image_augmentation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "'''TASK DETAILS:\n",
    "Make a copy of:\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "and add your own image augmenting method. Some ideas of possible image augmentation include image enhancement \n",
    "methods using histogram modification techniques. You are welcome to do any enhancement, \n",
    "if the code and enhancement is good enough we will try and help give direction with merging \n",
    "it into the main Keras code.'''\n",
    "\n",
    "''' Jump off point from Ben: https://github.com/aleju/imgaug'''\n",
    "\n",
    "from __future__ import print_function\n",
    "import keras\n",
    "from keras.datasets import cifar10\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras import backend as K\n",
    "from matplotlib import pyplot\n",
    "import numpy as np\n",
    "#from keras.preprocessing.image import ImageDataGenerator\n",
    "#print(keras.__file__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\"\"\"Fairly basic set of tools for real-time data augmentation on image data.\n",
    "Can easily be extended to include new transformations,\n",
    "new preprocessing methods, etc...\n",
    "\"\"\"\n",
    "from __future__ import absolute_import\n",
    "from __future__ import print_function\n",
    "\n",
    "import numpy as np\n",
    "import re\n",
    "from scipy import linalg\n",
    "import scipy.ndimage as ndi\n",
    "from six.moves import range\n",
    "import os\n",
    "import threading\n",
    "import warnings\n",
    "import multiprocessing.pool\n",
    "from functools import partial\n",
    "\n",
    "#from .. import backend as K\n",
    "\n",
    "try:\n",
    "    from PIL import Image as pil_image\n",
    "except ImportError:\n",
    "    pil_image = None\n",
    "    \n",
    "def dropout(x, p=.5, per_channel=False, name=None, deterministic=False,\n",
    "            random_state=None):\n",
    "    print(\"Calling Dropout\")\n",
    "    \"\"\"Create an augmenter to set a certain fraction of pixels in images to zero.\n",
    "    Example: aug = iaa.Dropout(0.02) - drops 2 percent of all pixels.\"\"\"\n",
    "    \n",
    "    \"\"\"Create an augmenter to set a certain fraction of pixels in images to zero.\n",
    "    Examples:\n",
    "        aug = iaa.Dropout(0.02)\n",
    "    drops 2 percent of all pixels.\n",
    "\n",
    "    x : input tensor\n",
    "    p : float or tuple of two floats or StochasticParameter, optional(default=0)\n",
    "        The probability of any pixel being dropped (i.e. set to zero).\n",
    "        If a float, then that value will be used for all pixels. A value of 1.0\n",
    "          would mean, that all pixels will be dropped. A value of 0.0 would\n",
    "          lead to no pixels being dropped.\n",
    "        If a tuple (a, b), then a value p will be sampled from the\n",
    "          range a <= p <= b per image and be used as the pixel's dropout\n",
    "          probability.\n",
    "        If a StochasticParameter, then this parameter will be used to determine\n",
    "          per pixel whether it should be dropped (sampled value of 0)\n",
    "          or shouldn't (sampled value of 1).\n",
    "    per_channel : bool, optional(default=False)\n",
    "        Whether to use the same value (is dropped / is not dropped)\n",
    "        for all channels of a pixel (False) or to sample a new value for each\n",
    "        channel (True).\n",
    "        If this value is a float p, then for p percent of all images\n",
    "        per_channel will be treated as True, otherwise as False.\n",
    "    name : string, optional(default=None)\n",
    "        See Augmenter.__init__()\n",
    "    deterministic : bool, optional(default=False)\n",
    "        See Augmenter.__init__()\n",
    "    random_state : int or np.random.RandomState or None, optional(default=None)\n",
    "        See Augmenter.__init__()\n",
    "    \"\"\"\n",
    "    \n",
    "    if x.is_single_number(p):\n",
    "        p2 = Binomial(1 - p)\n",
    "    elif x.is_iterable(p):\n",
    "        assert len(p) == 2\n",
    "        assert p[0] < p[1]\n",
    "        assert 0 <= p[0] <= 1.0\n",
    "        assert 0 <= p[1] <= 1.0\n",
    "        p2 = Binomial(Uniform(1 - p[1], 1 - p[0]))\n",
    "    elif isinstance(p, StochasticParameter):\n",
    "        p2 = p\n",
    "    else:\n",
    "        raise Exception(\"Expected p to be float or int or StochasticParameter, got %s.\" % (type(p),))\n",
    "    \n",
    "    x = MultiplyElementwise(p2, per_channel=per_channel, name=name, deterministic=deterministic, random_state=random_state)\n",
    "    print(\"returning from dropout\")\n",
    "    #raise ValueError('WTF???')\n",
    "    return x\n",
    "\n",
    "def random_rotation(x, rg, row_axis=1, col_axis=2, channel_axis=0,\n",
    "                    fill_mode='nearest', cval=0.):\n",
    "    print(\"calling random_rotation\")\n",
    "    \"\"\"Performs a random rotation of a Numpy image tensor.\n",
    "    # Arguments\n",
    "        x: Input tensor. Must be 3D.\n",
    "        rg: Rotation range, in degrees.\n",
    "        row_axis: Index of axis for rows in the input tensor.\n",
    "        col_axis: Index of axis for columns in the input tensor.\n",
    "        channel_axis: Index of axis for channels in the input tensor.\n",
    "        fill_mode: Points outside the boundaries of the input\n",
    "            are filled according to the given mode\n",
    "            (one of `{'constant', 'nearest', 'reflect', 'wrap'}`).\n",
    "        cval: Value used for points outside the boundaries\n",
    "            of the input if `mode='constant'`.\n",
    "    # Returns\n",
    "        Rotated Numpy image tensor.\n",
    "    \"\"\"\n",
    "    theta = np.pi / 180 * np.random.uniform(-rg, rg)\n",
    "    rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0],\n",
    "                                [np.sin(theta), np.cos(theta), 0],\n",
    "                                [0, 0, 1]])\n",
    "\n",
    "    h, w = x.shape[row_axis], x.shape[col_axis]\n",
    "    transform_matrix = transform_matrix_offset_center(rotation_matrix, h, w)\n",
    "    x = apply_transform(x, transform_matrix, channel_axis, fill_mode, cval)\n",
    "    return x\n",
    "\n",
    "\n",
    "def random_shift(x, wrg, hrg, row_axis=1, col_axis=2, channel_axis=0,\n",
    "                 fill_mode='nearest', cval=0.):\n",
    "    \"\"\"Performs a random spatial shift of a Numpy image tensor.\n",
    "    # Arguments\n",
    "        x: Input tensor. Must be 3D.\n",
    "        wrg: Width shift range, as a float fraction of the width.\n",
    "        hrg: Height shift range, as a float fraction of the height.\n",
    "        row_axis: Index of axis for rows in the input tensor.\n",
    "        col_axis: Index of axis for columns in the input tensor.\n",
    "        channel_axis: Index of axis for channels in the input tensor.\n",
    "        fill_mode: Points outside the boundaries of the input\n",
    "            are filled according to the given mode\n",
    "            (one of `{'constant', 'nearest', 'reflect', 'wrap'}`).\n",
    "        cval: Value used for points outside the boundaries\n",
    "            of the input if `mode='constant'`.\n",
    "    # Returns\n",
    "        Shifted Numpy image tensor.\n",
    "    \"\"\"\n",
    "    h, w = x.shape[row_axis], x.shape[col_axis]\n",
    "    tx = np.random.uniform(-hrg, hrg) * h\n",
    "    ty = np.random.uniform(-wrg, wrg) * w\n",
    "    translation_matrix = np.array([[1, 0, tx],\n",
    "                                   [0, 1, ty],\n",
    "                                   [0, 0, 1]])\n",
    "\n",
    "    transform_matrix = translation_matrix  # no need to do offset\n",
    "    x = apply_transform(x, transform_matrix, channel_axis, fill_mode, cval)\n",
    "    return x\n",
    "\n",
    "\n",
    "def random_shear(x, intensity, row_axis=1, col_axis=2, channel_axis=0,\n",
    "                 fill_mode='nearest', cval=0.):\n",
    "    \"\"\"Performs a random spatial shear of a Numpy image tensor.\n",
    "    # Arguments\n",
    "        x: Input tensor. Must be 3D.\n",
    "        intensity: Transformation intensity.\n",
    "        row_axis: Index of axis for rows in the input tensor.\n",
    "        col_axis: Index of axis for columns in the input tensor.\n",
    "        channel_axis: Index of axis for channels in the input tensor.\n",
    "        fill_mode: Points outside the boundaries of the input\n",
    "            are filled according to the given mode\n",
    "            (one of `{'constant', 'nearest', 'reflect', 'wrap'}`).\n",
    "        cval: Value used for points outside the boundaries\n",
    "            of the input if `mode='constant'`.\n",
    "    # Returns\n",
    "        Sheared Numpy image tensor.\n",
    "    \"\"\"\n",
    "    shear = np.random.uniform(-intensity, intensity)\n",
    "    shear_matrix = np.array([[1, -np.sin(shear), 0],\n",
    "                             [0, np.cos(shear), 0],\n",
    "                             [0, 0, 1]])\n",
    "\n",
    "    h, w = x.shape[row_axis], x.shape[col_axis]\n",
    "    transform_matrix = transform_matrix_offset_center(shear_matrix, h, w)\n",
    "    x = apply_transform(x, transform_matrix, channel_axis, fill_mode, cval)\n",
    "    return x\n",
    "\n",
    "\n",
    "def random_zoom(x, zoom_range, row_axis=1, col_axis=2, channel_axis=0,\n",
    "                fill_mode='nearest', cval=0.):\n",
    "    \"\"\"Performs a random spatial zoom of a Numpy image tensor.\n",
    "    # Arguments\n",
    "        x: Input tensor. Must be 3D.\n",
    "        zoom_range: Tuple of floats; zoom range for width and height.\n",
    "        row_axis: Index of axis for rows in the input tensor.\n",
    "        col_axis: Index of axis for columns in the input tensor.\n",
    "        channel_axis: Index of axis for channels in the input tensor.\n",
    "        fill_mode: Points outside the boundaries of the input\n",
    "            are filled according to the given mode\n",
    "            (one of `{'constant', 'nearest', 'reflect', 'wrap'}`).\n",
    "        cval: Value used for points outside the boundaries\n",
    "            of the input if `mode='constant'`.\n",
    "    # Returns\n",
    "        Zoomed Numpy image tensor.\n",
    "    # Raises\n",
    "        ValueError: if `zoom_range` isn't a tuple.\n",
    "    \"\"\"\n",
    "    if len(zoom_range) != 2:\n",
    "        raise ValueError('`zoom_range` should be a tuple or list of two floats. '\n",
    "                         'Received arg: ', zoom_range)\n",
    "\n",
    "    if zoom_range[0] == 1 and zoom_range[1] == 1:\n",
    "        zx, zy = 1, 1\n",
    "    else:\n",
    "        zx, zy = np.random.uniform(zoom_range[0], zoom_range[1], 2)\n",
    "    zoom_matrix = np.array([[zx, 0, 0],\n",
    "                            [0, zy, 0],\n",
    "                            [0, 0, 1]])\n",
    "\n",
    "    h, w = x.shape[row_axis], x.shape[col_axis]\n",
    "    transform_matrix = transform_matrix_offset_center(zoom_matrix, h, w)\n",
    "    x = apply_transform(x, transform_matrix, channel_axis, fill_mode, cval)\n",
    "    return x\n",
    "\n",
    "\n",
    "def random_channel_shift(x, intensity, channel_axis=0):\n",
    "    x = np.rollaxis(x, channel_axis, 0)\n",
    "    min_x, max_x = np.min(x), np.max(x)\n",
    "    channel_images = [np.clip(x_channel + np.random.uniform(-intensity, intensity), min_x, max_x)\n",
    "                      for x_channel in x]\n",
    "    x = np.stack(channel_images, axis=0)\n",
    "    x = np.rollaxis(x, 0, channel_axis + 1)\n",
    "    return x\n",
    "\n",
    "\n",
    "def transform_matrix_offset_center(matrix, x, y):\n",
    "    o_x = float(x) / 2 + 0.5\n",
    "    o_y = float(y) / 2 + 0.5\n",
    "    offset_matrix = np.array([[1, 0, o_x], [0, 1, o_y], [0, 0, 1]])\n",
    "    reset_matrix = np.array([[1, 0, -o_x], [0, 1, -o_y], [0, 0, 1]])\n",
    "    transform_matrix = np.dot(np.dot(offset_matrix, matrix), reset_matrix)\n",
    "    return transform_matrix\n",
    "\n",
    "\n",
    "def apply_transform(x,\n",
    "                    transform_matrix,\n",
    "                    channel_axis=0,\n",
    "                    fill_mode='nearest',\n",
    "                    cval=0.):\n",
    "    \"\"\"Apply the image transformation specified by a matrix.\n",
    "    # Arguments\n",
    "        x: 2D numpy array, single image.\n",
    "        transform_matrix: Numpy array specifying the geometric transformation.\n",
    "        channel_axis: Index of axis for channels in the input tensor.\n",
    "        fill_mode: Points outside the boundaries of the input\n",
    "            are filled according to the given mode\n",
    "            (one of `{'constant', 'nearest', 'reflect', 'wrap'}`).\n",
    "        cval: Value used for points outside the boundaries\n",
    "            of the input if `mode='constant'`.\n",
    "    # Returns\n",
    "        The transformed version of the input.\n",
    "    \"\"\"\n",
    "    x = np.rollaxis(x, channel_axis, 0)\n",
    "    final_affine_matrix = transform_matrix[:2, :2]\n",
    "    final_offset = transform_matrix[:2, 2]\n",
    "    channel_images = [ndi.interpolation.affine_transform(\n",
    "        x_channel,\n",
    "        final_affine_matrix,\n",
    "        final_offset,\n",
    "        order=0,\n",
    "        mode=fill_mode,\n",
    "        cval=cval) for x_channel in x]\n",
    "    x = np.stack(channel_images, axis=0)\n",
    "    x = np.rollaxis(x, 0, channel_axis + 1)\n",
    "    return x\n",
    "\n",
    "\n",
    "def flip_axis(x, axis):\n",
    "    x = np.asarray(x).swapaxes(axis, 0)\n",
    "    x = x[::-1, ...]\n",
    "    x = x.swapaxes(0, axis)\n",
    "    return x\n",
    "\n",
    "\n",
    "def array_to_img(x, data_format=None, scale=True):\n",
    "    \"\"\"Converts a 3D Numpy array to a PIL Image instance.\n",
    "    # Arguments\n",
    "        x: Input Numpy array.\n",
    "        data_format: Image data format.\n",
    "        scale: Whether to rescale image values\n",
    "            to be within [0, 255].\n",
    "    # Returns\n",
    "        A PIL Image instance.\n",
    "    # Raises\n",
    "        ImportError: if PIL is not available.\n",
    "        ValueError: if invalid `x` or `data_format` is passed.\n",
    "    \"\"\"\n",
    "    if pil_image is None:\n",
    "        raise ImportError('Could not import PIL.Image. '\n",
    "                          'The use of `array_to_img` requires PIL.')\n",
    "    x = np.asarray(x, dtype=K.floatx())\n",
    "    if x.ndim != 3:\n",
    "        raise ValueError('Expected image array to have rank 3 (single image). '\n",
    "                         'Got array with shape:', x.shape)\n",
    "\n",
    "    if data_format is None:\n",
    "        data_format = K.image_data_format()\n",
    "    if data_format not in {'channels_first', 'channels_last'}:\n",
    "        raise ValueError('Invalid data_format:', data_format)\n",
    "\n",
    "    # Original Numpy array x has format (height, width, channel)\n",
    "    # or (channel, height, width)\n",
    "    # but target PIL image has format (width, height, channel)\n",
    "    if data_format == 'channels_first':\n",
    "        x = x.transpose(1, 2, 0)\n",
    "    if scale:\n",
    "        x = x + max(-np.min(x), 0)\n",
    "        x_max = np.max(x)\n",
    "        if x_max != 0:\n",
    "            x /= x_max\n",
    "        x *= 255\n",
    "    if x.shape[2] == 3:\n",
    "        # RGB\n",
    "        return pil_image.fromarray(x.astype('uint8'), 'RGB')\n",
    "    elif x.shape[2] == 1:\n",
    "        # grayscale\n",
    "        return pil_image.fromarray(x[:, :, 0].astype('uint8'), 'L')\n",
    "    else:\n",
    "        raise ValueError('Unsupported channel number: ', x.shape[2])\n",
    "\n",
    "\n",
    "def img_to_array(img, data_format=None):\n",
    "    \"\"\"Converts a PIL Image instance to a Numpy array.\n",
    "    # Arguments\n",
    "        img: PIL Image instance.\n",
    "        data_format: Image data format.\n",
    "    # Returns\n",
    "        A 3D Numpy array.\n",
    "    # Raises\n",
    "        ValueError: if invalid `img` or `data_format` is passed.\n",
    "    \"\"\"\n",
    "    if data_format is None:\n",
    "        data_format = K.image_data_format()\n",
    "    if data_format not in {'channels_first', 'channels_last'}:\n",
    "        raise ValueError('Unknown data_format: ', data_format)\n",
    "    # Numpy array x has format (height, width, channel)\n",
    "    # or (channel, height, width)\n",
    "    # but original PIL image has format (width, height, channel)\n",
    "    x = np.asarray(img, dtype=K.floatx())\n",
    "    if len(x.shape) == 3:\n",
    "        if data_format == 'channels_first':\n",
    "            x = x.transpose(2, 0, 1)\n",
    "    elif len(x.shape) == 2:\n",
    "        if data_format == 'channels_first':\n",
    "            x = x.reshape((1, x.shape[0], x.shape[1]))\n",
    "        else:\n",
    "            x = x.reshape((x.shape[0], x.shape[1], 1))\n",
    "    else:\n",
    "        raise ValueError('Unsupported image shape: ', x.shape)\n",
    "    return x\n",
    "\n",
    "\n",
    "def load_img(path, grayscale=False, target_size=None):\n",
    "    \"\"\"Loads an image into PIL format.\n",
    "    # Arguments\n",
    "        path: Path to image file\n",
    "        grayscale: Boolean, whether to load the image as grayscale.\n",
    "        target_size: Either `None` (default to original size)\n",
    "            or tuple of ints `(img_height, img_width)`.\n",
    "    # Returns\n",
    "        A PIL Image instance.\n",
    "    # Raises\n",
    "        ImportError: if PIL is not available.\n",
    "    \"\"\"\n",
    "    if pil_image is None:\n",
    "        raise ImportError('Could not import PIL.Image. '\n",
    "                          'The use of `array_to_img` requires PIL.')\n",
    "    img = pil_image.open(path)\n",
    "    if grayscale:\n",
    "        if img.mode != 'L':\n",
    "            img = img.convert('L')\n",
    "    else:\n",
    "        if img.mode != 'RGB':\n",
    "            img = img.convert('RGB')\n",
    "    if target_size:\n",
    "        hw_tuple = (target_size[1], target_size[0])\n",
    "        if img.size != hw_tuple:\n",
    "            img = img.resize(hw_tuple)\n",
    "    return img\n",
    "\n",
    "\n",
    "def list_pictures(directory, ext='jpg|jpeg|bmp|png'):\n",
    "    return [os.path.join(root, f)\n",
    "            for root, _, files in os.walk(directory) for f in files\n",
    "            if re.match(r'([\\w]+\\.(?:' + ext + '))', f)]\n",
    "\n",
    "\n",
    "class ImageDataGenerator(object):\n",
    "    \"\"\"Generate minibatches of image data with real-time data augmentation.\n",
    "    # Arguments\n",
    "        featurewise_center: set input mean to 0 over the dataset.\n",
    "        samplewise_center: set each sample mean to 0.\n",
    "        featurewise_std_normalization: divide inputs by std of the dataset.\n",
    "        samplewise_std_normalization: divide each input by its std.\n",
    "        zca_whitening: apply ZCA whitening.\n",
    "        zca_epsilon: epsilon for ZCA whitening. Default is 1e-6.\n",
    "        rotation_range: degrees (0 to 180).\n",
    "        width_shift_range: fraction of total width.\n",
    "        height_shift_range: fraction of total height.\n",
    "        shear_range: shear intensity (shear angle in radians).\n",
    "        zoom_range: amount of zoom. if scalar z, zoom will be randomly picked\n",
    "            in the range [1-z, 1+z]. A sequence of two can be passed instead\n",
    "            to select this range.\n",
    "        channel_shift_range: shift range for each channels.\n",
    "        fill_mode: points outside the boundaries are filled according to the\n",
    "            given mode ('constant', 'nearest', 'reflect' or 'wrap'). Default\n",
    "            is 'nearest'.\n",
    "        cval: value used for points outside the boundaries when fill_mode is\n",
    "            'constant'. Default is 0.\n",
    "        horizontal_flip: whether to randomly flip images horizontally.\n",
    "        vertical_flip: whether to randomly flip images vertically.\n",
    "        rescale: rescaling factor. If None or 0, no rescaling is applied,\n",
    "            otherwise we multiply the data by the value provided\n",
    "            (before applying any other transformation).\n",
    "        preprocessing_function: function that will be implied on each input.\n",
    "            The function will run before any other modification on it.\n",
    "            The function should take one argument:\n",
    "            one image (Numpy tensor with rank 3),\n",
    "            and should output a Numpy tensor with the same shape.\n",
    "        data_format: 'channels_first' or 'channels_last'. In 'channels_first' mode, the channels dimension\n",
    "            (the depth) is at index 1, in 'channels_last' mode it is at index 3.\n",
    "            It defaults to the `image_data_format` value found in your\n",
    "            Keras config file at `~/.keras/keras.json`.\n",
    "            If you never set it, then it will be \"channels_last\".\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self,\n",
    "                 dropout=0., ##############\n",
    "                 featurewise_center=False,\n",
    "                 samplewise_center=False,\n",
    "                 featurewise_std_normalization=False,\n",
    "                 samplewise_std_normalization=False,\n",
    "                 zca_whitening=False,\n",
    "                 zca_epsilon=1e-6,\n",
    "                 rotation_range=0.,\n",
    "                 width_shift_range=0.,\n",
    "                 height_shift_range=0.,\n",
    "                 shear_range=0.,\n",
    "                 zoom_range=0.,\n",
    "                 channel_shift_range=0.,\n",
    "                 fill_mode='nearest',\n",
    "                 cval=0.,\n",
    "                 horizontal_flip=False,\n",
    "                 vertical_flip=False,\n",
    "                 rescale=None,\n",
    "                 preprocessing_function=None,\n",
    "                 data_format=None):\n",
    "        print(\"IDG init\")\n",
    "        if data_format is None:\n",
    "            data_format = K.image_data_format()\n",
    "        self.dropout = dropout #############\n",
    "        self.featurewise_center = featurewise_center\n",
    "        self.samplewise_center = samplewise_center\n",
    "        self.featurewise_std_normalization = featurewise_std_normalization\n",
    "        self.samplewise_std_normalization = samplewise_std_normalization\n",
    "        self.zca_whitening = zca_whitening\n",
    "        self.zca_epsilon = zca_epsilon\n",
    "        self.rotation_range = rotation_range\n",
    "        self.width_shift_range = width_shift_range\n",
    "        self.height_shift_range = height_shift_range\n",
    "        self.shear_range = shear_range\n",
    "        self.zoom_range = zoom_range\n",
    "        self.channel_shift_range = channel_shift_range\n",
    "        self.fill_mode = fill_mode\n",
    "        self.cval = cval\n",
    "        self.horizontal_flip = horizontal_flip\n",
    "        self.vertical_flip = vertical_flip\n",
    "        self.rescale = rescale\n",
    "        self.preprocessing_function = preprocessing_function\n",
    "        \n",
    "        #print(self) #########\n",
    "\n",
    "        if data_format not in {'channels_last', 'channels_first'}:\n",
    "            raise ValueError('`data_format` should be `\"channels_last\"` (channel after row and '\n",
    "                             'column) or `\"channels_first\"` (channel before row and column). '\n",
    "                             'Received arg: ', data_format)\n",
    "        self.data_format = data_format\n",
    "        if data_format == 'channels_first':\n",
    "            self.channel_axis = 1\n",
    "            self.row_axis = 2\n",
    "            self.col_axis = 3\n",
    "        if data_format == 'channels_last':\n",
    "            self.channel_axis = 3\n",
    "            self.row_axis = 1\n",
    "            self.col_axis = 2\n",
    "\n",
    "        self.mean = None\n",
    "        self.std = None\n",
    "        self.principal_components = None\n",
    "\n",
    "        if np.isscalar(zoom_range):\n",
    "            self.zoom_range = [1 - zoom_range, 1 + zoom_range]\n",
    "        elif len(zoom_range) == 2:\n",
    "            self.zoom_range = [zoom_range[0], zoom_range[1]]\n",
    "        else:\n",
    "            raise ValueError('`zoom_range` should be a float or '\n",
    "                             'a tuple or list of two floats. '\n",
    "                             'Received arg: ', zoom_range)\n",
    "\n",
    "    def flow(self, x, y=None, batch_size=32, shuffle=True, seed=None,\n",
    "             save_to_dir=None, save_prefix='', save_format='png'):\n",
    "        return NumpyArrayIterator(\n",
    "            x, y, self,\n",
    "            batch_size=batch_size,\n",
    "            shuffle=shuffle,\n",
    "            seed=seed,\n",
    "            data_format=self.data_format,\n",
    "            save_to_dir=save_to_dir,\n",
    "            save_prefix=save_prefix,\n",
    "            save_format=save_format)\n",
    "\n",
    "    def flow_from_directory(self, directory,\n",
    "                            target_size=(256, 256), color_mode='rgb',\n",
    "                            classes=None, class_mode='categorical',\n",
    "                            batch_size=32, shuffle=True, seed=None,\n",
    "                            save_to_dir=None,\n",
    "                            save_prefix='',\n",
    "                            save_format='png',\n",
    "                            follow_links=False):\n",
    "        return DirectoryIterator(\n",
    "            directory, self,\n",
    "            target_size=target_size, color_mode=color_mode,\n",
    "            classes=classes, class_mode=class_mode,\n",
    "            data_format=self.data_format,\n",
    "            batch_size=batch_size, shuffle=shuffle, seed=seed,\n",
    "            save_to_dir=save_to_dir,\n",
    "            save_prefix=save_prefix,\n",
    "            save_format=save_format,\n",
    "            follow_links=follow_links)\n",
    "\n",
    "    def standardize(self, x):\n",
    "        \"\"\"Apply the normalization configuration to a batch of inputs.\n",
    "        # Arguments\n",
    "            x: batch of inputs to be normalized.\n",
    "        # Returns\n",
    "            The inputs, normalized.\n",
    "        \"\"\"\n",
    "        if self.preprocessing_function:\n",
    "            x = self.preprocessing_function(x)\n",
    "        if self.rescale:\n",
    "            x *= self.rescale\n",
    "        # x is a single image, so it doesn't have image number at index 0\n",
    "        img_channel_axis = self.channel_axis - 1\n",
    "        if self.samplewise_center:\n",
    "            x -= np.mean(x, axis=img_channel_axis, keepdims=True)\n",
    "        if self.samplewise_std_normalization:\n",
    "            x /= (np.std(x, axis=img_channel_axis, keepdims=True) + 1e-7)\n",
    "\n",
    "        if self.featurewise_center:\n",
    "            if self.mean is not None:\n",
    "                x -= self.mean\n",
    "            else:\n",
    "                warnings.warn('This ImageDataGenerator specifies '\n",
    "                              '`featurewise_center`, but it hasn\\'t'\n",
    "                              'been fit on any training data. Fit it '\n",
    "                              'first by calling `.fit(numpy_data)`.')\n",
    "        if self.featurewise_std_normalization:\n",
    "            if self.std is not None:\n",
    "                x /= (self.std + 1e-7)\n",
    "            else:\n",
    "                warnings.warn('This ImageDataGenerator specifies '\n",
    "                              '`featurewise_std_normalization`, but it hasn\\'t'\n",
    "                              'been fit on any training data. Fit it '\n",
    "                              'first by calling `.fit(numpy_data)`.')\n",
    "        if self.zca_whitening:\n",
    "            if self.principal_components is not None:\n",
    "                flatx = np.reshape(x, (x.size))\n",
    "                whitex = np.dot(flatx, self.principal_components)\n",
    "                x = np.reshape(whitex, (x.shape[0], x.shape[1], x.shape[2]))\n",
    "            else:\n",
    "                warnings.warn('This ImageDataGenerator specifies '\n",
    "                              '`zca_whitening`, but it hasn\\'t'\n",
    "                              'been fit on any training data. Fit it '\n",
    "                              'first by calling `.fit(numpy_data)`.')\n",
    "        return x\n",
    "\n",
    "    def random_transform(self, x):\n",
    "        \"\"\"Randomly augment a single image tensor.\n",
    "        # Arguments\n",
    "            x: 3D tensor, single image.\n",
    "        # Returns\n",
    "            A randomly transformed version of the input (same shape).\n",
    "        \"\"\"\n",
    "        # x is a single image, so it doesn't have image number at index 0\n",
    "        img_row_axis = self.row_axis - 1\n",
    "        img_col_axis = self.col_axis - 1\n",
    "        img_channel_axis = self.channel_axis - 1\n",
    "\n",
    "        # use composition of homographies\n",
    "        # to generate final transform that needs to be applied\n",
    "        if self.rotation_range:\n",
    "            theta = np.pi / 180 * np.random.uniform(-self.rotation_range, self.rotation_range)\n",
    "        else:\n",
    "            theta = 0\n",
    "\n",
    "        if self.height_shift_range:\n",
    "            tx = np.random.uniform(-self.height_shift_range, self.height_shift_range) * x.shape[img_row_axis]\n",
    "        else:\n",
    "            tx = 0\n",
    "\n",
    "        if self.width_shift_range:\n",
    "            ty = np.random.uniform(-self.width_shift_range, self.width_shift_range) * x.shape[img_col_axis]\n",
    "        else:\n",
    "            ty = 0\n",
    "\n",
    "        if self.shear_range:\n",
    "            shear = np.random.uniform(-self.shear_range, self.shear_range)\n",
    "        else:\n",
    "            shear = 0\n",
    "\n",
    "        if self.zoom_range[0] == 1 and self.zoom_range[1] == 1:\n",
    "            zx, zy = 1, 1\n",
    "        else:\n",
    "            zx, zy = np.random.uniform(self.zoom_range[0], self.zoom_range[1], 2)\n",
    "\n",
    "        transform_matrix = None\n",
    "        if theta != 0:\n",
    "            rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0],\n",
    "                                        [np.sin(theta), np.cos(theta), 0],\n",
    "                                        [0, 0, 1]])\n",
    "            transform_matrix = rotation_matrix\n",
    "\n",
    "        if tx != 0 or ty != 0:\n",
    "            shift_matrix = np.array([[1, 0, tx],\n",
    "                                     [0, 1, ty],\n",
    "                                     [0, 0, 1]])\n",
    "            transform_matrix = shift_matrix if transform_matrix is None else np.dot(transform_matrix, shift_matrix)\n",
    "\n",
    "        if shear != 0:\n",
    "            shear_matrix = np.array([[1, -np.sin(shear), 0],\n",
    "                                    [0, np.cos(shear), 0],\n",
    "                                    [0, 0, 1]])\n",
    "            transform_matrix = shear_matrix if transform_matrix is None else np.dot(transform_matrix, shear_matrix)\n",
    "\n",
    "        if zx != 1 or zy != 1:\n",
    "            zoom_matrix = np.array([[zx, 0, 0],\n",
    "                                    [0, zy, 0],\n",
    "                                    [0, 0, 1]])\n",
    "            transform_matrix = zoom_matrix if transform_matrix is None else np.dot(transform_matrix, zoom_matrix)\n",
    "\n",
    "        if transform_matrix is not None:\n",
    "            h, w = x.shape[img_row_axis], x.shape[img_col_axis]\n",
    "            transform_matrix = transform_matrix_offset_center(transform_matrix, h, w)\n",
    "            x = apply_transform(x, transform_matrix, img_channel_axis,\n",
    "                                fill_mode=self.fill_mode, cval=self.cval)\n",
    "\n",
    "        if self.channel_shift_range != 0:\n",
    "            x = random_channel_shift(x,\n",
    "                                     self.channel_shift_range,\n",
    "                                     img_channel_axis)\n",
    "        if self.horizontal_flip:\n",
    "            if np.random.random() < 0.5:\n",
    "                x = flip_axis(x, img_col_axis)\n",
    "\n",
    "        if self.vertical_flip:\n",
    "            if np.random.random() < 0.5:\n",
    "                x = flip_axis(x, img_row_axis)\n",
    "\n",
    "        return x\n",
    "\n",
    "    def fit(self, x,\n",
    "            augment=False,\n",
    "            rounds=1,\n",
    "            seed=None):\n",
    "        \"\"\"Fits internal statistics to some sample data.\n",
    "        Required for featurewise_center, featurewise_std_normalization\n",
    "        and zca_whitening.\n",
    "        # Arguments\n",
    "            x: Numpy array, the data to fit on. Should have rank 4.\n",
    "                In case of grayscale data,\n",
    "                the channels axis should have value 1, and in case\n",
    "                of RGB data, it should have value 3.\n",
    "            augment: Whether to fit on randomly augmented samples\n",
    "            rounds: If `augment`,\n",
    "                how many augmentation passes to do over the data\n",
    "            seed: random seed.\n",
    "        # Raises\n",
    "            ValueError: in case of invalid input `x`.\n",
    "        \"\"\"\n",
    "        x = np.asarray(x, dtype=K.floatx())\n",
    "        if x.ndim != 4:\n",
    "            raise ValueError('Input to `.fit()` should have rank 4. '\n",
    "                             'Got array with shape: ' + str(x.shape))\n",
    "        if x.shape[self.channel_axis] not in {3, 4}:\n",
    "            warnings.warn(\n",
    "                'Expected input to be images (as Numpy array) '\n",
    "                'following the data format convention \"' + self.data_format + '\" '\n",
    "                '(channels on axis ' + str(self.channel_axis) + '), i.e. expected '\n",
    "                'either 1, 3 or 4 channels on axis ' + str(self.channel_axis) + '. '\n",
    "                'However, it was passed an array with shape ' + str(x.shape) +\n",
    "                ' (' + str(x.shape[self.channel_axis]) + ' channels).')\n",
    "\n",
    "        if seed is not None:\n",
    "            np.random.seed(seed)\n",
    "\n",
    "        x = np.copy(x)\n",
    "        if augment:\n",
    "            ax = np.zeros(tuple([rounds * x.shape[0]] + list(x.shape)[1:]), dtype=K.floatx())\n",
    "            for r in range(rounds):\n",
    "                for i in range(x.shape[0]):\n",
    "                    ax[i + r * x.shape[0]] = self.random_transform(x[i])\n",
    "            x = ax\n",
    "\n",
    "        if self.featurewise_center:\n",
    "            self.mean = np.mean(x, axis=(0, self.row_axis, self.col_axis))\n",
    "            broadcast_shape = [1, 1, 1]\n",
    "            broadcast_shape[self.channel_axis - 1] = x.shape[self.channel_axis]\n",
    "            self.mean = np.reshape(self.mean, broadcast_shape)\n",
    "            x -= self.mean\n",
    "\n",
    "        if self.featurewise_std_normalization:\n",
    "            self.std = np.std(x, axis=(0, self.row_axis, self.col_axis))\n",
    "            broadcast_shape = [1, 1, 1]\n",
    "            broadcast_shape[self.channel_axis - 1] = x.shape[self.channel_axis]\n",
    "            self.std = np.reshape(self.std, broadcast_shape)\n",
    "            x /= (self.std + K.epsilon())\n",
    "\n",
    "        if self.zca_whitening:\n",
    "            flat_x = np.reshape(x, (x.shape[0], x.shape[1] * x.shape[2] * x.shape[3]))\n",
    "            sigma = np.dot(flat_x.T, flat_x) / flat_x.shape[0]\n",
    "            u, s, _ = linalg.svd(sigma)\n",
    "            self.principal_components = np.dot(np.dot(u, np.diag(1. / np.sqrt(s + self.zca_epsilon))), u.T)\n",
    "\n",
    "\n",
    "class Iterator(object):\n",
    "    \"\"\"Abstract base class for image data iterators.\n",
    "    # Arguments\n",
    "        n: Integer, total number of samples in the dataset to loop over.\n",
    "        batch_size: Integer, size of a batch.\n",
    "        shuffle: Boolean, whether to shuffle the data between epochs.\n",
    "        seed: Random seeding for data shuffling.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, n, batch_size, shuffle, seed):\n",
    "        self.n = n\n",
    "        self.batch_size = batch_size\n",
    "        self.shuffle = shuffle\n",
    "        self.batch_index = 0\n",
    "        self.total_batches_seen = 0\n",
    "        self.lock = threading.Lock()\n",
    "        self.index_generator = self._flow_index(n, batch_size, shuffle, seed)\n",
    "\n",
    "    def reset(self):\n",
    "        self.batch_index = 0\n",
    "\n",
    "    def _flow_index(self, n, batch_size=32, shuffle=False, seed=None):\n",
    "        # Ensure self.batch_index is 0.\n",
    "        self.reset()\n",
    "        while 1:\n",
    "            if seed is not None:\n",
    "                np.random.seed(seed + self.total_batches_seen)\n",
    "            if self.batch_index == 0:\n",
    "                index_array = np.arange(n)\n",
    "                if shuffle:\n",
    "                    index_array = np.random.permutation(n)\n",
    "\n",
    "            current_index = (self.batch_index * batch_size) % n\n",
    "            if n > current_index + batch_size:\n",
    "                current_batch_size = batch_size\n",
    "                self.batch_index += 1\n",
    "            else:\n",
    "                current_batch_size = n - current_index\n",
    "                self.batch_index = 0\n",
    "            self.total_batches_seen += 1\n",
    "            yield (index_array[current_index: current_index + current_batch_size],\n",
    "                   current_index, current_batch_size)\n",
    "\n",
    "    def __iter__(self):\n",
    "        # Needed if we want to do something like:\n",
    "        # for x, y in data_gen.flow(...):\n",
    "        return self\n",
    "\n",
    "    def __next__(self, *args, **kwargs):\n",
    "        return self.next(*args, **kwargs)\n",
    "\n",
    "\n",
    "class NumpyArrayIterator(Iterator):\n",
    "    \"\"\"Iterator yielding data from a Numpy array.\n",
    "    # Arguments\n",
    "        x: Numpy array of input data.\n",
    "        y: Numpy array of targets data.\n",
    "        image_data_generator: Instance of `ImageDataGenerator`\n",
    "            to use for random transformations and normalization.\n",
    "        batch_size: Integer, size of a batch.\n",
    "        shuffle: Boolean, whether to shuffle the data between epochs.\n",
    "        seed: Random seed for data shuffling.\n",
    "        data_format: String, one of `channels_first`, `channels_last`.\n",
    "        save_to_dir: Optional directory where to save the pictures\n",
    "            being yielded, in a viewable format. This is useful\n",
    "            for visualizing the random transformations being\n",
    "            applied, for debugging purposes.\n",
    "        save_prefix: String prefix to use for saving sample\n",
    "            images (if `save_to_dir` is set).\n",
    "        save_format: Format to use for saving sample images\n",
    "            (if `save_to_dir` is set).\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, x, y, image_data_generator,\n",
    "                 batch_size=32, shuffle=False, seed=None,\n",
    "                 data_format=None,\n",
    "                 save_to_dir=None, save_prefix='', save_format='png'):\n",
    "        if y is not None and len(x) != len(y):\n",
    "            raise ValueError('X (images tensor) and y (labels) '\n",
    "                             'should have the same length. '\n",
    "                             'Found: X.shape = %s, y.shape = %s' %\n",
    "                             (np.asarray(x).shape, np.asarray(y).shape))\n",
    "\n",
    "        if data_format is None:\n",
    "            data_format = K.image_data_format()\n",
    "        self.x = np.asarray(x, dtype=K.floatx())\n",
    "\n",
    "        if self.x.ndim != 4:\n",
    "            raise ValueError('Input data in `NumpyArrayIterator` '\n",
    "                             'should have rank 4. You passed an array '\n",
    "                             'with shape', self.x.shape)\n",
    "        channels_axis = 3 if data_format == 'channels_last' else 1\n",
    "        if self.x.shape[channels_axis] not in {1, 3, 4}:\n",
    "            raise ValueError('NumpyArrayIterator is set to use the '\n",
    "                             'data format convention \"' + data_format + '\" '\n",
    "                             '(channels on axis ' + str(channels_axis) + '), i.e. expected '\n",
    "                             'either 1, 3 or 4 channels on axis ' + str(channels_axis) + '. '\n",
    "                             'However, it was passed an array with shape ' + str(self.x.shape) +\n",
    "                             ' (' + str(self.x.shape[channels_axis]) + ' channels).')\n",
    "        if y is not None:\n",
    "            self.y = np.asarray(y)\n",
    "        else:\n",
    "            self.y = None\n",
    "        self.image_data_generator = image_data_generator\n",
    "        self.data_format = data_format\n",
    "        self.save_to_dir = save_to_dir\n",
    "        self.save_prefix = save_prefix\n",
    "        self.save_format = save_format\n",
    "        super(NumpyArrayIterator, self).__init__(x.shape[0], batch_size, shuffle, seed)\n",
    "\n",
    "    def next(self):\n",
    "        \"\"\"For python 2.x.\n",
    "        # Returns\n",
    "            The next batch.\n",
    "        \"\"\"\n",
    "        # Keeps under lock only the mechanism which advances\n",
    "        # the indexing of each batch.\n",
    "        with self.lock:\n",
    "            index_array, current_index, current_batch_size = next(self.index_generator)\n",
    "        # The transformation of images is not under thread lock\n",
    "        # so it can be done in parallel\n",
    "        batch_x = np.zeros(tuple([current_batch_size] + list(self.x.shape)[1:]), dtype=K.floatx())\n",
    "        for i, j in enumerate(index_array):\n",
    "            x = self.x[j]\n",
    "            x = self.image_data_generator.random_transform(x.astype(K.floatx()))\n",
    "            x = self.image_data_generator.standardize(x)\n",
    "            batch_x[i] = x\n",
    "        if self.save_to_dir:\n",
    "            for i in range(current_batch_size):\n",
    "                img = array_to_img(batch_x[i], self.data_format, scale=True)\n",
    "                fname = '{prefix}_{index}_{hash}.{format}'.format(prefix=self.save_prefix,\n",
    "                                                                  index=current_index + i,\n",
    "                                                                  hash=np.random.randint(1e4),\n",
    "                                                                  format=self.save_format)\n",
    "                img.save(os.path.join(self.save_to_dir, fname))\n",
    "        if self.y is None:\n",
    "            return batch_x\n",
    "        batch_y = self.y[index_array]\n",
    "        return batch_x, batch_y\n",
    "\n",
    "\n",
    "def _count_valid_files_in_directory(directory, white_list_formats, follow_links):\n",
    "    \"\"\"Count files with extension in `white_list_formats` contained in a directory.\n",
    "    # Arguments\n",
    "        directory: absolute path to the directory containing files to be counted\n",
    "        white_list_formats: set of strings containing allowed extensions for\n",
    "            the files to be counted.\n",
    "    # Returns\n",
    "        the count of files with extension in `white_list_formats` contained in\n",
    "        the directory.\n",
    "    \"\"\"\n",
    "    def _recursive_list(subpath):\n",
    "        return sorted(os.walk(subpath, followlinks=follow_links), key=lambda tpl: tpl[0])\n",
    "\n",
    "    samples = 0\n",
    "    for root, _, files in _recursive_list(directory):\n",
    "        for fname in files:\n",
    "            is_valid = False\n",
    "            for extension in white_list_formats:\n",
    "                if fname.lower().endswith('.' + extension):\n",
    "                    is_valid = True\n",
    "                    break\n",
    "            if is_valid:\n",
    "                samples += 1\n",
    "    return samples\n",
    "\n",
    "\n",
    "def _list_valid_filenames_in_directory(directory, white_list_formats,\n",
    "                                       class_indices, follow_links):\n",
    "    \"\"\"List paths of files in `subdir` relative from `directory` whose extensions are in `white_list_formats`.\n",
    "    # Arguments\n",
    "        directory: absolute path to a directory containing the files to list.\n",
    "            The directory name is used as class label and must be a key of `class_indices`.\n",
    "        white_list_formats: set of strings containing allowed extensions for\n",
    "            the files to be counted.\n",
    "        class_indices: dictionary mapping a class name to its index.\n",
    "    # Returns\n",
    "        classes: a list of class indices\n",
    "        filenames: the path of valid files in `directory`, relative from\n",
    "            `directory`'s parent (e.g., if `directory` is \"dataset/class1\",\n",
    "            the filenames will be [\"class1/file1.jpg\", \"class1/file2.jpg\", ...]).\n",
    "    \"\"\"\n",
    "    def _recursive_list(subpath):\n",
    "        return sorted(os.walk(subpath, followlinks=follow_links), key=lambda tpl: tpl[0])\n",
    "\n",
    "    classes = []\n",
    "    filenames = []\n",
    "    subdir = os.path.basename(directory)\n",
    "    basedir = os.path.dirname(directory)\n",
    "    for root, _, files in _recursive_list(directory):\n",
    "        for fname in files:\n",
    "            is_valid = False\n",
    "            for extension in white_list_formats:\n",
    "                if fname.lower().endswith('.' + extension):\n",
    "                    is_valid = True\n",
    "                    break\n",
    "            if is_valid:\n",
    "                classes.append(class_indices[subdir])\n",
    "                # add filename relative to directory\n",
    "                absolute_path = os.path.join(root, fname)\n",
    "                filenames.append(os.path.relpath(absolute_path, basedir))\n",
    "    return classes, filenames\n",
    "\n",
    "\n",
    "class DirectoryIterator(Iterator):\n",
    "    \"\"\"Iterator capable of reading images from a directory on disk.\n",
    "    # Arguments\n",
    "        directory: Path to the directory to read images from.\n",
    "            Each subdirectory in this directory will be\n",
    "            considered to contain images from one class,\n",
    "            or alternatively you could specify class subdirectories\n",
    "            via the `classes` argument.\n",
    "        image_data_generator: Instance of `ImageDataGenerator`\n",
    "            to use for random transformations and normalization.\n",
    "        target_size: tuple of integers, dimensions to resize input images to.\n",
    "        color_mode: One of `\"rgb\"`, `\"grayscale\"`. Color mode to read images.\n",
    "        classes: Optional list of strings, names of sudirectories\n",
    "            containing images from each class (e.g. `[\"dogs\", \"cats\"]`).\n",
    "            It will be computed automatically if not set.\n",
    "        class_mode: Mode for yielding the targets:\n",
    "            `\"binary\"`: binary targets (if there are only two classes),\n",
    "            `\"categorical\"`: categorical targets,\n",
    "            `\"sparse\"`: integer targets,\n",
    "            `\"input\"`: targets are images identical to input images (mainly\n",
    "                used to work with autoencoders),\n",
    "            `None`: no targets get yielded (only input images are yielded).\n",
    "        batch_size: Integer, size of a batch.\n",
    "        shuffle: Boolean, whether to shuffle the data between epochs.\n",
    "        seed: Random seed for data shuffling.\n",
    "        data_format: String, one of `channels_first`, `channels_last`.\n",
    "        save_to_dir: Optional directory where to save the pictures\n",
    "            being yielded, in a viewable format. This is useful\n",
    "            for visualizing the random transformations being\n",
    "            applied, for debugging purposes.\n",
    "        save_prefix: String prefix to use for saving sample\n",
    "            images (if `save_to_dir` is set).\n",
    "        save_format: Format to use for saving sample images\n",
    "            (if `save_to_dir` is set).\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, directory, image_data_generator,\n",
    "                 target_size=(256, 256), color_mode='rgb',\n",
    "                 classes=None, class_mode='categorical',\n",
    "                 batch_size=32, shuffle=True, seed=None,\n",
    "                 data_format=None,\n",
    "                 save_to_dir=None, save_prefix='', save_format='png',\n",
    "                 follow_links=False):\n",
    "        if data_format is None:\n",
    "            data_format = K.image_data_format()\n",
    "        self.directory = directory\n",
    "        self.image_data_generator = image_data_generator\n",
    "        self.target_size = tuple(target_size)\n",
    "        if color_mode not in {'rgb', 'grayscale'}:\n",
    "            raise ValueError('Invalid color mode:', color_mode,\n",
    "                             '; expected \"rgb\" or \"grayscale\".')\n",
    "        self.color_mode = color_mode\n",
    "        self.data_format = data_format\n",
    "        if self.color_mode == 'rgb':\n",
    "            if self.data_format == 'channels_last':\n",
    "                self.image_shape = self.target_size + (3,)\n",
    "            else:\n",
    "                self.image_shape = (3,) + self.target_size\n",
    "        else:\n",
    "            if self.data_format == 'channels_last':\n",
    "                self.image_shape = self.target_size + (1,)\n",
    "            else:\n",
    "                self.image_shape = (1,) + self.target_size\n",
    "        self.classes = classes\n",
    "        if class_mode not in {'categorical', 'binary', 'sparse',\n",
    "                              'input', None}:\n",
    "            raise ValueError('Invalid class_mode:', class_mode,\n",
    "                             '; expected one of \"categorical\", '\n",
    "                             '\"binary\", \"sparse\", \"input\"'\n",
    "                             ' or None.')\n",
    "        self.class_mode = class_mode\n",
    "        self.save_to_dir = save_to_dir\n",
    "        self.save_prefix = save_prefix\n",
    "        self.save_format = save_format\n",
    "\n",
    "        white_list_formats = {'png', 'jpg', 'jpeg', 'bmp'}\n",
    "\n",
    "        # first, count the number of samples and classes\n",
    "        self.samples = 0\n",
    "\n",
    "        if not classes:\n",
    "            classes = []\n",
    "            for subdir in sorted(os.listdir(directory)):\n",
    "                if os.path.isdir(os.path.join(directory, subdir)):\n",
    "                    classes.append(subdir)\n",
    "        self.num_class = len(classes)\n",
    "        self.class_indices = dict(zip(classes, range(len(classes))))\n",
    "\n",
    "        def _recursive_list(subpath):\n",
    "            return sorted(os.walk(subpath, followlinks=follow_links), key=lambda tpl: tpl[0])\n",
    "\n",
    "        pool = multiprocessing.pool.ThreadPool()\n",
    "        function_partial = partial(_count_valid_files_in_directory,\n",
    "                                   white_list_formats=white_list_formats,\n",
    "                                   follow_links=follow_links)\n",
    "        self.samples = sum(pool.map(function_partial,\n",
    "                                    (os.path.join(directory, subdir)\n",
    "                                     for subdir in classes)))\n",
    "\n",
    "        print('Found %d images belonging to %d classes.' % (self.samples, self.num_class))\n",
    "\n",
    "        # second, build an index of the images in the different class subfolders\n",
    "        results = []\n",
    "\n",
    "        self.filenames = []\n",
    "        self.classes = np.zeros((self.samples,), dtype='int32')\n",
    "        i = 0\n",
    "        for dirpath in (os.path.join(directory, subdir) for subdir in classes):\n",
    "            results.append(pool.apply_async(_list_valid_filenames_in_directory,\n",
    "                                            (dirpath, white_list_formats,\n",
    "                                             self.class_indices, follow_links)))\n",
    "        for res in results:\n",
    "            classes, filenames = res.get()\n",
    "            self.classes[i:i + len(classes)] = classes\n",
    "            self.filenames += filenames\n",
    "            i += len(classes)\n",
    "        pool.close()\n",
    "        pool.join()\n",
    "        super(DirectoryIterator, self).__init__(self.samples, batch_size, shuffle, seed)\n",
    "\n",
    "    def next(self):\n",
    "        \"\"\"For python 2.x.\n",
    "        # Returns\n",
    "            The next batch.\n",
    "        \"\"\"\n",
    "        with self.lock:\n",
    "            index_array, current_index, current_batch_size = next(self.index_generator)\n",
    "        # The transformation of images is not under thread lock\n",
    "        # so it can be done in parallel\n",
    "        batch_x = np.zeros((current_batch_size,) + self.image_shape, dtype=K.floatx())\n",
    "        grayscale = self.color_mode == 'grayscale'\n",
    "        # build batch of image data\n",
    "        for i, j in enumerate(index_array):\n",
    "            fname = self.filenames[j]\n",
    "            img = load_img(os.path.join(self.directory, fname),\n",
    "                           grayscale=grayscale,\n",
    "                           target_size=self.target_size)\n",
    "            x = img_to_array(img, data_format=self.data_format)\n",
    "            x = self.image_data_generator.random_transform(x)\n",
    "            x = self.image_data_generator.standardize(x)\n",
    "            batch_x[i] = x\n",
    "        # optionally save augmented images to disk for debugging purposes\n",
    "        if self.save_to_dir:\n",
    "            for i in range(current_batch_size):\n",
    "                img = array_to_img(batch_x[i], self.data_format, scale=True)\n",
    "                fname = '{prefix}_{index}_{hash}.{format}'.format(prefix=self.save_prefix,\n",
    "                                                                  index=current_index + i,\n",
    "                                                                  hash=np.random.randint(1e4),\n",
    "                                                                  format=self.save_format)\n",
    "                img.save(os.path.join(self.save_to_dir, fname))\n",
    "        # build batch of labels\n",
    "        if self.class_mode == 'input':\n",
    "            batch_y = batch_x.copy()\n",
    "        elif self.class_mode == 'sparse':\n",
    "            batch_y = self.classes[index_array]\n",
    "        elif self.class_mode == 'binary':\n",
    "            batch_y = self.classes[index_array].astype(K.floatx())\n",
    "        elif self.class_mode == 'categorical':\n",
    "            batch_y = np.zeros((len(batch_x), self.num_class), dtype=K.floatx())\n",
    "            for i, label in enumerate(self.classes[index_array]):\n",
    "                batch_y[i, label] = 1.\n",
    "        else:\n",
    "            return batch_x\n",
    "        return batch_x, batch_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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GcH/++nXK05DNC0DfRT784V/PZrMlSKS4Z5hLIaXEMO5BlC5mSpftBvbPfvjD30EQrV4/\nKnahVc17VZy/9wGrcZ7v/M5/k/1+azHLfuHJSxnNPt2JIjHQL5dIV2m7jg9/x6+n73r6rqeLEWJH\ntzxhcXJKkUAGNETWZ2ds8hnjODoFLTx3bUkuxSaSYoMb3FOhFKPWFI8dQPGkk/dcXzHudk4h+sTa\nQLU3z7tbELsloVsaDRw7XrhxnV/4zM/Tdz2v3r37izO4b0ERlF/5NR8ET2iTYhMRTHHB6rGqMyT1\nXr713PM+OdkLQdwUV99fQbMnyuViYCrCrWffbYyIW8lmiMsE2u6BFtTtSIUAAeH5F77Ckp2iAWNR\nC4fUWCYOpC22BKDw/PPvdWd7TnkZTLcEpPtcneeefYGi2cBQDFCLlNnnZ3s3Khhu3br15g7UO0h+\no1zzbF1qblGLZ7aQvFn80BkDQlQ+2j1rwJIDlMreRdDOrTmbEGUGmGjho3EFKc8AMTTwbo9u9Few\nrEf46I1n7wFT93Zsj+r9FjUv2r3S3yHXuZ9Xal8EEgSNHjMN07E/dus5DwU7kFbvdEbvTp7pJB9j\nyURhz2+OR5enCEw7Tpb2w0KMLZu25ERKI+OATSNd79mEcWb5G5AGUTRADDbuxe/tUipP78kT9foC\nQcy702JAmnOaUXBKiJFFF1nIkhA6YuyJwRJ++m5h8c7lCatrNzi5cYMMpFJIaslQ682a0Tl70yvP\nyhTLEA1aLO5fcErNgL9ubVIVaZNsdY2kPoboHm6HhB4JPSFE3wKdU8hnl+tfnMF9C0q98dSpLi2C\nOpi2m5S5Va+VUQIqQ+IzjRRCtc4dYA1Is3mZPraqphs2wXkJVN2CJ4YYz2chjmJeokY7Y/GsXVWf\noGrss2XYWrxSGq3rv3XmPRqIVqOBAzCttG09lhCcPlGEgJSASL4vALfynOob3Bekj/JQUc/YVSxZ\nKCjNGg8W/qplLFop0IK5ihVMc0SysxYle1zVmLymwG7Yk/LkwVYliaFRq5OrygSqMO0vtBfFAbiG\nCcQBrwGpTlHce7eZuBPBjOKdPjbzdOdULzWp6T7Hq+KJTZaU8Hjy1IApWggh0Pe9XRPPmBxVyTkx\njorVtFnJi9a6TOncGmokgmdP26Qg1DzuK56pW+MSAl3s0C6TcyQmL69JFjuVIJZ5G51S7a1kpSUJ\nxY6uX7A4OaFfrdjstqx3W9bbDRcX5xAjqQ6mquXTOYWsEozVo8xj+9DA162B9sbcttI6r9I0Sixz\n1LiPWndKK4243O+/RIP51peafGElHxVUbTLQmqpvOzZArfbvpGMVUidOrKb4Sy1U0Dyj3QTNDqwC\nYAlFInECbwdkrWDqcUcN93qRbT818BZPzQxSGRA75yDTpFR/67wsRxsF/DAEnMp07vfOnNI9SNY8\nouojiPqYGd1ugfA6r1XvwOPRFVijMQeUYOUiWc0rzQXJM0D1XBM8PGDeowIZxjly+ncEN5Dmnme9\nJVqGGveoQ/ViUZ1KbOb30wzs7nnmsdIJ0K9wyMoEpLkcJh89EKDdgAQ3AB5fH59EO8E/iDVwnstP\nq+qvfNjn1pcXbNaXKJ44UQoiRpFpyYxjRksgl0LOhb4U+t4AyUBkmmzMIJGDGPTMmfB4pDawqTRa\nFyO66ChlYJcGK4/RYrHJGFmuTmGlROlQyZScyQRyUTwMgISO2C/olitCt0BDTyE0+lgoRPdKo6g/\n9wJq8YmtTUzxHmWc1E7vVT/FLEtR5pObFKNFhnF4jBF9a8vj6mPONr6qh3pFAxia7tQRa7e2WUQ+\nMAZ4VCPK60VLhJSUHJSsanpdqf6UKSV48bxt9RsUyMlYFFQp1VtmoutUlZRS83yLGwSIODUcpslX\n1ZgR1z8D0WL34Hw6a5TvHNRL+z4zOupHGjLP3F45mMOmphCvdyTfHvK4+ghMYDIHsQoAAtPkV6x2\nU2SqEQ0CITrYCCR/X2vGdofUOLsDkdQM21yA5PoVQK1RAtVDPDD4/TyaLtRjqNeU4tnGHBoBAAdA\nVyfr+cWjZfAeeMdzjKzHrp7pQz3dmW4+xTTvT2F1U/XM0mt94PLyks16Tex6JERqTWcAtGRSyeQE\nXS4+mSgQkVgpLdusg9FkpNimNRejxU6rBxiwgYkxoF0HZIZBGMeB9eU5pSQrgYkRzZkuRJaLFVoi\nRQoiHvtSp5BDJC6WdMsl0i8gdmSJZDUjAFU6B9IigSJYUpUIMYSmIEah2bW5Cpr2KByqYZ0cy2yi\n9DcFyJDG1xyGt6s8sj7mnMg5ezbj5OVpLclqo8BBTPDwVqwgJzODLdCHQC4QQyAlJaUMmi1RTQNa\nElkEDR0aIkUMTKvXO4WCTJet+Y0ldmgxRc85m0FQKV61bMia7XuQETxj8uYe6TwLd+6lQlWxCUjL\n7D0DzrkOurcu0+vNw36noanJI+sj4OAYOATRGZDOPbNQGvBIrMCJjUMFUoSawSPiDkxxMB2zVyNU\nbza74sXD85nHLuu4K4ePjsWHscyrP+4Q7O5xFYxU8W1mVMx2OaR4mQD7UQA1PH1gml5PmvdcdtsN\nm/WFJdOESBoHy4ZNySYa781WihKiUR0hdoTU0VrpeZ2VqngDKjmYeKYuLRb/shBU1TBFNTu9vGe7\nvuD87A45DQ1MKYW+W3CyPEWIQEQkNooPEbq+Z7lccXp6nWvXb3Ltxi32+4FxP5CGPZqL658dodG+\nMCmkuAfqHk5TAb2iYo3h0PbHRGnMxP/MLbf5HSePrI85W3KQ1WuWif1xSqrGMyuQBnFS3sdE3CKv\nIYcuBpbLnpPVktPVklKKt7sc2e52bLfKtoyun1hMTAta3DtVC1u0zMMaYZqDm58rqJ17mdpkVs+0\nxd4PKNzJ2ZlTvLWWtmb5HiYkVTCugDoBaXUW6t9TWGXybrUcGiXvMHlkfQSQPphHOI9H1kcHwlYy\n47NGY0JbCY20ecP0UyaPz2q7zCOV0YemxlXtvcl7kxqZMG911jawUaxzQEuK1C5+efr+CewmmQys\n+vuYzvGA5p19aBbNa17UAfXxMD2be6ZPX8z0q0XkF7CWTP8P8F+p6mcf9oFx3LO+PLPEilzQnNxS\nL9bYwJsbqCqhqMUyRyuWr2UDXWcgZHEEjy863Zu1kEsm5UTnShaKUNsPpjSy227Yri84u/0qd199\nmTsvf4E0jnQx0ncWZ1h0C05PTgGLt0Y6RGyCjTGwWi65cf0GOY0M2z1kuHntFvvdjv1uR05jq2Gl\nPdbWh+XAyq90Wy3ANz2ZWW5X9cM92ma4Sq0/tJrFrrzzavpcHlkfc87kkhooiVusdr8FYqjlKrWW\nVAgylWJVyIshsOgjq0XPrZs3eO7ZZ3juuWegFMZxYBwH7ty5y53bt7md9z7eXv6Cj3Mu6FT/gHo7\nEJOarc4MTPFYaWmgVRsv1Phlkxny2f1Smt4Z5k61rTCf6DBAZsaEXD02811n8V6t9bUPyhZ+28sj\n6yMAqw4WnRtEXPFKnZrVWQ1qfa8UA4l5kk2EmpLRJozoqKu07nLEZF5qyu07SI6G6uVgUdsxWtvA\n3Gq+7LGC6EE5zIOMqUNzzM6NCUgbzTvzTOcges/x7+eRzr/LddwyQl9zGB4kTwJMfxj4XcDPAO8F\n/hDwD0Xk61T1gemkwzhweXnOsN0yDntPmbb44uJkxfJkhS6WBDVaV0IgdR0pdZ4c5HRF+38OqDY5\nZc/WrUBDsFhm8fKb3WbD5fkZ53de5ezVl7nzxZdI40DXdZYRK4HTk1Nu3rxFCJYhi/bgnglBWC6X\noNftPDIs4pJnbjzHdmudlIb93nv9jqQ0mvedx+aF55QaRac5e2zW6Wvcc7g6A0l9qMlNoXkhtYY1\nxkhXHp/CeAvLY+ljKdljjpXq9RZrs+xaa7ARWsZtkWrg1xYMShRY9JGT1YJnnrnOl7/33XzFV7wX\nUWUY9gz7HV9YRsg7thd3SBlyUTIWQy1aPC4aAPNQcSaida9Rb5hQfEJg8jybYVY9x0bpmpFV47B4\nzNQ8UzcgmroIc4ycYqf10Y9QmZXZfleB+JAufkfSvI+ljwCsIrLsWvOC+8ZK1ebN2jYSKphK804t\nOSlY6Kt5t7jH5y5m8OcxuJeqkGbfU0rzBjXMjl+BNHndjtV9TV7qvK70vkB3FUiZqN042656pjo7\nfvVIm2eqV3acH3z2/GmjeVX1E7M/f0pEfhT4NPDbgb/4kM+RhoHdds12vbYazFyIIZDzNYpmFqUQ\nukTorLtRzVKtyUOlz1gWqzVXCFEJJaLRbupSCjknQoASbMJJaSSNgwHpxTlnd25zduc2l2dnbC8v\nSOPQVnY5Pb3OdrNmv90QotV1dv2iFdkjQhc7ZHVi1HMRFt2SmzeeYbfbstturU3huGccB2sSMQ6M\nqT63cxkdaMdxJI2JMdljyg62LTOTFosLIXofYuucFGeNIDpvlt9vtrzTFud4XH00Xcltk2AGXGhx\noQlADjKxHeKcqyUEWPQdJ6sFN6+f8tyzN3nPu59DUPb7nXXe2q/ZXp6xOTthTJkxFVIqjFkZk5K8\nI1jWUo9Obc56tXaulYA1iHSwnbufM9BrhiXMYqR4iG0+6c28Ts8gbRSxH8iZ5NcYDyYgffiub0t5\nXH0EoA+wrDFOZt16PLxTggFpEcvUhQls69WugBGrIriuWocYCMGaFzRw8efqn50DaXYEC5NeSMum\nnYGjhgZ2Ur2bA4Ps/s+a1HM+SBKaW3ezY1bD4r5e6f2AdH4Dy2sr8EPkiZfGqOqZiHwS+KUP2+9v\n/q1P0HfRvLUxgRZ+6fu/nK/5yvdRsIlkzJnYL4h9Is/4e4vLzxoYxI6+63xyCKQUPEtWKSVRiqAa\nyLkw7Hfsdhsuzu9y984dXn31Fe7eucN2szbPMGfLtEyJYb9jv92y3WyQzvrghm5hTcRrrScQY2S1\nXBJuCMvFiuHGaEDpwJ2SP0+z5/X1cWTY7xj25rXs9nujiPd7drs9+70t9VWp7Rit4cRiUWtf7fEz\nv/B5fv6zL04xvRAYxvFJD/dTL69XH//pT32Sn/7Zz9B3xnpIEL7hV32Qb/j6ryZnjysW7/95kHTh\n8Ua/eYNYQ5KTkwWnJytOT5ecni4RCjFkOkncvH7C889eJ+2eZT8khiGxHxK7IbHbZ/ZDZkgGrFqm\nGPp8Umm0fmW+mgcYKKjVEdqejaqueoHgy/hhhfQCUmTKAlb1UIE4aPpvbM7u5E08GFCFi4szzi/q\nuhy2f5lWjXlHyuvVR4DvffmzPBO6g5Z3H33mXXzsmedAsd7l6oAaZ91/7vH0KoXr4JhreQyTHpvi\nTmAqAboEKRnNW73dmUdsXjAT5Qq0zg4HeHaV1bh6jjr7cw504V4gxc+3erxldlw9cFfvuZ4/yMj/\nxs6skiFCDpw9TaUxV0VErmOK8r88bL/v/u7fys3TFWe3X+Hizh3Sfk/a79luLskUkhb6nOmWS7pF\nsib2THWUnS/JZg3ke/qua43GQwg+8dXl2QKlREDZDzvW6zXn52fcvXObV195hcu7d9hvN74STGl1\nevvdjt12y3a7IfTWC7dbLAml8yXXvB41RKTrWfQruI5PgDa4RjWPttybN6TI+RBs99stu+2G/XbD\nZrNhs1mzXm9Yr9es12v2w96aRcRIv1hwslqxOjlhuVzZCjuLJR/44FfzHd4hqV8sWPQLXvriF/mj\nf/qhC1O87eX16uPX/bIP8JVf8QK3bl7jxo0TukWg76PT8Bj13hLamAEq9p8WFEs2W/SR1WrJyemS\n05Mlp6cLRAuRRCRx88aKtLsBacduP7DbjWx3A+vNwGUcEBlAMkUzuWYUu3fZ6jcFT4jy03AgNMrZ\nqWKPoVMTi2oDhzADU8FiW5UtLgUtTMu/zRzzytAKHEx+964cY3Lz5jPcuHHrgOLd7Tb8wudeO1z4\ndpXXq48AP/DeX8KHTq/buDSWwI2pxjzMPdTqRbrBUjEoWsgACQaONa46x7XWRckBNUYYIjoEkOQr\nc3n273ybe3xXgbJh6P3A9KrMlWsOpPfZ7WCbK+eDPFM7zsdY8jHpgR5WK/RkxY93hV/z0k8/aAge\nKk+izvSPA38Toy6+AvhvgRH4wYd+zpu6G21rN2QeR1vvMEaIkSJC0mLlMbNWgyklWzptv2N1cspq\ndY2Tk1MEmDDnAAAgAElEQVQ3mqyGr9S6wVLo+46uG9CSOT+7y/mZNag/Pz9jfXnJdrslD8OUEelb\nTtkBMLXSiVwKKrlZ7fOYWuiCN60PPlnVhCcvWyjWb7jG53Kypdy2m0u2a9sWi8upr3DXs1gsGVMy\n2rbvWS6XrFYnnDiY9g6mMXZtCbqu71n0C3bvQM/0cfUx58xuvyeuoejoiwpYu76UISVhty/sd3tK\nSphr4KDkS1mZm1YXuA90QYgBOq+dpgyUcUsoA4uQubYQogbfIlKs2D6U6OVU0AWlEMkaKcTJUp9R\ntKo22RZVQimURtFK08MJhOWwPRwchNHc34TaR9qpPp0RztN+LYjmfN69oFrnRq3f8QZotbeiPK4+\n+ocbJdmycJk9tgvrnmWu72mLMQoOxN4xTRXUE9ymhiPi+Re+ClHfmT63OGqE0QE1+eZfc+B0tkCm\n0kD2gY7f/QG1JdpVhqTSyO161O+d0btaG94cFLRe+a6Z3klt2nD48qPKk/BM3wf8ZeBdwMvA/w38\na6r68PW/JBDitKTYGPYUVQcw20iJrDDmzJBGA9B+y3q9pu8tfnl6ep1r125w7foNrOm3WTQlJy/C\nLy2+mHPi/M4dzu7e4ezubS4uztjtt4xpNHBTPRwjZsX69bxrlmJRqztFSBjNFkLnHUggzJa7MoYl\nELSjY1YM7zWiXdcTJYJisbMxkVKh6xacnF4HYLFY2LZcsFysWC6X9AvryhT73pue24TZeUP85er0\nCQz3Uy+PpY9DGrncbBjGHeu1r4sbzUgqRSgaGBNsdpnkYFoXArc1TTNFxbpcUWlXq93TNJCHHfv1\nOZvzu+wvz8i7CyRt6EpmoRlCRmNBFtCpWEvLXtgnGHNkyB1Jg2eqSwtptQlTxUslBEStAXrzLsM0\ngcABeta+1tSscMTfn1Fs4oX+MltOq3kfZYautXkrtBcFX9JLUfEs03eWPN78CNwDnDDz1uZ/6zRW\nLSZa24rYviIBjR2a1WqZgxldRa0Ll9W9T4mV0s8SkvoIqUNGo3zro2XP6b1dcu6LVPX53GuUCYRl\n9npNoEsKo0LI0Lse13itMpuovS3ixDnf+z33ubSNTn5MeRIJSB97nM9JBdPOFr7eB6sZnZJAEqRg\ngDgKEgZC2NkSZuKUhQSuX7/BjRvPcPPmhtj15qHF7iAzs8aW0jhy986r3L19m8vzu+w2l+x3O8Zk\nK73UmNG0PfBHG502L/CPSmx8P4AYroogRNd3z7atdLTTbzFEKx3LmTRmhmEkpcLJ6ZRQtFqtjNZd\n2uowfd+3eHGI3XRTKb7KTMfy5J0Hpo+rj2NKrDcbNmoLLtaMXRErTUEiRSO5BLJ3hbEYpI1tVrHS\np2AWf6tzziOaBtJ+w7C5YHN+m2F9QdmvCWlDzMoC6/wlXSGqshBhmYVlH9llYTdGdimyT8GSJhWy\n1ppCXzhNp3pSW/nFM7yDLfk3ATyIeFvC6gnUjmJ1ItUKqrW+VR2gdYaROs2ZU2di7KU5CNTP4h7s\nGxndt548rj5OMnmn94JqNXbm4DSxBzUGIPgKKSFCKBSJFMkkFSsRxcvovMZG3CGQLpqXWvv2DsmA\nNEYYEkgGSdagoVLLB57pfNPZYwXcqxOsvdfCZMliwRK8VC3Odpt3VKqxUr3qmd4HTA9Oq167x5On\npjdvqbEcb8peKaWpV2hBSqZQKCKWHAQ2kRQlZyXlwsXFJefnF9y9e9foUV9r0mKf5vlVGrbkxOX5\nGZcXZ2zXl4zDjtGp5ZIzJU19Kuf9RWsh+5yBqC5sCaUN/jz5B2i/6aqFiFNypRRIyna3Z73ZcHG5\n5uJyzeXaYqf9oqfvF0iIRl23GtlkOQO5IDETYp7AFDdUwsB2u/3SDupbWIrXJFOyLTgv8yYNsU1o\nNvZTgYn9OTVYyEUZx9Hj7Rt2mzX7zSX7zQW7S9vSbk3ab9FxT12aKhRYBIiLwLKPnMYFJSwY6LjY\nFM63ynqn7MaCJq8xrOeETjFSVUpQdy5t/dwQO9M3NxiRqRUEdV9RNCihhMbGOCxOxuWMRVQtiMdz\npwwZm7hUDqav9s4b9QTecXI1k3X+95yWOBytez7fFsEWA1UNgRIjewJbAqMIyyCsAiwQOrGubQ10\nQpwG3lkNtRgEDOLVDYLmZJhWe/3Ozms+C7a3XG909mJjBL0ZhKTsS8AJxIBkudL5yAH0vjHTq9ez\nPtErfz+ePD1gqpNVHEI30ZTNjXfL3pXGKGArX9iPif1+YLcf6bozFosVi+XK468GzHYM06fee+0K\nsN9tGHZbxmFH8Zhlq/dMiYAV3nd1wWZPaGrxowaqbj0VbckBpRRCqDVf02dxBUEnYyGN41TEf/s2\nd159hTuvvszFxTkXFxdst1uWy2XbhmHFfhjo+97Px+LNEiMSa1conN4xuX1cgu11Sy4eEnBvM9Y4\nuJhFL3UxgRJIRTx50XSgsk1W317Y7wc2a1hfLFhfnLA+XzHs1mzXF+w3l+T9jjLu0HF0hso8yy5G\n+hgJXU937Qb96U20W/LK3T393T3hYkB2ibwb0aTtfrF8Eu/L6+v8qtJqo2PXWb6BCClnpDXht9rA\nCSCnEAZAXfKj1To3r2e+0kwF1Kn0ZT5H2WWaDI+jPIJUoKxzzhxID0CVK4aK1/q22GA1nXxRhRDI\nsWMbI3djZBMi10vhuhau+VhFVaInvhXUvEJ1UI5ACmjvPXt31h1RRSmt9esswx3vRX4A+lif9amF\nE1AZFGy5v9qW0JfGJEX/bg5XiWke6dVM3kONa6ZG+8o3ppFPEZja5Z4nIjXAYvJONXibwFwYR4ub\nbrY71ust683WFlIQL1WRwOECzTYhLhc9y0VPF6wPquaMllzVy1oKpkQaR6II0gl0cpAdXJeyqlLj\nnm6L+xJZYdZk3LKOoy+RZR2N7DHnwn7Ys92YB3r79qu88vLLvPryS1yuL1mvL9ntdpycnHCyOmF1\ncsJ+GFgNS7q+oylfMK9D6vVzGrlSjHfu3vnSD+xbVEqxJvfW7UgOanetDMrAVJM3kqk5GA1IDYhy\nyuz3ezYysr7oWJ+vWN9YMu7XbC8v2K3XaNojaYQ8th7SKPRdoOsDi9MFN567yfV3vZuwukZ3ckGJ\nF4yyJoc9+6xkcjs30ylBSrYkJj+vGDti39P1veUgyNRQoZW5VO+7UtMVTCueevxKdDISrYZbjPmp\nGZ1OKVap8HnQKu4ojyhz4ISaD3KwwfS8ZlU7akwdp2bzlgglRorCZtFzZ9FzHiPjaJ2PYspECktm\n3ZVqtUtXOykF6F3xa+N7hUIhldFJfzPabDVe2yc0FLODKmrg7E14hep4eDvZWraFojEiyUG9NdGv\nYHrA+86213eJH1ceGUxF5FuB/wL4RqyDx3ep6t+4ss8fBv5D4BngHwH/iar+y4cdN3ZTrK9WGdQM\nxGppS8HosxCsJZ96iystlvCRk6/gksxSrhmLBI9NOr2benLq6KMdh2xAavHKMGXvFoWA0alaM4gt\ng7dZWwcJJnYMaxwxTWKlKCVlUhjJknz5rLro+cg4jKzXl1xcnHN5cc7tV19u29brWofB1nfdLrcs\nNksWi57FwmLCUK+Xr2nqa72GK1T5yy9/8VGH+6mXJ6WPN29c57lb1+lioAuh1TEHN9IItlrQZpvY\n7hIZW1LK2vfNDqRqa/IOsPcOW3dXAU079usNZRy9xABEQ/sMQNdFlqsFJ6crTq4tOb22QJYLTq8t\nOLnes9r1bMZE3EJINCq6Us0TFV09SMvZDKh7rMVt/6sE3GS1O4vtp2XT4NQ6Qg/nHj14sOO1eb4C\nd6n5MLbHG1g/8mmUJ6WP/sn2TO9HkwqNucPnPXvdR1LF6HtoTF/KhV0pbFU5B+4I3AmCrdtt811p\neqLeCMw9TKdfpCp8bdYbA3QRKZFQ+raiUPGJ3foGTOs7T7Aaml4pxnBkVRLFjEVVuqJ0REIqyJgR\nKTCqhzp8XdYDivf+169eRadg2m8QvfqZ1y+P45leA34C+J+Av3b1TRH5L4H/DPgerN3Ofwd8QkR+\nhao+cA2wrl9Yez6PH1b/2xxTjydmjCt3q9jAjClxJ1oWpdFQ1huy9uoV9cxBVV+BqEAWA+WcjIKP\nEbqu0VhW7O4x2VlHnJSSL4Fl3511or6CWGlPCDPLKhcSqdFltSxmt981sDw/P+Ps7C53797l7O5t\n287uWqOIYU8aR7a7LV3f0Xe9LQvnfYlxr74lmPiEHxrtZ97E7benZ/pE9PG5Z5/lhXfdIgahC4HO\n17SNNaEIGFMhxC1ZtwxlJKk0tgGfLMw9LRTJ7Hdb1meBOyERNFPSQBmz5XlrIIo1WChiHkXsvPTp\nZMVi1dMtIrIQ+lVgedKxOu3ot8Hr6msLQ5oROW8WaEriNYfNazVAnUBRDrNz/T4UnZo0VEC0SYsZ\nV3YPkjYQrXkGU39erxEX9WYqbyt5Ivo4O4JhxcTgPpzupbqSZYKSmh2rmTFntqlwXgp3SuG2Kq+q\n1fWP2fMGVOlVm8Nhy5mafoQKpoqFuNTPp4uE0tn3FMhFTPWofZlN7wKhGXi+dIiFSkTJKKMUBs0M\nkugJLDU63WtAKlqziK9kEL8er7TpbaWTaoumx5NHBlNV/SHghwDk/kVivw/4I6r6t3yf7wFeAr4L\n+CsPOm5fwZRg3WWgWS6qBc1QrSv8xhStC4HjVJxQRChUb7VOE8W9CbtoSRTBAtmV5g0C9H2z7OtS\nbkbZFlOEkr2udbRm/Lj3XGlbjxMZkEWzHRXbt6jVkXpLw5RGNps1FxcXnF+cc/fObW7ffpXbt29z\neXnGxcU56/WFxVJTIqfcQLrSzKF2J6H+zlqE7/R2zT5Vo53X68tHHe6nXp6UPr7r2Wd44d3vanpV\nOyGFEF03lN1+JBXYDokwZKN6G+BU/bWa0pJGhm3hUhJ92rDsxGpHA16HbPXInoFEwUqkFssFq1PL\n2u4WAXqhXwaWpx3LbaS/EM8HmVZ/QauNLwcTqNQJIye34rODqcet6s5OCYp7Hkq12N2A9Sb1tuPk\nTVf9px5xBqTBww2llZ7WGO/rHem3hjwpfZyOTxsnS+yq8VMOwbTu5K/JlU5Emq2EacyFTc6cl8Ld\nUrhdCq8QGEtmLJmcMr0qp0XpgC4KMRpwRE86kxbbcCwKgnaBUDp7rQglGYjmnMlSSFjDk4gSCUSg\nr+afCCOFBOzJ7CSz08SSgBToRJCUCW1Fm9JaGNYQxP0BdD4c8xabOm1vIPzwpsZMReQDwJcBf6++\npqrnIvIjwLfwEGVZrqzhwNw7TV5Pqhk0CBIDse+I/XTa8xhPa6d2hXsy/ZuSI7RYRrAIDZBF8O+0\nzNgxJVLKqAhWLioMw8B6veHu3TMykaEI2yFVPQIRb+lnGcTNN1BtDezHWevA9WZt1O7lOednZ5yd\nn3F+dpftbmON8XfbaV3KVtIz/c7pXnVrldmN064JjYbebXdvyji/VeSN6GPXBfou+iIVxnrE4JO/\nXVBEdLYYh7Tn1SMUsc92IvQhEFXRlEi7TOwC0lkTCA2+UoxMRpwiHnefWJGUEhpSo96Kes/e2gRE\nvWGESmM/yqzzVxAvMwv12J7Ny3T/zOP5FSBp0868TKx6IZa0VBdfmPTyfqVk2o5hqvv4E9dbUd6I\nPgJT0wTwOU4O5rp2vWX2t/fQTaWQtZCKMgIDgRHhlVJ4pRReVuWlnHg5B+6MyjAObIYd62FkX5R1\nKdxCWEVhFQMrhCXKUi05qdG9xY1JfywiaAwMRAbpGKQwYOWiWYsvRRmICD2B3gnlQWGg2KaZPYXr\n2DKSwZfktIVQJo9U7tGn1wOOk042j/0x5c1OQPoy7OxeuvL6S/7eA2W1sg4+0eOmuRjNsB8Gp76U\n0EUWskS66n3N2I5mlM2s8RkFpe2i1fVMsRo68NgiRgF7V6MxZVIqlGD1oVmE/X7kcn1JvLNglwrr\nXeJkvZ2SpmL0toYGpkb0WnOI7XbHbrdlP+xa1u5mc8nl5QWXlxdsNpfWNnCztve9T29tWVdmpQ+H\n9hWNpZiZqO3nz2U/vOM6ID22PgZkAtJKazXGo9a+WbbgHFTN0FFv0TEB6SJGOrHyrjwq2bsc2Uow\nblyHmtXuWZPFyr1SsoS4MCZURtPNPIvhOx0nZZ5jULyzVplWvsHLwlzXtXoqzfAKB/p2YL3PntfP\nmcda47L1ytU8hUkP1b31ObhWkL4P4r6d5bH1EQCxpMw2JnMgnfO+NSO26mkujB4b3ZfMRmGDsAa+\niPIFlJdQ7pbA3SRcamEzDtwddtwZBs5z4XZRbinciJHrMXJdAqcIp0CnIMVjqE75zsunNAb2YmC6\nD8pOhZ2a91lTjSJCr4Eeywzfq7JXZVTzYhOFPSCaWSCgmVgyvXhLw/vq0WvQvHUXqZ61fmlp3icl\ny9UJi+XKPVOzdMeUGIaRTCZpIfaR0EV6XTBnhybokMlgk6vX1yZDJUyxm2IFyRVMc3YPIFtgfkxW\ndJ/E4mb7YeDick1WYbkfWW72LC7W9Avr2tT3C7rYWTwzRIsHSGAcRi9xOWe7tWSiYdyz2VyyXl+w\nXl+yH3b2+rCnxt2m+NvhtWrv6zSpHcxLOmVptmskQsqFo7w+CUEakMYg7Yar196ydZ3XnYWrLEIl\nPknYEmx9DCyjtRGkFPIwkktA6Q00awJjXTLQnpKzZXqnlAmjFcirRKP9na3IuZByJuXaUBcLjXq7\nTW2gmt16L9TFzQ9ZnYCIfV/1ZqdJe4LRAwtNsF+qE8hWsuTq3Ha49JrvffD3UV5TaqOFWbnHlbv8\ncH8FzUpJypgL25y5zJlzlDPgDPicKJ8T+HyAbU5sk7DXAuMeGfb0w47buXAzmWf6XOx4NnTcCpEb\nErhJoFcsma0cAlfN4yhR2EtkL8quCBuEjQp7zS2iGxAWat5pKcpOlV0ppFKo/5IaFXxKptPC0rPH\np+Xm6hV5vXRtNei4QvU+nrzZYPoFbETfw6H19R7gnz7sg3/hT/0p+i76slRb9rsdLzx3g5ur2NZk\ntHZXtLhLykpKxa109z3FJ4ZoIdI6vlPsyHMRVXztWsHcCsxTFUFULLtSbNJRrM3WkBK62zEqxP1A\nuFwTuiVd39N3C7q+txIKT1SpmWopJbabNZvNhv2wayvF7Icd+92W3X7v/X6t1vZQHarlPx9km9xr\n15l7PHHBmqXvx/kn3Nt4R8lj6+MP/f1/zMlq0UJRCHzD136Ab/i6D9qoRiXEqVTqMDw2jWDNoG1L\nMWIWvKhaly3BEo4k4ZFPG3cRpA+UCEmU09gjq2tIgP2usL4cuLzYs98lLEfEfWFxvXadLoJPReLn\nIw3wJxD1jUpjz/VQaV0XGhU006OW6GSxX5uP7FoUX8GjXprzM0usmyhgo5rfQfLY+gjwvZ/5FzzT\nxXbtAD76rvfwseerUzsHBpla/I3GbFzmzCs58YrAKwFeCcJtEW4H4TIEBpTBY6VlTJQ0IuPIkDOb\nXLgALrRwtyvcIHI9mIfai3g9fjU47VyUWvIIoxTGoIxB2SPsiKS57iBEFaLj2b4UhpJnJaO+xF+x\nEMVWCgOZUkZ6xJpLHAD59Exms+T8Lv1Bkq0ag8AQbNWY1zmQ95M3FUxV9edF5AvAh4F/BiAiN4F/\nFfgzD/vs7/3e38+7nr3FK1/8PC+/9At8/sXP8PkXP8PtV7/olJXF/2qzCy3qfWtt/UcriaE1cQ41\nFb9SDs6rGygF9+qnwmVL3FFEAyFmoy2CZQDXtslDyoy6Q4aRTPCqKYuTdt66MHoZhYTgPUiFkjP7\nYc8wWKy0NriviUgpjW11Gm39TBuPxqQLriL1ZhImRala4rucnqw4PVk1pRJgHDOv3Dl/E0b6rSFv\nRB9/46/7Rt7/3pqAhDWI9zUVg9Mf0dfSnZp4zCxdZ0JEbUQbmKp5rOKrseSsLeZeE+aqTpYII4VB\nCyyv0V8vdJ2w32U2lwPriz27XcacTmmGHzADVHU4t9hmbT6vSGteHxqgWr0h4rkFbW6ePNmqh02v\nZkkKwfWwNbC4YuXfvPUc12/calQyFHbbDb/w2U8/5gi/teSN6CPAD3zgl/Ohazf82j3Ai6oIVoot\n0D3aNo6ZizzySk58LsDnu8DnJLAhsJXANgQr1XRvcPT1lMs4sMlGqa4Q7lK4LoXrdJxK4ZpEehGi\nKMGBvOqMOUC2ZWdgrJJCSBLI0ky6xiuaA6SkIqRsOQAhC6GIMYeiBvpkVBMR4dSJ4lgvwLyvb33p\nPtfzY/R8jBMgot0Slgt+PBZ+zdmnHmVYmzxOnek1bMmgen4fFJGvB26r6meBPwn81yLyL7HU7z8C\nvAj89YcdN0qk7xcslytWJ6f0yyXSd2iM1CEpYp5pLjblpFQYfatgCjbpBSxzMGjt1uI3sCoqBdVa\nOVV7rRqIiTN6EjwDWHxiQ0gpk8dEKoUhWeelMWUD0dgZtetAPq+RtSL6iZqb4rf1cR5vq5NTVQaf\nwGZpfAf+6ExLZP73QYCq7vD2o3mflD6aeI60TDd7dVODQIjqa9lWz24+yU2lKtb5zLJ3QzFAFWzC\nUyyJKKnrMDhrEkgUYk4MpdBf23Gyz9DBfptZXw6sLwcG90zRgIrV66lnnal4qY4UigSsNaZR0Mw9\n0trgxOuxpQSQMjFfdqE9I54pQQkD7IDMrlGdyxyQ55RkDa8cJDG9vdiSJ6qPUucpuJfOlOY4SClo\n1skzHTJjylymxO088IUu8NkQ+LRGcmUygsdYvZXqfhzZp9ETQDOlJDoRTkQ5CcppUE60cCKFXiwb\nNwTx8hjTj6xO0JZiaesiEAMl1DaG86rmGVHrzlNxIO2y0CVhlMyQMltvo9qRWCpARw8s3RGZOxAH\nl+++r9ZrN6MxH1MexzP9JuDvM43kx/31/xn4D1T1j4nIKfAXsKLk/wv4Ta9VQ1VyNt58seTk5JTl\n6sSye/sexepGVS2uOY5GiY2pLodWvFettqzCiWowIK0rsqgWtARKKLUKAQVymAa0dS2qx1DPqCzq\nGXEWp8ruYYJPEGHWw9UVpFlp3trtIDtSp0lmkgqkk9S3K70zp3kOP3fldbVverASvS3kieijyUS5\nV67SqFCbGOri7F23oOsKIaRK0lKzY6MoXRC6KPShswxIrOualYxYSz9SRlNuWba5ZNQteY2R9cWG\n7tUz4rZwcfeS7eWecZcpyT3fIA3JBFqXGoufe7s/Ea/rs/3r94u4tyzWYD8EiF7gX5e5rGUs4sZl\nTUIKV9LnG8uIv1ybpUtdVWdihsyIfJvVxjxhfTwA02Zk63TbFzUgHTM6ZmTMhJSJo3mXIRck2Lha\nY/tiS6lhc7D6UpB5GCkpHSSuiaWtgwSyCKOvKpOdrQmIZaZ7gm1hykQ3fm/SgynQ4OzGLHFUW+9r\nM+qKCCkEtsl0fBBbOIZcSJp4N1a/CoGomUj9vplBi9WwHs6Tk96Kz9Feg/lY8jh1pv+AeT+q++/z\nh4A/9CjHLSkTJLDoF5ysTn1FlCVdv/CGDTawqRRLxFA8y9HLAnJNmnARB6mafVmBVNXqUItdyITF\nEmvcqzY5aMfw99EyAanXijZPUyHUJdik9vOgTcD6QACs56iuavOcvPtZSJNlf7/37nPw2fXQ+x7x\nrS5PSh8nmVnOLcuothQsxNg7zZ/NU5XcJo0KWjEE+igsOlgKnIRgGcIOZsM4UsRKBfCEoZQKRZxZ\nQVhfbNBwjqwz53f3DqaJkiGotC5uiPuCItSmJUWDNevXYF6yTFm9xlxXD1xr//AGxGDfHwLT/cFE\nlDSabkbtTnW2U/hhYl4qHV5DK28vI++J6mOlnq4CKnrYlzYXdMzgSWsyJkJKxGKAKr6M4KjeAc5O\nijImchopw2jrOafkge/KsHgoIQg5wOj3wxho7IQWIYu6Pk9gGkVt2cmgFupQ8WV0ZVoLGqi1fBau\nUzteEEopbAUG4NKdkayFoRQySlS1pg4kFhS6mmjngyHOQN6rbdJ+v+TiKP148tRk85ZsFFTf9axW\n1qi+Xyy9j2hCUvQ2fEryLkcp1a5E5dCblLkXV0G0zICrgqSDqxg1EkIg1JULal9L9+6aYhTLnKzf\nWdTiYgbR8zUgZz9OphlF5o9MlFf1e6YaPP/ymTwYSO+V+X4Vx99ulNqXRA7KPDzt1uOLIQRflagn\nxkxwIKXuKQaYXRS6GOi7wDIGVjG2OGwIRrNllFQyIVuGcMlW41xEKJLYrrckLtAucXmZ2W8yaXDv\nEKHzLkjqySCCkN1hicGoY8FLfRxEI0pzaMUzA0KlqC0qkEWsFtvPFW+kr36PCZMuTyhbrx3t3Vob\nfeiZqjeqOMrrEqMI4GB+qS6+YqVbMltFpSC+xWJbV2zhA4oBVKF+VMmjgWgeBgPVnNGirse0nAEV\nzFsU80lCpfj99SxCESVVz7QUYlZbfaZ4e04MfKT9rLmXCvUHahG0BLQURmodthucouylEBVWwIrA\nCZlTCguKd1ayXIXaXJ8D9ZwxKqpTO8LHlKcHTGtjcSwrsAuRvutZ9IvG4+faIk8CJSsSdGp+7BNX\nzfyt1Gq9SFTAEmZgZWLUgwFozRSu8Z0ZEeB0szYQnRUMuFTqQFve0H1qdA49Ru4PcsrcO5V7zvl1\nSzMw3l4ewJdExGxadfC09R8DiNeHGlTRbN9WbkLr89xFX5y9j/QL6GOh79RpVpugtM5WMRLCiMiA\nyIhEW4VDosVjcxooRdBstGwfrElch4GoGNdm8X21pjBjNval9sqK7nmGeubSsgZ8MrQl23wu9t7h\nrn9+kFLMuKza7lFYa2juFHDTf2YOvdASpepUdv8mQUe5r3jJ0rRQO7QM67qAO9IqZ2qeLBKIIywz\nXE/KNeC0wGpMjDGSg5JDcfD0JSRnjskVa6kZQrXhB6ptmcmJ3aaxzzaXSmsyIr5lDyvYUn+lAWoV\nmR2tEiUarAnEvijnauC6Ulj4ed+kcIPMNQo9tjZw7xraHVC8XPkmmBYVfzx5esDUaVrAVuqIkT72\nLEM3tt4AACAASURBVLoFpTcwnVKphUy2lTEqTVUnsjmQltKeV8L1QAcPgPWwdnN6tfqMpjS1m8z9\n6j/tA3OgrMUrs/euKMx9vcU2C1U6TO/B49clqvd/fpTXITaLaF3FQmzZNRGrg1ap9X6ROaACjUK1\nGtVg3ZT6SN8Li05Z9AambYnBEA1Iu54gg3m9Eh1kfYksCiUN1oYtW8+Yrk2kgFipjkRLgxoVUvUq\n8NabeA9yEYJYL9SgytRkQls3JmVqd5qrR+R2oTX1L22FJrtcRvEZM1xpOzcspd6nTgXqBKJHMH39\nUisS6rxwxUpxFZx1SJq9HwMsB7imyjUyJ6VwMtoAa4ykKGguNg+3ZvH3Ymmd9mrZk7ens/28ZeTh\n/Knu/aq5sWKMh2T7HRVIazJSmOuOXAHSGq8PgX00g26nQp9Nj7MUnlPrmpQonKBU0rbDymfs11TA\nPPh1/pvfDmBaDtufCWKA2veUnNA+OYnacmE9ecLbtgXLU7QWvl7jJF5rStWte2/gBwFqHcwKpe17\nG1hX4K32OW0fAU/40GadNbnilb626ME5P2zyeZCH+7D3j/IA8Ru3AqlKbcVt8aaCMiYlJVuYvsy8\ngckzhRitFWEXDVS7Hvreu8L5hNeJe7/BsszBejurx55UYEAYMdqtNoIQryWt3+dLrZKxioihmOdZ\ne0IIQucLQlhP1ELEvN7qobbSGqw5eVvVyr2eojAmJWaLjVWDw+b5gHreAEygOQfSKWMdO6Mjzfu6\npZX4eQORg3BSvaYxQIkzB3EC0xXKdVVuFuEZHXm2KD3Kxkc9FSWpbRVWLOo1+YctmRJXTH+/zYA1\nnFQToyY3xpJlq2fqTQAUB9JiS1zWabOWnxk2z0JzIpQQGIIyBGUblYVa7XZ2IM1ayGLtB23hL6HH\nGBv1485Zv9nZf2lp3tdaYkhE/iLwO6987IdU9Tc/7Li5JPb7LcOwY7u5ZBj2oMUmoi5ScucgNQM8\nt+5jCNTgjzWl96WD8tRSrWYfTjRFO992w9fXa9F5Yyt85wqg89KA+Y6KzMpCqzc5U/jXkFrP90al\nne8bPtLTL09KH5lRZEpAi9FUJVl6/pgy233i4mLH5XrHbrtnHJPHAaXFS+uC4jXmKJUl9pCBLYNV\nEzUUCYHFwjppGa4qKjAijAqDBha5Z5E7cokGpDCF0sQo3l1WQjJatw+BMYrHbwN9CHQBOoEuqGfy\nFsKsBtW8DwdTal6kgeYwJoYxMSYll0DSQCogtiYIIF4XfgikxgQqeBcyS3h6e4Hpk9NHKsJYtm7O\n1FK32qjDlEAtqUizLdLeARL/f/beL9aSZEvr+62IyMy9T/3v7tt9e+6dwcPMgEHyCGwjPLKQx/IL\n9oPNGzN+GI0lP4CFhHhClkaAAAnhF5CMkfxkyS9GWMhCwowHy2NLYEu27EEGxoPmj8EGLnP7dndV\nV9U5e+/MiFh+WCsic5+qru6qrnPdt25HK/ucsyt37tyZkfGt9a21vkUUuBDhfoi8nxeWHIll4bEI\nTyTwVIRLgUupXIXKQT3hx9e6HlMUa+gQarX5LULQQA1KrOftKJsEqknE2vmr0OlepVrynLbEtsZi\nBK/9d25j4xG30Shf0cAhVB6phSOywrHCU4W3gBPGpARgwjgke1Tc4GvP+WtYLV97CzYfPw/8LCuM\nnD7roCXPnE4HDodLri6fMM9HFCXFSE0JrYPFdGoLQNc1rd+bN4fYkpS856iLxJdS+s1tuqMdkMUT\nKzqduqmF64BoP1vpzXafNrpHCmfxomeCRzzrITaB8dU+aru+OrB+H3mhNzIfO5g6oFq6vzVfuDrO\nXB1PXB5OXF7OXF7NHI+ZZTH6P+BeYtiUoIgDaWgZ461nbnX5Su1gOsRk+xsPCwEWlEVhUWGqibkk\nisaN19mMPFiKEheDvxSgqFDUxCWGFBlitOziCEMQgphgePA2zr2owBeoqk24xHq4nuaF02nhtFRO\nRThlQYovoJvnqmXutmfMSnTaa+7Fv2HZvNzYfMTYEsVo2NpyOvw5d4dCRWk19RLUFpJoSXAXIRLj\nQJ0TSSJ3NPAwCI8EHgEfi/IwVB5qhZpZsHln86p6EpvJUoYQCO5NhlAJQakeN90meiJCSLGziZ1I\n9RrrZqQFdfEczBDV9szQfaAOqH2OSkADHCJUrab5W4WnojxR5UQLxAg7lDt4Zvp6QenUjp35F4qG\n3UQLNoCTqn7nZY57Oh24unziWrVPOB0P1JINLNvWHjwNhLi2IwMY0sAwDFibnxVIc86WXdYTh8JZ\nSctqOePJEZyBbafRO32x+qHPXpz1xzbOsOVAnnevth7py3qn10HzuVRv91TfPIC9qflotZTSqw2W\nXMlZmZfM06sjT6+OXB5OHI/ZgbR4HMkAouUUmTqSdBYlNDD1+s2ia310VWUQS1gaUiIk61gkERbV\nDqhjjQamNRBUe0zUDE1lKdAUvbKLOWiIxBCtlVyMDBHGKAwBgpieV5DirIwDaavWU/POqwRKUY6n\nxHGIHOfCYVbCArIUVAOlFkLLSxE7j+ahVzWFG/XzRdgkrrwZ46bmI0BLPFIPaou3HlP7o4ftrRLB\nKi+bhxeAMVg3JK2RoSTuRHgk8DDAowB3o9HAe1WmUIjVFIZmn3cmwGDryeqZBkK1kpd+L329CSFa\nHL+1LaT2PtE9VNZUuRrFizc1UWcbN4G2Xv2j2mOohMCskCOcUBaEgwoHR2FXwOYC5R7KiIFer3Ld\nxvm2HtErjJuKmf6kiHwbeAj8IvBzqvrxi97w+PHHpKRcXT7lcPWU0+nAMh+odaEU33I2t9wpjRBD\n18GdxolxHI028/6fiyyWeNEs5hCslEUg55WG2E75ZkFVr4/yGQka2JbXvGjIp/3RaYvnv1+v7f7V\neG3jpecjGHWJJ5yd5sxxzhxPmcNx5uo4czoZ1WnSfMlsbPFU/Kg9W7cxKOJWoUR/kJu6n9WmO1Vr\n9CdirMwwJtIYWbQ6mCqnbMowueDCI2t5lYUxA8RA0gDRmjDEYSRGy4YPQSxeKmogSrEmy+r6wNr8\nndATsNR/rxV2U2SeI4dTZjhWwqkgB3uelqVQKJ1yNkPCWCPzRtwzUPd/vz8TkF5hPuIZbaZWpVVN\n6KMUS+bR4q5bhVpQb1O2rmhm3QiBqYKGyjAKkwRuB+EtEd4W4etEHkni21X4QOA7CI9YeKiZK6fx\nixaqWLKQSDVA1UCsoVOzdu8DMQ2EYaC0spNaPCt5bSvZdKNdws7EH0TdE25gKlZBUdeeuJ28C274\nEjkZeYyijFWJKEkrFwj3KIxuccQzMF0B3ZIUXu2m3gSY/jzw14B/BPwI8OeAvykiP6Ev4B4fffII\nyByunnI8XNnF1kwtmVpMv7aU4uozwZOOAilZy7NpGtnvdtZFY1kMSNGeTh7Ab1BwsGxxG/t8Effc\nGt+vFnelQmuse9ZJ40UXvPO929duftH4PqJ2X2a80nxUCZSKt0CrXB1OXB0cROfMac4s2WUp1YXu\no2vviroQvnZPVDqgOtWLJV1IEEvIEEvKEK8XFCopBaZpYNyNDFg7qqVWQq7IooRssnGKWfmtZlBE\nSHFgFwaSy3Pu9nv3HKo3c86+sGXbNEMNGypNLFO3Sdg5qFaEZYksS2J3ysS0IMH0ppdFOUpBKLSe\nukjw9oQJpPaYGmrJHs8ro3/DxyvNRzAvnxS9jtSARfCU61whZ7QUtGa0ZFSzVzQUM+JCQmJkDJEh\nBG4N1vllDpFZAlcSOcjAEzL/LMK3inJHlH+qyonCSSGriYmoNu8Op3oD1fNXggSi2DMRUmIYJ6QW\nci1oCVSXKFR3dFokrHmrrXY1OJjKxglprKH0ayKWWyDBxCIInDSyqFo6aoEB5T7KJZVbQCRYZu/6\nQPrRdDVwX2G8djBV1W2D218Wkb8P/Abwk5jM1nPHkyePCMwcri45nY5EL3Rv/H/buqfoaNYSkFJK\nDMNADG75xEItJjpfg0WDbOq1Jtt0byG0TFkaIFlsVb1rgdnp8Sxe+pljs0bIBrEbjfsi7/Ta278a\nX2C86nwsRTnNmXkpnObM1eHE5WHmeJoti7eol2a5cRekt0IMQYlRCbGaFyqyyWfy2CnOrDjoGAWs\nxGjt+2IKVps6DIzjaC2usN6NKgWlWEgCpXgHpBA9ZzcOhGEijjum3Z79xQX7i1uEgM/pQi0LNS9Y\nroqXtRTpQKpAkOi6vSbvpt4VZolCToEQjKYuWllKYJ6F49EoN/MgGuvTFi06KAvNFf/+Gq86H6HN\nneDJRtYZi0QPQ3Ue1GFOq5gaUrG5QrSSphgrkix5bcLl/0SZ1RLdrkJkCol9GJmCHXz29epSK5da\nyBZFtXXKqfumDS2eDGqeaSKOI5oztYjLvboeQPEktNA0qdfSxEoTFFl90x4m8yS/bqCKeNjA+gQb\nlRx5ghK0sqvwDsonVC7UwHSHuDC+rBrB6tsrjhsvjVHrlPAhJv78qZPlF3/xb1uyUclUNYv+R374\nm/zob/1Gj7lYIFy6hdKUMFQjYO/R/oxqCy/7DW/qSZ6ABDSx78b1a/VJ1wLoZ4lGqyyhHf1zjq3r\nCy8E0i8yrh/zeLSF3/8V4PuxBdsz4/POx7/1i/8LaUiudGVz57333uOdr32d6gAYkgOpW7ntMYzB\ngNRatOlZjf02RJEEJAVSUXSwxSXG0Nv4DUMixkaduaauCDHCoDbDsxuXNQghJkIcSMPEMO0YXUls\n2tkmKNW9lpyVEgp5CVaCU6IL03vsHo95hWTPHvZ5pbaysEpKyjQIF5Ow5MA8R05zoqhCtoYULSmJ\nqnz4wbf46INvdYMVVWrJN3SnvzfG552PAH/s1/4e99JAN7VV+YNvf5OfevADSCloqUgpkDPkTMgz\ndVkI8wxavNZTzXPNBQmzdxYyozC5crMgvB0gDQM7gViEJJFbJfGBznygC5eUrr9bUe/2Yg0VolOv\nEgISIyEl80Bd5KHlr2gp1CDUGvrabVrzNgutlHUNrVlyknuqcQVsQa1Dl1YLJ2DPW0a5VOUTCg8V\nPlJljwk53Ab+az3yV8pja2fjM/yTL7Nog4h8E3gb+Ocv2u93/Us/xq2LidPxRMkLFxd7Li72PSsw\nxuCeYXP3Xc6vFqrHBrq32W3rljVo+NjkAKuDcrsZdgPUj+OjP+/awbV//md8588T+7wpUG1jtxvZ\n7caz15Yl8/DRkxv7zO+F8Xnn4+/9Pb+Li1u3Or2bi1oDjqyEKEgYCOFaor3PtRiUGM0DCLFlVeo6\nKcSoMYkmq+YcF1ScJrM8gDQkQozmtQqoRmKoJAX1UkIq1uSewDDuSOPENO2Z9hfsLvaM48Q4WnIe\nFEoWarF2fAsRUaFKMOWjastBm5XWDcm80waKUrTLeg5RmUZQArlYHPV0itZfWCuSW2KVHfT+2+9z\n9/7XLLEQi6Edrx7zD3/l77/em/w9ND7vfAT4Cz/24/zuu291lqCtUV3xSC2sJUtGc4Z5JhxPaIiw\nLJAXo4K19Lhqy/AMYsaYxsQQEykIdyVxN0TSEhhJTERQ4bJ6ZjmtbaRluhtfqB4icCD1TUqhZyNX\npRZbw0OpVH8+OuuneNlYq76wUF30qo3UAVNMHazF370TmLgHtajVnk4oD2vlIzUQvY2JOfzBcMEf\njPeRONpF1IW/Ww78nuXDV7qXr7UFm29/EosJ/Kbv9+eBXwV+4UXHPR4ORArLvFjyzzh66rd0DdSg\nULPVkZpikm5oAHXV/3Ng7THQ1vWlqFPiwalypx6aJ7qR0gqy0l5rKJ8NoMomCn4+zgD1U7zSFwow\n0Ojlz6YdPi8ov4nJHjc1Hw+nGULi6njicFpQDV3ts7cqczBtIgUt63wF02IZ6FJ6x6CWI0Js7c+8\nHABWyxuLO4UYnN3a1DZr+wxhICJDcM83Me4uGKc9027PtN+z2+9JybzbEIJZ9ylQJVrnpGgi4lsF\nm+0IIXbPuFTPwLWAHT2uG5Qhwm4M7KfErf1g4buaOS3Z1dnW7PgO1dpe/yJ3/8s3bmo+9uNDa8Sz\nsiJxY6VpRVNEygApWcJSiui8wDIbqJYFLQuaxYyaFnvFUs3QSpDEJCbyMacBXE5zLsJVAeqJSy08\nrdmYiGvnuF1rOpPo63Z1p0ZLpUQTXDCa1o3G2tZYf59rBwihd3Lr9C6yllmhXkhqn11qpWjkSpXH\nqnwkVh5zH6tFStIqRVYq+YtMx9fdgu0/An4c+BmsvdC3sEnyJ1R1edFBl+ORJUDJlkGrxiEAAZHk\nVC+oLi5wb0ATYzTljFrJZXZJwupA6YlELkxvLdSckmsF0KqUXF0m0AQebMESs+hQShUzXJ7jla7k\n3ucDqm0JzKePFUTX0p3zyfnZx3j+576B40bm49XhBJKYl2JAIp6VKrG3YAOPY4mDn8/RFCCFYjWm\nMYO6HGaGkryetLMo0X+3+D7gTeXXuH2hxSZNQg0RYqOKY2TUgKSRaXeLaXdhFO84MYxjr2nNJa+r\nlH2Kh0x8MYrRBNA3xuF23qlnt5fs3ZJKplbLII2ijFHYj4l8saNoZC4nDrOipTlOJv2wzthz8ZQ3\naNzIfAQ6Q9YMERNtCOcGvYiBZyvJSgEdR8SpX3JGlxnJC7rM9lpxT1Yrmj1xKRaIhRgSd1NChwGt\nA6csnBaIOfBBPTGjnJr8l2oHUhFMPD8XZFnIy8yyzCzLwpKzgalWQq3etctrsr12lGKZw6JmBHpm\nnecW+PMma4eu5nhYz127LlqjZZgH5akqH9fKHYwGuAIGYBDPmfkCsdI2bqIF2+9/lROZTyemINbQ\nOwTTRFNBVIz2ikKpoJrJpVJzdbEGK3BXVXJezMOkdrUVVbVFoFpSSVGcurLbUPLidam56/napAhE\nt/iqT5SW96QbsLPx/Bvx+fzKTxuvzyt9g4H0xubj1eEEYTADrEKInqkYkovd2/MX2sMdgsc7AymY\n9m4KQghm8ZdiBmDp4gwW/9SgHmpwT9Q4sz7ParXsW7OynWqNkZiiCeGHEYkDcdgz7W8x7W8xjDsz\nMlOi1sqSZ0q2pKVAo+PaAiIdNLfg2ViURu9Wf47yBlBNaces+yEKu2lAxepfD3Pl8mD5D0XV9Hyb\nlpI/R8AZvL4J46bmox/cEzG3NVV+/VqMgdDxVTUhOiLV6F9ysYSkeUbnGU4n2+YTqic0n9DFsoFJ\nBbSSBribBi6GHSMwz8KMyfrNWXmkhQU27S+btpDntpRMXharsFgWlmXuPahVK9HZQNQNypjwyW8s\noeh6RM9vib4FmgCDG3zdnvBZHu3YiyqXtfKRCHcUHmNguhOXGmy88BcE1C+NNm/OmZytBYwgvc6z\nlOoERLNA1nTp4Jm89uBXSllnsfUndYu6VqrHerSuNImKmEfqXWka1dueb3EatyshdUt69Ua3l/+1\nLQubheb6IvdZ2cBtvMkA+t0YuVQz3hAHu2Tt1lKi12EKXjcaOiUaQySE5oW58pZHkmxOY/GiYDV6\nUgMaWuE94D/V6bbW7N7eW7u0vrErnrA0DCSPi6aYXCjCn4tmULa+lF1ZpsVBNtZ+WClrkbVFoZay\nznNtgQ9tX9+aSwRhsj5wzFNlPyZ2Y6RqZaniJTwr00IQqPLVPH2Z0ejQqmigU7NbdmzVk95c2+jZ\nrrEaqMYIMUEaIA3oMCBpQOdk4s55doOxiT0EphQoEninDjwtI5daeFQXLiSSpZJFKLoCn51vhWKe\naciZwcFc3ECsagL1aFNzWtXogtS+7pl6VuhGa+89fTZ3rq3FbX2XQJbAlVPIj0R4jPIU5QKjenfN\ns++xwVcbXyIwtabbVgCsXi+ayWlxpQuxySSWpCF45w1Pj8bjokaBhX5TqltHHUwVNHsfUhGzruuz\nQAobtQ3tzsJmbNL++/hsOH3VpCMRcbrj5ROXXoUW/mpEJCSP4zhgpZGYEsUTcPCHP3iXo+giIgGB\natZ3lkoN1QBT1elSQcKa/CHVGBhVWSk8NrHSSm+ybIuO9hinqPbsX2PWCpRiGZaoe8SWENSs+FZi\nIWvwyQyGvkjZfiDUWvrra0w4oC0GW81YiChDiDAI+zGyHyMX00DVDIvXB7qXIRG0mvv0BsoJ3uBo\n1nxFart3zmA16cDm9vcU8g0N3MMTdr9x+laWCfYLcjzC6QjzyWjW6s6NOy5jEO7GwNdi4nFJfBAS\ntyUwEzgJvSlDw1NRJZRKIDPlwuDszFwx1SJs3ssmlN7LFMUVcwXvCoE/X8bgtLmrG/A7m0lu7apY\nf9WTf+/HwBOswfgdUaagaBSCG85rzenLjy8PmLqeLmJFuLWaFOCyLJ6xFR1MpXudoc8Pp6NKderL\nkidWj7XYw2w8MaoRpZrl0nr4tRpW1jhOZ9zaoqbXSamXWwheFdTa/Ny2b9uW6Xw1Xv9Qb8ESgmWz\npjSShtEyv4tRpgodhOxBT6QYEc1ulJsaUE3Vyk6qtRos2ZMepCKhUkMxIf1rEfiqLsrQvNJinmrz\n7qI3f2ggjhjoVskEopcdOjNTtTd3FvdcW/9LS4QKnjlMT+rwK9HZkO4piHWeMfEHa2ZuJwKBwDJE\n9mOyGGoxrewlV6IYPWiLHFC3fTC/Gp85HHSkhZw8jmhUhnbjbF0kGg3srzlQSAhoSjB6uUqx2Lwc\njsjxiB5P6PGIno6WHYzlAowB7oZAjonHMfEgR24TuZJCQQ2wHOAUujB+VCXkQvCk0aMavTr7V1oV\noZt/uSrTNU3r4AZj90yvX5utR+kGaeNPKsIRYca80icoT6jcp3Lb7Y1+Hb+AZ/pSM1lE/mMR+d9E\n5LGIfFtE/hsR+W3P2e9Pi8i3RORKRP57EfnRzzp2rUZFVS2Umsl5IS8nlvlIWRa0Zky4W5jGgd00\nsttN7KaJcZxIyUsV1Gi0ZcnknDf9+QpC9eL3QtBKpJKiMA6JaRhIKXUrXGmi+uqL0kr1bkF0SzVc\np6xelcLS9t/npHLPwP77CGRvcj42PVpLgAvnD1sIPeU/eLxTPOGjeQfa54x7sLKhqaQJgntD5pap\n2xkyi/vXWihl6TGnkhdqzhaz9L+p9azqBoyBKaVafLPYUhXcMLBziF5+s5YuxL4l/17Rs4DjxvO2\nsgT76W3lgpBiS7oShigMSZiGwG6MTCmSohD7pVnrv78orfZlGzc5H/2d7QAdeM6vnqkJmapQdRhx\nGb+eRq5n4QlSgmlEdnvk4hbcvoPcuYPcvoPcug0XF8huD+OIDANDGrhIiVsxchECexEmhEECKQSS\nBBLCgPUPnSrsFO5J5L0w8INx4htx4gfCyLsycJfAqCBVe6/ouvm9rWXCJkTQXpH1eqwgvpLfETu3\nPYHbErkbBm6HgVGsorbpAfeOOyFsMqNffrysZ/r7gP8U+N/9vX8O+Fsi8jtU9WDfS/448EewjLV/\nDPxZ4Bd8n/m5RwVLUqgOeKLkMjPPgFQGqseHEilF0pC6hxqDUVElL+SymOVfLCt3WTxRQo2jD35s\na5FVLTszWCKH9WksLIt7HU6NNUpPm4faz/gcRPtNf02A2vKP2mdqszz9mDddp/o9Mm5sPlrU0zVp\nMQq2iQ+YpR7XWGqIq6cnYpnfzRhy69oAS10QvMW9zZOMtToLFzwbHWdaCnnJFG/t1jVJnfKPMTkY\nt/poZz4wb1BL9TXH2hhanV6LQ5nEX0qDJXRsvNvO8rbOykRPzovg2ta1eMyLSiGYBBwmrz4EYYiB\nKUXGVLwzDa6YKv0838BxY/MRezNrotF1MPHXYKXWznTxtpzHZv/evtLZiiGhkwFnGAfL8k0RHRKo\nEiqkWhmXaLFUEUYJJiAvQhKT6hsVJoVJYEJ4O0TeSoE7QbjMM5dEHtfMR1jFxaFWtFTPm4Gcn21G\ngq/Ba25m87jXa2FMosWIkwoDdm57gX0IfA3lngqTYuFAWZMJbWLHl7jd5+OlwFSv9dwTkZ8FPsB6\n9/0df/mPAn9GVf+G7/MzwLeBPwBspbTORmmlKQhFKjk3JdBKEEjRitxTTMRh8MXJsrpKzsxzgBmW\nMrPkyjLP5GUxhRVtDaQqiFcKilnLcYikYTKK7bRQdfHEE5fZqms244vG88DteUD6KgBoXRKk09yf\nts+nfeabOm5yPtrxmw9lfTzFGzOHGLqH16intVxGetizgallKUKMJjMYgxFQ1UMTqvYYSrCnWqV2\nr7XkhWX2qolW1mUnR1kWT5xrVdD9upjaVzEaNQ3Rnpu4xj1XbzP15uWxF8NvvIF2zFrpSmMYaJps\nm4mcF1Gqx7+GAKN7p+MQ3DNVsqgnnzZAfbNA9abnYwfQMzDd0LrrmWwu7XVA3fTwdA+VFCEkGBKi\nk3WiGUZkHK0uVcRi5LUQijKUwhgjkwQmhEmEI4Ek2sF0UphU2Kl5hm+HgW+EgXc08oTIU418zExl\n4alWjq6IVMSEF1pYA3im/lnP1kFZv5cI6spMQWFQYa+BWyLclcCdkPgayt2qTChJYo/Rdoq8fvc8\n0+vjPnbbPrbzkR8Gvg78D20HVX0sIv8r8BO8YLJozdQavLu7t6iqlqigWnpsxrQcsTZVQ2JIA7VW\n0jAwDiOXXLLMmWXJ1GrW+zRNDqba6aVWNygeW90+1/qcbbWnz73D7XgekF2nYjfX5dp+z7kmbVO9\nNoFePD7tvL4PgPa1zceYBtIwnGUPqrJJclstZkvmsQe+VM+YbPdXLOHHvEJ1QQcD5d5yezOXzgvc\nCyV7pnmbdwQrkakVCcnLVCpJrfcGTiOrJwU1wI/Ja7WxmKt5pXaOrZNM8CS/FmgQP/eg5rlCMuM9\nCDGbhGHJtv6EUr0e2zWGxWjfKEY5mvi6em/V5mGof7c3dry2+Qh0D3J1Mlcgsb/bmnLtORc8UadN\n2rUJgQRXUwqbYwVF6mBzIKXV6V0yNS5kpPc6zT7PpxC4GwL7ELgjkTsSSWIimEMV7sbInThwRwbf\nN3GrJJZ64qqaZvosgRl6CVitq6qSRDmzv1TdtmQ1LkVhqhBVmDRyLwj3Q+CuCvdUuAs8qPB2fz6g\nuAAAIABJREFUrdwrlYsQGKLlRqjHlk1b4NXGK4Op2NP/F4G/o6r/l7/8df+63762+7f93z51aMlo\n8QWlt8IRC6J7eyFxMBWBGIVhaEAplFwoO6MGDlcHliWjYAL4KTm927vnOUDZxVsBa83aNTZhO1Ed\nUD+HMf1ZoPV5vNPOZGwyeD/tuNfjttuF+ftlvO75OAwj4zhaco7QH24T53ZLXe1hbgXkLd6Dp/5b\nIk/oiXLdM3UwVSeRY/AMYM94U7fKt315m06SUm0CS0WCtSU0AQUDsBCCeRminuBjNakhRqKIGaMA\nqT1HpkvqM4a1oYRujE4TRxGxwvramkeUQAkmkp6lIC6UH8W6hQX3wNFC9ezmqkKp0uPEpb6ZYPq6\n56Mf1EEPzjzTdQc2Kwdbr832dxANK5iuv2/2F4Eh2Z+p9jArCDVEZoGTmvj9yUMMuxDZpcQ9ibwt\nibclUdXAVlW5EwMXMXGRBm7FSIgDd8vAIUeeZiHXhcdiQvtV17hpW/voWwNS0+NVUWvlqhZGmCpc\nVOEOgXfjwNfCwNshcVcC9yRwW5VbpXK7FHbVep02nTvrg/j/TzbvXwZ+J/Cvf4Fj9GGWa8vv2YJp\ntAA6TSbQNgPTgd1uIkj0+LpyOs5IeMS8ZFIM1nUjJWsFKHg8tnqpgZIVijsT264wG3XfTsmrZ1J+\nGkZ9Hs/vpctaWGmNzwOoz0uI+j4B1dc6H61uc+y0as7ZWgKqEmroD3YDU/F4Z9MRDaq9K8Uznqk9\n+T3RzDLPQ6eGW431Cqh1tcC38fmQvQC+xU09090z2kWbZ5pcKUy6NxwlmeJS8o5MtXq/yOZY92CV\nxVijCadoDWgM1BKpRSiiFO/E1OTfWk/XKE2ezhWTSqFUB1PoGfhv6Hit8xFYAdD+2GzwqZR5j7OG\njWe7/X0DpNuEsBQtQcllJEPFFI2ieY8nrb5ZZvFOArs48G4YeJ/E12XgVDJXtTDXwp0xcism9uPI\nvsBFglPJPJ2FT6hcFVik8gSrpW7avOYwr7R1z0XwTkmo16pWE9vfVfNE35HID8SRbw573k0jd2Pi\nXojsFFIuxJKJORNKNs3i3sXouwymIvKXgH8H+H2quhVo/k3s1rzHufX1HvB3X3TMjz6Zefx0WYvK\nw4lvvnuHH/7GXcZpZBwGUmwct/ZMx2VZSBFisL6m4zAyjTv2uz1DiozjwDQkN+oUtKKnmWVeWMrC\nUpSlVouzZheJaJ0KMEt+ZeykB8Q31+Jlr901cDsvQ3hm/83/P+tYW0A9HE4cDsezo76poHoT8/Ef\n/r3/w8HU/lZV3n3/B3n3/R80WGtzgnbdbcemltX+VVqMs/+N10Z7Mo7H8GndWLq4yKoTrbVpbhlg\n9vQS94RbF4513m7pvmvhCFlFT1qf3loLVQpSzOK3BuXyrNGozfdpBuZW3s1yG9bX26crlpFV+M6H\nH/Lhx49aD2jAyofetHET8xHgj/3a/8n9NAArl/BT7/0W/v33fwvn3igrMLbEuK03ugXYZnD7vBX1\n4pTWwkXN83M6z4BVYUC4IHJfIjkId9LEnWHHu3HkvTDy9TBwXBaulpljztyWyB4hqrEb0bvLjCWx\nj4mdZgbooTiBXkPaDS/X6S3ix6mWRLT3c7lF4O2UeDsk3okD7w073h0nHqSRixC5iIFBIZRCKIW/\n+tE/4a8++ueg1RkU5ZP66l2MXkXo/i8B/x7wb6jq/7v9N7V2Qr8J/FvA3/P97wK/F/jPXnTcH3n/\nFm/dnRiHwDgmhmFyfdGJNIwWvxqS0WtODy15QeYTOghhNAt8HCd2uz0X+wujgceBaRxwDo1aC3NR\n6mlhLpV5Kcy5sJRinUFcEL/nSWxoXpQOrl8k/rhdqM4Ps1qZnwKf/f2f5qm2f7u42LHfT2ex2mXJ\nfPzw8Suf95dx3NR8/J0//q9y/613+vUtuZBLtlhlA4o+SVaD6JqddP0X25e1iQJNLk1Bi9eVFvVu\nMNo1oWnlM80iR7oHuQKqZUZKrZ4mtJ6TbpOXpJ2Dbs670VwGtq2FRDfE2ObhnhtwtgV/f3N22jWx\nRUpr4a37d7l95y5z9fPBGlz8P//o1190K76nxk3NR4C/8KM/zr989y03wq6B53oC9JsQxBSNtpnm\nIfi/23sbA9dp1HYYp1mlBServWZVEcqIcFsCb4UBCYEHcceDtOdrw8S7ceRraeQYZq6IHOqJQRID\nYjWnErzRg5BSYiqJXU2MqkQtXuq1MUIbY6MmeAJWsxyBSYR7IfJAIm/FgXfTxLvDxDvDyP1x4v4w\ncjslhhAYg4Ew1Z6Rnx5/mJ9++5uwLEiZIS/80uETfu9v/spL3PF1vBSYishfBn4a+HeBSxF5z//p\nE1U9+u9/Efg5Efl1LPX7zwD/FPjrLzr2bozc2iXGKTGN1p19HHekYUeII5JGCJGC99CrhWVZUNfu\nnYaJmBLDOLKbdlxc3GIYErtpYByHfjNyXgjHhaLCnAunZeG0mJShr2HNIKNNOpHGNDRv9bOv1fNk\n/54Ponrtb//cZwD1eYpL52Nd2L4/6N2bnI92fPo1lyBG7zag2HAGDTxgje9oWxC2tPsmZHB2bICq\n1Ox9HuuqV6oNVHvIyGNi0oB0BdTWrip4S6vWi6YNXc/Cvo+XrIAdo9Ric3Qbi1sTCNayBH9t3cuA\nVDwmK25t9KXaswm1FqopzPUrUvXN8Uxvej4C5yHRZ8/AVo2WMBfcI21hBKd2n1kVFGjlVQ1UqyWO\nre2yxDy4qkS1kpc7Hh8dYuJraeJrwwXvjBPvDLYdOXJVhasMRSJZxTp9JQf1ALFEphDZxchQCsE9\nY+n/cylOxdW23IsUA68J4V4U3kuJ92Xi68Oe96c970w7LsaB/TAypnjeU9gTBHVJlsk8R5ib0/Td\no3n/kF/a/+na6/8B8F8CqOp/IiIXwH+OZbP9beDf/qwaqovdyMXFxDgkhnFgGCZXnEnWNSMvZM1k\nrWS1Hu9BIiKRw3TgdDFzOs6cTidUlWm3IwSbC/OysOSFZcmc5pknT694ejhwOJ5YvLYpuzda1Xvu\ntYXjmhX+MgD1vH1ls0Z9ugd6/fUm4mCe8XqEtr26l/w9Pm5sPua8sMyzZ/JaY3rz5ixbVlhl0LQC\nYWUAGuh1+tUBJGcli/VZ7Nn+QpNStXBNtQWsyXjb58W1hVtt6jdNPN8y14ecid7dxuTWIkjqJTyr\nfKB/qLr2Lri4Q+mtrtTbZDXN6tYEogGiluIdlnLfci7MWVkWZcm5Zz1vDTyr716TAN/AcWPzEaDH\nO1e0WX/CGbWrW3o3bN7XRmc6oFlq0n+nG1Db44YYzVnZ7bhH5b0QCMnKru5NO+5PO+4OI/thIg4j\nQdVEclS5FOWJKFeaIVe0zBxRvr0c+aAufKKFUzDadpRIwVqzqdZ+3oMIuxDYhcj9MPIgDDyII+8O\nE+8NE18bJh4MI/eGif0wWOJp9O/fg65tvVTXxkzWPqbFME7nPaBfZrxsnenngm1V/VPAn3qZY+93\nA7f2O4YhkYZETHZDkERZKkvOzLkwl8JSsosp2LXZTXsD0uOMYGnWu/2OUjLV+yoej0cOxxOH45HL\nyyueXh05HueVHoM16ah5AdsJ5eOLiCVsgbQxYOsr1yDxOovjgf5O9X3aKfSAwyud4vfUuMn5uHjL\nqOhqQM3zbG2fxDsatQnT5mI3dRRvol2pLm5fRFkwkYawurS+HtrDbPXP0kG7bUpThdGexl+bIbgs\n5JwZXG/aFIySl32ZVyKy9adZ6f/eorD0ZCetZQOk1iWmg6gnEjUQLQ6quVSWrCwZFte+pn1FB9Ie\nS+31pm/WuMn5aMM8SxHOS11Wbn2lcrebXCv32Nz7Fgc9n7zXP9bYComRYRzY1x0ahJASt8aRGAIX\nw8jFMLL3LQ6jlWZ5PPJYFh6WzEc1c1TlSOWqFp6Uhcc180QLc1BiCIwhkjFtgFqby2BiILdcyejr\naeL9tOfrw463xx1vTzseDKMpM8XILpnCV2ylj2eLoq/ATfFoG0ceh5e/LT6+NNq8+2ng1n4kpoE4\npE7tVg1onllK5jjPzEvmtCy2iJRCyZXDeGI+ZeZTZrfbMY4j0zRxmivLsXKaT1weDlxeHri8uuJw\nPHJ1PDEvCx19NnRcA9T1L/vn5wHY5wbWDTiegaY+ZwZf45F1C7aqHYlVr02Os/ewzp8G4m/iCnZD\nI2dLbtOqpATBC7xNF7TVtuGeqaJBuvFl80d7in/1pvRZKlktPr+W9XkMAUCUISZCcnWlDZgWrN2R\neZP2nnImG2iyhBLCRiYwmYfa5rh7HoKfszbJQs9uryZu3rKSt6U5NWdqMRGUVo5Tivc2rZ5vUGAp\nkIudm30n2QDqauuJtKn5fcuqvPw4Sx7aguhm/boOpO6Vtrj7um3qoVuLtjaUs+NqM4RiYBhGgkIK\nkX0aeDCOBBGGGBlS8vV7IKTBvNJqHua8KI914YNS+KQsPC6ZpzWzaLUEUFEWMc3nUXEJxEgV01dX\nYB8id6MlF70/7Pgt4wU/NN7i/m7HvWnH3XEkSWv6vTEqwMMN/UKuYZCIxZWbcz98lzzTmxwpRbsB\naUBC8vrPYDFSbUuA0w0SELG6Ojz71xogL5wW80yzFo7HA4fDgcPhiqvDkcPxyOF05LQs3px2Ay7m\n8PlYvc9rcflXGJ0rWW9Y/5/vcfYBeg6A239RobbFUHVNHBBLFdHnLE0NiK9L9H81Xjy0bm68dqLd\nYpJO88LWw2tNGLz7S5E+dyrirEJridZaq9kmG4SREIliSl8aFR2M3i+KKdN4OEL9OSBEJPrilUZi\nMoM0BGsZ11L9Wxa6scnW6aUWeqy1zw8/zxV/XVlGrLi/yStWxEIirN9zQ6BZH9O6iuy3um5Yz+N1\nNGT+/hobL6rfo2uAugVcsMWzBT59TmtTxGlzfLu1oddA25lSCRBSJLqanASvle4UfkW0QF0QrcSA\n6TkXN+5KJtZCVBgrjJhs5wJcqXLlcz1gGb9TiuwlsAuBd9LEe8OOrw873hv3fH3Y88645yJZRnAM\nQug6xAqSQD1WrM1o9c2zk70m0n4KvBFdY+KQiENC4gBhQMXEBPNmMWqZhq22r12kRr3mvGCF4JmQ\nF64OV1xeXXJ1dcXxOHM8nZhPC7kUazB+PfWyrwYNSDdA6OPTVIyen927LlD94A1Qn9m9WU4NJOl1\nVK2GrwOjboXw13NsgNqWtPVfvwLSlx0rSJ6/hpszKmtTgXalhaavawuatmcWBx2p9tyituCoSQE2\nYQREkDQQREgpri0DKyxFIVSTGnSAq7ayIcGYnNi2NNprwb1Spcd8TXEJA9DmPXvC1Lo4swFQaEbs\nFli7jLpej3/63Kut/VsraWikiqzOuDzvOfhqfPpod/Aa0G3/bvWjyLqA9KS4BqAbr7StIZ3kWhkF\n1L273gQaS1xL0UDUa6ala58rQunhj+BZtyk5mBZvoEAgqTBWcT1fWBzo5lpZgIj1+b0IkQdp5K1h\n5P1xzzemC74xXvBgnHgwTNxPIwnxzcp4xFtqEqIvt9t4sRsSiLXeLLW34ARMbeQVx0u9Uz5HVwQR\n+S9EpF7b/uZnnkhK3Su1rN1A9kUkq/ZM26Yr2WHVi91LsVKZ0zJzOB25Ol5x6WD69OqSy6PRu8f5\nZAkSWjvY2Lap6WuL5HauXcOj1XPdAJxeX2C3F2b1DtfwhpwvKP2Gn7/7DESvfUZnb555z5s/bnI+\nboG0l67061431/+8DrkJODix1MGmule3umVWe0lZ0LqgpTjVpt703iQAY1q1qAnB1MG8uXNII3GY\nSOOOOO6Iw2SeaRyJ0b1TB9UmyL+K8ttkq7W4Z+oC++g6D5unc1arGFCJnUJrUorrZotwVRdH2QDq\nep3W5s5fpMTsyzZucj5uDkBPRGpGTts2bfVswXFv1JPKKJYJp5sNb8HGRgZzuw6219X/TQRCtCS3\nlCLjEBmS6S8HMROLmqEsiJYuKzmGwBQi+5C4kGRdXAjcJ/KWb7cxTd8RYQqBXRi4O0y8N13wQ7vb\n/PDFXX741j1+6537/OCtu7x36xb3L/bcnkamZApfAc9C1qZZ3a7ZxvBojpPigLpsAPXV5+Nr7xrj\n4+eBn92c2ekzT2Q0IC1VWJZqyUbZfi7Zko7yUsjZEyZyi0e5eH1VQilrr8YgnObZhBhaATz0U1qf\nYafl+qkqW5q3vbISWM+O5yoTdUv/+u0xuqvvL8HoEm3AbG98Zo3ZeKr2md2R9X3PJ8t50f3Wg32j\nxo3NRwsleEnBxrJttGb1GGZ0mja02JIqUgNZVirUmv6ZdKXEZEptBVvE3BtszEvwxt0GVgpNui9G\naohognHaMex27G/d4c6Dd7j94B1u3X3Abn+bOOyQMNJqP7tohEDLxkWLUdbtv1pM3aktKOoUntii\niSRaYouEiKRELIPp6paM1kzJhXmpzEuFeaEUmOfCabZepqWsmfLoGxspvbH52If2/12zwmXziF+j\nbps32ijQuoLnFj565xScOmiMWrMKN4tSa+tmp9PKaJxtaSSfCkkDkwoPRKkhcCsOnAbhJJElFp+T\nlUtzn1AJHIXe8u+dceIHxj3fmFqi0cTtcWDycMjqTTRjYP2ewub7rBfPL11j7xz8275l+dy34vq4\nia4xACdV/c5LncgwEWJiLsq8FA6nhcM8c3LBemvRptRcKXmVWSvV5DkkF6PKWglACF4SU7yYfQNA\nvrp0JRvPhliZjuYJ9m/eDb1r1+O5v2+Bus3PTreqsP3YlRzwydsmgb+/KZO08znzPnXzMHWAtkV0\nVXCScwrvDRo3OR9FLBsWiaxxlHYX7T9R6fcxtPo943qNdHevtCDWpiwIYYA0BGKp6JL9e9B7MQan\nfNtc1mh0bw2REgIaheHiglt37nH73gPuPHib2w/eYX/7LrHXZRu927Jnm5C91kzF6km1wakakLbM\n97PlOXj5WfNAQ0Si9QQ2QYlilFq1NnFIpuqCSKVUOC2FeS4sWa0TUzVjpGVGWwDnzRk3OR/9A3Cu\nvH0Cz1jrPc5/HUg3P1utVmv3197b2rH18uR2rEKPb7c1pOUJtPe6EIIZayZRKUSiRAKRt0TZh8g7\nKVAkkePEEgunkjnlzKO6oCJkhDkG9uPEfpx4d9rxzWnPN6Y9t1NiPyT2KRGxrPoO5tXBvLq29HoT\n7Pnd1NFKv3b4NcnrdftuKiBdG2ddETbjJ0Xk28BD4BeBn1PV6/ucjRgHVKIlEc2Zw2nm8nDkOC8e\nq2nGR7VeiqXFZFowxlx58R5TEgLLsrg8oK6Nmn31ky2/eg2k2FB3fbq8hGPXTgdWj1VWstb32sQ+\ngDVdeHOr+2vaY15nzub29/aPGye0K9lsWmq94eO1zccQ3LPrC//GIPIJU7tmtI31/nQRPYpi4Qo1\nUCVE4hCIoQALaDTxd4IZRyGsyT4BNAARZKjEYsbY7vZd7tx/i7sP3uLi3gMu7txj2t+CaOxOo5lX\nILXvY1KHBXeqndLtqVW2EDbqtdGwjelwMQhdHwz3wjNSM0tcyPXEnAEWclHm2crSTAubnrBkxonF\nrd5YH9XGa5uP61C3ydu6wbNztAPpOX17/rr/3K4/fjxku5i0j90uMucSmSswqQNqhpoRsSb0SOK2\nmmKSBgsTaAzkqNbbVGZ2NZjBSWRJkTvTntu7C94dd7w/7fiBaccYAxKFEMWVweRaZxI7576COpBK\n82a2OTA903l7Xc1AfNXxurvGgFEYfw34R8CPYFTH3xSRn9AX1JEUtebcx3nm6mClK1eHE6d56SIc\nZmSpg2prU1WdHnPr2ev/pO3j+3fmo9nGG9rOf6GB1ma9eN439+/v+zznKz1vfvZJ3qypLeksgWaz\nqwOwL2HrntJPcf1b1+NvQVfr+oWlxw3ebDB93fNRxPWDuje/ub5qzRZUV+EE9Rui0NuiabW+n0sp\nHOfKnAJLDVSJhDAisSCDWmuyCksVogaiW/Q1BEhKkIFdmhj2IDFx7/4D7t5/i1t37zHsLwgxUsVn\nltNzMbQyHoDa46Itc1fEOsYMQyJ4r9/S1YhWalhYmZ7V0HPAVUVLNqpXIxIVlUpWYSnKcS7Mi5XN\ndNm6DTX51Xz8/PPx2tE3P2TFswaCDTg8Xtq90A44PlpJyDPJS5vPaL+H9lnNAHKWrR0zJAiNfnBj\nrlGuZEQjqk1MxBKDYhSmGGEYUU0gExdUSkrsxon9tONOGriTorXyw3WCG6BrK9thcw0CyODfJz7L\nxok429S+gxsJJds1Cq/uX772rjGquu3J98si8veB3wB+EvgfP+1gWh1MTwtXBxNXOBxPHOelJ3Fs\nk4G0mneqtVptXfTaOnVAFQxsu2g43dhqMcZusdifZ/NMz35rALouBKv3uX3f+q4tkD7jRurmqNu4\nR39PE4KTFUR9Arcj2RzYAH9DWk/x7rm82vRXP01t6Y0Zr3U+NmOmPaNnxi9rJnWthZxNBq0ZYiZm\nUL08RJlz5SRwGiK5RirJ6tuoHgIwIFu0OpgaoFYB3BLfpYGYRoZxx51797h77x67i9uQBjRFByq3\nzAXrVJOCAV5jdDxWqqKWTxQDiYRosa2GdS4rmySh0BOrgrTG6HbskjM1F0oNhFhQWSgaWLJyWkxo\nJVc1Qrcv/r4cf17s+N4cr3U+9vd3Y9xnZrum1/ZqtK7WAnkDplWNvYstqUw6vfsCy7JvjbVozMT6\nkRU0Gb1bPVtW2+cWrBeueakS1NhDiTBEEsGE8yXwjgQ0JdJg/YTHEBjFaketraFCaedz7pV3MG3s\nR0jPgumZVyrrvnmxeOl1gYuXGK+7a8wzw8WdPwR+lBdMlr/1t/8BIQjLUlgWW4zu3JnY7wdblHqw\nW6AtXA6UITg0uYdnRpR5Bp0GoBlh1y0VnmEzzh7yTtOub1i1b6EtXqrne61/bdFuQ5X0XxXZiCmI\nWB1gcEDdnsM22ayzNo2S3gCmUDm4d9/PQxot+eaNm5iPv/Fr/2DTgs0Wmvfe/yHee/+H2pHQLspQ\nbJHrYLrG6SumCHSkclyEucBSgzXnDpPH+CsSjEYN0wVhvEDGfe9xGiQw7vZMuz27/QUXt2+zv3WH\ncbejiCU76dqyxdYxsVrPrnCUF2q1uGjV4uwFHSxXSnvDzlTpj5UEIUpcW7l5MXx1yrgQWapwWpTj\nUmybLSmpNY74+JOHPPrkUb9+AKW8eozqyzpuYj4C/LFf+yXupYHVGxV+6gd+Kz/1zR/b0FbNG61o\nLi7dV5Bt3TRrdra43J4GS55bGTToi40DT88Cb0ltW09X9QyMBTUMRaFghp4oBDW8CiBRGERIEhgk\ncCskVBJES3JrjcmlZb43b3hDQVvZoF2Lns3cMuq7YMnZzXHlo8R/9c9/nb/yz361Gx5o5ZPl8+eC\nXR+vtWvMp+z/TeBt4IWT6t/8iX+RcYg8fPSUR0+uOBxnrk4zx9mzq7Yg57HP5tmFRouq+ubxIpol\nZxJteiaIusG3s6HP/rm5H5+Vw7Mx4tbXzvZosSLc+l8bSbdhBdDuvJwZnw1JN2Dq16WJONhaqty9\nGLlzYd1y2vnMS+Zb33n64i/wPTZuaj7+tt/+49y995ZL8llCV23uqU8++9Pi8UHrtXtic69UyMCs\nynGpHObK1azoGJjiaOUtY2BPZJLIbr9n2l8w7fZd4agC+/0Fuwt7fRgnNESyYrGzED05yEphwFie\nuS6um5s7mKpmVAutUjTQ8g+sFrQWE1vQWhFZ6wm1ChorWtuENENiXhbmOfPk8sQnT488fHLgk6dH\nLg8zp6WweLwUhAf33ubB/Xf8Cluc9urqkt/4v3/tle79l3Hc1HwE+As/9q/wu++9beVRTUJLxDxP\nXxu1VgvSF6uhVI+PqrACZ/SetzGeL1ZhG1hq3p6cJXW2/Tvoqq6JS8WPmRLkwajTsnhSj9Aym9TL\nH7clV8FjqWsm+zYUBz1RU6AlW/YmCo3SxX+27YzG234v2376m7+Dn/7Gb/fyGDvXX3r4bX7PL/38\nZ92K547X2jVGRG4BfxKLCfwmZm39eeBXgV940bFLzpRAb67cgFNEzvo6np0PwjWDvG9N2rj9VBGi\nx5BqB+ZtucxzkbX/69nPDTp+GlPlhmOfnL2b+4ZuFVr/QOnHFQf+tq1U2/YMcAETfWYBX1U+6D8b\n+1FevR75Szlucj52oRmtm/nCSqf6Ze4MSZtLm8uvaqIGS1GoleNSuZorl3MlxIGYEtOQSONkNaLD\nxLTbMe32jNOOJZuIfdXK7tYtLi5uMe12vpgJWW2mG+hFryeNlKrkZWaeZ9fttZ+qBVXruxSDKYjF\nAOoJfbVAKWrZ8rV4izYTytcULN7qsolVIefCcc4c54XHDqaPHEyfHhaOc2Ep6s3AwzNzuBuVb8i4\nyfm4+RRgZcXWpKLVI7WuCi1e6fvFYKVZg4NYDA5A0OjSswrBvoA1jzScgWk/rmKfY4s3lGSfn7x+\n02PqGwvTPci4escOrCuQBmdXVg95/e1aeK7loUjs9ddG74ae3WuusW4AogGGl71F84YpEcbp892G\n54zX3TWmAD8O/AyWyfYtbJL8CVV9YQFPLdX1P617hW4uvmwnzTPjule6eqet2dCq4tJUk9xa7guk\nfioo4rs9zyN92ZBPmxJh4y02TzKEln3ZmudasXNTx+mZlY3yrRXtcbrnCUU00PaEprCdkm/MuLH5\nqK1QfdPj5MzcckNpZQbsfoCcGTdVXQoQOC3K1Vx4esy2gEQh1sgkIyHtCbsLGEZKGFg0cirWiaXU\nSizCSGQIGyFuWRM6lEBeKmU+Mc8Lh6srDocD8zxT8mJi9e6NIpUUhCEGhijUvFCXmbosTglbAlUT\nZAghMKSBlAZCCD35bynVSthOC4+fXvHRo0sePr7i8eWRw2npXukzLQ3PrvSbBKc3Nx+vD0FXycuy\nBdKWweuTLuDgGa3dWDr3CNcDXk/kkXPQ2QLp2WLo1KuIlaV4yzdiNXAqeQW1tq6LuzvJ2sE7AAAg\nAElEQVTd2w3dUxVZQX1VdINnXRo2D6SXADYPNbZYqbIq5es5ADfHo33pZljE71IC0md1RVDr2ff7\nX+VEaslWnL5V5qito8F6j5V6tqL1WlE1T8AC4zR6nr6zx5UUS8gIqi4eDo3v/3Rw1PX6s3op7djP\nvm9Dg1xbPASP+Yt62YKuANq3QIrBPOktmLYEAAAiq7C6PnNO3SsWPMYFKb16QfKXcdzkfCx5oZRs\n3Vc8KeE6L9L5hqobuHXq1xt24xSnEJgLXJ0KgZPRvyoUDewo7ENll7BEiIP16T2ejhyPR0rJPJgr\niwbuEhmGgWEYSdHoMpXIslQur664vDrw9OklT5884emTp8zL3A0DETVvM0CKwpACQwzUvFCWmbLM\nVrvtBm2LpYZgWsHRVZSq6+0uuXB1WqyM7erE46cHHj+54unVzHEpZD2XHdyyMisr9OaMm5yPNlZP\nXhVr4O1x0dUb1RX8ohi1G6N5om1z2kW2wHhG7W0Wj/b3s6ey/qLtPWFd4NrBJPTaT6nVqwu2/25b\nK/XRZoFe85Ll/EOv/c76fXqmyeb4a0wGcGNDMr2gdrWKnz3uS4wvjTZvLYUi1XonNkD1L2gxxdVD\nbbjRWaPWxJb19SAmaRb8Fd1MClVBQ0FKk/ymxxyvVXOeP/DXUPPzeKZd3HtDuQbBKTa1+S1CijBE\nMRDtP8MGSK9JsMn6aJ1r9LJ5BpwGD+IG4BvG897gKNnijYLHl6CzWk0coz2k9oxu5oy2XqbNEBSK\nCHNWLo/WdaUoZAKFyEXI5FQpg3eryZllMe/y6nBFzpm5BjROSJy4uBWJQ4QwosGyOeZ84vGTAx9+\n+DEPHz7k0aNHfPLoE5Z57mth9HkQo5BSYEyRIUVKnsmzg6l3jKneG7Vl8saYLFtegrc/rCy5eG6D\nAerhaF7qyUtiiq5A2mlJ/NJ1tum7cz/fnLEBh1KRpaCLZ+xmXwRH87RkcE90SLR4mHbQ6bTYliI7\n/9k+DvNaz/sSyLVfA0il9w4V8dhuZG0OX9cM4M19bytjwzvZOiFCX++u41yHvrPvc+45n72lYl4W\ndcWN67T1FxhfHjCtBTR4BwJ78FMAjd5DUtXb8XSHz8ACWdP1Rfx99tMyGgUNVjDfwuA2OQJCpUqg\nSqWKP9tVOwCuYCnPhdcGu9t78cy89Pqo4OCX/PyGaN8v+fdMMZCi6VzG2DZLEGhxgnNg3S7qz/FK\n/aHpYCpW+/fV+HxjmReWZUaxbMNVrH5rI6+RcGi/XNd1FqoIooGslVNutdGZykxW4VgjVyWwW2BZ\nDEiXeeF4PHA8Hqm1MO3vsL89s7+ViaMyVkGqMM8L81J48vSSD77ziA++8zEPP37I408e8/jxY3LO\n3kt0BdMQzFhLKTCkYOCeZ2sU0UrJnI5rRlwrjRFC97otZrpwnBeXDTQJ0E7v2sxnjSj7XNXneRpf\njc8cZxk52hk5aR5VW3yilZyQEqSIpLgCqR1oi1TPeKPP3hWncq8jYHfqNobRZs3sx22x/VB5xoBq\n7FoX4G+JlBuAbDXOK3r6+2SdSz2PoQHE5r3rw7j+3gQrtoZDO+4rji8NmKLVumVEGIcAooQQiC7s\nXzrQQRNeCKwPegjWsieEQBSjSG1Rc9FttezYrEoMQi5CCeJyjmEVeFBx1a3rNZxN2u/aXNiCqy9a\n283oXDp9O0RhTNG8ghhszrfFzb3SVscX4honteOFDUiGDU2zPYf1p+23gvBhfnV1j++3MS8n5tOR\nNYBjST4Q6DEi4Hkmc39u+7+ZqlFV64KEwNVcybpwzMpwrKSnJ+L41PuIFm/8vZDzgohwdcoc58ox\nK2OGMUOh8MmTpzx+8pRHjx7z0Ucf8+GHD3ny5AmHw5HjsVCLbtYM9c3zT4IQo5g2rzf6RldjwOZe\nyzQv6xre1yNlzpklV+9narR1azVnK10FMTGHvuivV+qZa/fVeMGQda5JZZ2HIRpjmTAPdEy2beOj\n0u7n5jhn3ue10W2fjaGusnlN+m5nCQIbxqEftgFdo4E79apdiUmk+O82B1cVLgfD9j02YC3bBbqf\nU1947fdN5ySCA3Y/x+tgCnyB8sGXzeb9Q8AfBv4Ff+mXgT+tqv/dZp8/DfyHWID9fwb+sKr++mce\nXAtBrPvAOFgGWchGhRYNHkLVs4u5Nk92AGq/iwFrr/tTZVFrUhsUSrV9ag2WEVu1g2lTTKoqHbyh\n3a/WegvaiVxfDhqAmlHUgBSGZIA5DZFpGNiNiSFFhti8VAPbGM+90O1DsHoJsunWsbHxO5A2r8L3\ndTC+PL1ZNX03OR+X05F5PtnDLMHFYlz4fT0DngXStUypeaatl6mJt9gcy7VyyjPhuKDhSE+O68dZ\n/d4hJS6PhcNcOC3KaVGmrMwl8/DRU779wYd8+NHHPHr4CY8ePubqcKRkawphC1MDRO2UG401CQ08\nq4v3txOgsyLA2pe0FeTTANXqSFtmeW2eqLQNWjnY5io9c93ehHGj6+N2bD1ACd7g2hN6WpLREDu1\n+/wY6NYgZHPA9uv2782keIYNY80w07q+t2PbmXXPWb9VBUpxxtXnalnbEnZDoZ+7JwRKq/z215vm\ncPuc0AxeB2DEgFSuySqykVNs87F8l8AU+CfAHwd+zT/9Z4G/LiK/S1V/RUT+OPBHsGy1fwz8WeAX\nvGvC/OJDW2+8IQmqgVSEIQmliqu3ODgitDqj655po33bz7aoVVVSVVKtpsaiQtXQ9XqtvAGPBXnv\nxU0PxtXY2soNrqUQ4jfb5o6swiKihFBJAUv28JZF0zAwjQNDNONhiM2zlp7Va/0tG5C2YzfPtCUm\nhdXa9LExXrsX2+QJ0xfIVPuSjhubjyEGRNVEBZaZ2OjeXmtF7/SzbYxwXvPbboashjiWA1A9FwCg\n9W8xAmQ1fpqkIQKH48LjpweGh084zZZFqwrf+fBjvvPhQx4+fMzTpweujgvzXDvjAnaebdHp4fVW\ndiDt6xglayd0bUJpK1ssVme6QjPansuec9A88gbiWInDp8WxXq4L5Jd93OD6yAqi2iQtG+Bskota\nslFTNnrBWCGxeQz2P7329zPnIJvfoYOTNqm/Pu034N1BdPOzJxt5wwPVTTmLn2HQTcF9fHa6bIBe\nr7/Wro2IfU5sXukGfK8f7oVX7MXjZbN5/9trL/2ciPxh4F8DfgX4o8CfUdW/ASAiPwN8G/gDwF/l\nhcO6so9J1oxBBz1L6KhurQcH0xVYwhZwGg1CAz9b4JZSWSpkTxTp5TJuRVVter8roPaOPn4cusex\nXvI+qRuIOxCaR1ptc8BsgDqkgXFIFhcV8ViW9NiWhHUxbcdt39X+Dpt/u3YZO5iutantisQ3LAHp\nJufjbhyJMbgk5QwE6w26sWzbnLBknQakqwFmC0BYF5823f4/9t482Lfsqu/7rL33Ob/f7w7vve5W\nTxp6EC0hCdlgJjMaDMRKqAqGciwsqKLsistlsKsSVyqkXPkDTAanoJLCcQJxKjGEcpAgKQ+4iskx\npIAgobKRAGE0dGtoqdXdar3Xb7j3/n7nnL33yh9r73PO777Xb+zX07vr1Xn3d3/3zGedNX7XWjU0\nL1uiDFNAx2uLIavnaDNw7vwBQ4Ll4oDlYoECFy4ccOHiJQ4PN/RdIqkD1zBK3nIedkTD1mrtdl1h\n72Vd3TqTSiYQsyQURy5ebh03Z2uWkpsifAWd+LKG62p0ZXaPjxuCr3a6vfKRUYnWrm5KMbqRSZmW\nQR+1V/REYhGrcfrLPMo38zwv+25OMhlh8wgGs2qCLU+U8fmPio2ZMq1pAEZGN6Eb0wSqdIGqSMVP\nHu+k0+s7VI97pWMy80TVAiV5di+2vJGbl5G30ujeAe8GdoDfFZFHgQeAf13XUdWLIvJ7wNdzTWYx\nZKs0NgNvqpLUMcFtN9CG74AbQ5iXvZFVqDEJvSEJQxJiyqUpvhRGNKWlWjrBZJ3KotJMQHK5Rzpx\n1CQAzSAsqElnxfEWebFhusGXJYQSmt72IF1hhik6M+VJETf+PtOajGcy8zaO/x3F+qm+RunF5sd2\n0eC9IxVgjbiADxkNuuUhWMs9LT2/p0hHOafxp+nPCmCaBNo25+p2aG781rHeDGS9xOFRN/KPqrLZ\n9Kw3HcOQiiK30JYdppzHaE5VRVqWokjHCTJM3mXNQVUOzxhQbwQSSRGsebY/ZqEcLLBdjUDdEt5z\nznxt8uSLLx8LVXBY1Wk1HTTrbjSFdWdUonoyPoeyzmT5bX8ef5+tWz+P+cmq1LefLTVneZlnOpXl\nTPKpLPV9StmGJ9RaWa9T3apWw7S6SuNZXFnmjd652H7mDdrnudGaO71Fy+5m2gm+E3g/sAQuAd+j\nqh8Tka8vZ/rssU2exZjoOvZd00blpsj8ttXnUifZ1dh5vYHFQplZSlvCrQgYy0s6nDeAD0zH2Arv\n5gJO0nr0eS0nW8eTmVK3Tlj1pxaFOh3Xe4ev3Wrqi8BMoc4+m8yqA56rR+rG+1O33bIhRzksbIlq\nBX8LExFeqXS7+HEyihzOTTnHy5GLk3E1Ns4YBZc75qVhCtViKaMQmPunI3Jb5jWFQoygRGLUqdE8\nWC/raEAjan/dUYgx6W0EG1k1O5lZY3zzEhgF6kyMbvP4GImp8md6By3KlycVPDcKZtcz/azRltcO\n3U75mGOyutLRLZXiSFVlJNZk3s0GFkxnVp7LJCuBKyvTLUeBkW+3dc20XuUDLT/H9oJVkQJj7nK+\n66KUpTSlV99AY/XN1Px8aZNpXY0KoMr5ctzyzsycrXG/xwaE65hLdZCLQteZhCy4gSnEfON0M5z8\nUeDLga8Ffhr4ORF5202fwYwe/+zFyUOrDQyKR9c0nqYJtE2gbcvS2PdPPHW+/N3TNAb5r0tVYNs5\ny8CybfjcM5dYtoFF61m2ntUisFo27C4bdnca9nZb9ndb9vca9vcaTu23nNpvee7C4fj59Kl2+rzf\ncGrP1t/bte13VwtWqwXLZcti0fDEUxcJjScEQ1LaUlG8NiHEFiuN8c6Qyf/uU8+ZEeCmspnaPs68\n3UAIZRk/e/74k89u/f4apNvDj6qcO3e28F8owCO5zEibWjqW37Py7NOfRmALKFatcSsVqR6qjTQo\nbWp47pmniqHoETyMi7O61KT0Q6brM12f6Poy3kyFp5/+zCjIatRlXko1egd1zFYxzJ559nMFc1AG\nOYs3LML4zwxXRwX4mTF79jk71wkE6EYMw+gLjzHvSfFeOP/F8veasrg1b+AVSLdNPv7qc59DY0Rj\nLGPVrNRBckZSRlKyz3VRRRTe99QTZtQp43dVAV3eOW5a3vfU42PTnLllNvHGaNabYpc6QN7btqWv\ndR2iYMESRXJd6rYOfEDCgvedexpZ7MJyF1nuIcsdpFkiPtjiAuKLgq3N8L3jvc88UWxIGztZr3+0\n62bOyOicAO99+nFTyjkjOSG3MHjhhl0VVY3AJ8uvHxKRr8VyAT9e7u39bFtf9wMfutZ+3/+Hz3L+\noOfxpy5C2dFbHj7N2x45U2REyY/WF3oWLvvEZ8/zp95yL8c90gmhm8vNBFSKMvM88bkLvPOxexk7\nPmwxzdysV5DJEvrMMxf5M2+/r5z5zOIevUy2mK1+B/DxJ8/z5W+9f8pnzjzxyYifPfSy/NHjz/EV\nX/qGYslvh3rn+bUaIv7wR5/iwx9/is8+c56PfOIZUKahAa8hul38+MSnP816vWF394ujl/m6e9/A\nfQ88PIXZJlO4RKmM15575knuf+ARU2Z2kthrXorFa6NLmbhHgC88+zkefP2jM1FV914jJUoNz1Z+\nriCUpz//GR5842MTo9WIWM0LqRTf0RUT2jzSZ599kte/4dGJf3IuketjCFwBp37sl/rF557i/nsf\npkZSzDEqgniem6t/F+HC88/x9NOf4fyFs+OeU3ptlWvdLn4E+MmnP8bvXfoio8hAeM+9D/Ge+x45\ndhKYsVJExC98/gm+7/VfAtfjmc4APe/7/Cd5z4Nv3jreC1FNQ1XP8Rc++wm+79F3TOHb8nNMIYyh\nYpk8zsbzvuc+w3ve9I5R2Zedzzxe2/9WpDjBLzz9ON/34GPUEK5mmYV167o1UmLL+55+gv/s4x/g\nF555Yjyn8/ElnBpzBXLAoowSegb4duAPAUTkFPBngf/5Wjv5hj99P//mT77Id37Dm0aL2rnjS5mr\nKH5L2IjYoOPjytRecRMKggF8QAjeW3chsRrPmhOdW8lzg/l4iYoTaOclEvOwV12/ftbpD5Uf/Ryp\nW7Yf91AE5AQwqkr1+Pfm2Wwp8Nl5fuU73sRXvv0h/vG/+AB/7S9+PSA89ezz/IOf/81rPYpXO70o\n/Pjoww/x5Oc+z1vf+k5s8HWDuIbpcW6jeMs30w5Gg2qrEnlLMMm43jGbv3qT4z5lttPp8zwUe9kZ\nzNJYlW+r2NQa0Z1fsG6fyzwoK9SuiHqFjepqMsmpMrWpXou9i8KZu+7lwvkv8ugjbx+NgfXRIR97\n/MO8hulF4UeAty/2+b8f+dpRHtb3XYc45kzxJQS6pTyOhUFHmoV0awRhFuafHLsXCP1uGfAy5ijV\nmfGkFWQE00syKze0A8jkxDhrfq/tYkupT8eUsen+1ARfy+8ym85WjNaUC4J3dgxxY//gv/KGL+W9\nzz7BP/+Kd1kP4Tjw++ef5Wv+8Nev53FcRjdaZ/rfYpPinwT2ge8HvgX4C2WVn8QQbI9j0O//Cvgc\n8C+u7wCTwrC8UAn3ejcq03FQ8VwEiVxRmZZCBHtgxaoRLKTqyz4clNrROld0rqgmBThL/SBYCJp6\nBlcIVcllHyYhNYKMqvKcKdV6PcyU5qQw3bHvjudctxVqTXP4Ipjday8/ddv4MZfciRN7ybVA7FWZ\nALBCEVKTypuroDH6ANZKzU56MrZmRvOoyKSGVqcGlBOPTcbTtKFu/Zt7zLoth6YvpZ7vMfzucSO+\nCCEVq1AYs0ljbm1+M2YmQ9nWFe+o1pxuoSdnyva1QrdbPqqWUq0SetcSulURJJf6UpgeYEH6KqVG\nfzxRtvOWM4V6nC9GB7Kq1S2eklFha4kcak0jALOM5Lj/CgidDlD3KdZHWARtWkYw5XiO5fiuGpo6\n8WGedcbW8rtQwuCpXkDhxaK0vdpnEbL3pXHErY0CuVHP9D7g/wAeBC5gFtZfUNXfsOvQHxeRHeAf\nYUXJvw38B9dTQ7UNspkr1JonvJIynbb13s1yV3lC4RbEpTiHw4+dg8qUH7yUOZUjElbLOWRG5OxM\n0FXFGmYF9lxJHlzlqVj4ryjF6SKmHY3KdI7klVEBzxXpZQCmUfFP67rZ+q8xum38OCJynUNcmHKd\nTHq0Cp+xLW+9/9THKePA5cmInywznf0/qZkppzOey/i9G7edtpxCWVOPZpkJPR2FW4n/1QucybNt\nIIadx8xzLoq0drKenBRl6+LGK6m8N+fLmQSvivqEH6+bH8v2pJQQMW6ktH4UBG1mfFT8B6lDwLWA\nea50v+fh1JmWq/9XQF01wLY2Lc9QXPX4iiIt8sY801lNZ3lZtpzhujjQsg9tm2IMMCnHPPHO1nnr\ndF2GQyj1jLWuNGemnsG1X7CCWmckpc5XTUyJvJujG60z/evXsc6PAj96A7tdAjx/qaMbEs89vwY5\nFtot3qm42pRh20Lf9JGny9DreWOFnJINV84ZoQArxI2o7c0QeebsIVlrqYDtbwqzTUKhfo/Apk88\nffawHP0FBMILPJVNH3n6iwfTvuteLlOm2xDyTRd56gsXRoNjVI4zwbutTO3c1t3AZ7/wPCA8e+7i\n1j1/tdPt5Mf1ekNMkYODS4jvyotWWpOJNzStTM25ofxJhBh7Ll08V4y+Whto1vOWMj3mLMQ4cPHS\nuZEv5rJGSm/cbWVa/5qJw8DFC2fHvW2li6rxWQ3NmScZY8/Fi+cm4+C451CPlW3bCriKaeDg4HzZ\nv5qazbnUg1tdeMaEfdJUeq9ajvTo6NDgRyJ0m6Ot+/5qptvEj1DuzUFOfGh9keCFUHvOFmVK70ov\n3ql5g5X+OS7Egd+/eHZm5W2dUT2x7d/Btrtwtig2d/m21futJS+zcWoXhg2/f+6ZSRmqTuPhymGm\nQ9o7RQhc6Ds+dO6ZGTS2WHJ1O5l9XxVqSlwYen7/wnNlpmqyfYYWmmYaySYyKeCUQOFi7PjQhS/Y\ntKah56NHNy8jZbvc46UnEfk+4P98WU/izqPvV9Wff7lP4pVIJ/z4stEJT74AnfDky0I3zI+vBGV6\nD/AuLIeweVlP5rVPS+AR4NdU9ew11r0j6YQfX3I64clr0AlPvqR00/z4sivTEzqhEzqhEzqhVzu9\ntuCdJ3RCJ3RCJ3RCLwOdKNMTOqETOqETOqFbpBNlekIndEIndEIndIv0ilCmIvK3RORTIrIWkQ+I\nyNdcxzY/IiL52PLvrrDeN4vIL4nIU2Wd77rCOj8mIp8XkSMR+Vci8ti1thORn7nC8X9ZRP6uiHxQ\nRC6KyLMi8s9E5K3XccyfuNZ2L3DMj4jIH4jIhbL8roj8+9e6vmvd3zuZTvjxlvjxl0Xkb57w5ItH\nJ/z46uDHl12Zisj3Av898CPAnwH+ABuY+7rr2PwjWG/LB8ryTVdYZxf4MPBDXKH6U6aBvX8Da059\nCPwacPpq2xX6lWPHfw/wzcA/xNqEfQfQAL8uIqtrHPOHgJ+62nYvcMy/hw0k/krgq4DfwAYSv/1q\n1yci7Qtc0x1NJ/x4y/z4HqYh2Sc8eYt0wo+vIn48PgXjpV6ADwD/YPa7YC22fvga2/0I8Ps3eKwM\nfNex7z4P/J3Z76eANfDua2z3M8A/vY5jvq5s/003eMwrbXe9xzwL/LXrPdbJcsKPt5MfT3jyhB/v\nBH58WT1TEWkwS2E+MFeB/wcbmHsteksJMzwhIv9ERN50g8d/lCsM7AXqwN5r0beWkMNHReSnROTu\nK6xzBrPczt3gMbe2u55jiogTkb/CNQYS38D13VF0wo9XPeYN82PZ/wlP3iSd8ONVj/mK48eXO8z7\nOmxg480MzP0A8FexYua/CTwK/JaI7N7A8R/AHsjNHP9XgB8Avg34Yayh9S+LzJr52eefBH5HVWu+\n4prHfIHtrnbMd4rIJaDDQiHfo6ofu8XruxPphB+vcMyb4Ec54ckXhU748QrHfKXy44sxgu1lIVX9\ntdmvHxGRDwKfAd6Nufu3+/i/OPv1j0Xkj4AngG8FfrN8/1PAO4BvvMHdX3G7qxzzfmwg8WngP8IG\nEv+5GzzmCd0CnfDjZcf8bU548mWjE3586fnx5fZMvwgkTBnM6X7gmRvZkapeAD4O3AgK6xksB/Fi\nHP9T2PU8BiAi/xPwncC3qurT13vMq2x3tWO+WVU/qaofUtX/EgMp/Ccv5vXdIXTCj8eOeZP8+Jiq\nxhOevGU64cdjx3wl8+NtU6ZyHXBuVR2Af4sNzK3bSfn9d2/weHvYg7rqDT52/E9hN2x+/Dqw90aP\n/0bgHuDp8sD/IvDnVfXJGzjm215ou2sd89ifxoHEL9b1vdrphB9fVn6EE57cohN+fA3y442gla53\nAb4Xa8j8A+UG/CMsUfy6K6z7buDo2LpngXuvcYyfAP4c8DDwDcC/wuLc9xxbbxdz778CQ3/9p+X3\nN5W//3A53n8I/CngnwOfwBLcV9yu7PPHyw1/uDyIfwP8CfC/AM9jEPD7Z8tydk5XOub5q213lWN+\nEQtjPAy8E/j7QAS+7RrX196OZ/9KXE748SXlxz8B/ruy3QlPnvDjHcOPt4tZbgjOjdUQfRqDI78f\n+OrrOMZ7yz7X2GT7nwcevcJ631Iedjq2/OPZOj+KwaOPsBqqx662HTZZ4Fcxi2YDfBL4aeDeF9gm\nAT9w7LyOH/Oq213lmP+kfF6Xv/16ZZKrXd/LLVBeyuWEH19SfrwX+N9OePKEH+80fnzRp8YUOPcR\n8JdU9Zdm3/8scFpVv+dFPeAJndBV6IQfT+iVRCf8+Nql24HmvRqc+0uPrywns/peSroTZ0ee8OMr\nm+40nrwhfoQTnnyJ6ab58ZVQGvMuTqbIv9T0/VjY54QupxN+fHnohCdfmE548qWnG+bH26FMbxTO\n/WmAB043XDiK3LvfWKNHVR55XcMj97Q45/De4X2gaQKhCTRNQ9O0NE3DL/72p/iB73g73gecE0Qc\ngkO84MTbT+cQV37iEISf/mcf5G/9pa9DHCQcUYUeTxfhaBDWvZBSRnJCNNF6YdUI7/vV9/M3vvsb\naT20Hqzmt8TeRVAEEFQAKZ/Lxf7U//U7/NBf/nOAgDg7V3GIlN+xPxkplipQ/uF7/zU/+Jf/PCkq\nOUHOiuZyTKeIKDgQEUSEf/snn+H3/+RJPvP5szz8+nsAYd31PPHZL4z3/A6hm+LHpfcMObPwHi1P\nb6cJrNrG1hLKM/PgBOc8IQR8CHz+ubO88f57xx2KrUzwnqZtaZpAzpmUEjlFKKmWTz71NG996I34\nYPtq29aWJhhvB49DGGJPjAPDMDD0A8PQ88dPPMk73/ooIdi7sVgsWLQLQhNw3uGdt2MOkRgjOSVy\nSrz/wx/ha9/5NrIqWRXEgQjiPN7b9ThxDOV4fR/p+w0f+djjvPmNrweMS713+BAIfvZ+hgbn3ciT\nn3rys3zs8U+yXC4K72ZiynR9P973O4Buptzl0/WDTMLBfheHKzLGfrd15mUafRpoQ2vfO1fkqcc5\nR85aeEFJaj+zKt47+tyzs9wnhFD4yHhJnKNpPG3bslgE2iawbAJt8MShp9ts+NgnP82D99zNpuvo\nuqFIMbuCUPjbfjoa7xExGfnk55/h9Q88UN4tAVUkZ1BFixxVYLWzx3J3l9XODqFd8Ed/8CG+5uu/\nsfAu5JTZbA7ZrNfEocdhMtI5wXtP8IFnv/Acn/j4E5w6dYoqv4chcunipa17fr30oitTVR1EpMK5\nfwm24Nz/4xU22QB815++i//34xf53q++m6xKypmUk72ozuO9IzQNbdtMQqYsq2Mr6lYAACAASURB\nVDbw6AOnCKEZmcWJR5zDOY/zbrYIoqZQd3da3vKm1yFOiDg6dXTqWUfHYe84HDwxJjQOaIqsAuy1\nws6y5S0P3cdOK+w0AppR7IFnTJmqSGEgQRWymgLcXba8+Q33oCqIeJz3iPPlpTDFihT1K4pIRkTZ\n31nyjjc/SI5CSpBSJqWMajZhL3kU+gBvfsO9vPvf+xp+7H/9l/zoD343Io4nPvscf/vv/9x4z+8E\null+fOP+Hs8eHfHQ6X3qnVW1n1IMM5wrysfhvB+VifeO1WIBgHPOlKz3puDKklJiGDr6vrdnmDPN\ns567zuwXIdWyWi5ZrVYsFguC9/jgAaXvOrpuQ9/Z9l3f8fhnGx543RmapqFtFyyXSxbLFU0Tyjvg\nQZWUsgnPGEkx8uGPfoI3PPgAKWcz0IoRqAhZTc/HnNG1MgwDOSeyKvUfUsW74ERwTmh8YNm2LBdL\n2rYZFev+zi7PffEsb3v0Ebq+YxgGLhwc8viTnxvv+2udboIfodwbQWjCYrYvmPeYl3GR8XNVYKLO\nHA0EJyYfvfMIGTKoJJOLkm17HxAiTbugWSxpFwszBNuGpm1Y7SzY3V2xt7vD7mrBzrJhZ9GyWR9x\ncPEiTz79DA88eC+Hh0ccHq2LEjSHYbFoWLQNbdPQNJ4m2LmoCM89f55HH3mjvWPi0ZzJyXg1ZbVF\nYf/0aU6duYv903fRLhZ88olP8Na3v52UEzkn+r7j4NIFDi9dpN+sgYxoxldlGgL33HMXz587zzd+\n89eRkx3n7Nnn+e3fev94z2+EbleY938AfrYwzQeBv4P1QvzZF9pANY/CqnxT/7D1WVXNmsp5jsQq\nP3XaVLYdvC1GE5DxQEJWU3xZhaSQ1JHFoxLI4kiiZDIBJWr1Q8E7R9u48RRVMxlHRsgITgsELYNo\n2RC1B6fF8AKciglnUZPNRVAJaudq5ibeOVxweIEoJrxSFlQTGbPisk4esqudu3JGPOa93pl0w/x4\nnKqlLFKiG8VCRwQtRpArXl0lVUXEXl5TkKbklsslKUWcA1BTUDkhIoQm0LYNi0XLcrVgtbNktVza\ns/cOVHEoaAIdTTe8E5aLlqZpR2W8aBtCCEUwWTQmBOPDFCNxiDjnadoFLiViNr7MalGPIUb6fqDr\new6Pjjg6OmLTdcQYiSlxsNmMitQ7R+gDTfDEIdn7me0+eOeR4AjlHBrnyc6jLuPk8nt9B9BN86OO\nsq2qyvoEmAk84Uqvus5Eav2zYYkLbxtDIoAPAclCWCxol0vaxZLFckG7XLBYLtjdW3H69B6nT+2x\nt7Nkd9Wyt1xweHCJtrUI4qkz++U9MedCMU96uWhHhdqEoky9B3G0TeDuu0+ZF1yiKXGIxGEgpsyQ\nLJqxd2qf/dOnOHXmlBmNbcPpu+8iDj3D0OM3jjhsGLqAJl8CfDoafFJtYcAVb1ZUCLfQeeG2KFNV\n/UWxEUE/hoUvPgy8S1Wfe+Ftxm3Lsl22k3MuobGMc5GUHDGaMFBVYkyIOLyD7BSXwXnwZb+SGTWq\nqIzHSimjTkh4kmpRpkLGo65BXUadkp0SNbKJ9lA3/cCyUVIOo6J2zgEWZnYl0FIt/JQyyZlwdUIx\nBuwZmxOqqHhEa2RYi7KtmroaDLkIdcUJ5kUUrZwRHGqKmhLyLeflynHvRLopfjz2s4YqpSg1807N\nK9UaohfbovJy3aZawt77cQElNAHVtnh72cJnbUNoG3wTCE1jS9vgS4SFErUJRfGJc4g3z3O52hnT\nH4vFgnaxwDtfT6ayEagSkaI8lU3fM8TIMCRismWIiU3Xl6WjKx5wHKLxnEIab5KSciKmTD8Iw2DK\nNg6JlBJkcFiUB8CLIzhPcgkv/kV7zq8Wuhl+hMmIr2mDWZvbUY+6avCJeaEKiA74pikK1/jRl5SE\nVyzyUmUuagpvteLc+ch9999HaM1Ia9qWZmHRwb29Hc6c3ufMmX12lg2L4GiDI7ae5SLgnGN/d4UX\nZdE4i4gUB6jxjsZBoxmfwaeM02RRHlVk6BHNiA94BBeExreknIlZGbLSBCGngc3mCPGuGK6K80JQ\nT9uEcdEYTOarK0rUopdS3tngpTgjMreFb5huGwBJVX8K66F4XZSLGas1hDQ+XLOUKQo1pVS8g1SW\naEoxJpw41IFzSpaiSAPGeDNlipbQlELKGVXzPqNCzKZMVTy4BrwxQFYl5kQXMyln1v3A7kJI2RS4\nFOvGi0NL6A8sX6QKyZkhICLmWeYSPCwhYMllk1GZMgrn6qnWG2LWlClS58yzFhEka7k2HS0u8xrM\nGnN3qjblxvlRx6WEdkVKmsDjnN/2TGuOnMkopG4zy1FVhWpGl6fRBhGKZ5pxztG0LaEpgq4o0tC2\n+LKPuTJVpYSXm1GZhsbCqm3xUJ3UNMMUwTE2srBZzkrXD2XpzRMdBvp+4Gi94WizYbPpiClZCE0V\nH7ylYrQaeViqI1m4uvOeYUgMfSRnxeEJvkFzAqUoUk9wHn+H8uSN8uO4HVU+zDxSGBVr5TnvCg4D\nQQYhNM24D+e9GWkhzCIubtyZ8569/X0Oji5w7/33jblzHwJN4wmNH5Xp3XedYtF4PBlPJrWB5aLB\ne2FvZ2k4k0VDHCLDYOFaD3gRHBmnGZ9AsnmvkhMydIhmnKrhBHxAgicrxKxEVcQ7Uh7YrA/xbSgp\nB8uJSnCQPW1ji0ZvEUnNk5MxOj6C91KULa9MZXrDpMo7HliMod7y6m97pyUZXZVqXb7qS+4mpVS8\nVCFnSght8g5IXBYG+ZYvf8TyRK7kNNVCvSVwZq6tOlQyKplBhRwTX/bYGzjqetYtbBpoilXmsFCF\nc34MrRVIUQnLOt71Z78UX8IMFYRhoZuqVRWchX3H0K/Ad3zt2yhZh3IFFWzAlvLMUrxfZ0r027/m\nbaZMvSn9E7o+UpRTi3bLM3WuKlP7SXkhR34t0YO79ve2t5kBPraEnnd4fDGIMg+94QF8sBw64mz/\nrvxejqcAPoBvICjiPM5nvvSxN+PbpeVtq0fbmDLNmpGaGineQUqJvh940+sf4HC9YdP1dF1H1w/2\nue85Wm9Yrzds+n5KwTihEcdqZwd1rhh3giZImkkxkVM2IzFlnDgab8pdUN5w770lb1dzdydMeb0k\nzp7tZbzE5FG5Gc/VaMSKXZqSw68h3KYoU+emKEuVlSEE9k+f5iH/KHfddZfJosKvTWMh3L29Ffv7\nu+zv7dI4yLFDh4gj0zjlHW95mGXrCNLQeOgd9GSiCqKKaDJlmSc5pggP338XEnszzjSBtBZhdOak\neBwNjgRETfTDhsWw4kvf8TZSjjgwAy14A0k1AVIwRVrzDiVMrsCXvPlNtr4Tct729m+UXjHKVFHe\n8eCSWSBj9rOsc6zjRC6W9Ve9+W5TsNncTykJBBGz4kkyKWevSPEYv+UrHqFaJ04cTm0xgNKEkKPE\n+3NSYp947OEHuXi4IZAgR3aWLTurlpULeDGkmHMeV69AjRmEzHd+wzvZ9ANNgiFlcjbv2NCUmaSD\nvSzB44MrXqXjXd/0ZUgWiwtn8+S1gkakQJ1KXtU7RgT0u77hy0av1LsTwXW9pCinlu30pjsZgUfV\nI60IxLLB6PWd2d+bcqt1vVlkJcbIlCutWAHlTa+/n5QVjYmsQugTvk1Io7iUcc6O1w3QRRiSoNmh\nKjz86JfQJSGBRWcaT+sbC0sXK38YBtKQ6PqOw/WaiweHnDm1z/kLl+j6jk3XE+MU5k1JQRzeNyMy\nXZx5OavdXVPcroStc2boB2Lfo9lALEmhHxJHmw7nDvFOuPfM3QYs1GoAnvDk9dLu3j6rnd0C3Gmo\nVQCTvLP1Ko4k50xOmeXOylJgJbISQkNTjC5g8nKdycEQPDu7K+67/z7aNpCzRSVElTaYrNvbWbBq\nPa0XSD1xfUR3dInN0SHD+pC3vuk+hm5NGgZyP5C6jtj1xH7AYZE1EVNi3lu4NSs8et8pNHbk2DP0\nDjcEwtDihxbf2NI2LYOaMwKgaeCxt7yZ7uiQJpiyd6IEbzlYyQ1oQmbRQC3pmMe+5CHyPL98C7CS\nV44yrWUeWxg0C2XCpEjr55yrUs2mhHLGpQxaw5kWUpVUfTcdf5ohUvJcDqbyFI+oR9RZfD27kmy1\ns4lJGfrIsO5N6eZIjj0xg4RA09o+cAVGjhoQQLWEbR1BhZAhZMikwqiZGBNDUmJSQhsIGmjEGEPE\nyipq4DFny51q9TTGHKuUciA3hgW9Lyg+B97fmSG1m6FckbvUsJCMCpUxasBlL18FHc290vp9nkVV\noOaQtlHYOdX8o+L7iO8z0hRedhlVoRuULkKMk8HnFJI4IkAWWjzqLDzmSuQi5kzMmU3Xc3i05uKl\nA86fvzTLifYjgtcQ9YqKw4USBnRShHEYvZu2bVg0LaihjHvfkIZhjBp1Q0Q2GzQrbfCWL/Nulle+\nrY/xNUWrnR1OnTnDarlksVwW4EwtjQEQVKeoXYxWBhWHAed9yadb1KIq09EpUS1GtyN4z2pnh+Vq\nQdsEhiGTUgQ8bSPs7izYWy1YNYHWwdBH4uaI9cULdOsjYrcm9xty35OHntQPpE1H2nTEYTCjvpYp\nBl8cD3MU6nsSkxKzIsEThxY/NCxWOwQvNK6dFGKGnAb6bm0ydrmg8Uu8k6JMPZKDWZjZQHsVpJlK\nmYUp5epu3Ty9cpRpucCaJAe2wELb68GY0RpzNlfwXFUtjyg1llA8CeeQWpvJFP3VrGRN5OzJmkg5\nkqKFrlJMDENk00e6zYAmJfZCtxGiOvAB51tW+DFClzOgpWTGflhOtuRlY4Y+ZbohMcRsS8q0Auoc\nLk+lGAY+yeXB59GQUK2AJLMsfcmXmDIteT5zkWcv3Qldi8aXavRIt8Nh26jdbd4bwUqzxdbTokjt\nCDknVBMVrQ1CpiCyBdyQkD6RfDRedQFF6AfoByGmGkGx0/EKTpQsim+VJioqlrRwKF0fOdpsuHRw\nwMVLtly4dIl+GAw0FJO9G4WBxVlkxI95tQrAKgLQWS40NK3ZFUpB8AoqgwHvFPoholmJpR4R9ZSA\nzYggPaFr0+7ePqfPnGG1s2K5XBVgoSnTKiZzTqX2eBiVaYoRHwLtYsFi0ZbcfSB4b4Z8SgbsLDLC\ne0e7sLKVEATNggahCcKiceaRBsFpIncb4vqI4eiQ/uiA2G3IQ4+kwZbYQ+wh9ZAGWyiOCxaZ8DXX\niZaqByVLRjShKZOHjOZI8I7cNLBosaSaoE5Gp2booHGQgoH1BCuF0QLcQwRVS/FpifAhlNSY3T+9\nBW36oitTEfkR4EeOff1RVX3H1barpS01K/jC11QUx6wcoSbaZ0nRywScveiCSlHGWtZXzLvDwlt9\nhC4lupzpNDFEJQ4DQxzou47Npme9Gei7yJFkLrhMl0xJxgT7e5mUhbgATWn0HmtOdoiZfkh0MdH1\nkU0X6fqBmA2FmzIQwGVoyvVWQEGWTKYoUJ1Cu+JkbEzhpYKNTMA6ySNoybT7nUU3y492X0vNcrWe\nZ4q08qch7nXKKdrW9di2lH/V6h4jKjmhOY1K2jnLBWV1ZBE2fSa5gXXqENeY+ymeIUPMnpwLYg0A\nHUEbXY5k15NxNF5wJJxmDg8uceH585w/f54LFy5y4eJFDg4PzRsFcA7nmxFsUm4C1HOv3rqIGZ4x\nk4KFEqUIKqSgizEwHmp83ecMMVqmqtT7maF3ZynTm+VHgP1TpkyXqyWLxWJUppZusGeTYqRvelzf\n0yQDiogwNfIo21XjKMZYDKlYjPWqVLHQKNA2Qusb2iawajyNKMSerl/Ta2JYH9IdHZD7DpcGHLmI\nYzurjJIEshfAPF/vHcE7msbRBDPQKuA0ZaUp3mkqEZKUI3noif2GYePAtzhvpTUIkBOaImnoGTqH\nekccenKJAs21ZLFbDcTpBI8jlVSZXkXzXItul2f6EawIuWq3eO1NdFSoV1Knk0cKNc9pifepg1DN\nHdT1thVq/bwdRq5wxKyJNCT6PtPFgT4n+lxCr9GsvG7Ts9n0HHUDOfZo7JE8MGQhqZDUEbMgEqx2\nNUbrcpMzKZsyjUnpY6KPma6PrLvIph9QNTQbOFyCRq3UpeapnHdotIvSknOq1zGCDrzVoNZ+Ak4m\nwJLdlDtPmRa6CX4sOSRvnYC2ymGgNDao0Qw9xp8zRVq8TilgkJxyCU+lwhtW3uVDgGDI7AQkEeKQ\n2eiA9M7cTq/gjC+y+FJWVV5/TWaJ5cwiKZmeqNA6cHlAcuTw0kWef/48z587x6VLBxwcHHLp4BDn\nC2gptBa+bRc0TVu8UzM6U7LSFzMALDSWMEWaklKwSEWZBnz5mVMilY5LoxGoyXJb7tYAH69iuil+\n3Dt9ilN3nS7NMNqp21vJN1qJYMR1Gyih9KYJNGFSpMul4VJqk46hILiHYSDHgZSiGXhmfSMKTSlN\nWbaBZetonELs6NZr+s0RqVuT+g156HA54ch4Z+9IRkmaaQQ0WKMaHyq63dMER2i8hX6L8VYb3CSF\nYYiWfugjeeiInaMXwS8wpewcCZPfGiE5YRDI3pHiQM6xRDzVUmFzk1dKpyg34R9uZfDL7VKm8Vo1\nU5eRMlMQ8zKDmrmqn5lewBrenK0xYSunG3g5Ve9CLIyQlC4p603kaBNZJ89AZkBJCRMEKVonjpyJ\nCYbewilp6PHNEYgjJh29zd3VAk2pgExM4NWmEDXUOyQti9UqGjDKkXCkYhRs5cYLSCnPjI6aD60g\no2pVmgM1eS3znPMdSDfMj66WsVTk5FhXOqUg0DF7UL6qhelTOUr1RLNmHG60fFO2PFQcIs5PUQlr\na+mIKHlwZO9QL7jW4xuxEgHnUfEoMkYqNBeJmhIxK8pAjIlAQlKPpJ6jg4tcuHCJS5cOODw6Yt11\n9DHSiLd6vlJmE5qGpm2nkK8IkhJSWhCaMs0FXCfWhCRb682UJw9WpRTry3x+lnkboYZL7jDPtNCN\ny0coIEJfWjc65gA3V+WnKCE3NNkiUpYnDbSLtijUtoQ7TR5sSmmS5mSlgGpcumgdi8azaKxSYdFY\n27/GOzzmAabuiP7ogDx0SI54Mk4UDyUyYcha74TshYC1DZyUaWlLGJyh2MXeMcWNkbzel3oIVdQJ\n5MTQd4gPkNryRukYdcwRIpnsnDkdOZmhWd6WLYk6xzxMmZabptulTN8iIk9hLZneD/xdVf3stTaq\nylNHD2rmYtYky/z77a1H5enE8qFV0FUF42q1wazmckiZISYO+8TBuufS0UCnnuwVDQ5VZ+EOzQXI\nYdZgLkJvSMKlw4GcDlivOy5ePOLszkVWi4bawQgAKTAQKSU3zupYpZQ5SOnxijjUV2Fpl56yFkE1\nFT6XXVILtO0aa76O0QLTopEVE+x3KN0wP4YQ8E0tHSgeAJMirbn9+gymvH35nUyK0fgvlZy3q9sK\ntWFIjBHJpoTEZYYs9OoYsiN7JXsgOBqBtmnwfoU6AxdZdy3zcJXavUbJeSBvBrpNxKUehg3EDd3R\nIUcHh6w3Hf0QScn42ZDfAR8sxBtCgw/NDGcgBHF4F9CgFaFExSxYR69cGj5Y/SoUk1az8X3phGPp\nCBnrdO9Mx/Tm5GO/2dBt1mMXy6n5QHnb1RC8Dqvl1ZJ/TDEyVEBidFsNRFIcGCxkwqL1tE3LovWs\nFoHVwrNoPMEpwYHTjMZITj0aN2jsIPU4TXgH3ntErVaUGaDJOz/GAp2z3rzWHtYZKK3xpUzHcp2K\nIdQzUrxPq5AYsoGS4jCQG2vxSopFJlutqEbrB2DyXUs5THWqpiCuzpaK5pVbxJXcDmX6AeCvAh8D\nHsSGrv6WiLxTVQ9faCNjBh2ZAti2EuYe5mVKtT6qWj4/eWZurlCrIhVGBowxse4GDtc9B4cdlw43\nDATcwuEXwWr8slk1phwNhKHiSCr0CdKR1eSdvwCrNrBqPYsmEHyFo1evxix/37aEZkG7WNGuAm3T\n4kptoYgl1asQq96NRfBKKYxmakMIQ41WxTpbppu6lTu+A+mm+LEpSqUy04jereZrUYqUdAMzZVrv\ndcwRxIRMSsle+oIEnpRpyem4jDhPl6BLQpccOZQ8UxNYNYJoi/gV6gO5KNMhDsQ8WOMOs+kZkuXh\nGY5gOEL7NdofETdr+m7DsLGWgDkX8FoBFQVv11yXeZ/ekgWuqTAE804NvDRYB6WiTCfPdP4u+mJH\nFkValOsdqE1vih8BNpsNm80R3pdGA6X8TUoHlyo7BQuBWlvITIwZkYHoPdEXcGITWCwahs7jRCFH\n2tCyv7tkf3fJzqphZ9mwbK20SjSRY093mApvbSB2FvXQbPnP4Cw9mZScdBwsUtNPiODVvGofnLUS\nbDxtCBbq9cH6T4u3en8V40nX0PjIuh9Yd9aJKw0RjRFSGvO/TsSigalgi6qsr9AC0ZJKG12vCfNQ\nHJ9bCZTcjkb3vzb79SMi8kHgM8C7gZ95oe3qy1q9qCmMVl62uR4QmW1T2khJ/ZlL6FPHvKFz5cGW\n9cp5ErPlLQ/XPZcOOw7XHUebgShK4wbaEA1dVst21Ho7hiJ4vG9wwULAfUz0muiHxHpjwA/vHSFM\n9V3OOZrFgoUKC9fgEFof8O3CGj1Qi6Mr0rhAuHO25vYjyEXGXNYUZtRReda8ce2aVAX8LUHVXqV0\ns/zoQrDGDNgLOHaKYbJWpITm6/dzRZpytjATNexp/Z3rejEn+hStIUKJdiBCl4Q+CX12qDaAIE0g\nuxbCCml2UOcRF4qH6MpQhYxkIUdFh4h2PbnbQL+BoYOhJ/XRmioUPMaUSpkhdWfAvjEoJjIqW+8M\nRVkFly/9ecdmK2p52yqozBOxd2DhHcsgLAv4JHhHn4bb8dhfsXSz/AgQ+55u040tIp3341QeLXnG\nnDMpWurKygAL5kJWiEgx3B0xJtLhEZu1GVhx6AnOmtafOb3PsnUsG0cTtHiAiRizJaA0WtOF0vVI\nBIKD4KoDYHLclzIbDQGXMy5Z20xfFLrxgLcpQ8GmInlvwLec66AFgcZyrdW5SKosW+vtu2wDWYRU\ngrip9BsweVes33lgUw3kqgUYlwvw09YpaY2bpNteGqOqF0Tk48BjV1vvNx7fsPBdDU4Cwtvub3n7\ng2VKwlyBzj0w0TJex7qtOKdXWCpCrWat7IYPUTjqI5eOei4e9RxsIusukZ1AEw2ZVstRsoFGnJjX\n0jaN9ZtUJQ5CBJIB4ohZcVEJrk4pEHxpwrBwHr9QFs7hmmBFyIulIeyKdPM+41wy6796pqnWQzF2\n3nHH3FBjELNCfv0DH+XXf+9jMLujh0fdi/hkX510vfz45BfO4iukHkCEe07vc89dp0akr0UGahef\nYtqVXCkpmjHjBJyVqGQbzwGi9DnSxYGjYZjSGAhDttRBVI/4jFt4XFjgmhWu3cG11hAii0Nz6UGd\nlCyDCYYYyd2A9j2562AYcCnhSsmKUFDDuaILpjrXYp5V/9OuB7PivQ80i4U1DCi9dTUlvA8T6nno\nDSGa4wjK8sHTtta8/+D5czx97ovmLZSjDOm6sDevWbpefgT4zKc+ydOf+9yIghYRHnzTQ7z+oYdI\nMVnDjcGGEwx9RJwU0FFL2y5oQsPe7j5DHDg6OuTo6IhufUS/PiL2G5xgyvTUHl4SXkxx5hxJsTfQ\nZY5jyssLBO+ogxZGEFoJS3jv0GCKX5KVd+Wsk0wsyjaUmldXMArmNWZIFlkM3hnyltbqU0NgubNi\nubtisVqWXLxhDvoYGQrw07i4WI2zCEhVpB//+JN84vHPzgCq0Ns4wJui265MRWQPY5Sfu9p63/6W\nXe7b86MVP8aHtne2nSgec6NavM5cSkPUvLuiTKV4p9UzTZpJZPoE627g0lHxTPvEuo8QBDckQooj\ncrG2/nMiNMGTykzK2qg+JROWMZtViGa8KF4y3pcZfo1HfcNC1epIQyC0Le1iYX2F1SC/3sWCxC0K\nMmWiszZcFXRVQxtGOoXIi2X47V/zVr7tq98y5VcFPvaZ5/iP/5v3vViP9lVJ18uPb37j/ewslwb4\nyrqFyt1qyiC1ZMaN9z8Vj1Q0Q0U1iilUqxHGvNI4sO57M5KKdZzU2ULAL9Ry6WGBNCtcs4tvdwui\nwDxDlzLiEoI3QO8QSX1P7npS1+PiQCit20SLMhWPiNW75lFdVk97rlArqIWxTnG1WNL6QOus61Ht\nYCQyRZFcql29Mk3wLBcLVssFrzv9EG97y6MsG1d4O3Pu+fP8xm9/8EV+yq8eul5+BLjv/jdy6swZ\n2jIKrQLkjg6O6Hvrp9x1HZt1x2a9IYTA3v4eeX+P/b1TBN+wu7vHwcEluk3PuXPnSH1nACJNeFF2\nVgvOnNq19oCpsw5GamMoc+yLx5AMaOSwOk9Kr12pzqAZ+d5X3hCT02SS6OiZeufKXNNm7C/tvCPn\nWsVnNdg2y1oQZ2MIQ5tZrHZY7qxYrFbE4q0OOSOdopqscnCCOIxUozGalS958xt55OE3EpOOMICz\nXzzPr/zqb97Us7wddaY/AfxLLHTxBuDvAQPw3qtuV0KWU+ypWsa6rTRmearLiuNrT9tR6DHmuerN\nylnphkgfMwebxMWDNRcPN1xa93Qx0yfzckOK+GHAuTy18CtCz1Bn1jPVu2ReZw54VVzxJFG1YmQy\nwQmhsfxA07bGPMWitxBZ3sovVRRccOApuWCd8sG4yXOo92XMh47h3SnkWO/wrdRQvVrpZvmxRD/N\nl6v5Uric5+a8V59QycdrBRw5Z0ZijMQCGFpvOjZ9T5+S9RQB3Bh+ymRNeBHEB1yzQEILTQuhHfnA\nicPliE+J7DvL5WvNs2vpkZvLtZSe0cGAIUkzxGHW4tA65ngXcT5ac/HSNceFYJ5N0xTU5Wjzj/1a\nU26p3cicoxghmaYNLJsyv7KtpRDWjUbznddI5Gb5EQzoVYcHDEMclSkiY3lLVaoxZoK3sO7Ozi5N\n05Ji5uDSIRcuXOT8+QucP3eexpdGDMulDaEX0BTRZJUKqXYxir2VOBVwL9H9IAAAIABJREFUkYiF\ncQnBPFNvMmuKsWD/axFoBXPidZZHDQEXGlxocaGZuouhpaY6W4K+DHWwTnCWMlsuW1arBcud5aRM\nUx5raJ24aSb2WBYxlb9sT8lhC0B4s3Q7PNM3Aj8P3AM8B/wO8HWqevZqG02hnyryp6sqxvFIkxV9\nuVCD7c92A50ltEscft1FDjeJi4eRCwdrLh2uOdxEBhUignOZPibcMOBdxloLllIandnuxVtx3hGa\nMJ5kZaiCS8O7Uu/VeBbLJU3b4oO3vBNWIiHqR6/AlxxE44TgdFKo43VPD320tMrfraZKtpSpcGcJ\nrGN0U/xY37+tDkYc627E3HCrNX+uCANw2ToaIRb6HWKk60vrvs4mtcSUcIjVZY74gPJmOyuF8W2D\naxokBAj2yjrFWv2liA+J7BuiM8GKTqU5UkJc1ptVDFWrmZgTdJsRBBXjwNAPOAk4N+Cd5bGatqUt\nPGtTRiyWl2rZAdYYvc1taU6ScZGSK85WltFawX/jPcFXJH3pUHbnseZN8SMUnEfpVCUlj+28WWxD\njMQyQi8ntQEDzYKd1R6nT93Fol3R9wNnz57j/PnnOXf2HM+fO8+ZUzvsrfY4tb/HYmE9bId+Qx5s\nSf2GOHTWACEOaGmCIIJhQTBlWvTgCPCx8wUtXVCFYKMntTgiozJtkbCwqoZitFpXsIpSNp7SZDM1\nvbPOTctFw2q5YGdUpphnWtMvztH3PXnI49Sx8Zx0civmAVClgjtvjm4HAOk9N7Wh1IuaaYnRE60o\nX52FOF/YSxhdUjGVBgWLW8KxR93AxYOe85c6LhxsuHi44bDLqPdkHwgu42IsytQa4zu16ikZ82MT\nmtZ7Z7NIC8Juqv2qylRMqDSe5XJB2y7GiQ0Vum5hadvWCQTBQExiXYymshw7ulKrE0zwKscU5wg6\nYvJI7zzH9Kb5cfToZ8p0xLSOvMbIb9sAHjOwsnqSTs0O+hhZbzYcHR3RD6X/aFKCc1OwYXxUBTQX\nAq5pkaZBQmOegNZwasIHG22WQ2OgpBJuthmVNcgzq0F2Hi/QR8tv1nZywxAJfsA5K1HwPtDKikXb\nslytrCuSLwZgtGvSZMX9IQRMOSbzqF0VWErTBBZNQxsamiDFgzE/pSr6O4luWj5iock4pBFgU1HY\nYPOSY6kBNoPO0zYLdnZ2OX3qDN47NpsNly4e8Pzz5zh39nnOP3+enUUghMCpU3ss2gY0M3STMs19\nR4y99VsununYMrOWO1FxKzb6cqTaTrxUGnhfXKVSo+h8Y3iA0EJoGHMdDvNKnUACLTXZXrDcfRnz\ntrNs2dlZjnOoh5zH99HkY2aIkbHn+wiSm3mgNegk9mXWmxeSr5jevPMwWnVFqwKtUmbejm3sOlOK\nxGtoy8ICZZOC99BsLalihn7IXDzsOX+w4fzBhoOjgXVvHYmypTpJImhvdYHeJZyEglrzo/dRJ37U\nU6xeoKMUV3tPECnhWje2z/K+1C4i5DJpQxRyWT87x1LFpimIEMQS/Z4xZYzlRWeee/lfxEKFIs54\no5xcRaCeTI25fkrJhEat5auAj/nPKWxUt9JpMEP5JmfzSPu+jDjrBvq+1GKq5Y2c1EF91TouP0VM\n8HhbshcDxxW0pDqrSZbQ4NoFi509vESGAEMjDA5kWONyJGtt5WcRjFp6YCjQqlAHE1ahMQt9lm+P\nw0Dfdyass+Jy6aMKIPZeCpQ8WZhNNcoMfU9OkVj6u0Yv47loumNrn2+YquKihNAdNUdp77UTywvV\nqVg5Z44O15w7ew5xYt2Oho6hH1i2C+65+x7uuedu7r77bu666ww7q4bgHZojKQ6kvoR5U0RnNeoi\npVubWFtJBLLMhod4Sj9nC9OKK0qyogpCQEKDbxYsVru0qx2cD8TY29J3xk9JRp9IpHZ6KxOwvCup\ns4Bkw5pkgdAE2pxL2DfRDxb9mdp+VoTAdC3200zQW+kS98pRpjAG20UmYQSTl1AVqM1lnLoBjTmi\n+QDwPMocMtANmS4q6y5y4aDj/KU15w86DrvEZrBcqck3U14qoKIEn/EuEyQgLjN2KSoW2ugXagkR\niBvLARofaHwY0WiutKcbc6UpEdWYNYijcUISIUlA2oYgjcnRmnYol8ZMUc7bClJyd37eYGD60x3X\nB/VWKBVjqc6ONKCEeWc1pAtYo4I45WVksv1KJMQMps2mY73Z0Hc9wxBRyti/MjFDauxep6iDClZz\n7B3qrP7YlCkWykVszqkGvC7xbp/FwhEXnj4InSh546Ffo72StDTZr2UB2Hnm0jyil57QNIW3S+/n\nciFDb/V9MUY8jiB1HmlJ4RdG88VTsjGBSk6JLg6oZhovtI2jbcpoQRGb3HFC10VeitDXOi3KUlfi\nrASmhnxryVbOmYODA5v1LJWnE00TWK1WnD59mnvvvZvX3XM3d911F63PBJfHFpBDbwrV3MPC3+Jm\nFScm/8oI5rG2U9TbeYrVsKrTMZqICL5d4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dQZvybs/rGf+Fn+xk/8LNeXTnj46KXGvPR6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\nKpSqaYQ1rdDSBHvqxc6ogIG5s126wztvYJp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A0uamc87AW7i6d49clC5spuNKY2eWeUZE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wqpOh/qwbQ5anEUKcMVIfShu7Vh53nmYs7zHbn4Mpjo5WpznucRCIExaK9WOQ5h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\nsO0IVPWTcy/3ish/AweBd2LL/e0e/6/nXu4Tkc8BDwJvA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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1132229b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#batch_size = 64\n",
    "batch_size = 32\n",
    "num_classes = 2\n",
    "epochs = 10\n",
    "\n",
    "# input image dimensions\n",
    "img_rows, img_cols = 32, 32   \n",
    "\n",
    "# the data, shuffled and split between train and test sets\n",
    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()   \n",
    "\n",
    "#Only look at cats [=3] and dogs [=5]\n",
    "train_picks = np.ravel(np.logical_or(y_train==3,y_train==5))  \n",
    "test_picks = np.ravel(np.logical_or(y_test==3,y_test==5))     \n",
    "\n",
    "y_train = np.array(y_train[train_picks]==5,dtype=int)\n",
    "y_test = np.array(y_test[test_picks]==5,dtype=int)\n",
    "\n",
    "x_train = x_train[train_picks]\n",
    "x_test = x_test[test_picks]\n",
    "\n",
    "if K.image_data_format() == 'channels_first':\n",
    "    x_train = x_train.reshape(x_train.shape[0], 3, img_rows, img_cols)\n",
    "    x_test = x_test.reshape(x_test.shape[0], 3, img_rows, img_cols)\n",
    "    input_shape = (3, img_rows, img_cols)\n",
    "else:\n",
    "    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 3)\n",
    "    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 3)\n",
    "    input_shape = (img_rows, img_cols, 3)\n",
    "\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "x_train /= 255\n",
    "x_test /= 255\n",
    "\n",
    "# print('x_train shape:', x_train.shape)\n",
    "# print(x_train.shape[0], 'train samples')\n",
    "# print(x_test.shape[0], 'test samples')\n",
    "\n",
    "# convert class vectors to binary class matrices\n",
    "y_train = keras.utils.to_categorical(np.ravel(y_train), num_classes)\n",
    "y_test = keras.utils.to_categorical(np.ravel(y_test), num_classes)\n",
    "\n",
    "# Look at the first 9 images from the dataset\n",
    "\n",
    "images = range(0,9)\n",
    "\n",
    "for i in images:\n",
    "    pyplot.subplot(330 + 1 + i)\n",
    "    pyplot.imshow(x_train[i], cmap=pyplot.get_cmap('gray'))\n",
    "# show the plot\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IDG init\n"
     ]
    },
    {
     "data": {
      "image/png": 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dh5HRd0gJ577j6XPElApH5cEiMI+OMfWBccxsU6Vy6MJV5pTrHmMCAmny9sgGdSqdOU42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\nv+VdLHNkFcRXjvV4HNmPE9ePn84xBp5NMP0p4C8AXwK+HvhTwP9ojPlue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Xwoa8BJlcN5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UQphXmZN7eFjRurOsCpvHnBlNe2HzfH78vSoNpE6GWuLU+7dRTaz26oDkFbVhOoJoALG13B\n2Pb1a8FHJM6CsdgQkDCt+aORpE0qVHUMMpZu0PYwhmmeOceVtRQqMnu3V8AQp5xT55zQxvYHjrVi\ntVINwTHs9hyjotpL4Xw+M03zlsA5a3AGvDF4qniUxkh+bmgs7wmDvN+ux7igiM9WNGVqSm9cMOU1\nno8CDc/bKEBE5q+Dadt9Dpz5NoWWJvpUqhGReoMEUeMHqgmUaslFvKOtc6JM1AXgeKWy5UjLTOge\nC8ajZqwT7vuwP7A/3rI/3ABiS5hKJebKEjPzEpnnyDSvYLwoecXENMlo4f7uRIwrFoM18PTpHV9/\n55u88867PH7ylKfP7nj67I6Cfq+s1b2uyWeNxCT7NOsersp3bmd3LoaUDdlZpW61FtSrAZFehwXb\nn3ipV6LBSIQQTHuujSyeUqLkgrOANeSYRBljmgnBcTjsOOwHRdoKTPvhW29hnZNgmqUyMywCRtKW\n17LMzPOkAA0vCjHVqDZkISXhLyXVeNwyaytcVwqb0Ywx8nptkepavk5yBa2RbMk5ozqsgWlypBiZ\nTBEtYKXf9IMI3AvfL4s7yDgq8bronFVVaVJlWkYBUMkr26AxtV65eWhAfdPWa9uPxmrl2GaiLVti\nAzSIXaiEO/Pcp3+JqkXnUxUHJmBdj/M9JgxYF6Qt55Q36EQC0/iArQoaygmbIjYXjC2YIkjJGBMF\niwuSeOZSGOeZp6cTU1y3/eGcEy62WmI1sn1OCYuh84GuUxeNJkW4k+/L/emkgTtuiaRzDm8twYCn\nYkumxlVlMK/AeNuy0mUxToFW8hmVrPO+9PKAj4/jem37EaTIqEmVjcQBZpuB1qtqsxpBrF5XpFtU\nbRl+4wtra9gGqvESaFJTfpNqLQQpUpyX89E5R+omndnKdRQ/3MDucGR3vGV3vBEQ3TqT08qaKvOS\nCeNK1y+EfqZUy6Jz0vM48fTJPc+ePmWe5s0B6d33HvOVr7zDV77ydc7jxLKuzMsCVhNTFwQoqhSb\nnBNLTLgrxLFoieifpkGzzGa9Jjv34v76suvjo80LytFUnqa6T5QiFaFX94xaUIUM0Z7tQoe1kGJi\nHKcrNxkrEOxhRzcMNFEGEEsz70VyahonpcAklesTYecmIwgSJAVUJK4xos1bSZvXlhpt2UrJ4lEq\ncym3OcU4J0a54s8n37UQPIfjAeed6AKviXWNBO9xtgGWzOZN2lrItV6EoEsVdZk20GsbqG2mWo1m\nj9Lu+/76YKv4AQ6PcN0OFzrKOlPiTE0yKsAi6lJlxeSGkmyOKk03VQKXoxJqxtqKC4Ew7KDfYcKA\n6QacKh3ZEEQHNwqwY82ZZZWWWFrlllPGOBkbFJNgnmVUUCvzeCZOE+uyMKcoh4oT8FlzgemUf90O\nDkMTEZFEa10Wplnau9TCru94cHNDQ296Z9kPPZ1zuFowOVLXGahY3+N9J041DW3qO6zvqS6IIfh2\nuFfxw0wfSPzn+wsoeVU+MiraEFRVSPihxnmdgyMjCZpk4FW3t4HljNzRTDlMTmQsFMNqFs73J571\nTyi5bMWHc34DWFolhPX7B/pcigzv9vh+R+h3oqhkPSZG1ux4dj8zrs/IVcBSMRWmWYLj/f2Zx4+f\n8PjxE7ETVLu5Z8/uuD+dmWMiZvWENvKeFanQ3orsKe3g1KY5XKvgWSwEbxhCZehgF6AP4J3823OS\nsC+5PmbBVPiZzX8uJRFaFo88ASXFNZPixQi8C4FSBaAU1wWM2QJqN/QM+x39NGi7s26AHKfWVNP5\nzHget2BqtBqpOpT36u3YdWGbmZWaZa5QxbOx6Y0YU3C+YnPB+4L3ELylcTxDkABu1ZLK+8DhIEbl\n8zQzjhMwSxaoXFJJImXu2dLLizBElmCqu8noPNgqCVmUbuyWjaXyyaPGvOwqbgf7t3A3Dwi7I+n8\njDo+o8znS6wkUaPOrnLRVq3DXBUmBrC14Ek4C7Xz1N2e2u+hG6hhEPCQ3tZlFqRkFKeMeY1My0Ja\no6Bnc8YVcF5UqksVtG4qmXmeifPMMk2c9Ga98JBjTBx2e0HehnB9DIliVpL9tCwLs85KKQLK8waa\na6Yzhn3n6Z3F1YpJSRswBuclWFvfbSAkqRx6sN2mBmWagtj3g+mHW3mhooYfzoHtQMcFpoHKTIUa\noQnZb8CvegmopuE6VBWpZIpJ1GpUR9xwvj/jnGONUXRvQ6eFhnT9+qHj5mbHzc0DzNZSrbh+j+/2\ndP2e5BLGdmClWrw/LdT7BWM9PnTkXDmPE+M48+zunnfffcy77z6WLlyWCvw8Ttzdn1nWSCpV7OKs\niFSIJrR5oaKUSrMZL0jDuuKMpfMSQIcAu86IApJpACT93d9PANLrW9IrrUh1mkvjG+VNlcVYI/vD\nGYqpmw5jSpF1LaqKIW0LaypxjdvhkEshqSVam1nWUpnOI9N5FBF4Y6nGicKLkpG7Xh3jQ7gEsSwH\nUBU3I5qUVvMqtU4AT1Rt+hnRiqxaRdZqqaXIrKpJJSpiuGRwzgvp3oigg1NP01Z5llogRUoq0nnU\n1hnmQuMwOsSzV2i9+goizp+4Neyxh0f427fpjrcY34FzGOc3zWNqwjiLWRFvyGqgWtFabl9vnVda\nY8A7TN/D4UDt9xTfU8Kg4uIWnNtAc6UWYk4scWVaFnJMpBipueC8gCZc8ZASxrCZhadlIa4L6zwz\njaO03xRl3vkAtRKc2FVdlF8KZFEvKmmlpEhJUUBGnRekEWiiauidpbeW4OR92XZgOad6wv02w6rW\nSwvROlqDrTXEZb3C6fUJWzWLVaNRJHZ1AVwvAdUZ9QtUJE7J29x+q7Ze/KhbNVcyhSRJdzHUYpnG\nEQwsyyrX1Es7tbnVHA4HnPfsjzdY40hVvJl90nlkhpQhZsOa4TRG7k9iRm8U9DTOC+fzxPk88ezu\nnvfee8K77z1mHidVgVPP3XlhWZMigEGAKhZTzZYovPi29I3reSv3yqxfwXOu4i3qqsXGi/59m5ka\nY/4d4F8BvgBMwN8Afq7W+lsv/Ny/D/ybyJD9fwP+rVrr33//R3cSBCs4V0T43nvVUDBUCs559scd\n/kbmmln76jGum+sFxqj9mhCDc4FpXlT3VC6GKxaXBVooEHDJbmKMpDxjlFQceuEZ5iRzS2Br/xpj\nKMotFYF9tmq3iOwLSynEuMjBuK6qIGLoOlHuaBSFZCT4hi5wvHV6+EqFczgeqbXS9YN6SVpyyYzj\nifN4Yl2Xy2SkXgSn9TpsM6pXmwZ8PNfr3I/DzSOG24d0hwNhGHDmAaHz5MMNRSkJJUdMOmDSAnGm\nrjNlWeQgMFZmMdYpj9Rj90fs4RZ384ASBrLzZBfUBHklT0moMHElqy9tA8gJNSzpTL5gk5jCi+Wg\nv9gAAs4KcORQq+zlELQT4tgNAw9ubtjv9lJFInNfUwumZgZbKZ3F7wNlLZRYqSkjknWitNSHTg2Z\n/Xawu9Dj+70AWbzQy1qlLiCs1mWUz4Wux5YjbnfzUW+Lf2zr9Z6PSHvfCbXDeAHAFXuZjFaahGAn\n4JxSqOaMGNbLXP/5REZXa4dezVNjjDBOpJQUqeuVdiNJUooRjEj8GWeJSYqZw37H+TwxnidxKlJ+\n8jhHpiUSs/zc6Twx9D3TvDDNC+M4cz6dOJ3Oav0mTIQYE+uSyDFLtam8wVor2QiGwBqLrWpvWS/Y\nv3YmSrezsEbD4qBPlSi20XjBal1oiM/Z2X249WEr0z8O/MfA/6m/+xeBXzLG/EitdQIwxvwc8KcR\nxNo/BP4C8GX9mfdBG2gv3lbwUIqjFJlTGiMyf8YZ9vuB4/6IxbLMguhdFr85tBhrlS4gdmWlSjCF\nSwJTnKXYJk9YtlZBTpF5njers94MmvVn6tKM0tqSLNAYQ6hggsWh2qRVhL1lnikV9rLMrKtwqHb7\nPXvEJLmR552TWdZuJxVow7scuMH5wG5/xHqpYFJOuCeBpIN0mQerw0deNZhWLjJfQpB/lazrY7pe\n237sWzDd7QnDQO0CdX+g5EheF1JcKHERIEiJ1HUinZ5Q8lNKXSnGbehc4zts6HC7G/zxBn9zS/Ed\nSYFyeRpJcWU+nzeKlvCIRUpwXRYRGlFjBkjSPrZOKC4d2wEDAibqO7Fuq9aKWhLi9bsbdtze3NB3\nvSRzRnRbbc2YGhlsxXWOgY5kElkPIgFJyWP1w0A/7PGhx1gjnRzfYfo9hB5ckEDakrhWIemfxRhR\ngDJH/O72o98V//jWazwfpfB0ruJVTVC0p1osvDQ1sUoxKEmSIJS+Rb00TPS/pvV+S6WquUgtVbjE\nWdr+jesuXtIOjGddVmLOjLN09NaUialwPOwYzxPTWWzTplFGBmuurBlShdN54r3Hz7SNnJR+GLdE\nUvAhcl7nmHS0J1q8xinFiisUriuYq+p0a2crYLSUSsqVNRlChNVX1iSfpzXCm5VgiqiCveT6UMH0\nRc89Y8yfAr6BePf9it79Z4Cfr7X+gv7MzwLvAH8SuJbSemEJcMNa+YB88Po52c08vvGe9vsd1Nba\nkv3XFIusdyIbGMJWjSaVPGv1f7ZGhfPRdoLw8HKMxHnB+kTXdQKC0gMgy5vZevXPDatr3WgRVQFJ\nVRG0pYoDzbJMLPOM1wDqzLXwfCV0A50x8rpz2fiBGLHLcj7ggiQJqWRRDqkF64Iq3ERSXSlF2jD1\nKtOsuK2qfZPW69yP4XCk2x/0s/dUayjybaMY8c7FyRwdKmUZMTlTllloIi5Ii9N3EDpM6DC7A3Y4\n4Ia9tIvr9n0nxsg8jZutWi6ZZV7Uy3FRRHbZQHSAOilZBYZIp8UAXr19uw6dW8qMbdd3DMEzeE9w\naolcC+SVmmZMWrA5EXLCkUhksikUWyWYeo/rerphRzfs8d0gYvbGgg/UMMj7bcIBLy6NrdUYjFLL\nbFNOegPW6z0fwTuxLPNBxlSuGhwGpYVSlf/RxDgKnuw9aRPFv5yBl1aBgCgrGVMkYaoYarbiFKPn\nXTs/jPUYkynVkHGsyVAwpFyIuWhgzKxLZF5mpkmCaq6WVC3FWKZpxfsRY6xICCYFQdWCQYCWDZhc\nVKin5LoBqy5by2x76rndts2F2SruXCCmSnTyZ0oSB4rb0ooNb/Oy61Vnpg/1pT8GMMZ8Hvgs8Ffb\nD9Ra74wxvwp8kffZLLXom2/ltsqmtYtNFdK5MXYTgD+fzzx7dkdcL8HSY7CuCv2zosAjBTw0ztFW\nrSGWZVXUhWJMYmRbiwgkhJGsWb1pgdQ6FXwoOgetatqbKUFmvEUfs11h8dVTqk2trPOCQ95nQ5H5\nsOLnhdDNpFxI6n/qtMXifMAUu82mjscbfOjYH87cPXvG3bNnpBSlundBuGga1DFs/Nw3fH1k+zF0\nQT5zgfoRl5l1nkjrrPNA2Q9WxTusAeY9dTiAC9gwYMIgaFYnvbk67Kmhp1hPwaqohnQuYhSwUVY0\nb4qR0/2J8TwyTdOWFoHZ3IZAvjdVM3lTK8GI1q5RZxd3BW663e/oLdRlpBZp2xnnqPOZMt8Tp/vN\niq0U0dO1uQhH1Xfin9sfCP0O3w3YMFCtk5vzVLUH2ygXPD+mk1mz3Ftbi/LN3pMf2X4E6HvLrg9q\nGt9T8BQjNSemSACxVShU1hBdZUqBsuzJtQIL116m+gqQWZoAkGSsVsWhRoGcWHcxEDeCxK3Gk6tl\nSRLccjFKdcmY8yTJvSrXxXx5lmrQjlrZqIRiD9deT5NJVLERPbO3REw+NIyThNE6p7QY/Q4aqc/N\nddtaO4aZ2rRPiAk6L1WreMPqU/x+zUyvl3r1/SXgV2qt/4/e/Vl99e+88OPv6L99x9U+UKsHlHMB\n7+0G3ikl67BYspllWTmdR54+vSPnjHee4OWiu81eql4uirbJRCxcAZmbqkIVh44Ut7nUuiw4JwIK\nzXHGqBaVsU7kC3NS8+R8qURzIpfEte1ZUTRuzsIPXVlA9XXba7Te48KM9Z209lR5abcJTxhstbgq\ntJ7D8Yab24eM00gtlfF8ZkGkGEGlrktS43Fp815LHL5p66Pej3JgSfJWayGtC/PpGct0xnY7bD/g\nVIzAdEFUZ4YDdR6pvscMB8JwEC5pm/P0B6rvJfjQEmfViY6RZV2J88K6LKzLzOkkwXSeZm3cSNXh\nnSR4WB0pqAC+1ZmSVX1U33WEviMMg4iGh8BgKywjtQTwHksgzWfS/RPi/Xu07wNUnA04FZgIIeD7\nHX63x4Ydthswvqc6T1GAUVOGEiCcgrRgawopa0FnqdKarG9oLP2o9yNA33l2g+ItQqe0GKPVmvIl\nTRGusjXMBurimf2emlqpt77QEq1QM1vaUyUoSyBVHrR1Chry2/9X60lVhOrlcaStv6yid35CqlqJ\nbhZs3VTCinZEtPl/BSDSVnVFcAm5CEq+1u3fpOunc9LgccGr+p0imWF7XNNu2qGkXqpS8US1z6na\nXf3yS61XqUz/U+BHgX/uFR5jW7VcnFq8dxfT2M2VXd5nc8VYY5uXqm5pUMBNKhiVWmvqGjHG5ypT\nCaYqWG4F7l904C2zSplXpVV8RNvMAOtEuNu656yqGtWlHYxFg6lzXo29lSeaBV2WYsaUetUuBlsq\nKResy6xrZFmlUm6HbqHSF3W0UWRm14uTQ98PdF3PGkRqUIbwonJiKFsi8IZr836k+zGnlbiKo0nN\nmWW8ZznfsUxnXIrYHPElY+wBZ1SEYTjijglbCmE44Ie90gn0KOh6ivNEVWZJUa7XGqNqjRYhpY+C\nMBf+c9R2G5IUqRk4aOAETKna8m3fIYt3hj5Yus7Td56uFyMFlxMlreTowDuq9+TxjjzeU8/3cpCi\n3zfXYXwvZIicsLVgK5sOq3H/f3vvHmxLftX3fdbv1917n3OvZjQaicEOgx6MZDAOEAtJNgiQpZSN\nnTJOUikS7CoC+cPBJFVO/oE4oQIxeThypYqUeSauUHb5kVCJQQ6RECCMEYyFNAMGJIQ0T41eI+nO\n3Lkz95yzd3f/fit/rPX7de9z348zd+6dXrf2Pefs3bu7d/fav7XWd631XZEcrL9R3YkTgLqoeQd0\nLQgp1ZKzY9yBifSM2HsAACAASURBVHyXm6qPgFdFd2TpCNIRoxCj1Fa4UnxTar5SK3RdYNVFJEdy\nH8hZ3FeaihKnPvXkCKmgtEiJYguLV7AiUUMfgkWyaR41WnAwOiGHbWpkCjiXtdoiPsvdFqNXnEtb\nEG0dngqmJBQjMIOdfUrN8UoWUEeD/d2iRFFigDao0WCK2YBinB30efH7TEXkx4G/BHyLqs4Jmp/2\nz3Mfu97XfcDvXm6fv/mrv856b282KFb5mq/7Wv7k130thRgheDEQOTOMg49Ns/dntT4nxkSmZ0iZ\nzba3aRf9ljoiSKba1hgCXdsQWoe8Ssl5SSXlBMnaXcQhFHKyvELOFc5NaWQcA5BrFG2iFH5MS6ob\nK5JKJuWJkaMaOTfk83Lvoe8rIf/+6bEOdQbD9zUlYoisVnsOdws5JT7zqU/x2Sc/BV7AJRLu2BFs\nJ6GPP/F/vYdTPhLQCC9G3vr6+3jz/feShy1hc0jeHhpPbbdHWK2Ie69g5YTjsV3RdCuHxswTzCIk\niVZUMQz0vRWlbTYbBueEHp1a7fwLL9D7AIamVuvmOgKwIi5hZBAbrdY4GUmMYp6/ZIJkomXPCElR\nR2AyMDqfNUcvoIcHhKFHxNpcREByQrKx7aTY1DLSGBqkXQNSo1FFKs+96VxZoKEscR966CE+9PDD\ndfET4PDozptnehL6CPDMmac5e/YZz4kGQoBXveaP85r7/g2L5vDoVAOiAZVE2win1kqr6gPqAzmp\noQLeilgjxJnrblSFFs2KkyFMK2fxjnyhZAbtS4DohULuxEuYSEzqNdLJ0Invqxg1dT4A26VHtjAz\npNOQhmoFZzUxu/fCDGfXwKpR9jtlr7Ne08999lN85rNPWduaSz9cP73l9RDd/zjwV4BvU9Wn5q/5\nOKGngXcBv+/b3wW8DfiJy+33m9/5Tr7y9a9jf39N13Vs+y19v6XvR6IzvihK0kRSnYwphUjB5r2m\n0RucZODo6IjDwyM2myNfaKx9pjT7Nk2oI63M4Hii3lmOcjKlsnyE58mS3x2m/qWU7HhZvZdUJ5g5\nqE0YMSYnUxzjILYRBrUH0dwi596c3t9vt6TNEeLtM2U8XBChCQZTxBBZr/brMIB+u+ErXvc6vuJ1\nX4lgr7dNy/PPneN9v/DPr/WWv6TlpPTxu97+9bzuvnt9R5nx6IDx6Dzp6HzVhWa7T9PtkU7fQwgd\nca8j7uEV5a2TgJSZt9CXQd99z3a7Mc7lzRH9ZlMHLIzjwOboiPPnz1ekJDYNjD5UPmf7DojR+5n7\npDQxsuqsmKdtoxWUFGMqmUYTISW035A2Nk3DoLZM6I+I/RGx7wlBCWIPzSM69mgayN4vSuiQdl3T\nJbb82vxduyH1v0K17hw8wjd94zfy9re8xZZjNaP9xKc/xd96d6Gvvf3lpPQRYPXKN9Lu3YtEy+ef\nWsOpNRCs/SOrkxpoAI2ojDStcGqdaVTZKOgojEDOQkq7k49shTX0qxjSKcdajJp5TFJtWOm59ic8\nFVENn+wa0YpKlL3WSNAjUtfxnR4XAev2kMo3YCgMs/diqZS678myhiCsGuVUp+yvYK8z8oavesP9\nvPGBr6yMdADPPPMsv/jeF2EEm4j8JPBdwHcAByJyn790TlXLlN8fA35IRB7FSr9/FPgM8J7L7psI\nKk7Hl2pFqxa8mzzdNK9sRHAuXi9pDp6D9N6iYUhGUt+PO329sXCgVsjCIsSm7dgTqdR9yc/BolaZ\npl54kt6uieet0oixg+A65eXWdV7g1LpQPrF4Q78VuXgE5H2qhUaxQMkKbDdHNk5Ns/NebgGxAeBe\nmBRDIAb7MtkXQC2PGuRijtttLSepj8PRAf1BW//O42DLwGxkWC2+kQaNbfWYQ0E54hSV1mxOMrIO\nEC+k8yH1gw0Btxm8Pf1mYgYq8FdN8ZdnVcnFt0O9+ARnhvXWLE2VMATNTvgtlnJIA5oGGHsjf9DS\nt5cJvkATcE5oZ8pJA7nfkDYHlvvs9jBdbuti5uGFkYvYjQJvzTIjKhb1aoI7iOj+JPURsKIfGsCY\nzfosNBl0pDIQ4WtOdGhW4orQnSImCEkIoxCG0XqHj3F1q3cdmyEs/cHlby9GkjKRRuZvnKwjMyh2\n9piCj+KCFb9Ldt6q4ukv8eCipAKEulaGMFUsl3TBzonIseOIj15zGmw3FRUGntA+dqrlr1WuNTL9\nPj/DXz/2/PcC/xBAVd8tIvvAz2DVbB8E/uKVeqhEIjlbmJ1y8mIhAKkLgjeomAeDEc03XWdl0x7l\npaxOtWerTmEngll+MhhTS9vZUObskGm3XrOWPZvBtzli61WUTWNEEL63Ce6oZdpqUWnKZtDEe11D\n4wOfIZOqkycxVMLokjzPmlHvTc05OdHz6FGqeWpDv+HwQBmHLZvDAw47KxDJDtkEUaKUc1Xsi2H0\nhbbQ3Wnm9OT0MfVHpI0bUxErwOjWfm1NYrf23srOmX4sYs0S/HdvvypeNxBiQ4vQN0bgUQY5bPue\no82G7dbIPcZxoPrYFYGzxWLKBfl8yWitZE0biV2wYchi7TXWn2oDmMvUj6ZbGcqTjJ5Q0m6fHszB\nPJlQGY9E0rCBQwhpQPZTTZFosEK3gs5oyfV6ZC5qPwNAdiO+3XAHyYnpI2ComUO8KsKogaNB6LOl\nolK2VFjXlVvWoHGf3EZy6iB1SGoQjiBvAHNkJsModgxpQRpEmspWVB7F2NZClmOxLfNn5qiwyqxA\naDKgxwPXUPSneKD+YiWrj+KVvB6dlnnSOhnO3TOp7l39u4LZiteuTOd8QUHSNci19pleVQWLqv4I\n8CPXsu8QzJgO/chwrOiotp8wVZ2pZsTJ43OYIAHVbFvlyRMSZyoq3n0Ikbbr6LqVFeoAiNCt1qzX\na4behs8eJJuaISFSqLtNEcJkSKVg/UaTFr3Qp4mR6PmljDJixUzFSw+h8UkdphSlOCl5zrVMTTAL\nbI+h3zCOPZujSBPtGG3b0nZ7dN2etc0Em04j3lemM+W600zpSepj3m5IG/96SCDunSZ0a0K3X7cJ\n3QpZnUJbI3K377/l5ctw7CmqtHxWcLLwJja2nc/Q7d2Ybnxm7TDsjtaqTmFZLby3OUQhNtENaSS2\nwajlvO2gTEtKORuJRIx0EslpRBCGlJECq2HeuhTwVqi5skIAApk8HJHHLWHc0oRI062RpkFzNIRR\nC/GEOb+FnL0WTAmQBnQw5qg7RU5SHwHKwHcvtmDIgTTGWteRshKDghM7xBDJcZ/MPtqtIbVIjkgW\nGGxQuN3pkpO0VJdIa4/QzB5xFq0en7FyoUE9LlKiRZ3GnJUCNCnGNkz0f5qNkEZUKypohlTqmllS\nKDuR7s5vuyueXvDQ6qRWsPkGKFdfMty8MQpt62TNwWnxtJRFA1o8XodBMQ7cdtUaSXcyIvsMnly3\nJajMcWzb1mn8Wrquo3FYLnpRUmydBzdG6Dr290/VRTB6hbF4rlJCsH5StQiaUhwCoJ63zaBR3FsX\nn96x53Mj25qnLfnZueEvSmd0YJMRB619sSkLOgZrak42ik3V4MiAGe1aRVlgxivr/CIuoVsRVnv+\nRyDuv4Lm1CuJe6enbRprFwkhmgHSCdLNtf6m5JMcVnKdGvstR+fP88Kzz3L+/AscnD/g4OCA7eEB\naehrrkq9CtLgsKkKFnPpiAqNJmK2ilsdgxNMSK2MLDlKiwrF2f3UiunG3hxVtZxsaf4PdcXxvNnY\nQ39kHzza+CvNBUkxg6wehWdVkupUtKm2/k+LmJts1RuC1V52IrMIsaSoPDpUKQiszcAd1fKiWe3v\nJIo22YLOYUTiERQo1SNB3xLEUEDVBHlEcuNcv7EGA1INKm4VCwQ8y1vuwL/2xPHIsXxF5s8VqPYC\n+LYcqkZabkxr/nZ2HJRGlCYoq0ZZN7BqoG0M5i3nV5zdIi8mzHtiEmNgtYo0rUVrBepKSf2mRXBa\nqDJ2LMRAtwoMITP0PjS7rl1+sYN546vVyvo11+sZSmHFPG0TaVqjEDQO04ZTp0+zWq8rtAZM8yBD\nYBgHhqFnGPtZObdN3hjSwMBIG+3mNdEGlq9WayPZ9wG7WQ3iS6m0swRHCi0qmUbAlUR+9Z8sX1ao\n55IybEdXcHst1LyB+Z8Lxf21SVifJu7f7X8E2tP30L7iHtr9GZdsCMRubXrhkR+YAcRz7iU3EyjV\nuBaxDUeHHJ47y7kvfYGDgwMOvViu32wYe+sFLIuVAfalpcvjVIdLm5xpkpX+l7FwWUCDMRaBqU20\nlOrks2u2nOmwRdVqBArlXMmrSimgyyOMG3QDkhKyOoWElkLQkBQjyg/uGOIjAlUprS9lrcu+WFrR\nUiDtrq6LXE6CESbgnN6UCluxCUKGihia0GdPMXhgoaJoVKQL0G8hvOAmpyTiFStnM29Ks+dI8eEf\n9TEVGE2G79iDGbg6s02zJWnHms1sYN1m/rM6gzNnchdlnhvU4kBm2qism8xep+x3sNcpXVPnSdgh\nZ1XEUH3Y65KXkDHFeuLWDbEJbLYZNoMPHQZ8wLKm0UepWSFRbFuQZANt1fKSc2S+9CJ1qzWnTp3m\n1Kl9Bm8PUM110kXbNl7MZBym6/WaJjZe1ZtITrJQHtvtlg3ivVDqQWMmedFT1ow2AlkIq4bVqmV/\nf4+mLHAi1qKQM1oRPfF5gVbpZIT682T+NPA3J6t8s8h4RHVLCJHoubPdOr07Ml96ohL2jJQeDOZv\nTr+S1SteRXvqGJfsDHYtjfOFDq2sBFG0RqXkhI4j/eaIg3PP8dyXvsjh4SGbrc11NKRh1kQ/wyV8\nxLg/42xYmokpWcQpNpMli6BNw9RTPT3q+pOzV+tud2AumyshiArRI9Ogig4bZBwhjUhsYLVv1Io+\nb1e9B7qgIMWBK8ecR6QZg8ET9ljk6kQqeULhyWVCKgpcKkImGq3o3FExPlZEOqQ9dCrMYFHprMUF\nRsr0I02uNTlCbiBHb3NhZhlnUamUkM/leNi5Y03nRnRasXW27RxI3lm+qoWV6e9iUP2ziGTaoOy1\nmVOtVfDudUZsPz+BAilPp/IiRaZXMxVBRH4W+I+PvfWXjvNWHpeUR4ZhgxIJ0VoEkhO250L8XdpL\nPJmaNZPHgXEsBUpKzc1ECDkQ1Sa5BG9nyU7C0LYtpQLWUFX3UJB6T+pEmRBpatRh25UIdqWryRvP\n2cu2Lersmo7WDbWNZZum1IAVDYnzvI6jksaR0dsnctZqRGd3wH9aT2vxKrNiFZdA8LL4uVJb0GIM\nOXeSnKQ+tqfuZnXXvb6PQLt3mtB2tpBxDL3CnkjozpdRVUl5JI9bxnGLppGczTHTw/PEYcMqjyRN\nqBoVpUimJBZThlGVRKYVGx8VQ4lGLIKMJZJEnbA+E9NIGLYg6t+rLZttRw6REaGXgB6dt4HnPuPW\n1iarBxBhotssr0qJUBqIRt4vsbVmejKioxECSLBpJv75cxAvL5C6WIq/pj4A+k6Rk9RH8O94nbm4\na5RsdSu6wRyCMFuVzHGyR2/Qe2ydsSrNYNzSHmj3tc5FzdbfXx8E19VQ1yGdpTo803RF0QlKBBxR\nk2wkC9H3ybReZrUARWRi/JrtbefXQtST3bnNGTQUeNjWc7PDM02/AaTkpk+NcXkf8D1Mt/SKE4BT\nGthuNwyjFz4UoECtj7NGcQZm2EKRbCzVOE78u6UoQwWiBrIGSl1AMWYxCqFpq4E1g5SgtrHgPaYT\n+1GIjbeqpGoQCwdqyXnarNRI07SocwlbHtaKjXLOjGPhHi7emOV0Rx0ZfLpMSjb0u3if9aT8HZOv\nT4U17EeYjP288khKZHLHQWonpo+r03ezvtv7TBFCuyI0rf/lDCqzyzlhBuWWeHHH0JOPzpMPXzBY\ntRQjHZ6nGbbsYc3x4kVmIRhTS1Bhm5VtUgZV2kA1qCVSLecCdr9jiVTziPRqKE6/ZRsjGiNDCFYw\nFCLSHxH6IySPXs/iALIIUaTOeQzgnOE2m1RjR4idTcJp3JhqQvNgi3BwQgCmDjLZ+WeiOaMpkcc7\nikjkxPQRqLlt2alzmsGqzmok1RFy5y4bWUfuN+T+gNxv3Zh2GN7hbV9ewQsFB1Ug2f1V+2mG1SK/\n0itYV1EFEb3g3HZ/1Z0fJT02FSY5aUiwHmlVI/LPWvLsgmYhWvC8u68LDmVBWE5GdG9+gHgxFWZU\nwYq6ioTrXyNPYmoMwFZVv3Qt+05poO+PKBW70Ydig3HzFmNayI3Ve017z6umsppheiBRCCrEbP77\nFJlmmqalbVpiE0hpIKVxYt0oHo1an10Trfo3hkDy51NKFQ4mVnuGqtLkxkgcUGITrLI2iN9IraTk\n5mlR87YK3pKzqdG1hMBcUyySLarrnr7nSdWLoCYvb3af3Lm70wC1k9TH9vQra2R6XEQKHRk7Rq0Y\nEKZeFvLQMxweMD5/ljwMlPuZj8yY7pNByuKUbXix58BisohTEnSidMEGG1erjdQoMIgZ05ZMyNj4\nrX5rOclgsy9tzq+1a7V5pE1bGh0RJ1YLEizCFZvxWIIcwKuTnbjBI9PQNLYgaUKzGHF99m39WgQt\nxvV4b6Ia7+odFJmepD4CHh0m0GnZLi65LQlTSkCkrGXZjGkaycOGtD1Ex94cwrgCCeRU3tv4I/q9\nV4eBbd5tjUpDYUZyky2Tvqu3PxX08FgQffx6UbjJawU5WolGYiioW7DUAAIqRh/siEdNUCjTODns\n3AtHgSGbfvpqs08pV6qcq8tLZmrMTN4hIl8AzgK/BvyQqh7fZkfGsWcYrVx7nhwW8BaRyQcrRiME\nYzHSnMiSyUxfTAGaJhBjhxDouhXdakXXtjSNG1c3gAUiHkcBVR/PZr1Mo8MXpYVhHA2G3W1KLm0D\nUm1ZXU9dgjiCgtQeKePJNJ7WUpJdqAUnz2nO9DEt3TFiEItaGbwVe0zjuOYKorP/73C5afoIEwZw\nfDEo0drsq1y3D84gk7UUffmNDy2qNgs191t0c0ijif2uIeaGlkwnhQHGFoA2mLPURKURoQlm5Mrv\nUo7jXn3bCF1jC8SYIWm24qPs0W7OBBKSI4FsxjdGn6IU6rSm8r2oQYZ4FMmABmuJCcMGGTqLYGNZ\nRurSbnrujmKI0fqqxeKggKIhEpuWpum4g+Wm6qOW6KxU0s6geJ39ZykqmSFWCgQjcGiT0ULqHuhA\nzj059eQ0oONIHkd3cGaEDcHhfWmA4GubR5OqM6NZ1qpiUHdi04uI955KWRdtnWxioA1KFy0IiUl9\nOo1CsGkziqIpMGZx26A+NSnRhEQXEm0wgzwVLM1JJI67wTcuN3tqDBiE8f8ATwBfhUEd7xWRP6uX\nye7a1Ja+fmhVJWQzrHMCBhxOVewixRjIMTOGTPZxueUyNXFF03T2s20ddm0rMX2BQazKMjF6HicH\nY0iKWjh4E2lMPqx59FFnEytIqc4thNPMDKl5/FIjw0LSHES88tg5Vou3VKofL7j5vj/fLoRmZ5Gv\n9OflCzC/uCUtcQfb05utj3BxOLd8cWerV5Uwex9aolTv+Q0tWYVx2zMcPE/eHNLmRGwjrbZ0oqxF\n6cfEdkxsVSoht4q1wZSIcRWFVWOvp5Txsbd00QyqKmxV2TrZR0SJJQevE5TbBKHxdq86Gaks0F6U\nob7WqIxkAjr25GFLGjbIsLKiFp0M4nxxLI5mDJEmNHb+mgmavSrVhqbfiXIS+jj1dpbioqlgpzjM\nqljRY9HZOtoyIKEjtCCskaCIZDOmoxnUcXMeVUtHVFjC8+RWve1Ra1lPZguLiKO+Zf2q/8GxX3ef\nLJEs6qQihgauWmHdGkWs9MVtzXVdHBTGrAweaZaIVmKilZEuZJqJO+WCoOfCE7nsmV6V3PSpMao6\nn8n3MRH5A+Ax4B3Av7jUzsZsU1jKmDXNikrevSvzyklXrZLbwakR5jBniCvarmHVravBi9FwWcWN\ncmlX0IwVyxq0AREhGoSRA4LN5jOjP9bCoXKDymzSkgOYCpqwcw3TuUqNOOfu2zzSLR9VZoZ/l46w\nTIEphtraYibIeUcpbqAR+TaSm6qPQM0Z7gA/xZFVPWZKp8pZMI7UCrpJRGIHCHkcGY8OkbGnESWu\nWlpRVgHGAAf9AGr0cLYQiPNSm0QR9lphvxVagZSENCZEoY1CG40FLLjDqR6BRvfcix8QYyTSWP5U\nwqwBvl44W4PVYDz7joxoGIxScBzI2SbJFIRx9onNeZRQ6S1jjBaV5rJwBogNMU6UjXeY3HR9ZEZO\nf5zOr6R6BNBQ8tVaddV0ULwaGJv/2Yjfy540bm09HDfkYeNECcWQWlQqtTCJutAUBEOAMhN0LhdD\ndur3psKt1DVP3KC2rbDqytLlETaG/IgASRmTQ9Tm5kFIhGJMo9XGRF8jQ11UjyN2lzi365CbPTXm\nAnFy5zPAA1xGWT78Gx9hte78i2gR6ANf/VW88WseqM8VUGOiYUsMY2IYbBr7PBr0YztePlKmWEwV\nXhaNKgkJ0Iqx0hgzzQST7tQ0yqQYxfDFaLXWxtiRbc7pOKAkyynRFJfNzytarsEXmtVqRdt2lLmZ\nw+B9qwVe1ilS1WOGOjtFHGE6H/GG6sceeZTHHnl0diOg76/MWHY7ykno49//pz/H6X0jbSga8Ofe\n9hbe+Wfe4ovXhR5sRRqU2kwvYnAm3Qpd7aHrNeytkTEgmgmakGZL2G4sTxkCbYzsDRFx0nkJhQPL\nHLJ1I6ybQCNWKJcb68EOYlSWo2RSDpY+INDUXKtzP2smuiKXiUh1QYOa76pgRnHQykZNS1jtE1b7\n0K5t8LlT3RVOasQoDaNYO08AHvzIh3nwI7/tF8u+f4eHL6upMRfI1eojQH/2YwzPP+KUgnb/mrtf\nR/vK11UnyUgMpLJOGXwXzLAGAS3ctjJ5i9HWDePwHbBK3QLrtljNuFf2IpaAJ7iBtZy5M0NUJSr9\nrxfEfLXAZE5m77qoPqwkT2QeIYCNCz6WQhgN8g2Df0e8ArhrMjFYVXwTIm1U2mhUq2hGcwDJZOCp\np57iqac+vXuNXypTYy6x/VcA9wKXVaq3fes38eV/7MuM1MArX7M3kyO4AbLuuhKFpXFk2I6MY7Ih\ntTvwqlTC+JQM/p2Mqz2nmidu0xjp2pa2tTxQ9txVTRux62GVaNQmDkiFa1MaGNMWJdFkZ0Xyody2\nMCkqASEbs1JoCKFFgWHsiZtYW4FUtVaaF6mT5+t5UKNXqzqGIJEH3vQAX/XGB2Y5EzjzpTP8v//s\n5690y24rOSl9/Bt/9Tt502u/csoLFck6M6QzL1dL36/3moo6tBaITYesMgxr2K6R7RpSrHmeEKPN\n1MWmv+w1kdxFpBQm2VJmpXkirJrAKhovr/rge03+HVGbiJRDJnsVURMN0rX8fHJY2As4PKqRBCI6\njQMsAcixByI253S1R1ifQprOcnAe4YZ5v2Fdq23Be/tb38bb3/JWM6T9ltwf8fgTj/NDP/0zV7pt\nt42clD4CdPd8LXH/1X69m5qxT4MbOdyY+ppUUa0Z9FrXsbKYBUGIiDSEbvBK3cLB68NHskDKqA4W\n9iLec+ppg1xyuMn0o6amws6xqxZptulYtWPBqoe1GlMqeUIMZkjbdgIpBUF621dQqtMZguVMg1cC\nxyCO1mgZ9OXti0aycv/993P//ffvXOOzZ8/yqx/4tSvdiovKTZ0aIyKngB/GcgJPY97W/wx8Enj/\n5fYdfFJBiQpF1DygAlm6x4WIzerzkudxyD6MVh1/LxFByQPZIqGaTSnQWsELmSANQiTGQNM0dG2L\nYBM9EmkXsvWJBmUEUIjB4V3ce/fjaEIZnYA+MNGmqXl8wT9rjLTdmq7bI2liszkyaKUk9cs+/RrN\nI1J/xhwLnQYEi1nX4/dt5+edIieqj0xYwtXAQcqxyNT5+AKCxgaRFaz3YDhFGI8sL+XUkALOxzzQ\nlrUlAZLcw88khIS1fDUx0EbLe2oIaFTjds5e6YjYxBb7gyaK1RbkbNMJwb4rWAFTHaflTqE4Zlfb\n02ZXQsAhQFvQCY1V+fr3dyIT8Naakt6YX7cZDd6dJCepj0XMwbdpQP7M7jl41FqYsEJNJ1ENHTvv\nFB/sHpG4R+iKMbX7owqMoxV3poKYlTznPAXixB1h6lX2WLm+LnW7ZG0+2dJpBIt21RHJMcOoRuTv\nYI0vaQWdFJpGWWV3Rt1/C7PeayFXeDd4MKZZyAQrEpTgVci7kl8sonuuPBUhAV8HfDdWyfY5TEn+\nW1W9bPwcYzAUQjMplWjKL7/Tqqi/nr3HtHxwaxGJCKEaxaaJtLGjaYzKT3MZb1bItw3yEFeYsvaU\ndhUzxuJTaKy/VLMSotCF1qoUQzBd8IhAVFCNKE3NW6Y8osNI8og4SEfXBmg7uhDpViv2T52iH3tC\n05IQSurWPvsEecwJI6CcJ9N1wscJjaOTC+xW9d5hthROUB9LkuaC/DOT0dwlaCjPAaiNM3NkwVaD\nFWF9mkYzEiPaH6HbI7TfQOx9QXMD4wopTBAslhkArzKv5qnA/JiTp9l6UTU0hGj7qsFinkWZCoWu\nqAQvZVh9jXLsDCoaaGWiCYYe3R6Suz2D3GJnFb2liCmUwfcwH45+Qwmp20NOTh+vUkruNOds9y9Y\naWJwqlKd1Z0o1JZAG8IRIayQNrjhDaY/cUTCYL3BBWiViERrtSpR5ZQJUM9Rqhssma1Q9t3QlJyE\nQtFgTkAp500IfYocDg0x70bSxZgGzYSY2F/nWT2W2Fg5a/4gpcxABjW0s4mhNIGZcdbd77VdlhfJ\nmOoVpiKozez79us5kVg8Km+0nRYwq76Y+vdAyKTRCoeK0a05hNiy6jpWq5XBqD4KbRwHBqcQLBFd\ntS5alIop+vXItkCupYI3xsLhG+oqZJCZR840INlIvsWndoyp5lJDTCAdoVEkRtrViv3TpznabpCm\nJTnLhzARlc+NKewa+2M3wAxxSsaG45BvRXTusEbTk9RHqmGEHTgXd+jKmL+Z5122L/159nsgOywX\ngtgCtN4j2PplqwAAGx1JREFUHz5PDucsIo3GOqOu6+X9grUBBNfXOCstrk5UwFMgGMQbqKhJm6Ox\nvxS2LBEcXPb2m3LOpY1FKghUPknRtVrQ4sY0b46Q9pAQOnInBBzmPTa82UjZ5x25d66cqD5e9Umw\nY0zr2hSx4GG+luhkSI1SLyJxDbGrES4ohAFCj6apkNEoDRukFHR6DnwqJFJfGw3RmyBexUg+bKaq\nBvUqv4g0ikgkaWCbIzr6hBumKLqY070mcaoV9lqDuOuQhSEYS1Iybmw7N2gbQyAncfRTd566oWLN\nlww3b/nylYHgAHWCCth92IGdxL30OC0sEui6js6NaSjDt2cl/zWQq4U9FsGZ0Z5mnmZXtFRzrFYd\nhuAUhPa7ipFDlLadrPa3EYVk5xK2SuUxjcQciXEgRiehiJG2W9Gt1nSrFe1qxdgrabCxWYaWlOth\nJ18MaYlMSzSEb2uQY6ne1Om63QiL88tMJmj9eEClEwHHVDp9kXc71OGeO7FBYkBigNxa5OqTWGK3\nRteGfNBHpIdp9J71QNc81AyJqLFAKfpQy4NaKkKJ2WBcY5ApUYV9p3Ly1gi0GlJzzoo1ratihd8E\nX4RzRgq9HNg+g+e9pFSYT5Xr5UIW10Ox5zX4+xa5erkcPC4FyStrARSC5NJOmGdkDpUdqbQeikBo\naiqrkMaIlu6DGcGGO4Y1LeV1KeWe22kW/ZxxUzv8XIyuaiGysRSYBCHlwJBBU0ByKPHsDC+BJiip\ndFvgx5p9aY2BzJ1eIGWxSvIC6Pg+p0sn1RG5XnnJGNOrkVoh5sltq3S1xcH6PCNt09C2rZHUZ/NO\nNKca7YYQa94BhUBwhqOmGu+cdWqDSSNptIVOw9SXKj4AOpSeTrNgKImUR+cWHq30vIxno0FzYBxG\nthyx3R7ZEGhVmrbl1KlT3HPPPZx/4RwHzw9s+4HW55ZaodPsWszyH5YTPoajldxqrZjDmZkWuTqR\nK8O5MydmRtHtUJvntdQXpFB0NWAVlRGNLaHpYO8uQrdPc/pV5INzpINz5MNznlftrRrdjyeFo88O\nygxcY/aknUd53TcPjuWKmC7nbPlWYd5iNm+9kCm36VAeEpDYENoVsVtB4xCvBOPldacy+CIu2ant\nHYrWgiLFSGg7Qre+SffrzhcNjbFL1fs+k531YApAaiziCJd6tFYMTs0pKa4bs9a8Cg76QPIw6Z14\ncZ1UdCzWSHbSxMmoFh9Q1ebvQoeEkZSUPGY0eYSasEENIaBh4qkuKG45rZSFwz4wpmi5UjGWJkkZ\nGRNBk/l45bN4+hBsgtLOdWJ3Pb1eua2MqcFR0SEIfKEyqj8jZWgt9xoCMQrjMJJTZhxGVwCxgblq\n9FKowaAxGMWaSERVbETWYAObU0p18QxlIZ15YCGGHeW0uZBj7Uc1aFmJoTF+XwLDMJLGQ7b7R95G\no7Rtw6lTpxjTPaRx4OD8C2yHHpHOcsAx1rh8Zh/tGZ9ir7McgALkOSeUM0ktcpVixmOCcyc57rxK\nZeadNlBNPo831/YWQy9sEcpO7adNR9PuOWl8Q39uDw3R2lr6DTKIDyowPjQJJTlVV4QLznt2Irso\nVihAl0BZU/O0QAmOyFB6/WfGtEDJEpDYmiFs19C0tZq3Fir5f+IDTev1kbIoB4PcJCDt6lpvzMtX\nQqxj/nZE5KqMQcl5ForB2YgtUIjOtVtmVNYozhGHHUUJMqsM8hyphgLrgU7MQ/NslCKFo9W+B1ub\n3auqlusUnH41kHPwEYZTMWnhhUgZjnLgqBcaSTTeGtPk7BSZ4wyRlAp9zy7ZMWMapkr265RrereI\nfJ+I/J6InPPHgyLy7ce2+dsi8jkRORSRXxGRB65m37sRwIVQRmGwwH9aNWxjDEftiq7tfOi3wRS5\n5gHKDZUJ9p3t3wqP1Ph9x+QMRyMp5SmnJFNf1lQZV3Yx5QMU6+ErkO4wjoxDIo3Z8qBieSXN6rSE\nvY1y2xyx3W7o+w3j0JPGwQqsUq7wS6F624V47STm8O/UbzqPoo6TSNwZcpL6WGD+7Poxf9RcgONo\nqhd7lCZ6X7TSaMYQEIzEQdo9wvo0Ye8u4v7dxNP3EPfvIqxPI90+0u2h7ZocO/Kcym1mImVePVfO\np+qkb4NHCdj3wNrBvFgvBm9uF2d7staaUgVZSCsmLmKFPJKHLXk4QoctjFtIg/PGTkcv56rlHCnG\nFVuwQ7DZi3eInKQ+ghuiCvVf8hx2HvPItNBU6vxRUkhhd+2Y7bHsePcxTy/IzLDOHxesl+X5ea18\nPUHXk2NrVp5F0VowE9OxMUOfhCHBkISUIKcyntK7KsrEsZQqHaytz8kGpfgjO3p5IyvktUamnwZ+\nEHjEr8L3AO8RkW9Q1Y+LyA8C/zlWrfYk8N8D7xebmnBZxgAr9ClRV8Gvpy+eVabWsi03KpYvNYi3\ncQg3kceptzQGoem6Gl1mh53Avt8jyceyZW9mLlGv1ordsjzEJiKNVbEZ7FW8HTvZjOVYU8re+5p9\nwoxBwTEYz2XpFR3HkaPNIS88/xyHR4ece+4szz13lvPnX6DfbqpyF1YoynnPjKLIFJnO5WLR6mW/\nhbennKA+loh05rpzaTj3Yo5KEC8oGkeEbXWmkIA0K8IaI41ncrSkWRHbNc1qz2aCquU1ycZQY0PI\nmXJQVLfQ//e/SqWu87kWmG0OBnu2pFT3Te8WnMTceXSZvnOqCe0PSOcN4Ykp2YxVzdCsoF1R+qpf\nhnJi+nhNUgycR3XALB84RY6oTkxqMUzrzDFDvIOv+r4mb93QCynbUSE8SoBR9+HfHivoTFYXkrKj\nzFPtinj9Sqk2z9lZloKDkVIcQ6zHtOyeyYlLqkjyFMPsqxl10s08i/Kjf5dKt8f1yLVW8/5/x576\nIRH5G8CfAT4O/E3gR1X1FwFE5LuBLwD/LvBzXEaSN/Hu3DWHmoKTLcvsw0+RWckpGlRbPI5+u7W+\nUR+DVoycliG4HkHkpKRSeekrTIzBeXyj3UD/F6IpXUnMK1oJ6sGimZQzY0qMKdfoNgSdjL/3pSqJ\nYRw5Ojrg+RfOcv6FF3j2zDM8+8wzPrx8nHmYpvD2HTgGKTIZ1J1oZGZk77CAtMrJ6mO+AN69OJxr\nbFp5VtxVWhGql5+8WCe2VinZRIvIQkvo9us0DlU1I9utrCjJI04lo6NBx0oyw1VWCJ399OHklFoC\nh93mscYE3ZVztf9UZ8a0Zjd916KlO4+sibw9ZEwJxsGcxGgN/Ar2GV+mRUUnqY/XIsejUnDAv6IY\nWgE1C0bCVFQJs/ftrilzmWoGfKtquAVmRU62oa+5wYkZFNKYGYfkzHElyvWAqdYWTMcpLl2oeeGp\nE7rCv37iWQ2xKfOFC3sSQA7T93T+vFZjeguqecU+9XcC+8CDIvJ64MuBD8xO8HkR+W3gz3JVyrKD\nnx57ZZ4TKFCvFXIE94iyD1+2AiF1z8VgUXUPKldYlOollV6rUsUGWKSqwXpIa5l/mJTUXaBKT5iV\nlMbajyoiNI3NkYwhOsG+nXMOtjihMA4DmyODecdxQHMiAG2MNF58FEJpk6E6EDUSOVbpO7v2M4Na\n79mVb8FtKjddHys0WaQ4YrtfwFIhvWuwTD9j7R2tr4AEskRfK6ItB/Ohy6t9wv7dxJzRziO9bYcM\nR8jQwriBPKC5R9TQlzIw2gMFP7lyqqas9SyUmSEuiqE762gJPI5dEIeKDebVQSD6oOlxsJFvs+ul\n6qxziveP25OS1auTjsOJd5aczPoIlzJuF55A/e8S75ujGqVyd3qvIfRag45CHlMi0HmaacJGZLYO\nKQX2UC2IohdPicH7Ei2Ha0xcHrnWjgojPyl5d3y7SjJS8wgefUomYPUJMTgzkkzBcomQC+QL1LYb\ngMbTDTdSV3LNGVcR+VMi8gI20PYngX9PVT+BKYpintZcvuCvXWnPPPpHj1zFGexOa2li5BN/+FE0\njwzDlpwGBKVpog0Bl93FbO6ZP/boIxSKtVz4emuuLBv1mpah41Ou9Q9//2PTLhXLtfY9/WZLGh1e\njpGuW7G/f4r9/VN0XUcIgUc+8UlvII6EYLBCP2zRlIxKbr3H/nqPvfWa/dWarmkRER6tPLtzJb6y\nPPH4E1e13e0qJ6WPgvIbDz1UgKkagRodnz3yzJCWfHyQwG8+/HtWcBZaYpweEhorLgqB0X8miaTQ\nkGLLrz/0EOydQl5xD/GV99Hc/WU0d7/aHnfdS7jr1cjpV8HqNMQVqtEWKg08+NnDGTQmk6HUudkq\nEHAJaJUHP3feXirEDmG2CF1GHvr8uUu+Vpc475murURZ+eBHfhuyR9coEq5Oj28XObn1EdLhFRkH\nLyrDc1e5BhQ/XQCUzZlHyV67odnhYV9/CsmCzHOos8fm7JPghUvqUWdRLImR2HbEbmUD5mPjzFnC\neP7TNcVic6ptVnXSGW9vmmhh1RKlHJx5kkgiik2LMdYvqTzE8wrnlK3vfxxHnn76C14oanUu4w1E\nptdTvvRHwNcDbwV+CviHIvLV130GVYRHP/HoFbcpd9yqcCNNE/nExz6K5sQwbElpRCT7LNPAhAzP\nC3Xsmccf/aTBDGliOKrGVA2uLUnpecn4x//Ajal7UAYr92w3W+fJNE9ntVqxv7/P3v4+bbdCQuCR\nTz5CKMZUvHK435JzJsbA3npthnS9Zm+1pmsaggiPPfKY55CF48xGl5MnHnviigvjbS4npI+Zf/nQ\nQ4APRnaayDR7WMuTQ/gzY/rBh/81ITTE0O48QmzR2JgRDYEUIjlEcmzITcsHP/xhdH0aOf0q4j33\nEV/5ZTR3v4bmrlcT73o14RX3IqfvgdVpNKxQ9aIkhAc/d2ilGT5ZBNgJRkofdIXNXM//1ecPqGM/\njhnSy+nNw09f2piWQ+dqUEvxaOa3PvIR0ASOHt2B4ekJ6SPkw6ev633DuSevfmOr1kRRNs8+7oZ0\nVqxUELpQaliK9Z07bML22SdmBtR0VF25QmwIbUvsOkLbeg2K7ScdftYDHHYMadEje874qMuDnHnh\n2aeIJBpRmqi0jRvU0r4oJXpWz9lap8WXzpypabU0WvfH9co1w7yqOgKP+5+/KyJvxXIB7/YreR+7\n3td9wO9eab8P/svf4tzZs/zSe95bn3vgTX+CB77mTyAyXewalRWYfZ5f1VoqgWATNdJoBM1WzZVr\nUZDlT7XCwlacNFsc1RfH4KPbGps+XyCIcUxGMu6ekpVvR2ccKoPLJ9pBg57tzLqum+UobJHLJQUv\npR0heF9rZgae7Vyz0nc7uzs14njisSd44rEnOPOlM3zgl424+U6cGnNS+vi//98/z2e/+EX+9k/9\nb3Yc4Fve/PW8/U9/Q93GjKj1N4e5XyoCIRqz0cxaJAm1TKimrdyeiHh/ZyEyiK1BvDoSJBstnCiq\nI9p0ZI9ybYdlQZu3ac1zuzDPi05FITp/mfK9uhSSKCjBo3RRJaQB3R4yHp6zBStb3zbdnn+GZkZf\nB7/1Ow/z6Kc+xd/5qZ+uVvvw6OhKt+K2kpPSRwDtn6f/3G/uPBfvei3NXa+d1VfITOV0upfzJOe1\nfJ65B3Ypz0cKjj/LLJTnnZJw/lZDdMX69GNEG2ONK2x2tU7KdzUvDyidN7Xv2iNWVRiS9b3GiyB3\nBT2aN4s988yzHBwc8uijj9drl8YXqQDpEhKAldoooaeBdwG/DyAidwFvA37iSjv5pm97Ow9/6MN8\n+3f8Ox4JFjg3zn6/NlqyMY2Mw8gwpErrl9JU5FQg3pIPSDkhWTzfaQVNTbRCpKaJXmBk5f9jPzrD\nUfYcaaR1aDk6pRtSKBLtfEqlsjE0rW0/o5dmq9Q2h1oxWnMX02eaOIsdMpl/4FKEosobHngDb3jg\nDXzg/R/gXX/hXagqz5x5hl/8heM1Enec3BR9/E/+/b/M//m+X+G//uvfe8ltLC/aWCX5TnFc8Ag0\nTsYEPGdPzXGWiE1EJlOsBpEqoDFC2wGJoKPlJceW3DQQG8u9loVMBOOMG3FSyao2FpXaduVf8dPt\nx5SXuuRnrdtCMMp9wrhFj15gzJkwjsSc3RFNaDGos8//zW9+Mw/+zsP8V9/3n9prIfL4pz/DD/4P\nP3qFu3Fby03RRwDp7qL742+fPXGsHS4cWxPc8a9e20lUIs71W2d/ClZrUlMLF3HyBCQGorRIshbC\neWxw4dpnaIev3pRQalSjEOxTQNR6qeNxzLVco9kOX/OaV3P27HO86U0P1PM8ODzgox/9+HVdimud\nGvM/YpPinwJeAfw14NuAP++b/BhWwfYoVvr9o8BngPdc7THKta69pGWiwcyQXi1smVLyPs6t94+a\n4YqxIcam5kbtuB4tZJ0VIUVi0xCbSGwb8D6lnJVxSDWfYNFn4yT79gjBZ67WcWr1InoudR/NSr/Z\nsk1bklpFubhhRwr6drFQoSjSsQtRNpXdQeJ22DsPTztJfSwQbvLpHLs9yuX4c/asWQWrCBoMzs0X\nu+41lQAjNoa+mNMpfhCr+JUOCYrkwR5DB7FFQ6FyK5RqYYoCvJ1GJWMDGEq7wXz/8yj1MlEHM7Vy\nM296acY0aSb1G2ToQZzdJlhkTrPi+jJJt6e8GOvj7GAXGNILK3iZ4/rX+akufw71OPWYu4M46mLt\nhZp1nS1a7hSb1uUw7p77XFQ9jeEOqEcagjJmc1K3YyA2YgxKx5dGmRdHzZ4vEWupk7mBAqRrjUy/\nDPgHwB8DzmEe1p9X1V8DUNV3i8g+8DPYVIQPAn/xCj1Ua4Czz55lu+0588UvAVDGOZkx9f6j2cIU\ngljDeWjYbjd84emnGcceEfNKRIShH9hse/rtdgbzmjENoaEfep599hnboapXjStN03C4t8d6vaZd\nNTStwbwpJYZhoN9u+dIXvmgXPluvVvSxak0jzsJk+dCUSwOx3aztdsvTn/88e+s9VJV+2zNse8Zh\nYBh6htlw2gIpJzJ933PmzJnZZTNjKjueodEkzqGMvh945ox9xnPnnt+55neAnJg+fuYLX+TwaMPj\nn/4sUCDdgo5MEkPrkelkTA8Oj3j0U08yhnixwRSAF1SoMe8GhCiBw8NDHn/yCWu3SgkYQQdIPbI9\nJGyP4Og86fxz5PPnyRsbrK0Kh0PiyXMbNPeeGvDkgGHHM2NajKfBxkej8uTzPUWfFAuc8+ztx0UR\nDsfMU89vSGEkhR45gnZsaLYQ9rbo6gXoThn1INavGgIcHh3x+Kc/bYY/RD77dEU87wSdPAl9hHJt\n8kjenLVnag2HTIb1WDGXdaVkNPWkzbOzF/xFBWJAmwYdYsk3GCKWM5oGxqNnJxwVIccyzKCwH810\nxH0yEcipZzg4Mxlcy6NRifXn7EvY8VKf0DyQ+3O273HOqy5osLU9F59BbQJNQNE00B+dJcQNxA1j\n2L2kO3VSMxlT4uDwsHZkHB1tdq/5NYjcalYcEfmrwD++pSfx8pO/pqr/5FafxEtRFn28ZbLo5CVk\n0clbItesjy8FY3ov8Bcw2GNz+a0XuUFZA68D3q+qz9zic3lJyqKPL7osOnkFWXTyRZXr1sdbbkwX\nWWSRRRZZ5HaXl091wCKLLLLIIouckCzGdJFFFllkkUVuUBZjusgiiyyyyCI3KIsxXWSRRRZZZJEb\nlJeEMRWR/0xEnhCRIxH5kIi85Sre88Miko89/vAi232LiPxzEfmsb/MdF9nmgoG9V3qfiPzsRY7/\nXhH5WyLyYRF5XkS+ICI/LyJvuopj/t0rve8Sx/yonOBA4pejLPp4Q/r4XjnhIdkvN1n08fbQx1tu\nTEXkPwT+F+CHgX8L+D1sYO6rr+LtH8W4Lb/cH2+/yDangH8NfD8X6UGXaWDvX8fIqQ+A9wN3X+59\nLu87dvzvAr4F+HsYTdi/DbTAL4vI3hWO+f3YlIlLvu8Sx/zvsIHEfxp4M/Br2EDir7nc5xOR7hKf\n6WUtiz7esD5+F9OQ7EUnb1AWfbyN9LFMj7hVD+BDwP86+1swiq0fuML7fhj4nWs8Vga+49hznwP+\ny9nfdwFHwHde4X0/C/yzqzjmq/39b7/GY17sfVd7zGeA773aYy2PRR9PUh8XnVz08eWgj7c0MhWR\nFvMU5gNzFfhVbGDuleSNDjM8JiL/SETuv8bjv56LDOwFysDeK8k7HHL4IxH5SRF51UW2eSXmuT17\njcfced/VHFNEgoj8R1xhIPE1fL6XlSz6eNljXrM++v4XnbxOWfTxssd8yenjrYZ5X42Nuriegbkf\nAr4HYwb5PuD1wG+IyKlrOP6NDOx9H/DdwDuBH8AIrd8rMpHl+u8/BvymqpZ8xRWPeYn3Xe6YJzaQ\n+GUmiz5e5JjXoY+y6ORNkUUfL3LMl6o+3owRbLdEVPX9sz8/KiIfBj4FfCcW7p/08X9u9ufHROQP\ngMeAdwD/wp//SeBPAt98jbu/6Psuc8z7sIHEdwP/ATaQ+Fuv8ZiL3IAs+njBMT/IopO3TBZ9fPH1\n8VZHpmewwRn3HXv+PuCaxsqr6jngk8C1VGE9DXVg740e/wns8zwAICI/Dvwl4B2q+vmrPeZl3ne5\nY75BVR9X1d9V1f8GK1L4mzfz871MZNHHY8e8Tn18QFXHRSdvWBZ9PHbMl7I+3lJjqqoD8DA2MBeo\nIfy7gAevZV8ichq7UZe9wMeO/wR2webHLwN7r/X4XwHcC3zeb/hfAf6cqj51Dcf86ku970rHPPZS\nHUh8sz7fy0EWfbzgmDdLH2HRyWuWRR8vOOZLWx+vpVrpJB4Y7HCIYd1fjc36ewZ4zRXe93eBbwVe\nC3wT8CsYzn3vse1OYeH9N2DVX/+F/32/v/4Dfry/DPybwC8Aj2AJ7ou+z/f5br/gr/Ub8RDwceCn\ngbNYCfh9s8d6dk4XO+Zzl3vfZY55BoMxXgv8KeB/wmZOv/MKn6+71ff+pfhY9PGG9fHjwN/x9y06\nuejjy0Yfb7my+If5fmy80BHwr4BvvIr3/FOsRPwIm2z/T4DXX2S7b/ObnY49/o/ZNj+ClUcfYj1U\nD1zufdiYnl/CPJoN8DjwU8BrLvGeBHz3sfM6fszLvu8yx/xH/vuRv/bLRUku9/lu9T1/KT8Wfbwh\nfXwN8PcXnVz08eWmj8sItkUWWWSRRRa5QbnVBUiLLLLIIossctvLYkwXWWSRRRZZ5AZlMaaLLLLI\nIosscoOyGNNFFllkkUUWuUFZjOkiiyyyyCKL3KAsxnSRRRZZZJFFblAWY7rIIossssgiNyiLMV1k\nkUUWWWSRG5TFmC6yyCKLLLLIDcpiTBdZZJFFFlnkBmUxpossssgiiyxyg7IY00UWWWSRRRa5Qfn/\nAXX2AmAxle9YAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a5faf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "TASK DETAILS:\n",
    "Make a copy of:\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "Add functionality to ImageDataGenerator() beyond what is offered. You have flexibility here, \n",
    "one idea would be to add image histogram modification methods such as this: \n",
    "http://scikit-image.org/docs/dev/auto_examples/color_exposure/plot_equalize.html\n",
    "\n",
    "You are welcome to do anything you want related to ImageDataGenerator to make it better, \n",
    "but histogram modification could be a great starting point. With your work it helps to show before/after \n",
    "modifications in your notebook/blog. In the end this work is for you, to help give you visibility, \n",
    "so focus more on something that can be shared on LinkedIn rather than just a notebook. \n",
    "\n",
    "13 seconds per epoch on a 2 GHz Intel Core i5.'''\n",
    "\n",
    "# Dunno what this does yet:\n",
    "#K.set_image_dim_ordering('th')\n",
    "\n",
    "# Lets try and implement a new function - Invert, sharpen, dropout \n",
    "\n",
    "# the data, shuffled and split between train and test sets\n",
    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()   \n",
    "\n",
    "#Only look at cats [=3] and dogs [=5]\n",
    "train_picks = np.ravel(np.logical_or(y_train==3,y_train==5))  \n",
    "test_picks = np.ravel(np.logical_or(y_test==3,y_test==5))     \n",
    "\n",
    "y_train = np.array(y_train[train_picks]==5,dtype=int)\n",
    "y_test = np.array(y_test[test_picks]==5,dtype=int)\n",
    "\n",
    "x_train = x_train[train_picks]\n",
    "x_test = x_test[test_picks]\n",
    "\n",
    "if K.image_data_format() == 'channels_first':\n",
    "    x_train = x_train.reshape(x_train.shape[0], 3, img_rows, img_cols)\n",
    "    x_test = x_test.reshape(x_test.shape[0], 3, img_rows, img_cols)\n",
    "    input_shape = (3, img_rows, img_cols)\n",
    "else:\n",
    "    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 3)\n",
    "    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 3)\n",
    "    input_shape = (img_rows, img_cols, 3)\n",
    "\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "x_train /= 255\n",
    "x_test /= 255\n",
    "\n",
    "# print('x_train shape:', x_train.shape)\n",
    "# print(x_train.shape[0], 'train samples')\n",
    "# print(x_test.shape[0], 'test samples')\n",
    "\n",
    "# convert class vectors to binary class matrices\n",
    "y_train = keras.utils.to_categorical(np.ravel(y_train), num_classes)\n",
    "y_test = keras.utils.to_categorical(np.ravel(y_test), num_classes)\n",
    "\n",
    "#######################\n",
    "\n",
    "datagen = ImageDataGenerator(rotation_range=90)\n",
    "\n",
    "# fit parameters from data\n",
    "datagen.fit(x_train)\n",
    "\n",
    "# Configure batch size and retrieve one batch of images\n",
    "for X_batch, y_batch in datagen.flow(x_train, y_train, batch_size=9):\n",
    "    # Show the first 6 images\n",
    "    for i in range(0, 9):\n",
    "        pyplot.subplot(330 + 1 + i)\n",
    "        pyplot.imshow(X_batch[i].reshape(img_rows, img_cols, 3))\n",
    "    # show the plot\n",
    "    pyplot.show()\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# # Lets try and implement a new function - Invert, sharpen, dropout \n",
    "\n",
    "# # the data, shuffled and split between train and test sets\n",
    "# (x_train, y_train), (x_test, y_test) = cifar10.load_data()   \n",
    "\n",
    "# #Only look at cats [=3] and dogs [=5]\n",
    "# train_picks = np.ravel(np.logical_or(y_train==3,y_train==5))  \n",
    "# test_picks = np.ravel(np.logical_or(y_test==3,y_test==5))     \n",
    "\n",
    "# y_train = np.array(y_train[train_picks]==5,dtype=int)\n",
    "# y_test = np.array(y_test[test_picks]==5,dtype=int)\n",
    "\n",
    "# x_train = x_train[train_picks]\n",
    "# x_test = x_test[test_picks]\n",
    "\n",
    "# if K.image_data_format() == 'channels_first':\n",
    "#     x_train = x_train.reshape(x_train.shape[0], 3, img_rows, img_cols)\n",
    "#     x_test = x_test.reshape(x_test.shape[0], 3, img_rows, img_cols)\n",
    "#     input_shape = (3, img_rows, img_cols)\n",
    "# else:\n",
    "#     x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 3)\n",
    "#     x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 3)\n",
    "#     input_shape = (img_rows, img_cols, 3)\n",
    "\n",
    "# x_train = x_train.astype('float32')\n",
    "# x_test = x_test.astype('float32')\n",
    "# x_train /= 255\n",
    "# x_test /= 255\n",
    "\n",
    "# # print('x_train shape:', x_train.shape)\n",
    "# # print(x_train.shape[0], 'train samples')\n",
    "# # print(x_test.shape[0], 'test samples')\n",
    "\n",
    "# # convert class vectors to binary class matrices\n",
    "# y_train = keras.utils.to_categorical(np.ravel(y_train), num_classes)\n",
    "# y_test = keras.utils.to_categorical(np.ravel(y_test), num_classes)\n",
    "\n",
    "# #######################\n",
    "\n",
    "# datagen = ImageDataGenerator(dropout=.5)\n",
    "\n",
    "# # fit parameters from data\n",
    "# datagen.fit(x_train)\n",
    "\n",
    "# # configure batch size and retrieve one batch of images\n",
    "# for X_batch, y_batch in datagen.flow(x_train, y_train, batch_size=9):\n",
    "#     # Show the first 6 images\n",
    "#     for i in range(0, 9):\n",
    "#         pyplot.subplot(330 + 1 + i)\n",
    "#         pyplot.imshow(X_batch[i].reshape(img_rows, img_cols, 3))\n",
    "#     # show the plot\n",
    "#     pyplot.show()\n",
    "#     break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# # the data, shuffled and split between train and test sets\n",
    "# (x_train, y_train), (x_test, y_test) = cifar10.load_data()   \n",
    "\n",
    "# #Only look at cats [=3] and dogs [=5]\n",
    "# train_picks = np.ravel(np.logical_or(y_train==3,y_train==5))  \n",
    "# test_picks = np.ravel(np.logical_or(y_test==3,y_test==5))     \n",
    "\n",
    "# y_train = np.array(y_train[train_picks]==5,dtype=int)\n",
    "# y_test = np.array(y_test[test_picks]==5,dtype=int)\n",
    "\n",
    "# x_train = x_train[train_picks]\n",
    "# x_test = x_test[test_picks]\n",
    "\n",
    "# if K.image_data_format() == 'channels_first':\n",
    "#     x_train = x_train.reshape(x_train.shape[0], 3, img_rows, img_cols)\n",
    "#     x_test = x_test.reshape(x_test.shape[0], 3, img_rows, img_cols)\n",
    "#     input_shape = (3, img_rows, img_cols)\n",
    "# else:\n",
    "#     x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 3)\n",
    "#     x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 3)\n",
    "#     input_shape = (img_rows, img_cols, 3)\n",
    "\n",
    "# x_train = x_train.astype('float32')\n",
    "# x_test = x_test.astype('float32')\n",
    "# x_train /= 255\n",
    "# x_test /= 255\n",
    "# print('x_train shape:', x_train.shape)\n",
    "# print(x_train.shape[0], 'train samples')\n",
    "# print(x_test.shape[0], 'test samples')\n",
    "\n",
    "# # convert class vectors to binary class matrices\n",
    "# y_train = keras.utils.to_categorical(np.ravel(y_train), num_classes)\n",
    "# y_test = keras.utils.to_categorical(np.ravel(y_test), num_classes)\n",
    "\n",
    "# datagen = ImageDataGenerator(featurewise_center=True, featurewise_std_normalization=True)\n",
    "# # fit parameters from data\n",
    "# datagen.fit(x_train)\n",
    "# # configure batch size and retrieve one batch of images\n",
    "# for X_batch, y_batch in datagen.flow(x_train, y_train, batch_size=9):\n",
    "#     # Show the first 6 images\n",
    "#     for i in range(0, 9):\n",
    "#         pyplot.subplot(330 + 1 + i)\n",
    "#         pyplot.imshow(X_batch[i].reshape(img_rows, img_cols, 3), cmap=pyplot.get_cmap('hsv'))\n",
    "#     # show the plot\n",
    "#     pyplot.show()\n",
    "#     break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# # the data, shuffled and split between train and test sets\n",
    "# (x_train, y_train), (x_test, y_test) = cifar10.load_data()   \n",
    "\n",
    "# #Only look at cats [=3] and dogs [=5]\n",
    "# train_picks = np.ravel(np.logical_or(y_train==3,y_train==5))  \n",
    "# test_picks = np.ravel(np.logical_or(y_test==3,y_test==5))     \n",
    "\n",
    "# y_train = np.array(y_train[train_picks]==5,dtype=int)\n",
    "# y_test = np.array(y_test[test_picks]==5,dtype=int)\n",
    "\n",
    "# x_train = x_train[train_picks]\n",
    "# x_test = x_test[test_picks]\n",
    "\n",
    "# if K.image_data_format() == 'channels_first':\n",
    "#     x_train = x_train.reshape(x_train.shape[0], 3, img_rows, img_cols)\n",
    "#     x_test = x_test.reshape(x_test.shape[0], 3, img_rows, img_cols)\n",
    "#     input_shape = (3, img_rows, img_cols)\n",
    "# else:\n",
    "#     x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 3)\n",
    "#     x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 3)\n",
    "#     input_shape = (img_rows, img_cols, 3)\n",
    "\n",
    "# x_train = x_train.astype('float32')\n",
    "# x_test = x_test.astype('float32')\n",
    "# x_train /= 255\n",
    "# x_test /= 255\n",
    "# print('x_train shape:', x_train.shape)\n",
    "# print(x_train.shape[0], 'train samples')\n",
    "# print(x_test.shape[0], 'test samples')\n",
    "\n",
    "# # convert class vectors to binary class matrices\n",
    "# y_train = keras.utils.to_categorical(np.ravel(y_train), num_classes)\n",
    "# y_test = keras.utils.to_categorical(np.ravel(y_test), num_classes)\n",
    "\n",
    "# datagen = ImageDataGenerator(zca_whitening=True)\n",
    "# # fit parameters from data\n",
    "# datagen.fit(x_train)\n",
    "# # configure batch size and retrieve one batch of images\n",
    "# for x_batch, y_batch in datagen.flow(x_train, y_train, batch_size=32):\n",
    "#     # create a grid of 3x3 images\n",
    "#     for i in range(0, 9):\n",
    "#         pyplot.subplot(330 + 1 + i)\n",
    "#         pyplot.imshow(x_batch[i].reshape(32, 32, 3), cmap=pyplot.get_cmap('hsv'))\n",
    "#     # show the plot\n",
    "#     pyplot.show()\n",
    "#     break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# # the data, shuffled and split between train and test sets\n",
    "# (x_train, y_train), (x_test, y_test) = cifar10.load_data()   \n",
    "\n",
    "# #Only look at cats [=3] and dogs [=5]\n",
    "# train_picks = np.ravel(np.logical_or(y_train==3,y_train==5))  \n",
    "# test_picks = np.ravel(np.logical_or(y_test==3,y_test==5))     \n",
    "\n",
    "# y_train = np.array(y_train[train_picks]==5,dtype=int)\n",
    "# y_test = np.array(y_test[test_picks]==5,dtype=int)\n",
    "\n",
    "# x_train = x_train[train_picks]\n",
    "# x_test = x_test[test_picks]\n",
    "\n",
    "# if K.image_data_format() == 'channels_first':\n",
    "#     x_train = x_train.reshape(x_train.shape[0], 3, img_rows, img_cols)\n",
    "#     x_test = x_test.reshape(x_test.shape[0], 3, img_rows, img_cols)\n",
    "#     input_shape = (3, img_rows, img_cols)\n",
    "# else:\n",
    "#     x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 3)\n",
    "#     x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 3)\n",
    "#     input_shape = (img_rows, img_cols, 3)\n",
    "\n",
    "# x_train = x_train.astype('float32')\n",
    "# x_test = x_test.astype('float32')\n",
    "# x_train /= 255\n",
    "# x_test /= 255\n",
    "# print('x_train shape:', x_train.shape)\n",
    "# print(x_train.shape[0], 'train samples')\n",
    "# print(x_test.shape[0], 'test samples')\n",
    "\n",
    "# # convert class vectors to binary class matrices\n",
    "# y_train = keras.utils.to_categorical(np.ravel(y_train), num_classes)\n",
    "# y_test = keras.utils.to_categorical(np.ravel(y_test), num_classes)\n",
    "\n",
    "# # define data preparation\n",
    "# datagen = ImageDataGenerator(rotation_range=90)\n",
    "# # fit parameters from data\n",
    "# datagen.fit(x_train)\n",
    "# # configure batch size and retrieve one batch of images\n",
    "# for X_batch, y_batch in datagen.flow(x_train, y_train, batch_size=9):\n",
    "#     # create a grid of 3x3 images\n",
    "#     for i in range(0, 9):\n",
    "#         pyplot.subplot(330 + 1 + i)\n",
    "#         pyplot.imshow(X_batch[i].reshape(32, 32, 3), cmap=pyplot.get_cmap('hsv'))\n",
    "#     # show the plot\n",
    "#     pyplot.show()\n",
    "#     break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# # define data preparation\n",
    "# shift = 0.2\n",
    "# datagen = ImageDataGenerator(width_shift_range=shift, height_shift_range=shift)\n",
    "# # fit parameters from data\n",
    "# datagen.fit(x_train)\n",
    "# # configure batch size and retrieve one batch of images\n",
    "# for X_batch, y_batch in datagen.flow(x_train, y_train, batch_size=9):\n",
    "#     # create a grid of 3x3 images\n",
    "#     for i in range(0, 9):\n",
    "#         pyplot.subplot(330 + 1 + i)\n",
    "#         pyplot.imshow(X_batch[i].reshape(32, 32, 3))\n",
    "#     # show the plot\n",
    "#     pyplot.show()\n",
    "#     break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# datagen = ImageDataGenerator(horizontal_flip=True, vertical_flip=True)\n",
    "# # fit parameters from data\n",
    "# datagen.fit(x_train)\n",
    "# # configure batch size and retrieve one batch of images\n",
    "# for X_batch, y_batch in datagen.flow(x_train, y_train, batch_size=9):\n",
    "#     # create a grid of 3x3 images\n",
    "#     for i in range(0, 9):\n",
    "#         pyplot.subplot(330 + 1 + i)\n",
    "#         pyplot.imshow(X_batch[i].reshape(32, 32, 3), cmap=pyplot.get_cmap('hsv'))\n",
    "#     # show the plot\n",
    "#     pyplot.show()\n",
    "#     break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IDG init\n",
      "Running augmented training now\n",
      "Epoch 1/20\n",
      "312/312 [==============================] - 5s - loss: 0.6942 - acc: 0.5092 - val_loss: 0.6926 - val_acc: 0.5190\n",
      "Epoch 2/20\n",
      "312/312 [==============================] - 4s - loss: 0.6906 - acc: 0.5249 - val_loss: 0.6827 - val_acc: 0.5655\n",
      "Epoch 3/20\n",
      "312/312 [==============================] - 5s - loss: 0.6856 - acc: 0.5382 - val_loss: 0.6783 - val_acc: 0.5935\n",
      "Epoch 4/20\n",
      "312/312 [==============================] - 6s - loss: 0.6834 - acc: 0.5452 - val_loss: 0.6722 - val_acc: 0.5885\n",
      "Epoch 5/20\n",
      "312/312 [==============================] - 6s - loss: 0.6759 - acc: 0.5724 - val_loss: 0.6636 - val_acc: 0.6205\n",
      "Epoch 6/20\n",
      "312/312 [==============================] - 5s - loss: 0.6657 - acc: 0.5883 - val_loss: 0.6491 - val_acc: 0.6365\n",
      "Epoch 7/20\n",
      "312/312 [==============================] - 5s - loss: 0.6599 - acc: 0.6061 - val_loss: 0.6413 - val_acc: 0.6375\n",
      "Epoch 8/20\n",
      "312/312 [==============================] - 5s - loss: 0.6538 - acc: 0.6234 - val_loss: 0.6347 - val_acc: 0.6460\n",
      "Epoch 9/20\n",
      "312/312 [==============================] - 5s - loss: 0.6489 - acc: 0.6273 - val_loss: 0.6261 - val_acc: 0.6535\n",
      "Epoch 10/20\n",
      "312/312 [==============================] - 5s - loss: 0.6505 - acc: 0.6251 - val_loss: 0.6263 - val_acc: 0.6640\n",
      "Epoch 11/20\n",
      "312/312 [==============================] - 5s - loss: 0.6444 - acc: 0.6329 - val_loss: 0.6239 - val_acc: 0.6620\n",
      "Epoch 12/20\n",
      "312/312 [==============================] - 5s - loss: 0.6412 - acc: 0.6353 - val_loss: 0.6200 - val_acc: 0.6605\n",
      "Epoch 13/20\n",
      "312/312 [==============================] - 5s - loss: 0.6379 - acc: 0.6398 - val_loss: 0.6179 - val_acc: 0.6645\n",
      "Epoch 14/20\n",
      "312/312 [==============================] - 5s - loss: 0.6415 - acc: 0.6408 - val_loss: 0.6180 - val_acc: 0.6770\n",
      "Epoch 15/20\n",
      "312/312 [==============================] - 5s - loss: 0.6364 - acc: 0.6468 - val_loss: 0.6114 - val_acc: 0.6645\n",
      "Epoch 16/20\n",
      "312/312 [==============================] - 5s - loss: 0.6396 - acc: 0.6392 - val_loss: 0.6144 - val_acc: 0.6795\n",
      "Epoch 17/20\n",
      "312/312 [==============================] - 5s - loss: 0.6402 - acc: 0.6337 - val_loss: 0.6132 - val_acc: 0.6740\n",
      "Epoch 18/20\n",
      "312/312 [==============================] - 5s - loss: 0.6333 - acc: 0.6430 - val_loss: 0.6051 - val_acc: 0.6835\n",
      "Epoch 19/20\n",
      "312/312 [==============================] - 5s - loss: 0.6335 - acc: 0.6443 - val_loss: 0.6030 - val_acc: 0.6785\n",
      "Epoch 20/20\n",
      "312/312 [==============================] - 5s - loss: 0.6348 - acc: 0.6416 - val_loss: 0.6053 - val_acc: 0.6885\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv2D(4, kernel_size=(3, 3),activation='relu',input_shape=input_shape))\n",
    "model.add(Conv2D(8, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(16, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(2, activation='softmax'))\n",
    "    \n",
    "model.compile(loss=keras.losses.categorical_crossentropy,\n",
    "              optimizer=keras.optimizers.Adadelta(),\n",
    "             metrics=['accuracy'])\n",
    "\n",
    "augmentation=True\n",
    "    \n",
    "if augmentation==True:    \n",
    "    datagen = ImageDataGenerator(\n",
    "    featurewise_center=False,  # set input mean to 0 over the dataset\n",
    "    samplewise_center=False,  # set each sample mean to 0\n",
    "    featurewise_std_normalization=False,  # divide inputs by std of the dataset\n",
    "    samplewise_std_normalization=False,  # divide each input by its std\n",
    "    zca_whitening=False,  # apply ZCA whitening\n",
    "    rotation_range=0,  # randomly rotate images in the range (degrees, 0 to 180)\n",
    "    width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)\n",
    "    height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)\n",
    "    horizontal_flip=True,  # randomly flip images\n",
    "    vertical_flip=False)  # randomly flip images\n",
    "\n",
    "  \n",
    "\n",
    "    datagen.fit(x_train)\n",
    "    print(\"Running augmented training now\")\n",
    "    history = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),\n",
    "                        steps_per_epoch=x_train.shape[0] // batch_size,\n",
    "                        epochs=20,\n",
    "                        validation_data=(x_test, y_test))\n",
    "else:\n",
    "    print(\"Running regular training, no augmentation\")\n",
    "    history = model.fit(x_train, y_train,\n",
    "                        batch_size=batch_size,\n",
    "                        epochs=20,\n",
    "                        verbose=1,\n",
    "                        validation_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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