HeadPose_ResNet50_Tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Head Pose Estimator\n",
    "\n",
    "Tatsuya J. Arai @araitats, Sunil Mallya @smallya\n",
    "02/01/2018\n",
    "\n",
    "\n",
    "## Introduction\n",
    "\n",
    "In this notebook, the details of construction of convolutional neural network based head pose estimator are described. \n",
    "\n",
    "## Overview \n",
    "\n",
    "When there is a face photo presented in front of you, your human eyes can immediately recognize what direction the person in the photo is looking at (e.g. either facing straight up to the camera or looking at somewhere else).  The direction is defined as the head pose. A convolutional neural network model is trained with thousands of such face photos and their corresponding head pose labels, the model will be able to estimate various head poses when a new batch of face images are presented. In this notebook, the head pose is categorized into nine classes (i.e. the combinations of looking down, straight, and up (tilt angles) and looking right, middle and left (pan angles)).\n",
    "    \n",
    "## Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "import cv2\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.7.12 (default, Nov 20 2017, 18:23:56) \n",
      "[GCC 5.4.0 20160609]\n"
     ]
    }
   ],
   "source": [
    "import mxnet as mx\n",
    "## Python version\n",
    "print(sys.version)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.7.12 (default, Nov 20 2017, 18:23:56)  [GCC 5.4.0 20160609]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11.0\n"
     ]
    }
   ],
   "source": [
    "print(mx.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0.11.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper functions\n",
    "\n",
    "### EvalCallback  \n",
    "\n",
    "``EvalCallback`` class is to keep track and save the best model found during the training based on the metric you specified.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class EvalCallback(object):\n",
    "    '''\n",
    "    Attempt at a Earlystopping solution using the first metric.\n",
    "    \n",
    "    pass an instance of the metric or the instance name to specify which metric to use for stopping\n",
    "    \n",
    "    1. epoch_end_callback: doesn't provide the metrics to the registered callback function, hence we can't use it to track\n",
    "    metrics and save\n",
    "    \n",
    "    2. eval_batch_end_callback: while it provides us with eval metrics, there isn't a clean way to stop the training, so the best\n",
    "    thing to do is track and save the best model we have seen so far based on the metric. \n",
    "    \n",
    "    '''\n",
    "    def __init__(self, model_prefix, metric, op=\"max\", save_model=True, patience=0, delta=0):\n",
    "        assert isinstance(metric, str) or isinstance(metric,mx.metric.EvalMetric), \"Metric must be the name or the instance\"\n",
    "        self.metric_name = metric if isinstance(metric,str) else metric.name\n",
    "        self.model_prefix = model_prefix\n",
    "        self.eval_metrics = []\n",
    "        self.save_model = save_model\n",
    "        self.metric_op = np.less if op == \"min\" else np.greater\n",
    "        self.best_metric = np.Inf if self.metric_op == np.less else -np.Inf\n",
    "        self.delta = delta #min difference between metric changes\n",
    "        self.patience = patience\n",
    "\n",
    "    def get_loss_metrics(self):\n",
    "        return self.eval_metrics\n",
    "    \n",
    "    def __call__(self, param):\n",
    "        cur_epoch = param.epoch\n",
    "        module_obj = param.locals['self']\n",
    "        name_values = param.eval_metric.get_name_value()\n",
    "        \n",
    "        names, cur_values = zip(*name_values)\n",
    "        if self.metric_name not in names:\n",
    "            print(\"Metric %s not in model metrics: %s\" % (self.metric_name, names))\n",
    "            return\n",
    "        name, cur_value = name_values[names.index(self.metric_name)]\n",
    "        self.eval_metrics.append(cur_value)\n",
    "        if cur_epoch >= self.patience:\n",
    "            if self.metric_op(cur_value - self.delta, self.best_metric):\n",
    "                self.best_metric = cur_value\n",
    "                print('The best model found so far at epoch %05d with %s %s' % (cur_epoch, name, cur_value))\n",
    "                if self.save_model:\n",
    "                    logging.info('Saving the Model')    \n",
    "                    module_obj.save_checkpoint(self.model_prefix, cur_epoch)\n",
    "                    param_fname = '%s-%04d.params' % (self.model_prefix, cur_epoch)\n",
    "                    os.rename(param_fname, '%s-0000.params' % self.model_prefix ) #rename the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Color shift in HSV space  \n",
    "\n",
    "``shiftHSV`` shifts colors in a randomly selected input image.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def shiftHSV(im, h_shift_lim=(-180, 180),\n",
    "                 s_shift_lim=(-255, 255),\n",
    "                 v_shift_lim=(-255, 255), drop_p=0.5):\n",
    "    if np.random.random() < drop_p:\n",
    "        im = cv2.cvtColor(im, cv2.COLOR_BGR2HSV)\n",
    "        h, s, v = cv2.split(im)\n",
    "        h_shift = np.random.uniform(h_shift_lim[0], h_shift_lim[1])\n",
    "        h = cv2.add(h, h_shift) \n",
    "        s_shift = np.random.uniform(s_shift_lim[0], s_shift_lim[1])\n",
    "        s = cv2.add(s, s_shift)\n",
    "        v_shift = np.random.uniform(v_shift_lim[0], v_shift_lim[1])\n",
    "        v = cv2.add(v, v_shift)\n",
    "        im = cv2.merge((h, s, v))\n",
    "        im = cv2.cvtColor(im, cv2.COLOR_HSV2BGR)\n",
    "        im = np.uint8(im) \n",
    "        im = np.float32(im)\n",
    "    return im"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Obtain a pre-trained ResNet Model from model zoo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import urllib\n",
    "def download(url):\n",
    "    filename = url.split(\"/\")[-1]\n",
    "    if not os.path.exists(filename):\n",
    "        urllib.urlretrieve(url, filename)\n",
    "\n",
    "#*- resnet-XX -*#\n",
    "def get_model(prefix, epoch):\n",
    "    download(prefix+'-symbol.json')\n",
    "    download(prefix+'-%04d.params' % (epoch,))\n",
    "\n",
    "#get_model('http://data.mxnet.io/models/imagenet/resnet/34-layers/resnet-34', 0)\n",
    "#sym, arg_params, aux_params = mx.model.load_checkpoint('resnet-34', 0)\n",
    "get_model('http://data.mxnet.io/models/imagenet/resnet/50-layers/resnet-50', 0)\n",
    "sym, arg_params, aux_params = mx.model.load_checkpoint('resnet-50', 0)\n",
    "#get_model('http://data.mxnet.io/models/imagenet/resnet/152-layers/resnet-152', 0)\n",
    "#sym, arg_params, aux_params = mx.model.load_checkpoint('resnet-152', 0)\n",
    "#get_model('http://data.mxnet.io/models/imagenet/resnet/200-layers/resnet-200', 0)\n",
    "#sym, arg_params, aux_params = mx.model.load_checkpoint('resnet-200', 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_model(s_fname, p_fname):\n",
    "    \"\"\"\n",
    "    Load model checkpoint from file.\n",
    "    :return: (arg_params, aux_params)\n",
    "    arg_params : dict of str to NDArray\n",
    "        Model parameter, dict of name to NDArray of net's weights.\n",
    "    aux_params : dict of str to NDArray\n",
    "        Model parameter, dict of name to NDArray of net's auxiliary states.\n",
    "    \"\"\"\n",
    "    symbol = mx.symbol.load(s_fname)\n",
    "    save_dict = mx.nd.load(p_fname)\n",
    "    arg_params = {}\n",
    "    aux_params = {}\n",
    "    for k, v in save_dict.items():\n",
    "        tp, name = k.split(':', 1)\n",
    "        if tp == 'arg':\n",
    "            arg_params[name] = v\n",
    "        if tp == 'aux':\n",
    "            aux_params[name] = v\n",
    "    return symbol, arg_params, aux_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Change the number of outputs\n",
    "\n",
    "``change_num_output`` changes the last fully-connected layer with the new number of output classes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def change_num_output(symbol, arg_params, num_classes, layer_name='flatten0'):\n",
    "    \"\"\"\n",
    "    symbol: the pre-trained network symbol\n",
    "    arg_params: the argument parameters of the pre-trained model\n",
    "    num_classes: the number of classes for the fine-tune datasets\n",
    "    layer_name: the layer name before the last fully-connected layer\n",
    "    \"\"\"\n",
    "    all_layers = sym.get_internals()\n",
    "    net = all_layers[layer_name+'_output']\n",
    "    net = mx.symbol.FullyConnected(data=net, num_hidden=num_classes, name='fc1')\n",
    "    net = mx.symbol.SoftmaxOutput(data=net, name='softmax')\n",
    "    new_args = {k:arg_params[k] for k in arg_params if 'fc1' not in k}\n",
    "    return (net, new_args)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load preprocessed datasets\n",
    "### Dataset \n",
    "\n",
    "\n",
    "Original Data: http://www-prima.inrialpes.fr/perso/Gourier/Faces/HPDatabase.html\n",
    "\n",
    "> N. Gourier, D. Hall, J. L. Crowley,\n",
    "> Estimating Face Orientation from Robust Detection of Salient Facial Features,\n",
    "> *Proceedings of Pointing 2004, ICPR, International Workshop on Visual Observation of Deictic Gestures*, Cambridge, UK"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have to preprocess the dataset using ``preprocessingDataset_py2.py``. This may take some time. \n",
    "\n",
    "> python2 preprocessingDataset_py2.py --num-data-aug 15 --aspect-ratio 1\n",
    "\n",
    "Preprocessed Data: (6.7 GB (Aspect Ratio, 1:1) or 5.0 GB (Aspect Ratio, 16:9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "trn_im, test_im, trn_output, test_output = pickle.load(open( \"HeadPoseData_trn_test_x15_py2.pkl\", \"rb\" ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "((33480, 3, 84, 84), (1674, 3, 84, 84))\n",
      "((33480, 2), (1674, 2))\n"
     ]
    }
   ],
   "source": [
    "print(trn_im.shape, test_im.shape)\n",
    "print(trn_output.shape, test_output.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification of Head Pose"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make mirror images (data augmentation)\n",
    "\n",
    "Head-pose images were flipped along the horizontal axis and the signs of corresponding head-pose pan angles were changed accordingly. The mirror image procedure effectively doubles the size of training data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trn_im_mirror = trn_im[:,:,:,::-1]\n",
    "trn_output_mirror = np.zeros(trn_output.shape)\n",
    "# Tilt\n",
    "trn_output_mirror[:,0] = trn_output[:,0] \n",
    "# Pan\n",
    "trn_output_mirror[:,1] = trn_output[:,1] * -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([-0.33333334, -1.        ], dtype=float32), array([-0.33333334,  1.        ]))\n"
     ]
    }
   ],
   "source": [
    "im_idx = 200\n",
    "print(trn_output[im_idx,:], trn_output_mirror[im_idx,:]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trn_im = np.concatenate((trn_im, trn_im_mirror), axis = 0) \n",
    "trn_output = np.concatenate((trn_output, trn_output_mirror), axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "((66960, 3, 84, 84), (66960, 2))\n"
     ]
    }
   ],
   "source": [
    "print(trn_im.shape, trn_output.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From (normalized) angles to angle classes (Tilts and Pans)\n",
    "\n",
    "xxx_output[:,0] and xxx_output[i0,1] contain normalized agnle data (from -90 degrees to 90 degrees -> from -1.0 to 1.0) in tilt and pan directions, respectively. This process is to convert the normalized angle into one of three angle classes in each direction. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Threshold Angles -19.47, 19.47\n"
     ]
    }
   ],
   "source": [
    "n_grid = 3\n",
    "angles_thrshld = [np.arcsin(float(a) * 2 / n_grid - 1)/np.pi * 180 / 90 for a in range(1,n_grid)]\n",
    "print(\"Threshold Angles \" +  (', ').join([\"{:.2f}\".format(a * 90) for a in angles_thrshld]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Head pose is classified into 9 categories (the combinations of 3 tilt and 3 pan classes). The head pose contained within +/- 19.5 degrees in tilt and pan angles is labeled as a center position (i.e. Grid Class of 4, Tilt Class of 1 and Pan Class of 1). The threshold angles, +/- 19.5 degrees split a semicircle (i.e. the distance between sin(-90 degrees) and sin(90 degrees)) into three equal arch lengths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def angles2Cat(angles_thrshld, angl_input):\n",
    "    # angl_input: Normalized angle -90 - 90 -> -1.0 - 1.0\n",
    "    angles_cat_temp = angles_thrshld + [angl_input]\n",
    "    return np.argmin(np.multiply(sorted(angles_cat_temp)-angl_input,sorted(angles_cat_temp)-angl_input))\n",
    "\n",
    "### Dataset ###\n",
    "trn_tilt_cls = []\n",
    "trn_pan_cls = []\n",
    "for i0 in range(trn_output.shape[0]):\n",
    "    trn_tilt_cls += [angles2Cat(angles_thrshld, trn_output[i0,0])]\n",
    "    trn_pan_cls += [angles2Cat(angles_thrshld, trn_output[i0,1])]\n",
    "\n",
    "test_tilt_cls = []\n",
    "test_pan_cls = []\n",
    "for i0 in range(test_output.shape[0]):\n",
    "    test_tilt_cls += [angles2Cat(angles_thrshld, test_output[i0,0])]\n",
    "    test_pan_cls += [angles2Cat(angles_thrshld, test_output[i0,1])]\n",
    "\n",
    "    \n",
    "np_trn_tilt_cls = np.asarray(trn_tilt_cls)\n",
    "np_test_tilt_cls = np.asarray(test_tilt_cls)\n",
    "np_trn_pan_cls = np.asarray(trn_pan_cls)\n",
    "np_test_pan_cls = np.asarray(test_pan_cls)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From angles classes to 9 head pose classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "((66960,), (66960,))\n"
     ]
    }
   ],
   "source": [
    "np_trn_grid_cls = np_trn_pan_cls * n_grid + np_trn_tilt_cls\n",
    "np_test_grid_cls = np_test_pan_cls * n_grid + np_test_tilt_cls\n",
    "\n",
    "print(np_trn_grid_cls.shape, np_trn_grid_cls.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shift colors in the training data (additional data augmentation)\n",
    "\n",
    "The color shift procedure would simulate the changes in the lighting condition, skin tone, and background."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i0 in range(trn_im.shape[0]):\n",
    "    im_temp = trn_im[i0,:,:,:]\n",
    "    im_temp = np.transpose(im_temp, (1,2,0)) * 255 #transposing and restoring the color\n",
    "    im_temp = shiftHSV(im_temp,\n",
    "                       h_shift_lim=(-0.1, 0.1),\n",
    "                       s_shift_lim=(-0.1, 0.1),\n",
    "                       v_shift_lim=(-0.1, 0.1))\n",
    "    im_temp = np.transpose(im_temp, (2,0,1)) / 255 #transposing and restoring the color\n",
    "    trn_im[i0,:,:,:] = im_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VvnqcZMEKoRBCEQQRWVYQhCFSKZTWTZ5CaxA+9dRTTCYTHn30UYqiYDqddkq3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd0ad66b650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "im_idx = 390\n",
    "n_panels = 4\n",
    "for i in range(n_panels):\n",
    "    i0 = i + im_idx\n",
    "    plt.subplot(1,n_panels,i+1)\n",
    "    im_temp = trn_im[i0,:,:,:]\n",
    "    im_temp = np.transpose(im_temp, (1,2,0))\n",
    "    ## BGR -> RGB \n",
    "    plt.imshow(im_temp[:,:,[2,1,0]])\n",
    "    plt.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the model\n",
    "### Make iterators from nparrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size = 300\n",
    "\n",
    "trn_iter_grid = mx.io.NDArrayIter((trn_im.astype(np.float32)) * 255, np_trn_grid_cls, batch_size, shuffle=True)\n",
    "test_iter_grid = mx.io.NDArrayIter((test_im.astype(np.float32)) * 255, np_test_grid_cls, batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(DataDesc[data,(300, 3L, 84L, 84L),<type 'numpy.float32'>,NCHW], DataDesc[softmax_label,(300,),<type 'numpy.float32'>,NCHW])\n"
     ]
    }
   ],
   "source": [
    "print(trn_iter_grid.provide_data[0], trn_iter_grid.provide_label[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training \n",
    "### Modify the ResNet 50 model from model zoo \n",
    "\n",
    "The number of output classes is modified to 9 to match our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(new_sym, new_args) = change_num_output(sym, arg_params, num_classes = max(np_trn_grid_cls) + 1)\n",
    "shape = {\"data\": (batch_size, trn_im.shape[1], trn_im.shape[2], trn_im.shape[3])}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 2.38.0 (20140413.2041)\n",
       " -->\n",
       "<!-- Title: plot Pages: 1 -->\n",
       "<svg width=\"413pt\" height=\"18877pt\"\n",
       " viewBox=\"0.00 0.00 413.00 18877.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 18873)\">\n",
       "<title>plot</title>\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-18873 409,-18873 409,4 -4,4\"/>\n",
       "<!-- data -->\n",
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     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "mx.viz.plot_network(new_sym, shape=shape)"
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  {
   "cell_type": "markdown",
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   "source": [
    "### Fine-tune the model\n",
    "\n",
    "We run the training. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Already bound, ignoring bind()\n",
      "INFO:root:Epoch[0] Batch [100]\tSpeed: 314.94 samples/sec\taccuracy=0.844554\n",
      "INFO:root:Epoch[0] Batch [200]\tSpeed: 312.73 samples/sec\taccuracy=0.932467\n",
      "INFO:root:Epoch[0] Train-accuracy=0.936812\n",
      "INFO:root:Epoch[0] Time cost=214.388\n",
      "INFO:root:Saved checkpoint to \"chkpt_Res50_1_3x3-0001.params\"\n",
      "INFO:root:Saving the Model\n",
      "INFO:root:Saved checkpoint to \"chkpt_best_Res50_1_3x3-0000.params\"\n"
     ]
    },
    {
     "name": "stdout",
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     "text": [
      "The best model found so far at epoch 00000 with accuracy 0.823333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Saving the Model\n",
      "INFO:root:Saved checkpoint to \"chkpt_best_Res50_1_3x3-0000.params\"\n"
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     "name": "stdout",
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     "text": [
      "The best model found so far at epoch 00000 with accuracy 0.826666666667\n"
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    },
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      "INFO:root:Epoch[0] Validation-accuracy=0.807778\n",
      "INFO:root:Epoch[1] Batch [100]\tSpeed: 312.65 samples/sec\taccuracy=0.959472\n",
      "INFO:root:Epoch[1] Batch [200]\tSpeed: 312.64 samples/sec\taccuracy=0.964700\n",
      "INFO:root:Epoch[1] Train-accuracy=0.967246\n",
      "INFO:root:Epoch[1] Time cost=214.357\n",
      "INFO:root:Saved checkpoint to \"chkpt_Res50_1_3x3-0002.params\"\n",
      "INFO:root:Epoch[1] Validation-accuracy=0.774444\n",
      "INFO:root:Epoch[2] Batch [100]\tSpeed: 312.65 samples/sec\taccuracy=0.973828\n",
      "INFO:root:Epoch[2] Batch [200]\tSpeed: 312.58 samples/sec\taccuracy=0.983000\n",
      "INFO:root:Epoch[2] Train-accuracy=0.982029\n",
      "INFO:root:Epoch[2] Time cost=214.377\n",
      "INFO:root:Saved checkpoint to \"chkpt_Res50_1_3x3-0003.params\"\n",
      "INFO:root:Epoch[2] Validation-accuracy=0.748333\n",
      "INFO:root:Epoch[3] Batch [100]\tSpeed: 312.64 samples/sec\taccuracy=0.982475\n",
      "INFO:root:Epoch[3] Batch [200]\tSpeed: 312.67 samples/sec\taccuracy=0.985133\n",
      "INFO:root:Epoch[3] Train-accuracy=0.982174\n",
      "INFO:root:Epoch[3] Time cost=214.360\n",
      "INFO:root:Saved checkpoint to \"chkpt_Res50_1_3x3-0004.params\"\n",
      "INFO:root:Epoch[3] Validation-accuracy=0.779444\n",
      "INFO:root:Epoch[4] Batch [100]\tSpeed: 312.62 samples/sec\taccuracy=0.986304\n",
      "INFO:root:Epoch[4] Batch [200]\tSpeed: 312.68 samples/sec\taccuracy=0.988100\n",
      "INFO:root:Epoch[4] Train-accuracy=0.987681\n",
      "INFO:root:Epoch[4] Time cost=214.360\n",
      "INFO:root:Saved checkpoint to \"chkpt_Res50_1_3x3-0005.params\"\n",
      "INFO:root:Epoch[4] Validation-accuracy=0.781667\n"
     ]
    }
   ],
   "source": [
    "import logging\n",
    "logging.getLogger().setLevel(logging.DEBUG)\n",
    "\n",
    "# p2.8 instance has 8 GPUs. \n",
    "ctx = [mx.gpu(i) for i in range(6)] \n",
    "#ctx = mx.cpu()\n",
    "num_epoch = 5\n",
    "\n",
    "net = mx.mod.Module(symbol=new_sym, context=ctx)\n",
    "net.bind(data_shapes=[trn_iter_grid.provide_data[0]], label_shapes=[trn_iter_grid.provide_label[0]])\n",
    "\n",
    "### Checkpoint\n",
    "model_prefix = 'chkpt_Res50_1_3x3'\n",
    "checkpoint = mx.callback.do_checkpoint(model_prefix, 1)\n",
    "\n",
    "### EvalCallback\n",
    "model_prefix_best = 'chkpt_best_Res50_1_3x3'\n",
    "ev_cb = EvalCallback(model_prefix_best, \"accuracy\", \"max\", save_model=True)\n",
    "\n",
    "net.fit(trn_iter_grid,\n",
    "       test_iter_grid,\n",
    "       arg_params=new_args, ### Fine Tune \n",
    "       aux_params=aux_params, ### Fine Tune\n",
    "       initializer=mx.init.Xavier(rnd_type='gaussian', factor_type=\"in\", magnitude=2), ### Fine Tune\n",
    "       allow_missing=True, ### Fine Tune \n",
    "       optimizer=\"adam\",\n",
    "       optimizer_params={'learning_rate' : 0.0005},\n",
    "       eval_metric='acc',\n",
    "       batch_end_callback=mx.callback.Speedometer(batch_size, 100),\n",
    "       epoch_end_callback=checkpoint,\n",
    "       eval_batch_end_callback=ev_cb, ##\n",
    "       num_epoch=num_epoch\n",
    "       )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Learning Curve\n",
    "\n",
    "The plot shows the changes in validation accuracy during the trining. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd0a43b7990>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pYmdZg8fnev9gDTHhwcxJd2TdhASZeOymeUxOiuQbzxRw4MSpAY/9x9ZjBJtM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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd0a4434e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "val_metrics = ev_cb.get_loss_metrics()\n",
    "plt.plot(val_metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Take a peek at validation results\n",
    "\n",
    "Let's load a model and make inference results of validation data.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_symbol = model_prefix + \"-symbol.json\"\n",
    "model_params = model_prefix + \"-0005.params\" \n",
    "\n",
    "sym, arg_params, aux_params = load_model(model_symbol, model_params)\n",
    "\n",
    "#ctx = mx.cpu() # USE CPU to look up validation results \n",
    "ctx = [mx.gpu(i) for i in range(4)] # USE GPU to look up validation results \n",
    "net2 = mx.mod.Module(symbol=sym,context=ctx)\n",
    "net2.bind(data_shapes=[trn_iter_grid.provide_data[0]], label_shapes=[trn_iter_grid.provide_label[0]])\n",
    "net2.set_params(arg_params, aux_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Infer validation data iterator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1674, 9)\n"
     ]
    }
   ],
   "source": [
    "preds = net2.predict(test_iter_grid).asnumpy()\n",
    "print(preds.shape)\n",
    "### Head Pose Prediction (9 classes)\n",
    "pred_cls = []\n",
    "\n",
    "for idx in range(preds.shape[0]):\n",
    "    pred_cls += [int(preds[idx].argmax())]\n",
    "\n",
    "### Tilt Prediction (3 classes)\n",
    "pred_tilt = pred_cls % max(np_trn_tilt_cls + 1)\n",
    "### Pan Prediction (3 classes)\n",
    "pred_pan = pred_cls // max(np_trn_tilt_cls + 1) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion matrix\n",
    "\n",
    "Confusion matrix is one of the useful ways to visualize the validation accuracy. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "lst_angl_lbl = ['<< -19.5 dgrs', '-19.5 to 19.5 dgrs', '>> 19.5 dgrs']\n",
    "lst_angl_lbl_tilt = ['<< -19.5 dgrs (Down)', '-19.5 to 19.5 dgrs', '>> 19.5 dgrs (Up)']\n",
    "lst_angl_lbl_pan = ['<< -19.5 dgrs (Your Right)', '-19.5 to 19.5 dgrs', '>> 19.5 dgrs (Your Left)']\n",
    "\n",
    "cm_grid = confusion_matrix(y_true=np_test_grid_cls,  # True class for test-set.\n",
    "                      y_pred=pred_cls)  # Predicted class.\n",
    "\n",
    "cm_tilt = confusion_matrix(y_true=np_test_tilt_cls,  # True class for test-set.\n",
    "                      y_pred=pred_tilt)  # Predicted class.\n",
    "\n",
    "cm_pan = confusion_matrix(y_true=np_test_pan_cls,  # True class for test-set.\n",
    "                      y_pred=pred_pan)  # Predicted class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion matrix helper function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.3f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Head pose prediction in the 9 classes\n",
    "### Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[142  28   0  10   0   0   0   0   0]\n",
      " [  4 207  56   0   3   0   0   0   0]\n",
      " [  0   5 174   0   0   1   0   0   0]\n",
      " [  0   0   0 113  10   0   3   0   0]\n",
      " [  0   2   2  10 114  22   0   9   3]\n",
      " [  0   0  13   0  18  71   0   0  24]\n",
      " [  0   0   0  11   0   0 144  25   0]\n",
      " [  0   0   0   0   2   0  32 188  48]\n",
      " [  0   0   0   0   0   0   0  13 167]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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481xmBdAOuAdAVVcCnwGrMGKqt6lqcqBjWFVZQ2aqss8DRTHDMgCOh5HXsVgs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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd09d543e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute confusion matrix Head Pose \n",
    "cnf_matrix = cm_grid\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=[\"Grid Class\" + str(i) for i in range(max(np_trn_grid_cls + 1))],\n",
    "                      title='Confusion matrix, without normalization')\n",
    "\n",
    "plt.rc('figure', figsize=(10.0, 5.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalized confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized confusion matrix\n",
      "[[ 0.79  0.16  0.    0.06  0.    0.    0.    0.    0.  ]\n",
      " [ 0.01  0.77  0.21  0.    0.01  0.    0.    0.    0.  ]\n",
      " [ 0.    0.03  0.97  0.    0.    0.01  0.    0.    0.  ]\n",
      " [ 0.    0.    0.    0.9   0.08  0.    0.02  0.    0.  ]\n",
      " [ 0.    0.01  0.01  0.06  0.7   0.14  0.    0.06  0.02]\n",
      " [ 0.    0.    0.1   0.    0.14  0.56  0.    0.    0.19]\n",
      " [ 0.    0.    0.    0.06  0.    0.    0.8   0.14  0.  ]\n",
      " [ 0.    0.    0.    0.    0.01  0.    0.12  0.7   0.18]\n",
      " [ 0.    0.    0.    0.    0.    0.    0.    0.07  0.93]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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aSKOQUCeZLXGnCKxxPbU9y3FdSaF3lB9Lt+Z2NkG1KlC5XCli9p9wqHuSimWv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\ncVKHOy/HcRwndfw/lO3zZ8Sxm3UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd0a44e1e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=[\"Grid Class\" + str(i) for i in range(9)], normalize=True,\n",
    "                      title='Normalized confusion matrix')\n",
    "\n",
    "plt.rc('figure', figsize=(10.0, 5.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Head pose prediction in Tilt (Pitch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[423  63   0]\n",
      " [ 46 525 131]\n",
      " [  0  36 450]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.87   0.13   0.   ]\n",
      " [ 0.066  0.748  0.187]\n",
      " [ 0.     0.074  0.926]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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zGks5/OGk03jx9fd4bsTb/P2Ka1l3/Y244NKr2GKrQTzz5OMAPPv0Eyy+xMAy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\nwMt583nAeRFxo6QD6x13VWD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dX2AZUtfu5yNN+XUMMD4iLilftJVJ0jz1Op1sTLrn34fA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcf400eebd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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p0u0g7vybj3vkOKFbE8bPWs52C0YLzjuqBf+dv5KV6/O2zlIriHOPak6PO99l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\nFxERXSkBLiIiulICXEREdKX/Ajs0l0wTi1VfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcf401d6850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute confusion matrix Tilt (Pitch)\n",
    "cnf_matrix = cm_tilt\n",
    "np.set_printoptions(precision=3)\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=lst_angl_lbl,\n",
    "                      title='Confusion matrix, without normalization')\n",
    "\n",
    "# Plot normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=lst_angl_lbl, normalize=True,\n",
    "                      title='Normalized confusion matrix')\n",
    "\n",
    "plt.rc('figure', figsize=(5.0, 4.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Head pose prediction in Pan (Yaw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[616  14   0]\n",
      " [ 17 358  39]\n",
      " [  0  13 617]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.978  0.022  0.   ]\n",
      " [ 0.041  0.865  0.094]\n",
      " [ 0.     0.021  0.979]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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UAPhI0n+Ai4F/mdkASfcRvrP3xr+zzWwzSX0IubHL/YdXBct61yiSrgXmmdmt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\nYsBKUhMDVpKaGLCS1MSAlaQmBqwkNfkAnLBqVgQ3zY0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcf400fe550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MEb325/mRr1dYuyDsUZUqa7QkStpNdZikF4s71uUhZvazmU0gBGVJpDXwsZn9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\noo8SVCMi+ihBNSKij/4DeAJO1NxLkTAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcf36547d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute confusion matrix Pan (Yaw)\n",
    "cnf_matrix = cm_pan\n",
    "np.set_printoptions(precision=3)\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=lst_angl_lbl,\n",
    "                      title='Confusion matrix, without normalization')\n",
    "\n",
    "# Plot normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=lst_angl_lbl, normalize=True,\n",
    "                      title='Normalized confusion matrix')\n",
    "\n",
    "plt.rc('figure', figsize=(5.0, 4.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of false predictions: 354\n",
      "Validation Accuracy: 0.789\n"
     ]
    }
   ],
   "source": [
    "i_test_count = 0\n",
    "\n",
    "for idx in range(np_test_tilt_cls.shape[0]):\n",
    "    if abs(np_test_tilt_cls[idx] - pred_tilt[idx]) + abs(np_test_pan_cls[idx] - pred_pan[idx]) >= 1:\n",
    "        i_test_count += 1\n",
    "\n",
    "print(\"The number of false predictions: {}\".format(i_test_count))\n",
    "print(\"Validation Accuracy: {:.3f}\".format(1 - float(i_test_count)/np_test_tilt_cls.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Predict a head pose of an arbitrary image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/src/mxnet/python/mxnet/module/base_module.py:65: UserWarning: Data provided by label_shapes don't match names specified by label_names ([] vs. ['softmax_label'])\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "sym, arg_params, aux_params = load_model(model_symbol, model_params)\n",
    "dshape = [('data', (1,trn_im.shape[1], trn_im.shape[2], trn_im.shape[3]))]\n",
    "\n",
    "ctx = mx.cpu() # USE CPU to predict... \n",
    "net2 = mx.mod.Module(symbol=sym,context=ctx)\n",
    "net2.bind(for_training=False, data_shapes=dshape)\n",
    "net2.set_params(arg_params, aux_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load, crop, and resize a head image "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aDHlPDG9ZjKibVbxWrTW27x1hVIZRBt03lTmZLfDXbzAajfjyl79M09R85rOv\nU5Y5N29exznHSy+9RLm/19+vDhUnVT2N4j5KqdN9JyD3Y8sDsgln9U24b9OkTFp+/og9hX/Pe39d\nKXUZ+C2l1Fvpm9573xuM+0RtTZ3+uOJdz0GwgZy0np/yx3/8Pa59+BFf/ervcnh0CoSHt8grLJ7G\nrjfc8TiARViBvUj8Lm3JrLWBsdiHD+IBiMstRUiCJRRlTjU6DwTDk7IQ0+OLkZFVWMIB4Ts0TcN0\nPAkj35xHA10XQpyqqjg9PZX7jtF5BO+kl8BoNOoLjnxkJkaQUbyTXgklJAqK7fHeRtCvbVu61jEu\nK1pnI3Yg3aAk0zKkF8H26U5jDAofgdz5fB7DkZSfYTsbQ66h2UuG90OjWfH4rLUcHp9iZgu+8Y1v\n8Gf/7C/hvefLX/4yBwd32NvbCWFhWWAd7OzvJ9/u07AQH1eexb6esHtSSlJ6Spr3xzIK3vvr/c87\nSql/BPwScFsp9bL3/qZS6mXgzgM++0ynTm8afs+9e/c4uH2Ha9euRe5B6np3frPMWHLsWmts026g\n6WnFYKrEETDsV3PpiQAkqTUVj5k2OvV9IVCaXRBPQZiO0qdAjIUosYBskcvgBkMi7neswOyNlYQJ\nImK0Yvahf73skX0BOUUhBc8Qhey6cH+8YiMNKoYjzZLI8Qa+RBNTo65/beO7VApjNM51A6eivzZw\nuCTMkmsSw7lYLLh16xa7e9OYKh6Px1y4cCHMwByPeXbFT89atqnJbP39MdTkR8FoVEpNAO29n/W/\n/2Xgvwf+CfDrwN/qf/7jpz3GE4n34C3eNty9dZvv/ps/4ve++nvcvnuXxXzF6WLZk4E0XitGxYiq\nqjg4ONgIAyRzJg1R054E8hBKNyMBxsbj8X0kof4eRQag1jpSjQUnkPhfQgYBOItsqD9IGYlKKaxr\nGU8qqlFB29joddSrNaOyiqt05z0ei3UOowZC1Gg02qigrOsamzAY13140XUdvvecRLmlKet4PMY7\nFcFVSYfK9TR9WOK9j8ZXPCwJQZRSKO/JJ5N4zvJ+alzS+wU9lqD6HhB9XwbBJ5xz6Lzgvffe4/qN\nq4xGJa+//jre3+OVV16jLMveczmrrPhJuzE/iTxov9vHfJShSg3DlgdxlofwBIYglY/jKVwB/lH/\nwGTAb3rv/1+l1NeBv6+U+q+AD4G//jGO8fiiAh/h5O4RR4cH3L55i4O7d3Fd7/ZqhTIaDfHhkNRc\nWNUM3ius36wclPflIRfAbVuhBXCUmF0MRGok2rbdaJzivUEpH42BeAFpnYCch7Aiq9FQXJTlGl0P\nDVdEkbIsQxkHmI1zhaHkGsBHPScmAAAgAElEQVSrzeYs6T6UUmAMmTFkRYGJvAWFs5DlZsMTSDMF\nUiKd8g7k+pRS4Fyg4fQZCOF2yPmJAUkNQQxzlOozQh7nIOvvkbUe7x1N3aEqRek13/nOd3j99deB\n0F1qZ38PrVtMdtZj/zz6IzxOyfSTyJaSP0jpz3r9R+EpeO/fA/70Ga/fA/7S0+73qUQFYpKtl/zg\n7e8xm824du0abdMxXy6whH4IXecYV2V0jwMV2QwtzhXozm90KE7z/alyiWKWZRnblUllpaTthHMg\noUnaWEUMgCiD1hlaG8pSo/qJTpIWlbSd9x7btLExSWctk+kIZ2E2myWufRcHzIriimJKeba0S/Pe\n0yTKC0P/Ra01nVLYukYlacWwSg8sxZTWnBKyJDVrIR7Xe0+RDQohIU4abghwOhjswVORe55mKNIQ\no65rmnbNYjFnWS/55//8t/jiF7/IfD6nmoQWeKX3ZOUooTyrMEVrY6E+y3N4Hp7EWSGDhDd+M33q\n/RAnbyt5ih+cZQB+xDyFH7/48N9yPufo6Ij33nuPxWrJYrWksyEOraoiKIF1UdFCqtD0fPycrg0P\n8Xw+jw8fEOnMQtgRd1W8gTTXDkSWn3gaZ6Xctr2PiFd4+lXP4toOo0OzkI3BK1U4r/l8Dk1YgXd3\nd1mt6kGZ3YAxyP4lxJFVW66hqiq6/pomk8kGFhJYgM2GwSnLEuc78GAyhVJ9qNJzPoBI+S6KgrIK\nmZG8N6C2V3a5DxIiScWk1FkIRTt6Gb3n09Rd9BhM1neVdhblHbnJyE0eWuR5xdvvvMO777/PX/2V\nv8y//e/+O1R5gfMe26yDx2CC16Hv05kfUeWjNKBVKjnmGW3lvI/P+YP39WxSlj9FRgGOjo44Pj5m\nPp9zeC/MGUAFoEwxuMbSKTmlJqcKC0PbMlmthXknSDsMaTh5uFMeQapYMOTXxRjI70BcUYMbPXRf\nTqsLI8Bo29i8xTmH78G40WgUZ0WI1+O3HpK0zmD73JQfip5gqPK01tL0MyQEF5HrTBmNwasy8TpS\nqnNqRNP7l6Ybtw2qlILL++FcB08m3h/tY3o4DVV8f13j8ZidnR2+//3v88Ybb2BQfOpzn91I3Vkr\nHuHzZDs+qE3aWdttUZrjUJyHfPYZGQT4aTEKytEtlqyXC+7cucOtW7c4nc8oR1UoE7YqrByuQ5s8\npgWllkF4BTAw6gRzEEVLCT6yakvzEPEehIiUphpTenBRFLF6UpROlFj2kRvpL6DBuridrLJkwTNp\nmgaj8xi/i2HI8zxQnvu+jTAYB6lwFMVMWYZiJE77uZfyep7n5AnlezQaxdBDPAw5hu9py2IkITSC\n7VZ9uJIUSaXt44VRKalQ51zsV+m9x+TBOHmnYjt6qS9pey9GGsV01mLrFpMpZrN5ny5e85nPvs4f\n/N6/5vhnQ73LxYsXgwE0mrKq0I9Nf35Q09ZHPqSPsR9NqBhIxQ8/YnThzw4XHpqg+BPVT8FB/xC+\n+eZbXL95i9b2gFRmaF3NaFzSrNZY71jXy5iKO7+3z7mLFzg9PQ1NWY2O2QZ50AT4khBgMpnE2FxW\nMVn5Ii+AYRUUd9t7H4k54hlIzCzZCYC2DoVWWkNhsngO1oVOTM6GOo20N4GEBUWR0TTroKxLG7oe\nq4EmnKZd67reSDsaY9B9xgWGFuxtXbO2Q0k1ED0op4aU6aiaYNQwgUq8DLk2mX7lvce2HZk2ZIXB\n9tiL3Gcxcq6TcXMDh0G8vqE+JY/GTLIwxhg0HjpNp8OoutlszquvXeHly1c4OTnh2rVrNE3D/v4+\nu+f20dLw5v4Y4v5n7ZmKg7Par0fSTYIpiOJv/3wO8gk3Cj17sc+Vv/veB2iVMR5NOeSE9apBKY3t\nPJ0LsXpbN9GFL0ZVzBiAPOw+egHpqidKLHGycPpFWUSxUhxBVu+UBSkrpYQuYkhGVWjFpkyH8aHe\nQucZje0wmcF4D16Ta4VBUXeht2TKBhQDs1wuybKwypZlxfHxcTx+Wkdgvcd2Xdh31+FWK+bzeUwh\nilHxdph+LWlaMZLOOJRXdF3/IHuNdw7bebTOmM+G4rLVsu7BTYWzHpSLhlPCB+dC8RY67N/64C1Z\n73C2C/MpbVCIul5SlEOGJoYOOuhM5jK8U3jtuXnjNj/3pV+IreLkvkuJuNIZpoCB6XiWPOvuzJ6N\nFvVSGi1G4Cww0T0klPBsYhCbH37ss/rkGgXXhRugPLOje7z5x29ycHDA3bsHyXCToc2XoOMCZgmQ\neHJyQtu2IUtgHWVJbEAqCi3xrbjO3vuhs7O48sYwHo83cIA0ZpfVVdJvAq45FzoPCQNRXOMsG7oS\npVwH23bkVRnBRgkVuq6j6dp4nZIVWCwWXLp0ibqumc1m0XAVRRH3maYlOzc0URUDoWGDtTmfz6Oi\nR3ymSVKODBkM4UWIByF4iFIK57voaYnnY23oaZHlQzNZObchFBODV9J2q40MUfg31E9orXFecffu\nXd59910y/QWuXLkSu2NfvNLPrTTmua6+jyd+08tPFV8MxaOyCB8zHQmfZKPQGwSs5Xt//Me884N3\nOTk5iZONyrKkTsBEWd1EWbTWERPIsixQhLWJK4mspmn6Syi5knYENkqIZYpT6gEITyCl5ApTMXXn\nQ83EZlfl8Xgc6xmkf6MxhoqhUKioSio9iuCkXM/5/XN9+jNjOV9EbyZtMyc9J6WF23K5pOu7M1dV\nFUu4J6NRvN7ZbBYzBFmWxb4NeT6UTKfei5yX1FikmZc8KzfCEjk/CGCwhASyT62yeE9DCFaizQAK\nDxmV8IgMmR7PZLLD0cER35p9i8uXL8Yw5fz585hRlazA8b+nkxjfPwaguI0piu6ehRlsG4WzvIg0\ntLgvJHn8S/jkGgVvQWmWi0UEBKXWIEX15QFM015AVPS0V0CWDTGquLXiVYhLLau3SOopSGGRPIyC\npKfx/wadmc2+jOm2su/U5U/3IQDpfD7fGPgSKc5F2Xsyweis12tUP89SirVkZQ7hRvCgzvVekngA\n1loODw9jg5jUWKQzKo0Zji+KL4oqgKEMoInpUe03Q5GEB5L2sxAjuVyst6jUXSy0Sr9TY8RjkcIx\nzcHBAV/83Od5+ZUraB1a8y+XoTU/TYPJn6Yj0/Yz6Td/f9ZM6g2+gnq4AfoYXs8n1Cg4bNeh8NT1\nCmstp7Pj+LBmWcZyuaRuh4El6USi5XJJVYWW5NKnQJmwkp6cnCTAXRGVQwg1sj9RqLQpioQM8tCL\nYYEhAyA4xcaKuTWRqXMWOk/XObJMx9AkKE2SQlUwGk16TyUog6Thir7L8Xy+jIVcAFVRoqsRzbqm\nczYCfFIhKufXJMxHYwwXLlwIn0/Ay729vUjGiuGSH0qs0+vVWlON+iG5TQgjbDekHFNilPcdXTOw\nLAXMTNOO8X4zGAX634uiCPfHDlwICJ7QtWvX2JlM+cWf/wWm0ynz+Zy9vR26xpI9aUn1I72CsxRT\nDW/5xFVIlXjbGxADoPtJWNseg9pyN1KDsb3Px5BPplFwHabIwbbcvH6Vg4M7vPvuu33T1LBirZoa\n2/m44qSzASaTCaPRKCq8IPHJQjVUJSYpsMViEXEA4S7IA7q90qdAJAyrnUiqCCn3X97LcvFo6Ps7\nBKUvijyyJWWSlIxaE49lvV5zfHpCWZZMp1O8H5q7iJRlSdmHONLWXlbxNHyBwahKe7mhDbuOxlW4\nCOIxiXt+cnKCMYaizGILu3odjIF1bfx+xMsSzGW9Xsc+mNIXUjAd8QhjRWVCxkqnbRXZUI/iPZye\nnpKvcr7yla9QFBlf+tKX+Llf+PlwfbbD+aHQLcsyVGYISptmHTwQm1k++BlVPWj4IGryWSv5WWlG\n+X0bZNw2GGefxNn7e4R88oyCcv3UYQ/GMJ1OuX71WlQs6eyzWq3Is3IDwJJ/+/v7GGOCNyEj3LoO\n74eZimkYISu/ZA/koRO3eJuiK4Zim7sv4UvqZYirLscTAlGmh+EtadwtnodzDt8RvQN5+IF+atU6\nYgrVeERW5Jwen8T9rNdrRpMxRVWyXK8GnkTPRNT9sQLL0mMTD6dt+v4GWJSqN9K23ns625DZLAKN\nYV7nUGGptUZpj3ZBaaWJS9rzcrt+omuHTkuC29TNCudNDNOAjUYu8t0IQDoej3n99depRgVN17Jc\nrwKhqyzIzAiU2VyJPdw/wv4BhsD7rW0fsTJvK/PjruSP/MzHDyU+eUYhiqNeLHjnnXe4ceMGt2/f\nZjFfYW0Y3CIglqxuopTysAm6vm6GGY5N00SarzxMi8UC5xx7e3txP6LsaeFSyoQUF1ZibnGRZfsU\nLJPPpUVRZ/H6BbhMmZhVVVBU4XqmMgylBzZ3d3f77QNm4L0nK0KLeTn3dc9UFPDUuXDvpGtUmg2R\nlmyyrbSeQ7nYyFbCJWstx0en0XCabBgQo5SKjWjkuxAAVfafYhKD0R5mU2S5psgysnwSrznSu3vW\npjEGZ4f6lWpU8MP33+Pk9Ihf//Vf59OfDlWTYfRdE4bJWLs1HwLODAHOAvLuMx7bHsIZj/CjwMNU\nzvISts9J3o/He4xsxRnyyTIK0lXJdXRNza1bt3jrze9z7doNTucznIXVKrjJdVvHlVZcbdtPNxas\nQBTu9PS0d5VdBOWE7itKenBwELMO3vuNZiVpukw+J8eONQ3JzxRXkG0XizCEoWkadnZ2KLKS5XIB\nDIh+0zRkSqNUGE3vXBZxgOBiJyCf0hgUo9EkFmWdnJygM4Omny+phvMUxdrb24srr7A1j4+P42rb\n1B0rXcd7U1Zhtc/LkioxFm0xVJemHoi4+xIqpZ2cUoMrMyolS1TkgVRV1zWdbTaAX5lpmU6iAsgS\n2nLbNqxWcHpqeOutt7hw4Rx7e3uM+5F/9TKU1qv7VuLtFMGW3OchcD+j2W9vv50ZOMvIpDUPW6HE\nw+SszMXjfC6RT5ZREOlXcNuvmvP5nHrdxpVCHpgU8Mozg1FDy3aJS9MsQVnmkW0oYYSs3mkBzzaW\nIA+4iLj78jnxEtJ6A+EqyD5S11gkrN7dhvGweEw2gHdK6+Scsz6rMTAP5R4IvnBwcECmAzi6XK82\nshZANJbiVUmptxit7elOnQ04Rmsto76TtZy73CNZraXTtfA75PzSsmr5TJolMsaw7ou9pN5BREIx\nMcZitJ1zmDycg8kUus+8LBaBCj+fzzk9Dd4MWUaZ57R1Ta5UH56SZBAeQQTyfmAgPqmcZRCe1ece\nCYSeLZ8MoxCBFgsK6tkRb735Xd5++22+9offom4s1muaxtJ0fdlxj2xLzt1bhykKcm3Qfkj3aRS5\nybDWU1VZbJoiVOC0iEeUPzcZykNnNzsDpcU7XdehswyjFJMquLl1s4qGIK6oSUFVGjqEFXXoKgSw\nM55QjIpYN1AUBeWoigq4WKxwDpTJ6Pry7bZtYzgwnU7Z399nvV6HrsgmPMhKK5quo+uGlKZzLmYS\nijIMzJXzTpU5gpfOcXD3MBYoTSaToW7DZBTTnY3U7mg0ijyGlDzlrUOhcEnaFSDLdQznJFRIW8bL\n9xSMrMG5QObKexIYPqMocqqy4ubNm9R1zY0bNzj37h4/86e+CAryKgfn8M6itIyy31aoLc9AeA3b\nK7F/iIdxn6u/5Z2IdyDvC76xsa0K1Oyo8GdQodP9PwEZ85NhFJRwxAHX8e677/Lhhx/y1ls/iICa\n9QPv3vtQJWj95uAUIMx28AEVlzRflmU41MYAmBSpl9dEKeq6pl7VeDWMdBfjIA99oOgOcxjzwsRt\nNtzinjKdgoqConda06zrWDcwmUwiBiAhgQCsWVZEQtR8Pg8FVG1L21+ftZbj4+NIVlosFqi+e/Vq\ntUIZTZYNdOmQAdFoMzSolfhf6iNSbkEaHsiK7FyoYVBKMZlM4nbee1rbxZVfMB0A6zaBWvG45Nok\nAyEeW+plSHo4fD6ja1owgeqs8n5+RZ5z4UJoCf/yyy+HepSuC1iCUqBNr8qPCBvCU5X8vkUc2sYL\nzpKHhRLbYUM85FZo8LCwYMM4/LSFD8n3s14uOTg44J23f8h6uRrKa7MiGoPODQi0SjIEskrLQ911\nHdYHvrtJin1gAPbSkWYpjdfh0WoAD9MYN806aKUC0t6HEOIJyGor55OumKIswdsYWH7BqOhQZ4Cj\nafqhtaM8dnP2PtRt5FmGVYrT09P7eBSizKYn/mRFHo9dFHmvaEMWRkICwWPivUvqFtK0Zdd1aAav\nAIjnJvd0XQcCWOoNGGNChywUmRoGwqRMSUm7CgEszfqk4ZnsL1au5ppcm1jotrOzw8WLFzdnacRC\npAc8fGnFIunLj6uYD5GzshFneR+Ps5+zjv8E8skwCn012cnd21y79hFXP7wWuAV1x3weRsS7zoFW\nGJWUFvcrtsPjJRWmwsAXWX2rarThBovyCBq+zTmQVVM4/aLE23TmlLcgoJ2cj9CAZRq1eCBp3l3q\nBMSbKYqCRV8KnJ7ret2wXK7Rly5y8eJFlFLcvn071i3IFKu0walUbq5Wi40CqbRnRMA5hgxOep1y\nb4STAKEGQwyEt46mN0DRe2gDm7Fdr7D0PACj470VbybP+nmdZmhwm2JFKUYhBkqMj3hlWZZFasH+\n/n5kUsr38O6779J1Hbdu3eJnf/Znwz7aFpVlW6uvf/gq+6CV/HEU8WEhhPydbvOg0OBR+30K+QQY\nBRe/mFu3bnDn1s1QkltNWCyuR6Cpc5aiKjdGrYkiSmUfENNVMZdtQ4NSr4gKVPQDYgSQ1FozHo1o\nuqHBaloMlJb8yvsp0CnKKQomBUJ1XUfFkXqLVEEktJD4+aVXXqZZt8xmJxvkp67ruHf3gLZuGI1G\nvHzlJY5HJ3Ewrii+3IOIzvfGKZY0JyXdQcGEj2GiAq7X6ziBSu5zNHa6N8LKReOG7XA+bdMOpvci\nfGdjBiStVNVqABPFUxGvQYyqEKWkPkK8H2kAg4e878NQVRVtNxjfPM+5fv06ly9f5s6dO4x3guHN\nJYQ4SwlT2Vbkh2UORLb38zA84azPbnsJzj1c8VOj4j1PEj4861rQ5yQe2lDlNxqNuHDhXMydO+fI\nyiKuoKkrn8boIrIKpzUIKSYQj+gHl18UMI1dRSFS1Hw8HsfVXn7CwOOXfcrvQhlO059paXVKwtE6\nzJgQr0HO0XsfMwSCqEs1pNQqpLwLIf6kGZNtpqUQrOQ9uU5jQrs2uc60AxUM6c1t6nLq1sfwKwmz\nUs8qDfVSwpl4CXJvhLNRVdXGfMzFYhGNtXiD0sgl/U5ffvnlDQxEa43rurOVd/tfuPnD+/L39r/H\nlXS/j+tpPIknkO7/MeQT4CkAnWUxP+XShfNh9PtizZ07d+g6eWBUGBvfDGnG9EE1xgRUWw1zHKTh\namTYebBtCAFqu4qrpoQuSnmMApMFZZsvFxilqcbVRpwrg2XFa4ChxZgcV8BRCHH6ss+RC6tPhqrI\nPrz37OzsBIXrbBw4IwBlVVXs7u7GkOH09JS8LKLXcu7cOfI85/T0NK7ywTD0K3efJWjrhnoVqh6n\n40loemstVTWO3ou47ufOnWN3uhNDtcPDQ9BDmXOVZ3itsHYwAloNhU+mNxRt7y2IMRCDkpLI5D7K\nvRCll+8wNWRKKbpGZlQMxKb9c7sURR49sevXr/PSSy+xWq1YzpaMd6bkZZEoj32wIj3MA3jQdiIP\n8iq2P7ftsTxNOLDhWfy0AY39qre7txMfrLTzkdeKumn7ebJDyXJEsbuhEm87UyASVyQshS4wKLQH\n7cHoQASy/SqTZRlGadCh0aqsOBK7puXUcfutQighQhljMDqnXi9B+zjzQRRQPiuous8cdb3aYlI6\n8nzgQQgA5/pJTELRHo/HsbdiMJqD95LnOa7ztK3tC7GgzCtaG1ZkmUIt57RaLFE+xOzSf2I2m6Gc\npbMd1g5NZ3TycKccBOscynd4B85arB+qReU4qWFI04/yPcMQEsrr4o0E707jvaNuGnZ2ptimpSgK\ndiZTqqJkPp8H0LRpyXt8JMgW7+ApALv7MgVnSZpWjNukKcgnP+x98oTn/kijoJT6u8CvAne89z/f\nv3bmuHkVvo3/AfiPgCXwX3jvv/VEZ7Qt3kNC6jk9PWGxWLBYLLh37x5OhThXywPjA5PN6l6ptNmg\n7UJgxQUwqkN5FzCFfoVL24hL+tH0q5v2gHUczY/6mD8U8RSVlCk38WHOsoyqGKo2xSiIyyzhwXrV\nsLu7i9EBrBMsQfgCk8kErTXnzp0LVZxHx3FVlLZtTROQfMlgBMMUiELlqIqgphiqlMiU4hySJk1x\nBjEowg/Y29tjvVyRl+HR+eCDD8jznPPnz6P9UFa+nbGRgielFKZPF4OmVDleq/56e6Cy15G0OlUw\nGxh6SgYDb+PvYlBSfCcYhhA+Wmspx6E3hIRc5/fPRWOHtZthgt9iKj7sGX1UfH/W76kxOMvFf5QX\ncuYxPbED09MYMh4PU/hfgL+69ZqMm/8C8Nv93wC/Anyh//cbwP/0VGeViuqX6zynbTpG4wlZkXP9\n5g2sDmFCnmWgDHXb4PCxDLoqyk2Ayxi8bVEu9Et0dkit2bbDti04h7Mt2juqPCMzCu87fD+ZKS8M\nuVGUWQgtTD6U/FprKfOCqu9lgFbozGysdLu7u0ynU0ajMKGqrHLqZkXdNpSjitFkHLkH0/GEcTUi\nN4FdeO7cOaa7O4ynE6x3TCajWIcQQpbQci7LCrquoWnWge3ZG0GhDkutQTrzsW1bOmcjFhGLwmxI\nfxZFhjGKxWKGMmB9x8nJEfv7u+zsTDg9OqSp69DN6NJ5irLvtGwUWkOeKcZFxjgzFFqTK02hFZlR\nYB2+k5HdQ/+FlJEqWQ/vQ8WnZDu8V/eFHp2ztLZBZwqvHDoLRm6xXMW28aPJlAuXLvPZz3+O5XLZ\nf1/984YDv91AtZczQwLDhio5H/49KMMguJ/bMgYpHuHc2aHDWaBjimG4ZN/S3s1DtLSPIY80Ct77\nrwCHWy//GmHMPP3P/yR5/X/zQf4A2FdhnuTTi9wghpmPi9WSdTvQWVM0vT/n6GqmoUKamtt4v/8S\nhb+m6TskeYtWAwAoypO6qGnKTuJuySo4QGdDtyBROImXt8E6aVUmaT5ZWSF4Q1VVxS5JKZ1YlEhC\ngXSFFj6ElI0DMXOSpiLlOuRcZNUVTCZlXMr9q6qK+XxOs1rHhranp6ex6c24GoUGrUbjO4vCBc4G\n4cHTWpFpQ5FvDpNJjbicg/xMSWWyvbyfgrkprTylqqfXMZ1O47mu1+tBER8GGn6MVN8GmLh5EQ9e\n1R/03sO8gG0j84TytNmHB42bfxW4mmx3rX/tPlFK/YZS6htKqW/cvXv3wUdSKrDNejR5sVjwO7/z\nO+zs7Gw8sPch4O3mVGIBxNqmby/eN3B1ncV1FqMUmdJkSpMbjVHDXAd5mARALIoisP20BhcavXRd\nQ1WUceUWbCE1IHt7e0g2RMqFgY1ZCZKelId/3dSRwHR4eBhTalmW0fZVgOHvQu5rrwjhn/JEAHM6\nncb3twlUco5pNkYAvPVyRZkXGKXZmUxZzhe0dYPvLOOyQgGL+Zy9vT3Onz8fvI4mkIaKMifXpnf4\nNL4Lg25yoyhyg8GincPgGeWaUZaRm1D0JQBt6jGIpJmZ9FwFOxIjk5bCQ6C33717l2vXPuLo6B6r\n1YoLL73Elddee3CmYVseqMAMrvuDPp8aBqWG6seHyaMMRgpKfhyj1cvHBhq9f/C4+Ud87vGnTncd\ndR2IPovFgvF4Gkk/1lq003g82mQs62V8gGQFT1l4eZ/yw3vaZh3ASaPJ8wAeFrnB93n0rh4avipB\n7CHOOnBd8FYyI0VMDuU8je02jil9DDcQ8v5BF7Dx3Llz8eGVNmG27dg7tx97AZyennJychLjfilZ\nbruO1WK+0R9S+8C6TMuRj46ONpiVqdGU14MR9ExGY+p6hdYK5RTLWdj/uu14+fKVUHa+WoVu0MZQ\nZDmz2UkEYcVzyvMMkxcxI6B0aN2W67CSj4qSxvZFak1HbcOkp9B+U9iGmrptwyA3d/8wnbTMOxjd\n4POlBiGUUlt0ZiJwurOzE7ym5TyAjEoNbv+DFDuGAOKep69vP8YP4Cuo5LUHKfGDwoNHyfZ2j2t4\nEnlaT+G2hAVqc9z8deBTyXav9a89vSgPZijEkXZkWZZRFGV0K5ULZBgB+WTllfclu9B5mfjchZBB\ng1H9TMOsH9JiFLqPLZWz4R+A9zhrwzRmws0rspwi02QalPNRIcNpK7L+XFNSEAwx8lnU567rYgo1\n3V73lZUp+FZVVShWMkPYIAopypOGBUKk6r+7jTAjpApDeta5LvIn4qSoZA5lKJAK96jrGrIseE3O\ndhR5htEKZ4eWbnlZoIzujSuo/l4rAoCb9ZRxrUL4Fr9+NfBAxMinrFEBCSXNKx5BmqFIh+IIm1NI\nZ9b2YVQaw/ffdfjH5r8Yo6cPaV/Sf5ac6fpvf37rmE8i953f1nEfZXzOkKc1CjJuHjbHzf8T4D9X\nQf4ccJKEGU8v/cM+Ho+5ePEin/rUpzAo2nWNrW1UJu89ynkMaoOOK+W0QgxyvgvpL6PItSLLNGWR\nkeeGXKv4z9kWvMf37m1dr2jbGrzFu47MGMbjUXxAt9FwUYiqqqJiCflGSpGLoohtz7z3oX26Gla3\ne3cPOLp3yNHREavVaqPPQJ7nrPuU4/7+flztxY1O8QjBY1IykIQhcm9CTG5RysfWduMykH52d3dj\n8Vfb1XhCj0YZTa+8Z1pVVCanzMJ9zBTU9Yr5ao7FUowKyvEI9ADMZVkW267lJkMxNGsVLy3lNaRZ\nEvGMxKCm9RdiGKX82/UZLKUU+/v7LJdLTk9PEQJbU9ebXZfEHU9X2W2Q7ywl3gALz8AkNrbd+vss\n0FFeT3/GfZ9xzHRfZ4GSjyGPk5L834G/AFxUSl0D/jvgb3H2uPl/RkhH/pCQkvwvn+hs7jt4/4V0\nHaenp3FFCCnGMlKYU9YwO+UAACAASURBVDQ960k7Q7XcMKXYWkvT1UzKCq0VXb2myDMKY8hUKKPX\nyuN971J7UM6S98VW2nl8nwzRSpHlGd6FkKRtO0wWxrSDZTLZpXM2urRpq3lh32mtmU6n/WwHz2q1\nwNuO0WgCdiAuGWM4PT2lqqoIOHrvWS6X8YF/+eWX+eHsnQGl14oqD8boZHYaS62tDRhK41cRjxDg\nMdMGLR2VrKPpiUwoF7odlZOhtgDIMoMx4bOj8QjddSF7oKCcjsP34HPatkb5FiU9EH2v6NqgtKYW\nD6YqGSlF3TaozrFqQtbEekvRpzRlAUgVXxq7xuxDD5TKohDAyA6lwrUul0suXboU08fQd9UWpqvW\n97vv8vOsVN/DVnef/LKdhXjUZzeOzabia33/eZ11zk8hjzQK3vu/8YC37hs374Ov+18/9dk8SEzo\nZnxwx8bVSvLhsuIJ9VVy3mmMmRb9KAJz0feTlgyBXJMZgzEq0qchkJbyosCYnHU7FFit6zoqpsSW\nWqv+exiar7iujV7DWYVTKY+/adYbhB27xZtQSsXhNKvVKl6z7lcz4fhLk1MJTUajEYvVciPbkGIJ\nSqlIpAp8BRUNqbjtoohSJSnnMuvLz40OQ2qMUrj+fmQ69J2o6xrVE7KMAu8Vznm8d7RKqh81zkHb\nDdWQnR3G4WVZhnXDsF+5FvFy0mY1AXweMkNphWX6vIxGI/b396MHlzaP0d6HUXJnKfFZf4eb+ujn\n+FkAgY/6/JNmKs6Qn1xGo7Rew0GfXhqPpxFRXy6XIb/c9ZRmk6HzYfrwdurR+b5M2eQhbu15CIXR\nGK0YZRkm66saURgTWq1XsaMPOOuo1+tA3PE2KAPDNKa2bcHr+4qhxGU3xkRjJgNRpQNUs67JdMhK\ndHVDY1uUMhtusDGGUVlFerOUS2sd5hpAT+xxHpUFIDIrcq5cudKHH4ueQ9FiLdHLkOyCUkM1o8lM\nMJZZRlUGKrZGUYxGQBgme/HCBU5Pj8m1wWRhpF1QzI7VYkFVluyUVTwvCUlWmRhHS9t5WhtKtZXO\n6ImiFLlB24GvsFiu8GowaGKoRNkFUMwyvTHLY6gBKaJxKMuSy5cvs7Ozw3i8E1wXP7TPe7rMw1bY\nEV4csMjU1d94zrc5DP7s19PXYLMgKn39JyH78NzFA33jkLZtuXHjBvfu3YuKaAXF77kB4ONqEFuz\nZQqlMpQLClqVOVoZcqUYV2FFrMqQ75/XcyA8ZIXWrBaB7NI5S9P2FNnpDm1P9LF1i4LoViuCAavr\n0IRFeAtS45Ci/ziPxUVgrcoLfGf7mRbgXYciC678umZ8+TLWtrFbswyCcc6FFGEEIHNMXkRC1HK5\nZDqehMGrvSjnqdcrjAm4gtC56T2BzBiUCp2cjdEUeUa7WnF+dzc0jVEa37UhJalUcL3x5GVG1zjO\n7+70oUpLpjWFUYymY7ou9HEwk2CQ5qslXWMD0cj3IZsiZCQ64SwEDAE9eC7ee0zfdTotpJJ7LZ6Z\nYAZFkVEWeayrEM8gxue9wXmgyHs2zPB8oKu+bQTue563jMejsIDtVGNqNLY9j2fhifCTXiXpAddR\nLxfkRnN0dC8w8tarwAYsRmA0nR/c8abpev5+Fx+eTGm0D66j7icLF0ZjMlDKh7y4gdZ2sYRa4tEy\nM6G4x3ZMJyP293aiy09PnzWooMTW9QzIwTvQWqORmNajdWjLnhuNckFpunpNkemQ8fAWa1sky+u9\nRec6ZhfyvIwZlpSYozODQ8bbLXGdJdOGrqnpmpp6uWBnPAmMTm8D1mcd2I5Mhfvg3BCXGxOwgSIz\naO+Yjioyo2jqFZlRdG0dH94sqc0IYUYYIpvnJbkxZJkOx1OOsjJMKkOpHdMy49LuLrujgmmRkxGy\nO7bHBOjje6fA+VDDogksS9fZ2GQ1rXwVRqeAyqYw6HzgOmgNeR6mTJ8en+Bsi/c2Li6+B5Y561+6\nmm//k9dTNqOAgdt4gGyb/tx+Pf39QWHMgwzTWcbmCUKIn3xPQQ8ly1IqLS7hfHYcw4OmaWMsPKQh\nZTXQGBOQbm8lvWjJVMg6FEVBW/cj1LXhdD6P9QGd89RdSANKn8KgEIrW9ai1Umiv0Nn9pb8S4xoU\nYc1TtHXoA9ms1rRdCBt2pzux0EuMEsqACbMTluua49NTbD+8RngKs9lsSD+6oUxc+gw0Td8gRm1S\nhQFMFtztPDd0zUBYatuWIq/IxOh5x950h52qZL1eUmAgUxSZofWOerUky4oIZEpFXqYNPsvQypJl\nhtxkZFpjiuDFtbZjMhoxnVxhtW5Y1B1lEwq25nVHqyyun8VRlb3nolSsAE1Tk2m1ZNd1ZGVOpobx\nfFk+jK4TbCjP8+htARj8BhMSzvAexEvYXs1TxU1X8fT3s7yCx8UIztruLEWXsOKs4z2m/OQaBe/o\nu6PGHHTTNNy8eZPxeBybn9TLVR9X6oGLEFuEATjQQ5OOUV5SmAxjPGVZsDuZojW0OpRgz+dzxmUV\nHjClWdiGdb1iMh1T5kUIJZqW1nZYB1WeUXe2X1k1vvcaXNvh8JFD0faVmlb1zWC7gEuM+mOlzMs4\nwDUvaV0/t0EFVmKzavruxOEhn0wmYXJV3whFeAwRFO2nVovyCNgJfQNaFPr/5+5dYmXZtvSsb8xH\nRORjrbWfZ9e5Vad8760XYAO2KdmWaIBFA+GORceiAxghQcNuINHA0KJjyR2M3AGpkBFYAhlLIGEh\nN0BIFi8Z/KYKjIWr7Kqi6j7PPWfv9ciMx5yDxpgzIjJXrn32uVWNcz2lpbVWZmRkZOScY47xj3/8\nA6Ft42zMhEKrrsK2wZHHgRgCu5sby/4ETxp7nl3f0PcmHx/cthSPaQk/bKcXhU1sICd0HJDoaYKj\nCQ3jOIB4tiFANtrzEBwpH5myaTkO48RQRGwBcHav+tFCkwqKVlCx73uGNJxoSdaNYrMxT+vq6mpm\neFb6t4gUeGHRedACHM/ZAxFKOe7TO/g8hy/s1OuswfteW8cXvcf7jv+yry3jqx8+YGBczbVXjn3F\nFGYr7xetRFgIO2vL7xCriizFLlUvYckI2Gtj8DPdGJhz4hWhF7GFVCd+jEvqq77vOvtRx5qbv35u\nTdCpO1i97kq8WshbC/16TUaqC33N4FuTk+rPumKzpiOrEVkLwxijMJ1kS6LztG00zyINOAfkxK41\nlex1DQLAME4zbyKlRFPeeyZXUdvGJ3KaEDKaRrxaGBac3WfnFk2Kdbl013UndR/n97l6lfWe1Hte\nQdp1Ner59wGcnLM88PTu+z7D8MNmK9433rfQVR+Tsb7E+Gp6CpJXQI21FasL9OOPP+Z73/vv5zRa\nDSnq4lgXPYHt2CkU6TGttfeOtm2IBQWvbrU9J7SbFnB8/vk9h/44x/Dj0cRNJCvbpgURJuNJk5IS\n28gP3r1DfMEc0mTNW8jklCxkCYE2NuRkLrUYqZqcE6qZzaZjt9szpol3dw+zIRjHES/CJJlxGOds\nRyU6VcBxpjm7hSYMNtFTNi4CPuOA4D3gZ0wkIxCL7NrDA13RSRiOPYMXNE1st9dsN5E0lIzC7eeo\nCj/26iXf+fwtw9E8IWKkjYHjMLDtDMQ9DoMZBnF4cYRW8MHSr9OUURFScgw5sWkbslqmaRx7q2UJ\nxks59IXAFRp8WVxrIlPTNIx5LH8Hq2mpm8OkvHjxiu9///vs93ti9CcU9GrU5yrRdUZiXuBieMwa\n/c/ZQMaT7APvz2KoLj0m6mPnxz8FHj71+HkG5Cns4j3jq+UpCAt6aygjkPn000/59NNPGceRX/mV\nXyHnzPPnz3n+/LntALq0lK+74ZoHkNUozfYFK7Ex0k0IAeeFtmsQB/cPd4TguNptaKOVSO+7lmfX\nV6TS8DTGSFt2ZnHYeXMm+nCis1gBxanUR9R4uG2ioQuquAI8QkZzngG7ypOoDWMsPLLYfRpG8pSY\nhpE2Lg1XUgHMINOGhToN0IZIFxuLxgYLD7q2JUgBXJ3F/w7jEuRpZLvdWNu7lNjstrNGwbu7WwCc\nF5oY2HSRZ1dbbt/9gNc3O57tt0RR8tQzDGZQHx4eGKaECxHxkaSZ43Dg/uGBlMZi+DJOEl0Xubq2\nczTesYlhLp7SyfgEa75E9XJqeDl7PmUirUV1Knh8c3PDRx99NHsS1fNcewvVo6iGIqdVj8oat9eF\nVlmPX9aLOI/7z/9fn/88HFgbk/NQ5tL7uQ9f6l8po/DIXQOmw4F3b9/iVu7f1W7P69evjWa7bWeX\n2xD0VWMRXTQa7Z7pHEa4chOD80TviN7c2bZpmMae/vCAQ2nLpJQ5/CglvysKbi26WpOCqotqHooV\n+MSwuLJLdsHgx+q+O+fox4XxOO/8rHpYIkhWSyOiOF0EYPJohsCLEr1Vfq7vrVJqHCgdpoDozAA2\nwbwaIRuOoJPt0imRUNTJiuTkmaaBGKyqNAbBoTTBse0ay6JMA+Is5DONAxPYlSJpt9lsyCibrdVw\nrIVogjutR6mFTidhAkspdX3deqevWac66ryoNGk4BRLXBuE8tFu/R/nn/UDe+fMf4j2sn3/fzv5F\nzz91PR84vjpGQRbtgDl0SIlPP/2U/X7Py5cvef7sGS9fvuQP/sE/yE//9E/P8fF2u6Xt4gnQCAtx\nSZPFsJuuY9eaCMo2RjYx4ETxCgGTQmtjw3Q8EiSTxyPbJrJtG55d7Uljz9ibUIcCqGU72rYls7Sq\nW0/UquA0c/XViqyiqzF8aRyTMwqzFkNVSupiw9QPJhfmfInrq4ZjKapyDjSVMGU0ALM0TVmXlNfY\nWXIqqUSr8fCF3t1Ej+TEpok03rHrWkSUsT/O1OGr7TXjsUdTpomRXdfiJPHm+TUxHwlp4Gbb8Hy3\nw+VEf3/H8XBPiJH745HDOBBiy1S+4/1+b9fk1IRcSlrWOYgemuhoY0Q0W6pSsxWylddXb6oaVeDE\nmFa2ooVWi1dQi6eqkXAXdtI1J6Ked5bVq5tP+Vm96DTzcHrCy6nCSz+Xxvr59XuuU6aXzvEl8YWv\nJqawGm3bzl9AVertug3393dzF6Kq3Gtf8iLIMU2Wg68hSSXlmGfgjZY7ZaQYl5CU8dizkq+h27Q4\nJ7imIatwKE1Mqlsq4kgoeTzOKku5hCrOm2DsGuxSPVUwtu/rVDmoeho5T8RSCaqq1ljGL6IjdUeL\nTUNKS1+JRYZsmfTeOcY0IarzXI1zNyTmMKRt2zkD0jQNToQ0WpqUVEhDEpiGia6wHp3zaJ7Ybqz6\n8P7hnufPrQfF29t388L0pUak0re1VFHOJeUl7HIYy1TIqFrqtG1bkvZk1bnzVtb8SJxlEYQ53elr\npeqmVEjWknJYxGzqPV2nJOu518ede7Sq+riX1Dok+K2Mp3CG9e8vGh+S+lyNr46nAEDJDNRCKO95\n9uIV+6urGTN4/fo1IThub2/nyr0KBJqnYaW8FY8wS7+USzuU1heXVxw3V9fst7ui7SgED9tNi3fw\n/OqaNgaa4GnKYhyGAR8DWUDCsvO8vb3n7du3RoAqn0bTiGKTPjbhJDOwkGmWqkARmQt7AKIXxslS\nr00B0ryDtglsugYBmhjp+6VRrO16Qn+wLszijBA0HHs0TcRgrrPTzFhCAbKSpzTv/qFoD2yKS1/r\nQQzIVbbbfaFIw9QPOFGCE7rguOo6I3v1PW+e3/DRs+d0TcOxAMUiwmefvUWzEFojoM0LGlNz2rad\nKTaJM+4ASnCOrll0K7y3RTzlTKpgICz3ACn6GJFmtfivr695+fIlv/qrvzpXnNbXrsPOOtZh6Npz\nqMZjDi/WC/WpsOH8+bVHsf5ZH7dezPVczi18iTXYeYlp+UXex4Xx1TEKuhTr2B+AOGgNmAptw098\n8gkfvX5p+AGZZ9fXvH7x0kqjs+kmBofFo9gOHWKJ4UsLeyXTtZ79piF6JbrMpnFsu0AbhWfXW0iZ\nqR948fyKTQy0XeShPzBM/VyZ6Shl2jFwe28FSuKL7sB4NO4+HiZA1HblKeNcQFVwscE5ozBLVkSF\nlDLTZFoNwTlElSYEhERKAyGIKUh7wBmlWDWVEMJAzE2MBAQnmSYEmhDnFm4OwWsmimEG265lE6z+\nY9NEuq5ht+l4dn3F2C8t6JpuQ38cuL03AZu+761tnnOMSbk/TqgzarUEz36/ZROB4YGrzvNqv+XN\n82ekopx1HDP/37e/yw/e3nNMI6GEVn3fk4sqt4VbnjZalshJRhzEEAgSLOyiYj3MuFKdQzUkqEZC\nNfPixXNevX7BN77xjblVXw3XBB4Zg/V8XBuGasgfGYx0QSm8uvrnC3PNeag1PpcWrypzrHRuMM5Z\nljWkOPcMvqSn8JUPH8DixpubG7bdhvu7u7nJ6fEwsN/vrGJwSLP8+xwDInY/KMCTM4AOysQRN2sc\nVmTaO8cwWClz0zR4F5lET1qcy2SxuneBNfll5g2UqsDK7KsdkeoEMwqvTZyE0bBT7WCt1h+hehTr\nXSnnPNN8XSEFQVGvzpO9j7NJplp3MTcXEjkxL6IakeiNBTlzEwTIiSZ4utiAUXdoNxvuS8Wn99Za\nbpwyN7vdPCmPxyMxbBEx9WTV0k2r7fBqAjjP9jsejgfu+4FpGLh/uKWJmTEYLwPvGKeEQwnlMTkT\nm7EFr4CC9+S0PL7evZ2cuvn12l+8eMF+v6fpWppuKalXdwrIro3BuRdyyZNYj9kwrEhQqycfL9jZ\nGDyRYtTVa9ePc/q4qiIFV3mEW3yJ8dUxCuv23uuRM7vra3COw+dveXh44Hp/xe/6Xb+LX/u1X+Pv\n/covWx5605hwibpZkh2YNQqjFxoX2GxaNm1neop+0V3QbFyCPiX6Of1oN1gHI95MatV8dw8HrnZ7\nfIjkFcCFGEsSMAWiJpKnyeTJpoIXFNVi8W52e1VPy3+rO5oLtrDeDbx3thCy7fwpKwRl121MXg6b\nWmkYyQoh+lJ3gaUgg6cLwQzkighUJ3vSjCbjTjiFpokchxI6DCN3esf11u7NOI7stxvyNJGScv8w\nFLDQA4lYQi/vPZucyQIuPOPtQ8/tu885HA589ukP+NrXvoaIiehU7cyhpGhDhCabypT2PSNpRf5K\n+GL06vdd6zZEF2ozYGB0G4lty83N1Sy+UovEanpyvdOfZx7qcXCalaj/n/+dc15KsMscW47Ly685\n/Cjd1Z8KG9bjAtYgUvkSq3P8EJjGV8coABclrdzSlKNSm51zfOtbv8Ev/uIv0h+OfPOb3+Tb3/0+\nh8MBXzgEa8Q4BosvYzANxuhdCcusWEmccHV1RXCe73//uwQn7LemRHwcrNagFvq8e3dHSlYtOUwj\nb29vyWq4wnEY8SHMO4RHiN7jyDhvHZnq9SddJmGaCslJhFh2yJwzY150GCorUMQIP6ZGZLiGAagy\nGyincBwHHA7xnqG38moXPduuJaAEKX6AAmnCS5h3WFToQiSrEp1DimuPE7poysfjAFe7DlGdOzXd\nHw48PCRTsappVgeCeV2Hw4GhH3i+23OzfUPOE7d3D7z9wWcz2Ng2TWE7mhenTBwntRqNHPFTIqsS\nokcHRX2N6ZeeEg5zD6uRNe/mwE/8xE/wsz/9zVleP+eMeJmzU+sFvw4R5lRwNf489hDWHsXaQFUv\nYD5W9KJD8Gh80e5+Kcz4bRpfMaPwxFCFUjvftu3MZuy6htcfvaQfTPXo+vqau7fv5pfNkwLDGcxw\nWuqraQNuGpbuTtNSELTZbGwXcYu+oyHWrXVnUmEYjhz6geF4oO12NhGKOGlKiV23WYWCdVdZdqE6\n6VKysKdrilLUKmuQUpr5D5UbsAbG1kQtWCjd6zlX8QTShG/NIHrNeAVZFXCBpezElea42YRUK9/C\nOcfD7R3Be5w3/kBVUDoeTeLd+yKFfzzMHpHVI9nnnlmXJSUqznF9tQMqu5KlaY04FMNMwHgLfWGE\nDoWLERvPmJbQChawcB1OuFIfsdls2O12hpOsmgOd/16m3eIdnDMc12Hdk+FD+YzVKKiqpY/XY40T\neA9JvxwPQS54FsuFPH6fDxhfIaPwPuFLISF0RQ+x7pRXV1e8erUhNJGfdYFf/we/xt/8m38T5yHG\nDjQZpdlDF62/QBttogWJdG03YwlV6u3+nVUdNj4YSUiNZguLi7nptmhKkGsKceR4PDL1wyxIalrK\nOtNw0zjN6TsRYUzJKiwxjFXIM5lo7E1DkpRx3jaWaRyIXUfwjilZP4phHOadMOcVsBYdDQ2IZ9Jc\n9CYnurYhSiFfpSIt5z2x6+inkRg8KjD0R8tuiOKCfb6rTceufcHd7Vtev3iJ98LhoehMhsjw8MBu\nExEP3XbDVBva7PfzvQvBuCReE6KmVNWESHx2M2eXHB1OYQDujqMBqNFzHAO7nWN8e08TTYnJiWPX\nBMYhQfkO5+8pCK4YOFCudjs+fvOGV69elUKpXIyJ4vDFi7Mpt17o64W/lr4/Dy/qsWu+Q0oJz7Ip\nzEZlPb/XBqBSpfNicIDH0mv1dav/Z/ziDAS1l7sv5Ul8RYzC+QWvLZwHUXzToGrcg88++4zrq52p\nLnsrRLp5/oJf+/v/AO8dzhn9N7jGMjViLD1RLMUYI6i1CHNa0mqK1TRsNsRV6zQjJ23YScPtsYiC\nThM+Fqn4nAhNi0dmzn3XmHajU1AnTGNVbm7QEiaMx6PJ0jtMqzAr3kkpvdfiAjsjI6VsmgdFVTpK\nKQBLtruawEleeTalsEmUxgUm52CCNniCt/QqMkHdYSXTBIeS58KuMY04D+NwZNN2MzGqi4HD4R4n\nxpoMRfh2t9vjpDAMxWL4SnFWVba7HVOynTLNDXltwQYwroVmDiV9LCmx2204jBPHbBkGcjYZuIdD\nMZTmyTgJ8/2qat0eXwymLZL9fs83v/l13rx5Y8YOA1mdrHGEhcSUs8X4594DPI03nHsNa49ifgwz\nROKKZ5J0fmae906Qko1bZyQ053nR1+dOrsU526w4wzy+jOfBVyklCfCk9rWNcRxn+bGrqytevHhB\nZQ2O/UDwUnoE2vGVglxj8q5p6VornZ53VV/TQsxAXte2tKUM+bwysaL61WuoqLYds5B+7HRayriX\nOn2yKT7nnGcxjkq6EbFKRCUxpcFqNtJk/ApRvDN1pOCLVJoAyY6JzgqbGh8KtblyNKysO/jaedsm\nJCwUb9FsXIwi9NLGUGo/fJGpF9oY2LSNZTmclVo754wdeOxn4tQilmr8kBp6HA+HQkZSEI/iUJIJ\nvjgL67yc7qpQdDLjQv6KhYQ0sxbVQo+6S4uICbrM88nG65cv2G223Fzt6Q/HC1mDx2lGZiH/s1mq\ncvKzPK6Pznv+2JKZKD8nx68Mgax+VobhfJwsfMxTODEcYIbiR89TgPcZg4qiOhcKitzy8ccf85u/\n+Zvc39/inCv053+G29tbvvWb3+Ht7e3sqlf++2K5E855mhgtvUd9vLjsztF1nQnEjmo57TGT7w/L\nrqCONnp8bLg99AXhl3nCNk1gGgZUMyEUXGCy3TkNxiw8DqaNsG07spwSZ0RMmMVETbNJsylEJ7Ne\nRBuieZsFqU/JGuVOOpGdeViarSDKeU8aRyS3qwWUaYpSNa6UaY/TQpLyjrBp8SIzILvfmnhsGpWb\nq72pME1ToaObVH2N4VM2I1ul8IbDAbxju92RcyLlCU0jORsW0zaG5Yw5wTjx9nBgSkqMDdutoIcH\n3t0d6GJD9oF7PYAs3Z9SMjWmWPQYq5HYNDZfbm5u5sY6Vd/Rvrea+nUlPJeVd67zY2YEsjFcVwtP\nJKzwh8fdsev3Wc93muKsS7AWPa2OmbEChbIZnaQ5K16Rc5HvW0LwtSL1JW/nfeMLPQUR+U9F5Lsi\n8kurx/59EfkNEflb5ecPrZ77d0Xk74nI3xWRf/5LXc2TF+EhZ0JjzMCXL18Alv77yU8+4Z/6Pb+X\nn/u5n+PZs2e8efOGm2dXJYwwgRARQ827GE4EOWYPouvYxMh+YzUUgcxYxEko8m3DMJCmgTYGxtFa\nuU0lxfTu9pYxJxIJ52tzlIY0KaKOccqM00QiMUwjx8EqCJtgIi1N9GSxxXwcj0zTQB4HmtZBmmgd\n7NqWKEoMQnSKEwsjtjGyb1s0ZSP6eNOJTGkkjQNeJ9w00Xrrr1Dd69j4uf1dExzbxrNtPF20LI0R\nqhKb2BBcCRVQnl/t2XctwSuaJzxKFNjvNggWAsXgQTOxGK4uNnaPNxu2bUd/uEPTaGXUsaVrtzRx\ny8PRsjy12WxlqubJjJ0XV1Sse7JkNqUJTtLFk3IeplotK47OORrMwG06U+YOTSRrYirKXHORE+ZJ\nqS4VkZUyXeXq6vEVY6i1LiaGO86vWRv3c9zhJPVcWLwL2UmWHxyalIvNYVchwTkGUv4gT9NpBecH\njg8JH/4zHnedBvgPVfV3l5+/VC7uHwP+JeB3ltf8R1L7gP9Why4obr0Jbdvy8uVLbm5uuLq64ubm\nhp/92Z/l5uZmBu1UlaZdaNA24RYhEzhFl5sQCxiUic7NtOC5fsFpUQfuEPEM0xLDNU1jkvGlijFP\nCroIqhgfoZ936k3XsS1ZDlgKbkh5RvejdwbGFY0GKZ/fi9UweLeQnGYxlBiLVqCFKL5kYLwsIiWn\noiwGfrkSftWUrd3nNHMiqhcUG2MakoxoVSXw1izCUBbwOhtQ72EVR1m6Yp3K2c908NI1CoquZYzF\neyq7q9iubee3deL9op1Atl4dXjOffuc79A8HvDjGflh2etHV4jzFAy7hBvXvdfry/PhzI/BUWFHn\n3nnm5Mmx3vF1CSueMkDz31/CIMAP33X6qfGHgT+vqr2q/n2sKczv+1JX9PgK7Jc3vneNvdu25fr6\nmq9//esGiI09Nzc3hOC4vr5mt9mudgAYp/6kz0GtmagTd5omckrsdjs2bWcT0rlZ3HQaepoQTwqX\nakw9jiMPD0ckC8eHnjQkxmGgbUwefW5Dp0t1YvBFpnzoy44OASFglYub2BKwhRzEmZtdBGHq4g9F\nkahpo+k2CLN+d+QO1QAAIABJREFUIQm6prWqymDeCDqipcbBu7iahEbhqUBl27bcXO+LqKl1Z153\n3A7OqN4xmo7BNE3FSNp0qmDlus1cSmnGQ7w4usZShL4YHms6uxgFY1yWz+jMmOecabtYyrWNX9D4\nQCxzwjlvTEhK6IUSvdJGx3D7lr/9V/8av/x3/x/a2BF8XJGV8rwZrzeddbXrIzSfZRFWJa9LhuHc\nKFzKVqzTnevS7HU4oMUAPJU2haVgaw5f1tjClwghfiuYwh8XkX8F+GvAv62qn2Edpv/K6pj3dp0G\n/g2An/zJn7xwRL0h65sIinD97IZ/8uUrrovc+Pe+9z0eDgc+++y7vH79mj/wB34fv/RLv2STfJyY\ngvUr6ArHwYp8Sukyp30JRU1x2DQAE40TRCJtjByOIznBdrclqdU9JM242ND5wKbd4rD4fxp64yo4\nZVKLNcnMRT0mfS64onkQfERVOB6PbJvWip2cWLdn75kmNUPTttawVsAV3CCnid2m4zD2iCghOsLo\nTbfRwa5t8M7YjNKs0HItE1MW+bbQlCrDDNd7a3vvPHNp+pRMq7HKq00F/FXVme5tqTiZvQHvDMA0\n19zqLqY0QXalnLyqX49IwTaCOJLzVvcShCxHpjQyTaarkMfMJgb6lNl0HYeHo1Gbs5jerQgxwMY7\n9tHT5Ux/e8vn3/8+0zDgGvPpNCsUX3a9qE7HqTGoi3uprnycZTib6/Pr6u/13+dVmutjT67igqex\nfo9H171+v/xEyv/C+GGzD/8x8FPA7wa+BfwHX/YEqvoLqvrzqvrzr1+/+qDXSNFkvNpfs9lucavu\ny9M4cnd7y26zNRVhtckTopsbsa61D9fkofUOMKPepXPyugwX7OYP04Su0l81LVa7Vw3DYCknZ6lD\nj4GdIXpCtMYp4jDXdZWTdrIKW7zpSdbH6qKqugkCoKVxjUgBzdJcQ+EKvk82rQKTts+4InFetRXq\n53Ziku46JcMTsiI54bSAj6XqMJXS57k02Tmj8q7uperpDrkuY57d9gL4khPBWSk7nC7MGnJUg1OV\nmtcxvZ0qLZ20nFVIlogc7zKNN65DcCXxJ3YHtXg2a5LZl1nYOVtm6KkF/L5FvJwjv9cQXHr8kgE4\nP8f7PssXjR/KU1DV76wu8D8B/rvy729T1+n3EJm8o910pbgsM6XE52/fzrjAd773Hb733U/5+OM3\nfP+73yM42BWGYlNcX+9OY0HnHBICQiaNFpeqsx13SBOaM0201OV215HFc3t7y/3xwHHo2W63qCp3\n7z4vvSUd+82GJjo0w3EYCU3EO08/jfPErrupKKTJ+AhdcHRNxRYmvFjuyhdugk6JTdOWz2t6klNK\n5EKOStNEVmXbNDRNwOnI1aY172WaChDoOA5jKbIzg2QZDeZ4PaWJGDwiFlqouEVROVmzmlS6ZFHw\nnTrJ0zCCN12HWOjDZjgrCAbOFxGUnBEpDMUSymWUIWVErNlPHSGYtubdQ0/OkHQiQwEPg1WZFiq5\nR2lFeLbbsG0My7h+/oxut4WwCLl6KQzQShFYeQN1VE+gPr4W1a1z6DyWX7vx69dWvYv18+fvt/5/\nNqRiDXcuHVuPq6zX9aiPfZkMxA9lFETkY126Sf+LQM1M/EXgvxSRPw18DfgZ4P/48DNfMga6PO49\n4gUiJdZPbK+v2d/foznz6tWrmT/wT/zjv5P/9X/6n0nTyNV+a5V33naLGKOVFGeLJb33uFJEM4nF\nYrkoKIcx4UV4tttx3w8gkXeHnu12Rz+NpkUwHJgmUzTaxoLqo+RxABzXe8M3xmS9JnyIpczbmI/b\n3ZY8jEwpsW0b29VCsHPOE87Nu6OgxOBLq3jLwkzTxDZG7g8PNDHSRjGyklRlpYDXgqST2bQNh+M9\nPghdDKzJM9XARueLstGEb+LMB1gDs/UnJSNotSGQJM+T0YzOwpsQEbKYdzN7AnLmWVB3uiIRoJk8\nDXgcbWxIG+OsjMnu6TRZSTxYAxpNA6qJTWh4se3ouo7di2fsnt/wyTe+bloYmgBL2Sbs418yCNXz\nWT9uHsrCU7mUXVgf/yQA+MT/54t+bXyqYTkHOmFhLuaV57uIzvw2GgW53HX6nxWR343Non8A/Jvl\nw/xfIvIXgP8bUxL4Y1pRmB96CGtcYR5a87PWffn29pb7+/sZG3jx4gUvXrzg/u6WcezZba3DkcMI\nRKJLWbIBW1afjy5UVhEDBBXYdC2djyRxBG+xvtNM1zaMU0LTYB7C1qjT5u6bS1urGqP36DQRYlO6\nPEPGEZxjQkwDwlkhVTCRAPuoYvfgPO6cv3CHXWcyLYJpmqCwNp2YwpIXR5+nGdiKMTCliFNom6aQ\nwhawyyPW1/HMla9eRd3917tmztnEWc8Q+fr36denjxZK7d6lqhQVCQu7vGlFDP0IWsRlvScpuGpM\nMI5BbfITBdrYsG83tI2lPXdXe3xjjXl9LCzIVT3KUzvwGrCu99/7RfnqvMR9/Xv9ec/vyaX7cv7e\n62ubd/yze3oCgJ6957lH8iHjh+06/Wffc/yfBP7kB1/BFw5Dx21kUCGPPf3DgZwzbz/7nM8//5xx\nHPna175G3/c8f/6ctm35xtd/B9/61m8QdOLli+dE59l5YdM2RO8YjgdEqpCNVUw6BznL7Kq3wZur\n6pRt4+lHZeMD/d0teRitGjCIVSA2kZv9FikLK6cCIulEcMKYs0mYOcV52G035u6pkvJA6x2t90Zp\nTolN40FdSVsXd7soU4tYnAyeh+PArttwe/dA9FPRhVCa4I1T4HxptT5ixUXWQPdqa/0c7+/vud7t\nSclIUUFswjdNY1oNU57FYCpxJ8Zg1ZJFqi1GK62uEuvRB3LKSGGPWi2IZQhcSQMqxe1daTrURadK\nqdOwtnEiniE0fH73wFQas2QnCA29WuWkSd0VHME5nm227JsOcY6mafmpb/4MVy9ecDeMpHEyz1GE\nCWDl6q8NWTUAdXeuXlLOy8ZRN5FLQOK5UVwDi5eer8ecG46Ta7sQclQv0rAhmSnsPwyu8BViND41\nziypmF5io4p3nt1my/X1Nc4FRJTDg7Vdv7295dXr1wzDET/2vHz+AqaRUJDxqrMwYwoltnTBI8kA\nt1h223Ec6drIi+trPnv3DlzL8fN37DcdiuN4fCA0kRc3e17fXM+Vg9NYcvTBtAdkHMgpE52n61qc\nZgKO+8MdHthsGppQvATMY3ClrfqEI/qMZD+3p/PevJtd53noj+y2RhAapx5XQDxzvRN4I249PNwZ\n+CngisAMWvtOLJ5TBfHGNOG8zGFLzhZO9X3PZlNk4Mtk9UFIk6XOYhNmrUn72lY8/arKrIr4paX8\nSckxiqrpMjTB0TQdWY2r8P13txz60dSfFJI4soOxhB6N82yi46OXr9jEwN2x5+VHb0wkdpzoYuD4\ncCCHgBQyGxVb4BS0q/diXT699gS+0J0/O+f676cwhfXjdadfF2GtU6X1fc49mvVrv6xh+BEwCpeH\nb+IMDm33V+Q8cf9wByKEGHn16hXPnj3j/u4dn3/7N61WIEYrVPIOLag3rpYPT0jRIxzTQJZMLqBd\ndddqWfX0MOAxsGxImVAowJ2PhehjhUKTJpoYrX2cFxr1+DbO56qTJjjTXYjesdm2eBZ5MO89qfDk\nJ82IJqL3TFMy9BvLgjjnICfGcWK/3dii97WcWiFPFk4o5HFCYgNkNOus1ZCKqKzTEpqUyZRLKtgX\n9B+YO3jX7lQ5Z6JvUO0LKm+3rRobkcUA11AIMpoyKssxqmoi81Loxc4RRAxVyondtmGYOqJ3fPcH\nb3HicU44DiNQvRjHtjMV7rhpiCI0O6uwdd6jSgGrnTWcAQsbWb6TU9dc5nNLEZCRs514HTasMyys\nwoVzT8Kv6hTOjcBjXOPUeFSP5eR8xevU+TM9Xd79vvEVMgofnkedhwDemUuqjpv4nOpZ5Jz5qZ/6\nacbhyOff/k07XGxii2C/VRmrPoEEc41FF4Xg0v/RYnelbQxJH/zE1bZFfEM/DORgjUx3XaQLwpCU\nGBzaOCBZ23sfoWsM8U/5xLon57nabWdwz7vFzQwh4FVpRNhfbUmaub29tXLoyZqnhCzkEHBkDn0P\nOdF2pl3QRE8aehILExPVuSuUIOTiSjdld+/7Hgkmf69aeAylwUxXQM0qIR9jpPGLVxB9mNWgYclm\nnLNH6xco4ooBWVx0cQVnKffBZZ27bssw8er5M7IK2+2eYz/yrbdv+fzuHlUIZFwhn+33ezb7jnjj\neP7yNS4GCzMAKTXp5rBUd976iZ5mHk53+5TGR+Sl+bovgI5flE5c7+6PwgQepxovnWP9WvSxx/EU\nXvLU+AoZhS851jellv2JWlZCi4S4E7Jae/JxHOk2HZIdTkBYo9zr4pXH8WD1JCxeNXGPbWrIOMjC\nkE36q2tKazqxiVarMTNGj/bB2IZpzLNQDCS6ZgHncs7EVaOZuXelK81NnNGep2miNodNqmhOJ0zC\nNlpo4FSR4p6LGJ8hzazIxzTdma4sMjekMeOl1B4VNUW27pu5/F6AN2AODc7j7jp05YnVc1Ssoeos\namnbq0AbPYfBCtpu9jtiHDgo/ODtgbEf57TdNE1ksXRlt9tZWCgeLQ2DLhF0Li3K88VeDfn5/+tz\nrD+brh6/ZDQ4+/8S8PgUALqeuyfvX/8/m8cfOn5EjMK5F+GWxyqAXG9wcaF2ux2vXr3iG9/4Br/0\n1/8q727v2bYNTfA4VciJLEtHISO/eOs54O3ciREl44QirtrjHWybACXlNnjlgQnnAlEU5xSvDtfE\n2WjXnaUfjjQhEDaFiuwUXCg77imC7dTIWqmUk5vupGUPtm2D35gasS/yXpNm0w5wjuN4xLu9FYA1\ngXfv3hWegLnPAuQpEbwt/lm9Sa3IyYmW+odC5pJcgLWM5sGIXaWLFFq8Kbd8T845psF6K4iaXkSl\nkq+zGPYKnbt1GX28eGkp430oSlSl30QITClBdGRxTGoA6ugCN2/v6B96dBxoRHh+tefb3/42V1dX\nbLoNV9fXjNME5+pVnC/UhQtgyts1JV43CFk9vwCNy+tXi3SFU5w/p7qobK2ff8owrDGF9bHr81av\njrOQ4R8yT+GpkGKVkaguUy28KQYiKXSbDc9fvWR3tef+8MBDv2Ozaw1P8IFpWL7Y+pOyMk4ZzaVV\nmioopfIv0gJTMDKTqjIVefhxHIuFtl2V7GaMNIQAktm2V8woMdCEhpwTh8NI2wTSVHgIueyOYlqJ\nWgA5gDQMxv5TpQ2BcbCKze2mtbui2foujAMEj2TH9W5nWhBY9qDxgYSA5pMFUkVF2rbFiwPJ6DSh\nTub0p3OOrMlEQMrr5oKsaenmrV7IOeG8AcBNaHCrzl0igorJxdVq05xBJRfV6cL6RIklzSk54Vwk\nBDMmiYYkjtgbeSulRFfk5tI00nUd3/7e94nPn7HZX1kWg4UkdeKiixpFegXqpTSWaz0NIc4XbTUQ\n87m4nHVYx/7zPFgZhvMMTB1r7+QchFzfz3Ow8/y4Dx1fMaPwobjCqXuECIpjGA6mYpxzaVqyZbi6\nomka7u5vy6FS0k/nVW6+COuWG8xiLFAtAJ+h6zkVVSW1Bq2SrXOTupJlkNKnoBSI+hrdkK3mwkH2\ntgiIQko2SSuBqqYGu9LUVXMmYB6F8wYg1tCi1nC4YA1mgnc0bSQWgM47mKZk1+CYDU7wDlUpwjFK\njH5mOAKkXBaEX6pNDcAyNGLShCvA4DBYqXithxWnSILgg90ngazT6a44f4XLbhZCnOXocYK6aqyt\nCtI5h1IWLxC8Q9Qv59HSMwMDVofDkauXL0F8MU4lS1Ba3FMxxLPxVCag/l+N47pv5aVj67nWYcM6\ny3Iyo1evvZSynL+XFRvykWE7u/4vm3Wo4ytiFKqL9iXHKnYS72m3W9gAOZOGgavdlt12w8/9o/8I\n/9tf/st8/vaWlxWFtsIEpmxfbEYhW73BPDGxQiXxdn1pHPFB2MeOaTTJ8mmylF2jRq6RpilfvOBd\nyTR4cC4uMaszZWf1oOrY7zYMw2SqzqW8WLU0tBVnUvEoTYykZO64ikNalpJub81UG2/dnGJwtAXj\nsHb1ZQ2ItVuryk+mdqQr197a2AuLZwCg2U5Qc/XB+dV9Yv7+nLdQyxVQ1nbEJZ0nIsY+KqsxrXZZ\n8bOTTmKuU5p3bxHBq4VLHlOvzkkYppF+OJBSRoKzBjfAu9vPeR1+km63XSjUqviVBNt6LjmRs4dO\niUjnsfvaO/gi1uClRf7Ujr7GNtaPf9Hr3/ee/xCFD2CT7QIs9OhDFnmz2oAlNgwPDwzDwOvXrxfw\nSyuRhtkgzOEDxn2AhuxKLjibXJktoNPcNM4xjjKn6Wqz05QS45ARyu6YMVd95QJ6lCyOlK0+wBGB\naG3pBEKMBGcdmAAjAxV03+olDL+QQkW2OgOrSmx9KTFXELXmLtNkRkzVWJMOz0kXZ3+a75YKKJZe\nmKUNEyF4ROxa5lRo9aqa0rpOl0mb0Dl1zOylKZAWb8wvO+3MVTgRFnHlEouGpSsuvQgqxpkYk+k4\nbmKDz5nUH9lttoj3iF9K7it46VYLSoH1Za4X3KVQQWSpM7hkCObFKsyVqOfnmJ9fz+Azw/JFbv85\nbgCn2ZJL+MOHjB8Bo3BhnH9ANTAOsIlXcIZqwd+9e7e4bU4Q9QiLG3Z6Kl0tDocTj6iQNeG8n3fK\nahTWnabHNOFcOzdNTdOp9/OYh27vH0JAWHbM2r9RS3hQv9DofPE63Klxok5ue6xpmlmgREuHpRCC\nyZxRkf3TyeMskbL6fzEK5ztOfd5zioAv17QSQ2VRQZ4n5jqMUH3kwT81gRcQ1vL7qSy4GgLVzxG8\nFFEZXWlGlvf/goX2FNC3Hpd26HPA8OLnfGLnfsrwfJEncOkcl8KIH3FMYT3eUym5/pDz/8UYUGSy\nWdy7+7sDNzfPkZreE1cKaJYhYl5C3QXyDApFo+XqYK64X9qd15udCoHHjxXPcOQccRtbWMMwznn8\ndWyYkxJDQEWIjUOnRFeaodTmJMMwEHxbXN/JPp4Yah7cwoVvus40H6OlLFNKeFFi2xl56qwk2W6x\nnngHdXMWH+bPiPNIKf4BirahY5wmky+3W41zxs9IKTH1A7vdrtzZRNUwNAzYtmS77owT/yiGrtWU\nTq32woulkW2xeCP9eMc0OVQnDv2Alm3XjGKg8w3brjXRnM2GZtPZ59SFgXhyL8Qk5E536lrwdLoo\n1wVh64V3vniFU2DwfeHA+Zx6Knx430I3b/exB7HePD5kfIWNwhMfQgRIi6+3HqrMVDqFh4cHjsee\n1x9/jV/+lf8XVzs6O0Az3kfyVG+Y1ddnVYvXvUPF+gEkTP/QB1caoNRyXgtFvFpX5KaEDwBJZS4n\nxglGy9e5q3Tf99wVIVhrReuQaAskFMalwyTpwQyCd3UCl1x7sP+rN9GERdZtllsLHi0ko1m1qRi3\nLBNgrc1CtLRk3/fGf3Am02ZeyQK6xtYMTpUon8MhFwjBkVrTYYBSTTgDhIuaedKFd1CVrisaLyJI\nLU4ro4qr1gWaRUv5mDAqTP0EKZOcM8Fa5/He4X1ks9vSbbcgModv1WM4mUI5nXAKbDEtZdvmhSyd\nzes0q0xNrTEIzBuOZJk1Pk+nqc4L+NzLqt+frI6tZz73bNfHr19/ybP4hz98WI+y87C2hOWxcRy5\nu7ub3fb91kqY26YxBL03CbOM2k6jAGLNVhRSUnJWfHCoDyX0ADDhEBEPuSLaReuwVnmrzrurgYta\nJpItuKZpuHaOlO06awMQWBDmtYWvgGJFvGGZvGv3/Dy2NPBwEZYRXSZX8LaD116SwEnn5nkSrhaS\nK70bnfelUKmmF81bE1GQcp2+AJxZyFoju0WC7NxV9+V1KWXUmSTb+fGqRimv1wiLtqGmpTzbu4gL\n3rgOfiFQXcIK1rvv5R34XGb0cU3Dye5/4fn1d3Y+1obhqfG+EOApr+L8mA8dPwJG4VIYIRgypItR\nKPLp88+4uOtXNS15d8dWN+y2XSHHeFzhAWSFVPoUTtNE0Su1XPmU6dOAaCZ60x+IRbZdnaLDWOiz\nK4q02C5dpc3qDrNebJbzNwn5/nA0zMCZ7qOI6S0uuWc304qnaTopWTb8YSmSWXMvcDJrJAYvSKUd\nI/gmzmFNNTbVKJwsIOfmHhjee3xTUqgnrrOgrrjVvgCMYM1WnJKSGdHKOMy5QMgiuJLZUBFTiqqY\nhfcnLvXsApd7CViKOJtMv8N0F5qmodtt2dw8Y3fzjP3VzSPj8pT02rmhql7k+fOXdm3Vov9wwfAs\n9+nUADzFb1i/phxwGhbwfoPzPszii8ZX2Cg8JbhSRyUHlZChwsfFUFRXues6rq6uePnyJd8vjWIR\nIaMc+4G+CKFoQfcPxyOff/4OVeu5ALZzT2NPG4UuGCty27W0MaKFU+BEiCWPLs6amNSFqyyybcC8\n49cCFuccrlmUi6yhjeXp5/fPaaYMr+PDaoTELW74KV/f6h9qCLAGLdNqgq9fW4/Lq4m1bnBTF7AV\nTZUFK4KKudXqHKG1qsZxTChKdgVHUSvNtj4XYN6F1aKQFYltKahajMP82/iP8yyoxk5VZ6m1JnjE\nR0K34fqjN7z+2ie8+Ogjah/OcwzgErB4Ho+LcGKYqjGu5zmtnHy6XPlSbL82MuvrWV/XJU/CsJxT\nbKLiNpdMwJcxDF9ho/BDjOo5rG4mMOszxhjp+4MZBjWUHueZktKPiX4cubt7YMrmkk7AOJZWcWkE\naRGspZpQukiTCcFZS3Q1afNK6qmeSp3U62t6BK65pSBoHSLUiVwR9MonWL9Oy+d+nwuqq3DGHjhN\nX63Lf+u50ce74nnarH5G59zMW5hSAvFkEYYKropnzBamJTXNBVRJeWKcJppsEvYxBFQXgtD5IjoN\nq1YVgSwLNLYNzXbD/tkzdld72qIbsV585/dmvRDXBmD+bKvXnDMHT8/9+HzvG5XVeC6jdmmcA5vr\n619/jt/q+IoZhffRmteHKXPwbjTEwk8Q899UCUXG3TlHaFuePXvGeH/P+DabfuA4Mo4DWYXjOPLZ\nuwczFi6QNTNp4ng40g8GDB6PRz57+0D0gd22o99BGyc2jWcXIpOCTInoi2sehDROeIr7jpGRclnA\n3nsjQBUj4FlKceefFS9/bkNf8BFYuZzkmUdfJ1d0FvNXl/8SCHX+t4F8CxbRzHqNaYnVV3TnlDGO\ngVkD0lj6F3jPpJBypk8wjJl3D7eAGU8vSr67tw7i0bPbbHAIbz9/SxtDMeD+xFvIOc/Vo1Uoti7E\nnCd7LiXadkN7dUV785w3n3zC89evaWJH5lTf8DwMuHQv7LFTtuL7jK51HnMnRuSpMKGO+vg5VrQ2\ndOfeyYKtnOIy69/nxuMSi/Kp8RUzCh84qkew/r/k1MkFS8hYee808e3f+I25q/TU99bSzAFZOU62\nq+GEBOQpk8Qxpoljn7h7ONKP1tvAMg9HNseBz97ds+kCu23LftPjnbM2a06I3pm8elj6HUKePRRf\ni5ZXO/20mhR1RqzR6VjkztdfeC0yCs6TdSHTLN6JPNrl6oRK55M1C0kzvhzjoqk6qKqlJVfp2jEL\nmpIBeBLIWa1+AcdYWtgfcmYaM3eHHgmeYcilV7BpWTYxcLg70LaRw3GgayP7Z9eldqUaoMoCzTMr\nE5asgYgYOauJdNuWNHoTu3nzho++9jGvfuInCF1bdCJO3fT1Il/jFuv7ZO/xeCE/3pFP+zOsPYlz\nT+sSGHxpXDr+/JyXvteKUwmn3sSPaPjwlCVb6c5V70Dk1CgAM7xdimnIWIs44P7qimEYjP67RvDd\ncoON5AJDyoz9wDCNHIaeQ38s+ES1wNCXWDbVUEVMA3EKnq5xtnh1YsrBpNBKf4ZUtBqzWF8Ca2yc\nTzgC9okfW3zA2HEINT03Vx7qqWhLzpk0aWmqVSew8QLOY2ooUExxxbPNJgSPZp3rQWasIRW+gZji\ncpoMk4ltw7FP9JNyHBJ3fY+q8O7uARXbCUPb0IbI8XDg+fUV3WaLoKQ0MI6O+7sD+92GGBqCe+y2\nm/e0UnA6WyDee0LTcf38Bd3+iquba/NsqJ7kYwDufEf/osVzaiDqzv3+XfrSOS49tz7H+0LBk9eU\nCzjnJ3zRe71vfIWMwqVxvvBlMQwnh5XQQbVur4AQmgaBuZPUeH/PZ9/+jeKOQt/3HIeJYbTjU0r8\n4Aef8fnbWx76o8W26hAX0JzpxxLvD6Z1GA+ezx8cu+2RNkSuNg1BDQqLXthttjTR09TUmpqitKUF\njTYNWFPYnMhidQMUmbU1OHkSx5bPGVzERVmUk4pb3zQNTJDQk9hc1c23NGsBXOedzbQQtfA2Emnm\nFdhCTIg4sghjcav7lJiSCdUMdw+MirWTHxO39wdUhftjv2Q43t3RNpGXz2743qdvCQ6e3ezZdA25\ndHB+eDgSveNq16121IDmtHy/GICakvXtBEpzmshHb36MH/8d3+TF6xdsr2/mtn6+el9nC+7S3+9b\nRJcWnoVdcN7z8VKodglAfDydbbM592rWuM8atLwkBfe+83/R+IoahTNrvGoVXy3+nGnIOuMI9njd\nFe1f30T2+z3d9ppmvzeGokJwgegDt+MBVU8aJ9KYOR6MHde4liFlhuGIlgUzjMf5ratbO6TMkI84\nd+R7bzONt4atV23LkK2N+6aNgCkkx1KG7J21iBMxBaU5JJBM1EWLb/5S1bgKUyp9EMvHbUtDHCh8\nB2eLdD0h+nFYuePORFkIqCbytEilmVegJkSiLM1uUKYxFe5BsKauOfPu4cBYOjX/4N0tQ8lwjGky\nFqcqYyo9LnxEnGfSzOFw4PXr1yiJ+3e3DMfeZO1vrqyTtgO8m3kQtaO3K0ZsrjkY7HOJxxS0vOeT\nb36T1x+94c2Pf43aqXk9p84zDue7+iWX+9zdt+mWZw9BVQtH/PHOfun8l0DCk9CG04X+OBuy8EcA\nCxfOzynV5TbFAAAgAElEQVSPC7w+dHyIxPsnwJ8D3mCf+hdU9c+IyAvgvwK+jsm8/xFV/Uzs6v4M\n8IeAB+CPqurf+KGu7vIFLYah/v8+Nytnuu2WzXZLEzvaTWPCIt6Mwqaxxe8UYu0tiTDKxJiP9SRz\n+CCyxLQ1GzDVkmYHk3qSFqIN0MVosbo3jyEp+KxEJ4XwZJMpa55tn1+1V19PjjWpaQ3ArUOgel31\n9xpgsgXO6hhhPW9EDFeQSVBnadCUEkP1JIDh2JtWpAsMWbg9PDBlZVTHoe9LDYi9bppMWVpETqow\nbx/uubq6IkTHbrcjpwFXiVQxzjJw9Trl7JrL1dq9qrTz4PAxsL+ysCG2zfy8qlpR28Xp9PTCsft8\n+r/9vkAZv3DOS3H/abr48WK+BByev//5/+8LU36Y8SGewoT1ivwbInIF/HUR+R+APwr8j6r6p0Tk\nTwB/Avh3gH8BawLzM8Dvx1rM/f4vfpunPsAFUHGO5eUx6Lg+DkAETRMff/wxLif+z7tP8d4Ww3a3\nwbeR4zChWejHiRfP9twfe45Dj/OmSjxOidvD0XgCWebFklIyybeU0UlBk+k6OmXXNty1E10IFlYE\nx6ZriWKLvg0m2Gr8frtUp8Y3yBidtuowOD0V3lhEQBLZG55hH3kBIKsIa8UJBEx6ntNcuEMMZFXQ\nbGXdCgxjYphGyyDg6ItOxW1J2Q7TSJ8ysW3JON6+fUtVLaoybvVvKbt7TpkpQddGvveDT9ltWrqm\nZd81c/NeESEET4yBmnJcFlPhdIiYYIyI8UGcKUJfXV3x8qPXPH/50mojiieZU0Lk8mJ7atd+n2sv\ncmp4z897sptffP1lr2F9jkuvraHg/P3lXMi8T8jcXfiMHzI+pO/Dt7B+kajqrYj8Haxp7B/GmsQA\n/OfAX8aMwh8G/pzaVf0VEXkmpx2l3jNOXb2ZibFszau/33vRy+/igsYY2Ww27Pd7pD/iVHFqis3B\nwW6/wfcjD0PPceoJkyBkci71EskkycxVtGtVMTd6LnRSJZUW8E4yY+o5+pFJJxofGLPSBU9wjpQc\nbZRS8LMU/zgVJNUdyoqBau/D4CgcijK56k62AhS1XEMigzrDAbTUGmiayUbFQbEGri4yFdKN4bWO\nSeEwWOXnoMJxsnt6NybuHw6IdzwcB1w/EHyDD9ZxqgKftaZBCjjoiycjGJaz3W4Zx5FtZ1WlhMoj\n6dHscbLqwATUdN95X8+10djtdqX79eJFaOm9ucytOjUu8zretygvve9TBuE8a7B+3VMGYz7PhU1O\ny5f22IjZd1sXRHWi12f/stjCl8IUROTrwO8B/nfgzWqhfxsLL8AMxq+vXlY7T58YBTnpOv0JJ6t/\nfU8uLf7ZWKy8hrkQamUQtOAN3qTZ37x5w7tPPuHbv/LLRtHVhDCRxwRTIqDsdy3T1NE6B9kmYoqJ\n4D39MDJNmVETU87G4NOEy4mUTIGofgG3g3kWzgsP/RERwxa2XccmBvZdyyYFnMcaxKhlxH2AWMq6\nrXzAQEIvkMR29Uppds7RjyPO7ufMIcg5M2qmalFUDKRmFgDyTG9Wk2vPVrB0GCeSKkNK9FNiGBN3\nhwfGKZNU6UdlGBPTdGAobE7VO+uWvd0SnTdPY5jwXrnaWbt56xthPS9ctyONPZNz3L+7Jd5c83C4\nYzgoL57tiaGb296bIShiu1J0EEpBVAjWlKZpGmJK3Lx4zv7ZjbX+A0ublimz3k2rR3LJ/X78mN3D\n02MMt6rexzl4+VRG4xKWsVoPJ4bE8dgwScFUFrzJDPgJEe7Ccqmf+UPHBxsFEdkD/zXwb6nqu7Mb\nqXJeX/oFQ1V/AfgFgJ//+d9rLsDaI7hk2erz1QjUv088g7PXldh2LOj+y1c/xvd+7dfR4WidlVOG\nMVlZdVa23uP21/TjgFcIHBnTRBDHkUwOnoNkhklpFHL2hNZc36G3PH1GOPY9/XiE7HkYbJd+OPQc\ntxNNcLy981ztWtrYsOtanNpu2sZAGz0OGNJEDKZbYCFGntlvNU4WxVz+nGetw2pQVJXEVHZ/QZ0n\n6UhVIJqyVRqmaWKYJqakPAwjCeHtu1vGyYRUhuFoIi0qPBzHmbmomqGEDNF5k8KTcs3eDN3bzz7H\ne8eL5zdMfcJ7Zz0uu72pVqFGIW8jP/bRR+y3nQnIiFVbimZ88GUPWKPsMutOxOj5+NnHfO3Hf5yr\nmz2KFbl5WaoNz9ma57vnU8CjeSHLDr0syOKiFc5Jzo+l2SrxSfXye6//fuR1FOzqSdxhfa25eAxi\nONUlY/fb7imISMQMwn+hqv9Nefg7NSwQkY+B75bHf3s6T5+7UOf/n3/Ipz60LDl28Y5uu6NtW8Y0\nMpXnnAcGtfJbVdI4wmSEmSZ4yInkIDkhaaLxRmcWF7ACydI5etOVxqfKOxSOB1Qzk327aJ7Mm5g8\nOSS8FwsHsA7JodZLTJngDHPIJQSiYA+PZL/0dAJrwQ1ASsZktIyMDyjm0YylQcuYjcSUVejHzDCN\n3B17MnCcEsNgFORpSgUoLErIPlBbtGnOpUuTlr6TpoJsSo6ZrmlBlDSMtLEhNh6nmegibRfxTtht\nG3RK3H7+lsYJMQa8LJ/T2IynArvnIOn19TUvXrxY6TicT4PLsfvpFHtsENa/P2RcCknOxzq1eClc\neZ+7/5SHc/4Zzh/7bTUKYmf7s8DfUdU/vXrqLwL/KvCnyu//dvX4HxeRP48BjG8/DE94YpzgCE94\nE/X3OSC5uhE5Z3KC/fUV+/01nx0PtgBzwmvGa1WDtoo7UYji6ILHq6kjhyYgEklp6Ta8MBZBxOoo\nEsqh33J3PKJZuO+tDfxx6G1BpsSojoeDudl972m9IzphmCbaGGmjZxMD2RsYGQRCBRjHIkvvTbR1\nnNWRFkxh0sQ4JYY0mU7ElJHYMCYjUQ3TyP3RwMRhmJiwXP+70qNzmjLjNFHrEExn0fCVkBIqRdbN\nWeOW4D3ohCZFJthstnNbu6ZpuLnakdOIiJGzorfW8l3XsGsbGh/o2sg0jeQ0oX6Z9G3TWBilBSeQ\normgmbZtccHz5s0bPvnkE2t2U79zFKk2VU8FZd4XMjxG/RN2lsfl3uvxeGHX/wwkhaXW4ZKgyqVr\nemrXr57TU/jEb8WwfYin8E8D/zLwiyLyt8pj/x5mDP6CiPzrwK8Cf6Q895ewdOTfw1KS/9oHXcn6\nmteL/Ang5dHzX/Ch27aFEBnve5pNV3o05jKZlRisTbvtmgPiMuISbQw0wdMEzzgcyxcYC1V5Qfgt\npo8cCydg4zfcbKxP4TCZ93B7/8Dd8Whu+DCSNDFNiZQEaSO9Kv2U2LUNQwgM0bNrrcmrBiMjiWaC\nSqk9WAquarxZr2dSC5emrAzjgIpHk+Xzx2niMIwMo9JPI33fczj2M4BpBWCYvLuaFmQUa0bTbTra\n6KyJrVdC9Hjxc5kzMOtUmgEAnY4c7zNXuw2Q2Ww6urbFezMY1ijXis/M0AlBKECpELxJ4XtXXGop\noGzweIVXr17x5s0bnj9/XjgcNi9qWnitk3hpsZyLlCzTcNUCjss78zIcqvmiB2DnrRyHU+Nw/n6X\nxtpouNXnsk0A8hmmsf586/n5oeNDsg//C0/jF//cheMV+GMffAVPv/GFx544Tp54rjxfsw8uQDo4\nYrRYdKBmNsuOn/O8q6gmDK2357vW+lCqaqmxWApz5i/MQRdNsCQ6P2siep1wmhmDQNcZ7RYD9XJh\nMw5DWdwKA860FTXSOEECiHNEMXd9ongPqzLiZaKUiZigPjJNE1p6M/bjyJitb2TOBjIaJqlIEZrx\n4oiNY1uMmiPhi1HYtg1d40CUpnGEaOKOWYXYWiu5qTaXwbybioV0bcQ5iNETnZWmiybDiOcwKEOy\ncGdN3fYXdmp7Hp6/fMlHH33E1dWVhWlqn1WdFLHXitAv39WlXbneu9Pps34uo2o08zrhnnrtpb/t\nfU/P/bR3coY78P6MwtpwfJHn80Xjq8NoXLv79UZnZb4buvr9CG/g8WtnD8OUf72V2NG0LaHb4EO0\n3QaxHg3eUpTuONJg8mkOwQUhNNHkvQLkpDAlmKYiwV4sd4mBFRMpmUKmyabRkL2wVeX/p+7dYi3b\n0vuu3zfGmHOutfalLqe6+5z26W67u21122ljA7HilwhFggckiJBACZeIB4RRQAqShSLBC3lwHpC4\nvICCFAhygsBYkRBRFGQIBDWXQNxuYuO2nLR97GP3udWpql1VZ++91ppzjPHx8I0x51xzr11Vx46l\nyiiV9t5rzcuYY47xje/y//7fpmvpUyRm5XTVcr3b08fEELMVtc0ZSZE+g2KCaz8k6mIgKB4taFol\necWJI43KUknXBkrx53HnjzmxTzt2fW8yjam0utdEa+TINN5SmFehJThT1zdtGE2FNjS2k7sp7Kiq\nI19BSoJfb0aSFc2Ca2QkbwHAKUoixkxwBopyzhXC1bI7izkYnROQbCaDGEw8pgytEJoVbSN8/+c+\nz5e+9CXu3b1jmYNl7khWK1c/EwKTAL+pHdhiv8moPPpqMsXpuKBOW+zSk+PwUEMZQ4hyM1pxbDEv\nz1U12r/luYfL6KaQ+gMNSf6Bt3lEYd6WguBQ3Np/56awZP18LkkLEUvTrTi/c4/L08dcffwRXmwf\naQongqhjSBnVwL7fWWnK5Gjalu6kMQ/7fjAbvKqKrrA+z16WV0+STJZMXz5vdELYOTdY9eic2fUD\nu95YoWsmpEPYbq8hGquzX69QFSIZV/gesnpyqSxdk7zq/VPKI1DJOUfAIiY2OYqJEHsrYNt4Vm2Z\nCinSeseqc3RFQASnrLuGUJihumbyqYhMyVjzalMJg0/X8alJXHhHJE4TvuyCTsquniOKI/jDqeln\n13MFro0LnK43vP3938/3vfkWWsyLeZqyzn7a1Cn9W6YqFy1luXgOfQA3PfuHC3oaE/vsZqhxqdIu\nBcP8s+VxzjliJZXxU5nBY9rP8pr/8KdOLxb0y/wFR30KRzQPESNjrYQrFuc7LNvlnCXPtEVIbPdb\nckoGiimqNSVKkJ2YKWGzYLyGpW5bn+puPAqLgiVovGNwGe+ENldWII/macJVx2KFDSc3pXnUyVZf\n9nxBVsegyjSZrA2TacQhbVtlhm6aprBIeUKwOg+rYN+HEOgaMwEmp13CO1Mz5tpAoBDI1kVTeQtF\nqCzHAFpJRkomo5vVsFwCleaTPmNIRhVPs1pbRelsIKWDhVsW6AE0/ugUuj1zcamGLzWNwzGeq/K3\nE6e8SAAsNY/62TEW6Xkfj33+e2mvj1BYCoFc9d/FcccWP0xaRv17JigOwB2+oV2tWZ2couJxwWod\nppTIZNrgaYIQVDjfdAzDnt31jmfxgre/7/tMHfc919fXrCSQ3VAWdAkEFqebBGMg8kkJBcc839Gd\nc1YmPoM6K9hioSpPzGUhVao3rHCNDmbbOwdOHUECpEzIrlC3FR9C1jEdu2Yo1gnlJZNFaB0oDs1K\n17VYwhZ0bUNwRmu2aRvL2Wg8XWhwBRvQtS2qE+TWhluLdmDw7CqsPN4yMQWyVmdoTWDLJapgpfE0\nCI0LmEJRa1PUtOAqVCz6gCrtyQnnD97k7PycfogjFFrEkIx1XlUOx2nKHO7OL+M1mBb/MLv2HI+w\nFCo36d+nnzcjBsds/6WjsppoI6VfLZQ7e6alYLvBpPWK7fURCktEItz8+zYBIWLne18xwNO5MiUO\n5ZwNILTesDk5M/rzaJWYm+BI0SbQuqs4/A7v77EfIk+ePufy8pLT01M0OFwTkGHAKkiYaeIkgINM\nZshWf0KC4FNVYdWo0bXShWemojQeR0lPJppK7QOhLDBNGV924sIWj9ea5qyFAGaR37+YlIa7qGxK\nmYSVdE9pYFUo7JvGWxGVIKOm0HUdTckxaIpa34W2mDBF4MjEGl2ZkLw3CjbU0RR2Kgq0PA49SsZp\nxrcB5z2rJiDONCQTfpUpmoJkLDuibyA0nJ6dc3Jyxsn5+bi4M/NFXJynYEQxs4VxmC9QhUK+sbDq\nsXUcVackrGlxFzbwg6m5TJGuTrHDY44df+zvefIbMJL3HvNlwLGl8hJte9ZeH6FwIABm4YS5P6EO\nknl8Ds9Z+hRK0xmlVz1XfCB0LRPirHAr+sw2WS0EETGb39vOttls2O52iHN0mzW+CaDZ+pdtYcc0\nIBIsNVmn5CMt5kPOSowFXlygxVkFFY/3k6lhzk+HcRj40aZ1o8062+nyRM3l6jBhWAUnljNRSKbw\nIgw5Wfk7rF5jNQVSjkh25Th/oyTauABKcda6c1XVeVw4iO3kLqAI3gMqpskoaFTSYLiNlCONMwEZ\nBBClKWxLKQ94KczT1TdRC8d48yf4pmN1sil8EpbybQ7ldKPvS+1gGTqsjsHlLl5NtEmIWJ3Quak2\n8UUeJqZ5P+/DTfNi3p9lxuv8Oscci/UZRh98PcbV6NMi8e0F2tCyvSZCYWYqLLWDY60a1nC7PwFu\nDOT4MpyjXW8IbUfqr/Hiisk5TIVIy07fegdE1l3D9T7yyfUVl33P5mSFW3VojgYVTonQdKSYEacM\nfT9OvGGIlnCUYUgGohpSZruPY2RA08TPOHIiaiKLmr1fIgnGxxAmtbIIJNRUcbB6FcbgbBM4kXBi\nAskEhMGj21JERlNESoqz4kgqRp4STCsJybAKmgpPooeIImSkMTizYfCN6CUjVvnJOcvBEMg5sU8R\nTYlnTx7TD3tWq5bz+3c5XW9KjkcBhFHqROjcXp4K9agKbdOyOTvn9OzM5owzc2QYjJ5OYNQs5m1u\nfy8/rxvRfJd2zngn5tEGOCRrnQuM+T0O5tzBfZhd/1CQLB2HUubzjf4eTPdZ3YfqUxE98N18mvZ6\nCAUFKjHKwWLnUEgcEwRFrVweczxEVPj+Gkez6ujWK64vHTDVWPTes2pWtliJpKQEFZrG42JCFT7Z\nXvH33rWcr8985jOcn53RdoEhg4qxM2m7QrPtiLGxLETU0e/2qFd2w45BhaRKTBT8OuRCQGumDpb6\nW9TaUKjmXHnk6vQUV1ie1fD+vkQCVEEKuYtzHvUlU7IIHM2FiENqKraSSwn6nAMZR2i08DdaGFIl\n0ahFEZzImMac0mD8jt485DEWZ2YoYT6EYbsjpcTZ6Qnr9X2a1nO6XllJeSmOxjEBTEa+CYs0CIIj\nitCtNmzu3OH0zjnr9ZomBEsUUghucu4dqswVlUgZXx3fd130N9X3ikdwxSQ7nva8JESZL/hj9SWW\nguFY9KAKg1HzXWx8878OFn6e1oHUJcWhf+Fl7fUQCktp9jJNYSkE5oO3+P42j7JzxitY1W4dJbOM\n4TW7fCpQ2UzTeKMszxa1uHj2lI8eX/Dg3n26rmOzsryK6iFOpbycQYsh58iut+vGJIX7sYQMy8LY\n7/djGI0SPZjvmKgVcA2VQyEbkKqGC21BF/VSsb6WuHrM0fwY9VlHlfKm9zpmgzOLCBqw7MyyONXJ\nuLxqzc2UM9mBU0u8kpKMZLsYDP2u1Ld0rNct63Vn+R3ezxKOJqFgdSTkoK99Gatcjmuarryjmzv8\n3EF3zBk4X9DHnYWMn805DOZz6Jhav+zPsbl37LNjjsllP5ZC69ixy3vaea9uOsDrIhRuMwGqplD/\nrq1qB7NjNR8HndjXC4bd0NCtVmxOT7ksuQu+5BH4XDD/Ajil84HsFU2ORCYhXPeJ89Mzmrbj4aOn\nvPfRI1Rt0XzyySdWBVkK9l4snFd9BR7jVGyLCaSaxizB85NT7pyfW+JVEtZNoPGOxlt40DtonCEL\ng/c0sxLrfmY7igh931soUxu2w0BMiitVsBsfLIGrZO81TcCFlqSZ/X5PzMquV1KEEBKKhwCNd7Sh\nJRdnZRZzsCkKq4YcE3vNpAzX15f0fWR3+Qmqift3zg3t6YWuawrTNQYKK0xTqUYMqmAuE7qaNV3b\nEkOgXW9Yn51ycnqKL47P+SL37iaeYD4f5u22RTpvk3kwRXHmwmTuiDxmNswX/THhsQwzzhPA6nHH\nTJ7ldZbXHP8ukbFXba+HUFi2ak6gNwFJ9efoaT8ilZc+hFlL0a4tTcf5gwd88Fu/gajgs0GLNYLz\nlgrMEMoOmwhJWXnw6oitJ7dKF1rcG3dIec+uH0hZuPfGgwIyUa73O6saLaW8vMC6a21RpjiiBL33\nNKWYq+RMQPHOyFdCxU00ntZbxeXGSeGC9LQl+9G7ME7elJKZSN5x3Q8EEZxTUiOIs1Jxg8CQLOFJ\nxPIzrrZbnl9ekhM43/EwXZUyetNiaFYd6/W6eOCzoQZzJjSe2A/kHAkoq67hbL3m7vmGtmkNWt74\nMVMyBNMGrPRkAXfVZCcR1CkePwkF54qgdrSbDav1Gc36BHSqo1kXTj6y+KepcSgslppAvZYr8GzT\nvOJ4ztxEqL/P+RmWmsiNucntmkBtrqAyxz7N5vXyWed+EDvXDj10pOqn4mt8vYTCMYDJK7zg+YDO\nKxYdP768PCe07QoJDQz7afAljxWeajn46mmnAG3a0ND4QEwDwWVOVyvWbcezqx19v6dpGs5Ozzg/\nP7UK09dXiAhnJxt06E34iMXnvRM2xa52Cvt+C9kiD86bB9sShGoVqQlBWOHAThyupC4rZgohbiTq\nqOaSd46QATLqIGdHygnwaMo03vOZNx6UUm8OLXUc+iHy/OqSGCOXz57w9LmnkqmGAjZaNS1esFBm\n29C2DZvNhpOTE+NGENOQQgg0FSFY+6fTpB0dd6WwrwkFb7R3IuBKiHS1Ht/rbbv90nyYz5VXXbTH\nnITLY5cC4bbfj517Wx+O3VePfF8vsdSMK/ryMHLyau31EQr55k5/IBCOmBjHzIVjL2l+TGUfSlFL\nDkRLv9+ZR18VUXfArhycRxoB8aRkjEadd9w/OaVfRTRH/L1TnAvcv5N5+vQZu/3A9vkjMyFiMjYi\nVXapR7MRtnRtw8l6UwhfjVAkhECzOSENPUqiccKqaw1d6Etxl6w0Yqp81/jJsZYVwYSdwxGz4SK6\nEoWIWWlSwgchN56UArLdE8VAR3fPN2SBoU/I2tMPkcvtDq8J75QH9++NYzsMg8GVY0RyGkOUrTfh\nebJec75Zs2o9ncUaWbUN667FeWiDkbPWdzLu8LOENKA4A7NVuHaeJA5pWs7O73J+587Be6+rY5kW\nfcwUmH/3IrV8eTwcCpVjC3mpyi+jC7e1eR+OhSYP7idyME6HOArb+JYC59P4FV4fobB0FsLkMDzi\nbxgXvUVfyHoYdz48fB6yUVBHCDa5Tu7cIfd7iHuSGpGpztRC42FISIbOBXwotR19oMuB4O5x8ckl\n+yHiGwjnG2Kxq1PBKfTbHsisuxVaHJgGmbaEpsqL4Jwja8RlRXA0zrFurKCMkpCcaHwwISEGJGqb\nQvLKFBrb9QMOT8iZ3EN0EVXMJxFMiCTn0JjQrrEqWZpofSB0jZ3roG1OGYY9+6Rst3uLnvS9CZ5B\nWLctsSAbT083nJ1s6LqGrg0QB9McxLIiN+uGrtSocNQqWfYuc+GDaLrWUJ+qZToYqCsjRBxRhZPN\nGavTMzZnp6PG9yra5OG0kqOL++ZCumkKLHfk21Kr5wt6fs0lHPpFbdnH5aZn7zvfiCwsBdaLhN6x\n9voIBXh51AFGQXHsQasD6GUqopbFh3d0Xcdl8OhgTrNRpV04h1LKaCFgMcfhYAspNJysVzRN4unz\nT4xVSKAJAr5kTBZorqiBimq/QqkBIW4WxlKM5lwNa9A1rb38BEnTQd8qHRlMIc36uVTIMWYP+4J3\nsIpJiqqxImWMQyKhkJWuNY++laNTXNPQNJYKrgn6xhOjRWK6pkWxPJKzs1OMdQlcYWVqQ2DVNrRd\nMJi0K+zL8zmcdczb6GoIcnzVpVQdhk1QcbTditVqQ9t2lnbCC8JzHO6yty2WY36GY9+9yAy4bYG/\n6PylU/KYEKp/zxf+XCjdtvhfxY9xW3t9hIJMO8etbaY+jbkRVUg4ORAIdvgR6aoWFdBkMOKze/d5\ndnFBvN7iXKFeK5loh9erziSjSrNkqkB0mdPNhpSSOfaudwzDwG7fT0SusxB5U0KPIVgGopeqkSRD\nRmYlhOKUawNNMRtwnuwF1OFah299Zfck+CkMafZ9YN8bzboPPW1qTNVvql8ioI3SN9nSkNkxaFkM\nqez8mzXDMBiV+9CzagzncH66HpOm2rYlpaF4ywuvYkkePznd0IYwmgyQDQZecBihMjXL9K6saIwv\nRCJgel0BZIkD17I5vcvp2XkZM0bSkcOpdJNw5NhCO7YYj82fYyZH/X3++fy8G7R5i74s77XMI5lf\nMy+c6sf6suyHjGM4mWGv2l4foXBbmwuL+QBIiVjOPj/2UqfLHAqdLCYU2tWabn3CcPFkdq8SLszz\nl28FTquqnsT2KFXFi3nQN6sVTh1DGPA+EPp+zNMql7IkJvuk9C8bMhDjbred3lK5u+BL7QeLwNTJ\nWUNYrgCXLIRXgzKTY3TMqnOKOCVUpGCBA4tvCMkIV0iRnEq0l7LjNx7nQbzRxqkoOQ30ORbtxSDh\nAJqVdRPwzrFqOzYri7KEZgZMKpLRHdjyE1x6FMTFSSbOcCOKkbj4pqFbr2iaptBrZFTdWMl7udhe\ntGiXGsTxqbc4R5giJkeEyvy8l1372H2OaTSHkYapqVqdT7e4/LRvStkvPx2XArxOQuGYhlA/Ky/9\n2MtOOsWNl61K3/l3rgyWOI8LLWf3HnDv8orHv/uuhQJLQlJN9LEah5aVVu8/pDgiBq3wijEoobaQ\nm+BYdR1DTIZkhGKCJIbetJCUE1nNJCElq3MQrA7ketVayLFpSr2JPJKWgJkYHoMD+4Jy1JhwNd4v\nMzu9afBxGHeuwOS/iFnpmhbfBJ5/cknUSJ8KtboahgDvWa3awndoQqlxU5l4qaxKTUPb+FKo1zIr\ng7gxc1KCjAL1QCBwWPmq35uGk50HzTgHSTx9Vs7Wa7rTc7KbCTw8h0Ticsvvh22uus934qXN72oo\nFCUAtREAACAASURBVAonh5Hv1ISkY6bDbWbKMT/D/O/bNjRYhhin71OZsy+KXty2Pm5rr49QWDZd\nOBhl8rjW3XA5QFNyyu1OHIMvG4Y+pszq9JQHn3uT316vidstsR+g5B4Y4jCP90yF3DVqHouneJQh\nVnMjIlmKACuTOuexzLymRMrlmuXa4gyT4JyF61ZdQ+stfCi5JDoVaHOKar6BEEb6csl2vpTqVXUc\nCrPaCMoax0cq8tHjJYIoXRM4Pztlt98T+t5KxQ29bUPe4VXpmsYo4Zy36IFztI2ZN1AFcAEfaUay\nM6yBY0ySci4YijKnMq6WTVp5GCw+X96bE6sPmaGXgdW9u5zcucvJnXOGFIsmVOwyd7ijH7OtK3LU\nptK0SJde/uU1stkok2YmYlwZs+vfFv5e+iiWpsOxe0/T/aaJMT9u/owZBQFZTPl6/m2mzG3t9REK\nS/OgCoV58pPI4T7wio6Z+efqKxdjge2Kx3crJASSZlKKOFWrbDQTQDVNWNVCfTpTWacuV4EwnTsm\nV8H4u86OEWzhNj5YjF+mhJvEVEy17qwyU1/HiZMzPlhWYp2kXhh9I3UimQNyYR8jEASfhSYEUiFr\n9fhiVmRyKW9nFr6QkxGr5GzcDFYhO+HCBMW28dCCoaiIxSmr0xZS9XFUwpJpIhvM3CHekdUR2s7q\ngbbtyIY0huX09qjT8u+lJnCbar6cN/PPZfHZMafkbZvSy9qLfQQvD2uW4RhNtGPP87L2+ggFuKEZ\nFKNoVNvskENpO5eEc8k4P375e1PUrZiV7FtYrbn71vfxvetrNGdSn8eJqSmZhz4rKRaNIQ82uUs2\nXuOacs9MxBYKYkQotvAsl2LI0UJxImiYeA+bxhMK6qwJs13HGXmKK+XWmsbjqVmSHtQYpA2jYMQu\nqkoaLB+iYj+quSBYRqONW0ZCa+M1ZFrnaToTJjFG9kNPjNko5zSx3+9t8TZK13QkTTiVwstgDtSK\nRPWzfAbL7kwjDCUC6i0r1RVcRSyZnoIY50KJ8Owy9H1iaFpO7z2gOzml6VocVpdSELLoAVuzDZs7\nstBLpuVi1xy1TgdCMMFeoh3jokrVicsogOaLdZk/cQwCfbARLO5fr3Pb/D4AH83wHfanjOEXRckT\nQ8DBevo0guH3U3X6zwH/OvBxOfTfU9W/Uc75d4F/DUtN+zOq+guv1Ju6y8x8CHLEHDgWl13+Pn8J\n82a7cxnwAvTpujWf/8IXidtr3nn0iJAtCSqV0J0JiGzFU1Iix6H0w5xzlTdhKFTqALt+GBdFLOaF\nd1btujrcRHQMR3oOK0bXiTGnTMOBy87K1WEvPsZI27YGlCpmShbIKY8p2OPuWKphu2IkazbmHu9s\nMSrQNQ1tCLQhlLTuPOZyqKpVk8pKkxukybgwsS5rVsRXDaX2G6ptPz6LmtmjgEqmlox3juJUNOp5\nbVfFIexZrVacnZ0j4u0+RYjI0tM2f89H5kWlYr9hYpTEsWOaw8uuPadJW55bE+uOmTT1/R675jFt\nxYTNpDUcY5MiY96mmbb8B+FovK3qNMB/oqr/4aLzPwz8SeBHgM8Df1NEfkhHg/OWJnKDUq2qokvb\nay5Fj9lYtd0mHefn2KRzrDen3Ln3hsUBpNQrmEtaJ+Q4oCMePpWoaM15rwKnqviWTamAq/0sUGQv\ndYHUfiiuMDbnxAFuYak+SvAGaXbGUG1jUKDKt6i9tdWIhBThVvkXotWYK2nU9i5cMA7KoJkSqR3H\ne1z0an4E+3zSisqI2Y+kULMftdi/s+IoaN0LlCz1eQRVIQ6JqA5/0tBt1oSuLSnlNUPUHKrLcVo+\n/825YaaLzavb58/UjPtxTjG/XMTLcT/wSdzyPpbnLNsxH0ctboMy+V8OTrJ3+KL+vaz9fqpO39b+\nOPBzqroHfktEfgP4CeBvv+RGB+HHFy3o5UCLyGg/T/bpzQGtLWF4BSMv9Th1nJzd5bNvf5Hm7C67\njx8ab2CqGkshQq3mCjrWTVCdQpcOIcWBjLE2jXgHtR05YHyN1T/gxMKFqjbBx77qYdbd/L8jMyYJ\nqY4hP8uRaMpuPhwKyxkjkHnTlVwqVc3HOaVUYNcO7y3XIKgjuI7c6FjYtqrHTjC+h5xKWnNAJB6o\n78FNu9yoPvtiGoqFWnOF4BY/QszJ6lP4TCLwufO7nJyecX5218bJTe936eSbL9iXLWC7Rn0fN/0C\nx48//Hu5YS3b0rm5PP+2c26YGqqzVPRDk+JQw7jlGT5FqddPlWgtIt/PVHUarDzcr4jIXxKRe+Wz\n26pO396qyZDzOFHFOYtTL7SCY2pWjFMWW/05nyxzh9+42zH5I4ackdBwdvcN3v7KD7LTzHWMhrfP\nEJMVZLU9j5H+ap6UlNUShJqmGatKNT4Y4nHVcbq2pKmm1EzwYpEEyTra+aNT0gmIN+COGJfASITq\njYAkOKu6bCHA5gj+3TaNOZ/gnDVI1TSihNWWTBi5ao2sVGdeCJ7GC60TuuLTWDWWj+EU8hANLq0W\nfYkxHpgtMUPMk+kw+jekApNcqReRGIZSxi5mq2wVLVW92ayMG7NoEWkmzJZzo34+f+c3nLtH5tSx\nBW9JX8cdidMxh2zdc7/WwXwrY7K8x/GFrTeer86/5TteCpsq3ubP+GnbKwsFWVSdBv4C8BXgxzBN\n4j/6NDcWkZ8SkW+JyLc+fvS4fnggGXU22PbRyx/w2Eua/1+20R52HvGBzfk5VjTd6i0mzPcwv/d8\ngdXf55N9/lmFIlt15ObWlz+/9jxisbxn3SW8cwf5DnUi1ph//T93es3HZX7e8tmOjeUcMFXPPxhT\nyTf6OXcAL1Gi80kPoFnGMHBKiX3FVjjHarUitCuLFi3GbvkMxxbL8vflDj9vx4RLPe9F5sJygd9Y\nrHJzg/uDarcJm1dtv+eq06r60ez7vwj89fLnK1Wd1nkp+n/0x3RpMlQH31Kyl3OPApOOSdi5ZvHC\nBZgsBfnNL36Rk/sPePLBewQRfMrmJZdJBc8lzo7q6EhyztF0wXwCuS68w1CkuKnfY7gwC9kZfFpE\nrBhMtuc1H8AEB3bOERC6ti28iHoQnUmpYB9wBbBi1x/HT8okVyElKzyj1RQaKnX4bCLFQgNXINlg\nOR3jmHtGk08sDjaOeYxxdIKKGOZgrCbVzBaOC8RsoeB+SGOpvSQKoaFtW87v3LM0dlUqgnIp8I4t\nwvnvtwm+xZw8esxoLs3Cp/Njjh27bMeE1FKQHbvG8loWbJhC0zfOZUKCzvE8n0Y2/J6rTkspQ1/+\n/OeAXy2//zXgvxGR/xhzNP4g8HdepTNSMAk5p9nAH09dXUrb29SwYz/Lt8y94ql8d3p+lx/+xo/y\nq0Pk0QffI++2BDfViMQ7vDSIGNqugdFhB+DC4a4374s4PfB95ASJXDIz7bgUE64JUNR6UQ84Wmea\nRiuOUCZGEwLO2dqMWvqhSkz5UNUsQmrCLQhDipYCTXFeFTV56Asbk/OYvV3IZ9y064cSmajcj3U0\nXVPMHGXUilTVwD/eqNyqQzahaBJwA+ICKafRbEmayQK+Ebbb/eG7NZlm95RcEI232/TA6JzMZVzM\n5JstujKeuHm056ZPqgrz+Tyrwqn+Pteg5pvW/Pj5fJwfW78/NocP5+34cssDzj8r58w0N1ke85L2\n+6k6/S+KyI+VXv428G+Uh/iOiPw88GtY5OLf0pdFHl7SlhJ8qR7dFnq5TTMo344vbcwTEAEnnJ2e\n88Ybb/Dw/d+1LMQSevTO4u++EKCqplGVB0bPvkpN6Z3s+dG2LCSxOVlEg+TGgrOVuCTGeKBRiAj4\nmkDl8G6apL6EnwLCMOcoAByBLP2BymxaSyHfqM41tAR+1IBbC58MTGQd1Syq95+GUy2oWs6rTFAV\ng6Clz+abrDUvjQovDsMosFLx3xjBinE3vPvuu3z/D31teqelWK4WJN984c7f/XzcxWXgJn5hOT+W\n5y/b8vPbBNGrmge39fv4wccdnVVIioj55urhVfa9OsIZ+P1Vnf4bLzjnzwN//tN1BXKtiHQjY6ya\nAjfuMxt8HaGsS61hLt3nJoWIGL4fxbmyo6rj/ve9zVfF8Z3vfIdhv8c7R58yUQez5QXaAFkcDisJ\nX30KKoom43fwXQAcediTC8NxxqEiZEmknBBnadX73gqzxMIPqTGSZwAa361pfWDVNDi1SIemofS9\nMERlwUlDloTmBKI4DzFn1MsYVsyiqAiD5pHfwZUoAc6owBI6mkFgYVtxoYxxERpO0GjvLEvljYam\nLULWQ9d045gbXbqyH4ZCBZ+KUzGhefJ5ZKf4EEjYuPwv//Pfwrcn/OE/8ofxjUNUDq43zZObDMqj\nMNSSIISOzjjKzzn6b7qWRYjmZsoykrD0TRxuOje5F5dawbzfL/JvjEVfVEbBMBd68zZW7x6XxU3n\n/Mvaa4VoPGYaHLOvjjmDnJvDYw+PnTvVnDPGoGmgZn6IYrev1mvufeYBn//C23z87jvGhJQsLBlF\nkBzZE2lWa3zTApa6XAtwkKwMWk2GkkoqIm7UFESmsmipaBy1//uhH/u7WXesmpZNt6IR8/5X6rKm\nbfFi3nsXWvq4R9UElFdDt7kcgASp9MGFEuGJ9H3N7SiRBgdaysg7dSMBqnn8zRtf+Q9CCDgVvDMz\nIYhxMoyORBwpW5TFmhtzSfKQ6WMkSS4kscWMKRRsrrAs7a57rveRJ0+e8N/9tz/P//X//N/8mX/7\n36RtVqZx5Mnsmc+hY/OkajfzeXZsTh2q74ep3XNtayl0jp2/9C8sF+f8/HmrkZ/5MZPwuPkM83l+\njIvxABH5Cu3TcT//AbZjLxOmKs3LqMJtNtj8enNP9zyHYS71jw2uBE+33vDGgwf4xtT2XG21ko9Q\n60LW685rNs4n47FISG3LnWN5LVHLiBRlZGeq58js2s5NFZ8PSprpdJ/5uC7Ho+97hmE4COHNw35z\noVz/3/asuSAgU0qjs7H+H68vEzV8TSxbRly8bxhSZL/fs9taktb33v0d/r9f+c5oxiwxKcfe+3zM\n58cuz7ntu9va8vjbzrltfs7vszx/jvN40bVf1K/lM32a9hppCjc7X18wVIl5u4PRtAV/8Hc9b6k9\n2N/HKwUrpqaFVccXvvxlfu2Xv414jwsZBlusA5m2VJyORX22mxZ7XA+vmXN9+XBo1+aZIJgWYy2K\n0nhPg6MRZ6xJUlKlAXUOmaVl27OUnb9cq/oqrChqscNLURoRb7U0BUh+XLQr8QQnxMKp4Be7pcCI\n1CwmPaZmQy1ZlmLGhSk+HwscGzf5IoZsDsWkVvRGAQnmyAW47geu94mHj5/Sx0R/eYl4+Cs/+7Os\n2pavf/3ruNAezIHalvkGtR1z4i2/X25OOZs2Oc2xmybErar8kc9u68vyOeYC2SDk+dBnoMBcy9Ab\nLofx/PnG9CrttREKS8l7W2hn+RJsh7TvLFR4mPZ6c0coHAaz75e7Si5e8jc+9znO7t3n+ZPHqAxW\nJj0rOSWSVkdYGndwh4UBay5KKuE1FmjI+qKHVNOoE2kG1+0aozhbdy1t0xCcpw3GyWgWv5lLOc4m\nZ8xWul7EysO5QM5p5F1wWCm46vJtfSCHzKAwIGQfQNQiEk1DW2xzFSHMBZmbBGvMCUlT+FTCBE6q\n42s+FCUpo/8ixmj8EE5KFKO8I++RUgi2j8L7Dx/x8PFjEoE4DGhvdSn+i7/4l/jKV77Cn/7TfxrX\nNcwp2JdtvtCX9n+dE/Pj5nNlOrYKGbCEqZuO7cPjb0Ya6t/Hjpn3YzkXxzk+3Wkxn2+aL8tnepFw\nOtZeG/MBDgXDoQp14FQdj7XFNYGFjtlsdUCW6ultKpYUoZKBtm25e/fuaC6MC1tlrG4ccyZWlVtz\nyaic7qVqdROiTkQpqloKsUyAoDaEgu+zKIT3wqZblcrP9ppCST4SBXJZiOV6wzAwDMMYuYCaHRnG\ne4iIPR9mjjRNM17T+iVG+5YtOrJEBNbrZMyuSWmgRmGqbZ9SsvAjHExq5z1Rlai1LD3ErCXrcyqi\nixPEBfZD4vJ6S0xqpfViNDar0PL8+SV/7+99l1/8xV9kt9uxnMbLdzufA0sQ1XJRLj9fzsv5d8c3\nneNq+1xQIgZ9nzsN5xvUbclVy/vN18ixPh0TQq/SXhNN4eYgHkrOw8+mz6eXfluEobZp0G46Iuti\nQa38WcKEQ9Ou+MpXf4jt5RVPPvwebWvqat7v2e/7qaCrMlY5Modi0UTUUpgtHKcjS1R55NJP07s1\nRbw4+jhw0q44Xa+4d/ecJlTmZo8ING0gDYdaDlSvu0UVnIJYZhXOyQhJxmV88MZRkDPOtUhWdghD\nMmKWlBLqamEaj3dmUhmGQM0B6Q07UZ2M3jmDpfuAEykRFCnRioFaP6LCtVPJJQHwjdVrlHo8gSE5\nnl5e8uTimTlGS7QkDoeL+i//5f+az/zN/4mf/umf5uRk/cLS7Mt5UN/9Mlqw/HxuYrrq5C2aJrfs\nwGMYedGPOtfqtVNKIz/M3IE5F2STeTA5xmtbhuKPOTZ/L+210RRuW/DzaOj84SlpzVUaVqk539kq\npt5eciLndCAMYoym2pfy8JrNCg9W843L51dsNqe89dZbOB8Y2YqDx3edKfKqxJRGgtM+RWKGIaVS\nts3o2mqJtrlmMWoy1Z/QD6yalnt3z/nMGw842aw5Wa1ZrVbjrr7ctepP3wRcQR6qKLUKrchUn1EU\nutbYnbrWyr+vm0C3akahOlf/RQroCHcwnmDMkuKD+QmcNyar8mxa3sPcETtqWeW5QzPRwvm2wbcN\n4j1DdlzvIxfPnrOLaRy3nDN9igxJud7t2e57Hj+94J133uFnfuZnuLh4NvZzuVsvVfNjNvZ84c7/\nVx+PKKMTddyJZ7PzmL9i+eymZaYbfVtqCPPzlxru/Jzl37eZ25+2vRZCYd7v5cMeHndzIOrkq4t/\n+XKWnvN5GxfL7PNhGPjkk094dnHBo0ePeOedd/it33r3qOSvZKw5GxmJ2dCLqMei/8uXXAVarYPQ\ntS2np6ecrDdWh6HkTdR7Lvtbx6Tv+/G6y0k0N6OmCVQciYX1aTlelSOiPud8Es8jF/NoRf1+LrzH\nMa5p3uTR1Fg6BFMxq4YUubzaHgp5OeTcHIZh/P7Ro0d885vf5OOPPz54zvm1j/19THDM+35sHF8W\n8Zjf49j9jrXl8UuhNdd4XnSd29pSI3pZe03MB2vLFwTcGOz60y14+V70Quz3STUb1a4Mu74n9gMX\njx9zdXXFu+++y4fvf8DV1RXPnl7wyccf0DaOL372vu1aampfFmwXFVOva8TB1G8HZMOgOwM3LaX5\nELOBkMS4A7yDpm043ZzwmTceEBpfhEJFCJZ8hiGOz1afI8ZICIHtfm+dqAujJHM5tfBkaAyyLJpp\nQoNzyhAzbVuKsBThVgVP1ExOsAoBkTA5YtWAXrliBMru7zFhM1/IWZUhKSoQSaVwqiJSqOGkplJ7\nsnqut3sePf2EJ0+fI8GDWJ3LCkqyxzNh0A8DLtm4/MIv/ALf/va3+bN/9t/h9PR0HCMRGX0w9Vwn\nYjwMYtqUls8RAe9wRt9iWKGF6j8HR9Xr1fNftFsvfRfj7zNBvzQHloLcuYnDUmbHjXM9G0r1mBP1\n07TXQihUZxkcSlaPjOXJDhe4AVdUK9rLOAbmNhk3rll+AinZghz6gYtHF+y2W37jN36Dx48f8/DD\nj9jv9wzDwLMnFwx7i7cPMSPOk/o9bWjRoSeV0myqaoIg2aKSnAleisqZChekCRTFjTuoZAw2jQIB\nR2azWVlGZWhogh9fsvPFCeqFNDDa8gZ0CeyHngpfzijqrcKzQ0ZG5eAakg4Y92HAOyVJLI5UISdb\nMJqzFXQFcsnjqPb63KGrMSJNGP8enXQlz2Ga9MqQrFyeLdKyILVOfEsIS+q57ge2QzH5EoAaH2Sh\nn8dJYZpTXLbk6zRkUsg8eXTBr/7Kd/jJn/xJu3cR2P0wQDr0D1RTLOVh9AE4CWSxvA8fBEqG5khJ\nX5/d1BYKN3UZLxubPg50XWemaa73nAr0arGIxzF0Bn13OkPyCqCTZlidk0o+GGs/ywsSGyoQ20jn\npuZtwuq29loIBQu53uRaTLYt31CXaq4C2C7pEfLIiHQIUvLF8qv3UCfsdnv2+z0fv/+Qb//SL3Fx\nccHHHz2k9YHdbkeMkb7vefbsGf3+Gi+Jk67hC2++geCM8VkFCR4tO/dEqGLcgSSHzFRwANWE1FBm\ntEWixQPflijAZnPK+uTUOA8pPIZlt0sykOMk/TOQsmlBKeaDnWZ8Xq0YAsV5bz+dM95HJ7TJ4cLe\nJpHkA/BTZTUamCo55zpRkyVu3YCk15BlnoBKdj2tG/Po60CqaeJAApf7gcfPrvjw4wua9RqJEz9D\nKJiEGtK1OWLmnnOO67xl6CM/93M/T9et+fKXv8yzZxc8fvyYJ0+e8PDhI/bX2/E9eOdomsKtKUI/\n7Ih9Gk211aql6zrOzs64d+8eTdPwxhtvcHJyQtM0rFYrVhtzbp6enuILT2cjDTkqjW8LgKs/mNNS\nhOHYKoalLGayjl+LCKkWIcqHG93y9/k94GYU4tOYHK+FUIBDKX5oMjCCf+Z+gTHakNUYgrwjJUtI\nciKE8lIsL2HKWnx28ZyPPvqIDz/8kF/95V/h4smTkVFoN5hAePLoMX3fWxqz8/RD4uGTp9y7e07r\nC/eCgBSnUdJDtCXlMy/mK9AMS8x6F5oCSc0EKZBlb4CirEZN5itRKJA1ld2yOP90JjBrLkLFRVAK\n43o3Sz4C3wQ0GjuzFdp1JDUeRucCrhDFVOfnfKzH3I5yvSwZh9n/k2PSo7kS0iRLy04TRfp4befR\nZMV5s/MMzpNUuNgO/PZ7H7Ld7jg9P6frOpxzo6CuiT9zdVucsN/v8d7T9z37/Z6/9F/+V9y/f5/r\n62sePf6Yfj9MfBcFkGUEMgEnynq9HudXvXYXapZnERRlXNebzSh8z87OLGz9xn1+8Ad/kDt37vDm\n59/ivPR9dbLCyUSvb5sSVgYA7L1RzJti7i2jDxrzGOKd7/gy0zjqdwZjP9QIPq1AgNdIKCxhtZPq\nY9/PB2qODaeE+VIyYItBavNoZ4tYqm/f7+h3Ax999BG/+fe/y4cffsjFkyfkaNfa7/dIVq6urkbV\nN8Zo2gAWDtv3kWbVlQUveOdJM2fY0gasb07UQklSbNSx0ApY/oB3Y6rxPC9DzK4yhsAXqH9LW1Rn\nu2m1Pa1/k+2s9dql77XG5OTIPKRQm6uho79iYStXUFjt03g8HAqF+rczZqYkniiOfZ+4vt6Oz1sz\nMseFsoCm23NPanQ1cZ4/f06MkcvLy1GTGCMhJW2rzo2kadSOvGsQl0YN03b1NNr9dSNCLHuzMlw/\nefKEDz74gM1mwxe/+EXefvtt7t27x1vf9yYn52dWkGdWsWscyyoQZm25gG+NKBxZ5/P1Mr/WP5Tm\nA9z0uk4CII8TA0x49Nsdl5eX7HY7Li4u+N733uf68or33nuPjx894fr6mt2uRzWNNQ9PTze2a+v0\nIi6fX1Hz5q+vr8eJE/eRpOAao0DHB2JSLi6e0Tx4gy4IOUVyqbsYSlguxlhUYyGnWpSW0bsfnCfM\nHGYexXmha0PhWPTEPPDk4hGbzYY7Z+fGuCwFRyEZZrkCNk5Kiok0i3MYgMjCtlnVdmZnKr2KaQ/b\n7X4EPF1f74rPwVu+hVgF6BiNam1gYMgDXjyhpHCnIbHr9weMUxXBmSWPi76YxweRnurfESnjJg3P\nrnt+96PHqHiaYg9PYKPiIE7pwLyxjcHcgjVpy66b2G73rFYbHj9+zwhaqEV0Jn9C3TjS5dYiqzKM\nQLWhT4hT8y+IPVcTAru4H9/xUFLdhyGx2/U8Sg/53d9+dwSENauOBw8ecP/+Xb7+Iz/M5z73Od56\n6y1Ozk/NAR2rU/rQUXmgLYixdC816TmTs9NluH6i4VPVkZjlVdtrIxQAvJ+cI5IV7wP7OLC93LLd\nbvnoo4+4uLjg13/913nnnXcsdPjsGXEwfEEcpjyCvrcCr947gnPcu3eHzWbD+enZiElwznF1dXWQ\nvKNq9R2qxI3R0qUHMhfPLun3A1/+0ptWmDUbb0LbtqOmUK9hMAHjmXTedn2blMWhlDLeCT4YKjOE\nig9QLi8/YbfbEuPA+emZZSuWcvEpDSMnYsoGm9ZqVrkCYPHGLSkKUWDo9yPxy27oiTGy6/fsdrtx\n8plwmyjk7LNgKdd9PhDaqrbANMXp86LKZsDJZGa4Cmya7VQmfAWcZ/ArLveOy73y4cdP2W4HmpA4\nPd2MTrYaxaiRn2VYMIstjGrmqCp9b7b8/fv3ubi4ICYlDpnQOIYhkTurStUmg4e75AitIw4Jn8sY\nZDOzROyYGHe22NzkBKw/R8GY0sg2FWPkdy+vePed3+Lb3/67NE3Dgwf3+frXv87bb7/Nl7/6Fe49\nuG9Ca+ZPqwAvWxP+QCCoOZHwTZiFgWfawCxMPrZ/KH0Kamq05kJ0UiHEfeLpE3MWPXv2jF/+5V/m\n8eMLHj58yNOnTy3NeGuT3Aq1FKblqGy3WzSbvR5ON1aBqVZfmmU0jhiHLAi+RAu0+H/msWMYIlzt\n9lz3iUa8Mf+oFWt1JfqQnflApLCCO4EQDDgkTue0JoiUmgnBj0VgKbUPomaeXX5CcJ6ubQniiNEI\nUioaMGs2YtlSPi2p+a0qW7OIMCjsY31e5Xq7G4u9VOaltmnMETgLZzXek9SiKkGn8nnF/TsKGZiD\nnG43cw7NjJILoZ6oDYNm+jSRy+Sc2W63nJydG0iqLJRjGIGDaVR2/3oswGqzZn2y4dnTT6wfzp6p\nrplhGJC2LazUJmDNwVo5IxQpz+6wsa2wpZRKP3Kma3zZSKaMVU0Z3wQ7PkHMkUcfPeJbz7/Fr3/n\n1/na997nG//IH+L83l0+c/8NK5wbszFv1RKAMZlPpjhUqtk5Z/VKVM1JpsSp2ZhInkMAX95ewoP9\nnAAAIABJREFUC6FgDqkEWYhx4OnTp3z0/gdcXFzw3e9+l/fee49hGNhu92Y+9JHGNWhUUM/Q98Sc\n2O127HfD6FRsQqDpWs5ONpyUUF/VIHJKXF/txsGt54gvUWpRcoplV4Aowj47sja8/2TLZz97l4Ye\nwWjERDE4cy5VDZxO0OdKYJIiQ5lgwTX4zhOaQNM1hKLuxQy+caAWoXj/44d0vuP87AwpWYWak30/\nDAZP1kTOlB3ZG2KysDL3KZW0aKYUaSw02nVd0WqssIiRopT07CC4bBpTDbEdgK6cQZ4P7XvMTzFz\n2FlUw+osjEJYlZ22DCmwVeHpNvPk8ppc+tQEQ0dut1tT5YeBpmmYA6TgUNDkmaCoxV+HZO/z7OyM\nq8stfW9Vr7quo+97E0DOwT7ig7Ar4cTqtxIpeRnlXsE5XD5MvTekqJLK8/Z9P0Y1nHPIEMdxAEhJ\n2O16nj59zgcffMQ3v/lNNpsNb731Fl/96le5d+8eX/jCF3jjM/dpW4uA1PRyE45a7nszgau+g6XQ\nnAvJV2mvhVBAledPP+Hhw4c8fvyYX/3lX+F7v/Me2+2Wftgh+FlefraYfJrDmM0DfX19zdCn0XF2\ncrLm7GTDgwcPCN6ShvaFWGS/H9jvdqPKZ7tdIeLIGSda6gtICYMForTsB+UknJC7U7S/JKc9QvEQ\n52IaiOA04Zg7xvJYp1FEaFrPer0mBE/bGkOThZ6SFaTJmYiQ1TPs9zx59tw84qGo+mK73VjVGkfO\nAznvLeGo7K6Xuy27IZY07zI5iuddAngtRWCLJjMlJhmnASKk2AOMPpe6mztnpRrMlJl8P27mTNRS\nnKSaVblEJwY8g2u41pbrtOd6sPJ8vvGExqPqxgVWF9ucQ8HGNSHiR9sbGCNJMUZ8E2xODAOf/exn\nef/998cQoffeQFwE07CyCcVUkJIxl5BgMip+5xzqPZLT6CNy5d36ENgX4Jj3nmHf45vAUMxgVWOb\nooxJU0w0m4d7ts+vuXj4hO/+2t8Hp6xWK+7cucPJ+Rk//uM/zk/8xE+wPlkRimB0zo0p+0vNbO7s\nrt9/WpKV10IoXG+3fOtb3+J3fud3uLi44IPvvUcaCs6cRIoG4e37/mCS1UHte5twwzBYifcETXCc\nbU44OzulbSy9VnU6ZxjSKBDGiT6zV0XKQhO1xS4ghSNxaNa4kwe2eK+GohGYIzGHqlKK1TwsXn+d\nSfs6uefZgZW+PEbLHdCSiYlzpKzs9gO7/UBoSkKSs1enCs57ch7K34WaXoWYBy63OzMvSr3J4Iqo\ncubnqOZMVT9rqC5RxqK8o7rgK4GKc66gMacokYiM2lGcRScqZiKVpLCsjiiOHseQHUOqjsIEBAOA\nMYWdK1HssYm99KxXG7++26pprFdw9+5dnj57Mn6eC+hIMbxESnn0z9SckbrDz7WSIUVaaUri3M28\nhJoSnkewUwGx1fEozFW1qldlZXbONMSrqyuLhn38MY8ePeLJkyf8yDd+mG984xtjNOVYZGEuEJZ9\n/jRCQT5tDPMPor1x547+U3/kJ8aJl1Iy1bFtyEM/PVzCFksBreScudrt2e337OOe7dWV2Xddx5uf\n/Syff/MtNutujGGnlNjudmOkoQqGeZsP+CAl+iGOlQq6WrHLgfD5r/OVr/0o6+EJ8ui73M1Pacno\nYF56shGHiNjEy2koL8+82U3TsFp3bDabUa2zciyOGJM5xbJlX8YhkZIW88lUYCi7fduhTJDk1A9o\nzuz7HnGFKj0lQrBn8t7TNg1OrRZFGyafRyi2e/AmGBpnAiuLhfD2cWJTSqlENVQWk1MLIlJQb5qX\nzqpZbdOAKuy046k751pbLvaOi0cfsX12weXD92maTNO2xBIZERFWq9XoFG2a5kC9X7a6wOYQ5dpq\nYtnjx48ZhqEgR7tJO5IpMzKU5/eiB+nltjHIeMxoJhzxeyzvn0dtyv723k+1RL2HWnpP1fxQYiTB\nTWv+nR/5kR/hX/5T/wonJyfmmG5CcWj2o/ZWQV41hDppE5kHX/zhX1LVf/xl6/G10BSqg6ii1+pO\ndHV1ZcVThxLqKtDmfj/QF8/3vh/YDT3D0NO2LZvVis1mw2c/+1k2mw0i5oneF6FwfX194KQ5QAGK\n1U7wYizEQ63FkJU94AVojD/wWltk8yac7tDHV5z4npOQcQlUPDFDgyM4YZjFul3TWDZj04LzpIJk\nExqDUw9q9062q+6HaUwShiK83u6JOZG229HUSMoI3AJFXJrt+gbyqdqUSATJKEJwAq4xkJTL5Cx4\nD77z+OBxeIZhT1OIXwwgNBSHVqn0BlCK1AYxWyRb/bnyfjNxSAwKEc8VLZ+klm02ePawvebq+QUx\n9aBCP1zRhg7NYvkYQenatfWdkmqN0eHXRTXfLe2XQ62hAptijNy9e5fnz58X39J+NFGa1o9zUaVo\nj96jkvDYQqtMj1Uw1WhH1QDnTFxDsnlWBUrWwk8/OlsNDl+vNZYfdI793q7TdV3JpxG+/e2/yzvv\n/DZvv/15/tCPfgMROD8/52tf+xp37tzBzcoDHvpfPh2d86vUfVgB3wS6cvxfVdV/X0R+APg54A3g\nl4A/paq9iHRYlep/DHgM/AlV/e2X3WcuXeuOJM5AQ8MQx5BNzUisrEX7OBBLcZau61it1rRtNx4L\nVkC1XtMEyywxys2SXMpuOX+ZKSWjQAtm9yc1zMB1VmBNu7qHa08J6ZJV3pXd1aS9SOHcr/UOxH5X\nzDSJqURLUsIXk0FcIKVMzNEcTFhsMamRlxjVmYekpCGRyrMMKaElpyM0HhlX67STzWPbGRMgScEn\nJRUPd1bzdsdkgkW8Q7H/Bo9WlGE2P+ZQbgfqSiJWiaaUwjZJDZKdxLF3LXtt2SH0MaEpQo6Wp6FY\nspWbhPUwDKxWq0nlnzvYshRQU1H7PWP17WPzqy5a2zCEoZ+OzQmM0i8b7iNXbcOIZ3zRoqjakdg4\n1kU+Zn3KzXvX+ZRzpmkaoAoC48GgRLvs0FzmaQZnyXcaTdN78uQpu92Ohw8fkbMJuPffe4+vfe1r\n/OiP/ihDtHlj4KxD0+5V26toCnvgj6nqpVilqP9DRP5H4KexqtM/JyL/OVZ6/i+Unxeq+lUR+ZPA\nfwD8iRfdQIsqGmMaHUp9rJIukkpZ9br4RYTtvkQiorEN1XDjHD1Wc9/7fmC73Zn3uVZ3wngR4Lhz\npobwKieieE9Myj4N5D5xrYHBnxFC4u7pF8jbR5zExJo9ToUcssGGAdEK7xXwjiSwSwNeJ6+xFNVz\nv9/Tl6hHP/SkXNOhi6YRSoRDHepyqZmgY+q0iLBxDavOl508lWK64LqShq3VOQhJMzH2hOCKSizk\nmOnjNWEIhLZh3YQivKJRsHkr515hxoJY/8WRySX/IxgDdsaYrZOjz569b3ma1zzTlj7Dfrfj6vlj\ndNjTNJ59zjh1o3pvz26RiLZtcc6NodTluzM/R0OWfCAEqtlR/VAiQteuOTu9w+X1lV1PbYH7PEUV\ncmHGHol0ixruHAQ5RLKmlC0aBhTC7gOn7FybqUhI5+YZoEbxL86uVTErOWe217H4oIpZ219x/ck1\n3guPPnrEB++9x//5v/3v/NF/4o/yT/+z/4yZWCkz6FQy4dO0V6n7oMBl+bMp/xX4Y8C/VD7/WeDP\nYULhj5ffAf4q8J+KiOgLnBdV3Rl39rKgxZuNrdkGKqrFkmPu6QsIR9UWrkUbTmjbdhQMVYXaDz3b\n/e6AnWm0I6nOIbMFY0pjKrA4R+oHoxHLmewh5YDEgeyEXc4k18H6LU7DhscfX/IGiU4U0YHMFNaK\negjkyVoqVJdnrXBr65MhIbvQMBQhlrQg+1K2XdU5Wu9ItrEz9NEyM7Nxn7bh0EkmajHvpIw2bU1u\nEkzVdWoaQXI2GbvgrI9xQtQ558hFa5jjCuweFg3w3hMrsAlPzsI2O65ouY4NT33LthTKSXFPv73E\n6UDKrghBNZRo6XsNR+73+7GE3RzqXufN/LMqJKqpODcZY4ys1g4fAmdnZyMs2nAYUxZiFThzx2V9\nXgoQrfaxajBzoeWbZkybdzOfQ8zZ/E4l3FlXRnUieu/pY42ClLwTZITyA5ydWA6GKAz7SOwTf+fv\n/CIn52f82I//OHfv3sWF4/VCX9ZetZakx0yErwL/GfCbwFNVrSM9ryw9Vp1W1SgizzAT49GL7rHf\n78fFWtFo5qSrhKy2A6WUyLMEJBvoKVmnLiw7juJQHMbFWa89j+vC5OmGCZJryHqDKQ85W0l5CQYo\nyUYWkiSwlRXOD7TNGcOwxekAKbFqazIMpcZDnbiBECaNZH7/GpHIxcioVakBYr+35065QJ4L8rM4\nBikmRuMKZLlMWodYxSpqMtFs4KsWIXIgnHN5Pu+t8K4UarlxZ76RAVo83qrF+VIcrUkt2kBglwNb\nDexK6aIU90iemIgUHdmjj8XW54t0XJyz72ys88jqNLfvqwCrY17nW73PiJ5cwIHnz3gQmdLpyGWO\nyLwv877VOTePDuTZe19GD+bX2g89XqZFPgwDXdeYQC84ju3VNe+//z6fe/NN7t27dyDY/kGbD6iV\nffsxEbkL/PfA1175Drc0Efkp4KcAVm0LzhHHsJOMlGU5M77cakLEYjLUCRJCoPGe4CZy1Sqx9/2e\n7bamzOrohVeRG/DP8bvSD+c9oVQharoWISBhxZUmUr+jO4Gt81z6EwZa5PRL+Kc9m3TJXZ/p894y\nNMV2f4A+ZZxm9nFv6c4l09KXnREpiUsFTKU54dTReQ8xkjTZOdkSstp1a/H8dYtqxjvHZmN5HjUZ\nDy3ebamT0j52xSlYadhNAPjRgSYuEFOmH/YjH6WpzmU3dlJyIW0x55wtmUkzSaxOZp8d++S4iIHn\nsmHrO3oCDQnXb4n9lqaxZ4spm1+lLLi5N78u2tq3UXjOduKq3udif9dNpi5O884HnG9GIJd576e5\nJMyqe8sEzKoCxeZLceTVPIxy3Xps1TrdbHFrjSyI4PFkjeQYx2jKsu5o/Z8pWBIxU6NqB9cl/T8E\nx907Z8VnlvnN3/xNnHP8wA/8wGRKy3Fm9Nvap4o+qOpTEflbwE8Cd0UkFG1hXlm6Vp3+nogE4A7m\ncFxea6w6fef0VOtkzTM8P84w/7FwFIzmxcw2DCXE1xYOw1VjKcjb6x37/Z6UD52U2aBGFBfgUfWy\nqejCajeLJytsumAYgv2W6+cXrM4f4GTNzjl69eRwn7B+k7h7RBN7Vl7ph4imUmiF8mzZIgpxGOj7\nnhACd8/PrJq0GKrQ1TwGCnw7QVw1hSathG33xlXoupZQdlc00TqlbbryNBP8OGmmxxYAIiO4Cj8h\n5A60LTV8bNWwoCTahMIshSt8AFY2rnIzara6llnhKgm7LFyy4oms6WnITpDdJ/h4zfb5k9GnlEVH\nSHiZIyM+oZoNczXeMBCWE5KVg4WbUrIEprQfTZC5dllNkO12S+jaUtx4mp8GST+M89veKIVgZYYc\nlEkgzjWc+c5fMTAjLiWZFtWPhWCrVmQYjZpM5uq9nWNIqcDqJ6EIytNnn3B6emqZoc+e8+GHH/LB\nBx/wpS99yXx0TakS9ortVaIPnwGGIhDWwD+JOQ//FvDPYxGIfxX4H8opf638/bfL9//ri/wJtdUQ\nTYU815c3V4FUjcRE1QAflaKsqQSgi2zKMQ0ZV8qd5zH8ZNecYKG1iyM3YTE3tJReH+JAFyz7UPOA\nQxmGBI0WqjFh5wJDWBObDXHwaPYGSS4VnOMwlNJqyjBE4n5gv+8RGThZrwyx6BTwBVMQsFJtggbQ\nNliiU9ndqqBrsAlb6zEa+7MgUjIYC0hmKPkQc85FsLwTC70ehmdrM0r3JRBGoLwv2xUN+VnDboqQ\nEIakDCrsgK04knpa5xAyGnsryadKwvo7MYyYbwEoukhlPyqqt7NUbdGJdGfuzAPGXTVni9hMbEuz\nKlwzv4DmDAXM5HRKLoIqTJiiDzO/gwncQ7NhrrZLCROP95mN/RxOPR/f2telqZsxQZE0o0mNsi5b\nrs/p6cZwLvuB3XY7rZn8D15TeAv42eJXcMDPq+pfF5FfA35ORH4G+H+xcvWUn39FRH4DeAL8yZfd\nYJxkzOwusNt5JUfzI1RPuRQUXwiB4AyTvu46uq4lYeCdvh9GAYKAV8H7wBAjXVd3UbFQnhoYR8TC\njUNR6SUZy7DXhO6x1Fwiu/6aVYZtvyc1J+RCdz64DQ/DXVY0RN1z7+JdOiLeW7HYGCNDv8Xgxz3X\ne1Mbh6tLrj655DMP7vPZN+7jSi7DahXIBTrdhDA636pK7B2QbAcXaY07Uc3H0LZWuj5lSuTAaMRc\ntHFNmolabW4jhJMQrMZjUsPz5zJpnUJONK5hyIOF0EpI01chSrakNO9JTthmGNRxoSc8Tw2PnWlU\nTiCgSNyRh0s07ie0Yoq4xrIXJxb1aVHUXfigNJ6buBSquTBfCDUDMaol26UCDR+iRaYcFHr/OhZl\n8dSoihQsB+Yv0SpMCqBp6TeY+4myOR/MTHXUVDL7J5P8mz9ffb/DkMa+ex+m+zjAF2h+CMSYOFmv\nEbGcitU68Mknn7Db7SaAV+WjfMX2KtGHXwF+/Mjn7wA/ceTzHfAvvHIPSpsz60gdzMPrHgxanQCV\n7bhOlAr0qepuPa6qW2k4dAhVz3b1J3jvzTNYrjXskhVezbbTeqdozgz7PV2plWDVmR2Ip/crRJVt\nOGXjO1yM5NiTccSsBzDh1aoh9j30nv1+z8XT5yawTjZs///2zizUti29679vjDHnXGvvc/Y557Z1\nU8QkpVGRQjQEE0F88Cn6oD6IGBBFBLF50AfFBEFQ8EERBUFQwQZBxBZSDxFJTF6MGI2mEivGaqyy\nutSte+ueZu+9mjlH58M3xpxjrrNv3VOVqjqnbvYHm73WXHPNNdpvfO3/m0ZEzlVaCeguKYEu2kSN\nNNz2wyIOIzAHsKiOqQYKQ8wBEYtzBSwk6olZN3cyFJQmAMPkw1zEJmfPYLWGg8kQclB1Q0SDK6EE\n3mi06ZQyY3aMyXAd1eswSYcYh+s6JI7k4MnBowdv48fPQSP7WDba6vNmE4rIgmHZrIk6t1UlVKPj\nYuA7DZmudoL6zNNTtd3wInJjKFBrPHzK+No881RobqWJVvVpjaCrqMly0NlikLei//u+n5/tm2jQ\nm37zveiFiGgEcH03b/wphOLWMXOefrUj5KzBIpUZbDab2RUZY+Tq6mqWPBKLu3IMS0LK7CYs2XQt\nPLxzjuh9YfBSLM3VljGRsgAd2U9kf6STjKecQsaSzJYoEZPvIN1r9OEh92Mk5QlSxPQ9EjKExMYa\npNsydZr1GELiS1/6El+YomYtGsO9B/cZup6z4Uytz0Q2G7WbOCs4d8Z2GDBOY/xzUvfi1dUVKXmy\nEXzZEMrUVH1KIXAMsTBGW+pWePbHAyEELi8vZ0bZD4Y7Z+cAbPtBI/+ipoynpNWpTTZMZGI27Dxc\nZst1dDySMw4MeHOmocMRTI6M+x3HqyuSD+o5QU/BFOKcK1EX9aloXT9TBr7egK3o3fc9x+NxVfBW\nN5xVL4kuiFmqMIY1Y8hPb6hTewGsvQwtbF17kNV6nqfGz2rDqf2s99TDrV6rgXegAD45B4Z+SwwT\nu53GWmy3KgGP3mvkbpwahvRNMjR+M6lOXDu5KWk+QM1bUDIYkxUQRIR79+7xwTe+A4Crq2vGUVOj\nfVTLrnWu5LQvk9canGpgVI1vEBFc3+n1UNpQrP0pw+T3uM0ZfZfoLGTJxQNXQFasI3GHJ96TN8JW\nzuHJAUmwlUAngukSd5zDidbB9G4gBUNy4O2A7xLeR/ZHz+c+/zZODDZXaaeMgoF7F3d5+eUHGLPn\nzsU5Ipot+vjqkqtr3QzHorMbseyPqmeKgRwTGVtsBVUHToizxMmrWtZpVua262a36BSDgtJKJqes\nsO0RyFkxEcRyGRxvJ8d1NDxxjuB6+n6gI+HyRDrsOO6vOOyvZleoBnAVHILMbM2va2H2DsgSjBOj\nMs92s4loBGSV+upzrnbXs4qY89PuzlOGc/pZe9rfZDto4yPWJ3SDuNzYxur366aPc5LUGkimulKN\ncbM7XW3wluPxyPnZGfQ9h8OBy8tLzs5fQUS4vLpimiY2m803xabwzac6yDCXKFcvgfriUwSyipaz\n2FiMjFViSCnPgR3AvNFnnRTNKdCTsYhaObG/2qvO3qgRdXKc6wn+CLka97xKClnjFExOxLmgbZEq\nkgYmie05So+YLUd7jsmRjiMdCWu7AgNvgYR1g27GCMFqkpczHc4O9FbbGo4jfpzIUkvUZZ7kS8Zx\n1IShSWP4D4cDl9d7rht4uZgXjwVlpPW1QYwjo0lTSOZs2yPO0nXq1cFkuhIyG3PJIrSq3WUxau+R\nTA6RKQkjwiEPHJIwYsH1SOeKz0e9KdEf8NO+bOo1SlBVCerruuFanXsl+tf/JeS4RqyGpFFdtnOI\nNUum5epb7RKU+Sw9Fenbe2juaTf3KhCOliGk1TNbOmVEbXDdqYSySBiBnMHagZQz17sdLz24xzAM\nHI7X6qEomaGLZKQAtM9KLwZTyFodqFqRaz6AblywTkOg6+bs+46+73FOIdnfefRQ4/abqLLeqTqi\nQR7DavCrmFcZx2zczBoOq+GyjrGgAIsoorAapNWwOe6u6QqABsYSQxFpDZqP4Ab8cK65j3feYJjO\n4dGBlCcGm7l73iGm2kcsm8FpLoPLDN1GTwoxXO8PWk9iqpOc5yzPMEWeHK7YuQOPH13hOqOoU+OR\ncVQwlc1mowCzMbJxg4Y5l1qStQiMcwYxmb7vcB0Fc0Dz+nNxPcbiTo0mE2KNzVfG4lMmYrhKwiFZ\n3oodj2VL6rbYzQaxPZ1VtaGXzGF3yfH6ijyNxKcOsAJhzZopvKt+3FyvJ2797qFY4AHu3bs3z/OC\nzRFIs5dxCStWrAW9rvNbgq2yJmLdhG40p5O3EZ6ybndtW9ufqiJUVbauyxlYdg5uElKJZNU+JVLW\n948fP6LrOl7/wKtlXjJvvfkW49GzGc7I8nSZvK9GLwZTYInoawdPT4cFO9GWACUR0QQloyjODx8+\nmsX/zWYzqxtDp2mk0QdKmYE5Yq1KBpURWWtnrMXgNZax6zr24143JQVvQBykgE2e6bhnawxa90Mt\n8b0zJAMeSxZH6M54kl+ipyMNV6R9YO8PTNlz77ynw9LbjjAH5UDX1QWTuX93Q44QJ89xfyCExKa/\nq7aDOQfAzIhKxoHhnPNNwljLMAwMgzID27k5LuLs7Gw+SdRW4oFMmiZ6Z7RSUgl6SVkLz5BTCR4L\ngGH0BVbNWKZs2Jlz9vQ88o6x32K7M8hWczxiUP13uiZcXyEl0Uwt+aU+RwXVNU5VtubUra+rvaBe\nP418bN147UaoIdKgSFK1WHCMUTE5BQXWaZ7dSgt1E+tvVQ9HeKpN7cavGBqtobQeVFUiXedPLN9t\nI2yBOT06JgWb9WHCzS74zOi1Tsnrr7+OMcJnP/t5PvnxT/LhD3+Ybui+/dSHDCsO2xpu2rDQNvgD\n1gugQndVPXK73a5EtmqJrkEsh8Nh9j7U02NxYcWGI5fTKZs5mCUFj/cTeTziatiwaHJRhe4SKQlQ\nWLwZyGYidHfwZoNNgXHacbAGbzNmm3Gooas1blUrc7YZN3QYpOA0Lu65ajhzzpFLfYWqh1qrKkCt\nbNRZB/3CeBePTTWqFSYpmd45KNJBSqViVOljSpBiIiWDL2nMU4ZjFo5REaM76zBF1dG/qAph8KQw\nIXMQ+UI65sxz/14L+SbxvjXk3bQhTw2Vxhhsr7aVzi1qSvRLpuypLaENnprXB6yerWv36diFOmez\nHeVdVIV2D5wyuHkdN0BBtlEZKqbCRz/6UXLOfO9v+o3cpDK9G70QTIFcQTFrrL7FpyXhqbX41o07\nD3Ad3Jy5vr7mztn5Cs+vtfTGGMljntWJEAPb4uPd7/ezYadO5PF4LHaNCkyqujk5Me132PGAxAmR\nQUXyUEE0ITnIdkOWwJiPIFuyu8e9Yc8gHeIDcXfEiHoHtp2jE8u2c5A0v6GzDud6chZ623P3TkdO\nFXMxMR51jGq9hlbHreqVT3E2tGqSWa8oQykRSvp5kgrrpkFbfTcoinMugVkZpilqde0YmCYVu5NY\nxmzJJnEdIl++fsIhRg5YNncn3Nkdzoatzl/KpHiN+B3H3aXCkiPkmrSDBjzVGBVrnrY11A3bGhYN\nhaEVZG1n7FxFqhrx6pisEppYDM4U3AxrOrZbbe90HGfjN23oc2PfWLwja4awSLtmxbhO3al1TQJz\nuHP7ect8arxKzpbgI11nEGtJRQXWbZS5urpiHHvu3bvHZz7zWT73uS/wnR/9RS7u333m7fhiMIVm\n0Ork1dfej/RO9fzqXtTBthijee461hl/PJJSYtMPbDYbxv0Bk6HvepJVRnJ29w6Xl5fEGDk/P59/\nr56uIWlYc66/Hz1OHNYYQipiXzcQxz05HZmOR7LrESNY15OD1+xDB95omnI3bAjWcAh3MeMlvosM\nNsD0mJQ9T66vOFhH32/YT4Y7mzOFSY8Jl7UGpBE715cYhoEUM9ZoTsfoJ6xTuwe5KXSCuuViSQEO\nMZKSIjvFkmwVQuA4HlTULXUO/DjR4cjGEoNmL+7HUXNTxBBC1mzKbIndhijCO0+u+cpXrtgddgTJ\n+M9/FvoNFy//Ol55/QN84LUHHA9XpPGKHAPOWlIoFb2cnROjjFHjZ8sU6mY5dfd572e8xFMdva0I\nfuoRyHGRIoyzs43gcDjMEmW9p6IuVaZUmUTLdFJa1N2WaTjXz9fa622AVatSVMmtYlNW1VbfD6RU\nY00cOZe+YkpUrEDfcfnokqFUvKrt3e0OX9N2fDGYAuBLDnAtM1YnR8RiagmvYmFuRTeoXBqyWC6v\n94RBw1pN5zTy0S7ptnHyXFxccDgc2O/3ALPKYU2HlBgJfWbEopBYi9sOfE4axNRIIyHUcLvaAAAf\n30lEQVRGRNIMqCLZFCTjTEwOB0RjCW5gTFt2aceZ6XBZvRCjj0zhQNoM5HSgc46NMez3ezbDQGd7\nlVqAlNVF1Q8du8MeY9V3jbHEmEtEctLTM5sVo/XHAuxajXkUOHgnIBX1KTDliB02JMAHjw/qvs05\ngSi2RLAWj2M/eqZs2XnPlOqpa4nTxOVbnydOV7x28ZswYSLs91ouroZOp1QSPhc8xxS9gqc0enzL\nIKo01zKIeT0YmQ3C7Wn+1D1pCVWmjIQ1C4iLMWaupQkKhJJSjSVY1yuNMZfT2j8lRdR4g4oF0TKp\n2vbTIKVWtVuMo9Pq2maz4fr6WhOmVGmleoQO40g39vPvHI7Tqhjte9GLwRTyUrG35cSw+Kur2lA7\n1+pum82GnAvQiA+qChC5uLjAWkVNHsdxFtEOh8NsXDwVURd9UCUGRckpG6KIuNZabHFrSlYjWRQN\nl0oimvCTRCsyS0KmXlWj4Zwp3MOLQDzSdRGXHVsrHPORGBO7wxFvA9YYRtux7Tp8THgXGawjRk9n\nusUlm9V2kU3GeopNo6SOk/Bpwlc7BBmfFDMi5BLOLEIsmziJukhxAz5l/OiZsqorcdIFb8Rihy04\nyyF3XHnhGByP9zsCglinEEaiGZvZJKbdE9754udw2TPtrzQewa6rWJ/OQT7ZPKc2gXpvmjMjaiTs\nOg0e1vaqltFUMb+d8xYD8vT34GmVpq7FNgBuFv/zYjNoU7frd9r7q8ch5zyrA6d7oP6GMWY2nOas\n7mB1qVoS6qW7urpq9s4SGv4s9GIwBUAtugpJVnXm1n3Yvu46y1Amr+vcvLk7O2BQo+Fut9PsSddp\nLYHiary+vlYkohOds4pwOvBaHakatXMWRGLJ0cnEMJGnIy4GqGXdhAUWDAHRbDeTDL0tWBBWSK4j\npzO8O2d33AGWB/2GO2WCp+NRo/Cy4CUy+oARx6YLnPUDNoHNB0Rxx3TijSCGuWxbPCZq0MzRT1qK\n3VjGGMilTPwUcwkrz2RriuEwlXbbpVpWTupWNUVdMx2j6TkkYRoumKzFS+A6XC6bx6jdBYGzbcfZ\nZoM/7Mh5Ytrv4cTg166B1h1Z56XV5WtsyvL9XLTPdeJQu9luMgi2zKG+7t0Sq0LBu2yf2UoHLTNb\nTn2NB5jtGEVtq8bjleG60NpukFb9bTEWKnJU/b0QwhzFi7P4ELCF2YsoGE9fVaj4bVpLEpbBaj0O\nsARutP9bq23LvdvEmBDCHOZaB3rOnjSiOAB2QeSNYW3UPE1SMZIglvTu6CGpFV0ThsrEa08wKeNE\nyKJJVIienlPWHPlx9OynQGfAdzDYkq04LN4Q7xU+zkopjpoFh4KumJIC3eU412qUXCHIFMhFRFQq\n0CRnIpkQNPQ5kJURkEvcQmPULW5g7Xg17qo9Y8qGQ0FSCvTYsy3CCCVfLovWU6jztOm1fmMnEMcJ\ncmw20bsv1lPvTyuWt3NUMxjrfaf2g5vWWHvfqTtzeS6r1+2zblp37ev6vSUcau0+valdX83jUNet\nMWaGK6zuzPrsGusQC9pXJs0uayP2xt98N3pBmIKscxtgxcnr+95ZrBX6Ep1mrSWF8p88J4EYY3j5\n5Zfn/IHr6+uZq1YpBDEzglMdaNUd07yhjHGEMJUNkrAlxDoAMXnGwyWkURGbS8muIWc6AROP5GlP\nHK85Pv4i/rDn8q0vs398qenCfiIfH9LlI/I9r/DrX9rQS2S7cZwPGk9wnBLXuyMhjUyHPQfvSrjx\nIg46v9R+rJDjOo6l8lXOTCGW0vPCmALRl0rTJS1ckp2zIW3UIBn1gQNkumHAZ4V+e5IcbyfLRI/3\nwisXF3RZK20Z6yAbog84Z9gMA+InDJHxOGHChMl5LjEH6xO0Fe9vUinqyd+epsv+Ve9Ja+lXY9/N\nS/w0ZbmqY62er7iVzBmNxmjadI5p1ZbqDlRDqCUWKDrM8jtiijcnF8SsE1W19rNN6GrDnes+6Pt+\nVoVb75wa5X1Rf5JKjtXbktMKL+S96MVgCrJUEGrz3CUnOmuwArUsiS2DaABSwpVUWyeWzfmwshNU\n3TKSZx0sJc38k6iLs+pyxhgtVNtMVIxrCWO2U2mAG5lM8hMmJXoRnMmYaQ/hyO7N/8ebn/sEh+sn\nPP7KmwR/wEyTRtCZnn6zYbu9R5IzPr+z3DuD+4PljMimd3RO6IfIsOmYxoAfD5r9mTRi0Xqj4dKV\nebI+DYIiU+gmzapOZCCUxQqQQ0aylp2zpU/OWEzXYwwY15EE9mNiHzLHKDwSx6PUY8/u8h0f/BAb\nK4wJNv3AsYSEG3EYLc7B5qwn+ZEUJy1PVzYHPH2yk/IcXFZPyxsNhXXZiOYxtIa99nun6mHLMGbs\njVRQmlhnKdZoTT0kFK5uzp1o1tapBPBukkp76tcK4IL+/lryYdXulnmsjKzOYoVZ1enKPqgwfla0\niFBMkZQU3PdZ6cVgCrmoDKmU2E6ZmqAaY1QJwQhDV0p8N4kuNW/BYmY31OlkGGPwUYvQ+tTofI3h\nRk+dRWecjY3OYsmkVPVFrYIURZCYwUdc0lJoHSP+8IjDk4e89bmP86VPf5zxsCP6ESGzHTqyWJII\nQRzR9GAc+2h5cvB01tAPHYieUtY6rEl0xnKUSIjqKlSdsYicsXg/wmKHyQIajxQx4shNkJAKtWr5\nd+WdcRYtktucVqJ1FpOAB65D5hDgCkjDQOc25YSKBQ3azRtGN0gghEgKlhwiTkoB1FwRjKqt5ul8\nAFirjLD28bfSRUr6nNY4DawYj/b73QOBWoNm+/5UclkrBMztaf/rolyrR+8muuesafhtv07VmdbI\nOs9tXqNEx6JCijUwGzwNMdZyf3mGEngWeiGYQspanVdCLGKYcL69w3a74e7du7ORsMakhzm1um6I\noGHPKXMoBT+GYTM/v3L1ymVt5+YJqWrFNE0YLNYYUvRFjzaK/28thIQ1bjbGdUbLssnxinMOOH9g\n//av8Klf+h+8/eYX2T/8FXqrngkpYbEhd3R3LzBuINJxNKLVsK3jM0/e4cmU+fWvbhEmeon0xhR3\nKAg9KWll47OhJ6cCn15AKGIx0IKeNMc4kpJFWJcyr8hSiUxvHGJyYUAl8jMKWQxjtowJjj7z9pR5\nEmDMHQc5Y3t2wTBsmA7XPHn0mCeP3mF/HLGmU7Bc75GcEEmE4xFn1a2bc54RpFfegCby71QyaMOY\nq8RW1cxTHb3adG5iNO0Y1N/MrKWPkKKCz6TI0C0YHfU7OS84j9VOBYt3oKoZOT3NeG5qS6veVGNi\nOzb1nnYc6iE4juOq33M5ufK+ethyzhpsdmLz+Gr0QjAFKIAmhZtdXFzwgddeZbvd0neW6+trIDEd\nlzyIdtBrBKMxht45RQ06yWuYJ8NIkzOwTJK62+pkBaoVvOqmJd5PDXM5k5Mnhh2EJ8jxbfyTx3z2\nY/+Ftz/9KcZxZHAduK78foF0Nz3b4S5iO6xYtfSL4hkeuwveDoH+8Uh/z3DHdUiaFPkJ1WVj9JC1\nGI5YS4fBF6OpkDVcF13A53m7nJKmeHaMFLCVcpKJ1diPFEhRM0BjNoQM+2B4dMyMU+Lt1HG052A3\nnF+8gjOCyZnHb73J5cNHHA4HLSpTpQxjcMlgyNgEIpGcLVgHkhRXYhaZVdqYT9YG7KTSEg+wfG9m\nDklmlKRFyng6m/LUeGgLyG8F6Mk546dx8XzZtS6v7VviJFopYBxHtSe4tdjf2shaplTfxxgJcY0y\nXW0UbTxDlZpSSnOmr3OuyV3ROR3HUeeaqNmr8/M014Unj59pL74wTGER4UtAi7QRY6kMviY6VV9y\na3xyZokp1xNjmYz6rBAC1tgVSlOrg7ZGn1YvbRO0dOFqebkkCcJEPlzjrx9z+fAdoBioxJHpMBa1\nxosB12NNB8YpMOe8MTKhHDOXh4nL3mG2Whikq0wwaWBUjlWs1gpTNXovJY8xdeErfoI+u+mXDq2q\nZoK2H1Ng11T0nxIkDGMQdh6O0bBPhv7OXfrNOcZajrtLZU6H3eIRcJbkPWIKGpLmRJd5kFm1qONX\n21bnaNlMiyjdnpL1/rppRKQUwIlzX2mee/q9m+hURThdC62dQdu39ibU/6d/7e+dqiT1xF82+trg\n2X6vvXeWHIqaWFPBTxGpYwhUYPAadzEMywHxLPRCMIWcM6lAc6UUsXYDKRN9YDcd2O21ZPzk1Uuw\nOxxmxOW6oEJjpU1QcUbnpKBqHa73zD7frMk9pIzp6sJQpmNyRpLWPwjGzSXCQBN9fA7I9UNk6Nh/\n5cvsjoHYbcniSdLRuQ0ipV6kgBUNHba2Q7omKEsifQwQIy+/8QEevHyGCSPvvP0FHphBN5foZgtF\n9DW5bDJUXE1Ri8ES1SjmrUGsZv5Za+Zo0FSKsKSyWX3K7HxinCBj8FkYQ+StQ+RhPCO4ge7B6xhr\nkQRx2hHHkeNhpyeV9+ScSvVltSV0VjApYjshm6h5FClhkuCyTk4tkrLK8xeqgHbjKX/KLKoFvpaX\nXzZaY2xsDpu6cSrWZaUqDVbpo+97hcyQth3aOBGZ3bg5a1Rk5/q1DSGV/to1k6kMrT2MjFN8zJgT\nzqyrUWmbE87J7AUZhkFLFxT1Qcpart/r+uKSLxmyVXru+w3PSi8EU1DhYA2iUX310a+TV5ZEJ51Y\naxerb7U7VKmjBrTU3HlgzvoDZQgxLcAqVaSbs9hWBq3StiK6Rz9iTeS4e4S3jv1xh+s7xAtZHBiL\n67ckDK78bsaSjMVYh0iJqTcgOGz2XJyd8cHXP8C9+z1CIEjmV978Ap0IG8n01hJTJkdREdzWaDpP\nTIvRFWPwWaMtIdNhSwh0xKdISGqIOo5RmcLRsw9CTJHLCcZkeDQK3N3Qb++y3W45Hvfsx4nD7pro\na5ZjnSs09RlmgBQrBieamdn1FgLkWJhqLoFBLGK2nsztmlgb3tr3daNVpCzb6PitdJCSRlW2+nhd\nK6dSiG/g9ltYtHZDzyI+awNie8gYcSDVY+Xn+9pkrJWXIS6RmGBwbp34pQVmPQq3KbOUUOtWpEaS\nrr9TgYfqms7Ct6FLkiWIRDuXVpNnWNxYFFyDmVPztBiq39NztKoXdVKs0Tz38sUVRuMcqlr80TQT\n2XJ4U6DIXIY4jhrdOHtDrCIRG0d2hhIggCZENOCiFTI9AWKw4hiGgbPzLf0gXO1GHh8mvB30VCaz\n6XTBabmyTPBVb0wlOtFqIde4eCFyagqNZGGMimSVc+IYktbHDIbjlDj4wOMRjlEImws2m3P67R0N\ngApBqxlJ1lMrLQlAIuqJWWZIQWVJWhfSJNPkbWQktdufUil7bfU/FbvrWmij/OoGc8asGMIMtMLT\nUkbOWd2mJzr+UyvyRK2opNLB8ro1jJJNi76mKmSzRgVDThRmSMG4DCvDoylAtlU9qGNcbQk1XDqn\nIpHkuGpjZ91KRZ6NmWY95l+NXhCmwFPBGACksGYORhNT6ulvjCHkEuqMzNljs3GwLJI54cnaFbeu\nHL7iMKildiKzuMJmj0VJOAEhh4ixBkfEXz8G6xg2dzjfnnE4eqJEVRNcT0qZOBVUZWOREo2G6zVF\noBQyyaK2hft3Bu5daNGZd66v+dAbH+SV+/fYPXrMJz7xSaw19EZF1sHpIiL62Y8+xiIZlfFy/UD2\nOiaHcWT0hVGGjA+ZMUT2B8/Dw8QhwN6ek4cz7r/8nWzv3idh8OMOP05EP66ksmrthxJVJzWgKKsx\nMhlIaS6NVitowxqIdZYG0DGoNJ/2JyL3uzGOOYirgp7ktYt5VjHsUtMTEcLkV1Gv9bcrnUoV9Tdb\no6cRNyMtt/fVdlfDYJVY2nadXqsSa3v6W7eMmRbEXSqJx2adK1fSGJblkLw5uvPd6D1Tp0RkIyL/\nTUR+QUR+SUT+Wrn+z0TkMyLy0fL328p1EZG/JyKfEpFfFJHve5aGtHrRyujIErpc76mDcyq+taLj\nqRGpXq/fbSekPXlgHWZ9em9lLE4MRjSUORwPSIzc3RYYNWOhpHa3ElCVGer7tl3ZOjKGzhrONhsu\n7mzY9oq5mMTwwe/5EMP91xhlw6U3XI2Z62Dx9EwyMKKQ6scoBOnwyXCYoqoGU2A3RqZomSLsx8D1\nceJqN3K9G7naj1wfA4cAdFuGs7sMZ3fBlNMqRo15KAxtbRi8IT8FwRUcSfNV5qKO6U3zM4/Lifp2\n0/y2z27h2Npkupug0FrGX9ddqz6sVIbWZnCywdrPb1qDrbHwtB+nBsuqNrf9advcPutUmqmHXw1k\natfuaZLYV6NfTSl6gL+Uc/63J/f/XuB7y98PoJWof+BZGtMuEGsttreY4mN1pVBJtbC3gxNCxFqH\nlvBePBnAwmntDSXHbtBB9Xp6avJO/byiKCHkOCKHzNVuz937r3E83/B4fyBbTcXunCGnY9FZj3R9\nj5+O9IOZi7eknDkk+OI7jwnG8dorL/Fqznz3D/9hri53jD7w5qOR6/5lvjRpBJvNCTt6TA5YZ+ik\n5D5EIARszohYUlhQsqcQOYz63sdInBIxG65DJG/u4TZ3uPvGd7G5e59ue8Z0POIM+CIaG0OplL3E\nR2jymCXHgEkZyRFrFsRkkQK8K42LjWWj6cldR/Xpk71u7Hahz2pcs16qjYJyypIzNKpGncf6v17X\nsQiknOhMiQpMaVZpTqWEmpY9t7h5dpVo6qlf12n7emZaJpNywM1u67o+w+q5WeA4HRZDYVbVdn5u\nMji7Zgrtul32yDfQppC1dTeVon83+gPAPy/f+68icl9E3sg5f+mr/Y4xpsExKAbByDxp9R5gNcgi\nMoOvtEbC9tSpA9yCt9SJbuPX6/f6QcXPknQ4L8B5YdRCKDljyXRaGpvDk7e5e/EKw+aCL7+zpz/v\nycbSi0HcSN4fmMaRIRvMAIhiHPoY8Fh6c8G//fH/zEt/9PfzGz70XcQw4XzPlT/w6S8/4RNfOpDM\nfbVqE0nTASuJNHokB2wUJMPx6jFx91izOb1nqJsuZTCWbDRuImOJ2dJdvETa3KO/uMf5S68QEpp+\nHj1khVHvrJaUj2FcNheObMrcZEVLslk9D8aoLciaal0XoMKQVUCRypgXia9l3u3p2m6+tf1pvRSr\nJEcjYdT5PZVKQghM07SC4psNmSxMYW5T1szXU8aVk1BBXm86/eWEwYhRQ68xZo6Nqfk1Nbmt2hAE\noeuGlRG03l/VEWQxuta+tYfcKQN9L3qmO0XEishHgbeAn8g5/2z56G8UFeHvikitxTaXoi/Ulqlv\nn/mnROTnROTnQpPeCo2IVwAz6uC2ItZsoY2LHlf/txh4q4rVYW2jaAdxLv7aeEJiQVoKOelGKq5F\n0BMyUSzaeJwFwsR49QSCxzYSi3U9zik8Wm6CSmbR0lmM6wnZcGDgx37iZ/jJn/l5Lo/Cw13kzUcH\nPvOFt4jSkYwjZCEkS5BeE5PMhkl6gtsSuzPM5oLgtoy54xgzuzFw9EEL64oUfdQg3YbkLNKfsX3w\nEtu79zWGQoQQdbOEmAEzG81OxefVvOU0eyX0w6RIwic2rnYb36RStJvqVJU7WUOrU1w3aElZP2Ec\n7bppn9Pq8cZocdtadWpuj1Sbz4nqkM2s27fta/vU3q9G7DVGg7pknx6nmZHkvNg/8mnCVilFYDX+\npTK+dlxOD8lnoa+rFL2IfBj4UeBNoEerR/9l4K8/6w/npur0ne02t1FdWoZ+rbO2r+vkVENXKwqe\nhqbe9LrV0epklzaVAWwMSYaSeLxQHezBDWUxRWL2dBgevvNlonmCu/sqPkVc32MrAnUG7y/x44jk\nTMKAFUJK5KAZbpO8yifemvjUT3+cj/zk/8JZ4Th69gh22OJSLpW0ASxRLMZ2RG+0unX0WHtO91KH\niyNyvMI/eYfsJ0LKOGdxqN0j9BvOthdsXv4A3UsfQLqeQ0Gvng7HAvZhiGnSWImChiRLdAwgpWwe\nOJFSeakwaJbYfhXJ9WvWWl3sLLp/u/Fj2dinVE/xVn04PYlP1cP5sxC1clf5rD04ulKxvE2MqmSt\nLQlla7doimo4PZUiazvaNuuXk3rN5rWrSVfG6O/aJgArhDAHtuWsEZN912mV6mlaGdGrdFPb1+6N\nllm+W7boTfT1lqL/oZzz3y6XRxH5p8BfLO9rKfpKbZn6m0nWluO++FhFzJJoU8TMltvXCXK5Fm9x\nqwXRWnFbOwPA1CYQ5SXGQa/VGIeiZoQJzFJlqkZVRiLRZ3rbYQVimOht4nq84tGbI9spcfel13Hn\nL5NNj2DpN4HDccfV5UMuHrxKTNBbRyoIvMZaorGK9GQ6jinA0DNUzh8CsYrGAiUFSRey6xBjETLT\nGMh2YLizYdNtMX5k9/BtPFb74nq4/wbu/gO6Ow9wfUdIWTNFcyz91ZNTgDEGZQxZId4AkrI1rAEn\nIDFAnjR7L8WSQGRnUVh1fo2faJl9WVs3GvPa6L2WCeSclUmZRcQ/PZXbaxG1M7Tfr/kCdWPPBkYW\nI50RhxRpM6dMZ3t8nNQOktdMqkLxkSsWY9mwKVND2pdNq2bnVlKZ219UGz35Nd/GT/HGk79GK2oN\nlH0DQFMjWBem96z0LN6HV4uEgCyl6P+PiLxRrgnwB4GPla98BPhjovSDwJP3sifUSWxP8Eo36ZMt\nZzwNSFmLZsvCak+Z1hJd72vVifrs+l1YAEGrhTqlVABIykIrrlJnan2ITJg8YYrYYUC6gWR7ctch\nXc9+vyflQNe7WS+UsjBn1ylovINY5oxCI0+dDrPXpAQVJzHYrgdx+ATGbXGbu7jNGSEbojUwbOnO\n77C98wDX93OSWSoSgXMdVhQtWQozDnmJJFSVq/E+SHFJqtY9z+XKjce6sOvpHLcBRpXaTdvOSzu/\n9aSepTuelv7MydjOJ35hOi2aU/1++1evnapOtc2na+kmlef0+a1L8xSBqR6SbSxIu25z1sCmigVy\nOBxYcECWBLKvlSHAr64U/U+JyKvoQfJR4E+X+38c+H3Ap4A98Cfe8xcabttu3MrNK2c83fR1Mlp8\nu9ZA1U7Y6YCm1HD4hm4SB3OSlZ941k/9iBW3OgFcP+CiZ5Ce4zRyOOx4+d7LdNst2RoOlw+5evSQ\nh5/7NA8fPWFzNnHnlVfBaxJLCNPcTtyS86/tsYikua82acZmCEHhvlPCdcXDIkLnIPsJ70cShrMH\nr3N5eYn0PdsHr9FdPMD0PZ4K2LoYcDvbM41HYpi0MjY1AMeuxHeLhajGR2cLo0gskYQWxQ4sMSfA\nKiLwlLG375fYlKcz/OrJrR4nswJzPWXoc9ubDV6fX6tkndoD5vbYqAxbnnZ5xhDX142GI6dUD5zE\nfPjT2K8at3u77udakeimMiKzHSgmPZSq+rDdbkkpcX5+pjUlz7cFZUyllM1mQ0x+ZWd4VvrVlKL/\nPe9yfwb+3DO3gBLhpZjeeupUSDXWbp46qd77lSW2bpLTzp/qna3XwormCiyqyZKiW0W3odeBNibP\nIl0lLSIT6HsHlFMGA+IQC5YOycLxeCSkzL37L3F0Pffuv8rFq1dICLzzxf+Hc4797gpjB1KCWmos\nCphuKIhIII5582ek4P91eK8py8YqcEdGjVNONPA4iUNsBykSkoc7PanrGF77IObsHB8C3oeZCTun\ngWA5BqIPxKRFXObTB0FCNdBZYgpY1GCW4kRKE07U06BSFE8lp6WwzhMQqWHrGikqYmbmBmu3YHtq\nU/5OT9Z2vluVsbXG1+8Aq++20sCyZhbGJCKQLUYMEb9Eyhb0r6pynkoU7YHX/lYrGVUGuLKJZN0H\nuTC+OkegJQEPh8MsMdTfqerW9mw7j1ebGfxeJDeJc99qEpG3gR3wlefdlm8hvcJtf9/v9KL1+bty\nzq++100vBFMAEJGfyzl///Nux7eKbvv7/qdv1z4/e0TDLd3SLf2aoFumcEu3dEsrepGYwj963g34\nFtNtf9//9G3Z5xfGpnBLt3RLLwa9SJLCLd3SLb0A9NyZgoj8kIh8XBR/4Ueed3u+USQi/0RE3hKR\njzXXXhKRnxCRT5b/D8p1ka8Dg+JFIhH5ThH5aRH536K4G3++XH9f9lneHWfke0TkZ0u//pWI9OX6\nUN5/qnz+3c+z/V+V2pjzb/UfCsHzf4EPoYlVvwD8lufZpm9g33438H3Ax5prfwv4kfL6R4C/WV7/\nPuA/oIFsPwj87PNu/9fR3zeA7yuv7wKfAH7L+7XPpd13yusO+NnSj38N/JFy/R8Af6a8/rPAPyiv\n/wjwr553H961b895YH8n8B+b9z8K/OjzHpRvYP+++4QpfBx4o7x+A/h4ef0PgR++6b5v1z/gx9A8\nmfd9n4Ez4H+iYEJfAVy5Pq9v4D8Cv7O8duU+ed5tv+nveasPz4S98D6i1/OSHPYm8Hp5/b4ahyIa\n/3b09Hzf9llOcEZQqfdxrtV9132a+1s+fwK8/K1t8bPR82YKv2Yp65HxvnP9iMgd4N8BfyHnfNl+\n9n7rc8455px/GwoP8DuA3/ycm/QNoefNFL527IVvb/qyLCnnb6AnDLxPxkEUw/PfAf8i5/zvy+X3\ndZ9BcUaAn0bVhfsiUhMN2z7N/S2f3wPe+RY39ZnoeTOF/w58b7HY9qgB5iPPuU3fTPoI8MfL6z+O\n6t31+teMQfEikWg63j8Gfjnn/Heaj96XfZabcUZ+GWUOf6jcdtrfOg5/CPipIjm9ePS8jRqoFfoT\nqD72V553e76B/fqXwJfQSu5fAP4kqkP+J+CTwE8CL5V7Bfj7ZQz+F/D9z7v9X0d/fxeqGvwiiq/x\n0TK378s+A78V+PnS348Bf7Vc/xDw31A8kX8DDOX6prz/VPn8Q8+7D+/2dxvReEu3dEsret7qwy3d\n0i29YHTLFG7plm5pRbdM4ZZu6ZZWdMsUbumWbmlFt0zhlm7pllZ0yxRu6ZZuaUW3TOGWbumWVnTL\nFG7plm5pRf8fpZgn8ae7PUIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcf3657b790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(360, 360, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": 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AdQp4HuHz6w+Gi4oYuaxDWSK1W0RWumHickDHfP7EKQBAk1qS/3+f/HMAwCvveXU2trAU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\nNhUlba+HTMUL65ax+OgXg3fAOnCcqagE6cyceT1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AH5btOwB8BsATAH4XQPVaz28X13Ev\ngIfkM/pDAAf28+cD4N8A+AqARwD8JoDqlfp8UuZeQsIUIpF7CQlTiPTDT0iYQqQffkLCFCL98BMS\nphDph5+QMIVIP/yEhClE+uEnJEwh0g8/IWEK8f8DP6Gpz8U93gwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd0a45d4310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(84, 84, 3)\n"
     ]
    }
   ],
   "source": [
    "###\n",
    "im = cv2.imread(\"testIMs/\" + \"IMG_1247.JPG\")\n",
    "im_true = im.copy()\n",
    "\n",
    "if trn_im.shape[2] == trn_im.shape[3]:\n",
    "    ### Aspect Ratio 1:1\n",
    "    crop_uly = 62\n",
    "    crop_height = 360\n",
    "    crop_ulx = 100\n",
    "    crop_width = 360\n",
    "    im = im[crop_uly:crop_uly + crop_height, crop_ulx:crop_ulx + crop_width]\n",
    "    im_crop = im.copy()\n",
    "    plt.imshow(im_crop[:,:,::-1])\n",
    "    plt.show()\n",
    "    print(im.shape)\n",
    "\n",
    "    im = cv2.resize(im, (trn_im.shape[3], trn_im.shape[2]))\n",
    "    plt.imshow(im[:,:,::-1])\n",
    "    plt.show()\n",
    "    print(im.shape)\n",
    "else:\n",
    "    ### Aspect Ratio 16:9\n",
    "    crop_uly = 62\n",
    "    crop_height = 360\n",
    "    crop_ulx = 0\n",
    "    crop_width = 640\n",
    "    im = im[crop_uly:crop_uly + crop_height, crop_ulx:crop_ulx + crop_width]\n",
    "    im_crop = im.copy()\n",
    "    plt.imshow(im_crop[:,:,::-1])\n",
    "    plt.show()\n",
    "    print(im.shape)\n",
    "\n",
    "    im = cv2.resize(im, (trn_im.shape[3], trn_im.shape[2]))\n",
    "    plt.imshow(im[:,:,::-1])\n",
    "    plt.show()\n",
    "    print(im.shape)\n",
    "    \n",
    "im = np.swapaxes(im, 0, 2)\n",
    "im = np.swapaxes(im, 1, 2)\n",
    "im = im[np.newaxis, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 3, 84, 84)\n"
     ]
    }
   ],
   "source": [
    "print(im.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict a head pose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  1.067e-04   1.848e-06   4.576e-06   9.997e-01   9.798e-05   2.742e-05\n",
      "    6.086e-05   5.204e-06   4.315e-05]]\n",
      "Predicted Class: 3, 100.0%\n"
     ]
    }
   ],
   "source": [
    "from collections import namedtuple\n",
    "Batch = namedtuple('Batch', ['data'])\n",
    "net2.forward(Batch([mx.nd.array(im)]))\n",
    "prob = net2.get_outputs()[0].asnumpy()\n",
    "pred = prob.argmax()\n",
    "print(prob)\n",
    "print(\"Predicted Class: {}, {:.1f}%\".format(pred, prob[0][pred] * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display a head pose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V6Eopa3anOX8ZeIsxvCrcl37zO1L4nXeauCWxQijAazP+xgTeELAqdJzKb1qfoPCl3xSw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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcf36571f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_grid_cls = 9\n",
    "n_tilt_cls = 3\n",
    "\n",
    "### Tilt Prediction\n",
    "pred_tilt_pic = pred % n_tilt_cls\n",
    "### Pan Prediction\n",
    "pred_pan_pic = pred // n_tilt_cls\n",
    "\n",
    "extent = 0, im_true.shape[1]-1, im_true.shape[0]-1, 0\n",
    "Panel_Pred = np.zeros((n_tilt_cls, n_tilt_cls))\n",
    "Panel_Pred[pred_tilt_pic, pred_pan_pic] = 1\n",
    "Panel_Pred = np.fliplr(Panel_Pred)\n",
    "Panel_Pred = np.flipud(Panel_Pred)\n",
    "plt.imshow(im_true[:,:,[2,1,0]], extent=extent)\n",
    "plt.imshow(Panel_Pred, cmap=plt.cm.Blues, alpha=.2, interpolation='nearest', extent=extent)\n",
    "plt.axis('off')\n",
    "arrw_mg = 100\n",
    "arrw_x_rad = 1 * (prob[0][0] + prob[0][1] + prob[0][2] - prob[0][6] -prob[0][7] - prob[0][8]) * 90 * np.pi / 180. \n",
    "arrw_y_rad = 1 * (prob[0][0] + prob[0][3] + prob[0][6] - prob[0][2] -prob[0][5] - prob[0][8]) * 90 * np.pi / 180.\n",
    "plt.arrow(im_true.shape[1]//2, im_true.shape[0]//2, \n",
    "          np.sin(arrw_x_rad) * arrw_mg, np.sin(arrw_y_rad) * arrw_mg, \n",
    "          head_width=10, head_length=10, fc='b', ec='b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## End"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}