SCTM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d66274d1",
   "metadata": {},
   "source": [
    "# Self Consistent Training Model For The Hybrid Autoencoder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9702be75",
   "metadata": {},
   "source": [
    "### Library imports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2f6a2621",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Strawberry Fields: a Python library for continuous-variable quantum circuits.\n",
      "Copyright 2018-2020 Xanadu Quantum Technologies Inc.\n",
      "\n",
      "Python version:            3.9.7\n",
      "Platform info:             Windows-10-10.0.22000-SP0\n",
      "Installation path:         C:\\Users\\Bartu\\anaconda3\\envs\\qml\\lib\\site-packages\\strawberryfields\n",
      "Strawberry Fields version: 0.23.0\n",
      "Numpy version:             1.22.2\n",
      "Scipy version:             1.8.0\n",
      "SymPy version:             1.9\n",
      "NetworkX version:          2.6.3\n",
      "The Walrus version:        0.18.0\n",
      "Blackbird version:         0.4.0\n",
      "XCC version:               0.2.1\n",
      "TensorFlow version:        2.8.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 0 Axes>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras import layers, losses\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "import os\n",
    "\n",
    "import strawberryfields as sf\n",
    "from strawberryfields import ops\n",
    "sf.about()\n",
    "\n",
    "from matplotlib.pyplot import figure\n",
    "figure(figsize=(12, 6), dpi=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2acf3aef",
   "metadata": {},
   "source": [
    "### Training parameter definitions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4c5c5c42",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of modes\n",
    "modes = 1\n",
    "\n",
    "# Cutoff dimension (number of Fock states)\n",
    "cutoff_dim = 5\n",
    "\n",
    "# Input vector length.\n",
    "input_len = 5\n",
    "\n",
    "# Number of layers (depth)\n",
    "Qlayers = 25\n",
    "\n",
    "# Number of steps in optimization routine performing gradient descent for the quantum decoder.\n",
    "reps = 200\n",
    "\n",
    "# Learning rate\n",
    "lr = 0.01\n",
    "\n",
    "# Standard deviation of initial parameters\n",
    "passive_sd = 0.2\n",
    "active_sd = 0.01\n",
    "\n",
    "# The gamma parameter in the penalty function, given by the reference paper.\n",
    "norm_weight = 200\n",
    "\n",
    "# Seeds for the RNG functions to be able to reproduce results.\n",
    "tf.random.set_seed(155)\n",
    "np.random.seed(155)\n",
    "\n",
    "# Number of iterations to train the classical autoencoder.\n",
    "classical_epochs = 100\n",
    "# Number of feedback loop iterations.\n",
    "sctm_iterations = 3\n",
    "\n",
    "# Phase space circile restriciton radius.\n",
    "alpha_clip = 5\n",
    "\n",
    "save_folder_name = str(input_len) + '_inputs_sctm'\n",
    "os.makedirs(save_folder_name, exist_ok=True)\n",
    "\n",
    "train_state_select = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1d5d28b",
   "metadata": {},
   "source": [
    "### Initialization of the classical encoder and decoder networks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8f5d7905",
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_dim = 2\n",
    "\n",
    "class Autoencoder(Model):\n",
    "  def __init__(self, latent_dim):\n",
    "    super(Autoencoder, self).__init__()\n",
    "    self.latent_dim = latent_dim   \n",
    "    self.encoder = tf.keras.Sequential([\n",
    "      layers.Dense(input_len, activation =\"elu\"),\n",
    "      layers.Dense(5, activation=\"elu\"),\n",
    "      layers.Dense(5, activation=\"elu\"),\n",
    "      layers.Dense(5, activation=\"elu\"),\n",
    "      layers.Dense(5, activation=\"elu\"),\n",
    "      layers.Dense(5, activation=\"elu\"),\n",
    "      layers.Dense(5, activation=\"elu\"),\n",
    "      layers.Dense(5, activation=\"elu\"),\n",
    "      layers.Dense(5, activation=\"elu\"),\n",
    "      layers.Dense(5, activation=\"elu\"),\n",
    "      layers.Dense(5, activation=\"elu\"),\n",
    "      layers.Dense(5, activation=\"elu\"),\n",
    "      layers.Dense(5, activation=\"elu\"),\n",
    "      layers.Dense(4, activation=\"elu\"),\n",
    "      layers.Dense(3, activation=\"elu\"),\n",
    "      layers.Dense(latent_dim, activation=\"elu\"),\n",
    "    ])\n",
    "    \n",
    "    self.decoder = tf.keras.Sequential([\n",
    "        layers.Dense(input_len, activation=\"elu\"),\n",
    "        layers.Dense(3, activation=\"elu\"),\n",
    "        layers.Dense(4, activation=\"elu\"),\n",
    "        layers.Dense(5, activation=\"elu\"),\n",
    "        layers.Dense(5, activation=\"elu\"),\n",
    "        layers.Dense(5, activation=\"elu\"),\n",
    "        layers.Dense(5, activation=\"elu\"),\n",
    "        layers.Dense(5, activation=\"elu\"),\n",
    "        layers.Dense(5, activation=\"elu\"),\n",
    "        layers.Dense(5, activation=\"elu\"),\n",
    "        layers.Dense(5, activation=\"elu\"),\n",
    "        layers.Dense(5, activation=\"elu\"),\n",
    "        layers.Dense(5, activation=\"elu\"),\n",
    "        layers.Dense(4, activation=\"elu\"),\n",
    "        layers.Dense(3, activation=\"elu\"),\n",
    "        layers.Dense(input_len, activation=\"elu\")\n",
    "    ])\n",
    "    \n",
    "\n",
    "  def call(self, x):\n",
    "    encoded = self.encoder(x)\n",
    "    decoded = self.decoder(encoded)\n",
    "    return decoded"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f05b5803",
   "metadata": {},
   "source": [
    "### Initialization of layer weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "38596bca",
   "metadata": {},
   "outputs": [],
   "source": [
    "def init_weights(modes, layers, active_sd=0.0001, passive_sd=0.1):\n",
    "    \"\"\"Initialize a 2D TensorFlow Variable containing normally-distributed\n",
    "    random weights for an ``N`` mode quantum neural network with ``L`` layers.\n",
    "\n",
    "    Args:\n",
    "        modes (int): the number of modes in the quantum neural network\n",
    "        layers (int): the number of layers in the quantum neural network\n",
    "        active_sd (float): the standard deviation used when initializing\n",
    "            the normally-distributed weights for the active parameters\n",
    "            (displacement, squeezing, and Kerr magnitude)\n",
    "        passive_sd (float): the standard deviation used when initializing\n",
    "            the normally-distributed weights for the passive parameters\n",
    "            (beamsplitter angles and all gate phases)\n",
    "\n",
    "    Returns:\n",
    "        tf.Variable[tf.float32]: A TensorFlow Variable of shape\n",
    "        ``[layers, 2*(max(1, modes-1) + modes**2 + modes)]``, where the Lth\n",
    "        row represents the layer parameters for the Lth layer.\n",
    "    \"\"\"\n",
    "    # Number of interferometer parameters:\n",
    "    M = int(modes * (modes - 1)) + max(1, modes - 1)\n",
    "\n",
    "    # Create the TensorFlow variables\n",
    "    int1_weights = tf.random.normal(shape=[layers, M], stddev=passive_sd)\n",
    "    s_weights = tf.random.normal(shape=[layers, modes], stddev=active_sd)\n",
    "    int2_weights = tf.random.normal(shape=[layers, M], stddev=passive_sd)\n",
    "    dr_weights = tf.random.normal(shape=[layers, modes], stddev=active_sd)\n",
    "    dp_weights = tf.random.normal(shape=[layers, modes], stddev=passive_sd)\n",
    "    k_weights = tf.random.normal(shape=[layers, modes], stddev=active_sd)\n",
    "\n",
    "    weights = tf.concat(\n",
    "        [int1_weights, s_weights, int2_weights, dr_weights, dp_weights, k_weights], axis=1\n",
    "    )\n",
    "\n",
    "    weights = tf.Variable(weights)\n",
    "\n",
    "    return weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9278c98e",
   "metadata": {},
   "outputs": [],
   "source": [
    "autoencoder = Autoencoder(latent_dim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2b8c5a1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The target state for the training is chosen to be [1. 0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "def GenerateTargetState(input_len, f):\n",
    "    state = np.zeros(input_len)\n",
    "    state[f] = 1.0\n",
    "    train = np.array([state])\n",
    "    return train, state\n",
    "    \n",
    "    \n",
    "x_train, target_state = GenerateTargetState(input_len, train_state_select)\n",
    "print('The target state for the training is chosen to be ' + str(target_state))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5af0f231",
   "metadata": {},
   "source": [
    "### Compile the classical autoencoder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "09be67b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "autoencoder.compile(optimizer='adam', loss=losses.MeanSquaredError())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17a80bd4",
   "metadata": {},
   "source": [
    "### Initialization of the feedback loop that contains the training of the classical and quantum layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "88c1899a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "1/1 [==============================] - 1s 891ms/step - loss: 0.1906\n",
      "Epoch 2/100\n",
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      "Epoch 8/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.1710\n",
      "Epoch 9/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.1685\n",
      "Epoch 10/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.1661\n",
      "Epoch 11/100\n",
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      "Epoch 12/100\n",
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      "Epoch 13/100\n",
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      "Epoch 18/100\n",
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      "Epoch 98/100\n",
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      "Epoch 99/100\n",
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      "Epoch 100/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0172\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Bartu\\AppData\\Local\\Temp\\ipykernel_13352\\2927410443.py:52: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rep: 0 Cost: 1.2257 Fidelity: 0.0039 Trace: 0.0214\n",
      "Rep: 1 Cost: 1.1691 Fidelity: 0.0054 Trace: 0.0191\n",
      "Rep: 2 Cost: 1.1427 Fidelity: 0.0074 Trace: 0.0177\n",
      "Rep: 3 Cost: 1.0594 Fidelity: 0.0101 Trace: 0.0172\n",
      "Rep: 4 Cost: 1.0121 Fidelity: 0.0126 Trace: 0.0169\n",
      "Rep: 5 Cost: 1.0102 Fidelity: 0.0131 Trace: 0.0157\n",
      "Rep: 6 Cost: 0.9421 Fidelity: 0.0134 Trace: 0.0142\n",
      "Rep: 7 Cost: 0.9381 Fidelity: 0.0126 Trace: 0.0131\n",
      "Rep: 8 Cost: 0.9670 Fidelity: 0.0116 Trace: 0.0124\n",
      "Rep: 9 Cost: 0.9532 Fidelity: 0.0111 Trace: 0.0118\n",
      "Rep: 10 Cost: 0.9458 Fidelity: 0.0108 Trace: 0.0116\n",
      "Rep: 11 Cost: 0.9561 Fidelity: 0.0109 Trace: 0.0119\n",
      "Rep: 12 Cost: 0.9515 Fidelity: 0.0115 Trace: 0.0122\n",
      "Rep: 13 Cost: 0.9285 Fidelity: 0.0125 Trace: 0.0129\n",
      "Rep: 14 Cost: 0.9198 Fidelity: 0.0132 Trace: 0.0135\n",
      "Rep: 15 Cost: 0.9122 Fidelity: 0.0137 Trace: 0.0139\n",
      "Rep: 16 Cost: 0.9187 Fidelity: 0.0139 Trace: 0.0143\n",
      "Rep: 17 Cost: 0.9205 Fidelity: 0.0138 Trace: 0.0143\n",
      "Rep: 18 Cost: 0.9183 Fidelity: 0.0140 Trace: 0.0146\n",
      "Rep: 19 Cost: 0.9158 Fidelity: 0.0143 Trace: 0.0148\n",
      "Rep: 20 Cost: 0.9165 Fidelity: 0.0143 Trace: 0.0148\n",
      "Rep: 21 Cost: 0.9019 Fidelity: 0.0149 Trace: 0.0150\n",
      "Rep: 22 Cost: 0.8974 Fidelity: 0.0149 Trace: 0.0151\n",
      "Rep: 23 Cost: 0.9072 Fidelity: 0.0147 Trace: 0.0150\n",
      "Rep: 24 Cost: 0.8983 Fidelity: 0.0150 Trace: 0.0151\n",
      "Rep: 25 Cost: 0.9039 Fidelity: 0.0154 Trace: 0.0156\n",
      "Rep: 26 Cost: 0.8958 Fidelity: 0.0159 Trace: 0.0161\n",
      "Rep: 27 Cost: 0.9017 Fidelity: 0.0163 Trace: 0.0165\n",
      "Rep: 28 Cost: 0.8901 Fidelity: 0.0168 Trace: 0.0169\n",
      "Rep: 29 Cost: 0.8906 Fidelity: 0.0171 Trace: 0.0172\n",
      "Rep: 30 Cost: 0.8907 Fidelity: 0.0175 Trace: 0.0177\n",
      "Rep: 31 Cost: 0.8799 Fidelity: 0.0183 Trace: 0.0183\n",
      "Rep: 32 Cost: 0.8902 Fidelity: 0.0188 Trace: 0.0191\n",
      "Rep: 33 Cost: 0.8870 Fidelity: 0.0194 Trace: 0.0196\n",
      "Rep: 34 Cost: 0.8919 Fidelity: 0.0195 Trace: 0.0199\n",
      "Rep: 35 Cost: 0.8835 Fidelity: 0.0199 Trace: 0.0201\n",
      "Rep: 36 Cost: 0.8842 Fidelity: 0.0201 Trace: 0.0204\n",
      "Rep: 37 Cost: 0.8819 Fidelity: 0.0207 Trace: 0.0210\n",
      "Rep: 38 Cost: 0.8716 Fidelity: 0.0219 Trace: 0.0220\n",
      "Rep: 39 Cost: 0.8667 Fidelity: 0.0227 Trace: 0.0228\n",
      "Rep: 40 Cost: 0.8704 Fidelity: 0.0231 Trace: 0.0235\n",
      "Rep: 41 Cost: 0.8813 Fidelity: 0.0232 Trace: 0.0238\n",
      "Rep: 42 Cost: 0.8595 Fidelity: 0.0244 Trace: 0.0245\n",
      "Rep: 43 Cost: 0.8794 Fidelity: 0.0251 Trace: 0.0257\n",
      "Rep: 44 Cost: 0.8816 Fidelity: 0.0262 Trace: 0.0269\n",
      "Rep: 45 Cost: 0.8460 Fidelity: 0.0281 Trace: 0.0281\n",
      "Rep: 46 Cost: 0.8760 Fidelity: 0.0278 Trace: 0.0288\n",
      "Rep: 47 Cost: 0.8817 Fidelity: 0.0288 Trace: 0.0302\n",
      "Rep: 48 Cost: 0.8553 Fidelity: 0.0316 Trace: 0.0322\n",
      "Rep: 49 Cost: 0.8485 Fidelity: 0.0337 Trace: 0.0341\n",
      "Rep: 50 Cost: 0.8548 Fidelity: 0.0356 Trace: 0.0362\n",
      "Rep: 51 Cost: 0.8230 Fidelity: 0.0385 Trace: 0.0387\n",
      "Rep: 52 Cost: 0.8504 Fidelity: 0.0398 Trace: 0.0416\n",
      "Rep: 53 Cost: 0.8650 Fidelity: 0.0405 Trace: 0.0438\n",
      "Rep: 54 Cost: 0.8519 Fidelity: 0.0441 Trace: 0.0468\n",
      "Rep: 55 Cost: 0.8132 Fidelity: 0.0498 Trace: 0.0507\n",
      "Rep: 56 Cost: 0.8059 Fidelity: 0.0526 Trace: 0.0530\n",
      "Rep: 57 Cost: 0.8089 Fidelity: 0.0553 Trace: 0.0561\n",
      "Rep: 58 Cost: 0.7767 Fidelity: 0.0611 Trace: 0.0613\n",
      "Rep: 59 Cost: 0.7933 Fidelity: 0.0682 Trace: 0.0700\n",
      "Rep: 60 Cost: 0.8028 Fidelity: 0.0757 Trace: 0.0797\n",
      "Rep: 61 Cost: 0.7635 Fidelity: 0.0851 Trace: 0.0869\n",
      "Rep: 62 Cost: 0.7209 Fidelity: 0.0928 Trace: 0.0930\n",
      "Rep: 63 Cost: 0.7235 Fidelity: 0.1010 Trace: 0.1017\n",
      "Rep: 64 Cost: 0.6893 Fidelity: 0.1174 Trace: 0.1178\n",
      "Rep: 65 Cost: 0.6661 Fidelity: 0.1405 Trace: 0.1416\n",
      "Rep: 66 Cost: 0.6461 Fidelity: 0.1631 Trace: 0.1639\n",
      "Rep: 67 Cost: 0.6427 Fidelity: 0.1828 Trace: 0.1844\n",
      "Rep: 68 Cost: 0.5753 Fidelity: 0.2149 Trace: 0.2154\n",
      "Rep: 69 Cost: 0.5819 Fidelity: 0.2431 Trace: 0.2452\n",
      "Rep: 70 Cost: 0.5468 Fidelity: 0.2730 Trace: 0.2747\n",
      "Rep: 71 Cost: 0.4950 Fidelity: 0.3168 Trace: 0.3184\n",
      "Rep: 72 Cost: 0.4875 Fidelity: 0.3737 Trace: 0.3782\n",
      "Rep: 73 Cost: 0.4497 Fidelity: 0.4265 Trace: 0.4324\n",
      "Rep: 74 Cost: 0.4007 Fidelity: 0.4530 Trace: 0.4549\n",
      "Rep: 75 Cost: 0.4576 Fidelity: 0.4531 Trace: 0.4591\n",
      "Rep: 76 Cost: 0.3507 Fidelity: 0.5096 Trace: 0.5113\n",
      "Rep: 77 Cost: 0.4081 Fidelity: 0.5736 Trace: 0.5885\n",
      "Rep: 78 Cost: 0.4213 Fidelity: 0.5918 Trace: 0.6162\n",
      "Rep: 79 Cost: 0.3877 Fidelity: 0.6034 Trace: 0.6224\n",
      "Rep: 80 Cost: 0.3179 Fidelity: 0.6208 Trace: 0.6258\n",
      "Rep: 81 Cost: 0.3311 Fidelity: 0.6197 Trace: 0.6245\n",
      "Rep: 82 Cost: 0.3179 Fidelity: 0.6554 Trace: 0.6600\n",
      "Rep: 83 Cost: 0.2382 Fidelity: 0.6575 Trace: 0.6581\n",
      "Rep: 84 Cost: 0.2773 Fidelity: 0.6797 Trace: 0.6832\n",
      "Rep: 85 Cost: 0.2258 Fidelity: 0.6855 Trace: 0.6865\n",
      "Rep: 86 Cost: 0.2392 Fidelity: 0.7095 Trace: 0.7108\n",
      "Rep: 87 Cost: 0.2353 Fidelity: 0.7109 Trace: 0.7123\n",
      "Rep: 88 Cost: 0.2200 Fidelity: 0.7364 Trace: 0.7381\n",
      "Rep: 89 Cost: 0.2379 Fidelity: 0.7204 Trace: 0.7227\n",
      "Rep: 90 Cost: 0.1740 Fidelity: 0.7531 Trace: 0.7535\n",
      "Rep: 91 Cost: 0.1819 Fidelity: 0.7614 Trace: 0.7627\n",
      "Rep: 92 Cost: 0.1524 Fidelity: 0.7748 Trace: 0.7751\n",
      "Rep: 93 Cost: 0.2251 Fidelity: 0.7804 Trace: 0.7863\n",
      "Rep: 94 Cost: 0.1961 Fidelity: 0.7850 Trace: 0.7885\n",
      "Rep: 95 Cost: 0.1896 Fidelity: 0.7829 Trace: 0.7855\n",
      "Rep: 96 Cost: 0.1818 Fidelity: 0.7999 Trace: 0.8013\n",
      "Rep: 97 Cost: 0.2003 Fidelity: 0.8111 Trace: 0.8165\n",
      "Rep: 98 Cost: 0.1919 Fidelity: 0.8180 Trace: 0.8237\n",
      "Rep: 99 Cost: 0.1493 Fidelity: 0.8194 Trace: 0.8203\n",
      "Rep: 100 Cost: 0.1666 Fidelity: 0.8259 Trace: 0.8291\n",
      "Rep: 101 Cost: 0.2138 Fidelity: 0.8476 Trace: 0.8504\n",
      "Rep: 102 Cost: 0.1262 Fidelity: 0.8465 Trace: 0.8470\n",
      "Rep: 103 Cost: 0.2488 Fidelity: 0.8388 Trace: 0.8501\n",
      "Rep: 104 Cost: 0.2846 Fidelity: 0.8303 Trace: 0.8395\n",
      "Rep: 105 Cost: 0.1939 Fidelity: 0.8554 Trace: 0.8600\n",
      "Rep: 106 Cost: 0.2550 Fidelity: 0.8810 Trace: 0.8856\n",
      "Rep: 107 Cost: 0.1888 Fidelity: 0.8657 Trace: 0.8709\n",
      "Rep: 108 Cost: 0.2447 Fidelity: 0.8555 Trace: 0.8655\n",
      "Rep: 109 Cost: 0.1961 Fidelity: 0.8873 Trace: 0.8896\n",
      "Rep: 110 Cost: 0.1818 Fidelity: 0.8917 Trace: 0.8966\n",
      "Rep: 111 Cost: 0.2961 Fidelity: 0.8832 Trace: 0.8878\n",
      "Rep: 112 Cost: 0.1959 Fidelity: 0.8846 Trace: 0.8855\n",
      "Rep: 113 Cost: 0.1444 Fidelity: 0.8998 Trace: 0.9010\n",
      "Rep: 114 Cost: 0.1844 Fidelity: 0.8878 Trace: 0.8930\n",
      "Rep: 115 Cost: 0.2400 Fidelity: 0.8779 Trace: 0.8878\n",
      "Rep: 116 Cost: 0.1652 Fidelity: 0.9038 Trace: 0.9057\n",
      "Rep: 117 Cost: 0.1493 Fidelity: 0.8946 Trace: 0.8990\n",
      "Rep: 118 Cost: 0.1966 Fidelity: 0.8930 Trace: 0.8964\n",
      "Rep: 119 Cost: 0.1744 Fidelity: 0.9044 Trace: 0.9070\n",
      "Rep: 120 Cost: 0.1600 Fidelity: 0.9142 Trace: 0.9162\n",
      "Rep: 121 Cost: 0.1690 Fidelity: 0.8966 Trace: 0.9048\n",
      "Rep: 122 Cost: 0.1983 Fidelity: 0.8883 Trace: 0.9022\n",
      "Rep: 123 Cost: 0.1553 Fidelity: 0.9071 Trace: 0.9111\n",
      "Rep: 124 Cost: 0.1460 Fidelity: 0.9130 Trace: 0.9165\n",
      "Rep: 125 Cost: 0.1641 Fidelity: 0.9132 Trace: 0.9197\n",
      "Rep: 126 Cost: 0.1117 Fidelity: 0.9131 Trace: 0.9146\n",
      "Rep: 127 Cost: 0.1308 Fidelity: 0.9079 Trace: 0.9102\n",
      "Rep: 128 Cost: 0.1458 Fidelity: 0.9149 Trace: 0.9166\n",
      "Rep: 129 Cost: 0.1681 Fidelity: 0.9185 Trace: 0.9236\n",
      "Rep: 130 Cost: 0.1658 Fidelity: 0.9195 Trace: 0.9220\n",
      "Rep: 131 Cost: 0.1493 Fidelity: 0.9141 Trace: 0.9205\n",
      "Rep: 132 Cost: 0.1832 Fidelity: 0.9113 Trace: 0.9198\n",
      "Rep: 133 Cost: 0.1311 Fidelity: 0.9198 Trace: 0.9211\n",
      "Rep: 134 Cost: 0.1523 Fidelity: 0.9280 Trace: 0.9340\n",
      "Rep: 135 Cost: 0.1440 Fidelity: 0.9260 Trace: 0.9331\n",
      "Rep: 136 Cost: 0.1276 Fidelity: 0.9196 Trace: 0.9210\n",
      "Rep: 137 Cost: 0.1951 Fidelity: 0.9139 Trace: 0.9194\n",
      "Rep: 138 Cost: 0.1449 Fidelity: 0.9153 Trace: 0.9193\n",
      "Rep: 139 Cost: 0.1314 Fidelity: 0.9256 Trace: 0.9264\n",
      "Rep: 140 Cost: 0.0859 Fidelity: 0.9323 Trace: 0.9331\n",
      "Rep: 141 Cost: 0.1245 Fidelity: 0.9263 Trace: 0.9296\n",
      "Rep: 142 Cost: 0.1208 Fidelity: 0.9224 Trace: 0.9272\n",
      "Rep: 143 Cost: 0.0714 Fidelity: 0.9354 Trace: 0.9359\n",
      "Rep: 144 Cost: 0.1959 Fidelity: 0.9304 Trace: 0.9424\n",
      "Rep: 145 Cost: 0.2086 Fidelity: 0.9303 Trace: 0.9456\n",
      "Rep: 146 Cost: 0.1298 Fidelity: 0.9324 Trace: 0.9375\n",
      "Rep: 147 Cost: 0.1334 Fidelity: 0.9280 Trace: 0.9303\n",
      "Rep: 148 Cost: 0.1308 Fidelity: 0.9263 Trace: 0.9293\n",
      "Rep: 149 Cost: 0.0830 Fidelity: 0.9370 Trace: 0.9378\n",
      "Rep: 150 Cost: 0.1206 Fidelity: 0.9409 Trace: 0.9439\n",
      "Rep: 151 Cost: 0.0885 Fidelity: 0.9385 Trace: 0.9390\n",
      "Rep: 152 Cost: 0.1658 Fidelity: 0.9374 Trace: 0.9424\n",
      "Rep: 153 Cost: 0.1142 Fidelity: 0.9355 Trace: 0.9397\n",
      "Rep: 154 Cost: 0.1450 Fidelity: 0.9390 Trace: 0.9397\n",
      "Rep: 155 Cost: 0.1050 Fidelity: 0.9438 Trace: 0.9470\n",
      "Rep: 156 Cost: 0.1654 Fidelity: 0.9439 Trace: 0.9472\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rep: 157 Cost: 0.0994 Fidelity: 0.9375 Trace: 0.9390\n",
      "Rep: 158 Cost: 0.1120 Fidelity: 0.9384 Trace: 0.9403\n",
      "Rep: 159 Cost: 0.1432 Fidelity: 0.9483 Trace: 0.9503\n",
      "Rep: 160 Cost: 0.0690 Fidelity: 0.9482 Trace: 0.9487\n",
      "Rep: 161 Cost: 0.2532 Fidelity: 0.9315 Trace: 0.9371\n",
      "Rep: 162 Cost: 0.2373 Fidelity: 0.9332 Trace: 0.9386\n",
      "Rep: 163 Cost: 0.0813 Fidelity: 0.9495 Trace: 0.9504\n",
      "Rep: 164 Cost: 0.1511 Fidelity: 0.9521 Trace: 0.9555\n",
      "Rep: 165 Cost: 0.1072 Fidelity: 0.9477 Trace: 0.9507\n",
      "Rep: 166 Cost: 0.0933 Fidelity: 0.9456 Trace: 0.9472\n",
      "Rep: 167 Cost: 0.0960 Fidelity: 0.9476 Trace: 0.9487\n",
      "Rep: 168 Cost: 0.1070 Fidelity: 0.9504 Trace: 0.9521\n",
      "Rep: 169 Cost: 0.0918 Fidelity: 0.9523 Trace: 0.9545\n",
      "Rep: 170 Cost: 0.1112 Fidelity: 0.9535 Trace: 0.9546\n",
      "Rep: 171 Cost: 0.0977 Fidelity: 0.9494 Trace: 0.9516\n",
      "Rep: 172 Cost: 0.1182 Fidelity: 0.9505 Trace: 0.9525\n",
      "Rep: 173 Cost: 0.1148 Fidelity: 0.9565 Trace: 0.9581\n",
      "Rep: 174 Cost: 0.0760 Fidelity: 0.9547 Trace: 0.9556\n",
      "Rep: 175 Cost: 0.1091 Fidelity: 0.9513 Trace: 0.9529\n",
      "Rep: 176 Cost: 0.0899 Fidelity: 0.9551 Trace: 0.9563\n",
      "Rep: 177 Cost: 0.0747 Fidelity: 0.9581 Trace: 0.9584\n",
      "Rep: 178 Cost: 0.1113 Fidelity: 0.9520 Trace: 0.9531\n",
      "Rep: 179 Cost: 0.0822 Fidelity: 0.9559 Trace: 0.9563\n",
      "Rep: 180 Cost: 0.1030 Fidelity: 0.9598 Trace: 0.9612\n",
      "Rep: 181 Cost: 0.0766 Fidelity: 0.9575 Trace: 0.9585\n",
      "Rep: 182 Cost: 0.1082 Fidelity: 0.9539 Trace: 0.9552\n",
      "Rep: 183 Cost: 0.0743 Fidelity: 0.9578 Trace: 0.9590\n",
      "Rep: 184 Cost: 0.0723 Fidelity: 0.9596 Trace: 0.9609\n",
      "Rep: 185 Cost: 0.0718 Fidelity: 0.9570 Trace: 0.9577\n",
      "Rep: 186 Cost: 0.1013 Fidelity: 0.9591 Trace: 0.9598\n",
      "Rep: 187 Cost: 0.0813 Fidelity: 0.9611 Trace: 0.9621\n",
      "Rep: 188 Cost: 0.1342 Fidelity: 0.9566 Trace: 0.9577\n",
      "Rep: 189 Cost: 0.1272 Fidelity: 0.9544 Trace: 0.9559\n",
      "Rep: 190 Cost: 0.1203 Fidelity: 0.9588 Trace: 0.9609\n",
      "Rep: 191 Cost: 0.0869 Fidelity: 0.9612 Trace: 0.9630\n",
      "Rep: 192 Cost: 0.1592 Fidelity: 0.9563 Trace: 0.9594\n",
      "Rep: 193 Cost: 0.0779 Fidelity: 0.9591 Trace: 0.9599\n",
      "Rep: 194 Cost: 0.2048 Fidelity: 0.9589 Trace: 0.9637\n",
      "Rep: 195 Cost: 0.1927 Fidelity: 0.9615 Trace: 0.9654\n",
      "Rep: 196 Cost: 0.0803 Fidelity: 0.9624 Trace: 0.9635\n",
      "Rep: 197 Cost: 0.1104 Fidelity: 0.9578 Trace: 0.9602\n",
      "Rep: 198 Cost: 0.1069 Fidelity: 0.9621 Trace: 0.9631\n",
      "Rep: 199 Cost: 0.1012 Fidelity: 0.9650 Trace: 0.9666\n",
      "Epoch 1/100\n",
      "1/1 [==============================] - 0s 425ms/step - loss: 0.0163\n",
      "Epoch 2/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0156\n",
      "Epoch 3/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0148\n",
      "Epoch 4/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0141\n",
      "Epoch 5/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0134\n",
      "Epoch 6/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0128\n",
      "Epoch 7/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0122\n",
      "Epoch 8/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0116\n",
      "Epoch 9/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0110\n",
      "Epoch 10/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0104\n",
      "Epoch 11/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0099\n",
      "Epoch 12/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0093\n",
      "Epoch 13/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0088\n",
      "Epoch 14/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0083\n",
      "Epoch 15/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0079\n",
      "Epoch 16/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0074\n",
      "Epoch 17/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0070\n",
      "Epoch 18/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 0.0066\n",
      "Epoch 19/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0062\n",
      "Epoch 20/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0059\n",
      "Epoch 21/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0055\n",
      "Epoch 22/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0052\n",
      "Epoch 23/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0049\n",
      "Epoch 24/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0046\n",
      "Epoch 25/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0043\n",
      "Epoch 26/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0040\n",
      "Epoch 27/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0038\n",
      "Epoch 28/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0035\n",
      "Epoch 29/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0033\n",
      "Epoch 30/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0031\n",
      "Epoch 31/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0029\n",
      "Epoch 32/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0027\n",
      "Epoch 33/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 0.0025\n",
      "Epoch 34/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0023\n",
      "Epoch 35/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 0.0021\n",
      "Epoch 36/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0020\n",
      "Epoch 37/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 0.0018\n",
      "Epoch 38/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0017\n",
      "Epoch 39/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0016\n",
      "Epoch 40/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0015\n",
      "Epoch 41/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0013\n",
      "Epoch 42/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0012\n",
      "Epoch 43/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0011\n",
      "Epoch 44/100\n",
      "1/1 [==============================] - 0s 1000us/step - loss: 0.0010\n",
      "Epoch 45/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 9.6318e-04\n",
      "Epoch 46/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.8413e-04\n",
      "Epoch 47/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.1062e-04\n",
      "Epoch 48/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.4236e-04\n",
      "Epoch 49/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.7908e-04\n",
      "Epoch 50/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 6.2048e-04\n",
      "Epoch 51/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 5.6630e-04\n",
      "Epoch 52/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 5.1629e-04\n",
      "Epoch 53/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 4.7016e-04\n",
      "Epoch 54/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 4.2763e-04\n",
      "Epoch 55/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 3.8843e-04\n",
      "Epoch 56/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 3.5233e-04\n",
      "Epoch 57/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 3.1910e-04\n",
      "Epoch 58/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.8859e-04\n",
      "Epoch 59/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.6064e-04\n",
      "Epoch 60/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.3510e-04\n",
      "Epoch 61/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.1178e-04\n",
      "Epoch 62/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.9049e-04\n",
      "Epoch 63/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.7105e-04\n",
      "Epoch 64/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.5333e-04\n",
      "Epoch 65/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.3720e-04\n",
      "Epoch 66/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 1.2256e-04\n",
      "Epoch 67/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.0931e-04\n",
      "Epoch 68/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 9.7342e-05\n",
      "Epoch 69/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.6541e-05\n",
      "Epoch 70/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.6796e-05\n",
      "Epoch 71/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.8013e-05\n",
      "Epoch 72/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.0112e-05\n",
      "Epoch 73/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 5.3021e-05\n",
      "Epoch 74/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 4.6675e-05\n",
      "Epoch 75/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 4.1006e-05\n",
      "Epoch 76/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 3.5952e-05\n",
      "Epoch 77/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 3.1454e-05\n",
      "Epoch 78/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.7456e-05\n",
      "Epoch 79/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.3908e-05\n",
      "Epoch 80/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.0764e-05\n",
      "Epoch 81/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.7983e-05\n",
      "Epoch 82/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.5529e-05\n",
      "Epoch 83/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.3370e-05\n",
      "Epoch 84/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.1477e-05\n",
      "Epoch 85/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 9.8210e-06\n",
      "Epoch 86/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.3752e-06\n",
      "Epoch 87/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 7.1146e-06\n",
      "Epoch 88/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 6.0164e-06\n",
      "Epoch 89/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 5.0633e-06\n",
      "Epoch 90/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 4.2399e-06\n",
      "Epoch 91/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 3.5329e-06\n",
      "Epoch 92/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.9288e-06\n",
      "Epoch 93/100\n",
      "1/1 [==============================] - 0s 1000us/step - loss: 2.4143e-06\n",
      "Epoch 94/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.9768e-06\n",
      "Epoch 95/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.6057e-06\n",
      "Epoch 96/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 1.2926e-06\n",
      "Epoch 97/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.0305e-06\n",
      "Epoch 98/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.1323e-07\n",
      "Epoch 99/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.3474e-07\n",
      "Epoch 100/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 4.8913e-07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Bartu\\AppData\\Local\\Temp\\ipykernel_13352\\2927410443.py:52: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rep: 0 Cost: 1.0892 Fidelity: 0.0001 Trace: 0.0034\n",
      "Rep: 1 Cost: 1.0758 Fidelity: 0.0003 Trace: 0.0025\n",
      "Rep: 2 Cost: 1.0680 Fidelity: 0.0003 Trace: 0.0024\n",
      "Rep: 3 Cost: 1.0561 Fidelity: 0.0004 Trace: 0.0019\n",
      "Rep: 4 Cost: 1.0435 Fidelity: 0.0004 Trace: 0.0015\n",
      "Rep: 5 Cost: 1.0437 Fidelity: 0.0004 Trace: 0.0015\n",
      "Rep: 6 Cost: 1.0369 Fidelity: 0.0005 Trace: 0.0015\n",
      "Rep: 7 Cost: 1.0280 Fidelity: 0.0007 Trace: 0.0015\n",
      "Rep: 8 Cost: 1.0230 Fidelity: 0.0008 Trace: 0.0017\n",
      "Rep: 9 Cost: 1.0147 Fidelity: 0.0011 Trace: 0.0018\n",
      "Rep: 10 Cost: 1.0055 Fidelity: 0.0013 Trace: 0.0019\n",
      "Rep: 11 Cost: 0.9950 Fidelity: 0.0015 Trace: 0.0020\n",
      "Rep: 12 Cost: 0.9913 Fidelity: 0.0018 Trace: 0.0021\n",
      "Rep: 13 Cost: 0.9797 Fidelity: 0.0022 Trace: 0.0024\n",
      "Rep: 14 Cost: 0.9747 Fidelity: 0.0025 Trace: 0.0026\n",
      "Rep: 15 Cost: 0.9709 Fidelity: 0.0026 Trace: 0.0027\n",
      "Rep: 16 Cost: 0.9631 Fidelity: 0.0029 Trace: 0.0030\n",
      "Rep: 17 Cost: 0.9593 Fidelity: 0.0030 Trace: 0.0031\n",
      "Rep: 18 Cost: 0.9574 Fidelity: 0.0035 Trace: 0.0035\n",
      "Rep: 19 Cost: 0.9510 Fidelity: 0.0037 Trace: 0.0037\n",
      "Rep: 20 Cost: 0.9556 Fidelity: 0.0041 Trace: 0.0042\n",
      "Rep: 21 Cost: 0.9564 Fidelity: 0.0044 Trace: 0.0046\n",
      "Rep: 22 Cost: 0.9518 Fidelity: 0.0050 Trace: 0.0051\n",
      "Rep: 23 Cost: 0.9471 Fidelity: 0.0056 Trace: 0.0058\n",
      "Rep: 24 Cost: 0.9419 Fidelity: 0.0064 Trace: 0.0065\n",
      "Rep: 25 Cost: 0.9381 Fidelity: 0.0068 Trace: 0.0068\n",
      "Rep: 26 Cost: 0.9326 Fidelity: 0.0079 Trace: 0.0080\n",
      "Rep: 27 Cost: 0.9296 Fidelity: 0.0088 Trace: 0.0090\n",
      "Rep: 28 Cost: 0.9209 Fidelity: 0.0094 Trace: 0.0095\n",
      "Rep: 29 Cost: 0.9219 Fidelity: 0.0097 Trace: 0.0099\n",
      "Rep: 30 Cost: 0.9068 Fidelity: 0.0123 Trace: 0.0124\n",
      "Rep: 31 Cost: 0.9084 Fidelity: 0.0141 Trace: 0.0144\n",
      "Rep: 32 Cost: 0.9020 Fidelity: 0.0151 Trace: 0.0152\n",
      "Rep: 33 Cost: 0.8928 Fidelity: 0.0166 Trace: 0.0168\n",
      "Rep: 34 Cost: 0.8842 Fidelity: 0.0194 Trace: 0.0197\n",
      "Rep: 35 Cost: 0.8749 Fidelity: 0.0205 Trace: 0.0206\n",
      "Rep: 36 Cost: 0.8706 Fidelity: 0.0240 Trace: 0.0242\n",
      "Rep: 37 Cost: 0.8599 Fidelity: 0.0279 Trace: 0.0283\n",
      "Rep: 38 Cost: 0.8455 Fidelity: 0.0303 Trace: 0.0305\n",
      "Rep: 39 Cost: 0.8369 Fidelity: 0.0347 Trace: 0.0348\n",
      "Rep: 40 Cost: 0.8286 Fidelity: 0.0429 Trace: 0.0435\n",
      "Rep: 41 Cost: 0.8260 Fidelity: 0.0513 Trace: 0.0525\n",
      "Rep: 42 Cost: 0.7958 Fidelity: 0.0563 Trace: 0.0566\n",
      "Rep: 43 Cost: 0.7984 Fidelity: 0.0617 Trace: 0.0625\n",
      "Rep: 44 Cost: 0.7705 Fidelity: 0.0797 Trace: 0.0808\n",
      "Rep: 45 Cost: 0.7507 Fidelity: 0.0921 Trace: 0.0934\n",
      "Rep: 46 Cost: 0.6921 Fidelity: 0.1042 Trace: 0.1043\n",
      "Rep: 47 Cost: 0.7437 Fidelity: 0.1164 Trace: 0.1197\n",
      "Rep: 48 Cost: 0.6745 Fidelity: 0.1518 Trace: 0.1541\n",
      "Rep: 49 Cost: 0.6798 Fidelity: 0.1871 Trace: 0.1922\n",
      "Rep: 50 Cost: 0.6442 Fidelity: 0.2053 Trace: 0.2087\n",
      "Rep: 51 Cost: 0.5858 Fidelity: 0.2310 Trace: 0.2327\n",
      "Rep: 52 Cost: 0.5496 Fidelity: 0.2967 Trace: 0.2999\n",
      "Rep: 53 Cost: 0.5292 Fidelity: 0.3570 Trace: 0.3634\n",
      "Rep: 54 Cost: 0.4960 Fidelity: 0.3815 Trace: 0.3857\n",
      "Rep: 55 Cost: 0.4237 Fidelity: 0.4156 Trace: 0.4171\n",
      "Rep: 56 Cost: 0.4275 Fidelity: 0.4999 Trace: 0.5065\n",
      "Rep: 57 Cost: 0.4203 Fidelity: 0.5514 Trace: 0.5577\n",
      "Rep: 58 Cost: 0.3193 Fidelity: 0.5627 Trace: 0.5637\n",
      "Rep: 59 Cost: 0.3170 Fidelity: 0.5966 Trace: 0.5991\n",
      "Rep: 60 Cost: 0.3341 Fidelity: 0.6509 Trace: 0.6576\n",
      "Rep: 61 Cost: 0.3027 Fidelity: 0.6769 Trace: 0.6819\n",
      "Rep: 62 Cost: 0.2834 Fidelity: 0.6774 Trace: 0.6846\n",
      "Rep: 63 Cost: 0.3177 Fidelity: 0.6885 Trace: 0.6942\n",
      "Rep: 64 Cost: 0.2613 Fidelity: 0.7191 Trace: 0.7236\n",
      "Rep: 65 Cost: 0.2932 Fidelity: 0.7258 Trace: 0.7328\n",
      "Rep: 66 Cost: 0.2743 Fidelity: 0.7291 Trace: 0.7328\n",
      "Rep: 67 Cost: 0.2646 Fidelity: 0.7379 Trace: 0.7413\n",
      "Rep: 68 Cost: 0.2584 Fidelity: 0.7510 Trace: 0.7567\n",
      "Rep: 69 Cost: 0.2427 Fidelity: 0.7653 Trace: 0.7691\n",
      "Rep: 70 Cost: 0.2401 Fidelity: 0.7560 Trace: 0.7591\n",
      "Rep: 71 Cost: 0.2776 Fidelity: 0.7705 Trace: 0.7772\n",
      "Rep: 72 Cost: 0.2257 Fidelity: 0.7811 Trace: 0.7815\n",
      "Rep: 73 Cost: 0.2512 Fidelity: 0.7941 Trace: 0.8003\n",
      "Rep: 74 Cost: 0.3823 Fidelity: 0.8300 Trace: 0.8312\n",
      "Rep: 75 Cost: 0.3768 Fidelity: 0.8234 Trace: 0.8276\n",
      "Rep: 76 Cost: 0.3117 Fidelity: 0.8034 Trace: 0.8093\n",
      "Rep: 77 Cost: 0.2401 Fidelity: 0.8205 Trace: 0.8210\n",
      "Rep: 78 Cost: 0.3610 Fidelity: 0.8251 Trace: 0.8396\n",
      "Rep: 79 Cost: 0.3302 Fidelity: 0.8281 Trace: 0.8430\n",
      "Rep: 80 Cost: 0.2613 Fidelity: 0.8357 Trace: 0.8370\n",
      "Rep: 81 Cost: 0.3078 Fidelity: 0.8345 Trace: 0.8386\n",
      "Rep: 82 Cost: 0.2350 Fidelity: 0.8540 Trace: 0.8563\n",
      "Rep: 83 Cost: 0.2278 Fidelity: 0.8679 Trace: 0.8684\n",
      "Rep: 84 Cost: 0.1826 Fidelity: 0.8611 Trace: 0.8649\n",
      "Rep: 85 Cost: 0.1674 Fidelity: 0.8636 Trace: 0.8653\n",
      "Rep: 86 Cost: 0.2324 Fidelity: 0.8647 Trace: 0.8694\n",
      "Rep: 87 Cost: 0.2047 Fidelity: 0.8650 Trace: 0.8699\n",
      "Rep: 88 Cost: 0.1757 Fidelity: 0.8709 Trace: 0.8714\n",
      "Rep: 89 Cost: 0.2054 Fidelity: 0.8733 Trace: 0.8779\n",
      "Rep: 90 Cost: 0.2872 Fidelity: 0.8810 Trace: 0.8864\n",
      "Rep: 91 Cost: 0.1476 Fidelity: 0.8854 Trace: 0.8858\n",
      "Rep: 92 Cost: 0.2837 Fidelity: 0.8678 Trace: 0.8743\n",
      "Rep: 93 Cost: 0.2958 Fidelity: 0.8679 Trace: 0.8753\n",
      "Rep: 94 Cost: 0.1235 Fidelity: 0.8872 Trace: 0.8879\n",
      "Rep: 95 Cost: 0.2667 Fidelity: 0.8851 Trace: 0.8923\n",
      "Rep: 96 Cost: 0.2813 Fidelity: 0.8758 Trace: 0.8876\n",
      "Rep: 97 Cost: 0.2272 Fidelity: 0.8841 Trace: 0.8896\n",
      "Rep: 98 Cost: 0.1178 Fidelity: 0.8925 Trace: 0.8931\n",
      "Rep: 99 Cost: 0.1753 Fidelity: 0.8867 Trace: 0.8905\n",
      "Rep: 100 Cost: 0.1637 Fidelity: 0.8864 Trace: 0.8889\n",
      "Rep: 101 Cost: 0.1031 Fidelity: 0.8947 Trace: 0.8954\n",
      "Rep: 102 Cost: 0.1442 Fidelity: 0.8988 Trace: 0.9002\n",
      "Rep: 103 Cost: 0.0975 Fidelity: 0.8969 Trace: 0.8975\n",
      "Rep: 104 Cost: 0.1236 Fidelity: 0.8946 Trace: 0.8962\n",
      "Rep: 105 Cost: 0.0868 Fidelity: 0.8999 Trace: 0.9002\n",
      "Rep: 106 Cost: 0.1874 Fidelity: 0.8977 Trace: 0.9033\n",
      "Rep: 107 Cost: 0.1825 Fidelity: 0.9010 Trace: 0.9073\n",
      "Rep: 108 Cost: 0.0798 Fidelity: 0.9044 Trace: 0.9047\n",
      "Rep: 109 Cost: 0.2125 Fidelity: 0.8861 Trace: 0.8925\n",
      "Rep: 110 Cost: 0.2403 Fidelity: 0.8801 Trace: 0.8911\n",
      "Rep: 111 Cost: 0.1764 Fidelity: 0.8945 Trace: 0.8988\n",
      "Rep: 112 Cost: 0.1285 Fidelity: 0.9044 Trace: 0.9064\n",
      "Rep: 113 Cost: 0.1782 Fidelity: 0.9058 Trace: 0.9112\n",
      "Rep: 114 Cost: 0.1471 Fidelity: 0.9064 Trace: 0.9094\n",
      "Rep: 115 Cost: 0.1078 Fidelity: 0.9033 Trace: 0.9042\n",
      "Rep: 116 Cost: 0.1400 Fidelity: 0.9022 Trace: 0.9049\n",
      "Rep: 117 Cost: 0.1333 Fidelity: 0.9057 Trace: 0.9082\n",
      "Rep: 118 Cost: 0.0886 Fidelity: 0.9117 Trace: 0.9124\n",
      "Rep: 119 Cost: 0.1333 Fidelity: 0.9079 Trace: 0.9104\n",
      "Rep: 120 Cost: 0.1218 Fidelity: 0.9076 Trace: 0.9090\n",
      "Rep: 121 Cost: 0.0987 Fidelity: 0.9126 Trace: 0.9135\n",
      "Rep: 122 Cost: 0.1168 Fidelity: 0.9153 Trace: 0.9163\n",
      "Rep: 123 Cost: 0.0793 Fidelity: 0.9133 Trace: 0.9137\n",
      "Rep: 124 Cost: 0.0757 Fidelity: 0.9149 Trace: 0.9150\n",
      "Rep: 125 Cost: 0.1176 Fidelity: 0.9165 Trace: 0.9176\n",
      "Rep: 126 Cost: 0.0846 Fidelity: 0.9169 Trace: 0.9177\n",
      "Rep: 127 Cost: 0.1183 Fidelity: 0.9165 Trace: 0.9175\n",
      "Rep: 128 Cost: 0.1180 Fidelity: 0.9177 Trace: 0.9192\n",
      "Rep: 129 Cost: 0.0812 Fidelity: 0.9192 Trace: 0.9199\n",
      "Rep: 130 Cost: 0.1226 Fidelity: 0.9177 Trace: 0.9200\n",
      "Rep: 131 Cost: 0.0887 Fidelity: 0.9196 Trace: 0.9204\n",
      "Rep: 132 Cost: 0.1165 Fidelity: 0.9202 Trace: 0.9223\n",
      "Rep: 133 Cost: 0.1391 Fidelity: 0.9187 Trace: 0.9216\n",
      "Rep: 134 Cost: 0.0734 Fidelity: 0.9230 Trace: 0.9234\n",
      "Rep: 135 Cost: 0.1060 Fidelity: 0.9219 Trace: 0.9232\n",
      "Rep: 136 Cost: 0.0838 Fidelity: 0.9241 Trace: 0.9248\n",
      "Rep: 137 Cost: 0.1219 Fidelity: 0.9256 Trace: 0.9270\n",
      "Rep: 138 Cost: 0.1342 Fidelity: 0.9231 Trace: 0.9245\n",
      "Rep: 139 Cost: 0.0968 Fidelity: 0.9244 Trace: 0.9256\n",
      "Rep: 140 Cost: 0.1500 Fidelity: 0.9260 Trace: 0.9286\n",
      "Rep: 141 Cost: 0.1077 Fidelity: 0.9265 Trace: 0.9284\n",
      "Rep: 142 Cost: 0.1170 Fidelity: 0.9262 Trace: 0.9264\n",
      "Rep: 143 Cost: 0.1365 Fidelity: 0.9217 Trace: 0.9248\n",
      "Rep: 144 Cost: 0.1771 Fidelity: 0.9254 Trace: 0.9284\n",
      "Rep: 145 Cost: 0.0929 Fidelity: 0.9293 Trace: 0.9302\n",
      "Rep: 146 Cost: 0.1757 Fidelity: 0.9253 Trace: 0.9275\n",
      "Rep: 147 Cost: 0.0777 Fidelity: 0.9290 Trace: 0.9295\n",
      "Rep: 148 Cost: 0.1345 Fidelity: 0.9299 Trace: 0.9316\n",
      "Rep: 149 Cost: 0.1261 Fidelity: 0.9293 Trace: 0.9302\n",
      "Rep: 150 Cost: 0.0986 Fidelity: 0.9295 Trace: 0.9304\n",
      "Rep: 151 Cost: 0.1629 Fidelity: 0.9321 Trace: 0.9339\n",
      "Rep: 152 Cost: 0.1274 Fidelity: 0.9349 Trace: 0.9354\n",
      "Rep: 153 Cost: 0.1009 Fidelity: 0.9313 Trace: 0.9319\n",
      "Rep: 154 Cost: 0.1202 Fidelity: 0.9281 Trace: 0.9291\n",
      "Rep: 155 Cost: 0.0890 Fidelity: 0.9322 Trace: 0.9325\n",
      "Rep: 156 Cost: 0.1135 Fidelity: 0.9330 Trace: 0.9351\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rep: 157 Cost: 0.1212 Fidelity: 0.9322 Trace: 0.9343\n",
      "Rep: 158 Cost: 0.1124 Fidelity: 0.9334 Trace: 0.9343\n",
      "Rep: 159 Cost: 0.0980 Fidelity: 0.9318 Trace: 0.9329\n",
      "Rep: 160 Cost: 0.1052 Fidelity: 0.9346 Trace: 0.9362\n",
      "Rep: 161 Cost: 0.1059 Fidelity: 0.9372 Trace: 0.9382\n",
      "Rep: 162 Cost: 0.0911 Fidelity: 0.9331 Trace: 0.9339\n",
      "Rep: 163 Cost: 0.0866 Fidelity: 0.9326 Trace: 0.9338\n",
      "Rep: 164 Cost: 0.1091 Fidelity: 0.9326 Trace: 0.9351\n",
      "Rep: 165 Cost: 0.0961 Fidelity: 0.9342 Trace: 0.9360\n",
      "Rep: 166 Cost: 0.0671 Fidelity: 0.9347 Trace: 0.9351\n",
      "Rep: 167 Cost: 0.0908 Fidelity: 0.9362 Trace: 0.9375\n",
      "Rep: 168 Cost: 0.0841 Fidelity: 0.9379 Trace: 0.9391\n",
      "Rep: 169 Cost: 0.0839 Fidelity: 0.9368 Trace: 0.9372\n",
      "Rep: 170 Cost: 0.0980 Fidelity: 0.9351 Trace: 0.9365\n",
      "Rep: 171 Cost: 0.0952 Fidelity: 0.9355 Trace: 0.9366\n",
      "Rep: 172 Cost: 0.1004 Fidelity: 0.9383 Trace: 0.9396\n",
      "Rep: 173 Cost: 0.0915 Fidelity: 0.9367 Trace: 0.9376\n",
      "Rep: 174 Cost: 0.0861 Fidelity: 0.9379 Trace: 0.9389\n",
      "Rep: 175 Cost: 0.0728 Fidelity: 0.9387 Trace: 0.9389\n",
      "Rep: 176 Cost: 0.0850 Fidelity: 0.9394 Trace: 0.9405\n",
      "Rep: 177 Cost: 0.0802 Fidelity: 0.9409 Trace: 0.9418\n",
      "Rep: 178 Cost: 0.0955 Fidelity: 0.9403 Trace: 0.9409\n",
      "Rep: 179 Cost: 0.0972 Fidelity: 0.9377 Trace: 0.9393\n",
      "Rep: 180 Cost: 0.0986 Fidelity: 0.9353 Trace: 0.9374\n",
      "Rep: 181 Cost: 0.0871 Fidelity: 0.9401 Trace: 0.9409\n",
      "Rep: 182 Cost: 0.0890 Fidelity: 0.9417 Trace: 0.9426\n",
      "Rep: 183 Cost: 0.0676 Fidelity: 0.9424 Trace: 0.9425\n",
      "Rep: 184 Cost: 0.1200 Fidelity: 0.9380 Trace: 0.9394\n",
      "Rep: 185 Cost: 0.0736 Fidelity: 0.9421 Trace: 0.9427\n",
      "Rep: 186 Cost: 0.0937 Fidelity: 0.9437 Trace: 0.9451\n",
      "Rep: 187 Cost: 0.0971 Fidelity: 0.9421 Trace: 0.9425\n",
      "Rep: 188 Cost: 0.1090 Fidelity: 0.9373 Trace: 0.9400\n",
      "Rep: 189 Cost: 0.1154 Fidelity: 0.9389 Trace: 0.9413\n",
      "Rep: 190 Cost: 0.0956 Fidelity: 0.9429 Trace: 0.9435\n",
      "Rep: 191 Cost: 0.1073 Fidelity: 0.9441 Trace: 0.9455\n",
      "Rep: 192 Cost: 0.0733 Fidelity: 0.9425 Trace: 0.9429\n",
      "Rep: 193 Cost: 0.0825 Fidelity: 0.9424 Trace: 0.9435\n",
      "Rep: 194 Cost: 0.1017 Fidelity: 0.9450 Trace: 0.9462\n",
      "Rep: 195 Cost: 0.0720 Fidelity: 0.9450 Trace: 0.9458\n",
      "Rep: 196 Cost: 0.0821 Fidelity: 0.9426 Trace: 0.9433\n",
      "Rep: 197 Cost: 0.0572 Fidelity: 0.9444 Trace: 0.9446\n",
      "Rep: 198 Cost: 0.1492 Fidelity: 0.9428 Trace: 0.9462\n",
      "Rep: 199 Cost: 0.1535 Fidelity: 0.9452 Trace: 0.9483\n",
      "Epoch 1/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 2.0708e-04\n",
      "Epoch 2/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 1.6337e-04\n",
      "Epoch 3/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 1.0800e-04\n",
      "Epoch 4/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 6.0531e-05\n",
      "Epoch 5/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 3.1808e-05\n",
      "Epoch 6/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 2.4442e-05\n",
      "Epoch 7/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 3.3948e-05\n",
      "Epoch 8/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 5.1465e-05\n",
      "Epoch 9/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.7833e-05\n",
      "Epoch 10/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 7.6788e-05\n",
      "Epoch 11/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.5817e-05\n",
      "Epoch 12/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.5720e-05\n",
      "Epoch 13/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 4.9893e-05\n",
      "Epoch 14/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 3.3221e-05\n",
      "Epoch 15/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.0407e-05\n",
      "Epoch 16/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.4259e-05\n",
      "Epoch 17/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 1.4788e-05\n",
      "Epoch 18/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 1.9596e-05\n",
      "Epoch 19/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 2.5290e-05\n",
      "Epoch 20/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.9040e-05\n",
      "Epoch 21/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.9505e-05\n",
      "Epoch 22/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.6831e-05\n",
      "Epoch 23/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.2089e-05\n",
      "Epoch 24/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.6633e-05\n",
      "Epoch 25/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 1.1688e-05\n",
      "Epoch 26/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.1603e-06\n",
      "Epoch 27/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.5203e-06\n",
      "Epoch 28/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 6.6906e-06\n",
      "Epoch 29/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.0482e-06\n",
      "Epoch 30/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 9.6353e-06\n",
      "Epoch 31/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.0542e-05\n",
      "Epoch 32/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.0274e-05\n",
      "Epoch 33/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.8900e-06\n",
      "Epoch 34/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.8752e-06\n",
      "Epoch 35/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 4.8450e-06\n",
      "Epoch 36/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 3.2756e-06\n",
      "Epoch 37/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.3774e-06\n",
      "Epoch 38/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.1163e-06\n",
      "Epoch 39/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.3084e-06\n",
      "Epoch 40/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.7103e-06\n",
      "Epoch 41/100\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 3.0769e-06\n",
      "Epoch 42/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 3.2092e-06\n",
      "Epoch 43/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 3.0015e-06\n",
      "Epoch 44/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.4775e-06\n",
      "Epoch 45/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.7830e-06\n",
      "Epoch 46/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.1216e-06\n",
      "Epoch 47/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.6325e-07\n",
      "Epoch 48/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 4.7624e-07\n",
      "Epoch 49/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 5.1759e-07\n",
      "Epoch 50/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.7645e-07\n",
      "Epoch 51/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.3616e-07\n",
      "Epoch 52/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 9.1762e-07\n",
      "Epoch 53/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.9063e-07\n",
      "Epoch 54/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 7.6439e-07\n",
      "Epoch 55/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 5.7265e-07\n",
      "Epoch 56/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 3.6339e-07\n",
      "Epoch 57/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.8766e-07\n",
      "Epoch 58/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.4610e-08\n",
      "Epoch 59/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.7381e-08\n",
      "Epoch 60/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.1691e-07\n",
      "Epoch 61/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 1.9207e-07\n",
      "Epoch 62/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 2.4963e-07\n",
      "Epoch 63/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.6355e-07\n",
      "Epoch 64/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 2.3203e-07\n",
      "Epoch 65/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.7135e-07\n",
      "Epoch 66/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 1.0398e-07\n",
      "Epoch 67/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 4.8420e-08\n",
      "Epoch 68/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 1.5163e-08\n",
      "Epoch 69/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 6.9694e-09\n",
      "Epoch 70/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 2.0031e-08\n",
      "Epoch 71/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 4.5253e-08\n",
      "Epoch 72/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 7.0478e-08\n",
      "Epoch 73/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.4648e-08\n",
      "Epoch 74/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.2037e-08\n",
      "Epoch 75/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.4764e-08\n",
      "Epoch 76/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 4.0797e-08\n",
      "Epoch 77/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 1.9527e-08\n",
      "Epoch 78/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 7.1611e-09\n",
      "Epoch 79/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 5.0189e-09\n",
      "Epoch 80/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 1.0474e-08\n",
      "Epoch 81/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.9279e-08\n",
      "Epoch 82/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 2.7429e-08\n",
      "Epoch 83/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 3.2132e-08\n",
      "Epoch 84/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 3.1979e-08\n",
      "Epoch 85/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 2.7121e-08\n",
      "Epoch 86/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 1.9319e-08\n",
      "Epoch 87/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 1.1356e-08\n",
      "Epoch 88/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 5.8985e-09\n",
      "Epoch 89/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 4.2830e-09\n",
      "Epoch 90/100\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 6.0636e-09\n",
      "Epoch 91/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 9.4742e-09\n",
      "Epoch 92/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 1.2517e-08\n",
      "Epoch 93/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.3908e-08\n",
      "Epoch 94/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 1.3317e-08\n",
      "Epoch 95/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 1.1218e-08\n",
      "Epoch 96/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 8.4040e-09\n",
      "Epoch 97/100\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 5.6812e-09\n",
      "Epoch 98/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 3.7134e-09\n",
      "Epoch 99/100\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 2.9039e-09\n",
      "Epoch 100/100\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 3.2277e-09\n",
      "Rep: 0 Cost: 1.1499 Fidelity: 0.0000 Trace: 0.0061\n",
      "Rep: 1 Cost: 1.0816 Fidelity: 0.0001 Trace: 0.0037\n",
      "Rep: 2 Cost: 1.0812 Fidelity: 0.0001 Trace: 0.0027\n",
      "Rep: 3 Cost: 1.0694 Fidelity: 0.0002 Trace: 0.0030\n",
      "Rep: 4 Cost: 1.0649 Fidelity: 0.0003 Trace: 0.0035\n",
      "Rep: 5 Cost: 1.0528 Fidelity: 0.0004 Trace: 0.0023\n",
      "Rep: 6 Cost: 1.0492 Fidelity: 0.0004 Trace: 0.0025\n",
      "Rep: 7 Cost: 1.0507 Fidelity: 0.0004 Trace: 0.0028\n",
      "Rep: 8 Cost: 1.0413 Fidelity: 0.0006 Trace: 0.0026\n",
      "Rep: 9 Cost: 1.0335 Fidelity: 0.0009 Trace: 0.0024\n",
      "Rep: 10 Cost: 1.0250 Fidelity: 0.0012 Trace: 0.0024\n",
      "Rep: 11 Cost: 1.0104 Fidelity: 0.0016 Trace: 0.0023\n",
      "Rep: 12 Cost: 1.0006 Fidelity: 0.0019 Trace: 0.0024\n",
      "Rep: 13 Cost: 0.9923 Fidelity: 0.0022 Trace: 0.0026\n",
      "Rep: 14 Cost: 0.9798 Fidelity: 0.0024 Trace: 0.0027\n",
      "Rep: 15 Cost: 0.9795 Fidelity: 0.0025 Trace: 0.0028\n",
      "Rep: 16 Cost: 0.9774 Fidelity: 0.0027 Trace: 0.0030\n",
      "Rep: 17 Cost: 0.9640 Fidelity: 0.0031 Trace: 0.0032\n",
      "Rep: 18 Cost: 0.9671 Fidelity: 0.0033 Trace: 0.0035\n",
      "Rep: 19 Cost: 0.9720 Fidelity: 0.0034 Trace: 0.0037\n",
      "Rep: 20 Cost: 0.9612 Fidelity: 0.0037 Trace: 0.0039\n",
      "Rep: 21 Cost: 0.9658 Fidelity: 0.0038 Trace: 0.0040\n",
      "Rep: 22 Cost: 0.9716 Fidelity: 0.0037 Trace: 0.0040\n",
      "Rep: 23 Cost: 0.9683 Fidelity: 0.0038 Trace: 0.0042\n",
      "Rep: 24 Cost: 0.9572 Fidelity: 0.0042 Trace: 0.0044\n",
      "Rep: 25 Cost: 0.9559 Fidelity: 0.0043 Trace: 0.0045\n",
      "Rep: 26 Cost: 0.9532 Fidelity: 0.0044 Trace: 0.0046\n",
      "Rep: 27 Cost: 0.9453 Fidelity: 0.0047 Trace: 0.0047\n",
      "Rep: 28 Cost: 0.9453 Fidelity: 0.0048 Trace: 0.0048\n",
      "Rep: 29 Cost: 0.9404 Fidelity: 0.0051 Trace: 0.0051\n",
      "Rep: 30 Cost: 0.9427 Fidelity: 0.0054 Trace: 0.0055\n",
      "Rep: 31 Cost: 0.9423 Fidelity: 0.0056 Trace: 0.0056\n",
      "Rep: 32 Cost: 0.9410 Fidelity: 0.0056 Trace: 0.0057\n",
      "Rep: 33 Cost: 0.9343 Fidelity: 0.0060 Trace: 0.0060\n",
      "Rep: 34 Cost: 0.9369 Fidelity: 0.0064 Trace: 0.0065\n",
      "Rep: 35 Cost: 0.9369 Fidelity: 0.0069 Trace: 0.0070\n",
      "Rep: 36 Cost: 0.9285 Fidelity: 0.0071 Trace: 0.0071\n",
      "Rep: 37 Cost: 0.9296 Fidelity: 0.0070 Trace: 0.0070\n",
      "Rep: 38 Cost: 0.9290 Fidelity: 0.0073 Trace: 0.0074\n",
      "Rep: 39 Cost: 0.9241 Fidelity: 0.0079 Trace: 0.0080\n",
      "Rep: 40 Cost: 0.9276 Fidelity: 0.0082 Trace: 0.0083\n",
      "Rep: 41 Cost: 0.9181 Fidelity: 0.0083 Trace: 0.0083\n",
      "Rep: 42 Cost: 0.9248 Fidelity: 0.0085 Trace: 0.0086\n",
      "Rep: 43 Cost: 0.9192 Fidelity: 0.0088 Trace: 0.0088\n",
      "Rep: 44 Cost: 0.9155 Fidelity: 0.0091 Trace: 0.0091\n",
      "Rep: 45 Cost: 0.9177 Fidelity: 0.0097 Trace: 0.0098\n",
      "Rep: 46 Cost: 0.9151 Fidelity: 0.0101 Trace: 0.0101\n",
      "Rep: 47 Cost: 0.9119 Fidelity: 0.0102 Trace: 0.0102\n",
      "Rep: 48 Cost: 0.9082 Fidelity: 0.0105 Trace: 0.0105\n",
      "Rep: 49 Cost: 0.9084 Fidelity: 0.0113 Trace: 0.0113\n",
      "Rep: 50 Cost: 0.9091 Fidelity: 0.0120 Trace: 0.0120\n",
      "Rep: 51 Cost: 0.9014 Fidelity: 0.0122 Trace: 0.0122\n",
      "Rep: 52 Cost: 0.9014 Fidelity: 0.0127 Trace: 0.0128\n",
      "Rep: 53 Cost: 0.8929 Fidelity: 0.0137 Trace: 0.0138\n",
      "Rep: 54 Cost: 0.8966 Fidelity: 0.0141 Trace: 0.0141\n",
      "Rep: 55 Cost: 0.8906 Fidelity: 0.0144 Trace: 0.0145\n",
      "Rep: 56 Cost: 0.8870 Fidelity: 0.0152 Trace: 0.0152\n",
      "Rep: 57 Cost: 0.8885 Fidelity: 0.0162 Trace: 0.0163\n",
      "Rep: 58 Cost: 0.8824 Fidelity: 0.0169 Trace: 0.0169\n",
      "Rep: 59 Cost: 0.8784 Fidelity: 0.0175 Trace: 0.0176\n",
      "Rep: 60 Cost: 0.8753 Fidelity: 0.0181 Trace: 0.0181\n",
      "Rep: 61 Cost: 0.8739 Fidelity: 0.0191 Trace: 0.0192\n",
      "Rep: 62 Cost: 0.8683 Fidelity: 0.0202 Trace: 0.0202\n",
      "Rep: 63 Cost: 0.8686 Fidelity: 0.0211 Trace: 0.0212\n",
      "Rep: 64 Cost: 0.8666 Fidelity: 0.0222 Trace: 0.0222\n",
      "Rep: 65 Cost: 0.8677 Fidelity: 0.0227 Trace: 0.0228\n",
      "Rep: 66 Cost: 0.8604 Fidelity: 0.0236 Trace: 0.0237\n",
      "Rep: 67 Cost: 0.8588 Fidelity: 0.0248 Trace: 0.0249\n",
      "Rep: 68 Cost: 0.8639 Fidelity: 0.0258 Trace: 0.0259\n",
      "Rep: 69 Cost: 0.8547 Fidelity: 0.0267 Trace: 0.0268\n",
      "Rep: 70 Cost: 0.8631 Fidelity: 0.0274 Trace: 0.0276\n",
      "Rep: 71 Cost: 0.8501 Fidelity: 0.0292 Trace: 0.0293\n",
      "Rep: 72 Cost: 0.8475 Fidelity: 0.0307 Trace: 0.0309\n",
      "Rep: 73 Cost: 0.8396 Fidelity: 0.0313 Trace: 0.0315\n",
      "Rep: 74 Cost: 0.8341 Fidelity: 0.0327 Trace: 0.0328\n",
      "Rep: 75 Cost: 0.8359 Fidelity: 0.0349 Trace: 0.0351\n",
      "Rep: 76 Cost: 0.8240 Fidelity: 0.0364 Trace: 0.0364\n",
      "Rep: 77 Cost: 0.8387 Fidelity: 0.0367 Trace: 0.0370\n",
      "Rep: 78 Cost: 0.8271 Fidelity: 0.0376 Trace: 0.0378\n",
      "Rep: 79 Cost: 0.8244 Fidelity: 0.0397 Trace: 0.0399\n",
      "Rep: 80 Cost: 0.8297 Fidelity: 0.0425 Trace: 0.0429\n",
      "Rep: 81 Cost: 0.8126 Fidelity: 0.0449 Trace: 0.0451\n",
      "Rep: 82 Cost: 0.8223 Fidelity: 0.0447 Trace: 0.0452\n",
      "Rep: 83 Cost: 0.8147 Fidelity: 0.0469 Trace: 0.0473\n",
      "Rep: 84 Cost: 0.8123 Fidelity: 0.0509 Trace: 0.0515\n",
      "Rep: 85 Cost: 0.7979 Fidelity: 0.0541 Trace: 0.0544\n",
      "Rep: 86 Cost: 0.8386 Fidelity: 0.0541 Trace: 0.0558\n",
      "Rep: 87 Cost: 0.7950 Fidelity: 0.0575 Trace: 0.0579\n",
      "Rep: 88 Cost: 0.8428 Fidelity: 0.0593 Trace: 0.0621\n",
      "Rep: 89 Cost: 0.8271 Fidelity: 0.0637 Trace: 0.0660\n",
      "Rep: 90 Cost: 0.7900 Fidelity: 0.0667 Trace: 0.0676\n",
      "Rep: 91 Cost: 0.8121 Fidelity: 0.0664 Trace: 0.0688\n",
      "Rep: 92 Cost: 0.7548 Fidelity: 0.0722 Trace: 0.0724\n",
      "Rep: 93 Cost: 0.8330 Fidelity: 0.0755 Trace: 0.0805\n",
      "Rep: 94 Cost: 0.8012 Fidelity: 0.0805 Trace: 0.0843\n",
      "Rep: 95 Cost: 0.7911 Fidelity: 0.0812 Trace: 0.0827\n",
      "Rep: 96 Cost: 0.8059 Fidelity: 0.0816 Trace: 0.0838\n",
      "Rep: 97 Cost: 0.7534 Fidelity: 0.0900 Trace: 0.0909\n",
      "Rep: 98 Cost: 0.7783 Fidelity: 0.0978 Trace: 0.1007\n",
      "Rep: 99 Cost: 0.7405 Fidelity: 0.1016 Trace: 0.1025\n",
      "Rep: 100 Cost: 0.7805 Fidelity: 0.0912 Trace: 0.0954\n",
      "Rep: 101 Cost: 0.7966 Fidelity: 0.0913 Trace: 0.0977\n",
      "Rep: 102 Cost: 0.7243 Fidelity: 0.1089 Trace: 0.1106\n",
      "Rep: 103 Cost: 0.7579 Fidelity: 0.1285 Trace: 0.1334\n",
      "Rep: 104 Cost: 0.7686 Fidelity: 0.1360 Trace: 0.1446\n",
      "Rep: 105 Cost: 0.6987 Fidelity: 0.1400 Trace: 0.1420\n",
      "Rep: 106 Cost: 0.7620 Fidelity: 0.1318 Trace: 0.1375\n",
      "Rep: 107 Cost: 0.7357 Fidelity: 0.1406 Trace: 0.1459\n",
      "Rep: 108 Cost: 0.6564 Fidelity: 0.1674 Trace: 0.1685\n",
      "Rep: 109 Cost: 0.6754 Fidelity: 0.1817 Trace: 0.1844\n",
      "Rep: 110 Cost: 0.6568 Fidelity: 0.1828 Trace: 0.1846\n",
      "Rep: 111 Cost: 0.6290 Fidelity: 0.1948 Trace: 0.1967\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rep: 112 Cost: 0.6347 Fidelity: 0.2097 Trace: 0.2120\n",
      "Rep: 113 Cost: 0.5966 Fidelity: 0.2304 Trace: 0.2327\n",
      "Rep: 114 Cost: 0.5693 Fidelity: 0.2575 Trace: 0.2604\n",
      "Rep: 115 Cost: 0.5749 Fidelity: 0.2748 Trace: 0.2788\n",
      "Rep: 116 Cost: 0.5070 Fidelity: 0.2892 Trace: 0.2899\n",
      "Rep: 117 Cost: 0.5095 Fidelity: 0.3207 Trace: 0.3228\n",
      "Rep: 118 Cost: 0.5267 Fidelity: 0.3310 Trace: 0.3345\n",
      "Rep: 119 Cost: 0.4906 Fidelity: 0.3770 Trace: 0.3803\n",
      "Rep: 120 Cost: 0.4471 Fidelity: 0.3990 Trace: 0.4013\n",
      "Rep: 121 Cost: 0.4198 Fidelity: 0.4444 Trace: 0.4471\n",
      "Rep: 122 Cost: 0.4503 Fidelity: 0.4491 Trace: 0.4545\n",
      "Rep: 123 Cost: 0.3632 Fidelity: 0.5090 Trace: 0.5108\n",
      "Rep: 124 Cost: 0.3726 Fidelity: 0.5423 Trace: 0.5449\n",
      "Rep: 125 Cost: 0.3440 Fidelity: 0.5699 Trace: 0.5715\n",
      "Rep: 126 Cost: 0.3067 Fidelity: 0.5982 Trace: 0.6000\n",
      "Rep: 127 Cost: 0.3622 Fidelity: 0.6347 Trace: 0.6431\n",
      "Rep: 128 Cost: 0.2920 Fidelity: 0.6476 Trace: 0.6504\n",
      "Rep: 129 Cost: 0.3103 Fidelity: 0.6533 Trace: 0.6609\n",
      "Rep: 130 Cost: 0.3007 Fidelity: 0.6926 Trace: 0.6986\n",
      "Rep: 131 Cost: 0.3344 Fidelity: 0.7183 Trace: 0.7192\n",
      "Rep: 132 Cost: 0.2516 Fidelity: 0.7368 Trace: 0.7381\n",
      "Rep: 133 Cost: 0.3741 Fidelity: 0.7197 Trace: 0.7357\n",
      "Rep: 134 Cost: 0.2716 Fidelity: 0.7551 Trace: 0.7594\n",
      "Rep: 135 Cost: 0.3445 Fidelity: 0.7569 Trace: 0.7820\n",
      "Rep: 136 Cost: 0.4037 Fidelity: 0.7613 Trace: 0.7906\n",
      "Rep: 137 Cost: 0.2481 Fidelity: 0.7921 Trace: 0.7975\n",
      "Rep: 138 Cost: 0.3261 Fidelity: 0.7734 Trace: 0.7900\n",
      "Rep: 139 Cost: 0.3219 Fidelity: 0.7835 Trace: 0.7973\n",
      "Rep: 140 Cost: 0.2194 Fidelity: 0.8097 Trace: 0.8144\n",
      "Rep: 141 Cost: 0.2549 Fidelity: 0.8161 Trace: 0.8249\n",
      "Rep: 142 Cost: 0.2232 Fidelity: 0.8248 Trace: 0.8311\n",
      "Rep: 143 Cost: 0.2459 Fidelity: 0.8212 Trace: 0.8303\n",
      "Rep: 144 Cost: 0.3446 Fidelity: 0.8322 Trace: 0.8382\n",
      "Rep: 145 Cost: 0.1936 Fidelity: 0.8431 Trace: 0.8470\n",
      "Rep: 146 Cost: 0.3656 Fidelity: 0.8482 Trace: 0.8526\n",
      "Rep: 147 Cost: 0.1847 Fidelity: 0.8483 Trace: 0.8533\n",
      "Rep: 148 Cost: 0.4192 Fidelity: 0.8448 Trace: 0.8467\n",
      "Rep: 149 Cost: 0.3987 Fidelity: 0.8297 Trace: 0.8433\n",
      "Rep: 150 Cost: 0.4874 Fidelity: 0.8196 Trace: 0.8457\n",
      "Rep: 151 Cost: 0.2505 Fidelity: 0.8542 Trace: 0.8570\n",
      "Rep: 152 Cost: 0.6040 Fidelity: 0.7912 Trace: 0.8365\n",
      "Rep: 153 Cost: 0.7242 Fidelity: 0.7404 Trace: 0.8220\n",
      "Rep: 154 Cost: 0.4587 Fidelity: 0.7825 Trace: 0.8361\n",
      "Rep: 155 Cost: 0.4243 Fidelity: 0.8518 Trace: 0.8589\n",
      "Rep: 156 Cost: 0.4127 Fidelity: 0.8447 Trace: 0.8637\n",
      "Rep: 157 Cost: 0.5209 Fidelity: 0.8217 Trace: 0.8575\n",
      "Rep: 158 Cost: 0.6210 Fidelity: 0.8164 Trace: 0.8529\n",
      "Rep: 159 Cost: 0.4381 Fidelity: 0.8325 Trace: 0.8540\n",
      "Rep: 160 Cost: 0.2074 Fidelity: 0.8548 Trace: 0.8585\n",
      "Rep: 161 Cost: 0.3669 Fidelity: 0.8319 Trace: 0.8458\n",
      "Rep: 162 Cost: 0.4050 Fidelity: 0.7944 Trace: 0.8307\n",
      "Rep: 163 Cost: 0.3873 Fidelity: 0.8072 Trace: 0.8328\n",
      "Rep: 164 Cost: 0.1858 Fidelity: 0.8434 Trace: 0.8478\n",
      "Rep: 165 Cost: 0.3035 Fidelity: 0.8340 Trace: 0.8483\n",
      "Rep: 166 Cost: 0.3498 Fidelity: 0.8284 Trace: 0.8463\n",
      "Rep: 167 Cost: 0.2810 Fidelity: 0.8395 Trace: 0.8494\n",
      "Rep: 168 Cost: 0.1879 Fidelity: 0.8502 Trace: 0.8523\n",
      "Rep: 169 Cost: 0.2464 Fidelity: 0.8363 Trace: 0.8450\n",
      "Rep: 170 Cost: 0.2954 Fidelity: 0.8251 Trace: 0.8374\n",
      "Rep: 171 Cost: 0.1663 Fidelity: 0.8437 Trace: 0.8460\n",
      "Rep: 172 Cost: 0.2724 Fidelity: 0.8448 Trace: 0.8540\n",
      "Rep: 173 Cost: 0.2659 Fidelity: 0.8354 Trace: 0.8538\n",
      "Rep: 174 Cost: 0.2834 Fidelity: 0.8467 Trace: 0.8568\n",
      "Rep: 175 Cost: 0.1319 Fidelity: 0.8618 Trace: 0.8626\n",
      "Rep: 176 Cost: 0.2854 Fidelity: 0.8536 Trace: 0.8629\n",
      "Rep: 177 Cost: 0.2786 Fidelity: 0.8441 Trace: 0.8621\n",
      "Rep: 178 Cost: 0.2797 Fidelity: 0.8533 Trace: 0.8656\n",
      "Rep: 179 Cost: 0.1535 Fidelity: 0.8729 Trace: 0.8745\n",
      "Rep: 180 Cost: 0.3816 Fidelity: 0.8591 Trace: 0.8730\n",
      "Rep: 181 Cost: 0.2995 Fidelity: 0.8594 Trace: 0.8741\n",
      "Rep: 182 Cost: 0.3010 Fidelity: 0.8754 Trace: 0.8770\n",
      "Rep: 183 Cost: 0.3921 Fidelity: 0.8659 Trace: 0.8733\n",
      "Rep: 184 Cost: 0.2176 Fidelity: 0.8652 Trace: 0.8761\n",
      "Rep: 185 Cost: 0.3743 Fidelity: 0.8794 Trace: 0.8836\n",
      "Rep: 186 Cost: 0.3892 Fidelity: 0.8839 Trace: 0.8861\n",
      "Rep: 187 Cost: 0.1652 Fidelity: 0.8811 Trace: 0.8859\n",
      "Rep: 188 Cost: 0.4412 Fidelity: 0.8727 Trace: 0.8791\n",
      "Rep: 189 Cost: 0.4569 Fidelity: 0.8751 Trace: 0.8762\n",
      "Rep: 190 Cost: 0.3811 Fidelity: 0.8708 Trace: 0.8814\n",
      "Rep: 191 Cost: 0.3516 Fidelity: 0.8638 Trace: 0.8847\n",
      "Rep: 192 Cost: 0.3242 Fidelity: 0.8790 Trace: 0.8864\n",
      "Rep: 193 Cost: 0.1679 Fidelity: 0.8770 Trace: 0.8815\n",
      "Rep: 194 Cost: 0.3457 Fidelity: 0.8618 Trace: 0.8771\n",
      "Rep: 195 Cost: 0.2252 Fidelity: 0.8738 Trace: 0.8822\n",
      "Rep: 196 Cost: 0.2351 Fidelity: 0.8897 Trace: 0.8905\n",
      "Rep: 197 Cost: 0.2514 Fidelity: 0.8882 Trace: 0.8906\n",
      "Rep: 198 Cost: 0.1446 Fidelity: 0.8863 Trace: 0.8872\n",
      "Rep: 199 Cost: 0.1651 Fidelity: 0.8901 Trace: 0.8911\n"
     ]
    }
   ],
   "source": [
    "fid_progress = [[], [], [], [], [], [], []]\n",
    "loss_progress = [[], [], [], [], [], [], []]\n",
    "\n",
    "\n",
    "for j in range(sctm_iterations):\n",
    "    history = autoencoder.fit(x_train, x_train,epochs=classical_epochs)\n",
    "    \n",
    "    encoded_st = autoencoder.encoder(np.array([target_state])).numpy()\n",
    "    #decoded_st = autoencoder.decoder(encoded_st).numpy()\n",
    "    \n",
    "    def layer(params, q):\n",
    "        \"\"\"CV quantum neural network layer acting on ``N`` modes.\n",
    "\n",
    "        Args:\n",
    "            params (list[float]): list of length ``2*(max(1, N-1) + N**2 + n)`` containing\n",
    "                the number of parameters for the layer\n",
    "            q (list[RegRef]): list of Strawberry Fields quantum registers the layer\n",
    "                is to be applied to\n",
    "        \"\"\"\n",
    "        ops.Dgate(tf.clip_by_value(encoded_st[0][0], clip_value_min = -alpha_clip, clip_value_max = alpha_clip), math.degrees(encoded_st[0][1])) | q[0]\n",
    "\n",
    "        N = len(q)\n",
    "        M = int(N * (N - 1)) + max(1, N - 1)\n",
    "\n",
    "        rphi = params[-N+1:]\n",
    "        s = params[M:M+N]\n",
    "        dr = params[2*M+N:2*M+2*N]\n",
    "        dp = params[2*M+2*N:2*M+3*N]\n",
    "        k = params[2*M+3*N:2*M+4*N]\n",
    "\n",
    "        ops.Rgate(rphi[0]) | q[0]\n",
    "\n",
    "        for i in range(N):\n",
    "            ops.Sgate(s[i]) | q[i]\n",
    "\n",
    "        ops.Rgate(rphi[0]) | q[0]\n",
    "\n",
    "        for i in range(N):\n",
    "            ops.Dgate(dr[i], dp[i]) | q[i]\n",
    "            ops.Kgate(k[i]) | q[i]\n",
    "    \n",
    "    # initialize engine and program\n",
    "    eng = sf.Engine(backend=\"tf\", backend_options={\"cutoff_dim\": cutoff_dim})\n",
    "    qnn = sf.Program(modes)\n",
    "\n",
    "    # initialize QNN weights\n",
    "    weights = init_weights(modes, Qlayers) # our TensorFlow weights\n",
    "    num_params = np.prod(weights.shape)   # total number of parameters in our model\n",
    "\n",
    "    # Create array of Strawberry Fields symbolic gate arguments, matching\n",
    "    # the size of the weights Variable.\n",
    "    sf_params = np.arange(num_params).reshape(weights.shape).astype(np.str)\n",
    "    sf_params = np.array([qnn.params(*i) for i in sf_params])\n",
    "\n",
    "\n",
    "    # Construct the symbolic Strawberry Fields program by\n",
    "    # looping and applying layers to the program.\n",
    "    with qnn.context as q:\n",
    "        for k in range(Qlayers):\n",
    "            layer(sf_params[k], q)\n",
    "            \n",
    "    def cost(weights):\n",
    "        # Create a dictionary mapping from the names of the Strawberry Fields\n",
    "        # free parameters to the TensorFlow weight values.\n",
    "        mapping = {p.name: w for p, w in zip(sf_params.flatten(), tf.reshape(weights, [-1]))}\n",
    "\n",
    "        # Run engine\n",
    "        state = eng.run(qnn, args=mapping).state\n",
    "\n",
    "        # Extract the statevector\n",
    "        ket = state.ket()\n",
    "\n",
    "        # Compute the fidelity between the output statevector\n",
    "        # and the target state.\n",
    "        fidelity = tf.abs(tf.reduce_sum(tf.math.conj(ket) * target_state)) ** 2\n",
    "\n",
    "        # Objective function to minimize\n",
    "        #cost = tf.abs(tf.reduce_sum(tf.math.conj(ket) * target_state) - 1)\n",
    "        #return cost, fidelity, ket\n",
    "        # Instead of the Cost function, maybe it is better to break it down to components\n",
    "        # at least, when the Fock basis is insufficent, it will be visible\n",
    "        difference = tf.reduce_sum(tf.abs(ket - target_state))\n",
    "        fidelity = tf.abs(tf.reduce_sum(tf.math.conj(ket) * target_state)) ** 2\n",
    "        return difference, fidelity, ket, tf.math.real(state.trace())\n",
    "    \n",
    "    opt = tf.keras.optimizers.Adam(learning_rate=lr)\n",
    "    \n",
    "    fidp = []\n",
    "    lossp = []\n",
    "    best_fid = 0\n",
    "\n",
    "    for i in range(reps):\n",
    "        # reset the engine if it has already been executed\n",
    "        if eng.run_progs:\n",
    "            eng.reset()\n",
    "\n",
    "        with tf.GradientTape() as tape:\n",
    "            loss, fid, ket, trace = cost(weights)\n",
    "\n",
    "        # Stores fidelity at each step\n",
    "        fidp.append(fid.numpy())\n",
    "        lossp.append(loss)\n",
    "\n",
    "        if fid > best_fid:\n",
    "            # store the new best fidelity and best state\n",
    "            best_fid = fid.numpy()\n",
    "            learnt_state = ket.numpy()\n",
    "\n",
    "        # one repetition of the optimization\n",
    "        gradients = tape.gradient(loss, weights)\n",
    "        opt.apply_gradients(zip([gradients], [weights]))\n",
    "         # Prints progress at every rep\n",
    "        if i % 1 == 0:\n",
    "            print(\"Rep: {} Cost: {:.4f} Fidelity: {:.4f} Trace: {:.4f}\".format(i, loss, fid, trace))\n",
    "            \n",
    "    fid_progress[j] = fidp\n",
    "    loss_progress[j] = lossp\n",
    "\n",
    "    x_train = np.array([learnt_state])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3840b76",
   "metadata": {},
   "source": [
    "### Produce fidelity plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cbc5c720",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 12), dpi = 100)\n",
    "\n",
    "# Set tick font size\n",
    "for label in (ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    label.set_fontsize(16)\n",
    "\n",
    "for i in range(sctm_iterations):\n",
    "    plt.plot(fid_progress[i], label = \"Fidelity of iteration \" + str(i), linewidth = 3)\n",
    "\n",
    "plt.ylabel(\"Fidelity\", fontsize = '20')\n",
    "ax.yaxis.set_ticks(np.arange(0, 1, 0.4))\n",
    "plt.xlabel(\"Epoch\", fontsize = '20')\n",
    "ax.xaxis.set_ticks(np.arange(0, reps, 40))\n",
    "plt.title('SCTM Fidelity', fontsize = '24')\n",
    "plt.legend()\n",
    "plt.savefig('SCTM_fidelity.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e783394",
   "metadata": {},
   "source": [
    "### Produce loss plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cd05f41b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WZc0H3nVrGmaMGV2K+9QwxnwN/Kuk11SCYTjrJXQGOuQXRzsdua/B7ldMtfdX0YOWl/zMkU/cmkYWU9huGM6CjacUy7I2APPcmt4xxhS1m8O/gVr5r1OBz9xPGmOi84MfJRHv9nqf+4lS7B5Q6BgiIqczBQRERKSkzgVm52/3dZ8xptCHO2PMecBvnKiwD/ByEWnzj2Dfu/wpY8wCY8zFhT1kGmMaG2OeA7YC15TqnVSw/EJoS/K/lhd/RbX1Cycq4zcFPjHGRLl3MMZEGGP+B9xDyavPn2le5cQWnPWBn4wxTd07GKf7cK5391yScqp4khNr8S8EJhtjarl3MMYEGGNeBB52a36ugB04BgAbjTEjCis2aowJNMYMw/73/6NHt2RjzAfGmO6FBRiMMZ1w7mRQ2BgiIqctv6qegIiIVDud8r/+a4zZDKzFWRAsF+de5O2xby8HMBX7f3DbWJaVlZ998AlwbX5zN+Bn4LAxZhHOT+2ycH7K3BxnMTB3BVYpl4pjWdZhY8wY4Jn8phuBy4wxfwI7cRaR7IGzmnwucB/2T8MFZwDJGHM3zvoTPjj/vjYYY+YDf+H8/l3AiU+xH+LE31O516YwxqwoRfdd+UX+sCzrd2PME8Ar+ef643wg/xXYAUQDPYEabtdPBV4vZOwm+WO9YoxJBlZx4tP72sD5gHs2xaT83U3cBePcVeAu4Gj+e9uOMzhVE2gJtHbrvx8YWey7FhE5TSggICIiJfUzziwB94f9JvlfhckAXgRetCwrt6jBLcvKMMZch3Mt77M4P3EG50NEnyIu3Y3zU9NCAw5SoUbhrBZ/vD5ADM7q7+6OALdRwJIRcbIs6xtjzM3AB0AY4IszmNLDrVsW8AAw163Nffu98uJZ+6MoUe4HlmWNMcYcxvk3GQEEAn0LuC4PeAd41LIsq4DzaTh3IDm+i0UDCt+9wAG8jzNQUtA4Yfmvw3FmLlxYyDgrgesty9pVyHkRkdOOAgIiIlIilmV9iHOv7zZAd5yfzrXEuZVcJM7/cD8K7MH5Sd6vwNeWZR0uxT0sYKIx5gvgEqA3zk9G6+D8NM8HOIxzmcBinNsazvLY614qUf73/tb8Og53AefhDOIcxllE8lvgY8uydhWW+i1OlmV9lp8V8ABwOc4HYAvntpWzgPcty9qQvyTnuCOVPtFiWJb1kTHmW2AozuBQc5yBoqM4MwXm4PydKLS2Rn6ApA7OfwO64QxSNOZEACIF2Ihz28FPixirBs7dLbrjrOnRDGeWURDO5S5/A0uBycD003Q3EBGRQpmCg7IiIiIicioyxgwF/pd/+L5lWfdW5XxERKT6UlFBERERkerlOrfXi6tsFiIiUu0pQ0BERESkmjDGXIUzvR0gE6hbmmU5IiIi7pQhICIiIlLFjDFdjTEfGmPaF3I+0BjzEPC5W/P/FAwQEZGToQwBERERkSpmjOmBsxAnOAvvrQD24izWWQ/ogrN453HrgHMtyzpWaZMUEZHTjnYZEBERETm1xOd/FeYnYLCCASIicrKUISAiIiJSxYwxPji3xuuHc3u841ttRgCpwC6cW+x9YVnWb1U1TxEROb0oIFDBjDEGqItz710RERERERGRyhAO7LKKeOjXkoGKVxf4u6onISIiIiIiImec+sDOwk4qIFDxjgLs2LGDiIiIqp6LiIiIiIiInOZSU1OJj4+HYjLVFRCoJBEREQoIiIiIiIiIyCnDp6onICIiIiIiIiKVTwEBERERERERkTOQAgIiIiIiIiIiZyDVEBARERERkWJZlkVubi55eXlVPRWRM56vry9+fn44d7kvOwUERERERESkSNnZ2ezevZv09PSqnoqI5AsJCaFOnToEBASUeQwFBEREREREpFAOh4OtW7fi6+tL3bp1CQgIOOlPJUWk7CzLIjs7m/3797N161aaNWuGj0/ZqgEoICAiIiIiIoXKzs7G4XAQHx9PSEhIVU9HRIDg4GD8/f3Zvn072dnZBAUFlWkcFRUUEREREZFilfUTSBGpGOXxN6m/ahEREREREZEzkAICIiIiIiIiImcgBQRERERERERKqFGjRrzxxhsl7j937lyMMRw5cqTC5gQwfvx4oqKiKvQeJyM9PZ2rr76aiIiIQr8fI0eOpH379pU+t+L06NGDhx56qKqnUSEUEBARERERkdOOMabIr5EjR5Zp3MWLF3PXXXeVuH/Xrl3ZvXs3kZGRZbrf6eKTTz5h/vz5/P7774V+P0aMGMHPP//sOh4yZAgDBw6stDkWFryZMmUKo0ePrvD7Jycnc/nllxMSEkKtWrV47LHHyM3NrdB7apcBEREREREpMYfD4nB6dpXOITokAB+forc+3L17t+v1l19+yTPPPENSUpKrLSwszPXasizy8vLw8yv+8Sg2NrZUcw0ICKB27dqluuZ0tHnzZhISEmjTpk2hfcLCwmw/l/KSnZ1NQEBAma+PiYkpx9kULC8vj8svv5zatWu7gia33HIL/v7+vPDCCxV2X2UIiIiIiIhIiR1Oz6bjv+dU6VdJAhK1a9d2fUVGRmKMcR1v2LCB8PBwfvzxRzp27EhgYCALFixg8+bNDBgwgLi4OMLCwujcuTNz5syxjeu5ZMAYw9ixY7nyyisJCQmhWbNmTJ8+3XXe81Pn46n9P/30EwkJCYSFhdG3b19bACM3N5fhw4cTFRVFjRo1ePzxx7n11ltL/Wn5e++9R5MmTQgICKBFixZMmDDBdc6yLEaOHEmDBg0IDAykbt26DB8+3HX+3XffpVmzZgQFBREXF8c111xT5L0mT55M69atCQwMpFGjRrz66quucz169ODVV19l3rx5GGPo0aNHgWO4LxkYOXIkn3zyCd9++60rq2Pu3LkA7Nixg0GDBhEVFUVMTAwDBgxg27ZtrnGOZxY8//zz1K1blxYtWgAwYcIEOnXqRHh4OLVr12bw4MHs27cPgG3bttGzZ08AoqOjMcYwZMgQ1/zdlwwcPnyYW265hejoaEJCQrjsssvYtGmT63xJfsaeZs2axbp165g4cSLt27fnsssuY/To0fz3v/8lO7viAnAKCIiIiIiIyBnpiSee4KWXXmL9+vW0a9eOtLQ0+vXrx88//8zy5cvp27cv/fv3Jzk5uchxnnvuOQYNGsSqVavo168fN954I4cOHSq0f3p6OmPGjGHChAnMmzeP5ORkRowY4Tr/8ssvM2nSJMaNG0diYiKpqalMmzatVO9t6tSpPPjggzz66KOsWbOGu+++m9tuu41ff/0VcD7Av/7663zwwQds2rSJadOm0bZtWwCWLFnC8OHDGTVqFElJScycOZOLLrqo0HstXbqUQYMGcf3117N69WpGjhzJ008/zfjx4wFnyv3QoUPp0qULu3fvZsqUKcXOf8SIEQwaNMj1IL179266du1KTk4Offr0ITw8nPnz55OYmOh64HZ/cP75559JSkpi9uzZfP/99wDk5OQwevRoVq5cybRp09i2bZvroT8+Pp7JkycDkJSUxO7du3nzzTcLnNuQIUNYsmQJ06dPZ+HChViWRb9+/cjJyXH1Ke5n7GnhwoW0bduWuLg4V1ufPn1ITU1l7dq1xX6/ykpLBkRERERE5Iw0atQoLr30UtdxTEwMZ599tut49OjRTJ06lenTpzNs2LBCxxkyZAg33HADAC+88AJvvfUWixYtom/fvgX2z8nJ4f3336dJkyYADBs2jFGjRrnOv/322zz55JNceeWVALzzzjvMmDGjVO9tzJgxDBkyhPvuuw+ARx55hD/++IMxY8bQs2dPkpOTqV27Nr169cLf358GDRpw7rnnAs617KGhoVxxxRWEh4fTsGFDzjnnnELv9dprr3HJJZfw9NNPA9C8eXPWrVvHK6+8wpAhQ4iJiSEkJKRUyyfCwsIIDg4mKyvLds3EiRNxOByMHTsWY5zLRsaNG0dUVBRz586ld+/eAISGhjJ27FjbUoHbb7/d9bpx48a89dZbdO7cmbS0NMLCwlxLA2rVqlVogcZNmzYxffp0EhMT6dq1KwCTJk0iPj6eadOmce211wLF/4w97dmzxxYMAFzHe/bsKf4bVkbKEBARERERkTNSp06dbMdpaWmMGDGChIQEoqKiCAsLY/369cVmCLRr1871OjQ0lIiICFcqekFCQkJcD4oAderUcfVPSUlh7969rodzAF9fXzp27Fiq97Z+/Xq6detma+vWrRvr168H4NprryUjI4PGjRszdOhQpk6d6ipgd+mll9KwYUMaN27MzTffzKRJk0hPTy/1vTZt2kReXl6p5l2clStX8tdffxEeHu6qORATE0NmZiabN2929Wvbtq1X3YClS5fSv39/GjRoQHh4ON27dwco9ufrbv369fj5+XHeeee52mrUqEGLFi1c31so+md8KlGGgIiIiIiIlFh0SABLn+pV5XMoD6GhobbjESNGMHv2bMaMGUPTpk0JDg7mmmuuKXYNt7+/v+3YGIPD4ShVf8uySjn7kxMfH09SUhJz5sxh9uzZ3Hfffbzyyiv89ttvhIeHs2zZMubOncusWbN45plnGDlyJIsXL67yrQ3T0tLo2LEjkyZN8jrnXvDR82d77Ngx+vTpQ58+fZg0aRKxsbEkJyfTp0+fClmjX9qfce3atVm0aJGtbe/eva5zFUUBARERERERKTEfH0ONsMCqnkaFSExMZMiQIa5U/bS0NFuxusoQGRlJXFwcixcvdq3bz8vLY9myZa6CeyWRkJBAYmIit956q6stMTGRVq1auY6Dg4Pp378//fv35/7776dly5asXr2aDh064OfnR69evejVqxfPPvssUVFR/PLLL1x11VWF3stdYmIizZs3x9fXt5TfgRMCAgK8Mgw6dOjAl19+Sa1atYiIiCjxWBs2bODgwYO89NJLxMfHA85aCZ73A4rMakhISCA3N5c///zTtWTg4MGDJCUl2b63pdWlSxeef/559u3bR61atQCYPXs2ERERJzVucbRkQEREREREBGjWrBlTpkxhxYoVrFy5ksGDBxf5SX9FeeCBB3jxxRf59ttvSUpK4sEHH+Tw4cOuNfMl8dhjjzF+/Hjee+89Nm3axGuvvcaUKVNche3Gjx/PRx99xJo1a9iyZQsTJ04kODiYhg0b8v333/PWW2+xYsUKtm/fzqefforD4XBV6/f06KOP8vPPPzN69Gg2btzIJ598wjvvvFNkEb2SaNSoEatWrSIpKYkDBw6Qk5PDjTfeSM2aNRkwYADz589n69atzJ07l+HDh/P3338XOlaDBg0ICAjg7bffZsuWLUyfPp3Ro0fb+jRs2BBjDN9//z379+8nLS3Na5xmzZoxYMAAhg4dyoIFC1i5ciU33XQT9erVY8CAAWV+r71796ZVq1bcfPPNrFy5kp9++omnnnqK+++/n8DAigvAKSAgIiIiIiKCszhedHQ0Xbt2pX///vTp04cOHTpU+jwef/xxbrjhBm655Ra6dOlCWFgYffr0ISgoqMRjDBw4kDfffJMxY8bQunVrPvjgA8aNG+fa8i8qKooPP/yQbt260a5dO+bMmcN3331HjRo1iIqKYsqUKVx88cUkJCTw/vvv8/nnn9O6desC79WhQwe++uorvvjiC9q0acMzzzzDqFGjXBX8y2ro0KG0aNGCTp06ERsbS2JiIiEhIcybN48GDRpw1VVXkZCQwB133EFmZmaRGQOxsbGMHz+er7/+mlatWvHSSy8xZswYW5969erx3HPP8cQTTxAXF1doIclx48bRsWNHrrjiCrp06YJlWcyYMcNrmUBp+Pr68v333+Pr60uXLl246aabuOWWW4osRFgeTGWvVTnTGGMigJSUlJRSpbSIiIiIiJwKMjMz2bp1K2eddVapHkil/DgcDhISEhg0aJDXp9py5irqbzM1NZXIyEiASMuyUgsbQzUERERERERETiHbt29n1qxZdO/enaysLN555x22bt3K4MGDq3pqcprRkgEREREREZFTiI+PD+PHj6dz585069aN1atXM2fOHBISEqp6anKaUYaAiIiIiIjIKSQ+Pt6rar9IRVCGgIiIiIiIiMgZSAEBERERERERkTOQAgIiIiIiIiIiZyAFBERERERERETOQAoIiIiIiIiIiJyBFBAQEREREREROQMpICAiIiIiIlJCjRo14o033ihx/7lz52KM4ciRIxU2J4Dx48cTFRVVofc4Genp6Vx99dVEREQU+v0YOXIk7du3r/S5FadHjx489NBDVT2NCqGAgIiIiIiInHaMMUV+jRw5skzjLl68mLvuuqvE/bt27cru3buJjIws0/1OF5988gnz58/n999/L/T7MWLECH7++WfX8ZAhQxg4cGClzbGw4M2UKVMYPXp0hd9/+PDhdOzYkcDAwEoLjPhVyl1EREREROT04HBAxqGqnUNwDPgU/dnm7t27Xa+//PJLnnnmGZKSklxtYWFhrteWZZGXl4efX/GPR7GxsaWaakBAALVr1y7VNaejzZs3k5CQQJs2bQrtExYWZvu5lJfs7GwCAgLKfH1MTEw5zqZot99+O3/++SerVq2qlPspQ0BEREREREou4xC80qRqv0oQkKhdu7brKzIyEmOM63jDhg2Eh4fz448/uj6RXbBgAZs3b2bAgAHExcURFhZG586dmTNnjm1czyUDxhjGjh3LlVdeSUhICM2aNWP69Omu856fOh9P7f/pp59ISEggLCyMvn372gIYubm5DB8+nKioKGrUqMHjjz/OrbfeWupPy9977z2aNGlCQEAALVq0YMKECa5zlmUxcuRIGjRoQGBgIHXr1mX48OGu8++++y7NmjUjKCiIuLg4rrnmmiLvNXnyZFq3bk1gYCCNGjXi1VdfdZ3r0aMHr776KvPmzcMYQ48ePQocw33JwMiRI/nkk0/49ttvXVkdc+fOBWDHjh0MGjSIqKgoYmJiGDBgANu2bXONczyz4Pnnn6du3bq0aNECgAkTJtCpUyfCw8OpXbs2gwcPZt++fQBs27aNnj17AhAdHY0xhiFDhrjm775k4PDhw9xyyy1ER0cTEhLCZZddxqZNm1znS/IzLshbb73F/fffT+PGjYvsV54UEBAyc/JIPpjOn1sOMnPNnqqejoiIiIhIpXjiiSd46aWXWL9+Pe3atSMtLY1+/frx888/s3z5cvr27Uv//v1JTk4ucpznnnuOQYMGsWrVKvr168eNN97IoUOFBy3S09MZM2YMEyZMYN68eSQnJzNixAjX+ZdffplJkyYxbtw4EhMTSU1NZdq0aaV6b1OnTuXBBx/k0UcfZc2aNdx9993cdttt/Prrr4DzAf7111/ngw8+YNOmTUybNo22bdsCsGTJEoYPH86oUaNISkpi5syZXHTRRYXea+nSpQwaNIjrr7+e1atXM3LkSJ5++mnGjx8POFPuhw4dSpcuXdi9ezdTpkwpdv4jRoxg0KBBrgfp3bt307VrV3JycujTpw/h4eHMnz+fxMRE1wN3dna26/qff/6ZpKQkZs+ezffffw9ATk4Oo0ePZuXKlUybNo1t27a5Hvrj4+OZPHkyAElJSezevZs333yzwLkNGTKEJUuWMH36dBYuXIhlWfTr14+cnBxXn+J+xqcKLRk4w63dlcLlby1wHfv6GDb++zJ8fUwVzkpEREREpOKNGjWKSy+91HUcExPD2Wef7ToePXo0U6dOZfr06QwbNqzQcYYMGcINN9wAwAsvvMBbb73FokWL6Nu3b4H9c3JyeP/992nSpAkAw4YNY9SoUa7zb7/9Nk8++SRXXnklAO+88w4zZswo1XsbM2YMQ4YM4b777gPgkUce4Y8//mDMmDH07NmT5ORkateuTa9evfD396dBgwace+65ACQnJxMaGsoVV1xBeHg4DRs25Jxzzin0Xq+99hqXXHIJTz/9NADNmzdn3bp1vPLKKwwZMoSYmBhCQkJKtXwiLCyM4OBgsrKybNdMnDgRh8PB2LFjMcb5zDJu3DiioqKYO3cuvXv3BiA0NJSxY8falgrcfvvtrteNGzfmrbfeonPnzqSlpREWFuZaGlCrVq1CCzRu2rSJ6dOnk5iYSNeuXQGYNGkS8fHxTJs2jWuvvRYo/md8qlCGwBmuVniQ7TjPYXEgLauKZiMiIiIiUnk6depkO05LS2PEiBEkJCQQFRVFWFgY69evLzZDoF27dq7XoaGhREREuFLRCxISEuJ6UASoU6eOq39KSgp79+51PZwD+Pr60rFjx1K9t/Xr19OtWzdbW7du3Vi/fj0A1157LRkZGTRu3JihQ4cydepUcnNzAbj00ktp2LAhjRs35uabb2bSpEmkp6eX+l6bNm0iLy+vVPMuzsqVK/nrr78IDw931RyIiYkhMzOTzZs3u/q1bdvWq27A0qVL6d+/Pw0aNCA8PJzu3bsDFPvzdbd+/Xr8/Pw477zzXG01atSgRYsWru8tFP0zPpUoQ+AMVyM0AH9fQ06e5WrbnZJJXERQEVeJiIiIyBkrOAYe21x8v4qeQzkIDQ21HY8YMYLZs2czZswYmjZtSnBwMNdcc40tFb0g/v7+tmNjDA6Ho1T9LcsqpHfFiI+PJykpiTlz5jB79mzuu+8+XnnlFX777TfCw8NZtmwZc+fOZdasWTzzzDOMHDmSxYsXV/nWhmlpaXTs2JFJkyZ5nXMv+Oj5sz127Bh9+vShT58+TJo0idjYWJKTk+nTp0+xP9+yOBV+xiWhgMAZzsfHUCs8iJ1HMlxte1IyIb4KJyUiIiIipy4fHwitWdWzqBCJiYkMGTLElaqflpZmK1ZXGSIjI4mLi2Px4sWudft5eXksW7asVFvRJSQkkJiYyK233upqS0xMpFWrVq7j4OBg+vfvT//+/bn//vtp2bIlq1evpkOHDvj5+dGrVy969erFs88+S1RUFL/88gtXXXVVofdyl5iYSPPmzfH19S3ld+CEgIAArwyDDh068OWXX1KrVi0iIiJKPNaGDRs4ePAgL730EvHxzoedJUuWeN0PKDKrISEhgdzcXP7880/XkoGDBw+SlJRk+95WFwoICHERgbaAwN7UzCqcjYiIiIhI1WjWrBlTpkyhf//+GGN4+umni/ykv6I88MADvPjiizRt2pSWLVvy9ttvc/jwYdea+ZJ47LHHGDRoEOeccw69evXiu+++Y8qUKa5dE8aPH09eXh7nnXceISEhTJw4keDgYBo2bMj333/Pli1buOiii4iOjmbGjBk4HA5XtX5Pjz76KJ07d2b06NFcd911LFy4kHfeeYd33333pL4PjRo14qeffiIpKYkaNWoQGRnJjTfeyCuvvMKAAQMYNWoU9evXZ/v27UyZMoV//vOf1K9fv8CxGjRoQEBAAG+//Tb33HMPa9asYfTo0bY+DRs2xBjD999/T79+/QgODvbaBrFZs2YMGDCAoUOH8sEHHxAeHs4TTzxBvXr1GDBgwEm937/++ou0tDT27NlDRkYGK1asAKBVq1YntW1iUVRDQKgTGWw73qOAgIiIiIicgV577TWio6Pp2rUr/fv3p0+fPnTo0KHS5/H4449zww03cMstt9ClSxfCwsLo06cPQUElX9Y7cOBA3nzzTcaMGUPr1q354IMPGDdunGvLv6ioKD788EO6detGu3btmDNnDt999x01atQgKiqKKVOmcPHFF5OQkMD777/P559/TuvWrQu8V4cOHfjqq6/44osvaNOmDc888wyjRo1yVfAvq6FDh9KiRQs6depEbGwsiYmJhISEMG/ePBo0aMBVV11FQkICd9xxB5mZmUVmDMTGxjJ+/Hi+/vprWrVqxUsvvcSYMWNsferVq8dzzz3HE088QVxcXKGFJMeNG0fHjh254oor6NKlC5ZlMWPGDK9lAqV15513cs455/DBBx+wceNGzjnnHM455xx27dp1UuMWxZyK6xhOJ8aYCCAlJSWlVCktlWnUd+v4OHGr6/jKc+rx+nXtq25CIiIiInLKyMzMZOvWrZx11lmleiCV8uNwOEhISGDQoEFen2rLmauov83U1FQiIyMBIi3LSi1sDC0ZEGpHBtqO96QoQ0BEREREpKps376dWbNm0b17d7KysnjnnXfYunUrgwcPruqpyWlGSwbEa0cB1RAQEREREak6Pj4+jB8/ns6dO9OtWzdWr17NnDlzSEhIqOqpyWlGGQJSYA0By7JKVbRERERERETKR3x8vFfVfpGKoAwBobZHhkB6dh6pmblVNBsRERERERGpDAoICLUiAr3atGxARERERETk9KaAgBDk70t0iH2LDBUWFBEREREROb0pICAA1C6gjoCIiIiIiIicvhQQEABqR2jrQRERERERkTOJAgICQO1Ie2FBZQiIiIiIiIic3hQQEADiPHYa2KsMARERERERL40aNeKNN94ocf+5c+dijOHIkSMVNieA8ePHExUVVaH3OBnp6elcffXVREREFPr9GDlyJO3bt6/0uRWnR48ePPTQQ1U9jQqhgIAAUMcjQ2C3AgIiIiIiUo0ZY4r8GjlyZJnGXbx4MXfddVeJ+3ft2pXdu3cTGRlZpvudLj755BPmz5/P77//Xuj3Y8SIEfz888+u4yFDhjBw4MBKm2NhwZspU6YwevToCr33ypUrueGGG4iPjyc4OJiEhATefPPNCr0ngF+F30GqBa8MAS0ZEBEREZECOCwHR7KOVOkcogKj8DFFf7a5e/du1+svv/ySZ555hqSkJFdbWFiY67VlWeTl5eHnV/zjUWxsbKnmGhAQQO3atUt1zelo8+bNJCQk0KZNm0L7hIWF2X4u5SU7O5uAgIAyXx8TE1OOsynY0qVLqVWrFhMnTiQ+Pp7ff/+du+66C19fX4YNG1Zh91WGgADeNQQOHssmKzevimYjIiIiIqeqI1lH6P5l9yr9KklAonbt2q6vyMhIjDGu4w0bNhAeHs6PP/5Ix44dCQwMZMGCBWzevJkBAwYQFxdHWFgYnTt3Zs6cObZxPZcMGGMYO3YsV155JSEhITRr1ozp06e7znt+6nw8tf+nn34iISGBsLAw+vbtawtg5ObmMnz4cKKioqhRowaPP/44t956a6k/LX/vvfdo0qQJAQEBtGjRggkTJrjOWZbFyJEjadCgAYGBgdStW5fhw4e7zr/77rs0a9aMoKAg4uLiuOaaa4q81+TJk2ndujWBgYE0atSIV1991XWuR48evPrqq8ybNw9jDD169ChwDPclAyNHjuSTTz7h22+/dWV1zJ07F4AdO3YwaNAgoqKiiImJYcCAAWzbts01zvHMgueff566devSokULACZMmECnTp0IDw+ndu3aDB48mH379gGwbds2evbsCUB0dDTGGIYMGeKav/uSgcOHD3PLLbcQHR1NSEgIl112GZs2bXKdL8nP2NPtt9/Om2++Sffu3WncuDE33XQTt912G1OmTCny+36yFBAQAGp7ZAgA7EvNqoKZiIiIiIhUjieeeIKXXnqJ9evX065dO9LS0ujXrx8///wzy5cvp2/fvvTv35/k5OQix3nuuecYNGgQq1atol+/ftx4440cOnSo0P7p6emMGTOGCRMmMG/ePJKTkxkxYoTr/Msvv8ykSZMYN24ciYmJpKamMm3atFK9t6lTp/Lggw/y6KOPsmbNGu6++25uu+02fv31V8D5AP/666/zwQcfsGnTJqZNm0bbtm0BWLJkCcOHD2fUqFEkJSUxc+ZMLrrookLvtXTpUgYNGsT111/P6tWrGTlyJE8//TTjx48HnCn3Q4cOpUuXLuzevbtED7kjRoxg0KBBrgfp3bt307VrV3JycujTpw/h4eHMnz+fxMRE1wN3dna26/qff/6ZpKQkZs+ezffffw9ATk4Oo0ePZuXKlUybNo1t27a5Hvrj4+OZPHkyAElJSezevbvQlP0hQ4awZMkSpk+fzsKFC7Esi379+pGTk+PqU9zPuCRSUlIqPDtBSwYEgMhgf4L8fcjMcbja9qRmEh8TUoWzEhERERGpOKNGjeLSSy91HcfExHD22We7jkePHs3UqVOZPn16kWnbQ4YM4YYbbgDghRde4K233mLRokX07du3wP45OTm8//77NGnSBIBhw4YxatQo1/m3336bJ598kiuvvBKAd955hxkzZpTqvY0ZM4YhQ4Zw3333AfDII4/wxx9/MGbMGHr27ElycjK1a9emV69e+Pv706BBA84991wAkpOTCQ0N5YorriA8PJyGDRtyzjnnFHqv1157jUsuuYSnn34agObNm7Nu3TpeeeUVhgwZQkxMDCEhIaVaPhEWFkZwcDBZWVm2ayZOnIjD4WDs2LEYYwAYN24cUVFRzJ07l969ewMQGhrK2LFjbUsFbr/9dtfrxo0b89Zbb9G5c2fS0tIICwtzPXzXqlWr0AKNmzZtYvr06SQmJtK1a1cAJk2aRHx8PNOmTePaa68Fiv8ZF+f333/nyy+/5IcffijxNWWhDAEBnKlOnlkCe1RYUEREREROY506dbIdp6WlMWLECBISEoiKiiIsLIz169cXmyHQrl071+vQ0FAiIiJcqegFCQkJcT0oAtSpU8fVPyUlhb1797oezgF8fX3p2LFjqd7b+vXr6datm62tW7durF+/HoBrr72WjIwMGjduzNChQ5k6dSq5ubkAXHrppTRs2JDGjRtz8803M2nSJNLT00t9r02bNpGXV77LkFeuXMlff/1FeHi4q+ZATEwMmZmZbN682dWvbdu2XnUDli5dSv/+/WnQoAHh4eF0794doNifr7v169fj5+fHeeed52qrUaMGLVq0cH1voeifcXHWrFnDgAEDePbZZ10BjoqiDAFxiYsIYtvBE3/oKiwoIiIiIp6iAqP47brfqnwO5SE0NNR2PGLECGbPns2YMWNo2rQpwcHBXHPNNbZU9IL4+/vbjo0xOByOQnoX3N+yrFLO/uTEx8eTlJTEnDlzmD17Nvfddx+vvPIKv/32G+Hh4Sxbtoy5c+cya9YsnnnmGUaOHMnixYurfGvDtLQ0OnbsyKRJk7zOuRd89PzZHjt2jD59+tCnTx8mTZpEbGwsycnJ9OnTp9ifb1mU9We8bt06LrnkEu666y6eeuqpcp+XJwUExMWzsKAyBERERETEk4/xISao4quuV4XExESGDBniStVPS0uzFaurDJGRkcTFxbF48WLXuv28vDyWLVvmKrhXEgkJCSQmJnLrrbe62hITE2nVqpXrODg4mP79+9O/f3/uv/9+WrZsyerVq+nQoQN+fn706tWLXr168eyzzxIVFcUvv/zCVVddVei93CUmJtK8eXN8fX1L+R04ISAgwCvDoEOHDnz55ZfUqlWLiIiIEo+1YcMGDh48yEsvvUR8fDzgrJXgeT+gyKyGhIQEcnNz+fPPP11LBg4ePEhSUpLte1sWa9eu5eKLL+bWW2/l+eefP6mxSkpLBsTFMyCwWxkCIiIiInIGadasGVOmTGHFihWsXLmSwYMHF/lJf0V54IEHePHFF/n2229JSkriwQcf5PDhw6418yXx2GOPMX78eN577z02bdrEa6+9xpQpU1yF7caPH89HH33EmjVr2LJlCxMnTiQ4OJiGDRvy/fff89Zbb7FixQq2b9/Op59+isPhcFXr9/Too4/y888/M3r0aDZu3Mgnn3zCO++8U+oiep4aNWrEqlWrSEpK4sCBA+Tk5HDjjTdSs2ZNBgwYwPz589m6dStz585l+PDh/P3334WO1aBBAwICAnj77bfZsmUL06dPZ/To0bY+DRs2xBjD999/z/79+0lLS/Map1mzZgwYMIChQ4eyYMECVq5cyU033US9evUYMGBAmd/rmjVr6NmzJ7179+aRRx5hz5497Nmzh/3795d5zJJQQEBcPGsI7FWGgIiIiIicQV577TWio6Pp2rUr/fv3p0+fPnTo0KHS5/H4449zww03cMstt9ClSxfCwsLo06cPQUHeO4MVZuDAgbz55puMGTOG1q1b88EHHzBu3DjXln9RUVF8+OGHdOvWjXbt2jFnzhy+++47atSoQVRUFFOmTOHiiy8mISGB999/n88//5zWrVsXeK8OHTrw1Vdf8cUXX9CmTRueeeYZRo0a5argX1ZDhw6lRYsWdOrUidjYWBITEwkJCWHevHk0aNCAq666ioSEBO644w4yMzOLzBiIjY1l/PjxfP3117Rq1YqXXnqJMWPG2PrUq1eP5557jieeeIK4uLhCC0mOGzeOjh07csUVV9ClSxcsy2LGjBleywRK45tvvmH//v1MnDiROnXquL46d+5c5jFLwlT2WpUzjTEmAkhJSUkpVUpLVfhx9W7unbTMdVw/OpgFj19chTMSERERkaqWmZnJ1q1bOeuss0r1QCrlx+FwkJCQwKBBg7w+1ZYzV1F/m6mpqURGRgJEWpaVWtgYqiEgLnEeSwb2pWbhcFj4+JQ8NUlERERERE7O9u3bmTVrFt27dycrK4t33nmHrVu3Mnjw4KqempxmtGRAXOp4BASy8xwcSi//ipsiIiIiIlI4Hx8fxo8fT+fOnenWrRurV69mzpw5JCQkVPXU5DSjDAFxiQ0LxMeAw20VyZ6UTGqGBVbdpEREREREzjDx8fFeVftFKoIyBMTFz9fH6+F/r3YaEBEREREROS0pICA2nlsP7lFAQEREREQAFSMXObWUx9+kAgJi47n14B5tPSgiIiJyRju+lVp6enoVz0RE3B3/mzyZ7Q5VQ0BsvDIEFBAQEREROaP5+voSFRXFvn37AAgJCcEY7UIlUlUsyyI9PZ19+/YRFRWFr69vmcdSQEBs4jwzBLRkQEREROSMV7t2bQBXUEBEql5UVJTrb7OsFBAQGy0ZEBERERFPxhjq1KlDrVq1yMnJqerpiJzx/P39Tyoz4DgFBMSmjooKioiIiEghfH19y+UhRERODSoqKDZxHgGBo5m5pGfnVtFsREREREREpKIoICA2nksGQMsGRERERERETkcKCIhNaKAf4YH2lSQKCIiIiIiIiJx+FBAQL55bD/59OKOKZiIiIiIiIiIVRQEB8dI4NtR2vOLvI1UzEREREREREakwCgiIl44No23HS7cdrqKZiIiIiIiISEVRQEC8eAYENu47SkqG9psVERERERE5nSggIF7a1IskwPfEr4ZlwfJkZQmIiIiIiIicThQQEC+Bfr60rR9pa1u2XQEBERERERGR04kCAlKgTh7LBpYoICAiIiIiInJaUUBACtTBIyCwYscRcvMcVTQbERERERERKW8KCEiBPAsLpmfnsWHP0SqajYiIiIiIiJQ3BQSkQDXDAmlUI8TWtlTLBkRERERERE4bCghIoTo2jLEdq46AiIiIiIjI6UMBASmU57IB7TQgIiIiIiJy+vCr6glI1dqXvo93V7zLwYyDHMo8REp2Ct8N/A5jDJ0a2QMCO49ksDslgzqRwVU0WxERERERESkvCgic4fIceUzeNNnWlpqdSmRgJE1jw4gI8iM1M9d1bun2w1zRTgEBERERERGR6k5LBs5wMcExXm2HMg8B4ONjvLYfVGFBERERERGR04MCAme4QN9AwvzDbG0HMw66XndsoICAiIiIiIjI6UgBASEmyJ4lcDxDAKCjRx2BtbtSSc/ORURERERERKo3BQSEGsE1bMcHM09kCLSPj8LXx7iO8xwWK3ekVNrcREREREREpGIoICDUCPIICLgtGQgJ8KNVnQjb+aXbDyEiIiIiIiLVmwICUuSSAYBzGkTZjv/al1bRUxIREREREZEKpoCAeC8ZcMsQAKgTad9m8HB6ToXPSURERERERCqWAgLilSHgXkMAICrE33Z8JD27wuckIiIiIiIiFUsBAfHKEPBcMhDtGRDIUIaAiIiIiIhIdaeAgBRZVBAgMjjAdnz4mDIEREREREREqjsFBMRryUB6bjoZuRmu4+hQe4ZAamYueQ6rUuYmIiIiIiIiFUMBAfFaMgD2ZQPRIQFe51O0bEBERERERKRaU0BACPMPw9/HngXgvmwgMtjf8xIVFhQREREREanmFBAQjDFFFhYM8vcl2N/Xdl5bD4qIiIiIiFRvCggIUHxhQc+tB1MylCEgIiIiIiJSnSkgIIB3YUHPrQejQjx3GlCGgIiIiIiISHWmgIAA3oUFD2Z6ZAh41BE4oqKCIiIiIiIi1ZoCAgJ4Zwh4Lhnw3HpQRQVFRERERESqNwUEBPCuIeC5ZCAy2L5k4IiKCoqIiIiIiFRrCggIADHBxWQIeBQVPKwMARERERERkWpNAQEBis8Q8N5lQBkCIiIiIiIi1ZkCAgJ4FxU8knWEXEeu69hrlwFlCIiIiIiIiFRrCggI4F1U0MLicOZh17HXLgOqISAiIiIiIlKtKSAgAEQHRmMwtjb3ZQPRoSoqKCIiIiIicjpRQEAA8PXxJToo2tbmXljQM0MgLSuXnDxHpcxNREREREREyp8CAuLiuWzgYKZbQMCjhgAoS0BERERERKQ6U0BAXDwLC7ovGYj0yBAASMlQYUEREREREZHqSgEBcfHKEHBbMhDg50NYoJ/t/GFlCIiIiIiIiFRbCgiIS40ge4aA+5IB8M4SOHxMGQIiIiIiIiLVlQIC4uK5ZMAzIBAd6rH1YIYyBERERERERKorBQTExTND4FDGIdtxVLDn1oPKEBAREREREamuFBAQl+IyBKJCPDIEVENARERERESk2lJAQFw8iwoeyjyEZVmuY8+AgIoKioiIiIiIVF8KCIiL55KBXEcuqdmpruPoEPuSAW07KCIiIiIiUn0pICAuMcExXm3uywaiPAICh48pQ0BERERERKS6UkBAXAJ9AwnzD7O1HcxwCwgEa5cBERERERGR04UCAmLjWVjwUOaJnQa8th3ULgMiIiIiIiLVlgICYuNZWNA9QyDSa9tBZQiIiIiIiIhUVwoIiI1nYUH3GgLRHrsMZOTkkZmTVynzEhERERERkfKlgIDYFLT14HGeRQUBUlRHQEREREREpFpSQECcHHmwbjo1PH4l7EsG/D2v4rDqCIiIiIiIiFRLflU9Aali2cdg+ST44104vJWYNr1tp90zBHx9DBFBfqRm5rraVEdARERERESkelJA4Ew3/zWYP8Z1WGP7n1Az0nXsniEAEB0a4BEQUIaAiIiIiIhIdaQlA2e6TreDz4m4UI3sTNtp96KCAFHBnlsPKkNARERERESkOlJA4EwXWQ/aXO06jMmz7xqQkZtBek6669izsOBhBQRERERERESqJQUEBLoMc72skee9jaB9pwGPDIEMLRkQERERERGpjlRDQKBOOzjrItg6jzDLwt+yyDHGdXrCugk0iWpCdFA0IUHhtkuPHFOGgIiIiIiISHWkgIA4dXkAts7D4MwS2ON34lfjsw2fuV77m2B8Au/CkVUHUIaAiIiIiIhIdaUlA+LUtBfEtgSgVq73soHjcqwMAuOmAxagGgIiIiIiIiLVlQIC4uTjA13uB6DvsfQiu/qFbsU35C9A2w6KiIiIiIhUVwoIyAltB0FoLDelHuWVfQe4PvUo/fxqckG9C4gIiLB1DYydDVjadlBERERERKSaUkBATvAPgnPvwuDMEvjXwcO8vHkV77V/lIc7Pmzr6huSjG/oRo6k52BZVtXMV0RERERERMpMAQGx63QH+AWfOHbkwle3MKDBpdQLq2frGhg7m+y8PDJyCq85ICIiIiIiIqcmBQTELrQGdBxib9u3Fv8Zj3FPu3tszb7Bf+Mbtl6FBUVERERERKohBQTE2yVPQ61W9rZVX3LFob00jGhoaw6Mnc2hY5mVODkREREREREpDwoIiLeAUBg0AQLthQT9fvoX99bvY2vzDdrNrO0/VubsREREREREpBwoICAFq9kUBr5rb3Pk0HfeuwTk1rI1f7LpJf674r/kOVRLQEREREREpLpQQEAKl9AfLvDYXeDoLq4/GmBrs7B4f+X7DJ09lH3p+ypzhiIiIiIiIlJGCghI0Xo+BWddZGt68Mif+Ke09Oq6eM9irv3uWpbvW15ZsxMREREREZEyUkBAiubrB1f+D/yCXE0B5PHYviyy9l6GZdl/hQ5lHmL4L8NJzU6t7JmKiIiIiIhIKSggIMWLqAOd7rA1Xes7n9qHW5K+/W6CTA3buSNZR5i0blJlzlBERERERERKSQEBKZkLHgK/YNehn3HwgO9UHBkNaW09S6e4TrbuE9ZP4Gj20UqepIiIiIiIiJSUAgJSMmG14Nw7bU1X+i6gkdlNWnog/3fe/9nOHc0+yqT1yhIQERERERE5VSkgICXX9UHwD3Ed+hkHD/hN5XB6Ns2im3Fpw0tt3Sesm0Badlplz1JERERERERKQAEBKbmwWDj3LlvTQJ9EQlK3YlkW95x9j+1canYqn234rDJnKCIiIiIiIiWkgICUTtfhOPxDXYe+xuIOx1ekZOTQPLo5vRr0snX/ZO0nyhIQERERERE5BSkgIKUTWgPLI0ugv89C9m5bD1BglsDnGz6vtOmJiIiIiIhIySggIKXm22046QSdODYWwYveBqBFTAsujr/Y1n/c2nH8mvwrlmVV6jxFRERERESkcAoISOmFxDAn9HJbU73tUyF1F+CdJXA0+yjDfx3OsF+GsSN1B2nZacz7ex6vLnmVe+bcw+iFo7WsQEREREREpJL5VfUEpHpaVvdG+mycTqDJAcDXyoXf34a+L5JQI4FeDXoxJ3mO7Zp5f8/j912/47AcOCyH7dzmlM183OdjfIxiVCIiIiIiIpVBT19SJlFx8XyV193euHQ8HDsAwKhuozi39rle1+U6cr2CAQBL9y7lm43fVMRURUREREREpAAKCEiZ1I8O4YO8/uRabr9COenwx3sAhAeEM7b3WF688EVqBtcs0ZivLX2NPcf2VMR0RURERERExIMCAlIm8dHB/G3F8q2jm/3Eog8hMwUAYwxXNL6C7wZ+x82tbsbX+Lq6+fv40y62ne3SYznHeP6P51V8UEREREREpBIYPXxVLGNMBJCSkpJCREREVU+n3Ow8kkG3l36hidnJ7IB/4mPcfo9qNIOAUMjLAf9g6HgrdLiF5NRkFu9ZTP3w+pwdezZBfkE8teApvt38rW3sV7q/Qt9GfSv5HYmIiIiIiJweUlNTiYyMBIi0LCu1sH4qKihlUjsiCH9fw+a8esx0dKaf76ITJw9usnfeuQRys2hw7lAaRDSwnXqs82Ms2LmAg5kHXW0v/vki59c+n6igqAp8ByIiIiIiImc2LRmQMvH1MdSNCgbg3dx/FH/BjMdg/XdezZGBkTx53pO2tkOZh/jP4v+UyzxFRERERESkYAoISJnVj3YGBNZYjfks9+Jielsw+U5I/tPrTO+GvekZ39PW9t2W75j217RymqmIiIiIiIh4Ug2BCna61hAAeGLyKr5YvAMAX/L4v4QD3JGQB77+4BsAu5bDov/ZLwqOhjtmQ81mtua9x/Yy8NuBpOWkudoCfQOZ1G8SLWJaVPh7EREREREROV2UtIaAMgSkzI5nCADk4cvc3NZw7lDoOATaD4bL/gMdbrFflHEYJlwFaftszXGhcTx9/tO2tqy8LB6e+zCp2YX+/oqIiIiIiEgZKSAgZRYfE2I7/vtwhr2DMXD569Cst709JRmmDweP7JR+jftxQ8sbbG07ju7gXwv+hcNylNu8RURERERERAEBOQnuGQIAOw9n4HB4LEHx9YNrx0PdDvb2jT/Cys+9xnys02O0q9nO1jZ3x1zGrRlXDjMWERERERGR4xQQkDKLj7ZnCGTnOdh3NMu7Y0AoDP4SQmPt7T8+ASl/25r8ff15tcerRAdG29rfWv4Wy/ctL5d5i4iIiIiIiAICchJiwwMJ9LP/Cu04nF5w57Ba0P9Ne1tWCkx/wGvpQO3Q2rx00UsYjKvNYTl4cv6THMs5Vi5zFxEREREROdMpICBlZoyhnseygb8LCwgAtLwc2l1vb9v8Cyz1Xg7QtW5X7mt/n61tZ9pOXlr0UpnnKyIiIiIiIicoICAnxXPZwI5DGYX0zHfZSxBe197201NwaKtX16Fth3JOrXNsbdP+msbs7bPLNFcRERERERE5QQEBOSmehQWLzBAACI6Gf7xtb8s5BuMvh132GgG+Pr68cMELhPqH2tqfW/gc+9Lt2xaKiIiIiIhI6SggICfFc+vBYjMEAJr1gg632ttSd8LHfWH1N7bm+uH1+b/z/s/WlpKVwlMLntJWhCIiIiIiIidBAQE5KV4ZAkeKyRA4rs/zULO5vS03EybfAT+PAseJh/3+jfvTu2FvW9eFuxfy7V/flmnOIiIiIiIiooCAnCTPGgK7jmSSm1eCT+4Dw+G2mdDwAu9z81+Fb+9z7T5gjOGZLs9QK7iWrduE9ROwPHYoEBERERERkZJRQEBOimeGQJ7DYk9qZskuDq0BN0+Fjrd5n1v5OSz7xHUYGRjJU+c/Zeuy6fAmlu9b7nmliIiIiIiIlIACAnJSYkIDCAnwtbWVqI7AcX4B0P8NuPxVMPZx+PEJ2J/kOuwe35348Hhbly+TviztlEVERERERAQFBOQkGWO8tx4sbqeBgnS+E64db2/LzYBv7oAcZ8aBj/HhuhbX2brM2j6LgxkHS38/ERERERGRM5wCAnLSvLceLEWGgLtW/3AGBtztXQ1zRroOBzQZQKBvoOs415HL1L+mlu1+IiIiIiIiZzAFBOSkeW49+PehMmQIHNf731Crlb3tz/dg4ywAooKi6Nuor+30V0lfkefIK/s9RUREREREzkAKCMhJK7cMAQD/YLj6I/ALsrdPuxfS9gF4LRvYfWw3C3YuKPs9RUREREREzkAKCMhJq+9RQyD5UDoOx0lsBxjXypkp4C79AMx4DIA2NdvQqoY9i+CLpC/Kfj8REREREZEzkAICctI8MwT2pGbS5aWfeWraauZt3F+24EDnO6HF5fa2ddNg3bcYY7i+xfW2U4k7E9mRuqP09xERERERETlDKSAgJ82zhgDA3tQsJv6RzC0fL+KWjxeRm+co3aDGQP83ITjG3v7Do3DsIH3P6kt4QLir2cLSFoQiIiIiIiKloICAnLTIYH8uaVmr0PML/jrA/L8OlH7gsFjo94q97dh+mPk4wX7BDGw60Hbq8w2fk5yaXPr7iIiIiIiInIEUEJBy8c7gDgy/pBmNa4YWeH7zvrSyDdzmau+lA6u/hg0zuK7FdfiYE7/C2Y5snv/zeSzrJOoXiIiIiIiInCEUEJByERzgyyOXNueXET2Y80h3OjSIsp0/eCy7bAMbA1e8BkGR9vbvH6ahfwSDWw62Nf++63d+2v5T2e4lIiIiIiJyBlFAQMpd01phtKsfZWs7cDSr7AOG14a+L9nb0vbAL/9m2DnDqBViX67wn0X/IS27jBkJIiIiIiIiZwgFBKRC1AwLsB0fSDuJgADA2TdA00vtbUs+JvTQdh7v/LiteX/Gft5Z8c7J3U9EREREROQ0p4CAVIiaYYG24wNpZVwycJwxcMXr4Oe2xaHlgJ+e5NIGvbig3gW27p9v+Jy1B9ee3D1FREREREROYwoISIXwDAgcPNkMAYCoeOg23N62ZS5m40z+77z/I9D3xD0dloPnfn+OnLyck7+viIiIiIjIaUgBAakQNbyWDGSXT/X/bg9CeF1726x/ER9ci7va3WVrXn9ovZYOiIiIiIiIFEIBAakQnhkC2XkOUjNzT37ggFC49Dl726Et8OcHDGk9hMaRjW2nPl7zMb/v+v3k7ysiIiIiInKaUUBAKoRnQADKadkAQNtroX5ne9u8VwjISOGlC1/C38ffdupfC/7FwYyD5XNvERERERGR04QCAlIhggN8CQ3wtbWddGHB44zx3oYwKxXG9SUhZR8Pd3zYft+MAzyV+BQOy1E+9xcRERERETkNKCAgFaZmuOdOA+WUIQBQvxO0u87edvAv+PQf3LR+HhfEnWs7tWDnAiatn1R+9xcREREREanmFBCQClMhOw246zUSQmp4NZvVX/HvVT9Twz/c1v7a0tdITk0u3zmIiIiIiIhUUwoISIWpEWrfaWB/eS0ZOC6iLgyZAXU7eN87/TAv7N5pa8t15DJ29djynYOIiIiIiEg1pYCAVJgKXTJwXK2WcOcc6DcGAiNsp7oePcJNwY1sbd9t/o5dabvKfx4iIiIiIiLVjAICUmEqfMnAcT6+cO5QGLYEGvewnbpz0yKCfE/MI9fKZdyacRUzDxERERERkWpEAQGpMDXD7EsGym2XgcKEx0H/t8Bt28EaOZlc7Rdr6zZl0xT2p++v2LmIiIiIiIic4hQQkArjmSFQIUsGPEU3hI5DbE1DNi/Bz/i5jrMd2Xy67tOKn4uIiIiIiMgpTAEBqTDeSwYqOEPguItGgF+Q67B2TjYD/GJsXb5M+pIjmUcqZz4iIiIiIiKnIAUEpMLU8FgykJaVS2ZOXsXfOLy2s6aAmzu2rMAH4zrOyM1g4vqJFT8XERERERGRU5Rf8V1EysYzQwBg/9Es4mNCKv7m3R6GJeMgOw2A+Nxc+vlE8b3jsKvLp+s+Zc3BNYT5hxHmH8ZZkWdxacNLqRtWt+LnJyIiIiIiUsUUEJAKExHkR4CvD9l5DlfbwWPZlRMQCK0B598H8/7jarozeS3f1z/xsJ+Rm0HizkTbZWOWjKF9bHsuO+syejfqTc3gmhU/VxERERERkSqgJQNSYYwxXssGDhythMKCx3UdBkFRrsMmObn0OpZe7GUr9q/gxUUvcuk3l/LZ+s8qcIIiIiIiIiJVRwEBqVBVstPAcUGRcOlztqZHDh0hKq9kdQxyHbn8Z/F/2JW2qyJmJyIiIiIiUqW0ZEAqVE2PDIGDxyppp4HjOg4By4IZI8CRS3xuLt//vZs/ggJJCQwjrUVv0mJbsjdjL3N3zCU1O9V2eZ6Vx8T1E/ln539W7rxFREREREQqmDIEpEJ5Zgjsr8wlA8d1ug1umQ4hNQCIdDjok57BoMP7uf2PSQxfOYPnW9zC3EFzeefid+gU18l2+eSNk70CBZ4cloNV+1ex59ieUk3NsqzSvRcREREREZFyooCAVKgaVblkwF2jbnDXXIhr430ueSG81w3/ea/QvW43RncbjY858aeRnpvO5I2TCx16R+oOBn03iBtn3EjfyX35adtPxU7nUOYhHpn7COdOOpchM4fw5+4/y/KuREREREREykwBAalQXksG0ip5yYC7qAZwxyxod733OUcO/PYyTB9G/bB69GrQy3Z64vqJ5OTleF32+67fuf6H60k6nAQ4lxg8t/A5jmQeKXQa+9L3cdvM25i9fTaZeZks3buUO2fdyT1z7iHpUNJJvUUREREREZGSUkBAKlRs+CmSIXBcQChc9QHcNBmiGnqfX/k5LPmIW1vfamvel76Pmdtmuo4ty+LTtZ9y75x7vZYTHM0+ygerPijw9rvSdjFk5hC2pGzxOpe4M5Frv7uWpxOfJiuvir9PIiIiIiJy2lNAQCpUjdCSBwQ27T3KvROXcveEJazdlVKxE2vaC+77A7o9BMbXfm7mk7TLyqFDrQ625vFrx2NZFrvTdvP4/Md5ZckrOCxHgcN/seELtqVss7UlpyZz68xb2XF0R6HTsrCY9tc0nl7wdFnelYiIiIiISIkpICAVqma4fcnA4fQccvK8H6Ity+LBL1bw45o9/LR2L7eNW8yxrNyKnVxAiHNbwkGf2NvzsuHrWxnS9Gpb88bDGxn+y3D6Te3Hj1t/LHLoXCuX15e+7jpef3A9Q2YO8So6WD+sPjWDa3pd/+O2H1mxb0Xp3o+IiIiIiEgpKCAgFcpzlwGAwwVsPbjtYDrrdp9Ivd93NIvJy/6u0Lm5JPSHrsPtbSk76L7wYxpF2JcVzP17LrkO70DFfe3v47bWt9naftnxC4v3LGbyxsncNOMm9mfst51vGtWUCf0m8MOVPzCs/TCC/YJt519b+pp2IRARERERkQqjgIBUqOiQAHyMvW1/AcsGVv19xKttfOI2HI5KeiC+5Flo2M3W5LPlV27JDS7kAqcQvxDe6PEG9559L0PbDSU6MNp2fvgvwxm5cCTZDnsQJCEmgXF9xlEzuCYh/iHcffbdPHDOA7Y+y/ctZ+6OuWV+SyIiIiIiIkVRQEAqlK+PISbUvmzgQAE7DazYccSrbcuBY/y2ab9Xe4Xw9YNrxkFYnK25/7rZxBRQJiDAJ4DBLQfz3ZXfcUnDSwAIDwjn3vb32vql5aR5XduhVgfG9hlLVFCUrf26FtdRL6yere2NZW8UmJEgIiIiIiJyshQQkArnuWzgYIEZAgUXERyXuK0iplSw8Di4drytyGCQZfHQwYOu4wAffwa3HMyMq2bw5HlPUiuklm2Ia5pfQ6OIRoXe4qaEmxjbeywRARFe5wJ8A7yyBLakbOHbv74t2/sREREREREpggICUuFqhHlmCNgDAjl5DtbsLDggMG/jfv7ad7TC5ualYVf4x1vg4+9qujLtGJ/t3MOL+w4wc9tWnlw5i7gln8LeteCxxt/fx58RnUZ4DRvqH8qr3V/l8XMfx9/X3+v8cZeddRkJMQm2tndXvEtGbsZJvjERERERERE7BQSkwnlmCHguGdi49yhZuQVv3wfFZwlk5uSRW8DOBWV2zk1wzwKIP8/V1DY7myuOpROb54Bdy+DXf8N7XWH8FZC623b5RfUv4rJGl7mOm0U15YsLxtDbEQib5kBOZqG39jE+PNzxYVvbvox9PPjLg0xcN5EV+1aQmVv49SIiIiIiIiXlV9UTkNOfd0DAniFQ2HKB46Ys28k/+7QkMsT+ybplWTz33Tom/LGdmmEB/OvyVvzj7LrlM+laLeG2mbDkI5jzHGQXkqWwfQFMuBJumwEhMQAYY3ihyXVctnM9eYe20H3bXPyX/3LimuhGcO0nULd9gUN2qduFrnW78vuu311tC3cvZOHuhQD4+fhxSYNLuK31bbSu2bo83q2IiIiIiJyBlCEgFc57yYA9Q2ClR0HBcxvF4Ou2NUFGTh5fLE72Gnfaip2M/30beQ6LvalZDP98Oc98u4as3LzymbiPD5w7FO7/E9rfBIHe6/4B2L8eJl0LWWnOJQSLx+I3ri89N86j14G/8bc8shcOb4OPesOyTwu99cMdH8ZgCjyX68jlp20/cf0P13PbzNuY9/c8HJ73EBERERERKYYCAlLhvDIEjtozBFZ6ZAj0bh3HZW1q29o+XbjdtiwgLSuXF2ds8LrXpwu3M+j9hfx9OP1kp31CZD0Y+F94bDPcPA3OvRtC7cUE2bkEvhgMU++GHx6FPO+dFGzysmD6A/Dt/ZDjXR+gZUxL7mp3V7FTW7J3Cff/fD+3/ngrhzIPleJNiYiIiIjImU4BAalwsZ67DBw7ERDIyM5j4157Ov7Z8VHcfsFZtradRzJ4ZVaS6/i/v/7FvqPeuxWAM8Bw+VsLWLKtnB+Q/QKgSU/o9x8Y+jNE2LcIZOtvsOrLwq/3DfBuWz4RProUDm31OjXsnGF8O/Bbnj7/aa5seiVNo5oWmjWwYv8K/vnbP8lzlFN2hIiIiIiInPYUEJAK57lk4GBaNg6Hszr/2l0p5DlOVOr3MdC6bgQdGkRzdnyU7boPftvCtOU72XbgGB/N936AdpeSkcODX6wgM6eCHpCjGjizBUJqFN2vyzC4ex48vg2e3Ann3+fdZ89q+F93SPrR61TjyMYMajGIUd1GMXXAVOZfP5+HOjxEbHCsV98/9/zJeyvfK9v7ERERERGRM44CAlLhPJcM5DosUjJyAO/lAs3jwgkJcNa6fOaKVvj72j8Rf3zyKh78cgXZbssH/HwM797YgbNqhtr67jySwbLth8vtfXiJbQ43TYaAcO9zAeEwaAL0eR7qnA3B0c4Mg74vwjXjwN8+VzJT4PPr4edRUMSn/JGBkdzR9g5mXj2T0d1GUzO4pu38/1b9j8SdieXx7kRERERE5DSngIBUOM8MATixbMCzoODZ9aNcrzs2jGbUgDa281m5Dq9rhnRtRL+2dZg+rBtNa4XZziVuPlD2iZdE3XNg8JfgF3SirVZruGsutPpHwde0uQru+hVqtvA+N/9V564Fxw4WedsA3wAGNh3I6z1ex8+c2CzEwuKJ+U+w59ieMrwZERERERE5kyggIBUu0M+X8CD7Dpf7jzqL7q36+4itvV18pO34hnMbcPP5DQsdu2ZYAMN7NQMgPMifS1vF2c4n/lX0g3W5aNQNhv4K590Dvf8Nd86Bmk2Lvia2BQz9BVpf5X1u628w9hI48Fext25fqz0PdXzI1nYk6wjDfxnOuyvedX19lfQV6TnlWGhRRERERESqPb/iu4icvNiwQI5m5rqOD6RlcSQ9m20H7Q+p7hkCxz3TvxWb9h3ljy3eRQIf69OCiCB/13G3JjV5b+5m1/Gqv4+QkpFDZLC/17XlKq4VXPZy6a4JDINrPob482DWv8Bx4vvD4a3wUS+4/jNo2LXIYW5pdQvL9i7jlx2/uNrWH1rP+kPrbf0mrZ/Emz3fpFFko9LNU0RERERETkvKEJBK4VlH4GBaFqs86gcE+vnQorb3enx/Xx/evbEj9aKCbe3t6kdybcd4W1unRtEE+J34tXZY8OeWSsgSKCtj4Px7YMgMCK9jP5dxGD4dAKu+KmYIw+gLRlM/rH6R/bakbGHwD4NZsHOBq+1gxkEmrJvAE/Of4JnEZ3h1yauMXT2Wrzd+zR+7/+Bo9tEiRhQRERERkepMGQJSKTzrCKzdlWrLGADn7gL+vgXHqGJCAxh7ayduH7+Y3SmZ1AwL4OWr2+HjYy86GOTvS6eG0fy++UQQ4PfNB+ndunY5vZMK0uA857KDzwbBnlUn2vOyYcpQ2LUCLv4XBIQWeHlEQASv9niVW368hay8grdjBDiac5T7f76fW1vdSvLRZH7b8Ru5Vm6h/Q2GsyLPom3NtnSt25W+Z/XFxyiOKCIiIiJyOjCWZRXfS8rMGBMBpKSkpBAREVHV06kyT09bw4Q/ttvaAnx9bLsFDOnaiJH/aF3kOGlZuazblUqT2FBqeGQdHPffX//ilZ+SXMfNaoUx+5HuJzH7SpSVBpPvhI3eWxAS2QCueA2aXVro5WsPrOXrjV+Tmp3qatt0eBPbUreVy/TOrX0u/73kvwS5F1EUEREREZFTSmpqKpGRkQCRlmWlFtZPGQJSKdrHR3kFBNyDAQBnexQULEhYoB/nnhVTZJ+uTWrYjjftS2Nfaia1IqrBQ2xgGFw/CWY+CYs+sJ9LSYZJ1zgLEV72MoTV8rq8dc3WtK5pD6qk56TzdOLTzNo+66Snt2jPIh6f9ziv9XgNXx/fkx5PRERERESqjjIEKpgyBJzyHBYjp6/1Cgq4++XR7jSODSv0fEnl5jk4Z9RsjmadSIV/47r2DDyn3kmPXan+/J+z2GBetve5sDi4aTLUbluioSzL4qM1H/HWsrewsP/NB/kGcXGDi4kJiuFI1hFSslI4kHGAv478RY4jp8Dxrm1+LU+f/zTGGLLzspm5bSbL9i4jx5GDwbmMw9fHl9Y1WnN1s6sVPBARERERqUQlzRBQQKCCKSBg99vG/Tw5eRW7UjJt7eFBfqx8prdXTYCyuvOTJcxZv9d1fG3H+rxy7dnlMnalOrAJvnsIti/wPhcYATd8Do0uKPFw8/6ex7//+De7j+2mXc12DGw2kL6N+hIe4F3MMTsvmw2HNrBq/yreXfEuR3PsBQbvbHsngb6BfLHhCw5mFl648epmVzOy68gSz1FERERERE6OAgKnCAUEvB3NzOGFGRv4fFGyq+22bo14tn/R9QNKY1ziVp77bp3ruG5kEIlPXIwx5RNwqFSWBcsnwqynIPOI/ZxvIFzzEST0L/FwuY5c8qw8An0LrsFQkMV7FnPP7HvIdhSQrVACr3Z/ld6NepfpWhERERERKR0FBE4RCggUbtHWQ3y3chf1o4O56fyGhAaWX0mLjXuP0vv1eba2X0f04KyaBVfprxbS9sPXt8L2RHu78YHL/gOd73RuY1hBZm+fzaNzH/VaclASEQERTP7HZGqHnuK7PYiIiIiInAYUEDhFKCBQNSzL4twXfmb/0RNb8P17YBtuOr9hFc6qHORkwuQ7YMP33ufi2kKPx6HF5eBTMVsDfrHhC57/8/kCz51b+1xa1WiFZVkczjrM9M3Tbec71+7Mh5d+qHoCIiIiIiIVTLsMyBnNGEO3JjWYtmKXq+33zQeqf0DAPwiu/QR+eBiWfWo/t3c1fHkTxLWBnv+Clv3K/fbXt7yew5mHeXfluwD4+fjR76x+3NzqZlrGtHT1syzLVWzwuMV7FvPJuk8Y0noIO47uYOPhjRzJOkJ0YDQ1gmtQI6gGNYNrEuIfUu7zFhERERERb8oQqGDKEKg6Xy3ZwT+/WeU6jg7xZ+lTl5Zb4cIqZVnw6/Mw75XC+/R6Di54qEJuv/bgWvak7aFdbDtiQ2IL7JOanco1069h97HdrjZf40uAbwAZuRkFXuNjfLio/kU8dd5TxIXGVcjcRUREREROdyXNEKiYvGKRU0C3pjVtx4fTc1iy/XAVzaacGQMXPwWDJkDN5gX3+XkU/L20Qm7fukZrLml4SaHBAHDWDXjxwhfxMSf+mcmz8goNBgA4LAdzd8zlhh9uYPX+1a727LxsPlr9EX2+6UPfyX2Z9/e8QscQEREREZGSUYZABVOGQNXq8cqvbDuY7jpuWiuM7x+4gCD/02gduyMP1k6F3/4DB5Ls52o0hbvnQ0DVpeG/tewtPlz9YamvC/AJYGTXkYT6hzJmyRh2HN3hOhfkG8SUAVOID4/3us6yrOq5m4SIiIiISDlRhoAIMKiz/YHxr31p/GdmUiG9qykfX2h7Ddy3EC542H7u4F8w59mqmVe+e9vfS6e4Tl7tMUExtKnRhvph9Qn2C/Y6n+3I5v8W/B8P/vqgLRgAkJmXyeiFo3EPaB7MOMjQWUNpP6E998y5hwMZB8r/zYiIiIiInEaUIVDBlCFQtXLyHFz5biJrdtqDYpPuPM9rScFpIS8XPu4NOz2WCtw8FZpcXDVzwpny/8OWHziafZQmUU1oEdOCmsH27/+BjAM8Mf8J/tz9Z4nHfeGCF+jfpD/pOenc9tNtrDu4znWuQXgD/tf7f9QLq1du70NEREREpDrQtoOnCAUEqt6mvUe5/O0FZOc6XG11IoOY+dBFRAb7V+HMKsiBTfD+heC+Vj+8LrS8HPatc37lZsH598Ilz1TdPAuQ48hhzOIxfLbhswLP+xgfHNaJn2NUYBST/zGZpxOf5vddv3v1rxVci//1/h9NoppU2JxFRERERE41CgicIhQQODV8vGAro75fZ2u78px6vH5d+0KvOZaVy/xN+5mzfh9zk/aTnp1Lm3qRXN2hHpe1rUNE0CkcTPjzf/DjY8X3u3kaNOlZ4dMprW82fsPzfzxPrpULQK2QWjza8VEC/QJ56NeHbH2jAqM4knWk0LEiAyN575L3aBvbtgJnLCIiIiJy6lBA4BShgMCpweGwuPnjP0n866Ct/c3r2zOgvT2lfNuBY7wwYz1zk/aTneegIIF+PlzaKo5rO8VzYdOap95Whg4HTLwKtvxadL+Ef8B1EypnTqW06fAmvt/yPfXC6nFF4ysI8XcWRnxk7iPM3j67VGOF+IXwz87/5MpmV9p2PQDnzgbZedkE+QWV29xFRERERKqSAgKnCAUETh27jmTQ5415HM3MdbUF+fsw5d5utKrr/Nls3p/Gte8v5NCx7BKP27BGCDed15BrO9UnKiQAcGYX7DuaRVxEICEBfuX7RkoqZSe81wUyUwrv4+MHD6+D8LjKm9dJ2p++nwHTBnA056jXuWC/YF7v8TrvrXyPlftXep1vF9uOp857ipYxLVm5fyXTN0/np20/cTT7KOfVOY+bW93MBfUu8AoaiIiIiIhUJwoInCIUEDi1TFu+k4e+XGFri48J5rthF5CRk8c17y1k55GMgi8uRqCfD/ExIexNyeRoljPo4GPgmStaMaTbWSc79bLZvRJmPQUZh6Fmc4hNgAWvQc6JrRi55Fm48JGqmV8Zfb3xa0YtHGVr8zW+vHXxW1xU/yLSc9J56NeHWLh7ode1PsaHuJA4dh/bXeDYDSMackPLGxjYdCCh/qEVMn8RERERkYqkgMApQgGBU8//TV3NZ38m29ouaFqTPamZ/LUvzdbu52M4r3EMvRLiiA0PZPqKXfyatI+cvJL/3fgY+O2xnsTHhJTL/E/atPthxcQTx9GN4IHl4FN9PhV3WA7u+OkOluxd4mob3W00A5sOdB1n52Xz3MLnmL55epnuERUYxZ1t7+S6FtdpOYGIiIiIVCsKCJwiFBA49WTl5nH9//5gefKRIvu1qx/JJ7edS3RogK390LFsvlu5i88XJbNhj3faekHu69GEf/ZtWdYpl6+/l8DYS+xtVbwtYVkczT7KK4tfYWfaTgYnDOaSBpcU2C9xZyIvLnqR7anby3SfWiG1uPfsexnYdCB+PlW0/ENEREREpBQUEDhFKCBwatqbmsnlby3gQFpWgeebxIby9T1difEIBrizLIsl2w8zYeF2flyzu8isgRqhAfz+5MUE+vme9NxPmmXB+xfA3jUn2loNgEGfVt2cKlh2Xjbj1ozjw9UfkpV34mfeKKIR/Zv0JyYohs83fM7GwxsLHaNRRCP+77z/o0vdLpUxZRERERGRMlNA4BShgMCpa/G2Q9zwvz/Iddj/BupGBvHNvV2pGxVc4rH2Hc3kzy2HyHNYxEUEketwcPNHi2x9CtrRoMos+hBmjDhx7OMHj6yHsFpVN6dKsDNtJ5M3TsbComd8T9rWbIsxzh0iLMti8Z7FfLruU377+7dCx7ii8RWM6DSCGsE1iryXZVmusUVEREREKpMCAqcIBQRObZ/8vo1np691HceEBvDV3V1oWivspMe+/n8L+WPLIddx50bRfH1P15Met1xkHIFXW0KuWwHFXiOhyzDYudSZPRDTuNotIygvq/av4q1lb/Hnnj8LPB8ZGMmdbe4kLCCMzNxMMvMyOZJ5hJ1pO11fGbkZXFT/Iv7d7d+EBZz875OIiIiISEkpIHCKUEDg1GZZFv+bt4WxC7ZSNzKIl65uR0Kd8vk5fb9qF8M+W25rm/nQhbSsXfD4m/en8eXiHdSJDOKWLo3w9angT5en3QcrJp04DggHLMh2K6zYeSj0ewXO0E+6F+5ayJvL3mTtwbXFdy5Ez/ievNHzDW1lKCIiIiKVRgGBU4QCAmeu7FwH3V7+hf1HT6xZv+n8Bvx7YFuvvlsPHOMf7yzgaKZzu8Irz6nH69e1r9gJ7lgEH11afL9uD8Glz1XsXE5hDsvBNxu/4Y2lb3A0p2RFJD092OFB7mx7ZznPTERERESkYCUNCOgjK5EKEuDnww2d421tU5ft5Ghmjq3NsiyenrbGFQwAmLp8Jz+s2u015o5D6fyx5SCZOXknP8H6naFWq+L7Jb4B8189+ftVUz7Gh0EtBjH9yun0bdS3TGO8vfxtFu5aWM4zExERERE5OcoQqGDKEDiz7TqSwQUv/4J73cLRA1pzc5dGruPpK3cx/PPlXtdGh/gz6+HuxIYHAvDBb5v5z09J5Dks2tSLYNId5xMZ4n9yE1z2KUx/oIATBvD4t+GyV+C8u07ufqeBBTsX8HXS1+xI20GwXzDBvsEE+QUR4h9C3dC61A2ri2VZvLDoBRyWw3VdVGAUX13xFXXC6lTh7EVERETkTKAlA6cIBQTkrk+XMGvdXtdx87gwfnzwInx9DKmZOVzy6m+2ZQXu+rSO4/2bOvKfn5J4b+5m27m+rWvz3k0dbJXsf03ax4SF22kQE8LjfVsSHFDMNoeW5cwA2PwLRDWExj3grO6waRZ8e593/ys/gLOvL+lbP6ONXT2WN5e9aWtrEtmETrU74WN88DW+1A+vz5VNryTEP6SKZikiIiIipyMFBE4RCgjI/E37vbYg7NK4Bm9c35735m5m/O/birz+3LNiWLT1UIHnnvtHa27t2gjw3jHhsja1ee+mjmWf+J8fwI//tLf5BcG9v0ONJmUf9wzhsBw89OtD/Lrj1yL7dajVgbF9xuLvc5LZHiIiIiIi+VRDQOQU0a1JTc6qGWprW7jlIJe9OZ9PF26ztZ/fOIaY0ABbW2HBAIDnf1jPmp0pjJ2/xRYMAPhxzR6WJR8u+8TPuxsuftrelpsJ3w4Dh6Pga8TFx/jw/AXP0zCiYZH9lu1bxvsr36+kWYmIiIiInKCAgEgF8/ExjB7QxmsbwUPHsm21BQL9fPjP1WczekCbwsfy2P0vO8/BjWP/5N8/rC+w/6uzkso8bwAufBQ63WFvS/4dFo89uXHPEOEB4bze43VC/UOL7Dd29ViW7l1qa7Msiz3H9qAsLhERERGpKFoyUMG0ZECOW7r9EA98tpxdKZkFnh/RuznDLm4GwLDPlvG9xy4D/r6GN68/h5U7jvDBvC0lvu9nQ8+ja5OaZZ949jF4twsc2e42mVC473eIblT2cc8gW1O2MmPrDFKzUsmz8sh15PLtX9+Sa53YWaJOaB2++cc3hPuH88PWH3h96evsS99HQkwCY7qPoUFEgyp8ByIiIiJSnaiGwClCAQFxd/hYNo99s5I56/fZ2hvHhvLjgxcS6OcsAnjoWDa9X5/HgTRnscEgfx/ev6kjPVrUIifPwaAPFrI8+UiB9wgJ8CU9+8S2hB0bRvPNPV1sxQdLbes8+KS/ve2si+CW6XAy457BCio62L1+dzJyM1i0x15zolZILcb1GaeggIiIiIiUiGoIiJyCokMD+PCWTjx1eQL+vs4H6ZAAX165pp0rGAAQExrA1/d0oV/b2vRpHcc393SlR4taAPj7+vD2DecQEeTnNf5Tlyfwf/0SbG1Ltx9m7sb9Jzfxsy6CTrfb27bOg6XjT27cM9htrW+jc+3Otrbf/v7NKxgAsC99H7f9dBvJqcmVNT0REREROQMoQ6CCKUNACrPrSAaLth7inAZRNKxR9Brzgsxet5d7Jy4lN78QwfEdB7JzHVz86lz+Ppzh6tumXgTfDbvg5LIEso46lw6k7DjRFhDOsTvm8cqfGSz46wAXNqvJk5clEOCnWGNJ7Dm2h6unX01qdqFBWxtlCoiIiIhISWjJwClCAQGpSKv/TmHhlgN0bVKTNvUiXe3fLP2bEV+vtPU9p0EUx7Jy2X80i9TMXCzLchU1DPDz4dJWcbx67dkE+fvartt/NIuPE7diWXBL7F/U/f5G2/lVvq0ZeOxJHPkJR7d2achzRRRGFLtZ22bx6G+PerXXCa1DWEAYmw5vsrXXCqnFmO5jOKfWOZU1RRERERGpZhQQOEUoICBVITfPQe835rFl/7FSXTekayNG/qO16zg718E/3lnAhj1HAecuB1/ETeTcIzNs1z2fM5gP865wHX88pBMXt4w7iXdwZhm9cDRfbfwKAD/jx62tb+WudneR48hh6KyhrD/kvYvEP5r8g4c7PkzNYGfByAMZB1ixbwUZuRlc3ODiYnc2EBEREZHTlwICpwgFBKSqfL9qF8M+W16qa/x8DLMevojGsWEAfPDbZl78cYOtTxjpzAx8gvrmgKsty/JjQPa/2WA5U9lrhAbw40MXUis86CTfxZnBYTn4YcsP7D62m94Ne9MospHrXEpWSqFBgTD/MLrHd2ftgbVsS93maq8bWpexvccSHxFfCbMXERERkVONAgKnCAUEpKo4HBaDx/7BH1sOleq6S1vF8eEtndiXmknPMXM55rZjwXGdzQa+DBiNjznx78d6RzwDs0eTRQAA3ZvHMm5IZ3x8tAvByUrJSuG+n+9j1f5VJb6mdmhtPu7zMfHhCgqIiIiInGkUEDhFKCAgVSktK5evl+xgT0omNcMCiQ13fkUG++PrYzAGvli0g/G/b7Nd9/nQ8/l66Q6mLNtZ6NiP+33OvX7f2do+yL2cF3NP1Bh44rKWXNC0Jn8fzmB3SgaRwf70bFGL6NCAcn2fZ4JcRy5fbPiC/674L2k5aSW6RkEBERERkTOTAgKnCAUE5FSXmplDz1fmcvBYtqutXlQwO49k2Ppd3q4ONUID+HxRMjl5FgHkMC3gGVr5bHf1cWC4PuspFln2rQ/dBfj6cFnb2txwbgPOOyvm5HY+OAMdyDjA60tfZ/rm6V7nDAYL+7/pdULr8HGfj6kfXr+ypigiIiIiVUwBgVOEAgJSHUz4YztPT1tT6PnwQD9+GdGD2PBAdhxK59OF29h5JIPbmmbQedaVkHcimLDVEcdl2S+RSWCx921cM5R/X9mGrk1qlmne63ensvNwBhc2r0mgn2+RfS3LYu2uVH7buB9/X8ON5zUkNNCv2Hus2ZnCN0v/plGNEG48vyH+vqfGlorL9i7j8w2fk2flcXbs2XSq3YnaIbW5Z849bDhkr/sQFxLHCxe8wLl1zq2i2YqIiIhIZVJA4BShgIBUB7l5Dvq+OZ+/9hWciv7U5QnceWHjgi/+/R2Y9S9bk+fSgaL4+Rgm39uVs+OjSjNlvlqyg39+41xT37J2ONOHXUCAn/fD+u6UDKYt38XU5X+zce+J93dhs5pMuOO8Iu/x3cpdPPzlCnLz92d84OKmPNq7RanmWdmOZB7hzll3knQ4yevcoOaDeLjjw4QFhFXBzERERESkspQ0IHBqfNQlIlXKz9eHf/UrOM2/aa0wbu3aqPCLz78X6ts/eR7q9yPtzGbAuVVhncggzo6PIqCAT9dzHRYjv1uLw1Hy4OT+o1k8N32t63jDnqPMXLvH1seyLEZ9t46uL/3CyzM32IIBAPM3HWBfamah9/h8UTLDv1juCgYATF76d4nnWFWigqL4sPeHNI9u7nXuq41fceX0K1mwc0EVzExERERETjUKCIgIAD1axHJBU+/U/Wf7tyo6Td7HFwa8A74nCgX64GBy3UnMf7QbSf++jIVPXsK393fjj/+7hKcuT6BJbKhtiOXJR5i6vPAChp7emLPRa/eD35L2244Xbj7Ix4lbKSoJKmnv0QLb/zdvM09OWe117a6UTHZ51FY4FUUHRTO291g6xnX0Orfn2B7unXMv98y+h7UH1hZwtYiIiIicKRQQEBEAjDH86/IE26f4l7etw4XNYou/OLYFdH/c1uR/cAPxa9+3BRNiQgO488LGzHjwQs6qaQ8KvDRzA0czc4q91V/7jvLF4h1e7fM37cd9CdSMNbuLHcszawDg1VlJvDBjQwG9nZZuP1zsuKeC6KBoPu7zMU+e+yTBfsFe5xN3JXL9D9fz0K8PsenwpiqYoYiIiIhUNQUERMQloU4E42/rTL+2tbm7e2NeHXR2yS/u9iDUbmtvmzcGdi716hro58szV7Syte0/msU7v/xV7G1e+nEDeQUsL9h3NMv1ib9lWfy8fp/tfPfmsXRtUsPWtnGPPUPgt437ebuYOVSXgACAj/FhcMJgpvxjCufVLrhews/JP3PV9Kvo/U1vHvvtMSaum8j6g+sreaYiIiIiUhUUEBARm65Na/LujR158rIEgvyLrtxv4+sPA/4Lxu0aRw58fBn8+T888+97tqzFxS1r2do+TtzK5v0FFzYE5zKAOR4P+u7mbXQuG1i7K5XdKfb6AP+6PIEeLezZDhv32QMCXy3xzjxoXddeDLQ6BQSOqx9enw97f8hzXZ+jZnDBOzrsPrabmdtm8vLilxn0/SDu//l+0rIL/1mIiIiISPWngICIlJ86ZzszBdzlZcGPj8Fn10GafZ3/01e0wt/XuI5z8pyFAAva/cThsHhhRtGfXM/beACAOev32tobxITQrFYYzePCbe2b9qbZ7rVmZ4rt/EO9mvGvy+3FFtftTiU9O7fIeZyKjDFc1ewqZlw1g4c7PkxkYGSR/ef9PY87Z93JkcwjlTNBEREREal0CgiISPnq/jg06OLdvukneK8r7FjsajqrZih3XGDfzvC3jfv5z09JXssCvl25k9UeD+zdm9s/8V+07RAZ2XleywV6JcRhjPEKCKRl5bIrP5MgJT2H7QfTva5rHx+Fr8+JoEWew2LFjiMFvPHqIdgvmNvb3M6PV/3IPWffQ1RgVKF91x5cy20/3cb+9P2F9hERERGR6ksBAREpX/5BcMu30GWY97lj++Dz6yDjRNr9sIubUis80NbtvbmbGfrpElIzcziWlcsLM9Yz4utVtj6NY0N547r2tof17FwH367wDhz0auVcmlAnMojwQD/buY35dQfW7LJfE+DnQ/O4cEIC/GhVx75sYFk1XDbgKTwgnPvb389v1/3G1H9MZWSXkVzZ9EqvAMFfR/7i1pm38kvyL8zcNpNvNn7DxHUTmff3PLLysqpm8iIiIiJSLvyK7yIiUkp+gdDneWjSE6be6wwEHJd+0FlssM/zAIQF+vH0Fa144PPltiF+2bCPAe8kkpmT51UPAODJyxKIDg3gnPgolrg9oI+ZlWTrFx7kR+dGMYAzbb5ZXBjLko+4zm/cc5SeLWp5BRESaocT4OeMmXZsGG07v+Q0CAgc52N8aBrdlKbRTbm6+dVsS9nG0NlD2XNsj6vPjqM7ePDXB72uDfUPpUd8D3o37E398PpsPrKZTYc3semIc9eCmxNu5tw651baexERERGR0lFAQEQqTtNecO/v8NUtkPz7ifY/P4BOt0ONJgD0P7suRzJyeG76WnLdlgpsPXCswGH7tq5NrwTnp/4XNY+1PaAfSMu29e3ZopZt68PmceH2gED+1oOr/7YHBNrUO7HGvmPDaMb/vs11vGz7YRwOCx+37ITTRaPIRnza91PunHUnyUeTi+x7LOcYP2z5gR+2/FDg+fl/z+e9Xu/RpW4BS0hEREREpMppyYCIVKywWBj4X/ANONHmyIE5z9q63Xx+QybdeR41QgMoTICvD8MvbsqbN7THGOfD+EUedQQ89WoVZzv2KiyYv9OAZ4ZAu/onAgKdGkXbzqVm5vJXEbshlNb+o1ncN2kpA/6byHcrd5XbuGVVJ6wOn1z2Cc2im53UOHlWHo/+9ijbUrYV2zczNxOH5Tip+4mIiIhI6SggICIVL6YxnHe3vW39d7Btga3pvMY1mP7ABV5b/QFc0LQmMx+6kEd6tyDQ78TWhm3rRRIV4l/gbf18jFfhwYJ2Gjh8LJvkQ/aCgu4ZAnUig6kXFWw7X57bD474eiUzVu9h5Y4jPPTlCq/dDqpCzeCajOszjt4NexMVGEWt4FqcFXkW7Wq2o3WN1hhKlh1xNPsoD/zyAClZBb8nh+Xg3RXvctGXF9Hlsy78tO2nAvvN3TGXV5e8yuI9iws8LyIiIiKlpyUDIlI5LhwBKz5z1hA47qf/g6FzwedEbLJeVDDf3NOVF2asZ/Kyv4mPDmHYxU25ol0dV1aAO18fwwVNa/L9qt1e5849K4bIYHuwoHlcmO04IyePH9fssbUdLyjorkPDaHYeyXAdL9l2mBvObVDs2y7Opr1H+W3jiSr+eQ6L93/bzDuDO5z02CcrMjCSV3u8WuC5/en7mb19NrO2z2LZ3mVYWMSFxNE0uinHso+xYv8KV99tqdt49LdHea/Xe/j7nPh5pOek8+T8J/llxy+utifnP0mzqGY0jjqx+8RXSV8x+o/RAExYN4GxvcfSqXancn63IiIiImceZQiISOUIjoIeT9rbdq+EVV96dw3wZfTANqwe2YefHr6I/mfXLTAYcFxhywZ6JcR5tcWGB3plFExe9rftOKFOhK3uAECnhvZlA8uSyydDYMIf273aZqzezfaDBddPOFXEhsQyOGEw4/uOZ9GNi1h4w0LmXDuH93u9zweXfkBCTIKt/5+7/+SpBU+x9sBaHJaDvcf2MmTmEFswACDHkcPTvz9NniMPgL8O/8XLi152nc+z8vjviv9W/BsUEREROQMoICAilafjbVCzhb3t5+cgO73A7r4lLNp3YbOaBbYXFBAwxtC8lv3Tf8/0/3ZuywWO6+gRENh64BgH0k5u2720rFymLNvp1e6wYOz8rSc1dmUK8gsiLOBE5kWIfwhvXfwWNYPtP5cZW2dw/Q/X0/3L7gz6fhDrD60vcLxV+1cxaf0ksvKy+Of8f5LtsBeKXLJ3Cav3ry7/NyIiIiJyhlFAQEQqj68f9P63ve3oblj1xUkNWycy2GspQPO4MBrUCCmwfzOPvp7aFhAQaFk7nJAAX1vbspOsIzBt+U7SsnILPPfVkh0nHXCoSrVDa/NmzzcJ8PEuEnkk6wiHMg8Vef3by9/mqQVPsenwpgLPj1s7rlzmKSIiInImU0BARCpXs0uhcQ9726KxYFkFdi+p3q1q244va1On0L4taocXeg6gbX3vgICfrw/t46NsbSdTWNCyLCYWsFzguKxcB5+6bXVYHbWLbce/L/g3fqbocjUNwhswqusoW6HCzLxMZm6bWeg1c7bPITm16G0RRURERKRoCgiISOUyBroMs7ftWwvJC09q2Ht7NHGt8z/vrBju6d6k0L7NahUeEAj086FZrYIzCDzrCBQUEMhzWIxP3MrdE5bw2Z/JWIUEOhZvO8yGPUdtbU097vvJwu0cKySDoLq47KzL+LL/lwxuOZhGEY28zneu3ZlJ/SZxZbMruaHlDYWOE+ATQIjfiYwPC4tP1n5SEVMWEREROWNolwERqXxNLoHos+Cw2zr5RR9Cw65lHjI00I9v7u3KsaxcQgOL/qfNc3mBu1Z1I/DzLThW2sEzIJB8mFlr99C7tTM7wbIs/m/Kar5csgOAn9buJTY8kEtbedcy8Cwm2LBGCO/e2IHer89ztaVk5PDl4h3cfsFZRb6fU13z6OY8eZ6zoOSutF0s3LWQDYc20DiqMdc0uwZ/X2eRxwc7PMhvf//GzjTvugqPdHqEvel7GbfmxFKBaX9N497293rVKhARERGRklGGgIhUPh8f6HyHvW39dDi696SHLi4YAFAjLJCaYd5r26Hg+gHHdWgYTZD/iX82LQuGf7GcZcmHsSyLf/+w3hUMOO7HNd7bIe47mslMj/abzmtI87hweiXUsrV/tGArOXmOYt9TdVE3rC5XN7+af53/L25oeYMrGADOYoQju470uubCehcyuOVgbkq4CT+fEz/fbEc2n2/4vDKmLSIiInJaUkBARKpG+xvBL+jEsSMXllVeCnhhywbaFBEQiAjy54GLm9naMnMc3PnJEp6dvpaPFnjvDLBuV6pX21eLd5CTd2IpQaCfD9d2qg/gtdRh55EMflyzp/A3cpo5v8753NrqVtdxvbB6jOo2CmMMtUJqcUXjK2z9v9jwBek5Be9SISIiIiJFU0BARKpGSAy0vcbetmQc5FXOmvnCCgu2K6CgoLv7ejTh2o71bW2HjmXz6cKCCwT+tS+NrNw817HDYfHZn/ZieP84uy5RIc6MhU6NYry2OFy4+WCRczrdPNrpUd7q+RZPn/80X/X/yrYkYEjrIba+qdmpDJk5hOmbp5Odl42IiIiIlJwCAiJSdToPtR8f3QVJP1TKrQvaejDI34emsUVvSWiM4YWr2tK9eWyJ7pPrsNi0N811nLT3KLtSMm19bunSyHbcK8Fec2DL/jTOJMYYejboyaAWg4gIiLCdaxLVhO71u9va1h9az78W/ItLv7mUd5a/Q1Ze9d2uUURERKQyKSAgIlWnbnuo39netujDSrl18zjvDIFWdQovKOjO39eHd2/sQJt6EQWed68zALB2V4rr9fLkI7Zz9aKCvbY5bBIbajvevP9YsXM6k9zZ9s4C2w9lHuKDVR9w16y7yMnLqeRZiYiIiFQ/CgiISNXyzBLYNh/2ra/w2zYvoIZAUQUFPYUG+vHxkM7Ujw62tT9xWUsubmkvDOheR2B5sn2rQs+dCwCaeGw/eCAti5R0PeAe175We1688EXqhtYt8Pyyfct4belrlTwrERERkepHAQERqVqtB0KIx7ZxP4wAR16B3ctLZIg/tSOCbG1FFRQsSK3wID4fej6XtKxFi7hwXrqqLfd0b0KrOvbMgbVuAYFlngGBBlFe4zaICcHPx9jaNh84s5YNFOeKxlcw46oZvNHzDc6rc57X+YnrJzLn/9m77zC3rjpv4N+jNprei7s9cYntJE4lpEFCSAIhCS0QYJfee4flpbywu1nKy7IJsLsktNAhBNIgvUFIb25x93jc7el91M/7h6QZnXOLpJFG5er7eZ55xvfoSjruur/7K/sfKMLOSsfB8YMYDY6mP5GIiIgqFgMCRFRcnirgjHepa/v/ATx23by/9WtPnb3DXF/lwSXrOm3ONrekpQY/fddZuPfTL8NbXrIUALB+oRpY2H50DLGYxOhU2JD+f9pSY4aA1+3C0tYaZW1vHwMCOrfLjYuXXoyfXPoT/PLVv4TPpY6S/OpjX8WBsQMWz3a27z37PVz+58tx/u/Px627by32doiIiKhEMSBARMV33ieAxqXq2sP/ARx+bl7f9nOXrcHXrliH95y3An/+yLkznf5ztW6hmiEwGYpi/9AUNh4aUdZ9HpchmyDpBK25Yc8A+wjYOa3jNHzxJV9U1ibCE/js3z6LQCRg8Sxn6pvqwy+3/XLm+NqnrsVYyDj+koiIiIgBASIqPn8j8IYbAZHyT1IsAvzpfUBw/u6Me90uvOf8FfjaleuwyqTJ4Fx11FehrU4NLmw7MmboH3Dyokb4POb/DOsBAWYIpPem1W/CFd1XKGs7hnbg2qeuhZQy69eTUmLn0E784/A/Si6oMBYaw7bBbaYTFfaM7EFUzpbcBKNB3LPvnkJuj4iIiMoEAwJEVBqWnQNc8Dl1bagHuOeL5ueXMCEE1mllA9uOjuJ5bcLAaUuaLF/DOGmAAYF0hBD46ku/ihMaT1DWb9tzG77y2FcQiUWyer0/7PwDrr7zanz4gQ/jg/d/EKFoKJ/bnbO9I3tx5a1X4pq/XIOr77ja0CfgyMQRw3P+vPvPhdoeERERlREGBIiodLz8i8YxhC/8GnjxtqJsJxd6KcDWw2PYqGUImPUPSNInDewfnEI4GsvfBh2qxluD7134PVR71OkPd+y9A596+FOYjkzPrB0YO4C7eu7CzqGdhteZCk/huuevmzl+vu95/HHXH+dt39n49fZfYygwBADoHevF7XtuVx43Cwi8OPii6c+TiIiIKhsDAkRUOtwe4A0/BnzqxTDu/AQweqg4e5qj9Vofgcf3DmAsoN6hPn1Zk+XzT2hTfw0iMYkDQ1N525+TdTd149rzr4VbuJX1vx36Gz50/4fwX8/9F15722vxmltfgy8++kVcfefVuHvf3cq5f933V0yG1b4NN26+EVPh4v8e6Bf2u4Z3KceHJsz/rty257b52hIRERGVKQYEiKi0tKwALv+uuhYYBf78wXkfRZhPemPBcFStYe9q8GNBo3oXO1VjjdfQh4B9BDJ3ybJLcP1F16PKXaWsP9/3PH629WfoGe1R1r/zzHdm+gRIKXHzzpsNrzkUGMJvd/x2/jadASklesd6lbWD4weVY7MMAQD4S89fci572Dm0E08ceQLRMvq7SERERNYYECCi0rPhLcBJb1TX9v8DeOz64uxnDpa31qLG57Z8/LSlTWlfo5uTBnLy8iUvx42X3Ih6X/qGkQPTAzNBgM0Dm7FjaIfpeT/f+vOiduwfDg5jPDSurGUaEBgJjuCRg4/M+b1/u/23uPrOq/GB+z+AD97/wTk1aiQiIqLSwoAAEZUeIYDXfM9kFOG18z6KMF/cLoETu6wvRE+36R+QxEkDuTu983Tc9Kqb0F7dnvbcn279KabCU6bZAUljoTH88sVfWj4OYF4vlPeP7Tes9U/3z5QyBCIB9E/3Wz7/1j23zvm9U0cZPnXsKWzq3zTn1yIiIqLSwIAAEZWm6iaLUYTvn9dRhPm0Xps0kCqTDAFOGsiP1c2r8avLf4UzOs+AW7ixsmkl3n/y+/Gv5/6rct5QYAg3bL7BMKKvzqsGZn617VczTf1SSSnxm+2/wSv++ApccesVeObYM3n/ufSO9pquJ/sGHJ08avv8x488jmOTx7J+35iMGZ63dWBr1q9DREREpYUBASIqXaajCPcCP700Pn0gXFqz4XV6H4Ekj0vgpEXWwYIkfdLA3v5JpmnP0aK6Rfj5ZT/HxndsxK2vvRWfOP0TeP2q1+P8Recr5/1s688Qis3W2XtcHvzgFT+AKyUwNRWZwk+3/FR5XiQWwb8/+e/41tPfwsD0APaP7ccX/v4FhKPhvP48zDIEgNmyAb1coM5bh1rvbGApJmO4Y+8dWb/veGgcUan2DbAqqyAiIqLywYAAEZU2s1GEfS8Ct38U+K91wIP/CkwZ79aWAn3SQOq632vdXyBJnzQwOh3G4GRuTeEqmRDCsPbRUz9q+5xLll2CM7vOxBXdVyjrv9/xe9yw6QaMBEYwFZ7Cpx/+NG7epZYaDEwP4LEjj+W+8RSWAYGxeEDg8MRhZX1J/RK8avmrlLVbd9+KmMxuhOVIcMSwtnOYYwyJiIjKHQMCRFTa3J546YA+ihAApgaBR/8TuPFCIDxtfLzIVnfWw+0yXoSelkH/AABY1FwNn0f9Z7qnn40F8+mktpNw4eILLR9/y5q3AAA+tOFD8AjPzHooFsIPN/4Ql9xyCd78lzfjkUOPmD7/rp678rldw4SBpGSGgB4QWFy/GG9Y9QZl7dDEIWwf2p7V+5oFBPaM7Ml7BgQREREVFgMCRFT6WrqBt98GNK8wf3xkP7Dr3oJuKRN+rxsr242BjEz6BwDxxoTdbewjMN8+cupHTNdXNq3EaR2nAYjfaX/j6jcazglEA5Z37QHg4YMPzzT8y1VMxnBg7IDpYwfG4+t6ycDC2oU4ue1kLKpbpKzvGtqV1XuPBEYMa5FYBHtH92b1OkRERFRaGBAgovKw5Czg488Bb/0D0H2R8fEjzxd+TxkwKxvIZMJAEicNzL+1rWtx8dKLDevXrLlGKTP4wllfwKuXv9r2tRp8DXCL2XKQQDSAhw4+NHMckzHcsOkGfOiBD+FPu/6UVU+IY5PHlP4Gqax6CCysWwghBNY0r1HW943uy/h9gfi4QzPbB7PLNCAiIqLSwoAAEZUPlxtY8yrgHbcB531KfezIxiJsKD29sWBbnQ+Lm6szfj4nDRTGhzd8WDmu9dYa+gb43D585+XfwR+v/COu6L5CKSEA4nfjf3X5r3DOwnOU9dSygeuevw4/3PhDPHb4MXz9ia/jp1vV5oR2rMoFgPh0gXA0PDNtIGlx/WIAwAlNJyjr2d7ZN8sQANhHgIiIqNwxIEBE5Wnxmerx0U1ACXbgv+jEDqT2srt0fZdpczsr3XqGAHsIzIs1LWvw+TM/D5/LhzpvHb5x7jdQZ9a3AsCJLSfimxd8E3e/8W68e/27cVLrSXjdytfh15f/Gt2N3bh8xeXK+Y8feRxDgSHsHNqJX774S+Wx7z//fTx66NGM9mhXmhCTMfSM9hjGIS6sXQgA6G7qVtb3jmQZEDDpIQAwQ4CIiKjcedKfQkRUghacqh4HRoDhXqDFos9AkZzQXofvv+U0/PrJ/ehur8UXLluT/kna81MdHJ5CIBzNaEoBZecd69+Ba068BgICPrcv7fldtV34zJmfMaxfvPRi+N1+BKLxsZhRGcXd++7GXT13GUb3SUh88dEv4vev+T2WNiy1fT+r/gFJTx19yrC2sC4REGhUAwJHJo5gOjKNak9m2SpWAYGdwzsRkzFlLCMRERGVD/4PTkTlqXExUNOqrh3dWJStpHPlhoX4wwfPwTffcAqaatJfaKbq1koGpAT2D+anSR0ZVbmrMgoG2Knx1uCiJWqfi+ufvx6bBzabnj8eGscnH/5k2uaDdiUDAPDk0SeV4xZ/C2q8NQCAFY0rIDCbmSIh0Ttq/3qphgPmPQQmw5M4PH7Y9DEiIiIqfQwIEFF5EsKYJVCifQRyUVvlwYJGv7LGPgKl7/JutWxgOqKOxUy9OAfiI/y++thXEYlFLF9TLxmoclcpx88ef1Y5TpYLAEC1p3omWyCpZ7TH8r10VhkCALBjeEfGr0NERESlhQEBIipfC09Vj0s0QyBXnDRQfs5beB4afMYJE0nff8X3sbJppbJ23/77cNktl+G/N/43jk0eUx4LR8M4PKHeiT+zS+2joQcd9ACAXjaQTR8Bu4AA+wgQERGVLwYEiKh8mWUIlGBjwVzpkwZ2Hh8v0k4oU163F5cuv9T0sSu7r8SFSy7E9Rddj3pfvfJY33QffrTpR7jsT5fhUw9/amaM4MGJg4jJmHLuBYsusN3DovpFyrE+aUDPENjcvxnffOqbpuMQ7QICnDRARERUvhgQIKLypWcIBEaAEetO7OVKnzRw15ajeHhHX5F2Q5nSpw0AQGNVIz531ucAAEsbluLbF3zbtCFfTMbw4IEH8amHPwUpJfaPqn+uW/2tWNuy1vb9F9WqAQE9QyA1IHBg7ADedc+78Nsdv8XXn/g6fr3918pebEsGBlkyQEREVK4YECCi8tW4BKhuUdcc2EfgFSd2wJVSch6TwCd+9wJ2p2QKSCmx6/g4/rF7AJFozORVqNDO6DwDC2oXKGufOeMzaPHP/pm9YPEF+MmlP8Ep7aeYvsb2oe145tgzhv4ByxqWYUn9Etv3N5QMaKMHD4wdQDgaBgDcvvd2hGPhmcce2P/AzI/HQ+OG7IRUfdN9GJwetN0LERERlSYGBIiofAlREX0ElrTU4NOvXK2sjQcjeO8vnsXQZAh/29WPN/3oCVz6X3/HP//0Kbz5hicQizmvdKLcuIQLXzvna6j1xks+3rjqjXjdytcZzjur6yz85vLf4JYrb8E1a64xjAL8/c7fGyYMLG9cjrbqNtuxgXrJgJ4hEJXRmUDDE0eeUB47PnV85sdm2QFel1c53jnEsgEiIqJyVPYBASHEm4QQjwghhoUQk0KITUKILwghvOmfbXitWiHEl4QQzwohxoQQYSHEMSHEX4QQV83H/okoRxUwaQAAPvaKlbhqg3rH98DQFF7+nYfxzp89jWf3z46Fe/7ACP6xZ6DQWyQT5y86Hw+96SH87Zq/4evnft20PCBpTcsafOWlX8EnT/+ksv7QgYfw3PHnlLVlDcsghMDi+sWWr5c6ZQAA6n316KjuUNZ6RnswGhzF1oGtynrfVN9MHwF95GC1p9pQrmA3aWAkMGI5tpCIiIiKq6wDAkKI6wDcDOA8AE8DuAfAUgDfBvCQEML61onxtVoTr/EfANYAeALAnwEcBvAaALcLIa7P5/6JKA/0DIEjLziysaAQAt+5+hRsWNyorI8HzcfUbTk8WohtUQZqvDVKmUA6V51wlXLnPyqjhgyBZQ3LAABL65eavkarvxV+j9+wrpcN7B3diyePPgkJ9e9MOBbGcDB+Ea9nCDRWNWJNyxplzaqPwPef/z4u+MMFuPSWS3Hn3jtNzyEiIqLiKduAgBDidQA+CWACwNlSysuklG8EsArAFgDnA/i3LF7yawDWAXgOwLLE610jpTwD8YBABMAnhBAvzeNPg4hypWcIOLSxIAD4vW78+B1noqvBeKGn23ZkLOPX1TvKU3HV++pxZfeVtucsb1gOAJZ9BPRygSS9bGDfyD5DuUBS31S8caUeEGiuasaJLScqa2YZAtsGt+HHW34MAAhEA7juuev4Z42IiKjElG1AAMD/SXz/lpTy+eSilHIAwEcShx8TQjQanmnuFYnv35ZSDqU+IKW8C8DDicNz5rhfIpoPTUuB6mZ1zaFlAwDQ0eDHT955Jvxe9Z/vGp9bOX7xSPoMgds3Hsap/3ofzrr2QTyw7Xja86lw3nLiWywfExAzgQDLgECteUBAHz24Z3RP+oBAYERZb6pqMgQEekd7MRWeUtZu3nmz+nrTfbbTCoiIiKjwyjIgIIRYBOCsxOFv9cellP8AcBBAFQDj3CdzgQzPY2EuUSkRwpgl4MDGgqlOWtSIP3zgHJy/sg0XrWnHf12zAb9539nKOb2DU5iwKCcAgK2HR/HpP2zEyFQYAxNBfPm2LZxOUEJWNa/CGZ1nmD62sG4hfG4fAOuAgD5hIGlF4wrleM/wHhyZPGJ6brKxYLJ0IKnJ34RVzauUfggSErtHds8cj4fGcde+uwyveWzymOl7ERERUXGUZUAAwGmJ70NSyn0W5zyrnZvO3YnvXxRCKMWeQojLAVwE4BiAO7LZKBEVgKGPwMZi7KKgNixpwq/fdzZ+/u6X4PWnLcbaBQ1wp84mBLDjqHnZQCQaw5f+vAWpgwiOjwVxaHh6PrdMWbLKEkj2DwCApQ3mPQSsSgb0DAG9d0Aqq5KBpqomVHuqZ8oWkp488uTMj+/ceyemI8Y/T0cnj1q+HxERERVeuQYEkrc4Dticc1A7N51vA7gXwBkA9gsh7hFC/F4I8SyAvwJ4CsCFUkrbPFwhRJUQoiH5BaA+w/cnorkyyxA4/iJw5yeBH54F/ObNwOihYuysYPxeN1a21ylr2ywCAjc93mvadLBnYGJe9kZzc/HSi9FW3WZYTw0IdNV0wePyGM6xKhlo8beguarZ9DGdVclA8vkntZ2krP9s689mphP8cdcfTV+TGQJERESlpVwDAsmL7Embc5KfbBsyeUEp5SSAKwF8F0AtgMsAXIN4gGAQwAOITxxI50sARlO+nH0VQlQK9AyB6WHgf88FnrsJGNgF7L4XuP//FmNnBbVuofrPnVljwYNDU/jP+3aZPn9vn90/qVRoXpcXV6++2rCeGhBwu9xYXGccPWhVMgAYywasWGYI+JsAANesuUZZn4pM4XvPfQ/P9z2PPSN7TF/z2FTuAYGp8BT+d9P/4t+f/Hf0jvbm/HpERESVrFwDAnknhFgA4DEAHwfwFQDdAOoAvATxyQP/F8A/hBDp7vh/E0Bjypf1kGgiyo+mZUDiIsXS7vuAmLNr5NfrAQEtQ0BKia/evhXT4ajp85khUHquXnU13EJtGLmiQb2gX1xv/G9mQd0Cy9fUywasJAMCeg+BZIbAKe2n4HUrX6c89teev+KbT33T8jWPTeQeEPj6E1/H/2z8H/xh5x/w3vvei3AsnPNrEhERVapyDQiMJ77X2pyTzJ3NdPbWLxBvVPhVKeV/SCn3SSknpZTPALgC8VGGGwB8zu5FpJRBKeVY8itlr0Q0X4QwZgnogmPA4G77c8rcugVqQGDHsXGlUeBfNh/FIzv7LZ+/t58ZAqWms7YTb1v7tpnjJfVLcNaCs5RzltarfQQ6qjtQ5a6yfE199GCSHniwKhlorJod3vOp0z+Feq8aJ985vNPyvXPNEDg0fgh377t75rhvqg+b+jbl9JpERESVrFwDAr2J7+btldXHem3OATAzteCSxOHv9MellGEAtyQOX5nRDomosLovSn/OoWfmfx9FtFYLCIQiMfQMxC/yxwNhfOPObcrjQu1BiB4GBErSZ8/4LK49/1p86vRP4Vev/hW8Lq/yuN5Y0CxjIFV3k3lA4PxF5yvHI8ERTEemMRpS+000+2d7ELRWt+Kjp3007c8hKdemgnf23GlYGwwM5vSaRERElaxcAwIvJL63CiGsiiHPTHx/PoPXS/00ZZVRkPxE1GLxOBEV00veD5x4BeCtBZacDbz+RmCNNnX00LPmz3WI5lofFjb6lbVkH4E/P38YAxNB5bHPXrJaOR6YCGJ0munXpcbtcuOqE67Ce09+L1qrWw2PX7rsUvjds7/vV5xwhe3rndBoXjKgp/8DQM9ID2JSLbVpqmpSjq9Zcw1WN6t/lpJWNq1Ujvun+hGJWY/DtBOTMdy+53bD+lBgaE6vR0RERGUaEJBSHgKQvNX3Nv1xIcT5iGcIBAEYByEbpTYLPNvinJcmvluNOSSiYvLVAm/5DfDlI8B77wM2XAMsPUc9x+EBAcCksWCij8CfX1B7ol64ph0feNkJhlGFPf3sI1Bu2mva8ccr/4iPnPoRXHfhdbh6lbERYaqOmg7UetWKO7/bjwsWX4BqT7Wybpb+rwcEPC4Pvnz2l03f6+OnfVw5jsooBqYHbPdn5fnjz+PwhLG373Bg2ORsIiIiykRZBgQS/iPx/V+EEKcnF4UQrQD+J3H4w9QxgUKI1wshdgghHkx9ISnlAcwGGK4XQixPfVwI8c+ITxwAgN/m76dARPNq8Znqcd+LQMjZafF6H4FtR8awt38Cmw6OKOvvPHc5fB4XlrbUKOssGyhPyxuX48MbPoyLl10ModeCaIQQhiyBM7rOQJW7Ch01Hcr6rmF1IkW1pxp+j5qFAgCnd56OK7uvVNZWN6/GRUsugs/lU9bnOnrwjr13mK4zQ4CIiGjuyjYgIKW8DcD3EW8e+KQQ4m4hxC0A9gA4GfGJAV/VntYIYA0As3zJ9wAYALAWwHYhxMNCiD8KIbYC+BUAAeDXAH4zDz8dIpoPC04FUhulyRhw5AXL053ALEPg1ufVu6ptdVW4YGV8vv0J7eqdYk4aqAyntJ+iHF+4+EIAMAQEdg6pGQJ6dkCqz5z5GSxvWA4g3qDwC2d9AUIIdNV2KefNJSAwFZ7Cvb33mj7GgAAREdHclW1AAACklJ9E/M79EwDOBXA5gEMA/gXAK6SU01m81lYAJwH4NoBdiE8ceB2ADgD3ArhGSvl2KaXM58+BiOaRrwboXK+uObxsYN2CRuV4aDKEXz+1X1m7asNCeNzxf/672+uUx/b2MUOgErxr/buwtmUtAOCCRRfgDaveAMAYENAzBOwCAm3Vbfjda36HG155A+594704e0G8Am9BrToCcS6NBR888CCmIlOmj7FkgIiIaO48xd5ArqSUNwO4OcNzbwJwk83jxxEPJvxLPvZGRCVg8VnAsc2zxw6fNLC4uRr1VR6MB2cbt41MqY0C33D6opkfd7cxQ6ASddZ24g9X/AGhWEgZUagHBMZCap9du4AAANT56nDuonMN75VqLhkCZs0EkxgQICIimruyzhAgIkpL7yNw6FnAwYk+LpcwjB9MtbqzDutTygr0DIHewSlEY8799aFZQgglGAAAnTWdFmfHNfmbsn4fvWQg2wyBIxNH8PSxpy0fZ8kAERHR3DEgQETOtvgs9XjiGDBm7FTuJHofgVSvP22x0nRO7yEQisRweDjjaityGD1DQNdc1Zz1a+olA9lmCNy5905IWAepRoIjiMaiWe+LiIiIGBAgIqdrOQHwq3X1zu8jYB4QEAJ43WkLlbWWWh8aq73K2l6OHqxY6QIC6UoGzOTSVFBKaZgucOGSC9VzIDEaGgURERFljwEBInI2lwtYpJcNOLuPgFWGwLkntGJBozpnXgiBbi1LgAGByjUfJQN6hsBwcBiBSAAAEI6GcePmG/HxBz+O2/fcDr1v776xfTgwfkBZe9f6dxneY2iaZQNERERzwYAAETmf3kfg8HPF2UeBrOyog8dlnEX/htMWm57f3ab2EegZ4KSBStVa3QoB45+dpLmUDOgZAgBwfOo4AOD3O3+PH7zwAzxy6BF85bGv4Pm+55XzekZ61P35W3F6x+mo99Ur68NBNhYkIiKaCwYEiMj59D4CR14AomHzcx3A73VjZYd6kV/tdeNVJxkvzADghA5t0gAzBCqW1+VFa3Wr5eNzyRCo9dai3qtewCcbC97Vc5ey/sjBR5Tj3rFe5bi7qRtCCLT61T0OBgaz3hcRERExIEBElWDRGepxJAAcf7E4eymQ9QvVvgmXre9EbZX5pFlDhkA/MwQqmV0fgbn0EACArjpjH4HJ8CS2D21X1ntG1YyA3tFe5Xh5w3IAQLNfzVTg6EEiIqK5YUCAiJyvpiXeXDCVw/sIvPf8FUhWDdRVefCxV6yyPFefNNA3HsR4wLkZFGRvXgICNcbRg5v6NiEq1ekAeomAniEwExCoYkCAiIgoH8xvFxEROc3iM4GhvbPHh58D8P6ibWe+rVvYgL99/iI80TOIl65oxdLWGstzl7bWwCWAWEo/t57+SWxY0jT/G6WSY9dYcK4BAb2x4PHJ43g2apz2cXjiMAKRAPwePwCTgEDjcgBAS3WLsj4UYFNBIiKiuWCGABFVBr2PgMMzBABgSUsN3nzmEttgAABUedxY2qKe0zPAPgKVqr263XS92lM9c6GeLb2x4NHJo3juuLG5p4TE/rH9AICRwAhGg+o4QasMAQYEiIiI5oYBASKqDHofgcE9wHjm89CdrrudfQQozqpkYK7ZAYAxILB/bD+2Dmw1PTfZR0DPDvC4PFhYtxAA0OJXMwTSlQw8cvARvP721+Of7vonbBvclsXOiYiInI0BASKqDF0nA1UN6tque4uzlxLU3ab2EdjLSQMVy6pkIJ8BgcMThxGKhUzP3Te6T/metKR+CTyueKWjHhCwyxDYO7IXn37409gzsgeb+zfj2ievzXr/RERETsWAABFVBrcXWHmxurbz7uLspQQxQ4CSrDIE9M7+2dADAnaSGQLJ0oGkZLmA2V7sMgT+89n/RERGZo43D2xG2MFjR4mIiLLBgAARVY41l6vHPQ8Doani7KXEdGuTBvYNTCKW2mWQKkZHrXlAoLGq0XQ9E501nRAQGZ1rVTKQGhDQMwRGgiOIxtSJBQDw+JHH8ejhRw3r7DlAREQUx4AAEVWOla8EhHv2OBIAeh4p2nZKyQlahkAwEsPhkeki7YaKqd5bj2pPtWFdb+SXDZ/bh9bq1ozO3T+6H9FYFL2jvcp6csIAYAwISEiMBEeUtWgsiu8++13T9zALCIwERrClfwuzB4iIqKLkFBAQQniFEOsSX1Umj/uFEP8phDgohJgWQmwTQnw8l/ckIpqzmhZg2bnq2s67irOXEtNW50O9X51Eyz4ClUkIYVo20ORvyul19dGDVkKxEA6MH8CB8QPKemqGgNle9LKB2/bcht3Du03fQw8IbB/cjituuwJvu+tteMMdb8BkmCUzRERUGXLNEHg9gC0A/mbx+K0APgVgEYAqACcCuE4I8cMc35eIaG7WvFo93nUvEIsVZy8lRAhhaCx4YIjlFJXKLCCQS4YAYN1H4ITGEwwNCx8/8jjCMfVO/bKGZTM/9rq8qPfVK48PB2cDApPhSfzghR9Y7kUPCPx2x29nRhz2jvXill23WP9EiIiIHCTXgMBlAASA26SUwdQHhBCvSTwOAIcQDw4cTpz/YSGEdpuOiKgA9IDAZB9w5Pni7KXELGs19hGgymSaIZDDlAHAenrBGZ1noLuxW1l76MBDynG9r95QJtDqV0sQBgODMz/+2dafKcc6PSCglyc8d/w5y+cSERE5Sa4BgdMBSJhnCLwn8X0XgPVSyjcCOAnA9sT6+3J8byKi7LV0A+0nqmssGwAALNcyBPYPMkOgUhWyZOCMzjOwonGFsqZfkC9vWA4h1KaEVpMG+qb68IsXf2G7Fz1YMDA9oBxv6t8EKdlUk4iInC/XgEDyE8Oe1EUhhAvAxYgHC34gpRwHACnlKIAfIp4lcE6O701ENDerX6Uec/wgAGB5a41y3DvIDIFKZXY3f75KBk7vPN2QIRCV6sSA1P4BSXrGQDIgcP/++xGMziYtuoUb5y08Tzl3aHo2Q0BKaQgIDAWGDGMPiYiInCjXgEBb4rveivpUAA2JH/9Ve2xr4vuSHN+biGhu9PGDfduAoX3F2UsJ0UsGDg5NIcrRgxXJLEMgl7GDgHmGwOK6xeiq7UJ3U7fJM2alThhI0jMEkmUAO4Z2KOuXLrsUZ3adaXouEO83EIgGDK+/sX+j7Z6IiIicINeAQDIE36atvyzx/ZCUUg+xjye+u0FEVAyLzwRqtH+2dt1TnL2UED1DIByVOMLRgxVpPnoImGUInNF5BgAYMgR0qQ0Fk/SMheRF/q7hXcr6ye0nG7IJUgMCenZA0sa+jbZ7IiIicoJcAwLJi/2ztfUrES8X+LvJc5L/K/fn+N5ERHPjcpuUDbCPQEutcfQgywYq06qmVaj3znbxX9uyFn6PP6fXbK1uhcel/vlKBgS6artQ7am2fK5ZyUBrtdpUcDgwjEgsgr0je5X11c2rbQMC/dPmH0de6HvBcj9EREROkWtA4GHE+wF8XAixFgCEEFcBuDDxuNkn7JMS34/m+N5ERHOnTxvofQyYHjY/t0IIIbBcKxvoZWPBilTjrcG3XvYtrGpehQ3tG/CNc7+R82u6hAtndJwxc1zlrsJ5i86beczsoj8p0wyBA+MHlP4BALCqeZVpQCDZNHBw2nwaQc9oz8woQiIiIqfKNSDwAwAhxJsLbhVCDCA+XlAgPmLwTybPuRTx7IHNOb43EdHcnXAR4K6aPZZR4ODTxdtPiVimlQ3s5+jBivWyxS/Dn6/6M359+a+xtnVtXl7zKy/9Cl664KVY3bwa/3H+fyilCVZ9BBbULjDNTjCbMqCXC7RXt6PF32IICASjQUxF4sEuqwwBID5tgIiIyMk86U+xJqXcLYR4O4CfAajFbDnACIC3SilDqecLIboAXJI4VIcMExEVkq8W6DoZOPzs7NrooeLtp0QYMwQYEKD8Wd64HD++9Memj61oWGG6bpU5oF/kjwRHsGNQbSi4unm16blAPDOg1ltr2UMAiJcNvGzxyywfJyIiKnc5BQQAQEr5RyHE3wC8BkAX4qUAd0gph0xOPwXAbxM/ZsEuERVXw4J4LlPS+LGibaVU6BkCLBmgQrHKEDArFwCMF/kSEs8ce0ZZSwYEarw1qPZUYzoy2yRzKDCEpQ1L0wYEiIiInCzngAAASCn7APw8g/PuA3BfPt6TiChn9doYtPEjxnNCk8CeB4Hm5cCCUwqyrWJa0aZmCBwYjI8edLtEkXZElcJq0oDZyEEAaPI3Gda2Dm5Vjlc1r5r5cYu/BYcnZiOAg4F47wC7gMDWga0Ix8LwuryW5xAREZWzXHsIEBGVr3ptDJqeIRAJAj+6ALj57cCNLwe23FK4vRXJMq1kIBSN4diYcUY7Ub4trV8KtzBOJLYqJfC6vGjwNShrMRlTjpMZAoAxoyA5acCuh0AwGjSUIRARETnJvAcEhBCdQoj3CiG+KIR4sxCiJv2ziIgKoH6heqwHBHbdCwwlRpjJGPD0jYXZVxG11flQ61MvynrZWJAKwOv2Ykn9EsP6skbzkgHAvDdAkkd4lKwDQ0BgOh4QsJoykMSyASIicrKcAgJCiLVCiJuFEH8QQjSZPH4VgL0AbgTwHwB+B2C7EOLUXN6XiCgv9AyBMa1kYEidZ46Rg/O7nxIghDBkCbCxIBWKXjZQ5a7CgtoFFmcbJw2kWtG0Al73bKp/a3Wr8vhQYAjhWBjDAXXc6MltJyvHG/s3pts2ERFR2co1Q+B1AK4GsFBKOZL6gBCiA8CvAdQgPoYw+bUEwJ1CiLoc35uIKDd6D4HpoXiZQJI+dWCyH0jMLncyvY/AfjYWpALRGwsubVgKl7D+qGKXIZBaLmB27lBgCEPTQ5BQ/06/ctkrleONfRshK+DvPRERVaZcAwIXA5AA/mLy2EcA1AGIAPgMgA0AvgAgBmAhgPfn+N5ERLnRMwQAtWxADwjEwkBgZF63VAoMkwZYMkAFclbXWcrxGR1n2J5vlyGQSUBgIKA2FPQIDy5ccqGy1j/drzQjJCIicpJcAwJLE9/NCuzeiHiw4JdSyuuklFuklN8F8FPEMwWuyvG9iYhy428EvFpbk/Gjsz/WAwIAMGndkdwplrNkgIrknAXn4N3r341WfyvOXXguPnDKB2zPb67KMSAwpf59bqluwYqGFYbXZdkAERE5Va4BgY7E977URSFEG4D1icPfas+5I/F9XY7vTUSUGyFMJg2kBgRMegZMWnckdwo9Q2D/4BRiMaZM0/wTQuAzZ34Gj1zzCG645Aa017Tbnq/3BUiVUUBAGznYVt0GIQQ2dGxQ1jf2bcxg90REROUn14BAdeK7X1s/P/E9BOAf2mPJT9tNOb43EVHu9D4CyZKBwBgQGDWeP9FnXHMYvYdAMBLD8XGOHqTSY5Uh0FTVhPZqNZigBwSGA8M4PnVcWUs+Z0O7GhDYN7ov160SERGVpFwDAkOJ70u19YsT35+VUoa0xzyJ7xM5vjcRUe70gEBy0sCYRc1wBWQItNdXodqrjx5kY0EqPVY9BFY3r4YQQlnTswkkJPaM7FHW2qrbAACdNZ3K+nBQnURARETkFLkGBDYlvr8tuSCEqAbwJsT7Bzxk8pzkQOHjJo8RERWWoWQgkSFg1j8AqIgeAvHRg1pjQfYRoBJkNWVALxcA4lkDut3Du5XjZNCgsapRWR81yxYiIiJygFwDAr9HvEHglUKI3wshPgbgPsR7C0gAvzN5ztmJ7/tzfG8iotwZSgYSVU1m/QMAYNL5JQMAGwtSecgmIOBxeQxBgf1j6keRZMmAXoowEhzh6EEiInKkXAMCv0S8R4BAPCvgegDnJh77uZRyh8lz3oB4sODxHN+biCh3DRY9BCwzBJxfMgAAy7U+AvtZMkAlqMnfZLpuFhAAjAEECfUiP1kyoAcOQrEQpiPTc9skERFRCcspICCljAF4NYDvATgEIALgIIB/A/Bh/XwhxBUAlicO78rlvYmI8sIyQ8Cqh4DzSwYAYDlLBqgMeF1eNPgalDWXcKG7qdv0fLupBEBKQMAk0DASHJnTHomIiEqZJ/0p9qSUkwA+l/hK5zEAKxLPY8kAERWf3kMgNAEEx60zBCpgygAALNNKBvYPTkFKCSEEpkIReFwu+Dy5JpkR5a7F34Kx0NjM8dL6paj2VFueaycZEKjz1sEjPIjIyMxjw8FhLKxbmIcdE5WXyfAkbnrxJgQiAbxz/Ttn/p4QkTPkHBDIhpRyGABb9RJR6dAzBIB42YBlD4EKyRBoUzMEpsNR3PD3Hty+8Qi2Hx3D4uZq/PgdZ2LtggaLVyAqjBZ/C3rHemeOrcoFkufaSV7oCCHQWNWIwcDgzGNsLEiV6ot//yL+duhvAIBHDj6CO153h2GKBxGVL97eIaLK5q0G9PTg0UOz4wd1wVEgEpz3bRVbZ70ffq/6X8S37t6B7Ufjd2IPDU/je/fvKsbWiBRdtWqWz9rWtZbn2gUE6r318Hv8M8d6HwGOHqRKFI6F8ejhR2eOe8d6cWjcIoOOiMpS3gICQgi3EOKNQoj/FUI8KoR4MfH1aGLtjUIId/pXIiIqMD1L4OgmIBa2Pr8CsgRcLoFlLbW25zzbO8TO61R0V6++Gl6XFwDQ4GvA61a+zvJcu4CA3l9A7yPAHgJUiabCU4jJmLoWYZNZIifJS8mAEOJVAG4EsCh1OfFdIj554AMADgkhPiClvDcf70tElBf1XUD/9tnjQ8/Ynz/ZBzQusj/HAZa11mDn8XHLx4enwugfD6KjwW95DtF8O6vrLNxy1S3YObQTL13wUjT7my3PbfVbNxXU66L1DAEGBKgSTYaNDWUjsYjJmURUrnLOEBBCvB3AXxAPBojE134ATya+ks0DBYAlAP4qhPinXN+XiChvGrRGYWkDAs7PEACAi9d2GNb0stEdx6wDBkSF0t3YjVeveLVtMAAAWqqtMwTaq9uVY0NAIDAy1+0RlS2zgEDYLoOOiMpOTgEBIcQyxDMDXACmAHwFQJeUsltKeW7iqxtAF4AvA5hInPtjIcTS3LZORJQn+qSBieP250/2z99eSsgbTl+Mz16yGuetbMV7z1+Bhz77cpyyuEk5ZycDAlRGsioZYIYAEQMCRBUg15KBTwKoQvxC/2VSyo1mJ0kp+wF8UwhxF4BHAdQmnvvZHN+fiCh3ZpMG7FTI6EGv24WPX7wKH8eqmbUTO+ux6eDIzDEzBKic2AUE2mvSZAgwIEAVaCps7BfAgACRs+RaMnAp4j0C/p9VMCCVlHITgO8iXj5wWY7vTUSUH9kGBCokQ8DMmq565Xjn8TGLM4lKT523bqYBoc7QQ4BNBYlMGwiyhwCRs+QaEEim/T+QxXPu155LRFRc6QICVY3qcYX0EDBz4gI1ILDr+AQi0ZjF2USlRQhhmSXQ5mdTQSKdaclAlBkCRE6Sa0AgOUYwmsVzkmHFvI08JCLKid5DQLdwg3o8WRklA2ZO7GpQjkORGHoHOYKKyofeKyCprcY+IDAaHJ2vLRGVLPYQIHK+XC/KDye+n5vFc5LnHsnxvYmI8qOuA7OTUk0sPE09ruCSgZZaH9rrq5Q1NhakcmKZIZBm7OB0ZBqBSGC+tkVUksxKBhgQIHKWXAMCDyP+KfpfhBAL052cOOdfEO878FCO701ElB9ubyIoYMEQEKjckgEAOFHvI3CMfQSofJgFBDzCYwgAmI0wZNkAVRqzDAH2ECByllwDAj8AEAPQDuApIcTVQgi3fpIQwiWEuBrAEwA6E8/5YY7vTUSUP1ZlA9XNQPNydW2yH4hVbt38mk41IMBJA1ROWv3GkoGW6ha4hPqRqN5Xb1hjQIAqDUsGiJwvp7GDUsqtQoivArgWwEIAfwAwIoR4HkAf4pkAnQBOB9CE2Zzcr0opt+by3kREeVW/ADi6ybjeuBioVceRIRYBAiNAjfUIMyczThpgQIDKh1mGgF4uAAAu4UKjrxHDweGZNQYEqNJw7CCR8+UUEAAAKeU3hRCjAL4DoAZAM4BXaKclAwFTAD4vpfzfXN+XiCivrCYNNC4BaowXC5gcqNiAwNoFamPB/YNTmAxGUFuV838pRPOupdr497a9ut3kTKCxSgsIBEbma1tEJcm0hwCnDBA5Sl46/Usp/wfAcgBfRHys4DEAocTXscTaFwEsZzCAiEqSZUBgMeD1m4werNzGgis76uDSejDuYpYAlYlMMwQAYx8BZghQpWHJAJHz5e12jpRyAMD/S3wREZUXqx4CDYvi32vbgNSxYxU8etDvdWN5Wy16+mc/KO48No7TlhqbsBGVmmwCAo1aIDA1W4CoEjAgQOR8eckQyIYQ4kohxPNCiOcK/d5ERJYaLAalNC6Of9f7CHDSgHLMxoJULrIJCOiTB0ZTg4JEFYBTBoicr+ABAQAtAE5NfBERlQarDIHGJfHvdXpAoHJLBgBgTafaR2AnAwJUJrIqGahSs16GA8wQoMrCpoJEzleMgAARUemx6yEAGDMEJiq3ZAAATlygZwiMQUpZpN0QZc7n9hkCAAvrzDOE9JIBZghQpTFtKsiAAJGjMCBARAQANa2Ay6uuCfds5oChZKCyMwT0koHhqTD6x4NF2g1Rdt685s0zPz6l/RSsbVlreh6bClKlM+0hwCkDRI7CGVFERAAgRDxLYPTA7FrDIsDljv+YPQQUS5prUONzYyoUnVnbcWwcHQ3+Iu6KKDMf3vBhnNZxGkaCI3jFkldACGF6np4hwIAAVZJwNGyaDcAMASJnYYYAEVGS3kcgWS4AmAQEKrtkwOUSWNWpZgmwjwCVk5cueCletfxV8Ll9lufoPQQYEKBKYpYdALCpIJHTMCBARJSUVUCgsjMEAODETk4aIGfTpwxMhieZLk0VYzJiHhBghgCRszAgQESUtOAU7XjD7I/1gEBwDAgH5n9PJWyN1kdg5/GxIu2EaH40+ZsMa8wSoEphlSHAgACRszAgQESUdMa7gUVnxn+89FzgtH+efUwfOwiwsaA2aWDX8QmEIrEi7YYo/xp8DYa14SBHD1JlMBs5CDAgQOQ0bCpIRJRU2wa8934gEgA8fsCVEjP1NwEuD5BaOznZDzQtKfg2S8W6BerFUigSw+ZDIzhzuXHOO1E58rg8aPA1YCw0m/3C0YNUKSwDAiybIXKUjDMEhBDRfHwB+Nk8/nyIiHLjcgG+GjUYAMSnELCPgKKpxmcYP/jYnsEi7YZofuh9BIYDzBCgysAeAkSVIZuSAZHHLyKi8lPbph5XeMkAAJy3Uv01eXxvZQdJyHn0PgLsIUCVglMGiCpDNiUDfwcg52sjREQlr7ZDPa7w0YMAcO4JrfjpP/bNHL9wYATToSiqfe4i7ooof/QMAQYEqFKwqSBRZcg4ICClvHAe90FEVPqsSgYG9wJjR4AlZwMe65nmTvSSFS1wuwSisXi8OBSN4dn9Q7hglUkTRqIyxIAAVSo2FSSqDJwyQESUKb1kYKIPePDfgB+cAfziCuCmy4FYtDh7K5J6vxenLG5U1thHgJzEEBAIjBRlH0SFNhVhQICoEjAgQESUqTqtZODFPwOPfhcz1VSHngF6Hi74tortvBPUQMkT7CNADsIMAapUliUDnDJA5CgMCBARZUovGTBrrHR0c2H2UkLOPaFVOd5yeBSj0/zASM6gNxVMHTs4HBhG72hvYTdEVCCWTQUlmwoSOQkDAkREmdIDAmb6d8z/PkrM6cua4fPM/ncSk8BTPSwbIGcwjB0MxscO/qXnL7jo5otw5W1X4muPfa0IOyOaX5Y9BJghQOQoDAgQEWVK7yFgpm/7/O+jxPi9bpy5rFlZe3wvAwLkDGYlA+FYGN96+luIynjPkFv33IqNfRsLvzmiecQpA0SVgQEBIqJM1S80rnmq1eOBXRXXWBAAzlupBkseZx8Bcgg9IDAeGsdTR59SSgcA4P799xdwV0TzbzLCgABRJWBAgIgoU/WdwAkXzx53rAPec496TiQADPcWdFul4Bytj8Cu4xPoGw8UaTdE+dPsbzas/Xn3nw1rD+x/AFLKQmyJqCA4dpCoMjAgQESUjbf8FrjyeuCqHwLvfwhYeCpQo5US9G0rytaK6ZRFjair8ihrT7BsgByg0ddoWHv4oHGayJHJI9g2VHl/98m5rAICkViEwS8iB2FAgIgoG14/cMa7gNPfDngT5QIda9Vz+iqvsaDH7cLZK1qUtcf3MCBA5c/r9qLOW6esRcwmjCCeJUDkFFYlAwAnDRA5CQMCRES5MgQEKvMu4bl6H4Ee9hEgZ2isMmYJmGHZADmJVVNBgJMGiJyEAQEioly1n6geV+DoQQA4V+sjcHBoGgeHzFNOicpJc5Wxj4CZ3rFe7BnZM8+7IZp/oWjIMhMGYB8BIidhQICIKFcd69Tjgd1ABd49WdNZj9Zan7L28M6+Iu2GKH8a/dYZAn63XznmtAFyArvsAIABASInySkgIIRYOoevJUKIdiGEL/07EBGVgQ4tQyAWBgb3FmcvReRyCbxsdbuy9sB2BgSo/FllCHTWdOLq1VcrawwIkBNMReyzu+yyB4iovOSaIbBvDl+9AI4BmBZC7BdC/EEI8eoc90FEVDzVzUBdl7rWv704eymyi9d2KMdP7h3ERJAfHKm8NVU1ma6/YukrcMmyS5S1PSN70DvaO/+bIppHzBAgqhy5BgREjl9LAFwN4C9CiHuEEJkV6RERlRpDY8HKDAi8bHU7PC4xcxyKxvCP3f1F3BFR7qyaCl689GKc2nEq2qrVhpoPHOC0ASpvViMHkxgQIHKOXAMC7058PZU4DgC4HcBXAXwo8fXVxFoAgEyc+14AnwbwUwCDiAcHLgFwW477ISIqDgYEAAANfi/O7lbHD7JsgMqdWclAY1Ujzug8Ay7hwsVLL1YeY9kAlbu0GQIV2CeHyKlyCghIKX8B4BwAL0H8on+5lPL1UsprpZQ3Jr6ulVK+HsByAHcmzj1PSnm9lPL9AJYC+CXiQYHzhRDX5LInIqKi0AMCFTppAAAuPrFTOX54Rx+iMY5io/Jl1lTw5YtfDo/LAwB45bJXKo9tG9yGwxOHC7I3ovmQLiDAHgJEzpFrU8HXAfgAgCcAvEFKaXkbKPHY6wE8CeDdQog3J9YDAN4D4PnEqW/JZU9EREXRrgUEBvcCkWBx9lJkr1yrBgQGJ0PYeHCkOJshygOzDIHUrIAzO8809Bn428G/zfe2iOYNewgQVY5cSwY+gngZwPVSyrS3fxLnXId4NsAHUtZjAH6cWD8zxz0RERVe+xr1WEbj4wcr0NLWGqzqqFPWHtx+vEi7IcrdorpFynG1pxrnLjx35tjj8uBli1+mnLOpf1NB9kY0H9JNGWBAgMg5cg0InJL4vieL5yTPPVlb35z43prTjoiIisHfADQuUdcqtI8AALxynZol8CD7CFAZW1y/GK9a/qqZ44+d+jH4PX7lnA3tG5Tjzf2bQVSu2FSQqHJ4cnx+Q+J7u+1ZquS59dr6dOI7/4UhovLUfiIwenD2uEJHDwLAK9d24H8f2TtzvPP4OA4OTWFJS00Rd0U0d99+2bfxzvXvRI23Bt2N3YbH9YDAoYlDGAoMocXfYjiXqNSxhwBR5cg1Q+BA4vtbs3jOP2nPTUoOr+Z8KiIqT5w0MOPUJc1oqfUpaw+wbIDKmEu4cFLbSabBAAA4oekEVHuqlbUt/VsKsTWivOOUAaLKkWtA4A7E6/7fIYT4WLqThRAfB/B2xPsO3KE9fHbie2+OeyIiKg4GBGa4XQIXrelQ1lg2QE7mcXmwvnW9srZ5gGUDVJ7YQ4CocuQaEPgWgOQnvOuFEE8KIT4mhDhHCLEy8XVOYu1JxBsKAvEsgG9pr/UWxAMFD+S4JyKi4mg/UT0e7gVC9h+qnOyVa9WAwFP7BjEe4IdIcq6T29X2SMwQoHLFKQNElSOnHgJSyiEhxCUA7gGwAMBZiS8rAsAxAK+SUg7NLArRDeDpxNefctkTEVHRtK9B/J+55NAVCQzsBBaeVsRNFc8Fq9vhc7sQisYAAOGoxLt//gy+csU6nLqkqbibI5oHp7SdohxvGdiCmIzBJXK9/0JUWOmaCrKHAJFz5Pw/lJRyC4B1AK4HMIr4p2GzrzEAPwCwXkq5WXuNHinluxNflTmni4jKn68WaF6mrvXtKM5eSkBdlQdnd6sN1Z7dP4zX/fdj+MTvXsCh4crNniBnOqVdDQhMhCfQO9pbnM0Q5WAywgwBokqRl5C1lHJUSvlpAF0AzgfwIQBfSnx9CMAFALqklJ+UUg7n4z2JiEpSxzr1uL9yAwIA8E9nLzNdv2PTEVz6X3/H0/uGTB8nKkcdNR3orFFHbm7q31Sk3RDNHccOElWOvOawSSlDUsrHpZQ3Sim/nfi6UUr5mJQymM/3IiIqSS1aB/KxI8XZR4l41Uld+NE/n4HFzdWGx6ZCUfzvI3uKsCui+aNnCWwZYB8BKj+cMkBUOVjURkSUT/UL1OPxo8XZRwl51UldeOAzL8f/ufxE1PvV1jW9gywbIGfR+whs7uekASo/ekCgzlunHDNDgMg5GBAgIsqn+i71mAEBAIDf68YHXnYCfvTPZyjrA+NMHiNn0ScN7B7ZnTb9mqiUSCkNf2YbqxqVYzYVJHKOnKYMpBJCbEC8V0A3gHoA7jRPkVLK9+br/YmISoKeITB2FJASEKI4+ykxnQ1VyvF4MIJAOAq/N91/GUTlYV3rOriFG1EZBQDEZAwvDr6Is7rshjARlY5wLIyIVC/4m6qacHjisHIOETlDzgEBIcQaAD8D8NJsnob4XC4GBIjIWRq0gEB4EgiOA/6G4uynxLTVVRnWBiaCWNxcU4TdEOVftacaq5tXY/vQ9pm1LQNbcFbXWRgODOP2PbejzleH1658LbwubxF3SmTOrH+AniHAgACRc+QUEBBCLALwdwBtiF/kA8AEgGEAsdy2RkRUhvQMAQAYP8aAQEJjtRdet0A4KmfWBiZCDAiQo5zSfooSENjcvxm7h3fjA/d/AAPTAwDiQYJvnPuNYm2RyBIDAkSVJdceAl8G0J748U8AnCilbJBSLpNSrkj3leN7ExGVHm814G9S18Yre9JAKiEEWmvVLIHBCfYRIGc5uU3tI/DMsWfwrnveNRMMAIC/7P0L67CpJOkBAQGBBp8a1OaUASLnyDUg8CrEU/9/KaX8gJRyVx72RERU3gyTBo4VZx8lqq3epxwPMCBADqM3FhwLjWEsNKashWIhHBo/VMhtEWVkKqI2FKzx1hjKW/QeA0RUvnINCCxMfP9lrhshInIMvY/AGDMEUul9BAYmQkXaCdH8WN6wHPW++rTn7RvdV4DdEGVHzxCo9dTC61YDAswQIHKOXAMCw4nvIzm+DhGRczBDwJYeEOjn6EFyGJdwGcoGzPSM9hRgN0TZ0UcOmmUIsIcAkXPkGhB4NvF9da4bISJyjPou9Xj8aHH2UaKMGQIMCJDznNJ+imHN7/Yrx8wQoFKkZwgwIEDkbLkGBL6P+HSBD+RhL0REzmDIEGBAIFVbHXsIkPNd2X0lPGJ2mNNrT3gtPnrqR5VzGBCgUqT3EKj11sLjUgeTMSBA5Bw5jR2UUt4vhPg2gC8KIf4XwCeklPwXgogqG0sGbLGHAFWCpQ1L8evX/Br3996PE1tPxKXLLsXjRx5Xztk3ug9SSgghLF6FqPBMewjoTQU5IYPIMXIKCAgh3gFgO4DHEc8SuFIIcQuAHQCm7J4LAFJKNiMkIufRmwqOHwViMcCVa1KWM7BkgCrF+tb1WN+6fuZ4RaM6cXk8PI6B6QG017TrTyUqGpYMEFWWnAICAG5CfOxg0gIAH8/wuRKcTkBETqRnCMQiwNQgUMcP/YBx7ODIVBjhaAxeNwMm5GwLahfA7/YjEA3MrO0b3ceAAJUUQ4aAN/9TBgamB/CNx7+BntEevH7V6/Hek97LTBmiIsnHpy+RwxcRkfPUdgBC++eVfQRm6BkCADA0ybIBcj6XcGF543JljZMGqNToUwZqvcaSgVwzBG7cfCMeOfQIDowfwPXPX49tQ9tyej0imrtcMwRWpD+FiKjCuD3xoMBESu+A8aPAAmPX8UrUXOODSwCxlPyy/vEgOhv81k8icogVjSuwY2jHzDEDAlRq9KaCNd6avDcV3Ny/WTne0r9FKa8hosLJtang/nxthIjIUeq7jAEBAgC4XQIttVVK7wD2EaBK0d3YrRxz0gCVGkMPAU/+ewiMh8aV42CU/wcQFQsLNomI5kPDQvV4jAGBVMbRgywZoMqgNxZkhgCVmkxKBnKdMjARnlCOQ1H+H0BULAwIEBHNh/ou9ZgZAor2ek4aoMqkZwj0TfUZ7sgSFZNpU8E8ZghIKTEWGlPWUhttElFhMSBARDQf6rUMgfFj5udVKMPowXEGBKgyLGtYBpfWdJRlA1RKJiPzO2UgGA0aMgyYIUBUPBn1EBBCPJT4oZRSXmyyPhfKaxEROYohQ+BIcfZRolpr9ZIBBgSoMvjcPiyuW4wD4wdm1vaN7sNJbScVcVdEs8x6COhyyRDQ+wcAQCDCDAGiYsm0qeCFie/SZF0iuxGCyfP11yIico6GBeoxMwQUbVrJwCDHDlIF6W7sVgIC7CNApUJKienwtLJW661FKKb+G51TQCBsDAjor09EhZNpQODvML+At1onIqps9VpAYLIfiIQAj8/8/Aqjlwz0s2SAKsiKxhV45NAjM8c9IwwIUGkIxUKISDWdv9ZbCxlWP+7n0lRwIjRhWOOUAaLiySggIKW8MJt1IqKKpwcEAGDiONC0pPB7KUGcMkCVTJ80sG+MPQSoNJg1uKzx1hgu2MOxMKSUECKbJOE4s5KBYIQBAaJiYVNBIqL5UN0MuNW74CwbmKVnCAxNBhGNMeGMKoMeEDg4djDnue5E+WAaEPDUGKYMADBkEmTKrGSAGQJExcOAABHRfBCCjQVt6GMHYxIYnmKWAFUGPSAQkREcHD9YpN0QzZoKTynHLuFCtafaMGUAmPukAdMMAQYEiIqGAQEiovnSwNGDVlpqjb0UOGmAKkVjVSNa/a3K2r4Rlg1Q8ZlNGBBCwCOMVcZzzWphDwGi0pJpU8GMCCHqAawAUA/Ane58KeXf8/n+REQlRc8QGGOGQJLX7UJzjRfDU7MfKAfGQ0CXzZOIHKS7qRuDxwZnjtlHgEpBIKqO//N7/ABgmiEw18aCzBAgKi15CQgIId4P4CMATkbmIwhlvt6fiKgk1TNDwE5bXZUaEGCGAFWQ7sZuPHPsmZljThqgUhCNRZVjjyv+Ud2sh8BcMwQYECAqLTldkAsh3AD+BODK5FLOOyIicgpDD4GjxdlHiWqrq8LuvtnUUQYEqJIYJg2MMkOAii8q1YCAW8QTfvMZEJgIm5QMcMoAUdHkeof+QwCuSvz4OICfA3gOwBCAWI6vTURU3vTRgwwIKFo5epAqmNnowbmOcSPKF70MIBkIYIYAkXPlGhB4R+L7NgAXSCmHc3w9IiLnaNADAiwZSKWPHmSGAFWS7sZu5XgyPInjU8fRVctGGlQ8+ijBZIaA2+WGgIDE7HjYfE4ZCEUZECYqllynDKxFvBfAvzEYQESk0TMEgmNA0JgqWan00YMMCFAl6azpRI2nRlnbMbSjSLshitN7CLhdsz3C9SyBuTYVNCsZ0JsZElHh5Gvs4M48vQ4RkXPoPQQAZgmkaDOUDDAgQJVDCIH1beuVtc39m4u0G6I4/SI/2VQQME4ayGfJQDgWRkyy2pioGHINCOxOfG/JdSNERI7jqwWqGtU19hGYYSgZGGfKKFWWU9pOUY439W8q0k6I4vSmgh6REhBw5ScgMBEyz5RjHwGi4sg1IPB7xCcLXJGHvRAROY+hjwADAkl6QGBwMggppcXZRM6zoX2DcrxlYIshZZuokPQMAbuSgbkEBGIyZloyALCPAFGx5BoQ+D6ATQA+LIS4IA/7ISJyFo4etNSm9RAIRyVGp+d2x4moHJ3SrmYITEemsWdkT5F2Q2RfMpD6Y7NzMzEZnlQaE6YKRNhHgKgYcgoISCmDAC5DfNTg/UKI7wghThVC+POyOyKicqc3FhxjQCCptdZnWGMfAaokrdWtWFy3WFlj2QAVk14ykJwyAJhkCMxhyoBZ/4AkZggQFUdOAQEhRBTAUQDnAPAB+CziwYFJIUQ0zdfcWpMSEZUTPSDADIEZfq8b9X71jtPABD8QUmXZ0KGWDTAgQMVk21RQDwjI/AYE2EOAqDhyLRkQKV/6cSZfRETOZsgQOFKcfZQoQ2NBZghQhdEbC3LSABWTbVNBfcrAHDIErPoHAAwIEBWLJ/0ptr6Rl10QETlV0xL1eHAPICUgGBMF4qMH9w1MzhwPjMc/EG49PIq+8QDOX9kOnydfE3KJSo+eIdA71ovR4Cga9QklRAWQTVPBufQQYIYAUenJKSAgpWRAgIjITvsa9Xh6CJjsB+o6irOfEmPMEAjhe/fvwvcfjE+13bCkCbd++Fy4XAygkDOtbl4Nv9uPQHS2odrm/s24YDF7NVPhZdNUcC5TBuwCAql/B4iocHjbhYhoPjUtBzzV6lr/jqJspRTpAYGbnz04EwwAgE0HR/Bkz2Cht0VUMF6XF+ta1ylr7CNAxZJVU8E8BwTYVJCoOBgQICKaTy4X0L5aXetjQCBJDwj0jRtTRrcdHSvUdoiKYkO7WjbAPgJULNGY1kPApqngXEoG7HoIMEOAqDgYECAimm/tJ6rH/duLs48S1FZvHD2o23nM+o4SkRPoAYEtA1sQk7Ei7YYqmX7X33bKwBwyBCZC1gEBZggQFUdGPQSEEO9I/lhK+Uuz9blIfS0iIscyBAR2FmcfJUjPEDCz6zgDAuRsp7SrkwYmwhPoGenByuaVGA2O4vEjj2NV0yqsbF5ZpB1SpbAtGcjDlIGxkHXGF5sKEhVHpk0FbwIgE1+/NFmfC/21iIicSQ8I9G3npIGEzAICE4jFJBsLkmO117RjYe1CHJmcHUu6qX8TRoIj+MTDn8B4aBwelwf//Yr/xrmLzi3iTsnpDFMGUgIC+WgqaDt2MMKAAFExZFMyIBJfVutz+SIicr4OLSCQnDRAWNRUbVi7aE27cjwdjuLQ8HShtkRUFHqWwO93/h4feuBDM03YIrEIbtl9SzG2RhVEzxDId8kAxw4SlZ5MAwIrEl/dFutz+dJfi4jImZqWcdKAha5GPy4+cXYE4ytO7MANbz8TzTXqB8+dLBsgh9P7COwY2mG4QDoycQRE88lu7OB89xBgQICoODIqGZBS7s9mnYiIUrjcQNsq4FhK5/C+HcCKlxVvTyXkR28/A/e9eBw+jwsXn9gBl0tgdWc9nto3NHPOruPjuGRdZxF3STS/9AwBM4MBjuCk+WUXENBLBuYyZYA9BIhKD6cMEBEVQsda9ZgZAjO8bhdec8oCXLKuc6ZPwJqueuUcThogp1vbshY+l/3UjaHpIUg519ZNROnZNhXMR4aAXQ8BBgSIioIBASKiQmhfox4zIGBrdacaEOCkAXI6r9uLta1rbc8JxUK2F1REuYrGtICAK79TBlgyQFR6GBAgIiqEdu2DfnLSAJnSMwT29k8gHOVcdnK2K7uvVI4/cupHDOcMTrNsgOaPXgaQmhXgEblNGQhHwwhEA5aPc8oAUXFkOnYwLSHECQCuArABQBuAathPEpBSyovz9f5ERCXNdNLAAFDXbn5+hVvdoQYEwlGJ3oFJrNIyB4ic5E1r3gSPy4MXB1/EpcsvxUsXvBS/ePEXmAxPzpwzGBjE8sblxdskOVpEWo8dNGQIZBkQGA/bZ3oxQ4CoOHIOCAghagD8N4C3wxgAEAD0W2DJc3hrjIgqR9MywOMHIil3R/q3MyBgobHGi64GP46Nzf567Tw+zoAAOZpLuPDG1W/EG/HGmbUWf4sSEBgKDJk9lSgvDCUDNj0Esm0qaDdyEABC0VBWr0dE+ZFTyYAQQgC4FcA7Eq81CGATZi/6HwWwDUA0ZW0ngL8B+Hsu701EVFZcbqBttbrWxz4CdlZrZQO72FiQKlCrv1U5ZskAzSc9QyCfYwft+gcAsC0nIKL5k2sPgTcBuCTx428A6EI8OAAAkFK+XEp5MoBmAJ8BMAmgBcBXpZQX5fjeRETlpV0rG2BjQVtrOuuU451sLEgVqLVaCwhw9CDNIz1DIJ8BgXQlA8wQICqOXAMCb0t8f0JK+Q0pZQwmpQBSykkp5XUALgZQD+DPQoiFOb43EVF50fsIMCBgyzhpgN3VqfIwQ4AKSS8DSC0ZSA0OANlPGUhXMsAMAaLiyDUgcCbiAYAfZ3KylPIZAP+LeNPBT+T43kRE5YWTBrKiTxroHZxEIBy1OJvImVqqW5Rj9hCg+VTMkgFmCBAVR64BgbbE956UtZl/HYQQ1SbP+Wvi+xU5vjcRUXlpX6MeJycNkKmVHXUQKa1qpQT29DFLgCoLMwSokAxNBV3WUwaybSo4FhqzfTwQYYYAUTHkGhBI/kuQmgOU+uMuk+eMJr4vyfG9iYjKS/Py+KSBVP3bi7KVclDj82BpS42ytpONBanCsIcAFZJ+kZ/XDIGwGtCt9dYqx8wQICqOXAMCRxLfU+dmHQMwnfjx6SbPWZX4nvPIQyKisuJyA22r1DVOGrBl7CPAgABVFj1DgCUDNJ+iUmsqKPLYVFDrIdBW3aYcB2PBrF6PiPIj14DApsT3k5MLUkoJ4KnE4UdSTxZCeBGfNgAAu3N8byKi8qP3EWBjQVtrtIAAJw1QpWnxqz0EJsOTTK2meZNVU8EcAwJ6sCsYYUCAqBhyDQg8BEAAeJW2/rPE+oVCiEeEEB8VQnwBwNOYbUR4c47vTURUfjhpICurtcaCu1gyQBVGLxkAWDZA8yerpoJZThnQmwrqf7aD0SAkG+0SFVyuAYFbEb+4v0gI0Z1clFL+GsA9iAcFLgDwfQDfBHBK4pSNAL6X43sTEZWfdi0gwEkDtvQMgSOjAYwFsvsQSlTO6rx18Ll8yhobC9J8sW0qmGvJQNi+ZEBCZv2aRJS7nAICUspjALwA/FLKHu3h1wO4FsBxxAMDAvGGgv8N4CIpJfPdiKjy6AGB6SFgcG9x9lIGVrTVwuMSytpulg1QBRFCcPQgFYxtD4EcpwzoGQJ6QACIZwkQUWHlmiEAKWVMSmkYDC2lDEopvyqlXIj4eMIuAK1Syo9LKe3njhAROVXzCqBOG8Cy+97i7KUM+DwudLernah3HuPoQaosHD1IhaLfoc/nlAF97CADAkSlIaeAgBBiaeKrxe48KeWQlLJPsjCIiCqdywWsvlRd23VPcfZSJlZpZQM9/QwIUGXh6EEqlGxLBrL5aK+PHWRAgKg05Joh0AtgH4C35L4VIqIKseoy9Xj/40BgtDh7KQNLW2qU46OjrDijysIMASoUvQwgtWRAnzIAGJsQWpFSGpsK+o0NMxkQICq8XAMC04nvz+S6ESKiitF9IeBOaRIWiwB7Hyradkrdwka/cnx4ZNriTCJn0kcPsocAzRdDDwGbkgEg80kD05Fpw2s3VjUqAQeAoweJiiHXgMDhxHe37VlERDSrqg5YfoG6tot9BKwsbKpWjo8wIEAVhiUDVAhSSsNFu1tYlwwAmWcIjIeMzWDrffXwudUJGswQICq8XAMC9yW+n5/rRoiIKsrqV6nHu+8HYob+rARjQKBvPIhghL9WVDlYMkCFYHZxr2QIuOeeIWAWEKj11sLvUTPAGBAgKrxcAwLXI1428DkhxKI87IeIqDLojQWnBoDDzxdnLyVODwgAwPFRfmikysGxg1QIekNBwL6pIJD5pAG9oWCNpwYel4cZAkQlIKeAgJRyN4C3AagB8KQQ4m1CCF+apxERUfNyoP1EdY3TBkw1+D2o9amVaUdGWTZAlUPPEBgJjmQ98o0oHb1cAEjfVDDTP4d6hkCdrw4A4HczQ4Co2Ix/sy0IIWIAYgBOkVJuS6wlu2D1A1gB4FcAfiqE2A1gGIBdTqeUUl48p10TETnB6suA/h2zx7vuBS7+avH2U6KEEFjYVI3dfbN3mNhHgCqJ3kMAAIYDw+io6SjCbsip9AkDQAZNBecYEGjwNQAAMwSISkDGAYEEoR1fCEBqj1cBOMnmNWTivMwHlxIROdHqVwGPXT97fHwLMHoYaGQFlo4BAapkTVVNcAkXYjI2szY4PciAAOWVWUAgtWTALdwQEJApH+HNnmNGLxmo81pkCHDKAFHBZRsQ0P0dvLAnIpqbxS8B/E1AYGR2bfe9wJnvKdaOSpbeR+DwSKBIOyEqPJdwobmqWZkuwD4ClG9mJQOpUwaEEPC6vAjFQjNrmWYIjIXGlON6Xz0AZggQlYKcAgJSygvztA8iosrj9gArXwlsvWV2bRcDAmYWNqp3kZghQJWmtbpVCQhw9CDlm9ndfr1MwOvWAgIZThmYCGkZAokeAlXuKmU9FA2BiAor1ykDRESUC338YM8jQGiqKFspZXqGwFE2FaQKw9GDNN/STRkAjI0F59pDoN4bzxDQAwKBKLO/iAqNAQEiomJaeTEgUv4pjgSAg08Wbz8lylAyMDwNKVmxRpVDbyxoFxDoGenBnXvvxGhwdL63RQ4SlsaL+9SSAcCYMZBxQCCsBQR85gEBZggQFV6uPQSIiCgXNS3AwtOAw8/Nrh3dDJzwiuLtqQQt0gICk6EoxgIRNFYbu14TOVGLv0U5tuoh8PTRp/H++9+PmIyhsaoRf339X9FY1ViILVKZM80QSBMQyLipoFXJgIcZAkTFNpeAwM+FEJN5eG+OHSQiAoCuk9WAwPEXi7eXEtXZWAUhgNSkgCMj0wwIUMUwZAhY9BC47vnrZqYRjAZHcdue2/DO9e+c9/1R+dObCnqEB0KoA8bmnCFgMXaQGQJExTeXgMCZeXhfjh0kIkrq1Ca1MiBgUOVxo62uCv3jsx2oj45OY+2ChiLuiqhwMukhcGDsALYMbFHWdg3vmtd9kXPod/v1fgHA3AMCVmMHDT0EIswQICq0ufQQEHn4IiKiJD0gMLAT4CxmA44epEqWScnAXfvuMqztGdkzb3siZ9EDAnpDQcCkqWCGUwb0sYOcMkBUOuYSEDhJSunKw5fxXxkiokrUuU49jkWAAd7V0y1q4uhBqlx6ycBQYGimNAAApJT4a89fDc/bN7pPOY/Iil4yoPcPAPLXQyBZMuBz+5T1YJTBcKJC45QBIqJi8zcCTUvVtWNbi7OXErawUc0QYECAKoleMhCVUWWKwI6hHegd6zU8bzoyjcMTh+d7e+QAGZUMuLMvGYjGopiKqON0kyUDfrca6GVAgKjwGBAgIioFhj4CDAjoFjQxIECVSy8ZANQ+AmblAkk9Iz3zsidyFn3KgEfkp4eA3j8AmC0ZYIYAUfExIEBEVAoYEEjLWDLAHgJUObxu70yadVKyj0BMxmwDAuwjQJmIyPQ9BOZSMjAZNg4nm8kQ8DBDgKjYGBAgIioFnevVY04aMNCbCh4bCyAa48AaqhxWowefO/4c+qb6LJ+3d2TvvO6LnGG+pgzoGQICAjXeGgDMECAqBQwIEBGVgq6T1ePJfmDC+gN+JdIDAtGYRN84swSocliNHrTLDgCYIUCZyaSp4FymDOgZArXeWrhE/BKEPQSIio8BASKiUtC8HEjcMZlxbIvpqZWqtdYHn0f9b4t9BKiSmGUIhKNh3Nd7n7J+zoJzlGNOGqBMzFeGwHhoXDmu9dbO/JgZAkTFl01AYAWAbgCchUVElG8uN9CxVl1j2YBCCIGFjewjQJVLbyw4FBjCY0ceM8x4/9hpH1OOA9EAJw1QWnOZMjCXHgLJ/gEAUOWuUh4LRUNpX4+I8ivjgICUcn/iK7OBo0RElB02FkxLLxtghgBVEr1k4Jljz+BbT39LWTut4zSc3HYyGqsalXX2EaB0MikZyEcPgeSEAcAYEAhEGOQlKjSWDBARlQpDQIAZAjoGBKiS6SUDB8cPGu78X77icgghcELjCco6+whQOplkCBh6CGQQEJgMMUOAqJQxIEBEVCq6tIBA/04gwg9HqfSAwGGWDFAF0UsGdLXeWly2/DIAwMqmlcpjzBCgdKKx+ckQGA9b9xCo8qgBgYiMZFSGQET5w4AAEVGp6FinHsfCwADbtqQy9hBghgBVjs6aTsvHuhu78f2Lvo9mf3P8uKlbeZwBAUonIufQVHAOUwbsSgYAZgkQFZrxbzoRERVHdRPQuBQYPTC7dnyrMXOggukZAkdHGRCgyrG2dS2WNSzD/rH9AACXcOHCxRfibWvfhpd0vQRCiJlz9QyB5KSB5Lg3It18lQxMhLQeAjYlA0C8CWaNPnWHiOYNAwJERKWkc70xIEAz9IDA8FQYU6EIanz874yczyVc+MWrfoE/7PwDajw1uGT5JVhUt8j03BOa1B4CgWgAh8cPY0nDkkJslcpQRk0F5zBlwNBUME1AgBkCRIXFT1BERKWk6yRg192zx2wsqFjY5DesHRkJYGVHncnZRM7TWt2Kj5z6kfTn+VvRWNWI0eDozNqekT0MCJAl/eLe7ZqfKQNKDwGzDAFOGiAqKOaNERGVks716vExZgikqvF50FSjfiBlHwEiI7NJA3tH2UeArOlNBfWLf7O1OU0ZSOkh4HV5ISCUx4PRYNrXJKL8YUCAiKiUdJ6sHk/2ARN9xdlLiVrYyNGDRJngpAHKht5U0KxkYE49BGxKBoQQHD1IVGQMCBARlZKWFYBHveBlHwGV3kfgyCjTS4nM6H0EGBAgO3MqGchgyoBdQAAwjh4MRPlvOlEhMSBARFRKXG6gUxs/yLIBxaImjh4kyoQeEOgZ7TGkhRMl6X82PCL92MFMmgrqYwdrfbXKcZWLGQJExcSAABFRqWlfqx6P7C/OPkqUniHwyM5+HB/jHSUinR4QCEaDODJxpEi7oVKnTxkwGzuoTxlIVzIQiUUwHVGDtvXeeuWYGQJExcWAABFRqanvVI8nB4qzjxK1YUmTcjwwEcSHfv0cghHe+SRK1epvRVNVk7K2Z2RPcTZDJU+/uDcLCGTbQ0DPDgDUKQOAcdIAMwSICosBASKiUlPTph4zIKA4e0ULLlil/hq9cGAEX7+DIxqJUgkh0N3Yraxx0gBZ0TMEzJoKZjtlQO8fAKhTBgDA5/Ypxxw7SFRYDAgQEZWa2nb1eIoBgVRCCHz/LadhSYtaOvC7pw/iN0+xvIIolT5pgBkCZEXvIZBRU8F0AYGQGhAQEKjWGuf63WpfGGYIEBUWAwJERKWmtlU9ZoaAQXOtDzf885nwe9X/xr5+x4t4tneoSLsiKj3dTWqGwIGxA0XaCZWCcCyMn275Kb706JfwxJEnlMf0BoGmPQSynDKglwzUeevgEuq/23qGQDAatH1NIsovBgSIiEqNXjIwNQiwM7jBuoUN+M7VG5S1cFTia7e/CCllkXZFVFqW1i9Vjg+OHyzSTqgU3LT1Jlz3/HX4S89f8MH7P6gEiCJSCwhkMmVA2k8Z0EsG9AkDgDFDgAEBosJiQICIqNToJQOQwPRwUbZS6q7asBAffJl6B3Tb0TEMTDDllAgAltQvUY5HgiMYDY4WaTdUbI8efnTmxxISTx59cuZYzxAwLRnQpwykyRDQSwbqvHWGc5ghQFRcDAgQEZWamlbjGssGLH3+sjWo8qj/ne3pMzayIqpEi+oWGVK0D40fKtJuqNhGgiPKcWoDP72HgFmGQLZTBgwZAl6TDAEPMwSIiokBASKiUuPxAVWN6tpkf3H2UgY8bhe629W7Tnv6GRAgAuJ3dBfULlDWWDZQufTskNQLesOUgQybCtqVaBl6CPiYIUBUahgQICIqRbV6HwFmCNhZ2aF+yNzLDAGiGXrZwIFxNhasRFJKjIXGlLVQbLa8ytBUMIMeAoB9HwE9Q8CsZKDKXaUcMyBAVFgMCBARlSI9IMCSAVsr9QwBBgSIZugBAWYIVKbpyLThoj+1B4ChqWAGUwYAYyAhVSY9BBgQICouBgSIiEqR3liQAQFbJ3Sodal7WTJANEOfNMDRg5VJzw4AtJKBWPYlA/pr6DLpIWAICEQYECAqJAYEiIhKkd5YkCUDtvSSgaOjAUwE7cdhEVUKPUOATQUrk9l0idSLeUPJgEmGgNma3aSBTHoIMEOAqLgYECAiKkUsGcjKirZauIS6xj4CRHFLGtSAQN90H6Yj00XaDRWLWYZAKDrbQ0BvKmjaQ8CdW4ZAJiUDqXsiovnHgAARUSliyUBWqjxuLG2pUdbYR4AobnHdYsMaswQqz1jQvmRAzxDIS8lAJj0EPGpAIBANGM4hovnDgAARUSmq4ZSBbOllAxw9SBRX461Be7UaZOSkgcqTLkPA0FQw0ykDNk0F51IywAwBosJiQICIqBTVaj0EmCGQ1gl6QIAZAkQz2EeA0vUQyKSpoFu4IaDWZ+XaVNDn9inHzBAgKiwGBIiISpFeMjA1CGgf1kiljx5kDwGiWXpAgJMGKo/plIGoTVNBkwwBIYShsaBdQMCQIWBSMuB3+5VjZggQFRYDAkREpUgvGYAEpoeLspVyoWcI7B+aQigSK9JuiEqLHhA4OH6wSDuhYkmbIaA3FTSZKAAYywaspgyEY2FD80qzgIAhQyDCDAGiQmJAgIioFOljBwGWDaSh9xCIxiT2D05anE1UWZY2LFWO2UOg8pj2EIil9BDIoKkgYJw0YJUhMBWeMqyZ9RBghgBRcTEgQERUijw+wN+ork32F2cvZaLB70VHvdqcin0EiOKW1qsBgaOTR23nx5PzZFsy4BYWAQE9Q8AiIKD3DwAyGzsYjAZNX4+I5gcDAkREpYqTBrJmmDTAgAARAGBxvTp6MCZjODJ5pEi7oWIwKxlIzRCYa8mA1ZQBfeSggEC1p9pwnmHKQCyEmGS5F1GhMCBARFSq9MaCLBlIi6MHicw1VjWisUrNOmIfgcpimiFgM2XAKiCQaVNBPUOgzlsHIYThPD0gALBsgKiQGBAgIipVtVqGAAMCaTFDgMjakjpOGqhkpk0FU0sGZPopA0DmJQOGCQMm/QMAY1NBgGUDRIXEgAARUanSGwuyZCAtffRgT/8kYjFZpN0QlZYlDZw0UKliMobx0LhhPfViPuOmghlOGdBLBmq9tabn+T1+wxoDAkSFw4AAEVGpYslA1vTRg9PhKI6MTpue+9ieAXzidy/ghw/tRjjKelVyPo4erFwT4QlIGIOjqan5c20qaNlDwKRkwIxphkCEAYFKMxocxbHJY8XeRkUyzwUiIqLiY8lA1jrqq1Bf5cF4cPYD6p6+CSxurlHOOzg0hX/+6VOQic/HkZjEp165upBbJSo4fdIAAwKVw6xcAJjNEIjJmCFgoF/4J+kX8GbTBMzWa33mGQJmPQSYIVBZfrv9t/jm09+EW7jxsdM+hved/L5ib6miMEOAiKhUccpA1oQQhiwBsz4CD+3omwkGAMDfdnGkIzmfniFwaPwQu7lXCLOGgsBshoDeUBCwLhlYVLdIOd47stf0PL1koN5bb3qeS7gMwYdgjAGBStE31YfvPPMdAPFJFzduvhFT4aki76qyMCBARFSqmCEwJ3pjwb0mkwaOjgaU47FpzmMn51vaoGYIhGIh9E31FWk3VEjpMgTMGgNalQysal6lHO8e2W16nt5U0KqHAAD43WofAZYMVI479t6hjLycjkyzdKDAGBAgIipVekBgahAwuYtDqkwmDRwfUwMCE0HzGlgiJ2n1txrmwHPSQGWwyhAIx8KQUioXZElWYwf1gMCe4T2mmSaZ9hAAjGUILBmoDFJK3LbnNsP6UGCo8JupYAwIEBGVKr1kABKYHi7KVsqJPmnALCBwTMsQGA8wIEDOJ4TIqLGglBJ/P/R33LDpBuweNr/7S+VlLGgeEADiTQHNSgYsAwJNakBgKjKFIxNHDOfpJQNWYwcBYx8BBgQqw/N9z2P/2H7D+khwpPCbqWAMCBARlSp97CDAsoEM6BkCw1NhDE2GlLVjWobAVCiKKMcTUgXQAwIHxo0ZAn/p+Qs++uBH8cONP8Rb//pWHBo/VKjt0TyxyhAA4qUjEWkMilqVDHTUdKDB16CsmQWO9JIBuwyBKg8DApXoz7v/bLrODIHCYkCAiKhUeXyAv1Fdm2Tzu3QWN1fD51b/e0vNEpBSGjIEAGCCWQJUATKZNPCn3X+a+XEwGsTDBx+e933R/LLLEAhHw6ajA60yBIQQWNm0Ulkz6yNgmDJg00OAGQKVZyI0gfv332/6GDMECosBASKiUsZJA1nzuF1Y2qqOGTw0PNuxeCwQwXTYmB47HmRjQXK+xfWLlWM9IBCTMWwf3K6sDU4Pzvu+aH6NhsybCgLxDAGzHgJWGQKASWNBkwwBQw8BlgxQint678F0ZNr0seEAyyMLiQEBIqJSVtuuHrNkICOLm9XGaYeGZz906A0Fk9hHgCqBPmlg/9h+pX58/9h+TEXUkV926eZUHmwzBGLmGQL6KMBUq5tXK8d7RvYYzjH0ELArGdADApwy4Hi37rnV8rHhIAMChcSAABFRKePowTlZ1KQGBA6nBATMygUAThqgyqCnek9HppWLuW2D2wzPsRpZR+XDtodANGTaVNDtyjxDoHe0F+GommWVVQ8BZghUlL0je7G5f7Pl4yOBkcJthhgQICIqaYbRgwwIZGJxs1YyMDJ7x1NvKJg0HmDJADlfW3UbFtUtUtY29W+a+bFeLgDYp5tTebAL6oRjYYRj6r9/AgIuYX2ZoAeWIjKCntEe5TUDUfXf2lpf5j0EQtGQxZnkBLfuts4OAJghUGgMCBARlTK9hwAzBDKyqDn7DAGWDFClOKX9FOU49U7dtiFjhoBdujmVB7sMgXA0bOghYNVQMKneV48FtQuUtdTGgpOhSf0pthkCPrdPOdaDCeQc4VgYd/bcqayd0HiCcsweAoXFgAARUSljycCc6D0EjowEEEuMFbTKEGDJAFWKDe0blONkhoBZQ0GAPQScwC5DIBQzlgykCwgA9o0F9YaCgH1AwO/xq3tihoBjberbZBgr+M7171SOOWWgsBgQICIqZXpTQZYMZGSx1kMgFI2hfyJek3qcGQJU4fSAQO9YL0aDozg0fsj0Qo49BMpbOBY2NIpUHo+GEZHqv392EwaSVjVZBwT0/gEu4UK1R/13OZXPxQyBSqEHA5bUL8Hpnacra9ORacsJBJR/DAgQEZWymlb1eLK/OPsoM211VfB51P/ikqMHLTMEGBCgCrGmeY2hZntz/2bThoJA/G6vXmNO5WM8NG77eCgWMkwZmFOGwIh1hkCttxZCCMvXYoZA5dD/LfF7/Gj2NxvOY2PBwmFAgIiolBmaCg4BJt2gSeVyCcOkgeToQeuxg7zgocrgdXuxrnWdsrZ5wDogAKS/qKTSlS7Dw6yHQEYZAlpA4NjksZnykmxGDgLGEYecMuBcekDA6/Ki3ltv+DPHxoKFw4AAEVEp00sGIIFp/ieZCb2PwKHhaYQiMQxMmN95GmcPAaoghj4CfZtMGwomsbFg+UrXA8IsQ8Bu5GDSioYV8Ag1k2DPcHyEpZ4hUOezDwjoTQWZIeBcZtkoQgg0VTUp62wsWDgMCBARlTK9ZABg2UCG9AyBwyPT6Bu3rktlDwGqJPqkgS0DW0wbCiZx9GD5SpshEAsbmgrqd+zNeN1eLG9crqwl+wjoPQTSZQjoAQGWqDiXWYYAAEPZADMECocBASKiUub2Av5GdY2TBjJiliFgNXIQYA8BqiyntKkBgYnwhO2dZDYWLF/pMgTC0bAxQyCDkgHAuo+AWQ8BO3oAIhxlQMCprPpV6AEB9hAoHAYEiIhKHScNzMkiLSBweHjKsqEgwLGDVFk6azvRVduV8fkMCJSvdOUe4ZhxykAmTQUBYHXzauU4mSGg9xCo99bbvo4+ZSAUY8mAU1llCOglA/o0Apo/DAgQEZW6Gq2x4ORAvI/AMz8Fnv8lEGHzJTOLm2uU48Mj9hkCbCpIlUbPErCT7i4zla505R6haMhQMpBJDwHAfPSglNKYIeCzzxAwlAwwQ8Cx9N/bZECgxd+irI8ERwq1pYqXWfiPiIiKR580sO124JFvzWYKbPwd8PZbAa/f+NwKpvcQCIRj2HbU+qKGGQJUaTa0b8B9++/L6Fw2FSxfc8oQEJldIuglA+PhcRyfOp51DwGvWy0ZYIaAc1llo7CpYPEwQ4CIqNTpAYHeR9WygQOPA3d+ApCysPsqcZ0Nfnhc6tzr5/Zbf8AYYw8BqjB6Y8FU9T41xZtNBctX2ikDJhkCmZYMLKhdYLjY3z2821AykG0PAU4ZcC6rDAE2FSweBgSIiEqdXjJgZvMfgEe/O/97KSNul8CCJjVrYv/glOX5oUgMwUjU8nEip1nXus6ym/zZXWcrx8wQKF9zyRDItKmgEAIrm1Yqa7tHdhtKBvQAk07vIcApA85l2VSwik0Fi4UBASKiUqc3FbTy0L8DL946v3spM3rZQDrpJg3EYhJHRqYhmY1BDuBz+7C2Za1hvcHXgLWt6jozBMpX2gyBWMg4ZSDDHgKAsWzgwQMPYjw0rqylyxBgD4HKYdlU0N+krDNDoHAYECAiKnUtK4xrjUuAl/8LILR/xm/9EHD4ucLsqwzojQXTsesjcHR0Gi//7sM491sP4XX//RimQ8wmoPJnVjawrnWdoZ6XUwbKl/57V+1RA6XhaHjOJQMAcEbnGcrx5v7N2DW8S1lL20NALxlgDwHHsgoImDUVjMlYwfZVyRgQICIqdd0XAovPiv9YuIFzPw585Engoi8Bl/67em4kANz8TiDKengg+wyBcZsMgf97+4s4ODQNANh0aBS/e/pATnsjKgUb2jcY1ta2rkWDr0FZY0CgPOwa3oWvP/51fP/5789kBugZAq3+VuU4HAsjKrWAQIZNBQHg0uWXYnHdYmVNf72sMwRYMuBYViUDehAyJmOGTBOaH5wyQERU6jxVwLvuAo5vAZqWqU0GX/oRYGAX8NxNs2ujB4H9/4gHEirc4mbrgIAQQK3Po2QFWAUEevoncP/248racweG8R6YZG8QlRGzgMC61nWGgADHDpa+QCSAd939LoyH4xdRRyeP4psXfNMQzGmrbsOhiUMzx+FY2HABnk3JgNflxYdP/TC+/I8vW56TroeAYcoAmwo6liFDwG3eVBAAhgJDaKxqLMi+KhkzBIiIyoHHByw6wzhxQAjg8u8CTUvV9Ym+wu2thC2yCQi01lahuVb9EGpVMvCTf+wzDHHYYTPCkKhcdNV2YXnD8pljj8uD0ztON3wIHwuOsXdGids6sHUmGAAA9+y7BwPTA4b0+7Zq9f8R0ykDWWQIAMBrVrwG3Y3dlo9nO2UgHAvzz5tDGTIEEn/WqtxVqPGoZX4jwZFCbauiMSBARFTu3F6gYZG6FmB6LwAssekh0NVYhboq9UPoeMCYpjowEcQtzx0yrO8bmEQgzD4CVN6EEPjaOV+bGR/3xbO+iI6aDjT61IBAREYwFbGe0kHFNx2ZVo4jMoJ7e+81nNdabSwZMEwZyCJDIHn+R0/9qOXj6XoI6FMGAOOFIzmDVYYAYMwSGAoMFWRPlY4lA0RETlClpvcyIBDX1eiHSwAxkxtNXQ1+jE2rHzjNMgR++XgvQhFjY6OYBPb0TeCkRUxnpPJ2VtdZuO/q+5S1Bv3fFMT7CKS700vFY1Z3f+feOw1reg8B0wyBLJoKJr1y2StxYsuJ2DG0w/BYtj0EgHhjQb2UgMqfHuhJzQ5pqmrC4YnDM8ccPVgYzBAgInICv/bhnTPDAQBetwtdDX7Tx7oa/aj3qx969R4CU6EIfvnkfsvX386yAXKoOm8dXNoUE/YRKG1mnflfHHxROa731sPvUf9NNMsQyLZkAABcwoWPn/Zxw7pbuA2TDXR6yQDAPgJOZTVlADBmCHD0YGEwIEBE5AR+7S41MwRmWPUR6Grwoy5NQOCPzx7CyJR1t+sdx9gBmZzJJVycNFBmwtH0nfkbqhqM9frRsOGubbYlA0kXLLrA0Kiy1lsLIYTt80wzBBgQcCQ9IJCajdJcpQUEAgwIFAIDAkRETmAoGeCdvKTFFn0EOhvMMgRmP6hEYxI/+UeP8rj+mXbHMf46k3PpjQUZEChtmYzqa/A1mI74y0fJABDvSaFnCaxtXZv2eWYZAhw96Ex2JQN6hgCbChYGAwJERE7AkgFLi5osMgQa/Yamgqk9BO7ZegwHh9QmXe88Z7lyvP3oODthk2MZMgRCDAiUsrlmCISiIUSlGhBwi7llCADA2QvOxufP/DyaqpqwonEFPn36p9M+x6qHADmP/ufUtmSAGQIFUfYBASHEm4QQjwghhoUQk0KITUKILwghcu5CIoS4XAghE18P5GO/RETzghkClhbblAzoGQITKSUDv3qyV3ns5EWNeNe5y5W1ockQ+ieCedknUanRGwuOMdBY0jK5gG70NRoa9YVjxpKBuWYIJL1j/Tvw6Fsexe2vvR3r29anPd8t3BBQU7AyCXBQ+TH0q0j5s9ZU1aQ8xoBAYZR1QEAIcR2AmwGcB+BpAPcAWArg2wAeEkLYdzCxf+1mAD8GwFs/RFT62EPAklUPgc40TQW3HlYvft53wQosbalBjU+9c7bjKPsIkDPpoweZIVDaMqm5N80QiIUsZ8PnKl3vgNTzzEoZ7ISjYdyy6xZc++S12Ni3ca5bpALLKkOATQULomwDAkKI1wH4JIAJAGdLKS+TUr4RwCoAWwCcD+DfcniLHwDoBPCjHLdKRDT/9IAA7+TNMCsZqPG5UV/lMQYEEiUDE8GIYQThKYub4HIJrOmqV9bZR4CcSu8h4NQMgZ1DO/F/Hv0/+H/P/D9MhCaKvZ05y7iHgEu78I6GjSUDc2wqmAt9X+kCHL/b8Tt844lv4Pc7f48P3v9BHBg7MJ/bozyxnTLApoJFUbYBAQD/J/H9W1LK55OLUsoBAB9JHH5MCJH1gGghxOsB/BOA7yGeeUBEVNpYMmBpoUlAoKvRDyGEoYdAsqngsdGA8TmJ8YUn6gEBZgiQQ+k9BJw4djAQCeC9970Xd/bciV9u+yW+8cQ3ir2lOcskQ6CxylgyYJYhkEsPgbky25ed2/fePvPjqcgU/rjrj/OyL8ovu/KUJn+T8thUZArBKMvy5ltZBgSEEIsAnJU4/K3+uJTyHwAOAqgCcHmWr92GeFbATgBfy22nREQFomcIhCcB1l8CAPxeN9rrq5S15MV9XZXWQyCRFdA3pgYEGvweVCdKBU7sUi+StnP0IDlUJUwZ2Ni/Ufl5PX7k8SLuJjf6hZYZswyBSCxiyBAw6/o/38zGIVqJxqLYP7ZfWbu39142eS0DdhkCLVUthvNHAiPzvaWKV5YBAQCnJb4PSSn3WZzzrHZupv4XQBuA90opjbeIiIhKkT5lAACCvFBN0hsLJgMCZj0EpJQ4pgUEuhr9Mz/WMwT29I0jHI3lc7tEJaESAgL9U/3K8VRkqkg7yV1GTQXNMgSiIcNFWlFKBrQeAnY/nyOTRwx3jo9OHsWm/k3zsjfKH7sMgXpfPVxCvTxlH4H5V64BgRWJ73bFQge1c9MSQrwFwNUAfiClfGwuGxNCVAkhGpJfAOrTPomIKFd6yQDAxoIplrXUKMfJAIEeEIjGJALhmCEg0NmQGhBQf63DUYme/sl8bpeoJFRCU8G+qT7lOBKLZFSLX4r0kgH9wgqw6CEQCyMaUzMEcp0yMBfZZAjsGzW/H3hP7z153RPlnyFDICVA5Xa5Df/usI/A/CvXgEDyItvuE1iyK4zJp2QjIUQXgP8GsBez/Qnm4ksARlO+DuXwWkREmfHVAnrNp0MbgM3Fm89cAlei2bXP48JVpy4EANT7jWmx48Ewjms9BLpSAgKNNV4sTMkYANhYkJypEsYO9k/3G9aCkfKsWdYvtE7vON1wToOvwXCxH4qWRg+BbKYMWAUE7uu9zxDcoNJiVzIAGCcNjARH5ntLFa9cAwLz4UYAzQDeJ6XMJV/smwAaU74W52FvRET2hDCWDTBDYMa5K9tw60fOw9evXIe7PnEBVnbE48p6DwEgXjZglyEAACcu0PoIsLEgOZB+p24qMuW42fB6hgAABKLlWTGqZwhsaN+A5Q3LZ45rvbVY0bjCcOEtIQ3p98XIEMhmykDPaI/pev90P57ve970MSoNdiUDANBU1aQcDwWG5ntLFa9cAwLJT161NufUJb6nDWcLId4J4EoAP5JSPpLLxqSUQSnlWPIrZa9ERPOLkwZsbVjShHedtwIrO+pm1nweF6o86n+FE4EIjo2pH447tYwAw6QBZgiQA+kZAoDzygbMAgLTkeki7CR3+p3XKncVvnnBN3Fy28lY2bQS377g2/B7/KYNA/Wfs0cUPiBgyFyw6SFglSEAAPfsY9lAqYrJWNoGli1+tbEgMwTmX+H/tudHb+L7Eptzko/12pyT9PrE97OEEI9oj3Ulvp+R8thbpJTHMnhdIqLC0TMEHJjeOx/q/R4EJ2Y/eI4HIrYlA4AxQ4CjB8mJ9AwBIF420FbdVoTdzA+9qSBQxiUDUWNt9kltJ+G3r1EHcukZAoAxK6IUmgpaZaNIKS0zBADg/v3340tnf6koWQ5O9MyxZ/D4kcdxZueZOG/ReTm9ltkkDEOGgDZ6kD0E5l+5/k15IfG9VQixwmLSwJmJ79nkDZ1p81gTgJcnfuy3OY+IqDi0juDMEMhMvd+LgZSAwOh0GP0T6gWBHhBYq2UIHBsLYHgyhOZa4wdtonLldXtR7alW7h6PhZzz74qUEn3TDioZ0O6oW40OzCRDoBR6CFhlCAwHh20nXgwHh/H00adx7qJz87q/SrSxbyPec+97AAA/2fIT3PDKG3L6dTXrC2HoIVCl9hBgQGD+lWXJgJTyEIBnEodv0x8XQpyPeIZAEMBdGbze66SUwuwLwLsTpz2Yst6bn58JEVEe+fWAgLNSe+eL3kegd3AS0Zg6y7qzsUo5XtFWC59b/S90xzFmCZDzOHn04EhwxPSOpVNKBqwCAmYZAlNhtX1WKfQQsMoQ6BlRswN8Lh/WtqxV1jhtID8e2P+AcvzggQfTPkdKiQf2P4CfbvkpDo4fVB4z+z1lU8HiK8uAQMJ/JL7/ixBipo2qEKIVwP8kDn8opRxNeez1QogdQoj0f5qJiMoNSwbmRB89uKdvQjn2uATaatWAgMftwqrOOmWNfQTIiZw8etCsfwAABCJlmiGgNeEzu/AHMssQKImxgxZTBvaNqYnByxqX4TXdr1HWHjjwgOMaYBaD3tBvPJw+8P2LF3+BTz/yaVz3/HX4p7/+kxJEjMgMSgbYVLDgyjYgIKW8DcD3EW8e+KQQ4m4hxC0A9gA4GcBjAL6qPa0RwBoAJxRwq0REhWFoKuicD+7zSc8Q0AMCHfVVcCVnFqY4sYt9BMj59MaCTsoQMBs5CJRvyUCmGQJm6/pzi1EykDqPHrCeMqBnCKxoWIHLll+mrI2HxvH4kcfzu8EKpJcIpeuvEYgE8OMtP545Hg4O4+ljT88cM0OgNJVtQAAApJSfBHANgCcAnAvgcgCHAPwLgFdIKcsz54uIaC5YMjAndWkyBPQJA0n6pIG9/ROm5xGVMz1DwEk9BMwaCgLOzxAQQqTNAChKyUCGPQT0DIHupm501Xbh1PZTlfVHDz+a1/1VIkNAIGYfELhv/32G50yEZv9vzKSpoCEgEBiBlGoZH+VXWQcEAEBKebOU8uVSykYpZY2U8mQp5bellIZ/RaSUNyV6ACzP4vWTz3llXjdORJRvLBmYkwa/endiOqyOROqsNw8ILGquVo4HJ61HZBGVKyf3ELAqGSjXHgL6xZZek5/pY0CJlwyMqAGBFQ0rAABndZ2lrPPOcu7GQ2rmW7oMgZt33mxYSw1UzaWpYERGMipVoLkr+4AAERElGEoGGBCYISWw6Q/AnZ8EdqrNpvSSAV2XRYZAqzZRYGCiPEeVEdmpyJIBh2QI6Cn4mT4GAB5R/KaCZiUD05FpHJk8oqx1N3UDAPwe9d/qYJT/JuuklDg+edxwoW9lTLuxYPdrunNoJzb1bzKsp2Z66AEBl3AZRlzqPQSAeJYAzR8GBIiInIIZAta2/gm49QPAczcBv7sGuP9r8SABjE0FdZ0NFgGBOrXR4HgggmAkanouUblq8GkBgUpoKlimPQQyHTsIpM8Q0C/SCkEvGTCrN+8d7VWOBQSWNSwDAFS51X+T2VRQJaXEl/7xJbzyllfislsuy6jHgp7+b/d344+7/mi6nhrY0bNYzAJPNd4a+N3q/7tsLDi/GBAgInIK9hCwtvs+9fix64HbPwZEI4YeAroubeRgUlud8QP1EMsGyGH0koHxoHNSd502ZSDTpoLpHgOK1FRQ25NZD4GeUbWh4MK6haj2VJs+nxkCqp3D0KArggAA+dVJREFUO/HXnr8CiE8LuGHTDbbnh6IhQwDAqmRgKjyFv/T8xfx1bDIErDJVmvxNyvFwYNh2r5QbBgSIiJyiSg8IMENghllwZOOvgZvfjkav/V19qwyBBr8XHm36wMA4AwLkLE4eO2jVVLBcewhk2lQw3WNAkXoIuNP3ENg3qvUPaFwx82M9Q8BqSkGlOjR+SDnWfy11Zg1ErTIE7tp3FybDk6aPpWZq6L+nVn/O2qvbleMD4wds90q5YUCAiMgp9JKBaBAIl+edrrwLmX9Qwc67cP4T70ctrC8AuiwCAi6XQIveR2CSd6TIWZzaVDAai2IgMGD6WLmWDGSTIVCSUwYy6CGgZwikBgQynVJQqfRfz9HQKGIyZnm+3j8AsM66sCoXANQ/l5n+GV3dvFo53jqw1fL1KXcMCBAROYVeMgCwj0CSVUAAQFP/s/isx/rDjFVTQcDYR2Bwgh9AyVn0HgJjoTHbi4hyMRQYsvx5lGPJQEzGjFMGcsgQKErJgJYhYBYQ0O9qdzd2z/xY/zmxZEClB0hiMoaJsPW4XLMMAbPfkxcHXsS2wW3W72vTQ8AqIHBS20nqewy+aPn6lDsGBIiInEKfMgCwbCDJJiAAAGe5dpiu1/s9qPFZ3ynT+wgMctIAOYyeIRCTMcvU4FLQO9qLDz3wIbz9rrfjyaNPWp7XN23ePwAozwtJs/R6u8aB6XoIlEKGgH7xGIlFsH9sv7LGkoHMmf16jNr0GjItGYgEIBMNeZNu3mUcNai8r00PAas/Z+tb1yvHB8cPOiY7qRQxIEBE5BReP6Df9eF/oHF6QGDD25TDNmEeOLEqF5h5np4hwKaC5DB6QACIlw1s7NuIjz34MXz5H1/GscljRdiZuf/7+P/FY4cfw8b+jfjMI5+xvLi36h8AlGcPAbOO+najBdNmCJTAlAH9jvaRiSOGC0q7DAEGBFRmQaOR4Ijl+WYX4BJSeZ1oLIp79qmjfPUJAam/D5mWDKxsWmkIENllIVBuGBAgInISPUuAGQJx+h3NTvXuQxtGIWBMH7ZqKJjUqvcQYIYAOUyNp8YwGmxz/2a877734W+H/oY79t6Brz/x9eJsThOTMbzQ98LM8XhoHLuHd5ueazVhACjPkgGzevlcpgyYjYObb+lKBvT+Ac1VzWj2N88c6xeQ5ZjpMZ/MgkZ2AQGzDAFA7bExFhrDVGRKefyipRdZvq9h7KBFhoDX7cWaljXKGssG5g8DAkRETsLRg+b0DIGWFcqhV0TRCGMadNqAAHsIkMMJIdCgBRr/4+n/UC62Hj/8OCZC1rXIhTIdmYaEms48HjIfk9g/bZ0hUI4BgawzBGzKCYAiTRlw2U8ZsJswALBkIB2zoJHd1BCrgEDq6EGzvyudNZ2W76v/ObULTOllAy8OMCAwXxgQICJyEn3SAJsKApEQoH8wbF5uOK1dGD8YdTVWGdZSteo9BDhlgBxIbyyopxJLyJJI550KTxnWrJqm2ZUMlOOUAbOLPdseAjbBAqA4TQXTTRmwmzAAmJcc6PXulcy0h4BNWaHZlAFA/fsxHTWW19T76i3fNyIzyxAAgPVtWkCAGQLzhgEBIiInYcmAkVkDtOrm+FeKdjFiOC19DwG9qSDvSJHzmPUR0G0Z2FKAndgza3ZolSFgVzJQlj0ETOrDcyoZKEZTQe2CPtsMAf35MRkzXIBWMrOgkV3JgNXfHbsMAY/Lg2pPtbKmjB3MIUPg6ORRDE4PWp5Pc8eAABGRkzBDwChkvGsIXy1Qp6Y1tmPEcFr6HgLGkgHekSKnKZeAgF7LDDirZGAkMILP/+3zeP3tr8dNW29S/q3RL7Tcwm3bGDBdU8GSKBlI+TlJKQ0ZAqkNBQFjyQDAsoFUpj0EAiOW51uWDMRmAwJ6n4Zqd7VtpkemTQWB+O+vHlxglsD8YECAiMhJ2EPAyGzkoLcWqOtQltpMSwbS9RDQP/jEMB7kHSlyFr1kwEwpBATMMgSsSgZsmwqWaMnAT7f+FPf03oM9I3vwn8/9p3JxpF/4prvgT5chUJSSAZspA8Fo0BDcWVK/xPb5AAMCqcyySGxLBjLoIaBn0/g9fttMj0ybCgLxSRdrW9YqawwIzA8GBIiInES/k8eSAUBvduauAtweY4aAWUAgywwBABgYZx8BchazDIEaT41y3DfVh+OTx21f5+D4Qfxm+2+USQD5ZJbqb9bsMBwNYygwZPk6ZrPWS8Gm/k2Wx5nOd08qhwyBUHQ248osa6POV6ccm/2cOGlglmkPgTk0FUwNmOm/L36P33ZaRDYZAgCwrnWdcrxtoPi9SpyIAQEiIidhyYCRniHgq41/r1UzBPQeAm6XMEwRwNY/AXd9Htj7MACg2udGrU+9kzY4yTtS5CyNPmNA4N/P/3fUemuVta2DWy1f4+jEUbzxjjfiW09/C++8+5145OAjed5l5j0EBqYHbF8nKqOGO5mlQL/4Sr3Y1evD000RSJshYFNuMF/0C0kJiaiMAjDP2tBLBFgyYC/bHgJWTQXtegj4PX5jyUDK+2aTIQAAJ7WdpBwzQ2B+MCBAROQkLBkwMgQEEneVtJKBdqi/Vu11VXC7xOzC1j8Dt7wHePpG4FevAw4/B8Bs9CDvSJGznN55unJ8ybJLcMmyS3BSq/phfeuAdUDg7t67Z+7gS0jcsfeOvO8z05KBvmnrcoEks+7pxaZfFKcGBAzN2tJMEUjbVFAUoamgSRAjeUFvliHg96gZXHbPJ/MeAnMqGUj5c6f/max2VxtLP+wyBNL8OdUbC/ZP96fNRKLsMSBAROQkhikDDAgYpgwkMwTSlAx06v0Ddt6tHm/6PQBjH4EBThogh3lJ10vw8dM+jpVNK3HVCVfh38/7dwDGu3d2fQQOjx9WjufjQ73p2EGTkgF95GC9t95wTik2Fky9M6sfZ50hYHMh5hZuCCEsH58vZin/yQtI/cLTIzyGoIYQwrCW2gCv0mWTIRCOhS2nbShjB7VzqjxVhj97qYGIbEsGljYsRZ1XLQ1hlkD+MSBAROQkLBkwMmQIJGqfDU0FR5TjrgYt/VTvxnx0MwDzSQNETiKEwAdO+QBufe2tuPb8a1Hjjf8dOrntZOW8FwdeREzGTF9Db+I3GMj/+LDJiEnJQNhYMqDvZUnDEsM5pRgQyCZDIF2PALuAQTEaCgLmF4czAQHt96PKYywPAIxlA8wQmGWWITAZnjRdtyoXANRfU0PJgNukh0AOJQMu4TL0EWBAIP8YECAichJDhgADApY9BLSAQCvG4UZ05tjQUDCo3Wk8thmIRdGmZQgMTvKOFFUGPUNgIjyB3rFe03OPT6kZAXZN/eZqOpxZU0F95OCiukUQUO+Il+KkAb1BnhIQyPLOq93jxegfANhPCdB/P/xu84avdunqlc4sQwAwbyxoVS4AqEEA/c+k3+M3bQ6ZlO2fUwBY36aWDTAgkH8MCBAROYlZD4ES7JZdUPoFwUwPAbVkwCUkWjB7N9FQMqC/TngKGNxjKBlghgBVis7aTnRUq4E1qz4CekBgOjJtmuKfi4x7CGgZAp01nYZ69FLMELALCOgXvulqs+0yCIoxYQDIroeA/vs18xraz4tTBmaZZQIA5n0E7AICSg8B7fel2mPsITDXsYNJeh+BFwdeLMkpIOWMAQEiIifRSwZkNH7hWslC2s8/mSFQ0woI9b/B1EkDhgwBkzuNOLoJbVpTwQE2FaQKcnK7Wjawpd/YRyAYDZpmBAwHh/O6F9OSAZMpA3oPgY6aDlR7qpW1UgsIRGPGyQfzlSFQjIaCgPnFYfKudqYZAiwZsGaVIWDWR8CuZMCuh4DfbZwyEI6FZy7g55QhoAUERoIjODJ5JO3zKHMMCBAROYnJvPCKLxvQSwYS9c9wuYGaNuWh1MaCaUsGAODoJuOUAY4dpAqSSWNB/Y580tB0fssGzDIOwrGw4S6xXjLQXtNuuMAstZIBszvdSlPBaHZNBUsxQ8CsKaBVD4FMMwQYEJhl9WthGhCwyxCIWE8Z8Hv8ts0h9aBWJgGBRXWL0FTVpKy9OMCygXxiQICIyEn0DAGAkwasxg4ChrKBtpTRg2lLBoB4hkCtXjLADAGqHHpjwZ3DOw0Xr5YBgTz3EbAqQdCzBPTyhY7qDkOTOqsO68ViFqBQSgZi2ZUMlGIPAcB4QZ9Mc884IODKrmRgOjKN65+/Hp986JP4+6G/Z7vdsqLfnU8yKxkwy6xJSv2zaGj26K6y7QWhly1kEnwSQhiyBLYOWo84pewxIEBE5CRu7+wd8KRKnzRg6CFQO/tjrbFgaslAZ2qGQMyi9OLoJrTWqh+sh6fCiETNO60TOc261nVKQ75ILIKdQzuVc6xGDOY9IBAxDwikNhacjkwbLnZMMwRKrGRAHzkI2JcM5Dp2sFj0fScvJA3N6/JUMnDj5hvxky0/wUMHH8JHH/woDo4dzHbLZaNQPQTMgk3JgNVcSgYAY2NBs9IkmjsGBIiInIaTBlT6hbwSEFAzBJIlA2evaEFdVcqdCz3LICk4hvaIsZZxiGUDVCHqffVY0bhCWdPLBvQ78kn5Hj1o1lQQUBsLDkwNGB4vhx4C6TIE9Iu9XDIEMr1Imw9WJQOGWvVMSwYs6uaTHjn4iHL8+JHHM9hlecpXDwElIJBhyUAyMDOXpoIAsKF9g3L84uCLhteiuWNAgIjIafSyAZPof0WxGjsIAHXtykOntQTx1pcsxfffepr2GiblAglNI9sh1IllGOCkAaogeh8BfdKAVUAg3xkCVgGB1IyAvmm1fKHGU4Nab61xykCReghEY1HTDuqmPQRyaCpo10OgmBkChhn28zhlQEqJwxOHlbX5GIdZKqyyJbLOEIhYZwhY9hBIBKzmmiGglyZNR6axe3i37XMGpwfxub99Dtf85RrcsfeOjN6nUjEgQETkNGajByuZbcmAmiFweksY33zDyWq5AGDeUDDBdWwTWmq0PgKT7CNAlUP/sK4HBArWQ8CqZCAlQ8BswgBgTEEvRg+BH236EU7/9em48OYL8eyxZ5XHzDIW7MYO2l3wA+XTQ2A+pwwMBYYMv8/5zlopJVY9BLJuKmiXIeD225YMGJoKpslkSWr2N2Np/VJlbVP/Jtvn/OCFH+De3nuxbXAbvvyPL+PQ+KGM3qsSMSBAROQ0LBlQ2WYIqAEBTJhfuNhlCMQnDeiNBZkhQJVDHz3YO9ar3HW07CGQxykDUsqMmgrqwYn2mniWkCFDoMAlA0cnjuJ/Nv4PYjKGocAQrnv+OuXxdFMGss4QsOkxUKwpA4B1ycB8TBnQswMAZ2cIWPUQyDYgYNdUsNpTDZdwGUZXzjQV1P6cZjPiUi8b2Ny/2fb8jX0bleMX+l7I+L0qDQMCREROYygZqPSAgF0PAbWpICbML1zSBgS0DIEBThqgCrK6abXhAnPX8K6ZHx+bOmb6vHxefIViIURl1PSx1ICAfge4zR8fPWroIVDgkoE9I3sgMVsqsHdkr/K4aUAgNvexg3Z3ZrO5SMs3wwz75JSBDDMEspkyYBYQGJx2ZoZATMYQkeY196YlA3Y9BFICUVa9HfQ/X8lAgCFwlWGGAACc0n6Kcrx5wD4goP+ZsSopIgYEiIichxkCKruxg7VaQCAwAph087YrGcD0EFb6R5SlQTYVpAridXuxvHG5srZvdB+AeIrwwLSxkR+Q34CA3Yf91JKB4cCw8lizvxlA8TMEUvdo9v6mJQORuY8dtAsYlFTJgEUPAX1MZLrnmzFLIXdqhoDdr0O2PQSUDAHtojtZsmH1+zDXpoKAMSCwf2y/4e9zKj0YxICANQYEiIichj0EZklpvLufOpZRzxAAgMl+45pdhgCA9WKfcjzIDAGqMN2N3cpx8g734PQgYtJ8DOdwYNjysWzZBgRCGQQEitxDQB+FGJER5W6qVVPBZANC/UIrbYaATUlBUUsGLO4s6xee1W41oyMpmykDlZQhYPfrMBIcMTSyzGXsIGAyPjLHsYMAsLp5teHvqT7RRNlnhAGBTDEgQETkNCwZmBUJAnoacWrJQHUzoH8gMSsbSBMQ6A7vUY7ZQ4AqTXeTGhDoGe0BYD1hAIhf9OoXwnNl1T8AUC+29TvALf4WAMYMAbtU8/lgdgGWerFlVsIgIWcusPQ7wGnHDto8XtQpA9q/xzNNBTPsIaA3FbT7fTw0YcwQGA+P295NL1dW/QOA+EV6agAsEovYXjwnf01jMmb49U1esOuBmeT7G5oKZhEQ8Lg8WN+2XlnT+wSk0v/OFKNRaLlgQICIyGmq9AyBCg4I6OUCgFoyIIRJHwGTxoJ2JQMAFgV2KccDLBmgCqNnCGQSEADy19XdasIAoKbjWwUEDD0ECl0yYBJ0TL2A0e92zqwnLsiyvfNaqhkChjvLViUD7vkpGQCcWTZgNWEgKbWxYLogXfLPnFmwZaaHgEVgx9BUMMs/a4bGghZ9BKKxqOG9mCFgjQEBIiKnybRkYLgXuPfLwH1fAcaOzvu2isLsA0BqhgCQWUBA/7CufdhpG9+hHLNkgCqNHhDom+rDeGjccsJAUjaTBqSU+PHmH+MNd7wBX3/860pWgF2GgFIyECyPkgEgfYYAMHtRlu3YQbvHi9pUUL+znLio0y8+9QDOzPMtAgq6SCyCY5PmzS6dOHowXdZDah8Bu3IBYDY4ZRY0SwYErAIzuZQMAMY+AlsHtiIaMzYTNSuRYEDAWvH+xhMR0fzIpGQgEgJ+91agb1v8+Ogm4B13xO+YO4lZhkBqDwEgs9GDeobAotOBg0/NHFYF+tGOYfQjfnHBkgGqNMsalsEt3Eqn/32j+wxj/nTZ3I197Mhj+P4L3wcA7B7ejYV1C/GBUz4AwP7D/ng4frEdioYM51k2FSzwlAGzgICSIWCR+j4TENCbCuaQIVDMpoKGsYOJVHOrbvY6PXPA6kL4+NRxy6kU+RyHWSrSBQSyyRAIRAOQUpoGBCx7CFg0Fcw2IKBnCEyGJ7F3dC9WN69W1s0yaiYjDAhYYYYAEZHTZDJlYO9Ds8EAANj3d2DsyPzuqxj0gIC3BnBp//VlMnpQ/4DUdYpaegDgJFfvzI+nw1FMBiOIRGO49YVDuOFve3F0lPWL5Fw+tw9L6pcoaz2jPZYjB5OyCQg8euhR5fj548/P/DiTpoJm79VSFS8Z0LvWF7pkIBm0UPaQEpSwLBlIrOs14ukyBEq2ZMCiKeCcSwYsmukdHjc2FExyYoZAupIBJUMgg75DoVgI01Hj/2nJ3xfLsYPR3DIE2qrbsKhukbK2ud9YNmAW0LPLIqp0DAgQETmNWYZATOvkveWPxucds5/pW5b0VH+9XAAwjh40DQhoFxv+hnhQIMVJhkkDIXzz7h349B824Zt378BbbnwSYwH7D2VE5WxF4wrluGekJ33JQBYBgV3Daq+O1IuYTHoI6BMG3MKNhkQAVe9aX/CAQJoMgXQlA9mmYrtdbsvmgaXUVDB5AZlxyYAWELDKrDBrKJjkxB4CdlMGADVDQC8ZqPPWQReIBAx/Rzwuz0wwyTJDQM597GDSKW3q/72b+jcZzjHLiGBAwBoDAkRETqP3EIBU73AHJ4Cddxmfd9SJAQHtA4BZQEAvGTAbO6iXDPjqgIWnKkunuPcrx5sOjeCmx3tnjvcPTuG3Tx1Is2Gi8nVC0wnKcc9oj6GpYKPW9DTTiy8ppSEgkHoRk8mUAT0g0FjVCJeIfxQuxZKB1AuudCUDekAg3dhBwDqLoJTGDuY6ZcAqVd6qoSDgzNGD2ZQM6AGB9pp2w/nBaNAYpEkJqpllCERjUcOY0WwzBABjH4FMMwTYQ8AaAwJERE6jlwwAatnAzrsAsw/PjswQ0EsGzAICmWQImAQEOtYqS8vcA8rxf92/C9GYOtv554/tQzBiXrdKVO70xoJ7R/YaegisbVH/3mQaEOib6jNcqIyGMssQCMfCCEaDhlTw5IQBwHiBadZUMF3adS7MpgwoTQUtMhaSFz7Zjh0ErC/8S23KQDgWNtxZznXKwOEJ65IBJ2YI2I0dBOybCnZUd+inIxgN2vZ1sPp91M0lIKD3EegZ7THs2azExu7fiErHgAARkdNU1RvXUmsCzcoFAIdmCGRQMpBRU0Ht7l1VHVDXpSy1CvUDSc+A8W7E8bEgbt/owF4NRDAGBA5NHDJcBKxtVQMCmd6N1bMDgPhd9WSTsnR3/8ZD44YMASUgoE0ZCETijdOA+N3kq++4Gi/59Uvw6Yc/nbYMYi7Meghk0lQwl+7tVlkExSwZMJsyYHZxl+mUAZYMxOXSQ6Ctps1wfjAStM3aMOvloDcUBOYWfDqx5UTD7/OW/i3q/kx+35khYI0BASIip3G5TRoLJv6znxwA9jxo/rzRA8CUwz4I6RkCpgEB7e5HaML4PMPr1AG16oekxtgIADUjwMyP/96DWCz9eUTlRu8hoHMJl6EbeKYXX2YBAWD2bma6D/sToQnLkYOA8QJTQs6kq/9ky0+wc3gnIjKCBw48gDfc8Qbcs++ejPadiXAsbJqRkJr2bFXCkLwoy3bsoN05pdZDwOznrgdwkjItGbBtKujEkoEcegg0+hpNAy3ZBATC0bB5hkAGmSxmz1nXuk5Z08sGzAICwWjQNChBDAgQETmT1aSBF28FLEYtAQCObbF+rBzppRGZBAQAY5aAnmlQVWd4ngdRNCL9HYjdfRN4eKf9KDaiclTjrcGC2gWWj7dVtxnSj3MNCCTvbJpdUKeaCE8YMgSaq2YDAmY16ckLnt3Du5X1sdAYPv/3z+MLf/+Ccmd1rszKBQAtQ8BqysAcmwranVNqUwbMfm/1qRB2z9dNR6ZtJwk4MUMgXQ8Bu5KBel+9IdASiAYMgRqlh4DL2AvCNENAzO3Pmt5HQG8saBVAY9mAOQYEiIicyGzSAABsucX+eUeN3XrLWiYlA766+DjCVHpAwKypoEkaZZswvzhor1c/TN3wtx7T84jKnV42kKqrpktJ0wfiFx/p6puB9AGBTEoG9As9ux4CwOwFuZ5ZkHT3vrtx9Z1X29ajZ8Jq7ntWTQWzHDtod05RmwrqF5LRkGkwJNMeAma/bnbZAUA8IKA3vyt3WTUV1EoGGnwNhgBMMGLsIZB6jlkvh3xlCADA+tb1yvHB8YOG9zPDSQPmGBAgInIis5KB4f3AwSfV9cal6rHTGgtmUjIghH1jwVgM0C82fHWA1w9oHdPbYJzffE53K75w2Rpl7eneITx/wPwig6icdTdZBwQ6ajrQUt1iWLe64E4KR8PoHe01fSx5IZPug75phkBqyYDbWJOevCAfCYxYvu6xyWO4aetNtu+dTiYBAbuxgzEZMzTdy2TKQDlkCIRjxpIBv9s/Mx1CpwcKIrGI4eJeD+Dod6mjMmq4KC53aXsIhKwzBBqqGswzBPSSAbd9U0GzDIG5NBUEjNNK9L1YNeFkHwFzDAgQETmRPnpw883Aw9dq5zQB53xEXcukseBEPxAu7EiuOcskIAAAtVpAYDIlQ8DsA0RVYi6z1keg1SRD4AMv78ZrT12Ezgb1A9WNzBIgB7LLEOis7USjr9FwMZcuRbtntMdwwZs0kyEQyT5DIDUg4HF5DLXzgWgA4VjY0PBPv9g5OKHencyWWUNBIPOSgbl2b7e6O1tKPQRCUWPJgFW5AGCe9aDfLdYbCq5qXmV4jl1JQTnS/4yklssA8ayAaCxeTmgICPgaDD0bzMYOpmbZmI2PNPtzOtfgk2EqSFT9M2KVUcOAgDkGBIiInEgPCBx6Gtj8B3Vt/euARWeqa4O7gZDFnbZYDPjDPwPfXQlcvwE48kLetjtvzMYFmjFkCKQEBPRygdTXqVXnM+uTBtZ01uPC1e3weVx4z3lqw7V7tx3DPpNJBETlzDYgUNMJt8uNpqomZX1o2j4gYFUuAGSeIZBuyoAQwnCREYgETHsEvO/k9ynHVj0AMmWVIZB6IWyXIWCWHp1JKrZV0MDtKq0pA3Z3og3PN8mM0C8OD42rAYEVjStQ71On8zitj4D+Z0SfHCAhZ/4cZlQyYNJUMLUxp/77YNZU0C3clpke6Zj9XdX3Z4YBAXMMCBAROdHSl6Y/5+Q3A53rgdT/kGUMOP6i+fl7HgC23xn/8cQx4O/fzX2f800PblhlCBhGD6aUDJh92E8GBOrUgIDeQ+D9L+uGEAIA8Nazl6K+avZuiJTAPVuPWe+dqAylCwgAMPQRSHc3Vm/qlyp5wa4HBBp8atnUSHDEcCde34dh9GA0YFousLh+sXKca0DA6vmpQQDLHgIRi4BADmMH55rGnQ+GC8lY2PBztxo5CJj3FtB/ffSSgUV1i9Dqb1XWnJYhoP8atFe3G84ZCY4gJmOYCKt/HhuqTDIETHoIpJ6jB6TCsbChZCCX0hS9xCccC89kOADWf1/YQ8AcAwJERE502tuBcz4GmDTKAgA0LAaWngP4aoBWLV3ymEVjwYNPqcfHt+a+z/mmlwzozQOTDBkC/bM/Dmp379w+wJP40KplCCz2zb7f4uZqXLVh4cxxg9+LS9argYdjo/ad0YnKTZO/yXChndRZG//zr198pbsba5chYNVUMPleSXrTMUAtGQDM7zrq/Q3qffWG+mWrlP9M6Snaqe+flG3JQCZNBUuyZEBPNTcrGbBoKAhYlAzE7AMCi+sXG4NUDhs9qP8a1PvqDRf5I8ERjIfGIbXxuQ0+ix4Cem8Hj30PgblMwrBi1gQ0NQhg1UOAUwbMMSBAROREHh9w2bXAZ3cCr/lPYOFp6uMv+yzgSvwXsEAd32PZR6Bvm3o8djR+m7uUGXoIZFoykJohYPMaWu+BCxdJnLa0CeevbMNN734JfB71v1l92sDwVPru6kTlxipLwCpDIJeAwEhwBOFY2HDB01Gj/t08MHZAORYQaPSpF/aGDIFIQOm+DsRrr/X08vkqGUhe1EgpbUsGzKY05JIhUGo9BOwuPHVmAYHUC0UppXmGQHV2Qapyo1+M+1w+Q2BrLDRmGpwyCwiYlQyknmM2/lHPEMh3QCA1cGQ1ZYAlA+aK10aUiIjmX3UTcNb74l/Htsbv8reeAKx4+ew5XacAW/44e2w1aUAPCESDwNQQUNtqfn4pyGTsIGBSMpDSQ0B/jarUgIDWVBBjuPUj51lup6VG/ZA0PGU/CoqoHHU3duPZ488a1pMX6fqkAbuLr+HAMPqn+y0fHw2OmqYBd9V0Kcd6hkBTVZOhVt7QqCwybbiQavI3oc6rBhanIlOIxqJzrr3XU7Rn3j/RKM2uQ3xOGQJlMmVAz46wCwgkm0NG5Wz6eOrF4UhwxHBRWAkZAmZjKZuqmnB8ajb4PRIcMQQEPC4Pqj3Vhh4CgYhxyoDSQ0D/fYyGDXvI5c+ZWR+J1MCRVQCNAQFzzBAgIqoUXScBZ70X6L4wPmovacEG9bzjLwL6HafgBDDca3zN8SP53mV+6RcK2fQQSGY/6E0FU+8OaiUDmLS+cAGAZgYEqAKYjR5s8bfMXCRYZQhsG9yGt/7lrbjqtqtw//77Adj3DwDi49LMAgLJbIQk/aJbLxcATEoGosamgk1VTYYMAbPXz0a6poJWFzdAoqmglh3hEZ6MmrVZlQwUMyCgBymsxg7aMdydTgkI6NkBbuFGZ01n1mUs5Ua/Y+5xeQzNPUcCI6YNBYUQ5hkCNpkbhkwPkykD+c4QUEps2FQwKwwIEBFVuq6T1eNoCOjfqa717zB/7tjR+dlTvmQ8dlC7sI8GZ3sH6B/WU1/DrvdAUmAUGI/fhWmqUT8ADU+yZICcx6xkIPUC3RAQmB6ClBL/8ui/YOvgVuwb3YfP/e1z2D643bZcAIjf1TSrC26vMTZNS5VRQMCkh8B8BATS9RCw6h8AmE8ZyGTCAGAzZaCIJQNmF/OG5nU2GQJmr5F6caiPHOyq7YLH5TGUDDiuqaAWNPK5fWioMjbeNBs5CBiDMGYlA0oPAZPfx3w2FXQJl6HkJZOeG+whYI4BASKiSlfTAjQuVdf0sgGryQNjh83XS0EslkVAoM24lrzbr7+GUjKgXXSExoFwyofXXfcC31sP/Odq4M5PoqVW/QAzwgwBcqCsAwKBIWwdiAcCkmIyhn994l+xfWi7cu7yhuXK8Whw1HDXz+/2G+qjdWaND/XO5YGIccpAc1Uzajw1hjvwufQRsJwykLjAsc0QiBhLBjK982pVVlDUkgHtIi8qo4YMkHQZAlUu9W52asBEHzm4uC4+McIsSOUkZj0E9AyB0eCoZUDAdOygTeaGWS+IfGYIAOYlPqn7M8MpA+YYECAiovSNBfX+AUnjJZwhEJkGtG7JlgEBX61xAsFU4g6RoWTAuocAAGByYPbHD/7bbIbBczehPdCrnhqKIhiJgshJOmo6DHX2qV3/zQIC9+2/z/A6Wwe34q6eu5S1M7vOVI6nI9OGxn813hrD++vMAgKGC4zotDFDwN8EIQRqveq/JVZ3+TORrqlg1hkCGV5oWWYIzLEXQj6Y7Un/9ck2Q8CuZCA5QjLbRpflRv8zkuwhkGo0NGooGaivimfDmI0dzKaHgGnJQIaZLFbMSnxm9seSgawwIEBERPHGgqkyzhAo4R4CIZM7AVZTBgCgRru4n8kQ0JsKpqQL+5sA/QPsZKIhYTQM9Km/bi1TPYa3HeGkAXIYIYQhSyC1679erx2IBvDXnr+avlZEqmnGL+l6ieGcoxNqYLLGU2Oa1p8q05IBsx4CAFDvzd+kgXQ9BKwubpKPGe7+ZtBQ0O68ovYQMLlINAQEsuwhEIzN/vodHjdOGABgKBmYikwZShXKmd7Qz+vyGrJo7EoG9F/TQDRgW8qhZ3qEo2FjyYDI7c9ZagACUANnlk0FIwwImGFAgIiIjBkCx7bEU+6BeHM9qwyBkg4ImHxA17MAUul3+5MBgaBNDwEhTBoLJjIERg8BMqY8VBMZUfo5AsDQJMsGyHkuWnqRcnz+ovNnfqxPGQBgO0kg1RmdZxjWDk+qF3m13tr0AYEqk4CAydjB4YCaIZB8Xp0WXMypqWDYPCAQkRHTpnqpzMYO5pohkOuFWi7MghT6r0+6DAG9AZ5SMqD1EEgGBMwyRpyUJWDWQyCbkgHTHgLan0u7sYPhWDj/GQJuY0ZPktXYQZYMmOPYQSIiMmYIBMeAkV6gpTs+gm/KosFSKZcM6LX/EIC32vRUACYBgQHz19GzDOra1WkLyZGFJlMZ3NNDaPAvxej07AcjThogJ/rntf+MQCSAHUM78OoVr8a61nUzj9V4alDlrrK9822mxd+CjpoO1PvqlbvGeoZArbd2TiUD+h3HQCRgKEdo8jcBgOH1re7ypxOTMdvsgkAkkLZkIN8ZAkVtKugy7klPY59ryUA0FsXRSfXPSrJkoM5bB5/Lp1w4D04PzgQMyp1Z/b5phoDJlAHA2EMg3dhB/WLfrIdArpkoZhk9Mz/m2MGsMCBARERAw8J4yvxUSv37vkfjAYE+i3IBoMQzBEwu5PXb86ms7vQbSga0Cw2r0YMj+43vMTmAllqfEhBgyQA5kd/jx8dO+5jpY0IItPhbDBdnSW9c9UY8dOAhQ/3+6ubVAOJp+6kX4Ecm1X+Hqr3VeSkZGAuPGccVJjIE9Nefa4bAZHgSUu91kiIQCaRtKqjf/c05Q8BpJQOJwNNkZNKQtt5eHf/3WwiBluoWHJs8NvOYozIEMughcHzyOB6afkhZSwYNDNkz0YAhoJd6jh7YCcWMUwby3VQwoykDzBAwxZIBIiKKXygvP19d25lo5nXcolwAAAIj5rX6pUC/E2DVUDBJzxBIBkcMTQW1Cw2rQMKwSUBgasAwepAlA1SJzO7QJ71p9ZvwubM+Z1hPBgQafeqdTUOGgKcWPrfP9G5zkmlAQLvoOT553HDOTIaAXjIwxx4C6Z4XiBgvvFIFY/MwdtChTQXNLgZrU/5f0HtbDE47Z/SgWeNJPUNAQhou2mcyBLQyDD2TALAfOwgYR/7lGngyTAXJoKkgxw6aY0CAiIjiTrxCPd77cPxi2Kp/QFKplg0YMgRs+gcANk0FbXoIACYBgUTJgEWGQHMNRw8SWQUEFtYuxLrWdbiy+0pDA8GzF5wNAGj0qxcyev+B5AQA/aI93fvrF5pmAYHkBZKhZMCiD0A66aYTTEWmDKnZqczGDtoFQlJZBQ6K2UPAJVyGC0X911a/ONVZjR00Sxev8cz+v+DkSQNmZSV6hoDOJVw4Z+E5AIwlA3qzzf/P3nnHSVKV6/+p6jjdk3PYnDPsLiwZkRwERDCiKF7Ua8CseC/ee8Xsz3SN14ioIIIoICA5L2kDsDnvzu7O7k7OnUP9/qgOdd5zTnX3TE/Y2fP9fPgwXVXdXR1mtt7nPO/zAvahggAvyIzWIUDPKZ+xg6plQIwSBBQKhUJhMv8iwHohlogA+56WTxhIM1nbBjhBIJdDgBb2PeLHybdlQOQQEAgCfcdLy0AsBDz9deDBTwFduyb6bCY9hmHgty/sxxU/fRFfvm8TBsPHyec8TsgEgYtmXgRN06BpGv7fuf8PpzWdhnJ3Od6/+P04p+UcAMhZyPhS4aF2bQOix6CCAC1Ey93lmWKVaxkYoUMgV/aAyJptZTRjB2XCwUS2DAC5z59mPVCkLQOkGPQ6vMxrpZMGChEEdvbuxBOtT4xq/ORYIhKNKj2VGdeNFQ0aFlcvxs/O/xka/Y0AePeMSACzyxAAii8IiEJA08h+ZyKJCOeCUKgMAYVCoVCkKakEZp0D7H82u237P4Gunfb3m7SCALX624eMyacM5Hic0nr2drplQOQQCHajirQM9B0vLQP3fTjbRrLzEeALuwBnfiuRJyIbDvbhW//aAQDYdnQQTRUl+NxF/MX3iYpo0gAAXDzr4szPNSU1+N3Fv+OOoS0DlPSqryxYsMJTISx6c/WmW9sMiuUQyJU9kKtlwIDBFVr5hgpOxpYBwDx/u5F/I20ZoIKAj0ydoSJVvi0Dj7c+ji89/yUYMNBS2oJ/XPUP7rEnGlFbiaZp+NF5P8JPXv8JhqJDWFKzBKsbVuPk+pMzTpg0+XynrM4N0XeLjvwbq1BBwzBsf2cCsQDXLnGiowQBhUKhUGRZdAURBB4AqJresBzo2JK9bU3YHzwKPPU1oP8wcMYngMVXjuXZ2lOwQ0CQIWAYglBBmiFA7jfcaYoIAcEYtWAPLwgcDy0DB1/OigEAEOoFjmwAZp45cec0yVnfyq4urjswdezHxYD2awNAo78Ry2uX57xvvg4BWcuAaOQgkLvQtD7vuDkEBGnu3GMQMSJvh4CkyJvIlgEgd8tDzpYBOnYwKc4QSLeWpKHfyXwdAn/Z8ZdMMOSR4SNYe2QtI2xNBmQukpnlM/Gj836U8/65xDKn7mQKfGGGQJEdAtxUkFSGQDwZR5KM/LUSioeUIEBQLQMKhUKhyLLwMvY2FQP8dUATGVE4aMkQePxWYPM9wKGXgb/fBPQeGJvzzAcadphLEKAZAsk4EOrL7TSgLQPBbuHIwfRj1rvZi/vjomXgue/y26hzQsEwHGZ/d4Yix8HnPI6IWgYunHEhNLtJICnKPeW2+9OFXplL3DIga1fIZUW3CgLUITBWgkAoEco5npE+92hDBaday0DGIUBWqKkgQF0rPeH8HAIHB1k3WHeoW3LkxEEnUeTrIklD+/UpNOAvnwyBsXII2E3lAFSOgAglCCgUCoUiS8U0oHmlfH/9EqCsid02eMT8fyLOriLHw8DW+4p/jvlCL9BdBToEALHtnwsVJC0DRhI49qb0aep1tgCY9A6BQ68CB57nt9tYehVAIEIEgbDqW7UiKsrzXVXN5RDIFSoomjAA5F4FZQQBd3FaBnIKArFQzgKHth2MNlTQoU18y4AdIx47SFsGnPYtA/k4BMLxMCccTMYke9o3n+93JE2u95wW5w7dwX2P6PtS9AyB1O9JLgFNCQI8ShBQKBQKBcuiK+T7GpYC5UQQSE8Z6NlrigBWdjxU3HMrhEJbBlwl/Oq/aKWftgz4eOsz2tZLn6ZGI4LAZM8QELkDACBmX6Sc6AwRQWAwpBwCVuZWzoWGrBugyd+Ek+pOyuu+OVsGcmQISAWBHC0D1vuNa6igZKa67DGOe4dAjvPPtVrNtQxIMgSoqENbBvrCfTkD6I4F+Ck7k3HWPW0ZKNghkKNNQ/S7Q5+j6KGCEoeAEgQKRwkCCoVCoWCh4wet1C8GylvYbemWgfYt/PHHNgH9h4p3boVQqCAA8C4BkSBARQOnG0jNJs/QtlH6FFVgxzUNhuOIJ+T9jmPGzn8BT90GHJaLFzi8js2UsKIcAraIHAKGYUzQ2Uw+Gv2N+PhJH4cGDVWeKnztjK9B1/K7LM3V/5tryoCsZaCQDAEqNgTjQSSSCdv7i8gnVDCnQyA6MoeArCic6FDBUbcM6OKWAS5DwEkyBMiUAQMG+iP9ts91dJgP1bULRJwIEskEEgb73Sy0GM/1uyHaT5+j2KGCXIZAWhDIIaBNRsFmolGCgEKhUChY6hYBVbPF++qX8i0Dw+1mu0D7JvF9djxc3PPLF/qPfq4pAwCfI0AzEBxucbI+nTTQKR/VWJbo57b1j/fq8bYHgL++F1j7I+D2S4D2reLjZO4AQDkEcjBMBIF40kAoVnjBOJX5+Mkfx8b3b8RT73wKZ7bkH1CZUxDI4RAYaYaAnUMAyF3ci8g1pi7XlAHR8+Zb7EkdApM8VHCkLQP0faKTACo9lYxrBcg9aeBogBcExqplIJaMYUP7BrQOtBZ0P5ofAOTvIkmTy1FAMwRE9+EcAgWeA4W6FkIJU4jJ9fsyGVs6JholCCgUCoWCRdMkbQMaUL8IKG9mNxtJINApdggAE9c2wIUB5jEGigYEUoeAzGVA72eTcOyL93Pb+sc7R2D7A9mfjQSw61H+mMPrgX1Pyx9jkq2C2bGlbQAfv3MjvnzfJnQOjY+QMRzhi3+VI8DjcrgKti/nmyEgcwhIpwzkKDStQoRIbBiJIJAzQyAeKrhlIN/3U1Z4T3TLQK7zL7hlICluGaChgk7dyX23cuUIiBwCY7ECnTSSuOnxm3Dj4zfi7Q++HX/c9se87xtL8oJzoRkCOSc7CD4T+hxcqOAohSfVMlA8lCCgUCgUCh5R20DVLLMg9tUCdGVp8KhcEDj0ijmKb7wZUcsAyQPgBAFxgcEJAjY4Q70o9bAXQuM+aWCYjEQM9/PHPG/jDgCAHEXKZCEcS+DGO9bj0a3tuHdDGz5/j8TJUmSGw/xnOiTYpiicUlepbfDdSEMFcxWaViHB5/Jxq8kjyRGg96HFeCheeKhg3hkCslDBiW4ZsDl/p+bM6YCQTRnINXYQ4NsGRiIIjKRloCPQgY888RFcdN9F+PWmX3PtRZu7NuP1ztcBAAkjgR9s+AH+ue+feT02zQ8ACs8Q0DXdVkQQiWn0OeIGK4iO1iFAXQlp4UxNGSgcJQgoFAqFgmf6Gt4+37DU/L+u820DRzYCQZm10mCnD4wXnCCQR8sALewH2tjbHsljFCAIINiNSh97IdQ73sGCIXKRS1e0hruAvU+x2+gKUSyPi97wALDpr2aLQnICchIAvH6wD93DWfHi5X3dSCbHvpc/IHAIDISUQ6AYaJpm2zYw0pYBl+6yXR2vtGSF6JrOPX6u1X4RdDpBXQn7tyScyN0yQPfn2zIgK/AmesqA3fnn6mUH8p8yIBIE6HcjZ8uAyCEwAkv699Z/D68eexXtgXb8/M2fY3vvdmZ/Z5AX1f/npf/By0dezvnYsQQvRI4k0M9OMBN9LrmcJsUOFUy3DIgEECtKEOBRgoBCoVAoeHQHsJi4BKadmv2ZThrY/Zj9401EjsBIHAJUBCFBTFJRoRBBINCNaj97wTruLQNBIgjQ92qQCCG6E1hyFbuNTpSgJOLA3e8D7v8Y8LcPAo99ZWTnOkp2d7AFV9IYH+s+zRAAlEOgmNgJAjlbBiQOAUDcC525H2k1oA6EYrQM1PmIIBAPZ6zQ+TLasYOjLdRGi93557Kui47JTBkgoXY0QwDgJw3kdAiIMgQKbBkYiAzg2UNseOuOnh3MbdGqd9yI43PPfS5zbG+4Fy8feRkvtL3AFL2iDIFCHQKAfUuNKH8j13OMtjVF1jKQ6/dFZQjwTGyTkEKhUCgmL+f/F7D/OdM237gCOPWm7D6aI9C6lr2tOwHruKb9z5mrxV77MLCiQotcwcUfR67CXiYqlBYmCFT62AulcW0ZMAzeIUDfK5G7ooSsquZyCOz6F3DQ8r14/Y/AJd8GHON76bG7ky/SBkIxVPjGruhJJg0EorwgMKgyBIpGhVv8t8SpOzOFrnTsoCRDADBXQemqPQBo0FDuLme2lbnLmLFzhToEDMPg7lNfwgaU5hMqSBnt2MGJbhmwKyTzcgiMcMoAAFSTv3M9YblDIJaIoSvYxW0vtOB86uBTnJ2eFrWyIjcYD+IjT34EJc4StAfaM9tnV8zGPW+7ByXOkuI5BGzEGGHLQA5hqtgOgVgyhngynjtUUE0Z4FAOAYVCoVCI8dcCn34T+OwW4GMvsHb5MiIIUIveinebifxpkjFg9xPFO7eNfwR+81bg/n/nV7vTjKhloMZ+/0haBugotWA3qkgx2mdpGegejuAHj+/CL57di6CgqBw10QD/edELpCid0OAHqF0016rl+t/xxw935H+eRWJPB1+kDYzxVIdgLAHRhEHlECgesmBBqw1c5BAoc5fZFsyyVdByTzlXKI+2ZSCSiHCBb9QhEIqHChYERj12cBK3DOSaBAGMrmWAOk8GI/IpEO2Bdhjgf9ELLTgfbeVDXakjwC6XYCAywIgBAHBg4ABeOfoKAN4h4NJd0DQ2/yIfbAUBgVAz1g4BkZsnkoioDIERoBwCCoVCoZCjaUDlDH47bRmgzDwTCHQBeywiwM6HgBXvHP05tW8BHvq0+fPR103XwWXfY49JJvgU/LxCBXM5BGShgvXi7QBQvwTosIz1C3SjqoQIAqmWgWTSwPt++yp2d5ir2m8e7sdvbzgl52kXBHUHALwAwE1o8AMucvFl5xDo3gMceJ7fPngUqGjJ7zyLgGEYmffSylgLAgFBuwCgpgwUk3JPuXC7ddVXJAjI8gPSyFagRa4C+viFtgyIBIR6H/u3JJQIcavDTt2JeFL+XRq1Q2CCBQG7QnJELQN5ThkAwLlA7MZCHgkcEW4vxCHQHerG+vb13HYqABTaNgIg416gPfUjaRcA7DMERJ9Lru9hsR0CgPm+5cwQiCtBgKIcAgqFQqEoHNoyQGlcDiy+kt2258n8guhyQcPuDrzAHyNaoRlJhgBF6hCwuV/LavZ2MoYGD1uQplsGth8bZArYp3Z0IFzs2fUiRwVdMaHvn8sH0IsvuwvUDbeLt9NsgjGmaygiLP7HWhCQFf6DY/y8JxIyh4C1L9ztcHOr5XbtAoB8BVr0fFyGQIFTBkStCbUl7N8SUcsALVop+ToEnLoTOnEwOTXniFaPi0mxQwWjiSgMw8hrygB9b+1cH8eGjwm3h2IhbkqAjCdan0BSMKaWaxkgq97ntJyDeZXzbB87LUxQF8pIC/GCMwTGuGVAJELkk7mhWgZ4lCCgUCgUisKhLQNWdCdQtwhYeDlrl48FWcfASOnaxd6mkwAAvl0AyNMhkEMQkD2GnbOACgIAmpzsRWa6ZWBfF1tQGAZwbKDwlSFbQn38Ns4hQFsGSvN3CESDwJt3ifcN8gFcY4nIHQAoh8BUIB9BAOCLdrtAQUBecFonDGQem7YMCAp8O2ix6XV4OddBOB7mikG7QEWgsHFutCgbrY27GNi2dIxwykAkEeH69EftEBgWOwTiRpwrwg3DQEegg/vMH2sVB/LmyhCo99XjzsvvxGdWfQY3LrsR3z7721jTuIY5JiMIkAyBfAUjymRrGRA9Zz6ZG6plgEcJAgqFQqEoHLuWgbrFZr+5vxaYcSa776nbgNgoC1wqCEQGzcBCKyMVBJweQGJFNh9D4hDwlPEr6GnqlwBk9aRWJ4JAqmVgfxd/3kf7i+CqsCJqGeAyBGjLQAEOga1/5z+PNAPiC+ixYpcgPwAYe0FANGEAAAZVhkDRkBXFNCiOFtg5WwYkq6AiAYJrGSjUIUCKwzJ3GdcXPRwb5toDcjkECll5pcXhRAcKAvYFq90qdRpR4dof6ee2iaYM0FYUW4dAQOwQANhVaMMwcOvaW3HhfRfikvsuwWMHTBHg2PAxvNH5hvD+uTIEvE4v/C4/blp+Ez6/+vO4cu6VaPKz/y6nz4HLEChAMLJS6NjBsXYI6JrOfdZp8cdKegxpGuUQ4FGCgEKhUCgKp8xGEGhcnv355Pex+3r3AWt/NPLnNQyzP51CXQL0wlx3siGHdti5BDySDAFNk7sEqmZyj1kNdtUp3TJAHQIAcKTYgoCoZYA6AkQtA5xDQCIIbPi9/LkHx1cQEAUKAhMnCCiHQPGQCQKcQ8BVHIeAqNVgtA4BKiCUuctQQn7PRKF2uRwChfSI0+JwovMDgOJPGQCAvjDvjBI5BMpcfC5EIilu25I5BAA2R2B33248tP8hAOZ35CsvfgXPHnoWj7c+Lr0/lyFABAKRMEK/++mV8GJlCNiJMcIpA2PsEAD474MohJOKgGrsII8SBBQKhUJROE6PvN/eKgic9F6ghYTirf0x0LV7ZM87eBQQrdjQlWduwoDfLNrzwS5HwG5SgUhIcPlMocDHTi+oALuC3h+MIpk0xskhIGgZoBZKUcsA5xAQnNeRjcBR8YoXgHEXBHZPlCAgKfzVlIHikc+UAYAvBmhoH0WaISBoGRitQ4Da0UvdpVxhRW3uQHEdApOyZcAuQyAPh4CoEO2LsH/3dE0XPpYorFLmEpBlCADsKnTrYCuzL2Ek8MXnv4i7dkpaq8ALAqEY7xCg0O9+uvAVTRkYCXYtA6Lfm1zfpdE6BAD++xBO8BkC9G+AahngUYKAQqFQKEaGLFiwaUX2Z10HrvxfwLrqlIgCD38OwrlsuejeJd4+cJi9LSpo88UuD0AWKgiIJw1UzhC6B8rirCCQNMwidX83X1AUXRAQOQSScSBuuWgUtQzk4xBYb+MOAMY1Q8AwDOyRZAiMdbhfQDIucjCkHALFQrZKTguTt815G7PvwhkX2j6urODMxyFQqCBApxKUucvyWgHP6RAooEecEwS0iRcERusQEBWu1CHgd/qF4YmiyRQiQSCejKMjKB+jal2FFhWg0WSUGxVoJVeooKgAp9b4tIgwWTMERtq6YIW+D+F4mHNEUFdQJBGxndJxIqIEAYVCoVCMDJkg0LCMvd24HDjjk+y2g2uBTXcX/pwyZ0GulgFBr6gUu5YBuxwCkZBQOVP4mL44v0q//dggwjE+bfpof7FDBQWCAMC6BPKaMkCEimCvmR9gZfFV7O2hY0BifC7E2gfDGJJY9ydqyoByCBSPfB0Cl8+5HL+56Df44ilfxD+u+gfqfPajRWUFp6gIp4GFow0VLHeVSx0KzHG5HAIFFFq0aJsMGQJj4hAggoAoPyD9+PT5RcGCncFOJAz5BBirIGCXQyAjV6hgPi0DsikD4zV2MJfwMFYtA1Q8EeWGKJcAixIEFAqFQjEyRDkClTOAkkp++3lfASpmsNsevxUI9BT2nFKHABUEBC0D+WIrCEgyBACgVFBoVKUEAdIy4Az3wuti/wne0Cqw8mOcHAIA66rIa8oAESr2PsUGDeou83O3YiSBYfmqWjGRTRgAJm7KwKDKECgasqJY1Bd+RvMZ+ODSD2Ja2bScjyvNEBBkDxQ7VFDUMkDRNV34Gq2MyiEwCVoGRusQEAkKtGWAujvSaJrGfa4iQcAuPwBgWwbyKT65cZN5hApSqENAliEw0pX5gscO5nIIjFHLAM0QEP3uqmBBFiUIKBQKhWJklLfw2xpX8NsAsyC//PvstlAvcN+H5OPrRNAJA2lobzonCIxHy4BIEJiV2kdEhkA3qnzsxdKGg+JC/Uh//jOt80KUIQCwroC8pgyE2LaPIdJPO/MMc+IEvSgcpxwBWaAgMHGhgsOROBLJIn6WJzAlzhJh4UuLopE8rgiRI4EWlcF4UBpAJ0I4ZYAKbwSPw5NTNBiVQ2AShAqO1iGgaRq3Ys21DNiIKvmMHrSbMAAU7hC4ai7rpsoZKigSBCQOgfHIEJioUEHqWhCNHRQ5BFSwIIsSBBQKhUIxMkSjB2WCAAAsvBRYcjW77cALwD0fAOL2c4MzyAQBmiFAV2QKcQiMOFTQpmWAPmagC5VEEHjzUL/wYSPxJHoDUeG+ESFrGYjmaBmghYqRBKy9qVTY8VaYGRK0tWScBIFd7ZNPEADkgYOKwtA0TVik51o9z0UhGQKifnOaC2CHSBBw625okAegeh3eoq68TkWHAMC7JPJtGQB4QUBU0I/GIfCO+e/Aitrsv5Wr6lfhtKbTmGM4QSBeeIZAZuwgnTIwwgwBu/detC/X97AYDgE6pjOSiCBCridKnCXc77VqGWBRgoBCoVAoRoaoZcA6YUDEpd/j7PPY+yRw34fZ4lJEsBcIdov3DR4FrCtznEOgWBkChToExBkCCPag2s9eDMn63YEi5wjIWgZiOVoGRBeD1otW+p6nCzPqJKETIcaI3Z3ywmwwHENyDFfq7Yr+QZUjUDREifC5VthzIeqT1sDbyAGx7XxUgoCrDJqm2RZeHqcnZ1E8mrGDk0IQsClY8xYEyHvQG2b/7tkJR2We3C0DdhMGALagp9+Jel89/nDpH/DfZ/w3vrLmK/jFBb/givlJmSFQ7FDBYrQM5DF20Ov0SkcyKkyUIKBQKBSKkSFqGWiycQgApqvg/f8AaEDXzoeBf3yULeopMncAYKbkD3dmb49VhoBdy0CpaMpAWhAgYkGgG5Ul+V8MHSlWjkAyAYQHxPuiOVoGRIWWNUeAcxWkjqffk3GYNGAYBvbatAwYhr0AM1pkUwYAeeCgonCEDgFn8R0CFZ4KYdiez+XjVvMLyREQTRkA5G0L6fMbS4fAVGgZAPjilWYIFNIyIHIIHB22/ztmdQjQ70SpqxRuhxvvXPBOXL/4epS6S/m0/ESYaRXLq2VAMGXAMAx+ysAYCALCDIEJCBUUtQy4HW6pe0JhogQBhUKhUIyM2vlsUGDzSrFIQGk+GXj/3/nV9m3/AJ74qvx+skDBNNZgQa6gLUKGgO4CbFKWUbcIKLeEls08G/CmLiypKyIRQaM3/8KwaMGCoX4AkpXxQqcMAGyIIOcqSDsExr9l4Eh/CIGofS/3WI4eVA6B8WEsWgZEhY1sooGu6ZxLoJBEebrynJ5aYFf05pMhUEjBR4u2yeAQsMtAGKlDgGsZsMma4DIEIrlDBelnYu1Rp8KPyFlCv3dJI5lZ2TcMI79QQbIKHjfiiCaj45IhIJwyMBGhggJBwOvwcn8XAnHlELCiBAGFQqFQjAzdAVz/N2DpNcDydwHX3Q4I5joLmX6qeV/ax/nar4BBiRVTNnIwjTVHQFac5gMt3tPYuQMA8/34wP3me7HqBuDa32b3CVwHza78L0iKJwhI2gWAwqcMAKwgQC2Y6c+WcwiMvSCwh0wYKPM64dTZ7+ZY5gjYZQgoh0DxEI0CtOsNzwdRoSVKKU9DRw+OpmUgXYjaOQQ8To9tYQYU6BBwTD6HQFEyBMhjDERYZ9RoQgUTyQTag+3MtjmVc5jbjEOAfCf8gn+PRK8rLQLQAhfge+cB8WsKxoL8lIGRCgISQdypO4VC0rhkCAicFbS9wuPwcO+NcgiwKEFAoVAoFCOnfhHwzjvM4rd6Ts7DGWaeCbz3bsB6cWskgU13i4/v2mn/eIxDQFKc5oPDBXgr+e35uAzqFpjvxVU/Y1fG3aXs6wRQ78i/cDg6UCRBQJYfAGQL+mRSEMroMwUPegFnDRKkoYLp3IaK8W8Z2E3aBRY0lKGctGhYBYGBUAyfu+dNnP/D5/DDJ3aNOl/AThAYS2fCiYZIEBiLlgGZQwDgBYF0kb+/fz8+/tTH8cFHP4jXjr3G3S+ejHOrvumWAdvwNofXVhBwak7oWv6X95PSITAGLQMGcUbZZgiQvAgq3HSFuhBPsr/j8yrnMbcZhwBxrJW5+DwKO0GAFriy40Wuh2A8WLQMAdl7LxIngNzTLsYqQ4AKIB6HR2UI5EAJAgqFQqGYOOacB6x4J7vtjTvZcXZpuolDgBbo1pXn0bQMAOIcgUIfw4qmcY9Zq+dvLT5SrFDBfBwCcYH4kL6Yoi4Bu5aBjEOAtAwMHQMSY7tKvosTBEpRYSMI/OGlA7j/jSPY3xXAz57Zixf2dI3q+QMRebvCkGoZKBqiQn20oYKFtAwAfHGXXg2+de2tWHtkLV7vfB1feP4L3IqkKGsgbSW3DRV0eKQrtUDhM+aPt1DBe9Ydwzcf3o7DvfYrvLmKzUKmDFCHAB056HF4MK1sGrPN1iEgECNERXVaCKD5AYD4OyLaJnIIFDtDQPZ9nagMAfp+eZweTixRggCLEgQUCoVCMbGs/AB7u3cfcOgVdltkmB8tOOsc9radQ6CQlgFAnCOQq2UgF6QVocrolx46p5Y93+K1DPTJ96XfM1rYA1kxhF74MQ4BWcsAe6EMIwkMd+Q+11FAWwbm19s7BNYdYIWSDa0271MOkklDtQyME0KHwGjHDgqKm0qRYygF1zIQHUZ/uB9be7Zmtg1EBrCpaxNznChrIC+HgNPeIVDoqutkDBW0K1j/uq4Dv1t7AB/6wzpE40npcbnaKkR9/GlyOQRofkCTv4lzpqRX9+2cIFacupN779OFLb0/IP6O6JrOCVqBWIBzCIx0ZV72vZQKAjafo1NzQsu3xdAG+jmLHBGiDAGrg8OKYRjY0L4B69vXM6GOUx0lCCgUCoViYpl+GlAzn932+p/Z2z17yJ00011gxSoY0P7AQgUBUY7AaBwCACcylCf5oKo0Z89n3QRdQxFE4vYheXmRT8uAKCU9bf93kQs/21DB1H18NQC9MBzDHIFk0sBeMnJwQUOZrUOgc4jt0e0a4nt28yUYyxFmqBwCRYMKArqm520plyHMEPDYZAjQUMHYEPb27+WO29W7izvOilNzZoo5u8A7j8M+Q6DQ1V9OEBBMUxhvbAvWpPn69nUF8MxOubCY632wzRDw5HAIkJGDzaXN0pF/opVokRihaRrfD592CJCWAafulL5HosJ3whwCNs9TqJNFBn3PaFZE+jy4UEGJQ+Abr34DNz5+Iz78+Ifx9Ve/XpRzPB5QgoBCoVAoJhZNA1YRl8D2B4Cw5SKMBgpWzQRq2Z5NxiFAV8Ing0OAtAyUJvqlh54zn3/+9oEitA3k0zIgCltKX3RRO7VdhkD6AkzXx3XSQFtfCCFSlOdqGegcZN/b7uGRCwJ2EwYA5RAoJjVeVrgrd5ePetWx0AwB0WqySBDY0buDO85Kqbs0c+65pgwU0yFAi7Zi9HWPFrti0TCyNvO/bWiTHper6C20ZcC6WixyCHBj7VKCgChkkrpK0oj64QG+ZUDWsw+IRw8WyyEg+97JzseuZcCpFac1hf6uiCZCeB1eXrAR/Dt3cPAg/rb7b5nb9++5v6AxosczShBQKBQKxcSz4j2A1S4ZC5pjCNPQkYO1C4GK6ey2YI9ZlA608Zb0yhkoiGJnCACAj33Mkpjcln7S9AqUedgLpiPFaBuwdQikLpC4LAC/WdQD9g4BURBhGjppYGDsBAEaKFjpc6GuzIOKEvb9TAsC4VgCg6RIH5UgYNMuACiHQDFZVrsMDb6GzO0LZlww6scsOEOACALD0WGhILCzlw1FpYKA9XFG0zJQ6OpvXQkrPlZ7qwu6/1hg+xqS2X3P7e5C55BYKM3VMlBIqCC1/dMMgZbSFmnBSQtKDZrUAUKL2/Rz5jNyMI3IqTDWDgFZpoWdsFMshwB9L/oj/dwxHic/ZUA0dvDBvQ8ytxNGAr1hm38zpxBKEFAoFArFxFPWACy4lN32xp3Zn7uIIFC3gC8yAbPQPPQqu81baQoIhSByCIy6ZYBdzXSGe+Fy8KuZZV4n6ko9aK5kC5OjxQgWtHUISFoGrIW9XYaALFQQEIweHLtJAwdJ2Ni8OnPlVeYQ6Bzki//RtAzkEgSUQ6B4OHUn7rr8Lty0/CZ88ZQv4tbTbh31Y4qKHtuxg3m2DLQOtjKF3UgFAbfDXVSHwMWzLkajv9E8B1cZrpl/TUH3Hwtsw+gsDoFE0sD9r4vFxZwtAzbTKKhDAGDbBo4Os3+/mkqbOCEp/VlTh0Cpq1TqYhEF5Fn/LzvOiig8L5okgkCOsD8Z0gwBiaNlXBwC5JzoawVSYwfJ500dAkkjiYf3P8zdt5AxosczShBQKBQKxeRg5fvZ223rgc7UqhonCCwyLfw07GvgMHDwZXbbjNOzK9z5IsoQGHWoIOsQ0ILdqPTxF0xzUwVscyV7oVOUYMF8HALUSumyEQTSF6rJJD+dgBEEaMuA3Oo7Wqhlv9pvvsdUEEiP/xOtMHYPR7lAqba+IG57aBt+9ORu26I/kNMhoASBYtLgb8BnVn0GH1z6waKsOjp0B2ZXzM7cdutu5jZF5BDY17+POy5pJLGnL5uFwgkClmkFdi0DXocXDt0hTWgvVBCo99Xj71f9Hb+7+He4/+r7sbRmaUH3Hwtkr8FIugCwxfTfNrYJw99yFb12DgFRj3/68zIMQ+wQoC0DEoeArF0A4N0p6VaBQgQBOmUjGA8iliAtAyP8PTkeMgQoGjS4dXfOsYPr29dznysgngYyFVGCgEKhUCgmB/MvBkob2G1v/BmIR4He/ez29Io/bRsYPMJPKJhxRuHnMiYOAfKYgW5U+fiLojl15oUq7xAogiBgO2VA0jJgfd10pFvaISDKHbA6CyrIpIExdAgEo2zBXZpqvZA6BARugGgiyRTu8UQS7/71q/jDS6346dN78OX7NnH3SZPLATAUUi0Dk51bTr0F1d5qeBwefPHULwqnGaShxePBwYNC2zLAtg3QPnRrkJ1df3u6KJMVZyOxg5e7y3Fa02lo8DfkPngccOgO4bQDUxBg2ds5jDcP93Pbc7UM2L3HDt3BjZNMOwT6In2IJNi/GU3+Ju7xwokwEslEXiMH08gyBGjLgF2GgGglnGYIjNQhIM0QkBTlduJUsbIqcoWIehweaJqWUxD4575/Cu9/ojgEJn7YqEKhUCgUAOBwAie9B3jpJ9ltG/9orkIbJLm9boH5/4ppQMeW7Pb2rUDndvbYEQkCY5AhQB8z0I2qOrFDAOAFgaJkCNgJArIpA3YtA+mVKxooCGRDBQHeITCGGQJ09d7nMQsLmSDQMShuxegejmTus/XoIPP+P7GtA/FEEk4Hv66iHALHP2e1nIXn3/08DMPIGVJIV3xlYgDACgLr2tcx+xZWZduabB0Cqd9Bj8MjTEqfDKGAxcDtcPPj9gzxa/vbxjasnMG2dYxmygBgOj+skyDSDoGuYBdznAYNNSU1QpdCKB7iVphFIwfTSKcMJApoGcgjQ2Ckq/OapsHj8HCCiLRlwG7soMThUih274X1HOjnbf1uBWNBPHnwSeH9TxRBQDkEFAqFQjF5WEmmDUSHgPW/Y7eVNgLe1IpdBelN33Y/e9vpBZpPLvw8xmLKAG1DiIfQ4OVH1M1NOQRaxsIhYNcyIJsyYL3AlDoEBCOcrMfSDIHhdiAxNoVxMMq+p/6UQ6C8AIcAwOYIHCPvfTxpoJ0KCYYB7HoUc7f9FEu1A5nNXhd7qVVoqOBQOIZndnbgUI94brZi7MhnYoFdgUdJCwJdwS4uZ+D05tMzP9sVOWPhEJiMCAtGgUMAAB7adBRhMlnE7n3wODw5C1LZ6MHOYCezvaakBi7dJXQcBOPBwhwCpLBOCwEFhQoKWheKlSEAiN9X2flMBodAej91TljFtCcPPsmLTylUy4BCoVAoFONN7Xxg9rn2x6TdAQBvRR9uZ2+3rAYkCci2lAiStovtEADQ4uELaZlD4Gh/GIZhIJk08Itn9+LDd6zHH146gGg8md/zx0J8nz+zP4+WAZlDgN4HsA8VNJL8Z1UkqEPA7xa3DAyGYkgmDWGoIMBOGjgmGPnY1kfeyy33AXe/Byfv/zXud/83ZmtmP2pzBfs5RuNJROK8ECSiPxjFxT9+AR++YwMu+vHzeGVfT173U4wfon5zGbv7diOejOPVY2zwaamrlOndH40gMGUcAoKi1ZA4BIbCcTy+jf17YtcykMsdAIjHSQK8IJCe0iCaHBCM8YIAbUWwwrUMpARXLkPApggWTTsolkNA9twjEafGyyGQnoBAP/NIIoJ40vy3QtYuACiHgEKhUCgUE8PVv7AXBawTA2iGAGUk7QKA2b5AV/S9fPJ0QXjKAXKx3uRkBQGHrmFGjXlBR0MFQ7EEBkIx/ObF/fj+47vwzM5O3PbQdlz187V445BNKwCAA90BfPr2p+3PL58pA5xDIN0yQAQBZwkb5OivBejF4RjlCFDLvl+SIZA0gOFoXDq2rNviEBC1FXCCwOt/zPzo1hK4RF8PAGis4C9Y85008NDmYxkxIhJP4s7XDuZ1P8X4UYhDIJKI4ODgQbx27DVm+ymNpzAFkl1QmrVlQMRUcQgIX0dS/tr+toENKrV7H/IRBOikgfR8+84QKwjU++oBmEU2LXKD8SAXHjnWoYKcQyBevAwBQPy9k31fnboTuiYuNYvmEMglCKTOV+TgCMQCODJ8hGvfsaIEAYVCoVAoJoLKGcAHHwI+9gJw8vV8Ibns2uzP1CFAmTlCQQAA5l+S/dnlB6atGfljAYCmcS6Bep29WJxeVQKP0+x5byj3QieO5X1dw/jNC2zA4s72Ibzj/17GbQ9tk/avf/m+TdjTesj+/GRTBtyWi2fOIZAqiqPE6eAmF1+aJsgRGJtJAwHSMlAqyRAAgIFgTOoQ6MrpELC8T8kEcOR1Zn+jZrZnNFXwF8uDeQYLtnaz7ysnQigmnEIcAgCwo3cH5xA4vel05rZdaFzGISCb/T5FHAKi1yFzCADAS/u6md/JogsCkpaBtCAAiO36NOfB7vtCV9/TNnaaIWAnGIkyBOiUgdGIRqIC3HZMpkR8KNb3NFd4pJ0gEIwF8dC+h2zvH6D/tk1RlCCgUCgUislJ00nA238JfG47cOFtpjjw7rvYIp9a0a1o+uiK+Mu+B5x5M7D8XcCN/xp9hgDACQJzS9kC75RZ2VYFl0NHQzl7ofWzZ/aiN8DPWTYM4A8vteLSn7yATrKafaA7gPWtfajUcqx0JKJmXz+1/1svnqUOATpyUHDBXT4+kwaoKOJLtQyUepxwEIVlIBSzcQhk32cuLwCkOO/cweUo1Gn9AICaUjfcTvZyK1+HQDsRIvoEn71iYvG5fNAgzxqwhgUCwOMHHkdHsIPZdkYTK1zaFVjponHKtwwIHQLZ17aosQxl3uyKvGEAr+3PZqTYFYoiez+FOj/SggANFazzZfNmRMV4QWMHXeJQQS5DwK5lII8MgdF8R0Tvq933VdaeUKyWAV3Tbd+PjCAg+MwDsQAnCNDfZWuw5FRGCQIKhUKhmNyU1gFnf9YUBxa/jd1X1mQW/iIalo3O5u8tBy7+JnDtb0cWTCjCxwoC8yK78IHTZ6LE5cBJ0yrwmQvmM/tpjsBzu9iLUcrh3hD+8/4tzLZHNpuFdxXIhY1olSkWGOGUAbKKQoUDgHcIDI7NpIEgEQTSYwc1TUO5l70I7R6OoC8oXq23ZgjQwhwAjlgFgbb13P46bQCAmWFQ7iX5BXkGC1IhQiQGKSYWXdOlq76lrlKcO41tf3qu7Tnmdl1JHWZXzGa22WYIOE+MUEGhQ8AiCFSUuLCsmR0HOWT5vbKzxRfVIVBi4xAoUqhgQS0DRJQYig4habA5M6PJEBAKAjYF+Vg7BIAcAlpqn1N3cuf5aOujODTEOufeMv0tzG3RJI+piBIEFAqFQnH84nACZc3ifSPNDxhLqucwN7WNt+MbszZjxzcuxYOfOhvTq9mLOSoIAEANBvAX1zexy3MD/tFwO/xO9mLvmZ2dzESChzeb4XZV1CEgareIBu1bBmihLwsVpC0DAD8RYowEAW7soDs7z5y2DeztlLsm0oKAYQgmCgBo67e85iMbuP116AcAlHqdnBAxUofAcCSedyChYvyQrfrOrZyLJTVLbO97etPp3DQD5RCQFK2WlgGPy4ESy+82AIRi2b+Fo20ZyDdU0K5lIBQL8aGCNpkTXKigpGWgkAwB0RjM0WQIiJ7broVB9jkUyyEgOyfR81Ox5PYttzO3Z5XPwpnNZzLb1JQBhUKhUCiOB2Q5AqPJDxgrTrkRoBdC//w0cPBl4eE0WBAAPuR8HGc6tsOjxbFq4Ck8d3FnZhUcMMPy7t1wGIBZ8O5sNy9kK5CHIBAL2rcM0AuvzNhBm/ukoe0dA8UXBAzDkI4dBAoTBNJjB/uCMeEkh2P9YcQTqe1tAkEg5RAo9TgYazPArmTKSCQNYZhhX6CwsYWKsUcmCMyrnIdF1Yts72sdN5jGztJ+oowdFE4ZsDgEvE4dJS4qCGR/90c7ZUA0djCWjKE3zI5uZQSBPFoG7J6bCxWMix0CdgU4fXwaagiM7jtSyNhBQC5QFdUhYONQsO6jv1dxgxVmb1p+E+f2UaGCCoVCoVAcD9CV5zST0SHQuBy4/AfstmQM+Ov1QO8B7vAWgUNgucYeV9f5Eq46mXVJ3Lv+MBJJA4+k3AGAwCFQ1gho7AU1ojlaBmQOAc5VIChoqCAwBhkCkXgS8aTBbLMKAuVEENhj6xCIwjAMHBsQB/nFkwY6hiJAeADo2sXtL9XC8CGMUo+Le97BUG6HQM9whHstANATEIcgKiYO2Si5eZXz0FLaYjtq7rTG07httgVOqvgayez34wlh0Wpkt3lcDniJIBC2CAJ2Ra8oYI5CWwaGokPoCfXAAPs7yWQICPr3Cxo7KAsVLEAQoK+Nni8wSodAAWMHAfnnMJq2Be6c8hjTCdiLMavqV+GquVfxgoByCCgUCoVCcRwgWumumm0WvJORU24ETv8kuy3UC/zl3WZxaYHOsAeyYXUZ2tbjvafOYDYdHQjjhT1deGRLtujmBIGSarYdADAL+0KmDKQdApyrII8MgeF2M8SwiIimLPg98paB3R3ywKhoIonBcFyYH5DmSF8oNV2Av+gGgFptAP4ROgREbQqAcghMRuxaBjRNw8LqhcL9sytmo8HfwG3Pp8CR9mYXsdCaSMQOgezvkcepo8TNljGhaH6CwEjHDtJ2AafuRJWnKnObhgIGYgE+Q4D+zbUgcwiEEiMPFRRR7AwBO4FCJlAVtWUgj1BBQP65OzQHbj39Vmiaxv0uK4eAQqFQKBTHAxXT+W0zz+S3TSYu/gY71hAAuncBL/+M2STKEEhb0TP0H8LyygiWNrMXsN97dCd2d2QvZippqKCvCqArZdFAjikDslDBPFoGqHBjJE1RoIjQdgEATDsFFQRy9fJ3DUWkhTmQGj0oyA9IU48+lHlFoYK5hRDRqENAOQQmI7JQwflVZkiorG2AjhtMY7fimt4nEw1Gs/o7mRBnCGRfm9elw+uUOwTs3sN8RkVSQSAYD+JogHU11ZfUM/kPov79eJL9Xbd1CJDPtBihgiKKnSFgGyoocwiMU6igdTyn7L25fvH1WFC1AAD/3QjEAjAMseA7lVCCgEKhUCiOb0QOgRniC+1Jg+4Arv0dUE8Cx3Y/xtykLQM6kqjGIP94bRvw3jWsSyCdHZBG7BAgF0ixoLRl4HBvEN976iA5Pu0QCAjvw+CrAehFepHbBmigoKaB6TOmgkAuuocjtg6Btr6QMD8gTZ02AL/HyTkE8pkyIHteNWlg8iEKiqvwVKDGWwOgcEFA0zTpqmu6+BmPQmsiyTVlwOMUhQpaHAI2RW8+RbPoM93fv5+5bc0PED0uHS8J5Bg7SD5zWctAIaGCIkazOl/o2MGJDhW0ihUih0B9ST0+cfInMrfp55MwEtzYx6mIEgQUCoVCcXxDe9MBYMYkdwgAqbGG32C39R00B2qnKC9xwm+56K3CEJwaH3CHtvW4+uRmLmTLSiUJFQy7KviV/GhA2jLw1Qe24qWDpPCXOgQEF6WaZooCzEkM8MeNgmCUFQT8biezgld0QaA3kEMQ6Eepx4kyb2HOBEDuEOhTgsCkQ7TiPLdibua7JxIEdE3HKY2nSB9TJAi4dBf01JhV2arslAkVFGYIWAUBfcQZAn5n4aGCALC3fy9z25ofAPDFeFeQHxNr507gHALxMAzDKChDwOv0QoMm3e/W3dxUi0IoWBAYh7GDJY78phyIxJIvnfolRigQfT4nwuhBJQgoFAqF4vimbhFQaunDrV8K1MyduPMphGpynpFBINSXualpGi5akn1tMz2Sfsa29SjzuvC2FU3Sp6rSWMdAv1HKr+SHB4EEKThTF0vrDvQiDHJxFw+bAoZd7oAVD1l1iwjcDqNgOMK2DPjICmKhgkCuloFozwEg2C3dX6cNoNTDjx0cDOV2CIgmDABAjxIEJh2iVd90uwAAzKmcwxVAy2qWcbZ0K6KC37rtRHcIeF2OMZ0y4HF4uGK2UIdAV4gVBNy621aooJ95wkggnowXlCGga7p9T/8oMyYKzhCQPN9EOARmls9k9p3edDoumcW27okEgaGYPGtmqqAEAYVCoVAc3zjdwHv+Asw5D1hwKXDd783V6OOBiml80n9fK3Pzq29bgg+cPhNXrGjCdy5mL0AzHH0DSCbwHtI2kGZahQcVYFc5upOl/Ep+gF/RgtuHUDSBUCzBCwKAKQrkEyoICASB4l5oBUnLgDU/AMgtCNDju4cj0pV6AKju22z7eHXoT7UMjMQhILapqpaByYeoL3xuZVbsc+kuzKucx+w/rYmfLmBFVORY+6GVQ0DnWwbyDBXMp2UA4F0CBwfZlilOECAr0HREoV27ACAurIdiQ1wOgV0BDNi/vtFmTAi/l3ZTBsbBIZBvhsDV867O/B7OrpiN2868jXNLuBwu7vUEaEvcFKR48oxCoVAoFBPFtFOAGx6c6LMoHIfLFAX6LReafQeAllWZm7WlHnzj7cvMG5t2ix8nOgx07cSqGUuwoKGUCRMEgHcsLYPjdTYYqTPu41fyhYKAPxNkFzYEF3exUH4tA8CYCwI0Q8BfoCCwpKkc61qzF/HdQ1F02AgCM4PbAXmXBhr0AbgcOj92UGUITClEhR4VAK5bcB2+8arZIlTiLME1866xfcxchddUdwiIpwzkcghk26lGO2UAMHMEukNZBxCdW19XQloGcggNucIMRZ95f7if22a3Ig/Y5wgU2yHg1J22q/0THipoOd/aklrc+7Z70RHsQJ2vTipk+F1+RBLZ8FblEFAoFAqFQjG2VM1ibxOHAMMwH1KVoW09NE3De07lXQKXzeUvfI5GS/JzCLj86Bk2i9AwBBdx8bAgVDDfloHiXmjRsYO0ZYAW5pQlZFJDa08AQ4JRhmlWaGxPcdjPBlw26GZGAj920N4hYBiG1JmgBIHJhyiAjgoC186/Ft8++9u4fvH1+M1Fv8H0csF0FAuios/qCpAVQVNl7KCwyEwWkCFgsxKeryBg19IB8A6BXIV6rucV3b8v0sdts2sZyPU8o3UI0CLarn8fmFxjBwHz92Na2TRbVwP9fVYZAgqFQqFQKMaWggSBTvm+tvUAgHesakGlL3sRtqSpHIvK2RXpsOFCe1DnMwSoIKA7Aac7U4RGRC0DsVB22kAaqUOAXGAXWxAgYwcLaRlw6hoWNLAXgtuO8hkHLodpMXUjhiVaK7OvvekC5nad1g9AJAjEbEdZDYRiiMQF4ZEA+oJKEJhstJSywaZN/iZUeauYbQ7dgSvnXomvrPkKTq4/Oedj5moZkK28TpWxg6LXZ1gcSh6XfcuAQ3fAqYmLzrESBHIl/IuEIyvW0Mg0fWGBIJCjZWAsMwToc+c6l4l2COQST0TQ78cwnbwzBVGCgEKhUCgUE0nRBIGNAIBKnxu/u+EUnLugDpcta8TP37cSWoi9qOxDGbqGIgC1OtPHT63092QEAYlDYMQtA8UNFeQcAlQQ8MkvQuvKPKgrY1eNaAtCpc+F6dXma1uiHYRHs+7XcLDuXOb4aqMfSCZRTjIEkgYvXlixyy3oC8aQTE79udjHE3Mr5+KimRcBAJyaE59a+alRP6Zo5ZVxCEgKnaniEBAWktaWAad9qKD0MVBYy4AduUIFC31eTdO4z1XkELBb3c51HqMVjKhIkks0mWiHwEgyNWgmyHBs6gsCKkNAoVAoFIqJpFgtA107zTF+3gqcMqsaf/rwmuy+o2y4Vb9Riq7hCFBNHQIkMT91AdsbSPdTaggbLng1i+MgJmoZGLsMgb2dQ7j1/q3oC0bxmQsW4ArLZIUg5xBgC4ZStxO6ZhbklPpyL2pL7S8eG8u9qC/3Yn9XACv1PezOuoXodLE2cAeSQKgX5d4K7rGGwjHOwZDGbtRhImlgIBRDlX9qrARPFX74lh9id99ulLpLOcfASBhphsCUcQiIMgSsoYIuHV4Xu65JBQGPw4NgnBUrdU3Pe9XYrtj1OX1cgT9ahwBgru5bz5lmCJQ4S3KODbQ7j9GGTp7SeArqSuoyExQum32Z7fHj4RDINYaxUE5Eh4ASBBQKhUKhmEiqZ7O3B9qARMwMHKTYOQRgAEdeB+a+ld8V5AWBzqEwX7hHSYGe2p/OEACAMNzwwiIIxAsIFfSOvmXgqw9sxWsHzNfzuXvexFnzalDpMy866Yq+z81e5ui6hooSF/qCfKhfvcAhQGms8KKpwrz4PFnfx+5sOQXdhqCAGO5AaV0Nt3kwFEcTrxMAgO2oQwDoDUaVIDDJ0DQNC6sXFu3xRjplYKqECgpfRw6HQDSeRDJpQNfNglnklvA7/TkL6jR2BTx1BwC5BYFcoYIA/7lTh0A+YoadQ2C03w+Pw4N7r7wXD+97GI2ljbhk5iW2x0sFgSI6WfINFcwXGhJ6IjgEVMuAQqFQKBQTCXUIGElg4LD4WOoQoCMLj2wQ3y/ECgJ9KDVbBnJZZ0nLAABu9GAkMAgkSF/7GIUKxhNJrG/NXiBHE0lsPZJtOwhG7acMAPIcgfoyD2pLcwgC5V5MqzIFgZM0IghMOwWDMQ09BnmNwx1w6BrnBhiymTRg1zIAqGDBE4FcoYJSh8AUHjvIOwT4ER/heNYlICoG8x05CAAVHoliB7EgUCIbt5oin1YF+rlTh0A+K962oYJF+H7UltTiQ8s+hEtnXZpTXJE5VmT5DiOhkFDBfKDCjRIEFAqFQqFQjC0lVQC1lIvaBhIxrrDHrLPY220yQYBdZeo3StE9HEUyxwVstmXAIgiQ0YNHjx0R3C/PloFwYRkCxwbCSBC//0AoW1gPR+xbBgA7QcALr8uBMomNHzAdAmlBoFEjn0XDMgyH4+g0KtntKVdHIZMG2gdC0n0A69hQTE1ERY61uJnqUwaEr4OZMuDgQgUBNlhQVAzmmx8A2DsE6nx13LZitAzQz703wv6dyUcQsB07OM4OkinhEDgBWgaUIKBQKBQKxUSTT46AaCTgwivY223rAVF6fZB3CCSSBgLJHBdL6ZYBG4dAZ8cx/n5jNGXgcF+Q29Yfyp4bP3aQL+5lowcbys33wq5toKnCi2lVPjiQYHMUAMBbjkAkji5OEDBdHTRYcHAUDgE1aWDqI3QIWAofWaEzVVsGDMMBICsAeF061zIAsDkCoveiEEHALkNA5BDwOr3QIF8xz+e5aXHLOQRG2TIw3g4SaahgER0Cxc4QoA4BNXZQoVAoFArF2EMFgd4D/DGidoH5F7Hbgj1An+C+tGUgZWvvT+QoHtw0VBAIk0kDA72CoMMxChVs6+VXzq0OASoIiEL7pA6BlCBg1zbQkGoZ8CHC73T7MRSJowvE7TFkvj/UITBo4xDoyJUhoFoGpjzxOF/sJhPZ75BMEJiyoYJJ9vfH43SIWwZixWsZsM0QKOEFAV3TbYvTvBwCVBCI9DO37R4/n2PG+/sxHg4BOxfASAQQ6hAYihV3PO5kRAkCCoVCoVBMNPk4BIaJQ6C0HqieA/hq2e2p8YMMNFQQ5gVPXyzHKk1qRctqUY/QDIEB6lzQANmqDBUEokNAMml/DhbaBA6BAUtAYCBKHQKFtQwAQG2Z/AKyqaIEdaUeVDgEBbnbb+sQ4ASBUP4OgZZK9gJfCQJTnw0H+FXJcDR72S51CEyRlgFaGBvEzeRx6nDoGtxOMmkgmv17ImwZcBbHISBqGQDsBYeRZAj0hfts9xf6POP9/ZA5BIrpZLFzAby8ZxC3rz2Aw738vx0yOIcAnaIzBVGCgEKhUCgUE01eggBZiffXAZoGTDuF3d62nr8vyRDoM8wLnp5IDkHA7UM4lmDG+dEMASfpcYXbb56XCCoIAEAB/ZmH++wdAkEuQ6CwUEEAqLNxCDSWe6HrGmaLssZcfgxH4ugyyM50ywB5XlmGwHAkzu1b3MQWJkoQmNoMBGN4dR+/KpmwOASm+tjBk+pOYorfRGA+s9+TcgfQtgGmZUA0ZaCQlgHa4mShwdcg3G7Xv5/v2EErdGziVMkQcOrj0zLwqbs24+sPb8c7f/VKTudVGvodUQ4BhUKhUCgUY49IEKBZAFQQKE1dkFJBgE4aiAyZowwtdBpV5v9zCgJ+Jj8A4DMEqjRS0NsFFYoEgQLaBkSrPP1Ba6ggcQjkKQjoGlBTat8yUOJyoLzEfLyZZexnk9CcgNOdEgQq2TtKQgVlGQLtgvyAJc1sYUI/E8XU4o+vtCIS490tMUsbgVN3Cvuwp4pDwOfy4Y5L78AVc67ANXOuR7jjKma/J+UMsBMExjtUMH3eMvLKEMiREZCXIDCJMgRkAlVRHQKy98zQkc6daB8M4xfP7s3r8ejnrjIEFAqFQqFQjD1UEIgMcqv66cIyQ1oQaFnNbm/fAsQtBeOxzQCyBWzMcGC3MQ0A0BHKcRng8qOXJNrTloEqkILerkdXdIFdgCDQJnAIpEMF44kkInG2/SDfKQO1pR44UrPLayWhgo0V3syIrWl+VhCI6KYIMhyOowuV7B0loYIyhwBdxaoocaGlkswmV4LAlCUQieP2lw7ASPKFVJSIBB4n/12dKqGCALCkZgm+e853cf38TwKClgHADBe0Yp0yICp+CxEESl2l0pDAuhKJIGDnEHAVniHA7c8nVNDmHMbbQSITqIrpEJC+Zwb73H9dd1gouFLodyQQCyCRTEiOnhooQUChUCgUiommYroZEmiFtg0EqCCQCrVqXsluT0SBjq3Z20ffYHbvMqZnivpjwRyXAW4/ugNsgJ5BLr6qNVLQu+UX3L97+RACBili8hQEIvEEOob4i7mBkFlYB6L8BZs/T4dAOlAQkDsEGsuzr7vZxz5XCOZ9AhHB2MFwPxCPoIwTBMQOAZof0FThRbWfPSfVMjB1uXvdIdP1kuS/p9Q1QFfAnboTujb1Lu2tQYGAKQakxTkaLBiJWwQBQfFbiCCgazoXMAcAVZ4q6Up7iY1DSvRYlFyCQD4ZApPKISALFSyicCUNFSSCQDSRxK+e35fz8UTCDW3dmGpMvb8aCoVCoVAcbzhcQMU0dhsVBDiHQEoQKKkCquey+46+bvmZFQQ2J2dnfj6SUxDwcQ4BjVzw5usQiMQT+MlTezAMckEbGbQ/hxRH+8PCiYoDqRF8dMIAIB47KBQEyrIX4bKxg00V2WMaS1gnwnDSi2TSQCCa4DMEAGC4M+9QwfYB1gXRWOFFtZ+9qO4JCKYcFJGe4Qj+7Y71OO3bT+Fbj2xHMil44xVFJxJP4HcvmlNCDIMvpKyhggBfCE0ld4AV6vzxWIIES0hwqNUhMNopA4A4WFDWLgDYr87b7UuTq+DPp2XALjhx3DMExqFlQNM04ftmJPm//39ZdyhnloBfIGoPF5B1czyiBAGFQqFQKCYDuYIFuQwBy9irllXsviMWEYAIAluMOdldwwZgMzcbLj+3Gq272Qsvp0amBEhGDq7d042hSBzDBnv/LfsPy5/fgiwlOh0qGIzygoBfMGWAhvsBQAPjEBBfwDZYBIE6N/tcQ0k3hlPPPwg/Iga5EB3uyDtUUOwQYM8pHEsyhY+VWCKJnz+zBx/90wY8vPmo8Jhc/PzZvXh6Zyc6BiP47YsH8PxuOklCMRbc//oRtKeLFYFDIBRhf1dpwTveq7/jBXUIWF0BdhkCo20ZAIonCPhdfjh0/u8RJacgkEfLgJ1LYbwzJsYjVBAQvy9Jwe9QNJ7E/z1n7xIQCSrDMSUIKBQKhUKhGGtyCgJ07KAl5bqZCAJph0CoH+hlL342J7OCQNdw1NbiLwoVdHhyrHJJVuAe3doOABgiDoH7X9mJTkErAOWwYOQgYLYKRONJDEd4W7HTwV/miBwCdRaHgKxlwOoQqHaz78mw4UFrdzp4ShPmCOQbKkhXrxrKeUEAkLsEbl97AD94Yjee2N6BT/3lDbx+qE94nB1UAFjX2is5cmoyEIrhO4/uwK33b8GhnvGxCieSBmNnFmUIBCPKIQAAHktuwEQIArIJA4DcgZDv8xYlVHAyZQiMw9hBQJynASrMpsjlEnDoDu49nOrBgkoQUCgUCoViMsAJAgeyP8dCQGSA3e+3OgRIsGDXTiAyDBzbxGw2dDd2G9MztwdCMRi2IYA+9AyzhadrBIJANJ7EE9tMQYA6BLToEL5w76actnRRoGCagVAMQdIyIBo5CAAVPlHLQPZi0utycMU7YBbmacp0tpgPGF48szPb0tEtGD1IQwUHQ3EYgh4IkUOg3OuEU2dXh/sCYkHBeh6AuepcCEPhGPZ3sRe/B3um9sUw5Qv3volfP78fd712CO/97auIJ5K57zRKdhwbRKtVfDD47+lwmDgESBE0VUYOUqy5AADgcWZFAC9xAYVztAzY2elFiEYPygIFAXkxnk+gIFCcDIESZ4k0DHGyZAiMh0NA5LIBzH+PcmUJ0LyHoejUHj2oBAGFQqFQKCYDdg4Bmh8AsC0DjcvZUEIjaYoBpF0gUb8UMbAXYgm7vlZBy4Dbay8IDAt6n1/Z34PBlEV+GOz9SxHCi3u6cftLB7j7WZG1DACmIEBHDooCBQGg1O0Eqa0ZQQAA6gQuAatDQCerRSF4cMfLrZnbotGD9DFDsUTmPbFCU7AbK0qgaRqq8swRoMLJ2r3dwuNkbDkywG1r7Z7agVpW4okkI6oc6Q9h29H8ci5GQ9cQ+3lW+/gAuqGQfcvAVBk5SAnHWEHGOwqHQKEZAqLRg/W+esGR9o8v6ksXkXPKQB4OAU3TpOcx3i4SqUOgyN9VYYaAxCEAAH957RA6bVwCpS729085BBQKhUKhUIw9VBAYaAMSqVVgKgg4PIDXsgrt9gH1S9hjjr7OCQKOaavgJjb6mG5zgSloGfCU2F/YPrZ7iGsBeGzrsczPNFSwVDML2O89ttN2JfqwrUMgiiDpqfcJ8gMAQNc1zKhmL5bn1rMXf6K2AeuUAUTZ8wwYXjMZPgU3aWC4Aw0VHmhEiDjaz76mSDzBvd9pIaKGCAKiSQOxRBLHSCjhge6ArZhC2dzGCwIHewJCN8NUpDcQBTWrHOmXf/eKRYBkYFR4S+BxsL8rgwEX46Q5cVoG5A4BW0FglFMGAHHLgK0gMEqHQDEyBOzOY9I4BLQiOwREQonFZePQNbgc2T/AkXgSf9vYJn08ziEQUw4BhUKhUCgUYw0VBIwkMJAK3ONGDjaAqy5byPjBI7wgoDWv5FL0I7rNBajbxxWeJT77C+qusI4P3r4+E/YXTyTx+LZsIOIQaRkohVlsxRIGHtokD8E7IskQAMQOAVnLAADcfP78jAX/HStbMLeOvfij75FT11BjFQmIIBAEe3wXaMtAJzxOB+cSoIJA5yC/6p9uVaA5AiJB4Fh/mCtmgcJcApvb+rltgWgC3cP8801Fuob5z+CIjRhVLOiUjDKPC5fOuDJzOx6Yi0S0mgmjPFFCBSN2DgFuykD2WFHLAF35zYXIIWAbKjjKDIFiTBmwO49xDxWUTRko8nmIhBLrlIFyrxNvW9HM7M/mvvBwDoGocggoFAqFQqEYa0qqAA8pJNNtA9yEAcEFKc0ROPA80H+Q3da8ErWk2A1BEMaURtAyUCKwMlsJGh7sODaIz93zJpJJA+tae5nHoA6BMi1bbNHe9cxjRuO2BWl/MMYVVD4bQeDa1dPw4i1vxZOfOxc/fNdJ5sZQH9C2EUjEuEkDDeVeOKx9BrkEAYFDAACaK9nXTgUBmh/gcztQnsozoC0DIkFAFrz44p78pwSIHALAiZMjIPqejYtDIEIdLk7852n/gdDhDyB05N0IHboRANAbzJ7fieIQCNtlCDjZUiZc5JaBQkMFZQW9SFgQkcsBkE+GAGDjEBjnnInJ4hDwuZ2cM4yKyFaoIKAcAgqFQqFQKMYeTQOqZrLbMoKAwCFAoZMGgj3sbacXqFvErVIHDbkgENa9/Mp7aQ5BIFUcP7OzE794di8e3dLO7PeXV7GPh2yx1SopOnOt0PYHYwiQloFSj/2Ir6aKEsxvKIOmaWbewk9XAr87H/jZKiwx9jLHWscSAhC2DFjpoqGCQ6Yg0EIEgSP9rABA7f6NFV7z/JBfy0CbRBB4aW8PEjlCGwGgZzgiDW9sHae0/YmGhmgC4yMI0LGZfo8DPo8TzshyxAdXAqnsjz4bQeBEcQh4LCIADRUs9pQBWsg7NAeqPFWSo+WFeL7OhJwZAnm2DMiEg3HPEBA4AZy6M/N3rViI3jfDEiro9zg415itIOBWGQIKhUKhUCgmgurZ7G2pQ0DQw1q/2Cz6ZTSuABxO1JPidkgw3ixNb5RfxSkrtV/pCiF7Dj96ajfuf4NNuZ/T0sTcLrU4BA5Kik7ZyneagZDAIeAuYAVq3W9MhwAA9B/COzd/BG/X12Z2nz6nhj1eECpoRegQMAw0V7KfD3UI0FFY1tyCKl8eDoFeceE6EIoJwwIpm22OOXTCOAQmpmWAjs1Mh2LSz70voBwCXpc8Q8DqEKDvj8fhKTjdvraklrnd4GuAQ5eLjaMNFSxWy4BM+Bhv0cipObmJB2PxPRW+b5ZQQb/HiVJv/oIAff/UlAGFQqFQKBTjg2zSQD4OAYfLLPplNJsZA9QhMJiQXCA6S9AbZC+YHLoGX46WAWtxbBj8RdfiWS3MbatDoCcQxWCYH6cnK3TTDIRi3AqrXYYAR/8h5qaejOJ/3b/EDyruxXUrG/Cxc+eyx1OHAIhDAJXs8YkIEB4ouGWg0TLZoKZ05C0DALA2j7aBzYflgsBkdQgkkwbWHejF7o7iXLBPVMsA/f6mBS1OELCEV3IOgak6dtDGIVDIlIFC3QEAsKphFVpKs3+z3j7/7bbHj3WoYN4tAxJhYrwFAU3TuOcs9shBQJwXYW0Z8LudvENAMOUlDf28prpDoPifiEKhUCgUipGRryDgl4RatawG2tZJ9pktBTQwrz8uWa0RTBio8rmhu+xXqC5dORv3bxDvW9RYhoY69tKjXGMLzYPdQSyfxlruZVb4NAOhGOLEEu/P0TLAEBYXwtdFHsB10UHAeRcAy/sUZc+norwS6Mve5loGAGC4E82VbD8yJwiQFgLrqEMuVDAoahmQF64v7OnGp86fL90PAFuO9Ev3TcYMAcMw8JE/bcDTqTGBX796KW44Y9aoHrN7iHcIpEMrCxKZCoQPxTS/v/RztzoESlxscThlWwbiNFTQ4hDgQgWzgsCCqgXQoMGA+bdhcc3igp/bpbtwz9vuwSP7H0GDrwHnzzjf9nj6maTJV4woxthBYPJkCKSfM5LI/l6NhUMgV8uAz+0YlUNgODY8yjOc3CiHgEKhUCgUkwUqCPQeAJIJQcuAJNSqZZV4O5B1CBBBQNQWAABw+7h+6tpSNyC54E1z8UlzcMlS8fldtqwJ8LArL9ZQQUCcI0AdAtbxUQDQH4wiOJqWgbDNnPl9zwAv/D92G3EInLl4BnM7AjdCDrIiONzBZQh0DEUQT2SLHfraWyqzF/XVebUMyIWTNw712V4AG4aBTZJAQfPcJp9DYOuRwYwYAAD/99y+UT+maMoAMPZtA0FBqCAAVPrY4skqBK1pXMPso7enCpEYDRW0ZAjYtAw0+htxy5pbUOOtweLqxfj86s+P6PkrPBV43+L34YKZF+TsfZc6BPIMFXTrbs5ib2W0DoHxnjIges4xaRlw5NEyUIhDgHxew1ElCCgUCoVCoRgPasgKbmQQ2PMkECB2b5kgQIMF07hLgZp5AHhBoEcmCAgmDFT73fY5BQA0tx/ff+dJmFPLr4hdtryREwT8CAHIru6LVqKpFX5BA/sY/QWOHeSI2AgCANBGLA/k4vD0RTNQRp4v5Ca5A8MdXMtAImmgM7UinUwanCAwpy77HlaTloH+YIwRE8KxROaxRMQSBl7b3yPd3z4YRpfN/QdCMfQLXAkTCc1FODYQ5qz3hSKbZkHdHMUmIAgVBHiHgPUzOKXhFHz9zK/jvGnn4eaVN+PaBdeO6TlOFNQhkG/LAABcv/h6PPfu53DvlfdiQdWCsTvJFKMdO6hpmq0LQGiNF52HRJiYiJwJ+pxj0TKQl0OACgLROAxDHLZKP6+p3jKgBAGFQqFQKCYLldOBppPZba/8HIiR1VlRqCAAVM/hRxcCQNNJQCoIi2YIyEIFDUHLQLU/t0MAbh/KvS786gOrmYv1lTMqMb++lBMEdBjwIVuIHujmV6KpFX55C/saB0IxrqDyufNsGTAMvmXg5OvZ23Q/+TxK/GW44Ux2QoS7spG9z3AnqnwupphpQRe8998I/PFK9Gx9EmHSK20VVWhhCJhCSBpRn/uyFrZF4cU93dwxaTaR/IAyjxNOnV2pnGwugV3tvJBzlLRdFIooVBAA2sZaEIhQQSDtEJA7QzRNwzXzr8HPLvgZPrrio2NSaE0GwtQhkGfLwEQw2rGDdo/h0l15f8aTJUNA9Jzj1TLAZAgIHAKGAQQl3xc6ZUCFCioUCoVCoRg/Vn+Ivd36In+MTBDQdaBlJb+9ObuNOgSCEK84heBBL1ktrcnDIYDUheiChjLc+7EzcNGSBrz95Gb89D0rTbuth5/rbQ0WpA6BwXAMAyE2aHApFQSCMc5ynbdDIBYCkmRVuW4Re9vqIEjEgTgpOt2l+PxFC/Gxt8zBabOr8b1rl6O0hg1PxHA7NE1j2ga+6/otqg8+Chx4AVUP3YhyZF97qcfJfFY0XA5gi0PaLlDlc+HSpawo8aJNsCDND1g+rQLTqtjCZLLlCOxo5y/S6ejGQkgmDWErBjAOLQPRBD7meAg7PR/EWs+nMW14KwCgmrQMWEMFTxQKcQhQUW28cekuYZ9+IYGGstGC+eYHAJMvQ8DKmDgEBO+ZYW0ZEIQKAvIcATomUjkEFAqFQqFQjB/LrwPsLh7dpYDdCCtR24BFEPC6HCi3hCuFDLEg0B93cQ6BmlJPboeAZWVq+bQK/PaGU/C/71mJ6dWp7R5+pazMEizY3LUW+NuHgBd/BCSTaCP5AZoGLGliRYUBQcuAL19BQBQoWDmdHGMRBEQXhm4/HLqG/7hsMe752Bl496kz+LaOVDBkum3AgQTO0Ldndjtjw1ij78zcnl3rZ/qVXQ6d+dwAIgiQgnV6tQ/nzGfDJ/d1BaTW980kP2DFtErMrGG/Z60C98ZEYRgGdgkEgdFY+/tDMSSSYgvxWE8aKA0dxZedf4VXi2Ga1o2Ttn8fAFBlcYZM0zrxzY5PAd+oBx76LJCc2OJ3vIjE5Q4BmiEQTSSZVpqJQLQ6XwyHgLBPvoBzAKauQ0D4niWtDgFHxnVjZUiSI0AdAuFEGLHk1BXjlCCgUCgUCsVkwlNmigIyZO6ANKJgwWbWNWBdeZY5BLqjLvQEWPt0td9tjjfUbOz4bvGFaAaHCyAXb2mHwBztKP438W1g2/3A07cBT3+Nyw9oKPOinrgc4kmDEy9K850yIMoPKJ/GH5PuNY0KimLRxTcVBIaOAQCaK82VrEb0wqmxhcsy/UDmZ2t+QBpu0oDlNbcRh8D0Kh+WtVSgooS9+F4raBswDIMTBE6aVoFZNezrmkwOgfbBMOccAYAjo2gZkLULAGOfITAvuhMOLStGlPWb4pDVGfIJxz+xILnXHGO58Q/AgefH9JwmC3TV38uECvKlTDg+wYKAYHWerjjbIXMCFMMhMCEZAjRUcAyCDUXZClaHgM/thNupM+4SgG/VSSP6vALRyfP3r9goQUChUCgUiskGbRuw4s8hCEw/HbCuyJS3AFWzmUNYQUB8kdkR0jn7dE26ILVzCeRjjZVMGrhQ3wjdUhTh1f9D71E2OX56dQkqfPwFJV3ZFa0GCaETBlw+wFfNbkvGzdYCgJswAMB0bVAqiKjQfxhA1iHQovGF+VLtYObnObX8Y1JBwCqC0JyFadUlcOgazp5Xy2x/YjuZWAHgUG+QK65XTBc4BCaRILBT4A4AgGOjKNxFIwfTjHXLQFO8jbntSISAWJj5zJfqreydOraO6TlNFuwcArRlAJj4HAG6Ou/UnHmHAQLFEQRkLQoTMWWAaxnQxidUkM0QML8nZXmOHhQJAkOxqZsjoAQBhUKhUCgmG80rgcbl4n25HAKldcCl3zF7/Uuqgbf92MwWsDDbElYXlLQMHAnqXOp8pjiRXZjqTsCZhyWVCAIz/OYFfJPWyx6XiGLhzv9jNk2r8qHM44RDtx//5c937CBtGfCUA15BMGPaSUBbBnSX+DVXsiGDGDgMJJP2goDFITBb6BBgP6s+pmWAdQhMqzKLkrcsYNsGntnZgTZyLB03WFvqRnOFFzOJQ+CQzVjD8WbnMfHF+dFRZAjIRg4CQMdQGNExWnmOJ5KYjmP8jlAfM3awEmT0majdZQpCMwSsDgEaKgjwIYTjDV2dL3WX5hxXaKUoDgGBa0mDNibFeC64loExECVytQykx3hSoVjWMlDiLIGuETfBFM4RUIKAQqFQKBSTDU2TuwRkIwetnHoTcGs7cMsBYMEl3O4PnD4LValCo6qyUvgQQ0kPl8Bck55QIHMISPpWOYggMNNvXpQ1aH3coSf1PIJZWrZYml5VAk3TuH56St4OgQgpqrzlwuDDjJOAOgRkLRKVM9jbiSgw3J4JFRQJAs1aL6phPo9obGO1n8yktwkVnJ4KBLx8RRMTppU0gLteO8Qcu/lwP3N7xbRKaJrGOQS6h6MYCk+OPlrRhAEAODaqlgH5WEXDANoHRjfBQEYgmsAcTSwIWB0CVdqJKQjYTRnwOiefIFBC/j4WEigIyLMCCsoQELQMuB3ugoSJYjEuoYKisYNMqKD5PeFGD0ocApqmcZ/bMBk3O5VQgoBCoVAoFJOR5e/Mrzddhs2F35Lmcjz9hfPwj0+ciV9+6BzhMaKwwZpcDoERCgLNPvOirJE6BAA4kMTnnH/P3J6WCiek49goeY8dpC0D3gpzxZ++xohMEJD0BpfW84/Rf8jWIQBkbeGzhYIA+5mkWwaGI3EufT4d4ljqceLaVezEg3vWH2aKJpofkB7rOL26hPsaHRzD0YNdQxHc9tA2fPFvm/DPTUdtCztZy8CR/pB0tngu7DIEAKCtf2xeezASw2yhINCLEpcDHqcOBxIo18jz0+/uFMVuyoCua1xfeGiSOQQKCRQE5KGCo80QmIj8AECQITAWYwdFkxmYUEFTCKCCgCxDAADKXOznNhxTgoBCoVAoFIrxxFsBLHsHv720jt82Aqr9bqyaUQVfqWA1HHzYoEPXsgF1LsmFaa5AwTRkBb7RYxazIocAAFypv4KFmrmqnR6FV14iv6h0CooEKaKWAcE5Zo6jgoBMBNE0oIJMK+g/hKYK872TCgJaKxrLvUKHQw3JEEi3DNAWAADMeMMPnDGL2dcbiOKRzWYB+ur+HqxrZYWYk6abgoDH6UBzBR09OHaCwJfu24Q/vNSK+za24dN3v4E133oK//3gVmw9wn5G0XgS+7rEF+eReFI6OjAXdhkCAHB0FO4DO0IDnaigxT4AhPqgaRqqfG5UQGBXPkEdAnSyAG0bmGwZAoU6BIrRMkBdCsDETBgAeAFgTASBXA6BAjMEAMBPpvmkBYFYYnK4pIqJEgQUCoVCoZisrL6R35avQyBfJAVtgIQNVvlc0NN9+5IVrLwCBQHOIVDrikBDEvXoFx6uawa+4PwbADM9HwAqbQQBv8eZvzWWThnwlrP/p8dxDgGb11xFcgT6D8LrcqC21C0VBJbprcIJAwA7gg7IOgQOk9GM9WUepmiaV1+Ks+bVMMf86ZVWBCJxfPm+zcx2n9uBU2ZlQxVn1bLfj7EKFuwcDOO5XV3MtsFwHH965SDe9rO1uOmPGxBLjZPb3z2MWELuAjg2Qms/nVRBGatgwUTXHvGOkCmQVfndqNIEjogxEgR+8exeLLj1UZz13Wew8aBYpMvF+tZefORPG3Dr/VuYrItCMQzD1iEA8MGCk84h4CrMISAVBESr4BJEIsREOQSoEDEWLQNl7jJoYP/mG8ns+1VohgAgcAhEh9Ed6sZZfz0LNz99M/62+29oD7SP9tQnBUoQUCgUCoVistKyGmi2jBF0eoGWU4r7HK4SAHzxTFsGmIR7mUPAbvqAFSIIVDkiqMUgXJr8Qv5ix0ascuzLrLBXCiYNpPHn2y4AiFsGAIFDoMCWAYDPEegzpwg0V3htHAIHhO0CAO8Q6E2NhaQOgXS7gJUbiEtgU9sAPvrnDVxQ4BcuXohyb/a9pTkCstGDB7oDuOmPG/CuX7+Cl/aKX5sda3Pc56kdHfj7RjOJXxYomObICCcN0JYBOtLuyBi1DGg9+8Q70oKAz8UHCgJjIggc7g3i+4/vQjSRxJH+EL79rx0FP8ZgOIYbfr8OT27vwF2vHcL//HPbiM8nljBAO0A4hwC5PdEZAjShvtCWAVnhX4hDwOvwcgXyRDkEaIbAWAgTfpcf50zLtr/FA/OARPZvVzpktpCWAS5DIDaMF9teRCgewnNtz+Hrr3wd1zx4DeJJ+WMcLyhBQKFQKBSKyYqmAe/6EzD7LUDtQuDqXxStZYB5DoFLgDoEaqz96zKHQL4tA2T1vVQLoZ60CxiajmGdPe6rpQ/B6TAvXSpyOATyRtYyIHMI0KRpu9dMBYF+s+1hYWkYXk1sO52td2BhlfjhrOMiAaBjMIID3QHOIZBuq7BywaJ6po0AAF7a28PcXjOrGjeeOYvZNquGOgT4otgwDHz2r2/gqR0dWHegFx/780bbC20Ra/fkFhH+/npKEJDkB6QZ6ehB2jKQzlJIM1KhIReOPokgEDRbOar8blTSQEGAd7eMhHgEePCTwPdmAXe9C3sOksDJtn4kk4VlMqw/0Mus0j+/u8vmaHvCcb64pw4BKhBMtEPg7JazmdvWQjUfZBkColwAGZqmca0LE+UQqPCwv0eFCiT58qPzfoSvrPkKPrz4swgdvoHZl24rKS2gZaCUiL3D0WG80PYCs+2M5jPGxPEw3ihBQKFQKBSKyUzldOCD/wQ+tQ5Yft3YPIegqA2RDIHq0nwcAiNrGXDGhrHYzxY8YXctfhG7ktl2UvR1IGle7Nu1DPgKEQRkLQN5OwRsXrNMECjptz2lZY6Dwu0LGspYpwaA+zYe5kYOptsqrDgdOt532gxuexqvS8f/u25Fti0kRT4OgZ3tQ8zowuFInOv7t8MwDM4h8Mm3zsWHiDixvrUPh3uD2CmZMJDm6AhaBgzD4KYMrJhWydweq5YBz8AB8Y6UQ6Da5+YnDADFcQjseAh4407zufY8joZttzO7YwkD3QH7bAVKxyB7/EAohoigsM+HSIwf9cgLAiRUMDo24yHzZU3TGnz/Ld/HVXOvwtfO+BounXVpQfcvRqggwAsIE+UQuGjmRRkxwqk5ccksfvJNMfA4PLh+8fW4oPlawMi+VrdDhzv1nSkroGWAOj36I/145dgrzLZzp5072tOeFChBQKFQKBSKEx2BQyBIWgYYu/poHQJEEEBkCIuIIHAoXoG/x85ktjmMOBAwC0e7UMFSz2haBipT/y9ChkAlyRAYaAOSCcx28tMUrMyKileM3U4dV5/czGz7+8YjONRDWwbEn897Tp0Ot0N86XfLpYswS9CqMIsIAh2DEQSj7EX0v7bwCfmF9PHv7hhGJ1mdv3bVNPzn5Ysz4zHT3P/GEewiDoF0G0makazkD4bjiCbYQnLFNHZl8+hAuODV8nzwDdkLAtKWgchgRiAbMUdeZ25Wd73GHVJomGLnEH98j81IRztE9v+coYIT7BAAgEtnXYpvnf0tXLvg2oJH/RUjVBDgLe/Uuj9eLK5ZjLuvuBtfWfMV3HnFnVhZv7IojxuOJfD4tnZOfAxE2M/fb/n3gLrHhiPygEDqEHix7UUEiEOMukGOV5QgoFAoFArFiY6gqKUtA/llCIxsygAiQ5jtZgvz1mgFulGBhEEupofM4tNu7GA6QCovpFMGKshxEkHAzhVBBYFkDBhqR4tmb6GuHNgu3ffO1ezkgvbBMHZ1sAWyyCEAADWlHrxtRRO3fc3sanyQZAykmSHII7DmDhiGgUcEgkAhRfmLe9j3o6WyBLNr/XA7dbxtBSuA3L3uECc2nL+onrk9kpYB0cjBk4hDIBpPFrxanpNkEqXBQ+J9llBBYcsAMPq2geEO5mbl8H4ArOhxtMD3kzoEgNwjHWXQQEEgd6jgRGcIjJZihAoCvNOAjv8bTxZWL8T1i6/H0pqlRXm8WCKJq3/+Ej7254248udrce+Gw5l9VLC0/nvAZwjIvyvUIdAZ6mRuL6tZhtqS2oLPfTKiBAGFQqFQKE50BIU8DRWsKc0jQyBvQYA6BAbR7GAzBNqNKiShoxukME8VMHYZAvSizxauZSD1fMVwCPhr+feq/yBq42wRFjfYyzG9fYv0IZc0l2NZi3hUZJppEkEAAG4gNvwSlwPfF7QKZPa7HWgoZ78Lrd1ZQWBXxxD2d/FtBMcG8i8iabvAOfNrM6uq16xqIY/LigEuh4az57EX5SMZD0jzA0o9Tsyo9sHlYN+XorcNDLbBmZSsnmccAm5UiRwCAO9wKRQiCJQkBrlpH4UKAp2D/PvflWOkowzaauDQtUyOSBrqGDjeBYESh/jvq6yVQMZkyRAYC17Z15MRQg3DnJqSJhCVOwQKGTtIBQHKudOnRrsAoAQBhUKhUCgUAqt/EDYtAzKHwEhbBsKDqE2yAXcdRnXq/yRhL+MQsAsVHE3LgCxDIOUkiJFQPbvXrGnCHIGyCDuq6tXkYvaYrp1ATF6EUZeAFV0DmirlK4knT6/EJ86bC4euwevS8eN3n8zlBFDo/kO9WQHgX5t5dwCQf1EeiSfw2n62heLs+dkCf+X0SunUBQCYV1/GTVXoHApnRhTmC80PqC11Q9c1NFWwRVjRgwV79sr3MQ4BSZDiaHMEiCAAAAv0NuZ2oa+Ztn8AI3cIhEmGgNfJly7c2MHo8S0IFKtlYLJkCIwFbUSYs/69CUbkDoFCxg7SlgHKuU1n2u4/nlCCgEKhUCgUJzoC23vQrmWg6A6BIZTFWNt4e0oI6DQq2WOHzALGLlTQn2/LQDLJOwRyTRmIkpXaHBeNIkHAPcwWXE8lVyNpbY0wEkCnvG3g6pObpVkATRUlcEn2pfnypYuw8asXYvP/XIJLlzXanz/4SQPplH9ZuwCQ/6ryxoN9TM+3pgFnza213NZwzcoW0V0BAIsay7jpCUkD6BCsUtvRQ1oBalOOGPrYRXcIyEYOApkpA9U+NyohHvc4FoLAQu0wc7vwlgH+vaeCS75Qh4DHxYt9kzFDYDRIQwULbBmgDoGJyhAYC3rJ72t/MJrJ96Cr/laBmLrHbDMEbBwCtUlg8eYH8z7fyY4SBBQKhUKhONEhq9wJOBAFe+GUn0NgZFMGYCTgGWL7qNthOgR4QcAsQIsydjA6BNovnWkZKMaUAUAgCByENsAKAruNaThgkML82Cb5Q/rcuGhJg3CfaOSg7DHcgtVWEXPq2AvjB988im1HB7C7Yxj7BO0CQP5FJB03uKy5AlVkkkIuQaDS5+KS5tMrhoZh4MU9XfjNC/uwv0tiuwffMpAWBJqJIFBocZwTO4dAPATEQqj0ucRTBoDRCQKxsPD+8zX2+1lIC0YiaQjdACNvGRiBQ+A4FwTGKlRwIjMEig0VmJJGdrU/SBwiVocAbRkIx5KIS9xEdoLAOYFh6Gt/BLSuLei8JytKEFAoFAqF4kSHrCQlnCUA2N5pNkNAFiqYZ48rLbYBaHG26Ei3Cgw4a9gDUyuadlMGfO48WwZE/dfeXA4B0jKQyxVRRYIF27dwroQjRi22GbPY445ttn3Y606ZJtxO7fPF4PJlTUwvfSJp4Ja/b8Y/Nx2R3mcwHLftz01D8wOs7QJpplf7cOqsKm47ACxsLIOmaVzhns4weODNI/jA79fh2//aiSt/tlY6DrGLtgyUmaJES9UEtgwAQKgf1XYtA6MJFQx0Cjcv1KkgkP9r7hmOQDSIoWukoYKx3A4BuwyB7UcH8eX7NuGHT+xCII/v42RAVvgXmiFABYFCHQaTmd4A7zjpC5rbAiRU0OoKKPXw/27IggX9NmLvucEQAAP4x0czTp7jGSUIKBQKhUJxokMufDRy2+PU2RV5qSAwQoeAgLQg0NA8i92Rcgh4XQ5uVThN3qGC3OqoBrhT5yZyCBiGwCFQYMuAIDDwmFGDrclZZKPcIQAA586vQ2M5/znIJgyMhhk1PnzivHnMtq1HBvHr5/fb3i9X2n9fIIotpEA/RyAIAMA1K8UCyOIm83PirP2p5/7di9mRfoFoAjff/YZQqKCr2mmHwDTyuLR3OcOW+4BfnAb84Qqga5f4GBE5BYFe+NyOsWkZGBYLAqZDIFvV9wSieQf1ifIDgOI5BOiEAUDQMpBaIR6OxPGuX7+Ceze04WfP7MU3H5G34UwmZKGChToEzmg6g7m9pmnNiM9psmEnCAQj1CFgHTvIC0pDkraBMpf43ymnYeCMUErALm/mc2WOQ5QgoFAoFArFiQ4RAJzeUqycUZm5/Y5VLXBYU+hlToB8QwWdHsDhke4eNrwYhvlYixcsYHcOZXueK0vEPbG+fAUBUX6Anro08pLpBskYEA8LMgQKbBkw2AKnw6hEFC5sNWazx3VsAxLy/laHruEdq3grfb4tA4XyibfOxbx6VvyIk6VgOm796IC91fzlfT0wLA9R4nJg9UyxE+CK5U1cbkKlz4X6MvN71FTBFkvH+sPoHApj21H2Mz7QHcD/PLiNe3yZIJCXQyDQDTzwcTMM8uBa4JEvCF8DRzwC9EtGDqYJ9UGLR+DTJAX1qAQBPj8AAEq1MFrAOjfydQnIshtGHiqYh0OAiATploF1B3oY8edfW9gwz8lKscYOnt1yNm478zZcNPMifGXNV3D57MuLcXqTAtH3qT9k/r2kDgFrC5koX0bmZKIOizSnhMPwQwPO/TJw46NAhVisPJ5QgoBCoVAoFCc6xPauuX2466bT8K1rluEn7zkZ33z7cvZ4qUOggNVpG5dA2h1QX+bBgnnz2Z3DHWYYIOSTBkrznTJAiylrmwAVBADTJVDIlAEAqJxpu/uIYa6Ib6MOgUQE6N5te9/rVvMXomPRMgAAHqcD37t2BVf0p2mq8GJxI+uqyFVErt3LBkmumV0Nj1P82VX4XLhgcT2zbVGqXQAQ9/o/v4t9/DR/f70ND7zBtjvwgkCqZYA87lA4jsEwEWraNgAJy4rloVdsxZwMfa2cQNRtEGdKqA8I2ViSRzN2UCIIAMB80jZAxz3KkDkEaEZDvozIIZCaTEBdCQOhGAaCeXwuE4w0VLBAh4CmaXjH/HfgR+f9CNcvvh66NnXKPpFDoD8Ph4Cua5yDTNZKUuYW/xt1btIDfPBh4PxbgSmSyzB1vhkKhUKhUChGBrW9u0vhcztx/WkzcfXJxB0A2DgE8mwZAGwFgfbUyMEbz5oNR3kTu9NIAEFz9VKWI5D3lAFaTFnbBAQ5Bwj1mS4BK7laBnw1tkJJWhAYQCl6XfkHCwJm2N/ly7P3mVHtY5wdxWb1zCrccLpY4LhsWRO3mm7XMmCG/bGr0LJ2gTQfPns2rF/Ft5+cdUg0C8YDPrdbLAgAwK33b0Frd9aG3z1Exw6aDoHGCr4I44SOQbZ4RjKee+Uf4NoFOo1KtBnkPQj2ZsYPChmDlgEAWEiCBakzYl/XMJ7a3sGtrsocAoPheN5tB1YisdyCAJchkGoZEE02ONgrab2YRHgk7qlCMwSmKoZhiFsGAhKHAPn3gAoCstGDbocbLoMPxDj3uruBWWcVdM6THSUIKBQKhUJxojPjNHL7DPFxaUYbKgjYCgLu6hZ89YrF+Ni5cwB/HWjAIYZM629liQs+hPEN5+14yP2f+LjjnwCM/KcM0JYBqyvA6eZf55BgxF4uV4Sm2boEjhh1mZ/7yxeyO7t22j82gO9fdxK+cNEC3HjWLPz539bkHDk4Wr506SI0C4rkK1Y0Cvr45avKezuHuX58UaCglVNnVeP/3r8aV5/cjG9cvZRxSFCHwJH+EDfBwEogmsCn//oGovEkgtE4l0yfFgS8LgfqytgCjRs9OCAIV+w9wG+jEEHggNGIfoP8XoT67EPLwv25n0eGjUNggU2w4NM7OnDJj1/ATX/agMt+8gKzwipzCABmFoEdhmEgHEvAsBRhVESgxT/ATxkIp0YV9ogEgZ7J3++taZqw+C/UITBVGQzFuXYlIOsQoCv+PuIYozkCduGnZeR5ZpU0YGb9ioLO93hACQIKhUKhUJzoNJ0MXP4DoHklcPL7gbM+Y3+8rPAvqGVAsAKf4tTlS3HTOXOg6xrgcAKlrFU8LQhUlLhwo+MxfMD5FJbrrbjF9Veco2/JXxCgxRSdLEDPUSQI5OOKoDkCFqwrwkYtyUvozhE4B7M/9uYL5uN/rlyKmTUFODRGSKnHiW9dw7aQTK8uwcrpVXwf/4DcIXDPenbWfX2ZBwsbcodNXrK0ET95z0p84IxZcFrEj+ZK9rmHwnEMhFh7+IWk5WBz2wB+v/YA5w4AgFqLCECFjnUHSIE+KBIE9tm+DgCcILA/2YR+kM8wZ8vA2DgEFmjs52MVBH79wv5MQXa4N4RHtmR/LzolDgHAvm0gHEvghtvXYdF/PYYrf7424zQYTahgT4B/vkO9k18QAPi8AA0a3Lo4M+VEQ/S5AkBfqh2Ejh2kjoBSL+ssG5Y4BJCIwZ9kH+uchlMLOdXjBiUIKBQKhUJxoqNpwJqPAB99Dnj7L4CSSvvjZStVRWoZQFkze7u0gb09nHII+Fw418GO5ztL3wr/SMcOUgGACgRCQSBHywBgKwikWwY0DaicvpTdmSNDYKJ466J6/Mdli1DicqC5wovvX3cSdJ0f/SfLEAjHErjvdXYF+ppVLZk8gJHQVGHvTlnYUIafvncl5tSx39G/rDuIrmG2iPW6dOY7tGoGG3R497pD7Cqk0CFgP4EBANDDigamQ4B8n0I5WgZGM3bQxiEwTzsKHdli/GjK7ZFIGtzoxt3t2ZGIHYPyot9u0sC9Gw5nWki2HhnEn15pBTAyh0Da7SF2CEz+lgGAdwN4nd5R/X5MJUTtAoB87KCPtAyUEYFA6hAI9WNRlBUVL555USGnetygBAGFQqFQKBSFURSHgI0gQHMDyshti0NgtsYmh0/XOovTMgDwAsEgEQR0p9lakAsbQaDXWQ9NA/79LXNRM5MIAn0H+HC6XY8BvzkP+NPVQOeO3M89RnzsLXOx7bZL8OIt5+P0OTUA+FX6owNhxv6d5vFt7egn4W7vPVX+HuVDiduBar/5WdSjD9M1ttg9b2EdfG4nvn8da/c93BvCY1vZ71BtqYcpvt5/+gwmTHEwHMffNlhW0GmGAMAV+0K4loEm9IMKArlaBoo/ZQAASrQopmtZB0Fa3DnQHeBWYA9Yshg6h2wcAjaTBl7bz77GncdMkSEfhwCXIRBLZwjwz3c8tAwAvCCg8gOyiLIhAGT+ptBQQSoQ590yEB7Ap3v7sSQSQVkiiU/09ePkljNHeNaTmzz/xVQoFAqFQqFIMcZTBjiHQBlxCKQEgRp3DPVaP7NrutbFrRhKsZsyILpNHQL5OiJsBIG7v/RORB1+VPrcfOGXjAN9B4HaeebtUB/w93/Ljj684wrgw09k948zOgmbpKv00XgSPYFoph8/zV2vsYF7Z82rwaza0bc7NFd6cWH4cXzLeTtcWgJ3xC/G1+IfAgC8ZaGZ1bBqRhXm1Zdib2d2fOTd61h7PD3fOXWluGBRA57akS2gb3+pFR84YxYcMIDBo/zJ5HIIhAe5gny/0YQWg+QehPrHpmXAMGxbBgBggdaGg4YZWnmkPwTD4N0BALA/JQgkkoatC8Bu37aj7OP2plZ7I3EydlAwhYK2DMQSBmKJpDCz4HhpGaACgBIEshTsEKAtA548WwbC/ZgVj+Oeo6nfU6cXcE3NHAflEFAoFAqFQlEYIoeAw232++cLLbatlJG0fYlDoDnBF2IztC6uUJVCWwZyOQQ4QSCPdgEAqJKECnor4SurMsUAAPBVAz4SrNezJ/vzoVezYgAABHuAO68BhuQrveNJfZmHm0hxjAQL7u0c5nrw37tmdO6ANNPKXbjVeRdcmllEfsj5BBrRA7/bgVNmmpMrNE3DNStbmPvRFUIqCADAR86Zzdw+1BvEk9vbzYkXCUGB0n8QSMjDymjGQMLQcNio51sGck4ZGDSL+0KJDHITM7oc7O/dAsukgUg8id5AVCgIHOoNpgrwCARZbxlkDoHBcAytZOU+XfSFyZQBr0uQISAQAIPRhLBwPDYQHtG0g/GGZgjQ2ycyvZIMgf5gDImkwX1nqEOgzJt/ywCDt7KQ0zyuUIKAQqFQKBSKwhA5BApxBwByh4Cm85kBkgyB+ihv1a7UhvOfzU5bBnJlCNCWgXxfs2zKQOV0flvtfPa2NUegfQt/fP8h4K7rRjePvkg4HToaaCI/yRG4ex3rDqjxu3HxEiIAjZA1rv2o0NjCcrbejrPm1cJtsZq/fWUL7Nqxa0v5NpA1s6uxvIUVjH734gFgQNAuAJjujgGb0YOkpaDNqEMULoSc5DsX6gOCNoKAkQCiI+iLF7gD3nSy7RT8pIEwth7lBYFE0sCh3iA6bfIDALnVe8dR/rvbO1yAQ0AgCLQPhJGQqBOHjwOXAHUEqAkDWWTTKvqCUc4dAIgcAnkKAjR0Nle2znGMEgQUCoVCoVAUhujitJBAQUA+ZcBfzzsNJA6BqrCk4Oo/mN855GoZ8BDHAO25zvc1l1SJ3QQVgpXxGmL/77Y4BNrZAEVm+z3vB+I2Y91GupJcIDRY0DppIBxL4O8kTPC6U6YxxfpoOCn6BretFgM4byE7XaClsgRnpHIPRHAOgUQM2hNfxe36N3C1vjazecPBPuzft0t+Qj02bQNH2XNtTVnzIy7yncs1ZQBA69Gj2VXvDX8AfrgI+NU5QPtW+Z3od9lTgU1J1gVBJw209QWx7YhYeDrQFbDNDwDkLQNbBYLAUCSOSDzBZQiIHAKioMG2PnnRfzzkCIhCBacSL+7pwofvWI//emBrZlxgvojCIgHTFdIfiHHbS0moIM2YyVsQUA4BhUKhUCgUihS6DjhI0VQshwANFAT4DIHhDiCZROmwpPDvy1cQyNEyQAUCg1iN8xUENE2cI5CPQ8AaPCdyCKQ58Dzwry/w2xNx4O73At+dDvz8VNNRMIY02UwaeGxr8cMErcwe3MBtq9MGcF4qP8DKO1ZNkz4O5xB4/D+BV36Ouq5X8RP3L7FEa83s2rjF5jOR5QgYBrDzEWbTm4YpBMU9leyx8ZA4o8DCR3/zDFZ/40n84L5nYfzri2ZrS/tm4M5rgUCP+E5UECitx+Yom90xVzsGJ7LF0msHejEkKZ72dw/bThgA5C0DND8gTX8wxtn7RaGCom1tffKRlwePA4fAVBYEOofC+PAd6/HMzk78+dWDuP53r7FTO3IgyxAAgLZ+/rOlGROltGVAliHAtQxUCA+bCihBQKFQKBQKReHQcCXZ5AEZMkGABgoCvEMgGQeCPfAOtYofI1+HANcykCNDgFKIK0IkCFSIBIEF7O20QyA8APS1svto0NgbdwL97Koutj8A7PqX+XPPHuCBT4ypU0A0aSDNX0i7QLHCBAEA4UFU9W3iNi8oDXKuBQC4dFmjNHyy1tr20H/YXHW3cKH+enb3sVb5OfVKJg107TQnSFh4IrEaABBzV/LHDxzmt1koRwCBaAK7X38eWtJS3Ay3A//8lPjzJi0DSX89NkXY3zO3FsdMy7SGJ7ax0xisHOgOcC0DdLybzCEgcx30DEf5KQOCz0zXNc45QFtVrBw6DkYPcqGCjqkTKvjKvh7EEtnv5Lajg7j57jcQTyRt7pVF1jIAAEeIEOR26JwDKe+xg6plQKFQKBQKhcIGWowW3DJQgEPAXw+ANH0Pt8PRJ1mBzcchkIgBMbKalGvKAKUgQUCQIyByCNQQh0Cw2wyW69jGbtddwA0Psu0bRpJ3ERzZyN5ufRHY/2z+510gzRVih8BYhgkCAA6+BI06OAAsLhPb2Es9Tly6TJxdwLQMvPS/QJJ1NSxyHsn83KRJVuABuUOAuAOOGDXYZswyb3jKwX3XDftCqUwz3+MGTZA1sOtfwIbf89uJQyBaUosBlKLdqGK2L7S0DVjFHcq+rgA6SMvAkmb292coEudW/MOxBPZ2DUNEbyCaV6ggwOcI2LYMHA8OARIi6HHyQZfHK0f7+e/RMzs7cdtD24VjSik9NuMrqRDk8/ACUt4ZAipUUKFQKBQKhcIGziFQpJYB6gYAzEwBP7F9d+6EJuutzschIArhyzVlgOIaA4dA1UxAJxkKPXv5Qr9uETDjNKDpZHY7WXnmXAUA8NRtY+YSoKvxaUHgwTePMNuLGSYIANj/nHDzDLe42ASAd6xqEW7PCAKDx4DX/8ztX+XryvxsKwj0SBwCacdGiqcSq5AWAXxed05r8rDB/u6Vw1zxbtQkvw+P3wp07iAPwjoEgi5zusXuJNtKQYMFZezvCqBzkC30ljbzr4O2DexsH5KG//UEInmFCgIiQcDOITD5BYEVdWzA44raFZIjjz+suSJW/vzqQfx+7QHhvjSGYWTGC4qgDgG/m598k3+GAGllUQ4BhUKhUCgUCgucQ6BQQUBSbJcLWgYAPkfg4FrxcUB+DoGIoG8515QBymhbBkTbHC6gig13Q/cePlCwcZn5/2pyLBUAegUX2MfeBLY/aHe2I6apgi1WO4ciiCWSeJzYza9Z2VK0MEEAUkGgMilP6D9zbi0ayvmV17q0IPDyT4EEvxpZFzkEHebKta0gIBo9OHiMc208mTwl87PP4zTHT9pw2GBDEj+4qgofe8scTHeKe/ERDwN/vwmIWQp24hAYcprPudsggoCWnyDQPRzBvi7Wij+vvhRuB/sZ07YBWX4AYDoEIsQhIMoLAACvO39B4HBfUCpCTBYunHEh3r/4/ZhZPhPvmP8OvH3e2yf6lIrGUZt2jm/9awce3XJMun8wHGfaDSicQ8DNC0jc2MFwXOxMUKGCCoVCoVAoFDaM2iEgKbbLJKvGpWR760vyx+4/mHsFnK7+6E4+B6GYGQI0G8BbCfgkSff02J49fGJ843Lz/1Wz2O1WAcAwxA4BAHjmm3yxWgRaiEPAMIBX9/dgdwe7Un/Z8iK6AwaPmX35ArRAl3A7ADh0DW9fyboEXA4N5SVOcwV9w+3i+yWjmKZ1wYEEGmAzElA0enD3o8zNsO7Ha8nFmdulbqc5lUJCzHDgqMF+b1bVa/iPyxbjshk2rQUdW4Gnb8veJoJAn8N8TioILNTs8wusHOhmBYGGcg8X0EhHD26V5AcAQF8gyjkERBMFAN4hYBc8F0sYtkXpZMDlcOGWNbfg4Wsexm1n3gZfoX9fJzGiloE0hgF89p438foh8e+VXbsAwAsC1A0A8C0D8aTBZVUAUKGCCoVCoVAoFLZQh0DRWgZkDgFSQPbsER8HmNkAge7sbcMwV2W7dme3iSYM0OH0uS4AC3FF1C8C5l2UvX3mzfzzpaklowc7d/CW74wgYOMQGO4wU+pF9OwBNt2d87QLpdLn4vq873iplbldV+bByunyordgJO4AAGZhn5QXyu9cPQ0OPfs5rJxRBU3TgJd/Zq6sS5ivtaEe/XBoRHjSXczNr/3xYfzhpQPZwLSdbLvArrLTEEO2QPF5HLaCgOarwqLZpNUk9V12BVgXRsQgxdC632RbBUjLQI9RaZ5eknWtzNWPoRx820W514mZNfbf/4ZyLxvQCN4hsN3GIdBTgENAFhAp49BxkCMwVaEtA+cuYNvBIvEkPvLHDcLWDjuhB+DdB35BhoBIJBC2DahQQYVCoVAoFAobqEOg0FBBp4crngCIQwUBuXNAhjVH4B8fBX57PvCLU4FXfmlu4yYMCNwAOR0CpYWd0/vuAd7/d+Cmp4Fzvyg/jgYL7n+et643pFoGqEOg/yCQTK2qytwBaZ77LmsjLwKapnE5As/sYovPi5Y0QNclYogVwzAnJzx6C3B4vfw4O0HASAAh+Sr+vPoyfOPqZWiq8GJRYxm+esVic1TfekEQn4Wlrna+XcDhybZypEh278VtD23H2362Fut3HTTHQ1rYWHImc9vvdgIl8pYBp78GLQ2kfSbtdhlirdZfj9+AhG5ZoU/GgUOvmt8P4pxoT5ri1w5jJsIG+3u5St8LyrKWCszJMSGivsyTbb9IYc0QiCWS2NE+JL1/byCKcL4OAYE13I6Dx0GOwFQkFE2gj4we/e+3LcZ7TmVFrp5AFB+6Yx36SV6A3YQBAFw7gU+QIUBbBgDJ6MEQEatUy4BCoVAoFAqFhdE6BDSNdwm4/IW3EshIF8OdO4At92a3P/89c8WYtgyI8gJyZQgU+pp1BzDvQmDaKfbH1RJBgK7yV0zP9pnTDIFENFsY0vwAOst8sE2cQD9K6KQB2r1xydI8P8v1vwMe/CTw2q+A2y8Wh/QZhr0gAHD2eMr7TpuBV/7jAjz6mXOwYlol8OovgZjF/q7pwOxzmfss93SgmQoC5c1A9Vxm06zU2L6d7UO4/Y+/Nz+fNLoTG1yrmeP9HvuWAZRU8c6V8IDpEoiyK/kvJpfjUOnJ7LFt6xHq7+QmFxxLmN/1GJzYZLCvYbW+G5TlLRWYUycXxHQNqCn1ZAIa/Qjhs877sHLX/2bcCfu6hhEVWbVTdA9HuAJP5hCQhQ2mqfazrQsHeyf/6MGpyFFBoGBTRQm+8fZlOGd+LbN9f1cAH/3zRqZtpGfYXhCg+AVCkcepw0kESc4hkEzyorFyCCgUCoVCoVBYmL6GvT3jtMIfgwoC5U1yGz3NEKDQYjftEGgl4YPhfnOuu6hlgHtMj7nqK6NQV0S+UIcAJd0uAJjTF+i0g7QQQB0Cs88FZp3Dbnvll0WfONBc6ZXuK/M6ccYcSXYCZes/sj8bSWDr3/ljunYBw6xVHhopAgKsQ0GGpmnme/H6H9kdy98JLLiU2TRPa+MdAhXTgBoqCGTP7SIHGQE562x0x1jxxJ+jZQC+arEgMMQHsXUYVdjhXMhsC+x/Ff/2S3bsITQdhyNZcev1JPv9W62xgkAZgvhg23/jM9vfjY84HhaeZm2pBw5dQ12ZByUI42/ur+Ozzn/gnM47TbdOsBfbbPIDAHGvuXTKQA6HwKoZlczt42HSwKSl9SXgng8Aj/0nL6zm4Bj5TMu9Tvg9TrgcOn55/SosamT/TVh3oBf/8ffshJXegH2GAMUnaA/QNA2lNFiQCgKRAQDk76LKEFAoFAqFQqGwsOYjwOobgcYVwFu/Csx5a+GPQd0AopGD+ewDgJlnsbfTkwYOv8Yf27WLv5CVORPsXAKFtgzki7/G1jbOCAKaxrcNpIUAOoKwajZw/n+x2wbbzCkGxSCZAOJRNBGHQJrl2n78qOZBuJ/5H+C575l9+htuBw6vEz9eD7Gqd2zjj9n/LHu7YjrvsBjOTxAAAAy1c3Z6nPUZoJYtrJtjhwQOgRageg6zaWbKIeBEHOfrb7DHL7wCgShbiPjdOaYMlAgEgcggMHiU2dRnlCICNzbE2TwKV8cmeINEPPDVojeUXYXdkGRDLU/W98GB7P5bnXei+egTKA8ewq2uv2Clxn9/GspNUajW78K3XLdjiW5p4Rk4DDx6C7aS/ABq5e4Y5AUBmk+RpkSyPc2qmazIMt4tA32BKO567SCe3tGRzZM4Hgl0A3++BtjxT+DVXwBP/ndBd6cOAWt7UZnXhds/dCo3+eMfbxzB5rZ+AHzLgGiKgBWRQ8Dczk8aYKCBgoBqGVAoFAqFQqFgcPuBK/8X+PcXgbd8ybTDFwrnEJAECgL82EErTi8w8wx2W9ohcEgkCOzk7aCyiz27HIFCRy0WAi1qrTSwfeq8IJASAmjLQNUs09lBgxtJXzsAcxXw5Z/nN8IRAPY8Cfy/OcC3m3F+Dx9WOFc7gr+7/wcX9dxljvN77tvAE18FHv4c8PuLgBd/yN4hPMiv7Hdu55+XtgvMeQtQyo7lK0gQ6N7F3nb5gLrFQB0rCHiSQZyskxaGCl4QmKF1YnaVB+d69qJSIzb1hZchGGV75HM6BEoqeZFK4BBoN8zHeG6YDQl0GxGcq5MRlqUNTFgbdQj4tAgWa+b3wIMornS8yux/q4MIHTDzAwBgddc/8A6HYETolnvh38cGLJ41l7WMxwWjAaUOAZtQQaeuYUVLJbPtYE9APGpuDAjHErj2Vy/j1vu34t/+uAGX/uRFPLuzc9yev6jsf47NM9n1qPRQEdQhQPNGmitL8PsPnsoV+q/sM8U32jIwp87epSUKEAQEowepQ4AGCurOsXOETQKUIKBQKBQKhWJioIKAnQug1EYQqJ4jSNs/CAwc4ce+ASmHABUERuIQGMMLRLu2AatDAOBzBDIOgVb+OE0zi2YrVBDY8TBwx+XAE7cCvzwjv4L60VvMi+hkDCt2/QSNYFfPr3K8AreWEN8XMMUHa4HUu58/pmcvELOsMCZifEvInLcCfioI2GcIMFC3RO18QNfNdgDSmrFCo4LANC5DwKUl8N5FGm6uZ4vww575QOV0rhDx5Rg7KG0ZIA6BdsN0GewPepAk58S1LpTWo98S9NaHcgTK2O/Uat18X87Qt8GvsUXdMq2VO836ci/QtgFLt3xX+lJu7PspqpH9PTxnQa302DQeiRPAa7NSXO13cxMRAtFEzoC6YvHY1nbs78qKQXs7h3HjHevxgd+vw45j9m0Tk46BNvb2cAf7O5kDOmGgqSLVXhSPABv/CGy4HcvqXLj6ZHYc6M5U+CSdMjDPJscC4J0AaejowSFOEBAECsra2aYAShBQKBQKhUIxMcw4nb09+xzxcQDgcAE+ScFQPYdfJR9oAw6+LD6+aye/AiRzAtg6BMaoZQDgRw+m8ZQDlTPZbfS19x4AIsP8Cnv6OBKQhwMvZicTAMBz38n+HAsAb95lf66hPqA3WxxrRgIrSTL9Ik0gzDCP0Wva9dPQdgHAzBHosqzgH32TC9LD7Lfw4hFtAbCjizgEalP2eU3jXBt05GCwpBHwVWMYbPF5dvAZnNT1T2bbWqeZwREkhYgZKmjXMiAJFeQcAtnHGK5dyexrIa0ORmk9V2iFGtiww3Sw4MX6Bu6Uluqt3LaZ3iBw7w3QkzFuX5oabRDfcv0e6V7tc+bVSY9NM5KxgzWlHjRXlsDlYAu68WobeGanWFBbu7cbl//0Rfx+7QHh/knJ4BF+W3+O320LR/oFLQPhAeD2S4CHPm06hv76XixuZP+2poUTKuLMzSEI+ARjBwFwGQIBKgjQloEpHCgIKEFAoVAoFArFRLHmo8DqDwENy4HzvwrMOd/+eJmDoGYeXyQnY8D2B8THizIEZIFRdg6BQqcMFELtAvH2hmXmirUVzh3Ryo5dTJN+j6ggEO4H2lPBXd17gY6t7P7Onfbn2sOv5i/W2efnBIGW1YCDTX5Hl+V5RA4BgM0RaCOjCOsWA6V15n9WCnIIUEHA0ipQt8j2rkeNGkQSSexPsgGYi3f9AjqyfeMRw4W/Rs5CMmkgIGwZqJQ/SUk1396SiHLvVweyLoNjZaTFhNCnVyEUI+6N6WxI6Cp9D3QkeXcBgAatH3Xot2wxcNX+27ji8VfxK/F4gp2wcZljPa7SX0aN343p1SUok1i8AcDt1M3gRwF2gkBtqRsOXcO0Kvb39dA4TBqIJ5J4bpfcYWMYwPce24mBkFw4mVRQhwCQe7yphWMDrLtkmj8J3PUu4Kil7WT/c1hVwgpcezvNiRQ0VHBu/cgcArSVgMsQoILxFA4UBJQgoFAoFAqFYqLwlAJX/gT4+Frg3C/xhS5FliNQMxfw1/IF+u7HxcdHh/iVYFnh77G5EJyIlgHaLgDwDoFQL3BsE7uttDGbeSCwtuPAC+b/t9/PPz4tkim9+7hNK5yHMz/7EcJMnRRFb/sx0LCU3Wb9TEQjBgE2R4AKAulxjtQhMFyAQ0DUMpCmTiLSpGiNVuJgTxCtBvv8Gkkr/0n8Hdg0XIl+QRHoz6dlQORaIaKN1SGwx20vZOwN8MKWZ/aZzO1pWjcu09ehThNb3K0ugZO1fWjufonZ/3JiCb4ffxdujf0bYl7WAfEN1x9wemMSmqahupSIRBa8EncAYD9lID36cEY1+zqL5RBIJg08svkYfvzkbuzpGGL2vX6oH4Ok2EznK6SJxpPYTe43EhJJA3e+ehBff2g7tpHAxqIhFATyyxkxDAPHLA4BD6J4y+ufBQ6/yh07r+9F5nY8aWBv5zDnZJlWVQK3zfdCFjpIhSdr645hGLxDYAoHCgJKEFAoFAqFQnG8UCYZPVgzz7R0i1wCMqidfkRTBsZQEKiaxY/PA8SCQOUMQCOXdDRsj+YMyHIEtj3IP373HvvRhILifYmedQQs1A6zOzWHufJOV9wZh4BEEGAcAsS+Pu1U8/8jzRAQje+zhgmSSQPMUxhe7B9yYF/nMA4Y8hGZW5Oz8JvEFQDMVU+K3+NMFR+SfuWSKvF3cohmCGRFhS3xabZuljf62ALVoWvwtyzmVkW/6LxH+hhLLDkCZ+msw6QbVfh07GYk4EA3KrBt5f8w+yu0IN6LJwCY/f4yPDYuAK/NvoaSJGAYXI5AsUYP/vGVVnzyL6/jJ0/vwTt//QoO92Yf9+md7HdvcVM5nv/SWzGLnMv+Lv67AAis7Db88tm9+OoDW3H7Swdw7f+9jE7BlIZRI2wZyE8QGAzHM44YJ+L4ueunqGx/SXisd9/jaCGBg+tbexFLsH+Hqv1uVPlc0uekWQGy7UMp0eap7R04+3vP4k/PkeBN1TKgUCgUCoVCMQkolRRa6dXuqpni/fkgs4TKhALdyVvei4nTza/8A2JBwOkGyqex26ggQB+Ltg0cfMVcZe7YAo7osLgQSCMo3uuTXSiHacleqBNBoHY+4PJyyf15OQTSgsBQBx8YmXEIEEEg2M1mJMjoJrkFmoOdGmDTMnDMqMHhvjD2dQ3jYFLsZIlDxy2xjyIOsxjZJVgV9nscplNGVoCUVANOD+AUj3ZM02FxCBzujwLNq6THvtrJFkdVPhd0hwOYtobZPluXCyvLLA6B0/QdzL5XSs5BN7K/X7/tWYGHE2x+yKk9/wQSMdTYCQI2K8EyQeALznvxxTcuAX64CKtdrcy+g72jFwQMw8BvX8i2a/QHY/iN5fazJD/ggkX1KHE7sLiJ/buyr4ttXwjHEnj/717D0v95HFf/4iUMhXO3FDy4KSsKhWNJPLLlmM3Rcl7e2407Xz2InmHWno9oEAj28HfIs2UgGyho4AeuX+Eix+vyg49sxOl1rBvgpb3d3GE1fg+qfPLvjE8iCNCWgUAkjkg8ga/8YzOO9Iegk5YBQzkEFAqFQqFQKCYBIoeAuyxbAFKHQCEUOmXA7R/71GmaI6A75UUpFUPoqjjNGZhFBIFYAHjyv+TnQlssrEj6/c/wmwXJueXEjZFuFeAcAjtMJ0Kw12x7EBHoNFsAjhB3gLs0+3hUEDCS4kKGQlsjqmaZxbf1tkQEOmZU41BvEPu6AmiVOATu816HbcaszO3d7awg4NQ1uB2pS3NZ24AvVejn6Gm2OgTa+kJItKyWHnsoyvZhZwoskiNgx9KUQ8CJeCaAMM3hMjbU8JHNx/CL+NXMNk+4E9jxkG1xZ+cCEGUILNUO4GbnA3AaUWC4HRdtuwUOZIWhgz35ZQjs7xrG/3tsJ/78SisicVZY2nZ0EEdJX/zfNh5GXyCKw71B7O5gV/7fusj8btIwvH3ELfKX1w5hbaoA3nS4Hz9+krSyEKLxJFq72dezua3wtoG/vHYI7/vda/jqA1txyf++yE7BIJMsMuTZMnA01S5wof463u4gga+eci6k9WKXte3JwKL9t+Ov7m/gk44HoCMJn9uBErcDlTYOAb+sZUAwdnDHsSF0p8YaVpARoQ/tCkhdHFMBJQgoFAqFQqE4PhAJAjVzsoW5nUMgVwBgoVMGXOMwk5pOGqhdYK6si6AtARTqEPDXmGGOVvY8Ib9/9275Pslq/s/Od+PFL78Vl9aRlb2MIEAcAqE+INAtDxRM07mNbxdoXgnoqYt/Xy04y316dKJhAM9/H/j5GuDBT5njztLQ10jPz+HksxdSHDVqcLg3iH1dw2JBoHYBnm+8kdlE+8Z9bkc2NE80acDpBVwpZ4BNK0tSd6MP2ZGeh3uDaC8XOEtSdBmVzO2q9Cr9DBtBgAgjM/VOlCOA5doB+DV2Zbmnlg0SBIAdxkysTxLBa/3vmAwBD6I4X38dNzkewdecd+BboW8CvzobuPM64DCbHyESBC7U2RVoX6ANl+uvZW53D0cxmGPlvXMwjHf9+lX88rl9+K8Ht+E7/2KzGp7Y1s7dJxxL4i/rDuFZEiZY7Xfj5OmVAIA5dezfj32k2HztACtg/WvLMbO3XUJrTwDxJLt/U1u/9HgZf3qlNfNz93AEj1pdBgOH+TsAZsuAXUtRiqP9YQAGPue8j93h8gHvuxeYfzGz+eRgVjS4wfEEPo+7cLq+A19y3Yur9Zcy7SUjcQiIxg5uOZIVUCrACgJbezVc9fOX8Nr+PITF4xAlCCgUCoVCoTg+EE0ZqLEUzXYOgWXvsH9smSXUziEw1sw8m7095zz5saL2AisiwYDmCNjRJZk0EOzlE7lTuLu3YXpVCbSO7eyOhlTqfeVMs8ilzyNrF0jTsV0QKHhq9meHE/DVsPvTjom9TwPPftN0A7zxZ+DFH1memwgCtYJgRyoSpDBbBoLY1zmMXpRhX9L6XdWAq36Ohmp2VZ8KAkyRInIIWEUCG4dAorQJVkGkLxjD63GxkBExXBgkYxKr0wVW8ypxjgUAnPReQGdXZpfoB7l2AdQthq9S3ELx5zhbAOLgS5hnmKvNfoRwr/vruN39A3zVdRc+5HwCp8XWmdMw9j4J3P1uJvitxM2XNGc5tnLb/t35EGAJeXxok2TVO8WPn9qNbot1/q7XWCv9E9vFbRR3vNyKx7ayYsF5C+rg0M3PhToEDvUGGffBTuIeaR8M26747+ngV6/3dwVyCh5WwrEE9hCnwo5jlvOQtQ1FBk0xLwfHBkK4RN+ApWQCCa74ETDzDGDh5czmuq5XUYIwfAjj00427PRixwbUpMIiK20EAZlDgI4dHA7HsNXy/pYTh8AASjEcieOG29fZTo04XlGCgEKhUCgUiuMDmh4PsCu2dg6BVR+yf2zplIEJFATmXwyc/gnAXwfMuwg4+/PyY2lLALd/Fr9tdiGCgMQhYFe8d2w1U8kjpJBJOwR0Bz9NoWunPFAwTftmdkwZwAoCAP9dCaQmDez6F7t9q2W1kjoERCGCEkHgKGoQS6THCGr4z9hNaE02IOGrB675FTDjNEyrYvv++4JssebLKQhYttkIAo6KZm7bY4eANqOW296FClA3RcYh4CkFGiUjC5deA9QvZjdprbwgMOusTMo/5dHkGgTdrHCzquMf0JHET1w/x0m6jVMk2ANszoYc0nYCP0JYqe2l98JS/SDOtoQe/uGlVunK+56OIdyznl0VjyUM/P11M2n/cG+QK9zTdA1F8PI+djX5/MXZVhbqEEga2akHw5G4cALCE9t5N0LmXDvF57GViAjP7+7Ch+9Yj6/9cxv6SGL/rvYhJIjLYFeHZaqEaMJAmjyCBdv7gvgsdQfULgBWvMv8ef6FjAClJ8J4q2s7Puh4ArVkusVC7XAmb8IuVNCX59jBQCTBOATKwb7/A4b5eUXiSXzkTxvwrxHmM0xWlCCgUCgUCoXi+EAkCOTjEKiabc69lwWxOUsAh+SiciIdAroOXPod4Et7gfffB5TWyY+1cwi4/KaoQJl5hplLIKJiBntbNnrQrnjv3GEW71Y8FUB5S/a2KFiwhxRy9HPb9S8z6NDKNGJLp+9V2iHQQVaNe/aaPdDxKN+qQDMcZNtgOgSsvGYsxuX4KbQv7gZOeg8AcKnpFKZI8QlaBnz5OQT0imbUkdF2L+7uwutJ3vFA2wUAoNpv+V0Q5Qh4KoBZZwNNJzGbV+j7cQrJD8DMs7hzSVPqK4HrVLaNYuaRh3Cb8w5c6HhDeB+GDbdnrOq0ZeB0fTtcmjhI8t8d/8z8vLdzGC/u4cPqAOC7j+5EUqAV/HXdYRiGIXUHiHDqGs6Zn/1OlnldaChn35d0j/qudvFoxye2yZ9P5BAAgE0WQeBIfwgfv3MjntnZiTtebsW3/8WKN9uO8s+70+oQsBMELDkCsUQS/3i9Dd97bCd2Wl7LtI6nsJgGjL7llmyrT0kVMJMdd3l9ySv4mPMh7ulmaR3m9AjIWwZqHCG4H/4U8LuLgC2sEEHHDvYFo4xjh2YIDCD79z6WMPCpv7yOezdIWiiOQ5QgoFAoFAqF4vjA6eaLfusse2+5eGV1xhlmcS2bI28X0OaR7BsPQaAQ7DIEqmeLAxA9ZaZQIuIc4kYI9gABQf+snUMgHga2/5Pd1rCUPRcaLNi9i3/MeRewt8PEcVA5gw8SpOLRcCeQTLJjC9Psf9YUAwxSQApbBsShjscMvoCfU+eHrmdfa0tVDkHAam/O5RCQOVcAoKwJ08lzDYbjeCM5jzu0y+C/30yBJRIEFlxiCmhEELjEsQFlWog9dqbcIfBvZ8+G67R/Y1aFnfEgPuB8ijmu3/DjvsS5eLH8beTkdwKHzBn2JcQafrbOtwtk9jm2YZmWFX/+8NIB7phX9vXg6Z1ia/j+7gBeO9CLJ8mKPR1rmMXAf9c+i4rfnAr84YqM8MQFC6YmDTA2fQt7OoelwXYyh8BmS47AI5uPIhjNfscf3nwM8UQyc3vbUb4loScQRddQqkXCVhBozfz4h5cO4PP3bsL/PbcP7/3Nq+gcCgPJJK7u/zNzl8HyeabTxAppGzgr+hIqNT78UdcMLNTN85GFCn7N9SfgzbuAtnXA/R9jpojQloFIPGnJYDC4DIFTFsxibicN4Mv3bcbta/nvzvGIEgQUCoVCoVAcP5z7RUBLXb4svoq3NItcAulwNFlCv01Am3RfrpDC8aakSi5s2LkH6PhBwAzlO/l9fKK+yCWQKwBwB1ndswo4AO8Q6NzJP+YSNpGeo4UPreMcEcOdQN8B3lkAAPue5dsFShvEo/9q5mW/fxaOEocAwBd806rsvzOMvXkULQMobxY+1xt5OwRyCAKLrjD/TwQBL1gLOmrmA2UNQodAmceJD5wxCyhvzj6egKjhwEejn8cXY/+OfzR9ng913HA7AN4hYCcIAOksAZNnd3UxhXYyaeA7j+4Q3S3Dr57fh3UHspMwKjGE756RwMIa9nfGjRj+1/UL3DD4G/P7d3AtcNc7gWhQOmlgxzGxQwAQZxbEEkkc6OaLZsAyaSAaRPumJ/Fpxz/wE9fP8QXnvUjEwoz4IHIIAMiu8tuNHrW0DNz5anYcaF8whr9vPILktvsxx2DHhHat+lzWHZBm4aXy5yDMTrQCEDsE/AjhYlgmGSTjwO7HMjdpqCB73zCcWpLZ9rkrT8X7T5/BHfv1h7czosvxihIEFAqFQqFQHD+sugG4eSPw0eeAd/2J3y/KEZiemnku6f+2dwjIWgZKxdsnElmOgK0gIMgRWHylOW6P6+8XCQI5+v1jpFDhBAEi0gQ6zZAyK9NPA/zEAWCF5gcAggyBTr5dIM3+58z2BiuS1gC4vJzo1G/4EQI//YEWfFU+lzANP3PKHqtDYOQtAyhr4vIKAGC7MRMRgy2EzAwBliqrIFAxjRUFyluAeReaPzcsBTfNwcqsswAAtaV8wXbDmTNRUZJa2V3zEelDfDX+YawzzKwCt8sFnMK2GGD7A0Cgh8kQaEQP5uukeF3Eugsuc6zDTC27wn/Hy62Znx/afJQL8FvWwv4deG5XV6ad4Ax9G9Z5PokznroWD8Q+hk84HkQ5AqjCIO50f5sfsdezF3j669JJA7aCgGCqwcGeIGIJtrfBiTjWaDvw3uE7EPv1+TC+Ox3/3XMLPu+6D1c7XsbNzgfwA9evsPGgKWokkgZj77eyq33IbM3Io2WgPxjFoV62//7Z7UeRfPY7zLYdyenwnUzcAQBQPQeoW8xvF9AcMYXDKj/vEDhX3wwPSKCiJXfEThCg+QEAoPuq8I2rl+Hj57GC1JcuWYgV0yrzOt/JjBIEFAqFQqFQHF9UzzHHzIls8NQh4K3MFncyh4Cd/drlFc+en2wtA4C88LcTBKadyvfoL327+X/aYkEFAcMAeshqfuMK+3NsII6O6tlcWj2Dw20WpQ1L5MfQ/ACAbyEY7gTaJYJAuB/Y8jd2m0wQALjvEc0PSEMFAU3TbNsGcocK5ikIlLcIHQJRuLDFmMNsOyIIGqy2rrhqmhmKeNJ7gSVvB959pxk2CJi/A6K2ijSpKRllXldm3J5524kPn2URr2adI/zd/G38ctybeGvmttelAycR50oiCrx5FzxOPfPn4Gw6XcBbAVz1M8CdHcXogIGPOB7J3L5vYxsGQjHsbB/E/3uM/Z7PqfXj9g+dCrdDVDYZ+IbzD3BrcQBASbQHX3bdg5c8n8bDnluxRpdkb7z2f1iVZM9zX1cAyaRhFuAS3jjcj87BMLNtb6ZdwMDl+qv4tetHeMPzMdzr+QY+5XwQrmMboSXj3GNd5XgFXTtfAgAc6B5GOJbkjgFSLQyhPiDGF8oZUg4BKqScqW/FN9o/DmfvHmb7TxPXob5c4phZeJlwMxWzaoOmGCmaMnCxYwO3zSoI0FBBKzQ/ANAATzk0TcMtly7Cly81heV/f8tcfPKtfBvO8YgSBBQKhUKhUEwdaME562wzPwAYWcsAIBYM3JOsZQCQ5wjY5Qu4vMC5X8jenncRMCvVRkBT9mnLQLCHnyCw5Cr7cyTJ9HC42GBIStVs01ZMP9c0ukssQogEAZlDAAB62IJF6iYBOKHkiEwQqOdFI7tgwdxjB/NtGWjC9Grx8/w2fgWSqVX9fkcNHk/wYgrTMgCYAtw1vwLe9UegZRW7j7QNMKQcAgDw/etW4IJF9Th3QR3+9OE1mZFxAEzR4fRPMHd9JnEyvhN/H7PN43IA/hpTmLCy8Q/QDANep+kSOIu2C8w+13RXEHfBdY4X4IeZeRCMJnDVz9fi0v99EUf62RyEWy5bhPoyLy5eyoeanq1vxTydH11YpoXQotnPrF+6/j8yzw+Y0wU2HupLTarI4nZmyzXDAJ7awWYb7E4FCn7E8Qh+6f6pOMtBwnltvwYgbxcAUpMG7NwBANB/CEgmM0n9M7V2/Mb1Q/zF/W0sJEGC25IzsaX07MwIRg6SIwAAcUPHj+PXMdvKBnYBhsG1DLgQxwW6IJSyd19mVKXLoZsCkwCaHwBvefbfEACfOG8e/vrR03HLpTZ/I44zlCCgUCgUCoVi6rDkqqyF3FdrplinqZwJOAQBZ3bFFSAWDI4rh0COkYTnfBG46Wng+vvMFeCMgEIdAqTPnob/6S5ggU0PcNXs7OqyFbviOy0W1EscAk0rTFGDQlsMgj38qEI77BwCJAjtiSRfVGsaMKuG/46IrPxpfNZgPJ9AEMi3ZaC0UZpX8HjyVPxkzu+Aa3+Pv53yVwyC/zyqqCBgh8wRUjXbzAdIMb+hDL//0Kn404fXYOUMwWtb+QHgrM8CNfPwuOdi3By7GUlSpnjThfEpH2bv27sfOPB8KljQ4PMD5qRcBqd/gpmq4dViWGEZbSga9XfqrCpcvMQUAt63hu8h/5DjMW6bkLJmYM3HmE3OwcP4L/fdzLaHN7HiQrXfnXn+NI+TtoE9ncNwIc7kIoiIGzoOJdlsjVONzeje/IStILC7YxiJfpKo7yS/c4koMHQMm9v6cba+BY+7b8HFjo3cY0UMJ26L3YDGShtBtWU1lwHyj8Q5eDS5hj2FcC8w3ImKEhdjFjtN34FyTeJmOLYp82OpR+xMqtBJzoi3kjvm9Dk10EQOteMUJQgoFAqFQqGYOrhKgBsfA25+HfjMm2bBmMbhFFuc7VoGZPtdk1EQEBT+mg5UTLe/n6aZtvv5F7HFNXUIDLYBEcvFMg3/q5plujBELRYAnx+QRubcAICaOfb3FQUKAoIRlQYwVMDscDtBoHmlKZ6s/ACil/0Y9ybO4w6ZXuVj+tozp2sjCPiLESrorwOcbjRXeoUdNQDgn7USWH4d5szi8zbcDp2ddpCLJokgYHEH5IWuAxfdBty8EXfWfxEB8O+TJ/1+zjid7zPfcDtKXA4s0g6jTiOulTnnmf8vbwIalzO7TtL4DAwdSUzTujC9zIHvXrsiU/idPqeGmSQwQ+vA+fqb7J0bl4PLVWhcAXzkaeDS7wIz2fflPfpTOFfPFqn/2soW+4say3DJ0kZm28v7ujEUzvbH7+kYwkX6BtRofKvBtuRM/CpxFd4f/Q+cFPktLop+n5uI4Xj2G9h2pJ+7b5poPImeYyRNv2Ep32rUfxBb2gbwJec98Gqkfx/AjuQMXB/9T6wzFqPJbgSnrjOOkaCzEj+JvwOHjHoEDSLodm6DQ9dQ7s0W9xfrgnaBNMfezPzIZHZYmFtGpo2IwkWnGEoQUCgUCoVCMbVwOIGaueZYPYpoNTpXy8Dx7BAon2aOaxwJokR9axo/DRSsmWu2AMgKfKkgYOMQSKfK1y0UpvsLAwUBczVddHwa3SXPLnCXMqvbQuZfBFz9c7hP+zDqykSBguLvh13LANPX7KngRSfrOckEgbIm8+5OBxoE5wUA8+pNV8DSZlGgoKuwlU+ZQyCVHzASaiQOBU/aIaBpvEtg5yM4T9uIs/QtzOagr8VseUhDxmye62dXvssQxKPer2Kt5zN4zv8VzDWy+3Vdw7tPzYprNziegK5Zwvw8FSkxciNw6keA5lXAGZ8CbnzU/Ox0Hbj6F9zn+jPXz3CqthMAsiP+UixuKsd5C+uY/IJYwsBzu7oAAPFEEvu7A3iP41nmfm8k5+HU8C9xRfQ7+G7sPVibXI4AShCBGz+Nsw6Xqr7NqD36DOwYbCeCQMV0c9yn9Zhje5EYOIqTdFYo7DbK8R+xf8MV0W9jg2H+bWiuEH83M5z5aeDa3wNv+QpeecudOII6GNCx25jGHpcaI1qVGj2oIYmLBM6EDNZgQa84R2BBOREEBA6BqYYSBBQKhUKhUJw4iIrVXBd8wgyBSSgIVExjLNEAgOpZI388l5cXGayCAG0ZSBfvZBU2w4gcAqnHdJWIswamrea3AWbugI8PzMtQv0g8Ug8wXSQFFMUzqnn7Mw0UTGPXMuC3rljqOtvzPu9C9rOQCQIW0UD2XPPqTKGsodzD5QWIRrjZ4qsGKngrfcEOAQvVfkFbDywOAQA46d3s6E8jgW+FvolPOh9k7jPYfDb7WTazGQinuFszAsS8+lLctWIzFqIVAODoPwD8+R1M//x1q6fBqWvwIYx3OZ5jT3DVB8yWmJq5wBU/AD76LHDJt9g2merZwCXfZO5WoQVxp/s7uEx/jXvNi5vKUeZ14cx5bE5Fum3gUG8Q9YkOrk3iPsel6EIl93gA8LB+PlqTrIPm44m7sUA7jE84HsTf3F/D8yVfxgcdj2f2x3pJy0DFNO5vQ3fbblzgYNtyBg0fzo/8EHcnLmBaQJpyCQIOJ7D8OuCt/4Fp80/ObN6ZJG6nju0AssGCK7T9aNJ6ISWPSQMzfWR8pnIIKBQKhUKhUEwhRKvRuVoGRMXXZBQEdAe3amc7YSAfaNuAddIA5xBI2/slAYCy7TVzAU1iU7eKADRHwFdjn4/AtQ1YaFwBzH2reB99zTkQCgL1YkGgxaZ3mmkZAICLvwm8/+/meM333sPuk31ncwgCHqeeaVvQNA1LmtjH4QIF84G2DVTM4L+HBVAjGFMIWBwCgPk7ufpD3DHVGtv/nZxDPmMSiugePoJXbl6OdbdegCc/dy5WRMjq8tBRUxQImkVmfZkXX7lsEd7heBHlTHCfBpx6k+3ryrD6RmD+JcwmjxbDL1w/5TIJFjWa4s3FS9i2gSe3d2AgGMOezmG80/Ec51Q41sw+vpX3nzUPPyIBfYv0w3jCcwu+7LoHp+q7MdNow22uP2JFqqXCMUTGOFZM40a8hjv343z9dWbbc8mTMAj+b2WzXcsAYU6dHy6HKersNMj3KhUUmnYIcNMF6HjYvtbMZykTBJo9rEsjZ8bMFEAJAgqFQqFQKE4chA6BkUwZmISCAMCvotsl+OeDbPSgaORgtU2/v8snFyecHtbWbb1PygIPgC88p62xX8kvrZPva1gGzD1fvM9ulJ6A6QU4BOrLPJnihuKjPc2aZjoDllxtrphacZWIWx7KrIIAf15z6kqZdPclzex3u65MvDpvy3Q27A1zzi38MSzIRAkuk+HCrwGL5VMtkoYG7/zz2I21CzjLvrvjTdSXeaHFQsDhdfwDde8C7n4vEDMFgJvOno2vNaxlj1lwqf00DyuaBrzzDi5NX9cMfM31J3zRaYo/Tl3D/Abze3Tx0ga4HBo8iEJHEpF4Eg+8eQT7OvrxLsfz7OOveBeWzBCLYV6Xjo+eMwf/wpnYQVfbBVzuMF0LZdEOdkd5CzfitaR/D+dUeDqxUvi4hQgCLoeOefWmMLLLIOfctQtIxDPOlot1Iuis/hAfgJjKERAJApoG1DjJhAbVMqBQKBQKhUIxhaiew9vqc13wHS8ZAoCZ1p7G5QOWXSc/Nh9kowcDXUCUhJjZtQzULzYdDDJEzo3qOWzBf/L12c9KdwJnfIK/jxVbh8Ayc2ReSTW/zy7TQIC4ZUD8/dB1TVoMcQ4BOzRNvHJZnhVQRKMH5xHnwjtXT4N1+tuVK3JkJ4hY9cFsyF9ZM3D25wt/DAuytgXGIQCYQtK7/gRc8UPh9JBtxixU1LAr69AdQPPJ7LajqVXtw68BST4Mz9z3KvDX64HXfgM8+y04e8mYytM+Jr6fDLcPeNefEV95I7frU84Hcaa+FXPrSuFJjVKs9bvxf3UPYIvn3/Ci5zO4XH8Vd687BOf+p3mL/OoPYsU08ar2WXNrUeV3Y0lzJX4Yf1fO03yLvgk6kqhLkhGKAofA7OhuJkwwqTmwq+x04ePmbBkgLE45JbiWgUQE6N2PSp8bc7SjmK8TJ8PSa/i/R6m2AVGGwOxaP1xRMnFBtQwoFAqFQqFQTCEcLnZlzlthpsbbcbxMGQDMsYs3Pgpc+j3gE68AFS2jezxaHPceAOJRfsKAw20WCYDZV15GCktZfkDmeQTODeoaKGsEPrXeDBv7+MvmfHk7/DkcAroDmPMWfp/dhAEBc0jxX1vqtrXey4IF/RILsxSRIGBxVIgcAvOIc2F+QxkevvkcfOGiBbjrptNw4RIbEUVGSSXwsReAT64Hbt6QzX0YIXm1DKTRUlb9jzyDdjdrJ3/Kea541j1pG8CRlCDQ+qL9ie17Gnj0S8AL32e31y7MTjIoBIcTzqt+jF87r+d2/bvjISxqsoSi7noUF/bfA7eWQIvWg1+6f4qru3+DBYfvY+7XXb4UaFyOk6ZXCp/yvEXmOM5VM6rwVHIVXkiwxXKPwQaxLtYPY4W2H04tyT6QIEOAEmk6FWuW8N8Fj1MvuDXlbSeZ3+s+lKPDqGR3dmxFlc/FTRcIeerMzIimk9nj04JAZuygkfoPWN5SAYT72eNPAIdAgX95FAqFQqFQKI5z3va/puU11GumWYvm2Fs5nhwCADDzTPO/YkCLYyNhZgfQQMGq2awDoGUVsNMyUz2X6CISBETtDqX1ZthYPsgcAuXTTNECMNsGtt2f3ac7xe0LNqycXomVMyrxxqF+AMCHz55tm9QvFwQKGPcHiL+XOTIEqEMAMNsGaOtAwTjdfHvJCMm7ZcBK4zL8fN7v0Lj5FzhP34RXk4vxRPU1+JzoWBIsiKOvmy0wB4ggsOht5rYIGWNIOe2jBYVQMmgaXmr+EHr3JfEfrrszm891bEFHWWoEoWEAz3+Xu+vHnQ9x2wYWvxe1ABrKvWgo96BjkO2Hf+tCUyQ7ZVYV7ni5FR+PfRbXJV+AB1G8lFyGXcZ07Cq/GY5o9jW/x0EmEDg8ZmAnteITPEuuwAX1DfjTKweZ7U0V3sImWQB468J6/PCdJ+G53V1I9iwBul/O7uzcjkr/apxN8gO6Wy7AdF3n//YcfRMAUOrW8TnnfbjR8RiOGLX4dOxTWN6yGNjUzx5/AmQIKEFAoVAoFArFiYW/BriMv8CWcjxlCBQbb7m52j9kKe67dvGBgrSIPuuzwP7nzbaC2gXA8hz2ZJFNf5QrzSitF29vtIQbLrjUdHvEAubtWeeYLpIC0DQNf/3o6Xh2Zyeq/R6smS1oQ7AgWrkHiu8QaKoogduhI5rIru4ubBRnG0wmco4dlOD0luIH8XfjB3g3AODMMkmfOnUIBHuAzu3AEdJ/fsqHgdM/Dvz5GiBBkufTlDUBK95je165mFvnxx27L8FNzkdQp2Xt6ud0/RXAZcDux4Fjm3I+TtDwoHLNezO3V0yrxJPbs73/CxpKM9+91TOrAAABlOCPiWwAodupQ5t3PrA9K5Jd6XiFfaL0CEVvOVBSBYT6hOejL7wUp1dVw+92IBDNjvJrqsg/PyCNpmm4dvU0XLt6GvDEalYQ6NiGxZXPYaW+l7nP4KxLzR+oIDBwGAh0Y3nfU3iL8x/mS9IO4b+cf4an5TrgtX72eNUyoFAoFAqFQnGCc7w5BIoNXfk99ArvEKDF+/RTgU+/Dvzbk8C/r2VHr4monQ+ArBpWj5UgsJw95l1/BGacYa4IX/mTET2Vx+nApcuacooBADIp/xSf3Qq4CCoIuHzMNrdTx7tOzc5tP2NOjTTscDJR7nUJrf7pfnr5/VhBpV4WkFj5/9u783C7qvrg49/fzU1C5pmEzAkEkDmQAAKBWAMqiIoy9MUBHKgD2mJrfdGWpw7UobVKFVGxVLC+6CtK1SIiYMUZBEEELSBCkFmGQAhDSMivf+x9ueeee84dcufs7+d5zrPP2nutddaBxeXs317Dos5rR/zinGL0S5uW0bDwQFh8CJx8Kex5XDGaZPGqYrvKuctht1fBiV/vvm93Y8dZE9nIGL68+YgO52f/8RJ4/F740cd7VM/lcRDTp7dvT7h6l45TZo7cs2OwqNFIlV1mT6Jl2ZoO5yZE3ar7U9r7VP3Cgm0eHTsfZi5jbOsoVi3r2I5m/b/Htq+bfnTv9ex144c6nHooJ7NpQTlKaubOHbeoBFj7E/a//VMdTr2w5Xfsvv1oeKZuRMh20/rW3hHAEQKSJEldGVf3g3DUmOJVFTN3gTuuak9f83mIumdKjYbZT9y++U15vdHjiqf1t32vSE/aAebtt1XNfd6EJp9dv/3hssOL1yBpdCM2trWF1lG9fE5XHxCYPLfT0PUPvWIPDtlpFk9v2syRe+7Q66HaQ6GlJZg2fjQPb+j4VH670V3/81mz22zO/uHtbCl34Duq2QKJEcUogduvbD/3m7ptHeft1x70W7CyeA2QtiDNV55bwztav8O4KL53bNkMF53Uvuhh6copx7L3Y1cwKzreuF47/WheVfPv94QVC/jV2nVc8bsHOXinmbztsI4Btn0XTePexzquqL/73Mmw44u7bnBtQGDa4udX7a/1+IIXM71syxsOWsRlv33g+WtH7jmnU/5emV23/eiGB6j/a3zmptdx6vgyCDCqtdhm9O6r2zN89z2Me+bhDmVGx3OMvu9q2PxMx8oqMELAgIAkSVJXZu/Zcdj8Todv/ZzhkWj+SvjlFzqey7pFxvo6vB/gVefAT/4FNq4v1nZo7WPQpdkaAo12QRhEjeb2N9sTvUv1U1lqt2gstbQEL92jjzdgQ2D6hDGdAgJjuxlBsdf8qXz1lAO56raHWLFoGi9+QRcLJM7br2NAoH53gSWretvkrbbj9kXgYR2Tuei5w3hD6xXtF++5tmPm6UsZfcSZvPz81XxuzFnsWw6T/+5z+5PzOwYtWke18MkT9iEzGwaC9ls4lf+68b4O53afO7nYqWL2HvDgzZ3KAHUBgcYjBCbudfTz7w/acSbnv3ElV936EAfvNJM/23UrFq6sNXMXiFEdR3TUuPK55Xx7y8G8t/a/qbnLOwYEnnq4c0GAW7/b+ZyLCkqSJFXcqFZ443fhZ58upg+s+puhbtHg2u0V8LuXwy2XNM/Ty4X4Gho/HV7yj32vp824aZ1vHEZPKBZAHEJzpmxHS/D8k2yA8b1dUBA6L7q4/Qv61rBhpNHCgtt1s4YAwAFLZ3DA0hnd5uu0sGC9xYMXEJg1cSyTtmvliWc2c95zL+N1o66kJbJx5lXvYdXOc2idOo9jH/sAq1puooUt/HzL7pw+e1LDIs1GhaxY3Hl6y25zy1EnO61pHhCYXLNzSYMpA08wnpm7ddy9Y/Uu27N6lx6OFurO6O2Kvt+2BWqN9TmOv9v0ZqZPGNtxykh3i5q2ufWyzucaTRnbxriGgCRJUnemL4Wjz4LDP1SJVac7aB0LJ3wFjju/83aCUKw2Pnl+5/NDraWl85SF2bsX54fQ6FEtzJnccYX2CWO24hnd7se0b6k2ZSEc8La+N26YmDGh8/z/7kYI9Er9woK1Ro2FBfv332d1IyJ4wZzipvOunMP3t6xonHHaYtjrBFpagj9fuYAttPCjLXvzwy3L2cgYdm4SEGhm1zmT2GFKez+cPmFMMUIAioBAE/909ZNceM0fueOhDTw5ofN/978dvz/R19E93Wmyjem/jjqZzRPmcOar9mB07RScngYENjzQMT1mYq8XGR2JHCEgSZKkrkUUN6A7HQ4//if4xWdhy+bi2j6vHfKb7KYmzIIn7m9Pz9mjed5BNG/aOO57vH2ucq93GIBibvNbroT19xbrJYxpvHvBSNRohEB3uwz0ysTtiyDW+ns6X1uwf7GmxSB6y6olXP/HdWzeklw66Vhe9tS1nTOtek8xWgk4fuUCzvrB73muHGbS2hLsOqd3AYHWUS186oR9+MB3fsvmLcnfH/WC9q0dFxxQ3Aw/u6FTuSvuHc3v77kJgMVxP1fVxW4enfdnvWrHVpm9G/z24o7nlq7mjNd/lDMajYiYsVPj7zNpB3jiAaDJiIwKTBcAAwKSJEnqqbETi1ESy19fTCEYOxn2OXGoW9XczJ3hgd+0p+cP3OJwvTFv6jiupX27tvFjtvLp96jRxZPjbcy0uoDAqJbo+MS3P8xb3jggMIjTBdocsfscLn/3ody97mkOWPJS+PLFcPc17RmmLoK927c3nD15O05/6a7846X/A8DbDtuRGROb7KrQhQOXzuCy0w7tfKF1DCw5rOGc+vuzfarBPTmLR3Mi06O40X46xzBut5f2uh29Vj/lY/QEOPrTzdd2aWkpRtPc9dOO51/6MfjxPzefHlGBBQXBKQOSJEnqrZnL4JB3w8o3D/rT1F456J0wvpxTPm8F7PGaoW1PaeH0jk/zp46v0K4VPTCjLiDQr6MD2jTbxWIQFxSstXTWRA7beVbxlP5Ff9dxJ481/9Bp6Pophy7lmve/mKves5r3vGSX/m/Qss7TBtbneDbQ3nc308oHNp3E0zmGJ3MsH9h8Ervv2HihwX619EXtgZtRY+CVZzdd4PB5Cw+sq2M17PZKWHRQ8zIVmR7mCAFJkiRtm+Yuh7+8oRgWPGPZsJnacPTec/nMD28ny5HKLxuBOwEMpPopAwMSEGi0sGDruL5vd9kflh4Gr7sYfn85LD4Edj2qYbbZdWtR9KtG2w9Omc9Rs3fgx79/iCeeKaYMfWfLwVyy8YW08hzHHrAj2w9km9q0tMAbvgP33wAT58CUed2XOfDtxTSDR+8oFhZtG1Gw6CD45bmNyzhlQJIkSRrhtpsy7J70LZs9iW+fejDf/+0DLF8wjTW79XErtm3MohkdR1DMmTIAo1Dm7tP53MIDikU0h4MdX1S8hsq0RcWUm4dve/7U5NmL+exri0DKU89u5pENz/LIk8/yyIaNTJswhuULpg5e+1paehe8mTATTr22CAhMXVjsVgCwsIsRAhWZMmBAQJIkSRpke82fyl7zpw51M4alPedN4dCdZ/Hj2x5iVEtwyqoB2CpyuynFqJFHft9+bgjWDxjWlh3RISDAzJ2ffzt+TCvjp7eyYPoIWsxyVCvM2rnjuUmzi0UHH7m9c35HCEiSJEnS4IoIzj95JTfc/RgzJoxh8cwJA/NBK94E339f8X7s5A4L9wk46C/h5m8WO3VMnF3889oWLTq4cUDAEQKSJEmSNPhaWoL9Fk0b2A/Z/y9gu8nwwM2w13EwZf7Aft5IM2k2vOv6YhX+mTtvuzfIiw6G6y/ofH6YTTUaKAYEJEmSJFXPqFZY/rqhbsXwNmY8LNh/qFsxsJrtNFCRKQPDY6lVSZIkSZIG29QFMGVh5/Pb6oiIOgYEJEmSJEnV1WiUgCMEJEmSJEnaxjUMCFRjDQEDApIkSZKk6lp0cOdz42cMfjuGgAEBSZIkSVJ1zdgRFq9qTy98IUycNXTtGUTuMiBJkiRJqq4IOP7LcO15QBZbUlaEAQFJkiRJUrWNnw6H/e1Qt2LQOWVAkiRJkqQKMiAgSZIkSVIFGRCQJEmSJKmCDAhIkiRJklRBBgQkSZIkSaogAwKSJEmSJFWQAQFJkiRJkirIgIAkSZIkSRVkQECSJEmSpAoyICBJkiRJUgUZEJAkSZIkqYIMCEiSJEmSVEEGBCRJkiRJqiADApIkSZIkVZABAUmSJEmSKsiAgCRJkiRJFWRAQJIkSZKkCjIgIEmSJElSBRkQkCRJkiSpggwISJIkSZJUQQYEJEmSJEmqIAMCkiRJkiRVkAEBSZIkSZIqyICAJEmSJEkVZEBAkiRJkqQKMiAgSZIkSVIFGRCQJEmSJKmCDAhIkiRJklRBBgQkSZIkSaqgER8QiIjjIuKqiFgXEU9GxI0R8d6IGL2V9e0XERdFxIMR8UxE3BkRn4mI7fu77ZIkSZIkDZURHRCIiLOArwMHA78ELgMWAh8H/jsixvWyvmOBq4FjgbuAbwNbgHcCv4mInfqt8ZIkSZIkDaERGxCIiFcBfwVsAA7IzJdk5muAZcBNwCHAh3tR31zgAqAVeGtm7p+ZJwA7A18BZgMXRkT06xeRJEmSJGkIjNiAAPD+8vixzLy+7WRmPgy8o0y+MyKm9LC+04DxwJWZeW5Nfc8BbwceB1YCR/Sx3ZIkSZIkDbkRGRCIiHkUN+cAF9Zfz8yfAncDY4Eje1jtMV3UtwH4Tpl8da8aK0mSJEnSMDQiAwLA8vL4aGbe2STPdXV5m4qISUDb+gDXNcnW4/okSZIkSRruRmpAYEl5/GMXee6uy9uVxTXvm9XZm/okSZIkSRrWWoe6AVtpUnl8sos8G8rj5F7U11WdPaovIsZSTFVoVLckSZIkScPCSA0IDGfvA/6h/uT69euHoCmSJEmSpKrp6f3nSA0IPFEeJ3SRZ2J57Mk/iSdq3k+g2FFga+v7KPDJmvQOwC0LFizoQTMkSZIkSeo3k+jiHnakBgTWlseu7rLbrq3tIk+bu2reLwRu2tr6MnMjsLEtHRFPAPPpGHQYjiYB9zAy2qrqsp9qJLCfaiSwn2oksJ9KfTMJuK+rDCM1IHBDeZwREUua7DSwojxe311lmbk+Im6n2GlgBY0DAj2ur67uBO7tTZmhEBFtb5/ITOc3aFiyn2oksJ9qJLCfaiSwn0p91u1/NyNyl4HMvAe4tkyeWH89Ig6heKK/Ebi0h9X+Zxf1TQSOLpMX96qxkiRJkiQNQyMyIFD6SHk8PSL2bTsZETOAc8rk2Zn5eM21YyLiloj4QYP6zgKeAtZExCk1ZUaV9U2lCEJc3p9fQpIkSZKkoTBiAwKZ+S3g0xSL/V0dEd+LiG8AtwN7Aj8DzqgrNgXYBdixQX33AScDzwHnRsTVEfE14Dbg9cCDwInlFIBt0Ubgg9SsfyANQ/ZTjQT2U40E9lONBPZTaYDFSL+/jYjjgVOBfYDRwB+ArwCfysxn6/KeDHwJuCszFzepbz/g/cAqigDC/cAlwIcz88EB+RKSJEmSJA2yER8QkCRJkiRJvTdipwxIkiRJkqStZ0BAkiRJkqQKMiBQcRFxXERcFRHrIuLJiLgxIt4bEaOHum2qroj4p4jI8vX3XeRbExGXRsTDEfF0uYvIP5ZbhUr9KiIWRsTZEXFr2d+eiYg7I+KCiNi7i3L2U/WbiNglIt4VEedHxE0Rsbmrv5UR0RIRB0XEhyLipxHxSERsKvvjFRHx2qjZ7L1JHftFxEUR8WBNv/9MRGw/MN9SI11v+2ld2ZaIOCkiroyIhyJiY0TcHxH/HRHv6KKc/VTaCq4hUGERcRbwV8Bm4L+BDcCfUWyx+FPgiMx8eqjap2qKiIOAnwBRvs7IzDMb5Hs38Ekgy/wPUiwGOge4FTgkMx8erHZr2xYRBwBXAJOAe4FfUexKsw+whOLv6ImZeVFdOfup+lXN/7vrNftbuRPw+zL5KHAdsA5YCqwsz18CvKZ+Meay/LHAV4FWiu2X7wRWlOUfpOjDt/fhK2kb1Nt+WlNuCvAd4FBgPfBz4DFgHsUuYn/IzBUNytlPpa3kCIGKiohXUfyh3gAckJkvyczXAMuAm4BDgA8PXQtVRRExHjifYnePb3eRbznwLxQ3ZEdl5mGZeTzFlqI/oNhe9PMD3mBVybkUwYBzgSWZ+crMfDWwE3AmxY/QcyNiu7YC9lMNkJuBTwCvBV4A/Ec3+ZMi6P8yYPvy//d/npn7A6uBJ4GXA6fXF4yIucAFFP37rZm5f2aeAOxMsaPTbODC7kYYqJJ6208p+9G3KIIBXwDmZebLMvP/ZOahFP3tbQ3K2U+lPjAgUF3vL48fy8zr206WT6rahmO9s4zUSoPloxRBqb8AHu8i3/soRg98KTO/13YyM58C3gxsAV4TEbsOYFtVERExA9irTP59Zm5qu5aZW4APAE9TjK56QU1R+6n6XWb+W2b+bWZemJm3UPSjrvL/ITNfnJmXZeZzddd+BHysTL6hQfHTgPHAlZl5bk2554C3U/ydXgkcsdVfSNuk3vbT0hspglTfz8y3ZeaGujqfzczrGpQ7DfuptNUMCFRQRMyjfZjghfXXM/OnwN3AWODIQWyaKiwiVgPvAr6cmZd2kW8McFSZbNR/7wJ+ViaP6d9WqqI29iLvw2A/1YhyQ3lc0OBaW99s1Ic3UAztBnj1ALRL1fOX5fGfe1nOfir1gQGBalpeHh/NzDub5LmuLq80YMrF1f6dYp7fad1k35niSQC099N69l/1m/IH5U/K5Jm1i65GRAvFCIFxwPcy8+7ykv1UI8Wy8nh/7cmImEQxJQbswxpgETEb2JtiitXPI2JpRJweEZ+PiE+Ui2CPaVDOfir1UetQN0BDYkl5/GMXedp+1C7pIo/UXz5B0deOycx13eRt65OPZeYTTfLYf9XfTgEupZjOclREXEfxw3U5xWJX/wG8sya//VTDXrluS9tT2W/WXV5c877Z7wX7sPpL27SsR4C3UKy/Ur/j1R0RcUxm/qbm3OKa9/ZTaSs4QqCaJpXHJ7vI0zZva/IAt0UVFxFHAG8FvpaZ3+pBEfuvBl1m3gq8ELicIgDwSorhp0uA24GrMnN9TRH7qUaCcyj68H3AR+quTap536wf24fVX2aUx+nApykWFt6Toh++ELiGYseAy8p1XdrYT6U+MiAgaciUi1aeBzxEsX6ANCxFxMEUO7DsAZxIsW3gdOBoiqdY50XEeUPXQql3IuIM4CTgGeD4zHxkiJukamvbAaAV+EVmHpeZN2fmhsy8GjicYlrhDrQvfi2pHxgQqKa24asTusgzsTyu7yKP1FdnAfOBd/ZiL3b7rwZVREwF/hOYBbw6M7+amQ9m5rrMvAR4KfAU8KaIeFFZzH6qYSsi/hr4EMWCmcdk5s8aZKud6tKsH9uH1V9q+9sX6i+WU6++UibXNClnP5W2ggGBalpbHhutKEzdtbVd5JH66hhgM/COiLiq9kVxkwXw5vLc18r02vI4tVxMqBH7r/rTURTBgDsy85r6i5l5B8VwVmj/obq2PNpPNaxExLso5mc/C7wmMy9rkvWumvcLm+SxD6u/3NHkfaM8O9Scs59KfWRAoJrathiaERHNFlhZUR6vH4T2qNpagcMavGaX1xeX6QPL9K0UT2OhvZ/Ws/+qP7X9yOzq6dLj5XF6ebSfatiJiFMp5me3BQO+2yxvuSbG7WXSPqyBdhvtT/tnNsnTdr5tTQD7qdQPDAhUUGbeA1xbJk+svx4Rh1BEUzdSrKotDYjMnJqZ0egFXFBmO6M8t7gs8yzQ9iO2Uf9dBBxUJv9zgL+CquHe8rhrue5FB+U2hPuWyTvBfqrhJyLeBpxNezDgkh4Ua+ubjfrwRIo1NAAu7pdGqrIyczPwrTK5pkm2w8vjL+vO20+lPjAgUF1tqwmfHhFtP2QpV249p0yenZmPdyopDb2PAQm8MSLapha0baF1HjAK+GZm3jJE7dO25XsUq1ePA75Y/sAEoNwX+1MUowg2Ad+oKWc/1bAQEadQ/L+9N8EAKNZ5eQpYU9bRVt+osr6pFA8YLu/P9qqyPkLxd/SUiHh57YWI+FvgEIrtXj9bV+4s7KfSVovMHOo2aIhExL9S7D+8CfgBxQ/eF1P84fwZcHhmPj1kDVSlRcT5FCtgn5GZZza4/m7gkxQ3XD8C/gSsophbeCtwSC8WKpS6FBGvA75EMcXlIYofl5sohqLOA7YAp2bm5+vK2U/Vr8og/jk1p3akGEp9D+2jWaBYLPD+iNiHYqh0ALfQvt5FJ5l5coPPOw74KkUA6xqKedgrKbaAe5CiD99eX07V1tt+WlPuJODfKR5aXkfR3/YAdqUIBrw9M7/Y4PPsp9JWMiBQcRFxPHAqsA/F1ll/oFjF9VPlkFdpSHQXECjzrAH+BtifYnXhP1I8of1ouSKx1G8iYm/gNOBQiiBAAPcDPwU+nZn1w1jbytlP1W8iYjXwwx5kXZKZa3uRn3K6VqPP3A94P0UwawpFv78E+HBmPtiTulUtve2ndWVXAv+Xor9NAx4BfgJ8otnf2bKc/VTaCgYEJEmSJEmqINcQkCRJkiSpggwISJIkSZJUQQYEJEmSJEmqIAMCkiRJkiRVkAEBSZIkSZIqyICAJEmSJEkVZEBAkiRJkqQKMiAgSZIkSVIFGRCQJEmSJKmCDAhIkqTKi4jFEZHl6+Shbo8kSYPBgIAkSRUWEatrboR7+jprqNstSZL6zoCAJEmSJEkV1DrUDZAkScPG54BzepDv4YFuiCRJGngGBCRJUps/ZebNQ90ISZI0OJwyIEmSJElSBRkQkCRJfRIRa8vFBs8v0ysj4qsRcXdEPFMevxQRu/awvqMj4hsRcU9EbIyIRyLiFxFxekRM7GEde0TEZyLipohYFxGbIuKBiLgyIt4bETv0oI7DI+K/ynIbI+LOiPhcRMzvSRskSRruIjOHug2SJGmIRMRq4Idl8oOZ+YGtqGMtsAi4APgx8AUaT0vcCLw+My9qUs92wIXAMV183H3AUZn56yZ1jAL+GTgNiC7quSAzT64ptxi4s0y+EdgFOL1J2YeAwzLzf7qoX5KkYc8RApIkqb/sA3we+BPwLuAA4DDg4xTBgLHA/4uIFU3KX0B7MOBG4A3ASuAlwJeABOYCP4iIeU3qOBd4N0Uw4H7g74AXAfuW9ZxR1t2VUyiCAT8CTgRWAGuAL5fXZwH/3k0dkiQNe44QkCSpwupGCPR0l4FbM3NTTR1rKUYIANwFHJiZD9R9zouAyylGDlybmfvXXT8KuKRM/gA4MjOfrctzCsUNP8DXM/OEuuuvAL5dJn9R1vFYoy8QEQsy8+6a9GLaRwgAfBF4a9b9UIqILwJvKZP7ZuYNjeqXJGkkMCAgSVKF1QUEempJZq6tqWMt7QGBYzPzm00+6xzg7WVyZWZeV3PtUuBlwCZgx9qb9bo6rqB4Wr8ZWJiZ99dc+znwQuApYFlm3tfTL1QXELi//I4bG+TbBbilTP5VZn66p58hSdJw45QBSZLUX9bR/oS+kdph9mva3kREK8XUAoDLmwUDSl8sj63A6po6ZgAHlsn/35tgQAPfaBQMAMjMW4ENZXJpHz5DkqQhZ0BAkiS1+WBmRg9ea5uUvyEzN3dR/6+BtmkAe9acXwqML99f000ba6/vUfN+H9oXEfxJN3V055Zurq8rj5P6+DmSJA0pAwKSJKm//Kmri2Ww4NEyOb3mUu37LusAatcmqC03s+b9/fTNU91c31IeR/XxcyRJGlIGBCRJUn/pj4WJXNxIkqRBYkBAkiT1l9ldXSzXCmh7qv9ozaXa913WAcxpUu7hmvc7dFOHJEnCgIAkSeo/+5Q3/c3sDYwp399cc/4O2ofpH9DNZ9RuV1hbxw20jy44tJs6JEkSBgQkSVL/mQ4c3cX1N9W8v7LtTbm2wI/K5OERMb+LOt5SHjcDV9XU8Sjw8zJ5fETM7WGbJUmqLAMCkiSpP30yIjoN+4+Iw4C/KJO/ysxr67J8tjyOAc6LiNEN6ngTcESZvDgz6xcP/Hh5HA9cFBFTmjWym6CDJEmV0NWwPkmSVC3bR8Qe3Wfj6cz8Q4PzNwK7Ab+KiI8CvwTGAkcC76b43bEZOLW+YGZ+NyIuAo6juOm/OiI+SbEF4DTgz2kfYfAo8NcN6viviDgPeDNwEPC7iDgb+BmwnmInghXACWVbT+7Bd5UkaZtlQECSJLV5e/nqzo3APg3O/xo4G/hceaz3LHBSZl7TpN43UPw2OQbYF/hKgzz3AUdl5r1N6ngr8DRF0GEu8JEm+W5scl6SpMpwyoAkSeo3mflvwCrg6xQ3788C9wJfBpZn5te6KPtMZr4aeAVwcU35dcA1wPuAXTLz113U8VxmvotiJMC5wG3Ak8Am4AHgcorRBe/p0xeVJGkbEJlu9ytJkrZeRKwFFgEXZObJQ9saSZLUU44QkCRJkiSpggwISJIkSZJUQQYEJEmSJEmqIAMCkiRJkiRVkAEBSZIkSZIqyF0GJEmSJEmqIEcISJIkSZJUQQYEJEmSJEmqIAMCkiRJkiRVkAEBSZIkSZIqyICAJEmSJEkVZEBAkiRJkqQKMiAgSZIkSVIFGRCQJEmSJKmC/hdcloxG2CszCQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 12), dpi = 100)\n",
    "\n",
    "# Set tick font size\n",
    "for label in (ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    label.set_fontsize(16)\n",
    "\n",
    "for i in range(sctm_iterations):\n",
    "    plt.plot(loss_progress[i], label = \"Training loss of iteration \" + str(i), linewidth = 3)\n",
    "\n",
    "plt.ylabel(\"Training Loss\", fontsize = '20')\n",
    "ax.yaxis.set_ticks(np.arange(0, 1, 0.4))\n",
    "plt.xlabel(\"Epoch\", fontsize = '20')\n",
    "ax.xaxis.set_ticks(np.arange(0, reps, 40))\n",
    "plt.title('SCTM Training Loss', fontsize = '24')\n",
    "plt.legend()\n",
    "plt.savefig('SCTM_loss.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "284197f1",
   "metadata": {},
   "source": [
    "### Definition of the function that plots Wigner functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ae4b1a60",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "\n",
    "def wigner(rho):\n",
    "    \"\"\"This code is a modified version of the 'iterative' method\n",
    "    of the wigner function provided in QuTiP, which is released\n",
    "    under the BSD license, with the following copyright notice:\n",
    "\n",
    "    Copyright (C) 2011 and later, P.D. Nation, J.R. Johansson,\n",
    "    A.J.G. Pitchford, C. Granade, and A.L. Grimsmo.\n",
    "\n",
    "    All rights reserved.\"\"\"\n",
    "    import copy\n",
    "\n",
    "    # Domain parameter for Wigner function plots\n",
    "    l = 5.0\n",
    "    cutoff = rho.shape[0]\n",
    "\n",
    "    # Creates 2D grid for Wigner function plots\n",
    "    x = np.linspace(-l, l, 100)\n",
    "    p = np.linspace(-l, l, 100)\n",
    "\n",
    "    Q, P = np.meshgrid(x, p)\n",
    "    A = (Q + P * 1.0j) / (2 * np.sqrt(2 / 2))\n",
    "\n",
    "    Wlist = np.array([np.zeros(np.shape(A), dtype=complex) for k in range(cutoff)])\n",
    "\n",
    "    # Wigner function for |0><0|\n",
    "    Wlist[0] = np.exp(-2.0 * np.abs(A) ** 2) / np.pi\n",
    "\n",
    "    # W = rho(0,0)W(|0><0|)\n",
    "    W = np.real(rho[0, 0]) * np.real(Wlist[0])\n",
    "\n",
    "    for n in range(1, cutoff):\n",
    "        Wlist[n] = (2.0 * A * Wlist[n - 1]) / np.sqrt(n)\n",
    "        W += 2 * np.real(rho[0, n] * Wlist[n])\n",
    "\n",
    "    for m in range(1, cutoff):\n",
    "        temp = copy.copy(Wlist[m])\n",
    "        # Wlist[m] = Wigner function for |m><m|\n",
    "        Wlist[m] = (2 * np.conj(A) * temp - np.sqrt(m) * Wlist[m - 1]) / np.sqrt(m)\n",
    "\n",
    "        # W += rho(m,m)W(|m><m|)\n",
    "        W += np.real(rho[m, m] * Wlist[m])\n",
    "\n",
    "        for n in range(m + 1, cutoff):\n",
    "            temp2 = (2 * A * Wlist[n - 1] - np.sqrt(m) * temp) / np.sqrt(n)\n",
    "            temp = copy.copy(Wlist[n])\n",
    "            # Wlist[n] = Wigner function for |m><n|\n",
    "            Wlist[n] = temp2\n",
    "\n",
    "            # W += rho(m,n)W(|m><n|) + rho(n,m)W(|n><m|)\n",
    "            W += 2 * np.real(rho[m, n] * Wlist[n])\n",
    "\n",
    "    return Q, P, W / 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d784a383",
   "metadata": {},
   "source": [
    "### Obtain the target and learnt states from the quantum decoder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "72aef1cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "rho_target = np.outer(target_state, target_state.conj())\n",
    "rho_learnt = np.outer(learnt_state, learnt_state.conj())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b83c178d",
   "metadata": {},
   "source": [
    "### Plot the target state as a Wigner function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bdc9184a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Bartu\\AppData\\Local\\Temp\\ipykernel_13352\\2788124896.py:7: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection=\"3d\")\n",
    "X, P, W = wigner(rho_target)\n",
    "ax.plot_surface(X, P, W, cmap=\"Spectral\", lw=0.5, rstride=1, cstride=1)\n",
    "ax.contour(X, P, W, 10, cmap=\"Spectral\", linestyles=\"solid\", offset=-0.17)\n",
    "ax.set_axis_off()\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7983923f",
   "metadata": {},
   "source": [
    "### Plot the learnt state as a Wigner function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f4b78c87",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Bartu\\AppData\\Local\\Temp\\ipykernel_13352\\4222121820.py:7: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection=\"3d\")\n",
    "X, P, W = wigner(rho_learnt)\n",
    "ax.plot_surface(X, P, W, cmap=\"Spectral\", lw=0.5, rstride=1, cstride=1)\n",
    "ax.contour(X, P, W, 10, cmap=\"Spectral\", linestyles=\"solid\", offset=-0.17)\n",
    "ax.set_axis_off()\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "44c46f89",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt(save_folder_name + '/x' + str(train_state_select) + '.txt', X)\n",
    "np.savetxt(save_folder_name + '/p' + str(train_state_select) + '.txt', P)\n",
    "np.savetxt(save_folder_name + '/w' + str(train_state_select) + '.txt', W)\n",
    "np.savetxt(save_folder_name +'/encoded_ '+ str(train_state_select) +'.txt', encoded_st)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f43e9c3a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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