code/day16 handwritten digit recognition neural network.ipynb

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    "# Practice Lab: Neural Networks for Handwritten Digit Recognition, Binary\n",
    "\n",
    "In this exercise, you will use a neural network to recognize the hand-written digits zero and one.\n",
    "\n",
    "\n",
    "# Outline\n",
    "- [ 1 - Packages ](#1)\n",
    "- [ 2 - Neural Networks](#2)\n",
    "  - [ 2.1 Problem Statement](#2.1)\n",
    "  - [ 2.2 Dataset](#2.2)\n",
    "  - [ 2.3 Model representation](#2.3)\n",
    "  - [ 2.4 Tensorflow Model Implementation](#2.4)\n",
    "    - [ Exercise 1](#ex01)\n",
    "  - [ 2.5 NumPy Model Implementation (Forward Prop in NumPy)](#2.5)\n",
    "    - [ Exercise 2](#ex02)\n",
    "  - [ 2.6 Vectorized NumPy Model Implementation (Optional)](#2.6)\n",
    "    - [ Exercise 3](#ex03)\n",
    "  - [ 2.7 Congratulations!](#2.7)\n",
    "  - [ 2.8 NumPy Broadcasting Tutorial (Optional)](#2.8)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_**NOTE:** To prevent errors from the autograder, you are not allowed to edit or delete non-graded cells in this notebook . Please also refrain from adding any new cells. \n",
    "**Once you have passed this assignment** and want to experiment with any of the non-graded code, you may follow the instructions at the bottom of this notebook._"
   ]
  },
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   "source": [
    "<a name=\"1\"></a>\n",
    "## 1 - Packages \n",
    "\n",
    "First, let's run the cell below to import all the packages that you will need during this assignment.\n",
    "- [numpy](https://numpy.org/) is the fundamental package for scientific computing with Python.\n",
    "- [matplotlib](http://matplotlib.org) is a popular library to plot graphs in Python.\n",
    "- [tensorflow](https://www.tensorflow.org/) a popular platform for machine learning."
   ]
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   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "import matplotlib.pyplot as plt\n",
    "from autils import *\n",
    "%matplotlib inline\n",
    "\n",
    "import logging\n",
    "logging.getLogger(\"tensorflow\").setLevel(logging.ERROR)\n",
    "tf.autograph.set_verbosity(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Tensorflow and Keras**  \n",
    "Tensorflow is a machine learning package developed by Google. In 2019, Google integrated Keras into Tensorflow and released Tensorflow 2.0. Keras is a framework developed independently by François Chollet that creates a simple, layer-centric interface to Tensorflow. This course will be using the Keras interface. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
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   "source": [
    "<a name=\"2\"></a>\n",
    "## 2 - Neural Networks\n",
    "\n",
    "In Course 1, you implemented logistic regression. This was extended to handle non-linear boundaries using polynomial regression. For even more complex scenarios such as image recognition, neural networks are preferred.\n",
    "\n",
    "<a name=\"2.1\"></a>\n",
    "### 2.1 Problem Statement\n",
    "\n",
    "In this exercise, you will use a neural network to recognize two handwritten digits, zero and one. This is a binary classification task. Automated handwritten digit recognition is widely used today - from recognizing zip codes (postal codes) on mail envelopes to recognizing amounts written on bank checks. You will extend this network to recognize all 10 digits (0-9) in a future assignment. \n",
    "\n",
    "This exercise will show you how the methods you have learned can be used for this classification task.\n",
    "\n",
    "<a name=\"2.2\"></a>\n",
    "### 2.2 Dataset\n",
    "\n",
    "You will start by loading the dataset for this task. \n",
    "- The `load_data()` function shown below loads the data into variables `X` and `y`\n",
    "\n",
    "\n",
    "- The data set contains 1000 training examples of handwritten digits $^1$, here limited to zero and one.  \n",
    "\n",
    "    - Each training example is a 20-pixel x 20-pixel grayscale image of the digit. \n",
    "        - Each pixel is represented by a floating-point number indicating the grayscale intensity at that location. \n",
    "        - The 20 by 20 grid of pixels is “unrolled” into a 400-dimensional vector. \n",
    "        - Each training example becomes a single row in our data matrix `X`. \n",
    "        - This gives us a 1000 x 400 matrix `X` where every row is a training example of a handwritten digit image.\n",
    "\n",
    "$$X = \n",
    "\\left(\\begin{array}{cc} \n",
    "--- (x^{(1)}) --- \\\\\n",
    "--- (x^{(2)}) --- \\\\\n",
    "\\vdots \\\\ \n",
    "--- (x^{(m)}) --- \n",
    "\\end{array}\\right)$$ \n",
    "\n",
    "- The second part of the training set is a 1000 x 1 dimensional vector `y` that contains labels for the training set\n",
    "    - `y = 0` if the image is of the digit `0`, `y = 1` if the image is of the digit `1`.\n",
    "\n",
    "$^1$<sub> This is a subset of the MNIST handwritten digit dataset (http://yann.lecun.com/exdb/mnist/)</sub>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "deletable": false,
    "editable": false
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   "source": [
    "# load dataset\n",
    "X, y = load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"toc_89367_2.2.1\"></a>\n",
    "#### 2.2.1 View the variables\n",
    "Let's get more familiar with your dataset.  \n",
    "- A good place to start is to print out each variable and see what it contains.\n",
    "\n",
    "The code below prints elements of the variables `X` and `y`.  "
   ]
  },
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   "execution_count": 3,
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     "text": [
      "The first element of X is:  [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  8.56059680e-06\n",
      "  1.94035948e-06 -7.37438725e-04 -8.13403799e-03 -1.86104473e-02\n",
      " -1.87412865e-02 -1.87572508e-02 -1.90963542e-02 -1.64039011e-02\n",
      " -3.78191381e-03  3.30347316e-04  1.27655229e-05  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  1.16421569e-04  1.20052179e-04\n",
      " -1.40444581e-02 -2.84542484e-02  8.03826593e-02  2.66540339e-01\n",
      "  2.73853746e-01  2.78729541e-01  2.74293607e-01  2.24676403e-01\n",
      "  2.77562977e-02 -7.06315478e-03  2.34715414e-04  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  1.28335523e-17 -3.26286765e-04 -1.38651604e-02\n",
      "  8.15651552e-02  3.82800381e-01  8.57849775e-01  1.00109761e+00\n",
      "  9.69710638e-01  9.30928598e-01  1.00383757e+00  9.64157356e-01\n",
      "  4.49256553e-01 -5.60408259e-03 -3.78319036e-03  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  5.10620915e-06\n",
      "  4.36410675e-04 -3.95509940e-03 -2.68537241e-02  1.00755014e-01\n",
      "  6.42031710e-01  1.03136838e+00  8.50968614e-01  5.43122379e-01\n",
      "  3.42599738e-01  2.68918777e-01  6.68374643e-01  1.01256958e+00\n",
      "  9.03795598e-01  1.04481574e-01 -1.66424973e-02  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  2.59875260e-05\n",
      " -3.10606987e-03  7.52456076e-03  1.77539831e-01  7.92890120e-01\n",
      "  9.65626503e-01  4.63166079e-01  6.91720680e-02 -3.64100526e-03\n",
      " -4.12180405e-02 -5.01900656e-02  1.56102907e-01  9.01762651e-01\n",
      "  1.04748346e+00  1.51055252e-01 -2.16044665e-02  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  5.87012352e-05 -6.40931373e-04\n",
      " -3.23305249e-02  2.78203465e-01  9.36720163e-01  1.04320956e+00\n",
      "  5.98003217e-01 -3.59409041e-03 -2.16751770e-02 -4.81021923e-03\n",
      "  6.16566793e-05 -1.23773318e-02  1.55477482e-01  9.14867477e-01\n",
      "  9.20401348e-01  1.09173902e-01 -1.71058007e-02  0.00000000e+00\n",
      "  0.00000000e+00  1.56250000e-04 -4.27724104e-04 -2.51466503e-02\n",
      "  1.30532561e-01  7.81664862e-01  1.02836583e+00  7.57137601e-01\n",
      "  2.84667194e-01  4.86865128e-03 -3.18688725e-03  0.00000000e+00\n",
      "  8.36492601e-04 -3.70751123e-02  4.52644165e-01  1.03180133e+00\n",
      "  5.39028101e-01 -2.43742611e-03 -4.80290033e-03  0.00000000e+00\n",
      "  0.00000000e+00 -7.03635621e-04 -1.27262443e-02  1.61706648e-01\n",
      "  7.79865383e-01  1.03676705e+00  8.04490400e-01  1.60586724e-01\n",
      " -1.38173339e-02  2.14879493e-03 -2.12622549e-04  2.04248366e-04\n",
      " -6.85907627e-03  4.31712963e-04  7.20680947e-01  8.48136063e-01\n",
      "  1.51383408e-01 -2.28404366e-02  1.98971950e-04  0.00000000e+00\n",
      "  0.00000000e+00 -9.40410539e-03  3.74520505e-02  6.94389110e-01\n",
      "  1.02844844e+00  1.01648066e+00  8.80488426e-01  3.92123945e-01\n",
      " -1.74122413e-02 -1.20098039e-04  5.55215142e-05 -2.23907271e-03\n",
      " -2.76068376e-02  3.68645493e-01  9.36411169e-01  4.59006723e-01\n",
      " -4.24701797e-02  1.17356610e-03  1.88929739e-05  0.00000000e+00\n",
      "  0.00000000e+00 -1.93511951e-02  1.29999794e-01  9.79821705e-01\n",
      "  9.41862388e-01  7.75147704e-01  8.73632241e-01  2.12778350e-01\n",
      " -1.72353349e-02  0.00000000e+00  1.09937426e-03 -2.61793751e-02\n",
      "  1.22872879e-01  8.30812662e-01  7.26501773e-01  5.24441863e-02\n",
      " -6.18971913e-03  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00 -9.36563862e-03  3.68349741e-02  6.99079299e-01\n",
      "  1.00293583e+00  6.05704402e-01  3.27299224e-01 -3.22099249e-02\n",
      " -4.83053002e-02 -4.34069138e-02 -5.75151144e-02  9.55674190e-02\n",
      "  7.26512627e-01  6.95366966e-01  1.47114481e-01 -1.20048679e-02\n",
      " -3.02798203e-04  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00 -6.76572712e-04 -6.51415556e-03  1.17339359e-01\n",
      "  4.21948410e-01  9.93210937e-01  8.82013974e-01  7.45758734e-01\n",
      "  7.23874268e-01  7.23341725e-01  7.20020340e-01  8.45324959e-01\n",
      "  8.31859739e-01  6.88831870e-02 -2.77765012e-02  3.59136710e-04\n",
      "  7.14869281e-05  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  1.53186275e-04  3.17353553e-04 -2.29167177e-02\n",
      " -4.14402914e-03  3.87038450e-01  5.04583435e-01  7.74885876e-01\n",
      "  9.90037446e-01  1.00769478e+00  1.00851440e+00  7.37905042e-01\n",
      "  2.15455291e-01 -2.69624864e-02  1.32506127e-03  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  2.36366422e-04\n",
      " -2.26031454e-03 -2.51994485e-02 -3.73889910e-02  6.62121228e-02\n",
      "  2.91134498e-01  3.23055726e-01  3.06260315e-01  8.76070942e-02\n",
      " -2.50581917e-02  2.37438725e-04  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  6.20939216e-18  6.72618320e-04 -1.13151411e-02\n",
      " -3.54641066e-02 -3.88214912e-02 -3.71077412e-02 -1.33524928e-02\n",
      "  9.90964718e-04  4.89176960e-05  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]\n"
     ]
    }
   ],
   "source": [
    "print ('The first element of X is: ', X[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first element of y is:  0\n",
      "The last element of y is:  1\n"
     ]
    }
   ],
   "source": [
    "print ('The first element of y is: ', y[0,0])\n",
    "print ('The last element of y is: ', y[-1,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"toc_89367_2.2.2\"></a>\n",
    "#### 2.2.2 Check the dimensions of your variables\n",
    "\n",
    "Another way to get familiar with your data is to view its dimensions. Please print the shape of `X` and `y` and see how many training examples you have in your dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of X is: (1000, 400)\n",
      "The shape of y is: (1000, 1)\n"
     ]
    }
   ],
   "source": [
    "print ('The shape of X is: ' + str(X.shape))\n",
    "print ('The shape of y is: ' + str(y.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"toc_89367_2.2.3\"></a>\n",
    "#### 2.2.3 Visualizing the Data\n",
    "\n",
    "You will begin by visualizing a subset of the training set. \n",
    "- In the cell below, the code randomly selects 64 rows from `X`, maps each row back to a 20 pixel by 20 pixel grayscale image and displays the images together. \n",
    "- The label for each image is displayed above the image "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Dplwus7JrpSmL1wm5cRcWFhCNRuH3+2EwGFgCcL1eR6lUQqVSQb1eHwqtKAF52avT6cT169cRiURw+fJlRCIR5g7v9Xqo1+us4gYAC6fdvHkT//RP/4RWq4VqtQrgaXw/Go3C5XIxZZxIJFj5rBI9Vy8DtTfQ6XSwWq0Ih8O4evUqJEnCw4cPkclkkM1mUa/XVW3QHYX80kCGjN1uZzlalEyfTqeRSCRQrVYVKSu5ziVDZn5+Hl6vF3q9HvV6HY8ePcLu7i5+9rOfIZ/PM0OGvo/WE/X8cjqdcDqdsFgscLlc7KLldDoRi8WQTqeh0WhYrxUlJsHKWzlUKhXWEuTy5cuYnJyEzWbD9PQ0kskkPB4Pq2ojr43S1snrotlsMkPGZDLBZrPB6XSi2WwyY+c49e9YPTJkoVutVni9XtTrdaTT6aG28mpvu38UlLzr8XgQDofhcrkAgB3snU4HuVwOsVgMxWIRnU6HJeopEVKy5MYHnlZsFQoFbG1tQa/XY25uDjqdDo1GA/V6HZVKBaVSCd1uF81mk/0sURSRTqdRqVTYzyuXy+wgItcnoO6YttVqRTAYhNfrhdVqRaPRwP7+PuLxOMtjANT57EdBypTi+R6PB+fOnUMkEmFdbnO5HLLZLPNcUaKrkpA/p81mg8PhQCgUQjgchiiKyGaziMViWFlZQSqVYhci8vACTw0Z4Gk322q1irt376JWq2F+fh7z8/Po9Xrwer1ot9sIBAJMR1UqlZMUwWuBLo71ep3lUNFFTKfTsURxzlPIk04ecWraetxG3thDS1qtFl6vF3Nzc4jH49jZ2WEWLZWynSUXnfzmZLfbYTabMTMzg8uXL8NutwMAizk2m01sbGzg3r172NvbQ61WGxpXoDQoxET5GwBYf4adnR38/d//Petx4XQ6kc/nkc/nkU6nkUwmh+Zy0fPn8/mhxGl5JYb8d6glKVEOPbPX68XS0hJmZ2fh9XoRj8dx69YtrK+vsx4YZy1viDx91JDTarVifn4ev/M7vwO/3w+v1wuNRoP19XWsrKxgZWUF6XQawNPkYCXJi0YvRCIR+P1+XLx4EZcvX0Yul8Pa2hpu376NTz75BOVyeahxpPwZ5XsrmUxCp9OhWCzC6/Xihz/8IQsPRyIRmEwmnD9/HmazGbFYDLlc7pmfcdoZffZer4disQitVotKpcIKK6hAwWw2s+7Yavf2Pg+SXbvdRrVaRblcRj6fR6lUUl+ODHlk6EZEeR/yeR5nCfkzazQa1svC5/PBarWy9vvypMRarYZyuTxk6SoFuXuRPChU9koHa7vdRqlUQqFQQDqdRrPZRLFYhEajYTci+aEklyF9Xl75ZjQa4fF4WDLwYDBAsVhknhwlKdrnQYet1WpluVVUQVCv11Gr1ViS71nGYrHA7/fD5/OxhHC6SRaLRaRSKdabCVBeR2jaFxqNBg6HA263GxaLhYWo5SMIms3mUP7YqB4mHUP/RwdTKpVCPB5Hv99HIBCATqeD1+tFs9lko0QOao+gBGgfSZLEiis6nQ663e4zHY3luURn8QyTQzr8IB2tKkNGq9UyI4ay4GmBnLVFIH+DaXDbRx99hCtXruDChQsIhUJDZaJ0uO/s7GBjYwOlUoktHKUdTDR5l0YwOJ1OVgGRTqfx8OFD3L17Fzdu3IDD4cD09DQCgQAMBsNQf4vRcQb0s8kTI4oiotEo3n33XRa263a7+NWvfoXt7W32/UqT3yjypmcajQYLCwv47d/+bWi1WmxubmJnZweZTIZ1aD1reWhy4xkA5ufn8e6772JpaQlLS0vQaDTIZDIoFAq4ceMGvv76a5RKJQBP15WS5EUVNwaDAcvLy2z/GI1GNBoNxONxZLNZ1iEcwAutCUmSkEwmkU6nodfrUSqVWO6IXq/H9evXMTs7iwcPHiCVSqFWq6FerytWRw0GA5TLZWg0GjYHkGZVAcPGixKf8XVCHbJpiC+AIY/5cTN2jwwdMHQIU0vwswwlv8rLrQ0GA3NvDgYDNJtN1Ot1NBoN1Go1xSeXyT0ydACTR6ZSqbDW6MCTJF/qDUN9Lg5y98sPHfL00CAzmpXSarVgMpmYwaMWaG9RiajP50Oj0WDuXXmM/ywhf17Kx3K73azposViYeX8hUIBxWIRhUJhqKpLqXtMFEU2BoYOGKrGoZlJ8ovAUc9JcqQKwEKhgHg8jsnJSbRaLRiNRtjtdpbsSV5WJa43uSxGPTKkfwglepyOE4q6kA4eVzPSsVUt0YNRy3Qq8yuXy6x87SxBrjeqCPB4PAiFQvD7/TCZTCz5GXjSn+Hx48dIJBKIx+Mol8tscJvSkN9g5ElztOApjCYPpVE+DHXtPepn04fVaoXL5cLi4iK+//3vQ6vVsmov6mpK/UMIpR5YtL/8fj/sdjsmJyfh8/mwsbGBb7/9llUFUimkkg3gV4Fy76gp3PXr1/Hhhx/CarWi1Wohm83iF7/4Bfb397Gzs8NyIZQmI9pbdDGy2+0sXN1ut5HNZrG2tobf/OY3iMfjrMjiRZ6TvoYuoaVSCevr6/B4PNjc3ITT6YTP52PdgpWat0eQLqpUKpAkiXlk5Mm93Ih5ClXTDgYD2Gw2uFwueL1e9Ho9JsvjZGweGfmNkW7LANjQw7O4KCgEQh0zHQ4HrFYriy/T13Q6HVYOWiqVWEmbEg0ZYDh5kpJwRxPtqBkVlUEajcYXCkHS/+t0Otjtdvj9fszNzUEURSQSCdYsT6fTqaJNv9y9TdWAtI76/T729/eRTCbZHDMlr5uXRW7YCoIAl8vFmsJFo1FIksS8MRsbG9je3kaxWHwmF0JJkE6h0RQWiwVms5l1Bc9ms9jb22Pzo6i/x4saHbR2qBlnLpdDLpeDIAjw+XzM66X0og3yItBAY/LK8EaSw8g9V5R3SM4Kq9UKs9msjvJr+QNQbbnBYGBVONVq9Uz1tZCXRlosFgQCAXz88ceYnJzE9PQ0zGYz81A0Gg0UCgXEYjHcv38f+/v7LNyiVmgkgcFgYAPHyuUyBEHAzs4OSqUSq0w6jNFqJaqOoh4aU1NTzDCkhnpKXnu0nmi8QyAQYNUDyWQSmUzmzCX5kseT2hnYbDa88847OH/+PGZmZqDRaJBMJnH//n3EYjE8evQIyWRS0b2s5M9MuWfUxK1Wq6FUKiGdTiObzaLRaAB49URmeWI97UUa7TAxMYGFhQWsr6+jUCi8tuc7CUaNFrkXmXoPyRN+zxr0zNRbiBLKqfp0XH3OxtoQz2q1sumYFHsc7eirdkgBiKLImpb94Ac/wOzsLJvzQoq01WohmUxib28PDx48wN7eHjt4lc5hm54MGXJRS5I0FErL5XJMOcp/xmgcVu4BpORiGro5OTmJdDqNWq2GRCJxvA86JkRRxPz8PK5fv876eVSrVebFIxfvWdhjwPA+8/l88Pv9uH79Ot566y1YrVZoNBrW1TYWi+Hx48coFovQ6XSK9STQM8s7FrtcLthsNuzt7aFYLCKTySCXyw2NEniVNTFqyNBlgcYY0IVD6cg9x6NVtyaTic2Bk/enOit7DBg+251OJ/R6Pfr9PksNoLw8VfSRkbt2Q6EQjEYjWq0Wi6vJm5apfRGQR4YmPXs8HpjNZlaRQ19DrmCqEqBeO2ppZiZPpCQjZDAYwGw2IxAIsC6jdCC3Wi30ej0YjUZWAvoi0M8d/d1qWGvUQNJiscBut8PtdsPj8aDRaGBtbY31GqJSfaUe0C+DPJREHY7n5+cRCoXg8/lgNptRLpcRj8exvr6OnZ0dZLNZVbSYl/dOstlsrC+V0WhknbJpptLr8B6QjCl8BTzJRyLdrobQLUEGG120LBYLHA4H6vX6MyHIs2TM0HPSiAIa/kwf41oDYwktkbclEongjTfegMPhQLlcRi6Xw/7+PiqVCkvIOwsLQJIkWCwWzM3NIRqNsrbfBA2wy+VyePjwIUvwpUmiSle4csigoI6+Ho8HBoMBqVQKLpeL9fXodrsHJhC+SMmo/KaqJiOm3++zBpMejweRSAQzMzP45ptvcOPGDayuriKXy6Hdbg81BFQzlF9FuVB+vx/f//73EY1GMT8/D7fbjZWVFfzmN7/B6uoqbty4wZQvrS+lrg152XUgEGAXAofDgXa7jUKhwCYSU0Lwq/4e4MneMxqN7PdQ1SE1Q1OL95igCyhdGsLhMHq9HpsFdxahvVIoFJBIJBCJRNiIikajMbZ+Z2N9Byi+KAgCS+aUT51VqgJ5Gcg9aTQaWfxafkBLksSqdaiLbT6fV1WvHTIs+v0+a9hGpaCiKLKqNpvNhna7jXw+zxITKWT0qqMs5AeV0tcb3Yg9Hg9rgGc0GlkSZrlcPjPNJuXPSH2Z/H4/gsEga3wnSRLq9Try+Tz29/eRyWQUPUH+KOSJznJvt3z9v0ozSPp5FFqhRnvUo6nT6Qy1iFAjdCk4K5eD50FJ8/KEaPoYVwRh7IYMWa80koA+1I68aZlWq4XP58Py8jJCodAzg9ri8ThWV1dx9+5dfPrpp6wLp9LDb3LvC+VHxeNxaDQa+P1+2Gw2dggFAgEsLS0hkUggm82+0swbeXdJ+v2kfJRYkULQOqDOrR988AHzODgcDpRKJdy9e3eoPbhS18yLMrq/Zmdn8du//duYnJzE9evX4XQ6WXn1l19+iZ///OdoNBqss60aqrnoGSgsTa7+VqvFyrGpaRl5b+T5H0dBBgwZxj6fDzMzM5idnYXdboder2e9n/b397G9vf1MU0ElIzcK5blBZ7HaVg69t/V6HYVCgYWy6UN+SVB8jgwxmg9BFts4OwCeJPIYK/U5sdvt7FCV942huUKFQoGVtakB+fvf7XZRLpdhs9nQarVYeJHCAh6PB61WCxaLhZXoj7opX2TdyG+gtN6UajyPGmV6vR5+vx9+vx86nY55ueSzYdRwkLwotHZcLhcikQhCoRAcDgfMZjO63S4bOJrP51XrDaYbstzjrdPpWL4MlWNTRcmL/Dy5oSgIAmtt4HQ62eWUBrpSR19q2ql0SG+Q11ie2Mt5AkVYyNCVG33j4EQ6+1IctVqtot1uMxekWm+OozeZaDSKixcvYnl5GXa7HQaDAQBYg7Z4PI6HDx9if3+fddJUSyMzMmTa7TZyuRw+++wzeL1elohpt9vZtN4f/ehHyGQysFqtyGaz2N7eZrH3Wq0GAEMeB7mcAbDptGQoVqvVoQ6u1I9HaTKlRoo2m431yZmamsLu7i5WV1fx4MEDZLNZ1uxPac/3MoweJrOzs1haWsLy8jK+973vwWazwWw2Q5Ik1u6h2WyyxHk1yUbukalUKqyDb6fTQSQSwcTEBJtKnUql8OjRI7TbbXaJOCwBX14BODMzA7fbjQ8++ADvvfcevF4vTCYTSqUSqwDb3NxkzdGU7uUCnuy3fD6PWCyGUCg0FDLhDEMXUepjNFqeflz7bezTrzUaDbs10sFNN3E1Q5a81WpFKBRCKBRCIBCA2Wxmrbyp9r5UKiGVSrGmVWoJD8gXdL/fR6PRwNbWFgqFArLZLKrVKuvca7fbsbi4CI/Hg0wmw/IZKBRJvYfo5xHyslt5+TUZT5STo9QYPj2rKIowmUysCZ7b7UYsFmN9YyjRUg0HyYtAe8Tj8WBhYYEl0tN6ogoK0jmj3j2l7y2CQrftdpt99Ho9OJ1OGI1GzMzMIJ1OQ6vVYnt7mzV9k3v65PuKjBFqYeD3+zExMYFz587hypUrLJTXbrexvb3NLhuUUKxEvX6QDOr1+tCwXu6NeRZaJ/Kmt+StO255jbX8msIpgiAwQ4as27OQOEUVR9QjRR6b7vV6WFlZQSKRwJ07d7C+vo5araaK2P1BkLKg2S8PHjzAYDDA8vIym9NiMpng9Xpx/fp11Go1hEIhZLNZ7OzsYH9/nxl8lGgGgI14uHTpEt544w2cO3eOza2ikEI2m0Uul0Oj0VDkATYYDOB0OnH58mVEo1F4PB4YjUbs7+/j/v37bKSDEp/tZZAfKJRwStVb1GVWnhTebrdRr9fRbrdVeRDRZafX66FcLrO5UeTlNBgMiEaj0Gg0iEQicDgcKBaLWFlZYbOXaE+2222YTCY4HA7We8lms+HSpUsIBoNYWFiA2WxGoVDA/fv3EY/Hmf5SclNBOfKLF7UKoZ5nalw/rwODwQCr1Qq/349z587BbrcjHo8f+6VxrA3xqPtfrVZjYSWljnp/WeQxZupaS9U3giCg2+1iZWWFTX5eX19nSalKvNUchVxBkJfl0aNHqFQqbPaU2WyG1+uF1WqFz+fDYDDA3NwcKpUKHj58iMePHyMWi7EO0bSGqF/IG2+8gXfeeQd+v5/1LaKyUGqrPq6uk68TuiE6HA5cunQJk5OTcLvd0Ol02N/fx71795BKpc6ElxN4uq+oLbrX68XU1BQ8Hs8zhkyr1UKtVmPvu9ryh+jSQ7lnpVIJhUIBDocDkUiEGTLhcBiZTAbBYBDpdJo1mux0Ouj1eqy3l9vtxtzcHFwuF9588014PB5cuHABfr+f6bBarYZbt24hFothbW1tqNxfybKVe2UGgwHrjEyXb27IHAz1bvL5fFhcXITRaMTNmzdRq9WGoguvm7EYMvTCybVLrn81ehpeFkmS2JyKYrE41D78LGwWUhjUBfT+/fsAnhgkly9fZqXYGo2G/Tk1NQVJkpgngprmSZKEyclJOJ1OLC4usiGKFFZKpVJIJBKoVquKzZGQT7gOh8NwuVzIZDLo9Xqs5FoNB8mLQg3g3G43XC4X/H4/6+BMuR+lUgnVahU7OzvY3d1lDTjVCO0naoC3tbWFTqcDu92ObrfL8hYMBgOrmHzrrbdQqVRYGIryF10uF6ampmC32zE3N8fK+/v9PtLpNCqVClZXV/H48WNks1nm1aHXoSbkM+H4ufUs1O1Yr9ezsNJojgxxHMbMWHNkKNmO4q1nqZHQaNwVAMvdIGt/b2+PzSdR221xFJIHlZzv7u5if38fW1tb+MUvfoG33nqLVZ8Eg0HWiEqn08HtdmNpaQmNRgPFYpHlFgFPcmuoKsxmszEPRq1Ww6NHj7C7u4t0Oo1mszkUzjztspaHUOgQunTpEjQaDVZWVpDL5bC+vo5EIsHm7aiZ0b4x0WgU0WgUS0tLmJ2dRbfbZQbMvXv3kM1m8dVXX2FrawuZTEa1+THA015U+Xwen3/+OVwuF7rdLmZnZzE9PY1IJMISxQeDAd544w22h8gAqtfrLERAlWCCIKBSqaDVauHRo0e4c+cO1tbW8Omnn7JUAbocqEWmwFNP11lp2Poy0D6kbsdkvFD+HuWAqqqPzKhVe5YXhbwyoNlsol6vo9FoDJUtniX5yJN4+/0+MpkM9vf3Ua1W0ev1YDKZ4HQ6YTKZWLiNmgoOBgMWLrBYLGyoGyn0SqWCbDbLqpWU2FxQbsjQdGsqKS4UCkilUqyxoNoO6KMgXWI2m2Gz2dhk80ajgXQ6jWKxyBJQc7kcC2mfBfr9PqtS2t/fZzqFSrGpGy/wtIkggKHbtE6nA4ChfVmtVhGLxRCLxZDNZllTQbWmCJCHSz4ihvOE0QIOmrNEuY/ykNLo97xOxmrI0E2ZavLPkotO/kaSdU/DM/f395FKpZBMJpHP59Fut5kVq+bDaLTDKABWafH48WP87//9v2GxWDAxMQGLxYLp6Wk4nU5EIhFMTU1Br9fD5XKxn0EeHo1GwwyYWCyG27dvI5VK4datW6w6Smnl7JRL5vP5cO7cOSwsLMDtdiObzeLWrVvY3t5GNps9E4nzo3uJGkxGIhG4XC5otVokEgn87d/+LZLJJH7961+zihOqWFKKJ+5VoHLpXq+HWCwGURSRSqVgNBpx4cIFnD9/Hj6fDwsLCzAajayhXTAYZAayIAhoNBrI5XKo1+vY2dlBtVplYaTNzU1sb28zT4xaKitHGW34Rj291Pis3wX57MRcLoft7W188803SKVS7DIKHN9+G6shIy9/7Pf7L9yQSW3IGwLKm3TRzBc1umePYjSxjqoEYrEYLBYL+v0+LBYLNBoNarUaM4jJ3U3fLz/gaNZHNptFPB5HOp1moQa510IpyJPFaeJus9lErVZj1SlnxdMwivz9JwOWxhCkUimkUimUy2WW/HsW8hzk/ZooD08URWaoUKNJk8mEVqvFctHICBJFkR3e8inqe3t7SKfT7NIlL9ZQs0w7nQ7rVisvbVdqY83XhTz/lbq1ywsq5B5wRXf2FQQBWq0Wg8EAv/71r7G5uckO8WQyCUmS2PwlNSNPwmu1WigWi9ja2kK5XMYXX3yBeDyOVCrFSiDVLo9RSD5kmLRaLSSTSYiiyPpePH78GHq9Hj6fD16vl7nA5YYQ0el0WE+e/f19NJtNZDIZNgtEaUqXeg3lcjk8evQImUwGW1tbqFarePDgAcrlMjus1M7o+93pdPDtt99iZ2cHX3/9Nfx+P1KpFB48eMBCtaPtDs4CcoONLgixWAylUgkWiwW3b99mYSadTsdCt+TRo/BAq9ViY0Ly+TwajcbQIa5mmZL8qAjj1q1bcLlc2NzcxJ07d1AsFp9JrlezPOTI11cqlWLdxG/fvo10Oo3V1VU2puC4L+ZjMWRICW9vbyOVSrHbE7npzoqCIQXc7XbZ8Lp8Po/t7W3EYjHUarWhSpqzIBM58uclgw8ASqUSW0cajQZWqxVWq5VV8MgPNnlfnl6vh2aziXK5zBQ5hZ+UJltSBI1GA5lMBsVikZWfK7WU/LsgD0vSfDLq/mwwGNiYD/IYkIF3VuRDjMqpVCqhVCqxDqwajQYGg4HlnOn1+qF+NK1WC91uF7VajY01oNJ+SihXs0xJt1CH5Hg8jvX1dezu7iKZTKJarQ5djNQsi4Mg+ZARU61WsbW1hWazybo7jyO6MNbQEoVO6NChuNlZgIy5bDaL+/fvs8FqzWYTGxsbrPzxrBoxo5DhIv83ANawi1qq09eMGjJ0gJGHi/5PqXKVG2iNRgOiKLL+OWdpH41CipR6oFB+2ehcHPras4z8+ck7LAgCO2xIdvS18vlClLB5loo15BckksPGxga7HGWz2TOZCnAQ8nVC+hkYn84dax8Z+aRZ+vxZ2RCkIHK5HLLZ7FBOBz3/WZDFi3KQkUIya7VazJt3VPURbSL5ZlK6fLvdLrsMUCiOrxuwXBC5XM6qF+YgRo0YMmQADDUIHDX85Ptm9HJxFpBfIABgY2MD6+vr7P/oQ2mh6tcNyeek9NLYm02cdcuVGJUBl8nzeRkZjSpgtcDXzeHIZcHlcjjyfLSD/u+gf3N5PmE0l4/zhIPC++Nk7H1kzjryDcDd3i/Giyjcl/1+JcOV6LNQc0luzLwYXE4vh1xGo14pLr8nnKReUnf7z1MO3wCvBpcb5yD4uuAcN3yNnU4EpXU45XA4HA6HwyF4rIfD4XA4HI5i4YYMh8PhcDgcxcINGQ6Hw+FwOIqFGzIcDofD4XAUCzdkOBwOh8PhKBZuyHA4HA6Hw1Es3JDhcDgcDoejWLghw+FwOBwOR7FwQ4bD4XA4HI5i4YYMh8PhcDgcxcINGQ6Hw+FwOIplrIaMIAh/KgjCt4IgtAVB+J/j/N1KQBAEtyAI/0cQhLogCLuCIPzJSb+m0wSXz9Hw/XU0fP0cDl87z4evn8M56fUz7unXCQB/AeBHAExj/t1K4H8A6AAIALgC4O8FQbgrSdLDE31Vpwcun6Ph++to+Po5HL52ng9fP4dzouvnRKZfC4LwFwAmJUn6f8f+y08pgiBYABQBXJQkae1fP/dXAOKSJP3Zib64UwCXz4vD99ez8PXzYvC1czB8/bwYJ7V+eI7M6WERQI82yb9yF8DyCb2e0waXD+e7wNcP57vA188phhsypwcrgMrI58oAbCfwWk4jXD6c7wJfP5zvAl8/pxhuyJweagDsI5+zA6iewGs5jXD5cL4LfP1wvgt8/ZxiuCFzelgDoBUEYUH2ucsAeCLZE7h8ON8Fvn443wW+fk4x4y6/1gqCYAQgAhAFQTAKgjDuyqlTiSRJdQB/A+C/CYJgEQThAwA/AfBXJ/vKTgdcPs+H76/D4evnaPjaORq+fo7mpNfPuD0yfw6gCeDPAPynf/37n4/5NZxm/iuelK5lAPw1gP/CS/uG4PI5Gr6/joavn8Pha+f58PVzOCe6fk6k/JrD4XA4HA7ndcBzZDgcDofD4SgWbshwOBwOh8NRLNyQ4XA4HA6Ho1i4IcPhcDgcDkexcEOGw+FwOByOYjmyzttisSiupKlerwvj+l0Oh0Nx8imXy2OTj9vtVpx8CoXC2ORjNpsVJ59GozE2+TidTsXJp1QqjU0+JpNJcfJpNptjkQ8/u47GarUqTj61Wu1Q+fCGR6eUlymLF4SxrX/FIJcflw+Hw/muHKaTuX45eU6tITMYDJ45jOhD7dBzS5I09HeCZEDykCTpTMjlRSCZHSWvs8hgMBj691mWBefVka8j0jsajfozFOi55bqF9g89P99PBzMqO/lZ/rpkdioNmYMOb+BsLpSDZCFJEts83Ih5ityIkW8a4GyuHYLvJ87rRL6eztIaOuiSRJ/nF8qDGb2Uy43A1ymvU2fIkIWv0+mg0+nY5weDAdrt9jOeGjUxaqzodDoYDAYIggBRFNnnJUlCr9dDr9fDYDAYkslZ2kijRgsA6HQ6iKIIrVYLnU6HwWDA5NTtds+knDQaDfR6/dDtudvtotfrqXYvHYVcufb7/aH/I/loNJoztUaeBx06BoMBGo0GoihCo9Gg1+uh3W6rch1JksS8CQaDgekVrVbL/m8wGKDZbA55HYCzpV8OQn6RpDWj1+uh0WjQ7/fZR6fTeS2/79QYMnI3pUajgdlshtVqZQuGDu6DLGI1cNBzabVaWK1WaLVadhD1+31IkoR6vY5ms/nMYjgrtwKSFxlxdCAZjUbo9XqYTCZYLBZ0u100Gg30+31m0JwF+RCSJEEURZhMJmi1WnZA1+t19Ho9djM6a9AhJNcpdGHgxswwcvno9Xr2odPp0Gw20el0VLeG6Jn7/T57bqPRyD4GgwH6/T7a7TY6nc6ZvUwexWAwgCiKMBgM0Ol0sFqt0Ov1aLfbaLVaaLfbQ5fL78KpMmSAJzdqvV6PmZkZLCwsQJIkdLtdVCoV3LlzB9VqFd1ul1nASl80B72Jdrsddrsdfr8fi4uLMBgMMBqN0Gg06Ha76Pf7SCQSSCaTKJVKiMfjQwe0mo0ZubxEUYTRaIQoijCbzdDr9QgEArDZbHC5XHC73Uw+lUoF29vb7PBWO5IkQavVwmAwwGazYXl5GRaLhf3/xsYGtra20O/30e12ASh/Lz0PWjvk8dXr9bBYLEMGS7PZRK/XQ6fTYXI5ywcU6RKS14ULFxAIBJj+TSaTqFQqz+RgKR0yai0WC/R6Pc6dOwefzweHwwGHw8GMmEajgdXVVdRqNaTTadRqNQBnd83Qc5NutlqtuHDhAmw2G3w+HywWC/b29rC3t4dSqYRarcbOru8iq1NhyNDNWu6J+a3f+i380R/9EbrdLur1OnZ3d5FIJNBut5lbSk2LRO6RCYVCOH/+PC5evIh/+2//LWw2G+x2OzQaDbsB3bhxAzdu3MDa2hpSqdSZOKBHQ296vR5OpxMWiwWzs7Ow2+24ePEigsEgpqenEY1Gsbm5iS+++AKxWAypVAr1el31eTO0n/R6PbxeL6ampvAf/+N/RDgcRrfbRbfbxU9/+lOk02m0Wi3m0VOrPICnMhkMBrBYLLBYLPB6vVhYWIBer4fBYMBgMGAKNp/PI5/Ps5u3PLx7ViCZabVamEwmOBwO/N7v/R7efPNN1Go1VKtV3Lx5E2tra6oI28pfP3lhQqEQnE4n/vAP/xDLy8uYnJzE5OQkOp0O6vU6stks/vEf/xGJRAK/+tWv0Gg0mNyUKofvymAwgMlkgtfrRTgcxr/7d/8OU1NTWFhYgNvtxj//8z/j5z//OXZ2dhCLxZjn5rtwKgwZOXq9HmazGTabDQ6HgxkuFKNUi9dh1BNDRhxtnkgkAq/XC41Gw/KDyE1nMBjgcrng9/uRyWSg0+lUn+8gz2sgt7bT6cTk5CRsNhui0SjsdjvC4TB8Ph88Hg9sNhucTidcLheq1SoMBgO0Wu3Qz1TyGjoMMoo1Gg2MRiMsFgvcbjfcbjeq1SparRZEUVR1vhkhP5zIe+fz+eD1euHz+TA7O8tCJXRol0olWCwWmEwmtFotlMtlZtCoXV6jUGjS4XDA6/Wyj263i0KhoFq9Qx6FYDAIr9cLt9sNu90OrVaLdrsN4EkY22azYWJiAlqtFsFgEPV6HbVaDfV6HYB6dcxh0FqgM8rn88Hv98Pn88FkMkEURfT7fXaBel1r58QNGXmug0ajgdvtRigUgtfrhcViYXFKym9QE/I3UafT4Y033sDk5CTee+89vPPOO6jValhfX0ev10Oj0YBWq8XVq1cRCoUwNTXFlO9XX30FURRRq9XQ7/dVVw5JcqL3n9bIwsICPv74YzgcDkxNTcFsNjNjUKvVQhRF2Gw2TE9PAwC8Xi/a7TZqtRo6nY5qS5BpPxkMBvj9foRCIUxOTiIYDGJlZQXlchm1Wo3lWKlVBvK/azQaRCIReDwevPXWW7h27Ro8Hg+mp6dZAudgMEA2m0WtVkM8HkcikcDu7i6++eYb1Go1JJNJtNvtM5M7Q+vIZDLh3XffxcTEBJaWljA9PY2trS3cvn0bW1tbaLfb6PV6QxdNJUJnEV2mQ6EQfv/3fx8TExMIhUIwmUzY3t7Gb37zG3i9XszNzUGn0+EHP/gB2u02jEYjVldXcefOHdy7d+9MVXeNGiRutxtvv/02IpEI3njjDXg8HqTTaSQSCTx8+JClibyuM/3EDRmCMuGtViscDgdLTpQkiSUGqTGhShAElsvg9XoxMTGBQCAAn8+HXq+HYrGIVquFZrMJnU7HDmCTyQS32w2n0wmr1Yper/dM9rwaZCTfIJSsSvHWUCiE6elp2Gw2hMNhlhUvf26Kc1utVphMJhgMBub+VYN8joKSxeXPTvuJcq3UdjkgqMKE3mOdTgePx4NAIIBwOMw8eD6fj7m16TJlt9tZXkiv14PH44FGo0EmkznJRzox5LKTJ2wWi0XU63XVrSF55azD4YDL5QLwJH+qWCwikUig3+/D6/Uyg8disSAUCqFWq2F3dxd6vX4o/4x+rtoh/WuxWJjn02azwWw2o91uo1wus49Wq/Xa9PCJGjJk8VOYwG6344MPPsDy8jIWFhZgMBhQKpXw1VdfIR6PI5/Po9FovJbkoJNitPTTbDZjfn4ebrcbv/3bv43l5WXodDpkMhmsra3hl7/8JQRBwMzMDJxOJ7s1WCwWGI1GLC0t4Q/+4A+QSCTwL//yLygWi6pIhpbLqdvtQqvVYnFxET6fD9euXcPVq1fh8/kwPz8PjUaDTqfDKgj6/T5MJhOMRiN0Oh0mJiYwGAwwNTUFSZLQaDRYUp6avFfyG6VWq8Xk5CTeeecdTExMwOFwQKfToVqtolAoMEUCQPE36YPw+/24ePEiSzg0mUxYWlpCIBBAKBRCIBBAvV7H3bt30el0UCgU2OFksVhgt9sRDAbh9/tht9uxt7eHXC7HSo3VbAjLEzb1ej3cbjcuXLiAaDSKfr+PWCyGnZ0dbGxsoFgsqsaQkVfwDQYDlsibyWRYIm+z2USz2YTJZMLt27fh8Xjw4Ycfwul0Yn5+HjMzMwCAVquFbDbLEurVulYIcjJQCPv8+fN455134PV6YTQa0e12EY/HsbW1hVgshnw+D+D1NRM8cY8MuX2tVivcbjdmZmZw4cIFuN1uiKKIRqOB3d1dlqjZ6XRUoXhJGWq1WuZdmJ+fx/nz55HJZJDJZJDL5bC5uQmj0YhQKPRMvxSTyQS/348LFy7AYrHg66+/RrVaHYpbq0FOpCg9Hg8ikQjOnz+PK1euwGq1wuPxoNfrIZPJoNPpoNFosOenUlq73Q6n0wmn04lSqcTyQ9RkxABP1xTlUtntdkxNTSEYDLKqN0pSJK+MKIqqS2IVBAE2mw1zc3NsjZAh4/f7YTQaYTAYUK1WkUwmUa1WEYvF0O/3ce7cOXi9XjidTgSDQRa+pXADxfiVflF4HqSX6bkDgQACgQC63S7K5TJKpRJyuRzzbqoFMmYkSUKn00Emk0GtVsPa2hqy2SwLQwJPDL1gMIi5uTkAYJfNjY0NrK6uot/vY3t7W/UhJnl6iMlkYvmbkUiEXaBo3WQyGZRKJTQaDaanFOuRoeoBk8nEkjHfe+89BINBLC4uwuv1snh1Op1GLBZDLpdTRWUOvek6nQ52ux2BQADvvPMOJicnYbfb0Ww2sb+/j0ePHqFer+O9996D1WrF1atX4XK5mJsbeCJHs9mM6elpaDQaRKNRaLVaxGIxlMtlaDQaxRk0o5UDZrMZk5OTcDgceO+99zAzM4NIJAKDwYBKpYKdnR3UajVsbGywRLtut4vl5WUsLS3BbDbD5XKx/kT0oUZobRkMBjidTvh8PkxMTMDtdqPf76NeryOTyWB/fx/VapW5gZWyNo5Cbux6vV5cvHgR7777LgtVU1iyUqlgZWUFyWQSmUwGKysraDabzCOztrbGytUvXLjAQrjtdhvLy8uw2+3Y2dlBqVQ62Qc+Rkg/22w2TE1NYWpqCl6vFw6HA9vb2ygUCshms6hUKuh2u6pZQ8ATvUNN7gRBwOPHj1ni98zMDKampjA5OYl0Oo1Hjx6h2+3i66+/htPpxA9/+ENotVp4vV5cu3YNOp0ODx48UI3H6nlQjuv8/DwmJyfhdDphNBqZB3xrawuPHj1CLpdj+/F1cWKGTK/Xg16vRzAYxOTkJD766COEw2EWZkmlUshms0ilUojFYqhUKqoxZCic5nK5MDExwZKiqMHU/v4+bt++jYmJCbzzzjtwuVy4ePEirFYrgKdKW27IiKLIklpzuRxKpZJib0ry5ohWqxVvvPEGgsEg3nnnHczMzMBkMkGv1yOdTuP27dvI5XK4desWSqUSyuUyCzP5fD643W54PB6WL6H09XMU8iRfp9MJj8eDcDg8lEOVyWSQSCRQq9VUJQ8y4pxOJ+bm5rC8vIx33nkHVquVFQ2sr68jl8vh3r17+PLLL5HL5bCxsYFOp4NWq4XBYMDCkXQZoHCBIAhYWlqC3W5n64x+r1pkSFDY22g0DhkydrudJUJns1mUy+WhsnSly4FePzUZ7XQ6WFtbg8lkwttvv41oNIrr16/jrbfewt27d9m59M0337BeM8FgEB6PB9euXUO9Xoder2fhbkCd6wV4Kju32425uTlmyAiCgEwmg2KxyAyZQqHAvFqKHFFAByspi3A4jAsXLiAUCrFKJUmS2GZZXV3F7u4uGo0Gi00rVfnKm3GJogi32836ErhcLhiNRqTTaVSrVVQqFRiNRtjtdoRCIdjtduj1egBAvV5Hu91Gu91Gs9mEwWCA1WplB7+8PF1JyGPTdBucnJyEz+fDxYsX4ff74XK5IIoi0uk0CoUC9vf3ce/ePZTLZSSTySGPTKPRYG34u90uM24oaVyp6+h5kBfL7XbD4XCwkuNGo4FKpcJu081mUxXVN/J9BQAulwuRSAQ+n4+1Rqf3nRpxbW9vM2OOSqpJFnTJosROcoGbzWZMTExAo9Hg/v370Ov16PV67PuVLkfgqSwpfOJyuTAzM4PJyUnodDr0+33k83nE43FUq9Wh71XL89NaMBgMMJvNQ/2pJiYmIAgC1tfXsbu7i1wux3LO9Ho9tre3YbfbYbFYnqmeVGurA3mqg0ajgcvlQjgchsvlYuG5bDbLjJlqtcoKVl4nYzdkJEmCw+GAz+fD8vIyfu/3fo/93Ww2I5fLIZPJ4M6dO/j0008Rj8dRKBTQ7XYVq3jlMVKaITUzM4Pf//3fRyAQwNTUFHQ6HT7//HOsr6+j3+/D4XAgFApheXkZRqMRwJP5OORtyWazyOVyCIVCeOONN9jNiBaUEjcNeRR6vR68Xi8++ugjTExM4Mc//jE8Hg9raf3o0SN89tlniMfjuH//Pvs83aQGgwGKxSIz9prNJjvIK5UKS75T4lo6Cnoml8uFaDSKcDgMh8MBSZIQi8WQyWSwvb2NjY0N1pdI6ZARQQdGJBLBtWvXEIlEmAeTkpu//vpr3L9/H2tra1hfX2ffBzxRxOSJ6Ha7KBaL2N3dRTgcZl1tL126hHA4jFu3biEWizFjWS1riQ5bamMQjUbxwQcfsB4gnU4H29vbuHv3LlKplKIvlgdBvYIoHB0MBvFv/s2/QTgcxtLSEnw+H7755ht88skn2Nvbw9raGprNJqrVKrRaLb766iskk0lcv34db775JsxmMwwGAytPV2OTPFoDVBUZiURw+fJlOBwODAYDll8Uj8exs7ODVCp1LOH9sRoy9OLJ00DNhlwuFyu1pq6a6XQamUyGNaNSOvKKEmrU5vF4WF5MvV5HtVpFtVqF3W6Hy+Vi3Xwpv6HdbiORSCCbzaJaraJWq8HhcAB4eogpdaPQa5db9lQ1Qomq5XIZlUoFqVQKqVQKhUKBJffKbzy0UeiGXavVUKvV0Gg0VN07hZ7ZarWyduo02K/RaLD5XNRkElDHTRoAzGYzjEYjyyMjI6bb7SKfzzNPVC6XQ71eR7/ff8aQk8uCPC2kezQaDZvfpdfrh2YyqQ29Xg+r1cpGfVgsFtYCg0pn2+22atbOqN6w2+2IRCIIBoMsVNTr9dgFMplMIp/PswsUGUD1eh2VSoXpGAqN93o9tFqtZwaUKh159R515Lfb7bBaraxTdqfTQbFYRKFQYOHb41g3YzFkRkNKFy9exA9+8ANEIhFcvHgRer0ekiShXC7jm2++wcrKCr799lvcuXPnGdevUqFKB6/Xi/n5eSwvL2N5eRn9fh8PHjxAsVjE+vo6UqkUFhYW8MEHH8BkMrHDd3V1FYVCAb/61a+wvb0Nr9cLj8cDs9kMAKz6REkJnKMNAakKIBQK4cqVK/jwww9hsVjQ7/eRyWTwL//yL1hfX8eDBw/w4MEDJlP52qBQCrWhb7VaWFlZQSwWw9bWFhtzIe8xonRImdBwtvn5efzWb/0W/H4/RFFEvV5HPB5HPB5HsVhkiYxKP4jlVVrz8/OYmJjAtWvXcOnSJVYpkcvl8Otf/xr7+/u4ceMGNjc3Wafwg/YJ/ZvCK4LwZFCrVquF3W4fan3wOvtgnDRktFEJ7dzcHBYXF7G4uAjgyWyuXC6HlZUVllekhupR4GnOIemMy5cv44//+I/hdrsxOzsLjUaDL7/8Eru7u/j2229x69YtdLvdoXQHjUaDQqEAURSRy+VQqVSg1+tx/vx55HI53L59m3XUVoPMCLoQzMzMIBqNYnFxEYFAAADQ6XRQLpfx8OFD7OzsoFKpHNuzj82QodkVRqMRbrebeWMov6NarbLZFVRnTpUVOp1O8W8+HdpUnma322E2m1nFRDabZX09qFJiMBgwL006nUYul0MsFsP+/j6z9uUNl5QaTiJFIIriUP8Ot9sNnU7HPCqpVAr7+/vMIzWaaEiHOa0zaiBIc2GazaaqDp9RqAkclRzT3qLbIsWnD/JGKBlqkujxeOB0OmGz2ViYkYb5pVIplEol1Ov1Fy45pzUlv7Er3fN5FPRcVE1Kc8w6nQ4qlQqrVFJTQ0m5zqQBq9TmwW63w2QysXEM+/v7SKVSKBaLAJ7sN/n307BRav9Axi/lo6kVQRBgt9vhdruZN6bX67Ep11Suf5zDaY/dkKGcB61Wi4WFBUxOTuLSpUtYWFiAzWaDRqNBrVbD7du3kU6ncePGDTx8+BClUokZMErdMPK+L9R1dnJyEpcvX8bk5CTa7Tby+Tzu3r2LbDaLaDSKhYUF1q9hZ2cHX3zxBQqFAtbW1lAul7G3t4dyuczKain8RIlV5MJUilFDHhWTyQSr1YpLly7h+9//PoLBICwWC2q1Gm7evIlUKoVvv/0W6+vr7DCSHzRUCTY3N8dyi6anp1lSHvW8oN4pSl5Xo9B7bTAYYLFY2FwcvV6PbreLarWKtbU1bG9vs1uRWp6dep1MT09jeXkZwWAQOp0OhUIBjx8/RiwWwzfffMOmNL9MSEjeGK7f77PQVLlcZiFNNciRnpMSVJeWlvC7v/u7iEQiMBqNKJfLuHnzJra2tpDJZNhzK/lwHu0YLooi5ubmcPnyZZw/fx6RSATdbhc3b95ELpfD559/jtXVVdbWQr6HqECBoMtqpVJhuX2jRo+SkYeUdDod63M2OzvL9E6tVsPOzg4bDJlKpdh4j+NgLB4Ziov5fD6WhEgPDDzpghiLxRCLxdiUa7XNDCJXtdPpZEmYNNk7kUggk8lgeXkZU1NT7EaZy+Vw584d5PN5bG5uDlXlDAYDZv3SYd7r9YbyRZTQQ0be+8RsNiMcDuP8+fOwWq0sPLC/v4+9vT3s7+8jmUwy780otMYmJyfh9/vhdDqRTqdRr9fRaDSGQlFqWltypUJytFgsbOBoq9Vi3UnVktsgTzQVRREulwuBQAA2mw2iKDKdQusmk8mwgoGXRZ5rRUM3aQ8qXZbyQ0mv18NkMiEYDGJ+fh5erxeiKKLb7TLdTJVeavDoyQ1Vakw6Pz+PSCQCl8uFUqnEPODb29vY29sDcHA+4miYnBKmqQcN6Wi1GDOEVqtlPdEo1YHWTKFQQKFQYPmcZAAr0iNjMBjYjI75+XksLCzA7/fDYDCg2WwilUohmUzi9u3biMVibKIqcLoP4OchLyemRkEOhwMzMzNYWFhAv99HMplklioA1k200+lgY2MD6+vrWF9fR6VSQb1eR6/XY1VPPp8PkUgETqeTZc7TR6/XO/U5RXIjS6vVIhwOY3JyElNTU/D7/SgWi/jqq6+QSqWYR6ZSqTxjhMjzQywWC6LRKC5evIhAIABBEFircGp4piZvBDBcMhuNRjExMcHmTlHYkpKjs9msKgwZeVPJQCAAt9uNaDTK+r3s7e1hZWUF33zzDdLpNCqVCpu0+zLPPtrSgAxh+lDCReFF6Pf7LLdqYmICFy5cwOTkJJrNJm7evMlu1olEAq1W69TrlhdFrpvtdjsWFxdx5coV1gojlUphdXUV+/v7qFQqR1ZpCYIw1DWb9pnVaoXZbFZsJelRUJUSNcGbnZ1lF/RKpcIuEWT8H2fRzrEYMnKFodfrMTk5CY/HwwwZn88HvV6PUqmE3d1d7O7u4vbt2ywZkQ5ipd6a5eXWtFm8Xi+CwSAzZJLJJHZ2dtgtGXhiyIRCIayvr2NjY4N9tNttVs2j1+uh1+vh9XoxNTXFOic2Gg1m+SrpwCZDZnJyEouLi4hEIvB6vUilUvj6668Rj8dx69YtFAqFocRe+Q2H+j6QIUOt6LVaLbrdLjNk1DiWgKCGiEtLS8yQqVaryGQySCaTLCzS6XQAKP/wJUMmHA4jEAggGo1ienoa6XQae3t7WF9fx40bN1ilmzyk+Lyfe1BODH2OwrZquV2TjhIEAfPz87h48SLOnz+PiYkJrK+v4/bt29jd3cXOzg4ymQwGg4FqvDGkJ91uNwKBABYXF3H58mUUi0V2CK+urrLGd4cZMvRvCu1TNRMA1pBR6fuNGM0pikajbLzOzMwM86JXKhXs7+8jkUgwQ+Y4z6TXbsjQBqeJzjRwLBAIsENKq9WiWq0ilUrh4cOHiMVibP6CmpqVkdubpsdOTEyw5LlisYjNzU0Ui0XY7XY4HA5YLBbodDqUSiVsbGwglUqxWyEtIBquSfNg5GE56jEj98icRjmOxqf1ej3rQkvx1Xw+j1gsxgy9g2bbyN3ilGNjNpthMpnQ6/VQKBRQLBZRLBZZGf9plcmrMJpETzNOqJKNOvnmcjlVhkNoICiN96ChmHt7e0gmkyyX5WU8MbQ+LBYLS16UJIlVQGWzWeYmV7IhI78EUMlsOBxmeTHVahXZbBabm5vME6OGNhjA8L4xGAwIhUKIRqNwu93QarVoNBrY2Nhg5xKV67+oETwa1lf6fiPkVYJ6vR5OpxOzs7OYmJiA0+mEXq9nyb2ZTIbpb3lBCqCQZF954qXb7UYkEsGPf/xjTE9PY3p6Gg6HA7lcDul0Gg8fPsTf/d3fsW6RzWZTdUPsaOja3Nwc3njjDbjdbtTrdezt7eGzzz4DACwuLrIZMQaDAbFYDJ999hlLzNRqtWwjRSIRTExMsDbQ6+vruHXrFvb397G5uckGm532Mr+DPCnLy8swmUxIp9PY2dlhYwfIy3RQWIlKaWkUARl6tVoN2WyW3SblMVo1QPtMq9XCbDbD4XAgEolgYWGBVbxVKhWsra1hf3+fTbtWSxMzmtV29epVzM7OIhgMwmAwsIKBzc1NpkSptP9FINmQB9nv90OSJNTrdTa5l5otKlmWlFNnNBoRCATg9Xrxxhtv4M0338RgMEAqlcLa2hp+/etfo1AosEaSSvaUA8OHMeWwXLx4ERcvXkQ0GoXBYGBtLpLJJAuNvMi5RPqIwo50CVVLKI50Dl2aJiYm8P777yMajWJqagoWiwW5XI6Fdu/cuYNyuYxms3nsLVSOZUVKkgSj0cgaClF+iE6nAwBUq1WW4FosFtkcJTUxmoDpcDjYzJ9Wq8VCQa1WC0ajEWazGf1+n3WhrdVqQwcPJXHSIEC73Q4AaLfbyOVyKBQKLA8AUEbogAwZajRmNpuZJ4U8dHRgELSZSKkajUZYLBYEg0EmF4PBgFarxUYZdDoddLtd1ZSMEnJvFCllSvKlMQ3FYpF56dRyoybkt2p5zxd6v+VTqp+H3MtjNBphs9ngdrthNpvZVPVCocC8W2qYfk36ye12w+/3s5YQ7XYbqVSK9UORT7hW8vPK0Wg0MJvNrPmoz+eDRqNhHly5d/tFPW9arZa1fdBqtSzRnvS4GqCzyOVywe12s4ujIAhot9solUpIJpOs8eS42l28Vo8MWaS9Xg+hUAh/8Ad/gHA4jLm5OTidTtYdkcYP7O7uYm9vj7WVP+1ehJeBrH7K5qb4a6VSYZN3S6US6/nh9/tRKpXQ6XSYW5wUsl6vZ0mNH374Ia5cuQKn08kqnm7evIl8Po9ut6uYRm/0fgcCAfh8PvbngwcP8PDhQzx+/Jh5EQCw8BrdJPv9PusQPTExgT/8wz/ExMQElpaW4PF4cOPGDfzzP/8zm1RMORKAOpSx/OClKq2JiQn4/X4MBgPk83k2iyqbzQ51s1XyjVqO/MZLyBu7vai3ZDRfwm634/z583jzzTcBANlsFnt7e7hx4wZ2d3dRKBQUaxTLDRK9Xg+Xy4UPPvgAkUgEs7OzcLvduHPnDn7xi19gY2NjqNJNDfpZHjGYnp5GIBDApUuXcOXKFezs7OCzzz7D7du38fjxY1Zg8bznprVAE+fdbjcsFgtSqRR2d3cRj8dVMWKH9pvf78e7777L1ozP52O9Ym7evIlPP/0UiUSCnUnH7Y0BjsEjQzkhVquVNTYzm82sORk1vaMJqq1WSzUJiKPIb4zUvlmj0bDGbOSFMhqNbJZJsVhEvV5nN0pqCOhwOOB2u9mhr9frWTk2lbiNdrk9rciTKQ0GA4xGI2tk1+l0mCtb3lwKeOr2p7waGo7o9XrZ4FFqjU2JrpQ8Lj/c1ALJwm63w+l0sjb9kiSxsRflcpmF5s4SL3sDpq8nbwzJlLx79XodpVJpqLGXUqF9IIoijEYjvF4v09PU14tGgNAeBNSjn0fz6ight9vtIpPJoFAosJYNR+kMeY6eIAjMw0MtMbrdLtt/ashNo7PdbDbD7/fD6/UOne2VSgX5fB6pVAr5fH6sa+e1emQ0Gg18Ph/8fj+WlpYQjUbhdDohiiLa7TZrUHXz5k3cv39/LLGzk4ZujFRvT+XA7XabJe3OzMzA5/PhN7/5DXZ3d7G1tYVGozEUv/7444/Z8LJQKMTa9N+7dw+FQgHNZlORm4V6ONBcjkKhgN3dXWQyGZb9T14EmnNDRks4HMalS5fgdruxsLAAk8nEEqjv3buH3d1dVCoVAFDNGpPH+E0mE7xeL95++21EIhFMTk7CYrEgk8lgZ2eHdSIlRaqGG/XrRp6YKYoipqamWAWUy+ViVW/k1SJXuVLXE3kkDAYDnE4nAoEA09UA2Np5+PAhyuUyAKgqZxF4mp9HRgzwpOIon89jd3cX6XSaXTQP8+rJq70oz+/q1atMP1Pl040bN1AqlVgzOKWtGfJWSpIEn8+HYDCIq1ev4sMPP4Tb7WYX8NXVVezs7ODu3btYW1tjKQHjeubvbMiM3pYdDgcmJiZYbozNZmPWaSqVwvb2NmtsNtqnQS3Ib4P0fJQs1uv1mMeFvApOpxMOhwPlchk7OzvMiyCKImw2G7xeL+uK7Pf7YbVaWfJhIpFAvV5X5OwTuVwoZNRoNFAul1k/HHkpuU6nYw27FhYW2KRVaisuCAJyuRzreVEqlVjZn9qgUnyr1coOX4fDMeTVKhaLbFgmoK4KiuNAFEW43W6Ew2Hm4RIEgY3IGDWslQwlu9psNgQCAQQCAdRqNVQqFRQKhaG2EGpcN3JvOQCml/P5PMvZHC01H83Vk+dBGo1GhMNhLCwsQKfTsZyqWCzGLhJKXTP0rCaTCX6/n6WLWCwWiKKITqfDCitoqDHp9nHxnQwZuVVqtVphNBqxtLSE999/H9PT06x6YmNjA6VSCTdu3MDKygri8fjQ5lDbJgGe5sg4nU54vV6YTCb2xlKlTSgUgtvtRqfTYbOmWq0WJiYmEAgE4Pf7ceHCBXg8HiwvL8NutyOdTiORSOD27du4c+cO0um04suKKXFSFEWEw2G8+eabrFpEkiTYbDYYjUZMTU3B6XRibm4Os7OzsFqt8Hq9bI3VajV88cUXWF9fH+pCqlS5HMSoR4YOolAoBFEU0Ww2EYvFcPfuXezs7LD8M0C9+2w0jETPKW9aN/r18guY0WjExMQEXC4X3nzzTVy4cAFutxvFYhGxWAy3b99mJchK3mfA0+enPKDZ2Vm4XC4YjUZW6ZVKpYZC30p+XkK+DkRRhMFggNfrhc/ng1arZQndlNxMBQWHvd/0M8xmM2vNT5er/f197OzsIJVKsZYHSkNenh8IBGA2m/H222/j+vXriEajMJvNkCSJ5XM+evQI9+7dQyaTOZGz/bV4ZKjvgt1ux4ULF/DRRx+xnA7qiRKPx3Hjxg08evQI3W5X1UYMMGzIeDyeZwwZ6kpqs9nYzader6PZbCIajSISiWB6ehpvv/02zGYzPB4PJEnCjRs3sL+/jzt37uDOnTvP9AZRqjxp8U9MTLBumzSlmeYuyfsRTU1Nodvtotlssp48qVQKX375JR49esSSztWSpChn1JAJhUIIBoOQJIn1Fbp37x7S6bQqh0TKOaxnhyA8ncE1+rVyA4fytBYXFxEKhXDt2jUsLS2h3W6jWCyyvZbP51Xj3ZMbMtPT0ywXKJPJYG1tDZlMBs1mE4By9clhUBTAYDDA4/HA5/MxTzl5gykxXm7MjKLVatlMpQsXLiAUCmFubg5TU1PY2dnBxsYGEokE8+yQN0ZJ8qTX7ff74ff78eabb+J3fud3WL8uKjZJp9N49OgRHjx4gHq9zr5/nM/6WnJk6ODx+/3weDyw2+0QRRGVSgW5XI6FkyhRTom5HK8CJZ1Swlej0YDZbMbU1BTreUIJqzqdDhcvXoTT6WQLx263o9vtolQqsT471AFZvkmUKkt5BRIZxDabDcCTNUVJ4G63G0ajEZOTk3A6naxpVaFQwPb2NvL5PKvOKZVKL121okRoyjXlDWm1Whb6qNfrKBQKqNVqANQbGuh0Otjb24MgCMwD7HA4MDs7i06nw4zhbrfLKvroJk3yI5119epV+Hw+NpGemkyurq6yMmSlez4JyuvweDxwOBys7QMVYYw2j1T6845Cg4wpIR54sp+CwSCWlpbgcrlYzt4oFAonT6jD4cD58+fhcrnQbDaxt7fHqnHz+bwivTHEqLeTQtkGg4E1iaSWBNQu5KS6yn9nQ4ZihNPT01hYWMDs7Cz8fj+q1Sprw//FF19gd3cXyWQSrVZLFTHmoyCrv9frIZPJoN/vI5PJIJ/Pw+Vywev1otfrMVlYrVaIosjCKTSGgAyYbDaLzz77DJlMBo8ePWKJsFQWqVRlI0lPJnZT/xuy/qempjA/P4/Lly8DABvPYDKZoNVqmYH88OFD/MM//ANyuRzu3buHarWKdrvNNpOavRDkGidvH8XlW60Wcrkcdnd3mSJW216j/dVoNFhJNHlUQqEQ3n//fZhMJqyurrLk8Xa7DYvFAq1WC4fDweaevf/++/D7/Xj//fdhs9lQKpVQLBZx48YN/PKXv2R5fUofm0KQnrDZbKzIoNvtotVqsZEEyWSSGTJKf15CHm7s9XqsQpSSfS0WC5aWluB0OhGPxxEMBpmOlRsjVGEZCARw4cIFWCwWhMNhaDQabG1tYWNjA9988w2+/fZb1oRz9DUoAfJc0rNT3x2PxwPgSVSh0WiwogLqeH1Sa+a1VS1RVQ7deuTZ/pS8KQ8pnQUkSUK73Uaj0UA2m0UikYDD4WANhKjcUa/XMyVJi4fKPff29tj35nI51nZdvrmUJlMyvOTyKRQKyGazsNlsTLmYTCb29cCT5n9U9ZXJZJBIJJBKpVhSK1XBnQXoRu1yuaDVPtnGtVqNucapfF/N9Pt9lMtl6HQ6ZLNZ1smXkr8jkQisVis7qCnXyuVywePxYHp6mnn5qCCBmijSfqOkc6V7Y2hfUMI8tYMwmUysaRvNalNrOwxCEJ40TiyVSjCZTOyCORgM4HA40Ol0EI1G0e12nzFkyCPjdDqh0+kgSRKKxSIGgwFryU/N4NQiR3kuLHnKq9UqisUiO+PlTf9O4nlfi0cGeGKx0Zus1WqRzWbx6aefIhaLYXt7G4VCAQBUl69wGBqNBv1+H/l8HvV6nTWYOn/+PM6dOweXy4XJyUk2zRoAO4xpWvHa2hp+8YtfoFKpsCnZ8uQxJd+WBEFAr9dDOp1mSbqpVArBYJA1/5uenoYgCGg2m+j1emx428rKClZWVlhSK+WBqHG+yWF4vV5873vfQygUYt1nV1dXsbW1xbwxauubQ5BC7Xa72NvbQy6XY7rm4sWLWFpawqVLl+DxeFgiYqPRYJ16aY05nU5MTEyg0+lga2sLpVIJn3zyCdbW1pBMJpFIJIa8e0qWJYVb/X4/QqEQzp07h4WFBWg0Guzs7DAvXjweZ55iNe4jei+bzSZu3boFq9UKjUaDzc1NJpNQKIQrV64AwDM5VuVymX1kMhlUq1VsbGywcSAUZqlWq+z3yf9UIrR2SL9SKJ/G7CQSCZTL5RONDrw2j0yv10O73Ua9XmclfKlUCtlsFs1mE51Oh4UIzgoURwTAStIo+RcAXC7XkCFDt2nyNiQSCezv7zOZjnZmVfLmkHtkBEFANpuF0Whkm6bf77ObcqPRYM2qqJKEPFUkF71erwql8SIIwtOmgKIool6vo91uI5PJIJ1Oo1qtDpWHqhFaP3QTTKfTMJlMCIVCaLVabOK80WhEpVJBq9VihgzloBmNRhiNRnS7XRSLRaTTacRiMZbP12g0VBFekVdqURM8SnCVJInlOdDIlKP6p6gF8uZRJ3WDwQCfz4d2u80alNIak0NG3mAwYA0Sab3E43EUCgXmVVf62hkNxzWbzaGzPZ1OI5/Ps3YhJ7lehKNc8RaL5bl+etoc5KKdmprC5OQkUqkUa3qXyWTQ6XTG0jOmXq+PTZoOh+NQ+cjlKggC7HY766Lp8XhgsVjg8XiYB0uSJNarolKpsJbPsViMJcTSz5L/3JelXC6PTT5ut/tI+ZCy1Gg0TCbU24JKq+nmTYlljUYD+XwehUKBhaUovwb47kZMoVAYm3zMZvNLx8HoQAqFQrh8+TKrnACAzc1NFAoFZDIZZDIZAK/fqGs0GmOTj9PpfCH9Q0YL9dSZnJxkoSUKJVF3bQrjCoKAfD7PvMW3bt1CsVhkIy16vd4rlR+XSqWxycdkMr3w+qELgsvlgt1uZ801B4MBa/a3srKCbDb7TCXY66TZbI5FPkedXaR7KIrg8/lgtVrZuBOtVsv6y9DXE/V6nX1QSIU6INOFXc7LyG+cZ5fVaj1y7cjDSdT4bmFhAQsLC6hUKojH46hWq9jc3GR9qo774lSr1Q794a/FkKFeDDSJ12q1sryHfr8/VJVy3JwWQwZ4ugFGM7+pHT8N+KNDmG5DlGRGSWnA6wvJnRZDBni2dBZ4mmtFBw8AdsOh+D190PiG18lpN2QIk8kEt9vN1g/F6qnD80GG7+vgNBoyI9/DLlRvvvkmPB4Pa5ooT4puNBrY3t7GF198gVwuh9u3b7NJvZTY+yrJ4qfVkJGPYDCZTKwb7WAwYB5PukgdpzfmNBgywNODGnjatNRut7PCC9K3o+ur3W6j0+mw6AO1eZAkic25e1X5nSZDBni6Zsjza7PZYLFY0G63We4YDVAdhwfvKEPmteXIyKfN0oE8OifnrELPTiETMlIo7ET/L+9dQH9XsmvyeRy0JgaDAcsDIncl3Sbl+UFqr3x7HjTHRZ44TeWPwNnZb/K9BWDIkGu32zCZTHjw4MHQhGzae5RMT5PoaT+qoTrpMChE0Ol0WFiO9pXamkc+D/mzStKT+WQkg9FBpMSofgbAOpOrMRxHidF0kZSfXfJGmyf93K81R4aUKVloapo2/CqMbhRaFOSyHu0xIM/xOOjvamT0ICIlQZ02R7+WPqhS56xCFRUHjcNQ83o5DLkx12g0WJiI/u8gRmVHf6qxbJ9kQIfQqEeC/jwra0e+T2gdtFot1gjwsEjFQbpZrXtOfsEGwLqEyw2907JmXutpIK9gOg0PdxqRbx5aDKNeq7NixBwEHUgHPfdZk8XzkHtj6N9nGbn+eZEy/FF5nRX5yeUk//dZ50XzDw/S02eBUU/VaXru12bInLU39WUZPXC4nIYZVSIHGTNcZk/ha+hguFyOhuvpZ5HL5EX7UJ0l+SlhzZxt//yYOc0L4bTBZcXhcMYN1zvK5MiqJQ6Hw+FwOJzTjDpT8zkcDofD4ZwJuCHD4XA4HA5HsXBDhsPhcDgcjmLhhgyHw+FwOBzFwg0ZDofD4XA4ioUbMhwOh8PhcBQLN2Q4HA6Hw+EoFm7IcDgcDofDUSzckOFwOBwOh6NYuCHD4XA4HA5HsXBDhsPhcDgcjmIZqyEjCMKfCoLwrSAIbUEQ/uc4f7cS4PI5GkEQ3IIg/B9BEOqCIOwKgvAnJ/2aThNcPkfD99fhcNk8H76/Duek18+4p18nAPwFgB8BMI35dysBLp+j+R8AOgACAK4A+HtBEO5KkvTwRF/V6YHL52j4/jocLpvnw/fX4Zzo+hmrR0aSpL+RJOn/AsiP8/cqBS6fwxEEwQLgjwH8/yRJqkmS9DmAvwXw/5zsKzsdcPk8H76/DofL5mj4/jqak14/PEeGoxQWAfQkSVqTfe4ugOUTej2nDS4fDuf44PvrFMMNGY5SsAKojHyuDMB2Aq/lNMLlw+EcH3x/nWK4IcNRCjUA9pHP2QFUT+C1nEa4fDic44Pvr1MMN2Q4SmENgFYQhAXZ5y4D4Il2T+Dy4XCOD76/TjHjLr/WCoJgBCACEAVBMAqCMO7KqVMLl8/hSJJUB/A3AP6bIAgWQRA+APATAH91sq/sdMDl83z4/jocLpuj4fvraE56/YzbI/PnAJoA/gzAf/rXv//5mF/DaYbL52j+K56U9mUA/DWA/8JLH4fg8jkavr8Oh8vm+fD9dTgnun4ESZLG9bs4HA6Hw+FwXis8R4bD4XA4HI5i4YYMh8PhcDgcxcINGQ6Hw+FwOIqFGzIcDofD4XAUCzdkOBwOh8PhKJYj67zNZvNrKWkarYwSBOF1/NgDaTQax/fDR7BYLIor+arX62OTz+taP+NknOvHbrcrTj6VSoXL5wjGKR+r1Xps8jmomvV16O1arTYW+dhsNsWtnWq1Ora1YzQaX7t8JEk61rO91Wod+sOPvWGNJEnHtik4nIOg9Ta67jQa7oA8i8jXAylb0j9cDz0LyekgeQFcZpynHKRrT2J9HKshc9iBAvDNwDkeRg+t0f/j6+5sIj+YAa5/jmLUkJEkiV0Czqrcjuq3dlZlQozqWtUZMmTJGwwGCILAHrbf76PX67GvOavIZQIAg8Fg6LAl+Y1+HecJ8g1EfxdFka05nU7H/n8wGKDT6RzqIVQDg8GA/Ul/p7U06o06S14JrVYLjUYDnU4HURTR6/XQ7XYhSRL6/f5Jv7xTA60ZrVYLrVYLURSh1+shSRI6nQ7T24PBQNVrRq5TSCcfpTNob2k0mjOzp+ic0mq1EASB6d1er8fO9nFyLIaM/DDWaDQwGo0QRZEtiFardSIPe5qhTUNKQhCEoY3BORhSNKSENRoNNBoNDAYDzGYz+7perzdkQKsR2l+kTOSGMCka4iysLfIk6HQ6aLVaWCwWGAwG1Ot1NBoN9Pt9bsjIoPWj1WphMpmg1+thtVoxGAxQrVbR7XbRbDafuXCpFTJ0SS8fFlmQ762zsq8IuiQYDAaIoohms4l+vz/2y+JrN2TkrlutVgur1YqlpSVYLBa2Uba3t7G9vT10AKn9zT8IefxZEASYzWaIosg2Tb/fZ14EzrOIosiMZJPJBK1WC6fTCaPRCK/XC6/Xy9ZYrVbD48ePUa1WUa1W0W63n4n9K4mDQmh6vR6iKMJsNsNkMmEwGKDX60GSJLTbbbbX5MaOWsMt9EwGgwFTU1OwWq2IRqNwOp1YX1/H1tYWGo0G219qevaXQZ4HYzAYoNVqEY1GEYlEYLPZ4PP5UK/Xcf/+fZTLZaTTaXQ6HdXJS76f6Nn0ej1cLhe0Wi3z5tEekn9fu91Gv99Hu91mHis17ik5g8EAOp0OdrsdRqMRExMTsNls7Gw/yCt8nByLISO36sPhMH7yk59gYmKCHc4//elPkUgk0O12h5SrWt/05yGKIrRaLQKBAMxmM7rdLtrtNhqNBvL5vOpduS/KqEFnMBjgcrlgMpnYYTU3NwePx4PZ2VnMzMywwzyRSOCv//qvsb+/zw6xUS+F0iCFSd4Xl8sFi8WCmZkZRKNRdDod1Ot1tFotpNNpZswMBgOUy2VUKpUhhQOoQ/HK14nFYsFbb72FcDiMjz/+GDMzM/i///f/4m//9m+RzWZRKpUwGAzOxE36MPr9PjQaDex2OywWC37rt34LP/zhD+Hz+TA/P49kMom//Mu/xM7ODmq1GsrlMjQaDURRPOmX/loYDSXRs5lMJpw7dw42mw0ulwtms/kZb1S73UYikUCj0UAikUC1WkWn00G322U/X23rinSq0WhEMBiEx+PBhx9+iGg0ir/7u7/D/v4+82SNi2OvWtJoNDCbzbBYLNDr9RAEAW63G3a7Hc1mE+Vy+cwaMYPBgBl8RqMR4XAYHo8H9XodtVoNuVwOuVwOg8FANUrjVRi93ZhMJphMJjgcDoTDYVgsFkxOTsJisSASicDlcsHv98PtdmMwGLDbktPpRKVSgU6nO8nH+U6QLChkIg+dBINBOBwOTE9PY2ZmBp1OB7VaDa1WCyaTaciQKRaLKBaLqNfrKBaLqrlQjCYdkofKZrPBbrfD4XDA6XTC5XKh0WioOmfqKOTPrNfrodVq4ff74fF4EA6HEQgEYLPZIAgCyy9rtVpjPZyOm9H3nS6UJpMJTqcTNpsN0WgUdrudXZjk60Wj0aDVarFwpUajQalUQrFYRKlUOhMJ5iQzq9UKp9PJznm5MTcOnXJshgxZtxQv02g0mJiYgMPhwMbGBvb29hCPx3Hv3j30+31otcduU50qyKo1GAyIRqPwer34wz/8Q5w/fx6xWAyxWAz37t3D1tYWer0ey/04C0qXkptHFQElIM7OzuLChQuIRCJ46623YLVa4fP5YDAYmKtTFMUh408URSwtLcFqtSIWiyGbzZ7U470S8qTlwWAAq9XKDpuFhQU4HA7Mz8/D4/EgGo1ienoa3W4XrVYLnU4H+XyeJWxKkoRsNotsNouVlRX84z/+I8tbU4PSJX0jiiJ0Oh1cLhc8Hg9sNhvz3l25cgWiKOLOnTtsfyn5mV8FCg94PB7Y7Xb87u/+LpaWlnDhwgWcO3cOpVIJDx48wM7ODh49eoT9/X3UajVVyEquR0nHkLE7OzuLDz74AD6fD5cuXYLNZoPZbGbFA7S+tFotOp0Ocrkc6vU6VldXkU6n8e233+LGjRvodDrMWFZj6wd5DpHT6UQwGITP54PH40G1WmWG7zie/VitBzJkut0uer0e9Ho9zGYzHA4H3G43SqUSgKNL29QMWaomkwk2mw2hUAjT09OQJAndbhcOh2NIYZwVOR10q9ZoNCwB0efzIRwOY3JyEtFoFGazGU6nE3q9HkajEVqtliVy0vdTGKpWqzFDh36X0pSyIAjQ6/VwOBxwOByYnJyE0+lEJBIZulH3ej202210u12YzWZ0Oh0AT/al2WyG2WxGqVSC1WqFRqNBvV5nMlMD9N7rdDqW40AhA7o9quVZXwb5/hJFEXa7HW63G+FwGJFIBF6vF1arFaVSiX1Uq1XU63VVVptSiNlqtcLtdiMQCCAajcLj8SAUCrH9odFoWGhJq9XCYDCwc63ZbKLRaMBoNGJnZwdWq5UllZ8FdDodjEYj85a32+2x/v7XbshQAmW/32du69XVVTSbTUxOTsLv98Pr9WJxcRHtdpsdOoAyD5VXRZ5kZzabYbVa4XA44HK5UK/X0el0EAgE4PF4WHhAzfKh5xqtDrBYLMzjsLS0BL/fzzwOFosFbrcbkiShVCoNHcJ0WzAajbDb7QCAy5cvY2pqCnfv3kU6nUa9Xke9XmcVYqeRUa+UwWCAwWDA7OwsPv74Y3i9Xly+fBl2ux1WqxV6vZ4pEkoWBwCn08meU6PRwO12Y3p6Gi6XC6IoIpPJ4KuvvkKpVEK9Xke73Vb8zZvkNhqvdzgcmJqaQiKRgN1uZwacmvcXIW9ToNfrEQgE8OMf/xiTk5N45513MDU1xfL06vU6kskkUqkUKpWKagxdeU6MKIosHH3t2jVcvXoVXq8XCwsLAIBCoYBkMomNjQ3kcjlW+eh0OofC2QaDAQsLC5iZmYEkSTAajdja2sKXX37J9qAavJ2jkL4mJ8XExASWl5exu7uLVCo1tqrAY/PIkDem2Wwim83CYDCg1WpBEARYLBb4fD7YbLZnEi7PgjIh5LX45E0go8ZutzOXJt2sKSlP7fKRV3Pp9XqEQiH4/X5cvXoV0WgUgUAAfr+fhS9brRYz/iguSxvMarWy23g4HGa3T4vFcuorwg7K39DpdDCbzfD5fFhYWIDf78e5c+dgsViGvocUbrfbZXF/eX8Qi8XCDvdKpYJYLIbHjx+j1Wqh2WwOGdpqQP488hwIo9HIQnBnoYJSLgedTgebzYbz589jZmYGU1NT8Pl8LHzZbrdRqVRYmICqlZQsn9GQEiXJh8NhXLhwAe+88w67IDWbTWQyGRSLRTx69Ai7u7tsT/n9fjQaDXg8HgSDQZjNZni9Xuh0OmQyGZRKJbRaLXa+qWkvAcMXT8rh1Ov1sNvtCAaDKJfLYz3Xj8UjI18snU4HiUQCg8EA2WwWxWIROp0OExMTiMfj8Pl8qFarqFQq7KA+i5DhR65Kp9MJr9eLUCgEvV6ParU6tnjjSUCGB4UB6KD2er14//334fF4MD09DafTiVarhe3tbRSLRezs7KDRaLCqHPntW5IkRKNRvP/++7DZbAgEAqxE+TQr5NEWBlqtFrOzsyyJ2ev1Ynp6GrOzs7DZbM/kl5HXpdFoIJPJDDX16nQ6GAwG8Pl88Pl80Ol0iEajrGqu2+2ytXZa5fMqkF4aDAYwGAxsf4XDYRSLRcRiMXS7XcVXsr0IkiTBYDAgEAggFAohFAqxUGSxWGQJnLVaDZlMBvl8nu0tNciHPDE2mw1WqxVvvvkmzp8/j/Pnz8PlcqHVamF9fR25XA6/+MUvkM1msbW1xZLie70ekskkkskkJiYmEIlEWNK9TqdDIBDAxYsX0e/3sbq6ilKphHg8zlo+KN2oId05GAxQKpXYWmk2m3C73VheXmYFBgDGEnE5Fo+M/JDodrtIJpPodrus3FGr1SIcDrObNgmi2+2e6gPmuKBDhqx9MmTcbjeCwSAAYHd3VzWK5CDksWdy13788ccIBoP44IMP4HQ6Wa7MxsYGdnZ2sLW1hc8//xyVSoUZMtTHgXjrrbcwNzeHwWCAcDjMcmhOu0Eo99aZzWYsLS1hbm4O09PTiEQi7OZDho788kCGTLfbRSaTQavVQq1WQ6/XYzfFixcvsr5FMzMzrGqlXq8jHo8P5RepBTJkjEYj3G43vF4vJiYmoNVqkUwmVX1RIEjXGI1GBAIBBINBpodrtRqKxSLLc6jX68hms0OJ4krXP7QGdDodC+VfuXIFb7/9NtxuN1wuF2KxGDY2NrC7u4uf/exnSCaTaLVarF0IGUI6nQ6zs7P4/ve/z7zoAODz+eBwONDr9fD48WOkUilks1kWkaDXoVQ5UoiaGiWKosgMGSpTz2azLJogb/R6XBz7iAJJkpgibTQaaDabMBqNMBqNsFgssFqt7A0+zW7+40b+Jsub5NGhpFYDb7SZ2+TkJCYnJ7G4uIj5+Xk4nU7odDr0ej1ks1nWnOvRo0dIJpOIx+NoNpus8+hod2R5JQ7wVM6nUZ5yTwwlKAeDQdjtdszNzWFxcZFVBZBBJggCC6d1u130+31Wjl2tVlmi5vb2NprNJmq1GvPKtFotFprS6XTMS0HJ0FQld9rk9LLQ4SXPN5J77Q5aH2plNIcxl8vh1q1b2NvbY3lCkUgEkUjkmTCMkpHvLepESzmbHo+HdTAul8tIJpN4/PgxEonE0H4h2dGFy2q1wmq1svYZJFe6gNjtdszMzECv1+PRo0dDFy2lrzN6/Z1OB+12G7VaDZVKZSi5ns4uQIGhJYKUMSVikhuqXC7DbDbD7XbD4/HA5/Oh2+1Co9Ewi/+sIn/jAajakCHXJG18q9UKs9mMK1eu4P3330c0GsW7777LShybzSbu3r2Lvb09fPXVV/jNb36DZrOJSqXCyhvlxjDJjpKk6XOnXY5026Pwx6VLlxAMBvH9738f58+fZzku9LV0IPV6PdRqNbTbbZYQnM1mEY/HkUql8Mknn6BQKDCl/ODBA0xMTODtt99mynZ6eho6nQ4rKytIp9NDSZGnWWajyG+9FAqQe+rkz/Uis3TUBO0LKhuu1Wr4X//rf7GLpcFgwA9/+ENMTk6qUib0/Ha7HVevXsXU1BRmZmbg9XqRz+eRzWbx+PFj/NM//RMKhQKy2Sza7fZQOJryrPx+PyvIII8xNafU6XQIBoN4//33sb29jZs3bw55RpWe60j6u9FoQKPRIJPJIJFIsI7qZMyMq63Ksf8WUhakUCheRnFYvV7PGuWddeTeBIq5WiwWmM1mRYRDXgZ56EQURXi9XrjdbkxMTCAcDsPlckEQBHQ6HaTTaVSrVezv72Nvbw/ZbJZ5YOTloKPhFVpjJDsK3VFp9mlV1CQXo9EIn88Hv98Pq9XKbn3Ak9lRjUaDde1ttVps9ILBYIBer0cqlUI6nWYHFhk8kiShUqnAaDSyfBgqw6US0tESdiVC4dpms8kqsU7z+z4u6BCiBom5XI5VMMm9cEp93w9DfkmghF632w2DwcCMu3K5zLpeNxqNAxsAyr3lAFgXdtpLRqORlWZTMz1q/dDpdBRfxSTXtXSZajQaqFQqsNlsQ183LsZiLskTWckNTnXndruduaTUED98VUZ7O4iiCI/Hg8XFRZhMJty/f18VA9tIiVJXYyob/uEPf4gLFy6wD7oxZrNZ/PznP0cqlcLt27eRSqVYOTrwJCRFPxd4OsHXZDLBYrHA4XDAbrfDYDCgVCqh0WgwhUKH9GmRJRn9JJdgMIjr16+zahL56yyVStjY2EAmk8Evf/lLFAoFVCoVdDodZrw1m00UCgW0Wi2W50AUi0U0Gg0sLi6yxoznz5+H3+/Hl19+id3dXTQaDcX1DRmtpqjX69ja2kKr1cLly5fZMyn9RvyqyHMXqY9XOp1m/ZmcTqcqL050BlksFkxMTGB6ehoXL17E1NQUHA4HJElCJpPBo0ePsLW1hVwux6r3Rr0x8srARqOB/f19NBoNrK2tIZ/PIxwOIxwOw+/3Y3FxEQaDAe+//z729/fxq1/9CvV6/aTF8Z0ZlUMikYDNZmPraNwXhrEYMvLYNHlk5J03KZ52Vsuw5dBzU+dIq9UKi8XCFIvS3eCjeSDU2joUCiESicDn8w0148pms9jb20MikUAqlWJVOHLP1UH5VVSmbDKZ2DA8upmTEXPa2q3Te0sT481mM1wuF9xuNzPY6GtarRZyuRzS6TTzUpGBRl6udrvNOvZS/hAp4263C0l6MvCOfqfVakW/34fZbIbBYDj15enPQ37YyI3X0/a+nwTyqfFyo16eDK9G/Uv5MSaTieW4iKLI1kmxWGTzkkbz6+TQ2up2u6yaKRaLIZ1OA3iqf/R6PSwWCzweD/N2qkWucsOOxg1RG4Nx642xGDIHJa9SZ0SLxQKTyaTajfMyjIZGjEYjbDbbkCGj5OaBdAjrdDqYTCZ4PB788Ic/RCgUwnvvvYdoNIp2u43NzU1sbGzgl7/8JXK5HG7evIlyuYxarTZk6Mk9eARtIp/PhwsXLuD8+fOYmJhAr9fDrVu3kEqlsL+/j2KxeOrK/Uk5GgwGVhbr8XjgcrlYe/R2u41Op4Pd3V388pe/RCaTwfr6OnNry8MCZPCRfChnDRh2j5MhY7PZoNFoEIlEkM1msb29jUqlwl6b0tYb3cJbrRarOuGhpSfQe0n6WBRFOJ1O+P1+tg5O0954Xcg9sPQnFaJsb2/jzp07iMfjQ1Vao99PFYHk7fzpT38KvV6PeDyOarWKRCKBeDyOVquFq1evQhRFTE1NsWna1LuIvJ3y16I0yJDJZrOsAvIkLgsnMuBIvnn0ej3zyHCexnFJPpS4OXpwK3Hhj3ocnE4nFhcXMT09jenpaQSDQeZd2N3dxZ07d5DP57G7u4tmszmU6CrPhpf/fMJutzP3Lg2LzGaziMViKJVKaDabp05Z0w2Z8lVoGjEZ+mTodDodFItFbG5ussGiJJ/ReVwHJTgflAMhCE+aD0qSBJfLBa/Xy26XSkQew+92u9yIOQA6hOhwNhqNrDu0EvXLq0IJ8IVCAfF4HPl8fmitjMqCqpMoR41CRcVicajEempqivUmcjqd6PV67FKqlk7S9Prr9TrTq6OXpXEwVo8MJV/KQwF0OMmrc84aox6r0UNHbcrXbrfj3LlzmJqawvnz5xEOh9HtdhGPx3Hz5k18++232N3dZXkalDciNzpGDRjqARIOh2Gz2XDt2jV88MEHCAaD6HQ6qFarSKVSSKVSrJLptMr1sCo1Sn6u1Wrso9lsHvo9B+0lem6aT2W324cuFjSLiA40NUDPJe+yCjz1gNGhcprXxDg4a89P+4PmkVWrVZTLZbanDto/o7kho2EUnU7HWhtQMrVWq4XH44FGo2GNKMmbc9jvURryHmg0SzEQCECr1aJerx97j7ixGTKkaOVJvWTYHJQjc5aQZ8AfhJqUiyRJsNvtbC7JuXPn4PV6sbu7i2KxiNu3b+Mf/uEfWD8HCkU9ryyfYvyhUAiTk5O4evUq3nvvPYiiiG63i1qthlQqhWQyyYyj0+SNOYiD3OA0A6derw8ZMnQZOEpZyA8qGnMgbzRIIV9KlFaLISO/JFEjLzqE6O+cp2FZNembo6B9QlOqyZCRe1UO+z654UsFLFRtSIYM5dnQwFrqWO71etFqtVAoFMbynOOg1+uh0+mwRoNutxt+vx8AEIvFjl3fjrVqqdvtsqZAB/X2OGuGDG2IVquFRqPBSvP0ev3Y6u/Hgdz7ptVq4Xa7MT8/j1AoBEmSWGUJlVdXKhWmTI5aE/JQDHWUXFhYwNzcHEKhEOtOmsvlWCJeLpdjfYtO43ojtzVN06WcGHk8n26NL5OUOeomd7vdmJmZQSAQgMFggCAILBm6UCggl8spenKvPBGxWq3CaDSyEnSdTge9Xg+r1coqLAwGA2shz1Efo8bZYWfOyxpxo97zw76G0iiouCGbzb7U71EK8ouQ2+1m86aI4wqnHXtnX+CptUZWr7wM9KyGlOQJmdVqFSaTCdVqFfV6ncWrR2/jSoVuLtTJORqN4nvf+x5sNhubwfX555/j9u3b2N7eZrkZhxlz9PPIQNbr9azi6Qc/+AEuXbqEQCAAu92OfD6Pu3fvYnd3F48fP0Y6nWaVPadNrvJEwnw+D4fDgWq1ypKcSVbxeBzlcpklzL/Mc9DviEQiLMHaYrGwhD2aX7WxsYFisajYNUiHR7/fRy6XQ6/XQyaTQS6XYy3kPR4PG7hJAzPloQKlPTPnaA4zZuT//6IeqcPCtgd9ThAEGAwGSJIEv9+PiYkJpFKpQ79HyRgMBjgcDni9XkxNTWEwGDyTc3UcxszYPDKDwQDdbveZsjZ5wuVZUxyUmEm372azyYZtyTeZWhY7uV4NBgNsNhtMJhMzbovFIvMCHNbCe7SqS6/Xw2AwwG63Y3p6mrltHQ4HtFot2u02qtUq0uk0mzlEiX1KQe5ZAIBms4lisYhyucymfb/M+qD9RhVxtNZoDlMul2MVYnThUOq+pNdNE70pb4GqUcxmM/x+P9tzer2e5TUo9Zk5ByPfR9TpmSqHqPkolWVLkvRS3sjRvCsq0jhoOC2dgUrvBzaK3ACkSwSNIhpHZ/pjHVEAPA0rdTodVCoVFAoF1thNEIRn+siQQNTyBh+G/MaYSCRQr9eRSCQwOTkJg8EAt9utGhnQe0pGDLn0B4MBHj16hFQqhdXVVWxsbKDX6z2TDzOaXEeVbuFwGJcvX0YwGMRHH33EpkI7HA5UKhXs7+/j0aNH+OUvf4l8Po9SqcRaiJ9m2VKvC+p6TaXXg8EAmUwGt2/fRjweRzweZ4bMUV5NuZKhnDSbzQa/3w+HwwFBEFCv13H37l3EYjGsrKxgZ2dHEXlEh0Hvcb/fR7FYRKfTQaFQQKlUgsfjgVarRSgUwkcffYTNzU188cUX6HQ6yOfzpzr0yHk15N7OSqWCUqmEQqHAvMRmsxnhcBizs7PIZDKoVCpDrS7oZxwF7VGr1YpgMAi32z1UkUtewZ2dHdaMUA3Ie+pQ9369Xg+3241yucyMuuNkLFqKFCmFmMgjQzd0qihQqtJ8FeQ3BGpcRvkQSm4L/zzoPachkeSRqVaraDQaQ4lzo99HyajkTfB4PGzI5NTUFKampmA2m6HRaNBsNpHNZll5crFYHPJeKEG+pBiBYcO3Xq8P5c+8iOEvN2QMBgOMRiNMJhN0Oh2AJ0q2XC6jUCiwJGJ5nwslI/cEy8teKQnT5XKxMnd5r52zhNyjoGYkSWKDDmkfAWD5K263mzXJe1EdMdrugDzOZrN5KL1CPi5D6d5OYtRhQZ4uSZKGPDKHNS59XYw1o7Tb7Q6NQ9fpdPB6vajVakOuXbo1nxVGG5epHXrWXq/HbkfyCbNUWSJ309JasVgsmJubw+zsLCYmJnDt2jXYbDaEw2HodDqk02lUKhV8+eWX+Oqrr5BMJrG/v88O/OPeUN8FevZ2u41sNguTyYR0Og2DwcDKymnYKnmXyIP1PG8MlYHSxN/Z2VkEAgG252q1GtLpNFKp1DNVXUrfi6RIRxUqGYdmsxnz8/PQarVsiq/akRsuz8sLOY175WWh95o68Wq1Wnz77bdIJpP4+OOPEQgEcPHiRRiNRty7d4+NQpEPeaSfQ4waf9RBfGZmBu+88w5mZmZY/lkymUShUEA6nUY+n0ez2VT8vpJDoejd3V08ePAA/X4f09PTkCQJTqeTVYMddlH9royt/BoAs9goRCCKIhuKSJU68oFaanqjn8dhykINSuQwaHBdq9UauinLlSuVXxsMBng8HrjdbiwuLuLy5csIh8O4ePEiC8HQBOhsNovV1VV89dVXaDQaKJVKLLR1mteUPBxSq9VQrVZRqVRQrVah0WhgMplgNpthsVhgNBqZgXJYOHa0144gCHA4HAgEAnC73WzAG10w6PeRsqHXpHRIrgd1g6bwtsfjQb1eh9FoPMmXOlYO88KMrhs1ID+D2u02Cz33ej20223o9XoEg0HodDqUSiVYrVamTw7aRwd9Tq6naD6aTqcbSquggg4lhLifh/y1Uy+eQqGAVCoFq9UKr9eLcrkMo9HILkyKrFoChkMomUwGRqMRS0tL6PV6Q0r4LHkkRqHnpy6R8uZBSl7oByHPd9HpdIhGozCZTFhfXwfwJJm12WwOlSueP38eDocDc3NzQ+Eks9mMbreLRqOBZDKJarWKb775Bvv7+3jw4AHK5TIbQ6CEtUWHLRky+XweDx48QLFYZJU2brcbS0tL6Pf7ePDgAesuOtoLRb5uNBoNzGYzzGYzZmdnce7cOQSDQRgMBpTLZcTjcezv7yORSCCbzaLT6byUa10p0IReaohICILAcpHU9syj0HPTnDu6KNBhM9rt+rDKHvo/pSH3zOzv76PZbGJnZwdOpxMmkwmBQACLi4v44IMPkE6n8fXXX6NcLg+dV6M/j4oO3njjDYRCIVy5coVVA7bbbRSLRdy9exeJRALJZBL1ev3UDaz9rpBs0uk0Hj58iImJCZbnSW03FJnsK4c2RjabRb/fR6FQGIpXn3VIOdA4eHl5+uhiV/LCl98AKSGMknMXFxcxGAyQz+eRz+dZHozX68VHH30Ev9/PmueRV6LZbKJWq6FYLOLevXvIZDL49NNPsbW1xaZBU16NEoyZUY+MRqPB/fv3kc/ncfXqVUxNTcHj8eDChQtoNBrw+/0olUpIp9PPtFSndUJeCLPZDJvNhmg0igsXLiAYDEKv16PVamF7e5sZMplMhiW7qiVnjWQhN2QojDl6mz7upMTTAlWuAU8OIaPR+EKNJwFle8vpstDr9Vgbg52dnaHeVp1OB++++y729/exsrKCZrPJogmjOkR+4bp06RKWl5extLSEaDTKcmIKhQLu37+Pvb09FrpVW7Wu3JB5/PgxAODixYsAnvYPU3xDPIISgUjpyqsz1DY2/kWQJ0p1u12k02ns7OwgEAiwGKrL5WKuTovFgmq1qrgkTHnCW71eRyaTwcrKCiv/dTqdWFpagtPpRKlUQrVahU6ng8Vigd1ux9zcHBwOBxwOB4xGIwaDASqVCvL5PLa3t1mvmHw+j3Q6jVqtporDuNfrscZZ+/v78Hg8GAwGcDgcmJqawvXr11EoFLC2toZarcYmXVPHbKoQM5lMmJychMPhwPz8PAKBAEwmE+tX8+DBA6RSKVQqlaFcJTUg9wiXSiWkUinMzc2x0DZ5wDKZDJLJJOuUrEbkBojP58Py8jLzJhiNRpw7dw6hUAg2mw2SJMFqtWJ2dhZ2ux3NZpNNo6/VaiyvD1DeQUzrodPpoNVqYWtrixlxdrsdvV6PDXm8fv066wherVZZ4rg8aX5iYgIOhwPnzp3D9PQ0649VLpexu7uLeDzOGnJS00WlyewwRkvPW60WKpUK2u02DAYDzGYzTCYTmxd3XIzNkCGPQ71eZ70aNBoNy5GRu5+UbPG/CjShd2VlBeVyGV6vF+fOnQPwZPBYv99HIBBgYYTntdA+bdDGpT4u6+vr+NnPfoapqSn86Ec/QjAYhN/vx2AwYN2NyfWt1WphNpuZotFoNMhms8hms1hfX8fPf/5zZLNZ3Lhxg/VWIbetvKHeaffGjEJJv5ubm0ilUvj222/R6XSwuLiIxcVFluCczWbx6aefIpfLsQRFyqVxuVzMi3P9+nW4XC74fD5YLBZ0u100m01sbm7ipz/9KUqlEis5pVurWqBnicVi6Pf7mJ+fHwqxdDodrK6uYm1tDeVy+SRf6rEx2uNjfn4e//k//2e4XC7mnaNbtdVqhSQ9mSD/4YcfolKpYGZmBsViEZ988gk2NzdZbpuS9BDw1OiiXjHtdhtfffUVVldX0Ww22b65fPkyms0m3G43isUivvnmGzZwli6WXq8XLpcLb775JjweD5aXl+H3+5n3Jh6P45NPPkEikcCdO3dQLBZZqFtNxgzwNKpQq9WQTCYxPz8Pm82GZrMJp9MJh8OBXC733MTyV2WsHpnRDHnaVPKBbmp6c18GSXrSqr9YLKJaraLVarFGTfLYtRJ77dBrJkXZaDSQSqUgiiIymQwkSWIJ32S9yyu5qESSPpdMJpFMJpmrNp/PM48EfY/cG6M0I4agyq52u41MJoO9vT3WNVOSJNhsNvT7fUQiEeaxq9frMJlMMBqNcDgcmJiYgNPphNvthsPhgCiK6PV6LLG3WCyycmu1Neki5G0OaE5VtVplbQDq9TprmKfm1gfAcEdxqhClsBpBuULU6qDb7arWcz4YDJh+SafT2N/fR7fbhcfjQb/fZ3tmamoKWq0W5XIZ1WoVZrMZHo8HDocDoVAIdruddWOnMSLJZJKFa8lbCijPg/WiCILA9BU16yTP53Hn3I3dkJFPmqXurLRx1PoGPw9StKlUCsViEXt7e0in08zqp/Lj0151cxRyYyaVSuHXv/41vF4v+v0+/H4/lpaWEAgE4HK54Ha7USqVmKs/nU6j1WqhXC6j3W6zydi5XA6bm5usLwTwNCcEUK4BI3/9dAB/8cUXuHv3LptJNTk5iWvXrsHpdCIcDrNqDCoVpb1lNpvZRGsAKBQKaDQaWF9fx8bGBh49eoRms8kqldR0SBEkz1qtBlEUsbOzg/v377Mk1/39fRSLxaEkTLVC3rZcLocvv/wSfr8fGo0GHo8HwWAQVqsVAFhuGfV7arfbaDabrCWC/JKgVHlRPlCxWESpVMLnn3+O3d1dnDt3Do1GgxkpgUCAecbb7Tba7TYLfWu1WtZ3ptVqscG3d+7cwdbWFn7961+zzu3A07WoVJkdBrXM6HQ6KJVKrFN7uVweS9uLsZZf042P4midTofdhs5y1RLJh9qnUy8LQRBgt9tZ3oJaZESLHXji7m+1WvB4PMxq1+l0qFQqyOVyqNfriMfjrIy62Wxib28Pe3t7zM1LVr/alAMxGAxQKpVQq9WQSCTg9/thNBqZK9xut0MUxSGPCnk7dTrdULMq6tuTSqUQj8eRz+efSWJUkxzlypPKzMvlMtLpNDNkstksuzEruSLnRSEdnMlkAAD5fB6CIMBisbCGgBQmoA/yZJGMlK6H5JcFauBWLBah0Whgs9mQyWTQ6/Xgcrmg1WqZ0UJ7iXJk6GcMBgMmp3Q6jb29PSSTSdaIU/471bq2qFCBKkkpukDyPU6jd2yGjCAIrKPhJ598wuLVVGMfi8XYjeistgcfDAbo9XrY2NjAv/zLv7A28rlcDvv7+ygUCopuny53a9MY+6+++gpGoxEPHz6EzWaDzWaD1WpFvV5HqVRiBw8l5tEmaTQarBeDWo0Y+SHc7/fR7/exvr6OQqGABw8eYGVlhcXzqTTbbDYzxUqemU6ng3Q6jWq1ii+++AK7u7vIZDIscfMsuLwBoFKpoNFo4KuvvsLW1hYL3dJ4ELpQKXV/HQWtJerhlU6n0Wg0YDabsbm5CYvFApfLxTx3ANiFs9lsIplMotFoIJ/PHzkPTYmQ8UbnE+kmq9WK6elp9qfL5YLf72f5fNRle3V1FZVKBbu7uygUCojFYtjf30ej0WBtEdRsxJCXj4zCarWKBw8e4C//8i/R6XSwsbHBQv/HVYAxlj4y8hsRHdTpdJptLGqPLh9dcBahAyifz2N9fR1Wq5WVEZfLZVYGqNQw3OgtqNvtol6vQxAEpFIpGAwGVglA09J7vR5TCFTxRgeNRqNRfbnsqDeTxi1ks1mUy2X4fD7Y7Xb4fD4mCzqsaJ00m03s7++jVCrh7t27WF1dZbdH+e9Q4pp6Eei5Wq0WgCeez0QiwdYQGdZKrcJ5UeTh3VqthlKpBK1Wi1wux8KQer1+SF/TqAr57CEAqjH25GufwmdUMm0ymVhzt3q9jmAwCACw2WxMNsViEQ8fPkQul8Pa2hoLpxzUHVoN8joK8sj0+32kUil2MSD9Le+Q/LoZa2iJ3EvymTq0sdTs3n5Z8vk8aylvtVrRarVQKpWOtTPiOJC/9tFnoFk4lJBJJenyMk95tr9SjblXRf6spBwSiQQ7YMxm81B5ulxm3W6XheW2t7cPnGx9lmQ52nNHDaGSl4EMOErcpdwqqhYk6PBRaxL4KPK8HwqHpFIp6PV6NJtNWK1W3L17F06nk31No9Fgoe9cLodms/lMHzD5n2pl9PloOCeAZ5oJHocshKM2sNlsfq27m34XKVr5A9EG+q4P2Wg0xrZiLBbLsWg/Mu7kvK4bUL1eH5t8XnT9jFaz0Z+jDaPGoQzGuX7sdvtLrx+5nEbXyIsyagi+jFwrlcqpls+LcJDh8roM43HKx2q1fif5jK6jUbmMrpHXsQ9rtdpY5GOz2V5JNgfJ5EW/Ty6fV2l2V61Wx7Z2jEbjsVnutI5GL6DAd1s7rVbr0G8ea9USIX+jOQcz+oar2aI/6NlGF76an/9lIS/Cq8qEy3I45H3WvHvE89bR6zZilMBRMjnKqDlLMnoeJ6G7x2rIyB/oLB3UL8tZVKxn8ZlfBblyeJVwCJcxN47l0L57nmF8lmR11Dn1Kj/jLPKqXqlX5cQ8MhwO57vB9xHndcHX0jBcHt+dccrwyBwZDofD4XA4nNMMT1ThcDgcDoejWLghw+FwOBwOR7FwQ4bD4XA4HI5i4YYMh8PhcDgcxcINGQ6Hw+FwOIqFGzIcDofD4XAUCzdkOBwOh8PhKBZuyHA4HA6Hw1Es3JDhcDgcDoejWLghw+FwOBwOR7FwQ4bD4XA4HI5i4YYMh8PhcDgcxTJWQ0YQhD8VBOFbQRDagiD8z3H+biXA5XM0XD5Hw+VzNIIguAVB+D+CINQFQdgVBOFPTvo1nSa4fI6Gy+doTlI+2nH9on8lAeAvAPwIgGnMv1sJcPkcDZfP0XD5HM3/ANABEABwBcDfC4JwV5Kkhyf6qk4PXD5Hw+VzNCcmH0GSpOP+Hc/+UkH4CwCTkiT9v2P/5QqAy+douHyOhsvnWQRBsAAoArgoSdLav37urwDEJUn6sxN9cacALp+j4fI5mpOWD8+R4XA4Z4FFAD1Ssv/KXQDLJ/R6ThtcPkfD5XM0JyofbshwOJyzgBVAZeRzZQC2E3gtpxEun6Ph8jmaE5UPN2Q4HM5ZoAbAPvI5O4DqCbyW0wiXz9Fw+RzNicqHGzIcDucssAZAKwjCguxzlwHwRM0ncPkcDZfP0ZyofMZdfq0VBMEIQAQgCoJgFARh3JVTpxYun6Ph8jkaLp/DkSSpDuBvAPw3QRAsgiB8AOAnAP7qZF/Z6YDL52i4fI7mpOUzbo/MnwNoAvgzAP/pX//+52N+DacZLp+j4fI5Gi6fo/mveFKWngHw1wD+Cy+dHYLL52i4fI7mxORzIuXXHA6Hw+FwOK8DniPD4XA4HA5HsXBDhsPhcDgcjmLhhgyHw+FwOBzFwg0ZDofD4XA4iuXI0kyj0ai4TOBWqyWM63dx+RyNxWJRnHzq9TqXzxGMUz42m01x8qlWq2OTj9VqVZx8arXaWOTDZXM0attbvMeEwjioykwQxrb+ORzF8aqVmXxfcTjK4MQNmcFgAOCp0uDK43AkSWIfo3LicuNwnoWMGNo3hCAIh14K+F7ivAqj64szPk7UkBlVLgBfAC8KyY3Li8M5mlFjhvaM/PPA8F7i+4rzKtD6OuiyedY4zBN6HHI5UUNGq9VCp9Oxf0uShF6vh36/f4Kv6vQhXxB6vR6iKLKbY7/fR6fTOZO3AUmSMBgMhgxiuWzUzEGH8WAwwGAwgCiK0Gg0Q1971hpfyo0TvV4PjUYDnU4HURTR7XbR7XYhiuLQepEkCa1WC91uF5Ikod/vQ6PRqHY90b6hPUSfA556ptT8/K+KXG6EIAjQ6XTQaDTo9/vo9/vsa+RyVDuj+uggTyjJQ/7v78qJGTKSJEEURZjNZqZEJElCrVbjhowM+cIgpWwwGKDRaKDRaNBut5niPUsIgoDBYMAUhnzNiKIIANBoNGdCLrR3er3eUKj2LCjOo5AkCRqNBnq9HlqtFlarFQaDAY1GA41GA1qtFiaTiSnWwWCAQqHA5EhritaTmpCHqfv9PjOCCZIJXTTP+loi5EZMr9cD8HSvGY1G6HQ6dDqdoTVERg59rdqRy0eum0lOtJ9ep44aqyEjvzWLogi/348LFy5AEARUq1W0Wi1sb2+jUChwRYxhT4xWq4Ver8eFCxcQCATQbDbRarWQy+VQq9WecZmrXXaSJMFgMMDlcg0p3Fqthna7/cyNSW3Ib85arRYajQYejwc6nQ6NRgP1ep19nfxrz4JhR9Cz6nQ6GI1GzM7Owuv1otPpoNPpwGg0wm63Q6PRQBRF9Pt97OzsoFwuI5/PI5/Po9frodvtspu1WvYVeQjoMimKIvMo0Jppt9uo1+tDFwa1PP/LQjKhtaLX62EymSCKIjNgvF4vTCYTqtUqarUaWq0WarUaer0ems3mgR4KpSN/FlofJJPRdUXGXavVQrvdHjJ2gO9m5I3dkKEDyGQyYXFxEX/yJ38CANjc3EShUECpVEIulzszrrjnQaECg8EAu92OH//4x3jrrbcQi8UQj8fx6NEjbG9vnyklQ+vIZrNhdnYWJpMJbrcbAPDw4UOkUim02230ej1VHT6jkGI1Go0wGAw4f/48vF4v1tfXsb6+/sytkf5UkyI9DLlb32w2w+l04sMPP8TS0hKThd1uRzAYhFarhVarRbfbxc2bNxGLxXDnzh3cuXMHtVoNmUyG7UOlQ++9RqOBwWCAxWLB/Pw8LBYLHA4HjEYjer0eer0eMpkMVlZW2IE8GAzOnF4meZEhp9PpYDAY4PF4EI1GYTKZ4PP5YDabMTc3B6/Xi3Q6jXQ6jUKhgJ2dHdRqNezs7KDZbA4ZxUpHrkfIo6fT6aDVauF2uzE/P8/kI4oic1YkEgkkEgm0Wi1Uq9XXcnadSGhJr9fDZrPB6XTC6/ViMBggkUiw/A/OMKIowul0wu12w+v1wuv1Ip/PM5ew2qz8F0GSJOj1eng8HthsNkxMTEAURRQKBfT7fRSLRXQ6nZN+mccKvedarRYGgwGBQACTk5NoNpuo1WpoNBooFotneo2QsUdhJKvVCp1OB51OB4vFArvdDlEUodVq0ev14PV60e12EQ6Hkc/nkc1mUalUWO6eGpLsae84nU44nU5EIhE4HI4hQ6bf78NisaBe//+z92dPcmbHeTD+vLXv+770ikajG+sAGHKGQ3KGQ1KUFHZQ4U+WIxz+In63dvgP8IW+G4f/Bd/b4QtHOEKyxCApUTOkNKPZB4PB2kDvXV37vu/b7wLKxFsFoAdro6vqPBEdPejppd6sc/LkyXzyyTpqtRri8Tja7fbU2OBpQVwOrVYLpVLJdnK5XFhcXITBYOBMTDAYhM1m44un2WyGQqFAqVRCuVyGSqXitQRMtv3GfQnZx2QywWw2w+12c6Dn8Xj42VutFmdoSqUSOp3OCKcIeD67vJaMjMvlwtLSEk6fPo3l5WU0Gg1sbGwIou+/YLzLwmg04s0330QgEMDZs2exuLiI+/fvY3d3F+l0mhfBrGRlKPq32+24fPky/H4/fvCDH0Cr1SIYDGJrawvXr1/H9evXR2r/02Sb8YyD0+nEu+++i6tXr2JjYwP37t3D7u4uPv30UzSbTTQajam5CT4N5OUQ4pXpdDro9Xo4nU44nU7+XkqHKxQKnDp1CqFQCOFwGG+88Qbu3r2LX//616hUKsjlcuh2uyNE6kkDrRuXy4W33noLgUAAf/zHfwyXywWj0QiNRsM+OJ1O4969e0gkEvjVr36FdDqNfD6PRqMx9ZkZyj5RSX9ubg4OhwNnzpzB6uoq3G43Tp06xQGLSqXiNUQZrVarhUqlglQqhb/5m79BIpHA7du3J95+45kYpVKJYDAIu92OlZUVnDlzBl6vF+vr69Dr9Rzc1et1dDodxGIxRKNR3L9/H3//93+ParWKYrHIGfTnwWvJyGi1WlitVpjNZhgMBj5oppnT8DwgB6vVauFyueDz+WA0GjkNXq1W0Wg0ZuqmTcEaEegoU+Xz+aDT6eB2u5HL5ZjEOanO4llA3X92ux1erxelUgm1Wg3VahUajQbdbhfA7HCngId7R6VSwWAwwGg0QqvVclCj1WrR7XaZS0TrxWAwQKfTsYPO5XKwWq3o9Xoj/JFJtiOVSFwuF7xeL4LBINxuN/R6PVQqFd+QgQecMwBwOp1ot9uoVqtoNpuv8+UfC+T8IfIrHo8H4XAYCwsLcDqdCAaD0Gg00Ol0UCgUaLVa6PV6I0GzyWSCQqGAx+NBu92GRqN53Y/23HicVIparebsntvtRigUwvz8PDweD0KhEFMiFAoFGo3GSDaqUChAp9Oh0+m8cMv6sQYydLDYbDZ+WJ1Oh3a7PTO1+6cB3Zooml1YWMC7776LcDiMdruNO3fu4M6dO7h//z4TfeUR/iQ72aNAhwjVYU0mE4xGI6fDqZSkVqu5q2kW1hStFzqAAoEA7HY7AODLL7+EUqlErVZDt9ud+uCOMnBGoxEOhwNerxe/+MUv4Pf7cfHiRQQCAQBAvV7HwcEBvvzyS2g0Gpw7dw4WiwUejwdGo5HLCN1ul2v6lI2Q+6pJsyW9bpPJhIWFBQSDQe7mGgwGaLfbSKfTSKVSHNiFw2H82Z/9GQqFAn71q1/h/v37aLfbfABNmg2eBNpHRFZ1OBx477334Ha7sb6+Dp/Px9k8yso1m01ks1m0Wi1sbm4im81yM0sgEMD58+dhNptx5coVhMNhRKNRFItFlgCYNPvJs7omkwnnz5+H0+nEW2+9hXA4DL/fD5/PB41GA4PBwAEe8JDT53A4oNfrUSgU4HK5AADZbPaFLgnHHsgAgMFggN1u55TcJKdqXwVoQ9Fh7XA4sLS0hFAohK2tLWQyGf54HJlzWiEnKmo0GibeqdXqR9r86PtnJZChz8PhEBaLBS6XCwcHBzAajWg0GiPfN+0g0iFlMc+cOYNgMAi/3w+bzYZarYZarYZsNotbt27BYDDA7XZjMBjA4XAw+ddoNMLr9WJxcREARjSvJh1arRYOhwM2m435DVQSKZVKSCQSMBqNcLvdsFqtOHv2LCqVCr744gscHByg3++j3W4DmB6fI+dUqdVqWCwWnDlzBuFwGOfOneMDWqPRoNPpoFarodPpoFgsolqt4v79+4hGo9yp0+12cfbsWWg0GoRCIej1elitVg4aSTZjUuw3LgWi1WoRDocRDAZx/vx5LC0twWw2w2KxPHK5Ah4mMoxGIxPw9Xo9dDrdZJB96cFJkMrpdGJ+fp6jsUm93bxMjIsGqVQqOJ1OvPHGG5ifn4der0e/38fu7i42NzcRi8VYCG9W7EaOhjghPp+PD6der4d2u41kMomDg4ORmuusBMrywG287XqaIX9W6uA6deoU3n//fXi9XiwvL8Nms3GZLRqNYnt7G/fu3cO9e/eYfOhwOFAsFuH3++FwOOBwOKDT6XD69Gno9XqcPXsWNpsNqVSKSy6ESbOxnBcEjAoqRqNRfPHFF0wBsNlsuHz5MrxeL9bW1tDr9bCzs4Pd3V3+2Ul7fjkoc0uHqtfrxZkzZ+Dz+bC+vg6PxwObzQa1Wo1+v496vY5UKoW7d++iXC5jd3cXlUoFOzs7yOfzLLJYrVbhdDphtVoRCATg9XoxNzeHbDaLeDyOZrM5EXaTB3gKhYL5QR6PBz/84Q/hdrs5swcAlUqFhVolSeI29fELprx096KaX8eakaF6mtPpxNzcHKe/Z4mEeBTkBw/pgly8eBF+vx9GoxHD4RB7e3u4fv06YrEYpyZn6aAeDAZ8g/Z4PMwb6vf7aDQaSKfTODw8RLFYZGXWWbEP4XFBzLTvL1obOp2OSYc///nPYbPZEAwG+Rbc7/cRjUbx9ddfY3t7GxsbG9waSlyYhYUFnDp1CjqdDhqNBisrKzCZTFhfX4fZbEa9Xke1Wh2x6aQd5vJAhtaKPJD58ssvudMrHA7j+9//PlwuF86cOQOVSoV6vc6BDDB5zy8HBTKkS7W4uIj33nsPHo8H6+vrsFgsXM6u1+toNBqIRqP49NNPkU6ncfPmTZTLZZTLZS6jSJKEWq0Gp9OJUCiEpaUlOBwOhMNh5PN51Ot1JJPJ1/zkTwd54KFWq+H3+/HOO+/A5/Ph7bffht1uh9FohFqtRrVaRbVaRbfbRbPZZP6efJ3JA6OJzMjo9XoYjUaYzWaYTCbodDoAD3r0C4UC8vn8I3L7swR5acBisSAYDGJxcREWiwXZbBadTgfxeBypVAr1ev2R+uqkOpKnBfGA7HY75ufn4fV6odPpmPxM5M1Z4YOMg55Xfrseb22cJsifiYTKKAOzsLAAm83GLbD9fh/xeBylUgmbm5vY3t5GKpXiQ6xcLqPT6eD+/fsoFArMt7JYLPD7/ayH0el02GlPKgeLyrA0joHWDJVIstksSqUSZ4a1Wi1yuRy3rAcCAdhsNuh0OvT7fSaTT9peo/eOSM4UwC4uLmJxcZHb0eVE1UgkgkgkgoODA+zt7TGxnkQ45YTwVquFVCoFrVaLVqvFZSnqDjvJGN9bkiRxBvz06dNYXV2Fw+FgOQNqpY7H40gkEuh0OqjX68yjoTNNq9WOUEx8Ph96vR6USiV3ij0Pji2QAcD99x6PBy6Xi2uznU4HkUgEOzs7j6RsZwHyG9FwOITT6cSpU6dw8eJF/OAHP0Cn08EXX3zB7Xv3798fycRMmgN5XtANkm6I8/PzsFgskCSJlY5LpRKy2ezEtzg+CyiAoQ8KYOSqtNMK2jtarRYqlQpra2t4//33MT8/j1AoBJXqgYtrtVq4fv06tra28MUXX+DLL78cqd8nk0lIkoRYLAaVSoXDw0MUCgWsrKwgFArBbDazcNydO3eQTqeZ8ApMzh6kILfT6fCoBkIkEuGyWywWY/u0Wi3s7u6i1+vB6XTC4/FgY2MDVqsVzWaTbTBJWRkK5iRJ4g7at956Cz/96U/h8XiwurrKnKh+v498Po9SqYQPP/wQv//971EsFhGPx9HtdlmdlugTZLdKpYL79++j0+mgVCpBq9VyWVyv179mC3w3qFJCfMT19XX88Ic/xPLyMn70ox/x6I9ut4vNzU0kk0l88803+Pbbb9FsNlEul+H3+yFJEsLhMJaWljgwlCQJDocDa2tr3E34In7qlQcyciayyWQaSUMBDxwMbahGo8GLa1Ygj3xJDNBmsyEUCsHpdDIJL51OI5FIsOQ1MeNnAXK9FGr1o9sAHdyUiSHNFFpHs7CWyHHKNXOIjEly4JN0yDwL5AeITqeD1WqF2+3mlk/KthCvIZFIoFgsPkJUlUtAdDod5PN5xGIx2O12tFotDIdD2Gw2dLtdmM1m5qxRx+UkYDgccqpfr9dzWp/sVK1WkcvluDQgD2Sy2SyXmaid3WAwcCA4aZkpyjapVCq43W643W74/X64XC5YrVYWZ6V9VSwWkUqlkE6nkc1mUa/Xv1MgcDgcnX8GPMwcTlK5m95vEtx0Op1sH/K5qVSKqwXZbBbtdhu1Wg06nQ6lUok7AAGwXlyz2eRS3YvSS46NI6NUKnHq1Cmsra0xebVeryOdTiMajbKk87S19D0NyBkbDAZotVpcuXIFf/ZnfwaTyYRqtYpoNIqPPvoI+/v7yGQyM9FqTSBnoFKpMDc3B6/Xi4sXL+L8+fOs/VGv17Gzs4NEIoFoNIp8Pv9CacpJAZUEKPNCt8NGo4FWq4V0Oo1YLMYy4HLC3TSAbtUKhQJ2ux12ux2nTp3ChQsXmBNTrVbxzTffIJ1O45/+6Z9w7949VCqVET0i4OElgrKi29vbSCQSqNfruHLlCqxWK9bW1rCwsIBr164hn88jHo9zBvmk25Wei9R8qYxCh1K328Xu7i6uX7+OaDTKWjGSJKFcLuPjjz+Gz+fDv/t3/w6Li4vw+/0Ih8NIp9PI5XITk/WT8z0sFgvMZjN+8Ytf4Pz581heXsbS0hKvBXkg9/XXX+PmzZu4e/cuDg4O+PcdxVGUz2Gi4IV4oif5Eko2ou4/Eol855138P7773OWs1Kp4NatW8jn8/jHf/xH7O3tsVQBdWUBwM7ODjqdDhYWFqBUKlEqlVAsFrG5uYnPP/+cR8q8iH86lkCGFBLNZjMcDgcMBgMLL1UqFVQqFTQaDR4kNasgESWHw8EpuUajgUqlgmw2yzwZeRlhFkAkM7pty1v3gQcOp1wuo1Qqcap7VspKwEMVWzknptvtotPp8HT0aQtiCPLSktFohMlk4nIj6aJks1m+KT5udMW4LsxwOESz2US73Ua5XGa/RJlk6rRQqVQTk4mgw0mj0cBkMvGHXq/nYJgUVuU3ZEmS0Ov1UCgUuIxAGR2r1cqk50lbW9QxYzab4fP5EA6HueRD9qCMG2WqUqkUSqUSWq0W+6QnPTfZhDLn9G9SCj6pgcz4elYoFLBYLHC73XC5XLDb7TwEk5orMpkMUqkUUqkUisUims0mB0Kkblwul7mLVF7aJF7Wic3IyCM6auGT3wIk6YGy37fffotIJIJsNotyufyIuNu0Qr5gBoMBT7aem5vDuXPnEAgEcHh4iM8//xzRaBSJRAKlUolvoNNuHzqUdTodzzB5//33sbq6ioWFBWg0Gj50Go0Gtra2EIlEeA1NaymFICf2Ugmp2WyyEyH1Wp1OxzyZabHJuFQBSToEg0E4HA5W7aWBj19//TUODw+RTCa5q4QOEnlmUx7MkCowlV3kY0AmDXLyN8nsLy0twev1QqVSIRaLoVgs4uDgAJFIBNVqdcQ+/X4fpVIJKpWKBx8Gg0G8/fbbuHXrFg+WpL91UtcYvTaaXff9738ffr8fZ8+eZZ9CAV0qlUK5XMbt27eRz+fx1Vdf4fDwEOVymbtwjnrO8Q5UCmKCwSD0ej1u3rx5ou1EHUUmkwmrq6u4cOECfD4fl2r39/cRi8Xwm9/8BolEArFYDOVymS9N9PydTge7u7uo1+uoVCoAwIGwQqFgTacXTWC80owMLRwazkZDDw0GA4AHqojEcibm96wJ5NEbrlAo4PV62cFYLBb0+30cHBwgFouhUqlwO9us2IdSwCSlTqVJkv0mR9Dr9ZDL5ZDJZNihzhIoA0MfVIcnDoBKpZrqAZoKhQJGo5EFtuhAojp9NBrFwcEBcz+O4pdRQEMHz1HfN0mgIIxE7ohjRkFKJpNhQmu73X6kLbvVajEvZDAYwGKxYH5+HqlUCmq1mrtNT3qgR4GMXq/H3Nwc5ubm4PP5ONNApdlsNotMJoMbN24gk8kw+bvT6TxzdpMuHQqFAlarFQqFgjVXTiok6YHgHQ19DAaDMJvNnK1Mp9OIx+O4d+8eYrEYms3mCHeTMixElFYqlcw1oxKbJEncOTde6n1WvNKMzHD4QE4+FArB5/MhGAzC6/VynSyXyyEej48o1M4K6JZEUugWiwWrq6u4ePEizGYzb56NjQ0uKc1KJobS+E6nEy6XCz/5yU/g8/mwuLgIm83GJaVZBx26VEKiD3ImnU4HrVZraoUTFQoFDAYDTCYTzp49izfeeAPhcBjD4ZAnNsfjceRyOZRKJXa0R9mBboZUxvT5fLBarTCZTJyZIU7SST+0gYcXJSL1ejweLC0twePxYDAYoFar4d69e2wreQmA1he1EjcaDfZL/X4fFosFRqOR/dJJtgeVXu12O8LhMA8FDYVCsNlsAIBcLod0Oo29vT188sknKJVK2NraQq1WGykxflcmhj6ITkFUikm4gMpfu8vlgtPphN/vh9/vh1qtRrlcZh0mkjMg4vzj9hbtGXn3JLXxkxRLq9UaWWvP46de6YlApaVAIIC5uTkEAgF4PB6USqWRQCabzTIxaFZAgYxKpWJJ+ZWVFVy8eJHnd0SjUWxsbKBSqTA3Bpi82+DTQL6Q+/0+9Ho9FhYWEA6H8e6773LZwGAw8M1pGg/nZ4E8kGm1WtyhRFkYeXBz0nUrnhXy9DdJ6L/99tssHNlsNpFIJJBMJlEoFFAqlZhw+TQlAbPZjHA4DK/XC6vVCqPRCAC89ohDAZzc/Ug+hm7XWq0WPp8Py8vL8Hg8GA6HqNfruH//Pvb397mcQp1NcgGzVqsFlUrFBHJSPqZ5OifVBgQin+r1eqysrGBhYQGXLl2C3+/n7FMul8Pm5iZu3ryJX//616jX66jX63zh/C5u4rjgm1KpZC4SZffoZ09y0EfPK1dP9/v93FIdi8Vw7do1viB0Op0nZi+Jb0T7hZIbdAkxGo2o1+svHOQdy9WWolH5TZGUEMvl8mMHH04rxmuner0e8/Pz8Pl8XHbLZDI4ODhAIpHg1Bv9zLTahzY/qakGAgGsra3xAESdTsct1nQgkVgXcHIPk1cJCuRoQBt90AE2zaCDwmq1wm63Q6/XQ61Wo9froVKpIJfLcVlWXlZ7mnUiP5BonQ0GAyZ7Eql8kqQiaK9QBkupVKLRaKBcLiOZTLKvGW8NlpdvSXCyUqnw4UwHGIkOnnRQWYN0h2gm0nA4RKVSQSqVQj6f50wm8OQ18zhirCRJ3J6/vLyM9fV1BAIBmM1mKJVKVCoVZDIZnrp+EjFe0jcajVCpVEzuzWazKBQKqFQqz03SHeeIviheaWkJeNivT+I5SqWS5ZnpxkRkn2ntrBgH2YZaAK9evYr5+XksLCzAbrfj888/53a2arXKZaVJSE0+L+TtoV6vFxcuXMCf/umfwm63s7DZzs4OCoUCC1hRu/o0Z6qeBDmZ3mKxsLaO3W5/ROhs2iDPZtK0XVKaLZfLPPfmyy+/RC6Xe66ZNvJsBg0J3NjY4IyEnEtykiEvLZlMJlitVp7eXCwWkUwmcefOHezu7nJHEkGeJaVOlXw+j1QqBafTyZcOku+nLOlJzJTS65LL4tPAWerwS6fTuHfvHiKRCCqVCss+yDMN8iCXPtM6ocAoHA5jeXkZa2tr+NM//VMuUw6HQ6TTaWxubiKfz59YO1HzydLSEhYXF7mbq1Qq4f79+9je3sbh4SF3tz3tuS2f60WfX1Zm6pUFMpRxUCqVLM1MD0LyxZRtmCV+jLwGqdPpYDab4fV64fV6ud26WCwinU6jWCxO/c2aQOuF2iGJkGg2m3kAWSaTQTKZ5Jos8EA88KQ5g+MEleKIu0H8Bo1Gw11LtL9OouN8XtChZDKZYDab2b9Qy3Wz2USlUuHSgPzn5JBzO+T8BrKhSqUaacmu1WpcwjvppSVg9D2XZ8WpBZY63uSjYeTPQ89O3LVut8uEeo1Gw50t3W53YoQX5SWi8WelC+P4x+N+B/DAprROSOx1aWkJCwsLCAQCsFgs0Ol0aDQa6HQ63JRAI2ZOIuQBH3UXDQaDkXbpTqfzVOe2PA6Qgy6uL+t8eyWBDBmCBIEcDgecTifrEJTLZSQSCWSzWdb9mKVsTL/fh9lsxvz8PMs9h0IhZLNZ3L59G9evX8fnn3/+CLlsGu1DzpNudWfOnMFPfvITLCwsYGVlBYPBAPF4HMViER988AHu3r2L8+fP49y5cwiHwwgEAjNRkhwHOQhqhxwOh6w1ZLPZWKl0bm4OhUIBmUxmavaZPCMTCoUwNzfHg/263S6q1SoL1lGnkvxAftx6ofo9fabuHtKk6fV6yOfz3N1TLBYBTM6elI8zUSgUaLfbKJVKPGOKurmelGEiOQTSmpEkCU6nk0vA6XQat27dOvFNCfTa5EEMBSt0iarVajCZTKza/LgWfTrfDAYDlpaWYLPZcPXqVYRCIQSDQR5Uajab0Ww2uY37o48+wr1795DL5U6kjeT8HuoyBoB6vY54PI47d+4gEomg0+mg3+9/pz+hCwFp55COjFyNnDJ5L4KXHsiMazFQ1E6kMCInkgDetA60+y7QpFWn0wmbzQaLxYLDw0Nks1nk83mUy+Wp0v74LpCD1el0sFgsPIys3W6jUqlwSjsejyMQCHC7/iyDHCtlrOgDwEhGhjR3pg3Udm0ymTgjQ1PQ5eTncYl4OeT+Sv5hMBj4Nk2t3NVqldddr9d77E3zpEKuZwI8bKkm+zyNDx4Oh9ziT4cdZZVJGO+k+3J6Vgpq5e8/jbigAYdH+RfKxJhMJvh8PjidTszNzWFhYQEejwdut5uzpPJ27lwuh3w+P6KcfFKhVqtH5k21Wi0eSfC0a0aeHZaXluSk+RPLkaEaG3Xj0DRaEuciglmpVDrxC/9lQJ66JtLd0tIS/tW/+lfw+XxQqVQoFov46quvcOfOHWxtbXHa7qTX4F8WKE2fy+Wwu7vLQx9rtRo+/vhjpFIpbGxsIJPJMKdqFtbO0+BxnTRU2ydCJnCyOyWeBvLbME0RDgQCPNRRrVajUChwDZ+0l+TDASnNT/V94hnZ7XZoNBo4nU4YjUb86Ec/wo9//GOoVCrs7+8jHo/jiy++QDQaRaFQeOQmepIPJGB0tpBarUar1WKhTcpYfdfPA0C32x1RYVepVLDZbEzCP6lrjAK5Xq+HYrEIm82GarUKs9kM4AFfcWlpCRaLBadPn0YoFOLyiTzYkTclmM1mGI1GrKys8Cwio9GITqfDatJ3795FsVjE7du3USwWsbu7y5fUk+7b5UGfvKP0abgt9L0ajYbtSTSAWq2GXC6HZDKJeDzOfKQTpSMjJ7JS/dTpdPJMD4rsqtXqRESlLwtyu2i1WjidTqyvr8Nut/OY+Egkgnv37iGbzT7CBp9WG423XdMiVygUsNlsKJVKuHfvHkvME0fhZRLFJhHyNs7xVC39f7qBn3SH+SyQl63VajXMZjOsVivf+GjAIZFxSbZAoVDwGAPKKMiJmtTN4/f7YbPZsLi4iNOnT6NYLLIqMHVByQeSTsq+lK8FOtDL5TKq1eozdRtR9xKtNTl59iTbgt4rythR1o6kCRQKBZxOJ2dkSESSSii0h2h/GQwGOBwOGI1GLC4uQqfTcXCbyWRYHfj27dsoFAq4e/cuqtXqyIiDSdiXR/nZ8a8/rnqgVqvh8Xjg8/l4Nl6n00GlUkG1WuUs54tWHl4ZR4be4Pn5eZ4oWq/XUSqVEIvFcHBwgHw+D2B624of12Lm9/uxsLCAc+fOYX5+HgCwsbHBN8loNIpqtTpTgyHlKelUKoUbN27AbDZjd3cXzWYTOzs7qFarLPYmr7nOKsZJnABQKpUQj8fhcDig0+lgMBhgs9m41k83LLlq6yRCXhagrAr9m9L9TqcT4XAYCoUC4XAYRqMRwWAQFouFnai8Q8Pn88FgMHBGxuVyIZlMIhKJ4Msvv0QqlUKlUuEb6aTsSfIj1O5LmYJKpcJBjZw7NI5xqoDdbmcSKzUnRKNRpFIpvlWfRFAbfbPZxP7+PjqdDr7++mskk0msrq7C7XZz1spqtWJ5eZkvB/R+y5+NLqR0Ca3X60gkEqhWq4hEIohGo9wR1mg0WBV4krpzKVilyfIulwsLCws8OobI3ePnHJ3/wWAQgUCAh06SmnGxWMT+/j4SiQSToE9sIGMwGDA/P4+5uTk4nU6YTCbkcjkUCgXE43FEIhFmv09CZPqioDfc6/XijTfewPr6OsLhMKtHHhwcYHt7G7FYbKJq7y8TpNWRyWQ4YKEsDTkAeXp81hV+x7tMqJ321KlTHMhYLBbukBjnBExiMCPPRMmzUfRvtVoNk8kEh8OBubk5GI1GvP3223A4HJifn+cLlbw9XaPRwO1288+qVCokk0kkk0lsbm7io48+YmIscWMmxWeRver1OnfNpNNp1Ot13k/yw0h+oMhLCgA4a+H3+2E2m/kQj8ViyGazbJuTeEjT62o2m4hEIqjVavjmm2+QSqV4ICK9p6RN9Lifpw8iqbbbbaTTadRqNdy6dQuxWAybm5vY2tpCuVxGKpUaIZiTbz/pe48uB/1+HyqVCnq9Hk6nE/Pz86jVatDpdNz5Rpd0eQcl7TdqRQ8EAo8EMqlUioeznlhBPGov1mq1fCiXSiUkk0kUi8URwaFpBi1YvV7PuhdLS0uwWq3cTbK/v49IJIJGo/HYlsBZgbyzBMDIJpE7V2oBnTU16CeBbtX5fJ4zekSyowzEpKiKPg1ojVCKmjpvhsMhrFYrBzCkZnv69GmYzWa43W4YjUbo9Xo0Gg30ej20Wi0oFArOWFB7P0n2HxwcoFQqodFoPPZmfpIhf53E9SBBNpVKBbfbjW63y59pXo4cFBy6XC5YLBYsLy9zoEzCaMVikS8bJ9k2cqJzu91GNBpFq9XCvXv30Ov14HK54HA4Ri6SdFmiEhO1rNfrdeTzec5I0aiHbDaLRCKBcrnMF/XxMuRJ3n9yLlE0GoVCocDCwgJcLhdsNhuWl5cBAIVCAbVaDdlslmeakY36/T4CgQBOnz6NQCAAt9sNs9mMVCqFaDSKzc1NbG9vI5FInOz2a+DBApDLMw+HQxweHuLevXuIRqM8CXOSHMPzgKJxu90Ok8mE9fV1vPPOO+h0Otjb28PBwQE+//xzHB4eslTztNvkKMhv2Y9TNKZui3K5zEJncvnwWYO8DXtvbw/5fB5vvvkmS4D7/X50Oh3uXJpkG8nXAAm0JRIJqNVqrK2tYTAYwO/3w2Kx8O1QqVTCbDZzFk+hUPCU8Hq9zo6YDqUbN25wSenw8JDXGoCJzJTS5YACvUQigfv372NxcRHnz5+HzWbDqVOnoFarEYlEuAFDnoXRarVYX1+H3+/HD3/4Q1y6dAnJZBI7OztcIqjVaiPcoZOMwWCAarWK69evsxL2zs4Orl69isuXL49MPqf3m7p1qOsoHo/j5s2bKJfLPI+J1hAd5uOdYpMA8qfNZhPXrl1DNBrF6uoql4mCwSDW1tawtraGYrGImzdvolQq4eDggDOW/X4f586dw89+9jO43W4sLy9DpVLhd7/7HTY3N/HVV1/h66+/Zq7Vy6BQvJJAhlJv1AJJLVZEFKN/T8Kifx48TnTLZDLB5XLBbrfDbDZzOzGlJeXdFdNok+eB3JnSv+Uf8vQnra9ZBomSEXlTo9EweVEu7jYNIOJmqVSCyWRCoVDgKbt6vX7kMKaDhcTxarUaS+2n02m0Wi0+hGiILd045b5qGkDP6nK5mB8UDAb5Fq7X67kDjsq7NpsN8/Pz8Hq9MBgMAB4c7CTaSTfxk5xpGMdwOOT26kwmA7VazUKcWq2WxzjQzCny06TVlEqlEIvFUKvVUCgU0Gw2uZuLfv8klSDloPO7XC5Do9HwpGuz2czik2Qn6gBTKBQolUrsj0OhEJPwiVBOXUpkr5fZefrKMjK1Wg0HBwdQKBSoVqvQaDQjb+60OIbvAqX4z549i9OnT/PNJhqN4h/+4R+QSCSQyWTQbDZnyi5H4bsidHknDsmLNxqNx6bGZwmkW0GBjM1mw7lz56DX6+FyubhT40VbHV836NbYbrdx8+ZNRCIRaDQaHB4e4uzZs1hbW+PyY7PZRCwWQ6PRQDKZRLlcRjab5UOYsi40HZtuiXSYA5h4LhbZazgcIplMotVqYTAY4OLFizAYDPjzP/9zdLtd3Lp1C8lkkjtJaAK93W7Hm2++CavVCoVCgWQyiRs3buCzzz5DNBrl4HlS/Bf5Dupqu3HjBo8muHnzJtxuN1ZWVlgBWW6bfD7PYy+KxSKvFbpUPW5O1SRBXloiiZS/+7u/w927d7G+vo61tTVYLBYsLi5iOBxieXkZvV4PhUKBeWdyjZ16vY6PP/4Y+Xwef//3f4/t7W2+INDfk39+XryyHUrD22jIGg0pa7VaEzFc7GWBDgwSBgTAU0SppZi4IJPiCF43yLm0221upRzXfJgljJdcOp0Oms0mhsPhyC1KrVZPBS+NnO1gMODBdalUClqtFl6vF7VajTkf9XqdtYei0SjvOxoASQcxdSRRSeFxCrCTDkmS0Gq1RvyyJEnw+XxQKBTwer0AHkz+pkCGOk7tdjsMBgMKhQKX5MiGkzpGhV53tVrl+X8qlQqtVospEUTojUQiPEm9WCyyng4ALj9Ny1qhciR1JSWTSQyHQ1YMlyQJbrebNYSGwyG0Wi13RwLg0m+z2UQymeTqQy6XG8nEvKyM1UsPZCj6r9Vq2N7eRiaTQavVgsFg4GFrqVRqKt7wpwGlwEnM7d69e/jNb36DaDSKjY0NLilNSkveSUAymcS3336LXC6HbreLYrGIRCKBUqnE4l6zZEt5Z0q73caNGzfwq1/9Cna7HeFwGBaLBW63mw94ucOZVNC+oim8H3/8McxmM65du4ZQKDQySyifz6PdbqNYLPJlisi+JOxGXXDTFrwQiEfVbrfR7XZx7949/J//839gs9mwtrYGo9EISXqgbkst6P1+H+12G+12G7///e/RarVwcHCAYrGIWCyGeDw+0nk6aTaTl6wpGC6Xy9jf38fOzg4UCgVfkHK5HK8ZubIxMD0BDDDazUiZps3NTRwcHCCZTOLevXtwOp1YWVmB2WzGqVOnYDAYoFaroVQqWXsnFovhm2++QT6fZx5NNpt9ZfPJXkkgAzxI2+VyOZTLZdRqNahUKt5ErVZrat747wItjHQ6jXK5jEgkApVKhXq9PhKdTmIt9XWhUqmwMBnJoxP5V04emzWQE0kkEtjY2MDCwgLm5+dZBZdumZMOefcHZZ4ajQaUSiXi8Ti2trY4O0US8ZShIdIrZUApeJnEg/hZQcEfDWDt9XrMb7DZbKwPQxPoiRPSaDSwtbWFYrGIra0tnpE3TRPWiQBcLpehVquRzWYBPChdUwaYLpzTKDQpB51ZRJgngVbiV7ndbtTrdTidTtaqIj4MlXOTySRu3ryJXC6He/fujXTkvorA75V6NZqpQOJLRMqc9tISLQS5w6XyB20A2iCzWAp5XpA9yYG2Wi3WxygWi0wgm6Yb0rOAnjkSiWAwGODOnTu4efMm6vU67t+/z2q303Royw8TmtAr15WhQ4j2GnFFprF09DSgjDkNgOx2u7h58yZL7hPRlYYmUgYrnU6j2WwyKVrORZsG+40Hs9QxKb9ovsqD+CSCnpO6jimrSX7XYDDg4OAAOp0OOp2Oh0L3ej1ks1ns7Owwb/FV20066iDV6XQvfMrKVTiPYyG0Wq1jW2FPax+5Dejf5FAIx7UxjtM+RqPxlURp4wJewMtLbdfr9Ym3DxEP5ftsnIT4vAH0cdrHbDY/9Ysc72YjyG0w/t+vAtVq9djsYzKZnnv9yO0kXwty+zxJnv5F/HitVjsW+7yIbYCH/JlXvV7kOC7bAM++t+jzuEji09hFPnn9Rex41N46ljzzuCOZhWh2HOPPLP/3LNrjRSDPdMn/LfAAs7zH5JnQJ30WeICnscfjvmcW/Pg0P9uzYnxfPe3PHPXvl41XHshM+4J/GghH+vIh1tWTMZ7tG8c0lzOfhrcg1o3YP98FYZtRyIOZ57XNq7Tp5DP/JgRiYwgIvDqI/SUg8GpxkvfYkRwZAQEBAQEBAYGTjOnsHxMQEBAQEBCYCYhARkBAQEBAQGBiIQIZAQEBAQEBgYmFCGQEBAQEBAQEJhYikBEQEBAQEBCYWIhARkBAQEBAQGBiIQIZAQEBAQEBgYmFCGQEBAQEBAQEJhYikBEQEBAQEBCYWIhARkBAQEBAQGBiIQIZAQEBAQEBgYmFCGQEBAQEBAQEJhbHHshIkuSQJOn/SpJUlyQpIknSvz/u13BSIWxzNCRJ+s+SJF2TJKktSdL/eN2v56RB2OdoCPs8GcI2R0PY57vxOs8v1XH9IRn+O4AOAC+ASwB+I0nSzeFwePc1vJaTBmGbo5EA8N8A/AKA/jW/lpMIYZ+jIezzZAjbHA1hn+/Gazu/jjUjI0mSEcD/A+D/Gw6HteFw+AmAXwH4f4/zdZxECNt8N4bD4V8Ph8O/AZB/3a/lJELY52gI+zwZwjZHQ9jnaLzu8+u4S0unAfSGw+GW7Gs3AZw95tdxEiFsIyAgICAwiXit59dxBzImAJWxr5UBmI/5dZxECNsICAgICEwiXuv5ddyBTA2AZexrFgDVY34dJxHCNgICAgICk4jXen4ddyCzBUAlSdKK7GsXAQgyq7CNgICAgMBk4rWeX8cayAyHwzqAvwbwXyVJMkqS9A6AXwL4X8f5Ok4ihG2+G5IkqSRJ0gFQAlBKkqSTJOl1dN6dSAj7HA1hnydD2OZoCPscjdd9fr0OQbz/hAftaxkA/xvAfxTtxQxhm6PxlwCaAP4LgP/wL//9l6/1FZ0sCPscDWGfJ0PY5mgI+3w3Xtv5JQ2Hw+P4OwICAgICAgICLx1iRIGAgICAgIDAxEIEMgICAgICAgITCxHICAgICAgICEwsRCAjICAgICAgMLE4sn3MZDJNHBO4VqtJx/W3jEbjxNmnXq8L+xyB47SPwWCYOPs0Go1js4/wP0dD7K8nw2w2T5xtqtWqWDtH4Ki1IzIyAgICAgICAhMLIegzYaB2+eFwCHnrvCRJ/CEgIPD0GN9LAMReGoPc7wDCPsADG4zbZTgcCtu8BohAZgJBjvdxzldAQODpMX4QySH200M8zk6zbB/5sz/NGhJ6ba8WxxLIyA/e8Y2gVCqP4yVMLMheg8EAAKBSqaBQKKDRaKBWqzEcDjEYDDAYDNBut/n7Zh1kF/maUyqVU39bkt8SaV3I/59CoZjq539WkD0MBgMUioeV9m63i263C0AcQpIkQavVQpIk9j9kn8ddqGYB8swUnWFkm16vJ9bOMeOVBzLyAKbX6404VqVSKRzrd0BuOwDQaDTQaDQwGo0wmUwYDAa8cXq93sw6lnEMBgP0+30Mh0P0+/2R0tu0rrnxW+JgMGCHSs+uUqnY8cqDnlmGSqWCyWSCSvXQHdbrdV4/s2wjKpVoNBoolUrodDqoVCq2D10WZhl0jpFtGo0G7zuB48ErDWRoE6jVaigUCuj1eqjVagAPnGi73Ua1Wh25OZIznvab81GQOwaFQgGlUgmHwwGNRgO/3w+r1QqHwwG3241Op4Nms4lKpYKbN2+iUqmg1Wqh1+vNpA3JdjqdDlqtFiqVCjqdDv1+H41Gg4O+acxcjd8SDQYDjEYjJEniQ6fZbM70TRp4aCeDwQCn0wmz2YyVlRUYjUa20+7uLnZ3d3l/AbNbStHpdFheXobJZILNZoNOp8POzg729vbQ7XbRarUAzIZ96BnpzNJqtfD5fDAYDHC73TAajdjf38f+/j76/T76/f5rfsUnA6+6dPtKAhl5FoZuOzqdDnNzc7DZbHwjTiaTuHPnDvr9PjtX+n8KhYL/exY2iBx0m1YqlVAqlTCZTFhdXYXdbsebb76JUCiExcVFLC8vo16vI5fLIRKJoFQqIRqNIpfLodPpTG3m4UkguwGA1WqFx+OB1WpFIBBAq9XC9vY2arUaCoXCVB5O5CxUKhU0Gg3cbjfW1tYAAI1GA51OB/v7+ygWizPtZMk32Ww2XLx4EcFgEL/85S/hdDrRbrfR6XTwq1/9CqVSCdVqFY1Gg/fjNK2X7wLZyWw247333kMwGEQoFILFYsGvfvUrZLNZNBoNNJtNDIfDmaIJUJbcbDbj3Llz8Hg8uHDhAjweD37zm98glUqh0+mg0WjwuTZrGOcOUfaOzveXSYx+JYEMpSINBgN0Oh08Hg/0ej0WFhZgs9nYIWi1WpRKJXYeVAYYDofodDoc3Ezj7flxIMehUCigVqthMBhgt9thtVqxsLAAu92OQCAAn88Hl8sFs9kMhUKBZrMJvV7PqfFZcrYE2hQmkwlqtRp+vx9zc3OwWCzw+/2o1+tIp9Po9XpQKpX8/dMGOlA0Gg1sNhvm5uagUChQr9fRarWQz+dRr9fR6XQ4azeLoEuW1WqF3W6Hw+GAw+FApVKBQqEYKTPNIsg+Op0ONpsNbrcbHo8HFouFM+uzuHbkfDu1Wg2LxQKfzwefzwer1cp+eNZsI88GEyh4o/OeOER0iaLy/5N+/lnwUnerPMLy+Xy4evUqHA4Hzp8/D5vNhmAwCLPZzN+fSqVw69YtDmR6vR6KxSIajQb29vawt7eHdruNWq02E1EtlTzsdjtsNhtOnz6N999/Hw6HA6dPn4bJZILdboder4dWqx25IT6O3DoLkD+rSqXC2toaAoEArl69iitXrkCn08FisSCdTqNSqSAWi6FUKqFcLk9dxorS3Xq9Hk6nE+fOncNf/MVfQKvVotlsolqtotvtotPpoFgsotVqzcS+GgftEZPJhOXlZQSDQT6ky+UyB33dbpcd7SxlhumQIb+zsLCAK1euwOfzoVKpoNlszlw2j/hktMdsNhtCoRDm5+fx85//HH6/H9VqFc1mkw/oWbiAy88cukTRuUTBntVqhVarhcfjgdFoRKlU4nO+UChwYCOvyNDH055lLy2QoT9ItxmTyYRAIACXy4X5+XnYbDb4fL6RQEalUqFSqaDT6XAgYzabUa/XUavVkM/nUavVOLU7zZCn3PR6Pd+CFhYW4HA4EAqFYDQamVBGIEJnr9eb6XIBlVMcDgd8Ph/8fj9CoRBvplqtxlytaSW5ym+LWq0WJpMJXq8Xer0erVYLlUqFSa3y1O4sQX7ZUqlUMBqNMBqN0Gg0UKlU6PV6I0HMtPudJ2EwGHDGij4sFguXSmYN8jLJYDCAWq2GzWZjrqLT6UStVkOr1UKn05kZErSc0wo8aEahS7ZWq4VarYbD4YBOp+Pzn4jjtVqNOYuNRoODGcKz2O+lBjLD4RAejwderxeXLl3Cz372Mw5gtFotPwA9uNvtxpUrV7jzhlqIu90uLl68iEgkgs3NTXzwwQdoNBrMlJ8m50u1VkmS4HK5YDQa8cYbb+DChQtYWFjApUuXoNPpYDAYuCRCQctgMEAul8POzg4ODw9RqVTQbrdHunSmGbTm9Ho95ubmYLfb8YMf/ACrq6vwer1QqVSIx+O4efMmEokErl27hmw2i2q1OrV8Bzqo6fnkXSe0BzUazcxlYQCM3Bqp889oNEKv17PviUQi2N3dRSQSQT6fn5m9JIeco2cymWAymaDX6/kSRfxFYPbK2HRWGY1GnDt3DuFwGC6XC3q9Hpubm7h79y62t7eZNzSta4eyJwaDYYQHu7CwwBSSYDAIjUbD68ZqtUKn06Fer6PRaKBSqSCRSKBcLuPOnTsolUqIx+Mol8uPSEd8F156RsZoNMLj8SAYDHI5xGg0QqlU8gEMPMjcGI1GWK1WAI+mqGw2GzweD4bDIb744gsMBgO+DUzTwiCnQfZwOp1YWFjAhQsX4Pf74fP5oFaruZ2P3uB+v49er4dGo4FMJoN8Ps83yWmz0XdBpVLB4XDA6/ViYWEBp06d4u64crmMu3fvIpVKcVmJMl/Tisc5T5VKNXIIzdL6IIxz0NRqNd8a6YJQLBaRTqc59T2LWldkJ+Ixko2I+zGLa4dA/poyDFQuUavVyGQy2N3dRaFQQLfbnXo/o1AoRrIuRqMRS0tLOH/+PLxeL1ZXV6FWq7k9nbhVVIGpVCqIx+PI5XKo1+vIZDIolUqo1+scKzztWnvhQGYwGECSJI62zp8/j7fffhuLi4swGAxQq9UjQQqRfSh1S901lHEAwL9PqVSiWCzi8uXLSKVSuHbtGtrt9sRvJnnKTK1Wcybmxz/+MZaWlrC6uopTp07BaDQCAKfehsMhZ7VKpRKy2Szu3r2LP/zhD8jn8ygWi0yannQbHQVyJlqtFhaLBV6vF++++y4CgQBWVlbgcrmwv7+PSCSC+/fv49atW9yWPs12Ich5UkSgl2c7KfU9ayC7uFwuXitzc3OwWq3odDqo1WpIJBI4ODhAqVR63S/3tYBIvkqlEmazGW63Gw6HA0qlEv1+H6VSCalUCqVSiS9OwPRnZuS8Kq1Wi3A4jMXFRTgcDpTLZXS7XcRiMUSjUVQqlan0M2QDakSxWq24fPky7HY7dyQHg0EEAgEYjUbuUKZgjgji8hK/QqGA0+nkxh+LxYKDgwNEo1HEYjEAT7e2XiiQkWcTzGYzbDYbzp8/j/fffx9ms3lEw0LOASFeR6vVQqlU4mhfXnqyWCywWq1oNBq4fPkyIpEI7ty5g3K5PNEZB/kBQ07D5/PB7Xbjxz/+MS5dugSn0wmHw8G3RApkqG6tVqtRLpdxcHCAO3fu4A9/+AOazeYjRKlpBWWk1Go1nE4n5ubm8M4772BhYQEejwcGgwHXr1/HV199hZ2dHdy5cwftdnuqb0dyyInfBFJiJWL9LHKpaM85HA6cOXMGKysrmJ+fh0ajGUl1RyIRlMvl1/1yjx3yg0qlUsFiscDtdsNut7Ni7eMCmWn2NQR5IONyuVgCQ6vVolwuo1wuIx6PIxaLTa2GF12K9Ho9zGYz/H4/3nnnHQSDQZw5cwYejwdarRZ6vf6xPkiezKDyLlVkTp06xRpXFosF7XYbh4eHT50RfeGMjLzt1el0cruwVqvlIIcOEKovplIppFIpdhwajQbz8/MwGo3w+/0wm81Qq9Usw2+1WmE2m6cmxTscDqHT6WC1Wjn483g88Pl8MJlM0Gg0AIBms8klo0KhwE5Gq9UyqZrSdrNwSFMWgbq2QqEQ3njjDQSDQY7qk8kk2u02dnZ2cHBwgEwmw1lDYPpvjiS373Q6OUuqVCrRbDZRq9W4Pk0H0LTbA3joo6gEEA6Hcfr0aQSDQfZT+XwehUIB2WwWhUJhZrJ345AkCU6nEx6PB8vLy9xsMBwO0Ww2EYvFsLOzg1wuN7XCko8DrQWPx4O1tTUsLCzAYrGg3+8jGo1yWYTU1adp3VAwQvw6n8/H5aOFhQW43W7odDoMBgOUSiXkcjluLpBrxBFXxm63c3A8fm7R1571PHtpgYzX68Xi4iJCoRB8Ph9nXagEADw4mDudDjY2NvDpp58ikUhgY2MDer0eFy9ehMvlwk9+8hMsLy8zr0ar1bIOiEajGWk3nsTFQlGp1WrF+vo6gsEg/vW//tfwer0IBoOwWCzcUVIsFnHnzh3UajWk02koFAo4HA44nU7OYNEH2XqamfJUIvF6vQiFQrh06RL+/M//HHa7HeFwGADw5ZdfYnd3F1988QW++uorJj8D0x/EAA+e0W63Y2lpidcTlQSy2Syy2SxyudxMtV2Tr3C5XLBYLHjjjTfws5/9DBaLBQaDAaVSCXt7e0gkEtjf30c0GgWAqbk4PQskScLi4iK+//3v48yZM3jrrbdYdr9cLuPbb7/FF198gXw+j3a7DQAzsY7ocD1z5gz+9E//FMFgED6fD5lMBt988w329/eRSqXQ7XanjoNG54rdbofL5cKlS5fwb//tv4XL5cLKygr0ej3q9Tra7TZisRhisRhyuRz29vY4oBkMBty19MYbb+DSpUvc3QSMVnfkpaenxQsHMnSgUruw0Wjkg5iMQN1GxWIR9Xod8Xgc8Xgc2WwWpVIJzWaTb9KRSIQ7mtxuN9rtNnQ6HYxGI8xmM8xmM9rtNs8empQFQwEMBR4ulwsLCwvw+/1wOBycxVIoFGi326hUKshkMohGo6jX68jn81Cr1cwRknd6zQqo7ZxUe6nt0Wg0otFooN1uM6mX1HtnKYghMqbFYmFVYxrw12g0WEdmljpx5A6SxMvsdjvMZjP0ej2ABz6qWq2iXC7zeI9ZyXI+Djqdjm2k0+mYHtDpdNBqtTijN6mXyWcBPSOV26xWK7xeL4xGI+r1Omcg5IHdtIGCDeLAkCArXZIajQbS6TSq1SoikQgikQiKxSJisRharRbrwA0GA5jNZiwuLqLT6XA1od/vo1wuo1qtIp/Ps2I08PR++7kDGXn7lcViwcWLF1k+nw5Ymu1ycHCASqWC27dvI5lM4tatW7h9+zbzZBQKBSqVCjQaDXZ2duBwOHDlyhVcvnyZlVkHgwHOnTsHs9mM7e1t5PP5Z3rQ1w2ySTAY5Lbqv/iLv4DFYoHD4eAuCgDcdn7//n38wz/8AxOirVYrfvrTn0KlUqHT6aBUKqFWq0293sU4ofzChQv4wQ9+gKWlJSwsLKDZbOKrr75CJpPB73//e9y/f59r+MDkrJHnhbx0otPpsLa2hnfffRculwtKpRKtVgt7e3s4PDxEqVTig33a7ULo9XpQqVQ4f/48Ll26xFLyALgbcnd3FwcHB6hWq1Od1XwS5MqqDoeD+WYajQa9Xg/1eh2VSoVHNkx7aZKkC2hvWSwWmEwmnDp1CpcvX0Y2m8X169dxcHCA69evIx6Po9lsTpU96Eyh1uorV67gvffeQygUwtLSEvr9Pvb29lAul/Hpp59yEBOJRDjwpQs32ZA6c0+fPs2K2vV6HdeuXUMymcSHH36IjY0NNBqNZ7pIvHAgQ9kSSjuRJgO1B7daLc68RKNRxONxpFIpFIvFR8iIFKGVSiUEAgEUi0VotVr+Gw6HA7VajUlAk7Jo5E7CaDTC7XbD5/Mxu5sCGBK0KxaLSCQS/NHr9aDT6QAA7XZ7xL5yEaFpdsCSJLFKr9Pp5Ju1Wq3mjFUikeDSCW2iSVonLwriT5nNZh6GCDw4yCuVCkqlEt+kZwlEGKSsMXHwqH5PHUsk0DVroPVAnDudTsfaMdRwUKvVUK1WWbhUzjubRozvEerSsVgsMJvNzKkqFAqcTQCmSwGazni9Xg+j0cgkZ5fLBa1Wi3q9jkKhgFwuh2g0ir29PcRiMcTjcQCjashkk263yyNS6ALe7XaRy+WQTqeRzWaRz+dHzsynwXMFMvL217W1Nfh8PiwtLcHv90OtVqPX66FcLiOVSiGZTOJv//ZvOeVPEuCPi7aGwyHy+TxKpRKSySQymQwvHpVKhR/84AdYXFxELpfjlreTfDMgljdFo1qtFpcvX8ZPf/pTVuqlemC328W9e/eQTqfxySef4JNPPmEpZ3q+VquFVCqFSCSCarXKjkdOZJ2mQ0quFq3VarG6uoq5uTlcuXIFly5dAgAuU/7TP/0TIpEIDg8POS05K1kHeUaGOFQulwsKhQLVahXZbBYbGxvY39/n1tBph1z8zm63cwcOSR1IkoR2u41sNotkMskOeNpu1U8DEr8LBoNwOBw8tkGj0SCfzyOXy+GDDz5APB5HJBIZEXubJsj5l/KDVKvV4o033sDq6iqWl5cxHA5RLpdx//59xONx1j2hi/i0gPzu1atXsby8jLfffhvnzp1Dt9tFIpFAMpnEr3/9ayQSCezu7iKfz6PZbI7IqIwraVPlgTJ9dNbfuHEDBwcHyOVyz1XafaFARqlUwuPxIBAIwOFwcM2s3++j3W7zLfnOnTssRtZqtVic63G/l9qMK5UKarUaOp0Ok4IWFha420ej0XA0d5I3FW0IuikHg0Gsr6/DarWyWBDZM51OY29vjxUiqfWapNO73S5qtRrPyaG5FtMO6tDyeDyYm5tDOBxGIBBAuVxGNBpFOp3Gzs4OTwCXZ/dmAbT+SQHaaDTCZDLx7YfEplKpFJfbZgFkF4PBALPZzOKclAHtdruoVqucraLL0Um9GL0KyA9sm80Gr9fLHW/9fp+nxW9ubiIajaJYLE4cP/FFQOTTUCiE1dXVkQ4uIs9TdmHafDEFH6FQiDu1vF4vCoUCJxru3buHw8ND5PN5NBqNR/yuPJih84o+6LwnYbxYLIZ6vf5c2b4XKi3RbcflckGn0/Ek5lqtht3dXXzwwQdIpVJIp9Ms6Ebqok8yHBmiXC5jb28PVqsVhUIBSqWSb5wGgwEajWZE/O0kgkhiGo0Gq6urCIVCWFlZ4XY1pVKJbreLdDqNcrmMb775BhsbG4hEItzGR1kFeSBDM3McDgeSySTbYprS4lQ+MxgMWFxchMvlwve+9z2sr6/DbDZzKvOTTz5BMplEKpXiTTCt4wceB7kaNkkg0DylSqWCw8NDPoCq1SrfdmbFPnq9Hmtra9wq6vP5YDQa0ev1kMvl8O233+Lw8BDJZJIP6VkIgClbTKrYRqMRV69exerqKs6cOQObzYZsNouDgwMcHh7i8PAQ8Xics53TCHk2mzKc5K8vXbqE9fV1DIdDbGxs8EehUGAO1jTsKbmWEGkInT59GmtrazCbzSiXy9jd3cWHH37I1Adqs36c35WT7SlW8Pl8cDqd6Pf7aDabaDabaLVaTJt4novEc2dkKJChwVl6vR5KpZKlh/f39/Hhhx+iVCohn8/zLflxmRg5KFIjwTe32418Ps9zUYbDIUsd0/eexAUkZ7vr9Xqsrq5ifX2dlWfpzep2u4jH40in07h27Rpu3LiBRqPBBCl5IEO1/Eqlgvn5eYTDYWxvb7OQ4DQFMuRo1Wo1a368+eabOH/+PEfvt27dwt/+7d+iXC4jn8+P2GwWIK9BA+DglrIOnU6HSybFYpE5INOecZBnGQwGA9bW1hAOhzmQoUYECmQSiQTrgMhFOacZRMKkQMblcuHKlSu4cuUKvF4vbDYb0uk0K2QfHh6yBMQ0rx95KcRoNOLSpUsIh8O4ePEizpw5g7t3744EMs1m84kVhkkD+RMSvfP5fPB6vTh9+jTW19fRbDY5wfDhhx8in88jmUxydeBJfpfoFQ6HA8FgEF6vFy6XizsFG40GBzPyy/uz4KVanw5mEt0iSfRn4W3QA5AYHLVjdTodOJ3OidhAcg6Rz+eDzWbD4uIiFhcXYbPZAID76wuFAjY2NpBKpViIq9/v86KQp8dNJhNPoqVW7Umwx7NArnis0+ngcDg4aDMajXwAbW9vsxw41eyB2Uh3E+iZacI3Tf4mOzUajZHW0FlQ85XfKKkM7fV64fV6mTBPLcR0yZq1Ti6SgaBy2/LyMjweD1wuFwwGA+r1Omfy6JI1TZekJ0GuPEsy/H6/H4FAADqdDv1+H9lsFtvb20gmk1NHeJb7E51Oh1AohEAgAIPBgMFggGw2i3g8joODA74YyQO/8d8lP7t0Oh0CgQAWFhZYDK/T6aBQKLAsizwj86x4KYJ48oehAKRcLqPZbD6zaBL9nnK5jEqlAqfTia2tLXg8HpjN5kce8iQuJIpqNRoNi9699dZbOH/+PFQqFYbDIUqlEjY3NxGPx/Hb3/4WyWSSNWNIwpnKK0qlkm9NtLGIsHgSn/9FQEGgXq/n8QPf//73MT8/D5vNhl6vh93dXfzhD39g4aVZy8QQaN9pNBro9XosLCzg3LlzcLlc6Ha7KJVK2NnZYUIiZUWnbc2MYzgcQqvVwuPxMLdhbm6Ou7gqlQrS6TQODw+xv7+PUqn0xNT4tIH8tdFoRCgUgtvtxnvvvcelN5vNhv39fRwcHGB7exs3btxgTt60Z2OAh5QJvV4Pm82Gs2fPYmlpCSaTCZ1OBzs7O/jDH/6AbDY7UgaZhkYLOZfFYrHg0qVLI353e3sbn3zyCXZ3dxGLxbgzdHzfkA+njDqp/ZP8wdzcHKuNU+myVCqhWq0eb2npu/AyFjsZYxL1UWgx6HQ6OJ1OuN1uHjZGb3Cz2UQmk+HJ1aVSiTk/ctY88CBCtlqtLFJlMBi4i2ka22mJuEqidzabDWazmbVz6KNWq7Hq5DQ71ydBLoFgMplgNpthtVqhUqnQbrfRbDbZQcxCNgYYFZ50Op3chk5K4STQmc1mecI1pbRnYQ3JM1bEWSDxUcpY1Wo1JJNJls0gZdZZAXXr6HQ66HQ6bjVuNpucQWg2mwBO5kX6eSCni+h0Og7k5BUE6jCi6d50gaSfl4My6nq9HsFgEHa7nbkxWq2WRTopYywfZPtaMjLyaJRuQnTYUnswPeizvEC6YavVahiNRuj1eh44KcdJckByJ6HX6+H1enH58mUsLCzA6XRymzVxFz7++GOkUins7++jXC4zS5zEhMgGBoMB586d44mrPp8PyWSS1ZFJrXWSIU9rSpKEYDCI999/H6FQCMvLy7BYLCOCijs7OyPO5KSsgeMCcRw0Gg3m5ubgdrtx5swZrK6uotPpIJlMIhKJ4O7duxwkzwqGwwcTrt9++20Eg0HMz8/z/iMRr48++gh7e3tIpVKcNZ52bgz5p8FgALvdznPKrl69CofDAeBBJ9fOzg7+8R//EclkEtvb2+xfZiXjqdFo4HA4mG+mUqmwsbGBdDrN5HDyt3IKwCSDqggmkwmBQADz8/M4deoUQqEQWq0Wdx9/8sknTMyVn710oSSujNls5nFFv/jFL+D3+3k+E11IDw4O8NlnnyGbzfJl63nLuy+8MuXZA7oharVaaDQa6HS6kflIz/L7qO5PkTHNaxpfMCfpAJOX2ej5aTAkDYKkSLRcLiOTySCXyz1SH5RPB6XWUdIGoQCx0+lwnXKaZixRJG+1WuH3++F2u6HVaiFJEk/epRsRHUAnaQ0cJ2it6fV6bi2mESGUkanVaixpMCt2ooyey+WC0+mETqfjluter4dqtYpcLodSqcTjTqZl/xwFuW+hbDHJZhAPggidlI0hruMs2AcA+24ah0NcRNJFo5E63W73db/Ulwr5uWswGJiTaTQaAYBV+Mf9Lum9EOGZytwmk4mFX+fm5jA3NwebzQa9Xo9+v49qtTrCUZNfxI89I0Oqj/KMgMlkgsfjwdLSEq5cuYJ0Oo3r16+z9PeTyEEEOpRdLhc8Hg+3fpFBySmPl19OCgaDAU/sttlsLG2tUqkwGAywvb2NW7du4e7du7h16xZqtRovDFLqJQfscrmwtLQEn8+Hn/3sZ3C5XNBoNMhkMrh58yY++eQT7O3todlsTry2A72noVAIp06dwoULF/D2229DrVYjmUyiVqvho48+wt27d3F4eIhKpTKTBF+CnJgodzp6vZ67k+SjQqYdZA+69Hi9XiwtLXEgPBwOOfDf39/H3bt3US6XAcyGcCIdOtQsQF2U1EDRaDRw48YNxGIx3LhxA4eHhzwBfBbsQ+VIjUaD5eVl/PKXv4Tf74fP54NarUYsFsPt27eRSqU4czBNGSrKyGi1WgSDQVbwdTgc3Ka/vLyMq1evolgs4vDwkG1G3csUHDudTgSDQVy8eBF2ux1nzpyBwWAYyexsbGzg/v37iEajPBOP1tnznOvPFcjIAxESwJOXVcxmM08kpgxNrVZjgx21Kej36PV6rt96vV5oNBru2T+pGQj54UL1QXKsVBbL5XLY3NwcSWuPl+d0Oh3cbje3/Xk8Hpw6dQoWi4XnncTjcWxubiKTyfDk60ndWPKglDq8FhYWMDc3h16vh42NDWQyGRYLrFarTD6c9nLAUSCbaTQazoJSBpS4WPJAZppLcGQLyuiZzWYexjouvpXP55FOpx9Jj08z5C3FLpeLGwcsFgsk6YHKcTweZ3I4dXLRz04z5GVtjUYDt9uN9fV1uFwumM1mVqpPJBIjF3JgetTU5d2ilI2i80un06HX63HzBZ3Fg8GAs51erxcmk4k7nUiBndabQqFgZf9YLIaNjQ0m+b6MieHPnZGh9PX29jaq1SoWFxdZWVSv1yMQCOCtt95CJpNBr9dDJpNh5dVWq/WIwqhcd0WlUmFhYQFvvvkmlpaWoFAoUK/XcefOHaTTaSSTSe7COGnOWc56pyBGrkJMbZ+UeVEqlQiHw9Dr9bBYLNDpdAgGgwgGg5yR0ev1UKlUaDab2NraQiqVYnlseQvcJIJKHpTGXVxcxLlz5xAIBNBut1EoFPDVV18hHo9zVxfJgU/qM78syEsFci5arVZDOp1GoVAYKQ1Ms73o2QKBAJaWlnD69OlH5irt7e1xl0S9Xmd9i2k4iJ4E2l8ajQZqtRoLCwu4ePEiVldX2f8QIXxra4t5aFRum+Y1Iwcd4jRXyOl0wmg0IpfLoVarMTmcLlDTwo0hkB9ptVqIRCKQJAl7e3tot9swGAyw2+04e/YsjEYjCoUCzp49yxIjKpWKG1rsdjs3ZxgMBvT7fezu7qLZbOLmzZtIJpOswVMul19ayfu5MzLUB06TqC9cuACbzYZAIMAMZbPZjEKhgH6/j1Qqxekrkr4mjKfJ9Xo95ufncfXqVXi9Xg5kqKyQSqVG6v4nZbM9LiNDbzQFMjRFdjAYwOVywWQy4a233oLD4UA4HB7RvTAYDKyAKHc29EEDJQknxQ5PC3mp0Ww2w2KxcAuxXq9nnYGvv/4a+/v73EYMPH07/7RC3vY5HsjIu3Kopn2S9snLhPxmLEkS/H4/3njjDZw+fZoVx2n+G00qpkCGavvTaBcC2YcaEBYWFvD9738ffr8fDocDnU4H8XgcmUyGAxm6LNDamnaMa1dRNk+lUrHEA3GG2u32VJbaqNGk2Wxy2Whvbw+9Xg/r6+uw2+0wGAw4deoUqtUq0uk020yuDq3VarkribTSdnZ2UCgUmGAfi8UQi8VeatnyhUpLg8GAU233799Hr9fD2bNnodFomPhjNpuxtLQEu92OWq0Gl8uFeDzOhzC1HAMPUnuhUAh2ux2nTp2C3++HQqHg4IXSnpSFoNdykkA3PDlHAXgY8YZCIVy+fBmtVgvlchkGgwGrq6swm81wu908J0epVKLdbiORSKDRaGBnZwelUolHGORyuZES20mzw9NA3pkWCoW4Nktr5d69e4jFYkin05yCFHgISoXbbDbY7XYeEzIYDJjESuWlaXS+cpBqON0KzWYzBymNRgO1Wo3b9mdp3hRhnIip0+kgSQ8Uw0l0lGbbUaZqmtfLkyBvJx4MBuh0Oux3KBMzjXaRU0UajQaKxSK2trZQq9V4pAcFLSqVCna7faRLiRIbzWaT4wJSy75x4wYKhQKfW41GY+Qi9jLwQqWlXq+HdDqNXC6H3/3ud/j666/xR3/0R9BqtXA6nVhaWoLRaITdbken08HCwgJyuRxu3bqFO3fuoNFooFAo8O/TaDT43ve+h3A4jHPnzmFtbQ0HBwf4/PPPEYlE8NFHHyGdTp9Y7RB5gEflMwpmaPLnpUuXcPr06UdY4tRJQAFMu91GLpfD7u4u0uk0PvzwQ5YMLxaLj3AfJg1y8SW9Xo+rV6/i4sWLOHfuHEKhEG7cuIHf/e53SCQSHMTNqnMdB2X9VCoVC5vNz8/DYrFwGaXdbrPOELVpTyNoHdFN0OVyYWFhAR6Ph5+5WCyiWCwiHo/j8PCQpQ6mfS3JM8RarRYGgwFWq5W5HwDQbrdxcHDAmYd6vc4/M6ugIGY4HLIfJxtSdnPaQPuh2+2ywvwf/vAHOBwODAYDnD59Gl6vl/WGHA7HSOt1Pp9HvV5HPp9HoVBAIpHAzZs3kcvlcO3aNZTLZT4PgZcvdfBSlH3lpaJUKoXDw0O0221YLJYRAiJtnlAoxPMV3G73SMtyOByG3++HTqdjQa9oNMqZiU6nc6KjYgpkOp0OWq0WisUidy1R9sFkMvH3UkoPeNDi1u122fHS0LZsNot0Os0TRkljZtLr+/TsGo0GFosFTqcTGo2GuUT5fB7FYnFkuuxJfd+PC/IMHGVkKItHqtG9Xo/XCX3vtILsQYEdyR6o1Wq+JVKbZ7lcHuHWzRLkJUgiP1N7fqFQQD6fR6vVGulwmyUbkd8mH9zpdJgzYzKZ+GNas3nyTBT5EOrqi8ViUKvVPA9Jr9fDarVCoVDw+Z/JZLikncvlkE6nmTRerVbRaDS4KehVXCJeKJCR11BLpRLK5TI++eQTRCIR5rhYrVbOzBD/IxwO40c/+hGLehGII6NSqZBIJPDNN9/g2rVr+Ku/+itUq1XUarUTfZjJuUP5fB4KhQKff/45Dg4OcOXKFQSDQeYAEYbDIZcBkskkKpUK7t69izt37iCfz2Nvbw+NRgPpdHpE/ZB+dlJBC5rWxcLCAs6cOYNer4dIJIK9vT3cv3+fA5mTHLweN+SZPIvFgrm5OczPz/M8mEKhgP39fWSzWQDTyyeSc6y0Wi2MRiPMZjNsNhvLNdRqNVy/fh3RaBRbW1uIxWJsv1laTxTAqNVqaDQaVqmNxWL4+uuvcXh4yJ0os7bX6Fnb7TbK5TIKhQKy2Sx3UPZ6Pdy7d48/076aJhuNX5AGgwEPU83n8zCZTPB6vfD5fDAYDHA4HCOBTDKZ5OG9uVwOrVaLRe6IPvIq/dBLUfYFwFL5hUKBa2ZerxfVahUmk4k7csjhWK3WkciMDEk1/WaziXQ6jUwmg3Q6zSquJxnyZ2m322g0GshkMgCAXC4Hg8HArdIEGlfQ6XSYUBaLxXgwVzweR7vdRr1e57kWk34wjW8alUrFwofFYpG5DNVqlVPdAg8hz0JQVoY642jdVavVkT0zTU73cZDX+FutFpPqS6US+xFq25crjk875Ol/Wh+kXVUqlZDNZlEoFGZO/VkOymzTodtsNlEsFll3Ry7QOum+92lA9uh0OpwNJ25Zs9mEwWBAqVRiW1AgU61WObvX7/eZW3QchPqXNmtJrtVAmYVUKsWt2EajEUtLSyyWEwwGeZyBJEno9/vo9XrcW37z5k3cunULqVTqWCK6lwG5M6UU9qeffgqdToeNjQ3YbDY+sAmUzuz3+zz7JZ1OI51Oo9vt8mTnaRrYJi+J0fNns1lEIhHs7OxgZ2cHW1tbnO6WP7/AaMDc6/VYG6Ver0OSJOagUbn3pO+b54U8I0xE1X/+53/mVLjJZEKj0cD29jYHNVTinQXIu0JIpffDDz/E3t4ed5VUq1UcHBywiOKs7jM6gwaDAba2tvA//+f/ZFV1ANje3kYul+OL+iyA1gE1DRB5l4I6Al3cKXihizplPY9jPb20QIZeMHFDSMtCo9EgFovBaDSiVCrB5XKh0WhAoVDAaDTygUYdTAcHB9xrfvv2bVb9mxRQrZWeh9o8M5kM1+6JEwM85BhRZob4Ic1mkx31y2xTOymgYIaE26rVKorFImKxGDY3NxGLxbgNdNpbZJ8XtHaI60Bt/clkkhVIgekNZICHzpa6S/b29pBOp7mU0u/3US6Xmfcwbfvou0DPSvyGnZ0dJJNJPnRoZINcrXbWMqDyrNVwOEQ2m8W3337LDSgAUK1W0el0RqgQ045xu1B2hvy2/Pto7cg7u47T77yS6dfyB6DsBInnxeNxlsGmgZD0fYPBAJlMhqevVqvVkQmbk4LHvYlyovL4/6NFQdGsfDT6NB9C1BUwHA7x8ccfY3NzE6lUCqlUCrVabSSQE3gAuXNptVrIZDL4+7//e5hMJuaMbG5uzsz8IAI9a6vVGmkfptviSe10PE7QfqPDiD4DD8c0zNKaGYc8o16r1UZ8NWXNZ2HcxzjILvI1Mr5O5BWD17HHpKMWrslkeqFVLS8fyP8NPLlmPy5w9ayGqdVqx2ZFo9H4nfaR24D++0l2eN5nfhbU6/UTZR/gIWlz3C6vI4g7TvsYDIYXPjXGb0fAq7Vdo9E4Nvs8j/953By24yzLnjT/M47x8S7Hvc+Oa3+ZzeZnts04nwjAI4He43idLwvVavVEr53XjaPWzivJyBAoensWB0LfO218kPGA5UnfO4uQ3wTl77/Ad+NxbfizbDv5+nlVrZ6TjHFbCNs8CvmaedJ5NMuZq5OIVxrIANMTkLwIhA2+G9NcQnvVELZ7CLHXjoawz5MxnsV7mu8TOBkQHlBAQEBAQEBgYnEkR0ZAQEBAQEBA4CRDZGQEBAQEBAQEJhYikBEQEBAQEBCYWIhARkBAQEBAQGBiIQIZAQEBAQEBgYmFCGQEBAQEBAQEJhYikBEQEBAQEBCYWIhARkBAQEBAQGBiIQIZAQEBAQEBgYmFCGQEBAQEBAQEJhYikBEQEBAQEBCYWIhARkBAQEBAQGBiIQIZAQEBAQEBgYnFsQYykiT9Z0mSrkmS1JYk6X8c59+eBEiS5JAk6f9KklSXJCkiSdK/f92v6SRB2OdoCPscDWGfJ0P45qMh7PPdeJ37S3Vcf+hfkADw3wD8AoD+mP/2JOC/A+gA8AK4BOA3kiTdHA6Hd1/rqzo5EPY5GsI+R0PY58kQvvloCPt8N17b/pKGw+Gr/huP/lFJ+m8AQsPh8P937H/8hEKSJCOAIoBzw+Fw61++9r8AxIfD4X95rS/uBEDY52gI+xwNYZ+ng/DNR0PY5/F43ftLcGRODk4D6NEi+BfcBHD2Nb2ekwZhn6Mh7HM0hH0EBF4dXuv+EoHMyYEJQGXsa2UA5tfwWk4ihH2OhrDP0RD2ERB4dXit+0sEMicHNQCWsa9ZAFRfw2s5iRD2ORrCPkdD2EdA4NXhte4vEcicHGwBUEmStCL72kUAgoj4AMI+R0PY52gI+wgIvDq81v113O3XKkmSdACUAJSSJOkkSTruzqkTieFwWAfw1wD+qyRJRkmS3gHwSwD/6/W+spMBYZ+jIexzNIR9jobwzUdD2OdovO79ddwZmb8E0ATwXwD8h3/577885tdwkvGf8KC1LwPgfwP4j6I1dATCPkdD2OdoCPs8GcI3Hw1hn+/Ga9tfr6X9WkBAQEBAQEDgZUBwZAQEBAQEBAQmFiKQERAQEBAQEJhYiEBGQEBAQEBAYGIhAhkBAQEBAQGBicWR7WNGo3HimMD1el06rr9ltVonzj7lcvnY7GM2myfOPtVq9djsY7FYJs4+lUpFrJ8jcJzrx2QyTZx9arXasdhH2OZoTJt9Xlsf/HA4hCQd2/smICAgICAgcAx41m7oF40FjjWQGQwGAB48JAUy9ADy/55V0JtP9iGQbWbdPgICAq8H475J+CSBx+FxZ9iTgponrZ/nWVPHnpGhBxx/OLEhHoBsMxgMRmwi7CMgIPA68Ti/LTLrAk/C49bLkzARGRla7DqdDkqlkj8GgwG63S5/nlVxPnpupVIJSZKg0WigVqsxHA7R7/fR7/fRarXQ7/cBzG6GRh7hU3aPQLZQKBQzZxcC2WcwGDxyYVAoHvD6aY3NIuT2ka+f8ewn8HAdzeI+k0O+fiRJglqthlKpRK/XQ6/Xe6bDahpBvki+3xQKxcj6mQWQDeiZ1Wo1dDrdkf643+8/cv5LksTr6Vlsd6wZGZ1OB51OB41GA41Gg263i0ajgV6vxwf2LIKyL0qlEiqVCiaTCSaTCb1eD+12G91ulwMZenNn+UAaDAa8VsYd7Sw5j3HID+p+v8+BMK0tso1SqXzNr/R4Qc6RPvr9Pnq9HoBH097y/aVUKmc+MKbDhexhMBig0WjQbDbRbDZH1tqs2okCY7KDSqUaoUxMO+TBHO0XjUYDi8UClUoFlerxYUan00G9XuegmC4XJ7K0RI5DrVZjbm4OXq8XTqcTLpcLtVoN6XQalUoFGxsbqNVqvBiA6V4EcgeqUCigUqkQDofhdDrh8/ng9/vRbrdRq9VQq9WwubmJer2ORqOBbreLbreLXq8HSZL4tj2tkK8HyuxpNBp+9uFwyJF9u90eyVzJP08j5OuIghWDwQCtVguVSgWNRsN26fV6vH5miZtGNtJoNFAoFHyhopuzHHQotdtttFotvi3OImh/6XQ6zM/PQ6/Xw+FwQK/XIxKJIBKJ8GV0Fnz2OIbDIZRKJUwmE5RKJe85CvIoYJ6mIE/ub+TZOMrUmc1mmEwmeDwerKysQKPRcCVGftECgGaziXw+j0ajgYODA9TrdVQqFbRarZG/9TS2O5ZAptfrQaPR4MqVK7hy5QrOnj2L9fV1pNNp3LlzB5FIBNlsljMPdEBPO+i2rFarYTAY8NZbb2FtbQ0XLlzA+fPnUa/XkclkkEql8Nvf/hapVArxeByVSgWFQgH1eh1KpZIP9WmEfLPQDdnpdMLj8fBB3e12USwW0el0kEql0G63R9K70+5kyTFoNBqoVCp4PB54vV5YLBZ4vV60223E43HU63Xs7u6i0WiM2Gdasw703lPG02AwQKfTIRwOIxQKcfqbnn04HKLVaqHb7SIejyORSKDZbKJYLE7VYfRdkGc5VSoVHA4H3nvvPfh8PoRCIZhMJnz44YeoVquoVquo1Wp8qM8KyC+p1WqEw2GYTCb4fD6YTCYcHBwgEomg0WigVCpNjf8ZD2LkQb7ZbIbRaMT8/Dzm5+exvr6OP/mTP4HJZILNZoNKpRrJig6HQxQKBRweHiKTyeD3v/890uk07t69i3Q6/czlymMtLWk0Gmi1WhgMBphMJjQaDVitVphMJqjVar4dTbvToOdTqVTQarXwer2wWq0IBALw+XxwOBwwmUxQKBTodrvo9/sIh8OciSiXyxzlUvlpGm0mdwAKhQImkwkajQZ+vx/hcJjt1+l0YLVa0Ww20ev1oFAouFw5rfV7+XOpVCoolUq4XC4YDAYEg0H4/X6YzWb4fD50Oh1oNBq+7Wg0GnQ6HXQ6HQwGA741ApPvbOWQB8BKpRIOhwNWqxXhcBgLCwtQq9XQ6/UjgUyz2US324VarYZKpeILgzz1PQugMgH5a7fbDa/XC5vNxhm/WcjmHQUqIzmdTthsNszPz8NqtaLRaCCXy42UL6fJTuSPNRoNHA4H1Go1PB4PzGYz5ubmMDc3B7/fz2uFsp+U7VSpVPzvZrMJAPD5fACAvb29ke99Wrsde/s1Oc5utwuFQgG73Q6bzQa9Xg+NRjOSVpqmN59ANtBqtTCbzQgEAvjzP/9zhEIhnDp1Ck6nEwaDAZ1Oh52vyWTCL3/5S7TbbRSLRTQaDdy/fx+bm5s4PDzEV199xfYEpuMwkt+kKQBeW1uDz+fD5cuXcfnyZc7IdDodFAoF1Go1/PM//zMODg6QTCaRTCaZUEa/U/55kiEv2dIaeffdd7G0tMQZB6PRCJfLhW63i3Q6jVqthlu3biGRSGB/fx8HBwcolUpIpVIYDAZcppuG9QM82GtKpRJGoxFGoxE/+tGPcPr0aayurmJ1dRUqlQpqtXokkOl0Ouj1epyRuXHjBv7qr/4K9Xod9Xp9hKc2raC9p9Fo4HK5EA6HcfnyZYRCIc5YUalgGvbS84Ce3WAw4M0338T8/DwuX74Mr9eL3/72t6jX60gkEkin07y3JhXjflOlUsFgMMDpdOK9996D2+1GOByGzWaD0+mE0+mEyWSC3W7HYDBAPp/nM7/f78Nms8FqtcJgMGB5eRkejwe9Xg/pdBqRSATpdBqdTmeEM/tde+61COLJu07kHA+5Q5lmZ0GRvMFggNVqxfz8PObm5uB2uzkTQzag2qPH40G/34fZbEar1UKj0UCj0UC9XodKpZpaorRSqYRer4fBYIDL5YLf70cwGEQ4HOayWrfbhdFoRK1WQzAYZD5Iq9VCu93mejXxaIDJXmPjvBiTyQSr1Qqfz4e5uTmEQiEEg0Ho9XpYrVb0ej0olUrU63UUi0WoVCrmXwFgRyN3WJNqG8K4jdRqNWw2G1wuF3w+HwKBAJPr5X5nnHuWSCRGLlizAioV6fV6GI1GWK1WWK1WDqDpe2YxkJEToLVaLZxOJ9xuNzweD/x+P6xWK/Nl6PunYU9RaV+n03GgMjc3B5/Ph3A4DKvVyhwZKiV1u12Uy2W02220222+XFDiQq/XYzAYwOFwoNPpQK/XQ6VScTbrafFaAhlaBK1WC4lEAolEArVajVnw05qylLfmeb1eXL16FXNzc1hdXYXX6+USAX2vvBVUp9MBeFCe6/f70Gg0mJubg81mw+bmJsrlMvL5PGdmJtV+tOkpSLHb7XjnnXfg8Xjwve99D+FwGC6XC06nc4TsazKZ0O12YTAYUC6XUa1WUalUkEgkcPPmTRSLRSaUU6vfJDoXugQolUrodDo4nU786Ec/QiAQwNtvv425uTk+fDqdDqLR6EhHwdraGk6fPo21tTVks1lsbW3hgw8+QKlUQiwWQ6vV4jU4abYZx3hNv9PpcBcg2YQyLHSZ6vf7GAwG3HGxv78PvV6PVqvFnJtJt8t3gUiqRPINhUKwWCzQ6/Wo1Wpsr0n2M88LWh9msxlerxfz8/NYWlrC3NwchsMh8vk8isUil3HlLcmTCNojg8EAwWAQwWAQPp8Pa2trcLlc+P73vw+73c7dbKlUCvv7+1AoFFAqlSiXy/jqq69QKpW4PEsZdYfDgfn5eQyHQ+h0OubZmEwmDAaDEft9F15bICNJEnq9HiqVCmq1GpN8p9VRyDkIdIsmJ+F2u+FwOLhLYvxmTIsCAOvLaLVauFwupNNp2O129Pt9FIvF1/Z8LxvEITKZTFhZWUE4HMa5c+cQCoVGAj5aK3q9HgC4bZ06u7a2tlCr1ZBKpbC3t8c8mkldZ3KtCo1GA7PZjOXl5ZHaNKHVavGa0Ov1XMvW6XRwu92o1WpQqVS4c+cOVCoVksnkVN+wqUWWHLMc1PFFz6/VaqFWq5m/N565mcS187SgYFmtVsNut3OGgXiM8oN5mu3wOIzbhkopdrsdw+EQjUYDzWaTS3DTAKJDmEwmBAIBzM/P49KlS3A4HFhaWoLJZOLvPTw8RDKZBPBgbeRyOdy4cQP5fB6dTgfD4RBmsxnBYBAKhQKBQAAAWJJFp9NBq9Vy6fLEcWTkbGU6nDudDnK5HAqFApcBgOncHEQ8DQQCCAaDOHPmDC5cuACXywWVSsWLnp59MBig0WigWCxyWUSSJCZ0Ulo8GAzij/7oj5BKpfDxxx8jn8+jUqlw584k2ZLWx2AwgNVqxenTpxEMBnHp0iX4fD7o9Xo0m01UKhVUq1V0u10mOlMd2uv1cmqT2m2plVae3pwkuwCjnSRE7F1ZWUEwGMTa2hr8fj87FCLyRqNRfPXVV1CpVJifn4fJZOLWY7VaDaPRiHA4jB//+MdIJBJMUqRU8CQTgGktaTQaLC4uwuVyYX19HWfOnIHH4+HvoaC3VCphMBjA6XRCr9fzmqKARx74TKI9ngbyNnUicoZCIQQCAej1ei5PFgoFlEolVKtVNJvNqbWHHHJ+iEajgcfjwYULFxAKhWCz2aDT6VAul9FsNlGtVlGv1yf6PJOf1RaLBWq1GisrK/je976HQCCA1dVVzsJ0u10cHBygUCjg2rVruHbtGv9so9HA/v4+l/cB4O7duxgMBjh//jyWlpag0WhgtVqhUCi47NvtdlEoFJ769R6bsu84KJApFAooFApoNpvodDrMaJ7EN/9JoPRcv99nh7q+vo4LFy5wUPK4klCtVkMkEhnhM6yvr8PtdkOv10On0yEUCuHnP/85otEoDg4OIEkSc2gATExLpDwLNRwOYbVace7cOczPz+PixYtwuVwol8toNBpIJBI4PDxEq9VCuVxGv99Hp9OBWq3Gm2++iVAoBLPZzG3pFMhQMEO13knCeEus0+nE+fPnEQ6HcebMGbjdbv4+Wi+xWAxffvkltFotADA/hA54jUaDcDgMrVaLWCyGg4MDJk9TKYV+5yTuRyKsLiwsIBgMciBDvDPK2tXrdUSjUS6nUN1ertg6C6BnpVux3W5HKBSCz+fjzhO6XFH5lrgMk1w+eRqQbbRaLbRaLTweD86dOwefz8eBTCaTYbs0Gg202+2RLN+kgfY98V5Onz6Nq1evskaMQqHg7NPOzg52d3fx8ccf4+OPPx75HXJOlSRJ2NjY4KzNH/3RHzF/TavVsoZaPp9/Jru9skBGngInQxBhTKvV8g3aZDLBaDSyBsakO89xyCN5esP8fj+cTiffjokURpmUXC6HXC6HfD6Pvb09tFotNJtNzsLU63UEAgEYjUZ2OLVaDU6nE/V6faQLZVJAdjKZTDAYDAiHwzh9+jTcbjdarRby+Ty2traQy+UQi8UQj8c5YKNAhm6Rg8EA4XCYbxIU9E2SPQjjm9lut8PlcmFpaYntQ6J31WoV7XYb0WgUsVgMu7u7iMfj0Ol0SCaT6PV6WFlZeUQh2mQyweFwYHl5GTqdjm/b8jLnpIAyKXq9nnV0VldXEQgEmPdCJMRCoYCDgwMOjgFgbm4OwGTeol8U5LONRiOcTicT6x0OB3q9Hmq1Gg4PD7G9vc26X5OY3XxWyKsJer2eSeOBQAAOhwPAgzJuPB5HPB5HKpVCrVab2IzMOLVBTpB3uVwwm80sGnl4eIhyuYx79+5hZ2eHu7SO+t1yn0KBHvGy6HLxrFIHrzSQoZ5xj8cDh8OBYDDIB3C/34dWq4Xb7Ua9XmdtAiIETdqb/yTQYiCOAnE9aBNQENPpdJBMJpHNZnHt2jVcv34dxWIR0WiUSyikIXP69Gm89dZbLMRkMpkgSRITp/b29tDv9yfm4Ka1MhwO4XQ6sbi4iKtXr+L999+HSqVCqVRCPB7Hb37zG9y/fx/JZBKZTGakE6nT6XDmIZfLQaPRYGlpCQaDATabDZVKZWLs8TjQ5g+Hw7h06RLW1tbw3nvvwWAwQK/Xc8twPp/HZ599hs8++wzFYhGHh4dcUvL5fDh79iyCwSDfoKnFVqfT4d1330Umk+F12G630el0JuK2Ta+PMp9msxnnz5/H/Pw8fvrTnyIQCMBqtUKj0XCG7uDgAH/3d3/H3X86nQ5nz57F4uLiTGnGAKOHtcvlwpkzZ3D27FlcvHgRWq0WjUYDlUqFSweUEaUM4bSDbGOxWDA/P49Tp07h3Llz3IRRrVZx8+ZN3LlzB3fv3kU2mwUweXPxxjPjSqUSKysrWF9fx/nz57GysjJyEfjqq68Qi8Xwu9/9Dpubm9w2/Tjl8KMuRZQFlvOLnsVur3QF0iFO2QgizdGBQpoy5Hwm7fb3XZBzDCwWCwwGAxwOB2w2G4xGI0ejFKhks1nEYjHWHyAuiHw2DADOXMnbAClbM2lOhWxEZEubzYZAIACn0wmtVot+v49sNot8Po9MJoNsNotSqYR6vc7kTbodkMCSXBhPpVJx5ks+n2oSMn7y/UCZO4fDwRk9g8EAlUrFmRjSPkkkEsjn89yh1W63USqVoNFokM/nkcvlYDQaYTAYmK+mVqthsVjQ7XZhMpm4vNLpdPi1nGR7ydcRSaX7/X54vV7uhiDf0+l0UKvVUCwWkUgkRpSgp1XG4GkgJ86TeCARL4n7USqVUCwWR0qPswB6VpLft9vtzDUjdeNCoYBsFPKhYgAAfbFJREFUNotGozHSfTuJdqJLjl6v54yM2WyGUqkcGWScTqeRTqdRKpW461j+O8ZBZW3yMUR9oMsFXSqelSj9ygMZ4CF5jD7kDiWbzSKXy6HZbE4kQfVJkB9CarUa58+fx8LCAt58802sra1xG3Wn00GpVEK5XMY//dM/4caNG4jFYojFYpxyI4VNvV4Pv9+PhYUFWK1WdrqTmmmgTIwkSZw1uHDhAv74j/8YDocDkiQhn8/jd7/7HQ4PD3H79m2Wr5YHifLgpNVq8QFOQ+68Xi8ajQY0Gs0IZ+ikH87AQ1G3UCgEp9OJN998Ez/5yU9YUKpWq+HmzZvIZDL44IMPsL29jUKhgGKxyDyPZrOJ+/fvIxqNwuv1IpPJ4OzZszh79uxIFwZpQSwvL3M2kLhZk2AnujGbzWasr6/jF7/4BTtho9HIXTeFQgHb29v49ttv8dFHH0GpVOL8+fMwm81TJSr5tJDzrxQKBcLhMK5cuYKFhQXodDq0Wi1EIhGkUinmQgyHw4m7ND0P5AGyQqHAqVOn8P777yMcDrNwKcns3759Gzdu3ECn05no6dfELXO73XA6nbh69Sreeust7szqdDqoVqtIpVL4/PPPsb+/j3Q6/cTZf3TpJn/kcrlw4cIFLC4u8mWK4oCDgwPs7u6iUqk8k+2OZSXKdQfkREu68VE6atKj2MeBnt1ms42MHwAealuUy2UUCgWk02kkk0kUCgU0Gg3OttDvoOyCwWCAWq0e4cFMajZLnrUj9rrb7WYn0Wg02C5E9n1c+7W820su7kZ2k5PIJ8FW8tdIPDMS3qJSEK2fbDaLZDLJNXoiNtO66ff7rEqbyWRgNpsRCoU42CN1W51Oh16vB5PJxGTpSYNWq4XFYoHdbueSNnWv0bqoVqvcZFAoFKDVapnPN02+51mh0Wi45dzhcMBsNkOSJPT7fc7GkFQGZYBnBfS8FosFLpcLVqsVSqUSg8EAtVoN5XKZM+iTvIbk3Faj0Qiz2QybzQaHw8Gle8rGkMBmoVAYGZMz/uzySyfxYml9kQ3lpSUabEs/8zQ49pUov03L67LTCApE9Ho9Tp06hatXrzI/gZRnM5kMfv3rXyORSODOnTssz6xWq/n30GGs0WhgMBhgNpv5/4/bc9Jq+/T6ictCN+hSqYTbt2/j8PAQW1tbXAKgTqTHLfDHaVt0u12eIN7pdPjwPsnORr7x6T2/cOEC1tfXWYiqVqsxmfeDDz5ALBbD4eEhp3fpkJFzR1qtFu7cuYNYLMaOyuFwYGFhgXUb1Go1fD4fFhcXkcvlXpsNngVye83NzeGNN97A2bNnsbCwwCWyarXKZPHNzU1sbW1hf38fvV4PRqORNUHIWR/lkKcJ8ozc+vo6AoEA3nzzTayvr0On06HZbCKXy+HmzZs4PDxEqVSamqz5d0Eu6+B2u2E2m7G4uIiVlRUO+iuVCmc7qYX/cVPVJwly2YJAIAC73c6VFAD8zDTihEppj1sX8qy72WyGTqfDysoKd5dSl6Q8gfE8vvmVBzLjL2yc+DOtzoFAN16Px8MKmaRGS9mYe/fu4eDggNnulKGQLwLKZNHcIfltiIKBSeMZyYMwGk5HgRrxFxKJBLLZLAqFwkgmRo7xRS9fY/1+n0mrlPWbBEcznqny+/1YXl6G1+vlcQz5fB7JZBLb29uIxWI88HD8+eRk6lQqhWKxiFOnTiGZTLI9aU0Sv8ThcMBgMLyux39qjJcY7XY7t1s7HA7WPqGukkgkgp2dHU6H03qgMRjyzsnxvzFtkPtg0mCiWV1er5enG9dqNc74UaZvFgIZ4OE+pE4u+gAeXA6azSZSqRRSqRQajcbEX8zlJF+Hw8HZcTkvs9lsIpvNMh+IlNKftCbIHjTagOZ3ORyOxwY/z7O2Xnkgo1Kp+JZN6STgQVRXKBSQSqWQzWZH9AimDXLiFKXxyUk0Gg0kk0k+iMY5H1SvXFpagsfjQTAYZKInABaIi0aj2Nvb424COan6pIKeValUwm6388RUtVqNZrOJ/f19JBIJjtif5nfJD3EqqVCXE9VwT/oak7cQk0bM6dOnMT8/D5VKhUwmg4ODA1y/fh3xeBylUolVoZ90WyabUJfX9vY2VCoVVldXYbPZON1LYnutVgsWi+W4H/2ZQYew0+mE0WjE8vIyTp8+Da/Xy3Nebty4gVwuh88++wyRSASFQgH5fB6tVgtarZZVthcWFrgDMJ/Ps15RpVLhQ+qkr52nBR1Y1FFpMpmwsLCA9fV1+P1+1kW5d+8et1zH4/FHfNS0Ql6aVqvVWF5exvLyMoLBINRqNer1OjKZDOt3RaNRpgNMom3kAYxWq+Up8XNzczCZTFAqlZzRTqVSuHXrFpLJJBqNBvvVJ/1O2qPyoZI08kL+cy9SoXklgcw40dXtdsPn88Fut8NisXArHwUy8pHnwHQGM/JABnjIj2k2mzyp+XFigPI0H6lsulwuAGCBqlgshkgkgv39fUSjUdacOcmBjPwmTYGMz+dj7Rdqj81kMs/EYJfzYCiQka+xSVhb9Npp4nc4HMapU6cwNzeHQqGAXC6HSCSC69evI5fLcSDzpOBV7ly73S46nQ5rPtRqNZw+fRoOhwMWiwUajYZvnHTpOKmQH8ZOpxMejwdLS0s4c+YMtFotut0u8vk8vvrqK0SjUXzyySeIRCL88+S0LRYLwuHwI4HM1tYW62RQ+nyaQAeMPJBZW1uD1+uFTqdjIvnh4SF2dnaQzWYfS+acNozz0+giefHiRQQCAWg0Gs4YHx4eYn9/n+eUTUMgQ8OMQ6EQ5ubmYDQaoVQqWUsolUrh9u3byOfzzL0bP2/kNqT/tlqt8Hq9nNCgQGY8gHmeYOaV6sgAD2fmyNuvAbD4DfWMT3I67nEgJ6FWq7mFkSBJD9R3aTwDdR/RJpDbjgTdSJyKZgoB4I6UdDqNTCbDRLyjbuYnDfTMNGdDoVBwy3Cj0eDneZbfJR+8SV1hlUrlxOsTyW+BFPh6PB54vV5+3yn7RsJbpVLpmTSD5AENEanv3bsHv9+PQCAAk8mEWq2GSqXCrdcnFfL32+12IxwO8wT5Xq+HarWKYrGISCQycmMenx1DP2ez2dDv99lZ7+7uIpVKjQh0neT18zQYP6QNBgOWl5fhdrt5cjMAFItFZDIZxGIxpFIpnpPzOB7aNIL8NzVXmEwmDvQBsHAptaJ/V3llUkD7SS5bQVWETqfDsxFrtdpIm7kctMao49Jms8FgMODUqVNYWlqC3++HVqvlzmUSpEwmkzx65sQI4hGoG4IE7+QLod1uM0t52m47wENHQREt8ND51ut17Ozs4ODggNvOCeNpPpvNhnPnzmFpaYmVJOn7isUi7ty5g3g8jmw2i0qlMvJ3TjrohmcwGLiU0Wg0+BAql8tHBiDyoA94qCNC3RakRlooFCaitERtrfS+r6ysYHFxETabDUqlEqlUCtevX8f9+/exsbHB6qFPO4qCnp+6Dra3twEACwsLWF1dhSQ9GPRGTuWkg4K+lZUVnD9/HqdOnYLb7UY+n+fb8vXr13FwcMABH12irFYrtxkT/6hcLiOTyWBjYwP//M//zNIQ5JSnAfQspEv07rvvYm5uDmtrawgEAqx4vLW1hW+//ZZHyIzvtWkFlXaJL0ZNCDTHDXgggLe7u4toNMozluQ6VZMICsRIJsVsNsNisXDQQbPYSM+L1L/H6RDAw+5RjUaDYDAIt9uNd999F5cuXWLidK/XQ7PZRD6fx61btxCNRpFKpXitPYsUwksPZMZLBiqViol0445g0olRR4HeiPHFQC2glUoFmUwGhUJh5KCWt7AZDAbmF1ksFh6GSN9Ho85pdDypIk9KNgZ4uAZ6vd4IIVf+8aQ1Ml7C1Gq17Hj6/T7fmCizc9JvTPJ6slar5bk/Op2ORf8qlQry+TzK5TK63S6ndZ8W4/wrUkeWC1PWajUuV51UjJOhqT2dlFYpDU5ZSjnPigjzVM70eDzQ6/V8Q6zX66jX6/yz0+ajyDdrtVomsbpcLk71U6Yun8/z3KDxA2sWoFQquZXfbDaP8BKr1Sry+TxKpdIILQKY7EDvqNcul0+R+5Hxc1zuwwwGAzweDzweD5xOJ6xWK68zymplMhkW1nve/fbKODJKpZJ1Qebm5jA3N8cta/I07SS/6U+CnONAc17cbjd0Oh2KxSKKxSI2Njbw0UcfsWYM8HDCLg0mW15exnvvvQefz4eVlRU4nU7m2PR6PfR6PaTTaXz99dd8YE+aPclWxWIRqVQKlUqFD1b5WgEev8mo1ZjmeJ07dw6XL19GLpfDb3/7WyalUZR/ktcc2UKtVrNmDBFxq9UqKpUKC7nJA+AXSfdTTZycC02yvXv3LjKZzMt+xJcGylxZrVbY7XasrKzg3LlzsFqtPIyWOGNUniSHS6NS3njjDfzJn/wJ7HY7Z7wKhQKSySTzqiiYO6lr5nlABF+fz4dwOIyzZ89ifn6eZ+hsb2/j448/xu7uLmKxGKtmT3rG4bswfoAaDAYmPy8uLsLn86FcLmN/fx/379/H119/jUKhMKJyPOn2kXc4kqI8XS5J5ddut0Ov16PdbrPC+vgFibqTPB4P3nvvPZ6Y7fP5uEMymUzio48+QjQaxR/+8AfkcrlHSPVPa89XwtqiF0LBDLXVklbFrECSpJHaKvE/KpUKyuUystksZ1LkP0MSzg6HA6FQCMFgkAdE0iIgHZp6vT7CAZkUjEf08oyMnCvyXaDon9q2bTYb7HY7BoMB0uk0isUi2u32M0tev07QbUar1TKvTP5+UxeNHE+74eUEWfn+pDlnNDn7pGdkCHJ9JRpDQM9BpUk5B42GZLrdbr4pUrmW9mahUGCZ9PHb9qRCnu1TKpXQ6XS8V6xWK4xGI7fWEpGVArlnFSebdMj3Bg1pJf9L4oCVSoUFAqctW0WZ8PEBjpTFI+4McRrlz01ZUhpzQY0+pK5N1BIaE0KXhmKxiFKpNOKnn8Wer6y0RLVrnU7HYlNqtZojtmmHvN5IB5JarUYul8O9e/ewtbWFWCyGer3OqohErDp37hwuXbqE5eVl/PCHP+S5OHJWN2libG1tjaTAJ2VDyQNaWvw0hoGe12AwoNFojKgXjwdAAGA0GnHlyhWO+oPBILa3t5HJZJhjI/9bJxVybhSVlIhXRgdKp9NBu91+ZrkCee2abkxGoxErKyv48Y9/DIPBgP39fbTbbdy5c4fX1UmU7B8vX8vJ0SRHsLOzg08++QTZbJbHLHg8HhiNRrz11lt45513EA6H4fF4MBwOWdDsgw8+wMbGBosLEq9m0i9gtLboYrW6uoqf//znPNVYr9fjzp07SCaT+PTTT/H111/z7Xjau5QIZCOdTsezui5cuIC5uTnY7XYAQDabZTE44sac9JL104C4ilSSpYzmYDCA2+3mgatUYfnhD3+IVCqFzz77jC8Lw+EQDocDDocDKysr+OlPfwqn04lz584xxYLUxWmkw9dff82ZGPlreVa8stKSvFtJzpGZlSAGeLg4iIBKhKl0Oo1cLodyucwTZOW3AJpSTNoWarWaI2NK+5XLZUSjUeTz+ZGDbdIgJ+nSeiGyGWUjnsSKp58nQtn8/DxcLheThmu12ghJcRIg3zv0QZlM4rRQqvdpf58ctH6IV+J2u7G0tAQA2NvbQ7FYRDqdRjabnQhyp5yLp1KpMBgMUK/XmbBaKpWYTG80GuFyuTA/P4+zZ8/yuBDKQqTTaWxtbeHWrVssLjgJNnga0CFNujl+vx/nz5/ntnsAyGQyLBYYjUYBPBzmOunP/7Sgi4TRaITNZuNuPuoarNfrnOkl7tW0jLagjj6SBSkUCrw/qLRI3DI6l+j/0/lkNBrhdruxuLiIK1euMA9Np9Ox36KuwGQyiWg0ynQCeg0nJpAhPK6XXI5xIuekLwQCvRn0plUqFbTbbfT7fXg8Hly6dAkOhwN6vZ4XAJVHtFotLly4gLW1NdhsNj7A1Go1+v0+YrEYisUivvnmG3zyySc8rEv+tycZ1LJOk8K73S4SiQQ7YloztGkcDgfC4TDOnTuHYDCIWq2Gzc1NbG9vY29vD4VCgX/vSQetm36/z1NgyVlSadJiscBqtWI4HCKfzz8Vd4H+n0Kh4DlNa2trOH36NHw+H5RKJY+EIG7IpKXLx4nSpI0CPJjmrNVq8cMf/hCLi4s4e/YsvF4vVCoVKpUKcrkcvvnmG8TjcR6UOWkq2UdBvm+sVitOnTqF+fl5lnMgmfmNjQ3cvHkTqVRqplqtx2G1WrG2tob5+Xn4/X7Y7XbusN3d3cW3336LZDI5Mil9mmxEl4F79+6hVCpxVoou5U6nE2+88Qbm5uag0WhQLpeZz0iz4Obm5uDxeGAwGLicVK/XeZ198cUX7J9fRiPGa5n6NU4omhaHIQcFMvV6HdVqdSSQMRqNmJubw+LiIpOpAPAwSL/fD6/XC0l6KOym1WohSRLS6TSLoX388cdMyAIm47B+Emg9yHkbdrudB9QBD4NhykpotVqEw2EsLS3h7Nmz8Pl82NraQjabxc7ODvb29iauvk9DHqnTSh7IUFeWxWJBq9ViOzzJCcgPIro1ulwudkTvvPMOr696vY7bt2+zwvSz8JReJ+RdE0T8pm4vs9nMHBqTyYQf/vCHuHjxIvMems0mSqUSUqkUvvnmGxweHiKRSKBarXIWdVpA2VybzYbFxUUe4zAcDrG/v88l72+//Zbb7icpkH0ZoHVksViwvr6OUCjEQq7xeBzlchk7Ozv49ttvmUA+Lfah56B1Uq/Xcf/+feRyOeTzeXQ6HeZokqBds9lEKBRCs9nkblyr1QqLxQKDwQCn08nZUvlwzXv37uGjjz7igZO0b1/E1xxrIEMOtd1uo1QqMcHncf3okw5JejAYkjo/SIGVOgaI4ErlAiqRUBeJnETV6/WQzWbRbDZxcHDAjmdaRJgAjKRnSYiKpPIVCgUvdtJ2MBgMmJ+fx5tvvgmPx8Mqrtvb24hEIjzaQE70POmg5ycxt1KphFKpNDIR3OfzYXV1FUajkVuwn+QAKLulUqlgs9mg1WpZi8hkMiGXy3GqnHQhKEN4kvciBfhykji1SxPXyu/34+233+YBrFqtFnNzc7BarVCr1Wi328hms7h37x4LDGYymZFSwTSAAlIK6oLBIJaWluB2uzEcDtFoNBCJRJBMJvl2TP5oWmzwNKBsHmXG3W43k8Db7fbI3Dc5ARqYDN/ytKD3fTgcjjST5PN5WK1WLvUT35Wy5hTIEI2EhvuSnEO73cb+/j6y2SyPVaFxFy9jv71yZV85KZNedLVaRSwW449GozEiZDbpoOfsdDrY2tpCKpXC/v4+a1Z4PB6YzWYeNXAUaEFtb2+jUCjg888/x/b29oiQ3uN4JJOGcT6R1WrFysoKR/SUgVGr1QiFQpifn8elS5fwb/7Nv4FCoUAqlUIikcAf/vAH3Lp1i9PlxKGYBNBr7XQ6SKVSUKvViMfj0Gg08Hq9sNvtWFtbg0KhwM7ODisgP44fJefBGI1GrK6uwmKx8GEejUaxs7ODw8NDfP3116jX63zzmoShmuRsm80mkxMzmQy3rJ8/fx6Li4sjnTpmsxkajYazpFtbW/jrv/5rpNNp3Lx5E5VKhdfbNICybaR8bLfbceHCBbzzzjswGo3o9XrsU0g8sFwuT9SeeRmg84l8j8Ph4LEdwAPxuxs3buDOnTu4f/8+22gS9snzgC6OlUoFw+EQsVgM+/v7PK5ArvprNpsfqazIM8HNZpOzWZ999hn29/e5hE0Xppdhw1eyY+lB+v0+t1lVKhW43W4AGCEtykXcpmVRyDtrOp0OWq0W0uk0Dg8PAYC7k3Q63cibTkEf2UVuu/39feTzeWQyGW4ppuh50oMYAPy8FN2rVCouAfj9fs5UUTmJpqcCD25MRKAuFApcyptU0BrodDrI5/MwGo2oVquw2WzQaDRwuVxoNBpYWloaaVmXrzsATLTX6/Ww2+3Q6XScDc1ms0gmk8hmsyiXy1z6nCSQnbrdLmewLBYL3xhpVpR8j9DIikwmwwNri8UiOp3OVKn3joP8DbWpU1aq0WigVCqhUCjwGALg5JcUXwVonVB7ukaj4U7BcrnMml/TSIV4HKgFO5PJIBKJcIZXo9HAaDRytyB9ncri9HMkHBiJRFAsFnmsSrVafemZ8pceyMiDmHq9jmKxiLt376JSqcBms404F+AhX2baQIFJp9NBtVrF73//e9y5cweXL1/GG2+8AbfbjZWVlZHOHPogBxOLxfDNN98gk8ngH//xH1EoFFjfg4TTJhXywG0wGKBUKrGaaKVSgVarxfnz57mOX6lUeGIqtfj1ej3s7Owgl8vhww8/RDqdxt7eHkqlEoCnl+0/KZDvCUmSUKvV8Nlnn2FnZwdarRb1eh12u52l+K9cucI17cf9HnIonU6Hy0hff/01Z0Kp/EZdCZS9mITAmNYPHSzb29tcQpqfn+csDICRNdZsNvHll1/i2rVriMVi2Nzc5OenmTLTBDp09Xo9LBYLTyCWT2++f/8+9vf3Ua1Wp+Zi9DygIIa4aGq1mv3R9vY27t+/z/SAabUT7SvgYTXg97//Pa5du4YzZ85gdXWVux31ej0CgQAMBgOCwSCLUdL8v/v37yOVSuH3v/890uk0YrEYKpXKY4f3nsjSkpyk2m63kc/nodPpWLhNLhs+7dEt1fGJ0+L1euHz+TAcDuF2u6HRaB6Zek3S6lRPTKVS3K1EGayTPt36aTFej6UMFJF+abAfKdySrLrBYEC5XEYul+NWvkwmg3q9jl6vN9H2kXNlisUihsMhMpkMZ1Wojk9y/HLVWspuAuCBbI1GA/1+H61WC9lsFrFYDJlMBvl8fiQdPGnOWc6Toe6jcrmMer3+SCBD31OtVjk7SoNW5VyzSXr+5wENC6VyXLFYRKVS4X0j8BCDwYBtRb7ppA9SfRkYrw7QqApSADebzWi328zlJE0qAOxvcrkcn12RSIRJw41Gg7mOL3OvvZKMjNwI5XIZn3/+OUwmEzY3N+FwOJjEmEqlAGAqb0LAQ94HAHaw3W4Xu7u7sFgs3PoqHygJPCiVtFotlEolFuaq1WojN+ZpsRfZJ5lMolgswmq1otlswmKxcEnJ4XDwLbLdbmN7exuHh4colUo81Tgej3NwPA1cKxKnSiaTyOfz+O1vf4vPP/8cy8vLWFxchMPhwPz8PADwIUQlNcrSlEolxONx1Ot15qLFYjGUy2WePSS306TZjHxNr9fD/fv3kU6nUS6XcXBwAKvVimAwyPX+TqeDb7/9FvF4HFtbW9ja2mJxwWmZbP04kG+h8R+9Xg83b94cKVun0+mR6dbTaIenAZ1ZVA5RKpWIRqOsMURt+ZOStXxR0DO2Wi32u+l0mkvWNNhWq9XynDNSAy6Xy0gkEuxzqMuLzvqXbb9XmpGhm/bh4SFP7qV5SyS5DmCqFwY9F7W0NptNJJNJaLVaWCyWEW4QfS/xRKg9dFzTYVpsJX+OarWKarWKvb09aDQauN1u9Ho9WCwWLC4uQqvVotlsot1uIx6P4+bNmygWi4hGo7yeAEwF10peGqpUKpAkCZ1OBzqdjsdbhEIhGAwGSJKEUqnEfJpGo8Et/dlsFtvb22g0Gkgmk9zOPZ6xmuT1JEkPZA6I66NUKtFut+H1etHv95ko3mq1cPv2bezv7yOZTCKRSHDWRp6NmqYMsdxnVKtV1Ot1NJtNVmylfVOtVp9ZKXoaQRnNVqvFmVC6YFWrVc7GzIKN5GVuylhms1nkcjkuwRFvUaVS8fgB8j2tVotV1enn5VnSl41XRs+Xv9lEAKpWq1yPJsLvrIAcpfyNlesQyO1F9plF55LNZtHv92E0GrG7u8tRv0ql4ja+ZDLJB/NROirTAHmZaDAYIBqNolqtYn9/H9vb25AkiYMTucIm8Ufy+TwT72i/TUPGiiAn8fb7fc5A7e3tYXt7mzMy/X6flX7r9fojAQzweNHOSYecs0j6IJR9IR80jRzFZ8E4Xy8ajeK3v/0thsMhcxKJGyP/mVnAk0rOlL1qNpvcoUvK/eMK5MdhK+mozWs0Gl/Kzh5XZZUb52Xfnuv1+rGtMKvV+sz2kQt4yUlVcsiDm5d9ay6Xy8dmH7PZ/NT2kdviaR2rfIO9rExVtVo9NvtYLJanss/4mnmewE2+517ETpVK5USvH7la+JOe81VmgI9z/ZhMpheyDzAqlngcqNVqx/KHnsU243icDzqO7OVx2QZ4PvvI1824vMrjzrBxm72o7Y6yz7EJJow70VmJaB+Hp2Vsz6qNnva5Z20tvciNcNptRLfqo55z2m3wNHjZ3SKzgFnzM0+D8f0mL8s+zk+9atsdSyBDDzFp7bCvAmJTPB6P2xBP+zPTDjl/40WeedrtNU38sZcN4XeeHsQBERiFfO2MBzFP+3OvCtMhYSkwVRDO9vEQdhEQEDgJOGm+6EiOjICAgICAgIDAScZk96kKCAgICAgIzDREICMgICAgICAwsRCBjICAgICAgMDEQgQyAgICAgICAhMLEcgICAgICAgITCxEICMgICAgICAwsRCBjICAgICAgMDEQgQyAgICAgICAhMLEcgICAgICAgITCxEICMgICAgICAwsRCBjICAgICAgMDE4lgDGUmS/rMkSdckSWpLkvQ/jvNvTwIkSXJIkvR/JUmqS5IUkSTp37/u13SSINbP0RD2ORpifx0NYZ8nQ+yto/G67XPc068TAP4bgF8A0B/z354E/HcAHQBeAJcA/EaSpJvD4fDua31VJwdi/RwNYZ+jIfbX0RD2eTLE3joar9U+x5qRGQ6Hfz0cDv8GQP44/+4kQJIkI4D/B8D/NxwOa8Ph8BMAvwLw/77eV3ZyINbP0RD2eTLE/joawj5HQ+yto/G67SM4MicHpwH0hsPhluxrNwGcfU2vR0BgmiD219EQ9hGYWIhA5uTABKAy9rUyAPNreC0CAtMGsb+OhrCPwMRCBDInBzUAlrGvWQBUX8NrERCYNoj9dTSEfQQmFiKQOTnYAqCSJGlF9rWLAATRTkDgxSH219EQ9hGYWBx3+7VKkiQdACUApSRJOkmSjrtz6kRiOBzWAfw1gP8qSZJRkqR3APwSwP96va/s5ECsn6Mh7PNkiP11NIR9jobYW0fjddvnuDMyfwmgCeC/APgP//Lff3nMr+Ek4z/hQetaBsD/BvAfRevjCMT6ORrCPkdD7K+jIezzZIi9dTReq32k4XB4XH9LQEBAQEBAQOClQnBkBAQEBAQEBCYWIpAREBAQEBAQmFiIQEZAQEBAQEBgYiECGQEBAQEBAYGJhQhkBAQEBAQEBCYWR/Z5GwyGiWtpajQa0nH9LbPZPHH2qVarx2YfvV4/cfZpNpvHZh+j0Thx9qnX68dmH7vdPnH2KRaLx2Yf4Z+fDGGbozFtvkdkZAQEBAQEBAQmFkKZUGAqMBgMRv4tSRIk6dguOAITBtLPGv+sUDy429H6mRWdreFw+IgtAPAeEvtJ4CRDBDICU4PHOWABgXE87tCWf5avnVkIZuTP/rhnJXuM20ZA4KTgxAQyg8FgZCMpFAq+Hc0a5LaQZxroViRJEhQKhXAq/wKFQgGdTgeFQsHrp9frodvtvuZXdrygNUPrhvbQrK4TCkJoTdBeUqlUUKlUvI8UCgWUSiUAoNPpoN/vo9/vYzAYjGQkpiWgGbcL+RSDwTBil8FggG63i8FggE6n88jPCDyAfH1RsCf/EHhgo36//8hlk/bdi9rpRAQy5Hjln2lDzeJCoDd9MBig3+/z1+mNpwBPngafVZDjICdMzrbRaMxsIEMHMa2VWbxJjz+v3JGq1WpoNBoolUqo1WoolUpoNBoAQK1WQ6vV4u9/3O+aZMizK/LATqFQwGg0Qq/XQ6FQQKVSodvtotVqodvtot/vo9fr8Rqb5QD5cZD7bHlgLGz0qF8ikH96GTY6EYGMUqmEwWCAUqnkTdVqtdihTMtN6EkYT2/rdDpotVqo1WpotVr+vsFggGaziW63i16vh16v91pe70nCcDiEwWDAhQsXYDab2enu7+9jf3//kazWNIICFbVaDYVCAa1WC61Wi06ng3q9PvXP/ziQ35AH/zabDRqNBi6XCx6PB2q1mgNgi8UCADg8PESpVEIymUQqlRr5fdMAeSCi1Wqh0+ng9/thMpkQDodhtVqh0Wig0WjQbDZRLpfRarWQSCTQaDSQyWRQq9UeuWTNMugwtlgsUKvV6Ha77IfoMjWLAQ3tP8p6OhwOvjAAQLfbRbVaHbk0PC9eayBDm0qj0cDj8cBgMMBqtUKn0yEajSIajfJNAJjuxSA/cK1WK3w+H+x2O4LBICRJ4lLJ/v4+isUiyuUyKpXKTKd76dntdjv+7M/+DHNzc3yj/ru/+zvE43H0ej2+iU97qVKv10Or1cLv98Pn8yGTyWBzc5PLA7OUmSHnqFQqObBbXFyE0+nE+vo61tbWYDQaYbfbYTAY4PF4AACffvopDg4O8OmnnyKXy40c2JNsO3rtg8EAg8EAWq0WNpsNLpcLP//5z+Hz+XD58mUEAgEYjUYYjUaUy2Ukk0mUSiXcvHkTuVwOn376KQ4PD1Gr1VCv12c6aw48zJKr1WrMz8/D5XIhl8shl8uh1WqhVCrNpH+Wn0t0IV9dXYXP5+PAJpVK4e7du2i322i1WiOl3GfFicnIOJ1OmM1muN1uGAwGVKtVvhFNa+ZBvsAptatWqxEIBDA/Pw+bzYZQKDQSyACAxWJBMpnkGnaz2Xydj/FaQIGJWq2GXq+HzWaD3W5nx6LRaGYi/S3PPBgMBlgsFvh8PszPz0OSJBweHkKSJM5uArPhUCVJgkqlglarZZ8yPz8Pj8eDcDgMv98PvV4Pq9XKh/pwOITH40G73YbD4YDRaESn00Gj0Zj4IHCce2gymTjgDQaDfHGirIIkSdBoNDAajRgOhwgEAjAYDIhEIuj1eshkMhwgz2LGD8Ajma1QKAS/3w+lUskHs3x/ziKIv6jX6xEIBDA3NzdCj9jd3cVwOES73X6hv/PaMzL9fh96vR5vvvkmAoEAFhYWOL2ZSCRQr9fZCU/TwSQnZJLDPX/+PPx+P9544w1cunSJAzuFQsGlpEQigXK5jC+++AJfffUVcrkcdnd38f9v7z2aG8uyNMEP4gF4AB60JEAAJN3pTqeLcBWRITIiIzKrxqrSyqa2PTaL2c7Y/IBe9Gqs/8Lsx6wXs6tcZE9VV6aVdcoK4RGuBbWC1lqrWXif4w8Id4YLOskHvs+M5oqg4x3ce+6553znO8PhcKbscxjIeYqiCJ/Ph7m5OQQCAfh8PgwGA75xCoLAzmSWMRqNoNfrsby8jFgsxuvn+++/RzqdRqlUQiaTQb/fh06nm+k1Ir8Fms1meL1efPXVVwgEArh58ybm5uZgtVphtVo52KGswmg0wqVLl7CwsIByuYxEIoFCoYDd3V2+LSrVdmQXURQhiiJWV1fx61//Gj6fD7dv34bNZuOyfrFYRKlU4kyDzWbDl19+ieFwiEAggIODA3zzzTf49ttv0W63OTOsVNu8DejsEgQBPp8PLpcL//iP/4gbN27g97//PX7/+98jk8kgk8mwbz4rkBOeqXTp8Xjw61//Grdu3eLg7uuvv8bOzg6KxSKazSb6/f5bn2GnIiOj1+vhdDrh9Xrh8/ngcDhgtVqh1+uZNDWLhxFlYsxmM0RRhNfrRSgUQjgcxvz8PCwWC9xuNzQaDZfYdDodHA4H9vf34XK50Ol0zpQDIRAh3Gw2w2q1wmQyMYHzrIH2hiRJcLvd8Pl8CAaDcLvdXG46K6BATRRF2Gw2OJ1OzM3N8VcwGIQgCEwMp6CYunKIn+ZwOGCz2bh8Mgv+ZzweQ6fTcSYqHA7D6/XC4/Fw9mkwGKDT6aBYLEIQBJhMJpjNZgQCAeh0OgQCAYzHY2xvb8NqtQLAmQxkCOS/7XY7/H4/wuEwHA4HjEYj9PoXx+tZsg+da5Qtdzqd8Hg88Pv9CAaDXF2g4PkoLlcnFshQhkWn08FiscDv9yMQCPyI9CtvqZ0FUIaAyh8ulws///nP4ff7cfPmTYRCIbjdbtjtduj1evR6vYlo3ul0wmq1YmVlBYPBAI8ePcL6+jqnd8/ChqH1YLFYsLCwgEgkAkmSYDQa0Wq1UCwWUavV0Ol0MBgMZtYe8lKRXq9HKBTiOrQoihzkNZtNbqed1UCPeFDygOWDDz6A0+nE6uoqJEmCIAio1+tMlm+1WqhUKuj1eigWiwCAlZUV+Hw+uN1uXLlyBUajkdPfSlxHFISRfWw2G+bn57GwsIBz587BZrNxxhd4fjDv7OzgL3/5CxqNBnK5HBwOB7788kt4PB4Eg0EEg0H0+30IgoDt7W388Y9/RK/XO+EnPT7IyfU2mw2rq6uc6aNOr1qtNlHyV+LaeRNM0yR8Ph+Wl5fh8/nw+eefw+v1IhKJYDgcIpFIIJFIYG1tDcVikcnj72KjE83IkAOmWqzNZmNWMwU5s7gAqBRgMBggSRIuXLiASCTCG0IewJETIjtQvdHn8yEajSKbzXLN8azUqiltaTQa4Xa7uRtFp9Oh2+2i2Wyi2+1ymeksQKvVwmazwe12czaTuk/kGZlZuhTIQdkVs9kMv9+PxcVF3LhxA5IkIRgMwmAwoNvtot1uo9VqodPpoF6vI51Oo9VqIZ1OQ6PRcCbLYrEgGAwin89zK7KSQfYxmUxwu91wuVxwuVxcYqPM1Hg8Rrlcxvb2NnK5HDY3N+F2uxEMBjE/P49oNMoUgEqlglarxfaZ1bUlx/SBbTQaEQgEMDc3B5PJxLxF8j/0vWcBlJwgDlY0GkUoFML169fh9XohiiLG4zEqlQri8ThyuRza7TZTR96ldHsigQxtGMrEEEnK7XajWq3yTanZbKLX683EQpBrNoiiCL/fj5WVFebE+P1+OJ3OCSHAbreLWq2GwWDAN55AIACLxQKj0QhJkmA2mzlVTs5EqbfHnwKtG6vVCovFgqWlJdy6dQs+nw9arRb1eh3r6+t49OgR4vE4614As+tM6ICi0iPV4ykjI0kSGo0GH1azDMo+9Ho9VCoVbG5uwmAwYH19HQBYuqBWq6FWq3HGgW7RRqMRc3NzLAdx8+ZNDAYD3LlzB5VKBeVymev4SgGtf7rsOJ1ORKNR+P1+mEwm6PV6DIdDdLtdpNNp1Go1bG9vI5VKcaACAN988w22t7dhMpnYJ124cAG1Wg1+vx+1Wg3lchmDwUBR9nlTyANmt9uNcDiM1dVVhEIh9Pt9xONxpFIp5HK5M9nVRZWVc+fO4fbt23zRJMHS0WiEbDaLhw8fYnd3F9VqFd1u951904kEMpRlIKb3/Pw8/H4/3G43crkcCoUCBzR0gCt9QVCAIQgCLBYLwuEwPv30U06Bu1wuPoiA5zfsXq+HfD6PXq+HarXKka7ZbOZsDv2ehKtmNYgBJgPgQCCAxcVF3Lx5k2+V9XodGxsbuHv3LnK5HJeVZrWcIi8byL+IB2E2m1nkbFrNddZAa344HKLX66FUKmF7exuj0QiVSgWDwYBb80ulEhMMi8UikxIlScLFixdhNpuxurqKS5cuodFoIBQKwWAwoFarMZdGKXtsulvJ7XYjFotxIENZzHa7jb29PaRSKQ5kWq0WWq0W2u02vv32W1itVvh8PvT7fZw7dw4XLlxAoVCA3++HVqtFrVZjHzRrlAAC7TeDwQCv14twOIxLly5hbm4OW1tbrD+Uz+cnfPlZgEajgc/nw+XLl7G8vMy+2eFwQK/X84UrnU7j4cOHyOVyqFarGI1GMBgMyiktyW8HgiDA6XTi/PnziEQiMJvN0Gq1qFar/IDdbneirKJEyB0JAF7858+fx+LiIhwOB98gK5UKOp0OrFYrJElCs9lEs9lErVbDxsYGhsMhzGYzpy/p+8xmM/r9Pnq93kzeusmGRqMROp0OkUgEly9fxvnz52G32yEIAhqNBmq1Gtux2+0qPvh9XdAzUiaz0Wig3W7zV7fbfefU7WkHPVej0UA2m2UbiKKIQCAAg8HAgdzW1hbfAqeHRMp/FgDmQbRaLUUfzgaDAYIgQJIkbqag56nX66jX69jZ2cH29jaSySRfIsk+lLXK5XJIJBJM/nU4HFheXobNZkOxWJxQ/50lyDO7giDAbrdjcXERkUgEVquV+Ve5XG4ioHvZmpolkF2ItOvz+XDu3DmEQiFuwqB1Vi6X0Ww2US6XmcN4VD7pWAMZulGbzWaYzWYsLi7iF7/4Bfx+P1wuF7RaLbLZLDY2NpBOp1Gv17k9WYkLQS5yR1Lo58+fx2effYalpSV8+umnAIBCoYB8Po/NzU0Ui0UsLS1xC2ihUEAqlcI///M/c4mg0WggHA4jFArB6/XC7XYDAKskKtFWh4EOHKvVClEU8eGHH+If/uEfmHzY7XaRSCSQyWSYSKbT6WY2EzMNchSlUgnJZBKBQADVapXLIdVqdeYFAenZyuUySqUS+41QKDRBohdFEX/4wx84MCHRO3LE1P4p56T5/X7uGFTaAU0kSsrO+f1+RCIReDweVu/N5XLIZrP485//jO+//54DG+KBjMdjJs5vbW2h1WohEonAYDAgHA7jV7/6Ffb29rC9vY1erzdBsp8VXyS/hNO6+vnPf465uTl4PB4YjUZkMhmsra0hk8mg3W5z586s2OBloPVlMplgMpmwvLyMzz//HHa7HW63m/cUld0ymQx2d3eRz+fR7XaVPaLAaDTCZrPBbrfD5XJBkiQuq1SrVRSLRRahApQdzdIH5XA4YDabmfXvcrmg0+nQ6/VYPXNvbw/FYpE/fCJCkfJhu91GpVJBsViEx+Phziez2cydKUpztK8Dcoh0mySyIol1tdttZLNZpNNptNvtiZv2WQJl5agrh0pNs7gmpiEvLVHZ2mKxcEu6x+OB0+nk1mqr1cqHtfz1csdKFxF5yVdpkHfYUCu11WqF0WjkZ2s0GqjX65zFovLZy35Ws9lEvV5nfS/guUCnzWaDyWSCIAgz28FE7flWqxUul4ulQnq9HpczC4UC84qUfG69LqhkabFYYLfbYbPZYLFYOBMDvMjmlUolPuuo1f+oMlYnEsj4fD5cuHABly5dwvnz5yEIAgqFAiqVCh49eoTvv/8e5XJZ0dEsORDiK3zyySdMTr116xaA5+TDZDKJf/qnf8LBwQG2t7dRKpUQiUQwPz8Pj8eDpaUldrgajQZ7e3vodDqw2+24cOECrFYrYrEYTCYTDg4OZorsKw9kDQYDVldXsbi4iCtXriAajWIwGKBUKiEej+O//bf/hv39faTT6TMjDAhMlkSoE6fT6UyQf+US+7NqF3kJV6PRsOR+NBrFz372MwSDQXas0WgUCwsL3FUy/VrK5A2HQ9ZUKZfLiitzUyBGlwDKYM7Pz7O0Q61Ww+7uLpLJJB/C0+KacuHAYrGIdruNg4MDHBwcQK/XIxgMotfrwe128/qjy4SS7PUqkB2pvX9lZQU3btzA7du3odFosL+/j1KphDt37uDu3buoVqt8ds3C878MdMbo9XqYTCasrKwgEolgeXkZHo+HNXT6/T5KpRJqtRq++eYbJvk2Gg0AR+eTji2QkR9KxPh2OBywWCwAgGazyenwWq2maI7DtL6HnBjm9/vhcDjQ6XRQLpdRqVSQTCZxcHDA0apOp+OBYx6Phwf/Ufah0WhwN5der+cIeFaEu+Sg9KzZbIbL5UIgEGAWPBERiReRTqfPrEAg8MLhTn/NImfhVaBsJrVd03qRJIntQXwRedaOfI28JEnETro9KtmGxEs0GAwwmUwAwD6GuGW9Xo8zeNO+l/5MUvK1Wg3VahVWqxU2m43pAqIoTgjBKR1yX67RaGC1WuH3++HxeHhIbaVSQS6XQ6lUOnP8PCq1ORwOeL1eSJLE42GA52uMeJ7FYhH5fB7NZpMvBUdV/j+WFScnBGm1WiwuLuKzzz5DJBLhdr4nT57g4OAAiUSCmcxKXgyj0QiCIDCH5erVq7h58yacTicGgwESiQS+/vprxONxrK+vI5vNotfrwWg0otFoIB6Po1Ao8ODMcrnM9iB9ECotWSwWJksr2dkS5Nksu92ODz74AD6fD1999RWWlpb4dl2r1fDs2TPs7e0hHo8jm80ead1VKZDL8ouiCACsl0Ilg1nJ0r0KdACTpsfVq1fxd3/3d1yCHA6H3H6dy+Wwt7eHfD7P3CGDwQBRFOFyueD3+2G1Wn/U8aNk+8mDWgATCr5PnjzhsjYdMNPPSgcTBXVbW1v4wx/+wBpYPp8PH3zwAdxuNzqdDprN5rE/41FjOqNlMplw9epV/OpXv4Lb7Ua320Uul8Of/vQnxONx7O3t8XMrea38FMguNFrHYrFwppxI4LSGWq0W7t69i1QqhY2NDaRSKeYPHaWNjjV0JgKe0+lELBaD1+uFXq/HYDBAOp1GMplEpVJBu92eGE+gRJCDpBtLIBBAJBIB8NzpVioVbGxsIJlMIpfLoVKpTIi6tdtt7uCi4IWyLkQcpsPeYDBMEKJnIZiRC04tLi4iFAphaWkJi4uLfKMkbgzZT052nmVHIof8s9br9RAEAQBYe4g4VrMeyJBzpRtzJBLBxYsXuaNEPmC1Xq+jXC5PDIOkmyVxSKjLCXgRyCgd8o5G0ttptVrIZDIsDEglIXreaV9C5bh8Po/d3V243W6WlJibmwOAiY6od1VsPWnQ81N7/tzcHJaXlyEIAmezdnZ2WBOl1+udqUYDCmYoWy5J0kQnYL/f54oDyaq8Dx/93gMZWsw6nQ7hcBgulwvnzp3D/Pw8j/ImuWKKaJV++5FHrETupamyJMZFmZh8Ps8dEXLCIYCJwIRKSTqdjoWsiBBMAkwkRqVk2xEorQ+8GANPnRflchkHBwd4+vQpvv76axafmgVy+NuAHIPD4UAgEOAZOETmlB9gs2ab6db8aDSKmzdv4ty5cywWSRL8+/v7rFYbj8c5U0VBDNX7LRYL615Q5qLdbisyGKQLAZFUSa9Drj9EA2np+w8rUdPfdzodFi0dj8cwGo2IRCIwGo1wOp0QRRG9Xo9LUUqym/zZjUYjjEYjrl+/jmg0itXVVXg8HhSLRaytreHg4IC5eWcpGzxdcqOSLj0/7ZtyuYx4PI79/X3U6/VXZvzeFceSkSGRrlAohFgshqWlJYTDYZTLZezt7eHg4ABra2v8sEpfDMRJ0Ol0rFwsSRL0ej1nERKJBDY2NlglUx7I0MKgn/OyQIbIZN1uF6lUigWYlGw3OeQEO+q4sFgssFqtODg4wObmJh4/foxvvvmGuyiUeNAcBShQcTqdCAaDkCQJwIsAeJal0ukzNxqNMBgMiEajuHHjBvx+P0RR5H00Go1wcHCAra0tbG1tIZFIsN0oU0xZTznPgwi/r+rkUQKoPdZisfyIv0CEcOIAvc4aoXZsUv4dj8cwGAyYn59nLpsoihiNRtzZpLS1R5dvEh69du0arl+/jkuXLsHr9bI6LQUyhUKBx+qcFcj3A3EZKaAZDodoNBo8juDg4ACNRoNjAUUFMvSg5CB8Ph9PdSbGfCKRQDqdRqPRmJkUON2CKFVtNps5g0IEO3Igr+Mc5SREo9HIJabBYMCt2dQpQN+vVEwTpUnEi1KWtEFyuRyrs8pvy0p+9rcF3YQos6DRvJidA7wIrGcR8tS/1WqF3W7nJgKN5vnUeBpHkEqlcHBwgEqlwq+jQHlubg5+vx92u51bk9vtNjchEOFeaWuM1gW1oVssFt5HpBfTarXeSiaeLpzTQqdKL8VR4CsIAs+kCgQCzM0rFArIZDKciTkrc6YI8izo3Nwc80CJ6DsajdBqtZBKpZDJZFCr1SYIvu8D7y2QkZeUTCYTbDYbLly4gJs3b3KbXiqVwr1795BIJFAqlSZmUyjJWRDkhzAFHS6XC263eyL4IMchP2AOe17ixRiNRnbWOp1uwtHW63XF16MJlM2iw8nn88Hn83FdOpfLYX19nQmK/X5fsaKJ7wJ5CyR1pBiNRj6o6KY9i2rPwKSPsdlsE+3FpGPRbreRSCRQLBbx8OFDPHr0CLlcbuL1ROKMRCIIBoOwWCysEE2HVq1WU1zGky5UBoMBgUAACwsLcDqd3HpNQnjUKUpt168LCmRofVETAu1FJdlKDiq76fV6xGIxBINBrK6u4vLly8jn89je3sazZ8/w3XffcVZKnk2fZcjPK4vFgmvXriEUCmFxcRF+v58zUuVymTNWmUwG5XKZ/fr7wHsvLVHnicPhgMPhgN1uh0aj4bQTta29SWpTCaCbEGVQ5BkZko0fDocTnQQvA5GGSdzL4XDA5XJBr9ej2+3yF03nnQX7jcdjbisnsjS1z7ZaLW7lo+DtLN2GCPJnpmwd8TyopESZv1kuLQHg0pLZbIbJZGLSPA1DLBaLyOVyLI1OvA3ao6IosmieKIrMqSFByjfJnp420GdO60N+kNDzUwblbfeSXP2XOsOUuC/lhzR1AHo8Hvj9fu4KbTabSKfTyOfzPLvrrHDz6OItiiLsdjt8Ph9LHJjNZi7j9vt9NJtNFAoFlMvlH43OeR92eq8ZmfH4+TiC69evs5BQNBrlG/WjR4/w9ddfc+qJoOQFQYEHEQfdbjePMNfpdKjX60gmk8jn85yZedXthW4GoijiwoUL8Pl8+PDDD/HBBx+g1Wohm80in89zx84s3Lzl3Sc0j2plZQVzc3Mol8soFot4/PgxvvvuOx7ZcBazMcCLtUYDIq1WK6xWK5rNJmcT6MYob4mcJVDWwel0IhAIwOVywWq1Mj+jUCiwzMHTp09ZNBJ4Xo6y2+2IRCK4fv06FhcX4Xa7eZJ6PB7nlmSlH1bTnz2Rcp1OJw+ipfQ/ff/06+WcPeAFN4uCRhoTQiXffr+vqDVHz2Y0GiFJEsLhMD7//HMWJx0Oh1hbW8Pvfvc77O/vI5/Pc1npLHBj6Dyan5/H3/3d38Hv9+Ojjz7i8pter2e5h/39ffzwww8c8BEP9H2VHN/LT5XzFQRBgMfjgc/ngyRJEEWR1f5KpRLfkmbhECbIy2N0S6aDZDweT9zwfurWQjZ0Op3weDxwuVwTpSXKxsxaRstgMMBms7GcvCiK6Ha7nPKXp3Rn5ZnfFHJuAqX0qduG2q7la21W7SQvr8lbX6lzolAoIJvNol6vTwyipXEFTqcTLpcLNpttwn71ep1HXigtuzCNaV9DJSf5unmdbiX56+UyEKPRiIfc0rpTYqmbssGU/fZ6vfB6vQCei7aWSiWkUimUSqUfEemV9qxvC7PZjLm5OZ7153Q6YTAYADzXGaIBvjTnjWz0PnHkGRm6UZPAVDAYxEcffYRYLIZAIABBEBCPx/GHP/wB+/v7qNVq3BGg9G4lAtlA3rpJ6Vaz2Qyfz4dUKjUx70R+0JDTEQQBVqsVwWAQH3/8Mebn5zE3NweTyYROp4NMJsMbSqmp75eBWok/+OADhMNhGAwG9Ho9rK+vY2triyWuZ4Hc/LagNSLXKjKbzTAajWg2m8w7m5U1cRiorEFZgNFoxEFvNpvFvXv3sLOzwxoWoihCkiQsLy/jq6++QjAYRCQSYcVtmjZ/584d7OzscGpcieuMSN+UpaPATKfTsZyBKIowGo0ckEzPmwLAmRq6VQeDQVy6dAnz8/N8qSIpDfLpBCXZTaPRIBgM4osvvsD8/DyWl5dht9vx8OFDpNNp/OlPf8KjR49mpjHlTUAlSKvVyiRfi8UCo9GI8XiMbreL7e1tPHz4EM+ePUM8HmdtovfRqSTHewlk5ClvSZIQiUQQi8W486RcLmN7exv5fJ67bd73gx4XpoORfr+Pfr/PfBgKTkRR5AFr0xHrtA1tNhui0ShisRhsNht0Oh36/T7fGOlnT78HJYJKBSSw5fP5mLOQzWa51ZFKcmchpfsqyLsCjUYjBEGAXq9noS654NusQr7PKBNAWU/KqqRSKSSTSeapGQwGFs67cuUKvF4vHA4HRFHk1DipahcKBcVntEaj0cRUanqWV2Vk5B1d9Cv9nsrmNpsNc3NzcDgc3MRAow5o1hftZSVAXjq02WxYXl6emGydzWbx9OlT7O7uIpPJsO85K5kYOo8oEydJEusSEXl8MBigWCxiZ2cHyWSSBQKB95+xei8cGY1GA4/Hg+vXrzPr22azIZlMolarYX19HYlEgruUZiUTQ5A70kwmA5PJhPPnz2M0GsFoNMJut7PiqE6nw2g0mhAKoluU0+nEwsICotEoR8C9Xg/FYhFbW1v4/vvvsbe3p+gbI4HS90TsjUQiWFpagiRJ3Jq/t7eHjY0NlEqlk367pwLTRHC6XVN5RJ6NUfLaOAx0+FYqFWi1WmSzWWSzWT6Q5ufn8Td/8zdYXV3lAzkQCMDv9yMWiyESiUCSJAiCgPF4jEajgVKphEwmg3g8zuNSlAjKDA+HQxQKBYiiiFKpxJdHURThdDqxuroKSZJw9+7dCaIuaesIggC73Q6DwYBQKAS73Y4bN27gxo0bkCQJlUoF6XQaW1tbrBeilMBPrlfl8Xjg9XqxsrKCy5cvw2w2I5FIoNPp4MGDB9zxdlaCF4LcNzscDvh8PvY1RBIniYL9/X08e/aMdc0I79teRx7I0Bt2uVy4desWwuEwgsEgrFYrfvjhB2xtbbGyJn2/UqL2nwI9OzmPTqeDfD4Po9HIU2UFQeBR5xTNUnmJAjrKsJhMJs7EBINBeL1ensS7tbWFu3fvolwus/NRclaLgjer1YpQKIT5+XksLCxAr9czgXBnZwebm5s8OfUsgxywRqPhzKd8aN9ZKCnRgTIej1GtVtHv93lkBY0FAYBf/vKXPMyPyIrhcJi/RxAECILA2irEqYnH47y3lAq6VBUKBWg0GpRKJXS7Xej1eoiiCIfDgYsXL8JmsyGRSPDwyH6/zx1dFouFs8HXrl3jduRLly6hVqshl8shl8the3ubRU2VBPK3LpcLy8vLuHjxIlZXV9Hv93H37l1kMhk8fPgQDx48wGAwULREyNuAAhmiOdBwSApkxuMxd5Lu7+9jbW2N9xpdtt43jiyQIcdJToFURt1uNxqNBlqtFra2tvD06VMUCoUzIWBGqTbiLfT7fRgMBtjtdgQCAVy8eBGFQoEVf8ke1CoZjUZx7tw5hEIhmEwmjMdj5PN51n+geTGAsm/cdPvTaDTwer04f/48QqEQjEYjBoMBO8parYZ2uz3hTM4q6AIwHr+YiE7kb4PBALfbDZvNxgJVABRzS34T0Dqglup8Po/9/X0Eg0H4/X6YTCaEQiE4HA4moLrdbpbRJ4VsktNPpVIT+kRK046RQ75HGo0GdDodUqkUtre34XQ6EQqFIIoiIpEILBYLcrkcD0Psdrsc6IiiiGg0CqvVilgsxp1hNHTy8ePHrNWjJOFA+Zml1Wrh8/mwtLQEl8vFYoFra2tM7qWA57Q/11GB7EO+2el0IhqNcru1IAgs75DNZpFOp1EsFjkQBo7vXDrSQIaIdA6HA+FwGJcvX4YoinwI/fWvf8WdO3eYdDeroE3c6/WYmFosFtFut3n4WK/Xw9/8zd8gl8vhr3/9KyqVCr9ekiTY7XasrKzgq6++mtBR2drawvr6Op49e4ZEIjHx/ylxg5FjIIn5ixcv4ssvv2S581KphI2NDRwcHCCdTqNWq81kOfJtQGndYrGI8XjMHYBmsxlLS0tcTqCBiUpvIX4Z6LbXbDbRarWwvb2NO3fucMbAarXi6tWrE1kVuXYKDa2tVquo1Wp4/PgxHj58iN3dXb5cKJmHRe+9UCigVCrhwYMHLE4aDodht9tx+/Zt9Ho9hMNhlEoltNtttNttSJLEGirBYJD3qCAIqFarqFar2NjYwG9/+1vk83meX6UEm8kzmkRYXV5exmeffQaDwcBDjH/3u99xGz6NiZmVCsJPQc6lEgQBi4uL+OijjxCNRuF0OqHT6bhTbX19nf30Scy9O/LSkslkYolwYjKTME6lUkGj0eAFMUsOdRqU8ib13VKphHw+D4fDweJdlNZeXFxErVbj11qtVtYxsNlsEEWR7ZbP55HJZCYGcJ12p/E6oCwUqfkajUYWNCsUCkwMPwuZvNeBnFQuV/AlTQsShTtLGI1GaDQayGaz8Pl8yOfzrK1DRF95i3G/32ctp0wmMyHQKde1UjLoeSmbUKlUkMlk4HA4UCwWOSul0+ngcDggCAKPPLFYLPB4PBNriUaiUFaYNLGoxC3fn0q5rJIGk91uh91uR7vdRjqdRiqVQrVaRaPR4AzDWQP5EypFEhePOGUkUFooFJDL5U6sm/RIAhk5oz0cDuPatWuYm5tDoVBAq9XCnTt3eOx7tVrl751lENelXC6j2+1yJurGjRv44IMP4PF48PHHH6PX6+HmzZsTG4W6T8iRNJtNPHz4ELlcDn/+85/x5MkTVKtVdsxKPtTl6V0aweByuSAIAiqVCpLJJL799ltsbW2hXC6rmRgZyA6U3pUPQZSr2866si/w4tmo/bdUKkGn08Hn8+H27duw2+2cDqcOi1KphN3dXZTLZdy9exfFYhF3795FKpVijsgs2IwaCMbjMfb29tBqtZDJZHiW0NLSEiwWCwKBAJciabgfKYgnEgm0Wi0UCgU0m02sra1hbW0NmUwGT548YZtSwKOEIIa6SGOxGMLhMJe079+/j9/85jfI5XITGYZZP7PkoIwVTU63WCzw+XyIRqPwer0soEiZ8jt37uDBgwcnNiX+nQMZeRAjF5kyGAwcrZECLRFelT5U7HVANxJqCy0UCrBarYhGo2g2m9yGTaReOcObXk9tx6Tim8lk+PZDWa1ZA2VhAHCrfqlUYjLnLD7zu0DemdLv9/lAoRv4LIi5vS6IK0OqxolEgnlqtBcpkKHuv3Q6jVKphIODAxbppPLILKy16cxIu93mkQ3JZBKdTgcOhwO9Xo+FO+XaMcPhkAOYRqPB2WCa+kz2omYDpYHI8sQnoyxDKpVCoVDglvWzcGa9CnQWyYeC0n6qVqsol8tccTkpX3MkgQwtBoPBwN04rVYLT58+5bpssVhErVY7Ezdq+fNR18P9+/extbXF7P5gMIgrV65MqGMSqF5fKBSws7ODSqWCJ0+eoFKp4ODggEWtZsGO5Bwoxf/nP/8ZiUSCJ6ZXq1Wk02mezjsLz3xUoEOq1WpBq9Vie3sbFouFA5pEIsHaKnq9nl8zi5Bnp4bDIeLxOP77f//vsFgsePToEfsn4hUNBgOeok5ibiSqNytBDDB50QTASuAbGxuo1+ss0Em3bhI3k9+qqaTd7XZ5VhUdXnS5UNIhL7cJZRzMZjP71kePHmFnZ2fi4j0r6+FNQM9Mml2krdRsNqHValEsFnHv3j0kk0kUCgX0+322qSIzMhrNc4lwEuXS6XTodruo1+vI5/NIpVIol8uKW/DvCvltOZ1Oc1RLgmXBYBCiKHIbG4EUSQ8ODvDtt9+iUqnwxqIb96zcEOTOEgDPMCGeB9lK7VSaxHSwTB0kyWSS11y1Wp0YTDoL6+VVkEsfUDt2rVaDwWDgMop8TAjJI9AcGNJWIS7NrEEe6JFAXqVSgV6vh9Vq5Wnz8tKQ/DVyO1Epk/yQkmed0fvX6/WoVqt8WSTFdOpqU+rzvS3kDSTD4RC9Xg/NZpPPcUmSkM/nkUwmWRNOroV23HjnQIY2PYmybW5uol6vYzAYoNfrodVqodPpnLmFAEy2yALg9unhcIhMJsNqo9POk5jg1WqVb9XUdjxLoxzkoOehyalEYqbyyFl0Jj8FOpQ1mufKqk+ePEEmk2GbFYtFAFD0QfO6eFWXxGAwQL1en+CTUcaBynFyWf5Zt5O84490PhqNBrRaLWf2gEl7UtcbZbJoPyqVQ0RrQKvVYjgcYnNzE/l8nv+9UCgAwJlS7n0ZpoPfZ8+eodVq8fRrqrrU63V0Op0TvQRoDqtpmc3mNy54TUtcyxfCcSyIVqt1bKtOkqTXtg/Z5E05C3IbHkWbdb1ePzb7iKL4VutnWvfkOGvv7Xb72OxjsViOpKA8vbbk++2oHUuz2Tw2+zidzneyz0/tuaPaU3KUy+Vjs8/b+Gc55PaR//llmLbT29rruPzzm9iGzix5SY32zXEGMcd5dr2p75Hbh/48vR7et60O8z3vZUQBQf5gZzWqncab2uE4F8ppgbpu3gzyzIyKF5DbZZr0epyXq9OO17HBcV9ITwKz/nzvArLJqzKfJ22z9zKi4KQf6jRCvknehtl9Vmyqrp+3g/ywVu33Aup6ejXUg/s55GtE3T+vxnFnx98E7zUjo+LlUDeKivcFdW2pUPH2UPePMnEoR0aFChUqVKhQoeI0Y/b6DFWoUKFChQoVZwZqIKNChQoVKlSoUCzUQEaFChUqVKhQoViogYwKFSpUqFChQrFQAxkVKlSoUKFChWKhBjIqVKhQoUKFCsVCDWRUqFChQoUKFYqFGsioUKFChQoVKhQLNZBRoUKFChUqVCgWaiCjQoUKFSpUqFAs1EBGhQoVKlSoUKFYHGsgo9Fo/k+NRvO9RqPpajSa/+c4/28lQLXP4VDtczhU+xwOjUbj0mg0v9FoNE2NRrOv0Wj+l5N+T6cJqn0Oh2qfw3GS9jnu6dcpAP8ZwP8EQDzm/1sJUO1zOFT7HA7VPofj/wbQA+AH8AGA/0+j0TwYj8dPTvRdnR6o9jkcqn0Ox4nZ50SmX2s0mv8MIDwej/+3Y//PFQDVPodDtc/hUO3zY2g0GguAMoDL4/F443/83X8BkByPx//xRN/cKYBqn8Oh2udwnLR9VI6MChUqzgKWAQzIyf4PPACwekLv57RBtc/hUO1zOE7UPmogo0KFirMAK4Da1N9VAUgn8F5OI1T7HA7VPofjRO2jBjIqVKg4C2gAsE39nQ1A/QTey2mEap/DodrncJyofdRARoUKFWcBGwD0Go3mvOzvrgFQiZrPodrncKj2ORwnap/jbr/WazQaEwAdAJ1GozFpNJrj7pw6tVDtczhU+xwO1T6vxng8bgL4JwD/l0ajsWg0mk8B/M8A/svJvrPTAdU+h0O1z+E4afscd0bmPwFoA/iPAP7X//H7/3TM7+E0Q7XP4VDtczhU+xyO/wPP29JzAP5fAP+72jo7AdU+h0O1z+E4MfucSPu1ChUqVKhQoULFUUDlyKhQoUKFChUqFAs1kFGhQoUKFSpUKBZqIKNChQoVKlSoUCzUQEaFChUqVKhQoViogYwKFSpUqFChQrE4VGPCarUqrqWp0Whojuv/Uu1zOCwWi+Ls02w21fVzCNT9dTjU/XU4jmt/SZKkONvU63V1bx2Cw/aWmpFRoWIGocoqqFCh4n1Ao9FAo3m9mOtNvvddoKp+qlAxIxiPxxzAjMdjaDQaaLXqXUXF20G+ngjHdTAdNzQazcSzyvfR9PfN4vO/Dl723HI7kc85CRupgYwKFTOClzlfci4qVLwJpteSfA3N4pqa3jOvCmQIs/b8r4M3+dyPe42cukBmNBrxQhqNRtBoNNDpdGcqEqbNMxqNMBqNAABarfbEot3TClojcsdz1tYK8MJp6PV6aLVa/hoOh+j3+zN58LwJ5PuJ1sp0xop+Pct2IpC9tFotDAYDgBe2Gw6H7JfJVkq1GWVhpoMVrVYLnU4HrVYLQRCg0WgwHA4xHo8xGAwwGAzYN8vXENlklsu68mczm81YXFyE2WyGyWSCTqfD3t4eDg4O2D7yLM2rfs5R4NQFMnQ4DYdDDIdDPsDPiqOhjUA26Pf7fEjRryqe24lsRA6WoNPpADx3SLPsVKah1+uh1+shCAIEQUC32+VABpj9vfMyyAMXOoTlDpYCXwCKP5iPAvJMhE6ng8lkAgA+vMmGs+aP5XuEAjidTgeLxQKdTod+v4/BYIB2u43BYMBBjfwCQTgrwYwoirhy5Qo8Hg8cDgeMRiP++Mc/Ip1OYzAYsE8+MxwZ+YdOkZ3JZIIoiuh2u2g2mxiNRmfidjkajfhAMhgMsFqtGI1GvIG63e7EoX1WMR6PYTQa4XQ6+eYEAI1GA91ul4PBWYbc+er1ekQiETidTlitVlgsFmQyGTx9+hS9Xo+dL33/WYE8Q2UymSAIAnQ6HQwGw8R+6nQ6E0HOWQTtF1EUYTKZ4Ha7sbCwAI1Gg263i16vh3w+j0ajgWaziVarNZHRUNq6ms7i2u122Gw2WCwWuFwuGI1GuN1u6PV6tFot9Pt9lMtlVCoVtNttVCoVDm7o4j2drZoFvCwwkycbNBoNAoEA3G43MpkMCoUCyuUy4vE4XzTJLvKf+bL/B3i7bM2pCGQAcPbFZrPBarVifn4e4XAYuVwOz549Q6fTQbVaxWAwAKC8TfM6ICdqMpkgSRL8fj9WVlbQ7/exs7ODRqOBdDqNWq02c5vlTUDOU5IkLC4uQhRFuFwuAMCTJ0+QyWTQ7Xb5xjSLdpJvdo1GA6PRiFu3buHSpUvw+Xzw+Xz4/vvvkc1mUavVUKvVeI/NOuSXHa1WC1EUYTAYMD8/D7vdDofDAYfDgXq9jnQ6jWaziXQ6jU6nwxcG+c85KyUD+rLZbAgEArh06RJ+/etfQ6/Xo16vo91u4969e0ilUtjb28P+/j6GwyEHyUrcZ7R3BEHA0tISlpeX4fP5sLy8DJvNhlgsBoPBgEqlglarhXg8jng8jmKxiO3tbdTrdezu7qLVanGwo9frOStM/4dS1448Sykv0VKGinzKysoKLl68CJPJBLvdjrW1NRSLRbTbbb4kyPfSUWf0TjSQkX+4Wq0Wer0eLpcLHo8HkUgEsVgMOp0O+/v7it0obwJyJHq9ng/nWCyGXq+HZrMJk8mEYrF40m/zVGA8HsNgMMDtdkOSJIRCIeh0OpRKJQyHQ5TLZfR6vZN+m8cKg8EAs9kMu90Or9cLl8sFq9WKwWCAer0+wQ2ZdQiCALPZDIPBAI/HA5PJhGg0ykGM0+lEtVqFyWRCs9mEwWBAq9VCpVJBt9tFp9NBp9OZOOBnFfKMikajgSRJCAQCCAQC8Pl8EAQBVqsV7XYbhUIBgiCg0Wggn89z+ZJ+jpLWFr1fs9kMi8UCv9+PSCQCj8eDQCAAi8UCh8MBQRCg1WphNpv5cmSxWDAajVCv1zEYDNBqtVAqldDpdNDr9SZ8jxLXzjS5+2W/p0pBs9nk4F+SJMzNzaFeryMSiaDRaHDmil5LPKNX7au3ycyceEaGIjqj0QiLxYJPP/0Uq6uruHDhAs6fP49vv/0W+/v7KBQKKBaLGI1GE9HuLIFS22azGYFAANeuXcN/+A//Ad1uF3/5y1+QTqdRKBSQy+WYO3QWQXZyOp24ceMGgsEgPvnkExiNRoRCIWxsbODu3bu4e/fuBGF6Vu1FKV4qR3q9XiwsLKBYLGJlZYXTvf1+n8mbswa589NoNHC73bh06RI8Hg8+/PBDuFwu+Hw+SJIESZJgs9nQbDZRKBTQarU4M7O+vo58Po+dnR3s7OxwaRvATPod+WFCZbeLFy/iq6++QjQaxaVLlyAIAnNCYrEY6vU6fve736HT6aBYLHLpX2n2IR7Q4uIiwuEwPv/8c/z85z/ns4hIvxS4AEA4HOZyUqVSQbPZxM7ODur1OtbX11EoFLC+vo7NzU0uvwAvmjWUGNQQpoP6breL3d1dNBoNHBwcwO12w+Px4IsvvsCFCxewuLiIcrmMp0+fol6vo9PpYDAYIJvNchDcbDb5c3iXQPjEAxng+YdssVi4nBIOh+Hz+eB0OnlByUsESov8Xxe0SHQ6HYxGI9uj2+3C4/Gg0+nAYDAoejO8K+TpfkEQ4HA44HK5EAgEYDKZ4PV6USgUIIrizJaVgB+neqf5MnTLtFqtMJvNr+zQmDUYDAbo9XrOKvh8PsRiMbhcLrjdbpjNZuYQtVotGI1GdDodCILA5QGDwYBarYZ8Pg+tVot2uz2TfCt5dxL5YOLG+P1+OBwO/jcKZigY9Pv98Hg86Pf7fAgpFZR58Xg88Pv90Ol0/LzE8ZCTgCk7Q2uIMjO0ngqFAhKJBPr9Pmf1AGVmZqarJuRHqKmg0+lwJrNUKsHtdsPpdGIwGCAajcJut6NarXJpstfrcWmq2Wyi2+1O7K23PdtPJJAhh0oLw+1248svv0QoFMInn3yCxcVFJJNJ/Mu//AsePXqEeDyOWq2GwWBwJjIRRI7q9/ucuqXNdRae/1WgdSMIAvR6PR9IJpMJg8GA07nUMjnrxE16xtFoxOnter3O2RkKhs1m88y2pcu7j3Q6HVZWVrC8vIxYLIYPP/wQVqsVoVAIJpOJ906n05lwoCaTCeFwGMPhEF6vF51OB8vLy7h48SJ2d3fx9ddfc62fyI1KB2ULBEGAx+OBJEn45S9/iaWlJcRiMUSjUZTLZfz2t7+FwWBANBqFxWKBz+eDy+XC7du34XA48PDhQ9RqtYkSgpLWmUajgclkYj9CGUsqlVC2s1Qqod1uQ6fTQa/Xw2QywWazMU9vMBggGAyi0WggFArB7/cjmUzi/v376Pf7igqEX5Y50mg0HOiJogiHw4F+v49MJoNqtYp//dd/xXfffYcPP/wQt27dgslkwvLyMgc01HAwGo1QKBRQKBSws7ODP/7xj8z97PV6b83hO9GMDN0erVYrVlZWEI1GEYvF4Pf7sb29jbW1Nezt7aFcLvOtSCkb5G0wnXWidC61OyotdXvUmNa3EASBiXryFuxpG8465IFvr9fjci115xgMhpkWNKNLkU6ng8/nw+XLlxGNRnH58mWYTCYOYii13e/3WdaAXieKIrRaLTweD9846VB79OgRxuMxut3uCT/p0UHeSWI2m+F0OnH16lXcvHkToihCFEXk83k8efKEsxBOpxMejweiKGJ+fh4WiwXtdpuJ9rVaTTFrS+4X6LOmC9K0rMNgMEC1WkW1WuXvIdI4ZT8BwOVy8YWCbPH06VP+WUr3RUQCt9lsCAaDqNfryOfzaDab2NjYgFarhcPhwMLCAnw+H3d8+f3+CQ0n6vyyWCxcjsvlcu/U8XWsgcz0B+lyubC0tIRIJIIrV64gGAxiOBwinU5jc3MT9+7dQzqdZjY4QQkb5V0wLfKm4jnIJmazGW63G4FAAMFgEA6Hg1tp0+k0B790OzwLnTrAi0BYfqOS/90s7hv5wUm3a4fDAUmSYDKZoNfruXV4a2sLhUIB9XodtVqNMzSUlRBFEQsLC3C5XHC5XFhZWYFWq0U2m0Uul8N3332Hbrc7oT2jNNAeMplMsFqtcDqd+Oyzz+D3+zE3NwedTofNzU3s7e0hHo/jzp070Ov1yGazsNvtKJfLmJ+fh8PhgN1ux9zcHK5cuYJ0Oo18Po9Wq6WItUbvT6/Xw+l0IhgMMoGX9k6n08Hu7i6q1Sru3r2LVCrFAU8gEMDq6ip3NlEpW6/XY35+nknTmUwG5XIZOzs7aLfbisioy0vWGo0GVqsVJpMJKysr+NnPfsZlpXw+j3v37nEJbTgc4tGjRxiNRvD7/djd3YUoirDb7RwAUmXBZrNhfn4eH3/8MVKpFAqFAmeW5QmL1+UVHXtGRs5xcLlcuHHjBiKRCC5fvgyXy4X9/X3kcjkOZNrtNusVkCjcrEIevKhBzI9Bt0iz2Qyv1wufz8fdBcPhEK1WC9lsFgcHByiXy5yZOGuBzPSfZ3nPTIPaPymQAcC6J0+ePMH29jby+TxyuRy/xmw2swYPcfXcbjdCoRCMRiMqlQr29vbw4MEDzo4q1aa0h4xGI/MRP//8c8zNzcFut0Ov12NzcxO/+93v2A9rtVpsb29DkiT0+30sLS3h+vXrmJ+fRzAYxOXLl2G1WnHnzh0Mh0PFZI7lnbLBYJA1uwBwCXJzcxPpdBr/9m//hs3NTc4CLywsoNVqsX4KZXV0Oh3C4TACgQCMRiP29/eRSqWQSCTQbDYV5YvkXV12ux0rKyv4xS9+gW63i2q1yurH/X4ftVoNnU4Hjx49QiKRQCAQwMHBAWw2G5aWlphTJAgCQqEQ3G43wuEwfvazn2F3dxfffvstGo0GWq3WWwnpHVsgIz+kLRYLLBYLQqEQzp8/zwSrXq+HRCKBg4MDZDIZJgedFWdMDtJutyMQCMDhcECjeS6P3Ww2udXvrILs43Q6EY1G4ff7+dZN5YJms4lGo8Glg7OwbqYxHA7R7XbRbrdRr9e5q4QwSzaRq4yazWYmf1utVgBAr9dDLpdDuVzG7u4utre3UalUUC6X+WcYjUb0ej2+JQJAKBTC3NwcDAYDC6UZjUa+TCmlhCKHPEsnSRLOnTuHUCiEQCAAp9OJcrmMZrOJeDyOTCaDWq3GGYpGo4HhcIhkMgmtVov5+Xl0u11otVq43W7UajWYzWYYjcYfZQRPG6ihwmw2w2azwel0wul0clal2+2iXq8jm81ibW0NyWQShUKBNYaIG/Ls2TPk83lIkgSXy4VwOAybzcblXJvNhnA4DAATxGAlQH5eU/bObDZDFEXUajVsbW0hlUqhVquh3W5zeZd8MHFgzGYzyxuQrybuDJWifD4f77tEIoFqtTrxPl4Hx15aGo/HcLvdiMViuH79Or744gtYLBYWXfrhhx9w//59rK2t8QPNmhz2q0ARbiAQwOXLlxEOh3lxFAoF5PN5dDqdk36bJwayz/z8PD766CNEo1HYbDZoNBomY1YqFU5xK/nm/KaQC02RM6lUKshkMsjn8zMZABOHQc7fIJ6dyWTCaDRCo9HA2toa0uk0vvnmGzx9+hTdbpf5LvIOOLPZDEEQkMlk8PHHH2Nubg4mkwlzc3Not9uwWCwwGo0T5E2lrK/pUmMgEMDPf/5zzM3NYWVlBUajEQ8fPsTa2hr7XwrWxuMxisUidDod7t+/j3g8jrm5OVy7dg06nQ6xWAzj8RhOp5MPtn6/fyqzD/LOUI/Hw5mBSCTCwW+j0cD+/j52d3fxr//6r4jH4/xMZL9yuYy9vT3Y7XbE43F4vV58+eWXWFpags/ng81mg9/vx40bN+B0OvHXv/51QjRPCZcsaqm32+1M8rbZbNjc3MQf//hH5HI5pNNpNBoNJkGTqGSxWMTOzg70ej2PeQCen+WffvopKpUKYrEYbt++DVEUcevWLfj9frRarYlA5nVxLIGMPKqinnyv18ty6oIgMJkql8shl8txf7m8VjbrkNdt6fZHJE6q68/igfRTkJMSDQbDxK2bOncoE0OlSOouOQvrBvixUBXdHEl9cxZLlcRJ0Ov1cDgc8Hq9kCSJ9840CZo6J6bnwNAaogMql8uhVqtxsEPlJjqoq9WqYi8URqMRRqORb8LUKjsajVAqlTgTQ/wyOoDIlsRFkzch0DgZIlbTiJDTmrWiQMZut8PlcnGAqtFoMBgMWOmZDml5uUOupUIHfSaTQb/fRzqd5moDZSgoi0FZGmrHPo12mQbtDerq0uv1nO2t1WpcIZAHyXLbEGmaLli0x5rNJgsHTje4yOegvQmONSNDfIVIJIKPP/4YS0tLcLlcqNVq+Pd//3ckk0l8++23WFtbw2Aw4E2khA/9qEDOmTZWq9VCuVxmAl69Xj/pt3isoM4tmiXk9/tx7do1XLlyhVPBzWaTU50kHy4fbHeWQPbq9XpcXpJnH2YFdBhJkgSLxYLr16/jwoULWF5ehtVq5WwNzb8hjp3RaJxIm9NgURrQur29jWq1inA4jAsXLkAQBITDYeh0Oty+fRt+vx8//PADkskkgNNv0+lLZCAQQCQSwfXr13Hjxg3o9XouvX333Xf4/vvvUS6Xf6TdRdlQanMHwG3+TqcT9Xodc3NzrIhMpN/TBvqsjUYjrly5gvn5eczNzUGSJB6Ds7Gxgd/85jfI5XIoFossfzHtT8bjMZrNJh4+fAij0YjBYIBnz57hb//2bzE3NweNRgObzcYXL9KaUco8JuIQ+f1+LCwswGQyoVKpoFAoIJ1OT/AQCRT80v4EXrSyUyaz2WzyrC5SBibe2vQ4g9fFsQcyOp0OVqsVHo8HNpsNWq0Wg8EA+Xwe6XQapVIJ9Xr9pQtn1kEfHjG7NRoNa8k0Go0zy5Ehe5D0vtPphCRJPAl8OByiWq3yMDfSIzjtjuIoQbceem75jUgu6jUrNqHnMRqNPM7D5/PBarWyT6EDlcqOchu8jAhNXBAqc7daLe7YoEGCjUaDB5QqCfSsFouFRctsNhuX38rlMkqlEsrlMmebXkYeJ8gzEtTFQjwI+c38NIKyJRRkEHdjNBpxMJPJZFAsFtHr9X7E95E/FzUZdDodZLNZ6PV6tuFwOITBYIDRaOTMjFKI0ORPBEGAxWLhziPK9MpHUxBelUmRZ2mmbUk+isaCvG2r+nsPZOR6BXa7HRaLBdFoFMvLyzAajcjn84jH47h//z729vZQrVbPVDkJmBT1otY0n8/H9slmszxR9KyQn2mBm0wmhEIhOBwOfPXVV7hw4QIPciOH2Wq1sLGxgf39fVSr1VOd1j4qyB0CHSBOpxM+nw9ms/mlgdxpPVjeFORTBEHA/Pw8vF4vVldXceXKFVitVvT7fSQSCdy9exe5XA5//vOf+aJEQnjyPUR7j7J7w+GQtS3G4zH8fj/MZjOvu0ePHiGTyXCQSD/jtEF+IFDb8OLiIj777DMsLCzAYDCgWq3iyZMniMfjyOVyrNc1vX7o91RaoswL8LxcJf+e02gLgpwjI0kSHA4HRFGEXq9HpVLB7u4u9vb2eDjvdGZ3OsNFmlYajQa5XA6tVguPHz/mzpxYLIZgMIjV1VVIkoRKpcJDf08rKHtpt9tht9tx5coV3Lx5EwaD4aXByOv8POB5d6Ber0cgEGCtGeB5MEilX9pPb4pj48hoNBqIosgscbfbjeFwiEajgVKphGQyiUQigXa7fSYO6peB0rbEEqcbInWeyGu1ZwFyUp7f78e5c+ewsrLCt265c6WDR6nchXcB3YpFUeQMwizvITqMiOTr9Xrh9/sRCAQ4AK5UKtjc3EQqlcL6+jrK5TL6/f5EdkoO+jM500ajgUajwWRyg8EAp9OJbrcLURQn5g+dZjvTs1J7sMfj4UNEp9MxtyOZTE5kfF/1TCQQR12ClBVVIojTQzOmaHYUBRsk+3HYnCQ5j6jRaKDT6SCTySCRSLACsNVqhd/vR7/f55E7wOkVppS3XZMIXiQSYbKy3CYvey3w4+wVZU+NRiNsNhuPCwFerCniXU0HiyeqIyO/FQuCAJPJhCtXrmBpaQlLS0swmUxIJBL44YcfEI/Hkc1meSOdtbLAdFu6x+OBz+dDsVjE7u4ut6RRIDPLJTcqiVAK3OPx4Msvv+QontQ0VbxwOFTLNhgMEEURALh7gNLe5HxmAfJnpj1D4zuo1v7kyRPcvXsX5XIZ1Wp1goD6qp8JgJ1pNptlVdYLFy5wUE2iXkrxT5RRoEOJWl0FQUCpVEI6ncba2hp2d3dRq9UmXvtTz0iHDPGxKPhTCtFeTtwFXmQG+v3+G5U36DkpUM5kMnjy5Al0Oh1u3LgBrVYLq9XKLfw6nW5C9PQ02UkeeJBmjtFohMFgmFC2/qk28ulgRK/Xs2TGhQsXEIlEADxXgyaSeTabRa/Xe6v99V5PBHkgYzabcfXqVdy6dQuxWIyFpr7++mukUilks1nUajXWaThNH+77hLzWTHV4t9sNr9eLSqWCRCKBRCLBbY2zyh2S19WHwyFEUUQsFsP8/Dy++OILhEIhuFwumM3miTHwZ2WdvAp0I6RMHmVjKJChTq5ZsxUFMqIoQpIk5pQVi0Wsr6/jyZMnuHfvHlqtFvMcXsdB0u0wm81ifX0dVquVB91RIDNNhD2tkJf1aTCi3+9HKBRigmU6ncazZ8+wu7v7xlpDxMWiUhPd2JUWyBCIq0Gk1DcJZCg7Nx6PkU6nOYNHGTur1coddad9yOZ0Fo94PpSNedP3TjHAwsICzp07x7PQqIW9VCohm80im80ysfplfKTD8F4CGfl/bjQaEYlEWDDI7/cDAA+OKhaLqNVqEyWT074BjhJybkwgEEA4HGalSOqoIJzmxf+uoA1Cg9tI32Jubg5OpxMmk4lbrCmYoy4U4GytGYKc8EpBDKnZEiGP5PkJSrcTZZa0Wi2MRiPcbjfcbjcEQcBwOGQhs0qlwjfkNz1Uf+p7lbQPyb/YbDZuTydbkf99k6GGdLiZTCaeHJ7P55FKpVAul9FoNDirrkS8y/6gwNFoNLK0iE6nm1k6wKtKbfRv8owxNfm4XC6IosjcxmQyyeK37yITceSBDL0Regir1Ypbt24hFArh2rVruHDhAhKJBLa2trC9vc2zLJS8+N8WcpKvKIpYWVnBlStXEIvFuG57mtn/Rwm6AdGt8erVq/j7v/97OJ1OhMNh6PV6bG1toVQqsQS9XEkUUP4h/SaQ37ZtNhtcLhe3IlNLY6vVYh2MWSgtybkxNMMlGo0iGo3CZDKh3++zGms8HuchkW+SqqbbNTlfeZlbvhdP+56cLjvOzc2xirrFYkEqlcLW1hb29vZeSfCVg9aayWSCJEmQJAlWqxXlcplF8ogwTOU3JeNtfAmVxe12OyKRCLxeLweNSoW8/Pama19+vhmNRgQCAUSjUdjtdoxGIxSLRTx48ID5We/SbfreMjJ0Wya5fVLJHI/HqNVqSCQSyGaz3C47a+2hbwJymmazmQUCgReaB7MqaCYHOV1JkhAIBOD1evlwpto1KUnKa7U0xuEsQk6it1gsLEpGvBgSLpvF9UOkXuJnELeFBtj9FCfmVaB1SDObqAPsZZ1OSgAFMnKOBpVBms0mBzGv4mvIbSgngNIB3Wq1kM/nObOjtHU2/ZlOB62v8zyUzSKeFs3qkiRpos34TcpVpwUvC16m2/Dlfz/9/XKSL61B6g6sVqvIZrMolUrvLCvyXjIyo9EIkiRhcXER0WgUv/zlLxEKhSAIAiqVCu7evYt//ud/5qmyVFtU+o3xTSC/XVKq1uFwwOPxsAx6p9OZcDazCFrwNB314sWL+PLLLxGLxXD+/HmMRiMkk0mUy2X8/ve/x5MnT3DlyhVcvnyZxazOGjkceBHk6nQ6ngs0NzcHr9eLfD6PZDLJCtmdTkcxvI7DQO9/OByi3W6jUqlga2sLnU4HgUAAdrsdnU6Hu0dIDO91ML0Oo9Eobt68iUgkwuXL6QzNaYX8EDEYDDCbzZifn8elS5fgcrk4iCFZh1cFILTG5C3Li4uLuHDhAguZHhwc4N///d9RKpXQ6/X4MFcCXjZQVk76prLky/5dHujQyIFgMAhJkrC6uorbt2/D6/UysbfX63GGUCm+XK5FRe+Z9oC8k+tl4qP09waDAV6vl7vllpaWUKvVsLGxgUePHuHrr79GvV5/58ngR77i6MMVBIHbrD0eD1wuF0ajEer1OgqFApLJJC9+ecvfaXcS7wP03FR/JmdNol5yUaZZhfwmbLPZJjJTtVoNxWIRmUwGyWQSxWIRjUZjIjNz1iC/QVO3Et18xuMxrxu5su2sgA7YwWDAnVlyqfR3wcsyMrQ/lXSrlhM2BUGAJEk80JBIrYddkuTBEBHJaSinx+OBTqdDvV5HtVpFqVRCtVrlrJVSQIctgbJXgiBwQEvfJ3/N9OdPr6NMjMvl4tEHpBjdbrd56ORppwtQgCJvtZcT5qkL2Wg0TgQ007QSnU7HQ1dJxNRisWA0GrGAKbW6TxPNT0XX0nj8fDDkhx9+iHA4DKfTCZ1Oh8ePH2NnZwf37t1DIpHgAEYpEfz7Bn2ApGeQyWSwt7eHfD7PUf+sgurIhUIB29vbPPSx0WjgT3/6EzKZDJ4+fYpcLodGo6GYA+V9gw4r6lwigjjpfNDNUu5wZmEdTSuCAs+zKQ6HA+FwGIPBANvb26w++lPPLR8CSVlS4mABz/VlSKuo0WhwkHhabUnZXponND8/j6WlJQiCgEajgVwuh7W1tYlBtHSw05fRaGQ+zKVLl+B2u/H5559jYWEB6+vr+O1vf4vt7W1ks9mXCg2eRsiDUlIzpkDYbrdjYWEBjUYDV69eRalUwubmJlqtFu8t8jtyyQOv1wur1YqPP/6Yxz+Ew2H0+33Ojn733XfMITrNQyPpPQ2HQx5BsLOzw0NZPR4Pzp07hy+++AKZTAZ37txBrVbjiyXtH6vVyvpONLIhFotBFEUUi0U8fPgQe3t7XBZ+10znkQYy8nSb2WxGNBpFMBjkmTjpdBrr6+tIpVKs4EtR72n8UE8K/X4frVYL9XodpVKJu7pO6+J/F0y3XTcaDRQKBR7xXqlU8OzZM57iTGWD036rOQ7Q88tTvfLblJz7MEuQH7jUik83QMoaWK1WHnL3Oj9PnoGQH1LyuULNZhO1Wm1CWO80g8i51Hrtcrm4k63RaCCfz6NcLv8omyUvF5jNZjidTpw7d461nObn53H//n08efKEBysOh0NugVcCKGtJ7fmj0Qgmk4lHXYRCIeh0Ouzs7LAtaJ3Qn6lzzuPxsI3OnTuHcDgMu93OQ5DL5TLi8Tj29/dRr9d/klh9GjAej1nuo1gsIpfLsTK/2+3G4uIiTCYTNjc30e/3WRGbGlQMBgPcbjcCgQBWVlZY6Viv16PVavE4IuqYm+YnvSmOJJCRp5ToQz1//jyi0SgsFguSySTa7TaePXuGtbU1FAoFRRLn3hemyWa1Wo0HcxWLRdTr9ZnmEMlJYplMBvfv34ckSdje3ka73cbW1hbq9Tq63S6nK+WHzFkF2Y0cMrVakyaRJEnMG/mp2r+SIL9VNxoN1Go15h+EQiF88skncLlcKJfLqFQq2N/f52F0wI8dJbUnG41GxGIxuN1uLC8vw+v1wmg0cnC9sbGBg4MDPvzpvSgJtH+sVisCgQD0ej1ntUjyweVywePxsMS+zWbDysoKRFHE7u4u1tfXcf/+fRwcHKBer3MQqQTQe+12u9jY2EC9Xsfly5fh9/uh0+ngdrtx7tw5fPnll8jn8zCZTFw663Q6sNlssFgszGm0Wq04d+4c7HY7Ll68CK/XC7vdzvPftra2cHBwwLOb+v3+qQ9iyK9QSSyRSLBQq16vh9vtxgcffIBoNAqDwYBKpYJisYhms8nZYYfDgVAoBI/Hg1gsBkmSkM1msb+/jydPnmBjYwP5fB4AjsQe7xzIyIOY8XjMQcy5c+cQiUQwGo2wsbHBqcxnz55NpKBO8wd6XJi2BQUymUwGpVKJxzbMaiBDGI1GyGQyyOVy7HApSyMXJKM69llW+JVnX+ggIjEvrVYLi8UCq9UKu90Og8HANx8qySi9xESBTL1eh9ls5kAmGAwiEolAkiRkMhmk02nujqTnByaDGZq7Y7PZsLq6inA4jPPnz8Pr9fJts1QqYWNjA3t7ezzuQD4JWgmgQ1zeHajT6VAqlQCA91YsFmMNp6tXr8JsNjNJ+L/+1/+KtbU1rK+v4+DgYOI2rYT1RH601+tha2sLxWKRVeVpiKTVaoXP50OhUIBOp0M+n8fOzg4qlQqT6iVJgt/vhyRJOH/+PK8f6syVBzLxeJwDmZcRjE8jKJAZj8dIpVLQ6XRYWVmBTqeDy+WC0+lEq9WCz+dDq9VCsVhkOgANmgwEAjCbzQgEAgCA7e1txONxrK+vY2NjgzsLj2LdvNNJMO0MNBoNvF4vFhcX4ff7OY20v7+PVCqFSqXCtWUlfJjvG6PRiFsjHQ4Ht9B2u13k83lUq9WZOHReF3IWPABO+dK/AS82WKfT+dH01bMGSnfXajWIooh6vY56vQ6DwQC/34/z58/j888/R71eRyaTQavVwsHBAQ8eVTK0Wi3X8cfjMTY2NriDKxgMwm6349KlS/B4PKjX6yiXyyiXyxzQUFaK6vkrKytwOBy4dOkSgsEgPB4PtFotut0u0uk0XyoqlYqixqiQfAMJJNJlwOPx4NatW6jVaohGo+h0OhyYhUIhhMNhWCwWGAwG9Ho9bG5uotlsYmdnBwcHB0zuPe3ltZeB/Eyr1YIgCEilUtjb20M4HOb5PzQTaHFxkQm8jUaDibxUciNSuFyxl0Y1JBIJbG9vI5PJTIhSKgHywLRSqUAQBGxvb+Phw4ewWq08K9HpdMJiscBsNrNfIXK4zWZjnmOv18P+/j5zqujicVQ4sowMMb1jsRg++eQT+Hw+mEwmFItF3LlzBzs7O0gkEmg2m4q7ybwPyNtn/X4/fD4fvF4vHA4HWq0Wtra2kE6nuV3vrAR+FMiQ8wUmN9VoNEKn00G1WuVMFQXRSjhYjgr03MPhkGeUpNNp5HI5WCwWXLx4EQsLC7h9+zaKxSL+8pe/IJvN8pBWJc0LmgZ91v1+H/v7+8hkMpAkCZubm/jFL34Br9eLQCCAv//7v0etVsP8/DyKxSIeP37MxHkqwQmCAI/Hg7/9279FIBBALBaD0+nkm3Oj0cDa2hr29/exu7uLbDY7we07zSC9mF6vh2aziXq9DkmSIIoiZ8xJi0cOyszUajWkUink83n827/9GzKZDB48eIB0Oj3x/UpbR9QVWi6X0el08OjRI/R6vYmWaVEUIYoiHA4Ht08TD4jKJ9RqLudR0X7c39/HvXv38Kc//Qn1eh2tVmsic6WEAJCeK5FIIJfLQRAEVKtVLCws4Gc/+xkkSWJ9uOmuI/LjrVYLu7u7KBaL+O6773hyPE0Al/uhE+XITLdaUW2e5KubzSYqlQrK5bLiotLjALV6kjQ/tRzPqpDZm0BOZqU/y7+mdQ6OMsI/7XhZaYkmpZMwnkajwWAwgMFgwGAwQLfbVbTK6MtAAW+5XIbBYEA6nUY6nWblX0EQ+FLVarVgs9m4pZSyE06nE4FAAC6XCyaTiQOYZrOJVCqFZDLJc2CoxHnaQYcCZS9zuRzi8Ti8Xu+PiL3TaLfb6Pf7KJVKSCaTPIgzn8+j1Wqx7ZR+uaJAj4YW0hcFMNS9BoCnnVOASxdRsu9wOOQscSKR4HIS6TgpOatOZPByuYxkMslK0ZIkYTwec2cfgbKlRAuIx+MoFos8jmhaNuPES0vA5ORmSZLg9XoRDAbR7/exu7uLvb097O3tIZFIoNvt8lBIFS+Idx6PB4FAADabDaIocjADnE1tnZ8igcv5QnS7brVarNNwVkDtoDRXaHd3l2/ci4uLGI1GaDabKBQKePz4MeLxOCqVykytJ7pdb21tYX9/H61WC4lEAvPz87h16xYsFgsuX74MQRDw0Ucf/UjHg7IrkiRBq9WiXq8jn8/j4cOHePjwIRKJBO7du8flAiVksuQttJS5/Jd/+Rc8ePAA165dw9WrVycI0/IuwPF4jGQyiUQigVKpxFOx9/f3OcCZpcG+g8EAjx8/xu7uLnK5HPb39xGJRPDhhx9y+YhI0MCLNv1Wq8UZzq2tLe4Co+CXZk/V6/Uf0QOUcjmVryPaY4lEAm63G48fP4YkSVhYWODZSfLXkQZNs9nE5uYmarUadnd3USqVmFLxMrytbY40I0MkTBr5TZmYWYhKjxJkB7IbCZnJdQreRJX0rIGk6LvdLk/cJWFFpTiJd4U8PU3Zlmq1imKxiGq1ytkZEp2izotZ0yOS8x0A8GGk0+lQKBQwGAzg8/lYNJCCYHk2Qb7nWq0WarUaMpkM9vf3uVyn1GwW7QnSL3G73fD5fMzxIS0eeas+tQqXy2UcHBywivKsBTG0dkjSIZPJ8PiFQqHAwa3BYPhRU0u1WkWhUEC1WkUikeBhpfV6HblcDrlcjieBK00oUP5e5QEuXRaJEkFikURwlr+eKgqtVgvxeJylC7rd7kQ5Tv7/vAuOrNBLh3K/30ej0cD+/j7+8pe/IJPJTAyFVNIH+j5BN0FBELgeS5uBUpzVapW/V7XbC6TTady7d4/l1cvlMpPJT7PY1FGDHDE5jrt372J/fx/3799HMBjkA6harWJ3dxeNRoO7mmYJ8s86m82i2WwiHo9jZ2eHb42SJDH/xeFwwOFw8Gu63S7frh8+fIhUKoXt7W0ef9BoNNiZK2VdyW//w+GQR1ZUq1U8e/aM/03enUJ/R6qr1H5LAdwsBTFy9Ho99Pt97OzsoFAo4NmzZ3jw4AHPB5IfumSndrvNLdkkCEj7i36eUhs15O9Z7mMIFJzo9XokEgno9fqXBiK09ihTTiXJ92GPIw1kyKGSMu329jYKhQJrOMziJnhTyD9wikyJNNbpdFCr1fiLFDfPus2mQUNHh8MhJEliqXRyumfJXnJiHREz4/E4zGYz78V+v49arcbCcfS6WQLZgTq3yuUyCoUCzGYz8vk8bDYber0egsHgj7q2ms0mEokEKpUKHj16hL29PebaAC90LuTqyEoBBSskA0+lD/lNmwIVei4axCl/5lngxEyDPksKOIrFIvNl6IA2Go0v3SsU5A0GA9TrdRZmJA4VZf2UuGaAyREV0yWxXq+HdrvNHKOXPZvcz1AAPL2GjtIm7xzI0IdVr9fR7/c5C5PP57G9vT2h+DdrzvNtILcBCXrdvXsXVqsVT58+hclkws7ODorFIpOiVLs9B9mByghEJu/1ehOttWctYKZnJUdKtyAiQBMpelaDGILc4Q4GA5ZNJ7n9crnM814sFgu/bjAYoFwuo9vtcmtxs9l86aBNpR1IdIiSnyZODOFVys9yxfVZXS+E6ecjbsdh3WnyvUUZC8pMyLuTAOWtmZ8CBSWvk22S8xnf5z7SHPYDrVbra/9v8tTktNM8zs3QaDSObde9iX0Og/x2BPx4tPxR4jjtY7FY3ssOlttK3tl0FLZqNpuKWz8E4lTJnSvwY8HFd4FS9td0hxvwZntJ7oDfBKd9f8l1mg4j078vf31c+0uSpDeyjTxrIrfRYeej/Gw7CnvV63VF7K13wbtkpw7bW+9FDGHWb37vC0pMQZ4Epm866jp7jul69lnfh2/z3GfBZi97tll+3rfB62Qa6FfVdq+P93W+HSlHhn5VD5g3g7oZ3hyqzX4Mssm0sziLdjoLAcnbQN03L8d0141qI2XhvWZkVKhQcfxQ958KFSrOEg7lyKhQoUKFChUqVJxmzFY/nQoVKlSoUKHiTEENZFSoUKFChQoVioUayKhQoUKFChUqFAs1kFGhQoUKFSpUKBZqIKNChQoVKlSoUCzUQEaFChUqVKhQoVj8/yj/tVat1mnIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x576 with 64 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "# You do not need to modify anything in this cell\n",
    "\n",
    "m, n = X.shape\n",
    "\n",
    "fig, axes = plt.subplots(8,8, figsize=(8,8))\n",
    "fig.tight_layout(pad=0.1)\n",
    "\n",
    "for i,ax in enumerate(axes.flat):\n",
    "    # Select random indices\n",
    "    random_index = np.random.randint(m)\n",
    "    \n",
    "    # Select rows corresponding to the random indices and\n",
    "    # reshape the image\n",
    "    X_random_reshaped = X[random_index].reshape((20,20)).T\n",
    "    \n",
    "    # Display the image\n",
    "    ax.imshow(X_random_reshaped, cmap='gray')\n",
    "    \n",
    "    # Display the label above the image\n",
    "    ax.set_title(y[random_index,0])\n",
    "    ax.set_axis_off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"2.3\"></a>\n",
    "### 2.3 Model representation\n",
    "\n",
    "The neural network you will use in this assignment is shown in the figure below. \n",
    "- This has three dense layers with sigmoid activations.\n",
    "    - Recall that our inputs are pixel values of digit images.\n",
    "    - Since the images are of size $20\\times20$, this gives us $400$ inputs  \n",
    "    \n",
    "<img src=\"images/C2_W1_Assign1.PNG\" width=\"500\" height=\"400\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The parameters have dimensions that are sized for a neural network with $25$ units in layer 1, $15$ units in layer 2 and $1$ output unit in layer 3. \n",
    "\n",
    "    - Recall that the dimensions of these parameters are determined as follows:\n",
    "        - If network has $s_{in}$ units in a layer and $s_{out}$ units in the next layer, then \n",
    "            - $W$ will be of dimension $s_{in} \\times s_{out}$.\n",
    "            - $b$ will a vector with $s_{out}$ elements\n",
    "  \n",
    "    - Therefore, the shapes of `W`, and `b`,  are \n",
    "        - layer1: The shape of `W1` is (400, 25) and the shape of `b1` is (25,)\n",
    "        - layer2: The shape of `W2` is (25, 15) and the shape of `b2` is: (15,)\n",
    "        - layer3: The shape of `W3` is (15, 1) and the shape of `b3` is: (1,)\n",
    ">**Note:** The bias vector `b` could be represented as a 1-D (n,) or 2-D (1,n) array. Tensorflow utilizes a 1-D representation and this lab will maintain that convention. \n",
    "               "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"2.4\"></a>\n",
    "### 2.4 Tensorflow Model Implementation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tensorflow models are built layer by layer. A layer's input dimensions ($s_{in}$ above) are calculated for you. You specify a layer's *output dimensions* and this determines the next layer's input dimension. The input dimension of the first layer is derived from the size of the input data specified in the `model.fit` statement below. \n",
    ">**Note:** It is also possible to add an input layer that specifies the input dimension of the first layer. For example:  \n",
    "`tf.keras.Input(shape=(400,)),    #specify input shape`  \n",
    "We will include that here to illuminate some model sizing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"ex01\"></a>\n",
    "### Exercise 1\n",
    "\n",
    "Below, using Keras [Sequential model](https://keras.io/guides/sequential_model/) and [Dense Layer](https://keras.io/api/layers/core_layers/dense/) with a sigmoid activation to construct the network described above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "deletable": false
   },
   "outputs": [],
   "source": [
    "# UNQ_C1\n",
    "# GRADED CELL: Sequential model\n",
    "\n",
    "model = Sequential(\n",
    "    [               \n",
    "        tf.keras.Input(shape=(400,)),    #specify input size\n",
    "        ### START CODE HERE ### \n",
    "        \n",
    "        Dense(units=25, activation='sigmoid'),\n",
    "        Dense(units=15, activation='sigmoid'),\n",
    "        Dense(units=1, activation='sigmoid')\n",
    "        \n",
    "        ### END CODE HERE ### \n",
    "    ], name = \"my_model\" \n",
    ")                            \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"my_model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 25)                10025     \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 15)                390       \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 1)                 16        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 10,431\n",
      "Trainable params: 10,431\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><font size=\"3\" color=\"darkgreen\"><b>Expected Output (Click to Expand) </b></font></summary>\n",
    "The `model.summary()` function displays a useful summary of the model. Because we have specified an input layer size, the shape of the weight and bias arrays are determined and the total number of parameters per layer can be shown. Note, the names of the layers may vary as they are auto-generated.  \n",
    "    \n",
    "    \n",
    "```\n",
    "Model: \"my_model\"\n",
    "_________________________________________________________________\n",
    "Layer (type)                 Output Shape              Param #   \n",
    "=================================================================\n",
    "dense (Dense)                (None, 25)                10025     \n",
    "_________________________________________________________________\n",
    "dense_1 (Dense)              (None, 15)                390       \n",
    "_________________________________________________________________\n",
    "dense_2 (Dense)              (None, 1)                 16        \n",
    "=================================================================\n",
    "Total params: 10,431\n",
    "Trainable params: 10,431\n",
    "Non-trainable params: 0\n",
    "_________________________________________________________________\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><font size=\"3\" color=\"darkgreen\"><b>Click for hints</b></font></summary>\n",
    "As described in the lecture:\n",
    "    \n",
    "```python\n",
    "model = Sequential(                      \n",
    "    [                                   \n",
    "        tf.keras.Input(shape=(400,)),    # specify input size (optional)\n",
    "        Dense(25, activation='sigmoid'), \n",
    "        Dense(15, activation='sigmoid'), \n",
    "        Dense(1,  activation='sigmoid')  \n",
    "    ], name = \"my_model\"                                    \n",
    ")                                       \n",
    "``` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92mAll tests passed!\n"
     ]
    }
   ],
   "source": [
    "# UNIT TESTS\n",
    "from public_tests import *\n",
    "\n",
    "test_c1(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The parameter counts shown in the summary correspond to the number of elements in the weight and bias arrays as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "L1 params =  10025 , L2 params =  390 ,  L3 params =  16\n"
     ]
    }
   ],
   "source": [
    "L1_num_params = 400 * 25 + 25  # W1 parameters  + b1 parameters\n",
    "L2_num_params = 25 * 15 + 15   # W2 parameters  + b2 parameters\n",
    "L3_num_params = 15 * 1 + 1     # W3 parameters  + b3 parameters\n",
    "print(\"L1 params = \", L1_num_params, \", L2 params = \", L2_num_params, \",  L3 params = \", L3_num_params )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can examine details of the model by first extracting the layers with `model.layers` and then extracting the weights with `layerx.get_weights()` as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "[layer1, layer2, layer3] = model.layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 shape = (400, 25), b1 shape = (25,)\n",
      "W2 shape = (25, 15), b2 shape = (15,)\n",
      "W3 shape = (15, 1), b3 shape = (1,)\n"
     ]
    }
   ],
   "source": [
    "#### Examine Weights shapes\n",
    "W1,b1 = layer1.get_weights()\n",
    "W2,b2 = layer2.get_weights()\n",
    "W3,b3 = layer3.get_weights()\n",
    "print(f\"W1 shape = {W1.shape}, b1 shape = {b1.shape}\")\n",
    "print(f\"W2 shape = {W2.shape}, b2 shape = {b2.shape}\")\n",
    "print(f\"W3 shape = {W3.shape}, b3 shape = {b3.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**\n",
    "```\n",
    "W1 shape = (400, 25), b1 shape = (25,)  \n",
    "W2 shape = (25, 15), b2 shape = (15,)  \n",
    "W3 shape = (15, 1), b3 shape = (1,)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`xx.get_weights` returns a NumPy array. One can also access the weights directly in their tensor form. Note the shape of the tensors in the final layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<tf.Variable 'dense_2/kernel:0' shape=(15, 1) dtype=float32, numpy=\n",
      "array([[-0.45587707],\n",
      "       [-0.52920926],\n",
      "       [ 0.5405378 ],\n",
      "       [-0.1849108 ],\n",
      "       [-0.6035058 ],\n",
      "       [ 0.541251  ],\n",
      "       [-0.02397978],\n",
      "       [ 0.54912597],\n",
      "       [ 0.25174904],\n",
      "       [ 0.44561964],\n",
      "       [ 0.03553385],\n",
      "       [-0.08671862],\n",
      "       [-0.27646336],\n",
      "       [-0.07121998],\n",
      "       [-0.38104838]], dtype=float32)>, <tf.Variable 'dense_2/bias:0' shape=(1,) dtype=float32, numpy=array([0.], dtype=float32)>]\n"
     ]
    }
   ],
   "source": [
    "print(model.layers[2].weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code will define a loss function and run gradient descent to fit the weights of the model to the training data. This will be explained in more detail in the following week."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "deletable": false,
    "editable": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.6357\n",
      "Epoch 2/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.4767\n",
      "Epoch 3/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.3223\n",
      "Epoch 4/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.2154\n",
      "Epoch 5/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.1527\n",
      "Epoch 6/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.1154\n",
      "Epoch 7/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0915\n",
      "Epoch 8/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0749\n",
      "Epoch 9/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.0627\n",
      "Epoch 10/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0536\n",
      "Epoch 11/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.0462\n",
      "Epoch 12/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0406\n",
      "Epoch 13/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.0361\n",
      "Epoch 14/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0325\n",
      "Epoch 15/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0295\n",
      "Epoch 16/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0271\n",
      "Epoch 17/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0250\n",
      "Epoch 18/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.0232\n",
      "Epoch 19/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.0217\n",
      "Epoch 20/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0204\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f1e840c6d10>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(\n",
    "    loss=tf.keras.losses.BinaryCrossentropy(),\n",
    "    optimizer=tf.keras.optimizers.Adam(0.001),\n",
    ")\n",
    "\n",
    "model.fit(\n",
    "    X,y,\n",
    "    epochs=20\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run the model on an example to make a prediction, use [Keras `predict`](https://www.tensorflow.org/api_docs/python/tf/keras/Model). The input to `predict` is an array so the single example is reshaped to be two dimensional."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " predicting a zero: [[0.01251164]]\n",
      " predicting a one:  [[0.9826013]]\n"
     ]
    }
   ],
   "source": [
    "prediction = model.predict(X[0].reshape(1,400))  # a zero\n",
    "print(f\" predicting a zero: {prediction}\")\n",
    "prediction = model.predict(X[500].reshape(1,400))  # a one\n",
    "print(f\" predicting a one:  {prediction}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output of the model is interpreted as a probability. In the first example above, the input is a zero. The model predicts the probability that the input is a one is nearly zero. \n",
    "In the second example, the input is a one. The model predicts the probability that the input is a one is nearly one.\n",
    "As in the case of logistic regression, the probability is compared to a threshold to make a final prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction after threshold: 1\n"
     ]
    }
   ],
   "source": [
    "if prediction >= 0.5:\n",
    "    yhat = 1\n",
    "else:\n",
    "    yhat = 0\n",
    "print(f\"prediction after threshold: {yhat}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare the predictions vs the labels for a random sample of 64 digits. This takes a moment to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 64 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "# You do not need to modify anything in this cell\n",
    "\n",
    "m, n = X.shape\n",
    "\n",
    "fig, axes = plt.subplots(8,8, figsize=(8,8))\n",
    "fig.tight_layout(pad=0.1,rect=[0, 0.03, 1, 0.92]) #[left, bottom, right, top]\n",
    "\n",
    "for i,ax in enumerate(axes.flat):\n",
    "    # Select random indices\n",
    "    random_index = np.random.randint(m)\n",
    "    \n",
    "    # Select rows corresponding to the random indices and\n",
    "    # reshape the image\n",
    "    X_random_reshaped = X[random_index].reshape((20,20)).T\n",
    "    \n",
    "    # Display the image\n",
    "    ax.imshow(X_random_reshaped, cmap='gray')\n",
    "    \n",
    "    # Predict using the Neural Network\n",
    "    prediction = model.predict(X[random_index].reshape(1,400))\n",
    "    if prediction >= 0.5:\n",
    "        yhat = 1\n",
    "    else:\n",
    "        yhat = 0\n",
    "    \n",
    "    # Display the label above the image\n",
    "    ax.set_title(f\"{y[random_index,0]},{yhat}\")\n",
    "    ax.set_axis_off()\n",
    "fig.suptitle(\"Label, yhat\", fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a name=\"2.5\"></a>\n",
    "### 2.5 NumPy Model Implementation (Forward Prop in NumPy)\n",
    "As described in lecture, it is possible to build your own dense layer using NumPy. This can then be utilized to build a multi-layer neural network. \n",
    "\n",
    "<img src=\"images/C2_W1_dense2.PNG\" width=\"600\" height=\"450\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"ex02\"></a>\n",
    "### Exercise 2\n",
    "\n",
    "Below, build a dense layer subroutine. The example in lecture utilized a for loop to visit each unit (`j`) in the layer and perform the dot product of the weights for that unit (`W[:,j]`) and sum the bias for the unit (`b[j]`) to form `z`. An activation function `g(z)` is then applied to that result. This section will not utilize some of the matrix operations described in the optional lectures. These will be explored in a later section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "deletable": false,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# UNQ_C2\n",
    "# GRADED FUNCTION: my_dense\n",
    "\n",
    "def my_dense(a_in, W, b, g):\n",
    "    \"\"\"\n",
    "    Computes dense layer\n",
    "    Args:\n",
    "      a_in (ndarray (n, )) : Data, 1 example \n",
    "      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\n",
    "      b    (ndarray (j, )) : bias vector, j units  \n",
    "      g    activation function (e.g. sigmoid, relu..)\n",
    "    Returns\n",
    "      a_out (ndarray (j,))  : j units\n",
    "    \"\"\"\n",
    "    units = W.shape[1]\n",
    "    a_out = np.zeros(units)\n",
    "### START CODE HERE ### \n",
    "    for j in range(units):\n",
    "        w = W[:,j]\n",
    "        z = np.dot(w, a_in) + b[j]\n",
    "        a_out[j] = g(z)\n",
    "        \n",
    "        \n",
    "        \n",
    "### END CODE HERE ### \n",
    "    return(a_out)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.54735762 0.57932425 0.61063923]\n"
     ]
    }
   ],
   "source": [
    "# Quick Check\n",
    "x_tst = 0.1*np.arange(1,3,1).reshape(2,)  # (1 examples, 2 features)\n",
    "W_tst = 0.1*np.arange(1,7,1).reshape(2,3) # (2 input features, 3 output features)\n",
    "b_tst = 0.1*np.arange(1,4,1).reshape(3,)  # (3 features)\n",
    "A_tst = my_dense(x_tst, W_tst, b_tst, sigmoid)\n",
    "print(A_tst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**\n",
    "```\n",
    "[0.54735762 0.57932425 0.61063923]\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><font size=\"3\" color=\"darkgreen\"><b>Click for hints</b></font></summary>\n",
    "As described in the lecture:\n",
    "    \n",
    "```python\n",
    "def my_dense(a_in, W, b, g):\n",
    "    \"\"\"\n",
    "    Computes dense layer\n",
    "    Args:\n",
    "      a_in (ndarray (n, )) : Data, 1 example \n",
    "      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\n",
    "      b    (ndarray (j, )) : bias vector, j units  \n",
    "      g    activation function (e.g. sigmoid, relu..)\n",
    "    Returns\n",
    "      a_out (ndarray (j,))  : j units\n",
    "    \"\"\"\n",
    "    units = W.shape[1]\n",
    "    a_out = np.zeros(units)\n",
    "    for j in range(units):             \n",
    "        w =                            # Select weights for unit j. These are in column j of W\n",
    "        z =                            # dot product of w and a_in + b\n",
    "        a_out[j] =                     # apply activation to z\n",
    "    return(a_out)\n",
    "```\n",
    "   \n",
    "    \n",
    "<details>\n",
    "  <summary><font size=\"3\" color=\"darkgreen\"><b>Click for more hints</b></font></summary>\n",
    "\n",
    "    \n",
    "```python\n",
    "def my_dense(a_in, W, b, g):\n",
    "    \"\"\"\n",
    "    Computes dense layer\n",
    "    Args:\n",
    "      a_in (ndarray (n, )) : Data, 1 example \n",
    "      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\n",
    "      b    (ndarray (j, )) : bias vector, j units  \n",
    "      g    activation function (e.g. sigmoid, relu..)\n",
    "    Returns\n",
    "      a_out (ndarray (j,))  : j units\n",
    "    \"\"\"\n",
    "    units = W.shape[1]\n",
    "    a_out = np.zeros(units)\n",
    "    for j in range(units):             \n",
    "        w = W[:,j]                     \n",
    "        z = np.dot(w, a_in) + b[j]     \n",
    "        a_out[j] = g(z)                \n",
    "    return(a_out)\n",
    "``` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92mAll tests passed!\n"
     ]
    }
   ],
   "source": [
    "# UNIT TESTS\n",
    "\n",
    "test_c2(my_dense)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell builds a three-layer neural network utilizing the `my_dense` subroutine above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "def my_sequential(x, W1, b1, W2, b2, W3, b3):\n",
    "    a1 = my_dense(x,  W1, b1, sigmoid)\n",
    "    a2 = my_dense(a1, W2, b2, sigmoid)\n",
    "    a3 = my_dense(a2, W3, b3, sigmoid)\n",
    "    return(a3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can copy trained weights and biases from Tensorflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "W1_tmp,b1_tmp = layer1.get_weights()\n",
    "W2_tmp,b2_tmp = layer2.get_weights()\n",
    "W3_tmp,b3_tmp = layer3.get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yhat =  0  label=  0\n",
      "yhat =  1  label=  1\n"
     ]
    }
   ],
   "source": [
    "# make predictions\n",
    "prediction = my_sequential(X[0], W1_tmp, b1_tmp, W2_tmp, b2_tmp, W3_tmp, b3_tmp )\n",
    "if prediction >= 0.5:\n",
    "    yhat = 1\n",
    "else:\n",
    "    yhat = 0\n",
    "print( \"yhat = \", yhat, \" label= \", y[0,0])\n",
    "prediction = my_sequential(X[500], W1_tmp, b1_tmp, W2_tmp, b2_tmp, W3_tmp, b3_tmp )\n",
    "if prediction >= 0.5:\n",
    "    yhat = 1\n",
    "else:\n",
    "    yhat = 0\n",
    "print( \"yhat = \", yhat, \" label= \", y[500,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following cell to see predictions from both the Numpy model and the Tensorflow model. This takes a moment to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 64 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "# You do not need to modify anything in this cell\n",
    "\n",
    "m, n = X.shape\n",
    "\n",
    "fig, axes = plt.subplots(8,8, figsize=(8,8))\n",
    "fig.tight_layout(pad=0.1,rect=[0, 0.03, 1, 0.92]) #[left, bottom, right, top]\n",
    "\n",
    "for i,ax in enumerate(axes.flat):\n",
    "    # Select random indices\n",
    "    random_index = np.random.randint(m)\n",
    "    \n",
    "    # Select rows corresponding to the random indices and\n",
    "    # reshape the image\n",
    "    X_random_reshaped = X[random_index].reshape((20,20)).T\n",
    "    \n",
    "    # Display the image\n",
    "    ax.imshow(X_random_reshaped, cmap='gray')\n",
    "\n",
    "    # Predict using the Neural Network implemented in Numpy\n",
    "    my_prediction = my_sequential(X[random_index], W1_tmp, b1_tmp, W2_tmp, b2_tmp, W3_tmp, b3_tmp )\n",
    "    my_yhat = int(my_prediction >= 0.5)\n",
    "\n",
    "    # Predict using the Neural Network implemented in Tensorflow\n",
    "    tf_prediction = model.predict(X[random_index].reshape(1,400))\n",
    "    tf_yhat = int(tf_prediction >= 0.5)\n",
    "    \n",
    "    # Display the label above the image\n",
    "    ax.set_title(f\"{y[random_index,0]},{tf_yhat},{my_yhat}\")\n",
    "    ax.set_axis_off() \n",
    "fig.suptitle(\"Label, yhat Tensorflow, yhat Numpy\", fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a name=\"2.6\"></a>\n",
    "### 2.6 Vectorized NumPy Model Implementation (Optional)\n",
    "The optional lectures described vector and matrix operations that can be used to speed the calculations.\n",
    "Below describes a layer operation that computes the output for all units in a layer on a given input example:\n",
    "\n",
    "<img src=\"images/C2_W1_VectorMatrix.PNG\" width=\"600\" height=\"450\">\n",
    "\n",
    "We can demonstrate this using the examples `X` and the `W1`,`b1` parameters above. We use `np.matmul` to perform the matrix multiply. Note, the dimensions of x and W must be compatible as shown in the diagram above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 25)\n"
     ]
    }
   ],
   "source": [
    "x = X[0].reshape(-1,1)         # column vector (400,1)\n",
    "z1 = np.matmul(x.T,W1) + b1    # (1,400)(400,25) = (1,25)\n",
    "a1 = sigmoid(z1)\n",
    "print(a1.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can take this a step further and compute all the units for all examples in one Matrix-Matrix operation.\n",
    "\n",
    "<img src=\"images/C2_W1_MatrixMatrix.PNG\" width=\"600\" height=\"450\">\n",
    "The full operation is $\\mathbf{Z}=\\mathbf{XW}+\\mathbf{b}$. This will utilize NumPy broadcasting to expand $\\mathbf{b}$ to $m$ rows. If this is unfamiliar, a short tutorial is provided at the end of the notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"ex03\"></a>\n",
    "### Exercise 3\n",
    "\n",
    "Below, compose a new `my_dense_v` subroutine that performs the layer calculations for a matrix of examples. This will utilize `np.matmul()`.\n",
    "\n",
    "_**Note**: This function is not graded because it is discussed in the optional lectures on vectorization. If you didn't go through them, feel free to click the hints below the expected code to see the code. You can also submit the notebook even with a blank answer here._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "deletable": false
   },
   "outputs": [],
   "source": [
    "# UNQ_C3\n",
    "# UNGRADED FUNCTION: my_dense_v\n",
    "\n",
    "def my_dense_v(A_in, W, b, g):\n",
    "    \"\"\"\n",
    "    Computes dense layer\n",
    "    Args:\n",
    "      A_in (ndarray (m,n)) : Data, m examples, n features each\n",
    "      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\n",
    "      b    (ndarray (1,j)) : bias vector, j units  \n",
    "      g    activation function (e.g. sigmoid, relu..)\n",
    "    Returns\n",
    "      A_out (tf.Tensor or ndarray (m,j)) : m examples, j units\n",
    "    \"\"\"\n",
    "### START CODE HERE ### \n",
    "    Z = np.matmul(A_in,W) + b\n",
    "    A_out = g(Z)\n",
    "    \n",
    "### END CODE HERE ### \n",
    "    return(A_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.54735762 0.57932425 0.61063923]\n",
      " [0.57199613 0.61301418 0.65248946]\n",
      " [0.5962827  0.64565631 0.6921095 ]\n",
      " [0.62010643 0.67699586 0.72908792]]\n"
     ]
    }
   ],
   "source": [
    "X_tst = 0.1*np.arange(1,9,1).reshape(4,2) # (4 examples, 2 features)\n",
    "W_tst = 0.1*np.arange(1,7,1).reshape(2,3) # (2 input features, 3 output features)\n",
    "b_tst = 0.1*np.arange(1,4,1).reshape(1,3) # (1,3 features)\n",
    "A_tst = my_dense_v(X_tst, W_tst, b_tst, sigmoid)\n",
    "print(A_tst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**\n",
    "\n",
    "```\n",
    "[[0.54735762 0.57932425 0.61063923]\n",
    " [0.57199613 0.61301418 0.65248946]\n",
    " [0.5962827  0.64565631 0.6921095 ]\n",
    " [0.62010643 0.67699586 0.72908792]]\n",
    " ```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><font size=\"3\" color=\"darkgreen\"><b>Click for hints</b></font></summary>\n",
    "    In matrix form, this can be written in one or two lines. \n",
    "    \n",
    "       Z = np.matmul of A_in and W plus b    \n",
    "       A_out is g(Z)  \n",
    "<details>\n",
    "  <summary><font size=\"3\" color=\"darkgreen\"><b>Click for code</b></font></summary>\n",
    "\n",
    "```python\n",
    "def my_dense_v(A_in, W, b, g):\n",
    "    \"\"\"\n",
    "    Computes dense layer\n",
    "    Args:\n",
    "      A_in (ndarray (m,n)) : Data, m examples, n features each\n",
    "      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\n",
    "      b    (ndarray (j,1)) : bias vector, j units  \n",
    "      g    activation function (e.g. sigmoid, relu..)\n",
    "    Returns\n",
    "      A_out (ndarray (m,j)) : m examples, j units\n",
    "    \"\"\"\n",
    "    Z = np.matmul(A_in,W) + b    \n",
    "    A_out = g(Z)                 \n",
    "    return(A_out)\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92mAll tests passed!\n"
     ]
    }
   ],
   "source": [
    "# UNIT TESTS\n",
    "\n",
    "test_c3(my_dense_v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell builds a three-layer neural network utilizing the `my_dense_v` subroutine above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "def my_sequential_v(X, W1, b1, W2, b2, W3, b3):\n",
    "    A1 = my_dense_v(X,  W1, b1, sigmoid)\n",
    "    A2 = my_dense_v(A1, W2, b2, sigmoid)\n",
    "    A3 = my_dense_v(A2, W3, b3, sigmoid)\n",
    "    return(A3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can again copy trained weights and biases from Tensorflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "W1_tmp,b1_tmp = layer1.get_weights()\n",
    "W2_tmp,b2_tmp = layer2.get_weights()\n",
    "W3_tmp,b3_tmp = layer3.get_weights()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make a prediction with the new model. This will make a prediction on *all of the examples at once*. Note the shape of the output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 1)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Prediction = my_sequential_v(X, W1_tmp, b1_tmp, W2_tmp, b2_tmp, W3_tmp, b3_tmp )\n",
    "Prediction.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll apply a threshold of 0.5 as before, but to all predictions at once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predict a zero:  [0] predict a one:  [1]\n"
     ]
    }
   ],
   "source": [
    "Yhat = (Prediction >= 0.5).astype(int)\n",
    "print(\"predict a zero: \",Yhat[0], \"predict a one: \", Yhat[500])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following cell to see predictions. This will use the predictions we just calculated above. This takes a moment to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 64 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "# You do not need to modify anything in this cell\n",
    "\n",
    "m, n = X.shape\n",
    "\n",
    "fig, axes = plt.subplots(8, 8, figsize=(8, 8))\n",
    "fig.tight_layout(pad=0.1, rect=[0, 0.03, 1, 0.92]) #[left, bottom, right, top]\n",
    "\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    # Select random indices\n",
    "    random_index = np.random.randint(m)\n",
    "    \n",
    "    # Select rows corresponding to the random indices and\n",
    "    # reshape the image\n",
    "    X_random_reshaped = X[random_index].reshape((20, 20)).T\n",
    "    \n",
    "    # Display the image\n",
    "    ax.imshow(X_random_reshaped, cmap='gray')\n",
    "   \n",
    "    # Display the label above the image\n",
    "    ax.set_title(f\"{y[random_index,0]}, {Yhat[random_index, 0]}\")\n",
    "    ax.set_axis_off() \n",
    "fig.suptitle(\"Label, Yhat\", fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see how one of the misclassified images looks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 72x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(1, 1))\n",
    "errors = np.where(y != Yhat)\n",
    "random_index = errors[0][0]\n",
    "X_random_reshaped = X[random_index].reshape((20, 20)).T\n",
    "plt.imshow(X_random_reshaped, cmap='gray')\n",
    "plt.title(f\"{y[random_index,0]}, {Yhat[random_index, 0]}\")\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"2.7\"></a>\n",
    "### 2.7 Congratulations!\n",
    "You have successfully built and utilized a neural network."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a name=\"2.8\"></a>\n",
    "### 2.8 NumPy Broadcasting Tutorial (Optional)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "In the last example,  $\\mathbf{Z}=\\mathbf{XW} + \\mathbf{b}$ utilized NumPy broadcasting to expand the vector $\\mathbf{b}$. If you are not familiar with NumPy Broadcasting, this short tutorial is provided.\n",
    "\n",
    "$\\mathbf{XW}$  is a matrix-matrix operation with dimensions $(m,j_1)(j_1,j_2)$ which results in a matrix with dimension  $(m,j_2)$. To that, we add a vector $\\mathbf{b}$ with dimension $(1,j_2)$.  $\\mathbf{b}$ must be expanded to be a $(m,j_2)$ matrix for this element-wise operation to make sense. This expansion is accomplished for you by NumPy broadcasting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Broadcasting applies to element-wise operations.  \n",
    "Its basic operation is to 'stretch' a smaller dimension by replicating elements to match a larger dimension.\n",
    "\n",
    "More [specifically](https://NumPy.org/doc/stable/user/basics.broadcasting.html): \n",
    "When operating on two arrays, NumPy compares their shapes element-wise. It starts with the trailing (i.e. rightmost) dimensions and works its way left. Two dimensions are compatible when\n",
    "- they are equal, or\n",
    "- one of them is 1   \n",
    "\n",
    "If these conditions are not met, a ValueError: operands could not be broadcast together exception is thrown, indicating that the arrays have incompatible shapes. The size of the resulting array is the size that is not 1 along each axis of the inputs.\n",
    "\n",
    "Here are some examples:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<figure>\n",
    "    <center> <img src=\"./images/C2_W1_Assign1_BroadcastIndexes.PNG\"  alt='missing' width=\"400\"  ><center/>\n",
    "    <figcaption>Calculating Broadcast Result shape</figcaption>\n",
    "<figure/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graphic below describes expanding dimensions. Note the red text below:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<figure>\n",
    "    <center> <img src=\"./images/C2_W1_Assign1_Broadcasting.gif\"  alt='missing' width=\"600\"  ><center/>\n",
    "    <figcaption>Broadcast notionally expands arguments to match for element wise operations</figcaption>\n",
    "<figure/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graphic above shows NumPy expanding the arguments to match before the final operation. Note that this is a notional description. The actual mechanics of NumPy operation choose the most efficient implementation.\n",
    "\n",
    "For each of the following examples, try to guess the size of the result before running the example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(a + b).shape: (3, 1), \n",
      "a + b = \n",
      "[[6]\n",
      " [7]\n",
      " [8]]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([1,2,3]).reshape(-1,1)  #(3,1)\n",
    "b = 5\n",
    "print(f\"(a + b).shape: {(a + b).shape}, \\na + b = \\n{a + b}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that this applies to all element-wise operations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(a * b).shape: (3, 1), \n",
      "a * b = \n",
      "[[ 5]\n",
      " [10]\n",
      " [15]]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([1,2,3]).reshape(-1,1)  #(3,1)\n",
    "b = 5\n",
    "print(f\"(a * b).shape: {(a * b).shape}, \\na * b = \\n{a * b}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<figure>\n",
    "    <img src=\"./images/C2_W1_Assign1_VectorAdd.PNG\"  alt='missing' width=\"740\" >\n",
    "    <center><figcaption><b>Row-Column Element-Wise Operations</b></figcaption></center>\n",
    "<figure/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1]\n",
      " [2]\n",
      " [3]\n",
      " [4]]\n",
      "[[1 2 3]]\n",
      "(a + b).shape: (4, 3), \n",
      "a + b = \n",
      "[[2 3 4]\n",
      " [3 4 5]\n",
      " [4 5 6]\n",
      " [5 6 7]]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([1,2,3,4]).reshape(-1,1)\n",
    "b = np.array([1,2,3]).reshape(1,-1)\n",
    "print(a)\n",
    "print(b)\n",
    "print(f\"(a + b).shape: {(a + b).shape}, \\na + b = \\n{a + b}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the scenario in the dense layer you built above. Adding a 1-D vector $b$ to a (m,j) matrix.\n",
    "<figure>\n",
    "    <img src=\"./images/C2_W1_Assign1_BroadcastMatrix.PNG\"  alt='missing' width=\"740\" >\n",
    "    <center><figcaption><b>Matrix + 1-D Vector</b></figcaption></center>\n",
    "<figure/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><font size=\"2\" color=\"darkgreen\"><b>Please click here if you want to experiment with any of the non-graded code.</b></font></summary>\n",
    "    <p><i><b>Important Note: Please only do this when you've already passed the assignment to avoid problems with the autograder.</b></i>\n",
    "    <ol>\n",
    "        <li> On the notebook’s menu, click “View” > “Cell Toolbar” > “Edit Metadata”</li>\n",
    "        <li> Hit the “Edit Metadata” button next to the code cell which you want to lock/unlock</li>\n",
    "        <li> Set the attribute value for “editable” to:\n",
    "            <ul>\n",
    "                <li> “true” if you want to unlock it </li>\n",
    "                <li> “false” if you want to lock it </li>\n",
    "            </ul>\n",
    "        </li>\n",
    "        <li> On the notebook’s menu, click “View” > “Cell Toolbar” > “None” </li>\n",
    "    </ol>\n",
    "    <p> Here's a short demo of how to do the steps above: \n",
    "        <br>\n",
    "        <img src=\"https://drive.google.com/uc?export=view&id=14Xy_Mb17CZVgzVAgq7NCjMVBvSae3xO1\" align=\"center\" alt=\"unlock_cells.gif\">\n",
    "</details>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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