Advanced Learning Algorithms/C2_W1_Assignment.ipynb

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    "# Practice Lab: Neural Networks for Handwritten Digit Recognition, Binary\n",
    "\n",
    "In this exercise, you will use a neural network to recognize the hand-written digits zero and one.\n",
    "\n",
    "\n",
    "# Outline\n",
    "- [ 1 - Packages ](#1)\n",
    "- [ 2 - Neural Networks](#2)\n",
    "  - [ 2.1 Problem Statement](#2.1)\n",
    "  - [ 2.2 Dataset](#2.2)\n",
    "  - [ 2.3 Model representation](#2.3)\n",
    "  - [ 2.4 Tensorflow Model Implementation](#2.4)\n",
    "    - [ Exercise 1](#ex01)\n",
    "  - [ 2.5 NumPy Model Implementation (Forward Prop in NumPy)](#2.5)\n",
    "    - [ Exercise 2](#ex02)\n",
    "  - [ 2.6 Vectorized NumPy Model Implementation (Optional)](#2.6)\n",
    "    - [ Exercise 3](#ex03)\n",
    "  - [ 2.7 Congratulations!](#2.7)\n",
    "  - [ 2.8 NumPy Broadcasting Tutorial (Optional)](#2.8)\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "_**NOTE:** To prevent errors from the autograder, you are not allowed to edit or delete non-graded cells in this notebook . Please also refrain from adding any new cells. \n",
    "**Once you have passed this assignment** and want to experiment with any of the non-graded code, you may follow the instructions at the bottom of this notebook._"
   ]
  },
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   "source": [
    "<a name=\"1\"></a>\n",
    "## 1 - Packages \n",
    "\n",
    "First, let's run the cell below to import all the packages that you will need during this assignment.\n",
    "- [numpy](https://numpy.org/) is the fundamental package for scientific computing with Python.\n",
    "- [matplotlib](http://matplotlib.org) is a popular library to plot graphs in Python.\n",
    "- [tensorflow](https://www.tensorflow.org/) a popular platform for machine learning."
   ]
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   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "import matplotlib.pyplot as plt\n",
    "from autils import *\n",
    "%matplotlib inline\n",
    "\n",
    "import logging\n",
    "logging.getLogger(\"tensorflow\").setLevel(logging.ERROR)\n",
    "tf.autograph.set_verbosity(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Tensorflow and Keras**  \n",
    "Tensorflow is a machine learning package developed by Google. In 2019, Google integrated Keras into Tensorflow and released Tensorflow 2.0. Keras is a framework developed independently by François Chollet that creates a simple, layer-centric interface to Tensorflow. This course will be using the Keras interface. "
   ]
  },
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   "source": [
    "<a name=\"2\"></a>\n",
    "## 2 - Neural Networks\n",
    "\n",
    "In Course 1, you implemented logistic regression. This was extended to handle non-linear boundaries using polynomial regression. For even more complex scenarios such as image recognition, neural networks are preferred.\n",
    "\n",
    "<a name=\"2.1\"></a>\n",
    "### 2.1 Problem Statement\n",
    "\n",
    "In this exercise, you will use a neural network to recognize two handwritten digits, zero and one. This is a binary classification task. Automated handwritten digit recognition is widely used today - from recognizing zip codes (postal codes) on mail envelopes to recognizing amounts written on bank checks. You will extend this network to recognize all 10 digits (0-9) in a future assignment. \n",
    "\n",
    "This exercise will show you how the methods you have learned can be used for this classification task.\n",
    "\n",
    "<a name=\"2.2\"></a>\n",
    "### 2.2 Dataset\n",
    "\n",
    "You will start by loading the dataset for this task. \n",
    "- The `load_data()` function shown below loads the data into variables `X` and `y`\n",
    "\n",
    "\n",
    "- The data set contains 1000 training examples of handwritten digits $^1$, here limited to zero and one.  \n",
    "\n",
    "    - Each training example is a 20-pixel x 20-pixel grayscale image of the digit. \n",
    "        - Each pixel is represented by a floating-point number indicating the grayscale intensity at that location. \n",
    "        - The 20 by 20 grid of pixels is “unrolled” into a 400-dimensional vector. \n",
    "        - Each training example becomes a single row in our data matrix `X`. \n",
    "        - This gives us a 1000 x 400 matrix `X` where every row is a training example of a handwritten digit image.\n",
    "\n",
    "$$X = \n",
    "\\left(\\begin{array}{cc} \n",
    "--- (x^{(1)}) --- \\\\\n",
    "--- (x^{(2)}) --- \\\\\n",
    "\\vdots \\\\ \n",
    "--- (x^{(m)}) --- \n",
    "\\end{array}\\right)$$ \n",
    "\n",
    "- The second part of the training set is a 1000 x 1 dimensional vector `y` that contains labels for the training set\n",
    "    - `y = 0` if the image is of the digit `0`, `y = 1` if the image is of the digit `1`.\n",
    "\n",
    "$^1$<sub> This is a subset of the MNIST handwritten digit dataset (http://yann.lecun.com/exdb/mnist/)</sub>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "deletable": false,
    "editable": false
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   "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`.  "
   ]
  },
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   "execution_count": 3,
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     "text": [
      "The first element of X is:  [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  8.56059680e-06\n",
      "  1.94035948e-06 -7.37438725e-04 -8.13403799e-03 -1.86104473e-02\n",
      " -1.87412865e-02 -1.87572508e-02 -1.90963542e-02 -1.64039011e-02\n",
      " -3.78191381e-03  3.30347316e-04  1.27655229e-05  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  1.16421569e-04  1.20052179e-04\n",
      " -1.40444581e-02 -2.84542484e-02  8.03826593e-02  2.66540339e-01\n",
      "  2.73853746e-01  2.78729541e-01  2.74293607e-01  2.24676403e-01\n",
      "  2.77562977e-02 -7.06315478e-03  2.34715414e-04  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  1.28335523e-17 -3.26286765e-04 -1.38651604e-02\n",
      "  8.15651552e-02  3.82800381e-01  8.57849775e-01  1.00109761e+00\n",
      "  9.69710638e-01  9.30928598e-01  1.00383757e+00  9.64157356e-01\n",
      "  4.49256553e-01 -5.60408259e-03 -3.78319036e-03  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  5.10620915e-06\n",
      "  4.36410675e-04 -3.95509940e-03 -2.68537241e-02  1.00755014e-01\n",
      "  6.42031710e-01  1.03136838e+00  8.50968614e-01  5.43122379e-01\n",
      "  3.42599738e-01  2.68918777e-01  6.68374643e-01  1.01256958e+00\n",
      "  9.03795598e-01  1.04481574e-01 -1.66424973e-02  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  2.59875260e-05\n",
      " -3.10606987e-03  7.52456076e-03  1.77539831e-01  7.92890120e-01\n",
      "  9.65626503e-01  4.63166079e-01  6.91720680e-02 -3.64100526e-03\n",
      " -4.12180405e-02 -5.01900656e-02  1.56102907e-01  9.01762651e-01\n",
      "  1.04748346e+00  1.51055252e-01 -2.16044665e-02  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  5.87012352e-05 -6.40931373e-04\n",
      " -3.23305249e-02  2.78203465e-01  9.36720163e-01  1.04320956e+00\n",
      "  5.98003217e-01 -3.59409041e-03 -2.16751770e-02 -4.81021923e-03\n",
      "  6.16566793e-05 -1.23773318e-02  1.55477482e-01  9.14867477e-01\n",
      "  9.20401348e-01  1.09173902e-01 -1.71058007e-02  0.00000000e+00\n",
      "  0.00000000e+00  1.56250000e-04 -4.27724104e-04 -2.51466503e-02\n",
      "  1.30532561e-01  7.81664862e-01  1.02836583e+00  7.57137601e-01\n",
      "  2.84667194e-01  4.86865128e-03 -3.18688725e-03  0.00000000e+00\n",
      "  8.36492601e-04 -3.70751123e-02  4.52644165e-01  1.03180133e+00\n",
      "  5.39028101e-01 -2.43742611e-03 -4.80290033e-03  0.00000000e+00\n",
      "  0.00000000e+00 -7.03635621e-04 -1.27262443e-02  1.61706648e-01\n",
      "  7.79865383e-01  1.03676705e+00  8.04490400e-01  1.60586724e-01\n",
      " -1.38173339e-02  2.14879493e-03 -2.12622549e-04  2.04248366e-04\n",
      " -6.85907627e-03  4.31712963e-04  7.20680947e-01  8.48136063e-01\n",
      "  1.51383408e-01 -2.28404366e-02  1.98971950e-04  0.00000000e+00\n",
      "  0.00000000e+00 -9.40410539e-03  3.74520505e-02  6.94389110e-01\n",
      "  1.02844844e+00  1.01648066e+00  8.80488426e-01  3.92123945e-01\n",
      " -1.74122413e-02 -1.20098039e-04  5.55215142e-05 -2.23907271e-03\n",
      " -2.76068376e-02  3.68645493e-01  9.36411169e-01  4.59006723e-01\n",
      " -4.24701797e-02  1.17356610e-03  1.88929739e-05  0.00000000e+00\n",
      "  0.00000000e+00 -1.93511951e-02  1.29999794e-01  9.79821705e-01\n",
      "  9.41862388e-01  7.75147704e-01  8.73632241e-01  2.12778350e-01\n",
      " -1.72353349e-02  0.00000000e+00  1.09937426e-03 -2.61793751e-02\n",
      "  1.22872879e-01  8.30812662e-01  7.26501773e-01  5.24441863e-02\n",
      " -6.18971913e-03  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00 -9.36563862e-03  3.68349741e-02  6.99079299e-01\n",
      "  1.00293583e+00  6.05704402e-01  3.27299224e-01 -3.22099249e-02\n",
      " -4.83053002e-02 -4.34069138e-02 -5.75151144e-02  9.55674190e-02\n",
      "  7.26512627e-01  6.95366966e-01  1.47114481e-01 -1.20048679e-02\n",
      " -3.02798203e-04  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00 -6.76572712e-04 -6.51415556e-03  1.17339359e-01\n",
      "  4.21948410e-01  9.93210937e-01  8.82013974e-01  7.45758734e-01\n",
      "  7.23874268e-01  7.23341725e-01  7.20020340e-01  8.45324959e-01\n",
      "  8.31859739e-01  6.88831870e-02 -2.77765012e-02  3.59136710e-04\n",
      "  7.14869281e-05  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  1.53186275e-04  3.17353553e-04 -2.29167177e-02\n",
      " -4.14402914e-03  3.87038450e-01  5.04583435e-01  7.74885876e-01\n",
      "  9.90037446e-01  1.00769478e+00  1.00851440e+00  7.37905042e-01\n",
      "  2.15455291e-01 -2.69624864e-02  1.32506127e-03  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  2.36366422e-04\n",
      " -2.26031454e-03 -2.51994485e-02 -3.73889910e-02  6.62121228e-02\n",
      "  2.91134498e-01  3.23055726e-01  3.06260315e-01  8.76070942e-02\n",
      " -2.50581917e-02  2.37438725e-04  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  6.20939216e-18  6.72618320e-04 -1.13151411e-02\n",
      " -3.54641066e-02 -3.88214912e-02 -3.71077412e-02 -1.33524928e-02\n",
      "  9.90964718e-04  4.89176960e-05  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]\n"
     ]
    }
   ],
   "source": [
    "print ('The first element of X is: ', X[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first element of y is:  0\n",
      "The last element of y is:  1\n"
     ]
    }
   ],
   "source": [
    "print ('The first element of y is: ', y[0,0])\n",
    "print ('The last element of y is: ', y[-1,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"toc_89367_2.2.2\"></a>\n",
    "#### 2.2.2 Check the dimensions of your variables\n",
    "\n",
    "Another way to get familiar with your data is to view its dimensions. Please print the shape of `X` and `y` and see how many training examples you have in your dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of X is: (1000, 400)\n",
      "The shape of y is: (1000, 1)\n"
     ]
    }
   ],
   "source": [
    "print ('The shape of X is: ' + str(X.shape))\n",
    "print ('The shape of y is: ' + str(y.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"toc_89367_2.2.3\"></a>\n",
    "#### 2.2.3 Visualizing the Data\n",
    "\n",
    "You will begin by visualizing a subset of the training set. \n",
    "- In the cell below, the code randomly selects 64 rows from `X`, maps each row back to a 20 pixel by 20 pixel grayscale image and displays the images together. \n",
    "- The label for each image is displayed above the image "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 64 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "# You do not need to modify anything in this cell\n",
    "\n",
    "m, n = X.shape\n",
    "\n",
    "fig, axes = plt.subplots(8,8, figsize=(8,8))\n",
    "fig.tight_layout(pad=0.1)\n",
    "\n",
    "for i,ax in enumerate(axes.flat):\n",
    "    # Select random indices\n",
    "    random_index = np.random.randint(m)\n",
    "    \n",
    "    # Select rows corresponding to the random indices and\n",
    "    # reshape the image\n",
    "    X_random_reshaped = X[random_index].reshape((20,20)).T\n",
    "    \n",
    "    # Display the image\n",
    "    ax.imshow(X_random_reshaped, cmap='gray')\n",
    "    \n",
    "    # Display the label above the image\n",
    "    ax.set_title(y[random_index,0])\n",
    "    ax.set_axis_off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"2.3\"></a>\n",
    "### 2.3 Model representation\n",
    "\n",
    "The neural network you will use in this assignment is shown in the figure below. \n",
    "- This has three dense layers with sigmoid activations.\n",
    "    - Recall that our inputs are pixel values of digit images.\n",
    "    - Since the images are of size $20\\times20$, this gives us $400$ inputs  \n",
    "    \n",
    "<img src=\"images/C2_W1_Assign1.PNG\" width=\"500\" height=\"400\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The parameters have dimensions that are sized for a neural network with $25$ units in layer 1, $15$ units in layer 2 and $1$ output unit in layer 3. \n",
    "\n",
    "    - Recall that the dimensions of these parameters are determined as follows:\n",
    "        - If network has $s_{in}$ units in a layer and $s_{out}$ units in the next layer, then \n",
    "            - $W$ will be of dimension $s_{in} \\times s_{out}$.\n",
    "            - $b$ will a vector with $s_{out}$ elements\n",
    "  \n",
    "    - Therefore, the shapes of `W`, and `b`,  are \n",
    "        - layer1: The shape of `W1` is (400, 25) and the shape of `b1` is (25,)\n",
    "        - layer2: The shape of `W2` is (25, 15) and the shape of `b2` is: (15,)\n",
    "        - layer3: The shape of `W3` is (15, 1) and the shape of `b3` is: (1,)\n",
    ">**Note:** The bias vector `b` could be represented as a 1-D (n,) or 2-D (1,n) array. Tensorflow utilizes a 1-D representation and this lab will maintain that convention. \n",
    "               "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"2.4\"></a>\n",
    "### 2.4 Tensorflow Model Implementation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tensorflow models are built layer by layer. A layer's input dimensions ($s_{in}$ above) are calculated for you. You specify a layer's *output dimensions* and this determines the next layer's input dimension. The input dimension of the first layer is derived from the size of the input data specified in the `model.fit` statement below. \n",
    ">**Note:** It is also possible to add an input layer that specifies the input dimension of the first layer. For example:  \n",
    "`tf.keras.Input(shape=(400,)),    #specify input shape`  \n",
    "We will include that here to illuminate some model sizing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"ex01\"></a>\n",
    "### Exercise 1\n",
    "\n",
    "Below, using Keras [Sequential model](https://keras.io/guides/sequential_model/) and [Dense Layer](https://keras.io/api/layers/core_layers/dense/) with a sigmoid activation to construct the network described above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "deletable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"my_model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_4 (Dense)             (None, 25)                10025     \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 15)                390       \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 1)                 16        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 10,431\n",
      "Trainable params: 10,431\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# UNQ_C1\n",
    "# GRADED CELL: Sequential model\n",
    "\n",
    "model = Sequential(\n",
    "    [               \n",
    "        tf.keras.Input(shape=(400,)),    # specify input size\n",
    "        \n",
    "        ### START CODE HERE ### \n",
    "        \n",
    "        tf.keras.layers.Dense(units=25, activation='sigmoid'),\n",
    "        tf.keras.layers.Dense(units=15, activation='sigmoid'),\n",
    "        tf.keras.layers.Dense(units=1, activation='sigmoid'),\n",
    "        \n",
    "        ### END CODE HERE ### \n",
    "    ], name=\"my_model\" \n",
    ")\n",
    "\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"my_model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_4 (Dense)             (None, 25)                10025     \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 15)                390       \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 1)                 16        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 10,431\n",
      "Trainable params: 10,431\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><font size=\"3\" color=\"darkgreen\"><b>Expected Output (Click to Expand) </b></font></summary>\n",
    "The `model.summary()` function displays a useful summary of the model. Because we have specified an input layer size, the shape of the weight and bias arrays are determined and the total number of parameters per layer can be shown. Note, the names of the layers may vary as they are auto-generated.  \n",
    "    \n",
    "    \n",
    "```\n",
    "Model: \"my_model\"\n",
    "_________________________________________________________________\n",
    "Layer (type)                 Output Shape              Param #   \n",
    "=================================================================\n",
    "dense (Dense)                (None, 25)                10025     \n",
    "_________________________________________________________________\n",
    "dense_1 (Dense)              (None, 15)                390       \n",
    "_________________________________________________________________\n",
    "dense_2 (Dense)              (None, 1)                 16        \n",
    "=================================================================\n",
    "Total params: 10,431\n",
    "Trainable params: 10,431\n",
    "Non-trainable params: 0\n",
    "_________________________________________________________________\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><font size=\"3\" color=\"darkgreen\"><b>Click for hints</b></font></summary>\n",
    "As described in the lecture:\n",
    "    \n",
    "```python\n",
    "model = Sequential(                      \n",
    "    [                                   \n",
    "        tf.keras.Input(shape=(400,)),    # specify input size (optional)\n",
    "        Dense(25, activation='sigmoid'), \n",
    "        Dense(15, activation='sigmoid'), \n",
    "        Dense(1,  activation='sigmoid')  \n",
    "    ], name = \"my_model\"                                    \n",
    ")                                       \n",
    "``` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92mAll tests passed!\n"
     ]
    }
   ],
   "source": [
    "# UNIT TESTS\n",
    "from public_tests import *\n",
    "\n",
    "test_c1(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The parameter counts shown in the summary correspond to the number of elements in the weight and bias arrays as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "L1 params =  10025 , L2 params =  390 ,  L3 params =  16\n"
     ]
    }
   ],
   "source": [
    "L1_num_params = 400 * 25 + 25  # W1 parameters  + b1 parameters\n",
    "L2_num_params = 25 * 15 + 15   # W2 parameters  + b2 parameters\n",
    "L3_num_params = 15 * 1 + 1     # W3 parameters  + b3 parameters\n",
    "print(\"L1 params = \", L1_num_params, \", L2 params = \", L2_num_params, \",  L3 params = \", L3_num_params )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can examine details of the model by first extracting the layers with `model.layers` and then extracting the weights with `layerx.get_weights()` as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "[layer1, layer2, layer3] = model.layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 shape = (400, 25), b1 shape = (25,)\n",
      "W2 shape = (25, 15), b2 shape = (15,)\n",
      "W3 shape = (15, 1), b3 shape = (1,)\n"
     ]
    }
   ],
   "source": [
    "#### Examine Weights shapes\n",
    "W1,b1 = layer1.get_weights()\n",
    "W2,b2 = layer2.get_weights()\n",
    "W3,b3 = layer3.get_weights()\n",
    "print(f\"W1 shape = {W1.shape}, b1 shape = {b1.shape}\")\n",
    "print(f\"W2 shape = {W2.shape}, b2 shape = {b2.shape}\")\n",
    "print(f\"W3 shape = {W3.shape}, b3 shape = {b3.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**\n",
    "```\n",
    "W1 shape = (400, 25), b1 shape = (25,)  \n",
    "W2 shape = (25, 15), b2 shape = (15,)  \n",
    "W3 shape = (15, 1), b3 shape = (1,)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`xx.get_weights` returns a NumPy array. One can also access the weights directly in their tensor form. Note the shape of the tensors in the final layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<tf.Variable 'dense_6/kernel:0' shape=(15, 1) dtype=float32, numpy=\n",
      "array([[-0.22111148],\n",
      "       [ 0.20680875],\n",
      "       [ 0.37256718],\n",
      "       [-0.5701437 ],\n",
      "       [-0.03297621],\n",
      "       [-0.21993354],\n",
      "       [-0.45910195],\n",
      "       [-0.14314401],\n",
      "       [-0.42263383],\n",
      "       [-0.46516976],\n",
      "       [ 0.61230177],\n",
      "       [ 0.30566472],\n",
      "       [ 0.46071762],\n",
      "       [-0.47927153],\n",
      "       [-0.19699094]], dtype=float32)>, <tf.Variable 'dense_6/bias:0' shape=(1,) dtype=float32, numpy=array([0.], dtype=float32)>]\n"
     ]
    }
   ],
   "source": [
    "print(model.layers[2].weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code will define a loss function and run gradient descent to fit the weights of the model to the training data. This will be explained in more detail in the following week."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "deletable": false,
    "editable": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.6149\n",
      "Epoch 2/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.4417\n",
      "Epoch 3/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.2911\n",
      "Epoch 4/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.1953\n",
      "Epoch 5/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.1397\n",
      "Epoch 6/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.1055\n",
      "Epoch 7/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0832\n",
      "Epoch 8/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.0679\n",
      "Epoch 9/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0568\n",
      "Epoch 10/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.0486\n",
      "Epoch 11/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0422\n",
      "Epoch 12/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.0374\n",
      "Epoch 13/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0334\n",
      "Epoch 14/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.0302\n",
      "Epoch 15/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.0275\n",
      "Epoch 16/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0254\n",
      "Epoch 17/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.0235\n",
      "Epoch 18/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0219\n",
      "Epoch 19/20\n",
      "32/32 [==============================] - 0s 1ms/step - loss: 0.0205\n",
      "Epoch 20/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0193\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f48140e7e50>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(\n",
    "    loss=tf.keras.losses.BinaryCrossentropy(),\n",
    "    optimizer=tf.keras.optimizers.Adam(0.001),\n",
    ")\n",
    "\n",
    "model.fit(\n",
    "    X,y,\n",
    "    epochs=20\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run the model on an example to make a prediction, use [Keras `predict`](https://www.tensorflow.org/api_docs/python/tf/keras/Model). The input to `predict` is an array so the single example is reshaped to be two dimensional."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " predicting a zero: [[0.00993636]]\n",
      " predicting a one:  [[0.9818276]]\n"
     ]
    }
   ],
   "source": [
    "prediction = model.predict(X[0].reshape(1,400))  # a zero\n",
    "print(f\" predicting a zero: {prediction}\")\n",
    "prediction = model.predict(X[500].reshape(1,400))  # a one\n",
    "print(f\" predicting a one:  {prediction}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output of the model is interpreted as a probability. In the first example above, the input is a zero. The model predicts the probability that the input is a one is nearly zero. \n",
    "In the second example, the input is a one. The model predicts the probability that the input is a one is nearly one.\n",
    "As in the case of logistic regression, the probability is compared to a threshold to make a final prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction after threshold: 1\n"
     ]
    }
   ],
   "source": [
    "if prediction >= 0.5:\n",
    "    yhat = 1\n",
    "else:\n",
    "    yhat = 0\n",
    "print(f\"prediction after threshold: {yhat}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare the predictions vs the labels for a random sample of 64 digits. This takes a moment to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 64 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "# You do not need to modify anything in this cell\n",
    "\n",
    "m, n = X.shape\n",
    "\n",
    "fig, axes = plt.subplots(8,8, figsize=(8,8))\n",
    "fig.tight_layout(pad=0.1,rect=[0, 0.03, 1, 0.92]) #[left, bottom, right, top]\n",
    "\n",
    "for i,ax in enumerate(axes.flat):\n",
    "    # Select random indices\n",
    "    random_index = np.random.randint(m)\n",
    "    \n",
    "    # Select rows corresponding to the random indices and\n",
    "    # reshape the image\n",
    "    X_random_reshaped = X[random_index].reshape((20,20)).T\n",
    "    \n",
    "    # Display the image\n",
    "    ax.imshow(X_random_reshaped, cmap='gray')\n",
    "    \n",
    "    # Predict using the Neural Network\n",
    "    prediction = model.predict(X[random_index].reshape(1,400))\n",
    "    if prediction >= 0.5:\n",
    "        yhat = 1\n",
    "    else:\n",
    "        yhat = 0\n",
    "    \n",
    "    # Display the label above the image\n",
    "    ax.set_title(f\"{y[random_index,0]},{yhat}\")\n",
    "    ax.set_axis_off()\n",
    "fig.suptitle(\"Label, yhat\", fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a name=\"2.5\"></a>\n",
    "### 2.5 NumPy Model Implementation (Forward Prop in NumPy)\n",
    "As described in lecture, it is possible to build your own dense layer using NumPy. This can then be utilized to build a multi-layer neural network. \n",
    "\n",
    "<img src=\"images/C2_W1_dense2.PNG\" width=\"600\" height=\"450\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"ex02\"></a>\n",
    "### Exercise 2\n",
    "\n",
    "Below, build a dense layer subroutine. The example in lecture utilized a for loop to visit each unit (`j`) in the layer and perform the dot product of the weights for that unit (`W[:,j]`) and sum the bias for the unit (`b[j]`) to form `z`. An activation function `g(z)` is then applied to that result. This section will not utilize some of the matrix operations described in the optional lectures. These will be explored in a later section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "deletable": false,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# UNQ_C2\n",
    "# GRADED FUNCTION: my_dense\n",
    "\n",
    "def my_dense(a_in, W, b, g):\n",
    "    \"\"\"\n",
    "    Computes dense layer\n",
    "    Args:\n",
    "      a_in (ndarray (n, )) : Data, 1 example \n",
    "      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\n",
    "      b    (ndarray (j, )) : bias vector, j units  \n",
    "      g    activation function (e.g. sigmoid, relu..)\n",
    "    Returns\n",
    "      a_out (ndarray (j,))  : j units\n",
    "    \"\"\"\n",
    "    units = W.shape[1]\n",
    "    a_out = np.zeros(units)\n",
    "\n",
    "    ### START CODE HERE ###\n",
    "    for j in range(units):\n",
    "        z = np.dot(a_in, W[:, j]) + b[j]\n",
    "        a_out[j] = g(z)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return a_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.54735762 0.57932425 0.61063923]\n"
     ]
    }
   ],
   "source": [
    "# Quick Check\n",
    "x_tst = 0.1*np.arange(1,3,1).reshape(2,)  # (1 examples, 2 features)\n",
    "W_tst = 0.1*np.arange(1,7,1).reshape(2,3) # (2 input features, 3 output features)\n",
    "b_tst = 0.1*np.arange(1,4,1).reshape(3,)  # (3 features)\n",
    "A_tst = my_dense(x_tst, W_tst, b_tst, sigmoid)\n",
    "print(A_tst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**\n",
    "```\n",
    "[0.54735762 0.57932425 0.61063923]\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><font size=\"3\" color=\"darkgreen\"><b>Click for hints</b></font></summary>\n",
    "As described in the lecture:\n",
    "    \n",
    "```python\n",
    "def my_dense(a_in, W, b, g):\n",
    "    \"\"\"\n",
    "    Computes dense layer\n",
    "    Args:\n",
    "      a_in (ndarray (n, )) : Data, 1 example \n",
    "      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\n",
    "      b    (ndarray (j, )) : bias vector, j units  \n",
    "      g    activation function (e.g. sigmoid, relu..)\n",
    "    Returns\n",
    "      a_out (ndarray (j,))  : j units\n",
    "    \"\"\"\n",
    "    units = W.shape[1]\n",
    "    a_out = np.zeros(units)\n",
    "    for j in range(units):             \n",
    "        w =                            # Select weights for unit j. These are in column j of W\n",
    "        z =                            # dot product of w and a_in + b\n",
    "        a_out[j] =                     # apply activation to z\n",
    "    return(a_out)\n",
    "```\n",
    "   \n",
    "    \n",
    "<details>\n",
    "  <summary><font size=\"3\" color=\"darkgreen\"><b>Click for more hints</b></font></summary>\n",
    "\n",
    "    \n",
    "```python\n",
    "def my_dense(a_in, W, b, g):\n",
    "    \"\"\"\n",
    "    Computes dense layer\n",
    "    Args:\n",
    "      a_in (ndarray (n, )) : Data, 1 example \n",
    "      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\n",
    "      b    (ndarray (j, )) : bias vector, j units  \n",
    "      g    activation function (e.g. sigmoid, relu..)\n",
    "    Returns\n",
    "      a_out (ndarray (j,))  : j units\n",
    "    \"\"\"\n",
    "    units = W.shape[1]\n",
    "    a_out = np.zeros(units)\n",
    "    for j in range(units):             \n",
    "        w = W[:,j]                     \n",
    "        z = np.dot(w, a_in) + b[j]     \n",
    "        a_out[j] = g(z)                \n",
    "    return(a_out)\n",
    "``` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92mAll tests passed!\n"
     ]
    }
   ],
   "source": [
    "# UNIT TESTS\n",
    "\n",
    "test_c2(my_dense)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell builds a three-layer neural network utilizing the `my_dense` subroutine above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "def my_sequential(x, W1, b1, W2, b2, W3, b3):\n",
    "    a1 = my_dense(x,  W1, b1, sigmoid)\n",
    "    a2 = my_dense(a1, W2, b2, sigmoid)\n",
    "    a3 = my_dense(a2, W3, b3, sigmoid)\n",
    "    return(a3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can copy trained weights and biases from Tensorflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "W1_tmp,b1_tmp = layer1.get_weights()\n",
    "W2_tmp,b2_tmp = layer2.get_weights()\n",
    "W3_tmp,b3_tmp = layer3.get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yhat =  0  label=  0\n",
      "yhat =  1  label=  1\n"
     ]
    }
   ],
   "source": [
    "# make predictions\n",
    "prediction = my_sequential(X[0], W1_tmp, b1_tmp, W2_tmp, b2_tmp, W3_tmp, b3_tmp )\n",
    "if prediction >= 0.5:\n",
    "    yhat = 1\n",
    "else:\n",
    "    yhat = 0\n",
    "print( \"yhat = \", yhat, \" label= \", y[0,0])\n",
    "prediction = my_sequential(X[500], W1_tmp, b1_tmp, W2_tmp, b2_tmp, W3_tmp, b3_tmp )\n",
    "if prediction >= 0.5:\n",
    "    yhat = 1\n",
    "else:\n",
    "    yhat = 0\n",
    "print( \"yhat = \", yhat, \" label= \", y[500,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following cell to see predictions from both the Numpy model and the Tensorflow model. This takes a moment to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YtBSG8mJgYGBgYGDQUhjKi4GBgYGBgUFLYSgvBgYGBgYGBi2FobwYGBgYGBgYtBSG8mJgYGBgYGDQUjyU8iKECAoh/q0QIieEmBNCfPUOxwkhxK8IIeKbr18RQogdzvvVzfPlhBDfEkIEH+Y6HxeGfO6MEOJvCiHOCiFKm5u93em4I0KI14UQMSHEXctBCyH+mRDihhCiLoT42m5e86eN0X7ujNF+dsZoOztjyOfOtErfeljLy68BZRr7hPxF4J8KIQ5vc9wvAH+axsZpx2hsBf+L251w8/u/Afzc5nnzwK8/5HU+Lgz53Jll4O8D/+Iux1WA3wd+/h7PewH468C5B7+0Jwaj/dwZo/3sjNF2dsaQz51pjb6ladoDvWhsglYG9uve+03gv9/m2PeAX9D9/fPAB3c47z8Afkf399jm//E+6LU+jpchn3uW098HvnEPx403mus9n/eHwNce9/0Z7cdoP0bbebJehnzuWU5PdN96GMvLfqCqadpN3XsXgO2018Obn93tuNuO1TRtis2G9hDX+jgw5GPwMBjtx+BBMdrOzhjyaQMeRnnxAOmm91KA9w7HppqO89zBd9h87E7nfZIx5GPwMBjtx+BBMdrOzhjyaQMeRnnJAr6m93xA5h6O9QFZbdOG9BDnfZIx5GPwMBjtx+BBMdrOzhjyaQMeRnm5CViEEPt07x0Hrmxz7JXNz+523G3HCiFGAfvm/2slDPkYPAxG+zF4UIy2szOGfNqBhwzo+T3gd2kEQL1Iw0R2GBgGNGB487hfAq4BfUCExkP+Jd15ZtkM4Nn8fhp4efO8vwX83uMOXjLks+uysQAO4B/SCJZzAJbNzzTglc3fxeZnE5vvOwC77jzfQBdUBtg2j3kX+Gubv5se9/0a7cdoP0bbeXJehnx2lE1L9K2Hvckg8C0gB8wDX918/+XNh2rV3eSvAonN168CQndDGeCg7rxf3TxfDvg2EHzcD9SQz67L5uubDV7/+jowsDkAhDaPG97muFndeb4P/DXd3z/Y5vhXHvf9Gu3HaD9G23lyXoZ8dpRNS/Qt+RB2FSHE3wGimqb9xj0c+xLwNzRN+9ldv5AnFEM+d0YI8ZeAw5qm/fI9HGujEd1/TNO0yiO/uCcEo/3cGaP97IzRdnbGkM+dedL61iNRXgwMDAwMDAwMHhXG3kYGBgYGBgYGLYWhvBgYGBgYGBi0FIbyYmBgYGBgYNBSWHb6sKOjoyUCYlKp1B13+XyUeDyelpBPNpt9LPJxu90tIZ9cLvdY5ONyuVpCPvl83uhfO/C4+pchnzvjdDpbQjaFQsFoOzuwU9vZUXl5XMgg4h12HjfYw9wtyNxoNwYGBgbtzROjvNyWwy3Elle7Iyfker0OfDIB75X7fxDu1F4MGujlYzI1PMR7VT7b9S+jvRgYtC5PjPICWyds/c+9hP6e9+L93w+GrO6MYb3cilTeLJbGkNdUlGtPci/3brQfgyeVx668yJWQ1WrFbDbjdDqx2+1Uq1UqlQrlcplisQi0Z0eSVgOLxYLFYsHtdiOEoFwuU6vVKBQK1Go1Y5XI1gnZbDZjMplwu91YrVaKxSLVapVSqUSlUsFkMu0peUnZ1Go1oDFJm81mJZ9cLke5XKZer1Ov1/dMe9I0DavVSkdHBy6Xi0OHDmGxWJiamiKdThOPxykUCnuqvegVt3q9rn7qkfKQP/dKezFoHR6r8qLX/K1WKzabDZ/Ph9Pp3KK0yJ/tihACm82G1WrF5/NhNpvJ5/NKgZMTksEnmM1mLBYLPp8Ph8NBJpOhVCpRq9WoVCpKKdyrWK1WLBYLXq8Xu92uJqlKpXLbRNXOyHbgcDjo6OhgeHgYq9XKxsYG9Xpd/ZSWmb2KEGLL4mAv9x2D1uCxKC96c63T6cRms/H0008zMDDA4cOHGRoaYnZ2lrm5OS5dusT777+vBl9ofQuMfsWjaRrBYJDTp08TDoc5ffo0LpeL9fV1UqkUb7zxBgsLC8TjcfL5PCaTaU8PtNVqFZvNxtDQEMFgkC9/+csMDAxw/fp11tfXee+997h69SqwVTlu9TZzJ/SWO6vVSnd3N263m9HRUfx+P0eOHMHv93Pu3Dnm5+e5ceMG8/Pz1Ov1trfoyf5lsVgIBoMMDAzwYz/2YzidTorFInNzc6ytrVGr1TCZTJjN5sd9yY8UKQ+z2YzNZsPhcBAIBLDb7QQCATWuSKWuWCyqn+VymWq1uqfHn3aZf3YDOX7I3+ET16zZbP5UxpXHanmRFge3283IyAgTExM8//zzHDp0iEuXLuH1eolGo0oQ7eSf1t+Lw+FgbGyMoaEhXn31VbxeL4uLi8RiMSYnJ8lkMqTT6T2/QtQrvYFAgP7+fl544QUmJibw+/3Mzs5y8+ZNTCaTUhD3wkAj3WgOh4NwOIzf72diYoJwOMyLL75IKBRC0zRcLhfRaJSVlZU9Y9GTwcoej4dgMMi+fftwu910dXWRTqexWCx7zhJlMpmw2Wy4XC7C4TAej4e+vj6sVisAlUqFpaUlstmscjVKa+ZeRX/ve12JaZaF/LvZ2v2ord+fmvKi98mbzWZCoRAej4fTp08TiUR4+umn6e/vJ5VK8fbbb3Px4kUuXrzI1NRU2zQS+aDdbjd+v5/Ozk4OHjxIIBDg0KFDdHR0EI1GyWQy2Gw2wuEwr776KgcOHOCHP/whU1NTbGxskMlklD96L9A8aFosFkKhEOFwGKfTicViwWazYbfbsdlsWCwWqtWq+m67tJ9mpNISDAZ5+umnCYVCPPXUU/j9fvr7+/F4PPT09OB0Ojlx4gT9/f2qrV29epXr16+ruDJ5vnZCKrBycTA4OEilUiGZTBKLxVhbW1PxUe2M7D9WqxWHw8HAwAAnT54kGAwyMjKilBgZJ1Wr1VheXiafz7OwsEAqlVKWzWg0SjKZVOdulzajn4D1yqzegiDjf+Q43qz8t3NskJSPyWTCYrGocUSOMfV6nbW1NTKZDNeuXSOTySgX9aOSx6dqeZENw2Qyqcn71KlT7N+/n9HRUQKBAO+//z5Xr15Vyks6nQbap5MA2O12uru7GRkZ4XOf+xxutxuv14vJZCKZTGKxWBgYGMDj8XD8+HFGR0dZW1sjn89TLBZJJpNtb+K+E1Jp8/l8+P1+7Ha7in+RcR5yNV2tVtuq3ejRu4t8Ph9PPfUUkUiEF198kY6ODrxeLxaLRQ24Y2NjDAwMYLPZ6O3tpVwuMzc3B0CpVGprOVmtViKRCN3d3UpZ29jYYGNjQ7lC2vX+JVIOTqeTgYEBXnjhBbq6uti3bx9Op5NQKITZbMZqtaqJKJ/PMz8/z8bGhrLsFQqFLcpLOy0O5PwklRKpiNwpaLlZ0ZHuknZFP+aEQiFOnjxJV1cXR44coVqtcvXqVdbX11ldXVUJFPrv7TaPXHnRa7R2u53x8XECgQCf+cxn6O3t5amnniIcDuNyuajX6+TzeVKpFPF4nPX1dZV1A62twGiaRldXF/39/Wrl4/V68Xq95PN5Ll26RD6fJx6PYzKZGB8fp6Ojg7GxMTweDxMTE/h8Pmq1Gul0mlqt1hZyuVdkB7Db7Urb7+/vx2KxqPiFK1eusLa2RrFY3FL7pd2Qg6bFYsHpdOL3+xkcHCQSidDR0YHT6SSfz1Or1chms1SrVaXIBAIB3G43xWIRl8vFrVu3OH/+PJVKpS2z+mSMh8fjUWOMTAYoFott7VrUuzdMJhN9fX0cPXqUQ4cOMTExgdvtxuPxKEtlsVgkGo1Sq9WwWq2YTCb6+/sJh8NsbGzgdDrZ2NhgdXV1S8xUqyNdaSaTCbvdjtfrxWq14vF4sNlsKi4zEAhgNpspFovUajVisRj5fJ5kMkmxWCSfz6ukATn+tFN9JSknqewePHgQn8+nFowHDx5kcHAQp9NJLBbj7bffZmlpiVKppBaSuymHT8XyIs1sNpuNgwcPMjAwwFe+8hUGBgbw+XzYbDblXy0UCqTTaRKJBOvr62pVDa3ZAPQuj66uLp566ikOHDjA5z73OSqVCrFYjEQiwfvvv8/6+jozMzNomsbhw4cJh8N89atfJRQKcejQIRXIPDMzQz6f3xMmb4k+a8TtdhOJRIhEImowmZ+fV5p/sVjEbDYr61Qrtps7Ic3W0oLpcrmU8tLb26ssLslkknw+z8rKCoVCQWXx9fb2EgqFcDqd9Pf38+abbzIzM0M2myWfzwO0jVVPX6DP6/XidrtV/EahUGh7JRc+Ud7MZjORSITnn3+e8fFxDh06BKASB2q1Grlcjlu3blGpVBgZGcHr9RKJRLDb7WQyGTweD9evX8disVCpVB7zne0OegVPWjH1LlcZ1OxyuRgcHFRlB4rFIrdu3WJjY4P5+XkSiYRaOEnFTm+xaXX0wd4Oh4NQKMSBAwew2WxkMhksFgv9/f2YzWb279+v5CKtm+VyedctnI9MedEPCk6nk3A4TFdXF88++yw9PT2YzWbS6TTz8/OUy2VcLhc2m0357WOxGDMzM2qV1IoNQD5wh8OB3W6nv7+fI0eO0NPTg6ZprK2t8eabb7K6usri4iLZbFZp7aurqxQKBc6ePUs0GmVsbIxQKMTw8DAHDx5kbm6OxcXFtjLbNtMcGCbdRcFgkK6uLkKhEMVikUKhwPLyMgsLC+RyubZ2A+gHW4/Hw759+xgdHSUYDOJ2u6lUKuRyOc6fP8/6+jqTk5Ok02lCoRBer5fTp09jNptVexwaGmJkZISVlRU2NjbaNihTn60I7aXQ3g1pAbDb7Wqc1VMqlVQ8y5tvvkmxWOTZZ58lHA7jcDiUha+jo0NZsPL5/COZkD4t9JloZrOZ3t5ehoeH6e3tZWJiQi0KZGCz1WpVGVmVSoVKpUIkEiGfz7O8vEwymWR6epqVlRXS6TSZTEZZY+TkDa3b7uRcXq/XKRaLLCws8Prrr2M2m0kkErhcLk6ePInf76evrw+LxaLk1lIxL/oIZGmynZiYYGhoiC984QsEg0FSqRSJRIIPPviAWCzG/v376erqore3l0OHDrGyssLVq1dVESlovQcvC4I5HA6CwSBjY2Nq8qjX68zPz/O7v/u7pFIpCoWC0mwB5ufnVSG23t5e/spf+SscOnSIQ4cOqfovCwsLj/kOPx30loZgMEh3dzd9fX10d3ezvr5OMplkZmaGyclJqtXqp5aq9zjQB875/X4VEyUDc/P5vDLZTk5OcvbsWWKxGJFIhEAggMPhoKuri2AwSCQSYW1tjcOHD2M2m7l161bbKS+yDWxXiK3d0W+dIRVWj8eDw+HYckyhUGB6eprp6Wn+4A/+QFnBR0dHGRgYUFarUCikFBiZfdSq7UUqs9KSMDY2xuc+9zn27dvHiy++qBQ9+MTa2ZxlJN+LxWJks1k+/vhjJicnWV5eZnl5mWg0ysLCgopVhNa1akoFuFqtks1mmZqa4vd+7/eU4hsMBimXy4yMjLB//35cLhcej0e5Hx9FO3lkyku9Xsfj8dDf309vby+nTp0iHA4DkMlkuHTpErFYjPPnz5NIJKjVaiSTSZxOpxpcBwcHAYjFYo/iMj8VhBDK/NrV1UVHR4cqFma321UcizRh678HqABe6V5yOBz09fURCASwWq1bBqh2Rpp1g8EgoVAIt9uN0+lUspQveWy7IVc9Uhnu6Oigr6+P8fFxIpEIQgjy+TxXrlxhZWWFmZkZlpeXKZVKAORyOYQQyspntVqVNWZgYIB4PI7dbt9SxK5d5KjvI7IYptPpxOFwqIG13fuQPuBU3me1WmVjY4OVlRUuXrzIwsIChUKBSqXC8vKyWlWHQiFVpbi7u5uBgQH13VajuRBfOBxmYGCAiYkJDhw4QHd3NyaTiXK5TCaTUcfKcUa62DRNU1YVKc9gMMihQ4fo6elhZGSE1dVVent7WVhY4MqVK1Sr1bboWzJuLJ1OU61W1X1JOcmCoYVCYctnu82uKi+yYchMD7/fzwsvvMDY2Bh//s//eRwOB+vr66ysrPCHf/iHTE1NcePGDTKZDCsrKwwODtLT08OhQ4cYHBzk1KlTANy6dasli9TJhxkOh5mYmGBsbIzu7m5KpRLpdBqfz6dMbLLSp/ye1NBl6vTMzAzd3d14vV6OHj3K1atXcTqdVCoVNUG1ilweBFkTqK+vj4GBAfx+v4phKJVKlEolyuXylliXdkE+V9mvPB4P4+PjHDlyhBdffBGPx4PJZCIWi/H6668zOzvLmTNniMfjQGO1l0qlSKfTTE5OEgwGcblcDA8P09XVxbFjx8hkMip4PJ/Pt91kLu/H6XQC4PP56OjoYHl5mXq93nZtRk+z4iLTXbPZLLOzs0xOTvKd73yHWCxGJpNB0zQVP/bSSy+pBUMwGGT//v3KUry4uHhbZd4nHX0clMlkYv/+/Xz2s5/lxIkTvPzyy2piTqVSzM7OqtiVer2utmopFotKwSsUCoyPj9PZ2cnIyAjd3d1q8b60tMTU1BQffPAB8/Pzqm9Ba8iqGfmspeU/m82q9+WcJRdIgIq7e1TFMHfd8iJXhrLw0cjICL29vSrzYXp6mrW1NdbW1kgkEkqbzWazxONx0uk0+Xwem81GT08PwWBwy15HrYZc7TmdTmVC0wdd1mq1bc3Z+tVRuVwmGo0yPz/P6OioqnEyMDCgAsXaGTng2mw2Ojs76erqUgW19CuhdkUfLGexWOjs7GRiYoLh4WFlfVpcXFRxPysrK9sGo2qaRrFYJJvNUiqVtlgjpIuyFQfVe0H2MbvdruKFpNLXyu6Pu6F3jbhcri3bRcjsolgsRjqdJpfLqe/pEydisRg+nw+3243dblc/W7GtyASHzs5OfD4fY2NjjI+PEw6HsVqtahG0sbHBtWvX1N9ykSSVm2q1SiwWo1wuk8/nCQQCVKtVNE3D5/OpLBxpLW2OuWon5Bgjt7iRc9uj7le7qrzIyTgYDHLixAkOHTrEa6+9pvYSiUajfPe732VpaYmrV6+SSqVUWl40GiWbzbK4uMjKyoqqcbK0tEQgECCXyykzZauskuRDle4hh8OxxQQpG74+RVUOCPJnpVKhWq1y6dIlksmkWikfOXKEXC7HhQsXWF9fb7uVsh45wXq9XiYmJhgdHcXlcqlVgKwn0I73r3cXycnn6NGj/IW/8BcIBAL4/X7W1tZ44403mJub47333iMej9+2+aLepCv7mpSbHHTatfS73i3k9/up1Wr09vaSyWSwWq3UajWV0dhO6N2MgUCAcDhMf3+/CuxOJBJcvnyZmZkZ1tfXVbC7rDdVKpW4ceOGKoQoC4tKy92jimV4FMjrlGm9R48e5fDhw7z66qt85jOfUX1AKm03b97kd3/3d9nY2GB9fV0pJnrkwlsWx/zJn/xJnn32WU6ePMlTTz2lylrkcjlV0bodxqjtat7IfdS8Xq8alx/1onJXeqy+gqOs1rhv3z76+/ux2+2USiXm5uZYXV1leXmZ9fV1pcU2Wxhk/QW73a5WhPr/0WpIjVQf5S83DyyVSkp5uZvyISedRCKhypqHw2E6Ojq2aPjyf7YL9Xodm82G3+8nGAyqAmwmk4larUYmk2FjY6Plo/nvhD5AV6Zx6tOdS6USmUyG1dVV1tbWVMzCnZQQab2Rn8udy2V9inYLam22PsnFgBxn2j1VGlDKi9frxel0YjKZlAUuFouRTCZvm5zlQlRmzOjHlla00Ml783g8uN1u+vv71SIol8up2llSJjIIN5fLbckCha1uXGj0oVKpRDweZ21tjWw2uyUlX9Yqa7e+BZ+EONhsNhXYLTMepcL2qObuXVFe5MQZCoUYGhri9OnT/NzP/ZyaVBcXF/md3/kdVldXuXbtGoVCYUsBH2ik68nS3fF4HJ/Ph8lkolQqqWp9rdRh9H5VGVzZ0dEBQLlcViXKZXpvszwkckW4uLjI6uqqqogpq6pKk26xWFRm31aS005Il5DL5eKpp55ieHhYZdbIAfjmzZtcu3aNRCLxuC9319Gnc1qtVg4ePMhrr73GgQMH6O/vp1gssry8zNTUFB9++KGqirpTDILH41EraJvNRi6XUwsLWWTrTt9tZcxmM5qmqezF+fl55ufnKZVKWCyWtrtfOf4AdHd3c/jwYYaHhwkEAuTzedbX15mfn+fKlSvEYrFt4xKkOz+ZTKrFgWyTrbSY1Gcrjo+PMzg4yGuvvcaLL77I/Pw877zzjpp3arUa1WqVxcVFNe/o42T08tErMbVajenpaWq1GkNDQzzzzDNqwSmL2Mn4mXZA78r2eDwEAgEV03nx4kVWVlbUfT+xAbv6lYvH46G3t1dtDlcsFllaWlKrwvX1dbUylGZqeQ7pI5M/5WTeilq+HmlCk0FeMugyn8+raOydzNb6FWOtViOVShGLxXA4HDgcji3xCu1C88BoNpvx+/34/X5V7bJSqZDP55VPXg6u7YhUVqXZPxgMqowI2a+SyaRa8cHtyofsRy6Xi46ODhX3UCgUSCQSyo3UjqtDPXKi0bsb243mrEUZc2ez2dTeRdKiImMM5bHbnau5P7aS4tKMrN/i8/nwer2Uy2VWVlaIx+OsrKyo+41Goyq2BdjWOtf8d6FQIB6Pk0qlVF/0eDxq/7VWltudMJlMOBwOnE6niuuUlixpzYVHsxh6KOVFKhoWiwW73c6BAwf40pe+xODgIHa7nbm5OX7/93+fxcVFrl+/rjqJXnFpPpfZbFaCcLlcuFwuHA6HMum1CkIIZTJbWFjg7NmzWK1WJiYmVF0SWWb7XpBK0Pz8vPLbyoqGejdAO+JyuRgbG1MrR7vdrgK/L1y4wMWLF0kkEi2t5O7E+Pg4J06c4Nlnn+XFF1/EarVSLpeZnZ3l29/+NouLiywuLqp6SHpFWPYrOYiOj4/z1FNPEQwGyWazzM3N8eGHHzI3N6dqDbVj/Ie+7pS0ZMkCWu04qUhkiQG73a6eq1R65SuXy22xLOgXpHLDU/340kr9TG+FlBsCy9o1JpOJubk53nrrLRYXF5mamgIabUQuOPVu2zshP4tGo2oTyytXrmC321UG0zvvvEM2m92Sft3q1Ot1tW9YJBLB7XZjsViYn5/n5s2bpFIpSqXSlvCP3WRXRilZsj0UCqmbyGazJBIJFYAry9k3V9zTx8vIqHgZyS79062K1OILhQIbGxvkcjnl+3xQX2CpVFL+REm7+uzl4OlwONRKSbaffD5PJpNR1Sxbza14r8g6Qb29vapOULVaJZfLqViXaDSq9g9pVjxkG3M6nXi9XmXBkntCyUD4XC7X9lYX+ER5sdls2Gy2tmwzeqTrVb9HXLVa3VJeoFKp3NGyIPtf8+TTanIzmUxqfnG73VsKrsViMeLxuFoA6a399zO2Sst6oVAgn8/jdDpVrJHcNBZa23LVjNlsxuVy4Xa71disD/V44gN2Dx48yMmTJzl16hSHDx9mdnaWb37zm9y6dYuPP/54y4pQn7YJKF/rvn37VD2U4eFhFhYWuHXrFrOzs6RSKcrlcktO0vV6nWQyyfz8PNFolEKhgNVqZWBggLW1tfuymOhTrOVE004dQd6LvEe3201vby9jY2McOHCA3t5e7HY7tVqNlZUV5ufnSaVSj7SWwONCWkAsFguDg4M888wzaiPKRCLB5OQk165d49q1a6TT6S0xDvL7emuD3Ln9wIEDRCIR0um0qrm0uLhIJpNpa+ud9Lv7/X6q1So9PT2kUqm2zDaSfUBu3rq6usqVK1fo7+9X7lZpJWhWWPVtxm63Mzg4yIEDB/D5fOrcrdTHpBVJKmFdXV1EIhEqlQrz8/PMzs5y69YtCoXCbZW57/c+9WOzTDQIBAIqvqyd2pi8V5vNpjaFlUrup9U+HkiaelOaEIJgMMjIyAhdXV04HA6KxSIzMzMsLi6STqeV4rHd4CgLJgUCASKRCH6/H5fLpTYtlN9vxTQzKafmqHMZe/AgtRLuNHi0kxIDjfuxWq34/X4CgYAqSw6NlWM6nd4SCNdO6NM6ZRS/rCoMDeubzIjIZDLbFpWT55AWBjlo+/1+HA4HiUSCjY0N0um0aput1r/uF+lCkfv86EsXtFv/gU8U2GKxqPba0cfgyVo/22U6yvHa7XYrywF8EjPUSnVLpGIv92iSxS3z+bzKKNJbnx60H8gCmdIt2c6LAdha+FFupbBT7bLd5qGUl+7uboLBIKdOneLVV1+lUChw7tw5zpw5w9tvv006nd5Ssr05Ut1isTAyMkIgEOCLX/wiBw8epK+vj0KhwK1bt3j77bdVRgDs7HdsFfRxPc0bpN0LsgPKwaTdJhy9YhwMBnnhhRcYHR1laGgIh8PBysoKsViMDz/8kKmpKRKJxG01TVod2UdCoRBdXV309/cTDodxOp3UajUWFxd5/fXXWVhYUH1M3r8+YNdms3Hs2DEikQhf+tKXmJiYIBQKUa1WuXr1Km+99RZXr15lY2OjrTIh7oTe6isnmnYvzicVNr3bQmY7yqDS7ZQQ+b7L5cLn86lA31gsxuzs7JY28yT3Pb3lxel00tnZSSQSUdmemUyGcrn8UHWypPy6urpUAckjR46QSqW4ePEi09PTapEA7TNm6/tTtVolmUxSr9dZXV1ldXWVSqXySC0xDzRaycHV5XKpjfL6+/uxWq2srKywvLzM0tIS8Xj8jpHqUhPu7Oykv79fpcE6nU6KxSKJRIKlpSVVNr9VtPydkKY2+ERLv9/vy9V4O5kgm5EDrqzS3Nvbi8/nU9uvJxIJtflZ855Q7YA+TkX6zOUOrfV6nXQ6rdKbZaxLMzJIU1a5li9ZhyEajTI5Ocna2tqWeIh2ptmVJq0w7TKZNCPvS4618l5ltVhZZO1O35X9UMYGSWtFKpVS/a5V+p583tLyAqjM192YX2S2rdztPhgMAqhK8lLe7dbW9AqM3M8ok8mo+M5Heb8PNAPKCTgUCjE+Pk4gEEDTNFZXVzlz5gw3b95UhY1kfQW526/cUG90dJTOzk5ee+01hoaG6Orqwmazce7cOSYnJzlz5gwLCwstve26XMmWSiWSySSJRIJoNKo6ktyOXV+p8k73KQN8u7u7mZiYoKenR21Xv9P3Wgmp3AWDQYaGhpiYmODYsWN0dXVhNpvJ5XJcunSJ+fl5JicnWVpaaun2cSfkvQwMDHDixAkGBgZwuVxks1mi0SgrKyusra2pmAVZslz2L4/HwzPPPENPTw+f/exn6evro7+/HyEEN2/eZH5+ng8//JCrV6+qDMB2RLo+KpUKq6urOBwOCoWC2iOrWq0SCASIRqMtuXfaTkhLgiwzMDAwQDAYVGOPLALa/B1plQoGgwSDQbWdQKFQoFAosLy8zOzsLMlksqXkJduCjH0RQlCtVpUL8X5T5/VlPWQm3wsvvMDzzz/P2NgY6XSa69ev893vfpe5uTk1mctraQdkG5P3L/duisfjxGIxJd9Hdb8PvHzXNA2v10s4HFal2lOpFHNzc0SjUeUukmYj2dAdDgder5fh4WH6+vo4efIkIyMj5HI5tdnXxx9/zMzMDMlkUkWJtyqyk+TzeZUhIiPe9WZrvY/wTjEt9Xodn8+nLBF6k1yrrIDuhrToRSIR+vv7iUQi+Hw+hBAUi0W1K3I8HmdjY0OlcbbLgCCRwaWRSIRAIIDNZlN1ftLpNNlsVqU2A2qhIDOzDh48yPDwMMeOHaO3t1cds76+zo0bN5ibm2NlZaVttwSQyJIFqVSKVCqlMh47Ojro7OxU7tvmit+tTLNSIXchl9V1pQJzp3FVTkgdHR1qgVQul5W7KR6Pt5zSq68bpk8ckX9rmnbflhH9fllut5vR0VGeeuop1Z7W1ta4cuWKsrxIS1Y7IcccWb6hUCiQy+WUsvYox5b7kqQ+CNBsNqtMEKfTqfYuWllZUXuG6FOfOzo68Pl8HD16VMXJBAIBisUi165d49KlSywtLXH+/HmmpqZIpVItPynJa5c+1aWlJS5evMjg4CDHjx+nr6+PF198kbW1Na5du6bM93qtXmq3Xq8Xh8NBd3c3PT09CCGIxWKkUilyuVxLl8dvLqrV1dXF6dOnlXVO0zRmZmZYXl7m8uXLzM/PUy6X1SDUive8E3LF7PP56OnpwePxbNkFOB6PYzKZ8Pv9DA4OYrPZ1MZ54+Pjajf3UCiE3W5Xvvfl5WXOnDnDjRs31I7A7SY7PXLCyufzXLx4kXQ6zfPPP084HKazsxOr1UpfX5+yiLZSHamdaI4vzGazrK+vq6xNt9vNgQMHKJVK+Hw+stms2l/NarXidrt5+umnGRwcVDu4z8/Pq3RimUTRCuOzvL5CoYDJZGJ2dpZr167R0dHBiRMnWF1dVZXOb968eU+xL7Jdyf3qnnvuOUZGRjhx4gS9vb0sLy8zMzOjxqxSqdTSC/DtkHOU3Kams7NTLdRlGn5z9tZu80DKi9VqxWazEQwGiUQiKhYhk8mQTCZVOrDFYlE7TPf399PZ2cmpU6fo7u7m6aefxu12c+7cOVZWVnj//fe5ceMGKysrqtR7q68I5YOTFT2j0SjT09PKddbV1cXhw4fxer3MzMyoCqf6XUjlalBWRpXmXLmSlANPK2Zj3YmOjg4mJiaIRCJ0dHSQzWZZXl5WqY1LS0uPPBjscSEzI0wmk6oGKq10xWKRtbU10um0Kss9MjKiKlvLATkYDHLgwAFcLpeatC5fvsylS5e4dOkSs7OzbdVe7oScZEqlkirdHo/Ht1QZ1vendkJvkS0WiySTSZVV43A4VKkGGQMlrXiyYur+/ftV+Qq3202pVGJjY0PFM7RK/IZ+ASmEYG1tjbm5OZ599ln27dvH9PQ0c3NzaqsR4K4KjH5x4fV6OXbsGMeOHWN8fJxQKMTc3Jza3T0ej7elxQU+icGUG8TCJ/v2lctlHA7Hk2N5kZjNZmVZkZHcctX3+c9/Xk3WDoeDUCikNgZzOp309fVhtVq5cuUKhUKBH/7whywsLDA5OUksFttijmyFznGvSEvJuXPnKJVKhEIhzGYzJ0+eZGxsjM7OThXTUCwWicfjygdrsVjYv38/PT09HD58mM7OTlZXV5mcnFRR3a22KeN2gdx+v5/Ozk7GxsZUO5menmZ1dZXvf//7rKyssLS0pDZRa5V7vV/kirl5Wwm/38/+/fvx+/3KXStr3/j9fux2O11dXVgsFhYXFymXy3z88cesrq7y4YcfKquNvq20qwwlMtBUFuST7rbu7m7cbjenTp3C7/ffJm/53VZHCEE6nWZhYYHZ2VmmpqbweDxEIhGOHDnCT/7kT7KyssJHH30EwNDQkKq3NTg4iMvlUjFVMsBVWodbqf3IsXRycpJaraZieeR+R5lMhqWlJbLZLBsbG1vGJ6nUSRdjOBzG4/Gwf/9+QqEQIyMjdHR0kE6nmZqa4ty5c7z11ltMTU21/AL8XpAuN7l1zaeVubjryot8X74nC2tZrVZVbTaTyfCDH/xApXzOz8+rTtGOqYvSjBuNRonFYhSLRfx+P2NjY/z4j/84ABMTE2SzWSYnJ0mn08zMzJDP51Vg7+nTpxkaGqK/v59QKESxWGRycpL19XW1qmhV7V5O1n6/nwMHDjA6OkokEiGbzXLz5k2mpqZ44403iMVin0oK3uNEthW5K62sglqv1+no6GD//v2MjY3xzDPPKKVFBmDqI/4vXrzI2toaf/zHf6xWl4lEQsVZ6esutUu81J2Q1ge5k7KsfurxeDh58iSRSITZ2VnW1tbUDsHtMOnI/pFOpykWi8zOzjI9Pc3o6CgTExPY7XbK5TLT09Osr68DqEDvQ4cO0d3djcPhQNM0yuUyxWJRtcVWaTNSBnIPuVu3bpFMJpVV12QyMTY2puI119bWVDCy/h6lJdTtdqvF5nPPPafKhTidTlXx+vz587z11ltqA+J2Ryr+n1agruS+Zjt5MTK4bW5ujsuXL3PgwAH8fj9er5fR0dEt7pK1tTVVVEtm3eRyOS5cuEA8HiedTivXyKP2kT1O5D3V63USiQQXL14klUrh8/lwOp0qAn54eJh6vc7g4OAWi4rb7SaTyXD16lU0TVNZWbFYrKWVPX3NDbn7bU9PD+VymfX1dX70ox+xsLBANBolk8ncVkm2XdE0jeXlZa5cuYLNZqOjo0MVhAJUVVi5QWM0GiWfzzM3N0cqleLSpUvE43FmZmbUxpXbbeDZKpPQg6J3nxQKBRVAWSgUcLvdqmaJtHi2k0VPf++1Wo1oNMr58+cpFAp0dnYC0Nvbi9Vq5Qtf+AIA+/btU2O5tBbn83lu3rzJzZs3icfjLWmZku0+l8sBcO7cOdLptJpv9NW65XvNfUMuKOLxOMViEZPJhNfrVXW38vk8xWKRGzduqPL4rSSje0FfasDj8agQCKvVqhZbn9aY8kDKi9y74fr169RqNVwuF+Pj4wSDQUZHR9VD1rs2zp49SzqdZm1tjXw+z9ra2pb9D/QrwXZFavNra2vE43Gmp6fJ5/N0dXVx/PhxAoEAJ06cwOfz4XK5MJlMqhLmhQsXWFxc5MaNGywsLHDt2jWuX7+uskxaESkPGRs1ODjI888/j8/no1gssrCwwPe+9z2i0ShLS0tqwm5XBVci5TI7O0ulUlFlxjs7O+nr61NWNtnPisUiU1NTrK+v8+abbxKLxbh69aoajOVK6E77Hu0VstksH374IV6vl+npaZVoILMcc7mcmnDapX3JSbhararSE8lkklAoRE9PD0ePHmVwcJChoSGV4Sb7WLVaZXl5mfX1dT7++GOuXLnC8vIylUql5bJA5dySTCZJpVIkk0k+/PBDZdmWFItF9fyb24B0ny0tLSGE4MaNG1uyROUu1HLMhtZS8O4VuS2A1+tViQIWi0VZfT+trL0H8jPIC0smkywsLHD+/Hnq9ToulwuPx6M6SyKR4Pr16ySTSWZnZ1WqsN4Urj9fu6O/T7lL9vT0NIlEgnK5jM/nIx6PK0uMDDasVCrcunWLaDTKwsKCivpvJfPtTtTrdarVKisrK5w9e1YFeU9PTxOPx8lms0q5baeJ5U7ozf0Wi4UrV66gaRodHR10dXUpWcgVtcxCkruVy+qpsghZu8vrXpEVYrPZLNVqVa2WK5WK2piyneSlzzqCxqIzHo8zOzvLmTNn6OvrU7tGy9RpucO0zGI8e/Ysq6urKl5KutRaWUYyRkMu/PT3I5MmtovJk8qJVHD1GaHwyV5S7VbPpRnZR+TO7DJV2uVyqXT8T2NeeuCYF4DV1VXW19eZmZnh+9//vqrGKB9qsVhkY2ODcrmsitZJDXivTETNNLuP4vE4ZrOZDz/8EJvNplY+0oUm94mQWVwyoPBOq4NWQl6/XNFcuXKFxcVF4BMzfywW2+JW3AvIZ7q+vs76+joLCwu888472O12tYeIRB8sV6lUVDCzvn+1u0XzXimXyywsLKBpGtevX9/igpQuuHZDP96k02k2NjaIxWJMTU2p+loy69Fms5HNZimVSly5coVoNMrrr7/O3NycipsB2qIf6t0beoX1TmNqvV5Xip2keYK+2znaAamsyaw0j8ejqp8HAgGy2awqTPuoeagIT6mk6NPs9Nt+S5O2jE6XtPPDvV9kcKZ+00Z9QKVMm87n81vqwLST/GSHKJfLZLPZLQGrdyve185IuciBtlwubzFHyz5VrVYpFApq1Whw77RbX7obckEkd5a+fv06q6urqjaX7Hezs7OkUilVkE7v3jdosBdloY8HqlQqZLNZFhcXsVqtqmbOpxXr89DpKfV6XcXAbIe8iXYqY78b6FfFsiHIegt3Ol7SDpk2+uuX7g9ZhVi+Zyi5DTnJ6qY7rWaknNphVfyo0GfktVJp+91ALorkgkjupjw/P39b/Irsj/qf8hztIq8HuZd2uv+HwWQyUavVVAr+D37wA0wmk8rkKhQKn8octSu5tXq/X7OFRf40Hvqd0fumm+Wol5tenu1Ic3piszl3L7Nd/2pmr8voftirstKPNTLW7G7HG+O3gR59EHihUGB+fh4hhKpS/Wll7D208qJv2HfyARrcHWOAMGRwJwy5GOwmD7IgMNqfAWxNvy+Xy6ytrfHv//2/V+/Jn5+GBXhXq5oZDdzgQTDajYHBp4vR5wweFulWlPvqSVeRfuPLR4loh1RbAwMDAwMDg71D++UGGhgYGBgYGLQ1hvJiYGBgYGBg0FIYyouBgYGBgYFBS2EoLwYGBgYGBgYthaG8GBgYGBgYGLQUhvJiYGBgYGBg0FIYyouBgYGBgYFBS/HAyosQ4m8KIc4KIUpCiG/scNwRIcTrQoiYEOKuRWWEEP9MCHFDCFEXQnztQa/vcWPIZ2cM+eyMIZ+dEUIEhRD/VgiRE0LMCSG+eofjhBDiV4QQ8c3Xr4gdKmgJIb66eb6cEOJbQojgo7uLR4chn50x5LMzrSCfh7G8LAN/H/gXdzmuAvw+8PP3eN4LwF8Hzj34pT0RGPLZGUM+O2PIZ2d+DSgD3cBfBP6pEOLwNsf9AvCngePAMeDLwC9ud8LN7/8G8HOb580Dv77bF/4pYchnZwz57MyTLx/9hoAP8qIxwH7jHo4bb/y7ez7vD4GvPez1Pe6XIR9DPoZ8dl0mbhoD637de78J/PfbHPse8Au6v38e+OAO5/0HwO/o/h7b/D/ex33PhnwM+Rjy2foyYl4MDAxajf1AVdO0m7r3LgDbrQwPb352t+NuO1bTtCk2B/GHutpPH0M+O2PIZ2daQj6G8mJgYNBqeIB003spwHuHY1NNx3nu4JdvPnan8z7JGPLZGUM+O9MS8jGUFwMDg1YjC/ia3vMBmXs41gdktU279UOc90nGkM/OGPLZmZaQj6G8GBgYtBo3AYsQYp/uvePAlW2OvbL52d2Ou+1YIcQoYN/8f62EIZ+dMeSzMy0hn4dJlbYIIRyAGTALIRxCCMvmZ5oQ4pXN38XmcbbNvx1CCLvuPN8QulRQIYRt83gBWDePbzkly5DPzhjy2RlDPndG07Qc8E3g7wkh3EKIF4GvAL8phBjelM/w5uH/B/BfCCH6hBAR4G8B35DnEkLMik9Sxn8b+LIQ4mUhhBv4e8A3NU1rqZWzIZ+dMeSzMy0jn4eISP46oDW9vg4M0PCXhTaPG97muFndeb4P/DXd3z/Y5vhXHncEtiEfQz6GfJ6cFxAEvgXkgHngq5vvvwzMAtbNvwXwq0Bi8/WrgNj8zEbDZH1Qd96vbp4vB3wbCD7uezXkY8jHkM/tL/lPdg0hxF8CDmua9sv3cKyNRvTxMU3TKrt6IU8ohnx2xpDPzhjy2RkhxN8Bopqm/cY9HPsS8Dc0TfvZR39lTwaGfHbGkM/OPEny2XXlxcDAwMDAwMDgUdJSvm4DAwMDAwMDA0N5MTAwMDAwMGgpDOXFwMDAwMDAoKWw7PSh2+1uiYCYXC53x10sHyUul6sl5JPP5x+LfIz2szOGfHbG6/W2hHwymcxjkY/H42kJ+WSz2U9dPoZsdqYdxp4dlZdPg+aAYXHn3bQNDG5jp4Bzoy0ZGBg8Koy5687oZfOo5PLYlJfbcrY3b1AIoV57kXq9Dnzy8E0m056Vxf1Qr9dVO9rL7QfQ12rYIoe9IhN573o5SIz+ZPCwyDbVPObA3uljd0Lf5/RzGOy+bB6r5WW7By5/1ys0ewkhxJ6994fBGEAa6JWWT2P18yTTfM97XandjmYl17Bk7kxzn2qWyV4eu5vHHqm0yPd2m09deZEP12q1YjabcbvdWCwWKpUKtVqNYrFIuVz+tC/ricBkMuFwODCZTJjNZgDy+TyVyp6oL3ZfNFum3G43drudcrlMrVajXC6rdrQXBhO54pGWO5vNptqREEL1r3ZFP6mYTCYsFgtWqxWXy6U+r9VqZLNZarXajpN0uyNloW8vcqKR1oRmZD8zmUx71nolZVWv1xFC4PP5sNlsqm+Vy2U1Vu8l+TR7UOScbrFYEEJQKBSUjKTsdoPHZnmRg4vP58PhcFAsFqlWq2iapn7KxrJXGoLJZMLpdGKxWLDZbNTrdcrlspKHQQO9LMxmM2azGa/Xi8vlIpfLUSqVtkzU7b4a0ptq5cRit9uV4gKoNtTOcpCTq8ViwW63Y7fbCQaDANRqNUqlEsViUY0tRp9qsJ2F6m60e1tqptniYjab8Xg8OJ1OisUilUqFer2+5xaazS5qs9m8ZQ6DxthTr9e3hETsRtv5VJUXqfFbrVb6+/sJhUJ8+ctfZmhoiGw2S7FY5J133uHy5cvEYjGi0Wjbm3qlec1qtRIKhfjKV75CV1cXXq+XSqXCN7/5Taampshms5TL5baXx70i29HExAThcJjTp08zMDDAmTNnuH79OtPT08zNzamVYrsOtvK+LBYLTqeT0dFROjo6GBkZwWazcfXqVWKxGMvLy21tiRJC0NPTw8DAAF1dXQwPDxMIBBgfHwegWCwSjUb55je/yerqKtFolFKptEXBa3fkBOJ2uwkGg7hcLgKBANVqlVQqhclkIhgMqhWzEEIpeZlMhlKpRDweJ51OU61WqVarqn+1K1LRlX3Mbrezf/9+gsEgL730Et3d3USjUdLpNO+++y5XrlyhVCpRKpWUMt2ONFs6bTYbPT09dHR0cOLECbxeL2tra2SzWSYnJ4lGo9RqNarVautaXmRDCAQCRCIRTp8+zdGjR0kmkxQKBaW05PN5NTC368SjR1oPnnrqKQYGBgiFQpRKJd5//31WVlYoFAp7Qg73ipRFT08PIyMjPP/88xw8eJBsNsvGxgbr6+vbBmy2E80WKIfDwdDQEF1dXRw9ehS73U4ymaRarRKNRqnX621n8pftwGQy4fP5GB4eZmBggKNHj9Ld3c2xY8eAhvt1fn6e9957j2KxSDwevy1ZoN2RE7HNZqOzsxOfz0dfXx+VSoXV1VXMZjP9/f3YbDYslsbUIBWeaDRKNpulVCqRz+ep1+t7xiIs71EuEAYHB+nr6+OFF15geHiYubk5NjY2mJ2dZWpqak9YOWFr37PZbITDYUKhEIcPH8bv93Pjxg3i8ThLS0tYLJZdV+Q+NeVFP1BIy8vw8DBerxeLxUJHRwdut5sjR46o7ywuLm6JXG7HxqBfOTscDsLhMD09PXg8HsrlMuPj4xSLRS5evEgul2tbOdwL+oHS5XLh9Xo5fvw4R44cwel0kkgkWF5eZmZmhmQyuSesVHIlPTIyQiQS4Ytf/CLhcJhAIKDifzY2NiiVSo/7UncdObF6vV68Xi8HDhzglVdeIRwOMzY2puLHAJxOJ11dXbz88suMjIwAEI1GSaVSW2TTjtlqUmkJBoOEw2HGx8d58cUX8fl8RCIRpdyazWa6u7ux2Wwq5q5Wq6k2lMvluHXrFsvLyywtLbG0tEQqlSIWi7WdlUHOV2azGZ/PR0dHB6dPnyYcDvP000/T2dnJ0NAQXq+X3t5ePB4PXV1dypKVTqcB2kom8Elbku7ZQCDAyZMnCQaDHDp0CIfDQSaTYX19nUuXLrG+vs76+roKC5HflTxMP/vULS9you7s7KSnp+c2/9jIyAgmk4mbN28qc7/0lbUbzb5Cq9WK3+8nGAzicDgol8v09/eTyWSYnJzcE6ucuyHdbA6HA4/Hw/j4OBMTE5RKJTKZDLFYjNXVVXK53JbvtdNkJNGvpPv6+hgZGeHZZ58lHA4rV0C9XieTyVCtVh/35e468v7tdjt+v5/BwUFOnDihrLr1el0pJna7nY6ODo4dO0ZXVxeTk5NUq1VyuRyFQkGdU1qm2mmRIOXk9XoZGRnhyJEjvPrqq/h8Pnp6eqjVaiQSCUwmE11dXVitVjXByAknlUqRz+eJRCIsLCxw5coVdUw0Gn2ct/fIkH3L4/HQ3d3NCy+8oKx6HR0dSk71eh2n00kwGMTr9ZJMJm+bpNsBfVKAnLM6Ojp46qmn6Onp4fDhw2iaxuuvv87S0hLT09Osr6+rYF0Zh7hdGYcH4VNRXpoH2XA4zIkTJxgdHcXr9W6JQC4UCsqF1O5mf306q752gHxVq1WSySSxWGzPZmA1IweFQCCgfKwej4disUihUFABzrsZ1f6kIfuTVHb7+/t58cUX6e3tRQhBPB7nvffeY2lpicnJSRWj0E4uI03TcDgcWK1WDh8+zFNPPcXx48cJBoM4nc5tAyetViuDg4P4fD5efvllxsbGuHTpErFYjFQqRaFQoFQqUalUtgzSrYo+xkVatV977TUikQgdHR1omsb8/LwKZgZIJpNb6nI4HA4VA2MymYhEIvj9fgKBACMjI1y4cEFlccXjcaC1rQ3yuVssFlwuF+FwmM9+9rNEIhGOHj1KMBhUGUaFQkEda7PZGBsb4/Tp0zidThX3IuXayu1IIg0PLpeLrq4ujh07Rn9/P88//zx+v18tmo4ePUpfXx/Dw8Pk83k1Li8tLRGLxZQ1Rh80/yDy+dSUF03TsFqtjIyMMDg4yLFjxxgZGcFut2+xruTzeZLJJMVisW0tLs3oFRiZTiaVl1QqRTweb0uz//2iD/gOhUKEw2HlblxfX1eTj3SXQHvW9tAPsMFgkIGBAZ5//nkCgYBSXl5//XVu3LjBwsICmUymrQIrZX9xOp24XC4mJib4/Oc/T29vL6FQSGUsNn/HarUyMDBAOBymUqkQjUaxWCwsLi6ysLBAIpEglUrdlk7dqu2nXq9Tq9VwuVx0d3dz5MgRfvzHfxybzabioaTyYrFYqNfrakKWi4TOzk5cLhcdHR04HA76+/ux2+0MDQ2RTqdxOBwsLS2xsrJCLBa7rb5Hq6F3i3g8Hvr6+vj85z9PX18fExMT2O12crkc5XJZLQpCoRBOp5OxsTGsViuFQoGVlRWlELfTGGS1WvF6vQwODvK5z32Ovr4+nnvuOZxOJwDlcpnDhw9TLBZVuYZMJkOhUOD8+fPcunWLS5cuKUX3YQJ4H7nyorec2Gw2IpEI/f39eDwerFbrbSbabDZLNBolk8molU+7PPhmmt1GMjceGnJIp9NsbGywsbFBpVJpWzncDX28lN1ux+12Mzo6ytDQEIFAALvdTqVSIZvNksvlKBaL1Gq1tpaX7DfS0iSDBGVtF32toHazXsr7cblchEIhurq66O7uxuv1brlXfT0T2R7kYikcDuN2u1U8x+LiIqlUiqWlJeLxOPPz8ywvL7d0yQZpLens7GTfvn1EIhFcLhelUoloNMrs7Czf+973KJfLWCwWNE1Twbi1Wk3FwMh4Do/Hw/79++nr6wPA4/EwPDzM6dOnuXbtGouLi1QqFaU4tpK89GOxjOU4cuQIo6Oj9Pb2EgwGEUJQrVZZWloinU5z69Yt0uk0J06cULIdGxtjaWmJtbU15TaR528leUjk2Guz2XA4HEQiESWXw4cP4/V6SafTJJNJ1tbWyOfzzMzMkMvl1Djk9XqV16WnpweXy4Xdbmd1dZW5ubkH7mOfWsyLHDhGR0dVOqc07+qVlGQyycLCAqlUimq1qup4tOKDv1dkELM+IjuZTBKPx4lGo8RiMZV6185y2AnZ+R0OBz6fj8OHD7Nv3z66urqUmTadTpPL5cjn80B7Wl3gk/uSyot0dVSrVVwul2pLZrO5LV2v8n5kLEJfXx9DQ0MqtRcargtZOKxWq1EoFFTfslqt9PX1YTKZ2L9/P/V6neXlZVKpFDdu3GB+fp4f/vCHrK+vb3FBtlpbkoui3t5ennrqKUZGRvB6vRSLRZaWljh//jy//du/TaFQUJYXqbxI60NfX5/KSgoGg3zxi1/E7XbT0dFBIBDg4MGD+Hw+nE4nH374Ifl8nkwmo/5/KyGVf4vFQldXF88//zyDg4MMDw/j8XiwWCwUCgVmZmZYXl7mjTfeYG1tTX1/eHiYnp4eMpmMKrZ68eLFlrZ66pUXn8/HgQMH+MpXvkJfXx/PPPMMpVKJqakpNjY2+Oijj9jY2GB6eppcLqcWDEePHiUSifDcc89x4MABlZX00Ucfsby8rPrY/fJIlRd54xaLBbfbTWdnJ729vXR3d6vOAiiNVgbY7YWVs0TKp7Ozk87OTux2OyaTSU1KpVJpz6Qk7oRUXjweD36/n1AoRCgUwmw2U6lUSKfTxGIx8vk8tVqtpQeMe0UWMZSvWq2G3W7H5XLh8Xjwer2Yzea2LcgmC102P+dyuUwmkyGVSjE7O0uxWCSdTitLgszqczgceL1erFYrNptNBf263W42NjZU1oTMemwVGcpxVbpUh4eHGR8fx+fzkclkWFpa4ty5c0xOTm4ZX/TWB/mSyozJZCKdTnPp0iU0TWN4eJjh4WFqtRrBYJDu7m6Gh4eJxWJks9mWc/nL+3c6nYTDYQYGBti3bx/d3d3Y7Xbq9Tpra2ukUimuXr3KwsKCstbF43ESiQSRSESlDbtcLpWE0irtZiekUietnRaLRaWInz17lng8zo0bN8hms6yurqpikHJhHovFlOXF4XAwPDzMwsICbrdbFfmDJ8zyIgMLZQDPxMQEAwMD2O32LXEJMlZBmv6b0xfbEWkuczgcjI2NMTo6isvlwmw2UywWVeG+UqnUcoPBbiEHFZm22NXVRSQSYWhoiMHBQSwWC6VSidXVVWZnZ5WLTW4/0a5IK0M+nyebzaoS3NJE293dTTKZxGq1tqXyIpV+GVAqB0qz2ayy86anp/njP/5jUqkUKysrWK1WDhw4gN/v5/DhwwQCAfbv308gECAYDBIKheju7laZOZ2dnZw9e5ZYLEalUmmZIn/yeff09DA0NMQzzzzDiy++SKFQYG1tjUuXLvFv/s2/YWNjQ8UW6jNI9PeXTqdJpVLKwpBMJjl//jwvv/wytVpN/Y99+/bx9NNPMzU1xdzcXEtuRVGv1/H7/Rw5coTjx4/z4osv4na7sdls5HI5bt68ydLSEq+//jozMzMkEgnq9Tpzc3OEQiGVKet0OlWMUKu6i/ToMzw7OjoYHBwkk8nwox/9iNnZWb75zW+ysbGh+klzhfzp6WmcTifd3d309PTg9Xp5+umnWVtbIxQKsbGx8UCp5Y9EedFr8DLAZ3x8XNV1cTgc6lj5YIvFIrlcjnQ6TTqd3jPVZKWs9G6jdr/ne0XfjmTnGRgYYGBgQA0O0mWyvr6uNP69IEOpzEkLi9PpVAsCabGTE1O79SOpzMpUX4/Howa9Wq1GOp1mdnaW+fl5VlZWVPyY2WxW8QrSF59MJvH7/YyPj9PZ2Ynf78fj8ajJKB6PMzg4SDKZZHV1tSWUQPm8PR4PwWAQj8eDzWYjnU6TSCRIJBLKxao/Xv7efC69VSaTybC2tsby8jLz8/NqFW02m7cUt2sV9G4Ru91OT08P+/fvV4HJJpNJuaRv3LjB0tISiURCWXilvNpxgQBb693ILUcqlQrJZJKbN2+yuLhIMpkkm83e5v6RsqlWq7fF5sl4mOb6QPej7D2yliYtLm63m4GBAb785S8zMDBAX18fLpdLBdLJ1XE8Hmd9fZ35+XlmZ2fJZDJtNeDuhNweQAYwS+SD3Sty2AlZyPCzn/0sIyMjDAwM4PV6mZ+fJxaLceXKFT766CMymcxtcmw3ZECl3W5XPvlIJEIwGFTZetKULV1o7YScKIaGhjh9+jTDw8OqLlKpVGJhYYE/+ZM/YWlpiYsXL25JE4/FYphMJn70ox9hMplwu904HA5+4id+gkOHDvHss89y4MABRkZGGBoawu/3A3D9+nWi0egTXT1VTp4yTrC7u5vx8XHC4TBOp5NMJsO1a9dUoTmZBLCTcqvfXgNgbW2N1dVVrFYr5XIZk8nEiRMn1OTWan1PWp18Ph+Dg4OcOnWKP/Nn/gx+v1/FZCYSCWZnZ/n2t7/N7Ows6+vrlEol5SJqRlod2kGZaS7LYLFYSCaTTE1N8e1vf1sF6upd9XK80d9/tVqlUqmomi/6tvqgPDLLi1RM3G43Pp+PUChER0fHtsG39XqdbDZLIpFQwU4yhaqVOsJus5fvXSJTgmWQYCgUIhgMYrVaAUgkEqyurqoiWnI11O6y03d+/e7R5XKZYrGo6iu0szxkfQ1ZhwQ+2YCxeTWor6kk91iRQb2lUonFxUXsdju9vb3K+uJ2u1UcTDwex263I4RQdWCeNJnqU8gdDgehUEgVAq1UKspqIpMh7kex1VsYarWaspTrXWlPmjzuBX0NMp/Ph9/vV8kk1WqVfD7PwsKCSqWXBR/l85cLT5kWLGlFWeiRbUkq/dLK2dHRQbVapVAokM1mt8Q33cl6t915d4NHprxUq1VsNptaGcoN08xm8xZ/qNyJc25uTgVCbWxs3GZO2gu0g6a+m8gUV7mfyOjoqErVtNvtlEolzp8/z9WrV5mZmVGugb3UbvTbzNfrdZLJJNFolJWVFVZWVqhUKm2drad3tcoA5nQ6zeLiIul0WsVx3MmCKV1s7777Lh9//DGpVIrV1VWOHTvG0aNHVVkHk8nEe++9pwLD9VbjJwWpqHZ3d6sy9i+99BIWi4VEIsH09DQffPDBlk067/cepJJSqVRu21qh1ZCKS7VaVYHNMqsKGuUq5ubm+OY3v6kCdPWxGTJgvKOjg87OThwOx5Zst1buc9IAIXdn37dvH5///OfxeDxKeYnH4+Tz+S1FDe/EnRSbh4mN2nXlRR+nYLPZCAQCdHR0bIm+lggh1CpIny3SjhvI7YSUldTe96Lipqe5/o3T6aS3t5eenh7cbjd2u518Pk8ulyMajW6Jbt9rCqDeTCsDePP5vAr0bnd53Mk8r3eH7GQlkd/N5/NUKhVWVlaYm5ujp6eHXC6HEIKOjg68Xq/KjNBbcZ4k5DXJLCqv14vP56NQKJDL5UilUiQSCbLZ7AP/DylTGeOiV35aKamgOdbQ5/OpopcWi4VKpaJktra2RjQapVwub5mbrFYrDocDl8uF2+1WgePSOvUktpH7RT5naYXUNI14PM7GxoZaON3vXC0td/oSD/ditWlmV5UXOXmYTCbsdjudnZ089dRTDA8PK1OcfLiyBoXMlrh+/TpnzpzZsk/GXlBeZCBqJBKht7dXBUXtVWSH1+8rMjY2xp/9s39WpdpZLBY+/vhjlpaWeP/997l27ZqyuuyFNgOfKHZ2u10pvbKA1uLiotpIrx33WJHIOi6yVhQ0JiNZ/8VkMqndo++ElI30x585c4br169TKBRwuVxEIhHGxsZUKrDVamV1dfVTub/7QY6rQggCgQB9fX10d3fT2dnJrVu3mJqaYnJykqtXrypX7IMg210gEFBFIuETd510qT3pSHl1dXXR2dnJM888w6uvvqr2dpI1fyYnJ7ly5QrxeFzFCMk5rrOzUwV2j42N4fV6qdVq5PN5UqmUUnRbEX0mbCAQUBXNb9y4wfe+9z0WFxfvuTquPJfso1KJlpWZ9bFX98MjGdVkIJPb7SYUChEIBG7bElveUDabJZlMkkwm91yWkRBC+e2lj11qse1+/9shBz35U66IgsEgvb29hMNhLBYL1WqVWCzG2tqaqsexF2Ok5CJBH/eh3+NJb5JtR7lUKhXy+fyWOkhypejxeHA6nfd83/rxKBaLqYwc6XaT6a9ut/uJtbxI5JgirQqyfpa0yj3MGCutwk6nE7/fr1wlMtaqFdLJ9RZal8ullJBQKITH41HykkHvshSBPnZKCIHX61WbMbpcLrWAkHWFpPLyJMviTujjVh0Oh2pP5XJZjbv30gfkOWRCitls3hKTJsepB5HRrlte5F4a4XCYkZERnnnmGVVMTH+zlUqFYrHImTNnmJ2d5ebNm0Sj0T2R6tqs1fb19TE6OkokEsFms22RU6s2/odF0zRCoRCf+cxnGB8f59ChQ7jdbpXu+cMf/pCpqSlWVlZaepC4X/Rtw+VyMTg4SH9/P36/f0sgnTTHNi8a2gGZ/bK8vMylS5cIhULs379fTapdXV1MTExgs9mUpeFu1kzZfqS5P5/PK+uVpmkEAgFOnjyJ1Wrlww8/3HbjxyeFnSaVh3GtSouDEIKBgQFOnTpFd3e3ihO6du0aa2trT7T7SO+SBtS+YEePHmVwcJBqtUoikWBqakplrMm99mQbkdbMkydPMjExwdjYGH6/n2KxSDKZ5Nq1a7z55pusrKy07GJUthM5RzkcDrUBp4z9uZs7Vi6eZAkCuffY7OwsH330EVNTU6ofPcic/0hGNWm6latmn8+nlBepuUoNdW1tTW0HoK9L0c7IhiFEo9y90+m8baUotxBvF9/p/SLdaTL40Ov1Yrfb1apGpmzupQ089UjzvcvlUvFkcpUtM2zaWS5CCAqFAhsbG2pVDKiVot/vV1aSe7GU6I+TLhCZTSPrgMgspFYYn3Z7zNDHiEgrVDgcxmazqTo6cjXdCuOVtCB5PB7C4bBKjTaZTGqMWVlZIRqNbnFLwieWLVkwU24dIPdX29jYYG1tTZX7aIX2sh3bFYKUGVjSwna37wO43W4CgYAapwqFAuvr62r/wgfN3Ns1y4u+wmVXVxenTp3iyJEjBINBVZROmpDq9Tqrq6tEo1E+/PBDLl269FC+r1ZE7zbSZ0PIIkCLi4uq/LIcENptBb0dMtbF5XLR09PDxMQEkUhEVdK9du0a8/Pzag+aUqmkYl3avd00rxqlgme325U7TSotrTCBPCjy/qUSK907si/JWJWNjQ18Ph/ZbFYFL98tLkrKb3FxkXPnztHR0cEzzzyj4kRkhsmTbHmBrZWp7/T5/fQX2baOHj2qqumOjY1x48YN3nnnHS5cuMDU1JSa1J70sUq61Hp7ezl48CDhcFi5DRcWFpidnWVqamrLBsHVahWr1apioA4fPsyBAwfw+XwAzMzMcOvWLa5fv87q6qpKx29F9MpdT08Pfr9fuX3udk/6NGuLxcLExAQTExP09vZSq9WIRqPcuHGDRCLxUF6WXVFemiuhejweIpEInZ2dylem32lUNhJpblxeXt6TWUaw1Tcs713TNCWf5po37SwbfdyCtEaFQiH8fr9Kz5SxLjJGaq+lRjcj+1xz22jndiL7SLlcJpfLqRgfec8yfVXGIsgKn/fqo5fjk353e0Dt/C7b3JNq2ZLZLnolVr/b+IO0DakIBYNBhoeH6e3txefzUalUVCVj6Up40gvV6eMwZBVimUxSKpVIpVLqlc/nt4y9ctPG3t5eOjs7CQQCan5LJBIsLi6qFOJWHa+lYitEYzNlWcjxXveL04/jVquVzs5O+vv7VSHJXC6nLKYPI6NdUV7kakWa0Y4ePcrTTz9NOBze0sn1LpFoNMry8rLyz+sF1u7oLSzr6+v4/X42NjZwuVwAynQpzdbtGruwHXJfmfHxcfbt28fo6Chut5t8Ps/q6irvvvsus7OzZLPZLQPxXmk3+hV1uVwmFosRCATIZrMq3kyWg5cZJU9iQbWHQY4TiUSCUqnEpUuX6O3tZWRkhMOHDxMMBjl8+LCKkVpdXeXs2bNkMhni8fiWgNXmMUe6aoEtirHczFFOWCaTiVQq9cQoMHolVk6iKysrrK+vYzab6e/vZ2xsjCNHjqiqqHez5koXmqY1Niy02WyMj49z8uRJgsEgmUyGaDTK/Pz8XbO6nhTk8+rs7CQcDtPX16dqtFSrVeLxOOfPn+fWrVtqTznp2pdbSHzhC19QWWg+n08V/jtz5gwffvghc3NzW/pcK/W95jIVPp+PSCSCz+dTiv3dvi+LH0YiEUKhEIcOHeLgwYPcuHGDd999l3Pnzql9oR5GNrtmeanX6zidTiKRCH19ffT39+Pz+W7zN8tVQTqdVhuD6Tdo3AvolbhMJkM2myWfz28p+CQ3gnvQ7cJbEdlO7HY7XV1ddHV1qR1Mk8kkqVSKmZkZZmdnKZVKe8IatRPVapVMJrOl0qms9ilLxLcjckyR9WyWl5eZnp5W442+ptSJEyfUliNyYm8OiG+edOWkrleOLRYLHo8Hj8eDy+V6Iuu9SEVEBhunUim1j1MgEKCrq4u+vj5MJhPr6+s7xhvoM/9ksTK32612XJYykDEe+toxT2p/1N+TzDKSsVGyr+RyOZaXl4nFYmpekrspDw0N0dfXx7Fjx5TSY7PZSKVSLC8vMzs7y+TkJJlMRv3PJ1UWd0OOqw6HA5/Ph91uv+e2LttUMBikp6eH7u5uurq6eP/997lw4QILCwvkcjnlhn1QHkp50ZuHZKyLjL6WW4lL5AZXy8vLRKNR3nnnHWZnZ4nH41tcSnsJ/cTbfO+tqLXvBpqm4fF42LdvH319fVgsFsrlMktLSyryX5ak3muyga3toVwuq3TOQqGA3W4nHA5TqVTw+Xw4nU5KpVJbLg5kZlCtVmNubo73338fk8nE4OCgChDs6OjgyJEj9PX14fF41IaNcv+nYrHI+vq6yigCCIVC+Hw+5ac/fPgwNpuNUqnE/Pw8y8vLT2wcmrwW2R5u3bqldn0eHBzk6NGjCCE4f/480WiUfD5PJpPZYvXWV0s1m82Ew2HcbjfPPvssw8PDPPvss3R3dxOLxZifn+fmzZvMz8+TyWRaQlluLuTncDjUdcstEwC8Xi+HDh3C4/Fw9OhRgsEgx48fV24zl8ul2s67777LzZs3uXHjBhsbG1v20mo19Nbder1OJpNhZWVlSxkP6WmRi2r9T2nl8/v9fO5zn2NkZASr1crMzAxTU1PcunWLVCq1KzW5HtryIv2HUrsfGhoiEokQCARUnAJ8MtgsLy+ztLTEpUuXmJ2dJZlMUqvV9lSBsXtlL8pD0zTcbrda2chAuWg0SjQaJZvN7pmstJ3QNI1KpUI6nVYBqXLztGKxqPzUMtajHWUlB9C1tTXK5TK9vb2srq7S1dVFIBBQBQ4LhQKhUIhcLqdcjgsLC2qnYDnhaJrGvn37iEQiHDlyhCNHjtDV1aUU6PX1dWKxGJlMhkKhADxZfVSmkEtrrgz6D4fDeDweRkdHCQQCVKtV3njjDer1unJ9NS+kpCITCoXo7Ozk9OnTHD9+nN7eXgKBAIuLi8zMzLC0tMT6+npLBafK7DFpoZNb1ugzO91uNyMjI4TDYV599VU6Ozs5ePCgqvBdq9WYmZlhbW2Ny5cvc+nSJRYXF8lms21h9dTHfsXjcSKRyBaFrDkYXGYiyQ1Be3t7efrpp9m3bx+zs7OsrKywvLzMysqK2tDzsSovev+YTB9zOBxq0zz5GaA2QZP7riQSCVKpVFvuens/NK925M8naVB81OhrCrhcLnp7exkcHFR7YeVyOa5fv87MzAy5XI5KpbKn24xEro4lcpfkjo4OAoEAfr9fuSPbsU3JAbBYLLKxscHly5dxOp309/dz7NgxOjo66O/vR4hG1VlZ4r9cLjM4OEihUODw4cMq1VrTNLq6uujo6FDmbiEE6+vrLC0tMTMzo7Iin1TlWb9ynpycpFwuK3e+3Jl93759/MRP/ATr6+tcvXqVYrGoivEFg0HsdjuhUAiXy8W+ffsIBoMcO3aMSCRCvV4nFotx8+ZNPvjgA6anp1tOQRZCKOubPlHEZDIxMDDAa6+9piouezwe9u/fj8vlwmq1UiqVmJubI5VK8c4777CwsMCVK1dYXV2lUCi0xSJc34YymQyLi4uMjIzgcDjo7OzkwIEDrK2tcevWLRWgbbVaCQQC+Hw+XnjhBXp6elTW3kcffcTs7KzKDt2tqt+7YnmRKVF65aU580EG7sq9aKRPdq+Xw5fsteBTPfqifXInXGl6lAPNtWvXlK9Upiy244R8P8h+JfuP2WzG6/VSLpfx+/34/X5VNKwdlT35/EulEoVCgatXr7K2tsb4+DiFQoHBwUGCwSAul4tAIKBM2oCKtWuOKZMptDKzKBaLsbKyoiwNMgX2XgrfPS7k5DM1NcXc3BwDAwMcO3ZMxZGNj4/zxS9+kYWFBcxmM4VCgVQqhc1mY2RkRAXMS0XH7/fj8/lwOBwsLi4Si8WYnJzkzJkzJJNJVeLiSZWHHikbWVBOptDL3aH7+/vp6elRx8vUe03TyOVyZDIZbt68yfLyMt///veZmppSSlA7WFyakZucZrNZNT7v378fu93OwsIC9Xodl8uF0+lk3759dHV18cILL9DV1cXq6qoqOXD58mVSqZSyujx25UVOHLI89PLyMh999BHFYpH+/n61YZXMqllbW2Nubo7FxcW23+32XpCTdrlcJpFIqIlHv2dLK61oHhSp5cty9zKQq1QqsbCwwPz8PKurq8RisS0TcbvL5U7ISbter1MoFFRZdrmTu3w96Smru4VU4qQbbX5+Xg2u+Xwer9dLd3c3TqeTUCiEzWajo6NDWYibAxFlSn4qlWJhYYEbN26oGh6pVKol+qR000sl5q233uLgwYPK5dHb24vT6VQuMVkvSVZTDYVCqlyBxWJhfX2dUqnExYsXmZ6e5sqVKySTSQqFQssoxvrFjkyFn5qa4vz587jdbrxer5pYrVYrLpdLbROQz+eZnp4mmUzyox/9SFnjcrmc8h486W3ifpB9qlQqqVixjz76iHw+r6xxMjvW7XbjdDoZGBjA4XCQSqXY2Njg6tWrKjZKLjp3c8H50MqLjEmoVCrMzMyoFN8DBw7Q2dmpJuTp6WkWFha4du0aS0tLqqhUOz3w+0GuAOQOm7FYDJfLRT6fB6BQKFAoFB5qy/BWQ7+PhtlsJp/PMzc3x61bt5ibmyORSKh9MPZqu5HIySmXy5HL5dSmeB6PRxWukxs2PkkZMY+CZgtMMplkbm6Ojo4Obty4gd/v58CBA3R0dLB//378fj9HjhxRtSv0yoh0oSwsLHDz5k0uX77MBx98oAZhWXX0SZ6w9dmMlUqFy5cvE4vFeOWVV1TMysjICEIITp48ueW7+mBN+CQ7dGFhgeXlZf74j/+Ys2fPkkgkiMfju7aK/rSQbUXWcJGuxs7OTrUxrtxnzmw2UywWmZ6eJhaL8dZbb7G2tsZHH32kYjX1xVnbCdkv5Bx08+ZN3nzzTYaHh3n66aepVqs89dRTKp3aZrMRDAYpFov80R/9EfPz87z55pssLS0pS6XeSrwb7EqqtOwspVKJjY0NZmZmeOedd/D5fPT09FAoFJiamlImWPng9/oEBJ+UOb9+/bqqOAhw/fp1FdzU7hYqOaDI7JmpqSneeOMNtQmYrAfUKtU7HzV6N2wul2NlZYV3332Xjo4OPB4P+Xx+SyZfu7cfPc2LgpWVFbVxp8fjIZFI4PF4WFpawu12b9mqA1AWz4WFBfWSK295/lZCxgTF43Fu3rzJW2+9RXd3N/v371dl/uVmlvV6XVUrlm61XC5HsVjk8uXLKh04lUq1fByVXBSurKxw+fJlfD4foVBIbX/gdDrVZLy8vEwqlWJyclJtuKivXdaqMrgbsi/J+jc3b95UmycDam6S2wc4nU6KxSJXrlxhfX1d1Z6S59r169tpVeZ2u+9rySY1dpfLRUdHh6oNUC6XSaVSyqy722a2XC73WFqPy+V66CWtzNZyu92qMqgQgo2NDcrlstJ8H0ZW+Xz+scjnXttPc4Vmmeoq3SLlcnlLme7d7giPq/3cb//S01wFs7OzU1kE6vU68XhcWWOai0TeL49LPl6v96HkI5UYIYSy5snAS7/fv22NCZl1JDN2qtWqeu9OY2Umk3ks8vF4PPfcv2RtE6/Xq1Kew+Ewhw4dwuFw0NHRQblcZmpqinw+TywWo1AoKOVPxhPJ/ng/q+hsNvupy+duspEykXEqMmZT/i739KlWq6qQqkww0XsMHnYsehyygQcbm4UQKn7MZrNt2WFdWvlkiEipVFL95mHG7J3Gnl3fVRo+2aq+VCqpOhP5fF6VrDbYiqyWqtdS5cqn3U3+sLVQmNyOXiorUi57QQ73gz5lURarkxYWWeZcL7d2XR3eK1IW0i0EqMlXb3nRHyeDOXdSXFoFmVqfy+WIxWJMTU0Rj8fJ5XLY7Xa1jYLcpV0GV0qrUzqdVpki0D7tSV+zRC6OpEIj71du0dLcBtpFBveCfO5ybLZarcoCIz+rVCoqXvPTCHfYVcsLbK1iqP/ZrKnu5oNvZcuL5G7yehiedMuLpLlugJ5HmYnVipYXSXO7keymvFrR8tLMdlV0d6I5W3InnnTLi0QfGC9fUoHTK2+wtfBYs/J2v23qSbS8SO7Uf7bjftrEvfKkW14kO43NzXxaY8+uWl5ga4443D4ZN/9u8AkPO0i0C9L9+CgV3nZhu/5myOt2tpPT3bKG2lV+MkX8XpFm/3aO79DT3JeMuev2fdVgeyXm02wju668wNbCa9t9ZnA7e2Vg2AnZQbaL3t/rstmJ7fqbIa/b2et9bK/f/3ZsJ487yWivy062n/uxWj5KHonyAsaDNngwjHbzYBhyMzB4cIz+c+88KbLaMebFwMDAwMDAwOBJY28XzDAwMDAwMDBoOQzlxcDAwMDAwKClMJQXAwMDAwMDg5bCUF4MDAwMDAwMWgpDeTEwMDAwMDBoKQzlxcDAwMDAwKClMJQXAwMDAwMDg5bioZQXIURQCPFvhRA5IcScEOKrdzhOCCF+RQgR33z9itih0o0Q4qub58sJIb4lhAg+zHU+Lgz57Iwhn50x5HNnhBB/UwhxVghREkJ8Y4fjjgghXhdCxIQQdy1qJYT4Z0KIG0KIuhDia7t5zZ8mRtvZGaP93JlWkc3DWl5+DSgD3cBfBP6pEOLwNsf9AvCngePAMeDLwC9ud8LN7/8G8HOb580Dv/6Q1/m4MOSzM4Z8dsaQz51ZBv4+8C/uclwF+H3g5+/xvBeAvw6ce/BLeyIw2s7OGO3nzrSGbPSbLd3PC3DT6Bz7de/9JvDfb3Pse8Av6P7+eeCDO5z3HwC/o/t7bPP/eB/0Wh/Hy5CPIR9DPp+KnP4+8I17OG68Mdzd83l/CHztcd+f0XaM9mPIZvvXw1he9gNVTdNu6t67AGyn3R/e/Oxux912rKZpU2x2xIe41seBIZ+dMeSzM4Z8DB4Uo+0YtD0Po7x4gHTTeynAe4djU03Hee7gW20+dqfzPskY8tkZQz47Y8jH4EEx2o5B2/MwyksW8DW95wMy93CsD8hqmzakhzjvk4whn50x5LMzhnwMHhSj7Ri0PQ+jvNwELEKIfbr3jgNXtjn2yuZndzvutmOFEKOAffP/tRKGfHbGkM/OGPIxeFCMtmPQ/jxkQM/vAb9LI0DsRRomxMPAMKABw5vH/RJwDegDIjQ6wS/pzjPLZgDP5vfTwMub5/0t4Pced/CSIR9DPoZ8npwXYAEcwD+kEYzqACybn2nAK5u/i83PJjbfdwB23Xm+gS4oEbBtHvMu8Nc2fzc97vs12o7RfgzZNF3nQ95kEPgWkAPmga9uvv/yZqO36m7yV4HE5utXAaG7oQxwUHfer26eLwd8Gwg+7gdqyMeQjyGfJ+cFfJ3GgKl/fR0YoDHBhjaPG97muFndeb4P/DXd3z/Y5vhXHvf9Gm3HaD+GbLa+ZCPdVYQQfweIapr2G/dw7EvA39A07Wd3/UKeUAz57Iwhn50x5HNnhBB/CTisadov38OxNhrZM8c0Tas88ot7AjDazs4Y7efOPGmyeSTKi4GBgYGBgYHBo8LY28jAwMDAwMCgpTCUFwMDAwMDA4OWwlBeDAwMDAwMDFoKy04f+ny+lgiISafTd9wF9VHicrlaQj75fN6Qzw48Lvl4PJ6WkE82mzXazw48rvbj9XpbQj6ZTOZTl4/Rt3amHeSzo/JiYPCkslOg+faVzQ0M9i6yv8i+cS+JGkY/MniSeeTKy906idFBGkg51ev1Le+bTA3PniGnT9DVDVDyEkKol4HBndC3F/3PdkY/Bjf/3jw+6+WiadqekM+D0FQXBWiM1Ya8Pj0My8sThlRW9NytQ+yVQWY7RdhkMu2Z+zd4ePTtpN3bzG4sHI2+dWf2UlvaDbZrjw8jt0euvMjJWHYCi8Wy5YJLpRK1Wg2z2bznGoCmaVSrVYQQWK1WzGYzHo8Hs9mM2WxG0zRyuRyVSoVarbbFKqNpGvV6fYu14X5Mwq2GvKdqtQqAzWbDYrHg8XiwWq0Ui0UqlQqlUknJdK+0J/3zrtVq264G95I87oTJZMLlcmEymahWq9TrdcrlMrVarW1ls52FQAiByWTCbDbjcDhU29A0jXK5TL1ep1KpoGmasTjYBik/t9u9ZT4rFAqUSqVtLVp7FSmLer2uXhLZBh/UYvVIlRd5cfCJWd9ms6kOoWmampj3MiaTCavVis1mw+fzYbFYsFgsSrmRD78Z2Yn01pp27jR6v73NZlPycjgcZDIZisUitVqNSqWijmt32vl57zZmsxm3243ZbKZQKFCtVlVbaWfk5KDKqguB2WzGZrPhcrnUwlHTNPL5vFoo6cdlQ4H5BLm4dLvdOBwO9b5U+ur1utEvm2heQMn29MRYXmTnkEpLMBjk0KFDOJ1OOjo6sFqteL1eTCYTxWKRYrHI+fPnWVtbI5lMks/nt2hi7dZZ9KugWq2Gy+Wir6+PUCjEyZMn8Xq9dHV1YbPZMJvNVCoV3n77bRYXF1lYWCCZTAKNhuD3+/H7/Xg8Hjo6OojH48zOzlKtVttKGZSDgKZp2Gw2ent78Xq9HD16lFAoxJEjR/D7/Vy+fJnl5WXef/99rl+/vuW77dSO9CuZZqubzWYjHA5jtVqxWq1omkY8Hld9rVqt7jm/vKZpmM1m7HY7XV1d/PRP/zRut5uzZ88Si8W4cuUKGxsbWCyWbV22rYhs99I6OTo6yujoKMlkkmg0isPhwO12EwwGmZiYwOFwYLfbqVarJBIJstks586dI5FIsLKyQi6X2yKbvdR+9NTrdWw2GwcPHiQYDPLyyy/T29tLuVymXC7zxhtvcOHCBTKZDJlMRi0u9yL6sbejowO73U4oFMLr9arFeSKRYHV1lWKxSCaTAbYPm7gTj8zyYjKZ8Hq9jI6O4vP56O7uxm63EwgEEEKQzWbJZrOsrq5SKpXIZrPU6/U9YaaUE5DVaqWzs5P+/n6efvpp/H4/4XBYDTrFYpHZ2VkKhQJra2tbtHmXy0VnZyfBYJBwOIzZbGZhYYF6va5cK60uw+bVi8Vioauri87OTo4fP04kEuH555+ns7MTt9vN1NQUt27d4saNG1tWme2MfkVjtVoJBoM4nU4cDoeybAJUKhVlxZPf20tYrVY6Ojo4deqUUvZNJhM3b95Ubut2QT5ji8WCw+EgEolw9OhRVldXsdlsajHZ19fH6dOnlTJTrVbVQjIWi2E2m4nFYoYVga0L8+7ubvr6+nj++ecZHR2lUChQLBaZnp5mcnKSQqGg5rK9iH6Mke41n89Hf38/XV1dWK1W7HY7c3NzZLNZNE0jnU7f9//ZFeVFPlhpNfH7/fT09LBv3z4+85nP4PP5CAQCyjUCjViXUqmE0+kkHo+zsLDAxsYGi4uLxGIxkskkyWSyLSwwUj4OhwOn00lPTw8HDx6ks7OT/fv3K0tLPB7n/PnzlMtlNfFcuXKFWCxGsVjcYvr1+XwMDQ3R39/P2NgYVquVCxcuKMWlXajVathsNjo6Oujp6eFP/ak/RSQSYd++fXR0dOD1epUlQsYxtCNy4DSZTIRCIcbGxpQCa7FYMJvNOJ1OxsfHcTqdyu24uLhIOp3m/PnzLC0tEYvFSKfTe0KJke3C4XDQ1dVFd3c3fr8fl8tFoVAgnU63XbyLvGdN0xgbG2P//v0899xzPPfcc8zMzNDZ2Uk6nWZtbY2lpSXefvttpdC4XC7GxsaIRCL85E/+JMlkku985ztMT08Ti8X2vAVGznGRSISRkRE6Ozvx+/3k83ny+TzlclmNQe0wbz0omqZhsVjYt28foVCIEydO0NfXR29vL8FgUM1hH374IWtra1sWVffDrlle5CrXarXi8XgYHBxkfHycY8eO4fP58Pl8avKV/tRarUZHRwfZbFYpLx999BGTk5NUKhU2NjaA1u8k8p6tVis+n4+RkRFeffVVurq6OHjwILlcjqtXr5JOp/noo49IJpMqFiiTySizpF5+Ho+HcDjM0NAQhw4dIhqNbvFdQ3vITU7aPp+P3t5eXnjhBQYHBwkGg9hsNqXk6QPC2m2lqA8ANJvNyl0WDAYZHR3FYrFgtVqV8uJwOJQFc2VlhVQqpdpPsVgklUrd5ntuV+SE4/f7CQQCeDwebDYbpVKJXC6nVsjtJAPZB/r7+zlx4gQnT57k6aefxuv1UqvVmJubY35+Xlm+TSaTUvD2799PMBhkeHiYUqnEtWvXyOfzZDIZ0un0np6U4ZP+19PTo9z20FiMN1s39zImk4nBwUGGhoZ46aWX2LdvH+FwGL/fT6lUolwuE4vFcLvdWxZT98NDKS/6AdVutzM0NMSBAwfo7+/n6NGjdHd3K39Xswktl8tRrVZxOBxK869UKni9XsbHx/nwww8plUoUCgVyuVxLdhq9e8hqtXLw4EGee+45BgYG2L9/P9lslrfeeotEIsGlS5dIpVLcunWLUqmkBiAZ/a9fIWqaRjKZZGlpif7+fvx+P8FgkGAwqNxNrWyB0Ct7fr+f7u5uPve5zzEwMEAkElHxU0IIyuUypVKJ5eVlpqenSaVSSlbtZrbt7OxkcHCQiYkJXnnlFTweD52dnZjNZiyWRlcul8tks1mmp6cpFAo4HA7MZjNHjhxhdHSUs2fPMjk5yerqKqurq4/5jh49sv91dXURDAaVxTcWixGLxahUKm2hvOgVXLfbjc1mY2BggPHxcUqlEufOnePjjz9W4410MctxQmbu1et1urq6ePHFF/H5fDz77LOMjo7y+uuvc/36dbLZLPl8fk/VgZFyle4OaUGHhjt2ZWWFqakpVldX1UKhHdrU/SIXAl1dXfj9fk6dOsWBAwcYHR2ls7MTp9OJEIL5+XkmJye5cOGCUqIfZKx+aOVF0zTsdjtOp5OxsTG+8IUvMDg4yPHjx1VGiD4VT35PmtkCgYDyucqAzEQiQT6fV+bKbDYLtJ4lQU7CFosFl8vFgQMH+NKXvkRHRwfhcJirV6/y9ttvs7KywsWLFykUCsqKIGXWnOoqZZjJZFhcXOT48eNKcens7ETTNNbX1x/znT8cMqDZ6XQSCoUYGRnhtddeo7e3l97eXpxOp1LuqtUqhUKBlZUVZmZmSCaTbZV6r1+RhEIhDh8+zMmTJ/nMZz6DzWZTCwOZQXP9+nVisRgffvghGxsbHDp0iM7OTg4fPkxnZ6fKMKnX622vvMjxyWKxEA6Ht1VeZBBzOyDbisvlwuPx0NfXx9jYGAsLC9y6dYsf/vCH/NEf/RGVSkW5pmU/ksGoi4uLhMNhIpEIY2NjnDp1CqvVysrKCtlsdstks1cUGHmPcj5zuVwqy0gqL9PT00p5keETewk5ZpvNZrq6uujt7eXUqVMcP34cr9er5KVpGgsLC7z77rtcuHCBhYUF4MEK/D2U8iL/2cDAAGNjYxw7dozx8XH8fj9CCNLpNKurq1QqFRXEJM38CwsLlEolFWDY1dWFx+NRwYa9vb0cOnSIyclJotHow1zmY6enp4exsTFllYrH47z99tvcunWLyclJ5SaCrXU54PYUM/mzWq1SLBaVCc5ms9HX14emaUxPT7ek6VLvJpJm7GeffZaRkRH6+vrweDwsLS1Rq9WUyV8GgEkzdyKRIBaLUavVWr7ei37ylebq0dFRFaANkM/nqVarZLNZNjY2eOutt1hbW1NZD9lslkAggNfrxeVyEYlEsFgsRKNRrly5siVzqV2R9aWkzLarfdIOyOfo9XqVid7tdpNMJrly5QrLy8vKHd28IJLjTj6fJ5FIcPbsWVZXV3nppZe2xOa9++67ytop4/Daue3o46YGBgbo6elRPzVNI5PJsL6+zuLiIrlc7nFf7mPHbDYr5dfr9WK32xFCUKvViMVipFIpbty4wfXr11lbW1Pf+1TrvOjztMfHx3nttdfYv38/x44dUw88kUjwwQcfkE6nWV9fVwW0yuWyMmt3dnbicrkYHh6ms7OTU6dOsX//foaHh8nn81QqFa5evaoUn1brKEIIhoeHefnllzl58iTj4+MsLy/zrW99i4WFBS5cuKCCUmX9hXs5Z6VSIZ/Pq8JIDoeDsbExarVay2r9euXF5XIxMDDAF77wBRWUXKlU+OCDD9jY2GBlZQVN03jllVfo7e3l4MGDhMNhlVYuY4XuVaZPInrlxeFw0N3dzcGDB4lEIlitVqrVKplMhlwux+zsLEtLS3zrW99iaWmJpaUlSqUSN2/exOfzMTo6Sm9vL6Ojoxw5coQbN25gNptVWn0r9q17RSov0rUGtysw7XDvUnkJhUIMDAzQ2dmJ1+slGo3yox/9iLW1NUql0o59IpvNUigU+P73v09nZyeRSASn08nJkyc5efIkuVyOaDRKPB4nm83uCSuDLNOwf/9+FcvZ19dHoVAgmUwyPz/P1NQUqVTqcV/qY0HfjywWCwMDA4yOjhIIBJR7rVqtsrCwwPT0NOfOnePs2bMUi8WHGnceSHnRx7qYzWaVCeLz+TCbzSpraH5+nqtXr5LJZIhGo2qgrNVqRKNRKpUKxWIRm81GLpfD4/HQ3d1NT0+PMvXKom36+iVP+kAj5eN0OrHb7fT393Pw4EE8Hg/r6+usra2plES4v9x26UqqVqvkcjmy2SzpdJpyuaxia550+eyEpmn4/X7Gx8c5ePAgg4OD+Hw+pbWfP3+eaDTK2toaJpOJ8fFxbDYbDoeDnp4ewuEwXV1d1Go1FWDYyhOzvk6J0+nE6/VitVoplUokk0lu3rxJMplkamqK9fV1lRUCqFpB+Xye5eVlpqam2Ldvn7LCdHR0kM/nyWazLSufe0VmarXzferru0jXhgxqly7pZkuJ/nd9P5Ft6OLFi2SzWSYmJlSCwDPPPMPly5fVZN1OCqAe2fc8Ho8KZh4cHFRV0BOJhLL0xuNxSqVS27exZqShQuoBMth7ZGQEt9ut5qpqtcrKygo3btxgZWVFFUN8LEXqZCCcnDT27duHx+PBZDKxvr7Om2++yczMDG+++aYyaUurgF7z12eHyDS0gYEBbDYb4+PjXL16FYfDocq/t8JKWgbB+f1+VZPklVdeIRaLMTk5yc2bN7l16xb5fP6+N16UxxWLRRWxvbq6Si6XUxUfW7Hz6E34vb29vPbaa+zbt48TJ05QLBa5fPkyc3NzfOtb32J5eZlYLKbaiMVi4dixY4TDYfbt28fc3BzFYpHl5eWWzqqRKxqbzaaKEQaDQex2O9lslrm5Of7dv/t3rK+vc+3aNZU9UqlUMJvNatuEcrnMpUuXKBQK+Hw+pRD29PQQi8VUgahWk8+9IJ+7dBvd6R5bsX00ow/Y1bsKpSIvJ1dJ8/3KsVXTNBKJBBsbG/zhH/4hgUCAX/iFX6C7u5uTJ0+yf/9+zGYzU1NTqqJ1u7mP9O6iSCTC0NAQn/3sZ5VFSwjBwsICs7OzTE1NMT8/3xJz06NAeg4GBwfp6+vjmWeeUeEjUnkpFotcv36dH/zgB8zMzJBOp9UY9aA8kPIiH1I4HKazs5NwOKzMQ+l0mmg0yuzsrNKwZHDYTo1bKjHJZJLV1VXC4TBer5fOzk5GR0eVNacVfNXyPkOhEMPDw/h8PqrVKtFolEuXLqnJVcZkPMj59bVNqtUq5XJ5y94arYS+TpDdbsfv9xOJRAgGg9RqNbLZrBog0um0cieaTCY2NjaIxWIqwt/tduP3+7fs2dLKSDeaDBaUgW+5XI5EIsH8/DzxeJxkMqmy1PToV9IbGxsqRsblctHb29uS7eV+EULgcrnUvkbt7uaQCoW0Vj/M85WZnnNzcwSDQQKBAKFQiN7eXoaGhkgmkypBoN0sMDKIeWBggIGBARVDJCfkVCpFPB4nn88r68NeQp+F5Xa7GRwcpL+/n0AgoOQkA5rj8fhtZRsetp3cl/KiN0taLBZVQ+D48eMEAgESiQSLi4tcvnyZt956S7k0AOx2uzpP80XLrBFZVOujjz7iueeeY2xsjEOHDvGVr3xF1UEpFosUCoVtz/OkIB/MoUOHePnll4lEIqRSKc6ePcu/+lf/io2NDZVB9aANXk74tVqNcrlMJpNhdXWVjY2NlpuMpCImS5aPjIzwzDPP4HK5yOfzzM/P893vfpfl5WWWlpZU8He9Xmd+fh6bzcaRI0cYGhpSZsuZmZktMQ6tiD7mRbqM/H4/6XRapWe+8847aoKBT+I7JPL9WCym4tAKhQLd3d08++yz1Go1Ll++vGUfsnZBn0XT09NDd3e3suLqM/fgyR1L7gc57hSLRXK5nFrMSIv3fZVe32xDyWSSdDrNm2++ya1bt/jpn/5pjhw5wtNPP02lUuHChQtEo9GWG3PuhtynyO1289nPfpbBwUEGBgbweDwqbnNubo4bN26QyWRUX22HdnQv6BecLpeL7u5uPvvZzzI8PMzw8DB+v1/N1e+99x6XL1/m3LlzKhZvN8IbHmh0N5lMWCwWvF4voVBI5W9Ls7X0u5dKJfWdnS5Ur4WlUimWl5eJx+PkcjnsdjsDAwMkEgl8Pp/aF+lJzJDQa6JWq5VAIKAyQ2TFYL3i8jD/RwYg2u127HY7mtYosSzLLbcCzdep3/LA7XYDjUlXBghK16M+5T6bzZJMJimXywCq5sn9uuOeZKTyIhcN0g2QzWYpl8tUKhVlfr3T/Tbv6ipdUbKgXTvI6U40u43a9V7lfckFjWwb9ztWNpe1qNfrJJNJlTK9traGxWJheHiY9fV1AoEAxWKRfD5/27W0KiaTaUucmdyTx2QyqYq6snCfdJvtNer1Ok6nk0gkQn9/P5FIRG1vA7CxsUEqlWJ+fp7Z2VmVVbtbNcge2PIizc779u3D7/dTqVSYn5/nzTffZHJykkwmsyUl727IVd+tW7eYn5/H4/HQ09NDT08Pr7zyCk6nU6VXJRKJJ3KClg+lo6ODjo4OxsfHOXLkCIuLi1y9epVbt26xvLy8JTXxfpGDidyfJBwO09PTw+LiIjdu3FATfKt1JiEEPT09PP/88xw+fJhQKMTq6ioffPABk5OTTE5Okkqltux/JS110vIk20S7bOwpg7NlX5BbbBQKBaamplhZWQG4p0lZfiZl43a76ezsxOPxqE0c283sr6fdFTT45Lnl83k1cUgF9377g1Rg5DYTCwsLrKysEAwG2djY4KmnnuKnfuqncLvdWyzugPpOK8pbjq92u10lonR3dxMKhdTCQQbGz83NMTMzo4r2teL9Pgj6kAWfz8dP/dRPMTQ0xOnTpwkEAthsNsrlMmfOnOHWrVu8/vrrXL58ecvCczdctw98Bml9keafWq1GsVhkY2ODXC6nLvR+A1FlTRgZoAsNl5PVasVisbREA5FykdUYZdyGjHN5GOVCPnyHw0FHRwdut1sVKpN1X1oJfUCljOr3er1AIyh5bW2NWCxGqVS6bWNB6TbTa/PtOIjIWBdpbavX6xQKBWVtgntTOPTWKLkXksPhwGq1tn0cCKBcY/KlH0+exMXQgyCEUDFwcgsWq9WKy+VSSur9nEt/zmKxqPahy2QymEwmOjo6GB4eVituveuxlWVqs9kIBoNqJ2TpXajVaiQSCaLRqIq/e9DYxVZFCKHkI2u69PT0qEKzMsNxbW2NxcVFpRNUKpVdjVl94KAA/T4yUuHY2NhgdXWVRCLxwKYhObBKH225XCaVSqnXk1wISJ/rLicbk8lEoVBQlYJ3w92laRo9PT2cOHFC7dptsViUj7tV0FuRnE4nfX19HD16lFAoRDqdZn5+nh/+8Iesra2pOJfmeA4Zsa6fmNslKFMqYk6nE7/fr9KkNU0jl8upOgn3ir7tybIGoVAIn89HoVBo2UrW94JUkp1OJ9VqVVlHZZHHdoj3ke0ln8+TSqVUYHswGFQFP1dWVu4rK6a51MCNGzeUZby3t5euri7+8l/+y7zzzjvMzc2RTqeVZbzV+qB+YRQKhXj11VcZGRlRmbRCCDKZDO+//z5TU1PcvHlTlWxotXu9X/SykVnBL730ktq7SFbKr1arzM/Ps76+zttvv61KW8g5fTfHloeWuJyA5P47D+rT0qdLS6uF9OMXi0X1ktaYVkA+KLnfjFQsHvYByuwJuTlhPp9XsmmVWjiwVdmz2+243W61F5bcOE9m0tzJime323G5XGoVLa0x7bJBo1TGpCKsj2t4GN+xXslrhbbyMMgFVq1Ww2KxqL3U9BtYtktbgU+qb8uMI5vNRiAQwOVyPdR5oeGSSiaTxGIxVXi0q6tL7bAsNypsVaQV2O12q2xap9OpikJK69P6+rpSDtuh7dwL0o0oEyuGhoYYHBxUG55K78v6+rqKfZXxiI/CIv7ARer0Jns5sTSvgu8HqQB1d3fT2dmpNF5pfpIbycmA1FYYcKX5dnl5mcuXL7O6uvrAKzw5GcsJJxwOc+DAAXK5HN/5zne4fPky8Xi8ZYLHZBuq1+t4vV76+vro7+9nYGBA1TCZm5tjcXFx28wsGek+NjbG+Pg4gUAAIQS5XE4FjEtLQ7usiuQAIO/pfl2y8InCKOWUTCbJZrMt027uBymncrnM2tqaCr50u92MjIyoiUjWnGh1ZDtPJpPk83n1fAOBAE899RSpVIof/ehHW4Jx77cNyW03Ll26RLVa5aWXXlJuoxdffJHp6WmWlpZaapEJnyzC5Wano6OjHD9+nHA4jNVqpVKpsLy8zOrqKrdu3WJ6eppSqdQyoQwPi5zvZVs6ePAgX/7yl9WmwLVajYWFBaLRKL//+7/PzMwMN2/eVLGIj2KR9ECjul5xkb5VqbzIjAj9sfd8MZt1OoLBIB0dHXi9Xsxms4rubvbzP+nIDlEqlUilUhSLReDBrCJyIHY6nfh8PrxeLz6fT6Xsra+vb3kWT3KHam4Tdrtd3ZOsD5BKpUin0zs+c+lzlxsO1ut1lSYqawu1OnrLplRg7/e+tju+Wq2qTQp3MwPgSaRer6ttFGQWSUdHB36/v+UrUuvRW3qb9z0LBoO4XK4HHhv0sUEy+2hhYYFEIqGy3eR+Sq28WLBaraptBINBfD6finWR2aKpVEolpOwVyyWgkgf02UWhUEhlF6VSKWKxGLOzs8zOzpLNZqlWq8Cj8QTcl+VFXoCcUCYnJ/F4PDz99NMMDAyozRSh4RvV747c3Gn0K29oZOi4XC5eeOEFnnnmGY4cOYLP51NWl0QiQblcfqTC2C305njZ8PXXfj9IN5DctPLHfuzHOHHiBJ2dnTgcDqLRKO+++y7pdLrlJiApo56eHo4ePcrQ0BBer5f5+XkuX77M9PT0liBduD3jbXR0lImJCQBWV1eZmpriypUrbVF7QvaRVCrF0tKSKohVq9W2uFXvdg7Y6iaSwd3S6iDrnrR6XZxm5IIqn89z7tw5MpkMX/rSlwgEAkxMTODxeLh69SqJROK+EwyeNPSxKZVKhUqlQiaTIZlM4nK5CIfDynUkF536797r/5DtSAZhDg0NqQ0+h4eHSSaTLam8yLkoHA7z0ksvsW/fPvr6+lTx1Uwmw5kzZ5ibm2N+fl5t/truyosMB5HV4p955hm+8pWvEA6HcbvdKrMsk8nw7rvvqto3a2trVKvVHataPywPpLzIjJlEIsHy8jIHDx7EYrGoOh0yGPB+zJMulwu/309fXx/j4+N0dnZitVpVTQs5aLfqhPSwioXD4cDr9TI2NsbJkydVHYdcLsfKykrLmWklMn5HBo7abDZVjTidTt9maZBtScYtyIqf9Xpd1XyJxWJbak7I/9OKyIJYcsM86abV75J8LzTHzVQqFRX028qbed4NmSkTjUbx+/1q0g6FQhSLRZWFo6+B06ptRSow8l6kFVJWF5Z7HT3MJrfyO6VSiUKhQCKRIJlM4nA4Wnp7EkAFdPf19dHd3Y3b7VaxLqVSiZWVFZaWlshkMhSLxT3hMtJntwaDQXp6ehgbG1MlFgAV67K0tMT8/LxyRdtstkc6rjzw9gAy979YLDI+Pk4mk8HpdDI2NkapVFLpdOvr6+rh6wcIu92ugn9cLhcvv/wyY2NjPPPMM4yNjVGv11lZWeHmzZv86Ec/UiX1n+Qodr1lSu/yCIVCHD16lGvXrqk6Lzsh3XKyHofT6eS1117j4MGDHD16lO7ubs6cOcPZs2e5fPkyhUJBDUat1JnktepjpWQmzfz8PGtra8rypJ9YnE4np06doq+vj/3799Pb28vU1JTalkIG07VDUTIhGsUfl5aWWF1dJRqNYjKZmJiYULuJb6cY6029JpOJ4eFhxsbG6O3txel0sr6+zkcffcTS0tIjXR09TuSzr1arxGIx/H4/8XhcpYfL1M52u3/5zKemprBarbz88svs379fbRC7trbG5OQk8ElNlntFKj6yz7pcLtxuN7lcjoWFBRYWFlSfbQXk4kiWI+jt7WVsbIzu7m4VLxWLxVhcXOTatWvMz89TLBaf2Dlot5BykfPzxMQEzz33nNpgWAa7Z7NZbt26xdzcHBcvXmRhYeFTiwV6KDux3E8lkUhQLBaxWq10dXXR29urquLKSrvSMiBdSVarFZvNht/vp6Ojg4MHD3Ls2DFGRkbo6uoiGo2SSCRYW1tjdnaWWCym8umf1IYjH5bcLVtWuHS5XEQiEVVU7F7Qa7w+n49Dhw7x7LPP0tnZidvtJpVKce3aNVZWVpRcWjXosDkAVe6YLIOz9aW35f5Hsgy13AMrk8mwtLREIpEgk8lscVm2OqVSSVXzlJsodnd3EwwGsVqt/zd7fxYjV5on9qG/E/u+ZkRGRO4bM8kkWVyKtbG2HtWM1NOaGcHzYLtH0hUgSDZsP9kv14AvIAiCryXAj7Lv+MEYW2N74IFHmmnLo1Z3dVdXs1hVzSoyuTMzmfsS+74vGXEfyO+rk1nJ5E5mRJ4fECAZefIwzj++5f/9V/R6/SPdHkJmfr+fwcFBmVZfLBZZW1uTZv5ekNN+CLdtuVymWCxKC5aIz1OncHaz20iNWB8TiQR6vZ6zZ89Ky3Y4HJaHQPW8ehrEAVL02xLWiXg8Tjab7Tr3tTqLxuVyEQgEcLvdUvEtFAqyDIg4jPfK2vIo9hbrC4fDHDt2jIGBAemyVhRFJqRsbm6ytbUl5fMqDgTPrLyID95ut4nH4ywtLcm+NC6Xi76+PnK5HOvr63KhFM0D9Xo9g4OD2O12wuEwTqeTM2fOMDAwgF6vJ51OMzc3x7Vr17h165a08Bz2zVl8WUJhW1pa4saNGxiNRi5cuECxWOTGjRv71tRQW04MBoPs/Pv2228TCoU4duwYJpOJb775RvZ/WlxclJ2pe2UiKYqC0+lkZGQERVFYXV0FvlPizp49S39/P7/9279NOBxGp9MRi8WYm5vj6tWrbGxsdEVc1JOgzpYpFAqk02m2trbweDyEQiHZFNVgMJBMJmX1YUC6gvr7+3G73Zw9e5bz589jt9uJxWJsb2+zvb0t+yJ1u6weh+hVk06nZeAqfHfChO4fLwLxHMLtGo/HSafTeDwePvzwQ5xOJysrK1SrVWm1fdQ99ip2omDb5OSkrCAeiURIJpPcvXuXWCzWdcqLTqcjHA5z6tQpZmdnd8VzlMtlvvnmG9bW1r6XxdjrKIpCJBLh1KlTvPnmm3L9EOEcIhbv0qVLbGxskM1mZVLKq+CZ3UaA7GgsYl9cLpdcVIeHhykWi2xubpLL5aR7o1gsYjAYmJmZwePxMDg4iNPppL+/H4fDQSqVIp/Ps7i4yOXLl9nc3CSZTHaFZUHtNup0Omxvb7O0tMTs7CzT09MsLi7icrlkEKb6dwSiCmgkEiEcDvPBBx8wNjYmXSrz8/NcunSJzc1NNjc3ZbXQXkBsolarlVAoJKt4wgPlxePxcO7cOYaHh3nzzTfx+XyyD9bi4iI3b94knU7TarV6otGg2pLXbrdlNL+If3K5XHi9XhqNBslkcleNH7HAejwe+vv7mZycZHZ2lkQiQTablVVCe8Xa8Dg6nY48Refz+a6vR/Io1EqZOERls1kKhQIOh4PTp09TKBRwuVwAj9yMH6XQCgvOzMwM7733HuFwGJ/PByDjHbolLlE99v1+P8ePH2dsbEyWuNfr9dRqNRYWFlhdXZWxLr2UobYf6u8vEAgwMzPD9PQ0x44dk/NI1OFKpVLcvHmTaDRKsViUmWevQj7P5TYSD7m8vMxnn31GNpuVXSZ9Ph8Gg4FwOIzH45FR7vV6XdYpEadpg8EgG13Nzc2xtLTEN998w8rKyq4NrFsQikY8Hufu3bv4/X7GxsYYHx/nj/7oj1hdXeXKlSvUajWpzLndbiwWC5FIRFoYRC+bWCzG3bt3icfjzM3Nsbm5SbFY7HpfvTj15nI5tra2GBoaolwu43Q6OX/+PMFgUCohAwMDeL1e3n77bVwul8yk+PLLL9nc3GRhYUE2/uqVipfqjajVarG9vc1XX33F6dOnOX78OC6XizNnzrCxsUEqlaJarUol3+1243A4+PjjjxkdHWVkZASTyUQikWBxcZF4PL6rkF83j6OnQZTMF3JSb9K9pMgJd9nOzg4LCwt8+umnjI6OyoPUj3/8Y5LJJHfu3KFer0tXknh+EZMoikYKOfX398sgeXjQi+7q1avcvn2bdDotT96Hff6JtUe0cvF4PEQiEemKbbfbZLNZUqkUsViMdDrdlT3jnhaxHoh2PMFgkPHxcfx+v4zFbDQaxGIxvvjiC5aXl3cpra/ywPjcuZGdTkea1KrVqswDF5Hn/f39AAwPD+9KjRYBmuLfW1tb5HI5vvnmG77++mtWVlZYX1+XPUi6CTHRE4kE7XabyclJqtUq4+PjnD59mrm5OVmpcmtrC4vFItuIv/HGG/j9fs6cOYPNZuP69etEo1F++ctfcuPGDdkjqRcsC2IBERY69Rg6e/YsQ0NDuFwurFYrx44dw+l0Mjg4SKfT4caNGyQSCT777DPm5+fZ2toin8/3lCUKvrOidDodWfzLbrfLpminT5/GbrczNzcHIIMPBwcHCQQCfPjhh8zMzBAIBDAajdK8L6qjHuYYsheNaCYnLFS9ouQ+ClFmYGlpiWazyQ9+8APOnz8vT9IbGxt4vV4ZTyXWYr1eL3umDQwM4HA4ZGyQaBCaTqdJp9Pcv3+fS5cukclkSKfTwO6eSIcVtfIi2m8IK5LRaJR9+tLptKwW+yylLroRdQBzMBhkbGwMn88n06ZbrRaxWIxf/OIXbG5usr6+LoN0u0p5EUFNlUqFjY0NfvOb3xAIBIhGozidTsLhMHa7nUgkIsv+AzLtU1TNFbnht2/fJhqNymyRblxc1LEvYqPV6XQMDg4yNTWFwWDgnXfeoVqtksvlZLqw2WzGZDJRrVa5cuUK7Xabubk5YrEYm5ub1Go1GddwmBeGJ0U8QzabZXl5mXA4zI0bN/B6vTKeZXZ2FkVRZMuAW7duUSqV+Prrr4nFYqysrMgaQN04Vp4EdSBzJpNhbW2Nq1evYjKZ6O/vx2Aw8Mknn8hyAkajkfHxcTweD8PDwzidTra2tiiVSty4cYO7d++SSqVe81O9GkS8hrBeiWJ1IyMjADLzqBfmk0A8s7AAF4tFtre3uXHjBna7XW7U9XqdsbExWQlcHasi/i7qnFSrVVqtljxhi+BVEaQpOit3i8sIvqvSbTabsVqtUmGDB3VdFhYWuH//PslkUj53r6y9j0KdKOJ2u3G5XHg8HqxWq4y/S6fTJJNJWX/tdVksn0t5ER9YVHRcWFjYFfvi9/uZmZkhFArx/vvv43a7dxX9KRaLfPvtt2xvb8sCQCJbBOhat4j4zPl8nmw2S6VS4fbt21y4cEG6zP7m3/yb8lTdaDTIZDLU63USiQT5fJ6rV6+STCa5fv06qVTqe+0Bupm936loMW+xWPB6vczOznLs2DH8fj+jo6NUq1W2t7fJZDJcvnyZaDTKz372M+LxOIVCgWaz2XMWFzXCkicCvefn5/n000+ZnJzkd37ndxgaGmJwcFBuQkajkdHRURwOB3a7HYDf/OY33L17l6+++oq5ubkjkTGhRgTsZrNZPB4PLpdL1iYRXdm7aeN9HGrrRz6fJ5fLyVjCiYkJ3n33Xfr6+jhx4gRGo3GXJbzdbpNMJmXJh3q9TqlUolwus7y8LA9TW1tb8iQuLHjdsjbttbyITCOLxQI8yKS9evUqy8vLbG5uUi6XZWZfryNSpP1+v+xbJeZIrVYjGo2yubkpwzpel1L3QvwxYuCKwkjiISqVCu12m1gsRqvVwuFwyECxTCZDpVLh3r17pNNpotEohUJBlnXvhYVVPIMoJrexscGVK1fw+/1sbW3JL7zRaMi0c1FFdWlpSdaK6eW4BPFM7XabdDrNnTt3KJfLsomeXq+nXq+TSqUoFArcvn1bluiu1+s9FafwOMR4KpVKLC0tyRpCYuE1GAyy83S73aZSqbC9vU2lUuHGjRssLCyQTCZptVpHJmNCIObZ/fv3KRaLMs03Go1+L96jVxGWO1Hg0e12E4vFZNyHQJTCr9frVCoVGo2GDFaNxWLkcjkKhcKu9Pxuk53Ys1qtFqVSieXlZX79619jt9vxer1sb2/LrtHqLL6jgDgoibCGhYUFXC4XPp+P1dVVrl27xsLCwq7WLa/j+1cOOm24XK6nOoqoawcIbUy4ikQEt+iDILqeik1IdH19Fu29UCi8lpljs9meSD57g8NsNhter1cWemq1WuTzeZrNJqVSiZ2dHVnUT1RFfR4qlcqhlo+wKplMJtkl2uv1yucWG3Gz2SSXy8kstxel8b8u+Tgcjqc+6qsVWZ/Px4kTJxgaGuKHP/whXq9XupFEEKZIY/z8889ZXl6W8+xpNpxSqXSox8+TIFwEojCdSCiIRqNSWX7WgMzXNX6cTucTrz/iz52dHbnuiEJ9ezM5O50O9Xpd9q8TVit1t3Z1zN3jZFYsFl+5fJ50bon9xuFw4Ha7ZSVqYQ1vtVq7ioC+aF7X3HqUfNQBu3q9ng8++ID333+fSCTC6OgoV69e5c///M9Jp9Osra299OrcB8nnpUbCqqOTS6XSruBbMRnq9brciLpRg38axCJQrVblpDEYDOzs7FCpVGi1WnKx6CUT9uMQ37kYK+r31a419WKq/r2jhIgrECW54/E4AHNzczgcDnw+H3q9nkajQb1e5+7duySTSakcd1sNjheJaLUgLAs6nU4eEnrZ8rI3m6rT6cg2E2KOiZdYd8RYEdYVobjsvWe3I5JI6vU6hUJBrsvCBasuP3AUEGNAfNfxeJx79+6RSCRkR+1MJiMLiL7Wz/oiLS/7odb69/0ALyAy/bBbXtQ8Sh771Vl4URx2y4tAXTDsIPk86u/PSjdZXgRqC4xQgtWZQ+LnwkX0PGnRvWB5EewdXy/iwHTYLS8C9Zx62o3nUTJ6EtkdZsuLQL32CF7FYfqwWV7UCGulsG6L0JBX2UfvtVle1BxUxfGosd9EERxVmah5UvlosvqubonaYrWXvafro04vVtV9EvazwDzLPOtVme2nvPSyRe5JEIcfgdrY8LrjgF668iIe9ihEaT8J2gZyME8qn6MuQ21ePRva/Nu9KT+tLHpVdnvjfjS+myvqTLzD9P13V/U3jZ7mME0MDY1eRptrGk/DYRwvB8a8aGhoaGhoaGgcNo5O8rqGhoaGhoZGT6ApLxoaGhoaGhpdhaa8aGhoaGhoaHQVmvKioaGhoaGh0VVoyouGhoaGhoZGV6EpLxoaGhoaGhpdhaa8aGhoaGhoaHQVz6y8KIryXyiK8o2iKHVFUf7kgOtOKoryU0VRUoqiPLaojKIo/5OiKPOKorQVRfkHz/r5XjeafA5Gk8/BaPI5GE0+B6Moik9RlH+tKEpZUZQ1RVF+/IjrFEVR/rmiKOmHr3+uHFCRTFGUHz+8X1lRlH+jKIrv5T3Fy0OTz8F0g3yex/KyDfwz4H9+zHVN4P8E/uET3vc68J8BV5/9ox0KNPkcjCafg9HkczCafA7mXwINoB/4I+B/VBRldp/r/jHwd4A3gNPA7wH/yX43fPj7fwz8vYf3rQD/w4v+4K8ITT4Hc/jlI5pzPeuLBwvInzzBdZMP/rsnvu8l4B887+d73S9NPpp8NPlo8nnFMrHzYOM5pnrvXwH/3T7XXgb+serf/xD46hH3/W+B/13174mH/4/zdT+zJp+jJx8t5kVDQ0OjtzgGtDqdzoLqvevAfifn2Yc/e9x137u20+ks8XCTe65P++rR5HMwXSEfTXnR0NDQ6C0cQGHPe3nA+Yhr83uuczwibmHvtQfd9zCjyedgukI+mvKioaGh0VuUANee91xA8QmudQGlzkO7/nPc9zCjyedgukI+mvKioaGh0VssAAZFUaZU770B3N7n2tsPf/a46753raIo44D54f/XTWjyOZiukM/zpEobFEWxAHpAryiKRVEUw8OfdRRF+fjh35WH15ke/tuiKIpZdZ8/Uac6Kopieni9AhgfXt91SpYmn4PR5HMwmnwORpPPo+l0OmXgL4B/qiiKXVGUi8AfAP9KUZTRh/IZfXj5/wr8l4qiDCiKEgH+K+BPxL0URVlVvksZ/9+A31MU5QNFUezAPwX+otPpdJVlQZPPwXSNfJ4jIvmfAJ09r38CDPHAX+Z/eN3oPtetqu7zKfCPVP/+bJ/rP37dEdiafDT5aPI5PC9NPo+Vjw/4N0AZWAd+/PD9D4BVwPjw3wrwL4DMw9e/AJSHPzPxwKQ/o7rvjx/erwz8JeB73c+qyedoykf8Jy8MRVH+LjDb6XT+6ye41sSD6OPTnU6n+UI/yCFFk8/BaPI5GE0+B6PJ52AURflvgGSn0/njJ7j2feA/73Q6//HL/2SHA00+B3OY5PPClRcNDQ0NDQ0NjZdJV/lyNTQ0NDQ0NDQ05UVDQ0NDQ0Ojq9CUFw0NDQ0NDY2uwnDQD202W1cExFQqlUd2sXyZWK3WrpBPtVp9LfLRxs/B2O32rpBPuVx+LfJxOp1dIZ9isfha5ONwOLpCPqVS6ZXLR1ubD6YX5taByouGhkb30+l0UB7dpV5DQ+M5eFTSizbnXi6a8tKldDod2u02iqLISSL+rmWQaQja7fYu5UU9XjQ09mNPvY9d40en02nj5yFCPmKOCfauyRovB0156WL2myRHWXHRFLfvoy2ij2fvxqOxWw57ZXLULXl715ijfCDYb719VbJ47cqLeFChvbbbbdrtNnq9Hr1ev+sabWP67lRks9mw2+3s7OzQarVoNpvU6/XX/fFeCurJIMZAq9Wi0+nIk6BOp5PXauPkuw3G4XBgMBjk/KrX6zQajSO72KrpdDq0Wq1dFky9Xi/H0lFCbWkR48ZoNGI0GtHr9TQaDXZ2dqhUKtTr9e9Z844CaksLfGeFcjgcmM1mOXZqtRq1Wo1WqyXnWq+NKbUs1PIQf76KMfHalRc1R2USPA9CRlarFbfbTaPRoFarAXxvUdE42iiKgtVqxWQySWWv1WoB2ukZvjsxH+WTsxq1AudwODCZTNhsNrkhN5tN2u22HEtH/ZAgDtgOh0POM4PBQKFQQFEUarUajUbjdX/Ml8ZB3/+rsMi8duVFaG02mw2TyYTf78fpdJJIJIjH49okeYg4Off39+PxeLh48SLvvfceKysr3Llzh7W1Na5evUqn08FgeO1f6wtlr1nfYDAwMjKC2Wwmn89Tr9cpl8uaRUFFp9PBaDTy4YcfMjQ0RDabpVQqcfXqVRYXF9HpdNKyeRQRm/TExAROp1OelFOpFLlc7nV/vFeGUGINBgM2m43JyUl8Ph9vv/02fX19+Hw+zGYzmUyGcrnMvXv32NjYYGVlhZWVlV0n716ce+q1R1h6XS4XFouFmZkZ+vr6OHPmDAMDA9jtdiwWC0tLSywvL3P37l2uXLmyyyre7RYYsR/rdDqMRiMWi4VgMAhALpej0WhQLBZpNpvfOxTsPSg873h57bucmDxCyw8Gg/T399NoNEgmk98LhjqqiEHjcDjo7+/n9OnTfPLJJ1y9epVKpUK5XJZut15GLLR9fX04HA4URaFUKkl3iMZ3C67YnE+ePMnW1hbZbJbFxUXpJjnq1he9Xk9/fz+BQIBKpUKtVqNSqUjlpdflIxvcPZxTFouFkZERwuEw7777LpFIhFAohNVqJR6Pk8/nsdlsOBwOyuUyGxsb0u3Wy6jdaTqdDrvdjsPhYHx8nKGhIS5evMjExAQulwu73c6NGzfwer1Uq1Vu3boFQK1W65mxJMaM0WjEZrMRCoWABwfsarVKqVR6IsvL886v16a8iMVzZ2cHRVHw+XwEg0E++OADTp48yc9+9jMymQyVSoVCoSB/56ghBoHwPU9PT3PmzBkGBwdptVoUCgU2NjZIp9NA78poZ2cHg8GA1+vF7/fzox/9iEAgwBdffMHW1hb1ep1SqXTksyHEgmCxWHA4HHg8HjweD+vr6xQKhV1xUUdZTjs7OwBMTEwwOTkp16JGo0EikWBnZ6dnLQpCadHr9RiNRnw+HxcuXCAYDPLee+/h9/sZHh6Wh4NGo4HNZsNoNHLu3DnGxsbwer243W5WV1eZn5/vOQu5WrHT6XTYbDaOHz+O1+vl1KlT+P1+hoaGpNXu7t270o1kMpmYnZ0ll8uxurpKNBrl7t2738sO7SbU361erycYDHL27Fn6+/t588030el0xGIxSqUSCwsLFAoFarUa7XZbzqV0Oi3fF+tQVyovAvGFulwu+vv7OXXqFO+//z5ra2tcuXKFdrtNPp/vyi/8RaA+RZvNZoaGhjh9+jSBQIB2u025XCaRSPS0gqdeSBwOB4FAgLfeeouhoSESiQTtdpvl5eWuXhxeFEJOZrMZq9WKw+HA4XDIYEsRr3CUUVsow+EwU1NTGI1GFEVhbm4OvV7f89YEkRRhNpvx+/2cP3+egYEB3n33XVwuF3q9HkVR5OYjxpPL5QKQiQI7Ozvcv3+/Z5U94SKxWq3MzMwwMDDAD37wA/r6+rBYLADcu3ePRCJBs9lkZ2eHU6dOcezYMba2thgfH6fVanH37t19U8+7CSELnU6Hx+PhjTfeYHh4mI8//hiDwUA8HqdQKODz+chkMvKwJOQixsnOzo60RD2PLF6b8iI2JBHRPjg4yPT0NH6/X1oZ1Kfobv3CnxcxYCYmJgiHw8zOzjI+Pk4mk+Gbb76R8S7C6tCLiNOP0+nkzTffZGRkhFAohNPplBNBKC5HGbEp6/V6+vr66O/vx+v1SuWlUCjQarWOtJz2KndmsxmDwSDdRqVSSQam9hrqeAWTyUR/fz8XLlxgeHiYc+fO4fV6sVgsNJtNVlZWKJfLVCoVGo0GFosFo9FIKBTC5/PR19fH2bNnqVarrK+vk81miUajQHcrLyKGx2Qy4XA48Hq9vPHGGwQCAd555x3cbjc7OzvE43GWl5fJ5XIsLi6STCalfMvlMoqiUCgU6OvrIxaLdX18mXg2u91OOBzm2LFjnDlzhkAggN1uR6fTEQgEpOJbrVZlHJmQaSwWI5PJsLi4yNLSEqlUiu3tbTkmn5bXqrwAmEwmzGYzAwMDTE1N4fP5MBgM31NejiJqi8Pk5CSzs7NSednc3OTbb7+VyksvpuOpEVH958+fZ3x8nFAohMlkkum/wg1wlMcLPDgRG41GAoEA4XAYr9eL0+mk1WpRqVRoNpuAJiez2YzFYpEZIuVymXw+T6lUotFoyLWn1+QklFuTyUQkEuG3fuu3GBwc5OzZs5jNZlqtFuVymaWlJZLJJKlUimq1is1mw2q1cubMGSwWCz6fj/7+fvL5PPfu3WN9ff25NqLDgnAf6vV6vF4vY2Nj/O2//bcJhUIcP34cvV7PzZs3SSQS/OpXv2JlZYXl5WXS6bS0Suj1ejweD61Wi76+Prmhd7tC3G63sdvtjI6OcuzYMd544w0cDgcWiwVFUbDZbABEIpF94y9zuRyVSoXLly9jt9u5c+cOm5ubz2yBeW3KS7vdltqax+NhfHyciYkJ9Ho96XSaXC5HsVh8Ib6xbmRnZwedTsfg4CAej4cTJ04wOzuLyWQilUqxsbHB/Pw8iUTiSMhGBBW63W7cbjetVotWq0U2myWVSh35YF1xGDAYDJjNZvr7+wmHw5RKJba2tuSrUqlIl8BRQ2xMZrNZKsBjY2MMDAywsbFBPB6nWq1KK143b8J7EUqFiFeYmppienqaqakpvF4vAJVKhZWVFWnVjcfjpNNparUaZrMZo9GI2WzGbDYTCoVwu924XC76+vrI5XJyE+pGK7nYbA0GA1arlUgkwvvvv08kEiESiWA2m7l79y6lUomvv/6aeDzO/Pw8mUxmlwtkZ2eHcrlMMpnE6XTi8/lwuVzyoCVKFXSjfER9MWH5FlbLR7mh984fu92O0WhkaGiIQqFAsVjk9u3b0pX0tOPmlSkve4uHCeUlEokwNDQkN+dYLEY8HieVSpHJZPZNuToKiBPS5OQkIyMjXLhwgdOnT5PJZNja2mJhYYFr165RKpWA7psMT4raz2oymQgEAvj9fhqNBvV6nVgsxvb2NtVq9XV/1NeGesMQGQDDw8MMDQ2Rz+fJ5/MsLS2xtLREs9nsehP2s6I+VZ88eZKJiQlmZ2cZGhri888/Z2Njg0KhINemXkHtLjKbzQwPD/PJJ58wNjbG6dOnMRqNtFotisUiV69eZXNzk08//ZRoNCo3Z51OJw8PTqdTvvx+P5FIhHQ63dXrtIjtEValY8eO8Qd/8Af4fD5CoRCFQoHPPvuMzc1NfvrTn5JIJKRLRFjpRNxPLpdjc3OTyclJwuEwfr8fi8XS9XWWOp0ObrebmZkZRkdHsdlssgCmOGyrEXJRu5ycTifHjh3D6XSSTqe5dOmSdNc+bQr1K1Ne1MFK8KDImtVqZXJykomJCbxeLzqdjlQqxfLyMslkUlZ17LYv+XkQk6G/vx+n08nJkycZHx/H7/ejKArxeJy1tTW2t7cpl8tSuetlRPaMMPPrdDoSiQS5XE5Grx+1cbIXsTk5nU48Hg/hcJhwOCxTXAuFwq5YjqMmK3Xgu8iw6evro9lsksvliMfj0jLVS+5qdZqv1+tlYGCAY8eOMTk5SX9//64Np9lsEovF2NraolAoUK1WZVaSuFcul2N7e5uxsTFardau2lxms1mOsW7ZnIXFRYQviLjCY8eOEQqFUBSF+fl5kskkd+7cIRaLUalUnmgeibHWzSEQaouLw+EgEokQDofx+Xzy58JqIlz3Op2OTqdDNpuV8YidTkd6WURavs1mw+l0Asj06kNpeRGIk4/b7cbv9/Pee+9x+vRpIpEIOp2O5eVlvvjiC5aXl+VC0kunoIPodDo0m00sFgvHjh1jcHCQH/7wh0xPT8sBce/ePb744gvu3r1LNpvtefmIxdPpdOJyubBareh0Ou7du8f29jbRaHTXabkbF4gXgViAA4EAkUiE48ePEw6HuXHjBgsLCySTSWq12q62G0cFdRqv0WjEarUyNDTEyMgI1WqVra0t5ufnuXXrFrVaDYPB0BPjSG1x0el0DA0N8cEHH3DixAkuXryIwWBAp9PJgMpqtcr8/DwrKyskk0lKpZJ0FYkstVgsxp07dxgfH6dWq2GxWBgaGmJjYwObzSYDNbtFeRFWA6vVSigU4ty5c/zhH/4h/f39TE9Ps7Gxwc9+9jM2Njb47LPPZFYnfJdIALuVRPHcer0eq9WKxWKRshbXdoNshGLXbrfxer1MT08zOzvLiRMnsNvt6PV6uWeJysvwYI7t7Oxw7949ksmk/PmFCxdwOp0YDAZZxiEYDNJut0kkEsDTFfF7pZYXcYK2Wq1MTU0RCoVkUGG1WqVarRKNRtne3t41SI4CQj5Cwx0cHGRoaEj6SxOJBPl8no2NDba3tykWi6/7I780xMQWGrvBYCAYDBIIBKSpMp/Pk06nZfrvUUe4jAKBAP39/VgsFnQ6HeVyWVa+FNcdRcQ48vv99Pf3S2tBMpmkWCySzWap1+s9lW6vLmzpdDoZGhpiYmKC/v5+Op0OpVJJuntMJhP5fB6LxSKzRw665350k9zEc4iA7UgkwszMDOPj4wQCAQwGA6urq6ytrbG5uUk0GpWegIOU/73y6RZ57GXvd+xwOAiHwzK7yGKxyEzPzc1N6vW6PESKn928eZNkMonNZsNsNkvXvlopep7SDa9EeREmJaPRiN/vp6+vj//gP/gPmJiY4NSpU/h8Pq5evcrGxgZXrlzhm2++oVKpdNVkeB6EfET6YjAY5N1332V8fJz+/n6MRiM3btzg7t27XLp0ievXrwP0fOCl8BGLLIexsTGCwSCKosiA5VwuR71el+bZo4b6xGez2Th9+jTDw8O43W70ej2JRILV1VVpxTyqdDodLBYLJ0+eZGhoiKmpKQKBAJ9//jn37t1jZWVFBjP3ipzEJjEwMCCtLX/zb/5N4EHF142NDX7xi1/IIMpmsylLvd+7d+97B0ixHu9dd9QhAd1SQ0hsnn6/H7/fz7vvvsvv/u7vEgwGmZiYYH19nb/8y79kbW2Ny5cvy+SRJ8lC21sVvtvXaEVRZFq9mDedTodqtUoymeQXv/gFqVSK+/fv02g0cLlctNttvv32WzKZDKdOnZIZbaJ2kOjJV61Wnzn04aUrL8KiYDKZsNvtjI2NEQqFGBgYoK+vj1arRSaTYXNzU+Z+i/zwbv/SnwR13QV1JLcofy+isre2ttjc3CSfz9NqtXraXaTekIV/1O/34/V6MRqNcpEUi4T6evXvHyVEQKXX68Xj8UhLZqlU0urg8J1lKhQK0d/fT61WI5fLEYvFiEajPRXwLca/yAbp7+9nbGyM/v5+rFYrpVKJRCLB5uYmq6ursmt0u90mm81SLpcfOV6EZcZsNqPT6WQKvog7O+wxVerYJ2GJm5qaYnh4WLZCKJfLpNNp1tfXZRxUo9F4InePWLPMZjMA1WpVlnI47LLZi7BWijIVbrdbWuWq1SqpVIp4PM76+jqpVEpapwqFAp1Oh3w+T6VSAdhV/kR0267X61Sr1WfuSffClRd1VpHaoiCi0v/+3//7DA8Pc/z4ccxmMzdu3CAWi/FXf/VX0swkHrhXN2eBWj7hcJhIJMKPf/xjIpEIw8PDGAwGvvrqKzY3N/nFL37B0tIShULhSMR2iFgXm81GIBBgdnaWwcFBbDabTE3cTw5HSXFRK21GoxGXy8X09DShUIj19XUymQzb29tkMhl5GOj1cbMXseEYDAZcLhfvvvuulE8+n+fLL79kYWFBZmH1gnyEQi+suB9++CE//OEPcbvdmM1mlpeX+elPf8ri4iL/7t/9OzqdDj6fT27owh2glocYa16vl9HRUfx+PyaTSboNxLp92F244jnsdjtWq5X333+fH/3oRwwODjI5OUk0GuX69etcv36dn/70pxSLxV3p8/uNj72HJ6fTKS1Ym5ubJBIJ2ThWcNjHmbBMuVwuPB4PAwMDjIyM4PP56HQ6pNNpvvzyS+7fv89Pf/pTWcNFnTjRarVkvZzBwUFcLhcGg0EGyadSKWKx2K616bUG7O41lxmNRux2O5FIhJGREQYGBggGg3Q6HSqVCtFolLW1NVl9Tx3s1csn6b0WqYGBAQYGBmQFS9FOfXNzU25Ej+rW2YuoNx1hlbJarQCyLoDwl/byOHkcnc6DKtVerxefz4fb7cZms1Eul8lms1Sr1e/J6aihKIpsleD3+/F4PESjUdlpW51h1AsyEs8isouCwaAs/lmtVsnlcmxtbZFIJCgWi3JsiHYBwqqploW4p8gQMRqNtNttarWa3OCFdaEbDlfCouvxeAiFQtjtdprNJoVCQRbcU2dcwcEKR6fTkbVw3G43Ho8HnU5HsViUm3q3WF7U66iQkdvtxuFwYDabpXKbTqdlGwBRmVo9dkQhSI/HI9PFFUWh1WpJi5QI5j0Ulhf4ztcqcubHxsb4+3//7xOJRDh79iwGg4Hr168TjUb58z//cxYWFuRE2lscqhc3JBGoZLfbpUn3xz/+McFgkMnJSXZ2dvjpT3/K+vo6v/rVr9jc3NxlXjvsg/95UWdJWK1W7HY7Ho8Hh8MhF8tsNks2m5X1BXpdJvshYoL8fj8ff/wxY2Njsk/P9vY29+/fl+mKRzEmSFg2bTYbU1NTTE5OcuLECZxOJ5cvX2ZtbU3GMggXSzcjNkeHw4HVauW9997j/fff59ixYwSDQba3t1lZWeHKlSt89tlnu4L+y+UysNv1qraiizIFwWBQ1vgQxdju378vT9Dq1OrDiHgem81GX18fAwMDTExMkEwmuXbtGleuXOHP/uzPyOVy0uLyuPVFKCbj4+MMDw/z1ltv8fbbbzM/Py/r5ohg38PuTdhrfBgcHOTcuXOcPHmS0dFR4EExw2Qyyb1793bNIaPRCCDDGgYGBvD7/bz11lu89dZbhMNh2u02hUKB7e1tcrmctNQdqvYAoiBSX18foVCI4eFhgsGgNBuJugrRaJR4PC61U3XAXC8qLsCuU47f7ycYDDI4OCgrXdZqNWKxmDQ5ZrNZ+XtAz8tHjZCVGBeNRkMGe2mxHMgTjsjGEguIKHcvTkNHEaEEGwwGfD6fjJmCB/IRvZ56BaFwmM1mbDabLLDmdDpRFIVqtUo8HieZTEqlFpBBlOpxoq7tAt9Vbrbb7TJNttFoUK1WKRaL0mLeLQgZWa1WjEYjjUZDxnCIGKjHlfNXP69oVhgOh6V7rtlskk6nZQxRN8lHrKlWq1W2GLFYLHL9rVarZLNZisXirmcT67WIvwsGgzJeUez9pVKJTCYjFeZn5YUoL+rUVlFvwmazcezYMf7D//A/lNHunU6HmzdvEo/H+b/+r/+L9fV1lpeXZZXY/XysvYSwSJnNZlwuF2NjY/zu7/4uQ0NDTE9P02q1+PLLL9ne3ubSpUsyQHevP7AXZfM4dDodOzs7ZLNZksmkrMTcbDaPnOVFvSk7HA6CwSAnT54kEAhIC+b8/Dz379+nXq/3TN2Sp0VYXux2OxcuXCAcDsu0zuvXr7O0tES1WpWFD7sdsS6IhIjR0VGGhoYAiMfjXL9+nZ/85Cesr69TKpV2Kf57LS4CUXhMmP77+/tlqnU2myWRSLC9vS1rTh1mxLzpdDr4/X7Gx8elNXdzc5Pf/OY3LCwsUCwWZezGQXEuYtMWDT7Pnz/PRx99hF6v5/79+1y/fp3PP/9cbtJCPt0wF8Vn9Xg8DA8P4/F4AGQ5k7W1NW7dukUulwOQbkSdTofL5cLpdPLRRx8xPT3NiRMnCAQC5PN5MpkMc3Nz/OxnP2N7e3vX//m0cnkho21vipzBYMBms+H3+5mYmGBoaEj6UmOxGBsbG6yvr7O5uSktLnB0LAomkwmXy0UgEJBR7qJEdzQalX1Wstms1khPRafToVarUalUZDGsXqrL8TSITUbEcoju0SLWRfih1YvwUUK4HY1GIxaLhWAwiMfjoVgskkwmyeVy5PP5nnQ72mw22XfIZrPtqumysbFBOp3e1UtG/ez7pUAriiJdt06nU8aeVSoVKpWKbGbZTVitVlwuF0ajkWazSbFYlLW0Wq3WE1fsFnPQ7XbT19dHOBxGp9ORTqdJp9OkUqld7rluGmfqLFiTyQQgLSfFYpF8Pr/L3ShCPkQBuoGBAVmrTFi3CoUC6XRaFhdV/+7T8lyWl/0sAYqi0NfXx4ULFzh58iSzs7O0Wi2uXLlCNBrlL/7iL4jFYrL2hHAVqf2rvcZ+FqlPPvmEoaEhzpw5Q61W42c/+xmxWIx/+2//rezmKhaEw36iedmIU069XpeuRhFsCUdPPiJmyu12y4amw8PD6HQ6Ll++zObmpswAETEI3bRoPi9ivHg8HiYmJpienubs2bMAfPrpp2xtbbG2tkY6nZZKTrfLRx106/f7GRgYwOfz4XQ6iUaj3L9/n4WFBRYXF2XX7IMQB0qbzYbFYuHNN99kdnaW48eP09fXx8LCAktLS1IZEhbQw4rYW8R3HYlEmJ2dxW63y7Tomzdvksvldlmf9stmFO4ks9mMyWTi4sWLHDt2jJmZGUwmE8vLy1y6dImVlZVdrrluQl3TR10dOJvNcuPGDRYXFymXyzJFXt0094c//CHDw8NcvHiRgYEBrFYrjUaD1dVVFhYWmJ+fJxqNytiYZ+WFxbyIL1yYjQYHBwmHw3g8HvL5PNvb26yurnLv3j0SiYR86KMQ4yIQFqlAICArDHs8HpLJJCsrK2xsbLCyskI2m5V1BdTNrY467XZbav0iUv0ou0QMBgMej0eeiFutFul0WrqORKDuYd5UXgbCEix61YTDYfr6+qhUKsTjcTY3N6V8esVlBN+Vq7dYLLKVhjjxigB3YeZ/UoXWYrHI6qqjo6P4fD5MJhP1ep1UKkU+n3/ijJzDgk6nw2634/P5pHxEJeqnicMQmaLDw8McO3ZMFmfLZDKsrKxIC5fImuw2xHhS79G1Wo1UKkUul6PZbO7ymtjtdrxeLxMTE0xMTBCJRPD7/dRqNer1+q7SDeVy+bkPDc8l0b2VFUOhEBMTE5w5c4bf+73fkzUF1tfX+bf/9t8Si8VIpVLU63XgaFSIbbfbOJ1O7HY7J06c4K233mJ0dJQzZ85QLpe5fPmy7J+RyWTkoDjqMS77IbJrxKubGsC9CPbWdfF4PExPTzM6Ooper6dcLhONRtna2nruU003sre4od/v5/z580QiESqVCqlUinv37rG6ukqj0egZxVc8t3CRif5WVqtVlhTYm5Z6UByHoiiyINn7778veyJNTEyws7PD4uIiX3/9tez50y0pwIJOp0O9XqdcLhMKhYhEIoyPj3Pq1Ck2NjbIZDLAdw0Gxe/sTQH+6KOPGB4e5u2332ZoaIgbN27w2WefyWrxjUajK/c4UX5BZJdNTEzI+i71el0qea1WC6PRKJWUDz74gFAoxLvvvksgEMBsNlOpVLhx4wbRaJRf/OIXzM3NEYvFdq1NzyqfF6IOCuXF4/EwPj7O1NQUs7OzlEolFhYWWF9f59atW1LjEmamXre4qE+AXq+X8fFx3nnnHbm4rK+vs7S0xMrKyq5AMbWpv1dl8yyIBURo+0e1sq44EVmtVvr7+/H5fDKgOZ/Pk8vljnysi16vl6din88n4xpisRiJRGKXu7rbUddEEnW1RJyBmC8ijkMEVe53D3hg3TQYDPIUffz4cY4dO8bExAThcJi1tTUSiQQrKyvcuXOHcrncVVYX+C6QW1huRbxKJBKR8Sn7rSVCzkLGwlU0MjKC3+8nlUoxNzfH6uqqDGDuNouL2r0mEgL6+vqwWq3y8ChqkAklR8T6vPHGG7Kem8vlkhaXra0tFhYWuHv3Lnfu3Nml7L5Wy0u73ZbBYSdPnuQHP/gBgUCAbDbLysoKP/nJT9ja2iKTych+RUchq6jT6eB0OrHZbLz55pucO3eO8fFxBgcHKRQKfP7556ysrPD555+TSqX2reLYi7J5FvbzPx9V2YgFw2q14vP5GB4exuVyyYady8vL8tR31Cwv8EA+NptN1iMZGxuTMXcbGxvk8/muCy59HGJeCCWlVqvJYG1hmle7n0XMlHqtEZtVMBjE5XLx3nvvEQ6Heeedd4hEIlSrVRYWFrh8+TJ37tzh1q1bMri1G5QW8ezitb29zc2bN+nv7wfA7/czOztLo9Fgbm5OWqoU5UEhUaPRiM/nw+FwcPbsWYLBIGfOnCEQCHD79m2SySRfffWVPIR2u2KszrRSH6StVivhcBij0YiiKHg8Ht5++238fj9nzpzB6XRiNptpNBoyROTSpUvcvXuXzc1NefB8EbJ5buVFLBaBQIDx8XHOnDnDzs4OxWKRzc1NvvrqK9LpNIVC4chZXESthZmZGT7++GPcbjd+v59MJsP169dZWVnh+vXrsgCdFuPyfQ46JR41xLjS6/Uyy6i/v182YNze3iYajcpA3aNmeRHysVgshEIh2bVeuIs2NjakubtXXEZqhJVFnHj3K7suZCQsc2JjEhaFvr4+AoEAb775JiMjI5w4cQKv18vNmzfZ2tpibm6OL7/8kkwmQ6lU6qoea2oFJpVKsbq6Kl1EbrebsbExotGozNASjRj1er3MWAsEAly8eFG2czGZTHz66adcvXpVbtDdJJMnQb0nidpkVqsVp9NJIBDgt37rt3C73bIbtxiD6+vrrKyscOPGDe7cubOrP9RrV17EAjA1NcU777wjo63X1tb4+uuvWV5eJhqN7turqBc3IPFMoozyuXPnmJmZ4c0332R4eJhMJsONGze4ffs2X3zxBel0WvYD0awK+yNaAahTg+12O0ajUY6/oySvTqeD2+3mxIkTTE9PMzAwQKlUkn52EQjfS4vn0+Lz+Th79ixDQ0OkUik2Nze5f/8+0Wj0mTvYdgM7Ozs0Gg1yuRzxeFwqaiLrKpFIsL6+LhviCXe2xWKRjWAnJyfxeDycPn0at9sta7l8/vnnzM/Pc/v2bdLpNLVarSuztMTGmcvlZMmOpaUldDodU1NTtFotcrkcuVyO9fV1TCYTAwMDOJ1OZmZmcLlchEIhDAYDV69eJZvNcvXqVRYXFykWi7tk0m2yge8UPBEnlU6nWVlZIRAIyAy206dPy+aKwnBhMploNBoUi0Vu3rwpx4ywCIvGny9SJs+svIgToNFoZGpqio8//lhWsIzH4/z7f//vSSQSxONx2u32rqyHXt1sxJdjt9txu92cPXuWDz/8kLGxMYaHh0kmk9y6dYurV69y+fJlWd1TmHXVAWIa3/mmRao5PKjPIOoOdLtp9lkQ7siZmRmmpqaIRCJsb2+zvb0tC7CJcXgUFZhOp4PX6+XMmTPYbDbS6bR0p6VSqZ5UXsSGI5R8obyIfjMipT6fz5NKpSgUCiSTSZxOp4xPmJqawuPxcPz4cVlFt9PpyDYun3/+OVevXpWdgoVFotsQa6xwH66trbG0tMTY2BjHjh2TDRvj8Thzc3M4HA5OnjyJz+fj1KlTmEwmGQZx9epVFhYWuHPnDvF4fFdWTjePMUVRZIxUKpViZWUFg8HA0NAQfr8fh8MhrXbC3SjK/udyOS5fvszy8jKXL1+WbSNErNWLXJOeSXkRpjeR1y26l+bzea5fv869e/eIxWJHpleRQDybKEAXCATo6+uTk2R1dZXl5WWSyeQLC1rqRYRMROO3dDrNzZs36evrk+0mwuEwlUpFNvPsxlPgsyKayplMJhnD0Gg0jnQrAIG6NkW9XieZTLKxsSFTo8U1vTZW1M+TTCYxm81Eo1EymQwGgwGn0ynjf0R/MKvVKoMx+/r6MJlMshzB1tYWlUpF1g3a2tqSyRbd7hZRFEXOlaWlJWkFF4ckj8cj2weIomt6vZ6NjQ2azSbXr18nkUiwsLDA1tbW99afXhhb4gCUSCS4c+cOJpOJ4eFhjEajjHcRa3ShUKBcLnPr1i0SiYR0MaqrOL+M9fmZlBex8fr9foaHhxkZGWFwcFBq6KJh094TYK8vrOL5vF4vQ0NDDAwMEIlE2NraYnl5mTt37kgtXR3Vvff3NR7Ipd1uyyZgX331FUNDQ/zhH/4hXq+X0dFRWe2xXC735Ib0KMRmpO6yXa/XqdfrR3oMqat8Go1G8vm8jEMQHbbNZnNXb7wHISwwsViMUqnE+vo60WiUcDhMIBDA5XJx7NgxqfCK/mpiE2o2mySTSYrFInNzcyQSCX7605+ytrYmM0VFn7FuRawR9XqdWq3GrVu32NraklbLgYEBZmdnsVqtvPnmmzJ2o1Qqsbi4SDqd5v/5f/4fWSJfuIqETLp9DVLHBbXbbdbX12Vm4/Hjx2Vsi3rMZDIZYrEYn376KZubmzImSn3PlzHnnkl5EQ/YaDSoVCoUi0XpJ8zlclLjEml3RwWhXeZyOba3t/n222+pVCokEgmSySRLS0skk0lKpVLXD/JXRafToVqtsra2RqvVYmNjQ6aAmkymIxfzoiiKLEEg0jpTqRRra2skk8kjmyItUBSFer0uG5pub2+TSqWA3q/ErN6YFUXh3r17mEwm/H4/Pp/ve2UGRHq12IhEvEelUuH+/fvfi53pJfmpZVUsFllbW8Nms7G2tkY0GsVsNsu+R4lEQtYJKhQK0qK1N2OrF+dcuVwmFouxsLDA119/TTAYZHx8XCot5XJZ1sZZWFggnU7LOm7wcmXyzMoLPOhtIQK6tra2ZLZDNpvd1a31qGwwQnnZ2toimUyyurqK3W6nXC7LUsoiePkobzCPY29mRLFY5Nq1a2xvb8tKliJtr5tPgU+LGF/xeJxf/vKXGI1G/vqv/1p2r202m7RarSMZCwTfjZtiscji4iKpVIrbt2/LNhK9mGGkRigXoufQL37xC37zm99gNBoxm837/o56XRbuR9GDRrgie8FVtBe1rESX41u3bsk+RaKvz87OjnSXVatVdnZ2ZFsbIZNeG1NqZUzdn2ljY0PWKstms7Ix48LCgqxQ3Gq1Dmyv8CJ5ZrOIOOEUi0WWlpb4+uuv5YIhUuiOGup6C6LkdKPRkCb9vV+sxuMRshIupPv372Oz2dje3qZQKPTcifBJEAupenNpNBoyYPOoUyqVWF1dlc0phTvtqM078dwGg+GRFnARAiAQgZr1el0Gy/e63NQuELXrUa/Xy55q4ufq5IGjcgDtdDrSfS8KGAplplQqUSgU5P62X6fyl4Vy0GJns9kOXAnFhzMYDPKLFgvo3knxMqlUKq9lBFmt1gPlIywHsH+htVdFtVp9LfJ53Ph5WvYWTBLj7Hk37Nc1fux2+zN9cHXJcsHLHF/lcvm1yMfpdD6TfMTm86rWoWKx+Frk43A4nnj9eVpe5HgqlUqvXD6PW5vV7DefxL/3e/4XKZvXtTY/6dzau8aINVid3bZfheUXtQ4dNLeey20kPrj6ZLNfUaSjgto9pq7mKN4/Clr6i2TvGBJxVqKYn7jmKI61veMLetPn/iyoK8jC0Tkh78fejflJ5HDUZKVen9XzCnjk/DoqMtrvoNhsNr93zX5/vmyeOVVa8KiF4ShtJIInkYvGk7PfGHqUi+gojbf9FDZtrH2HOB0edTQ5PDlPqpgcxXn2pErJq5bN0UkF0uhZjpLispejuJhqaLwstPm0P4dRLgfGvGhoaGhoaGhoHDaOVpqGhoaGhoaGRtejKS8aGhoaGhoaXYWmvGhoaGhoaGh0FZryoqGhoaGhodFVaMqLhoaGhoaGRlehKS8aGhoaGhoaXYWmvGhoaGhoaGh0Fc+lvCiK4lMU5V8rilJWFGVNUZQfP+I6RVGUf64oSvrh658rB1S9URTlxw/vV1YU5d8oiuJ7ns/5utDkczCafB6Noij/haIo3yiKUlcU5U8OuO6koig/VRQlpSjKY4s2KYryPymKMq8oSltRlH/wIj/zq0QbOwejyedgtPl1MN0wfp7X8vIvgQbQD/wR8D8qijK7z3X/GPg7wBvAaeD3gP9kvxs+/P0/Bv7ew/tWgP/hOT/n60KTz8Fo8nk028A/A/7nx1zXBP5P4B8+4X2vA/8ZcPXZP9qhQBs7B6PJ52C0+XUwh3/8qBtRPc0LsD98uGOq9/4V8N/tc+1l4B+r/v0Pga8ecd//FvjfVf+eePj/OJ/1s76OlyYfTT4vSE7/DPiTJ7hu8sF0fuL7XgL+wet+Pm3saPJ5zbLS5leXjp/nsbwcA1qdTmdB9d51YD/tbPbhzx533feu7XQ6Sw8f8NhzfNbXgSafg9Hko/GsaGPnYDT5aDwPXTF+nkd5cQCFPe/lAecjrs3vuc7xCN/Y3msPuu9hRpPPwWjy0XhWtLFzMJp8NJ6Hrhg/z6O8lADXnvdcQPEJrnUBpc5D29Fz3Pcwo8nnYDT5aDwr2tg5GE0+Gs9DV4yf51FeFgCDoihTqvfeAG7vc+3thz973HXfu1ZRlHHA/PD/6yY0+RyMJh+NZ0UbOwejyUfjeeiO8fOcgT1/BvwfPAjwucgDE9AsMAp0gNGH1/2nwF1gAIg8fIj/VHWfVR4GNz38/QLwwcP7/inwZ687iEmTjyafVywbA2AB/r88CJazAIaHP+sAHz/8u/LwZycevm8BzKr7/AmqgETA9PCaL4B/9PDvutf9vNrY0eTziuWjza8uHz/P+4A+4N8AZWAd+PHD9z94+KGNqgHwL4DMw9e/ABTVl10EZlT3/fHD+5WBvwR8r/vL1OSjyecVy+afPFwk1K9/Agw9XAD8D68b3ee6VdV9PgX+kerfn+1z/cev+3m1saPJ5xXLR5tfXT5+xH/yQlEU5b8Bkp1O54+f4Nr3gf+80+n8xy/8gxxSNPkcjCafR6Moyt8FZjudzn/9BNeaeBDdf7rT6TRf+oc7BGhj52A0+RyMNr8O5jCNn5eivGhoaGhoaGhovCy03kYaGhoaGhoaXYWmvGhoaGhoaGh0FZryoqGhoaGhodFVGA76ocPh6IqAmFKp9Mguli8Tt9vdFfLJ5/OvRT52u70r5FMul1+LfGw2W1fIp1KpvBb5aOvPwTidzq6QT7FYfOXy0cbOwfSCfDTLi4bGEUWVvqihoaHRVRxoedHQOGyIzXbvn4qiINpp7N9WQwO+k1e73X5QK+Gh3NTy09DQ0DjsaMqLRlew10KgKIrcfMW/1ddqG/HBaMqexn48zhKnjZVHo5adJqeXj6a8aBxqhIVgZ2cHAIPBgE6nw2w2o9frMRgMKIpCrVaj2WzSarXY2dmRm7NOp9MWEr5zEen1enQ6HXa7HavVSqvVol6v02w2qdVqQO8svOpqnGL8CMQ40vgOIaf95CXGjWal+z7tdnvXS6fT7XppvBw05UWja1AUBb1ej16vx2q1YjQaMRqN6PX6XQuqFsfxaPR6PUajEYfDgdfrpVKpUCw+aOoqlJdeRFjqxN9Bs9CpeZxlU0PjsPFKlBfNFKnxNIjTstFoxOVyYTab6evrw2Kx0NfXh81mY3R0FIfDgcvlwmAwsLa2RiaTIZFIkM1mSaVSpNNp6vV6z1kUnga1xUWv1/PGG28wMTHByZMnOXHiBLdu3eLy5ctsbm5y48YNOp2OtGZ1K+KZDQYDNpsNq9VKMBiUz9VsNllZWaFcLtNsNnfF/qitC0cBtXXKaDQSDoexWCwEAgFMJhM7Ozvs7OyQTCYpFouUy2U5n466ctNut1EUBY/Hg9lspr+/H7fbTT6fJ5/PUywWyWQyXT+exPhot9vyPTFXxN9fBy9deXmSU/DeE5GGhjC/2mw2nE4nQ0ND2O12BgcHcblcnDx5Erfbjc/nw2g0cu/ePWKxGGtra8TjcXQ6HbVajU6nQ7VaPbJjS628mEwmhoaGOHXqFO+99x7vvPMOVquVjY0NyuWydNF1M+rPLyx0breboaEhTCYTer2earVKNBqlWq0C34018ftHxeqgzjYTY8Tv9+NyuRgdHcVms9FoNNjZ2ZEuEOFmVP9er8tpP9TPb7PZcLvdDA8PEw6HiUajRKNRWq1W1+9t6ud81LO8rmd8ocrL3gdVn2T2xh7szXoQmp1aEL3iL9zPJCsQ2uzea9RardrkfRQQz+pyuXjjjTcIhUJ88MEHOJ3OXZYYs9mMyWRCURROnDjB6OgoMzMzlEolbty4wc2bN7l//z75fP7ILbLqOWgwGJidnWVoaIiPPvqIM2fOEAwGKZfL5PN5earuBfkoikIgEGBqagqfz8fU1JTcjI1GI4qiUK1WGR0dJZPJyDipUqlEvV5na2tLWuzq9XrXn5r3Q4wNnU6HwWDA4XAwNjZGX18fFy9exOv1MjAwgMVikTFkGxsbZDIZrl69yuLiIslkklQq1ZPyeRwiHshkMmE2m3nnnXeYmZlhcnKSSCTC3Nwcc3NztFot1tfXu3IvE3uxwWDAYDDQ19fH4OAg7XabVqtFoVBgY2NDKmj7xUkJ1Hv/ixwrL9zyolZEhLYuFlDY7W/eq8Co39fpdF294QiFY1cL7z0ZHntlsPf31c9+VBQY9TPa7XYmJycZHx/n448/xul0SmVFLAZi7LjdbgAajQbtdhuj0Uiz2aRUKnHr1q3X8iyvk70b1NjYGKdOneL06dOcOnWKZrNJtVqlVCqRz+elFaJb55t6fvl8Pk6ePMnAwADnz5/H5XIxNDSE0Wik0+lQq9VwOBxks1kajQbNZpNkMkmhUKDT6VCpVNjZ2elpd6OYZwaDAafTyfHjxxkYGODDDz8kEAgQCASwWCzSbRSNRsnlcjQaDarVKrVajWQy+Zqf4tWjdp8YjUasVisnTpzg4sWLTE1NEYlEaLVapFIpotFo167Z6j3caDQSDAaZnZ1lZ2eHer1OLBZje3tbBngDB1puX0bixAtRXtTWEjEZQqEQDoeDSCSC2WzG4/FILQ6gXq/TbrfJZrPUajUqlQqNRoN4PE4ul6NcLlMul+V9uw2xceh0Onw+Hx6PR2rqZrMZh8NBtVolHo/TaDTI5/NysLTbbarVKq1Wi2az+T3FBtjlm1ebvLsZseG6XC5CoZCMzQiFQuh0Our1Oul0mp2dHSkTh8OB0WjEbDZjNBoxGAy0222CwSDHjx8nHo/j9Xqp1+tUKhWArjsFPQ1iDIhMrPHxcfx+P+fOnWNmZoZAIIBerycajbK1tcX6+jq5XI5KpdK1qdPqcdPX18eJEyd477338Pl8DA0N7bLQAVgsFiYnJ6nVatKyUC6XqdfrDAwMsL6+zs2bN7lx4wbNZlNaYLodtUJrMpkIBAKcPXuWUCjEu+++i8/nIxAIYLPZAGg2m/J3xcFhamqKRqNBuVxmbW1N3he6b9w8LeI5bTYbZrOZmZkZgsGgVFr0ej3FYpHt7W0WFxdJpVJdl+0oxojdbsdisTA2NsbExARjY2OcPHmSnZ0dGo0GiUQCv99PrVaTcVDRaFSOGbUnpVQqSfejiBN6ETJ5YZYXcdo1GAz4/X5mZ2fp7+/n9OnTuFwuBgcH5eYNUK1WqdfrbGxsUCgUyGQylMtlbt++zerqKrFYjFKpBHTPpNhrTVFrrRMTEzJ+w+l00t/fTzab5d69e9IEJ5QXobnXajWZArz3C9fpdDJ9sZcQm9D09DTT09OcPHkSp9MplZdYLEa9XqfRaAAQiUSw2+0YDAaMRqNUGAOBADqdjuXlZbxeL/l8Xo6nXpOZGrE46PV6LBYL09PTjIyM8MYbb3Ds2DHcbjc6nY50Os29e/fY2Nggl8tRrVa7Vi5ioXQ4HIyOjnL8+HHeeecd7HY7drsdgFarteskOTExgaIou9zWnU6H8fFxEokEAKurq5RKpZ6xwAjTvl6vx2w2E4lE+MEPfkAkEuHtt9/GarXuOgi1Wi15OHK5XHQ6HSYmJjAYDKysrEjreLcfmp4EtSXCZrPhcDg4ceIE4+PjTE5OEg6HqdVq5PN54vE49+/f70q3mpgPdrsdr9fLmTNnuHjxIsPDwxw7dox2u02j0SCdThMKhSiXy6RSKQqFArdu3ZIGB0AevLe2tigUCrTbbRk/9dqVFzFwTSYTRqORSCTC6Ogow8PDnD17FrfbzeDgoLS8iIwHQEay6/V6eSpuNBr09fUxPT3N7du3uXv3Lvl8nnQ63RV+w70nkEgkQiAQ4OTJk8zOzmIymbBardhsNrxeL6VSCZ/PR7VaJZ1O02q10Ov1NJtN1tfXKZfLZDIZarWaTAcWlEolubCWSqWumyT7od6EJicnGRkZwe1202q1uHr1KoVCgfn5eSqVCq1WC51ORyQSweVycerUKcLhMA6HA4vFgs1mo91uMzAwwLFjx9jc3CSTyeyqLNtLiLEnrFDj4+P09fXx1ltvMTo6ysDAAHa7nVwux/b2NlevXuXSpUtsbGyQz+e70rog1h+r1YrFYmFqaoqLFy9y7NgxrFYrer3+e3544aMvl8vyd9Vzy+VyodPpmJyc5NSpU6ytrZHL5YDuVV7UQdtms1laC6ampjh+/Dher1dapoRCVygUaDabMhtLZPmZzWZ5KjcajdJydVQwmUyMj4/T39/P2bNnGR0dxev10ul02NraIhqNsrq6SiaTkYkC3TJuxBhRFIXh4WHGx8eZnp5mbGxMuuWF1c7lcjE+Pi6tlbVajUgkIg+VnU5HxpMtLS2RSqWIxWJks1mKxeL3DBPPIqPnUl5EUR7xMCdOnOCTTz5hZGSE8+fPS0uMuFb9pyAcDu8K6E2n0+TzeX7xi1+g1+u5f/8+iUSi61IYdTqdNLVduHCBCxcuSGuJUGKazSbFYpFWqyUzY+CBS00obtFolEql8j1tdWNjg83NTemLFrEN3YxYZL1eL2+88QaRSIS+vj62trb41a9+xdbWFt988w2lUkmavwcGBnC73XJcRSIRrFarLMI2NjbGuXPnMJlMzM/P02q15P/VLWPpcahPhSaTCafTyYULFxgdHeV3fud3ZLCqsETdv3+fzz77jJ/85CdyDnfD4WAv4rPb7XaCwSBnzpzhRz/6EW63G6vVCrDLeiD+3Wq1yGaz7OzsEAgEdikwfr+fvr4+Tp06JeOAbt++/dqe8UUgxodwV4+NjfHbv/3bjI2N8eabb8r4MEBaftPpNMVikWKxyM7OjlyzLBYLbrcbm80mY4iEjHsVtWXJZDJx6tQpJiYm+OCDDxgdHZUK3NLSEnNzc9y+fZtoNNpVBf32egumpqZ47733ZEmFnZ0deWA0GAwyaUL8rhgHalkJt+z169fZ2trixo0brKyssLq6Sj6f3yWfV6a8qB9UbCDT09PyS+3r68NoNEoNXh38JoqKiY02n8/TaDQwGAyygJbdbmdoaIgzZ86g1+ulFis2rcM6GERl176+PtxuN9PT05w+fZpIJLLrZCN8xq1WSwYHii9eURR2dnbw+XzY7XbcbjfNZnNX1pZOp6O/v5+BgQEWFhZkZkS3udkEaquB1WrF6XTi8XgwGo0kk0mi0SgrKyvEYjEKhYLcVHQ6HYlEgnK5zN27d6UCY7VaMRgMmEwmLBYLLpcLm82GXq+XG16vITID+vv7pfVydHQUl8uFXq+nXC7TaDRYWlrixo0bRKNReWLulgV2P0Sqal9fn4wts1gs8ucGg0G6S6rVKvfv36dYLLKxsUGj0WB4eBiPx8PAwIAcc2KjFnOwm2UD3xV39Pv9TExMMDMzI7OxSqWSVOaE8tbpdCgWixQKBenWFsG77XYbp9O5yyUnUqe7VU4HoU62MJvNclz4fD7MZjOKosj08XQ6zebmpnSRdFOFXTFWxDN6PB78fr88BIgxJNxGIutIZOrt7OxIBVhY5cQ6HAqFpEsyFAoxPDzM1NQU6XSaWCxGtVqlUCgAT+fSf2blpdPpSMvK2bNn+f3f/32ZqipiP0qlEtFolFqtRiaTQafT4Xa7MRqNOJ1OOp0Ot27dIpPJyAc+efIko6OjnDlzRgYbivvcvXtX/v+HcaKIE8jY2Bijo6P84Ac/4IMPPgB2Z2EVCgU2NzdptVrSzKbWQA0GA+FwWPpWRYonPND8DQaDLML2xRdf0Gw2iUajzM/PS9NfNyFkY7PZ8Hg8BINBGbW/tLTE/Pw833zzDel0mkqlsqsuhygCBQ9OyAaDQW5idrsdp9NJMBiU464XlRcxrkwmEzMzMwwPD/Pxxx8zOjoqF1gxXi5fvswvf/lLUqmU9D9323gRiHHj8XgYGxtjaGiIcDgsF1hxihSHp0wmw89//nO2tra4evUqlUpFWvg++eQTpqenpbInkg08Hs/3YtkO49qzH+Izi4Ph+Pg4v/3bv8309DQXL16UMWTZbJa7d+9iNBo5e/YsRqORdDpNKpXiZz/7mUz3rVQqjI+PEw6HCQaD+Hw+AAqFQtfI5FkQVl673S4V3aGhIbmxC9f9ysoKN2/eJB6Py4NBtykvDocDj8dDOBxmaGhIxjoJ5aXVasngduH+WVtbkwdocbC2Wq1MTU3hdrs5duwYBoOB06dPU61WyeVy5HI5vv32Wz799FOi0SjZbBZ4uoPUMykv4gsJBAJ4PB4GBwflBxZBpiJWZWFhgUqlQiaTAZBZNw6HA4D5+Xny+TxWq1VGwItgS5fLRSQSYWZmBr1ez+rq6i4/7GGdMAaDAYvFIrMcRIBpoVAgm80Sj8dZWFiQmQyA3EAURcFoNDI4OCgtLyJDSa/X43a7sdvttNttLBYL/f39HD9+HKvVSjqdlplbh1k++9HpdLBYLPj9fhwOh4xLWFtbY2tri2q1KjX7vc8l0l+LxSL1el0GZ4qfiXS+XgwuFAurzWbD5XIxPDzMyMiIVHqFLDY3N1lfX5fWq0aj0dUWF0BmL/p8PoaHh/F6vcB3m3aj0aBYLMqidPF4nJWVFelqFRkStVpNBhsODQ0RDAZpNpu4XC68Xi+BQIBqtborGLEbEN+vcIVNTEwwOjqK3++n3W6Ty+W4c+cO6XRaKi+isF+lUpEZjzs7O2SzWWKxmHTzW61W2V6i1+bUXsRBPRwOEwqFCAQCeL1eaUUXsS7qudVNWUbi8KMoijxAulwumShRrVZlG5FCoUAikZDKS7lcZmtri0ajIZ87Ho9js9mo1Wp4vV76+vpwOBy7LOFGo5FQKMTIyAg7OzusrKzsWqefRHZPpbyozftCkzp58iTvvfceJ06coFwuE4vF2Nra4tatW6ytrfHLX/6SUqkkNStxAhbm2Hg8TrVaxeFwYLVaZUXM/v5++vv7OXfuHKFQiF//+tfcvXuXYrFINps9lBYGde0EobyYzWb5/Pfu3ePKlSssLy/z5Zdf0mg0dvWTEV+Y2WyWWqvf75dmcYfDIU8+TqcTh8PB7Owso6OjzM3NUavVSCQS3Lt3Tw7GbsLr9TIzM0MoFGJnZ4dYLMYvfvELotEoxWJxl/sMvtu4ASqVikyxF75WQFq3hMLbS6hjGYLBoLS4jIyM4Pf7MRgMcoH59a9/zddff838/DzxeFyepLptjMD+KasfffQRwWBQKmudTod8Ps/i4iKJRIKvv/6aeDzOpUuXKBQKcnwUCgX0ej0LCwt4vV4+/vhj3njjDQYGBhgYGGBycpIzZ86wtbUlXZOHbd3Zi3od0uv1sqKyWKt3dnYolUrMz8/zp3/6p8RiMRYXFzEYDExPT+Pz+Thz5gxms1luJOJ0PTQ0JF1O4+PjVKtVlpaWXvMTvxzUBx6r1SqD30+dOkUoFJIlLj7//HNZmG57e1tau7oJsV8EAgFpxYxEIuRyORKJBCsrK9y+fZvt7W1u375NtVqVh8V8Pi9DH4QCZDab5VgS1s3Z2VlGRkakC1/IyeVysbi4KO/5UpUXkTEjNCev17ur7PbGxgaLi4tsb2/LGhLlcll+IIPBIC0OIgccHmw0iUSCra0teQo3Go243W48Hg8ej4dOp0MulzvUG9FeX6cwlcXjcdbX19ne3t4VzS8Q8hFFs6rVKtVqFYvFQqlUkhaXQqEg/a4iONXv9zM8PAw8sGZ1A+qaJEajUZpkbTYbiUSCWCxGPB6XWUJ7UQd6u1wuAoEADocDs9mMTqeT7oJCoSBji3rBZaR2X4iTjMjy6+vrk64PkXKfyWSIxWIkk0npdutWxQW+e35hxvf5fLhcLiwWi3QZlUolUqmUDPhfX1+XsXPqOddsNtnZ2SGXy9FsNmU2xM7OjoxvGBoaol6vYzAY5OnwsMtOWAtEP7DBwUG5nlYqFTY3N9ne3pa9wERGYzqdptFosLm5KdcdYX0Rc0dYh4Wrv5cRFYh9Ph+RSIRwOCwDvJvNJpVKRcZulMtlGfcI3eNehN1xrCJ7WLjZq9UqqVSK5eVlEomEtLyIuVStVneND5FpFI/HqdVquFwuqtUqTqdTrvNerxer1So9LcFgkFwuJ+Nan4QnVl7UUeWRSISBgQEuXrzIhx9+iNVqpdPpsL6+zqeffsr8/Dy//OUv5cIgTsiKokhLgwguFR+0VqvRaDSYm5ujUqnw8ccfSxeTmHwidXF7e/tQb0J7i8clEglu3LjBV199xc9+9jNpcRHxLXtpt9tsbGxIXzs8OGWKTBKbzSb9rufOneMHP/gBY2Nj/N7v/R5Xrlzhq6++2rVAH1aE5cBut8t+RX/rb/0ttre3+dWvfsXy8jJzc3NSuVWfePcqPrOzs5w4cYKZmRn6+/uljz4Wi3Hv3j02NzdlRlc3mXT3Q8xFu91OKBRidHSU/+g/+o+IRCJMT0/LImPlcpkvv/yShYUFrly5wvz8PO12u+tOhXsRc39kZITjx49z4sQJqbiL4FNxUvzTP/1TeXoUQfH7WU5SqRTJZJKtrS0ikQgTExP4/X5OnDgBwFdffSVPnNVqdVfc1WFDHQvU19fH6dOn+eijj2RW0crKCn/913/N/fv3mZ+flwkTAFtbW9LSIgpmCkVXKCuihpDT6ZTWmW5F/dnVa4I6Du/kyZMMDQ3xwQcfyNIMnU6HTCZDJpPh3r17zM3NyQP53nt1EyJgXR3ykEqluH79Ov/6X/9ruafvRb2miuD4hYUFFEXh/v37WK1WFhYWmJ6e5vz585w5cwa73c6pU6cA2N7eZnl5WSYSPMkB4alXMVHXRQRDulwuWRtAxLrkcjkZcS2CTdXFj/YbMGIjy2azbG9vs729TSwWkz5nu91OIBCQKVaHecIIE5o4sai/CKGhqk/P+z2PWGjVRbTq9boseW8ymTCZTNIEbjAYcLvdMpaoGxDPJQIrARm9Ho1GSaVSMoblUZ2ORVaR1+slGAxK36ooYV4oFEilUtIceZjHzdMgsh/6+vpkgHMwGMRisUh3UbFYlGW8RS2XbsqAOAhhyvd4PDJtV2R95PN5NjY22Nraktlo1WpVmqn3G0dinop+T8L1aDKZ8Pv9OJ1O6YY57GNIjHOLxYLH45EuZlGSQZRgSKVSuxovArvcreJwJTIl1XW6hKzVGWvdyH7r717LZjAYJBwO4/V6cTgcUmaJRIJ4PC7Hi9rq0q3sTV8We5CwZqpls7f/nnovB6SrXriXYrEYNpuNkZERarWarMclkipE8sULt7zAd34xp9Mp04FFmXvhr0okErv8VntPuY8KEhQTbnl5mdXVVQqFAqurq1y8eJEf/ehHRCIRPv74Y2w2Gz//+c/lafwwIZ61WCzKejXFYhG73c7ExATb29uMjo6SzWZ39b3Y78sSi4T6Z6Ihljh553I5IpEI6XQao9GIw+HAZrN1zQQSFRfFIhmNRvnss89YWVnh8uXLspz/fi4OMb5ExeLp6WnOnTtHJBLBYrGwubnJ8vIyV69e5auvvpKWrm4OUhULibA2iQqpY2NjnD59WpZwFynR0WiUzz//nDt37pDL5br62dWoLQuDg4MyxVlU+7x27Rr/y//yv5BOp2U7CXVA/H6IlOpoNIper5eZk51Oh/7+fnw+HxaLZVf588OKOPD09/dLS6TRaKRQKLC9vc29e/f46quvZHFLtWz2rjvi8CCybYTlJZvNygaN3WjJVIcwGAwG2YpFPcfMZjP9/f38zu/8DoODgwwPD2MwGLh37x7JZJKf/OQnrKyscP/+fan0dZsc9rJ3TxJ7uKjtstd9uN/zivfUB9JWq8WtW7dYXFzEZrMxNDREf38/brebYDDIO++8g6Io/N//9/+9y4J1EM9kPxZ1WoQmLjZT4Q5RZ4U86ZcpNC5x4hF+xGKxCDxIEfZ4PDgcjkM7QMTnElpqpVKRvmSXy4Xf7ycUCgEQj8efqNrrXouVaJQmoruFGU98H910qhbjR/hXa7UasViMRCIhYxBg/wVByE343l0uF263G5PJRKfzoLleMpkkk8nIWkOPulc3IRQ9m82G2+0mEonQ39+P3W7HbDbTbrdl7EYqlSKbzZLNZrvCjfi0CP+8sKaoY5zE2iGe+3FBtmKtqtVq5HI58vm8rG+idpcc9vGz12ogXDui2rDIHBHWKHGtYD/XibBYiUKHIpNLHa/YbYjvW6xBags3fBfr4na76evrw+/3y+tEDFk0GmVzc7MrszufFqHEPE5x2fs78J0yLcacONSLjC11OMTT8MzOb/FliXSxQqFALpcjnU5TKpWeSRtXXy8WEXUzPbPZ/Eiz72FALJCiFP3Zs2cZGRkhGAwyOTmJxWJhcHCQr7/+mlgsJhcR+E5J2at87HWxiZeolikiu8XJ8TCfCAXiM4ZCIamQOhwOisUiX3zxhWx78KgFQZ3a5/f7CYfD9Pf3ywaO5XKZxcVFfv3rX7O4uLiremg3oo7vMRgMBAIBpqamOH36NB9//DEulwuDwSBditlslitXrrC0tEQsFpNWp248IT8OdaCh6HSczWapVCpP1fJAbMqpVIpcLsf8/DwTExOEw2FGR0df4hO8ONTjRASxB4NBnE6nVF5ENt7eDsCPkpM4WIi0cRFLJvrRiYae3TKu1OunUMjEAVw9lvx+P2+99ZZML3c4HCSTSfL5PJ9++ikrKyvcunWLZDIprcfdIoOD2Os2Em7mvUqtuPZx7A2ZaLVaxONx7t69i8ViYWZmZlefvqeR4XNV2BWI6nqNRkP6QZ/3i1THe8B3mt9hTlMUzyyisAuFAoVCgb6+Phmzs7Ozw8bGhqxlsneTPkiDF8GGohaD0FaFee6gluSHAfXiKmrWBINBuSmLKpXCp3zQgiDGgzgdiSwjUVNHuOaEm63bUccGiQJqopqucBWKmIZSqST98SIT4DAr/c+D+rttt9vy+3+WzDJxEBPVqtWWhW6SnZgbZrNZrg/CMiXi5h63Vqgtm+I+oryFqE8lXErdKBsRyyP2FGH5F5Zgp9PJwMAAoVBINn4Vbsjt7W22trbI5/NUq9Wus3gfhNh31W1DhKxsNtuuOKenXVfVngmxRz6P0vdcFXbFBFCXChbNmJ7lA6k1X4/Hw/DwsDTXAYe+CZiYAEImoiqwCPoyGAwy3/2HP/whiUSCO3fuyOqfe9PO9mqtAFNTU7Idw/T0NDMzMwwMDFAqlYjH40/sL3wdiOeZmJggGAxy9uxZJiYmmJub48aNG5RKpQMzOYR7UqfTyViXt99+W/r1d3Z2ZCr6jRs3uH37tkzR76YF9lGI8gHHjx/nb//tvy3Lbov5Ua/XmZ+fZ3Nzk1u3brG6ukqlUukKd8ezolb6RVkFm80m3ajiZ09zP51Oh8VikcHfT3uP1404DIjDgQg2rlarbG1tkUqldm0ee1HHfYyPjxMMBnnrrbc4f/48rVaLWCwmXZJivTns40t8fyINWFh8hYuw0WjQ6XTw+/3MzMwwMzPDH/zBH+D1evF4PBQKBb744gvW19eZm5uTFs1emVtivS0UCsTjcZLJJKlUCoPBwNDQEO+88w6dToelpSUuX74sU6UPOmzvjTEUXgLRoVoc5kW7HHV/vyfhmZWXva3kxXsiav9Z7gnf1UgRNRysViuKosiI573Nnw4bagWmVCqRTCYpFArUajWpvYoeI1arlUwmQ7FYlCc+ofzt94ydTgeXy8Xg4CBjY2NMTU3JDUx8+WISHjbUFjSv18vAwAAjIyNMTEywvLwsKzM/SeCbojyo8CkqMA8PD8veK8LikkgkZLBmLywuwjfscDgIBoOy06uwqIhYl2QySTwel5tLt52MnwUx30SQpci4elorrdqyKSwO3ZZWrk6UED2ahOVF9FIT8+ygdG9xH6/XSygUktVlo9Eo+XxeuuW6bXwJS4sYJzqdbte+YrPZiEQiDA0NMTY2JoOUW60Wm5ubrK6uSteikGs3Pf+jEM8gKpWXy2XK5TJ6vR673U44HOb48eM0Gg1ZGkXETD0KsZcLd5Cw3omqu1arVSovz7J3PdXMFF+UOuimXC7Tbrex2WyyF4/wqYoHOOjL3et3Fe3GP/jgAy5evEggEKBQKLCyssKvf/1r7t27d+g3JBFYOz8/LwuDVatVRkZGmJ6exu/38/7771MoFJidnaVarcoursJaICwQArGpf/TRR5w6dYrBwUEGBwep1Wrcvn2btbU1Wb33sC0owvwoKihfuHBBdnquVCqylYRoVikW372KsSjR7XK5ZM2Fd999l0gkQiaTYWtri5///OdcuXKFtbW1rjT5qxEHBJEWf/z4cT766CNmZmYIh8OyGF+j0ZBN4X7+85+zsbFBKpXa1UajW2VwEOJAIw5MVquV/v5+xsfHeeutt9je3ubatWvSTXKQHIRlMxgM0tfXx8zMDLOzs9JNoi57cJgRhychm2q1KhvpCfl4vV4sFovctNUyEQqNSK++cOECMzMzDA4OYjabZRX1YrG4qw1HtyAULlH8UrRAEArN0NAQ7733HkNDQ9jtdnZ2dtja2mJ9fZ1vvvmG9fV1qtWqPDT0wrwS6y1AMpmkVquxtLTEwMAAkUhEHhBdLpcsFCsKQAqPy150Op0cZy6XC6vVysDAAMFgkOPHjzMzMyMb6t6/f59Lly6xvr4uLetPItenVl4A6d4Q/mXYXdxGBI8+7b0V5UF54omJCaampjh+/LiMC0mlUszPz7O1tXWoXUeCdrtNPB4nnU7L3iJms5nx8XHZP6JWq9Hf3y9rU+RyOXZ2dsjn87JTp5C5GCCTk5PMzMzICrubm5uy67IIIDtsC4oYC6LT6PDwMMePHyeVSsn6I0Lz3rvJqBUY4U4MBAKcOnWKkZERWQpfVAy9d+8e165dk3UXuj1IVWR6WCwWQqGQLF3vcrnkgiPGTCqVYmFhgfX1dTl+uv35D0IEAAolz2AwyDIOo6OjMtbnSXqmiJ87HA4CgYBsTyKUazH/DrvyAt9ZxtWtMYTyL+LkhFweZeUVbjPRAVhY+er1OrlcTrq3uwW1+12c9tVpwSJr0ev1MjY2RiAQkEX9MpmMrI6+tbUlFbxemldiLSmVStTrdZLJJIlEAp/Ph8FgwOPx4Ha7abfbpFIpYrGY3P/3ZpwJC2YoFJLzyeFwMDk5KRWYQCDA2toasViMlZUVrl27Ri6Xe6q964mVF/WGIgo5FQoFisUier1eukOGh4fpdDq7lIxHuUDEn4qiyM6/b7/9NhcuXGBychKPx8PGxgYLCwvMzc1x7do1GYB52AeO2pS/tLREpVIhlUrRbrdlIzlhoTEYDLKWhN1u35UCLRDyEkrQxsYGN27cYGFhgW+//ZZ0Os3a2tq+6Y+HBdFuPRAIyDYAxWKRDz/8EI/Hw+bmJouLi7JPkUiPt9ls0uJy8uRJfD4f586dw+PxUK1WWV1d5dKlS1Ie6s7Th1EOT4NOp5MVX0+ePCndRfDAxCsaff7qV79ic3NTnoofZ2noZoRVbmNjgytXruByuWS6uGhO+Vu/9VtMTEzIQMv5+XlZi2q/YFWz2YzRaOT48eOcPXuWoaEh4EHF2bm5OW7fvi3dv4ddrkJZS6VSrK2tEQwG2dnZkbEeoh6QsCjB7s1dxCWEw2FGRkaIRCKy3PutW7f4zW9+w9ra2q6g+sMsj0chgpjb7bYcN6Ojo0QiEWw2m2x386tf/Yr19XVZELRXgnP3QxwGbt26RaVSIR6Pk8vlCAaDDAwM4PP5eOuttygWi0xNTR1oeXG73TJdX7SxsVgssh7T7du3+frrr1lbW2Nzc/Op46eeyfIiAmzK5TKVSkVmeohKefl8fpef9SDlRWwyog33iRMnuHDhgswgqVQqLC4usrCwwOLiYledJsUiubW1RSwWAx40HxwaGpJBdOoKsQaDgYGBgV359LA7A6lQKFAul0kmk9y+fZvr169z6dIlGTNz2FJi1SdecboRtRNE5gxAf38/N2/elDV+Go0GDoeDoaEhfD4fJ0+exO/3c/78eTweD5FIBIPBwNLSEslkkps3b/Lll1+SyWRkIN1hzkx7HOrvvK+vj+npacbGxuRzAzI7a3Nzk2+++YZYLCZN4WpTcK8hNst4PE6r1WJycpJ4PE4oFJIVhx0OB319fVQqFTY2NkgkEiiKIt0dArEO2e12bDYbw8PDnDp1imAwSKfTkWNLBD/vt1AfFtQuo3a7TT6fJx6PSyucsEyJDCR1fRO1hVc0+hSFxPx+v6xSvbq6ys2bN0kkEnLt7uZxJuQlWq6EQiH8fj86nY56vU4mk+H69etsbW311KFoL3vTmVdWVkilUnLd3tnZIRQK4XQ68fv9NJtNxsfH5QFcvc6rg94NBoOMuxI14LLZLKurq9y9e5dvv/2WVCol56fQG56Ep1JexCAV0dkLCwt4PB5mZmbweDyMjIzw8ccfMz09zcjIiGyOJhQY9Uu8Z7FYMJlMnDp1ioGBAWZmZnA6nTLY9c6dO1y7do21tbVdG3o3DB6160P4Tr/44gvcbjdzc3PSjeJ0OhkfH8fpdDI6Oiq/dIBMJkOj0aBYLFKr1eRCvLq6ytramswwOqy9VtSnMhGhLtxcdrtdBoMJH3s4HKZSqZDJZLBYLPh8Pml5sVqtBINBjEYjuVyORqPBtWvX2NjYYHV1VRa26/bUxb2ZEaJnUTgclkX4arUa0WiUX//616ytrXHv3j3y+fxzpx92A+LZSqUSzWaTq1ev0mq1OHnyJICcU6FQiLfeeouxsTGsViupVIqbN2/K+krCsqnX65mcnCQYDHLu3DkmJiYol8t8++23fPvtt1y5coVUKtUVTRnV371opjc7O0utVsNoNMqaU++//z6JRIK7d+9KC4rYcDweDxcuXGB4eBiv1wvAxsYG6+vrrKysEI/HqVQqXb2Jq8s2iMPzyMgI/f39WK1WqtUqsViMWCxGOp2WFv9uXleeBGHVFIq6sDiur68TjUbp7+9nampKehbgu5gZUbJDeA5EiyBhzYlGo2QyGVZWVlhdXZWW4kqlIve7pxlPz6y85HI5FhYW5AYzMzPDyMgIQ0ND5HI5Tp48STqd5v79+7IuAHyn6Yq0KK/Xi81m4/z58wwNDeFyubDZbCSTSVZWVrh79y7Xrl3rSlO4+KziJLS1tcX29jaKosgAMVH86c0335Qbs0j37HQ6bG5uyl4k+XyemzdvSq1YaMbi/zqsE2uvxS4ajbK6usrk5CR9fX04HA7ZofzMmTNS+1dXUDWbzQCyntDW1hbZbJbr16+zuLjI+vo62Wz2mbJMDiMiUFcobtPT04RCIVnLRlQjvnTpEpubm7LBnuhD08uI5ysWi+RyOa5du8bm5iblclmenoPBIHa7nWAwSLFYJBAIkEwmsVgsZDIZ2ddH1Px45513GB8fl+n7v/nNb7h27Rrffvst33zzDfDd+nfY5SssMJlMBnigxIhsR6fTyeTkJBcvXmRtbY1yuSxjFkQBRL/fz5tvvsnQ0BAejweA9fV1mX4fj8e7/oAAu7PLvF6vLCgqXBtCeRFVuoXycti//+dB7FmVSkXWOlpaWmJlZYXNzU2OHz8uqzarC/3BgzgpkYVUq9VIp9MyBKBSqXDnzh02NjZYWVlhZWVFxmSJ/fBpeaY8QPHliUp5DodjlzmtWq3KDyyCUNWR+p1OR04YYVG4e/cu0WhUdrNcWVlhbW2Nu3fvUigUZFO5buKgTA8RDyNMurdv32Zra4tWq4XD4cBisQAQjUZl0bFSqSQnU61W66rFFB4oHrVajXv37gEPlGCxoYiXKIRlNBppNBqyjYLwrYrCUPfu3ZMm3Wg0KuuZdNsYOQhhkhcLrHi+RqMhTa3JZFL2LRI1Jw77eHhRiO9a1ElaXFzk888/Z3BwkGKxiM1mw+fz0W63ZTG/999/X1pems2mlPHY2Bg+n0/GqN24cYMvv/yStbW1XfLsFtkqiiJbJaytrXHjxg0ikQijo6O4XC5mZmZkIKVwhen1emkBjUQiOBwOtre35Xy7e/cumUxm1wbeLfJ4FHvTykVdH1HoUlgPxLVHhb1dokUvKxHzJZJzxCFcxFMB0poi9inhyhZNLDOZjLTGPM969czKi6IorK+vy4CelZUVucA+TRBXrVYDHjywsMrAA01/Y2NDptF2q29VXd9E/Z6oiaPu8moymVhcXMRisUhLQzqdlq0ShMIiBla3yENtednZ2ZEp3dFolMnJSbxeL16vl0gkwvj4uKyzUSwWWV9fp1wuywwHUdnyq6++IpVKkUwmZZXLbqvJ8SjUNY9EXJTRaJRWJdGhNRqNSnmolZejgpgLlUpFxrIkEgnGxsbIZDIEAgFOnDghq6WKgFxhmVAj3rtx4wbz8/NcunSJv/7rv97lJuoW2aoTK0ThQqfTyblz5xgeHsbn8xEIBGg2m7z99tu7ZCHWcJvNxs7ODjdv3pRlGEQQp1h/ukUe+6Fel8UGLGI3hVtWpFOrD6Hd/MxPg7qnnkgtj0ajrK2tsby8LNdo0QPKZDIRDAZpt9usrKxQKpXI5XLU63Wy2SzVanXfvet59rBnVl5EHEe9XieVSskPotbYntTEplZaREyM0Hq70eLyJAgZqhfSdrst+/qIjVicEHtlAolnbLfbLC4uUiwW5Wmvr6+PhYUFuYAWi0U2NjaoVquyVLuoB5NKpb6XTt4rqJU9EbD+q1/9Cp/PJ1vHr62tsbKyIutUqH/vqCEWQ3FaNhqNzM3N4fV6yWQyuFwuxsbGsNls0jVrsVhkwGq73ZaWYuGGjEajXRdjtxdhsYzFYty9exeDwSCLf0YiEYBdFvF2uy1jHbLZLOVymW+++UYWfRSxdb2AOrhZxGdEo1GMRiP9/f1sbGwwPz9PPB6X3oFeefanQb1PKcqDmkfC0isOU8ViEYPBQLFYlEHutVpNVnFWe2Ve5P6lHFS3wOFwHFjUQPyucAu9SJ7GwlAqlV7LyuJ2u1940Ye96eUvQkPN5/OvRT52u31f+YgFU2juovmb1WqVXcPFRBEdoUUNGFGfQWzYL8IHXS6XX4t8bDbbE80vp9Mp0wxtNpssPibqA4kN+GVRqVRei3wet/7sRV0PSKfTYbVa6evrw+PxcOLECRnLIWoFmUwm2dLk9u3bbG9v8+WXX3L37l1ZCuJJFtvXtf44nc4D5aOufyNK/Z88eZKpqSk++ugjWbpAHVAvYscuXbpELBZjaWlJWn2Fm+1p51uxWHzl8nnSsSNSpUWW6/DwMOfPn+fWrVv88R//McVikVKp9NKKPb6usfO0c0sgFL79LJfqa8R7z6uwHCSf3rCz9xCPMmk/6mfdjDj1CBeaCNQVg73VaslKwyI7yJ3E+gAAg1VJREFUrRuqnL5IOp2OTIEXZbSFMqfO4tP4DrG4iiy9drstN2VRPdbtdssU0FarxerqKul0mng8Lq2d3Y5YN4Q88vk8Gxsbu7I81QHezWZTxtZtbGxI64s4OPQiYq0plUpsbm7Kw9Ha2hqVSuXQtls5bLwOGT2X5UXNi/7wT6Op9ZLl5WVw2CwvAvWYedz42TseXuQJ6LBaXgRqBWVvDMarcGd0i+VFjXo8CTmpOwirZadWisVJvBvWn8dZXgR7T8IilupRiQTCsql25T/PWDvMlhdgl7VOyEccrODlugy7zfKyH/ut3S9KZq/E8tKNPmGNw4V6g4ZXtzl3A2rZ7K01osno0ajj6Z6EXpar2r34pN3nezHecC+KokjrLuxW1nptDLwMXpeMNLeRxmujlzeKF4nIiNhrTdB4NN2WIfQyUcvgWeppHAUZanOs+9CUFw2NLkFbTDWeB238PB5NRt3DgTEvGhoaGhoaGhqHjd53aGpoaGhoaGj0FJryoqGhoaGhodFVaMqLhoaGhoaGRlehKS8aGhoaGhoaXYWmvGhoaGhoaGh0FZryoqGhoaGhodFVaMqLhoaGhoaGRlfxzMqLoij/haIo3yiKUlcU5U8OuO6koig/VRQlpSjKY4vKKIryPymKMq8oSltRlH/wrJ/vdaPJ5/EoiuJTFOVfK4pSVhRlTVGUHz/iOkVRlH+uKEr64eufKwdUk1IU5ccP71dWFOXfKIrie3lP8XLQxs/BaPI5GE0+B6OtPY+mW8bO81hetoF/BvzPj7muCfyfwD98wvteB/4z4Oqzf7RDgSafx/MvgQbQD/wR8D8qijK7z3X/GPg7wBvAaeD3gP9kvxs+/P0/Bv7ew/tWgP/hRX/wV4A2fg5Gk8/BaPI5GG3teTRdMXaeuT1Ap9P5CwBFUd4EBg+4bh6YVxRl8gnv+y8f3rf2rJ/tMKDJ52AURbEDfwic7HQ6JeCSoih/xYOJ///ec/n/C/jvO53O5sPf/e+BfwT8//a59R8BP+l0Op8/vPb/A9xVFMXZ6XSKL+dpXjza+DkYTT4Ho8nn0Whrz8F0y9jRYl40XhfHgFan01lQvXcd2O/0M/vwZ4+77nvXdjqdJR6csI4916fV0NDoFbS1pwfQlBeN14UDKOx5Lw84H3Ftfs91jkf4nvdee9B9NTQ0jh7a2tMDaMqLxuuiBLj2vOcC9jOv7r3WBZQ6+3cVfZr7amhoHD20tacH0JQXjdfFAmBQFGVK9d4bwO19rr398GePu+571yqKMg6YH/5/GhoaGtra0wM8T6q0QVEUC6AH9IqiWBRFMTz8WUdRlI8f/l15eJ3p4b8tiqKYVff5E3U6lqIopofXK4Dx4fVdp2Rp8jmYTqdTBv4C+KeKotgVRbkI/AHwrxRFGX0oo9GHl/+vwH+pKMqAoigR4L8C/kTcS1GUVVXq3f8G/J6iKB88DMz7p8BfdFPAHGjj53Fo8jkYTT6PRlt7DqZrxk6n03mmF/BPgM6e1z8BhnjgT/Q/vG50n+tWVff5FPhHqn9/ts/1Hz/r53xdL00+TyQjH/BvgDKwDvz44fsfAKuA8eG/FeBfAJmHr38BKA9/ZuKBWXZGdd8fP7xfGfhLwPe6n1UbP5p8NPkcnpe29nT/2BFfwgtDUZS/C8x2Op3/+gmuNfEgOvt0p9NpvtAPckjR5PN4FEX5b4Bkp9P54ye49n3gP+90Ov/xy/9krx9t/ByMJp+D0eRzMNra82gO29h54cqLhoaGhoaGhsbLpKt8lRoaGhoaGhoamvKioaGhoaGh0VVoyouGhoaGhoZGV3FgbyOHw9EVATGlUumRXT5fJk6nsyvkUywWX4t8fD5fV8gnk8m8Fvlo8+tgrFZrV8inWq1q4+cAXsf40WRzML0gn2duzKihoXG4UQfjK/tWM9fYm7CgyWl/tLGkcdjQlBeNrmafGgUA6HQ6ucgetcV2rzw6nQ56vf7IyeFxdDod2u32rvfU40YDKR/xp6Iou169xt55s5defvZu47UrL5pGr8ngWRFy209J0eS4v1w0dqPJ5tF0Oh0URaHT6aDTfRce2esye9Tzqd8XstH4jldtxXxtyot40FarRbvdlifDo3TyUZ+QW60WiqJgMBg0zf4xyAqLioLZbEav12OxWDAajfKaWq1GrVZjZ2dHyvYoIDYaq9WKwWDAaDSiKArFYpFGo7HvafKootPpsNvtGI1GdnZ26HQ61Go1Go0G0Pub9EHs7OygKIocR+LPRqNBq9WiXq9Tr9df98d8YYi9R6/XYzQaMRgMWCwWqby1222azSatVotKpSLXFbVVU7yO0hzrdDrs7OzQbrd3WTEVRUGv16PT6XYpvi+S12p52XtyPspoMngyxJgRSq5QWhwOBxaLRV5XLD5oJ1Kv1+Uic1RkrCgKNpsNo9GIxWJBp9NRr9flpqzxAKG8WCwWms2m3JA0OT1AzC+TyYTb7cZsNlMqleTm3UvKi1BcTCYTVqsVs9mM0+mUP2+321QqFTlOxEFgZ2dn132OkuKiZr8Dd89ZXoR2ptPpMJlMDA4O4nA4yOVyVKtVSqUStVrtVX+sV4qwHBiNRmw2G1arlWAwSKvVYmNj40huuI9DLArtdhuj0cjIyAhut5szZ84QDAYZGBjA5XJJLX9xcVG+5ubmvhfb0EuoLVEWiwWXy8Xv//7vE4lE8Hg8KIrCn/3ZnzE3N0er1ZIL7lEbW0JOOp0Ol8uF0+nkD//wDxkbGyMajZLL5fjiiy+4deuWvB6OlpyEFdzn8+FwOPgbf+NvMDw8zPDwMC6Xi8uXL3P79m2WlpYoFArSYtEt7LUOtNttOp0OXq+XwcFBhoeHOXfuHG63m4GBAfndN5tNkskk5XKZxcVFCoUCS0tL5HI5CoUCtVpNWmaOiuW83W5jtVqJRCJYLBa8Xi96vV4eAjY2NigWi1Sr1Zdi/X6lyot6kRXmuXA4jN/vZ3Nzk1wuR6PR6HnlBXab910uFyMjIzQaDVKpFDs7O9KMrfEdQh56vZ5AIEAwGOTMmTMMDw8zMTGB3++XFhm/34/JZKJYLMrFRB102GuITUdYoU6fPs3U1BR9fX0oisJnn312pFyyj0KMIYvFgtPp5OzZs5w5c4bFxUXi8Tj37t3bde1Rkpd6vbHZbHi9Xk6dOsXs7CzT09P4/X4KhQKZTIZYLEa73e4qxUWgjuURLiGLxUIwGGRiYoKLFy/S19fHxMQEOp2OdrtNvV5ne3ubQqGAy+UilUpRr9cxGAwy9EGs2UdhzKgP4P39/bhcLiKRCCaTSboVi8WitGQ2mw/aG71I2bwS5UUMEL1ej8fjwW63c/bsWQKBAG+++SZ+v5+bN2+yvb3Nb37zG0qlktSIofc2GyEPg8GAz+djaGiI3/3d36XZbKLT6YjH48zPz1MsFo98loh6QbVYLIyOjuLz+fjkk0+IRCJMT0/j9XqxWq3SoqDT6XA6nQwMDODz+TAajXKB6TXUbjQxr8LhMOPj40QiEdrtNtVqtSef/VkQ887r9dLf34/H48HhcGAymXbFLhy1g4P6eU0mEzMzMwwODnL8+HHGx8epVqusrKywsLDA7du3SaVSr/HTPj3iew2HwzgcDhnbVCgUKBQKOBwOIpEIXq9XKiu5XE7GjbXbbVwuF1arlbfffptqtcrY2BjZbJbbt28TjUa5d+8eGxsb8vDZyxYYEadpsVg4deoUoVCIM2fOYLPZpPISiUTY3t7m8uXLrK2tfS8j9Hl5ZZYXsWg4HA78fj8XLlxgZGSEt956C7/fj9lsZnl5meXlZRYXF+VJuRe/fKG8iE02FArx9ttv02w2WVpawmQysby8LK/pRRk8DWLAm0wmhoeHGRgY4M0332RwcBCfz4fFYpHKiRgzNpuNvr4+HA4HBoNBKsO9KkthxZucnGRkZIRIJILf7yedTves4vYsiLHkdDrxer04HA4ZHyTmmhgjR02BgQfPbDAYGBoaYnJykuHhYSKRiLRMbW5usr6+TqlU6rq5pNPp6OvrIxgMks/nKZfLNJtNisUiNpsNv9+P3W6n0+nQbDYplUoYjUasVqs8HAgFCGB4eJhSqYTdbmd5eZl0Ok08Hpduf+i9g7dA7GFms5nx8XFGRkZ49913cblctFotarUaiqKwtrbG4uKiVOpe5Br80pUXtS/e6/Xy0UcfEYlEOHv2LMFgEIvFIjXgdDotT4m9vNEIxCQRG4vJZCISidBsNjEajUdy8RSoay04nU6mpqbo7+/nb/yNv0EwGCQSiUjFRG3+FWZs8e/9asD0Cmqri9FoxOVycfr0aUZHR3E6nTLav5dPgE+KkJXInBkdHZVy0ul0pNNp1tfXyeVy0gJ6VGSmtgQHAgH6+vo4efIkk5OTOJ1O2u02iUSClZUV4vE4+XyenZ2drpCR+ns3mUxMTk4yPT1NOp2mWCxy/fp1kskkiUSCq1evsrCwwK1bt7BYLHg8Hvl7IkbTaDTS19eHzWZjcHAQm83G9PQ0AwMDGI1GBgcHuXv3LouLi9/LwOkl1JnB4jkbjQb1el3+2+fz0Ww2sdls6PX6Fy6Ll6q8iE1DpLL6/X4+/PBDRkdHOX78OA6Hg2q1Sq1WI5/Pk06nqVQqPfuFC9SmaXUApdFoJBQKSeVF7To7aqiVF4fDwfnz5xkeHuaTTz7B5/PJ7AD19epxo1ZeBId9oX0WhJIvMkJOnTrF2NgYNpvte0pLLz7/k6COtRPKy8jICJOTkzgcDhRFIZPJsLm5Kf30BoMBg+G1l8F6JYisGZPJRCAQYHBwkBMnTkj57OzskEgkWF1dJZFIkM/nMRqNmEym1/3RnxiRSTQxMcH58+dJJBLkcjni8bh0g5VKJSkLo9EoFVuDwYBOp8NisWCxWBgfH8fr9fKjH/2I0dFRjh07htVqxWq1MjQ0RKfTYX19nWazSb1e79l5J5QXIbNGoyGzsNrtNl6vV1qsXoai+0osLwaDgVAoxMDAAP39/TKIsF6vs7m5SSaT4e7du9y5c4dsNrvr93v1ixcaa61Wo1qtyrTDXt9sH4c6zsnhcBAKhRgaGuLkyZOEQiFsNhs6nY5qtUqz2WRra4tyuYzZbMZgMDA4OEhfX9/3LA5ikvWqTPd7rk6nQ7lcplQq0Wg0jrQyLCwLLpcLr9fLwMAAkUiEVqtFJpNha2uLlZUVisViV1gUXiRingjL7+DgoHSp1et1ms0mGxsb3L9/n0KhIC163cjeCsHipU5/Fi8R7C9qlQiFtl6v43Q66e/vJ5lMcvbsWQYGBuS6s7KywurqKrFYjM3Nza7LyHocItlEWKOERWrvnNHr9RgMBux2O263m3w+/0IDd1+65UX4xUZGRhgbG2N4eJhQKMTOzg6VSkX6w65cucL169e/l8rWqwjlpVQqUS6XqVarwNH0swvUJ2SdTofH4+H8+fOMjY3xzjvv4PF4cLlccmEpFAp8+eWXRKNRgsEgLpcLh8NBOBzeFXzZSwvH09ButymXy+RyOWnOPYrjS6xDiqLg8Xjo7+9nfHycsbExarUauVyO+/fvc+fOHUqlUldvzk+L+rAg4hfGxsYIBAK4XC5poVhYWGBubo5sNistEd3M3nVBBNmqqVQq3/u9Tqcj4xIVRWFkZAS/34/P5yMUCjEyMkI8HieRSHDz5k1WV1flRt8rgeBCeTGbzdIaZTabd8WKiWv0er2ML6tWqy/0cPBK7KJ6vR63243b7ZZferlcplgssrS0xPLyMtlsdtfg6XXFRQxkYVpsNpvSDNvLz/4kuN1uwuEwIyMjvPHGG4RCIex2O3q9nlwuR61W48aNGySTSebm5kilUpTLZfx+v9yohbnS4XDgcrkolUpSQTwq8m232zKtVRQW69UMvschNufR0VGGhobweDxYLBaSySTpdJpCoUClUjlS1ZgFQjZ2u51wOEwkEsFgMMjaJolEQsqo0Wh0rWVK/ZkNBgNms1lmme3Nbt3PSqs+WAEkEgk6nQ7Xrl2jUqkwPj4urcPj4+Mkk0npdturGHUrahkJpUyv1++bFSusVUajUVZCf5G8EreR0WgkEokQCoWkhpbJZEgkEnz11VfcvHmTdDotg8C6Xat/EoSpUsT8NBqNXeXtjxrqoNv+/n4++eQTJiYm+OSTT2Qhv3q9zvr6OolEgr/6q79idXWV27dvk8vlOHPmDAMDA8zOzjI1NYVOp8Pn8xEIBAiFQnIBFieCXkV9smu326yvrzM/P086nZYbTy8//17UG46InRofH2dgYAC3283Vq1dZXl6WgahiIT4KqGMSHQ4HfX19nDhxgtHRUUwmE7VajeXlZVZWVlhbWyMWi/WEVUokkACyQvfOzg7NZnOXm3m/34MHiRXCArO6ukqxWCQUCvG7v/u7XLhwAZfLxdtvv00+n+fbb7+V1s9eWnsURZFxT3vdRuJwLtxGFotFBu2+yOSJl6a8qAMJRcEjj8cjK/Dl83kymYyswCfy4o8aYtLU63UZpHvUEIPZarVis9mIRCKMjo4SDoflIpNMJikUCty+fZt4PE40GiWTychS7pVKhXK5LF1wYlEWL51O1xMm20fxqEVBXTzrqCFkImJd/H4/oVCIYDAIPOh/FY1GWV9f39dFcBQQ8hFFH91uNw6HA3hQVTadThOLxeSc6tZxJA5GomiaiMMQFjhh/X7SuDh10Utx8I7FYsTjcTweD16vV+55nU6HTCYD0BPKi1DCTCbTvm4jgdjbKpUKxWJRKoeHOlVaDBRR/r6vr4+pqSkikQhGo5FGo8Ha2hqrq6tyUxI+6aOiwIjB32w2qdVqFAoFmX3UrQvE89DpdOQ4uXDhAr/1W7+FzWbDYrGQy+WYm5tja2uLP//zPyeRSJBMJmUch+i5Eo/HSSaTpFIpHA4HDocDo9GI0Wjs6dO0mG/7Kb7qas1HbVypY+6OHTvG6Ogo58+fJxwO0263SSaTXLlyhWvXrpFIJI7U+gPfFRqz2Wy88cYbjI2NyYD3ZrNJuVzm3r17XL9+nWw229XyEYpLuVwmn8/LkgJzc3N4vV7y+TyVSuWpnk+s4evr66yvrzM0NITJZOKdd97hjTfeYHx8nOnpaWm5Aro+g21vgVW/34/f78ftdstqxMLtVqvVKJVKRKNR1tbWqFarL3QdfqmSNBgMMtbF6XRis9nY2dmhVquRyWRIp9Oy8y8cPT88IPtA1Go1aY4U9Ppmo65TIqovj4yM0N/fL9PrSqUS2WyW9fV1tra2yGQyFAqFXfVxDgrK7eYF90kQFiYxz0RXcrHIiNPPUbPoqeeO2WwmHA4TDodxOp2YzWZSqRSZTIZMJiOzIHp5nOxlb40gj8eDx+PBbDaj0+lkTKKoQNvt8hHrbLVapVwu0+l0ZJyPx+OhXq8/czyYmGvFYlHuaSKg1el0yiJ3vbaeCxf0Xlf03ud8Wdbfl2p5cTgcHD9+nGPHjjE0NITT6aRcLpPJZLh9+zaLi4tkMhmazWZPRLA/KWJyqCeUMD2K93ttoO+HCJKz2+04nU5OnTrF7//+7xMIBLDZbBQKBebn51lZWeEv//IvSaVSbG1tydgN4YIEZAdls9m8r6WlmxfeRyFOzh6PhzNnzjA2NobL5cJkMsmuv8ViUdYuOSqorUwi9umjjz6SlYd1Oh23bt1idXWV5eVlotHokapmrc7qM5vNuFwuJiYmGBkZkVVkNzc32d7eZm1tja2tLVkxtdvkIxT5RqNBq9UiFouxtrbG6dOnZV2tEydOALCxsfFM9xeu6Vgsxu3bt5mdnZXF2YaGhshms3JO9nK5hr2og3pfxjO/cG1BvemKNCmHwyEDo5rNpkyZKhQKPROF/ayIAj/qjqRHrSqq1WrF7/fT19cnS/qLNN+trS22trZIpVLkcrldbrVHyUiccoTLpJfL44vAOZFGLgLn6vW6jANS+5uPCupYF6/XSzAYlH1rqtUqiUSCaDRKuVyW4+MoyQceWMadTqeUkejKvrOzQ7FYJJfLUalUqNfrXR2TqK4CW6/XZZdjkUxit9ul1ftZDo1iHRLFVkulkqzb5XA4sFqtuwKde+FgKixLJpMJg8GwrztIuOpe1tr70txGIqI7HA4TCoVksO7a2hrJZFIGNzWbzSPbfFCdMl2r1ajX6zKzxmKxyLTyXk1vFUrG+Pg4Fy5c4Ny5c7K7djqd5s6dO/zZn/0ZqVSK7e1tWeBov/Giju0QkwaQZuJeqnSptipYLBZcLhfj4+MMDQ3Jk/PGxoY8Cd66dYtCoXAkFGJ1kTGPx8O5c+eYnp7m1KlTOBwOotEoiUSCX/3qVywuLpJKpY6c1UXg8Xh44403mJyc5MyZM3i9XhRFoVKpcP/+fZaXl2XlWej+9UcUbczlcpTLZRqNBiaTCZ/Ph81me2ariPidTCZDuVxmfX2dzc1N2u02w8PDbG9vy2xJodR0M51OR1YTHhwcxO/343K55F4l1vVCoUA2m92lLMKLG0cv1U+jzvMWmqcoISw266NkRnsUnU5nV60X4f7Yq7z0EiJWQ5S1D4VCeL1eTCYT7XabXC4nG52lUikajca+nVqFbERanhhzooaOGG/CqtUrY01d7t5iscgTtAgIFCfnQqEgrQu98uyPYm8NCpG51t/fj9PpxGg0ksvlSKVSJJPJXdlqRwG1fPR6PTabjVAoRCgUwuVyYbfbZXaImH+1Wu17NVC6EfG5G40G1WpVrgnArpCFF7XWik1cVOXtJeVYWHuFV0VYX/buVbVaTTa/fBkFMl9qqrT672rfY7ValYFgopvrUUVRFBqNBpubm3Q6HS5cuCA19nq9zvb2NuVyuaesU8KMKFIJT58+zUcffYTb7abVarG5ucnPf/5z7t+/Tzwel/72veNEbeb1+/0MDAwQCoXw+Xxks1my2axM9ew1y4vIohHpv+Pj44TDYaxWK41Gg8XFRe7fv080GiWXy+1q0tiriPFgMpmw2+2Mj4/zox/9iP7+fmw2G7lcjkuXLrG6usrGxgaZTEYq0UcBMW5sNhtut5vJyUl++MMfEgqF8Pv9tNttGQN05coVlpeXyefzcv3u5rEj1o5sNivjU5LJJOVyeVc23rM8o/h90VphZmaG8fFxVldX2d7elgUiuz1EQihkRqMRn8/H9PQ0w8PDWK1WjEbjrri6nZ0dGVOWSqWoVqtPtf48iaLzQpUX9X+4t/qe+Hmr1ZLxHTs7O12fOva8CH+s8JOKyHRR6l4U9ulFbDYbPp+Pvr4+AoEABoOBVqtFoVBga2uLRCIhLVKPsrioC5D5fD7pvxaxDaIAYK9ZHsSma7fbZcCzGC8ApVKJQqFArVY7Ul2ShVysVitut5uBgQG8Xi8A9XqdaDQqDwTCZX3UDk8Gg0HOl0gkgt/vR6/Xyz5PouqwSKaA7rW4CMTnr9frlMtlKpWKbAIsPAPPq6A5HA4CgQBut1uu48LK0+1JGEK5E9Ykk8mEx+OR3euF3MQzdjodGdsqrHdPI98nue6Faw5i8RCnwomJCSKRiGxCmEgkSCQStFqtI7do7IeotFupVKjVani9XulPrFQqbGxssLOz0xOnw72p0bOzs5w/f54TJ07gcDhIp9NsbW0xNzfHV199JVNY956IRDCuoij4fD4cDgcffPAB586dY3JyEoPBQD6fZ2lpSSpAvRSwKxYSu93O1NQUExMT9Pf3y5gFIZujFvgND2TjcrmYmpqS1iidTsf6+jqrq6tcu3aNjY2Npz4JdjtiDun1evr7+3nvvfc4ceIEw8PDmEwmUqkUiUSCn/3sZ2xsbLC+vk4+n++ZtUfsNaLFQSKRIJVKYbFYmJqaYm1tDbfbTbPZfKzCJtYxoZCIDEfRsToQCMi1+5tvvuH+/ftUq1UZW9WtiL1dFJ0dGhqir6/ve/XJ9h4sn/aZRQG8x83Nl6K8CNOSxWKRFRtFDILQeHvBFPm8qLXVer1Oq9XCZDJhtVplkTVR+Kebtfa9COWlr6+P0dFRfD4fJpOJer0ue6lEo1GpsR90H4fDgdfrZXBwkPHxcZkxUa1WyWQylEqlnqojpB4HBoNB1uew2WyyyuVRjiMT6b99fX0yELPZbMoYDlHEsJuzZ54W9aFBzJnBwUFCoRBOp5NOp0MymSSbzUqXmuhE3isKnjpsod1uy31IuEDcbjc2m01aSsTvPAr1Bi3WbNGc0WKx0Gg0KBQKRKNRstmsdKl0s/IC38Wxms1mHA4HNpsN2J1+L/6t/vNp3EUipuaVKi/qSWI2m7FarbhcLqxWK7VajXQ6zTfffMPq6iqVSqWn4jieFRHEvL6+jsVioVQqYTQapWmul4obieew2+3YbDaCwSDhcBiz2UylUmFlZYVf//rXLC4u7lI64Ls4GVHdcWRkBLfbzfvvv8/g4CCnT58mGAxSKpXI5XJcv36dX/7yl0Sj0dfyrC8bcUiw2WzYbDbZXwS+y2I7KoeDvYGo4XCYH/zgB4yMjGAwGMhms1y9epWVlRXy+fyuTfkoyEekCQcCAcLhMGfPnuUHP/gBPp8PnU5HJpPhm2++YX19nbt370prZa/JRlEUGbJQLpcpFAqMjIwwNDRENBqVlXDn5uYOLBYq1me/34/D4WB2dpZIJMIHH3zAqVOnyGaz3Lp1i+vXr8umjd2utDwJQkEU7W5isRgbGxtP3HpDKECi/s7jQkpemuVFbL5ms1l2KBWmtI2NjSNXd+JRKIpCq9Uil8uRzWZ3xSj0WjyQWAREQKXo+GwwGKjX62QyGZaWltje3qbRaMiBrP5dMb78fj/BYJCTJ09K16TD4SCbzZLJZNja2mJxcVH2ZOnFsSaytcRiKg4DvaLsPi3CTO31epmamiIQCKDT6ajVamxsbMhia2q32lFBpLeGw2GGh4eZnJyUfcMqlYp0q8ViMRnU2ovKnbBi1+t1WdU8GAwyMDDA+Pg41WpVzqG91gP4/7f3ZrFxpel99//Uvu97scgiKUqUqK013Wr1NtOTMWYQY2YcBMiFJwlgwIgDJLlKrgLkwjCCIPZ9EjgXwSRGDDhw/I2TzBgzdnvaPT3d6m4tLYmUSJGsImthFWtfTu3bdyE9r06VSIqSSIl16v0BBYnk4WGdp97leZ912PLgcrlgt9uxtLSE+fl5LC4uYnp6GuVyGclkEjs7O0gkEiwrcFKgtjeUKv08GX20vlOxzYM4FonSh0Xpq9KAXem/nCdQAaV0Os0WVyrsJ5dFlj53g8EAm83G2kYoFIqhIG7gyemGglDJPOtyuWC1WvHBBx/A5/Ph0qVLcDqdrHrmjRs3cP/+faysrKBarcq2ZxY9j9TCQotyvV5HuVxGpVKRXYr4XlAWjc1mg8vlwtTUFNxuN/R6PcrlMqvMvLu7O1GVhsla6XA4YLfbceXKFXzrW9/C7OwsDAYDU+oikQhu3LiBVCrFLC5yHTNkyc5kMnjw4AF8Ph96vR58Ph/effddOBwOtNttlEolJJNJKJVKWCwW6HQ6eDwe6PV6uFwuGI1GzM7OwmazIRwOw+l0QqfTIZfLYW1tDZ999hm2trZkK0fg6TWIoKKrxWIR2WwWzWbz0PejvWBnZ+eZsVbHpryQCXfUZ8oVmL2hwmrSU49GoxmS4bhbEKSWF/KX6vV6dLtdprhIay9otVrYbDbmq6cAVbfbjWvXrrEaHgaDgaW+rq2t4csvv0QsFmOnKDkEHO7F6Fggn36z2WTVdSehcqw0Bdjv9zMFVxAEiKLIFJhCoTD26arPA1kPLBYLQqEQzp07h/feew8mk4k1M6UWAOvr68jn82i327IeL/Rs1C+tWCyi1+vB4XDgwoULLHg5lUqhXq9DpVKxnlhnzpyBxWLB9PQ0zGYz5ubmWEynRqNBPp9HuVxGPB7H/fv3kU6nZa28AMNrEO1RtI5TrSkKgD7s/brdLvL5/KsP2AWebMSUokpxCjqdDm63G7VajRVAmmSk5sler4dGo4F4PI52uw2r1coKAel0uiEz5rhOBrIgUQfoRCKBra0t1j7+1KlT+O53v4tKpYJMJsNOO1TokEy8er0eNpuN1RJot9u4desW4vE4bt++jVgshkqlIptgQ4LGgEqlYifAhYUFhEIhKJVKFphaKBSQTqeRTqdlFXQ5ymhWg8PhwOLiImZmZmA2m1GtVpmbOpPJoFgssowJOcpjFFLq6NBDhwGtVsvKM1DD02q1ikajAWB815fDILW8tNtthMNheL1euFwuBAIBhMNhfOc730GlUsEbb7wBhUIBq9UKnU4Hr9fLmi2SZZg6J/d6PaytrSGRSODrr79GPB5HrVaTrfIiLYNCzziaXPEyz36YQ8aRKy/0EO12m6WdUbodKS+iKLL6JeNuTTgKRpWXTqeDpaUllkev1WpZrYBxhj5nURTR7XaRTCaxtbUFjUaDYDCI+fl5WK1WVovBYDDA6/VCrVZDq9WyGI9er4dCocCCfHO5HP7mb/4Gq6uryGazTHGRm8WF5otSqYTJZILT6cSpU6dYbEev10OlUkEul2PKy6TUeKE4KIo7IOUlkUggHo8jm82ygmtyccM+C6nyAoApLwDQ7XZZDOLOzg5EUWTdkOW64QJPNtRsNotsNgu/388am546dQpmsxnz8/OsYS7wpAKvXq9nloFut4tsNot6vc6yGm/evImVlRU8ePAAiURClmsQ8SzPCl3zImOJUq+fxZEqL1JLAlle6vU6M6uZTCaEQiH0ej3cvHkT/X5fth/ui0ApfGR50el0sNlsMJlMLH4BGN+TEb1vMiOur68zK53BYGCpv71eD2azGRqNBgaDYaiIHy0YiUQCoigiEomgVCphe3sb5XKZ1Q8aVxkdhDQYl0yzVMRvMHjUH2tjYwPxeBylUonVt5GjLAhpyQW1Wg29Xg+VSsWKFFJ7CSpRLvXTTxLSTaVarWJnZwerq6tYXl5mdbcmTTaDwQC7u7u4c+cOer0es3S7XC6o1WqWBkxB3ru7u2i328hms+ygWa1Wkc/nIYoiNjY2kEwmUS6XZWvdo2DcUqmE9fV1eDwedrikMIejKPx4GNkdq9uo1WqhWq0y14cgCFhYWGAPK7f6JS/CqOtIFEU0Gg24XC4WgGiz2dBoNFgMx7hC750alH399ddYXV3FYPCoiV4oFMLCwsLQJi0IjxrFpdNpFAoFfPbZZ8jlcnj48CELqqPePb1eT9anHYLGCnVop8W1Xq/jzp072NraQiaTYTEvcrc0kNuIstioVDm5RVKpFKvvMUlZHwSlSpNZv1gs4s6dO1heXsYXX3yBer2+ZxVrOUPry/b2NiucWi6X4ff7sbi4CLvdjrm5OQCPsrEajQZrl3D//n0Ui0Wsra2hUCiwhoyNRgOtVmuoqrycIK9Kq9VCLpfD119/jampKXg8HhbUTPGJL1Kc7nk5FrcR5XqLoohMJgONRsPqK5RKpaEsEM4jBOFRZdRcLgeNRsNOQ1TBUY4bEFlgtra28OWXXyISiSASiQxdIwgCq8xcqVSwvLyMarWKdDrNukVTpctJGE+0gHQ6HeRyOXz11Vew2+2IxWKoVqtYW1tj/aCkPmi5ykb6XJlMBrdv32aZRfF4HJubm8hms0PjQ66yGIXiO6gMwf379/FXf/VX2N3dxerqKmKxGJs/kwbtU91uF81mk8kkk8kgn8/DYrFgfX0dwJPDFrWV2N7ehiiKSKfTEEWRKX/kkpTj+JIWn+t2uyiXy3jw4AGKxSJrNbGwsACNRgNRFFEsFtmh8rgyPo/cbQQ8MWmTaanZbCIcDrMUqFQqNXGa/kFIU8Q2NzdRLpexuroKn8+HwWDAAsTkgtQC02w2WXqzTqeD0Wh86jryz7fb7af6rYy+5MxoSnQ0GsWf/umfQq1Ww2QyodPpsCBdaVVhucpFaqHr9/tYX19HJpNhlt1ms4lcLscWULma8veDTr/U46pQKDBLS7VaZU1yJykOSIogCKzzfDQaRSwWY5Xh1Wo1LBYLADCrriiKzNpJB0upwiL3ei4039rtNlKpFNLpNKxWK+LxOHw+H773ve/BbDajXC6zDD/qH3Uc8+7YUqUBoNFoYHt7G/V6HTqdDv1+Hw8fPkQul5N1FsSLIN2UKpUKS7Xb2dlh6WZylRUVMKSmnaP0ej2WuTZJVpaDINcRpXNSTAcFyE8K0nHQ6XRQq9VYITFKGJBTX6sXgcYFbSQUjCqnthkvi7Q/D3kOpFW9qTcfHcyl2Z9SpAq1nKFgcLKKd7tdfP311zAYDKjVaqjVaiiVSkxWx3GIEg4StMlkeqlPgSwK0jiEXq+Hfr9/pAusKIqvZfaZzeZjG6XU24Emzn6T5TBUq9XXIh+Hw3GoN3zY2j+jNQWOikKh8Frk87Lza68xcRyukdc1v/R6/XPJZ7QqqnTBPM4NutFonPjxs9cce1VWudcxfl5kbo3Opf3m1uj/X4bXNbdedu0BhtsBkdVpNIbzZQ8OB8nnWG1cpKSM1iiZFDP/i0CyajabT5kk5cx+ypl0E5pU8/azkJYckPs4eRb7bdKTjFQmcq04fRRIN15g/zHEZTcMZcFS3A/J57g9K8eqvEg3ndHvc/ZmVGaTICu+mL4Ye82vSZYjWXpHvzfpSK1Pkxa0/LzwtejwkJxeV2bVK4ku4oPh+eEy4xwWPlaewGVxMFw+HLlwYMwLh8PhcDgczkmDBxBwOBwOh8MZK7jywuFwOBwOZ6zgyguHw+FwOJyxgisvHA6Hw+FwxgquvHA4HA6HwxkruPLC4XA4HA5nrHhh5UUQhH8lCMINQRBagiD8+IDrzguC8HNBEHKCIDwzL1sQhP8qCMKaIAh9QRB+50Xf3+uGy+dguHyejSAIDkEQ/j9BEGqCIGwLgvCjfa4TBEH4Q0EQ8o9ffygcUNBDEIQfPb5fTRCEnwiC4Di+pzge+Pg5GD52DobLZ3/GZW69jOVlB8C/B/DfnnFdB8D/AvC7h7zvHQD/AsCtF39rJwIun4Ph8nk2/wlAG4AXwD8G8F8EQVja47rfA/APAFwCcBHADwD8871u+Pj3/xjAP3183zqA/3zUb/wVwMfPwfCxczBcPvszHnOLejm86AuPHvLHh7ju1KM/d+j7fgrgd172/b3uF5cPl88LysWIR4vracn3/gTAf9zj2s8A/J7k698FcH2f+/4HAH8q+Xr+8d8xv+5n5uOHjx0un5PzOulzi8e8cDgnk9MAuoPB4KHke3cA7HU6XHr8s2dd99S1g8FgE48X8pd6t5yTBB87B8PlIwO48sLhnExMACoj3ysDMO9zbXnkOtM+vvnRaw+6L2c84WPnYLh8ZABXXjick4kIwDLyPQuA6iGutQAQB49ttC9xX854wsfOwXD5yACuvHA4J5OHAFSCICxIvncJwMoe1648/tmzrnvqWkEQ5gBoH/89jjzgY+dguHxkwMukSqsEQdABUAJQCoKgEwRB9fhnA0EQPnz8f+HxdZrHX+sEQdBK7vNjaTqWIAiax9cLANSPrx87JYvL52C4fA5mMBjUAPwFgD8QBMEoCMJ7AH4LwJ8IghB+LKPw48v/B4B/LQhCUBCEAIB/A+DHdC9BELYkqYn/E8APBEH4QBAEI4A/APAXg8FgrE6HfPzsDx87B8PlczBjM7deIhL59wEMRl6/DyCER/5E5+PrwntctyW5z0cA/pnk64/3uP7D1x15zeXD5fMaZOQA8BMANQAxAD96/P0PAGwBUD/+WgDwRwAKj19/BEB4/DMNHpmtFyX3/dHj+9UA/CUAx+t+Vj5++Njh8jkZr3GZW/QhHBmCIPwTAEuDweDfHuJaDR5FZ18cDAadI30jJxQun4Ph8nk2giD8OwDZwWDwx4e49n0A/3IwGPz28b+z1w8fPwfDx87BcPnsz0mbW0euvHA4HA6Hw+EcJ2Ply+VwOBwOh8PhyguHw+FwOJyxgisvHA6Hw+FwxgquvHA4HA6HwxkrVAf90Gg0jkU0b61W27dF+XFiMpnGQj6iKL4W+ZjN5rGQT7Va5ePnAF7X+OHrz8Hw8bM/Ho9nLGSTyWT42nwAB63NByovHA5nvHiR7EFhzzYt8mYvOU2iHDicceWVKy+9Xo/9XxAE9uJwOEdHv99/piIzOvcmbR5KimdNtBwOAysMxtdrzjPo9/tDXx/XmHnlyotCMRxmwyfC3uy38UyyvLhM9mdUNrRgcJntD1dYno10/JCMSInhcPaC1p3jHiOvTHkRBAEqlQoWiwVKpRK9Xg+9Xg/1eh2dTueFzN1yhk7OvV4Pg8EASqUSCoUCCoVi4hYOGhtSeQCPxpRSqWT/n1RILqSwKJVKWCwWqNVqCILw1IEBAFqtFtrtNjqdDlqt1tDP5CpLqfVApVJBoVBAr9dDqVSi2Wyi0+mg1+uxk6Nc5XAQJCOaZyQDnU4HlUqFTqeDbrfL5KRQKKBUKidSVpxh+v0+BEGAVquFIAhsD+v3+09ZY46CV2p5USgUMJvNUKvV6Ha76Ha7aLfb6Ha7AF7MX8+ZPPhC+Yi95otSqYRarYbZbGYbzl7yqtfrqNfraDQaE3l4UKvVUKlUMJlMUKlUQz1TjmOhHVdI+dVqtdBoNACejDvarDgcgpQXlUrFFNxWqzXkoj0qXqnlxWq14vvf/z6cTicEQUCz2cRf/uVfIhqNot1uD50eJxH6gBUKBdxuN7RaLbxeL3Q6HdLpNKrVKkRRRKPRmBg5SQe8VquF3++HRqOBQqFAv99HKpVCvV7fN35BbkitUIPBAN1ul1k0DQYDZmZmYLfb8eGHH8LlcrHDAvDEFz0YDJDNZpHL5RCNRrG8vIxCoYBYLDYUKyMXOY4umjqdDm+99RY8Hg/Onz8Pi8WCX/ziF1hfX0cmk0GpVJooCycpbLTxaDQa+Hw+6PV6WK1W6HQ6hEIhWCwW5PN5lMtl5HI55PN5VCoVFAoF9Pt9dLvdiZEZ5xFSa6Zer4fZbMZ3vvMduN1uZt29efMm4vE46vU6Wq3Wke1dx668jJoez507h6mpKahUKtRqNfzqV79CLBY77rcxNvT7fSiVShiNRlitVszOzsJsNrPFpdVqoV6vA5DP5rIfo5uoRqOBx+Nhpv5ut4tiscjkIXdGN2HppmM0GmGxWDA9PQ2/34/33nsPoVAINpsNWq32qY6syWQSOzs7sFgsKJfLAIB4PD4UJyO32AZ6LrVajZmZGYTDYbzzzjtwOp14+PAhdnd3USwWj+WUeFIZ6tIrCFCr1dDr9fD7/bBYLPB6vTAajTh9+jRcLheSySTy+Tzi8Th0Oh2USiUqlcqQ9VxOY2aUg8aFnJ/7IGjP0mg0MJlMWFpawvT0NBqNBhqNBuLxOHK5HLPAHJWcjl15ocWw1+uh0WhgfX0dtVoNoVBoyK86qR+8FJKTWq2G3+9HIBDAd7/7XQQCAfzd3/0dNjY2cOvWLeRyOeazlzMkD6fTibfffhtutxtvvPEGNBoNIpEICoUCEokEWzzlasaWLpiDwQAqlQparRYulwtLS0tMaTGZTAiHw7BYLPD7/dDpdKhWqyiXy8w1ZDKZ2Ilaq9WyE/OdO3ewtraGdruNdrsNQF6Lcb/fh1qths/ng8vlwje+8Q2Ew2GYTCZ0u13UajUmp0lYj8jCq1KpoNfr4fV6YTabce7cOdhsNpw9exYWiwU2mw0ajYaNl1AohGaziWKxiEKhgJWVFXz++efIZrOIRqMAxnvcjCr50rFwmNieSVF6gSfPSuEg7777LoLBIC5fvgyfz4disQhRFGE2m1nMJv3eWFheiF6vh2aziUQigW63C4fDwXzNnCeQad/lciEYDOKtt97C/Pw88vk8ut0u1tfX0ev1WKCqnCHlxWAw4MqVKwiFQrh27RpbRLRaLTv90clPztCCqlQqodPp4PP58MEHH8DpdGJubg5GoxEejwcajQZ6vR4AUC6X0Wg0htyyarUaJpMJdrsdvV6PWbC0Wi0rZSC3EzTJjebVwsICZmdnmSWz2WyiVqtNhOtDallSqVQwGAyYnp6Gx+PB+++/D4/Hg8XFRVgsFhiNRqhUKnY4AB4pJ41GA81mEzqdDqlUCoPBANFolAXxjjOjsU/kQqRxsd/4GD1kTAqCIMBgMGBpaQmzs7M4deoUnE4n9Ho9SqUS9Hr9eKdKKxQKqNVq2O12OJ1OaLXaV/WnTzyjsS52ux1LS0uYm5uDTqdDq9ViJrhJWFyl9Pt9aLVanDp1Ci6XC6lUCtVqFb/61a+YOZIUF7nKhaL1HQ4HPB4P3G435ufnEQqF8MYbb8BoNMLhcKDf77PDAQXF3717F/l8Hu12G/1+H2+88QaTpdvthlKphMFgYC+ykMoF6Sai0Wjg9/sRCoXgcDhgNBqxs7ODTCaDbDaLSqUia8uLdJ0hF+yVK1fgcrlw8eJFWK1WnDp1CkajERqNBp1OB5lMBt1uF1tbWygWi+wEHQwGEQgE4PV6ceXKFSgUCty/f38oAWMcZChVVMg6R0HcGo2GxZNptVoEg0Go1eqnlDP6OhKJIJVKQRRFiKK4b6afnKDsRo1GA4fDAbvdDrVajV6vh2g0ikQigWQyiVKphHa7faRz65UoL/RmVSoV7HY77HY7tFrtxG3EB0ETx+Vywefz4ezZs5ifn2fKS7PZnDjlhRYVnU6Hubk5mEwm3LlzB4lEAp999hm2t7efMuXKUTbk2rFYLDh9+jTm5uZw7do1uN1uXLx4kWUUVSoV3L17F9VqlcVH/fVf/zVisRhzBZHVTqlUwuFwsHRhUl7oOjkwWqNErVYjEAhgamoKDocDJpMJ+Xwe29vbyOfzqFarQ+ZtuUGWTNpsAoEAvvOd78Dn8+HKlSvQ6/XMYieKItrtNrLZLKrVKq5fv46trS2o1WoolUp885vfRDAYZMpLrVaD0WiEIAjodrtjYbmjdF6aX51Oh8Vt6PV6FgTv8Xhgs9lw+fJl9oxSVKpH2+hHH33EUslLpZLsxxJB48lms8Fut7NMo2g0irW1Nezs7LAg+KOUxysL2CXz5MzMDILBIDqdDprNJjNHTpKZTcroyTAUCmFqagqBQAAulwuNRgOlUglbW1t4+PCh7BfYUShmihaXvepMPMucO67QqVCv10OtVmN6ehoXL17E9PQ0U+YEQUCtVkMsFkMmk8Enn3yCUqnENpGtrS0UCgXmDlpdXWVxVaFQaMga6vV6AQCZTEaW6cJ0SiSXa6/XQ7FYxO7uLprN5mt+d8cHjSONRgOtVguPx4OFhQXMz89jbm6OHSap3k2r1cLKygqKxSIikQiKxSJWV1eRzWbZ2mO1WmEymWA2m9nYCYVCyOVyqNfrJ3780J5jNBphMBhgNpvhcDhgsVjg8/lgMBiYh8BisUCv12N6eprVMJFChwefzwebzYZKpcLkNA5K3ItC8XcUG2W1Wll8S6/Xw87ODiKRCERRPJb9/ZVZXsgct7i4iGAwiOXlZVagTlp4bFIRBAE6nQ6nT5/G7OwswuEwvF4v1tbWsLu7i/v37+Prr79mLoFJgmoFqFQqFlAqd6VXas4mt9Di4iLef/99+Hw+nD59msmlXC7jiy++wPb2Nn7yk58gn8+z+4zOra+++grr6+vwer24evUqO2mSO6Xf72N9ff11PPIrQalUss2m3+9jd3eXpXHKlX6/j16vB5PJBJfLhcXFRXzve99DMBjE+fPnWXp0r9dDrVZDsVjEr3/9a2xvb+P27dvIZrMolUpPKXj9fh9XrlzB4uIiQqEQTp06Ba1Wi0Qi8Zqe9HDQvOr1ejAajZiamsL8/DwuXrwIu92OYDAIk8kEn883VBxUq9XumUJPxSDv3LnDEgmktYMA+R2sgCfKi8PhgNPphNPphM1mg1KpRKfTQSQSwb1791AsFo/l77/yInW08RaLRWQyGdRqNbTb7YnMOpJuKhT5T4NApVKh3++jWq2iWCxOZDExGg/dbpfFbWg0GhgMBiiVStnKQpqqDAA2mw2zs7MIhUIsK6TX60EURWxvbyORSODevXtIpVJoNBosMHe/e0qVPlrEybIlx8BnOhiYTCZ4vV54PB4olUrmMpDr4Yk2T61Wy9LDl5aWcOrUKczPz7P4hH6/j1KpBFEUce/ePWSzWTx48ADpdBqlUgmNRoNl8tE9K5UKEokEwuEwixMymUzQ6XQneg0nZYRcQwsLCzh16hSmp6dx+vRpGI1G2Gw26HQ66HS6p6wnewUk03wLBAK4ePEi1Go1dDodcrkcs2JKe/rJBVJenE4n3G43TCYTtFotcrkcSqUSqtUqms3msWWBvjLLC2mvGo0GSqUS8Xgc6+vrbGMmX+okQvKhqP+ZmZmhgLlEIoFyuYxWqzVk9p4EFAoFms0motEonE4nLBYL86vKHVI0pqamcO3aNVy6dAmnTp3CYDBAu91GOp3GJ598gkgkgp/+9KeoVqtDtRSk7rTRzZl+TtYbiqmSU8wL8GSBNZvNcLlcOH36NMLhMMusIgVGjsoLPRu5QN566y388Ic/ZJY72phrtRoSiQQSiQT++3//70gmk8waRYodHTzJ4pnJZLC8vIyZmRnU63UIggCHw4F8Pn8ilRdpjR+NRoP5+XlMTU3h6tWreOutt+B0OuH3+4esJTQuKIZHWuRR+i8pJufOncP8/DxOnz6NtbU13L17F7/61a/QarVQq9UAyMcCQ/LQarUIh8MIh8Nwu90wGAxYWVlBMplEOp2GKIro9XrHUvTxte0A1NtIrgvHYaGFgQImTSYTDAYDi+vIZDJIJpOyNmsfhDQ1WKfTwWAwoNls7rkhyw0qs+3xeDA9Pc0CbMlKUi6XsbW1hWQyyXrzAHt3cSVZGY1GuN1ulgJLmWyVSgWlUgm1Wk1WcpWeDp1OJ6xWK4xGI7rdLlqtFiqVCqvvIifouRUKBQKBABYWFjA3Nwe32836ywGPNuhGo4GtrS3E43FkMhnk83m0Wq0941ZGx5VUQT7JsXg0pql9hsfjwdzcHHw+H6xWK0vn7XQ6QxW76cBNzym1wozKgtxKVF02n8/D5/OhUCiweSU35UWhUAxlGSkUCtYv7bh6GhHHprzstwCOmqknXXmhbBq32w2/3w+fzwen08m09Tt37uDrr79GPp+fqJLlBC3CLpcLHo+HVRuWa40gaYEsm80Gm82Gixcv4sMPP4TBYMBgMGCbbjwex8cff4xisYharfZU/R/pgkvzzO/348KFCwgGg9BqtSiXy0gkEtje3sbGxoasFlnKrtHpdDh79ixmZ2cxPT0Np9OJcrmMYrGI7e1tFlQoh2cGntSKogPRu+++i9/8zd9EIBDA3Nwcu4bkk8vl8Nd//deIx+NYW1uDKIosLmgvZYRiGHU6HduwpKnFJxFpXRuj0Yjz58/j29/+Nis9AACdTgelUgnxeBwA2KEyFAqxIFR69r3kolarMRgMWA0hrVaLarWKzc1NpNPpocJ34w6NHSpjMTMzA4PBwLIcyYp7nPv7kY80+mDIJElvXmp24zyBJoPb7YbL5YLRaIRarYYoiiiVSiiVSrI8GT4P0iwR6mIrZ2iyGwwGOBwOmM1mlnEEAO12m40Laq6434lQai6nU5Lf74fJZGILMFlB6SU3qN2GwWBgfbFKpRKr7ULuETlsKsATy5vNZoPb7YbH42GKP1nuarUaq5QbjUaRzWZRLBaZi4TucxDS4m0ajYalFp9EOdL7tFqtcLlcsNvtMJvN0Gg0zA3bbDaxu7uLzc1NZnWR1iWj+ZFOp4eK9tG/ZNUjxc5ms2FqagqlUgkajWYoyeAkyuiwkGyoSCi99nqm4zxgHqnyQpo6RfRTVV0Ae2pg4/wBvgyj9ScsFguuXr2KcDiMQCAAvV6PBw8esBPxzs4O2u32RFpeCJVKdWJPdUcNKftkJQkEAiyVFXgU7H7v3j1sbGygUqkwN9periJSRqgo24ULF/Dee+9henqaxaFJNyG5jC/pHFOpVHC73XC73dDr9RgMBrh37x6i0Si2t7eRy+UA4MS6PA6L1DWiUqlw4cIFXL58GdeuXWOxUp1Oh9W2SSQS+PWvf41MJoN79+4xd8l+lgXp36G1XqooLS0tQRTFE3W4kFpcVCoVFhcXcfr0aczPz8PpdAIAGo0GyzpbWVnBz3/+cxanMTc3B5fLBavVil6vh2q1ip/97GfY3d1llgWaY9/85jdx8eJFeL1eeL1ehMNh6PV6aDQa3Lx5E6Iosj5i4zrPpLEuNpsNPp+PHbCAJ53GX0X5iiPbDci8r9FoWGR/o9FAq9V69IdUqqGCYuP64R0V0vo3Op2O+Q0VCgWrP0E1E8i9NqkMBgNmYaDqjXKEFlraFMxmM+x2O8uuAsBOzbu7uygUCkMnwIPQarUwmUwwmUysYqhckVoO6BRsNptZgTHK4qP03263e6i+NScdem4qNufxeBAKhVg8QqPRQK1WY32IkskkYrEYa25Ka/VBcpDKltZ04EmhspNaxkGpVLJ+YMFgEBaLBWq1mtW1yWaziEQiiMfjyGazzBJnNpuRyWRY1kylUkEymUQqlWItNyhDr1AoDBUS1Wq1rB6OVCEcZ3e3NBZIp9NBr9dDp9NBo9EAeGTFpXIW0uDn4+BIlBfSxgwGA1wuF0KhEC5cuIDt7W381V/9FQDAYrHAYrEMDfhJheSl0WhgNpvhdruxuLgIr9fLFpivvvoKGxsbyGazE211USqVaDQaWF1dRalUwtmzZw+9YY8T0omu1+tZFP/58+fh8/mgUqnQaDQgiiIikQg+/vhjpNNplho9Oj6kGRGCIMDlcmFqagperxcOh4NVUpVm2xz3YvMqoflFTSoXFxfh9/sBgGXXxGIxFiskh/lFm+bc3BxCoRA+/PBDvP/++9DpdADA6kXdvXsX/+f//B+Iooh8Pv9UuvhB5n9au+jARRszKT4nbfyQJcBsNsNms+HatWv41re+BaPRCK1Wi0wmg1gshl/+8pf48z//c5Z1R65qAPjZz37GLHb1eh1ffPEF6zXX7/fRbDYxGAxw4cIFnDlzBi6Xi8lIpVKxzu60wY8zZM2lJqcUo2mxWNDpdFCr1ZBOp7GzszPUK+zEp0pLYxNI06RsEToNyOGEc1SQXIxGI4traDabbFGhNuJSJkl20toKZHmRNoeTqyxo/uj1epjNZlbVk9ywoiiiUCigWq3u6Y6VbjRkLrfb7SzThOI++v0+y66gk6UcimpJMyGMRiOzNlGWETVhFEVxrPrwPAv67HQ6HSwWC6xWK+x2O1NK6/U6crkcdnd3sbOzwzZq4EmV2P3kQN+nzBubzQan0wmTycRqLrVarRMbm6dSqYYakpKySuOhVCohlUoBeGL5VCqVEEUROzs7TPGga2ljpjkE4CmLMGUqUdaXHMYY8CTmhSxttOdTmwU6gI/GT53IVGnyf9brdaRSKZTLZcTjcYiiiFarBafTiQsXLmB+fh5ms/mZPtVnMZpnPy5I3+9gMIDJZMLZs2exsLAAn88HjUaD9fV1pFIp3L9/H5FIBI1G46muppMITRDqiSW14MlhwwWG/fNU+dZutzOlv9FoYGdnB6lUCul0es/aEXSPbrcLhULB3Abf+973cOnSJczPz8NisbAid9FoFL/+9a9x//59iKLICo6NM1Sbg3pizc/PIxwOw2g0IpvNYnd3F5FIBNFoFO12m23c4w599lqtFgaDgZnzW60W2u02UqkU7ty5g83NTZTLZfT7/SEr+Oj6BDwJRqWNiip/v//++3j33XdZ8Hc6ncby8jK2trZOtFtXms5NSq40+5VcjcAjZaRcLuOrr75i6+9gMBhqXEqp0UajEYFAAB6Ph1lpgPFfk/ZDaqUlI0W73Ua9XmfKcb1eZ2v1ccSTHZnlRXoypNMcVYQl05nNZmMPKvUVPi9y8B1S7RKXywWXy8XqDFBF3XK5zBrsyXUCPA9U94YCwuUWYCqFDgNkNaHnlTaQa7fbT51yRwPBlUolLBYL3G43AoEApqenYbVamZmfLDg7Ozss00QO7jhaWJVKJeu/Q5lGtVoNlUoFlUoFoig+1R9rXJF+9jR2SOGnsUNmfXI1AvufiOl7pNgZjUbodDp4vV7MzMwgFAohFArBaDQyy2gul0O5XD7R6zIpLPTcJCtS+IDh/WUwGKBarQ6lOZMCRHVdHA4HbDYb64FEKdM0n1qtFlu/5Ib0s+71emyMjVpejoMjdRuRUkIPQYqL0WiE3++Hw+FApVJBq9VCNBpFNBpFs9k89MlHej8yrY8b/X4fer0eVqsVCwsL+O53vwu/3w+73Y5qtcq0VlpgKMNm3BfXF4UsDvfv30etVsOHH3441PmVCh3KST60wHa73aEmlKTsUvlyCt6Vpl7Sadrj8cBiseCDDz7A7Owszp49C5/Px8rBP3z4EHfu3MHNmzfx8ccfs3RhKeMuU2o6abPZ2FjZ2NjA1tYW0uk067ky7llGz4I2aerXUy6XYTKZ2GGArgEwtEGrVCrMzMzAZrPh0qVLCAaDCIfD8Pl8sNvtsFgsyOVyWFlZwZ07d/DLX/6SNQE9aWOHGgEXCgVks1m2fjidTtabyOfzIZ1O4+HDh2wP6/V67CBOcS6091y6dAlerxeXLl3C1NQU5ubmEAwGoVAo0G63EYlEsLy8jK+//hq5XG7frMBxRZr23e/3mWzJOtzpdI7Vqnnkygv9Syca0mxpsLRaLdTrdZRKJVQqlUMHy0mjnNVqNdRqNQwGw1gtPPQMarUaFosFLpcLMzMzbAIBj1rRVyqVoQwjuQz254Umeq/XQ6FQgN1uH4qponR8uSGtiyStDaFUKmEwGFiEvzQeihYR2nisViscDgdCoRBmZ2fhcDhgMBjY4aJQKLBU4WQyOaQAjdOcGkV60qOYMp1OB6VSiW63O5TF12q1JqYtiTT7hSxRFKS71/qi0Wig0Wjg8/ngdrtx7tw5tjk7nU4W6C2KIuLxOOLxOBKJBEu3PilrFs0Jmkv1eh3lchlqtRparRZarRZ2ux0zMzNQKBTY2tpiygq9qEcPZSaR1TwUCmFmZgYXL17EzMwMi1skN10+n8fGxsaQC0VuSN1ptLeLosjaAhynsnashTMUCgX0ej1sNhtmZmZgt9uRz+eRz+dZUSSp9v8saFLQ5n/mzJmxs74MBgO43W689957OHv2LKanp6FUKhGLxZBMJnH79m3EYrHnskjJFdrAlUolnE4n7HY7C5yTxgGdpMXyZaBnoODZZDKJ9fV16HQ6ZjWxWCwIh8P44IMPEI/Hcf36dZayqdFoWDbR3//7fx9TU1O4ePHiUJ2KVCqFfD6P27dv4/r160ilUrLxz0vdRVTgb25uDoFAgAWsSk+FcnAXEVI3aq1WQ6FQYM3xgEfzZXp6Gn/v7/09LCwsIBQKMcueFJpLZN1bWFiAw+GA1+uFyWRi/dYikQgikQhisRhWV1exu7vLXI8nSab0XqgB8Mcff4xYLIZr165hcXERFosFZrMZXq+XdZheXFxkY6ndbrNikJFIhMVIabVaXL58GV6vF36/H1arFbVaDaVSCbFYDLFYDMvLy7h+/TpKpRJzoZwk2bwolCZvMplY1pZSqWSB8KVSCcVicagW0HFw7MqLRqOBXq9nFQ2pU6koiqjX608VtTnsfcn/SmmA4wBtEmazmXUydTgczI22u7uLWCyGnZ0dFnDJeaIEkz9ZDnEZo0jHP1ndqPO6NDaDUlQXFhYAAHfv3oUgCKzjtsvlgt/vx5UrVzA7O4tAIACj0chcT5RVEY/Hsb29DVEU2d+Xw8JKyotGo4HRaGT9jPr9PtuIyuXyiXRtvCz0GVJrEbIWkJWS1mCr1coqvkpru9BhgdpxUMCzxWJhm1AqlUKlUkE0GsWtW7cQj8dZWwnqv3aSiknSZ0wxYuvr6yiVSizNl4qtUQVmt9uNcDjMNt52u41isQhRFGG325m1TqPRYHFxEXa7naVEF4tFFItFxGIx3L9/H2tra1hfX2fWznGfY9LihFSfjPqvSXsaUWYoWTyPi2MZZTQJqAGWy+ViNV7i8TiSySTrgHvYD5SERqbfdruNW7dujZXlhZ6TCkg5HA6msT58+BBbW1vI5XLMnTbOA/2oILdRuVxm7jS5K3UU7BeNRtHtduHz+XDp0iXmMvN4PLh27RpmZ2fhdrvZwqzRaDA1NQWLxYL5+Xk4HA6WZi2KImq1Gm7cuIFbt27h3r17QydlOYw1Oi3TCXpubg4LCwvQ6/WsgeXm5iZisZjsLC8AWHBuKpVCrVbDl19+icHgUa+d+fl55nJ3OBw4e/Ysc6XQ7wJPFCBpk71sNsuK2ZEbZHNzE5FIhLlh6LB1UuVJ76tQKKBer+Ozzz5DOp3G3NwcZmdn4fV6MT09DeBJ9gwpwuQOohYBFLBLDS7z+Tyr/7K2toZYLIZEIsFiZKQu3XGF5EEd2sPhML7zne/A7/dDq9WiXq+zPYxca8f9vMemIpMGT1lG5GPNZDLMB9jpdFiw1GGg6rPkg6TibeOGRqNhJZWVSiXa7TYrmlWpVFCr1Z5Zd2ESkAah1ut1ZqmTK/S8FMeTSqUgiiLeffddtNttaLVatvmYzWaEQiF4vV50Oh2Wkuh2u6HT6Vg/FjoFU5zZw4cP8cUXXyCdTqNarbLATLkwGAyg1+vh9XoRDAYRDAbR7Xbx4MEDxONx7OzsIJPJnDj3xlFACgi5jFZXV5l5f25ujh0AyfpCSM37JBMaUzRO1tbWsLW1hZWVFUQiEVaHimKspPc4idlG9FyUaXbv3j3s7Oywjs+9Xo8F25KVk5QO6tdkt9uHXNUko1KphHw+j7t37+L69evI5XIoFouyqZsEYMgqR4HfV65cYdWb2+02c5e9qgPmsVlepCnSVqsVOp0O1WoVGxsbiEajL6SdSc1Wg8GARW+fdEgeGo1mKGhOo9EwP2G5XEatVpNd5szLQsF2ZJZutVosUFeuHcmlsS+DwQCZTAaJRAJ2ux0ej4cFXwqCgFAoNGSWNhgMLMV6MBggl8uh0Wjg7t27LIYmm82i2WzKtmCkXq9HKBRiNTekfngq5SCX+IO9oKzP7e1tdkDs9Xrw+/2YmZkZanJKc6lWq6HT6aBSqaDRaCAWi6FarWJrawvlchmbm5vI5/PY3d1lbRWkJQukf3scoAKF1GhxY2MDq6urrJniaDsbYDiuqN/vI5vNol6vY3V1lWUpZbPZoWKa9HtyQJowo9frh3oadbtdiKKIarX6yrwGx+o2oqwH6qVSqVQQiUSwsbGBdrv9wosnaXXU9PGkI1VeqPIlVU4lH2GlUmEVUzlPoIWCqoG2Wq2hGgrjslg+D1LlpdVqIZfLIZlMQhAElplGHV0tFgvbrIDhjL9er8cC4+/cuYP19XXWcoJOzHLEaDQiGAwy5YWsTqVSCY1GY2wttoeF1ptYLIZ0Os0OehRgSsotvXq9HuuuHY/HUSqVcP36dezu7uLhw4csloO6lwMY2+7uNLeq1SrK5TJ2d3eZVYXiXd58802WbQU8rZCRB4DiZ27fvo1UKjVUtoH+jlwUF0IQHnUQNxgMrIAmZVJVq1W2h70Kr8Gx24vpIcikeJQbzrgMDNpIDAYDOxFaLBZmzo5Go4hEIshkMkOp4+PyfK8C8h2bTCYYDAbMzMxAFEUkEglW64SQi9zomSKRCP72b/8W4XAYmUwGBoOBHQio3D+5AaggFpn7b9y4gVQqxczkpVJJdmNLejigzKy5uTn4/X4Wv+HxeCCKIsuMkKPSK4VixdrtNpLJJAs+7Xa7sFgs8Hg8TAGp1WrY2NiAKIosHnF9fR3lchn5fJ7VnJLTpkx7EtFqtVAqlViFYKk7dXSskNuIrJqiKO5ZBn/cZbQX0kq65XKZuc5arRY7bL2quXWsyov0w5MGcx2VAjMug4NKUJvNZszPz2N6ehpOpxPJZBK3bt3C5uYmVlZWUKlUZG3OflHIkgc8avBps9mwsLCAXq/HMtfkJDNp3EC/38fKygqi0Sjm5+dx+fJlWK1W+Hw+WK1WzMzMMOsmbVCiKGJ5eRmpVAr/7//9P0QiEWSzWYiiuKc5fNyh8aHT6eB2uzE9PY2LFy/CZrOxlhJTU1MsA0KlUh179c/XiTR2qtvtYmtrC/F4HLFYDNFoFIFAAOfPn2cKTi6Xw+eff45yuYxUKoVms8nM/9KDlJysVaMKPFl2d3d38eDBg+e6h1RGcppXo9B4oVCHXC4Hk8nEmsaSa/9VhT4ci/JCGj1p/YIg4Be/+MVQYbpJQjqoqXFVsVhENptFPB5HOp1Gu91+Zea2cYOC41qtFisw5XQ6EQwGYTAYxtJ8/SxIgREEgbXbyGazWF1dhdFoRDKZhNFoxPb2NoxGI9bX1yEIAiv//uDBAxajIKfOyfshXViTySQ+/fRTGI1GWK1WNJtNpFIppFIp2Qd9jyJNga5Wq0gkEmg2m2wNpiJs6XSapbhS2wnphizXcTPK8zzrJMlHGuvTarWQTqfxySefsMNAPp8fKusw1sqLWq1GrVbD8vIyHj58iNu3bzNztpwK9hwWQRjuYBqLxbC5uYm7d+8ik8kMtQOYJLkcBop5EUUR6XQagiBgZmYGRqMRn3/+OauLI7dgZ3qWdruNVquFzc1NRKNRdjhQq9UwGo3QaDQwm80QBIG5BorFIqv0SSZ/KisgJxkBTxbWTqeDcrmMr7/+Gtvb2+xUTH19Wq0WywKR/q5ckT4bxT/t7u5Co9Hgzp07TC69Xo+Z+6WZQ3KytDwLqQIix8PQy0Jjghq6PnjwAOl0mq1F1D6BalSNbbYR8ETjb7VaTMsn94lczbX7QR98s9nE7u4ua22QSqWQy+WGCoVxhpG6GkVRxM2bN+FwOJDP51GtVpmrbRKg2CkqzU7KGikpVAeJlL3RRotyH19Ut4QOCPS80p5rkySP/ZAqK9IxMqny4BwO6XzqdDrMXU9Kzatu7CoctPAbjcaX3hWk2Q/A8fRNqdVqr2XWmUymQ8uH0szow1YqlUNFoqRdS48aURRfi3zMZvORahVkPZDKSNoD6kXlV61WT/T42WuOjn5vr6BC6b+j/38eXtf4ed71RyoDmk+EVB5HPcfGYf2Rstd4Ok7F5XWMH4/HMxYnmkwmM5Zr8+j8Ao5nbh20Nr+S6lR7PeikIc20kioqk+Q3fRlIZlSCfC+5yVV+0hPPKKOHg9HfGf3/JCAN8B5l0mSxF9KAcPqaw3leDlpzXgWvLFWas/dpmHM4aMEd9UdPkiylm85hx9KkyYfYL25hkuRxEFwOnJdhr+wz2SkvnGH4ovHicNlxGRwGLiMO5/h53fPswJgXDofD4XA4nJPG5OTCcTgcDofDkQVceeFwOBwOhzNWcOWFw+FwOBzOWMGVFw6Hw+FwOGMFV144HA6Hw+GMFVx54XA4HA6HM1a8lPIiCIJDEIT/TxCEmiAI24Ig/Gif6wRBEP5QEIT849cfCgckiQuC8KPH96sJgvATQRAcL/M+XweCIPwrQRBuCILQEgThxwdcd14QhJ8LgpATBOGZeeuCIPxXQRDWBEHoC4LwO0f5nl81fPwcDJfP/nDZHAxffw6Gy2d/xkU2L2t5+U8A2gC8AP4xgP8iCMLSHtf9HoB/AOASgIsAfgDgn+91w8e//8cA/unj+9YB/OeXfJ+vgx0A/x7Af3vGdR0A/wvA7x7yvncA/AsAt178rZ0Y+Pg5GC6f/eGyORi+/hwMl8/+jIdspL12nucFwIhHi8dpyff+BMB/3OPazwD8nuTr3wVwfZ/7/gcAfyr5ev7x3zG/6Ht9nS88GgQ/PsR1px59HIe+76cAfud1Px8fP1w+XDYn98XXHy4fucrmZSwvpwF0B4PBQ8n37gDY6/Sz9Phnz7ruqWsHg8EmHi9UL/FeOScPPn4Ohstnf7hsOJwJ52WUFxOAysj3ygDM+1xbHrnOtI/vefTag+7LGV/4+DkYLp/94bLhcCacl1FeRACWke9ZAFQPca0FgDh4bEN6iftyxhc+fg6Gy2d/uGw4nAnnZZSXhwBUgiAsSL53CcDKHteuPP7Zs6576lpBEOYAaB//PY584OPnYLh89ofLhsOZcF5YeRkMBjUAfwHgDwRBMAqC8B6A3wLwJ4IghAVBGAiCEH58+f8A8K8FQQgKghAA8G8A/JjuJQjCliR16n8C+IEgCB8IgmAE8AcA/mIwGIzV6UcQBJUgCDoASgBKQRB0giCoHv9sIAjCh4//Lzy+TvP4a50gCFrJfX4sTVcTBEHz+HoBgPrx9WNXr4ePn4Ph8tkfLptnw9efg+Hy2Z+xkc1LRiM7APwEQA1ADMCPHn//AwBbANSPvxYA/BGAwuPXHwEQHv9Mg0dm2UXJfX/0+H41AH8JwPG6I69fQDa/D2Aw8vp9ACE88tc7H18X3uO6Lcl9PgLwzyRff7zH9R++7ufl44fLh8vm5Lz4+sPlI3fZ0CQ+UgRB+HcAsoPB4I8Pce37AP7lYDD47SN/IycQQRD+CYClwWDwbw9xrQaPsh8uDgaDzrG/uRMCHz8Hw+WzP1w2B8PXn4Ph8tmfkyabY1FeOBwOh8PhcI6LsfLFcTgcDofD4XDlhcPhcDgczljBlRcOh8PhcDhjBVdeOBwOh8PhjBWqg35oMpnGIppXFMV9W9wfJ0ajcSzkU6vVXot8+Pg5GLPZPBbyqVarfH4dwOuaX1w++8PXnoORw9rDLS9jAM8I43A4HA7nCQdaXjivn36//6ggz+M+coIgsP9zOJzngw4CksJZQ/NLoXh0npu0Odbv9wHgqXWGH5w4z8tIwbtjm1NHrrw872CftEXieZEuJpPOYccWlxXnWUjHCJ9je8tj0hWX0eef5PHxvIzK6jhkd6TKy+hpRvoizV6pVAJ4otlP6knnWdBp0GQyQaVSMQtMq9VCu92eKHlJx1Sv13tKswceafcKhWLixxSdlqXzTionks+kWvCUSiUUCgWMRiNUKhX7utvtotfrodFooNVqAZD/5k2fv0qlgsFgAAB0Oh30ej10Oh22Zk8i/X4f3W53aAyoVKqh9YUzDO1ZWq0WSqUSOp0OSqUStVoN7Xb7qbXoZTl2t9FepxrpwkkPPLrAch7JQa/XQ6PRsInU7XYBDJu6Jw0aL9JxI5XFJMvmWYvDpMoFePTsKpUKarUaZrMZGo0GGo0GCoUC7XYb7XYbwKONq9frodfrveZ3fPwIggClUgmj0QgAaDab6Ha7bL2Z9PECTPZ68jyQnDQaDdRqNSwWC1QqFZtLowfOl+VIlBc64anVaqjVaiiVSqjVaqhUKuh0Ouh0OjidTgwGA2SzWbRaLbRaLXS7XdRqNablDwaDoVPhJA+YwWAAtVqNb37zmwiFQigWixBFEbdu3cL6+joUCgWzYskV1oDr8QKr0Wjgdruh1WphNpvZWAOAbDaLWq2GarWKRqOBXq+Hfr8/UeOI5qEgCDAYDFCr1bDb7dBoNExO1WoV9XodjUYDjUZjouRjsVjwwQcfwO12Y3FxERaLhY2jUqmEZrOJjY0NJJNJPHjwAA8ePHjdb/nYoPXaYDBgamoKP/jBD6BUKrG5uYlCoYDr16+jWCxCpVJBoVDI3goFgO0/CoUCVqsVgUAAKpUKGo0G7XYbyWQSzWaTrS/cAvMEWqd1Oh3efvtteL1eXLp0CTabDX/zN3+DtbU1xONxZLNZJuOX5ciUF/rgtVot1Go19Ho9077MZjNmZmbQ6/WYGalarQ4pMUPdIidc46WFQqlUYn5+HufPn0cymUSxWMT6+jrboCZBPvSMKpUKWq0WLpcLJpMJHo+HmSUBQK1WI5/Po9vtotPpPOUykbucCOkiotPp4PF4YDKZ2KFgd3cXCoUCvV4P9Xqd/Z7c5UPm7LNnz2J6ehpvvvkmHA4HHA4HNBoN8vk8RFGE3W6Hw+FAPp/H6uqqLDdt6Tqr1WrhcDjwjW98g52YU6kUbt++feQn5ZPMqAvaYDAgFApBq9VCp9Oh0WigUqlAEAQ0m032O3KfN4dBKju1Wo2ZmRnMzc2xg0IsFkOpVEImk2F711HwUsoLDW6ysiwsLODSpUswm81wuVxMszcYDAgEAuj3+0gkEuzU12q1EIlEUKlUUK/X0Ww2kUqlUCgU0Gq10Gw2Jy6GQbr5mEwm2Gw22Gw2xGIxVCoV5o8H5CsPUjzIF+90OnHlyhU4nU4sLS3BZDKxTUelejSEE4kESqUSdnZ2UCgU8ODBA6yurqLdbjOZyU1e0k1IoVBArVbD4XDAZDLhjTfegNPpxMLCAqxW65DyUqlU8NVXX+HmzZvsJAmAnbDHfcOi99/v99Hv92EwGDAzM4Pp6WlcvXoVwWAQLpcLWq0W/X4f7XYbGo0GFosFZ86cgdfrRbFYxM7ODorFIjKZDADI7qStUCiY60yj0cBkMuHChQvw+/1YXl6GRqNBLpdDs9lk40fOSK28LpcLV69ehcPhwNTUFBqNBmZmZrC7u4tPP/2UHZSOcjMeV8hwodPpYDab4fP54Pf7odVq2c+PI37qSJQXpVIJvV6P6elpvPPOO3A4HAiFQlCr1Uxz9Xg86Pf72N3dZYpJs9nEysoK8vk8isUiW0TJ79rv92W3YDwL6YlIr9fDZDLBZDKxk/JoEJkcGXV/uN1uvP322/D5fHjjjTdgsVhgtVqH3EbpdBrVahWxWAzZbBbtdhuxWAwA2ElJTouMNIgZAPM1u91uOJ1OfOMb38DU1BTOnTsHh8PB5lEul0OpVIIoitjY2IAgCEPuI0EQxjrWYzRYudvtQqVSIRAIYGZmBouLiwgEAix2jOJbyOU9PT2NqakprK2tIRAIoNfrIZ1Oy2rsAMMWTQpcNhgMsNvtsNvtbMMul8uo1WoT4V6UWrwtFgsWFxfh9/uxtLSEVqsFhUKBWCyG5eVlVCqVobVY7rLZD+lhh/Ysh8MBp9MJtVr91DVHyQspL6NvZHp6GmfOnMGbb76JS5cuQa/Xs9OeUqmEUqlkv2M2m2EwGNjColarUavV0Gw20Wq1EAwGEYvFEI1GEY1GUalUUCgUhgQg14FCCy5p/l6vF3a7nSkvNGHk/PykDOt0OgQCAVy9ehWhUAhvvPEGzGYzlEol6vU6stksU3ABsBiPQCAAj8eDdrsNq9WKjY0N3LlzB81mE6IoAhj/EzTJyWg0wu/3w2w2Y3Z2lv1rs9lw4cIFWK1WGAwGdLtdNBoNdDodCIIAh8OBc+fOsdiXWq3G5JhOp3Hr1q2xGmfSWkgKhYIF5ZL7mpTe6elpaDQa9qzSdYx+n07eNpsNMzMzqFarT10rByjDqlKpoFQqoVgsMresSqWCx+NBqVRCNBqdmKwjQRDQ7/dRr9dRKBQQjUYxGAywtLTE4sdqtRp0Oh3LPOKuoyfxUzabDR6PB8FgEIFAAKVSCalUCltbW9je3katVmPZfUfBkcS8TE9P491338WlS5dw8eLFp7KHBoMBOp0OAMBkMkEQBKjVagiCAJ/PN3RSCofD2NnZwZdffgmVSoVYLIZiscjuI3foFOh2u+H3+2G322E2m9HtdlGv15kc5TphBoMBVCoVjEYjpqam8Bu/8RsIBAK4fPkyFAoFC8zd2NiAKIrMJbS0tASfz4dAIACTyQSj0Yjp6Wl88sknzKVUqVQAjH+hP5orBoMB8/PzCAQC+OCDD2C32zE3Nwej0Qin0wmVSsUsmuSa9Xg8sNlsWFpagtlsHkqL7Xa7uHfvHu7cucMUnXGArCd0UCJ3o8lkgt/vx9TUFK5cucLipEjZkR6GpHF7AGCz2TA9PY2dnR3ZbVD0LHQgKpfLKJVKsFgssFgsMBgM8Hg8KBaLUKvVsnAlPgup1bHT6TDFTa1Ws83ZbrdDFEXmrpbTmHhRpFZyu90Ol8uFUCiEQCCA27dvI5FIMOWFgpxfe8zLYDBgwbk+nw/z8/NwOp1sEWw2m2wgkD+M3vhgMEAmk0Gv12NBlzqdjvnsVSoV6vU6y1ZKp9NotVrMfAnIb/OmxYECU71eL/x+P0RRRDKZZK96vQ6lUim75weGLQrhcBizs7MIh8NwOBwYDAZot9soFouoVCpIJpMsCKzdbqNQKMDpdOLcuXMIhUJQKpUIBoMIhUKYmZmBUqlEKpUa60VYqrSQovLee+/B4/FgYWGBnQjb7TY2NzfRbrexvb2NcrmMVCoFURRx+fJlzM/PQ6FQYHZ2lsWFVCoV7O7uMjfcOMmJFHyDwQCz2Qyz2QyPxwOz2YxAIACXy4VwOMwsd8Czn89msyEUCiGZTCIUCqFer6NSqchGkaF1uNPpoNPpsFRxsrJoNBo2niYh3oUYdTlKXajSBIBxmh+vCrJ60iGC9v/jcq+9lNtIr9fDbDZjenoaFy5cgMFgYLEZxWIRzWYT9XodWq0Wfr+faavtdhvRaJSdBEnT12q18Hg88Pl8sFgsCIfDEAQBy8vLKJfLqFarzKwrJ6SBlxTkPD09jVAohHK5jHK5jM3NTWxubqLT6cju+QlaNMxmM86ePYtz585hcXEROp0OnU4H9XodmUwGuVwOm5ubyGazWF1dRalUgtfrhcViwW/+5m9CEAQEg0HMz88jnU7j9OnT6Ha7WFlZGVp8xmlBJpN2r9eDyWRiwfHf//73YbPZ4HK50Ov1kMlkIIoilpeXkcvlcOvWLezu7jIlplqtotfr4ezZszhz5gxztcTjcZRKJRYAPU643W7Mzc3B6XTC5/PB4/Hg1KlTsFqtCAaD0Ol0sFgsUCqVQ67GvaBx4XQ6sbi4iEKhgPX1dRboPG7jZj9oPFFAe7vdHipMp9PpYDAYJtLKQIoLyUSq0Ixa7ThP9AEKE6GstW63i1arxcpWHLXMnnulkn5wdrsdfr8fLpeLFVMTBAHVahXr6+sol8uIxWLQ6/UIh8PQ6XSwWq1ot9u4desWKpUKS309f/48/H4/DAYDtFotNBoNbDYbgsEgzp8/j3g8jmKxOOSvltOEIpO12WyGzWaD3++H3+/H7u4uyuUyKpXK0OIit2cHnlR/pVgOu93OzPiUGWGz2aBQKLCwsAC32w0AyOfzaDQaqFar2N7ehsFggEKhYONyamoK2WyWKUHjliEgzZoxm804deoU3n77bczOzrJKsZVKBaIo4vbt2ygUClheXmZ++1KphHq9zuKJtFot25B6vR6azSby+Tw2NzeRSqVOfIyDNEBQrVYjHA7jzTffhNVqhcvlgsViQSAQgF6vZyUbABw6EHkwGLDAQ7fbjUAggFarNfbuxr2QlqXo9/toNpss5qXVasFisUCn0010Zs1oEoXRaITRaEStVnuqvMekIg34HwweVUIXRRGVSgXtdvvkBOzS4hYMBnHx4kVMT0/DbDaz4jPZbBaffPIJtre38fnnn0On0+HChQuw2WzsFPzTn/4U2WwWdrsdFosF//Af/kNcvHgRPp8PdrudpQpfvHgRvV4PN27cwMbGBqsNQ8hl0PT7fZYtEggEcPbsWfj9fty9excPHz5ENptFs9lkJjk5QlY1u92Os2fPIhAIMOWFXIvBYBAej4dtKMFgEJlMBr/85S8RjUZx69YtbG9vQ6VSYX5+HhaLBRcuXEC5XIbFYkG9XmcBquNiDqdToM/nw9mzZ3H16lX89m//NkwmE+x2O1qtFhKJBFKpFP78z/8ciUQCq6urQwqvzWaDyWRi6Yx00Gi1WqhUKohGo/j000+RTqdPfLYRbRh6vR4WiwVvvvkm/tE/+kcszoWKZAJPTP2HVchokbVYLHA4HMjlcjh37hw6nQ5u3br1VKPUcWV0M5FuODqdDqdOnYLT6cRHH32EdDqNcrk8MSnTwN4Vu00mE6xWK6sPVCgUeLE6PJEVBeP2+310Oh3k83mk02nU6/VjKRj6UjEvtEhQDMZgMGDxLtVqFaIosgq6Ozs7KJfLzPRdKBRYJD+lTDebTZw7dw7T09Ow2+2w2WwwGo3MFGy321GtVo9Nk3udkMvI7XbD6/WyGKFarYZSqcRKl8tt4ZCmttLm43K54HQ6YTabMRg86udUKpXQaDSws7PDxlez2WQVQYvFInNX0sQplUrodDowmUzMBE5yHAekFgadTgev14v5+XkEg0GYzWZWt6VcLuP+/ftIpVJIJBLI5/OsVhC5B0i29FKpVExO0WgUW1tbyGQyKJVKJ35zpliMmZkZBINBTE1NwWQyMRfH6GYifZbRTemgdYQsgZOyYQNPeoTRmFOr1RP1/HsxGo9IlpdxjA87amiOaDQaptQBYHGI6XQazWbzWNaUl4p5oQ2XlBcK/KpWq8hms6xqZa/XQ6lUGopXoSCeYrEIQRAQj8eh1WrxW7/1W3j33Xdx4cIF2O12uN1uGAwGiKLIYhjIfSQHC4RUlgaDgVmyrFYrlEolMpkMtra2UK/XZafhjxYTs9vtWFxcxNLSEs6cOQOtVoter4disYhbt24hm83iq6++QqlUQjqdRqPRYAUNqVJzqVSCUqnE0tISotEoS99zOBwwGo0sW2scILm43W64XC68+eab+P73v89ctbu7u/jqq6+wvb2Nn/70p8jlcojH4+j3+wgEAnA4HGz+kUUzGAzC7/dDrVajXq9jZWUFP/vZz7C5uYnl5WX0er2hwpAnCVoAHQ4HbDYbvvvd7+Ktt97C3NwcbDYbu4YOUcDwqVC6/pBsqefKfn+P7iH3zVu6Cen1etYMllz4oxmkkwRZDUwmE9rtNrxeLwuNmHRIBzCZTFhcXMTMzAwEQUClUsGDBw9w+/Zt5PP5Y3GvPZfyIg3MAR6VApb2Ten1emi1WqjX6+xkPLqYSFN96VQIAPV6Ha1WC5lMBvF4HFNTU8wkp9PpmP9aq9XKciGhSG2yOFEVYlEU0Ww2Ze1vNplM0Ov1CIVCmJ+fh9frBfBoTJTLZeRyOaytraFQKCCZTKJSqSCfz6PdbjPLnnQTovFG2QLSgLFxWoBpE/H5fJiZmUEoFILb7YbJZEKn00G1WkU8HkcikUAul0O5XEa73WZxQwaDAUajEb1eD0ajkQXUUUZSvV5HLpdjVa3pQHGSDwWCIMBiscDtdsPtdsPn87Fnk7qGpLEcvV6PBSq3Wi0MBgO2bhkMhmcGKct13kmh9ZjGCCmxcs3ufBFojTYajTCZTENWqZNurTwOqKyFxWKBy+VihyxSfHu93rEWVX0hywulQxmNRhbQpVQq0Ww2kc1mkU6nWTl7Ou3sd5KTnoS63S7u3LmDdDoNm82Gy5cvs8FisVjgdDpZxpEcGLVgUXlyn8+HWCyGQqHAyt1T0TA5PPvoYF5YWMCFCxdw4cIFvPPOOywANZFI4G//9m+xs7OD69evo1arDRVUG2Uv2ZDLiWrCkBJ9kqFNJBAIwOv14tq1a7h69SqrENtoNJDNZrG2toaf//znyGQyiMViaLfb6Ha7rC+Lx+OBxWKBWq1GPB5HtVplczGXyyGZTOLu3bu4ceMGiyM7iRYXYPjgtLCwgHPnzuHSpUtYXFxkCgox6hqqVqu4c+cOSqUSNjc30e/3MTMzA5vNhjfeeAMul2so2FDuxTCl0PosLfBHLkXOsGuRSnf4/X60Wi0WOybXJJKDoAOixWLBlStXMDMzg7fffhtutxsqlQq1Wg0AjtXl+EKWF6pgSX50jUbDfk6N8Si/W8qzHmIwGKDRaLD4BRoU5Ic9qm6UJ4nBYMAKIDkcDlYVtVarsQJjcm1PT1VQ3W43wuEwfD4frFYrRFHEzs4O4vE4YrEYMpkMS72nRZUW3FGZUECzRqNhvTWob9az0mRfNzS/KOMuGAwyi4vP54PNZoNarUalUkE6nUYmkxmK7aHTMlkULBYLbDYblEolqxyazWaRTCZRKBRYi4695upJhbI+DAYDdDodNBoNs7IR0roloiiiUCggEomgXC5ja2uLuYwoq49cthqNZmixlcZjjZPF7rAIgsDSWilzi9ZXslgeV5rrOCDNoJF+TTVwpGuQnC3je0FzgrwFDoeD1VqivoWjY+e1xbxIJzAFLE1PT+P06dNwu91PDfrn3SioKVytVmPuAOnDj6PZ/1mQsud0OvHhhx9idnYWCwsLUKvV2NnZwcbGBtu0pX18xh36LKempuDxePDhhx/ihz/8Ifv5w4cP8Wd/9mfIZDJ4+PAh24xI0ZNCE0LaJsBkMsHr9bIU6UQigWw2O1SR9yQuNFTVdm5uDqFQCD/84Q9x7do1Nt9UKhW63S4ikQj+9//+3yzQlp6JlEGLxcKUHrVajV6vh1wuh+XlZXQ6HXzxxRdYWFjAqVOnoFAooNfrAYC5VE6ibKSoVCrm9iFlQ1oHJ51OY21tDalUCjdu3GCNOklZA8AUoFgshtnZWVy6dAmBQAB2ux1GoxEA2Do2Lha7w0LrqU6nY3FQwWAQPp8PKpUKg8EA9XodoigOFRk76ePiqNlLcZE2s5Rm10wiFBN1+vRphMNhuFwu6HQ6xONxpFIpVqKBDlVHzQu5jeh0Rxr7ftaQF1E06EGlZmA5KSyj0EnS4/HA7XazzblWq6FcLrMiSXJB+iwmk4nFLrjdblSrVRSLRRSLRcTjcRQKBZTLZRaH8Sz/O8UyULyHXq+HIDxqPEiWl5OeBgyAxaVQKjRl0LRaLRYMTwoZbaw0J6lOkMPhgN1uR6/XQ7vdZu4AqunidrtRqVTYZn6SkY4Z6Wl4dDxQzZpyuYxEIoFEIoFIJIJisYhUKjWUpUjZWLFYDIIgIBQKwWq1slRrckW12212kpTbPCQrAlkpyfJCygsdJA8KapYz+xWlO6kB7a8asnCbzWaWwUgZstVqla1Nx6X4PpfyQm+g0+mg0WigVCohl8vBarXuaRV5kTc8ujDJEanJzWQywePx4Pz583C73chkMqhWq1hbW2N1beRS4ZLGB51Yzpw5g3fffRcLCwswGAzY3t7GV199hbt37yIWi6HVarGNhBaLveRAG81gMGDVVqempmCz2RCPx5HJZFhWkvQUcNJkSgpaPB5HuVzG/Pw8tFotS2+ORqO4f/8+IpEI7t69C1EUmWwokPXb3/42pqam8K1vfQtutxsPHz5EoVDA+++/j3PnzrHNand3F7/4xS9YAPRJrlchzXQZdeXQwqhSqZDNZrG9vY0bN27gz/7sz1iwN7mVpHEtwCNL0/Xr17G8vAyj0YjBYACz2Qyfz8eK/sXjcdy/fx/JZJKNsZMqpxdBut6SW77X62FjYwOxWAzpdJo1hJ2ElGmSQ7fbZdXNRVFkB/XRuKhJg6x2Go0GdrsdHo+HlTEhpXdzcxPRaBT5fB71eh3A8WTsvZDlhcypVP7/KGpnjKYl7lebQS7QoqvX69kJ22QysVgXWjxHo/7lAFkJrFYrPB4Pa9ZJgagU80QbzkGL5ui4MZlMcDqdMBqN0Gg0rF0F9W05ySn2tCk2Gg0MBgOUSiWUSiVmLcpkMtjY2MDOzs5Q7R9CpVLB6/UiEAjA7Xaz9PB6vc5cIrT4pNNpViPmODMCjou93m+v10Oj0UC5XGbFsShwcNRyRydqsj5Vq1UWX0YbVKfTYdmTZLUZNzk9L/Ts1G1a2gx2UiAZUAkGcp3JNdP1RSB3s16vZ/FnFAZBlXXpsHhcyv6hlRfph9ZsNtFut5FOp7G1tQWv1/vUaYg2isMgjWkBIPtmYJRZZbVaMTc3h/n5eUxPT0OhUOCzzz5jJu9sNvuUy2SckX6+ZG50OBxQKpWo1WpIp9N48OABEokEc5cdlMZK4wYAC3S+fPky3nnnHQSDQVYHZmtrC7u7u08F+5501Go1KxQGALu7u7hx4wZbHKQmbVKGqcghxb+cOXMG4XCYxY9Rd+nd3V22OJNyNA4y2QuyvJlMJoTDYRQKBVy7dg2lUgmpVAqtVgvlcpnF45G8FAoF66Pm9Xphs9mYwkuxQx6PB+FwGN1uFw8fPpyI+AYqIkoKYKvVGgrmlTN0WOp0Oshms3A6nSiVStBoNDAYDLI7SD4vtOZSQsHU1BR8Ph+cTicEQWAxYlJ343FZK5/bbSSt2UL1XF7G8jJ6kpEG48l5oNDGbLPZYLPZYDab0e12kc/nmeuIAnXlsGjs5TOmqH0a9FQhV2pxouufdV+9Xg+r1Qqv14tQKASj0cg6kZfLZWbNOMmMWpFGK1hTvAodHoBhRUylUrHTEB0AbDbbkKWB7kfBv3TIGOe5RgukWq2G2WyGy+XC1NQUq3EjDTylZx7NlLBardDr9SwzSzo+zWYzG6f098ZVVodhMBig2Wyi0WiwLLZxbNj5otB8IRnQZjxpGUX7Ia3vYrFYYDQahywv0gSL4+Sl2gOMWlrotEL9HxQKBarV6jPNreSv12g0OH/+PGZnZ7G4uDjU0VSaVTCuSDcntVoNm83GTsZkfUilUkgmk8zHLGcOajH/LIVFGj+jVqtx5coVnD59GleuXMHs7CxSqRSLDXnw4AFEUTzxFhd6JuqrMzU1hZmZGQCP5OH3+3Hq1CnkcjlEIpGhDZaKRPX7/aGsIcrAWV1dxe7uLr788kusrq4iGo0inU6j0+mceCvnXmODak1RQTVaUI1GI86cOQOr1Yp6vY58Po9qtYpoNPpU52SlUomzZ8/C6XQiHA6zdE/qzUJ9nzKZDOsoTX9f7sg5RfwgaI+hwoaVSgXlcplloHEezRuTyYS5uTk2b3Q6HTt4P3z4EBsbGxBFEcDxzZcjU6fJvUGt1E0mE5rNJltA9/sdGiw6nQ5GoxHhcBhLS0vw+XwHFrcbZ0gZ0+v18Hq9TNHr9Xool8sszmHclbXDIF0gpYGDB10v/ZcsdaFQCOfOnUMwGITD4cDW1ha2t7eRSCSQyWSGAg5Pqkzpmei0b7PZWMZQt9tldUlI4aBof4PBwKpbkhWL7kUuSkq9/+yzz3Djxg1miTjpistBjH6WJBO3283kRBuQ2+1mbjLp2Llw4QKcTiezVgFgCh8lJlCV60nayCdRcSGklhd6jZYRmETZSJ+fDkwUX6hUKllF9N3dXaTT6WPvI/fSyovUtK/RaGCz2TAzM4N33nkHkUiEPQRdJ62XQBu4VqvFO++8g7m5OVy5cgWnTp1itWNarRYajQZyuRyy2SxLnR1XyLyt1+vhcDgwPT0Ni8WCeDyOnZ0dRCIRxONxVuZdLkizPGhjaDabqNVqrD6Aw+HAzMwM614rjZva7/R95swZeL1evPXWW7h8+TI0Gg2SySSWl5fxq1/9CpFIhJkwT/omTe+vXq+jVCohn88jl8vBZrPB6XTi4sWLLKvm6tWrbP4YDAbWrHF2dpZlJ/X7fSQSCZRKJdy8eRMrKytIJpNMHuOiuEjf42AwYIXmqNcMLaLS62id0Wq1sNlsWFxcZBYaafFLu90+VO5B2qetWq2yPlq07ozz2jPKaNr5aPq5nNaf50F6kCwWiyiVSswiJx1jkxD/REgzZLVaLex2O8LhMPx+P4BHjRipun4mk2FZfsdp7X4p5WU0JoE6kfp8PiwtLbHAHvrQyScmXTgpFfTcuXP4xje+gcXFRUxPT7O/QYsIWSRqtdpYbER7QQNAqVSyLCOv18saMO7s7CCVSrFA3ZNsJXgRaAzQyZZqaNA4MZvN8Hg8SKfT7Ln3Ul5ILtRZeHZ2FqdPn8bCwgKSySRyuRyi0Sju3r3Lqs9SDMlJhp6Z6rlQxofVaoXZbMbc3BwcDgfK5TIWFxdZ91+9Xg+fzwetVguLxcLSy2lBSaVSWFtbw/3795HNZtHpdJjLZdw248FgwIJw6ZkFQYDL5WJrirQuCdUwodif0ZMzBYZL1zKlUslisEiBoXVnUthLoZkE6FmpD5YoiqhWq6ziOxXx28/dLWek3hWLxQKfz8e8BlRfqVAoMIWPrJnHJaMXLlIHAKVSiTWFq9VqLBfearXi7NmzsNvtsFgsLHCy2WwiFouh0+nAarVCp9OxDspXrlxhQXbNZhOZTAaZTAaRSAT3799HLBZDPB5nqY/jOqEGgwGsVivOnTuHM2fOIBgMQhRF3Lp1C/F4HM1m80TX3HhZpJtLPp9HLBaDx+NBv9+H1WrF6dOnUavVMDMzg0ajgVqtxtyKFOCs0+ng8XhgNpvxwQcfIBgMQqFQIBqN4tatW9jY2MC9e/eQz+fRarXGRpY0pilld3Nzk8VuUIo3lf4PBoMAnhS0I3dROp1Gt9tFLpdDtVrFp59+ing8jvX1dRSLRaa4SC1h4wSlkDebTXzxxRfY3t5GJpNBo9FgFio6HZIbSboB72V9oRgXCujtdrtYXl7GysoK7t27x2oEyWkjJ9cIBVdKa9hQDx8qgT8u8+eoobFB9V5o7pBLlzIBJ8kCA2BojKjVaqboRyIRRKNRdDodtiYd5xpzJMpLPp9HrVZjPVmsVisWFxdx+vRpvPPOO2i1WsjlciiVSvjyyy/RbDYxNTUFs9mM8+fPD9XlkCo59+/fx40bN/DRRx8xFwP9/XGdUFQIa3FxEQsLCwgEAtjZ2cHOzg4SiQRarRazVI3rM+4HDWZKWc3n80gkEjh9+jT6/T4sFgtOnTqFcrmM6elp5j9VKpWw2Wys4SApf06nE1evXoXb7cbGxgai0Si+/PJL3LhxA8lkEvl8nm3444BUeanX69jY2GAyI1Ot2+2GwWCAzWYD8MSaR5as3d1diKKIhw8fIpfL4Re/+AW2trZY5si4j6vBYIBisYjBYIBMJgOlUolOpwOtVoupqSmm3FGWnnQRJYuf9ORMCg71vxJFEfV6HSsrK/i7v/s7Vul53OW2F1TLhipPU5YVna7pwCAnpe2wSAN3S6US69hOngJSXqhD+7h6A54XskzSAYGUl1qthmg0+pTycpw8t/IiNSWWSiUMBgPcu3cPNpsNU1NTmJ+fZxkQlA2hVCrhcDhgMBhw5coVVuOEzNxqtRqiKLLTJtX7WFtbYwsvFY+SA6S1ajQatqhSJsQ4noafF/oc8/k8IpEILly4gEajAZVKBafTiYWFBfzGb/wGqtUq26Aooj0YDEKv18PlckGj0SCTySCdTuP27duIx+N48OABdnd3Ua/XT3x20X7Q/MrlchgMHjUrTSQScDgcCAQCLEgZeKK8UIAhWSeTySRrJSBdXMdRHqNQCXvgkVtxe3sbX3zxBSKRCDY3N2Gz2RAMBp+qzaHRaBAIBFgDxsFggGQyiVqthng8zooCiqKIe/fuIR6Po1gsykJmUkgR6/f7qNVqyGazzFKp0+nQ7/eRy+VYLaFxnUcvi0KhQLvdRiKRYHsUHSIAwO12w263o9vtTpQCQ9CzUmZerVYbaqh83LyQ5YUmfi6XQyaTgVqtRrFYxJUrV1izr0AgwLR44FEfGwCYn58H8MRXSFo/dQ7+7LPP8PXXX2NzcxObm5vMrDkaTDbOqFQqmM1m1hCP/Kty65+yF9ITLCkZb731FkRRhMFggN1uh9VqxezsLOuCrFKphoJ6qUdPo9HAJ598glgsho8++gjr6+ts86FS5+M4Vkg+yWQSsVgMy8vLUKvVrIYNdXUHhrOvms0mNjY2WFO9brc7VPBxHGWxF/QspLRtbGwgHo+zE7Hf78fi4iKr/UOuD8re0mq1Q00uE4kEbt++jWQyyZICCoUCs7jIRW5SpOnA6XQav/71rxEIBFjMVDqdRjabZUGXkwatU61WC5ubm6xwn9FoZC7rYDCIdDqNarWKarX6ut/yK4f0AEqqqVQqrNXIq5gzR5JtVCqVEIvF2KLgcrkwOzsLi8UCv9/PAgNHTa+9Xo/1VolEIsjlclhZWUEsFkOhUGA1GaSCGPeFRBAEZtanAZ/L5bC9vY1sNjsRKdK08VAcwebmJj7//HO2OUshvzttzvF4HJ1Oh1kWbty4wUrd12q1sUiJPizS+ULF1tLpNHOHAMMZD91ul7mHAIyt8nZYpHEs0k70pNCp1WoYjUYmB7PZzCrxkvJy7949ZLNZ1ouFgjNpbMoZGl/tdhs7OztoNBq4fv06lEolIpEICoUCOzjK/VC1F1K3UTabxcrKCkqlEhYWFqBQKGA2m+H1ehGPx2U/VoAncVKtVgu7u7v49NNPWVBuPp9nityrKnopHDQoTSbTgSNW+ruDwQAWi4W1WL9w4QJmZmbw7W9/e8+gJpo0v/zlL7G9vY27d+8ilUqhUCiwTX0vhWcvRFF8LSPHaDQ+94weDAYsWlutVsNkMqHT6SCfz7P04aOu5Fir1V6LfA4aP9IiWOFwGOFwGOfPn8e1a9dgtVrh8/meajdfr9fx4MED5PN5XL9+HZlMBtFoFJVKhQUdksXleXhd48dsNh96/EjdQ/vNWZorR71wVKvVEz+/pPLZK1CX3EbUOoHGVi6XY7U8pPVxnifG5XXNrxdZf/aDMkksFgvLHqEaQXt1Vn4eXod8nrV3HQbpWDCbzbh27Rr8fj9+8IMfwOl04tatW9jZ2cH//b//Fw8ePJDt2iOFYl70ej2cTiebR5QkQAaHo1J2D1p7jqRIHQ1sSvGkCqD1ep1VyR0d/KS83L9/H5lMhp16RtvWy1GjpQZyFOdCJ8dJaj0vPc3VajVkMhlsbm5Cq9XCaDTC4XAMuQdofG1tbaFarbLmhGRpkKvcJvXU+zJQAPNeFttyuTxU6p7m4ejck+O6cxB0SKDCoqTIHeVGNG5I155Op8MqUt+8eRMWiwWRSATFYhGNRkO2e9VeUIym1Mgwqui+Cl7K8kKMWmDo1EJRyQdBmho99Iu4iMbJ8gI8XSUWeLpmzlFyEi0vwNM1Eigz6CB3B2VF7KfovYj8xu3086oZB8vLXuw1PvYqdij990WQg+WFkB4cj4pxtbwQ0mB3QXjU2kWq2NCm/SKM69qzlyXuOLLxjt3yQkhdAVRF9bDIKa7lMEhlJZdA5OeFnpeUV+pu/DxIFZ1Jkx/nYKTj4aCiYnzcDHMcCsw4Iy3xAICVvZdLbN2LMjqXXnW21ZEoL0eteMh9MEgHPF8onvhRX+b3OZyDoDEyiZkzz8Mkb8YHQWvUJLsWpbzsmn0UHKnlZZI/zBeFy4zLgMPhjAd8rTo5HBjzwuFwOBwOh3PSkFe9aw6Hw+FwOLKHKy8cDofD4XDGCq68cDgcDofDGSu48sLhcDgcDmes4MoLh8PhcDicsYIrLxwOh8PhcMaK/x+UsI7Md3FUJQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x576 with 64 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "# You do not need to modify anything in this cell\n",
    "\n",
    "m, n = X.shape\n",
    "\n",
    "fig, axes = plt.subplots(8,8, figsize=(8,8))\n",
    "fig.tight_layout(pad=0.1,rect=[0, 0.03, 1, 0.92]) #[left, bottom, right, top]\n",
    "\n",
    "for i,ax in enumerate(axes.flat):\n",
    "    # Select random indices\n",
    "    random_index = np.random.randint(m)\n",
    "    \n",
    "    # Select rows corresponding to the random indices and\n",
    "    # reshape the image\n",
    "    X_random_reshaped = X[random_index].reshape((20,20)).T\n",
    "    \n",
    "    # Display the image\n",
    "    ax.imshow(X_random_reshaped, cmap='gray')\n",
    "\n",
    "    # Predict using the Neural Network implemented in Numpy\n",
    "    my_prediction = my_sequential(X[random_index], W1_tmp, b1_tmp, W2_tmp, b2_tmp, W3_tmp, b3_tmp )\n",
    "    my_yhat = int(my_prediction >= 0.5)\n",
    "\n",
    "    # Predict using the Neural Network implemented in Tensorflow\n",
    "    tf_prediction = model.predict(X[random_index].reshape(1,400))\n",
    "    tf_yhat = int(tf_prediction >= 0.5)\n",
    "    \n",
    "    # Display the label above the image\n",
    "    ax.set_title(f\"{y[random_index,0]},{tf_yhat},{my_yhat}\")\n",
    "    ax.set_axis_off() \n",
    "fig.suptitle(\"Label, yhat Tensorflow, yhat Numpy\", fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a name=\"2.6\"></a>\n",
    "### 2.6 Vectorized NumPy Model Implementation (Optional)\n",
    "The optional lectures described vector and matrix operations that can be used to speed the calculations.\n",
    "Below describes a layer operation that computes the output for all units in a layer on a given input example:\n",
    "\n",
    "<img src=\"images/C2_W1_VectorMatrix.PNG\" width=\"600\" height=\"450\">\n",
    "\n",
    "We can demonstrate this using the examples `X` and the `W1`,`b1` parameters above. We use `np.matmul` to perform the matrix multiply. Note, the dimensions of x and W must be compatible as shown in the diagram above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 25)\n"
     ]
    }
   ],
   "source": [
    "x = X[0].reshape(-1,1)         # column vector (400,1)\n",
    "z1 = np.matmul(x.T,W1) + b1    # (1,400)(400,25) = (1,25)\n",
    "a1 = sigmoid(z1)\n",
    "print(a1.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can take this a step further and compute all the units for all examples in one Matrix-Matrix operation.\n",
    "\n",
    "<img src=\"images/C2_W1_MatrixMatrix.PNG\" width=\"600\" height=\"450\">\n",
    "The full operation is $\\mathbf{Z}=\\mathbf{XW}+\\mathbf{b}$. This will utilize NumPy broadcasting to expand $\\mathbf{b}$ to $m$ rows. If this is unfamiliar, a short tutorial is provided at the end of the notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"ex03\"></a>\n",
    "### Exercise 3\n",
    "\n",
    "Below, compose a new `my_dense_v` subroutine that performs the layer calculations for a matrix of examples. This will utilize `np.matmul()`.\n",
    "\n",
    "_**Note**: This function is not graded because it is discussed in the optional lectures on vectorization. If you didn't go through them, feel free to click the hints below the expected code to see the code. You can also submit the notebook even with a blank answer here._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "deletable": false
   },
   "outputs": [],
   "source": [
    "# UNQ_C3\n",
    "# UNGRADED FUNCTION: my_dense_v\n",
    "\n",
    "def my_dense_v(A_in, W, b, g):\n",
    "    \"\"\"\n",
    "    Computes dense layer\n",
    "    Args:\n",
    "      A_in (ndarray (m,n)) : Data, m examples, n features each\n",
    "      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\n",
    "      b    (ndarray (1,j)) : bias vector, j units  \n",
    "      g    activation function (e.g. sigmoid, relu..)\n",
    "    Returns\n",
    "      A_out (tf.Tensor or ndarray (m,j)) : m examples, j units\n",
    "    \"\"\"\n",
    "    ### START CODE HERE ### \n",
    "    Z = np.matmul(A_in, W) + b\n",
    "    A_out = g(Z)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return A_out\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.54735762 0.57932425 0.61063923]\n",
      " [0.57199613 0.61301418 0.65248946]\n",
      " [0.5962827  0.64565631 0.6921095 ]\n",
      " [0.62010643 0.67699586 0.72908792]]\n"
     ]
    }
   ],
   "source": [
    "X_tst = 0.1*np.arange(1,9,1).reshape(4,2) # (4 examples, 2 features)\n",
    "W_tst = 0.1*np.arange(1,7,1).reshape(2,3) # (2 input features, 3 output features)\n",
    "b_tst = 0.1*np.arange(1,4,1).reshape(1,3) # (1,3 features)\n",
    "A_tst = my_dense_v(X_tst, W_tst, b_tst, sigmoid)\n",
    "print(A_tst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**\n",
    "\n",
    "```\n",
    "[[0.54735762 0.57932425 0.61063923]\n",
    " [0.57199613 0.61301418 0.65248946]\n",
    " [0.5962827  0.64565631 0.6921095 ]\n",
    " [0.62010643 0.67699586 0.72908792]]\n",
    " ```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><font size=\"3\" color=\"darkgreen\"><b>Click for hints</b></font></summary>\n",
    "    In matrix form, this can be written in one or two lines. \n",
    "    \n",
    "       Z = np.matmul of A_in and W plus b    \n",
    "       A_out is g(Z)  \n",
    "<details>\n",
    "  <summary><font size=\"3\" color=\"darkgreen\"><b>Click for code</b></font></summary>\n",
    "\n",
    "```python\n",
    "def my_dense_v(A_in, W, b, g):\n",
    "    \"\"\"\n",
    "    Computes dense layer\n",
    "    Args:\n",
    "      A_in (ndarray (m,n)) : Data, m examples, n features each\n",
    "      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\n",
    "      b    (ndarray (j,1)) : bias vector, j units  \n",
    "      g    activation function (e.g. sigmoid, relu..)\n",
    "    Returns\n",
    "      A_out (ndarray (m,j)) : m examples, j units\n",
    "    \"\"\"\n",
    "    Z = np.matmul(A_in,W) + b    \n",
    "    A_out = g(Z)                 \n",
    "    return(A_out)\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92mAll tests passed!\n"
     ]
    }
   ],
   "source": [
    "# UNIT TESTS\n",
    "\n",
    "test_c3(my_dense_v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell builds a three-layer neural network utilizing the `my_dense_v` subroutine above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "def my_sequential_v(X, W1, b1, W2, b2, W3, b3):\n",
    "    A1 = my_dense_v(X,  W1, b1, sigmoid)\n",
    "    A2 = my_dense_v(A1, W2, b2, sigmoid)\n",
    "    A3 = my_dense_v(A2, W3, b3, sigmoid)\n",
    "    return(A3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can again copy trained weights and biases from Tensorflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "W1_tmp,b1_tmp = layer1.get_weights()\n",
    "W2_tmp,b2_tmp = layer2.get_weights()\n",
    "W3_tmp,b3_tmp = layer3.get_weights()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make a prediction with the new model. This will make a prediction on *all of the examples at once*. Note the shape of the output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 1)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Prediction = my_sequential_v(X, W1_tmp, b1_tmp, W2_tmp, b2_tmp, W3_tmp, b3_tmp )\n",
    "Prediction.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll apply a threshold of 0.5 as before, but to all predictions at once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predict a zero:  [0] predict a one:  [1]\n"
     ]
    }
   ],
   "source": [
    "Yhat = (Prediction >= 0.5).astype(int)\n",
    "print(\"predict a zero: \",Yhat[0], \"predict a one: \", Yhat[500])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following cell to see predictions. This will use the predictions we just calculated above. This takes a moment to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 64 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "# You do not need to modify anything in this cell\n",
    "\n",
    "m, n = X.shape\n",
    "\n",
    "fig, axes = plt.subplots(8, 8, figsize=(8, 8))\n",
    "fig.tight_layout(pad=0.1, rect=[0, 0.03, 1, 0.92]) #[left, bottom, right, top]\n",
    "\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    # Select random indices\n",
    "    random_index = np.random.randint(m)\n",
    "    \n",
    "    # Select rows corresponding to the random indices and\n",
    "    # reshape the image\n",
    "    X_random_reshaped = X[random_index].reshape((20, 20)).T\n",
    "    \n",
    "    # Display the image\n",
    "    ax.imshow(X_random_reshaped, cmap='gray')\n",
    "   \n",
    "    # Display the label above the image\n",
    "    ax.set_title(f\"{y[random_index,0]}, {Yhat[random_index, 0]}\")\n",
    "    ax.set_axis_off() \n",
    "fig.suptitle(\"Label, Yhat\", fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see how one of the misclassified images looks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 72x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(1, 1))\n",
    "errors = np.where(y != Yhat)\n",
    "random_index = errors[0][0]\n",
    "X_random_reshaped = X[random_index].reshape((20, 20)).T\n",
    "plt.imshow(X_random_reshaped, cmap='gray')\n",
    "plt.title(f\"{y[random_index,0]}, {Yhat[random_index, 0]}\")\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"2.7\"></a>\n",
    "### 2.7 Congratulations!\n",
    "You have successfully built and utilized a neural network."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a name=\"2.8\"></a>\n",
    "### 2.8 NumPy Broadcasting Tutorial (Optional)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "In the last example,  $\\mathbf{Z}=\\mathbf{XW} + \\mathbf{b}$ utilized NumPy broadcasting to expand the vector $\\mathbf{b}$. If you are not familiar with NumPy Broadcasting, this short tutorial is provided.\n",
    "\n",
    "$\\mathbf{XW}$  is a matrix-matrix operation with dimensions $(m,j_1)(j_1,j_2)$ which results in a matrix with dimension  $(m,j_2)$. To that, we add a vector $\\mathbf{b}$ with dimension $(1,j_2)$.  $\\mathbf{b}$ must be expanded to be a $(m,j_2)$ matrix for this element-wise operation to make sense. This expansion is accomplished for you by NumPy broadcasting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Broadcasting applies to element-wise operations.  \n",
    "Its basic operation is to 'stretch' a smaller dimension by replicating elements to match a larger dimension.\n",
    "\n",
    "More [specifically](https://NumPy.org/doc/stable/user/basics.broadcasting.html): \n",
    "When operating on two arrays, NumPy compares their shapes element-wise. It starts with the trailing (i.e. rightmost) dimensions and works its way left. Two dimensions are compatible when\n",
    "- they are equal, or\n",
    "- one of them is 1   \n",
    "\n",
    "If these conditions are not met, a ValueError: operands could not be broadcast together exception is thrown, indicating that the arrays have incompatible shapes. The size of the resulting array is the size that is not 1 along each axis of the inputs.\n",
    "\n",
    "Here are some examples:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<figure>\n",
    "    <center> <img src=\"./images/C2_W1_Assign1_BroadcastIndexes.PNG\"  alt='missing' width=\"400\"  ><center/>\n",
    "    <figcaption>Calculating Broadcast Result shape</figcaption>\n",
    "<figure/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graphic below describes expanding dimensions. Note the red text below:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<figure>\n",
    "    <center> <img src=\"./images/C2_W1_Assign1_Broadcasting.gif\"  alt='missing' width=\"600\"  ><center/>\n",
    "    <figcaption>Broadcast notionally expands arguments to match for element wise operations</figcaption>\n",
    "<figure/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graphic above shows NumPy expanding the arguments to match before the final operation. Note that this is a notional description. The actual mechanics of NumPy operation choose the most efficient implementation.\n",
    "\n",
    "For each of the following examples, try to guess the size of the result before running the example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(a + b).shape: (3, 1), \n",
      "a + b = \n",
      "[[6]\n",
      " [7]\n",
      " [8]]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([1,2,3]).reshape(-1,1)  #(3,1)\n",
    "b = 5\n",
    "print(f\"(a + b).shape: {(a + b).shape}, \\na + b = \\n{a + b}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that this applies to all element-wise operations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(a * b).shape: (3, 1), \n",
      "a * b = \n",
      "[[ 5]\n",
      " [10]\n",
      " [15]]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([1,2,3]).reshape(-1,1)  #(3,1)\n",
    "b = 5\n",
    "print(f\"(a * b).shape: {(a * b).shape}, \\na * b = \\n{a * b}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<figure>\n",
    "    <img src=\"./images/C2_W1_Assign1_VectorAdd.PNG\"  alt='missing' width=\"740\" >\n",
    "    <center><figcaption><b>Row-Column Element-Wise Operations</b></figcaption></center>\n",
    "<figure/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1]\n",
      " [2]\n",
      " [3]\n",
      " [4]]\n",
      "[[1 2 3]]\n",
      "(a + b).shape: (4, 3), \n",
      "a + b = \n",
      "[[2 3 4]\n",
      " [3 4 5]\n",
      " [4 5 6]\n",
      " [5 6 7]]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([1,2,3,4]).reshape(-1,1)\n",
    "b = np.array([1,2,3]).reshape(1,-1)\n",
    "print(a)\n",
    "print(b)\n",
    "print(f\"(a + b).shape: {(a + b).shape}, \\na + b = \\n{a + b}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the scenario in the dense layer you built above. Adding a 1-D vector $b$ to a (m,j) matrix.\n",
    "<figure>\n",
    "    <img src=\"./images/C2_W1_Assign1_BroadcastMatrix.PNG\"  alt='missing' width=\"740\" >\n",
    "    <center><figcaption><b>Matrix + 1-D Vector</b></figcaption></center>\n",
    "<figure/>"
   ]
  }
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