6. Modular Network.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "    <div style=\"width: 150px; float: left;\"> <img src=\"https://raw.githubusercontent.com/DataForScience/DeepLearning/master/data/D4Sci_logo_ball.png\" alt=\"Data For Science, Inc\" align=\"left\" border=\"0\"> </div>\n",
    "    <div style=\"float: left; margin-left: 10px;\"> <h1>Deep Learning From Scratch</h1>\n",
    "<h1>Modular Network</h1>\n",
    "        <p>Bruno Gonçalves<br/>\n",
    "        <a href=\"http://www.data4sci.com/\">www.data4sci.com</a><br/>\n",
    "            @bgoncalves, @data4sci</p></div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import watermark\n",
    "\n",
    "%load_ext watermark\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.10.9\n",
      "IPython version      : 8.10.0\n",
      "\n",
      "Compiler    : Clang 14.0.6 \n",
      "OS          : Darwin\n",
      "Release     : 22.5.0\n",
      "Machine     : x86_64\n",
      "Processor   : i386\n",
      "CPU cores   : 16\n",
      "Architecture: 64bit\n",
      "\n",
      "Git hash: 0a19814306bc315e122d1b21878507f2b920977d\n",
      "\n",
      "matplotlib: 3.7.0\n",
      "numpy     : 1.23.5\n",
      "seaborn   : 0.12.2\n",
      "watermark : 2.4.2\n",
      "pandas    : 1.5.3\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%watermark -i -n -v -m -g -iv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('d4sci.mplstyle')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.load('input/X_train.npy')\n",
    "X_test = np.load('input/X_test.npy')\n",
    "y_train = np.load('input/y_train.npy')\n",
    "y_test = np.load('input/y_test.npy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preprocessing and Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_layer_size = X_train.shape[1]\n",
    "\n",
    "X_train /= 255.\n",
    "X_test /= 255."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define the initializatino function as we'll have to call it more than once"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def init_weights(L_in, L_out, epsilon = 0.12):\n",
    "    return 2*np.random.rand(L_out, L_in+1)*epsilon - epsilon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the layer sizes we'll be using"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "hidden_layer_size = 50\n",
    "num_labels = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize the weights. In this case we use a array of weight matrices so that we can easily add/remove layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "Thetas = []\n",
    "Thetas.append(init_weights(input_layer_size, hidden_layer_size))\n",
    "Thetas.append(init_weights(hidden_layer_size, num_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Utility functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One-hot encoding to define the labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def one_hot(K, pos):\n",
    "    y0 = np.zeros(K)\n",
    "    y0[pos] = 1\n",
    "\n",
    "    return y0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Activation function base class. Here we must provide an interface to both the activation function and its derivative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Activation(object):\n",
    "    def f(z):\n",
    "        pass\n",
    "\n",
    "    def df(z):\n",
    "        pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The various activation functions simply extend the base class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Linear(Activation):\n",
    "    def f(z):\n",
    "        return z\n",
    "\n",
    "    def df(z):\n",
    "        return np.ones(z.shape)\n",
    "\n",
    "class ReLu(Activation):\n",
    "    def f(z):\n",
    "        return np.where(z > 0, z, 0)\n",
    "\n",
    "    def df(z):\n",
    "        return np.where(z > 0, 1, 0)\n",
    "\n",
    "class Sigmoid(Activation):\n",
    "    def f(z):\n",
    "        return 1./(1+np.exp(-z))\n",
    "    \n",
    "    def df(z):\n",
    "        h = Sigmoid.f(z)\n",
    "        return h*(1-h)\n",
    "\n",
    "class TanH(Activation):\n",
    "    def f(z):\n",
    "        return np.tanh(z)\n",
    "\n",
    "    def df(z):\n",
    "        return 1-np.power(np.tanh(z), 2.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forward Propagation and Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The forward and predict functions are also generalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def forward(Theta, X, active):\n",
    "    N = X.shape[0]\n",
    "\n",
    "    # Add the bias column\n",
    "    X_ = np.concatenate((np.ones((N, 1)), X), 1)\n",
    "\n",
    "    # Multiply by the weights\n",
    "    z = np.dot(X_, Theta.T)\n",
    "\n",
    "    # Apply the activation function\n",
    "    a = active.f(z)\n",
    "\n",
    "    return a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The predict function now takes the entire model as input and it must loop over the various layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(model, X):\n",
    "    h = X.copy()\n",
    "\n",
    "    for i in range(0, len(model), 2):\n",
    "        theta = model[i]\n",
    "        activation = model[i+1]\n",
    "\n",
    "        h = forward(theta, h, activation)\n",
    "\n",
    "    return np.argmax(h, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy function is just the same as before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy(y_, y):\n",
    "    return np.mean((y_ == y.flatten()))*100."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Back propagation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def backprop(model, X, y):\n",
    "    M = X.shape[0]\n",
    "\n",
    "    Thetas=[0]\n",
    "    Thetas.extend(model[0::2])\n",
    "    activations = [0]\n",
    "    activations.extend(model[1::2])\n",
    "\n",
    "    layers = len(Thetas)\n",
    "\n",
    "    K = Thetas[-1].shape[0]\n",
    "    J = 0\n",
    "\n",
    "    Deltas = [0]\n",
    "\n",
    "    for i in range(1, layers):\n",
    "        Deltas.append(np.zeros(Thetas[i].shape))\n",
    "\n",
    "    deltas = [0]*(layers+1)\n",
    "\n",
    "    for i in range(M):\n",
    "        As = [0]\n",
    "        Zs = [0, 0]\n",
    "        Hs = [0, X[i]]\n",
    "\n",
    "        # Forward propagation, saving intermediate results\n",
    "        As.append(np.concatenate(([1], Hs[1])))  # Input layer\n",
    "\n",
    "        for l in range(2, layers+1):\n",
    "            Zs.append(np.dot(Thetas[l-1], As[l-1]))\n",
    "            Hs.append(activations[l-1].f(Zs[l]))\n",
    "            As.append(np.concatenate(([1], Hs[l])))\n",
    "\n",
    "        y0 = one_hot(K, y[i])\n",
    "\n",
    "        # Cross entropy\n",
    "        J -= np.dot(y0.T, np.log(Hs[-1]))+np.dot((1-y0).T, np.log(1-Hs[-1]))\n",
    "\n",
    "        deltas[layers] = Hs[layers]-y0\n",
    "\n",
    "        # Calculate the weight deltas\n",
    "        for l in range(layers-1, 1, -1):\n",
    "            deltas[l] = np.dot(Thetas[l].T, \n",
    "                               deltas[l+1])[1:]*activations[l].df(Zs[l])\n",
    "\n",
    "        Deltas[2] += np.outer(deltas[3], As[2])\n",
    "        Deltas[1] += np.outer(deltas[2], As[1])\n",
    "\n",
    "    J /= M\n",
    "\n",
    "    grads = []\n",
    "\n",
    "    grads.append(Deltas[1]/M)\n",
    "    grads.append(Deltas[2]/M)\n",
    "\n",
    "    return [J, grads]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Definition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = []\n",
    "\n",
    "# Layer 1\n",
    "model.append(Thetas[0])\n",
    "model.append(Sigmoid)\n",
    "\n",
    "# Layer 2\n",
    "model.append(Thetas[1])\n",
    "model.append(Sigmoid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training procedure\n",
    "The same basic idea as before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 3.1414411559743445 3.139513941838044 43.919999999999995 39.5\n",
      "20 2.93984434443716 2.9488624622001014 55.92 50.7\n",
      "30 2.630329366414158 2.659496665706168 61.88 57.199999999999996\n",
      "40 2.2976239824356446 2.3531360808164856 67.06 64.0\n",
      "50 2.030130345217811 2.1059028777874564 71.88 66.9\n",
      "60 1.8303312966774767 1.9185109781516132 75.14 70.3\n",
      "70 1.6753269033916727 1.771408362242612 77.75999999999999 73.4\n",
      "80 1.5489571028606763 1.6508990904167629 80.17999999999999 75.5\n",
      "90 1.442295708692224 1.5493279936153703 81.86 78.2\n",
      "100 1.3503472691122536 1.4622622105228154 83.64 79.5\n",
      "110 1.270076144710735 1.3868463082179112 84.58 81.0\n",
      "120 1.1994725995214515 1.3210662046931427 85.61999999999999 81.8\n",
      "130 1.1370857584867715 1.2634032942919886 86.28 82.3\n",
      "140 1.0817770213890463 1.2126505749435397 86.98 83.1\n",
      "150 1.0325999075485859 1.1678145737014727 87.68 83.89999999999999\n",
      "160 0.9887464775653624 1.128063403141761 88.26 84.6\n",
      "170 0.9495212583430607 1.0926953224069318 88.53999999999999 85.3\n",
      "180 0.9143241292130148 1.0611154401932834 88.84 85.8\n",
      "190 0.882636285222267 1.0328167636468173 89.08 86.1\n",
      "200 0.8540083274131192 1.0073649725390637 89.46 86.2\n",
      "210 0.828050474518476 0.9843866248333263 89.72 86.5\n",
      "220 0.8044246581526955 0.9635601336969776 90.08 86.3\n",
      "230 0.7828380326656315 0.9446086858672541 90.2 86.5\n",
      "240 0.763037433115303 0.9272943912795245 90.48 86.6\n",
      "250 0.7448044565620981 0.911413201224772 90.60000000000001 86.6\n",
      "260 0.7279510002039501 0.8967903606164879 90.75999999999999 86.6\n",
      "270 0.712315200282027 0.8832763078371536 90.92 86.9\n",
      "280 0.6977577681948811 0.8707430048011147 91.03999999999999 87.0\n",
      "290 0.6841587313874377 0.8590806971146164 91.22 86.8\n",
      "300 0.6714145771412344 0.8481950966075881 91.38 87.0\n",
      "310 0.6594357824611213 0.8380049641518578 91.56 87.2\n",
      "320 0.6481447006655429 0.828440058559723 91.74 87.5\n",
      "330 0.6374737676484438 0.81943941037169 91.92 87.8\n",
      "338 0.6293436290905756 0.81943941037169 91.92 87.8\n",
      "DONE\n"
     ]
    }
   ],
   "source": [
    "step = 0\n",
    "tol = 1e-3\n",
    "J_old = 1/tol\n",
    "diff = 1\n",
    "\n",
    "acc_train = []\n",
    "acc_test = []\n",
    "J_val = []\n",
    "steps = []\n",
    "\n",
    "alpha = 0.5\n",
    "\n",
    "while diff > tol:\n",
    "    J_train, grads = backprop(model, X_train, y_train)\n",
    "\n",
    "    diff = abs(J_old-J_train)\n",
    "    J_old = J_train\n",
    "    J_val.append(J_train)\n",
    "\n",
    "    step += 1\n",
    "\n",
    "    for i in range(len(Thetas)):\n",
    "        Thetas[i] -= alpha*grads[i]\n",
    "    \n",
    "    if step % 10 == 0:\n",
    "        pred_train = predict(model, X_train)\n",
    "        pred_test = predict(model, X_test)\n",
    "\n",
    "        J_test, grads = backprop(model, X_test, y_test)\n",
    "\n",
    "        acc_train.append(accuracy(pred_train, y_train))\n",
    "        acc_test.append(accuracy(pred_test, y_test))\n",
    "        steps.append(step)\n",
    "        \n",
    "        print(step, J_train, J_test, acc_train[-1], acc_test[-1])\n",
    "        \n",
    "print(step, J_train, J_test, acc_train[-1], acc_test[-1])\n",
    "print(\"DONE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accuracy during training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Cost function')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 3840x2640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(1, len(J_val)+1), J_val)\n",
    "plt.xlabel(\"Iterations\")\n",
    "plt.ylabel(\"Cost function\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fefb098d6c0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3840x2640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(steps, acc_train, label='Training dataset')\n",
    "plt.plot(steps, acc_test, label='Testing dataset')\n",
    "plt.xlabel(\"iterations\")\n",
    "plt.ylabel(\"Accuracy (%)\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Difference between the training and testing accuracies. We can see that it hits a minimum around ~200-250 epochs and starts increasing afterwards, indicating the beginning of overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fefb97e6da0>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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nTxLKBAAAqBcWgUyXTbHWLMzos6/NalVH+mt5IxPSJ+8I9a1NIes9L7J5X6zbt9uEVRsD6e1rWLsBAAAAAACYbQQyAQAAUuYayBwLVXcPLksGO8Q2BQRFAADVoT3vXmOwNLl7hZ5+93uKo9o9ZX2us/UuyHha6hjMHY+kZ0aMDqjO3fhkrNCgr++0Iz0tbObvGwD8jKc/PSvQ0e3258QvPxDpgV1MDgIAAKgHvYM2EzJdzGvydM1FgX5t1cy0iP3H47E+dls46fXKWhcnib6ywW5Tlt86zle74wZ/AAAAAAAAcEcgEwAAIGXNWfeHYsWywYFUkVGD35cJmQCAamHRQNNfmtx/t6Xfvfm/ewZ21Ed16HJsCJSkviECKa7KUaKfGE1bu6SbCQsA8JyGwNNHzgu0oGBbN06kz90dausA10AAAIBaZzMh0721K8h4eufJgT50TqB84FzuZT2yN9EHf1rWo3u5572tN9aTAzbh1AUFzViwFgAAAAAAAC+NVRoAAICUNTlOyJSk4oR7jWpSKrs/mCSQCQCoFtU2IXNVB8tJOKirzT2QaTEpot7d9VSsoTH3Op2tno6fT+AaAJ6vtdHT5edn1WywtvN8Y6H0iXWhni1yHQQAAKhVUZzoqf1u93vZjLS4xeiAJJ21NKPPvCar5QabbL2cwTHpqltD/efjkZKkPu97R8uJvvWQ3XTMS08OlPNZuwEAAAAAAKgEdNABAACkrDnn/mBsZKK+HlS6TsjM+Qd3+wUAoBpYTMgcmEQgM04Sk0DmSiZk4r91mkzIrK/7XGtJkuhHPTbTJi7uzsjz+PsGgBdbMsfTX5wbKDB+ojY0Jl27LlSxztZ8AAAA6sWuA9KEYxZvWasn3/h51+I5nv76VYEu6Eq/ZSxOpG9uivTXd9bnfe93H41MNtGSpBMXeDr9SNZtAAAAAAAAKgWBTAAAgJRZTFEoOgYUq03J8ffNBzbHAQDATGgzCWS+/H+za9h904O2Rmlek1sN1A6LQCYTMt08sS/RNoPXcE6D9IrlLBUDwOEcPz+j/3W6b153x/5En74zVDnieggAAFBregfdN1DqSmmSZUPg6f1nBPqj033l7G9zf8l9OxN98KdlbR2w2VSqGuw6YLeJVsaT3nmyz0ZaAAAAAAAAFYQuGwAAgJQ1Zd1r1NuusaOh2+9r8ZoDADBT2hrdawxOYkJmT797A1D3PI/GH/yPQs7T/IJbjcExaWisvu51Lf1ws+Oojf/22qMzyvn8bQPASzmv09dbTrDvVn94b6Iv3hcpSbgeAgAA1JK+Iff7O4vNsF7KRUf5+qtXBVrYnOo/I0naU5Q+8rNQN22tj3vfr20MFRrlT197dEbLW2nxAwAAAAAAqCSs1gAAAKSsOef+sLQ4YXAgVcR5QmaWZnIAQPVoCDznidr9k5iQ2dPv3ui0soOlJLyQRWOgRYNiPdpbTLR+p/trF2Sk1x49A+MwAKAG/NZxGb3qKPv7odv6Yn3n0fqZFgQAAFAPLNY7utrSf97V1ZbRZ16T1elHpv9vlWPpi/dF+r8/jzTuuDlrJdu4O9b9u2x+v+ac9ObVrNsAAAAAAABUGrroAAAAUlYwmNY44hhQrDajjr8vEzIBANWmPe/W8DS5CZnuTUCrOtj0AC/U1ea+vNg3WLsNeGn6yZZIscFLd87SjPM5CADqhed5eu+pvtYstD9vfvuRSLf22kw+BgAAwOxKkkTbDNY7lqc8IfM5hZynvzg30O+t8ZWZgX/ytr5Yf3FTqJ3DtbcmFMaJrtsYmtX73RN8tTSwbgMAAAAAAFBpCGQCAACkrOA48UqSRidq74Hk4cRJojHH55R5ApkAgCrTnnf7+ZEJveSu8mNhoqf2u91PZDxpRTvNP3ghiwmZvUzInLKxMNFNW20mqV28iiViAJiKIOPpQ+cEJtfAF/uH9ZEeeoZJmQAAANVucEwaHnersbBZasrO3Fqc53n6jWN8XXNhoLbG9P+9p/Yn+tCNZd31VG1tSvLjLbF2DtvUWj7X02tWsG4DAAAAAABQiVi1AQAASFkh5/6wdGTC4ECqhGsYU5KaAsIiAIDq0mYwnW5w7PD/29aBxHmS3vK5nhq5xuJFugzCKH0EMqfstt5YRcep8pJ0zDxPR7ezRAwAU5XPerr8vMB5U40XixLp03eF2j5EKBMAAKCaWax1pLEByGQce0RGn/uVrE6Yn/6/PxZKn7s70lc2hCpH1b8+tH8s0bcfsQuYvmutL38mRpYCAAAAAABgyui2AQAASFmzwYRMi2brajFq8LsyIRMAUG0smvkHRg/ftLS5372haWUHzT/4ZUcUpCbHe6+dw8lLTnjFC8VJoht6bJr7Lun2TeoAQD3qaPJ0xfmB8oFt3dGy9Il1oQZKXBsBAACqVd+gRSBz9lq6Whs9ffSVgd543Mwcww09sa68JdS+YnXfA1//cGTynFOSzlzi6YQFtPUBAAAAAABUKlZuAAAAUtbgS75jfqE4Ud0PIKeiZPCg0jUUAADATGs3mJA5MHb4+4Wefe5TlroJZOIQPM9znpIZJ9KO4fq533X14DOJdh5wrzOvSTpjCX/XAOCiszWjD50TyHpozb7Rg6HMUpnrIwAAQDXqNZiQ2dU2u9/Z/Yynt5wY6IrzApPNZ19OT3+iD/y0rI27q3Na/LbBWD/banPs2Yx06RrjnV8AAAAAAABgikAmAABAyjzPU8HxQWVxwuZYqsGoQbNhPktjOQCgulgEMgdLh/7/J0miHoMJmd3zWEbCoXUaNAj2GkyOqBdW0zFft9KXb50gAoA6tGZRRu87zX7icO9gos/dHSqKuUYCAABUm75B92Ce6wZYVtYuzuizr83q6Pb0j+fAhHTt7aH+7eHqug9OkkTXbYhkdcS/cUxG85sr4/0HAAAAAADAodFJBwAAMANcA5kjdTQRgQmZAIB61J53rzFQOvT9wr5RaWjMrXZTVlrc4lYDtavToEGwz2ByRD14ejjRxt3ur1XOl151FEvDAGDloqN8vfE4+/Pqht2J/nlDpCThOgkAAFAtxsJEuw641WjOSR1NNsdjYX7B0ycuCvS6lemvJSSSvvNorGtvD7V/rDrug+/aEeuxZ22OtT0vveE4+w1fAAAAAAAAYIuuGwAAgBnQ7Dixsa4mZIbuNfKBew0AAGZSm8GEzIHDTMjs6Xffkb+7w1PGY1d2HFqXwYTMPiZkTorVdMwLujJqaeBvGgAs/e4Jvs5fbv/Y7adPxvr+E+73cwAAAJgZT+1PnCcldrZ68ipsLS7re/r9UwL96Vm+GmfgOdymPYk+8NOynthX2ffC42Gibzxos14jSe84yVdjUFnvPQAAAAAAAH4ZgUwAAIAZ4DohszihupmGUDKYBtrkGIAFAGCmtTZKrlevgdFDX0N7+t2vrd0dXFtxeEvnePIdPyJ9Q4niOrnfna6RiUS39do0IV68kkkLAGDN8zxddrqv1fPt75u+uSnSnU/ZNXkDAAAgPb0Gm05ZbH6Vllcs9/XpV2e1dE76xzhQkq68OdQPN1fu1Pj/fDzSvlGbWsfM8/SKFDZ5AQAAAAAAgD1WcQAAAGZAIef2UDJKpPE66bsbLbvXyGfdawAAMJOCjKe5jW41BsbSC2Su7GAJCYeX9T0tcWzCK4XS3hGjA6pRP9sam3wnWLPQ05K5ldvYCQDVLOt7+vNzAy2ZY1/77++N9PizlT0dCAAAAAc3nXLV2VrZ39uXzPX0qdcEOm8GwoNRIn11Y6TP3BVq1GBTV0vPFhOzafaepHev9StuMioAAAAAAAAOjW46AACAGVAwCAiOTLjXqAYlApkAgDrVnndrthks/fJE7XKUaNsAEzKRvk6DyQ0WDYu1KooT/dcWmx1aLulmOiYApKk55+mK87Jqddxs48XCWPqrO0LtGuZ6CQAAUMn6DCZkVnogU5IaA09/fKavPzjVVzAD3Wf3Pp3oQzeW1TdUOZuUfP3BSBNGG+peeFRGK9pp4wMAAAAAAKgWrOQAAADMgOace43iRH003FnsbtsUVP6DagAAXqw97/bzY+HBKYPPt31/orJjj9KiFqmlgWsrXlqXQaNgbwU11FWan+9MtG/Uvc7iFmnNIv6eASBt85s9feS8QA3GGfiRCema28vaf5jJ6AAAAJhdUZxo+363e7UgIy2ZUx3f3T3P02uP9vXJVwU6oin9f2/3AenDN4W6ZZtRCtLBo3tj3b3DZi2rKSu99UQ20AIAAAAAAKgmBDIBAABmQFPO/cFpkQmZk8aETABANWpznJApSQMvCmz17LOYjsnyEV5el8GEzF6DCRK16obNNo2GF3f7ynjV0dQJANXu6PaM/uzsQBnj0+6e4sFJmeMh100AAIBKs2fk4KZpLpbO8ZT1q+u7+9HtGX32tVmdujj9456IpM+vj/QP62fvnjiKE31lg10o9E3H+2ptrK73HAAAAAAAoN7RUQcAADADmg0CgkWDoGI1GHV8UC0d3EkWAIBq024RyCy9sAmpp98ikEkzEF5ep8GEzL4hgiWHsnUg1uMG4eqmrPTKTpaDAWAmnXZkRu9eaz/ppqc/0f+5N1QUc+0EAACoJL1D7hMTOw02vZoNLQ2ePvyKQG870TfflORQfrYt1kd+FuqZAzN/T/yzbbHZOtbiFulXV7JeAwAAAAAAUG1Y0QEAAJgBhZx7jZGJ+miyK5Xdfs+MJ+Xsex0BAEhde969xi8HMt2bwAhkYjJaGjx1OH6G941KB8br4553Kn7U4/53LEmvXpFRPsvfMwDMtNet9PXrq+wfx937dKJvbLKbygMAAAB3FiE9i02vZkvG8/SG43xd/cpArY3p/3u9Q4k+eGNZ9z5ts3YyGSMTia5/yO4+/J0nB1U3ERUAAAAAAAAEMgEAAGZEIef+IG20TiZklhx/z6as5Hk8uAQAVB+TCZljv/i/h8cTPTPiVi/nS8uruAkMM6vLYIIDUzJfaKCU6K6n3JsKM97BQBAAYHa8Y42vM5fY31P9cHOsG3oIZQIAAFSK3kH3dY2uGliLW70go8++Nqvjjkj/dxktS5++M9TXHwwVzsAE+W8/EunAhE2ttYs8nbKY1j0AAAAAAIBqxKoOAADADChk3WuMGD3cq3SjodvP5wOb4wAAYKZZTMgcfN6EzJ5+9wako9o8BZnqbwLDzLCY4EAg84VufDJSaDDk4fQjPc0v8LcMALMl43n64zMDrUph8vh1GyKtn8GJQAAAADg8kwmZBhteVYL2vKePXRDo9cfOTGvaD56IddUtoQZK6a0t7dif6MdbbO69fe/gdEwAAAAAAABUJwKZAAAAM6DZYEJmcaI+mtNLZbffsylbGw+qAQD1p81gQmb/6PMCmfvcm4O6UwgNoHZ1trkvNVpMkqgVE1Ginz5p0+R3STfTMQFgtjUEnj78ikALm23rJpL+5p5QW/oJZQIAAMym/WOJBkpuNY5osnmmWCn8jKe3nxTow68I1GSwee3LeXxfog/8pKyH99jfGydJoq9uDGU1hPPi7oyOnFM77zUAAAAAAEC9IZAJAAAwAwo59xrFepmQWXb7+fwMPNAFACANcxqkwHGlZvB5TV9bDCZkds9j6QiT18WETFN3bo+1f9y9Tlebp2OPoMEPACrB3EZPV5yfVYvBOtHzTUTSJ9eF2jPCdRQAAGC2MB3z8E4/MqPPvjaro2bg99s/Ln3stlDffTRSnNjdH9+/K9GDz9jUm9sgvel4Ns8CAAAAAACoZnTVAQAAzICCQUiw6Dg5shokSaKSYyBzJnbYBQAgDRnPU2ujW42B0sH7hThJtGXAIJDZXptNYEjHgmapMXCr8fRwonJU+/e9LydJEt3QYzUdMyPP428ZACrF4hZPf3leoKzxE7r949K168oameA6CgAAMBt6DQKZFptdVaqFzZ4++apAr16RfqtanEjXPxzpk+tCHRh3f1/K0cHpmFbeeqKvQg1NQgUAAAAAAKhHBDIBAABmgJ/xnJvTR+pgQuZEJLn23+ezPMAEAFSv9rzbdWxw7GCQa+ew+9Tp9rzU0eRWA/Ul43nqdGwcDOODocx699iziUkjZ2ujdO4yloABoNIcMy+jPz7TcaHoEHYOS5+6I2RzAwAAgFnQN2gxIbO2v8PnfE/vOy3Q+8/wlZuBAZEbdif6wE/L2tLvtunVj3piPTNic0wr2jxdeFRtv88AAAAAAAD1gBUeAACAGdKcc/v5omOoohqUDDaXbbLvZwQAYMa0591+PoylAxNSj2OTkSR1d3hM1cOUuQYyJanXoIGx2v2oJzKp85oVGWV9/o4BoBKdvSyjd5xk34X+6LOJPr8+UpxwPQUAAJhJTMicvAu6fH3q1YEWt6T/b+0blS6/OdSP/z97fx5n+VXXif/vz723quvWre6u6qWyp6uydJZuQsAAYQs7UQM4oqKOI8Rx+DpfZhwcfjoK7qOIqIw683Dc+ao4jtvoPBxQRJQJCARZQrArZIOqzp7qJFXdXUt3Lffz+yOCLFmq+5y7P5+PRx4+HnrvuacqaT+3zzmv87pjM8rT+I78yGoZfzKTZ50mIuJfP70aFWuuAAAAAD1PIBMAoE0aic2Ny2v9f5AutckrIqI+lD4GAHTKRGJDZkTEwytl3P5w+veG/bstG3HqpifS/xuey3CAsZc9uFTGx+9N/x3UKhHXXtSGugkATts3XFqJay/K/53rQ4eb8Yf/mO/QOAAAT2xts4x7j6X9XX50KGKykWlCPWDfeCV+/uVD8ZzzWr8GudGM+M1PbsYvfXQzVtdP7d/T/7h5M05kuFA2IuL5+ypx2V5rrgAAAAD9wCoPAECbNFIbMtfyzKObneom6GOpJwZfAaCTdmcIZC6sRpZA5sW7PVM5dTkaMgc9kPmXd2xGM8Ov4HnnV7KEvAFonaIo4t88vRpfc3b+/3/9p7c0428+J5QJANAOdx8tk/8uPzVeRDFgzYn1oSL+f8+pxnc/vRq1Npxg+9BdzfjBv1mPu49u7V/WHQ834wNzzSyfva0a8Z1PdXEWAAAAQL8QyAQAaJPUQObqRsRmjpPZXSxHQ+aohkwAethEPX2M+46XWz5U9HgqRcSFuwbrABh5nL+ziErifzqzC2WUZX9/7308q+tl/O3n8xz0u26/pV+AXlCtFPGmZ9figgwt01/p1z+xGTfdn+e5AgDA45tdSF/HyHHJVS8qiiKu21+Nn3pxLfaMtv7z7jkW8Z/etx4fnHviy0uaZRm/9al8F5y8+vJq7BkdzH/HAAAAAP3IqRwAgDYZy9DcmCOw2M1WM/x89Vr6GADQKbsytNl9/N5m8o38+8aLGKk5IMSp21Yr4uztaWMsr0ccWckzn17zgdlmlu/8l+8t4sJdln4BekV9qIi3XJP/AHqzjPj5D2/E7IJQJgBAK80upgcyp1twQUcvuWRPJX7h2qG48szW/x5Obkb80o2b8euf2Ij1zcf+d/fBuWbc8XCeC8MmGxGvusQ6DQAAAEA/sdoDANAmo4kNmRERS2vpY3SzlfX0jc3RDMFXAOiUHIHMmSPpz9P9uz1POX3T4+lLjnMZDjL2mmZZxnvuyNO8cN3+apZxAGifXfUifuQFtRgdyjvuiY2It35wIx5aGbxnKwBAuxzOsI4xqA2ZX2rHtke/E3/bwWq047fx13c24y1/uxHzS1/+7291vYx33ZyvHfN1V9Zim8vvAAAAAPqKQCYAQJuMDadvtC1nCCx2s9WN9DHqmQ8uAkA77aqnj5HajhkhkEmaqQyNDnML/f2997F86v4y7j+ePs7e0YhnnuPPMEAvOn9nJX7webWoZd69e2T10VBmjouwAAD4cs2yjNnEdYxKEXHeTn+Xj4ioFEW85mA1fvSFtdixrfWf97lHyvj+963HJ+7951b5/3XLZiycyDP+wckirj7Xv1sAAACAfiOQCQDQJo0MQcH+b8hMHyN3kwQAtNPoUMS2Lii227/bkhGnbzpDo8PsYvPJX9Rn3nNbnuaFr9tfjWrFQT+AXvWUMyrx/z4j/xfCw4tl/MKHN2Ijx+0dAAB80fxy+oWj5+4oYrjq7/Jf6sozK/EL1w7FJW24OG5pLeJnPrQRv3/zRtx3rIy/uC3PulSliPjXT69GUfh3CwAAANBvnK4DAGiTxnD6GCt9HshczdDUoCETgF5WFEWWlswUY8MRZ23v7BzobRoyT91dR5tx84PpP/NILeKlF1jyBeh1L5quxrcdzB/K/PQDZfz6JzajLAfrOQsA0Eo51jByXG7Vj/aMFvFTL6nFKy9pz1rHn322Gd//vvXYyHRP2MsvrMTUuHUaAAAAgH5k1QcAoE0aw+mbqUtr/X1gLvUG4YiI0ZpNawB620S9s8+yi3YVUXFrOwnGR4oYH0kb48HliOU+/+77pd5ze56Tfi+cqsRYhr93ANB533KgEi+azr+N97efb8b/umXwmqgBAFplbjH9u9W+DJdb9atapYjvelotfuC5tajXWv95JzLsVUY8eundtz8l/yUrAAAAAHQHgUwAgDYZy9DcuLyePkY3W8nw82nIBKDX7epwIHP/bgfASDed4SDh4cXBCGQeP1nGDXN5gjHX7XfQD6BfFEUR/+8zqnHFGfm/m/3BP27GDXOb2ccFABhEsxnWLzRkPrlnn1eJn792KPb1yO/q2w5WY/u23pgrAAAAAKdOIBMAoE0aw+lj9HtL0Op6+s830obbcQGglXbVO/v5+/dYLiJdjoOEOQ409oK/+Vwz1jJkYp5+VhHn7HDQD6Cf1CpF/MBza3H+zvz///1X/mEzDj2oKRMAINXsQvr6xVSPhAw77eztRfzsS2staZLP6bydRVx7UXfPEQAAAIA0Vn8AANqkMZy+maoh84mN1CKqFZvWAPS2TjdkXrzLs5R0OQ4SzmU40NjtNppl/NUdeRrKtGMC9KfGcBE/ck0tJkbyjrvRjHj732/EPUf7/3kLANAqx0+W8dBK2hi76hE7R6zHbdW2WhHf+6xa/LtnVmO4S5dCvvtpVfuVAAAAAH1OIBMAoE0aQ+ljLK2lj9HNVhMDmaMZfscA0GmdbMg8e3vE9m0OC5FuaiJ92XFuABoyP3ZPMx5eTR/n3B0RV57pzy5Av9rTKOKHX1CLkVrecZfXI376g+uxeKL/n7kAAK1wOMPahXbM0/OSC6rxtpfW4syxTs/kyz3znCKuONNxPAAAAIB+ZwUIAKBNRmoR1cQ91eW1/j4gt7KR9vPVBTIB6AMTHWzI3L/bUhF5nDUWyS0Fdx0tY6PZ399/331bM8s41+2vRlE4wAnQzy6YqMT3P6cWuYt25pcj3vrBjTiRuCYDADCIZjMEMqcn/H3+dE1PVOLnXz4Uzzq3O36HtUrE9U/LfIsKAAAAAF3JKTsAgDYpiiIaw2ljLGvIfEKjte7YcAWAFLs6GMi8eLdnKXlUK0XsS2x4WG9G3Husf8MhdzzcjNseTv/5xoYjXjBlmRdgEDz97Er8P1+TeOPBY/jcI2X8l49sxGafX4QAAJDb3EKGQOa4v9OnaAwX8Z+eW4vXXVnNfnnJqXrVJZU4c8z6KgAAAMAgsKoHANBGyYHM9f4+GLeSGMjUkAlAP9hV79xn7xfIJKOpxEBmRMRchqaJbvXu2/O0Y770gkqMuJgEYGC8/KJqvPqy/Nt7n7ivjHfetBll2b/PXgCA3HKsW0xpyExWFEV8w6XV+KkX12JipDNz2FWP+KbL81+eAgAAAEB3EsgEAGijxlDapupSHzdkbjTLWNtMG0MgE4B+sK1WRKMDz7ThaiQ3GsKXmhbIfFyPrJbxkbvSA5mVIuLrLnbYD2DQ/MsrqvG88/Nv8f3VHc34P7fluTAAAKDfrW+WcfextHWLkVrEmWOZJkRctrcS7/jaoXjKZPvXOL/zqdWoJ+4DAwAAANA7BDIBANpoLLUhcy36tqlgNbEdMyJiVDMQAH1iV739z7SLdhVRq3iWks90hoaH2YX+/O773js2YzPDj3b1uZXY2/DnFmDQVIoi/v2zqnHZnvzPgN/99GZ89G6hTACAJ3PPsTI2Er827dtZRKXw9/qcxkeK+LEX1uKbL2/fkbj9u4t4/j5H8AAAAAAGidUgAIA2Gk28GXWzjDiZ2CLZrXIEMjVkAtAvJurt/8yLdzv8RV7n7ywi9b+qucWy7y4kWdss432fyxN0uW6/5V2AQTVcLeKHnl+Ls7fnHbeMiF++cSNue0goEwDgicwtpq9XTGW4zIqvVq0U8S+vqMWPXFNLvix3K7776VXBWgAAAIAB48QOAEAb5dj0W1pLH6MbrWykb1yPCmQC0Cc60ZC5f7dlIvKqDxVxZmJI5NjJiEdW88ynW3zocDOOnUwf58JdRVzagmY0AHrH9m1F/MgLhmLHtrzjrm1GvO1DG3H/8f66FAEAIKccgczpcX+vb6Wnn12JX7h2KC7a1brf84umK3GxdVUAAACAgWNFCACgjRoZApnLa/15GC5PQ6aNawD6w64ONGTu15BJC0yPpy8/5jjg2C3Ksox335ancewV+ytRaF8AGHhnjhXxlmtqMVzNO+6xkxE//cH1OHayf57DAAA5zS5oyOwFk40i3vqSWnzdxfmPyI3UIv7VFZm/iAMAAADQEwQyAQDaqDGcvrG63KcNmTkCmRoyAegX7W7I3FWP2D3qABj5TWVoeshxwLFbHJov4/DR9J9nfCTiOedZ2gXgUft3V+L7nl2L3N/m7j8e8bMf2oi1zf55FgMA5FCWZfIFUpUiYt9O63HtMFQt4vVfU4s3PbsaI7V8437LgWpMtHkdFwAAAIDu4NQOAEAbNTIEBpczBBe70cpG+uG+esZNVADopHYHMrVj0irTGZoe+qkh8923b2YZ52svqsZQ1Z9bAP7Z1edW4vqn5W/nufWhMv7rjZvRLPvneQwAkOqhlYilxAtUz9oesa3m7/bt9Lx91fi5lw3FeTvSf+9njUW8Yr9jdwAAAACDysoQAEAbjQ2nj7G01p8H4PI0ZNq4BqA/TNTb+3n791giojWyNGQuNjPMpPMeOF7GJ+5N/y5fq0S8/CJ/ZgH4aq+8pBrXteBQ+Efubsbv35znUgEAgH6Q4/Ko6XF/t++Ec3cW8faX1+KafWm//+96Ws1lWQAAAAADzOoeAEAbNTIEBlf6tSEzw89Vz9BACgDdYHebGzIv0ZBJi+yqR+zYljbGA8cjVtd7/1KSv7xjM3L8FNfsq8T4iD+zADy266+sxjPPyf+c+N+3NuOv7xTKBACIiJhdSP8bfo5LrDg9I7Ui3nh1Nb7nqmrUTuPk3L+4tBJXnePIHQAAAMAgszoEANBGjSwNmeljdKMch+xHBTIB6BPjbWzIrBQRF0w4AEZrFEWRfMCwjIi7jvZ2IHNlvYy//Xyeps9WNJ8B0D+qlSL+47NrcfGu/N/vfvOTm/GJ+/qjuRoAIMXcYvp3oinrcR1VFEVce1E1fualtZhsbP19V51dxHdcUW3dxAAAAADoCU7vAAC0UWM4fXN1ea23D6M/njwNmTavAegPtUoROxNbBbdqaryIbTXPUFonR+NDjuaJTvrAbDNWN9LHOThZxPSEJV0Anti2WhFvvqYWZ5zCwfKtaJYR/+UjG/G5R4QyAYDBlmOdYlpDZle4aFclfuHaoXjlJZUYeoIll9GhiO96WjX+0/NqUa34dwcAAAAw6GqdngAAwCAZy9CQudy3DZnpY2jIBKCf7KoXcfRk60No+3c7QERrTWdofJhd7N1A5mazjPfcvpllrOv2a2AAYGvGR4r44RcMxVvevx5LGdeSTmxEvPWDG/GzLxuKyYbvkQDA4FlZL+PB5bQxxkciJuq+S3WLseEivutptXjlJWW8947NuPtYGfcfL2OzGTE9UYkLdhXxoqmKf2cAAAAAfJFAJgBAG+UIDC6v9+5h9CeyspH+c9V9uwWgj0zUI2YXW/85Fwtk0mI5GjLnejiQ+an7y3hgKX2cMxoRV53tzysAW3fujiJ+8Hm1+Mn/uxEbGUstF088Gsr8mZfUojHs2QQADJbDGdYocqyVkN+e0SL+1VNtNgIAAADw5CqdngAAwCCpVYoYSdzHy9lq0E1SGzJrlYihqg1sAPrH7jbduH7JbstDtNY5O4oYSvzP7PBiGZvN3gxl5mrH/Pr91ahWfN8F4NQcmKzE9z4rf8Py3UfL+Lm/34j1zd58PgMAnK7ZBYFMAAAAABh0TtwBALRZI7ElczkxuNitVhJ/rhztowDQTSbqrf+MseGIs7a3/nMYbLVKEeftTDtouLYZcX+Glsl2O7zYjM88mH5Qc6QW8eJpS7kAnJ7n76vGd1yRP5T5j/Nl/OrHN6MshTIBgMExm6Ehc3pCIBMAAAAAeplTPAAAbTY2nLbJurzWn4fcUhsy64nNowDQbXa1oSHz4t1FFIUDYLTedIbmh7mFZoaZtNd7bs8z5xdPV6KR+PcIAAbbqy+rxMsuyL8t+H/nmvFHh/K0QQMA9IK5HIHMcce1AAAAAKCXWeEDAGizxnDa+5fX8syj26ysp21gjw45oA5Af2lHIHP/bktDtMdUhuaHHAce2+nYyTI+eDg9kFlExNfvz99qBsBgKYoi/p+rqvG0s/J/x/zjmWb83eeFMgGA/rfZLOOuxPWJ4WrEWdszTQgAAAAA6Ain7gAA2iw1kLm68eiGb79Z3Uh7f30ozzwAoFtM1Fv/Gft3u9CA9sjRkDnbY4HM993ZjLUM2ZSnn13E2dv9WQUgXbVSxPc/pxZTGZ7LX+lXP74ZNz/Qe23WAACn4t7jZawnfuU5f2cR1Yq/5wMAAABALxPIBABos0aGJseV9QwT6SJlWcZq4s80KpAJQJ/ZPdr6g1kXC2TSJvtyBDIXeieQudEs47135mkKe4V2TAAyqg8V8cPX1GJ35ss/NsuIn//wRhxeFMoEAPpXjrWJHJdWAQAAAACdJZAJANBmqQ2ZERFLa+ljdJMTGxGpW9j1DEFXAOgmO7ZFtPKy/HO2R4wNe37SHo3hIs5opI2xeCJi8URvhDI/enczHllNH+e8nUVccYY/pwDktXu0iB9+QS3qtbzjrqxHvPWDG/HIam88rwEATtXhxfTvOVMT/p4PAAAAAL1OIBMAoM0aGZocl9f762Db6kb6GBoyAeg3laKIiZHWjb9/t2Uh2ivHgcO5HmnJfPftedrBXrG/EkXhoCYA+U2NV+IHnleLaubHzEMrj4YyV/ts7QoAICJPQ+aUhkwAAAAA6HlO3gEAtFmOJqp+a8hcWU8fI3erAwB0g1311h3Q2r/H4S/aa3o8fSlyNkMTRavd9lAz7ng4fZ5jwxHX7LN8C0DrXHlmJf7tM6rZx51dKOMdH9mIzWb3P7cBALaqLMss6xICmQAAAADQ+5zoAQBos8Zw+hgrfRbIzNGaMDpkAxuA/rNrtHVjX7zbs5P2ynHgsBcaMt+TqR3z5RdWYlvNn1MAWuslF1TjWw7k3y781P1l/OYnN6Msu//ZDQCwFQsnIo6dTBvjrLGIuv0sAAAAAOh5ApkAAG3WyNKQ2V+H2bI0ZA6ljwEA3aZVDZnD1Yh9Ox3+or2mJtL/m5tdzBN2bJWHVsr4yN3pc6wUEV97cf7GMgB4LN92sBovmMq/Zfi+zzXjz2/t7mc3AMBW5bgkKsfaCAAAAADQeQKZAABt1sgQHFzOEGDsJqsZfp5RgUwA+tDESGsOaV20q4hqxQEw2mvvaPp34fuOR5zc6N7LSd57x2Y0M0zv2edVYs+oP6MAtEdRFPGGZ1Tj4GT+Z8/v37wZf3/XZvZxAQDabXYxQyBz3DEtAAAAAOgHVvoAANpsbDh9jOW+a8hM/3nqQw6sA9B/dtVbM+7+3Z6btF9RFDGd2ATRLCPuOtqd34VPbpTxN5/L0wL2iv2WbQFor6FqEf/pebU4d0f+sf/rjZvx2SOaMgGA3jaboSFzetyaHAAAAAD0Ayd7AADarJEhONh3DZkb6WPUa+ljAEC32VVvzSGt/bstCdEZ+zIcPJzL0EjRCh883Izja+njXLyriEv2+DMKQPuNDRfxI9cMxfhI3nE3mhFv+9BG3HusO5/hAABbMbeYfsHEVOJFVQAAAABAd3CyBwCgzRoZGjKXMhz07iYrGQKmo0PpYwBAt9k12ppx9+9x+IvOyNEEkaORIreyLOPdt2dqx7zEki0AnTM5VsRbrqnFtmrecZfWIn76hvVYPNF9z3EAgCdzYqOM+4+njbF9OGJ3Pc98AAAAAIDOcroHAKDNRmoRlcRz6Mtr/XV4bXU9/eepZ2geBYBu04qGzD2jrWvehCcznaEJohsbMj/zYBl3H02f1656xLPPs2QLQGddtKsSb3pOLXn96is9uPxoU+bJje57lgMAPJHDi2WkfoOZGi+iKKzJAQAAAEA/cLoHAKDNiqKIscSWzGUNmV9FQyYA/agxFDGcuZ3o4t0OftE55+4oopa4Ijm3WEaz7K4gx3tu38wyztdeVI1a7vQLAJyGZ5xTie9+euYvohFxx8Nl/NKNG7HZ7K5nOQDAE8lxOdRUhkuqAAAAAIDuIJAJANABjcTw4HKGRsluspohkFmvpY8BAN2mKIqYGMk75v7dloPonKFqEefsSDuAeGIj4sGlTBPK4L7jZXzyvvTv58PViJdd6M8nAN3j6y6uxqsuyf9s+tg9ZfzezXkuMwAAaIfZhfS/90+PC2QCAAAAQL9wwgcAoAMaw2mbrkt91pC5upG2kV1ExIhAJgB9alc972Gt/Roy6bAcBxBnMzRT5PKXt29Gjtk8f18ldo748wlAd3ntldV49nn5n0//57ZmtoZpAIBWy9GQOa0hEwAAAAD6hkAmAEAHpDZkrqxHlGX3HEJPtZLYkFkferRBDAD60a7RfM+4ahFxgcNfdFiOA4hzC80MM0m3vFbG383mmcsr9luqBaD7VIoi/sOzanFJCy71eOenNuMf7umOZzoAwOPZbJZxODGQWatEnLPDmhwAAAAA9AunfAAAOiC1IXOjGXGyj0oEVhMDmaOJAVcA6Ga76vnGmhovYlvN4S86aypDQ2aOZooc/m62GSc20sd5ymQR+8Yt1QLQnbbVinjzNbU4ayzvuGVE/JePbsQdDwtlAgDd64Gl9D2583YWUatYkwMAAACAfuGUDwBABzSG08dYXksfo1usrKcdqK8P2cQGoH9N1PM95/bv8cyk83IEMmcXOh/I3GyW8Ze357kl5bpLqlnGAYBW2bGtiB9+wVBsz7Cm9aXWNiN+5oMb8eBS55/tAACPZXYx/fKI6QxrIQAAAABA9xDIBADogLEMh9eW1vrnoNpqYqvQaC3PPACgG+0ayTfW/t0Of9F527cVsWc0bYyHVyOOn+zs9+FP3FfGg8vp45w5FvE1Z/mzCUD3O3v7o02ZQ5l3F4+ejPjpG9ZjuY/WugCA/jGX4VKo6Ql/7wcAAACAfiKQCQDQAY0MjY4r6xkm0iVSf5b6UJ55AEA32j2a78DWxbstBdEdcrRkzi12NrTxnkztmF9/cTWqFQczAegNl+6pxBuvzn8z1r3HI/6/m/I8WwEAcsqx/pBjHQQAAAAA6B5O4QEAdEAjS0Nm+hjdYH2zjI1m2hijGQKuANCtJup5nnNjwxFnjWUZCpLlaIaYzdBQcfqf3YxD8+mfX69FvPgCS7QA9JbnnF+J115ZzT7u380249aHEheJAAAyy7H+IJAJAAAAAP3FaR8AgA7IEchcXutsI1AuOZo+NWQC0M8mRvKMs393EUXh8BfdYWo8fVlytoMNme+5PU9Y5MUXVFwuAkBP+oZLKvG1F+XfZnz/5wQyAYDusXiijIUTaWNMNiIaw/7uDwAAAAD9RCATAKADxjIcul7OEGTsBqsZfo5RgUwA+lh9qMjyrNu/2zIQ3WM6QzPEXIcaMhdPlPGhw+lhkSIirrs4f7sYALRDURTx3U+vxlVn5w0X3HhPMzab/XEJGQDQ++YyXAalHRMAAAAA+o+TeAAAHZCnITN9jG6wspG+mV2vZZgIAHSxiXr6GPt3O/xF95gcS/8Od8+xMtY32x/YeN+dzVjPUN511TlFnLndn0sAele1UsSbnlOLCyfyPc9W1vMEHwAAcshxGZRAJgAAAAD0H4FMAIAOaAynb74urfXH4bQcDZn1DI2jANDNdtfTn3UXC2TSRSpFkXwgcbOMuPtYe78Tr2+W8d47N7OM9Yr92jEB6H0jtSLefE0t9ozmG3Nmvj/WvACA3jeb4aKI6QlHswAAAACg31j1AwDogMZQ+hjLGYKM3WAlw88xmuH3CQDdbF9icO2cHXkuhICcpjK0ac1maKo4FR+5uxmLJ9LH2beziIOT/kwC0B921Yv4kRfUsq3PzBzJUEUNAJBBjobMaQ2ZAAAAANB3BDIBADqgMZw+xnKfNGSurKf/HHWBTAD63FPPSFvCufpcS0B0n9SGzIiIuQxNFVtVlmW8+/Y8AZHr9leiKBzIBKB/nL+zEj/4vFrUMnztvGW+jM1mf6x7AQC96+RGGfceT/tOMjoUsbeRaUIAAAAAQNdwGg8AoANqlSJGamljLK/lmUunreZoyKw5zA5Af7t8soixhAsdXjJdzTcZyGQ6Q0NmjqaKrbrt4TI+90j65+3YFvH8fZZlAeg/TzmjEm94Rvr3zuX1iMNHBTIBgM66+1gZqXdETI0XLmQCAAAAgD7k5A8AQIc0ElsdlzIEGbvB6kb6GBoyAeh3I7UiXnfl6R1uf/75lThzu4NfdJ/zdhRRSfxPc3axjLJsT2Dj3bflacd8+YWV2OZCEQD61Aunq3FwMv05NzMvkAkAdNZshkugclxGBQAAAAB0H4FMAIAOGRtO24RdXuuPg2mr6+k/x6hAJgAD4MXTlfias0/t+8PUeBH/7zO1Y9KdttWKOCcxLLyyHnFkOdOEnsBDy2XceE96ILNaRHztxf5MAtDfDk6mbz/OzOe5CAEA4HTNLabvX02NC2QCAAAAQD8SyAQA6JDGcNr7l9fyzKPTVjI0fdaHbGgD0P+Kooj/9NxavOzCrS3nXLKniDc/vxYjmvjoYlMZmiJyHJB8Mn9152Y0M3zMc86vxK66P5MA9LcDGRoybzlSRrNNLdgAAI9lTkMmAAAAAPA4BDIBADokNZC5uhGxmeNUeIetZghkasgEYFAMVYv4t1dV403Pqcalex77QNeuesS/eXo13vqSWuxtOPRFd5vO0BQx2+JA5omNMv7mc3laul6x33IsAP3v4t1FDCcWQi+tRRxuw6ULAACPpVmWyRdAVYuI83ZYmwMAAACAflTr9AQAAAZVY6iIiLTN3JX1iO3b8synU1Y20g/X1X2rBWCAFEURzzu/Gs87vxr3Hivj7qNlrKyXUUbEpXsqcfb2R18DvSBHQ+bsQjMiElMfT+CGuWYsZWinv2R3ERfvFsgEoP8NV4vYv7uIQ/Npaz4z82VMT2SaFADAKZhfevRi1BTn7ihiqGqNDgAAAAD6kaPrAAAdktqQGRGxvNb7gczUhsxt1YhqxYY2AIPpnB1FnOOmfXrYVIaGzNTGiidSlmW85/bNLGNdd4kwJgCD48BkJQ7Npz1DD8034xWXtO7SBQCAxzObYa0hxyVUAAAAAEB3cgoIAKBDGkPpYyytt+7webusJAYy6xl+jwAAdMb4SBETI2ljzC9HLK+15nvxzQ+Ucc+x9HF21SOuPtdSLACD4+BkegDhliNlNMveX/sCAHrP3GIzeYzpDJdQAQAAAADdySkgAIAOGRtO34hdXsswkQ5bTQyVjgpkAgD0tOkMjRGtasl8d6Z2zK+/uBo1re4ADJCLdxcxlLgLubQWcddRgUwAoP1mFzI0ZApkAgAAAEDfEsgEAOiQxnD6GP0QyExuyKzZ0AYA6GU5Dii2IpB577EyPnV/+rjD1YiXXWgZFoDBMlwtYv/u9Gf8zLxAJgDQfjnWGaYyXEAFAAAAAHQnJ4EAADqkkaMhM7FdshusbqS9v64hEwCgp+U4oJijueIr/eUdedoxXzBVie3bHMIEYPAcmMwRyGxmmAkAwNYdP1nGQytpY+yuR+ywFgAAAAAAfUsgEwCgQxoZgoRLPd6Qudks40RiIHNUIBMAoKdNj6cvUeZuyFxeK+MDs3kCINfttwQLwGA6OJn+DJyZL6NZ9v6FZABA78ixxjCtHRMAAAAA+prTQAAAHTI2nD7G8lpvH0hLDWNGRNSHbGoDAPSyM8citlXTxrjraBkbzXzfjd//+WaW76pXnFHE+TstwQIwmC7eXUQt8TF4fC3i7qO9vf4FAPSWHIHMqXF7VwAAAADQz5wGAgDokEaGIOHyeoaJdNBKhvlryAQA6G3VShH7Eg8qbjQj7j2WJ6yx2SzjL2/fzDLWKy5JTJoCQA/bViti/+709a+ZeYFMAKB9ZhcyBDInHMcCAAAAgH5mBRAAoEMaWRoy08fopJX19E3tei3DRAAA6KgczRE5DkxGRHz83jKOrKSPc9b2iKefpREDgMF2cDL9WXhovplhJgAAWzOboSFzWkMmAAAAAPQ1gUwAgA4ZqUVUEvdjl9Z6uyHgxEb6GKMZmkYBAOis6Yn073RzGQ5MRkS8O1M75nUXV6NS+K4KwGA7MJm+FXnLkTLKsrfXwACA3rC+Wca9x9K+d4zUIs4YyzQhAAAAAKArCWQCAHRIURQxltiSubyeZy6dspJh/vWh9DEAAOisHA2ZOQKZn19oxi1H0scZHYp40bSlVwDYv7uIWuIj8djJiLsTgxEAAFtxz7EyNhLLufeNFy5oAgAAAIA+51QQAEAHNRLDhMs93pC5miGQOSqQCQDQ8/aNF5F6VHF2Ib096z23JZ66/CcvuaASdU3uABDbakXs353+TJyZ7+01MACgN8wupH/nmM5w6RQAAAAA0N0EMgEAOqgxnLYpu7yWaSIdsrKevrHtoDsAQO8bqRVx1va0MY6vRTy8evrvXzxRxofuSg9kVoqIr7+4mjwOAPSLy/emr90cms9zaQIAwBOZXcwQyJywbwUAAAAA/U4gEwCgg5IbMtcjuQWok1Y30scYraWPAQBA502Npy9VziUcnPzrO5uxkSHrcdXZRZwx5vAlAHzBwcn0Z/zMfHoTNgDAkzmcIZA5pSETAAAAAPqeQCYAQAelNmRuNCPWNjNNpgNW1tPHqCeGWgEA6A45GiTmFk7v4OT6ZhnvvSPPF+tXXqIdEwC+1CV7iqgl7kgeOxlxz7E88wEAeCxlWcbsaa4rfEGliDh/p0AmAAAAAPQ7gUwAgA5qDKePsbSWPkanrKyn3zQ8OmRjGwCgH0xnaJCYXTy9isu/v6sZR08mf3xMjRdx+V7fTwHgS22rFXHRrvTn46H5DFXWAACP48hKxHLiRaJnb3/0uw8AAAAA0N8EMgEAOmgsQyBzOUOosVNWNWQCAPBPpjrUkFmWZbz79jwBj1fsr0RROHgJAF/p4GT683FmvnfXwACA7nc6awpfaXrcMSwAAAAAGARWAgEAOqiRod1xuacbMtPHqNfSxwAAoPMmRiJ2bEsb44GliNVTvLDks0fKmM1w6HLHtojn7bPcCgCP5cBk+jNyZr4ZZSmUCQC0xtxi+veMHJdNAQAAAADdzwkhAIAOauRoyOzhQObqRtrmdrWIGK5mmgwAAB1VFEVMj6cdXCwj4vDRU/uO+e7bN5M+8wuuvagSw1UHLwHgsVyyp4jUx+TRkxH3HsszHwCArzS72EweYypxXQMAAAAA6A0CmQAAHZQjkLm01rvNAKkNmfWhRw/uAwDQH6YzNEmcStvl/FIZ/3Bv+vfpWiXi2ovcFAIAj2ekVsRFu9Of8zNH0oMSAACPZe4U1hMeT451DQAAAACg+wlkAgB00NhQ+sbscmKosZNWE+c+OpRnHgAAdId9GZokTuUA5V/duRnNDPebPPe8SuyqO3QJAE/k4GT6s/LQfO9eTAYAdK/ltTIeXE4bY3wkYnzE2gAAAAAADAKBTACADsrRkLm8lj5Gp6yspx2iq2cItAIA0D1yNEnMLW7tO+bqehnv/1yelq1XXGKZFQCezIG96c/LmflmlKVQJgCQ1+EtriU8Ee2YAAAAADA4nBQCAOigxnCOhszePYR2YiPt/aO1PPMAAKA7nLO9iKHEFcvDR8vY3ELt5f+da2Zpm790TxEX7rLMCgBP5pI9RVQTl8IWT0TcdzzPfAAAvmA2QyBzalwgEwAAAAAGhZNCAAAd1BhKH2OpRxsyy7KMlcQD8PUMvz8AALpHtVLE+YkHGNc2I+5/kqBGsyzjL2/fTPqcL3jF/mqWcQCg39WHirhoV3pQ4dB8noZrAIAvmFvI0JApkAkAAAAAA0MgEwCggxrD6WMsr/VmQ+baZsQWioue0OiQzW0AgH6To1FidvGJgxqfvr+MezO0a+0ZjXjWub6TAsBWHZhMf27OzPfmWhgA0L2yNGROOIIFAAAAAIPCaiAAQAfVKkWM1NLGWO7RhszUdswIDZkAAP0oR6PE3JMcpHx3pnbMr7u4GtWKQCYAbNWByfStyZn5ZpSlUCYAkMdGs4y7j6Z9txiuRpw1lmlCAAAAAEDXE8gEAOiwRmKocDlDsLETVjPMe1QgEwCg70xPZGjIXHj8g5T3HC3j0w+khzi2VSNeeoHlVQA4FZfuKSL1LoOFExH3Z2i6BgCIiLj3WBnrzbQx9u0sXNgEAAAAAAPEiSEAgA5rDKdt0C6t9WYjwMpG+rzrNZvbAAD9Zl+LGzLfc0eedswXTldi+zbfRwHgVNSHirhwV/rz89B8YmoCAOCfPNEawlZNZbhcCgAAAADoHQKZAAAdNjac9v4VDZkAAPSR0aEizmikjbF4ImJh9asPVB4/Wcb/nc0T4Lju4mqWcQBg0BycTA8szBzpzQvKAIDuM7uQ/r1iOsPlUgAAAABA7xDIBADosNRQ4cp6xGaz9w6h5QiS1gUyAQD60nSGZonHarh4/+ebcTJDQeaVZxZx7k6HLQHgdByYTN+enJlvRln23noYANB9NGQCAAAAAKdKIBMAoMPGhtM3aXuxJXNlPX2De3TIBjcAQD+aGk9ftpz9igOVm80y/uqODGnMiHjFfu2YAHC6LttTRCVxSeeR1Yj7l/LMBwAYXGVZJgcyi4jY59ImAAAAABgoApkAAB3WGE4fY3ktfYx2W83RkFlLHwMAgO6TpSFz4csPVH7snjIeWkkeNs7ZHnHlWQ5aAsDpqg8VcWGGZ/3MfDPDbACAQfbIasSxk2ljnLn90e83AAAAAMDgEMgEAOiwxlD6GMsZ2ibbLUerZz3D7w4AgO4zNZ5+kHF28ctDGu++PU875tfvr0alcNASAFIcmMwRyOy99TAAoLvMJrZjRkRMjzt6BQAAAACDxqogAECHNYbTD6At9WJD5kb6JveoG4cBAPrSntGIscQm+fuPR5z8p++cdz7SjFsfSv/+2RiKeOGUJVUASHVgMv15emi+GWUplAkAnL65hfTvEjkulQIAAAAAeovTQwAAHZZ60DwiYjlD22S7acgEAODxFEWRfKCxWUbcdfTRg5Xvua35JK/empdeWIm6S0EAINlle4uoJD5SH1mNeHApz3wAgME0l6Ehc2rCOgEAAAAADBqBTACADmtkONC9vNZ7bQCrGQKZowKZAAB9azrDgcbZhTIeWS3jw3enBzIrRcTXXVxNHgcAiBgdKuKCDM/6Q/N5Ll0AAAbT7GL6d4lpDZkAAAAAMHAEMgEAOqyRoyFzLX2MdlvdSA+RjtQyTAQAgK6U2pAZETG7WMZf37kZGxmyGs88p4jJhkOWAJDLgcn05+rMfO9dUgYAdIfV9TIeOJ42xo5tEbvqeeYDAAAAAPQOgUwAgA7LEchcGsCGzHotolI4EA8A0K9yBDLveLiMv74zT3PWK/ZrxwSAnA5Mpm9TzhxpRln23roYANB5dx0tI/VbxNR4EYW9KgAAAAAYOAKZAAAdNjaUvlG7nBhu7ISV1EDmUJ55AADQnc7dUUQtcfXy8wtlHDuZPpcLJoq4bK8DlgCQ02V7iqgkPl4fWol4cDnPfACAwTK7kH6pQ47LpAAAAACA3iOQCQDQYTkaMpfX0sdot9X1tI3uUYFMAIC+NlQt4rwd3XGw8br9FY0XAJBZY7iI6Qwhhpn5PG3YAMBgmV1MD2ROT1grAAAAAIBBJJAJANBhI7VIbgNYWkvfNG639IZMm9wAAP2uG5omxkcinne+ZVQAaIUDkzkCmb23LgYAdN5chkBmN6xbAAAAAADt5yQRAECHFUURjcS2x+XEcGMnrG6kvX+0lmceAAB0r6kuaJq49qJqDFU7Pw8A6EcHJtO3Kg/NN6MshTIBgK3bbJZxODGQWatEnLPDegEAAAAADCKBTACALjA2nPb+5R5ryFzfLGNtM22MemKIFQCA7jfd4aaJWiXi2ossoQJAq1y+t4jUp/1DKxHzy1mmAwAMiPuXInmf6vydRdQqApkAAAAAMIicJgIA6AKjw2kbtstrmSbSJicS2zEjIkaHbHIDAPS7TjdkPv/8SoyP+N4JAK3SGC5iOsPzfma+mWE2AMCgmFtI/+7Q6UukAAAAAIDOEcgEAOgCY4ltj8vrEWXZOy2ZK+vpY2jIBADof2PDRewZ7dznX3eJ5VMAaLUDkzkCmb2zLgYAdN7cYvp3h05fIgUAAAAAdI4TRQAAXaCR2JC50YxY28w0mTZYWU/f6K7XMkwEAICul6M163RcvreICyYsnwJAqx2YTH/eHtKQCQCcgtkcgUwNmQAAAAAwsJwoAgDoAo3h9DGW1tLHaJfVDA2Zo0M2ugEABsF0hw44vmJ/tSOfCwCD5vK9RaQ+7Y+sRMwvackEALZmdkEgEwAAAAA4fQKZAABdoDGUPsZyhtbJdlnZSB+jnuF3BgBA95vqQEvl3tGIZ5zjYCUAtMPYcJEl0DBzREsmAPDkFk+UsXgibYwzGhGNYesGAAAAADCoBDIBALrAWIZN2+WeashMD4+OCmQCAAyETjROfP3+alQrDlYCQLscmEx/7h6a753LygCAzpnL0Y45Yc0AAAAAAAaZQCYAQBdoDKeP0VuBzPQx6kM2uwEABsFko72XcYzUIl56gWVTAGing5Ppz95b5jVkAgBPbnYxQyBz3LoBAAAAAAwyK4QAAF0gRyBzaa13WgBWNtLHGK2ljwEAQPerFEVbWzJfNF2JRoYGewBg6y7bW0Tq0/fB5Yj55d5ZHwMAOiNHQ+Z0G9cpAAAAAIDuI5AJANAFGhnaHpcztE62y+p6+mZ3vY0tSQAAdFY7A5lff3G1bZ8FADxq+7Yi9mV43s9oyQQAnsTsYvr3hakJgUwAAAAAGGQCmQAAXWAsQ0Pm8lr6GO2ykiE8OpohxAoAQG9oVyDz6WcVcc4O3zMBoBMOTKY/g2+Z15AJADy+kxtl3Hc8bYzGUMTe0TzzAQAAAAB6k0AmAEAXyNOQ2TsHzlazBDLTxwAAoDdMt6l54hX7tWMCQKccmEzftjykIRMAeAJ3HS2jmbidNjVeRFG4zAkAAAAABplAJgBAF2hkaMhc6qmGzPTw6Egtw0QAAOgJ5+0sotLis47n7oh46pkOVAJApxzYm/4cfnA54qHl3rm0DABor7nF9O8JU226NAoAAAAA6F4CmQAAXSBHIHN5rXcOm6U2ZA5XI4aqNrwBAAbFcLWIc3e09vvfdfurGi4AoIO2byti33j6s3jmiJZMAOCxzS6k76VNZ/i+AgAAAAD0NoFMAIAuUKsUyY2Py73UkLmR9v66dkwAgIEz1cIDj2PDES+cslQKAJ2WoyXz0HzvXFoGALRXjobMaQ2ZAAAAADDwnDICAOgSo0Np719ObJ1sp9X1tA3v1N8VAAC9Z6qFBx5fekElttUcqASATjs4mb51OTOvIRMA+GrNskwOZFaLiHN3WD8AAAAAgEEnkAkA0CXGhtM2cJfWeuf2/5XE8OjIkM1uAIBBM92ihsxKEfF1F1dbMjYAcGoun0x/3j+wFPHQSu+skwEA7fHgUsSJjbQxzt1ZxFDVHhUAAAAADDqBTACALtFIbH1MDTm202riXEdreeYBAEDvmGpRIPPqcyuxt+EwJQB0gx3bijh/Z/pzWUsmAPCVZhPbMSNad1kUAAAAANBbBDIBALpEYzjt/SvrEZvN7r/9v1mWsZp4A3E9MbwKAEDv2TlSxK56/nFfcYklUgDoJgcytGTOzHf/GhkA0F5zC+kXNkxPCGQCAAAAAAKZAABdozGcvonbCy2ZJxLDmBERo0M2vAEABlHulswLdxVxyW7fLQGgmxycTN++nDmiIRMA+HJzGRoyc69LAAAAAAC9SSATAKBLjCU2ZEZELK+lj9FqOUKjGjIBAAZT7iaKV+yvRFE4TAkA3eTyvenP5vuPRzy8oiUTAPhnswsCmQAAAABAHgKZAABdopEhZLi83v0HzU5kCGSOCmQCAAyk6fF8y5kTIxHPOc/yKAB0m50jRZy3Mz3sMDOvJRMAeNTxk2U8vJo2xp7RiO3bBDIBAAAAAIFMAICu0RhO38Rd6oWGzI300Gi9ZsMbAGAQ5WyiuPaiagxVfa8EgG50cDJDIPNI919cBgC0x9yidkwAAAAAIB+BTACALpGnITN9jFZb0ZAJAMBpOmMsYqSWPs5QJeLlF1kaBYBudWBv+nNaQyYA8AWzCwKZAAAAAEA+Th0BAHSJsQwNmctr3X/z/2qGQGZdIBMAYCBVK0Xs25n+vfn5+yoxPuIgJQB0q8szNGTedzzikdXuXysDAFpvNkND5vSEI1YAAAAAwKOsFgIAdInGcPoYy2vpY7Taynr6pvfokMPzAACDanoi/bvgdfstiwJANxsfKeK8HenP/Fu0ZAIAETGXoSFzWkMmAAAAAPBPnDwCAOgSOQKZSwPSkDmqIRMAYGA985y0Jc2Dk4VWCwDoATlaMg/Nd/9aGQDQWuubZdxzLO07Qb0WMTmWaUIAAAAAQM9z8ggAoEs0MrQ+LmcIO7baSoY51gUyAQAG1lPOKGJ85PTf/6pLq/kmAwC0zMEMgcwZDZkAMPDuPlbGZuIdDfvGi6gUGjIBAAAAgEcJZAIAdImxDA2Zy2vpY7Ta6kZ6M8FozaY3AMCgqlaKePVlpxeqfNa5RVx1tiVRAOgFBybTn9n3Ho9YWNWSCQCDbHYh/bvA9IR9KQAAAADgnzl9BADQJUZqEZXE/dzl9e4/YJajIXNEQyYAwEC7bn8lnn3eqX15HhuOeP3X1Fo0IwAgt/GRIs7ZkT7OzBEtmQAwyOYW0/fOpsYFMgEAAACAfyaQCQDQJYqiiEZi0HCpFxoyMwQyRwUyAQAGWlEU8e+fWYurz93agcjzdhTx9pcNxa66A5QA0EsOZmjJnJnv/gvMAIDWmdOQCQAAAABkJpAJANBFGsNp719Z6/4DZiuJLZ6VImJbNdNkAADoWfWhIn7gubV407OrsWf0sV+zY1vEqy6pxM++rBZnbXd4EgB6zYHJ9Of3zLyGTAAYVGVZxmxiQ2alePSiJwAAAACAL6h1egIAAPyzxnAREae/MTwIDZn12qONSAAAUBRFPG9fNZ57fiXuPRZx84PNWNuMGKlF7B0t4qlnFjFU9d0RAHrVgb2ViNhMGuOeYxGLJ8oYH/GdAAAGzZHliJXEfalzthexreZ7BAAAAADwzwQyAQC6SGMo7f3L64/e9tvNgcWVjbT31xN/RwAA9J+iKOLcnRHn7lSlDgD9ZKJexDnbI+49njbOzHwznnu+7wkAMGhS2zEjIqYmunfPDQAAAADojEqnJwAAwD8bG07b1N1oRqyllQa03In1tM3v0SEb3wAAAACD4sBk+nbmzHx6GAMA6D1zGQKZ0+P2pQAAAACALyeQCQDQRRrD6WMsr6eP0UorifPTkAkAAAAwOA5MpocgBDIBYDDNLjSTx5gSyAQAAAAAvoJAJgBAF2lkCBsurXXvAbOyLGN1I22MUYFMAAAAgIGRoyHz7mNlLJ7o3jUzAKA1cjRkTk0IZAIAAAAAX04gEwCgi4wNp2/qLq9lmEiLrDcjNhIvI67XbHwDAAAADIpd9SLO3p4+zi1HBDIBYJAsr5Uxv5w2xsRIxPiIfSkAAAAA4MsJZAIAdJHGcPoY3RzIXFlPH0NDJgAAAMBgObA3fUtzZj7xljAAoKfkaMec1o4JAAAAADwGgUwAgC6SJZC53r23/a9mCGTWBTIBAAAABsqByfQwxKH57l0zAwDyyxHInBoXyAQAAAAAvppAJgBAF2kMpW/sLnV1Q2b65vdoht8RAAAAAL3jwGT6lubdR8s4ekIoEwAGxexChkCmhkwAAAAA4DEIZAIAdJGxHA2ZXRzIzNGQOaohEwAAAGCg7B4t4qyx9HFuOSKQCQCDYjZDQ+b0uGNVAAAAAMBXs3IIANBFcjRkLmdooWyVlY30Meq19DEAAAAA6C05WjIPzTczzAQA6HYbzTLuPpq2X7atGnFmhgshAAAAAID+I5AJANBFGn3fkJkeFq1ryAQAAAAYOAcm0y8yu2W+ey8yAwDyufdYGRuJ9zCcP15EtZL+/QMAAAAA6D8CmQAAXSRHIHNprXsPlq2sp49Rz9AiCgAAAEBvydGQefhoGcdOdu/aGQCQx+xC+vN+etx+FAAAAADw2AQyAQC6SK1SxEgtbYwcocdWWc0wt1ENmQAAAAADZ89oEWeOpY+jJRMA+t/sYoZA5oRAJgAAAADw2AQyAQC6TGrgcGktzzxaYWU9fQO8nhhYBQAAAKA35WjJnDnSzDATAKCbHc4QyJzSkAkAAAAAPA6BTACALjM2nLbBu7zWvbf8r26kjzE6ZAMcAAAAYBAd2Ju+LnRIQyYA9LWyLGN2Ie15X0TEPoFMAAAAAOBxCGQCAHSZRmJD5vJ6nnm0wmqGuaU2iAIAAADQm3I0ZB5eLOP4SaFMAOhXD69GHF9LG+Os7REjNYFMAAAAAOCxCWQCAHSZxnDa+1fWIzab3XmobGU9fV4jtQwTAQAAAKDn7G0UcUYjfZxbjnTn2hkAkG4usR0zImJq3HEqAAAAAODxWUEEAOgyjeH0G3dXurQlM7Uhc6QWUa24kRgAAABgUOVoyTw038wwEwCgG80tpgcypyfsRQEAAAAAj08gEwCgy4wlNmRGRCx3aSBzZSPt/XXtmAAAAAAD7cBkekBiZl5DJgD0q9nF9IsXpsYFMgEAAACAxyeQCQDQZRpD6WMsr3XnobLV9bR5jWb43QAAAADQu3I0ZB5eLOP4ye5cPwMA0swtaMgEAAAAAFpLIBMAoMs0htM3eZfXMkykBVYSmzvrQzbAAQAAAAbZZKOIyUbaGGVEfPaIQCYA9JvV9TLuX0obY8e2iImRPPMBAAAAAPqTQCYAQJfJ0ZC5lBh8bJXVxHlpyAQAAAAgR0vmoflmhpkAAN3k8NEM7ZjjRRSFC0IBAAAAgMcnkAkA0GXGsjRkdt8N/5vNMk5upo1RF8gEAAAAGHgHJ9PXz2Y0ZAJA35ldSH++T00IYwIAAAAAT0wgEwCgyzSG08dYXksfI7fVjfQxRms2wQEAAAAG3YG96VuccwtlLHXhpWYAwOmbyxHIHLcXBQAAAAA8MYFMAIAukyWQud59h8lW1tPHGNGQCQAAADDwJseK2DuaNkYZEbdoyQSAvjK7mP5sn9aQCQAAAAA8CYFMAIAu0xhK3+hd6saGzAwh0VGBTAAAAAAi4uBk+jbnzHwzw0wAgG6w2SzjrqNpe1FDlYhztgtkAgAAAABPTCATAKDLjGVoyFzpwkBmjobMeoawKgAAAAC97/LJ9HWimXkNmQDQL+4/HrG2mTbG+TuLqFbsRQEAAAAAT0wgEwCgy4zUIlL3epcytFHmtpohkDlaSx8DAAAAgN6XoyFzdqGM5bXuW0cDAE7d7GJ68/XUhDAmAAAAAPDkBDIBALpMURTRGEobY7kbGzI30g+31RN/LwAAAAD0h8lGxJ7RtDHKiPjsEYFMAOgHswvpz/TpcYFMAAAAAODJCWQCAHShxnDa+7vxZv8sDZlDNsIBAAAAePRSswMZWjIPzae3aQEAnTe3mCGQqSETAAAAANiCWqcnkMvDDz8chw4disOHD3/xn7vuuiseeOCBWF5e/uI/KysrUavVYmRkJBqNRkxOTsYZZ5wR09PTcfnll8dTnvKUeOYznxmNRqPTPxIAMMAaw0U8ekf/6VnOEH7MLU8gM30MAAAAAPrDwckibphLG2NGQyYA9IUcgcx9GjIBAAAAgC3oyUDmPffcEx/5yEfi5ptv/uI/99xzz5bfv7a2Fmtra3Hs2LG4//77v+r/XqvV4qqrropv+IZviG/5lm+JCy+8MOf0e8rU1FQcPny409P4Kvv27Yu5ublOTwMAWqaRGDxcWosoyzKKons2jlfW0zfC6wKZAAAAAPyTRxsyN5PGmF0oY3mt/KcL0gCAXrSwWsbiibQxzmhEjA75PgAAAAAAPLmeCGTecccd8cEPfjA+9KEPxQc/+MGYnZ1t6edtbGzEjTfeGDfeeGO8+c1vjhe96EXxxje+MV71qld1VagBAOhfY4kNmRvNiLXNiG1d9G1vdSN9jNGa72IAAAAAPOqMRsTuesTDq6c/RrOM+OxDZVx1tnUnAOhVsxnaMacnfBcAAAAAALami47oP7aZmZk4ePBgR+fwgQ98ID7wgQ/EVVddFe94xzvimmuu6eh8AID+l9qQGRGxvN5dgcyV9fQxNGQCAAAA8AVFUcTByUrccLiZNM4t88246uxKplkBAO02lyGQOTXuuwAAAAAAsDVdv5pYlumLprl84hOfiBe84AXx7/7dv4uVlZVOTwcA6GON4fQxltfSx8hpNUMgc1QgEwAAAIAvcWAyvc3q0Hz37EcCAKdubiFDIFNDJgAAAACwRV0fyOxG//2///d41rOeFXfddVenpwIA9KnGcPqm79Jadx0kW1lPm0+tEjFUtRkOAAAAwD87MJm+3fn5hTJ57QoA6JzZxbS27IiI6XF7UAAAAADA1ghknqZDhw7Fs571rPjsZz/b6akAAH1oLEdDZoZGypxSGzK1YwIAAADwlc4ci9hVTxujWUbcekQgEwB60cmNMu4/njbG2HDEntE88wEAAAAA+p9AZoIHHnggXv7yl8fhw4c7PRUAoM80MoQPl7utIXMj7f31Wp55AAAAANA/iqKIgxlaMg/NpzdrAQDtd9fRMpqJW2JT40UUhYZMAAAAAGBr+uZY+9DQUFx66aVx8cUXx/79+2P//v1x0UUXxcTERGzfvj127NgR27dvj5MnT8axY8fi2LFj8eCDD8bNN98cN998c3zkIx85rbbLe+65J17zmtfE3//938fQkNomACCPxnD6pu/yWoaJZLS6nrYbXh+yEQ4AAADAVzswWcQHE+9PnZnvrsvNAICtmV1If4ZPjduDAgAAAAC2rmcDmbt27YpnP/vZ8dznPjee+9znxjOe8Yyo1+tP+r5arRaNRiPOOuusuOSSS+Kaa6754v/tpptuit///d+P3/zN34zjx49veS7/8A//ED/90z8dP/mTP3laP0uvm52djampqU5PAwD6SmM4fYylLgtkrqynvX/U3RcAAAAAPIYDk5WI2Ewa43MLZayuly4FA4AeM7uYHsicnvD8BwAAAAC2rtLpCZyKK664In74h384Pvaxj8VDDz0U7373u+PNb35zXHPNNVsKYz6Zpz3tafGOd7wjbrvttvj2b//2U3rvz//8z8c999yTPAcAgIiIsQwHv5YTGylzKssyVhMDmXWBTAAAAAAew1ljEbsStwqbZcRnH+qe9TQAYGvmNGQCAAAAAG3W9YHMXbt2xX/7b/8t5ubm4uabb46f/umfjmc+85lRFK1bDD3rrLPiD/7gD+K3f/u3o1LZ2q9odXU13vGOd7RsTgDAYBnN0JC53EUNmSc2IlK3w0drNsMBAAAA+GpFUcSBvenbnjPzzQyzAQDapVmWcfho2g5UrRJx7g57UAAAAADA1nV9IPPss8+Of//v/33s27ev7Z/9r//1v453vetdW3797/7u78bJkydbOCMAYFA0MrRBdlND5upG+hgaMgEAAAB4PAcm04MUM/Pds54GADy5B5YevRQ0xbk7ihiqCmQCAAAAAFvX9YHMTvuX//JfxvXXX7+l1y4sLMQNN9zQ2gkBAANhqFrEtmraGN3UkLm6nj7GqEAmAAAAAI/j4GT6tuedj5Sx2kWXnAEAT2xuIf25PT0ujAkAAAAAnBqBzC34hV/4hRgfH9/Sa9///ve3djIAwMBoDKe9v5sCmSsZDrLVh2yIAwAAAPDYztoeMT6SNkazjLj1IYFMAOgVs4vN5DGmJuw/AQAAAACnRiBzC3bv3h3f+I3fuKXXfuYzn2nxbACAQdEYTtsAXlrrnsNjGjIBAAAAaKWiKLK0ZM7Mpwc7AID2mFvUkAkAAAAAtJ9A5hZ90zd905Zed8cdd7R4JgDAoBhLDCAuZwhB5rKykT5GvZY+BgAAAAD968BkeqBiZr57LjkDAJ7Y7EL6c1tDJgAAAABwqgQyt+hZz3rWll73yCOPtHgmAMCgaAynvX9lPWKz2R0HyFbX0+cxOmRDHAAAAIDHdyBDQ+adj5RxYqM71tQAgMd39EQZj6ymjbFnNGJs2P4TAAAAAHBqBDK3aPfu3VGrPXkt09LSUhtmAwAMgkaGDeDVDM2UOaxkaOusJzaGAgAAANDfztkeMT6SNsZmGXHrQwKZANDt5hbTn9fT2jEBAAAAgNMgkLlFRVHEnj17nvR1jUajDbMBAAZBI0MAcWktfYwcVjMEMkcFMgEAAAB4AkVRZGnJnJlvZpgNANBKWQKZ4wKZAAAAAMCpE8g8BcePH3/S1+zevbsNMwEABsHYcPoYy2vdcZv/ynr6POpDNsUBAAAAeGIH9qavIR2a7441NQDg8c0tpD+vp8YdmwIAAAAATp2VxS1aXl6O5eXlJ33d5ORkG2YDAAyCxnD64bHlbmnI3Egfo15LHwMAAACA/nYwQ0Pm5x4p48SGUCYAdLPZDA2ZUxMuAwUAAAAATp1A5hZ99rOf3dLrnvWsZ7V4JgDAoGgMpY+xtJ4+Rg4rGeYxmuH3AQAAAEB/O2dHxM5taWNsNCNue0ggEwC61dpmGfccS3tWjw5FTDYyTQgAAAAAGCgCmVv0v//3/97S617wghe0diIAwMDI05DZHQfHVtfT5lFExDYNmQAAAAA8iaIo4kCGlsyZ+WaG2QAArXD30TKaiVtgU+NFVAoNmQAAAADAqRPI3IKNjY344z/+4yd93ejoaLzoRS9qw4wAgEEwNpw+xvJa+hg5pDZk1ofCpjgAAAAAW3JgMn0daWa+Oy46AwC+2txi+nN637h9JwAAAADg9AhkbsEv/dIvxR133PGkr/uu7/quGB8fb/2EAICBMDqUPsZyYjNlLqupgUztmAAAAABs0cEMDZl3PFLGyY3uWFsDAL7c7EL6M3paIBMAAAAAOE2Otj+Jz3zmM/ETP/ETT/q6oaGh+L7v+76Wz6dbnTx5Mm688cb4+Mc/Hp/+9Kfj85//fNx1111x7NixWFlZiVqtFvV6PUZHR+OMM86Ic889N84///x46lOfGk972tPiiiuuiOHhDDVgANBHxobTN4KXuqQhczXx8NrokE1xAAAAALbm3B0RO7ZFHDt5+mNsNCNue6iMK860LgUA3SZHQ+b0hGc8AAAAAHB6BDKfwCc+8Ym49tprY3l5+Ulf+2M/9mNx0UUXtWFW3ef666+PT3ziE0/4e9rc3IyTJ0/G4uJi3HfffXHTTTd92f+90WjES17ykrjuuuviNa95jaZRAIiIRoa7Cla6JJC5ktqQmaEtFAAAAIDBUBRFHJgs4qN3p4U1Zo4044oz09s2AYB8yrJMDmRWiojzdgpkAgAAAACnxw7iY1hfX4+f+7mfixe+8IXxyCOPPOnrn/3sZ8eb3/zmNsysO91www1bCq0+keXl5fiLv/iL+J7v+Z44++yz43Wve1185jOfyTRDAOhN9dqjG8IpltbTbwjOYTUxkDkqkAkAAADAKTg4mb4NOjPfHWtrAMA/m19Ovwj0nB1FDFcFMgEAAACA0yOQ+SWOHDkSv/IrvxJPecpT4gd/8Ae3FDJ8ylOeEn/xF38R1Wq1DTMcDKurq/F7v/d7ceWVV8a3f/u3x+c///lOTwkAOqIoiuQg4nIXNGSub5ax3kwbQ0MmAAAAAKfi8r3pIYvbHy7j5IZQJgB0k9nEdsyIiOlxYUwAAAAA4PTVOj2BdtvY2Ihjx47F8ePHY2FhIW655Za4+eab45Of/GTccMMNsbGxseWxrrzyyvibv/mb2LNnTwtnPLjKsow//MM/jL/4i7+It73tbfG93/u9URQWxQEYLGPDEUsJocrltc4fGFvd+terxzVa8x0AAAAAgK07b2cRO7ZFHDt5+mNsNB8NZT7lDGtTANAt5hYSbwGNiKkJz3YAAAAA4PT1VSDzec97Xnz4wx9u+ecURRHf+73fG29/+9tjZGSk5Z836FZWVuKNb3xjvPe9740//MM/jB07dnR6SgDQNo2hIiJOP1S5vJ5vLqdrJcMcNGQCAAAAcCoqRRGX7y3ixnvSLiybmW/GU86oZJoVAJBqTkMmAAAAANBhdg9P0dVXXx1/+7d/G7/8y78sjNlmf/VXfxVXX311HD58uNNTAYC2aQynvX9p7dHW6U5aXU///FGBTAAAAABO0YHJ9K3QQ/OdXVsDAL7c7EL6s3lKIBMAAAAASNBXDZmtMjw8HC972cviP/7H/xgveclLOj2dgfbZz342XvziF8cHP/jBOOecczo9nS9qNptx8uTJTk8jydra2pb+dwC0V70aEXH6m8IbzYil1bUYrmab0ik7uhKR8jNERAwVm3Hy5GaW+fQqz2oA6F6e0wDQvTynB9v+8YjUdak7Hm7G8ZWTHV1fA+hXntOcqqW1iCMrac/2iZEyRoq16PEjHgAt5zkNAN3LcxqAQdRsNjs9hS9TlJ2uTMroec97Xnz4wx/ONt7IyEj84A/+YLzpTW+KHTt2ZBu3l0xNTT1uI2WlUokDBw588Z/LL788pqamYvv27bFz587YsWNH1Gq1WF1djcXFxbjnnnvizjvvjJtuuiluuOGGuOmmm067sevSSy+Nj33sYy3797K0tBSbm1sPfSwuLsanP/3plswFgMH2kbUDccfm+UljfMvI38Vo0bld5bs398bfrV2VNMazhw7F/trdmWYEAAAAwCAoy4g/OvGSOBnDSeNcO/yxOLP6SKZZAQCn64HNXfHXa89KGuOcyny8dNsnM80IAAAAAGiHK6+8MsbHx7f8+mq1GmNjYy2bj4bMJ3DixIn4yZ/8yXjXu94VX/d1Xxevfe1r45nPfGanp9VRZ555Zlx77bXxtV/7tfHyl788du3a9aTv2b59e2zfvj3OO++8ePaznx3f+Z3fGRERDzzwQLzrXe+KX//1X4/Pfe5zpzSPW2+9Na6//vr4sz/7s9P6OQCgVwwXG8ljrJVDHQ1krpfpXzmHIv33AAAAAMBgKYqIMyqPxF3NM5PGeaC5SyATALrAI+X25DF2VY5lmAkAAAAAMMgqnZ5AL/j85z8fv/IrvxLPetaz4oUvfGH85V/+Zaen1FZDQ0Pxzd/8zfG+970v7rvvvvid3/md+LZv+7YthTGfyJlnnhk/8AM/ELfeemv81m/9Vuzdu/eU3v/nf/7n8Wu/9mtJcwCAbrct1pPHWMsQiEyxnuEOkKEMwVQAAAAABk+OIOWDzbQ9MQAgj0eaO5LH2FUczzATAAAAAGCQCWSeohtuuCGuu+66eMUrXhEPPPBAp6fTcj/2Yz8W99xzT/zJn/xJvOxlL4uiKLJ/Rq1Wi+/+7u+OW265JV71qled0nvf8pa3xJEjR7LPCQC6RY4g4loMZZjJ6dOQCQAAAECnnFFJD2TON8djs7StCgCdtpAhkDmhIRMAAAAASFSUZVl2ehK5/MzP/EzcfvvtT/ia48ePx9GjR2NxcTEeeuihOHz48Gl/3p49e+Kd73xnvPKVrzztMfhyZVnGD/3QD8XP/dzPbfk9b3jDG+JXfuVXss5jaWkpNjc3t/z6oihi27ZtWefQbmtra/F3f/d3X/a/e/GLXxzDw8MdmhEAEREfuSfiv3087UKEN3xNGc8/P9OETsMf3RLxv29L+xl+9sVl7NuZaUI9yrMaALqX5zQAdC/PaZplxPe8J2JpPW196kefX8blezJNCoCI8Jzm1Gw0I77r/0RsNE//mb6tWsY7XxlRyX8XOUDf8ZwGgO7lOQ3AIDp58mScSgSyWq3G2NhYy+aTXlfURd7ylrec8nsWFhbiE5/4RHz84x+PP/3TP42bbrppy+996KGH4hu/8Rvjf/yP/xHf+q3fesqfzVcriiLe/va3f/F/bsU73/nO+PEf//GYnJxs8eweX6VS6flA5mMZHh7uy58LoJfsHG1GJLZDrpW12LatmmdCp2G93IiIZtIY46PDsW2b3fGv5FkNAN3LcxoAupfn9OC5fHI9/uHetDtq71ioxdPO6dwaG8Cg8Jzm8dy/2IyNZtqe2dR4JeojQ5lmBDB4PKcBoHt5TgPQ79bX10+peK/VKp2eQKdNTEzEy172snjLW94Sn/rUp+ITn/hEvP71r49abWtZ1c3NzfiO7/iO+OM//uMWz3SwvO1tb4tXvepVW3rtiRMn4td+7ddaPCMA6IyxDJdWLa2lj5FiZT19jLq9cQAAAABO08HJ9C3RQ/NpF44BAGlmF9IuV4iImJpw+ScAAAAAkG7gA5lf6Wu+5mviN37jN+JjH/tYPPWpT93SezY3N+N1r3td3HrrrS2e3eAoiiJ+67d+K3bt2rWl1//P//k/WzwjAOiMxlD6xvDyevoGdYrVDJ8vkAkAAADA6Towmb7GdvvDZaxvdnadDQAG2dxihkDmuEAmAAAAAJBOIPNxPP3pT4+Pf/zj8cY3vnFLrz9x4kS89rWvjY2NjRbPbHDs3bs3fvzHf3xLr7311ltjZmamxTMCgPZrZGjIXO7xhszhakStYoMcAAAAgNOzb7yIscR1trXNiDseFsgEgE6Zy9CQOa0hEwAAAADIQCDzCQwNDcUv/dIvxZvf/OYtvf7jH/94/Nf/+l9bPKvB8vrXvz727t27pdd+4AMfaPFsAKD9GhmaITvfkJn2/lHtmAAAAAAkqBRFXL43PYBxaF4gEwA6oSzLmE1syCwi4vydApkAAAAAQDqBzC34mZ/5mfjGb/zGLb32F3/xF7VkZlSv1+M1r3nNll774Q9/uMWzAYD2G6oWsa2aNkbHGzI30jbI6wKZAAAAACQ6MJm+LToz38wwEwDgVD28ErGUuN919vaIkZpAJgAAAACQTiBzi3791389xsfHn/R199xzT/zpn/5p6yc0QLYahr3ppptaPBMA6IzGcNr7Ox3ITG7ItDkOAAAAQKIDk+lrTLc9XMb6ppZMAGi31HbMiIipCUekAAAAAIA8rDZu0d69e+NNb3rTll77u7/7uy2ezWC5+uqro1arPenr5ubmoixtggPQfxrDaYfFltY6+3xcSQxkasgEAAAAINW+nUU0EteZ1jYj7nzEXhQAtNtchkDm9LgLQAEAAACAPAQyT8HrX//6qFarT/q6D3/4w7G5udmGGQ2GRqMRU1NTT/q6kydPxgMPPND6CQFAm6UeFFtODESm2GyWcWIjbYxRgUwAAAAAElUrRVy2Nz2IcWheIBMA2m12oZk8xpRAJgAAAACQiUDmKTjzzDPj6quvftLXHT9+PD796U+3fkIDZHp6ekuvW1hYaPFMAKD9xobT3r+y/mgwshNSw5gREfWaDXIAAAAA0h2cTN8anZlPD4QAAKcmR0Pm1IT9JgAAAAAgD4HMU/Tc5z53S6+76aabWjyTwbJz584tvW55ebnFMwGA9msMpW8Qr2YIRp7W52Zo56xryAQAAAAggwOT6etstz5UxvqmlkwAaJeV9TIeWEobY+e2iImRPPMBAAAAABDIPEWXXHLJll730EMPtXgmg2VkZGsr4ydOnGjxTACg/RqJDZkREUtr6WOcjpWN9MNpowKZAAAAAGQwNV4krzWtbUbc+YhAJgC0y+EM7ZjTE0UUhYZMAAAAACAPgcxTtHfv3i297uGHH27xTAbLVoOWWw1uAkAvyRHIXF7rzCGxPA2ZNsgBAAAASFetFHH53vS1ppl5gUwAaJe5DIHMqXF7TQAAAABAPgKZp2h0dHRLr1tcXGztRAbM0aNHt/S6RqPR4pkAQPuNDadvEi93qiEzQyBTQyYAAAAAuRyYTN8enZlvZpgJALAVswsCmQAAAABAdxHIPEWrq6tbet3wcIYqK75odnZ2S68bHx9v7UQAoANyBBKXMwQjT8fqRvoY9Vr6GAAAAAAQEXFgMj2QcetDZWw0tWQCQDvMZmjInJ5wPAoAAAAAyMeK4yl65JFHtvS6sbGxFs9kcKysrMTc3NyTvm7btm1x1llntX5CANBmORoyl9Y6c0BsdT39c0eH3FoMAAAAQB7T40XyBWgnNyM+94hAJgC02mazjLuPpj1zh6sRZ2/PNCEAAAAAgBDIPGV33nnnll63Z8+eFs9kcNx4442xsfHk9Vr79u2LohDYAKD/jGUo3u5UQ+ZKhs+tZ2gIBQAAAICIiGqliMv2pu8nHZoXyASAVrvveMTaZtoY5+0solpxlgQAAAAAyEcg8xR97GMf29LrLrroohbPZHD8+Z//+ZZed8UVV7R4JgDQGak39kdELHesITN9jBw/PwAAAAB8wYG96VukM/PNDDMBAJ7I7EL683Z6XBgTAAAAAMhLIPMULC8vx4c//OEtvXb//v0tns1gOHHiRPzRH/3Rll77nOc8p8WzAYDOGBtO3yheXsswkdOwsp4eBK0P2SgHAAAAIJ8Dk+nrTbc+VMZGU0smALTS3GL6s3Z6wj4TAAAAAJCXQOYp+JM/+ZNYXl5+0tft2rUrLrvssjbMqP/99m//dhw5cmRLr33BC17Q4tkAQGc0htPH6FQgU0MmAAAAAN3mgoki6rW0MU5sRHzuEYFMAGilHIHMKQ2ZAAAAAEBmAplbtLm5GT/7sz+7pde+4AUviErFrzbVww8/HD/xEz+xpddecMEF8fSnP721EwKADqnXIiqJe8XLGZoqT8fKRvoYqYfjAAAAAOBLVStFXLY3PZwxMy+QCQCtUpZlfH5BIBMAAAAA6D5Sg1v0C7/wC3Hbbbdt6bXf9E3f1OLZ9L+yLOP1r399PPTQQ1t6/bd+67e2eEYA0DlFUSS3RC51rCEzbaO8WkQMVzNNBgAAAAD+yYHJ9G3SmSPNDDMBAB7LwomIYyfTxjhzLKI+JJAJAAAAAOQlkLkFH/jAB+JHf/RHt/TaHTt2xKtf/eoWz6j//eiP/mj8+Z//+ZZeOzQ0FP/23/7bFs8IADprbDjt/R1ryFxPe3996NFAKgAAAADkdHAyfc3ps0fK2GhqyQSAVpjTjgkAAAAAdKmuDWS+5z3viV/91V+NjY2Njs7jhhtuiH/xL/5FrK9vLU3wPd/zPVGv17N9/vXXXx9FUTzpP9dff322z3z7298ei4uL2cY7FWVZxo/8yI/EW9/61i2/5zu+4zvi/PPPb+GsAKDzGom39y53rCEz7f31Wp55AAAAAMCXumCiiJHEtacTGxGfzxAWAQC+2txi+jN2eqJrj0UBAAAAAD2sa1cejxw5Em94wxvisssui9/4jd+IEydOtPXzy7KMX/7lX46Xv/zlcezYsS29Z8eOHfFDP/RDLZ5Z673tbW+Lqamp+JEf+ZG499572/a5CwsL8c3f/M2nFMZsNBrxkz/5ky2cFQB0h0ZqQ2aHApkric2co4lBVAAAAAB4LNVKEZftTV97mpkXyASAVpjNEMjUkAkAAAAAtELXBjK/4M4774zv+Z7vifPPPz++//u/P26++eaWf+YNN9wQz3nOc+L7vu/7Ym1t6+mFt73tbbFr164Wzqx9jh49Gm9961tjamoqXvOa18S73/3uLbeEnqrNzc34vd/7vbjsssviz/7sz07pvf/5P/9n7ZgADITUQOZ6M+LkRvsPh60mlp3Xh/LMAwAAAAC+0oG96VulM/PNDDMBAL7S3EL6M3ZaIBMAAAAAaIGuD2R+wZEjR+Id73hHXHnllXHZZZfF93//98f73//+WFpayjL+fffdF7/6q78aV111VbzwhS+MG2+88ZTef+2118Yb3vCGLHPpJhsbG/Enf/In8cpXvjLOOuuseO1rXxt/8Ad/EA888EDy2PPz8/GLv/iLcfnll8frXve6ePDBB0/p/S9/+cvjjW98Y/I8AKAXjGVoilxuzd0Kj6ssy1hJ/MxRgUwAAAAAWuTAZPqa2y1HythsaskEgJxObJRx3/G0McaGI3aP5pkPAAAAAMCXqnV6Aqfj1ltvjVtvvTXe8Y53RLVajYMHD8bTnva0uOSSS+Liiy+Os88+O84888yYmJiIkZGRGB4ejmazGWtra3H8+PGYn5+Pe++9N26//fb4x3/8x7jxxhtjZmYmyvL0NksvueSS+IM/+IPMP2X3efjhh+Nd73pXvOtd74qIiH379sUzn/nMuPTSS2P//v1x/vnnxxlnnBF79uyJer0e27Zti4iIEydOxOLiYtxzzz1x5513xqc//em44YYb4pOf/GQ0m6d3o+GFF14Yf/iHfxjVajXbzwcA3Sy1ITMiYnktYlc9fZytWtuMSD2LVs8QRAUAAACAx3LhriJGahEnNk5/jBMbEZ9fKOPi3daxACCXu46WkXrdwfR4EUXh+QwAAAAA5NeTgcwvtbm5GTfffHPcfPPNHfn8888/P9773vfGrl27OvL5nXT48OE4fPhw2z93amoq3v/+98fExETbPxsAOqUxnKMhs4yI9m08ryYcZPuC0Z7/tgoAAABAt6pVirh0TxGffiAt8jEzX8bFuzNNCgCI2YX09umpCWFMAAAAAKA1Kp2eQC+74oor4qMf/WhMTU11eioD49JLL40bbrjB7xyAgdMYSh9jaS19jFOxsp4+Rj3Dzw0AAAAAj+fAZPp26aH5ZoaZAABfMLeYIZA5LpAJAAAAALSGQOZp+lf/6l/F3//938fZZ5/d6akMjFe/+tXxD//wD3H++ed3eioA0HZjw+ljLK+lb16fitX19M8bHbJZDgAAAEDrHJxMX3+69aEyNpvtXXsDgH6WoyFzWkMmAAAAANAiApmn6Jxzzok//uM/jne9612xffv2Tk9nIOzevTve+c53xv/6X//L7xyAgTU6nL5pvKwhEwAAAAC+zIW7ithWTRtjZT1iNkOTFwAQsdks466jac/VWiXinO0CmQAAAABAa3RtIPPyyy+Pq6++OiqV7pjirl274qd+6qfi9ttvj2/5lm/p9HRaqii6Y1F6dHQ03vjGN8Ztt90W3/Vd39Xp6QBAR2VpyMwQkDwVqxk+b1QgEwAAAIAWqlWKuHRP+t7YoXmBTADI4cGliBMbaWOct6OIoWp3nH0BAAAAAPpPrdMTeDzPfOYz46Mf/WgcOXIk3vOe98S73/3ueN/73hfHjx9v6zyuuuqqeP3rXx/f+Z3fGfV6va2f3SkzMzPx/ve/P97//vfH3/7t38Z9993X1s/ft29fvPa1r43v/d7vjb1797b1swGgWzWG0jeNl9baeyhsZT398+oZfm4AAAAAeCIHJitx84ObSWPcMt+Mf3FpYtUmABCzi83kMaYm7C8BAAAAAK3TtYHML9i7d29cf/31cf3118f6+np8+tOfjo9+9KNx4403xkc/+tGYm5vL+nnj4+PxnOc8J1760pfGq1/96ti3b1/W8XvB2WefHa997Wvjta99bUREfPazn40Pf/jD8clPfjI+9alPxT/+4z/G6upqts8riiKuuOKKeOlLXxqvetWr4vnPf37XtHQCQLdoZGjIXFlLH+NUrCbeXhwRMdr131YBAAAA6HUHJtP3pW45UsZms4xqxR4XAKSYXUi/8HN63PMYAAAAAGidnjriPjQ0FM94xjPiGc94RvyH//AfIiJifn4+br311vjc5z73xX8OHz4cR48ejaWlpS/+s7GxEdu2bYtt27ZFvV6PvXv3xuTkZJx11llx0UUXxaWXXhoHDx6Myy67rKvCgL/zO78Tv/M7v9PROVx22WVx2WWXxb/5N/8mIiI2Nzfj1ltvjdtuuy0OHz4cd911Vxw+fDjuueeeOHr0aCwvL8fKykqsrKzE+vp6DA8Px8jISIyOjsYZZ5wRZ599dpx77rlx2WWXxVOe8pR46lOfGrt37+7ozwgA3a4xlD7GUobGylOxsp4+Rj3Dzw0AAAAAT+SiXUUMVyPWEkoyV9Yj5hbLuHBX9+wzAkAvmlvMEMjUkAkAAAAAtFBPBTIfy+TkZExOTsY111zT6akMjGq1GgcOHIgDBw50eioAMDCGqkVsq0acTDgUttzuhswMAVCBTAAAAABabahaxKV7ivjMg2nrWTPzZVy4K9OkAGBA5Qhk7tOQCQAAAAC0UKXTEwAAYGsaw2nvb3cgM0dD5mjNhjkAAAAArXdgMn3b9NB8M8NMAGBwLZ4o45HVtDH2jkaMDdtfAgAAAABaRyATAKBHNBI3j5czNFaeitUMgUwNmQAAAAC0w8HJ9ODGLUfK2Gy2dw0OAPpJjnbM6QlhTAAAAACgtQQyAQB6RCMxnLjU7obMjfRN85FahokAAAAAwJO4aFcRw9W0MVbWIw4fFcgEgNOVI5A5NS6QCQAAAAC0lkAmAECPGBtOe//qekSzbN+BsNSGzJFaRLVi0xwAAACA1huqFnHJ7vS1qJl5gUwAOF1zCxkCmROOQgEAAAAArWUVEgCgRzSG0g6ElfHoLf3tkhrIHE1sBAUAAACAU3FgMn3r9NB8M8NMAGAwzWZoyJzWkAkAAAAAtJhAJgBAj2gkNmRGRCyvpY+xVSsbaZvmdYFMAAAAANro4GR6gOOzR8polloyAeBUrW2Wce+xtGfo6FDEZCPThAAAAAAAHodAJgBAj8gRyFxaa99hsOSGzJobjAEAAABon4t3FzFcTRtjaS3icIZ2LwAYNHcfLaOZ+AidGi+iKOwvAQAAAACtJZAJANAjxobTN5BXEkOS7fwsDZkAAAAAtNNQtYj9u9PX4A7NC2QCwKmaXUh/fk6NC2MCAAAAAK0nkAkA0CNGMwQUl9bSx9iKjWYZa5tpY+T4eQEAAADgVBycTN8+nZlvZpgJAAyW2QwN09MTApkAAAAAQOsJZAIA9IgcDZnLa+25nX81QxNnfcimOQAAAADtdWAyfU3qliNlNEstmQBwKuY0ZAIAAAAAPUIgEwCgRzRyNGRmCEpuRY5ApoZMAAAAANrt4t1FDCXuoC6tRRzO0PIFAIOiWZYxl/jsrBYR5+0UyAQAAAAAWk8gEwCgRzSG08dYaVND5spG+ufUaxkmAgAAAACnYLhaxCV78rRkAgBbM78csbqRNsY5O4oYrgpkAgAAAACtJ5AJANAjGsPpm8hLaxkmsgV5GjJtmgMAAADQfpfvTV+XOjTfzDATABgMcwvpFxlMj9tXAgAAAADaQyATAKBHjGVoyFzOEJTcipUMn1MfSh8DAAAAAE7Vwcn0LdSZ+TKapZZMANiK2cX0iwymJgQyAQAAAID2EMgEAOgRI7WISuJe8vJaew6Bra6nf069lmEiAAAAAHCK9u8pYihxF3VpLeLuowKZALAVc4saMgEAAACA3iGQCQDQIypFEaOJrZHLa3nm8mRyNGSODtk4BwAAAKD9hqtFXLw7fW3q0LxAJgBsxexC+jNTQyYAAAAA0C4CmQAAPaSRGMhcytBcuRUrG+lj1BN/VgAAAAA4XQcn00MdM/PNDDMBgP52/GQZD62kjbGrHrFjm0AmAAAAANAeApkAAD1kbDhtM7ldDZmrGYKfqW2gAAAAAHC6Dkymb6PecqSMZqklEwCeyNxi+rNyWjsmAAAAANBGApkAAD2kMZz2/vYFMtPHqA/ZPAcAAACgM/bvLqKWuJN67GTE3UcFMgHgiWQJZI7bUwIAAAAA2kcgEwCgh6QGMtebEWubrT8EliOQqSETAAAAgE7ZViti/+70cMfMvEAmADyRuYX0Z+XUuONPAAAAAED7WJEEAOghjQytkUttaMlc2UjfPK/XMkwEAAAAAE7TgckMgcwjzQwzAYD+NZuhIXNqQkMmAAAAANA+ApkAAD1kLLEhMyJipQ2BzNSGzKFKxFDV5jkAAAAAnXNgb/pW6sx8GWWpJRMAHsv6Zhn3HEt7To7UIs4cyzQhAAAAAIAtEMgEAOghozkaMtdbfwBsJTGQWR/KMw8AAAAAOF2X7CmilribeuxkxN2JQRMA6Ff3HCtjI7FMet94EZXCJZ8AAAAAQPsIZAIA9JAcDZnLPdCQOSqQCQAAAECHbasVcfGu9IDHzLxAJgA8lrnF9Gfk1LgwJgAAAADQXgKZAAA9pJEhkLm01o6GzLTPqGdoAgUAAACAVAcmBTIBoFVmF9KfkdMCmQAAAABAmwlkAgD0kMZw+qbySmJ75VasbqS9f7SWZx4AAAAAkOLAZPp26sx8M8pSKBMAvlKOhszpCYFMAAAAAKC9BDIBAHpIYyh9jKW19DGeSLMsYzUx9FnP8HMCAAAAQKpL9hRRS9xRPXoy4t5jeeYDAP2iLMvkQGaliDh/p0AmAAAAANBeApkAAD1kLEND5vJaa2/jP7kRkfoJ9SGb5wAAAAB03kitiAt3pa9VHZpvZpgNAPSPh1bSLxE9a3vEtpo9JQAAAACgvQQyAQB6SGM4fYzlFjdkriS2Y0ZEjGrIBAAAAKBLHJzMEchs7SVpANBrZhPbMSMipscdewIAAAAA2s/KJABAD2lkCCourbf28NdqhkBmvZY+BgAAAADkcGAyfUv1liPNKEuhTAD4grmF9Ofi1IR2TAAAAACg/QQyAQB6yFC1iOFq2hgtb8jcSN9AHx2ygQ4AAABAd7h0TxHVxOWqxRMR9x7PMx8A6Adzi83kMabH7ScBAAAAAO0nkAkA0GPGhtPe3+pAZpaGzAxNoAAAAACQw0itiIt2pQc+ZubTgycA0C9mczRkCmQCAAAAAB0gkAkA0GMaie2Ry+vpG9xPJEcgc1QgEwAAAIAucmAyRyCztetyANArltfKeHA5bYzxkYiJukAmAAAAANB+ApkAAD2mkdiQudTihsyVDIHPemLoFAAAAAByOjCZvq16aL4ZZSmUCQCHF7VjAgAAAAC9SyATAKDHpAYyV9cjmi08+LW6kT7GaC19DAAAAADI5dI9RVQScx+LJyLuO55nPgDQy+YyBDKnJwQyAQAAAIDOEMgEAOgxY4ntkWVErKznmctjyTF2fSh9DAAAAADIpT5UxEW70oMfM/PNDLMBgN42qyETAAAAAOhhApkAAD1mNLEhMyJieS19jMezup6+iT6aGDoFAAAAgNwOTOYIZKavnQFAr5tbyNCQOe7IEwAAAADQGVYnAQB6zFiGQObSWusOfmnIBAAAAKAfHZhM31o9NN+MshTKBGBwbTbLuOto2rNwuBpx1vZMEwIAAAAAOEUCmQAAPaaRoT0yR2jy8axmGHtUIBMAAACALnPZniIqiUtzCyci7l/KMx8A6EX3Hi9jvZk2xr6dRVRTH8oAAAAAAKdJIBMAoMc0sjRkpo/xeFY20m41rhQR26qZJgMAAAAAmdSHirhwIj38MTOfmEIBgB42u5DeFD01LowJAAAAAHSOQCYAQI9pDKdvMi+vpW92P57UhsyRWkRR2EgHAAAAoPscmExftzo037q1OQDodnOLGQKZGS5IAAAAAAA4XQKZAAA9ZmwofYylxNDkE1lJHHs0w88HAAAAAK1wcDJ9e3VmvhllKZQJwGDK0ZA5LZAJAAAAAHSQQCYAQI9pDKePsdLShsy0setDNtEBAAAA6E6X7i2ikrh89chqxANLeeYDAL2kLMvkhswiIvbttJcEAAAAAHSOQCYAQI9pDKdvMi+tZZjI40huyKzlmQcAAAAA5DY6VMQFGVq5Ds03M8wGAHrLwomIYyfTxjhzzOWeAAAAAEBnCWQCAPSYxlD6GMuJocknsrqR9v56hp8PAAAAAFrl4GR6COSW+bR2MADoRbML6c+/qQwXIwAAAAAApBDIBADoMfWhiEriXvPyWmsOfK1vlrGReLn/qFuNAQAAAOhil0+mb7Eemm9GWQplAjBYcgQyp8cddQIAAAAAOssqJQBAj6kURYwmtkgur+WZy1daydC8qSETAAAAgG522Z4i+cK0h1cjHlzKMx8A6BVzixoyAQAAAIDeJ5AJANCDGomhxaX11ty+v5ohkJkaNgUAAACAVmoMFzGdIQwyc6SZYTYA0DvmFtOffVPjApkAAAAAQGcJZAIA9KDGcNpmc8saMjfSg571WoaJAAAAAEALHdibHgY5NN+aS9MAoBud2Cjj/uNpY2wfjthdzzMfAAAAAIDTJZAJANCDxobT3t+qQGaehkw3GwMAAADQ3Q5Opm+zzsw3oyyFMgEYDIcXy0h96k1PFFEU9pEAAAAAgM4SyAQA6EGjQ2nvX29GrG3mP+y1kiGQWU/82QAAAACg1S7bW0QlMQ/y0ErE/HKe+QBAt5tbTN+XmhoXxgQAAAAAOk8gEwCgB40Np284L7WgJXNlPX0zPTVsCgAAAACt1hgusoRCDs03M8wGALrf7IJAJgAAAADQHwQyAQB6UGM4fYyVFgQyV7M0ZNpMBwAAAKD7HZhMX8eamU8PpwBAL5jN0JA5PWEPCQAAAADoPIFMAIAe1MgQWlzK0Gb5lVY20seo19LHAAAAAIBWOziZvtU6oyETgAGw2SzjrsRAZq0Scc4OgUwAAAAAoPMEMgEAelCOhszlljRkpoc8R4cyTAQAAAAAWuzyvUWkxkKOrETML2nJBKC/PbAUcXIzbYzzdxZRqwhkAgAAAACdJ5AJANCDxjIEMpfWWtCQuZ4+Rj1D+ycAAAAAtFpjuIipifS1rENaMgHoc7OL6c+6qXH7RwAAAABAdxDIBADoQY0MocUc4cmvtJphTA2ZAAAAAPSKg5Pp63QzRzRkAtDf5hbSn3XTGS5BAAAAAADIQSATAKAHNbI0ZKaP8ZVW1tM31Ou1DBMBAAAAgDY4sDd9u3VGQyYAfW5uMX3/SEMmAAD8/9n78yg7z/s+8Py9712AqlsACyBR3ACiCtwJkJIsydookiDVkiJr8aaFsmfi2O5MErXjk+k5aTs+7hl3K2n7xJ4Zx530zLGdHs1Yq62WrCWKWpFAbbT2hQS4k1UgAYIogAAI1AJU1b3v/EHTliguQD1PVd3l8zlHx7KI+7sva8V5nuf7fAEA6BYCmQAAPajVTN90nl3If/P+6aW016+rRdRKG+oAAAAA9IZrtxSRupo1PRsxPaslE4D+NZmhIVMgEwAAAADoFgKZAAA9aCRDQ+bsijRkpr1+qJHnOQAAAABgNWxYV2QJiGjJBKBfnThdxfHTaTPGWnkuKwUAAAAAyEEgEwCgB7UyBBdnF/Pfuj+fOHNYIBMAAACAHnPdWI5ApoZMAPrTlHZMAAAAAKDPCGQCAPSgRq2IZi1txkxXNmTaUAcAAACgt+waS99y1ZAJQL+aOpEeyJzY5HgTAAAAANA9rFgCAPSokWba62cTw5PPZX4p7fXD9TzPAQAAAACr5botRaReM3Z4NuLIrJZMAPrPZI5ApoZMAAAAAKCLCGQCAPSoVmKb5OxC3gNe7U4VpxMDmUONPM8CAAAAAKtlw7oiLssQFNGSCUA/mjqevh81LpAJAAAAAHQRgUwAgB7VSm3IXMjzHM9IDWNGRAwnhkwBAAAAYC3sGssRyNSQCUB/ObNUxcFTab/fhhsRW1qZHggAAAAAIAOBTACAHpUayJxbjOhU+Q55zS2mz9CQCQAAAEAv2jmWvu2674iGTAD6y2Mnq+gkbkVNjBZRFC70BAAAAAC6h0AmAECPGklsk6wiYj5DiPIZc4vp4c5hgUwAAAAAetB1W9KDIk/MRByd1ZIJQP+YPJ7+e218kzAmAAAAANBdBDIBAHrUcGJDZkTEzEL6jGfML6XPGKrbVAcAAACg92xcV8T289LXtrRkAtBPpk5kCGSO2jsCAAAAALqLQCYAQI8ayRDInM3QavmMuQxtm0MaMgEAAADoUTvHMgQypzVkAtA/cjRkTmjIBAAAAAC6jEAmAECPajXSN6BnczZkZgh3DgtkAgAAANCjdo6lb73um9aQCUB/6FRV7E9syKwVEds2CmQCAAAAAN1FIBMAoEe1cjRkZgxk5mnItKkOAAAAQG+6bkv62tahmYgn57RkAtD7pmci5pfSZmw9r4hGzd4RAAAAANBdBDIBAHpUq5m+AT2zkO9w13yGQOZwPX0GAAAAAKyF89YXcdl56Wt2WjIB6AeTie2YERHjo8KYAAAAAED3EcgEAOhRI430GbMZQpTPSL3lOCJiKMO/EwAAAACslZ1jOQKZGjIB6H2Tx9MvGJgQyAQAAAAAupBAJgBAj2o102fMZmzInFtMnzXcsLEOAAAAQO/aOZa+/br3iIZMAHrfVIaGzIlN9o0AAAAAgO4jkAkA0KNazfRN6KwNmRlmacgEAAAAoJddtyV9ze7QqYhj81oyAehtOQKZ4xoyAQAAAIAuJJAJANCjWhnCizML6TOeMZchkDkskAkAAABADxtdX8S2jenhkX3TWjIB6F2nzlRxdC5txvlDERvWCWQCAAAAAN1HIBMAoEcNNSLKxH3o2YV8N+3PL6XNqpcRDX87BQAAAKDH7RxLD4/sndaQCUDvytGOObFJGBMAAAAA6E6OvAMA9KiyKJIbJWe7qCFzqB5RFDbXAQAAAOhtu8bSt2Dv0ZAJQA/LEcgcH7VnBAAAAAB0J4FMAIAe1koNZC5mbMhMDGSmhksBAAAAoBtcl6Eh8+CpiGPzWjIB6E2Tx3M0ZDrSBAAAAAB0J6uXAAA9rNVMO9w1k7UhM21zfajhpmMAAAAAet/o+iK2bkyfoyUTgF41qSETAAAAAOhjApkAAD1spJn2+rnEVssfpSETAAAAAJ62cyx9G3bfEQ2ZAPSexXYVB55K+x22vh5x4UimBwIAAAAAyEwgEwCgh6WGGBfaEQvt9INdVVXF/FLajPX15McAAAAAgK6wcyy91WuvhkwAetCBk1Wkbj2NjxZRFhoyAQAAAIDuJJAJANDDRprpm9GzC+nPcaYd0UncXB9u2FgHAAAAoD/s3JK+DXvwZMTxeS2ZAPSWyePpv7smNtkzAgAAAAC6l0AmAEAPazXTZ+QIZM4tps8YSmz7BAAAAIBusWmoiEs3ps+554iWTAB6y+SJ9EDm+KhAJgAAAADQvQQyAQB6WCtDq+TsYvrG+HyGQOawQCYAAAAAfSRHS+beaQ2ZAPSWqQwNmQKZAAAAAEA3E8gEAOhhORoyZ7I0ZKZvrg/Vba4DAAAA0D92jaWvd+2b1pAJQO+oqiqmEhsyyyLisvPsGQEAAAAA3UsgEwCgh41kCGTOLqSHKU8vpT+HhkwAAAAA+sl1Y+lbsQdORpw4rSUTgN5wZC5idjFtxiUbIta5xBMAAAAA6GICmQAAPazVSN+QTt0Yj4iYyzBjSCATAAAAgD6yeaiISzakz7lnWiATgN4wdTz9d9bEqKNMAAAAAEB3s4oJANDDWlkaMtNnzC2mb7APZwiXAgAAAEA32ZmhJXPvdCfDkwDAyps8kb5fNL7JfhEAAAAA0N0EMgEAelirmb4pPbOQvjk+ryETAAAAAH7CrrH09bt9RzRkAtAbpk6kXyIwMSqQCQAAAAB0N4FMAIAeNpKjITNDmHIuw4xhgUwAAAAA+kyOhszHnqrixGmhTAC639RxDZkAAAAAQP8TyAQA6GGtDCHG2RwNmUvpM4brNtgBAAAA6C+bh4q4eEP6nHu0ZALQ5WYXqjg8mzZjdH3E6Hr7RQAAAABAdxPIBADoYY1aEc1a2ozZhfTnyNGQOaQhEwAAAIA+tGtL+pbsvulOhicBgJUzdSL98oAJ7ZgAAAAAQA8QyAQA6HGpLZkzGcKU8xlmDAtkAgAAANCHrhtLD5fsm9aQCUB3yxLIHBXIBAAAAAC6n0AmAECPG2mmbU7PLaRvkM8tps9YV08eAQAAAABdZ+dY+pbso09VcfKMUCYA3WvqePrvqXGBTAAAAACgBwhkAgD0uOFm2utnFtKfIbUhc6geURY22QEAAADoPxcMF3HxSPqce7RkAtDFJjM0ZI5vcowJAAAAAOh+VjIBAHrcSGIgc24xolOlbZLPLaU9w1Aj7fUAAAAA0M2uy9CSuXe6k+FJACC/pU4Vjz6VttfUrEWWCwwAAAAAAFaaQCYAQI9rNdKaJatIb7icX0zbZB8WyAQAAACgj+0aS1vDi4jYpyETgC518GQVS4n3BmwfLaJWpv++BAAAAABYaQKZAAA9rpXYkBkRMbuQ9vq5xEDnUGKoFAAAAAC62c4t6duy+5+q4tQZoUwAus/UifTfT+Oj9ooAAAAAgN4gkAkA0ONaGdolZxIbLk8vpb3/cD3t9QAAAADQzS5oFXFhK33OviMCmQB0n8nj6b+fJgQyAQAAAIAeIZAJANDjRprpG9QpDZmL7SoW2mnvP5QhVAoAAAAA3WzXWPrW7L7pToYnAYC8cjRkTmwSyAQAAAAAeoNAJgBAj2s102ekBDJT2zEjIoYbNtkBAAAA6G87x9LXwPZNa8gEoLtUVZUcyCwi4rLz7BUBAAAAAL1BIBMAoMe1cjRkLi5/o3xuMfntNWQCAAAA0Pd2ZmjI3H+iilNnhDIB6B7H5iNOnkmbcdGGiCGXdwIAAAAAPUIgEwCgx7UyhBlnEhoy5xLCnM8YFsgEAAAAoM9taRVxYSttRhUR9xwRyASge0wmtmNGREyMOr4EAAAAAPQOK5oAAD1upJk+Y3Zh+Zvl81kaMt16DAAAAED/y9GSuW+6k+FJACCPqePpgczxUftEAAAAAEDvEMgEAOhxrQxhxtmEUOXcUvLbx3A9fQYAAAAAdLudY+lrefumNWQC0D2mcjRkbhLIBAAAAAB6h0AmAECPa2VpyFz+a+cX0zfahxvJIwAAAACg6+3ckr49O3WiipkFoUwAusPkifTmZg2ZAAAAAEAvEcgEAOhxQ42I1G3qlANccwntms8YytDyCQAAAADdbmykiLFW2owqIu45IpAJwNqbX6ziiVNpMzaui9g8lOd5AAAAAABWg0AmAECPK4siuSVzNiFUOZ8jkFlPnwEAAAAAvSBHS+a+6fQ2MgBItf+pKlKvCBgfLaIoXNwJAAAAAPQOgUwAgD7QaqS9fjapITP9Nv6hxOcHAAAAgF6xcyw9dLJ3WkMmAGtv6nj676OJTcKYAAAAAEBvEcgEAOgDrWbaZvXswvJfO7+U9NYRETHcsNkOAAAAwGDYNZa+RTt1vEq6ZA0Acpg8kf67aHzUHhEAAAAA0FsEMgEA+kByQ+bi8l87l/DaZwxryAQAAABgQIyNFHHBcNqMKiLuOSKQCcDaytGQKZAJAAAAAPQagUwAgD7Qaqa9fqEdsdBe3qb5/GL6Zvv6evIIAAAAAOgZOVoy9053MjwJACxPu1PF/qfS9ogaZcSlGwUyAQAAAIDeIpAJANAHWs30zerZheW9bj6xIbNZi2jUbLYDAAAAMDh2jqWvh90zrSETgLVzaObpCz9TXHZeEfXSHhEAAAAA0FsEMgEA+sBIYkNmRMTsMoOVc0tp7zukHRMAAACAAbMzQ0Pm5IkqZheEMgFYG1PH05uax0eFMQEAAACA3iOQCQDQB1qNHA2Zyzu8Nb+YduhruJH0cgAAAADoORe2Ii4YTpvRqSLuPSKQCcDamDyR/jtofJNAJgAAAADQewQyAQD6QCtDQ+bMwvJeN7fMZs1nDGUIkwIAAABALymKIktL5r4j6e1kALAck8fTA5kTGjIBAAAAgB4kkAkA0AdaGVoml9+Qmfa+GjIBAAAAGEQ7t6SHUPZOa8gEYG1MZWjI3C6QCQAAAAD0IIFMAIA+MNJM37CeXUawslNVMb+U9r5DApkAAAAADKBdGRoyJ49Xy75oDQCW6/h8FSdOp824sBXRyrC/BQAAAACw2gQyAQD6QKuZPmN24dxfczoxjBkRMVy32Q4AAADA4LlwJGLzUNqMThVx31GBTABWV452zPFN9ocAAAAAgN4kkAkA0Ady3CA8s4yb9OeW0ar5bBoyAQAAABhERVFkacncN93J8DQAcPYmMwQyJ0YdWQIAAAAAepPVTQCAPtDKEGqcXUa4cl4gEwAAAACWbedY+kVre6c1ZAKwuvYf15AJAAAAAAwugUwAgD7QaqbPmF1WQ2b6hvtQ3YY7AAAAAINpZ4aGzEeOV1nW6QDgbE2eSG9nHh+1PwQAAAAA9CaBTACAPtCsFdGspc2YXTj31+RoyBzWkAkAAADAgLp4JGLzUNqMThVx3xGBTABWx5mlKh4/lTaj1YjYMpzneQAAAAAAVptAJgBAn2glBhtnlhGunFtKe8+IiCGBTAAAAAAGVFEUWVoy906nN5UBwNl49KkqOon3AExsKqIoNGQCAAAAAL1JIBMAoE+0mmmvn1s4993z+cX0m/eHGzbcAQAAABhcO7ekr4/t05AJwCqZOpH+O2d81N4QAAAAANC7BDIBAPpEq5m2eT2zcO6vmV9Gq+azDWvIBAAAAGCA7crQkPnwsSrL5WkA8GImjwtkAgAAAACDTSATAKBPtBKDjXOLEZ3q3DbR5zIEMocEMgEAAAAYYBdviNi0Pm1Gp4q496hAJgArbzJDQ+bEJoFMAAAAAKB3CWQCAPSJkcSGzCrOvfFyfil90324btMdAAAAgMFVFEXszNCSec90J8PTAMDz61RV7E8MZNbLiK0b7Q0BAAAAAL1LIBMAoE+0mukzZhfO7c9ryAQAAACAdDvH0oMpe6c1ZAKwsg7PRJxeSpuxdWMRjZpAJgAAAADQuwQyAQD6RCtDsHFm8dwObZ1ro+ZzGRbIBAAAAGDA7crQkPnQsSrmz3F9DwDOxWRiO2ZExPioMCYAAAAA0NsEMgEA+kSrmb6Bfe4NmWkb72UR0awljQAAAACAnnfJhojR9WkzOlXE/UcFMgFYOVPHO8kzJjYJZAIAAAAAvU0gEwCgT4w002ecayAztSFzuBFRFDbeAQAAABhsRVHEzgwtmXun04MyAPB8NGQCAAAAAAhkAgD0jVYjQ0PmOTZezi2lvd9QPe31AAAAANAvdo2lr+/tm9aQCcDKmToukAkAAAAAIJAJANAnWhkaMmfOuSEzbeN9OEOIFAAAAAD6wc4t6Vu3Dx2r4vSSUCYA+Z08U8WT82kzLhiO2LDO3hAAAAAA0NsEMgEA+kSOQObswjk2ZC6mvd/6RtrrAQAAAKBfXLox4rx1aTPaVcR9RwUyAchPOyYAAAAAwNMEMgEA+sRIhrbJ2XMIWFZVFfOJgcxhgUwAAAAAiIiIoihi51j69u2+6U6GpwGAHzd5Ij2QObFJIBMAAAAA6H0CmQAAfSJPQ+bZ/9mF9tM37qcYqtt4BwAAAIBn7BxLXy/bN60hE4D8pnIEMkcdUwIAAAAAep+VTgCAPjHUiEg9rjWzcPab6fNLiW8WGjIBAAAA4EftytCQ+dCxKk4vCWUCkNfU8fTfLeOjLuoEAAAAAHqfQCYAQJ8oiyI54Di7ePZ/du4c/uzzGRLIBAAAAIC/s3VjxMZ1aTOWOhH3HxXIBCCfxXYVB06m/W4ZqkeMjWR6IAAAAACANSSQCQDQR0aaaa+fPZeGzMX0Q13DDTchAwAAAMAziqKInWPpa2b7pjsZngYAnvbYySraidtC46NFlIV9IQAAAACg9wlkAgD0keFm2kb27MLZ/9n5DA2ZqY2eAAAAANBvdo2lb+Hum9aQCUA+k8fTf6+MbxLGBAAAAAD6g0AmAEAfGUkMOM6eQ8hybintvSIihurpMwAAAACgn+RoyHzwWBVnloQyAchj6kSGQOaoQCYAAAAA0B8EMgEA+kirmfb6hXbEQvvsNtXnF9M334cbNt8BAAAA4Edt21jExnVpM5Y6EQ88KZAJQB45GjInNGQCAAAAAH1CIBMAoI+0mumb2bMLZ/fn5s6hTfP5DCU2egIAAABAvymKIq7bkr7Ot3e6k+FpABh0VVUlN2SWxdMXDgAAAAAA9AOBTACAPtLKEHCcPcug5XyGQOawQCYAAAAA/IRdY+nbuPumNWQCkO7IbPolnZduLGJdXSATAAAAAOgPApkAAH1kJEtD5tkd1JpbTD/QpSETAAAAAH7SzrH0db4HnqzizJJQJgBpJhPbMSMixkeFMQEAAACA/iGQCQDQR1rN9BmzC2f35+aX0t9r2G3IAAAAAPATtp1XxIbEtb6lztOhTABIMXm8kzxjQiATAAAAAOgjApkAAH2klaFxcuasGzLT30tDJgAAAAD8pLIosrRk7ptOD9EAMNimcjRkbhLIBAAAAAD6h0AmAEAfGWmmb2jPnmXQcn4xfQN+fT15BAAAAAD0pZ1j6Vu5+6Y1ZAKQJksgU0MmAAAAANBHBDIBAPpIq5k+Y3bh7P5cakPm+npErbQBDwAAAADPJUdD5gNPVrHQFsoEYHlmF6qYnk2bsWl9xOh6+0EAAAAAQP8QyAQA6COtLA2ZZ3dAaz4xkDmkHRMAAAAAntdl5xUxkngB22In4oGjApkALE+OdsyJTcKYAAAAAEB/EcgEAOgjrUb6jJmzbchcStuEH87wrAAAAADQr8qiiOu2pIdY9h0RyARgeXIEMsdHBTIBAAAAgP4ikAkA0EdaiTfmR0TMLqxSQ2bDBjwAAAAAvJBdY+nbufumOxmeBIBBNHlcQyYAAAAAwLMJZAIA9JFmrYhmLW3G7Fk2ZKYGMjVkAgAAAMAL2zmWHmK5/2gVC20tmQCcu8ksDZmOJgEAAAAA/cWqJwBAn2klBh1nzyJo2e5Ucaad9j5DApkAAAAA8IK2jxYx0kybsdiJePBJgUwAzs1Sp4rHnkr7/bGuFnHRSKYHAgAAAADoEgKZAAB9ppV4QGt24cU31+eX0t4jImK4nn67PwAAAAD0s7Io4rot6eto+6YFMgE4NwdPVrHUSZuxfbSIWmk/CAAAAADoLwKZAAB9ptVM29g+m4bMubP4My9GQyYAAAAAvLidY+lbununExM1AAycyePpYf6JTcKYAAAAAED/EcgEAOgzrcSg4+xCRKd64U32+cX0TfhhgUwAAAAAeFE7x9LDLA88WcViW0smAGdv8kT6743xUYFMAAAAAKD/CGQCAPSZkcSGzCoi5l+kATNPQ6ZNeAAAAAB4MdvPK5IvYVtoRzz4pEAmAGdvKkNDpkAmAAAAANCPBDIBAPpMq5k+Y3bhhf/5iwU2z4aGTAAAAAB4cbWyiOsytGTunRbIBODsVFUVU4kNmUVEbBfIBAAAAAD6kEAmAECfSb0tPyJidvGFN9nnltIPbw3Vk0cAAAAAwEDYuSV9W/eeI50MTwLAIHhyPuLUi1ze+WIu2RCxvi6QCQAAAAD0H4FMAIA+02qmb27PrEJD5lDDJjwAAAAAnI2dGRoy7ztaxWJbSyYAL27qePrvi+2jjiQBAAAAAP3J6icAQJ8ZaabPmH2RwOVchkDmcIYmTwAAAAAYBOOjRfJ62kI74qFjApkAvLjJE+m/LyY2uZgTAAAAAOhPApkAAH2mlaF5cnbhhTfa5xfTN+KHBDIBAAAA4KzUyiKu25K+7rdvWiATgBc3daKTPGNiVCATAAAAAOhPApkAAH2mlaMhc+GF/3mWhsy6jXgAAAAAOFs7x9K3dvdOpwdsAOh/U8fTA/zjGjIBAAAAgD4lkAkA0GdyBDJnXrQhM/09NGQCAAAAwNnbNZYebLnvaBWLbS2ZADy/+cUqDs2kzdi4LmLT+jzPAwAAAADQbQQyAQD6zEgj/WDW7IsELueXkt8ihgUyAQAAAOCsjY8WyWtqC+2IhzO0ngHQv6ZOpP+emBgtoig0ZAIAAAAA/UkgEwCgz+RoyJxdeOF/PreYthlfLyMaNRvxAAAAAHC2amUR125JX1Pbe1ggE4DnlyWQuckeEAAAAADQvwQyAQD6zFAjInWbe/ZFApfzL9Kg+WK0YwIAAADAuds5lr69u2+6k+FJAOhXUxmalMdHBTIBAAAAgP4lkAkA0GfKokgOPM68WEPmUtr8oXra6wEAAABgEO3M0JB539EqljpaMgF4bpMZGjLHNWQCAAAAAH1MIBMAoA+NNNNeP/cigcz5F2nQfDHDDRvxAAAAAHCudmwqki87O9OOePiYQCYAP6ndqeLRp9J+RzTKiEs32AcCAAAAAPqXQCYAQB8abqZtdM8svPBm+9xi0vgYSmzwBAAAAIBBVCuLuDZDS+beaYFMAH7SoVMRC+20GZeNFlErBTIBAAAAgP4lkAkA0IdGEgOPsy8QuKyqKuYTA5nDApkAAAAAsCw7x9K3ePdNdzI8CQD9ZvJE+u+HiVFhTAAAAACgvwlkAgD0oVYz7fUL7YjF9nPfkn96KSL1/vyhhs14AAAAAFiOXWPpa2v3Ha1iqaMlE4AfN3k8/XfDuEAmAAAAANDnBDIBAPpQq5m+2f18LZnzS8mjNWQCAAAAwDLt2FTE+nrajNNLEQ8fE8gE4MdNnUj/3TCxSSATAAAAAOhvApkAAH2olSHwOLPw3P/73PMENc/FUOKBMQAAAAAYVLWyiGu3pIdd9h0RyATgx+UIZG7XkAkAAAAA9DlH4QEA+tBIjobMhSoifnLO/GL6ZvxQw2Y8AAAAACzXzrEyvn+onTRj33Qnfv7aWqYnAjg384tV/JdHIu5c2Blz1froRBnnlydj08GIV26r7COsgePzVZw4nTbjopGIYZ87AAAAAKDPCWQCAPShVjN9xuwKNmQOZ2jwBAAAAIBBtWssPexy75Eq2p0qaqXgDLB6ljpVfGxvO/7Tg52YWywi4rK/+2eHOhfE3m9FrP/eYrx2Wxm37SjjmguKKAo/p1bDZIZ2zHHtmAAAAADAABDIBADoQ60MgcfZ52nCnM8QyBzyt1AAAAAAWLYdm4pYX484vbT8GaeXIh45XsWV5wvPAKvjzFIVf3jnUnz38RcO/p1eivjSZCe+NNmJSzZE3DpRi1smytg85OfVSpo6niOQWWZ4EgAAAACA7mYlFACgD7Wa6YcSZp63ITN9Q3644dAEAAAAACxXvSzimgvS19j2Tqev9QGcjaqq4g++9uJhzGd7/FTEX9zVjn/8qcX4119ZjG8c6MRi28+ulTCVoSFzYpP9HwAAAACg/+kmAgDoQyPN9BmzzxPInE+4df8ZQxkaPAEAAABgkO0aK+MHT7STZuyb7sTPXVvL9EQAz++bB6v4wRPLD/x1qojvPl7Fdx9fio3rIm4eL+PWiTK2a2TMZvJEJ3nGxKhAJgAAAADQ/wQyAQD6UCtDA+Xs8zRhzi8mj45hgUwAAAAASLJzLH0N8N4jVbQ7VdRKARpg5bQ7Vfz/fpDhtse/dfJMxKfv78Sn7+/ElZuLuHVHGTdeVkar6WfZcp1equLxk2kzRpoR5w/neR4AAAAAgG4mkAkA0IdaK9iQOfc8Qc1zMZQhMAoAAAAAg+zyzUWsq0WcSSjJnF+KeOR4FVeeb70OWDl3Ha7i0MzKzH7wWBUPHmvH//r9drxmaxm37ihj51gRZeHn2rl49KkqUnd/xkeLKHzcAQAAAIABIJAJANCHcgQyZxaepyEzwyXWGjIBAAAAIE29LOLaLUX84Im0CM2+6SquPD/TQwE8hzsmOyv+HgvtiC/v78SX93fiwlbE7ola7J4oY0tLQPBsTB1Pv4xzYpOPNQAAAAAwGMq1fgAAAPJr1opo1tJmzC2e2/9+LoZcCwIAAAAAya7bkr7du+/IygelgME1u1DFNw+u7s+Zw7MRH9nbjn/y6cX4H+5YjK892o6FdnrgsJ9Nnkj/+IyPCmQCAAAAAIPBUXgAgD413Hj6Rujlmll47v99fjFtU76IiPX+FgoAAAAAyXZdWETcnTbj3iNVtDtV1EpBGiC/Ox/rJO1VpKgi4gdPVPGDJ9ox0mzH67eXcduOMnZscnf5s+VoyBTIBAAAAAAGhaPwAAB9aqQZceL08l8/u/Dcm++pDZlDjYiisCkPAAAAAKku31TEulrEmYSw09zi081oV2y2Zgfk96XJ7mjhnVmI+NyDnfjcg52YGC3i1h1l3LS9jA3r/OzrVFXsfyotkFkvI7Zu9LEEAAAAAAaDa/8AAPpUq5G28T37PMHL+cRA5nAj7fUAAAAAwNMatSKuviA9ALNvOr0ZDeDZHj9Vxf1Hu+/ny+SJKv78e+34tb9ejD/8+lJ8/1An2p3ue87V8sRMxOmltBnbNhbRqAlkAgAAAACDQSATAKBPtZppr59bfPpW5J/839MOJQzVbcgDAAAAQC67xtK3fPdNd0eDHdBf7phMqO9dBUudiDsf68T/+OWl+CefXowP3bUUT8wMXjBz6nj6v/P4Jns/AAAAAMDgqK/1AwAAsDJazSIilr+J3qmebsN8drBzPvGW5CENmQAAAACQzc6x9BDMPUeqaHeqqJUCNUAenaqKO6Z6J+z95HzEX93Tib+6pxO7xoq4daKM12wrY90AXDI5eSL98zQ+2v8fJwAAAACAZwhkAgD0qZHEhsyIiNnnCGTOLabNHBbIBAAAAIBsrthcRLMWsZBQRDe3GDF1oorLNwvUAHnsPVzF0bm1forl2Ttdxd7pdvzZ99rxusvKuG1HGVduLqIo+vNn5GSGhswJgUwAAAAAYIAIZAIA9KlWhuDj7EIV0fr7TfTFdhVLiRcla8gEAAAAgHwatSKuuaCIuw6nBWr2TVdx+eZMDwUMvD091I75fOYWI77wcCe+8HAntm0s4tYdZdw8Xsbo+v4KH06dSA9kjm/qr48JAAAAAMALKdf6AQAAWBmtZvrm9+zCj///qe2YERHDdZvyAAAAAJDTzrH0bd+9070fngK6w/xiFd94rL9+pjx2sooP/KAd//VfL8bvf3UxvnOwE+1OepBxrT11uopj82kztgxHjGTYkwIAAAAA6BUaMgEA+lSOhsyZZwUw55fSZ2rIBAAAAIC8dm5JD8Lce6SKdqeKWilUA6S587FOnGmv9VOsjHYV8a2DVXzr4FKMro/YPVHGrRO1uHRjb/7s1I4JAAAAAHDuBDIBAPpUjtuIZxd+fCN+fjF9Y35YIBMAAAAAsrry/CKatYiFhADU7GLE/qeq2CFYAyTaM9lf7ZjP58TpiE/c24lP3NuJay4o4rYdZbx2WxlDjd75OTqZIZA5Mdo7/74AAAAAADmUa/0AAACsjFYzfcbswo///3OLz/3nzkUvHUQAAAAAgF7QqBVx9fnp6277ptODOcBge2KminuOpP8s2TVWxG0TZazvkWvG7ztaxb//Vjt+7a8X43/+5lLce6QTVdX9P1P3H8/RkOnoEQAAAAAwWHpk6RoAgHOVJZC5+OyGzPSZGjIBAAAAIL+dY2XcPZ1QkRkR+6Y78bara5meCBhEd0ym/Rx6xjuuqcXLLynjV3+qijsf68SXHunEvUe7P+B4einiS5Od+NJkJy7eEHHbRC1umShj81B3XlapIRMAAAAA4NwJZAIA9KlWhibKmZ9oyEzfmB8SyAQAAACA7HaOpa8H3nOkik5VRVkI1wDnrlNV8eWpTvKc0fURL73o6Z9DQ40ibttRi9t21OLgySr2TLZjz2Qnjp9OfpsVd+hUxF/c1Y4P3d2Ol11cxG0TtXj5JUU0at3xM3ahXcWBk2n7PsONiLFWpgcCAAAAAOgRApkAAH1qJEND5tyzApnzS+kzh+vdcdAAAAAAAPrJlecX0axFLCSU080sROw/UcXEJmt4wLm790gVh2fT59w8Xkat/MmfQ5duLOKXX1KP26+v4vtPVPHFR9rxnYNVtLu8OLNTRXz38Sq++/hSbFz39L/frRNlbB8t1/S5Hnuqik7ix258tIhCiB8AAAAAGDACmQAAfWqoEVFERMpe+syzGjHnFpMeKSI0ZAIAAADASmjWirjq/CL2Tqela/ZNVzGxKdNDAQNlz2R6O2ZExC3jLxxUrJVFvOKSIl5xSRknTlfxlalOfPGRTjyW2Pa4Gk6eifj0/Z349P2duGJzEbftKOPGy8poNVc/1Dh1Iv3jNT4qjAkAAAAADB6BTACAPlUWRQw3ImYTQpSzz27IXEzfnB8WyAQAAACAFbFzrIy90wkVmRHxzQOdeOvVtUxPBAyK00tV3PlYeiBzYrQ6p+bI0fVFvP2aWrzt6jIeOlbFFx/pxNce7WS5YHKlPXSsioeOteN//X47Xr21jNt2lLFzrIhylRonJ4+n7/loVAYAAAAABpFAJgBAH2s18wYy8zRk2pwHAAAAgJWwa6yIjybO2HekirsPd+L6C88+EAXwjcc6cXopfc5Nly3vdUVRxJXnF3Hl+WX8o5dV8Y0DT7dmprYGr4aFdsRX9nfiK/s7MdaKuHWiFrsnytjSWtn9FA2ZAAAAAADLI5AJANDHWs0iYnb5G+ozCz/+2vkcgUx/AwUAAACAFXHl+UU0yojFxJK6D97Vjv/pDUUUq9TSBvS+PZPp7ZhldOK1W9N/7qyrF3HzeC1uHq/FEzNV7Jlsx57JThydSx694qZnIz6ytx0f3duOGy4s4rYdZfz01jKatbw/j6uqSg5klkXEtvP8ngAAAAAABo/j8AAAfazVSHv9s9s155bSb0seSnwmAAAAAOC5NWtFXHV+EfuOpK3jPfBkFd99vIpXXCpoA7y46dkqSxPl1nI6Nq67MMMT/b2LRoq4/fp6vGtnFXcfruKLk5345oFOLKXnR1dUFRE/PFzFDw+3Y6TZjtdvL+O2HWXs2JSnvXh6NmIu8RLOrRuL7EFRAAAAAIBeIJAJANDHRpppr19oRyy2q2j87YZ6akNmsxZRL23OAwAAAMBK2TmWHsiMiPjg3e34qUuKKLVkAi/iy1OdSP+pE3F5/WBE5A1kPqNWFvHSi4t46cVlnDpTxVf3d+KLk52YPJ7jyVfWzELE5x7sxOce7MT46NOtmTdtL2PDuuX/fJ5MbMeMiJgY9fsBAAAAABhMea7OAwCgK7Ua6ZvhP9qSmXpb8rB2TAAAAABYUS+7OM8W8P4TVdz5aJdXyAFrrqqquGOynTxnfZyJreWRDE/04jasK+ItV9Xij97UiD98Uz3ecmWZfMHlapk6UcWff68dv/bXi/GHX1+K7x/qRLtz7uHKqePpP9+3bxLIBAAAAAAGk4ZMAIA+1spwgGBmIWJ0/dP/fX4x7cbkIYFMAAAAAFhRV51fxCUbIh4/lT7rw3vb8ZptZdRKoRvgud13tIpDM+lzJmqPR1msflvljk1l7Hh5Gf/Hl1bxrYOd+NIjnfjhE1WWxs+VtNSJuPOxTtz5WCfOH4rYPVHGrRO1uGjD2f281pAJAAAAALB8ApkAAH2s1czQkLlQRcTTc+aX0mYN123OAwAAAMBKKooifuaqWvzpd9Mb6w6ditgz2Yk3XF7L8GRAP7pjMk+T7hX1g1nmLFezVsSNl9XixstqcWS2ij2Tndgz2Y7Ds2v6WGflyfmIv7qnE391Tyd2jRVx60QZr9lWxroX2JOZOp4eyBwXyAQAAAAABlS51g8AAMDKGcnQkDm78Pf/fW4xbZaGTAAAAABYeW/YUcaW4TyzPravHYvtbu+KA9bCmaUqvv5YeiBzU3EyNpcZan0z2dIq4l27avHv39qI39tdj5u3l9HskVz63ukq/t032/Grn1yM/+XbS/HAk52oqh//GT6zUMWRubT32TwUcd56gUwAAAAAYDBpyAQA6GPDGQKQs4tPb9S3O1WcTm3IFMgEAAAAgBXXqBXx7l21+J+/ld6SeXQu4n9/uBM/c1WPpJGAVfOtg53kixwjIi5f43bM51MWRVx/YRHXX1jGry9U8bVHO/GlRzrx4LHuD6nPL0V84eFOfOHhTmzbWMStO8q4ebyM0fWFdkwAAAAAgEQCmQAAfWykmb4hPvO3DZmpYcyIiKGGDXoAAAAAWA03j5fxifvacfBk+qy/2teOWydK63vAj9kzmd6OWUQndtQez/A0K6vVLOJNV9TiTVfUYv+JTnxpshNfnurEyTNr/WQv7rGTVXzgB+34ix+24+WXFDFUT/9ZPrHJ7wMAAAAAYHCVa/0AAACsnFYzfcbs3wYy5zPccq0hEwAAAABWR60s4vbr89zP+9SZiM8+kB68AvrHk3NV3HU4vWnx0vJIDBULGZ5o9WwfLeMfvawef/r2RvzLG+vxikuKKHsgn9iuIr51sIov70//eT4x6rgRAAAAADC4NGQCAPSxkQw31s8uPn2gYm4p/WDFkL99AgAAAMCqefXWIiY2FTF5PH1t76/va8ebryxjpNkDqSNgxX15qhOd9B8tcUX9YPqQNdKoFfHqrUW8emsZx+aruGOqE196pB2Pn1rrJ1t546N+FwAAAAAAg8uVdQAAfWy46xoybdADAAAAwGopiyJ+6fpallmzi0+HMgGqqoo9U+k/D0YaVWwtj2R4orW3eaiIn7+2Fn/ylkb869vqcdtEGev79JLK9fWIC0fW+ikAAAAAANaOQCYAQB8byRDInFn424bMDIHMoUb6DAAAAADg7L3s4iKuvSDPRWmfub8TJ05nqMQDetqDx6o4eDJ9zmu3RdSKTvqgLlIURVy7pYz3vaoef/6ORrzvp2vZfgZ3i+3nFVEr++vfCQAAAADgXAhkAgD0sWatiEbi3/ieCWLmachMnwEAAAAAnL2iKOK9N+RpyTzTjvj4PVoyYdDtmcwTorzpsixjutZQo4jbdtTiX7+hEX/ylkb83LVljK5f66dKN75JGBMAAAAAGGwCmQAAfa6V2JI5s/D0/51bTL/5fqhukx4AAAAAVtvOsTJeelGetbnPP9SJI7NaMmFQLbSr+Nr+9EDmtvOK2DGa/jy94tKNRfwfXlKPP317I/7V6+vxqq1F1Hp0y2R8tEcfHAAAAAAgE4FMAIA+lxrInF14+nDV/FL6swxpyAQAAACANfFLmVoylzoRH9unJRMG1XcOVjG7mD5n93gZxQDm+mplEa+4tIz/7sZG/Ok7GvErL63Fto299YGY0JAJAAAAAAw4gUwAgD430kjbGH/mYMVchgMWwwKZAAAAALAmLt9cxqu35gnR7JnsxOMntWTCIPrSZHoguywibhp3XGV0fRFvv6YW/89/UI8/+K/q8cbLy67fRymLiO3nCWQCAAAAAIPNCjcAQJ9LbcicW4zoVFXMLaYfsBpKDIcCAAAAAMt3+/X1yLFC16kiPrJXSyYMmmPzVfzgifS9gpdeVMTmIfsFzyiKIq48v4x/8sp6/Pk7GvGbr67FrrHu/PhcvCFiXb07nw0AAAAAYLUIZAIA9LlWM21jvFNFzC9GnNaQCQAAAAA9bdt5RdycqZXua492YvJ4J8ssoDd8ZX8nOhnKcXdPOKryfNbVi7h5vBb/w62N+A9vbcQ7d5Zx/tBaP9XfGx/1uQMAAAAAsFIKANDnWhlCkLOLEXNL6XOG6ukzAAAAAIDle/euWtQz7RJ/+G4tmTAoqqqKOybTQ9itRsQrL3VU5WxcNFLE7dfX4//1tkb89zfX43WXldl+fi/XxKh2TAAAAAAAR+IBAPrcSDN9xuxCFfOLadde14qIZi39WQAAAACA5btwpIjbdpTx+YfSg1XfebyK+4924uoLhKug3z1yvIpHn0qvx7xxexnNmlDfuaiVRbz04iJeenEZp85U8dX9nfjiI52YPJGhrvQcjW/yuQMAAAAAsDMGANDnhpvpm+OzCxFzi2kzhhoRRWGjHgAAAADW2jt31rJdnvbBu9pRVasfCgJW154M7ZgREbeMO6aSYsO6It5yVS3+6M2N+MM31eMtV5ZZLuY8WzsEMgEAAAAABDIBAPrdSCN9xsxixHxiIHM4w3MAAAAAAOk2DxXxlivzbBXvna7irsMCmdDPFttPtzKmunRDxFXnC/TlsmNTGb/+8nr82Tsa8X9+bS1eelERK/nRveaCIkbX+/wBAAAAANTX+gEAAFhZrSwNmVXMLaYdqhpq2KQHAAAAgG7xc9fW4vMPdWJ+KX3Wh+5qxw0XFlEU1gChH333UBWnFtLn3DJR83NiBTRrRdx4WS1uvKwWR2ar2DPZiT2T7Tg8m/d9brzMne8AAAAAABEaMgEA+t5IM33G7EIkH8wadhUIAAAAAHSNDeuKePs1tSyzHjxWxbcPasmEfrVnsp08o4iIm8cdUVlpW1pFvGtXLf79Wxvxe7vrcfP2MpoZftRfdl4Rb7jc5w8AAAAAIEIgEwCg7w030mfMLFQxt5g2YyjDcwAAAAAA+bzt6jI2ZLjQLSLiQ3e3o90RyoR+c+J0Fd97PP17+4YLi7hgWDvmaimLIq6/sIzffE09/vwdjfg/vaIWV25e3se/WYv4F6+pRbPm8wcAAAAAECGQCQDQ90aa6Rvkx+cjUs9SDTds1AMAAABANxluFPFz1+VpyXz0qSq+/mgnyyyge3x1fyfaGbLWuyccT1krrWYRb7qiFn/wxkb8P95cj7ddXcbGdWf32gtbEf/TG+qxfdTnDwAAAADgGVZMAQD63EiGG+6PzKWfthiqpz8HAAAAAJDXP7iijM1DeWZ9ZG87lrRkQl+5YzI9aD1Uj3jVVsdTusH20TL+0cvq8advb8S/vLEeL7+kiMZzfGrOH4r4xevK+MM3NWJik88dAAAAAMCPciweAKDPDTUiiohIOQaVJZDZSB4BAAAAAGS2rl7EO3fW4v/9nXbyrCdmIr70SCfeeEWe1k1gbU0e78TkifT9gdddVsa6epHhicilUSvi1VuLePXWMhbbVUydqOLxU1W0mkWcty5ix6YiaqXPGQAAAADAc3GNHQBAnyuLIoYTw5BHZ9OfY7hh4x4AAAAAutGtE2Vc2Moz62P72rHQ1pIJ/eCOqfR2zIiI3ROOpnSzRq2IK88v4+bxWrzikjKuPL8UxgQAAAAAeAFWvQEABkCrmfb6xQxnLjRkAgAAAEB3atSKePeuPK2Wx+YjPv9QnhAXsHaWOlV8OUMg86KRiGsuEO4DAAAAAKB/CGQCAAyAVnPtDzuktnQCAAAAACvn9dvL2LYxzzrix+9px/yilkzoZd8/VMXJM+lzbhmvRVGs/R4FAAAAAADkIpAJADAAWl0QhhxqOHABAAAAAN2qVhZx+w15WjJPnon49P1aMqGX7ZlsZ5lzy4RjKQAAAAAA9Bcr3wAAA2CkudZPEDFcX+snAAAAAABeyKsuLeLyzXkuVvvU/e04dUZLJvSiU2eq+M7j6d+/u8aKGGu5rBEAAAAAgP4ikAkAMABaXdBOOdQFLZ0AAAAAwPMriiJ+6fo8LZlzixGfvC9Pwx6wur66vxNLGUpud2vHBAAAAACgD1n9BgAYAK1uaMjsglAoAAAAAPDCXnJRETu35FnL++wDnTg2ryUTes2eqfQ05vp6xKu3OpICAAAAAED/sfoNADAAWs21D0NqyAQAAACA7lcURbz3hjwtmQvtiI/foyUTesmjT3Xi4WPpQerXbCtjyEWNAAAAAAD0IYFMAIABMNIVDZlr/QQAAAAAwNm4dksZP3VxniDVFx7uxPSMlkzoFXdMprdjRkTsHnccBQAAAACA/mQFHABgAHRDGHJ9fa2fAAAAAAA4W7laMpc6ER/dpyUTekG7U8UdU+mBzLFWxHVj2jEBAAAAAOhPApkAAANgpLm2Bx+G6hFl4fAFAAAAAPSKHZvKeO22PNvJX57qxIGTWjKh2/3wiSpOnE6fc8t4aU8AAAAAAIC+JZAJADAAWs21ff+hLmjoBAAAAADOzXuur0WZIVPVqSI+creWTOh2X5pMb8eMiLhlPE/DLgAAAAAAdCOBTACAATDSWOOGTIFMAAAAAOg5WzcWcct4ni3lOx/rxCPH84S9gPxmFqr49sH079HrthRx0QbtmAAAAAAA9C+BTACAATC8xg2Zw3WHLwAAAACgF71rVy3qmXaVP3SXlkzoVl9/tBOLGTLTuyccQwEAAAAAoL9ZCQcAGAAjaxzI1JAJAAAAAL1prFXEf3V5nm3l7x2q4t4jWjKhG+2ZTP/ebNYiXrPNMRQAAAAAAPqblXAAgAHQrBXRWMO/+Q0LZAIAAABAz/rF62rRrOWZ9cG72lFVVZ5hQBYHT1bxwJPp35ev2VrGcKPI8EQAAAAAANC9BDIBAAZEaw1bMoccwAAAAACAnrVpqIi3XpVna/meI1X84AmBTOgmeybbWebcMuEICgAAAAAA/c9qOADAgFjLQKaGTAAAAADobe+4ppZtne9DWjKha7Q7Vdwx1Umec8FwxK4xlzMCAAAAAND/BDIBAAbEyBq2VA7V1+ytAQAAAIAMNqwr4h3X1LLMevh4Fd88IJAJ3eDu6SqOzafPuXm8jFopkAkAAAAAQP8TyAQAGBBr25DpEAYAAAAA9Lq3XlXGxnV5Zn3o7qVod4QyYa3tmUxvx4yI2D2eJ7ANAAAAAADdTiATAGBAtJpr2JDZWLO3BgAAAAAyGWoU8QvX5QldHTgZ8dX9eYJgwPLMLlTxzQPp34dXn1/EJRtdzAgAAAAAwGAQyAQAGBCtNQxFDgtkAgAAAEBfeNMVZZw/lGfWR/e2Y7GtJRPWyp2PdWKhnT5n94SjJwAAAAAADA6r4gAAA2KkuXbvPdRwMzYAAAAA9INmrYh37crTknl4NuKLj2jJhLVyx2T691+jjHjdZY6eAAAAAAAwOKyKAwAMiOHm2oUih+tr9tYAAAAAQGa7J8q4aCTPrL/c144zS1oyYbUdOlXFvUfTv/detbWM1hruPwAAAAAAwGoTyAQAGBAjjbV776E1fG8AAAAAIK96WcR7MrVkHj8d8bmHtGTCartjqp1lzi0Tjp0AAAAAADBYrIwDAAyItbyheqjhdmwAAAAA6Cc3bi9j+3l51v3+t3vaMbeoJRNWS6eqYs9kehB681DESy60/g8AAAAAwGARyAQAGBAjzbV772ENmQAAAADQV8qiiNtvyNOSObMQ8en7tWTCatk3XcXRufQ5N20vo1YKZAIAAAAAMFgEMgEABsRahiKH6mv33gAAAADAynjlJUVcuTlPGOtT97Xj5BktmbAacrRjRkTsnsgTygYAAAAAgF4ikAkAMCBGmmtzS3WjjGjU3JANAAAAAP2mKIr4pUwtmfNLEZ+4t51lFvD85her+MaB9EDmFZuL2HaetX8AAAAAAAaPQCYAwIBoNdfmfYfWsJkTAAAAAFhZN1xUxvVjeUJZn3uwE8fmtWTCSvqbxzpxeil9zu4Jx00AAAAAABhMVsgBAAbEcCNiLe6qHhbIBAAAAIC+9t5MLZkL7Yi/3KclE1bSHVPp7Zj1MuLGyxw3AQAAAABgMFkhBwAYEGVRrElb5VBjLWKgAAAAAMBqufqCMl5xSZ51wP/ycCeemNGSCSvh8EwVe6fTv79eeWkRG9ZZ+wcAAAAAYDAJZAIADJCR5uq/53B99d8TAAAAAFhduVoy21XER/dqyYSV8OUM7ZgREbvH83y/AwAAAABALxLIBAAYIK01aKtci1ZOAAAAAGB1jY+WceNlebafvzLViUefyhMcA55WVVXsmUwPO4+uj3jpxdoxAQAAAAAYXAKZAAADpLUWDZlrEAIFAAAAAFbfe3bVosywHFhFxEfu1pIJOd17pIrDs+lzXr+9jHqOb3QAAAAAAOhRApkAAANkLQKZGjIBAAAAYDBcsrGIWyfybEF/40AVDx3Tkgm57JnM8/20O9P3OAAAAAAA9Cor5QAAA2RkDdoqhwUyAQAAAGBgvGtnLeqZdqE/dJeWTMjh9FIVdz6WHsic2FTE+KhjJgAAAAAADDYr5QAAA2R4LRoy66sfAgUAAAAA1sYFrSLedEWebegfPFHFvmktmZDqmwc6Mb+UPidXAy4AAAAAAPQyq+UAAANkpKkhEwAAAABYWb9wXS3W1/PM+uBd7aiqKs8wGFB7JtODzbUi4sbLHDEBAAAAAACr5QAAA6S1BuHIIYFMAAAAABgoo+uLeOtVebai7ztaxfcPCWTCch2dreLuw+nfQy+/pIjz1q/+pY8AAAAAANBtBDIBAAZIq7n67znUcEADAAAAAAbNO66pZbsg7oN3t6OjJROW5Y6pTuT47tk9UcswBQAAAAAAep9AJgDAAGk1Vz8cOVxf9bcEAAAAANZYq1nEz16bJ8A1ebyKbzwmkAnnqqqq2DPVTp6zcV3ET13s8kUAAAAAAIgQyAQAGCgja9KQufrvCQAAAACsvZ+5qozz1uWZ9eG9S9HuCGXCuXjgySoOnUqf8/rtZTRqApkAAAAAABAhkAkAMFBajTVoyFyD9wQAAAAA1t76ehG/uDNPS+bBkxFfnupkmQWD4kuTeb5ndk84WgIAAAAAAM+wag4AMEBaGjIBAAAAgFX0xsvLuGA4z6yP7m3HYltLJpyNM0tVfP3R9EDm9vOKmBh18SIAAAAAADxDIBMAYIC01iAcOSyQCQAAAAADq1Er4t278rRkHpmL+MLDWjLhbHz7YCfmFtPn7J4ooygEMgEAAAAA4BkCmQAAA2RdvYjGKv4NsCwi1uU5awUAAAAA9Khbxsu4ZEOeWX91TztOL2nJhBezZyo9vFwWETeNO1YCAAAAAAA/yso5AMCAaTVX772G6uHmbAAAAAAYcLWyiPdcn+fmthOnI/7Tg1oy4YUcm6/ih0+kB5d/6uIiRtdb4wcAAAAAgB8lkAkAMGBWNZDZWL33AgAAAAC612u3lTE+mifY9cl72zG7oCUTns+XpzrRyfAtcstEniA1AAAAAAD0E4FMAIAB02qs3m3Ww6v4XgAAAABA9yqLIt57Q55w18xCxKfub2eZBf2mqqrYM5n+/THSjHjlJdb4AQAAAADg2QQyAQAGzIiGTAAAAABgDbz84iKuPj9PwOvT93fixGktmfBsDx2r4sDJ9Dk3XlZGoyaQCQAAAAAAzyaQCQAwYFa3IXPV3goAAAAA6HJFUcQvZWrJPL0U8Yl7tWTCs+2Z7GSZs3vCcRIAAAAAAHguVtABAAZMazUbMutuzwYAAAAA/t6uC8t4yYV51g3/84OdODqnJROesdiu4muPpgcyt26MuGKz9X0AAAAAAHguApkAAANmVQOZGjIBAAAAgGd5b6aWzMVOxF/u05IJz/j241XMLKTP2T1Ri6IQyAQAAAAAgOcikAkAMGBazdU7RDEskAkAAAAAPMuV55fx05fmWaf84iOdOHRKSyZERNwxmR5QLouIm8cdJQEAAAAAgOdjFR0AYMC0VjEkOVRfvfcCAAAAAHrH7dfXIkcks1NFfHSvlkw4cbqK7x1KDye/5KIiNg9pxwQAAAAAgOcjkAkAMGBGVrUh06ENAAAAAOAnbR8t4/Xb82xXf3V/J/af6GSZBb3qK1Od6GQoi92tHRMAAAAAAF6QlXQAgAGzqg2Zq/heAAAAAEBvefeuWtQy3OlWRcSH79aSyeCqqir2TKaHkocbEa+81DESAAAAAAB4IVbSAQAGTKu5eu81LJAJAAAAADyPizcUcduOPFvW3zpYxYNPaslkME2eqGL/U+n1mK+7rIx19QwpaQAAAAAA6GMCmQAAA6bVXL3DFEMNBzcAAAAAgOf3zp21aGTatf7gXVoyGUw52jEjIm6dcIQEAAAAAABejNV0AIABM6IhEwAAAADoEucPF/HmK/NsW991uIq7D2vJZLAstqv46v70r/uLN0Rcdb5LFgEAAAAA4MUIZAIADJihen++FwAAAADQm37+2lqsz7SW+MG72lFVVZ5h0AO+d6iKk2fS5+wer0VRCGQCAAAAAMCLEcgEABgwtbJYtebK4YbDGwAAAADACztvfRFvvzrP1vUDT1bx3ccFMhkceybbyTOKiLhl3PERAAAAAAA4G1bUAQAGUGvVApmr8z4AAAAAQG9729W1GGnmmfXBu9vR0ZLJAHjqdJ4A8vUXFnFBywWLAAAAAABwNgQyAQAG0EhzdQ5WrK+vytsAAAAAAD2u1Szi566tZZm1/0QVdz7ayTILutnXHu1EO0P2ePeEoyMAAAAAAHC2rKoDAAygVqab5l/I+npErXSjNgAAAABwdt5yZRmj6/PM+vDedrQ7WjLpb1+aTA8eD9UjXrXV0REAAAAAADhbVtUBAAbQagUyAQAAAADO1rp6Ee/cmacl89CpiD0ZwmrQraZOdGLyeHro+LXbylhfd7kiAAAAAACcLYFMAIAB1Gqs/OGK4caKvwUAAAAA0GfesKOMsVaeWR/b147FtpZM+lOuwPHuCcdGAAAAAADgXFhZBwAYQKvRkDm0CqFPAAAAAKC/NGpFvHtXnpbMo3MR//vDWjLpP0udKr66P/1r+8JWxLVbrOUDAAAAAMC5EMgEABhAq9KQWV/xtwAAAAAA+tBN28u4dGOeWX+1rx3zi1oy6S8/OFTFidPpc3ZP1KIoBDIBAAAAAOBcCGQCAAygkVVpyFz59wAAAAAA+k+tLOL26/Pc+PbUmYjPPqAlk/6yZ6qdZc4t446MAAAAAADAubK6DgAwgFqrEMgcXoUWTgAAAACgP716axE7NuVZY/zr+9oxs6Alk/5w6kwV3z6Y/vW8a6yIsRHr+AAAAAAAcK4EMgEABlCrufKHLDRkAgAAAADLVRZFvPeGWpZZs4tPhzKhH3zt0U4sZSh91Y4JAAAAAADLY4UdAGAAtVYhLDkskAkAAAAAJHjZRUVce0Gey+U+c38nTpzWkknv2zOZnsZcX494zTbHRQAAAAAAYDmssAMADKCRVWnIXPn3AAAAAAD6V1EU8UuZWjLPtCM+fo+WTHrbY09V8dCx9GDxq7eW1vABAAAAAGCZBDIBAAZQq7ny7zFcX/n3AAAAAAD623VjZbzs4jzBsc8/1Ikjs1oy6V17JvOEindPOCoCAAAAAADLZZUdAGAAtRor/x5Dq/AeAAAAAED/e+/1eVoylzoRH9unJZPe1O5U8ZX9neQ5W4Yjdo5pxwQAAAAAgOUSyAQAGEDNWkR9hf8mONxwoAMAAAAASHf55jJevTXPeuOeyU48flJLJr3nh4erODafPueWiTLKwvo9AAAAAAAsl0AmAMAAKooiRpor+x7DGjIBAAAAgExuv74eZYYMWaeK+MheLZn0njsm09sxIyJuGc/TOAsAAAAAAINKIBMAYEC1VjgwOSSQCQAAAABksu28Im7anmd7+2uPdmLyeJ5wG6yG2YUqvnkg/Wv22guKuHiDdkwAAAAAAEghkAkAMKBazZU9dDFUd6gDAAAAAMjn3btqUc+0w/3hu7Vk0ju+/mgnFjNkiG+ZcEQEAAAAAABSWW0HABhQI82Vna8hEwAAAADI6cKRIt6wI88W93cer+L+o1oy6Q17JtO/Vpu1iNduc0QEAAAAAABSWW0HABhQrcbKNlgOC2QCAAAAAJn94s5aNGt5Zn3wrnZUVZVnGKyQx09Wcf+T6V+nr9paRqu5svsCAAAAAAAwCAQyAQAGVGsFGzLrZUTD3zQBAAAAgMw2DxXxlivzLD7una7irsMCmXS3PVPtLHN2T1i0BwAAAACAHKy4AwAMqJUMZA7VI4rCTdsAAAAAQH4/d20thup5Zn1ISyZdrN2p4o7JTvKczUMR149ZswcAAAAAgBwEMgEABlSruXKHL4YbKzYaAAAAABhwG9YV8Y5rallmPXisim8fFMikO+2druLJ+fQ5t4yXUSsFMgEAAAAAIAeBTACAAdVawdDkUMPBDgAAAABg5bz16jI2NPPM+tDd7Wh3hDLpPjnaMSMidk/kCTADAAAAAAACmQAAA2tEQyYAAAAA0KOGG0X8/HV5QmaPPlXF1x/NE3yDXOYWq/ibA+lfl1edX8SlG12iCAAAAAAAuQhkAgAMqJVtyFy52QAAAAAAERFvvqKMzUN5Zn1kbzuWtGTSRf7msU4stNPn7J5wLAQAAAAAAHKy8g4AMKBazZWbPVx32zYAAAAAsLLW1Yt45848LZlPzER86REtmXSPPZPpX4+NMuJ1lzkWAgAAAAAAOVl5BwAYUK3myoUmNWQCAAAAAKvh1okyLmzlmfWxfe1YaGvJZO09caqKe46kfy2+8tIyRlZwLwAAAAAAAAaRQCYAwIAaWcmGTIFMAAAAAGAVNGpFvOf6PC2Zx+YjPv+QlkzW3h1T7Sxzbp1wJAQAAAAAAHKz+g4AMKCG6is4u+HGbQAAAABgddx4WRnbNuZZk/z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      "text/plain": [
       "<Figure size 3840x2640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(steps, np.array(acc_train)-np.array(acc_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "     <img src=\"https://raw.githubusercontent.com/DataForScience/DeepLearning/master/data/D4Sci_logo_full.png\" alt=\"Data For Science, Inc\" align=\"center\" border=\"0\" width=300px> \n",
    "</div>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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  "language_info": {
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  "toc": {
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  "varInspector": {
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