{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 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._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "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": []
   },
   "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
   },
   "outputs": [],
   "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`.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "deletable": false,
    "editable": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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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\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": 13,
   "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",
    "        Dense(25, activation=\"sigmoid\", name=\"layer1\"),\n",
    "        Dense(15, activation=\"sigmoid\", name=\"layer2\"),\n",
    "        Dense(1, activation=\"sigmoid\", name=\"output\")\n",
    "        \n",
    "        \n",
    "        ### END CODE HERE ### \n",
    "    ], name = \"my_model\" \n",
    ")                            \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"my_model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " layer1 (Dense)              (None, 25)                10025     \n",
      "                                                                 \n",
      " layer2 (Dense)              (None, 15)                390       \n",
      "                                                                 \n",
      " output (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": 15,
   "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": 16,
   "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": 17,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "[layer1, layer2, layer3] = model.layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 19,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<tf.Variable 'output/kernel:0' shape=(15, 1) dtype=float32, numpy=\n",
      "array([[-0.38629478],\n",
      "       [-0.4512433 ],\n",
      "       [ 0.522036  ],\n",
      "       [-0.2840382 ],\n",
      "       [ 0.20888507],\n",
      "       [ 0.09745187],\n",
      "       [-0.49855843],\n",
      "       [-0.42101437],\n",
      "       [-0.60057336],\n",
      "       [ 0.3380137 ],\n",
      "       [ 0.26243353],\n",
      "       [ 0.15161824],\n",
      "       [ 0.44996935],\n",
      "       [-0.53803927],\n",
      "       [ 0.4013558 ]], dtype=float32)>, <tf.Variable 'output/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": 20,
   "metadata": {
    "deletable": false,
    "editable": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.6475\n",
      "Epoch 2/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.4900\n",
      "Epoch 3/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.3222\n",
      "Epoch 4/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.2082\n",
      "Epoch 5/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.1446\n",
      "Epoch 6/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.1079\n",
      "Epoch 7/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0845\n",
      "Epoch 8/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.0686\n",
      "Epoch 9/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0572\n",
      "Epoch 10/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.0488\n",
      "Epoch 11/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0423\n",
      "Epoch 12/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.0373\n",
      "Epoch 13/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0332\n",
      "Epoch 14/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.0301\n",
      "Epoch 15/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0274\n",
      "Epoch 16/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.0252\n",
      "Epoch 17/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0233\n",
      "Epoch 18/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0217\n",
      "Epoch 19/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.0203\n",
      "Epoch 20/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.0191\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7a32f3930050>"
      ]
     },
     "execution_count": 20,
     "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": 21,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " predicting a zero: [[0.01274332]]\n",
      " predicting a one:  [[0.9859272]]\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": 22,
   "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": 23,
   "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": 27,
   "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": 28,
   "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": 29,
   "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": 30,
   "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": 31,
   "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": 32,
   "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": 33,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
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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": 34,
   "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": 35,
   "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",
    "    \n",
    "### END CODE HERE ### \n",
    "    return(A_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": 37,
   "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": 38,
   "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": 39,
   "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": 40,
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 1)"
      ]
     },
     "execution_count": 40,
     "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": 41,
   "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": 42,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ajUwmw8bGBrlcrutlI4TAbrfT19fHCy+8QCgUIp/PY7PZsNvtOw4U+81DKS9GpcVqtTIxMcHp06cZHx/n+PHjWK1WtZg4HA41ERqNBqFQiFKpxKFDh0ilUszOzpJMJtna2iKbzVKr1ahWq12fLy/dHHa7HYDx8XGEEGxvb1Mul5/krR0oZOZMMBgkGAxiMpmU1cpqteJ2u/H7/dhstp4MeJYBy3LeyGyHXpCFcaOVC+nw8DBer5eBgQF8Ph8TExMEAgGmpqZwOp0q9k4quM888wwjIyPqILG5ucnm5qaSaS8owxKptEmLy9jYGM8//zzT09NKvvF4HLPZ3BPjSyL3Mr/fj9vt5tVXX2VqaoojR44QiUTY3Nxkfn6e+fl54vE4hUKha/ct+GqcOJ1O3G43brcbp9OJEOKxZMw+lPIi0zPlzU9MTPDqq68Si8U4evQo5XKZRCKhXEoysNBisXDkyBGazSZPPfUUmUyGTz75hIWFBa5fvw5AJpOhVCphMpnUJOm2gWCc+DabDZvNxujoKACXL1/WwYO3kc9+J+XFYrFgNpuV8mKxWHpmMYWvagW1Wi210dbrder1ugrS7VZ2f85yYx0fH+e5554jGo1y7NgxPB4PQ0NDOBwOgsEgFotFyUbKzu/3K1eSy+Xi0qVLFItFisUi5XJZBah2+zw0Bjfb7XaefvppTp06xTPPPKMCMn0+HxcvXlQW9V4IBDfGKfp8PsLhMC+//DJPP/00g4ODeL1ebty4weXLl5mfn1fWu26Vi9ybpPLicrnU1+NS8h9IeTG6O0wmE0NDQ0xMTDA5OUkwGKRYLPLpp5+SyWSYnZ1VJjaZXeN0OgmFQthsNpxOJ3a7nVOnThGLxRgaGiIejzM7O8v8/DylUol8Pn/HqqqdivSjN5tNstksuVyOarWKzWZjbGwMi8WCx+PBZrOpZ+5G5e2bMFr2+vr6iEQiBAIBfD6fMufvrlKsuUWvjBXpdzeZTMRiMU6ePMn4+DjPPvssXq+XaDSq6nBUKhWuX79Ou93G4/EoN7fVasVsNmOz2Th06BAul4tgMMjAwAArKyvcuHGDarWq0l8l3Spjq9XK2NgYkUiEQ4cOMTY2htlsplAosLKywvXr10kmkz0T3Gys5+JyuTh+/DixWIzh4WECgQC5XI7t7W0uX77MuXPnlDvNWG23W5HufJvNpp5TrsWP+uD0wJYXuWiYzWbGx8eVxSUcDjM3N8f7779PPB7n/PnzmEwmZb49evQogUCA06dPEwqFmJ6epq+vj1gsRqvVYn19na2tLd5//32sVivxeJxyuawCDrthIEjlpV6vs729TSqVolgsYrPZOHLkCH19fQQCARKJhDoNdsNzPwjGmI7h4WGi0SjBYBCz2awKsgGPZbJoDhbSciIXz6mpKX7t136NsbExzpw5syPWLp/PUygU+Pzzz6lWq0xMTOD1ehkbG8Pr9aq17NSpU5w5c4Zjx46xsLDAp59+SqVSYWtri2Kx2LVxMEalzGazceLECcbGxjh9+jRTU1NUq1Wy2SwzMzN89tlnrK6uqqKQ3SgPiVxXTCYTNpsNn8/HSy+9xPT0NIcOHSISiXDp0iVWV1f55JNP+PnPf06hUOgJxUU+o3TXynHwKONcjDyQ8iIH7ODgIKFQiCNHjjA1NUWj0eDmzZvMzc0xMzNDJpNRJtdUKqVSNmXcS19fH5lMhmAwSDQaVacg+Z6tVov5+XnMZjPpdJrV1dWv1YbpVKS5v1qtUi6XyWQyWCwWbDabOglK81u3KG33i1w4ZDbNxMQETqdzh1nS6DIxZtT0IsYxYrfblQUhHA6rWJhuwTgnvF4vwWCQoaEhhoaG6Ovro9VqUalUyGazZLNZbt68SSqV4sKFC+owEIlEGBgYwOv17qhDBbfiZ4aGhpicnOTkyZMsLCyQTCZpNBpKae6meSmVQOmWPXr0KGNjY8rKmUwm2d7eZmFhgcXFxUeaRXLQkLIZHx+nv7+f8fFxYrEYzWaTdDrN7OwsN2/eZH19nVKp1BOVvo0ZxrLOjc1mA1Du60e9Ft+X8mJ0F5nNZp566ilOnz7Ns88+y7PPPsvHH3/MT37yE2ZnZ/noo4+UjxAgl8sBMDMzg8Vi4cKFC3g8Ho4ePUo4HOa1115T6Z6Dg4NEo1Gee+45Lly4QCAQ4Nq1a6ysrCgtuJMXDnnfzWaTQqGgUg/b7TYjIyM4HA5sNpuqQdGryJLTbrebs2fPqiwju92u6gTJLzlhetlKBV8F0Xm9XgCGh4fVGMtmsx09byS7XTfRaJTDhw/z1FNPceLECYQQ1Go10uk0MzMzLC0t8ed//uckk0m1/jSbTSYmJjh06BDhcFiNI1ncz+v1Mjg4iNPppK+vj08++YQbN25QKpUoFArq/+4GjO7Zw4cPMzw8zLe//W3Gx8dVUb+ZmRmuX7/Op59+yueff94zVk4pG6fTyQsvvKDiqQYGBkgmkySTSd5//30+/fRTlpeXyWQyXV+mwegWcrvdnDlzhomJCVwulzoAVCqVg+U2Mkb1y9oJo6OjOJ1OisUi6XSajY0NstnsjvgU42Ijq8vKVMX19XWKxSLhcJhCoUA+n2dgYACn06kWjtHRUQqFArFYjHK5rIrfdAOyUmM6ncbhcDA4OPjYzG6dgLFSs8Ph+JqJWlpc8vk86XRaZYZ0y8byTdwp5keOqUqlQrFYJJfL7eh51C1IK2UsFuP48eMMDg6q6sKpVIp4PM4XX3zB+vq6Sl2V7Teq1ar6N0CpVKJSqbCyskIymSQWiylXdn9/P7FYjPHxcba2tiiVSl2pJFssFsLhsLKCO51OCoUClUqFpaUl5ubmSKfTXTmWdmMMXHY4HPT19SnLnkwBXl5eZn19nfX1dVKpVE+sPUaLSygUIhKJEAwGcbvdqr+cjIV61NyX8iIVkkAgQDgc5uTJk7z88stks1kWFha4ceMGFy9epFKpKIuLcbMxPlAulyOXy5FMJpVm7/f7OXXqFFNTU5w+fZpTp04xNDSE3+8nGo3SaDRYWVnho48+ol6vd7R2a1TqyuUy8/PzVKtVxsbGVIprr2K08Emlxe1243K51HiSC0SlUqFUKrGyssLMzAy5XE4FcPYC8gRkzPpot9sqiHBxcZHZ2Vny+XxXLKrGaq8+nw+fz8fLL7/M3/k7f0dVz93e3ubTTz/lypUr/Mt/+S8pFouqjlSj0cBqtSqlTlpyk8kkiUSCt956i08//ZSzZ8/y0ksvMTExwYkTJzCbzWQyGa5fv87KykrX9NEyrjNOp5OnnnqKiYkJ+vv7cblczMzMEI/Heffdd/nss89U0HK3b9LwVQHIWCzG2NgYL7zwAiMjI7hcLkqlEj/96U+5cOECly9fZmNjQx20ulkucr3x+XycOXOG8fFxJicn6evrU1W9pcvowLiNjDfjcDjweDx4PB7cbje5XI5KpUKlUqFard61MuzuaGT5JYQgn8/TaDRYXV1VqYvBYBCn04nH4yEUCjE6Oqpqv3QTzWaTfD6vZNDLiotEBoTLqs12ux2r1ar+JpGbtlRipL+5F5BF2GR8i6yxAF8tMsaS992CsbyAXIPcbjdms1lZ4dbW1kgkEuTzeSqVytesv9lslu3tbTY2NlRF4s3NTRKJBJubm6ytrbG4uIjH46Fer2O1WolGo2xsbHTN+JLrr2xTEolEiEajhEIharUauVyOlZUVlpeX2d7eVvOrV5CutP7+fqLRKH19fbhcLhX8Ld1G5XJZHRy6+dAkLS4ul4toNMr4+DiRSGRHBW+5P8sMv0fJfVtehBAEAgGGhoZU5kcikSCbzaqF4pvy/o1au1xUpCUmkUgAMD8/z9zcHM8//zy/8iu/wuHDhwkEAnz44Ye89dZbyuTbyQuJHOy1Wo3FxUWazSaVSmXH33sR6VqUrSZkYLjf799hsZIN9mQ2iXQbdUNA914YYxTcbjfhcFhVkJVBc92krOxGPpvf7ycWi9HX16f6FRWLRebn53n33XfZ3NxUp0BppZUlCq5evcrKygqhUIjr16+ztrZGOp3m0qVLLC0t0Wg0WF5eJpfLMTw8TLvd5uTJkxQKhY62+BqRMWV9fX289NJLKp4jEAiwsbFBOp3mL/7iL5S1yVjvppvZbd179dVXGRsbY3x8HLvdzgcffMDKygpffvkls7Oz1Gq1nrG4hEIhzpw5w6FDh/j+979PLpfj/fffB+DEiROqYK2ck4+SB4p5kTEIMitGToIHaWwmXyfjPKTfLJ1OK21fanIykLWbBomsRiiLi8lgL1nUTwYnd7qidr/IstPSpyp7rMBXWR7tdptyuUw+n1dt6LsxFuFOSBlIy5RslbDbrdattNu3uoz7fD7sdjvtdptiscjW1haJRILt7W1VH8qI0d0ohFCB8rJtiezAXSqVyGQy6kAmeyRJ5bDTMVo2ZVxhLBbD7XZjsViUG21zc5Otra2uylTbC7nWmkwmrFYrHo+HcDhMKBRSlr2trS02NjYoFAo74qZ6AZms02g0yOVypFIpNjY2VOFH2YrkcRwgH0g1MiovsgWADHirVCqq4NP9IDdpqQyVSiXVVE5aJHK5nKq10E1Ipa3VainT5OjoKOVymUKhQLFY7PrNCL6ezRaNRnnzzTcZHx9XgeFwK0jX4XDQaDSYn59naWmJ2dlZVlZWuiaVfi/kKchisdDX10c0GmV0dJRoNNo1m+teyNg7WUE3EAhQq9W4dOkSP/7xj5mbm2Nubk4pstJSYhwT0r393nvvYbVa1cGrVCphtVqV+0kqNC6XS9WD6fSxJceP3+9nenqa6elpfv3Xf1256XO5HO+88w6zs7NcvXpVxSV2i8VpL4xW36GhIaanpzl9+jThcJhisUg2m+Wjjz7i5s2bJJNJdeDsdmuUVEhkU+X19XU+++wzyuUy8XicgYEBvve97xEKhUgkEo8l6eSB67wYtSvjv42VT+9nkhs3LlnvxOFwqAkjF5NyudyVyov8sOVzy5iiXlgwjMhxYzKZcDgcBAIB+vr6VPsEWYejUqlQq9VIJpNsbGyQz+ep1Wo9sZBIZL8nOV/sdnvHb6z3ipwrTqdTtYQoFoskEglSqZRyv0rT9W65yE1KlnAwWoClwiO/ukkZlvPLbDbjdDoZHBxkYGCAaDSKx+OhWq2Sz+eV5aVUKqmYn15Zi2QcUF9fH319ffj9flwulzpAp1Iptra2Hkstk4OEEIJGo0GxWFTWyXq9Tj6fx+fz7ViDDly2kRE5mYUQeDwe+vv7iUQiqrOm3GS+acDLh5TuJo/Hg8Ph4JlnnuHNN9/k0KFDKoPgk08+4ebNmzsqq3YTZrMZv9+vzLjVapVr1659LT6oWxbSO7E7RXF3OwCr1Uq1WuXq1asqO2RmZob19fWu2mTuh24PFNzN7oB/+VWv1+9oyt8reeBOyAJ2sg7M8PCwcu128mYlD0l2ux2Px8Phw4f5jd/4DQYGBohEIlQqFc6dO8fq6irXr19ndXWVWq32WIIvnzS7x0sgEODZZ5/l0KFDDA4OAvDll1+ysrLC7Owsa2trPRHrshuZ2SmVf2BH9WGfz3fwextJS4EMHPR4PHi9Xnw+H+VymXq9rl57L8jJIc2z0WiUkZERfD6fCshMJBKk0+mubAQmTeEWi0UFYno8nkce9HQQkYqLbPjpcDjU36R7MZPJsLW1RTweZ319nXK53HVj4l6R89FIN8rCaJ2V1gO5eUi3j+yu/U3sPhDs/pvT6SQYDOLz+VQXapmM0MkKDHzVdNHn8zEyMkIoFFKF+zY3N4nH48pFL1PJu3E87cYYRyYP5OFwGLvdTq1WI5VKkUwmKRQKKni5F1LGjTSbzR17u1RcpMdEZoQeuK7S8oMqFAqkUinS6TSZTAaXy8Xk5CSNRgOPx8ONGzd49913VczGXi4k6Uvt7+/H6/Vy/PhxRkdHeeGFFzh69Chra2v89Kc/5dKlS3zwwQcqCK+bBo0MeJZBz0IIlSrucDh6SoERQjA4OMjzzz/P0aNHOXr0KF6vV21QUhayaqysHNut1ri9MI6ber2+w+IgN/dumSfypCeEYGBgAL/fz5EjRzh8+DAej0dZXO7XCiVN3LsVo6GhIU6fPs3IyIiy9pXL5Y4OXDUWGItGowwMDDA2NobNZlPp4R9//DHLy8ukUilV8qIbxs9eyLHVaDQYGBjg2LFjPPXUU3znO99Rh/FEIsGHH37I4uLijiKp3S6bO2GscP8kx8cDKS/1ep1SqUSpVKJYLGKxWFTaojyhuN1uFXhr1MJ2n3jk6cnv9xMKhRgZGWF6epqBgQHlg11cXGRlZYV4PK5Mt902aGQBrWazSavVwuFwqKJbvZBxZJwMPp+P8fFx1bVVWl6Mzy8Xm3q9rk4C3SqbvTAuvHLs7O643S3IOeB2uwkEAupLNjm9mwJ7p3lzt81HKj9ut5tIJKLWMenj7/TsEhnP4XK5VFwdQD6fV1WJNzY2qFQqNJvNrghQvh9koO7w8DDDw8NYrVZKpRLZbFZV05UhEb2MVPqf5Ni4Z+XFeJJbW1sjk8kwMDBAqVTiqaee4tixY0SjUVwuF1arlXK5rCp8SguM0d0jTU5jY2P09fXxzDPPMDIyoky16XSaH//4x1y4cIH333+fdDq9o19Ct0woGQQlWytcu3aNXC6nTLkyTbhSqdy1+F+nI03xXq+XQCDAsWPHeOONNwiHwzvSf41dhI8cOYLf7+fnP/85yWSSYrGoarx0O3Iu1ut1Njc3cTqd3Lhxg2KxSDQaVamu8Xh8RwxIp48d+QxOpxOfz6fiw6S7SAZ1y1ousgyBcaHdrXgY3bXSCmys7FwsFpmdnWVubo6PP/5Y1YDpVFlK5W9iYoJIJKKC3n/4wx+ysrLCwsICmUym6xWX3QkifX19+Hw+Tp06xXe/+11isRhOp5N0Os27777L8vIys7OzqjlnL6wz94pU7svl8teMFY+S+7K8yDRmaaqfn5/H6XQSjUYRQqg4jVqtxokTJ0gmk8CtIJ/Nzc0dp0KZ6nno0CH6+/t5/vnnmZiYUKer9fV1rl27xvXr17l+/bo6ZXdbcKJcYKWCt7Gxgc1mo7+/X8W+yCybbra+tFotbDYbfX199Pf3c+jQIdxut/KhGjv5mkwmwuEwQtzqUO5wOFQWWrfKx4jR+iTdZ8lkEofDQb1ex2Qykc/nyWazVCoVGo1GVzTS211nSvZYq9frKnhSfsnXGy0suxUX489yE5PKiywDUa1WSSaTLC4u8vnnn5PJZDo+5s5msxEOh/F6vdTrdTKZDJcuXWJtbY3t7W3K5XLX1dO6G7LonsfjYWBggJGREY4ePUpfXx9Wq5VKpcKNGzdYWlpia2uLXC6nFF3NzuB5magj1+hHzX0HUxh9xAsLC5RKJdV3JhgMqp4YZ86coVQqcfjwYarVqmrWuNvyMjQ0hNvtxm63s7m5yc2bN1lZWeHmzZvMzMyoXHqj4tMtGBdUmYJ29epV8vk8Xq8Xq9WqgqC6eSExugOGh4eJRCLY7XaVAitfU6lUuHbtGvl8ntnZWba2tlheXlbafq8EFsJXY0e6zcrlsrJM1ut1EokEa2trqsiavKaT5SM/32w2i9VqJZ1OqxoswWCQI0eO8O1vf5vFxUUcDgeVSoVMJkOj0VCxKlIGMoNGKnWyAF0oFMLn8xGJRCgWi6oH0vz8PGtra19rNdApSIuly+UiHA4Ti8Ww2Wxcv36dxcVFlpeX2draUrW2uh255thsNtxuN0ePHuW1115jcnKSWCyGEIJ0Ok0ikWBhYYH19fWeiQG6V4yHqGq1SjweV20CpAX0Ua7JDxwJ2m632dzcJJ1OMzAwQH9/PyMjI3i9XlwuF9PT06q4nPQX73b5SGuN2WxmdXWVVCrFl19+yYULF1hZWWFlZWVHw7luHTQyW6RcLrO8vEyr1eLMmTOqtkK3p+PJ53c6nUQiEfx+PzabDbPZrMZMu32rb8bc3BzJZJIvvviC7e1tNjc376klRTcizbXS8iAberZaLdLpNFtbW8qdJq0KnYw8vBSLRcxmM/l8nlKphMfjwefzMTw8TLPZxO/3UywWyWQyrKysUKlUyGazO2qcuN3uHcqLrOA8OjpKf38/gUCAarVKOp1mbW2N9fV1kskkrVZLHSY6ZbzJjVrGuvh8PkKhECaTSfUu2tzcVFalbnYXSYyFHp1OJ2NjY7z00ktEo1EikQilUon19XXlzt/c3Oypvk73gzwwbW9v7yhp8ajH0UPVeZG1WWZmZqjVakQiEc6dO0cwGGRsbExtvPKEuDuXvtVqkUwmKZVKbGxskM1mmZmZYXV1lXw+r2IcunkiGS1ZjUaDVCqF0+nEZrPh9XpV+rkxwr1bMAZtm81mwuEw09PTDA4OKuXF6JtutVpsbm6yurrKzZs32draIp/P72g82M1j5W4Yn1mIW80ap6encTqdXL16VXVV7vSTo7zvarVKoVBgc3OTlZUVzGazqg81MjKC2+3G5/ORyWRYXFxke3ubixcvqmwki8VCf38/NptNuSVlewDZaG9hYUEVvstms6RSqY617MmNOhQKcfjwYY4fP8709DSpVIoLFy6oYnS1Wq0jn+9BkVYmmRzh9/ux2+3KVXju3Dnm5ubUOgN0nfX/YZEJA5VKhfX1ddrtNseOHcNkMvHOO++wsbFBrVZTMWj7Ob4euMIufJXxMTMzo+Jf3G43sViMEydO4HQ68fv9Oya9MaukXq9z7tw5EomEOiXKdERjDn0vTCij8iJjPaTi4vV6uzZdWp4IrVYr4XCYQ4cOEYvFVIaRsdihtPYZlRe73d5VKcH3y52yZWw2G4cOHcLv9zM4OMjm5iapVKrjNyep6EtrrhwLsi6U0+kkEAgwODjI0aNHyWQyzM3Nsby8rDpMy7oU4+Pjqh+UEIJiscj29jbJZJKtrS22trZIpVLK8vu4/PiPAqPycurUKZ566ikOHTrE7OwsiUSCRCJBuVymVqv1TJVm48HJbrfjdruVxbder7O1tcX58+dZWlpSCi3QsQrso0QqL/F4HLPZzPe+9z18Ph/9/f0sLCyQy+UeSZD7Q++I8oZkBUq4dYqRfmWHw3HHm5aWG2llkeZtY+xCLw0SuTBWKhVKpRL5fF5Zn3pBFq1Wi62tLa5cuUKr1WJqakqliNfrdbLZLPF4nM3NTba3t9Wpqdvl8k0YLU7yy2w24/P5aDabDA0NqXL5xWLxCd/tg2PMGJJlBZaXl7Hb7eRyOba2tujv72d0dFSVcpDWXmmRkb+Dr9pLyKKX+XyeRqNBoVCgVqtRLpeVRc8Y+NupyEQHaWmSDU3j8Tjb29tA71kVds8dk8lErVYjm82ytrbGzMwMiURCu4u+ATk3ZR8+KS9ZcLZUKj2S+fNQyotx42i1WlSrVeVfXlxcBL651sSdLCyd7pu/X+SzG83U6XQat9utCrMZ3UvdhHyuVqtFPB7n448/RgjBCy+8oFxHxWKRxcVF1tbWWFlZUY2/ZNBlrysw8NXmJJWXQCCA3W5ncnKSWq1GPB4nmUx2tBvWeFBqNpvcvHmT9fV1ZmdnicViPPXUU7z00ksUi0XS6bTKhHS73Rw+fFj1pqlWq6yurqo6VfV6XRVlS6VSZDKZOzYi7FS5SYwNcyuVCul0WmXRyL93+jPeL8bnlWvN2toa8/PzXLx4kXw+39N1pO4FeaDI5XIqQB7A4/EQCoVIp9PA/fc7/Cb23RdhDMa919cbFZdeHSBGf34+n+fzzz9XWQDpdFqlwHaTfIzZVu12m3w+z+rqKpcvX+YnP/mJipkql8sqjVPGRkkfaq8jZSiLQ0rffCKRIJPJsLy8rIqOdYsFzzhXADY3N5WVRWYW5XI5HA4HwWCQVqul6kxJOSUSCZVNJC01Mui52+YZ7KzFIS0uiUSCbDarTsbd9sx7IZV9OVZmZ2f56U9/SqlUIplMMjc3R7Va7YoSA48aqbxks1mcTifr6+vqgGHsi3Xg3EaS3UGDvWaCfBiM8pJVPP/Vv/pXWCwWZcauVqtdaZEyWpRkYbXl5WUuXryoUu1kR/F6vU4ul1OuND3GbtFqtZSrMZlMkslk+Pjjj9nY2ODzzz9nY2ODQqHQNZuyVMLK5bKysgghuHbtGh988IFSYGw2Gx6PR1lgZH2Yer3OzZs3yeVyypUkZSPrvXQTMqhSBjpvbGxgMplUzIvsC9ZL80muLVJpfe+997h48aJqsyHdrJ0c6/Q4kJbeVqtFIpFQTXNljJ3s+v4o6K5Z2sEYA5pbrRbFYlH5YLulyNi9IIRQfme5oMqAMGMMQjdswg+LlE+9XletMz799FMsFgvXrl0jnU6TSqUolUpd2/vJOG+kBUUmEsg5U6vVlBXParXSaDS6qnjfvWCcVzK9fnNzs2vHxb0iFTupwMnGg9L1odeZe0eGjszPz5NKpVhfXyeTyTyyRAGxVwyFz+friACLXC73REaYw+F4JPLZXRnU+P1BqFQqT0Q+Xq/3vuVjdCPdiUfh+sjn809EPh6PZ1/Gjzz9yO+A2pj3Y3MuFAoHcvzsVTF3N/L0vLvVxJ243/H1pMaPy+W6p/Ejx8HAwACjo6PALVlls1kWFhYeeY2kUqn02OVzP3PrTmvO43KxPqm55XQ6H8neZVyL5D72MJ3Yy+XyXeWjLS8HlN0TqVesDbsDk42p9fLvmp0Y6yjtVnylvLrR9G20ukh2zxP5N2lhuFNvtG4fU3IcSMuL0dqguYXcZHtpXDwKZPyd0d32qOSolZcDyG7/c69Nojv533tNBvdLtwTjPgh7bTjfJJNekJk8DcvyC0Z65VC0F7uTRuTvNA/G4zooaeXlgNLrk6fXn19z/+gxc3dkRogxxsXoZtTo8dNp7BnzotFoNBqNRnPQ6D5HuEaj0Wg0mq5GKy8ajUaj0Wg6Cq28aDQajUaj6Si08qLRaDQajaaj0MqLRqPRaDSajkIrLxqNRqPRaDoKrbxoNBqNRqPpKPZNeRFCBIUQ/1YIURRCLAkhfvs+rrULIf5ECJETQmwIIX5/v+7roKDlc3eEEH9PCHFOCFEVQvzpfV77LSHEu0KIrBBi8dHc4ZNFy2dv9NzaGz1+9kaPn705qPLZzwq7fwzUgH7gaeCvhBAX2+32lXu49g+BaWAMGADeFUJcbbfbP9rH+3vSaPncnXXgj4DvA877vLYI/AnwL4D/ep/v66Cg5bM3em7tjR4/e6PHz94cTPkYm7k96Bfgvv1whw2/+6fA/3CP168D3zP8/N8C/3I/7u0gfGn53LOc/gj40we89rvA4pN+Bi2fxy4TPbfuXVZ6/Hz9ufT46VD57Jfb6DDQaLfbNw2/uwgc/6YLhRABYPD26+/r2g5Cy0ejeTTouaV5GPT42ZsDK5/9Ul48QG7X77KA9x6vla+/32s7BS0fjebRoOeW5mHQ42dvDqx89kt5KQC+Xb/zAfk7vPZO18rX3++1nYKWj0bzaNBzS/Mw6PGzNwdWPvulvNwELEKIacPvTgPfGNDTbrfTQPz26+/r2g5Cy0ejeTTouaV5GPT42ZsDK599UV7a7XYR+DfAPxRCuIUQrwB/k1uBPQghxoUQbSHE+F3e4s+APxBCBIQQR4HfA/50P+7tIKDlszdCCIsQwgGYAbMQwiGEsBj+3hZCvHmXa023r7Xe+lE4hBC2x3Hfjwstn7uj59Y3o8fP3dHjZ28OtHz2MSo5CPw5t1LrloHfNvztNWARsN7lWju30vFyQAL4/ScdZf0Iora1fO4umz8E2ru+/vD230ZuP3foLte+eYdr33vSz6Tl81jlo+eWHj96/PSYfMTt/+CRIoT4AyDZbrf/8SP/zzoQLZ+7I4T4u8Dxdrv9D570vRxEtHz2Rs+tvdHjZ2/0+NmbJymfx6K8aDQajUaj0ewXureRRqPRaDSajkIrLxqNRqPRaDoKrbxoNBqNRqPpKPZszOjz+ToiICaXy4kn8f96PJ6OkE+hUNDy2YMnJR+3290R8ikWi09EPn6/vyPkk81mn4h8XC5XR8inVCo9dvkEg8GOkE0qldJrzx7stfbsZ1fpe+JuAcJCPJHPUKPRaDSaO6L3q4PLY1VeDLnftFot4NYgkANBD4hb7MqTv5XTfls2JpOpJ+UkhNghD6N8oHflcq8Y51273cZkMmEyaa+xRnMnjGvLbgXGuBbpNefJ8diUF+OHLRdPiRwAxk261zHKYbdMelFOxgXkTrLpNXk8KCaTqSfHj+RBSkP0qqyM3EluWi6aJ8ljUV7a7TbNZhMhBFarFYvFgtfrxWw2UygUqNfr1Ot1ms1mz56gjZYEIQQejwer1YrVasVsNlOr1Wg2m5RKJarV6o4NqFfkJYTAbDZjMpnweDyYzWbgluzy+TzVavUJ3+HBQ44pOY5cLhdWq5VSqUSxWNzxmm6n3W7TaDRot9s7LFDS+rv7RG386lWkPBqNBq1WS8nDZDKp+ddN7Lbumkwm7Hb7jjFQr9d7Zs58E8a51Gw2ga/2I4vF8kitu49cedm9KdtsNmw2G36/H6vVqhSWZrNJs9ns6VOhXBjMZjMejwebzYbL5cJkMlGpVKjX67RaLbUA99IEkuPCZDJhtVrxer3YbDYlh0qlopWXPbBardhsNrxeL263m3a7TalU6pkxdCfTv/y+l5VT8xW94uKXa41ci+12u1Jy5XrTbDbVpm2km+XyTTxud9ojVV6kViY3G5/Px5kzZwiHwzz33HO43W4++ugjVldXuXLlCvF4nFqtRr1e7xkLjJwoFosFl8vFoUOHCAaDvPjii4TDYYLBIHa7nVQqRbFY5Pr166ysrLCwsMDCwgKtVmtH/FA3IseRyWQiEAgQDAb5rd/6LYaGhmg0GlQqFf75P//nfP755z0zbu4FaWkQQjA9Pc3o6CgnT55kfHyct956i7feekspw9Cd48cY62Oz2RgdHcXpdBIMBjGbzaTTaXUwkCdHk8lErVZTY6tQKKjf9wpSbjI2amxsDL/fT7VapVqtkslkyGQyQHeMm93xcx6Ph1gsRiAQ4MSJE8r6Ui6XOXfuHFtbW8TjcYrForIG3ykUoluRB0npRXE4HAwMDCgvQb1eZ2VlhXw+T6PRUHPLeO3DWjUfmfJiHAxmsxmfz0c4HObo0aOMjIzwne98B6/XS7lcxu12E4/HSafTNJtNarVaT1hgVIOp28qLw+FgbGyMwcFBzp49y9DQEAMDAzidThKJBNlsFpfLhcfjoVgssrKyosy53Y40WcuN5/nnn2d6epparUapVOLtt9/eIU/NLaTS19/fz9TUFM899xzHjx/n6tWrmEymnhg7EovFQiQSwe/3MzIygtVqZW1tjXw+rxQYuLVeVSoVKpUKAPl8vmfHlLQ+RCIRhoaGyOfzlMtl6vU66XRanba7QT7STSTdq4ODgwwMDPDss8/idDoxmUzk83nW1tZoNBokEgnlOZDXQ/cH9Bqt4DabDZ/Ph8/nY3JyErvdTqlUolwus729fUfX9H7J55EoL9Ia4HK5iEQi9Pf38/rrrxOJRDh58iR9fX3Y7XYAjhw5Qjgcxmw2c+jQIS5cuMDCwgKNRkOdGrttAMhN1mw2Y7Va1WYcjUZ5+eWXCYVCjI6O4vF4EEJQq9VUrMIzzzzDxMQEgUAAv9/P4uIiN27c6Ak3kslkwul04nQ6sVgsahJId6PmK6Q8bDYbdrudyclJnn76aWq1GpcvX1ZWzm5dZI0WF6fTyfDwMNFolB/84AdEIhEGBgawWq0kEgmKxaKytEg/fT6fJ5/Pc+3aNT799FPK5TK5XA74uqup2zBaOiORCH19ffzgBz/g5MmTLCwsEI/HAVhbW9txTSfKZPe6GYlEOHr0KENDQ7zwwgsEg0EmJyex2WwIISgWi6yvrxOLxRgcHCSXyynFd3t7m2w2q2I4u9Ed2Wq1aDabuN1u+vv7GRgY4I033iAUCnH48GFsNhu5XE5ZXBYWFvB6vbhcLlwuF3a7nc3NTRKJBIVCgUwms8N7cD/su/IiB4N0F0WjUSYmJnj99dcJh8MMDw+rgdButxkZGaG/v59Go0E0GlXmOKndQ/d88EZarZbyp4ZCIZ599llisRhnz57F5/NhNpsRQqjBYrfbcTqd+Hw+AJrNpjLHzc7OKh8sdKe85IYhN2OLxYLZbN4RMKb5CqPy4nA4iMViTE1NsbS0xOrqKtvb2+pw0K3uELkW2Ww2hoeHGRkZ4ZVXXmFoaIhwOIzFYmFra2uH20gmFKTTaWVZmJmZQQih3CTdGKi6Gzl+gsEg/f39vPDCC7z++ut88cUX3Lx5k9nZ2Sd8h4+Gvr4+jh8/zvj4OC+//DJutxu/36/W41KpxPT0NH19ffj9fkqlEqlUilKpRKvVolqtqu/d6MKWh0Wz2awO2a+++ir9/f1KeUmn02QyGb744gvK5TJjY2PK3e/xeLh+/ToWi4VEIkE+nwd48sqLXCysVitut5vR0VHeeOMNtXC43W51YpYfqtPpxGazEYlEaLVa+P1+HA5HV0Z0GyPYbTYb/f39PP/884yOjvLMM88QCATUsy8sLFAsFimVStRqNRwOB1arlYGBAYLBIOFwmDNnzlAul1leXiadTqsTUbdNGLnBulwujhw5wtjYGMFgEJfLpRYNzU7kyXlwcJBQKMTg4CDBYJDz589z+fJlEomEek23IU9yDoeDYDDIyMgI3/rWtxgcHCQcDuN0OpWyb7PZVEB8o9GgUChQLpeVtWZkZISxsTFMJhOrq6tA9yovxvXJ7Xbjdrt58cUXOXLkCIODgwCUSiUymUzHB8fLvUXGqwwNDTE+Ps7hw4d5+eWXCQaDuN1ubDabGityXR0dHSUcDjM6Okq1WqVSqVCr1Th58iRbW1vcvHmTmZkZCoUC2Wx2x//XqWuzHBt2u51gMMjY2Bjf/e53GRoaIhqNYrVa+fLLL6nVamQyGWq1GpOTkwwODqp9TcrU5/MxODjIxYsX2draolqtPpCh4pEoLxaLBZ/Px8jICK+//jrRaJRYLIbValU3CajMEYBwOIzJZFLKi8yE6NQP+25Ii4vNZmNoaIhvf/vbDA8Pc+bMGex2O41Gg2KxyNzcHMlkkq2tLcrlMi6XC6fTydNPP60GQn9/P9lsluvXr7O8vMz6+vrXauh0C9IPffjwYSYmJpTyYswA0NzCaPYfGBhgeHhYKb3pdJpLly6xubnZtXKTp0ObzUYsFmN6epo333xTBcCbzWaVsSezHwEVnLu1tcXU1BSxWIyRkRFGR0cpFos9oSTL8eB2uwkEArz00ks888wzDAwMACj3mYwH6lSMyovNZmNsbIw33niDQ4cOcfbsWWVpMaYAS0ZGRoCvCmNKy+/29ja5XI533nmHarVKPB5Xyks30Gq1sNvtRKNRDh8+zHe/+136+voIh8MUCgUuXbpEJpMhl8shhOC5555TQbwy7tXtdjMwMMDU1BSVSoXPPvtsh9v/iSkvcGtQOBwOIpEIkUiEYDCI1+tVDy+/l8tltZHLuI5uVFYkxmCwaDTK9PQ0R44cYXp6mkAgANw61SwsLJBKpTh37hyJRILt7W0qlQp2ux2r1YrdbsdutzMwMIDf71eB0JlMZsdG3i1yNMrN4XAwOjrK2NgYNputJzaT+0V+9tLH/PTTT3PkyBFarRZzc3MsLy+zsbFBsVjsuiq7xlgyOUfOnDnD+Pi4OhTJzUgumNIte/nyZdbX15mfn2dzc5Nvf/vbytrgdrtVnJUcc900x4zI+TYwMMDg4CD9/f0Eg0EKhQLpdJobN25w8eJFNjY2OlIGRmW93W4TDocZGhri6NGjHDlyhGg0qubEbqUFvp4ubvzZ5XJhsViYnJwkl8up2DJj/GanImu0RaNRnnnmGQ4fPkwgEKDdbnPhwgW2trb44IMPlLJmsViwWCwEg0FKpRKNRkMl60gDx8OuPY9EeXE6ncRiMWVSknUlZEpmq9Uil8vtMD3uTqXq5A96N7sLHo2OjvLd736XiYkJTp06hdVqpdFokM/nOX/+PKurq/zsZz8jHo+TSqWoVCoqLc3v9+P1etVXKBRiaGiI7e3trpObEYvFgtvt5siRI0xNTamNSBcS24mUicfjIRgM8vrrr/Pcc89x/fp1Ll26xMzMDIuLi+o01E1yM1pcfD4fo6OjvP7662oDtlqtO5QXqbhUq1U++ugjzp8/z82bN1lfX6evr4/nnnsOIQR+vx+3263maTdirF9iMpkYHR1lcnJSxSReuXKFtbU1zp07x/vvv7+jRlAnK8CDg4M899xzPP3005w5cwar1aqy8IwJI3spLfK7jI05ceIEfX191Ot1Lly4sMMtYnx9pyAPBBaLheHhYb71rW+pTNj19XV+/vOfs7y8zI9//GPy+TyBQACn00kqlcLr9bK6uko+n+cHP/gBzz//POFwmFAo9NDrz767jQDsdruKUrfZbDsyQ/L5PIVCgU8//ZRcLsfo6Cg+n09lz4TDYQYGBqjVaqRSKfW+nfaBS4zmsEAgQCwW4/Dhwxw6dIj+/v4dE79er7OxscHa2hq5XI5yuawGjnyvTCbD+vo6ExMTNBoNbDYboVAIr9eL3W5Xhew6WWa7uVtFz90bUa8jLQkWi4XBwUEGBwfxeDwqXuPatWtsb2+r13fL+DAeDsxmM8FgkKmpKSYnJxkaGiIQCGA2m6nX66ytrVEqldjY2KBarSoL3tzcHKurqxQKhR3uNKvVSl9fn6p4Dahq4d2C0VorA1SnpqaYmppSB8/t7W1WVlZUcGqnZ4LKk38oFGJkZIRQKKSU20qlQi6XY2lpSVkb7HY7fr8fIQTb29uq4jncCvJ1Op1qzDgcDvx+v4pNzGazHRsGIePHvF6vSpcfHh7G6/WSy+XY3t5mfX2djY0N5Yqt1WoAbGxskMlkdtRSulN9lweVyb4pL/KDa7fbeDweJiYmGBoaUu4OIQSVSoXNzU3W19f5Z//snxGPx/kbf+NvcOjQIaLRKIODg0xNTbG5uUmpVGJlZWXH+3faB29cVE0mEyMjI7z22ms89dRTvPLKK2oCyQFSLpe5ceMGCwsLJJNJCoWCchXJgbGxscHVq1eZnJykUqngcDgYGRlhZWUFl8tFuVymUql0pLzuhHETsVgsX8suajQa1Go1rbywMxPg+PHjTExMEAqFMJlMXLx4kZ/97Gckk8muy4IwZjc6nU7GxsZ48803mZqa4tixY9hsNhqNBrlcjs8//5x4PM5HH31EJpMhEolgs9n44osviMfjO5TkdrutfPShUAin0wnwwD76g4ocN1arlUAgwODgIC+++CJHjx4lEAjQarVYWFjgwoULrKyskM1mlVug057feMCWbuinn36acDiM3W6nXC6TzWaZn5/nrbfewuPxcPbsWXW4BlhYWCCdTqvD5enTpxkaGsJsNtNqtXC73ZhMJmKxGJOTk6ysrJBIJNRe0Elrs7RAyWJ9p06d4uTJk5TLZVZWVlhcXOTSpUtsbW2p9PByuUy5XCaVSqlnlcXrpMVX7olP3PJiTEmUdUtkdoPRstBsNsnlcmQyGbLZLLlcTlkKpFtEljHvZFOkRMrF4/Hg9XoZGRlhamqK/v5+2u02hUJBuXtsNhvZbBaHw6EG/17veSc6+SS0FzabjWg0SjQaVWNDBsql02kSiUTHBxA+LDIbwu1209fXx9jYGGNjY8o9m0wmyeVy1Gq1rhknRgXCbDYTCAQYGhpicnKSyclJNc9KpRLJZJJkMsmNGzd21JloNptYLBYVg7e715F0K8mTdjcGOEusViuDg4MMDQ0pa24ulyOVSrG6usry8jKFQmFfqqM+CYxrp8/nIxQKEQ6H8fl8OBwOVcdlaWmJxcVFFhcX6evrI5PJqFYkQggKhQKpVIr19XWq1Soul4tKpUI0GlXxi3IflBbyubm5HRbiTlBgpNXfbDbT19enrJhSQVlaWmJlZYVCoaDWX+MzGTOU7HY7brdbWYLlnHqYIqv7pry0Wi08Hg8DAwMcPXqU559/XhVZk4tCrVZjeXmZ5eVltra2VEqVFJK0Msi04N3C6DSkXGKxmLK2fP/73wduZTasrKzwzjvvYLVaGRkZoV6vE41GAbh+/boqiiWRC8ZuX6HR6tVNi6t8Fq/Xy3PPPcfExAQ+n09ZrGq1Gjdv3uTGjRvKxdiLyHEmM9hisRhvvvkmY2NjvPPOOywsLHD16lUSiUTXNdQz+uOnp6f59re/zeHDh3njjTeAW9kxyWSSX/7yl6yurvLDH/6Qra0tSqWSslIZM0bk/JLvWalU1Fol06mhew4K8pllSfwXXniBiYkJDh06RDgc5ty5c6yurvKLX/yCTz75RI2zTkWukWNjYzz11FMcPXqUwcFBLJZbW2E8HucnP/kJs7OzvPvuuwwMDHDo0CF1nbR+z8/P8/Of/5zNzU2Wl5eZnJzklVde4fjx4zgcDlwuF9PT0zgcDvr6+rhx44ZKne6EjFA5H2RR0PHxcV588UUGBwdpNpvE43F+/OMfs7q6SiKRoFqtqnkjkf8OBoP4/X5isRixWIx8Pk86nVahEfJAdb88lPJiDNiS2tnY2BgDAwO43W4cDgeAqp+wvb3N2toaq6urqjMy7CwX7HK5CIVCO7oGdxpG06TFYqG/v5+JiQn6+/txOp0UCgU2NzdZXV1VwZPS9ZFOp1Va5p0+UGmZkc3CGo0GpVKJSqXSVYXqjO42Wak5FAopxUW6i3K5HOl0uuPrTuwHshibLATZarVIJpOsra2pMXXQF837QW4mTqcTj8dDNBplZGSEcDiM1WpVbup4PM7i4iLxeFy1ApBWlN3KiHwvuXbVajXS6bSy0txtXnYiRre2bAIrx4/FYlExeEtLS2Sz2Y622hn3KpPJpGI4ZCxTo9FQro6NjQ1SqRSNRkNZLR0OB/F4XL1Wvo/JZGJ7exuz2czo6Ch9fX0q09ZutxMIBFQTWXkg7yRkO5ZwOEw0GsXj8VCr1SgWiySTSdLp9F33HKkY2+12VepDVtiV8TDS8/IgPLTyIn18LpeL06dP89f/+l9nZGSEvr4+darJ5XKcO3eO5eVl/vIv/1LduKySKmMZms0mY2NjOJ1O5ufncTqdapPqpAkjF4X+/n6i0Sivv/46v/Zrv4bf78dutzM/P8/bb7/NzMwMP/rRj2i326r+hMViodlsUqlUdlhY5OQLBAKMj48TCoWw2WxUKhVWV1dJJpMqkK7TMcYw9PX1MTg4yJkzZ4jFYni9XlW+PZPJsLCwwOzsrOqh0UnjZD8wLsqBQID/4D/4DxgZGcFkMrG+vs65c+f44osvyGQyXZVdJOdYq9VS1t6XX36Zb33rW2oOxeNx3nvvPRYXF/n3//7fk8vlKBaLO/rRSORJc3BwkPHxcaLRqApUvXr1KisrK0pBljF83YCcZ6FQiLGxMb7zne8wOjpKs9kkkUjwi1/8gi+++ILV1dUd2X2dhNEiLUvUj4+Pc+LECQYHB7HZbGxvb6v4jc8++4xisagsb59++ilzc3PUajV8Ph+NRoO+vj7Gx8fVXvXFF1+Qy+WYn5/nlVdeoa+vT7lJFhYWVOzQ1tZWx1jH5Xw4fvw4zzzzDGfOnKFUKrG1taWaKUtL0p0MDVIp8fv9yuXk8XhYXV3lpz/9KTMzM2rdfhBDxb5ZXqxWKy6XS5UAlgul1Gg3NjaURpvNZpWvudls7siQcTgcygfZqYGF8r5ldlE0GiUYDCrfeiaTYW1tjc3NTfL5/A5ztd1uV4uy8dnle7pcLrxeL1arlVarRaVSUc3S5MmwU+VmRE4Ir9dLX18fwWBQtU0AqFarlMtlisUihUKh4+soPCjy5OxwOJTbNhKJkM/nVXxZt8lHrjvSChcOh5XFxel0qkxFadJfX19XGR+7SzLsPpHLgFWZZVOtVnf49LsF+dyyfpKs6+L3+3E6nayvr7O1tUUymWR7e7vjLZtGa7jH48Hv96see+12m3K5zNbWlrJ8VyoVNT5k0bWVlRVl9d7dCkBu6mtra2xvb1MqlZT7SO6LnXYIh1vWXI/HoywnlUpFVX6XLh+486HRaHlxOp2YzWaazSb5fJ5kMrlj73sQHjrmRW4yMiBH1kSwWCyqVPDy8jI//elPWV9fV32LJJlMRlWRlQ2fLBYLHo9HKTedgtQ0PR4PTqeTl19+mVdffZXDhw8TjUZZX19nYWGBzz77jPfee0/1dQC+ZjmQH6ox/U4Gro6Pj+NyuZTpbnZ2dkeqWqe62yStVotarUY0GuXkyZMcO3aMp556SlnzarUaiUSCRCLB2toaa2trlMvljlsYHhZprZTdyKenpzl9+jQej4c/+7M/4+bNm8zPzysrZ7e4jORBRwZIvvbaa/y1v/bX8Hq91Go1FhcX+eyzz5iZmeEv//IvKRaLFIvFHQulsaAjoFxFzz77LG+88QaRSIRarUa5XCadTlMqlbpmfBlT6mU8w+/8zu+osveNRoMPPviA+fl5rly5wsrKSscfiuQ+EovFGB4eVgVCzWazCj795S9/yezsLLlcThVQbTQabG5usr29rbLRZG++arVKs9mkVCphNpuZn59nfX2dUCjE8PCwajkwODjIa6+9xrVr17hx40bH7GlCCJX27Xa7MZvNlEollpaWiMfjKgbsTvuNMSREpqO3Wi0VL3TlyhXK5fJD7VUPrLwYTV8WiwWHw6GiimUlymq1SiqV2qHBywZoMmVM9sqQMR/SinOnmh4HfeLs9vEFg0EGBgbwer0qQjuRSChfoTFCe3dzQWO6JtySsVQQ5UCSi6v043eKOfKbkJuKLCktNX+Hw6FiFUqlErlcbke8Ty8iT879/f2q2WC73WZra4tEIqEOBZ2u0O5GxqeEQiHVuV6Oi3Q6zcrKCuvr66omx+4CYxJjKr5cqGW8Qr1eV71rus1yJeeXx+Ohr69PFaOr1+tUKhXW19dV3ZtardbRigvsbFTq8XhUqEOtVlPW683NTeUVMCIr5FarVYQQX+vPJ63ksseR7Kos13fZfFfWiekkjAki0pNSKBRUT7m99hxZ403WvAHI5/PqMGGMN3sQHkh5Md6wEEK1DR8eHiYUCgGwtbXFwsIC//7f/3tWV1eZm5tT1gXp8mg0Gly5coXNzU3Onj2ryph32gcskXIZGBggFosxPj6u+mAkEgkuXrzID3/4Q5VyaHzW3RYXibEQUigUor+/Xy3U6XRa1c1Jp9Ndc7IG1CIhFRcpE9lJemFhgbm5OeV6kxkTvYA8OTscDgKBAFNTU/z2b/82fr+f69evk8lkuHDhAgsLC1QqFVU1tNMxujpMJhPT09M888wzHDlyhHA4zOLiIteuXePChQu89dZb5HK5rxVT2722yPnldruJRqNEIhHVbiMejyt3t3QjdOraBDuzEmUT3GeeeYajR49y8uRJLBYLH374Ievr6/zoRz9ieXmZTCbTsanRRuS6KhMepPIii37evHmTS5cuqVRwI3eKj7rTe8u/ZTIZVlZW8Hq9NJtNPB4P09PTKrC30zA+b6FQYHFxUfVGuxPSODE2NkYoFOLVV1/l2WefZW5ujosXL7K2tnbH1Or7ZV9WNKvVitfrVadjk8mkWoUvLCyoXHDpNzXW6ZBpU6VSiXq9Tr1ev2NNhU6aOC6XS/UdcrlcO2q6rKyssL29rZ5vr2qDRtObrN/h9XpVsaxSqUSpVFKno25BtlGQC418XnlibDabZLNZlb7ajR3I90LKwWKxqMZoo6OjRKNRldGXSqXI5XJ3DE7tZGSMjwzmlj2+LBaLygwxZjPcyxoi55ff71cBnfV6nXQ6TT6fV+Xd76YAdRryNC1jXaLRKD6fD6vVyvr6usrMSiaTXbWuwFeZscbkCBk7J1N3gR2f9Z2yaIxfu39fq9UolUo7yoDITJtORT5bvV5Xndfh7nPBZDLR19dHNBqlv7+fSCRCs9kkmUyqzMeHXbP3pc6L2+0mFAqptLB4PK56qZw/f558Pr/D7Lo7ILXVarG5ucnCwgL5fF751aRPsRMWC2PQbSgUIhaLqaaU8Xic2dlZ1SpdmmH3Qp4IpUL43HPPcfz4cY4dO0Y4HObmzZvMzc0pZUiWXu5k5LgIh8NMTk5y9OhR3njjDeUOkYuCtOrNzc2pWIROGCMPi1GZlX1GfuM3foNYLEY4HCadTvPWW2+xtLTE+vq6Ot10+riAr+J7AEKhEH19fRw7dozTp0/j9/vJZrMsLS1x7tw55ufndxSdg68fCuRpUm4qZ86c4cSJE0xMTOBwOJiZmeHtt99WmWyd4Lb+JuRzy3IUJ06c4Ac/+AGRSEStwT/72c+Ym5sjlUrdVX6diHHuGOeD/PlObiCjlcQYf3i395cJE/IALuVXLpdZW1tja2ur4yqByzHTaDRU2wTZc+9uzyLDG1599VWOHTvG8PCwyn68dOnSvjX13BflRZblttvt2Gw2ms0m6XRaRfzvTvuFrwaNrKpbrVbJ5XJsbW0pn2Gn9ayRzyQzppxOJ1arVdWKSKfTZDIZgHtOW5VZJDKFMxgMYrPZqFarbG1tkc1mVZlqeQ+dTLvdxuVyqcC6WCyGz+dTlrpSqUSxWCSTyag6AZ3+zPeDPMlJX7IsJibH2fz8PEtLS8qn3G3p0XBL4ZAVUiORiNogstmssrhIf/o3xWrI+iYDAwOMj4/j8/kA2N7eZnZ2lkQioQ4GnWjy3027fasSeiAQIBKJMD4+jsfjoV6vk8/nVdVUY3ZRt4yfOyHXbDmndiv692Id2G3d232YqtVqZLNZpQR3GlJxk3GsMrt1N7trBsViMXUYkO05ZOuf3VarB+GhlBf5QTscDtUYUHZqlUFO0hVkbAEgs2a8Xi9vvvkmIyMjTE5O4nK5mJmZ4cKFC8pC0Qmn6t1Bf7KBldPpVLKQpmep1d8ttUz+XUZ4v/rqq6on0tTUFM1mk5mZGT755BN+8pOfqEwA6OxFxhi0HIvF+MEPfqAsCrIgX7VaZXt7W8UhJBIJVTCqk5/9XpFzKxwOqziFU6dOkcvl+Bf/4l+wurrK+vo6hUIBuHcF+aCze3MYGBhQvdNkrMvy8jJffvklly5dUoelO72HMQDeZrPxwgsvMDw8zIsvvsjx48fZ3t7m888/5+bNmyqLrdOVFmM6uNlsZmpqit/4jd9Q9WwymQw/+tGPWFpa2rG5dIPFTiKfpVwuk8vlKBQKFAoFnE4nw8PDHD9+nNdff521tTWuXLmiAnnvpJhIjO5bk8nE+Pg4kUiEl19+mVdeeYVgMEg+n2d2dpa3336b1dXVjqnDJeXl8/lU6QBjcTpZ30VanKQS6HQ6eemllxgaGlIHq+vXr7OxscH58+dZWFhQPY6eqOVFfpgyE0ZmOshN2NjHwRjhLosiRSIRTp8+zaFDh1R55mQyyfXr10mlUipL4qBPImkCk1Ykt9utfMhGZc5Yh+VO7wGoFEa3200gEODYsWMcPnyYqakpBgcH1QIjS74btflu2aj8fj8nTpxQNYNkZpqsEZDNZlXUeqencN4vsijk+Pg4Y2NjDA4OUi6X+fzzz1ldXVWVULslSHc3Qgi8Xi/hcFgVAqtWqyplfmNjQ81Fye64BGPMx/j4OIcPH1YprWtraywvL5NIJMhmsx2dQGDEGCvU39/PmTNniEajuFwuNjc3uXz5MsvLy+Tzeer1+o4Mk25BCEG9XlfNa6vVqoobGxwcZHp6GoDFxUWVPQQ7A3J3/9so10gkovpqTUxMAKj6MdeuXSOTyXTMeJKfvfQiyObAsqaNUbEzzim73c7ExIRS5Nxut2omvLS0xPb2tmrq+bDsi/IizUSypbjT6SQWi1GtVvne976nLCgyNc/pdHL8+HGCwSAnT54kGAyytLSkXE1yAnXKpiTvUSoplUpFlROXzyC/G32IxsVBWqVk8NzLL7/M4OCg0mLL5TI3b97kww8/5OrVq1y+fJlsNts1KZyyZ4oxFdwoLxmk+95777G0tKQyQDplMXgYjLEKPp+Pp556il/5lV/BarXy/vvvMz8/z/z8vOri2ondfu8F2bQ1Go0yOjqqKnBvbW0xPz/P1tbW1+aDMfDSZDKpLJOnn36aaDTKG2+8wcjICMVikfPnz/Pee+9x4cIF5ubmOuaUfDd2ZxdJi9VTTz3F+Pg41WqVTz75hLm5OS5cuKACdO/k+uh0pPVMrhtXr15VzQYHBweJxWK89NJLjI+PMzo6SjabZWVlRVV4l+tPvV6nVCoBEA6HVZaax+Ph1KlTypplsVhYXFzk6tWrXLp0SfX/gc44ZMrP3+VyEQgEVNKERB4mZU8sr9fLmTNnGBgY4M033yQcDqtD9meffcbVq1dJpVL7Wm/qodUf4ylGprHa7XbC4TCtVovnn39eFU+zWCyqbsepU6cIBALKXz8/P8/29rbyp3Xipiw3WanVGxWU3dq6NLUZTzhWq5VwOEwkEuG5555TzcMCgQCXLl1ibW2NCxcu8NFHH5FKpVRaX6efsI3mV9kTa7fMWq0WhUKBixcvqsJrsu5Cp42T+0VaMaVLcnJykmeffZZ4PK4aL8bjcYrF4teao3U6Rt+4zWbDbrer/jHyNCiDCGWNjt0KC3wV++J0Ounr6+PUqVOMjY1x+vRpotEon3/+OcvLy1y4cIGPP/54h2LcLfPL7/czPT3NxMQEAwMDrK+vc/XqVWZnZ5mbm+vK7DSJfCZ5OF5aWlLukOHhYcLhMF6vV8XabW9vc+PGDVXnR9a+kRXSW60W09PThEIhJiYmCIVCHD9+XCmF5XKZzc1Nzp07x+LiorqmEwKgjW5Gu92uQkIku61Qst6SdME+/fTTuFwufvzjHzM/P8/169e5ceOG8irsFw/1TjJGoVAosLGxwcDAAK1WC7vdTiQSweVy4Xa7d8RkyEqxAwMDOBwO5Uebm5vj8uXLSkPtRJ9rs9lUVYUTiYQKmuzr62NqakqVK5eDWzbucjgchMNhPB4Phw4dUour3+9XtVx+8YtfcOPGDa5cucL29jaVSqVjLFPfhFTmvF6vMt97vV7VHK9er5NKpVQa7NbWFvV6/Qnf9aNHzi+bzYbNZuPkyZN8+9vfJhAIcO3aNRYWFvjlL395R4tDNyItm+VyWbU8kIUMh4aGVDkC+KoWjMvlUgGqLpeLiYkJ/H4/zz77LH19fSSTSTY2NtT8mpubU4X9Ol0xlkqLLFF/+PBhXnnlFWKxGMVikXg8zhdffKEy0zols/N+MT6TLFFx8+ZN6vU6xWKRarWKz+cjGAzidDoZGRkhFAoRDAaV5aXRaJDJZGg2m8qCIttRGNeqeDzOwsICCwsLXLt2jatXr6okjU6RrTH+MJVKsbi4iMViYXx8nKGhIZ5//nmy2SzxeByv18tTTz1FOBzmmWeewev1qiSdL7/8krm5OVUaZL/ZF+VFVifM5XLK8iI7a05OTn7tOulqarfbJJNJ1dDq6tWr6sFl08ZOQLo35MSQykuhUKBer+P3+5mcnCSbzapsqmQyidfrZWxsDJ/Px/T0tEr/lK6TdrvNxYsXicfj/OIXv+D8+fOqR0u3ZD/AV5YFj8fD+Pg4/f39qoeI9FPLSs2bm5t7pul1E9LiZLPZ8Hq9HD9+nN/8zd9kdXWVzz//nKtXr/LRRx+pdvTdoswa2b3xSLO9LL9gMpmU8iIzjuR18tTo8/kYGxsjEAhw5swZ/H4/0WgUIQS//OUvWV5e5oMPPuDLL79UgfXdYtGU7sZoNMr09DRnz57FbDZTLBZJJBJcunSJra0tZWnqliDv3ex27c/OzhKPx5VXYGJiAp/Pp2INAaampmi32+o18ssY6ymEUFb2eDxOPB7n888/56OPPmJ9fZ35+fkd1sNOka1cX1OpFEtLS0SjUZxOJwMDAzz77LPk83ni8TihUIg333yTvr4+YrEYzWaTTz/9lPX1db788ktmZmZU9p9co/Yr4+qhY14A0uk0CwsLDA4Osry8jNvtJhgM3vWDkh1fK5UK165dY3Nzkxs3brC2tqb6RHTKhywx3m8ymcRut6t8eIvFgtfrZWJiQsXE5PN5nE6n0t7D4TA2m025R6QsPvzwQ1ZXV1lbW9vREbfTF9bd7K5hIoOdU6mU6m67tLSkylJ32vh4EKQLaHp6mhMnTjA5OUm5XCYej3P+/HnW1tbuWKW5W5GWgVwux/b2tqrlEo1GOXHiBAMDAzsOS7J3jyzPLhvlycrehUKBX/7yl6yurrKxsaGKHXa6Erg7QyYajXLs2DFGR0fxer1sbGxw+fJlrl69ytbWlspO6+Rnvlek4iGL7y0sLNBqtVRRPp/PRyQSUUGlFosFl8sFoGJfZBkPuQZL683q6irJZJIbN26wsrKyo3ddJ8q23W6r1GhZDNTv93Py5EnK5TKTk5Mq5kcIwc2bN8lms3zwwQesr6+zvr6umqFKWe1nqvgDKS/S0iBJJpPU63WCwSCHDh1SKa4mk+mOJ+RGo8HKygrJZJKf/exnLC4ucvnyZdX4aj/9Yo8TKZeNjQ0KhQLLy8vE43EGBweJRCL4fD4OHz6sTkQyOlua6er1uuq2eeHCBTY3N3n77bdZWloilUp1ZUzDbozp9LIb+c2bN/l3/+7fqd4jUnbdijH13mKxcPr0aX7rt34Lq9VKsVhkcXGRd999t2vcG9+E0bLZarVUkzzZYmN4eJjh4eEdJ1zjCVnGltXrdRKJBOl0mk8++YTV1VV+9rOfsb6+vqOcQ7ccDOSzy1Rwad29cuUKf/VXf8XKygrxeJxms9lRlu6HQX62Mpbl6tWr3Lhxg/7+fmWdi8ViqvyH2+0mFoshhKBUKlGtVtUhSiI7US8sLKi1f3c8YifOz3a7TaVSIZPJqINzIBDgxRdf3FE0Em7VRTp//jzLy8v88Ic/JB6P7yjU9yjm1ANrCcYPQ5YMXlpa4pNPPiEcDrO8vKw25d3U63UWFhbIZrPMzMyQTCa7oiuwvH8ZSHr9+nVsNpvyn8qAXtiZXi3lJP2qpVKJ2dnZr8XOdMuiuhvpApOVKC9dusSPfvQjKpWK6hwt3Yu9YnWBr2IWNjY2uHjxoho/V69e7ajq0/uFHP/b29vYbDbm5+fp7+/H6/Xi9XqBnSe7RqOhNhxpqVlbWyOXy3Hp0iWSyaTKCoTO3GD2wmiRE0KQSqW4cuUKN2/eVJW5ja/rJYxBp81mU8VtZrNZcrmcyqB1OBysrq5iMplU48VEIqF6XbXbbWV5SaVSXdXEUwhBPp9ndXWVGzdu8Pnnn+P3++nv71cWqEqlopouf/nllypcwjinHpUsxF5mHJ/P9402HqMv0Ol0qtbycjG5EzJDoFarqbiQO6U33iu5XO6JjBSPx7OnfPr6+vB4PFit1rv2tTDKv9VqUavVVGfSWq22owv3gw6CQqFwIOVjRMZKOZ1O1Ruq0WhQLBap1Wqq7s+jUOCelHzcbvee8pEnlmg0SjgcplAoqP4r0iT9OBTaYrH4ROTj9/t3yEcehGQl7+9+97s888wzTE1NqboaRvdjoVBgfX2dZDLJJ598QiaTYWFhgXK5rLpN745jeBCy2ewTkY/L5brj+DHKQAjBd7/7Xb7zne+oRIHLly/z1ltv7ejq+yg321Kp9NjlEwwG72ntMe5fRqudrN1iNpuV26jZbNJsNpUVwqgAwc7quvc6L1Op1IFce4wZfjabjampKU6ePMmhQ4d4+eWXKRaLLC8vq7mVSqWYnZ2lXC7vsGI+LHutPfuSKi0fVGYCyOq6d0M2w5Kv2/3hdwsya2qvojy7LVOymJ08WfeKpUFan+SGIq0MMtK/FwJ070S73aZUKpFOp1UDuYdtJd+pGIMua7Ua6+vrOJ1OlfkAOw8DUkmRNTuk8mesdt0LbG1tcf36dVU1Nh6Pq2SLbrXm3g9yD9tdF0euO7JYnUwskLLbffDvpvlorLFVr9fJZDIsLy+rwFuZCi5bcsjGy3I/N1r99jPOZcc9PqzlBb5ewXL37+74H98hyPBBP/yDanm50wC/V/YzCLMTLC9G7hSd/ygXhoNqeZHsHkePW8k/KJYXibEOhcxguFvVarnRGDNGYH/WHclBs7zsxpjlYawzBY9nwz3IlhfYe6+609/uRWb3KteDannZjdGiJCvpG+cWcMe59bA8UssL7PQfyu/3smnv5wJyUNmtzO3nwO8mjKefbj/V3C9yoeg2y+TDIBfN+633s1sZ7gV5yiwq+CrWTltcvmL3/rX7b/d6AO3WsWRUVIAdTTuNPO4xte9pPfe7wHbrBw5fVR/WfDPd6jrcD7opA2Y/2A/Fo5fG2J3mVC89/72yWyZ7HTh7SX4HdU3eV+XlID6gRqPpPvRac+9oWT0YWm4Hmz1jXjQajUaj0WgOGtoWrdFoNBqNpqPQyotGo9FoNJqOQisvGo1Go9FoOgqtvGg0Go1Go+kotPKi0Wg0Go2mo9DKi0aj0Wg0mo5CKy8ajUaj0Wg6in1TXoQQQSHEvxVCFIUQS0KI376Pa+1CiD8RQuSEEBtCiN/fr/s6KAgh/p4Q4pwQoiqE+NP7vPZbQoh3hRBZIcTio7nDJ4eWzd5o+eyNls/eaPnsjZbP3hxU+eyn5eWPgRrQD/wO8I+EEMfv8do/BKaBMeBbwH8lhPjVfby3g8A68EfAnzzAtcXb1/2X+3pHBwctm73R8tkbLZ+90fLZGy2fvTmQ8tmvxoxu4G8BJ9rtdgH4QAjxF8B/CPz9e3iL/wj43Xa7nQbSQoj/Bfhd4Ef7cX8HgXa7/W8AhBDPAcP3ee2nwKdCiO8+int70mjZ7I2Wz95o+eyNls/eaPnszUGVz35ZXg4DjXa7fdPwu4vAN1pehBABYPD26+/rWo1Go9FoNL3HfikvHiC363dZwHuP18rX3++1Go1Go9Foeoz9Ul4KgG/X73xA/h6vla+/32s1Go1Go9H0GPulvNwELEKIacPvTgNXvunC23Eu8duvv69rNRqNRqPR9B77ory02+0i8G+AfyiEcAshXgH+JvBPAYQQ40KIthBi/C5v8WfAHwghAkKIo8DvAX+6H/d2UBBCWIQQDsAMmIUQDiGExfD3thDizbtca7p9rfXWj8IhhLA9jvt+HGjZ7I2Wz95o+eyNls/eaPnszYGVT7vd3pcvIAj8ObdSo5aB3zb87TVgEbDe5Vo7t9KpckAC+P39uq+D8sWtdPD2rq8/vP23kdvPHrrLtW/e4dr3nvQzadlo+RyELy0fLR8tn96Tj7j9HzxShBB/ACTb7fY/fuT/WQcihPi7wPF2u/0PnvS9HDS0bPZGy2dvtHz2Rstnb7R89uZJyuexKC8ajUaj0Wg0+4XubaTRaDQajaaj0MqLRqPRaDSajkIrLxqNRqPRaDqKPXsbeTyejgiIKRQK4kn8vy6XqyPkUyqVnoh89PjZGy2fvXG73R0hn2KxqNefPXgS648eO3vTDWNnXxozajQajUbTi+xOehHiiegjB5J2u/3I5PHYlBfjB2zIAVcIIdSXRrMbIYQaM7vqCABgMpn02NFoNI8Nuf60Wi21Scs1qJfXorvJZb/39ydieZEPYdx85EM9Sk1N07ncaawY/63HjOZe0WvMvaOtCt+MPnTvRO7tj1qZe6TKS7vdptVqIYTAYrEghMBsNmMymXA4HJhMJsxmMwCFQoFKpUK9XqfRaKj3kAIwmUxYLN3j5dptSWg0GjsWCikv4wDo9Zo8xvHj8XjU2Gm32+TzearV6hO+w4NHs9ncMW6k/KD3xlOr1VLzTFrqzGaz3nj2oNFo0Gq11Jgxjp9eZfdByufzYbfbqdVqNBoNarUatVqtZ5UaOa+8Xi9Wq5VGo0Gz2aRSqVCr1fbt/3ks2oAQAqvVislkwmazYTabcbvdWCwWLBYLJpOJZrNJq9Wi2Wyqa+T3XhgE2vK0N1IuJpMJq9WK1+vFZrMp91GlUtHKi2ZP9Ly6P+Qmra2bd8Z4kPJ4PBSLRWq1Gu12e1836U5DGhrcbjcul4tqtaoUmAOvvMhBb7VacbvdBAIBTp06hcfjIRQK4XA4GBgYwOl04nK5EEJw8eJFVldX2d7eJpfLKe2tVqtRKpXI5/NsbGzQarVotVqP4rYfK0bt3eVyMTw8jNVqxWKx0Gg0WFtbo1wuU6/XlfWqV5EWPJPJRCAQIBgM8lu/9VsMDQ3RaDSoVCr883/+z/n888917MttpHVhYGAAl8ulTsvJZJJsNttzcjKZTDidTqamprDb7WSzWSqVCpubm5TL5Z6SxTfRbrdpNpuYTCYOHz6Mz+dTa5EcP/Ig0WvIddtisXDkyBEikQhvvvkmk5OTLC8vk0gkOH/+POfPn6fRaFCv14HuV/zkGm2xWIhGowQCAX7zN3+TiYkJFhYW2Nra4v333+fy5cs7rnsYuTwyy0u73Vamo0gkwokTJ+jr62NwcBC3283Y2Bhutxuv14vJZMJutxMOh1lfXyeVSilzbrlcJpvNkkgkSCaTXWfqFkJgt9sZHBzE4XAo82Mmk6HZbO5wofUyUoFzOp0Eg0Gef/55pqenlXL79ttvf9Wwq8sXintByqGvr4++vj4lk0KhQDqd7glrpkRutA6Hg4mJCRwOBxsbGxSLRTKZDKVSSb1Ocws5fvr7+xkYGCCTyVAul8nn86TT6Z5WXOCW1WVwcJCxsTFefvllTp06xdWrV1lcXCSRSHD58mVlgemVcSXl4/V6CYfDvPDCC5w6dUoZJq5cufI1a97DsK/Ki9w83G43oVCI4eFhXnvtNSKRCKdOncLlcik/mNPpxGw2Ky1/enqagYEByuWy+sBNJhOlUolCocCFCxfIZrPk83m2trbUybITkc/sdrsZGBhgdHSUX//1X1e+01KphNPpZG1tjRs3bqjFolcmwd2Qp2en06ligqQsu02pfVgsFgtOp5OXXnqJyclJ0uk0pVKJdDrN8vJyT5yc5Xpkt9vp7+8nFovx1//6XycQCHDjxg22trbIZDIUi0U1hnp1ju12y46PjxMKhfgbf+NvMDo6ys2bN9nc3CSXyxGPx5/07T525DpjNpsJh8P4/X5efvlljh49Sl9fH8VikZWVFa5du0YikVCxQr00nuSeHAwGiUajuFwurFYrgAoH2U/2TXmRG0mr1cJutxOJRDh06BDf/va3CQQCjI6OYrVa1cNUKhUV59Jut4nFYlgsFuVHlB96qVRSJ6Nf/vKXtNttpbx0KlJOVquVaDTK+Pg4r7zyinKp5XI5FhYWMJvNLC0tPenbPRBIS4HNZsNut6uxImXZyeNhPzFuQna7nWPHjnH69GmWl5dJpVJ8/vnnXeN6vRfa7TYWi4VgMMjw8DAvv/wykUgEn8/H2toav/jFL7BYLDtk0ksbjhE5dux2OxMTE4yOjvLyyy9z6NAhXC4XCwsLnD9//muv7xVarRZmsxm/38/AwADHjx/n1KlTAJTLZRKJBIuLi6TTabW39RJyPHi9Xvr6+rDb7TuSKvZ7jd435aXdbuP1evH7/UxNTfHGG28wMjLC8PAwDoeDRqNBqVRiaWmJQqHA6uoq5XJZuUWGhobw+XyYzWbMZjN2ux273Y7P5yMYDOLxeIhGozQajY6fMMYYDmlJsNlsSrmz2+0Eg0EikYjKyupl5Gbscrk4cuQIY2NjBINBXC4XpVKp5xaJvZB+54GBAcLhMENDQ/T39xOPx1X8FPTeBi0XT7neRCIRAEKhEH6/n0wm0xVry4NgjFcIBAKEQiFeeOEFJiYmiEQiWCyWHXEwckOS13azzOS4MZlM+Hw+fD4fr7zyCqOjowwNDWG327lw4QIrKyt88sknfPnll6TTaWV56QWMcUAOh4OhoSFGRkaUl8VkMj2Sw+W+KC/yxjweD6Ojozz99NP82q/9Gn19fQwMDNBqtcjn82SzWb744gsSiQRffPEF2WyWer2OEIITJ04wMDCA2WzGYrHg8/nwer0cOnSIgYEBPB4PkUiEYrHY0ZPFmFV0J+XFZrPhcDgIh8MUi0WcTqf68Lt9odgLs9mMy+Xi8OHDTExMKOVFWvy05WVnaQLpJhkYGCAajaqUxV5ZUO+EVF5arRaRSASbzUYoFMLn81EoFHp6jslxEwgEGBwc5Nlnn+Xw4cOEQiFVoqLZbKqyF9JS1QuykmPC5/MRjUY5e/Ys09PTSnm5du0an376KefOnWN2dlZl0fZKXJmUj1ReBgYGGBkZwePxYLPZHtkava+WF4/Hw/DwMAMDA/j9flwuFwDVapW1tTXi8Tjnz58nkUiwvLxMuVxWfsTZ2VmSyaQyt4VCIUKhEHa7nSNHjtBsNncMik7FaNa32Wx4vV7cbrdyl9XrdarVKplMhu3tbeVe65WJsBup5JnNZhwOB6Ojo4yNjWGz2Xp6I94Li8VCKBRiYGBApZQbT8u9hEwc6Ovrw+fz7XBN2+12leHXi3NrN1arlUgkQn9/P16vF4fDodajtbU15ubmlFWhl9YjGTc1PT3N8PAwY2NjDAwMUK/XKZfLbG5uEo/HqVQqXwt76HakUiKtvZFIhNHRUWKxGK1Wi1wux/LyMjdu3CCTyQD7Z/V9aOXFWKq9r6+P6elpRkdHCYVCKr6lVCoxNzfHwsIC7777LolEgnq9rhYWIQSpVAqr1UqhUKBcLjM0NMTg4CB+v5+zZ8/SaDRUrEOnI7VUu91OIBDA7/djtVoRQqhiPslkkkQiQalUotFo9PwCK+sGHDlyhKmpKRwOx9dKT/cyRquBxWJheHiY8fFx/H4/DoejJ5UXozskEokQiUSUGdtqtVKv15XF81GZtjsJq9VKLBZjZGSEvr4+5ZYtlUrMz89z+fJlkskkjUZDufe7GaO1wOl08vTTTzM5OalSpBcXF0kmk6ysrLC0tESxWOwpiwt8FcjsdDoZHR1lZGSE6elpxsfHabfbpFIprl+/zvnz50kmk3esjv6g7IsmEA6HCYVCTE9PMzk5SX9/v1occrkca2trfPnll6ysrFAul+9obpT+eBmV7Pf7lRJkt9uVVaLTU4flBmO1WnG5XEQiEQKBgBr00hxbq9WoVqs6k4avYl52L5Zy4vRicNydsNlsai5OTk4yNjaGxWKhUqmQzWZJp9PKTdsLyE2k1WpRLpeVpde4KTWbzR2xQL2E8dQsXSLy8Cmt5qlUiu3tbTY2NkgkElQqlZ6wLBhl43a7iUQijIyMEIvFVP2x9fV11tbWSKVSlEqlnrOQS6u4zWbD4/EwNTXF6OioCtaV2cHb29skEgnK5bK69omnSssFYGpqiueee45Tp07x4osv4vF4sFqt5PN55ubmuH79On/xF3/B9vY22WxWuYqMyMqE0hoTi8V45plnmJiYwOVyYbFYVBp1p27mxgnhdDoJhUIcPnyYoaEhbDYb8FUJ83K5rKwuxgZXnfrsD4qx4KEMHDRmF8ly3L24+ezG6XRy6tQpRkZGOHv2LLFYDLvdTqFQYHNzk5WVFYrFYk+VeJfWzFQqRTAYVIqKHEfValW5ZnsRmfUo3SGvv/46w8PDBAIBAJaWllhaWuLGjRvMzc0BdIX1+14wymZ8fJzTp08zPDyMxWKhUChw5coVbty4wcrKCplMBpPJ1DPzSq6/Mm4zHA5z9uxZxsfH6e/vV6U+Njc3WV5eZn5+ft+t5A81CqUG7vV66e/vJxAIqNzuRqNBsVhkdXWVeDxOPp+nVCp9Y0Cc9El7PB6CwaCKBwG65oQtqwdLjdXpdO74m7Q0SPn2em8jm81GNBolGo1is9mUXFqtFul0Wp0IexGpDMv5cuTIEWKxGH6/H7vdTiKRIJ/PE4/H2dra6pmTsxFZLEwqudLy20sy2Aur1Uo4HCYSiaj1SCp9m5ubrK6uUigUVLZRL8jNWCNodHRUWRScTielUolyuczGxgZra2uqlEev0Wq1VIDu0NCQ8iIIIajVaqyurrK4uEgul1Np5vs5dh5YeZGLpsViYXBwkJMnTxKLxQgEAjSbTcrlMuvr6/zyl79keXmZZDJJuVxW/uU7vR+gsm8GBgaYmJggHA4r5aXRaHSF20gGC8r0b/mBS6QpTrrLenWhNVZsfO6555iYmFBBlyaTiVqtxs2bN7lx4wapVOoJ3+3jx1hXaXJykvHxcX7913+dWCxGX18fzWaTL774gpmZGT7++GPm5+cplUo9c3KGrywv2WyWXC53x6avvTy/2u02LpeLo0ePMj4+Tjgcxuv1YjabKZfLXL58mXPnzrG5udlTSp+cW36/n29/+9uMjY0xOjqKw+Hgxo0bbGxs8Pnnn/Pll1+SzWaf9O0+doyFVqXF5dixY4RCIer1Otlslp///Oequu6jGDMPtIrJQS/ruoRCIQKBAG63m3a7TalUYnNzk/X1ddbX11UW0V4PIBUav99POBxWm7rNZlMulFwuR7lc7ngLhPzgq9Uq2WwWq9VKX1+f2pSNykuvdr2VSp6s7xKJRFTapslkUu6iXC5HOp3u2aaMUk5utxuPx4Pb7cbhcFAoFCiVSqytrbGyskIul6NSqfRknRchBC6Xa0fgsuw7I+PsOn1NuV/kuJEu7HA4TDAYVEkWuVyOTCZDJpMhl8upHj3djrGui8PhwOl04vf78Xq9SumvVCoUi0VKpZKaU72i1Bkxrj1ut1tlNeZyOdVGIpVKPbImlQ+kvEj/8JEjRzhx4gQvvPAC09PTKqh2cXGRd955hxs3bvDBBx8ok76cGEZ2xzScPn2a06dPc/bsWY4dO0Y+n2d9fZ3FxUVu3LhBLpfr2FoM0h3UbDYpFAqsr6/z2WefMTw8rFxkskidLBZlLIXfKxgrEMt+WGfOnCEWi6leWPl8nkwmw8LCArOzsxSLRaC3NmW4JSur1Up/fz/RaBS32w3A5cuXWV9f55133uH69evk83mq1WrPLbLSMiUzIVwuF2azmUKhoPr1NBqNjl1THgR5eHI4HIRCIUZHRzl9+jRDQ0O43W5arRZXrlxhbW2N2dlZ1tfXVRpwt8vImD0TjUZVraRwOKzq2ySTSdUbq1qt9ky9GyNyjZZlCPr6+rDZbLTbbVZWVojH48zPz7OyskKpVHok684DRRcZzY2hUAiv17ujFHC1WiWdTitLyb2cimUciGzqJPv8tNvtHVquTLHuZOQEqVQqbGxsqFgEYzCyPBH2aul7GbgtS00Hg0FVgRlujbFyuUyxWKRQKPRsdVSZGi2LOrZaLSqVCvF4nNXVVVKpFPl8vivmzf1iTB93OBzK8iKEUNZcmcEoZdMLY8h4YPT7/fT19eH3+5XlvFarqQ06n8/vsNj1ClI2Pp8Pp9OJw+FQWaC5XI5UKtXz2aDGLFA5r2RBWnkweJTK3QO7jVqtFsFgUJVqt1qt6vfNZpN0Ok0+nwf2XhDkpLBYLCowc2JigkAgoE5IstW4sWljpyI/4GazSTwe52c/+xlHjhzh1KlTRKNRBgcHaTQabG1t7ajzIuvA9AJykYhGo5w8eZJjx47x1FNP0dfXp9IUE4kEiUSCtbU11tbWKJfLPSMf+GoDMpvN+Hw+Tp06xcDAAMVikc3NTd5++21mZmZYXl5WQbrdXpfjTkjTttfrVQX7AOLxuOpgL9NceyFTxKikBQIBnnnmGVWXw+PxUCqV2N7e5qOPPuLmzZusrq6Sz+e7PkPNKBfZJuHUqVNMT08ri2+xWCSdTqs4smQySbVa7YmaN7uRxga5b0vvQL1eZ25ujrm5Oba2tiiVSo8sbvOBR6Ms9OR0OtXGKjcd6ROsVqv3pJUKIXb4FmVQZq1Wo1gssr29TT6f76ry5vKEIwMJq9WqOh1Ly4zx517S7o0p8z6fD4/Ho2IWZDExGQMlLXK9mOoqA79dLhd9fX3K8lKtVlV9Dp1Gzo5eaXL8yNNhpVLZYXnpBeRGIt2yfr9ftSip1+tUKhXS6bSKJesl64K0JjgcDqLRKMFgEIfDoUp1yLolcm71ilyMSGumzWZTsZmy4rm0TGUyGWq12iPt1n5flhf5QclKty6XC6fTidlsptVqsbW1xdraGteuXePatWtkMhl18tmNXFDNZjNWq5UXXniBw4cPc/bsWY4ePUo+n+fmzZt8+umn/NVf/ZU6XXejf9GonMgTssvl2tHYqtue+ZuQJyCpuMjnl52kFxYWmJubY3Nzk3w+rxadXkCOF4/Hw/j4OEePHmVsbAyfz0e1WlVKvlToekUuRozFIN1utypZbrVaqdVqXL9+ndnZWTY2NigUCj2R1We0LpjNZtxuN4ODg0SjUbWOS5dIMplU1XR7Jeau1Wphs9lwuVzEYjHOnj2rWmw0Gg3m5uZYXV3lxo0bzM/PU6lUVAJBryDXFZfLxcDAAOPj4xw+fJhIJEKpVCKfz7O0tMTi4qJKIX9UY+eBpC7NRdJcJj+8Wq1GJpNRlfWMFfWMGLVVq9WqtNzx8XEikQher1edjqRClMlkujL+w/g8RgXmTnVeegVpUXA4HLjdblUHx2iVymaz6uTci/Ec8FVF3UAggMfjweFwKItdt9REehjk6dBo1RVC0Gg0yGazKm6hlwJ2jWuMzDQyZmHVajVVjbhSqexowdHNGLOMpAITCoVUFqgMhdja2iKbzVIoFGi1Wj2luBiRVjvZ3sblclGtVsnn8+RyOXK53DdmGD8sD2R5ke4iGQQnfV7VapXNzU31ARsLh8mFdHeMy/Hjx+nv7+db3/oWTz/9NOFwGJPJRKFQUPEMa2trKui32ybR7l4PxmBemQkho7q7HRkzFQ6HmZyc5OjRo7zxxhsq0r9Wq1Eqldja2lKWl0cVyX5QMfYvCofDvPDCC0rpl6dDWVcpn8/3VGExiaxS3dfXx6FDhzh27BjPPPMMoVCIWq1GOp3esa700vgBvrZB+3w+ZfKXfXq2trbU+Okl2djtdiKRCOFweEdRus3NTT744AMWFhbY3t6mXq8rxaUX5GMMfrfZbMRiMb7//e+r+jftdlvVdLl58yYLCwtUq9VHarV7oIBdi8WC1WpVTc2k9aXRaKjqg/JEA19tyvLfUgAOh4P+/n5GR0cZHh5WfSNkkTtZY0Bqud2K8cOVg6TRaPSUr1kis9hisZgaEz6fT8VUlUolisWiqkHRS716YGeXbZfLpcz+DoeDUqlEKpVia2uLYrGo6iv0knxgZ6p9KBRSm5Hf71f1bwqFQk9uznIDku56p9OJ3W4HbtW+yeVy6uBpHD+9IiOr1arqJcnwCBl7uba2xurq6o7aLr1meZGxrl6vl4mJCWKxGB6Ph3K5rNyNqVSKXC73yF1q96W8SAXC5/OpQnLhcBin06k0dxlAKavhyq6uLpcLl8vF1NQUHo9HlTB/9tlnVXlhGamcTCY5f/4858+fZ35+Xvnuu3ECyYXW2L9Iuk2kYtiNz70bqZy0221isRg/+MEPiMVihMNhFWhZrVZVkzjZKK7RaPSMZcEYyCxPzdPT04RCIZrNJplMhosXL7KwsKAU/l7aeIzsDiqUAYWymd7y8jLr6+vUarWemWO7x08wGGRyclL1MSoWi8zNzbGwsKAysHpFNnBLPuFwmBdffJEjR44QDAZpNpssLi6yuLjI0tIS8Xi8p9YcidynAoEAY2NjnDhxgtOnT+P3+2k2m6qPobS4PA7Z3LfbSGYGeb1ePB6PasIoA3mMdRN211nw+XxMTEyohoSBQEC5ilqtFvV6nc3NTebm5piZmWFmZoatra2ursGw27JiDEy12Wxd+cx70W638fv9nDhxgmAwiMfjURkicpLImKpisaj8zr0iJ2N7CZfLRTQaVVlGsiWHPB32ShzH3ZDuNXkCbLfbZDIZtre3SafTqklsL44fGQsk6ye1222q1SpbW1skk8kdjXJ7RTaACu4eGhrC5XKp9OitrS01Zno51sXhcDA4OMjQ0BCxWAyHw6HipDY3N0kmk4+tGvN9KS/yA8vn81gsFlKpFKlUSjVS9Hq9jIyMkMlkmJycVG4jn8/HsWPHiEajnD17lr6+PkKhEA6Hg2AwiNlsZmVlha2tLd577z0uXLjA+vo68Xhc1XXpxgkkG6IFg0G8Xq9KO5d+13w+rxpddjsy0l+WmpYnPmPtoGw2y3vvvcfS0hIbGxs7zLe9gLHX06FDhxgbG8Pr9SqXkQyWMzZA7RXZ7IW0bFarVWX6LxQKVKvVnlJcJMYsPhmrmEgkVBXz5eVlarVaT8rG6XQyODio9qVKpbLDGmUshtkrsjG6GoeGhnjttdeYnJzEbrdTLpe5du0ay8vLXL9+XVkzH8fYuS/lRd5MuVzGZDKpqGK/30+73cbpdNLf308ymWRwcFCdaiKRCC+++CL9/f28/PLL+P1+VRumXq9Tr9dJJpMsLCxw4cIFPvzwQ2q1mioAZLFYui72o92+1dhS1rUxRv1bLBb8fj+BQED9zuhW6jbkCc9isajePMaNVyowhUKBixcvMj8/TyaT6clgS0DFBPX39+N2uzGZTKqipcwS6SWlbi+MFavr9Trb29tsbm5SLpep1+s9Z92U1ihZ10VmXyUSCeLxuCrt3ouuEbiVwScPk7L5qzxIy3pSveRKk8h1NhgMcvr0aSKRCDabjVwux8LCAouLiypR4HGNnQdSXmTmjyxGE4lEEEIoy4vNZlOmbACPx8Pk5CRutxshBPl8XnWZlnU6Pv/8c5UfLlNfZSBwtykucMuK5fP5mJ6eZnJyUk0YaW3opYqo0iXk9XoZHx9ncHBQWRQA6vW6CgaT2Wy90ijuTtjtduVSk7EuH374IYuLi6pibDfOmQdhdzC8dGvv/lu3I9dTq9VKLBbj9ddf5+jRozgcDorFIvl8vusKgT4IzWaTYrGoDgBut5uTJ08SCARYWVlhc3NTKTK9oNzJg2UoFGJwcJBDhw4RiUTw+/2qFYlU7uSB4HFx38qLrAzbbDaV8iJTor1erzoVPv300zuaN8mgXpkjPzMzw/b2NtevX2dra4uLFy+yvr6u+o106+ZtDMr1eDyqNLdszGisN2Cs9dLNSLO+LLomLQp2u11Z52QWzebmJqlUqqcXWKPy0mq1SKfTfPTRR6ysrJBKpSiXyz15OtwLY42gXquoa2wlYbPZGB4e5o033qC/v/+uyksvycdIvV5XvfRk5uPx48cJhULMzMzg9XrVAaHbrb7GuNVAIMCJEyeYnJwkEomog2W1WlUJFFJ5eVyF++47VVp+YEII1tfXuXz5Mm63W3VAdjqdqliNbJctN59SqcTs7CyZTIbz58+rzJFCocDW1pYqRd3NG7Z8LukGmZubo91us7m5qbpzyo2nW2VwJ3b3FpFB4KlUikQiwS9+8QuWlpYolUo97xKR/udGo6EsUuvr6yr7qpdlcyfkmBJCEA6HyefzquR7r8jKGLdgt9uVq1rWlEqlUqTT6Z5T7HaTzWa5fPky5XKZyclJVTPI+NVr8pEufRnaYLVaqVQqrK6uMjc3pyxSj3vvfqA6L/IGFxYWyGazqsy0TJsuFovE43HVj6ZUKqlGTe+//z4bGxt8/PHHpFKpHUXYeqWirNRms9ksFy9epFQq8fzzz1Ov11VFx93m7l5BVv602WyUy2U2Nja4efMm/+7f/Ts2NzdVtH83WuXuFbkJ1et11ZxycXFxhzut2+fQvSKtnHJMDQ4OUq1WVSB8N8eS7cYY7xIIBHC73TSbTUqlEhsbG2xubqpeWN18gLwbQgiSySQff/wxhUKBkydPYrPZVBXvWq2msrB6YZ+CnfGIHo9HJZVkMhkuXbrE3NwcN2/eJJPJPPbmpg+kvEiTfT6fp9Vqce3aNex2u8oiKhQKbG5uKvN2rVZjbW2NbDbLzMyMcjUZXSi9MBAk8lmlRWp9fZ2rV6+SSqVUd+6lpSVWVlaUpaGbkS7CcrnM2toaly5d4kc/+hGVSkVtzslkklwu1/NWF4BKpUIymaTdbtPX10cul6NWq/VkLMfdkHWDyuUyxWKRbDaLxWLZUem7F5t5yrifQqGgUuuXl5eZm5tTgbq9OH6kMlKtVkkmk8zNzfHzn/8ci8VCpVIhk8mwvLzM9vZ2z8jI6GXJ5XLMzs6qXlfZbJYbN24Qj8dV5t7jVuoeWHmRjRiTySTb29t88sknquS0LFZnMplwuVyqPke9Xv/aBtSrJ2ghBJVKheXlZQqFgmp0JYtmffbZZ6yvr5NKpbrelSafLZVK8eWXXzI7O8svf/lLGo2GqhRrlEOv1liQn790N+ZyOex2O4lEYkewXLeOk3tFLqAyvX57e5vV1VUcDodKBpCxL8bebN2OjEGUym+hUODGjRusrq7y4YcfqjUaem8MyUO0nFurq6tcvXoVQNUvk3WBoHf2LWnljcfjpFIpzp07x09+8hPVH0xWH5bW8Mc5bh5IeTEiA3iLxSLValWlk8lcb9kkTv7eaKbttQlyJ4QQ1Go1lWJ28eJFTCYT8Xi857R8WaXZWNtFWhS63fp0r5hMJmWRkqniqVRKmfs1t5AxQZlMhrW1NT799FOsViubm5tsb29TLpd7ag2SyRYyLfrcuXOUy2VWV1fZ2tpS63OvuNDuhDEeUVqn5M/NZrOn5SPXZols4fMkZSL2iqfweDz3FGwh38P4XnKyGH82ft9PCoXCExlNLpdr34JRpPVBavTGKsUPS6lUeiLyudfxsxvjcz+OglBPavw8qHzkpiutUHJTgkcTH/Wk5ON2ux/qYYxyslhundPk2NrPTuTFYrFj1h+jPKQFSn6Xf99vnsT68zBj5077GTyataiTxs6d9qNHfQDYa+w8tOVFcreN9k4KjObryM1ndzO9XjFpS2XX+LX775pbGDcbSa+Mk/tBWvAajYYq52CcV704pqSiK9cZY9yhZieP6/DdCRhlcVB6pu2L8nIQHqTT6fXFY3fXcc3e9Pp4uVeM1hfNLXbLQ8+3ndzt4NjLcjIqbwdFkds3y4tGo9FoOoMnvfF0AlpGd+agyGXPmBeNRqPRaDSag4a2pWo0Go1Go+kotPKi0Wg0Go2mo9DKi0aj0Wg0mo5CKy8ajUaj0Wg6Cq28aDQajUaj6Si08qLRaDQajaaj0MqLRqPRaDSajmLflBchRFAI8W+FEEUhxJIQ4rfv41q7EOJPhBA5IcSGEOL39+u+DgpaPndHCPH3hBDnhBBVIcSf3ue13xJCvCuEyAohFh/NHT5ZtHz2Rs+tvdHjZ2/0+Nmbgyqf/ayw+8dADegHngb+Sghxsd1uX7mHa/8QmAbGgAHgXSHE1Xa7/aN9vL8njZbP3VkH/gj4PuC8z2uLwJ8A/wL4r/f5vg4KWj57o+fW3ujxszd6/OzNwZTP7mZ4D/IFuG8/3GHD7/4p8D/c4/XrwPcMP/+3wL/cj3s7CF9aPvcspz8C/vQBr/0usPikn0HL57HLRM+te5eVHj9ffy49fjpUPvvlNjoMNNrt9k3D7y4Cx7/pQiFEABi8/fr7uraD0PLRaB4Nem5pHgY9fvbmwMpnv5QXD5Db9bss4L3Ha+Xr7/faTkHLR6N5NOi5pXkY9PjZmwMrn/1SXgqAb9fvfED+Hq+Vr7/fazsFLR+N5tGg55bmYdDjZ28OrHz2S3m5CViEENOG350GvjGgp91up4H47dff17UdhJaPRvNo0HNL8zDo8bM3B1Y++6K8tNvtIvBvgH8ohHALIV4B/ia3AnsQQowLIdpCiPG7vMWfAX8ghAgIIY4Cvwf86X7c20FAy2dvhBAWIYQDMANmIYRDCGEx/L0thHjzLteabl9rvfWjcAghbI/jvh8XWj53R8+tb0aPn7ujx8/eHGj57GNUchD4c26l1i0Dv23422vAImC9y7V2bqXj5YAE8PtPOsr6EURta/ncXTZ/CLR3ff3h7b+N3H7u0F2uffMO1773pJ9Jy+exykfPLT1+9PjpMfmI2//BI0UI8QdAst1u/+NH/p91IFo+d0cI8XeB4+12+x886Xs5iGj57I2eW3ujx8/e6PGzN09SPo9FedFoNBqNRqPZL3RvI41Go9FoNB2FVl40Go1Go9F0FFp50Wg0Go1G01Hs2ZjR4/F0REBMoVAQT+L/dTqdHSGfcrn8ROSjx8/euN3ujpBPsVh8IvIJBAIdIZ90Oq3Hzx48ifGj15696Qb57GdXac0j5G6B1UI8kbGv0Wg0PcHutVevuQcDrbwccOTEabVaOyaREEJNIj2ZNHvRbrfV+DGZTDvGTq9inE9aLt/M7nXIZDJhMnV31MHX6ooY1lu99n6FcWwAd5TTo+CJKC/3kp7d64PiTtp+r8vkm9it3Gm+wmQy7ViAjf/uRYzPrufWvbN7HHUj37T2dvOzPwyPW0aPTXmR2muj0TBW37srZrMZs9nccwvLbi1Wngg9Hg92ux2z2YzJZKJSqVCpVGg0GtRqNYQQXX8SuhPGcSVlBmCxWNT46WXk+PF6vdhsNkqlkho3zWaz58ZNq9VCCIHb7cZisWCz2TCZTJRKJer1OvV6XclF89V6ZLVaMZvNuFwurFYrpVKJYrG44zWdjlxL5Ocvx4fH48FkMtFqtWi1WlQqFVqtllpzenWsSCXWYrFgsVhwu90IIajVajSbTcrlspLlo5DRY7e8GB9iLw2+15SWuyGVOI/Hg9PpxGazYbFYyOVyCCGoVCrUarUnfZtPHG3G3YmcWyaTCYvFgtfrxe12q78BNJvNJ3mLjx353CaTCZfLhc1mw+VyYbFYEEJQKpVoNps0Gg1AjyUjVqsVm82mxlG73aZUKnWV4mLEZDJhtVpxOBz4/X6EEEp5EUJQr9fVz72MEAKbzYbVasXn82E2mymVSjQaDXUQeFQ8cuVFLqJmsxmHw8Ho6ChOpxOn04nZbP7aoJE/b2xskEgkqNfrVCqVrl5IjDKQ/mSfz4fD4eDo0aOEw2GefvppYrEYbrcbh8PB3Nwc8/PzXLt2jc8++4x6vU61WgXoiZO0lJnD4cBqtTI+Pk4gEFALyvr6OvF4nGaz2XObNNxSTMxmMxMTE0QiEb73ve8xPT3N5cuXWV5eZnZ2loWFBUqlEvn8rQ713TrH5GnaYrEQCoXo6+vj137t1xgYGGBwcBC73c6HH37I/Pw8V65cYXFxUVmsehlp0RRCMD09zejoKCdPnmR8fJy33nqLt956S1kgoHPHj/EQ7fV6CYfDBAIBpqenCYVCHD9+HIvFQqPRoFwuc+nSJVKpFJ988gnJZHJHTEynyuBeMcbPtdttgsEgL730EtFolJdeegmXy8Xm5ibZbJZ33nmHlZUVtre3KZVK+x4n9dgsL1J5GR4exufz4ff7sVgsX4tTMAbSZbPZHUF13Tww5HPK07Lb7cbj8TA5OcnIyAivvPIKU1NT+Hw+3G43X375JYFAgHK5zOXLlwG6XskzIseD1WrF6XQyMjLCyMiIUlYajQbJZBJALcC9hJRPJBJhZGSEl156ieeeew6fz0dfXx+NRoNUKkWr1SKXywGdu/nsxe6AS6/XSyQS4ZlnnmFqaorx8XEcDodygaysrOy4thtlcj+0Wi1MJhP9/f1MTU3x3HPPcfz4ca5evapcKZ2Mcf8xmUw4nU6i0SixWIynn36agYEBXnrpJex2O9VqlUKhgNlsZnV1lWvXrrG1tfW1gN5uxygzh8PB1NQUY2NjfOtb38Lr9bK6usrW1hYzMzPk83lyuZwaR/vJI1NejIuGz+fj+PHjDAwM8P3vf59gMKh8zhL5wcvN5+2332ZzcxOga0+GxkEvTdnHjh0jEAhw8uRJQqEQIyMjeL1eKpUK165dU24km83G8ePHyWQyLC4uEo/HuXbtmjJrdpusdiMVlyNHjjA4OMiv/MqvcOTIERXLEQwGabfbbGxssLS01DOxHdLCMDo6Sl9fH6+++ipTU1NEo1GazSaxWAybzUYikeDKlStdKxM5D0wmE3a7nWAwSDAY5Dvf+Q6Dg4McPnyYcDiM2Wym1Wpx+PBhfD4fW1tbZLNZMpkM2Wy2J7Jq7oTcoGw2G3a7/f/P3n/+Rprlh73456mccxWrijk12bmnu6dndmdmJ+zOSpZ2tUow7LUvrv3Cbwy/MmDjGvAPEHz9LxiGcX8QBNmA7CvLkgVppdFqd2cnh97Ogc0ciqzMKlbOdV/0nDMPOd3sxA5VfD4AsTtsFlnPt77nnO/5RiYmJjhz5gyNRoMbN24Qj8dpNBq7Ll29hvqMikajjI+PMzk5yauvvorH42FkZAS73Y7T6cRgMGC1WrHZbLz00kuMjIwQj8fxer0sLy+TzWYB+nb/FXKy2+14PB4CgQCzs7N4vV6OHj2K2+0mnU5TLBYxmUyEQiHefvttZmZm+Oijj1haWiKXy1EsFg9sL36qnhfhYrJarRw5coSxsTHeeOMNgsEgFosFvV4vf1Z84M1mk1arxeLiIp9++imlUqnvrVoRKrJarczOzjI4OMjbb79NIBDAYrEAMDc3RyqVknHEkydPcuTIETY3N5mYmKDVanH79u1D46kSocjh4WGmpqa4cOECJ0+elMbL9vY2a2tr1Ot1VldXn/fbfSaI9SY8LpFIhJMnTzI7O4vH46Hb7RIMBvF6vVy6dAmj0Si9n/2kK3svBWazmYGBAYaGhnjttdeIRCIMDw9jsVjodDq0222GhoYIBAJcvnyZxcVF6vU6uVwOOBxh2L2ojReLxcLg4CCTk5Osra0Ri8XIZrPSo9mL8lHvk51ORx7CJ0+e5Hvf+x42mw2Hw4GiKLtSH2w2G9PT04TDYW7evAlAJpNhe3v7UHhgxFoaHx/nu9/9rjTudDod+Xweg8HA8PAwDoeD06dPMzExQTKZlIUC+Xx+17n/JBy48aLeNLxeL+Pj4wwPD/P6668zMDCAzWYDkIdMuVym2WxSKpVoNBp4PB7sdjvNZrNvksH2IvIyRCa71+vl9OnTBINBXn31VdxuN+12m2QyyfLyMvl8noWFhV3x1XK5jKIoFAoFAoEAiUTiwJTiRUVsNEajkYmJCXw+H6+88grT09O4XC5ZBdDpdGTliIjHHyaMRiPj4+OMjY0RjUbx+/2YTCa63S7r6+skEgkWFhZIJBKUSqW+uymKg8bj8TA1NUUwGORb3/oWwWCQkZERnE4nnU6Her0un91sNsvcqVOnTtFsNkkkEj15MB8Ews0fiUTw+/1EIhF8Ph+XLl3ixo0bJJPJpxIKeFaIvWRgYIBQKMQrr7zC97//ffx+PwDxeJyFhQUMBoO8RAaDQZmcarVauXDhAhMTEzidTpaWlpifnycWi+2qpu2HdSUuPUNDQwwPD3P27FmcTidOp5NKpcL169epVCpks1l0Oh1TU1O43W4mJydxOBwcO3YMl8tFu92mUCjsykN8EvkcqPGy98bj9/t55ZVXGBsb4/XXX5flmoqi0G636XQ65PN5yuUyiUSCYrHI1NQURqPxG03Z+gmRQKjX66WB94Mf/IBwOMzRo0fR6/Vcv36dVCrFL3/5S1ZWVqRrUrixxebcarUIBAIy07vXY9APotVqYTKZmJiYYGxsjAsXLjA9PY1Op6Ner+8qnRZl+YcB9YZpMBgYGxuTITW/3y/Lo2OxGNevX2dxcZFEIiG9fv2CuoGax+Ph9OnTjI2N8Vu/9Vs4nU7MZjMA1WqVVqslS4AtFgs6nY6RkREqlQqbm5uHNmlXHOw6nY5wOMzQ0BDhcBifz0cul5N708O0vHhREZecYDDIyZMnefXVV3n33Xep1+sUCgUSiQR/+7d/i8lk4ujRo3g8HjqdDg6HQxozFy5coN1uYzabGR0dpVqtEo/Hd5VQ97L+qD/bYDDISy+9xMzMDN/97ndpNpvS4/Tpp5+SSqVYWVmh2+1y/PhxQqEQP/7xj/H7/Rw9epTR0VFWV1dlkUCz2XzifedAjRfxYdlsNtxuN6Ojo5w+fZpwOCzDROoyzUajweLiIqlUilwuR61WIxgMEo1GD/JtvTCITUHET6PRKK+//jrRaJRoNIrZbOb27duUSiU+//xzkskkd+7cYXt7WybjCsOnXC6TTqdxOp34fD5cLpe8Xfd69v+9UB/MwnUZiUSw2+0YjUa5kdbrdRqNBtvb28TjcRlj7SdZ3ItutysN2kAgQCgUwu/3y4tAuVymXC6zsrLCzZs3SSQST7UHw/NAHEg2m41gMMjU1BRnz54lHA5js9nQ6/WUy2Xq9Trr6+s0Gg3GxsZwuVwy/87pdBIKhaQHWPRRgv5aT/dDXD5tNhs2m40zZ84wMzNDp9NhaWlJeu7K5XJP5gOpw2HCWD1z5gzRaJROp0MqleLKlSssLS0xNzeHTqdjZ2cHu93O6uoqdrtdeu/Gx8dxOBzyfIvFYjQaDWKxGLFYrKf1ReynFosFs9nM0NAQJ06cIBwO0+12SSaT/OIXvyCRSBCLxSiVSrTbbZlnWK1WuXjxIul0msnJSfx+P2NjY8zOzsrQ45OG2J6K8WK32xkdHWVmZoY33ngDh8MhPS7iAG82m1QqFa5duyZjzO12myNHjuz6ff2EiK9bLBZ8Ph9HjhzhRz/6ET6fj3A4TKFQ4P333ycWi8mEZWHFi1ug2mMVi8WYmpqSt2uLxbLLeOmn+Ku6IZKIwY+Pj+NyuTAajVJOtVqNYrFIMplkfX2darXaVwf0vRBrymQyyfLfoaEhaRB3Oh12dnbIZrPcvHmTzz77jJ2dHdrttvTi9QPCsLfZbExMTHDixAnefvttuf80m02KxSL5fJ5Lly5RKpVk6wGj0YiiKLjdboaGhggGg7hcLmns9LP+qBHrzOFw4PP5+M53vsP58+eZm5vj+vXrLCwssLq6uquJaC8hDmWbzSZzMd98803cbjedToeNjQ3+5m/+hvX1dS5evEi32+XGjRuYTCZcLpcMgwSDQd59911GRkYYHR3l6NGjFItFrFYrH3300a7wUa/JCL6+CIizSiQyC+/++vo6f/Inf8LOzg7ValVengDW19dlM9VIJMI//+f/nKNHj3L06FHZ/0Vd1fe4HIjxIj4kq9WK3W5naGiIM2fOMD09jdlslk2gxM24VquxtLRENptlYWGBjY0NRkZG8Pl8spuh+Pl+QH24mM1mIpEIx48f58iRI4TDYRRF4c6dO6TTaW7dukUikaBSqXxjVsS90Ov10vXdr25uoV96vR6/308wGCQUCuHz+WTCqeg3sbGxwfr6OpubmzJU0o8yEYjN0Ww243a7OXPmDENDQwwMDOB0Omm1WpRKJVZWVlhbW5O61Wq1+kpfut0uJpMJu91OIBBgcHBQ5iiIbsvNZpOdnR0ymQzLy8sUCgUZchweHpbJqU6nE7fbjdfrlXH6Xj2EHgW1ZzgSiRCJROR+LEqDRVUN9OblUlxkxOcbCATweDw0Gg1WVlZYWVlhdXV1V36hyL8UeZkihO/xeNja2uLs2bMMDg7KnjAbGxvMzc1Rr9epVCo9m9QsWgtEo1GCwSBut1vKw2w2yzyWWq32jZYngEzgFeElcen0er27vOWPq0cHZrwId200GuX06dP89m//tnS9ig+u1WpRLBbZ3t7mb/7mb1hdXeXKlSsUCgWOHz/O6dOnCYVC0qLvl/wN4XGxWq2Ew2HOnj3L7/3e7zEwMMDMzAwbGxv89Kc/ZWNjg/fff1/23QB2Kb46CUx84Hq9HqvVisViwWAw7PrZXtxc7ke325UJlaKscWRkBLPZLBPAms0m169f58MPP+T27duyC3EvbhwPizDq7HY70WiU3/md32F0dJRwOIzJZKJUKlEul/nss8+4fPkyd+7ckYdxvxgv6tu03+9nfHyckydPMjo6KhNxDQYDnU6HeDzOxsYGX3zxBel0mlAoRLlclgaLw+HAYrEQDocZHByk1WqRSCT6Qk4PQp2Ld/z4ccbHx/H7/eh0Oq5evcrPfvYz0ul0T+rN3r0zHA4zMTHB+Pg40WiUW7du8cUXX3Dx4kUuX75Ms9mUP99oNGg0GpRKJQA2NzcxGAysrq4SCATQ6XSYTCbGxsY4duwYyWRSVocWi8WeDK8J50EoFOLYsWNMTk4yMDAgc4JcLheDg4MYDAZyudyui7bwwIjS6ZWVFXmZOnnyJLdu3cJqte5qrPo4+nTgCbsijm40GqUrVnhcSqUSc3NzpNNpGTsVc3lMJhNWq3VX75deR2yq4vYXjUaZnZ1lYmKCYDAoF4CIAYreCWIDedDvFfTaRvIoiOcUZYqies3tdmOxWGQoLZvNUigUiMfjpFIpudH0M+r4/eDgIENDQ3g8HpxOJ0ajEZ1OR6lUIp/Pk0wmicfjlMvlvqmEUD+H8MrNzMwwOTnJyMgIgUCAbrdLtVqlVCpJT+/m5iY7OztUKhWZN1atVgHkOAWXy0UoFJIlsP2OeuaTx+NhdHSU0dFRCoUC9XqddDpNoVCQ+3Wv6o6YwxMKhZiYmMDlclGv18nn82xsbJBOp3fNt1JHAMT/F+ecyKfb2NjA7/czNTUlc6ZmZ2cxGAyk0+ldxSe9JDd1E1Cxn6hTP0QKw71eB8h8MXHeT0xM4Pf7CYVCDA8Ps729TTKZfOz3d2CWgvCUiA9eHRNtNptsb2+zubnJn/zJnxCLxVhYWKBcLstRAeLmI3Jj+gERN/T7/fj9fr71rW/xG7/xG4RCISYnJ1lfX+d//+//zdraGp988gnFYpF6vS6VZD857K3G6heZ7UXdAycYDPLrv/7rjI2NMTo6itVqpd1uU6/XuX37NisrK1y6dImbN29Sq9WA/pWLoNu92wTy9ddfZ3R0lOHhYfx+PwaDgWazSTweJxaLcfPmTa5evdqTt+b7IQ4Ss9mM2Wzm6NGj/OhHP2J4eJhTp04B0Gg0yOVyXLt2jVgsxp//+Z+TTCZJJBJ0Oh2uX79OOp3mwoULAPLyNDQ0xPnz5ykWiz1dEvwwqMPa0WiUwcFB3nrrLUZHR/n5z3/OysoKt27dIplM9myOlDhkRWrDSy+9xLvvvovb7Safz7O4uMhHH30kR9IAMt1BvV7UF8d0Os329jYffvghW1tb/PCHP2RgYIDjx4/j8Xj4+c9/TiwWo1KpSG96r8hO3UJAjKoRhkuz2aTRaMjeLeqfF/8fkK0qrl+/Tj6fJxgMcurUKU6cOEG5XObq1auyau25el7EGxAeF+GqFQ+RSqVIJpOkUinS6bQsl3K5XNhsNux2u+y6K7KWxe/tNdTeAjFPRcwGCYfDWK1WyuUy2WxW5mdUKhXZsfJBH6RIXFWXfYqE54fJk+kldDodFotFVhf5fD45WkJRFOr1OtVqla2tLVZWVshms9J71S8y2IvYQA0GAw6HA7/fTzQaZWBgALPZLF3dtVqNTCZDMpmkXC7TarVk/kc/4XA48Hg8hEIhWSkk2rkXi0Wy2SxLS0tsbW2RzWYpFouyz0StVpM5QAK1J9hoNAK9uQ89KiaTiaGhIYaGhjCZTHQ6HdLpNJubm5TL5b4w4gwGg/QmiEGllUqFUqkkvXFw/8HA6u8JHRL5HKI7s8FgYGBggIGBAcLhMNvb2xSLxZ5L4BXrQAwxBWR4XuTziHyX/Z6pWCxKQ69QKEjPl9jH1QMuH0U2B5bz0m63MZlMeL1evF4vHo8Hq9UK3E3c+eSTT1hdXeXWrVtks1k6nQ56vV5W2oyMjDAyMgLcvS2JHh29uGmI92y327Farbz++uv85m/+JkNDQ0xNTRGPx7l69SpXr17lvffeo1gsUq1W9+0NsNftKNyTALFYjFQqRblc3jVhulcWyb0QHheTycTAwADf//73GR4eZnx8HJ/PJ0uA0+k02WyW999/n08++YRSqSRvA72+0d4PcftxOp0cO3aM6elp3njjDQKBgPRGpdNp8vk8X375JXfu3CGbzcqk7n5BPMv4+Dhnz57l3LlzHD16VN4Sy+UyCwsLLCws8Cd/8idsb2+zs7Mj9xadTier09Q5DuLG6XQ6ZQVfL6+l/RD7imgq+oMf/IDh4WF0Oh1bW1syB0R0Ru1FOajPEYvFgsfjkZVDYgzE5uamLHV+kHEvZCD0b3FxUYZF3G43U1NTHD9+nHK5TKFQ4ObNm7L/i3g/L7Ic1b2S3G43g4ODuN1u4O7ZnM/nyWQybGxsyGap99pXhPMiFouRSCSYnp7GarXicrl46aWXyGQysrmomC32zI0X8UdFx1gxMbrdbpPL5UilUmxtbcn6b3UFiMlk2vVVLpep1Wq7vAkv8ge9H6Ks1+PxEA6HZefgQqHA+vo6W1tbFAoFWWoG+394wkVuNBpxu914PB50Oh3FYpFKpdI3nhfhxjYajbID8dDQEJFIRCYmdzodGo0GmUyGRCJBNpuV5b8v+ubwpKirrxwOh+x2abfb0ev1tFotue7S6TTpdLqvyn3FWhGVjGL4pDDeFEWhVquxs7NDLBZjc3NT3vr2Ni7cG7cXuiPi9f3eoVkcVBaLRfYsCQaDcqBePp+nVCr1fNWeutWC+ku0V6jX64983ogwitCTbDZLIpEgGo1iMBhk2X0ymcRisVCv13tKn0TbjVqtRrPZlBWdlUpFnuNiltq9UIeP2u22rPSzWCyyPcGTXKieyHgRm6iIgwaDQWZmZhgcHMRisZBKpfjss89YXl7mvffeI5/PU6/XZbM6oUzCRWu1WmVJ58LCArFYrCcXjdgcbTabLNucnJwknU5z+fJlvvzyS/77f//v5PN56XF5UC6C2GQnJiYYGRnhwoULvPLKK9y5c4dLly7JW4Po3dHLCMNEDKg8cuQI3//+9/H5fNhsNjqdDqVSiUKhwE9/+lNu3brFwsICtVqtJzP7Hxez2UwgECAYDMrbJEC9XufTTz9leXmZzz//nPX1daB34u0PQuw7oVCIYDDIa6+9xg9/+EPZs2VnZ4etrS1u3LjBf/tv/21XqEjohnD5C0NZrFmRiJhOp1lYWCCTyfRVnpAa4TG3WCyMjo4yPT3N6dOncTgc/PEf/zHz8/NyPIm6krFXEZc/UVEmKhWLxaK8AIoz6WE+b/EzIuxx69YtKpUKNpuN2dlZIpEI3//+9zEajVy8eJFcLkc8Hn/hK/1EEUS322VjY4OLFy9iNBo5duwY+XyelZUVmTP2MAgjaH19XRqNQ0NDMq3iuRgvApEw5/V6CYVCuFwumeW/tbXF1tYWuVxu15DFvaVr4tARcepqtUqj0ehpT4LZbJbNkIxGo/QUiMoPYbjsh/qWKFqeRyIR3G43ZrOZZrNJNpuVMeleDLPdD6PRiMfjwefzycNZLIRKpUKxWCSRSEhZ9tOzPwixyYpOqKJjqPAYbG9vk0qlKBQKVCoVzGZzXxkvgHx+0WVa3OLEOkulUrLLstiM926URqNxV5GAMIyE50Z0tu7F/edhEKMRBgYGCAQCsm+S2Keq1eoDqx97AXHuqM8bcQ6JBpcPc4nci1pvRHNMMT3ZaDTi9Xrx+Xx4vV6azWbPtAAR66BarZLL5SiXy/ICrc5JfRTq9bqcZSh4krV1IMbL+Pg4R48e5cKFC3z/+9+XLZVXV1f54IMPSKVSMqyxVznUAhHD0kqlEvV6vScPY/Ghd7td/H4/ExMTOBwOarUasViML774gvn5ebmhqhfUvX6XkIHVasVsNnPu3DnefPNN9Ho9i4uLXL16lQ8++EDGDMXm3KubrThgzGazHKw3PDwsEyf1ej31ep1bt27JxllLS0syVt2rz/0wqMNFZrMZv9/PkSNHpHxEyXgymWR1dZXV1VXq9fpD3yR7FXUOgqIoxONx/v7v/56VlRXS6bTMA1MbLkLPxsbGGB4exuPxAF/vR5lMhsXFRdLpdN8ZL2JfsVgseL1eJicn+fGPf4zb7WZubo58Ps+VK1dYWVmhVqvJMtl+415VRI9z3uh0OrrdLqlUiu3tbS5dusTAwABTU1OcPHmSwcFBvv3tbzM/P8/Kyor0+r3oiE7u6+vrpNNpqtUqRqOR4eFhWXn2sKhLrIXx9qRn+4FopNPpJBKJEA6H5eTocrnMzs4OyWSS7e3tXVMk1Uoj3Ejq+GG1WpUxtl4zXtSI5CSj0Shbk6dSKZk0+LDxVUVRsFqtuN1uAoEAkUgEnU5HNpslm82SyWQoFou7fr4XUYchhdfK6/XKkevw9SIQSWPFYlFW0vTbIXM/hMfBbrfj9XpxuVzS1SvyFHZ2duS8kX6ViagcEUaLiMkXi0W2trZkrs+98gxEqFusKYvFIv9NhC1LpZK8LfcT6mo1MQtrZGSEUChENpvdlSPUD2Hoh0F9iXycM0fkWYnqNjFoWOxlXq8Xh8PRM7okZNBsNmUUROwlNptNVjU+Cg8qRnlUHtvzItxwOp1Ozj4IBoPo9XoymQwfffQRt27dki59dUay+pASsVaHwyFnHly7dk1mZ/dSXwF15r6iKESjUY4fP47dbpdl0aLmXZ1UuvcDFRYq3A09mUwmXnvtNY4cOcLs7Cwmk4nl5WU++ugjeTu61+/pRTqdDj6fj/HxcU6cOMG5c+d2jQEQCWSxWIyVlRVZKdKvt0M1Qi9CoRDnzp1jdnZWjqfvdrvkcjn+7u/+jvX1de7cuUMqlaLZbPad50XsH+FwmOPHjxMOhzEajaTTaba2trh69SrXrl2TBr3a2yvWndvtxu12c+7cOY4fP04wGPzG33mR8xIeB3WoXuQd/M7v/A6Dg4MEAgFyuRw/+clPWFtbY2trq6+r9sRaEu09RMKy6NYND7+fCoNHvHZra4tr164RDAbl36nVarsqQXsR4fW1Wq2ydPpREAOJhRf9SdfWE4WNhCVls9lkaXS3e3cGxMbGhpw+2mw2v9F8TlHudsV0uVz4/X7ZV0DUhIscjl7dPHQ6HXa7XZb1NhoNyuUy+XxehngeBjGvZWRkhCNHjuByueh0Omxvb8u+JsIi7vXuxOJQEjF48SXmqwiXY6PRoFAosLOzsysv6rAg9EEMEBTDFyuVipzNksvlZAJir66h+yH0RMwxstlsKIpCtVolkUiQSqXIZDLfaD0uDhKR5+FwOBgYGGBwcFDuXepQba97fu+FOIBENczU1BSBQEDuUcvLy6ytrUlvZr+EYvfqgAgPio7KYq6VusLscZ5b5Ink83nZM0bsWY+bK/IioL5Qiwa0j/p6IeeDOqce67eID0DM1AkGg0SjUbrdLouLi1y/fp2PPvpI9nNRJymJwV/Dw8P4fD5eeukl6Z0QzYJEp9leXjTdblcmKIXDYaLRKBMTE5w8eZKNjQ22t7eBr+Ol4jVCXmazGYvFwptvvsnIyAivvPIKw8PDXLt2jffff59Lly6xsbHRl7keAwMDvPHGG4yMjOByuTCbzTKZW5SY37lzh6WlJer1uhxD0a+ojTqr1cr4+DgXLlyQDQ8bjQYbGxuyj9LGxoZsd9+PchHyED0onE4nnU6HRCLBxYsXuXPnjgyZwe7qiUajgcViYWxsjKGhIUZGRhgYGJA9qcT+I0rMRR+LfkAc2oFAgNnZWU6ePMmpU6coFAqy8/nW1pYcrdEv+4ra65/L5Wi329LAdTgcnDhxgkwmw8LCAltbW8zNzcnD9kHPr87jEG1CZmZmOHfuHIODg7IvysrKCltbWz1z0RLyEqMTtre3SafT0vAVzS7V59f9ZCXW3sDAAMeOHSMcDsuWF/u97kE8UdhIdCp0OBy43W7K5bJ03a6srMgKEHUcUYSLRHO6wcFBBgcHAWStvZgG/CQP9rwRbkThthex9Wg0Kt3Z97LChbyMRiN2u12GikZHR/H7/WQyGa5cuSJv1+Lm0C8oyt1JpmIQmMVikVZ+q9UinU7Lbs3pdLqvbof7ITZTp9OJ3+9ndHQUr9cr+ylls1lZXZNKpfo610V9eRJVd8JrG4vFyGazMmdOvTbEmgTw+/2Ew2GZM6TerIUBIwoHoHf3ob10Oh3sdrscsRGJRKhWq/zqV78iFotJb2a/hWHF51epVOh2uxQKBUqlkvTuDg4OMjw8LENl6vPqfp+9+me63a6MQITDYUZHR3G73XKqu8jP6yXPi+h3VKlUKJfLFItFLBaL7CcljBe1QbZf4YnL5SISieByuXbt2c8050X8MZPJhNPpxOv1yumj5XIZs9mMz+ejWq3uipuazWZGRkbweDycP3+eUCjE4OAger2e69evs7m5ye3bt0mlUlSr1Z47lISRJr62tra4fv06AwMDAHJkeqPR4MqVK9TrdZkQaDKZMBqN+Hw+HA4HL730EqFQiDNnzhAMBrl58ybpdJrPPvtMViv1mnzuh9CnYDBIJBJhdnaWgYEB2dVRXbZ369Yt1tbWyGazD+xK3C8I+QwMDHDq1ClmZ2cJBoPSW1CtVllZWWF9fZ1ardYz1QyPi7pyQRhpZrOZiYkJvv/97zM3NydnXqmTdQ0GA16vF7fbzTvvvEM0GpX7lvpmLqorCoVCXyTsClnZbDZcLhfHjh3j3XffxWg08uGHH7K8vMzy8rIcQtlvOVLw9aEqRrAsLi7y2Wefcf78eekV//Vf/3VGRkZotVrk83m2trZ2hXr2tu0QHogjR44QCoWYnp5mcHCQqakpJicnyefzfPDBB1y6dImVlRU536gXcqn2ymtzc5Nr164xMjLC6dOnGRwc5LXXXiOZTHL79u1dYTERbhWGn+hULcYlKIoiRymou8I/qkyeyPMimsuJ2SKiSZhorS16KMBdF6QYiS0aSwWDQXnrWV1d5dq1a6ytrclOqb1o+asNmEwmw+rqqgwRud1uxsfHicfj2Gw2GVoSSckWi2VX461IJEI0GsVkMvGzn/2MS5cucfv2bWKxWF82Y1OXRvt8Pnk4q3tvrK6uykTdfgyZ7UV9K/F4PBw5ckReAIRHqlarkUgk5JT2x+lX0YsIT4rI94pEInLmlwidiSnaiqJgsVjkZFtxORCVWkJWpVKJVCol+1L1g2EswhoixD8xMcG5c+eIx+Ny8KKYOP44+Qy9hKjyjMVi2O12JiYm0Ov1RCIR7HY7JpOJjY0NNjc3d3nw1OtQ6IR6IOjMzAynT59mcnISj8eD1+vl4sWLXL9+nbm5OeLx+K4RFC864hnb7bb0eC8vL8uRN8FgkOPHj+N0OllZWdnVK0edMyZyYt1uNz6fD5/Px87OjqyGFJetx5HJYxsvojSsUCiQyWRk58DBwUG5UbRarV3hH+F5cTgcDA8PY7FYiMfjsq/AlStXpMt7P3fdi4744MWY9fX1dZaWltDpdExPT0vLXtTQm0wmGbufnZ3F5XIRDocxGAxcunSJXC7HpUuXWFhYkOV396tU6iWEkjscDux2OzMzM7z11lsMDw9jt9tlVnq9XieZTBKLxVheXmZ9fZ1Go9HTz/4wiA1A5JYNDg5y5MgR2X5cjJpIJBIsLS2xubkpwxz9jPjck8kkc3NzOJ1OotEoiqLg8XiYnp7mN37jN+QAOYHRaMTv92Oz2WQiuKhiE6M6fvWrX3HlyhU2NjaAF38OzX6I9SVGr5w8eZJ33nkHr9crp7B//PHHZDKZnuxk/iioQxTdbpdEIkG73WZ8fJzR0VEcDgcul4uJiQl+8zd/k1wux/nz5+WhLEZutNtt2f5ClAxPTk4SCARwuVxUKhWpS1evXuXDDz+Uk6p7Jd9lL8JTcunSJer1On6/H71ez9mzZ+Wzl0ol0uk0tVqNbDYrz33hmRKVgYFAgEQiwcLCAolEYpdcnonnRXgX1MZLIpEgGAwyODhIOBzmxIkT93ydyWSSTbaazSaXLl2SXpfLly/LOFovLySRxCTix2traywtLTE+Ps6RI0ek9ZpMJrly5YpMGvP5fJw8eRKTySR7LVy6dIn5+Xk5kl49C6KXZQRf35yFJT8zM8N3vvMd2cNE/Ey9Xmdra4u1tTWWl5fZ2tqi2Wz2vJ48CPWN2ePxEI1GmZ6elqXjYqMQxksymZRu3n5G3RTszp07DA0NyS7Cbrcbp9PJ2NgYwDfi8SK5W6077XabeDxOLBbj8uXLfPbZZzI/oZf1Sxi/Irx//Phxfvd3f5dYLMavfvUrbt26xaeffipHtvT7ehJ0u13ZCXdycpLR0VHGx8cJBoOMjY0xPT1NvV4nl8vJ0KQoFmi32zLB2+l0ymICRVHk9Og7d+5w9epVbt++zRdffCFLqNWJw72COOvT6TSZTIZarYbH42FycpJf+7VfA+DYsWOUSiUWFhYoFAqsrKxQqVRkWO3VV19ldHSUoaEh/H4/tVqNhYUFUqmUvIQ+Tt7mE3leRAOb7e1tYrEYer2eUCgkjRT45jRkuBtHW19fZ2dnh8uXL7OyskIul+sLF61AyKfb7bK0tMTHH38sq686nQ4ej0c2YtPpdDgcDvR6PRsbGzSbTa5evUoqlWJ+fp7NzU0ZkusHj8t+CH0xGAzU63Wy2SzxeJxPP/2UWCwmOxMfJkRI0Wq1YrfbZav/VqtFJpMhk8nIluQiIb5f9QO+1v1sNovBYGBubg6/38/Q0BDT09O7cmL2Hhbiv8WwuGQySaFQ4PPPP2d5eZnV1VXZ3qHX9yMRApqenubEiRNMTExQrVaJx+NcunSJzc3NXe0oevlZHwURDmk0GiwuLmI2m1lbWyOdTuN0OgkEAt8wbsUMKOGFEU1YxfDhSqUiDSIxn089/6eXdUm8b9Gi49q1a+zs7OByuaRXWFEUxsbG6HQ6jIyM7PKo2O12isUit27dotvtyijCk84Ne2zPC9w1QhqNhnQDmc1mxsfHMZvNMl9BnVDZ6XRoNptUKhWuXr1KLBbjF7/4BSsrK+zs7Mj8hV79kAXi/YvKqRs3bsiR6/V6ncHBQY4fP47VauX8+fPUajXW19el9ZrNZvnJT35CPB5nbW1NhopELLrX5bMXdZIz3N10TSYTpVKJra0t5ufn+clPfkImkyGfzx8Krwt8fXM2GAzY7Xbp2nY4HLIvx9bWFpubm7Jzcz8M0HsQYo9IJpNks1msViudTodXX32V2dnZXc0w7/VaUTJdqVTkmIn33nuP27dvUy6XqVarPZ1TJp7baDRiMBg4ffo0v//7v4/RaKRcLrO6usovfvELObeoH/bch0VtkLRaLZmTMj4+zvz8POFwmKNHj+J0OhkYGNilA+o8xWazKauzbt++TSKRYH19nVQqRalUolQq7dqze1WXBGJ/FmtueXmZSqVCMBjk9OnTeL1ezpw5g8vlkhdyMa1bnPV37txhY2OD27dvMzc3J3suPS4HUmObyWRkhr9oHiUmAItsfkAaOrlcjuvXrxOPx0mn03LuUb8tIrURUywWWVtbw2azsba2RjwelxNOa7WanP+UyWQoFArE43G2t7e/0fa+3+QjBumJScC3b98mFAqh0+lIp9MsLi6yurpKPp+nVCodiuoigaLcbeQo5DM/P8/PfvYzWa6YyWS4du2anL7e6xvkw6I+gNThI3XZ5v28c+qQd71eZ25ujkwmI0cJ9NM+pM7vuHr1qgyB3Lp1a9ezHmbEhTqfz7O8vCwTta1WKz6fb9eaUl/aRRJruVxmY2ODfD4vKyD7cVSJ+lna7TalUklWqDUaDVwul7xIWCwW2Zer2WzKGWEbGxtks1m2t7cPpAGkst8vcDgcD/XbRda1yHnx+/2MjY0RjUZ55ZVXMBqNdDodCoUCn376KYlEgl/+8pekUiny+bwcHve4m2+pVHouWmK1Wh9KPiJUZDQaZW8ct9uNTqfDZDLRbrfl1E5xG9o7yPJJFkK1Wn0u8nkY/VGHOY4ePcq5c+eYmpritddeY2Njg/fee4/NzU0+/vhjarXaUynjfF76Y7fbH0o+4gbtdDql3uj1eprNJuVyWTaSelqHUblcfi7y8Xq9+8pHbIBiXYmKBnhw7whRdZLL5WR/KWEAPqoMc7ncC6k/wnsUCoVkUmWhUKBare4anfC0eR7687Bnl9rjC8hqNYPBsGve1d7XiI7WYoq70J1H3a+f197zsPK5F2pPuV6vl1PtPR6PbF4nQnNiuKNom6IOxz6MjPaTz4F4XkRCU6FQwGg0yjcpsrMNBoP8sOfm5sjlcnLc/GG5SavDZuJ5xSEkpmmLf1dP3ux32YibMMDOzg7r6+tyI0ilUtJa7+VREU+C0Bth2IrvwdfJzK1Wq+eTSx8HdSxeGHJq2eyHyGMQe1W/6le326VSqZDL5WTpeK83AH0aiMNY3R/oftVBwmgWvU16tYroSRFGnHpoozBc1ONcKpXKrj4wB6V3B+J5EQ8C7DqY92YRC+VQd7o8CF50zwvs7sa49/v3+jAPMkz0Inte1Kh1Rxi86sZHT4sX2fOiZu8tEQ5WT+7Hi+p5UXO/9fUgDkJ+L6rnRbBXb571hehF9rwI1PJ5WB3ab99+WHrR86LmYeS2VyaPIqOn7nlRbxzikHnQgzyK66gfEB4G8b/qDUVtwKjlcVhkI1B7nGq12i79OCz5HPdjP52Bw6cr9+JRjNzDJDt1w7B+f9bHRe0BFjzsYXy/7x0G7neeiX9T/9ze7z0pB2K89GMy6dPgYTfMwypH4XbU+CbawbM/aq+dxm56uXLqWXKvc+x+FWsaX/O89qb+mejXQ2jKr6GhofHio+3VLy775rxoaGhoaGhoaLxoaL5EDQ0NDQ0NjZ5CM140NDQ0NDQ0egrNeNHQ0NDQ0NDoKTTjRUNDQ0NDQ6On0IwXDQ0NDQ0NjZ5CM140NDQ0NDQ0egrNeNHQ0NDQ0NDoKQ7MeFEUxacoyp8rilJWFGVNUZQfP8JrzYqi/KGiKAVFURKKovzrg3pfLwqKovwrRVEuKopSVxTljx7xtW8rivILRVF2FEVZfTrv8Pmi6c/90XRnfzTd2R9Nf/ZH05/9eVH15yA9L/8JaAADwD8B/rOiKMcf8rV/AEwDo8DbwL9VFOXXD/C9vQhsAf8R+MPHeG35q9f9mwN9Ry8Wmv7cH0139kfTnf3R9Gd/NP3ZnxdSfw6kw66iKHYgB5zodrvzX33vvwKb3W73/3qI128B/6zb7f7dV//9fwPT3W73Hz3xm3vBUBTlPwJD3W73nz3Ga78H/P+73e7YQb+v54mmPw+HpjvfRNOdh0fTn2+i6c/D86Lpz0F5Xo4ALfHhf8VV4IHWq6IoXiDy1c8/0ms1+gZNfzQeF013NJ4ETX96lIMyXhxAYc/3dgDnQ75W/PyjvlajP9D0R+Nx0XRH40nQ9KdHOSjjpQS49nzPBRQf8rXi5x/1tRr9gaY/Go+LpjsaT4KmPz3KQRkv84BBUZRp1fdOAzcf9MJut5sD4l/9/CO9VqNv0PRH43HRdEfjSdD0p0c5EOOl2+2Wgf8F/AdFUeyKorwG/Aj4rwCKoowpitJVFGXsPr/ij4F/ryiKV1GUWeBfAH90EO/tRUFRFIOiKBZAD+gVRbEoimJQ/XtXUZS37vNa3VevNd79T8WiKIrpWbzvZ4GmP/uj6c790XTnwWj6c380/XkwL6z+dLvdA/kCfMBfcLc0ah34serf3gBWAeN9XmvmbjlVAUgC//qg3teL8sXdkrrunq8/+Orfhr96dv99XvvWPV77/vN+pgOWj6Y/mu5ouqPpj6Y/L9jXi6o/B1Iq/SAURfn3QLrb7f6Xp/7HehBFUf4pcLzb7f675/1eXkQ0/bk/mu7sj6Y7+6Ppz/5o+rM/z1N/nonxoqGhoaGhoaFxUGizjTQ0NDQ0NDR6Cs140dDQ0NDQ0OgpNONFQ0NDQ0NDo6cw7PePVqu1JxJiqtWq8jz+rsPh6An5lEql5yIfp9PZE/IpFovPRT7a+tofj8fTE/LJ5/PPRT52u70n5FMul5+5fFwuV0/IplAoaLqzD/vpzr7Gi4aGhoaGhobGQXCvAiFFeTz77ZkYL6qa73u+ebj7AOqvw1AFJT409bN2Oh0AdDrdvj+noXEv9vZC0Ol0ck1p3OVe60isO9i9F2ncRcis0+lIvVLvURoaD+IbfVpUa+xx1tozMV4e5o2pf+awHNLiA1SjbZiPhtAVTW5fo94QNLl8zX77yr3kdK/1edjR6XSHVi4H6TU4bKhl96RGi+CpGy/dbhe9Xo/VakWv12OxWOQCUP9MpVKh2WzSbDZpt9uHxgOj3gh0Oh1msxlFUWg0GnQ6HXnT0dhtue9FO7DvPrvFYsFgMGC1WtHpdJRKJRqNBu12e5d34bCwV2eEfqg9UoqiYLPZMBqNtNtt2u029Xqder1+6I1j8fxGoxG9Xi/lVKlUKJfLu36m39gbLdi7F+/VIY17I+RmMpkwGo2YzWbMZjONRoNarUa73abRaACPts6euvGi0+kwGo3Y7XaMRiMOhwO9Xv+NjVRRFKrVKp1Oh1arJb93WNDr9RgMBux2O3B3wTSbzUN54NyLft0gDwrhyjebzZhMJrxeLzqdjna7DUCtVjuUN+a9Roter0dRFAwGgzx8dDodTqcTi8VCo9GQ+0+r1aLb7crN97DJTo3RaMRkMuF0OrHb7fLCqa1LjQchLg9GoxGr1YrD4cDhcFAqleh2u499SXhqxku328XpdDI6OkooFOLb3/42brebcDiMyWTa9XPtdpuLFy+ytrbG1atXWVxclP/W73Q6Hex2O5OTk/h8Ps6cOYNOp+OXv/wlqVSKdDpNtVqVG+1hQn3zEYezw+G4pyyEx67ZbNJqtQ5VTF7Ix2az8corrxAOh3n99ddxOBz83d/9Haurq9y8eZNEInEo9EgYHMLTa7VaGRoawmw243a7MZlM+Hw+TCYTer0evV7P+Pg4Pp+PUqlEpVJhYWGBxcVF4vE4a2trh2Iv2ku326XVaqEoCtPT04yMjHDy5EnGxsb4yU9+wk9+8pO+u2zu/ZwVRcFoNGIwGPB6vRgMBoxGI91ul0wmQ7VaPZR7zsMgLt4WiwWTycSrr77K0aNHGR0dZWRkhCtXrvDRRx8Rj8e5ffs28Gie86divIjN1GKxEIlEGBsb49vf/jaBQIDR0VHMZvOun282mwDY7XY2NjZkWKnfbztikzUajYTDYSKRCC+//DKKojA3N0elUiGbzdLpdA7totjrprVYLBiNxm/oRa1WkwbM3qSww4LJZGJsbIzx8XG++93v4vF42NzcpNvtsrKyQqfTOTQubnW4w+FwMDg4iMPhIBAIYLVaiUQiMpRtNBo5duwYAwMD7OzsUCqVsNlstFotms0mGxsbMoR7GGSnRuw9AwMDTE5Ocv78eY4fP86tW7fQ6XR96RlWJ5OKyIHFYsHn82GxWDCbzXQ6HSqVCq1WS3ro1K897KhDtSaTCZvNxuTkJC+//DKzs7PMzs5iNBpZXl6mXC5L76bB8PAmyYEaL+IN6/V6TCYT0WiUN998k8HBQUZHR7Hb7SiKQqvVkoeMeLOTk5O4XC4ymQytVot4PE48Hgf6w6K/H8J4iUajRKNRwuEw7Xb7G4f0YVoUe2/Odrud4eFhXC4Xp0+flqE1uBtu63a75HI5yuUyCwsLxGIxdnZ2yOfzB5Yc9qIi9ELklY2OjjI6Ooper6fVauF0OvH5fBiNxuf9Vp8J4tLkcrnw+/0cPXqUYDDISy+9hM1mk+Frt9u9K3Tk8XgwmUy43W7sdjtnz55lcHCQL774gmq1yvb2Nuvr6zLk1O+oDx6z2czExARnzpyh0Whw48YN4vE4jUajb3KC1JcdvV6Px+ORZ9LY2Bg2m41oNIrJZMJkMtHpdFhdXSWfz3Pr1i0SiQTZbJZCoXDo82CEQStSIc6ePcvExASvvvoqJ06cwOVyUa1WqdVq1Gq1x/bcHfgqVMfe/X4/p0+fZmBggIGBAQwGA61Wi06nQ7PZlMqi0+mIRqP4/X4WFxfJ5XLUajW2trYe66F6BbFgDAYDfr+fUCiEz+ej2WxKl/ZhSFreiwgl6vV6zGYzLpeL6elpwuEw3/3ud3G73VInhPGSSCTI5/MYDAbp7s7lcvJ39qsOCYShFwqFGBgYkHllNpsNt9v9DW9nPyLWkwgLDQ8P8/LLLxMOh3n55ZexWq3SYDGbzbsKB8S+ZDKZ0Ol0uFwuJicnqVarzM3NycMKOHTGi8ViYXBwkMnJSdbW1ojFYmSzWRlS6hevsDi79Ho9LpeLmZkZBgYGOHfuHE6nk4GBAUwmEwaDgU6nw9raGrlcTl60qtUq+XxeGsSH6cIpUHtcdDodJpOJI0eOcPbsWY4dO8bY2BiNRkN+iXX3OBz4KlQUBZ/Px5EjR5idnSUSieB2u4G7G0SxWKRUKnHp0iWKxSLhcBiHw8HIyAgej4dgMMjw8DDr6+t9syjuh7DOW60W6XQas9lMuVyW2dei4uiwWPFC8c1mM4FAgEAgwPHjxwmFQrz88st4vV7Gx8d3HcRCR+x2O/V6HZvNxtGjR7l69SpXr14ln8+TTqdlPgz0jyGjPqxDoRChUIhgMIjX65XGS6lUIp/PU6/Xn/fbfeoIj53D4WBycpLJyUlOnTqFx+PBZrNhMBjkWlJXX6n1QYSHdDodBoMBj8dDJBKhWCxKmR6GQ0nIIBKJ4Pf7iUQi+Hw+Ll26xI0bN0gmk30TzlYbLcFgkImJCYLBIIODgwQCATweD3a7HafTKY3bbrfL0NAQgUAAgJMnT/L5558zPz9PIpEgkUj0vY7sh8FgYGxsDL/fz+zsLDMzM3Jf2t7eZmtri4WFBZaWltje3kav1z/63zjINyw+LK/Xy/Hjxzly5AgDAwOybLPRaFAsFkmlUvz85z8nHo9z7NgxQqEQfr+faDRKMBhkZGSEmzdvHooPX6fT0Wq1yGazWK1WWX5Yq9WkW/YwyAG+PnyEB2FycpK3336baDTKK6+8Im/OiqJ8w1oPhUIAjIyMUK1WZbXNysoKpVJJyhP6x3iBu4eMwWAgGAwSDofx+/1yk6jX65RKJXZ2dqTh1s/sNV5mZmY4deoUZrN5l7EiPHOATLIU4QLxcwaDQSb4RqNR0un0roO6n9elkKNOpyMcDjM0NEQ4HMbn85HL5bh+/TqpVOq+bQt6CbWXwGAwyIuS0+kkHA7jcrlwu93YbDZcLteu8KvT6QRgcHCQRqMhK2kuXbokUx4E/aor90KspeHhYcbGxpidneXIkSPYbDZ0Oh25XI6FhQVpvLRaLWm8PJdqI5Hrotfr8fv9HD9+nOHhYelibbValEolbty4wdbWFktLSySTSRRFIZVKcebMGYaGhrDb7YTDYTweDxaLRSbN9fOH3263KRQKlEol4K7VKmKre3vi9CPqeLPRaJQbyOjoKLOzs3g8HhkeEj2A9vbuEIgeJ5OTk3Q6HVwuF6VSiUwmw/r6el8m8up0OqxWq+zBITaCTqdDMplkfX2dSqXynN/l00XsP8LoECXj4t/Uhgt83WytUqns6qnkdDqxWq0AMmdoamqK7e1tQqGQ7G/SbzokEM9ls9mw2WycOXOGmZkZOp0OS0tLrK+vk0gkKJfLfVFdo/ZeejweWc3Y6XSkt03kZ4rPu1KpyBCjOPNMJhNDQ0NUq1W2trZkRVI/JjTfC7E3GwwGIpEIHo+HCxcuMDk5SSQSwWQyUSwWaTQarKysMDc3RyKReL5hI3ELFomnFouFcDjMhQsX8Hg80lJttVoUCgU+++wzVldXuXbtGtvb22SzWdxuN2+99RZHjhyR1m0gEMBms8lStH7cLNQu7Hw+L3M5DAYDFosFi8Wy66Dut+eHrw8WoT8mk4nh4WG+973vMTg4yEsvvYRer5c6IPqW3KukEb7uR3H8+HFmZmZwuVzkcjmWlpbY2Njou6oAcWg7HA6ZjCq8U+12m42NDe7cuUOhUHjeb/Wpoc4dE5UNDocDs9m8q0+L2tgVB9TOzg7lclkmDo6OjuJyuWSjOq/Xy6lTpygWiwwODrK9vS2Nl35ErAuHw4HP5+M73/kO58+fZ25ujuvXr7OwsMDq6qo8tHt9DQn9sFqthEIheVESe7JIfFfvF6VSiWazicPhkE3XTCYTk5OT+P1+7ty5g8ViodlsUq/Xe15GD4PwZhoMBiYmJhgeHua73/0uR48elZcqEU67desWly5dIhaL0Wg0HjsJ/omNF3WGdiAQYGRkhLGxMbl56HQ6qtUqa2trbG5usrKywsbGhnzTosteNpslkUjgcDjweDwMDAwwPDxMMpmkVCr1Zd6HegPsx+d7EOJQEb04otEo4+PjHD9+nMHBQXw+H/DNRmMC9f/f68LW6/Wy+ZjP5yOVSvWdF2tvSfi9dOgw6JToiuv3+xkbG2NmZoYjR44QDAZ3hRjV8qlUKtRqNa5fvy73mHq9zqlTpxgbG8Pr9cq+HjabjXA4zJkzZ+Q+1o83anGQi9tzJBKRnohYLMbt27fJZrPy5/tJt0S/GpE/ZrVacblc6PV6kskkyWSS27dv0263yeVytNttPB6P9PIGAgG537hcLhwOB5VKRTZg6ydZqVH34PJ6vbjdbpmY6/V6MZlMssv3rVu3WFhY4M6dO2xtbVEoFJ6o79QTGS/q0miDwcD4+DjvvPMOR48exev1ylLfQqHAF198werqKp9//jnpdFrGVGu1GgCrq6sEAgHOnTtHNBqVGcrXr19nY2Ojr7La9yKMv8dJWupVhBel3W4TCoUYHBzk9ddf54c//CF+v5+hoSGZIKnur6HOT1ArvXA/qjdgvV6P1+tldHSUTCYjK5H6yYBRJ5Dea32IRMR+3DzVlUL1ep1oNMp3v/tdjh8/zne+8x15g4avD1pR8ZjL5chms/zVX/0VN2/eZHt7m3q9zltvvcWZM2c4d+4c586dkzfrmZkZDAYDX3zxBZ988klf5hCpq/yOHz/O+Pg4fr8fnU7H1atX+dnPfiZzf/pFn4RB2+l0ZN7K9PQ0gUCAyclJkskkf/VXf0U8Hufzzz9nZ2eHWq0me9+4XC5+7/d+j1OnTuH3+/H7/QQCAYLBIJlMhp2dnef9iE8VoTNGo5Hh4WEikQjvvvuurA41mUysr6+TTCb5+7//ez766CPS6bS8TD7JmXcgnheHw4Hb7WZoaIjJyUlCoZB09efzeeLxuPS4iFkG6v4lonRaXV0jYtf9XpYorFbRMlm4/MW/9SPCgHA4HLKx2vT0tMxOFyMk1IexuOmKhMpqtSpj0YBM5lUf1GpvRL9stgJhsIgwSSgUIhAIyAvD/TxU/YR4Lp/PJ3vcCI+det9QhwqLxSLVapU7d+6QSqVkiX25XKbRaJBMJllZWWFiYkJesEQyp81m2xXK7SfEvmu32/F4PLJfUKFQoF6vk06nKRQK0mPe6zqlzn0SXoPJyUkGBwdlg8Ll5WXi8ThLS0ukUikymQzlclnmYIqoguiELqIGHo+HkZERWq0WsVis52V1P9SXJpPJxMDAANFoFJ/Ph9PplPv0+vo6y8vLbG5uks/nqVarB/L3n8gyELe+aDTKsWPHeO211/je974n4+75fF7GSd977z2y2axMSgXkJiCMl3q9Lm9KYh6SyWTq2w8fkMlig4ODRKNRLBbL835LTxV1DkIkEmF4eJjXX3+d1157jVAoJD0uIkFXbDLCaBGNjRYXFymVSrLXxMzMDMFgUDb3U4dU+m24pXgmsUbC4TBnz55laGhIGsDQv0aLGkVRmJ2d5cyZMxw/fpyXX34Zl8v1jQRdgHq9ztraGqlUij/7sz9jbW2NhYUF8vm81JGbN2+yvr7O6Ogor7/+OkajUeZRuVwubDZb38lV6JNoLDo4OMhbb73F6OgoP//5z1lZWeHWrVskk8knvi2/KKjbMphMJo4dO8Zv//ZvyxYNsViMn//858RiMX75y19SKpXk7D24q3f5fB6TycSNGzdQFAWv18vw8DDT09O8/fbbKIrCtWvX+rLni3pdGQwGnE4nL730EmNjY4yNjREKhchkMhQKBd5//30+/fRTVldXpQ4dhFPiiX6DuOX6fD5GR0fl4QF3D5tSqcTGxgZbW1vs7OzIQV4PY7mbzWYcDods4d2vCHmIttP9lpehRu0lEYm5U1NTDA8PEwwGcTqd0vMkEsCq1Sr1ep3t7W2azaZMrpyfn5f6pNfrGRgYkF1S1X9PPVJA3LKEwdPrGI1GfD4ffr+fYDC4qypLyK/fDDf4Wo+Ed87v9zMxMSFzNEQHVLh7yAgPcKlUYnFxkVQqRTweJ51Oy5wEoRui3FXokdCTVqslda/f5CkQFTNDQ0NShul0ms3NTdnCvV9C92KNWCwW3G43fr+fgYEBdDod8XicjY0NediK3BV1iFasL0VR2NnZIZPJyHEBooOzSFTttyIBgTB4/X4/4XCYwcFBIpEIBoOBZrNJOp2WISLReHZvnueT8FjGi3gDNpsNs9nMmTNn+NGPfkQgEJCbRblcZn19nffee49EIkE6nabRaMgN517VIsIFpdPp8Pl8TE1Nsba2hsPhkE3bDuKhXxSEDAwGA263W7YsFz0o+oW9i3dkZISBgQF+8IMf8OqrrxIMBgkGg994nThsEokEP//5zykWixQKBWq1Guvr69RqNTk+wOv1yjb4otRVdJiNRqNypg18PS0YelOXxE3Z7XZz9uxZZmZmOHPmDB6PR3b/rFQqFItF6vX6N6ol+gF1ddGpU6f4vd/7PdleAL5usmY2mykUCnz88cfE43F++tOfkk6n2draku5rnU6HzWbDZDLJ8OXg4KDsxgtQKBTk6Il+StZVh0+8Xi8/+MEPGB4eRqfTsbW1xcWLF7l8+bKsvOkHHVJ7ZEXU4OzZs5w4cYIbN27wl3/5l6ytrfHRRx/tmni89xLdbDZ3zb46ffo0MzMz0oMleuPUajXZqqBf5CdyhLxeL2+88QZjY2O88847MnE5m83y/vvvMz8/z5UrV9jY2JC5MXAwcnhsz0u325Wtx8XBIZrQiJ4uwiLN5/O78hMehOj3IbwRe/Mf+gW1wWaz2aSXqd1u9+3ziiTacDgsO8IKj4tIzm00GlQqFdLptPTcra+vy+7MjUaDbDZLs9mUw9KEHPfmC4m+LxaLZddh1A+I6ga73S4vEiKBrlqtUi6Xdxkv/YJYN6KBmNfrlQ3DRAm0eF4RasxkMsTjcZLJpLwFqicBC49LOBxmdHQUr9e7S5cajQa5XI5isdhXslR7J8XzB4NBeVEQHqt+8VaqDRExBiAajeJyuYC7VWiJRELmt4gDd2+0QK0DzWZz12BY0fdF9FwS3tB+kp/ISfV4PESjUSKRCC6XC6vVSjqdJp/Ps7m5SSwWo1gs7kpwPyg5PJbxIvJSRFmrmMgqDoZcLse1a9e4fv06S0tLsmvsw4R/hJKI4WriQBfemn5QAIEw0hwOB8PDwzLnpd9GAgiFF3NSTp8+zblz56TeqF2wer2eVCrFl19+ydLSEj/72c/I5/NsbGzIXi/qhkgTExMMDQ0xMjJCMBjEaDRK+QkDW9yARO+FXm4ypt40xebhdrvlZilGS4iSXpFnJg7qXnxmgXrjNBgMXLhwgbNnz3Lq1ClptKhd++12m1qtRjKZ5Fe/+hWxWIzNzU2q1aqcJi36cIyOjjI2NsZv/dZv8a1vfUuGGQHZAfvGjRusrq7uKjjoZcQ6slgsjI6OMj09zenTp3E4HPzxH/8x8/PzLC8vy5lh/WL4i2IBq9XKmTNn+I3f+A2MRiPr6+ssLS1x8+ZNOWBReJvu9XmL77XbbTldWnSLFZdR0WhV/N1+2HcAPB6PLIn+7ne/K3vktNttPvnkE1ZWVvjpT3/K8vLyrj5tB/nsT+R5sVqtu2J7gMwwFnGuarVKo9F45I1TfZNWP3Svfvh7UX+YYqiemNkjSoj7wT2tVnqR7Cey8oXeiEUv5CH6/qTTaWKxGKVSiVKpJA8NtcfK7XYTDAZlgzb1BisMGCHLfiuTBu65RuBucqoYifAoXs8XGfEMwisr5qCJW7P6Z4THZXt7m3Q6TTabJZ/Py2Fwey9SoimbKHMVIQHR4btYLPZlgzqx9wwMDBAIBGQ7gUwmQzKZlFV9/ZJ3KPYAq9WKx+ORn3mpVCKbzZLL5eQ4kUc9Z/ZW0PaTnsDuLtZOp1PmRwWDQdxuN+12m3K5TCKRYHNzk+3tbQqFwlMzfJ8oYVfdQ0LcelqtFpubm3zwwQdsbm5Kq2sv6oOkHz/oh0WUhYskVpGrILoP98ONWSD0RXhghDdtbxJXvV6XjaFSqZQ0fsUCECXCDoeDU6dOcfLkSSKRCGazWeqhWGTZbJbLly8zPz9PLpeTAwp71bOlLtMVg05Fma/IKRM/o8416geE8RqJRAiHw9KDJ8oy4esE3XK5zMLCAn/2Z39GIpFgYWGBSqUivZ3i59VdnUVHZhG2NZvNbG1tcfv2bX71q19x/fp1SqVSz1+gxOFqsVhkifCPf/xj3G43c3Nz5PN5rly5wsrKCrVa7RuXgl5GRA2mp6c5ceIEx44dw+fzsby8zM9+9jMWFhbY3t7+xkXpXgg9ELlXojKtVquRTqfJ5XIydKtu39CLiEug2+1mbGyMY8eO8U//6T/F7/czOjpKs9nk6tWrxONxPvjgA9bW1tjZ2ZFRk6ex3z6W8SLeiMFg2KXYnU5HWl+pVEqWIO5FnSwpmkCJTUR9oPXjBrwXtfdF5AuJrsO1Wm1Xg7Ze53HKl++1gQh5iam/ospNnQguXtNoNGTcXty6+0We6unj/ZbXci/UhQIej0fm2qm9AjqdTub8ZLNZFhYWyGazu+Luaj0R7n2RO2MymXZ5RavVKslkUubu9UPRgFiDYv2Izuhms1lORRa35n7Kv1OfJS6Xi0gkIkOu4sKUy+XkhVvdc2s/RAhS7OH1ep2dnR0Zru11D7p6TxVjFKLRKGNjYzJfsVarEY/HicVicr2IfjhP66L4SMaLeAhRejo8PMzRo0cJhULyAYrFIvl8nnQ6zc7OjrwJq/M4FEXBbDbL/IdIJMK3v/1tjh07ht/vp91uy74ve8s9e3nTeBDdbld6XnZ2dnZtHr363Or3LTb+1dVV3G43gUCAaDS6a1Npt9sMDAzwzjvvMDk5idFoJJlMcvnyZTlETywis9mM1+slEAhIr4v4XSJUUqvVpBu4nw73brdLvV4nkUjgdrspFosYjUacTmfP6sp+CINXJOoGg0GZqLs37Ci8bdeuXeP27dvy9gtfG752ux2r1cqFCxdkd+fJyUm8Xq/MUVAUhUwmw9zcHOvr6xQKBdm9uRdlrN5DDQYDQ0ND/M7v/A6Dg4MEAgFyuRw/+clPWFtbY2trS3Y/7wfjRV1VpSh323sMDw/jdrt35TY9arheURT8fj/Dw8N4vV4sFgvz8/P89V//Naurqz2dXwfIs9tms+F0Ojl9+jS///u/z9DQEKFQiFqtxo0bN4jFYvzpn/4pm5ubu577aYYbH8vzIlz/TqcTr9crGzd1Oh0Zaxe18YK9HgYxBXd4eJjR0VEZOxOHkDqDW52E10/sLQ8HZKfhfiwNF0ZpoVAgnU7LAWfq5xMLZWRkBEVRmJ6exmw2c/v27V0yES5aMRdJvUjUuS7Ci3W/8GUvI/LLxPBSsVb6RV/uhyiHFx7bvftDvV4nk8nsyl+Arw9tg8GAw+HA6XQyMTHB+Pi4PMDF7xNe5GKxSDabZWdnZ5fu9SrqvAW3283U1JR87kajwfLyMmtra5TLZZkb1E/6pM4xFPP3xH4hPvMH7RN7w9w2mw2XyyX7dOVyORYXF8lkMtKo7lUDUHjpjEYjLpeLgYEBjhw5gt/vx2KxUK1WSSQSxGIx7ty5QzKZpFgsyoKKp6k7j+V5UbfMFl4YvV5PtVolHo+Ty+WkG1uUVIkp0ZFIBJvNxtjYGB6Ph5deeomBgQFZalWv1ymVSly+fJkPP/yQubk5OYK8XxaRWChiDLvb7ZYJpyK5UDRF6tXcDDXqPI1Op8PNmzdJJpNYLBbK5TKTk5OMj4/vkovf78dsNmO1Wtna2kKv17O9vc36+jqdToexsTECgYDUJ1FtIuRVKBTY2dlhfn6eL7/8kng8LnWyVzeSvRiNRrxerxwQtzePqNfd1fdjv4Om3W5jtVoZGhoC4Ac/+AHFYpFEIkG328XpdOJ0Onn99dcJhUJMTU3Jlu4ibA1w5coVLl++zO3bt7l27RqFQqGnjRb4OqwfCASYnZ3l5MmTnDp1ikKhwJ/8yZ8Qi8XY2tqSXdD7zXCBryvVxJcIMVYqlV2t/++HOvRtNpsxm81MTk5y6tQp9Ho9m5ubMmxSrVZ7Vn7iQiDkNDMzw7e+9S2OHz/O8PAwer2eTCbD2tqa7EScTqcfqbL4SXnshF1hvKhHo4usfNH8SeS0iDiZy+Vienoat9vN8ePH8fl8zM7OyumtOp1Ohp02Nja4ceMGiURCxqp7VRHuhThEbTab7NGh1+tl2EhszP3yzOrbjciHmpiYwOVy4fV6mZiY2FVNIsKKLpcLj8cjF4eiKLRaLTnJ1eVyyUNFyEsM/Mzn86RSKVZXV6XLX/1+ehlxgxRGi7hAALsMl37zNsE3J4jD1/olkrk9Hg+dToeTJ0+Sy+WkR8Xn8+H1ennttdcYHBzE7XZjNptlhZ/4/bFYjM8//5yNjQ1p+PZDxU2n08FutzM2Nsbo6CiRSIRqtSpLyYWHqZ+SdAXq/UHdfkN4LUWawoP2hr29TgKBAIODg9LrUigUZL4L9OZeI/YQdTWaGNbp8Xio1+typtP8/DzxeFzmFT6rsvpHMl7Ude2NRoNSqUQ+nycQCNDpdHA6nYyPj8ufFaWGVquVaDSKw+EgEolIY0a4/FutlrTa7ty5QywW41e/+hVra2t91ZlQIBTDaDQyMDAgDbt2u838/Lx02/bbwSM+Q9HM6dq1a6TTaZnLIhqE7Z2E7Pf7efXVV6lUKpw5c4ZOp4Pf75cDCdWJ48LzkslkuH37Nqurq2QyGVmxpH4fvUy3e7dx2vb2Nvl8nkqlIhvx6fV62eJdNI8UvSj64dnh60Gd6jw6YdSIwzkcDhONRqlWq5w+fVr2/bFYLITDYRnuFoZLu92Ww/i+/PJLrl27Jm/jvez5FfuNCG8cO3aMd999F6PRyIcffsjy8jLLy8tsb28/UqJqryHOJLW3RZRNi5yOXC4njdi9uYZC50R10dmzZ4lEIkxMTOB2u7l27RpLS0vcuXNnV45VL6E2zPR6PSdOnODEiROcPn2as2fP4nA4aDabbG1t8fOf/5zV1VWWlpYoFAoyJPmsnvmxm9Q1m02q1SqlUkmGOERDMDEvQmwIwnITCZYiX0FRFHlwZTIZstkst27dYn5+noWFBRKJRE/HC++H2ExEtr/oeSJmQa2trck4PezuH9DLqI3fVqvF6uoqW1tbslMqwOjoqPzMxaHkdrs5evQo3W5X3mbUs4rUeUPibxQKBdbX14nH4+Tz+V2/s18QpdJiDID6kBVtup1OJ2azWRrD/eDNu184TOTdiXyGbrfL0NCQHJcgNldRBr3XU9Vut9nc3OTmzZvMzc2xvLy8axBhr8pN5PCI3jgTExOcO3eOeDwuBy/G43HK5bL0pPcbwrgVoeN6vS4NDOHlFe0qhPGydzK50DuhP1NTUxw5ckTO1BLjFGKxGM1msyeHWKqNF6PRyPj4ON/+9reZmZlhenpaVsNmMhnprUsmk9RqNelxeSGNF/GmRAVQMplkcXERr9fL0NCQ/FBFBrdaEHa7XX6QIrxUq9W4du0aqVSK+fl5UqkUsViMTCZDOp3uO6NFjXDJuVwunE4nVqtVJpeqB1gKet1wUaM2Yur1OgsLC7TbbbLZLIqiyIZhwmUp+uDA1xvK3r4Jot17Mplke3ubzz77jM8++4z19fWnWq73PBFJySLEqPYOiMTvfmlQJ8JkiqKQSqXQ6XRcvHiRTqdDOBwmHA6j0+l2tXJXe2RsNpv8PerfJfayWCzG9vY2X3zxBZcuXWJjY2PXRtyLuiPyFsTMp5MnT/LOO+/g9Xq5ffs2KysrfPzxx7sSS/sVsQZMJpPsgCsqzsxmM4ODg5w/fx6bzUYqlaJer9NoNHbleYocvBMnThAIBLhw4QIDAwOsra1x69YtLl26xOLiIjs7Oz13UdrbkmRsbIzh4WE588nn89FqtchkMiwsLHD79m3m5ubY3t7eNbDzWT7zY3texEGxvLzMyMgIpVIJm80mJ7K6XK5v3HZFNZJwd+/s7PDhhx+ysLAg81vUpWq99OE/CuIGLG7GwnipVCrSeFHLoB8OHzXiABEHx8LCAmtra5RKJaxWK8FgkPHxcaxWK16vV86dUd9i1HoiSqKr1Srz8/MsLS3x2Wef8cUXX8jS6n7TJaFDQoZ7++YIQ1jdaqDX5SA+/1QqRalUwuv1UiqVuHDhAsFgUObhwe7iAkAObFRv0uKr2WwSi8VYW1vj4sWLfPLJJ3J99rLM1AnwTqeT48eP87u/+7syLH/r1i0+/fRT6vX6rmZi/YjaeDGbzdjtdmm8iEGK586dQ6fTsbCwIDvuqkNFXq8Xr9fLm2++yfDwMEeOHMFut/M//sf/4NKlS1y7do2VlRVZzdWLCDmNjo7y8ssv89JLL3H8+HHpmcxkMly+fJm5uTnu3Lmzq5z+WevOEzWpEzcgl8slXbRTU1NYrVbZf0GUt+bzearVqpwtsrW1RaFQ4MaNG2xtbVEsFg9NPxc1Ii9IfcD0s8dJjXhWcQhvbW3x+eef43A4uH79uuz+6fF4mJ2dlQ3E2u227JYrQiaim+Xt27fZ2NhgfX2953MV9kOESEQzQ+FlUU9BzuVyMizbD5VH6lLobrfL4uIixWIRQOa6eDyeXQm9ez0nagOv3W5LQ+jWrVskEgnS6fQ3elL1KiIEJLrJTkxMyIrQS5cusbm5uWt99PKzPgh11AAgkUiwtLQkZ8t1u12i0aisdi2Xy6TT6V3RA5FnJ0KyV69epV6vc/36ddnbRD1EuBfkubfsW1QFT0xMMDU1hd/vx2AwUCgUyGazrKyscOXKFba2tp77tPrHMl7EhxOPx0mlUhSLRVZXVzl37pzMvhbGSz6fp1AoyPbsly5dIpfLyZv21tYW5XL5G67efkbtzhbGnfAk7M3d6GeEHonb7/LyMqurq8BdGQ0ODsqqEFG1JvKtRJLYxsYGOzs7pNNpisUia2trpFIp+TvU/9tPiE6yosRTdNkVwxlzuZzsudAvnZrVIcJqtUo+n+f69evs7OzIwoFIJLLLq7L3mdUeqGazyc2bN8lkMrLSJpVK7VqHvYi6as9gMMjGYkajkXK5zOrqKr/4xS/k3KLDsueqK4vW19dxOBxyTo/P52NsbIxgMMjk5KScaq8eIxEIBGQxwM7ODh9//DGrq6vMz89LQ2dvWLtXEKFUMSH6xIkTnDp1ShZElMtl1tfXuX37Nh9//LHcc8Rrn8fzPpFvS9yC8/k8sVhM5ie43W4GBgZkbot48FKpJG9L29vbu0rTeu3DfhKEotTrdWKxGADvvfeeHGqlHkJ4WBCbi9pDUCqVWFtbo1gsyhJY4aURCYapVIpKpUKhUJCHWj8kpe6HWC+dTodyuUwymeSTTz7B6/USDofpdDrcunVLjqbvxzCs0BdxmIgE/83NTeDBOWIibCn0SwyRVbc073WEAZdIJLh69ap0/d+6dYt6vX4o9xjxvJlMhsXFRXQ6HaVSieHhYSYnJ6XnRAyQFWtH5HjWajUWFhbI5XKsrKyQSCRkjqL67/Qi3e7dDu+5XI6bN2/SbrdlWoMYkjs3NyfD0c/7OZX9FrnVat13B9j7WlFuJobmdTodGo2GNGKazSalUmmXW/YgvAzVavW5SNHhcDx2Moqw0m02GyaTCa/XK2OKItnyoFz9pVLpucjH6XQ+knz2Jo3p9XpZFWKxWHblTlWrVVqtlvzfvVVH8PCbSLFYfC7yedD6ehh0Oh0mk0lOBLZYLAAyXJTP52WY5XF5XuvL4/E89JsWF4JHyTUQuVLqQ/xx9qN8Pv9c5GO32/eVj1gLoVBITk4uFApUq1UZbnsW3qVyufzM5eNyue4pm72NVkWT0BMnTvDqq68SiUQ4cuTIrvBPt9ulWCzy2WefkUgk+PDDD0mlUqRSKarV6hOFigqFwnPVHfVFTxRLmM1m2VJArAdxMaxWq+zs7DwzY20/3Xkiz4u4/YgHEdNchcEi3HTtdltuEv1SrvmkqMNGnU6HfD4v5XUYhuzth1qv9s4bUcvtXnNI+jlUdC/EJPdisSi7XANSPqLss59Rh4ke1eBXJ333o86ob9PValW2/YfDs0bUqD284hIEsLW1JfOeMpnMN8KGlUqF27dvk8vl2N7eplwuf2Nt9aI8975n0RBW5NMJRP6q0J17vfZZ80Sel73svTnv+kNP8VDpNc/L3uRBtQGoXjAHVWnUK56XvaiffT+detD3HkQve17g3h1n4eBi0b3geXmSdfKkMnpRPS+CvfrxrMNiL5LnRbB3b1F3ir9XbxaRIqH+XzWPK8/n7XnZy94z/H6J789Kf56a5+Ve3Gsjfd4W2ovMvZRFLa9+LJV+VB4mf+Gwc7/NZu//71ced50cBtmoewAdhud9FNQeXuF1eBD9Xlyi9mSqv6cOrb4IHKjx0q8f5kGzN84I9x5kddiNFrWMNIP4/tzvtnjY0HTi3vRy5dTT4iCM+37Ut14Ku/dmJ50+4LAbJo9KLywmDQ2N3kLbV75Jr8hk35wXDQ0NDQ0NDY0XDc2XqKGhoaGhodFTaMaLhoaGhoaGRk+hGS8aGhoaGhoaPYVmvGhoaGhoaGj0FJrxoqGhoaGhodFTaMaLhoaGhoaGRk+hGS8aGhoaGhoaPcWBGS+KovwrRVEuKopSVxTljx7xtW8rivILRVF2FEVZPaj39CKhyWd/FEXxKYry54qilBVFWVMU5ceP8Fqzoih/qChKQVGUhKIo//ppvtdnjaY7+6Ppzv5o+rM/mnz250WVz0F6XraA/wj84WO8tvzV6/7NAb6fFw1NPvvzn4AGMAD8E+A/K4py/CFf+wfANDAKvA38W0VRfv1pvMnnhKY7+6Ppzv5o+rM/mnz254WUz4GNB+h2u/8LQFGU88DQI772C+ALRVG+d1Dv50VDk8/9URTFDvwecKLb7ZaAjxRF+Uvg/wD+r4f4Ff8n8M+63W4OyCmK8v8A/wz426f0lp8pmu7cH013HoymP/ujyWd/XlT5aDkvGi8CR4BWt9udV33vKvDA27OiKF4g8tXPP9JrNfoCTXc0NA4hmvGi8SLgAAp7vrcDOB/yteLnH/W1Gr2PpjsaGocQzXjReBEoAa4933MBxYd8rfj5R32tRu+j6Y6GxiFEM140XgTmAYOiKNOq750Gbj7ohV/lKsS/+vlHeq1GX6DpjobGIeQgS6UNiqJYAD2gVxTFoiiKQfXvXUVR3rrPa3VfvdZ49z8Vi6IopoN6by8CmnzuT7fbLQP/C/gPiqLYFUV5DfgR8F8BFEUZ+0o+Y/f5FX8M/HtFUbyKoswC/wL4o6f/zp8Nmu7cH013HoymP/ujyWd/Xlj5dLvdA/nibslhd8/XH3z1b8PcjUv77/Pat+7x2vcP6r29CF+afB4oHx/wF9wtrVsHfqz6tzeAVcB4n9eauVuOVwCSwL9+3s+j6Y6mOy/Kl6Y/mnz6UT7KV3/gqaIoyj8Fjne73X/31P9YD6LJZ38URfn3QLrb7f6X5/1eXjQ03dkfTXf2R9Of/dHksz/PUz7PxHjR0NDQ0NDQ0DgotIRdDQ0NDQ0NjZ5CM140NDQ0NDQ0egrNeNHQ0NDQ0NDoKfadbeR0OnsiIaZYLCrP4+86HI6ekE+pVHou8tH0Z380+eyPy+XqCfkUCoXnIh+73d4T8imXy89cPh6Ppydkk8/ntb1nH/bbe56Z52VP+ZSGhsZTQFtjGgeFpkcaj8Oz2oMObKr0fnS7XdrtNgA6ne5ujbaioCjPxejU0OhL1JuGtsY0Hpdut0un0wGQOqTp0dfGnJCNTqfT5KJCLZ9utyvl87Rk9EyMF7j7QQPyQbQP/fG4l0V7GGR5P0v+MDz746DJ5eHQ9OreqPfpwy6Lvew9yzR2s9fgFRepg+apGS/qm5/JZMLlujv7rFKp0G63aTQadDodTQEeAnGj7nQ68kug0+nQ6/V9ewsQz95qtXbdCMXzGgwGbYMFedOxWCwoiiLl1Wq1tHV2D4RetdttqVdChuJLr9c/77f5XNDr9TgcDnQ6HbVajVarRbvdPrR6JJ7bYDCg1+ux2+3odDpKpRLNZvPQh2rFWW8ymXbpTqFQkOe8kOFB6s9T97woioLRaMTj8cjvNRoNms0m8PSssn7kfhZtv8pPvSFoHrv9URQFnU6H1WqVh0673ZbhWm2d3RtFUXbJ5rDqmXovEQeQwWD4RqjksCIOZ6PRKGXTaDR2rbHDjsFgwGg04na7MRgM8oyv1+tP5+89jV8qLFGdTofZbGZ8fJx/+S//JYqi8Mknn5BKpfjkk0/Y3t5Gr9cfuo3iYREbh6IouN1uzGYzfr8fp9OJwWDAYDCwvb1NIpGgVqtRLBaBr92avYq4Cet0OgwGAzabjWg0isViweVyodPp2NnZoVarEYvFKJVKu2R1WBDPLDwufr+f3/qt38JqtfLFF1+QzWZZXl6mWCxq64zdOUFi/UQiEex2O06nE7PZzM7ODqVSie3tbba3t+Vr+1l2Yr0ZDAYCgQCBQIB//I//MX6/nz/7sz9jcXGRVCpFqVSSXql+R70HWSwW3G43b731Fn6/n7GxMQD+5//8nywsLFAul2k0Gn19kbwfnU6HVquF2WwmEokwMDDAP/yH/5BgMMiHH35ILBbj6tWrJBKJA/fePfWwkcFgIBgM8vbbb6PX6ykWi6ysrHDx4kU6nc6hdc0+CPVhrNPpsNvtuFwuhoaGCAaDGI1GzGYza2tr8vAuFArP+V0fDHLw1ldeO5vNxuDgIHa7ncHBQXQ6HYlEgmKxyPb2NpVKRbr9D5uHQS0nt9vNuXPncDgcxONxut0u6+vrtNvtQ3HgPAzqfclkMhEMBvH7/QSDQRwOB1tbW2SzWRqNxi7jpd/1ShwqDoeDYDDIa6+9xuDgIF988QWJRILt7W15mB8WxGduNpvxeDwcP36coaEhZmZmAPjlL3/J2toa1Wq17/VjP4TueDwewuEw3/nOdxgZGaFcLuNwONjY2CCdTkvv3UHJ6pl4XiwWCwaDAavVyuTkJHq9HqvV+tQTenqZbreLwWBgenoav9/PmTNnGBwcJBKJ4PP5pIw///xzksmkzHHoZcQzCQ9LOBzm/PnzeL1epqensdvtBAIBGRYpl8v8zd/8DcvLy8zNzZFMJvs6/+dedLtdjEYjoVCIaDTKyMgINptNrq/DIof7oW7RYDAY8Pl8OBwOTp8+jd/v5+jRo/j9frxeLxaLhWvXrrG8vEytVmNzc3NXjlm/hmmFfIxGI4ODgwwPD+NyubBYLLRaLRmC7LfnvhfC42K32xkYGJB7bzAY5NixYzgcDhqNBpVKhVqtRrPZPPQhNb1ej81mY3JykvHxcQwGA51Oh6mpKRwOB3Nzc6RSKenVPCgD+Kl7XoThotfrMZvNRKNRWq2WTCzUuD86nY6RkRFGR0d5/fXXmZ6eJhQK4fF4qNfrNBoNMpkMdrudQqHQF8ZLp9PBZDLh8/kYHx/nrbfeIhAIMD4+jtVqxev1YjDcVdtKpUIqlcJisbC5ucnW1tahuhnC10au8B4MDAxgsVhk8txhRm24iNuhy+UiGAxy/vx5hoaGOHbsmAzFms1mmeC8uroq5ac2XqB/Q0h6vR6/308oFMJut2MymaTxcliSdYW+iLNqZGSEd955B4/HIy9OuVyOcrlMvV6XCd+HQTZ7Ec+t1+uxWCwMDQ0xODiIXq+n0+kQjUZxOp2EQiFcLheVSuVAveNPxXgRoY5Wq8X29jbZbFbGvHw+H/V6HYfDgcVikXEw8brDjnDNBoNBPB4PL7/8MjMzM0xMTBAIBOSNen19nYWFBa5evcr6+vqBWrTPGhHycblc0svyxhtv4HA46HQ6bGxscOnSJfR6PdFoFIfDwfT0NGazmampKdxuN5ubmzQaDXZ2diiXy/J397NO7Q2vGY1G+f3DjFouImdqcHAQn8/Hq6++is/nY3R0FKvVyuLiInfu3CEYDGK32zEajRw9ehSTycTU1BSZTIZUKkUymWRpaamv9yt19Z7InxPVRv3uXRA6I7y7ExMT/Pqv/zpms5l4PE4qlSIej2MwGDCbzVSrVVl4cpjXm4iuOBwOQqEQoVBIGi+NRoNarSYv2gdtAD8T4yWTyZBMJjEajYyOjtLpdHA6nVgsFmmN9dtG8DiI0k29Xk8wGCQSifDyyy9z+vRpKS/xcxsbG3z88cdcvXqVjY0NoHebJomSXofDwdjYGOfPn+cf/sN/yM7ODrdu3WJra4u//uu/ptVqMTk5KW9AkUiEqakppqenuXnzJjs7OywtLVEqlYBvVpL0K+LQEdUh6vyfw4h6HRkMBlwuFydPnmRoaIgf/ehHeDweSqUSlUqF69evk0wmGRoawuPxcOTIEWZnZxkfH6fRaLC8vMytW7e4desWsViMRqNBo9F43o/41BB7SLFYpN1uy9BIv4bMBOpw0djYGKdPn+aHP/whuVyOv/iLv6BSqchqo7GxMbrdLvV6fVcrgn6Vzb1Qp4ZYrVZcLhcDAwOEQiFMJpOUT61Wk0beQRvATy1spM5nud9mepg+7EdBr9fLHAbhzlYUhXa7TSaTYWdnhzt37sg8D0GvyVPthjcajQQCAY4ePYrP5yORSBCLxXj//fdJpVIkEgk6nQ7tdputrS263S7BYJCXX36ZQCDA1NQUNpsNh8OB1Woll8uRy+UOxaai7qp7mFHntthsNtxuN1NTU4TDYV5//XW8Xq88mDc2NiiXy/JSEI1GcblcUr8qlQrVahWdTsfJkycxGo1kMhnS6TRzc3N9lQMiLpsigdnr9cr1UygUqFarffW8+2G32xkeHpaJpolEguvXr9NsNpmensZoNErvlLrH1GFce91uF71ej9vtxufz4fV6cbvd6PV6Wq0W6+vrbG5ukkgk2NnZOfCKrKfa50VtuBz22+CD2FvGOTw8zMTEBF6vF6vVCkCr1WJjY4Pl5WUuXbrExYsXqdVqPe1dEAtAp9MRjUa5cOECOp2O9fV1rl27xp//+Z/LxoaiekZRFK5cuYLT6URRFE6ePMmpU6d4/fXXMZvNGI1G5ubmyGQyhybvQ1tXu0t+3W43ExMTfO9732N4eJh33nkHvV7P2toa+XyeO3fuUC6XmZ2dxe/3MzAwgMPhYHl5mfX1dYrFIsVikRMnTvDqq68SDAbpdrvcvn2b+fl5ms1mXxjG6nJ7kecxMDBAOp2mWCySzWZlSLofnvdBuFwuZmZmsFgszM/Ps7Kywscff4xerycSieByuTCZTLKNg16vP7R5L+KiIHLtQqEQfr8fg8FArVaTF+zV1VXZFuUg9+NnNh5A4/6ITVdtxY6NjTE+Pi67ObZaLVqtFvF4nDt37hCPx+Wh3quLRl3lYLVaZVJcLpdjbW2NRCJBs9mUTaDUTfqazSbVapXr169TLBaZnZ2VeQunT5+mWq3K19fr9b7feA/z5UDtwhab6cmTJxkdHeXIkSM4nU5SqRStVotYLEatViMSiQBIA3hhYYFarcbq6iqJREK6vG02G1NTU3Q6HYaGhshkMj2bW7YfwvPi9/vx+/1sbW3tynfp9/Ujnq9YLHLnzh1pmCSTSRqNBlarFYPBICsdRU6HqPLsZ9ncC7Hm9Ho9TqdTNu4Tcuh0OuRyOdmD7GnwXIyXw7jBPoh2u43JZGJkZITBwUHOnz/P1NQUHo9HGi+1Wo25uTnef/99VlZWKBQK6PV6majZawijzWazEQgEiEajjI2NsbOzw6VLl9jY2JCJXmJh6PV6mSNTLBZ57733MJvNvPLKK4yPj/Otb32LV199lVarxebmJtlslmQyKV/bz/R7UuX9EDkuwgg+cuQIv/3bv000GuXMmTMUi0W+/PJLdnZ2iMVimEwmfu3Xfo1AIEAymWRnZ4cPPviAGzduEIvFSKfT0tPQbrcZHx/H6XRy8uRJueb6CRECsVgsjI6OMjAwwMbGBqVSSXaR7dV8uodF7A+JRIKf/exnsvmauCRZrVZMJhMGg4FisSjLpUUo5DCi9rwIj4sw7NvtNpubmywtLVEul5+K8at5Xp4zwpAzGo3Y7XZGRkYYGhrC6/VKr0uz2SQej5PNZonH408lfvg8sVgseL1ebDYb3W6XarXK9va2bL639xnVMWaRCJZIJACYmJiQDe0mJibQ6XSk0+ln/kzPEtG6XLizDwtCB/R6PSaTiYGBAUZGRpieniYcDmM2m1lfX6dQKJBMJmk2mwSDQaxWK41Gg1wux/z8PKlUirW1NRkqaTQaspNss9mUB7gIb/YjwmslcjrE4X3YDOJ2uy2LSNrtNq1WSxo2VqsVi8VCs9nc5ZU6LF2H9yI8LyLfxWg07jJyRY7i03JWaMbLc0TterPZbAwMDPDmm28yNjbG2NgYHo+HWq1GtVrlk08+4caNG1y6dInNzU3q9TpGo7EvjJdAIMDs7Cw+n49arUY+n2dlZWVX1dDe51Rb+K1Wi2vXrnHz5k08Hg82mw2fz8cPfvAD3n//fRYWFnYton6QmUDoj8fjwePx7Lr99DvqChGn08mFCxf4zd/8TSKRCLOzs6yvr/PXf/3XshOzz+fj93//93G5XCwtLTE/P8+f/umfMj8/Tzab3VViL3Sl0WhQrVax2+3P6zGfGnvDbcL7IvrdNBqNQ+clF8YqfLOPic/nw+PxkEwmyeVyu7xShw2x9iwWC9PT04yMjGC322WZNHwdyn5aaMbLc6bT6WC1WolGowwNDRGNRmW5GUAul2NnZ4f19XVWV1fJ5/N919VRbJoinizK61qtFvBwxoYYkJbL5dja2iIcDhMMBnG73dhsNpnD8LC/r5cQ5YoWi0XOMOq3Z7wfiqLgdDqJRqPyy2w2k0wmicfjskrN6/Xi8XhkuHFtbU327ygUCtTr9V19KHQ6HUajEZPJhNlslhUU/TiETxzQwoARzyqasB027lUVK5qsqg07cSE6bAaeQO35VM9Oe1Z7j2a8PCeE5dpqtXC5XPzgBz9gdHSUV199Fa/Xi8lkotFo8OWXX7K4uMh7773HjRs3dnkQetniV3tBRLJup9MhmUyyvb0tm0A9CJ1Ot2vzuHPnDoVCgd/8zd/k29/+NmNjYwwODpLL5djc3JSv6QeEDhmNRoLBIMFgEJPJ1FeG7f1Q9x0RTQ1Pnz7NqVOnuH37Nn/2Z39GMpnk+vXrDA8P88Mf/hCr1cr6+jqZTIb//t//O2tra+zs7FCv16X3QYwDsFgsOJ1OAoEAkUgEnU5HuVyWc2z6CRF2tFgs2Gw2zGYzpVKJfD7fF2NHHhWxP6g9L0ajEY/HIzvFbm9vU6/XD51s1IhnF2vmXrJ4moaMZrw8J8SGIdonR6NRwuGwbMvdbDapVCokk0lisZhsSa0uN+uX27XoMwHIDqaPcqNR91poNBoUi0VarZaM3/d7q3xhAArvVb9vqOqWAmJe0eDgIA6Hg3q9Tj6flwNLjUajPJT1er3slCr6JalDI+r1JGaxCbmquzf3m3xFmbQIQ4t1pPZG9cte86iIvUkYMAaDgWazeeg76wIy12dvGb1ad55mGblmvDxD1JukaHX/+uuvy9lFYjicaPCTSqX44IMPuHLlCul0um+HDooNQv1cj/OMiqLIKoBmsykTOcXGrG7m1i8ybLfbGAwG2ZrbZDLJ3j9PO+b8vBCeJZHnc+bMGd555x2SySQff/wxn3/+OX//93/PwMAAb731FuFwGIBMJsNf/dVfsbm5STwelx4XtctbyM3pdDI0NEQ4HCYQCLC8vMxnn30m86f6BVHJNzAwQDAYpN1uU61Wyefz5HI5mbB6WFGP3hCGbLlcJp/PH4oqrL2ovf5CHqJYQFSCJpNJ0uk0+Xz+qTY47A//eQ8hysvsdrucsTIyMoLX68XhcMiW3KKrbCaTIZ/P91V1kUDdt2Xv15P+Ttjd+K8fD3H4ukrEarViNpuBr7P8n2am//NEfL4Wi0W68h0Oh+yDlMlkZPJtIBCQrv6dnR3S6TSZTGbXzVmtL8KQttvt+P1+HA4Her2eer0u12K/yVT06rDb7btm0lQqFc3zosoHMhqNsindYfa8iCRvs9m8awis8NqVy2WKxaIcn/C0CiU0z8szRJTfeb1eXnrpJWZnZ/nhD3+Ix+PB5/PRbrfZ2NggnU7z//6//y8rKyvMz89TLBalwvTjJiJcssJyf1xl73bvToN1u90YjUbq9TrValUupGedUPa0UFepiaaG4XAYv99PvV6nWCySy+X67na4N0FwamqKEydO4PP5yOVyzM3N8dOf/pRiscjIyAgzMzNcuHCBcrnMBx98wNbWFvF4nEKhIH+PkIsw9ETOx0svvSR7wRQKBdbX1/nss8/I5XLyJtnrMhX7kc1mk7Of6vW6fF7RZ0ndfOwwIfZc9dgRkYtYrVYPzaRtNaLHls1mY2RkhNHRUSKRCMFgEJ1OR71eZ3V1VeaWlUqlp+Z50YyXZ4Ta3eZwOHZVF4lFUa1W2dnZIZPJsLq6yurqKqVSiVartWuj7TfudxA8ys1G/KzZbJYle6LsUQyX6yf5iY1VhMWEDolbYb1e/0YFTb8gjF2v10s4HMZischQRzweB8DhcOByuXA6ndTrdWm4CF0QJeV7DWWbzYbT6SQYDDI4OIhOp6NQKJDP52Wr/H4KOwojWMhKeF4qlUrPd/A+CNRhIyEHdRn5YZONurzeZrNht9ul11cYNjs7O+RyOWq1mpbz0usIN75of3/+/Hl+9KMfEQqFsNvtchpwsVjk448/Zm1tjTt37pBMJvvecAHkgasOczyqS1bkd4yMjHD27FlcLhdbW1vy0Gq1Wn1VZSQOHYfDgcPhwGazSQNYeF6El6BfPC8i1yQajeLz+Th79izf+c53KJfLbG5usrq6yp07dxgcHOTMmTO4XC7W19eJxWJcvnyZ7e1tmQslXNzidzocDiwWC2+//TanT59mdnaWgYEBbt68yWeffcbc3Fxf54AIY070TRJfhxX16JKBgQF8Ph/1ep1ms0k2m5W60C9rzgnk7gAAX+hJREFU62ERF02j0YjT6ZSDTUXn4Xw+z/z8PIuLixQKhV0yOmg59cdu/oIjDmLR6CgcDjM5OUkkEpEWvch12dzcZH19nXw+T6lU6subs0BsEOpSu8d9VvG73G434XAYo9FIsVikUChQLpep1+tA74eMBEJW6li88DaJnjZiBks/ID7fbreL1WrF7XYTCoUYHBzEbDZTKBTY2dmRfZCcTicmk4lCoUAulyOdTrO9vS07ogrEpmq1WnG5XIyOjnLixAnC4TAGg4FCocDCwoJM8H2U3kMvOmqvk7piT1y2+jXh+2ERHgbhXRC9b6rV6qENGwlE4z6z2Sw7ewuPXSaTIZvN7iol1zwvPYbYbO12OzabjWPHjvHKK68wOzsrb3o6nY5SqcTi4iJra2tcu3aNjY0N6vX6oYo1i6oHkXPwsDcascHa7XbMZjNDQ0NMTU2xsrLCnTt32Nzc3NUTpF8QB404bOBud9Dt7W2ZWFosFneVofc64rBwOBwEAgF56ysUCiwuLpLNZuWt0OFwoCgK6XSabDYr+waJNSUGoYpO1q+88gqjo6PMzs4yNDTE3Nwc7733HtevX+fSpUtUq1Xg/iHOXke0EhBGoGgUCf37zA+i0+lgMpkYHR0lGAxSLBZleLJQKPTlvnI/1EaI0WiU07cnJibweDzo9XpWV1fZ3NyUnpdqtfpUowaa8fIUEQeLSCKNRCIcOXJE3haF16XRaLC1tUUsFmNzc1NOwO33cJFAHUdVy+Vhnl281mKxYLfb8Xq9+P1+5ufnWV9fZ3t7+xk8wfNH6Fq5XKZUKlGtVqnX69IQ7Be63S4mk0kauUajkWq1KiuMRHWIyWRCURRKpRKlUolms/mNNaUoimxCd+bMGY4ePSo7MmezWb788ktWVlaIxWKyqqtf16PQEeG1u5e8DhNiXxF9hNxuN7VaTXpya7WanOVzWBB7tMh3CYVCBAIBbDYb7XZbjuFIJpMkk0kZTnpaaMbLU0ZRFKLRKCdPnuT8+fOcO3cOu92O0Wik3W6zs7PD5uYmH330ERsbGzLRqd9Rl6eKpmJ+v59AIMDQ0BAzMzOk02mWlpZ2TZVWV4cIjEYjJ06cYHx8nIGBAWq1GvF4nFu3bpHL5fo2Lr23LFx4XrLZbF8mWornrVQq5HI5isUilUpFJsGbTKZds8JKpRLLy8ukUikURcFisciqkYmJCfx+P6+//jqDg4MMDw/jcDi4ffs2GxsbfP7559y8eZNSqfQN3esn9h7QwvUvhg4eRuNFhM1EiFCU44uO6IcVsbZEG4GpqSnZQ2lvBd6z2HM14+UpoY4VB4NBZmdnmZmZ4ciRIzIru16vUy6XyWQyXL9+nXg8TrFYpNls9s3QxYehVCqRTCZpNBoyqXlkZASAxcVFOdEXdodLxGLS6/VMTExw5swZvF4vjUaDbDbL6urqN3Ic+gW1fgmZtFotCoUChUKhL/tziGep1WqUSiUqlQrVahVFUbDZbBiNRuDrjrE7OztsbGyws7MjZxXZbDZcLhcnTpxgaGiIN954g+HhYZnPsra2xqeffsrt27dZXV2VPT76FbGGnE4nDoeD7e1tWSVyGHNe1KFYkZwtwv7CqDtsMoHdRQJinMvg4CA+nw/4unHkvRqOPi2eyarcG5vvpw31XgjlFu2kQ6GQvOmJCodGo0EikeDjjz9meXlZJukKBTkMCEUXU6Tn5+e5efMm3W6Xd999l/n5eTKZjMxib7VaNJtNAFmeNzs7SzAY5NSpU4yPj7OwsMDFixdZXFzcdUvqN50TN2bRZVY0U4OvRyz0G+IzrNfrlEolyuUy5XIZj8fD8ePHpRE8MTGBzWYjEAhw+vRp6aUzmUyywm9qagqHw0Gj0WBlZYWbN2+SSCS4dOmSzJ8RHa37GVEsUKlUZIixn421eyFyoMTeK5JQnU4nk5OTcsBru93G4XBw7NgxHA4H8Xh81yTyw4C4BFgsFtxuNw6HA9g9a+xZ7bVPXUsPo5UKX8fmLRYLoVCI8fFxfD7fLpdkIpHg5z//ObFYjPX1dZmke5iMF4CdnR0KhQJLS0vcunWL8fFxvvvd7zIwMMCNGzdIpVKUy2VZUi1CAG63m/PnzzM5OcnJkycJhUJ89NFH/OIXvyAej8uy2H6Tp7gI6PV6vF7vNzaRfjVchL40Gg2ZyyKMl0gkQq1WY3t7G5/Ph81mkxVEFouFwcFB7HY7Q0NDWCwWrFYr7Xabq1evkkgk+OlPf8rNmzdlW3MxNuAw0O12qVQqMkfhMBkv6rCrGLUh9paJiQnGx8cJBAK43W4MBgNut5tjx45ht9spFouHynhRVzgKGdntdumceNbFAU9NS8XDiLHyYjOBr6dQ9uMmC7tLo91uNy6XC4/Hg9VqlSVl2WyWdDpNIpFge3v7UDY8gt2bRzKZ5PLly1SrVcLhMDqdjnfffZdsNsvIyIhs4qfT6RgeHsblcnHkyBF8Ph+Li4vcuHGDubk50un0U890f96IG3OhUJAJuoqi7OrP0W/PLp6nXC7T6XRYWFjA5XIxNTXF1NQUw8PDvPnmm3L0hvAo6HQ6LBYLiqKQyWTodDrs7OxQqVS4ePEiiUSClZUVOYZD7fbuNxneC3EZsFgswNcD9w4DwuPicrnwer0Eg0GmpqZwu90MDw/Lobl6vZ5kMkkul5Pr7Wk2YHsRER6qVqslq65E64JutytbFTyr2V9PxXhRd5MVLib17Awxd+VJe3u8yIjyXb/fTzAYJBAISLesSCiNxWKsrKz0ffv/+yGeVejBxsaGLEMcGhpicHCQf/SP/hGlUokrV65QKpXIZrPodDpmZmZwOp0oikKz2eR//s//yZUrV1hZWSGZTD7T2OuzRjxbq9WSA/RKpRJ6vV4mWvYj4kAtFosUi0WuXLlCPp/HaDRy5MgRpqenOX/+vPQ+CQ9NvV6XifCbm5sUi0WuX79OOp3m008/JR6P02g0ZEO/w3Jww9c5L2KCtug+fFgQh7HX6+XkyZPMzs7y1ltvyX5RZrMZl8tFqVRibm6ORCIhe3CJS0K/nmH3otPpUKvVKJfLZLNZ4G4Iv9PpyDYNz6p531PXUkVRKBQKfPzxx9L7kM/n2d7e7qvprHtRFEVap6L23eVy4fP5WF1d5fLly8zPz8v+E+I1hxF1BVG1WmVra4uLFy8Si8Wo1WrSQBE5HnC3H0WxWCSRSMg+H6lUSv58vyWrqhHGS6fToVQqEY/Hef/999Hr9dy4cYNMJiMnJvcjIkE5n88Ti8W4fv06VqsVr9dLKBSi0WjIxoSFQoF6vS69KiIhdWVlhZ2dHYrFotyH+lln7oUoKy+Xy1y+fBmn0ykN4lqt1teeS4FYS7VajVwux/r6OleuXJEXbjH0tFqtcuPGDfkzuVzuUO3b6j2nXq8Tj8f58MMPcblc+P1+yuUyV69eJZVKPbO9R9kvJ8XpdD52woo6gcdqtUrXW7fblcmqoiT4SS3XYrH4XLTH4XDcUz7qhF29Xs8bb7zB66+/TjQaZWxsjEuXLvGnf/qnZLNZ1tbW5I3vaVEqlZ6LfB5Vf8SNWXjrAoEAJ06cIBQK8fLLL+NwOGTi3Pz8PNlsll/84hfEYjG2t7epVquPdXN+XvpzUOvLbDYTCoVQFIVyuUyr1aJYLB5Y+Oh5ycflct1XPurnj0ajRCIRwuEwY2Nj0qitVqtks1mazaaUR6PRoNVqyRwqdXjoceVUKBSei3zsdvsTJRSqeyQFAgE5pqTVakkD+CAMunK5/Mzl4/F4Hko2QgZibxHl9OKQBmQ4NpPJ0Gg05Mywg9iz8/l8z+096pxD0Vi03W7L8RsHORNrv73nmfgHhTUvLLe99fL9ZrmKm6Fw3yeTSebm5kilUiQSCRYXF9ne3pZD3jR202635XTkra0tmUxotVrlxrK+vk6xWCSbzcoD+zDJUp0rJEqkRcNDdT5Zv60tgbpkvlwuk06nZUi6Uqmwvb1No9GQRoto595sNr8xjqJfZfQg1B7PSqWya9/qx1L7e7HX66ue6yQqG4XxsreE/DCFi9SInlLlchm9Xk+tVqPb7e4axPgseGqeFzXigxb//6Dd+i+a50WNiCmLGKAw4MTCeBb0iudFoN4YxNe9mtSJCgH1jJHH0ale9LwI1OtKcNCHzovoeYFvVjKKNabuA3Svn1PL5iDk1KueFzV786QO0hP8Inte9qLWm708jSTuXvS8wL3l9DTk89w9L/DNxXGYLNa9SZTqD7lf8xIOAmGcAPc19tQH9WHSKYH6sFb/t/j//cxeQ+VeTdXuZagcdo/LXvYeREKuh0U+ez3lDzJeNO6y1yEBz1ZGz8R4EYlhe793GBCbpE6nOzSHypPyKAaJJstvGsGHSSZCVx7kqj5MMnlU7rU/HybU1bEaD4fQmb1G77PkmXletM1Dk8HjoMns4Tjscjrsz6+h8Tx4nutu35wXDQ0NDQ0NDY0XDc1PpqGhoaGhodFTaMaLhoaGhoaGRk+hGS8aGhoaGhoaPYVmvGhoaGhoaGj0FJrxoqGhoaGhodFTaMaLhoaGhoaGRk9xYMaLoig+RVH+XFGUsqIoa4qi/PgRXmtWFOUPFUUpKIqSUBTlXx/U+3pR0ORzfxRF+VeKolxUFKWuKMofPeJr31YU5ReKouwoirL6dN7h80WTz/5o8tkfTT77o8lnf17Us+sgPS//CWgAA8A/Af6zoijHH/K1fwBMA6PA28C/VRTl1w/wvb0IaPK5P1vAfwT+8DFeW/7qdf/mQN/Ri4Umn/3R5LM/mnz2R5PP/ryQZ9eBGC+KotiB3wP+f91ut9Ttdj8C/hL4Px7yV/yfwP/d7XZz3W73NvD/AP/sIN7bi4Amn/3pdrv/q9vt/gWQfYzXftHtdv8rsHzgb+wFQZPP/mjy2R9NPvujyef+vMhn10F5Xo4ArW63O6/63lXggdaZoiheIPLVzz/Sa3sITT4aGhoaGr3GC3t2HZTx4gAKe763Azgf8rXi5x/1tb2CJh8NDQ0NjV7jhT27Dsp4KQGuPd9zAcWHfK34+Ud9ba+gyUdDQ0NDo9d4Yc+ugzJe5gGDoijTqu+dBm4+6IXdbjcHxL/6+Ud6bQ+hyUdDQ0NDo9d4Yc+uAzFeut1uGfhfwH9QFMWuKMprwI+A/wqgKMqYoihdRVHG7vMr/hj494qieBVFmQX+BfBHB/HeXgQ0+eyPoigGRVEsgB7QK4piURTFoPr3rqIob93ntbqvXmu8+5+KRVEU07N4388KTT77o8lnfzT57I8mn/vzQp9d3W73QL4AH/AX3C0dWwd+rPq3N4BVwHif15q5W25WAJLAvz6o9/WifGny2Vc2fwB093z9wVf/NvzVc/vv89q37vHa95/3M2ny0eTzonxp8tHk84TyeSHPLuWrP/BUURTl3wPpbrf7X576H+tBNPncH0VR/ilwvNvt/rvn/V5eRDT57I8mn/3R5LM/mnz253meXc/EeNHQ0NDQ0NDQOCi02UYaGhoaGhoaPYVmvGhoaGhoaGj0FJrxoqGhoaGhodFTaMaLhoaGhoaGRk9h2O8fvV5vT2Tz5nI55Xn8XafT2RPyKRaLz0U+dru9J+RTLpefi3wcDkdPyKdUKmn6sw/PS3+0/ef+2Gy2npBNpVLR9p592G/v2dd40dDQ6B3UlYOK8lz2RI0+437VqJp+aTxvnrnxompeI/8b7i4G9ddhL+EWzy/kpdPptA1D456o15T4X01fdiP2FCGfTqcjv6/tOw+m0+ns2oe63a6mXxrPlefiebmX0qu/p20gX3PYN4i9unDY5XEvNJk8GPUlSRzCew9gbd+5P2oDT2M3h32Pel7P/9SMF/VNp9vt0m630el0WCwW9Ho9BoMBnU6H0WhEURSq1SrNZpNmsyl/VgjhsGwqQk4AJpMJvV6PzWbDYDBQLpdpNBrf8Fz1M0Iesh20omAwGA7d5rAf3W4XvV6P2WxGp9Oh1+vpdrtUKhWpSxp3URQFk8mEwWDAZrOhKArlclnuO51OR/PAfIV4fr1ej6Io2Gw2TCYTlUqFRqNBp9PZ5b06rIg9SsgC7spMr9c/x3f1dBH7cavVAtj17HDX6yv+V1GUp+YFfmaeF/EQwnjZ+7/5fJ5KpUKn05EH1mHdRMQmazQacTqdmEwmWq0W7XZbyuYwsVfxNZf1XdTeBLGOzGYznU6Her0uXf0aSG+LWFcejweD4e72V61W5b6j6dXuy6I4iO12OzabbdeFQuObHCb92S98+Czk8FSMl263S6fTkZup1WolHA7jdrs5ceIETqcTj8eD2WzG7/djNBq5evUqm5ubXL58meXlZfk7DoMyiGc1m82Mj4/jdrs5duwYXq+XsbExbDYbP/vZz5ifn2dzc5NMJoNOp5MWbr8hNkar1UooFJKeunq9Tjwep9FoPOd3+GIgDhKPx8Nrr70m9aXRaPCXf/mXJBIJCoUCjUbjUOfACDmZzWamp6cJBoP88Ic/xO/386tf/Yp4PM6nn37K2tqaZvB9hdi/x8fHCQQCvPHGG4yOjvL3f//3/OpXv2J7e5vt7W3p7TtMqPPK9Ho9Q0NDuN1uadRlMhkymUxfhdmEt0Wv12M0GnE4HITDYSwWC3a7XZ5FnU6HYrFIo9GgUChQr9fZ2dmhVqvJ33VQe9FT87x0Oh0MBgNmsxmHw8HQ0BDBYJCzZ8/i9XoJBoPYbDbC4TAmkwmTyYTH4yEWi7G2tiZd3v1+y1ZvlAaDgXA4TDAY5NSpU0QiEY4ePYrD4WB9fZ18Pk82m+1ro06deGoymQgEAhiNRiwWC9VqlUwmQ7PZlD/Tr3J4WDqdDiaTiYmJCcLhMKdPn6ZWq/Hpp59SKBQolUq7wm6HFbHxBoNBxsbGePPNNxkcHESn07GysiIvBurw0WFG6EsgEGBkZISXX36Z48ePs7q6yuLiIqVS6dDLSUQTAoEAkUiEVqtFq9WiXq+TyWSe99s7cNShe7vdzvDwMHa7Hb/fv8t4SaVSVKtVUqkU5XKZWq1GvV7ftbe/kMZLt9uVMeWBgQHOnTtHKBTi7NmzuN1uRkZGsFgsWK1WDAYDFosFnU7H1NQUXq+XtbU10um0tF779caoNlrsdjsjIyOEw2H+wT/4BwwMDDA5OYnD4cDj8Uj5tFotcrkciUSirw8k9U15eHiYUCjEmTNnKJVK6PV6kskkGxsbVKtVGZM/rCiKQqvVYmtrS+ZI2e12xsbGaLVaFAoFKpXK836bzx0Rig2FQgwMDKAoCu12m7GxMVwuFwsLC3S7XdbW1kilUs/77T53RJhtcnKS2dlZXC6XDFsfdu+UMNrEWnvjjTc4c+aMzAP64IMPaLValEolcrkcQM96ycU5Y7fbcblcWK1W3G430WiUV199Fa/Xy/j4OEajEUCeUbVajWw2S71elx7gpaUl4vE4uVyOfD7/xJ6pp2K8iE00HA5z7tw5hoaG+Pa3v43dbsdsNu96w61Wi06nQzQaJRgMMjo6ysLCgrTcDsPBZLFYmJiYYHR0lDfeeINwOIzH48FoNNJqtWg2mwwODtJqtbh8+TIGg0FuJNB/cVYRRjMajYRCISYmJnjzzTcpFArcuHEDvV5PIpGgXC73rXH7sAjjJZvN4nQ6MZvNWCwWQqEQxWKR+fn5A7/x9CLixuj1evF4PMDdQygSieDxeJiYmKBQKLC9vU0qlTrUsoKvjZdIJMLExARWq1Xm3e1N0DxMqAsmLBYLLpeLkydP8p3vfEf+ezab5c6dOyQSCba3t5/n231i1CkNPp8Pp9NJKBRidHSUEydOEAwGOXHiBCaTSf5sPp/fFTZKJpMUCgU+/PBDABqNhgw5PnfjRbxpEQ4Kh8McOXKEaDTK0aNHcTqdNBoNeeC2220ZF8vn87RaLUZHR3G73fj9fqanpykUCqyvr/flBiIWgNfrZXp6mkgkwve+9z1CoRBerxe9Xi+VXlRFhEIh6YGJx+Ok0+m+NO5EElin06HValEul6nX61itVnQ6HePj4wDMzc1RKpUOdc8J4bZuNBrEYjFp7BqNRsLhMI1GA4fDIW9FcPgMGHGZstvteL1eBgcHiUajGI3GXZVa0WiUYrHI2toaGxsbcp86bLISRksoFMLj8TA4OEgkEqFer1MqlchkMmxvb1Or1Q7dxUGd62IymZiZmWF4eJhoNIrdbiefz1MqlWS4VuTm9bKMxB5Tr9fJ5XKUSiV2dnYolUrYbDYZMXA4HNIxISofU6kUxWIRh8OB0+nklVdeYXR0lM8++4xGo0G1WqVcLsu/86gciPEiMvVdLhdTU1PMzMzw1ltv4Xa7iUQiANTrdXkY1Wo1Njc3KZVKbG1tUa/XMZvN2O12AoEAU1NTrK6u9mUimNpyd7vdnDt3jrGxMd59912cTqf0qqRSKZrNJuFwGKvVSjAYxOPxMDU1RSKRoNVq9fUNUZTiCePFYrFgsVgYGxsD7hp1er1e3gL7VQ4PQlEUabxYLBba7bbMnep0OtJ4ER7OwyYjYaC4XK5dxovBYJBJqYqiyJyFy5cvYzKZ5H4FvX34PCpCJoFAgHA4LI2XxcVFUqkUmUyGnZ2dQ50ErjZeZmdniUajOBwOKZtisUipVKJer/e8fERop9FokMvl5MVye3ubTqdDLpfj3LlzdDodWcFnMpmkwVMul4lGo7jdboLBIK1Wi2q1yvLyMplMhlKpJP/Oo3KgYaNOp0Oj0WBnZ4fV1VWMRiMrKyu0223ZH0BY7fF4nGq1Kt/82NgYFosFt9vNyZMnWV5e5tatW5TLZYrF4mM/4IuIw+HA5/MxPT3NmTNnpIHSbDaZm5ujUCgwPz9Po9HgxIkT+P1+QqEQdrud8fFxabWKG2Kz2ewb2cDXC0boTaVSoVqtYjKZCIfDNJtN/H6/vOk0m82ejSk/KeJwdrvduN1ujEbjoc8DEqhd3tPT04yMjMhLgOiHIy5e2WyWra0t8vk81WpVGsOHSY4i18xoNDIwMMDQ0BAejwebzUan05GJl8KLfpjkIy6dBoOBSCSCz+djZmaGmZkZbDYb9XqdtbU1bt++zerqqjTw+kU+nU6HZrOJyWSSlUbHjx9ndHRUXiTz+TzdbpdisUi5XOazzz4jnU5z/PhxwuEw4XBYXiCmp6cBiMfjjy2jAzFe1IdNo9Egk8lw+/ZtacgIl2OtVmNra4tyuUwmk6HRaMjk3cnJSSwWCy+99BITExMsLS1x9epVEokEOzs7fbNQFEXB5XIxMzPDqVOneOONN3A4HFitVrLZLJ988gmxWIwvv/ySWq3Gu+++y/j4ON/61rfwer0cP36c4eFhUqkUV65ckc39+s3zoNPpaLfbFAoFCoUC5XIZvV7PyMgIZrOZUChENpuVG2q/6MejIjbUQCCAz+eTzQ3V8jiMclEURYYerVYrp0+fZmxsjJGRETwej0w6FR6+ZDLJ8vIy6XSaUqkk+8HA4WmSCXdzEBVFYWhoiMnJSQKBAA6Hg06nQ7lcplqtyvV2WC4M6pwxg8HAxMQEg4ODvPTSSxw7dgyAWq3G3Nwcv/jFL7hz547M6RC9hHodEUa1WCz4/X5GR0d55ZVXCAaDOBwOFEUhk8lQqVRYXl4mm83yt3/7t2xtbVEsFpmamsLn8xEMBpmYmODMmTNUKhWuXr2KoiiPFWU5MONFp9NRrVZJJBLSbeZ0OolGo/KNlUolqtUqiqKQy+Xu23JaNN1yu93k83mgtzcQsUmKnjdDQ0OcOXOGiYkJjEYj9Xqdzc1Nkskkt2/fJh6Py5LgpaUl/r/23vPHrjvN8/uem8/NOVfOYlGkqKIotaRRaIUWenrcjcFgButd2G/8zq8WsAEDa2Bh+18wDMOYxcI7DcxggNaOMTPdmpG6FSiRSiRLFCunm3PO8fgF+/nx1BVZJFVVZN1zzwcgSFbVJe/53V94fk/4Po1GA9PT03A4HOA4DkajEXq9HjqdTpKGC0Fia3Tba7fbrBxxmOfDSaNQKFiiLh0oo2y4AIfzE0iXgvQoxGPSbrfRbDZRKBSQyWTQbDYPea5GYZ6JQ9k8z8NoNMLpdMLlcgG4ezCn02lEIhFWej8q80qsWWY2m2GxWHDu3DlMTk7CbrdDqVQil8uhXC4jHo8jlUqhVqtJ5jIl9jjp9XoEAgGsrKxgamoKwWAQBoMBzWYT1WoVX331FQqFAmKxGEqlElKpFMttFQtqajQa6PV6lnf2VD0vJJhWLBaRz+dZZv+5c+fw5ptvwmazwe12I5/Ps4fLZDLodrtQqVRQqVRQKpWHNhaj0Qifz4d8Pj/0i4XcsVQFsry8jF/+8pdMPTeTyeDTTz9FOBzGhx9+iFwux1yOrVYLFosFMzMzLCHaZrPBYrHAaDSypLBhHyMxZAz3ej1Uq1VUq1XU63Vm6JEWx6gjTrDU6/XMfUvrSDwfpDI3HhWxJoVWq2VVRnSRokTver2OSqWCaDTK9EuodckoGC6E+IB2Op2YmprC1NQUOI5DsVjE9vY2bt26JUn9kqOgvVulUsHtdiMQCODdd9/F/Pw8HA4HVCoVIpEICxltbW2h1WpJJh+IvJdGoxEulwsXL17EX/7lX8LlcmFubg7tdhuhUAihUAi//vWvEYvFWLURne9igTpK6DWbzdDpdAB+/AXhVEql9Xo97HY7AoEAAoEAS5YDAKvVikqlcshNJL4l0obc6XTQbDaZINkwQ0qMdrsdU1NT8Pv9MJvNEAQB8XgciUQCOzs7iMfjaDabh3rS0E0wlUohGo2yOnue5+FwONBsNiVluIgRBAHtdpt5XprNJrLZLNLpNNMvGeyDJXMXcb8e8c16VCBPp9lshtvthtPphMVi+UFDxkajwapD6vU6C5uMGpQ7Rfl1TqcTVqsV5XIZnU6HJes2m82RC0nSBYHOMbPZDKPRyM6oWCyG3d1dlgoh7pE17KhUKqjVajgcDkxPT2NsbIyFEjudDorFIra2thAOh5HP51Eul9kZdtr7zomVSgNgsSu/34+XXnoJy8vLuHLlCnQ6HTiOY+JZgiBgdXX10O1ZLHdPN+5MJoN6vT60E4E+PKqUWVxcxM9//nNMTk7C5XIhFovh888/x/7+Pv7pn/4JpVIJjUYDAFislKqzvvvuO7RaLRiNRgQCATidTiwuLqLdbmN7e1tSMWix56VSqbAwZLvdxurqKhKJBMLhMFKpFJs3wzg/ThJyb9OfKRmebkBSNXDvB607k8mECxcuYG5uDgsLC7DZbFCpVGwsSJMjkUggmUwinU6j2+1KZh09CuIDRqvVYmlpCRMTE1hYWIDP58PVq1cRDodx+/ZtbG1todvtHvJejQKCIECtVmNqaoopWZMafC6Xw9WrV/HVV18hFouh0WhIonULzQnqafXss8/izTffxMzMDGZnZ9HpdJDL5bCzs4O///u/RzweRygUQr1e/0FDRvFY0Hw7CaHDEzVeNBoNeJ6Hx+PB9PQ0fD4fSyAkcSPKYaCNlrwSlA9CVQDihxxWxPFSEjRyuVwsTlgqlRCJRFgSs1hCWax3AoDFFSl+SMlgUiwnB+4lXLZaLTSbTbTbbRYSoRJpuuEM+0ZxktC8oXGjLu3DegH4MdCcIKFMakVCOhRi44W0Jqg0epQ8VOK9Rq1Ws/Hyer2svJ5yXUqlEsuvGwXo/FEqlbBarXA4HPD7/fB4PFAqleh0OkgkEkxzq1AooNVqPe23feKYzWb4/X4mMWCz2VgZdDweRzweZ89/v/VzmnvOieq8kDjdCy+8gD//8z9nCXKdTgfVahX5fB4HBwcIhUIsLqjRaFgGs9/vZ2V55MUZ5sOZDlie52G32zE+Po5z586h0+kgEong+++/x29/+1sm/jMYAqENlqpuSHaZXHJS9TjQM/V6PSYl3Wg0mOIuAHaDBqSV73NcaM6QUmytVkO73YZarZZM5cNRUE81agXw0ksvwe/3w+FwQKvVHupd1O/3meeFEgtHrcyclKxNJhN8Ph9eeeUVjI2NQaFQIJPJ4Msvv2TNK8krNQqXBdp3yXsXDAbx9ttvw+v1guM4pFIpfPjhh7hz5w5u3ryJWCzGziwpzB/6jJeWlvD6669jaWkJKysrUCqVTFfqgw8+QCgUwsbGBmq1GptLT+w9nsQ/Qh8a1X+TlgKpw3Y6HZRKJXYQVSqVQ7dmSqojZT5yKQ17Dw1a6EajER6PB1arFTzPo9frIZPJIJPJoFgssg/+ftAY8TzP+kHRmEpFxfF+iD1P9DuFHk0m06GbNDAaVSGPCm28JEw3zN7Lx0HsSaC+aZSfIC4fF+fUFYtFZLNZ5vUcNUNYXGrvdrvhcDhgMplQrVaRy+WQz+dZAuaoQHOAPOZerxc+n4+NDc2bdDrN2pRIJTQr9jhptVpYrVZ4vV7Y7XbwPA8AKJfLyOVyrEL2UQUdxfmsJPHxsNccxbGuYuISYK1Wi/n5ebz99tsYHx9nlQ+CIKBcLuP69es4ODjA2toa0uk087yo1WpoNBoYDIZDUuatVov1RhhGBEFgIbPl5WW8+eabmJ+fh1arRTqdxgcffID9/X22MQx6UWgjVqvVUKvVTNDO5/NBpVIhnU5jdXUV6XRash4Y4J6GEJXPB4NBOBwOJtaXSCRQr9clc+M5KcSh11FCXNZJqp6k60LhM0EQkMlkkMvlcP36dXz//feH1pHUx0zssRSEu21K3nnnHUxOTmJ+fh5qtRr//M//jIODA2xvbyOVSh3KYZAyZPiTGNv4+DjefPNNBINBTE1NQalU4vr164hGo1hdXcX6+voPyuuHGZobVqsVVqsV8/PzeP7552EymVhZ+M2bN3H79m189NFHzLP7oLC0OPWB1mA2m8Xu7i5yudyx5tSJGC9kodJmYbFYmOFCVhZlq1MCKr2OdAW0Wi00Gg2Au+ECqjAZxux/mgBKpRJqtRpWq5VJSHc6HVQqFablQrfjQVcsWfGUC0Rl0lqtluWCkOdFivkM9ztAOI5jDeJI52bUSlplHo5KpWK6LjzPQ6vVAjgcXqzX66hWq0zeYZQ8C8C9saCLo8/ng8fjYftLPp9HKpViCtZqtXpkwkXA3TlkMplgsViYR4rygLLZLJLJJIrFIur1uiQ8LmKoyISe32w2Q6VSsWaLsViMPX+r1ToytWMwlEYq/NQ+4Tgc23ih/inUB2NycpIp7lWrVYTDYezt7eH69etIpVKsmobiY+fPn8fMzAwmJiZgMplQLpdRLBaRSqUQiUSGtj+EeAJMTU3hueeeQ7Vaxc7ODjY2NnDr1i0m+HS/TYG+Pjc3B6/Xi5WVFVy6dInp6VQqFdTrdUmUkj8K4rARADgcDjgcDsRisaEPL8qcDLQfORwOXLlyBc888wz8fj94nmd7CCVbUgPGeDyOfD4/UiX3dAM2mUwYGxvD4uIiVlZWYLPZUKlUUCwWsba2hq2tLda5HZBeaFqMeP/gOA5OpxOvvvoqZmdnsbS0BIPBgFwuh1wuh88++wzb29vIZrOH5o0Uxoe8JH6/H8vLy+xczuVy2N7exrfffovf/OY3bM0c9dxKpZJ5QcV9+6jB53EriY9tSpOb1mg0stuOVqtlGh35fJ5pc+RyOeZJoVwXp9PJvBJqtRqdTof1sjmJuNjTQBzy0el0MBqNsFqt4DgO+Xwe+XwepVKJddS83+spbu9wOFi81Wq1AgAqlQqazeZINNsTa/+QQUeLgvJ/ZGSAw1VGdFvW6/XQaDSHwkHdbhfFYhG5XA6NRoNpc4wSlNhstVphs9mYfgnp3lB+Iu3Xo7DOaH9Rq9UwGAysushoNEKj0aBaraJQKCCZTCKRSPxAfE0K0D5rMBhgt9vZs5OmSzabRTweRy6Xe6jHieaNuPCGRO/o/DrOuB27/IDj7vbqoQPWaDQCuCv+FIvF8PHHHzPvCyU2cRwHu90Om82GCxcu4OLFi3A6nUzNMRqNolQqsRv1sE0McdyQLNder4dUKoVbt25hb2+PCWIB9yY+xVupq6vVasVPf/pTnDt3DlNTU9BoNNjd3cX6+jo2NjbYGIn/DakwmP/TaDSYGmqtVkOpVEKpVBqpG/ODGMXclkEo947neUxMTODFF1+Ex+NhRgtVPaZSKWSzWXzxxRdMWEzcXVrKDHoXjEYj5ufnMT09Dbvdjn6/jzt37iASiWBvbw+xWIzpukh9bAhqaXP+/Hm8/vrrsNvt4Li77Ww+++wzRCIRbGxsIB6Ps71HapBGks/nYyGjer2OaDTKeg0eVSgymFMFgMmlUMioVCqhXq8/8N94FE5k5EkjQKfTsVtOt9tFpVJBOBxGNBpliqiU30HeCLfbDa/XC51OB0EQmP4J5cUMKxQ2MpvNzBNVq9WQTqdRLBaZ/gbwww+PNhYqr56enobJZAIA5PN5RCIRFAqFoc0JelzIzU2qsVQpIu6ZITO6iHPvSIGaOtjS96k0ulKpIJ/PM40K6iANSO8CcBSUp+hwOGCz2Vi+YTqdRjweZ55h2q9HYWwop87tdrOu2i6XC/1+H/V6nbUBEEtbSM0rJS7CoSiKQqFAt9tlKtR07jwM8rqQHhnlwJJa+lP3vABgyaOtVosl2xYKBUSjUdasiaqLKIv5Zz/7GSYmJjA/Pw+r1cqqizY2NnDjxg1Eo1E2OYYN2gyNRiMcDgd0Oh36/T4ajQby+TwqlcoPGiqSG5fUG9955x2MjY1hYWEBdrsdsVgMhUIBX331Fa5du4ZEInHfRF+pIlaPPUmVxmGHwrZ2ux1Wq3VkEivF0FygHkYWi4WV0hMcd7frfTKZRDQaRTweRzKZZP1XRqXKqN/vw2Qywe12M++CxWJh4RBS1K1WqyPhcREf1jzPY3FxEb/61a8wNjYGt9uNer2Ob775BpFIBJ999hmSySTTnJLyOqO5IhaT1Wg0h0L14vNL7GWhs2xhYQEejwfnz59navD7+/uIRCJIJBLHzmc9tvFCD0kN88i6qtfrKJVKiEajrNmZSqWCwWCA1WrF8vIyZmZm4Ha7odfrWQO+ZDKJg4MDFIvFob4RCcJdSWnSZhEEAd1uF/V6/ZDCMP0sGSJWqxUejwfLy8uYmpqC2+0Gz/MoFosIh8M4ODjA/v4+S3wexrDaj0E2Vh4M6QDp9XpJJQ8+DmTEkR6SVqv9QRUEtZsoFosolUool8vQ6XTs56Q+t8QHNTUZnJ2dhUqlQi6XQzabxd7eHsLhMJNvGAXEc8fj8eDChQtwOp0wmUyo1Wos7WFvbw+ZTIblcEh1jYnzDIF7uUDiPMPBnyHEGjkejwcTExMsdygcDiObzaJQKKBcLh/7fD+RnJdyuYxoNIpYLIZ4PM4STRcXF/EXf/EXqNVqbNFMT0/DZrPh3LlzcDqdh4Rv0uk0otEo9vf3USgUjvvWzgz0wdvtdszPz0Oj0aBYLAK4l9Tr8XhgsVhw6dIlOJ1OXLx4ETabDe12G6lUCt988w2+++47li8zCrke9GztdhvJZBIajQbLy8tQKBRytdEfGfS8aDQayc+LQSi53eVysQoJg8HAQiG0yfZ6PZYrNei1lPL8ESe7q1QqjI+P4+2338bk5CTbiz755BN2uDQaDckXAhA0NlR0YjKZWJJqvV5HsVg8ZNARUh4bmiu0PgRBYJov1WoVi4uLKBaLiMfjh9TwDQYDdDodpqamYLPZ8MYbb2B8fBwTExPgeR65XA5bW1usSgs43jgey3ghC6xeryObzbJflL0+NjaG1157Dc1mE81mEzzPY2FhgfVLoBwZAGyipNNppFIpSegu0MKgD9dsNiMYDKLdbsPr9QK42w/KYrFgcXERLpcLr7zyCux2O/x+P9RqNeLxOAqFAra2tnDjxg2k02k0m82RkekG7laHFAoFWCwW1uXUZDLBbDazWOooIj6UaOMV3whHwQNDz0+FA5OTkyyHbtA46fV6qNVqP8jlGJX5Q5U0brcbKysrsNvt0Gg0qNVqWF1dZbmJ4jYJozB/xMKGer0eBoMBKpUKrVYLlUoF8XgcqVRqKCtfj4M4pcFoNGJsbAyZTAZjY2NQq9VIJpOHjGKz2Qyz2Yzl5WX4fD5cvnwZY2NjMBqNUKlUzMlBl4fjzq9jGy8KheKQeM3333+PqakpeL1eWCwWLC8vo9vtotvtQq1Ww+l0QqvVQq/XQ6VSsXLo7e1tbGxsIJlMsrDKsC4ees9Uz06dRt1uNy5evIixsTFMTU0BAAst+Xw+6PV6+P1+aLVaJpz1xRdfIBqNYnNzE7lcjuUODeO4HIfBuSBX2BxGbLAoFIpD7TakOl/ExptarWaNT8mopVYjrVaLJaLeunULkUiEqaJKmcGqD5fLhYmJCSwtLcHn86HT6eDbb7/F7u4uNjY2mGjmqIUe+/0+zGYz5ubmEAwGYTAY0Gg0sLu7i52dHUQiEaTT6aHNwXwc6PkqlQpSqRQCgQDTI7NYLJidncW7776LXC6HxcVF5kHRarVwuVwwGo1YWFiAxWKB1WpFt9vF+vo6isUivv32W6ZmfRKciOeFsoej0Shu374NtVqNlZUV9gD3ew0po1arVVQqFezs7GB1dZUl8tDmM6yQRyqXyzExHqfTCYfDgW63y9QFKRubOmqTiNb29jZyuRy++OILbGxsMFGkYR+Xx0FsoAy6McW/y9yF1hYpp1KCnXjcpLj5Uh8WSpAnDxQAdrmKRCI4ODjA6uoq4vH4yF0CBEFg4eiFhQUEAgGEw2F8++232Nvbw/b2NkvSHZX9Bbi3x1itVszOzrLmwOVyGVtbW9je3kY4HD4kyyBlBo0XqqyiKj4yUsiLQsYuNUKlai21Wo1arYZGo4HNzU1sb2/j5s2bWFtbQ6PROBHj+ESqjeiNFItFbG9vQ6fTYWJiAjabDcFgkHV5BcC8MPl8Ho1GAzs7OygUClhbW0MoFEK9Xh/6TYXee7lcRjwex8HBAdbX12G1WuFyudhmS0Zcr9dDPp9Hq9VCMplEpVLB+vo6crkcNjY2mCDSsI/LoyI2WHieZ+E2j8eDTqeDVqvF1C5p8YwitO56vR6KxSIKhQJKpRIUCgWy2Szy+bzkFZjp+ZvNJjKZDLa3t9Hv95lrW6lUolarYXd3F6FQiMndS9WQE0MHs06ng1arRSAQwPLyMlwuF5NcuH37NuLx+MitI3G4iLx2TqcTRqORJXbv7u4iHA7/QCxVyvOGnArxeJyFDuv1OgKBAKanp6FUKqHRaNieLNZxofAQXc7Jy/Lll19id3cXsVjsREqkiRMzXmjDrFarqNVq4Hkek5OTMJvN0Ov1hxou1ut1bG5uIpvN4tq1a4jFYlhbW0MymWT5IcMMTYB8Po9yuYw7d+7A5XKxvBbqok0/S1VWuVwOV69eRTqdxq1bt5DP55FOp9FoNA6pFI4C1D6ClB5nZmbgcrnQbrdZ+4hkMolOpyPpzP+HQZpK2WyWJcUpFAokk0kkk8lDeklSGyN6HtKOiMViuHHjBgRBwDPPPMMqsCqVCm7fvo1IJIJyuYxmszkS6sxUocfzPOx2O+bm5vDiiy+i0+kgkUhgZ2cHV69e/YFmidTHBThsvOh0OthsNvj9flgsFna5vnPnDuLxOOshp1arJT82ZMBS+4xEIoHt7W0899xzUCqVsNvtCAaDMBqNcLvdPyiVJsOvUqngiy++wNbWFq5du4ZwOHwohH1mjBeCmi7l83msra2x/gc6nY5VFbVaLbRaLezt7aFUKrHukqS2JxXoRggA0WgUN2/eRCaTQSaTORQC4TgOzWaTCUPdvn0b5XL5UNb/qHhcxFAiJZWXk6ufkugajQZrQ08/P2qIc86i0SiazSY++eQTcBzHyjpHQSFVnGMWi8VYR2CtVgudTodUKoVQKIRcLjcSVXpiyLhtNpsIhUK4evUq2u026vU6tre3D7VGGJUxAQ4no7bbbdRqNeRyOWg0GuRyORQKBbbPiH9+VCDDt1gsIhKJsEiB1WpFIBBgnhdq7NlutxGPx9leXSqVsLa2hlgsduweRg+COypvwGazPXJSASlYirOPeZ6Hw+FgeR00INRbhBbRcTfYQqHwVGaWyWQ6cnzIGlWr1VCr1Uw1d/CDJPXCdrt9KBRCh9NxqVQqT2V8DAbDj0pKERskCoUCHo8Hb731FlMcbjQa+Nu//VtEIhEm+HecA6lWqz2V8TEajcdO2qH1RvktZrMZAJiEd6/XO3bZa7VaHYr5Q32veJ6H1Wpl+xCtK1JnPulcqac1fx51/9FoNIe8mJSj2Gq1kM/nWQjttA7op7H/6PX6h44N/d7v9/H888/jjTfegM/nw/z8PNbW1vDXf/3XKJVKyOVyp1ZAUq/Xz+TeM5hXyPM8q2ikxO8333wTVqsV4+PjqFQq+Nd//Vckk0l89913rHcYeX5/bJj2qL3nRD0vYhcSxb3K5fIhTwOp9jUaDbaxShky6trtNlPYHTxoSZWYGi2OeiKquHyVWkaEw2F2G6LqtlErXbwfYg9Vv99HtVplSfS9Xm+k5hK5rQf3nW63yzwMozQewL39h/bjYrHIesyIJd5HbQ2J9xjK19zb22N5Y5FIBLVabWS0XQYZ3INJeFZs6H733XcwGo0Ih8NoNpvY3t5GoVBgkZROp3OqXr0T87wM8rCKEHFp53E5q54XMY9SIXOSYyJm2DwvYmixDHqixCGj4zLMnhdicH6d5FwaFs8LMVhGf9p5HGfV80Ictfc8iRyXs+h5EUPeS/L+U4n9Se4xD+Ksel4GGfSekFdTvCeTsSJuqHzcufXEPC9iyHJ7mPEySjxsPGgRyRyGLP/BxQOM5jx6EOL5NWrjIr4pEoN/Fxt0o+aBIcQ34VGbIw+C8hPJEyW+MNH3Ze5CewwZd0dx2uN2asYLcK+rpIy8WfxYBo2Vwa/L3IXm16gaL4/jaRk1w4Xmxf3241GbJw+CKkTFyGNzj7N4fp2q8SIjc1KctYVzVpHHSeZ+yPPi4chjNFwcmfMiIyMjIyMjI3PWkBMsZGRkZGRkZIYK2XiRkZGRkZGRGSpk40VGRkZGRkZmqJCNFxkZGRkZGZmhQjZeZGRkZGRkZIYK2XiRkZGRkZGRGSpOzHjhOO5/5DjuG47jWhzH/efHfO0bHMf9geO4EsdxByf1ns4S8vg8GHlsjobjODvHce9zHFfjOC7Ecdy/eYzXajmO+08cx5U5jktyHPfvT/O9Pg3k+XM08vw5Gnl8juasrq+T9LzEAfwfAP7Tj3ht7Y+v+59O8P2cNeTxeTDy2BzN/wmgDcAD4L8F8H9xHHfuEV/7HwHMAZgA8AaA/5njuJ+dxpt8isjz52jk+XM08vgczZlcXydmvAiC8BtBEP4rgNyPeO1XgiD8FwB7J/V+zhry+DwYeWweDMdxBgB/DuB/FQShKgjCVQD/H4B/94j/xH8H4H8XBKEgCMI6gP8HwH9/Km/2KSHPnwcjz5+jkcfn4ZzV9SXnvMjInG3mAXQFQdgSfW0VwENvhhzH2QD4/vjzj/VaGckgz5+jkcdnSJGNFxmZs40RQHngayUApkd8Lf38475WRhrI8+do5PEZUmTjRUbmbFMFYB74mhlA5RFfSz//uK+VkQby/DkaeXyGFNl4kZE522wBUHEcNyf62gUAdx72QkEQCgASf/z5x3qtjGSQ58/RyOMzpJxkqbSK4zgdACUAJcdxOo7jVKLvCxzHvf6A1yr++Fr13b9yOo7jNCf13s4C8vg8GHlsHowgCDUAvwHwv3EcZ+A47mUA/w2A/wIAHMdN/nF8Jh/wT/y/AP4Dx3E2juMWAfwPAP7z6b/zJ4c8fx6MPH+ORh6fh3Nm15cgCCfyC3dLxoSBX//xj98bw924ouMBr339Pq/9+KTe21n4JY+PPDbHGB87gP+Ku2WHYQD/RvS9VwEcAFA/4LVa3C1VLANIAfj3T/t55Pkjz5+z9Esen4eOz5lcX9wf/4NTheO4fwvgnCAI/8up/2dDiDw+D0Yem6PhOO4/AMgIgvB/P+33chaR58/RyPPnaOTxOZqnub6eiPEiIyMjIyMjI3NSyAm7MjIyMjIyMkOFbLzIyMjIyMjIDBWy8SIjIyMjIyMzVMjGi4yMjIyMjMxQoTrqm0ajcSiyeavVKvc0/l+DwTAU41Or1Z7K+Gi12qEYn1ar9VTGx2QyDcX4VCoVeX0dwdNaX3q9fijGp16vP/Hxkc+uo5HC+BxpvMjIyAwP96sc5LinsjfKyMjInCpPzHihjZV+5zhO3lgfg36/D+Dw+CkUctSPEM8vQRBGanwGRKHY8wOAQqGQ15mMzCkxuPYAec09KZ6K50U2XB4fjuMOHUry+N2fURyfwWcd/Lt43sjIyJwM4ouk+HeZJ8MTMV44joNSqYRCoYBarQYANJtN9Ho9+QN/BDiOg06ng0qlglqthlKpRKPRQL1ev2+oYJSg56dx0el0UKvVbHzEPyNFBEFgz61SqcDzPDiOQ7fbhSAIKJVKaLfbAEZ7cyXjv9/vH/pdpVKNjIdO5mQQ7yccx8FkMkGr1bI51Ww20Ww2Jb3vPAhBENDr9dhY0MVJbAOc1D506saLIAhQKBTQaDRQKpXgeR6CIKDdbqPX6532fz/00Aev0+mg0+mg0Wig0WggCALq9TrblEcdtVoNjUYDg8EAvV4PQRDQaDQkPTbizYHneajVapjNZigUCnS7XXS7XTQaDWbIyB6Yu8g3ZZmTgA5jo9EIg8GAbrfLDu5ms/m0395TRRwpOK11dqrGiyAI6Ha7sFqteOmll2C1WhEMBtFqtfD+++8jHo8zC03eSA7T7/fBcRz0ej14nsfPfvYzLC0tged5aDQafPDBB/iXf/kX9Ho9dLtdAKO3GdOBrFQqsby8jPHxcSwuLiIYDOK3v/0tfve737HxkeLY0PpyOp149913YTKZmBFjMpnQ6/XwySefIBqNIpFIoFwuj2w8vtfrQalUwmQyQaPRwOPxQKVSIRaLoVqtskNnFHlcA38U5w9wL/yq0+mg1WqxvLwMt9uNS5cuIRAIIJ1Oo1gs4rPPPsPnn38OYLTGiiIrfr8fer0eZrMZarUatVoN7XYbyWQSpVLpxPbkUzde+v0+NBoN5ubm4PF4sLCwgFqthg8//PDQm5cNmMPQeJA3YXl5GS+//DJ4nodWq8Xm5iYUCoW84eLuBuH3+7G4uIgXXngBCwsL2NjYgFKpPJToLLX5ReuL53ksLi7CbrdDo9FArVbD4/Gg3+8jFAqh3W4jn8+PtPeF5gvP89Dr9QgGg9BoNCgWiyyEPapjA/wwf+NhPzuK40RjRHvy9PQ0Jicn8corr2B2dhYHBwdIJpPY3d1l4zMKYyUukKC9x2q1wu12Q6fToVgsol6vo9lsolarndie/ESMF51Oh8nJSbjdbnYjBMC8LsBoWaiPQr/fh0KhQDAYhNfrxdTUFMbGxlCr1dBoNNDpdH6Q5T5K0MR3OBwwmUx49tlnceXKFbhcrh9UtkkNiitrtVq4XC74/X54vV5wHIdPP/0U7XYbCwsL4HkeLpcLOp0OqVQK6XT6ab/1p4ZSqYTNZsN7773HLlEcx6FcLrPQWqfTGQnPlLhCpt/vQ6lUQqvVstwfQRDY/kJuf/HPD4YDpD5edNgqFArwPI+VlRX4fD689dZbmJycRCAQgE6ng81mAwBMT09jeXkZmUwG8Xh8ZCofFQoFdDodzp07h7GxMSwuLsJqtaJSqaDRaOC3v/0t+v0+stksstksFAoFlErlj/7/nkjOi0qlgtfrZZZYo9EAADlh9wGIjRKHw4GxsTH4fD643W5Eo1GUy+VDht8oQhuoyWSC0+nE7OwslpaW0Ov12C1aqtAholKp4HQ64XK5YLfbUa1WcefOHRSLRfR6PTidToyNjcHtduPatWtP+20/VSg34YUXXsDU1BQWFhbQ7/fxu9/9Dnt7e1AoFJKeM4PQ/tHr9VhOoth4oXWkUCigUCgO5SeO0oVTvBdTyGhubg6zs7N4/vnnMTExwdaj0WgEx3Hw+XyYmppCr9dDNBqVvPEi9rxotVpMTU1hfn4eKysrcLvdzOuyu7uL3d1dVKtVlhZxHJ5YqTQtFp7nWehDo9Gg2+0yy3YUFsPDII+L1WqF0WjEuXPnsLCwALPZjG63i3A4jM3NTcRiMXY7AkZv7GjB2O12eL1e2Gw2mM1m5PN5FmOVomdK/HmbTCYsLCzA6/Uik8mgWCwin8+jWCwiHo+j1WohEAiA53moVKqRkyigQ8VgMGB8fByTk5OYmZlBMBiESqVi1WhSPljEiHPEtFot8xbY7XY8++yzUCqVLIwWiUTQbrcxMzMDs9mMTqfD9p9UKoVKpYJyucz+XSnOK1prKpUKOp0Os7Oz8Hg8uHTpEiYmJqBWq1GtVtFqtViCvCAI8Pl8uHjxIlqtFjY2NiS3B4mhNcZxHNRqNXQ6HfR6PfR6Pfr9PlqtFhqNBhqNBlqtFjqdzomlOjwx44Usd3o4qpqRK47uIT5srVYrHA4HlpaWcP78eVgsFnQ6HYTDYdy8eRPRaJS5uUdl8xVDG6bNZmPGi8lkQi6XO2S8SInB5zEajVhcXIRer0c+n0c6nUahUEChUGDGC4VtR7UkuN/vQ6vVYnZ2FjMzM5ienobH42FJusDo6E7RQaNWq6HX62Gz2TA3N4fx8XH84he/gEqlwsHBAUqlElZXV9FsNvH6668jEAiww+f69etYW1tDJBJBpVJh/64UDRh6JqVSCaPRiOXlZYyNjeHSpUvw+Xxot9uoVquoVqtot9ss38zr9UKtViORSLALuhSLKiicKPbe0fnO8/wh46Verx8yXk5izT0x42VUNojjQgnOgUAAfr8fgUAALpcLzWYT9XodkUgEBwcHKBaLT/utPhXIM+VwONiGMj8/D6PRiEajgVAohM3NTSQSCXYTAqSzaYhvzlarFePj4+j3+9jZ2UEymUS73Ua/30elUoFKpYJGo4HZbGblnO12G51OB4B0xuRB0KFK4TW9Xo+trS1kMhk4HI6RSdIVjwNVg5w7dw4ejwfnz5+H3W5HIBBAt9tFIpGATqfDxMQEer0e7HY79Ho9nE4nVKq7x0UgEMCNGzcgCAKq1Srbi6QyjjQnVCoVTCYT5ufn4fF48NJLL8HpdKLRaCASiWB/fx+lUgnlchnNZhM2mw1GoxEmkwkmk4n9udFoMENPKmME3JNBMZlMMBgMWFpags/ng8/ng8PhgEqlQrfbRTweRzqdRiwWQy6XQ7PZPJHcMtl4OSMMJtCJb4rBYBDb29tIpVLY2trC2toaSqXS037LTxyxle/1euHxePDiiy/i/PnzMBgMqFar2NzcxNWrV3FwcIBOpyOpeLO4es9kMsHlcmF+fh65XA4ffvghM156vR4KhQIAQKvVwm63w2azwWq1olAooNVqSWZMHoYgCOw2bDKZcOPGDfA8j5dffhlqtVpy3rn7QeuG53lYLBbMz8/jz/7szzA2NoYXX3wRKpUKnU4HpVIJ29vb4HkeCwsLUKvVcLlcMBgMmJychM1mw8TEBCqVCvR6PYrFIqLRKPL5vGT2drEXSa1Ww+l04rXXXsP4+Djee+898DyP27dvI51O48MPP0QkEmEH8vj4OFwuF65cuYLLly/DbDbDZrOxxHCpQXuRxWKB0+nEysoKgsEgpqam4Ha70el00G63sb+/j+3tbezu7iKZTLIL2Jk3XkjtM5/PQ6fTweFwSO42fBIIggCNRoNgMAin04mlpSVMTk5CqVSiXC5jc3MTOzs7iMViqNVqrE5+1MaQ3JNerxfBYBAOhwNmsxmtVgu1Wg2pVAqxWAyVSkWyt2qe5+F2u9ntRhAEVCqVQ8/c7/fR7XZRr9dRq9VgMBjgdrvRbDZRLpdHYu6Q4dpsNrG5uQmTyQSr1Qqr1cqSC6Us8kgHMeUgTExMYGFhAdPT0/D5fNDr9UgmkyxXodfrwWazQa1WI5/Po1KpIJFIQBAElMtleL1eaLVaqNVqBINBXL58mRkxrVZLUjlERqMRY2NjbMwcDgfK5TJyuRxWV1cRj8ext7eHdDqNSqWCTqcDr9fLcsv6/T5bg1KUsxB78+x2O1wuFyYnJ+H3+6HRaJj3t1qtYm9vD2tra0yuATiZs/9UjRfaPNrtNkKhEHq9HsbHx0cu1vwwxHodL730EsbHx/H222/D7/ejXq8jlUrh448/xrVr1xCLxVAsFlksdlSgSU8qzQsLC5iZmcHExATcbjfW19eRTCaxs7OD9fV11Go1Sc4tQRBgNpuxtLSE8fFxljeWTqeRzWZZWK3b7aLVaqFYLCKXy8HhcGBxcZEl80rJI/UgyPVfLpfx0UcfwWQy4dKlSwgGg1AqlTAYDOA4TpLVabSn9Pt92O12jI2N4fXXX8evfvUr8DwPg8GAcrmMO3fuQKvVYnx8HCqVCsFgENVqFaFQCIlEAl999RVyuRxee+01zM7O4sKFC5iZmcGFCxcwNzeHjz76CKlUCtls9lBoZBjXHs0BCku//PLLmJmZwWuvvQaFQoG1tTXE43H8wz/8A/b395HJZNBoNFjV7MzMDHQ6HavOovJ78XknJcir6ff7MTk5iZWVFWbgdrtdpFIpZDIZXL9+HdeuXWNtSk6KJxI2IrnkVqt19z9VqQ4lEEr1hvwoiF2UPM/D6/XC6/WyHjV0KKVSKeTzeTaGowZZ+Q6HA06nE+Pj4xgbG4NGo0Gn00GxWGS3IKm3nuB5Hh6PB2azmSUMUjni4CFMBxjdrpVKpeQO6ochCHfbkXAch2AwyFS+c7kc6vU6yxOSyh40+PnSQUo5Co1GA+VyGfl8Hnt7e1CpVCgUCqxcvF6vY3t7G+l0GplMBuVyGXt7e2g2m9DpdOj3+8yDZbFYYLPZUKvVntLTngzkSeB5HmazGX6/HzMzM/B6vaxaZm1tDbFYDNlsFrVa7Qfl41qtFkajkXn76vU6S1SVyty6H1Q0olQqWTiI9p1er4d2u82KB06SJxI26vV6KBaLsFgsTE+ASqWl/KE+DFowCoUCBoMBDocDzz77LCYnJ2EwGNDpdHDjxg1sbW3h+++/RzgcHsnqIrpFarVaXLx4kd0ixeWKe3t7uHPnDhKJBBqNBvNASBGbzYbnnnsOGo0G6XQa8XgchUKB3XxJeVksRqbT6WAymViexygZMIJwt42CTqfDm2++iWAwiEwmwyq0KpWKJMOw9Dl3Oh1Uq1UmcJlKpbC9vY1MJoO1tTW0221m+CqVSrRaLezt7bGqvX6/j2QyCaVSiZ2dHTzzzDN45513sLS0hLGxMUxNTaHT6RwqCx62CynlBdlsNly8eBHLy8t47733AACxWAzhcBh/8zd/g1gshlKpxOYLNRoWBAFWqxV+vx8qlYp5PJPJ5KGfGwVo3pHR3Gq10Gw2WS+ok+KJJewOiv3IHG66KK4sMpvNqNVqaLVaiMViiEajJybsM2yIw0U6nQ5utxt+vx9ms5kJHtKGTOWbUlZuppYRRqMRvV4P2WwWxWKRNYV7UBa/1MbhceA4jumaGI1G6HQ65PN5JJNJ1Ot15pWQ2hjRTZgMmGw2i83NTeTzeYRCIeTzeaRSKXQ6HWa8UMiRPAZiMTsAqNVqqFar7PDW6XSwWq0wGAxQqVRD6/EUl0QHAgG43W5otVrU63VEo1FEo1EUi0VWYi9WGaY1p9VqwfM8FArFobJgqV6iiAddhKhM+rTCsk/EeKGbs/jvow5tFFQJ8d5772FiYgLnz58Hz/O4fv06otEofv/732NzcxPNZpMlg40KdGNWqVQwGAxwuVx45ZVXWDa7TqfDwcEBUqkUPv/8c3z++efM6wJI78CmDVav18Pj8SCRSODmzZuIxWJoNpusUo1+Vvy6UV1zFJd3uVws1KZUKrG6uoqdnR2k02m0221JtQWgz5u60FPCLWm0tFotVCoVNJtNFIvF+xr64vwP8dcoBaDdbqPb7cJisWBhYQHlchkGg4Ed2uL3ctYhj5Narcb4+DjeeustOBwOAEAqlcI//uM/snLfRqNxyCikcJFKpYLNZoPL5UKtVkOpVEKz2ZScN0+MuEKWDBSaN/1+H7lcDolEgpVGnzRPxHihxDn6IKX6YT4q4nI8avLl8Xjgdruh1+uhVCrZAiB3r9jSHxXEqo0ejwc+nw9OpxM2mw0KhQKdTgeFQgGpVArFYpFV20gRsQQ36XUAd2/C9Xpdss99HMSKslarFWazmVU/lstlFAoFSYgZig0NhULBRECpMqjT6aDT6TAvZbfbRbPZZKWsdJES8yAjuNvtshwG8syo1WqWwzhs+5M4rEolvy6XC1qtFqVSCYVCAZlMBrlcjgnNiV8LgHlcqOknKV03Go2hG4/Hhc72wYu1WAPopBN1iSfS24hyOijmDoBN9FG8FYpj8C6XC2NjY7h48SK8Xi90Oh2azSYT9alWq+h0OidSFz8siG80VqsVPp8PP//5zxEMBnHhwgVYLBY0m01Uq1VcvXoVGxsbiEQiklUcpvWhUqmg1WrZTa/b7bK2APc7gEYZcbk4z/MsV6rX66FUKiESiSAcDqPZbD7tt3osBi9COp0OV65cwfj4ONsv9vf3sb+/j2KxiGw2C+CeUULCc/dj0HABgHK5jFQqhWQyiXg8zvpo0fwbtjlIXoOJiQm8+uqruHDhAhYWFpBMJvHpp59id3cX29vbKJVKLAREY0FVRoFAAD6fD9PT0/B6vVhdXcXvf/97RKNRSe/bNOdIR0qtVrO8ln6/j93dXdy6dYtpTp00T8zzQv2MgHsVEOIOpaMIKTiazWZYLBamgFqtVlEqlVhiGDEq40QeBp1OB6fTCZ/Ph4mJCfh8PhiNRqhUKtRqNRQKBaTTaZa7IGUjmKqteJ5n5ZiCIKDZbA79AXwaiD1VWq0WTqcTVquVjRd5rKTUHJYqPux2O4LBIAvtxONxdkiTcCN5Vn6ssUHhAvKO0qE1TGMpNrgsFgsCgQAcDgfzViUSCaTTaVaRBtzbg8Xzy2q1wuPxQKfTAbjrDc1ms6hWq0M1Ho8DeVxIwdtkMjFDjXKkarUaS4Y/DU7VeKEJrtPpmFXaaDRQLBZRLpdRq9VGLglVnIBqt9vx/PPPY25uDj6fDwqFAt9++y1isRg+++wzJj89bJvCj2WwMmZmZga/+MUv4Pf78ZOf/AQGgwFarRaNRgNffvklQqEQrl27hr29PRaLluI4kQfB4XDgmWeewfT0NOsdQv2MaCOmzWPQkBsFXRdCLKBlMpkQCARw+fJlGAwGpvIZiUSQzWYloZckzllRqVSYnZ3FysoKQqEQUqkUarUaIpEIer0e87Q8zjqhfZw0hnw+H/x+PyYmJmAymcDzPCKRCNP3aDQaZ/5SSs/jdDrhcDhw/vx5XLlyhQn3bW1t4Q9/+AOy2eyhZGZ6LQB2kXjhhRdw+fJlaLVahMNh5tUjQ1FKiCs/rVYrJiYm8JOf/ARerxdmsxkKhQKVSoWF8UlQFTj5y/ep72bisBGV/7ZaLdZjRcq35UHEsWmVSgW9Xg+v1wuHw8HUPjOZDJLJJNLpNPL5vCQXwFFQgqXFYoHL5cL09DQmJyfhdDphNpvR6/VQr9eRSCTYAVQqldg4SXGsxGE06ulECYOUPAn80M1PBzP9rLjXk9ShfYcE2ex2O0wmE6uwodv0WT9kHwf6zI1GI2w2G3Q6HctToVwXcd7hozz34Hyhii2j0cjyPCgxeBgvWeLO2k6nExqNhmngkLbWoD6JONeFCgm8Xi8AIJ/Po1wuo9FoSLqHGF0MKEnZbrczte96vc4Swk9TQ+mJ6LxQVQ3HcSxRjoS11Gr1yNwI6QZtNpvh8Xhw7tw5vPXWW7Db7RAEAYVCAbdu3cL+/j5rOw9IQ277USCrfmJiAr/85S8xMTGB5557DgaDAWq1Go1GA7dv30Y8HsdHH32E/f19FAoFySeC07hQfJlctIIgoNFoHAob0WFM4UgSPVxdXcXu7i4qlcqJ6y2cNchQs1gsmJ6exvT0NFwuF6rVKq5du4b9/X3UajW2J0nFoFMqlcyVr9VqmaEvrv55VMRaHTT3qC3AuXPn4HQ60e/3Ua1WkUwmUSgUWLn+MEB7hd/vx3PPPYfFxUUEg0Hs7u7iiy++wNraGutZBICFaenParUai4uL8Pl8GBsbg9VqxY0bN3Dr1i3s7OxIVjeILt6BQADvvvsuxsfHEQgEmHRDuVzGZ599hkgkgvX1dcRisVOr5ntixgttmJSpTm3Cj0oYkyoajQZWqxVOpxPBYBAGg4FtNMlk8tDNkDbYUcJsNmN+fp7p3tAYdDodJJNJRKNRhMNhtjBGBUrYFWf2i/VdgHvGCyVvUgVEs9lk1TVSNlyAe8aeSqViVUZarRbVahWJRALxeBzdbncoPQVHQXst7bf9fp9pjQwiNtgGx+B+IUeaT1arFS6XCzqdjunBlEolpjg7bPpK1PPLZrOxfTgejyOTyTDPwaDAnFKphEajgcvlQiAQgMFgAHDX63JwcPDA8vNhRpwYTl6XyclJZrhotVoWVYnFYjg4OEChUECtVju1dXYqloO4RJESelwuF6xWK4rFIts8pfLBPgwaD3JhT05O4oUXXsDS0hJMJhNarRa+++47hEIhrK+vI5FIoNPpjIyuizgsQqERr9fLXJGk0JxIJPDJJ58gHA4jk8mwVglSP4zFiA8o8S+CtF6oWVq73UYul0M4HMbOzg6q1apkc4PEUM6L1WoFz/Mol8soFovMUyXFZnmDUJIuHTjkkSJoDgyWuJKxolar4Xa7YTAYMDY2BrvdjldeeQXPPvssms0mvv76a9y5cwfXr19HOBxGrVY7FOY+63NM/D7JA0cXSMojA8C8SZTrce7cObjdbrz99tsYGxtDKBTCrVu38OWXX2J7exuNRkOSexKtKbPZDIfDwcT8NBoNADBDNhaLIRKJoF6vs5DRacyFUx1hseqg0WiEwWAYiY1zEDqcNRoNTCYTXC4Xpqam4PP5oNFo0O12mTchmUwil8sdqZYqRWhhGI1GVn2l1+tZ2V2tVkM+n8fu7i52d3eZyqdUXP6PyuCNWTxHxIm6lJfQ7XZZZRYlH47KvBJrnlCvGfL6Sn3eiJ+PwhwU+qDviX8f/EVePpfLhWAwiPn5eZw7dw6Tk5Pw+XzodDo4ODjA1tYWbt++jVAohFarNTRhIzHitdDtdlmi6eD4AGAhk+npaczPz2N2dhaFQgG3b9/GwcEBMpkM6vW65EJGBOVqGgwGWCwWln8H3FPULRaLT0RD6dQ8L2SlWiwW1sRLr9ezZJ5RqjKiG5DNZsOzzz6L8+fP4yc/+QlMJhPrGr26uopIJMKys6U6+e8HzReXy4Xl5WUsLi7C6XSyrr/VahXfffcd9vf3EQ6HkUqlRsq4o8TbSqWC/f192O12VsVHLeiTySS7VSuVSoyNjcHv90Or1bKS2VGRKlepVNDpdAgEArh06RIMBgNisRhisdihREqpQGug0WgAAHZ2dlhY+p133oHH48H4+DiTFqCSe+p5RLktANjl0mAwgOd5XL58manGVqtV/OEPf8DHH3+MRCLBtF4SicSh8O0wrEl6j1TZqFAoWGlvuVxmRj7P86xly8LCAqxWKy5dugSTyYSdnR3cvn0b169fx9bWFvL5vOT3JBqHmZkZuN1umEwmAHcNl0gkgmg0ikgkgmQyeerRg1NLOBmsMhKXuZI7aVSgzcFkMmF6ehpzc3NYXl5m3ZDJo0Ay7+KSxlGAbjYWiwVzc3MIBoOwWq2sgqHZbGJvbw97e3tMlE3clVzqkIelVqshkUigWCwCAIu7k5YH5XEoFAp4PB4Eg0EmZifW5ZDq5krPqFarodfr4XA4MDc3h36/j62tLaTTaTSbTXS73UMKssOctCv+LEnxNhqNwmaz4dVXX8X58+eh1+thsVgQj8ext7cHnU4Hs9mMRqOBTCbDGugBYB4aypW6cOECfD4fbty4gWKxiK2tLUQiESZ3QZo5lMg5THOLwmPk4SUFYso35Li7vZu8Xi9cLhf+5E/+BG63GzMzM1AoFHj//fexvb2NtbU1hMNhySfCA3dzhMbHx+H3+2G1WllFW6fTQSqVQjweRzabRT6fP/U9+tR1XkhGutVqQRAE1Gq1Q/ouwzTZHxexq1GlUrHyX4vFAo7j0Gq1WNOvTCbDVBylPCZixOE0lUoFr9fLEnXFcuP9fp/lKgzrIXMcaAOo1WoIhUKsKqLb7eKtt95CPB4HADSbTej1etjtdjz33HPwer3Y29tDNptllWujAAnTmUwmeL1elMtlxGIxxONx1huKkFIIiXJadnd3mTR9NpuFVqvF5OQkq0CjLtImkwkTExNoNBqIxWJQKpUYHx+HWq1GpVJBp9PBzZs38c0337Au1CRNQHIXZAgO454lCALa7TY7j3iex/j4ON544w2Wv2EwGDA7Owuz2Yzx8XGoVCocHBygXC7jzp072NvbQ6VSkexZRhcCs9kMm82GxcVFvPDCC/B6vUzMj8rKv/rqK0SjUWbMnjanYryIbzPtdvuQpsug8SJ1SLdErVbDbDbD7XbDaDQySz8SiRzSKxklmXfxTdloNMLr9WJubg5ut/uQ1U56JmQAjxoUNqrVaiiXyzg4OMDu7i4cDgd++tOfIhKJIB6Po9FowGazweFw4OLFi7Db7VhdXWUbrNQhY5iMF5pT/X6fGS/kVaC5R/IFw7wX0Xun8DR5cbvdLpLJJK5cuYLLly/D4XDA7XYjl8shFArBbDZjZmYG5XIZ7XYbGo0Gly5dglarxe7uLvL5PD788EMWBiiXy6x0WqyoO4wif7SPtNttprSs1WoRDAbx+uuvs8opk8mE2dlZqFQqNJtNlMtl3LhxA+FwGGtrawiFQpIOx9I6MRqNmJiYwMLCAlZWVqDX66FWq1mpfCgUwtdff81a2jwJY+7UPC90Y261Wsy1SIk+1NlV6geRuOqKxJ2o+qFeryOTybAqo1qtdkg0a5g300eFnrPf76PdbiOfz2N/fx+CILCQh1KpRK/XY0aw1OfMw6Db9FdffYVgMAiLxQKdToe3336bXRBUKhWLPa+vr2N7exvValWyG+yDoI2X53ksLS3B5XLB6XSiUqlga2sL5XKZGcRSWG/0DDQPIpEIS6KlZG0ygNPpNEwmEzKZDCqVCtbW1qBSqdBut5l3oVKpIBKJsORLcYK4uDfdsCIIAorFIkKhELxeL1MzDwaDzLDlOA6FQgGNRgPr6+ts7aVSKXbZBDD0Y/EgxBpTFosFJpOJiRKSAGIul0Mul0O1WmXe8ScxFqduvNTrdeZtoe6u9Of7yZhLDfK86PV61gODSjcjkQiuXr2KdDrN+hgNqwv2x6JQKFiX21QqhbW1NSgUCjzzzDOsdJq+T2qNowptkIlEAh988AGWl5exsLAAn8+Hv/qrv4JCoWA5MZ9++imi0Si+/vprhEIhAKNVUg4cvjWurKyg0Wgw9dRSqYR2u806kUvBgKHPl8L0Ozs72N/fx/b2NpxOJ9PWolAJz/Pwer1oNps4ODgAx3HY3NwEACSTSdZ5WtyQUCr7E33mmUwG6+vr8Hg8yOVysFgsmJ2dBQDWfXx9fR3xeBzvv/8+4vE4NjY2DnkXpGq4AD9U96Yq0EHtLfLM1Wq1JzYepxY2Au4lqubzeVy9ehU8z7NknmazKZmFcBQcx6HX66HZbCIUCuHzzz9nSWK7u7usgRf9rNTHQwyFFmme5HI5bGxsMFVLCrclk0lWhihlF+2jQPOp0WggmUziyy+/hNPpRCaTgUKhQC6XQ6VSwerqKrsNiQ9mKc8vWj/UFC6VSuHrr79mmy0lFZK3odFoSDJ8TeuKDP16vY5sNsu+RgZMv99HJpNBp9NhFVilUgnA3XAKVSCJ9yWpjBU9B3nANzY28Pvf/561VgDAJBoODg6Qz+cRDodRLBYP9eqRyng8jF6vx6IolUqFFZTkcjnE43FWXfQkLwHcUZ4Po9F4LLeI2Gpzu93slt3tdlEoFE6sb0+1Wn0qM8hgMDx0fChspFAoWNhIXNqYzWYPKVOeBrVa7amMj1arfaSHEofXVCoV60VDxm2n00Eul0On02ECYye5QFqt1lMZH5PJ9KM+dLEWh16vZ12TOY5jpcDk6qeE1ONstJVK5cyur0HEz+rz+fDKK69gYmICf/qnf4pGo4G/+7u/Yx4pqto6Lk9rfen1+iPHh/YUakJIiHMSaS0NNh0Ul/wed63V6/UnPj6Pe3bR3qxWq1l3aDL0qHfaaXjHn9bZ9ajjQ5Vos7OzuHjxIp555hm88847LJwfCoXw61//GslkEpubm2g0GidadXbU+JxqtZE4kYwysimWOCruf/Em0Wq1UC6X2fc6nc6pGy7DgNhTRyGiUqnENtVut8vKQMU/P+r0+31WWl8oFADcnVN0sz6tbq5nGfFcajabLO/j+vXraLVaTJ35Sd8ShxWpj484cZf6ftXrdQBg51Sj0WCJyqMG7cEU1tdqtbh+/ToTD02lUkin0yiXy0/cK36qnhdiMLflpN1tZ9nzIuZ+OT5P4sM+654XMYPqn8Rpuq2HzfNCHJUzdpLjNUyel0Fo86X+NJTDcZIXhrPqeSHu96yD2jZio0/895NgGDwvT2otDXLWPS8E6UcNVpcJgsAuAqdxCX9qnhcxDzqMRo37bRgyhxmUMB+l2PKPZdArJY/XvZw74G4Sq3hsRmlOPeg57/f1URmT+zF4cRKPxSiPC3CvDP9+RsrTWk9PxHgZVi2Ak0Yeh4czSofKcTkqkVIeQ3m9yTw68lx5OGdtbx4dDXoZGQlzljYVGRkZmdPmyJwXGRkZGRkZGZmzxugKZsjIyMjIyMgMJbLxIiMjIyMjIzNUyMaLjIyMjIyMzFAhGy8yMjIyMjIyQ4VsvMjIyMjIyMgMFbLxIiMjIyMjIzNU/P+dOQLkYajSzAAAAABJRU5ErkJggg==\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": 43,
   "metadata": {
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   "outputs": [
    {
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      "text/plain": [
       "<Figure size 72x72 with 1 Axes>"
      ]
     },
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    }
   ],
   "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": 44,
   "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": null,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "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": 45,
   "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://lh3.google.com/u/0/d/14Xy_Mb17CZVgzVAgq7NCjMVBvSae3xO1\" align=\"center\" alt=\"unlock_cells.gif\">\n",
    "</details>"
   ]
  }
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