P1.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 0x106df82b0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sp0ffUORL7j18RD67YLQ9IlOKd56+z8sXLzg48JZC7hRWhhideD0x4g8qsoBuS28No4c6PKsBU0hp7gVmegMjjKlmTAaFrrHXSAOotKSSWWlqxhw7W9tUO986mHHEwKF01oU2i8eRg4PrJsdiJYi0xkTO0SDdQ5/apnSf8Liurpv6Jj2+gGrHnrOAh8Di0NSaBwrW2N76q3DB8lmxTdwWagEZwyqCd7z2MZx2v0R65k6wvHo+upRCBjYVgo06oKG4dFlPdaTQpvegzZBUYJplO53zvpo2xh63oabyu7QFeGsM+iwh/3AdXunXRTS3+r4nNnJESPh4oTjQ0dZFY8NT1X1Rrnr/ujnb5XSt+hG9J1JHgnmfXemwTwZs7+6zszUkn82QPOP5i2cMBwlPHj3l+fPnDBJFkVtWRcH2ZIvhdIc0TXGFxRnL7mSLi5Mz9vbvce/gIcevX6EHjsViwapYM9zaZjweIgPh4uQV1q25PDphJ9VsTSe8fP4CrTWHh4csM0gGE7YmU5QekLm8EtWhP0qpTt7TR28Vo+/dQKH8YhAHVtnKWapKhud3I0rjeT+nVDRh/eQUJZWiXy1c53y4UqCIIegebciVhVhxNaYbhSU2no0FhpJSQ3atqIOy6piRVDHdVByvK9InKZ2nVX1SLsQOS8JTcJ5BeMgXX15X+FAzAKdQ4l1W10VtBOc0lFqatQ0GXza+U1jEw1UxuEhzFah4nIS6SiEXnrE1729AP6pj/4DXDzeFfqVM0MMsIkYVLgX4grJOG4HQVfuVfzkImCCUjWpCCeEbuLizoQxjqxBQl9UOeCs+UieE9rqyb9UuXxW+r0NaYVMiHmYM/atWgNCpOEjtmEKk3njm0LXwjPpvoLQMosLinbGt8uM4fMFj2IEaG/CiMmKNvYuaSp1p1CrKoY1BnEOSbYaDEUptUaxmqMEpSoHWwnA4YDWbocSxt7PPdHsHNxyS5wWfffxjKDK++9GHFNmK9XxGnq3IM8ujp0/4+PRjhsMh+/u7HO4fcHJ+xQpDamZoN+Py/CuOjzKOXp6we3Cf0faI0WSPRI8gHbE2RRl6TtWPuj+/aoxe6g/Shi7iydCIMS/vhQXeeI8mcwjPB4qxTVUuhKBd96Y86CEdOUXaWlhD+pZtrdGdm8MqGwKjSzsvmUxBDXNsPBM9202lxuQ8zg5gCosQYuEVgsW4ooFxNsv2zN3jh9e04RakRBoWUKCu8WpAYhFvbDZt8x1VYrONcDvnNgQO0TPXzY0mhn3zN62K6ZifSrXmf4cVGm9uk/YmJbqtjThaqXE9Erxx3UHh6IJzwtiFOWeibx7DLWFHba9f7BekJo+I+9Ix9tdEQIgIaA1OMd3ZRTlI8gwzGPPBh5psfkqSKvb3phyvLhmPp8wzv1ktHQw5OLzP7OyYy5MX2GJJOrCs12ekacpAW1arGTs7O4gITx495uz8iOOjY0Q5Tl6fMx0N0c4wvzhjPIBiveD1i2fcfzxi+wCyokB0nXkrng9vSm8Fo4+dUF2Ton3Nrye3wTgDdWmeAbvsRba0qgREXJ/ueb5SjiR2jjU16/BrrHPEjreuXawNeKFVZ2U5VBciBiASYfQdDRZbabkuMqfr8vTGQhE0iBdjohX0WFwVRHWLKICuha7EQwtAZa1J7FxuvR8uv+l0jyE5A1V0V92uOqyl1uJtCZcBldZNpd1aoVPI3mYxNh2F8fV+5hTvEI0ZaycTbzBe6MLqtQZjTC/0Fe++bUR+xgw2spqiFtXWSMf8hs0x6sPQ+6k9H99sRgjaI0POYIx3curBiCEOXVxxeTrn6vyYaQrZ+orC5MxWcPDwXbbGI5zN0Dpjdy8Bd4a4JbnJsIVPTTC7gsXVnPuPHnN2fgoU5NkpzjkGyRCb+bh8l81wVkjSlCKD5eoKpxxKCyZ2ZL/B3GrTW8HoHVQaoYqcsjbqUOW0gSrKool71o7XiqFJdcX/0rF+OrWfaDLa3tQBAX+unYvWdePPqlxwsT4ca2kiTby+k1SHiGphyrE23TUZqrwlHQvPOdd04pU5ShqM5CZlLHZA9uwoDQ+EUFapJFoQet6XohowSBOnbVhQ4Ru3No+EplbvSNOS6/JtxFh6NX6lYLESGGeoO37xekYff+e2EhHPvzi0sP2eqDA/PNQYRgIRlKbaftGYu9a2FJvNb2KMK63i1seNoKAS1WqQU1ESuca3lo2xjfscK9iN+a7L9CPRGPWtB1cOknLSOZeb1J2zKAguP68TFA7tVgyU4fmXH/P665+g7AK7vmBVQsN5Ltw/fMIogaPnnzCdjBF3gTOnrIoMjMEYQ24UuAFKKQ7u7fL111+wXC/Y3p4wHCgEjTGOQaI93LOaMZ3ukg4SinXG66MXPHn/u1gZ4BLBWR/MG4/LRrqJG+itYPQNcq5Sb+JQy+AkraxkLY2kVjVZQv6WinHdlI6gRTEDoWc3YMUwXWw+SrVgGuZ2VF7bQdmut8/BUjGsWMLX1TYYRLxTc2PRBMW1VW6MX8a6nbeE/Ga260L1qvj20Ja+RRo9j+NmAed7RJc2uhGHHgS/ddUAqxLrV2wy3+DfgVLJ0GUOnLIfvo46xDS0uRZOZesiyyAuv+5zzzdVruH87+xUR3k33Y8FVuO9LuEr6sY0aHUZ3WtBb8xZP/62ct7eUEFEViKo8xbPxm2RjvlZy+vN8fNCzKFKv4iYglcvv+Sbrz9G55eIFBhjEZ0yGqWcX67I8yVfffUpT548YH7xkuXlMQlLCld4S9Q4lPXJ0ZQqyPIZ060RiYZsvWYy3GI83eLl0SmrpcFJwXR7B5P7MNvZYsHO4QHr9Yp05GEfrWjMi19ZjT6mLhMlhjMqHNG63gVUT/TNUL4YR75Ve3R3NsnOxaFVxGSlYsphR+NtYvD7KNasN8eltAjKh9qaqyfT0WpFM7WyX1pap5U2GPBy5W5eeLeihtbfZX14pm4Fz5g2dlcqmmBYuCrVfzGE1GextbU8D8Fs3mvPsOod5aJY+iqKHkdHQrjNJmzQmyzdTSHyiwmHN6VYmIUxdgJJKeE3IdYO5norSGtTsPRRnA2zq454CGLfhlcwyuuiwDoGOuXs7ILRKCUdbZMt5iRJwmSSUJRY7ZNHDzi7vOD05UtE+Z3JNi8QHMN0iMl8WCUOFosFolNEKS4vTzk4eMBisWI82eNg/x5Yy8XsnCQdcbC/zXT3kMn+IcPJLokW7wyuOrE5lm/yfd8aRl+ZeQAubJAqmVD5jA4YT/VSU8OuqcxACRDgh8g6qNICSBOyiElcy8xswAuCIymjGsoMklbKrIqxtleZBWWyNMHWAdpRa12Z6t0LrxAtIVEpEoW2teEqbwT5beoCUQSSimKKdfU+KoyrAdERdl++VWSVSNDVJPNpI7pI47HEymoRX3cYt4aTNtQRMeTNCRu2Eqs6/QWmDEeMTaj62+kw7s71+lXCW0FgtYWmijaYVc7RYOVI1E7rfGnVlufQP9sZ5eMi60t3YNeaKHWy6LqFjW2kPecX0BpY6NVk+qCNzmedaQqUyBnry4pLiODLMHNc9b+NNlTWVAek4svwpONIsl6Hblcdrb/D+2KrN8RvufBlmQIh5+Wr5+xtTUi3nmLzBdtPUz7/+MfM5mvG4zHvPn3M5cUpo2HK0lwxSockpFwtFM440mSEGiRcXl6wuzP1lrzzGxvTJGGcDrg4P0Ofn5OOxhw+fcTs5ytWc8NVpjjce8jAOIZbBxg19HPBlSHnyjSsVNyboRRvBaMX6mxyEiZMlMPlNh1qZ3100lwnjTzvLQbjFZL+NL5VjgxXa9DOedNbRJVM3MEtkgy1BchmfYFb1sLottS9ZDZT5EbsoZN1v6kmGDvFoclzQqx/0LtMuF8usq5ACapnPSNop8dtCOZbKn9xn1QDPgv/950I2l7f+3Gcf6AYHlAdm9U2s/aw8UzN/GsNva9L1zk02/cbc6gjFLFLMJUv9tTeeixqQ1uL78LafxHYoV3Gm1JfndX8E2F2cYVyws7+ASljjl5cMZvNSBRczRdsTcbk6xVJOuDi5Bzr1pjCka3WrJaGrcmU2dWKrb1d7h+OQTRZYRA9IMsylCuYzRYopVivlyzWK+49fIjWA6xRzBc5g8EWy3xJNl+ihop0aP3yFbsBS90GBYjprWD0UCdgkgiaqU387onSlRtERLDONhJltUlhsXG2P2lvJ6+pHacbHCGFMygJphUbMdSbBalq+/Imo4g0F2otp30IiXOu0c64tgZcg6Nm8JvMXkflmobSGIRts946nn0TElIOnHYYYxvlxpCL7fhmXgAokP79E1WESTwmpSXQjJbpx8HjZxrMugPeCppmOCmrLLhqt3fcSWm0+fMT/E1LEGVdykk4K0H1oyyocpx1VGa8N+RNYLNYMDoVJYwz18ecX0dtptIHgSpsI3CibEX1m/EDCeW+gsp11OELa8jBnvUSr5GbBHT7erzrfjGb8/jePVjPODt6jTNrjk6eo9yS4QhW2QUKx/pijcOiEkeW56zXjmG6xdb2PU5OXjMa7uJQ7OztcblYYpzw8ugVFDk7aoBSPjxzd3+Pzz75lGxdcO/hU568/xtkboBTXsHZ3t5hZQz0MHS/mexXzBkrUm6jF2rnp0h9lGCX1gJQatPhbynNd4WAdVgdMVBXwy5SQgDOxWkA+qR+mVelRgs8MwgOXgnlql5hUTdCam0yEgrO1biwkRq2iCe/ildFSRX0ETEQr1UGs65m9hUzc/VYVB2qYImyHdERjk41eVPl/wg8Q6KcPbYZhlfxqZjRRonlbtLugmBEaojNySb84aG0biZWfdv4OLsWk4+hhI026AArOqwt4QytUDbe+Rw08bhPdR0V421Nj4aAD5CT2Ma3qNoZz4WGdVoLqEDW1cfyWVM/q7vmZ2wJdzDDZnub0TVtYRvKUK0xaIx3VHbzO2z2o09j7WP6XUy9YW1E5Yc5GiL9kiTBOcvx8UuuTo9ZL48RMpKBYTAQTLFktV4z0BpJNM4qslWOKTQYy+VshhPNq6NjtnZ2yE4v+e3f+SvMVyteX1yyu3tIgfNKj3Pk66U/utA5n+9IaZROmQxS9HAITpEkqsyqu5n23BjzqwfdOFcv5FhT7IrL9rs2/e/tBR+HxAFo3czxHWuF4n+py+2dVEnDXHfOeIaN8R+gZDBdESDNPrry2VBpzZ2DlRCYknP4U25KnagPWlJh569QtyOKuABX73ANTlBpirQgGMK7fmt1vTCCheWgTDLV1H4Bv+kmOG2VKuE3L1AL53d0VrHxEc4bGEWfRu/bEMJTa4EU6k0k+r59mnKs8Zm63Jiugz9CsjhxoJSud6d21OHbdnstq322b5v5xY7GvkXdla/IF1F+p2ZIz8b7DZitJyV31T5FJTD7eIwPlghzpIye6TzDuLsfbb/JTfSLQkGBwobLw8NDUhzvvf8Rr5OCb748I8sNaeKwNi/XScJ8uWIymZDoEZldo0XIxWJdQW4ysDkvX87JDdgk4b2PPuLg4ID1Yg254epqzng85OTkBGMMe/cesb27hbEFeuhYrlckaYoVg5YEfxjTpgB7UyTrrWD0AnVSpwaM4X+U8pq7OMhdnenOqprpNGLiSlKuPjyjsfW+/LhKwCFQxm4DiGs61AwFUs/dum4naAQJQ3iNttZoU7UwI22m3NMvApq00i6rZGFiMR1fNmkwuJ5UDUHTJKmFRbSgq9fFoSpv6mY5AuiwyAWfsjaEJlJbG2VWFazykEtT92zn5Il3ZW7m1rcqpFFw0a5Nh5goKV31+M3an9LXM4X4k7UhmDg8VkRw5bb0dhx+r8SJ2hPKbJ+f2oe7tqGmThiiT8ttBKV0OHRjjb5RaVND9lbsoLauNpoZNnDVllVXXqPO1AotRvYm2LONpmyCbIxVm+p7fjNgUeQosRibMxLHZ5/9jO1JwvsffI8/+2f/mMFW6k9mVTAYaJRKSJKEVCfsPdjj1eszivWCQZoyGAxYLFYM0wGqsGSLc45fPiNNhLPlBd/56Df53g++z6ef/IxnX33OoydPSIcDrDV8/fUXzDLL0fEJyWDIv/Y3/02USlCU8fIS7btxDiXqjY5PfSsYvcMvpGordWwSBg0VSgbd7bXvYqxtp2sX+bKdz83746p2AAAgAElEQVTSUVanuUu3ydyF+3a1zakIwuhoXzX5/8WUlUbZLhJepVLafC4SoGHRbpZTR9b4Rd1dV20DdPQt+t2VSa/aGHqgmCk07lzjpO9a6LfV+jacmM41NOaA33uYpW5YH3Pu+r2dyO4m5taeY2/aj5vm53WOyuue3Sw3ggulPsErHsM2jNJX55v0tzHneqCkLhoIWOtYr1esV3NeP3/G43v7nJ1doJnycG+b73z4m2TLUy6On6M0iDgKY1AqYbZYkBWWPM+xOJ/gzHk4eJ1npGkKwMnJCeeXV+xs72GtJcvXDMcj1nnO2dkF21Z49vJj7j18inOa73z4EcPpGFOsSSdTTFY02u0zmPrcQl2hpX30VjD6oF5v7Nosd/41HnVU+VBKa748ecXfjxe6dFxrlBXMcImvNRlsr2YeXJ5KVQmkrst6GP4NqRPax8vVfS61ZAeoKhAwRjzixl1bX+MajU7WwSpduHQHBuxv1O/FScTiersYV6M9LUdagMVK24p48G9a77fBcK9jwjdpjm2GHPevcSpWPLT2+v5f5zC8rv6+59tMrc/R/KbUJWBiBStm3OE4yuodt2mphd9vU+ebnMHbTDnSD8FtvOd82OMo0dgkYZIOGKUaXI5yhvOTU45fn/DO40O0szibsc4WDLGs10tmyxVTpRmPx6zWBev1GpUMGI/H3H/wiIvZnMFoxHgw5uzikswUHB8fs5jPmEwmTLa22T+8j3MKrQ0XZ+e88/5HTMdjZus1L559wzvvj7GS+CMO2RSOv3IYvbgoH7WLOqD8pI2xeD8HbLmLsTQhxVamvX/VVWVB90LxTiMfKSOArrLs1QdQXEsdJuhtGG9glV0RLKHc60IO+8q9+ZnrcVLPIJR3pDXudx9S0ld1sL5iIfumfagOEOmY3NCEUW5TXpsZ9j3Tx9jiMqqxiOZkPBlu0lT7mHAXU+/T/GMLo6uutmDqElTXtbfvet8u5hpya3+vJuO+DWN6szlNM01z2Nl8i3qc8z4HhZCmAx7eP+Ti+Oc8fbTP/OKU2WyBsQWff/45owFsb499hJozGOPY3trFOUeSpjx8+JDcWFbLjNHWlFVeIErjSNje2eNRYTk/v+DZ11/x+PFjnDMMBgO2t3c4v5xxcHDAX/70E37wO79Lmo4YqW1mqxyF9ruLRSPSRC9C1t3b0tvB6MWbUpQ52oM5bDcV+ir8zJU4FZRwzoY+2JL2VV3NxdO3uSZ+ppMi7bYr1KxzQQRZcg1VQk2a58bdlnH21R2uhRS/gUx5r9pw1BJAEv3+JkeXdTGpuh+306RDmuYbn7vhmb7IjOuY+3UMsA1bxTlrbmrRTYz8Nu/1XbtOk++zDq6rt92u9s7S+Lm6XOl9v68ft23PTdSlkPQ5ggULZs0wMaxnF2wNhePlCcvFJVdnZ5y8PGF2tWBra4vHjw5Z5pYXL18zHqXs7GyTqJTlasV8tuT88orpZJfRaMw6h1VhSEcTLq9WrPLXPH33PZbLjGK9Rgss5wuSJOWTTz7jg48+4vXrEx7ev8c//kf/N7//+/8Wy9kcWwgJDpTGao21Rc3gsZgbzopu01vB6KHU0lx0gpQIzSwc3Vr5dbjebU3zvusi0n+iVSvPjnI0HL4NPFNtaoa9WTSvi8Vvtbk/9v8aARfdFwdJ0LZ6w0u7GUTVhi4hB4QjA6MrUT23c1r+MsjG5gVUllybIYQ6r2OejfkWJfUKZd6G2nsjuuq4Ld1mjLosgfjdm+rus/7a0T4b5TlVaaHNE5/6rZ+bHKk3URfs0zXeAAkrVrNjTo+/4ezFVzy5v0+xnJEvZuxtjVmNJ+xs7TJIR4wnux5JkDEXF3OurjLee/ddtB7y6NF9Li5/xqNHj1msChbLNQWQzTJIBnzwG/8SSil+46Pv8OMf/Tnbkym5NUy3dvnmxUuwlu3tKecXc7QYvvjiE64WS0aTXabbO2zde4hzlPmm/Pqx1pIk/ccrdtFbw+gDhQMhvFlyPZO5cYGU5yyKdQ0M3emk1Mqui73uhyf62tSnOTb+DgeWXFNOF+kS0goZ90IUkpN6+3xdR3kYSayNWYUTWzG+gFG2UxMAlXBrWEfOoaMTn0I5Bp9dJiZ/r1t4BMy/OS7tMSizfZb1uUgB6IcC+hhkU6iEsatuRa95+M1fq8ZSWversYg3h0WFqDgtRTl/w32R5rORYAn9ax+eXVfR2vtAGAvvkGv6CepnVRWZVqdz6KPGIS5d+c9C2104bMf/qa3UcGcFnRoM8Z6ObmoLoEZWzMjn0DyLorus4CsI5VXzsIQlxTq0cThbsFgc8/Xn/5xsfozNLvnis6/YHg65ODlB33sIiebF8Qn379/n1Zdf8P3vf48Pf+u3+cmPfwTWcDVbkGjh9PSURw/vIyKkaULuYHaxwCqYjMZcnJ7y+NEjPv3kGybjKdPpDs9fvuTd9x+TZQW2yDk9fkXhHDjLj//iz1BKMVsVJKMh54sZT9//COOkTI5okcQx0AaK7NrvGdNbwuibUr8X9riGur59mJhOSRsJAbE424/Xxv92TdQqbW1r4bbbE2LM22X/IuQ1zqictpXQKls1NGm7sbEq5GCJGWm7nABvdWp7UEqbm9sd3k/K49yq0LDWuw2GJT5+PoZKbhL6bc26Lcr9c7fD1NvtiR6ABiQQ3YogxPr3N9DSY79SLHx72heEvep59hehkBZgo21R/Y0D3ap+2ip/ut/YpipGex1Jx7qBlgXQA0v1WSGVD67MJKSdQbkccQV5vub5Z3/J6uKYsTasVysuj18zvveA6TjFFCseHB6QJCkXV5fsH+xweXXO2ckxaZqyWiw4uH/I9mTKX/zwz5hMJqyzgtFkiohikGoGwynL9Zrz83OWixmvXr1imCou51dorfnpT/+S2fyK3d1txiNfj8lz/tpf+z0uTy6QwYgHO9sUolhfnDHc3cfaHMRishVnFydcnr66dlxjejsYvUQLqqE1NTUqR2urf88H7/r4AT4REZAIQIggjRub2dJEb2tq9mn/N5nPVVRDvPipnYFx+GjMSKsUENGyqfFVMJTH0gmI2+xHzDQdUSoCt3k8YMw2rxuLqn4sgmqMn4RjEqV1Zi81swyBZL7v/i9jazYuKmK80efX0grH7W1h85vepIX2dzVo20Gz6K9Ri4rSYdepAzqFT9vyKCk+Ia3Zgk26KV1AOC3NOZ+oesMalZAMTzfmloSwWxftP5BIaFF/3+v8JW0h3O7rTX2Ko45C+3UpMJVdcnn8guNXL3jy8BEP9/eY65xJ6ng+v+Rgb5/Vcs7O3jaXV1d856PfYm9vl29eKqZbY/YOdkjTgoFO+ef/9J/x4sU3fLmc8/TpI169fM7l5Sm/+3t/lcwoknTIYpkxUIr3P3iX46PXFCbj4d4jXrx4wcHBAcYWKAzOZnz/+9/j2Tdf8cXnP0fbFT/7+Ef89X/5X+GHf/qP2N57yIe/+dvs7I5xYlhnKz7+0Z+yvjhFmfW1YxPT28HoO8hje1GGSdlcMn2LsWsCNeKXy+e0kuqQ7r4FGc5jDc7foIlaNidhg3lJ7bBKuiwUFy2AyLKINdAKfpGI+Ur9TrWooNwqLRUm6plae7H6sqrEW+WYhqyNQaConi37MYZd7y8QrI1THYdnN3dsljeAliOpjiKtNbGe9e2cQ1dZTWOTv86WGb6XVJPGi/V6bDZP9qqEagvjlWjsm1En12upzXK6O+MhSrxjX6TKm9QmP+7dkSuuPEM5Ph1MdQ2e67EsGhiNrsKG/XhFLxOPlxfWVfRPFTfv22lKgRqMxBBSLK45Ll2KjhKp0z00TKU+ht+E7xRUlq4WwFkGUnD86iu++PgvONzf4/Tll9y//5DDg99ivZpxdbXmxbPPyNdztqxld2/Cl1/8GKUS9qYpSbLg/NVrClMwX+dMRhmHBynGaLLsnIcPdpgtVlizwBSafLnm5PgM6xKeP5v6NBoKVuslx8fHKAXD4YC93fsMlOLjn/wFR6+fs7O1zddffsz77z3gpz/+p+QG3nl6j8lwzZ/9kz/me9//PlcXF+yPE85nhq2tUc+YbNKNjF5E/gHw7wJHzrkflNcOgP8Z+AD4Evj3nXNn4r/YfwP8O8AC+E+cc39669a0yHIzLHAdDZTf3OJsk3l4bFNQSleLtx19U2u5rvEjlVayOUmlPBA7/vGMugsegHoB1QzFRDskq3TKrl504V/VzutO0HodBusPPKYWlvHpV/FYhEO9+xzJ4LxjlZZ1VT2zaZn4Pluu/XhRrnmRWuhUbYRGWtzq2EW3af1Bk9G5wqd0dYC4ctdzEPat79GlvcffNIkZfdxP3cO8zfXO9MazN1gPDUWizIGzoexQfheJ50O3hXhbusmiCRTDrQ3HdEd5gRqujbisjuoaFsgbRnwF2HaAZXZ+zIuff8rq6hS1N2E8mvDTn/6Ug4N73NvbQw+njLf3UWJYG0vickxRoLVmMpqi7BpnZygs25OU4t6ERBeMRops6cjzFdOtEbP5OculY740bE8nJOmY8XCAGmgGiWa5XvHg0X1+8IMfcHp8wtXFCctsydGrFxRFxnq58H015xhjGA6GXF0c8elfznAMeP385zx9713y9ZX3173BCVO3ic/574C/1br294E/ds59F/jj8m+Afxv4bvnzB8B/e5tGCF57TUShKR2NpcYafsL9vp+KOUZaLTS13PDsoPxdOe/8vS7EMpQRygvtCe81frRuOIREpHf3Wty3xoIp348nfrwh5bqf9nN1LndA1/dCn7TUiykcXLKRETRuh25qYXF57fGKx+26Z33BUR+of+L3tCi0+LESLRs/jeISjejyWVUKHbFVsrbQpjDmcX/ia4G6nhHlOn9Q4vujVfUT19f+7vH3v25cRXuYxwVVufzxGVObP3G5N419Oz67qx2dPyo+xco1fSsu/ERZO3+J1DtGrXUPIBScHL1gmCgGynDy+hVX5+dMRim7W7uAZmfnPo8ev8t0dw+LcHR6RJoqinzBeGQZqDXbE2EytKRJxpOHW2iVUawvEXJwGcv5BVdXl1hTUGQr5lfnFNma9XrFO4+foLUmTVM+/PBDHjx4QJIosmzN5eUlIo6BFpwp/Jo0Odn8ivXsnGJ2RrG8olhecn5+hBbL++++w8HBAScnp7cesxs1eufc/yUiH7Qu/23gb5a///fA/wn85+X1/8H5GfNPRGRPRB47517c3JRYQwwMqM6fUS02mowzMNWAVTYmlTSfCb/X9nHzKLk+3LxNCsEpGpp23J7wb7WAWqF40M5NUmvbFRbdc/xhw+kW1R07TDVCeyttw8kX/naCE+MPYXAqwtpj7h7V5xwi3myvy/JQzAaFvDhYpFLjBFReHk6hSidei6lG36xM07bRji7G4aJsklUGSPHtqL599a1dMB8aZZj+Tx7114+DjRrUbM8mLFSfEgTxiV6FxH6WTU26MQddtKchUkyGAT7xEqDRBqiyRWxQFUCjkuhasFxrOKYspfx/NJZlPd4K9VEtlQO2iixLqjwtoT96I/VHvRaBRpZOJGz/V42jIAUhHEoTLF+f1tkh1qco8D6+jKK44vXRM/ZHjtViydZoQpGv+d5v/Q6z1RqlFdOtPX7y6iv2d58wffKI5XwHKRasUsN8fsIg9QqDEkuWZayLFcIAax1aDTk4OOTzz37O3t6IVZ6Tr1doGXF1csT2OGU9O+XwYMrR0RHvv/uvcnr8Gsua7a0Ri8UZhwe7nJ68ZpAo8nyNpUASx2R3hLFL7DzDAYWs+X//n/+D997/DQ4mQ44l7/64HfSLYvQPA/N2zr0QkQfl9afA19Fzz8prNzL6WLOEFnNGE/LibuxaFWmEW8Xnd24y+ECbnMKFrHsRXi7Oa8QbTD/UsXG9dfRdBK34dvVpwHVGyxiW+AWs7rIZHcwi9Ms1GUU1nlXsqdrsV/WsbkTuYL2DNnbAVfVWcltDXJzoUlBEe4S7OuooE5ZttkW5zWuO8iBssc1xi/sbhHk53K6jnNuQVz5qamizqh5n6LCQoixjqnmjVFji9NUx0xt0tsWWvhAtcabL/na3f28qE/V901ovHp7ROFdrztb6yLWkSmWtMJGQcbaV2x+QiONUIaNOoYIwoHaqUpYZ2htbyt4P4JXBQIktuDp/zWg0YTIccXVxws8+/nMG4s+O+L2/+lf49GefoBNYzU9JVMKrF19zmjgmacHi6pTVfMUgWZFQoBMQJyxnc/I8930xgpCSjqYopVitMorcsLOzhzWwXq9x1iIDR6oHzK5O+NGfn2IoODjY4Sc//hPmsyWDVCjWGb/3uz/ghz/06PZ8uWA4GDEaa8aTlMFAoxODiKGwBiFFrPDy2U/Z295iMrz9+QK/bGds18rpnHYi8gd4eIe9+0/qo9SiGO5gvnvHot4orBMbhuoAB4kWeWNC65qpxfNZxGvqMYShSrA3PkquN9pGhCrLnIsiKsoIjK5DpFsFVD1sR990WRrtdlTOsSirZzjDtqteER+748e4ZvguWlBalM+XLQE3AO/08lZD0LTCEY29pGNtrK6/bPhGuzzU0nVmbBin5lioCr5ppbsNR7FFjMIGTbHi+EFj7arKNp4JZFuBm7XNUkZE3YLZth/yjuKIGcfvdSwtH6jQjI73grgvvXFXozado/4d23rKWwz+EdvMX+Nqwdc4NrJloSilsNRaaDUUYqqABP/jh0ZTC7daWPjxVaUA0WJxrkCcZXV1xOz4K15dXvHg/iFboyHbieHR00dcnL7k4uSI3ekIheXy5Bt/HvR6hslyjF0xUBm72yOcUxRrizU5xhTkecF6VSDiyNaGySRlYJNyFVhWa38k53y+wlqNsQprQCeOJ08ecX7x2vtSzBWvnp/4MRdhMVvy9defARatB+SZAVeQJClawf7OPucXx6RDXfrkMkaDIcvlOc8vXpMm3/7O2FcBkhGRx8BRef0Z8G703DvA864CnHN/CPwhwDvf/UGPMIhzt4dr/VpYfM9S58EIuGx9LyQmj05fUnUahZtUaZ99sutOfdqlFRM5G6M2Kx9V0AyMaC7+Pvz4Oqdh199919rXY5O9vueqRa2UhztChEcMh9QpFLq1xDAiPh6+1tqkPJAZKE/LiRhOmadBbaRhDX6SzXoaUIP4+lwQ9j3j56EH3bRmNgeq/DfK7U9/VlO/ecxj2KoqL7StFdGj2t8TpNjUusvGRa2umuTHtJSd1QlWDfins5kN7fh6UhuwH+gKgvJjqqg1b12qZLaKhIrnsa5UetvQ9KuU3BXESbfaiEIw4CxKDNrlOLdmdn7CN5/8kGlqccszTl+8YpkkpNZy+uIMazK2p2PO5j6l8Hp9RmHWWFugVcEgMejEsVhcYvIMYyxKEhDBFJoiLxgOhxT5ijwD65aMRiMG6YSrqznvvPcdXjw/4vXxJYPJhOn2Pu+895SBWjJcnaO1Jc+WZMsznKQICdl65XukhMFAs7NziKCZjreYL65wkqBUwjAdsVwuUU5weUa+XDDQCal8+9kr/zfgPwb+6/Lf/zW6/vdE5B8CfwO4uA0+7xd+x1etFml9MPV1YXdVeZUjpnsy1zy/nqAW52EagqZLhDaWc06kOsc01rzr8r3gcOI3ANRtjTfXJOUB4fERf/WsDjlnAjwQY/3W2YZmJ9J9vmnXGF3njHOu6dwK1kvw7Id4+kpvk5KBxEcOlmMmpcwIkFdVJv7cyxCR4xxoFQ51kbL/EgmdOAQzPgUs/ioNAKV6tu5v89tX2/bjA7ibBzJ2jk91L4Y++hz4Ij76SnT1fD01bQO6kZYJ5GGweElGc9o1obEuCgeUi+rW6LtywXdHwAqNk9UEb1E31pgfQz//Q311iCtogsyq3pI4CZmmAYC1Nf9Opab8PtbPIXEF6+UZF8fP+ebnn5IWCy4vLxCXgU0pjPLRLOs1SZJwtoJEK0y2xGJIBwq0xbLEFDnGGrJ8wXq9RquRF9paoZIhxhVczXOMFda5IXEFShVczRZMtnZZr3KQAXv3HrHIHfcfvsPTd97j5PgLcusQZVDaMp4MEVKy3Adw7O7ukgwTCmvI84IH9w/RWnN68Zqvvjrl8P4+WZazWq3KsVYkqnSAu2YK4+voNuGV/xPe8XooIs+A/wLP4P8XEfm7wFfAv1c+/kf40MpP8eGV/+mtWlF++5CDJDhWVMcitR0LzDnDoIEFlpNEQzgtPcb2Y91eh4M5orivwOgD+62O7IsrLaGIuI3OBVPTIuLQmNIUj6GDHLXBsOp6dXmiVXmh6o6TSOuhNNvJ/YlbrslUJTb/wzULxPlJggasdMWUNYIztsok6h+VyjGclButBHzqA0WJ6VvAeMy+5tl+1IID1ApYh1VlptIAzwgkTmFKuENEyiicBGcKvOCMN8K4Sr4EB7qHCzaZm3fKFWjdtAxNoUr3nq0ceVAjOXEZhYu0JokEZsykbYuZipS7sUthHhVhpdaEu6CuClYitpCaeZ90LEArZ2ytWTuJNpJFk7a9dvx87MZ5g9PUSlh7zd2L1fnI4kofSzmeUvpkXOl4LSWFLcNtTQWTlUICPISaeKtOnCox+7I6a0AsBQVaHHZVYG2B4Bgmhsvz5ywvvkHbc0w+x2YL0sSijEGUQpmCgaw8b2GAkgEFGc4VIIkP9LAGk/tTpJaLnPUyYzwdsF6tGE932Z6OmM+WFLkXajYvSEYj8nyNsZmHGJXjcnmFTre5d/8J051dLIqn77zHN88/ZzabMUhgNErJClBO8/SD9zg7O0ErocjXOCe8evWK/b09sILWQ4q1YZCAyQ05OdPplLDtT8vt9fTbRN38nZ5b/0bHsw74z25de/VinbXRKyOBIdkKP65jhZuQQwzJbGZziw4TKa/EjrJQRni3GRlTPy/R79oF7bUfJqkYlr9KLCJqrH0zbr3dtoYWLi1BI9X/6nbKZj5v55xnREr71M7Vdam0tRjiCmPRGf3hutvoQnlE79OCoCRYLKHZtRXjc9vXVpJ3oFK+Iw0G62vZ5JBdFotnCG2cGhIVNPoSPup4t/JRqPrvqklQKQ6xpu0T25WKRdUmV8X+V3MpbNLrUKe7LBAvE5ttrNsTmKwfHehG4kN58fvXxfGH6xopBXgzkKDO2NkMTq7LctH0tBV2H+znyhpQqkwTUr4ltQz1mxJBoRloh8kWvPz6M2y+IE0VW1sps7MXzC9PsEXG7tYu48E+F2dHGJNj7BrBonU4pENYLRdYVyAiZJnFYVAKitzzinztjwhNkyELk4G1zJcrtre3WQ/g9PSclTEYHEmSMB4PWS/nTLd2GQ4Uk8mI3a0R7zy6jx4ozs9ecnZyAvmMyTBhOBwiTrOzs8Pl5SVZlmGsz1mzWuYolXByalkulwBkhaYoDMPxyM8ja/2mMhF0z16OLnordsZKwFIbOXRjRlNf13GTlRcGMSPpLF/8odyWUjh0aX9B0XU1MxER0M3j8Oql1GSOvh+61FKbuyKbf9cMoP67Lr9LCMUN9NZJXE43k6vKtiHM0ZQhgSGqKeK31AxbiSZOoLVRdnkilOD8PsoS+kGaDFhTpzqrrYrS0Vu1P5jihhgOKmHn0pNSHk4YhIY1NMM7S6vLRKF40Y7h7o1bUf9U/S1tvOmq/E9XGn0ICmiWECyKeLz8tTDOPlJFOXDWlqG2QchuatjiQtxKU9ha2YTo/Pm8gg/PrSNQ+hz+zZDJEJLs19Yms1dlBFH5l7U+BDdqcnXmb9wNcRBp7eVFKLPOVNgegk4qtb2jnQqnCpT1wkFcDuaSgT1hdXXMxXqO3RmTLxesrs7RCMOdMZPxiHy1ZL06w5g1WisKU1AYsEbIsowk0YholsvM74nRmiIXVsslWg1J0xRhwN7OPoUJjlIhy3OSwZDRULO7t41zhiRRTKZjzk+ekyYak5+xuoKf/eURKM1qec693S0uzmZ+rIw/kvDB4X3WKy9kZvMLhsMB+/t7XF3N0IlgjGEyHaESjbOWNB2ynC8wZs1goEmUj8O/Lb0VjB5q5hc0+VrbaKbj7HNKdjkC289USzaGaapfbOOClFywvZW/wsVdyVBiM7nkVq5M0eoXa/OwhqBxxyFkzciEvnwsJWdWsS3Rc1pRZGV7uMMvxsZxgiKlRukXVRzWWjG1GA4SwUUHBCQVY4n3EvRrxqEMwWIrhhH7KcKBEa4KtUuUKnW/0ofgaMBPIn6DqoeVYuitZDaR8InnR2UVtoVy/B0q52A7eVkLpimVggrCc4YQeeWVCgXlHApWgD+dSpox41ElIb7MVhBfUHbaY+udkhLDgE41IoKagjpeR2UdffnaxR9S7zc9hTUYz00B8S3ccAlKFxwkm/eC+t5wgod+Gh8W6SyJGFZXrzl+8TGL06/Q6zVDHKwdu+MpEz3h3r37FEXBen6JtVAUXjPPc4sxxjN0lVLkGev1gslkC6UGOOdYzHNc4bB2gDX+WxZ5QWENW1tbKJViE2G1WlAUhtFkxOXlOdZadrcnXOZz7/Zxjp2tBOGC3FoSNUaPDculQcRbmM4K1mrOzo84P3vtoVdTIGhWyyumkxEOIcsXTPXIQ5vpiGE69A5Z8WvVAqvVrxqjF8r4WgXOeXw2gjPa0IZU2u2mxrk5sW9n3oSzQCHC5MVrpSHVamB8qsSTsTGTK52lIY46gjFoMdiAz0okVdoMsS2wgql8m0yIumTpTkqtzNWwwUbaXQnxKYAVlKI88KW9qct5zDv+O8BFVQhiLUoUcYKy0ioq4SYphVYIN01UgHf8sLkqV48hRH1I+DLOkTSYfU9ETcDdaSoBIhJFu7TC/7pA8/Z4hXKogse9AIyeUfF3tAYpD58PR2D6sEChvanNj1TcnnpeNFhhpCTUSkMtvHRHyGQ5ehsKUbxze0OBciHtgisXW9jLEkFHDStx8ztsOpDbgtPHM0vYYBf5DFSxpFjMmV+dcnn6FaevPye/Okbj0GqEswpbDFmvc5ALppOxP9KN99YAACAASURBVNIPC1aDDFEoCrvEoTA2IR1MWWeCKRzD4ZgkSbBmyWI5R6uU0XjEZDLBuoLZbIZ1A/LMsVxkLNc543RIOhoyX8xZLhckA8cohfEwRacD8vyKbHnGOi8Yjaek4wnjScKHHz1lNbtiuVziXM5sfkaaCOkwARR5nmPyHINGDYQPPnyX8XhMURQYY3AYxuMxzjlMXmCKMirolvR2MHrAKVc61+prYYErKeOLI2SjDVvEk6xmmt2MPp6K4X1bYoqBkYdy2s9VIYESIlWSiqko0dS7eGt4odEn8WZZUkZGxALEWwubfYvr7TtIobuHtmY8VnkHYaU5l6S8ThrGL7Y4YophkqodgeEEplrCL3UkTfNvb20IThy2L32zx/Aqy6T+jj3jUo1hPeYbcJoL1675rk4B10cxxN/KhnkQleH3FnhNvLJIMSiSCpcXaOQValPF/EoK4aux0z+2vmr7Lp5z3eXHbW0oT1H/Aik2AxhsJTyB4DcLNlel3LSFRRPucfHuZGqfW1CxwAtc5xyL8zOOnn2BWV0ynz3DZhfk6zlGOQaJRgrLzt6U3b0hapBweXLO2ckR2xNFlmVoNWA4HLE1mZBlGXleeDhOpYgojHE+As8Jw3TMusgpCststiDLMp82JLPkhQ+ZHY+nLJdz1q+XpEPHzt4Oe7tTEm2AguVqhiuc30RVWE5PT9k5uM/9B4/QYhiOEnQypsgtxgrJ9pTF8gqlQKsye6jJfWTf0sM6Simc8c7oNE3J85zCOfIsu2b9b9JbwugdyjQZmWc8XuO0zmFV2PhTThDx2F5l+DdM7Dqmt6J4t+ggRDyo2ny25SR0tUXpfQet8MxSGAQttiE0nG1oZJW5G2AH33KUTlrP1NTsf4BWvHbe7kdjH0BETvlImaAHWgdK2Qh7F0KagMLUeLaIxgK6jIaJqV2Tc7aSo/E4B2drjM6WhpgfGe2ZXYIpb3akqwo5UkKoYQwNld+98Umcg7C5RmxrTE1VRKUF26Q+QCVsplJuQyDXPW80zpdR3S/DLsVixFROUw9FB6vHazAKV2rxbiNlL9R4f50mQXtHeVkWVY1C2BRVacAV3g6uBQlVAtLZasJW3zJK9ayi59vWsg8JjtZTBDGKdIWE1hFw8deouxEETjPzqYhCTA7miouznzO7es7eZMTcFAgpSu+iENLRBJQmyzKKIiNRlqOjL3C24PJC4dyY9dqHI9+7d4CSJUpWFGaJcw5rDIKjwMM6hbIsljlZ4sdXKZ8Ta75YsV5n5MaxWq1I04RhmpCmsDUdY11GVm4ozDK/NyArHM4ppls7fPc773Mxu+Ty6pJECelwAMrhMhhuO9xghLJDFrMl1q3I87W3QNwEnQgiFmvBFZYkydGSoPWA8+UMaC3Sa+gtYfSRptG46iWpc2V+bGk+G+8JjO/XpTQ1V6B1TkaM0Xom7ZxrWA7lWxvtjfN1b1zvGP/rHKaNBlZdUIhYnAtha12WScvJG4SJBIEVbyKqmWNDcwtr19XOvE5TXMIzN/ency9Z1OpNuv2E3XjTS6i61Crcs6NmV/cvRMuEyAUvBOP0AzfUK/UeDFcmFovTYoToGxFFvJ+jskhd5Aco21bqHh6eLK2QyrqMvp3/Lk0cvKmMbO4pcG2FZWNM3oyaaaZd1cbal9BtKWygYw2/gBdWSimKwvHq1SvE+A813dpjcXHBINE8ePCAZbZkucgBy2q9wOQrrC0YKA3iNzjpZMS4PA7QWMtyOcdahzVgS2s0HCZuLD7U2HhOEFJhZ3mGMxZxloEWFIbReMjh/hbOGdZZTpat0FqznC3RgxSlfI6f+/fvs16vEGtIkoTF7IpksF0KhRVJOmU4HPL04Qe8eHZEcXJMURQ+aKFwfj+G8mHLWVaQ5zlpMkQkJE/stwzb9JYw+nhjTcSEyskU44hWwsacUutzgFgsqsFkDa5xnF4wdx00cqWE+eo3DTVDC0OcdhyZUpOttPtAzpYhZ85rbmGxUloKnVjyTSNTQhdhAbmWZtfA3MN1RwXThBtVlnHXzjNeQho9W/1rTYsIJtgk59wNDL7dymvGo6ctNcUbc26oLSoj7EOoonaIoUA2mKe/3r37sL5uwZUHuQTfThVAYIAWpNijCPgy65O3glbtXDjysT5TYROibM6F+rSw8rkSlrzNB7pOgPfdU9gyRDZSsaT+Po3snMY2onQaUGzpxBYco1FKkg555+n7UOTcSx/ztfmCxfklkkwZ6zF5cY5oOH5xRF4s/HNP3uHybM7jJ++QJCOUUsyXc/LVCovz4YzG496r9YzBYICIMBxN2Nm9hykK1us12WoNeUZhMrTyG62mkzHjScr29pQ8u8Q5V0JEKTjIM0thnA/nTLzCcX5+jjE51uSA4vTkrBwx74vUA83R0UsGwxHj8RhrLcvlKdvb2yyWM3b3xlhrMMaHCuvhCOUUSZIwnU5v/qAlvSWMvoYoGlKqdGRB0GhUFbLlsfQSPkBKv5Y3kV2Zjjg4Pf2b0nwPiLfyO+XqbDqVTQrYvu3zmyRlGSY4r/BRIWERqLLs5nprbuaprlXZwyxt5hgNUORg9Zi3xpt7baftZg73gDHHncUvNlePVVuwBIqvh520MUvs5ylRcq5qqINAam0g63U8V9I58h1E7WlAMB2MOt5Q5gJs5Teo1GWUhfQKm9oCCI5u35s6iVvYPUo1nvGzYHsESzVnxZTzxSFugKL0fUgQzFLtIPUROGX5MSONhrTTltrQqN+cnGhk49tFNbZ8IvEpYtVsdGVQgHj1xFnFw6cfMUxHiLFsbY15YIRX8oyff3NEVqx59+kTdvd3ubi44vLKcXY242Bp0IMBxhgsa1arBYv5FamCPF+Tm4L5fFbBN3meMxqNOL84ZWv7PqvVmvfff58sy3j98sX/R927/N6SbHden4jI1379XudZL9e99r2+vrivRUs2NgY3lhg1kx7BDNEIqScwQGJAi7+gR0g9QrLEgJaQAAFSM6BBNNDIxnbT3Vf2fdatqlu3XqfO8/faz8yMF4PIR+Teuc85ZRl0HFLV2b/MyMjIyMgVK75rre+irhxSOaZFihAeJSybzQpjS6x1FMUUJXPKskQmBUopkkRy994ZSkp2Vd0syAlYi/QZxjlkIhrq4oT1dsd2c0NVadJEcXHnjKraMJ0pptOc9XqFNhrhJdZNSJMU6yo82Wu/ozdD0DdYLRFMESZd656oog+p0WA8RHKnBzFao1CrYYp2N9zDOQPDX0enK3rNnUjTkMEPu83C1N/mUAC0bJeqmawyOh6u6Z+if95YQB3SK8TC5PB+bbLvRmAL30LYff2Ob2d4n3bwhOh61NWXcujV0XU6kkuD/uwJfaJn3687FtncM13KYT+PCPp9OKNtub/fSNuDG9rhAiZiw+KwjfjyoeGyrRvDapJWg+92qGIY1TqcQ2OLUISZx/CID4lyeuO36Pvu/eCeA5bpyLNnLCfKMeH+dXaeoQ3VXtgcdMMX0T9GOB3dIwRTiSjRjESphPO77zBJM3CBIvjF8mNkXnC73ZIXGdn0hOn8Dr/0zZyPPvwRb79zRpYVGL3j+eWzQHTnK3RV40RwgqjritoEF1hFgGPm04KbpUHvNpydnHBzc4WUkvliyqPlC+7eOSVRkOWS7XZLWYeAqyTJyJIFxjhOT+6SJgXa7ZgvChwOZ3TYUWy2WOvBumYhSJhNZiglqXVJkibM5gXL1TXLqmaa5qgEHj64S5I6zk4X1PUV3oC1mqvVhixJMH/VkoMLREMg1B9r8U4XCT6w2EgBjqNkx1zjhp/R4JPtfzWudvtp3MTIL+g/jDakO66jfO/NHYtwJeLtuO8d0va0INkE00DvzdK7sR0KhZY/PbhPhsiSsCXsvVx850niuz50230fuHsGQqIhGNv/RmV7j70SNLDe22VMbMTCpAskil6Hj/5oXUFDpO2xfUHv5dNfyJ6m3ohXYYjfYevuGQua9nI3MJq3fR/3Se9ZJlW0OHm8N+MLjTjsc9z9ti+JODwG4KXpro8DolqvnDGhul/UiDvnYGxikrExz6Bju5toXsSupqOMqbHK1WU/83gXvmchASVwPiWbiDbMCudq8tkZO2u5/843OZkvuPvg7RABiwI54c7FnIlSXF49oSo3JMKihAgMlxK0tWjr0NpS6x33Ls6xpma73Qb4yZdIUeAcbDdVWGidQJuS2WLOer2krC1SZJxfXFDXGk9OluVU9ZZiWiCqGl1VWBeEvK4ddeVYrVYspgsyVeCsw2lIkoTbqxdYa7j34AHCV4HyObGcnp5RlhW5c8ynKbNfesDTJ5ds1tcIlSPTfHRHeKy8EYI+Lr0m2qz2DGGA/SjEts74xnSkXYba+Jjmsu+Ctn/94d/dZjxct/c9xRDIy9vZvybqS6etHdowYPiRykiYxxh7dz7qoGJPSLxkGI+54r3qWcbq7AtaGO58Qi7f/j286t7toHfYsAhBJWG+9AvyPo1uXFoajq9VIu16rM3+wV7ecDdHjsy5r2fZ+YuVV4/xsV6M93lsXfGeDnJz3oJobWOqsyh0baEiBSThvV/6Bl98Zrm4uCBJEvLplM1ySW0sl9e33Lk4x3nPV4+f4pzGKwtKoZLgRZMpgc09WmuqnaWuKs4Wc4ytkYARHucDuViShDHIC0WSBtgny1K8AClSFiczVsst2+2Gs9MCbRzb3Zo0kex22+6ek8mE7aamTQyjmwju3bZiW5ZcXd1w794dqt2Oe/fvUFeGxXyKMY5pkVNWS0ytmc4WzOcn1JXHixy8YLvZvtZ7hTdG0LfbUUA05Fp48KF7rUY8/KTGHAv3Wh3VYEDGAS5t3UHVVijDPrNjX2Nk0stW+x4W0WqRIhJ0EYVx/FG5CDYhFtYtVBBlq4oXrNjAtR9523atNewOdi7+cEE7Zs2PjZVxv+VfRAzF60u3uPcwhHN7CcTjS1+2oLSP1xmvD7N7yUh1iH3FW9fIYwypg9tEQVfDMooVvUadw+PD5+xhjmGdkVa+xuuQ8a7hFdDk8WC9IwMWEbgNq/rwnTc5m1tDtoiS/4Q+xAuoIi0KECknFxfkSYrHURmDEwm1EyT5FF1ueP+Xv8nzJ59TbW4pdUmapiSuif62Dl2V5GlGKgXaVCRSks0yltsNztVIFYys6/WWs7MzityjsmBvmS+mgcGy2oGwVPWG61vN7e0Vnoq333oAwgWaYynZViXeC5RKMcaSpRM8FotAV4ZyV7PZbKiqkvliymyWgtPgHNYIiixnVRlWy5K6NixO77BZ1+xKjYxsSq8qb4igP9QmgvvbEJ9uea5Hrx85NrDuD2b/Ye0BlOCj8P94qz9wzDwsbTadMa+I+DkO7h0db9PO9UExCoHANBDMUOuMP8zD9sYx5SDsYu35dY1wvVweRgq7sdEYywLlh/QPbTtKNYZz2QQi7WXi2ncPHOttLHYH88b33P+dMTluu/n3MCriyBiM4c6D9/yq3WAY/zgZStzGsTchhBjYpcYVlObcWIYxwPJyr6JX4/JHdhtHrmvhxuHm13XXuPiZhYhe4nB+BnngUEJSFAXWCLZVibOaujZIKfnGN74J0nP/rYc8+nxFURTo3QajA02ArkN0qbMWqw1S0bhxGpI8ROJPi4z5tMB7z8pqZhNFkRG0exzeW6wNAtZZHWJs0DgLut4iVXBhTtOQhMRaT115nJN411AZ5IHKwLswBkmSsV5vyTLF4mSClGCruvPcSpKE05M7fPjRJ1y+uOX9b34LkBjjjuajHitviKAXnTuiiFZz2XKExEayEeimbWK/DLWfVwvcQe0RYXkgkLtjrZYehEnHpS16ygSEwNlIS2017L3ncQwFADQTItKw2sxbbmA8jFc1d6DW9TYM+crnd/YIde1+HtpO8I5Jm8ND7fsM62cbKcvhwtjAL/7IO2u7EV/XXtPi+00gdcc+CbKzRnZ5Rvb62GXmGn36/n7hHoGeYax/I1ceHHmdBfaYx9PwqnG7yV+kDOf6aI2veY+x7UbvMaWaD9R6HyiIiWwfXuIb6mvnDMIahPTcPz/nFx99yPnFKWenc3arikdffMrv/d7vsVm94IMf/jnbzS3T+ZTN7S0qTVhtNpycLLAWrNPMT2ZhARCOJFHIRDTRzJab20vyPKeYKLJ8gsdQNxTGxjmKQoG37HYlUgbhvF4vOTufcXo2I0kgSQuEdDx/dsVqWZJnC4rpHGMcL65vqOua2SynmOTcu/cAbyu03iGcot7VbDcrinyKNQUaT1XX7LaaxeKMsqxRSYH1UFd/xYyxnfHR9xGHIcjENiu+6Ax2x3Segx3BSz6kgYLR1nHDYy18IGRfZ/DhNT6TQVg35xstLebNGSwOr1iBOx9376MPe6hzdofE8bGI79v3vRXevR/9fjKLDopRR6AbZ6PcvrbrW+st8TKbRgubDNbmxqUu1I8Scvj2+YbPMzBO7sNNjaut9CJw5bSv1ZlmLF0IDvM99ULQ/ul+izhew4xpv/vjHd6JaiKd28jW8DuqNbrbGo/EPWqDifr8qnJM2x7LiiUOSNua68Z2va+xIx28F+kOjsVZ10TD0NllpPIhB7FCNVq9QGDx1rDbrsiVpNws+aV37yGV4+r5p3z12c8pN2v+8f/y3+OcI8dR64pyGxYQbdu8tb77/qSCxXRGohT1bovWEtckMgk7Lc90WqCUCAFMza5aa02SOJzTaFPhnWAymXBx5xQImdiKSYYQHqk8yzwlvzunKi11XSNJG5fKDOccaRr+1qXHWs16taHIM4rJgrPTc4zVrDZbtmsbBHwKufW8+97brLc/f2Uimri8EYJeCEHehGjKGLboiI6icOv9ANJOe947IQT4cY+JQZZN76FBbf3exySahAvjQT3hn3gLrtQYZn4sXdvIh9TW94Bok6y0DIEhy1ZYEF23KPpmnRHNdjdwnx+2LZtXLUTvce4bod/p+q0mPSKwQ+BXP13iZ+0w7+i+kWm4eX9tdqzoPQnoPXn2cdzh+4zhHiWaEPbm+YXw2GbWO9GE+fu2n2kfudm06kawf7nXhRZiUiOvPqxZ8RzqaRZEv93oTicuxA7EdNveSwz2YG6IEftKOB52EN61CkyvCOwrIi5miIyaH2aXbXlrjli6xqb8iMJzrE5Tse1hdNc2b0EINOvu7yEVsln4g3YtG8VHpim1d7hqQ7V6hk8cu90tzx5/itAbUlmH+S81iUxJEoWpLTsXWCmFUFRaU+sdwlum02nINGU1FotzYbwTFbR2X+0QwpOmTcyHtzirUC7F1QKhJKYMi0CmDFZYnHCkWcJ6s0Gb4LHz4K0HCDK225IvPn+GFI4HD99nvVlSFBlCCKxzXN5ckyWSySRHG48QjvV6TVmWGA9lVXNyfhL87FOB94Zf/da3+OyLL46+h/3yRgj6trTeIvt458u8FkIo8N659u9BouX9OvuRJH07AT5oJ+n49rz9GIe+zlG/2vtJMeSnaavGH6CICMWaq7v/t3CCb57i4Fmb/3wseA9hiajn/W8JY57tLfvhmCY6VkadKg8Wi7bvrbgdQiFjrQ4XnPa1+gbX7/TAIAzGjIe+XTSbMWyJ4UbcBL33DZ9LX9rgs7aPXX+lYJDEe0QADrD3aCMXFokQ0NYG9fn4+oaqQvTdbs41vPOycdztNOMwJsMxPL6TPYj+lYeLCoDw6cGxjjfoJZrkq3D+LrCxm7j9jvUgj4MSCO+QwuHshuvnX3D55HN0fYWud2zX19TVjsViEZYO67BIpEixTpBlEq89q806+KzXJXmRMp/O0GbbjL2krjRKKfIsZbVacXo2pygKEqXQWhOC4IJW7xzBhkSKs4bdboeQOUkKWEdVVwHHlxIpErSuESJlPp+y2ey4uX0RiMlMzWy2AKAqDRQZmRGkaUhOcnt7TZIkrLcbrPHM5yfcv3+CSlIuLy/ZVjXaaF63vBGCXjD02+0FbogQ9UejE6M2RqCbeEYO8fzWf6cNaxKdoGiQwk5DHniaRL7srV7SYe1NSwceG412vl9UlNez3cGERBJ7z9Xp+a1QjBc92UMcEQQR/9sW2bgbDoSR50DIBrYG1z0PRARlR77w0Q1PV7c3k/bG2DDuotmCyIMGDh1mWwWgtdkIhjQDcZ6jTvuHLtWihM6wMpZnVwgGCc7bdloopc0rENqOR2fvmdt0ioPx6SN/O8+S6F7tQh984mO66r4NF1FihwUhaPiKHh551U6+XXDHnATiY337++XVHAr7i9N4G/H86ue+9w7bfpfCNd+bx7maq8uvePboA7ZXj3Fui5Lgqg3C1GxvK5IkAxxpmiPTsBgbZcmKBLEN36WXiroyQXjaELFa1xZrQ1KVXVmSZRlSyhBZaxrKikZGaO3RuwohNFYbqrJmNl+QJAlCWKqqxtQ1znmSSY4zYI1ntbrk9OycO3fOuVluEV5ys1pSaQ/OoNKMb37jW1xfX7LelFgjQGaggq+9x6GN5/rmlrOzC5bLJdpZnPtLzBn7/0fx3oOzvSBoP/FmpytfpkJ0jfQ/BQGPOEYHO6pzNBK2NRjKRoLKSAM9vFAMjws3KhzlyCfY5hESPtAkdA3tVZWN9tZnWxLdbiRkcZJ79QlGrJF+tCnW42PCDznkxzIfvdK2P6rRRuPWHfRR/Vira6GPjmGtM7AOb9G6qEpwrtGO46fs63amFz+kfguL2yFkcqCN+xZmiTBlEVgunfd7Q9Rrxa023hPwQRxSHJr2UR9991xJq3u0i0s04doIV+dsxN8Tuwm/GlahEaMBHuypRcYEvxrx0LF7gjumI+lGYmB4OPz+nGjI3/bmhm2gOOFdl7bQuwD5eVvz6NEvWD17hNnekCUC60OAonfgjKOqNCenc2QiqaoNSqWkWYK3jvnpnKo0VKXh5PwMgUJ7j64MuqqZTHLAo0RCMZ+SpxJnLNZalEio6sBTI0hCf5zHOItQgXMm2OQgTVKMhsuba7gpEUKR5TkqScM9tWa1vmW3rUAkTUIURT6Z8ez5JUpKtPZU1YrpbAJCkqQ5Qnq+evwcgNvVDqUCa+exfL9j5Y0Q9EKIIZkYTUj6y3eBB20clvEGeu1PtRdj6D/mNst83277wUcfA+7gA4l9slV8bIzT5ODHftejHQ6uWQz28dkIHz/CLLmvLQshumOu0QJldE3c+j6V7liWI2Ag5F5dGm1+qK5354KQ7UnH+ptEP0X0jO24+HFhE3sKdUZeD4xptbTwRg8pHchNL2FPCA7mwQgc1Y/bkOfft5wE8lDL3i+9/UgNFoB92CjsZI9p3rFbbAP/NE4Qr/Pq4nsFw26be/dY6bX1ro1GkfCeTnOmUVc8QQ60MedC+CZxS1BP2rR/UiikklhtAE+lTZMUJ8PZIPTn8xwpwRc52muqVYk2jloHQrPaWJxxIBOEENy7exF2U97ivcVZh9GOymqstaRJHpbkkLyauq7JshTnHNYnCKmCC6fIsEbihWQxP8U6zWa9Q8mSyWTC+fk5UqzY7uqOLsV7z2q14vz8Ds4LhFAYa5mmOdttiReqgyq999iGo2dsZ3qsvBGCfrfdkkiJjtz6xEs08q7OMU3iFaUjmwI6jS3SiOLt8xH1/+D+x/oUwvAP68m9yJf+T9n7fbcKsI+R9HFX0/j4PuQx6I+U3aIq6WGOcOdgkbAtTLSn3R/lr/GHE87HeFJ/VWdraDVxiTg0HovGSBeH1g/eb9yTl7sXjr0+KbKu3mAhG7mq9RwZPsVwjxO3M/Aa61bMtg0L3iPptemurmyhqSPGfz/cWXSHRxYW1exKW0eBqJH+KZtbtGN/MJfH4NLWBbrb8dAkFjkS23IU6nOA6ubjxx9+xHvvvUeeT7DONbxwoluMRTNnnQtjn2QFdVninaTSnrI0ZLnCGkWpt1jnu/SBWZGRpBO0FtTaY63HOUtVG/I0Y1IUeFtRliU4Q5I0kKBUWGvAS9Ikpa4NRusQGSsls8UCbWry6YTJJKOqKm5vr3E+xTqJtbBcbrBW471DihKtLWmRsjg9IS+CkdgYwztvvU1VaZ5+9TjECbgK3fj/n56fs17vSPMM52hy3maB08f8FYNuZrMpFo1UfSIQK4ARASJHtqvhj7FF4ZjwPzyexprWAM+PPuqI02RfiIVEJUPWyHYPMKYIuyN2B4E79PQQx6KAh/d6VXHtmPohHNPyv7SqXftkQsSBWy9xb2xTO0YGiuC3b3uB0SYEsfswy5F31G3xuwNRf30HM3kEVuzNhUH7Qw+U4M4XQUTRGPaQVn/JmMyViAE8JkQ/aL0TUc+c2nESiaEwb4nyfOhKB0UN7z/swMHUGHFtbX92SdI7LV7u1fURx9OQ8yj+nlosWI1wYgfPrch2Jdyg/f2+Ca8aaMs1mrMlSRVZniJkeOdSSKw1KAnWWDCWSTKhzOaIRHH5+Cmz2QxrLdYJVJKz3VZ4f0WaBEI0ayRZmoJPUTJhks+ZTixVHSJMvUi4c3FBkadIXyLQWFMhSQNUaj1VbZlMMjw5TlSQBqpgYzX3Tu+y2axIkoTVas16s8OTIZCcn9/l8sU1u22FSgSTSUFRzFBKYCuLSGBSKG6XG6RMePLsKeV2h7GGichRKjCpbrdbqkpTVxZTViRJQpYrzs4WVFWF1q/z1YfyRgh6T1hlsY5EqrA1ZyjU98v+tntMeIsjjqZDDWSoiUGj5Yr+97H7D+GH5n9734IS42S7R7nUj584cjyUAR/QsRYio1vb2n60ZOCHkR1O2mr/sTE0biuUZgwVe8eGMIL0vo92amu9DLI4MvaesDi1z3l0ZI6QsB0m/G77cRglKsaYuXjZwnJYf9/G1DkK+NYwPd7neMz3jc1tcWL8+OBebd1Iw+/jIcaD7mICu9aV9viueX/xHu4S9iEG723A3yUkUoCzfRCbs1gX+NutqbD1DoHG1BVCCKbzM9aTLUKlKOnRO43wCVJ4jFOYnSEvUjKjWG8tUnpmsxzvYTG/Q+E0292asHZoMgAAIABJREFUu3fvksrgp58IyXZ7g2tUNaMtm3VJWRryTOIICcaVStBaM5nOQhYpkfDixQqtNWma881f/hU++PFPyDIQImlcIS1JklGWJXVdkmUpZ+cn5HmKzhOqUqOdYbm8DYsXFmcMxmiyPGG92qJ1GL8sS5hOJxSTFJXAZrM58j4Oyxsh6IX3IUE0wQATHMfDh3c4uTq9K/x/T8gLogl7RDMafnaHPuCRgjb4+IcT1nWVuiAZuff5B8C369/Qx3h/oWr/OMYzc4jLHs0NGtkEBoK5pTHGd0PWURLQj51wHkGP3brGyVx6OVQ3BwJMNG0G4WSJBfzB0wz7e0TQH5P/7ftpT6uDyvEc2dOIfU+9sN+mF2MLwHgfjvU/XjheeZ3fzxE2XEC632MLRXuP1g242dV47w/opOP6rw1xji6SI9UY7uTib8e1HYnTOzaHduUWKSFLFRKH9A5nHdvthiJP+fGff5/FVIFzmHrLV199ycN7F7zz4CHlquTqKiTnEHIXIsQTyNIJVlqyfMKuNOyqNd/6lfdJs4KzixllWXN9fYnWnunkhERCXW0o6y211nhtUdKzWm3YVRaBotIOY2qstWRZjtaacld30bLee7SDrJjw8UefYY2k9uFaazV1XfPw4UMur56z3W5J0hl1XWJMycl8jikcu7LGnS1QMqU2mjunpwgh2G43eA+JykiUIE8zlJSYWiNwg2C3V5U3QtA77zpNSDXGGBmA6UPNKdIWjrmUvdw/+7AEA2V0faR1Dtwro69SjmxjA94c1+nPjd2zqxfzvxzzcdlzd+sMas19ht4Or37ubrFohHNsF9gXBUeU2ri1QZvhmva3H9QJq0cMvx26lEadHPzZPqPaF9IQXC47gTb+7uPdw9j5sed0R3hj9u/f3WOk7jF4qg2gGrb7enO2LfspLQViwEE/vN/r8RqFHV+k4HhzcN1QOYlOtFAUrlNmhJfdd+x87/F0e3NFnig2qyUvnj+lmMzYlFvmswvyPOPqxVPOL04xtibLMn7xiy84nZ+zWtXMZhfsyh0qKXC6wjoLIqXWNZmx3K5WLJe31NWPuP/wLTalRWuLMTVporhz9z7zWc4HP/kBL569oMg9Uki08zgkdb2j3NWkeQ4CZKLQ1iBUIEcz1ncY+XQ65fLZC/JsEmwJ3mBsoCdwzvH8+SVltWU+nzMtpggvqMsKJWp2uwptLNZ4ZA431zfIJlhLSolMMrxXLE6mtMGTZV0xmUzIiskr32Vb3ghBf319HSJiRfBnlziclEipBtpXFwDTlnhbuqe9v3Ri7pe9hBchuvTl1+0fbTMVxTS47fHhbuFwK067YAmBHzH+hfsN8VaIvIfaf1oD28AtbxiJ2pWWUygEAkTcPcGzYD8/r492JvFz9A3LPWgn9GT4BArf+H+LNlDHy6OOG/v0DLFn0GEZE+CHO4fOv37fECtGPGw4vmsaQHTxPBypKv34YuHFISR2VNDvBd1575tvZqxrr79YHGMIje8nm2CheLxaN+S2y/25iE/oAF4NbqJCCBaLGd7VnM5nPHn8iLsXZ2gP09MH4C3/0l/7df70D58AsN0GOl6tNS+eX7E4O+fRo0e0tAlZlpP6kA7w/fff5wc/+DOEdCxOT0hSyXQx5Wb9nF1tsHWNKAqur6+5vbHM53OsvUtdLVlMCpbLJZPJhJDKfUMLQVprO9tKC70ppcgTBdaRpglVvSNPM6qyJJESlaakSqGrmvv37zeu0hDszYqqNDx8610+/fRTnIP5fM7Dhw/J85zPP/+cJAnkZcaY0NfFNKQldBbXJDl/3fJKQS+EeA/4B8DD5i3+gff+7wshLoD/FvgG8Cnw73jvr0V4o38f+LeALfC3vffff9k9FosFwnnyJME0FinRaH6Nwtn0BcaDMEAOPgQB3vfeI0IOBMWxyMjomSMq4HGIxY+4NrX+8nK/K62O08AmktY///DeHVWBp3NvDA6JLm6wuVDQh7LHtxw+ixMgvO1hAi/7MW1gGeEDw55E4BLbYRY9A6HYo9vp79JSNQTYphug/pk6YeL6vjvRCSSv4ijlxrvEC5BxsFpUZQ+a8K7XmoMQH7sqMko60cBSol8dPYMEKB2D5t6xsQ6N2SviciwOJNgLRFA0osCnZgCGfd+fsr6dVw2td/zRf40t/b5tquubihbtxkMmfowBtu/7dlonAykl1h8mtZdJDxWeLM64ub6kyOcYB3kWyLpEKlFOcPfhQ87mC/Ikx+wsJ5Nziizh8dPn7LRG0bBGyjlJIsmyjCdPwuKglGI2z1ksFkyLlOl0SlYIltc3OO/ZrJc4UzIpUi5O5qyWNVYHKgVkEOItZ5W2FluHBN9COlQSloEsz3A2aPSmDjYFkQclJkvzhivLkuSeaZGz3m7IsqCpW8B7yfXVLcgEZ2tWqw0nJ2colaIrQyIVRVFwe3tLpaBwjjQJHj5l+ZfvXmmA/8R7/30hxAL4F0KI/w3428D/7r3/e0KIvwv8XeA/Bf4m8O3mv98G/ovm36Mly7JuXrf5JP0+HPGKMhCWMvi7qG6i9lGswDi2NeK9EOoyapgdW2+OKUd9DtYezhnufCON0Ntud9DpeFIc0XqPGdL6jrS7jAM8ParRxTH1fF99e9Fw7Gt/vYbsm3u2O6/jmuIopNL9iPznj8EdHqRSg4XbNeHzr5ov3e5ksMj1/VEDT5PGO2ngqRJr7uNG5NFj0e+DPgq/Zxxuawtelm59CL3tKSFHxuGo9j5y3QDmaRY9d4Q7igj7j6m6e26fWKmJ+i0c5+fnfPXlI5QSJI3HjPYVT58+ZTKZUe5qTs8vWMzm5EqyXt6yqyuyXKGE52Q+Y1duuHz2lGKS8OTJM+4/uIOQlvl8glSwWl2TphJbGhAWoyuMqSm3axbzO4DDOYeua5IkYVfq4ArZcN1IKfEyuIJaZ/DGMp0WpGlKaQybzQZja4o0IUskIstRWYKuLc4nFFlBWdZsNjustcFjpqqZTE+wWRibPM+Zz+fBJTTLqKodqomkTJIEYwzOBaI2azy7cveXK+i994+Bx83vlRDip8A7wN8Cfr+p9l8B/4Qg6P8W8A98ePN/KoQ4E0K81bQzWgKu2Hcl4MQON4JXK0YkLODHKBS6naUYkkVJf1g3/sD2MM9W+u1DCXFyjJdBQ0oENsVOrjjPftBN358+jZ+g2ThG2tTgI5ZDgd7+f5BWsbsuvke0KCBayTq8afecER3vCIgtBAgbNOrYCP51ovZav/KhAdARG0X6lHzNOXoPomDjsKEve+8h/hY6d0MRC57x36K1KrapD1vCrZHArOF4joI3w3a765uxjWFDdwibNZXH24j60kFSxxbZEfk/2KTsLRZCtJ5lzbyKaZ0GEypu2HX17EhMQNgdhLmRpimmqklTRapUEKQiGGXzfMKnn3zGw/t3mS/OuX7+hNJrzu/dQX3gmOQKZzVXl4/Z7TY4r3n8+Bc8fHDG2fkJ2+0SIWp0bfAkzKZnpGlKnkmEs6xuXjCdpZS7FcIbiiKj2m2o6xIpM4RwSKkAgS5rLB4lAwuUNjXOpOzsjiTJ2G1LTk8XKOF59uQJ0/kcb8J3O8nysCvQGms8deUwWiJESlmGzFPZpOB2ddMYYLcURcH9+/cxxmCMoaoqimRKWdbkSYoxDrwc5Xc6Vr4WRi+E+Abw14F/Cjxohbf3/rEQ4n5T7R0gplX7sjk2EPRCiL8D/B2Auw/exjmHEq1xNXDcdShAPFeOTPgYM2y35KLdJrqhhiIY+biPQTSRkG0/JOdcxxTZYd2yh1EOi4x2BuHCMVoECO5yY2e6ZWjw8bcd28d3ozGKFrux58PJ0d1J1/OB0Bh/vp7WuA9Cex2jX297sdG7ifPkRvX23o9oYK19w+uYN03Xe9fmXVVR3fFdUad5djaKhnRL0GisNjrfr47jRuBx98ODVRWJkNFzD2wescdMvLDsP2fPz/M6ZUQn6IqzltvlkvPzc7AOF9mPBqkrOXRLDQ06DobDBhoGIYOilCSxohRAykQpzs8u+I1f/x63tzdIKUnylN36iq8efcTN7VO8M8yLlKq6RgjLyckUIVLyTIDfkCbhHe12JUJmWLvh7OIBt1eXCCU5PZnwzlv32e6WXD6/RmvN+fk5z569QNcGrU0jk2SUBQvmp3Omk1O2603QsHWN0RW2zpiezBqtXGGMZj49xQtYLpdsNxVVpSlLTZqmgVMHwWw2I8kz3nv/XX7+858HriTvqXXNbDYD4OZmGZKVeB8SjAvFJJ9i/f8HAVNCiDnwPwD/sfd++ZIPeezEwczz3v8B8AcAv/Jrf80nSYK1TSjznsdG7A9/dGLKSCB3Gmr4OMN3HQn34YMdtDt8mOF2P1wSOE9EfzlCghL9cA62VbE8bi868jG+WjzGdXt8OS5fh6dadAuLG+U9ea3igsDsr3u9LWW3+EiP7/DceKBbIdcI2u5EbzcQzeJ51Ggad3PfPXYPfonngBSNsJcNT3ozn5xzeKIMSG2PO31hLJJ2r2+D1/bysWqN2kOMfGRnNXiyPsjtVe/y2PZfiJBy8fnTx2QJzGazoa1hsAuxo7BPHKAVK1Rht+QQzvZkgt4iSbEN1YH0gvl8Tp6lpKlgWiR8/JOPePbsc168+JL5pCBXBYkyFJOErAhyQmHQdRWoCRwkiaLWmt/93d/kiy8e8WS7CoFY9YZHX25BWJy3AZLxAudbDvrgPGCtQ2tNbQ1ltUEmC+aT4Ad/eXmJUjlCBEqEqxclSaK4d+8+m80GbRzOBSPyer1lu92RFVPO8zxE7qokQEBFjreGLFEhraAuyZK0g46qqsIYg0Kx04ZJPuH09Byp/hKhm+bFpQQh/1977//H5vDTFpIRQrwFPGuOfwm8F13+LvDVK9oPKb2SBJqPyDtoGfpibdSPaeO0QqPVBmNt7HVCiQ77c3CMXoBKKYMxVcZ+/vuaZKx5N9e2guslCtcx7fRV2G9f3KAnQ6+ZV5f9+7yWP3j7UbdCzsuvhR/S8bJ7QHYIkRMRPDKoHzS/2Cf+dcatM8Y3/z/Igbo3oIFds51LRArIeCDW17EpDdrpmBobBaAZRynCUu4bPHvsXY7NJaXisfj68x8CwZpX4fuZTHLKcstkMusrDHbZhPgXmgVChP14F1Ud/V9KhbE1XorGocEjZIA28nwSAieFCh530iDzFLzm5vqS7WZJgmFxMmGSKopMkCiFlx6jt4HBUgTXSKUyql2FxuGd5E/++A+ZTmdMpwla71BZgvOaqtw1cido3kmSsFyuydJJaNs3tCFeUps6wE3GkCrB6dmCyxe3CAK18Gq5BDzL9QpjDFprpAikZ3lesN0EWgPnHEqFAMPlcklapyyXcOfuOQhHVYfdxHq9bpgxBffv3+f6xSUgyLKMWpckr8Hq273PV1VovGj+S+Cn3vv/PDr1PwH/HvD3mn//YXT8PxJC/DcEI+zty/D58OCWNA3ciiJY8pCJxNYtoVHI91jbmuXVC+bzOZWuOTs55Uc/+hHPHj/h27/2Hb75zV/GITqOvkPexWbSu0Oh7GONcFxxiRgQosjZQRuDRgf3HZyCoxqo3cO2jyaGwDHGiQ4yusLFHRxc25ZBKD8C9rhLWqg61B0vHjPE770/Fvc1XgbwkI9YE8yegA0nEhobR4SAWOKxiJ8vJoeLLRnhuqEn1QjsMkgk75Ei0Ca0rQaf9Ui994r9YCMjx7fYMu5IS987GLd+1+gHn2oLeXmk77X3Dgo7eAy3d7+4E1Grkc3BK4c3FrwhTRKUSIbeTvG4NlQOLX2Ba9pq6Rm6Bco6arPFGIP3FiWD8RVt2a435NMJiZRYU2JMhdMlWlfocsWXn/6UeaEoV4Z756c8e/oIXXpm0xyFwlqLsSFEK8AuAms9xoHWNav1M6w7ZVJkaFNS3t6iMkWSJME7SKrGS0iRJkXglXE2JDFpdpuqyTG73tZkqSDPJ2RZgiLFecPF3fNgbG1gFusslbMUsylaKMRyiVLBipMkKdbBbrfD+Zq7d++wW2+odyVCJtx7cJcvvvgi2BXylO1qS1FMqWvN4vQE52zHh/M65XU0+n8N+HeBHwoh/qw59p8RBPx/J4T4D4DPgX+7Ofc/E1wrPya4V/77r7qBlBJdl2QqIWjwCqNr0tSxvl0GLhynefL5p/zoh9/n3r17XF9f8/u///t8/smP2a7W/PPbZ6TK8/Dtd1EqwwlJTMA71PL2QrLZ1wJfEsTTtTjAicLPI6Jwz/7b1B0vh9mh2o+vfYbeT9kNFoXxbbrvKIDH79g6b/bNuIGgehmG25VWMDc88wiPd+OBX2OeH46XJVAI3jzHErx0fTvko2iq9uPlCdG9x5LGq5FFdV9E9547TWYx9qJNRwbpmD2GI/0Yr9vvlmS0i9m3vRzaHaIbjdgKWl6nlqyss5U4B0I0rIkeqQTORuO619+D+a0kuD7zFoBxmrLcAVBWO6pdSSokm+2am5scmSg25ZrTxYRPP/kZubCkiWSzvqHeLnn44JTNbRBZk8kErMG7sLBY40E6jK6aBbuB91wT2OQA53Fao4RvNOcaT0aiMqTKkSLFNDQRZVkhJUynp4jdNsw/kWCNZ5Jn5Hnox8XFBbb21LUmz/NOC8/znMvrKzzBC2m1WiFEwOSzLMN5y6askMqTTmY457i8fEFZluR5wfnJKV/wBfhAhey9w1nP2UlrH7C4v0yM3nv/Rxz/xv/Nkfoe+A9fuweA0TWXj3/BO2+/zeeffdlsdXJ++Gf/jCePH3G2mAcyIluTSsVX6yuqquL/+Ef/kHpXkkvJ++/+En/0T/4R+WTBb/+rfwOZTbj74N0WYW8+8nA/y+GEj/Vg7/pNvXqFxI81qaOBKnvQc3OX0apKjn9M0rkQF9B+8AIQ0YtutvlCiCZOYO8Zo13M0F866lELmRwzukZdPggKa3802o8cTVzBqFfKAd9L3Jy3SCGGcQejkr7Fx/YP93UToYIWf8RNMN7o9cbhYZsd/BNBdmPQ9QDKeS1E5xAWit/TfqaDblESh/3r+Itepa28rDcevLN4q8EZBMPgxfi2ColXUbyFAOscyreZwBr3Ra2pG//vNEsp5inTvGC3XDMtJpyfnHD3zgkffvB9Xjz+BPSKIsuZFnnA1D+/wRuLdpY8yUmyAmPrkDhEJEHx8Yo8z7DWU9Y7vAsUdrWtqXZrvAn9mc+nOGfIssAEWRtYr2vW6y1SKqbTwA65Wt8iJaRpgnMhgOlkccZmfQMQ4CxbkySK1WrZ7CYsk1nByckJSZaz2ey6iOCyLFFKYV3ITpWkAphxvbzm5vaKPE8RpGSp5Px0znZTIoRH5YrpdE5Z1ljtqOua9Wb52u/zjYiMvb254k//8H8lyzImWY6UkqurK05mU96+t2AxnfHoq2tSa7hz+hbL5ZLvfu83+PDDD5mkGUVRsLx+gSm31GXFP/t//oTf+Ov/Cmofn2wx9hGhG3tRDHXRl2PNgziVY4yUwg8ibdtt7mjdgQ03hh0cCXtCTvZaWGwr6JnVIw8WOf6RBpfBgI23GHnX/EvkxL7Qb6P+wDfej0fGbWTXcZhhqq0q+vrRuxzNidveuy2d0B+HrLpqx/Du1i/8yCB0axt7Qr15bh/dWh6R9G3+1wHsEp3fd4Pt3p8PzyKEGF2Ue4h+z94yutvwg6pdrgLnAue882zXG4piisxkZ5AdjpsP/EhCEK9HwdAa+mdt4I7P0zTAFdpR5AWPPvs0eMzpClfvSJOUWZ6y21xhd7e46ZRUnqBESLlXljXG1Egp2FY7jKnRNghsIQSJEk1gE6jM4GwAfttkHXjFdr3krQf3ESJrqAYSrNdI51GNS+hifhrOJYpnz57hjEU0kO3z51cILEoJtF4ihMebwCmfpinn5+dYp0lTRZIl1LcV3geWTo/j5HTB9fU1CM92u8PaU6ZFwcXFGd94/z1SKdiub8hSwcpuefjwIVk65dmzF80c8VinB7aYV5U3QtBX5Y6nX3zGYjrjab3j9PQ0WKr1jsvtjtl8Ck3OzMvnzxBC8OnPPwYcV7dXTOoJv/Nbv8Onn32BSCX3797jwb174Hoj3THO7LbIaL4PDXvj9cf5S3rsdFDXC1SrcLb+zseEqAg+sqHELmzNEd8/S0xDe6x0kIfwA2HR3Y4GhPe+w8LHZFuERB/eo11XBo8xDt2MvYfj0zXy4Y9qJvZVuyxoe9suCvEVxyJVB2wRTR19AN40dZsMTXK/cWK3y1CObbBVlO+43Y3FRuxBUFhjG4ijmo89i4/fczTRxqJg27iUMShGEIzq56dnVNogIvbL4V0tQgnwrjPKJlLifYgJsThQkkwkIbEHis36hknqSJVD1yVaa66e17z3jXdYr19Ql7fU5ZKy3FDXO4qsIMky7G5HqWvyJEEoGTxj6iooG15wcnqOc0ETX23WSB9cGefzAJlsl7ckWYHWlslkQl0bnDPoKlAQ51mCkJI75wssnro2JEoEBkkXItc36x1SucYjSCElLFc3WGs5PVtw994ZT58+Jcsy1usldV0hvOX85AylFMvrK6Z5znptUEpQbXeczafcv3sP4TwOxXq9ZrfeUe9Kys0aNRO8+/Z96sqQZRPybMLN7RX/9J///MjsGpY3QtBba6i2tzw8W1CoGWZTM59PmohHwWq5ZjabYeoaoUKAzONHX/Hw4UMKleIqzScff4SSkr/xr/8eb7//LVSR4aUGEizhZQTyL0FLECaEaH5KfEOx20I8vW93L3SD/Gg+zhH1SB3R6Dvh9ho7aQdDrbBrJPwTCxbnjjUYLRCtoOgWsuPeIa3h+5joHaOOCN0xB52NjbxxkFvsE9TaLo6RcPkosGyw+IrDILUQbt564Vh8E80p3JC9cd87ZuiVcwibqFGDN81q0tIWNPlkg3NgNwaDqiOlhbE6Ue9CApLYFbFrxTdIf7vBGfX26oVwDzH1MQcQc+P0SknYxcaLgAt2Fu+C8VmE6HWrTVBU2sVDCpABu3fOsd1sMMZQV5p7d+7jUotCIm0ARq0QoDQ+gY8++HN221u++d67fPnll1w/f8bdu3d48fycslpR6w0ejbeG7cYyKTKcN1gXYJIuOQmgnSaxCSLJuF2vgo+6yDCVx/oShydF4Dxk+ZQ0gVpX4HXwWa/rJjYiUDLLRLLdrUjyYLjNJxlJkuC0AykRDowxWCdYFFOsrXDOoIRitbrl00+r0Ick4+kXX5ClOUWWUpVrJpOG4+f0jNsVLBZnADx59pS33nqLFzcrvA38NpPZgu9899dDMvEqROrOppKyLDG2HrXJHStvhKBPk5R3Hr7VpcsCqOsKj0BrTVEUbDa75uGzxmCRs1qt2Gw2qETw/LkilYo/+ZM/5r2nL/jt3/03sNbjvSHNiiCmvUOpHN+55rVkZgJvXSMUXa91QwS6Nj/3GAcHrn12fNfwsoCe1y1j5FWvdZ07XDQG/RnZ+ssRo2S48EjfRx33IxKyAfbd/hGgopBqcbzd2AB7LIK1LaqJjsU3PD2+gaX2qK5fFiew3+5+3XhBco1rYHgmQXCRFK+JxzelsbF04XPCkxwBeg4gmlZ4t4t+tIsZLtS+G14nhsK96ztBmA/sOEikFFjjsTr4lSvVZCZTgeTLOYF3EmM0Vb1jeXtLWW65vrzh/GyBIkOKoMilaYpwltvlc+aLCd/6lXf50//7Y54+tSTC4uyWr7665fkLgbcl3hmssyFgyRuqakeapty9d8ZqvW34XlqBqkiyFCkUVV2zWm8BiVAJSQNfWmubRUqRZxm6CotznueY2mCcQYhAP5DnOdZbdFkhPaQqYZJn1LsyRKtaj3UV1jrA8ODeOVkCq+WSk9NTbm5uqCqP98Fdc7PZNHNZcnp6SlXt8I5AYJZm1HUdfPc9vPfOu5gqjNdms2F5vSTLsoDrW8tms6EoiuZ9vDKbc1feCEGvlKIs68byr/DeoXVNWbnAJ1HWVFWF94JddcVsNiNNU3blBo9Fyoyy2gY5aw0f/OiHvLi6YTY/ZbOp+M3f+h0evP0AJwRaB+OGEII0V42G2dIkt1TBrZBpE0GLBjryPctj869zvmPkO85NEmuF7YXjwuaIl+eed3wzbl/DVX1wu0H8wWE5ZkgdTS93pBUrWsPw8LSQDfzQGTHFUYz+2M5ikEy9rSkgJq8LyabVAcQWNPojtzt4f35gjHVxqkvRh/JD2H0I4Tsj6WBxGWwEYgNJm4HLRhOj9xB7lVLgneuM6fFuK8AYQ8hmv83WTuREu4PtrT9ChBy8woecpuV6RZophApYtydBIYPdh6Cp52nGycmce3fPePH0K7abaxIvKcsdzmq2uyVVteXjT36INju83lGuL3m0/oqzk1MUK5SCeleiUom1jfBLU7K0QNstiCAUb5ZrdrsdZVmGyNI0xzowXjdUxI6y3GGtp8hStruK2SLBO4O2jtvlJffOLygmGVWpcd6SJClpmjR5YIMDgFCKqqpQAkxVYq0Oc8Jr0sSTJJa7906Z5Ir1BiaTjCxPuX//fjDqioxpbvHG4V3Y9SgE5yfnzBfTsJA2c8qYYJytNxVVVaMTjRQhR24iE9I0DcJeh4TmWZZRZPlL50dc3ghBX1V1Z5UPHBOSJEtxu12ThLdAqTTkS0xTjK07vufFdEZZlljnESpFeMekyKl3K8rNDusVf/xHf8hsMWN2suDbv/pdLi4u+MEPfsB3v/tdptMCAJGE/I3CebI87bLxOAtSOJRSkWYajEx9tGIDCbzGs/YBPq+uHWPlrftbXEa9T16jrWGHxlaLY1jDce+Yg2OiMfTtY/ei2Rp52XD4HH+K4U7g5Vuanqis0TqFAmRnpxlef6yt4fGX3XOfy6djzxxoymPXRQ/le+29tY94YUE29WIsfqTP+xTU3YIS2TBEdM9ESEwDhyoEzmq8tcgkx7uIl8Z78JYiT9mtbvDmPqvNjqcvHlNVFffvPeD8/A6zxTlCKlSWYmqKfMF9AAAgAElEQVQLSqJ3G3bbJU8ffUqSKn72s5/yW7/5L5NkNT/56Z+zXb0gSQVCGKxZYayjKgVKhOjaRAY64ER6vBIURUaSBPoKY0UTxaobHhiHMY48Dx5Bxhg2613ovhdMiikCR11vmDWev8bWoMALQaVLqrrGeU+aSKQK763lm8nTjLUO9yp3dbPIWdI0YTEvcLrG1CXkM05OTljM5ySq4PZ2yXa9IU0dk3xCnuZk+YTZLLhRFkWB8wZvA5XKarVqgqhUA5F5hBckKiFRCcILvFMIlWI0GOMo8oz8r5qgT5OQ19FaR1luQAgmjQAOXNSyywDvsTgXsscrBJWoyLLA7tZS+aaJBFPjvCNRGcLUrK5KtqslX/7ic9I8pSwrfv6zD/jVX/1VTs7PuHPvDj/5yU/Ybba8//77fOc73wlRcyhqXXUraiBfc4Mwc4RAGz2gQDhWevx1/LwcOR3cDN2Bq58/GpX0cmKtYc0xT5TXJ8UKJ0aE0FEXzSHOvc9bPmj2mA1iZMEZBEZ1GmzDT3PQt/F2/Ug/9vMftO+v3SH1gruNtz1sY2w3Br3twgk6DyuDaRam4aJwdMnpvHX62A8xgMPcgK5BeYc1GlKJkp7PPvmQ6+trvv3t7zCZTILHChJkjdc7VrdP+aP/6xO22zXabsmSlK9+UZBPJrz3/q/wa9/9HtaAlJ6f/+zH3Fy/4NvfeJtPfvExm+0l1zeX/OP/8xMmRYqptwhf4TSsV7dgK2bTOaJNPCQFu7IiLVISpVhM50AIeNpVVYBdnWC7KdlsdhT5lDTJGwGZsllX3NzcApJ7dx8ghMIZOD09D8yYiaQoQmJ4lSZUVY1QjlQFbyJta6q6pqo11nuyoqCuDLtyQ6KCd1+SShaLOZ7g9bJYnOC9JVUZMk1wzqNkzltvvcd8doKuNM+ePQPv2ayCP76zNkDP61VDh5zgTE2WZ9RlTaaSoAw6Dzb4+yMti0VCkU3wqSdL8r96GH1VV/z4xz/l29/6ZRJVUOmazbokTdMmBNhjrcYYR2VC7sgkSZBKhu2Ut6RZRl1XSCupkMg0A5k2WK1DSkG1qUizKU7XJErijOXDD36GF6Ab/9Y0Tbl++pSf//SnFEXBdnNNbWt2u11jVT9jMplx5+Ih77//PlVVMZvNODs7w8sjkMeew6aIDHn7pX11nb2r+XvMpfBoKrGRvKzHyvhkOSJ4j7Yycp9jlZ2nd+WkMW4f80sZX3BGdyetMJMBP5e0zyYG519WXsftfJ9yYX/83MhYx2yiLqYkbgzNwWAaXHCVj3H+2JZyvD+hZj+erRHXezt4JmdLVutbrq5esLq94uOPfsbl88fUdc3HP/szyrLk4cO3+d73voetd6xvl9xefcVusyRJJEJo6lqw0ZdIqai3a5599TlKCM7v3KHcbNDlNR9++ITNZkVVrqh3a+pqQ11kZInEuhohPLPZBDWfYQx469Em2DzybIK2FdZb/C6Mc1XtqI1FNHQGaZozKSSz2RylGs8eaxFCkiQZZ6cXwWXS2gaKTahrjTSgkuBdtbIWTMhNG3B5SSYLttstaZHidPD9Pzk5YbvdBpoF7yiygjzPydJpw75ZMZ1NqFyFFClJmiBExaSYcfn8kouLu0GYCxmYVJ1nty4xtSZJg4JaFAWzyRTvA9FbmgREwdpggHXOUWSAq6nLdfD9Tyak6euL7zdC0Avg4t5dDB7vykBXagmZWFr6UhvSgClFEykmmv/C1r+qNHUdQoKl0RjrmZ7cgdSTJwXOKzIFkhrlQagU3UWcO1QSItiUh0Qpdrc3bG8M1ofclspaFFCtb6k3a5aXN3z68UdYPM4H/onz0wvuPbjP228/xHnB22+9FYxW3ndJpq1uYKBm222MCTS7NNgohqBzBghCh9mBaOwIUsqG+M13C8ZAq/Q9XEHTpvMeo0xwmWtA3X4hib1ZGiHUrldxrk96eGH8Je4xFbaeKPvX+AZHbzJMeezAs2VQlSYdGzH0IgdSr4sQtabru2yYRB1HNgu+x/K/DlNDV8Rxz6WxFa6nUZAoegiwi0ptz3pC9Ca+S2fZLmrGBW6U1rhurSWVQYCYiJMHAamXeKHDnJIKY2pqvePRo8/4wZ9/n8dPviBLCJ4lVuJMxdWzL5AIHlcrbp59TioCIddutyHPM4ytcKaBVZXAmJoXzz5js3qGynK25XOm04JdtWSzWbOryuCl43yg4zUWOZ8AAqFE40HjwSms8+iywgsoigJTOow3+MJTTCdk2QRttngfolyzJCedJQjlG2g3wzvI84w7d+6w3dQoFbhinHHMZlMQgT45z9NOGQCHtWCwyAzqqkbb4N9f65JtHZS4ew8fMFvMg1FUtF53jroybDYlzoXFprJbvA/2Cq8Db71zjkQV4b6N7UglYT4XWUa+OOnkWyvovfSkRYGva1a7kjRNqK1BWMHJ6Yzz81PyNGM+n7/2lH0jBH2apZydLUizYFmWXoC3OFcHq7ixDT9GMI4lSUK1rfBpGoRhw1a3WMyChd07wGLNlkx6kArVZMxpjWbCOaZ5HnynhYM0LCzOVbjaAhow+KrECYF3QR0wxiOkQsogSAKUIzBOc2NrLq+e8pOf/CC88DxHyoSLizPm8yn3798nz3OK6YzzxSlSStI8eBG1L7911bONDULgefHiGZNJ0CTSPMP7EKkYWAHpKJpFawDFddG/3gctUrnGJTHCluOk5kDng9nBLn6I8R5PUbfvXklYSVyvaToxhFakULjWw7ylToguDUtQghfNfqg51nS0ebbI4Bh76AgIydocvgnDH0TytsbKCDaKeV66dl6yEzp2bjS1Xxfo1LA8dm67hztAIQKg0+YfdoQtvCRs470Nbr/CueDE2rrPCpAqxTmD9Q5rK1o67Edffs4//xd/ynSW8ukvPghJN2Tg7dHGNPYoRzEpSKVD+rKxmVkEljsXJzx+8oiEIOiMq5EEimGJQwjNbnuL0Vu0LsmL4CgBsF5v0LXDWUgSi1SORTHF6ArnLIlMsUazKwPvOqcSJySJSkiTPCgiMqgj1lgSlSCbb9xZB94gSEJwlpQNudiOcruj3O2oa01R5KRpynw+5ebmirouuXv3bvCySyDLUqyBs8UpRTFBa83JScibK6UkSyW69nhnkThmkwlgKMsKpRLqyiAVZFlIRmJqjalMsxsJsG/b391ux3w+pygKsga6dNoghSDLAqvlrq7Q1pCmOdvNivOTBRd3zsNClWacL+YoCcK/jDpkWN4IQZ8kCV46vPCo4CKLt4Hgq6w2hBBQh9YVSqRsV+ug2aoEgUUqweJk0nnTiOAhiRB1lzyg3m0xbTBHQy1a+SV5npGksltNrTbNx2gQziKMbtgqBVrXaFMjkwypKmodtotKpsF4XKfk0yneeqQKCQKqasV2vSJJJR99+AEyUXgn+H+pe5Nea9czv+t3d0+z+t283ens47ItUyAUYIQoKUqUOYJJIkQyQcoH4AvANExgAAJFTAoYRKVIjFCkBASCzEhSJUSqUrbLPi7bp3zeZu+9uqe5WwbXvdZ7jsuuuCQiuZb06rzNPrtZz/Nc93X9r3/TNg0ff/wxfS831qtXr7i5vQXAGHDWSifmJ6Zp5B/+o/+Fv/JX/jKlKJ49f8nr1++4vblnsVhQlNyUJRdySeTq9X3Z9M9+RBfhQl+c+igilNfG1ferXBGfry4aLwdKQf0yNs7PYx4VfrgcWlnpa1erla02sELHETxaGE9Q2Sy/dHD4KlQlxT2Tc+LLeub3bggaVQMlVZYuuShEvMaFXXL5niH/GRqCP/36xYX+F8FKF4uHr+4+/nQox1c+lmoKVg+HMI78s9/9J7x9+5Z5nrm7u+O3fuu3hN9d6v6qVHiDiDGe2R8J88wffe93+cH3/h8A/DyTUsIqhVYFq6VLX/bi1mgorLsFhcRQBkKR0Xq72pLC5RoWTNtQayu2UcQSmP1ESJE4nZnnkfPJo3Bs1ktijAzThCbRuI6u75nGAT/Lzi0GRYqK83nGdYZ+vaSprJcQAgpJlFLV3iMFjzGOrm1l0lWFEBLn04RWkqdrjcF0msY5uq6DXPj0a9+gaZorLCwHmjSK5+ORRb/l9uaGzz77Y5bLFc41rDfLK53RaC2e8NrxOB5xzrxf9hctXvO2JeiZd28fsFYL9IPGNQINCzU1kpSqdUyEaeMk2bjee0IIbLeaRed4+eI5XW/p+xY/ToTpTLdcsFosfsV79dek0Pvgef36Net1z3q1JKdAu2iJsWCtUKoEm+vJCkJINVUoEMn0XY9rHcPpjPcerTVt36O1YjzvKw7s8DHKRj47IOOMYTpL0o1zMlp5Lxhi33WkLBdEOn1peGPK6OzfL7yUIZaREh3GdWQPWjmhWiboraaUTImeFCN+iljbMPgz/+L3H0gUFMJOSClJEpXW+GmipBnXGA6HPUZn/qff/u9ZbdYcDxNf/8a3sU66/JcvPmK93tK2klO53W4x2qK1HBYxRiiR5VLRZoBc4a/ylcXml31oLhPDxThJKUUx77H0Ui7JTgUu1DHj3i8r0XgfQcP5fOR0HpimiafHk3w/SHzacrlku1lwf38v9FXzXuSlanzbZTFVysXc7BJELoVZKVNpir/AjE3JHHKBSfLl3wTE/jlK4Xsq4r+ss7/+7D/3+kXl32p3ZdFkuDK6vuodc5ksELFZykKZLJk3797ys5/8iGe3W0qcefNmIoeJ/+0f/gO+9vVv0C82/Ma3v800jcz+zGc/+AN+/NPvE/yRw+HE08MTMKFVj8VSckEbmXhSiLiuwSiHNQ6DYhoT1mnaZkXJI3235oufvWOahL/etJa7ux3domWeRw7HI4mMMYqHpz0JxzBMhFljjabvWrabDe8eXuN95O2bJ9rOsN2siEDwgVQUIYHD0NoelTRTThjTorUixoBCobIR18Ysz61pHCkWtNKkMKIKWK3Y7LY8e/aMbrmQPV8WK+S+7+thl/FhAmRSiDHSd8sa0r1huxUhk9HuCh/nDOfTKMlRvcOHyORn1usl1jlSVozDzGq5pOs6Pv74Y3yKaH0ihozGypyqFeSC694fNBesPsZI0zrWmxX39/eQIrfblcDHGnbPblk0jvVqRWvdL7jbfvHr16LQp5j40Wc/5vZuxdc++ZimsXg/CW2rBJQFay6dIGijMTV6UCM89mmaROFWIsZ0eO9pW0vOiZIi1imMKkQy2iiRGifpFGY/E5M8jNZaUeAih8k4T2gEa04lkwo0tiUGwdK1rlh0LpSUKcUCCVVshYioYRWZFCdOpwNt29O2jtNJLFu1bUiLtXQZruW0f+R4eMJPZ6zVtJ0hxESYTzi15Ga3Js0TwSfIhc8++wytLUaLd/V3fvNfZzhPaG3ZbDYonWisYbFaU5IseXyURU8ifylc+sLgEGbRVwqeVEv59wqhlYzYwVLISRGzWBaUUpjmmf1+z3kc2B+PnM8jymhioBZ0w+AH3u1P2M8TNzfvKKWw3ixl2dU0dI3DOqGchRBorBP2BKruE7601C7l+uevsFX05Weqf5b2/ivYvS5UH3SuOOqX7SV+0cL6MuV8WW37Sxe0X7I1kGWtlh6hbkguX1PuEynyOSc+/+lPub3d8eMf/gA/n2ncjnevX7PbbDDG8MGLF3zvD34f0y7Y7W5oWsN4PvK97/4+czigmDkdj4QQ5BAOCaULzsr1vXyfKSVSycRqr1tiYZgHPvnkI0LKmMaBNijrSPPE/nhktV2CzxWyaPBJJl/rGsJcCD4z+UScZ1JSNI1l9jNQ2C6WTPORh3cHum5RhUealApaWZbrLSUFhnngPIykLBBF6zqxNeYywUiTZK2w8p7dvs9cFQ8bzeQ9OUhjoZUihiBkDmPAGFKllV5yWSF/ZfK95N82TUNKQZa+pdD3cg1yfD/lSkOlSDFjlcY6Q9aF9WbF6SDiLrk/CvM8s+otqILRcJ6GarUQuHvxAeM4sn945NXLe+7vb2kby2qxlIk9eCDjK7z7q7x+LQp9zgU/eQ6HEyFF8BWnnkVSfaE2ygb68qbLoqcoRQriZqetwShL13U8Pj5hTI/VBrTCNgaTM8QaA6cizlhKiXXRmzDG0PUNZrXk8LTncDjgcx3ZKjTQdB0xipjBWlMXREGw+5yIaWYOmbaVrMjFsmP0M97PxDDLWDYfGFUhxkxIha7reTztUcqw2W55/cUXfPjBc/743U/QvSVhOQ0eZwyf//QntO2a+exx/Yqnpyduds9puyW5QiW/97v/L2j7nlqoEmTPdrsVxkO/YL1Z8fLlS252KzCGkpIwK4CStSxRVSbn94ZvifdFLSexGQghcDxLlx68LJZjzLx+8yh/lwU6KfqyhDQUiugT6rVJRfH6YS9mdvsDWkMIM1o3NEZXTF+KeNd1rPuW29tbbm638m9avFUur68qf7/aYyu+pBa+TApK1QdaEed4DcFpmkb2FD+H48PleFHvf9WDRpUkzcWXu/2sxDpXy6FYShI4wkj60OX78d7zxc8+p8TI+XRiGs784R9MsmxMnuP+gecv7vHTzKJv+dFnP+B8OmJD4fOf/oQPPnhJ2zi+9ek3+L/+8f+KcpFxDExjoRRNSZ6SFUYZNIVYApv1grbtaBrL4+OeaZooWRHizOP+Ce9nJj9yOB0YhqEWz5k5eIxrsJX2TApMfhaiQI4cjmeM7lhvbglVJ2Ot5d3DWxZtR+M6FFno00XyIMZxZLtdc9ofOZ9PPJ2PlUdvKSWx7DY0pmWx7en7nqLVlZ0SY0Rndz14nDP4aaZrmusESdY4LXvARCKEJL/P75uay7WQawilRIoysntJGWMVeY6s12s+/PBDTqcTp/MRY5I8J0UsChpjMVZfodnVwohbphEIZpwGCSape4Xb21v6vme/31NKEVi3cbx88YzNqqOERIheDpqcZD/x53j9WhR6CvTdms45/BhRfUMhkUIk5XDtsCROq5Cr97OzMvrP80xMvlp+FmKaWK16uYi1IM1h5ur4l8VLYq4QQAhCsUpZczgEMJo5zGQDJQXxfkYedEfFTZWRkGASMclhkaZEzge0tkyDhBoMpz3aCGMm5YjKkVwU1jpMSThj8eNEKoVh8tLxW8O7N1/QNhL44KeArhfXZChp5nx8y0qDcQ05nQkBunYNaDAy6l7tw5XCaMe7xz3KwNuHJ/SfaL773c+u+4nWWT768AM++eQTlBEcN/ogi0CtiTExhapW1JZhGDgej8w+gsqk8j7gPRdFLLl2/BXDzOVKLbwUzktkm8pUd00oiEhN6Y6SC6FOG6pOTtNp5Hg88/nrtzIxUdhsNiwWC1b9Aus0i8Xi2j1pytWJMZVCzuCqhD/mjFW2MiBaYvbX7+/KkNAC82kUsaoYcxFfG2MMoXbiFziJkPAxgLoUj8L+8Yk3b19TSuGTT76JdpaiMiWoqv+Q9+fzH/0xh/1bGmMZhiPzOKBKxNieYTwRQuBMYpomDk8PAicgC0IVM3kOJF14+eID/sP/4G/wv/8f/4jDw2f1e4+QFE5rUor0qwVt09O0hpLf2ztYaxnOE95HHt4d2GxXqFompMFKdN2C/dOJEFp2uw3eT/jaKWsUnTWsupbhHCHOdBeSRTa8uLuFUnDGsVwt8NPMF1+8xirLcrmk61bYpqUMkdubBS9ePGO73bJaiUWvn6NAoDGxXq/FCTNNKIRaKXeQ42Z7y9me5fsqnpwj2mmMdYQpkb3UANR7B1FVMfPzWRbJqgislPGkMNf9AOQYMcbx9HQgRg9ZbCJcYwheMRz3rNYLEV3RcD6K6jXMZ9arHrtw3G9WvHx1f71P37x5zbuHI0TDdB744f57/Ft/6d9kGE4Mw4HOyYEa6vss/Pu/YBYIAF23kAVkUDSN4LBWFXKKhFl+ONUqosqEMLBer1G94uHx/JUfOFfsLmehP11Gr1LpVFpr0GLaFGOpVDvZjou6zmOtpesbWqewqrm6GspUocUatTJGQgiXfAVS7f4uvhUli9rWOoO26koPc84Jg6IUQgxMYyAVuYFi8ajGQmmIPjKeRtpW9ADW6CrKzaQUeXr8gk8++TqNyeQ4MVcOntGdsDeKdJFZFVJWIuivYeC5GEIp+Fnk3sM0Mfzwx/yL7/6Q3XZ9lZcbpTFOHvAo2Mb1vQ5ZbAZylMPCXMziMGRVKhb9nh0EShhDF4ijFGIpGJzAYypfF7hKKZTWJKENVQuiROscRotwLqWAs+L0dzgc5RpVVlbXdXRdy81uQ86Z169fcz6PzJPHuEtR6ei6Dmcs29VWYCFjCDESo+gqrLXyS9fFmdZ4X5WZZWaeZ87DwOFwqAeDvppk5RxZdJb9wwMhiCS/tT3b2zv6RUuKwraYpgmjYL3q+eInB2YtBTfHQAwzjTO1cw0MwwkA72P1aZnZ3L+g7RSPT2+IMXA8vsUYRQqZaZA4u/P5zN3dHaok+t5hbCGXUC08BCsehoHHh32FRUQh2rSWGDNaizWJvAcGawwpCnVyHCastRij6lI8s1otmacDJcuht1kt6ZuWohNN0/C4f+Jn3/sjFIYXz57x4vkrlss1ru1YLpfs93tCCpVUYGibJSlq/HzGoGlci86GxjiUhRjDFc4JQeyJ/RxJOcrzg6ACOURa65iKpHN9GW4zRpqSYTjJQZ8jKUVyvlj0ScxlSgnrFGPltJfa6JiiybkQc2KeZ6lRwpkl+oHlouP5/S13d1vubnfc7zY0TScHkE68ff0WpxtpXtZLvve9P6RpLLe3t+zWiytUuNls6lT4ryAc/F/165KS3nUdfdMTo8c1hhjFPrfvF7X7nlktN5SSeXp6kM7evYcpLnxUKcC6LlPFhU+pgrMNqmbTOqeZQyFF6dZfv/mCnDMvnz8jhoxrDH11rrOVBTPPMz6MGOMIIVByFna0MVgtD8elwF2Mk3yQUb1btCL00hqKqviiwerMPIzMMVx9tf04Mdc0HqM0xohaOObC5D2tc7jOMp2e2D8+0PdrZp8qbSyTi8ZpCUswpvqSlCxTRSkoI3uHXISnoZQh+IJSlsejcJbnJJ1NyqLCvdoYV4uKpmnEtc/IwRqScJUv0Au810Fc3CRBQao4sYKSohScVDnaFQ6jFGKJ7/n+dVGaU3UY1QqjLDll8QeqO5GiJEJuGCaOxzNv377DKH3FwFOBecqchj2FvbAoAKfN9fu9YL6ycK94ffU8Bxh9LUD1zIsXRpHWkPS1YTA6M532HB9eE9OMMYaf/vgHvHuQ7l5IAMJzv9muUQWMEkqoVVpCrOvPk1J1WgwzWmvWmyXBR7re8PrNH/MnP/uhfC8xsF7vZJJ4OtI2MtmuViuMKqQSUFpi8S5eKzFmbnZ3fPzxxywXa96+fRBCQt+jlRUyRBGDsxAEL9/dLLAGnHEktyCEQIrS+BSUcO5VIfiJze6GD188x1Dolgtc2/Dy+XPGTz6lby7PhBxGxkSmaaqHvSXnAIjCNcwRoyypXBbzFmsEBkwqUbIiJtnlTJNMpSihL/aLlrZtOaeM9yMoePXqFefzmdPpdCVXlFKEraMhlEguEXUhHtSmMUYPReFsQymFrruwXxTGBlolh93h+EjftDhT+ODVcz54+ZxPv/4RTSNswVISRRdCmEkl8vzVPY8PB3z2jGOgdYYwRzSF2XumWaCzS7rZL0pr+2WvX5tCn4Ngba9fP3A6DSidWPUdq/USyOQkEEvbCRsnhEApsFoJvcoYCe3VSsQTKaraWSX6vpWkeFvZD9rgvWzcrRVJ8tu376rbZa5dt0ErW7sBKVyCqVnatkdhWPQr+k4Wv+M4MowjnZOLn1JCmYbGNhRVCGKaQymF82liGmassjjX0riWvl+ioxx0cZ7qu6LEkhWhHaYkOZRymEEaAn4MNF2LKo7OwTy8JXUTKUrRRhmapgNTF0E5o5Qj+xnTNBj1fqklKLORrthZCnJoFpXlMCjyMSVpwuiZR6GDyQGQrzYVWr+PkdONBRxGS7eTs8AMF3GbXIMK75SErl1jzsI7vk5kFxZQ7dq6rqmfL9Uu87KMFb2prYvOUhLx4mipDY1zECGRiDkRU4WmEHbU7AN43j9IuWCdFO85RcGFkyarBBdtQrW+iKlgtaWg6DoNaWKaJzQBjUcXmAeYpgNt6/BaC+zhPa9/mmWfJO8aXtUCkDLn85lUZmIKXBhT41itcsPI+Xy4eqV0XUf0I0opWud4CiLV10ZTCCgl4eHOCXtludyyXq+hSJGe/UjTWKyprJgYmccJP83XjURO8PaLR0oe+eSTD2lci0oyRX1ZTeq+3jKOI1//5GvyjJyPVeRlUVYzjZ6cEov1WuC+WA/TiwiuCEE2zBI4InBagMuOKIttstEIEcLKs3GefU2ZSuToMV1D33eoAn3XoTEcTkf2T08SJ1iKTFd1IrdKo3LBaUMxEmISY6yUV0PbiXmZayy73Y7oZx6f3smE2DY4V6eXHLm7u2GzXLHdbsWRM3nGYazK/kzOFTrsW9brHu896TjQ9x3b7aZGD1ru7m/44Q9/iNb6uiv5c9T5X49Cr5Si6zoZiWrH23ViLTwHOYH7vqdpW4w2nM9nYvJ0Xctwnui6jlKLX8mFlDLDacAHCQLo+46YJvaVVaCsEe61UrTW0TaGu+c3lPReBNJ2jr61hOApRS609xGjwRgru4NYSEnEU85yVap5f4n7Cngf616goKwmpIQqhq5dXfHpUDv+d48PdF3HYrGoY+X7jjiEIPT0LMwNHwIlR5omE5Nn9me6rsFYTTy+gWLJGY7HEy+ev+L27gUpJcKcQDlJuc8QLzKooq8CopILMSopEGTZJdRieIFiUMJ+KLmGsSslHirZ4ilYikxT6eK8566USEWhNcJjjlfcvi7JC5K8hMAwqvLJY+3ILzf3PE3XBZrTiqjj9c9KKZLW1TlT/p9SFY1KabpGusKQIjGn63ua0vsdQim6Xt+EriIla7QEv5RSLTTl7129RsYaVBG7jbNIAYAAACAASURBVMZJJxnDnpIjunbqPgasdfgiaUPSIcvS27gOXxfBhHxd1JaiME4K4TCc2G7XhFA4HE4MJ3kWFqteDqMpkN1Qf47C3d0NsS7xZj/SdY7ddi0GYOcz93fL6h0z0LYtNzc3/OQnPyGGAgizpO9bvJ9Ydku2qzVdJzDCatXRd8Ju6fueUuB0EjhjGjzjONHYjnEUO5NusaLRCts4TsPEarEGlen7lhAzKbXybKWE5qKAL1cVq/j2ZLSRm0AOwkSMgVIyOYsqVbeiVFel4OtIGL00Uc51TOOTXOOUcVYmFmstrZba07oGrcTorHG9QD4V2jLa0S8XlQWl0CqwWre0rdAxl6ue2+1ObBWcZR4Huk4zz5I+5ecRrTXzPLNZtCiTWSwWvLx7xrpb0qiWEH/GbicHcIqem909bd+zu729TlTa8Bevo1d18zwMgr33fU/KUaCSKQh1y1pWmzXDcOI4jEDGNg3zMNK2PakoKOb6RsQkHPGmEYGC9+J2B8jWWgHI4dAYiRBTRtE4wzQFlBIpeoyynS9ZOjk/z7RtiyqaoBLGeEIQC2XnROxzwTjzNKMKzHEmxEzTL9DaYIxMCSklYqn5jydhm+gCOXi0k/FPRnbBUMdhwrYNoKWLK5FyLqxWS2yCrncScBA8MQSsViw6xePDT2gb2O12vHv9J2QMfb9m5+7J0ZCLFBcprAp1UfENM7n4q8YAZcAajHZo3RGzAWVxRRapxghLQWU5zHIIBD9SKoOlaRxKFVC6PqwZqy6FVewQSixY5wCNVuCVIZNQNmO5GEDJrkOWrJqic/X3UcSYRIKuFUVrrFKQLoHaSTZsSlYdFoEFCwV15UrnCguYuoOQQywXhZfcCVTlVItTJoQENmdMzaR1KpLDQJwOGAJaT1dSQM5JuuviSalAiQKh5ESICkogF0djDUVFYg4i+RozPgxM85F21iivGYYzMQkGb5Ql5wgkUkii+YiVJFDq17w0J3VqveyMxmEgBo9RUoCiDyzajmk4sd1usVrzzW98SmPFq93aprJ3pBk4nE+AZ7FYYIzDacfLZy/58Y9/JLuAEDGNq+JmOYglizljjaXEgs6KRst+pOQssKbOYBQ+JVRJVWGscNpU7F6o0yUFjAJjFbe3N9zf3PL27Vt8qXTfVFCx0LuG0zjgGoV/Elv0nDNWa6zV9K0Yl5XsaZzAfZvNui6rNetVX6egBl0G7nYtSmX6DpZ3zyhkFFbuv5yY5sA4nDBGySRZ1cTCPihEDUbJFGKUY5wDU5ohFyFCFFhUUZQpGqeEjlmKhNyrP0dIxa9FoddKYawilsjxeBQDsZKuo7fSlYETBK867E/Vsa6jbzpiEIpdzvG67Gw7R0qClV4sja+eOSHjkwgUUlGMc6jLPWFSrFYrSimM4yiUuyCYuyQ61YR4lWkaebgu3jupOkxGYuUuG5QyOK3QRqAUhWYcPSmFK0ulaRrW6zWHwwFtCtvdBsicigQvyBJTRmtlXaWEFnIu105KFU1jGrbbLSkHjocz4zjTtQ1D8jy8fcP5eBB1cDGM47l+Pcc0Tex2G1bLJTlHNssFjdOYFOqNNQtkkwslKGbA2A5oKNqgVYvSEvQwewmIeXjzUG9gfZ1SFosFSmfp2ir0cMGqL68Lq2C3u8G1S/xhpBgt0BHVXz6/57AvVgvO40CO8boHCJMc9hcWiEbJ18h1EV59cHKREImcM4267HrkmqSUMdqQKx3vUhRL0VyYk7lOGQaFMpoxBsHl04ROZ+L8REkjYRJ8PpPJKZDSZV8hfHkJSFGUEiQAI1R2WBFKXfCROU14P2CdZpgHlDLMcaa1cu/EkBmGagT4JUsLrTVWy2HunBMnyHGU90VbSqU9WuNYLpccHg989OoDPvrwwysffRzPNLZBa8s4zix6R9M4nJNOnyLq8JzEq+biynkJy7iwfHPOOCdY+TRNlCK5q+N4phTh2oNizlRqr7wPfdsJM6mUK52zqZCq95IvcfG8yiHS9z2rZc/T4zsa15GTqHa13nJ/s+PzL15jdJYcXK3RiK6jX3T0fU8Onq6RONPdbkvb9kI2MKqK+EAbxe5mzel0YhgGYqohKErjGvGjUqrw5md/Iq6gRhGjsLX6rielSAyhThQTOcsEE2bPq5fPpbnSAlG7quxdr9e8efPmOun8hevoY0o8Hfe8ePWydrfHKnuGtjXc724oRWCdmALr5fIq++/7JWSFj+E6tucS2WzWaBUY5+maTqOU2A/Mc6UJIviYnybpio1jnqRDErYBBJ+Z5xmtDc7KPiDnDKagrWEO/srlxcgSb5omDo8HnO1pGnG6axrLu8cngi/i01MvlGzwNZvNgg9fvSRnj/eCwS0WHeLCVzAxC60vK06nM03T0TVttW4OWCMPwDwHchxorKbbild/SY4UMzl6YVioxHB6ZP90Yrldyg02FQ7xiRg9w96wWPR0ravLVVnmOmvFGyVk0jyQsRSlKMoRU0EZoaYFP6GLJoVA0Zo0dwzxQAk9Vou3zsVaepokL/TCgjkez1A0j+8+xzU9y9UNzWIt3a+yaKWuthYFOA8jc4xXtk5RX1LslkLJgtpHMkYh3t4XM7yr4EmsB4Kf6uLeoutuhJxQJWM0wtYywuxSWV2dKrMSpoWqHjO6TDg1UfCM44nsA0GruszNxEq/vDBzUlV6p1TeT4e2xdqWUhIpe4wp7G7WV6VyDJm2a3Cqwc9R9Bm+kKLHokQTYRWmvtdt2zM1LcM0kqMourfrHZ1reXZ7XyfWxG61xXvPdrUVU7AQ0WiST7SLllwiOUZ0t6iL64a+pxajhhw9pSS895V1Asvl8nqwaV13BFYTfKBZdJU84dFKEwk4oyXVqoiwqWssqhRc45hTom1lQswlslpuSSkIXJoSbaP57h/+c/p2wXa9IcbI7d0znFV89OELjscjKgdudmu0tvR9z269AvL1fj+eIl23wSDXrOt6YpSC2/ctPkyyrymFEAylOIzRhDDjjAQhNY0QOFxjCHGmbTQ5C/aac7pqfZw2uFag2Vyb1OP+HZ98+nWUUmw3NwJbW8N2veJ8PLDf7yVl6pdEcP6i169FoQcRQS0WC47HYzUWiiyXDdvdmuWiJ8TalZuG1WZz7bCOx+MVF7y93RFj5LTf8/LlczCJtnHk3AKKnLmKFJxrmOdAnD0ioZeOXRarns1mTdc1+DJjrasb/EyKCbhwy+N1ARljJoZEShMqCwWqbXqhOWIJQTyoh+O+sijEeC3nTC4wTZbFsr9yZeHLS05R8Q3TEe8HcgbvR4YyXf1GnCmMVWCyXBpubm5wzqK7nq5peXg8XkU6MQr7xrjM+XBgueoJSqCFlAIjEMrE4Zh48eIFRYmz3+kUsLbBaIeiEPzIHDzzOGGbluVizaJTZGs4HY6oHMVOVc9Vvi7cesjMsdTC5okhc0oTqmw47t9d7ReUdfgw8qr/mL7p0MaA0pR4oV3K+K5zFopbFgy+VJ8dgdE0Jaca/RYhCQwmjKHEJZXKh7GKoDQlSzQdgKo02qKlg9fFoUqRLcRVE6UISfIQGp0wJdBaGOcRP0thLaXCQ7UrnGcpiBcWh5ALVKVPaoxx9N1SJrYMgXKF2AAaZ/niizcULayTYZgEOtKXcBFDnEacMzRa4bqe1Y3oDZy78OE75tPE+XS+isRs10HKhFkonxfBXElVwxIDyjXiO5OLLO41tM5gtSSLZUCrTNfKYf748FpoiKVg9BKtMm1j8DPsVh3zPJOsYbtd4muYyNt3j/SrBWH2LNqOZdeLQRjSGL14cSsCrHdvaNsNIQSGWZxvn9/uvtStK3Y3K5wplCR8+A9e3KGMw0eBsozO9K1D64v4LlPChHJO+PkGSs4YnaEITKSQ3YCzilz9bmKQha3VYDU0WvQZRunrIjsm0UFs10tW1fLgqsTOiexnpmHkgxcv6+FVaJ2jayw5arbrJSVJfbi/v/2VK+yvRaFXSgbsL/7kNcM0Ca1LW4xpWS62zHHEmIaua9Ba8/T0RMyJZd8zznNNc58q1CIqOdeYyvTI9F1HQXE+jxwOB/q+Z7FY4VLGrtZihnQWTrSM54lp9OxuNnVEba7qO6VE9p2gYv4X29gCSihfjbP0bUeMmaaOfajMQvWoW0X0hTdvH5imudI2U132epabJb11ssiL4iBIyYRcWK/XnM8zh/2RnCvl0EiHvL3b0DSanBN+Lhz2A9rIZKAwNE1LzpKoE0LCOFsxVcVmtUTVTqskU23AMmEKnE5Huq6T3ULjiD4yBTkcfZTl4uGwJ+bCZjOx2+2w1nJzs+V8PiNB0xFjLVZLgHeMEocWcyGGgg8ZCBjdkBOczwdJ+7Ga1WrBcHrHerMjZ0/jloSkxJTLUB+SQso1vD1rrhEtReJItI54P/HFT3+K08LH2W63GCs0V1m2QVEJ5zqUalGxspSKHOa6yOGqclMP7+baUeWUcXXpZ0omjANLW3k/xaAb4eMXZEHfW5HTz6NHaziPw3XC03UhmFLmPBwpJNrWkeeMP3sOU1U4G02jRNxktSOOHp8zu60s8e7u7rBW0beOeR5ZLBaklFivb6SRyAGHZggRq41YSuSC1QZfMil6nDUYI8pSP8vyeb2SKdUpVcVoCZUDjWlxRpFVtRXIEHxEEVElcz7tpetvLY0BnKa/3fLi+S1PDw+UYri/2VahnWb/8IjNCesMq66haQw5JJY3Aqt2VrFYOA4PmWWnOadIYyVkRTuNs9AYYaY0urBYiA/UWSmarkNZi57hOB8xTS9jGhoUWC3wbd/3V9gkhIZxHAGNtQo1j1gUy/UGvZHm7DE/ViKGON02veOjD1/J3/nA87t7YfWFQEnv2UlQ6LsF0zTx4sULvv3t77DoljgjsFwIAT9PWKNZr5aSLtW29H3/K9fYX4tCr+tYW4pitVijjWEcR/puwzxFjOsYxoHZXxSKhv3+wP7pSN813N7eCu/eOUrRLJfLKuMO7znrWiwU7u/vcdaitGaucvcYk8A4SLi0UoYQEo8Pe549v70KsrQG68QnJKGYfeDimQ/IuF2pXvMsENA4jiidGMdAqu525Mxut+Px8bFy8cWitWnuOJ9GQgjYupkv9fP3yy1aWw6HoXYB0k2Zyk/RSpZAMXmapjp2zsLa6PsFyog6drVaYpytOb0DKXvh+y5aGmWxWuOMIWeF61v8PDJPA5vNDmsdU5iIXvYcykjajtWG/fHEME34t2/Z7XY01pKKWBNccPGiBUcNWawRgs9MYyQpWV6vrSOhsNVR1FRRiq6Mi6ZdkkKubKKjUM+qsZOShQ5CyFPv2RqpkGPktH/ATwfmmh++XjgMdclcbF30eXIOrDctbStGdzHPlRse0bP4qhvjaKsNx+Rnump37Yzl/tkrzk+G4fQaoyUkYwqeeRjoqulczlKQZBKMlRrsmaapuhtWPre9LFgNy35FmJ84HY707YJs4GZ7y/O7nvV6w3qxlJzTCmk6ozid92IlgeF8HNhutzTGorLcNyWJVYcxRqL0tMZZzWa9xgCuuTC+FJolrnPMc8AZsbI2quC6htF7VouuelQVnOsIsaWtwRhv3rzBKNkBbFdLur7h8d1bUogs25a8XgCZ1bJFKcM4BZ7dCHNlHEeWfSuwiBNadMkZyort5p75/kaMCEvBVK2IBpxWrNcyPRhbHVmzLENb25LrfXmZ8C/vXU6iKTHGsF6vr/BU2/bXXVIphXl8Q9M0rBYr8W0aBj792jewTrPf76WBm72otdeb6+7JGktfaahOG0oUyDfnxAcvX1Z6aSOHLIbzWeiuFzGXkBrE6HG/3//KNfbXotCvVhv+zn/xX/HZZ5/xs5/9jHGUhV7bL/jWt77F//jbv813vvOb8gZXTPw8nEh1sXQaRnxpeTye2N2sWK12nMYBlBiPmWKYxxHXiKJPFotywR7ePjKOnlwXeVYbXGNY77aEHDhNE0ulobJwTBbFJkWJC2VVWy6XSwkmzgWjjPjfJI92Fm0KT8cDfdNRcmIeE7mY90ZK2tH0Qid1rqnQg1DqKILfDcPAPCemMVCKIWdPUQaF6AAO5wGM+IXs7AZbNOfzyDQPbIv48RtjcCURfeIwDHK4kmnbXgp3SmSbcfaiFhQfs1I0+8NZMPAsENccJg5PA7tY2OzWrI3hsD8JrBE8kw9oigigkqiH5zhjrKIEcKpjsWrJ5cjkI8NwxvCMOHusNtiiCXMmzOKYeDgNaG35+OOvcXw88vDwwKmxPL/f4ZwhIdi70S0xCUdeFpGW4/GR+XTAlpmQCiokTk9vuX92K5bYdQpEZ4xqGA9njkmBbnjz+i3b9Zr1ek2jHU1jKSrR6BllCo3OpDzTOgN4Hr444oxmu7nh/v6uXsvL4iwR4sjrL35CGE90jWaImejFA2m1WtE1DaYWIKUMVmk6u2DZL9gtt/y1v/zXqrVH4XA4YDC8+vBjPvvsMzrnICP7AiUCvlJ/+XnGGtAlYxBfIxEHOXEfjZpComkE/hKFuGS2nk4Ty6VMBAbYbVc0xsm9tttxPmc6p2gaUWEsly0haDYLWdbebnqOx6NANzmwdEtOlWTQWYfu1hinWXUrORiHL3j5/AbrGqapx1QzPGvrHm2euVlvIEaeP3smIsUS6ZJEjy4WC1arJctlR6wxoGKjoUUzgLDyYozc73ayQK6N1TzPtM2O280OWyyd7er1A7dYAzKVWv01pnlg1S9E6JWBKHm/VskEWBDq9OXguCRNNcairHh0OeMEGk2iphVCiGeapBHICsbxvR9SKYlxnmS/FWZ+1de/tNArpTrg/wTa+vF/v5TynymlPgX+HnAL/DPgb5ZSvFKqBf4H4N8B3gF/vZTy2Z/1NZqm4d/9rX+PTz79Or/zO7/D42HPfr9n9oHvf/8HTD7y459+jjIaa96r0dpuSbvYMI4jxoBSI21n0CqiTWGasrg1VpZGigXdmrpQLVUtSbW4fc/B3u12go0OiWVV5F58WkIWHjhFo5UVIVNJHE7nq0y+dwaKGKbFmDF1KTj5iNNGPOljXaQYTcmRzWZz7dSloIrni1iYZowpWNOQ0kVMpbn49hhjmMb5yjiap0BpLcY0+PnIPAW6XjpQ7yMX98hSaZHWOcSdvpBTIeRIUZDShRUjdDpfxTeXr28rY6edG8jQt+6K/Y7jucrixTAtl0COmpzF9CxnObS0EiaC1ppE4ng8sl6ucK7n4k1UChyOT8QgFsylJHKaOR2eeH63kjCVVMgx47ostmk5opVB5whpQmUpdPN5JMyewWWS72WyiEKZNapgtK6q5sjx9Ej0kb5/Rl+7cXG+1Hg/0DUNi66pbpuOxaJjngPTMILylJKrb4xH6UJKkePhLX46X1kZjTXY5YJ9DLRGs+gkXAYWlT2yoW0anHY0rpX7p4g9yKpfMAwT62XP3c1W4LDqB0UJWEPtUDWLZcOyd2iVsY2hJGmClquuGmWJMAwyo59pjUB7fd/zWonQavIJouZ2s6zEhsCibzgPHqUNOcnXVAS6VnYlfbckhMDp8MgnH3+MQdG1DctFy6qGaexuNtdCOPuZvnF0ztJ1C/JqhVKVTpvEy/5ikCZh3rBe9HTuFcdJLBucczgrjKL3FsPy/HYLwfpLlHu873tRnmst9Ohyca2M0rhF8b3XWuibOceqnVCYrn/Px6/Gixf/JriwjqoWQ0OOSSKBKnkgR6G5SlBMuor0BGWIYs+i1dXaQVhI722t7S/NjP7Tr1+lo5+Bv1pKOSmlHPCPlVL/APhPgf+ylPL3lFL/HfCfAP9t/e9jKeWbSqm/Afwd4K//WV/g6WnPf/w3/xalFPb7vSw+gePxjJ+FNrc/jWKXWqr/OIAyV/8RpQuFCWcVi0626T4k5vHMomvRpkrVkyxbQOPnSAiVlqc1bdtxf3sHCGc9hFCXsHJQQMY6uWmsaQhexu7eOoJPpCj4dw4ehSjYFmtJk9lsdMV2DTkJO4YRVJTF7cVcbb8XYUUMAv/EKJ4mr159yDhM1yU0XKxyL0EFCj1rnEv0vdyAwzBhTYfWjnmK15tFlk6ZZt0Rc+Z4HLFWk8LMMAwola9WwUoplLGUDH4uuPrzF225udng58g0xavFgDOaxmpuXj6j73vevXvkMA6kCCkFctYEHylJHrpSYbu2bauGoiUmjyqdKBStk26t68gOhuGEKRmtAoVAiWeKUoRJJj3dJDql8MVXjrbCn/ekeWI4jcLCiZ7erNituwqRrATDLtIYrFYbcjGMcwRaTqcT3gs0aJSIt/q2pbGOFCPH8UjKMyAiq1IM3aIFIMbEPEehPKrMutvw7niGrIhRONPf+o1v8sef/ZAXL16wu72RKMGsajhGXWIqS993GKWxtoq2GkenFW8+/4zeWjqjeHg4UlKi61ucgs5ZVosFOS+5v78BwBnRg+x2O4wRr52msTzun1gslnjf0tdnaLFYoBD7hXmemSbP8+c7dIGbjdwjN3efCL5vBfq7FL/Fask0iI3G1z98TtN0HPePWAvrjz+gqc6STWMxGMbq8Np3DbaRqSb4ia5xKCJtK82TNqBKoESYU0RVi4/b3Zb1cvG+wPpAokKgVSBnvKm4ODStsJqKEhpjrIwhVQrDKBTuRdeTUxB7hZBQSj5PjsIsWq4WhOhpbUMOkVTEpM5cLDUqFVJVWxajVM3FKNdYQq30lSl26eqhmsgV6m7gvfI8xUi6TKG/4utfWuiLVLhT/aOrvwrwV4H/qP79bwP/OVLo//36e4C/D/zXSilVvuLb+nNfA3h4eAKoGKYUVmdb/BRkWVtk+ahquMXFX+WSHZtLBuWYg8boBfVuEOaFtZDENyalAFk2+33f87Wv3XDYD+yPB3ab7RUnDSEyjILP3t5tMEa4s64R5d50PmJNBymh0DTa0GxuOB5PWC1UzvVaOtzTccRaU2Xnshj0cxTjqRQ4nZ9wTaHvO7pGYJa+aRnGE0pp1usl7969EWuISgkV6/dCzuq9WrV6ej8+HFgue0JIzH6iFKHbNU1DDJmY5koVS3ifOB7P9H3H+XhEa83N/W0NZZf3djhFhkHeP1eFa86J8Gu76wApwLM/k1MmhszxcCBexGsxkZOmFFMLWItpZE8Soychh9mLu1seb7dyeI0TmXylu/Z9T1QeS5FA5rbBdIZ5Gjjtz9fIts2qpXENqzqJbXZ3fPLhJ0gSmFhHb1ZrYdp4L9MFhdPpxHQc6ZoFw0kER1NIFGbO54nT6cT9s2cI6apwCmfIE5JHcOT+bsP+4cC3v/lvcDoHHvZP0p3lxLLp+OZvfMoffe/3SSnx4uYOKKQ08NFHH/H8+XMeP/+cddPSKs1cGwWnZeGptMKqQqsTSonXec4JYxO7u40UypqDumikANzf30IR8deqF3rjNEmMnTPiPnqz3WKMIqstBbi9214pn7o+iyUl7m5uUBhinSaNETrlctWSgqeo5mrClrMw02Y/MZ4PIhZqhOG2Dw9M08RmtQFmvJXPNw3Dleqayns/nVgT40KQBscme6lJFQqr9tK5Ok8mCR06D0dItQFK0viM43ilYIoGpxEvoegllzkLW8YZUbG7+jHOGbS2temLV8M6qVtVLWsMsb5vlx3MReV/wdZzlKat1H2efI5Ksb7mK0uJvPDkv9zIlZKqZqZUggWk/7959Epa6H8KfBP4b4A/Ap5KKZcj5SfAh/X3HwI/rhckKqX2wB3w9uc+598G/jaAq1mJ19QjqE6A8iaRokSalUTTisqsFMkpzUrUaFqJmjEVw5wyN9tnRO+gWKbpgc5JNzrMI11ja1f73mnQGc3h8ISvnuqC611EJQrTaLabBbkkppRoZetK6zQ+CCSSUiLOHqxGqUyO4rBonGUYz9zc7iBlOtdAka8dk2fpWm62wuXd9BtR5Ab45KNXAoMME9YJT3ccvRx8Qivh4rr35RvsPT9cOviLT8g8JzabFfs3e/pKNLCmQ6mB16/fQBbzq74THNP7wNPTgdlLfuVyuaQoy6ff+E2+9/0/JIYZaxXH45GPP37FN77xTX7v9/4Jb98+8PHHz3h82BNCFHzVOJR2xOKxtiGFzHA8EZK4ZFrEVeBmu6ZpGl6/fs3TwyNduyAUOB2P0rGnwqLtsK6haywvX9zR9Y7GyDV79vyOnCV5KufMYT+xP0z4mEhRHCY/8z+uY7KlW2wIIYkqe7Xh8ShWAKXaG1+6q7v7e87DkZubG1HNzkIZtbpwc9fx6uUCzcA8PTAOCT8d6NuOEkZ8nvjuHzyy6mRZrih0TUvT7rhdtxR/4uXzDffPVjJR9C2pZBarnlLEs2nVt0LVs6JcbqwIvFyjWHS9YMJa8er5uvoxtQKf6S9ZOayWMlWVwKJVDCfxPvcxEK5h6tXjPV8ovtJwCXNM3osSh2thSimSoxjLXbIBLrTRxjQoZZnn+fpe5lw4j4NkQcwFo50E1qiMvhQ4MqFCIGJ6WpPGssBNpUiwTRaqFMbZq63JRVh48ZuySqNKorEalCUE+Z59kJ9Rqep26hp09aIyRqONuFfOPktiXP2ZBFaDGDyq+tQA4hWfpUu/PIsyjYtxHFnqQwihevGLmR1w9cD6st2JTN6XHFxHKfb6nueaD9F13a9SvoFfsdAXSZ74S0qpHfA/A//aL/qw+t9fxOL/U918KeXvAn8XYLFal5QnbOnQphARF0I9iRotpkCYA3OENoyV7lcj5XSDqw95UoqSE7F4YsmYpsOlNeM0crtr2Swt2oq44t3DnpQC7x6f2D8eaNuuHjSXVBkxtGpdg+0Mi00LTUbNnmWrMd0KIXVkgte8eztBcTTOEkO6RhK6an17d/eS0/lI3/ccj2eO+z3DcKJvHNaAH0/c3Wxks29EqRhzwYfE0+EgizfreP78luNpYhhGUpIpw1bbBpFWi6/LRaWr0Ghl8EFw6EtXc3H4/M3vfIv7+3/7GqLwR9//AX/wve9DFuhnHCNxDixuVvhZog7/73/6fNOYEQAAIABJREFUe1BEl1CKTD0/+uxzbu5u+fRb3+EH3/vnvHn9wDR5QCwbJj8BEwVw04zThq51tFGzaCwvnj0D7/nk5Qe4rmXVL5hevOTpPLJdbdFZxtvdbsdisaDtHMfjnphm/j/q3jzYsuwq7/ztvc9wpzcPOWcNVGkqCXVJQoIAyxIgYZAAGVECWS1hN0gCAhx24Gh3dIDbHSYI0x2B3e5wMLRNGxpZNNBg0wIZyyAmSSWVVGoJpFKpsqqyqjIrp5cv33jvueecvXf/sfbe59wsAeIfh+pGZGTme/eee84e1l7rW9/61hv+1hvJlTgH4j1KcrMoClaWN/iDD/8pn/jEJzl96iytqygGy3g099z7Ej71qU9L9eVwzPrGKbJMM6sOOXPmNJ/5zGf4zOc+z+rSMru7u/zIj/5wyMsoPv6JP8W3NR7H0V5LcWbM0dEF9nZnuNaysbFFlsHKYMDa6iaurSkyT26WGA+HlIOcsswDY8tx57mvAy3wXRNw3eOZJN1sOSDXnrK0FFphm5phpsiNRysDzbFULWsFJsNbJzrsQdrYa5HAbmznDNTBwxRFT5G9DjW6IjOhOmE366QvQdi3KNVp9mtjcNqRZYF2Gn7uEaqpwBZaGD55EeiyUpmLMuhQgWatE8E9o8lNRjnIU9P6CFdGhVrv5I9WBpOZIF5maVXXhGSQF+IQomjrhmFZUpqMJi9QmcEHHFzhKHKDMS7kwjp5kvjdmWqxvnOq8jzHBYMc8XMAoxUE6QnhxTtqVyW6bNZr/ad1hhmodLDGKuC2bXFEtVfTU3UN440QNaz3f63mI38t1o33fk8p9YfA1wKrSqksePVngWfD2y4B54BLSurJV4Ddv+y6RmnWy/ECrex4PqXWLSZMXu4srXXMm1C8EcqrHSKFsLyyIqGMEr1say1LyxNcXkA7xztHUY5orHhamT4CrdlYX+bk1jaHR0dJHtVajwun7bw+ZoWSsiwYFBpTlmR4quOKBk+RFSjVcP78eS5f2WU6m7K/dwTahFZ4IuVbN3Nwlp0b1wW+aY5xtsYYw+bGNidPbTEsTRqDup3jQpJ4fXVZqm0PDtna2iLPS5xtJKOPSmqEbRubZXQhXfSQpNPNnLZVTEYj0ThvBHe+ceMGl565zLk77+LF993P57/whPR33TvAhYINkHD93Lk7eOTRx2ltCHezImhzzDk+OuDW3g7LkyX+9lu+g0ce+SKPfP4xWutZWhpIVaLyTCYjViYrFFnOqY0tHnv0c7jDmmu3LrOxvcnlvUuMliZsrG2zuooYqFooqLf296jmU+q6ZjgsMUaztrbB1cuXODg44PDwMBl7ay159iyvfOXX8PGHPo3KDLYSyd/ReMK73vUujo5ES/7xxx/n2pWr7N66zgMPfDd33X0HFx7/Im94wzdJ56crV5jP53zkI3/A4eEhJ05sY9CitWNbnrhwkY21bcblRCKLzU0Gg5JBkdHMKzKjqKaHKCvtLm0zY9ZW4tmqQNmzLfVcmnhIwjHHKelbqjzMDmeoQclkPMQY6U+gM53aaaKl14A0ew+VwLnBWY/TwpaKxjvCCk0j7KiiyIUhpkW4TTphhaKoqMtvOm+1aUXC1zlQ4fdCS24SvGCDBEM6FEKiOyVATUZjW1STo1SRWC9ZrinLIuxFm7xdkAOmCLBKygvlwsTJEA79YDCgMFk41BzaZOkelEMojQ4GgVqqAkRkQwOho6Mj8IqyLNlYW6NtHN62NLalbmsRN8y1EBfqeXrGaPCTCKFXlJlLB1VHmRX5lKZt0jMkinaeU7ddB7R4va5pfAddxWjiy3l9OaybLaAJRn4IfDOSYP0w8N0I8+b7gP8YPvLb4f8fC7//g78MnwcosowffO+7+cIXH+WhP/4I81nFss6wCo51xmFjaVqHVx6VKSkpD3ihMobWtdhBSTYYkuUZtXNMD/bZ2ljDoxmWqzz99BepqmU2t4ccH1+nnk8ZDoeMx4aVyYjzd2ylRPDm5iZ7ewc89thjeDx7t25QN8ecP3cKYzR1PccYRV23FKNREks7f/48167fZHlpgwtPPM729jbgKHLNv/7f/xXbJzY53D/ibQ98L4NBjlmDjY0N8kKzNBoj7Q0kvNNSwU9T19zaO8A2DUujMcYoRuOCNbfE0f4R1ksh1a29fby36KAqKc1PHCbTnDp1gqaas7u7S93OKTLD2vY6P/3TP836xgZ7e3v88//lZ3j6qas8+ugzvOL+1/DII49gW8EmR2XBubPnqZqKT3/607zspS9H64z5vObg4ICLFy9y4vwZrj17hbX1ZQbZgFe/+utoG8XerSNOnjzJU09e5MzJEwyLPMjMgmos0yvXOTNY4/T2afYO9jm4vs8P/t2/y4WLj/N7f/in3PXCF3L58lMsT8aYTJg/ly5dBUQI7+TJkyxN1ll58SaxO9Dh4WHCM6fHFcfTmrf87bfyqU89zNLqGsob8mLAT/7UP6csBxweTymHQ5668hRHR4f8P7/1G9xx5zn+yU/8U37l/b/K1soWOzs7fPKTn2QymbC0NKGu5pw5d4bt9TVOba6Sm5amnaGwItjlG1R7TNNa8iJ0XspbVK7xPsdaLSwMJ16hVSJw5topZZaHgjSRzjVaU5oClWmpxHR1iKYshTIoY1BKY33LfN5iG4e1Xj5rctHw957a9pq+hBaNRVHQOkFgjZKKY9tYXGA85UUhHHPkeoogM6EkmvBKM3ctddtglLDLMi3VHTrLBNoJ3rAwj0TDqg1y2wQdKBF7g2HeFQEpB4O8TEYyHlDxAIhsmniI+JCwdCFabZoGp5Q0G8lFzTI3Bq8URegzIYdWjdYG71qUyhiPlkM9g+wnncszeYQZNp/PoZEDJiskSvZOoONIqgAtgooyUeSDkiiQl4Wm5l5baNvAABJCSB00m4BEhoiJ2njwenwY265f7V/1+nI8+lPALwWcXgO/5r3/gFLq88CvKqV+Evg08G/D+/8t8H8ppS4gnvz3/lVfsL2+wTu++wGwjt868Ssc7e1x/cYN9vYO2Z0ecenaNfanM6a+pSwHIXyUZtQxMdJai/cO30rjAeUd9WzKwa0Dqv0j6rmlmlmauebqlR3uvOssTSXYt9YeVMNonDOfO1ANSresrk3I8mHA7hvq2mFyKZEfDgYcTfeAjGreYtsp48mYQTliZXXEzd09lpaWBPsdD3n6qadomzkHB0eCSWcZ+/v7olmTZ1gLRTFgXh3T1DAeT9g7mDKvLNVU8gjbJ06I9K63bG9sMipFC2fv4IjRYMjMz2ms9MJ1QWpgHDplba+fRAWtkdW1ZU5un2BtZYVr167x9NOXBGrIB6w46SR0111fRVEMGJWDIKswpnENH/3og1RfNeMbv/EbmVU1Dz/8ME8+/jhnT51m/+gWdTWn9XM+9KH/zGc/+1nauuXypQrbzDjatxy7BrzG4hl4zfEz11jJhjx2Y4dXv/rVuO1tPvBrv8HhfIad11x47FFaDU2bMT04Ig+FTJPJsuC7jeM/ffA/s7+/z9vf/na8U9za3Wfjni1msxm/9R9/m50bu6xtbDCralZXV7HWc/nyFY6mIuHgrUgCnzh1ktPmNMvL0mT6x3/8x7ly/QZaGXZ2dtje2OZVX/MKrl27RlkYjo4OyJXj8NYNMtWEhLtjaTQiM548g2GZ0bZBTtkEQTVrGQyHmJBjqdtGKl2tzJUKbDLp1CWhemEyMiMYeGqJGVJkzoLLhCI4GhoYSLLVBaljFai4fc+z7yWXphCmj5c2nVqbQDNVCYet65rGdZ/pdeglywrmswqLQ3uFCwaUgPNnRSaibYjj4kN3NZynGAq5wtkAB+FQWnrERlZJxLsjHx66NpTRwwXSIeZ7uSpUR0/E+9R9zlvLPFAanZViK+homLNAxGhb6VOMUmA0VTWTQ0xlKAxGZ3gnYzYcjUA5/EwOhpg7i558ZL3FZu2RRRNfsa4mipX1G9R3z9eT4v5rGHr1Vzjb/1VeZ5bW/Ud+7n08/OAneOTBT7G6tMyNwz1mSnPVzdnxLVM8VTi9CR1fXID+jTGCuylQOoPMBFqelw4tzjIZDaR4Y3uNleUMoxuOj28yPTpgfW0Tsq7DUdu2XLl6jc3NbeaV4+lLz5CZgq3NTZaXRlTHR4xGE4rBkOWVJfb2D5hXMJ2Kl7G0tCKKdZkWrRPXsr+3S55nzGYVq6vrtA2Mx8OwWUND4qalDtx1QGhhgUmDakVVM/YmRWNDos4HQxIlFQCcszjboLVjOCqltWEIIRNPF0S9sm7AZ9StxeHJgya40MY9SnuqquLy1UtkKmM4HPHN3/RG7rjjDt73q+8XD8c6ajdnNBqwOh5w512nJBF8PA0VhgL/lEoFaQQFdcv08g1edOpOvBclzmww4MLTF6lcCysTbs6OWNnaYnV1OTWmECxTKKDf/M3fzOte97p0fXn2mAgr0Urx6c/8Ob/+67+Osxp0xnxacXB8hPWRaipCdKuTIWfPnuVodsSjjz7K7u6uaBi1LUUhCbQXveAeXvay+9jbv8HFxy8wGY0YlxnGaDKjQkcsx8rSiGp2yNq6tH17wT13h25ILZtrq1L01Ip8cWsbvBdZisQg0R2TSikFoeG46M+0oUWdVONmWUG/EYUUSxnaxmEbaVMZ5zuyTkze6bBDoBeGZjHJ6+8Zk4iDx++VZKdATPM6qqk6NHoBVlFKBd0gn9a1CywXWDRukhiVAiIxsu0CuSAypeLnlFJMJhPqONfBCMb7a71ongopwqbEa8xPpabsreTtjBEnwjuV3h/vt2rqroo4l2KxaIiNMdJQJcDJqQo2F+VQqX4OTWGC1Im1Hq27zmTW2l7lrUp6TPFZ4nz4Vt5XW4Eyv+u9P/kp7/2r/iob+xVh6M+NV/1PfevbuXX5WQZH0mBjqh236pq7X/91XKHlqWtXuXJthycvPyP6L4FSaIxU/g0HExxedOa1JImUMqH7jGVjYwOvFFleorOCyVgxmWgm44E0gzAKpWTxohXT2RxjSmJ3K+nBimimW4vOMvCaSOk/nllsq0PBUIFRMBwUKO9onagS7u7uMBqMif5Q0zSUZZ6kfZva0roGo7VIIOMZlCLDG7XCm8YKLq4UTdgshIIN5Ttq1qDMqaop2jhOnNimrWaBvgbTqkIpTTkcsL97xGxek+WlaNxoMfRt21KYIulxHOzt89SzF6lnNa71jMcTdBZCaTIUjta1lKOc+dEhb37T6wNHWLBgXGBp1GIw8iyjmVY89vBn8Ufz4GlBYz2uMLTG0GqHHRRsnDzFnXfeldbLcFRwcCBdlb7u617D6173WvIQ4idqXthQ3iv+y+9/mOvXb7B784A6etd5Tl7mTEOV4ekTJxkPR1y/cTV5vbFzmHdigLIsk85FrmFjdYUXv+gFPPixjzILekB5YchNgfeWMgs6/7kiKzNmR4csT0omg4J7v+ouKc9HYTTC345a5YGBoXyAJQJEpWNRmWtC841QLOckeSqeYeftSeJOJZw6cr9jQrGq54lhkuc5rQ20QS+GWkU8vu0ZWtdBKF65QE9tqauGppZr2+C99g+L2ENVjKQ4EVELvihKqeS1QeyfcFC7Fq07XN5amyiSxhhWV1fTPHovgnHOW4l4Y4Gkl9aRnUhgNz6tlZaHIEWQ0p0uC152nu4DpAGOxSciQ1EUrK2tSSP16ZT9/f0uWgr8eq01g2GR8hsRfhEWXMW8ahiPikS/jM+ZnLjAQozJ84THW/m/RTj37/qxf/llGfqvCAmEosx550/8Y973P/0zdHFM6yx1PaOqpjAssAfHDI2hbmYMjKKZzZlbhzcZw3yMzgzH072Fkz9ywLNMU5QZzz5zC2UMg9GE0WQN3ITxeJtbt445Or6VDo7IRHABA9ShYi96FyALwwTqoneBpmmleEqFLkxZloGbs721Ea5b411L3UyJIaJSinktUr0esDSi3a29FJtUM7Jc0R5UzJqWPCsph6L2J/0zZNPkJqMspDlzWWQcT6eC22rL5voqS6OMy7f2OX/+Dvb3DxmWyylxJhrfIVGnNcYjTWKtw6lWsFjv2Npc58mLjzMajKnrmtFoyObmJqPRiNm8oq4bbt7cwdVyCDe1JPhar2mdCNfNqophnqHyjLn1VI3i6b1D6oNjioFgndoEOQMUfpAzWV5iNBMee+zdu38gcssKx2MXnuB3Pvi7XL36rEhWQ/AWZbNU84bt7ROUwzFlKWJ2TSNFNlU158TWNtvb29y4dp0rV64kIy9Rj4xxbFwrmxW80+zevMmDD36CF734JRRFzscffJCqqSlyWc/zuSXLDGauMLZFZ2OmtWY2a3jqjx7i/JkzzNs5B3t7DIcla2srbJ8+gUFyLtHIKOWZuYbcZAxCiz/vWzDSIL2lpvUWP5cmG5HiKLr04VArcwolh+N0Nk3eZZZloQLaonSG9g60dCwLnXlQoUkLisAGCV6zF2/V+payNEEaQuCmSLxzrbC0jDeUueizaKTpTIweAGnxmZMOprZtA8ylevUcEvFFoxe9X2MiiORwcd6skwjfe7AqYfwEFk+WSR4vHgDynAqHwL9CnTXpUGwJTDwlDKXWWY5n0pSlriusbdDGiA5XyDU412JtkItW0m0tiiYaY6SNoDaYTHB80b7R0nglQnKt1BW0bRvE+USN0ymYTWcch1aeX87rK8LQz+Zznvn/PsXnn3kCU0l2e27gprKca6dsba6yubnO3uEuzfw4VEeCzjTLkwEra2vcvHFNNCeyKGolTS+KwpAX4JWhtR7fzrhyZY/BYIQxVhIgTUttpSijKAomw7EIMfXwM+EHd9jmvGnwWjx9G7qxS09PTVEotGtxzZxZdYgGrGswmeD/WZZRT60sDi/Vix4HlIGVIkJJymYURrO5uiKnuA8h/ajE1g1OSdLVO8VwkDEqC2lCnmty49ADg1aWanoojJ6mEnkAranrhqZpyQc52iHCTYMRKwGfLsuS2CKuqio0ikuXr3Dy5EmOjo7Y399nZUWUKvPQSGJvb4/JZMJsNuXK9VuMRiOWl1cZjSbc3N2jKErK0Qqbm5vMZhXP1E/jihK9nrN9+pRsZC+NSVonuiVPPfM0+eiQk6dblldXODo45CUvvY+iKHjk849y4cKTfOqTn0UpeNGLNsOGFMNc5AOWlgu++OgFZrM5r/ya16BVRp4X1FXF/a98BRcvXuTRR7+ADzS26D2KwamTlxU9q1iJjTM0reMzn/1zJuMhD7ztbTz0yY/z2Bcfp3Wi2x6bfkSGiwEyU6CLNa7dnIXG00OOZo79o10ef/o6tW1D+X3DcDhkc3ODU6dOsTIekWewNByifJMwYNvDdKW4R2Ocoq2s8MKDF27wOO8ZjEZorSjC/HrvRSuobXFGBPJMoOjKupdiRWsb2tYH5o7oH2ldJqw51XG0PkRSoagpQGMRj3ZOPNVU9h+jUCXJYMHiM9DS7S0a3HgN4ZG3KEySC0AFKQHr8CpjWh+RexFR81o8/Xldc3x8TG3bcHiEJu4BytJKSYGfjyqiQoZwAS4sixw1j1BOy9WrV7l48SJra2s421DqMTZQVZ1rkyja7s5NKfazAueIIqhJOQnpfieibHVg38SD2FqLD5BbPPCappF8iHd/LfXKrwjoZqMY+P/xOx7gkUceYWU04dbRAX//v/8x/o9f+hXWtzb54uMXUGXOZGudg6Mjdm/tc313H28yilK6wpw7d5adG9dCs29hAAwKWcxZrlB5hkdTDoc8e/0m13f22L21x9b2STZPbCZcLc9zVlZWuHnjRsDGxOPN8rjIfFKPc0rEkbwX4+nbULqc5WTG4mzF2uoyuBprW7SWCZMGG9MFoaPIGFkaD0QnfC54+2gk8qVaa7JigHS4Es37FhXKu5XojGeao6Mj0RMZj/DeMixKBmVspKHRWSkVoiisVVglDI2mCTimF2lkFzjNybu1IuEb8U1RPCxFaTPL2NjYYH19HWmpmPP4xcdCqblmNJpIGbfXobevREPSkpGg+Z8luCQ23jh37hxnzp7i8ccfZ+/mTgrT521DWQ5papsqE1FOGjrnMcmlUlRXVVUw0hnzpsFkihfc+yKu79ygqqpA9TPpIG+aRWy4T2OL1/feiYCbt8zrY4zR3H///bz0pS/l/e9/HyBJQQCt8lAMkwXDpkO0KEyWWLhjjKIJ3HOALNdYKxHf9OAQrSwn1td52Uvv4/TJbYqioJlHnL9O+QYdkrVGi+qnCT8vM5OSgdrIWpQWfJpMEdoterx1yfvVoWCnbdtQih/HIMPZ2KC8l/MJciTKeRxdHi3OeQf9iJdsw1hHBpBSCq/cwiHW76Qk8yRrNerIxOuK5xx0YIxca+7kvvf292kCeSNGxeleQtI2U32apEoefVx3s5koy5osY1rNqKu5dPdqW5YmEznArDCltNZkQYUXSAVQ0thGfm7ygr79VSr0vM3MgvcPSN+BXhQS8xo/8I/+9fMHo18vBv71p+/CZJkEfZlhWs8ZqJxTp07BsGC3OmaegXWe/aNjLl/dIS8H3NjZwVrLS172UoySXouz6YFIqJpCJt83oKSJtdIGbwpu7Oyxf3zMZGmFF734BTSNGNPRaCSD6eMiEmpWnucSKrc1JhfaYVEOqUIiqnWWprZoJVo7ZaFp6mOGowKscI5N8JLW1taYTis2NtaCTo0Yzqqq2FiTzj7e+xAGyyYZDAaUo3GgT3Z4XlkOAC00rvBeb3Xg4wpPVzauZz5v8E5Tt0E6ImzEeF9KqSQlYIyRlm8By/Xe09SSIJpMJgEzDZsll+rHsixpKtF8iXireMmBweE8WYLBRL+mqufUzgKOtTXRYtnd3WMyWWI0GnF8fNRhzYHF0bQiFhcLaLIs60Jbrcni5oXU1CMyIF772tdy6tQp/u/f+PVU8SmMGKEKRuggGo9YkxKNffSstArQird4Lwm4vBBP7J3v/G/58Id/nwsXLhDL54uixDZt8oDzXDB1kcgNe1ArcBF2DIdtTH4iyV7ahqMgT9s0DctLY86cOcXZs6fZPrEV7i2sUxWqVINXnwf6nw9tOvPcoPAioaCkqYjrURnjuBRFkfJIbdtSzWqBD0PS2DlHNZ1hjESCgyL0OsjLZNDj/SYJAS3OiVEddh2TxLEK1YZ5SMbXy3zbthICQJiXePB4FVp+Wpmn2awCDYfTY4FHlBAc0Cop5EYM3TqRlpDoq6Nwxn7JWosTlcTHArZeVRXnz53jxo3rgqtr0lqzrTB9pDBLeg1Mp1OqqmI0mlC3M8qyJDdZIgRIe1OJjuIYK6USFNXeVlfw3/3Df/X8wei91pCXtMGjyxw4p6QxhIOdGze5UR3SaFhZX0ubrrUWwoBfuXaN82fPMjs8FKaEdcyZobX0BiWEkSgTFBgVo9FEkqveoZVnaTISz7JtKAcDptMpFhgUImmAEhzR+pY81wxKUSCsqhprHaXRaCNqenkBdWbJC43xGmsXObYrKytBsEpRFIOUfFEYRkPh8eZ5mXQ5vNLJ6DgnGh0eqOYW6T9pk8eCFTzVuS4B5YgaGRowqJDo0l6qIVeWRTP7zrvuoSiz0Iko59FHH+VoKhr4WVmgjOFoViXPIhbdRCOQZRnYTjXQGIP2UakTrLcYo7EWGiuNN4yXvIZ3jiIfMJlMiN3DNEookN7jCMbXunR4xEign4Rsg5GORkvGzHHqlEQHDz30EIXJ8JCMlXcipxEPtWjoYx9aYMEgARilQAmV1RihOc6mDe9737/nbW97Ky95yUv47d/+AN4T8ioiVdzh76LCSCx+cQCi1onSKKMTjVCrTAqfrGdlY4s8z7l16xazpuYzn/sCn/nc51lfXebs2bOcO3uK7e1t8rLEtQ11NRWtFg2o0MQc2SPGBFxZSbmp9jog7EbKT1vPtDrARNhgPpeeyeFQjWNu8gwXuPFOZRweHmI5QGcd9AKwe/MmDi+NMwYFZBllnuMJVEOt0Lok0yZ53T44XZJsb3EoisBLj/PbhL7Qxggj6/D4OBVgDYdDERnzTujZIK01rWcQdOaVUpQhogBxGgyhYCvQXd1I5l7YcKL7ZLTm+vVrImhmLQSJ77quJdrUcu3hcIT3jrwUwbaqqiSa0SYlpmN0LHvH4PK8qxvA00QYp8gXxvTLeX1FGPrheMyLX/5ymrlktds6GI5qxtWdG1w/uMXcaJxR1AQtaWexDZRDMZK39vdZW1vj+GgmIah1oA3aeUyWi8CZFVaAbaVxhjfS/ivPNCozDEpDWRbkYcMXmUhMj0cCUaA82igGJqMYDCiHI2bTBoX06hxNJpw8cYLN9RXqesrx9ACVKUqjExRQZoJrZkZKvNUoJJxC8wFvfaqObWopFDs6ntHYVrTug5hbpuI1FTbIETjnKcqBJHQ9mEx0XIWW1+lodPxjlTbLcDBmMITD6Yz9K7cWZBLiZorJ6LjIvFOpd+94PE6MBK0yEaqyFuc986YJDboLTpzf5u4772JjdY2Lz1zi0UcfJbfi7c2rhllV45xPmK8Om80Y6VyEzkRgTkuzlejxKroG3lFTBUSpMeZeqmrKdDqlaQTGUUaSxOLXdsY9/i0slsXNFENwhSSvQcrZlTLBW7Xs7x3yb/7NL/KCF9zDD77nvfz2//u7XLlyBW/BtpbMaNragrZY68iNzH+ENGKyU+ZAo5UO+DOYrGDeWOaNJSsGlGVJGRpZzNuaL3zxAo998clQCDRnNBiS54atjU3OnT/JmTNnpJcsPnW9Ojo8YDLOWF5aoZk3eGtRgeJYNXNsiOpsIwdnkQ9Q2jMeS2J+HiqsIROtFysFWfQojdHJGY1GUuuRi7HLsoxWN7hAFY7Yc5kXZFmeIB0gwSd5Pgly0V1hVMS1m8YynR2GCvGc8VBkwbNCagVqKw7JcDBgXlWhdemA1dXVNMexhiH2unWQ1uPR8YF8fjgMWjOeTJWJgWTbCo1Pe2V5ZcKgHImz6XVQCi2l4LLqpMakN+MwAAAgAElEQVS99yyNx6yurqJzw/HxMSMzStH1cVWhjeF4NqUJvRb0XwOM+YqAbjZGE//mF78q4W3WWmzwEqu24aiuOWjn1AacDmpyraV2ok7plKZpZpzc3qatG3IdWsOFSkDpcSCekvee6byiCSJQqIave839KC8TtbGxxd7eHjhPM58zGBScCCExkYoWCly8J9DKDHkuXojWIpwpxkhCQadjmbpwm12rqGuh9bVOKI/igQrnOXriRnc0L+cj1UzCY+n/GpJpmXTmkcVIqoSU94s409e85jU8/PDDCz+HWC7vQyI5Jpw7gx71RsSoioHUPrAvgmdrndDrIh/Ye88wL8QTDxs9CwnewXggWGdtw4FlaOg86Who+8UgtxeQxESc0j7xsaPXJ9IBJsEer33ta/mb3/A3+OM//CM+8pGPYPIM6yP+2hOKch3E0OeAx7EA0nhH7FaSsrHJe6g6DTBJ29aUA+lD+o53voudnR3+w2/+phyEIcEYv6OfA1Aqilt11Z/x2b2Xmob0fi+wj7fdYey1TwagbVvaRsS/ZrMZmVK0VqKjYSmd2e648yynTp1gMh6yubYqDDDnQqRnybTCtrXQmNMcxd6qor5az2cpL4DzqZpK+VikRTJm1jq877RdTKjqpWcc+3ROpRQ6k3xQnBsTtPKbRkQBo5ftwtqZTiuOZtKABC8HzLSakWUZK+tr5HnOkxcvpnFcX18X/SylgkaVzI32MBpNGI/HVFXF/v4tqZhtpLNYPIC07hL21XyKcy27u7vMZjM2N7ep65oT26dYWRFYtm1b5tWi5LhyntFoFAr6BDKOWvRoxa3jQ/b3RYTOWZLe19u+/6eePxj9SjHwX7N5FhMMRQz5K9dStZaptRw7i4+JMKQji3WgspzWWpyv2VjbxLeWO8/fwf7+Pkf7U8AlgSbvJRKYTo/wmSYrDPiab3rd11PPDzAmYzIRjfXlifSmFHpUMIoBJ8vzHJRKnpzWOjQGFw+maWvaxgZalxImRbIXGucV3oaNqXpG14MyMSTrYAkXKMaC9Q44OjrixIkTmEwoc7OZcJRVoL953el3xPnVuqOu9eGMaJxjYth7nzTFrffSbCHeu2qDcRaefeNC8UbTYFQnyaq8aKxfunSJeVNje0kvbUJVI12FZd32EmpmsRrwdoignxyN2L1zLoluRWU/70WJ8+zZs1y/fp3jY9FFt3i0ynssjs6jiq9o6FOiLs5cPGiUgmBwE7weFQiVNKdwztGGYjiP45WvfCVv+lvfyq/92q/xxS88mgT0Os0S3YOFgtGkK6vXWlpYRn3AOB4R33eu61GQlA+RVo6ybooU0c3nc3KTsbe3h3VzxoMh09khRWYYjUbceeednD93mo2NNfJQX4L36Zree7T3AgMZg9EOFZLTKrxPGzFeMrYyJ8fHx+BjYVC3BvsNsouiSLh9jCblGgJ/aa1pWsfx8XFom1mkQ1f0fmRcDqehoxWGcjTk4OCAPPQ9mM4raZhTlkHSeTN9l8A4EuXFiBJtaOsGraNkSZbW1HA4pCjL1NbPeTl49vb22N/fZ319PchxdJx/wfJNaMAjlbcx6TseDInpCGslp1HVM6b1PBV5DUbjFCG9/d3/6/PH0C8Vpb9/60xaSIPBgJMnT/LEM5eY1g2NUhy3FqcIJeFetG+0FEBZa2lszelTp2jrhrXlNUmeHBwmPCviXNPplN29mwDS5KKZ8q53fg/OzkSBD4kY4mkb/3ZOEiHSGkzRROtnJQEEJGwYNPh+ZaNAKGI85WN96qYKMqwaRRM8lOjVLC8vc/e991DPKiaTZVaCbEHTNMwby/Xr1zk4OARihZ0wg5xv0zWMMbhes5JoXG43ovH3SZNEiXcvOLEcHjiH0TknTpxgd39PErx01Zg6XCMmeG1MxmmVSt/7njsRLpGRkPsx/URuH4vsNLp99Ax7zAQQg7e5ucm9997Ln//5nzOfNynKMLG4RsXS8y50BsHYuyRujAa/9P5QpuM1y73GwzpEJMTotMEEg7iyssL3fM8DZFnGL/zCLxCDhbIMtRGqS77LnPj0M7wYT62F2x7HxzlpVm8ClAeSaO/Gw6W5jT1S034IyUNhcMCtW7dQuGBYOybL8mTE9vY2d95xmo2NLSlW8lHm16PxeNuSGyMGP2jOKB1ZNsHoe2k4IxWkTYoC2sbReoEjQxsJvPdU1SxRMOfzObPqmLIsmSytpIpaYwR9ns1m4ZmkY1NVTQPenQupILQdXVpaYlZVoiqbyz6PDJd507A0HtM2Dq0UeVakQq+ylJxApEk2lYjmzduGqqrY3N4I9sKGYr0qUGTHGGNS5W2kTQ6KMsF94/E4NfqZTqfSHtTLXLZOEuvOSOR+dHQkFOrBiFv7e7zzh/7F88fQD/PC37t+AoAm8Fxf/epX88mHP01jPXPraJyjdaTKQYsIBEVKVlXPWRpPEqUNkAIQoHEd7to0cyl4MobBsEADb/2ut9C0sqiwSAk0PhJHxOsjsmCUeOQEzzF48bKZhM4n3N/YUDwaK4MLY12HxUJkbqjQrNkrsgxe9apXiQzD9WtMp1OpzvQdlzuyQrSKwkbqOVBHihJUL+xXz9U78QHbjowG5xyeJmwaKeBaXVrmxIkT3Peyr2ZtZUXUHi88ycWLFzmcSkgcPy/j5TE+SDj3wvG+5xz/n0cIJN53UHMEkrZ3uOpzoJ14qGgtAlD9fEKipXlFvILWofE5nVBUX9/E9Q+g+BnV7Y+e/ewOyXRwR369ow0MoXitTEkvgdj+7t577+Gd73wHTz7xBP/u//zlkNAWzX8fKogTRONFjtt7kR/Q+nYoq/O0+1CWzHE3zzIWNh1skaZowv8dcf4750Z5qRSPSqDOVilxqbxnY2ON06dPcvbcGVaWJpRFRq4FKrVIoZ3WOjRFaUPUJQVE8TCIORiJ1F0Q87PCuAl7MgtRTZHl1E2FNp3mTZeQF2M/m0kv5MGoZDQapSjBua6St2pqylxYbnWIJuI95HnOzZu38F5RFlJUpjOhQ1698qwoYwbmmXjoYkv2DvaTREeW6VBUOBLNIQerq6s8/fTTnDx5UmQcnGdpaQUTKJhZJnpAorgqTK21tTW8chwdHYjj4IUVd/PmTbJM1vt7/tHPP38MfWEyvzEaY323we6//35u7u5RNS1N67h5a0+SaAEfxmiKskxlwqPJmNXlFRSS8cZrxsOSohiwur7BiRMnuHlzhw996EM4LdoYw+GQpaUlvuHrv57G1iH73SUbI9gpCRkJ87XKQoOEIKoUQtNMadCRF6vSJtdaY0L1YWbyUBjlQrFVZzkMwvwgheIS+ifI6ba2YbFcXTDpWLrdlYz3PfT4sz7nOP7OqPjv7l5a37KxscFb3vIdtG3NzvXrPPHEE1y9voNra/FOtEgW6FzG37sOAomGoA2wSvq595Ig7xmoTGvxApURDDwmPwPVMBr1TqbV4mPVZthk1lrG49g+cZoiMpBCufiKhj5GNd51STYAq7rDLkFFdAeo99InQSmFidBJz6M3xgTp3u6azjlo+w0jWkZjSZ6+5jWv4o1vfCO/93sf4o/+6I8p8oHos/fuV8AtySV1kE6cY4F5ImSRBQXGxd/TYfbxENCADe/THq+kfzCqp5JIHy8PbJrgYNRBmKxtaxHxCnOTG6kE3dhY46577ubk1glWVpfJtERGIqdgAx025B8CjTTXAbKCEC20ZFExU0mnrcjwivcYI7V40NlWqJPGmHQoZ5lOJAFhDIkUuTYylsfH0vS+LMvgRAkt0qIYDoc4LwiD957d3ZtCIx5EOYyS2WyWajkirVKiC7mvshyyt7dHpGIqpVheWsVozXg8ZjKZcPPmTXQvKiXRdQfcunWL1tbk4Tu11ql2B+BdP/K/PX/olSY3bJ/fCAUHlulxxaNPPMLK+hkOpsepobVSitZ7nA8NoLEobbBtw513nOPsmfNpok3gs9Zz0cyezWcsrSzT+jkZRSi2UCyvbjJtHNpF7FejVCZ8e29x0QNRSnBYJ7CByqQZtUwuWAv33HknBwcH7OzsoMP7weNQSTVPKYVCOmRF0ypGPTBN6BJXCbDwHnxnwJXy0g9VxTLtDOWjQBI0QqkBesJUzmF7HmuiIuID7Y9kvDIlfN+P/slH2dvbCyJkFqdEB8TksfNXpHOKJksymN53hVFIKzwdm0b4wMiIsq7EQ1UhrVJC3sKJdG2EEeI4xUNUGyiMeE+ve9238KrXvIIHH/wEH/rQh0SzB+lLoALEprXI19owri5gaE6BTwJi3cuFDmZ9zo0YobBOjBHOe5ojF1hBGryWBt1yt3ijcc7jW3EC5lVDnud8/OOfxHvPm7/zzayuTfjABz4AupRPOYWmQDB+C04LzS9Al8q7FHFa14Jz5EWZosYukayxaeziz0lRjXVCcRWRrZjsdmQBFvJ4tDFSm1AM8N6SlyXz+ZxCSdV0TEDX1tI6z9Xr+1x69iHyMmdra4Mzp0+ytrbCqVMnGeZDnPI4MjKtpEm5d1TeUxgRJsNblDdY1YoomXMY74Up5yXJq0KBlw0GXxlRgBU2g6VuhQ1ktGQDjgPdUmUx0pYxyXKNCnk7wrWsA0VO29bkuUA2o9GEshzhnKdparTKEgwWoZnJeBnrpX1hnuc4pD+s9Z6jvcNAmQ6QU1kybyrmtyr2DvaYTEadPTRGNKcKxdzOyLMyiM8F7R0tucZB2X3mr3p9RXj0Z86c9j/yQ+9mNjtO8qAH+4dMK8fv//6HZTHFtn7BoHjvQ5eeMiUTR8NJauJACFOFpuZDI+Kao+MDmcgwQWfOnGN5dZVmHvC/yTKrq+tpYK1fxGk7LY/uZ0Xgvkas7caNG3J9Ld6uUx0coLzHtg5UVyEakQLxGKMink5GJm7A2z32eCioEAnIXMo9R1gmefxejFqMBNJLS+Np8Zp4zvfkuUmhrYtkj96aiYlQ6/usDNL4xGRzSlwG6Cg2RlbeSfJNyR7tY/JN03XDit8bDy6NY21thR/50R9GKcUf/vEf89BDn0oVk5JrMR3zqhfloLu+ndZ2zxL/5b1PsNqXYipLMlZgBxN1XYKnGmNS5Ts4ILGhXBSoEuxbGxgOB9S25t3v/n7uPH+eX/6lX+HRRx/DmAyN6WAkHw7DwESJ61v+EPjxHaTTT5z2sf/4p/u91HX0E/TRsemvtegEyc/8whrIjUreckyiVlUV1rVL3i3KMxrmrK+vs7W1wdmzZzm5vRUankurQKnQlc/pMJretbT1XA7ooPsCLkgzhLXpoKrnuLYRhpB2HM9moiAf4MMsy/BaPOv4f+daCDo0WgmE2Tphlxkt9iUrBa7JTM7R9CiNn1KKWXUskamRZLcIDzYsLS2FNdyEjlahvWJg6iwvL3Nz90aKAEajUcrNSR5GNHqOjqbhujNWVlaweDY31zk+npHlOW/+O//z8we6mUwm/iUveRGHh4ep03xd19hW9TZHWGRpwYsBi5lsodNJyC7l+R20MxiMRK+iLJlMRuS5JD7G4zGuDZTBLBpjqWBLGKbJE7YZGTYAOtAZfY8aqHMpcBFYo2tyLgio6nlVXceY2zeUDtooArf2aI497LiDfDoD0r0C3hoMlTGKr77vpTjf8mef/Zz8rCiTHC6wgAdruQTOe0wmjSigMxJ9Jb2E/0aYItyB955MBS31YOgjztr/rO6Vx8vnuzGKv4/RRyxmitc4f/Yk73nPu3n88cf5jd/6TdrGB6kDn8rfI4SUrhcxYRU1SyJ19TapA9WNZ/x8f55kAci1IrQQm174cGDGyMu1NlU6Otc9q4ylUPR0IQb87rvv5ofe8152dnb4zd/8Dzz55MXeupGIxGs5XNJYpjXSWwKq0+0RGKYb++gkJY121ZMl6B0Evjcm/YM6HsRxT8hQ6MTeahqbrl3XbeK5z+czeQ7Vpu8yRpFrKQgclBl3nj/PmTOnOH1qmzLPaFpJhPq2QSvQBMaYbWnrChWgx5iLiLDSfD4TuAkPrsEGVc8sYN9aa/KskLE3CmwrUasTxdO67bj5eZ6jEI4+QdU1MoVicluomWaBrTUaibedpBYyk5qZpMRx26Qkr8xhR91tW5G+ODw8RGeip3XHHXdgch0SshJZffs7n0eGvigKv7G53dtswZiGBR0rLp1zKB8KKLRiMBhJcUUYrKXl5URViodAt6hMx1ntGe0oZqWzAAkEyqR0fImc5MVNAKCCOmI0xgAWSZRmpgjPEj6nfNKzke8XKtiClxleykHkoPQ3U/RslerDDC6NV38exdhI7kBrzfLyMlmu2bt1IBsSMerJe1Ydr9k7lSpZnZNcgvci1pQaRvRe0UjE5GgyBCFqiqFyfI4s4uC9Q0wE2wTWiN8r3YxiVBCNfcdQKjLNbDZFZUY6HfkeN53uQPbeByXRznj7Xtn9wtyq/oEZh9gtGLS4Rl1Yn9HoxgbWjlAxG96P87SukUPFRockwnNBNrgweOXIM8Gg3/62B/jar/1annrqGX7h536eqhJjoYxONQBx7Ix3wRmRaC48Ybeeeuurb7jT+AQsXquuwjhGgv33AykKi9fyvk9E0F3zEudkDwSaabpXY5hVNfNaJLPrSjD1thU8Pnr/eCftJldWOHVii1OnTrC1ucFwKC00cXPm1ZQiJJ+bVphqximaeUVVTWUN5ZosF/59tAW3j4PzrdSvWGlRWlVVSD4HZUslLLMoNFg1LU1oa7m6usr29jbXr++glEqd4aL9kYg4D5LCs0QYmE6lcG97c1OcTRe7ZHV7XQ6ZEHkqyFTGfD5jVs/TgWGygr/5XT/2/DH0Gxub/tu+7U0BculYKqrnLYFoc0hBQ8CIVWfgyrLkeHbMZDJhMpmwvr7BxsYGZ86cYX19nWeeeYZnn32WZ555Jhk8+awOXpJP/7eB4RK9ncik7HvOcTO3Pbz7dsMdmwFHsSkg4cM+dgnqJc8WDY8N2OmiB92BCZrM+FCdqhfC8ehN9g1U9O4ESgjPE68Zn9O5Hi00ft9zvfj46r5TSX/SNAaxqEanvICIt0k3H6UXWTjOObyKrKWuZZr2XuL4wDaJBSTK92CvQF/tsyBVyl2YFJHE5w8TKd8Tx0FBZLb0ny386y809Mp3iU4VckL9+Y+OivVOcjvxnn0UTGsSe0wpJfRJlPQiaBv+3t/7Pl7+8pfzod/7L/zO7/wOXgkmLTq4wfgi0FuCE3Tf4MfxiOtyEQKLuQ78bTRb5eg0d/xz5n5xDUhEHOERkdcW2W3iXHoSp55QdBZ56q13ondUzYn6PvTgI6FIKtqmZnNzk/X1VSajgo31NQalIdcKHYq5CmUYDaWHxLyeBYPbg7NQDAZShxL3ivOWppnjGsmZtW2daJLGGMbDoThOYXzE065xrmVpaYX19U12dnaC524ZFIta/LHZSN/ZjNECtiu8i1BrJB6UZSnqm6FnNt4HCWOhqWbFgN3dXb7tXc8jj35zc8t/57d/54JHL0yOMEEmLlRpzrCyssba2hqra2LMT548RV3P+fwXPsd0OmVnZ4eDgyPRHVddskQpqXwbDcZJBMzicV5hnA29LQ1thEO0xwcPMY5T3EzKiZhXxLuVMT0GxiLvWylhaUToQ/AMu3BwdN55FHWyIcNedwYGk8YHJGmmlEKroqMkhsz/gqHHLHQPShzxYDjEq3NBS8eIfASLcIbyHtejg3bFPaDIUEqKc+S9Ie+gVTDgpOf3oZQ/vkRLJBqS7jDVHjIVAVtHXmYhx9CSxQhCZVhvaJ2j0B0e3ffciO/NOpxau87z7N/M7YcYyPT2jXf8dzT0hsDywC0ctM655AE73xlX54RRIge8C9r7MdoT1kmWaXAtWaY4d+4c7/6BH6DIB/zMv/wXXHr2CmiTmEcZoaAOk5KzJuvvaZ32AHSKnPEZle7E8/oOgdHd4ZiiWNXPw/TqMbz0UvXes7m9BThu3NzBO9kTGXGsJWryPtZZhAQ5noj7R7XRaBBlz4jYWuxoJXCKJTeeMs/Icin0Wltb4QX33MupE1toDa13FEieZz6fkwWjGruwKaXASVQ+m81CTUwQlAvaU2WZM59LvQAhjhN4l6BNI5IfUnFsKUKBZdR/ilLC3rrUoSpG57btanX6e9e6Rpq1h2g74voCGdXM5hWzqej4PPAjP/P8MfQbG5v+gQfexnQ6ZTKZJO2J7a2TLC0tcfdX3Ume5zz88MMc7O1zeHjM7q09wfPrKpzcsQ1X59nOQ3FTNLBxo3XeX9cAJH62b0jlLV24qk3n1WnfkzTteXrQbfTIUddeNvEi7mlC6CubLRryajYLmF/Ww6tV6GTVSZ0KvGQSXBJf3QbsY60hcevbwFOWTlwenXjX/UNHqS7hHWmQz8Frw+HFbd/ff0XPd8Gw+EVjE+fBOwJbhqA94ygyw+tf/3qWlsd87GMf4+bubvACex+N3mjPGPXnwd82Nt770GCi76WG+bXBiIeEvgsG7/brhqt9ye+Tp+k9mdYdoBKipwTbOHEgYtTjleRUFOIpG+VTNPiOt/8dXv3qV/PFC4/xsz/7c1QzkXMuctObq+gY5d29LCRWFyO1/j2nHJjqGC23e/Jx7XWRYfjeIGEQYR/5XYsODlLMY8Tf9yHU9HeQM4n7qW2lOl36G8y4EWTDp9MpdeuCOmeoK1Ax59FQ5rlg/7lhbW2N3GScO3+WzfU1tjbWqOuKPDTpUZ7kSM3nM7wVTSaNHADxwNNRWJBOoiV2sYowWJT5HuQFg6IMSEBDa21qVN+0UksRWxt6skRRjdeRvS1Qk8miQ6XAyeFhraWq5yitybKC13/v//D8oVeur6/xile8gvX19YVExNUr17l06RIfe/AjIYvvRCdE69RbsihK2rZZrGoM4ku3JypdpOmlDbtofLsF3YVqcQJiRBEng57YlQIsBH1yn8JWhRcvRiTEOsYEgnubTP7WGtrWcf8r7ucV/839PPbYY3zsYx9PEIt45yL3292L7iCbGGJLCVF4dvHgIxwUPS6Cofc61gAsJls72Cc+nUAzi+MQvD8fjVe8z+caVa2EkZ0OHfdcnB+EMhuzHz5UW+rMcOPGDQ4PD1OZ/F92qMS/b3/PIq4c77dfQdoZ+/g+pVT4SXfA9Q/zhbvvH2RK4dRiErg3mL37i5IVveIsrSmyPBwGhnZeoQuhZr7vff+epmn4+r/xDbznPe/md3/3gzz22GMhIhMKq6y9HhdeeZzvHUC6//1xTCIWH98nrCl1m/OSCsRU17EpjWt/HFTPYYrf3RsL57tEcd/JCtY6HAby3qPplHI4pLGWpZUV6RXRthwdHTE9OgrFSC7UNoAnZ94qtIPZvOZ4toMximcuX6UsczY2VzmxtcnGxkbo/GZRGkalwaNRRvpMK5QMj/OYMvSMyHPaVuF9g1fiiDjvcNYFiFkOgyIvumfTGbkOdMo8w2QF9VwMu8kMeEWeDzGZomnqnuBgKygDebgWeBs1+K3IMCtNbr588/0V4dGf2D7h3/Sm7wQ6jznSJmPYHReXtwG39b0FrT3O6gXPRimVuMH9BdU3BLHIKOJn0VNZwGITBr94GEQGxWK4H+ERIx5Nz7OOhSVaw9LSCidPbnP27FnOnjudxI4uPvk0TzzxBM8+exXnYr4iJjPjZoketaF1vefre+axzWHgUQP4ADdBUKHUGaBw0XNJkM/tHqr6Ej+TVx+flnsLP/CSnOzGpVOjhMXCr3Cl7r2BVWKUlKgDqSWdtfY5hWZpHnuFRv379bfBDc45rO4ipT62r61O0Y5wzN2CR99fW39RwrP//xRt3ZYETPIUrssxxXuT+QzrKKynPDfJ69va2uQf/IO/z9LSEn/2Z3/GL/7iv+sVEQVqrO8Z6d6ajmuyX1nrvUtz338+EwvN1KLn368HiVFP7DoFpChWqa4+I35eexaai8e/+3vI0UXE1jUpsRmZLlpr6OXM2uBhV1XFvJHkr23qdH86PkZIlpvQC7cwnsGgYGVlmVMntlhZWeLUyW1RYW1aciN717aVUGiVS5CmkA3aNGcR8jEmxygpyhPb1XTjpKK9UbjQkc4ESeO2bWlDo3ZRye0S4cPhUJ7bm64fQIChj4+P+e4ffR5p3Wxubvnv/I63LvzMe2n64Hu4olcK5eKGW/RcvH/uRr/dOEWssoM3vvR7OoaLePF9BkL3nUG50TZp8+QZ1LWUQXsvcgt5nqOM55577+aFL3whZ8+eZTKZcPnSFf7kT/4kiJLNEi1NvosedBOBVynE6Uco3ncGwnufON19ymf6Ewx9fD6tM6yX6CXh4j0D0I1J58l2CewwluF39CGB4CXbUFT/3MOw69PZvXSax7hhRPe/w7ljjsFrdVviVT1nLvtryAeWUnzGaOiTIYjGyHmUC55mhBCUShWizz3oFr30yDySaKRPSfTpsEkJwATJPTfBqX2vWlf1qqtD1SmANp4X3vsC3vuD7waleP/738/HP/4QqscmSvfp+lFIF5FFNcoYsfSdniircbtzFFlQ8b0uzWt38HlCk3DboOnw6G6O/qKoSH62tLTE0dFRGCP3nDEDyfdYawUxT4eYI8+lUrWqpkyrWdgXKnRoC5/tcdWlilicMO0dZZExmUzYWt/grrvvZDQo2dxaZ1wUcpjMZ3jbih6P6ubL6JCY1zqRLOgz1IzUCagA90SntcjlXmITlKaZS/4p7F/luraMynf7FK/TAfimd//T55eh//Y3vzUtsmSc0KlIJyYr4+Slop8gGKVvZw7QJZfguaH9ovHxiQ6I6u4hQjv90L9/nXTIOOkT2TpJvpw+fYY3venbWF9fZzqVDP9jjz3GZz7zGa5evR6uHTA522GfWZbRplBeL0ys3K7u3VfHNw8DkTx7Gbt+Z57Ow44RhtZZYAzphXHqEsn9UH6RgZGM5G3zGOWR5WBZ9FJVus9Fjz5es1Mg1FirwBEioO4QiJz8xXnoch79eYp/u55xiwY0evQGldgrKnjYQKd2Ch4cEncAACAASURBVMnQ99eO3MPis6ckWxjffvFSHJN+slPur/t8ZK3EHIp4gb32e3TdrcpCiwxyWfLO73sH9913Hzdu3OTnfu7nOTg4oGnqLjrz/fXai8bSoR7zOv0900UgCwe77hwdeutFhMXC+3RXuKdCZBH3k6zx5+7JcEc9JySyyBYT5N2akYRpjESjXTBepzjLWmnEo4IHHxuBJK0o75M2UGS+5bo7jDVysJpMUWY5GxtrnD1zmo21VVbXlimKGG02aDyZBrzF6VB57RzO2SRaloVOXB106ShMlhQsBX9vUo5jPp/RzGtu3rwp7R618P6jfk+WiX7Pm77/J54/hn59fct/y7e8ZWFRRW86vuLPZLEU4CPF0ko45BYNu3wm8M67ckWZWAKk4VXP+23CW3qMDRCGQ6TdeSsVfD5gmtozGo85f8dZvvqrv5pz585R1zV7e3t88qGH+cIXvpCiDymuaBNfua+4uOB5q/w5i9t7H7AiFRKWfkHzpX+/3nsIFEbxwiJDx4uWUA9GyLTg/AssFcD09p9SKoXT8bxQvtvMTtEJcaWDuAdzBdVKHa8bWDSxeUKmslAZmWG9hKbxsZQzX9IoxLWgE/3Uo1THk16IelQHv6V7010yln5S3XZjmKCsPnTTuxdpbNKfpz7e38fFu2YisfgpVSDHmgKCQipgel6+cz7h1bHuIHYgSvvBV9xzz1fxnve8h9FowIMPPsgHP/hB9vf3Q+JQpCZEAG9xfwlzqZ+X6Sfbu6Y1cUz6Am8R9on/NiYLe7MrxOq/VymFbVocvhdldPMSD6J0L71I6Mt+aZWao9xOUojPAATVSZJ0ddPOsc7hnXjPeZBi8d6LoqYOz+uE8qy0Z215heGwZGNjg9WVCcsrI0bjkpWVMVqJImauRSYj6nNZ16QINR5oWmvaADVV1VQKtuqW8UAklL1rWV1dpWkajo9nnSyzF9bPm3/gnzx/DP3GxpZ/4xvfkv4fQ8l4+ncFBVGzO08GzAW+efQI4iDKK1QU9hOAzqbsfp95oVTwqHpYr/yg0wcviozVlRVe/vKX8frXv46qnnPz5g0x7J/8JBcuXAAIcgsdFbF1YeER8wH5Qul9vAcgMTDiZ9PvnEsNSmK4GbFYIND5ovESi5w+70JaQ4nhi4eMxnUHZv8+bsNl3W33IpCMjJfFp/voF8doumPa+VYKizzCiTfdNXOdpzG3XmR9o6HXPlvIl/TXRzT0nce86M1386fTekhGKyamw5yk67qubiGuNW16HvGC4VnE6W839P377P/pG/r+Kx1O/UYqvX/HSDNeJ4poQUteGLLM8I53vJ377rsPpRQf/sPf53d/5z+Flo8S5rcNC2urb7z7hjca3dgXNcFKqv/ZRa6+jq0pdYTzuqRrcsCcT4Y+OjaxMEnmsZf09x3ttrs/Fubx9lc0npvr6xwcHNDWTZK87tsAcahMYDl1rQ53d3ckwdt21Oc4V3F85L6tdCUNh1KWa1w7ZzjMWV1b5vzZs0HR1bIyWZFOVd6lBjxx78QqXdvGNojC+4/5CBAyQz2bSjV7bx/MZjNybXjbD/+z55eh/9ZvfeuCp9KHEKDvIXWl6xDDzttlABa95Ph/QLwJ7enkX0OFn4kGqlvcSqnggcoryzSnT0vy9MmLF1IT7zwv0j0qpVLvx7YJyTVF+q5MScFS387fPgd9gxl/LwJfcaMtPqO5bUP0n1s8EUK7XB2oFx1XPemdL4z1bbBWPEzCMyQNkt7P+568PG2ftmo7up72GKOpbYvRGcOypJ4FuC4cwNHQGxYPof4Yd2skjtVzE4rOOVSvIjqNr++4y6r/HT2P/ksZeqAfY7Jo6OVnfQN4u5FfhLBuM7p2cUwFdurnN2LXra44TqIOqRrPC8VsNuWFL7yXtz3wVs6ePcvx8TE/+7M/y1MXL4sD5OJ8qBSxJFH83tOliFovPrvWnQxC/xn7+Yp+tNsfW601WimJElI2VWCTGPXGtSb7UOauf9D8Ra+01sP/z58/j1KKJy48vlA/EV9aPIQFD18pxdrGOmUu+bUbN27gQt/ieWWZzqs07nKNrthPHBmPdaLrb4KufmEysixjMhqzsrLEyRPbLC9PKPMuT5DnOVqJQmYepJM9lnYuYnMK2bJN62itJGyVF0N/dHTIe//xl6de+WUbeiUW4ZPAZe/9m5VSdwG/CqwDDwPv9N7XSqkS+GXglcBN4Hu89xf/smtvbm77b//2B+L3LDTe0Ld5ZGnDJd2QDovu/z7+Lv67w7vDZPkOsnDOdRIItxsXrXobsrcQjSC43suC9r1N0Y1p541633m+Cz+7fZxv00OJ92cXNn2vhDt48ulw8vGeYxIsQ6Flz8YCpqiDI8voS8x1d1/9A5WF6tGuircvaJYO6yCDKzmU7uAshyVveMMbWNvc4PLly3z8ow8yPZwnQ++c6xQGfceA6u5tkebZH6fbX2Is/+KDNF4vvWxXM5A8OvPceXWA9qrPsA1f2FVx968dKysXvMPAG4//j0PuPQIjBO8xaQ31SAkxClFKJYaH1pDlktcoC80//P/be/Nw26rqTvQ35lp7n+ZyaS73gpQdGnwmvpdSE439M5Y0pbFFK+p7r8rvE0VLKyqYgEQTrVTKPJvEa4eoUQrBL1bsiST6/DSNJlZ4GBSMRIGIQBS9gMK9555z9lprjvfHmGPOMeea+9zD9wruBc483/7O3qudzZijb844A7t2HQ3vgXPPPQ/XXvN9NJMJutmQ5eqPfUWqBGURvWV6YtoOzm1dGneR7k2I3jIck5BzXQzNPkgvqViP6LUDYwAParSUZj9a85IBABIkZ3jCHLN9AcTbpQmSoazTJBLAruvA/RBzZG07fDuuvvrqmIdL+y1RwZ1IKvBRFeY4uSDL/x5NS9i2JAnSjjzycPyr+x6L5cUlLG9bxDB0aB2FFM4c0mkPgMadNA7dWihfyAOGocdsfR0v/I+b4+jviB/9awBcBeDw8PutAN7JzB8novMAnAbg/eH/T5n5BCJ6YbjuBRs9WJE7kEQsNZwlt7ywGJx0wfrbPqckCgp4el6shPo+a5Dy2X22JR32AApV7Qlecp2zGI3l+aJCSPhYdLlMIQycUl3MZLQqxuANQjFj9Jmbo/iae/biPw2S4B6iyFzZDJRQtQlRPZ9LMW41CKuXTpKOQsSsY0gYrRjcHIVYAQLge5ALlYIopLIg0UM/5SlPxqmnngoAuG3fXnzpS1+KRVXSGjbIsmualsZfEtSNmyX+yjzYe/V741xy4avMizYHBPXWGNnUEFC8R68Ja6GAEpU+zEDMJyNRyymYTm0CBDYqnU7f6RrM+h5EjKb3ePvb/xCPf/xj8dznPhevfe2rcfnll+Oij/13YCrj72aa41+ytDI0DkOrSwW4MYhe7WCA5r1JjIdtwtQw1EcfCLDMHAuqNy3JMnsXpkKjmmUPOfbRq8eq5ez65UydiY8hwuCTIV8ZkHI9nZPIXATiglADl4jQEMM3qr/vsLa+Hzt3HIn1UM6v7z3W1tawuir6fUlfnUp/eiJM0KLnHr7r4VwLrPXYu38FDa3gxzfvxXevvQ6TyQQLkwaHHbaMY47dhe3blrFtQRIublteBOAwW13Dup9h+/ZtGDwHQ3ETJfzNtE1x9ER0PwAXAPivAM4E8EwAewDch5l7InocgDcz8ylE9MXw/etE1AK4CcAu3uBFytFbriD5gaemwADA+KimpEr5daJTE4OHudcnLw17PZNwoM4kxAJErx05f8qfb7mEJGkkTxn9He1+g88QQYnQAURDp0VkRBRT+CrnC0hKAqKQWAsIhqOxykvHDccx+hQQjl41xtnGMUUnrBeIbDgPdoOoICSZe0a0gNxzxzngiCMOx2kvewkedMLPgfsB3/rWt/Anf/oJrO5fk4peausOKi5hsaTYRI2Ls9yk/vbFPNr5tb9LxsAS2xoNtMbYcu0UxsJIs3fO67ca40pEpXCitieG1A9Qn2rmLjARgckJRmSQhyMtlymF2oduFcvLS2AecMSR2/HSl74Exx9/PKaTZVx00UX4xjcux+pqKE49tFEV50LpSEHcTYa+rQpKXSgdxtKVjEd1/XlEtQPBG/VQZAaMZBmROftQJ0ALtXP2LNsvG60bn6E6dM7XIK6RUUNF54JA6EUyTm6nrZtIsZ2JQxdiexqS5IpL27ajaSa46cc/xurqKta7Dt36TNSzwf5AlGCNIKqiidPsuFKshkIAJcBw6LG4uIjDDlvG0UftwFFHHoF2cYrlxSVMJo3k+GkcZmur+D9e/tv/Uzn63QDOArA9/D4awM+YWZWfNwK4b/h+XwA3hAntiei2cP3N9oFEdDqA0wFg27bD4Hkm0ZGGo9Wl0WAPIPEOsqguuoXlGydMLrcgMBpq4CGJisgxQJMoYisX5Zzq2dlEBbKI4+HZTcgPThD/ambCEBD6oGqT6AJpkEoMrjLIhkwCLRgpRblZS0QANHDK0OkMgtw0uS8KNGWFuHXCgtYmYFLVoUf+MGxeRA7Tc5rHISJPXRcC9yI5OJIoPmaX8qUEH2OG2AJe+KJfx5Oe9CS0bYvrr78en//8n+OKK66MHkNSelFd2sQ9jpR4k48rLuttCX+QbJgkYZi6CRZXqOrDtnhN4DwBSAlKpjgXLvgzqzho56ehvBShwEdSFem7NY6gYQI1uZtn2ZQZGIzqRw2UAiHK0bMhpCxFeWkQKYqkaE4zWRb7UEO47Wd78a7d78NDH/pQvPz0l+J5pz4Hz37WM3DBBRfiyiu+g4UpYzbr0LpJ4BBDkB9xhEFVwTg0AZ5aeDCokVgH50XFEtNdIyF53QeRQQnUVILFQsZGJ/tJCqDrfCQiKgnAkoq2933GoWsbAoER6STlGyqvk0UQJMEBWfgCdjjAgWgU1oHg4eXgMZC4Jff9DHtv+ymYGQstsHDYNrBrYrlFjZGZzWbogp2o73u0k2kINJO95kii/LkP6SMwxe0rPfat7sNNe26HawhTatBOJJ7i8MO24ZhdO7FtaWEER/PaARE9ET0DwE+Y+RtE9Kt6uHIpb+JcOsD8QQAfBCTXzTAMAcmP06NaTh6GMjuT49xy/zFSz4dNwnZzSPVXMz4AJSecREYbfJJXOhIg9kbtkETNUi2QcxO6AWwrDbD6PVPvkBIly82px6QiAcoePU+VUL4700FrXwoOOFwduUd5forclfsIk4UpVlZWcNZZv4njjz8ezjlccskl+Iu/+CK81u/1xvc96nwRENqB+xw525j2IcDLBhJqRDhzzissiYFtSIigOL9xvzhxiXqsoaBiC30IyJosHOr3Sv9L9VFNUrG50DXID4RQfs/jyiu/g9ef8zv4jd94FR7wgAfg9Je/HD/4wQ141zt3Y3E6DQkAoxUh9CcRsNF7SeczRTtTdPf10OylppeyLzXarZSuILEoNptjtieMuqWlhLZsrWQ7R5rqN66n1+jVFEBXSr81NV0Yamzej6VM3Y9+EEK9trYfRISFhQmWlkLemxC4tba2FvLzS7FwYRwDMSKpsibzMoUP4yBP6LgDzXo4EPauzPDDm/bAbWKfaNsMR/8EAM8ioqcDWITo6HcDOJKI2sDV3w/AD8P1NwK4P4Abg+rmCAC3HuglihDjJJIH1PUtRIKJLjIBR7Kv5obYKGa2Wg1K/im1hy8RYCl6ltGcikjSe5T7gfpoF/flz8gBUKSDPMpU/5ccqEUuWvE+idC1AK5xMI8QicqkA9mG0vtrEa3SLDHKo2Wl7GKPSbuARz7y4Xje85+LpaUlrOxbxe7du/H97/9AuDyvul4bycxIhWUAuBAyXu8y9Eyemz+fs/oabqymtJKQiPKMefaCjZ5hjePCgeYulZrJM0oLMp1JGigISh6FmiNc+54U4RxgaejBPiA3D+y9fT/e/vY/wlFHHYmzzjoLxx13LN537ntw4YUX4m/++mviGklA6TQgLxJX3Fqzqbbt/+QOasc0XoPouWIS+TWNVYsB1FLY8JytuyVECcg55mzv+i66oipBtH3U95dzaqPDycyvPMvBdz67LhgcMGmkiLmO3zlgNusAmmBh2mJp8XAws2S9bBrs27cPKyv7JXsmEyTLU3BdHqR4kSRsFGl3IMkYgMEdEJ5tu0PulYGj/00Wr5tPAPgUJ2PsFcx8LhG9CsAvMvMrSIyxpzLzr2/03KOP3slP+7fPhGtSBkqQR+smGbX03odgJ+2PXfAx5yXFKIrz5CE1WxOSTXp+y73oe3Ldf4k8SkNxPJ7PXHZenlrX5ZZCvZ5vXdJ9D6zql7EBWZ5RIieK71OCZD0U7AcQZMsVwqCpkyPChyKUDsvLS3j0Yx6FU045BTt37cDq6iquvvpqXHThx7Gyst+opkzu9CBNMQ+ZLpVJXDYbs6b2vVpRKCH8NG82mZioC3J1CXPSv8Y+xTkRtYNnlhznJoWvbWXWTjt3Njpbj3dBw9m4lIs9vy4R+JrnSAZXhT84GSkzwpJL49Mye4AGW8k1/bCOU099Dp71zF/DysoKbr99H972trdhdW0GIoe1/asS5DekBGYNpgkWCLIeNJbAdW6sd44cQ8w9YyucAaL2YU9gw2lrYRPrj68IvJwTgYF0LPOoc4aJMM2q0hKTZOY2vLPJGDwJ6NPUGXGNVOVcMfQI0zCFph9X47J6E00mCzGVet/36IYZbr99H/q+x2y9D/0KeXK8j664RIRLvvC5Oz175dkAPk5Evw/gcgAfDsc/DOBCIroGwsm/cDMPU6OHOLKIiDd0fVhU455Y1LNM/8f+yz4Yf6yqAQh5zkNTSaIUT0uOpia+bsQx2uubGJ1qmvmh90quDNPP1hQxQNrgKrIRqWtdXkt2xNIXzaovEgAXBIsT0o1djjm5A0IL75xOF3G/B9wXv/6C50d32Ouuuw4fveAidF3Iq9+JWke5O4QQslICiTyfFMKt951dds6OhzBeC52bmsrDngdr3IH16Cqu2eB5436OiXn5LLlO3ApVRWFhryatln2v9U2ielVK1sppmuhPiuJcfPHnMZ1M8JSnPBnbt2/D6153Bj7/+T/HNy67HG3boOsE6aPS/+zdQXcf87xo/+I6pdWpSblAQvxaM1mQr4+pJUoYzQ3+de42Mokq6Rh/2IwwInHxZTyIzbkj58dE2FEbqchgnhfXPziEAA2GwZvplP6sr69i4iZg7zFxDdp2EdMdEzBLTM7evSvoO8mpr6lSMmlrE+2QCJjatesYfuYznoeh5+jPPmCACzpfpYCEBja+x4Uasdrs5AvQBIt2Rgg8HCcViGwkCjyvNxtUF7xBiTjmIfQY+WfexgS0NfG/kEAiF1LxYgAAx50AFAAuSrqV47c8hRKyJoi4seJSRTKJ7zV6yJyzTwVLAMRCDs859Rl44pMeLzm0mwbnf+QCXHrppcKpDj6WVpSNknO88bkAiFNSMSmfluZNrtdt1MZ+R0Kl82SOK0c/4ryNTSLbMJ5DVapUpzaLnCWK77C/7TpYhKzP7nX7c56bXVQsTUwjoeuSS5v5/hy5FZt01Pma+Wgc1XxK1rPFOaBphXgfvfMovP6ss2NJzh//eA/evftduH3fXrCnVCVsDkdfqreiHt5TBi+Ah2sC918JhlNYpQpnniM2Hw2edi48cgRNRCDnMGihndAtx8jiUhTJlzE72ixHL15nlHP0ZvzsxlKBWb2MgHtQjIRuxOgRvK/EhpWCs1qgccHeIoXX9+/fj9X9a/jMxZ+4+0TG7ty1i5/5zOeOueqIFIwrpMs3VFwsTOI1cSPGoYUiIICImr6JV6ZJNwCTRd1W6sUSRURZc+krOYWNzsfnFaKvTR0rfUjZG7W1WIDm1LboXQyVElY9gMG+AbU+5o93DCCMR3S/OmM6H1Y9ZsesUbUeve/xgAfcD6985StxxJHb0bYtLr/8cvzZxZfgppv2CFIf7PiTqqZsThxeAnHOozPVNVa4ukakO5ciV9OgNWo336QD8hz27ClK1xbRqwolziPlRMBykESUEcNaswTSB1c+6V9Ke83BLRLkQ/Cah/dzvESKYxbRb7SHMyJn5jYZ9FMSsX/3/FPxmMc8BtNpC+dafOrTn8all16GfftWototvDR7h6sQQ8tJU9yzeWxLbZ84U2mKTMChpvRQ7l6Pq9SrxCs9ywGDj0Xls71bSAc6H/a3zWCrMCjKUtkbDeXKECJNS0GYzTRYMPznFmTcRON6hPrXmuco9i/01SaDU0agaZpYnnB9fR3nffgDdx9Ef/ROQfQjLpXHyEER/YiDCoje6mhjtGFATnLBGNF771ME4hzuvUQKNY8Ue15/1yI79b+9ziY5s8/Sa5U7ta2Bgw9VqvJ7CeJ3zaHASAM0Q+QeFNGLeG/FUQ05n4PoIUay6XSKl738ZXjUox6B/atiSPrYxz6Gy//hW5LHp0cm4uYIMddtAoj9IuIYjVk4RBhEL2Mqg5pqHDGQI/qoThjy9R1JGKb2wLj/Sf9s16b8bhE9W3gw6g0P4dIsotd02yVyKtM7JERfSCVIum17rTAzObIDAEYXEKaUkGxawmmnnYaHP/wXMZlMcPMtP8X73vc+/OQnN6MLedSz+VYGILTMoI3S8Dmud6CnpLAOYkbJ8DRYF0vrYaPf1UtHnTV0zznXgjxLwFIhBZX71iL6mmFW94B6xJS59nMiIQRB1LAhJTe34KEzz81jL4C89jQKj6CSKOlv7z3e/6EP3n0qTAEw+rykvRuIMvEdSDDA5r9MTtDjVRAwWCipiMgAxcVM+mkbKm3/241if3vOjXraLMCk/7qISVy1iL3UC+pzSmIGykX56OfNeZGRmgGZxWk3RRUibXgtxlDTiWsjIrSNFDt+4QtfgIc85Odwy623YG1tDbt378YtN/8URG1E8t6Ll5CMyyxFSiWaxqbVPQwiIEoqJSHIJtK3glyja2Cx9qWeVu4fMwrMHNMdZH0r5iCOI3yNEcHhu2vS/Mb70gOAgvv1PgTKcF2vbxkMq8rQc3A0sv+NpF0zp2Vr3BSD74J7JeB7woc++GE86MH3xxlnnIGFaYs3velNuOyyy/DRCy5A30nOnVk/w2QiftzeIKmsClh8nw2ERIh90WM2UAopoBFeRL3Qej+YwDyXctiEtegGKV0oxldG04g6hFzp5qm+8X6018vr7DzLeqfI9PxSK01Lr/q+BwdVJ1gYw7Snm3BPgktbWJ4p9+u3uCDZCirRfRu0Q4ejf8bzRhvLa05v00VfIDEgiTsjZEy+uFYLdDQRYBK3g+pzSw6/3EQOdiPVr8+BQvVu84EqjqngyoRkqI4dSFGTQyw6ImMiqG+zELcGnqQeJhFJpSl9TwzKQTQIJvFyiH1tWoIWoGb2oEaMxbNuDZN2AevrvaqLI0FLc5XrKe082vEBSgDTaiRfeb3HR7iw987j6NklYi4IuIHjsrhMmLmmggkrrYTTUvrKOO4Nmg/Xu4CUxCNjnE1Un1X6fut7fSDi+ZPHfcsN69pfcx8JEm/bBpOQjvtFL3oRfuVXfgWeGYsLC/i7v/s7XHTRReg7n5B0qH07IoxuXLQlpSIujatJ5aKlLLN5NZ5E3sTNadrmvu8xdEOUxpxzEvlt6rGWxDPu/Qqjpf0lInh0gdEwxdPngIqOAQAGvy7X+wZNoXqKUp0yhBgTHOaUbjlj8EyMwLvPfd/dh6Mn5Hr4JFbyiMesqUvCl7gRoqhmPCaU62Ma4GOmwLF+faP/GSBoCHXl/swHlwiSAlk5/cr4K6xWSTwQ8pHk4zc65VE/VHxWblUSLHnvI1FIhCkMySmS8fG3cy5uMocUoewHRt+vAQDW+lmQrDR/kBrRAdkcebxqTXTOiWsdqQlnMx6rHYNKKvF84TWjf9b2splWSm7lOf2vuXLmXVtrov+2yG787Axh1+bPwaiT6nr+koCEo8HHfID3ovMGgG4mBs9P/Omn8NW/+Vu8/OUvR9s0+OVf/mU85jGPwbvf9V5897vfDZGcedHwGmOUXixrqNliE7ES1VwIjQ5zmCR29hSjg+0zvfeZSsdRAx/04Y5crBOtKYxLiZqZoxeLJf6WaSNjFxPNQ56axbZMxebqhKO2po2ZNw+OezXiEiMJOiI4ylVZB2p3LBrkTmx2YiN3x1IxSfS9IvZRUJlYo1sp3kZKPSB+2AdOYGgEMIMXunoU6DMdQog7EI7JlQ2xVJ3X34wYGFN+xlx5C8BVkbxeV2sW+GKOdiJJ9wrA+QGOk3eKcCc9bBWnBoTWie8t+eD1Ea5tyGGKFo6BhXaCxnmQuTdFrkqg09ALdzIMjL4fwOwwDIAfKNhIkvE8JN4LhUrEJVQNkrGv5DG45MmT3NuUKxJdq1W/MBPIB6+H8GFPES4sPDBzvMZRGzeLcJXKoTXxXdW1YS++6n6I3x0YDTo0GMJHwlyIAWqmQVQPenyvkmVQ4ekz2MNxA/KA89IH5ibre40QjZA3JwQv00YhHTWNPtofeAYPAhPOy1o5TMSLzTcSp+EI3dBjvevwgxtuwHvPPRd79+4FEaHr1vEbr/6PePwTHguA4HkdS8sTUUGRB6gHqMv2o10XQBHikOYlRr6berR+yD6152SceOPhqRevFfLoaQDDwcccQQmPdEMoghLmxrXC1NjCLtF/HxM01AoMw4ecPekzcKilEJPBqR2jgR9S1OtQkRqytSwIgSUInjyG8MdOChDZvFUHaoeE6sYmNSvVFdJcdXEt3rTJwPQ5NsrUcnB5WoNU+kuvs60MxEp9qC9KyflZEc0SJMtdlJyWvUepOheJwyikeK21GveXcRE6buegVY+apomeL55LY6fmDxfOjzHAw3A/0VXOlHL0bObC6GGtOi3o5FSVpvOiY69zoIjv0fX1JvK0JLS+uNdBpJHYH3OuaSseQcVmipvRqkcMkVCDncY6MHOme7SaEjbphlVtNG8/zpM+9Hm5obVOJHL1jo7H2D4yNVfSiSvXS8y47/2O9x46HQAAIABJREFUw2tf+2p432NxcRm33PJTnPu+83DrrT+N8Gr7HFU0amTnEBQJrQyWPJ7GkkBS/ZT7xnLe6aZkF7MRqwrzer+UfPRZ+UoLs1adI/l3UkbduI9G+DqoJMsKdWY8zEN07S377otro2SkEkLm+inn+77HBz503qZUN4cMR68Tq8BiXYtqAGQXozxmjS0HOq4tW3AgAyr7Xm2WCy2RfMnZ23fUVAB2DDoXdmzyOyRl8/Ip9bA1ImbP2X5b6UeBp/ddyLYniaCUA5f/LvpER2KFJn0oqZdKHXw5thqQ2/6Nx53PhZ3bGvCWhM22yAw4SMyE+ZBDFdZqjTm/O+fAZW20HkAZjTl6jpe8OjVOfrNcvZ2X8vy8Z2gbhgFd14USdlJrdnFxUXzq0UREqXD1ox/+GGec8Tp861tXYjKZYPv2bfj9//p7+Pf//v/CkUceUSDeJiS3EzhhL0XelZDYfVn2Ub5T9hG8V9SVroxvI65ZcYvWjpW8M+voug7d0KMbeim5SQ3ISf58fd48u5o0kQo3gr/kYpzGWDJ1di4yBwLkmos72g4JHT1QRwgyTkl0Vl7DLJ4vSvlc5TklaNekAmbOFnOMJPNNX1t0e8xyBMCYM63pbzNANe5yBEC9MUgRSnavy55fAoY8m+K1EYEyqQofBCkn572PAU8azm3HId87eE55TeKcORbx3ylS6JGXZDT6d6szj8Vh68CcEZbie3wGi8rGRiTKe/JmA8RiXziXfOzaRGRfbG6nqXe9XQeDoEI9YprjwaRVtEKnoEOxa1fbyPMQdbzWqpE5R/BxrDaaOKaiTjDLg8dsWMfQ9VhYnGDStgHxhGIcakh0LT796c/iq1/9Ks4880z0fY9HPPJf41GP/iX89V99FZ/85KdkTEVSQO0vw0f1Yxpb3l/ZXxTHo7YdsT8MUQq1Xiuqj1fuPUO6gamxnL4YdUNxI+bM4KtzRaSqUrE9xXeq9Fu6vZK4LAuBTwSSNcAK4k67EcL2LI4NZFJFD36MhzYmPHk7JDh65Qatzs5SfL2mxuFZCWAe5182i0RqXNC4b9LUX9eWPLRtHkeoSKREJKUUM+8jz0h6Rt+zfArkUCJHu2mUi7GucGk+GkyniymzH/fw3KMfZuiHGYaA4Mv3yXiCXhlDtT92Di1BrCG2khDb+3T+DgTcOp/ZHBRrW7q02XkruUtvPgOz+c0hJw8XfW2CIQ2iB+fy/MaIfDMcfXnfga7dmPsVv3AJypZAKe+BlX2rWFudxQhUi1iYgZ/99HbccMO/4MwzfxNf+9rXYuDPk/73J+Dd734Xfv7nHyr6bGJRc5GH9QOVebN7daju/9r8EFHmjqhtMplsuIfs/TWpx3uPoWcMvbhHKtevOeq9T/1o3CRKs9ZLJj6rkssqXuPHx7I2eLGl8YBukE/J6WdurJtohwSiBxCRUDZZcxA7MN4Y2uapLcpnb7QpbLN6vHnvqyEre9xGudp7au/LOGUae4fUVAE1acS+b968aN90M3sPDANHopCaCTizRwPRiMTDCVdUmyvmXOSu9RmQfN0lsi0J2YFahrANA1B+Sq+FzTy/hny85egDQVa40efWVHwlLB5IZTSvPxvBdHVMc6qMWYkUAGazGfbevgLnHKbTaQYF0+kChl5UeB//kz/FG9/4Rnz/+9fC+x7kGK94xSvwmte8Gs5JMFbr3Cg9RRhBNpd2Lmpjs6qXrusyRma97yI82vEDeawKc15xqmQaLXwMw4C+k3dZhjI+PyD7A+GJco71Pst46adtXXyehZVyTHcE0R8Sxtijj97Fp5x8ajpAOadt+zhUBmyRh71Wsx/WqKo1HMrvxGFaQyup72yB6BoN5tgwjW24l2zKVlnczA10zhpknDB35kweWTcCBsqNW0522OjZWnVe1ToxK6CZ14wgFQQs9obZIDUF4LQuudvgWOVl31Hj9OW7IgLN/lcY6H16ptdrCaDBj+a3Rqjl+xiJa6i7930wVjch8RZH9YfNX5NHdeo3ghba9tAQ/pRVMp+LJpuPcm3LNYlgSAgSBjKPtAwufDKgyzoXunEDywMnSRQQf/VJY+bceMeIt4rDMPT4Xx56As4880zs378PxA4LCwv4zGc+hy/8xRextLSEtVkXEHOHHL7HWuTa+AGA3FDAhu6rYXytXmd+O+fEM6eACz/IM3KDrwNzMu4mRD3OZ69MWUqFHNYMQxwfGYM4u3x8UcvqkxrR4gj9rWrWruvwgU1Gxh4yHH2NcpfcU+23fqxEcKBW43ISwOQiYc1YBiB49CK6ctVqsdYQzEYG4dp81MZaeijUn+Oz/yqe68bUj86d5bYPNG8KcDWOuzbuec+4Iy2J4Cn//YHWeiNDqLZ8ngeMVQjjeY6eOAWRn7cmqt7JpVONN7CqiiFTsW2mKYJXJLGZDW0R6LxWrvMwDJh1a8Gpb0B0a4UW0x4wDB7XXP3PePvb/hBEDeAIq+treN7zn483/M4bMFmYgkjSWou//oHhDUB1LmqSi5Wi7HXzcEsJixTsJ6qGrN1jOf3SKGzn1r67hBNLSGof+xxVFWt8g441szdsoh0SiJ4ZkQuD4dxqAF9D+AAy5GWbnVD9XXKSCcHn/rOy6GPRSjj8+YhGr1Gu3XoQJQouG7vMU2NVJHYjjsc2XrpybuyzPbRwgoqHLcBuQwJZ43xrYmMprtZE5wO1AxEZJW4bEaQIC8Vxax+5IwQmERTh0FrXZLABIGYxtMighkiZOWRXbKp2nOy6irfFvDYYJG/bvLWy8D0iunNUOhbJdV2XBUURUdRriw2J8L3vXY0zz/gtXH755VhaWsL6bBX3vd9xeNvb/wDPeMbT0DTC/cs8jr2qYv9VFdgg6fhJ0hfrNQoTfe+j2m9es4RDdfCZHQyDFC0PyF7fxSLMglmKgchH1DmlSidfq7EXjtoWFhcXRzBTwrXaB3RcNqfPdDrdFFMbx35HLr6z2o4dO/mpJz07AVBM8pS8ShJ3lFq24YqIPAAx7HiMZFMCr2hFZzJExlJ7AWAgqY3E/7xH2dTd3Kox5L6xzrnkFkfPYsrGR1pjtkC+ZRMiFC7FEIOEyImqxlELzgw7PeYGC2Xvz5GC51zPrvOrqaO9AfLGqOIU+EskZjehSh0loiyvtdDgfTLysjGaUVvPC2KJQhpbSlEb36FcPLVZn4fR0qkftf52o5QDzJx5ihDledblGuvON86QWBLfgfMw+fK8nTPr8klE0JQHNTj0PK7e5UydY0KDpaUl9CEFcLR3ZCkHPI499lic89tnYzKZYG1tDZPJBLfddhs+8pGP4KqrrsLCdAmA5Mm38y57qO7xRqT2IVE71uBBnxENviUhcakQuAZKZcZihRmN5KVC958VQMqN1VE9VDBdJB2Px/rScaRCsGsMqs5T3/f4yEc+cjfKXnn0Lj7lac9LwQzQcntNBCRAJ4PmTGQCdN3wDdURSuLghbPt+x5Db4oLcErdmoJPZFO1bYuBPRrWTH6Gsw2awLR5gp6tKqImwmPHiMa4QQ4JeUksjy8QfeICdPyTyQTiA2zeV6oYMmOnz8dQcGvld7tRAEmDTD6pg7wPCIhcjITVdZDNYmbAEF8dp/WIsetWm7+Se5qH6EsiMrCPeX+abPMkRB+N4KGPYJcxIp7zYi86lwoDHmTGnAadexxxdl6RccoVlGc0LRF9KTmUBK1cM50z5hDQRckgXc4vF57XQiCSvtiFFAbT6TTmntG50SZ6+wFN4/CL//p/xemnn45h6KIDwKWXXoYL/tuFKZYjcK/JGypnCtI4FOZDlkoKUe+UM1/OOUCJk88ZBTaMiFWTyAUhSNHr2oznl4o0CFaqtX3NGUbO4E2D+SJXH3mLjXGyZVwvuOCCuw+i33H0Lj7p5OcAyFUxNQ5FASmpJYIYalyW1Ne8IcruTYDSQ0VoRRD6bt3k+pta3XzKkU3Qdz4rc5f6PeaMJUlUG9OsChHIExspp+ecQ9d1o/7aDb0wmcZ5WFudJcByhgsOLo+TyQSTyQTr6+uYdfsToPoceWvfnXOhWk5I/ta26HuVqMSr16kHUdBnSoEQxLFrgimdX+dcRBDOueihUjLZOj92rLoRRsQ6blAbONZGJKqEw9YHtQjVI0VPjldMx6t9MZGjZq3zyOGc4Og74tiMU4C+V/o2X5I60DE7nrI/85oiGwCSEoMoFVmpqB0sYVFJAEj2krZto8eWvn7wnUFEOQJt2xYve9lLccJDHoyFBYHLhYUl7N69G9deey3aZgotHt/3HRCTD6Y1tXtUJZ6Y2iKkBc6Zuvpc6HNqiDndq3Cnx41BlHsTN+IxbSfh+1hVJvmP0h5I/0WqAJAxgw3lREiK4fTZ+kynU3Rdh/PPP/9uhOiD6gZIC1BOlp5LRQwKwDc1KH04n6XSyhbSersYpF4gVgAhq6xJLkQN+k7y3ZT9tEbSElHbZhG95cb0o8fLHPVElKrkNEAM7ybK3RoDt6JqAu89Br9uEKU+L2WKVISmUoRyGHZzERv1CFkRdYj3c5B61OumaRooy0dEhmvJkUjJnQKIuUFyggTjF69rwCCaxHmM7zKcj22KhC2MKNc1Xqc8nbSOueSu0z0JBrW5igpCEGtOPPScvW7MzSb4tPNXM0KW78zgPoT7D0Uwoh1HKY0og8DMMYOiPlvmJMBcQEplzVeBjQa/8Au/gFe+6hXwvo8MTtu2eOtb347rf3Aj1tfXA4JMNjqrFtHfUbXFQUqiFMyXz2cdmetYap5e9rdF9FHyDIgeCLnxY8qPZnS/PL/P1kjmRQjlysqK5Ccq5ln7R0RZGg1AYgZWV1fvXhz9UTt28kknP+eAIguQ80tpIXIqrEn8LUdvm5UGDigmuVS5Ru/xA0Cc5+GQTwLCeXphRSgWAZVSTH5tjlBcyLyYSiQGIOTke2tFQGYJ/gAljsBlZdx8tqn1Wd57A3yB+7XEy/RRe1Yai5OawBBRys9v1DThXBxjaCmvkZX4NkK+BRIznHWJ6PUZ6fsYhkpEmxOSMUdfInp9Rg3Rl61mRC4JpF6nba5x0+Zt4qQXLoNvlAGyiN4yGdZ7KJeiEtJvmiZy9+oxIpwtsLy8BNcAr33tq3Gf+9wHTSNzubCwhOuuux5vectbAsORx59YgpaIRyCsA2JaZLs+ObGsI/vSCyfN15ijV2bTMmq6x4gI8Pk8JhVkPsf6XVXHcCl/jUX0esxq8SxzuFkd/SHhdUOQzdsQZVkkbdOskkAO4DUOOn0vM+TpBm5QqzdrP6pz1owuGDygGSCLPpTAU3KS+ruGxGucmj5L71OrfuIEVf2RrP16rvxevtPOmSIb2z/9nryYzHwSRbdS20dt84Le9Npc3THfa8ket/OsTSW2YZgfWGM/JVIuEWX6ndwr1Qur1rdan+pIP/Q33//xulqEtV13IEdCZbOwamGlPFeTQEpJqYR/O55StWmvyz2s8hgA675r5211dQ2z9R5ve9s7cNVVV0WYXV9fxXHHHYs3vOENaCcOTSPxCkRA0yTCQ4ToAWRh3q6FlZCTigvwXvsxtnGURDVf50BQVY1PY+Nr+BH3CUgKiHhmwFHMLqrf9fmTySSqbcs+2LHZNa1Jqhu1QyLXjXMNDjvssBGwRRCy+kNTds4iKsvFqm5XkxGWdSZVrVAaHvX+HNDzTSz+q22GUC0wlEhKxjfW59oNYlU0JQGxlL/rOkwmEzFYtcn4KNx2EqNLxG2f7QfEupy2PxZoXFBXyfsT4JVukyUCKgFPgVLn0HtfZFkcP8MiNjZzV0pIJgN54LiwqVYiO56zYcoCEfaekkhHWwRJMfQSWaY+sykQU0pE6fmTyST2QdfQ+k1vtMHL9+r9pbFQvaaIKOZjt4ivadK7FFk6U0ugZkPTNWpbJZKExQXRJRMcJm0Tg52YgbaZ4oMf+Age/ohfxHOf+2xs374dbQvs3LkD5513Lt7//g/giiuuEFWpk7oG8o4aI8EakxbHo+ox5pJYJpWMdl/Tf8ybW++9OIlwsN9gInMYzkdiO4yJKAD4PuETndfGJffQgVM2TQs2dh+n/XTH8twAhwii995j//79IwqrnJvqZOV7cr0SABYjXG83Zpzo5MGRb9pxmLIicfUI0PsAyRypHFjbTkM+9lmG/KbTKdQdVIFmfX09C3RYX1c9uc8Awda7zIDDILfV1VUcddRR2Hf7XiwsLITcM30kOk2b1FiShRLJi4kZ7aQVIGw4KyOYEpiNEZt+HwaGc2N7Q47QyvsMsHsjjZDGbgLOCWeVFY4wAGw5UUsMB/ahwlZdz2/7abkh29q2FWRux0QeiKmDNYFW3iySt4g3zUMiUolhSXNuxXILK21IIGY50Ol0ekDOzRLhGmNRQzoAkoslcvVLmscmI2DOuei5Ypkk279cEmyijSh6jrUtGEOEp7W1GSaTBv/47avwj9++Co97/GPwohe9AESSSfMlL3kJbrvtNnz2s5/FNy+/oljX+UhZ3SZ1HOK9k3T1yiDqviYq17A+x84JPmATOzF4H1WaMu5C1RrUWLPZLOs/ByoT588lDzwbGav3lzr6uyWiBzj6lBKQyCx5ySIXM7+Jisd7XWQJWeahl7zqYcFSgoDcOGM5G72i1BVr6tLEsaeCyEQUk/231FaNOIJ8k4FSka9uxvT+tEEUGfd9H93eMkMsOyxMplhfXUPTNFheXkbXdWB1Awv9n0wmmM1mGNY9lpYWsX37dtx6663BzazB4AZMJuIdMJ1OhfAEHJuAyaOdSG4T9uI+t7q6jrW1NWw7fDucc/jZLbcCAI7YfjiWlpawb/9qNEb1fY/JwhTr66sZARuGAQsLCwA81tbW4jsXFhZijpJpM43z3HVD3LAyr2mua/YPeTbCvV0k3HpMid76+joWAwJdWljAwsKCye2zUHDsjGHg6BEk4rV649hIY8B7LVatDEbYqG2DCScX2maSqhm1rSKZZFSfThexurpa1c1rK4mZPWbniJlBTROlFjY+50SC9gg+en4o8icAnlMEaNw7RS3dyJioNE1S27WfDQDWIxGbTqdwbYO12QwLU2HSZLpcKGZDYHT4u7/9e1x55ZV49av/E3bu3IGmcTj88GW86lUvx/e+dw3OPfc8rOxblWDFTl0WFRby3DU1yZoZieB4MZzKvEsglHO5pGYRP7Okw1hcXMTefbfF7JvELuAjgMN/bY1z0CI0zhVxCV6cG9gRGOI16D2D4GIwpjICcn1iVtO+33yFqUPCGLtjx07+Nyc+K05s3GhKxdjowrCxPhSAca9MEzHm8EKmRpMjxV5X/lfEHDPr+tyAmYhJynORgl9cdq0+T5+fbWjP0KATvb5x4sI2mUwSh962Wb4OK8o6M496bPCJQ9bmvQdxMqJSk4xGiSC56Ns8IMQRzLowv0F9JBmB4jhW19cwmeRuibGfQ5qD6BXUhDk2cQNqQ7Hcoj6v9P22BDwatIARPNWep9fJ+WQwU6nRjl/nI50be9MoXPUGrhx8QeTHxvZEENnAUoJZ6+Fi4cUitvKYMj1qO1FuGwAmmqnUJ+7XFkthGhfqabJKZoaTr1Q6sgR6OhWX4LZtQUi1hTPdP7rIEbsGeOITn4gXvODfQfedqC0X8NnPXoxLLrkEjZuENczXVQl60t3X7SDiLJyIdglDJW5Rzr/rOrgGMYCuXKfsPUDKlUUJ5pgZ8FJ31ouoJ+8vcEP+/pQ0TdOtMDM+8IH3332MscAYgPVYTQytcTRA2noW+CPycybgxahu9Lm2lRyBtHEyLgtg9nmlfrxGTEsAKTesIvbpdJqVN9NNr9faMGkbpWgNo6XkUFM5qHFICAyCHSNJIcwEx1Ka0N7TDVIQhcnH4g0WKen8x1k065kkGY++t4SviWHfdiz6uzZ/OtZRgB2rX3Yf16b0StE502vV0GfnVO+161k+w8JC6xxag8hKnbZzLhJKGxwmUkQHLTSjaTJq3H0J4/psm/MlImmTDdHCqIXnMrAvqWHc6H3z9mtJdFSKIiIcdthhwVc+zySZEzqPbubxt1/7H3jta8/EDTf8C3Tv9f0MT3/6ydi9ezce8MD7oZ04tG0TDbXDMGA2m0VJsByzNtW3J3jPHRgUXlImVWHW1P7SuLErb43ptGtTquCs1FBrc21s3gO+rmLaqB0iqpv6RDkGYDgNYo5ufSMupri/aZoEuERA5JSS7l71dvNEvbE1H7GknzuAe6D0LSHUkiiU18ZNh0TgdGGbIPYp0on9Q58KSIS/wfvERWTjSps9mzczONHDCyFMevnkbuhcrn4qpQk4jnYDgEectxKoND/z52PeOSDfYOV9et5upBIxl31IHGpCfOpxpDRKi37ru2UKcn21HKesD8RJBLfXKTdKnEdOaxIr7Z/eW6qraohCn29VXpawSK4YAvtUyKJEUhofobaLjBjoXFKyNZT7piSyimhXVlYwmUxw9NFH4aabbgIDkZg2zQTwkodJ1WSz2QxEwDvf+S543+Pss38LxxxzTJAQJvjd330jvvnNK3D++efDD+lZlpGpzZX1Zkr9zzPJjhg4T/DBDVlcPsUWY1ttXziioM4ZS1qyszByU5ZrEvzWPJ6aponG3c22TXH0RHQdEV1JRN8kosvCsR1E9CUiujr8PyocJyJ6NxFdQ0RXENEv3aEemVZDwPajx0aDqhgqcq5HjTP1ZnW74wfVDXT6f7RxeAwQNR1zrZUbzXJjyt2WczJCvkUf9b6So7OERY+VfbdzaMfqqeB0iZDkK/kI0hznByqb9+N1Lj/zOKja/G3EBdvfNcnRfrdSw0ZzUr7DSqWlRBC5NpNK2XLyubtnLvWV3hjWpuOc2Aqcg6g/i6If9nsJJ7Z/tWbns4w8nvdhFpXfbbfdhj179mD79u1GwiEjsY3fvbY6w2zW4w//8J3Ys2ePEAUA+/btw8Me9jCcdtpp2LVrF9q2xfbt27GwsJAVJdkMnEg/xVNImBTFDy4SegsvNYndrn2tzdun+v4cP+X3lc+ocfsHandEdfMUZn4EJ33Q6wF8mZkfAuDL4TcAPA3AQ8LndADvvwPvyNo8RA/kYmVtUxLR3BrpB0KK6nWjrYZYdAOXSP1AiMj226qT5onCdrPoIpf+6vpcyzWWRKKG9Gr9ZGYxIM1RVZXIKt03VqloetWFhYW5Yyu/19ZlMwS+XHs9ljhwN7pu3v32+tp7LaG1xNYS8JKQ1ja43jNXQqp8z+ecszGW47HXhV5Vr6shjRoyGZhjEFs5z7XnlrDrnMPP9t6OvXv3YufOnVheXsby8jK2LS1Hr6NU2anLsteurKzgd3/3zfjMZz4TpEvJaX/CCSfg9eechf/0G68EM2NxcXGEF2p9rMFKKXXp3A3DAD+orSN/nmW4yjmznkx2LLX5tvik3O/2uTU42kz7/6OjfzaAC8L3CwA8xxz/KEv7HwCOJKLjNtMRh6TbbIqF0IENzGAiiW5E0MkXG9f7UP+UHRo0IZVs4uJZbgKY4nePRjISapQtQ8RTzxJ250VXqqkPSs5WJr9DsoS7+NmoTFr5X1MZMEvGxLaZCpBwj8F3GHwHSarVm/5JUif2DQZuxFjjRJWiufLnxQxoy7hGeHjuYn/VWEVoAW7A3skncD0ttbDGIjU+Ag7btm3HdLoI8at2GBjwIHhQ/O7YhVxFbi4C0eNi/JWeaWk/Jo3KZACailm+l+tcpgjcCJHba9Ka6jEC0IJoEu0Zcm8f4MJu1CSpBAiVjyOJPnYNGmqR19lV3X4L5Xr1mM6D9f3OmYwmzGXK0MoAMMg8O3ZwEGPgPIRhC8RrkfgWhCb8LmFfm5XjdN3ato1IbOIarK6uYs+ePVhYWEDbOvS+M2X5NEuoeKF4zxh6ANwC3OIrX/krvOlN/wW33XabUa/1OP744/EHf/AWPP3pT8NC8KayMJPmTssmJilL96ZKUwo7UaLCAM+zuBeICOAJCFMADXzwmGHmGBTlISqugQd442Fn4a0P/v2OSDx0iEDOxWdITLmDZxcDFT2LepYBdP2BJWRtm0X0DOD/IaJvENHp4dixzPyj0PkfATgmHL8vgBvMvTeGY1kjotOJ6DIiumx9fbW62UrEVC6a5WbNc0FEWF1djdfZc/Z/fq7gIskiGpfdE52BCopbEpt5lNdS63nnNVrOuljVOLrUAiIfIOql8PH6e4Om/ZEgoZT/Ro4P6T88yAEgBjkrspaRx9Kv9fVV3Hrrzdi37/asotA8zmQe117jcMvfpSGtxjEdiCOah/QS91ezHaS5qqnlSkQs7xhLFwfiiudxefa6cq70HJBsPxtxlXq9lX5UKrOl8koCXKr9yv7rffoM7z1WV1dxyy23YDKZYHl5ObNN2LxJacwMZqCbedy851b8/u+/BZ/73J/Fd3bdOhYWFnDSyU/F7/3ef8ZRRx0JH5gzJfzOYTTXZVWxch+Xa6mEezYTX391u9WArgNx3vPWqbx+I9jQazarAgY2b4x9AjP/kIiOAfAlIvqnDa6tzdIIopj5gwA+CIh7pR6PRixKhlK9XSjpWIytc1952UGo+sKJgcSzB4sHa+i0vG8Ao1HOnxDPMxMIbEaSW9JDb2Mf8gXKdXi6aa1HhI6PXIOmbSVtciN+uf0wg+ccQEoAUIM1yIGaNI+6sbTM2lg9IcYfbbnhL4meOXfkzbrkxq003eOIS9HRtyMbSm1T2L6WxtWae6UPrqZZWT9ZpXBNeq41kNk10fPp2Qybb8Vek5C+MzDbQvMYWbhNIryVqlz2HujzqYHd7Fbdos9Wn3FV+1h4spIbM6N1rcA6DxKKH47HvEVmXuzcawbT/B35/MS1aVyRZ5/i3rPERX8vLCwIohxm2LNnD5xzOGz7MhguRiSLJJPShUu6ixCr4T3WZwO+8uW/wte//nX82q89DU9+8pPFS6YHlrdN8cbf+W18/etfx2c+/TnMZpLlVV0vNYhRCYhd1xL5yvsR+yPur4PBci7DWTW8lNTte++MAAAVhElEQVRzeWqJpkmFicJB+afpHgYP69BhmyXIm2mbupKZfxj+/wTAZwD8CoAfU1DJhP8/CZffCOD+5vb7Afjhpjs0J1BkHscT+hX/248t+VWW/6q1eZyVttKAq5NdW2TbLzXqpL4JAE8mk2g4atsWbdtGd8reDyOjKSj3Ta/1TzhaH74jFHSoc4Z5H6WVRj4AUdRNIq2MofQlL6UYfV/TTKBqnhLBlq3keCxSt/YJ+/ys/4Ts/tJFsHz/PM5L36fjTO9KOv90zYENZBYWdC70uOV653P4HqqbLpvCdalftjCZVCK1Po3Hra20+ZQ2D+2zdelkzvNBKbdeughHYuw9ZrMZdu7ciV27dmFxcXFUHBsYEzUPYP/KGi7+3J/jnHPegKHXQjqiinzc4x6H977v3Xja00+BBEOlKHldr1IbUFsDF4Ijh6HOZDJzKKU4H6+E2Z07/2N8J6UY7bmaTXKz7YCInoi2EdF2/Q7gZADfBnAxgBeHy14M4HPh+8UA/gNJeyyA2zioeDZ4S2aY1EGUCHSjiZm3YYuxZM+eRxFLnX/+nvkEp0akFFnZcalusWkIk0kTP84BPfsY2WkRSB9KroFLwpInrLJjs3NoPxZYhOhgZIgqgUnekWwN2fqEsmuuQfTyYBYEf9RRR2P79u3Ze2uEed5almOzY7THeM799rd+t//nIVZ7nYWVhJz6iHjLcdWIyei70dnnBN2N7mWWOU1zWFdxlvfZcdXGW54rx64tcu7FHFk9t+2Hfbfea6+xUoJev3//flx//fXoug5Dz2hcYoLSeILtwwUjaM9YW1vHysp+rOxbxWte8xr85V/+ddbHtbU1nHTSSXj3e3bjmGOOydIi1+I9amOxRIGIAB4bbGvzZufPzG78VnuP9x6dT1HhNWZCr/+fzdEfC+BrRPQtAJcCuISZvwDg/wZwEhFdDeCk8BsA/hzAPwO4BsCHALxys50pgY8dScUlJ6K5Jx9NnMQcPw6QHNgY4MNncB5VhOBzfWlCFIKstHlA3okuftTIKaUBxeilhjjh1BqoNixxfx5SMk70hdNpi6WlpYAQB5DzWFhs0bQkBtahD7lqPDwTPItRJtoFCPG49jQiApdq0JYIIEf0QmRyLilx6Iq8hHlLRuwc6SckTDHHZ/oAHsvLi9i16+iYsM5yvbmKbsxd1wimRXoNhw+FakcYIzE7Lmtgq82J92IidsRwxMFcrMo86+qodovEldv5UCnASj1SdKIH0EMM9h0YM7CfATyA2Ae4FWN+0vWbNYz5VYb4PxrJC6ZFDdOWMGtqjnRteB5RNGzbObZGcw8CXIhLCYZCm4VRn28lPO2/9kO/N9SC2Ino5RrANeCgnhiGAbfccguoAabbFjLVX8YoaWpi7kGNFLPphgGTyRSf+NNP4ytf/hssLi7H9VcCcfbrfwv3uc+xAf4GU3RbDNfsm8iVC+w5gOW8Bkkp/NUYFO89hq4HcQMeLGEV9U+6nMFuiMGHMhaddx/SISgMJBVgiezvCEd/yKRAOPnk54w6PmDsz+44jzCMgEAUuR7o9SY37AjhG2QjJ9Rh1kQPcg/ytg9WNG0yRAHABL6kKkLKiUQO2zGWlpZw+OHb0bYOS0tLWFpawjAwrr32WvRd6pdVe5ccBBGBQqUsewycqjjZZqUO5cjL55acm0UiJUdon1mzkViOQ1RIQ9xUJTJXoLbvKvtn1TWePJyKyS7lH9Jm8z/p8+1vVWWV+mxCqhoUCZlWNIvVjvL+2ecmxJJLLbVAMSICh3q97CUnjhCjXNoq3yV9S2tZ1mLQPtjgrjSPSd3CgyBi74IKxRiSS5go19aOQ64ZGyAt8ctaVgnOPIs9+mGmL8XS0hK2LS8GV8uUf0pyMqXAvUyNF7q3tLSEww7bhtefcxaWl8XdcjabBfXoFN++8js477zzQESx0LcWFBIknjznNCeOqNjrMTA54h3gQnoGDsVJnLPpK9K81vh8okBA9ZkGlmuS7UfO/+CmUiAc0pGxNXG6pKQRWQBgdmB4sA8RgCZlqC6Qims2Y2J4euiJRNk1TQNypYXecG7MMQFbvmSprxFpuSFwWcADH3g8zjjjDIC6DGlu374df3bxJfjkJz4X3pHn9q4T5IpARh7WhbDcqE1D6LrcuGn/W3Hccol289rzFvEgzqDkDy9dAuU+dQmrp7Yox2oJRiQ8jFjLnZnFqF6BlVLy0H4rMi0JQLmOsnaKjTA6V75r0rTwlkkgQtOqF5NezwIIcbyJGMkwh8j5lTp3bSKNybMSsuPwLFcdtzAe42cpM5KV3WREZJMj9DE8CgMj7ydC4Fzz9dJ+iOoRIwKrbdIuYPAdPIsqxxFjeVk487ZtMZvNZG/SVKYRjVkXigRnZWUFs9k63vDbv4OfO+FBeN3rXmfsO2v4uRMeiPed+x58+I/Px6WXXhrmv0fTNhgG26cB4mbWxLECiCpU5ewtHAjDov/1mM6drpE+g2OSQbteQ1/mm5pPZDfbDhmO/pRTnj06rpya5Rxzbm1cQFpFRjQuS55UQ/SZH7KmEvCUuE/yIG8rw9jcJe1oEypS0vWJ50nyZhx++OG4z33ug5NOOhGzbr/khuk67N+/HysrK/jrv/oqZuvJUKVSQOllMuYmiw2ZnQtzNwRJueCCapuZK+8om10T5nEub2cSyilRcNSiH2bCHVNaP73GPtuO1RJ4QRLjgtZsuKA8o6s+1yLNevAUIeXDicjIJNYruWLbB838qIXtU3I7AMWGjQwAS18IDXqfctroM22aXzsXJTEujad2XtW/X+FIJQx5t0fHANuygJzmy66NMFPzdNB5tTX1RCmJtPce8CZS2O5vb+CF+3h/0zSYTqdYXFwEIDp3Dn1qmibas2T9fGb4BdRlmHDaS1+Mhz/84dBkg6IWIqyvr+PDf3w+vvOd72AYhBHJHQAAR1ORtkhVgMLpx/Una6D2ALeB0CfPLCtJO0NENVDMwrLW8XXOAT5dU9uT5/+3D919SgluhOhLMT7fxDlHa5N2ETG6IUeMcVOTHyGTCMyD2YwY0BjgTmmHGUy5OM4stgIBstTfyWQC13icfPLJuOqqq/CDH/wAy8vL8KHMGkg8DqTiPDD09RQLVqwu1RtWXA8XVee5HLNVU2TXFYglzrZLAqDeJ3Oec3yAIPqSA7R9dtTGDImlqsO2OnyOVQye0m8t7iHPtvcnZF2VIE391MiJupAJ06huZC7MM7xJHWEC3iKRceldOu+C9EJf0ASk5zP1gKM2vksZi1KdY8sAJgN8glPlJi2id85FRO9h8xf5RLA41YWNKsgmrX/uUjhkxn9rPMzmN0jZaTmMjWZIwT8S4DVgfa3L7l1aWhJjattg//79sRhP1OVDJHGte9xOJOhrGCTY8PjjH4Czzz47rrEWaG/bFnt+cgve+ta3YX19PSL6aJj1QnCbNhFwNmmDlTAJdz7AD2EfUo+FhYWYsE/eq3M1lr7impncR8TjehpWOv3ohR++e2WvrDVrvEsIyY+uGfpxCTXiHIkoIFqEaYGyFEdrzSIj1R3a98b+lC6AaPDFL3wJN954I5gZq6urmM16rK93WF/r0M0GzGb9KHeNfW65yPPmq9xcJTHaDGEXLnD8sV4JpXfCvPdpS+tiOT0/ukfHYf9vps+lG2XtHrtZyj6XUlMJA7V5LQlY6aoXI1StkZodhp5RC2JzLgUVWWJem2v9rV5O6vEkhT26EXyql08JBxbJoIgvsDBcpkm2nkZWNSPvFBgqvZH0fjuHek85136QwisqdQ/DgNtvvz2+e9u2bXDOxQjYeK9RvSQbmUPbTPGjH/0YZ599Dq655p+xbVsgGiHw76gdR+Ad73gHTjzxxJgCRdehaRq0ExeD/kqkS2iib74SWb2367psrS28zCOGpQtl16V0ELVMrptphwxHf+KJz8jEUGYGu1RCTQfeYKyuUeOnNgmuAKgtDFCKPD2NAL2B1Hb0sPri5MUC5MjWE6SOLHIfbU09y6HC/XQ6BdOAvh8isgS7kPvd6OUiZzZ23SqRvbR6VJxcn4fSy31jg6kgXuXSimIn5t6IgCEcsvc+SFvJ8N3Yje8T52PVTyoJ6fHY3znEwpaus81XjPQlzxK581glKnGZVmVjRWKydQ8csnOWm5cglkIK0nlyQbIiX0Xmeq33Hg2lwB1V+cAwHzUdvV2PkTFSz5GoD1TSEo5Q9oRFnCUHbuffYZzmw3qlKadvVUslQpeCIrkEoiqPeQQlSszMcY8ACAiTsbiwjMMO34Zt27ZhdXUVK/v3RkQoKhbjHOE9XGO9yFKA4s4dO/BbZ70ORxyxPVSCm6Lr5dp9t+/FO9+5GzfffDN48HCtcOqz2To0wGpeUkQNSgMn1YyVeCQ4UVONJC+oUsIu1901SXqzY7nowrtRcXBpHl23Dpu9D5hfT1V+J59u3cTWewPIOe2NXJOGCndaNrsIIwmCKPrmNk0DalLK2GHI/WCFi5GPBloo1wwkTqI2bhXf69JOjsRLzqBGMDQXjubnKD8D+/hBlr9HPX8ApxGboXh6KYpqemVRGQxw8Nn/+U1dDPNMjnnQUepPRBCc9P82V4t+twiuBgvlWo/XPF/3XFJQ9ZmLv5WzT2ssffZe8qJrwWmrAgJK//rUrxyWHcr5oMIjzLmxxKnfLYdZjqVEvDa1Rt8J161SSvmJOmyf1KEldz9PwkywnEeQMxy6wWPv3r342c9+JnAV3q+SgV5vpWMdq0j/Utf1pp/8GO9573tx2TcuRzNpMevXo1vq9u3bcc45r8eJJz4V1FgHAYqcu0hNHaxbqe5LWEYqBrmpXUXXOK8NYLn1GszVJOSaB9C8dmhw9Efv5BOf+nQAOQfLbuzupyHu+ls5HyBxBjqx4p6Z66GbpgEyy7q0FIRi3cJ83CTlRHsCJq6BuuoxM1onpfGcc5h1a+Z6VUUo94bYr7wPMi5bZ7bUd9ZUHjou5Qjie8z8kDPZJb0lhOIVUkN249aadw/RAKp2kyZU3ZHc7e1oTbz30V3RtlSJK811NKrHa3JbiYWTsqkQZhFLZsB1uYeRfb7eE7kms0RyXCsLDVmf9Xo1pIkhrolSnl6nz1EkH8fhk9RkcxuVMF4iS2/gNRntgsEwRkSrPSWXDm3B8UzKI4Lvh2LOKRqHMymv4Oiz/Ru8UlTXX9tHtabPsOmvHQuciLQubovTqaQm7vsea2tr6Lp8PM45uCbl8x96mZfJZILBr2M6nWAyabDj6CPx5je/Oea0V8cB9fTZvfs9uO666+JY+05cQctxiFTRRgbIGte9roXjLJanSliNxJo0GpJVV4mZwtxFF55/9zHGHnPMsfyrv3oKtCAxECiySwCvIqcg+kJE5JQGVIJnQpk0Amp+z864f8VqTQFbucw1jOMmsY2ZgcahQeGTPfiURyMTWbVKDaKqw4p+uWdCn3HiaUON9ZuKCEtOvWkmZl7U6Dsgy5sR4wVUtZWIyjy3LuVCmKX2rPc+Fj2WzREMhL2Z10KCStNAVR2jRfR6z1yjseE4Ca3IIBWEpZ460ZuGEoIqOV3VyeozrKqnJgHYa2wTg3wwUBopIptXF4pBG997K42Wnki1xla6MvNh5yAh+7Has8Zl23UpkX05J+X66j2yt+RY38/MvTlh1PtVV67wp3uy73tx/WQrtSR71nQ6BQAsLy9HQ6rNZwNWItNEuJIXJlhvWwfPPV72stPw6Ec/GrNZH+HDUYuFhSX8wz/8Az760QuxtrYWbR86Jq3xK4xMIzjKZqE19sEkZbmYeDExaKGeLScVk66Fcw6Li4tYXl7GysoKiAiHH3443vPuP7r7IHoi2gvguwe7H4dg2wng5oPdiUOwbc3LuG3NSb3d0+flgcy860AXHSIBU/juZqjSva0R0WVb8zJuW/MybltzUm9b8yLtEDLGbrWtttW22la7M9oWot9qW22rbbV7eDtUEP0HD3YHDtG2NS/1tjUv47Y1J/W2NS84RIyxW22rbbWtttXuvHaocPRbbattta221e6kdtARPRH9WyL6LhFdQ0SvP9j9uasaEd2fiP6SiK4ion8koteE4zuI6EtEdHX4f1Q4TkT07jBPVxDRLx3cEdy5jYgaIrqciD4ffj+IiP4+zMt/J6JpOL4Qfl8Tzh9/MPt9ZzYiOpKIPklE/xTg5nH3dnghojPC/vk2Ef0JES1uwcq4HVRETxKW+T4ATwPwMAAvIqKHHcw+3YWtB/A6Zv4FAI8F8Kow9tcD+DIzPwTAl8NvQOboIeFzOoD33/VdvkvbawBcZX6/FcA7w7z8FMBp4fhpAH7KzCcAeGe47p7a3gXgC8z88wAeDpmfey28ENF9AbwawKOY+X+DJI5/IbZgZdxsmPVd/QHwOABfNL/PAXDOwezTQZyLz0FKMn4XwHHh2HGQGAMA+ACAF5nr43X3tA+koPyXAfwbAJ8HQJCgl7aEGwBfBPC48L0N19HBHsOdMCeHA/h+ObZ7M7wAuC+AGwDsCGv/eQCn3NthpfY52KobXShtN4Zj96oWRMhHAvh7AMdyKKYe/h8TLrs3zdVuAGchJbs5GsDPmFnz2dqxx3kJ528L19/T2oMB7AFwflBp/TERbcO9GF6Y+V8AvAPA9QB+BFn7b2ALVkbtYCP6WgKPe5UbEBEdBuBTAF7LzLdvdGnl2D1urojoGQB+wszfsIcrl/Imzt2TWgvglwC8n5kfCWAFSU1Ta/f4eQn2iGcDeBCAfwVgG0RlVbZ7G6yM2sFG9DcCuL/5fT8APzxIfbnLGxFNIEj+Y8z86XD4x0R0XDh/HICfhOP3lrl6AoBnEdF1AD4OUd/sBnAkEWnKDjv2OC/h/BEAbr0rO3wXtRsB3MjMfx9+fxKC+O/N8HIigO8z8x5m7gB8GsDjsQUro3awEf3/C+AhwUo+hRhSLj7IfbpLGkm6vw8DuIqZ/8icuhjAi8P3F0N093r8PwRviscCuE1F9ntSY+ZzmPl+zHw8BB6+wsz/J4C/BPD8cFk5Lzpfzw/X3+O4NGa+CcANRPTQcOipAL6Deze8XA/gsUS0HPaTzsm9Glaq7WAbCQA8HcD3AFwL4A0Huz934bifCBEbrwDwzfB5OkRn+GUAV4f/O8L1BPFQuhbAlRBPg4M+jjt5jn4VwOfD9wcDuBTANQA+AWAhHF8Mv68J5x98sPt9J87HIwBcFmDmswCOurfDC4D/DOCfAHwbwIUAFrZgZfzZiozdalttq221e3g72KqbrbbVttpW22p3cttC9Fttq221rXYPb1uIfqttta221e7hbQvRb7WtttW22j28bSH6rbbVttpWu4e3LUS/1bbaVttq9/C2hei32lbbalvtHt62EP1W22pbbavdw9v/B48rs7cxFkAbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "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": 3,
   "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",
    "def draw_lines(img, lines, color=[255, 0, 0], thickness=3):\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",
    "    ysize = img.shape[0]\n",
    "    xsize = img.shape[1]         \n",
    "    \n",
    "    middlesOfTheLinesLeft = []\n",
    "    middlesOfTheLinesRight = []\n",
    "    \n",
    "    for line in lines:\n",
    "        for x1,y1,x2,y2 in line:\n",
    "            slope = (y2-y1)/(x2-x1) \n",
    "            midx = int((x1+x2)/2)\n",
    "            if slope > 0 : \n",
    "                if midx < xsize/2 :\n",
    "                    continue                  \n",
    "                middlesOfTheLinesRight.append([int((x1+x2)/2),int((y1+y2)/2)])\n",
    "            else:\n",
    "                if midx > xsize/2 :\n",
    "                    continue\n",
    "                middlesOfTheLinesLeft.append([int((x1+x2)/2),int((y1+y2)/2)])\n",
    "    \n",
    "    \n",
    "    # left line\n",
    "    if len(middlesOfTheLinesLeft) > 0 :\n",
    "        xl = np.array(middlesOfTheLinesLeft)[:,0]\n",
    "        yl = np.array(middlesOfTheLinesLeft)[:,1]\n",
    "\n",
    "        zLeft = np.polyfit(xl,yl, 1)\n",
    "        yLeflt = np.poly1d(zLeft)\n",
    "        lx1 = 0\n",
    "        lx2 = int(xsize*0.45)\n",
    "        ly1 = int(yLeflt(lx1))\n",
    "        ly2 = int(yLeflt(lx2))\n",
    "        \n",
    "        cv2.line(img,(lx1, ly1),(lx2, ly2), color, 15)\n",
    "\n",
    "    \n",
    "    # right line\n",
    "    if len(middlesOfTheLinesRight) > 0 :\n",
    "        xr = np.array(middlesOfTheLinesRight)[:,0]\n",
    "        yr = np.array(middlesOfTheLinesRight)[:,1]\n",
    "\n",
    "        zRight = np.polyfit(xr,yr, 1)\n",
    "        yRight = np.poly1d(zRight)\n",
    "        rx1 = xsize\n",
    "        rx2 = xsize - int(xsize*0.45)\n",
    "        ry1 = int(yRight(rx1))\n",
    "        ry2 = int(yRight(rx2))\n",
    "        \n",
    "        cv2.line(img, (rx1, ry1), (rx2, ry2), color, 15)\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)\n",
    "\n",
    "# filter white and yellow lines\n",
    "def filter_line_colors(img) :\n",
    "    # conver image to HSV, for easier color selection\n",
    "    # it is necessary step in this pipeline for an optional chalange\n",
    "    # to get rid of errors that are introduced by the mirrored hood \n",
    "    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n",
    "    \n",
    "    # set white color boundaries, values are estimated experimentaly\n",
    "    lower_white = np.array([100,100,210])\n",
    "    upper_white = np.array([255,255,255])\n",
    " \n",
    "    # threshold the HSV image to get only white colors\n",
    "    white_mask = cv2.inRange(img, lower_white, upper_white)\n",
    " \n",
    "    # set yellow color boundaries, values are estimated experimentaly\n",
    "    lower_yellow = np.array([80,80,225])\n",
    "    upper_yellow = np.array([255,255,255])\n",
    " \n",
    "    # threshold the HSV image to get only yellow colors\n",
    "    yellow_mask = cv2.inRange(hsv, lower_yellow, upper_yellow)\n",
    "        \n",
    "    # use mask on image and return    \n",
    "    return cv2.bitwise_and(img, img, mask = white_mask | yellow_mask)"
   ]
  },
  {
   "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": [
       "['solidYellowCurve.jpg',\n",
       " 'solidYellowLeft.jpg',\n",
       " 'solidYellowCurve2.jpg',\n",
       " 'solidWhiteRight.jpg',\n",
       " 'whiteCarLaneSwitch.jpg',\n",
       " 'solidWhiteCurve.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": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:71: RuntimeWarning: divide by zero encountered in int_scalars\n"
     ]
    }
   ],
   "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",
    "\n",
    "def draw_line_on_image(image):\n",
    "    # 1. copy original image     \n",
    "    img = np.copy(image)\n",
    "    \n",
    "    # 2. get image size\n",
    "    ysize = img.shape[0]\n",
    "    xsize = img.shape[1]\n",
    "\n",
    "    # 3. filter white and yellow lines\n",
    "    filtered_image = filter_line_colors(img)\n",
    "    \n",
    "    # 4. conver to grayscale \n",
    "    gray = grayscale(filtered_image)\n",
    "    \n",
    "    # 5. blur image for canny operator\n",
    "    blur = gaussian_blur(gray, 15)\n",
    "    \n",
    "    # 6. run canny over the image\n",
    "    edges = canny(blur, 30, 70)\n",
    "    \n",
    "    # 7. define a region of interest \n",
    "    left_bottom = [0, ysize]\n",
    "    right_bottom = [xsize, ysize]\n",
    "    apex_d = 30\n",
    "    apex_1 = [xsize/2+apex_d, ysize*0.59]\n",
    "    apex_2 = [xsize/2-apex_d, ysize*0.59]\n",
    "    \n",
    "    # 8. select region - trapezoid\n",
    "    region = region_of_interest(edges, np.array([[[left_bottom, right_bottom, apex_1, apex_2]]], 'int32'))\n",
    "\n",
    "    # 9. extract lines with Hough function\n",
    "    rho = 2\n",
    "    theta = np.pi/180\n",
    "    threshold = 1\n",
    "    min_line_len = 10\n",
    "    max_line_gap = 55\n",
    "    lines = hough_lines(region, rho, theta, threshold, min_line_len, max_line_gap)\n",
    "    cv2.imwrite(\"lines.jpg\", cv2.cvtColor(lines, cv2.COLOR_RGB2BGR))\n",
    "\n",
    "    # 10. draw lines on image and return resulted image\n",
    "    return weighted_img(lines, image)\n",
    "\n",
    "# process and save processed images\n",
    "for fileName in os.listdir(\"test_images/\"):\n",
    "    image = mpimg.imread('test_images/' + fileName)\n",
    "    result = draw_line_on_image(image)\n",
    "    cv2.imwrite(\"test_images_output/\" + fileName, cv2.cvtColor(result, cv2.COLOR_RGB2BGR))\n",
    "    \n",
    "image = mpimg.imread('test_images/solidYellowCurve.jpg')\n",
    "\n",
    "result = draw_line_on_image(image)\n",
    "    "
   ]
  },
  {
   "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": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_image(image):\n",
    "    return draw_line_on_image(image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try the one with the solid white lane on the right first ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": [
      "100%|█████████▉| 221/222 [00:05<00:00, 40.50it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] Done.\n",
      "[MoviePy] >>>> Video ready: test_videos_output/solidWhiteRight.mp4 \n",
      "\n",
      "CPU times: user 17 s, sys: 1.22 s, total: 18.2 s\n",
      "Wall time: 5.82 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)\n"
   ]
  },
  {
   "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": 9,
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(\"\"\"\n",
    "<video width=\"960\" height=\"540\" controls>\n",
    "  <source src=\"{0}\">\n",
    "</video>\n",
    "\"\"\".format(white_output))\n",
    "\n"
   ]
  },
  {
   "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": 10,
   "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": [
      "100%|█████████▉| 681/682 [00:18<00:00, 37.43it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] Done.\n",
      "[MoviePy] >>>> Video ready: test_videos_output/solidYellowLeft.mp4 \n",
      "\n",
      "CPU times: user 1min 8s, sys: 4.41 s, total: 1min 12s\n",
      "Wall time: 18.6 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)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "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": {},
   "source": [
    "## Reflection\n",
    "\n",
    "### 1. Pipeline description. As part of the description, I explain how I modified the draw_lines() function.\n",
    "\n",
    "My pipeline consist of 10 steps:\n",
    "1. Copy original image \n",
    "    - This step is necessary to not modify the input\n",
    "2. Get image size\n",
    "    - We need the geometry of the image for the following steps\n",
    "3. Filter white and yellow lines\n",
    "    - This step is necessary to remove mirror effect from the hood in the additional challenge\n",
    "4. Conver to grayscale\n",
    "    - A typical step in image processing to remove some noise, reduce the image size, the simpler structure of a grayscaled image\n",
    "5. Blur image for the Canny operator\n",
    "    - To increase the efficiency of Canny operator\n",
    "6. Run canny over the image\n",
    "    - This step should find edges on the image\n",
    "7. Define a region of interest\n",
    "    - Here we defined a zone in front of the car as our region of interest in which we expect to see lines\n",
    "8. Select region - trapezoid\n",
    "    - Black the region outside of our region of interest\n",
    "9. Extract lines with Hough function\n",
    "    - Find lines on the image\n",
    "10. Draw lines on image and return result\n",
    "    - Average and draw the lines on the image \n",
    "\n",
    "In order to draw a single line on the left and right lanes, I modified the draw_lines() function by:\n",
    "1. Checking the slope of the line, which defines with a high probability to which sideline belongs.\n",
    "2. Calculate the middle of the line\n",
    "2. In order to remove outliers, I check that center of the \"left\" line is on the left part of the image, same for \"right\", if not I ignore this line, else I add them to appropriate list.\n",
    "3. Then for \"left\" and \"right\" list I use **np.polyfit** function to build approximation.\n",
    "4. Last step: using the **np.poly1d**  function I calculate the beginning and end of the lines and draw them.\n",
    "\n",
    "### 2. Potential shortcomings with current pipeline\n",
    "From my point of view, few potential shortcomings are:\n",
    "1. The pipeline is sensitive to the quality of the lines color and line visibility. Different line color, rain or snow can cause recognition issues.\n",
    "2. The pipeline is sensitive to the sharp turns\n",
    "3. White or yellow car in front can cause the problem as well\n",
    "\n",
    "### 3. Possible improvements to pipeline\n",
    "Assuming the current implementation and used approach, I see two major things to improve:\n",
    "   - Select coefficients and color boundaries more precisely\n",
    "   - Improve approximation in **draw_lines()** function, the way how outlier is removed currently is not elegant"
   ]
  },
  {
   "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": 12,
   "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": [
      "100%|██████████| 251/251 [00:11<00:00, 20.96it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] Done.\n",
      "[MoviePy] >>>> Video ready: test_videos_output/challenge.mp4 \n",
      "\n",
      "CPU times: user 43 s, sys: 4.09 s, total: 47.1 s\n",
      "Wall time: 12.7 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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "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": 13,
     "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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