My Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Self-Driving Car Engineer Nanodegree\n",
    "\n",
    "\n",
    "## Project: **Finding Lane Lines on the Road** \n",
    "***\n",
    "In this project, you will use the tools you learned about in the lesson to identify lane lines on the road.  You can develop your pipeline on a series of individual images, and later apply the result to a video stream (really just a series of images). Check out the video clip \"raw-lines-example.mp4\" (also contained in this repository) to see what the output should look like after using the helper functions below. \n",
    "\n",
    "Once you have a result that looks roughly like \"raw-lines-example.mp4\", you'll need to get creative and try to average and/or extrapolate the line segments you've detected to map out the full extent of the lane lines.  You can see an example of the result you're going for in the video \"P1_example.mp4\".  Ultimately, you would like to draw just one line for the left side of the lane, and one for the right.\n",
    "\n",
    "In addition to implementing code, there is a brief writeup to complete. The writeup should be completed in a separate file, which can be either a markdown file or a pdf document. There is a [write up template](https://github.com/udacity/CarND-LaneLines-P1/blob/master/writeup_template.md) that can be used to guide the writing process. Completing both the code in the Ipython notebook and the writeup template will cover all of the [rubric points](https://review.udacity.com/#!/rubrics/322/view) for this project.\n",
    "\n",
    "---\n",
    "Let's have a look at our first image called 'test_images/solidWhiteRight.jpg'.  Run the 2 cells below (hit Shift-Enter or the \"play\" button above) to display the image.\n",
    "\n",
    "**Note: If, at any point, you encounter frozen display windows or other confounding issues, you can always start again with a clean slate by going to the \"Kernel\" menu above and selecting \"Restart & Clear Output\".**\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The tools you have are color selection, region of interest selection, grayscaling, Gaussian smoothing, Canny Edge Detection and Hough Tranform line detection.  You  are also free to explore and try other techniques that were not presented in the lesson.  Your goal is piece together a pipeline to detect the line segments in the image, then average/extrapolate them and draw them onto the image for display (as below).  Once you have a working pipeline, try it out on the video stream below.**\n",
    "\n",
    "---\n",
    "\n",
    "<figure>\n",
    " <img src=\"examples/line-segments-example.jpg\" width=\"380\" alt=\"Combined Image\" />\n",
    " <figcaption>\n",
    " <p></p> \n",
    " <p style=\"text-align: center;\"> Your output should look something like this (above) after detecting line segments using the helper functions below </p> \n",
    " </figcaption>\n",
    "</figure>\n",
    " <p></p> \n",
    "<figure>\n",
    " <img src=\"examples/laneLines_thirdPass.jpg\" width=\"380\" alt=\"Combined Image\" />\n",
    " <figcaption>\n",
    " <p></p> \n",
    " <p style=\"text-align: center;\"> Your goal is to connect/average/extrapolate line segments to get output like this</p> \n",
    " </figcaption>\n",
    "</figure>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Run the cell below to import some packages.  If you get an `import error` for a package you've already installed, try changing your kernel (select the Kernel menu above --> Change Kernel).  Still have problems?  Try relaunching Jupyter Notebook from the terminal prompt.  Also, consult the forums for more troubleshooting tips.**  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing some useful packages\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import numpy as np\n",
    "import cv2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in an Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This image is: <class 'numpy.ndarray'> with dimensions: (540, 960, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f7901dde3c8>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7901e5bd68>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#reading in an image\n",
    "image = mpimg.imread('test_images/solidWhiteRight.jpg')\n",
    "\n",
    "#printing out some stats and plotting\n",
    "print('This image is:', type(image), 'with dimensions:', image.shape)\n",
    "plt.imshow(image)  # if you wanted to show a single color channel image called 'gray', for example, call as plt.imshow(gray, cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ideas for Lane Detection Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Some OpenCV functions (beyond those introduced in the lesson) that might be useful for this project are:**\n",
    "\n",
    "`cv2.inRange()` for color selection  \n",
    "`cv2.fillPoly()` for regions selection  \n",
    "`cv2.line()` to draw lines on an image given endpoints  \n",
    "`cv2.addWeighted()` to coadd / overlay two images\n",
    "`cv2.cvtColor()` to grayscale or change color\n",
    "`cv2.imwrite()` to output images to file  \n",
    "`cv2.bitwise_and()` to apply a mask to an image\n",
    "\n",
    "**Check out the OpenCV documentation to learn about these and discover even more awesome functionality!**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below are some helper functions to help get you started. They should look familiar from the lesson!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "def grayscale(img):\n",
    "    \"\"\"Applies the Grayscale transform\n",
    "    This will return an image with only one color channel\n",
    "    but NOTE: to see the returned image as grayscale\n",
    "    (assuming your grayscaled image is called 'gray')\n",
    "    you should call plt.imshow(gray, cmap='gray')\"\"\"\n",
    "    return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n",
    "    # Or use BGR2GRAY if you read an image with cv2.imread()\n",
    "    # return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "    \n",
    "def canny(img, low_threshold, high_threshold):\n",
    "    \"\"\"Applies the Canny transform\"\"\"\n",
    "    return cv2.Canny(img, low_threshold, high_threshold)\n",
    "\n",
    "def gaussian_blur(img, kernel_size):\n",
    "    \"\"\"Applies a Gaussian Noise kernel\"\"\"\n",
    "    return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)\n",
    "\n",
    "def region_of_interest(img, vertices):\n",
    "    \"\"\"\n",
    "    Applies an image mask.\n",
    "    \n",
    "    Only keeps the region of the image defined by the polygon\n",
    "    formed from `vertices`. The rest of the image is set to black.\n",
    "    `vertices` should be a numpy array of integer points.\n",
    "    \"\"\"\n",
    "    #defining a blank mask to start with\n",
    "    mask = np.zeros_like(img)   \n",
    "    \n",
    "    #defining a 3 channel or 1 channel color to fill the mask with depending on the input image\n",
    "    if len(img.shape) > 2:\n",
    "        channel_count = img.shape[2]  # i.e. 3 or 4 depending on your image\n",
    "        ignore_mask_color = (255,) * channel_count\n",
    "    else:\n",
    "        ignore_mask_color = 255\n",
    "        \n",
    "    #filling pixels inside the polygon defined by \"vertices\" with the fill color    \n",
    "    cv2.fillPoly(mask, vertices, ignore_mask_color)\n",
    "    \n",
    "    #returning the image only where mask pixels are nonzero\n",
    "    masked_image = cv2.bitwise_and(img, mask)\n",
    "    return masked_image\n",
    "\n",
    "\n",
    "def draw_lines(img, lines, color=[255, 0, 0], thickness=10):\n",
    "    \"\"\"\n",
    "    NOTE: this is the function you might want to use as a starting point once you want to \n",
    "    average/extrapolate the line segments you detect to map out the full\n",
    "    extent of the lane (going from the result shown in raw-lines-example.mp4\n",
    "    to that shown in P1_example.mp4).  \n",
    "    \n",
    "    Think about things like separating line segments by their \n",
    "    slope ((y2-y1)/(x2-x1)) to decide which segments are part of the left\n",
    "    line vs. the right line.  Then, you can average the position of each of \n",
    "    the lines and extrapolate to the top and bottom of the lane.\n",
    "    \n",
    "    This function draws `lines` with `color` and `thickness`.    \n",
    "    Lines are drawn on the image inplace (mutates the image).\n",
    "    If you want to make the lines semi-transparent, think about combining\n",
    "    this function with the weighted_img() function below\n",
    "    \"\"\"\n",
    "    leftLaneLines = []\n",
    "    rightLaneLines = []\n",
    "    for line in lines:\n",
    "        for x1,y1,x2,y2 in line:\n",
    "            m = (y1 - y2) / (x1 - x2)\n",
    "            if m > 0: #and x1 < (img.shape[1]/2) and x2 < (img.shape[1]/2): # I mess up the sign of m but I got correct ans, idk why\n",
    "                leftLaneLines.append(line)\n",
    "            elif m <= 0: #and x1 > (img.shape[1]/2) and x2 > (img.shape[1]/2):\n",
    "                rightLaneLines.append(line)\n",
    "    \n",
    "    listMLeft = []\n",
    "    listBLeft = []\n",
    "    listMRight = []\n",
    "    listBRight = []\n",
    "    for line in leftLaneLines:\n",
    "        for x1,y1,x2,y2 in line:\n",
    "            m = (y1 - y2) / (x1 - x2)\n",
    "            b = y1 - m*x1\n",
    "            listMLeft.append(m)\n",
    "            listBLeft.append(b)\n",
    "    \n",
    "    for line in rightLaneLines:\n",
    "        for x1,y1,x2,y2 in line:\n",
    "            m = (y1 - y2) / (x1 - x2)\n",
    "            b = y1 - m*x1\n",
    "            listMRight.append(m)\n",
    "            listBRight.append(b)\n",
    "            \n",
    "    y_bottom_of_ROI = img.shape[0]\n",
    "    y_top_of_ROI = 325        \n",
    "    \n",
    "    y_left1 = y_bottom_of_ROI\n",
    "    y_left2 = y_top_of_ROI\n",
    "    if (len(listMLeft) != 0 and len(listBLeft) != 0):\n",
    "        avgMLeft = sum(listMLeft) / len(listMLeft)\n",
    "        avgBLeft = sum(listBLeft) / len(listBLeft)\n",
    "        x_left1 = (y_bottom_of_ROI - avgBLeft) / avgMLeft\n",
    "        x_left2 = (y_top_of_ROI - avgBLeft) / avgMLeft\n",
    "        if (np.isnan(x_left1) == False and np.isnan(x_left2) == False and np.isnan(y_left1) == False and np.isnan(y_left2) == False):\n",
    "            cv2.line(img, (int(x_left1), int(y_left1)), (int(x_left2), int(y_left2)), color, thickness)\n",
    "        \n",
    "    y_right1 = y_bottom_of_ROI\n",
    "    y_right2 = y_top_of_ROI\n",
    "    if (len(listMRight) != 0 and len(listBRight) != 0):\n",
    "        avgMRight = sum(listMRight) / len(listMRight)\n",
    "        avgBRight = sum(listBRight) / len(listBRight)\n",
    "        x_right1 = (y_bottom_of_ROI - avgBRight) / avgMRight\n",
    "        x_right2 = (y_top_of_ROI - avgBRight) / avgMRight\n",
    "        if (np.isnan(x_right1) == False and np.isnan(x_right2) == False and np.isnan(y_right1) == False and np.isnan(y_right2) == False):\n",
    "            cv2.line(img, (int(x_right1), int(y_right1)), (int(x_right2), int(y_right2)), color, thickness)\n",
    "            \n",
    "    '''        \n",
    "    avgMLeft = sum(listMLeft) / len(listMLeft)\n",
    "    avgBLeft = sum(listBLeft) / len(listBLeft)\n",
    "    avgMRight = sum(listMRight) / len(listMRight)\n",
    "    avgBRight = sum(listBRight) / len(listBRight)\n",
    "    \n",
    "    y_bottom_of_ROI = img.shape[0]\n",
    "    y_top_of_ROI = 325\n",
    "    \n",
    "    y_left1 = y_bottom_of_ROI\n",
    "    x_left1 = (y_bottom_of_ROI - avgBLeft) / avgMLeft\n",
    "    y_left2 = y_top_of_ROI\n",
    "    x_left2 = (y_top_of_ROI - avgBLeft) / avgMLeft\n",
    "    y_right1 = y_bottom_of_ROI\n",
    "    x_right1 = (y_bottom_of_ROI - avgBRight) / avgMRight\n",
    "    y_right2 = y_top_of_ROI\n",
    "    x_right2 = (y_top_of_ROI - avgBRight) / avgMRight\n",
    "    \n",
    "    cv2.line(img, (int(x_left1), int(y_left1)), (int(x_left2), int(y_left2)), color, thickness)\n",
    "    cv2.line(img, (int(x_right1), int(y_right1)), (int(x_right2), int(y_right2)), color, thickness)\n",
    "    '''\n",
    "\n",
    "def hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap):\n",
    "    \"\"\"\n",
    "    `img` should be the output of a Canny transform.\n",
    "        \n",
    "    Returns an image with hough lines drawn.\n",
    "    \"\"\"\n",
    "    lines = cv2.HoughLinesP(img, rho, theta, threshold, np.array([]), minLineLength=min_line_len, maxLineGap=max_line_gap)\n",
    "    line_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)\n",
    "    draw_lines(line_img, lines)\n",
    "    return line_img\n",
    "\n",
    "# Python 3 has support for cool math symbols.\n",
    "\n",
    "def weighted_img(img, initial_img, α=0.8, β=1., γ=0.):\n",
    "    \"\"\"\n",
    "    `img` is the output of the hough_lines(), An image with lines drawn on it.\n",
    "    Should be a blank image (all black) with lines drawn on it.\n",
    "    \n",
    "    `initial_img` should be the image before any processing.\n",
    "    \n",
    "    The result image is computed as follows:\n",
    "    \n",
    "    initial_img * α + img * β + γ\n",
    "    NOTE: initial_img and img must be the same shape!\n",
    "    \"\"\"\n",
    "    return cv2.addWeighted(initial_img, α, img, β, γ)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Images\n",
    "\n",
    "Build your pipeline to work on the images in the directory \"test_images\"  \n",
    "**You should make sure your pipeline works well on these images before you try the videos.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['solidWhiteCurve.jpg',\n",
       " 'solidYellowCurve2.jpg',\n",
       " 'solidWhiteRight.jpg',\n",
       " 'solidYellowLeft.jpg',\n",
       " 'solidYellowCurve.jpg',\n",
       " 'whiteCarLaneSwitch.jpg']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.listdir(\"test_images/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build a Lane Finding Pipeline\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build the pipeline and run your solution on all test_images. Make copies into the `test_images_output` directory, and you can use the images in your writeup report.\n",
    "\n",
    "Try tuning the various parameters, especially the low and high Canny thresholds as well as the Hough lines parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f78c09c9860>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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53gONwz5TgG7LhmXuasHutVYTdzFBAMibRHB/vf1ahCABMmcOEm4Vcvpg2uuATWuRGJbSFHqVTzQVnwuLYPFHeGDqtSCKGzOaJLKYoSnJ+cKBzfM7e9erxuo3WifqKNnUnFI7mgd6yDrH7rIOdbtaUtqVwqCfpGted72pesfJzXuNSIRZ/HXCpbQl0noupJVeA3WeIS0hgTmdIBZp1hWpUDmVewxN4+kSoeH0Un3wBpB15bK5xyWXMRPgtd+hIfnq9si55rybdaE0xmKWKmUdKd9SEiB0VA2gghhEwFCaTU2Q98E+gq3LyHzsqp1dKIOdsNU5m8yhNq8YjsRF9JALQc2dm8fX35HwtzQv4PWIeX8MU/ir+T1K6WKaKZU0TFYpJJolJPinktAjRYtwSBsZ1ZF8a86o46rrMj1pSghwCGEmITtlxrCDPl818ch0n8DX5htqEezOIprUSjALuCVVBwbrc9a7ymapKv1hMTmox73z4rkPPWaaVNsULWDa0/UDB0j01PwckOy3leNZ49isr/GZEh6UIoqAIv1qbWFmqJINuEMzqWO5qWc7pmLE0jja0GhlLOJouomnI7VIj9yetN9CbUZLZJ64uVyazsoWvq12hN+EEBNxaZbnnStx5VT+pZCghI+bt19pMdkM09kjTeJtOjopZd7SAuGl8s8IWdkkpNZmR4Nuta3RZYxZiLBFXcYr06cs2KoIwztEeL0ZhJ7tYEn4XMkCV4o6csVJo34Qi5k4c6KSYCebgRnhzfUqb4dHmVw7MfNFXogxxQiViuAfYq7owT6VVOzYXOV02aXdULVhBDnH+WM+6k/OVURKH2yi2WMJneOwIxOcVcPin07/atg1HrsiLjRk8xziyWWq1lbieIA6DV+0Hjl2R+hFWJXwxfROy3HcgH1LxDgmLXXb+d5dhFtNcLR5KBN4WW96D1HDjJNkD0ncx1m7SzZu0ayYc5sBAS7ln2kfXlR9IpkJdeiJyrdD+1yUJar6z4mx2t+BtobUy8ipgXtCFOJYGaEmxMOfMVdT0gRUbp5xSof69iR91PBmEHpZM4J4cgKi4rRSWMK2uOKkRebcvW2aE6ckLAntcgzlctrTBZo7eTKxP3xf7j2yvqvd3u/AfKzSQ5AwVEIh2L4Q7C4BbRCbTOAPWPgHQUszwd3METpyyslBE062bnBeNzD1FhPPIaatJurJ3DWLrdcCjUZTaVrNbuzTEHp4mDE0gcql3ZbC0aorb5UyPrUfQMxuRIDzc7Oi2RuZyJc6Y6F52/P1G+fOq/PjdSLvXbl8dFBA0eKUg1X9tm+4D9Es888GB6FllTDiQjHTlaf505BypE+NtMw9eDMIPYqEyKwITT7yrsHm+ZAh8jpHSObsc6lDpHogRdAoybQetpxqOIFepGJCUJnNZdpi+4JOTlXIGbeYRbFDmNVnm2VxTrAnah8CaebCcAWn2K/Ymo7AkZ3sOTEZzRAJMcyxhXd+VkI5ewwi55DXmo5iPHV/D4VeeQedu1woS9JiIPnSE/ZBCG4AeyGoWkrMk9rvX9bi5mUkq2Ecc4oRJokRFIFDzZ16pOffRrn4WeSVy7GtbmYW0COTK9d7I5V3DWmRW0yq0lgDR8pPARh8qjBXZXMPeVZr0thmxkJoWfBR2SbjL8nnVHatI2eJo4txSxK9Ze3jta6uX3OWEaSPIQSAHIitOcpS71A9khQsYdZPEYxcaO1h1QdmsJIAg5jvduVgruANIfTUJGQ83/939bOU90ifZosVSnrS3Zg1JBaDSXuwJSf1rIIGWM7dxqhlSoKSpg4eF0JaPEDbmtseK3MkfSZV3lHe3WVm6ZjWPg+Q+a+l9/yc5hFU+4CqMdrVl5zOlvcftdO1WIwkx1NJsHZXMGbG3ct6f12d+gVa+zu+1xW9s7lI3smaxaywykuFZCGY3xvz2hAZeKJVtP9wpIbipNdMuuDVml99CLFnmj0U3hBCz4WFMWeph1ven562oh2zQNMTb9bwLgKc8ovnCaicu1rYtTly4j8hyprY1wRZT5bXqr3STPZuWiG4VJFs0j2tdi4nGSMhy6QkfIWk+DpmOGhCZmtWjKPCp9cPGU/dTutZ5z3TRLtkTIY6e13aUM9QHcDbhXf9cpakK02oYohBEakaRAvVqZC1D2N28KiFS9YmkuScToVnNJvKWGFIgVU+ltcUqWZmDK7w3aEJ1XVos6f2CbjG2Ot3D4q6ayKhf2wf7KNG7qxDQJz+pp/eR9pSRbQRgOzHQOyT+C0kmCTWdXj7bwihL6BVtPaiYFNWSgTtoYbQcTWoitK7smYOuNCjjUGir90IB6m/HLhNm1aYmCpvHNEa5z3Z6UjZw9sSn5IeQAXfIKosWb7ZGAsTs64kfQNpLPRlDk2fgKqv2ZcZ1h3YJ3VWFXBNbFARdW3/rqvtEJUm8apMKPGnivB4lwUCh8/mfoSMS15jlJ8GJfGYNAMiIHAofg/dVc1Y8oJujIVyqALVKXYy2B0EzEXoawVTzOzuO8x+2swlJ6NLQ/O29Sly238VKkyAuXauElD2Ud+WPZ+cy4weiLb3TOSr6vJaxeFpHIA3kdAre2VrzMga2pqQ7zvttCFj7VhPvl2QmtnItpFUADq8a+/EosjVufyOte+9yl2vOH1rs9nDNbtVO4KSOHMDSj0URBXo4+i5EjSIl7PaD2YOpp+cmGlo9a+36A+Rv7JDGpU5r6spzcFx3LBTy37fYMbSFrzLoaMtvPJ7Zjj3zLUxL2rBoW5E1E1hdA0kO6BrMqYZ/XxPHSYCLDE9LZDp0OeaEZl68jxZBiGPRGvtOrpT8SB2Irji52i2x0qaqwj3bK73SSWYxdELszTCSJiMYLevzhreOEKPxM1AlI/4GjOH2raT2t5RGLUEXl+dlg+4MJfUClTUwKkaNHnTVQ6C6D/i6MjlamG7atLlOZe/xdnV675OxzrHR7enidPgHALL3Z7tBRDbluz38Zi6JKcPAOxBjdxKaT87+diQEIObbE4toeuDRrWqjZ6S3F/GeT+JEEVQTgw2OOW1oN5Pp9HhGdnhL+Uzm82bvw06A6ikOwaXNVALnhpk7UyK8oiZT0t2smaNPxMwC6N5GYf6fUBJ4lbfTBWdfFTOYmgTAsV0FVpy7BNumv0YY+VLGKWO2y9oqmtC1YISTUd+rwMDamidGs+/KaGuuaGg595m8szgkra3L+VA1b4OX87LXjEDAYnyq6P0fCjnapwWAF/Dj/VGEPpoVlDkQtlpa6gl0lJJg4c21CSuBrrOdzGrq568XLco4J0LgFtt5w3Tt4CGjtjRDpMsf6y5YP5++bmyBzaaO1Qqk2XcWLu2vs6cScSPy+Njx7sniQeFd3bOkeBhCXd3udR4zZBuVKDfV+1kiZx2E52Mf024qzpb+O6qq4beTUx12RkzmpnmSNPfA0H2bmcs1LiJE1raaNnX7+IQb+4RBbr/viFS/KQOzxr0PGTXraJtGktDhZwKeW6M/k+xM1aBJvIycUrSmPRtNrPkJTVotXX3AaZ+uJQqwzYxGGCJSw9ctQCJkjTdgg71mWfhqFQ85kJxGwu9oFhvnGJ/zNqRNuV2LmHI0od85wp1c+Km3acWMc7tGpxtKUkdwFW7SoAqJrZqLGT/CIO4GxFr1JO+y7qQy/60OWZqLK1wdwr6WtCM1vqMQZafaUmtDWC+R+QC+xjijHLYUAl9JhpIQbcvSnNugTf1zDe5FrK61xbeAXrnZ7RjWQPLsz3CV3zAf7SXg/+kYC9ziH+t6UaBUb/mEr2rUiTkevZw7h5nDyFkwsIprRx59f4e1a62Hb4WdJyDIKUq9kT6JLFxvjmtSFfkHDClLJS9Az6NzzEiIH6e1O82DlvNQ0sCBRnC2R4aNSfqSSv1hAZCWRtMjePqeZGh2ImNzbmSMCGaSOq/0DVYKV0CAuSzSGoc1P2yul4RQMyaPhwM6+7V0TQtlPdFyoxmMZpNxqGSpAhpeT64HP4ZkqlQtmZOSYxqPRKZudKmwKkxz7qvVtFXQpbuSyV8RBeAoiGtrhK16UWP4NKe+lDPB8DpUhWdVA9QqCv1WZtz9sEbR+jtceo08eZWqXL+rSfxlXwyvWPqbqdK2Iv7dc5lZ53cM6kXowmT3JNwaOiE4U6DvbUof4aiKBWeWk3uAgFEcv9Np0hDg7JjsbsJ2/82Q6pblxhpfWaCERe67xCWKaVN9t7HlK1a8jvAdCLtWpNtkbJMBJCyFRtVW6nX0YoXKyzZrvXcySN9e5g138WTkFXbHZuyTk+7T17QtHofmab0Hwk2aOLRgNrJX5THcksSs7rce482k4spIn0XuageQ+MjyNK0FeCAioj3/FzNCJG5kAm0zcxz/bLgKYKa+J27Wh/zna88fGMIfSHmZjvNyg2KmOgjwM1wsM8Y5LLjIn1UGQo7nvqmgN0xZ2iVUUvWcpG0iRBTtu27QCZclfRazA8FjKPUMFx1IlFGQSGiw/o0YdodJVJL0G1V23sf+5AmQ0dU6DBJAKCgietcQpJ1Jw7AXQwzq9ZZGk0RIeCkEcTMl0CcGxktzdMd5Og/21PX6El9UkCNoT50M83x7aXOaJkj6rMDsRk2Q38XzaIFQuwJVb75SoPpvr9DE9HgG/2bUtuzea209gPdAPvxazD4VCj+MSj0cdo1NM65KOz81CU1g3SKUwSDHBcGkNOQRlE6VJcE54x+prZYpicRgkPZtKmeSXEKT+qotoo9JkzJbiO6Z48/V6GPrf72nK4pPDF6+UvLQpy1tsZyry1gw8/M8UFJ3KW+qxQFJeROZfNTqV+ztlAZtPOlFYQSt01THi8z8rWGWUm9tTTYmrW5hMlRsiFnTu1GolJwZafeJYJHOTvgQDGtAwhMEt3hqiigIglSkroZjHyTJBRjpGK9I6RzmhRxyKacwDnXjhi38/6ulhQxt102DYe8fi/jw7BXPDb4Jqmf8rQk5Mv9LpZQMiop3pGJbpKSA5Bs71McV0KWVicqjM3ZpdU2wcB+kSneS+o4RaKRyl+U96+qkgBMZvBVc/MbWuqh1FrITJio+gJVFo0yeVi5CAlaE2FEs81dzmC8GYSeSkd+svvXU3UH2BLF1h7LkiG8QU2ja3pU5pcW1LB3AfZ4UP7P7Gn8NJvcPXpwAk9It4DqPDuHvZtbao2r2HLB6F692Nmx5FxazPtxaE1DlOpjGKBJHpf+zs4BVBA1AXuvwbzhUhcnaVwLCdDvU0nipZ29YvMmtrlOSPlN5sAlaoyA6rYRVYzzWDRx1xd6Tawc1/JBq4jxHabEyHMfksFPxjWNQ8MK0lCbkpajuiLNStF9yfsYlRaT/kOSeymZ7FpJvhxRlHxTgr5dxNEQXuV7a1mAZyi3uNNrQr57AepGu0a5uzT1ZhB6yDjNT3vV6qZNORDyijHxxwdZLtOnbL5orbZdBH3+nDqnuZhDp0yrWtp5Irac3kMkrCm2kPVqNNcRCg6cpUyNQ4tAOCUGegDBSf02cQglidTgFGuFziWo/dUjK/Rovjt6fpfKRgYg0T5GIvZsNlvvAFLWfiroERvtIFR54eqw/VJ/6pwc/DFLi5QTt2qDU9RU6/rMABg1venHmlFbKVee6wgcR5iZOEXJqB2h4vvIEnE2Vdn2BCP91ITecmLMSciCIvb7RbMCYjKLYbZFo22lC+ZUBigHHp1zCPti4js+tDKVqp9VkAKjno8DtBSNsxrrVqEsbh4g0Aq8EYSeZPOrjaRvLcrShKzQ/F704BNgstiVo95KStfZLZmMdQMMOG1ThkM5KKTyU8ZLKtORo8PtY0RZcS/45A1vmZtT97wW4hPTKtYSCakOiM26FaMfuxalKq+lbqJ8u/xukHto1XgaSrX7InUNbRt9u6yO3AkNaa2QYO6WbcZmy2UjKH6HbKKqmYBomlT5TxrMIpoE4nO54DkqPOpS6TqiSfwCSTI1bcg41JqAcTZWaoz6vR7ryVEx5QixbYA4qnPVae9EsxeXHJQtIuSKhuKYlXbMuf9ycEsT+FbGVar+CsgYOUcmXUJrTpwei040Xc76qRqy0cEtoq45PMqJ/caganarR6NHqIPWTjO+BWdhJncJr9xvYSD6W0T0KRH9c/XsHSL6e0T03fT37fSciOi/I6LvEdFGfNJFAAAgAElEQVT/S0R/7GBMdnAuAxzyv3hyL6UU5fKvLCDK/0TD6zNBl/8R9CJUz5ON9+4HFu5aXoP4Gxh+5+UFSapFQErOmv8nqi0CGaISmaQq16x4P0sL0IS/LZHKv33g1D/GlP+1ythnpRWieNqzO08cvTq7IijIuRjZQGXvBgr5HxGrfzRzAoMCyuXn6X+JD8g/vShziGiaPZb66noPAWm7cdJZhKoWU3OKhjlICmvK70URKPZZcv9GGWbv5sqD6BhJaDigG2jvnDR7WTsqUuBh9X3WwRrFhDWX4us+aA1RnP+t0+L1i3UkEWHe3i44RAz7HwD86erZrwP4XWb+JoDfTd8B4M8A+Gb69y0Af/MgLChKb3LtmE+fc4eAspkq0tQkyEL8DZEe1D8P1P9k4TPbduv/yZpW7eR/asPGEE6XpHmN0/6xKJtGNqqVRHQoVpaUOgwoq+NuyMQr36x0pwXPVf9axI3mK3uGj4Nz8d/OuoD2uFMZg8ERnCuSf3GKUib29ZgZ4lnNk3MeznmQ88gdEak72apaeFqYs6GcD13oY/on46CjmeAo2VUIo7C5HjMBmoSii5kWQp1DfRXdrA2RnDPRr77nuWjT+cJMKok4lOtA8/g0+qelYD1uRDHQohd1Us91ZqagdA9vf821oEeQy++lnCPK/2qc6s8aB91GpH8OXtEUGdzXTYK313TDzP8XEX29evyrAP5U+vy3Afx9AH89Pf9Njtj8AyJ6RERfYuaP97dTTmhKbhSrJAlBK5MbTShy/VhDbTPvK7rm9G9Sr7Z96492wg7NQm3i7/VCT990bIeWpj1L3hkd8eHgAqcYfkX0VXtFdZUSJSwkKaYKhySNVWs8fzXeJ2GjiYgnSTSyIVF5GcCUooDEZFEWpt1MSqIBz65Yk37UGoadh2Lgc7qvkDURkpkNpZMctZ0Ym+yyVCcmmfrUc861otolNZI2+2WJ43F6kQFwFMDJh8GOKmfolJ3I3hEm5ir2mvKk6MihcpqZynW92iekqnAUyvCYaXBpTIozOZsTieJ9xDJ38rsrleQ9pcJIMx6JGE+yBsIIoqgdLeARXGXXJsBEobHp4ax/Xgoxo7aExfr03ssPkxYnz8nMt+4/EI0DMi6tu5uDCRkuA1KWuuxHWaEhM0YZ85JzK+TIrACHgBEIRZuK/gwuBzWZEQ1ph+fEf10b/ftCvJn5YyL6Qnr+FQA/UuU+SM/2EnqBIjXsV/XjNQMxOqGljjk9UFAbQJX1eSLanLJFXnpAxj7XlhSKptCX7qXv+e5MLoux+xbrNufJmQ60oMeyrjA9MwedA2jxN1cRdM2QyDytRyabBwwWdev6rXlvQt2ebEjCwaq6deCSYpJlg7r8Pf4tzsa0gbPAUHY+a6ZOxT8gde9c560B01g2JNKe1GdPis8sN6Y+6ZOuvoenxmFKUUgMifRCkagJ8XpKTvtWeel1wjVyJfFbCzTe2q4uMQGNowUK//ijvb2t0adUlDQVB/aefs8TRnp2XdZKCgubotmLXcp9lBg+gMA+hqImAk/pt2g9i6ZJuRf7UPisnbGttpsjQ0TfQjTv4PTR47LAUMaoRZyEWxPJhKuUwTta7ttr9/xuyu6eZG+uMitla3JVbJSt+uIi8UBWradQtB0Tc86zNwFYDUJOjhpi23FK1b/pOlNh0zMJs3NgRLebt2pT/X4FhGoTRQTi2wzAl+WpTwG2Rs05UbELbSRy2dlMVEw8QVNw1bcWrtoO3BtD50TUKvhr+7h+rwx3oWSRILJiGPOeSpRK3PuKZOV9Y4m4ajwWIREvhO2kC6gzXrUGVSlDFWq6vFnf+h0RWB3lLyFRWJLzKC1IYaByPoKIrI+zQ2xDzk+i+uHTM+J04XtETBye2pfT2xdWIXXJRNNGPTqgY5vkkM5QijSP7Gcj9lIdXGBMIcCD4RxjChRNUwRM0waDd/AeGKcU+s1uf1hwBa9L6H8sJhki+hKAT9PzDwB8TZX7KoCPWhUw87cBfBsAHn/1F2yCwz1SWMlvAkg61X2cVh+jrtsCDpP69vFQrndY+SV/cumrbM/m6ThZmylO+JCMFnph6hA9l4haN1a9ihIgYxyqzACK9ZL5/fBFV/C0uoY63xWf0o6cJvn0qX465TXBgfLvYnyJGm9nDCoGZn4yphv7VyDWOyWtpi5d6Yrmi4xoNOjN2leEmCmu3xgSrlLWNnA2pmsTZx8gxoQwo+J9EE3A5DJSv1tCL2avUo65jII5X2A4h2JqWZMoRNikNzahSXp+XCTEWqPlkMsQiekmGVCTwNW8eWufLt8RIp1KLh8mSZ8hSyyAOd765RyByYGmLTxv4dYX8Bhxcf4KCCNOHpxhsVgicEDYbPDjJz/G0elDLI4eYnH2HpxfIexMv2rhdQn9bwP4iwB+I/39u+r5rxHRbwH4FQCvDrHPGxDJitAk3jHKASLagChukaYjUn2wm7wjSe9DbU8N8rRoGxHc7E3OZWZ2cpIEX/N0WloCk3ZaGOk4eRZR0Pzelkxz++qZr8o6VKllGQBSSgJtK9ex9fowmt6jirhnp1M2tAI7KGTCTfdzKv1y8T/MXFIcMDAFwc3NZrvWLbJQoAfZELodxAZQfSZ4bpvCXAx/gTD8uURP6r8+2b8J5l6wPYJKjwFr63f7PRWuqjTKut32u3V+mRhuGiiFvsq8V6akDKlpfdm3xsdV4bO5DumZ5nQkZ22ig1Rf9Zol+dyldqdaTM36BqxRy4l+6z3ExxMwgUBwWAEUEMIWQxgx8AYvn/wQN88/xtkRwYcR65sbnL9wcH6BaTNhChMeHB/h4sknuHZHePdL/xqG0/fgV2818W3BXkJPRP8TouP1MRF9AOC/RiTwf4eI/gqAHwL4c6n47wD4swC+B+AawF8+FBExyTCVBeLTEf8Azgct7CTGcnFfz6+v0A7PKfDOxbnrgEQmyjXOpSkAQJBbYATH5EV3PB/msrAZ1qYuRIGQg+1IHz9SuLF6qhe8I4Ctc9bNNsQc8r2WjsGBjc8h7hXKMdRaqBpdCniTE0wAvHFutwbektboFN2R2lanomCZF8W8UFT2aBWIE+qYkoNLmRV0nzU2WsDMZCPkOgNoJo3O/BJSeVYVbZiAPmkdPwqxSZqcK2cSJPWAaE36cp3SZENPpbQOQ2IM8h6pBBDaG51raK+RFrPoXRZTv1SyeKQwYQm0YK7OJSQzpct0OZqzMm4qzJfK0zJTAMmhPjORLoXqMhCipsjMcEN6qp3J6rWez85not7bT4omoYSAEqc1ToQwbrHiLXi8xBG2wOUTrOgabmJs15dAYEyjw3o7wTvC+naD4+W7WFLAOF3i+sWPgOtLvPPFf6OJYwsOibr5C52f/qNGWQbwnx/cegUBaB8iIIqS/ME1xcGdqtVatNUqFS2zPb6pX8sHMkq9QNkU+nIT1wj3qiVk+Z5lSmJwe00hqiJp4RS/rPpZS7SagCRbNZd4eou7rqLUMWXihpS/n0yDWmVmRSDEVERO2dT3mEk0HpwcTuK2qolu1dWcO85c7KBDEFW9UjdDtJvkwKO+FKyx40yYkPPTJGWy7QjNb6cx50pO1Ldt6daoOHFJBoCF3VtctXmtLZcH2Pt7RVhJjJ+CciFU0Vitkdhzmtv0o/rN1bi50k4wGmCZG1aIc2rA1Qy1gXVhvhqfohUxGORiiGWOtuv5GjCvw/7Q1pB7QJxMZrzFQCOefvR9jOunOKIRGC/hscH5q3OE8QbD6hibzYQHJ28BBHhP2KwvQX4Ab0eMNy9B24DbF02reBPejJOxRIlICne1A0eOc56Wrjh6l/YqJa0mxhZUmlo930nEN+ZQV4hCqjjXUdqSHpIq017EbKhdS32eE838PPsxAiIfqxhbgkkzpyqfCiVCVd5razd5E+4wCTVQLj0VPpQ0hnb6AvWei5K1SRHtPUII8c6ARPAly580IXgGsPIJlLVADZwlg6hHYTCkEKrXjsv+A1Yd0/1oj1FT5xETSP3clC99KhUjM6T4V7SGFM/Fh8S0aZhrDX2yq/FsaBt3gMxgWppM/iH3qk9w0zgPrkSGjY3+60i4hsek0XZuoPm8PhMQKIB4i83NK2wuP8EinIMQwJs1pjCCxy1WR8cADzhaDpjGEefnL0E04OzsIa7PL0HeAUw4Pl7i5qkOcNwNbwShF7Y9J/JqAzVe626ahqjGgHHyavt4bSu39kLllQ8cnSisNltFuGpaUdPxkpPG5z6Iw4a5mD4ihET/yIq0pkXdkHyk4gVDsf0SUba3agdR7mrdhCJmBMBLSAuSJiO/BzWoe0HbV/MnQ5D2RzfFCQjK3FYc9JTvhAXi4ZN892rWuml2scNeGzdbplDyxliul9eXIk46A2b30E311wmzrtozhRSYsxpE2SxUTynDRaekdCzsNcIc8LuOMIL6XC6U0agXxqNulKq1XrP3LcO09VEDs9Kqg5sJZHUd5Zk2ofbczfb9mnaYEde8l6J4ufBIkTa3WGDMeyeAMSyWgBtwe73GyckpwA6r5QkYwHJY4RrXUSCetri5folHbx9Ovt8MQg/OE22JtByJSkcJqMokqrln4+CEBtl3QndryfJ1FQWNrpgEtJOxxq2XTEkckhr3HFkEzA4WAajK6l8CKNmGZQ/F8D3OBzNE4reHQUzt8QEJsyC7cEvGk/rF1Od9lFPXlQghKjOXyT+kq0hS9tBuw0YpzQlETsFcv9fw/4w0AaFRtnODVm4jS6PBMKQeiDboEDJhMwpWbzgzMykg7znYPRJ30Q6tuCPt7JPL7dyosaipfP0el1QUzowhGa1lrhep9pq/2Ubz2HTXeqvetsbTMhNZptR7z4MwYskjLs+fYVxf42o8x8INcAhYLBbwS4fAAW7w2ExbOHgsjpbgANxOtxhWAzabLVaDB/GIq4tnuzuh4A0h9IShdt6gDGp0IaXJP8AeJmAyWgadolVaLZLDjGAnCOm9aP7w+S04lYUv9wJGChNKq5lXtrXrRJAVY8j1pYNLBIJP3mIdJqlNDdqGWQTXuA20wCT0WtxE7fGUaOM0PuklzWxYMaGWBtWbJ7vXaPaXWNqu6mAzKan/6mdD6dTgKnU9S8uK0NvkbGoMWeLvPZACAbI5pG67JRRPcl9jQEy7kTQg7UA3g5EIdkieCi6XmAiu+TUT/dMYZ9KOTitj1zVqH5OxVzeIotEaOmvP+BKST8P6j0rZgYZG1JcIY3HR5lu8jImtsAB7T4EIiz77Z7I2BUuQreNZ9L8CzXGFmn9ZUzw/xd0C7xgYN8B0ieuXT7FdX2NJDje3azgGxgVhuVpgvblF4BHjeoOT0wdg3kRH9dbF3IFTgF8dIXDA+vZ2b7sCbwihVzK1JpxevoZih+taBNV7RbEqz4wzVyifisfvTJbP+VJEikVRBSuVUFIXGCmDg9lMmZj4fKVA+n/r4Jc+y1f3DW1rDgCNleco2Y9yaQoK7gQPxlRoEwruMu5eBf6HOkJDzBQN6V2TFLOBjBoSP3vZaBzPRLSOpGumXbKT6rGact8kQksgVNqiPpmQ49TB0KFXhdkHoTx57B1b4k5KdA3Jy1miayge5MlEiKr3LBD5YmbMuDkTYWWWU+obexV+qJQtcyq3tXUMB9GpJMqbmeg5VqbPokkEw/UqYso1gXW50Wa2yRC1m1hqam5LVnMjN4OZPvGkNAJ7uKwm+BoygzkAatx1htKqZMRie4sjt8UP/uU/xXR9mdfzdjvh+OgINMRQTOc8OEw4Wi1xerzExcUWzCMCDYADlkcLbMMWIQQ4/1NnurGgNT6JGpGFzx3VaB9QJbkEAMkwDold31tHQwqYlyl/ZXHtq0+O7P8kWfXsIpuLmKSzmLGEnMn4lu2gx35m19xjHrszZC3DUK4K8/nz14ZKsp//1mJYvfmrxziOXFHdrXyYLWH71pC6rhJBbOnBiNj6Umie5vuhPhm9CwyNrrSbmfTaq2RvWoBO2+qzFQYaD3VZIzcVib2V6thVe5vqChRkYYKoWVcP+kReTp0wvGPcXj/Dgm8B3mC73eD47BTjOOHk5BTbcYvAE46PjzFuPfzgMW4DlssVQrL9bbdbbDbbeJAqBKxWP+WEXoAaxK93THmfG6mugwB7mYNJUtSuzUomDXwVXubygGZdxXRymAwRwWK2Z8ECQNJkxGZcjk6nXPRE5fo7Eol1bs6qW9Px4PlQiNkc3cwojSeCQP/KGHMIzLWIac9h32hRPZgnXZPn8skmTJP32VzeUihTEeT12kxSHXeIewO1LIHmetrnEvLHbpRTpYVVoC+sIWX/mhH96lU9/65xuCr3AXYdufwLGU23Jd1be3/5vTYr7do/OV0EpfDnIAnFSplcn6rImEL3EH1CjPCqUz/nIE4iTJsrPPnR7+PIbRDoFrwgOEd4/N5jbDdreMcYxxHgAYvlEuv1Ghxi9v9hGAAwhoWDcwuMY8DNzTWOjo524qXhzSH0hZkWgllp+XGy2tOqU2/nBbRngurTewXaRMqu/b76Kb+3QuPq96uHXcF1/61ZnXZEG8p25mJykfx3OthODiOlly1xR5toiN/AHsrqs7gaSr1kUzUYfqUICEnIpJkRpYk4ZbqYl7BJxkopEyqbPxgbTaYKrE7+yj0Acb7nCStkyTrYsD6nD4EJvhPn7yzOFSLL6HSnvJvZic24aKdqIz+9vqTeKQoa0xBoQgwwJqOPFKamTGGVmbIWdspF8s4Q0NaBvn17RxNWiVOfg06HoA9gqX4XCa1Zgz6olbPs1kVZxkZzi3hegXnE+vIpaHuNwQUcHy8xDQTvHTabaziHSMT9Ettxi2lkXF1fxSocANzi5HgB7x2c9xgGwhSWqE/k7oI3h9AnaBE6WSxqTx5Ux66j/rN3es7DHiFvla3q2R2fb9/bVW98LkRKE8L2ht6nIxCQHKvO+BpakovFrWXkLZuXD+hJT1cS0GSyF6wi/TOai+EK+s98NHq5gwz2uY7KXpWIb30bUbwXlZux+PqAk73ycg4x9BFJC5PYjx3zybvnWyTNQw6waWc7IaWQ1iYtI5lTFgQmXY/xwUi6AyWNA5A4fsO8jD9N6tJMVjHFmMEOEpbZ0qJKxUo7vYvqrCELgwRZwfq8cy+9SghRdHHY4NFRdLg6dri52mLcjjg+OcLNzTZpR4RpihGGnqKpLkxT9H8RgbDCNCYhhRmrYYmw/fzTFH+mQNTzcrPZtDOOqesweTMiEStlg3VcNm101q1a5+toYFaQl4/5UZ/Yl9wdnVW3hzEYNVmRQnN5ch3JgGReIeQUsZIy1Ujsxm5Qdap6bh/vkIih8oOgTZSCU5ux04beWMH5VLYVo2Wf1Am34g9clUuXPjQiUOKl4PlhGx8SorXLHSd9mrcBjaepQKK5J1DH8cYhZDMSNecvnRZmNvb6FuFnoy0nAq+jf6ywXkB7pz1VWgRQJQVOm7lIx0j4maaB6oKsKsZdzU8u7zg6KRVzs5FbZT/aMyOxzDRLT4Eqsk6lqVZvm5F0BArp9DERhu0WNxcf4cUn3wNub7HGFsujBZarY6yvtxj8MRYLj+urS6zXG4QwYlgM2G634GnC8ugoqVMOwzBgM064vb2F8w53MVi/EYT+deEuErtJPNV8r3EooiLG+04ycqNcDZImIdQLN7e5TwOYS9LV471Qn9gTyCkQnCZAnfZ0fR0pVkAWmWNgMrtX/AbqSUfCtKeSLfIzP45uwaY9bJYXbM2liVkrRCZcUTid99WDDpYYWwxS/+J6/qGw3V/5zobJ5nwXLcwoiGreqdx6lEM/77DGDDjdbgkZDkmging04o17Ztre/goxnDILDNWcyL7cJcC129vzu6prQ4zBe4QwYUkA317g6tlHuHj5DMO4RuAJDx48xNHRCsODBc4vzjGOWwzDAg8fLnFzc4PtdoOFX8CvVjg5OcFms8HNzQ2Y42HAaQrACBwfnxzchzeO0LeI8J0IerOuw0wo+r1DzS4askMGd8TT2Ix3m5AOWaBdc9MelIoJ5m7Qi8WfPaH6cU4nWXDrEB5TD9vfIy1WDInajEouTqmlMQcxB7TaYlMOrQitysJRt3EIiIbQfa9zbR7vuyBaHd1sjqdeK/qzi8TYO7/f7AmdL56URC59svqdoOFLUROiuc8xu8823dsrrVxUpg3dfadOQTduMunRJA8CQgz79Dzh+9//PTwcrrG5vMZ6vMFbZw8xjRPWtzd48OABlusl1usN1tsNfuEXvoGnT5/i5YvnAIAHJw8w8ojNZgMwYTNuYqpoIhwfH7VNVR14Ywh9PSm9TV6bRfTn+lb0ssm5e+N6D/YRVp8lIrVAO3i2wB4A3i+ZH8p87sqc9uH2umZNLYF3c5DL76p83xld1O7WfQW9sWmFD1p1XjPmNm6zOmMl5T0qhO61pV5d951eUIkGOOGVbNjxZ60hzWvf1d4svLY7N3qQw4yxRFN60gqgbGHpOsPO0Hf3kMTOs2JiuvyuPUDOIYzj3v1pAzUaa6vjH3MTxxvaKIApwBFh8Aucnj6Ap1MgTKB08PLm5gp+cDg6WgK0wvn5BaZpwhgmLIcBmzHGy3OITGrwA8btJl5aD8B1EjG24I0h9PXA3hmIqvdI2eVdd5G2Fsch7WcmUhU9VBOobYefBezqC6X/1EL1vp6aC6UP2BzNOlQysLt0lRqI9urobcqemar1u4NL+dKLXbp3F6upSzOQOzDank+jCZ2h9xSTWecQWRmhpHnsr7cnyDSa7uAQW0yE3Pk8hi7b0tkQ+/JeTIEw14ZkLnX4c9u0ulegajG3Q4RIlp4h4d9nInntORevSnQBAIPHW7z//hdw8/wjrI6P8fDBA1yeX2IzbrC5vIL3jNVqhYknXF1dI+ABrq9vsN1usfAOIxPCFJnSYrFA2Ma2B++xWg5YLn/q4uhprlbdcdNoySznVTf1NUQ7A/u3XUsSrAoAPLf/tRdHp6qu2FTy1BeUK3VXhLrGjuzhvm+Ee1emGdRa7xnPnf6jwwSTKaWYga0E2sFuSBeHhAMuR7ax4fOTxhZCyldEQMM80GUaLES+g0PX8a6l0d0mv33zVC7f2ZX2+vWgec7A/I4c6hhdhC75V+S9ODZyMNDUIU7VO5hnSeOk6EZIc6bvPDY4h5D35z4Tce1QLs75xt5KGhSHkM/OLULAdn2LVy+eYOUmXFzfYrMBjpZHAAHb6Rqb22uM0wbeOTgPXN9cYztusVosIwPfjgAhxteDsNlswBywXCwwLBYI009Z1E0T7ugwARSHFyYR5Bb7QyTs3i7VHxoyXTEY9+u2huf578YH2JE2xIbbEiVzHDVlyX0X7B/Xji69CzdCdYmFaVDVbOPSWUIVqZQodVT1sTyNR+OzUy69T6mQPX26x4bdmnduS4qWEJR4atJ47yWKCjd7+KOBW/moj/prrUVYVxPfeY1NnOry8dKPIkfkskJIK4exU0S8kHabpFjOBGjJ3BxYVO23TqqbC4fClMdZl/QNR7YZeyfRSXZkinN3LpwAIuwkDPN6mQumsRhjQQGeR9xcvsJyIHBYYjsRHpycAoPH0XKFMUy4uRlx9uAMLkwILqXZXk3Y3KzBzNiMI4ZhgPMe51dXYExYrZY4Plpg8C4dpDoM3lxC/xNAvFVdiN6BzKKnlsr7XBPhamMT5ePWM4lESxiZOLUZS48s+ZaG81mLbgKapzW6PCueM2V2C5iqc3U5FrqPxq5fnDlEdIAWtq8VFc6TT09337+bENKEfXd+qi7Fa2IjXp4tsZ9hdoB0vHdUGsMiNv5QCQIiYnCjbUekcg1VkvQeHLrgymmEXQzr4OpyJJyuRBhJ2WYmWqfTiCfGZn2BF5/8EBdPP8HRwuH05BQ/9wv/Jh6cnmG8vcV2cwOmCcwbLFcr3N5cgacJ4ziCxwkhBCyXSywWi9zOBMbt7U06NOVwu7nFer0+uI9vDqFvbajXJGT6JJsQe81/Wxdl9+12+VOlzlVSgapjVvvefpS6eod5Ij3kmYmrLEJ9RVyrLz0crNzMu4p2oElMmhm0ADMT1CdEwCFEen/Z13VOZ+ehazOTvZbAwxop9SmBoQWUmU9xyh8yPj2iv/PdRN1aDu9YT0DLPFi3UW6NOtwsU6PRfG+H4HDQ72j3vzbeyp4OkJj83XIjAyCe8OEHf4iXn/4ItLnB8tHbWCyO8ejdx3j58iUuXrzC9vYa280FjpZL3K7XuLm+QmBguVyCHOAHj9VqhSdPnoDBWC6WAALeeedtDMMA7wfc3K6xWv0UpkBweRFZYmrUucqWpr/XgqfYDfN7WkK5CwEx66u8NymJL+bVRokEiMgZaT/XoXC8Oz7U5RnFcXV3CTRLbKUyNDHtaT1SqithdnIKQVR+U3g/Y8xa1n4NohV90m1LMXXJTa9vrDJ1aH6l51fW5r6wR7TD/bqEMLCakTsQyzuaPwsicwNevHSHofRl29Ydm9hVvv7trkytVc8+J2xf+ylzv2vpRTOxx2Y7Yrk6xcnZGb721Z/DJ598gqP1NX78yYc4f/oUX//Gz2G6vQVvbnB9eQ0OAbebW2w2GxwtYtjkzc0ai8US1zfXmMYbnJyd4PT0BETRnv/45DGeP3u5d0wE3hhCL9AMfUufuXpuonQqbh2FkpZd+3BrDgBzOk/jpuOzdfvStr5CuEXUrbl3tw2/PP4MzAU1tBas+m8vkVsz6dPrEpXWBAmjbDmy098uuaslt7p+zYxbNad2iZW5YcYUPoe5ALrEaJ+Z5rMIqzX1qr9Sc+/SnNduo+rrZ92Hzwd2r75xu8Xxg7fw7PwcX/7i+9gG4Mc//hREAZ4YJ6dLXF+9AsYrON5iwhbMU7reknFzcwPnFmBmLJcLbMcJ47TF6cNTeL9AmEaEEPP032UJvnGEXkOW8qEkeh1VI8QV1gAxQgaio+Y2F1Rv1JSGofN116YbsjZK0oTfqLPz9g5R/YNMbMd/Zw5tNFbAtENaKbipq9HzyA0AACAASURBVN+4jH3+3Tg5dR1AZdnqn9pt2Gip1Q/iGK7GmO+pXH6GxaxPOpFVbY6JmpiamyotIiENQ0tS1Ck3dMbGxjD7ztqyedwbBUzumTtooa/JhLT9mfSBobzPyprW/eQ68c9PgKfeq6/rX7gru8gRX409SUQxZw1RNp/tasUNS3z5Kz+Pt8/egsOIcXuLd7/wGGcPH+BoAC5xi/PzZxjXrwBMeHC8BC0G0HbE5nYLRw5uAo6Pj3F9fYPFYoHTB8dYrY5xdXWFcdzg6GiF7Xa6E+N9Ywh9yb8SwYFSiFakcHI3ZySwsuCELFkl0ydzAHPIE2Ptc6rdmWqvZBiiKt9Gi0jp8uWXYq3oZ7DMtaoDLubUn2qc4Gfmd0OE9xy6ahF/wDKLwCqmmeanSPXuNjnPKbnidvDSQkNKnMTuE5BFqlZ6nambQOXC78oE09JDimO9LSDU9mwJq8tmKcVpa5txwTrhoyehK40zuFCU9H5Pa9BrTyJ+SDGv/ZrAjFlQvOhlFt9emfLKWQw9VrqR5scmCJpMpR8KwYwAc8UIGnlogLaS1d9vjf1bVwLZE8JsQr7prawadDtKTPDDgAePHsKHgHF9jqOrAddX5/jkR3+AL7z3Dk5OVrhYA7frG5y+9w4uLi5xfHSEze1VNBWCsF6vMfgFwIyJJxyfHuHjDz/E0fES260H4DBuftrCK5NmrK8StoaDtuQGs70aebC7ko1aNDmGrDwLQQjermvC5jPtOkyh5RAzUrxZrf10CDUmPUnRHPKRxX8A88/EwsSQ75cOY1hbXJClrtppbEkvo9zRGrpH2gNAzqyIXAfH9/R9s6UxVc41Mjfy3DYudRYchWAjS/Y2w2LbSdvSzlonUiOac/17J2HOz51U0M77YtaQ7ut8Hfagh3GrhNmRuj8NibOkEJ+v7xzpNDN47sBTTzvtW+dVymlUzcxO6Mlend/S1cuR6J3HxBPccIQQrnFx8RLnL56BphsgbHC7vgSHLaZpxGIY8OrVKzjnMG1CTBvO8UYzDsB6u8YYRjx4eIowjXj01ltYHR3j6vIaRA4nJw/2D1CCN4LQEwMIgBsGBJ5ibmnHUTUhmEMvOo7Vp2UBVMtCivdUm9aN2EqM2SUdZw3A6QJkUZt1sPHIXB5RNAhTWKdm5cYN1axuGtKvtTZhz/bfEscMY1KF5zm/TBs2dM7ikH8LJQ2vtO0bjDC+yAoBy+DJEVzQY1g2Y8gbFImok+nWftalWip35yUlY04gXOqJ43KUwBLY3v0G8xirIqUzyoAzmqJr/VnVXMBqQuUUZ8xxz9PUHA97KU/JMtmEDtNoXWh/6OhHTaot9HRrkO3TuvD2zlDqsBf5yM9tDQOiBXIAmLBaeXjaYrFgXGODZ88+AaaAhw8eYDl4rNdrTGNIN0ctcHN+AUcTvF/kPj969AiBJyyWK4zjhPPzC5ydvYXF4qcse2UIIwbcwvMCU0ibJJQNFBRhjh+jbq4lrGBY++yDWaR785FQm7rZNA2Vu5XlOjlprk2E8mO2+Eo8eXDtTQqiGd6hs/gNiRHCE9q4hWZfVfpjc5qrQ2BEylF19W7lqXnZ/Pe6DVGh5+YFI2GbNaKvRGyti44G0YzakIzwDIbPxN4KrhL22K57V/6emTVF+ZWiGU+EAK8L1bVUGkZbpa/PEzFPpq5u/P0+m3/v9z0Czr54/7v7GtTlOg0kdNuy7vVyZFn3tYDQmD+9bwyWPMIhgKcRm6tXePzwCPzuKV4+e4blcgCPE5iAq8tLXGPE0dEpCITlYoGjoxO8fPUSIUxgZvjBx1ukmBEmYDF4hBDvDt5sbjFOP2WXg9+ur/Dikx/ADyucvfU2/OI4Ll8/xNVpBrrNSXV+n3xq8A4LxdrodV3zMvq0rU6G1Yu5Do2V4rUWmRgXESHcISjb5KHRgjk1ytxp03QkhV4+hNx2b8Przd26xbS63KNZRWPDeq3xqNo45DEwjsJsm+/gObvToMIhnhdtGhZc/ttgFp3mJjN/AXJRRyT2cluABBMYRBSerfZ6/Shhgtm230uL3Dwx3O5Hd9p3nPaNaOgKuXx/TSG8mFUUQb+Da5YUM+WMD1n5RtrwcnWg3YcOE4hHbDfX+PTD7+JqcYuLF59gfbPGZjNGAdZFAr69vsLNxSWOj08wDAOYJxwfH2FzO2K5WuD05Ayr4yUYAYvlAi+exayWzhH8ANzefoYHpojoawB+E8AXEcWVbzPz3yCidwD8zwC+DuAHAP48M7+guJL+BoA/C+AawF9i5n+yqw0OAU8++RGOVkcYxxu89fZjDMsVCCtMWCLKUwGOYvxuXrueQYFAHG1bRK5sCAqVo2lfKuDGhmFLFOooF6KY/CpGb4yVyKRVvwbRcGL9lbKRfAzcydORHAB6/wWaANS50BkuTysps1DPDq45hD50tbuolbyT4za41E65gSnuBfWF5mzE3B1q7mgtVwJqbZydSF56Quz8ycUqTvtuUh3BtcfCBX09YKyvXAhCuc4YOKCYTGo+n/KlAEpzQOCuhO0qKZ2gAhAAyPG5KFeU3O3BjFHVOZTxjjXqKxitNM3szBBaLaylAWoGovvUpszF911uXAgdnwERDBOKp2o7AkDPSQsC4PJiiZeoKU1bR6Zp02l0dgBZewNAPuVgIgSSQJGkaflIOAPHS18oTCAHEAYEEMI44eWrpzi/eIpbvsKr58+wXCywWB7j4uoCZ8dLDH6J5anHRbjAdtpiYmDlHLxzODk9xnazxcuLF3jv6D1Mk8N0s0FImuRytYJzw2eeAmEE8F8y8z8hojMA/5iI/h6AvwTgd5n5N4jo1wH8OoC/DuDPAPhm+vcrAP5m+tsF7x1ePv8Aj995D1gP+OgHn+Ls0btYnj7G0dljOFqqtAaxs3Exy8W/HM0GicNGYcHBueTYqMIrrfQrXxqqWFo3uSxTNhdkuS5oB1KB15YqemYFpTnksokolNOmofytTEm2KmXmgN38QPvkMIAcshor0b94qSxvQoY+qJZu7IK9aq8Qnh4TUs5dfbNWy8RiJF7pe8ViSdBsS7zNe0v1unHldKp1mjql7YnKzyhX6R0OWgrWt1jlqByyK61FYkOap11NE8cUuvouXu0rmVr+A/1Zm4o03TXaBCViqYQUU23HdyECm4nX1Uy9+Vr22emwa60591aZaOguDNnMOqWrq4gJDgESKCJNT2PAMDiMY7pdjCdseYsBA0Ae5+fnmCbG9e0Gzh9hmgYcr05wfDzi+voGw9JjGicsFitc31xhufAgcnj06BHIeTx/9hyL5RG891gsPdY3G5yenmIcRwxuAGOCo8/wZCwzfwzg4/T5goh+D8BXAPwqgD+Viv1tAH8fkdD/KoDf5Dja/4CIHhHRl1I9TQjTFtvrc1y4ERhvsR5HXJw/x+rBC3z9m6dwSwKHKS6cYRUnR6bNA9m8k2O/JwgrEJMdVSps/dmxtksX2xxjzM99stQyczKx2DAwQ6QPUj9rCYqSpN+AkNoz0SzRlGCjCRKRJ9lcyqGXm1JESpsrxDlqmJSqmuaqvzxPjWV8XBp4cSwm7I0dXyRPPQpW9nXqk02AVePQh4Y2pe3EPWlU/vac2MY/UqRwzmykEPnezVxBP85lCyXU2TlLBkWuLvGem17soTwt4Kg580gOQ/XMaEVKu8l9ne+RiERDSCpdiusmySnaxFrfHlG/qad3UP0c1Z6cb7SQ/TVGOaquEyU9+I4Aoph1E/EaQA9A7PQUPLSeBADDwJjGEQswXrx4hodvPcTglmlcHL7ytW9g6QiPH/4SBiI8efIpPvnoD3G0PIULHqvlMfwR4/Z2DXaEgVy8PtBHE87bb7+NTRgBopgDh2NCuGEYgMBYLJYYhl7ClDncyUZPRF8H8O8C+IcA3hfizcwfE9EXUrGvAPiReu2D9MwQeiL6FoBvAcDRyTFWqwHHxytcXr7EsFjCe4/333sbp0cLbLYb8DThxatznD14C8enJwClpRGmTA/K4ajIYaO9EwDDZgqsth6hkkTSIohahA7PE9ldiDwDzqX4c3VMGkXyB6JzUPU7tqEWa2l7hxddCLjBO15gINJywVEYAJQBUW9GLdHraJV5GgWjaKudY+nfNJdyuYxFvsKPAMI0M6dRWJT2qIyLC21CK8wi8hLRMDQjmJCl6j0wJ9jV79pUptaCnJpNCKm2hcC7VMaao0zd5vNM3K3OKnRwblRdIoUi1gU37UOaR9Lo3httItvOa9OPaLftcFyJi2Og2kO5V6UFE2EW/w6aIRvNq+AwKW19xASvzH2mMtRzPe9eoKiFMXGkK0C023sAYcQ4xsgYdowBE54++wDjzTkwTcB4hsdf/CYm3iI4wur4Eb76jX8bfLsGAfjqg7dx9ughXn7wQ4zDEovBwbkJzjmsVksEDgjThHGcsPBRQ3TwuDw/R0DAanUM51yKSHTYbjefD6EnogcA/hcA/wUzn+/wiLd+mO0gZv42gG8DwMO33+JhcHCOsB03uLpZ49G7S3z0wQd4ePY+nr54geVqhXBzi4txg9XqixiGJQAGkQeHCY4cAm8TKfYIQYg7RUnCSKlCTAiePAKzjWDJKwlg9rlHLLk+mMFhjNJ8iOFpLqvsMEQesLbYvOC1ZM4iCffBUcRjam4qJUWCYI32bErGLy25S0WlmmcVEWvhhiK166cC9VtCJEp+I2Wi0QzJaVRbTEQ/10RI9aRJCHtrV0nK5VSWarcUYagEY6ZQ9A0hJJ8RVwS0aTaMZRxbXm5musOzmqdLO3n6W+Gv/Uibeey4ifzpQPdGLy/ikdIKtJ+joSH0ZsmTL9qnnhQXo/WyllZRc8vIG7W7CRymaGpiApij1d2NuLh4jtViiH5CYrx4/gE2589x/eolwmbE9dEZzk4eYXn2CM4tU2seRw8eRHMLlnj08G1Mj17i+mXANK0xIIa4OucwcoCjASBC2E5gBCyPVnABuB1vEcYp+jccp1uyJvjPOk0xES0Qifz/yMz/a3r8YzHJENGXAHyann8A4Gvq9a8C+GhX/cyMEIDzV9dYjwFTINxsRjx+eISnTz8FeIvn50+wvdni0ftfhsMIwhLEwDSOGLcjjo4XQGA4NyBM67SoimLulJMzOk4SaeQJg/PQvqXiRIqSt/gFxiQoOnIx4oMRN7RzyEekhbBqSdBIGEKeojoIJGku30XXOYjTGrhMy0NV6iC7UcRNSZEub0K9AVVzvahEJAe5K4MYDFEItiw4j9fsd9VgYFeIqDkxLNSWxOJl9Xw1Di322ZPztdYnZyl0FBSFwsIDPBLPN/TPibpPAUC8Nu4u6QsstGPxLUFuvNZjZOo1MRHZJKNa+tVrT0wpbem4D5zoJct9HF3Q3pRAlB2dLXCpTuhoJJZ1yHlMyrmGRshrQ5MJ04jBA2HcgsdrbDc3+PiTj3Fz+xxhugWI4TDCeWCzvsF2vcb68gpvnTzCdEt48emP8MWTtxAoSupjAOCiv4ECQBPw6uUrjOtrOAoYQBgWDsyEMcVyeDh4OIxjzGmzXC6iAJyk/XG8xTRu8eDBA9ysP8PwyhRF898D+D1m/m/VT78N4C8C+I309++q579GRL+F6IR9tcs+Hwd4wtMnT3H21iNMtwHX6w0ePngbX3z8Pk5OHuAHf/g9bNZXGIYVnnzyr3B2ssKjRx7jeIvzF0/x/PlzPH73bbzz1iNwCPjgwx/hy1/+Cn740Yd4993HePTobZDz8PC4urrAen2dufvR8Qn88giLYYWb9S02I+Pk6BS0WObNjOTYcq5IowMNYIzgRPjj82Q2cUPKQxEXmA67DCHAOYd4BiUaS7LwSN5s3NoGHUlIOk1K9ve6jdk8draZ1nTy9XnaDKJpiqMZTkDaKATAFRMM6fwvlSQ1I3zK9KHjRHQIng6TjNFQbAi+PWsh5is01Xhji56dhkxV5n6qscg5cqqr9szht1w4/gHDmThY9SKsRBvTLtTmBQYlohcyAYv1BQLiQiqmOgcHsMuWvtmh4Ez4age+5Q/azJNPV5CSxuGN+WYXOIQoqYaQfW3OOcBRrIejZ4PDiIV3mEJAIAdGjDnX/Bycbq8KIzwBYdyAQ0Bggl8MWPgFpsBR+vUDGNs0P7FH8bNDcCFpj2k/D0AYL/HBD/8At9evcHn5BNvNGsdLwmoAPCZsN7dRANg6rELAagnQYoQLl8AIrC+f4dRtcb2+xnY7YbNZg73DsyefYjkELLDFyYpxeTvB0YhxisJMCAGBUlQhObAPWAxD1PCSReL06BjOO1xf3+D58wucv9qmm6cOg0Mk+j8J4D8D8M+I6J+mZ/8VIoH/O0T0VwD8EMCfS7/9DmJo5fcQwyv/8iGIOARsrq9BfonlYsBqtcCTjz7Cz3/950HTGks3gqcJSzicLIGBb/D0+cf40Q++i3G7xnR1jJfDAn7wuF5f45m7xOWrV7h89RH4a9/A2ekZrrcjvvOd7yBst/BuivGoqyXeeecLeO/9r+DJj5/idhvw5S99DSv/EM4fpZ1BZcdwVMp4GjFOa3g/ABwvBJ7CGK/38iO8G1QGy7KZBgI4TIBsFKmToqw7BQm7dE3iLe847IiOaaS/DQ1ncxz3ucrcel9DV7IzREo5ElV9rTwlWu5mSo5bgnWgGwIpTkRFjLj6vWHrEN+LNzb1tmmqOJ1190rIJMK2WUdmJp1QWw02BXasxxzqZAco4hr7GCOYgkTZkwPgUS6dTu0ZCx5BHPTG6EVd1IyzMrvzjRRftMiuxqIPFTJHG/Q0AozoVCSPgQI8YojiuLnFy5fP8M67j+H8Mhki45gwpdBTmsCBolM3jHjx6ce4vryA9x7eL/HFr/4c/LAEIWDiKa2haHufmOHhEcYJw8AIUxS6HAK2V5d48vT7ePrJD+BpjfXVK3jPcGEJGiPTceOIcYrhtYsh7t+j1YCwHYHxGuP6FV48+SEuL25wfXOD8XaDh2enuHjyFGdvrXCzvcS0XsO7KR36jpeMRC0kjqFzyZQzjtiGETzGNCDjOIICwXnC6ekprm9ucHx83B731lS8CalBHz464z/+J34Zt+MWm23AcjjCsDzG7WbCV7/2JZyfv8Tt5hpnZ2cI44i3330PR8enePnyObaby2ijdwGD99jebgFHmMAYA4MDYbk8RhgB7z3GcYvBEZwL2NzeJsliwLA6wrBY4er6FowB/96//ycR6ARYnCBMDO8XYJ7A04jBe9xcXYAx4g++/1384i/9Ej7+6EO8/8Uv4fe/9118+ctfx1vvPAZjQN5kCYgI0zQCDnB+ABgp5znPJCQbBgmAkZK9xWejIRTtzSYEMLi23XYWTwxrzjF1aXU+KLVZpwnIuKs+G76h2ktUrVbRs203aAnSmmaKtUY88doZK1IuwCo23meG0zaJaf9HiU9XBI+GMka8LY5ZbQbIEVttZrhrv8XQzcqfQ/o+Jzn5OSC45JGRe3chqSVi1FMOGAiUe0NsupOYqTYJzbW7+FwGQJvmyg+eGz4oFFOYELPN7QbjNIE5YDms4AfGMAwY4MDbDf7Vd7+DgUaQG/C1b3wTnHKzTxxxiwJTlMw3N+d49exTPPvoAyw9wRPh7cePsd5s8aWf+3kMqxMEWoDZIxAwurRMNzd49ewpbm5f4eGDM5ycnmDpGT/4/nfw4uVHcLwBsMGQEg0OAHgcMW5jcMcYtpi2G6yOlpDTy4QJgz+CX5zA+2N453F5cQU3HMG7mLp4cITlKiqvYZpwe7tNJmTA+wHjFBA4YLk4wupogWkccf7qJYjjjVKAg6Powzw6PsZmcwvnPP63//3//sfM/Me7iyrBG3Ey1jvCOF5ju17jnXcfY/ALvHjxDIMb8OrJh1iuljg+OcJ4e4mAEa9ebnH+irBcAgvPmDBhCMBq4TAMjNtxg8WwwPPzc3i/wM31GsPSg52D8wFL7+AYODr26XTliKv1M2Dr8WB5BIbH//MP/0+4xQOsmfDL/84fw5MXL3F9dYPLi1c4OjrC4EZcX1/h+voC3/nnT/Dy5XOA/3WsLz/Fv/hnH+KXf/lP4OzhF4FhwDgV6Q8U4/vJOQQeASYsvMc0jSB4MI0IIdr4rIMqbt0c80OA74Rztoi+dROoWONGXLszG14zAq0VzGvX5hWvCH3gqLrP8FLRM7q9LNFDE9Lq3RxtkST7mRdQJKQhtcG5pR6p9ZhL4dphGlDmUacq0PbszJB0FEnlrC1dqM1RVN0jOyUzH+P500/w5Mef4Bd/8d+CxxLT5ODdgDFR78DR6h54woJcZKKE7PMJItnmaKyEVUdYYJ5HhRkNJNvr1HNuZ2oNgSM+KmLJIR06GxlEAZ/++ANM63OcnBxju13j4vkLnL33xSgjTRT3OIABAQOAixcv4NdrvHf2/1P3pj+SZFl23+9ttrh7bLlU1to90z3TJIdDQkNRC0AKAxAQBArQHzwfBJDUSGBTotij6dl6qepaMqtyjQhfzM3eqg/3mbtHZuR081uNAVURGe5ubu5mdt+9555z7ooSPZSMv33Bdrfll2+eyn2H5uzyCb//h/8ElRTKKF68/po3L54ybm+Yli2LriVGT/J7FiaRU4akq6ZAUXwkpECYIjHG+nVKRh+8J8XI5dU5KkuVZdQoI23zIIwb55h2Gz76wQ/IBMZxRFmFCgVjFCkVQvDEXIgxMk0Tfmq4OD+rlYfCT56zszOss/SlwVpL2ziC9/d82/dv34tArxScnzusCVyeWYxx9O0V+ymw6FuGYSD4QsoBSJhebvBhu0eVTOc62ralpAlItFaoVpcXPfshstntYARnLedXS0qJxJKxRaGKJZeALZlp3OFcpnM9Q9yR455l2/Hrv/w/SVFORFYwDZlF5xiGHbvtBnJPiYHf/OK/YK3FmZZf/Pz/4F/9T/+W9e1AQDIaYwy3t9c8e/aM5WJJ03b8wY9/Qi6aaT/SL1aEVLDaQJZS9SCHn2+m04bhaSZ8WvPfSZAr5POWfcEBmjjlFs+/nGR5d4PUe35/+/Vw15FSnUxouuO5U9/8Dn+7ZrBFFg4Rs/CWX8Dc2ptT1LfgAy3Arixm8Z1ju/uv+zH6w0JxB9m6+7pjU/nuX+fPcd9+3+eyedz16fk1qJyJwy2bV99Qxg1f/+r/48d/+M8wtmUMXqpCtLBFdMbmiZubVzz/7juefPCERx98hI/gXEdIJ1XdTB88/VruNM3vbrKonRxbudOBls/wTkNAfhiUUNWNlT5XyTgjgddQ+PqLX7Ndv4IU2Q4bVosrpuA51wm0kwotCWRqSwS/Z/PmBY3OTMMWpwshRhQB0iD2vUpkTt/evCKN13z6gx/RLxe8/PoXqOIp0y1DUEwoUglYpfHTgFYaowyxKEiRnCKxJCiZVluMNnR9S2MKxoFqW5IP1cPGgLakWHn/JaITXCw6wjQQk8dZS8yF1lliPQchBGKtkJ2z+Bh5fXNTh7QXlNX4KANKnHM0zuH9hO3+gY0STCkRwsjFxQqlMpvttVzkxuDjQCyeVIfntp2htYaQIsooOteTUmIMI0eoy+LjyHazZ7/PeC/loFm1aN2Q0gDSpsHpJEKFnGisFjMipNFTSiRMI12rSDmSYkBr6FvD5voabTRWJ0r0JD+hG03OlsZJufeff/pnXL/eMfpI1zWkFGmXLY2z7MaXvJ48r5/9AttaxjHx0Uc/5Pf/8I948XrD48cf18akImZQRsl9WGpuWstuRc0GTyLSqUR+vvVOm64i+qrf1SGrPsr6C8eM9S4d6YS5dG+QOnnqPcrJUsrd45h7AqpIQV7tE+anKNSxh3C6xjCDN+r43Dvxs3BIaAvvrEmJUj/kjHTL9o6wqWjueE/rOnWqJqcHemUpHBbUOQDeH8+5K6o7YuXHhq499De0VpiceXn9gjissSqzub3m22++5slnP2Y+q5pEIpD2a7789S8I+xtyznyzfsXti2f83j/6Y2KQRh9axEtyuej3qrZVhQjnHlIpGbQ0TlNJ2Hq9pZIO39vbFds8o8BqQ8mKZAy2no+cIkZDZzK7zUvCuKN1luXiHNc6zs5WrG+vefDgQxlAoyIvn33J8OYVjS7EcYNtHEoVJu+PDeOQUDlTVAKd6cisX33FV/6G1XJBr0dur9+gE2irpLIeR6LSWFUXpJKxyoDTaNcy90mstagseVSOmnE9kgn0TYu1ltXqjPPzS4zWaC2sutmmYLfdsZsgxUJJiVwSU4hobdDO4jKEGKQC05HdsMMaS0LumWn02GVP2zScna1YrT7kiy+/uPfc3bd9LwK9bFLCGGNo2462aVgPe8ZxEmUYhZQCKSp240jbNPgp4oyTaSsatDaM+z2rxQVt2+J9IoQIWNCKEALBB3a7DYtFhyJScm0KWUNREGMi+T2lKHIuZCLTtD+wZGIObK/3WGNo2pbtbsM0jTRNAzFjWlApYAyM22sWXYPf79jdXst77vc0pgNdsE0mlwm8psHw4utf8t03X2CaFZeLBtdfAFJ1HHnnc/hToNKh1/Zet8h7/6ZAzVnw/IwTbowSqOWw88N2KnA5OXP3vLe6w6S5e57v+12C91EqP3uLvz2Q5s6e7nZKT/4+Z6CQ1XGQ9fETauahNXfNQv/+1WsO8oBYX2g5E/lkz+pk4bz/85/uTx091A/N33z87DES457h9jUxTpUd0nBz84bzxx/SdOcoMlplnj/7NW+++xqVR1xVi8eQ2ayv+dXf/Q0/+eP/hlC9bYoqpBPc/t7v9i0oUBVFjgGlNaYoUvYYaw4aiuN2AtnNBVspVSQgdicqZ3QRYdB+fc3V+YLLT5+w26whaVzTEH1AN4YUg7zGFqZhw7C9ZusHWq0Zg6KxjhAjbefYDqPEihxxTQMpQkq0VpGHNZthQwiBNoOxlpQihszSNThnUbUy7LqezomXjHMWo1U1HSsoA41rsLallMw0yRQoYwzaaHLF2pVSGCO4es6ZRd8Rnwd2aS+LbQZrLCFIbNPKYq2TpisQWJ2rcgAAIABJREFUQ2TcDZyfn5NKJsWAUuLddHV1SVbQdr97M/Z7EegLGWOM0KlywraGQiYETyGhtCL6SEqJ3W4nK6t1rFarqhQTdzeArldQMiEkSfgU7KcR5xwpJbZbTYoFP2XBIVUECn1nMcYQYjowW2LMxJwPOK/EPoVVlhQj+2EgTIEQAs44jHPoDIUIpRDGPTFI3rTqDMveYloDekJlRWOEuV7KSFEWbRKvXt/gukt+/rM/5/Lhp1w9eszDJ5/hU822q/mSelsl+zt4gRyfrA7BRQzZ6gvqPoQaWT10TnZ7yvL5+3k5x4BXD/rkgXcPczYLo3Bws5iXtHIf7luRm9Newt24PGP73JnHqw/00VJbluVOX+HU3/6w/L3VHD8c24FnXxe/clKRvPU57wbT02M+2h0fF4r6HCV+K37csN3eoOKE0QZDwfuJzc0bPv54iY+RxmmuXz8j+y26eIEZUyFGg2p69uNAihFl7SFD1xUCvHup3CUNnHBmJPhMnu1mQ9O2uL5B6/YeCO9toZU+/E1RyGGPSoFvv/6cVdfy+uVT3rz4jutFj9UWa1qerBbsdtfo3GMvLzEGhu01N6+eEsctcdzSLnuUtmJxbgVCaxsDWFLIpGlAlUzbNPRNQ2OkjxMwNP2CRdexXC6wVeeoteb29WtUgcvLK1zXVeWp+NwopcilSLZuxGU2RsX58qLeR4mco3zaJHVSSgntHKqADwHvPdthR0qZWApTyKSUhXKtDNaJP73Tmr5riVrRNI5pmjCqEP3EqDLDsAXg+s0bftftexHoZRVtuLm+wWhpkCaV64csUs0YTW97tutbFr3Igb2XTFqFSt2KEa01Uw4HPHGx7Bj2ksmXWkpNfuR2s8VYRd/0xORZLtp6JArvAyUrlDJCwVIcOORGa1zTYotlP+wwxrLfj1KCaUtIkcZoYqVubXe3dK5neb7EGoPVmlwgl0iMhVgKOUec7aXLrzPT/oY3ePbDjpcvvuLx9Us+/uwf0/TnIiaJCWv0qZ3Ke7PGd7/sGfc+4CnHjPdAIT2W4XfdJE/hoeNmDs89bVyeQiJ3z/a7v9XArOczUP+iBLd8O+s8JMDvgdrvrjHHWb6nNhEHZetdPuNxH7UPcFopvQ/ZP77piU/9u3D/u8dWynFVu/N0TSmScLy5eU2MgU5B8BMKy6MPPuLybCUZdsm8fvlcMmCtIUGImRgzGIcxVkRbdZHTStodBwHTW6rtGYrKnMJ9Eox8GNhsbnnYPSJ6T2MbMEaYJ0oKIv2W1kKpgjEWP404DUTPt19/wXD7CrfqIYwsWoNOE61zbPcb3rz8joxmvFGcLTrOz88I4w4/DrRGPIC0KqTsQTu0KuScCNHjjIYIKWdSCFhr6F3DctFzcXHGNAVa62i0xjlbv4caK548lpmtugGjyDkdrhVjpFWvtbD3Jh/IOTGGINx3IkrJsKSUqicNpdKi5ftoGotCWHejn9C0Mjshy2fROIzRwvIjo7VQOE2F0YxROCdMPecsq8Xy7avwvdv3ItCjYL8f6bpOGp6MGONwrqXkQkyZruvw3vPwwQNyjPIFFsU4jFhb56nmQgxi+WmdoW1bxjHRL3pubm7o+wWgyAn6bolShaI0i+WS/eiFC18gpkyKM+6qiRGsqX6VVRqtFayWSybvaVwjSsNChVUsez/htOXRoyvIBdsorCnkPOK9h1RmyB1tQZWA1ZYnj87Jyla+7kTJgS9/8f/y+S//jn/zP/9vKLPCaSvZwvvUiffIyd//3Z8E3gOycaqWfRtgke2OYOgeVsb7KPD3D5iAY4P19DXv4vP3/LPuON15ghjPlcO8z9PjlEOYLYFPPv+dr23mqZ+8x5y8w3tLGnX3yOu+TqqCkwc0+WQi1eliK4wrlwo6RhqlmPxEyXDWdSx7x/r1K/buhsurK5599TkqTCgiKWRuhz0xZc5WHX234JMf/RhrLSFlqEKhqgaBOws5lJzrkPhyaIqP055l13D76gVh2lPCCtN0kjipagR2gBaPPRZpJmZKCCgir198R1i/4dsvf00ctqxd4fJshdMJozTr9Wsmn+g6R79YMYwDebhBLxRvvv2SRiecUUSt0CSMNUDEIBTGED1ow9XZivOPPqDvGhpr0UbOizOOvrMCReWIKbH2omQximhKETacyQZTh9PP4kSlFNMUSGmklFLvQQMqy8JslLhNOgconFGHKrJpWmIMPH/+HJMzJhUuLnq0dQzDgPeRlCKLrqXkiLVyj1uVafsWpQuLxVIWJ5Xw+4G76dbfv30vAr1SCucaurZBaUXKhRgz4yiCpJxhtxNc3MeCjxPGWfq+R5OZxonWOlIUMoGAMQCKECWLf/LBE6xzhDBS0Ew+gMpcXpxRSsTahpykMlDKVhhJcE6lJkrTHN0rkWaUNfIXqxXWykWulIJEvUgS1XyDnDy6aclJ9hFixrUNCgixntgYkVrSY7QmZ4/KisvecrPb8Pr55zx68nuQHdYuZ2ebmpm9B7p5d6zQncz7vvmwM0XwTkg+0BnffYvjW51yr095+6eCqXtfefLzJCiePHIX874HP3+7EagU6kRpOsNuANkcEvC7i9Od1x+x/uPn4zAG8Z2eyAEKuUeDcCpQu9uZFUokxwVAHeMkxsLN9UtGv8ePgZISIb9iHCcWqxXBe559/TnGQvB7+q5hiBUGMJrGaEryPP3yNyxu13zygx+RSZSciX6g7/tKWjwe51wtqcMXAITIPgzs1q+xpkFFz+vthk+XC9GWUASfzkngtJlfr4rAo2Viu73h269/xXR7jUoeZzKtMXJfgDBQVMZaxXZ9QwgeP4x8/nd/ybdfLZimARUmtLI8vjxn0TWin6kGYFYrPm07Fm2HM06ICqrSOKsNhbOio6lcFmaDjZLlntYoQeCwokSu966QRcJhpKJSVnoTRdjZympyqhbqRRZSrcVHCwQyLDnTW8f5coVzjq7raLoF1jUU4Msvv2K9XXO+XACZxlgxPFv0tNbhGoNtGnLKDNstu+2a7e363WvtPdv3ItBTQBvFersRto02+MljjIOsGLcDIUX6fkEuBeccMWSCkaaH0o6YNeMk/445kbMixj0lgTGOrmslyGnLsrekUlf5pmHY7tGN0JpCTLRtLxmQF1VayZCiAivXvbYWrYVueXZ2xjQLr8zsUSENMoGPLKhYMb1YZcsWZQsxJFLKKKtJWS64EL1kR8YIOpgVWhc6V/irn/0U3fycpjvnT/7lv6JZrKhFngzoLro6bp5uCdDoLHxvEZ7c73pfZuz+sExy+Pn2gOt7zbQq3ABvK1Xfh6XPf5w1kMc5oTPfRgJ84q5rzbsNz3vbAEq9Zd45Y/eqwuDqrfLgraj/1kJwSqV852NUts28OL3FxKz7OrUemBcjYfbMw1BUDQpaZVIcyCnQOUMOmqbrSEkYMMnvaZ2jaRq22zUxRnwwUBQlFVarBcooLi8veb3Z8+LbZ3zw5EMaZ7m9ecPf/s3f8s/+6R/RrVYorPC3x4m262hXK2KW7DSGSJy2DJvXpGlLLIVnX25pzy6I455m6dAlUmLBGWnN6lIO+gKlAm9eP+P5d88Yt9eQ9qTocVphjMCQu2GPa1q0lgFCpUT8bkPyCZJi7+U1xmgaZemMZdl0LFeLynDR1UpErAOMUlCq/1QRhg9kaXyWzDzUpKh6vMaRc0EZqcpjkvsQqqI8CwRjNHQ1NpAzMSVyyVV0Kb5IB2HjSY86l0zJkUW34Cc/+gnGGjKFGAuZwrAf6JctHzz5EReXFzTaMVWOvKn2xSF5SkyivEdsE1aLf2DN2Jwz+2ECFFlp/H5EKU3jWoKPlKxwuiUlyX7BYIzh1es3tG1LLoWuafDRY609BFRrLZvNhvPzSyiOEAI5F5EYp0CuEIxSipKrYm2c6LplbdRC23QH1DjlgLW6dtMTxlhhBCjpIyituNmuqy+FoukaaarkSMkCJfiQiAmUtqRUmKaJVvWEXEgRVInE5Gn7HqOtNJOHLSiDVo5xc804DPxf/+7P+ORHf8CnP/h9ludXpCSLij6YpR3pfhpA5ZpYljuUuruDuevPmonMN+uxIXq3MXnYDlVD7Wy99dzT7c7Eo4Oy1HA3mz8GRAn+9+Mkp5n93Xh9emzvKoLvDps/3cd9q8X92/tRM4GgTvclWa189jvfitZ1kcuY2g9QGJQpqBJ5+eI7YppoKbSNTFAuquD3W/weSkycPTxDlUjjHCUK39tomPYDKQW+/vzXPPjwEy4fXdHZwnfPvuJXv/wlZ63li7/+GR998inWWL744guUNRht+OHv/ZjLRx+h0YRxz7R9RZp2lLRFKU1OEZV6dAmYtCelxGbzhucvvhUIxU988tFHNFYzjhtePP+O4EdUGAhhRBvQ1hBLJFaF6DTupaqOkRAClMKy6ehtS9e1dN0Fq+WSbtHRNS1KidDSai3qWmNAC5ySfJBvutTvHvFyV0od7D1m/Jt5UTBQqk1CLoICzFRtXZCKPWfUrLE0wrRJdYALyghcmOvxI324ko/W1o11aGOrOVlktj6wSvPJo48kCZwSk0qHQJ9TPsBGGDDKsuwbWtdxj6XVe7fvRaAviDeTYl7RDTkrVDHEaWLaB7HsjAqtLCkKPm9dg7GO3XqNc5YHjx7hfaDLmZubG0IINI3DOUUuEaULbWMpxaBCJuVITBPOGUIQvM5Yg/cTKUPjWkA8MZQqWMMhU8/15MtJFYxymvbiE71aopXGGs083MFYQ04F7wPTlFEmkpM0zQpe/DxixuhCChmztHUupdA8GyPsal0C2Ufe3Fzz6uUzvvr1L/g3/8v/iut6coykrLFaRDSycCIcbQUzpXIOQuK0eGSNzA1sfRLWFTCzc+4MRL5neMcpQ+Y0pJ0GvVMb4ll1qlU5LjKH12RKvXnq0Z5cL3PFod75G/V4j8f57t3w3q7FfZ7G7+UX/Zbex+Hx07GDMzPn8AiFhCKQpwDaYEytbFJiHHaigNwN5CjNPqMNJWWhCTqH3w+MfiJMiaaxLLuGMYQaqBSb9YYffvKY86tH7MYdr55/gykT07BDKfjmN5/TdT1GBVKcSFrzq1/8nH/aGobdjt1ux831cywRkq/XvmN98x1Ofcbr777jN7/5gnHcCeVSQUmRv335G5qmwRmFc4Y4DuQ4cnV+QSmJ9XYLGfqmk+aiLkQfaY3mvFvSNA1PPnhC1zUyIJuCq5RHpXQ1cyu0jVgReC/HJj5SpRqdzWNgVG1KH89LninK5fRMCTPHNk4UBkV6FpIMZbSR+HDojRktne0sjJycxWHSh4jRmqOdhSKWTPaJGL1UDTGSszAJ4zxUSSlJ2LTc91LZaWSMqhEUWIu1sTMOzG+7Bo/b9yLQA4QkdsH7weN9oHEtuzwA4koXfKJftYiWTuyIz9pe8LkLSywRP0XJijI8ePBAPGWAFy+e8/DhJQDW9SgKXeOYYsZPAxSFcw5rJbvXtsEWRc6SaRgtjRnrNDEJA1kmtWtKiTjn0FozbtesFgtQquJ7tRdQiowYK9B0PTGNB1aK957z1ZkERmXYbXfkEuhjIk1SlayWZ5RUGDY71rc7un7BonOEmHjz4ht+9p/+A4vVipwVX331lAePn/Av/uW/lsokpZoppUp5rNYKStd+BIe/HVg1B/+U46bUXTjmDrvzxCZ5JpKcPpyr98pda2JOYuXRN2cO6ifUa04rFO789tsv9Pv92u9Px+9rbr8vaXo/t18SAa1tXThL5VJL9qayZJFaK7JSGJXZXL/h13/7V6QCf/Lf/48CJQDn55ekcc3r9e3B1C7HjLUG5wxKF6ZhL6Ka6uhojWNpLdoKAm3tiufPv2YcB263O6btLau+oXiND55x2LK+eUPTapbLBWSNxfLLv/kv5BwxVuNUxhRRnJeiBD6wkZ//5U8Zx4Fxv8Now9lyQVOdWdc31zx6cM6ybXj+/BULp7l6/AHWGqZxQpUGmekk16FRjrOrK87PVlyszlHW0FTuelHCNjFGY61AmjnKAJvNsD9cazKfV8mIRD1XVjKTICtFTpFS7zyBiKVZmhGGzaxUpWQabapbrcQbQfQzJSuKVhUZUEhLXc57SCL8s66t18IxASkR8fo5aILSIUZoexwgpOtxl5xR1qJKqV5YNQmol2hK+QAX/i7b9yLQK6SUKoUKvSSU0mirublZ07Yty+WCEJMYTxWqpaeRGyYHtDbEVJ39cmLR9ux2gRCOJZD8Uog5Cg2sllDGOrkYSuWO59mhUsrqUrSMO8yKlBNGC36ZazmYkgTRi/MVSgm0IzemxvuJTKHBCJaoLM46lDGYTqbBN86hXUPwnrDeoJUmZcn+U8rQGmYrhHHaY53DWYWxmTLuuX7xjNvXmv0U0cry/OmX/HXT8yf/7X+HVS0xDJiUQBW2mx392QLrHBKYNdao6rXzHgx9Pk/3MXSA03B89y+yidd4eRcTf19f9tAElZvy7b7nfTn2/ZZtb0FMp+/xO253TL/e44/+9r5NUeQ4zTuAoqXRhzrOLlDSuEvTyNPPf0XYb8k58/r5U5588gM0hb5v+XYcmYJ4uVgt8nyjFEpbYkooxAGyWRi0URQELpj2owSZYvju26e8fPmSlARuJBYoMmDDagg5YBCaophwTUQvVGWnG7rGUoomTqPMf8iJsNvjugaVRs4WrSzMOUpQNoqrq3P6RmModFbRdg3OaMI4YFAsWkspwoQ5W57RtUu6psUah62WzM7KlaO1OWg8YkyUIg3SMkMsNfM+QI31WpsTjJnRpVSdOq1rolMKKQsRQ2sjwVCBVWJoRsX49cG8DCgyO6PkTEaTcpLYQYWAkfu05CwLw3xslGroJm8i0NHdQH3wT8qZlKWpa7VBqbl9rOq9UXtm/xXX8fci0BcUMSQpcUuhbVtRm2nDxcUFymjGaSIG0R+XXBin8UAD9JMXjrFStG1LyoHlsq8BV/Pw4SMonoNUvSRilKaL7NAw7EYKwv6ZGzuNtRgZ905KuV5gHtByw+aCMVrKNT9hnWK5XBB9YEoeax2TjyhjKUpMy4ie4D39YolW0sAaxomw3QuVtJo8rddbaawhzKGSClLVFXL2BB84f3TJ2apHClENFvbDBpLlb3/2n1k1jp/80R+z3W742X/6c84vzkgx8+DhEy4fPGJ5cYE1nUBcbVeVwHC3efoWTeT+EyiPzgXBwUBr3o5CpTsGYHPcfDsWz3B9ZX7MKtZ5m0VKp9WBfR++fp/l8unM1TvUx3efe3cAx8lCd6e3cbq/2o+JAzll3ly/JEfNp59+ynqzpWlWNI2r04ESz1885fWLpzgtVte//Mu/5OMPPqKUzHa34fWbVwQ/CRynpKIssWCMlP9FKXIJvNxsePzooeg9cjpAFzkFtGlIfgKVyTkyJYkWwzhx1q84P79gddZhWsu43zKGAVUgZE/0htEZVsulLDKNsMI6Z/j400/45ulXDKOvDDnRCfddy6rvyD7ic2DRWXIORF8wSiClqweXWOvo2o6uW0hgLhldm6jWSGI0+7Hr6l0kmXBNNCrDp5yek5pFxxRB146QEsRsnsikDfgpHTLoI25/kkTkmo2nglLxWH2ocqjaTPWIiLXaSll49LkauM3XU8oZcgWL6vi/g71E3UIUN1Sd5XoyupJglap9kVzNDtWhOtbmH1hGX4AYi3jNFKFBtU17sB0opdC4hpQHStY4Y2XVCx6lZZp607TsdjtiylgLNzc3h8AT44RWiaZpmPxYY4g+UOhKUWhjRbEWxT0yhIjYexgWiw6tjeCibVtxYwVFMQzDwXa4xzJNnil4wuSJecI5GQc2+sjZomPvA9a6E969MCoHPwKqNnBA+XxQ4aUknX+tNA8fPKJpHLbCAa4xNLYjxoTGss+RMO1RRfN3P/8peXrNMGy57DX4NSomtq8Gbl98TlKGhOGHP/oDPv7499Cqr7SyI1JuimKeJVvMqR1B4d3f6qtOIBpBi+YRjHex/cPrT7DTQwCtWREVWjp9F13uCd7vhdLvy+jvf/Ldd5EqwSTp7ZQKs1AUWRVUOY6zK6p2RCrDY79+zeuX37Lbbkh+pGTF9vV3ZODjT35A/+AxJUSsK5g5EEZPDB6jHX6/Bm1pFDy8fMjT7ZYcvbimFi3XgzaCiTtFZy0lNfhhz2q1RFmLrnhvqvdTKjCMnq7tGONEjqIoXyzqzNIU2dzcMI4jfb9gs9lijGE/jLR9x6D9AS4BxersHL/39HaBzxFbDFYZrFaYDNEHcvAoCsvlCgX0bUPfraQXpgxN02KNRRct2bl22ArRoEplomXIBV8N2WZfp5TlWO5YRWepVE7n+hptKWQwilgCKSZUyAy7EWOMWAykUGnRAqeUubKv+5d4keWxfLx2fBTWTSpZRE+1cZpPjmvuDcwGfcZI3ynGeFdpro7TsVStKHTt/RljZEGv0NJcscxT0H6X7XsR6Cl11YNqzysYrTEa1zpKoZY9kpFb50A7UojkEtluNqyWZ3RdR0pHxk3TCJ8YIs52NK29w8ppm5ZxnNgPA9a6yowBn2U8obViqxBqc6ttOowVvjBF4CBFS4iKXBKNazHG4nJhn0Z2g6ftRFhVYpDyVmlaJ0wewfkMxmgWXYc1LdZ1jOOOxaI/dNu11jTGyEKUhZtfoA5fCMRYhDttwerCsF0TQuSsd/j9Ded9x7DZMo572kb0Bvtxou2XQObpr3/Oi2++5J//83+N7pYUK9hjYTY5zIgvumRXEpdPs9vjzyNz5277VP5f7rzuYET1PqxdHW/iu/H6npGH77u03keUPxz76eN1eaqfwyBCmllalKq3u6pCJ101CapCTaoUem14+vwZu90rwn4kTh4FhGmLdi3ffhNprUZpGSN3/fI7/LSDmCUIucjP/p//SIgFt+jZrN9gtCalzLgdWfS9VMCTZ9qPPD5/gDVQckDpQuMqTGQNMSZytgID5IxRmaurFVqdixHgNHBxLipzay3nqef2ds0nn3zKdjdwc3NN27QoZeja9gBbaG3p2g5rLZ3tOV+cMfqREAKT3wmanSKXV5cslkvcHOSUpe06SlH1e1FiHlbE64UilsaqKl2TqvbGWQJvzhmjNMqKGjdV1fpMFmD+D1GvzsZsUOmPoeCDRynF2flS4NcSBaJV8/xZjlqPihvmk/Rb1WZ6qo/nIswpSeJPzbAhFiFxFA22EgtSSocYhTk2dufeFrkchgRZrQ8CLBUCsUJIKDlf9yYx79m+F4FegeDWSgnjjESImeViBTWDUaVgrfB6c45YpfApoCqHfNrvRYCkFblKkFVdWTvX0piKbxaLsQ1OZcZpT2sdpjPC5kky8UXuYkvXtGitWbQ9MUZcK82hpu0gJYopBKVwVrwudIH9bqBpGzkZxhJ8JHaRxjXshi1OywjCnBXjODLPyzWuIZaItVok3+Ft73NwzshnQCZRlSwXe4oZ1zSkmDk7P6Nf9IfvwE8jq8UC5TREjWtbvJ948OCKs9UZ47jjZjNQlEKnNdpHSAuwlmwc5dCwqsrHQwZ1PH8zx77MJxMO4+7geD3OpfW85UNGcuroePrrEfI5fUDdA8fo91zzCVXFPHILCu/5HjiqHIVkqojZWcmpDthADLBcI00yyiHIV/8wUgqUVNjtNnz60Qd8/sVrohb+9CyKSn6kOztje/tKDPHixPWbl4RxwmjRELS2QSthX+1uXgtzy2qcbolTENgqJcZx4Gy1oO8t5Mjl+bLiugmUwnuh+XVdT1ECDTx+9ABNYthtCWFitTpDKxl1WLJwzdtGM+7XBB9wVtgtSikeP3xE13QSzLLI8nPK5C7hm4aCVMObYUPjLNZq2sYd2D/GGBonMGOYPNtxx7JfVUhKKNZKi91yqpYLMyRi6nVXUibkSPQjumbG1AEhQsIQCvEc5HM5GtrllFBa03VdHZpTaoYsWbRWilRkcKuu+guFZCulqGNsOFyz0h8syICjnJJYm9Rjn0eGliznI6u5B8gBLlLmOI5R+PFHyw2j5Hml5EoLn3U5tcopmaZp77/o79m+F4H+4EyZgnBNtdChSk6kKF7N5CIjBhsjhmUFHjy4ZLsd6JJjmEZW3RlaKZarJZMfiSXSOYftpFmUogQspQS/tMpRiBhnKMVgjcVoy3Y3kNOE1W3lzwIZwj5U3r3GaU3MkSkluqbBOKFl7sc9Pmc66+jOOoJPNM6xnybGYc9ytcK5hpACCVmIMhlVUs1S5ITruWGnKtUQIydYqwPPF21ISWZHxpTQVo6/aexBWp9z5Ob6FcM0EEOUXkYpTOPANGz54Wcf84/+4Ed4X/Dr52Ku5s5p+iXt8pyII9exh1ndVcfO2/sYKHJu39ru2kXKn+5l4nBoyipd4Yr5/bR656l3spsZWpvZLSnXXkrGWXcogWFulFVcVOkqWZfMy2jFi1ffsN/t0Cg+++GPKNVaMk4Dm/VtpfBaLs7O+Ju/+mtu3rxAI8pP5yxni4b9fiJNe1CWcbdFpcyz7S27YScKzyBLgdaGxmlC3GOU4dHlEu0Mr195fJiwFobhtg4WCTy4ekLOIzKEHBqrUFoaljFNdH3LYtEQYoISsQpaZ2nOV6TckUsmxIES5HoqCVCJzfqG0QcUCqcdOWvKNKGMo20alDaUWCR6qMKiddxubnHW8PjqAXP8tY3FKIuxhpKhpMzoJ26vb2hbGaChNXgf0MhiKg6yApFJ4M9EIEcJktZanBYiRE6ZmI5JwmzaPJMkxLxOemuz4ZxRQLVEOAwYn2cBawM5E7I6TDebufapNntLFU/FHGogliRkvpJ1bZyWIouXQDZZoFWlMa7OD1AH4O9oO1GkwjydMyFVWTh+Hq1omkbgrf+K7XsR6JVSNO0s9JFmW06e21sRKCljZcVUwkknJ9C2ypkL/aLFp8B+v8f7QN935Byl0+9aFAIH5XwU/1S7aJQSvD8n6crHDFY7shVhldG2ZhsGoyylyA2glZH3sA3OuHoxGZYMdwaAAAAgAElEQVT9ilwxuKIM3aoHo1hvdvT9Eq0dm/XAcrFAW1Npd0GYNoXqkyGbnuGTnPGTJ6UgnGKlSDEi8vM4M+ZROVUfbCgxEJLYGJs6i2o37DE6EIP4YS8WHcqAdQ2rxZmwDQiUdIu/vSVsrzH9iqa/QDU9UcmIxlTKQd4NcJ/s/2A1/M4jp1i7/MzvPql+/rkcf4t/P+P/79mvqmCLZD7ixzLtxcE0z57fSJCfPf0BUtxgbQOlMO133KzXfPvN55AiJWdcHnn8wYfs9lt+85tfsdtthKWSMtZopt2AwuKDp+lbbKVALntHcJoSCo+vzri5XuNInC07UvCsVkvI0HUN+2GNThFUIE4RpxoWvWHRdeLmWjSN1rTtgsKOcRw5O1vhWi3zRUk0tuXJk8coZbi8uuKLL77AnZ+htSLEUbJGlVFKk2I4KDdLFkvdpukI2xGrLIFE3y7Ybm6Bgp8cTdOiUcSUCEFIEM7aA3burKVr2zqjtX7XSpxC26YlpMh5uxIIIyRiDNLopKCtNGEpmqIhpQrjREkyrHM1iJaTPlupuHcNppbqkRVF0FQ9aKyr7DXm+bWJ2clUaY0qmlwUJWVh1sxVrK5MG6iBvoCaDcuoFgrC1DtQJMtMNZbFQVfVbMrSe5stJnIpovERwYt8Fq1pmgarDcnmA02c2oRtmubehOvv274fgR5QGEIMWCvUo5wzsUxY1Qr+iBjwK60YS8IZW2XKclKUghA9qRRCToRpQmkwq2XtUtcmTpT9NNYxxak25sRnO4bAuN+LM13KLJcrmrZlmkZKKhgrMmmnnbhb4kDnQ3ddIzawIoOOMHvdFEXXNpimZRw8foq0NtaMs6BtU8cN6opjF3LisF9VFPtJ/MibTsrKnNUdNkrJukr2pPsfQq5UtIw2BaMVTdMx7EQ3YIylsR2Tz3z59Fs+fQJt27NoO0xOOAsxbQm7ERUH3PIc6zqsbUhodEoo41DaEDKEmA6zLeWmEaxxhnwKtVkJx1ke+lgNVDYyJet7L+JSZmcfmH10DqrecsTiVRENi1HSkC9xpG07ttstl49kTKWuYpmiJctUJfL61StePf+KBxfn+DAxDgN+nCAGFJBC4PV3Twn7NSmPhP0anQO6ICZVqhG9hQKTFZBAJXGYRHosJRdu3rzGey/fkY7kPMmVXTLjMMpYu1qap+ixTrNonTAsSsvlxQUPL89x1vD026fk24AyUtYXJUGkZNhut2hn4VY81MmZmCPRTxKkChUvTljrUE5G482DqZ2pGT6F87MV52dnB4vsYbclRUlKtBG+udYKYy1925OBWIqcn0pbFj68qLJXqxVaW0Y/yvePTHlCCZxhCqRSaiKn51NMyZnJx5rMaKnWTpqvIlEole4MqkS53pRYkBstJkfyOqmOSy6kak1RsijkC4psJIBj6v0mXRpKmftFNdmQSSVSgaialChFqcH+YN2hpL92Ovs4l+rOikJX8VOZ91PJodaKLmGOcQfFu7o78eu3bd+LQD+vxiDNDm0k/motzU2tpKxzzslJbebMv3a9k4zYKiERpxEdZEWMMTLFwMK2tRkkWYJWGqsdniACFNcyjh6ykkDQOtbrG16/fEPf96xWC2yVPEMWLnq7kGPWGlLFAQv0izP85JmmkSkEEcVUbcDkxaWOXHG/mLDO4P1IKTWL8FItGGvEOiEVUkx1AZAEd78fsdZirKpSahFFOddWqmdgP+4BRBpeRGVqTItzwk5QSrHdjfhYGPaB25sdF8uOP/4n/7jqBwyttrg8EafANG3IrmdKirZb0PQLvvjqG5bnV3zy6e/JecyV/ppEpKK0xuiT7LvCUFI6V+xdqxr466JWm2u8jcuflLNa1cv2pPNVanmgAFUCpMibV8/Yr9d0bUP0ieBHPvr4U7GiVVLoJzIvXj7j81//HbZExvULqKV3SZCrytEqRUmwXt9ADiS/RxfQVhZxZx06Jfb7iYvlgnEcIOjqeioYuHWWrtOcn5+TKYx+z5vdhpxlgEfKic413K5vALh6cM75+YLb25vq6tgyjltS7nDKcn7WsZ+GGsQUymqcNvgY2O12pCGz2awxWsRRKEUIYjWitVw73keszdWaO2OMQ2vD2dmKYTuy7Be0bcfB5rlWnV3nBHKo2bIyFWoR/EG+28JcWwmTLUdSiDRNUzHvgtW6VsiKVKqiVWliDJhaJRQtDXA/+ZqVy5lOddD4TFoQOxKhYhpjRIikxGBw1r2kHOV4D9CoYlafS4UvGH+JAg2llKHSGJXSREHhZdGo1/BsRCdMGF2PB44W35LE5JywuoqdALHh1hirsVof4xtzIC8n/x2JKjlnjLlrB/3btu9FoE8pM/lAv+ikoRGrkrLyYK1WKGWrHLiIa6TSgEGrQKhNF9U6fJjIOeC6Dl0c2+2W5sygkcBonUMXw3o70FjLfhzIk2Q2bdtWGbM0qozR3NzcEmPi6uoStCaEjG06ximgtJFhFQXGaSIRIWvGcWQYJQPPY6DpNFZ3bLe3sqj4Hdv9TuiRyVB0EgpfVpQgvOjGyVCHXBcAihx/CBGMZRw91kj33WhDCoVd3FH5WXTNAjg2MkXwkpnGOCf+7HYDpvE8enxFnjzf3L7k8cNLHl5doMZC5yxGCVSAdmz3L2vVMnCzDrx69hXPvtZ0Tebi4hFKa6Z9xDlhcmQMJYt5lDrBJOcAPQ89EcxdcbQHKMfn1e2Q5Ks8p24HGA5mymWpk5k8m/VL9rcvZP5uyBQf2LzxTPsNy/aMz37wGWEKFDzFb9F5h4ribyT3ta7fvQyxSUl40d2iY9Eblq0lx1QnCk0s2wVt39Npg1Pw6NFjbKO5vb0V3LXRPHj8iL5rxb43J243hbTsWC6XfPbJxzjnePb0KT/5gx+yXC5BwbfPv+PZZsN62PD48WP8NPDZh48oKfLll1/S9D0la3xdXLeVUTbsRxl1aYx8VzkT6pALYxRWS3PUT4NoSqLnYrk60Ie7dsHF8pKu6TDGSSVsBGo0RhII2zWAZKYxVTM/c+Slq9rInp9TSv2ZEwqpKm31nCk5YBE6szKGRSMiphiDUJb9KB47qsdaGSKktRKaaKXzFitgp0FQAVOze+G2R4ylDluPpCi9gLlJD1Qv/fm6FBir5vHoSkowWh2u3bnynPHymJJcE1n6AiEGTF0ItS4VMRTmEEahsAcCgFG6YvASyGV0qlQmKSUKs3c+lBJJPlHeKxN8d/teBPqcC8MQaJrukKE65wRHtKBsh1bSbIkAJRMjoJLgXY1k/mhF3ztQGu893kecNTKUWIkqdV4EtbakYsixsPdeMgEto8O6tuf65g3GGPpuSUqJ9XorgwOqKEuacppYEn7yeO9pOkcI0hi1piXGSBgjPmZx2JwKZMHbrNXShNIO2ziZCB8zZf5JISUpCSfvD/RNaxy52hqAwDRFaXZ1jFrX9SglzbVcGRI5Z2ISTHG724qNsjXyGVJhfbOmxEijJKPb7z0qJ0owgr0qhXEZUzwxTVACm9evsGmHnwI/++n/zqc/+H2Kdrx4/pI/+Rf/A6vzh9JELkascOdsqd5AutTmmYKsJJvNSQa459ktUHHMWmpEjzFiq6uia+p3EGLVGwTCfuLp179m3L5m2m/wytLoVpKFXBg3N+xvbxmHG/y0R+nEfrjFxCRZdfDEAI1tq/pa6HWqJhchBLJTOGNxtiHnyKLtePLBY5wRIoEGUh3m3NYh2E3XglZMfuDNm2vWmzWUQmMdrdEs2pZpmri6uqCQpBekNUYXHlydszrrcG2DrqZlXddhjbg27vd7jK2Ort7jY6JKL0ipYIxiHMWTKdfKuWsN2Sc629E04iczL7zDbiC0AacacZBF9BzOCVQn3jOaGH0V7siwjG2dc7parbDOUkKU2c4FSFlG+ykZy6eVPgjXSsUljsFUHGz3o/QgQghopWg7S9doUo6UJGp420gIK0VcgyTTD/KcGjzngO19OmCFQtcsh16O1ubgO1+JxFWPVXs9iGiJMk+gE4jRoIhFEkU/TZjDIqeYVfUCNSqMFs0CgJqHihjDbreDygybOf0z115VRlIqgdn3v0bN6sf/u23fi0BfAKUdxi7Q1tdVNtNoS2MMOdYGWy2zwFJKwihTyybJ6GOIxODRVoyfxmnPavlQbgRjcLXJorFiz1Iyyjry5NGoOvv1DNcY+r6nlFQl0vJ+IRSMcWy3A8Zopp1QIIVCZpjGeBA4lQPmpyhJStnVsloPqAWkiC5aaFxFUxI426Oy4mZ7I9BCnWNrKxYuWKZ04p1rxFWv9ioUGmdbQohM03SnYSNjyiQL6vu+Dm4Rn52YE9F7Wm1ZXqwYx8SnH35G6zTjsGa7uSHlhI0T1jpaBTmNlLin1YFiIz4U9jcvGKeM3275i5/+ex48+oSPP/6M1cUDrGqqYVeDjyObm1tubm5YtT39aoEHhmHk4vICtzqn1KEq6iRjzylXFtbET//v/8if/umfktOELnC+WrKbNqjo+fLrX/Dym6+BSWiQaEqTyMaw307kIpL6OK5JMeAaLXqLIn5J4yB0OZpM1zvaxtG4QskwxYRzpjZxExdXK/rW8eBcxslNfkQXizEKbQVisg8vSWRu17fcrtfkktltt5QYOVsteXj1gEcPHpD8nu36luvNDa5pOD9fcX5+RsmeFPfCQouBcRx58fw7fvjDH/LowRW325Fpt8M10jDMsUBW1WtHhlGrooi+TmBT1YLBKS4uL1guF3Rdh7GGm5s3vHlzI0mWNoSc0dbVhqrGWoexhsl7nGsO2pdhGLi9vUFT+Oijj8jB4/0k0FfOR1hWgzMGXaGaSK2IUsaYgvcZrSMqSA/DOYOr/uxzph3DRClCw7ZG10pBHdTSRQnmVkohnngsaSWq1BgjKYWalVf/IAXGCoQSY6bESD7R7WgK5Cxam2Jqv0FGB4quQBIWZ2dCiap+9E72a4yo4K0jkbG1MpJ7cBKrchVrg/kIV4rXTl04tJRHudooWGPuEth+y/a9CPSSJXfs915YNkoy1cZWULra2BYluGIuYF1LCkKBU3DHeLBEKRPHcWSaJs6WZ8QYGcexZsma7XonNsAaMonLy6tDueWalgcPHsiAE+OQNb6QguwD5AKen4uW1T7FREwR5xRdLzhk1/eMU2K73eKsq3iplHRd1wv2VgSn3K13aCWZdgFi8JQi0u3GNcSY2K63dWFJUjKWjHwkXf177hqH6TpGLiVRBvd9jzXSdLq92TDGCWKiW3T4UPjNl9+w3W758MOHLDvH4vxchkBME2Pwh4vrfLlitVrx5ZdfQlRcLhecfXhFjIlvv3vOzXdfwrSl789AtbxZb+n6pdAcJxmssnYNt7eK680taM2wuWJxdsXV1SWLfkEmHDKYxjiyyvzFz/6CVa/583//Z5IBn1/w8ccfc319TfITu80alT37YVupeAofA2dnZ1ysWqxt2GwHhv0WRaKxPV3TkW3h+kYa1X6KaJ3QIUjSWRQGQ/KB1ipK8Li24cNHD6Ek0hRk1JsyNK0lRU/TNMJI0fDs6TfVCz0yToG+a/ns0x+z7BuBKUuUym3as16vsVZDSSz6JVcXD3n18prtekvJQg989fwVHzx8grUtw/YN0xSEOtqIdUijLZnCMAz1Wq0c8qbDKMXZ2RmPHj5k2f//1L3Zr63Zetb3G83XzmbN1eyudlWdU83xaTiAjS0bBdsQFBS4SAgxxIAUJVIk/oD8A8lFbnKVm0SJkJBCcmMSjIFYBClSgpvEQAwHG59jn7Zq195VtZvVzPbrRpeLd8y5T4KxKxKRTqZUWnvvmmuvuef8vjHe8b7P83tamYUp8XTEICeFqqw5P79k3syJPpwcqiE5bIxElZiG7lR9juOAUcj76HwWSrjT3OnYC0eLCiXEKCdRyOqUKA39JLBCCfGR2ZIQXKUtYrV4U2TRMPgps6/QMlTFZ/3765PgUQ9DAhVVVmIdq26dZ0KyoB9Z/ymrkF6vuceIxXBc9k8nBUOGI2iFtq+XU6VUXsuyECSHn9RFzaHv5T2fxpNyZ5qySSqfoJU6Ol/lBOSiFwWQ0cLmUa+zGz7L4wdioT8ONjabNSFE6rKgLit8XVGVRZ6Ek7UShuQdfppAaelZA6hEIEgVkURTbvPRLmoZEiojuYtjP2JsIoSRebtgHAdiCnkIE5imPca0ghwuNTHr/0pbYaw/UeimMaENDEPPNI00M1EchGQY3YTSBSGC8yOzZo42mr4f6A4d2mjmc5jNZ4DCu8Q0iSqiqisUimEY8X6iaWrKssA7T3fo0cYQ8k1vjBEVRz4SG6uZNa+zJL0L7PcbqrqgLMQrkO8wVqulVKrjSNcNdPsdu/0dh37Dq+vnvPX4HleXK87PVtR1jTaaruvYbre8vL3hwcMHvP35d7hbbxkOe7QCrQoKrXj04Jzl2YL9YWRyPQ/O5hSlxVjLnevoDh0+haxdFhXI3dhx8/JjnoTA1dVV7rYihi9EtWFDj3cDjB1+v+UQR567jkJZrIZ7y5YYS/yilVzN+VwYJ0lmO7vtloGIqHkVYRi47Q7Ml6LO0kXJwlT4GBiHIKKsCC5MEk6/7fmRP/QHWLYV0Q8UWqHLglDWTDayixplFB89e4YKnqQD680d86bl8uKS2WwmaI0EbvQnnIcYsRQqKJQy4BXT6LBac3V+zkcffEDbNpydLVmtVmAM+92B3WZLVbeE4NmtN6xWK3HGxkhdFIzjRFM2zOuWs/mSWTujruvvc/PK5VDamtWZpSorqrZBJ8PRjeoml9svr01u2iiqwrI/HLi7vmYxrylNTYruBOTS1qJDFJmvEta+z8ICHUTBZiXIQWYvKmLNEVntswxSGC94jweCEgmkLYuT7PDYcpJXLAur0UcPBdmElF93plIm/OkeyQ0cNBprZfDusx7g2HYhJgIJFT1WG8ZJhrrGGLkfQyJqaW+F4LCmgFywTTnLdpqkjRUA5xNKl2gV0DaRfBAENUf1Tjopw0xhUYHvO5HJzGAYps+8xv6+C71SqgZ+Gajy8/9mSuk/UUq9A/wccAH8U+DfTylNSqkK+O+AHwVugJ9NKX34+/2cozw+uMCYhIWxdRPzWUtZVUzTJIRFpSFmalwKpw/bGJFCOZfhQAm0lQu1qAqcDtndaKkVLOYLxtyySYWlLMs8jPH4yeP8hA8TKYBzEYWmbguKsqQfptw3CwyjYEfHyWPLY6BAxJiShHCyy6rMrJ7A5AOj9xRJ+pokxWazzxfV6wDrECMxy9/6vqesxAVXFMUpxSpG8qBQ2kvDMDCbzXIw8bGSErt91/U0TS0MoSSnD+cCKkk1NzlHSJHaWPphoh96ylr6ne1sxjjsGPqJcerZbLeEJP38EB1nyzndvmO73hB84m69pp7XHPYbru49JDpPnDpQkbKc8eDyjJ1VXG83BJxk4EapzUjSHtncvUAlzaxpGcOIm0bQmtAfqEpL3dSs2pLlcklVloKVVZqyLNDANIms0mfHpnOO9XrNdrvB+4h3AxfLJffuXVE3DT4lvvf0I6btSIzqhHDw3kulicJouDq/pMq5nWVdkoInGMU+OQat8EqBD7zYrLHeYy2sFkse3H8gFWJUouBBBpBTmDL/RJyOs9kcoTKWWCWo3uV8wQ994Yvcv3fFYjmnrhrqtsEWBZ///Lu8ePEK5x1+CozTyGHf4WPAFCXLdsZ8PqeqGuZNK9qmGLDanuTGIAE7jdWURYkLMifzQRRcwozRaKuy8oN8jUUsMG8rQXhYOWFKYzmhdJLhaEYgkARWBrK2+2MUIBIFGHOrRMLG1bFJTvo+s1yMnhA56c6PSA2VF3mtVTYdGREA6CSCCcgql6yQi983/4G8gMpCdFLFiDZUNnvRTL+WR2pL0paQFD4axlHmaDZA2zaZLpolorrCpSRZ0FPAOy/FR/CUVUlKgjfQSdy1CSCm46RAWlGk/LaKCi+q8H9zz/9+j89S0Y/An0wp7ZVSBfCrSqn/GfiPgf8ipfRzSqn/BviPgP86f71LKb2vlPqLwH8O/Ozv9QOUkp3XuSDcZueIQ6CqLGPvMaqmO0gyjYoySS+sxKhN0yQfgMmBAcrQj4M4RIs54+DYbLbYUvAIw9AR3MSDqzlYxW69zlWQtGJMoaAo6ceBGCQMoTtMxKiw3URdVWw3e7mxQqA20gtXusQHmDLedfLZ1esDhYYiOwGvri6pqpJCG+aLBQBDL4OW4CNuGkUK6BwRkZrO29kJ9HY2P2Oz31PYkn4YWd/dEQKyoKwuJOm+HziGp8cUOVuecXP3kkN3EG+BshhjxTMwTHlDMMzqGWUBbWuZppFxmljvt3zrg+9KhZGDSqSboRjdSFlVfPtb38P7xPnyHJQYZnyY6MbE80+fMZstKesCH3q2u1FUUzqwWtRcr+8kgs1akV6mSHCiWLLR0MwqcHsu53MSmpmBZlbTNPXJ0k5KjFMvSB4lhpvVvOVufUtZWjSCFNjvNnT7HfcfPOD+5ed4eHmJ1kIx/d6HH7Hf7ug7j0qGuq1JIWINuMmjreJsecaDqxWlURgF3jtS8kwxQdvgSfhCM3UOZS1XF5dcLVpslv6FEPGTA1uw3+9wztHMWrSRhQldiBKlKLBWTnC77R6j4I2Hb1GUihBg6Eb6YcRFCac+Wy5Z391ha8OsbTlfrZhGCbOp6oaqqqjLBnwQVUeIWXabMIU+Gdu00vIc8iKYAnUl7a8smJTc3OSJXmz5pYWL1QJ7rKCj56h4SFEWYJPlNwmPyn1oo8CW5tR6VYh5LSVIR/VVbvsU5nU7I8ZIUGKqtDm689gXF2XuUQopG40hZaOlKPaknSRKG5kdZBOlMUzRSUoXR1+Mygz4DCNTmtEFJgy9jxCgmS1RuuBs0WKN4nA4cO/hfS4vxSFcFCUpRTabNd/59gcsV0vOVtIOff7JEyafF30l3YGYGT4qAkpmQz56SlsxhoCbJo4hJZN7fSr5/R6/70KfZNvb598W+b8E/EngL+c//+vAf4os9H82/xrgbwL/pVJKpd9D9HmcMNd1xTRMYndWmrqZo1RBcA4/OYpSjn5lVaIwNM2Mi4srum7P4Ef6fsC5RAoaT+Ls7IwYPS6OxOhRSeFyn/nlzQuCT9y/umK/2wu7JiWKugZj6YeR/b4DH6mKueTYTk6Y8d4xTkITPLnjUmQaZYf10VGbQoZELgA2g9Y0WsP5+YLkFeM45opK6hFBGUQmH7CFpbRSOTVtJVrkMHEYBvABl0a8F03yOMpAR2s4HA5s7u6wthQ3oJYBVF21uMkRQyIhDtoUFYvlkuAcfTeijOLhvft0/ZrPvfmY89WcgCemmE9UcowNyZMSfPr8OWVTUrUtynl2vcwY1vs1F/euiCh2/UjTSARk3+0xRcG9y0u2Q0cClrOa9W5LyhmdonwRw5pXBQ8efJ5hvycm0fMX9nULqm1naK3o+kEGX6XhsDnQTT03N9fMZg22EMJoUViGUfgwbz56g3krOOkYJ2JSGeBl6TtHwmONDOiKouTijUfcvzqnaRvA4/sOHyDG7CoNk6iACgvxQB0iZw8fMLfFSWkSMrvFx8RuvWHX7YkuMrpIu5izvrtht9+TUpDCw1joRoGKAUkrxkkWNSN6JlkQk7S3hn4gxchqdYHR0Fa1nOaMwZhCVGXFccGVuQ7IMF/aJ4qjEc2iwCSKnHZlVGa3HA07SiBgSUEyCpTlaM83+rUyJCEKFXtaZtTJBW3MMQAnZiFMJjMmjz0Ogo4JY9mLIkq3SAye4CVM/PvjAaU9dPzZkZDlshIjKP7x48+Qga5sMRIUo6i0JWTImslsLGsthVX4lOimiKpnFHbG/vCKw77nDCgry29+45tYrfjKl3+I97/4h/nt3/ltnj19Ql239Iee9XrNzc0aa0tW52fEOFGVmmnsePTgAl0VhHA0sylsIRtWSHJ6d/2BGMHHlIGLNiuiPtvjM/XolWyT/wR4H/ivgO8C6ySBjADPgMf514+Bp/KGJq+U2gCXwPX/4+/8K8BfAbDWsD/sRF2CyJKOOmlbWEKUYVdMMqRxY6Spa8YhsN/dSC+bwOiGPDRK9JsdznuUMXjnmM0bmlkNlSUExzAecG5iGPZYCz7bNmMUF6TzgdIWHPoRTcgTec80DsLXiFmRY8TA0TQVLgYWiyU+DBS2ZH1zg/MONznq2rE8W56+J0RP3/f4SdiNEoySN7i65Px8RVTy9voowCy5SxRFZQkR5rMaezZnHEdur69lJlFomrqh6wcO+466KmmalroqiaVYrb2bMjYh8f5773F7c8NHHz4lxonbu1tmrRU9cwaznZ2d8cmnnzBMkwzTfGA2W9D1e6YoG0wIkbIQOd7q8oLC1Fzdu2I2W6FQvHz+ipvbDdoIo/2H3ntfvH+6RRVQVnJqI0n7res6Zm0N0dPUFWVdUJZNfu8tGI0bRjCavjvgPex3u4zXlT7t7e0tRVVweXWOUvDee++xOjsDH9lsb/HDkI/EhkdvPuSDpx/TzoTKWFUFjx7cZ5YrYnFej9JfzDro6IUNX1RFhtVl7QCA0RTW0J6dkSbP3d0dkcjgA7aqWJQF282GwU9MmzXj0HOxWtE2tbhNlSQZOR+IhGxyA1RE/JVi9+/7jm5/wBjN2eqc2azNn53ChyxRzfJBpcwJCS3+AEf0oyhcbEFRmlNrRiUZ9qXkxOIg8rDTP7Aw9vsWdHXStWt9NP1oyN6QmAOBjpV7VibnnFwpsFSS09xRTw6QdEQllbXkuU0RjyHsorY5bhhiZnzd4gkZN36EhCkSyujsbHqdhSwflVBtRZce8D6iApjaoAxEozGm5OWLl3z49BOWV4/57pOPSCj+zFd/nF/+5V/h7vqOuiz4sR9fcrvZ8rf+9t9FXLqZ+KkM0zASQsJ+/LEMpFPAKM/vfBv+3L/9Z1jffQoRdKmkJZgSU27TylFHvCkBL0yd+C+tnf+Fx2da6JNs9T+slFoBvwB8+VG26sUAACAASURBVHd7Wv76u4l+/oVXlFL6q8BfBZjN21RqyW2N3pMChBjoOkDfZ7vdnSqFmBTTNDG64xFTflw/9RRVhfcTuqiwpeLmZk/diKtvs7nh0aOHKCvNB6MMujQMY8dy1ooBIRju1jsJEbElTb3g4nwGWKl4U6QoC3RICGdGS9yZgrIsKXVkvmiQ4jSyXM2ZhoifJob9SHR3tMs5xkSKosJaxzg4Yjg6XEX/XhhLDEmO794x9QFtlYCgjLSryhwUZIyhKC0X9+/hQ8BPjrqtpO/sI/v9QQwn/UBRWB48vMezZ5/gfeD27k7aG1bx8OEF6/UtZ2ct56s5BFEKGaPoxoHnN6/QGjkZ5KSvhCwiMcI0TIxppKxqjK6IOvL2597hYvWAEGC/n3h5+5xxnLA2MaUBEuhkuby8IngobcP773+Juq7puj2fPn9KcBkKNzhCUJiiJgRpI222a25vX3LoBwpdSd83TBRlhTEVX/jyF0kpMrqetmm5u77m5tUN4+gwQGkAZTGFYbfv8N7h/UDbLnn48AEP7j8kjHKCm7wjeY+1Qi3UEc5WFxij2e22JAxlURF9h0kieR3GkdX5Bd04EUhMzhMUzGYNj998i8k5vvbrv85yNmcxn2NMcQoJdy4vUilRlAVJeXzwGKNweMLkmKaJyhTcu3/OYr7CWpMX4rxYJmHfSNZpng2kRGlgmiJlaQTfGxPWJkorVXGKAZ8EBBeiVMZaK6yycHSTqnCSAxY5hCcczUe58k/HY4N6je89qnyOLZMTU16DDtKnTwrpUftjXzpk8JhGq4A1IjnWRgayRkvlr63JLeCJEBJu8iSV8ualSE5MkW3Tchi6bGxK+DxwVT7gp0hMkT44TGmYoqUoCr7z3Rdstp6Hb73HN7/3EUYVTCGgy5JXN7dYY3CDo2laRAzn0dbiM0ZcKvWIT54QtLjHkT6/ivBP//k3ePfxA0YnG28KXgbJZMOUDxKUZGTz0Ram/fBZlm/g/6XqJqW0Vkr9A+CPAiullM1V/ZvAJ/lpz4C3gGdKKQucAbe/199rtGY+b5m8aOMrW3L96hXWGIbDgavL+2y3a8ZhEghSCMQwolI232hDNAkmuaH2u13uEwocqWlaDoc9T54+4+3PPQatUaagMJazeYMbB9wkBpfgJql+SGgTacoZ8dgyDCOzxYyUNIfDLic+ycD3sN8RdaRpSqZpwGjNcrFkLD13r9aQJImqtBaDwU0uc8ALOcPEIAPcFPAxcf3qFWVVUJYFKiYKU+DGiVjIsdn7cOJd9/0B5zyFlhzMfb9mPj8TZc9MjFv9fmC+aPj42ceyocZIXdd891vfBCKP33jIj/3YjzANB9q2Zjmbc7e+pXcdwQnIrKlatLFUlWYcJrQu5WbJtvJjIk6Mkeg03cFT2on9fk1MslgUZclsVmN0waHrGIc9VeuZz1fMlws22y2HriOliHcBo+D5p89RpmQ+XxLCiA+B569eEbzj8nzF0s64vb7JN3HDbDbjzbce8/LmJevtRuR7CuZVTVmUVAil8ONPPmY2m7M8O6MoSx49fJOzc5lzHA49IUT6UQaSh0NHCBOPHz1kv98yDAPtcokH2vmK+eyMsijY7Te8ev4pPojhyVYVs5i4vr0VO78PHPYHDvsd4zhxdXmJirIY+RgIWTddNjXjKPK7Q99TGEXX5dbOxSXz5QJbFMxnDUVhKI30eaOXAX5Miaqy2ewjD5X0SSxQV6Ki0tZwOHSZZBkoyoKI+D9MWRDilO8xkWwaK0E/0ZPRvKKGcjkIxCt10upHUh626tx6FGSCOJrTsVQQKbEPknWtZLE3SpNUzJuDoiqbfBNCUP4kQU6JE9l0GEST73zAO0fX9RijKKsyV/xQJcMweKZ+Em9Ckr745CdxSkfJhi3LElO2qKLg5asDL2572uUV3pR0/SC5vVXNi+cv8+IYUURmZUUKUJUlLgTEFihrlsAba/EfqMzT1wrvE2+/+QXeeHzJN77xNbSeIE4SdNTU2dXsiUkRJsGmDMN0GqR/lsdnUd3cA1xe5Bvg30AGrP8b8OcR5c1/APyd/C1/N//+1/L//19/r/48xw+yrricL0gx4YaJe/cu8Q4OXcdytTg996jOUTnzcZicLPpWktZBjEAxBtp5i3Mjt7cdPof53lyvmZ1VVKUWSd/tmsJo7t2/h8JQ1XtIIktTSpySfe9OGZVaKdp5yzQObNcbVos5pbU4n6hKi1aJ5VJSdPrDyH63pzscsgzSsd/uKetKLmBlqKqaYiZkwv1+z+Qc69sb2llDmEYGL65A50ZQUDYlbdtSz2vu1lux9DcNel4zn7XstluaZs7l5QVGifP3cNjT1RaFpmkqvB85W8p7ujhboPBsN3f81td/gy9+4V2WyyVtVbPbbxkODozME7TWFEbeB6NLhqkT2aWy0r4oSnHuRs2z5y/55V/9R7z7+be5uX2FUoF2VudFyPPBR09IKbHZbGnbOZ97u4AE16/uqKoaYzTf+ubv8OajN7BVjXdSKdZNxeAcX/nyF2lnFSnI8G9686G0R0KgaQqePP0uz549wxaSzzuNI8+d5w986Q/y1ttvYlAcuonNbsPsTPO5z70jzsQET558xHa9Y7VaUdqKFy9uefrxM/p+z/Yw8Nbjx3z1D32V6/WtUAmrhptNx9j3rM6XfP79L/Hhk+/inePJk2fMm5rVxQVGG4Z+wsXAfjugdGLZLk7ehMF76qbm6t59Zm3Ld7/zAU+fPmWxWAjQyyVWqxVtu6AqSvFkjClfq+6Ub6t0PBUgKeSTcAxE/3p4V5YlhMjQj3RdJyfSsiR6WXg0WthRXuItheMPystJsx8HMfFEDQZ8yIcJdSSNilNVhZRVRpn66H1WvkhrKsWIc/6U0GXLAqU0R+epXGtaXK8pD1C1qKFGJwVGaW12vxZEA8GPOCKYkslNsrnk/a5Tub+PwQcRdjg3ZjWbIBjm8zPW2x2/9ku/xurBWwyDJqiKPsD+bk0ylsM48OjigmlyrC5W1Lbk3FbYCKv5gj/9b/5pdFFi65pxmEioE4PoyZMnBOeZ3MQ0TjRVzXxxTt0sKMqG7folMfTCdFJOTnVVC0kzjAPTKJ9lZf/V9ugfAX899+k18D+klH5RKfUN4OeUUv8Z8DXgr+Xn/zXgv1dKfQep5P/i7/cDjq43lOyExMg4alL0eO+4u7uTIyWCC47ZYAIypJncgEqGoR9RRhO9kA6PxqEQAzoqlDYMw0hZK+qqJXpx4UY/4XJQsrGKsZsobIGfIskmfJAWS0qRqipxzmGs+AX3h4MM4TLHYhx6jK0hKbyfcG6iLIVNo1Rid9hTh0hZFVgr8seUvOzSY08KnrqpqKz0/qdpwhTC4F8u5lxcnGd+hqV94wG3tzeM48Dl5RXT0NM2lrqeS9KQVuiypCwNbhrwbqSuCkgNDx9cSSJXDITguDhfsLm74+nTp1hdoK+uZCYxDJSzRryRGoZpIPrI5fmMaZTEI1FNZXNMYbm6uOLtd7/A//G//xNQiuWyoet3jFMvpxfvKKsi91iNqBX2O7zLlVq0zBYL/tSf+lP86i//Cj/9kz9NRPH48WNi9Hztn/0micjt9UumqeO9z7/HzsuQfb3doHTCTyN1XdF1e/a7A1PwRCcpUc+efUxRFBz6gckHUJYPnjzlK1/5Kr/xG1/ncBgYxsBu17Nef8p6s6GqG2xZ8vLlmp/48T/GN7/7AReX59x/+IirqyuqshYruwrsd2uiMhR1gQG6fsptOTHDBQ/eicLLKCNRfyEgecAKqwvmsyXDJFJCW1T4aWIcPFU5z3F8mdGfuzUyeMwtFC/hKHLaShyDMGwht7u1FocghVEFZTWjrASvAbnnngJhCmJgqgTJPeY5kh8OLFdnKLKSyDmChlPYRu7TOO8Fzuf9iUmttcn9fwk2jxFCEqAZEaJLKCNSVqUU0ziJG9f3xOz5qMuKum2YlTLkFjNSRGmFjgllFcqDreQ+d246RQCaoDC2YgrTaY6RrBHmvTa0s5Z2seT53Y7BWT786JaiqimrhqpdoBK88eghF5crmlpOjz/8h/8wtTHUU2Q5m7E6O+eP/9RPExWMCfp+ZHITh3EghshiMafve+qqZjZbSPrUNDKNDqMtfe+wRoB5wzDljcxTNQ1tM+fm+hpjNM7/KzRMpZR+E/iR3+XPvwf8+O/y5wPwFz7zK+A4WIHb2zuMkrADay2TlozGYRhYrVbU9Zy+E+md7MQCELLWkpTGhygVRF4kra04X53z/MULhvEA3hMVVI2lmeTYVBYNrh/Ybnv6vmPoex5dvSGuw+RPSgSJaRMN+3Z3IIXX+uyiMJR1yfLsDFRi6Adh2RtNVWjm9VwMSd2BxWJBWQo8TQZGjmRLgUXpxND3nC1WvPPOO4TM23d+ZBycuHznDWVZMU0D680dTWmY1QsKDfPzZYZsRXkNRtNUGqLjYjUnBpFgmlIcunVdc319i1ICrCJp3v3853n27Bl13XBzd0vf9+jCspgtiToS/QRIe+bi4lxaT0qxXJ2xO3RYU/CTf+yn6YeJuxc7Pn3+jDcer6irls1OlEK9O6B1RYia+XzGrKmZtaUEvxhZVF69fM7D+w+oy5onT57ivOPu9oau71jn5CXSyMuXL9hvb4k+UtqKurIEP9K0FWdnb9C0Cz795Dnb7Y7RwXZ/oCxrxtw6q8ua0XkWyxUfPX1BP0RGp+iGwHZ3w36/Y7/fUzU1znlUCtyuN3zw0TPq+Rnzs0uqdknTNJiywWpZmCMfoLOJzdoWazRN04g72ljc5MDANPRSLTsHIWCNZXRwt+4I0XD/4Vvc3t5QFYYvf/UPcn5+jnOOkBKV1qQg7bIQ87AyRUCq5CMJ5ejwNIVwVk6acSB9n2JGIb3umFsH2miKwtJUlVyDbiAlaVfef/AGL1+9QifFlPZEr07Rn5nXifIyuA/JZU68oAtQCkXEaIk5TMZC0qLXNzJsDFldEhHq6pj9Bs3yjHnbgtaictPimMeQTzQJazW+8Ohk80lB2iSFtTSLBUPXY8tKEM2lRmuh2y6XK3xMDEGxvHjMT/zk53m1HmiaWrAP2fG77/a8fP6CxXLJYbdDKehRvPrgA/7ZP/41fvQnfox/7V//Kbb7Hd/4zvf48MkT+l5UZs5PstGOE24KKDRvvfUWbz14BOHAP/qHX+Pyck5baRn2o/F6ZL5cEJxw+Z0LBOcpivozr7E/EM7YlAc5IUT2+06wvCFRVa9zU8URKlr0dtaw3ewIQbS80zSKr+3IhciqhcIavJ9E28trK71RJckrmuWMYXfADZHBdSyXMy6XV6xWK2JIXJzXdN2IUmuRYBK5u7vjsB9f3yhRZG8hBYKfM1/MGJygY+u2oa1rUjA4PzHTDavlGfv9gTKT/5Qy9H3PMHTE6Pmpn/qjkgJ1NDulyA//8I/wO7/zbUIMHLZbmLdcXV1xc/OC84sLqqrkS1/6Mr/1W/+c0hR048gX3v8in376KXVd8/677/PBhx9SlAUPH77B5nZNCIGri0umYSKkyGJxhsZQ13PefPtzWGs5OzunGwf2hwN1U+NdIEVFUVR0h46marGmlMjGkDhfnqGV4td+9VdQukDFkXldsJzNQRfc3b1gvV7TZELi5GSRawrDzfUrJB1I4Z0oSv6Xj/8+dT3n5fPn9FPH9z78JkYrHj24oq1bDvuJ5byhtBpTaMqikEXBy5F+Np9xsbrPYnHJODo++PAp3gUO3Ro3Dbz7uTe5u93iRsezjz8lBAvaYouGq/tz6lLQ1JvthgSMfc/+sOU73/mA1fl97m63PPv4JdbUvHxxRzf0aAJ3t7es7w5i9kleEMVaIF5129B1g7BKcm/9KHctywplPX7Xsd4PnK2u8oZ6P7fIMspWDVnH71EgyIBQciylBcrliCGcWCtHFyzI6fmozDkGwVtjhb2uDUVTid4fUcRNwRGTwRbzk6zi1fUdKENVNackqGPr5tiLskk2lRihqgrhQw2HrBV3lFbmR/K9mahjDBGhQAZyEUeiTQ1aW5E0K3Fo9yGcEqdSxjQoa7DWsKisnHJSYphGqrrm4vycmCJmcpjiNSWyqCuB6SkjIUGbkcFptn1PN3qcH/noyUcsFguqqiI4z2p5yWo5x1YlQ7dlt16jteLi8oKnTz/m61//Ot0wkLTi3j05HWudi1GgLAt2mwMXq3POlktevfiYOHVcrs44P19SWZFsD6Pj7GwmHYDC4n1kvlgy9v3JGPlZHj8QC32MsrtrW+CsGGYm71E69/OcxwyOBw+kgnRO0qQSEec6UpKhUGG0JMooMBYqKygFqxRTBFIi4tlsNtT1Pfb7AT85zlcXtIsVVaGpKk2pLYMbxJZsoWlqIHHodsTBs1wu2O228uJTyvLKFmUKfEgYJek6OvPvUUhlVNeSXYniT/zkH+cX/97fYz6boVKSwfDVgq9+9av8+q//n3jvWc7mVGXB5m7LbD5j0Uqg8egGLs4veDe8T1XVRO/Zb/dcnV2hjWYREv3+QFu1aGW4vr7lfHWOczKUbeoGbQqGPnB+9ZDCWFCRxWLBYbfBjSPt1RXnlyuevfgY7wP9YSBEIFqGbqSqasZuRCdNWRWZrlkwDQ5rE4SJvtsS3cjLTz8B5QljZLU4w2jBNjy8esR+vye4QF23p4QgW2rKqhSna1FSlIZF0Cgt84R50zBrC86aS445nqq0xKhwDgqtKMoZWlmeffIpt3dbplHaeUkbkvcYEkO356/Oatj1/Ie2ZtIyFOwnTxENzo0CdMuhFecX5zx+/Jjddstf2x/g2SfwD/8xf3w+B62om4Zx7Ll58Yr33n+P+XzOvGkZeofzA4U13G1vs+GvImlOpj+t4Orikm23k1OU1igMZWHzgi6KLKVfB0M3bUuKQvHUSguW2Frh59uComwQdosUDSGKYUpnBZvSkut0dIumqAQTHhXaSCGC1mgMEr/5/VhchVUaY0tsDBlMlucBpNfwtBzlqLTCxYCxhRjYCkvyXj47BCOelBRpFkvQAX10sqZwQjErJ2EigcRsVjO5IHC0/DkppSltRT90jOOIQjGfz2mbVgafk8eFSSIzjaYsK8qykR59mLi+vuPJs2sevf0e3V3PfiPrTF3LKXxyI1VRMh62vDhsqZqG999/h/3dDWVd8vz5Cyg07aplfrEiKs3jN99mMZ8TQ+Cb3/423nsWyyXvv/s+dd3y5IMPuX51i5v27IcD763ewhjZaM+N5Yj4NtpgSkPXd1SNZZzGz7zG/kAs9CnBq5e3KHKEli0JHgaX9aI+nIY2MUgAQVWUlLagbWbUVcXdekvXHSiqgmEYBCsLDF0HIVFZuWFSSPg0ikomBC7OVvx7f+Ev8Yu/+D/RLpbURZLntw3OR0YvBiNrFYvFjKoU2eJmE/Pgr0IZzbyZM/YON3qqqhCKpYV5M6coKx4+eJOhH5nPJXRinCI/9kd+nJgi4zjStDVuGvned55wtjxHI6qGsqpIUWBIbTsnhphlfJEHV/dObSyS5uri6vts3dK/FFSt2MoLI27ZzXpPJDGNIj876pitVRgDw9jxzW/+Nu2soa4qZk3LerdBYaibis16BBPYrNd456jrilnV4MZOjuhIckxwjrKQkPWymFEXDSGMJBK7sEP7SGMLyqJiuZhjbYFWFu+c5AaYRAyewmqW83NpfZSF4KKjYnIua4sTQWvpfYeEj4ppEjzFNCaUrrAlp5lDChN/gwnuXcHLO/CR/9bAX0YRMtUzeBnymVI2b0Kg60devbrl79y/D1dXMJvDasUvxcSfRyB6hS05e3+FMYa+G5kGGbrVdU1dzph8R1EXJFKWwHoGN+K9Yz+8yOYgyWRN0aFthUtBIvqQIujB/XsUpVznm/VaNgWt5SScXkfcCQc9V9dWWjQm3xfaKMqizO2ZQrKQzZFSacSgRBT5bt5AyrLKSpeEiYoYA30vUkatdc58MKQcxJ3yJqJ0DvZQCXS2e8WEsSVGxq5Z4igLvYqSzBQQdU5KGjdNYDSDl/mczOpkVhWDRxsjSOPJo9AUlZV8iSAKo0Riuz+w3WwoyoKissznS1EFKYUyGhUt/+Rrv8FvffND/sSfXLBaXbKcLWVArDVGC1vrsNvxS//gl+i7jrppeP7sCW4aqLVBaVitlpJuZg3DOPHps0/ASILX/nA4tZutsegkLdTSCphtPl8yTj2LhTjhlbHs92vBmRfiEG6oRO5aaj7r4wdjoY+JcRg5P79gMZtxe32L1mCyRr5pa4wx3N3eZnep5v79+9xc3+LHkcXikrZt6MeBVy9fEmOgMPLBbLc7isISgmc2m9POWrb9hugis9mCwlRsNnum0YmL1cgbPriJtl2SSoU1jTC3Y2C/O/DixTV/5I/8KOWR4R0j+27gcDhIqIO11KX0nEWpUmJVzcX5CsmfTTKtX10wm89wo7hOQ5ywRqBIKXi0kTlAac3pBq0Ki3OeoevRmTnixgFbFBzGIWPAUg6IDoyTtMHaxYzD/sDkHM4f83CNaO+9bJxNZdFaMY2RaegwVtFWtQSvt3Mxd0XRVw9jT1FIbu/hMNEvZkzbUbJwZxqL5v7lihQD1mb7uZcFpKoqSq2z0xRSDNRVQVXWBOdo5gvKsiQp4XOLYUcyP+fzuVRUg6NqFhz2nfRqi4KYEl3Xsz8MDMPEODnGyTNOQeRpIVLg+RttA1/+Cnz3Y9hPYEuOUtyYJgmXiPLe+1xJA9RtjSss3N7A+gbmc3j0Btzd8TeHHpZLftZ7xmnK1bGwTgLQT45+WlNVFX0vrb/DIBvA5CLLsxVTP3B+dcl+v5cowATb7UHcoBrcFNEoPv7kubyeusrFkBYHpptomoaHV/ekxZEi67s7uq7HHSXCPgsfJk/XObHdh2MUIZAQpRJGAliUIoZMEc1ExcIWWFtSNzWFsZS1zEYiJSkmbFFKdZ519hGXYyaBTILVJy685BeblLnykDcDCRySlr6iLOpcsLzW43vnCClQWDkVre/WfO97H/DFL32RdnYuqrFBMB3OO5TS9MPA2WrF2epMlEApCgjRO9w4cXv9itVqybe++dvU1ZzSVhIKlAuq4MW13R/2LOZzFHDz6gVJwWgLmrrGx8h6u6dqG5QxxCDOfqUVdSGSZKWURBhG6WhUZUlp5jz76Ls8vL9i1s5JKdINA209Qxu5X2OKlKUMze3x1PUZHj8QC73SYgYyRtPWFerqnLZtSSlxfXvNNIwYpbCVpa7lQ00psDybcX09MLmeoZ94+vSZyMZSorQll/cuKWtRyeh85H3nnXe4d++K61d3IhPrD/zG136TN954m6IwWAVRB5bnLWXRUAePa7KV21qqtxt+9EcbtDaMx1663BHsD4cMCxNNLuSbKnO599sD2ggjZfK3ou+OvGZNR087q7MMKVCVFmM00ViK0jD1HU5BTELQ1CJTko1Ai+5UiV8GKzksQq8cO6raUJhEcAmfHCpqilrUDcTINO544+FbGK2IUWYGpdH0o8PmaMNtvxdtb2FY953MQsqC5bzFB8c0jZwtl2K4CZPYzlXK/BtNWc4QNl5kPrtgvpwLk8U7jLEoBYUpaOpCFgYF1VyUDV0/kFKiOwzcjltBXSCDzf3Bse+33NzcYYsKUxqcSwy9wwfpwxITuJ6fe3QOb74J3/oWTAZMBVHzszEQ8+kmZP65lbkmkxM2zDgFjLH8jNH8vPdwewc3t/DOOzBrYbPhb3R7aBr+giQmMvkE+TUoJSwUEMNPItJ1wo+ZhpGh73n18hWr8xUAm9s7Hr3xkM1mzTBOp6i+46K52w+EnII1uYgPEjiy230ISmXuuazwR9PQ+WrFZr0h5KryYraQ62lyjNNIjCkzfBzHmL5jS0bcrQqY0KYn3qzlWrU6FxiRmJKcyouCwhqKopB7W2uMNdRVJRr0Qob4aCUywhhlXpBAG5FLnvJWE6cCQ1Z5uQdEyqkyhx8ePnzIg/uPMv9Gsdt2eO857DuWZyuqRcHybIUP0ylpylgjRV4+vTR1TVvUbDZ7duudZETkIHDvvRg3Y6CuS6zVnK9WvHz5EmMtXerQuy136zXGKD748AOKwuCcy4olXnOAZKqNipL2ZW3C9R2PH1/x8I372EKMVk1TiKtXA0k6HEUuTOy/Ynnl/+cPSWJXJ51vky3nXdcz9gPDMKJqudhms5bdbkfbtmitadsZH3/8CUM/cnl5ifOOh48eApp79+7x1ltvobVmPp+f2DR11WCNOGaHfs5sJiybEBwqD4C6rsf5iLGWYRCZZwg9KW1JUePDRD9MGKWZ/GuKnM+DQK1yv9QKYjQlcm5nxOYKQekcsRfFFRuDZ+w9ZR4URS2VlS6lVz1OA2M/YgsZMssRtcgD54x7PlVmcmEN03C6SEHUQ01dZEgTJDSx0BCFU2KsomlqZm0DKdG2DdMkXHY3TZh8okohEpxDZYu9cIGSPCe7JVOKGC0qIKVLikJhKIkp0DQ1i3aGLysOh72QPEHaN5k57l1g6HckxL4foqCcXQRTNAyD5/mrV2x3B8Fb1y3WVuz6PcM4AXLUDn5Cq8TPn89lkX/+HHoHugRl+UvIRhxJpCjWemWkXy3QQmGoCwVA5kY/oxU/bwpwDp49g7KAxRIuLuDDD/kfi4KfUQiaOeQ0oyTVafAhywLl56Fg6HtsIS2d6+vrk8Hn5uYO58Q49e4XvoBSipvbG3bbHdpaopewmxhAI4uWmJJE9khenI+a9OcvXp7aOTElRies/qt7syMIklevbtist1LBZ65OTBItefSTCPn0iMn2eFm30NrQDxMMmQybpPV55C7FlNsWeZZQlJaqkF9rY/IcQr7qnAFrcr9fazFBapvxB9qikkQ+1mV70teXVYlRhvff/SIYJWYllAx600RTz+WDjSJrNUUJwaOs4vHjN7l//w2+9s++zjRuGYY9RuncmjqefgJVXXJ1dUldCYdqdE6CcpQiZVOajnJaJQAAIABJREFUBK/LZ+/yyf/7M2FjChgKCmPwKjIOe95//yeoyoIYJ2xZ4v10+vcfv9cWRtpXvxuD4F/y+IFY6Kuy4gtf+CG0NpwtJeygrEuaVvrv89niBBpSOmZAmEiyfAgM/ZAXYjkWukkCQbrDXo5MSnE33aKQimqa8sKX5MKMKVIUhSxSRZZ6aSAptPXEoE4DK7ngylMAiVTlwuYJIRBDwI3T6+dacyJuSiJPZN7UNPMWlRLTOOHckOWNAT86CCJQCzZSqJIQevo+0LY1Rmn6oTspjFISXHN0kYinqqpTvzORQ9OjHBk3mw1100ggQq4K3eRRquJ8sZAjYfJUpeXy4oL94cChG2nbGbvdgdlshveO1cU5ZVFyfX1Nf9jjug5F5P7FOWEaSFahTAX5RkeJ+iLFQMx9XOHHyEadEuz3e7yLeDfhnAwNC1tj7IzD3hOM5LaGkOj7gfX6Fbt9h9IWrcXisd33BN/hCQRkthN8z9+VGCr40jvwne/BXQdFy1+K4n8IOVJOELFyMx0HiEdnqeDrNUkpdIKoND+TIlEbfiFGOHQwDPDVPwj3H8B6w8/HAAr+nFxK2DwnMoUV/XjMKIksb/ReXNnayNxBWYsPAbRw23/nW9+kKAruXV3x6NFDnjz5SMBWCc4vz9nv9xmfHfN7gmz8pMyAEk57cJmbg+fmLnF9t87mp+zQ9R5tJMrTGkNK0LYtZ4sFr169IiGXPElYNba0BOeFEaOSDFqTBK9LJKTFO+R1GbJfYjjdI0bgOsR0HL7mxdBm5Yxc6EL1zCe/qpTNwhYFpERVlKRcpMkm6TE6EMgJcRFCIejiGATgIcgIKW5S0mireeONN0nJsFqueHl9m5k+ihAkASqmSGENq7M5n3/nLaqq5OXNNa+ubyAJo6bL7Vmji+xu9mhbiJT0OENTIjWd3IRDYId/9t/6M3z+3TfwUy8eAy+pcVpBCMLr8SHgOrlOiuL/Z1GCTdPw7jvvSQKUD2JS8p7t9sAmHLhWG4ahg6hQNmabvUj9jlKxsnxtBokhEeNAUlJ9pHzhF4VlcgFtxMhyxIEoZZkmTwhijkILe+aYaSnkvoQtKqy1hGnL4I4mGH26CI9402PVo5SCYyWd6XlG25wJnxiHDucdVVUQo8+hyXLCCdGjUonimPzugIpDt8VoTUwObeUUYpWRIZlOuCNnLknsorWaFC0hSYvAaHI2p2xy7bIW56EPnKBSPpF8FK19WZG8ZxoOrFYr7m5u2Vxf89abb1EZw+RG+u7AxcU5680dZ2dnXFRnIuUzAnQS1XQkRoVRAY0sHiFCGAM+WWaLK4Z+QGn5HLVSRAyT90zes17vmPxEP44QlMTcYU+LVlKGGORk5KOCMKGJ/IJ18NX34XwBv/1t2HvQLT8bbWbCGELmkMR8siKKAzo3jPM1AEdaYlIqB0skgor8uyGANfytGOHrvyVv8tUVdAfYbvkFXfDnFMRMslQkYc8EWcgS+nTN+ORPNMYizx2MkWjMtpJ25qsX19kxKkoVHwKHoZd+cIZdxeglPN4Y+sw5MipjaPJaKqeYXJREldk1cl0Q5eQyBSdI6s2Gu82WY96vDHdB64QfHZ97+3NcXF7IySVGNtstbhzYbLYiTXUTMSX0yZwrsXspRabsfpVWjeAqtFbEScxQcv8onEsi+1SRrjsWWpxiBI+mPWvVyTxgskdEG2kXHR8JQRakhKAgUBAd+75nsbxgtVqRvOPgRr7yla/y9a9/4zTcTgkuLi64vLygrhuMTkQ3Snstv7m2MFR1QQTZjPPGG9PrAPBxylhkDYXSvPH4AeMoGO+qbCRrIwncLEQlUY45ectae8qo+CyPH4iFfhxGPv3k+am6NqY4AYdSkK8qSiC2RlNXTT4aSe9MMi5jDimOODecJukpyXE/xikjfSdS9DkdPuYqWxQCwOkmCyGgURSFkBy1hrIoRDhQFKQkrPmqsLlqSbgQpIdWyHH2CGwyxpxCU4qizNpfzZQSVqssoXvdJjBGpHpFWYKKOBdZtDUaMSzNl0usE359aWxm65jsVPQYY/FBXHaGRNISVt22DYWVJCahTVqsVtRlycEf0DHIcCovdKWx1EuJRDzsLSpFZrVon3UInC8W7HaRy0cPGYaOy4sVj+4/YLU653DoGdxIaS1tuyQioSw+M8a91iQf8zBYEaPD6AqjC0KIrDcd68Md290ha73zQFcZ2X+D3FDHGwvEERqiDMv+tpqg0PDjPwx+gI+eyiIfS/68LnObQgMBkoCzODq0c2V/JC5CXiDz4ijHbrlWJN7NEEPg31GKv03ewV48l43iJ34C3njML/z9vw/DwJ/9v6h7syBNsuu+73fuvZn5LVXV1dODHsxgXwWA4E6KFElJlIMWCQwBOBjBxaJEySGJssMPinA47Fc/WA9+kf1kO7SEg7IpAiRBE4BmCCpoM2RJ3EGCAiUQBAEOMCtmeqmu+tbMvPf64ZybmV919WDokByDjOiuqm/J5ebNc8/5n//5n+wtwhFN8hVdeOd17sZkaoWwT3vI2shdROiTRmolMV+MmQuezXarRkjQ1pZ9pM+Zd73rHbz2kUfIGZ577lmef/559vu99R0O7KNKTguKU6WsUWJvrf1KAhYgSVAHwuAn57TSFMl88amn+PwXvjCMT1VVHC+PmS2OWB4teOa5Z7Xn86VnP6ONcbo+4z1kvAqCZcEbB78kisW8YSc6FwTtt+DEkpOVGvgh2qVIMmdiD21XGvYkRuFd239KRi7IvO4N1/nsZz9HTImj5Yw3vukNvO1tbxsUP5um4datF3j7294OZJMnyZwcn3B+fo4WrCWuP/oYy+WSlHre8KY363Nf18SY+Df/5vf50pef0gE3UbaTk2us1veo68Bi4djtNhS5fQmOhFDNZ1Si1ca79muslaCIDNnnbBSmrPRoilZ9cKrX4bxDXGa72VDXDbvtmkxDU9dUPnD77JbhoZHZ4oj9bk+73+O8Zx93lmjyA2YdY6RpZsPkzblob6s31MeO+XyurdFE8ea6CqhH0hP7boR1EGs+HC2BYpSs2NlDqZPCuZ7lvBoYDJVT4aWYPF0rzJraJF6TCZdpA2YvjtOjEyTDbL6gz5YTKI0SnGPX7sgxKmZfaaYep4vlYj6jDoHKa7UxOdMED6i0g3NoWXVSlc6Zm7Far6iripsPP6wLw8M3mFU1R6ZbE8KbWB4v2O/3vPTSS3ZDoZ414AVxjuXREdnEqjrDp7vk2KeEdzV9Tux2e3a7NWdn5/RdIkYhysj/TiZvmx2ErI2RU8kVRBPEij2Vz3zER1jW8M3fAM9/CZ66BWHOj/U10c0MysjgtHoyp4SzVnHl/EeDH83YOY2C8ugVgxb4OCAb5PFDqPPxiyX59uu/CcslfMd3wr/6l3ysV8nZH+x664ak7AsvHueVmz5VdSxFPVMLWd5z4ogkqlANEYjYYlWKpD73h5/j05/+NILw0EPXeeSRRzg9PeWLf/In3L51W2mFDCmdoSEJwQ1MkxLV2CpgcFZW6Cmri+ScdsmqqkbP1Xku1hdcrC+4dTdoZyRr+l6uoTz7zmkNQEyRRx55hKOjI55++mmN0NDFZhBJy6roKJjkcC6dqTBnSrV6NC+BRlejpdH8iwsIQSmlheePQks4zy998le4c/cOq/WGRx59lBe+8hX62JNNXDDGSE49v/Hrv8FLL73E+fk9nHO85z3v4bd/+zfp+0xVVXzd130dr3nNa4ZoTRP9md1uxzvf+U6++CdfoLbK8tfefITf+e3fHaK9qnZ4V5HotZo6eLwP1itW7cbF6uKV29ivojf2/8t246GH8nf82T/HbrcnDp3QNYyrgqMKwmzWEExnfLFYajNjCy/3+z11PcNZIU6RaT0+Oma1WrHb7wbsLvaRLsJ+v1MdbudZHB+x2W6HblUpq0frnBqUa9eO8OLY7TdI1pB6t9kq39uSRk4ceM9ms9ECGO/wPtDUNa4KKhW806YKPsDRvCFGxcNns5m2zRRYzOZ0fct2q9Wo6/UFVfAsZw1106hCpQh1XSnOGzuNUkS90pQT3b7j+vWH2G63SC59LCPL5RFeVO+9bZXFMZ/VA5sjWTs7Z59/6cUXCVXg+PiIpm4IzuO9Y9bMqGrFm6ugDAvvA5vNmv1+x3bfkfEkstHavFEpHTE7SwIL+82es4sL7p2v6dre7rm1WCwPpQgJLQUvycLaBSCz76MmvlKPE8/Pux7yDh69CV/3VviD34O7W34sHZNlphYiC3125ByHBRp7CLs8etPFYyybOGVHqCduC6uM3YecLWR97BRWNO//F0sbPBGogkI6R0fwuT8C7/ig02RiOZbTGMxOKw+SFuXvacRRDLpy0v14PRj2jWpElijVe00Mtu2eopVTvPXhHHPm3e9+D9vtnmeefnrUT9Ch048llfaVEknZ8aIVPpZdkaLBdhlEcw/FKYExH1IUT/U8M85UaX0ozbtlErkxzIvRwQIfAn2vGlVd3+rzWM6pKG7GsZWgQn/lPKItkPpeawQG1bOPbDYb7X6Xs4mKdfRdSwiei9UF292G2CsNt2v3kLUa+drJCU3T0DQNIWiNEMY+ms/m/Pbv/A459wTx/Lnv+A4ee/S12iPbiV47nqbRxkJdt7dGRdFaieq8+tmf/9lP5Zy/7WrLOm6vCo8+JZ3MVV0TTZrVWY/GqvLUlVbmLRbLQVyr8sK+cKO9Zdtrx2IxJ0XYtzvEurtXweO94vp1HQgZyMpNX8wXgJYki6iHXwqMckr4kJGUyKIc5j72zJsZ164tVRQqQ2W81u2u4+TkiJQyaxM7C8HTx5bYdXgnNHVlcF225iXCfKZc3ZQSfdzTzGpSrvAO5rM5zaxCshZoiQ+WuOupqoa+7/QB1ggaL56jxXKooKxDsKrTTtknlR4/1N70R9zgLLrk2bUdx9as5PxeTVXXzGdzZTnZw1boc12nkrBdn8BF9vuenFV0Lot6XMvlEnFBWTt9R+xhvV2zWm9Zrdb0KZJTAKkBoY9FKdERc8ZnlYlwgqodotWrBY8n9wQv/GyVod/Ce98E147g9z4DZx0/whESmkH9Uh/rPLbLs58ZS6yX5KX11xPzlNXoRi3oMUgj23clRvDWDcnMh+ZbHP+Jd3T9nidw0Hbw7LMwm4MlAj/e9Xww+AEfj5IGloxDtO9xThDTIOubLOcUU1KJYzOUxYi5jEZQxXsWMcaH5gi8nz72Kl8QUxqqb5955jmSQJdViz64oGPh3dCmNYRKZYdFR9SJaJMQLCeTkjpc9ncmWaNuNf4crC/q2BUKatli0bMnHxj18rfYopuBxWzG93z7d+O88KWnvsx6vbaKYsetW7esv0Ow/eoCVzmN2INv9N6KMnSqUOvcs3s8my80WhKNIBGnQoFdoVArm017M2ivC51aer+8EQacE7tGnSN/4Xv/otJKkyayn3/xliEYidA49ruOueHw0eQe9J8H/MCnfyXbq8LQgyZSqroi9EKf1NDWlSN4YbGoyWhBkeDo+pZZUxN7QULFbr8nVGJ4fGshp4JbIlOnRDPtfbunCdqJyTnPpt1BEoLTAqAS/jpvWtlJqWIimRxbcq4hC8FZcsUekFxrwqVt98xMQCqIo2sTlXMqgBZUX9o7h4SAQ9ju1hwdLZBkyeSu52i2pG23VA4U2M7MljPmTWOZfL1G9fIKMqRY73Kx0IbiwWtZOJp13u9NjjVqx3lllWgInhL4WnMQ88VMqaixo5IZonXxmiB2XkvIUb2SLJ62UyOSygPpFQoLodL328h239LuWjb7nXomCbouIxLMgy8VPU5lblPC48zLMwPcA2ZKYlbIwfnIz9JC2sJbXw8PHcPnvggr4Ue5rknskp9OxUzksRfRhGmTM7pAocm6TKYy3XNRi6XwItY8w/4lIPdanTns1jzlnDJVaPhQznzMjB+7Vr87q2Db8fHdlg81tVaYhuLd2qMpqGRwEHU4jMWEqJSBpEzP6PGbbQEguuLVmzcey4Uo5IQbDbd3phkPXKxXZgy1cXdEoxzfjwhSSso712InlfjoOi0KcuLocyJLoQC64TsUXB1dwAbK4JCsTAoFiRzALi4XSuKYOxAxOgpwcX7Or/7qv6CutSPayckxN28+Ql2r8NfZ2Rnr9Zqmrrl95w5f+crzSmjwAD1VHWj3JrniHKHkJ1ImSNDF1hZQ0AT0EBCmZKQON/TOBf3be2ewVhl3vW8qYlgYQkn7MduCXbajpdY0qDqsQT850bc6Brv915gEgla+6bofBEITqEJN1VQE00IPXvWrxWkHmeOTI9YbwTvHcrmg75SzqmGWGsamgd024l1iZoVTJycztpuEkzntvmO72hJJSKjAQfBCn3udQCmDC+SYENO8dtaQuK5mOCd4K2zKWZjNHe1+T11rs+0QlHc8p2a33VEvZpA6qkobiqSMFUBo2bgLjr7fs9lutLCkqgYvpO32Sq3c7sybL31ojXOeVUmzqRvWFyuFlIIyLkSUueMmkzDnTBUqcioh6Z6TpqGeV9y6fcbptWssj0/JMWmvUtmTRHnxPkFO2m4x5442JqVCVsqBrupA1/bcO1upaud2z37fEe16Y1S8XSljaeL9qOepC8bogZUEWireqyUPPxocpD28dglvfztsWnjpLsTAXzHAJ5uGYyxUTxi9yQMj/4CfBa+f0v4m7wu62CNCNqMTLaENGG1RD/ch8XwMpeKSEqzX8N3fBd/+rXzsySfhC1/g/b1ql4OxX4ZTFu2kRB6gmJy16YdCHaMOTcGsk3mfOY/e/uAxD87P+Hfp1JSzNRgxHaFyrDjRuin7OVou+M7v/HZuvfQSzz//PC++eEujFTKVFWylUoDmnQJiqTQfGfn90VKn6uyrJIIzGd6iX18+O1AwJ95/H6P2fSbywgsv8Nxzz/LZz/6hOXy6VVXF9evXaZqGN77pDRwfL7hx4yFOT6/zu5/6fZ55+rlhIRsatlg+wpW+umUeOjEiRz6Av6ZGPaVo96UzL940+bEpYK85W3ABjbbcyJH3lUZOoXEjvGe9dMt9fiXbq8LQpxRJ7V61oOdLDc2TFhIlhJR7dl3kaPkQxGSGS7HnUlhShUo58rstGa9CZKmjrgTvG7QN3hwhUleOvuvY7VZoDwIhZw9JaY0OdbyC99bOL+FFw9i6qi0hFUkZjpaNJsViJCkBw7BQNWJ17Zn5ALHTQoicWSxmNPMZ+1bzDCFoD57gMrOTI5zXGxrMowmV6ruUUNE5N3TVmc9VhqHb60KQk3bZqRr1FvyRZ7/fD+F623bWllEf7LaP1ikr0PdRQ/6YufXSHaNxalekTIdUnojDRZ1shIATzyKoPlHf96zWa1669SJndy8sCaUJ1UIFtAJ4clJzNk78YojFKH5uYF7F2OoyEO3hdvBRlyGu4d1vhseuwQsvwh8+zY/FGYQ5u17Qrj9h7GRkeUZnXm8yA1iSntm8xQGLLguBQTvF2OecB6gMSxCKCMmMgcJOpdn1BOsX+JBFix83tg6/9mvwuc/Cj/0InP9ZnvyFn4P1nsdNybPg8AWnHyALhDzIUZqwW4lEFZdS42kFWQcLmsgAVZRdjA22C9wz6uMUmvHU0Jfo4fzinE9+8kkeffQRbtx4mK//hq/jscdex927d/nMZ/4t5/fuKU8f8EF7TXiD4FIaDWR2435FhBzHxHLJm0yLhsomVvGrw+voUm+SBXq7QggDVBKjts904kjPa90NoqJvmpNrxjEGS3xCcG6cp06lIRKi65lkpBS9Yp6MnbdYTgSb133sVSVURvaWK/kxy7nkFCGWQjMMfoYcx+uEfpgDr3R7lSRjT/P3/6U/r4mGHFmtNhwtl6p5IbDdrqmbhoevn3Ln9h1i6rlx44Y+yDbBY1JRKe224zg9vcZsEUzfpaWqKpN6FfZty3q1GxKAfezxlRrMtuvNY4tUwdPUtfaQ9Z561qgkb85a0h8cp9eOiSmyWq2Jhk16w2tzShoiG2Xuxo0b3DNRpXpW0+72QKbrtsxmM202ElTUqxjzgs3VdU3fdpTO9UXVrySZN5uNevEmZTufz5TOGCMba3KgFa6JWdOQUmY+W9D2HSmpR9R2HdcfusFmdUHfJ6qmwQfD+quaJA4xrBGnBSB9l1htW27dusVutaGPiZS9QQvOCncyznn62BemtBrakoyjTGJnoWsyPrh6pym1VBIGSuFH8x5OBb7pLdBt4bNfhPWcn0gN+zxT3N87Ul+8cvUWtcnLaMBLmDwkOSe/l2RsMeLYsb0JiBWYSgyVH+Acf8iaUSdVRlhlCrNI5uMCxF5drr/0H8EbXgd/8Ifwm78NIrwfd4BplyRrOZ+yeBaqoWQUR2Yc3ywcyAgPgY1QKCwHyU3AkpkcRA/DYghDYVUIHqGzBcaK8IyCeOPGDd70pjdw87WP4ZywWW+4c/ecr7zwAl/60tOHJfxuPH4x9r5QarM+4KWgKsX7Iwv9jgfJQ2JcRBkqYoqd+jlD3A4aicehqKps04htoHW6MaLIpscjDkg2V5yQZLJYpXEuler0wuVXGGpyQIsW9Hr84FAM87KcU4kezbY88fF/+rWTjM0ps92pMH9Tzzg6WhiGDPuuA1v1InB6/ZqqU4qGQS6rd1rgjMKlr2pHU1fcbbUJyHIxV0w6W+PllBHvOV4uWW/WFk0Lnd2UygvLxRwnUNfzwXgH71ldXHD9+sPkXqmX680a7xykRAie/W5PVQVlagSPmLGsKuHk2kILKEjUR3Pl4FKzmM+0m1TfK7vDqIN1M2O5XGizCelAFLebNTO8q602QOmj3nDOIsFQ1w3rs7vEvmcnO2azGRcXF2ghlcpCN74mAft792iaOfVswflqQ2hqmsWRapZUtbIqsj44u67l7kt3OT+/YLPdASoaJ3icr9UbCiMMkFMmWUvEUh2olLxDLxJKMRxW4yD2GiA9HzWBWt5yHd71enjmafjic/x4u2Tfz9Qzc4mUNSqhRPfZOpROnJryMIpJE+QJbDTAA4A6neWzyWDubIwb1Dsr+wTtqee1nL8wmXI0Yzud84Ak+KDTvMAn+gRPfhJw0DTKOEiJJ1PicTNgBbIYF0hIriQlBWcJZG8yxkmsiIisePnBGUzQjzzCQMN7drzpWFRe9xvNw659MPmFgEiiNC5xAu0+8tKLd7h96w5ZPj0snA8/rLh5MJricLx+TLA6KfIccVyA/MjXcTIuSEzOOZkHEeN4r5OAlMhhYqirCfunML0OWEtlboJ62UC0+SwwtmWUPEZ+9r2Sp9BCLHViHn30Ud7xjrfz6d//fc7OzlTW2dhjJc829JS0ojGtVB7vFciwSOsc+Brj0WcArxhk0zS4Tvu51lXgYrXRwXSibct2W1LO3Ll9h+OTEzVoIeB8oOv3LJZLnnv+WeqZGrP5/Eh7UjrVsdfkWMU+t3gc5/fOOD4+prMEKaBqjrOaqlL/01sj8V23Vcx9UdM0NVGy6qmbQmNw2oGqmdUcHx+rlGgI+KRaH/WsIlGrLoy1Vps1tWbkg8dHlUGIqaNUS4YQjJEhVKbNPpVf8MEzd7OhejIEo2uKMnOU2qW4X0qJk5MTvFNRJO8CXUz0XW+t0uaQhes3bqrYlAtICLRdx3q14mK14+J8pQJYu71WsWbBiSYPvTdNcTKx70ZvLBk+mbMlLNXoONHfM2oJdcLLxJCJetHe8wt5BzPgve+ER4A/+Cy8sOLH+1M2+9owVOhyh0hQsyJa3KNelR+MQvF4bRBHb2k6Jyd/DwZv8l55V0DZQBaekzMSk0pfZLR6W5IlczOXDjMc53HgiarShKmekCrTRXjC5AYen3xv8GSjte8rBX92GtPNO6X/puLpF0+ywEpy//WXpKc4QUqRWyqvuTFvAahkiFOP2KKDECpU3TkO88AJPPf88+TEJeYPI4QxsIs4yA8UiPZy5JEmht7WLGtUP75ml6pjb/cpTnIagFWwTpLpk++Lm3j4zqKriFGi1fgW3n5KmRyzKa/G4Xuf//zneeqpp7TtqN0DZ3PUeYEspqSi7CWA5AyuQ4iGD42X6w7G4attrw5DnxNNVZOs+MX5QBDVBa+CY7vfcVIfKRbpQfrMbF4P9KI+JxUMQ1t1Hc3n5D6R+47Ge3zO7FYrLSd3DnLpvt5ZS8KOh29cZ7ff471js+5ZzGqapkKSNjWR3FMHR7vbU9c1lXfUC2Wm5C6rQa1nROskU7xV55yqYlqCarPZasMDr/Ss4+NjLi4uIGbVb69neJeNLlqbwNqK01OVXfW+ZrlcXuJAC81sxna1pmka5nONXrrYc3xyjZTRLlb7jt1ux2LZ4L2nxxGdo5rXLGdzYjY9lODJSVhv9pyd32K33dN3iV27t54A4H2D98GaKvfDOAImPWGFME5479e/l3f9mT/Dr/zKr3B2756FziqLoJsbWuHFqMYfyVQu8vMuQ72Ht9yA170G9iv4tafgnvAj6UTZNz5gJHmMlKjeq2SCeJV/MOSkQC4jRGNzUNS43QdlGr4taFjuC8ad0gTWsH0w4uG5V/lgJ05VMTXzW9Z3hvXPzlZy5gcRJDg+UQxxWZCcgz7yhEFNj4dKzztnoivnp5vmDiZ0RCt8OrikCcZfcPCr8O+UEy5puWwGY5yApDQY5ti7KSKlAymBmItR1n6nTpxVsTpDJi7FNzHr4ugU0sgCqR9zKJ110yqUyqFqWcZ75kxGYYqIFBqt5Ewven9cOoR8ys9B+O8ShFRQHinhj8iAxSt0l4c1zzl1Tgp+XmZY7SpSyvr82Pj11vqwLBZ6DDtOxiQr1EmqhsUVEKf5oK85j95wLOdV+te7wGxedBwywUFlNMEqVGTX431NVfmhb6uupqp/E0IgOMd2vR0kUnurRCwTrvIOl1UeNceevt1CUly+a/dU146og2LNfdcZTKShVHAKKTRNTbtXnRsxwNN7IVgSJqOruiTBeyuuGTA8/bnb7WhbpT2GUNmrTdKjAAAgAElEQVQDFKnris1ma91otJVYvDfKJeesiZ+MJqLFeViCDxXeBUIdcFGFwxyw8DXZbdh3kew8OCvecGqQexTjzCKsNnvWmy0vvXiH1WptEsKBVBbhMC4wJV9gpsBeH3uSkuFzf/g5zs7OWJ2v8Ci+7QSiGZk+o4u4hnY6li7x8+JA1vDIHN7+KGw38Pk/gZXjh1kSCfogWMJRo4M8PFzFUxVkfI1DI3OZdfNycxQYsF+xRB25JP4OYQ5SVk/euYHiGLNZerv/XHHIlNPguT+Rkhr5EOD1j8E73wkvfoUn/uAPeHwYb8sJxHJeNhxmMGzJU4/ammwUvN4u4OokZ2aomxiw42IAwXj3uqgkGY0jjF7puE/1+CUV+MgdGOjh2ic5E/EqzJTKYpyxJHnGxTi5BwXaUy89p6yRaNmnvUdWETEpdnoYvcPjHvy03+MkEQ7mvecCM7nhTozjOD4L47iMtFNE5Qy8LfRjTkcQKXQFwwXLfoq3b2Og7KuvMY/eOZXhbduOzXqD9xVHR0uCd5xeO2G321IFENFqtaOTI7brLeuLC2Zzk9Odz7h3cY8YO5oQaKoaZ11dtHjIcGnvoVctCh8S86BddoKvtfO6d8TTY+ZNrV54VA0OCUIb9wgVPjjz9lVqQdDKSE3kCoQwhGg5ZxyJ/aBo6ej7lu12S9M0qpUvjqZuAG2eLaK85FCpx3h8fEzOWRtQm5RrU2vTjr7vqMWTnNdCj74nI/Qmjbzve/Z9TxUC2TuOTk8t8ayYpniHr5Sbf35vze3bd7i4WFniDbxUkMB5QXw9VFem3GsydrtlOrHLNU7pfF3X85UXXqKPyZp6jwZEKYkqC6Ca6T0VwkfooY7w9a+Dx67D574Atzb8xD1hHxfEVJtKIyoXUQyeUhrU+FxKYhaj7vKh4S736apt8LQonjAHTI8HLg+5GImoHPHiNR/sezQow4nmNBjUD7jAJ/petXqefw5uvaSyyCnzhKmdfn9SvjppXIAAJDuFDwzKmVISR9Tl0MAfQAET7L5s0yTo8HkRoozMopIvkLLigAr5mQFOqWcwl1NvgImhNFmCypsYn4z3EcboSyUZeqqqppk1vP9976dt93z8Y/9Mn/MpvASWeB6bmQx5GpMgHqI7m5vl5jvn1Hu3gqdMxvVWdyDp4BAHmxvHxJUL8DZm0QrjDP7xznoHGFSJwXXlwqMVcE6ZYeKuPuxV26vC0GvGXujaVpvvxkjbtgSgdo4YgJzsvUwdPNusBmJWVTQhEKpAbRx3L0JMvbXomxsbBTbbLUg2rfXEbDZT7zeoZ9/HSNVUPHp0UxNukzZs6/WGftsSrIS53Xc4VLuF3vS2xZnCnLCYzbhx4wZVVfHcc88Ae0DhoqbWBGeMcdAk2bctKWWqSptFp9xri7G6Zj6fKx0Lz2KxMJikJ/aJQI3zlYbFPtDuI+ebLVkEHwIJr6JbOFxoNFdh0cZ217Lb7Wm7yMXFOev1Tpt/h4YkBrge4OXZ5FqTUu3UjVGwpGSNxKGKgwOegXMMVY9tNH2UbJINMdKESpsmOyHkzEdcCzcq+Ma3QdXC7/5beH7PX+GIPi1pkxayaPIU7f85eH6j0VAmSjx4OApeXLy1+zzxMifTxMJfnq9WNFUSdkMAMfGM83TcoobgBUnPg4dYMFiKu3rw9Kacedxw4yeMJ05VQ+2GOo9fFuXaf39Sg+O8M0B6DOvFucHzJo2GSe20wU+XPOw8HUdLLHPF++Q89EUur0sc/rAIQmG55Mzj9s6813FMnXMjNIT2Wp6yUg7GdrL6VqEmo7DL//1//epQRFTw8am2jndOJ6PovACLsjDnJE8GxrnJrR+P7Y1V5cah0P2jpIM4YfOQRsJBsjArdmnoFFWK80TyUCAnaOV3qTEYMHkZqZiFVfQybsZ926vC0DtxVKHieHnEvfNzFSgLirelvuPmjYfpY68JFFvJltaGzg36HRswD08ZA6o8l3MpfHI0lRrVxaxmVgfDurUAYtfuWM6agQPvvEECQ/GCqtvdPV8zI1OHhl3f6mpseiN9jux2e5bLI2JUds9qtQFgv+8G5o4Ez/Fioe3inFD7mvV6rau7y9oQO1Q01qrQu4rdfsPJ6ampeTqauiYG5cxHJ6zXO9i2Wogl2o8TEZxXvJTgyX1i22b6rmO1WnN+sWK3a+l68xZQTW79npbo6+vGHsGRYhwMVZl8YqG4BsKH2C/kITpIMYITPZ8kQFRll5SYhYqPSAfpNrz9dfCuN8L5Cn73j/jrq8COU/oU6KN6anHw8txkOdLdloYXImK8/REjH00waogyg0GKXIHRT7ac8oCK6pRIZkQxXJyxInaK/Uo/WUiSCdaN3hoZ9fRgxOWHo+h+3m8SCU/s9+opOg9NTQndftl73p9NhGxawGPXd2BVzbvPeZArG/Dmsk0o7pNE5EgBOYB78uGYjr9PDykDA0U55P6BYz1wxyc1FtOtGFkRoYtpuK93796lcNPHC7GK1gypjySJSk8Nh/eJqbR7zgzEfsYq6OGaRYZzK5BYTpns8nR2kVDYR6miul9vEafkUq+hEZnmMUwqIo1wlDMos/TfBcgmS/41B92IZFzcs9ucs6wrmlmgdtA7WC6OiF1iPluwb1san4ltq3TK2VxhmUrLyhfLheqqo49zNelkE3Pk2rWHAaiqeki+LJcLdrsdfa8GPUZNLM4sOZmT6WJYf1SlWql4Vdd3xlsP7Ha7gZ7W09Gnlhdvv6CMHxNpUp2Kamil1jQNOWf2uzUCg4zpybXr1lglcrHacLHZ4PCQA/tuS9+rN91nwAl9TFRz9fSjU5U7FWaAgqNvNj3rzUax8oudNkjpM9oA2iESCG4UxlLM0DR/irFwZbI5syWWdCpwgNPvkLQSVXKCZJXGWdsYaiOWSHYJ8QFPpPHwM/EM6g6+851wYw5ffhb+3Uv89XjEqnNkajKOaJr2ZM3pDI+Vecc+Q8YEq2xx0jkmpqmUhzA6OxkMa0nYXYbOC8ZfRnKctNbeTdIQ2ajNKAZi/HSciJbp+GkiuhiOJIwwwcSoWdyg52Fe3A8An8x56HNAFbDwiCdFIPa8T6pJKb0u2Ignpz1I6Sqc9Qj5/vh/wPZtPicj23tbQNNknHTc0mTQpgvAOBTZFqERXpoUgBXnwHDoacXnZezfrmioWHbmYfemXJlKO02xIijLZ5WF2dt+4gQG0QR2Gp4Y0Fo27LYMzKYSwYqQ7bhivNlyXWkyeYJ1ECuFVJk8eug5D2NQFuJB6yePydlIHoI8EY0mY6kVOETSXnZ7VRj6EDzzZU1VXaduarRvZc9sPmMxbzg7O+OonisjRCou1mvqZk5VB6pQsdtpc+xl3bBarRDRAqBMVM8WaEIzaL0UhkhrC8ZmsyGlntlsbhx9GVgv5+fnzOduUIxThcdaEz85E6xZt04AqKuaxnjnpSpOG0FoJyjntMnxarWyaw/s9j3b7YbrNx7Gu9KuTWi7lt2uIxNp5gv2d8+M6iUIqlXiTK41OcH7Wqt0k3oLfZ9ZX6y4OL9gvdqqDnlnwgBOGTNKvbeOnylbkVIRh2HwiBGsKrk8qEq907L80RMZGh/HotsezRZmxKlhcT4g3uF84qOxBW7De18Pb34t0MIffBm+vOHH44Jt9CRfQ5/Uq3Got2VGcoi2bS7ly5Ya7jM8xUgM12VbYeYcfEcmMcCBh2rPJ2MauhRYXfrowZZSnEAnE5gmHcQKl36zKEUxHk3WCjyRga7XZK0X9eQl80sos+x9gHgODGthbpTj+en1D9DJ4flPE4oigkuHBmpgmOR8v5G/9Psw7nag0bEYI4bS/UxS0ao6zCWUyDhKPjxM1jxPycGkrOyn6T0tC2kVxmbkvTF6NMjSa+sHnrydn0W2XgJpcj7TJLVeUj4Yryn4L3nyd0EMyrnZwnMZRsw5Q2KAmmyvA8z1SrdXhaFXw9BSNRVV5YhJBc4W8xnOO66dHrHdXliHF0/d1KroCDgysRtxueVyycVKy+9nJ0u6bo+ghrvvO2azGUdHx2y3a0KoaNs92+2GxXKmrdz86HG03Y7FYsFms2axWBJjz/HRMTFG6qYmeEfbGqUQxf5Lu7iD8BENs3b7LTEmlssFIVTcunVLrz1n+ogyZ3xgtdlTBWXHzJbH5Kwc5hAaJKjIVfHURYKa2ZiJWdj3PRcXazbbDbudJn1VHz8Qe3Cu0WbPFEOjsE3KWeUO8piEKtdVNu/DZFJrzgDEIA0rDMr6nUIhi0kFrCQ7xOs8V4prz8/1O61w/cb3wLHA7hx+/4v8rTNhF07pckBVDTMuKSRnANN9D/8ECDnYDuABUWN8FevmQd858MAu4/jZzPuAwOSJwR7vf1mMRkbGlGlxCZ64BHeMxyrY+/iBx22fT3advl7XcP2aHvDsHr/UavOVD8phFfBU6TJNDmgzypp8jOdyudT+AErJeWgEcvihEXq5/Pplww+GkpZzLAuh4doDldK2GCPZ2GIFx3dZI7TynXKoy4ccopG+P3hfRJupF0Nfzj2lEfwjZ/o0CrdpRHD5Pllx2kEkMh6oOAhkDmzEEB2JjDo7lM9rjc+UQHCwqL6C7RUbetEM2+8Az+acf1BE3gJ8GHgI+F3gr+WcWxFpgH8CfCtwG/jRnPNTL79zbbc1m80QPJV1ihHnaeqGe2fnowyANQoJztObpzybzVitLqiqwPJozm6zJgPdvqWqVYMlW/L17t27Q7/S3W6HuIzzOnn6rqeqq/uMdCk6apqZtjtMIx+4rpVVstvuuHZyMoSEfR+pazWqRVa27fbEXrXxm/mS0xs3aPctLniuX3fUsxkOT0hx6DSURUCcwjxWaNL30cSThISGeW3bst3uWK1WrNZbbd6SdXBzcob/O0IVhgckpoRETHhpZIRcfpCLF+9kUlBikywmhWdSwkq+Rw8sqTNCSj1evLY8zBmXMh9xa7gm8J43qbF/6QV48Zy/2wkdNV1fIgRwSYYyf8/0ATJvC5U3UEOYmBrky4Zdps/God00ry5fadTHJ5QrDL6NnoXd5ZyG8XN6brqsJjBtlSsZL5deK59xMHRomsJriPC4wBNdB7GFv/AX4K1v0XH4o8/D736aj5+d8fhWI9VCkX0gy8hyGSUZOBxnuFAZsfqJJML0GqbR0GFocP9xxrdkUsegbwS7p/nSmJe5EGMcF4UJ5DNANcOxLo3xpdeVwZNMZU9pmKVlYNFcsh0olHkJjy+FaAVXN7q+VX+PLKdkPXljNDHCKfQymXOHbBp3uGBllenLKd0/R19m+9N49H8X+CxwYn//D8D/mHP+sIj8r8DfBP4X+3k35/x2Efkx+9yPvtyORdzAgNntOnJKXH/oIUSEpp7z4ku3ePOb3gSiPVBj1GYAdV1Tz2ouLna02x0ny4fxqObEzBKZdaXdd4pI02zegGQypna5tWTprqOuK7yrlDppxm23bWmaevDSvQ/0pp3Tx0gwqYHj46NhAXCW0C3NCpx3LOfHLI6WtF3PcrGgz0I9X4KvIUGwRiI4iOLxVdCSc7xOMEC8N8VIx27bsm97Ntsdq82Kfdtb39uISyOs4RgLTLwPg37OMElNOlakmHo5NH5Z2+U58cODqpBUMezGU8+A80SjcqZ+D1GbHrvgkAiVV7zxw+05vO0Y3nVTn7Y/+hLcuuDvXX+Em2+8xvbLZ6zvRWL25KQPmjg1oupvjGyVApkUHXnh0JNMeTQo5fPTROkUailG/ioWjk1U+2GRz+Gbo7HXnR98Z/yUH97OkzB/CNsnYz8U+xR4wIzBEGRQjJfncaewxCc++ovw2EPwQz8EX/9ueNtb4Wd+hifW53xofmTSwCNMdBXTRg8+9TbHxaYYXqX3aWFgtgYjw7nabgp54gDO0bBBefYTSz/o8qCyJmCUQtvnNHehC14eRv3yvRoMo4wVzZcjvvuCjUv3ti/G2/ZfOOw+qNRI13X6DDAdPw688RDCUK2eDbUQnObCKLmP8azLNU7P7fIsHCi6f0ro5hWJmonI64GfAv4e8F8BHwBeAl6bc+5F5M8B/13O+ftF5Jft918XkQC8ALwmv8yBHn3kRv7Jn/iQ6rnYqppTIoSauql59plnqeuKpplRVQ0hCNvtRpXknBZN1UF/np6ecnZ2RmcdYOqmHgao8KoLTl9XNS/deomTkxOclESkEKpJG7XsmM20knR1saLre+W5eqHdt8M5lPldpHW906pW5xzBO0saGfU3QTIcMhttUiUgAhBNzE1I5imn7Egu0neZ1cWKi9WK3XZPiqJ9c8nGxikPvnqRguqeFGlVnZSK34J5Rrk8iGOlnXMTCoKNW5lgKr1akq4yCW1VKrmqKq6dXOPO2W0kVSjak6kl8WHZQwN88xvg4RrO7sJnb/O3zyNtH+jF0XVCxhFzTcyCtu9QtouXw3Mq1wDFzGbjk1sp+nBdupXFrxTAMBh82+1EI+VKQ2/H1h+XtFGkQDj+0sen0YUdltGAy+QzBVcuW1FPHH7PU7MyWQTABMYKhNLxRE7wXd8F3/M9ys754y/Cb30KvvgUH/IqMEempK2H/V113sP7k5fKgnQl7ZFD4z40755U1N5nDoaPH74+RDQTN7fw4MFYVpOitOmYlLG9yvSkzORzdj4yZdOnAa6ZMnBwClUVT316joXAoL/reTWN9oit65p3vetdPP3lL/P8889TVzW+Mpplur86NucSmbgBbjo8f13YP/nkv98OU/8T8N8Ax/b3DeAsjx12nwFeZ7+/DnjaLrwXkXv2+VvTHYrITwI/CXByvAQRmqrmvF0hkpnNGnKfWN27hyPhBU6OlpyfX1D5BbN6Ro4myetMBElgs9tqIjIrrpviaHz7vme+UKZLXdeklOhabTG273ZajHV0hFhRVd91OJ/o+xoRJWjogh3RZj1a6OW8o+8S4rS6tI09zz//LKc3rlOFmqOjpXLtg2fMuSVyUo4y1twYH4hJKVUpaeVoSpnNZsP5xZrVaqX8+VK4krQhQjK4Ilgx1dAc2RYrfdBGsvPo5E4900n1n5R3i9HSDH+iN+57mdhqXBO9eXlC7hN37tzB5UQIKgLnXM/PuDW8/SY8egLzXmWFP3+bv3buiPmEPmWiVbgqGqTeqx/OFBPmGg28LUEDhppS6Rw1hUJGw5owWuPECAkTA2xP2NSLPsRLR/gmW+Z2gKmwyGJCsBZEk6GDhslYoHXgoV4RQRQoonwuTonbBaMl44wH6UMxWoJzFT+YMv/stz8Fv/dp+OAHFM5551vh//k1PvZbvwXnLR8INci4uIxn4HCMBms87NTSj4vTYDBlbJwxUhFLTkkpy4ce8CXXNU8W7MvvTxeRyT+yyicnGWG94ZOX4bFL4zsi/8UFKseONpICk0hWP6nPU1FG1X2NC8FUw8dZHiFUgXe98x28+z3v4D/+vu/lmWeeYbVe8cdf+GOeffY5bt26BdkNHaRSQgUYYyZmFQssEYlGcdnO9d8jdCMiPwi8mHP+lIh87xVjNo7BV39vfCHnfwD8A4BHH3k4B68UxUJjvLg4J0itjJujI0SEzWZNFyMuBLr9Dsjs2hbvHZWv2Lf7YaKVZuEFny/C/6rwaI2/s4p+pZRZLBaakAmBdqs9NYOvhl6uXdfrcaoZKfVUlVap7ruOpqo5WjREn5nN5pzdu0c1m9F2icVyhgs1KUZthuPD0DRcJOhETQo6aA1MYNd17LY7zs/VsO92uwGzLtSrEjIj1vW+tNmLUUUG8sgdn3oaek8nYe0lrxTBEk026bNp3Ocx81+8DbuPJFH0PDjBiwMiITuCJD6Sd7DM8I2PqZvQXcDn78JTa/6zdETba4VrQkiG4WcKTDPOl+LdZg7pjgWuiYlRRWGYcIeY8WVPskQAkzk5jMNlnHf6PjDwoKeeawYrNx4Nn528vl90H7gfShiOYYnSPP3+FdvAfhouWA6Mb86Rx7sM/Y4nPvJzcPNh+Kt/Ff78d8H3fCd86tN84p//c+iED1WV5XTKtSSuYF0enufU5r/MOeq1TsfNaIgTXjigVeYDfGPfm17/FdGCCujZQqvWUSERGY8/RE5lvOz7Xq6OvGQ4/tWRznixk3zVZA71hSJpi54ygTz/9t/9Oz7zmc+w221ZLBdcP73G29/5Dt773vdyenoKwDPPPMNTT/0JL37lNrdu3WK/3yNOSRilMEzzATLII7/S7ZV49N8NfFBE3o/qB56gHv6piATz6l8PPGeffwZ4A/CMQTfXgDsvdwBNXvb0XaJqarq2o6pmdLuWtm1Zr9cDmwVE+c65rG4aEm63W7zzyq0evDD13KcMh75P1vkps1gc0bY9169fH7Tsu06xbu364oyeZlxccdaaLNG1iZuvfYTnnnvWVn31Dbs+s5gf8djrZpoX8DV97vFVTXDVQFfssyP2qocvOPb7lvPVBauLjZ1HZ0lUN1K9GGyGTiAZG2YzgQFSPKRqFXYIw0fFwtTD8u3BW86mJJgyOfcH3y+Lhsox2UNsGKokpT82XphXDT/lbsHrFvCOx6Bu4blb8PyOv307semP6VJNxpNzp3uKPYTqFfsp04QnBssNBpdLnvLBWDAsgNNjPciznr4/vn4F00TfGI6TxTz3Igo2jPODt6K8mHMemoOU/R5evPWOLRZ58n7skw6HgJOaD0givXiHJ/7+34dv+zb4nu+Gb/0W+PZvgRfv8rF/9L/x+PRahYmmz6FXf9X2IFR26ulPP5tztjrh6Xh+lf1dMvoih7pGunjkwfDr5ybHLJ9zpcI0DYvpmDwurRrzxJWY7OiK67t8rsO9t+rpZBIqfQdIxvma7baj6+5w++5vKqpg+v2PPPIIb3nLW3jXu97NzZuvwTnHtWsnnJ3d48Mf/lnu3r2rPQdQKRcVSnxl25+q8Yh59P91VtbNzwEfnSRj/03O+X8Wkf8S+Pqc839uydgfyjn/yMvt95HXXM9/8698iJiS8c2h3e/Z7jbcuXuHo6Pl0Ac19Zk3vvEN5Jzpuo47d+4gIrjK8dD1h4xGqVntyuiYuk8xuAZa065PUYWZmqZmZcqPMfZsd3vmiyVHyyNOr59yfn5O7CPnFxfMZjN0AWlomvnQaCAj1iQjgfMkMsFrEkaycs5ByFGLIFLWBPB2t+P2rbvs2lYnMH4QZysTTVlAo0EtWK3DJH31j2E+lkKKAyNuzA993Y1vTrsGaeaMEpVq8cnUwE/+iS58BeoJ4pXSGhwf9h0sI3z9DXjIaVPsp+7AUzv+RpzT5zn7CEk8pEgfRw9uOOfSZ3GcfHaOY3gvemIAqpVfZHQnnluBOYD7m0KQlY53acuX93HF5sOhy6tjIsOYaAJcS+y9d6PRsSRfaQxXjPrLee9qfMaIzMn97vbo22RtjSm6EGogp00qau/4WDQd5G/6Bvi+vwy50x62v/CL8OWn+ZBFuNHgA/LY/i5Nl6jJvZFXbkKszmFiiMdVefg5UEHd/UYbbD2fePXj68VROYSPpgtO+b0Uf+WsrDv1GC8vqlctVIcCZ2WOPXArC/9w+6YMnnbw/gWnrMK+B9E6n7quec1rbvC2t72N97znvdR1RYyJ9WrDH33+c3zhC1/gp3/qH/0Hbzzy3wIfFpH/Hvg94B/b6/8Y+N9F5I9RT/7HvtqOlCqV1UDmqAyXoD1Vlb2iTanrZka1bMiifUlb6xC137fUUlNVtTFdNPTSBh96c7R5hifFyHa7JecZu+2WupkR6oauT8S858aNGxyfes7v3eM93/CNxBg5ub5lvb4gohV1VeXJONppgwNUW8Z5D97h7WHPWcipp++szD7Bervj7OyMzXZH3ydStJyCBO1oRKA0yih9Xgv2Vybn0O2nLDSTkmyyGyZ1eUgL5CMiVmRVHoICl2Q916zjlbM9MJMKpElXtIMkompIJqo6EFILsoH3vE4Tru0evnwHnt/x490MZGEyBk51aNDkq54jxKTRiysYadmmOLJc5RVnIJnVOaSBDkbhirn3oATkVzO+Ux2WYnjLWjskWRHIiVR6n06Od+BNloVbJhRXRq/65Y5dms0U3ZxRfgLz6oVSENf3ifclbfr9xO98CrY7eN9fhuUc/tMfhn/xL/nYZz7DB1ql7xaG1mXteDg07gee+VeJx/R7owEeGT0jm6SwjfIYvh7ep2l0Oj2PSyvOFLIrMFd5ZqKUaFTHShgZT5NZADwgcivHn9wzO9j0zWEu6MKYGSuR9dkbP+rYt605MVBXc4IP3L59l2ef/Vf863/969y8eZObN2/y5je/mW/6pm/k277tW/jpn/pHDzy3g/P803j0/6G21968kX/ihz9AM1tw5+5L9F3H0dEJq/UZt27fpq4qbY+3WDBrGmbzOe1+jxPHvXv32G42LI8WHJ8cs7q4IGeo6oprR8dDgijGjIr7ZxKRummUE28qcjFp9ruqAhdb7UpV1Q2PvvZRbr72NQDcvXOP/V7zCM4Fo6oprp9AK/pEyFmQHI1DDrlXfP7iYsXd8zWbzQ7VrtDy9N5CPfFe+7F6B0kQp962qjoWtoLpdZteRiy0wmmTBhkfzDEc98OEd/gBXxaiGfZMRqmMg+ZLTpogHYAaPV8RTIPbevc68JL4mXyh1L73XofZFrZb+PwZP/lMxzovcXLMpo8DPpuS4e5xnIORbP07X8ZgGJ5QmorodZcIpJzn+JBPPXqyeqqDRz/RlinedRm3BzE2AORS0rXgxYQwLFE6r+JBt6KhV2geOdqFjaINRO4HDHLORIkHxytbKIvMhAWiSfwEwzgrHDEdUu/gE9nu7fd9H3zzN+mLPsDvfAp+41M8fnZm3dKUuZYeZMQP8K/Jy1csUsWTBvXYy3jcx97JY1V2znnIbZTDXXVfCmQr3o/fe8B5DEl8c6ZEhNLYCRwyLBqXGnzI4T4FSFNC/CS5MRh5oKgkiTk1GszY99QDG6InjQhN1rjvEMkDoQQgVJ7tdk1VBX7pEx/7GmolmLOKDHmFPZObE2kAACAASURBVPb9juPgqZo5oZqxOF6y3Ww4vnZKY71fz/pIu9szny/wPuCdsNtu2W53OBfIGdayYbFYsjy+BoaFaY9U9ey9D6TYsTxaamebBNk7Hr3+sOUEIgnh9q176l1IxtczEzpzOAnjDTdZgtRrE5SuT+z2ezbrNW3Xsd/19L2yX8QFcoqDTEBpFDEqZiaV7J6EhykZ9z2Xv00PHPMOLYYtcqfj5DSwVuIY2tKblySqRjltki0M0UIynLgsNBBtEVFYxVcVLrZUruenT4FveSssgbyB51bwhy/w1/dHtBzTU9PFzvqAiuH/do6TEH2sRHUHcMWwTUDjgoMX7xVRYTKZYrKXDYIw6o9wv5c/YKz2vVJAL1L+082nSXLPdpxBpRpk3BeMBggz8MWDLxBFkdRVD370H0foQTV8ptdw+ZxjHBuhqG7LSCFVvpXRY3NGRSs8H3TalvLJX/4k/Pqvw4/8iF7jww/D9/5Fnvg/PwaLJY/HXvd/6bjD3ZngJwfVnlcY42hedDm3l4PInFM4JUsmhTwkH6eaOwfnU05wmBMyRAOFZTPe94nBLhDOoIt0GAVONXCK/16esUmcqQv6eAoHyNQwJlPuvAr5q9NWiuoEVOfE0Esbz5jGBb7dR4KfD4Var2R7VRj6lLMaxf0OJLPdb/jSl59S2d1OxbgAvvSlLzOr/NDoO8ZI8IH5fKbiZ6nn+vWH6dpWYZ+uHxKdsY9mgDuapqaazZgbFTH4CldVkFX8rNy8qm5U18Jr0jNQqbeQkkkAC7FX+YCYEtvdlt1up+ygtqPtWtOX9zrxslbFMXSgKpiuWqoh3B82Gb3tgsBMPc480gwLSyBne+7KhHMw9Hij7MMMeQImnk8yjZw0wa2d9wpDiSDZ2gsKCBEf93xENvB1j8FbakgraDM8fRe+0PIT/Q3a3NAj2v81O41ARIzKaOco47VNH/yXQU6Gaz1wga/YDr25+43JgaEZVxl7k0k4L1d9/dLJYMbF1p1slZDFuOTM5arryYnYkQ8wkeEcX45K96Dk5TiXLlFEkSHBH1PSjlUXFzz5D/+hiqXNZiAe5nPYtzwhwuOmEluO9yBY60+Z8xu/d/k2ZZvXhof5QScmD8a5KEKOeP3oLZdFcqAaX7p5B+dZ1DIZJuJIOZVCTR5OWn+8zLXfB+fAlQtaqXVJl+fEwZdHkbw8XC+mG3XFSTxge1UY+pwzddXQRTXqwc+YH80UIrimWPd+t2ezWbPZ7XFdh/jArK7pOxUjSxlm8wXZdNmd83g8MSX27Z7ZbM58sWDfd1B6uVpz3pih65Ia3AkeriqNpdmvQRs506esieEstLuWfdey27Zsd3tVv0xJ+0Zmw+TMKANkEYJzA/6qUMvhxCiTsihxqjEe4YRCg8wpDwnZnEv4fmhMyqTTCEDZBvrAqq/Sl2KWGElZoQAno5a3toTz1lUrI/Tk3NO4zE8fR/iWt8G1PcQLeP4Mntvyt24JG7lGHzXPEnMPSdU4kxnCw8dHk4f6/JaH7YGTZfhRSryGdnhTW804XuRCv7tif+MqMwQLY/JuxMCnXn7Z98ttUj4jcuCNH/Dnr7q8iT5wdpdYJIyQw8F3LmHRIhr2j8GPGwrbykxzLhh8WOkyII7vB6Se8cmYgR6khdr61nYtOOH9k8XH5eKfPuhmfZWV+uU2mejJoHCburVyYMiZwCO+RLtXnEJZqGV84RAqmrymSew0PmfltXxYuTy9wjLPphCUTJ7DsRxjHJPi64gc1mvAFZ76gVU3aYj0NebRk+He2Tmgocrp6SnOusyEoFj44nTJ9dOHAG0d6L0a8tm1mcocVJ7tbqsNtL23CtRMn3pCU5N9oE8Z8TXiBbwj4gjGvVVP1SCZwdBrtrzti3BZpO81QbVeb+janu12pyyFnOjafnjYxsSmsXKtQXMTGpx3dG1n4Wca9Nrh6pVfX58mGLFnqxwD66dQ4IQRGSw0LyjVooUamYf/sYmsh04DhVMEqrq2JhcJ8RVNaPnpdg1vfgjefQruFuwF/uQuf+crPbv1jH2ckWRGnzRULS3/sozJa4GJdzRZmIaHwzHW4x3O82IrB3aGfpGrDIupyB7oqyPjg1+iffWq7td9PfDoX8F24JhmhZG+amgy/f5EvOrlQ4jxGAMckfPYPMPO5nCRuHymgidMIgddWN5P5skUwZhg1DMIM9h3PJkz5MT7rPLYiRyYpasWw1fi5V+56E08ZGAQLRuuY2Ls7QsA6jgZnncgMjDFntK435LczpaPOljUrRhxSJqXqKYobE6iAEseDIdwU037acRXrm9iqKfO2WVIanpt/1+3V4Whd97jg3LUq+BwXjH0nTXECFWgbmqVLug1OdR3HaWDS4qJXd+p5EFdabONlMh9pAlzE/93OC94JwM3HsaqvTwAJ4p3pphUilisL23fK4y03hL7yH63A6xiNJXO9RMBJNSz9k4jEi+K34lo/9pBOiAZg4bha8PkOcDZYZi8hwlDVHVv8jldaErZuc6RZD50prBp4LJ74kxEykswwy+QIt6uo/Ir/skNB298Hdx04Ndwbwd/tONv3E5s4zWEmiiRLmZ6a6BQPO4HsRemgWuBOUqIWraprZwuoJnx+Rq8runDNMFoDwzOK3xuDqGdSyfxCrZiIIp3Pz2H6R5K4m6cB/kVO8SXF6GSyynHP4w+xBYf/b1Ei+UcsjkQ77Peyr/cdtbZqsI8FhDHL/UZcuR96QpZ3cu/v5LrmMCFMq7kSrGUcW5mN0pBDx50+WLpCnVwv684n/HSD7cCoYrKexwkics8uuLapqye6b2IsWeomi0qmw/IYQzV7hxGAg8YLO4LX7/K9qow9No7NNH3LSlp1ei983NSShwfHWkSL6hAVmGZhLoi9okuReq6xqWMczVdhMZD28eBoidS4YIoVq4xtWHEQhLVoAmikruagMzECPt9JEtiu9VerhfbrVXaCsoCzMTeekiKwiL26OhxM0OrwMIkaPtOIRbEjDxkDm9suX1yAG4UgajLLIDiwY/GP+ekkIxadT1P28uwf1Hj7wbYRAluXoQUHc6r5nDt5/T9hjrs+amHgG94DTQ95C3sO3i6478483RxTmSmjRFMOKto05S2Z/dHKTou8dAPtp+m216SaJdExF7OS7yPwXF518OfBmaUB/nSfvSrE0MxhPNyYPQvwzjTBWh6vgKDzMLlrShTDt6lzYrLZ3VZ9XJcJifjky1JndTBuW+sSv1DCduukDvQpvLCD+TMJyOQeuvKJCBef4+OX8qRx4uYZUyT1n2212JrrzD2hwVT4weKIY8TD/l+Ebmyj+k5yzDG5V4dLHFTsbuD8zFnb8Jsyr7MjqT9d+3+ic0JN0CMeVCSFJH7KopzLk+eRQ8TL94PEOGl5/mSh1OYg9PzVf36V9555FVh6EWEWdPQG4zhneP4+Fh/956mCgNcQ1YMXfnluoKmjAqB+QBOq1NFAs471bAXD1bgkhAoBjaXLjOOPkdr/9ex37dsN1v2bUeXIm3b20C7wYhno8UVWyCG+w/47iRMHLz2nCFl2j4dJH5sEA4wSX3r0AMZ8NdJeC9WHVse6KJ9M7Rhc8pwkalByFByW9P/vRT4yiqDI7i45ef8PW0M8voZxDNIDm5v4alz/vatBZvoacWTUz/IJl+2LZeNzUFS8gGuu64P90cC+sxfGqsJU2IY6yvGUD9gY2MGkcn37ju/VwI7TB/Cqed+6aG97F1PjY1cMmTTqOzgEtxlg3dYwGNnb9GNznFxulgW8b2yz3FBvGKxmHzmB+yGfrJXyQ18tmYnDhw8kTP0PY+7Ea7EoLHh3pm3O72X066s04u8D86eLBTTvMv9BnD8WpFPmOZHDlCtg0VmdK3uO59XCLuVyLGIpV2OIAdG1NTjT4f3bojfp04E98+b8dS+muc/bq8aQ++qQEiJayen1sc1Ig72XUsIgUVVUVcV2iO5x4nDhyK/68mifU41KlXFyOLsJpx54NDnpBl8Co87K1NmvVEjv2tpu444yAhM8L6hJZzi2a603hOoQjXxPhhma1F7LKEfDADLA5NqDxqj8edYQZooBVQTw3ZJv6U89PaK7ufAFVLT4ETFnHyoyDFShx3/x3GCb3wznEbI57CN8PyKv/PsitX+hD4vSCjMRdaE78DdvnRdJfJJucgOZxuHg1O5ckwOJvr45v2vPWDcDg2+vSYlWte/H8iI+VNsxbtLOi3UwMpYzcwV13P5/Mp7RaCNXNQ7H2x00hUe77DODHNgLJqbHnfQXD8wMILT7KY5RZkfsH19MvcK53htlKOJ28wT6vHwPltg9LiHcscPgnmuSm67PC6GUzqspHzls3LZQF4+xoO2YnwvCWIM76YiIjaB0/rpeB9ibta0XimsZUvcf388l+C8q+ZqHnN845YGO/BKt1eHoXeO+XxJ13e0feTs7IymrglVwIeKECpSyuy7nuArfKUt88R7vUmCJlgHH0GbYgsTrjbGYhHoUmS72dN1LZvV9v9t792DLsuuwr7f2ufc7+vHaJ5C0owejITkISAIwiIWxgGMkEYGyUJlTJnYsUDYKjvYYMeVGJL8E1elKlSlbJxKijJFikAqjh+8JAtbgyyw40pViO0kOAoaWQIUNNK8Z3q6++v+vnvO3it/rLX23ufe+3X3AJpuZu6q6v7uPfecffZzvR9cPb7KNMdCJN/cjoQq1U2u3sHK4zmBiQVQ1RreHpuylhLsE4oR/rzbENLCzjnqqiqp5orjzB2yLLgV09EuVRanqfPSoN66VaIvOUO+xJlx5qfvvwMeOA/DMZQJHj2Bf3uR750OWM93cVJWzKxJJbLrDWbo1eVBrKqszr5ghG/7sNc9nxrX1t9j3Hpl76ypUpYHpra1ke5gQ4qS7jNdv5tR+xRkFNz6DlBPLrcV2evvW7ZdtsY30MZNsp3Sz4sWrRx6tLEFPTIVtvq6tPF087SYC0ckqoSROqTGivDL1LiagwMTtWbln2C1bW3f6SKVwdBzoXU6e1/3DsWKe/b0y6ZL203tr9+wRTA2xljBc+UDZndQRUpjvvr11+6dWdUqpG1KEi5xn+YJE2qjRdcrs8P2b90cFF162ES38+9xUrMvOqzXa3794U9x//33kxLc9rLbmeeZ4eCAlSN8aIhgSFFB3pD9kBJFClrLe5nPr2bLM7JeT8y5MK3XHF25wvHxCeu15b4vpee6hSY4CorpHAUTeWOPrmQgU8wzqJStzWAbMtfN3ecmCUVEXaINLqqHXVxfETvsuWQjPC4eL4s8R7DMMrBjoQfdeOfhOJDLmrOrNT/18kN44yvh5Wtzm5xG+PTT/IXPz1yebmPSFdNkLqIMK07UMvSFVkF348D2breLtDXtpJHop4aR2hB0H/m5KH8nskAkm0gruD8LnDIEPXRVlfujkq+hZun7519aXxdcaieO70CgW+3sgGjTVBCm+7Z8OepByk2dVWpXtveR2V0agmgS6W4Out9/fQ+TeKRp15aiPKjCQ1O2XqTR/p0/gJMTPnp8AmngXVrA7VfXHXPth7+jn6dTvGP7vvYS07Xa3/oearLEYvP26jdNwpnVAev12lQuckpbuNMFLNRxUZK+712vdtq0JyzGtsFY1Pz0NyCtBNwSiP7g4JA3PfCA1TYdV5wZRzNiDs4JOgIXEcRDgQWPkMOClbQeOKmLNeWJi5cvc3R0xJWjq0zT5FlkW+KwXGaf5JbTQsHTCFs747gijFOKJRxC8Krt9lzV4S/UMwa59AtusHD1gmsewPqsI25dbm2Sdt4quPHTqX2qA+pEeX+fjal4jqGZM+kK/+Ob74X7zwAX7OaLGT79BN/zTGI93c5JSaynwSWBDAUGEmjGCrsmVJYG411G2F3XN5FrGAwHL9pixLRDXAuPjx0GWG0HpXlGnOb7Y+/J2WwcuxKHnaZqWIy1NORekfyGDrYyAH5NexVFt38Csabg/ESQZPupvnNRLGMbegZkSJ268MZxRCM6/TPGOfAggCZH+NlyG61WcHAIRfmoJZXhwSDenXdJb2ta2iv6V0tlZkJ67/teT0K3d7T/LtLSNZ2yflaPF5CyPCPdOpZceN2XvY7HHn+co6Mr5L5Yd5W0XXI8hdNRWLqYLeznuvNz8kLomjcMr55H60bhlkD0KQnnzp4jjcNCj5tkMC4dSMNYD+IwrixLZdFqkDBvGTOQXjk64vJl85DpOfdAFsW5bXWdssIiTH0cBoZhZLVatRwkqrW9yE1SVAk21nKdtEPX16+0ZpeHnQUC6JC8tI2z8K3N5smS6qNp0b563VdDtFpfVOIdEVkkM0lGq2ilsEqJoRzx0y+b4GteA3dlmB6DcQWfP4aHL/E9V85xzDkmTWhJpEGb/lQTipdVU4Dsn7uETRu8yibC7DmbXu2TxCrw5Nw/0wyKqcs3U9RF8ch6qVQsKm60byq1ri/9e7NWtZd54zQ/+34do+RgrGn2MpM9oVGMACeEeWiqD1fL2j1ie6hXKxnC09isxoRE8lLcJiNN9afxw0LaWEpKFad12RkbOTmF0470E4FbRSGzRbyivQedAD9EgrlYu+OBqUXWax4SAVHe1a2DamNPpGxHn/Z1UWu0t0LvKdWDzWmnGuvOdCWUTrQWvupqGT03GWRrRkjjQBoKjz75BJePLvtUhsRozFfaeHYTCs0F0965TWtLKduJ7RzB9/eatLBkeq4HtwSiB0EGS0eQKaabVMsJk2QwnaR4DpfI5YIQxTYA1idrjt398fLlK5V7r0i+eMAQoMkPR6fSSAtk2/R6RQvTydRUNM4pR91VUUsJoEU4TRG+4PC6az7yJUeQzNCqsMBIlmo5rPqNY6mHWQOz7TLKuU5XTA5KGAJKKTNS+Kk7Fb7uS+HwCPIx5ARPHsGnLvJnLt9JLmeYoJbhs4OeqL5k2ubuRoxfPfd1DQHG0jlj+smh9r8LlOrf1bejjXHa2Zvu3lhPiy3YHXS1cwzWgfq5EqiNF1emKxD6pqpKtwlhe0kYcKOh3fppG9NSqohOLIhqh96j2VPnPxBjh5jbq2Qxsf3v5o4ZgW6zeeecObTUCnPho8CDN6hy2DSgbxottz6rtjlzsPyoIRHYvu8Ssi7HtYlNHU3MXj70uYvPsRrGigsiS2jUxsBVKtfa09ccbykwDAtccZo6R3VzVa4NtwaiF/c3H1KNJjPXyQEhmV97WSOSvIq9FectWTk5WTPPE0dHpnsHTLVCJM5yg6y2w1i0UX1QUjL1DDRktSiirda/7Am+WoBU5KJpCKhyebkwJCGloXKJC7mQjpPXxnFqbhxar4uuJehSvEs7TjjuM/XRLkiSzYCNIGRWzAzTZX7qgZfDH7gPylNmoLqU4VOX+QsXTrhwfBvHs1XESislTwOqMyJhaA5pps1l05psjzOuh1E2vHNOU/P0htHwxe/vWbgqBg5AWlEaNpg+jXt7vXV4pKhFL97gKe0J7dAlJavthjTh7qwx9l71lj06cxiGOqYlZ2ieY85Yet+ahxXUJdjZvy3Go+tfVi9TeErtilLv17Z1O5XbsAjvX7qlvssT8D20nm2Q4wDJfe9L4aECDzYRcLvvkeytX4vS1rzlf1pKvb2hP0BSO+spisXocp5P80avkrVYVLsWJYdhuiriqbjFDoYunq/zorYvyynba5MRrNc3/sb4Cpxq+N0FtwSiFxHEE4e1jSuesdEuTNNU1y8fmzpmmmauXr3K1atXybkwz7O7RTp3lkIt0HMAtoHSMCz+6jwvuGjVpVohiilHBJukcYt7rYbPhdqmS0rmEol3oyLtUCn1aqso3xd7JVLsgqmoFLVw8ECExMbqs/CZ7SKJUFTcV35ixQkvf5nwo3/wDXDbCeQvGH34wgn89hF/5pnzTOUMsx6QRmGehfUJSJA350SjRqdlTuw26QYrstjwG7ECp0kBPde4hfzD4InUeYs0zDZZWjnAvoZpD/Fc225xapfQq+BCxx/tRU3XPjinMdNKdjxaW1XdaahWr5W7CSnep526Ali4Ae6SIsOpwFn2KjHGeLzfRRXJy7UwgdbrPyluS9LoUNy5eJ3lA7J7BhIlGRL6dsRcLtcTMMFqNJdMgYdyAZR3UunXoh/X4lira6Ms2ZpY40XqYzYkyIU80xMtW4NNJkKxs1dyZjWOLDe5MzTRf6Gmn7ZBNRJSTKPZCp7sGtgW7miwkdnCmJPnITncEogevG6qwpDGymGVokzzRJ5npvXsFagK67UFNU3zVLnkPKtVVvJ0wSQT9YckxnF7eTBxnXtFkDmzzpnRF6dS1Q0kkUhVL9fDshZrGP2oz7lepyYcQ9y/WKRGy9XAmKLMlJqTJqWhErfwl7dDX3zOio8FGuvXefm4+97guvszcsL5wyN+/Kvug1efh3IByhp0gN+4xPd/Trl4fAfHZYXlvVcmDwobSMxE5K8Rj3lLNyktUGshUevW597NcNP9tF2LHPzLed/tyeLvV+M8k3ciDQ0RB5RQYmhTZzxfSLG2NIbA3Osm7+Rg9QIQxlOkhCFYQj3drTawyI2oxG6039HSNfGESLePW6CgfT9NoeBILmOpCpLwblU0ZX6RwZioeYZz50xkPznhl7KdhwdrtRSIwjmnjXkZbMeW+iZ1Z3lXdHG00fMjwZHvHFkTU7szFkTF3lkDF8e4TRbSVs8k3Ay4JRC9KuTsiD0f++ds1ZiOjjg6ugrgZbbMMGnJn+xhxXTpWmVcs1YvXJZiosUlBefYU7JKVJFLI5vlD9CmooCKUK0ot6lrcja3wjaOloRLUmrM1sq8iKCJW7P3JZKOmVRjREmSgspSvAz/aTFNo410ZhgOCMNZ1TCLgGZELE+/lDVnuMRrXrniv3rLl8LhVeApWA3wJPDrT/LBkzu4eDWxnkMaAJEB0VTHnDr+qViCbA7OniEfH9t8noI0pUOIS0SPE/cohIKL40Ke5wXBXBCLFPcWK/ZSLCRdo1SFqqkKBCNGQ6rE29ofqp61SlUoqXScXOWe7VCXZAQzirJELIURc5MNRJLnY8qE/3sa0gKJlw5hpqFdr8S+m+Pe3fM0pLyLo9UOIw2D27QGywlV/HdBGIZUzUB93n5FSblXqW0T70UB7l6FUFrWqOR5nmDgvYCWkQ+v13B0yVIhrw5BZsgzD7m3xDtKxvB452m3wekqTfXSI+s+HbQg5icxbDMLIQFvEpK0Iw2z9lKYNAFKu7Yqwe/uUWwPLjysunKeu5iXzXf3sGUnUTidOdiGWwPRAyfHJ1w9PmYcVuScuXp0zOUrVzy3jN0X0Xt9wq7N0GINPXEpluwrJi3EJW05qsPFMefMnKcm3kbgk0966OVlsILetmDOK6gZSq09vC9uQUeqd87S314N0TvS+JJ7Xs64WvH0009D0SrKL71D7PowNMIyjGO9JqJepQoSB0zrIw4PBw5kzWF5lp/4g/fCfechP+vy6Bn49BPwuSv8ufkujtYHTMURX61QlUhEgRZDbv2aWQWcmcPDw4Way+auOIckbY2EWmwl5hfPmOgMeYeExqq/39784t5QAznPVjJSEiIj4t4hYGqKYYxKYGE80yrRaWqeECqQuvULFZ3hnMQqVDVFKWJ69XmeGd2JAPEUFiJk8aIwC8QbM7pN9Ho47ejqafdoM9oF89LfHRWsJOxSGi7LlrwvWpPItkoQ+WXQWPVy8TY0NZvS2NtgYg53uH2KwHecOWSa4BfXE0i2NArjYFJlznysCO+IyPiYox3Sm/uY+bh3xCu4Gqa6c+ry+uJ+/5OLMZB9XeYtqARuY2C6y1Ae7poKMizGcZpMtGBOF689xSh+g3BLIPo8z1x47hLzPHN0dME5BFPFGCJ1FUpKXsjDNuFQdWHUSbRN3Ilmp6gNiirq2THtR+pvlUsXwQR93/DOfed5NhWT6gK5sdAFUp9BhprlsoJEAJDw1NPP+L1qnFdwm73RzTPr9Zu5FNPHlzmTPEwbhcIRZ1eFMj/Hfa884Ee+7gE4eAqOnwU5A49neOwS/9HFkQtX7uJkPsc6mzsjZc3cLJuGzPOJcZp9oJEqkL0KUHb/cep6FNfbN26nbcoFAqm6zuUaGU7ZkZALE2AUJeNZQBOo57iIWrvgAUJpe4sH8mt6d0P0NelV985RbNxZaCmEFWafiyjgYSbuXJOWFcyfWhaGfxZeOdfzm1gEhnW3LpB+VX9dO2Pj0jurqSrY0a6qJ8EzMQ3UmCJ7R9zr7SVZlILUrO3sLZC9VCQ7rla8WzIf0WJ7Ks8wHMDhIZyc8DHbeLxjh2shUFNXaDcunPOuCNF/P22KF1XGnPmztBVamY6KE3ZQ2YWDQQmGUGrdg5HUJLJg9Z2xNLvORsKFHQQNmj0iJLPod0EXZTCvB7cEop9z5sKzl0wHDy7+BcKwQsVznr0Mm/1LQ1oEtRTU1CWdvhRo1JbGRc3ZXBWt8PFQuT2pka4mcoMtItUPe1us69UL9TyLuWuKiHHoLEU1u14qYWi5UCwwI7iU4ht34SXiid6snx7sVCYYPCkZGcoxK9bc+6rb+JGvfiXIM7C+CONt8JTAJ5/gz83nuDwdME0jhURKxex3mnzMAkWIrB2ZXEuqLXTrJXkE+W6RcxPBV6mG5caO9bF5bWNtP7Y2tkrI9cEkQo2iTXRVkVCG4GQ9s+eCaKpW43JD6Fr98sUZjoYgdcHWhSF9SMI8Z7evxHxR1VKRW2fhiXUKLIhceCdt6KNDSqXjUIc01OhJpSsTGfvI7zdkGVLHEmZtnHBvbDXj5WCV2LRx//08axD5KNpSpJ0h3ytpHHi3Ch9xo6wnsTJk766YHxPlW32vLPdRZJPcYPKkk+67PXMaBFFIHt/QqyZFtjlooEqDPfTrVD2oihl5wxGjb2kzJUeP5OPcb56nYCgW0sgOwnAa3BKIHhUKyTZGnhnHoR7yxICkxJjM/XElqyp+Ay6CtgLMVTSnUfytfyUs88Zpp1rr0VgFw8N2QHLRcCfYoQAAIABJREFUGoEb4nmokPqi3Qv9My2oxrxnyuJARHpR8XvnbFLKPGcLdQcjEAIRgyXJ0jcXJ1ApJWQ85ODggDILwshKCknX3HXbzH/3da+HuwYoj8OcgdvhU8/AZyf+9PE5TvIh02wZP+d5ZhiNy/Aa6m5IsvEkgYGBotOWDjugbkq/HvpnMxy7P7ZLQJGrpc8cKRK+/vZUz5gqlsIgEFXqfQIXKgZPCeuBPeYW5yJ5qJBEqmRS3e0I5NAh3hJ1PKnIPtEC0DT6G1vYE7Vlr4IU6jwj3t1Wz6Ybl+rq19wXe2QNG66Wrt/VTrqtBKkjPD0h7Dl028ulGqlb/AZbEOckClTHWQjPrk1bjDE5sVeWgXxgUm0kAFQttRjL4eqA7xBzxfyQqO3TaW2J0sYEovxTLVCUb+08acwD2Q2tssMyFMviBLofV6hie0mn1qBNbO3vaxmFN8cJzogZt2GqVNcw5B1EIyQ2xeY61lJLY0RqWqcmSlTco7/v8tGj5OwRjKmFu6eUkEG8+HeqBTxEhGG0hGbTPJm7oVBVK70RB1xUz22qx2G14LjCIAd2XoZRmKcJSYkyl4qgI7d8zq6np4lr4qXbzL89m9hWChlxByD3yaUgMi48enLWmk4h40QjAVn9kHgUrg8guIZUlEMZOM6FUS6R5uf4ygdewX/25nshPwfrK4DC1TPwWxM/+MztPHWsHOcVsyqZgjjRmksiqdbI24okBIokyxnj8nAcluxBXFqc2HVqyA2epxLKXVBcagrRXpWqvgM8CpAqAW1yar1BsJTCENW4Ok53iPgHVaQ7IK6xNhqfpO6TkKqQdmBD3WLG3qa+sD3koeq1IlH0rSE/cS666hQCQUdX2UgR0NljekTe63FVHJmkkAoLwlD93IuGAVpYDStjIOr5aOtlxsKql6HkEcX73qVGFhGPUO+WU1Jdk0RyI2+kAJdFug8j8D7nnnIcge8chWmY+ZCqKeATMB5WwvxP15ZA7UE8F3zYejriXFejLeCSkDlemcvszJtBM+L6mnZqm02uvjKgMb+uqunfUVzSCs+c0zxuQqLYclboNGqbR0bcGy6xW+I4DW4NRK9YmtuC+e/6Ro5UAxEx1lPbeZ4pubBer9uBDCRRmcumJhh6j5F5veTAnQtKnru96toQtGQv4ituHwhiYBGx8xwIOxk3TBwkF8/GxEqGKiXUlARji4A7GC052TAMnu7XDt1qXLFamSRzss6MQ4GUuP3OOxlQnn3qaWDNXecS588OfP1bvpo/dUeB9aMwCMwCV8/B56/wwScOmPKKeTCJ5UCMM561zdXoqSUOBrEqXrMZOm2uM4dnbvP4BYsUPrs6w2233UbOxSKQNS+Mw+v1zNmzZ1lPEyfHJ5w/f47ZXUdFkhO3JumkZHO8Glesp3VVV8R6r1YmzeW5sFqtmn48mfooahZogXG1Yn1ywmq1Ypqmys2fnJxweHBQ98I4jkxOsER9v2khiWfyRKuasBCBdHNjRkICU2W1OiBnK0Ifh3g1rhrynwurg5Ujg0IaB/Kcl/UFSFXtshkM1Ku+wlY1poNFYFEphVlzrTebPMlRKVaIhySsxoFpHa7J+HulC6oWoKkQ4/3hF1SKMrpqw49eva+IMnvpzUHw6iHuWJDC1ReGQch5MoKkoKKshpH35YJS+IWSXaQRa+hwBfPMQ/PEgyZagFgqjnDOmESx1MClETh21/xlUdNhA5vqUuW4C59GDIt2yDnaUmnSWJN028wqVAwuHVYPpC+tWESDzqYQBv1d+ZhOg1sC0UMLglG12qIA49jCTSwYKte80CHGD517Y23NuaLQG4pQZ16kd7eyjRQLPk0zKQnTpJ5Iq/MQITZOe16VmrceZtbhfSPNU0QEpjK3AtahL6XUkn4hwsW74uCfpLW5qHmfB0mIZqarE5KvMAwryvwM97xi5Ee+7n44vAgnR+bFcGWGJ67AY1f53mdXXDyZmXMGGaAyyzOZ1tf1ZASwzDYXOc9VtSUpcXLxuCJCFWE6nrl85SpDVWM15LReWx2B4+PjyhlevHjJvFJcVVXnqLNDBMIHOi8ZYe4ITHhmze4Ka7YZdfXXbKL9eo0kYV7bvun984+PmweUpM6NUUstzhH5dQLXFs+vHnto4f7n+259MlslMDA9rFpwm6kWjZsPBF3U3T47QmuSUjuSW3YdaPlfgsNXM5wu95ZW1VFyUR/1dL9FmSfzaKnqQd3Ir97t4QTmbdX1JRd7p4i5Ty6kUyJmxGIv1DOxmyoNl8oMOQ/ehzijw2BEu4yFP4Hws6WEa12EysMw8FAyA/k7Z5NmzGalvgdA3TV5WbnM1iQNbsdzzj103SItDcqNQNhLksiiDGAjjC6RbRKYG267a09oDIUtwPPg5Q1uEUTfI7kBkRFJJsaq2EYZhyaOJhIDQ6NwkphmM06qahXFhoVKpolIwUmqhuGuWc3nea5iuMhy6XtRydw+I7jJ+zH2IntzNxQd6qGZw1KebA+H10fl3kRp7qNmGB5H80WO8R0ykcsFRq7ylq96HX/1y+6AdAHWF2A8D4/N8IlHeP/lER1fwdFayDpWnUzgeTEMTKgRFKWUGUhe79V8043pMI6ujsm5j2EwrjH5vaouifnGjKjZgnsMSKirgojqIhtjSnZwVC1607x+sruc2uRmN2SHWoZSmMpkRsKSsVSdIB5OXA+aEwWLx/BDGiofTxgXUhsyWrR2iTQY/r4Sfv656sirRChCVynDCJjjZdsYSxVGW+PCMIwurczN/rSDo5eUOpfBlnahDxIa6V1dexuJP1YKMjROl0XPatN+NYGaBLMKDjJR4wlsaM2VNtY2peRr7ExSgjLnahAHk6b68anmqiJVTfzJlUk3Pxu6+5Qs770IlIFfmiaY4d1iheStey3FxiZUPThNNSetA13kr3ts+dnc5fBodh/QTt21eVvJeUtauFEELaGZ6CWFjTHdOFm6RRC9IIuIvWpRlrYpcrgcdosYxj2jekPlBMO4giwnKDYTBLdvtWFDoBoG880Og+imHpNSWubLHePoc5CELrW/16TX5TXTbQ81kMaGEHpYr+MaCLlAUmHMl3jj617G3/jK++G8Qn4MdDad5v/3NHzyhP9wvotZbuPqGooMSJldJTCa1wnuTla5kezSw7I6VAqbSVlyESJ9cL+lawgdvg2MyrVSuSb7uc9v03O0YBs82cItDolI9zfUBN1BSsNgmQ+6aOImtUWXlJJnwt8hzqbSwpTEOWBF3QWuez4SoJVcU1kAFdkW37Ox/gFZTWWhVd0R/W8SguAqo0rwu0Ak34RhrAvvsoqoOqJXnROqBBDXQaRx3vVMoM3ou4GUwvPE4hOgeLIycUkiOUc+R/6lxuV0VR1oCFTSIlo0koX5LJJznBmpRE1z4X0CcnjIz507D1rgZbfDxQuwTqDCR0Tg8Ha+y1V3Tz75NEWV1XhAmebaj9jpIR2rE8pQkxmx7tKVd8zhJpihVJr9ho558keKdFNa57j706ls+nZj/5sLcetLhSFtSRLXgxtC9CLyWeASNlezqr5VRO4G/j5wP/BZ4LtU9VmxmfnbwLcBV4DvUdX/8zrtk4aBPBefqWIbojs0MajQvQWEDn7sIuBk47BtWtJDtOzzOS8QCbK41m6yw1gqHdrigxbvaVfriiL0B8iRlOfCt01mG7OUwpiEpIVBIc/CwVAY02Ve+6qRv/GW+2B1EcoRpAzzATxyBJ895v3z7ZzMB8yzImlEMs5J43m0Ba6RAK0lXgpR2InmRgasqi4IpCgbv4WnyIJCtHntCUr/HFCNvX5xZz/7dQ0vJ/9hs9Gdz/9uoCLLDrlf613uYVhJSkgs9fcgpNJhwvhZgiwFDPXdwcnXqODurroGNX/TNuI6Pb3uxlliud/Fs8oaXssLb5swPkoy+4BGAFltt9Iuc8Ko0lycH/ewKSYNDsMB0zTxH5TM311PcNcID74T/rd/AV/xZrj3Xjh7ln9w/jz/4vCAzz/yGD/xkz/pKrtTxiqNYC3wQ/dAr7ZtjzVEcSPG0IoLng/73TMMC1bDoGCEqpdArgfPh6P/o6r6VPf9h4CPq+p/LSI/5N//OvDHgDf5vz8E/Jj/vSYkr9pUNFtVp0DGrqMLn3fokMGQGLpkVsbhKyrurtQvUJf9jjhkXSkv9edr1KRRmqq/rfpK96oRWcGGiBgeiarOKdd1cBFfcU6V1hcJ7l1cT2i6SCNslpUlaUFSZkjP8ofe8nr+0v2HcPwI6No6fjLAJ5/iLz+94uL0Go5LYcoJJJtPLhnJCYbiWlPbPEk7Lsx61D5289IQaK9KaP7ZsRX7pprufYPobeDFTah65k49s3xely/aBTsIrWrzl+6LKp92WGvpt7C10p0rEWq6iy2asmNwG9MqrpLaQMvd77s6FFz9Bl2hQ8IaahQIN+HYY5YXya8j1TWvyw+2E2oPXdVlffT16UPBF3PQ7AUWPJUIU25w0qgZboXmMpowdZgVpjSD8bhaMY4jh4eHnKzXfFdKTF/4HD//M78J970KXnMvnDlrqp1nT/j3jy7DNPOTYvESfdd6QuUJbU314nOkuWUWlQWiXUqF8buoeGBcs3VsT4Y9s3CPxM/eKXNe++tPNXmBNoelLMsyXgd+N6qb9wLf7J9/CvhnGKJ/L/DTahjwfxeRO0XkXlV99JqtibI6GBfiSFR9sr1rKYtncj1xtQAELsYngaEhUvVjEFy/+CGpSaY6DtAOS1/yLFFK5MRw3b9vYBnGppccLMColPAAoGaeTK58KRJ63+5gJWHOkxnfXNSNFM2SBsbRkElKiuSr3HfHCd/4R97Id/AsTFd8pwywPoBf+wIfeOIsJ3o7V04EGFyNkLCcNwkGxRK+tbGX1ELgwQ3LIXYs1iYIYrNtSCd72quky4Pe7jl37hylFE7WJ+TZI0e3X7B4Tmv2SarKp3VFtwyH/Vr2h8pUMk2q25SiN0lcHZdq9Y5K3e/LTu4+ZNvChFZPE+Nm3QNHmu959C/etBPZh2pLmttezdcSRIDuc23a8kMtPDUredgllbZ+13E70q4OyuIBihFdPIqXKezm1vtlhvdFy/WcADDGHFskcXIkViRTSrE6zwTSM7XimAbeN2TKY4/zoY/8Irz2tfBrv2YTkjMU4aEC31SKxXOo1tiKgE1psqAL76d+rnbOkSNwXIUTfQz1ZfE1r2qgIBA+75GNdlNaMK+dZjuobpQ71GrPB24U0SvwS2I78++o6o8DrwzkraqPisgr/N5XA5/rnn3Ery0QvYh8EPggGDIIA5xtWOcKFhyz6e4WJ65fkxS/LcXcapRy5DGwMUESftRxPfyhFVWxFKXu2pTiECKNe4/8owKau/wc/Ss6vBR68JkIonIdIVh5vlkZsjImRcoxg17hLV/9Ov6TP3AeyqPAkfX15Dx89il4ZOLPy5dwpRxSysoMYEOYThWJnDxuiwhvg6K4b3yHZKs4ssWCVkkkDKgbU7jF+YhLJVePr5onCxb7kBcqnqZXDi6vqt+69ntPnNPUTX2fej14X/ijrrXRvLpTdh2ZqKC1Kxvn7wZCb7uLIAA1RUHYeIAt4b3vc7WdRMWjJN2gt6Ws7lvX2u5+2vOFwXXm/aqIc8JmW4vgsOVsNk+6/rWp3qtARMFbQRCBbH46SZMbqAvzZMQxYmkUJY0rBlH+xOUj5OGH+ZnVCk5mIpkdovxz93h4u8Ycdn3rEf1w44u7qYrRbr36fXeaiLTTSLzB7YvKUgr4PYAbRfTfoKpfcGT+MRF5+Br37urd1uicWPw4wF133a05m7fBnHu97LIp80NvHhp9OT2V8NTSbt3sKItvSDOytulrunrrXs5zTZRm9KEYF49xGgvj6saIiodyb3oxSHCm9XvjEYfhwCWFCRkOQIUxCYdpzZmDY77svjN8xRtfzfvOXQF+G5iAO+GpCR5+iu+5oBycvZ9nLs+ouieIbISvo04gu7mqyZaWYwiCt4kcl+PYgRiqqqt+DdYYaOHbkRjOskduuKQFd9Ppk5GG+EFOPTwGEf4enYh+dgZN5+/Va8KaAbz5wYucLkxvz9UO2DWpNFVGkxybxCGeNbU1IdvP7Wgv1qXtN/+et5/b1U6NsL0BCpZxD6jNoVWGiDrfvUtyqE+jb+FWGrNXsLQbgjLW3FG50vLYN8EcmfpTERlAzOMtnBveM038o3FlZQxFYyJAhI+jkIS3x54QWZYu1KUapfeY6+e4n68ewUcSRKR5z+1C6LD0fe+5/T7Sv3hkbH1nx0TFeVikar4BuCFEr6pf8L9PiMjPA/8e8HioZETkXuAJv/0R4LXd468BvnDN9nGueZnAuQ52aQTawdKLY/ngDpEqgkf51HgsRNH63liQWimqQyz0Lpqtf/URVytFvxYaha6bfeRtZOVLzl2quzeWPJFEOGDi3HiJP/w19/OB+wqUp4AjOM5w5m749HP8xd+4wlW9h6s68NyFCUkD2UXIknq1Qqt+lUPnLPGfGfB2HvM+L2tNn9uTyE18tsE9i5ge0XOMi7sjDkOqqrAtEONkelZ3S3++++zU53fep8uDpQAp1aRkNcJROtoAiES6gNPe191clv2+FkSNr2BOtprdQNCBSDc9kEL6LYFcOg61787SALnjHJ2CkKrnGrLb8+MUkB3rF8he1TzIxJmJAc/hTqEUjydRjU3a4imqasqQ4GCigMUlyIyqslod8p5sAYm/GFk5V6O5ZK7XkJWPSwItfEvZiI4uslX9KW0s77UHff252JyTreulMTb1FueVNp95vmobOF0O7hs9LyIvi8/AO4FPAB8G3u+3vR/4kH/+MPBnxeBtwHPX1c8THKNFtiWxf8Mg/i8xDKbT7EXa0L3vakcV1+rLYpAhXjUfeEuJusgsWZ9YJjBq6Yb9XynOOWS0ZAZxtzMMD8TmyUj9V/tJGFvt6plx5CCd8PpXw3sefAMfuPcizE+CHjuyug0+8Qz/8eMjl6Yv4ehkRckjpWTWs423VM4k1X+hglKX6Ita3dncb6hrrElT2/Ruqsv7NM6nSvfPEPw4jl5sPfl8b+tHzdHKG5HS/u2A5Tr9zqCEG2PNRc8W0i1Ft/q5gDZom+NN3/5rQCBpkzGcEfF/ESNU87l5+5Fxsxqpq6RzYwj4dwK919qWOlJkcRY3J3DTaN6QfgF3ujDwOIbQQw/hRSR1DSK3VG1L/PSI5WAaR5MGEiYRv/fggG8fBo/AElgduJHC3vHLp3irpMAZm9JbN9Z+Hep67LjvNGS867mYH91xJlVaQOXiPVx/n/VwIxz9K4Gf946PwN9V1Y+KyL8E/oGIfB+mV/iTfv8/xlwrP4O5V37vdd+gRlVtFPZnaRhpYhLE/un0u2xziaE3FjnFyOLIPi7PkU5WZFkarrPEUxF923dVJSfWP0s21DF8uTAkKJJIGkuWUITBv49pQOdneMU9B7z1Lffw3rOXLYMfxWpu5nPw6Yv8tfXdfP5CoehILuG3PZJEKWSUoamGVP09VKTb677N8+K0BYkD7uHpnXjuU79UB5RQmxTEuTRFOXf2HPfccw8nJyc8+eSTFJ0I4tnmTSzFsHNti3qovrYDvfqrk/R8TKFPr8/1B2PHYSj9ST5F3bL5+FK9LXSp+ReitEZcB8vfF66KEWjV37NDJ9urB3ZdB6qhtyJ9j7cILzGlIaAF4vXPcTa2kLnfHzaZvqC2esriFNx53OdFcxbDV2Xh5aQwhZSYzAc9i8mXOKJLyTjvdohsF8TzJKHMypmzB0x5quMZRAj31QS8G7XsmCqG6IvPgRY+LvBHs6XiiPcUpBWi6eZ06KpfhTRVusOTht3ptHfq4zcktq1n2HAPL82hIK5FapjnA9dF9Kr6m8C/u+P608Dbd1xX4PufVy/syYWeqmvPr6Umsvr1GPAmBY3PjWrK4qCGF802UvHvNWGVQngWbByEXQigHp4NffKJFkYx7xd197bMxNlhhHzMoV7igQfu4L/4qldBfhxO1lgA1MvgOYWHn+YHjm7n0ryiZNe5k6vntCHzEG91q4yo6UafP9e3i4uLv0ukEBigEYWsytHVK+gzwjRNzJ07Xn22kxCEhiAiDXPkWA8tXtXXd2q9weda1QNXZKliQraDSqT0+yGCjjbuuZZ4vCVJ7hDRu/nZnLcocG9iufPyPfewAxb90e1rRQMZaxX5r9Wn/lozIHZ66dKkZhwXBsKOaOIb3VJLzxJDwps68b7DsYeqXz2uFg3dfZkhCetpgk7KD6N1IjF4LqT3DjbnH87AgeWNYj1ByfyK59n5lnhxUXOu8N4IQXQ6rk7t/G56NsYcbnL4NwoL/OU6+UXg2w7m4fm0f0tExkLbREFJrf6q/WYIXXcOOhBBz+3HxonKUJZkqdtMHm6unZFD4vCqUmvVYQYj+7jp5tfeHxAFCDavj761VZUkBcrMmaSs8hEvvxPe9rVfynffIzA9AqwhrWBawaee4gceK2R9JReOlSvTCaJnURSz14Q0A7h0YvrONk/NgOwb9AZUC3U86sSuQ8h9u3XOuxJsYG6WmZmkiUuXL9UJU20534uWFr4v9nsg+9JxxVWa2BS3BSLXf6oc6w7itGNcqc4cDWm2j6dD6IqfJ82sueG19bfklsxMTuXOOif+biAxb8Pmedi4L7opbM9LG1MYmTYpXc9NW6O78uFsQi+pbV2EZscS6r5Ed2eZMRWgIbusE1KSn/WhFULppH0xNynLbCuWA0ewehffMRd+IRw9DlaQg2pkftkTE34LLZ1F9Ec7tdE1jeMOw64F4Pp6/gUTSTu3p53XqEtxo3DLIPpaPBuqKNySkpkmM9LZLsRFu8Pvi5QFhvSqBw8CqVMX+GGtRiLMJ7gursvlcTB76J/rQUQoyRJWJUmVGwU7kAXzXjhgxeE4M5YLfMNbX8trXnXAe8ZnYDrCAo9XcHGAX7/In790iA638+xzGRnOYs6R2Tl6IWlzRVUvDqEbiKhKRGwTyXRKBSe6wxMqsjr4XaKndqoLn5/RzY51AtWiV6u+UVvageqFI817J1RnvnoNabk6J5B6CmmlMl3Pj9PpiiNd12C1JGgNijPlsI0v67Pxd3NPyXLf9/ObYGv/QfOj7/siNMbzRoj5gjhwbYGicuAd5QjCut1w37d2uTIFEgFd0VTCUh4s+6Z4Ej/xMpl4TiCv9yvqhd+d1U/1fI9E/vyUktnokpJS4b0MrOeJf3LisTjjaPEljuh/2VOffFNZItgU6+ODEtnW49u+35Bensc+rJ42sly/PmHcZnvPx151SyB6o9KWgTC7q5IFTnUugRsb1xZ2e4PY3zg03W/FskUOXR56sIXZMhxJe373oep8nLsVL5gmMfSj/bJIUQ7HwiHH3HP7xDu/4ct5T3rKkpFx1cqpjbfBo4W/8puXee7y3VwtB8wnMB6cYZ5a6lVvsYnp0bf6e9fnU4jSrjltN8TGdqRZMeAOJA9V/RCe1iklpHOzy6W5uvUlB3viY0NZSm0djfCc5p0I3UlYAoaxRRidyF4P4pbrIfdTazv0Bo4bP8+VUNV9FRh2B6Zd8NQ7DCrqxHPwQjtxR8H0xgnx9NHLdAu/U9B+vm/k/jgv/bujAInd0LXWT7RJ1DlnS3wmIOq5aZziqCo62/kTMQlwUEtR7ElNGUerjGZu2baH0pB4lxOSh6YJxhXcdgCXL+NpNvnnAm+v0vIyDUSMZ5da81rzUouZ75oT3O0ymX1QN5wAFMeRIdW6W+nvO0SvSguY2pQ/OzBjafeZsnGvNiS/+Q60pjQOrtM+L5F8veb39WX8pNP1b3JEIkoSE9ySMy3tNzg7HsDJM7z5K+/mrV91J+/kIuSroLMx8nrGXCcfOeDKdBcXT6RVz9HQWRsHZBkuDcnnsCXoQEvbtDm/S68FYvy6NJR1D9SOL+anV9d0cyYkr9qV7TPCEKJ5N5dJxPtuF6R6vTQC1ROhUGlIrFkTyWIQnuytIcmtld8RwdoCeTb8zXdsu4VEtPi9tRuuetqJ/NeCGFvEM9RBisBGPqGQWPrUClG6Md4Xf3vJJ0my9NEi1R89d4ihFsVYTFg/V72+fvfcXA9O9Tzp1IjXglC9mredSX2iyuBR2CrhKCEV0aqnJSjZs96mvrCIpasexgEtyoOaKdPEx9ZrOH/eXJ2Oj0HEXTGVt5dCYVnQ3vbvdt+fv1p0+WyojXa1YIXpl/v2xt5kcEsgegDtlXf+13NidQyPVZeJIJPIcgcw01QW1ejVI3MioVe8w5M9VR12B71OLv5GymLFN4rXbC3NoyG5VKKJyt0KEwdkzsgR3/SNb+ADrymwfgTLNFYgjfCMwhdO+EtP38Zz01mOT1YeIm3tznrirp2KyOwEIG/4SJvol0QW6ohugttHtjdZ0VIPX7213mbzkZy4FDEJZeFDJC0mQJzwlCAkGrrU5DVo8eccybuoHKlZwxibfSAzeARmED7rT0HRWev892qQxrJ3Y60bhFbQWqjFxgnJITBmsFKLaYz2OgagWN6WXfdvPhcHOpCu5XUKm5Asy8N5ezFaa0DNBuPEToGhI2ZGL3zOhwE6D41aw9TbsT51BvKOqen7oe6zXgmBr/+1EE1F4rE2bSK8La2pA2xrdIxDLcPVmIQ+hiXSWosMtc+KMmGV5IZh8GpnM7N7IOWSScmycAoJTcWDI5V3qvJL07G1dXZlzhA+/o8PCkX45kokgxHV+t6q/lWL8a1D6piW/qkqeXXzkkS2VHXxfM2Oeo35vh7cMoi+22KNerrePnJ1AzXb3Fbkmmib1A5Tdcxo97lNZ8nFD0OXdrab7qQeHdtJEkmgRF3aTnmZyIiG7/3ASmYOh4Ez6TJve+ur+cC9GXgahrXl5OAAjlfw+SP+8rNnuHT1LOuystzxgwXB1PFoaRsnKxbzWgij1uKQLrhtV6v0CGgHMqqKlA2udCuIDTuTkiJXe9vsPddT65a6RKCqTli3287aXAFb00ndAAAIY0lEQVSbzniD2+4C1yrOQ5yoOrZeKJx9vfvuhwRRlm3HMxa8s1RP9VJkr0vv7wvbEIIXfl6K+X2CtkpDXJ1yPe+eWikp7pUmAYnP/cKdt5vzknO1DQGkKgk1G5V1a0FKKmfczdzpfdzqso//FBVFD/0vRXd4R/WxFNrV2MU8a6zthKqnISGImiHe4sxhiDtFZ4QRqwcSnL2dqXcpfBS1c3lgJRdNB2QT888KfKNqrYUsHZFqkocsMHVc68lWdf6oG9TvdWar/71IT+66SSubeO76cMsg+oUhCffvxTlIuopSksy9qo7RufVcKuKIYiDLIsB+90Ll4vUnJVXbAACpiYLiel9j5VNFEqlEXnvLCmg+rx6gIZkzoyLHT/LmL3stX/7APbzvzAWLcs1rrArDWXhU+eufu8BTl85xXM4xqzJ4wYupZKS06ERNJm5qsrDx4hyE+thqJjsRkixdQjcP22l657oxF2e8R3we2RkipnNLNdS9SkdqtW6HPm9QqJvssIoIc+giRWrSZNVWJm9BcHZwkBFJaRxq18ld6w2Uua1xn0WxjTOKl0fR7UbEgOV8dp0xw590zXTccCdmV48OV6kUCUkiCtpLRZCAl7BsZ8Ic5KkJxCQlkya0LASX7MkAa5onz5meaQnC+r+BnJtqbGPuoz/x9wbxy/OpsLRMgW0qrVKGpYpHnbsvZeGUEYxMcWI/SHDLiuTg/iP//MwoZ4CoYAdaMjlnvr3Yfv3HuZgWdLCEg+QZUuJ/tcnim2iODIK45NnZnHwuw/U70TyWFgzqopZuWdhhCsUL5/hcOvGiEuiyUCtfD24NRC+NQlbuyH9oi+l+70krZYyJNiRSKrKu4rEXSqgvgQWHbJMm1AxXAbmrBRr6GqhGkmma6sEaamIdNa4ygXDM2XHNg+/6cl5+xwnvyJ+HfMU2jJ6B4wE+/Rh/8fFzXMl3cDwfcKImyqY0URhAE1lL3chaPIJXPVIwpVocCrWigAB5youDPIxDy8URPe2JQF8hx3/L0iSgnkakAplcVSx1alz1sfA4sMbsbydOVa6lK5+jWjzVa5Rv3EDCsAiKagjJpQfBipx3vzVd/wYXhSO+1OahclcSSLEhYJFUEVumFZmINUHcVbIY4h56zrqbb9s/TVWISOMCfW+J11TtOcE6/ijh6NJl2KjU901L49LlRenjRNBKRMyHpa3UpqoT1e28Nh3Un0LC61RmvTSzk2DTqTB6ArDIldTZamhSVdRxDo+ahUNAR1CmPC0KkhshDetRYprWIEZYrR1j9kgmeT1YFEbhoWLMHQeDcfgnJ5BGhELWuXoAtYCr5SzNXSW7qk7t51WXmyW+1djRbggmnVzHieIaIL+Th36vQUQuAZ+62f24BeHlwFPXveulB/t52Yb9nOyGF/u8fKmqfsn1bro1OHr4lKq+9WZ34lYDEflX+3nZhv28bMN+TnbDfl4Mnl/ChD3sYQ972MPvO9gj+j3sYQ97eJHDrYLof/xmd+AWhf287Ib9vGzDfk52w35euEWMsXvYwx72sIcvHtwqHP0e9rCHPezhiwQ3HdGLyLtE5FMi8hkR+aGb3Z8XCkTktSLyKyLySRH5f0XkB/363SLyMRH5tP+9y6+LiPy3Pk//RkS+9uaO4IsLIjKIyP8lIh/x768XkV/1efn7InLg1w/9+2f89/tvZr+/mCAid4rIz4jIw75vvv6lvl9E5K/6+fmEiPwvInJmv1e24aYiehEZgP8e+GPAVwDfLSJfcTP79ALCDPw1Vf13gLcB3+9j/yHg46r6JuDj/h1sjt7k/z4I/NgL3+UXFH4Q+GT3/UeAv+Xz8izwfX79+4BnVfWNwN/y+16s8LeBj6rql2PFgD7JS3i/iMirgR8A3qqqb8Yywv0p9ntlGzZD1F/If8DXAw91338Y+OGb2aebOBcfAt6BBY7d69fuxWIMAP4O8N3d/fW+F9s/rKD8x7HiPx/BgimfAsbNfQM8BHy9fx79PrnZY/gizMntwG9tju2lvF+AVwOfA+72tf8I8OBLfa/s+nezVTexUAGP+LWXFLgI+RbgV4FXqhdT97+v8NteSnP1o8B/Sosqvwe4oKqR06Ife50X//05v//FBm8AngR+0lVaPyEi53kJ7xdV/Tzw32A1qx/F1v5fs98rW3CzEf2u1HYvKTcgEbkN+Fngr6jqxWvduuPai26uROTdwBOq+q/7yztu3cy1teu3FxOMwNcCP6aqbwGOaGqaXfCinxe3R7wXeD1wH3AeU1ltwkttr2zBzUb0jwCv7b6/BvjCTerLCw4issKQ/P+sqj/nlx8XkXv993uBJ/z6S2WuvgH44yLyWeDvYeqbHwXuFJFI2dGPvc6L/34H8MwL2eEXCB4BHlHVX/XvP4Mh/pfyfvlW4LdU9UlVnYCfA/4w+72yBTcb0f9L4E1uJT/ADCkfvsl9ekFALP3g/wB8UlX/ZvfTh4H3++f3Y7r7uP5n3ZvibcBzIbK/mEBVf1hVX6Oq92P74ZdV9U8DvwJ8p9+2OS8xX9/p97/ouDRVfQz4nIg84JfeDvw6L+398tvA20TknJ+nmJOX9F7ZCTfbSAB8G/Bvgd8A/vOb3Z8XcNx/BBMb/w3wf/u/b8N0hh8HPu1/7/b7BfNQ+g3g/8E8DW76OL7Ic/TNwEf88xuA/wP4DPAPgUO/fsa/f8Z/f8PN7vcXcT6+BvhXvmd+Abjrpb5fgP8SeBj4BPA/AYf7vbL9bx8Zu4c97GEPL3K42aqbPexhD3vYwxcZ9oh+D3vYwx5e5LBH9HvYwx728CKHPaLfwx72sIcXOewR/R72sIc9vMhhj+j3sIc97OFFDntEv4c97GEPL3LYI/o97GEPe3iRw/8P8tBX3Si5MesAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f78c0b82630>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Build your pipeline that will draw lane lines on the test_images\n",
    "# then save them to the test_images_output directory.\n",
    "image = mpimg.imread(\"test_images/solidYellowCurve.jpg\") # I don't know how to loop to test on all images\n",
    "gray = grayscale(image)\n",
    "\n",
    "blur_gray = gaussian_blur(gray, 5)\n",
    "\n",
    "edges = canny(blur_gray, 50, 150)\n",
    "\n",
    "vertices = np.array([[(0,539),(450, 325), (490, 325), (959,539)]], dtype=np.int32)\n",
    "masked_image = region_of_interest(edges, vertices)\n",
    "\n",
    "rho = 1\n",
    "theta = np.pi/180\n",
    "threshold = 20\n",
    "min_line_length = 100\n",
    "max_line_gap = 200\n",
    "line_image = np.copy(image)*0\n",
    "lines = hough_lines(masked_image, rho, theta, threshold, min_line_length, max_line_gap)\n",
    "\n",
    "color_edges = np.dstack((masked_image, masked_image, masked_image))\n",
    "\n",
    "lines_edges = weighted_img(lines, image)\n",
    "\n",
    "plt.imshow(lines_edges)\n",
    "\n",
    "#cv2.imwrite(test_images_output, lines_edges)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test on Videos\n",
    "\n",
    "You know what's cooler than drawing lanes over images? Drawing lanes over video!\n",
    "\n",
    "We can test our solution on two provided videos:\n",
    "\n",
    "`solidWhiteRight.mp4`\n",
    "\n",
    "`solidYellowLeft.mp4`\n",
    "\n",
    "**Note: if you get an import error when you run the next cell, try changing your kernel (select the Kernel menu above --> Change Kernel). Still have problems? Try relaunching Jupyter Notebook from the terminal prompt. Also, consult the forums for more troubleshooting tips.**\n",
    "\n",
    "**If you get an error that looks like this:**\n",
    "```\n",
    "NeedDownloadError: Need ffmpeg exe. \n",
    "You can download it by calling: \n",
    "imageio.plugins.ffmpeg.download()\n",
    "```\n",
    "**Follow the instructions in the error message and check out [this forum post](https://discussions.udacity.com/t/project-error-of-test-on-videos/274082) for more troubleshooting tips across operating systems.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import everything needed to edit/save/watch video clips\n",
    "from moviepy.editor import VideoFileClip\n",
    "from IPython.display import HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_image(image):\n",
    "    # NOTE: The output you return should be a color image (3 channel) for processing video below\n",
    "    # TODO: put your pipeline here,\n",
    "    # you should return the final output (image where lines are drawn on lanes)\n",
    "    gray = grayscale(image)\n",
    "\n",
    "    blur_gray = gaussian_blur(gray, 5)\n",
    "\n",
    "    edges = canny(blur_gray, 50, 150)\n",
    "\n",
    "    vertices = np.array([[(0,539),(450, 325), (490, 325), (959,539)]], dtype=np.int32)\n",
    "    masked_image = region_of_interest(edges, vertices)\n",
    "\n",
    "    rho = 1\n",
    "    theta = np.pi/180\n",
    "    threshold = 20\n",
    "    min_line_length = 100\n",
    "    max_line_gap = 200\n",
    "    line_image = np.copy(image)*0\n",
    "    lines = hough_lines(masked_image, rho, theta, threshold, min_line_length, max_line_gap)\n",
    "\n",
    "    lines_edges = weighted_img(lines, image)\n",
    "    \n",
    "    return lines_edges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try the one with the solid white lane on the right first ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] >>>> Building video test_videos_output/solidWhiteRight.mp4\n",
      "[MoviePy] Writing video test_videos_output/solidWhiteRight.mp4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] Done.\n",
      "[MoviePy] >>>> Video ready: test_videos_output/solidWhiteRight.mp4 \n",
      "\n",
      "CPU times: user 2.6 s, sys: 229 ms, total: 2.83 s\n",
      "Wall time: 14.3 s\n"
     ]
    }
   ],
   "source": [
    "white_output = 'test_videos_output/solidWhiteRight.mp4'\n",
    "## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video\n",
    "## To do so add .subclip(start_second,end_second) to the end of the line below\n",
    "## Where start_second and end_second are integer values representing the start and end of the subclip\n",
    "## You may also uncomment the following line for a subclip of the first 5 seconds\n",
    "##clip1 = VideoFileClip(\"test_videos/solidWhiteRight.mp4\").subclip(0,5)\n",
    "clip1 = VideoFileClip(\"test_videos/solidWhiteRight.mp4\")\n",
    "white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!\n",
    "%time white_clip.write_videofile(white_output, audio=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Play the video inline, or if you prefer find the video in your filesystem (should be in the same directory) and play it in your video player of choice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<video width=\"960\" height=\"540\" controls>\n",
       "  <source src=\"test_videos_output/solidWhiteRight.mp4\">\n",
       "</video>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(\"\"\"\n",
    "<video width=\"960\" height=\"540\" controls>\n",
    "  <source src=\"{0}\">\n",
    "</video>\n",
    "\"\"\".format(white_output))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Improve the draw_lines() function\n",
    "\n",
    "**At this point, if you were successful with making the pipeline and tuning parameters, you probably have the Hough line segments drawn onto the road, but what about identifying the full extent of the lane and marking it clearly as in the example video (P1_example.mp4)?  Think about defining a line to run the full length of the visible lane based on the line segments you identified with the Hough Transform. As mentioned previously, try to average and/or extrapolate the line segments you've detected to map out the full extent of the lane lines. You can see an example of the result you're going for in the video \"P1_example.mp4\".**\n",
    "\n",
    "**Go back and modify your draw_lines function accordingly and try re-running your pipeline. The new output should draw a single, solid line over the left lane line and a single, solid line over the right lane line. The lines should start from the bottom of the image and extend out to the top of the region of interest.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now for the one with the solid yellow lane on the left. This one's more tricky!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] >>>> Building video test_videos_output/solidYellowLeft.mp4\n",
      "[MoviePy] Writing video test_videos_output/solidYellowLeft.mp4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] Done.\n",
      "[MoviePy] >>>> Video ready: test_videos_output/solidYellowLeft.mp4 \n",
      "\n",
      "CPU times: user 8.55 s, sys: 678 ms, total: 9.22 s\n",
      "Wall time: 45.2 s\n"
     ]
    }
   ],
   "source": [
    "yellow_output = 'test_videos_output/solidYellowLeft.mp4'\n",
    "## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video\n",
    "## To do so add .subclip(start_second,end_second) to the end of the line below\n",
    "## Where start_second and end_second are integer values representing the start and end of the subclip\n",
    "## You may also uncomment the following line for a subclip of the first 5 seconds\n",
    "##clip2 = VideoFileClip('test_videos/solidYellowLeft.mp4').subclip(0,5)\n",
    "clip2 = VideoFileClip('test_videos/solidYellowLeft.mp4')\n",
    "yellow_clip = clip2.fl_image(process_image)\n",
    "%time yellow_clip.write_videofile(yellow_output, audio=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<video width=\"960\" height=\"540\" controls>\n",
       "  <source src=\"test_videos_output/solidYellowLeft.mp4\">\n",
       "</video>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(\"\"\"\n",
    "<video width=\"960\" height=\"540\" controls>\n",
    "  <source src=\"{0}\">\n",
    "</video>\n",
    "\"\"\".format(yellow_output))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Writeup and Submission\n",
    "\n",
    "If you're satisfied with your video outputs, it's time to make the report writeup in a pdf or markdown file. Once you have this Ipython notebook ready along with the writeup, it's time to submit for review! Here is a [link](https://github.com/udacity/CarND-LaneLines-P1/blob/master/writeup_template.md) to the writeup template file.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Optional Challenge\n",
    "\n",
    "Try your lane finding pipeline on the video below.  Does it still work?  Can you figure out a way to make it more robust?  If you're up for the challenge, modify your pipeline so it works with this video and submit it along with the rest of your project!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] >>>> Building video test_videos_output/challenge.mp4\n",
      "[MoviePy] Writing video test_videos_output/challenge.mp4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] Done.\n",
      "[MoviePy] >>>> Video ready: test_videos_output/challenge.mp4 \n",
      "\n",
      "CPU times: user 7.11 s, sys: 481 ms, total: 7.59 s\n",
      "Wall time: 36.9 s\n"
     ]
    }
   ],
   "source": [
    "challenge_output = 'test_videos_output/challenge.mp4'\n",
    "## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video\n",
    "## To do so add .subclip(start_second,end_second) to the end of the line below\n",
    "## Where start_second and end_second are integer values representing the start and end of the subclip\n",
    "## You may also uncomment the following line for a subclip of the first 5 seconds\n",
    "##clip3 = VideoFileClip('test_videos/challenge.mp4').subclip(0,5)\n",
    "clip3 = VideoFileClip('test_videos/challenge.mp4')\n",
    "challenge_clip = clip3.fl_image(process_image)\n",
    "%time challenge_clip.write_videofile(challenge_output, audio=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<video width=\"960\" height=\"540\" controls>\n",
       "  <source src=\"test_videos_output/challenge.mp4\">\n",
       "</video>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(\"\"\"\n",
    "<video width=\"960\" height=\"540\" controls>\n",
    "  <source src=\"{0}\">\n",
    "</video>\n",
    "\"\"\".format(challenge_output))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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