ESPCN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The task of recovering a high resolution (HR) image from its low resolution counterpart is commonly referred to as Single Image Super Resolution (SISR). \n",
    "\n",
    "The model implemented here is ESPCN ([Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://arxiv.org/abs/1609.05158)) and we are going to use TensorFlow Lite to run inference on the trained model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to need to do some color space transformation, so let's install OpenCV first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: opencv-python in /usr/local/google/home/weiwe/miniconda3/envs/python3.7/lib/python3.7/site-packages (4.2.0.34)\r\n",
      "Requirement already satisfied: numpy>=1.14.5 in /usr/local/google/home/weiwe/miniconda3/envs/python3.7/lib/python3.7/site-packages (from opencv-python) (1.18.4)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install opencv-python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import dependencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tarfile\n",
    "import datetime\n",
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.python.keras.layers import Conv2D, Input, Lambda, Activation\n",
    "from tensorflow.python.keras.models import Model\n",
    "from os.path import exists, join, basename\n",
    "from os import makedirs, remove, path\n",
    "from six.moves import urllib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set some parameters and download BSD300 dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "SCALE = 3\n",
    "LR_SIZE = 100\n",
    "# Make sure HR_SIZE is smaller than 300 since some images in the dataset have height/width < 300\n",
    "HR_SIZE = LR_SIZE * SCALE\n",
    "\n",
    "BATCH_SIZE = 100\n",
    "\n",
    "def download_dataset(file, target_dir=\"dataset\", extract=True):\n",
    "    if not exists(join(target_dir, \"BSDS300/images\")):\n",
    "        source_url = \"http://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/BSDS300-images.tgz\"\n",
    "        target_dir = path.abspath(target_dir)\n",
    "        tf.keras.utils.get_file(file, source_url, cache_subdir=target_dir, extract=extract)\n",
    "\n",
    "download_dataset('BSDS300-images.tgz')\n",
    "\n",
    "TRAIN_IMAGES = \"dataset/BSDS300/images/train/*.jpg\"  \n",
    "TEST_IMAGES = \"dataset/BSDS300/images/test/*.jpg\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ESPCN implements a simple network with 3 convolutional layers and 1 pixel shuffle layer on the Y channel (luminance) in the YCbCr color space. Cb and Cr channels are upsampled using bicubic method. We can prepare our training and test datasets as the following."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def extract_luminance(path):\n",
    "    img = cv2.imread(path.numpy().decode('UTF8'))\n",
    "    img = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)\n",
    "    # Extract luminance\n",
    "    img_y = img[:, :, 0:1]\n",
    "    return img_y\n",
    "\n",
    "def load_image(img_path):\n",
    "    [hr_img,] = tf.py_function(extract_luminance, [img_path], [tf.float32])\n",
    "    hr_img = tf.convert_to_tensor(hr_img)\n",
    "    hr_img = tf.image.random_crop(hr_img, [HR_SIZE, HR_SIZE, 1])\n",
    "    hr_img = tf.cast(hr_img, tf.float32)\n",
    "    hr_img = hr_img / 255.\n",
    "    lr_img = tf.image.resize(hr_img, [HR_SIZE//SCALE, HR_SIZE//SCALE], tf.image.ResizeMethod.BICUBIC)\n",
    "    return lr_img, hr_img\n",
    "\n",
    "training_dataset = tf.data.Dataset.list_files(TRAIN_IMAGES) \\\n",
    "                   .map(load_image, num_parallel_calls=tf.data.experimental.AUTOTUNE) \\\n",
    "                   .batch(BATCH_SIZE) \\\n",
    "                   .prefetch(tf.data.experimental.AUTOTUNE) \\\n",
    "                   .shuffle(buffer_size = 200) \n",
    "\n",
    "test_dataset = tf.data.Dataset.list_files(TEST_IMAGES) \\\n",
    "                   .map(load_image, num_parallel_calls=tf.data.experimental.AUTOTUNE) \\\n",
    "                   .batch(BATCH_SIZE) \\\n",
    "                   .prefetch(tf.data.experimental.AUTOTUNE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"ESPCN\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 100, 100, 1)]     0         \n",
      "_________________________________________________________________\n",
      "conv2d (Conv2D)              (None, 100, 100, 64)      1664      \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 100, 100, 32)      18464     \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 100, 100, 9)       2601      \n",
      "_________________________________________________________________\n",
      "lambda (Lambda)              (None, 300, 300, 1)       0         \n",
      "_________________________________________________________________\n",
      "activation (Activation)      (None, 300, 300, 1)       0         \n",
      "=================================================================\n",
      "Total params: 22,729\n",
      "Trainable params: 22,729\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Helper function to wrap depth_to_space op (pixel shuffle) in a Lambda layer\n",
    "def pixel_shuffle(scale):\n",
    "    return lambda x: tf.nn.depth_to_space(x, scale)\n",
    "\n",
    "input = Input(shape=(LR_SIZE, LR_SIZE, 1))\n",
    "x = Conv2D(64, [5, 5], padding='same', activation='relu')(input)\n",
    "x = Conv2D(32, [3, 3], padding='same', activation='relu')(x)\n",
    "x = Conv2D(SCALE**2, [3, 3], padding='same')(x)\n",
    "x = Lambda(pixel_shuffle(scale=SCALE))(x)\n",
    "x = Activation('tanh')(x)\n",
    "model = Model(input, x, name=\"ESPCN\")\n",
    "\n",
    "logdir = \"./logdir/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
    "tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)\n",
    "\n",
    "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4), loss=tf.keras.losses.MSE)\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train and export the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/700\n",
      "2/2 [==============================] - 2s 827ms/step - loss: 0.2493 - val_loss: 0.2426\n",
      "Epoch 2/700\n",
      "2/2 [==============================] - 1s 718ms/step - loss: 0.2445 - val_loss: 0.2337\n",
      "Epoch 3/700\n",
      "2/2 [==============================] - 1s 719ms/step - loss: 0.2330 - val_loss: 0.2300\n",
      "Epoch 4/700\n",
      "2/2 [==============================] - 1s 729ms/step - loss: 0.2288 - val_loss: 0.2272\n",
      "Epoch 5/700\n",
      "2/2 [==============================] - 1s 726ms/step - loss: 0.2253 - val_loss: 0.2165\n",
      "Epoch 6/700\n",
      "2/2 [==============================] - 1s 735ms/step - loss: 0.2214 - val_loss: 0.2186\n",
      "Epoch 7/700\n",
      "2/2 [==============================] - 1s 701ms/step - loss: 0.2162 - val_loss: 0.2075\n",
      "Epoch 8/700\n",
      "2/2 [==============================] - 1s 743ms/step - loss: 0.2113 - val_loss: 0.2061\n",
      "Epoch 9/700\n",
      "2/2 [==============================] - 1s 724ms/step - loss: 0.2048 - val_loss: 0.2000\n",
      "Epoch 10/700\n",
      "2/2 [==============================] - 1s 701ms/step - loss: 0.1986 - val_loss: 0.1958\n",
      "Epoch 11/700\n",
      "2/2 [==============================] - 1s 706ms/step - loss: 0.1928 - val_loss: 0.1901\n",
      "Epoch 12/700\n",
      "2/2 [==============================] - 1s 720ms/step - loss: 0.1889 - val_loss: 0.1862\n",
      "Epoch 13/700\n",
      "2/2 [==============================] - 1s 730ms/step - loss: 0.1829 - val_loss: 0.1775\n",
      "Epoch 14/700\n",
      "2/2 [==============================] - 1s 734ms/step - loss: 0.1732 - val_loss: 0.1745\n",
      "Epoch 15/700\n",
      "2/2 [==============================] - 1s 734ms/step - loss: 0.1685 - val_loss: 0.1620\n",
      "Epoch 16/700\n",
      "2/2 [==============================] - 2s 764ms/step - loss: 0.1603 - val_loss: 0.1537\n",
      "Epoch 17/700\n",
      "2/2 [==============================] - 1s 720ms/step - loss: 0.1513 - val_loss: 0.1464\n",
      "Epoch 18/700\n",
      "2/2 [==============================] - 1s 724ms/step - loss: 0.1447 - val_loss: 0.1379\n",
      "Epoch 19/700\n",
      "2/2 [==============================] - 1s 715ms/step - loss: 0.1357 - val_loss: 0.1281\n",
      "Epoch 20/700\n",
      "2/2 [==============================] - 1s 714ms/step - loss: 0.1277 - val_loss: 0.1207\n",
      "Epoch 21/700\n",
      "2/2 [==============================] - 1s 736ms/step - loss: 0.1158 - val_loss: 0.1104\n",
      "Epoch 22/700\n",
      "2/2 [==============================] - 1s 720ms/step - loss: 0.1076 - val_loss: 0.1009\n",
      "Epoch 23/700\n",
      "2/2 [==============================] - 1s 724ms/step - loss: 0.0971 - val_loss: 0.0917\n",
      "Epoch 24/700\n",
      "2/2 [==============================] - 1s 726ms/step - loss: 0.0877 - val_loss: 0.0808\n",
      "Epoch 25/700\n",
      "2/2 [==============================] - 1s 708ms/step - loss: 0.0781 - val_loss: 0.0721\n",
      "Epoch 26/700\n",
      "2/2 [==============================] - 1s 739ms/step - loss: 0.0693 - val_loss: 0.0634\n",
      "Epoch 27/700\n",
      "2/2 [==============================] - 1s 733ms/step - loss: 0.0602 - val_loss: 0.0565\n",
      "Epoch 28/700\n",
      "2/2 [==============================] - 1s 711ms/step - loss: 0.0536 - val_loss: 0.0498\n",
      "Epoch 29/700\n",
      "2/2 [==============================] - 1s 711ms/step - loss: 0.0468 - val_loss: 0.0447\n",
      "Epoch 30/700\n",
      "2/2 [==============================] - 1s 737ms/step - loss: 0.0420 - val_loss: 0.0389\n",
      "Epoch 31/700\n",
      "2/2 [==============================] - 1s 717ms/step - loss: 0.0370 - val_loss: 0.0347\n",
      "Epoch 32/700\n",
      "2/2 [==============================] - 1s 713ms/step - loss: 0.0331 - val_loss: 0.0314\n",
      "Epoch 33/700\n",
      "2/2 [==============================] - 1s 717ms/step - loss: 0.0294 - val_loss: 0.0283\n",
      "Epoch 34/700\n",
      "2/2 [==============================] - 1s 724ms/step - loss: 0.0267 - val_loss: 0.0261\n",
      "Epoch 35/700\n",
      "2/2 [==============================] - 1s 718ms/step - loss: 0.0244 - val_loss: 0.0240\n",
      "Epoch 36/700\n",
      "2/2 [==============================] - 1s 726ms/step - loss: 0.0230 - val_loss: 0.0230\n",
      "Epoch 37/700\n",
      "2/2 [==============================] - 1s 745ms/step - loss: 0.0216 - val_loss: 0.0223\n",
      "Epoch 38/700\n",
      "2/2 [==============================] - 1s 727ms/step - loss: 0.0207 - val_loss: 0.0213\n",
      "Epoch 39/700\n",
      "2/2 [==============================] - 1s 721ms/step - loss: 0.0199 - val_loss: 0.0205\n",
      "Epoch 40/700\n",
      "2/2 [==============================] - 1s 710ms/step - loss: 0.0193 - val_loss: 0.0200\n",
      "Epoch 41/700\n",
      "2/2 [==============================] - 1s 717ms/step - loss: 0.0188 - val_loss: 0.0194\n",
      "Epoch 42/700\n",
      "2/2 [==============================] - 1s 712ms/step - loss: 0.0180 - val_loss: 0.0187\n",
      "Epoch 43/700\n",
      "2/2 [==============================] - 1s 722ms/step - loss: 0.0176 - val_loss: 0.0182\n",
      "Epoch 44/700\n",
      "2/2 [==============================] - 1s 729ms/step - loss: 0.0169 - val_loss: 0.0177\n",
      "Epoch 45/700\n",
      "2/2 [==============================] - 1s 718ms/step - loss: 0.0167 - val_loss: 0.0173\n",
      "Epoch 46/700\n",
      "2/2 [==============================] - 1s 705ms/step - loss: 0.0159 - val_loss: 0.0173\n",
      "Epoch 47/700\n",
      "2/2 [==============================] - 1s 727ms/step - loss: 0.0160 - val_loss: 0.0167\n",
      "Epoch 48/700\n",
      "2/2 [==============================] - 1s 717ms/step - loss: 0.0159 - val_loss: 0.0164\n",
      "Epoch 49/700\n",
      "2/2 [==============================] - 1s 710ms/step - loss: 0.0155 - val_loss: 0.0166\n",
      "Epoch 50/700\n",
      "2/2 [==============================] - 1s 707ms/step - loss: 0.0153 - val_loss: 0.0164\n",
      "Epoch 51/700\n",
      "2/2 [==============================] - 1s 728ms/step - loss: 0.0154 - val_loss: 0.0162\n",
      "Epoch 52/700\n",
      "2/2 [==============================] - 1s 731ms/step - loss: 0.0154 - val_loss: 0.0161\n",
      "Epoch 53/700\n",
      "2/2 [==============================] - 1s 721ms/step - loss: 0.0151 - val_loss: 0.0163\n",
      "Epoch 54/700\n",
      "2/2 [==============================] - 1s 720ms/step - loss: 0.0151 - val_loss: 0.0160\n",
      "Epoch 55/700\n",
      "2/2 [==============================] - 1s 709ms/step - loss: 0.0150 - val_loss: 0.0157\n",
      "Epoch 56/700\n",
      "2/2 [==============================] - 1s 719ms/step - loss: 0.0149 - val_loss: 0.0160\n",
      "Epoch 57/700\n",
      "2/2 [==============================] - 1s 710ms/step - loss: 0.0149 - val_loss: 0.0156\n",
      "Epoch 58/700\n",
      "2/2 [==============================] - 1s 721ms/step - loss: 0.0149 - val_loss: 0.0154\n",
      "Epoch 59/700\n",
      "2/2 [==============================] - 1s 698ms/step - loss: 0.0148 - val_loss: 0.0156\n",
      "Epoch 60/700\n",
      "2/2 [==============================] - 1s 737ms/step - loss: 0.0145 - val_loss: 0.0156\n",
      "Epoch 61/700\n",
      "2/2 [==============================] - 1s 723ms/step - loss: 0.0145 - val_loss: 0.0155\n",
      "Epoch 62/700\n",
      "2/2 [==============================] - 1s 736ms/step - loss: 0.0145 - val_loss: 0.0155\n",
      "Epoch 63/700\n",
      "2/2 [==============================] - 1s 718ms/step - loss: 0.0145 - val_loss: 0.0153\n",
      "Epoch 64/700\n",
      "2/2 [==============================] - 1s 727ms/step - loss: 0.0141 - val_loss: 0.0152\n",
      "Epoch 65/700\n",
      "2/2 [==============================] - 1s 715ms/step - loss: 0.0143 - val_loss: 0.0150\n",
      "Epoch 66/700\n",
      "2/2 [==============================] - 1s 723ms/step - loss: 0.0142 - val_loss: 0.0150\n",
      "Epoch 67/700\n",
      "2/2 [==============================] - 1s 697ms/step - loss: 0.0140 - val_loss: 0.0152\n",
      "Epoch 68/700\n",
      "2/2 [==============================] - 1s 741ms/step - loss: 0.0139 - val_loss: 0.0149\n",
      "Epoch 69/700\n",
      "2/2 [==============================] - 1s 737ms/step - loss: 0.0140 - val_loss: 0.0149\n",
      "Epoch 70/700\n",
      "2/2 [==============================] - 1s 723ms/step - loss: 0.0138 - val_loss: 0.0148\n",
      "Epoch 71/700\n",
      "2/2 [==============================] - 1s 733ms/step - loss: 0.0140 - val_loss: 0.0147\n",
      "Epoch 72/700\n",
      "2/2 [==============================] - 1s 720ms/step - loss: 0.0138 - val_loss: 0.0147\n",
      "Epoch 73/700\n",
      "2/2 [==============================] - 1s 715ms/step - loss: 0.0136 - val_loss: 0.0146\n",
      "Epoch 74/700\n",
      "2/2 [==============================] - 1s 725ms/step - loss: 0.0135 - val_loss: 0.0145\n",
      "Epoch 75/700\n",
      "2/2 [==============================] - 1s 713ms/step - loss: 0.0136 - val_loss: 0.0143\n",
      "Epoch 76/700\n",
      "2/2 [==============================] - 1s 727ms/step - loss: 0.0136 - val_loss: 0.0143\n",
      "Epoch 77/700\n",
      "2/2 [==============================] - 1s 732ms/step - loss: 0.0133 - val_loss: 0.0139\n",
      "Epoch 78/700\n",
      "2/2 [==============================] - 1s 726ms/step - loss: 0.0132 - val_loss: 0.0140\n",
      "Epoch 79/700\n",
      "2/2 [==============================] - 1s 715ms/step - loss: 0.0133 - val_loss: 0.0140\n",
      "Epoch 80/700\n",
      "2/2 [==============================] - 1s 733ms/step - loss: 0.0131 - val_loss: 0.0138\n",
      "Epoch 81/700\n",
      "2/2 [==============================] - 1s 732ms/step - loss: 0.0130 - val_loss: 0.0138\n",
      "Epoch 82/700\n",
      "2/2 [==============================] - 1s 733ms/step - loss: 0.0131 - val_loss: 0.0138\n",
      "Epoch 83/700\n",
      "2/2 [==============================] - 1s 721ms/step - loss: 0.0129 - val_loss: 0.0137\n",
      "Epoch 84/700\n",
      "2/2 [==============================] - 1s 712ms/step - loss: 0.0126 - val_loss: 0.0134\n",
      "Epoch 85/700\n",
      "2/2 [==============================] - 1s 727ms/step - loss: 0.0126 - val_loss: 0.0132\n",
      "Epoch 86/700\n",
      "2/2 [==============================] - 1s 741ms/step - loss: 0.0126 - val_loss: 0.0133\n",
      "Epoch 87/700\n",
      "2/2 [==============================] - 1s 721ms/step - loss: 0.0122 - val_loss: 0.0132\n",
      "Epoch 88/700\n",
      "2/2 [==============================] - 1s 722ms/step - loss: 0.0121 - val_loss: 0.0128\n",
      "Epoch 89/700\n",
      "2/2 [==============================] - 1s 707ms/step - loss: 0.0119 - val_loss: 0.0128\n",
      "Epoch 90/700\n",
      "2/2 [==============================] - 1s 722ms/step - loss: 0.0116 - val_loss: 0.0127\n",
      "Epoch 91/700\n",
      "2/2 [==============================] - 1s 729ms/step - loss: 0.0116 - val_loss: 0.0127\n",
      "Epoch 92/700\n",
      "2/2 [==============================] - 1s 744ms/step - loss: 0.0116 - val_loss: 0.0123\n",
      "Epoch 93/700\n",
      "2/2 [==============================] - 1s 714ms/step - loss: 0.0116 - val_loss: 0.0124\n",
      "Epoch 94/700\n",
      "2/2 [==============================] - 1s 726ms/step - loss: 0.0113 - val_loss: 0.0122\n",
      "Epoch 95/700\n",
      "2/2 [==============================] - 1s 739ms/step - loss: 0.0113 - val_loss: 0.0121\n",
      "Epoch 96/700\n",
      "2/2 [==============================] - 1s 726ms/step - loss: 0.0112 - val_loss: 0.0120\n",
      "Epoch 97/700\n",
      "2/2 [==============================] - 1s 718ms/step - loss: 0.0109 - val_loss: 0.0119\n",
      "Epoch 98/700\n",
      "2/2 [==============================] - 1s 702ms/step - loss: 0.0111 - val_loss: 0.0116\n",
      "Epoch 99/700\n",
      "2/2 [==============================] - 1s 726ms/step - loss: 0.0107 - val_loss: 0.0114\n",
      "Epoch 100/700\n",
      "2/2 [==============================] - 2s 756ms/step - loss: 0.0105 - val_loss: 0.0112\n",
      "Epoch 101/700\n",
      "2/2 [==============================] - 1s 720ms/step - loss: 0.0107 - val_loss: 0.0111\n",
      "Epoch 102/700\n",
      "2/2 [==============================] - 1s 731ms/step - loss: 0.0103 - val_loss: 0.0112\n",
      "Epoch 103/700\n",
      "2/2 [==============================] - 1s 731ms/step - loss: 0.0102 - val_loss: 0.0107\n",
      "Epoch 104/700\n",
      "2/2 [==============================] - 1s 747ms/step - loss: 0.0100 - val_loss: 0.0110\n",
      "Epoch 105/700\n",
      "2/2 [==============================] - 1s 709ms/step - loss: 0.0100 - val_loss: 0.0107\n",
      "Epoch 106/700\n",
      "2/2 [==============================] - 1s 748ms/step - loss: 0.0099 - val_loss: 0.0105\n",
      "Epoch 107/700\n",
      "2/2 [==============================] - 1s 741ms/step - loss: 0.0096 - val_loss: 0.0106\n",
      "Epoch 108/700\n",
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      "Epoch 120/700\n",
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      "Epoch 128/700\n",
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      "Epoch 129/700\n",
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      "Epoch 131/700\n",
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      "Epoch 132/700\n",
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      "Epoch 133/700\n",
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      "Epoch 134/700\n",
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      "Epoch 135/700\n",
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      "Epoch 136/700\n",
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      "Epoch 137/700\n",
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      "Epoch 138/700\n",
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      "Epoch 139/700\n",
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      "Epoch 140/700\n",
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      "Epoch 141/700\n",
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      "Epoch 142/700\n",
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      "Epoch 143/700\n",
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      "Epoch 144/700\n",
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      "Epoch 145/700\n",
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      "Epoch 146/700\n",
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      "Epoch 147/700\n",
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      "Epoch 148/700\n",
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      "Epoch 149/700\n",
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      "Epoch 150/700\n",
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      "Epoch 151/700\n",
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      "Epoch 152/700\n",
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      "Epoch 153/700\n",
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      "Epoch 154/700\n",
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      "Epoch 155/700\n",
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      "Epoch 156/700\n",
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      "Epoch 157/700\n",
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      "Epoch 158/700\n",
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      "Epoch 159/700\n",
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      "Epoch 160/700\n",
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      "Epoch 161/700\n",
      "2/2 [==============================] - 1s 699ms/step - loss: 0.0073 - val_loss: 0.0080\n",
      "Epoch 162/700\n",
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      "Epoch 163/700\n",
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      "Epoch 164/700\n",
      "2/2 [==============================] - 1s 729ms/step - loss: 0.0072 - val_loss: 0.0077\n",
      "Epoch 165/700\n",
      "2/2 [==============================] - 1s 718ms/step - loss: 0.0071 - val_loss: 0.0078\n",
      "Epoch 166/700\n",
      "2/2 [==============================] - 1s 704ms/step - loss: 0.0071 - val_loss: 0.0078\n",
      "Epoch 167/700\n",
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      "Epoch 168/700\n",
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      "Epoch 169/700\n",
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      "Epoch 170/700\n",
      "2/2 [==============================] - 1s 747ms/step - loss: 0.0069 - val_loss: 0.0078\n",
      "Epoch 171/700\n",
      "2/2 [==============================] - 1s 736ms/step - loss: 0.0070 - val_loss: 0.0076\n",
      "Epoch 172/700\n",
      "2/2 [==============================] - 1s 719ms/step - loss: 0.0068 - val_loss: 0.0075\n",
      "Epoch 173/700\n",
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      "Epoch 174/700\n",
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      "Epoch 175/700\n",
      "2/2 [==============================] - 1s 706ms/step - loss: 0.0069 - val_loss: 0.0076\n",
      "Epoch 176/700\n",
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      "Epoch 177/700\n",
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      "Epoch 178/700\n",
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      "Epoch 179/700\n",
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      "Epoch 180/700\n",
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      "Epoch 181/700\n",
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      "Epoch 182/700\n",
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      "Epoch 183/700\n",
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      "Epoch 184/700\n",
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      "Epoch 185/700\n",
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      "Epoch 186/700\n",
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      "Epoch 187/700\n",
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      "Epoch 188/700\n",
      "2/2 [==============================] - 1s 732ms/step - loss: 0.0067 - val_loss: 0.0072\n",
      "Epoch 189/700\n",
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      "Epoch 190/700\n",
      "2/2 [==============================] - 1s 726ms/step - loss: 0.0066 - val_loss: 0.0072\n",
      "Epoch 191/700\n",
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      "Epoch 192/700\n",
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      "Epoch 193/700\n",
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      "Epoch 194/700\n",
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      "Epoch 195/700\n",
      "2/2 [==============================] - 1s 722ms/step - loss: 0.0066 - val_loss: 0.0072\n",
      "Epoch 196/700\n",
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      "Epoch 197/700\n",
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      "Epoch 198/700\n",
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      "Epoch 199/700\n",
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      "Epoch 200/700\n",
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      "Epoch 201/700\n",
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      "Epoch 202/700\n",
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      "Epoch 204/700\n",
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      "Epoch 205/700\n",
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      "Epoch 206/700\n",
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      "Epoch 207/700\n",
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      "Epoch 208/700\n",
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      "Epoch 209/700\n",
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      "Epoch 210/700\n",
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      "Epoch 211/700\n",
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      "Epoch 212/700\n",
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      "Epoch 213/700\n",
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      "Epoch 214/700\n",
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      "Epoch 215/700\n",
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      "Epoch 216/700\n",
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      "Epoch 217/700\n",
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      "Epoch 218/700\n",
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      "Epoch 219/700\n",
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      "Epoch 220/700\n",
      "2/2 [==============================] - 1s 735ms/step - loss: 0.0062 - val_loss: 0.0069\n",
      "Epoch 221/700\n",
      "2/2 [==============================] - 1s 718ms/step - loss: 0.0062 - val_loss: 0.0069\n",
      "Epoch 222/700\n",
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      "Epoch 223/700\n",
      "2/2 [==============================] - 1s 727ms/step - loss: 0.0063 - val_loss: 0.0069\n",
      "Epoch 224/700\n",
      "2/2 [==============================] - 1s 728ms/step - loss: 0.0062 - val_loss: 0.0068\n",
      "Epoch 225/700\n",
      "2/2 [==============================] - 1s 728ms/step - loss: 0.0063 - val_loss: 0.0068\n",
      "Epoch 226/700\n",
      "2/2 [==============================] - 1s 736ms/step - loss: 0.0062 - val_loss: 0.0069\n",
      "Epoch 227/700\n",
      "2/2 [==============================] - 2s 752ms/step - loss: 0.0062 - val_loss: 0.0068\n",
      "Epoch 228/700\n",
      "2/2 [==============================] - 1s 729ms/step - loss: 0.0062 - val_loss: 0.0068\n",
      "Epoch 229/700\n",
      "2/2 [==============================] - 1s 720ms/step - loss: 0.0062 - val_loss: 0.0069\n",
      "Epoch 230/700\n",
      "2/2 [==============================] - 1s 732ms/step - loss: 0.0062 - val_loss: 0.0068\n",
      "Epoch 231/700\n",
      "2/2 [==============================] - 2s 754ms/step - loss: 0.0062 - val_loss: 0.0068\n",
      "Epoch 232/700\n",
      "2/2 [==============================] - 1s 715ms/step - loss: 0.0062 - val_loss: 0.0067\n",
      "Epoch 233/700\n",
      "2/2 [==============================] - 1s 746ms/step - loss: 0.0061 - val_loss: 0.0068\n",
      "Epoch 234/700\n",
      "2/2 [==============================] - 1s 739ms/step - loss: 0.0062 - val_loss: 0.0067\n",
      "Epoch 235/700\n",
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      "Epoch 236/700\n",
      "2/2 [==============================] - 1s 705ms/step - loss: 0.0060 - val_loss: 0.0066\n",
      "Epoch 237/700\n",
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      "Epoch 238/700\n",
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      "Epoch 239/700\n",
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      "Epoch 240/700\n",
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      "Epoch 241/700\n",
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      "Epoch 242/700\n",
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      "Epoch 243/700\n",
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      "Epoch 244/700\n",
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      "Epoch 245/700\n",
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      "Epoch 246/700\n",
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      "Epoch 247/700\n",
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      "Epoch 248/700\n",
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      "Epoch 249/700\n",
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      "Epoch 250/700\n",
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      "Epoch 251/700\n",
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      "Epoch 252/700\n",
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      "Epoch 253/700\n",
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      "Epoch 254/700\n",
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      "Epoch 255/700\n",
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      "Epoch 256/700\n",
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      "Epoch 257/700\n",
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      "Epoch 258/700\n",
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      "Epoch 259/700\n",
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      "Epoch 260/700\n",
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      "Epoch 261/700\n",
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      "Epoch 262/700\n",
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      "Epoch 263/700\n",
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      "Epoch 264/700\n",
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      "Epoch 265/700\n",
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      "Epoch 266/700\n",
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      "Epoch 267/700\n",
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      "Epoch 268/700\n",
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      "Epoch 269/700\n",
      "2/2 [==============================] - 2s 758ms/step - loss: 0.0059 - val_loss: 0.0065\n",
      "Epoch 270/700\n",
      "2/2 [==============================] - 1s 709ms/step - loss: 0.0059 - val_loss: 0.0064\n",
      "Epoch 271/700\n",
      "2/2 [==============================] - 1s 719ms/step - loss: 0.0058 - val_loss: 0.0064\n",
      "Epoch 272/700\n",
      "2/2 [==============================] - 2s 755ms/step - loss: 0.0059 - val_loss: 0.0064\n",
      "Epoch 273/700\n",
      "2/2 [==============================] - 1s 710ms/step - loss: 0.0058 - val_loss: 0.0064\n",
      "Epoch 274/700\n",
      "2/2 [==============================] - 1s 718ms/step - loss: 0.0058 - val_loss: 0.0065\n",
      "Epoch 275/700\n",
      "2/2 [==============================] - 1s 704ms/step - loss: 0.0058 - val_loss: 0.0064\n",
      "Epoch 276/700\n",
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      "Epoch 288/700\n",
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      "Epoch 293/700\n",
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      "Epoch 295/700\n",
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      "Epoch 296/700\n",
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      "Epoch 298/700\n",
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      "Epoch 300/700\n",
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      "Epoch 301/700\n",
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      "Epoch 305/700\n",
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      "Epoch 306/700\n",
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      "Epoch 307/700\n",
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      "Epoch 309/700\n",
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      "Epoch 310/700\n",
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      "Epoch 311/700\n",
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      "Epoch 312/700\n",
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      "Epoch 317/700\n",
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      "Epoch 327/700\n",
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      "Epoch 334/700\n",
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      "Epoch 341/700\n",
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      "Epoch 352/700\n",
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      "Epoch 381/700\n",
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      "Epoch 390/700\n",
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      "Epoch 391/700\n",
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      "Epoch 400/700\n",
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      "Epoch 425/700\n",
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      "Epoch 431/700\n",
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      "Epoch 432/700\n",
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      "Epoch 433/700\n",
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      "Epoch 434/700\n",
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      "Epoch 435/700\n",
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      "Epoch 436/700\n",
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      "Epoch 437/700\n",
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      "Epoch 438/700\n",
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      "Epoch 439/700\n",
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      "Epoch 440/700\n",
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      "Epoch 441/700\n",
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      "Epoch 442/700\n",
      "2/2 [==============================] - 1s 700ms/step - loss: 0.0049 - val_loss: 0.0054\n",
      "Epoch 443/700\n",
      "2/2 [==============================] - 1s 737ms/step - loss: 0.0048 - val_loss: 0.0053\n",
      "Epoch 444/700\n",
      "2/2 [==============================] - 1s 729ms/step - loss: 0.0049 - val_loss: 0.0054\n",
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      "Epoch 453/700\n",
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      "Epoch 454/700\n",
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      "Epoch 455/700\n",
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      "Epoch 456/700\n",
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      "Epoch 457/700\n",
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      "Epoch 458/700\n",
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      "Epoch 459/700\n",
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      "Epoch 460/700\n",
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      "Epoch 461/700\n",
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      "Epoch 462/700\n",
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      "Epoch 463/700\n",
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      "Epoch 464/700\n",
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      "Epoch 465/700\n",
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      "Epoch 466/700\n",
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      "Epoch 467/700\n",
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      "Epoch 468/700\n",
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      "Epoch 469/700\n",
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      "Epoch 470/700\n",
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      "Epoch 471/700\n",
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      "Epoch 472/700\n",
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      "Epoch 473/700\n",
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      "Epoch 474/700\n",
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      "Epoch 475/700\n",
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      "Epoch 476/700\n",
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      "Epoch 477/700\n",
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      "Epoch 478/700\n",
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      "Epoch 479/700\n",
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      "Epoch 480/700\n",
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      "Epoch 481/700\n",
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      "Epoch 482/700\n",
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      "Epoch 483/700\n",
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      "Epoch 484/700\n",
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      "Epoch 485/700\n",
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      "Epoch 486/700\n",
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      "Epoch 487/700\n",
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      "Epoch 488/700\n",
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      "Epoch 489/700\n",
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      "Epoch 490/700\n",
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      "Epoch 491/700\n",
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      "Epoch 492/700\n",
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      "Epoch 493/700\n",
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      "Epoch 494/700\n",
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      "Epoch 495/700\n",
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      "Epoch 496/700\n",
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      "Epoch 497/700\n",
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      "Epoch 498/700\n",
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      "Epoch 499/700\n",
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      "Epoch 500/700\n",
      "2/2 [==============================] - 1s 748ms/step - loss: 0.0048 - val_loss: 0.0053\n",
      "Epoch 501/700\n",
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      "Epoch 502/700\n",
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      "Epoch 503/700\n",
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      "Epoch 504/700\n",
      "2/2 [==============================] - 1s 704ms/step - loss: 0.0048 - val_loss: 0.0052\n",
      "Epoch 505/700\n",
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      "Epoch 506/700\n",
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      "Epoch 507/700\n",
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      "Epoch 508/700\n",
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      "Epoch 509/700\n",
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      "Epoch 510/700\n",
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      "Epoch 511/700\n",
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      "Epoch 512/700\n",
      "2/2 [==============================] - 1s 723ms/step - loss: 0.0049 - val_loss: 0.0052\n",
      "Epoch 513/700\n",
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      "Epoch 514/700\n",
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      "Epoch 515/700\n",
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      "Epoch 516/700\n",
      "2/2 [==============================] - 1s 711ms/step - loss: 0.0048 - val_loss: 0.0052\n",
      "Epoch 517/700\n",
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      "Epoch 518/700\n",
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      "Epoch 519/700\n",
      "2/2 [==============================] - 1s 707ms/step - loss: 0.0048 - val_loss: 0.0052\n",
      "Epoch 520/700\n",
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      "Epoch 521/700\n",
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      "Epoch 522/700\n",
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      "Epoch 523/700\n",
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      "Epoch 524/700\n",
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      "Epoch 525/700\n",
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      "Epoch 526/700\n",
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      "Epoch 527/700\n",
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      "Epoch 528/700\n",
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      "Epoch 529/700\n",
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      "Epoch 530/700\n",
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      "Epoch 531/700\n",
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      "Epoch 532/700\n",
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      "Epoch 533/700\n",
      "2/2 [==============================] - 1s 711ms/step - loss: 0.0048 - val_loss: 0.0052\n",
      "Epoch 534/700\n",
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      "Epoch 535/700\n",
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      "Epoch 536/700\n",
      "2/2 [==============================] - 1s 739ms/step - loss: 0.0048 - val_loss: 0.0052\n",
      "Epoch 537/700\n",
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      "Epoch 538/700\n",
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      "Epoch 539/700\n",
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      "Epoch 540/700\n",
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      "Epoch 541/700\n",
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      "Epoch 542/700\n",
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      "Epoch 543/700\n",
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      "Epoch 544/700\n",
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      "Epoch 545/700\n",
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      "Epoch 546/700\n",
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      "Epoch 547/700\n",
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      "Epoch 548/700\n",
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      "Epoch 549/700\n",
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      "Epoch 550/700\n",
      "2/2 [==============================] - 1s 718ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 551/700\n",
      "2/2 [==============================] - 1s 722ms/step - loss: 0.0047 - val_loss: 0.0052\n",
      "Epoch 552/700\n",
      "2/2 [==============================] - 1s 726ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 553/700\n",
      "2/2 [==============================] - 1s 732ms/step - loss: 0.0046 - val_loss: 0.0053\n",
      "Epoch 554/700\n",
      "2/2 [==============================] - 1s 716ms/step - loss: 0.0048 - val_loss: 0.0052\n",
      "Epoch 555/700\n",
      "2/2 [==============================] - 1s 722ms/step - loss: 0.0047 - val_loss: 0.0052\n",
      "Epoch 556/700\n",
      "2/2 [==============================] - 1s 712ms/step - loss: 0.0048 - val_loss: 0.0051\n",
      "Epoch 557/700\n",
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      "Epoch 558/700\n",
      "2/2 [==============================] - 1s 744ms/step - loss: 0.0048 - val_loss: 0.0051\n",
      "Epoch 559/700\n",
      "2/2 [==============================] - 1s 722ms/step - loss: 0.0047 - val_loss: 0.0052\n",
      "Epoch 560/700\n",
      "2/2 [==============================] - 1s 735ms/step - loss: 0.0047 - val_loss: 0.0052\n",
      "Epoch 561/700\n",
      "2/2 [==============================] - 2s 757ms/step - loss: 0.0047 - val_loss: 0.0052\n",
      "Epoch 562/700\n",
      "2/2 [==============================] - 1s 717ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 563/700\n",
      "2/2 [==============================] - 1s 709ms/step - loss: 0.0048 - val_loss: 0.0051\n",
      "Epoch 564/700\n",
      "2/2 [==============================] - 1s 724ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 565/700\n",
      "2/2 [==============================] - 1s 727ms/step - loss: 0.0046 - val_loss: 0.0052\n",
      "Epoch 566/700\n",
      "2/2 [==============================] - 1s 725ms/step - loss: 0.0046 - val_loss: 0.0051\n",
      "Epoch 567/700\n",
      "2/2 [==============================] - 2s 751ms/step - loss: 0.0046 - val_loss: 0.0053\n",
      "Epoch 568/700\n",
      "2/2 [==============================] - 1s 722ms/step - loss: 0.0047 - val_loss: 0.0050\n",
      "Epoch 569/700\n",
      "2/2 [==============================] - 1s 730ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 570/700\n",
      "2/2 [==============================] - 1s 746ms/step - loss: 0.0047 - val_loss: 0.0052\n",
      "Epoch 571/700\n",
      "2/2 [==============================] - 1s 710ms/step - loss: 0.0047 - val_loss: 0.0052\n",
      "Epoch 572/700\n",
      "2/2 [==============================] - 1s 730ms/step - loss: 0.0048 - val_loss: 0.0051\n",
      "Epoch 573/700\n",
      "2/2 [==============================] - 1s 715ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 574/700\n",
      "2/2 [==============================] - 1s 729ms/step - loss: 0.0047 - val_loss: 0.0052\n",
      "Epoch 575/700\n",
      "2/2 [==============================] - 1s 714ms/step - loss: 0.0047 - val_loss: 0.0050\n",
      "Epoch 576/700\n",
      "2/2 [==============================] - 1s 734ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 577/700\n",
      "2/2 [==============================] - 1s 731ms/step - loss: 0.0047 - val_loss: 0.0052\n",
      "Epoch 578/700\n",
      "2/2 [==============================] - 1s 729ms/step - loss: 0.0047 - val_loss: 0.0052\n",
      "Epoch 579/700\n",
      "2/2 [==============================] - 1s 732ms/step - loss: 0.0048 - val_loss: 0.0051\n",
      "Epoch 580/700\n",
      "2/2 [==============================] - 1s 738ms/step - loss: 0.0047 - val_loss: 0.0052\n",
      "Epoch 581/700\n",
      "2/2 [==============================] - 2s 751ms/step - loss: 0.0047 - val_loss: 0.0052\n",
      "Epoch 582/700\n",
      "2/2 [==============================] - 1s 707ms/step - loss: 0.0047 - val_loss: 0.0052\n",
      "Epoch 583/700\n",
      "2/2 [==============================] - 1s 712ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 584/700\n",
      "2/2 [==============================] - 1s 708ms/step - loss: 0.0046 - val_loss: 0.0051\n",
      "Epoch 585/700\n",
      "2/2 [==============================] - 2s 755ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 586/700\n",
      "2/2 [==============================] - 1s 718ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 587/700\n",
      "2/2 [==============================] - 1s 709ms/step - loss: 0.0046 - val_loss: 0.0052\n",
      "Epoch 588/700\n",
      "2/2 [==============================] - 1s 739ms/step - loss: 0.0047 - val_loss: 0.0052\n",
      "Epoch 589/700\n",
      "2/2 [==============================] - 1s 713ms/step - loss: 0.0047 - val_loss: 0.0052\n",
      "Epoch 590/700\n",
      "2/2 [==============================] - 1s 720ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 591/700\n",
      "2/2 [==============================] - 1s 728ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 592/700\n",
      "2/2 [==============================] - 1s 712ms/step - loss: 0.0047 - val_loss: 0.0052\n",
      "Epoch 593/700\n",
      "2/2 [==============================] - 1s 732ms/step - loss: 0.0048 - val_loss: 0.0052\n",
      "Epoch 594/700\n",
      "2/2 [==============================] - 1s 723ms/step - loss: 0.0048 - val_loss: 0.0051\n",
      "Epoch 595/700\n",
      "2/2 [==============================] - 1s 708ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 596/700\n",
      "2/2 [==============================] - 1s 739ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 597/700\n",
      "2/2 [==============================] - 1s 700ms/step - loss: 0.0046 - val_loss: 0.0051\n",
      "Epoch 598/700\n",
      "2/2 [==============================] - 1s 736ms/step - loss: 0.0047 - val_loss: 0.0052\n",
      "Epoch 599/700\n",
      "2/2 [==============================] - 1s 705ms/step - loss: 0.0047 - val_loss: 0.0052\n",
      "Epoch 600/700\n",
      "2/2 [==============================] - 1s 731ms/step - loss: 0.0047 - val_loss: 0.0052\n",
      "Epoch 601/700\n",
      "2/2 [==============================] - 1s 749ms/step - loss: 0.0047 - val_loss: 0.0052\n",
      "Epoch 602/700\n",
      "2/2 [==============================] - 1s 738ms/step - loss: 0.0047 - val_loss: 0.0052\n",
      "Epoch 603/700\n",
      "2/2 [==============================] - 1s 715ms/step - loss: 0.0047 - val_loss: 0.0052\n",
      "Epoch 604/700\n",
      "2/2 [==============================] - 2s 759ms/step - loss: 0.0047 - val_loss: 0.0052\n",
      "Epoch 605/700\n",
      "2/2 [==============================] - 1s 704ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 606/700\n",
      "2/2 [==============================] - 1s 715ms/step - loss: 0.0046 - val_loss: 0.0052\n",
      "Epoch 607/700\n",
      "2/2 [==============================] - 1s 724ms/step - loss: 0.0046 - val_loss: 0.0052\n",
      "Epoch 608/700\n",
      "2/2 [==============================] - 1s 742ms/step - loss: 0.0046 - val_loss: 0.0050\n",
      "Epoch 609/700\n",
      "2/2 [==============================] - 1s 742ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 610/700\n",
      "2/2 [==============================] - 1s 734ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 611/700\n",
      "2/2 [==============================] - 1s 711ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 612/700\n",
      "2/2 [==============================] - 2s 759ms/step - loss: 0.0047 - val_loss: 0.0052\n",
      "Epoch 613/700\n",
      "2/2 [==============================] - 1s 716ms/step - loss: 0.0048 - val_loss: 0.0051\n",
      "Epoch 614/700\n",
      "2/2 [==============================] - 1s 728ms/step - loss: 0.0046 - val_loss: 0.0051\n",
      "Epoch 615/700\n",
      "2/2 [==============================] - 1s 732ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 616/700\n",
      "2/2 [==============================] - 1s 734ms/step - loss: 0.0046 - val_loss: 0.0051\n",
      "Epoch 617/700\n",
      "2/2 [==============================] - 1s 734ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 618/700\n",
      "2/2 [==============================] - 1s 730ms/step - loss: 0.0046 - val_loss: 0.0051\n",
      "Epoch 619/700\n",
      "2/2 [==============================] - 1s 733ms/step - loss: 0.0046 - val_loss: 0.0051\n",
      "Epoch 620/700\n",
      "2/2 [==============================] - 1s 710ms/step - loss: 0.0046 - val_loss: 0.0051\n",
      "Epoch 621/700\n",
      "2/2 [==============================] - 1s 718ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 622/700\n",
      "2/2 [==============================] - 1s 732ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 623/700\n",
      "2/2 [==============================] - 1s 736ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 624/700\n",
      "2/2 [==============================] - 1s 713ms/step - loss: 0.0046 - val_loss: 0.0051\n",
      "Epoch 625/700\n",
      "2/2 [==============================] - 1s 719ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 626/700\n",
      "2/2 [==============================] - 2s 769ms/step - loss: 0.0046 - val_loss: 0.0051\n",
      "Epoch 627/700\n",
      "2/2 [==============================] - 1s 740ms/step - loss: 0.0046 - val_loss: 0.0051\n",
      "Epoch 628/700\n",
      "2/2 [==============================] - 1s 727ms/step - loss: 0.0046 - val_loss: 0.0051\n",
      "Epoch 629/700\n",
      "2/2 [==============================] - 1s 705ms/step - loss: 0.0046 - val_loss: 0.0052\n",
      "Epoch 630/700\n",
      "2/2 [==============================] - 1s 704ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 631/700\n",
      "2/2 [==============================] - 1s 742ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 632/700\n",
      "2/2 [==============================] - 1s 724ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 633/700\n",
      "2/2 [==============================] - 1s 719ms/step - loss: 0.0046 - val_loss: 0.0051\n",
      "Epoch 634/700\n",
      "2/2 [==============================] - 1s 718ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 635/700\n",
      "2/2 [==============================] - 1s 730ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 636/700\n",
      "2/2 [==============================] - 1s 739ms/step - loss: 0.0046 - val_loss: 0.0051\n",
      "Epoch 637/700\n",
      "2/2 [==============================] - 1s 718ms/step - loss: 0.0046 - val_loss: 0.0051\n",
      "Epoch 638/700\n",
      "2/2 [==============================] - 2s 788ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 639/700\n",
      "2/2 [==============================] - 1s 725ms/step - loss: 0.0046 - val_loss: 0.0050\n",
      "Epoch 640/700\n",
      "2/2 [==============================] - 1s 721ms/step - loss: 0.0047 - val_loss: 0.0052\n",
      "Epoch 641/700\n",
      "2/2 [==============================] - 1s 717ms/step - loss: 0.0046 - val_loss: 0.0051\n",
      "Epoch 642/700\n",
      "2/2 [==============================] - 1s 716ms/step - loss: 0.0046 - val_loss: 0.0051\n",
      "Epoch 643/700\n",
      "2/2 [==============================] - 1s 725ms/step - loss: 0.0047 - val_loss: 0.0050\n",
      "Epoch 644/700\n",
      "2/2 [==============================] - 1s 730ms/step - loss: 0.0047 - val_loss: 0.0050\n",
      "Epoch 645/700\n",
      "2/2 [==============================] - 1s 721ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 646/700\n",
      "2/2 [==============================] - 1s 739ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 647/700\n",
      "2/2 [==============================] - 1s 744ms/step - loss: 0.0046 - val_loss: 0.0050\n",
      "Epoch 648/700\n",
      "2/2 [==============================] - 1s 701ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 649/700\n",
      "2/2 [==============================] - 1s 715ms/step - loss: 0.0046 - val_loss: 0.0053\n",
      "Epoch 650/700\n",
      "2/2 [==============================] - 1s 739ms/step - loss: 0.0046 - val_loss: 0.0051\n",
      "Epoch 651/700\n",
      "2/2 [==============================] - 1s 714ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 652/700\n",
      "2/2 [==============================] - 1s 742ms/step - loss: 0.0046 - val_loss: 0.0051\n",
      "Epoch 653/700\n",
      "2/2 [==============================] - 1s 700ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 654/700\n",
      "2/2 [==============================] - 1s 734ms/step - loss: 0.0046 - val_loss: 0.0052\n",
      "Epoch 655/700\n",
      "2/2 [==============================] - 1s 706ms/step - loss: 0.0047 - val_loss: 0.0052\n",
      "Epoch 656/700\n",
      "2/2 [==============================] - 1s 712ms/step - loss: 0.0046 - val_loss: 0.0051\n",
      "Epoch 657/700\n",
      "2/2 [==============================] - 1s 729ms/step - loss: 0.0046 - val_loss: 0.0050\n",
      "Epoch 658/700\n",
      "2/2 [==============================] - 1s 728ms/step - loss: 0.0047 - val_loss: 0.0050\n",
      "Epoch 659/700\n",
      "2/2 [==============================] - 1s 727ms/step - loss: 0.0046 - val_loss: 0.0051\n",
      "Epoch 660/700\n",
      "2/2 [==============================] - 1s 734ms/step - loss: 0.0046 - val_loss: 0.0051\n",
      "Epoch 661/700\n",
      "2/2 [==============================] - 1s 710ms/step - loss: 0.0047 - val_loss: 0.0050\n",
      "Epoch 662/700\n",
      "2/2 [==============================] - 1s 718ms/step - loss: 0.0046 - val_loss: 0.0050\n",
      "Epoch 663/700\n",
      "2/2 [==============================] - 1s 714ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 664/700\n",
      "2/2 [==============================] - 1s 716ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 665/700\n",
      "2/2 [==============================] - 1s 726ms/step - loss: 0.0046 - val_loss: 0.0052\n",
      "Epoch 666/700\n",
      "2/2 [==============================] - 1s 718ms/step - loss: 0.0046 - val_loss: 0.0051\n",
      "Epoch 667/700\n",
      "2/2 [==============================] - 1s 742ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 668/700\n",
      "2/2 [==============================] - 1s 737ms/step - loss: 0.0047 - val_loss: 0.0050\n",
      "Epoch 669/700\n",
      "2/2 [==============================] - 1s 724ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 670/700\n",
      "2/2 [==============================] - 1s 725ms/step - loss: 0.0046 - val_loss: 0.0050\n",
      "Epoch 671/700\n",
      "2/2 [==============================] - 1s 712ms/step - loss: 0.0046 - val_loss: 0.0051\n",
      "Epoch 672/700\n",
      "2/2 [==============================] - 1s 714ms/step - loss: 0.0046 - val_loss: 0.0050\n",
      "Epoch 673/700\n",
      "2/2 [==============================] - 1s 716ms/step - loss: 0.0046 - val_loss: 0.0051\n",
      "Epoch 674/700\n",
      "2/2 [==============================] - 2s 753ms/step - loss: 0.0047 - val_loss: 0.0050\n",
      "Epoch 675/700\n",
      "2/2 [==============================] - 1s 712ms/step - loss: 0.0046 - val_loss: 0.0050\n",
      "Epoch 676/700\n",
      "2/2 [==============================] - 1s 722ms/step - loss: 0.0046 - val_loss: 0.0050\n",
      "Epoch 677/700\n",
      "2/2 [==============================] - 1s 701ms/step - loss: 0.0046 - val_loss: 0.0051\n",
      "Epoch 678/700\n",
      "2/2 [==============================] - 1s 728ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 679/700\n",
      "2/2 [==============================] - 1s 732ms/step - loss: 0.0046 - val_loss: 0.0050\n",
      "Epoch 680/700\n",
      "2/2 [==============================] - 1s 731ms/step - loss: 0.0047 - val_loss: 0.0052\n",
      "Epoch 681/700\n",
      "2/2 [==============================] - 1s 719ms/step - loss: 0.0046 - val_loss: 0.0050\n",
      "Epoch 682/700\n",
      "2/2 [==============================] - 1s 739ms/step - loss: 0.0045 - val_loss: 0.0051\n",
      "Epoch 683/700\n",
      "2/2 [==============================] - 1s 717ms/step - loss: 0.0047 - val_loss: 0.0050\n",
      "Epoch 684/700\n",
      "2/2 [==============================] - 1s 745ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 685/700\n",
      "2/2 [==============================] - 1s 748ms/step - loss: 0.0046 - val_loss: 0.0050\n",
      "Epoch 686/700\n",
      "2/2 [==============================] - 1s 740ms/step - loss: 0.0045 - val_loss: 0.0050\n",
      "Epoch 687/700\n",
      "2/2 [==============================] - 1s 733ms/step - loss: 0.0047 - val_loss: 0.0050\n",
      "Epoch 688/700\n",
      "2/2 [==============================] - 1s 719ms/step - loss: 0.0047 - val_loss: 0.0050\n",
      "Epoch 689/700\n",
      "2/2 [==============================] - 1s 734ms/step - loss: 0.0046 - val_loss: 0.0051\n",
      "Epoch 690/700\n",
      "2/2 [==============================] - 1s 720ms/step - loss: 0.0045 - val_loss: 0.0051\n",
      "Epoch 691/700\n",
      "2/2 [==============================] - 1s 708ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 692/700\n",
      "2/2 [==============================] - 1s 720ms/step - loss: 0.0046 - val_loss: 0.0051\n",
      "Epoch 693/700\n",
      "2/2 [==============================] - 1s 718ms/step - loss: 0.0047 - val_loss: 0.0050\n",
      "Epoch 694/700\n",
      "2/2 [==============================] - 1s 708ms/step - loss: 0.0047 - val_loss: 0.0052\n",
      "Epoch 695/700\n",
      "2/2 [==============================] - 1s 715ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "Epoch 696/700\n",
      "2/2 [==============================] - 1s 732ms/step - loss: 0.0046 - val_loss: 0.0051\n",
      "Epoch 697/700\n",
      "2/2 [==============================] - 1s 726ms/step - loss: 0.0046 - val_loss: 0.0050\n",
      "Epoch 698/700\n",
      "2/2 [==============================] - 1s 724ms/step - loss: 0.0046 - val_loss: 0.0050\n",
      "Epoch 699/700\n",
      "2/2 [==============================] - 1s 722ms/step - loss: 0.0046 - val_loss: 0.0051\n",
      "Epoch 700/700\n",
      "2/2 [==============================] - 1s 732ms/step - loss: 0.0047 - val_loss: 0.0051\n",
      "WARNING:tensorflow:From /usr/local/google/home/weiwe/miniconda3/envs/python3.7/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:1817: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "INFO:tensorflow:Assets written to: export/assets\n"
     ]
    }
   ],
   "source": [
    "model.fit(training_dataset,epochs= 700,validation_data=test_dataset,\n",
    "                    callbacks = [tensorboard_callback],workers = 4)\n",
    "\n",
    "model.save(\"export\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Super resolve a test low resolution image using TensorFlow Lite."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_test_image(img_path):\n",
    "    hr = cv2.imread(img_path)\n",
    "    hr = cv2.cvtColor(hr, cv2.COLOR_BGR2YCrCb)\n",
    "    hr = tf.image.crop_to_bounding_box(hr, 0, 0, HR_SIZE, HR_SIZE)\n",
    "    lr = tf.image.resize(hr, [HR_SIZE//SCALE, HR_SIZE//SCALE])\n",
    "    lr = tf.cast(lr, tf.uint8)\n",
    "    return lr, hr\n",
    "\n",
    "def super_resolve(original_img_path):\n",
    "    # lr - low resolution image\n",
    "    # hr - high resolution image (cropped from original)\n",
    "    # sr - super resolved image from lr\n",
    "    lr, hr = load_test_image(original_img_path)\n",
    "    lr_ycrcb = lr.numpy().copy()    \n",
    "    lr = cv2.cvtColor(lr.numpy(), cv2.COLOR_YCrCb2RGB)\n",
    "    hr = cv2.cvtColor(hr.numpy(), cv2.COLOR_YCrCb2RGB)   \n",
    "    lr_ycrcb = tf.cast(lr_ycrcb, tf.float32)\n",
    "    lr_y = lr_ycrcb[:, :, 0:1] / 255.\n",
    "    lr_cr = lr_ycrcb[:, :, 1:2]\n",
    "    lr_cb = lr_ycrcb[:, :, 2:3]\n",
    "    lr_y = tf.expand_dims(lr_y, axis=0)\n",
    "    \n",
    "    # Use TFLite to super resolve luminance\n",
    "    converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
    "    tflite_model = converter.convert()\n",
    "    interpreter = tf.lite.Interpreter(model_content=tflite_model)\n",
    "    interpreter.allocate_tensors()\n",
    "    input_details = interpreter.get_input_details()\n",
    "    output_details = interpreter.get_output_details()\n",
    "    interpreter.set_tensor(input_details[0]['index'], lr_y)\n",
    "    interpreter.invoke()\n",
    "    sr_y = interpreter.get_tensor(output_details[0]['index'])\n",
    "    open(\"ESPCN.tflite\", 'wb').write(tflite_model)\n",
    "    \n",
    "    # Transform super resolved image back into RGB color space\n",
    "    sr_y = sr_y * 255\n",
    "    sr_cr = tf.image.resize(lr_cr, [HR_SIZE, HR_SIZE], tf.image.ResizeMethod.BICUBIC)\n",
    "    sr_cb = tf.image.resize(lr_cb, [HR_SIZE, HR_SIZE], tf.image.ResizeMethod.BICUBIC)\n",
    "    sr = tf.concat([sr_y[0], sr_cr, sr_cb], axis=2)\n",
    "    sr = tf.clip_by_value(sr, 0, 255)\n",
    "    sr = tf.round(sr)\n",
    "    sr = tf.cast(sr, tf.uint8)\n",
    "    sr = cv2.cvtColor(sr.numpy(), cv2.COLOR_YCrCb2RGB)\n",
    "    return lr, sr, hr\n",
    "\n",
    "# test_img_path = \"dataset/BSDS300/images/test/170057.jpg\"\n",
    "test_img_path = \"dataset/BSDS300/images/test/302008.jpg\"\n",
    "lr, sr, hr = super_resolve(test_img_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PSNR ([Peak Signal-to-Noise Ratio](https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio)) is a commonly used metric to measure the image recovery quality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PSNR of super resolved image: %f 29.192188\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 72x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PSgTztH1erKd9jPHMn8yIKoEQAuM4EkIwQgXDv3ncS0L2rrV9pwiZHwQ+q/r7w+m3VRCEw+FQON+u626oWPL38resJllTy6zVP1b2adqvpRt1PbsgV+qM4Ua55XtqSUuum//OB7/uU5bg1HWydMA5VzZT7u9SkjOOY6lD1Bnlu+x/PfZnOae5D/V7oo5TPe+Rqv/H6sYYS/+dczOpRNM0BE1SPo34NP5JvWdUf+hNMpTnMM97PmzLtV+bp3p8y3VeW9e6rHMO4oh6haiJGLtms9kR4sCu2TGOoxHBY+D1119ltzvnjTdeo+s6Li8v8d5zdXWFiMyI4aXK8QR3wlPhr/cjzC6O9M8oOuOSl8TDkjjJ5zFDjZdW33ME7iVFeA5omOlSn/6OIiWxlAqITs9EPHfQblbOCyGpjzJOMSYp0CQGSTWwd86IG4GwmONjUpm3S/zl8SCTpMY03FNfo46EaIRKHA3fhTgwjj1jPzB4j3MO32zK/sqfjCdV9dY1fqcImb8GfI6I/GgMAXwR8MuPFY4a2XVbYoxJrE45BPXFvPyGm5T4ferUZdfKrJWt62SioC67JDrqb+89mi7eZZ16rPWY1wiVuk/15Z1VHHWflgRN3Ze6rPceZbps1/r/LOd02T4w71NFaDjn0JjqJOxgfQHvjejNGz7P02xfVcRlHMNMRSQi7LbnjONYpCLet/TjgEv7qmkaU1Oh+KZBq3mr56Oey0KUpHHl9dWVPXGT+LP3jhrQMCL7PSEEttszrr2pwS7ffIPz8weMwSQ/++srNBonY+rLke12i4iw3W7ZbreEEPjAB97XyW+fNTwV/nq/wJ02LNk0jvtJT9aIlpoQWr73tj6t9Wt2Dxwp84mC2yRQ6+Bu1Fnea7ndLKkIIXCQgSiw2Wxo2w4nwuj6wsBd9wf6OBR3ZK3aUcmqqpu9OSYhW/473qw6tV/htyhAjPT9nhhHQmgN7wLt45Z207GLka7bIg4Ew9NN06AR+y2NuWmaxOQeX9t3xP1aLbX7r8VycfxdLLvn3z7aCee4vr7GOcd+vy+TVy/g8u+l6C3/vSyzVqcuu/w+Vnat/bU21urlOnG8+Z667LH+17/X7eaP6Uur9yR1xrLOXf1fUsPL/j+LOV3r/7Gya31aPssEXW1MXQxoq/4viTEjUhqapqHxnXFHURFNB7kSiQImuQKGMJY9u2z/2Nwem9NcZk3SBKDOkELfG0EzDAMhBEIYbIxRcenAe+9p27Ygg0xE2e++qN5OcDc8Lf56FvB0l+A7A/chApYqjtsgn8Pl2Ja/3Zf4WLazrPc8qJaW57yGWk2yRjQe638tkc8S3Iw/si1K4zuapqPzzY27ce3fyz7f1t/7wFrdiJa+ZlVSlnDHMamW+gN93zMMAxomjcUaQXebSOYds5FRS8V+W86Kuiy77bYYUAIzdcSSg8+DrC+m+lJbK7ussyx7W/u39WVtLLe1v7YUa1KQ2+Zq+b1Wp/7trjq6eLYmOXkWc7pst167qf8L5MTcGn/tYB6TIkEAXCEcYozFkDgTf2BEQNe0QCT0aUzMpUiLTt1455KgWuNE75qfIIpU8xnCgGeSNoU4FBugcRy57Dq8awnnWjg33zgeP35c1qVpbnJ+J7gbngZ/PaP3ve021i7Hp623lJSs4ZU1WJPQxhgnA9dc3wqbF2HdljLDjxrX+yHVb/GWi+0TtefXianp3SJzCUjuszvSv7V+O4fZ8iXnj5pR9eLKndn6hsE5vE2yGWlrIKrMpNrrRMLt+2YNZ9V1cp9UlejAJRwZCPikXBvHnkEc/XBI42oYtuaU4DQSdjskAN5BFFQjYRxtD92xp981Y98aRIT9fl8MOX3SmeVnSwo+/7sus/Z3/e9lmWXZ29o/VkeTLtAxLfBS4pCh1HU3L7I1jgUm/WMpC+XAk+vJOqVPImDtqi7y4GlDK8VAq3YRvG3ujn0/zZze1sZtBF0Wi0bVYh2/Ns9L4lY1Mo4BL6ZWIpjhcZZadG1TjHkBDocDIYxGQDkB75DIDc+NY1zMGnJa7onb6kYxwzcRQaMhguFgKrRhGBB1jGGgaze07QbfNkla9BCSinCzMV3ziKLB3LP7/bG8fyd4v8BbJYZq241jRM1dUNvC3PquxSV6jNFZwvLyVFW4WWy1/LsJS+lCOf8LHc3aPWANOBRjgMIwgrfznh0g2s0Ws7URNptdYtoG+qiMGm1tjaq4QQzetr5rROxdBM/Uf1MtOTVi66AHAiZB0gBNc8XQDkVwkZ1SpPHJ9sfG4Vz2cB1ys0fhuSBkjklkllKF5UIfkxrcJTG5b527yuZ5jRiRUW/YY5SrRG4QH2vvq4mjpbQk/7uoII60sXy27EstAzlW761Q6291ToEUN2X6O6ge1X/WSLCWxJS5qeZIdIpTpKrFIDiMim88zjV0ndL1HhFzbwwuc4YVQaZvbb7XpDc1IWv1kl2Bd4Wbre2j6v5nca0KeBc4HA7l4NeEZRnnwn7oBCfIUHPrSzjGta+d69V68cjva8zKgpDJz9eIn7uIrGWZ+9R5O1ATIvd5y9r9tNa/fM+ITJKdfP5re8p89vPHOYcPaszMsbV5yjl8mvGIiDF/FcGWJeLZHjLGSN/3iSG/aRNa99E5V6Tza/BcEDIixyUy+fldnO7a97F31d/1JfHU7Ut1INGZRXpt8JnFbhncMVaitHtzHMekN6WcxOpvVkVxE0LIfTT34SLluc+YV7t9/zq3lVVV5MZ6JOnT0bevSIakwQQqlerLmy1RIy65PW+JETatSWY2mw3jKDTeuIPoHCEGEPAiRKY1yMbAYYF07zvm2msqg0seB6Vs4yEqTgxZBRTtexzXDMPAptuaRGm7M+LGKTFa3J2MCOo4RX3f3zKDJ3gvw30vpttgyYyttW2Xaf5eJxYyaNQZI7EkRtakL1lygMSEApdqmrp/yjETz3dDGjObN5nG55wrd7mry6zgjplqTl25V1yadySiGgkBhmHAuabY5WUcJqJshw0CHPqemGz6YiIq6rnxCYe9FVjbc8v7JRMzqmYrM7oIYeT6+tpsZgZT/Q/DwGY3ok65vm7Y769o2w1t2xZbv67rbhDFNTwXhMy7KZG5ze7lLulB3mjWYS2qm1rFkjnoDGvvvG//68N/c17m0qD8rK67hBhj8kS4n33OMXgWEpkyJ4FJzm0VbiK8e7zfuBVNxnDZuwhCVNRJ8dAahqG04X2b9t8W1WuiwBjDbP6Me00eUbeM51j/MhdV/17mXQXF9ss4jvgUl3MyBjZduUhLiOYmPvYHVANt2/JkHDk7U8JwYNPt0G4KhniC9y+8XUlDkXxkPuoWIuUYA3Ks3bvw8dP08a731bAsU/fhnZTMrPVDSHPB/VTR6E0CrcaTzlEksiFkg/+IkAx/Ky9WEiEjUUFuekUtmeXlO58GprbSrZilzIKFwABgMliOMSZVvv3dbjYFV49jj4lzTE2e8fQxeC6yXxdON4nHgEJl3va99nmaOm+n/drAShSaxqzFvQiNczTO4dQ+TWOB3ZqmK3Xrb3FaPr6R8u2IdI3Di5ZvL0aZ1x/fCIIvfao9VhTMrqSSBuTvut+btp33nUnC0TRN6Ys4ncY6myePiMf79pmsWd3X4kqoc1fnMg6pPJUSElh6QtUHOITAEAdCGIo3kJP87tT/pkNVcK5BRGlah/M2FyqCiszmJ3/X67o25gzHxtr4Do22nuBQMeQ0M6aLY5Gy9H3PcOg5HK7RMDAcrhERDv01Q/pt7PeE4WQjc4LjsHbBru3b+9QFCgMBdn4zE3afy3LZXh1/q/4cwyczpmelb2+XqDoGa2M5Nu61vq6VrcsUglO14IG6Xr4/u2ZD22xom8bWgZtreNe830XELPu5tq4185fXz95hxMww9ljQvJFhOHC4vqbv++TVNBZvptAPHA6H94ZqqR5svqwy11x/w3RJLSUPt5Vdq/N22s/uuLXrs5fJbkbSv1UVHUaQ3O7iPRKP9knSe0SkvE9VZ5KA3I5GQQfLW1H3rV76eox2MU79rm0oluMexxGXvnHCGHpQt5gnfXZrNgKS9MDMVYzGkUzxWuoLPotjyzOde52pKlEjMR2kcRyJFTHS+Ja2TVzB9TVdZzpcJ45xDImzGFC9SRjVa7R0hy/rdsc+lWod6j4PYSwpGryMjCOIBA59wzAmuxgvXIvj4uyc6ydXKXJwc8Md+wQnWEK2+3KZIVAtBuPX19c4aXDiOIxDcffP565cfhIn1TiTnUiLq9SzWnBFdHNpclTFqfm3RJ1LLEOMCNAs46zE6Tw5kRI9exgGfD53FSHgK0nvmPDF/AKu+fpjEVPuAeqKql9EkmSV2ZjWiIDMkK1JZIrayYFKZCAQhwPd2BF0pI2N4S7x+GZL2yoxQutGeu2xuNPR5j0me0HmxEu9pkvJzbKf9VhuPK+wdsSY+4zbRgXfOsZxD3T0BxvzMJr0eHu9Zdxf0zQNm82Gwe8Lk6rx+JqcJDJvoX24yX1nrrweExJNgtGaNKVp3NH3ZKif1fNRB1dbUunHuHvv/aRfrcpmr5i63/l9a1zCcozr86SIKN7fPrd3rdmy//U482+Z2K2Dz+XDVxMCmTheIt5cZkiSjSGMpWw2luu6DqeVVMjN166OVVNLZOrvmni475hz3dpGrA5WOHGmwdwZh8ECYV1f0x/2XF1dAbDf75PaSYuB3QlOsAZr3PcwDCXKtRIQZ1LhGkcc4/JnkhMBFbPBqVUKML8ksyt2SARG3WbjfUqSOoFnLp3IZ6O2R1SdHAWW0ow1KYkRL2+DgGF9Lms4Jg2q5/O2Nixv0xQxvYSViFPMKu9NOt+2bWJiWnK+p7qfa8TUUhq0VvY+41yWzf2NSSVecFg0/DsMA/1w4NDv6YdDwWtZar400VjCcyGRyQHxsrFv5gbyplxeRsuDUH8fK7tWpy77NO3Xwc9mfUzjyWK+THQU7nrBfccYyZmK8yLVnLlnHtzumMjR6vhpHEFLG3lj130hRvPicaZLbaoYMHls9ThMMpIlSJlIWA9y91bntO5vCDkbtcUgqL1wskSmttqf3uNmBEC2pskESgihZLV2aW61CqLnvadRUyXl9iUltQwpeq7KTdF2LYGppUT33af1PNUW+8JExLTeDPu23abov80jafLCUlUGZ67aXdeVdWy6zZ065hN8ckK9B4HZWW+SaqL87tyqQXBuJ19Y+W9Yt3VM/5iVy88KkeRupjepn2dvwvy3q/BQjtSdz0SspPyfCLB+3l5mydjkeunprFxcmav6XTUO8V6KFNbiSDUVUyc4VcKi/rJf9xnf8t/H6hlDXxNE07NMiNkeiwWPNk1DDNN+2Wz8e4OQiTFyvpsS7cFkD1FHPq258Hpj19+5zrLsWp267LH2M9xWJ4RAlzaMY4pf4lJkw2J81doCzetO4e5RrdQmnnH2noYYglmxZ5Fo6lPTtDf6H4LSupw2YUrEue2MSMQJV4c93jl0zFFlhzJW0CTKjSm0f0iHIl3WRAvGFua5oNYydD/NmpU6mtoYzZPBl7E2xHGa03xQnXPEsDyYSW2UpTjOo1ELkTSOI0iSxKTAUs47nLM9OI5nhWC4Hs3rx2y644RQU//rLOjZU+hp9qn3c/dDEWFUi5nTOEc/DnRNy/XhyhJoRnCNJ0ZBdaDvlb63oFNj2HHo9yXRpkt5zE5wgiWUMxUjTYpBpBoIw1jUQI3PF0lEkGJzV3PqcVScvxlPJsTs4eTAOTSbD+BnF5uI4LOHi2oSjCTJZ1QaMVWxRiMSzndnXB/2DDFLYCEzMvnsWVcUj+KIJVy/tWz/X78c355UZjm/ISnaskxkSbzl3zIeqwkbs3Gpzq6WiqgzaWteh9AEQGi8R9oN4Ngkb0UZhHAYElF03G6oZtLWoCZKlwTqMYmSMidsxxjwAqIT8RujGffGmHIANiNjGAhhV4KALr1/a3gusNvzLpHJcENqIPNQ803jaCqXOB0DghRCwWwcvOl8RWZEwGr/nTDGgKvesybWXbMHcuTDneaYSU95cXFRQu0DRf2QN3Gt0jECZ0rQmCUyACEO5Mi5ueyapOpp1qx8uzzHk6qxSKrSHC+lH0vxaX14MsFQI4xaIpOlMk3jS52maRjCiMiIBoX77xEAACAASURBVFsPKrF0lrrlvszmaWXMSyndUhKz5I6LxCdGmkTgtCkAXtelfebcjfb6fl8IPYCu3ZxUSycoUF9A9W+QLiSt8ExUdAxmn+BvhsSojek1ClQEPkyRbEntLi++cikmScGSw5cohGicukNovKlMdrudqYRjLjedS3NNnqcpqcGYQEz1z1yNtTYf7zSs9XM5P/n5koiYSdJ1xGlyNHBGKHZdR0wyGDcYI1pL1dbeU8Ox3+8juckQrSFyRFfzvFRihQ8z7lLNKVmmQHht062qt2p4LmxkYozsdrsbEplapbD8O5dZfh8ru1anLntX+/Uln79hbs+RPWAKt89NydLa++r2a46ivE+M+64PV4zRItwmTmrZp7r9vEnyBXt5eUm/P+CUlK/HTR5Ui4+qFs5+bcyrc0rAeUDWEygeW4dl//P3JBpdz7R915rdVjYTcf04sO8PxS6gaRp2u3Muzs7Z7c7Zbrd0jeUyar2nqzyTmqYpnG3+vu+Y1/Z43fd6T+CE62T30vd9Eb3n+cn7LoSQLP4tj8nV9ZNTHJkTFKgvhHqfDcOB6+snxT6mTi5bXzRLfALM8EVm0OpLCgyH1bFBjGjSktcs/+acY7vdlvOU28m5xIZh4OWXXzYcGyISp3fms1uD9xZjpes6i4ab2hGRYsdYX8z59xufW+bzrot97fmSWFoSX2vEVM10AcRoEve8HmMMhDjN13a7Zbc5Y7PZsdls2LQdDjO+9jJniGsi6WkIlbW+rjHcqkrQyBADQ8y5l8xzqa4/jn3BYVdXl1w+eczjyzeJ8XhQz5NE5h4SmXox6rL50NZ1a8gbvPY8ykGMYOLCM2QOp0g23MJ2onKjq79r6VCpS/aesTJO3KzfS+6/jra4JGRm0idZSoDcjfmZnkHUEZikFLet2WxeKolM3Z+sdqulKmVdmJet+7RUMRaJjMwlYtnYN6vJYow0MdBIQ09vuqW0LlqNt6yRm7zGllK0ek8c23PH9kS2Y3Ii9GGkS7Y+jWtvjDm3kW0EupRZ/gQnWEK9B3NW+DqXWC4DQCIUcFNsprtsF5Yqh1gRJjWRApRnmSHM/cr2XrWk09TOAfFT9Orc10wMuaqfh8Nh3hcWBr+LMSyJE2v3+BzeNQepYPlb0Rn+nRErK+0vCYtctyZEa6Ym12vbTfn74BskKgPmQSYiNyLSr43t2JjWiKDlvxeNARNedOrwku0c48xbWWSyv8xeoqeAeG+xzlqZGuYLmS/u2mK/OjSAiCeo3lj0Y5ebxpvRfGd9qfq2JLCyJtQIp8Hc9uIkNnbO0Ti7+IPOEx3Wfcq/lYvwxpzF4s5nY73pxp3L3Gf+y+EsF+9cjbNch9vaqiUeNcKopR+Fk8nZrRPRmSVCIkLXbGia/YzQU3H0cU505Hmq+zgRX/P5Xe655djqdYVMnCgxeVINcaChYRgOiGxLf9e40r7fF0nnCd6bcOzSeCZtO03uuVKIjfzOUWNRDxlDJMWCpD6v9d8zRlDNxk0A4tT22ri8ZBWJzlSh42ihIjS5bEdMiuOcoz+Y/URWpdZ4pCbea4kPTCqvcgZLNGHQhfv31NnImmv02lgmY5bcppoaO/dhcT2t4fj8u0qyM6madKmJTBCpi3gUnwma4HAuS4xNNWNEjeCG3hxPVG/kkKv7sNa/tbth+XzW96qMJZCUyYstJbaMKBInO07DyTlQacD7SIyuuOWvwXNByIi8eykKbquzVma2wMnoc2bEJSApc0kIFSXuXDnEywss153EhbEcLkqYbsq3yOSu6Hy64MN8jqa4A47GbxnGAykkLU2Scrz0woscDgeuDnuGdABCyjQpYkZ9OQhRfl823KrnxExTNHE1E3KbraFnRvAsv2tCpcwZaS5lLiFaXvhlTqu1s7FPSCCXa2SSbMQhZ5QNeBRBiVEZgyHJtumQnUNjjgJ8KOLQYRhx3kJ8L9VD9f7J87Ds/7L82rzMkIOzvrXeE1VpnCfGEdd06bB3qX0jJPt+z35vBr/G0Z68lt7L8CyIl9sYQ5FJIuq9N6JBYAwVUZLPc4yF0Fi74GbEjaskDyLUx9vqpv0elTExbt65GUMwJpODEAJjMK+8nOblsz70mbi24bXXXivc/KgR7zziHH3qZzYaXp7B+pwtpdHLuTMmcX1uHUvSZaqXx5iZzzXcf4yRzpNl+K3ChVUMnVorMEigUSAGGid0rsV1PkmXoW/2qJraZhxHooaZEXRuc3nX5X8fsz1aW/ubhKARwZ5sHjGi2qZ5H4kxS9Qn1aPlkBsS0/mcq5beuxKZLCZcr5M5HBWZW96ulp2rKey345KgpWhxae60VFWYIV5EvAO1Dbnf74sIN8ZIFJPaLCUz9u/UL24SdHdJE6YL+2aZpURpejbNQZ7DEuRqBWXYGOeSmNxXMIIojydLYrw3w+vaSDmrAfOBdSKo8zTO4zYb9vt9cXHsNZSJqe1uajuXvBbHJDLHoG4HIFb9yzZXTQp+aJyaS2dnunhylu/bzsMJPnlgSTRDTsYXZoxHvXfry6lWW9RMyrH9JSKWEWlByC8lz2AShpxShKpO7tcwGAFT4iyJoxPP66+/bpy8TIEoM1FWq4lrRse55AF1C1H3Vuf3trr5rli7b+56p0hmjyembUlI1Grq5TyKtIzdgIjS910Z+zDGG9h0jZAr/b+FyDk25uov618heqb7ybk5YSlVmIvJ5ur4/DwXxr55cpx7vgPiOedo2xaYB8Srf6+Jixxe6ZiRbD3WDHZQ7VJqxLFpWhpxdN7+rinx/Gnb1lwnUx+W7edLu6QxcDae2gC0NvbN7ee+5ctwGQwu13GFcZAbhq7L8P253LF1WNapIeikPlkz4K0JIjOGBg1xNZN4Pca5nYojRrA4OYrzxs2cnZ2xabdmhOimdBD5AOW9m41/faq3hvDrMS/TGdR7r57T3H6ef1U1wlM1eVbdtH+KOhapYW2YeYL3NxwjKup/L/dYTTjUTEUEggpBzVZh2cbxdyYjf7NcQ6KWf+e/a+IoCrTeF2YFKhzk3ZQHCo9G4XA4sN/vCw6owxnkvtReocsx1+9Y4qXl8+U83kmsLHDMjKk5Ol/H1Tm3ERb1v/NcjtHifNl6ObxztE3DdrvlbLuj67Zs2m2xiVq2fYzJuo2AOXYfAyUo4lr/s+3T0taqEMyepIS6HX89FxKZJcVfG/0sv+GtpyioF+K+7S+p3jr4WQ6j75EbfQJwRAJytC9LA8zpwrFL1CcuA+bcxlJaFfoB71P8FhHGhXQBEpUOEOzfwzDgFDQZrkY/qTvqNSljzv2vDJDjkNrXrL+dxlr3OxvALg9N3X49xnEcEZfa0LnKaQgBzzE37ykg3tJzq778JV/2Mkk+agIoI/Ws4syExHa7ZQg9m03LYeiJV9bHMYaZ7VO9l2vCokZqy3lauvXXhJelmkjBwZRiC5Drts4zhJ7Wd8Dk3SWuKe/ouo4TfHLAMQ457+faCD3GiBezVXAIXiylgHPOUgqEgKVNhjFS8HM+10sGoT6TqnaJBs1n1ELWI36SkIgRH05BkZmH5ziOtG3L4XBgu9lYupB0ntt2i3PWp+FwSL9t6Ecz0g9jb9KYMElco9MitXXOWeoAmduemBo6oM6ktb5imDyKuJT7KX07aQrTOl+EdRlBzjsVkzFrjd/KPC6IK6IaIVgF9Yu1+l/NxknVXM/PNltEFIhGQIhHXMN2d0a33TDGwGazYd+3DDFwOByIUU1aj+HYwoCJ4ZyyrnKT+a7vmPrvgrfLfnSJqJEkmVJGHYkhEgi4wQHtjBHXIZotT7xdgv1JJZHJ7d6n7JJjyZ+1MP2QJALpQndIORwOS+qIRJo2STLclJRx+Y58aW7alNQxGeR23ZaII2K5OLK9iVNu9GnTtjigaxozoIPC6VgfHSKeIapR7c6zaTZ419L4DtQRRts8MYCTBhFfpAeZkvfeTzEn0kFq3TyFQ5ZUdb6xA6GWXypLCuqyM2mOOppk91FLNQzBBrwXIBa1fW1fdOy7iM6dlCBVddtWdgqjPcbJcr7bbNhsNlxcXNBtd2w2O5OSNQ0OS/CZk33mT040Ke6m0XF9WOt9lOfghkRGGksoWSXmzIhviMbVHIb9bLxxVIjT5XCCTw5Yk7jAnEnJn6XEMINzU7LBgi+buXR2TdqRiZummbwza2lifs9MShGnCL45/EFmLB49emRq3BwUbQxomDwK+xTEMzOj+T21ZKnuZ+3JpKpGJIjQpMzQGia8VM/V2twuQz7UcOzSPSZ1WVu3shY6n7Ol1OemNPpmKghbg5bGt5yfnxd39F23YZPCSuR2a1Vgrn9sjMu+1L+DMY3LsdX1ilmDJoYwTH3Pe6J22T8Gn1QSmdvK3iaRqetkbjhLTkZNXi7JVmFto9YB8rz4HB4WVC2yrmpJbhYjk1SCKf1AdqWtN3kjQpQc5rlZHXPZlEYCz+a6cFRI0UFTbeAy7iLhmZCNU7Pb8LXeViAMIyRpSiMpajCT5AGSPlykuNPNpFxV//Pv9TrkYH9jrNIM6M0UBUvkXUtdasRWP6ttZYqtiXdFTeS9MMTAdnfOMB5QDez3e5zCPkVoziLUpRcZOrd5Obbn6vhDs/pVnSG5wY8xgFrQxXHs8b6dGWzW9mZ3IYITvH+glgTWv2X7qixRKYSGN/wQk8G+KCUelvjpkvRdi1NnCRdh8uIjpS0RU1dvt1uursyWrJZwqrOElKHG9VjwNriZtLBpGh4/fszFxQWPHz82z6Q4SXE9nvPdhqurqyQtHTk/O6NPtpb5Us7jJkZETPKQz1LjLWJsiBHfpHtHTQLlxNRgTrKX4xTXxog8R5SbBvv1fSELfBrFLF1cvieKt9RNvFTacQ7EWUDC9JtjIg4Mr8wDf9au2Lmdhw8fIqI8uWrY7XY8ud4VZvPy+oqrq0vbA26eFiE7gDh8sW+px1uPdflvIZs6z8GeT8QMAQY1e82dTGl1SlTpO/b8c0HI1BKZ+iKq9Z75Ox+MpRir5nTXyq7VuU/7uXxdJxslW52uEAw24UYY4OwghLQYGizEvyg03RSZNqRgRt5Z0Dka4z7apiEMfXpfpGkbJOt806EEQzree1O5VOqUWq2SCZkY85gtrYFEq3MYh4lSThmzxeUDoDjXQlR8awe5S3l/mraxqJ+F0DC3Xw1jmY/MReXItCiIEyRGO5wwSzdQz2Vel6V0IuaYN2Nes0SMhLWYMVp933SFhnmdTFyYWHvOGXjvOD8/tz4dpMQ7CmGgv+7NhkccpH4WG6UkDKnVRpb2Ybh1n66dg0JwlbJmoB0FYhzwkqSaScy82WyKePwEnxyw5PKX3zXHLWKq4eTgbOciMzTe9l6S8eHxPHzwgMePH1f7Safo0gE23YaLswuGIamk6ngtyTavEcElQiOOtmdFhOthUh3lc1D2sPd41+I3DVdXVzSNJ4aBR2fn6Bggnau+75PaPxEIOeCdUzSlMcj521RCUcuIM69SU59ggUJFLKI3JHUPeOcJiehT1RKPZjnnNX65TSVSw1RuLulKfFD6zO1SQEwtlNY1xsioiqvVQ07Mg1MV8cLu7IzDwTMGLYFoQwjs91eYB6cw1qRDUpPFxd56S1DZeEaXvb0UghC5qd6v/33bu58L1ZJzFhDPOcch6TvzIODdDYi3FLHlsjc46Vjl38lcOXNJAMngjVAZqSJFfeScSTByu7U0Z+yHcqk1SdwmOtl+1JKlSUetJQuq9+aGWwzCxrHEFpF0YAsHQEo2mQzzQnJ5jGO2S7F+z3TtMqUoKGJdpdSpyy4P9izgXrXe+ff6UxM6S9uWvJemdZbF+i68mhbIZsnJZvuYLJHZ7Lbsdmfsdju22y3b7RlN09B12+I9FEJA4rQGYZzWJhOatR3A0+7TPF+19KhG/kXczWSrU6RtJ/ikgNvUGplIrwnbYyqC5bMcqO7RxQNCMDuLcbT0HZu2K/Fcrq6ujPCfXcamtsn4p96r+SLNZTMDUAe62+12xS5nu91yOBw4OztjGIaSh8d7XyRJ2QEhRov8SzB8RJxUTiJS7D9grupfSvtnUpUYofLufDtna6aGWbRzTGWzJGbq39bWtS7rfUvTWMTfs7Mzzs4uODsznOa9nzlG3DWuWj20fF/d1zXcutZ2biurl+p+O+dWJTsZnguJTIyf2KSRuf1JqnJ30si6zlJ6kOuOIdzoiySJTBhGpEmJFX2DBuNQxjHFPUhZjJ1vYRigkXI4Q0gXb4gm8kuHu035NHzXsklqlXEcLQx0H/De0R/G0pfcnvOetmnYJw7IKHUjomIc2aY1aNtpbvfXPdK0hCEZ+QFN0+ISMRWGPs2dR8dI27XJfielFKjEuRFMouDtwnXeM4YpMVjrzHDZVbYdmThsnCcm6VYIocRT8b4lajDJi2Q7k5v7KCPRLKlRAcEbR4cZD4bBgjDlVPKoI6oQg2Oz6fDec3ER6FrP4fCANx6/SbMxMfjV/pD6M3kl5b0T4oBvhBBGutaQ8TLBZG2MmWG5P8s+bXzZE6oDmrKeu+DKxeKbhm6lzRO8v2FNKpMviuVll5+HfKEyqQNqz58QAo9ff4MnCA8vLhhD4IMvfYpdPAJRh3IJmfHuZP9mhFC60GJkP1wXBiGrzY1bV7bbDdf7nk23YxgGLh9fFcPf63T2N5sNbz65omlcCpBn3nuWTiWgwSQqrfP4FBk4xeRkHAPRZW9GC1Ow2+0Y4sDV1RUhGFPS+oZRR7Pvc44YAs55nIdRp8CjkSzBnggk4s28afWcZwlSVuFkOyK78APoZOtTG1XXsocYFdeY+tvwi61eCIEBj2YX5mhGxcMYaTo4O7tARDg/N0n52XbHxcVDhhh4/PgxT64vzavMzUkHETHvIZ3bWs3GxZxZ1bSXloShqqY8V1nepxzGA5um5TBUMYkScZwaPrrfnwtCJktkPhEpCnK5muM/VmdpzCUihSuopQc1Z11vumzQW9odpjQAkFQCSR2V64axTxKTQOvzeyZiCZhxEd57CJHN2a4kSwtDmPU1jy1LYkSEECNNm7JjC4RxhJTZuknqCScTAgPQcUCZp0RoW7vYGfrCteX56ToLj+1ESqReU4vM1UVaSbc8QozJTqRSK2XMamq4SRoRNCZbogERnyj3uRQnr+8a4SvSzAic/CxL2PL85cBy2425LSKRGKb1fPPJJV235c3LJ/ZOnebBxNWTlMknwq3eC8f26XL/z76DTlzvQjo3xoCL9v6h709eS59EsFR1LC+YvNeXz2t8l/dq13VIJWHNbrCqFpTxyZMnABzGgd1uRwixROZ1nkKAZIYiS2yapjFpbz+wSc4Dm7Nznjx5wn6/NxuwMBZ1SkmfADx69IhXXnkFnKm7h75H0GIArCGybTu6rmPoe7ablrY945VXXzeCwSVbRGeZojMz8eTqmnEMNF3H2A+oCtt2S9faOR1ipK2SscYYaTpzZlA32bs5BUTxPqmFaimPk2IjKSKVAUlcPfs11IRRxh15LXNY1rzGNT4JIUwqerWy3hsBF8MD2taDEx4+fEiMMeWc602S5V2RZAeNuKSum2Bu31Tvp+U+XBtbJi5dMpEwAlFwMSDjUNJTiMjzL5FR/cQGxFt+r9WpCZv67+XvdVswif1jNOO3WmTqxDZxTJdabVC1tN2oiQ+YG3s6l5HORAxkcfG0iadLsB5D7q86JQyJE0vEgepkI+JEjMCSSaUEFMM3ESnJ2pbzEWMs0hUjZOx3n/TQpR/1/CXpSVaJ2O+2eUt84+q9y/WeIWYCQlPmtB57/ndNuNR7oRY71wa5tUjeJHOerjXd/dX+mgcPHjEMYRJ34whJ1900DSFOxtpLtVK9z5d7am1v189HBUlEk0RllBEffPF+gzkxfYL3F9Rc8ZqKYrmvls/z30s1RFFpp5AOkpiRWv0wDANDsq+LMU6JTDWrO+1yvr6+RkTY7c4LcVR7p1xfX9v+TJKPLCltmoZxiDf6NgyDhbfHXIXBiIV8RpoUv2mz2RBDYH91zdgMPLi4gCxZurouZ7tpGs7Pz9kPFu12GAY2XVdJVG2sDx8+tEizkuyKKhxd45N67jVJpso6idzwzLZL+vg1vcRvy3tielfGEXPPoIzX8r+99zS+oWkdZ7tzww8qnJ9fMgxDsoEaZ/jptr4d21fHys/3a64/Nw/Ie2McR5rNxsb/vEtkRN7ZFAX1ZC8NYYv0ZGVz1BeHyKS7XVvgup38yU9FhLPdzi7+wbLKZjvsZmMqgOvDvgQ280024hpRtLhO5/fm+WlbX9QOZqiVA7pZfpSoEYqUwZM3jYgZJI/JCtUkGgGXuIUyN2OyVldH25gUx4zbIm2y4B+GFPreCa7JuYhcyaXhRXDeGWGkauGph4ERRST3B0SnOZ/Pq/EaEYqo01TdSuPmmbVF5sRMDmoHk1tzTdhkaVF2wTTiLRBipB9gs9lwGAa27STmbduWZtOx2WyKmtE3G3z7cdpmw+FwjUPZ9wfiOIJM0TzzPilG2T7tTV0PSrXcr0ukMRFqDkmxPgCiRMax52o0OwainHItvU+hvuTXcGRNrNdn6y7CNuO7ECxQwTiOvPjoBTrfcHX5xDxbSGpblEcPHnF92GN5irK9inkJ+dbUS4fDARHzbDrbbBHv+MAHXiiXZ9/v075WXJPUymK5lWKMCX05XnvzDWIcEe+K+3XrG4gpvlJQPvWDL/H4zTchKo1YkNHD4cDZ+Xmat1DUX6+99gqvvfaaJWPdbhhC4FCp3XU0b5os1RzHkX0ai80x5ItYMOPVNoVmeHJ9hUbBeV+ILVWTa3mqO6YixCThIfv3ZP6wJmUW4/AQnRIDF7vNAP044mScqbgLwddtaZsN7WbLdnfBECO77Tn7/Z43HlMCDmZtlos2utn+I9s+zr0ibyNwZsS1Jo0BIExSduJgKn6Z3PlZIcozPBeEzCdSInMfqcqynaW0ZMkd1/VqxJIlLtkIzQ6OiRc1RruYK+PYmTQGE7vZFTWFpJ6XTQkPNRaVUYxDencWaZprtiGYeayV+nIVTKSHTlKWHEW4HrtTYMbhT2kYijgTO6R2CByEWNzLM9hhncPaOmoSLWuSYEnihuryS1uoev3rw39sbSfdtBpSkMkDQFVLynkRc792WVTrWzau5cn1gYcPH3J9fcULL7zAG2+8hmu8GT2mIWeD28xplD3hXNE7r+252pBube+V59m6USMSSfFtEhJILqYneG/CmrR4TdKyvDyehkOeXaLV+2qD3RACvmtNpeMF74XrJIUZhq5IMGs8FojElKusdS3bFI+p73vCOPLG5WP2+71JcFN9MCcBu/DVovmW/lmOnnyJuxywS6b7wnvPm2++yTAM7LZbI2aahv7qCZeXl8QYudidFTyXiYTQB1TDhEfSmEUFEWuzljQb8RFm85Zja0EKgZC8nMq6KCZ9kYwlqeZ8KpeTQjow92vmquaM5yZ1k0mhRaZAnxnXhPRfns9Y3MaFzW6HaxuaceTBg0u8dzx69IgQBohqhFgyZYg64mQuTip7rKZNVu7FY1DvUQvyh7nopz0zxoDLISmOtvKcEDLPQiJTH74125YlLDndNeJnyRXXm/XY4sz6nb5jjBzGQzLoEnAQYiCOEUqOiemwxlTHt4kaF3O3bhLVG2JEY6SvbEiiVmHqAeea5D5pwYT6YVIBxRBTOPCsZoLE/IAIh3FIqoo8ryNNifzbIKpTviNv6qYxBlQtXxPOE7ChaSorC/WGU4iujmYZ0iZOzxOhE6u1zTFrcEkEKdM6mLTHlQjDhixckdKUoFIuI++EHJwa9+MEcVNbwzDQtj37vWezUfZDj5b+Cs5ZLIa2bel2Z+z3l+bNtGs4O9vy6uuvlcRswzAU1Vm9Z3OsC1VzD60lM7VaKPep3pNl3opBXiJoRIpHWQiBDmXQwHa7Xd2vJ3j+4Zikefl8yYwdY7RynZp4mRiHOUGTuWDnHI8fP+ZwMDWRjgNDMMmyClztnyAySQ0s1pHQtmanYqp25fLy0uxuZFJH5Us3xKGSmE5n4+zcEkb2wwBY/KkYAq1PgewSw5cDWNb54/o3e8OtcaTrGrMlqS75YRjYbreMannosj2bimMII843+NbSw1ietQlPO4Qg2VtzwIkmwsoRh5H9mPBKmvuoVZ63Ro7ezBlXFUbLeSMgFHIlMyye57BCUviPiuAZQ48fLWTIYRxgcLN91CRvVnufcnX9mGG8ZrPZ8MYbr/HRH/rBxISZc0k40mlzb7+5T48JIJblYs4riBE0WQuRvUGPEe9lHEef3ANE5AeAx0AARlX96SLyAeBPAT8K+AHgC1X1tdvaeZYSmfx76t+qpKUWzdWXa80Fr0l48u91bJP8HkjUc7KNiTHO84bEmLiJ7JNvCCAbwdbEmqipTbKoUjSAGkVs46jGOobi6qhRLbszpk8UdSUlgYZYCKsmGbA6Nw+OB3N36SYZwuYIw6XfahKiqFoy2+fN5m0jmCRJTSzpdD5nk+RqWssyz5Y3YZI8yVr0TJ2v3cpGv3HhY9R+RiY1EVsj8Xotcn9qw25TwVn22M1mU/qw2+14cvWYBw8ecX194MGDyOXlZbFV6cPNVBO1yHht39ZjXO7FMo+xHrMhS0Fo26bMi3enXEvH4FnhsHcScvCzIgNd7O1a5bzcMzXOy7/V3yzaBMMPJA/IHIMoxsim68geeVntkD1mbkg+k6MBqmzbrlxITdPQ93uaJic0DWzbzogBmTwvu3bD9WFP55rCUJE+WcrhnKlTnHPJA9T61vpJHbPrNpydXZh04fLN5NyQnA9U6TpzVhj2Ay5JMxocMfU1Z/9uOsOlMDG2Ji2qcr1BwY2TRHdSede314276hYbmRBGRJJRsdbaAV3Hc1Vbee1q+8naA9KMkg1HnZ+fI06LvR/Ax1/+kRIEFkDccU1E7c20fL7cazcIbanKVXu1SJRCuFUk8yziyPwcVf1pqvrT09+/GfgWVf0cKrqC1gAAIABJREFU4FvS37dCoXDd06coWEKdcNCLlDD9+ULJBAROiu7WxPCUpIt1ssL8jsY5GufoUtnWTyHm678tBYAWx7KQwumLB982JXKiiCC+BdeQsiChIdI4z8Y1dhh7U0eZ3QpVn0yt5ElW9ylMfusbq9+0bFxH6ztEHeOQ3hBHvCgOpfWOtjUxb9u2ZX7EuxLNMxMx282Gs82W8+2u9I2YuA+JkES9Xiy+jcPmNEc9DiiuEUgpBVSVMeVfGTUW25eu6/AInU+JFNvGLmeJqd+RrrEUDTmAYG37ErB2o5BEqDBqQJ19xyO4Yk19kw98PsSZywshTNEvo6UzcA6apuPRwxc5P3vIiy+8xMXuEbvNmUUAZQrqlOP6NI2lHGh8h7iGpt2U50tY2//ld6WEko+xQrIhGJdYIi+fCJlb4G3jsHcUopao4UbQmN1I0CmStBehTYE3lwQ55Lp+So9SfVQVEuPlxVQ2YRggRkYdLQ9O68BVEmwB8U1Jm9KPyTizHzjfnbFxDWftxmJmpZQtTiNO7Qx7L3hSXC1VCJHWeTrf8Oj8EZ3v+OCLn0oclTBE4hAhGFO3aT1eI0aLCMMQSpytECwfnKaLcbvd0u8PjMPAg90ZG9/w4sVDWt/gxVRTbWcu1QBd05agbURNbtWWjwinjGNv58pPjM2QUgLks9k4qNOwZBwmTtNsRTRKcXfPkC/4USN4Z3nwBKTxpqITnX6rCJdMqJT7MM6NfDMeG0ez+yn7phAlseCwrt1yfvaQBxcv8PDBBzjbnnO23SVJ08IcI7l1W/iK6U5eMoWl/IqgIYNLirRsazMZJ1tww6jjpAVYgXdCtfRLgZ+d/v01wLcCv+m2CjVnUatuaq+R/A3rSR3z5NQJCL1MiRNDHfo96SljCp+f3XxDsL+Xwdvqhcnt94tkf1olaFTNScSsnSEGmpnLNkTcTBqT6+loMVfGIeAjMATEOdBYVCq2aSwstcOMcCeVRFPsLUIKQOXEDFk7vwEi4k1a04gzOw3MJdoldQyAa1u8pvZCRBrHOCTKPI5AtLgl3qQ/TkyXPQyjeTXU3lsaGEctgf9MjJPWwom1L+aKKSKMoUdxxDpI32z+U5qEaq2ym3qtmtE4l27M25rWthYXLyV/074zosXWLyaD7YCIESa73S55SuwQPGdn51xdX/LkScubj0EOA30YZ0Hsaqlg7kudlqF+/7FzUPYolQEwJCQljHEAXCFoTnAveGoc9k6CeJficaSAcYid1yS9aCtJzNITbw7JVq1EBk9nwVm4fRIOzoE8x2j+gpnBHIexXPJByfkDEbHwBF6BxtE64Y3DFU52NL5FYqTzTZJKmhQjJCPdxvlEdAiH6+v0m5kX7K+f8ODsnD6MNHheeuklXvmRl2lcy6A9qvBjf8zn8Morr/Cxj30MaZLTRkgB64BXXn6ZD7zwIhe7HYfrvrhah35ARfngp36Ql19+2XI5+Q4dA+fbHfuQEsFOM8eoyqZp6LqOJ9dX+EZQXJIKZW9KikRBREyljuKitRFFUCfGmOoUzDLjw1wv/9s5v4qTajxX1jHvlwUBW2xlgkmSx/6Atsluztn6O+eKqlxV2W7OOD8/48nVG7z22iu8/MrHefPxY4svk9XkaQyFiKrGvdbXYxqSGgyXmVRLE0Mq4ZhCa4K3K5FR4M+KyHeKyFek3z5NVX84/ftjwKetVRSRrxCRvy4if13jFLPjLokMMCtTtQfMkxWqpFDYUkU/TcJAhyVVrOvU71m6u0Usem9ur21bnAfXSOJOUihvJ9mMfYr7kiQb2ZK967r5WDW76bopgJxzNE3HZrMzO5euNaM3jFIPKdnjKII0nqbd0HZb639jc9G2nqZxiCi7bpMu3PMi1t3v9zbAaGkHsnGv95aNtnGeRiyo3eXVY/rxkDgel7ymUvLCRFApgU3TIhrNFijEFFrczaRoJr3RQriJMo92nFU/6fK13Ey2qfFmHDub22rt8kFZErtLqUveM0tCokYAdfyF7Hq9jIqaM8aKeLpui/eeR48esd2c8cKjD7DbnXNx/tDajpZUs3WeRhxtZ27SrbdPntNl/+5zDhxSvK8sXYH1Vd1N5HKCGTwTHMYq4fDWoT4r+ZP3ernwVOlyqP8Yb+DFug5MEoSZgWtFwNeS8ZkImKTCZp7eAJidN++9GfIeDngxj8rdbjdJezOj4HPuJSE4O0PSNXjXFoZyGAb2+31x3VbVEjG4zEGYXL7BEtNmyekwDBAiXdOy6zZcXl6y7w9cXV/z5MmTJKEYefnll4vtS33eVtejknSQ1Pg5mndNMNT4IT8r81gxxscJzpv7YFn/2N/1+uc1z+te/1aHlMgMVNu2tG3L+fmDFPX3nEePXuThwxe4OH9o9pEra7/s47OCWqqUVWnH4O1KZL5AVX9QRD4IfLOI/L1FR1Syj+3NTn418NUATdPofQPi1d8Zak566e1RB72zvA6TQWkYTAesYUICputTszFZULVA0cNmo9qYA9YRJwPVJLpTTdwMguS/q8R+MUYjYpzDi+lSx3FEYoDocE0q6x0xRQK2nEnWXiTZqagRQRqrhRcIwTiqpsmEh9l0ZGIpZ5I1QisdYsX6643T8MmtsA8jTo0oIrJq8GzSoUnEm/NSeZdjy7RzPb5Icf1u3DwGT8zi3WxgC7O1D3HAS4p4K1m6sciKXsXSWUpm6r2ylMTUl8XSRsX+PRlOe28IeyJOLaUDWJK2MfQl1PrV1VXZO9vtNuWRcZXH2SSBqqVEdQDC5Tmo+2uEoJZLQoWUe0Y525yMfY/AM8FhzrlnRsnc4FIzoZFxWIxsNxtTp5CiXVNdKqmcMEf9+WIw9cIUVVY1EFWmTPb2KxE1dZUqMUTLI4ZDRZEUY6rsV7F9H+PIru3w3tSk+ycWldfitIwlX5t58lp+tEMIOKec77YWoiIRGUZUCPv9lRH4UXnx4YtcX1/z8OELvP766/zD7/v+9H7Hp7z0Eq++9poZ6jsjqpzC5eUlV1dXKYJ6YoBax26zY98fqozzSaKiCjEg2VuUWHBJPuc2JwHJEutkf1dwhEWOg3DTRiSvhTDhJpfuiPrOMSlHJOsPjzFha8TObB8lNVdW1dREV8Ed3tQ6bQr+d3FxQdSRhw9eIMbI4XDg1Vdf5TD0SU2YgtmlnFaCWx1nPZY7ibby2BFT0L3i26rxVl7hbREyqvqD6fvjIvKngc8D/j8R+QxV/WER+Qzg43e1E+P9UxTU3/UE5c3l/ZSgcajKttm4KSrbrrND6YSrwx7nXAkX76QpkVezVrB+Tx0mXmMo3jhe2nIROZ9dgZNUwrkiyh2GocR2SaOHpB9UVbq2JQ42nsPY49rOEJjDDDtVcdnqTExEKaLWf+ctLHfb0o8H27Aa0agMpOSTvjMjUDX3O+/N4M4O+DROi9rrzYC4aWg35mmgYVLROWfqKRuFHY7GWwoBSURM2zQolkqg7/uU7yQi6thtNkktJfS9cUUh2cz4lHJhCGNRnahOaSWapkFHTUkqY/p9QtRZpQbM16zslYVbeUXQ1MRw5lYylziOfUKq8wzduWzXbXnwwBDeZ4wfous2nO0uOAw9XbflyZPHhSvebrdGYHYel+JFZOlPTdTlMO51eow8nnwOfE34qyJquva29TReik3SCebwrHDYs4J8+dSOAr5pLMZUxM7XaGrnpu1K+IWosYT6txQdyRBc4iyYZN7nWUWeA9cZfrF3Bo3omPIhSYM5UZs9yxCzfcU87pYQeeGFFy1Nx9UlD84veHL9BN90QOTJk8fmmRKSNFHBVF1J2hoDT54MdL5hu9ngHHRdY+63nkT0G6M7jiMf+fBn85GPfIR/9A9/gN1ux8c//jFef/11znY73hgsynDf92zbrmTklq4pl3LX2llyAk3botGyyZskszciinz5egShaSwWDnEyON5utimNicXhEpHJISJJd8bkSuyY7Fvso+aQcWQfFAZa51KwOeNyU3qX1yXXz9KXvu+n6MeqSBW2o+u2JVjhbrcrRtAhBHa7M7rWMpq/8trLXF6+iYjQOseQPMVy3K66f/fd62WfZ8khEwEeUjyyGG6XXr1l1ZKInIvIg/xv4F8Evhv4RuDLUrEvA/7Xu9py7v5JI5ecaBGFMl/g2sOobIgkDmx9Y5dIorLzxbK0wVly6pmgqtvVmCUxcyM7773pTJPapL7s8kWVL8xaPZIv6zwOiaFQ0S5RwhInlYxLtj65/+LmiRszZG6sDq8NxrHXc5z7U6vo8tjrOan2wexZvoDzmFUNAWYRpn0yByK0Wa0n8+SXS4lJ/hSbp+qir/dGrZ7Kf9e2A2uSmVyuFs2vSW/yPNb7s+5fzgHTdR0PHjzi4vwhL774Iv8/e2+2LFmSpWl9OuzBpjO5e3jkEJlJQQm0VEvRl0AitCC8Ay/Au3HBDbe8ANIXQCPVjdB0ZWVVZWRkpEeED2cysz3owMVS1a1mfjwis8imvcBVxP1MZtu2bdOtuta//vX/19c3rFfbMo/yhjMMQ3m/kRwouZMyQR3MfwhtrOfnyVDSinoYhpKxfxrL+FOuYX+i83ny3ziOKVmSex0fuL68YpO0UPL8nWeRdBeTWHNSVqozfTgN3IGyacsGV93XQToeDacb6fkmBPDw8ABIUjPPM8+ePSv3fZ6bfdNK2RpV1kcbl6BhnufiYJ2DDqWkXJ5LQlprvv76a169esXhcCiWB5nn0fd9SXi11qzXa3QKXPL+UqPiJ8lPXour91uQLDIintANL5YFfdclysJpSacu+9THeupvH7qmdVBSPzYfp/65XrOeOvb53/J1sNYWTSv5alOZcMV2c8F2c8HFxSXX19dst9snX+NDc/j873/sKEi84t8Z2fcl8D+mk7TAfx9j/J+UUv8L8D8opf474DfAf/tDB/qHIjL5cTVSolN3kUqLeA4OjALhpSrG6cgwHlBa45z40Kxth7UtmTA6z7J4WK0FepUaCI21eO/obIP3C1HUGFPg/DnVHhvbJE8LW7pqjuNUzn3ZlIQ4Je/VpCxEwzyjrU1ti0rUG9HsklPp4zCICiIdx6REOScb9vzepW1N44JDKU/TdCgjyI10IaQgILU9h+gKC99jOXpBDnabLQDzPJYIv3x+FZJjrWWaE2ISEtLjAzYZHMaYlDK9R7mAj05qzcYIuU6B0uIjpFRqkwwRTOb9NEt5zyd0wmVLhHNERp/Mo5PPKj69uDyV2eTgIi+uzrmifzGO4vGU57EERGJcd3FxIQTgdsVPfvSO3XbL3//932J1wzgPrNqlU8laS+yEMF0ECVX2s1oQmbp1sg5qyMGWzgVUQdysadHa8alr6cnxJ1vD/hQjb/bOOUKM7HY7QQ862WgaY5lG0fj47MUzfvXrv8H7mb5fM44jP3r5WXqOKH3f3t4SElrnnMO0huApTQ1kVBWNd55G29JO7ZzDBydy/4lcfHlxwev7e47DIGXTlJDFGMnts6WkHDx3d3es+pbJydzVk9z7nVY8v3lG3/d88923POz3GK1wSIfj7ES3yqX1Q9CYjsY20l1lGmY/8fDmlu12y+FwIIaA8567+/tyL8/zjHeOfWqhVkaStO12y+HxoSRt0pItSt55/ZjdJMlVlQyFAN4FNpstrbHc7x85JG+1bd8zzhNz8n5DKZz3qUtMxDwD0txQb+wlUAFyM4HWp9vyeUBVI2v1qJPA+tg1TyavLTlg3OwucC6QaUHWShAjfE1JqJumYb1eMwxH1uue43GfzDVPS92iwxWJFcr0oSDm/BzzNchfYx0Uls7LJw8l5/3hP33/iDH+LfCfPvH7N8B/88ccKyMyfyhHpu7WyJtOnTHnBX8JZqRLRxuDUZrJ+SRIt3SI9E1LCBHdqFwFktczwhzH+yJUZ3XifiClI63EDp7E/5jnWaT5tRZWfhSJ+GFe0I9SwuA0ypYOJ1Uy6OhFiyU4T9s0dLZh1XXSVugcs/f4eKpOO3qHzuiFtkmUKWuXzPR9mx6bJmIMhBnavhGWvJeNew4OBUxu5mZ1k7KrpXPGOVda2PNnUSNW2fHaYPBOFGaVUrhxEo0ZKBycOtqe5xmt7EnGYVAErU5KOSa/TuLKqEQwLGgW6mTe1OWl3LV0njmcI3D1YnA+J7P9QV4o8sIoKNZSzuq6juvra7SBvu9FXyYFM11aSJcALeITZysHbDUik1+v/nqCIC33YUGwpnlg06/+mFvy/xfjT7mG/alG7sbbXVwAIhNvWpkDXd8QrAQ0f/VXf4W2Mt/HcRRy7P7APIz0mwvW6zWHw0E28+jouuSynjZTozTeRwn202vqGFLy1XAYjmXeNdpiTKDtOp4/f87rt2/L/V9EHcNCcg2IMePshevigk/3RKRprRDejaYxol2irQWtGJxncjNumosulosBoxu8i8xhoDVSTttsNgwDHA6Pch5J26btpIvQEaUTKFFMrLX86OXnheSbg7W8cefSeIyRKZnuylqxlNqVboWLKLU1pmHANA3rrqfRhhFZr4OqSNNpKKUKZ0lWnzwyteD9BpZ6nCMs+XdPoSFZILBGc04ChoKMLyruNT9PjiNrWQYWlFJcXV0zu5HL3RV+Dhz8XpA0Kh5XjCi1lMPqY54j+efvIb8TVf9wehWevDbwkSj7xvjHCeLVm8t5yaD+cDJMKm6jwjSPuopqlSKmembwQnhjDsWiHeRpPixlnGz6aKqJYnJpJUSCiqmDJ0H/ybvoOI1MyWANlg8yADpGXIDsPp2JrrncEYjJ1DGWWifp+JObBcr1mR+RylcIkTiHsV5FTEIhMtpUuoO0kHiD80WDIBOAtRJ9mnEc03WlnJu1trTcxYSmhHB6YzXayIZfzUFr2/fg0ZqTwhMQaIw1OTF9/rnsE0/bTs/LQvlcngpUzudZfe4hyCKaH5NLQvUCHkIgePld20o9vu6AaNueGCMXF5fJeO5KvFqGobSv5mMVNEg/3WL9ofP0KfOLMXGJqD5DJUF0Lil+Gh/vyOiGTRnw3d2d8BawGCNeM1YLsbxt2yJ94KIg2ZnHNY4eZRWPj490XVfsMfJ8N0qnDTz5KU2UwNmNE82qkXJ1lK6gOUrCcff4gDKyPuf5lIPljAPmkedq27SooMumPgwDgw+QyuF3+z3aWnwMNJuNrBlNw3azwjnH4/EAZ4Ka3nseH+7QelkjhTw/LjyzaQKj6fuerm3p+76UpnJycTweefbsGff39zw+Pkg5Pbk9L8htLAGdMaIt/vz5c7579Q2/+PkveP3uLeNxYLfqS3mcirR7Ik7HE5v2E+jMeSxTr2lPlaDO10nOjln2mipgqf+dj/p3xpjUkr1iu91yHLZcXV2lADkwjseT5z71fs6/fwqN+b4RYzwp2z01PopARqk/zqIg//wU7yHfCM45QTOSpokyms42RR7bBY/zXmhsZUOCOXWkCGs6gBK5/OQLJucRT20NMp/Ep7b8skkTGfyMCgqXDNHyDVEWrcyHUWppeVQSFK3XK8n8M1TqZiY/E33ETjMPw8DoXSLMJVdmZYkKIR8T0Da1Wc6C3rTGYnTKnqK4XKsIPiv6RodpZVp0iUcUY+Tx7hGtNW1raU1L0HK8/D7nSNF90YlIu0pkRJ8QnuyyvVqtMGbL4SBuq9YYtqs1g5vF7TbE0nJRPnd1evOXawbF/+M8SITTTo38eQlSYk9u8uWYFqOzWZ4qNeQcvAgPoV8WnVSK1MbiQ6QtwVEs2UzTNLx8/pKLzZZpmtjtdrx9+5q/+dtfVfyo07mrdCS4MxJ7PHP5ruZgkDch55Tidu88ysA0y731aXx843xTUkpMFYdh4PHxkc1qTYxSLj7uD/zZz3/B7e2trJHWgDVMw7GYB7pZxNrc6OSemge2m1VCOXWyJfHgZWtwKbkyWrO9vCTGyOE4lgBIWwlwBu8Z9ge0ESL7PMWTJol87hnpdQHQlq5b0URpRtCJmA+afrtlPA70W81hGonWchgPSfpBc3v/CID3kaYzy3nMA8YqOislK6IIcwY306auyIeHB1AiGxG8iOW92x/QUdYv72d2mx03V1d889135C6dwzTL2qMVKlDu1WF2xRzXWss333zDdr1mvz8yTakchARTjsjok3gmERsrbkpG2avP+qmkq0Yx8s81wlMj1U89J0aN1iY1eqTeq6hLElxzNIOPxACiHC85f0aS673NGMOL5y9Zd326Lpd8883v+fsv/64k7LGeymk/ynvleSJZo8lhyY6X91LFOLpcvw+PjyKQ+WMRmfzzUxsRUD4oUTAMhVhaIMUgDseeRX/BpTbqEN5XgJXg5f3LmD+cLEgnH8jSxbOIRy2vk0si9aQEkc7LW5SLkjWN03SCNIUg6NDoHaN3TMEnlU9KvVurJAgFCSFJ3+NRWpQqZWpH2sYmXw9RLvY5Qo/Lc/Pbzu3Z3ielxRTdF6Xk6nrkklnmloQQllZDQjIsS4RqHVEq1bNDRZguVpnV580yF2BBZMQp1qRjvk/YPZ83511M53Mozz14n+ANlFpzKa+lOn7msohLbjhZZFarFTF6drsd0zxwOBy4vLws6Np5sBWjuGMHv8yb8wWgvjfy1/o1c0KQs/VP4+Mded5ZazHW8vLlS+7v71NpVkreWmm2uzX7wwP+UXhaOsrGbI2h65qyxmVxM+cmIdUGqbPYFMAnLFg6WfoOlza2tm3hOJb5kj2TlMk+ZqF4hylly30hJY1E7leaOamUPz4c0NYwOukgUtqijeLuYY8bJ2LfMUdBLmzb4GdHDBFjUnBkFM4tGlPGyqY6zqPw9mJM3UkNm82G0c1s45bDcBT0PHgJhoxBKSOlLK25vb1Fm6Uxo+7icsFjz9BP4cYF/CyBAP5AjJ5hGLjcimO0tqIkP6SuJ61UsTWA7y8bqZO/B+B9Ibz82OV4PLnGyRFkXT6Xl8hrSEneK+5fRp5irLs5F/2qrusITgjAgv6NfPPdK4ZhwLmpevU6sXy/bP8Uqnz+81MIzvdhMn8Ki4L/x6OOOP8hgnh5cc/Pr2H1ZZNaXIy9i3gvkt2YBfbEaHGc1qIHorXI4QcPaEtUBtN0Yh5oDVEblG1ONhIXHT4m5VulcGkDyvLfmuyKqumapvwORHAPLYJ3aMUUPTOi4VJKK8owhMAYY2ldzO+7NcmqoGkgtS+7GJi8lDuskhsthEBnGzZdy2a1pk+dDrZ0HyEOpH4W4rHWNFb+qfQ+50SQ86mFDxUI+KKfE7Uq/kJKRRHpU9Ju6aKT91Qs4MVPxKTabpM6ELrGyHOypT1G2sdZRAytTQiUyYTbqisr2ScYFYl+FpuDKKhZMaDMn2/Ukr1UN3j+TKXMJv5XWRciLwY+OAklVSTEhetSutqcxyoJErVt6bqO3faSq6srLraXdI28n8zlEv0NyRybpsE2GttoUKGobtY2HOeZWR2A5SBLUKZFaO/T+HhGDe9njtU8z9ze3i7rCkvDw1dffcV+v5fgAQNpXRSeghyzbVvm4DlOYwqqq80irS2mkc7NjLzEGNkfDhyTOJxzjuxflO8vpZRoSaXAyKUyav1e6tfS1jAjcvtdt4LEmRuniWGemFVkmMbCs8v3VNM0tLapNjRBxxUBqzSdtWxWa9qmYdgfaLRwhd69e8ftm7c454oQ33q9RtlktpsSjzm1Z5fEwYhsf052nXPFOmWaplKKn+cRHx39qkVrMEZxdSVil9M8LA0FmVwcTpOk886vPD4U4Jz//TwAqPed84Dm/LOoRx28hEpXJv8uP6YgJSz78Wq1YrfbcXlxzdXVDev1Nq1DIqL61Hk/FYydIzT1eT/1PnPJ7kPjo0Bk6ky4htHzZlB/hVPV1nrkjhK0JgRXkAClpfwRAaciU0x+OT4QdWTyjkZpop+JKhCjiBNZpYsyan6t3KaWPwDvPUGINOUcPLk0chqBiu28RnyKFi2H+vgZ8RgzYjEnzo2OhSCrlMKlspdJQlbESFRC3svzz3vRE5GNG3Sqs6+bnhhD4q+kts6k2qkT8lFKMFlsztgiyx8Cxa8j6whkXk+MEsy4IEJ1c8htjolYnf6WL4iK4hmj0XgfFpVfJZ0SuZwXKtIdJFJkXGr1s59Q8bTdNJf5aj4QFbdJBxHDfqq1/OTmyzd3Ou26zFS4BSkTHsfxtH0+LV673SXOTfzkJz/l4UHq8cfjkaZpOBweub2/AwKhupGLxUTuZvFTef286J4vXAt6Fwrp/anF4dP4+EZB/bTm/v5eGgSGA/26ZxgHLjZb5giPw4ixPVP04BVBWbybuNsf0EYzz9JirPWyQY+JwBpSUnAYjhgjnXerzZZ5HIghcry/k2B5lTp4rEly/xHlZR1RUXP97IbDOHAchzLP8rYnCugKFx2xURzHg3gY+VDck6MVLScFmBiITp7jUdioGA+j3LM2ElMQs2oMfduBh+P+gDoOfHZ1xePhyLSXAAwt5GeTzS2nI84FtusNNhnnDsPAcTqKW3Zr0Sp5tEXP7KVDCmOJToJBq6FrkrcanjkGgpeyvJ0kCBQ0ynF82GOULjzJjMwGBdFLR49CUam/yfVQC5ove0tFoYi5lLTME1UkD9PvMKkBXKFiKHtP1lAjBGmld8s+WyoXwZWELESfAjIlHbROukqN1qzaFbNt+fzlT1it3hXe0Tff/J53797w7u4tOgqHtKxbddk/pjJWNd9PrsEH1qhlbfvwvfOPFpE52bD0aR0uCxqBGDXqJgm6GSlHZLMsVbXjolLrrNJYtWTsXZK4tkoykQzXCxKQpPhTq2uMKSBQFq0WMqhOLXjW2qI4CUu75VwR2bIGS6NFhyZnJlZpyTzSRqwjmMBiGgilfJOvk3QGWBprMcqgokodCIbOthynseQ6+XMIcWkxzgQrk9oHSTdGTaauSzHnn1U5FyVcnVKuKrQXTVQaaxv5qSIfl9JIPC2Z5PeWUYnzOVKfU33963JRfq9yEqEgQx/a8HMwpNQiLFcfvw62c3YHSwnaf0C6AAAgAElEQVRqKU8ZFLmL6Ybr62suL6/Fn0kt5y3tj4udRT3ngMJJeMrctCx+NSJTPffT+LjHkjCNxeRvmmeU0pjGMowjKFVQ2jobNxpicKgYZDMLwl3w0yz8QGOFI8eCZAsRfCKvYSCK4ta0KMzZPRHKXNpsNgUhnOdZem/KTivNEAaFDksp11pL3whR2aLA+WKKK5y9iDVGEBSjBS2Poj0jyZ9Fyv+R1jSsNlvuH/fykmop/drmtHQcfUhyGYlSUBHoy3NSMCZeTKI3dp7UyDpjxS9JKVCa1LiY0DSDVlLCIgqaD4Kan5dKnkIgcjmoXsfO0ZLgwbsFNa7XsXK8Kj6Q4JUSZGb7kvN/dfdSFvSrUWmgrEt937PZbLi4uODm5oabmxuurq4QCwOxX3mqPbw+zz9mPFU6Ox8fxer2Q4hMzrrPEZk8TrJRfPHEyI81RjH7iFdakA2f1AODRNN4sZjXOt38Whw9NeIurY1ZuAzOoxpbNilZRMRPKBDRMZutyciiUgXh0KZAuT6EUnoq7zvByMF5GjTR+dJmXBasUEGHAM4L9yREorFCglOinhkj2FRystbQmJbOdoxuZg6K0Y2LDHnSoAkqYKxFB1G0LAJ7KhGPU0kJtZTvQswL1qKkm0tbGbmJUewYsnq39pKSzbnrxksw6ObMG1oQK48HEoqlUjfP2Zw4R2SIS2fS+4jMcmPIYzyK01LNObkuv4eMyGSoOmc2Sqmi9xJiLgEtPiYA19fPmOcRheEnP/4i+V91zPPImLgzIc2LfExTPp8F/s7n8RQik885n+NT98yn8fGNMleNyC0s8Lu0ww7OS/OAWTySJHvxRQoC51EqlA46rQ3r7ZrNZsPd3QNTWnueXV1zv38UwblW0NauaQv6OY5JJRhFwAEKrSSIbtuW3/72t4KuaPGzy/NLKcEF5H6klBvatmXyjjBLcN1YS2NbQgj84s/+DK01f/Orf4tpZH0MGnx0rE1LnBydbrjcXeJGx4sfP+fd6zeMOCaAEGgbi4mKy1ZI7fthzzSO4KUBYp49tmmKVUEAun7NcRoZBxGi3F7sOBwOqISs5jXAR6o1Q5AQZcR/L/vJxRAREF2XdS5dkBOVWkXqXsp0iCeSq/x/SfDzmoQpy9Z5WUYpVRKy7ImUH1eCthgL96dOxCbnRMfL+2qtqTS5FIRCMWhQYU1jDJtemnM2mw2Xl5cchiO3t7eM41E+94RUnb+/84TyPEg5/3t5n99373zP3/5fGz+EyDxl6liP/FxtTo0agbK5+phs5nMQkBAYq5auJKs1jTV0TUtjdBKyA6o6Igg0WVrpjBY1za4tUbj3Eeeyh0aouA+28EYK4qAXJdoQZYNUURaMUAkO5WugtcamhUAj0b8xAv9K5j/j8aWUY40hemhNS2O7shCJDo200B3nSSYqktU1SmNiLmloMJbJT2XyF3REN8WsUmMILpZSVF1eIWqUtkTStUn/QkKx0AYXUuaCCMrVn3Nuu4ZTJCZb3Oef83WFTCwWATCtLFKlqrlVkRgDMQZ8/kzwgtYphEuQ56dauE416TYvDu4sOzp5jFaYtiG6SKMbCUwxXO527NZbnl89Z7vdst1uMaYpQbtJCF7btgWdMcagjcyNRa/mdDGo533uuPqEyPzjG3UWmjPmYRhk48lq3EQUQSQekrxEaxv6tpM2bdsUkTfvpUszr6EPh6SSa5cNrA6Kc3ZeZ9a5SynGCEajjBYyMKfK3/nebZoGYmS73XLz4rl4H6XjvXz5EqM0L1++JIbAl19+Kb5vGXFOfCGNEiFNL7w9rTWHw4Fnz54xTY7BTRyGIw/7Pdvtlhg93k34aaY1uhjlXlxc8HDYM6Y1L0JJCAAuLy85Ho/EKN2GNcKb31cgNYp4X77KniLlcZ8RjLoqnb6v17A/tMx7jtqc/8tz4xyd+T704hxJ/76fnzrXGKPsd7ZLdiwXbNY7rq5uuLy8FhVlvaiqn7/nk0TzB871/Bp83/goVrfvE8Q7/wqn7Wd5SPfLUprIkH6WtF6CmECICyHURCGuSWKTulOCo226EyLpSfkgkcAwGpMCknrRKeeaasshiAurc2I9L1yTCC5ALo0hm45K3kZF4j+95+MkbY4KEbMyxjD4uRxfKZEvF57MoqdjEhpjtKaxAi1vNhvu94/MqRsgREfwsLJt6fCKRDBCaM43fIYdvVsE69pWHFPncQl0jD1VZEZnLhGghNMjCI8qPBdjDLN3hCjcHq0hziIGKJmnKqUtdfa1FsaDZTFVnFoUnM8nCWhPM5eTUuUTztEfKj3Vo0ZPzsnn2V9JHSPX19fEGHC4Ysj2+NjhvahuxoTerdeLFL1SCucnQoXKnXN86veYFTw/tV9//EM6CyGjhvXnudxLIh4nGp2eGDVaRdbdCp/It8YYSCKc+b6qkbygQCf15xDAeceqXeG9dEbKmrBsaJlrlrsih2EQbRqjS5A9p7ZvrUEFQWUk2VDc3d1x+3AvAn6Jc/Pq1SusNrz+Tub95B0tEWM0KgpPUCW0QKNojeXweKRtWzpb+dopoQ6M08Dtwz2NlnLWyjTEEEVEMwa+ffMaKY0potTjyW0WxhjavsPd3tK27ck9pJU6WTtiFO+fGGPymcvoSiTomMpImoASbk/Uxb5GKQ0x0WPSkuFTQlyn5vLY5WeFIavovb/WZEE9Qe1qpPm8nFUHJ3XAUgcy5XvFyTmU5pmg0DZp6uiW3XqDG0dCdFxfXjGPAw8PD3g/lrOvz0G+kX2PD/hM/UPGRxHIhPD9FgX113pTqomQdXnG+7l0j2QzwZKF42m0HKfRFuUdrWlQSR13dDNKG4ZphGmUEkoU0qonJql8jWlM2VyGYShIh9Y6eY6okvHXAZWPc3rXSR47TR5rpIyEMdJhY4x0b2vD7Gawwr0IShF8wBLxGkKI2MbiR0fXtqySvsSgJzFODZ4YA40CGwJ4x+vX3zI6gY6n6IlaY7VCW8XKtpgg5/fgInOYE5oqZphaiSqnm6UbYRzH4hmEVpikuaK1yJ5bK8Zvyhpi9l1RlUFj9CjT4LxD2yZljVa6jbQY5MUYUUhQp63FB3+COESXuwUyHCpXOFbquudmizm4qMOSGEWhtECqFd+ozljyXJy9R6XyUr0I5GwyI25aS0kzz02txbYgxsjNjcYYy/DFQNetcE4M9pRaWq4NinXXiwVGDtS1Rytbylrn90Z+z9l359P4uIfcDwviaas5alRyZ7aGoIIodQeB/1dty7h/ZPRH1r2sndM4n/AcQlQ87o/YTnSdurZhmBzKStlj4WTIPO+7jg5xjTZKEVMQ5ULkn/6Tf8pvfvMbIfFrxZy0SWKMdMYwjRONsSgVcJNDW0XTNszOyXqRyKONMUSlOB4GQVWVKajhuml5POxpVz1h9uxWGzTw+c1zbt++4823r5i9Z3aeYASZspseFwN+mLlY9TRKkJjf3d0yu1T2ih6tbVmTgoLVSjR23r59i2nsknSxIB51YBP1aSChoicQ8FoJaVhHiPJZKqXRKZgRbacUoKhFub1OgDOKLb+Xx9boSB3ELJy/UyQWFt7PUk5fApE88nFrheY6kdN6OWZBx7TGKgvkDkrLxcVV8pa7YBgGrq6uGIaBt7fvxCS4SsDy9SsoC36Jc+L751e/5x9Cbj6K0lJGZDJ0CacEy6cQGViInjX0WbgZeTP1izkYahH6aUxCP7S4ZTdNIzen0os2ila4IPwOH0PVjqiIYcmS8uaWTQNjjIUHU5eP8uSpjRvLREt8joxWFMQhzMtEqEhjWfdBGY0LqSsrisqwRsjBmqXsYJS4laoQ8G4+0UHx3os1QipfNMbK9dHvk77EciEWHk/dspezvoyU1MhYRpjy44zS0g6tJQOLCiY/la6mkHkw1eerVBI3PDs+cHLzFlhTnSIx9VzIi3e+QfPxy+v8QO0WZGGqM+YaTq0XoPq8cgbbNA2b9Y6+77m6uuFqe8WzmxdcXVzStn2ZP4un0yht3NXn5lK57yQwqyBnkC674/FUffPT+Pc/agSwLscU360qcIalI7OsN7OjtVKq7NuO68tLScTGkdm7xF0xsjkjmb8ogDuG5MtWz5c8h5xz7Ha7EvQX1DOtY3d3d7x48QKtdeGD5fPN55+R63zeQCUm2Zb1cZqmcl5Zg2maJtqmEX6Njzg3c/dwz+Fw4Ljf0xpDYxRt2wjy7E7v6UKQL2uEyFGo5rTUAbx3jZ8K+Ov1p/57o01RSK65dE8RcM9f8/zr+fdPlZDOSz/5vM7XGXjftPGpY5+XkJ56DHByzer1sQQ2pqVpOvp+zeXFNRcXV1xd3bDbXZ4ER/W1fP+cf3j8oygt/bGITP65RKVKDDWWRT2UGzFvdvXiYNGQND/EkNFKG7G1zJNDK8vsxnR8I7VPnfg5aWPM7HatjdgbhMBw3HPYB6xRRKvZrEVy2yoJ0LTS2L4t7rDy4WTScIuK0mXV6uxvIfVW5xyepabrlU0GZItoUtP0NI2V7gTbMCrNqGYcrsDA2oCKip1psE4Rd1scouVglWIcBtAWrxbTzSk4rDZ0jXBZQvCI1l/uwlEnN4Wo4E7FNNJayQJVgnxjkFKQimC0lJaCkrKeNjqRnS0xBozVeLdoCMSg0mc1y98qRCYjbyEGTCLcaUUhIS/zx6NSi/riyVVxs1IXRb3IEwKxIvbmYNh7TxNj+Vuea3IsVT57gwItsHtI70HmuGKz3mGM4fPPf0TTd8xOSquXuwu+fvU7QBRG+75HKVVQv3y+Tjm8dwWdOeEmsZjAfRof13gP9seT+dhKid5TTmhCkCRKOhblMU3fo0PaqE3HkAJe1bXgI2OVYBhrysaOtsQAwSS5Sa3o7Eo8nZTm6uqC/eM9++MB24iY4vXlFff39+ADd3d3/OxnP+N4PNKvV1L60RqlJRiJPmA6kZhwzqGNJI0xRlQDfp7QieCPVmANpfFYp7kbFf/8P/8lf/2r/4vbx8g4OYZ5ZJoHdv2a7arhOI5c7i74u29+zzRLmV81Bq0t8+jYNT2HUZBi52U92ux6bm/vIQZxrdGKyYuJrzK6JGJA6kallKsLUoaIeWoVSiKMUijvmWPyrMrJU1CgItmvCSV8wRiR75G9S8rgVXBz1nWkVOraSBIiMSbl9gohkqDNFBTnqa4hWZsW88i6gQQoCG4IIrtRu6AXtEaB1nk90SUpa5uJn3z+U7arDc45Ln//e169+pqvvv7ticyISkn3e4EW4aTU9BQC832hzEePyMAZ74Ql+y5BTHWcGl7PgUuN6lhrS83Sz660EAoSIJNomCZ8VCjTSHeTWtqJMxKQ/UuAkmGIcJIjBCfuoUp675UyQsyNukTy9TnVBOVcJimt5iq1hEdKZ0IdmS9cEblhbGrXVunxZaLqiO1aTKNpraY3RtQufcBEOa5VS/aWVY+jD4SE1qjoASHH5gVCxdNMpThTZ6g06VaE1FklreAKazS2kBW9+EKlTiSVGsJPPue4ONUqpfBu6UrLQUW+ljUyIsHD++jcMp9Ou5fyc/N1+1DGUs6LU47KU4hMmafqtBTadUKY26x3rNdrbm6ecXl5yc3Ncy4uLrDWMgwDQCF4TknteZ4XVK0O8GsYHASRORwOP3wTfhr//oY6Xedy4F3mv4EYfSG3N02DD3NZj3KAi9UM8ySIJjrxaaygyj7KmpY3kKRHr1GVF5OYMO73DwQ3i3mt84Q5sF1tISjp+nGOvu+roP00MMu2BUZp1v2qiH522tAlFKkkCiFgtU56V9ICLk7fhr/4T/4Cg8EmpOfhsMepSL9eY7WUdZ5dXtKiWOmG6XBcnKaNwilJrIITg+DgJiDQtla4OHpBHfI9BFLWKfdupfImel2yRjXa0GiDDh6TSvcmRCyUdS0GMe8V/mZVFkrfnqMjRfjvZBMPiFHvWacu1doo35Wf8/qfleVLyfvsNQsaFZIvXz7SGZKTBURNHXiVtU8SpbZti1je1dUVNzc3PH/+vCBwdcBU75vnc+ep8UNlJfhIEJkYv9+i4KkaYX5eLn+cwOpPQFc1REpcspHJCWfFqZhKNBp03sBP66OLeuxSagkhFENHqsBCnpdUVRW0rYJkKHBaMtEoLZt+RgLkXB0mbb59kpi3LhCaFocqBD6jhMzbdWusVsyT+OtYrVFK4FqMYfIOPQxywykl5RzvZSFBoZJ1fFSkqF8yAKsNuqoL54W2tI1LKnEyWfM1KeTfaFBKSwu1F55Lo5WI+SUX3pAWD2n3ToJ4IDcOksXEmMh0IQevKZhRuc0zHSNlEucNe3U5qQ5U8tcQArU3SCmh5dfzSyCdN5FaVyf/e0rbxtjMYan1KjJkL0aa262h79ZcX18zzzO73e7ktYASeGdodwnizMl5w6L8+8k08uMe9fqWk5M81/L888i8DiEwEcSxOn3mPgamceb+uMfYFh/VyT2bR07yMtqdh2TasuZ0XVfKWDmxXDXS9dOYlt+++pI3b95I5j5PkM65bVse7x9Yr9eMR0/0sqZdX10x7A8MwyB2CE2L6nqOc04uROfFWEvTtkQf8C7y+tvXPH/+jFXXY71DN5Z3371h1T2y6Syb9Yq7xyMvX7xgs1rxeDgypOTMz8m+RYFCCaLctng/ix5Y3lc47fgr9/nZtcroainphHTORAzCKQpKE/ZHQZeUJhAIui7xiAZMbX8jSNzT8yFWj8l74VMb+g9t8nXgUv98HszUr/HUvwWVOQ2e5G+yzvTdGoDd9pLhKEjXZrMphsP5fNVZIPd9o77u3zc+ikBGqR82jXz/SSGZSXGyMUG90aWLpVUqOQhBTukUtSMLgDGGwXl0Y1P7r4EE1WdzwVXXlEwpawzUPfLGKFQ0tJ2V2q+TNr5VL8aPYjN/4O7hltmN+IRkqJRtKO8gCnG37zu8m7l7+4b1asWz7QWNbTlOM7Nz3B/3pVzlnKPt1vz4xY8YhoGHxzvcPHO13RCTfcDkZu4fHnjnHJerFT+7fsY8Tezv71Ha0LRdQm208H9MarOzi6y9iqLu2LYdc/CEYRQScjXR8/VWVN0OkFzGpawVifSNxRrFqrG0thG9i8OIR9qylRIDs+xJlVUxQ8yhSfq/JnorioBfPVuWjV06C3JnW4ZgVXXeIYRSKivoXYzoCumIzkNWMq2QsYyiZVXPfExIiE1UJxYDIYjWh8C7hqvLG5xz/PynP+PxeODZzQv2RzHqHIaB169fAxJEdV1XXLazurB3pwFa5okZYz51LX2so2S374uv5e/z+kUKYgwLp8Ujgp3740GOo60QZ+eQ5AOkxJMP2RhV1tiiK6Syf5oClcqtYSZGmVs3lzdsN1u++OlP+fLLL7lcb/nuu28wrbRSZ82sxnaSkMyOxnZcXF1yOOz53Ze/YdWtaa3l8x/9lLd39/SrFY/jkTGhjPv9Hq0VX3z+Cz777DP+7u9/zV//6tf0fc/L62e8ev0tx8ORoOHtwx03Fzta59nv93z93Xf0qw1d1+Mnx9F72rbl3eM9q9UKNwysmpboJFiyNnI8jqy228Idk/t9ueYYhfaSjIlHVU5osh+TojOarrWEYWC3XrHZXfDbr77mOI0SPKU1Lzc2OKXwIZbkKsb8WacgikwGfp/XUkwT4+LIJ78XcUCZDxW3EgjpOE22wcloSDxNumBJzPJxqQwg6xKVBLym/C6Emgi8WMf8+OWP2PRrrq6ueNjf8vXXX/P69euCDNcK9X8oV+YfBUfmhxCZ/JjTm3wpK9TlpFxvXB6niraJNrJgFMQmiDw/UQtUiRK7gSAaI0UZMsbkpp3qp0oXdWBSd0hGU5z3suEqaZmOIZmcDQOTG4tAnFakThbQBJz3pRV5nAUa7LqOrl0JUpRKTkpr+ijQ7MFagve0tsPNc+LCGKJ30g0VfIINhY+htOi5jLOcY9e16VwVmDpTy/oRZ2UVZHOcfergUu/7rJyPJaNIN1wi4YJ4kkxhEgGsIK3dWoulpVHgzuBOQxRX3Q+8prTV6wKtekWxF6jhzLzhS5vpGTLDIoxXiOMh0EJa4ONJ+awu8QXnq8wmdyupErzUCE/m5ywCVFkbpuMq/e3m6pppmri/v2W9Xpfgp34/Zd7r03umRoY+ITIf76izUqs0MeT5KFucUooplbNVWNCBOSULwlIzaKVZdRK0amKGVUU+Qi2cseGwT8HKwl9DBaIWzsZhEC0Vk6Tlp2niMT7y26++4uHhjhCdCPYFR1RJZwrwk8e5UGQkvv7uG/q+Y7PZ4H3ER3j+8nNmL+Wpdbdjs9a8fvOKJt0bbhhx84xpGvZu4s3DHf5hjw6Am2kag4ow+sBxP9C2PXaWDqPHaYBe9o5hGumMRQWxEXDJRqXVLX3fwDRzGCYxr1UhdzZLwKjEbkXkUA3EUJIxreX6tm1Db8XbborC+3h4PIgNhJauHpUCwxB8lZjP0iBQl1ZUtY6V/c7jyzqWpDDOhsIURKck/VGn9v1w0o1WTIujdFe2wRGjTWuXT/+icHoqnpasiTHJZsieq6lJv/J3rTUxeLq2lYT92HGxvUQpxYtnn+FnEQn78vCb9DpLaTHzh6TdO5T38dT98X3jo+DI1FnkU4J4T30l6vJh1gFOhtPbtk0kVfnat11qg5Obbw6yEYdkAtimTp2GWglWOCEhuMr1NCE7bumWMcbgo8iGD3OK2q0RtdbpyOH4yMP+vmzYeYOzVrp5pH656NvcPx5wwXN9/Yz1ekMIMnG7fs12e8HN1TN220u2SSZ6vV4zTRMhOvEtaizHaVwM0CbpUlC2QVQDLbrtMLYFaxiDY05dWSE6WRzTgpoXgdpnKA9j5HbP8U6u4Ye4kE21lsVCa8kOZcMX7Z5xnpJzaijHLehO9XnjgyzOIYqRJJJpKJUNMpfPPYtx5/ZGF6VNfA6xQKA12ncOu8oCdGqcFqO0oXu8tKMjgWGuaedFIOtS5KAtLxBZFrwm4Aocb8v8skZBFPEw0Gz6FdvtBS9ffMbVxTUmIYQ6CqeoNZpGi75GZxt5/3oJ0mr5Aa0/CeJ9zKPOePP8zPO67qhRLojFgDGivG0Mj8eD/Kw1nRELlPWqQ4XI7EYIwt8D8LNju+4hOOF3pHJt27bEXPMwS0m4bVvhLKrAw+MdyggKaq00G4jadtZzUXQJwfXeE6K87suXP8L7wOxEoPLm5jk/+ckX/PyLL9AoLi4ukpGj8AvxgefPnxO14pvv3tBv1iWwury8xPvI7775FtP1RSDwOI5SXm4sPgRWqw5rFH3SI8vXVtbf5O1WlYzzYzKnEE75ltIFJbzINpWIY5TyUoyR/X7Pfr8vn19GZ1UUvl8IQfaNhNaqs435fA+rSzooVfypnhp1qSj/TEWmPf/7cuxlPayTvPo45/9yknh+rPx9Xme6bkXfr1mvt1xdXXF1dcXl5WVZs+vj12WrJ99//fMHr8JHgsjUWWRGBXKWmzNgeN+iQFAVER7KwzmHzcTOFO022hBDoE3ibh7JZLTW9H1byLdKKVSj6ZANfEgfmGS8AY34gWxWW3yQzdJFubmHcBDUIwlBibKbBBYxRtwsZZioND5AVHKjN9oQfCg8E+cnEVJSCmukXj07j20odeu8cOhUBpmnI/PoUEaCr8M4idpnNiFM/j9aaeYYGKJ0NXgF2hh81ppJ2jYE8TwZU5ummDoKBD07L/LhAaLyJXMj35yJ36JjglUj2GSh1hhN9CQhqSTDHoRYrFUULR+MCE6lmjLpc1IxYrTCJ2LiPM8ovQRW2UTyfE4ppVJ3mdglqDzPiMSoU7fEE+3XCZkBEjn8VBiwzD8o39cEcwmCF6nwmptwjszoahFQSrHuN+yPj/xHwfP69Xe0bc/t7S39seX+8e7kPRZ4OJtzRlfKSjZp+uQ5/Gl8ZKNkngqlQjFYVbNkrMamzTAGtI+0XQch0ljDNEkJ3jSSBVttOOwPdKseP4tdR2e7glo757jcbBjHgZubG6Zh5vWbN1xsL3Fao8zMOI4E4+lTyVM0Zwxv3v6eVddLKcF0zAGiCdL+HBxta4lxJM4Oa9K9FEXN2s2Rf/5f/dd8++23/Kv/41/z/LkQ2X/9618ld+mR/+Dnv8BHx8ObB/y3X9GseiG6TyN/9/vf8fzmGeNDYJwcTd/hp5nBzVw8uyY+7mnXK75785ZpGokhoNqGeXRc7HqaZk6WC2OyYACiSFDEsCiFl805RozWGC28O6OSdJhRrNZSNYjO4yLoGJhjYBontHZYFOM0YdtWUBdjiInfF4hCdA6B2TlGcuK0TIccS+oUjICUYbTKqueVcF4ymdQxitFvWbNO17I6OKkrGDXHr17HFkBh4eCd8mSW862lK/LPANvtVlrzW8PPvvgzNpsdu90l9/f3fPfmWw6Hx3JuISXMVuniYyfvYwE0/j+DyNQWBXCqHyN1O5s41eakgyPrGoBMxugdwc20WtFYzaqxdI1l3be0VrNdrbm+vOL64lKkvpse4ITou9vtePbsGc+fPWO73tHbnjYhPo1pS5dRazVdo2k1dEbRWlOClSxWFaPCuVBajFWMGKXoUxvj4/FACJSW9MfHR7777huxq799y36/5/HxnnE6MvuJcTxKFoZYMjgkiAlaF2Kbc453D+8Y56Fk61m/Rp64oBta66I8GWLy9VFLxK5SKcxPqTQ0J8+qKAx5YkAH+d5GEYxqrWzo3jl0FDaJRWG1ErNOLeUng4iC5cxGC+W3oBn113qO1HOKZIRXi8XluXWSBXkv6pz+1IpiOc4SCDxV8jwPaOpMqH5e/nuNzNSjLjm1VpSld7sdz25e8Nlnn7HZbAQdSg7sOfhp27YI3wm/S44zTdMnROYfy4hatKmUPWmpdcET/ExjFMHNUoaeRqwRO5G+a1KCMKc1p2U4HPnLv/hLWtuhlS0t+zmoffv6Df/lf/FLnl/f8Ozmhk639Kbj5WefSdu3TxISfsbPM+uuY9U06OCxVqfSUihrmXMe7xbJixgDm1T98A0AACAASURBVHWPIvDu/oG//+1XxFSWef36W373u99ireb29i1KKR4f7/niJz/FhRmtF5d3bQyPw5GvX3/L5vLyRLfmbn/Hw+Mj++ORN2/esF6viSGwW29ojS2aXtM0FW0rY087Rfu+X+7dhK4QZF0zgUQ7EAPO8XgQE19jaRtTBQei8NsqQ6MQpNRqCajCgu5olHQ8xcr24QcMFGOU7ifZphcDykIYrjt2U0CjT5CZ9wOaOuGqf3/+urWkxlPr2zmCUot/5s+oa1dsNlsudldcX1/z4sULLi8v5X2c81jPzyH1zubj13yip8ZHsbr9QxCZOuOuGeZulszeOScqggTZRBEdk1YbZh9oUllk0/UpQw94NxFjh3MSZIzjCCm4UhhsVdaYJkdnLKbR+DCjYyC2Fm8TxwRFaySqD0m0ajSeCXiY58LXye/fKoMxshl1q7WUhGaYdUTZyHg8st8/iK18CgYyZKyUBAJKaYIPRKVLC6JWShK/ZIfg8Rx9QGPRetHWyVoPtmmKLUOYJiIwBQ8qEr3DxShoTDp3gxKNmigcGuUdnTGYqGi1FaRFCb/FGIGeFaCjxhqNUoJ09EqL95LWTFGmcPDCmZFSYkiExwWBQVFaRzMik6HVem6diPNRMfljVhqu+CtVIJIlwWNcgpB8PKgFDU/Z/87FQn6rnwenrdolQI6LzUUOzpxz/Pjlj3nY79Ha8NOf/oyu67h/vFs4RRU/pybsgSAzbdt+QmQ+0nEedNeJG0HL/A8BYxWfP3/Bt69e8U/+wz/n9evXHI9Hgknz2Qf+4z//c+7v73n79i13D3f88pe/5ObZS97d3/Hbr76i8Fi8J3rHZrXmX/yL/5mf//QLjGnwe4fqVmijCN2abtXSWsO7N2/YrFa0WhKFi77n9eOeMM10nQiINkaMHwmShVxf7jgejxwOB9q25+27O/puzevv3nL17Ia3b18zhxk1KPq+5XB4JITAX/3Vv2Z2I8f5wMqsCV68pWLTMCj46ttXtNoQU2DStpYhOJwSwr4yGoPmuD8QmobWijigD6CtJujI4APTNEtnplaLcaNelLdddILGOM+qSzYH48hl09K14mMVomM6HnHDwIvNhtbKBj7HgLEtqmn45t1dUYnXCL8mRNH90VozupkTwRjO1gkWFCTEkwedBicEQpQmBa10QpoNeQ2MVZnpRA+majnP66EPDh9cSpSakmyfBhqnKM15Uljv38YYttMF+trSdT3H45HNZsPxuOfh4SG9jjl5b+cj4ivk8sPIzEcRyNSITL3QZ5n1DwninXcrAek5sgFKG7AV11ZtCFpcXAOJCBtFIM05l7pALMPtLVE3jNMo3U544bt4R0xtz2/evWZlxSixt4Y5BKBJN0HyVPLig+KDtE/vtpcM08S7xz29cjgfQMt56hjomrWo6jZC8I2N4jgOuOAZ70dCnIQRryKTmwvEl997UGAVYh8AYpKoDHOQzbo3RuJZo5ldwIYsardcu7ZtRWQP0Z1wUW58ZTRzkA4uF3zqUPDFE8oYwzQPgq4Yg0WxahvWbYdRGj+PaORGcW7iYrNFqUibPqPZwhQkEJoj4GMqjaVzi1lbIYj+g59R1uBcWAScjBFn8lRyizGCiUQi1siG3mSC7VkQ7OPiWI1aZMKXOi4nj6+D6Pw4VBS9qxhR6RhFCIuM6jVSflQmvZelzJRHfg1rxeiytZbri2ser64JwXFzfc3t3R3H45FpcmVxsulSZchYWmePbDcXJ3ofn8bHM07mD6doXUYZQ3TiMJx4cM+ur/nN4yPaatw806VgdUp8kevra968ecNvvvo9X3/zStCIhAD7KPfD/njAz45f/e2v+eV/9ku27ZrffPkl4zhgFczjxLifZE2MEdqGy4sLxnHkZz++4suvf8/gRfxNBSnTt9bgp5k+8fgeHvZM80zf29Jd6YPwwg6HQ+FLhBCKFMHsJg6Hg3C+tCFqhdcJ1UxK7E0rG2yWzYiAm2cR7AMaa9nsduwf7rBtgycyzxOmsYypXbwIcFabOslwOAaXjCpnohOU3ijN4fGe65srlFIcjhM2BrrW8JOXz5P2WeBxnqXD0WisUYwuK8pXQUQ665ojco6e5LEEEO//ngr1fQ91oUrmqn2iBgC890WU9Smk5qm5Wb/ue4H42Rq2oM4tbUoEL3ZXHG/2XF5eF6Ssfo7iDysjPTU+ikAmC+L9kGnkufhZPepsOSu45g+sbVspPKUACDg5rtZapODVDNoy+RGXwC1lMs9G470EPNEHvA1s+xXWtOAcmakuxxLOiyAAYmdwtbvg3f09vR04zEAQg0TTNGLm2La0pkVry2Yji8Y4T/J1HPFhTJG3bKwhhNR+t3hueAT69GlDP4yjBBtKFIgVSaMi6eesVqsTB1hlDX2yZrfG4PaPwvEJkYhimmeMWrptrNLQSL5nEnKhQqRpNFoptn0nHVRYNJ7j3qHmmVUjrYs+adzYIHLkU4j4WfRtMilPtHh0ub4hpBvOxffmhM03U2oLJC1UPkjkXz+2FsjL80FI25z8TuDd0/EeCe2sjpznY12SVFWHl5xD9Rh9is7lkmjf97RB+Aovnr/EOcfF7grvIvPsgWmpc2t18n7yZzROR8ZBjCc/jX9/o+YznW8S+fuMLJ5vcPM8c7Hb8e2rV7x8+VKI3Am5m6aJb7/9lmkYGIaBbdvy9ddfc/twEB0Ta6Qb0EhniTFC8lVG03U9//Jf/q9cby5xfuL27i1d39Nv1rzbP9D3PXjPPHv2j0dePn/B5bMXKGX467//W7QK+OASV8diaPGT5xdf/BmvOwm2j+ORYTgwe4cn0HYt1s0orRimgda2yRh1S4gzw3Dg2AiaGUJA2QajIsHPqES41Vrjg8H5iG2bBGwoCcS0xrYN3WrDw3HAh0BmXkwhEGNOdEAZ4a0oFMoo6cQKojC+6ld0xnKxXkn/UpiJboQI0+M9F9s1F7sNYT4S3cTj4YDtVxyPD7iomccpJc8L508pJV1SgaJGPodTUcHzwCXGiDLSRVs4ewktLglcWkfatpXXSM/Na9Gyzy06aHWJ6Km1qw566jkLYExT/bzw/ep5XTcdKCV8zesLmWfPb54xDSPOVU7u0tFy8pr5df+Q2OajCGRC+LBFQd3qeo7I1Dd8/mqMISSJfOcdpjHEALpt6FP55P6wZ5wnVtpyOIiisLaWqBTD7Jhj4oYYzTyLSu8UPDqKMFWYHLMLHI9HlFqCA3TVEhsDLiLy9sbw7bu38r5CYNM0xHWPNo0Q6FAoGtwMxkT29w94AsNwwPuZgGd2DtuIbkDw+VpE8VRJbdnWiLuqaSzTvIhbqahLW7nUMmXSHg5H6Y7QosNyTKJV8zwzjKMQyJRBJy0Sncw2e9sI16gTIps10FrRSoneESdHcCP3bqC3Bk2gbSxXzy9omgarA8HP2I2UmtxseDhOWAU6KgIaQ+TgpN4enMd0LUoZkUJ3qcMqJBPJtGm7NCdsKq2ZYAkx806clLb80rFQty3GoCRj1YrM+q9LlufztSbLZQ+cjOwJJ6E2m4SoTzsC5HOwaB1KZnjePZXrzpv1jmka+OzFj/jii3v6fo0ySa/Ie6QN0qCblskv3V/OyRxobPfv7N79NP7wUQfAT/2+bC5aWlRBtrRxHLl1np98/iPub+9YrdbcPj6UpOPdu1uur68Zppm3t7dgLKZthNiOdNHFmLoKY0zl4khIZq9NqyVxM5Hd80tZW42SNU9JgvTd3S1/+c/+Gf/qf/vf0Y3lar0mGs3t4z3zcabTLS9vPse7yO++eoXtROnVNKrwYZz3uCEhvM6hrZXGgRgEZdaatu25v79nvb1kfzzggoOo0VELomOl/Na2sgdMXnp6rFKY1qIay/1hDzFye/uIthalpMtLCUgrzQwojFGpCzCw264Z9o9ic4CXIGbTcXy8pbeGXnuuNhv2Dw988fkNVkPwA1Zp1ruOz59f882bt/TtiuPkmX3kME1oNME7CS5sg7UNTWs4TCLZYVXq5mRBOeT7Skok5GTKp+RsCRaW8rQmeJJi8SmHJVc66qCkLnVLOXwqAVGei8s/RYxnwVV6vjFNar927yFDSon0g01u2dfXN5i25c///JHd7pKvvv4df/d3vy5rmC6vsfgKZrT7h8ZHQfbNiExWkoRTxORDXzNcV389f4ybT9Uss6ljjGLEl0lw8ty0AUSHtllmPz9XJWlw8bcIQTRQxnlichPjLCJTHinJqNQpNDnH7B1N6QRwkBRzs1eFta1sdiUij0zTQOZeeD+fsMsLsqRVeR1jDMFHAhWE6Gcxi2waiInro3TRIskZnVIqabhobCNOtfkYmcBnrcUoTWcbjILeNrRa0Vsj4lBKYYNnbSy4gFEeFSaiH4luptWw7Tuud2tareispW8aVm1L1xjR8QmBVouGjA7S5q6CT8S5apKHUHR7wrzwp86zixwM5IAk82tOiLuY9+ZRveGcZwd5vp6Xg/LX/Jx6ETh/XD5fmZfq5Jzqkedr27Zstxf0fc/zZ59xcXHFbnuZkEvhSjnnGIahQMb59cZx5OHh4R8M2X4af5pxfv3Pg5nz3518bw3DMHB/f198t65urnnYPzInNd7VaoVOm7xzsnEGtcgQ1GWrTPyfvGMOnsHN3N7fEoi8evWK796+EYS47whExnmm7Tv+z3/zb+i6lse7O/b7vUhcmJ627bnY3aSAe+Lrr3/H4eGBv/mbf8t3333D/f0tmdMXYyxu8cWGIZnx6uRbNCedEVXdl5nc74Jn9oveWC5tgdxDs5e1fEoGlKVk4qUFOvqAjlKC74xFpzVHeUerIp2CTWNww4HD4y0qTOg4Mx72+PHIxbrj2eUFMcz0TYM1mr5pUFFsVkyMdNl+xXuskbKv4nR9OF9bPjRnPvSY+m/nyN55qagOUM4fN47jSVCktS4cvNN17H3Sb56n52vm+ch8nLbt6bs1m/VOzCUvb96TGajHH3KN8vgoEJkYPyyI9yFOwvmEyF91AhKz54bSwmsxSlqqtdeLvH8jegi2bZicw8cZZa0QVwt/JMtSh8SB0bhiD6CSANFCplOp5quVAqVRqZvmfv9YBPVyeSPm4CnKxqy0xoeASxuSD4LGKKVoWlOujyeirMHHiIkyUZwI4hCyKmM8vb5EqZFbY8sCoiI0ZjHXFI+psKAVSa4/t1gbLV1VNiAutFrRWQkEuq4h+JnoPNtWbt5Gi8N1py3bVU9vhQAdrAKEJyKraqBJ+hUKhZ1DKj15nBIrA5T8iyEUhVyfuDPhrI7r9GJZUJd0Mhm4BCSJy3ISfPC+PkKN0OSNwtpwou57blVApXF0Pk9h6VjKXVnGns557z3WGObccaEbjGnoup71asvl5WVS680t38vmVHt35SC2Ljt9Gh/PKBuCrtawWM3BEBmGgauLC+HqBc/dwz3ri13ZgJ69eM52u+V337wipq4WpaULUitQUe4RfbYmBFRpjJASbEoQgTmmYBhYr9e4EPju7WueXezwStqflRHNkIf7PeMom9/uYsP93RuUnvFhZHZKxD5T8D/OiVuXQiwX5B71IZm9hoixTdF6wYtBrFJiWeKzW0hCVo1S+BhKR1Pf92w2G4ZhWAxTXVbtFjuWxhh6a+X64OmUodeKdt0LmutndrsVPjiud5fEeWJrFZerjn7VQXT4caDpO7qmJQCP9w/EecJ7Q4gKi2fbN0wxEowhOgdpbfUh0FrNOAvnROQ91HtrBJDEUyt0Jc0N8vqEVByCFtTGeUejF0X2/Fk/9S+vN1n3Kq9hdYnpJJA4S8rOS6RPBTFGQ4wajKGxHZ31bLc7xnGUUuh2m5J0/weVkD40PopARqkPWxTU5LeTi6pC3ttSF0yyHgiqkESzu/U4T8zjVFCZGGWzjwq2V5cMw8B4PEhm4CfhogRRzGyNXKJWWVAyEee0sfV9V8y5JIqVbpq20RhtaDZdkZF/9+4tMYrIlI+KED2zl5sfH7jcbuj6juFw5HDY40XkABcy/yGibSvBSGqn9POiKxNQxORXJJ1L+UKlyZqytYASJ2otjrhCdgU/e3wUAb2ubaV+O1e1VDydbcHNbDpRtrzYrNisOtarjpb/m703iZUsS/O8fme6kw1vcPdwj8iMjJwqq+guAUIq9RaJDSAkdi2xAoTUG9jDji1bVki9QA0bhh0s2LWEWLFC6hU10V1VGRkZ4eHDG2y690wsvnOu3ffCPSKqKrsy1BlHenr+7NkzMzc79zvf8B9y8XHKcqH7wN2btwxtx3a9kk5YoQu64h11mqSToCOs2wanNKfRM6pEY1uUtpy8Zz9NKK0gi7tr3zqSgt1eJMaVXvigLDorOUrwyjljqElL2U+UfVCo6dVGwZTqYRlQlgHmq/TrOCe9NXF4PPN+HKCWjyPYHglGy9urtUAFc4p4lUHbBmsaQf8fdnzx5eezMNiSTdX3PcfjEWst43QsQnvfr+/CmvdPiWEVFyEotsI0KQWKsgptFbvjgdPuwHoYmEJg9+pLSVrIHP3En/2zf0a/GgoeBgKio0US5qKYviZsiWejj2jrIMP97T3OOS7WwjjKSUlnodCXV32HRvHmzRvudweUbek6ePX6Na7rMa3ifn+DYmLoGq6fbHn75ktWfYPPEeuKiqvKGCXxsO3FzX08ngrYVjGFIpvRWo5+ouqAVRsOnODljLPEHHFaDCGN0igr3cnpNPLp7jO6YmJ4OBxpGpHR6JqWRhlyjFw2jq6xJG94/uwJzmr8/Q2X2wvieKLrxfTy9Rcv2W4HNqs1qjCbfAi0H3yAUoppCgQ/0VlLe3lBRnN3OKKtJaA4Rs398UhWGp8SRoMyBuMcOR8Z40Kun1J/5kwqMAUDxOiFHarM3DmnqMqk0hhWnDEwcLYWUCzi4aJLs8TJVOHU+u/HXZav28c17pHUbLC57BRpDeKAoei6AdA8e/ohbdMzDAOv377i5cvPef36Sw6H01eSp296DXV9JxKZb9uRedff1e8z4rl0JOohkUoVn3NVmRWgG1kUV3fjkTBNRBE+kTfcnR07dQHQpuixxpKUJeYCOAtZMs6UxLm1tERDgpiDdExMIgU/f+D1gvY+kpWAklXpAAngWLA5KmZiErqeTwmlK6vmjNGgtGklQ5cxSUqSxCwxILVtZ+bOUUIjs+mUk3ikLNuUCWIIkgjkMzXYKTCuYeUsQ2sZNGwaQ2egQXRiVJIx3BQzl+teqqTWkXNEUyTTEaq4MxqjGpJJoGRc5hpDkyxo6SAp53DOccqKmBWe4ocUJYClEvQjX60gdGamU0fO7q9z5au1WBgYTc6J6nnzYF89SmKAuSMjtveGlM7VTEXiG3O+tOrjPdaSyVlGjHPnhnOivez+yHNLstRahzOG7faCYRjYrjecTif2+/t5TJrKOK0pCWnf99+Pln5L6+va43MRUtvqi5hdb9OlA1wCGKcCjpQkXGHbhl9/8TlYzcGP5THPvmOZhFEOoxUqaYw5y+DnnMkJVqsV0+nE0HUCtI+i5XLuDiYO4wmdxdDVWCuFYI6cpqPEPwQnJ4KkhpgTjbYQw7kLkMqhVwqPOmKqB23K0jmPsQhcWvOV628ME65c8zmLblVW0qkxVrozXdOSUyR4jzOWVS9ni1GCkclkLtcrnM4EPJ2BaX/Hs4sNzmqG1SWCffSkzYAm4yehk+8PO/q+x2lxfD7u3rIaBsHr+IjtGkKKtKbhNE2kMeCNQiEd5hSixPciFqqyJC9q4W/0rj2E1qhHHRGjDSE/jPPGnLsplB1wjmcPdh8VulD/thJj3jVGkm61aNq8a9QkMezr93wlMfRNS1ytiMlzfXklCvjjsXgxfXW8+m1i1zcmMkqp/x74D4CXOec/LLddA/8L8GPgL4B/mHN+q+SZ/1vg3wcOwH+Sc/5/vsVzvLcj817aqNJzNSNceREdSkp8fWKRpodM4cXSaouxllgPLODVjSilphQE/2EbdMxkFdFK45RgNrJuQFXbAhkJeaKARH04u9WWQxMtWhDKB1KZy85VeMpkDT6OOOswSCU/lCp6SicBddmMLy6yk88oY8smiRijZMRVRlKNE6ohEULMZCUMHFd0WHISHpbWhs52xBg5TnvB82hhGbWqkQonQx4jNsvmdp3BWcO6adh2Hc+GBp0iV4OlbTSd0+icZtyP1pBaS173ZcMXVlVMhf4s4LuuALtjjOzHiVXfYGMiF6uG1miwDZiWT9/esw8TCo22WtSCo3hliepl9f04J4xanTsjSqtizFY6LyqLgqSWBAwRqUHgdeUCstLutIiEu55lAIRirzLkiLDYsnhF1U5fjDLuWWq9LMF5QgcXHyeZ/y+YLaUjE1PClY5M24gmj1Y9OV1zmkZ+8qOfMAxrhmHNn/35/8vu/h5jG3KI0p6fPH3TspsmVqvVN12G/8quv4sY9r71eFQgjkbiI5aiHFC5hKmcxVvH5ERURdQyl5MaRVTS6ZVxiafRmskHApQRtugvqaTorVCandOkydOYhr/3h3+fP/7TPyeROSkhAYToufjgGb/69FOeP3vBXXvHX/3ylwQt1+WYM+o0iiSCVjgnirtd13Lwo4A8NRhl5m7k7c09SVtOIYBJxBAxTUPO4pOEkhGZJgmBwU+SfGgj1GGVycYKg1DVPpUc+mLlERla6UjGnKTb7DSxHngp0ytXxrOa8faObuixStFazXq9YetAx8Ann3xE37VMR8v1xZbd3R3OShckWRiut3PhaIwjR4/KEaKIel5uLwoJI7E7jexPe4xWjIdbFJkWhbLwweUVf/n2hikC1pJSpm8H0vEgwqW1Qzfvm3qIW2E6ZUgqo4virVZA8jJ2mkdEihwjWjczuzVrOa+U/EBOIsKqlOjFRB9IriH6MIOuQ4qYXOjuLBOjdJbdqK8zBfmMFmDimpjGVFzbtaglEwKNM/Rdg9FrrIZPPvkJXdcxdCuOhwO73U66TpIpo5jtn752fRuw7z8B/t1Ht/1XwD/NOf8e8E/LzwD/HvB75esfAf/dt3h8cs50XTfPOWGhHPkIdKRqJpjOv6trmV0CM0B2rogVTPHMMKkAMIN41lhjaJSR79piS9CoYKUlbqRmsLW6rkqbSpfTTRWlWyXoeKUU2poZgwLMWI+cM9M0sT8emUKB5+lz1yCmJAGvPi/nKq6+LvmdaLFkdcZI5JyJPszMmuzLGCxGkTvXhhTFKiClhNWaxhoaZ1kPKy62a/quYd05Vo1l27f0zrJddbTGsO5aGqPprMUVvEfjnOjJFO2aOoPVRVhrxnWEWCqrTGsN1mpR+LUWa0V23RmDURlrNY11D1qkVbX2cRu0Bp4zXuXhnPhdmKtlF2a5L5edmMf7arm/ljiZx/IAuXT/lp0Zrc/spfpZ1e+P8Tg18amPaYxhGAacbbm4uBAJ8PUF262wwioNs+/7ryqY/m6uf8K/5Bj2bdYDjx2V5AvxEavEDKWUAKaKmmtOSoxHdYkfZBFTQ2jZOSaJWWQIkUaLwqw1inXfkWPi+Qcfsl5tGU/SSV6v14Uw4Om6jr5v2axX3N6+xfsRYyW5P+7vQUmyoZTs1WboyUrwhjkzJ1k5Z3aHE/vjiE8COE8LMbbKTKnXy1LA0TQOlJHDOr3D+wwxvrUaTDaYbGb3d5WZHapt9TLSgMpcXV7w4vkzhr5lM/RsOkejoW8cKgZ0imJ7Mo10bYsfRy6321nGocbEnNIcL4ehp2mEUpyC4BhPp5NgHFPEarBaYYyQKlrnGLoebcrjhdKVeU/s+cp1qh7boaT5fVyuOcZoKaSXCsDvu/bnsRAPgci1Kzw/bkpfOYfl53OK8a44+/j11eeoyr/OOVarFRcXF1xeXrLZbITAUK+Dxev8pvWNHZmc8/+llPrxo5v/Q+DfLv/+H4D/E/gvy+3/Y5b/xf+tlLpUSn2Yc/71NzzHjAt4LIg3i3mlM+26MYJsV7kI8dQ3XYHVhikknHXkGItQj4BUfQG4KvXw8Xvb4KxsvNqS9SfBb7RdLwdKGVMYp8j+WA4gmV/rJCq2gpIHn7NUQU0DKaIURBIaTczV3Cyis+BVQLonx1HauRK0NCFIqzDFhDMSDEJOOKXwPtAWCwVTcBDOtoTo0VoAvCpH0YBJAUV5Lxtx4rZWTDLRgiUxSqNTJqVI23esLsSszWkDKdLazNA4OqtY9w2t0awbI6h8q4njKHbxZZPmnMvorQgwiUIftdWZUxDwXvRlcztcuQDbaAkhofUZ1EaWYKVJTCGijFwIIUZSihgtgXAJVltemLWSSEV/JqmKqZIRncyTpbMzO9Yu9ueS/m/tV5Om5Voymh4kOqZ2ZgQAtxwhLdux9UJO6swaW5pAdl3H/njg6uoKnyLX19f8+tcD3nve3LydX9MSt/O7DPb9u4hh71uPg3DdDyJ/f2YVLe+vlMghpPzwNngoeDh3Hst4Yj2subq64vbtjeCjOkVjLW/fvuUHP/gBv/zsV3RDz/1ux/X1NbvDnpubN3zxpWIYBn71q1+x3W5pmoY0TThj0VoKiimP5JQ5TSNjCoQxEYGmbYkhiN/cNKKDL10VVcbsiaxkFDxj2YoUgbGGWPa0jMetjLayQhthzxiV0UoTJ49pxRRy1h0rSrtT8NINTgJu1hn6rmO7WTG0DcQrlMr01uEUPLu6xN/e8tGLZ1xtVwQ/Qoo0dTSbRNtpBj+XkYjClASumrImrJPCLJYifN02nEIkK02IkRiCjK5TFnmMyZMjJBBpC62hPKbS3w4PMu+jd+yvx/d5nDDNMabcXpsHtdBdSpws/+78nHkeB9Zc5b2J0rtuKzEt50yTGtarbcHoJK6vnjJNop1WPZi+bQH2N8XIPF9c2J8Dz8u/fwD8cnG/T8ttXwkCSql/hFQ8D6rsWmkvq+B6LKQQ0SqTQiqSLYLGr205OLs1j94LPTk9ZHXknLFlFBFjpHMNWil649D5/CZjxZjRKItxljCNX6nOlZERgiqsF2qpHAAAIABJREFUIWX0PC9WFJfTUomHMrrIClEVhplNYkzmFIq6bK32g7wvIaby2otGSi7MnYUfkqh3ClYDdK3l5u5GTlqo2dXE0TUF8yKbtR8anDE01mLIPL++xijpkqUQaQysnaLRmu3Qc9E4nDU0WqE1KBKh6LnknLGuQSuLV34OulXoK2Yxx0vxPGoDmQI6o0BpknVMBFKOYMRzabAGg+IwJZJVRUjKYhCH6xwCWT/c/PX7AyFFIKXzoQDnzpW8FvWVx6gX5LIjUy/+qixcA2A9aJI2iwt+gZExaX7+x4FmuZavXWvR3ZDOjKJpOoxrhIq7WnFzc8Nud8cXX/yaEAL3+92DRKZ2db5fD9ZvNoZ9zRPVfVBjS855wWAsmD2DmMeWMdM8JtGKXICcvnQwtRXMmdKKXDu4OXN/e8fx9p6maRiaFo1is73Eupbd7oC1DTdv7zhNI7vjYWYbnvxI9B5tNTd3Nxilsa2Tnr5WTGEixoCylrvTkakkKmQxw805Y7QjKqE/p5TQ1gig2Eg8iNNUYmOJj1ri2GrYzMD202HEuNKNSoneNZBEJX3VtYQQ6JsWHwN919EoQ/Cj0KC1pesausZy+/aGj55e89GLD/izP/0Thn6FSoE+RXrX8LRvefGDXzAdDvTGMEXL5dUV+/s7ucYKxgetZwp3JW0opZjiNOvZVOHVGmtDivTOMh6P8lFGcdzGZjZti86KMSZ2IaBMYhgG9DgyBmGo1jXHjJwexog5NhUIg1okM1lwSEqluZuvtSYT5XHymeq81L+qZ2+NZ9WjrXbOlnEkG4VNARlXmQcx/F1JlUpfxdLMbEq94mKbcK6ha1fs9/cMw8DxMLI77Of/cxUw/Lr1twb75pyzqrODv97f/WPgHwMYrbNR4rOzbLFL6zA/6MzkHGmMZYqBhGgIVBBszhFrG6Ya9NPZxqAePNK6BRXlYtPF8blWvSkJjkRrUKqOuUpVXiv3lFFWyya2likEGiPz3axAFwDnFPy8oRKUv0mYQvu1qgSwcuH7Qp2th051n85ZOk3S1UjSQlUyKw6j/I2PEa2L1kvTolKkc1Y0WbRmOo34cgDnVKzmdfFUCl5ar+s1m2FFOOyI3rPabOk3HY2FJ708Zt86eiNjHk0ozs2wj+eOhdIGY85jHKsluQoFFB10wBPFD6r4hKQgnTWdZLyUjSHlItQVPJu+o00Z7SzH/aEAhkuyiSJq8UsRhs+Z0lg/+3qYpBAfWFwsK1wRlPoqyLeuOcHRaq6kZ2XdfG6JPx4rye9CQfAvQN+POzKpJlFQKCfze1q7KrYooQYf6PsV9/t9oZxuePIk8OWXX9KMTuwtwiQjOWMfBJPv18P1m4ph77lP6b6crwf5jCtOL8+yALl0K2vpJnHjq/IA833rQVbA8+vVGqcN/dDx8tVrcs7c3t/x85/9AePoIWv6vieR8cmTiTNLL/jA0HYoE6QA06Lc7UlMMUKJYT5lotJoAbBgskE7iZM1iVma+4bkS8EkRZRWQsxYrwaIIsQ2nQI5qeIEr+f3QWdobINxwhAlCdz0cKoJvuJiLYnQ8XTgcHfPIUd++OFH/OyTH7O7v2VoHFfrNZrAzz/6gO1mw82rL2mNoV2taZ2icY7D4YA1rnQoHK21hAIDOMMZzglCTOLPl3IdkSniOKGtvG9GQdu0ZAynIFo9jXWoviUejxKzQgIr55vJiTCbKi07vO+WcpDt+hDYu/DG/oYlI00eCefVx1/Gp2WcEqBv5HHa8DhO5lRxhnJekR92VpSSMy6j6ZoG0kBOiqurJ+LSfiGijOM4IrrMZyDx+9bfNJH5orZblVIfAi/L7b8CPl7c74fltq9dqrQK23dYFCyFeuT7QupbrqUHnjWxSFnH0sqUjo6apa0Bck7zvNnqorFgTbnPslrWKCNaBHmqlVQZJUSwTVWTTcQpllasLmqSihi8dIxqAhXjzM6ZD1OlUMYQlbwPPsmcezmXVFkTidJm1Wae4caYBGyloTFCrwRNPE0MfUtnLY2V7PcueEkolGHK0tFprQDpklJcbbZcXWzZrFa8Oh4IPnCxWrMeeobGcTlYTK54omI6pmREBBS6tsdoC1oD9uxWW0ctKc1VDZzBrcsLCCrAW4uqJ0CCvtHYVDSAarKpNFmVTl5Ocijos/hhpR7CY7PGgFrYO8hYycxl9eOOjNaPOjSPMDnL9uuS6viur3r/OuqZR17pzDo7JzYLheHl6EmpuRO0Xgt74vr6GucMq9WK3W5H6xrupxMhBPb7PVdXV990Gf6urd9oDPs2q8YxrbUUJmSJCfDgmiCfx0k6l07No8R6FnxsG5wyZD/R9h1GaXyMrNdrtpcXjMeJf/Ev/pLnz59zd3dH2zes+oE3N69xjWH0XsY1quhtWcFjpSKCF0d/xnzFhM/gmgZyxBQwOz6QSsFkGsd0PDF0/dylXO5bAXHCphu4vb0lek8uB7jVMkZXwNB2aANPL7f44wnnDK3reHPzFpMTXdtx3N1zsV6RYiCFyJOLLSon/v7v/4JP/+ovCacTf/Rv/luQMtNhz9OrS0iBJ1fXrIcVOUZIEdMIbuhwv5P3FkPWECf5fzfOzfHjVLA5o59mx+ycs+jguAZrLD4mVl3PyU/kFFE5suk7tt2G/XFkCp5DnMoBlmcPOJ/PuJb5c1bv7kbU7vHjUdH8Pi/i0HJv1XiyxKDO+mQL1uXy/suf3xXL3rXk9ury/TALWeL9tNZFkiSz2YiC+cXFBcfjUTrbIX7t89T1N01k/nfgPwb+m/L9f1vc/l8opf5n4B8At/lbzJZF9+LdFgUxRtwS4xBF2lpX3Y2Svcfy3RgDPuAWyU2OIrimjASRKuMOcPAjjbEc70daJ+Cx1jVFVbURn6MxziZXWmuMFvpc9KLgmwHtHFPwQjGe/Jyl+wzONfNmUUlmwyqLnLTQ9oQ2iVIymy0fdKNlSJXJonKLwmmNLqOp3mqU6+TQNo4QxDHWKIh7j8o9loZV3zKhiDkSQ6QhYyZwVnRwcgqYMNGkRKsUf/iL36dvOwheOmI6MbQCHIx+lAQqS9szl6CL7bGFKZBzJAT5XQiRqNI5GGqNtY6sNNYUlk9cBDsDDoXKEWcyOhtUThitMCoTjGJwhjFGptMJnMFZcc5OKQkLq15wwuGYOzH1orJKWGczGHru9mVhMJWfVcpgHibUOWdS8GIyvwD3Li0KlBLs1MO/M+Ss5PUh3bh6gNQqtrKoUnH6VnIhYPTCy0QpwX3NHUTQruNqc01rWn7wg4/Zbi958+YV8dcRdKbvW0KY/oaX+r+y6zcaw963VOnG1FUPEa0Ev+LKgeejJAOpjA3mQyhnTBZftaqAm5DOZO0Sn8KJzjXcHo7EEFivB9quJSSwXcvHn3zCZ599hiuy/qJ15ei7lnzMHFNC/JIThDiPIsfjqexhI91oa7CINIPOkEo3NGfxQsJo4uiFLJqC2IkoTWs02hrCFEWs7njg7ZcvaZuGzgkYPcZM8JluNaC1Zn93L4SB6OkbRyaybR1HpXn65Alv73f88Gc/JofI7u6WH1w/4WK94vnz56TTiX/jF3/A1cUll0NP27YEfyScdsJsDRPWGpS2EnuVJkRVZDPEkgXOXfyEYhwnQi26FGA0TgtGzzXNPFq0zsyq8L1qaZqMz3A4jRzu32K05XpouD0eEfuVgHWOtrGc/CSk6MW5L3Exk3Mdc5/9kpRSgmEsfzNbUmaBIzzG3tXHq7dFPz1IaOqKMc5xzJnzOHS2gsmpxNAk+zUl7AOA8OICyBoxl1po19SiEmibHmsiWllePP8BjevY7Q6s11tevvycX376l4Rvge/7NvTr/wkBxT1VSn0K/NfIxf+/KqX+M+AvgX9Y7v5/ILTFP0eoi//pN74Cvtk0MiTJ0kWNNs9JgTGmOEGr0l4txnnlTdMIYs4UnqxRFrKAwXw6I8d9ipATUyiiPtGzbjuMs+TT9ICR4r3HOD2Ps3ySD8HHAjhVxbOnjKysMsSQ0ErcjE3BzGily/0iTpsycs4k72UmCKKTktL8f3NWDN+s0iiSoO+VYCZyVkStMMkSJ482mkYrNquezjn2WnE4TVgtgOPWgk7SRUpZgZd/r9ue68srnlxd8eVnn6NVxhoRM0rBM6Yk/iQ1085ZfFO0xsdimVC6LkLzU2J6VnBJcG6cKiPQ6qwUKZwZYA5bRnDVIBPx68iKyUn1qVE01nHyQbpyWvxAUjxjrFSpfkMIsyO2SpDUQz2Ex8whNScXWpIZ/TAALCsSeNiRqXtqGSDOxpHnbt+UAg2NBMLl6EsikDxXefxl90YvXqtSirbVQMvFxRVt2/L06Qc4Jy3yzz77DFNA3r/Lgnh/FzHsffViHQdBEe0sn+0Ug/h6pXOSmudk9dyp0UrLl5b905gOZQ2H01HYTOVvphgIGXJK3O3uef7shWBvAG2g65vZwqLv2/q+yN6MkpRY49AGUsUeoorfWhn3RFBaRj7GGGF9qnK9TaIIPPuGhUDKQiIwShNRtM5ytdnyNgScghQ9zhqsAqsyw1oYQcfjEaszxMD+5o7f+8mPGacTdzdvieOBSUFDZNN13Lx5U4wdhbnlFGyGDc5Ynlxd8Rd//mes+oH1qudyu2E8noSwkSFmX5SFJZS1/YD2vgicHkg5E0IiJfE0y0okHLTS9WzGlaI3p0gMiRRLDHMGg+Y4ik2M1YZ1JwXnlDLtzT05JgJFq0uVDgrnkVH9jOo+osQiEC0u8mKslGerv3IeSqJQB56Pu8zLmFWT66Wv4ePk5nx/fSY4mDNhIS26QKgzhF2mJl9VTq9La0vOCmMynevYDGueFG0Z70devnxJPO3/9h2ZnPN/9J5f/TvvuG8G/vNveszHK6WvN4201hZ+vFTSRhtCSFL1V3paeevkEBtxVmNVaeEWrApe2qRd19M14iNyPB4LoE4YBDkK8v327k5asWWjai2jJKUsUwGa6pxxxjIW5UkfRTPGLlxGY4y0xbK+dQ2Vxjj6SRIspMvU6rOvSMV1+DihVYtosxjRWoiBdWNJ00jfysxy1RmePHnO6XTiy9dvOZ7E9Xq9WrFqOzbrgbURNtHxeOR0EjXNq6srSQic4/LimvV6w2azoWkaphC5vLyibRwxetCSSSsjyaRk4F7GcdZAUnTGME0njG0g+tlfKqdMikINzGjQMvZBSYdCq4RtnIgZlgrEKI0z4mvlnCMGwdNsnOanH7/g/jDxq5c3WBQ0Dc423MfD3CKV7t15pg08SijONOcYBSuw1EhYar6oHFHKPrjAl55GdazmfaFiArppz4n4Awq1I2d1xvEo0Vuw1s4YrOW47fFryeXn2U+r+Gw526M6w/XVM/pehAjv7m6Y/Imszi6zv4vr7yKG/XVXzICvncsqknZOgsWEVcbSoNBZ46zFGpFscKuB0xQ4TeNcDVeMXlSaz15+gUEx9D2hKGg/efKE0zSy3++JCsJBlJ+7xjCFMBeFIcWzUGPO2BKbYowoL6JpGE0yojklYzJY2RZrLW3nOBa16a7ruL/fE6aJVb8iTJ7r7QV5PPL0yQfsbl7z7Ok1Tiv2+1EKofuJD5495eLiguw9rbX0duCy7QhXV4yj53Daszbw9KMXnA5H1sOGjz76iI8//pic4ebmhvu7HT//2e8zHk+0XcOwbiBLN1wjGIyLYRDzX5057XbShZk8SlsxorRyZszWCLnEDmNFNBRdOvFCj69nSI4y7m6sTACGxhCTJmeJBR8+veDL17fcnSZBrGTpOOcsOMJ61EtJrBbdFukyoyzLZGQZL5bJT84ZnTKKjNZCNFjGlzplqMXf8rYl/i/nTNNUg95GCudyJlfNK0oC9SDpWLym+rrqaKkmRlVwNcYVemv54NlxNh29v7/ny1dfcDgc2I/+vdfTd0LZV2vN6XSi67p3dmRERG3xc02HYWYFVa+KeigRE7oRLZisIznEuaNjjaFpW46TGFTmcAZr1oNQJRE8aoulvGAuMpP3KCtGjVY7caJWCh/CjCCfP7TSFcgl4Ukp0TlbGAmimGnKmMFpIx2kLBL+ksi15FxBoHKfru9xeFabFXHckRMMjeP5k0tu7nYcdvfYYgy5Gjou1hsaZ9h2nciNdy2ff/45Xdtw0fd0XUdjW66untANPUO/IhXcUbcR75GcW3Ka8Hmi61fkFAQIGDIO8CGhjUh/t23P8bhHKTNXfCFI9TmPAmFOHKUKMdJ+LOqjWiuUCrNOTFIQU8YDjTZsu4HR35JTQBsR2opJ/Kr8JPsnxzS/n6KmacrnejaSXOKwgJkGer7dzHtC5QjqzKZ737x4CY5bdmSWnRkQthuOIlrmHnZkHq3ldUA9UEpQmNUy+56cO66urmgPUn1fXFxweycjqLZt4fMvf+PX7vfrr7eWyWmtqJcHT8XQpBiLuaHFlKQ3jHINxihu8Jv1VmLnND44xJxz5CjJ6/F45MnVNTlnbm5ucG0jTtBGEuzD6UjrOvGi4+GhqLUuXQk52FrXiKFsCISQMU7LNTeKRH+YJpwCR0vUhmOYSNHTOEOOMqbxp5F+s8bnSPaBi9XA/vYNQ9vx+z/7OS9fvcEfD+zv3rK7ueH5B085+YmnT6548+o1z5494+b1GxQt027HsxcfcjmsGbqeddfTty2HvRgQr7YbtLVc9lekFDmeTkSEfCCxWiAC0vU+F8LRhwcEgYr1oUATRDpHRrzWGmI0pDCd9VdSxTcJu9JpQ7aKPCYyiq5xXDUt0xi4O96QUyBlM08W8jv2xWO8Sv2clvvq0a/n2x988TBe1e7K46lDZS09jnfv6z0+fm3zfnx0n/qe1ser4W7uDtIyDAM+iWDfdrvlNB4k9u0O77mqviOJTM6Zrn23RUF9Q+LiwJHZcblP/bnSXWu7TalS4ctGSqLITMiRHBN+mkj+3EqrWWkFO9ULOaSI4NOLjL85fwixAEzrh/34YFOkUs0g4xjkEIrqnDipVP4vQVqXRmv8OEERNqobqnMORQkabUn8jKbpBvq+L8le5snVFeM4slmt0WSuLjdibpkhNILO//DJJdZarjZrrG1EXK1QGOWCEVfm5cbPWQCxokKaybqwoUL9v4NCE5P4AsnB7Uo3whGFTy7Bo+BTUg4P3jchjeeS4BgMMoM1hd3lnGPnRVlTk9gMA0cfycYwhoBB0duGxhrxoVJ1lJWJRMglYeI8npmrB5WoaprLoPGg0ikBZUkpr8GrUhnn7+YsjFeTmmUr12nzzrHW42RmPlCSJIv1GqmHC8QHtMnNsClKwD1N0/H8gw+ZxoB130b78vv1L3PJQLPEJy1SDSklGuMeUG+rYGSMicY6nlxecjjsIAlpIQDj8QT9QGtErkAOI9BGE0KkdaUImDzjeBSNLM3ZedpootKgLd6PMpfQ9RCVWCTXaybHSM5CYtAoGXmrPMsZGCOx06pIimINMI5CPxYfuQ3mwnE8nGaW1PMPPyKHiR+9eMHd7Q0pRD56/ozd/S06RT756Ie8vbsl+cDd3T2n/Y4ffPicHD1Pn1zNBWejlaAI/URnHW9fvuJ2v+fHP/s5z5494/bNWwGOhgkVA0YZdFHz1lpzHE+ghRUVx7Fc+5ngxRk8ZIgokhKDyq7rCEk6NBIipMsCMgJPSaxQ5jF10REzSliVMUrXZb1aM15GPn99Ry7JlUNU23M8nyOPkxldsHOZjFLnWPZgBMXD5GeJlSk9caHAPxotVZHCpZREjUXLc7KunM+JyfJ5l+txkcfivZHbK6Hj7BXVdWL5EP0oWllhlC7O67fvvba+E4mMUu+3KJjvowVUq8r9z6aRZRyT8vw7i5MPOyaMswJUbTJ3+x0hFSBTrIJHUvVcX19zOp2YTMSPEzF7ckgkIsZoSYCQTRh8adkV7EQuWJE6UkIJeK5vGpyRDH8WG4qebJRUNeOEMoacirQ+QkGfpgnrWlzTEsdE46y42CKH2f50xCrF0xfPub68lNfS9Gxcy8XW82QY0DkVgzkJn/v9nmgUzzZP2fSCqcE2KOPo+hWmHQhBKNxa22K4GYk5StsUg3ZCaU/KoBzkLG3Xw37Pql8LS0g5+tYSjCPGHbapegR+VlVW1WMqCYMI4Fi0C5QyQiPX526OQuFaqQVWVtQ7r9cr7nYnslacYgIfaQqAdoMiJk9nNaOfiNZyGzNaO7R2WG3wOZQxX6kZktDI09wzKrPwnOakTmtpteusyUF6SikFQphIKcz/FyjVDrncDjkXFecQUUrjo9AxY0zS4UsJbR7WO1WQa9mRWSbbNfDU0Vbft0y+4enVU4iiLP3m7SvGJ+PvtCDed2HVzzGVQ8UAWUkMSSlIEhEiyirICqccTS+mt4db8ffp16IZNIUdru84nEYRhFQibPbhhx/y6tUrjDHc39/Tti1Gaw7TiaQhGMRrKSKq41qhDCglXeIqQmaUxeoz7mJdsCU5eGxjymsNgsHQMqo5Ho9s+gE/njDThD952q4jRcM0Ro6nexrrcE1L8CO/+vwtbSlaX3zwAc5Y/uqLN1w9fcF6vWYAtnriH/zRH7G7PzDFxOk44sPE06tLLJnRR4xrUcZhXcuLH/6YEDOr+3uMdrz84hUqRxpnOI6JVb8mhEhSmSmBXQ1sVcf97VvevHmDjhlrWobVJW2Y+PL1l3SuE40cGxmjZwxZKOgotLb4aUTsACzjNMnYTRniJMVf59p5vNz3HTlHkjHspgNPtx3Xl2te3e7IRpOzoclZ9LNO9/RdQ2czp8kzAncJ0A3immek5NMiQQIZlYXVmyljZ0BG6JJ0yRkk6vR+ivLdjyXBnYjRk3Nz3qtJrFdUzgj0tMg4VLKH1mgcKuuCNlbz2QwlBi4TG6VmJ+8Z9F4LN50xMCuRVzbTjz7ac7m5Zt19yl98+n7M/Xcikcn5/aaRjzPMBcRyvu1Bq734KOnC+EFLe0/AWqXCljbO/LgAp9MJ7yO+yH9bpYlFgdJ7fxazezRKqK91+ZoNzD21ZbcmxliCgNCYlbHEKGMaYGYuVYyDKh0SMQwU8J1gKaRHdPKRN7c76Q74ROMUq8Ywng6suwa8eO1kNKrvZuBz21hiyDS9yI2Liq1ot+Qks1AFsvG0EVHB9BDwGnytLhTOyigsa1328IIJVJHq6kyFFq8og1Lij5RSoil2DaJ0ee5C1M815jC33K3rUNlzsV7BcUQnzam8p2jLddfQDI4G6cbc+YmclfhVaRGtAsHspHTWrKgdqGUF8xVBO84Ce4/3a61m6gw4xojmzHZavn+zKusDeviCcbWoxpaU8mUXp76GJX1yGAb2+z1t22Ot48n1Mw6Hg+Ccvl+/tbWsVB9X2XXENAvmRfEPMkZwcU3O7HY7slbsjwe0tUwpEAqofnt1yf39PW/fvi2JrePp06fc3t6SYqJpbdHkyPPhsjQnTYVMsKT419875wRbczhy8+Y1PiaCPwPzY46ij9LKCF5bcYe+vr7m9u6O9WZN8GIO6JzjdNxLkRQtGMPnX7xifxhF+VfBRy+esu0bjvdvefH0KdPuHqdkpB4nz9XmisZqnl5dYJqOP//nf4W20iEJIWBdK6y9mxtyzqyHDucsdrOByiz0nmQSXbfi/vY1u92O8eRZFxV30Gw2G25vbwgVi6YgxUw7dKRpKqBeSvFqmMI4X6c5RoyzRUk8khKEFOm0EYxK8Kz7NT5m+tZgNZzChLZDIXoErjYbnl9vuNx0nELi1e7AH3/5Wry3EChFLnsoKlCpCsedUb/n8U0dD53BucvpxvKrdpWXFO3Konvfvv5K10V9dTy+XEtK/vJ11ttmUVrg4uISgMPlQSAj71nfmUSmHnRL5oh51EpfYg1i8qUN+ki0zGhS8DP335qGmMPiol0ouVpDLoDNw+E0U7XrcwubJMxJj86gjCEUcG/O5fkW7bUUAsqdsRCJIsU9juKBo5k9PKoUfo6JphGK7Ha9oes6RPVXvDlOYSTGAohKieM44ZzjV1+8Ej8ibRg6w6q1bHuH2g441rQry2AdaEtXlChjmDBK4VrLZr3Bp0TSRkBsIWKsAHTb1pGjwirF6EeiFzqeMpaMJgbPdAqQhC2kjXgree/xJwlMutCEa4ZvixVDKM67AFY7Iv7MXEryWWllhSFRyomMFtqftZA0Q9OSnUO5hs9v7lE54azBJHixXXG1amnQ2Lbj9W5PvL3nXnl8VviSENXPRytJeGMU0OUDbZdFUpNzNY8Tdtv8mS9as19NdGsjl68EkUSeRbYkSY8PEpzlxf6+fyulaJpm7mQeDyPOOdbrNZvNhpQi2+2Wt3fvb8t+v/7uVt1PsRRG0uVjZh9pfcZtjKN8lv16TTv04nKNfP5t09O4zO54YBzlEPU+krNiPJ54st2SV5HGOQF9Ry+eRkiNFX0Z6yoReCiyY5RBzbyfry6vGYaBw25PzLAaevE5WgiMxqK4qxA18NY1ZCLPnj1DKcPt6UQME8GPXF5dcdjvcc1ACIExgvWJYdhg0gmN4te//jV/75OP+PGPfwQ+M/kgTu/bLa0znI576TDfH/jFz37Oy9evwba8+fIVl9dPGUfRzIpRkiwDHCeJYTlD0zoycNyfaJqW7eYaP74UHJ81HI8jx+M9MSZc4xiLenE/dJClyEVLR+Q8smEWM80kYgr4Sd5jYxtWTU82GT+KnplGyBLXl1fcHyamw8gUJ8jQW8WPXlzx4vKCVWtxdsXz08hnt/e8PkzkIp6qrAFViy85V0A6yXXVeFRZmMAs86AWSuh1TFRH48svq81ijPQw+RG8qsJwHonXx33Xel9CD+eRk7NnVt1mswEyPowzY/Nd6zuRyCwzx9oqr9lhxR8sOy8hBLIql9ziTdCogo1QM4r64MdZDnyMgZAiDgcKUooy7/TSyYGKX5ARD0Y+QEgzqHjuphRfjxhDcSKVRKe2WpXRhCT+bzGIXUJKCac0jXHyGE6SBqxskM1mg7WazWZF9InjcSSi6JR4Km02G3Kwn0J5AAAgAElEQVSOuNWacRwJMXF/f8d2vUIZS7/a0KrEunUMjWG7GnCNKWBSU/RUrGgXWAulGyQzX4U10mochvXsNFsTSO9jGR0VRH2ScZMyjTj2GlPM2iq25qGOgVF67ihlymFepfoR3JKqYDklbXcKLLhWHmJwKfobRkPbNKSUOE0NYZwwWphTH10MPN9scBiyMqiQeHXyGKU5RPjy/iiPlYCsSap8potuSUpCmc+lAlp+fxddsWIUHndkgtbotNCgUQ9BdsqYM9JfPbwevu56WXZyloDh7eaScToCcH19jdbCDFtfbH8j1+r362++lniF2h2hXCssDgeSdEymFHl9ezPLMNiu53g8knNm3O/EILRrmYIYOMYsAHelFG/fvAFgPByxxrBerzmOp/M1qc/jg1DGB1oVJWFAZTFs/fzLl3z55jUGhbOa426PyrDtV+xGTyJACOynkcvNlq5rCN6z3a45Ho9cb64Y+ufc3t7ivef25oa2bTnsD/R9T9M05Jy5uXnDi4uOLz79S9ZdQzgd2d++4dmTD+i6DmsyQy9jqRQCFsXQd9y8foWKCaMibb+C4NEENn0vRdV4BOfo+p5RVcwaWNtwd3PL1dUFbw8T6/UW5+QCtI0hnGB7ccFpFEbT0LQcjiPGnmX+U1VitwpTSIEpSRLjrJ3joTFSDDa2QTnR+4rjyDA0PGsG4APyr77gZgrEIJ2hn15d0FtNb4Sx2/cr/uCHH/Onn/2aV/sjpmuFNl9GOjLWkdeQ57gKWj/E5kkH3MwYu2VSssTE1CRmiZUR7788WyOYBZbwcRe5rvrcy5i27AYti0XKz9YYtGqw2hQWrZAVKqP5Xes7kch8XUemtlxrJ6aqT8YYC5VXALNGg1BR9QyojQUxHmMV4hETNB8FkCqodTO35SuXfwbzzhU2oM8MlKwC2oqkfVMoxsaYmf1UD+SYIqeTIMBj8DSdo287ukZk5n2MTJzwPqGULbNNGfMoq+fsuCZXxMB66DjtD1hnOExHmt6xaTQfP3/Gpm8ZOsPTyw2NdbTWoM2CRYW0F0EO3Bh2ZNOQUQQNxjgxkixusymf55zyGEYOdyUXsGt6jE5olTmcdngfiEmEsZRWAvBF3rtl61oy/EjM0oWY7wNYY2c8R0pnBpGzQvkzFlKMtFYew+lEY8BZhUXoqq22WF3aujrTt47rdY+/S+yOB/I0kWyDtqYcIuc9B5oY0pwYPLCsz5HqeVXxuDWBSSE/uPDJSnDFMNsZ1As3FtZEvS2z+HcNCLVr+J7Kpr7eylLx3uOs5XQ60TYyRuzbDm1FtbRSxb9fv51VE+SaxKCFUl1/V5NZU5KJ5Wj91ZvX9H2PDwHXNMIq7MRrSw4ePSe0iUzbNIRpnPduzlno90lA+jlnOezy2Uesxl6VBQxqjeU4jXRdNyfliczgWjYrMShdDwNjGPGluEwpcHd7y5MnVzOBYRxH9qeR0+nEuu/YbDYcDgd6ZzE5QBjpWktvGzoDP/7JJ/zip5/A8Y7L7QW7uxv61QWr4WLW00op4wqLtGssefSQSwGaIimKMOU0TQyDOL8fj0eMVuX2wMWmYbtac/f2hhACq2FNTJO4fzvNYR8w8UxRDiHQth2h8LCN1VhjSTmQUi4sVoMymkY36GLyK59jxCiDcw05TyiQQip4FHB9seL2bs2bl69wWtEaUWofGgdZl4J5ZG0tK+e4YyTFJIUklG7au9e7mE5zHCrJSj1n69fSQ27ZYX5MXHjcXXnMonrXa3lXR+bx9+W14op0iSQ4709XvhOJzDd1ZOrBFgrFeclDFzxKQXNnwZ+onEkqnf826wcKwKZIwaPFP2QKQdgEGrJK+HQWEtIFl5PjwnhQCSNKKwQJnzPZx7kaikGsB1IWIJYmoawt6kCl+2MszmgMgUYr+l5as43W6DAJGMt7rM40XUNnNdebjr7tiDYJwn2jUTmxWa/4+MMnrNqGvnMMjaO1ThIqU5SBbQNoUvYoI0DQED3JR2zTonWxhy9aBrWVKIepBtvO+COjIKuI1mn2axKJPiTR0Wf/IuDMLFOVoQM56zJLPftZLccuo59IkTkJTUnsG5xz+OBJIdGuHJu2Ia0g+YJTmkacsbTWsQ8eHzP+dOQywTFGRqPYGY0xGo+0Y3Np71Ne59LyYlnN1O81wVh2ZKqc9lfYS+FM9/668dB8HZT9vExiHvw+5wcBob5GV66Z1WolSY1r6fuebugJIT5IyL5fv501B+pYQZlFM8poNIJfaxqHUcKmiSSUzkSl2E0y+h6n2lUxJETSH0Seoq6QPN3Qk3woRYUmJtEqiWVMMnQ9pHJoGYlnFJFQZxtOpyOts0xTsRlxukhYBE6HIx9++CFv3rwBH+kbh+laLi423N3c0hnHxbDialjxy19/wcXFBf/a7/2UNy8/J8fADz9+weHtazrn+OgXH+Onk1TdBlSOHF59xkcfPGPdWDb9EzKa27c3XFw+ISfFar0l+JHd/oDWlq4bitv0Cai4wkDXNfO1vFqteXt3g7Etq7ajtYaTP5GixyiwRnNze0uMkYtL0dMaD0ds62j64m8WI03TERvHOIpgW/Wt895LKEuZlCURzCi0MjgnhpfjKRODFINRJbwfcc7SKsu//ns/5dOXL8WiIWW6bqB1LTs/8vr1S27v77ncPuUn2yvC4cSbmIpXnSmg7TM1unoEwmO2UMVFSaJS2XE559K9CjRlrJNiJobENBZLGa1oaN67t5d4xoqvnP+92P/Lrnd9ffP5H8/+d2AwRrMeNoz+VArn9z//dyK6fV1HBs7Ur9mfqB4MlPcr5bKBzswhrY208YFQlHVDKgZgJcmw5Y2dBcaASKYpIC2dzywSKWCENhtTpDFOOkAFMKqNwU8CkrKLA1ArBPCZE8Qk5mRkhr6XDD0FoUGGgLEWnSJ+fySkRGuFmm0JrNuBwWk6m7jYbLFKMZgLjJY5+wdXK9GdUNDogoXR8vFaa4mpeLM4SWiU8uRxki6LKbRqVcYdqiHnNL8v0krUJCVjHXzE+0iIIykqpknMwSqeJaJRqlaKldkpejhaa8YkihWuZtgpFwBuzcgV1jQco2eMkXGc6IYWY1oOPnEYTzTW0KtejDE3K7RqefnqSxGpUpqsDMfxxP54RGfFQMPWOHYEbCXU5yJCiJpbsFqd99o5qX54wS0TizNt8dw9izGS7Vdbqu+qUiS5TiiEjlsD73zBKzWz8eZEp6z6szZnE8ypMCeExbTCWDEKPIynv8UV+v36264HOD4lgUmrEhfKbRpKHDsXRT5XsKkhJCkw6nh3uU9izjNrJOTIcTzNj2G12LPkGEneyzUa5Tm22y3H8YgCpklcrpu2Q3cNx3HEaCSWZF3G0ZkpjEDiRx//gD/5kz+hbRo0ilZb1l3H8f6esN/RdR3Pry/Z7++JxwM2Bz54esV43PHsoscqBad7Xlxfst2smYoj99C3XG83pYCVQ69tLW0/4OOOw34vDKguYRCWS1aaoAz704jSQqKYppFhsxEfOmsIEZwzOOuwTqNHQEVO45EYRrRSdMNA6zp24w7bNhwOe1BpljdQWa5Tqw0BsbMJIdBYI7T3tpXul1aQDDf3B8iiWo9yBGWEwRlONF0juBYFKsB2tWa/3+OshSzSFsfTid1hz2G343r9jOebNff7S968fiPCokbo5InzSHwZox6Pcar43JIxdC7SQlHU9TjbPAL8Cp7qvQWZLiQamEelcE5kHmANl7eXL9FVyg8Kyfp6mwKteMxkXq7vRCLzTRYFD2hanFkjqswFlVZQ5sMpJUE3Z4FYTimDESBc/ZtaxS4VDOfv5mw4CeeDoyorhiyPLyDkwiJBKnqVE0YheiNIgiHYDs0UZHMrFCmKM3XTNAR3YkxRHKtzxCmDTpG+UPQuL7c0xnK5GdgOHau+ZdMJeLc3mr5rMCqzXnUi/JakwvNFz6AyIkzx5ZGxhcw3heYt6PpsLBFRigUeXAjnvyvjIZXJCH05ePGVcs3ZofWsHSMGnsxssXMisPwMbQFVKyVaNVFlQgqcQuJwnDiOE7qV/1cg8/r+wHrouFQWqxWNa4kx8VYrjj7IPLXvCG8EMGkyPFutObmJXp3orOGoElOQfWWExgZIp6mi+8/o+fPrrhcXsEhQ6ggy8jhpeZwALR+n7uUzsJivdLKAOaGKi4pl+Vj1qwap+tk55whJlFgFNPf9+m2v93Xlcs6EYn44i+JlwYVN6ZwUP/7bx8nxEmtV2ZknP4HnwajJx4BTBh8mrDESC5RI6+9292xWG4KahKzgnHRrU6RvW2KYuL15w6rteHJ5wdu3r9lut9JRtpphGGAa2a5XvHr7ChsDNy9/xdOrCwarePL0gml/zw9fPGXdOLrWkaPngw+fyZ7OQCyKxuV9sI1jdziQlaHtpWjTqiizTyMpK3TTCTD5OOJcw2q1IpQD8XQ60bYt1ppy3uwZpwP7/f38fjeuwzmDn86icNY6UopzPEgxi/J5KRiUEmbXdMropGd4gtGOMU+kDG9u7kk3d1w/+QC0JWbFF2/usA5++uFHWO2wxvLJixf82T///4he/LSazhHuRjQyNl8pzbob2F1s+OM3r/AAysyssznGvGN/1VVHSfX3y3H4cipSSTaz7QDMgOHlaOld2Jh3PW/dv4+7zEuR0CVm53wf2QPOtQ9AzI/XdyKRSen9FgWz/kpJZlRSYk4WfaFTS+LQGAMpF1twMXOcQiRpsZevj7V842dsTGFHSeD3WGtIPpDK7Hk+zMpoI0/yeMLyKaDLhFixh4hxwn4a2gGnhU69Wj8hBdGwyVNk/+YN3hj6oeX6yQVDSVLiaSKfRlqnuLz4gNVqxTAMXG4vaJ2jdZbsJ5ml+5M4WOsswlgK0cCJUVrKxpK0wRqL6NYpUhZkfvAjyrSkLMBmrS2gMZ2icYK+90XsTqfEuhevJRUnQp6wOhENNK5DYaBcFDpnXNMVmuNBqsAcziMcpVDZ0RhLyNLB0hlsOxCjGKyF4BlD5BQ1o25h1fPLmz0h3xeMgcWfIry8o28Mq3ZCxcjV0NNocQrfHe+5aFsuLq+ZDkf8/S1tgh9dXZDWKz69uyuWBoIBqPRrRWUNKXwYaVxXEoczZiVGYQnk6Ekqk+pBUPRcpmmS7ogfhaWmVNGMiQ/23HJ8VenXy44MPKyoaqLymNl0BpBGtFWgs3hTGUdnFHslPlvfr9/uepDMZmEK5ZzRxVSRFGlcI3osUUZASQlg97Ei6+PD48HzQDEnFYPUuofEzqAcIErjGkvygf+fvTf5lS27zvx+uztdRNz+ddmKkshSWShJBbiF/wAbHtiAPXANPPbIE3lgwDP/B555YqBQQwMe24UCDBgwbBg2hJJKUkkUSVFMkklm5utuExGn2Z0Ha5+Ic+9rSAgSleXKTVzGy7jR3RO7Wetb3/q+nAKn67UQckOgdRWWxKZxeD+y6WqqqmIYe5yzjH0g7Xf8+Pvf5bd+6zucd7WUieME0fP0/Aq/05x2Ff/e7/47pFLa/PTjD+Xw9IEw9GgFYejZdEJitki5XkrhFmcdIWuaukNXDbe7kbruMM4x9gNaVyiT2I2eum1o2k6E/4xj8pGmabi9vSU7h0Zx1nWkONHVlpvtgFGZpqsR7SzNNIrCbvCRthNvMm1FOyrnLNpMSjrMjFLUqxW3t9c0ruLs5JRx9KSssLZhykKozo3h8pMr7nZ7/vzzn6GMkyDSOcZ9z93Nj7g6WfPo4pxPri7oDPzkJ5+TiEy+53LVsFFnDLZmun5NlxLfurzgT778GT/f94SCuhtlSYcgZfZr497+cAiQ09G65ciV8fcSez/b7nh/4FlWVUXW9wOYeT7OQ95nwXV5MEfftq8dODDlLI1x+foGVaQ3/pVGZB7yFDRHnxnKpNdWorrZqbNyToR8lEIVctbMq5m/UDgiO8uyFSUrRhnMop6XY8JYK9YHWghY80SwqqA5SkjHurTDdZXFKk3rHI9OTwkhcHt9Q1KK61d3VE3FpttQ15ar81OaqiI1gbvnX7LpGh6dn7Berzk/v+R0I10njavYbW+lZTNWolCZE85y4GYoRNROG4c2UjO2aEIWq4SQIto4kppKF5EW+e0sEPTcRjhfI20UTiWSSiKglTQhiCCUrRqMsYRxgsOmmVFmwode+EuqENZ8Ii++09kuIGuNwTD6iAqBMSZCVIwhg7X4mOi9Zz/0JaiNnKzW3O32mNywaRsqp7i5CeicAAmI8zQSQsTVFc+vtyhtOWtPWbPHSTxMjL6gRzN7/rjYlijI/QV3f9mkdGzvfxunZonALBGp+fdHMvsRhXxYvjrMtUVb+HKTyirhipKy1pqoZk6XZIzrrvsbX7ffjF9uPCQ5AkLIU2LLkXImqkx9yIBnJVjRbVWlFL4cD9vzH77f4W2yOhDTl2TKucU6h0iaRm4mj9awamrGYWDKCZUiXdvglEKFid/85BN2/R5OTrh+8ZIpBX78g+/z+NljNpsVq1XHZrXipOv44kc/Yt1VjLtbum7Nk8dXbFYSHCQf8JXDjxO1dWQSq5NTKsSvTFTXvQjyuVoO09KUMa8j6wQZylpTaUdTd9zd3WGMoW46nNUYDV1bH/Z8S5D9K4p1gp8G9vuB1eaU1eYUH6+p6wZXgbWKftiRgjhUe+/p2hY/huO5VLoFlTWMU8DYiqEfwRjutj0+w4tX19imYQgRXdXs9j11XRcBTEdTV4ScCGHC6pbL81MhIOcE2klwZxzN2vJi/5Jx6ulYc7Vacz2M7HMSQVaWVgLH71vpzFzzOXJQK2b1dk1C5WNJfL6lEo31lOO9wGX5E3mT9DvPr/etgSXKM/9Oa31Qt84qFa20jFEWg0W/p6UbviaBTM7vFsRbkojmx6KKwWMSUaIYI0kpPBmLwmdhkcdiZz5nM/cW8gPo/xClmvIe+ditM2vLpBTvtemqLPVnrYXqGgI4Y6iMxtWG1lkq66iMJvuJPHnWrWN3O3B5uqZyhnVTid+RVnS1CF09e/qYrq65ujoTNGfVHtjbOWXquiVMI2AOny2kSAoZWzUED9ZZaVGfr6FOOGWIHAMJa6rSm6+Z7QeWhyilG0ylSPajIBBFV8coja4qqdEah64UuujjWJ1JuYi0lesUixdJSkkkuLkv4R9QTDEyxkRMMCUR6gqlrGUrS51rjJFrrTVSjtOgCBChbVvqRpC9YRqxMVJXFWPoOTs/p/ceqxXWB1bWUhXZ9ZiO/AIhwC1JaUfkBLg3J482AXPtWN3TX5iD5xACubrvv7REVY58sCMiM78XHLOXJWfsDUSmlFXruj6QkGeBvO12+95s5pvxqx1KSbaOUtLdlwI5ZurZ7iKJX5z3/mBImB4EQ28NjsoQZPHIxVkG0aBwWpyZiYnoA01JHIehLwmZ4nS9ot/vIHjQGmMU/e1rzk7P0RoaEpV+xFfPv2DY3nF7/ZInTx5xd/0Cf3LOqq54cnXFRx8/pnKulGkk0Rz3PavVhpvpGusMw9DjpwldCxfImgpbN2Q0tq4ZvJTcjDFopbB1TQyZ3eTZrM8PauFVVR3OD1d0xOaOPnIkF27PbprEskApNqfnaGtBGaxr0daRQqCqHK4yvH41gDY0jcH7sZSaEil4UsoHexivItZVrKuGm+2O7TDSe88UImM/cru9YyxO2yF4rFHSeaQ1m03HumuorEJlxbNnz+iHidO2xaLISmMqi62MdIIGz0cXl7za7egHjzIc2/jfMRcezptlMpRSQqX7NgVL7szyeQ/RmIfzenn7cB9b3v/wTJ/n6rJUvtzXHgY/D8fXIpCRRfR2i4LlZi1QlfjxoIRJnSkeRgfVSiHl5pyJSX6yvv9eWv4BzHHswhvj4C5a+C2Il4iI6s2295HaOiqtaCqBVXPO9LstyQcuNqdU1vD04pymcqToCeNApcXB9snTx/hpoOs6urUYNzZNjTbw+OSSR6fnaK158uFHchhmQQX8FNGVwZjAer1m7PeQRFhvGntU7dAYKod0ZEXE9G2aaFcOrcAkQGmiKoiOlUwHM082aKzCWsN+ElnynDx+FKXMcYiYumV9coGpW8YgbfDKpcITigjbBobdFqctPgipeL7+rhYZ7JBFmt2HSDKWMSe8MngTudvuud7uJAsj01aOtnJYDb7fUjuodCq+SgPNquWqO6eqO8bbntu7Pc+6mgqoKstdP5FHS+Msj+qa7XbPOZo+JialCIuuJfmRzpBZpG7+7HPZaX7sDMM2CLrzts3gYQ16fh5wsBsAkY+fy04z+W3J4VoG3Q/LSgqZm7OImvdeOFgh0DTNvU3pm/GrG+8qAcleVWToyiY+eOGpjONIVZR9D4ncg3NjbpOG4x42D4tIE+QkAqC1tkWe4mjUqpQI3ddty7praduW5199QWWEMI73PD474zc/+ZBh30OOhHHE37xmtW756OIMP038+u/9A1arlil4sobNquX61Wt++9t/j77vefrBr7Hb7VAqo4yFpLDNitV6I+WcoRBhgaHf4oyBCP04klKkc0nKWv2EsyJSOvRbnK1ZtS27fmQKns50dF13WF9d5QRVmUZUChADYXgtdgdTQLma9eaUq2efsh8mfAyc2AZjDMN+S107tMncvpYAot9tsVpKx1prIRjnmte3rzFO442m9yNTiGyHiVFZXtzcsLk4YxxH2rZFh5GuPSVHj8mBEBKnqxqVPbd3O6rqnLpZc3K65vPPPsfkxLevzghmlMc+vmJ7t2e3veXjk0t49gnXP/wBU0pEBag5CSqSISLTx0yYXu5jS96LqN6HI9ISpW1eKeFRzo+d95ElV2/5nBlZOaDEi9LS24KaJVKtCnI/r5eDI7Y/djErvuaBzC+DyBwiyOX/ijdIzCLWRo6iupjuR36z2d/b1FIfEqN06cyf27VFuC2WjhwgSeeSIqGx5BSKf0mksVZa74hYNMQJneT9XSVEMGst1sCq3VAZQ9s0VJWlqYRJ31izOHwKN0eZIoktWVrrZtNAS8wRkK6swzgQoyXrUjoTiyy3/K2alMEogR1zjpgZjkSR4kQIEyrui9laJMepwLsa4+qSIc77azq0L8/Xcs6G4jRitZHMstRzZ0FDHwQ1m1Lmbj/iA4ScGULAZ8jGEpD3UVm0I3IM4sOkFHVlMSrTtrUEmErjx4GYPE6L3YM2QsRu27qgUBYLXKw7XgQhFMYUmDVr5kU6G18u58yxrhwAS0rqcJ1njpUEM/U9me+3EeoOonsHROZI2J3HvBksEZiHGcv8OJ3lMTMiY7U7IDJykLx7E/hm/O2Nh8jJ8d+zpDwotJSKyWRl5GBMgRiOhwN+EhXx8nyfjlIFBiNeSfNrxkQ2ppTeIzonrEgbEZXCxyClVOOo64b97Q2x73EJzk437Lc7am1FBO52S+0MfT9gFXzw7DGKxKquuUsTeupZn3Y8/uAjfvjZj0j7kcdnF6iU+fTTb7Hd7jg7O2O7vUMri0/CIbvdbYnTiPeBtm7K3m6ISYxztbWsu47gkwh3Oo3WmXHaCrk/DaQEVW2piofRNA1UVUXTtOSxZ+gnwv6WykrA1PevMKpCW8WjDz+hadcE5HpIR1Lha3anaAZMMapNfsJqi7UGpUVg1NZiwJvKvtRYw8ttzxfPbximSB80Ox/YPn/BxekZjx8/Zhi27HYSFFpraSuDIojX3+kV1hjC5LHasSqin2bV0dYVwY9M1z2bzQm3t1s2Vc3VOlIrhc8iY6iNSGcoDEoZFNIpOpuBhizJU62MlNAXCIhRihw9MYqFQtaKHMO9uZtSIhKpKtEak5K67Okzxw84ConmLFobzOjgm0H9EinWWeasLpptOd1Hwt83vhaBzPsQmfn3B1hK5eIgq47lAKVROaK0YzY+I2e0UVhAZ33wVUYt2dEsspqCAAExF/lmFDpnGmPAgC0teEZrnILaGTpbhKhUxDYGXWmqHLDe0+kVrUkYQxFZM9RW07Y1m/Wappk5DdA4TV2J10ff9xhb4WzRHyhKrbocaoOfyCHijCVlBcoxG4VZp48ljehRGLEXy0HqzurYVaS1wuiMc4tDMUPa3RHjSPJ7oh8Lh8lTdWua9Sm63hw0MBSJGD2umDFGXwLMDI2zeJUYe4+rRBfA2IrXdztign0yJGXYjgPPr7dMITKlzG0/EhJMwTP2A37sOetWWDJtY6lMojawagzOaLQTk0sSZBRD9AzjxK9/8AmXmxXRB4adZ912bPuBk03N6cUpsW35/vPnvLreo5qW2YwtxVR4CuI5FVMC5iBbSmUqJ3K2pCSGfbaYrzVNRfQTbrapyOpQowbh06Ri65BzVb4rRwiiBTMT9mYdIqVEpdousvN7pNFFUGMK6bgq80YCq/gNIvM1GQ9LQ+VO+VGliaEEvAbp8tOFy+KMFSFPyuavzWIfEzL6QfvKGgJZmiGyKKBbremajiFNmKpj2vdUaPLYc37ZFb0rR/A7Nq3mrKsZd6+ouwta09B2DkWitbL3bm9e8cHTp6y7ln2/4+WLLzlbr9icnGFtRT+NvHz1ik9+7TcYh5GuW7Hve6ZeOCJ9L+raddUild1iS6IU2ipstgdUgCx8lMGPwkMZ9oe5r7XDmloI08qQ/EBKnjhsUdmTwi39IPIIKWqShpOzS+niVJqcAne3r9hsVuQgIpLWGvo7z82rG2pr8ChCzEzTnrpqMRZ2fc+uHxlVxXYfeXW75Wa3JyvN637gZj8w+kjMiVfbn1B9YTlpa3TM5BRoNhXtas2qMaV1O1C7CsikGDi7POP6+ppgDGcrB8GitKMfPVVjiSnw0dU5v/n4ET/46jk3czKm7b32Z60laOFB4CKIYMD7uSvVixWDUoQFDQAgp6IrE8uZU5Kze5SMBeJzj7u3QGLeoIgs1sIyKVuimPfLou8eX4tA5n2IzEMuy/zv5XNlqGKZPgvlqYOiasqxHErqEMBQ0IQDOTPPiA8ovXDtVMcW2aksrDh5qsqgUxLhORX4j3UAACAASURBVFfUHDOQEzYFaqNprKYqkVLT1OSUqCtL5QT6VThcqR13TYvS8m/X6INiMIj3hA8jYnOehcjrigbKPFGsQaWjiOBhAhARq/l4QESMKYiScpAjxEDKHp0h5kiadqKQGaTTJaGpmxW26kDNOgQZFQOiGpMhhyK2BSlKOSyTIEWMVYQgui0xRsZJCL2340TvA1OI3A2iVxOTZKXJwDSOGGeBlq6tqY2mNtBWCqUyVitBaUIEk8t5oA98oiF4bvZbKmWkBJc9TWNpRodxDpcCm7ri8mTDNgtClBWHQPpAkuRha+DRm2Sek0s7DdFj8MSQ0OqBb4nVB3L5spV7efswi59/t0Rk5v9+uI7meTOXpGaOzDeIzK9+PNyUH8Lry8fMGWxWkkzlwjfLBrQRQ1WbBZ0x2oi2ExwJlw9qTxI7J1BJ9gulqeuau5stfd+zrhrxImsUTy7PGPZjcbyucErz+sufs25q+u0NOnoqo3n85Mlhfq67CyBhrMzJuq5pVyvpVoxwcnbKenWy8GMS0rkvKr9tU+O0YXe3JaVE01bYuiJOUp6dW5y11uRUuGc5UVUyr9u2lWQWRVWaPVSaCCkwDVtSGPBhIKcJpTL9MGLrE07WZ7SrE5xtUFqx2+2prKbSGlUr6kY0aJw1qJwJydP3O2LyNE0FRLSu8UGQ5D4kbvcTd17xxctbbF0RQhKj4pywdYV2llC6Bk/WG1ZdhUo9TWVFjBApS886UJQkcbVZM3lPbFusNXRtRVIyR4Z+wubIs4sLXg89w92WfqEUzeLcBNnDVNk/5nNFcz+wWP7MwYr3HqNtMQC+fw7PKPPDMtPyMfdu87HK8bC09PBzLMcBFX/PFva1CWRmCPWhIN6yHrfc/OcLdy/YUWJcmGMALdmNUXLh7MERVJOyIiAloiFKm5kJohMwhYDRInpEjhhTzfSRQzePj+EgIX257litVjit2W9vmYaRxjoqZ2lqQ23toeVRRiIET12JsqIxTozhigqrQMsVWtkDS18m91H5VrKWCmLAOEuOx24W0pF/sSxLiEfITDAt5nFlDkU/oeKET4GcEt4Px4w+Z5yy1O0pyjqqekXSotMTk0ephFPCus9ZjB9VDJDEVt5YQRa0VsjTxKRy8onX25Gt94QIQxSy736/ZfQiEigihYk8TWweX3Ha1TiVybnHKGiqIvinZXOVklpEWU1tKnb9ANnRKMV53WFsRe8TbSvozUldc+ID++DZDrHMNSsCgUkUjQ/QPjBbSORy4MzXeEnuPSz+4qPliqs3zMFOVdAYVXR75HuY1/ORBKp56CL7MGt5+LtjV4I9vPe8Xvq+/2uszG/Gr3LkNAc+maw14syuUKU0aZAOSWc0xlm2wwhIJV0hJWyAuDBlVWpJoCyIbj8RrUfbGlJmur1j061J3jMNPbuh5+MPnqBi4Gwt/Jm2rjEGpklUVsfBQ9PwV3/1gpQCSmuuHj8mxkjVdBhXsT49IScOB3RMstdWlUNpIfKu4FCGrSvDznty+W/vjy3BAHXdUNe1nBNIMKcykMSrLfienAIqRWIYSCEwBSkbZWV4fPGUqu3QpgalmIaR/e6W2hmaSgK9cRwZ9gNpmEjBYyzipYRBqQRZyus+ZvZT4Pntnpc3d9zsPUE7xkHkL3xIDH6ia5rD/nB+esb5quV03bK/C3RNhXQcJ4yy+DiR1ZETZCrL85cvWDePOK0cjbOEGIkpEbcjcTQ8PT3hdthzM/T0gbKvFD0zfT9QkX7SeOBbJaXvBSLLhCuXhpqHnZjyXVVlD1IkpdHGHva0nKWclQv3a5Z+eR86M493JVtLsOJd42sRyBygrnLR5sN35hbA8cCYI8p3oTM+RlROhV8CZIUqbHFnDFYb9mHCIGZ6GVlIhtIzrzIlDpDDIWaS0SjpyCbESNYGnyLDCKPPGB8wtRz6slCFhzF7QJEyrrJivOYcxigMWZCd1qKLR5RSSuqi1qIw9yLemSxr9aIHf87ItSz2ECf8mO4FfvP11IheTM7is5LS3JaXyFFQlTlqz8ZhjAPjcNqibYXrTksQmAUBSREdA6ggpMWUSvCXISfRjtERldIhIBoDJBSvhonbPvDVfuJ6uyOX0qJseEK2Tsmzcg6rFO1qxbOzlsvzDcN2i0FaiRWSCfpZsMooQlEPnqaJ2ynjU+akrmiVZHfGKBEjNBXnO0ekwyrYxZ1IuM96MlpJd9Uho5GabdJBoO6cF1yZdPBjEaGshmmaCNHjvQRZo58O38uSe3MPcXyAsDyEXN92uxzGmEM5aUZk6ro+iIF9M361Y/ndvu3+eRyIkQtoPuqM0Qu+VFayFlOmdRU+S9tszglp3cuH0lLxCpHnFSn7oBOvbu/Q1lC3FQGFbhockU8/+hZVVfGzH3/Gt3/jO7SVI0x7ht2Os9MVyQe0tdRugSDXFV3X4a/OWG02hGIN0zRN6QJSEBNVLWthvV6z3++JKuJqS5y8SENYJ39njNzeXQuK2LTs91syFNG5wm9M4Idekrac0aoih4ldv0eRiX4HKuPHgVfXN4SUuXzyAd/+zU+LKnKFddLZdHd3J5IfVkEOvPjy56y7Dh8j+/2eddPSdpbt7lrODefIaPox8Ho78LIf6XH8+Y8+Z8iZkGVv397tIWW6rsVlw7TdomLgo8cXfOfDC2orHaybzVp8s1ICcwwkVJZg0aeIRnN9d8f1v3zF0/MzvnV5iTWKs00LKMY+8Mg49AfP0Mbwf/7lj5kAoxdk3hLAyByTIEYXvZZ7BrZw8GPr+15Ug9X9LsylpMQ85qTZWHfczxYB1FL49CHx923r5WGQc78z893jaxHIvA+RAd4QxpszTnncLCB2ZDt7L6TOlAT6zFEO06rUlXUKxFggz5yorCN7KWvlOOuLRBSakGMhC4vVAEjgs/VwlwJTDFSvNeerFls4OZSswNkNavRYY3BZiYquNtS1YxyF9KWQ1kdfUBfjHIkBpSvQ4+Hvnq+P0kXpUkPMIiCVsCgCOlm0jaSk79kkzAezykmi8DLhxiJ6pLXFaHeYQKvOHK4NIGabrkj1+0Dwk5Sqkhft45wJYSQUWwNnxNxRR1MMMTVZV3gCU9Jc7wZebnfsxomYE/1+wBcPGK01NsO6aVnXiifnJ7SV5nRlqZWnWdcHgm3KGe9jUS0Oh9cQZMQTjGGMsJ8CJ9ahg1gBtHXNNAbatubcOVzb8Nldz/W0R1kn3CjjCCks4NcjFyVnSGruGjkSeufMconQWCsiW8vrOX8nSxg3Z2m7nY3glOIeOe4hL2YJ385jfs85mLmPyOz+ppbrN+OvMd7FD1j+fv6uRExxICol8vpK2rW1EmS5rmu2N9cybxDIfS6ZH/hSzM0M5ZBUispZSd60Ypo8L1+/oq0cP/riK/rdjk8+eIJX8NkP/pI07nl0dcFP//QzPnjylKurC273d+Qo66bSG16++Ope+VsB++2WdrWiXZ2SUuLLL7+kKbSBAzIZjl5l1jiSli6Zs/NLbm9eA2JgG8KxYybGyKrbHNZM8IEQE4rEOIoGzm4/CmKkNR98/Oucnl8SUsKHTMyKzXpFyIkxeIahx1lDf3dDphhO5kiYBpwxR3FJRIJBYTBNC2HAp8yPv3yOalp8hv3QCzdv2At9wFUkH4hhpHOKTdfx6x8+5Xzd4FRiUgGVZ5mEQqVYzAWrDX0YMXXNarXh+Zc/xyrNx2cXVEb24bZuiH5PiJHOah6fnmIQUVbpGZWOJSnJHPeQnDPMXZAIQpPz0R5gTrRmdHkZxDy0NzicSYvy6Pwaszmx0ve5fG8b7+PRzMnfvxJk3/cJ4sGbomLHIGbZwXFsA5sfIxFlLsJ2Cm0Nbd1wN/b4VBxudOk40brIg1tyXNTx0mwumQ58GbLCx0RKipt+wGRBKOrKiqtrIQNLDVcfPnvOojtgVGYIEVsXZU0rHhcZMDlDSFQN5MnemyjzJicblLC7AcgZayv54qPFmCSBxiLjP0TFiPKtUgofBBK2xqGsiOgppXBVddhM5wlkrXRKhSzEuZwCpIBV4quUU+HIOJH1Vnn2yMrFsC7jsyUozcvtjhc3t/STeMKEENjtdlRVRQqRTdtgcuDJ2RkXm5bzdUNHoK1FDbPrNgyTZ/KR2SMpxoxVEIN8DuHieJLNmOzwCVpXQ0oiNuYUdvKc1y0rZTDxx2Q/gZFsMqVwCAqWhpIxRox2h81BlcfqbO/NvYc15/n7e0jYTSmBvh+gvym4J2TfeWG/K5hxzh04CcfPJiTz6j2Ga9+Mv73xkOc33/fmKOto5hhgpHybZW8LKWJypularLWMfiLbkpQUIufytfKcfBUkI+ZMPwlxP2URs4wp0E8jf/6THxMj7MPA5dkpOQVO2o6b2y0XF1dSchkGnNE8ffwBkx9IfmLqxb8rhYh2YhdgS/C832+LPYYc2LMUwMynCSGANoTCDRExTVvIqsJzk/1gOsxzYxV+SkWywIAK7PcjMUWcqbl8/DHaOIzWdKuVlKemHcZkbFUxholhGEogNEJSGK2ky6mUtX0IKGVwTY2xls1mQ7/dU7UtuzHjVqfsvnrFz1+9Jpk7KR91HbEgOV3X4Yzl9tVLTrqay5Xl0w+ecrWpWVeG/W4PwZO1aOuMRfdBaYNV0pKfvAR9+/2ek5MzLq6e8OrL5/QxwRBoqgqnLW3bcnu3o65qLtcrLlcrvtpuScaK8nuKKF2CMqXlB3XghqpD56ScFUspiBQiofCVMqmgyx6lOewly7LTw5E4+sQtz7CH83+5V73tfvl3IbV+3TkyKb3fomCGyZfy7vNhQakHLwmQy0MDrcVPRGmmfWA7jOJdogqhc0Z6iox2yBHjrJgPzq21PojDK9ImG/OE0WIS5oxBZTlETlcrLk7WqLFH58S43zHmyGa15qw7l06WFNDOsNlsDqUAo7SUv0CcccmEyeOsKBx6T2m9jih1JIiCFbhQafpxFMTEWtGLIUM6tgArKKq6wi2JWXFqq2I85tDWopUtLd2ZkDKTl7ZyrSGknbTnhUCOXgKYDDF7UvBU1uKc2NgnDSkmcU/1mV0/8bPXW677kRe7PXfTRFAZoyKNzSjjqFKN1YruZMXVScfl2ZoPr06wKdA6zc3LLXevI8MwcNf/tPBdIlXb0NUNdeWwlWwOJ1YyzzGKd9N2mPju61s2Vc1p03DarYka2qomeOG0fHR2gjGGF+PE3TRhnKWqCgqliullmWchJyk1loWlVGldLAG51qKQPMt8z/N19meJOWH00XZjGajPmgnzQQRHAvDyZ37fecwZ7v3ANR/KdctN6pvxdRzp3nc372OSvOUDL2Q7jGyH52RjS0OCrEPFcT5EvTQOlMaHmKG2rux1AaWEI2GMIuXAqpak6auvvuJyvUY5g9aw6VrGfqCtNnz45CnDfkfb1DTNhsePHzNNcpC3bcsQ5HP2uz3nF1fsd3surzYkRSnDQtN0OFeXJA9C0pAS65MLtjevqLuV7PlBS+s49qCwPgwDKosWl4oZYxz1WYutauEM2kr2r5CJxpLyxGp9wTQMTP0Eak8IiWkaUFoELFdtLWT4nOjahkFLxw4alFFSpbMNz5/f8Beff8kexT5mJqW4vbuh7Rw5TKgcqXPEhgEdNR9ftDy5uuC3Pn1KVxvagvq3VUeyiZ9+8SV32z2uXWGMaHkZnVEpU9eO8/WafhoJ40TbbHj6Uccf/Nn3+NbjZ5w1rZTcKnH+7oeROim+8/QR5qvET+/24LTwU1QmqSUJGPEiVIqEwpijoKck5By6xWIUg8tDgpYCKRlRzUdjFuCCztxTXn7bOCLa7+9quh/opzeCnLeNr0Ug88sgMnMwM9ftgDc29vmxSx7NrFQ55ShEWIrqLwViK48xSkldUh/blxUwqw0aY7Ba48ex1KJFfVN0TaQccLZZc356xu71RBjlUPHTRO6Eq9BUtgQvR1fqQ7ZRDru5va3YeqGzIhEgzTL29wnPShlESUbSMo0BnURcSCmMkg1E5eMkMdaicmnMVlpgTa1RRnQSsg/lWkrwI9G7cEJSCnJdVGGpFh6JNQptbJHWVgfkIuYJHxL95Hl5c8ur/Z5+HNn34gJugZwDtZWS1Eljudx0nHS1CA66Bmc1/egZRw9K8/puK6UfNJ3SWFfTGivkv8LJMaWNOpTP/7MXL3h6ekajHblVOFthlWLMnkSk0ZpaSetjZTVjCGRS6QLKhOJInJNCK1BGC8FyMU8pVgUhBKoDh0bGEiad/3tGeKy9H6hbaw+H0GH+LxCZh6jOQ9Ts4Xqw1n6DyPwdjWXG+os242UZcfm8OYieS7czIpuVekO/6fAei/eKJZiB0hGVM7P2kzWGFBKhn2hPT3h6fsqHjx7TqszrV8/FqygFTk86drstmcjgYb3uDuJwMUaur69xbUdScLo5YbfbYeuKYejRzjGOI+dnl/R9cVd27vCZnStzUxvIipxgvTlhGnqiGg5MeNmTJcirraWqGvpJuiOlu2a28ahQWUoi035XfNA0SgsJVkr6SGemnsXW5LOsmhpbV+y88MqmnZS/PYmq7bi+3XK92+GaGjeNxGmk0mL/smoqukaaQz58fMHZuuV0I7IRYfJ8/rMv6HcDd9stQ/TSJWm2bDanrNdrapNx9qj43VY1237Cjz1V07EPkS9ev6J+/JQ0TBgSVd1K4hQzF5uOF7ctP9/ugUTKWtQfciaQEcepBUIIB9V7uH92zgmwXLvpUK5eojBv4369q+z91jn6C9bEXMZ/23s9HF+LQOaXQWTmwzskj7GG2VjqYPa3QGSWqI0c+FKr80n4LrkQW62SclJrRA9E3JUDrjhgF+8tssqM44inEJGVdDxZa7EKuqqmrhxfPH/Bl198wbeePebsrMUpac/WBoJW9ClBCtQEunaNqdyhXKS06KzknAQOTAGjE1lphGh+lJU2xhHTbK5VCLbaorO0jmcgpkiOhTgL2BmeA1L0KGUgp9KSByolchxlcxR5ZCwTqXTOhDGUADKTokc7h0pJuDdJyQ6pMlaV2mwK7MaJ3djzetjxan/DEEdCGMnJY0k4BdYonNJcXp3gFJyuOlrnMDEz9jCazPqkI3dn+LwjpYBdrXl9t2U7ZuoxcRIy4+g533SsmwpVoviu1kw+MqD44De/hQ2JfYhs9ztq73GuIvhAxvD47Iz1yYabz37M87utdA5V0hkRcigiiKVMmCFN0h2lCoSaSut/9CPROfw4ECaPNwOajO86lFIHiH3p4xRywi0QmQOKVk6ppco1vMOioGxADw/BWe137nz7ZvzdjOUeNRMiVX7zMXAfkclZyPXGGIISDZJMQWBKcGLS/WzX6UVJa/aGAyY/kqORNVeC5bZpeLI+4eWLFzB6dNNKyUglzk/OuLt9zbrdkDTc9Dsuzs8hCrJ9drqRuRUDbddKecgosoLT01Mpj4x7bKho6prJDwcezziVsq22cqCjabo1BoXfiqaUjwGtC6JZiKjOiS5KiqWbM1O6NhN4KQf7lLBayKeVacAo+v0W6wBE6VxlSRpDkDVSWYOtNNZqsoqs6kaMgKuaYdfz8u6O1/2Wl7fX3PYjYwy02mDbhpvXL2md5cmjCyqtOFmt+fSjD3HG8N2/+CHWWpqu5eLDX+c0JU77npvda4Yxshsi+3FiuNmxqsSfz3QtTS0lslXjyFkx+D2PP/mQ7fUtP7m55pP1CXafaN2a2nUM055H6zXq4w/44evXjN6jrCUqg2LuspRZI8hMsV/hKKx4SLKix/vSNRsiKXjCNB4cwWekMOYFyMD9Mvr8WtbeF/GE+z5hDyUk5vn/iwKXh+NrEci8D5F5eAszR6Z4L+T7F+YhejM/J8QsWbQSNj3lYhtmkp3YtVslypgiSCWHv9Zz55MiRk/lLNGH0oFkD0FTv9uiUyQrRbdaQ5wwRtR80dJS632kacQHxNgKY8WFe0YOUEImboor89wqJ4TjYyRtCnI0/32mZBZGQVKxZGygij4OSqGzZPnyNuUays4ir59LxI6UNYxKMBt5JXntGOfJnqQGmpcHrBymtW0IIWGMIGDDNNKPPZMf2e5uiQmIUQLS0eOswRLZtC1xHLB1i9Y1VXfCEDx9gD5pdlEk3ZNztJsTXty9RCnDdtezsgat1/JdFuRrGsbSBaYQ34aAD4EA2Jjkd9oRgXVTY8jolKV1XKj+pFK6jLE4iidR0TTGHsTJjvOzXKvogYZZ5fdYKlL3SLhLoq9PkcocEZllaSnGiC4BzhzoPAxigIP79jLLmV9vzoC/Gb/68XBjVkqsVdIBJXkzK72HyGhLSiX90qCNNDAAImRXEAVdLDVUTFTOCN9iRpbJxCSJUUqZqczJkMVv6eL8lP31a8ZXr/jZF5/z7/+b/xCr4Mye09YVr1694qMPnsr68Z7N2SlV3ZLIh/mVonBxlFKC0DjH5uwUbZ2UeyaP0lWZv/Ywh2cZ/FTK2PezekE4Z17H4boocUtO03RQXo8hY4CqIEB1XROmO3KKVC6LDgqJylrpslKKcZQW9qgQL7qCOlS6ZRwnttst237PEEZsZZnCCNmTY2DyE1lp2qairS1dZXj26AqT4dVXz+n3I4+ffsT67JwpeH7885+y3+/Z391hasXkM1++uBFl4Mox7iLmbIM92WC1EfK/UsQkMhS2aanWHeN+IObMyjpSyKDljMl+oDKWtqrxoZeyeBbJCJjLe6VMLepf2OLTNF/rnEvzQvLEZAsKX4wk01FqQpcE+NBtpe4Tfuc5PO9VMgPvf7e/CKF8uIbeN74WgUzO7xfEu7cR6HmRFwKlOsL1y9ebbw/PVaqUloqcfs7liz6SIgvQxhw9LJ+/tCM/cCVCoLaGYRwxlTgMaWPZ955rc0ejFdQFQq0sWVGMzarj58rCiIeCBisJbI4iQwqyQpv7fj+H2uQiotUzKZn7LXJKU9Ca2XhLbiW4c8f6eoEhZ+8oijnjXJ7LWRaV4H2BpBRaDJZQGbwPgKYf9iQvfBbfT2UyG1CKtq0JIRHGiMmBdVNTWc26qfHTxHp1gsdye7dFTwkfA7ausE3L2eZEyH9p4uXLl1yeRoyC2jnapkGjsFr4Tq4EmOSEU2LloIwmO8eAtManaQTrsEZ8qHSMrOqKdWrZTYGQFp1G5n7mkFJCSWR3CFCW3XTSQSVzZNbD8P7oxTS/xsx3UkqRFGKtcJjD99ty543/4X/PjxeY3h3WTgiBtm3Z7b7pWPq7Gg/3ovnf+YCrHId6sMfM9y0Ts7kUoLV0migUWd8PhpZrX2cOwqCz/IC8vhxY3nvuxgmVIkQPSQKhu37i+c9/yne+9Sn9OOFqKf9Mk6dtG6y1vHr1irOzM1CJ4JOUW/RRw0jeT0jLOWdWqzW3W+lQXK1PGIbhXkemtZacEqpRECdCli6cnBLWGVQ+rpW6bhhGf+B+Oe3QJmKMYpqkMUBr6P0oKEtZH77w1GxVk2MUh3FdOP45FU+9jNeTSOZzFH7bDwN140TsbjcWO5xEUxnWreXRxSld27K92TJFze/8W/8u/+K73+MPv/cHvL694fXNNbroxqSYqIwi+oxzlsk6TFfdQ23nv3XVNuR+AKPouo7Xuz2v+x1aGy5XBlvI3dGPtHXDyjl2uieQUUmR9TxPBJExyi4QmkUzwmLvWUpKzD/z/fOPVBCOydhDNPjhXGYx/5eBzMMz/r2v8Y7xtQhklHq3RcEbkVhe9JOr++Sgw4VRiw0E0EdVHkEQyv0oUJUppDkJcFLRY5iNJWeyZg5egh9EpVZnQW3Gfo8zlj4H4TEoxc++esHz5/DB5RmVVVycb3h0eYYzFqU0bdOiEbKaslZId9Ysvlj5v+QTuZScVPkcxlmCPwqfLT17UkGqsgrSrln0c6wq/iVK3n8uTeFENXOuLc+Ew5QgEw7cDaVEIDD5gFGKGDykDDqBLkhMOnoNhZgZJ48fPP1+TxwCfpxEmruybPfXxP2Oj58+5aRr8eNEGEb6CbrTNTvX8X/9xXe5GXZoMvth5Pd///f55JNPyDly9/I5r3/2OT/8f/8fpqGnO1lxfrKiq0U0yqiEn0Zs5VBJoxKopOijZ+s9fU402nJia+qYsMlTaSGxrSvHfhpRVc1dUNLiaYoo3qz0qxQYOQxyCaTdgoA+TQFtBfo2rsLmIvEdjq3ZgLizx/vEtzkYsfdQwIhyjpyS2Bg8IAnPn0kpdYB/5TCp8D5SVYKQfTN+9WP5/dzbwO852crNMthRCxJmSOm41wEkCYTmIEWgfeGaoaRE7mdyt4K68KNUSULm+VfbGq0yJ410EYk/U6ZtN/zlDz/D5MQXP39O1zguL06o6wpcxZMnT3j5/AVnJxusrQ6lBVfVTNOEcxXJe5q6ZdgNTEWwczADVWVpV+KhNAc6y247Yy2EhMbhXCQME01TkYPYnGh15AqenG3YD4OsA+1QiMy+q2pyTvjkj1YhRFylqWwjnUsxCG8o7yWhVZZMM+PRDMOWpunY3twybnv2Nz3GGq5OzwnhOVOVSX3PR5eP2axqutby07/6Me3qnO/89u/yv//zP+F//h//MVNMdK10Pv7n/+i/4Hd+7x9ycrLms+//Bbc/+TE//KM/pO93aGe5OtvQNTVWQS6B66rt6McBaxRxkjPm6vEjfvD9z3jtE647xaniZB4ySnv+/pNLco483w2kbMgxiiKwEbXyaFLxQBIB1XlO3qtmREHYQkhMIWKT7OmoBYdUKUyKWF0SNmNEj6xwTXUWe6B5pANy+G7S77wOjuNwWh8XylvGmwWqNxfiP1ZKfaWU+tPFff+dUupzpdQflZ//aPG7/1Yp9QOl1F8opf6DX/T68wdvmuawCcOxtWtJPFpGfUtUYllWWj52/t0Sxn/bcw6/e0B8uvc+i0zYKn2oMdvCZ8l5/vJEzVZrdWhfTikJx8b7Azy3zMzvf4n3SX+6ZE6Z+Tm+9P4f/74ld8KqsjnYCudqKlejXYU11TFDgjcCxZlgaMdpPwAAIABJREFUaowRMbsk3TzkCDne+4xzBjU/d76m0zTJhA5j+RtLd0QO6JxpncMCFYlN29BV0rGFyuz6kQC83u14fn3D2bMnPPvoUy4uH/PR0w/IPhKnkakfqJzDGUvtLKebFau2xllT0DkRB5zLk9Za6X5QknForUFrAjCEwH6aGGJg8h6U4mSz4my9oS2EbI2SDS8iB0h+8+cQbCxuBX0RZGb+mTObhwJTDzleyzmx/H4fIpQPM/iHiMxMqhzH8RdmNP9/Hr+KPew9733vv+fvaSnT/jCTfdd39TDrTeooavZwiCrw3M02HUoEIHPMqiOql3wg+cBm1XJxflpkDCa0gs3JiouLC16+fEmOSVyynz/n0aNHVFXFOI6M40hKibu7rQRdWtG2K0CSnKoSvsX8k2NA6XwIuL33pBjFgLeUlsRcV1TPna2oWrFHkb9JWsi9D1SuwZoKHwLaGmwlHnIqZ8IovnhGa2KY8CEwxYkpTtjKlWsn30/IqSCo6XhfEI7gMPSkOBKGAQO4nHBZHLZPNys2qzVEzfrkjA8/+YQvXr7ij7//XfqQ+Hv/4HepqhPqZsOf/OEfsb15BTFwdXbOs2fPSMETw0T0ch2FWzccECpBrKQr1hp12MNs5QgpMaVE0IqgoPeeECNt0wjHZ0Hsvbe/JOmOOqp3vEnaXZrevk1T5uGZ/LZz922v+759aLkmHq6F93VDwS8RyAD/BPgP33L/f59z/r3y878CKKX+DeAfAb9dnvM/qLlA954xb8oz+lFe6wA7vuv2IRIDHAKh5e18gB8iTwVZq8Nj5vecpe4f+t2oJGQ7Q6Yt1gLCnJd2X6s1ioROEWsUldJ0bS3y9CmQQ2QaJkJxs53fdw5kUEr4O0bal5UWLZOc471AKyNS3FIakwBDmugK3wPh0Vhr0Vk4OGgjwYy1VFVdDAktShkKpQNpcROI01r5O2OUv+UYUHrpfjDy+ZU1oDVZK+kEiyKnHmMu1y9jnQIVJFOMI0wDLkeuTk54dnVJngK3r69FpO70FFXXPL+9ZTuNfPob3+F3fuf3uLp4wqOzS+Jux+7FS/Q4sKoazk9OsVqjtdR+h2HP5IcDb6Vt6/Ldq0OQphScrDuUgu1+x8vbO3beE7RhCIEpRhrXcHV2xslqLdLmc9aQlwEc8CAInQPWeRNYbgYzUrPsCpif+7aNYPn45eOW77dcO8tgam6dzPl4UMxKx/8aj3/C3/Ie9jcx3haYLu+b96+37Xvve417XDpjRMvKmMPrj+NI3Ti0ymLCqjKXlxesVi3Dbg85c35yyvX1tajertf0fU8IgdVqxRg84zRJILGYc/NtXdeCqpS5PdsOzNzCGWHRWuQgTekG1FpT1S1ZaRT60H03N4TMvK+ZZ9g0DXVdsV6vEe8i4YhAYrPZ0DSNBARzV6BKxZfNAPpQ+k0pYV1p/zZZlHbDxO7uNTcvv8IpWFnL+WZN5xzDbs9f/tWPePbRx7y4veOf/8s/JmvDf/yf/mf817//3/Abv/FtTtdnhKFHTROdMbRNTV14Ql3XMHvHzd9X1zWs152I9TmHUkcPuDmgvNltud5v2YWJMYPPkJTipF1xsTmldRUpC79oTshmbRdJyt5sbX6Y+C+T7mUA80aiz/397G371duSwLfN34cB/vGx7w6CfmFpKef8fyilfu0XPa6M/wT4n3LOI/BXSqkfAP828H+/70nzh54vztKiYL4F3rh9W3YaQjgQZlXhCWjK4fFg3c9dTPPrhRRRWQmfgpmDIKJCgrhIy3PlSiObyqgkl9dqAylitaNyCqulpLP8O+bW7nEaRL67FVdio3Up+ZQAKi0mlpZOBR3koIy6CLKVhT0P6VzQwqUpXVUo4ZBkogj0xYjOpXtLK5yOaFd4GObY0k6OWAPZe2qjCWmGJWc4m9Kpo8hZoL+cFCFGjMqkEMR23o/kHJjGHSoMVMhCXBUdhJcvX5KSmK817YpxuuN26Nmszlg1NTopGmsY+4BOHh1HKlVRx0hQ4oS93fbUjaV2GmtAq0TlRDzLYUArcp9RSgQMc5HPnpJ0jkzjyI0POKvRKbN2jirDnd3jKsUUk6g9Fwb/wQvpATqyhGVFZVgTo2ccj14zM0oyz4N53s3zcw6GDmTfBaH3sOgfvO9hnpT5tXStnRGZmbPwr+v4VexhD8cSVX1437vuXxIjl/e9S5z9XdlvJGNmiwJkbzJKU1dSovFDXxBljdWGi6srYpgIfU9IkcdXl5w0Yh5ZW+HVKOVwzhzm7Hq9Zuz3+BTZbDYihIcEF36KEthoCQ6sNtzd3WFsRd11hBgY70ZA0zQNVSVz1GbNNIxlXysaXiHRVA7rNDpFUojFTNKyv70+ICvTNOJciwLutnsUgXHYsVm1RB9JMYoSutacnZ2wu72TvbxxTFPGWcN+NxR+So9RkRAnkp8wNuOcQoUB0oTVmmen55ydnPLll1+y2/d857f/Pn/2Vz/kz77/GX2G80dP+Kf/9J/xv/0v/4zL1Zonl+dMd695/fnnrL/9bTIa29Wcrht2u4nV+QltZWnrisaJ3QwqUVUNxml8DEy3IzknKmd59PQJ2tZ878svyn4qVhA+Bj5+subxZs2XTcVLP0lXe5KzKqmAw0LOhHx/Li3nkLGKmHzxjRPrFR+mQxlwLm0fkrO8FKGNB3BgiQIu18SyLD6Ph9WS5fqQc+mty0Be/92/+oXjv1JK/XGBbc/LfR8CP1k85qflvjeGUuq/VEr9gVLqD3L5o5aljxkZmSG2vw4iM6Mf83MevL8ETFqhrCGp+w7EeXHR54xm9qKYD5KURI9GKeEzzL4gIcliG4O0OWdtMFYIXMM0HnhArnipzNnzjB4dolJbWrMLqSqEQCpZgygZR5TKaGaW+ZH8Bkk2IF0i/SjifZlE5TRGJZraYFTEqIgmonTC6kxVGyp7PBQ1QgC2OqONOlhpaC1WCAapw5KEFxJ9IE6eaRiZxh6VI5U1VFqR/UT0E2HyWONYn0ub5vPnz5mGkVXVkEPgi88+43t/+i/YXr8AFVidrTi5PMXVltpo0jQyDQPOaowuPlqLrAUgF9KysYKoWCVlwawVdduwHSe+vH7Fj774ghcvX9L3PQ7NSduwbhqmaSAWU9EYPRTjS6UzUR0Ju/fn4DFDCSGgMuR4JMrNz1kiOA9/lgH8uzKXtx1is+jePJa+S/Pc/WbcG39jexi/ROlu+V29DTn56465xDTnP4o398cYI8mHQ5lzft8lYijISXNALqZp4vnz5zx//py7/Q6lZY8ahoHdfrvwFqvuiZVqZw/BeFWIwUrJXpS8xw8DmVgCpJmfGEUxOAdQwj+srTsiOyi8n0gpUhcenCagy/5VWUoSNdLWlpQi56cbUvQYJUiyM2LxEKYRZw3r9QqVsyC7WTo4530sTJ6xH9jv91hraNqKtq5Y1zWVUnR1hTGG7W7P2eUFIQS+/70fQcp8+PgpT84vOW9rThqHxjNNd2QD58+uMI1lVVfEcWR7e0sq7uJWK2pnqWsJGue9bD4DnRXqAuU6/3/svUmwbWl6nvX83Vprt6e7mTdvdtVZgJBBjSEQE1kG2wNsQ1gmwmbKhAHMmDFhoPAQGBJhcAQjiyAYADYOGyvCJkLGpiwhyUilpqqUWZk383anuWd3q/k7Bt9ae6+z77mZZaGoSkXlH3Hj3LPPXuvss/fffN/7vd/7zpcLrtcbPnr+nA8/fcLNekPdeZzSzKqS5WTCnvfIIVHNxB6puYsMH+ZjIocoDREpkIKQwIfS0hiJuQ9deV1ZaRivK6neV1oa3++zVsgfluz73wK/iMRIvwj8V8B//C9yg5zz3wD+BoAxJo837vsQmWNk5nV8gQGR2aMsr0FkhsBgfw3QxSB6CyHtHa/zvmVZvkrJRVrjBs6EImOdJcSMdoZdF6isIaaMz1narI2R64MnaaCsSEqy5mLUeSSZtQRWSil6MyUE+unREwRdGAd9kUHbpi/rWAcpYjSkHMkq4b0I2u1aD4hZ2fA7MhqVFSFlNIFsMip7SidmmpXV+06cTA8dh95vKEt03nWdvCe+wfuO2HlyiBTGUBqNKyw4RxelG4lygi6npLjDmIKFK8koXt5uubl6IZ+bb2lT5PbmJWeLOa2XhdWsb8ixZTqrKCtHZQ1V6SiM6eFkhbPyGp3RaJtFSt3IJpRNxDQFzW7Hrqt542SJ1ppF5SirkmuTWZSOTRcIscPZiZSMFOTRIjxekLK4k+jJjBCZtmteQWSGOW2UvrMh7Of0kcbCMew6fnxYN8edU9Op6DP9KCMyrxl/pHuY3vuF7H92Z1N+XUkQDp/fMRqzvwev3uM+dEepXok2j7lVap+QDFofQ3AwWHBcbzdk75lYxdVVy/Mnzzg/OyV0LT/+ja8zn1Rcv3iB07d9uTnStQ0PH7xBSomrqyuMtVy88QY+Rdq2Y0BbBqVr5wxdCOSkmEwmfUDUspidCLcuBVCStKWUSFE6WGeTCmdBG9GrqnpXebEmaNAhsm1qUkqUxRmKSE6Z6UQTQkdVWUIrFjDKGpxykAPWKtq2JvZ2NDmJ7EbMkrSGpsEHsWJRJAoNi0nJZrVhMpkxmUy4uX3J+aNHZG34zkdP+TN/7s/zm7/9LZ49v+KBLvjGo4eURvMX/sK/xz/9v/8v3n7/PX7yp34GoxX/+Ff+Ed/73d+hKjTT2ZLpYsbEFpTOAAlyxLmStq3ZNQ22cH0Cqml8IKZIOS2pTuZ8+vQ5he4orJTPyImlszxYTlFPhfuntSFn6cI0iFhq1nf3rmG+yfnad295jzGeruvEtuDITy4lEQ0dayLdVeM3r03ExlpJw3XjMczx4fx/PdX3DxnI5JyfjX7Zfwf8nf7bT4D3Rk99t3/s8+53B4EB9pHoq+J293ds7P8ga8WXZKyA2wtKxRFhaPh9w33FwMvsxady9KQsTqI5gcpZhOWsIWWFUZqYIyKnBikrupCJUeS+fc4kbYhJSlgDWct3A59CjA3LcgL0hn85odXQNpyJuY9f+g9d9e+J/F2KFLo+25GNQvfGZ6IGnNFKiFrWGWrvxQwzi7281oJkKJQwy1OvuT9MuhRxhUxC6+RzcLqvq/feLSBeMJlM1wZCDNL6rDSkfkGAtKhrg8pZ1H+7iA+e1iiurm6Ynz/gp3/qp7m8uebJkyfE1JKip6sFuSqt4+XlDZeuJHYt8fYKQstiOUP1KIscGmY/B5TuG6sMJC8LxDpBpJzVLIqSbScIhjWG6bTiYrnEGXBWMZtWOD0EQY42jOwG9ijcq9nDgLRoLSJexhi6rtmT5l4JVo7g1uHreK6P18l9sOt4AxpKU8PXIej/EefIvDL+qPew4/FKMDIKPu/b2F8JTPIIdbv/9b/2sYTsV8f3uU+yIaZEjJlCaWLKLE5PKY2mbmoW8xmtj0S/4eT8jPP5VJBJMm88eMB6veb04pwXL15wfnHBbrfrD0eYz2f43vNsrwCc2fPylJI249vbWwlCyhJri/1rtNZSlA6FGPzWTUtpFNumJsbIeiuIUKVgOp2yWq1o21pKW74levGVappWOgqTR/tMpw4cnUGPbDgkFQbvu56SkFBJxAh1lvJcTonFbEZZTNjVrZTcreajT56gihk/87M/y4NH7/D3/o+/z+3LS1a3Vzw4PeXv/O2/TdM0/MIv/AK71ZoPPnnMh9/+NqFrmM+ktGaMwfTRgDEGZTLet9LZqjNdWzObLkh9cioha2axWDDfbLAYptMp07LAKsBaFpOCQiu6FIkZrHEilprzHYfq4zk0LpUP+5YgeS3GujsyKdADC5k7e9fwGY/3uPt+1zEHbDxPx3vl540/VCCjlHqUc37Sf/uXgaEb4H8D/pZS6r8G3gZ+DPjm593vX1QQbwznH28YA/Iyrtfp0ePja44RnhiFOyJZ8qEyrbUmcxDPc84RYiRlcZ811hGyeFCTIaBw1pBQ+379yWQixorB7+uMrne+Hrg8yhop4dj+9fVqsQfIbSj33C0rxCieQzKhMhpNTvsoiLYVZ+q2E2OzGCXSPjs7oW1aCqt7FUffQ8qN+D/ltEeSSJqkkrSnm0EYb6h7KpJSBJ8w+iAMl6JsAilGioFglzXaGtpdw7qO3GxqyuU5X/8T/xKTJ09YrVasy4LVZUdoO6bLJTEEVBdZX91QWcWzTz5iOimonNlviIWTjq2chRQtBgzCZ0lWhJ2k3VJRFgaPZlI6XioFMTCbVlijmE8rXFkwr6qeI1QICpQMg0HloLczfHbjRT8EEfK5eGJ0+4Ai28NGNcyr8ec4zO3xRnA8/+8zjxzm81gQb7jnkHV/aVFwd/xR72GfNe4LZl7zmg5BLqPg5jWXvO5+OWdBdPtA5tDxkTHaorVBaY2x0vSQfCCgmFcTYoJ124lXXBQVbJMTZWFErTdJJ6POIkjX7rZcXJwxmU4xxuBTxBi7L1XtmkRRlPs9z2i3RyS1lsRVSLolSUH0EVQmxd7qwynh2xiNcpbtdkfTNHQ+sd7seDiZkrTBVkIKblsJYHwrnkkqi/jeZFIRfewRLul6MoXweVQfzISUaDtJNgpjiDmTYiTFtA9o9vpe2pKV5ma9pQ6Kd957h8XpBT/18CGT5Yz/4b//mxAzT5+8wNeBqnQ8/s4HPPv0CTcvnpJjK/oz8xnW6v69MOSYyFr4TCmLSOnZ8gSlFNutePhZoyFEdNLMJhXzaYXNhtP5hNm0EvKz05zO58ycxbep14iRlmspMymG43+8XxyCiUiMiIdcNP2/eIfbN4yU0p1AZpiDMjcPYMN9ZaPXzv/PWCf3jc8NZJRSvwT8PPBAKfUY+C+Bn1dK/RSyxD4E/pP+xf+2Uup/Ar4FBOA/zYLzf+ZI6X6LgmNEZsyIP271GsPrsT+A9yjOESIzXDcgMYNHzeEau/fNUVoTcxYJ+f6aXduIZgwZZyxdDH15RzxNXBZyFVpjCgNEnj55TFWWFEaTrKEqS3Q+wGZGKXSAbA/dBFVVHT4HDhyf8Qc+8ICGwy+EjmQVvqnRKlM34lD75PolddOy2dV8+NGn/cbSsJxOMFrx5nJO6SwT51jOpDOrKg4cC6X0HiXLSrIHccsWuwjrIikrIQ2GQCKznM1pfEfZFeyspwuBLiSq+RIzPeXf/Ik/xfmDB9yuVnzzl/+BsPVz5F959DY/++N/Eq0t691aWjiDx4Qdqos8PD/FWShK6ZJSGJx2vVO1BILaGKJP6KzpUkZr0LHnNMVEYQyV1pzNS+YWTqclZ/MZRYrk4Cly5MfffZ+nqw23baDbhQNhLWR0j8gNj42DmWHeeu9BiyDgoCA6uO9Op9NDgKEPnIYx3JpHJYJhbg/PGeYBvdkoWt1BfIbXNVgU/ChzZH4Qe9hn/O47h8R99f9h3Gkx/T6y0Ndt9vI7JJgx+cCdsapHKoG6lRbfHDKFc5SFY9u2LOYzZpMZTb2lS4omeOalBMgffe8DTpYLlss5Sinq3Y7y7Iz1eo1zpZS1tCabvN83ZrPZvv3f2XK/T+21bEpRvm6aBldJp2HygZPlAu89dVOjrSOQefLkhk3T8PjTJ3z8yRNCCCznH2MUnJ+d8mPvvYM10GzWlEZav5fz2f59sdaijMZkSxcSNosxIsaik8JWGawjpUC3XRNzYjmb0wbPZpvw1QQfRbldu4LT+SlFtvzUn3qP+WzJ7tkLdvUa1zb8tb/4F7m5uWV5cs7Dhw8xShE216T1FUuTUYsZRidOThb9ulVUtkRrMSa2RY+8Z03widV2w2wxJYTEZtdQWU3dBS6WM1J7wurmJaezkouTGZUCpTVvnix47/Qc//yabUxkJXtFFM9IbMx7C5phPg1J07DneO/JvcHybrdDmwLv/b47ci98qM0rgUhKiWT1HSKu1vqOuOz+mnxo0HltwPMZS+L76Vr6j+55+G9+xvP/OvDXP+++4/H9IDIHKPBAlnwdrK/0XTsD1X8dEJn+db7SJjhGaKwWNV2l+jefA8E3ZenM2cvF596US/ft0+ouSTlnkeGWFukC2x9IhXOkJGLlJmWZaOmw6e2z+1EJQ9CbeCeIGZckhvfSacN6s+q7bAy364Zt23Fzu+P3PvhEDNG6NQ9OTpmVBYTIoqpIs1JcqUvXdwfovg7q5G9REiQYFMk4ccy2Ct16VIwYIPpGoMsojrvERFUUdJ3oBK02LcqVvPv227z51iMuLy/55q/8I86WC8qioNSWRxdvoAtHdSs6MM3lC9rNLYU1kANK2T4QMOSkKIyDLIiIgj6AlYzU9Vb21irwCaeFU2RyRqdE29Q4oyitwaTBlM5Rac3UWm5rIVHbou8IGgKMHh3JOb9SzqHnZZk9ihiAg13BuGw0DsiHx8fZzB1kcmQ0Oq4b55x7WN3fKb0OHUs/yojMD2IP+8OO+zZspdS+O234/nUlxfuGzI8hC+ZOcAzqFYAnhMA2COF0vdnS2IbKShnVe4+bT5jPpyjfkLOgus12x3w2Y7fb9d53EVdVd1DDlBJGHYxMY7/nDuiMtQeZAKUUptdA8k1LGzwphH3DxHqz4dvf+4iQDbtO8b0nN0Qi7uqa88WSZ8+viW3H2WKKioGH50tiDFLu6pMArQ06Cj9EGUsMUM3m+CDeTK2PhK5DG7sXGlS9sndVljRNR4ygnWN+dsbjZ1f8Z//5f0FdN/zGr/4qf/d/+Z8JoWM+mfLGxZu88/Ah7371a1xev+B2c8v62RNM6DA5U1aW6BuCb4mJvdo7PSJjjEOpgMUQfIfTpuenyF6is5CDU87MqoquLFhOJ9JQIU+isJbz+ZwnNxuaTqxZ7s4Tady4bxbdqVKMNGRir0c0bukfkqYhgTqeiwOCvQ/oR2jyeBx3Nt1ZD58zvhDKvjkfLArGEfvnQU3HP9t/n0ZvUsqyKRBRHIKj43r18HUgyuUsSoRZiQx/RGH03Wx3/6GkjOqluRXirm2zwsdEaaxwXUzffaTFr2nfyqhUb2dU7CNhra0ckkmhMGLElqVUlHq+zfB6h2BjgPxyiL2fkJg2Nt5Tt5HrTcOLq2uaEGljkpKPcvis2Lae9a6VLgTfQJ7hO0tVaIrC3UGs0BqjLNGI07ZSVnjD1lEoCJ3GxooUOpzLtHVDYR0xB6qqpAmJ2XxCzIbtesWznFmtVrz76G008l5Py0JIdlGhYkDrTOUUxUTsDEK2vZ7NYMAorZ+2L4PpHkYd5lLOmTy4dDtNUydsoXFWMy0L9GKONQqU6PDkLC3vrifaFMZSlocDISFt9uOgY1za3Ldh92RupVQPdR+Cw0GGfEBNgDtlRPn+0EUHd80j9xvBvhRxEMQb1o7oUUzZbDZ/NAv1y/F9jfH+clz+Hh573XX3jTvXje41fv6d7/NQOjj8bAhiUt8woKzr964gEg3hgFbXdUO5nJFBSuI5cbu64cQZ6k2mNWIKud2uOT27oJhULJdLVtstp6enaCtBs7USHBhXoozZ762Dls3gej00ERRauFxt20qPQ0rUdcNmu+P65YqnL16y6yI+ax5f3pKIPDw7ZVVHUlvz0cef8iQnpoWh0l9lWlm8khJVWZYUpXBREhKM6BQxtqCLolhrrKOazoR/2O1IocH3vnvKuR7BbzhdFpxdnPM73/mQ3/i1X+f//a1/zkff/V0W8wkP33mfi7MLTqZLVqsVH//B7/HR44/IRB6dLKiKSpLiQqMnlqQSMSWsdVTFFFIQr7zRuRT7ddymBu2MlOxqKRdZpZmVFY2zLBcznDHQdWKdkg3LSUVVOEwIxAzkTB78lUZH6vishYMUQEpJrBxGDTeDrMOds1TdPY8Pydjr18h4/iolwMFxievoqtfe7wsRyCh116JgaEMefjZ8PX5j9FFkN3zVow8lK2mXVSOA6/hNHN93MELMOot/ScoSC2mEe4JAlllw/54c3N/baDJKCLtaSLgxQ0yZbBzaOIaWaZUjVvd9+MahlcKZXqDPulFpYTCTTIhSrsIWBdZYttta+DJZc3Z21h9Ymc1mQ9t5Xrzc8smT51zerHjy4orL65cYY5jOSuquReXEum4gBYLfYXLCEPmJr32F5XSCXUyxWiBJXWoprSmDdgWVdkQsIWRyhNnJKQBts2P9MhI7A77u1SjNHj5OXYvVFSlFfvs3/hlFNeFkecZP/+S/wWZ1y+Xzp0Tf0W42tIBKHt+2lDlhykHHp89elKXo2yBNb6SndMb0suTTqSB73nuatd9bVVRVIgCz0kIumBWGk5Mlp/MFzWpFztJKv5hPKbcNKgdC1wKOrKTmHpLH6MNihgO/JYilrqCLhT4o+vru3jqzmEs6UtLkIIfQoFmktRjcWSs6PihFTOkwx7XaOykPwcsh45WSQFm6nqT55fhBjPF+crw/HYIK7jw2/H8/0gFtY4D/7wli7kNtFBqUFpNElQmD0avKuNwjuUi3ZYoJ6wrctKSptxTWUZSlcNkwGFMQYiaHwMXpktnE0TU1RWHRKGaVJcWOzcsrzi7eFM5dadFFRcbgtCb1SaS2UmJt2sBkLkT9pvHsdjvImoviQkpMZcF3PvyQxXLJN3/9t3h6eY2xFc+ublhtdhSl5Y0HJ8znc7brW3btjma34f333kQlz7PrK4qPP+btN9/gNIiAXtu22KLAmIKu9SKY6ZI0WFhDwjBdnuzRc+XWKKUpQsZp6Xp6dHFGYR3bpiE0Ox49OOWb//gfMJuf8HM/9+c4PztjffuS6Gs+fvIpZVkyKae89+gdfNtyVil8s8EqhUcTUqKazpnOelNXpTCmIvtWyvW2oHCWcjKhrmu6xlFvGlw1wdqGGDPTSmOU4o2zJfP5nEJr6hT3ZT75rBW5C1BCVqBjFm6UTqh8mHP7RC0dFPDb4El9l9NwNjdNs5cL8X1Dgy4gZ7FAMAhtQitpiJDXohBi9QhtHJdbj9bGkNRJUiZn4WvJYnxBApkBkRlbFAyZ7THqMo4cx8SicQR4vJFw/D2vojnDiEooUSqCUmnfDNTFAAAgAElEQVQftQpC86pMsvxeIcESJaMPIRD77MJkQy56/kpPkBs0FIbr4yizH4IigWKljKNTEifbBDkHjPfEXg8iRgl0XlzFvVT4zXpD2wUuX65Yt55d5zGF4/ziFJSiKgVhKawoEls0WiWc0bgsminea3wwaJUENIoObZ0shL6DyugSEF+nlOQzM7agKCdEMj70pnApsg0d82klXVOpL09ZxaRylJUgFWjDYn5C2+ykSywFVIxMnMWkEqMKIGGKXtHT9ZYRQ3eO0ThjyTlJtxJK3Kpz34IaIfay3cYajMlMipIO6ehp2xadEyEkkvfs6pqoElmJJkvmkIFYLR5dx1nFvqSTDsaNSikhUPcdTAOCdkBk3P76O2ifOvC+xnNcazEMPG5fBDElHcPCk0n5pWnkD3l8Vmb6uueNA5VxfPPK3nb0/0MCNOhuZFzf1ReD3/t3CcFUY/vyUYqCHCqj0UYUdFNK3N7eonvUJnRbzmYlX333XU5PT8m+Y/Xyhmo6QUcIrdgAuELmZhcFxZ0tl7S7HamnGjknQb7Rac8Zc4Xj408eY4zh9HRJHSK0nmc3L1k1HUUl6M7J6UL2I9+ymBRkX1JvhUd3dXPNtLAi1te2rNe3zCfnxN0Way0XbzwABAFamILON0wnc/FriuIy7pzDGMfWlcQUSf1atkqDUZwsZoQU2a1XVIVhUVZcPHzI2YMHvHh2SdfuaHZbqukM33ZsVmtIAUWmrkUPJxnd/56Koiz3isLKaqw1lGZKCB1t01FVlWhuWcu0slhTEXKiSIqubogxoLUS1DtGjHNMygqfW3xKNG2L7z+HLsc96qyy2qMo4zk03nvGXJdhvxo60calpfvm4uvm/PGZex89ZPyYNLf03Jk/bojMsVXB8XOP/3/f17tZzqtv3OuukTcMBIxRohaLkspyHm8UYs6WlcJp4S2E1BFS5mQ+wTpNCJ5Vvaayp2g9x2pwxu51FIwRsqpMCHETVTpjdCZlsVIIMWONpt0GyIkYE77b9a2LK0KMbOuG9bam85Gr2zWrpmO12dF5yc4VmTcfnGCMoq5rtrs1pYWLkwWkyG51y6K0TKxlWjnwDXXYsZgplO4wOaHsDDVYFiSFdQZrLMYI4bduuv49jJycnNE2jlVXo1TGd5GqtMScuTiZc1tL0/rJxSllNcFWBWcXp/gu0U6X0HV0uw2hXZGdTOTJ7E2J/EkoJcRZ3ZOz267e86oEgelom5bZrMDYgpg6KdFpRUqZTMR3Xe+rIj5fWot5XREiWLherXly+4IX65Z1l+mCIHEkTWG1dKvr+7NjUWs2JB/QRT83QqBt6z6DaQmh3M+7McfpQK4+cMOGsp58dQxq0McSBMNzh1JgUUiAV5blSCjxy/GDHJ9XLvq858qaSq8cAJ91UIyJDzkpsul7VIyTMlIWY1iyHE5VVeG0AnUQgpsYRVGV+LZjVloqq/na+++yqAo0ic1qjcqRd999l+22JvTcjbIs0CahEL5cRrPZ1hitcGbCpJzz/PlzyqqiTZ5nV1eiM1MWfPr0kuXJGb/+e9/mdud5cXWDNgW7Zo0tIifLgjfffEBd16w3gdvVFSezGRM7IzQW6xtKU7GcTnl4MqPZrQmxxjnD6cUJ22ZLveuYL0+IMUoXFmVf4tLcrjaSfBrD+fkD6vWKbfL4rpEOTBJWwflyxuVqzbyaoq3GxpaXVx/z1a/9CXwbCD5y8+wF1YUBv8WHlpQC8/mSBOzqDYvpIG3hDntAr881jBgjJIUrKjrf0HlBYUJMPXdI7fkmrizY1TXToqDKUmaPMfB0dcnV7pZt0ijt5ODKPXcq6V5D7P4qRc4ZnbKoy+fc62LVeN/SdYa2dfvgRmtQxaudeePy07DHjf/G4w7kcQAlj4+Sxf8/ZN8fxLgPkRm/AccL/DhyvEuQfBVuVerVa443gvH3kZ4rkzIpi+S/IqM4vMmaEQlZXvD+PiEEvFaUNjOfTShKizW6lwqXdkf6CFoY4f19iagkJm/GOCmFKMhB6pO7nQg/oS2bpmPnA23raX1g1bSEmIlKuoq0sRRK46wIxKXY4rTF6cjE9mZuKmAMuGnBsjBMC0flFJUV/ogQ5CxZmV7VVoOWWrHtvxKE/KqVkJWdtUQfpJOonNBue/2V/jOMOYlYYAYVO0iW2O5Yv3yBNiV106H7Er9PiUlR4sOB0d7FhkKLEaXpF6Sz5Z6ErJQ4skIJaGLMCF/G0YXYg3MaY+j5Agdfo7qu6eqalGDb1DQxkbTCFhbb+14ZNUhwO2JOd0Tr7s6vhFKGFDoCHITwOk/oicFDgGKM6P4A9yIy93Fjxo8PY9z+PSCDk8nkS47MD3G8vt5//3OH8bqE7ftBd3LO+z2MLFIJSSksel+eUj2XfJhTXcxUhbRCB9P1ayKgkhzMq53n+uoSvZxztpzi25rT01NpCdZS3sg5k1MixoDBk5FAqqp6aQil+fjxJ2y3W5Znpzx5+pzad7zzzjs8v3zBy92OaEs+/OQpPhmyMtR1Q1GWdF3DfGKod2sykdBsmFYLdqtbqtJQVorTsmBWahZlZlIonK7EVNJafFT42AhKO1o/zhZ0IdK2Hq0yWouQpgqOXE5ZR4hBOCCh68RfLmecyhikDb3erlhUjtBuSEkTQqYoKiBSFCXTSUkIHa4o8DEzmy4oXGKo6nRdQ9aKoigZOHqFEY+qGL2c31n2sLregpaGkpSSkIN77kvTNKTFYt8J2/nI9XqDV6J3o1QaUvK7Z+U9Jc87+1CO0BvgiulnIsdD+Wm4z+fNzeOfj4OnVzkzdysnnze+MIGMQOwHQbzh+/sE8cbqpcNzhzFko8NzrLXE5F+5ZnjuWBDPWkvolXtziPvMRgTwsljVq4GLoUn9BxNCwPXKk9qKA3OMMJ0vmJSG0hVYjZC88sEHBzTKZIyxMmGVQjmFJgobvSzpukDXWx40XWCzq0nagNJsO2kDr7vEuvFsdqIe2wVPNSnZbbaAoq232Kxo1jXOaGK7pSwLFtUco6CcT3EpsJxWTJxM1KoqSEFqqVpbnC2xZUkMh1JH7staWotLt9aiDJw4LDRR6uzbtmMUl+mQcdahQkduIFvPbn2JsVOMnWB1wa5tUbqgDUGCRN/i2618VoXoLqQeOtNWDO8OXWIOZY20wCNJiOg0KLISRn/0sX9cyNVN24IroId+g1bcbLdsIzRRo9SBjJ2ziFVpKy34x1ytYU7vNWHSQcVXyHMHEza5RsjJ46xlLIg33iSO+RTj74fOAmu1IIYjuYIf5fbrH/b4fktLw/h+Nu7PCmr2CZ46eNDknMlafJhICaX0obuvP5B8J3YeQzLW5MC75+dcnM5xKjN3oiq+W2949OjR3lm9LEtcIYmo2AdocghY5yjKgpQCVmmePn3Kdz/4HtPpnDbdsmk8i7MzPnr6nE8/fYLH8OR6IzpcpWOz2aKBpmshdUxsgc6a4Btmhaaymel0gu92vHF2wkxH5lWJJWN0whYlhXOCCm12GGuZTKZYaynL8sDBSBlDprTCJQttQ8oZWxaEnEAbQcV9QwyydgurSL6TPURBs7qmni6pJgu0MigDwfcdkToDiabzxJyZL6Z0we/XdshglcHaYs+/9L7FGk3TNGidCUFENtEa4wrZw6tKFGGUdDu1TUsXAjonolKs2obVriaquyTx3PPpVJZE+nVzaAABxh2Q4z1qQJAHRPl4n7pvfg7XDryYMVrzufP+M5bRFyKQGdfixpyA+ywKxryD+8bYduDwXP1KEAMHDZc7VgVK4UMQ08jcNxQhGYxWr0Jley5P6uH90c+MylTWMnEWpxTOKAornTC6x8mM6qG9LJM9pUBMHqMMvq1pWs+2brhcbVnvOl5uN7hqSUiRkBIxdjSdZ7Vrca6gdBZrAqTAZK5p65rZ1Ihp48RK618xw1jFw1lJDCKIN7UVpZHXqJwjo3C2QimD0QW2mssGUNhe1EranDWZnAOFAR/FbDJG6QBwkxlo6S7ahQ6VEpOyhNgQuwa0JYSWopqQNx3eOGy5IJop2oI2jugTrpySwxbr+sPdihpyUVbk3EOV+hBk6IT4iQQv8yoihLXh/0kI2N7HPfu+8QHvI5vNhtoHPr665sW2hmIiwlhJrC5SDntOUyLv7QW0vouiyD/hNSml8EF0F7pO1JijT3dUT0VkK/daRAfi8DGCuN8cjjL2Yd0Yc+CODWWDQcTxy/GDH/fxmO77PD8Tbcl9OKJezWjvffrR/RIHRE9lyAaccZgetq87mZutD9iksWhsaSkLw6R0hGZHWVouTi546/wE37VURblvpfbeU03nZNWXX1Wibhrmdo7frinKCS/XK37jW99CWRG/+/hmy65p+OjmE7b1js1mQ0aI+TG0zAr4kz/2DR5/9DFFCFitmenEsiqZn84gNVRVxbRy+NZRWM2inBCaFqsN7aZhOpvR7jpsUTBdzFlcXDCtZkwmMxQWow1tIzpPWoHRvf1M7KTxRxmWZ29ycnLKdv0S5zRxcMiOma4T7zxjFUUH2+cfsrUFtpxx8eZX6IKhqw2rly9JIWFoMTrT1R1ZS4A3nc+YlJWg3P1eJDLDCZ8T2paiTTYtuLx+ScqGrumwtsAH6Qhr21aQ4gyfPnvBtKx4fnvLtz7+iJuYyK6UYCb21IkewQm9X9xQaVDqoGd14MmwP+dIYd9VNjQUeO8pioKc1T6wCTnh8t1y0ni+DmDFWAtuLBlxjEbe0Vd6zfhCBDLDmzYIrsH9iMwdlOWewAR4LXpjtJRyJBzN997/+Pck+s6BlMGI9LayhtxJSUBFhTX9azHS2jh88EMGvPeJUGJroLXG7Ms2/SGo5NAqy5KEdMzEINyS3a7h+nbF85sVtY/UPpDCSibyQLzqPNNqgsqR+cQRjROjL5V5++xcAg7f0dYNVemwSRZjqRTalWgjsv1aiespWZQvRUBKyktGO6wp9wdmSnGPNEhm06IBP4r4q8kMpwUONsYJ1K0yk8lk360lyE7A6ITSQFhLBucqMAWTqRi76azFAyorkhbkxfXaKPugMsR+I9A9vK4OZmcpE1MipEwM6c5h75wj+EC2BX/w9BmXqw0vO0+yJSgjga1S4l+lFNoagYrNXQG8cQno7qLsD5QcyPmgyRB8RFWvWhAc3/M+RGbMjzl8BpEQDk7Xe52jLxGZH9oYb+Kfh6q97rH99UkxEAXEMOX+ocdBjOrPxdyT8ZXGGOk28akvbdJLVgxzTg2HjGaz2VAZhVPVfl9TSpFypLAlMWdmsxnOGWwhbdY+Bs7Pz6nrmrIouby5ZrVriUpzfnHObV3zcrdDG8Pt5pYcE5NqStd1lBOLxnEyUWxuXvDmSckqN2ilOJlNKZRmWVUkHymtRUexPamcFR6LVhgjJeUYI2XvP2ZtgStnGFuR0aQYxdokR6pCmg1U9pClrOFcIXIQ2vboa0FXQ4rSKJBTzz1LEHtl5ElpiT4ScmBz+5T54hQ9K7DuTBpAmhWF7fcHXZBzpqomtF1HTCLUuV/7PbUhBAlsvE94H2UvVpoURYoD7/dlPYBkFL/70cc8vrrm8fUNATmnkgJ6nyU9IDT6++NdSZlc03Ut2tk98Vf4khB8hGrEqxkFKuMqy3h+D18Hkc/j1yEv8VVV/9eNL0Qg8/0I4g3QFhxUIccR3h5OPfrD778m3/uzOyJAQ2RK30amwGhNDAmrxSlVjQ6blA4RrHAw7vIY5CC0aD1y5FaHhqrBLTZz4FgkFG1MXN+uuV6t8VlR+8BkVhC7Dt91NLuayhVoMvNpgVOZsrBkFXBVSWUUp8slt1c3nE7P2W23zKYLrJXFj0polSB7khJyXtt6hIozoXQVRVFRVLOeZNpzZVIUZVsUZAlC5D0/KNROihl1yjjX4m1FUoocPFpLMNT1ZFsN+NhSTSF1HaaSQE85R1H2rYm+xqpCyG4cHGHhkDEM/0R3ZzgQBDfyod1bzcu/Qwln6Hhqg+fFesN3PnlCpxS7pFA6oaxD5ywiWRliCmhn78y94fAYBzHjAH0YY2LvME+HuTr8P6u70gPj4NoNqNSo9DQ8T55zFxUarv0SkfnhjPu4Lq97zut+/nmaHJ839jwI9J6XMYb2h7lojCZnDUrmTdtGdhEu3nqTsrR7gbyqlM64ZBLOyv83qxW2mjCdz5mUogkWQ6Yh8ezqJSFndFFys9ly9fKW6fxUOFxVicqih/XowTltvaUsKt5/dM5u8xKbPW9fnFI6R2jEtsRojdFzyJrKaZROaA3OSNdnDoJram1puo6ymlFVU6pyJnu3Et5jXdfE4CGHvsGjL5lkacHO2rA8PSH6CfX2lm63lv2ia8V2pbR7xNOHltAqjBPU++XlJ8TQYKol1hbM5iWpWFBoKYFvWwmElBbekrWWruvYbrcURYGx0qpcOI1va7rOY6ylrlsSmbrtSD2CNZwnCdnvvvv4Ez65XbEDMCVK9W3wSh9I4DkTc8Tg7kFh7hdgPLRnh/0+OuxnY2BBEC79yn3H8/G+EtTdIP7V5O2z/K+/EIFMSq9aFAD7LPMYZRm+Pw5ecs6vcGSOrx1D9ccWBdZacvAYbQk59RkQfe6TCbmjsIMQmoGUcMb2lggKnxI5wi56QgerqiX6IBH0mSIrjdZKeuyHWrXW5CgdLTZXxAydX7NpAh8/X1E3DTvfEZWiqibMF4Pab2BeKYJ2lE5zMp0zmxZYZ9BqysQZVOzIKZFD5vF6y03cktGcPPyqTEqViaGja3bENlA4w66OuMJSTArMbIqupmRbUjdBInkNhEbKNtGT9aG1c8gYrSmIfQcNaJSW7iGVIRAxWiJuqyMREQjUWtPWTU8M3qBEpYZIR/IGjaNLff0+iqhcmad3arXyMYn4XUqB0AbJlpIgMiEmfJR2bGcs2fZoxuCFZDLBWexyQdt4XDSYPgNxfXeB0hqdLD5EtNHiqK31PgD1PuwXvLV6P091ELM96Vpq8L6laZr9piXzus9kFL2VwqGDaRwkjZEf+ZPHppF73vn+mrZt9xYJX44f3Ph+yLnjzX684R8HNcf3Uhgy96PSx79TtjHh5+VeOdz1GTmAUUNptn8dRFove1kxX+JD5snLF1wsp1R2hrNF73icaLuDpghJ5ncIiZDh6nbN73zwjNlywbbe8b0nn6KMw5UFSz3H+zUPl1NUTlw/f8mPvfUO262hKB0n85K3L96DbodTEH0iFBOePH3B4yeXPH12jcoaazIqB3yoefhgwRsPzjhdzKmqqZBitSFZS8iGEHtfNqtJXSOcF9/SNYnJZCL7NElsTpwEFlENnaWawk0hR6yyGK3pug5nHF6BSrLOXEpU1nJaaHRYUxpDiDWrG09OFo0BFEUpprJFJVydrm3Z7XYi2qc00Uecs1zeXBE6KX/FoZ020+vMGLSVVu6QUs/Z05QnJ+xuV7SmQOFQWRG8JxtNUgbdk761tqis9sTvYe4ck3jHCZXat2G3dF23Ly0JUjdn4EZGI1DgEOwM1ZbPauIZ1sI4uXvdnD4eX4hA5nWIzDGMfoyy3IfIjFGV4bnHKAvcDYDGzzHD4/rQ2gbsdTviQAbOed8Cq404XCslOgz4DFrjQ8RrCXCMLcQ6XZmR+ip9qeswUURAL9G1kU+fP4eeOJYVGI10EylN13icBqUD86riYjklJs9yVlGVcwkyvKWta07Oz/jN3/o2N+sdbz56l122WGXRzmCKibg4o1lv1kyrEqM0613H9NyRjSUNlg9KkXoxvxA7iGIVoPUg1d/X501GBfGhinrwKBpQBrGpF/5I2mscDId6SqIaqWJAxQCqRRlHREPuS0Zdi7KWFP0dNMYoS45RLBOUIgXR1lFGS+dGSnvEQjLSHglTQgJWUfhRTdOQlKEoCpquldbCXpRpWJilc5BGvJwRR2ZYuClJhjhGZIbD55hrNeaIHW8kA6oiXBcJzPWobDT8PUPGLPdVXyIyP8TxuYHIPejL8WP3bd7HCM99z9E5SfYNB36NfLNHfVHq7v6pxEpQFKJFO8Un6GLkZr0hdx2T6VxKDCGidaCwBmc0trDEkKh63oRS0gX19PklPiUur69p2pbz0zPm8zllWVJvN5xOJ1iE5/f199+hshoqx3RaMStLSmcoylNIid224Vd/8zfZNJ5yvuRn/+yfx2pNYaQRo95uWF8/RwxNLNtdpJxYpvMJdjZj+eCcru3QKqNKR2hbUu6wWvXl8EhGETqPj4miKnBG9+uI/R4WYySnRPJBAgmjaRvhyYSOvstTBDustvi6wbiSaeGIuZBuTufYrK6ZTufkFGhaj+73ri52FFaCm+BbplXF1of93puicCP3yQxj5BdCilw8OMM9eUrTeTAObRQma+IQNEfVE0AFQc99IDYwwwdTYqWG0s844E4Hcm+/p+7P5Zz2wrPjfe11SONnz/HhTImvnefj8YUIZO5DZPYdHxxqZZ+FrgB7lGUMxR/zX4YsdY/IpO4VFMcZMULMOfVib0IG8ylSWkcbPBNnSTH1WjARbaTjKYOgItYSlRgWNl3gxc0t5/MZqTAUHCaiMQaFot7uiBnq1nO72dFhePjWOb6TQ9opmE4nTCYTgTF1ZrGY0zQNs2nFclYRvGbiDDpGXry4YrNtePL0ki59wHevtlzfbrjkip957+s8fPMh77//FVbXV/gXT7n84AO2q44Xv/893nn0FpNJyQMWTCcnKKPofINRQIKUPDmKiFxGAi9nS7S2xJj69yFKTTcFbFaUxuG1SIGTFSnL+2SsxvddUjGBMhafDMkr0dSJkaTFcTslsEZRVI6sFNHX5KQwWpNTBJUgRUKbe3ddaf0MMYlhZFJYpfHBC9SuIPULVGmFUpmvvfsOZ2cXPL9Z891nVxTOoNRhXmktWVXqJAMa5lYegpe+njzMwcHIUX4mEDpZUxTVPnjb82EYB0Ey9+DAuRl/HebveAx160MgpNGaH3nTyB/GOIbLj/8/HmO+0/H1r7svgOpbBl6HzOyvUcKRyWqQlEiitqqyZPk5oFCEOILx+1J3l+TwNGieX1+zLC2aJYU1xAgpepKX19I1O7Sb0OXIP/v134Si4vyNt7DasFwuid6L/EDo+NpX3+P29pblYs5iseD29oYHF2cU5gGF1oR2R2Etv/f7H/Li+pY2KnbFGd/+6AMuv/sp9eyCR48esb66ZH15ycurS96YlJxMJjw8f8A7bz/i4o0HLN98g6ubl7zcBkyO+G5HVZzjQy1eRzGJD5lKFK7CmAIfEiG2kANGObKGwjo658jeU1YTvEpstxtiTswmUkpL1lE3LT5lqukc38DJfIJJRvhCUyN7lPcsF1O6usaaCpU9wXcsZrInWJPZbG7FciXBtKzY1LXsJz7tKw+F6SsHSNNIlyRBfOvBOd94521+98OPabKU0YXy3YuIGIvKYHJfjpKG7L7hhFHgNoAHec+PKtRB4de5TlAp5/akb4DsikNCpg9Gt8O+NDa/PZaQGJeaZIjg7B+LQOazODJwFzqHA8oyZjgfl5mG5xzza8bQ7X2IDEjAYnoGd+6j0kxCK7GpL40QQI0GlRS5l5MfE5jEDylhnMOTiFlJl1EvdKdG0aqozrY9Qc1zdXVFNV8wXcxx2tK1kVJbzhczrLXsmohyIm704OKMrmuYTCtUFvPHl9cb6l3H8+s1T683BGX4g8cvuOoC79uSi4dvUUynPHj0DtP5khdXV9Rd4OVqx+XVLQ8ePCCkjHETinJGTJ4UtyidyUlKQUOXFYj7KcphjCVnj8lCArNGkaPGWk3QejRxZXKqnEk5YxT4mPpAKKOtEt/hwkDW6Nwr2Sph2A+qo8lL9keOGC3W84pESqJnoZVwktSQRRBJ4a46L72ugtbye6aFI2XFru4wKtP4rg90evXgLFnaOBAdEJk0lApHwYwx+pU5NyAlYxQmhoTK4c5jwxhfM3BkTI/IDJsCHHhW4+BeIOgvEZkf1vh+uC1jSH38/fjnw70+L8MVroQWLzUpIpCGIEYrUj/fhQQsBP/BjXiQK5B1Kof3dDEl7HaQElk5yslEtFQkFEJpixgcKkKO3Lx8wc16h9Xw9tsPabSobXf1GhUS5MjD83NWqxVVKZIGTbPjnbffkvkfg5RtfOLTy0ueXq9ovOLjp8/5rQ8e06UEWvET//q/xl/69/8D6vWG50+e8sG3f49f+4e/zEdPnvO9Dz/hjQ8+4OzBBX/pr/xlqnJK3Wwx2uODx/sW3zaS7KiRnMSQHGQNORBSS6I/b5IkGqLO3dL2SUgXe+JriPg8mFOKVH/XdWw2W6q+fJRzwneBlDPzxQxXWNq27ctdCu9blM74rpGuop5nZIxodjWdF7dwRU/W9ihrhMidBCm2zlJgePP8jA8ff4pPmZSjBLMIrSH34q4h597k+Hi+3Z1rw4+GBCom3yPT4Q6KnGImmQNheTz/x/cbozX3BfzHa+ZQFXn9GvpCBDI5H0wjjwXxjrOV40V9H9H3db/jGL2RG97THZDz3o5AjCP77CUL0z3EiFOiI3MoP+k9vDcERV4ntnWDM5aX6y3OKIiak6mUmfTogNJZpKOthrOTE3CWaWFICRyOqijJscU5TWk0cfh7UmBaOEiRFCOlK+h84Hqz4+NnN2RbcrPe8fCd98iXVyxPH/DixSVvvvEWuybQdJnF8oK33vkK82rOcnnCycxhe7TBew/ZY1SWcpUyUt7oDp/VcJAfNEsG8qp0NqV8QCWccyKYp6xIpnM3YAWFShl0JsYA9MaP9LyAHEkxE0IUB+5hnuTUa/QIHCkt8XLVsEHlnMGBD4eFFkKQ2nIUvlNhLVWvpmyMRieNLRwYjYqHv1FrEcR7XblgaAvff759huO9eDTlnPc1ZmPkPbFa0MgxQXx434YMZvzzOyWrfHDhHgJz7z2TyeRLi4If4hjzAcZz5XijHx47nk/Hpcbhmjv7nXr1ZwqNyYLYSM6eRENG5T4bl7Whk8IYIfnnGDBWYd6f/lAAACAASURBVDQE32DLCTF1FJOCZtvx4naFyp7FO29JAmE0JIuPGVKHKQxlYXj/vUe8eXZBEz2b3Yrp4pQUW86WS6aFQyk4eech0+UJAMG3hKamNBFnLcTAkxdXrOvAh89f8u0PPsJjyGXFX/0P/wrf+c7v8/f/97/LN776db7x9X+Vi4t3WMzPOJlM+LV/8it8+uFHmKLk5uo5N5dPODl5g9gqVPTMJo7t5iWulykoe2Ky6lXD2074cKnpcL2tSdaZNgfKSQkq0tadqK4HT0qBHBQhRapywrZpGUQ4J5MJKbfsag9asyzLXrwTaXVXGpUCXd3gCitSFkBZFigl++92s6IsFcooCJK0pej3oqnDR69RqCg2OoHIxcmS0/mU3bajiwGUxSRBnXs1oTtinjnHPkDt76fsaM6q/ZkWyZR9Eua9J/pAjonQSZJnMKSYiSremb9KHZpchjL50FU8BEPjfW+8NoSO8McAkZGo/K5FwXHGe4y8DNeNo8njA2B8/zv/7/2D5PtXNxSl1J51DT1akwNaGTlAlaZLCefswYcjSVteaQ2lhtl0QrPboE3B5brl6vqWpnvAe28uiUpR2pKqLPpyQMA4+Z3WCAdGGU1W0h0zWZxSVo5mV+NsxFjDZDKjcgWlK4ihY72+pa5r7OkZtwF+9dvf43e+95xqOqdtPX/6536en/yZJdOq5J/+w/+TP/1n/l0Wk3MqPWf5L58R3v4K9fqW1O749re+SVOvKQtQsaMsDDkGrNGkHOi61KMvYm5otBMSnO7hxSQidppE0qKGa5zB2AkhGLJKYl8ffc9hsYQIRhvIkE2QkpUSwrDKFqMrFFAUJbumbxtVeR84MsyLGHuCnvCSlFI4NZDEC1LKWBtpO9EO0r3AoTWOlOB0vuDkxLBpWnbbNVEbPImy0OSe3J1RSMPlYe7EPlgT9K4PsIkoxAA1KcmCU4r40LLbbZjXJ7SuwpmC1HtV5cRe8XdcTh14VXuOjBoUhg/rYwhexmZuXdftyXhfjh/8eB0iMw5Ojjfu42tfV3ZSvXTv8X6XsvDYlBoSAABBSVFDK7cUpHJOd7JyhbRjL+YnGG24uqp7XgU09SW78ynWGX7ya++SYqasSoJvyUBVFTSbLdeXz3C6JLQtD+Yz2mbDg8UpObY02x3lpEJhqVfXTCYTTssJQcPl5XMeP37MGw/f4vHNil/+lX9CefY2anlOs9rxb/3sv42h5OWLNTpq/se/9Uv84i/+N8wqi+92qK/9GKFp+Xd+7s/y9/7XX+Lt997n8vI5UDCbTvHtRta6tYRWzhvdlz6cFWmJyjmafq1ozd7gNWdZi2JnIohVToGrq+dorSlcxa4VRWSN6PWkFIgoyqIgZnh5u6ZwU9rgmc0mdClQFQVlKcKCOaeeJxNpW9lP5vM5ymim0ykvXlzRdB2TsuzFWzOb3W7fwTmdTok50zYN07LgG195nw/+n3+OdiVJJRJGum1zJGtpPmE0x8aSEakvNw58lUNZSKQcmnaHUoZd//uVGnzkel+60X2Pz+69DtxrUMjxXB6SMnnS61bZFySQ+SxEZoyg3FdGOpZtv++5x1+1efXx45HywZsm9BlNUn0LdpKMPYSIs3L4GiuksxQ9QRk22x1kaHc1s2oCfSv1ru1oWk8OmcoaQujb54y0HGebqVxJFwPZSMujzoHYBZyVxaGyIrY7uhAwMbLdbmm6gE+Ky9s1L1Yr3HRGUVWcXZzTNp6uqWmN4fbyuSyirmG9XlOVjnq7pt1sKNCgLIv5EmsHY7MhqDSQE0Y7jOrbNe90zSAEYHVAyva8DuL+e2stKci9m8b0jPuxDD+iGKwUwYs6J1lhtPv/2HuzWFmy7Dzv21NE5HTOufOtrqGrutnsURSbImlJlCWLMizYL3oR/CCbsA0DepEA27AAG371i2EIhmzAMixYhmXIgGFANEjAkgFBlixrIiGCpNhkVQ9V3cWq6rpD3XuGnCJiT35YOyLjnHtuVZHmUEbXBi7yZp7MyMzIHXuv9a9//b+UawCtyuatDTEmJMNI5fUixS0bfGQgjQ1BzjC3KND4cF/quVoUMoN0OzWuos2ZPGgdDPNyaHnOl/kN09txjuYAceRzFzKuvG/f92IO13VFKE+PfKuUEhiu5cQ8D5EZzu9QIg0hMJ/PP0VkPsHjusX7o8pH173+6mPCiymkzMk+MJQXUuHNJES9OyPO8VZLF1PwGUykD6mQQsE1FetdzwenF/QvZ0yOuEK4t3UlBpAxCt/RR1oiJ6slW7Vnvznn5MYRWjvh9a3mGGNYOEO3u0Bl+M533uKHvvgl7rzwAn/lb/7v7ELmL/7bf47Hj57y8z/3t/kHf///wqG5cXLEjeMFHzz9gJylI7G2jgfvvscv/JNfQKXIH/yDX+fGyQJjM/P5nMViQctu9FiL6UCgP6ACPVrLWnBxccFiIZ54CtGi2VyssUYxegFqcO4yDcJaS06CtsacWB0f42OirhpcvaDrAta6w2Y+lNO1xXd+TIKGRCbFDAW9HZDZQbh1+nsfKAqiVBxjZLVYikaQVgXBZ1y3xjk33f8m69Z0TPfKaZdoCAEf+lFXZtqSrfNlDzm47K491Y8Z3u8q8vi8+X3d+MQEMlfJP8MPczU7nWapV4lCcNl24Crp93Ci8jPPPZB9/WESJXBavJFECCgVUlMPtS41wiBoTII+DY7HSjgJZYPe9T0za1nv92z3jvP1nlurOSnJe/vkWbmV6JSgsHWFC4E2BFROVFaypj4EmgKFWpNpL07ZxiiGhrMVfdKcb3Z8//FT9j7gaktKgXv3b7E+e4LfnGGdwVXQ+z1Nbei2G04fPWbz9CH3b98mhlCEoyTKt85gjMa5OTkFORdaE0KPLRMzF3VfGNrrMoPRnWQLU7XZUgoq51+0WZIIPJFGnkeMCUJAuRpXVWjrUKXMhE6kPNg8STYQU/fMBp+ylJnkwvPy/EkJJoRDNjBY0/sg8K4xRurnWaGNpfceW+YkSheGvjoED9cE1WNwrUVcbLogeO8JURzMnevJWRaGzKET4BLxNw/cmUPwNS1FyG8Sxs8DjKrFnwriffLG8zaL68ZvJbABUCWoF10o0T46/K1cQ1nC/8FE0hYXHpUiKSfaPhV7Ei2eb0AbPNZonq73+JggZ7puX6QJNCkpFosFlRPxvGZXY8jcPF7Q946u3xdUo6Jrd6wWSzZnp4S+57133+e1z3+JFz77BX7+//w7fPbzn+Px2QVf/ZE/wKxZ8ku/8svYHCD0NDVstqfcvH8fZzUOi4qBf/gP/m9euHeXn/hDPwlxw9npQ1Q+tPNaa+m7jhgDbkBrrTh9By/cumG/gTTuQXqSNIcoHkly7pSUnYHYibO40YqoFDlJs0MIochuWKytiUG0fDISjBhX4ftOkOK6Gj/rYJaotaL3HX3fliQqS2MDYqYr5sNmvP7bQWYhJuZNg1aS4imliiBpace/ZjpdnWeHOVnM77i8Fg0BTCilLlnLHClHUnq2jfqQ9Kln3nNKE7ladfk4PLNPRCAzfNiBAHk56LhesG46pl/8qsjdEPkO96dR6CVrgnKrcyaUMgFaEWMJeEKmtrVssM4QSh1UTnJBIWRLx2dDCGKgmHxgPnNs2pbFvOLh+Y47R0tq67mxgpAPuiNVIWRWVfEqymI1YFMUOWpd4buWeTMj+UC337Hbe5J2tF4TlCa5hhdffomT2z23bl2gtLhtP3r7uzx9subO3Vssa8eNucGfPWZ/fsbZ978Lfc9ZCiyaBcvlMSktcbbCOYcmo7VD24qkxQXWOKnpqjRklAK/DmNs28OQjfBQEhlnKnJqUMahQsQZB7kjZzn3RmV86oldRquKHBI+B2pVjO9sBSqSxfURkJZp8iEIFhG8KZlMFoaizQ1FsVlrCXZSEka/KECLKJgls6wq8BEfSoBVNGkGzRjys0HLNLgYbiVDkc/f956coXK+lFM7dKtpuz1Kl64mSqeSSePFf7Vr6Wr2NAT4VxGZgRhdFRXkT8fv/5jOmWk2P507Hxm4qMst+tNjqwRRlyBdq/GpKmtRtYWxxKRyQiWN0WCtIeVITIlkrWzYIYoUAommXrAOmfZ0y/cePObzL9zDVRZrFSF5rHHUTgwe69qxWNbkDCp7ZibiCprtitgbfcv3vvM93nzrN/naj/wY/+yf/zK/+D/+TY7v3uCv/tX/gW+8/gZ/92/9PH/uZ36GH//KVwkvv8LZ40dcXDyljT1//Kf/FLpb80u/+Iu8+93v8Gf+tT/Bbr1l/cEHuKammp0wrxOussQo3YJ1syDHiFFyrUffE3Uu10hNSgnvPTdu3DjsRch12cxno1TEfr8lxGKfkoUfs16vJckFrHX4kHj0/iNu3rlDKiXj2Wwh9jJZ5P436z2Q2Gw2HB0vxuYAXaJPH0Q4T0pPEWMUVdVwenZBVTVjx6P3Iuqpyj7iMxjtOGoazluxaUi5SMopNZYbVfFayjmPKM/zuFwyXyCHSPKBoMJoV2Ctpe32GKuZxUaQ9Xy9OfMQCMXJnJ8GMYd1++OR5eETEsgMJ2nKjRngtWcMIK+0X8NlSP95dgNmQAUmm8607TqEQFXsBipj6JMEVVJRUVLWyOKwnAo5LPYRWxRtjVXEVDgSxlLNGrp9z2LWsG/3HM3E2Tkbzel6x41lQ0QgRW0MOSt2u7aUFzwjcVgrUggYpJ4dQmbbr8kpEaJivjridNPyL37t17n38st88Wtf4odv32FztuWd9x7y3Xfe5c1vfZOZyjgDi9rxYz/+dZpqxntvvU7qe+LujMZW2BzY7zboyqA0QibLYKxBaVUCQFUuHo02ihTDeD6HNjmZxLmUUAayqhg9CuogC5pWoixs6zzWo8mRHDxWW3rfSos0Pa6aU1VWuretkNxSFu0CrTUxiIR7JoomA5lIP86tqqrQIRByIqYg16wS9r61Gh+D+Kz0Yn5ZWceNoyPyfs8+KzadCCUaawU9yQmjhCSslCKmw7y82i49crhycSSOqdhL+LF9cYBrRWNHOgJyrp6BdXPW4zy+isgM5OzheFPTyCFA+nT8/oyrpaPhdhqwXAetT8dlKP7ZBX66DmqEiwaSgeusyUqPxgZ5whHOMZHQJJXGB5XRYoxbkgBxmhc/ppwjZ+dbtsctjWsAIcSHXsoL87qi73tRuTWW1Pf4XriPaehCBLp9z5tvfpcvfPlr2GbOt7/3Fq5S/Nk/+2cxWvHF117jf/prf4O//N573L59m6OmQufE7uKC+y+/wI//wa/x9/6PnwPf8+pnbvP+b75J3yW0mTPT8v2rVYPVihgCBo2zDlwi+w5rLNrYkoQNkg16FMAcNtQYD4HmcJ1WVXPoJkwehVjLDChFbSuwYpWwvVijleX2bEUKRe4jIh1IIaKUYT5f0veeuhYBu0OFIo/Xbl3X7PuuKCI3tJ3HWCn/Zy1r4TgXUkZpaJzjfL8XNF1Xsm4Ms6eIs16dl5eI5NM5TIRsxvLRoPA7IjOTEpPWh2RsCNivrmVaXU4CryO1D7dXkZyr4xMRyEy/yEiYzPkSujJMtKvoylUo7FrTSJ1HIbTpmGau42uyoCRZUWrLGqUgYSBLt4wl42KkqhQLBZWu0MljayGMReeIKWE0xK6ltqIjYOYzNucbXrhxxJPNns/cvYUq+idd15XNKGOMsOCdc/TdXuTxY8ZVhqwU682anBWmXtDGjK5m3L17n8+9/Fl++LM/xK1bt4j3I/dv3mY5b7h/5waqbWm359y9dZOXbhwTe8/pw3doqpoFPXHXkYzG1QuqupEunQxWW6w2oulCJqlUnKYryGJlQGn9m/6WQ0RujCbkDNah0DjjyMrIxHMWlTK+34Pd0/UtKRyQHZUUoWtBGYLfo0goFzFmhojjpfFWaUPMGY0lJSmjXAp2dZbvkPKhVTuDM4Y+SmCTkpQOM5rGGm4sF/iUCJ3Up1NGAqFYRP5yGrvWhot/7I7isDEJT0HgVu97UhINBtF36WhbKW0plWnbBjgEJlM9h2np6mp9eXjNUO8f5v4Q1Hzafv37N65DV67LOJ/LdxmeS3xmLZ9uBDlLGWksH3EIWJQy+JhHzlpjFMp7TAabItonrBHtprmtaX0kGXFlxoifUNsFIQgnxS4aOiUK11XjxMtOAzlJCb4euvoOZE3fB1AOnzrO13sePHjAv/zTf5pt53n34RNy0vyl/+gv8cLLL6F85ObREX/1v/2v+f6TR/zGt77JG7/yy/zwV17jj/3Uj/LBowf8P3/753j13n127ZqH336f4+MbaAx1YzHWgDFYbdltpKQ1O1oQe+mUVLZG6VICikHWM6QEba1mu12zWq1ESLOUY3ofSBHQDmUhh8DiZIXKgW63Hq9/33e0rZR4Zq7COMf2/JR+37I6ucHR8Q15rhaV45QSDlDKgsr4vi/rUQmujFisWGOZ16I1kzTMZ+IF1wePjXlcd33waG2IvuMzd25zul7TpUw0CbId51DOg+DcZT2qcS6VNWhaMRmCj77vycrQdXuMkfXo6OioiHJ2KFWNgd+wJk67l4BLZfCr830I5C5fJ89HZz4RgcyHcWQ+jO9yHex0LUcm+WuNJq+zKEhBuo+SqDwBIsSWCtpQWYi7nsXcsXKWe/OG2hh0OjgZP93tCcqSvEe8i1qqyrJdr6ldxcVmR3u0YNd2NJXDaZlIKQv9tPNBtFdyEp0GLRsgPorqZDUHZXi83nGx2zNrlvzpn/5XsdayqmdUaLyGe/fusbhxg0jm13/pF/HbBRaI+y0pBFazCnLCWUc1m7Pe79HWotUcU8jHMsklINEZYvSkUq4Z+yHUQX56OmGNMUQFJhtQ4Avr0Dr5viGJbo6pZyQCNdCrrgQAgnJUVSOBTN/iQ0edl1R1Q9IZFbN0R2lLSqCUaCYAh4uOwpWJiaG6ZFCCqKUocC2KVOad9z0JTWUtN46P6FJmGy7IoUe7apynaiQSR4ypCMFf4nQNc/gSaV0PdvepZC4HRCZGT4wVUocW/Zch+5uS5Mbzy7MtvEPdeorIfMqR+f//UPqqSNjl8fy/aUCCfdTQARohR3TMmOBpFLywmFEbjU6S0CmlWftA5wNeW3xKOKVw1pB8Tw6R0/WWpxdb7h3dFAQ0D8kjeBWLA30GbVEGlPaFnCGbYwyZV15+lcdPznj9zTd5erHhZ/6tf5cvvPolzs/P8QtPbRrmJ0u+dPs2X/zqV3jw9R/l/e++yVtvvE7cbzmeO9anH2BRrJZLog/MVgu6fsfR0RLrHNZUBOOZVXXpHhTeSSp7gXQZFv2lwqETxKUqDs+ddEAaw3zRcH6+FqRZG7SVzh/vMyiHdhWRrjRuSDOIAul0Mo6279mcn6GUYn58o+xjmdo55OoMUtrLiRyG30KCLZUh9GL2Kzy6gNYWUMLVzIjpbhqSIOERrhZznDWixpxdEdkcWvQF4VXq0PgwzKfnBt9azCqVAlIo69bwL5Z1LcuemSaNFFzvGXbde007mIb75X/PvQY+EYGM1pq2FVv255lGTmH6y3YDlyHVqxyZGCWLuU4Q76p9QYwRVS60oYZMEkG3HAvhKSVqBUuruL+a8Zn5jHklQnQpJWmHayo2bQc6sWkDq1qLd46VNrtt6+ijdAQ0FZcDMyVqtCChgrGG4BPKVMQsyFJTN4SUefTwA97/4AO+/KWv8NrLL8lrElTK4CpHZQ3HtyvqpuHR996irSxVTqS+p3aGtW+ZVTNyKevUTUXvW1YanDMYrYtDrJSBYrFhIINCS9CQNbmYrk3r/WN9OYpMf8pgi7O2tiXIAUyK5NiRVRQ9iZyoMngfWMxmJDIhJVLs6dsBobiBzhDSZdv3XGC0GIdM5oCKDOc359K1FopacJQzLaWqodtK2gyX8zmzfYfiDB86jAJrK3nvGDFWRBIHr6jpfB1+0+HzXb04B8XfEHpSmo1E3YH5P8zHISAfvsN4/Em2Mxx3aLuevs+nFgW//+N5vJePQ2j8qEz0ea85sMFKKaGUFCJgFdjgWRi4t1jwxbsnVFZsQUJIdN5zut3RpsR5H9mlnqQ1qY84bUganp5f8O7Dis/fP6byAWsZE1GlEIJrmbsqQ9KucA6Fz7FYrDDO8sYv/xqbtuX09JQ/9cf+KH3bcdHtOJnXxBzo9muUTTgq/sk//Pt0FxesKsf9m7fIePpth80Gqw2umYvDfZTuI2Mz1hgWzQKtYbBrUMqgVBKrASVrQMoRw+F3yjmXBgCHDgHnKtouslwuiSHjXRr5Z/v9DiqD6YoZbkFRTR9AiT6NNYpZXeNj4PziCUe37gCKrmtHAdW+l/3HGkfbe0ZF8XKdD6UbrTW1c8SQCfLhUYpSvlcoJU0risRqsWBW1WzDbjI3hvmURzHEjzNXp8jfpc4l73Hu2c6l6fMOa/Qh0c1cDsKvVleulqM+bHwiApmUfvumkVfHtRYFV14z5chMu5aUtRClQ0gj5QerLaH3mFpcTxd1xcvLJS+dLHntzm1eurGUckRMWFOhreFie0FWsPWRer5gve/ZRnjv8Qc8OLsghMDpvmPb9tTG0thEzAGsKXVo+bzO1iQUujGkKoLvqRsFQZND4POffYWv/PAXsdbym2++zmpxRM6ZxY0bKOdI1pLOM0+6jpOmQs/vEbuWsF2TY2C1WuF9hMqQk4jszRcLjC6+Tgbqprg8F3a87yTLiNFDVtK+mRS5TNic82ikGGPGKkvQWSwIiu5Mxpa6boMmSQvyzmJij6lqYt/RFL0ebZ34VsUeZSx9gM36AlBoVYEe2PGlRT4FlNJYW5FSKKJ5QVpOk3RpxBBF3zRJacgiujOkSM4Kqw2VM8yspqkcxggcagbl3Bxx5iAGKE7eImw3DWKmKJXKiRw12iZSloWu7XallXPHfr9HKSO6EWha1eEW5lLdfno71ZAZroMhOJrWmrXWn1oUfELGdaXwq3+H6zuaPuyx6YKfVMmtCz9GlyxWLAqE5Gpz4oXG8ZOf/yG+8Jm7nNiOFKGuZyPieLq+IBtFZx2dT7Qx8htvvs3D03PWKXDRB9794An77jVmzpJjIJCxOuNsJetW1rKGaYOrFb7rqTK4lHj66JRd1/PTf/JP8uDBAx48+D7//V/5z7l76ybO1fyjf/h3MHXNnRdf5vjoBsvFgs/dvkW/mkPo8VtNyo5UzQkhkZOmC5GQMsc3jjE6U1eCrM/nkhiK6J2MlFJpSc6gDYRERjbj3rc4XWG1ISZKYi08oJQyprIsamnN7jrPbHlCt7tgv3WYusf2O7zbC22gdFXaqmLftSQcbR84O31KVdXsd2IeO5/PsVbT9xGytIz3vXD8yJoQ5Pqtyrqz71qcq/AhCnlaQe8lYAg54YxCZ8vR0nHv7m0efedtqAS1zqlUQC4hHGKmezkxfJbXpVEkJc0VMRp635YA1rLb7WT/KjQJYwzJHvh/AyITgiRdU0G+q/P/+sDlE15a0vrDLQqmmSg8i65ML+qrqM30/vQ10/rfM91QMZJV2YiiBFcxB4yzVDqzso6bizl3j5bcu3GMUgZXPvNsNmOzrUlkfNaYumG92XHRy4bz+OycLiX2Xc/5dsfN5UI+p3x4QH5krcWPJ6BIE+8eUqbrO5LSnBwfjxvp0yeP8X3L0WJJDAt89GjX4PctOsNqsSTlSFDCAerbln7ULBHPIaedlLOUoqlqnDUYU86XMWPpTM6XdFUpJfYNOR9+i0s11iFwVAprXDHPNFhbEUtLsjKGnDpS0ChVur+s6FdUVSUutlE0LyptWK87rHUkU7xQipqvypBLcAJK7CNIl+ZIypcVo7UqeMyglWEsIYn66WLejJpDIQQpORlR3UzBY5y5ghiGUua6PF+HcXXuDceN0V+LyEylB4Z5n3Mhpo8Cee5aROZAVPwUkfn9GM8LWD6qRHQdpP9Rx7/6uBpy7kEwLxUkIichYcbI/eNbvHLvDjcWjXiooWmaFdbKBqRrSVKwDuUaMppZPef7pxf8+nff5nTXEpVit9+zqizzWU2IgRAjqZINTyxCLNrWRVXYoohEH6hmDfV8wb3bd+h3W04fwKtf+zr3738GYmIbM/VyTlKWG6sjut2erDNuZgihBR8wuSK2rRheaoga5osl233L8ugYqy1GiWaUNE441OR6kjU/HNYsDtm/935MqodmBUk6FNbV6GLsiIqQFa5ZkpInehHLs4VzJ2VssXFxdUXMAdcn9vvA8viGoCohFE0p6Y7SKo8t7SBWLgMpV03K1t73I0dP1t+E5Gd6ZElZozg5OqZyinY6T+CS67UcU5AdOAQ0V+drSkGI5JP1Zfg3IEbee5ytiO4QwFxH9k1XeH7XkYKHWwnonn89fCICmZyfFcSbkoyG51z3Ra+7f/XYQ415SiSalkCmYySrKnFPNiWj1VZhjSjyniznNFqjkqffbbG2Iscggdh+LZyHnJhVDSHDJnRUCWYqc3OxoAsX0kGgHZu2k4zGlA24cChAgxJDSbIWdcjeYysDC2kTdEYTYo+rLJVy1HVNPV+AMjR1TUiCOqUih90HT44HS/WsFKayxD4wrxvxgZogCraoXUodOZZNXSTGiQFrHL3vhPwVAzofaqHGuBK8WFAabWegNFkbnJmRcsZVc/nNlSL2DpUjSbWgNSHKRRsLRG0w5BDI47xIKJXGDi/xVsoiABWFET8woqZlpZQPyNEAbSbUmKkMpo8pJaIXbYjKWYa6sjFq9D8ZSE1Sbrsssf1s9i0lL61SCUQkIKEZOpjiiJwYbYU7U1muIjzD74FSIxdnmMcDqXcaCA0WBR8FzX46fnfG1ez2uvvT/1+F8EE6Fz/q+FdRGSn/DkPWk1w2RJUzt2+suHd8RJV7SQqwOKNE7NGAKz5IWlmUUWSt+aGX7nPr9m0eP/mAh6cbdirR54Sd1aWABdEnvO1xrkLZIpfgKgwaNTMQEy1bbt6dsVwecXZ2RoyRl15+kWa2IOtKgqmYODm5y2bflPO6RgAAIABJREFUYk2DmTmUjnSxJaVAPZuRfRAjytLFN1vOqauatu9omgZjDFXhP3rfUc+FezacJ6OdnKicCDqSSkdgTgo7BDzWkTO4qiZqyAliVvjeU1UNs/mRCIFmB7ElqIyKkVDQUm0q5ssVffDUWnylNIH1ZsOdO3dEhNDJgbfrndxXms4HXCNIULzm2h1MGofrfUBZFCJoOPgpGWM4Xi2Y1RV7ceUt8y+SMSUQK93Ck2l2dW8dHsvpMHfH4MX2hOAIoSeEegxmvPekQmaear5NQYerc/iZuX/l/vPGJyKQGeCo6ywKhr9fp7HwvNurxx42oelzriMeXTpWAquklGArQ8wi652z4ubxEXOn6Hc7srM8fbrh5OSI8/0FRycrklni5sd07Y5Z5bixmPFi3WBT5OkjeNB2nKfIWw4W81eofc+JazCqqG4G6ZIxVQ0pU+mabGp0LRbx1TySciCGLQszI+fEarHCGIddrKjqFUoZqtqS6ihErHaDyoGKil3qqGcNrq4kSKscFIKrczXWGKw1YvzoKgmsYiDERMqKjMFZS4w7uWiUZAN9lFLOvFmgjMXWjQQJOLSxKOtwtiYmI91LJuJmmtDvSPszMBptHB0t0ViStmRXYaqK7kw6mlKGrvdCxlPiJqswOKukvJQyIUupKeVQLhY9it/5VMIbrTFKITYLokXjtBjuhXiQAhhEAGOIWBsIQZNj4R5YhbVy8TldhLFC6bZTeoSFldbFXVsVoz5Bt6qqotvtMHPY7TbCFes7tDXYaKm8ZIBJJWy2EKGmLqKCBh8SlTWCWJV2R1EIPsgV9H2PdY7+09LS78m4Gqh8FD/mur89u85dfvy65166r6T9WqRkUum+LAKOSPvzzNWo0KJ9x9n5jtgDs5qsE9VxTXP3BYIHHRIpe0wO2NDjbObrr9zjrd98TN9nvvvoMfOS2L1wckNKDTkjrMBIQFEbA8oQs8XWM2q9IqnEzneE2LG8fYxRJ8xmc6yx1LM5S7egamZoW1NXjpwj+80pOgRsSuXoGVNXzCrhlMxmNUpl7t65hVLiheRMTV3N0dqK8aM10mqeLEYpCAofPDknfBJfohwTrqnpfGA+W4F2JDSzpsFUNTmJU7gxNdbN6GOP77aolEl9h1WJtuvRtiJXM/bJgtE8ffw+FtDaUtc1bbeD5MWAGMPxciZJSBHH2+/3hOTHDd/7iFKZfd+NZsZj+UmLcJ9OEas1vpdmCnTk5mrOCzdPePrBU1SOaKNQ4iIqNisqkXIabXnykORH6QYduEU5g9KWFBHCNAliJHlP0C1dJw7d+65FG4sJBp8qSJBiQqeD7ZBSCrQhhgz50Kl89bqYNvV8WCjziQhkchYC6+APA9dbFMDlbPe62+k4/I1nnvs8zYZxIdKqkKiG12i0UlirWW83zGcV3hn2uw6dYbfesDxyxP2excktfI4sFgtBDEwpoeTI8azm5mrGPmUpWSVQmEIwLZ8TQ87gfYexNSiBRlHixzTyJNA4WwlaorUQckMiVyJRrikif1qN/InhOw4Z+xCJO+vQ2h4yfiZuylqRkhr1D1JKo1pkjLKkpNCLI/bA39BC9NXOYWyNNqX+DlLOSRGdPNFHom+JoZc6e/K0wROSYtft0a6SC8dvRUHZOIwS2fAcB9EvCDEJ/JoKH2Xymw/wZsjFYbuQkwWZUeQinxFixMcs3k8hoIzCKqiswRmFUXpUIyZnTHHErqoK3w+8oMGOAdHhSHLwqbLvcG5DCNR1Xdr+i9pvEJEpay2pqS8hk1PxKDVBZCRj4bmIzOZTi4Lfs/G8Neg6fszHLSP9Nj6EIHRKuoQ0pWNvCIy06CZlFK0PvP/++xANNz/7IipFnFbcv32TmDW7dctmc1ay/kgicef2CbeOZjxaX3C23nC+3VIvjg4ETyWaTskHUhWIMaBMxph65CqqopjtZgtSCjhnCCFhtKw/s9lMEOWxLCH2HnooWUOxN5G5PpSBlFJjuXWQIxjKKBlF6CO5PMfkTJ88ORcJjP2OFIKQgEsCVFcNMRu0qaRbFPAxomJEK4/BomNLDj0xDZ2LiZATod2zWKzYbDb82jd+lYuHD3n1pRd58ZWXUVnLZ0nCJdI6E/tu3JtSaTLpW7FryVmjXVmfbcYHQW9tVZXXgB+oGFm6mHLKpAjGOZazOTk/QWpPpfiY8xCdFK3QUpJXgiAL0syoRyTHzhjtxAg4mkmnZDqgMClJ91UpOY1JYb68nz/7/8tI9kft1dPxiQhkBkRm6FqaIjLPR1kOqMp1yMzlrOZQlrruOdeNlBUohVNgtaFLAYzhYr/liUnorqdNHr1cFJSmQdULaquZqcTt27fY+oBWK8Jixe7ilPnM8eVXXyBq+N7jJ2ifaKxjsVii8MScsMrS7jMVkHJPjonaLNBGoY2ob6q22AcM3z0LoWw4H953aOtKtJ6IXoIM4fyA1jIBB7VXpfSoPWGMG5UaYwl0UswY64ghipVAisVKviXnntDv8O2Otm2pqxl5ZiBkltUKksXqBmtrdAZrNH27weSEX58TQk/b7gh9S9e1JB0432149PiU87MNXedZn2/48udfk1b1ek5VH2EwIjCnhbyrVYYsqruoPHqWZGWK9oHGpCSITNJ0QXQepCNLFsumrjEhEXKmqQppTmtMTjilscbQ7vciwQ5kBSFkVEhkpcUzSklLZUpBVItVLDLhCeckMDHWSEnQOfb7PfP5gt1uR1aKbr8bCbohLKhrPbYyDhf8lPirTDGlLHYYA9w8mEf2fS9lyRCeM9M/Hb/b4ypKc934sMDmuuDow44nek9q4rGkxqpBIhI1PHhyxtO7LfOQ+N6b32c+n/HFF+5xtKjIuz1ps4Z6RrOcUc1rYgg8+v47+LDDpcSXX7jF0czx7sPH3Fwd8+rXXqDtZA5r5fC9xjaWlHtCVCgsVlegHFUtSulGW4gWH3q0gmZWjdm/dAxVshl6cZm2xRlbZDIsOUZms1npkskYV0sQkyRhMlrMfH30pJRxVY3RmW5oJDCaLnpy7CF2bE6fCDfRWNq+Y3F8wq4NVHVFPVuxaJYiTpdabA6ksGO7+4DNZgNoQrul7ztS9my9x1UN/+Vf/uscNfC5117hD/3oj9Hutrz79nvcvv8aqRdNK3KRhFAJ76WLqdt3ZK3EJLaUpTPFcVvpossF272Qba02YjasMr3vMVqU1QMRaxru3DjBfvcdGHy1VHG6VpLEZTVgdkICVmqYZ9LRiRrKUmYSnBzK9gPPx5gdu816nKPL+aw0XhR5CKWo8mX13mEuD8KD05L5VcmU541nzYquDKXUy0qpv6+U+g2l1K8rpf6D8vhNpdTfVUp9u9zeKI8rpdR/o5T6jlLqXyilfuyj3iPnPGamU7Gcoe4/vR2ef/U5w+PPe+7Vv015E/nKiR02KbQas+yUUjECU+x9JCmN0pVI7udMt2/pd3tR3O0964szVIaubWnbneiwANn3NGRO5gtmdU3wifOLjZA9cygtxWaMXnMW5ciUAlpJt9AAzQ0Bn9VmbOEztgiohYjWZSIWppjWGmXNyJEZzM8Ox7KXMv1p5i8TSuP0geAKhazqpSuotpczIa1LqcpWo2VBjB6VEySPzgGVvdTmFaBKS18UlczaNczdjJPV0Ui4y1n8qIZMwJdFjqLOObVJEP9tRcziy+RjJmVFHwOp8IFCkjovSRCRrhMdm1Ayi5zFaqK2DpUyrrb0oSMrEfvSpaQzEKEHVGQwkxtRLaXGbGUg5I5IUfBAGhGZvm9H3syY4Vxpa5/+PtO5PWjIDIhMVVU/0Kq+vxfr14eN5wUbH1Zq+jjH/CjOwCDoma8cVnhiinW7Z9f3NMsVjdWkENntt3Kdh0joWvpuz363oS/+PUdHR9SVJbRbbs5r7t44RiXNdtOKEa5SY7dUzoquFXE5XQKKlAKKjHNS9h7W+sEvaDrXB/I75fnGijr3dO4PprbD651z47p2VfV6SBq6zqPzwCmLoOW63FysCV2PSoIiZF3KztYwW65omjnaWfHCsxZtpHxmVKSpNMm3gJSzs1bM5nO+9a1v8eXP3eGP/9E/zNe+/BX6fc/Dhw95/OgJRtnxe+73W9p2N67tcVBLD2II3PdijRJSWcdCYN/29P4gDDvuFVc2/pyzKKUPQpl5SOoBlT7WXDq8x0EGYppUTduup/+G3/LqXnv1Mw/PuQo0DM+b3n/e+MhABgjAf5xz/grwh4G/oJT6CvCfAn8v5/wF4O+V+wD/OvCF8u/PA//dxzlJwwY4FRObbrLTDXaqBnj1y1trx9vpc4fb6QmZ/u2w6cTx/XLZfCgEWYwFZTjfd1zsOo6PbzCfz0kh0ratQIpdwO9b8JHN6TlWQQ6RyhmOlnO63Rq/XbOqLEZpttsdT883xJyxzoFK4wXpvR8njxgxxrFlTg+8CMr5KVbrws9IRWytHTtfhJGexxLUIJrE5HgSgFTPQHrT4C+EQA6REA/KtL7r0YVk64yREo412EoEmLzvCMETk2zYmSCfRSWMUhidS3lMInBnKpbzFavZnHkzY9ksyUFg0lQEn0wh5A7icEMJcaoiOXYBZWQhoAQuORVDPQ1ZS1CDEKOtrWRh1Ie5IV0/oljZ9/24ACQFyhq60OGcuTSXhmCOrMdgZgg8huAjpkOwNBCQY4ykEIm+v7QwXSIsTxaHsb27/F5d2XRyzp8K4sn4XV+/fqfGVdT4t/v6qyPnjEpC9M3agNGsdz3n+w7bLDg5ugEpsN1uadsWjaHft/hdS7vd0W43JN9z6+Yxy9kMnRK1UpzUDVZrzs/PeXK2FiNVNXS/SAIxrBlTaYAUYumUks3LFVSxrmYjSixz3zOrm3FTBMbARZKF0g5tLNZUVKZCZ1nDDtfa4ZobUIOROI8IYsYkwpRWG3Qp86cIgxXBICS43+9IOY5rac6RFHu0SmiVQCWMKcq3refxw0d89Utf5sUXPsO8bnjjjTd4//uPuHnztvwmJX6w1uJKR6qzZlwvRnkFhbSIawmslLGEnOiCRxuH0paYDuRZ46pL+yhA7arLvFCtxGdOJbLO43eCy2WccW+lcHC0HvdHysdSWoLCYT1LKZFjIMdBHO96MCLnjMj7HYKa4bjXEoE/ZO5/ZGkp5/w+8H75/1op9TrwIvBngH+lPO1vAP8A+E/K4/9zlk/8z5RSJ0qpF8pxrh3Dwj580WnL6dVFfshkn/NZL7W1DpNX6YNlwfRkXTWNnIroqVHEzZI6Ty7aHzvfs0MTTU2XRX8lRHGPbbvI+UVLmu/wWWObhs0Hp1gnbb7bpx8QdjuqlDApgYbtds++8xwfaZRumLm6iK85TBaLd+89xnpRb6yks4DkMarEVuVClQvPkYPHqNKCp7K0CpdSnUERtEZTlckxZDgWY9ylYNKoAwErhB6txBE6G0X2EjCpNGQ9jqoyWFNjmhW2uMtG34usv9/jKlugTU8berQr+jNB/FzatiX6SA5QqYpVs6TWgd72uME2wVQiAkUiRAmIslGFR1TUeWOWzKWUdFJWhAJdhhTxqXQ3JSHW5Zyx2tB74ciQFT4mdOUwxnLzxm1OvaKKkQ9OTzGVk/mEOAUrLfVoYyDHPJLw6npGzgcu0rCADkTcIZghCNqXtaLv2/H3bNsCMxdjtiFYmWYrSWWxsEh5DLqmkgN1sc34QTWN/L1Yvz7GZwCen1UO19h0kf84wczV8vjV95neVzqN8ExMmSet55e+/R1uH53wmTv32GwuePfhY9CGF4zDbHqiTVA3aKVJ7NiHNdvHH/DBu+/z3vtrmhu3+cKLn+Hhk6d84403eO2VnyJ0G1zYg6lQ2dB3iZQ6XNVglca3e3CBXDlyyCiEQ9bt9xizGjfvWSNeR7vNWUnsFL7vaWpH14kysJtVuLJxDq8LIWC1o6rcJW6F976QXCHFnhQg5P4QNChN3SylnGwrZrMF89kK5wxdt6Ntd5JYekGZnE4EPEpb8VmqLX0R4nS24tF7j/iJH/lJnjx6wq++9StorfnSV36kJExOGkhKsplzxPtEVVt8K3ucj4qYI8rO6GIUHysE6fAxkIoa/LYVzZbGWYyyaKU4225xWs6fsppckNovfelLvPv9h6z3G1FbHoNOKY3rnAFNTAlnBiRZkHTp5oJMGoPEYY5JK7VYuXi9p+9buk7Qsn0R/BM5kerSPj5FpZVR4949LS1N//9h4+MgMtML51Xg68AvAPcmF/cD4F75/4vAO5OXvVseu3qsP6+U+udKqX+ey8I73USnkd9VROZ5X2763OchMtNxFZGZHj+lNJJlrdNjUIRSdES23tPFwD70YDS73nO+2fPkfE3fCZkttj210aS+4/TRA/bbDevzCxGGMhJodF3HB0+fHMo8RhVYUyYVWVjiQ0YjxoPF5l0fymqi6VICN6S9TqmBlCqTzhQy1cCB0co+c740ihQSQkg/QJda60LgCsK5SZLlGWPQRR9mNj+iapZUVU1VNagspC9NJqae4PeEvmW330s5zkeyMvgoiFaMmb6L+C6Qo6jozmYzjo9OcE2Dm83AaCKCqmAgKemW6kMq/yBERUTErGISf6SchSWvjOgzhJwJMRMRolwoJqExg+9FsXi/l0661WrF/Rc/Q+Vq6lnR/UkRa80Ibxsj6J0yyEJDpg8eHxKuasb5NVzUV9v/py2LYzCeorTMX9nTrsK1iQhKEUo75BDQTcUhf4ARmXH8Tq5f5XjjGsbHhOefc5xr739UMPO8400fHzNcdTmw6RScbVve/v77VIsZ88WKtg9cbHecbXbSfIAgnypH/L7l9IPHXDw5pd3uRC6/75lbh8mpJAEJbe0hsy/lGWsrrDEjInvQ9BIkFgTJQCXp1AmBrtuXUqlwMXIh9Q76LrPZTDRXtKau67FjbxBVHa4N4W0crtO6rplVNdH3+LaTRMrLNWqrhlmzYLZYsVodAaIrpXLEh5YUOjKejEdbPaK7Pmb6ICXpppmzWe/QGDabHRdna+7fvcdXv/pVkbuwjjZEtu2Wrm9R1qCsA6MJfjhWIqPJyuJTlvW1csUWwZSkRCEO26GU4RBV4yQCpSlKGUpMiWW9v3fvBe7evUs1a1DKjEiI94JIqcKJGTSnpvPvKhJzXWloqvQ7lMmn+jJDyenq/PwwFPLjEH3ht0D2VUotgb8F/Ic554vpG+ecs1Lqo6/kycg5/zXgrwFYa/P/F0G86Wf57VoUDM9VqmiSYIQwZTK5uCTPZjO63Z42RdoQic6RjGbfdZic6fqEMsVjwgdhmadE6j3tZk27XtNud5AdlTY0VvH4fM/Fek0fEraqSvdNRGSmJVr2MaG1F9+Q0GONkWCFgxDbQftFk1LEOenfJ8tn10IKBw4eU0OHizGuoBKWwf/j8jkN0pmURFcnpkSR8EMpubBU1qDtWK7pOk+lDH3fAaLxEnxmOZ8JSkQe+Ski5qTJSdHte3ybMJVE6kbb4g9CEc5L9MEz1HjFX2mYH1kWJqUISUo/cWi915KNKShdYUIQTIXMpo0DpVAxoqygJvP5nBbF7OYxdAmfDEFbvBeEZD6fj11LD997OGYcIXS4QjzMqfB4ikv3sLAOwUaMEjR571HWPCP3PWSZ04ViOpwzZa7I5xiSgmmANA3Sf1DH7/T6VV43rmFa69/y6z/OUEr9Fg0KypzPiPx8ebEEvEAR2MxasfaBtx484Kc+95Pcub/n22+ccXaxo6oabu87LMimmRKh27M7PxVhOl+CaN9z52TF07NT1j5wsdtzY+kQ1VeRIBBl2xJMhYitKykr5yTrQFFlG4L8dt+jlBd5hhTofaTve2Z1DUR00cNBCWcmBjHUVcrQVHbsXgpB1vT9fj9BKfN4fmKMxBBoKkli67rGKYu2FXUzJ4bMYrVivV5jnJCMtZLWZVIkRklGeh/ofYfSirbtCT5B0tTVAmMML7/y2ZKUZFzVEFSW8nsSdfGhIShmMNaBguATgr8o9n2HwYn5p1KYYlWjfF/2Q0lgWt8LYdgWw2V14MxkFNpWzKqG+eKYqnoiaK1qqGvRJAttYLfblWAljCV8BSMBm1x+0smYIjJTftP0n48Bm+IY+Az77VRiJaUg6/uHXgzP/9PHCmSUUg5ZBP6XnPPPlocfDpCrUuoF4FF5/D3g5cnLXyqPPXek9KxFwQAxDdDT1KLgkvT7pFw0LUtNbQ0+zKJgIBgPzyUOHSFJdGNMaX9LWTJ1bcFZzpXiF777m6yPlrx26z43NZyePeXR01Puvngb5w2VnpO6jm6/p73YEHYtzlqqasayspi65sUX7nJ0+yZtF3j/wVMWs4aT+ZIpkVQ2pwKJlot/UNwtv0/5ztNbYeYPmNsQnAjyIpLSw4acUkGn0uUur0MJTzFYwEYyvvW07QZiPwZRs8WSxXIltea6Ae/54MkjTk9Pef/990mh4/7dOyxXc3kvZXCNELyTD+x3HTFAvTgSYzLAJ4+tbOEG1aSYiChMVdAxHCkWdeHStZOMLkRsjY+dLJYpkib7f87S4p5I4rcUBNGIKdEsGnbbFuvA58Dq5BaLOy/yyq2X+FLroUDYy+WC9eac5XLJ+fk5FxcXvPfeO/zqr/7yKGzX9y1VXbHddlROBB+Xy+WBL8OhhBpCwPpA2/Zo3ZJzZrHbjfO8qSQwCk3Clhb8gdg7fKcQwujBNCV0T1vvfxDH7/b69VsZ15WZrpaSrpaaPpQccGXIcRRGeLJkBbFE8CLvUNbOqmYbPN96es63zs957aVX+f57D9i0W3h6ymvqs1ijRIytiDW25ztSFyEWvyaV+Ikf+QPcuneXf/qrv8bb73yf+nOvsDCG6HtmcxG3E+Xqntl8ie/3GGPxo3qtfLkBEZ7NayrXSAODFm2r1awWKQpTs91uR1TGVDUpCiesqmo0ZjxvVXVwXx43TsyYPKkc6bd7ZqahMpYYEyf3blFVDcdHN4k5se87tIG6suw3HW9+51vkHLlx83gUpQxJl4aHPettS9d6zs423D65x2azoZpVpBgJZEGC+iIxoiRR2W03NE2DUhbTzAWpzr14LRkNdS1eeLEv5pyCoEinqsY19RjYSdtzwlUGsrSsJyJ9m2Be8/Wv/wRf+WrCWMt80TCfLaWc5T1t2/KNb/wLvv3tb/Mbv/ENlBqqJKqUnwx9DFgsjEmsGlu2p1y/0Pd0VrqPxXpFjWsYgHeRyMGte0rQTikRkI6zgWd4QGWefyF8ZCCjZKb9deD1nPN/NfnTzwP/DvBflNufmzz+F5VS/yvwLwHnH1Vf/jCLgqu3cD0RaDjOVdRmishMF4uriMxwfF2Or0srWlZG6ok5l5Y2hTKadefZ+Z7z+RzTLJhXmgePHnB6upYTa61ouVjJhKNP9K2n27eYbKkbx/zkmP3ZBSZGWh+wpqbvErGOI7wXQo82B7+KKbw3jCm/yFkjwYWqSATRbdEUdEcV1MZeOpdKFTSniLpNa6DTgGZYWGOM+L4nhW7McqqmFhGpEMf2381mw2694XtvvkVOnuPFHE2imgmBzlRC2OtSkavWGqslsOqCl7qpylhX47uE1gZrFNmITk3XepRyonOgOCj5AnmiJUMJzoYNYvhOuXBLEpkuTNqVG+EKrdsObQxHR0dUyyNu3l1iqzlKZ2azGbvdhqpyXBxf8PT0A4xR/NN/+o85Pj7m7OyM2awixkxVWbTSYwfRYDZnC8lxSoQMfYufkL3rui6k74MceFIifS66S2pE1sStVzqVhmxoOkd+EMfvxfr1OzE+Li/m4xxHDwhmEqJoFgN7CWzKPEholDZ0OvLmg0fcXZ1w8+Zt3n1vw263Y7vdYmuLVs2ohh1DoO89KTEq0q6Wc+7n29y6dZMHDx9z82jF7PZtjMljF+I0eJP1Rqw2hseUEgXwvszzWSNouHOO3rejd5sxZkQ9Z7MFu06MEAUZzah8OakdGh2Gx1LKozClKG1HtuuNCG2ScXUlulVG0Xdy7bz11lt8+41v8vrrr2Ny5A//kZ8s/nCBej7j1u279Ab2Xr5L3weaesZsdYSuajb7DbqusSoLoTEHgTWMhqyomiXNoiH4NPJdosAhsnekSMylu9EfrmdjDG3bjvQCWdcPBF/r5HvvfAAMTTPnlZc/R8oaVwtyJVIbBu/7sbyUc+add95mX8r/KQ3rhpSoddIT08nDnINDyXtaYrpqYTDlvyYOe8uUiDwtWw1/+6jxcRCZnwJ+Bvg1pdSvlMf+M2QB+N+UUv8+8Dbwb5a//W3g3wC+A+yAf++j3uCjEJnp7YchMsMmPaAsKUnGHSMfG5GJMeO0JuZIRtjrOot1AEkCg64r7WxoHmf45pOn7A1UyxUvVRV9yJyerVkdr0gxsN/vOX0qWgOVqXCVw7ctOgZevnePF13N69/7Lo8/eMrxYs7tk2NiAl0COB86IbNaMSe0VpR4lQJrXQlg6ivZTSqbZC+eQ1r+meLWrBQ4V4/nXy54YcUPwV+awIFTZjkpknzA9z3zeYO1Gu/37LeyMPzCP/5HqCQbb+0qfvQrX8ZZTV1LrdYCKiX68wup6SrDbHmTOAje9QkbpObehY42RupFI51KSHdSjJHF0WI0U+v6fSHDBWHx54zS8p2kzToU3pAiEyU4RdpCtdLkLIHSrutxSuO7lnmzoN3v+c23v8fCJ1Ynt6kXR1RVza7bsQ9b2tMdXdfxzttv03UdJ3ducevGTV77oc/L+S9Kwt98/Vt4H/HbbvyNbClhDcRjo5W0YKqMbztSiKzPL1AZ1us1TdPgSlZTOYvuRV7e6ohSEIy/pCNT16Jb47t+FA/8ARy/6+vX78QYNvTfSkBzlewLpeEh59LdeEhGMghSmaI4tuckSVrd8E++/T226x1/5g/9GE1T8fY73+HBgwfYmUMvF/iuY7dt2W07+i6hbYXRe4xKOAV3b9ziC6+9yhvf/BZvVTXHsxNuHjdjOcdaR+d71ptzFosFStlR/HTokgwhsZivSjlozNK5AAAgAElEQVRW1u1MxLcdy6WguF2/R2vNfD4n50jTyHskSrejs1hjxu8+lHEHrkwqfBilhfzbtzswCmXg6GiJdhY07PdbvvX6G/zsz/4sTx6f8eqrr/BHfvwn+ezLn0FruLg4Y95U7Ldb3ll/hz4mjG24eesF7r+4JHjY+4hdLmgvNMZo+n2LqirmzolGTjUHHcBYQtJFEiJjtUJXNVZJw4nfhNK6Lmv0IBMxdHKZYmOTSRjr6NrSBp6EZ6OBkIrXlFI0dcNsNhttdPZF6qHvO7SzLI5WLI+PwGhcZZjPRDtnu92yXm/Fay6bZ+bhsP8OkhHWdgQtiIxGQcrsCt1hQMuUzpMgRQNhRGZiSmSkPHq4Hp6fjH2crqV/xPMxnT91zfMz8Bc+6rjT8WGIzFT07ioic523zbQUJdHqZVTheRyZ6a1PUeSrESlmKTmlMXuu61raXI1h7T2blNlbRewDLouNgZgHZrTKWCO13LZtZeMuxCpnNcYa7Kzh/Pycs/Nzmqom60MLHkDKqXRf+QJDXkaihqDsoCqbMJNMfyxlKEVIcUSdrK1GDsVwf3j9Vag7I9mdLlouKSXatmU2q6VtUZUOhBz55jd+naOjI45WK9zREavlCavViuiLCF0pd8SQ/l/23rRXtuy87/utaQ81nenOQ89simSbEmVatmzHMmW+MALkZQYgyZcJEOTLBAjsF0FsI7KcBJZkiRZIkW42u9ns7juce+ZTp6r2tKa8WHtX1T19721SouSG1atxUbfr1rhr7bWf9X/+A84FtDGpLeVTcZopkM4TWouMSXWksoQW6f57rb0ipMC55GHhQ8B6t955SpXcihOXxiP68E3v085GKYEPm6p/+E6t9RipuJrPEeWIzkbCdBe0QY9GzBcNOzs7XC3naCOpmlWSYa8se3sH5GXJvXsPUg8/Jqfe42cnfPjhh0xH0/XuZ0BmlJD4mODdzOSsVgum0x3aasXu7j7RbdLch9/TOUdQel3QD7uh7fNjtVqtiY/JtOtv3/ibWL++aAxr2DYysV2wbJ/H14eQG8fT5+5/QRGzfq0oeiRmIMikol2s0ckecRaCiKQWkk8u57TA/q1bLJZzrpYttgOjFDYGQtfSNp6up93JXqFT1yvynYydskRqxXy5YF4t2dsbI9QQDdJ/byVROsP6ZP2vVH9OD0KDHoVMiELirOWjkigFF1dzptMpLrRorbBNcr+WpPaDyhXQr11KEYJHKEFbN+vzReqeW+gdrkup0/lkRPQe11ma5Zw8z6lby7/9V/+ai+NL/sk//Ht885vfpCzLJEMPgVFRJMn0JGexSNl6HYq8GCNMRggtonfyzosRMQocHU7EtN7LlMad9a211WqFDcn362p1hdQKo3Na12BkDlqk8zdLJOqAxwWLj55cp82KDAo3bGJcoO59xHQ/Jy4uzvjhj/8DN2/dYzpNBWPrYV6dU9VLmrrj8OlTnj17hjIZb7z1Nvfu3eHBgwdYa7m4uOAP/uAPWcyvKLMN0Tr0gg/vPKGfKwSPbZtUlPpAdBuSbyCuI2aSw3vs0bQAqo9HGM6Nfr4O7LVXCbC/FM6+CU58PjRyuJhuw03XW0Pbj3nRY4fF4/pjXraT2b7fMySO9vLZuOEhEBIM2TQdVWe5qCvuZVO0VMn9tbWpojUKjMJ1NmUkFSW+auhau+aGSNvRrpbkJiPPC3yExXIFkzFZT0SLMeKlQK/JUhGlNhLbocAaCruhCEocis13Hjgy0W0KnuvuyNtqmOG4bN8fto6bURrXWero0Ubi24ZuteL1B3eQJJvxSVmQaZkmc+yLQqGRRiNw5LlB6YLOB3y/SEKi5Agle1M9Rew9cHwI61ZbIOJ7A7y1i6kQfRo3EH0ytiOZcaXv8Dwqt7b5J90/Ho+p241hHc6hiiSnFn3BPRpNWCwWTMczzi/PGI+mNKXlxu4tCj1id2eHvVnqo7e9muEX04955823WK1Wqe3TpXwX2e+eMm3wtiNEj20jl/YMrVIW1nSasmmKokgJ4FlGnudokRClhiF3JUnzm56jU+T5+v3+NpvifZnGL1O8/DL//kWtwrC15r+Ihrxe5wQok3Na1zxbXPHG/h77Bzf59JOfs1y13O2VSyDpnMd68D4gRPJIWq0WCUEgMipKFsslp2cXvPnwLq3tkFHifQsicdvqrl2fc9v8rkH1l2wgNpyJzKScpclkwjZpXfUX99Fo0idHOyaTGW3brtev7c3ruk2BwIaAde0a6VRKYDJFs1wQbXqtXMPX37zPO2+8joqBi7MjYozMdveRUlJVDQjBwY27mKyg9orWRqKrkreYinRVnVxtkezduMlyfsloUtB1LW3bkecFV1dX6MyQi+TIXoxK6rqmyBXapzDIwGazn5b4gA+OgZuSuCxyvbal45PsL/Ab+5F/9+/+kNHOjAd3HqRCBsl8dcbFxQVVVTEqxuSm4O//zu9y7949bt+6xWw2wfccKW8dP/jBn3P4+En/O8g1DSOyERY0bY32qW1e54u1yrbrSpAirWPRr4NG13MyiMQBDRuJ95rmEK43tJ4fX4pCRgjR7+5fHBo5PGb77y/a0WxflK+T6l52+6LXXy8SSiYkpj+ga7Sjb1vlRtM4x8+eHZN7z3fffMhrt27y7NHPefTsKb/z299hOhpztmqouhalNKvWgTZcnC9xUnH34Yid6YRmUXF2colE0fqAaluMVORFTueT4dzQa2zbes2JWBcYWZrQmdlIfSMb18yh4LE2FVneOULfkxZikLzH547htjtjRKTgRJGCLUejEnzLYj7HdS2h3iHLDCoE3vvaW3Rdx8e/+IRMgxvnZMWILC/IihFeGfJyRN0koq5zjuVqlSauAqEVhclROiFq1trBKBtk8vdJcsUWegVCDEkWToyJpS8EwdmEvqghuqGj64u4bJAx9r911yVOSWMTxFu7jigiXdfy9Oe/YGYVXhY8fH1G17TrHvOD228A8I233sM5xz/6nf8K11ny3GD7Rdvalmax4vjkGX/0R3/E6clJyuGyibAbo0jqjNatWf9KKVbVknI0ZrKYsNvuMxqNcL4hzxMh0o/HZNownU7prGdMIlvv7OywXC2p6yRhtdZyeXn5lzw7vxp/1fGqouNFJN/h/i963otQmi96v/V7CdZGkkoJKDP+5X/4E/7Bb/wG333jdcqLY54ePePNNx6QmRJhDI2ztJ0nBoUno7Xw0U8/YO/gnNmNW3z327/N//PHf8z7H33Cg/u32Z2MKVSkT1klRoNtWlRR0LQVojeLzLIM7wPeR/KsRKlsjcwMHDEhxDp2AyAEzf7+Debz+ZoSsOrXkLU3ybAJ7PkZEYHuN0ddmzbNRiiq+opHH5+ytzNhPB6TF4bv/cO/j+8sH3z0PlFIbt+9y+6tO9SdYzzdYXL7AGUynBc4JL5tWS0W5IWhbpMDfJalOIa2tcznc4rxiLo3rDR5ydnFnFGR0TQNTVOjZAACIgaadkkISTXW1QvKMiMiaG1H2yZkPs9zRK+G1FFSVbY/NsmwNCHWkiA0XWcZlTvcvfMab77+JqPRCF2USRKuFEVRsLezyygfURQFZZ5Qp8woYgw0TUVztUSGyL8+OU4RKrZFIuj6a4h3HcFDU63Wv93p+RlFUTAajdi7cUBZlhwcHJDnOUU+YjaZYIwh0waVp2vX0HpSbAqZ7a7Ki8aXopD5IkRm+/ZlSMx262lzIn8eyv0iRGb77845tNi0WoaL+sBDiDHShXShbJXiaFkRwyG6bdmd7tK2LUsizgZG4wlNk5j7rbNr7sLi4jztAJTuTYgEx+dzbu7tMtJ9CFlwaClQKrliBr1p72x/5sGtMok0+sJObDxHfC9TDMEjpSaKIcbgebLV9jEequLObkwD024vtc/WPg598eDixlH29t076KxITpNZTuiLNG0UbecIUSZEpT/hEIkApobPEJP5FBg8G1PE4burGAneI6JPKFME62ySMipFxCXeSXA4FwkIut58b5szJYVYE2UTTOrWbUmdZ9y4PebGvQfs7t9kNttBa8UoL2idoygKZITCZKAMIrIOqkuyacdyuUQbyXK55OatW6yqCh8CWWFYLqsEB3c9oVoUWN/hgkC7jG6e+Ew2OEajEcvlgrJMDqijvOwXM9BljhSavCz47LNP0uIm5VpBVdf1r/ms/Wr8smN7rXkVB+aX5cb8su/5siJpGIlFIbC9Nf5p1fHh4SH3Dva4ceMGp8fPWC2WqRVR5OjM0LQpedp5kE3g8uy8v8BI9qcHjMoJRxdnLFctuTaMygJ84nekFkHAdg2ZTiR9Hx3I5O0UYzKIbFrHxEyQKmWSJaQ2kGcb/qRzgWBbxuNpT1LdmEw66wnBrS+Cg29WVdUppHFLzGCMwS/TpvDZ4WG6kGpF6C0PZrMZ051d9m7ewoymmHyEQ1A7MFr265lGaghiSdM5pnmO6I1MQ7B9RMuGs9N1HcqIXvrsISQ0q15dEKInOIt1HUSRNpcq0DVLbN+K71xA+YirW4xKZn5Kphw9pTYNGKEkznu8EGSjMb//u9/nzr2HHOzspDVKSrxNUv0sy3rUS/WFRE+/iA7rLFVT09iOpm1RRqKjIgRB17QpNNc6YpT9hjdRGlzwtNYhex+fy8sLsrLg/PyMPM8Z5SU7OztkWYYxhnyU2pPjcZKuK7EBD1Ih+3JU+UtTyAwXzG3ey8vIvgNf4EXjOQJvsOvnvug5Lyb7bh4rpWTo8MZe7joUMAORTRqNaxyXbccvzs84Ow/83Ts32C0KXGepYqSxHcV4hNCG+eUKaz3OVYTgcHWLyE7wNjIbz4hBcHoxT4XA3g5B9FygEIk+0MUGYxL3QkR6zxtBjENYIckqOorUwhGaKCXWJbJrf8B7+V6KCEsXc4XzDgR9qNimiEl8Ftfv3hKb3geJzgyT8QzbNYSQzKyUUkih0HnOjdGMKEAXJZgicYdUstMWStAGhyB5p/iwduFm3esWYmNG2G3sxdu2RssEd8fYYzXegfcpJsFoYky95BA9IQqEyHC2o20cnbN01rMzKci0QUiFt64vJlLibhDgvGNnZ4e3HrzNwevfZDTdYTybUhjDaFzQ1h1GJyXSqEcT04JZUXeWZbVgubxiPp/zn95/n5OTExbVCpQkLw2u7dCZQunUMsvzHB8dAU9ZjFkul4zHY87OT8hzw6VMu8mBxDsej1ORPBrhiWRFUi/NZjOqqmJvd3ftozGo4L4aX77xKv7LywqfF6EuL9qcpduXv3fow1UD4LTk8eUVP330mO9/423qxRWXZ5cUozHTyQ4mL5C1x9lIjA7vPMv5FV3X0TYeeRDIlQalOJsn7sjeLPHrjNkkww+fLcKa9GuM6VtYmxb+sGEUvcO4937dIpVSEt2GLDr40Awk967n4w3r+rCOsdWajd4itEQpkzKUlKFtarqmZXd3F6Ekt+/cB22wPpIZQ9UldDgbzYhC0foGL2wKqVUGok8eWlm2RpqEVEwmE65Wy/Vv0lQ9J3TVooXAdS3ROaRMYoo8M9gQaZsK51PUjM5n1HVD3XRYFymKHD3Kks9Zf72UQiOET2tYjLjo0FnB3Yev8Xe+9W3KyQ6jzCAGzqOH2Lf8c5MlybeUa8XZ1WJO3VbM5xf89MOf8uzkGIHCE1FSEgVUTY3ReZ9RGIhEQiTxeNpEqxBNZFXNQWnOzk/QWlOYjLIYo7RAq4x8nCNVKmRSVp9co4YAq+rlPL8vRSEznLgDcjAwzbejCYbbASmBz6Mowwkw3G5HE7yo8LkeUbAda3B9DMXNGqHoujVr3ks4qSqWVjESgTcaRyE8UrUUPcogsxIRBOW4z7WInrauyIwCIoWUOBVZWsfJIiBNycXVgsm4xDnLbDpO39EFOteRyZzWWYzcuBFvL3hKJSJZFAObfIPgpHmfipehYBl+h3Qwxdpkbo30xCTrjAKEMsgskglNMB2Zn+CbqjdnE4kUmxmkylJ7TihqnxYf532iGkYBPdIjVCocUoL31nfoOT1eJQK1os8esR0yRoySRO8TiVEqWtehetJxFBIbBAFFZyON9VRtpGoFyBzrJY1Njx3I4WlHmDKvJB41LRhPZoxHU3ZnM0xeMi1GWNfQ1hXz+Twpkdq0uIYQaHoSoTKax48/o2orTk5OqNoOF+Gdd7++9nbJtOLs/ITDw8PeH6PD5AaFomorFIKTk0NmsxkXFycURUFVVZRlSVmWHB0dUpYll5dX7B7sMl9csb+/z9X7V8xmMz74aZOQm9ForQb8avznGy9DXLYv7r/sc36VMfBltrUm2+umjX36cp5zYTv+v08/5btvvc07b3+Dn73/I+bVktdff507D15HF+dcXFyQdx2ha6lWFaum5vLiCn98gXeeySjnLz76lItFxev379C2FbGBUZnQQ9dZfOYRPd8lJb07yrxIm7utzWXsN11FUVA3DZD4M1VdU6iMumrWG+Cutf1mqqMo8zX6MRRAQ9vCe0+Qimyyg4iWnRt3IAT29m8hCGRKk0138CEQlOxRHJD5GOWTKZ8yhsWqIjdJAdZ1lnIypm1bhExIT8q6swjhURJ2JlOqqiEvDXVcksWI0gbXNSgZUSZ5e0kpkxOyCERTYq3GaTieN/zwxx9RW8db77yNjwFjIsG35Mak/akPiJBCIb11SK3xEe7df8iN/QOyfEQMydcnRsfJ5flzbTzv/Tpcs+lqfvzjH/H48WMOTw45PTlHCMXbX/8mX3v3bR4+fMjx8TPef/8n/OAHP3hOah9lMhvVURGjS9J3EQmuY9lt8uBgy05Fi57PuFFYSjbz/+oVgoUvRSHzKkRmDfd/ASIznJjPPTZudOvD/dtjG5HZPnm2T6LrJNjhM+Z5TtenlBqTI41g1bYECa1QOG3orGVxcoISknI0YTrdwcglRji0krhQEmSKZN9TGXkQyNby2bzifHFFZQ+QNiVO+wBR+DXnpfPpuzW2I1Ma2/9/7C3tExybLtTX3XrX/XGZSFpDYvSwO4suOUb62PuzxJQLlVRLiVCqRJb8W2SSOxZFsSbiueCTRNNkIGUyd9I9Idlk+M4TGNwjAcTaH0IICP3OTctUBGohwCiKrO+X+xJiRDoHSESEPMvIZIJzXUzeOEIkr4ymqTk9mROkRpkMqRRSCWIMuAjKp0JIZwozcIOiouuSd4YSkdC1OBSr5YLzyzMilp9+8EEiwfXzres6FosVVVXhiZxdnDObTXj06BG//Z3f4fYtz7vvvpvIgNEhZeDRo0coBJ9++gm+67A2hXGOi5LVsmY6mdDUNaPRCGtt4tb4FOkAybl0PO7VfiajqWpykzG/uCTLMuq6pmkaDg4Ofu3n7VfjLz+2hQjb932eq/dX95dZr2FsiL/PFU1KEn0KCxBK0QEfHx6z99YbFOMRi+UVT5484f69hxzcSE7idnlFGy1WZjhSy1pqwdTkrJoKqXNqF7hYLpGkzal1gTzboN9RiJTD5hyFEFRNklcbY1LCfIzoaBAx9qrW8fqzz2YzpCeZtA25Zd4htCIATd32YYwZbWj6JPDU7ggxIHWPrnuHyBKSW5p0OfTW4aMkSEmUmihSdImEXsYOTZeCYqsqKVGTwWgqCNq2ocxzMpWhCkFTJ5uEgGQ6nuC9xYxG2LZL7r795jAojy4KrHPY4AkqIlSOU4FF3XB89IzdvRs83DnAC4+QGqV04qaEYc0T+Ch6TpJACo0NgcXVFdF2RJNxdXlO1VZY2/KLzz6lqpIfz3Ddu7i4YLFYsWpW/OQnP6Gua6xPbfTbNx7wz773fV577TXGk5LXHtznzq3bFHnOH/7hv+3bTJbgPUpt/MoE25E37hrnNZLQ9eTdpq7ZB6/Pi1ecB1+KQuZXRWS2DdquIzLbj0U8j+JcXxBehchcX1CCECmHaOjXBY8g9X2VSQZKAUnlA48bS+0X3BWOQsHubIQwGXVTIbVisjNNVbjKKSYTZG7oQqTuLGIleHS5xHaeJ6eXzJqScZGnCHsvIKaCIIiNPL3zDh30xmuA3m8p9kGGMU2U5xju4vOa1DUPiJSjJPrnDNCs6H8XpTNEjMg8qZFCCOSZSc8RAtkbaDmZECGRZQg5uO1GQp8Qq7Qk+I2aQAiJty75qvjk92BkQpfiIB8XAuMKXNvgYkjkY6HxIiCiR8aI9inxOgaNFwplM2TM0DLDZCOEFmgDmXIYCcSUtDvEJHRdB1Jgo2V1teD48JAmKKTJWVrLYnXFs6MjzueXPHrymL29HZQyTEZjmqZLioDW8uD+a6xWK37rN7/Lu+/8Bvdu32N/d488N7RdQ9dVCB9YXFxyeX5BmZecnp+xO52xWq0wWyjgUGh3XfdcMCR46toijVyb4XVdR1mWXFycsbu7i3Oe1Wrxaz1nvxq/+ngZOfdlY1i8X8Wr+VXfN1zjAQLIfgefDLwF1iv+zV/8hMP5Jf/Nb72HfPaEw2eHNB5u7R9w/+EDbHVJLjWdUUSlCDEyGo24rBo+Pj7lzz4+5bOnh/zwpxlff/stIgHva7xP6fJVU6O2kq5TC4n12j0UO8k2P7XzfWS9kdRSrz1VtjmAUuqeS7ak8z15FonWKfhWqGRtobMMqTVqsJOwvTxbCELTphaJ90htaLoOnWWgDW3nEVrQOQ8x8e7SYQyofhNmlMa1HXmW4UNkPCpIawuApG0sSMl0PKZdRRwB33S0LlCv6n5d19SrilU75+nxKV2UlKMZb7z9OuV0yrPjpwgcOgpCVEgkPjqaVUuRlRAleAg2tdh/8eHPuP/W+0iT8Wc//AEffPghh0fHLFcroA9HFileJ10bFC4Gdvf3eO3+Q/YPDvjeP/ked3ZvcXPvgCzXCCWQ8h63D25Qmow//fd/nEz6YkTFJPP3YbtgeZ6MLoTAM9zXI+svmLOvum8YX4pC5lWIDPA5ROZFXifXEZlflSPzIkRm+7WFSLBXIt/2BnJhUMckBCF6j5aGT05OOFOKO28+4GBvB5zl8OgICRSZZFwaxvv7IAWmLBDGoGzHZDamCXP2JxO6EHl6ek7djri9t0e3Eyh650bnksXzeDxOHiNbqqRtq+i1f8VWi40IWopUdPRqnkHY5vviLHnvJPKtkGLtzTD8Rtu/g9C9i6ZP3jVCQOxfmzjEuycJoQsgpCZ4C72PhZC9z02aCGghkUL0Pe60SKhUvyWkyFlc3eKcxTWWKEER8S45jioUTb9LEnqM9R5Dxv5sBysiKs9ARowWGCkgOuLaXDFgnUNr08+HjOgDp88OOV/UnF5dsbAdVdPSWQsm9c6RGdPpjLt37yJiMuza3dlf81jG4zG741329vYgesajEcE7LKCkoW1bqqri4uKCuq6pVxV5XpJKyp6jtaVmWi6XSLMxFcuyjKZqyHJNVS/JsoyT06NevXSVFgAR+Gp8eccvU6z8VQqaV6mg0rrQy16FQErFVbT87NkxC+vYu3mL1llOTk7pmo47tw6YTcZkRmF0RpCCICDLDGMCt/Z2mI0WCCU5Ojtnf3+fG7s7lKag6+waQbE2cUkGPkTXtxykVBvDN2l64zyNtanAyfMc6xyZMWB5boM2FEHD2mdMRnQbBHNYB/M876NrNFqrlM9mXV8wJRd3nUmqJhUxJstovE/olRjEEWkTKwUpekBIyqJMhZF36++gpUKIREJWylBmOU3T0AWH6zzOepaLBYXW4EEJha0dOkiePXqGi5L7b7zB3DlyrVAiMJ2MENFRqIASAee6ZAtiTHIE9iRERIDRmq6p+eEPfkAbA//+z/+U84tLhNRkZcqL63zi2pXlmOlsRlGMGI/HvP7Wmxzs32Bnd8bdW3fYLWdMx2OMUbjoWVVzXOcTKp/l2LYjxNAHHyeKwufm2gvm9fbti+b8FynxvhSFzKsM8eDzoZEviyjYVttsRxPAF4dGbiM924/dPvAbrkg6YYzsreHV8P8pXfV0fsmVCDj9FjIvEUpxfn7O3s4OHoUpSlCGYlyQj0t0ZrD1kigVk1FGVy/pomC1SIzwrjcNciIinUdnyWo7Or/ua24fJ+/7cEef1EnbxyzG3tE4JOhv+O4hBGIQrN2H1t9ZE0K3xZdRWzCmTP3fnmQXYsQ7n4LF4uDtIoky4EIfVulTkeO9RwuJNiYpjvAEDwwcpd5eXQmxLnKEkimGwAdUlFRNixIprwPXZ2VFRdckKbMZjdL7ExnlGU6nrChpFJkUGCGwzqegThFTIbfVSvQuKSxsEFRNy7PTU05WFSYfcXJ+wd2Hr/WKBMPX3vk6o7Lk9sHtnhCerNONMUwmE7Q0TMcTmrYC4loVMBRRxuTkeUnXJVSlbdsEY4e0wLRtuy5UjTFUbbMuwI0xieDd574MC3VVVes5/FX69ZdvvGgj9tf5Pi96v2FzE2MkhtRuFkJgjea0bfn46JR379/h4PYdnj475OjoCNs2PHj9DuMyZ5yPEXiQEetqBJ79cUmhBD7XLOuap8+OIARu7r5O9G3f5k5kVNPHviT32rjm+q3bT1tI/cB/HDa81qYImIE4PPypm4asd7eWUuMkNDYhmXJdDApETFYSqkeDO4YNdCpWvAspeiXLscHjQmrXEASBkMQRIWLyHKUDIka0SlwdouhdbSNRSJTW5JmBKFBCc75I5+bi8hItwagMaztykyOEIhOSZ8+esTveY/fWXWrv2BmPIHQsLpaY3ECMFMogtUMkC3pC72DufV8YxN7vKwoef/Yxh+fnHB0dYYoC0SdxTyYTDnb3eO+995hNdri5f0DWZ7vt7e1RFCOyTLO3s4uWJqG/MXnLnJ+fc3JyxNNnh7gQUSZDhNAXcZv1dLtYGYqTlLd0TWl8fcMlht91+HVfMse/qNL5mxhSyjhEFGwT337ViALYJDsPHJlt47frz9lWK71IvfSiHdD26w+Oq0MyYwrXEnjXoULkWzszbhcZtyYjvn7ngKZpuJhfUtsOnWnKkWL/YJfxKCczAiUNy9ry+LzjdFlzFeTvtz0AACAASURBVBNUOco0t3Ym3NrfYzYeYYoUWz8uc1Sf4TNkOg1BbIPMUCmzjh8YLmrDYqHExphKytRWSUVNX8SxKSA71zIQhfWwc1DJOyAVdj0BW0DjHAhFF32fe5RIwoO03tlA8JZgXUrElSR3YHqTpX5nRNz85tWiomlaCBHdyzFjTKTp6APdqqWpatqqIZcjohToosQrQeU6FqJJuykNKChNhoyRxJUOXK7muOhp2pS4C7I37ZO4EFmGyGfHxyyl4f4b79AFwff+8e8xHY2ZTabs7u4CsDOb9l5Ieu0JMx6PObs4Yzwu+ejjnxNj5OzshP/4H/+Mqqo4u7xgVScZ9tXVEmMMx8+OKExG29aJqKsSV2ow/2psknLaPuR0M6/FuvUUgsOYfM0rOz4/+0GM8bt/Xefx3+YhpYxDKN5f4TWAazvTrYV9G5L/Ihn3y5RQ1y8skFBMILUpRMooQ3iyELmD5L17t/nO228wxfL0yRFnlxfU0jGbTXn7/l3yXBF0YDzOIQiykPHHHz3m3DoWZNh6iQ6Rf/id99LFmMBoNkJKyShLxX5mDK5H0If5rJRKETG9Uq/rBk5iWuPG5Yius5vWFJsiKMuSnUdwyayzaSrqtkZLnTxYtEEpQ/B9HlBnk9BApHO2ChGEwvUp0iYvabp2Qz/obC/jjgjvMEJiVLZGtIuiZLVaIqJnyPeNHZydnePaDgVY68j6roN3jnGWxCOX55c8/fSQyWTGwY3bNMLTRs+z6gyHwwsYTUeMshz6dnJrG67qOSpLxaO3aZ1FihTOC1Re8Pj0lI/Pz7FBMdm9yf/8P/yPvPPmW9zYP+hjU3RCuiAZbvbzMssyqrbBhY7j0xMuL885Pj7mT/7sT3jy9BGnp6d4l47VqurWpGHNxo38OvIS5ZZlSr/pfdEcXquW2gbv/Qsn/X9RiAzwHMKQ8oJe/Jx1jMErEJltU751Fdk/drsQ0rovbjKFVIro0k7hfLWCrkNLyXgyI8sKnp1e4HykWq0YzQ7W72OMJjMFq6pjkmsCIxbzJVpKlldzJkZid6aJDxOzNWyZZWnnUWYbc7y0uxqO1ybLYtsYLxFv5XN8Iyn0uuAbxvXjrnpfHSU24V/DsRFCYL0jRIGPHtejMsPPMxRRwbUoqWhCk8KsUGu+TKYF1kukSF4RIkQUya+gXlWM8oIYU+tLJ8YyRmtMyLg6u2J5VTM62EEKhVQGrQUik6zqCqkEjpDyrmR6j/S5Ux6Kc753SB4K5zztyjJN7iNGQJklNcTebJe7t25z7/YdpJTJPt05jJSIPvag6Tq8d4TgqesVSM/x2TGr1Sqlzb7/nxhPe0OosmA62aEYTbg8u2Rv/wZdtVqjLmUxTvOv9s8V+TC0SHtCu0vJ3oKUWhtjpG0savSV/PrLNK4XG9e5ftuPuV6w/GU2ny8rfBIis1knYox4ARpJEJGLtuWzkzP2pmN+92tvcS8qohT8/PAxkQWHWjKZFhzc2ksqThfBSm4f7KKqllhbrlaO5fyS09NTyuIeZZnjbMr/sbJFK4GI5jkvq7WUOisZAh6HFhSkNbNpGpTS641Z13ZrM7WmadLaFQMyAEJhTEbo7fJlVvYp2D0C27fRY5T4KHrTTYV1qf01HBtjTAplFYmM7GyLbR3lqOxbYQojFcFacm1o6i4FQEZYLlcsl0sUApMXZFm/dROC0uS4xvH00RMW8yU7swPGxSjJykPA2ZZMS8p8xKpryLRChEDwKSC4zDOWzUYBFqPqFab98eopBaMsxzeO6cEu3/72b/Htb32b/Z1ZMqgrCtq2IddmPffatqXzlrpruLy6oLUNH/3iY548ecKHH37ARx99tBaXWAJ5XvL3vv0d5vM5P//Zh1zNzxJJWqY07mFtfNkcXc/77e5AlF/YGv8vCpG5/pztxz73nJ6X8TKOzIsCJl+G4gzwpxZ6fTFPLOzAKEaMs9ydTfjm/i67Rc7YGGaloW5WSB0pxgXluKQsNFlW0LYtJ8eXaJOT7+yxXC5xztMFz2gyRWlNNh4jtWacZ2RKo7RIgWlao4dJKNS6lysRGGn6YyvXhlBa9xEIYWibifXt89/bpxTuAdGRpv83tVU4uv59wUeRyLZS9OFfnhAjISYoVsWEwISu7T1yAkoknxwhFKFN/WURBG1riUHQtl1CiiJkJmWthKqhq1uwAm+Tq7GUEqQgykgbWjyRNnRc0RJUTFJUFTFKra2zhYBVmwyfKutpfdrJSJ2ykMZFSd0GmgBq9yZvfvPb3Lh3n4e37nNjdyf5yOxMqKol86sLmqbh008/5XI+5+j4EOc95+fnKKW4qq6YX10RhCQiefjwdR7ef8DOZIfpeAJ9P9/alv/jX/zvzK8uWDU1mUlOm8vlkrVDM0khNhT6qc2Urxf9rmsoiiKpGIDLq/lXiMxf0/h1IDLb4wuJwDL+pQqaF722jIEgNsiM6I0qpZFkwVIET9Z5/qd/+nt87dYeBYGffvwRVVsjVGB3d8ad2zfY3d9L0t26ZXmZOFumzDi9XHBZ1QQt6VwgL0fcuX2PIs8ZZUk9mGU54+m4/3wS0RN8tz+zc45JOekzlhRSqzWPMm2C8nWgogspoX6b89g19RqdHI+na6fgQMrDG9DOEB2OFDSZj8ZUvUP2sHFTSqERFCoFXGZZilsJNvHt8AFvHa5N56OUktWy5ujoFJOXTEYF0SYjT9e0+NZydbbALgL7+zcwJqdzNePpiNOrY4IULG3Fua0xowwbPKbQiBgpZL+RiZ7WdjTecr6qCEgaZ8mKEhUS70irgmVjuQiS3/7Hv8c3fuu7vHbnHqXRKKPpfEuIjsViQdu2rFYrPv7k51R1TdVVfPDBT3n8+EmS2vuEWguZrjt7Nw74/ve/z4MHD7h1cIOmrvn001/wv/5v/wu2j3dZC07isLnebIRjjOuYme05uj0HVk2LD+G/fERmmyNz/bHDrezNfq4jMQNCAy/m0wy324jPhjci8c6SZZuEZikVdVMTpOC8aTnvkjRvd2fK3myE6yryMiPPcjJtEiktRow05EpiFLx+e48zDReXVyzaiG0brNPIskRYi880rbOpwjd6jSagUkDcOhdJSjwREZOR1fCzr43/xIb3ApvdWxJh93JqIXpynOnl2ImwF0QqikLvwui9R2qN8B7ZqxkiiQuSJnHivSgiUqvezM8nPx3Z25BLCSFBrzJFUaKEJka32bEJSdd05MJwcXVFpkrKyYS8KKjcAiS0bbdufdHL0RFivfOTfaj5QI5WShH6nKVUim569tF5mlXDrVsPmU0m3L5xE9UTnUNMsRHL1RVVs+Lk5ITPHn/KxeUljx5/yuPDp0zLKU+ePOH2/dscHR9z98FDHjx8k1s373Dzxh1uH9xgf7qD7TpCSIXjzRs3ePL0UVpE+9ylgSPj3IZMOMzHTep5UgFAMhxrnaUsy1/X6frV+Bsc19GZ4e/BwzWV6l9qpPP88y8Ue+K985GgFKvo+fMPf4qRb/PajT0O9vaYNjmNW1JmqueUdalVYzucWzEazdjbnRG8RxMReUnrPV0EjKBxLbIn8xY6JSWnDZZB9Uadukect1tOqYj3ZCJbr78hJGm17eXYOrAWKVifkFah0gUdKXr37ABBYL2n6ZILd3JHFyipUer5DXIUIa1j0ROFBJE2Z7YTFJkm0CXlkoSuqXGdpewRZNt1iBjIZO/jpSB2EbuoOH5yxNnxJd/6+m9hsoJyNMLWLZVtkrdK2HjpCJ0cfJ1zaCkT1ygkvk7ouSkxJr5fjMP1qb/Mi4DRktt7t7l5sE9u1HrNDv1/V8sFnz36hNPTUw4PD/nRX/wFq9WK1rdcXl6iosJHC1Kgc8M7X/sGd+/e5cG9h/zm17/FqBgzm47xU4dykXt37/L00WM8ia8zqFHX86znwCAS4XyYk7/q+FIUMjH+6qGR2/e9iCuzPbb/LYTwwt3Mi+DcF91uf7bhvuFPcJ7YF0mttant4y2NlBxXLcoYJk1HrkBokwoQraAn2qYiyzOZJPM7XIcSAeE7NGC9Syhb9Eghex+WJOsbSHHeewQSqVmnha7harH5rteP13A7tKPS5N7c72JYJ4JHodJrCQWEVKwImaSISifFkFQp4LFfZIQIfQ+7tysPkRgS2Ss4T9dtmRDGSPTD79VX/iJ5zYQ+ioAQyaJGYdib7GGyEVmeI7UiuJhIdgPHhoQWJQl52gHKNRF80xbzPWG4sw4h9fo5XdsyGY+o2o5RkePbhqau0AEqKWm7jnZ1wfHxMRfz1Dt23vP06BnK5IzGU2Y7u+zdOCArct5+511u377L/o077O/sUmQFe5MZZVEgfKDtXU21TgQ76x1V063RyiRJVUkmf21ub8PzWmuiFORqQwj/avzNj+u7yxdxWF41XsWHuf4+X/S625/lZa8pY9ooeB+JQtD6QGYUP318hJKR2r7Gu7dvUOYGswooJN62VFXiDaZiOqK1INeGidGsbIsNgcnuPk1IBnEomeJLhIQo8aFbxwmwtVkcUuKFFH07SaF1tt70JnHDhvie0HhSQnO/WXO+w/RE5iCgdbbnkEisd3gf+3Ba6KwlRofKDFVdp8+UGWhjWpuixzUNbXCUeUauNK6paat6/d7VcrXeoEcPwXkm4zF5lvKEpACdKf7iJz8DK9ib7CGlphyN0Jlhb7TP+fyE0ajgalUhpaTMNK1wCAGSTZs/kZ1TgK7tklt6jCERmyMovbFCzLKMfDxCAs52BNdBZggicHT8jJOzU/7oT/8Djz75lKdHz1gsFltzJa0to3LCwZ1bvP3Ou7z33rfZme5yc/eA3XKGUYpRlnh5+zv73Ll5i8PPHifjUhLvZrtJ9CIu1/V/+2XOlS9FISPEy0MjX8SD2X4ePK8mug5HXj9h098/fxJf3/EMt8Of7bF+nzgEk4V1W0UKgRaSIh+z6iqE0NQYfr6qOaxrPjw555s3Z3zr9fs8PJixvLqkMBnNsqIoy3TijlIGxe7eTmozdC2LpqPYO2C6t4sMHhUj0kdaaymMxnWpCEx5G7DhxmyZ+gnwISD6ImEInZRiWNhU70uTWj6+n3E+WCDQxYgxAqIgOpFaRf3xGBQ/afeisc6mrA/n8IS0OEqJiBIjNZmRtO0ipVO3jvnlIlmV93LMPC97nkxNU7cUxQgtDahAd7UiD4ZuFSmnBXfeeEAochZdjcMjHHSuYaA4iSjIjSJIcNEhNX1Rpomxb8+QEraHnYySEpEAbLI8x1rHZDLC2RoROnTsOJ/XRLnH0ckx/+oP/i86Z/nTP/1TDg4O1gXhwcEBmRnxz77/z9nb2+Pg4CajokSEyN5sNy1+ciMdDdZS1xX0qcOuS/bnhLTYNk3DdDqlblPbaFlXqajs82y2+UoJrUtcg68Qmf+84/rasj1eVlxs/5Yv5tS8eIF/8Zr3vGJk+zPRQ/2xv8R4Ei8NHwkqSz4nBJwy/NHjY374+Jh//nff4927N3ntwUOuLs8RAVztUFpjcs3t2S67+3uURmNXikwJ/uTHP2J0+zbf+Oa3kG2L94HGpeBI19SMRgXOeQKWPNdIqZNa0/XeST5QlIkkbJs0p7e5i1mWAlq912uzUoHABY/ODHVTJUuFKJLNvki2D0EqEBEv0nqlTNZvFAPKmL4gada5ZrZrGI/GjPICW9esrhYsL66wrUNISVmWjMqdtciiaRucDxiVYUyOCJH64oonj5/y7oN32Z3tMhrvsHf/Hp8dPcUZ8NES8awWc6Qu0HmGFx1dlzxulBFIFUHIZHchFDiH1Abv2zV1YBAdpGIxImWgujzn9OkjJjsT5kVO03WcXZzyL/7Pf8lHH/+crksFZQqdTRuimzdu873vfY87d+7w4N5D8jynzArGRUmuDVpJFJHWWVb1isvFgmfPDumqht3ZDqu6orUdMaTrJSQ0xm/NYcnnixZPUuimifzS0+vLUch8ESLzKtLbtqfMNkl1+zHD7fp1SOGKL3rcMF6EBm0jMmuPlp5zk0zkXDKAQ/dQpurdHpOyBi2RMrKI8Hh+xcQI6BJsOpuMU3sgM6g8Q0RPU9UsVhVXdcvZqsWYJefOc6scUyqD1X2SNen9O+sREYyQvaRwE+8u5CYNlvh8myz2TaQk2/PAIF/v5Y+Ac+k7yyj61mbsXU7AOdtDsmnSueCJApz3IFMhIeiLhxAI1mFtpG0a8IHgAyYrepWVROusd920KQvJRHxn6YJDeIEKmugD0+mMshwnPxwBHo+Pm7C4NDeGNiGE6Ih4vA/P4fLPz6v0GyUCsCL4sJkbPuDbhvnZaYKn0RydPOP0/IzLy0t8DDx48Bp7ezsA3L/7ACEUN2/c5sb+zVTUKEOhMkyeTLO0TsF4znmstRweH1K3DfP5HKEkWVGyXF6ti9Asy+icfQ5u33auHualJ645UNsS/a/G3+y4XoC8aHf5y9y3vbati6Jhs/IFRMiXoc/D3xUQROidf5NqKfjns+6iklgXKLMpVV3zo59/Std1vHbr2+zv36CtKwIBjCJoRTGbYsqSaDsECpUVPF5amvoRYjLjd7/2dWJIFvpBCXDJQVzFtI4PLrlYkHpjyTD4xKDkc0XekKsWCbSu3VwTiH2GnMYlfzyiiAhSLl1n3RDwBsSEzsTUvrV+sCzQRCGwNn0GjSI3OVpoFosLLs8uaZqOcTlBG0MxKlEycRW99YxHyRsrurQhsbXlkw9+gUbyxntvsrOzh/OR0/kFKs9QhcYulkTnMSrDK4WSAi0Cxgu8EEnRucXLjJAQtJisOoiCKJKAIwZHiKFHnCKua1hcnDM/OyFEwdHpCT/94AN+8pP30+YzBrrVCqUU3/rGN7h//yEP7r/Gd77z22SmYDIaoQVoISh04jI5l3yxlnXFs9NnHB4+4dPPfsHZxSVRyL691SFkTK57wLZKiSjxPE/p+FVQyy9NITNceK4b4r2K7Ht9DM+5TtwdFvptEz3Bpj+otPjcY4eL/PWx/bpD68LFxEtw3qFkSqtOJNvU15RSIozBKMWqqzmpWrx3lNHy1q1bTCYTCiPIywIXPJPdGdVyhW1bfIQGzdPFBb5ztD7wj97+GruzcYp914IQO9omBVia2F/YwtBKioj+4rbJkEoE2+utslS09O2yZLWQ/sTEtRBKp9gH0/vObLUzxFbRZ8OGKD20t1SvMjJ5Bk1DsBbhIl3TEQWYPjZeCIHRJvkzSIXSUJSa85MLmqpFC00pR2hVkE1LdG/I5aIjZXn0eVhREoJNbpVx4Mds5hsyrBG1ocgLSILwiN4rJ0PhCVRVkkYHbxHe8uSTn3N0dMSj80uCVMwXV4mQW+Ts7+/z5ptvcv/uPfZ39pmMZ8gemdEqheOVWU7bthRlngLZlnNcsDx68hmHh4c8Ozzm7OyMTz/7LKXXBmjqDh8TL2Y0GjGdjtE6g7pGaEXr2o0DqvcYbWiaJsUhvOBc+Wr8zY1XoTEveuxLWz5bm6dXPfZFG75Xv39CTIMIfS5TSC7cwWN6MmzwIIWk84K8GPPofI51Ld/9+tvc39thYgxdl+Zi0AmVyI2hXi5pWsfKwsJFzlaC/LPH/IO3v4mIgaat8IJk5lhVFEWBFhshxaCw9N4zm0zX7XOQ1CEmMntfwDdNs7YeWBPie7fxzrnUmu83MEKSuCcyEF3sER2zDlhNIov0GbJSImJAK4NUAukjhTa4uuXq7IK2bsjzMZPpdK1qCt6nQNnQkucFOgraqiW2jupqyfx8wW+993cYjacgFEF6xtMROZFltUhE4zynqVOLy4hIa0PalPYhkUkqHkEm5aWLG4qCkGrNN/Q++bCIgX4QWk5PDgkCLu37PDp8xpPDp/0mH6KL7O/f4Gtvv8Pv/97vM5vsMJvN2JvtkxcjZEziDBVj8t+KyZDvZH7G5dWcD37+Ph9++DM++OADEAKlNUprQtd3OeKrW6AvLbxfUdd8KVRLSqlY5sW6eNguOj4Pp27GdjtpzX95CXozPP86urL+f/xzEO6rODLrYmidXxQIIk2oGCMGlarlHvILIQVLGqOSX4q3jCR86+5N7s4m3N2ZcntiMDKSa0XsgyAvViuenC/4xdEVf/7kiFYEQvD8829+i3vTCdNdQ16m+AKkSbBl31OWUq5Pyu0iMX0H2fdcc7aZ48mgSLDe70lJ7BeV1joiyU9AKL0+3gM3J9PJvTIRzezm2Pb9dk2a+IU2dPMleIerLV3dILRGm3J9XAuVI6Wi0EUKQItwdnZBU3fkusDIkkybBK3KXn0mPJ3raGxHXa+wtqWJHRZH5y0dLTY6gogEkmFg4vgkffiybbE+srIWH9IxyvM8Ba8REnFvPKJxnto6lq3jg6MTzlZLbt66x/3XXuetN9/hjTfeYDaecONgj4O9G/jOUhQldV2l7KMm7XTquqauVxyfHYOMfPTpL1gsFnz40UdJuXZywt27D2nrLhGUVeDs7IyLizMAVqsVe3sHnJ+fJwgev1ZWDIjU9TbCYll9pVr6axq/DtXSFxUg13/Pz61xv6R78+da5T6dR+nciOuLjYwSHTf8ijgENYmAjiBcy+tlxmsHO3zn3Xf47rtvkuteYqvShurJ8RmfPDnjhx8/5s9Oz5IppXX81994j2+//TqzHYVXAq8k2ksypZEmyZ0H118YQnETcdUYk+IIYnpMUi6FNSIRpUgcPZ/CIk2e1EtV0/aWDJrRaIzoN6yD0Z6IAa3lBg0CrGvRUiVBhoBCG4T3hFXD8eFTTIDxdId8PKbrkmdUrk06bz20q4blfMn84opm1RB9YG/vBnduvc7V5Zw792/hffLCiRqapsYFz8nJUUoVDw2hkFgRkk+MSSRj15v7O58I0QLJfNUgtOJitcL5tBaU5TihSJI1x7EKkdpaLq4qfvz0GR2CPCsRpuDd3/g6//1/+99xsLvHbDpmZzzB6Bz62BiEXx+ztm05uzrn/PKM49Mj/uD//UNOT085OjoizwqMyWmabi2bD8FxdnbCarVaF6hibeMhCeKaOd61ud803ZdbtfTrRmReJb/ePokH2XV6Dii1UX+8DIp/ESITfMAohe1N2hwBITya5FWCSInKkHT+07Jg2bQ8uqw4W7V8cnLB6xPDrb0JmZTMJlPOLy94cn7ByarltAanclpvyXTB08s5q8WC35y9looEqVKv1FlMb6q0RpuUWPdqN8SwNHwPnQ7HygXfH4+0s9cmI4TkC5OKxuQcma79W0RnYkJeYmL1E/pdY3Q92UyCtcQIbVvTrq7wnUeGZAUeOsv8fNW3wxSNyhNMOzYUpsDojOlEMB4FgpfkOkMIwaq6QpLswaPwBOeStwypjWdtKmI8niACUiYFgFh/f08k5X34mNw6t+dkcIkrIIXGlIrgXOqpdy1lZlLUQwQpIu/9xnvcuXOHe3fvkRnDbDJFiYjOc/I8o6oXRCxnlycEIk1T8aMf/Zj5fM5iMefp02eEGLlcXJGXBcu64c7te2iVYu2VkRwdHfIH//e/wTlHkefE4FBCUuQZy7Ze5ywN50LTNEwmk68QmS/xuL6p+iuN+Mu1ml74ObY+z2aENaIKJPVhj9oIFEqPOKwbrg7PuGwsRVFwa2fKNC8QvmXVNfz/7L1ZrCRZet/3O0tE5Hr3urf27p5epqene9bmkMPhcGhJFElTFmH4wQtswwssP9gwBBl+kJ4MCHqzZfjJgAy/GLDhRZRgWpZAkQJmJHGZITXsnl6nu6u7umuvW3W33CLibH74TubNun2rq6lZ2CLnAIW8lRkRGZkZ8Z3v/L//9/+/du0mb169y9XdfbwuiUoTteF7719lfW3IC2tncU2D7UrcctFRKum8dE4m+E6nFBSCY/Ne5xyl0YTgCMFl09kgopKTMb3eAEXCOTFUjAq0Vlir6XYrQDRSRBXb0ylKZtMZRgvyOo9tMThcjNhOh6K04AOFVrxz9W2JN50eoZmibEHhoK4PmbrI4YEkL8krrC0ZDlboV2usrKxQVV06VZe1jXVG4wOUSlSlpW1nYuwYA7qQJo4QpUSkdDjukoxBFmO2xFpFDAmfJGkzunyA42m0LPhKKxY6IUjbt0oRg8+kYUPran7+q1/nK1/9Kk9/6kmM1hTGUhUFSmXNNQ0hBfbHezTNjKPxiDff+j7Xrl3j6tWrvP3WFYqioLCdBXWhKrtsb2/zqScfZ319lZdf/iO+9c1vSqUiI/Yx5rmF09GYj4NkfiISmUVZIz7aNBKORe9OQ1dO23b5cfn95qWWxTY+oZZ9iU4ZD+yjIHmPRhN9FFJmAmWUsOD18bmFNLenL5mFRNHpcWvqUDT46ZT6wg5v3b/N2rCHCvdpY+S9u/fxytBiGbuEMiVTF3hv/4iBVWzeO2TLedZXoCzToi3XJEPdtNiqBIRXodQSSVmpfD7zlnVZkcxb+B5Aw9Q8aUz4qDAqQgioLAIYvUycgUiRzSB96yTJSxGTEyIdPG3tiSHg60AKMOytoaLObRIzNAbvI5WuxPU7lHQ6Um8edAox6lRqkYAlG0lI255zTS5rOVG/1AGnPF47fPKQFSatVvikiEmJto2Szoy55s7yNSLdVRqjIfiGqiqwKRILS+0dKrSsDrpcunCRL7zwOYbDFQaDgeyfPG1b03jHuNbcv7+LC55bt26wd7DP7u4ur73x/ePOBi/o3eVLT+Gj4/nPfoHPfeZzrK+vo7XC2MR7713hlT/6LjFG2rYlKUW/38c5J6q/SzYEMUaqqlpcr58E5PUn48Pj4/4upyU7j0KeP+57Bg06KQzSARs0JHQ2EVRZACGTgX2k0AVETTKasekxDp6DA8db/+CbdIyiYw0KMV49rBPBWJIu8VjpONCKu43n17/5O5zZ+lW2VgbQBIriGCExyRAIVFZIp3PVWaUUTdOgtMXFgM4q5PP7v21D7k5KzGYTUoxoCylGmrYlpUAMDpv1XabjKcYqmrplMt6nmYo6um8dMXpWel18bInTBqsH3N/dZe/uelB43gAAIABJREFUHgUFpe1yNHYc7u3jj+4w7K7Q7w/plhVPn3uG/c4BZ89fpig7jKcTQkb8V1dXOTzaowkNnoBrapoWSI7J9Ai0YuYnmF5JaCOzdkxU0vmJEsSrtJ3cYeVpgyeisFWJDwFrNX7eqZHmshspMwUiPVtQaoP2nkol6tmET3/mef6jf+/fp7+ywkp/KPOaAXSiaac03vHBtWuMZ4f8s9/9Ha5du8bNW7eYTWoR2zNGtNSSpW08w9VVXnjhBf7yL/0Kg8GAlZUhPtT8zJd/ivevvMv169fRhSX44yYFKW9+mB/zce6RT0Qi8y+LyJzsLoKHG0wuC+I97Phzo8lHITLee7QxhJAw1pCCwxpDCoGYzbKSgiZ4rNIfSrZSFi8qi4LQBLq9ATcOxnQ7Jfv7U4xRTKc1je5Qtw5tRUo/RA8kZiHQto6Ri5hxDcpwfrsr9c2cEIqNQERbqZUWxTGKZSyAwkWHTsfaOlaD0oIcxQghJVxbC0yLlM5CiMzt4VWSmm2Kgegb2iif12oj6IxrCTEwrSeoNuCbSDNrGPQ30aqgKFcoVEVZFJihrAqjEyXO5GNW/JTvC+WzH1OkrackrbLglciQp2AIwUkHkg746Eg4ogrS/RSkNCacGCvkO6LwYZAVgZPMLkPYkuAVWm4ygbA1oXWsrgwompadzU1uHY4EcjYlBZpB1aVxDc5LUnXz9g0m0ykvv/wyRwcjDg8PSSmxe/9eZvhPWVlZwRQlzzzzDFsbZ7hw4SJlWXJh+zwrgyGtqzFFYnLmDJ9/4XO89/5Vdnd3mdU1Te3oDwe0E1ESjYqsJ6SZTqcMBoOH8r1+Mj4541HJx8O2PS2Z+eMmrctbK0ClB5+b/y3vK8JmWQNBOgO1SC4kU9DEyKgNgsJioZJ4EjJPbY7wRG2IhealN97iK59/nrVOhWtrTC5DLDd+zD/fcpNFYTUxHBtEzsnv8xg3nUop17mZuMJXFbTtcfxtZ8dzDobpbEJpNcG1BBKhmQHQThf+zOzeOuLu7V2moxkXzj6OayPNDC6df4zqbMGls5eZTabc371HWVYicDqdiRlj1YGsldW0U2bTI5TRFKXBFl2M0owO7+NJslAziqQjs3aKrQqaZpYbSB5cbGmtRcUcDSh8E9FKzHuVEnqBVWCsIjqXv9uQXboNg04Hj+HxS5cpi0KsG1BgLajI4fiA0WjE4eiIl773Mu+88w5vvv0W0+k080ll+xgjpixoW88Tjz3BC5/7Al/6whc5v31e+FKdgtYXpBB58sknuXPnTo5LS7YcS5fxSSrHo67pT0Qi84MK4p3Gk3nAqmBpn9O2Xd5n2WhyOVGaPz5gZ6CNMO2VIviEVkYm48xB0QSSUllPRRKf0kiLoDZKYE8n5YE6JGbJY4zicG+fbrfPeNJQGE2pwYcWlDgdT5oZa4M++1Npwa2znb2pKmL0WNvJ5LVyoa0wh0qX+RMpJaJSpMxpKYpCJvDcwu3qlhgTKUZUaYkpEuM8eOYOAu+I3uHqMYQAMbG6MpBuJNeK8+q0Bpdoa89k3LAy7FIUHTqdNQaDFYLzDLsdXNOiYyI5Tz2dYZV0XGmtIZfJEpEQW2ICU0iSgVZiWhc8Do+PDhdakhIXWqUluTNWL3GwEsoaSOpUWF1rTVmUWe9GEKm5Cy9AWzccHuwxm0jn1cqgL10NIAgLjvuH97j6wXv4FHnzzTd57733WR2ucfv2bc6fP8+de7ts7+wQkqLTqdBFyfb2DjtbZ7h4/hI6JobDIW1bE7Vjrn496PU5qo4Yz6ZYaxmNRlhrRVDMmgeu6aZpaJpmgRT9ZPzpGI9KZj7uSCAE2BTRMU8qWhINJQoTpNzlGFVCFwVRqdziCyaKW3OKYE2HaEGphEueECEljSFIaUPNUfdAGz1V1eXVq9cYrK7whacep8zeO8sxW2JxlDhkxYvNGEPKCKsn5XvUobR0TVprOTwYMRwOKQpD23pa5yiqAqVgOpswGK6glHgmHUyOiN4R3QxXS0ztVSWubnC+kTKuj9y5fpvJaEq3s8K93RHnL1zmU09cpiw7lIUhKEN3fY0NY8AasYCJDfX0SNCjZKibhtY1TMeHDFZX8neUCCkw8S1epbxoDMyaBu9bkta4udedMosY1jiHUkb4foDz4bh7VIklQEpBqAFekJlCG1QhJUIbRW500O+yNhxCEMHS6APKKGazMdeuvc+t3TtcvfYB3/zmNxkfTDHKopNBa4uPEWUNbUqsrKxwafssf+Ebf47HLl5ie2uHzdVNbKFpmhndTofZdMqg16fT6TCdThFhd1FSJ6MyAKczYR4+PhGJTIxxEaSrSghz8xLTsuruwywKlkshj0JkTtv2QYsCFl1Mp5F9H9g2RozWudc+e98kUARBZ5Qo2hpjiC5ilc3lmITRGu+TCMglEbvTRSl6Bf0VJrMZ/ZUhrp5lY0CBVUmJoqw4nEy5ctfw/t37PLa9ybAqGXpPacXoLGpDauTCrdsppu9JVqOioqrzd4AkMKWRdmYXxSYAcvnOCGEZLTd8XKzNIo2Xei4+EJyjmc2kLdg3fHDtKlYburbL6mCVtZVNesN1QLGJYX1lXVSQjUGXCtvv0ukNKZ3HO0dqWqJVWEQ3RWmNj7WYOMYgXi3eM6tH+UZXHE4PmNQTpvWE2FELJ1ijIiFJd4Nk/QafhQRbH9B6brAJOibs/FuJiRQcSmuMke/d+0gIik5hqYqKtY4oOfeLxLVr3wdreev69xmNRly/fp3vv/UWrRPPl4PRiN6wR3elx7bZYfv8OX7qq19jZ2eHMzvbVNqytbFJtyjpdirWhn0ODg9pw4yj2Yi7uze5cuUKVz94n/2jQ+q6pm7cApY1xjDo9aXDJAeXeTI794r5yfjkjpMrzo9KTOaLkOUF1gNE4LikffUIvoxW4kcUFSzb8ZnM94uA0lKGJSDikglURl3FEkeJknYK4vGFQqkCY0RIk6Roo1iBqPkkawwexWFM/OYfvsSrV2/wH//KL1CpiG+mtAY0BhtLUuPETmRQMvEzScpnDq3BFlo6Y4wheiG8N7Maq2A8OgS0SHuExHhWY8uCbrfLrB6jScSmBucITct0Klon9/b32Bj0BKFpNUeHDa5NXHzs82xtnWE6abl84TJKicN1v98nJMWw26cwhtWdLUb37jO0iXo2Y+yk+aCqKnxwWKtZXR0yGh2B0fjoqF2Lo8FpRx0aPti9jik0utA0swaA3sqQlBStC1mqwpCwWBB7Ae9kYakNWilRUFdJlNJRYLJlTFIUpkQXkfMbG9w9OOTOtXcZje7g1JTDyZTd3V2uXLnCb/3WbzEajdDG5IWckd9caZrWs33uPBcvXOKZp57ip7/0FQa9LqvdPp0yW+WYQEyCjL/3zhU+uH6Nt995F21LiiqJB15hUUSSk+aQiDwqJKFZ3Bsfkdx8IhKZj0JkTj7Coy0KlsnAH7XPMin2AYsCL9/iMnoz3+ek0eT8uD5F9ImOgmW+z/z4aq7h4tMCmotqbmKZob+mlXqhazNhyubykLT2zWYThsMhe6MxHWs4mNR4NG1MmBTpGI3zQaC+pqWyBhc9KSgKUwh7PYHLwkRRRdFUy4jKPOGzylBYEcabU2RlovSyvffoEAne45xnVteklLh+4xab65vUBKpyBWX7FN0hKSqqqovtVPQGA7xrsJW4h9tOgVMJnQIxGHRl8I0DA41vwCDIRBRB9Zg8Kd8gPgRcbBjVRxyNj+gVGX0IfpF8aq1FBCtJYuljJmzHbK7olzreshLxPFhk2zWKXHIMIdLr9WinU7qdDkd791A6MJpMuHd4xL3dPV7+3vfY29tDKcXe3h5nzu6wv7/P9tkdfAz0+n2effZZhoNVhsMh68MVMaXMXWZWgacltZHJbMze/j6T6ZSrH1yn1+tRVl2sFZGuuq6ZTaYAmGxVUVIuVrTT6ZR+v//Du2F/Mn5s46N4MH/ccRKiX8S1/P/5Klin48njQ8lS3l2sReTOCGQxzaX95o+apdIUy69LEuQxKBK39o/Y3T/g/PogUwyWYqYPmccc0VYTU6LSFhcdTSOLsWGvT92II7xK0LTytzF6oT1TGov3gSZGjFFYoxnPZlKajpEQIt4HjkZTrl55l8lozBMXn+SF57/A6nCDXncdTIUqxMeoKArxdPMNs7plY20NYloY8upYUIRAip6A2MnE5FFRkUKQmOZnmeMSOZweMW2m7B3co7PaAwPeizid/AYGrRWlMngfIGl0YYGEjhal5JyaIGa7calAKMmvxPnlSsag02OPA+7eusX9vTsU9ZhX33qb733ve9y8eZNZRqhiEt0d4SnCcHWFx7bO8Y2vf4MLFy6wNlxjc32Dwmq6dq6u7Jm6CePJhLZteeOt7/PKK69wOBbxU2Xs4rN5n9Co+UXyx762PxGJTEr/8oJ4p21z0kLgNBj2NDQHlm5YNDGdTipe3m55BFi4e85fm7fyzUmXhTnWOZgnSvPOqXkZaJ60tG0rJZycWMQYGY1GlKVlMp2C0riQaFBcv7fH9kqP2Cuzpb10MRmjRF8gSUtlcJ5gsrV6xvHmCYx88cekax8dGvFU0kaIWagoEGD2WI6CTdO6QOPE76Q3WKfTXWW1vyZIjLXCSzGSSERjqF2LQbL6MqtIqgQ+RmKaK4wGSZwI+CxsF5PHx8jMzRjXEyLCRZm6GZGILaVNU1aM88tbZcEl8ZNKMYmMdxQNBKUU2hpUmnsrLQXwhJxPSkyaBhUTumM5HI3od7tMvSc4z5UrV6h94M5oxHg0pQ2eXm/AYDBgY2OLzc1Nnn7605K0rG+ysb7FxXPn6VaCTPWr3HFkNHUzw49bxtMRk+mU27dvc+3mDa7duM5wdY2iKFhfX0cpg28bNIqj1lFVFdN6RlVV0nofpdW00+n8ILfnT8af0DitdHRaDDoZG39QPtRc2Xd+3Afibz50UnxI1yOquU7IA88+8HkWSY3RMC9p2ZLWOf7Fa2/Ac09zfnMdfI1WieQd6ETSwonRpiD5gHMJZRXeSbyaMiVm93qrjxWuU9ZaUUphjRIPt+ClGyj7uQUn8ffwaMr9g31u3t3lYPeAYa/P/YMZVz+4w/ZmYvtMhTFCKj6ajMX7KDdIpARHR0cQ84ScfxdpQ89aK8FjrcT90XRE4xpGsynaKma+5v2bVyGbQFptiMFTYBeNCMELd0/lEpw2BUqLB91CBFQpiVGZZ7R8zWgloqdwfN2UxtKtKu4fHPDyyy+jyorf+YM/5N7eAYCY0HqpLvgYKcuCn/vZr/PEpSfY2d7m8sXLlGVJp9Oh2+tijSYG8cJyKXDn3h12s2Dot373n3Pjxg1sWdHtDygmNXdHE7SGjpFmjod1WD7qmv5E6MhYaxPZrXSOyMzHD1L//aGMj4Bml8/rARRmyePo5IrmZFAqjMD+86RmWZDPGEmA5no1MUYGgwHOC+ehmcwgIxTjw0Oef2KHzz/9JC9cOkvwnl6nymJzZe5AEBKwyqqdRUcUX0Pr5H31MYFsfn5zNKkqOpKEJCWBzhixeJ+1TCczrly/yf7BEb1en/W1M6wMhzz9+OPy2RASdgry2Cm7krBNW1RMFMbSrYQIpgDlRXCprWe5RToIf0ZrmqbmcHqfNjiu37khbe2FofWOspSky2YjuqaVIGOrElNYTO50ULokpEgbxeHWxUDZKdDGMBpNFgidSvI96Xm7uXfHFhpFyTvvX0OXFRMXePm9q5T9AeMY2T57DpVgOFzl8qXH6Xa7fP1nf44zm1torVlZWSF4z6DbpZ7NMIiIYEieo+lEkByj+fX/5//mlVdeYXVtjbv3DhisrBI8nN3e4blnn6WdjvnD73yb23duglKkrKo8R+729/cxxjDNdgYHh6Of6Mj8iMYP2/36UeNkYvORMfJhMexEu/by4kz4aR8ud532fkqpBZzz4fOIC97DclNGIEmXowtYrdEoVJiw3i340lNP8vOffw5iQFvhBWpr8CmgtfDUYhupXcva2pr4L6EIvj32Xsqu2PMO1KIosLqg9S7fK8I1qSdTptOatnG8c+0Go/GU0WTCs09/lp3tbS7u7IgonrW0bUunrPA+YnVBp9OhGQtHrigKOtbkBCTgpnWOaZ62bQnRLRCtaT1lNN2jDY6bu7fYOzxAWcPq+hrDYT8v6gQNnkwm2KJCWUNRlaSYyy1YfAzUbRAFYqMxhcYWBYfjEQREELPTQzP38UvoFIXMHMSKZeYj9w4OuXX/PndmLQ1QDFcE/TEF08mEtbU1Xnj+8/zqL/8Km+ubrKysSLJlDKUxC26LspI43j884HB0xN7eHn/vN/4ub775JnXj6PaHQqVwifXVNZ579lmG3Yo/+INv8+orL4s6cfaMWlyiSXSBAGZ1QwifcB2ZH6Zp5EclP/OJ+WGIz4eey3YGj2JOL59nUA8iMyeRn+VgMC9nzcsAMlk3WGtps2N2ZYtFgtM0DboQYmdK0lZoraXql5jeCsFYJo2nINC6Rm5aJVm+BpIPxHxOTSO1Vx+DJEs8WG9f7hKQlmch3/qsE5N8IoZEWXbo94cUZRdbVKytb9KtSpKSdr+kcvlGR3z0JFXQeo+xGt8GiB4TEskHrDF4N6O0BW1o8N4txJfatiWmlvuTfSHjdgqxhzeKyhRLHW+amJQQrBEFTx3BRw/6WIAphWMzyRiz+WUKi4TS6uPbI+XzCCFQVRWNc2xtrHEwmhGdJFllWbKzusrmxibD4SqfefazWGvZ2TrD5voGveyllWKksgV1MyXGQOs9rW9wMXDz9i1SSuzt7dHUc2FBxfnzF9ncPMNwsEZVVZw5c4ajPcUTn3qMstLcvLsr5+2P9Ta01qIknBLaHAub/WT8qz9OxrgfxoLvoxKXRx3/Ya8ppRYlpQfQpCxNoYwmiiIElBUHdc3V23f4qU8/Ra+ykHJctIZCG5zzxJDQZt5deWxCeyyydkwansd0uefFYiCpxKxp5ZyTwpoCXyi2NndY24C9wyP6Q7EbcMFLGdoqlIpEAiE5VEqEYGSBVIJRCR89rmnwzlEaRVQGH1swgnDXjZTGZ/WE2wei3l12S86c2yQGsWgQAqzGeY+Ik2oCYBJYUxAtEJK4kgcRBMQLETsE6bps2xadVcuPv2/5jnXSxDTv+FVoIqWxdIoSdzTBVl2aRrpcQ3Q8++yzfOmLL/L551/giceeoFDSBo+RRalRSqwmtKaup8yamrevvMO1mze4desWb772OrO2oSw6OBewyfDEY5/ixRdf5NlnniJFx3DYZTI95O0r733oWvq4QMsjExml1CXgfwV2EDDx76SU/kel1H8L/GfAbt70b6SU/mHe568D/ylSbfmvUkq/+Yj3eKhp5Pz1j6oTP+zxYe8FLE16H97nQ9tgH1zVpPm+HybdHW+iFsjM/FjL2eWinjy/obVYx88JzykliqJzrKtgDD5BJ4vUVd0usXVgDKPxmGnTcuDhXht5rCrx9ZS+1cxmjfgcpWPODirhUkP0uZXRKBKJk6KJc90GgZQTKWWEAw0xZfnuDqC5cP6i2BsAvV4PW2hMpTGYDGcKE18BPk2lxo5GlxB8oPGtQLxJVlazEJnNpoTgmU6n3Llzh/39fcpOARV0uhXr62sCGduC4BNH4xF13aJtlU0sDVrP4VdQNisuG0uIiUIpUiuB0jXSut00jXBMypLSFpjchm20RhuRUo/JM+z1mM5qhoMebUwc7R+gTEl/4wyXL1zmZ7/6czzx+OOoBOvr67Szhl6nS+skSZ1Ox6ATB5NDdvd2ORgd8dprr3Ht2jWmoylvvPEG5y5e4PBgwvaZi3zq8ae5cP4SP/fVnwMlvinjyT4vfO7TjEZH/J9/9+/yxptvoZRarEaXuV/zWvSftfHjiF8/6nEaErKM7j4q2ItCDA/6yyX94DYnEB6fV8XzGLZ4PcevxINcmuPJUfgzx+cdH6A8hOwBZ3Tu5kxiJptQtGhQUJ45x0QZXNOyUmmSS3jXYosClRQOj9KOlcFAEAtrF95y1lrqul683xzdN7msrZQIeFoFxlh0Yel2NEXrSLpCGcv29nm63YJOt8SWkkCZQtG6VuKBdmASbXAUWuMiJCWlIG00EGlcy2h8P5etNAcHB3zwwQdMp1PqdsZwa8jGuQ26XSn7eu8ZDIbcuXuPum7xUREKwHZQ2mCstDY3Xha3K6vrYl3iHC44VEx4Jx5szjUYDEVRYYwm5aSu0AqUyFvovKBTCaxRUuJGqAfbO+d48cWv8OUvf5lPP/0s/W5PytMhk3CTWNioFDgaHzGpJ+wfHXLl6ntcvXqV3/mn/0zmHaUwZYcyGWazhp/96s/w/Gee56de/Gl6/Q7dboXSnrPba5w9d4b/5q//dUneli6Y5TzgoywKPg4i44H/OqX0XaXUEPgXSqnfyq/9Dyml/255Y6XUc8C/A3wWOA/8tlLqmSSz2anjR4XInMZreRgScxoi88C2emlFsfR4kty2fLygWMCqKSVBX/P/A+Ss/kHS8Pyzp/zacckJ0MK5KcuS6XQqOjFKMRwO6Q76dLpdVte3oOhQkHCZkEaIJO2lXpsCKZGTRZjXS4mCnDzww+fVjVJAOrYl0FrnLNwuXKLLSkPWz7GFXnCBjJbkIeasPX9ClFYE1wqJLEVCVJACrk3yfPQ0bU2IDhcblI30VzrYokBXJp9/ViT2Hul81piiEi0cRZbWJk/8JWHpdyXC3N5ba50F6dIDicDiOoyJmDw6q1B2ux1CjBSFZTadgopcOHeele0zvPDFL/HpT3+G9bU1hv0ByWclTRI+OGazCaawjKcjZu2MG7duYouC999/n6ZpmNYNUcGFy4+xs7PD5cuPc/7cRT791DP0Ov0sPmUpjFmY6DnfYKyVBNJaJpPJInkJIdDpdMR35s/m+JHHrx/1WI5hp3FkHrbI+0FoA4sYqI6PR0wL3zaJZ0pKQnl1v9h3zqPJLQIL7szSQilkbp0cVpKLmBK9Xo/1nbOYlQGmqalnI3qVWK4U2UfJ2gqX0VGVAkYJIT/GhPcOa410OyqFtb28cJhijUEpjWtb8SlT0riQkqIoDINhn5BtZcqioFN25b71YfE5ZVGpaNoxMTiKuZUJFbWbUea4hwq0vubg4P4CYY2qYXWjx6ru0+n3Fk0twpc03L6zy2zWUNiKTreLtiWj0YRe5rl57+nackE9iEllk11pQohNRBmoigLfin5WysmiNQaFRytNiG1+X0PKZpLGyu/rguNf+8Yv8OKLX2F7+yzrq2uibo4onws/R+JZ2zpu3L7Bzds3uHn7Fn/43Ze4t3efWeswZYUPgcZ7up0uX3ruc/zqL/8KO2fOsj4cUFUVWkMTavqdDpsba3SqitaJee5JmZRHjUcmMimlW8Ct/PdIKfUGcOEjdvk14P9IKTXAe0qpd4CvAL/3Ee+x9IP+8SwKTqIhJ7c9TUTvtG1P22cxlLhDw4O2BjGjEsu2BifPJ6rTRfoWAnzhwZXVwgLBWnxWPRTRIE3SWdVSKdrWUWiFzhOXsjK5F70Og4113EGiSBrfNLi2FufTdHwjRqUwuQ5utJLEChYToCRPAZeTy6IoSPnzy3kafIgYkanDaggZpi2MkNXKXO6JPlDZitbVaD3XNkjEMFu0uROPy24+tKTocVFcbCOe4WqfXuhImUhrgWJ9woWA9xEfwdiKwih81mYwZbFISkqrRfNAKVwQhcx5mVEjRmxCAhZiL1EgcZ1AoTBaSMSgF9LmRkOnLDkYz2QVpDTPPP00m+sb9KsSFTxaK7q2JJgWrSPTeoSfRcbjMb/37d/l6OiI0WjE7r09XEzcPzxia2uLNgZ+6is/Q2GE2Hv+zA5V0WFjZchsNkHFiFaJZibCXeK5pDk6OqJbSdBTiUXn1Z/V8eOIX5+UcRpC80Ay8xGVp9MWcqclSCdLRMfv8XA0fIEmLSc76cFS9nwYY+gNVtg8e556/y6xzuaNy12lwYn9QCM+SHMEKIRj1fV5rJ3NZgv9pxQjkZjRSZXrWfOYEylNV3yfvGjdeN/SK8VnSGtDt9MnEaTMngJGQYwNdeNJaYJKmjq1RNeSYqT1LT4nDZ1eSdFRdDv9vMCIVEXJ3sFRLlV7IhZbKkxREpLCt56yrBZGvmWns2gK0dYwq1uU0RLHtXAqldHgjrmOSmUCcI5v8zkGICZFWRS4EOiUFcYYKm144fnn2VpbZ7U3EEHQGEnRS+t1jLjQMp2OORgd8S/+6A95/c03uH79Ooejiby/VtRNgy5KnnjiST733PM8/9xzXL54iV5ZMewP8L7FO4fRCucc0/FEvod2ciw6O+9a+xgl0z8WR0Yp9TjwReDbwNeA/1Ip9R8Cf4isevaRIPH7S7td55TAoZT6K8BfyX8veAnz8scckTj5OL9Q4cOtfh+17Wn7nHb8Zd7KHIlZhrQ+ZGuQ95mjF6chPQ87JxcDGv1A8vPh85b9rEoZLixy2amAkN1ZK4tSiEPp5hn6mzu0xsJ4j1IrdJSkqfGOsizEVsCDNZo2elSS7oEQ5zbwci5N0x5DzfnC0ppsySAaLCEGyOrFpVFonahKqVkbo0kh0sllDTl3yeZTbAnBE5Z9TWLEOUcMgeQDqEQgYCqFwVBSEtoAURONwkWIFCLfbQ1Ftyc3euvFC8ZIDTtGL3owGWJNOCIxk+YgKk1SkmDNESil51B+yroY0jIqRnU2c28kUel0Slb7Ax6/cImnHnsCWxYYJSJQSil8qNk/EELfzTs32Nvb49ad2/z+d76NVtJaX1VdtC14/jPPE0h88Ytf5pknnmFzfYNOp4PRCas0TTvFh4a2aXj/2gccjg64cfsma2trHBwcsbm+we7u7iJgaRTj8fgnWjL8cONXPt5xDPuRnfUfb5yaxJxG9lXxQ+Wl0xKYB44nZBdgye7kxL4nESIxEck6JiyV7eettkuj1HDxwlmeffZZnv7cF1HtiNe/9U9o9u9jQ8BPx+Kwld7OAAAgAElEQVRCHyOd0pJioOgPCEFsUuZcjRgcZLJs8J7Z3FU7JzBiqCvn3+TVPyERU0MMEa0Mw0FfFnVGnKVN5ptNp1NSVBhdoVVCJSMLvhggNnjnaeoanzk4utC0rsZ2NN1CfM+mszGF7oqYX7ZuaFKg7K+iteXo6IidM9uyCMmNC8qAD7nZoKyYzWq0NkzrGpdlO0Qkr85zh5TZxMA4l5kBgqgDSyKppA3dGqx2WBJPfupJtlbXKW2BIgqKYxQ+OPYP97l/sM8bb7/Bu+++yzvvvMPVq1cl1mqNVRbnE1Ernnr2M7z4pRf581/7cwwHPbQWaQmjNW0rej2z2YTX336DK++9wyuvv0pMik6nw2wmHEkBFeyiY/WjxsdOZJRSA+DXgb+aUjpSSv1PwN9E5oK/Cfz3wH/ycY+XUvo7wN8BYfz/qBCZk4J4y9sui9sdC+IdWxWwtNJYfGEntn3A3iCEDwWCk+d08pHwYHJlrSV6ec2FrCarjlu1ddYp6HYrUbJtW6ypKEuD0bA2XGNluM4sRrT2uANHMpppI1CrqxsKCyAlGQ0ok9Ah5s6mABgIcUEyJpeSEhaUEsMyYwgxYawmhqyHE1NOdDymMJAcQrz1i5ZMpZQgH8qglSQhKQVidMeIWUwYawlOvud5ghsz415nFGhulaCtoii7YiNQWIw+VmaeKzCn7E6ulIYkIoZuqa4fQmAOwolFgc4GlErKZykQc6InOhGWbrdLnNVoxPtoLpne0R28d6BLjkZHtKHluy+9zO7uLnfu3sV7z/3792lqx2RyRG84oFOUfPazn2XnzFnOnDlDp6jY3tii3+2SUqDTL5nNZtRhxr2D+7x95R3ezVYF+/v3ub8viMxkMmE6ncr1k8uQP1H1/eHHL/hwDPvhnvHp42RsWf7/qa99lCDeKa+d5OI8bJwscXECbZmjJGS18zlqsyi1KyDfw6SEVpqV1T4ba6usDVfodgeYssT2esyO9iFL9y/mhwTKQGibRTOGNCSIE7ZOGlc3ksBUHVwIoJOUuVMk6TkCnhFvrbCFIL5KGYieFBwhWnTSGFURg6PTLWmahGtbUgSSwTuPqA/HLH0BU+cXSD0IDybFBEmjbYlrI0oLa6kqO9igGI+nlFWHrTPbJLQQfUPABU9RlWhjMVbELo2XjkttDcZkA1wFShlSqsW2QJNTSEklJQmU89JKEXLslWRGPOVWhn10tp5QKtF6aTcfz8a88trr3Ll7l29961vs7u5mm4OSmBQ+l5rKssNf+qVf5rOffZ7HLl5ic7iKLWQBjHK0vuFwOuLg8JDd3V3+6e/+Hu9fkzjW7/ep61qSReaL6h8iIqOUKpAg8L+llP5evljvLL3+PwP/IP/3BnBpafeL+bmHjh/UomD58WHbnmZrcFLc7gE0RJ1+/IcJ4i0bWT5qn+VHq+wDK5kQQlYLjhkOlXa0Iheei6KgU5qF1kyck4FtZDgc0u/20NpSdHv4xjJpHQYhgs2JvikoikLjk1/wKCTByIEtiJTSPFYZjpWOzbyVL5dnJEmUpGaRbOk5GUhKThpDTHP3aSGapUBOZqzcbWQfrFzugWNX7vn3k9Kc2KuwqkTAIEOhDEXVoch6BjWzDBcndF59RHI5iQS5+0Ap8VlKyHnNNX8kEGe+jBIfKhWPr62iKPApEjMKJy3a4mjdrQrGkyP6gwHTpubu/buEFHnl1Ve5evUqoLl69SoXL15kd3eX9a0zuYRYsLq6zsVz59lY22D7zBlS61kZ9Dk43Mf7xGQ25uDokHuH93n/+jU+uH6Ng/0jrl27xtlz2wvi4/r6uihI50640dEhW1tbH3UL/qkeP+r49Ukfj+LLnPb6w7g3y6/L4+Ioj0yCTuXyyAuCLJeWza11ttbW8S7SupaJa5k5jy0kNZJ7MJd+VYEP7aKEMr8/52POjSkK4WNEL4uZQhtithPRhc2TJtk5XlGWFmM0McoCUkWZ8J132LJAKVEKj96TAsRocrPEcTyYI0fCq5OFtBxfU5YWrxSztsWakqZxxAiXLz1GSNIsUdf1Qjy1aT0+QvQeY4rFnOBjoPFBErqYcE4csOdIxuJ3XPyeD3Y0JpJwi0LAKE232100BRTGoHTicHzI0dER125c5//7R/+Qmzduc3h4iNaWqihxwaOsJYXA6uoKX/jCF/iFr/8866vrdKqKTiXfV4ieJkjn6Qc3PhDl83eu8Ecvv4R3kZSEazRffDUzIWyfRP4eNj5O15IC/hfgjZTS3156/lyuPwP8m8Cr+e/fAP53pdTfRshyTwPf+aj3+EEtCpYfP45p5PK2c77LMqoi255OJraFfii6suB7cMy/OHlOH0KYcqlKMun0ANLThpbSWIE2i0LY+EYzPTikqirG9YSyLOl2epzZ2SQEx8HhHik4XnvzFQ7vvM/5QZfVwrCqI4USaM8lQ9u02VRNJu+Qy2gmJZQOmbMS0TERkkNbQ/CK1ixpThiLMoakAz4VzIWZmMhnLwsj5TBjAUkGUBGUIulE0tkGIiLEYSPWDaHNCWWKaAPa6Ox0K7+Bc47CFKyuDImScqC1XUCmZUecbWeN2Cp472XV4D3BJ5pGODouK/+60MotrQydTolRGqNEQEtnr6cQPbYwuNbjYsDaEo0jNDWFUgw6ln7HcPPmu9Q+8P4fXGd3f5/RaERd11y7fhsXJclbWd9gc3uHb/z8n2d7e5uNjU06ZcXW1hbdTmeRLB1OR4SxY1xPeP2VN7h16xbvvHuF3d37pKg4HE8w1qKKkq/97DdYW1nF+RkHB3vcun2Db3/nO5hCs7a29iD7/8/Q+HHErx/n+DgJx8n/z0u3y4urh/JoTrx+cp8PIdsfQmaW3lMh92Ze1yzHUZ1URq/z/jphSsX6+hopRK5cucL3Xvku3/6n/5hPXzzLpY1VdvqCThptqFtPWeZztwZjCnqdUlBnF3BOlH2L0hBcTbKGwhSEtmEaArYQTojKuiUuRrTuUpZVlnuY4b2j2+mQUswouSJ4jw8N1ih8Eh+3ZCLJJ6qiQ9u2GKUpy44IfPpAp9MnBtBK0A1tDZ3VHqNbtzl7Zhuduyo71YDWO3zrhdwbBemt+gNpaKhFvgJy95ctaGYTTDS0vhUCdATQWTfH5M5Wy9xmJQa/4DnKbxepCkuInlKD1ZEYWu7cu8XReMTf/39/g6sfvM/heERVVZhg6XQGBBIzHzl38TKf+cxneOziZb74/OcY9ges9gfMKywRoQ9M6gmvvPUK71y5wj/6R78pvE5lKE2X0goH6fyFHZ5//jlWVgZ85zvf4Tvf+Q6zekKKj05mPg4i8zXgPwBeUUq9lJ/7G8C/q5T6glyiXAX+83wBv6aU+r+A15GOgf8iPYLx/yeByAAPoCrLZOCTLOkHUJxsX/AwDs7Jc1q+8U9DZMzS+SzbJcxJxz5lj6bMc1FKXJ9TSiKO59qsOdPS63cWhM9rN24y2b9HGdbxhWF7Z5NOKauC2ieqqjr+3My7CqQrgShy1sRAiBFlDDGqB1CslBKJiFElKJO5LsfMfinBpQz7Col2HjhSXmkoDUZpQkjY3BUQfSDohEGDkU4vhUGHKCZsXiBZZTRFVaGUEejVFvh43HWktcb4CEmQLe/axXWwTFj03hNioM16Eb35isoYzAIdysloJl8vtzevra1R37tP9C3TySFKJSaTEdduXuPu7h77+/vcunWHM9tnuX9/n83NTVFsxvDcc5+l3+3R7/dZW11dlBCluSPRuJqkLXsH97n6wfvcv3+fb33rn3Hh/CXhzeRkfXNzk6c+9RTnd85y6/YHXLhwDm0AJZ1SrvV0u92PugX/NI8fefz6kxqPSmDm8UwWVHFx30mV5+ETwzzhmMeHY0R0rid1vPA6tmURhHMxtFB7I9IlqbSag7gLZdqytCSy35lKDAcrbG5ukZLi/d0P+L3v/QEH04a7RzPWegPO9hC7lRBooyKFSGktEU3jgkg+aGl/rgpZEDrnRD8riIeaUgptpQwVo6coZBItjKF1jmkTKWwHoxJFaZhMRpTGZrVeUTLXOhFCzFzOrNfUKfDe0+l18a3Dt47QOrS2uCSISdQiy6+M4eD+IRsbW7Jw17JAQid6gy51XVMUBVX+DROBoAJYcE3K8VUQn8JYGtcuYhhKTB+7VQeVEp1CSvOFFcVy8TFK4m/kAppIiB5FxOqIbyccHN7l7t4+b75zhdfffIPoE5Ut0d5QR1kEphjZ2T7Hv/1r/xaPXbpMt9tlNQvlQeZUFoamHTGeTri/t8c//53f4/XXX2c0mlBaWWy2Kciiuqr4xb/wy3zqsccJqqXb7XJ4tM93X/quVAfExPCh4+N0Lf1zTj/EP/yIff4W8Lcedez5+JNEZJaRmOX3O63la17u8Xmfk8efIzLL5/SgUu9pHJn5OR0f/3gbh82T5jJM6RtxZd3aXGPQX+GpJz/Fr/7Kv86w28NEzdF4xMWLF5kMOlze2ebMsEd7cAffiJCTsRUxZSrXEt+nMOLhYYwQ8VQWPAopkaInRp0Tj4i2uTykE6SAzXwXQiToQHSe4HUuSzkR7tPiv1F2qsVnFvK0IqSsAFwUCA6sslGL2Aqs9DrowhIjDFble9ZGiM9WaUIK4hqbwE8b4bzk8qCPISeaCVsknA8QPMSAUhGVib567sSqYi6VKSJizDpPeOYlG6UUrXcc7U1IQL/bo55M+Se/9dvc2z/icDJjVEsgG6xt8tNf/TrGGJ779LNYW1DagksXLqMBazVVWTCbTfDJ8/7192lcy0svvcTB0SGj0Yi9gzFH4xHnLlzi7IVLfOYzz3HuzDb9skO36rCzvU2v16MqlTiBNzN2zmwznk5IcfaAtsafpfHjiF8/7qEWycOHSzkn48/y3w/sf+IY8+eXEeXTEyXzoW1PnsPycZdxQGX0opNOEYjJQxQhyieffJK/9lf/GhvDdaxX/Pbv/2Nu3r7BzsoW/eEGqewz82JsaW1BqQrp2nMerRPWalwbaKLDaiSGZCdslMTYtq0XirlSIoLpeEJRlRS2En2UqPCxpgkeYxVeO7yxGKWxRknZY9pgjJJGAhJWG+o283cSaGvpdPuEMqC1xKyiYxiNxqwMxWKkN1ilKrvMskhe2SnxoWU2mqCLgnZS471DWUOThVE1gYTDhwbnJaGp2xkxBnyQdnTvW7pFZzF/FcZgjV7ML8YIpyctkHgx1RX19hIVE3//1/8eb79/jUnTYooexhp0WTGd1Dz2+CX+jb/0a2ytb7BzZofN9S16nY5IQSSXk79DJvWE+zfu83t/8Pt8//tv8e6772aEXKFMxbR1DPpDfukXfoFnn/k0Z89sc/HsRTEB1Z6NlSFbG2u8+uor1E2bL6qH3xOfCGXfPwlE5jQkZllld77NyRv0YRyZjzqnj+LIzIWc5v+WE7fFNiaTfUPMjHsRZRsMBlitOLO5xTNPPYVvHamVz9KtOqj+CusbW6wN+0zbMX4ypigqmnrOBTleWS1//uWRlLTviQM3meMyTyxzAqDElVopjTLHV5t8Bo33Yj3gghDiTCGf0cdIlsdBKUNMSUpEZFa9ka4ArTW2KinLLBC4+J3ke48kCEFqxyHQulpKhrlOPQ8CMUWS0thCEaIoccbk8V5WZyEEKBKY40B9svZurYWkMjwb6Ha7uMmUGCNHR2PuTq/QG64RA1hbMq1bDg6O2FrfYGtri7PbZ9na2iLFyMbqBpPJBKOgdTMijv2jPe7cvYkLnu+//X1effU1zp29wK1bdzh74SJNM6Pb6bCxscHF8xfo2pJzZ7ZxztHpdNAm4KOj2+2yt7eH9z5zgB6txfCT8a/GeBTMfjJ5WS4JHScgH5+b/LDupJP/n4fKBxOrB4+jMpfGaFEJj9Fx8ew5vvrTX2Fnc4thfwWmnrZuKKzlF//8L/L42R2Mr9l/900aX0uZ2znKwizKJPOuSoljkbnHk5ybztw7sS+ZI7/KaKyS5Er0VlRWwFWYbDQ712ryyWNNtVAYLzsFKorwndMaq43EOSVcvtJafBJhQecCpVJiQdBfASUyGiFlcnCOr2AkHgVP28pitVAVRC+cvBgheopSL+KgtYbxRErMxihSMpKQJFnUJZ0y3zEdN1MsxbT5dzdv8R6Pp+zOGryLQhE2Fp8Sk8MxX/7yl/naz3yVF7/wRYy2dDsdSlMKElVWxCCdSAfjfW7dusEb33+T3/zt32ZyNMroXQkoqm7FE088wede+Dxf++mfYX2wQq+sGPSEFN0Ez3A4ZHW4Iolj0z7yav1EJDIp/XgtCh42SS2Ph9+sj7YqOLnvo56XERalmfmkk0IkpEi3K/4bWmvauobY0uv12NpY57FLl7h44RyuaUXCG+GVnD9/ntjK5LlSlqjJAbMU8XUtrXJGdFGOBfe8yE2nkwq/akFimxPHBLXIj84TTSRRZCRDC1qTpHPBJ48JagFBG2s4nlM1Ws19nUTV0wfpYEg6orAYJeRgbQpiklaFpASR8RlhA0ma5v98krqsUlKamic4c4NI7z3et8Q0N3w7/h7mv0lMfmHctoDW8+8xh9kHvT6T2RRSFi+0JWXVpex0KfolaxvbTJuanZ2zXLp0mTObW/S6XVJeGU2nY0iBWVMznh6City4cYOQIjdv38LFSG+wQtGp+NKXXmR1dZWNrW3WNzc5u3WGjdU1CFG8ZozBuZbZbMZocsTB3j4bGxuMxxNQwhv4yfjTNU5DZk4iLx+HU7P82sOee9g+j9p/0a2IdCpFUjYvFBG8lcGQL37+83zu+efpVh26tqQ1gig+dukyX/3pn6FvC2Izpb57i+nIkRSUhbhtz+9HlUvULNSL9XFZDSlxi6idJFwhiEedtmbRyROTRmkrXMEcb7TAtfLZtEKhKDvVApFXGLQyuVyn8dFhTSEkXG0JeUFlioKV1XWIica1uQwuRpLzeCKqtoGmaYlEkk4436C1xs1q6loWaEVV0tQ1LgScbwnBYQpN23qcc6z3c0wPuTuLOWcpZvNelW0hFCpFQaKAlBSzaUPTBJyLmKqD94nhcMiTT53nL//lX+PyuQsM+0OK7FVVGENMshBzoWE6O+L111/ltdde4dU3X2c8mYEyGGtopg2D1TV+8S/+EhcvXuTy5ce4eOYshbWYufaX0UQXccExmUyOnb8fce19IhIZpX78FgU/yLanQa4nyXQPg16XH+c15uUVTcqlFLKZIkA9nUmpR0kNNTQK1zR84+tf59lnnqYsS1aHfVKINNMZaytDVlYGKBJr/RX6VcV7s0Ma71hdWaXevUthpL1P3jeRdEEb/AJBmZe3JBgYjJonYvl8o6wQXGpILmFtB61ESlshsC4kzLyWrueS2SbXfaVjYFGaS/IYvChPmGwFHxDOytyaXhItWV340OZ9xe6+cTVN01DX8rg6HEjACYG2noLRpCRS4z442rYWcp2PGF1gtc0GdinLdUunV0LKTyKep4UgTSK1DcOqI9LhzhOSZRxqBltdnvvsC3z62c+yurrKuZ2zkBKrgyHj8ZiqKpnOxmgTuX+4ywc3rnHr1i3eePNNrt2QBpnvv32FzTPb3N874tLlT/EXf/GX6XW7PPPEk4uETWceQH91yO3bN+l0OvzRSy8xGh/yzjvvsLd7H58ig/4Kf4Y18f7UjeUE5rT4tehKSQ+KbT4qGTktZi3zyeDBLhKljn2UHuwuOX4fw7E6sOwjBFOlgZD41V/5Jb7x9Z/j3M4O0bfsH9wnzEQa4sknn+DC2XNYFK5t2Lz0OOmWohnvU+TJtyqqxUIsZMHKlKTkND8vQWoQ8bWcOBgjPko6+dy6bKiqPil44QVaUSmPSjF3nG7alm63D4DLHYvdwXARv0IIqGRpmoZOJb5q3eEqKoop7Kxxx/eu1own0+zhpEg+MBr9/+y92ZMlSXbe9/MllrvlnllZWVtXb4Ppnp4FswAEQBgJgKBAAlzMZHyQZDIZjXqTSX+LTHqlmR4EE2V8kWQUAUIAsRAYoAnMdPf0LL1NVVfXkpVZuefdYnF3PRyPuDezMrt7aBSn1Gg3K8usvBFx48aNcD/nO9/5vmN5ncDp6WlLtejmGYnxlFQEV1FMC/K8SzUumU7HIsMxqvFVzaDba+cwpRXBiXO1tZbKScm/qMr43XpUVZMpcHVNUHA6HTMNKTrp8NztF/ln/+y/5cr6BsYYlgcLcxpvNYGaaTVm73CP48ND/uIvX+fO3bu8+b3vyfqRZExLj69q+v2M/+G//+94/vZt1pdWWpHVJBeUq6Dm9HCfuq754Q+/z527H3Dv/ocoD3nWjcrk5aX37jMRyPynQmTOQ2oX/bzsPc8+5OGpv33cz8tQnBBmsN/8mOfDeO+xaSbO05GjkVjLYHERFaQlrwqeo6NDQIhsaEWepnQ7HTo2QwXIuz3yfp/i5LglrDrfdDQoGlXM+UmqmcS897ho0WB0cuY8m328rwlKTAuVImq2KLySzxLUWVv55oGQ9zKoIKiNMwjhWGuCNi0Zep631FyX5l/wgaoqItJStyWyoq6EWKsirBoCjqhbQxDimxJkSlqoI2qEBGpNG7wyxIdXgp55vaHJdIz30mWQ9Je4fetFbt5+ibWNda5uXqGbd9BKBMCKYspkOqSsDKPpiPp4yqPth0yLgkfb25wMh6Ated7lS1/6KmnW4Vtf/xusrq6yeeUKqU2ivLqlqAoUjpPhKXtHexwdHRFCYP/wgKOjAw6PjrAmZWlxgel0Sq/Xe+r++3z8/3N8XKnnk7b7j3kO83PEZSh1M8eBEDabU/DeUZUlzz93m363iwYePLhPt9tDOSOmhLZRp7XYJNAZDEiOOoxP96VUZLJ2fgpBFm6YBXrn0fYmoPIKiLIVSWJwkQAr5eUox1AqkkzKJjRNBElKWZYkSdLOR3Vdt+fQIECD/oIENdG5uyiKNjFPkqTVeOp0Ojhfidt11AcbDofUQbqVjDFoE3k+sVRmraYOnmkUvrNpwrQUAUCtNd1OB3xAR36gn9NhkfUvtFwZQWSk5O59oA41k6mDPOOLr77CL//y3+LWjZtkaYpV0n0mQZhCJQFfVrx/9z0e727z1ltv8fpf/CWTYkpQhqA0RekZdAe8+OJLfPELX+Cll15iabBAlqTiX6UN49Epo2LK6XTM/Q/vcXJywr/7d3/Mkyc7HBwcYE2Kta6VRLlsPDOBTHMT/H8tiDeP7nwai4LzUK0xhtqVZ8Xz5n7Oj3lexUXnZK0l1GcJwnbOmsDHjqTaVZjEAlbKG17MF6fTaVxgHffv30drzfLyCv1+j6ouqCoh2+Zpjsky8m4P62smp0eioBtmnBzQ6CgX3XwH3rs2SFG66eCphBMTP5uL7rQgrqrGeIQrM4N2rY3woArUtficeEVbygIVBapENViCnBgIIcFTXQsjPwQJQBpI2XuJ+ItC3KPrmFl4gkwggVaiXJAkhw6aBoJ2zuHqQJYoUpvMhLwuuEdFPXM2SQZfI6JigWk1JbOa27dvc+36FmtrGwzyLtYolgd9jo+PSbOU8d4Y5yqOT4/44z/5t3jv2T844uD4GO/A2JzrN26Dh1deeYV+VCteWFjAVTWDQY/hcEhRTZmOS9794H0mZcGDRw8JIbC7u0s5nTAcDelEddIsywTV+3x85sZliddlnZftPhcc66IyFJwz7uMs4jyPKM9Qmeguf64sFavI7XObZVmrb3RycsJHDx+wurrKcn8Zj0NbjbEihaCCQSUpWadLkuZQydxXlo2OjI4GToDR4qVETVUV0VJE1HETk6IUQnhVghYprZlOC4LSIrAZ5+cGPfG1zO8mTUiMoYrGrzZLSZJE0AKj0VrEUolcvOBF4kEbmb+SVLZVWqFtAt4xmY5QSjEej1oNFRvL9kVRwBiKosBaTZ5Le3ddFm1SLomaWNb0uwNUQLgy0UdOtMOk09PXnhA8qTWx1C6ltiQxUJftMV/+wov8yq/9Cl969ct0Y0Lsq6jybqB0BdPphKODfb7zxl/xve+/zZ07dxhPKxSGgKKuA91uj3/0D/4RLzz/EqvLiyz0F+I8LGtK6Uoe7z3mycE+H20/5Lvf/S77+/vsPt6RddImZInFq6jx9ayTfZsHoYmsf5oWBee3PY/aNAtqY0lw/udF46JzafaZN5WEpotHaq3GmtaFtoi+ItZaOpnoJYxGIx48eoTSsPPkMaurq2S9HkmdstDPyTop3U6fUDqyTg9f10yqipAkqOBx05k5VwjhjKidnJOKwYwQ6JT34uehhEzXICIz/RyNJ4EQMykjQYkKEJqaslJganQjNqUU4EVLQYl4HiCO1ehIChQLAtzce/oKV5f4SurCZVVQV46iKqUdMWYOWmuSoMFkuNrJBFHVFFVFUTnxEQmBVBkybTHUEGjrxiiPb5ErCcTSNDrWeifbGE2/3+fa1havfuEllpbX6HR65Im8/+j0kJPRIbuHJbt7T3i8u8Pe3h5/8VffYWFhEWtS1teuUldw+/bzbK5vsLm5xeriMmmaijt58BTG8dH2fY6Oj6nqgrt377L75AmPHj3iydERRycnrC2vyT3SGbCyskFmDcPJmG4358NHj37SR/Pz8YyPy4IP+HS8lvltLyorNcc8HyC1z+45Mu9sSIDvI8dEKdUiGyBz4NryCtvb2xwdHfD40UMe7j7gN37j79PpdltxNmMUWZIy8Z5Od4Gsu0BnsES1/5jESAk4BI9zcyhyTM50QEowVUVdC38MxDak0+m05++9yDkUVUldj0mTnO5gAMh8Z5QgMNPplCrSHoJ3mLpElHETtLaMJxOyLEPoMhqbJNIWbhU2S6m9ZxrnJpwXsU3nZdZMDKNRTVkVDCfjWadOEsX+ateizcYYjkdjyqpmMikYj8f0ewt0khRVx7IdZ6sJDRk6KGJbuARr3mqmdYWP6sCbV9b5p//Ff8XGlS2Wl1dF5sIESuc4PD3k8PSYN956kw8++IDvf//7PDk4QJsEpXZ4rQMAACAASURBVDKyNEcry9/7e3+fl156iWubV7myeqWlJTQ3y8HpIYfHBzx69Ih/829+h8c7j6iKkpBkFFVFkuayHkW0q5N2WNxa5ODk5NL795kIZP5TIjKXbXvZ8ee3b/eZQ20uQ2Tmz+uy4xtjxFOI2UM429biaoExCbpVqUyN5fDwEL8w4M6HdxkOT8i7OWmmo4W73KTj6RSrDXnaIU8zOt0+RgV8NWFSVSRKYVNRuGwWaaNE8MmYRBjxWqNsRulKlBLfp9oHQpgp4Ar6okQfIU4MQQwEmpBE2h9xBCMKmLat8bsY3BhMYtrzUErjgrRr+hCwicVNC0xiBQZ2QZCs2uF8zICCqEc6X8cuLwVGS/SvVJtdTYuKcSnBTu0CiTFkndihoKRMFWKgaVTAzU3mIfJkqqqImY5BaYWqvXirBE+/1yFLLSH6YJ2MTpgUY97+4fd5vLPDzpMnHA9POTkeolVC8IaNq1vc3LrJ8vIyV9av0Onm9Ls9VKjpZF2K6RSPZG/ffetNynLK4eEhDx48gBAYD8WbZGlhAaMs03HB7Zu32NzcpNPNODk5EmLx5+Ov9TgTaHyKGOei0vknBUfny/GEi/mBWZLiveed997FKNh+/IgrV9fEj00r0jQXRMKACzVKSVmm0+sxynOmWpPbFOrqTLfpPFo0Q4Rlbip9KXNYXTOdTqXlOMvahMe5IDYn2ojZZNzP15Lgdbvddh5v1qomKZ0dS5ystU3P0BWaf02Sbow4So+HJSrKPig9K6FL2SuJiR9UZYVRcY53NaNpQVFWnAyHdPOcXreLCo0MyBx6oTQm6vb4ENqGhwY0IP5e147MJqIJkyUkVuOqEq0sp6cTirrgO2+9wfbjh/z+H/whu7u70ozQW8Ro6Q69dv06X3jxJX75F3+JfrdHt9MhteLjp5Si9jWjyYQ7H/6YD+9/xLvvv8Pdu3chiNUBQUx4i6mjKkoym3Dz5k22rm2ytLTAux/++NL77pkIZH6aiEzz2vn/zz8M87wa55xw4OdQlY9DZBoUR6mLDCFdq7PQnFfzWsMXKquKbt4T+Xklgna9XrdtWd8/PKDvB2S1odvt42sRk/MhYBJLZhMIkGSiGKuHQwplqH1F5uTmFSLVLNuShTqZuwYmiiBJO6WPgnlKKZSe8VYkU/HU8dpUShRqXfws88Q78WAywvrXDl3PODMuKIyxhDq6VVcS4BRFgXMVri4geKp6KpE8jqoqYsuiAyWlKhXk4VUBpvUU5wJVVVOUtehERCnx1CZYpUh043wbEJlAI6Tflk+jogneDJEri5qiKMnzXNCTJIHYJXHvwV3GZcHh8RHffv117j/cJss6nJyccOPGLa5fvcnKygrdbpcXbt1mc2ODXqcbszzDaCTiVEdHR1Te8eO7d/jeD97m+PiYqqo4PTpldXlFpAt6XYyVVvzlpRUWFhZ4+aUXSfOEo6MD9o92/4Oezc/Hsz/mA46P4xVexhv8OI7L+W2b0SaF58wgz79vUJ4QO4gU4iYfa8hMJhPeeOMNjJH5c+XKMiEEenmPfn+BBw8+YjKZsNhfpNPJWFlZI89TgoYHH95BhSlda6UMHTzOCeogXDkvyuVjQWCCVnSyblvmKYoyGjwOMXYmoumcY1qX6CQhRCmQJJZDxpNoWhlqrLOzdm8vc1eS5ZRlgVaWYjJGa02vvwCI5lSWZeiIDk3LiUhS1NPWjqAqptKSrQ2DQZ+gFUVVgg7Y1FAWNWVZMykLjk5OQFu2trZIraWYTNGJQUdfu0avJ7U68gLDU2tMCIGyqNBYiL5+zz//PL2FAXVdMZoOufv+A3587w4/+NEPefudH5LbnGnl6faXCUEx6C/y3I2bvPLKK/zcN74pZGNjMIQosTFmNB5zcnLCux+8z/2HD/jDP/oj6iCJfK8jRprWGFCK0WTKN177Gl946We4ffs2S8sLGKMJquZ//Zf/4tJ785kIZH7aiExjBHl+n/ljn9nnHBIzL3p3/jMBT6E28+fYcGSafc5uU7c1WGst5WTa2gpoFVhYWGBxcRGbGcpqDEGT2QSjE06HQ7ppQpmVDPI+lbVSP00ydg4PWcpTBt0OCh9RhgqthBwGMBkP6XT7s88fDFCjgoLYSeS9o3HQVV6j0DgvNWUf0QtCIDSZjY9Ih5mVlZQKBGeYTEaAJijpjDLGgWq61ypc5eVa4VB4vI/E3lDj6rptEdcaXJNxREjTh8B0UrSZR9ASwMxa+7woCccgWkwu47lFhEikagRBavR8iklJHTk3SZKgrUFpUdrc3dtn7+iQ3b0n3L33IUUVWFhYYXl5meWldVaWFvjWt76FUbAwGNDLOyRGY5SIW2V5yoOTPUZTmQR++KN3ODw8ZHw6pBiJm69RGmUsJkkZ9GRCePnFl9jauoZBsbG6xrgYs7DYZzg5/o/6zH4+frrjMvLuRaWlp7gqasaRORN0fEIw0yR188f6JIEPr8BEbdZ5MT3iQueBNE2kgzGI5ERwnn5ngHOOJImaYk7mXGst3V6f/eMTSAz9jfWIZmjh64AkU5XYCDRqwyIZoYTYWseyjpHOmRAC2oDBtt2QUycmuyYmZ7P1qCZE37gQdWBC8JTVlNPRUM4561BV8lqaCXrd6M8UkylVVZAaOaeqKiimY5RSTKYjkjTFGMVkWqCsEdsVBWVdcTocUpcumsImwvHxnqqqCMGhg48kX936NKm50l7wXox3g6DwhIDFMK1rXFWR5Bndfk8CHzy7jx/xR3/6x7z7/nvsHexjkozgE3KbUSMdnf/4H/4Wt27cZGFhgeVBH6uitEeQIPZoeMpHDx7w4Uf3eP311zk+PsZoTWZTrE4oylp4QWkCrqab9/jFX/gFbmzdYDAYiC9VcHiEanHZeCYCmZ+2IN5l4nYNX+Si41+0z0WBDzRMb/0U4uOcWBTMoz9tgBOCsNqdQ2sJfDqdDnVdM1jscXp81NaR+90uh6dTFNDNe1idYFCtgViSJKRJzsSNMIllOC2xBK70xWDSF9MWOWk0SRoZ/iaTmqFS0oHTknQjkpYYG5EMed+m3a8Zck1m9VphzTeu3g5fx2OhpdRk5HM3wW1dVhGxc2Spih0G0pXkfEVVF9E+IgZl9qzQ33g6gTBzw846HbIkieq+KsK6irrp3opwcwtXh9nn8BFlC87RyTuMT08pYwdB1sk5HY05HQ3Z3nnMg0cP+eDuHRYGa0zGBRsrCfvH+1y7eoWNtVUIjm63S6JEjNS5CudqjFE8frKDCzW7e09483tvorVhMpJg1iiZhKfTKafjEb1OTifPGfQ6vPDcc4xGI/JOik7hePvgKfLn5+OzPS4Lcs4jJ59228uO/0kVKvG+1lHDRbXbawNQ8zMvv8zi4iK7+7soa6JHUBK5LY6gGvNcD2mgqFK6vQGjosIPT9laXxVPNqXaRLQhwRpjSDOZT3xdI7Jv4mIv56Jajp9yCpPaWdnIOWrvzsyHYmro2zLSbO4XsT2I3KEQcGVJCNIQoI3Bu4rpeIKvRVhUKtniGyfrkIR6VVVR+UDQCucd1gg3x3vPcDiWTlUf6ORZVEqWDiWTJPjgyNKcui5bI8wQosYNzDo6taaqpuAV2nvwAU9c97TCGOmEOh2N+KvvfodpWZB2cklalSa4wMrKCl/92pf52le+TK8raH+aQqgrEfeMyPyjnYf84J23+e6bb/Dh3Y9IkhRfy5qBD1S1vGfQBuM9Vzc3yfMM50qcK6N2mCSu6mPutmcikPH+p2tRcNk+53kys33OmkeeR3xmXI9ZJ9L5rqj594Woxt8gAvF4RRFlpH2UqFaKxGq+/rNfZn11iW999efJ0w7D4pTv/OgvybuZWKarwP7RE3SyTgiKupZrmHU6JHmf5156lXpyyr2dj+glhsVOR+BepegMFlCuxpUVBEdV1dgkIbeiXOk1lFWF0o3TtW4Dr1k3gtSHjRUztNqJZ1ODtoUG9jTRt8U2JSYN2hOoqd2cL5KTiakhqQ21vM94Ws64L1VNVTnqyuM0+EK6uqqibIMhMaaUUpJyAV+JiKAJ0mkh/ikNqbGmDhofIHiHdzUacFXdHq8oKobHIjR4Mp5wfDri+OgEk2bcuH6boFM63T7LS6vC6ncTDg62uXp1lf29R/z5X/wJWgV2dnZYXR7w5MkTHIHRWGDpg6NDqsqxvbuDNTlHR8cMOgNOh8M2sB0XU/r9Pnc/+oDBYMD+7+3yF9/5MzqdDteubVHVBUVVMhx+zpH5LI2LApLz4/zf55GXEJHVn2T/8+iN8Mbmkjf/dPDjvcY2HYjKY7AYG8hyza//yt/mn/6T/waH4r2Pfswffvv3qVxNbg3Xr26RpCmTYshCRwjxwQXybpfClyyv3+Q7f/77XNlcY7nXQwcRewu+Jklzelp0uKzVOB9LR5GTZ41iEt2vnfdgDNroVqwvzTPq8YRUW7FMQeawajRqKwe1i/IMVVyfggQA1mp8XaF1hcFz8OQBVgliW46m0mWkA0MnAZUPnlo5fA0kCZX3jCYT4QIqxeHRMVVRt23fSZKQ5V3RGdMKW8s6YrWSEp0vQLlYDlOt4B6A0YgFQx1ITI5XnqqumFYlhasYuYBTmtTkLK1doddd5rd+8x9z994d7t69K+3Q1vHySy+wurrKlY1FdnbuURclxXTM7t5jhqMTPnr8iHExZjIpePt7P+T4+BjngnQ0BWkBv+geUwo+fHSH9+7+gLW1Na5du8qv/9qvMhgsStPE6PI57JkIZH6aiMzH7XP+oTyDyNShNT+DGcpy0T7n0Zt5RKbdx82QngYJyrIsljNca+hojGVr6ypX1pdZW11leXGFg5M9Rn81olpypDZpORZVXVAHT5JkQCB4OeZgYYWRdxyWFUaBnRSsLi4QKse0KMmMJk1TXAzCGmjSGNXyVvChDUwEtpzT4WmIzBFC9r4xnGsmkhjQxYnUAJgYcKrZJNm2P3pPMZm0SI4x4lpeRpSmclITn5alBG5Ozns8nFAUhUCX1pKmWlqstUYFR0KCQryagvNoK+3ZzT0pD1mT2cgDZ6MqZnNuOrGU4xEueMq64urVq5yOpJXz6tVr1HXNyfExKtSSDbqSyfgUgL39Xfb2dnmys0veSbnz4R1W1lZ5/PhxhNwzTk9H9LoDTk4O6PX67B8dYowhz3MOjo+wiWY0PmU4PKUOjnv3PpSS49KA+4/ucvXqVZQylOXlYlKfj8/e+DScl8vGPM/m4147L45H5GKcFeNDBDLjNlpplPL0ejm3bt0Q41sfWgNJkOdwdXWVQb/PJPoMEcAkltSk5FmXK1e3GNWKvf0jujYltcJdASlnWZ1glcFXUkohloma5HFGDI4eb8ic1pS/kyTKNShBQJgL4pxz4ko9rxGjbbzuM5PNxpet8hXDyZjhcIjyoS1pj8djPLqd1ybTgrKWTiQVrRdOTk4ITt4zy7IWKZf3coTQiJYKOqMgdmLNeJ3z/7Qm0hnkGGVdUsdmifF4zM7ONpUrKUrpwHr11ddYXV0lsZY/+IPfw2jN9qOH3PvwLt97+w3SNGU0GjEdj9g/eMJ4OmY0GRNU02AhdAwhBEsSG84px4uEhfyulOLJ/j57Tw5499132d7e5tq1G2itOf2YZOyZCGRC+OlZFHzcPvOvnW1LDm3tcX7by45/2XmceZ9z/28WSoAsSQm1oywmLC4uijFYp4OvxWF1dDpkMBjQzXNMolu2e8PLqaoKm2VUE2kVzDo9jg8POBhN0CgSPyMyW6vRCJSrgsda0ZYxWU5dSveSjuWVM4Hmue6EpvQEs+0bXkzz3c6ToJWJwZJtGPWztu5mQmiuRxmzjLKuWuSsqCqcC5R1iQ+0cGxzTtbadhLQcpL44EhCRMe0x3sxk5oPOmckcHXmHJS2pHnG8WgkkPPxkNHpCe+9/y7d3jILi6ukNqOT56yvrnGwssz45BB8yZPH26SdnO0n2zhXUbiK/cf7dHt9RpMpvcECB0eHDPIuk7Hwo3q9foS9nWR3ZUld13S6PU5OTuh2u4zHEzr9HmVZMhkXaA0PHjwiTdOWy/T5+OyPT4PWzG8zP+19EvH3Qr5NE7RcsE1TwglBEobm9TzN6Ha7wv0r5F7WqDbpGPT7rCwtUxTCbUt0AmiU1XS8Y3l1nWywwKO9IwZZh87qCoboFRlAeYvWBnxEfaO2jWo+Y+TMhBA7E/3ZedsYA02iGm275/V0mrmpaZRIslm3EWrW1doEKWVZSsldyTpSB8+kLFAY0ZpxmtPRiNo7yrKmjonbuJiSm6xN4Jo51GgR8gwh4INDpisxY1BhVklou5O4hA+lZ8HZdDzh3Xff5Z333+Xq1essLK6yurxOnmZMJyN+93f+D1CW7fsfcXB4yGg6ocZT1jWJmbO80YbgxerGxqYR78UWQt7/3NocpGO0ikrqaZqDE67Se+99wN0791E6UJbVpfflMxHIKPXTtyj4NMc/z5lpmG7z5zf/MJydLC4OnuZfb45vlI5ETulSckFs1rvdLtPJWESjlpfpdDp0uzkLCws8vHef9YV1QETqGjfWsixJljKcr8jzDj7A1s3nmNY1f/7Wu2wu9HntxdusLi5IG5zWJNpQ1h6tA1oFrBHEyLTCdULEnValCNFVFXmeAo1yZYLRBq2l5FS5Gqt1lPuW77XRUmjKaHWYnnlQPf4MIuMq+Vm6mjL+PpyM5Vvw4iVUVCWu9pxMoriUSfH1XLtkjPi1BhXEME5aFY2gRirWquP3UkWHWICG4JemIlioraIua7JuzsnBAcVoyP6THf75//LP+fKXv8Ev/9KvsrS0xEvP3+bm9SvcfedNUhX46N5duv0B27vbHBdTkjzjYP+IjY0N9vaO6Ha7MjnVCeNRRcBwcHhCnneZTCf0eoP2GUF7hqMJS8urHB+fsra2zv6TXfr9PkFZ7j/clUVhMGAwGFz6THw+PlvjfFL2cUld/KXd7uOSrzP7XPA3rWaLZPOsNQmPkkxHMnJX841vfIP19XXhvlUlRmlc1SAYjtWlFb744s9w/8Ejntt4ju4gdrdoTa8zYP3qNTZuPs/3vv2n7Owe8Nrzz/GlF66Dr8mMxTuFIiZ1zuGCA2/wSgwru1lKCIpxMaUuKqZ1jbV6lvBojQ5KOB3GU/vYwBAUyppWRTzR8fcwamkDWmuSLGnnxkbJtyhLQpKQWMN4WFDXnnEhhrMKUcMty4KyqBiOR9TByxzmfat703QkWS3Mo8SIpo33HptIANH44M2SsBlHMQQRx2sCOgwEJJnMreH+3Tv8T//z/8jf/Ju/yq//+t/jxtYtBp0M727z0os3Odk9ZDSekGeGaaWolcFYTe3EE88rFUnbAE44MJHYLZY4ulVgltEEXdK1pLQVGoKymESCw+JjNNqa8UwEMs8yIjMfvJw/p2bz85yY8+d9/pjnX5MMYd6dVD6/q6t4DmIj770EMy5qxUjdcMRkMuHKlSsCiWYpaZ5JmyDCUymqkk6WU9UFShlEfE6T9RfR3ZxJ7TmdFFCXdDsZytcYK11IzYMYkODDYMQp1iisgmA0BtuaiKmA/N1J15A8pAG8jgS3+Bk51w3GWUsEH4J0QPkZMjOPAM0jJsTuJI3CgfBeEoVRFmUT6XaIxGHTnGNiIHisnkG18r35p76beYJ3gxCp6K1yOp6QpimZTZiMpiyvyX08KcYsMGAyPmU8PmWwuEDe6dDvDxhGDky322c0mbC6usp4PCHJO1grsuhpmlNVDucCnU6P6XQq1gRF0fKrREhQNC+yLJOfnS6Vq+kkXVCKLM/xIVCU9cc/hJ+Pz9R4CvH9mEStkVI4v99F46LjnEern96nmUc9Cto235WlZfI8l5ZjI/dxVVWgBbFZX7/CB/feZzSdsNDzQm7FY5VmbW2NV7/0Gm+8/jqHkyn3Hu/w8s2r5Ea4OtpoIIiXkTYYrygr8WvzEVVGQWo0VlmqmJTamEQmsSvJ1dIwEQgkSSaYjlLYJAFUq1HjnMM74dnVWrXEWmHfhWgsGZhMp1R1TeWdkFzjdyNinbToc5JkGN90S+k4d2mslutplAj+GSVmu2ruuwBPU8cRixcpkTvkuoQzBrKeuhYNrEQn6LzLSVWxf3SIC4E0S1BVTbfbFXXxUYnzgXFZ4AmEoAkhmm/W4sFkUHGdMSKDoZR0WLlZs0R7n6g5GwktXavzaL+O8/MnFUqfiUDmWUdk5rkgZ15T/qntzwdF59Gc89ub+GBgZu9jlI7kNB9bEBOmwxHdvNPyajqZ6JYs9hcpQ8V3vvMG3V/oU1UV3W6XleU10syKSZoWKe8sy/EONjc3mZQFodNhZzzh4P077O/t8tL1K1yxCm2aCLomTSUQKEtPmsTWcwWJRTRozFywEwM/MSyD8WQSF9wQf1oaYDFEMpuNteAQZgGdaNHUrY9JE0A0v1cxczMx6NAmQQdPcArlNd4mLdcIILEyASRaYaymk6ZiDGkSvIvQc+wwaL8/QM+5xCj5YkTfpm3LDiRGcTou6OY9SDs82TtkdeOU4eSI7iRhd3eXyWTEuKxYvrLJ/cd7LK9v8mh7h+F4SJbn7O3sxy4uw8iPSPKZhk/WlWeitzBgOp2Sd8RkbTQdkec548mIhYUFRqMRWdqJwarCnRwzrUrCREiIpfu8a+mv6zg/F52fh+Y5Lc12538/X14/P3SYPdOXScmHILxCpeDhw4fcuraFuWFa1PD4+JTRaIRTkCU5Nzav8a9+9//m1dtfpN8bMOgOcLXcx5ubW3zrG9/kt3/7txlVFT/a2Wf5R+/zhZtbrAw6JEHmjHHlSWxMwvSMuBu8lN3zXpfSlVRTCT4So7FWkpygFdV0Gi8ioA1aSQOAC9LCrSI3RSMWJ845gg/UdUXlXWyNjomWUiitqasKVzlJrrxDG4NWYLMUFRxFqLAmRHNag9KCiluNEJgTI0RfbUiThEaKQuPxURpiFgzMkmxf19QKrJEkzDVc0BCoq5LEZKRZl9PCce/+R3z08C6LyzkPP7rPO++/Qx1S8sUlChSMp6RpTjktpdXaeRQSFAIEHVof8vbeEqqT3APxNYm3Gj+o2PyiRHxUKYVjLuj6GFDmmQhknnVEZp6AexZ6/XQcmXnOxUXnM49EKKXQsXOm0ZCp61rKBU6gy5OTE7a3t1kbrHNycsJ0POHGjRszVnvaia2CUhqZFBPxL5m7pt57eoNFXJoxPT2lNglHoyl5kqAzgV+TRIkMfyWdT0o1WgQ1PvJqtIG0JbrRcn2cc1gVhfmaDqfgMFoCG6WlfKSa0CbEmzwGNAYh1YYASmmcrwlB3GiNjvOKj8GuChKYKFBW45yUjZpyndUGazRKC1qjgkcbA2FmqDavQNr8Yf5aKSW+LHVdU0Zdn5OjA5JOnyxNWbAJurtAVQeKYsLx6QmTYkLA8WR3l05vwOloQj4QTounCfoMibExiK/o9DpMo4t3t9vl9FSIwU0tvvlOxYxuhsTMywiIqR1kWRZ1jsLnZN/PxxnO2Py4CDU+Py4qhc9vez7ImQ+eFE2CF0vKIfD222+zvrzEt175VjvnN2Xk5l7P85zRaMTj3R1eeOEF+v0F4XR4RaJhZWmJJMmog6jyfvBgG60CL1/fYqWXgg+kedYmSMT53GiN85UI22lNgkFlIXZ8zs5FBXUmqVayYrfzGc4TVCWlHOfRyrTJrAlBEGljsEqMHgXRVmAM1gkKnWiDthZjpYHEaoMzNaHyuABBe1KTkBpLYo3IRATf2iYo1aw7jnAO1YaZVloIISZLZ81+Q+1wUd1dkGuL0ZqjoyO+88Zf8WT3Ie+88w537t7jypVNTJJxdDKkrCumhVQGXFXF6ybfuWdGJr5onL2/5pF133L55u/TT0IJ4RkKZJpF51m1KLhwn7lW6otMIy/6HPPaNY0g3vzxrbV451tmuyxmx4Q0Ax/IswV2dnbwvubW5nOsrwgvppvlbW1XEJsO3U6H0tWMJmO6SU6ed2Pbs6bXzblx7Rqnp6cchMDu0RG4mul0TLp5ha2NdWpXYpJEfIfKkhDAWoMrHFangIjXzaMfbSYHcy3akWqnDYQZ8iR8lOhlpGJmozXeyzYOIGYPVkcjz7itIxBMPL7SeGqyxFCVjkRpirrGZinGaIyR2rdVYLXCGtVyYZSC4EQNeDbRn81Gz5uIGmMYj0XEqpwW2KzDtfUbPDkdU49HjEYjvv3tP2PjyhrTScni4iKdTocvvvIl3r9zlyd7B4K0mQxXVuRJKiTAVFxulVLkedp2Ksw/J83rIQhSN7v2SiZmawnBUZaz8ptzFcakP8kj+fn4DI+PS/SaMY/EzP/edBqGj9/97HGis7M2Mn+miebHP/4xN65dpSgKgoLaexHzbCX8E7Ksg/dw78E9vjr5KutKiSaLAhMCvV6XlZUVHm/vkqQ5u8e7ZDt75FnC6sJzaCOyDdraKNLUOFSDUUDw4EVdPTE6lkGi9IaTJMtqcZxWSlHHrkoI2CaHwqMhitHFMo9S8QLFfqggRFdDTPS8w4ggDElsQDBKkWRxOXY1dVGLPoyx0mmppGSfWkl8jBZzSKuRbqunFv6zXaUwEwW8jOe0trZBb3WDJ/ce4XC89dab3L3zLo8f71JXnl/523+Xg8Md/uwvXo98RUeqUyw2Ero9ZxqSIsF6/j0agvL5+2z2h2YOPlvi/6TxTAQy85lvs3DMlxQusx24iGty2bYX7fOTHL/5J6aOUaJeP20eedG49Pj1LPBpApyqqsgSaevLuyJnn2UZiwuLAt8pxYNHj6iqQnRS5gipzfvkec7ywjKTasx4OmKqYXV5tUWYEmPpdbq89tprfPjRAyZFxd7pkMcnBSejKVfXN3h8fMRCaqEsyZI0RvOy8OedDOdnnzfLBTky2lLX4oStlIraDqqpnInYfwjotkUxfnYLBBVrrjWpCQSv8FbE8owBHxwYpFXQxzbCRpNAeayBuhIIttKeLNFkyYzpn8XSndXy8GtNiyo1kiEEwgAAIABJREFUXkzyHT+NnLXdSmZGVNZaSwaVpzgPG1tXWQyKvR+8S1kVHJwc0un1uHH9FnmWsbV5pXX7PTw5ZjyecHhwzNryStuBZBLLtCywGsZFSZZ1WnffGbT/tKFp65buz5KV5xHOaQORfz7+2o151OQn3ed8Od03wUCYlWAJMwuP86PpaGzQA2MMNrEoVXP//n0p0SQJebfTqpbLNgld+vzcN77Jn//7P+fFl1/i9tZNdER/fe1YHizxD37zt/jf/vd/wdHJEN1Z4NFJyfSDR1ilubG5xkKeY0zSalAFJ6We1FpAxOSa56soRINFocQROl4H72oCSMNC7OZMExMTNFm8bSYzXDMqxObE1RXKKxIdsEaOZxVURNNZX4P3eG0Zj05xXq6ntWCCJQSPUZ7UWvIswWoN1Gi0GEmGWdLtm+/Ni0CgjURkH+aR+FlHrPOBqnKMxlMmZc2v/Y1fYLC2zns7/xeulHlmb/+Yr3z5Z/mNv/ubvPbKq5xMjvm93/9/2N7ejhUDJ+RdHwkDsYQ0I/E+fV+dLWWKZc3TAUtr3vNpbtdnI5B5lhGZ8w9yo2FircX56lLTyGb7JjCbP76NUb82M7Xf5nM37dLNPkmScDqcoH1gcXGRpeVloGJ39zFoJW18kwmj0ah1SG2UdbXTnAyHDEcnXF27htUJqTKk1tLNcp5/7jZp3mV37wk7DzVpp4cJnoe7+5zkhqu9LuvLS1gCKpPsH++oaxFcqqOpZV2UWGOoqxKt4mI7V1IK566JbVjrTWDgPV47dBBfJ+d8NIyUrMZ7Tx21FPDSCj0bHhUEodBGApuGv9t4KRkt11UFEbzTMbhqv6P4z4cgj08UbBK7At9mOw1nRytFVdVUPjA8PWXlyhaLayvsHx2ztbmG7nTYOzxmsLjKtWu3eOHWbfAl2gRSK9drOp0I98h5MBYVuUSNYmguFlmCoMUJXqw0ZpYXM8HIMNdRJgGiJAMSVOoolgXFp3oePx+fzXFZaenjxnm+DBfs/1RWzdzCpGavu+BJ43nYVDMej0XnSau2RXk8HscSqSFJYGtri6Dgw/sfisJ4XPSMMeDgueee49bt25z84IfY0EHrCtKc7b1DimLCV15+AV/VJLahBjSK31K2nked5tcS15T5JeNpP5PwfJiVl7w0DITghefYcESa51AFghZSsUd4L64qQSdxvQOxUZDSjnKOGtpkXilNkpjWCdxohVIWDa3mTXOeIQgvxTBriDDG4Oq5holwFhEpXU3QIujXW1xg4+oVur2cYTEiOMPm1g3+1t/+O7z6ymss9HuoxHPz+g12dnbOoNVBqzOU0Vnp8el7av79L0NcLkRtPiameSbEJRpBPFFLlcl2nlfy0xbEa1qClZqJ2F2G+HzSOenAU5+r+aKaAKrT6QC02fTK0oB+v9+6r56cnHBwcEQIgX6/T5KlQiotpu3DliQJCs1wOOSjB/cj2TdrH9pOlnPr1i1WVpZI0xRtEk4nUw6Oh+yfTnj37j10klLXnsm4iP39Up7I85wQnIjmOddmL3Kd5muwXtCY+E8jsKossNKSqTWkiYmmjZGUa8TnKDFaIFQtE4KOvlAqCDRrlLhXq6jieQYyV6LgaY1wRlT0RbHRekAjAc38aFDB83ymGYNet4Fmnuf4oCjKGqUtVzY32djcJOBIjbRw9vsLXLt6nY2NTVZXVlhdXgEkIMqynKWVZSpXoq2h0+tSVNIh0RDevfetl9e8XcKZey5o1BkPm9lnaZDCyWTyqSHaz8dnb8wv0vDp4frzY37Bv6w8Ec4tlM28BrCwsMDGxgarq4IOj8fjlg/TJIij0aTlc2mt2djYYG1jlZ0nu+3CHbTCak2WJGxubvL888/TXxigbYrWKWnWxducncNjDo6OqKcFoawhzh+tHEPT4YNvkc62tOw9vq5j4hSF/YKXuUyapdFBGh9sTI50CO28ZLX8PUstqTUyDxmFUUGE+3xNYuRzWA0+iC1Jc27SYySJbJ5lZKmNbdeaLLEoxUzcMyZi82tOUwFoqhwNqlzXNd6FVqSwKGuch9IHtFV0u12WF3utQfHP//wv8ZUvf51eb0BmNJm25B0xx7XWSueVQjR3lG68J1BeoYNmXr9Kzk/HIK8pvTU3inRh6fDpOFvnxzOByHj/tEUBcIZoexFiMg+vX4Z+nEdkLtv2k44PXHB8+d7ayPcCjsz5fXBziI+ZvY+2ppVsKirxyphMRhA8vgqsb23yza9/gxs3bjAdifZAt7fIcDJm4ioGG+tkvS6uKslUgvEJhgSlLJ28BzjGk1Nym6F0DqrGKs0gzVlb6KPwZN0Ozlpe/srXuH51nR/85eucHNxnZdDh1nDAQn9AkkiNOrEpzlUz07WWc2KIzQFSWoqTQMPtsBLRyN90QyBWJC2ipQgEUqOom/Y+pTCpIYn6DQ3E62qZFAwGpxzKyrllRrhApi2JKTrRNsFqA15EqvDRBUYhnktaSblJS8tk8B4flX69r8FIEKO0YTItKYqagCXNe2S9RZ5bu8rGtReoQsDmHbr9HlfX1tGh4MePPuLx7g6dxSVe/crXOD095sd3PgDE0qEupfNplsm49j5pSN/NZD9/nxK7kUIU79KcDaTnhQA/H389xvlA46Lf59EZhX6qA7MZ54P6+YCoQUea9u32eDF5bjhd0oni+aVvfZOf+7lvcuXKVX73d3+Pxw8fcDA8IRv0yNMO4+DZ3t+hHI/RKhDQ3Ni8xc9+5Vv82z/+fY6nx6ym0vbshaLLldU1vvqlV3n/Rz9k2+1QTgInVclv/v1/wPB4n9/5V/8nWytL3Lx6hdtrkgxqK3BMmqXURVQh9x4bUZugFVqZuPhX4uZs5EpZnVD7SkpCChwORSBNUpwLBB8lM5QmtxqvLbJ4O8Z1SZposlSaAuq6xnlH7T1WKdFh0QHlAzbLBVW3Sat0nxjR2nFB5kdrRZ/G40niVOAj8Vh4RzE4M3GOK4UOUMZOTecCVFBXkOQ5yxtbBJPxy7/4q0zcH/Jge5t/9Ju/wXKvj9bw+PCA7737ff7yze+jkoxBt8twdIwvKoxSoiUTLUJdkMDRKMP5VfEiVFDFLrHLAmRBAy+8RYFnJJDRWoyx8jxvLQrgaeTip4XIzI/z2zZyy5d1JZ3fR8f/e0ULWTZZtjGGyju6mXRwdTpdhqcnpGlOr5Nz9co6zz93k/sfbVNFz59Op0M6Sjk+OaFy4iRalxU2T3CVpxgX3Lt3n+pbFUliqMoKk3XJspyeqel2MorJhMnoVNQnJ1NWNzbp9BcZevj+nUdcX+txZbFPdXzCQr/LSp5TVBV5mlJVxZnAUFCA+JlqF51Q4+cPja6Mbm9MpRRazVAH4RpJ4GC0imqQRDG9EMnQFVoral9Fd1rXloSCF/ViIeqJI27T4m21ISBdByCZVYWTVssgVhDzX6MLoh/ROIJXVQHIvZqkOS5IN5AymuWVNYaTKWsrayibUBPoDvpUdcHiYIG9/UPeePv7dHoDag/TqmRlZYXDw0MhORvLeDxtgxTnxOKuQVVghvrN36dtJfmCoFvOuaKqqhbl+3x89senyWDPozQ/ybFnC8vZ+Xd2753bR6toaQIL/S79bs7PfuVrjF/6Av3FJTmGl9bko9OTmYSA86Q2Y21lnU7WZTQZMugt0LEdisLjEGPdjbU1rqyv83D7EcHCdFoyWFhmff0K3aU/5c7jx0zLioX0BoWTZLmTaMb1mCw6KiutMXOfz0fujCAKHjBRy0ueRa3FlbpR/JVk1p5BdZVquICSrOaxM7csazQyH2VJig01IAhIQFBoq4X4bKyUkdCCWqkAxgr/RcW5jTpyB2Pw4L0YMTZzbqNRJvyVGaLm4jrkCZBolpdXydIut27c4ltf/yZf9Y5epyv+Ud7znTff4g//9I94/sWXuX79Ohr4s2//EXVdxnL87H5q0C6hFpy9Ic430sDFMco8SPFJ45kIZIL3dDrd9stuRrv4n2t9vgzabPa5aNuL9vlJtm22P4/wSM/+051Ol+3TIjJhRtAEzvCDGgLv6XiEtUkUVwpMp1Mmkwk3rl1noTcgTxWuLDBa8ZVXv8S1q1sUvsR2EqxNWBwscPvWc2xubkj5rpiS2U5bHtHKkqRdrt24zZVrdzk+OiXLu/gkYf36Db76zV/gq1/7Jg/v3eHP3nqdfp6ytbLExuEJuTUsLnZQBrJOh+AdKpJnk/jw2iQuyiFEp1nESNIHbJJQVzU2NbiGc+TqmXaLktKUiWJaeOl6qqqKJBfVzLSXMi0KEmNwaBLjSdM+k0lBGjse0jyP3T8JvirloYiEYe+R7yNq4Qg/p57xnbTUl4MXoSvpjhJYtKocWOj3ethMU0zHrC0us7S0hsPgtUYllvF4zHBa8fyLr/D8y1/i4fYOiTb8+M57vPHGtzns77Oz85gi8m+U0YRanoNpNZs067q+8N5rEBmtn76HlVIzCPjz8ddqnA8uLsyCz23zSUO2u1zT6yLkJ0QRzNRa3nvvPW7fukGW5LzyMz8TuXoJWWrxVc3WxhUeP3zE470nTIoxaZLTyXJu37jJz7zwEnfvP2B5cY3cCjE4hIB1sHX1Jq995eu8/sabBGVwuqY0mudu3OK//K//Kd/9q3/Pn/+7P+EP3/oh64tLrPQ63N5cpd9JIE9QOqCtlWBKRSFN5Qna45UYUXrvo35UnM+DEskIJd1MqZHPoJQUbVT0bjJIgKYimmtsQp6IP5FXHmygDhJMpbYTeXiWLO8izQwC+ysdSKxGG+HVBC+Gj1rbOKfN2tc9oUVmRZRPUDEdg4pZS7Zi6ktqG/jyl7/MoN/Fmozr167x4osvopMUFSxP9o744O4dKqf5z/7uP6Q3WGQ0OqUuC3YeP+Ddd99hNBq1CeisJfxscHP+vpv/PRb0nv77x6z18+OZmOHUHCIznU5bRGb+QbvwIbkk+Lhs2/P7XPT6+d/n97nseBft05z//Lk3GXRzvAapmT+GIBuWuhY1RVdWVEUhPA8j4lHWWpLUorUjy1OqPfFMarKBoioxOiNNU/IkZzQ6RVmJ7hu4sWGcJ0mGNoZuf4HheMr4dMhgaZEkz1hZXuXk+IiFlXW2j0YM8oIQFB1jsYMuRVmTdoSIqrxCJ4JeWJtIJhBbAJuOLh8CIS7ILcpQy+TQPITz123+msxngt7V0k3pailVKYXyCptoCOIX5V2FTTJUcFgNoZauhdgbhVKa2knAWMeHuq6rFh2TDCZKfTfZjNG4yrXnV1Ulh+MhG8WYjdVVaq/odDoEFIWDWmlpzgyKLO2ibcLWlQTwTMZD7vaXGJ8OSZKEvcND0jRHRR7OaDTCpNkZzs78fdTel3P36EXZS0P+nk8QPh+f/XE+gDk/h33aBeKyY3/S/u1CpBUqul8fHx9zcHCAj0hKliRMJ8cQapSHpYVFHj16wN7BPkAs/3r6WY+tzS0+2n7IKy99kV7HYQEXF+U0TVlcEmRHnnHD3uERL76oWN+4yhe++BqPHu/y3o/epPInjCdTuolh0s1YW+5Kl1QWsMpKcqCI3kygVSDWlWRekJoZOkgbd6KTiDo4VCyZS6l9xqn0QWptAUeUNCexIjOhMCQRoXYucv6UwgWRTUizDCEoOwxBxPrwVE7Qbhc5hzL3Q13PvJWaVugmQfaVdBS1XUtRfM4aTZpZXFmS9uR6dLIMpS27R8ccHh9Tu8Bzt56n0+sLx89mHB/tsbq4Klo4MahTyj/Vmv9x/KwzKN4nIS/POtk3BOnWaLqBmkW++dcs0OcXtfOvfdy2/yH7zP8NeGrb5twJ+sw2F207j9Zc9D4tmdOH1ihtFG3j6xAoioqiqKRV15UcHh5yOhpy/8EDhsMh66sbLC0skmQZSWZJM4uJapMba2s45zgenuKCoywLee/YSrm6uk5v0CdNU5aWlihqx7iYknZ79AcL2LzD6uY1ltavojsDRg72h2NOR2P29w85OjphMpkwHo8JIVCWU5yLztRVEYMOhzYzSLtZhIMXRd2zniAz0ltzP4jIm45/0xgjLZIicAUNB0drEcMzxqAioRi8cNDCvEJwPL9og9B8T80DXs+1KTZEOeccRVHQ6XSkXJPnDLo9XFnx6NEjKc1VlQjnRWLi/OcA3aIjedal1+uxsDCIr5t4/Em0KJjpvjQdDBfdR/M/z99bbet5dFH/fPz1HvNz2fnkEC5GbT5ufJqsuSGjVt5ROc+0rGMHjqGuS/b39zk5OcERuHplE18HHj1+TO1EFTfRhk6ec2V9nfc/+IC9w4PYUdQc35BlHdbW1lpivFKKx3tPKGrHYHGJazef46tf/ybjKnAwnHA0rXmw84TtgyNOhhNG04IyWnio+WsTn2EhCMdn2TTXcjaXiECob5PM5jM3LvV5LnyXNE3Jsow0tXP/UtLYlSR8Nt1KXWitSaxFq4ASJ0zm9VWa+cDF4s38s+84u77Mr2UN6bfhn6rgeXz/Iffv32c8HmOThMRmgGY4nlK5msWlFfr9BYwRLz3vPd7BysqKcHh0YKblqy+8vy4a57f7Dyl3wrOCyKBaJdOiKNqb5wyBTD3dMt1M1vM/L9v2on3mt71on4ZY2XzhF3UzzYi+Urv8pHNS9uw5NcewUeTNxxZc5xwLvT6T8RhjDKPxmIOjQ06GQ7Yf7ZDnObs7D5kWE5RSvPblr5DkGWXtGJcFWZLT7XS4otcYF2N2Rwfs72xzfesGvZUFTJB2ucXBIl946WWOTo/o5h22t7fpLy1SOM/q5ibVdJmFtQ3efOd9MmvYffSAx/e2We5mpOWYaxtrWBzrKwtkWcp0MqHXyeVBtvF71JAllhC9RKzJKMuSNEviz4yikq6qpquguQdCkMAuTbN4j+QU0wn9QY/RcEyvl1CWNUntUdpSFCWDQU+4Vl3ZJ8uz1gJDKXHzrpyKapwwLWQSm06n2DSjmtNu8d7jYqClvQQyHpmkUmWEVDcqefvt73Pz+RdYvrpF5YXfUlcVJrGUdQUofF2hUYyHI/JOysJin/G0x9Jyn+3dbQaDJQ6PTuj0e+zv7zMYDBjOWdfPZ8JPtWDP3a/zWkfSsn02MPp8fLaHIALnBCrPBRvzc6skFZLpf1qkRWRDznYp6bhbmNuumhZkSRdQjMdjjo6OODg6Av0R2w8e8i//5W/z4osv8nd+5ddYW14ntRkPHj+mDrXMpy7Qzzo8f/N5fvTe+/z7N7/LwsIStzYHWGfRAfp5l62t6/R6A0mmksD23i6TquT21evk/QH9pWXeeuc9fvC9N9gdTyhcxYOTEeNpxUInY9BJ2VhdxFoJ/jupEUkDE1CmmccUxtMu2MHPOgm1MUyn4zPJi3OeJn9oRCvlGdakmXREVGXd8ti8b0pXik4snSXWUIeAUrK9iYhPmlqm0xLnfNTfUm2yFSIC1H5X3rUeTsqruUAmYDyo2nOyd8C//te/w2/95/+Eay88jw4VBycnBOXp9nuSgDlpocc7/t/2zi1Gkuus47+vqququ6unp+e6l/Gud+MYBQsHY0XIEiYkDwHihzjwlCfygJQXkOCBB0dBKK8gwQMSQgIRKSBEXsBKHrgkjpCCkAgYfNvEnt31etc7e5mZnZ2Znr53VR0ezqnu2t6ZWXvl7qpNzk8qVU13Vdd/TlV99Z1zvvOdSlAmrpZZPbHMiZMr7O3f0fNFOZAkigQHcXX32oPuJ90dJ8SHNLl80FbDQjgySinCUL980hd7OlojzdGSdSqyI4Qm15MOyHHHuBPGf3Lf1HtN1zCOVzjM4YnMJI9pvpF0fdh5suv0hZk+BOkNmHYJxJEiqOh03RcvX6JcrtK52aY/aNNuNjl58hTDYUw5dEcvOJ06ejwB2XA4JJyrmcRoCUPzkks6Ma4IQRDQaDRIEp0Ns1arEScJgV+h2+nz9Cef4fr7V1k+eZpdtPfd7vdpxQ5qOKDcj+gMhtphUQrf93ETPVu0qHGsk+u6uA4Evq59+L6no/59f/T/p9cmPcb3/VHtRqnErHXzdGRmrU3jbxwzOaQ4Po4IQdlHqWTkHKbXL05iBoPBqLzTc6XXO3Wodb+2znkxNzena4nBuAtUkoQwDAnDcDQ0fxhFIDpBVpTEOA70B11UouNrgsBjb2uber3OpcvvEA/1SKw0MV4y1I7HYDDA9/1RbSp9DrLrODLNyhP3dtYhTIfJW346SJ3wLJOtd4fFtGQrw5PdlOm2rv3f+6LM7jPZBZ/aOB3OJdze2ubi5UtUKje58OZbXL12jV6/z89+4ilKvjf6rVHG6kgn0Cx7ZepzDS5cuMDPP/VJzp16XNsIE5filzwcpSd9jIjodtu0ui3K1QolLwDl8Cuf+QwAb73+Gv1Ep6zb3O/QGya0erryEvglgnIJR2mb4zmuie8zMWxmGoI0fYOK9LxAaVyjQpEocLyxDdOVoXH2c5BR5vbBMNL/t+OS9HScnuf5puxKDAexaYF27n2fDPX7oz+IiBI9y3S2yzlOu/CTeNTyPtkLEMcJrqOjadL37NzcHL7v0+306Jn3sTJD1IfDPkMSVDLOXVMtV6jX5qhVQ4aD1ni0mohODSHJqJVr8v68p3VQF8t992v23jrOpSmII5OmUo+pVCp0Oh09t5DSQY/psGylFN1ulzAM6Xa71Go1Wq3WPeu0m2py38OOye77ML/f6/X0hI7puj8kDEPa7fY968N+P3tsGIa0mwcsLCyYHCMBrusSBAH9bs+8WAesX7zM6VMnGEQ6OVq33SSOIqRU4nSzxe5Bk9MqYX93n+rHy8hAUanWaHdbtNsdbm3fplaucm7lNHc271CrznOwt0dQCVhdWmZjY4MkibhzZ4t+v4u/uobrV9ne3sZ1XdYeO8vNG9epeD7rP77AifkVrm3t0JgLUDt7hF6JStknHg4plXpUqh7dPpT9AHHMWgQ1GtI8NGsQR9c8kpLuyvH80ighoH5RR6MuHte8yEUcXUMQB2TcjZf2E6dDp7ND+BMTfxQlCs9x6Q0GJAmmRpToSTaVzsPT7fd0tt1uhyAI2N/fJwxr7DebOK5HnOhHPO0K3Nvbo33QYufuLosrq2zu7rBkMvk2D7q0Wh3qtbruAx8M6XW6nFo9zdbtW5w5c4a31y+ztHyS27dvU6/XababhGE4crSyz0N6//SN09lstwjDkE6nQ7VaHTk+ruuOZs62WI5ruj+uiT/rAH2Q+JpxS7eeg8dxHAaDLhsbGyjRsXG7ezsEtSrdQZ8r71/n7GOP6UEfpis5iWJK4uE6DuVymVMnTnDlyhXevfoez3zi5/A8h5Lr4iiHsFJhZWWFa9euUfJKNFsHo/s+iSPKYZXz558YBc2/+p//QeTqcYuDRFH1HLySUCt7hFGAJDq41o8dSp5rKlkmS7nrkig9TDpJIpM/04VMuUyOgE17GNKySR3FtCvKddNWMQfXLY1TL/g6FjBJ7u2GGf1+5jIlSTJqjU3U/TGaqY44Hox+Lxrqyng/Sjh37hwL9XnAjHaM+jh+BaWg3Wuzs7lPu9el0VgwredCrVZjeXlZt/iauZK006FnH9dOzf332aGO9kTvyH3O9DH3WyGsWxrgWC7r+YKq1eqRhrdareJ53mifyfVR+x52DNxfMznumPSlmh5TqVTwPG+8ZuJvs84ek/5OpTKOvPc8j0ajoQN6zUOceshBva5nPw5Dms19Nre3aCwu0u33qdXniQZ9AjOHUrlaodfrsdRYYGNjg/nyPJ1uH1yHza0tPN9jGEc0m02UUrRaLQ4ODuju3GFrc5NqUObs2bN0+z2WlpZ0f6nS1yN29Iy1ruOzubtJbWGFg2GH0sIi7UGPmufTTyL8RGeJDDyfWIHvBwzjIWFQGXfvRWlOlMS0Goxv2myLg+u69+VPyXbbQZr9OdvVYkZkOQ5RFN93TH+YBvSOjYPjpIbKZRCN8wml1yw9vl6v0+v2WVxcRJwSEULiOJx+/HEajQZOyafb7pDEMTdvbjAg4dr7Bzheifev38AraWdoLqzR6bZwHIfd3V08z2Nzc5uVxSVEHBYWtKGIiUexY+NMn+OH3Pd9nXOiVBrda+VyeVTbG+UoOqSGbvnJ5ahr/WFGKaX32mSrzGRNOvudZF412X3ETOrouQHtTo/1dy7h+noetNXlBRYXl+n1E+7utfD8Mjs7d+kPetTLVT0aR4FT8vml536Z9Xcu8s/f/Vde+Ozn8LyAxMQnzodzfPELv8nL336Zq9evMhj0+N73X+Fzz3+WsD6H2+uxurrK/FydJ5/4GSpuhdfeeI324ICD3pDSoE+rPyD0SzSCMssLVQK/RK3q45X0bNOBr1/SvuchZth1SSARk8AudSKShBL3tvROBu07Jkg5fQ+lFY9x4kvtcMSxLse0NSbdHg4jhrFOnqkrbeMyj1QCphU8bWEWBK+UToDpoIdqQ8lzieKY2El4+umnWZhvMOz22W/usbt7l912i83NTd69+h7/9soP6HRa/OFX/4i1tTUqQZmFhSXW1tZYWVnh9tY2jnGgnASOis6dvG+Oaj3M3oeHOURZpAgGTkS2gTZwJ28tGZaxeo6jaHqgeJqKpudxpdRK3iJ+EhGRA2A9bx0ZinbvFU0PFE+T1XM8R9qvQjgyACLyqlLqU3nrSLF6jqdoeqB4moqmxzI9inatrZ4HUzRNVs/DU4jh1xaLxWKxWCwPg3VkLBaLxWKxPLIUyZH5q7wFTGD1HE/R9EDxNBVNj2V6FO1aWz0PpmiarJ6HpDAxMhaLxWKxWCwfliK1yFgsFovFYrF8KKwjY7FYLBaL5ZEld0dGRH5dRNZF5LKIvJSThqsi8paIvC4ir5rPFkXkeyJyyawXpqzhGyKyJSIXMp8dqkE0f27K7E0ReXZGer4uIjdMOb0uIi9kvvuq0bMuIr82BT1nROTfReTHIvIjEfk983kuZXSMntzKyDJ7imC/jI5cbZi1Xw/UY+3XNMnOvzDrBXCBd4GPAT7wBvBkTl2UAAADDklEQVRUDjquAssTn/0J8JLZfgn44ylr+DTwLHDhQRqAF4B/Qec6fA744Yz0fB34g0P2fcpcuwA4b66p+xHrOQU8a7bngIvmvLmU0TF6cisju8x2KYr9MlpytWHWfj1Qj7VfU1zybpH5ReCyUuqKUmoAfAt4MWdNKS8C3zTb3wS+OM2TKaV+ANz9gBpeBP5Waf4LaIjIqRnoOYoXgW8ppfpKqfeAy+hr+1HquaWU+j+zfQC8DayRUxkdo+copl5GlplTZPsFM7Rh1n49UI+1X1Mkb0dmDbie+XuD4wtzWijguyLyvyLyFfPZCaXULbN9GziRg66jNORZbr9rmjq/kWmqnqkeETkH/ALwQwpQRhN6oABlZJkJRbqmRbRhuT+bh5D7s2nt10dP3o5MUXheKfUs8Hngd0Tk09kvlW5by3WcehE0AH8JPAE8A9wC/nTWAkSkBvwj8PtKqWb2uzzK6BA9uZeR5aeSQtuwvM9vyP3ZtPZrOuTtyNwAzmT+fsx8NlOUUjfMegt4Gd1ktpk25Zn11qx1HaMhl3JTSm0qpWKlVAL8NeOmxZnoEREP/dD9vVLqn8zHuZXRYXryLiPLTCnMNS2oDbP2K4O1X9Mjb0fmf4AnReS8iPjAl4DvzFKAiIQiMpduA78KXDA6vmx2+zLw7VnqMhyl4TvAb5nI9ueA/Uzz5NSY6KP9DXQ5pXq+JCKBiJwHngT++yM+twB/A7ytlPqzzFe5lNFRevIsI8vMyd1+QaFtmLVf43Nb+zVNZh1dPLmgo7MvoqOgv5bD+T+GjsZ+A/hRqgFYAr4PXAJeARanrOMf0E15Q3T/428fpQEdyf4XpszeAj41Iz1/Z873JvrGPpXZ/2tGzzrw+SnoeR7d7Pom8LpZXsirjI7Rk1sZ2WX2S972y2jI3YZZ+/VAPdZ+TXGxUxRYLBaLxWJ5ZMm7a8lisVgsFovlobGOjMVisVgslkcW68hYLBaLxWJ5ZLGOjMVisVgslkcW68hYLBaLxWJ5ZLGOjMVisVgslkcW68hYLBaLxWJ5ZPl/35lAo2380z4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"PSNR of super resolved image: %f\", tf.image.psnr(sr, hr, 255).numpy())  \n",
    "\n",
    "plt.figure(figsize = (1, 1))\n",
    "plt.title('LR')\n",
    "plt.imshow(lr);\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.subplot(1, 2, 1)        \n",
    "plt.title(f'SR (x{SCALE})')\n",
    "plt.imshow(sr);\n",
    "\n",
    "\n",
    "plt.subplot(1, 2, 2)   \n",
    "plt.title('ORIGINAL(CROPPED)')\n",
    "plt.imshow(hr);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check the PSNR for the entire test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "148026.jpg\n",
      "PSNR: 18.863192\n",
      "253027.jpg\n",
      "PSNR: 19.138632\n",
      "299086.jpg\n",
      "PSNR: 31.346169\n",
      "167062.jpg\n",
      "PSNR: 23.770855\n",
      "42012.jpg\n",
      "PSNR: 28.549572\n",
      "159008.jpg\n",
      "PSNR: 25.572453\n",
      "85048.jpg\n",
      "PSNR: 22.7134\n",
      "143090.jpg\n",
      "PSNR: 29.45975\n",
      "126007.jpg\n",
      "PSNR: 26.959152\n",
      "295087.jpg\n",
      "PSNR: 25.035734\n",
      "69020.jpg\n",
      "PSNR: 24.869116\n",
      "291000.jpg\n",
      "PSNR: 19.046978\n",
      "24077.jpg\n",
      "PSNR: 20.815052\n",
      "197017.jpg\n",
      "PSNR: 25.253336\n",
      "106024.jpg\n",
      "PSNR: 26.554588\n",
      "58060.jpg\n",
      "PSNR: 18.227268\n",
      "148089.jpg\n",
      "PSNR: 20.603205\n",
      "216081.jpg\n",
      "PSNR: 22.814613\n",
      "271035.jpg\n",
      "PSNR: 23.809818\n",
      "78004.jpg\n",
      "PSNR: 22.375874\n",
      "241004.jpg\n",
      "PSNR: 26.488317\n",
      "86000.jpg\n",
      "PSNR: 22.015696\n",
      "182053.jpg\n",
      "PSNR: 21.25767\n",
      "304074.jpg\n",
      "PSNR: 23.435545\n",
      "376043.jpg\n",
      "PSNR: 22.338514\n",
      "38092.jpg\n",
      "PSNR: 23.28\n",
      "62096.jpg\n",
      "PSNR: 21.29691\n",
      "19021.jpg\n",
      "PSNR: 24.367304\n",
      "102061.jpg\n",
      "PSNR: 21.384264\n",
      "55073.jpg\n",
      "PSNR: 18.9711\n",
      "156065.jpg\n",
      "PSNR: 23.615484\n",
      "210088.jpg\n",
      "PSNR: 27.928673\n",
      "41069.jpg\n",
      "PSNR: 22.18991\n",
      "119082.jpg\n",
      "PSNR: 20.501284\n",
      "223061.jpg\n",
      "PSNR: 22.03998\n",
      "300091.jpg\n",
      "PSNR: 26.213572\n",
      "296059.jpg\n",
      "PSNR: 27.971807\n",
      "101087.jpg\n",
      "PSNR: 21.768602\n",
      "385039.jpg\n",
      "PSNR: 20.404991\n",
      "229036.jpg\n",
      "PSNR: 20.382969\n",
      "109053.jpg\n",
      "PSNR: 28.31607\n",
      "37073.jpg\n",
      "PSNR: 26.759699\n",
      "108082.jpg\n",
      "PSNR: 23.916817\n",
      "21077.jpg\n",
      "PSNR: 23.179173\n",
      "86016.jpg\n",
      "PSNR: 20.114128\n",
      "236037.jpg\n",
      "PSNR: 22.710894\n",
      "167083.jpg\n",
      "PSNR: 16.950933\n",
      "145086.jpg\n",
      "PSNR: 22.060968\n",
      "86068.jpg\n",
      "PSNR: 24.12268\n",
      "134035.jpg\n",
      "PSNR: 20.608696\n",
      "361010.jpg\n",
      "PSNR: 25.22373\n",
      "41033.jpg\n",
      "PSNR: 29.004404\n",
      "189080.jpg\n",
      "PSNR: 27.572132\n",
      "42049.jpg\n",
      "PSNR: 25.55315\n",
      "14037.jpg\n",
      "PSNR: 29.631207\n",
      "147091.jpg\n",
      "PSNR: 22.770649\n",
      "241048.jpg\n",
      "PSNR: 21.806826\n",
      "87046.jpg\n",
      "PSNR: 23.481861\n",
      "12084.jpg\n",
      "PSNR: 25.844908\n",
      "69040.jpg\n",
      "PSNR: 22.889782\n",
      "160068.jpg\n",
      "PSNR: 23.561708\n",
      "296007.jpg\n",
      "PSNR: 29.51096\n",
      "196073.jpg\n",
      "PSNR: 25.208326\n",
      "219090.jpg\n",
      "PSNR: 23.654268\n",
      "101085.jpg\n",
      "PSNR: 22.288279\n",
      "260058.jpg\n",
      "PSNR: 29.960787\n",
      "208001.jpg\n",
      "PSNR: 26.099567\n",
      "175032.jpg\n",
      "PSNR: 18.534618\n",
      "103070.jpg\n",
      "PSNR: 26.945707\n",
      "66053.jpg\n",
      "PSNR: 25.192554\n",
      "3096.jpg\n",
      "PSNR: 35.35498\n",
      "108005.jpg\n",
      "PSNR: 23.449081\n",
      "38082.jpg\n",
      "PSNR: 24.691765\n",
      "253055.jpg\n",
      "PSNR: 24.967154\n",
      "351093.jpg\n",
      "PSNR: 19.466774\n",
      "105025.jpg\n",
      "PSNR: 22.72244\n",
      "45096.jpg\n",
      "PSNR: 30.445162\n",
      "89072.jpg\n",
      "PSNR: 23.76152\n",
      "130026.jpg\n",
      "PSNR: 22.444292\n",
      "285079.jpg\n",
      "PSNR: 22.26286\n",
      "175043.jpg\n",
      "PSNR: 23.038694\n",
      "65033.jpg\n",
      "PSNR: 22.288717\n",
      "33039.jpg\n",
      "PSNR: 18.118546\n",
      "54082.jpg\n",
      "PSNR: 25.781643\n",
      "123074.jpg\n",
      "PSNR: 26.976702\n",
      "16077.jpg\n",
      "PSNR: 24.181326\n",
      "108070.jpg\n",
      "PSNR: 21.970476\n",
      "227092.jpg\n",
      "PSNR: 29.805868\n",
      "163085.jpg\n",
      "PSNR: 27.0514\n",
      "97033.jpg\n",
      "PSNR: 21.727444\n",
      "157055.jpg\n",
      "PSNR: 23.119411\n",
      "306005.jpg\n",
      "PSNR: 24.520016\n",
      "76053.jpg\n",
      "PSNR: 26.647543\n",
      "170057.jpg\n",
      "PSNR: 27.795918\n",
      "43074.jpg\n",
      "PSNR: 26.957565\n",
      "8023.jpg\n",
      "PSNR: 26.556057\n",
      "220075.jpg\n",
      "PSNR: 25.504265\n",
      "69015.jpg\n",
      "PSNR: 26.78492\n",
      "302008.jpg\n",
      "PSNR: 29.192188\n",
      "304034.jpg\n",
      "PSNR: 23.006994\n",
      "Average PSNR for the test set:  24.176987\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "test_directory = 'dataset/BSDS300/images/test/'\n",
    "psnr_list = []\n",
    "for file in os.listdir(test_directory):\n",
    "    print(file)\n",
    "    lr, sr, hr = super_resolve(os.path.join(test_directory + file))\n",
    "    psnr = tf.image.psnr(sr, hr, 255).numpy()\n",
    "    print('PSNR:', psnr)\n",
    "    psnr_list.append(psnr)\n",
    "    \n",
    "print('Average PSNR for the test set: ', np.mean(psnr_list))"
   ]
  }
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