1.Preprocessing and visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The purpose of this notebook is to run preprocessing of gaze data in the visual search task for a single participant. It outputs a number of different visualizations, some of which are intended as sanity checks. The most informative is the 360 equirect of each trial, with the temporal evolution of gaze overlaid. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import scipy.interpolate\n",
    "from matplotlib import animation, rc\n",
    "from IPython.display import HTML\n",
    "import imageio\n",
    "import quaternion as qt\n",
    "from colorspacious import cspace_converter\n",
    "\n",
    "import visual_search_load\n",
    "import visual_search_preproc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load single participant data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Set directories. \n",
    "direc = '/Users/angelaradulescu/Dropbox//NYU/Research/TaskVR/VisualSearchData/'\n",
    "fig_direc = '/Users/angelaradulescu/Desktop/tmp'\n",
    "vid_direc = '/Users/angelaradulescu/Dropbox/NYU/Research/TaskVR/VisualSearchVideos/'\n",
    "equirect_direc = '/Users/angelaradulescu/Dropbox/NYU/Research/TaskVR/VisualSearchSpheres/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of participants: 21\n",
      "P2_09262018\n",
      "['P2_09262018', 'P3_09272018', 'P5_09272018', 'P6_10022018', 'P7_10022018', 'P8_10032018', 'P9_10032018', 'P10_10042018', 'P11_10052018', 'P12_10052018', 'P13_10082018', 'P14_10082018', 'P15_10082018', 'P16_10082018', 'P17_10082018', 'P18_10092018', 'P20_10092018', 'P22_10102018', 'P23_10102018', 'P24_10102018', 'P26_10112018']\n"
     ]
    }
   ],
   "source": [
    "## Get participants. \n",
    "data_direc, participant_list = visual_search_load.get_direc_participants('Expt2', direc)\n",
    "n_participants = len(participant_list)\n",
    "print('Number of participants: ' + str(n_participants))\n",
    "participant_no = 20\n",
    "if participant_no > n_participants: raise Exception('participant should not exceed total number of participants')\n",
    "participant_id = participant_list[0]\n",
    "print(participant_id)\n",
    "print(participant_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Get camera locations. \n",
    "# These are the coordinates of the headset at the start of each trial.\n",
    "# There are 30 possible locations.\n",
    "camera_locations = visual_search_load.load_camera_location(data_direc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "replay_messages_P2_09262018_pickled.txt\n"
     ]
    }
   ],
   "source": [
    "## Open file with messages sent to the replay script in Unity which creates VisualSearchVideos, \n",
    "## the first-person visual experience videos.\n",
    "sorted_messages = [visual_search_load.get_sorted_messages(data_direc, p) for p in participant_id.split()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Select all times at which we asked Unity to create a screenshot. These correspond to the frame times in the \n",
    "#VisualSearchVideo.mp4 movies.  \n",
    "screenshot_times = [[m[\"event_time\"] for m in s if m[\"event_type\"]==\"Screenshot\"] for s in sorted_messages]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Load a file containing the gaze coordinates for each screenshot -- this is just a backup.\n",
    "# Need gaze_coords after interpolation. \n",
    "screen_coords = visual_search_load.load_screen_coords(vid_direc, participant_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/angelaradulescu/Dropbox/NYU/Research/TaskVR/VisualSearchVideos/P2_09262018/VisualSearchVideo.mp4\n"
     ]
    }
   ],
   "source": [
    "## Open video for first participant. This does not load in all the data, just opens a stream to the video.\n",
    "video = visual_search_load.open_video(participant_id, vid_direc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 777.6x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot a random frame.\n",
    "frame_no = np.random.randint(len(screenshot_times[0]))\n",
    "frame = visual_search_load.get_frame(video, frame_no)\n",
    "\n",
    "zoom = 1\n",
    "fig, ax = plt.subplots(figsize=(10.8*zoom,10*zoom), linewidth=6);\n",
    "ax.imshow(frame)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/angelaradulescu/Dropbox//NYU/Research/TaskVR/VisualSearchData/Expt2/P2_09262018_pickled_processed_Behavior.txt\n",
      "/Users/angelaradulescu/Dropbox//NYU/Research/TaskVR/VisualSearchData/Expt2/P2_09262018_pickled_processed_Eye.txt\n",
      "/Users/angelaradulescu/Dropbox//NYU/Research/TaskVR/VisualSearchData/Expt2/P2_09262018_pickled_processed_Controller.txt\n",
      "/Users/angelaradulescu/Dropbox//NYU/Research/TaskVR/VisualSearchData/Expt2/P2_09262018_pickled_processed_Highlights.txt\n"
     ]
    }
   ],
   "source": [
    "## Load data (slow, so doing it for one participant). We take all keys: 'Behavior','Eye','Controller' and 'Highlights'\n",
    "data = visual_search_load.load_processed_data_from_pickle(data_direc, participant_id.split(), ['Behavior','Eye','Controller','Highlights'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot RTs as a sanity check (should see a ceiling at 8s).\n",
    "# Compute RTs from dictionaries.\n",
    "all_rts = np.vstack([np.array(d['Behavior']['SearchOverTimes'])-np.array(d['Behavior']['TrialStartTimes']) for d in data])\n",
    "# Plot. \n",
    "fig, ax = plt.subplots(figsize=(20,4));\n",
    "ax.plot(all_rts.T[np.arange(10)], '-', linewidth=4, color='darkgreen');\n",
    "plt.xlabel('Trial', fontsize=30)\n",
    "plt.ylabel('Search time (s)', fontsize=30)\n",
    "ax.set_xticks(np.arange(10))\n",
    "ax.set_yticks(np.arange(9))\n",
    "ax.set_xticklabels(np.arange(10)+1, fontsize=30)\n",
    "ax.set_yticklabels(np.arange(9), fontsize=30)\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gaze data preprocessing. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Project to camera-centric frame. \n",
    "This step projects eyetracking samples to the trial specific camera-centric coordinate frame. These can be used to visualize gaze trajectories on 360-degree spheres specific to each trial.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load camera locations from file. \n",
    "camera_locations = visual_search_load.load_camera_location(data_direc)\n",
    "\n",
    "# Transform to cameracentric polar coordinates. \n",
    "gaze_from_camera_polar = [visual_search_preproc.get_gaze_direction_camera_centric(d, camera_locations) for d in data]\n",
    "\n",
    "# Finds all indices in an array A for which the absolute difference between \n",
    "# one row of A and the next is in the 99th percentile. Used to filter extreme events in eye tracking data,\n",
    "# which are likely tracking errors\n",
    "nonextreme_inds = [np.logical_and.reduce([visual_search_preproc.get_nonextreme_ind(d['Eye']['HitDistance']),\n",
    "                                          visual_search_preproc.get_nonextreme_ind(d['Eye']['GazeLocation']),\n",
    "                                          visual_search_preproc.get_nonextreme_ind(d['Eye']['GazeDirection'])])\n",
    "                   for d in data]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Project to egocentric frame. \n",
    "This step projects eyetracking samples from the world-centric coordinate frame used by Unity to the frame-by-frame egocentric coordinate frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get head and gaze direction at each screenshot time.\n",
    "gaze = [visual_search_load.get_gaze_direction(d,t) for d,t in zip(data,screenshot_times)]\n",
    "head = [visual_search_load.get_head_direction(d,t) for d,t in zip(data,screenshot_times)]\n",
    "\n",
    "# Rotate gaze to egocentric coordinates for all participants\n",
    "gaze_egocentric = [visual_search_preproc.rotate_gaze(h,g) for h,g in zip(head,gaze)]\n",
    "\n",
    "# \"Normalize\" gaze to find intersection point with a plane located exactly 1 distance unit in front \n",
    "# of the participant, with orientation perpendicular to their gaze\n",
    "# This is the plane on which Unity projects scenes to create a screenshot. \n",
    "gaze_egocentric_normalized = [g[:,:2]/np.sqrt(1-g[:,:2]**2) for g in gaze_egocentric]\n",
    "\n",
    "# In principle, we can deduce the scaling from world to pixel coordinates using the camera's field of view \n",
    "# (76 degrees) and the number of pixels (1080 x 1000), but in practice a regression is easier and more foolproof\n",
    "# We do this regression once for participant 0, and use the same scaling for all participants\n",
    "# I (Bas) have ran tests ensuring the scaling is consistent between participants, and that the resulting coordinates \n",
    "# in pixel space do in fact line up with a person's eye movements.\n",
    "beta = np.array([visual_search_preproc.get_gaze_to_pixel_regression(gaze_egocentric_normalized[0],screen_coords,\n",
    "                                              visual_search_preproc.get_last_known_input(data[0],screenshot_times[0]),i)\n",
    "                 for i in [0,1]])\n",
    "\n",
    "# This is a list of Nx2 arrays, one for each participant, where N is the number of frames of the \n",
    "# VisualSearchVideo movie for those participants. The coordinates are not checked for overflow \n",
    "# (outside the 0-1000 and 0-1080 range)\n",
    "gaze_pixel_coords = [visual_search_preproc.gaze_to_pixel_coords(g,beta) for g in gaze_egocentric_normalized]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(gaze_egocentric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4jssKuG1qarosYk4qZSPPT+9yBNLlCwKBAKLR6Ka1trDalWYmfhRFQTKZ1J5TxQ/t6E6pdajYoWw6xiDkYkzlhSwxpXYqt1rsMAwDWZYxPj5etNuqUlTLj9frzQi4HRsbQzwez8j0MsacUCpDDXjevn07gHRZg2AwqLW2AACfz6f9V414q2rHDRUjfkRRhMvlgtPppOKHUlNQsUPZVMpt+ZBLnBBCMD4+XnKncpZlLRU7hBCcOnUKLS0tm1KR2Szg1hhzomZ6bZbbZauhf485jkNzc7PWW03f2mJiYgIsy2ZUd7ZC/Gx0kLSZ+Ll06RJaW1s1ixe1/FBqBSp2KJuGas0pp1O5mWVH36n8uuuuKymY0krLzvLyMmKxGK699lqtmm8hrFio8p2DWcyJvqO4oigZae611FG8GDY7QLjQ+2eMtxJFEYFAAEtLS1prC31153ICgTc7I0w9tipsVMtPIpHICIam4oeyGVxedzTKlqAct5URoyWm0k7lDMNUHKCsKApGRkYQiUTg9Xrh8/kqGq+aGDuKS5KkiZ/JyUkwDKO93tDQcFlk4Wzmwlmq0LDZbGhra9NcrKlUCoFAAPPz8xgaGiqrtcVmix0gM/1d/QGjfnao+KFsJlTsUDaUfLVzSkEVJ3qBUUlcTKWWnWQyiTNnzmhuq5MnT264taGSc+B5PsPtorc8jIyMwGazacHOuRbfzbauXM44HA50dHSgo6MDQO7WFo2Njairq8t5/TdbLCiKklMYFyN+9B3dqfihWAkVO5QNoZjaOaXAsiwkScKLL76I1tbWiuNiKhEKZtlWm13zplLMLA9+vx8zMzOIRCJwuVwFF98rCauFhrG1RSKR0IKdc7W2qAWxU8oczMSPLMsZPerUAoc8z9OO7pSKoGKHUnUIIfD7/ZBlWSt+VymLi4uIRqM4fvy4Je6icgKU9Val/v5+2O127bXNEDvVPKbD4TBdfCcnJxGLxWCz2cCyLBKJRNFB4Vay2cKymsdnGAZutxtutxvd3d05W1ukUimt0vNmUYngMgt41osfhmEyLD9U/FBKgYodSlVRa+f4/X4QQrQsjXLRdyqvq6uzLC6m1JidRCKBs2fP5rQqXe6WnXyYLb6zs7OahUttaNrU1ITGxsYMEVjteW0mG3V8s0y7aDSKs2fPYmxsDKlUKm9ri2qSz41VKmbiR5KkjIbAVPxQioWKHUpVMAYhcxyn3aTKxdip/LnnnrNotqWJk2KKBG41y06h47pcLtTX1+Oqq67KqC48OzsLWZYz0twvt0yvYlB7m20GDMPA6/XCbrfj8OHDmvjJ1dqimuLTSrFjhIofSiVsvbsOZdMxq51TSTuGXJ3KraQYoZDPbVXOePpttxL66sI7duzIamgKQKsxs1UamtaKFU/9vhnLDITDYQQCAczNzUGSpIwyA1bWWNrIuCEz8SOKIkKhEPx+P3p7e7Oamm617xqleKjYoViKGoRsrJ1TbtG+fJ3KraSQGCvktjJyJVl2CmFsaCqKIoLBIFZWVjA2Ngae57Vu7pdrjRlg80VrrvdeLWCounxV8RkMBjE1NaW5l62osVSo83o1US3IsiwjlUqBYRiIophh+VGDndW+Xpv9nlE2Dip2KJZQqHZOOZadQp3KrSSfUFDTrw8cOKDVpalkvK1Ksedrs9nQ2tqqFVxUY7rm5uYQiUTgcDg0l8tGNzQtl1oQW8ViFJ9mNZbKtbxtpjtPRXWlqeJHRbU4q01N1Y7uquWHip+tDRU7lIoppnZOKQHAatn5+fn5vJ3KrcRMnKhuq2g0WtBtZTbeRrOZAquS87Xb7Rk1ZtRML7WhaV1dnSZ+roSGpuVS7nUxq7EUDAaxurqKsbExcByXIX4KiZnNfn9yxQ3pxY/6PVHFj/oDzej2omwdqNihlI2xdk6+X0bFWnb0ncqvv/76DbvhGIWC3m119OjRkm/gVlRkvlJxuVxwuVzo6uoCIQSxWAyBQACjo6NaarVa4LDazVWL5XKy7BTCzPIWDAaLam1RC9bMYlxp+grOQKb4EQQBAKj42WJQsUMpCzUYUJblosy/xcTslNqp3Er08y/HbZVvvI1iK7rOGIaBx+OBx+NBb28vFEVBNBqF3+/H+fPnIYoiGhoaEI/HIcvyps1zK4kdI3a73bS1hdHt6PP5auLzpyhKyUHvVPxsfajYoZSMWjunlAae+Sw7aqfylZWVkjqVWw0hBBcvXkQsFivZbWWEBihXB5ZltUyj7du3aw1NR0ZGMDo6mtFN3OfzbVim11YWO0aMrS0SiQSCwSBmZmY0i+hmVte2IkjaTPyoMT968aPv60XFT21DxQ6laPRuK7Mg5HzkEjv6TuX9/f0l3zCsWmQSiQTi8Tg6Ozuxd+9eS26WW1141AJqQ9P6+np0dXXB7XYjGAzC7/djYmIiQ/xcLg1NLzdUt2NnZyei0Sh27dqVUV17o2OuqhEkbZbmTghBKpXKCnjWZ3tRagcqdihFYVY7pxTMYlis6FRuhdhR3VYOhwM7d+6saCzj3IpBbWha6Q36ShZY6nnzPI+Wlha0tLQAyIw3GRkZKaubeLHHp4tb/tYWY2NjWmsLvfixGkVRql64spD4UX8YOhwOOJ1OKn5qACp2KAUpx21lRG/ZqZVO5YqiYHh4WHNbnTx5suyxyp1bLBbD2bNnIcsybDab1mLByoX4SsHsehnjTfTdxCORiGlDzXK4UkVmIXK1tggEAhmtRdT3wIqA82pWcc6FmfiZmppCXV2dJrw5jstwe9Hv98ZCxQ4lJ4Vq55SCGqAcj8cxMDBgSadyVUCVE5ehxha0tbVZ4rYyUozYWVhYwPj4OA4cOACn0wlRFDM6i7vdbk38FGP+32zLzuWw4Jt1E/f7/RkNNdVMr1J7StHFqzBqawuv14u+vj6ttUggEMgIOFddj+XEzW2G2DGifhdVYUMIgaIoSCaT2jZU/GwsVOxQTCmmdk4pqB2xX375ZRw8eNCSBp7lLu5WZFsVIt/cFEXB0NAQkskk+vv7wXEcBEHI6iwej8fh9/u1lGv1F3BTU9OGNdcslsvxRq13uRitDkNDQ0ilUkX3lNpsN1YtCM1y5qBvLaIGnKutLYx91YptbVELYgdIV6pW52Fm+VEUBYlEIiMYmoqf6kHFDiUD1dc8MTGBnp4eS4SOLMva4nHLLbdY1oun1KrMRrdVNQVDLrGjtyjt27cvZz0evflfTblWfwEPDg5CluWMrCM1JqAWFr3NwIrzNrM6GBde4zWnrGOF4NO3tjDrq0YI0V7P9R7UitjJZ3VW76vqPKn4qT7020rR0LutZmZm0NfXV/GYaqfyrq4uhMNhS5sOlrK4V9ttVczc1IDscixKxl/AsiwjGAxqWS9qefyGhoaaudlvNFa/p7kWXr/fn9VWYTN7QgGbb1mq1hzKaW1RK59/vWWnEMWIH31Hdyp+SoeKHQqAbLdVpRg7lXs8HszOzlow03WKtexshNvKiF7sEEIwMjKCcDhsmUWJ47iMEv+CIGB0dBTBYBAnT57UCr01NTVtSq2TrUi+hqaLi4sIhUJobW01rSxcbWpB7GyEyMjV2kJtKstxHCRJ0ix0myl6KrkeZuJHX/YDgFbgkOd52tG9CKjYucIxtnwwln4v5wu0UZ3KC1l2NtJtlWtuqVRKK7JWaUB2Pux2uxbT09nZqfWXUmudVDvd90rE2FahqakJkiRplYWdTueGFdfbbMsSsDmCy6y1xcDAgBbob7PZcra2qDblJk+YkUv8SJKk1RXSu72o+MmGip0rmHwtH8qtYbORncrzWXbUrK9S3VZW3bAZhkEkEsH4+HjZdYTKOaYq/sz6S/n9fi3dVw36bGxstNS1uFlsdqwSIQQ2mw0tLS1ZDU03orheLXUb30zsdjvsdjt27twJt9uds7VFY2MjPB5PVQVBKW6sUjELeFbFj/q63u1FxQ8VO1cshWrnqEKi2C9rrXQqB4DFxUWMjo6W7LayqkghIQSrq6uIx+Po7+8vOYXZavT9pfSBt36/H9PT01rQZ1NTkxb3UA6bLThqLWamUENTK+vL1IIbqxbmoM5D/QybtbZQg52j0SjcbrcW82O19c1Ky04hzMSPJEkQRRHhcBiEELS0tFzR4oeKnSuMYmvnlJLptFmdyo1zVFO6VZFRqtuqVIFnhmpGVxQF27dv31ChU2zAtj7wFki7HYPBIFZXVzE2Npa3q3W+Y1/JFLruesFpzK47f/48JEkqOcXaePzNfg9qwbIDQLNUm2EUoNW0vm3me6IXP/F4XLPiq+EKquVH39R0sz8/1YaKnSuIUlo+qFkNhVBv1rt27UJ7e7uV0y2IfnGPx+M4e/Ys2tvbtZTuSsYrh2AwiHPnzmH37t0ZxcOKPXal5EpjL4SxxYLe9B8Oh+FyubTihpVUGd4MZEVBOC7CaefgsvMYmw9jMZjA2HwIwWgSTV4XXn1tF3iWBQHQ7HWCZcs7v1Kui1l2nWpt06dYF9vQtBbETi3MQZ1HsQLd2NpCb31LJBLwer3a+1BOrFstXA9ZlmG32zM+Q2bix9jUtBbmbiVU7FwhqNacYls+FLLs1EKncnWOqtuq0mKF5YodtTT8/Pw8jhw5ArfbrbmHLkf0pn+zKsP64oZWlPcvF0II5v0xJAUZ08sxnFuaRCQFtDS4cOvhbigE+M9HzmMxmADDAH1t9RiZj+C587OYW4mCYxn46pz4tx8N4mBvA+pcNngcPHrbPPDVOXDLNb1obSjuc61+r0bnQjg9vgqnncMr9rWjub44yx7HcZpFAVi3tqnXXf96fX191mJeC0KjViw75c7DaH3biNYWG4Esy1limWGYLPEjCELepqab/fmqFCp2tjjltnzIJ3bUTuUNDQ1ldSq3kqmpKQCwJNtKbWlRCpIkYXBwEHa7HcePH8+omFqOlaUSqlFU0KzKsJn7xW63b+j5JlMS/uDzT+BnL00hmhAgKwpcDhu6Wjy4emcbBidW4XHZsRJJoqelDpGkiG89OYpWrwOBcBIOOw8QIJEUEIkn0dXsRp3Thh+fmICLZyApCv7t+6fx6ffdgqN7OgrOhxCCizNBfPvZWTjtHCRZwUujK/hfdx9Co6f0RdGsoWkgEMDCwgKGh4dht9s1a5vH46kJsVMLc7ByHmZFJs1aW6jWt1qraq5SjPjLJ35UoWOz2TS31+UofqjY2cJU0vKBZVnIspz1fCWdyq0KAAbSbqv5+Xk0Nzfj0KFDm+IGikQiGBgYwI4dO9DZ2Zk11uVq2ckHwzCor69HfX09tm3bphXam5+fRyAQwKlTp7Rfvg0NDVURwilRwl98+Wl8/5lhCIIEoigACGKiiMlUCrFYEszVvRAU4Opt6c8oz7JgAPijybToB0AYQJYVsAwDSVYwNheAJEpYjYlw2VksrYTxkft/hm984m70tNUXnNeT55bgq7Oj3p1e9KaWozg35cdNBzoL7FkYu92O9vZ2zVWsNjRVA23tdjtkWUYsFts0V2OtWHaA6riPzFpbqAUOZ2Zmslpb1Apmlp1C6MWPeh8TBAGCIOCxxx7D5OQkPvKRj1g+12pCxc4WJF/tnGIxC/6ttFO5KiYqzVBQ3Vatra1obm627MZWikCZmZnB9PQ0Dh8+DI/HU9FYVrEZx1QL7alm7x07diAYDGqFHO12u+bysiLVN5GS8A//dQLff3oYqWQq80WiAIRDPCni3Pgibrh6G/zRFFrqnbCxDFwOHoQQ2G0swnERNm7tejEMtrV5MDrtRziWgiIKCAQlKIqC0UQKH//8Y/jPP3kjbHzuzy0hBAQMjGdHqmTsMjY0XV5extTUVJarsbGxccOC5GvFsrNRsCyb4Xo0traIxWIYHR3VhP9mtRcpR+zoUd9TdYyVlZWSYxJrASp2thj5aueUgl7sWNWpvJIu5cB6tlUikcDx48cxMzNjqeukGLEgyzLOnz8PQgiOHz+et/fNVrTs5EI9X2ORN6MFQs12aWpqKivO68T5WUzMB+Gwc6bXWJIl2HgXQIB33LYH//P8JCYWQkimJLz7jr0IJmV876lhTAqriEcT4IiERo8T45cWAbAQU0kkogkA6c+VDAZPvjyOC5MrOLwrfwD+jfta8d3nZyDKBKIkQ5ZkPHd2Er94cRRXX9WKN75iN+w261ORGYaB0+mEx+PBvn37tFgTv9+PixcvQhAE1NfXo6mpqarullqy7GwG+grbiqLg1KlT8Pl88Pv9mJiYgFlri42gUrFjJBqNmv7Aq3Wo2NlCFKqdUwqqMFlYWMDY2JglrRbKiYlRUbOtOjo6tGwrq+NiCs1P7fPV29uL7u7uvNe3FLGzGZapjcJogVCzXUZGRrRaM2rsSTGLcDguwM6zuKrTh9VgAilRynjdznPY39eI1gYXBoZncfKli7gwuQyPy4HFuXn8wW/chJk+NwKLc1gNhQAA0VAEU7PL6QEY42JNkEik8MBPz+LwrtcAAERJRjIlweO2a+8dIQTXXtUMb50bL4+tQCEEJwYmMRNnUOey4eHnxxGJC3jn6w5XdkFzoLeq6GNNtm3bltHQVHW3VKOh6ZVm2cmH+qNOH3dl1toiX9C5lXOxcux4PI7u7m7LxtsoqNjZApQbhFyIS5cugeM4HD9+3JIqu6V2KVfJlW1ViXgyI594mp+fx8TEBA4dOoT6+sLxG7UoPDaKREpCJCHAV+fIsGTos116enqwGopjaSWAsellnPn5aawE43C53ehqa8JKREQwnERjvRN2O49DO9tweFcnOhrdSAoSdnb5sBSMYXI+BJ5jwLMMbDYefe0N6Gr2oKPBjgcfHcCZkXkABKFwHLOLATz90gQYBsj91hjf//TGP3zyPD76jldi+NIqvviDU5BlBT3tDfjwr70CLT63ttAf2t6EQ9ubcHZsCc+dHkdnqxcA4LLzeG5wFu94zSFwnPWLWj6hYdbQVN9ElmGYjEW3EssrFTtpzKwpZq0tjEHnanFPj8dj2X3castOPB7fkKKxVkPFzmUOIQQrKyuoq6uzrDZCNBrF3NycpcG/QOkBwEa3lVFw5QqirmR+RoGiKAouXryIVCqF/v7+okXflRKzY+TlkUV88UdnIMsEbieP37n7CBq9TjSsCZ/lYAw/e2EcT5yexPRiCPP+KILhBJJCOkaGKAqIop6D4VwIgcPGIv1xZGDjORzb3QmG56CkkrjjFQdw23U70dXixUf/+WFMzC4jmciOLSCEgaZ4jJ9t4/Vbe9kfTuDDn/0hEkkRjfVu1Nc5MLsUxuf/+wQ+8e5bs47BcyzGZ5dx6vwkPC4njuztBs+xZdfwKUQpVhVjE1nV4rC0tITR0VGtqGRTUxO8Xm/R49ZCy4paoRhrSq6g89nZWUtbW1A3Vhoqdi5j1CDk8+fP49ixYxV/oPWdyjs6Okq60RVDKZYdM7eVEasXd+N4+jns37+/pGtRC8JjownHBXzpsdPweRxwOWwYn/Xj3r/6H2xrr0cklsIt1/RhdNaP1VACY7N+RGMpEKIglhTTIoMQaALHeO3WIn1TwvrnJ5kS8ezpCe3xcwMzmJi+Fq84vA0PP3UeoqRkj8Uw6WNon0Mm/Z/63urnoCodhoHP40QwHMe58SW0NnpAQNDX4cPItAJFIRliIxJL4bc++R1MzAfXxgnh/Ngi/uVjv1Q1y0clVhWjxUEtKqkuusU2NK2FmJ2NLveQi3Kuhd7lC5i3tlDfh1Iy7qy2uMXjcSp2KBuD0W3FcVzFC6uxU/nc3JzlN45ixY4aJ1SoSKDVbiz9eGo2UbmFCksVO1bEO1RTYOWb38kLc/jeE+cxMbOMYBLobvFieGoFM0shSJKCxeUQWJbF6MwqnDYOu3qa4bLzCITjEEVpTXgQ/cEMf5N8fqcMvv7jl/H1H7+c70SyHzMA9NYk7TzTx2U5HrdftwPPDUxDkhW4XTbEkwJeGJwCFIKeu/4vOAa44/qL+I8/eyv+z5ce1wkdbST86Kkh/MqtB4s6j1Kx0qpiVlTS2FJBjbPSB5nXQsxOrViXrGgCamxtEY/HEQgEMDExUXJrCyvfF/XYlxtU7FxmmNXOKba1Qy7C4TAGBwczOpVb7SJSx8y3GOtdRsXECVkdoMwwDGRZxtDQEKLRaEWFCksVHpu9SOTi5ZFFfPWn5xBPiji2twPveM0BnLwwh09//VkEoylsa2+AQhREo0lMzvvhj6RwSpbXNEX6/HmOhctpRzwhQBJZTC0GEIokIAoiFFVsEKxZd0ysMUYyrC8WQNYtOGbH3tbmwROnxrDsj0AhwPnROcCQZC4B+NHTQ+h83f9df5LLvL1eWghYN2cD1RIa+VoqGKsKC4Kw6VWFa8G6pM7DStcRwzCoq6tDXV2dVtzTrLWFWbkBqz8XsVgMXq/X0jE3Aip2LhOMtXP02VblCpN8nco5jtOOZRX5LDvluIzKDXjOhSp0Ojo6cPTo0YpuElshZmd6KYx/eeg0Gr1OdDQ58Py5OTx7Zgq/ODkOAKh32/HUfAAgBDzHwOexA0TJnAMhkEQRCaJAEiWILINoNAGWZaDIBouObp/Mx0W8x1muqhxjqa8TgwVH3TYrGwuYmDUTKSbxPtpc19xispQheG45uqPgaZTLRllVjC0V9FWFFxYWtLT3chuaVkotiZ1qzsOstYX6PgwNDWWIUKt/tMZiMerGolSHQrVzyln0C3Uqt1pIALktMarb6tChQ2hoaChpPKsW99XVVSwuLmL79u3YuXNnxeOVMjfVLbnRCwMAxFMizo6tICVK2NvbhI6mdcF7aTEMQgjqnDYQQrC0GsapC7Pa53BZEDT7BsdyCEVTmm5gGAZEljSxIQkCAICs3XcVqUTLjZmYyXV9C113QrK3Y5g1DaQTMTlFli7AOeulNTOV/hgMg55WD/783bfln1cFbJYLSV9VGEjHndjtdi3WhBCSUVG72rVlakXsWOHGKgVjZXNVhPr9fqRSKbz44ouor6+3pLUFtexQqkIxtXNKdWMV06m8GmLHrCpzKW6rasxRbWjq9/vR2dlp2S+WYsWOGisVDofBcZyWelrOwlCq+IunRHz6my9iZjkChmFg41j84a9dh13dPgCA22GDQtLBt7GEgNHpVSiKDCiKJkUIy4Jda7sgyjIIFGAtYHddLOSIxwFyC4qCbqxczxU4fxPLjbq/5nozGyLje2eIIdKPSUjG9i1eO977K9fjvre9Mq8YCYQTGJlehd3G4cCOtpKKD740vIBv/OQ0BEHCm28luP3otk0RPoSQjMJ6wHpD09XV1YzaMmqml9WCoFbEjtVurFJRRajH48Hq6iqOHDliWa2lRCKxKY2fK4WKnRpF77YqVDunWDdWKZ3KqxWzo6/KXG6mk0qllh1BEHD27FnU19fj2LFjmJiYsEzgFTO3WCyGs2fPore3F/v27dPqn6hFx3ie1xaOYlJPS70eL15cwNRiGHaegT+cgKwoeODn5/Hn994IADh8VSuu2dmGsxPLiCUEpFKi1ocKQHphlyS43HYIgghJlNNrvP4aWiFutNctsOLldYkx5q4p9fh6a06Gq0yX2WWwOq0EY/j7rz6Bc6ML6OvwYWE1ir3bWvFbdx9DfV06rmJ6MYT//R+PIZYQoBCC3b3N+NN33wqnvfDt+eLUKv7xOydhY9Lf1S8/MgAbz+KWa/oK7ms1Zlk/uRqazs/PY2hoyLL0av0cakHsbLRlJxeq6NIXMASg3WuCwSAmJycBIEP85BNqtRIEXipU7NQgasfZYht4FmPZKbVTebUsO4SQst1WZuOVO0fVurVnzx4t5dZKt1ihsRYWFjA+Po5Dhw7B6/VCEISshSGVSsHv92e0WjDLgimXhCBjcTWKlVAMPMciJcoIx1IQ3n497DYOPMfid+6+Bl995AwGhhfTPihFH0CcPr9YXEhnLaluoGIETl7LzWalD6tzNwQsay+bWKhyWXxUaw8hSIoyvv/EedR7nLijfxeePXsJS4Eo/ur9d4JlGXzt4dMQRBldrfUghGBoagVPn57EHcd3FZzxixfmYeM5eOwsCAhcsOOZgdlNETvFLILG2jJm6dX6z3ip4qdWxE6tzCNXjR1jrSXVAqdvbWFWaLLY++O73/1u/PCHP0RbWxsGBwezXieE4L777sOPf/xjuN1ufPnLX8bRo0cBAI888gjuu+8+DA8PjwL4D0LIp8s8/Qyo2Kkxymn5UMgKo3Yq37t3r7aQFjNmNWpWTE9Pg+d5S6oylyNO1KDsxcXFLOvWRogdRVEwPDyMeDyuFSnMdUyHw5HVasHv92tZMA0NDVq/I5vNVvL8d3U3YHYlDK/LAZZNdxMnioK/+cYzmFkMweu2g4Dg4WeGEYwkIBhaM6gLe9rgka43k/16ntibQtsWQ7nvV97vVY5aP2YusIxAZ/3zSobgAYBwJIFHnr+IjiYv4gkB/nAcLb46rAZjqHPZ16bFgOdYBCIJw2HSrkSXw5ZRgdnl5CHKMgAWDBgIkgy3c3Nu6+XEDeVKrx4dHUUymYTH49HcXsVketWKyFAUpWo9yEqh2IKCxh9aoigiEAhgeXkZo6OjeOqpp7C8vIzbbrsNHMcVfJ/f9a534UMf+hDuvfde09cffvhhjIyMYGRkBC+88AI+8IEP4IUXXoAsy/jgBz+IRx99FFddddUBAC8yDPN9Qsj5kk/eeI6VDkCxhkpaPuSy7FTSqdxqsROPxzE9PQ2fz4fDhw9bElNQ6hxFUcTg4CCcTqepdavaYieZTOLs2bNoaWnB3r17Sy5SqGZf9PX1QVEUhEIhzfIDpINDFUUp+obf7nPDyQGz8ysQJRl1LjuioSgekyXs29aM6aUInnp5HKIogxDFENi7fm5EMVo8isiwSu9Y+MSrldGWa9x874k6X6PoySl4sp+Px0WMJwK4NBfAy+dnMDi5jKGJJfijSRzd25NO4pIVtPnqcHp4HrMrYfz0+VE89fIkkoKMeo8Tf/gbr8Sv3p6u13PrtX144vQ0ZpcDYFkG9R4PfumVu4u9CpZSqdAwS69WM4wuXLgAQRDQ0NCgWRzMfizVSsuKWnFjlTsPm82GtrY2tLW1AQC6urrwk5/8BF//+tcxMTGB173udbj99ttx++2348iRI1mC6pZbbtHcY2Y89NBDuPfee8EwDG644QYEg0HMz89jcnISu3btws6dO0EIERiGeRDAmwBQsbMVKNVtZcTMslNpp3IrxY7qsuns7ITT6bTsZlSKOFFrCe3cuRMdHR2m21h5zsa5+f1+XLhwAfv27dNMx5XAsmyGD14URczMzGBpaQknT56Ew+HQ3AFq1duUKOGRF8ZwfmIFjR4nVkJxKJIMSZbTbiwhLbaXVsNw8AySKRGCIBlq35QQbFyOwNngdP28x88Zv2Miekz307nEDMgE+I0//5a2PceyCITiOHagB0f3duIjf/8wFlYjkHRCkuNYJCUZH//Xn8Hl4PGGV+5FS4Mbn/ytm/Dw0wMgDIPb+/ehq2VzMmWsFhpmGUahUEgLslUURbNuNjQ0gOf5mrLsbGaAsopVrSK6u7vx7ne/G29729vwlre8BV/4whfw2GOP4XOf+xxeeuklHDlyBF/+8peLHm92dha9vb3a456eHszOzmY9D2AGwPUVnwCo2NlU8tXOKQXjIm1Fp3IrFn61bo3aV2ppacnS2j3FzJEQgpmZGczMzGTVEjJSDcsOIQSTk5NYWlrCsWPHMop9WYnNZkNjYyNEUcSePXuQSCTg9/sxOTmJidlV/OilFZwa8SOWktLuJkKQFCXs6mpEIBJHKiWm3VSEYGFVxMJKCBmZSaSEoONSRU6l17yQgMqVgVVwXLO4HONxTYKac6avm6Ssrz2WZRmyouBtd1yNT3/1ScyvRiAb3IKKQhCNp8AxLD76uUdx6uIc3vmGI+hrb8DNhzpgt9vRsUlCB6h+4KpR4EuSpIkfNc7EbrfXhOjZ7OPr52Gl6FKrJ/f29uLee+/FvffeC7U/YymY3Wfz3H8tuSlTsbNJGN1Wlfwi4jgOgiBAlmVcvHgRgiBUHBNTqdhRM426urq0bKtqdCnPN54kSTh//rzWAqPQl97KisxqNebTp0/ndJtZwexKBKOzQRzoa4adXb8eTqcTL1+K46GnL+GloQXUu3hEYimE4gIYosDGs0gKMoamliGkhDWjjYmg0VtzNlvklBu4bLZfKQIol5sq/eLa6yauLcA8gNloEVrbJhRNYHhqBf5QAgohYEAyLtFahBRkosAfiuPz3zuFb/xkAG+74xB+8/btm169eKNr/fA8n9XQdGJiApFIBKdOndKyGRsbGy3v81eIWhE71WgCavzByDCMluRRLD09PZientYez8zMoKurC4IgZDwPoAfAXPkzXoeKnU3ArOVDJbAsi0QigRMnTqC7uxu9vb0Vj1lJC4r5+XlMTEzg4MGDGdlWVscB5RsvGo1iYGAAfX196O7uLno8SZIKb1gEsVgMwWAQBw8e1Br7lcql5RiGZkMIRlPgWKCpzoH+va3wONMi9mNfeBJf/ekFKEQBz7L4vV8+jF86kq5v8tjLl/DlH5+BP5zAaigGfxgQBQny2vlJkgwQBSkZud1Tuaw5GyVyqpmVpR+7GOFTjJWn2ABm43Nrj4lC8LlvPwtZUUBk2bAfs3aM9MKlKGnByjDAQ09dxIXxORzc2YbfeP0x7O6t3E1aDpu9wNtsNng8HrjdbvT09GhdxGdmZhCNRrWGpk1NTSU10iwHq0VGrczDqr5Yd999N+6//37cc889eOGFF9DQ0IDOzk60trZiZGQEExMT2Llzpx3APQB+veIDgoqdDcXotrLixkAI0Uq1Hzt2DPX19RWPCZQnTPSWJTXTSE81elmZWXbm5uYwOTmJq6++uqRKn1a5sebm5jAxMQGPx1Oy0JEVgpfHV/CDE7N4cWwVDpZgcjECp53D7s56/PzsHD7+1mtxcmgBX/np+XTKN8tAkhX83XdO40BnP/buBZ4bnMHozCqW/XFI6uKpkhFsbGbNqZLLqphruxlp57kCj023zWPlySV4sjdEVizP2rjRuLA2F2NNn7V91qwnhKTFhSwrmF+JYn4liifPzuE/fzyIt7/mavS0NeCGQ93o31+c0LeCWmgEqhdc+i7i+oamaiNNNdPLqlIOueaxmVgdKF1sq4i3v/3tePzxx7GysoKenh588pOf1Na997///bjrrrvw4x//GLt27YLb7caXvvQlAGlr3f3334/Xvva1AHABwH8SQs5ZMXcqdjYI9cs2NjaG3bt3W3JTUN00giCgo6PDMqEDlC5M9G6rvr4+0/OrtmVHFVuiKOL48eMlVQUFKhdjiqLgwoULEEURR44cwblzpX1HU6KMv/zmGXz3+UuIJiWwDAOOyOhudgMMA6edx8xqDGcmVvHsYLptA8swgELAsSwkRcHzQ6u461VAOJbCwmo0XftGFTp6kaM+BnJbbbLqyhRII7/cRI7pHPIUFtS2K1HwmFp3cgkj9VplW37U19c9jCTdokM3djIl4Rs/OYs921rxlYfP4I037sZH3/FKuJ3Vb0VSCwu8oiim33uzhqbRaDSjoanaTqGxsbHitPFauBbqPKxMgS9W7Hzzm9/M+zrDMPjc5z5n+tpdd92Fu+66CwCuKmOKOaFiZwNQa+fIsgy/32+J0NF3Kne5XJifn7dgpuuUMsdcbisj1RA7qiVGrcjc2dmZU2wVohLLTiKRwJkzZ7TjS5JU8lhPnl/Ewy/PAQSwcyxkRUFKkLAcSqLN54KsKGABBCNJPH9hDlBIun0DA8gAwHHw1fFIChJCkQQUSYYCrAuVsoVOEUUBL3eho5EjBidrs3yCpwjXVaH9jGKIEGjd4de2Sdc1Sv/NsUy6gzwAUVKw4I+CYxh857ELECQF//u9t4Lnqrv41pplJx8Mw8Dr9cLr9WqlHNReUrOz6R8S+jpWpf5wom6s2oOKnSpiDEK22WyW9HKamprC3Nycll0UCoWqUgCwEHpLipnbykg1ApQVRcHi4iJGR0ctqchczvzUoo367LdyhNOsPwFFIWBYBhyjIJmIgygKIhEBdfZ0fIaNY7G4GsZKILomEtZFDEeAjgYH3von38YL52bSgxpFTTGCJsuig9yPjfsW2raY/WqBYlxSOQVM7pTzko6RMw5IgcNug93OAQyLSFxM24PWLjfLsqhz2qCs6aPh6VVMzgexq6ep8JwqoBasGeXOQd/QdMeOHZBlWcv0unTpEoD1dgrF9K2rhWsBVCdA+XLseA5QsVM1Kq2dY4baqdzpdGZ0Kq8kmLhcVLdVKQHRVlt2CCFIJpOYmZlBf39/xebaUt1YhBCMjY0hEAhkFW0sVewwDIPOBidikQiiCSHdg4phwHIcvE4OUER0NbkhxKP4i/98CklhvaN4Or4DkCUZf/WNlxAIJ9UJGv41sebkfb5AXR39vlnPX8ZCR6UYa4wpJeyTI1jZ9LHub0GUYOdZ3H6sB784PYtIPN2B3sazsNt5iJICSVawu6dp7XNt3Y+M3Kdy+Vh2CmFsaCqKYlbfOtXlZdbQdKuKnXg8TsUOZR3VmlNKy4dC5OtUXo2mnfko1m1lxMoA5WQyiTNnzoBhGBw9etSSa1yKQBEEAQMDA/B6vbjuuuuyjl+q2CGE4MHHLkKWpXQX8fST2Nbqxk0HOrAUjCMUCOJbv7iw9it+bWxZRtq6k47HWQ2K5bmtsiZUxUDky0HoACjaQmO2n87dtP50OeLJfA42nsUrru7Dn77zFXjlwVGcG1tAa6MHB3d14wsPD2FqMYS+9nqwLIvtHQ3Y3ukr4zxKnGmNiJ1qzMFms6G1tVVLsRYEAX6/H3Nzc4hEIlpD06amJs3Ns9nXArBedMViMU0AXm5QsWMhlbR8yDdmoU7lG2XZKdVtZcQqy87KygqGhoawf/9+XLx4ccMrModCIQwODmL37t1aOfVyx1IJxgQMTvrR4XOB41jM++MQBAlEFDF8aQWr4TieWw1rBQGhKDoBY3RB5RA6evKJGSp0iqds6w9QlpBaO57baccfv/MWbO/rwLv6enDhwgU0NTVBFEX82dsYPHpqGvMhCTu7m/GO1x+F3bYx8SObvcATQjYkVsZut6Ojo0Orxq5mel26dAnRaBSpVAqzs7NlNzS1Chqzsw4VOxZhde0cIN31+uzZswU7lW+EZacct5WRSsUOIQSjo6MIBoMl9/oqhkIChRCC6elpzM7O4siRI3C73XnHKgUbx8LGMxAkBXYbC7eNQTScwNRCAjOLAXAsA4asCRwlhzAxzZgqkFquf77Qa7m2ybdtMfvVMmWLmRxWIUNmVZoSxmcYgGHBMAz+4B8exg2HevHRe28GALjdbni9XvT09ODItYe1vlKXxocxLstazEk5AbeXC7Isb4qwMDY0feGFF6AoSkZDU7XA4UYWf6xGzE4p5Txqia35id9AyqmdU4y5t5RO5dXqUA6k56o2aDt06FBF6e2VBCinUikMDAygoaHB1G1kBfmuoyzLOHfuHBiGKaoac6nUOXm8/ba9+PcfDSKWkBAMxcBzDJq9TthtHOZXIiCKnF4sjdYb7ZoaY3QKxNsUu40VXI5CZ7PIG8eTrqwsKwr84QROXpzD5//7Rdx1tCnjO6EPuN2+fTtkWcbSyir8/gAmJychSAR2lwc7etvR6PNZZoXebKrdsqIYGIYBx3Ho7e1Fb28vFEVBNBqF3+/H+fPnIUlSRpp7JZXuC0FjdtahYqcCCCEQRVH7NVHMAqxaD3JtW06n8mqJHYZhMDg4CFmWy6pbY6TcearxSnv27Cm5LHkp5LLsqFat3t5e9PT0FBxnZjWOH56cwfmRMJLeBdx6sB0sm/1+KwrBD18Yx5NnZuB2cLjn1j3Y092An700hadOX0IkloLTzoNhGDgdPGJRcS37mJhYYIxZVzorT7FxOoXq6OTbvwYWOuupRFAXu6/Jdtq9Ifs1hmHAcxzcTjsi8RT29rXgzMg8Xn+kUbunEEIwMRdELClosTp/+80X8NLQAuw2FtfsbsOpoUWIkohmD4/fuLkLHc3ejJiTzXZHlUutBAbrYVlWa2i6ffv2jIam09PTIIRkWN2sFCfViNmhbqwrDLV2TqlByBzH5axqWW6n8mrcmGKxGKLRKDo6OrB9+3bLAoBLzXZSm2jmileyEjOxU2pa+0okhb/9/jkoBAglFHzz6UkIkozXHcmuZPvdp0bw1UfPw+dxIJEU8RdfewGffe9NuOVgJ95wYQbTC3EEwkCdyw6nnUccJH39cqWTZ8y9CKFTqlWnGOvQlmGjFnuTYGbNkrPmCst4Lf0wHE2C41hEEyns7mvR7kOEEPzjt17Az06Og2MZuJ02bO9sxODECjqbPVgJJfDlHw/g2L4OdLQ0YikQx5NjEj54qA//8F8v4vylFTTVcbj39qtw4KqukqoL14JAqgWxU+geZ9bQNBgMwu/3Y2JiIuP1+vr6is7HaktXLBajbqwrhUqDkFWxYzRdWtGp3CrUdgterxddXV2W3cRKseyIooiBgQG4XK6qNdE0ohc7qoUtGo2W1FR1aDaEuCCjr6UOYoRFs8+Jx88tmoqdR16cRGuDCy4HjzoHj9mVCE6PLeH5czMYvrQCSVZAFIJQJIGuljo4eRYJWc7MxjJ1WRVxjbMEShFWnXKbDxM1fuUyFEVWL+BZVhtjTBCj01k6wbO+NURJhiQrsNs4hGIpfPCtNyC6OgOGYXDi/Cx+emIMHU0esCwDfziBHz07guv2d4NhGEhr37+EkI7xa6534uKlVXz2v05jciGOjtZmhGNJfOnxOXyivQnLy+vVhdWYk2q6XSqlVsROKXPgeR4tLS1auIIgCAgGg1hcXMTw8DDsdntGmvtmispiKyjXIlTslIAVtXPMWhwMDQ0hlUpV3Km8UmRZxoULFyBJEo4fP46BgYEN6WVlJBQK4dy5c7jqqquy0uyrifreqIHhTU1NJae1cyyToRMkhcCWo3KtjWeRSKVjvdRWSE+fncZXHjkLQVLW43OIgsXlMGQ1EyufRafUooHG16rG5SR0SgwYLmt/VcQYtmXUYdXPkfYEnHYOgqjAbuexf3srWJaFw8ZhR1cjzq5Mg2EYrITiAKC5TevrHFAUBbGECJ+Xg51nAQLwa4txKJZCW2MdJhZCaG9MN8dsbnBjKRiDYq/HNXt2QlEUhMNh+P3+DLdLU1NTRoG9WojZqRWxU4krym63o62tTcv01Dc0jUQicLvdmvgp1NDU6veEWnauANQg5Epr56iWHWC9M3d3dzf279+/qYpdPxc128rqWKBC56fPdrr22mvzZjsZ97PKzZZKpXDy5MmiAsPNONTrQ3uDE9PLMQTjMuRICu959a6MbcLxFP7qq8/i5IVZ+CMp7Oltgo3n0Oh14OWRRV0BuLUUcxDIBBsX5Gsaq7P5C9nGsMHfwRyfW6JawxgGDAN46xyIJUUoYCCIMqYXw9i/o01bzNTvwLYOHxgmbf3hORar4ThedWQblkMpLAVikBUFh3e3QSEKlgIxeFx23PfW6/An//E0JFmBjeegKASyTOB2pH94sSwLn88Hn88HYN3tsrq6irGxMa0AnyzLmy42Nvv4gPXNN40NTePxOAKBAMbHx7WAYTXeyul0WnZcM5LJ5IZmk1kJFTsFsLp2jip2ZmdncenSpYoznKxAdVsZ51LNLC8jkiTh3Llz4DiupGynQgHfxUIIwczMDGKxGG688cay44M8Lhv+6E0H8fTFJZweiOKXX70f29vqcGZ8GQCwu8uH9/3tT/Ds4BxsPIeUKOPi1Cp+5cZtUAhwOhADxzKQJHktxTyHFafS3lYbTq27sfIFClc4Ts7x8hUtXH/eznNIiVJGGJY/nMDg6Dz+9LdvA7C+yB/a2Yb33H0UX/rhaciKgh1dPvzle9LbjE774XbacGB7C+ZWo4gmRPS118PjsuM3X3MAX35kvXHtq49uw/YO8/uS0e2SSqUQCAQgiiJOnjwJp9OpubwKWR6qwWbHDlVq2ckHwzCoq6tDXV0denp6oG9oqnoI1Ewvn89XtazVyxEqdvJQjdo5ADAyMgKHw2FJhpOeUhd9o9vKOJeNEjuRSAQDAwPYvn07urq6StpXnWMlX0BJkjA4OAie51FfX19Q6MRTEr73/BQGpoJoa3Di12/egY7G9X3q3TbcdbQbvuQldDY68fuffwIzy1EAQKvPiRcvLsDjsoHnWLgdPJYCEXz/2RFAUbASSqXXNEXvrsoTn1MqWaE6GymEalXwlPm9Nsba5HzN7BiFjrkuhARJQUeLB4v+ONwOHmCYdNyOQvBrr746vbXuu3/9wR786JkRjM8FsLAaw8xiGNfu6UBLw7qltK89M9j+LTfvwa6eRkwvRdBc78LxfR1F30scDgdaW1sxOzuLo0ePIpFIwO/3a5YHr3c90+tytQqUwkZal8wamobDYc3tFY/HMTIyYkl9pVqokF0JVOyYYKydY5XQCYfDWFxcRFdXF/bt21fxeHrURb/YXxSq26qnpwc9PT2m57cRxQpVC9fhw4fLCnyrVJDphVZraytOnz6dc9ukKOOrj4/jgSfHsRpJYk9nPVZCCXzqvyL4v+84gnp3dm+ubz0+jKnFCLpa0ud2aTGEpCCDZxlEYkkkUyJEUcJyEpliJqNAYI5g4qKsOgUCkTdCgKgBSRt1vKIo8H2u5Ptutm9Jw2VuTAjB4moUCkn3xHI6bLBxDAiAD332Bzi6twvXdDOalfPP//0XmFuNorvVi1hSxJ9/4Rf414++EaeGFjC/EsHebS141bV9Wd/5q3e04uod5ZV20Lv33W433G63ZnlQixuqNWYaGhq0mJOtWNxwM11pepdjd3c3BgcH0dTUhEAgXV+JYRgt3qq+vr5kC9TlLHi23ietQtTaOS+99BKuueYay4ptqZ3KOzs7Nd+3lagtI4r58OZyWxmppmVHtSpVWsOnnO7iKup1uPrqq+H1eiFJUt7z/cJPR/DAU+OYXo6CEILnw0k0uG2QJAVjc0H8xqt24tdftQtj8yGcGlnC1GQQfkmGy5E+t6QgYTkQA8sQLPqjWvDxOkRXHTmXVSfX/HKJCCuFhVVCRX+z3GjhU8SNuuDNnDEMo3tQzL4A0llYhnuLcV/dY/VjIckK4gkBCgCWVfDES5O4MLGMFzpduOH4MYRjKcwsR9DWmK6F4nHZsRKM4xP//hhmlmMACAKRJI7s7cAf3HMD9vU1F5hvceTqScUwjFZjZtu2bVndxBmG0YRPQ0PDZesi0WN1Ib9K5sHzPJqbm9HcnH6f1Yamy8vLGB0dLdjQVE+1+o5tFFTs6NDXzkmlUpZEsguCgHPnzmluq6mpqaoICNUKky+bq1SBUS2xY0XrCZVy5qgoCi5evAhBEDKuQy7hJCsKvvb4OP7uB+egyARJUQbHMCCKguWgBJeDh6/Ojm8/PYmVUBzf/MVFhGJJJBIpNNW7UV9nh8vB48ULcwjHU0jEUmsjGywzGcJGtYYU+xnMtV0hgbJBYiPDupPxQpXnUWlmVaFtihA6ppaeXEInxxhq7R2GSQsfBqivSwejLgfjkGUBK8E4mhrcsPEskoIEp52HrCiIJgQk5kNob/ZgYHwFKVHCoy9OYGoxjE+991Xo31ea69h8esXVc8nVTXxpaQkjIyNamnVTUxM8Hs9lubjWQpC0Og+j6DI2NFXjrdSGpk6nUxM/xuKS8Xi86IKCjzzyCO677z7Isoz3vOc9+NjHPpbx+t/8zd/gG9/4BoB0GMGFCxewvLyMpqYmbN++HV6vF4ODg6cBSISQ68q/CutQsYNMt5UahKwGElei0M06leuzsayk0KJfjNuq1DHLQRRFnDlzpuSO6bko1bKTSCRw9uxZtLe3Z2XA5RrrS4+N459+dAEpUYYkyulsKYZAkRXwHAOWYdDkcYBjgX/5/lkkUwKSKQmyrCCWiqIl5UQiKSAcS8HBKYgVjMHJYd0pi0L7FmmtySlWrKL4GJb1x+p++QJ9iz38RgqdfMcyKSSYcSwGYNLCJ5ZIwW7jwbIMRCm9wNp4Dn/066/Ep7/2NCLxFIgC3Hp0O05eXMBqKIGUmBZBKVGG3cbiSz8+a4nYKXeBNy6+apr11NQUotEo6urqNPFT7aKiVlErYqeY9cvhcGgNTQkhSCaT8Pv9mJyc1Kolz87Ooq+vDw0NDUWJHVmW8cEPfhCPPvooenp60N/fj7vvvhsHDhzQtvmjP/oj/NEf/REA4Ac/+AH+/u//PqOb+mOPPYaWlpZryztzc654sZOrdk4loiRfp3KO47RYICvJN99i3VZGrBQ7iqJgaGgIoijiFa94Bez27PiWcihljmq39FyFG1WxoygEoYSYzoqSFTz4zCW47Tw4hkEqnQmuGV8cNg6djS6wLINoQkQsKYIFAcMALEsgihKWVgUsrRAohKwJHTNxw2TW0NGe1z8so4aO5TEyJuMVK4QqEkz5Anwtjq8xO3bWZlYKnQLBzRkVlJW0VYgAoqRAlEWwLHDLoRa0+NIByLcc2YYdXT5MzgfR3OBGb3s9PvDZRzA0vQqFEKRECT6PEw47j0RKMp97iVgVy2GWZu33+zEyMoJkMgmv16tlehnvIbVQ5weoLTdWKfNgGAYulwvd3d3o7u4GIQSxWAzPP/88/uEf/gFzc3PgeR7f+MY3cPvtt6Ozs9N0nBMnTmDXrl3YuXMnAOCee+7BQw89lCF29Hzzm9/E29/+9tJPsESuaLGTr+VDuWKnUKfyarmGzMatNC7Gqrmq1pS2tjbU1dVZeiMoxrKjik+/35+33xjDMIimFPzNjy5iejUBG8fiqnYPeC5d0E1RCHiOgSQTcCwDp80GB8/CV2fH7GoM9W472nwuzK9G1jL50o07FVXgZBQENAqZXMHIGTMs8HoZMMzakMTwXInzKEXwAJtft6fYhblQVlUu8WImjnKKIvV/5VmoOIbBW2/ahrmVCBrqnPC47ehtb0CvLuPqbz/0avzfrz6LR14ch9dlR09rPULRFH71VdYkSlQjnkOfZq021IxEIvD7/ZidnYW81sldLW7IsmxNuL1qybJTyTwYhoHH48F73vMevOc978HJkyfx13/915iamsK9994Lv9+PG2+8EW95y1tw++23a/vNzs6it7dXe9zT04MXXnjB9BjxeByPPPII7r///ozj3nnnnXj55ZdPAfg3Qsi/l30SOq5IsVNM7ZxyxI5qOchXkK5abiw1QFmlHLeVESvEjtq9XbWmLC8vW1qHotAcRVHE2bNn4fF4cOzYsbxf/vlgAvc/F4dfSIFngavavXh5IoBmnxuzSxHY+PS+DhuHIzsakRBk/PYde9DXWgeFEBza1oh/++EAvvjwABZXI7qR8yzsWRlY+XYpJi7HKESKERVFCg8rs6qq7hbLccyKti0mELmAFSjvMQpZgszHkRTgM9+9APsPLwEAfu9tN+ANr9yTsU17kwf/+Ht34vTIIv7zx2cQT4q4544DeOut+3OcR2lsRLdxfSf3HTt2QJZlBINBBAIBTExMgGEYCIKAUChUMNi2miiKUhNZZlbX+xFFEdu2bcPHP/5xfPzjH0cymcRzzz2HWCyWsZ3Zj89c688PfvADvPKVr8xwYT3zzDNqm6LXA3iUYZiLhJAnK53/5r8jG0yxLR9KESWldCqvZsyOOq6azq1mGZULx3EQBKGsfQkhGB0dRSgUQn9/v2ZyZlnWUnNzPstOKBTC4OBgRsxUPr765CUsRgQEYwoURcH0YgRXddajvcEJp41DV3MdVkIJdDY44A8n0dnkxp1HutDocSCRkvDz01N4ZnAGLACWga4SMjItNxnWHb1FBdn6odRrlTWG8Qmzg5gECRclRgxjlSpgMtwzVRA+5fzKL1mAGLY1y9Iq6M7KsZ1ZEUJ9YDPDAGBAwGB2OY6+LiecDh5//+DzOLCzFTs6s1211+5uxz/dd2eO+ZfPRogdIxzHZWQaxWIxDA4OYn5+HkNDQ3A4HJrLayM7ucuyXBP1hKx2p6kxVCpOpxO33XZb1nY9PT2Ynp7WHs/MzOSsn/bggw9mubDUbQkhSwzDfA/AcQBU7BRLqbVzihUlqoum2E7l1XRjSZKk9bOyomBhuXNVXXmNjY1Z18Tq88813szMDKanp3HttdcWF1SnEAxOB7DgT0FS0j2uWBY4P7GCQKMLrfVOOHkGLW4OF6dWwTAMphbD+P7zk7jj2m585F+fwHPn5yArCpw8q8sgN8bhAFkiQTGzlpRyjfIIBaMAMdU6JYiUrG0NQqlcN5Xxe1OuaCqXIiwoeY+TSyQVFbdjkoZuNpbZGGv/RJISxmb84HkOnc0ezCyGTcVOtaiFtGSO4+ByubQaZmpxQzXYdqPaKtSSG8tKsVNsE9D+/n6MjIxgYmIC3d3dePDBB/HAAw9kbRcKhfDEE0/g61//esYxFEVRG57WAbgTwP+2Yv5XhNgxuq2K+VIWI3bK6VReLcuOJEkYHh7Gjh07ynZbGSlHmPj9fly4cCGnK49hmKo2F5VlGefPnwchpKS2EywDXFqIgAHR+gOlRBlQFHT6nOhp8WBqMYyXpwKw21jYeRY8B3zymy/h5NA8Tg7NI5IQAUIQ16eRa3EYRBdIbLKQM1jrg4UyhAKbp/5OSQOhKEuN6fMmoscYC1TSVDZg4SzGimO6nWGfXG6rYoKRS6n7s3bNWTatjzmWQWO9G/5IUrOsEAAL/hg6mje2M3UtiB2jyDAG26ptFdSyE/rihlY2YK4lsWOlO61YscPzPO6//3689rWvhSzLePe7342DBw/i85//PADg/e9/PwDge9/7Hu68886MH6OLi4t4y1veoj48AeABQsgjVsx/y4udcls+5BMllXQqr4bYmZ2dxeLiInp6ejICwyqllArKhBBMTExgZWUFx44dy/nLqZqWnXg8jjNnzpQVpyQrBK31Diz4GUiEgc3GwM4xkESgzmmDJCs4f8mvSZekIEPhWTAg+MXpKfijArKLBJpglm2VZW0pIjYnrzWmgOvKbF/tuRIED5BH9ABgdAG3lQgfyyghngYoX+Tk2reorK21659nf57jQQhBR4sXLqcdkYQAQZRBQEAUgvZmL3b35i8WqP5AsEqgbIYby0g+kWHWVkEtbqh2ctcXN6zEInK5ZmMVolixAwB33XUX7rrrroznVJGj8q53vQvvete7Mp7buXMnzpw5oz48WOZUTdmyYsfotir1i5hLlFTaqdwYSFwJeivG9u3bLf+CFStMBEHAwMAAPB4PrrvuurzXuhqd1AkhWlGyQ4cOFazfE09J+PZz0xiaD6PD58Q9N25De4MTR3c0IRwOISrZEEtKkAnB9hY3koKMeEqEICtgGdVWQxCNJcBCQTSSYxE3Ew8Zr6l/Z53V+gtlBfKaCRnTA5lsV4LgKfTapgufHDE0BXcrsN1arIxp9lSxqexZ2+vnypqPvUZ9nR0fefsr8PTZWQxPrYABg84mF9pbfIjEBbzp5r05jyxKMv7pu6fwo+fHwHMs3vW6q/H2V5d+HzNSC20ESrEusSyriRtgvZP7ysoKxsbGtMrCTU1NqkulpHlstvBT52G12Glra7NsvI1mS4odteWDLMslWXP0cByHVCqV8ZwVncqt6jeliq7e3l50d3djbm7O8vo9xQiTYDCIc+fOYffu3UV9EaoRoDwzMwNCSEYgdC4IIfj3n49hcCaEFo8Dw3MRfPaHF/GXv3IQH3z9XszOziBIPHDwLK7b2YRYQsT56SAkWUZLvROeNg8uzgQhCQKgpAuzJY3XKMtqApO13SwFXferviQLkeEgxQikgi6qEgWP6ZyM89P/aRifZP1RAiVabEyHKFKk5BJPBfcvtK1B3GRVWE7/z84BX/r9G9Ha2oxffdVuKKwdz5ydxme//gRSgoR921uwb3sbFlajpq6sr/5kEN9/ZhQtPhdkheDff3AaXS0e3HptX4H556cWFvhKrEu5OrnPzMwgEonA7XZnFDfMt6bUwrUAqmPZKbaCci2y5cROvto5paC37EiShPPnzwNAxYG/VrixzLKtqhH4nG9Mtd/X/Pw8jhw5ArfbbbpdKWOWSiqVwtzcHOrr63HNNdcU9V7HBRlnL/lxaSmKJ5bjYBnAzrM4N76CZq8D13fa8YG33oQHnhjF1x8fg41nIUgKunxubG9148J0EO0NDviDEjiGAccCHMsiljTJWlNFSzFBygDMBVAOsgKPi3msO4bZNlnPlxiHU3JwslEAGZ6rNpUKnGLG0Vtuij2m2TZrb4cgA5/7yRR2dwVx04Fm1PESttU78Cdv3YuvP+PH6EwIf/fg83DYePzj792J/dsz4+aePz8Hj9sOjmXTn12OxYsX5zG5EMZ/PzUClmXwjjv241du2VOyNaMWLDtWiQxjZeF4PI5AIICxsTGtk7ua6WXMvKq0vo1VVEPsVJLdu9lsGbFTTO2cUlBFSTgcxuDgILZt24bu7u6K51nJYq/2EFGDb/Wiy0r3mEquuUqShMHBQdjtdhw/fryka21VgLLaiqO5uRk+n6/oGy1DCE6MrMAfEcAgfUNQFKDVY4eNZ/Gdl8JYEF/CVx8bhcPG4ap2Lzx2Dj89NQlZliFJMhRCwADwuTjEUhIYMGAZFgqUTB2gihzTxT+PFaakwOCMDZDXMlKW4FHHXdtPtcrkm0e1U8oroexaO6W4qxhz8WYWEJ2Vbl54Ho88N4afMsA/fxeZYoph0NnoQEezF/FUCn/74PP4j4+9MWOoVp8b4/NB1DnTcYayrGB2JYYfvTCBRo8ThBDc/9BpNHlduP3ourVndiWKbz85gmhCxG3X9OCmQ5mpxLUes1MJ+uKG+k7ufr8/o5N7U1MTfD6f5e6jcrFadJUSs1OLbAmxU2ztnFJgWRbBYBB+vx/XXHONZea7cucWjUZx9uxZ9PX1obu7O2scq9xjxjGNwiQSiWBgYADbt2/PWTuh1DFLgRCCS5cuYWFhAUePHsXS0lLRbrGEIOPTPxxCMCqCEIBlmXT1fRAsR1JQFILRxQSmAmNgmXRl2uG5MGyMDEGSwRAFDLMWfkIIAjERDENASPoX8s0Hu3FxcgnLwfhajR29oDGes97ttPZnlsAoYBHKmRVVyJ1VpIvKNM1cNzZQnLtMz0aKn5K/a1ZZcYp9vsCTWmxQNkqOyzgfSMFbVweHncH03DJefPFFrcqwz+fD79x9Lc5NrGAlFAcI0Ndej5Qkw+2wwW5LL9D2lIRnBmc1sbMYiOOD9z+OaEIEz7F4/Mws/vCtR/D6/u3acWslZmcjBJe+k/v27du1Tu76NPepqSk0Nzdvaif3asTsUDfWJqJacyp1W+kRBAGjo6MQBAE33njjpqv0YooEboQbS61dc/jw4bIVfqWWLaNFqZjxVqMCxpZi+PKz03h+aGmtfQPSFhqWSffCiqaQSolISQQEChw2Nh2ULEiQJBmEpDNdGKR/wTJQoCgEnY0eeFw2sADOjMwjHEvlXIgyMQtaNtnMKCpKDhbOJWzMXFTFZF3pv1+6rKFig47zfT/Lqc1DcmQulTrO+oMitsn1mpmVJt94ZnFM+idKOa/1OSyFYmhr9KL/cB9Oz/NwLIVxZLuM8fFx8DyPT/76IUwHFdR769C/rxN/8+CLGJ4OQL2ziLICn3fdNfPUwBxCMQGdTemFLpYU8cBjwxliJxhN4bFzK2DOhHDD3nYc3WVeQb6abFasjLGT+4kTJ+D1ejM6uasur43s5E7dWJlctmJHdVtduHABe/bssexDrrpHenp6EAgENlXo5HNbGalGSrsqJPRZX/39/RXFLJUboKxatowWJYZh8p73yGIU//HEJYwuxzHtj0OQCXgbB1GQ19Z8Zm1ZSYtlB8/CYWchy2tuUVkBz7MQRQUKSffEAtJuLBaA08YimkhhYSUCSVZMLCGAqQjQiwti8lxJqeU5xtWmkC+epxhLUK7X9Iu1BdlW5SwCFe9ThqWm0Dj5RI7RelTxwsdAC2ZeGyoYFXH9gQZ8+4khCGL6u7F/Wwu+//9+FSxkBAIB2Fk/IpF5jA2H8PprW3Di4jwWg3EwAJrrXXjbbes9s+Q1i6YKyzAZ1cHDcQF/9q0LWAkLcDnt+N6zk/ijXz2MO49YVwajGGolMJhhGLS3t2tV29VO4hvdyd1qsROPx6kba6PR185ZXV21RCkbO5UzDIOVlRULZlseqrsol9vKSLUsO6Io4sSJExX12DKOWeo85+fnMTExYWrZyiWeCCEYmAnjL/7nIgJxEaJMIMkELM+jvt6NaCQBUZJht/F436v34kcvTKHZ60AiGsFkQEY0ngTHAq0NTvQ0MJgLSvBHUhAlCQ6eg4NnEIgkMDYfBFHIeiDy+gSQXvBVIWEs/FdAZOgzooxxQAVr5rDZ2xnJ5YbK557K67oyCh81fRrlix8rMIuTKWn7fNuU4uYycZHltRYhWzSZWbE0oZM5/mOnpwGGgcPGgQC4cGkFX3lkAO9/05GswFu/348/emMfTo/7UVfnxq1HdqCxbr122E0Hu/C1nw1hJZxujpsUJNz7mnUx9OyFRSyHUuhodMHhcCCekvDlR0euWLFjxOl0oqurC11dXVA7iQcCAQwPDyOVSqG+vl5Lgy+UTVoKVrsWaczOBlJp7ZxcmHUqV1PXq0W+D+LMzAympqZK6m1VjQDlpaUlRKNR3HDDDWWn2hspJUBZURQMDQ0hmUzmtGzlEk+nLgXx1w+PYmo1AY5j1rYFWMLA5nLA6bbDxjJo9znxztt2IRYTcXJ0BYJC0NNch+4mJyLRJJpcBL56Lxwu4DXH6vGeO/fi+8+N41uPDyGRTGdqyQwpci03WmTUxybPZVh5ABDd85qlJk/8TNah8oibjKeKyLwqGIBsEAMZi7ZREOaxUhWNUUgY51Fo91IETo5xi47Xyb8dw7DobPZg3h9dsx+S9XguhkGjx4EP/upx3P+dFxGMJtP7stnuM0GS1xrXctoVnlwIGg6Z2VX8putlPDd4CS+PLGByagaNdeu1Zv7h/TfhgcdGEE0KuP2aXrz2uvXgZVHK/P5xLANBqt69Mxe10oAzH2oncY/Ho3VyD4fDCAQCmJ2dhaIo8Pl8aGxshM/n2/QQCj2pVKomen6VS21/MnRYUTvHjFydyqvV1gFIf+DNxE4lKe5WBigrioKLFy8ilUqhrq7OMqEDFG/ZSSaTOHPmDNra2rBv376c73euRqBff24GCSFdB0dWCBRCwCLd74pjAZ7nUOfg8N5XbUeXz4U/e9s1ePjUDJ49PYSbj+yBXY7gL791DozNg5hAkBAkvP5oD164uIAXhxeRkuR0OwkGYMCYLNdGS02+c9Yv+BlmHPNN0idewGJiiK3J2M9M8OTLvMpznJIzr/QWoDwWkmpT7P2jkFgp2pKjPme2/fpzLMtgwR9bu5Qky2LjctqhEODs1z+Ah58bxe/+7SOQc7gXRUmBqIhgwIBhgGt35W6GqygEf/HV5/H04Bw4lgHPsfjMe25EfT2HpaUlhEIh3H3IjqamjowO1QBwdFcLHDYGwZgID2ERiov49VuvynmsalGrlp18sCwLn88Hn8+HHTt2QJIkLdh5fHwcHMdlFDcsNfPVajY7CL0SLguxU6h2TjnmukKdyq0ufqdHFVL6D67ebdXT01PymFa5sRKJBM6cOYOOjg7s378fzz33XMVj6lGtZvlYXV3FxYsXsX///qwbq9l4xvdJVghWoyLsPAuWYSAQAlkG6pwsDrV78IpdTah38Xjlrib0NKZ95g4bhzffsA09/DIYZhUurwufv+8OfPupcayGk7DzwCe/8QKSgpzO3gom0r+89ULAKEAKZjjpLDfaQ73YMLijSs2+yggeNs6zkPtKP65uXsWmnBfadiMp6d5g4m4qdbwsK9CaBSvLBaX5qrTHsvHya6FQ6e0EScb4rB9OO4+3vGof+joacNcffWv9O2AsRkjSRkEbz+HHL0zA53Hhtce3Z90vnzk3h6cG59DidYBhGETiAj7zrVN48BNvQGtrK4D12BN9Y82mpia0NDXhY3dfhYde9iOlsPjVm3bibbfszH+NqsDlKHaM8Dyf0cldEAQEAgHMzc0hEonA6XRq4sftdm+Y+KiFbLtKqWmxU0ztHNV9U4q5r9RO5Vajih21p1Y5biuzMSsVO2r2wMGDB+Hz+SoaKxf5RKS+v5aZADXD6BYbX47hxwNLiIsSlsICWAawsQxYAE0eO95/23Yc39EIAPjZ6Tm86a8eRSgmYG93A/75t49g1R+A5O6AkHLhhycGML4QxuxKDG4Hh4XVKDiWQVeTCwADoroYiE6YmHhrDCeZ60TWYzIKFQM07lPSc3p3mMl8Mp43sQ6Vkn1l9r2qpgAq93tcrIWpoBVH/+Yz+cdVX8saU73uRtGV/oythJI4PbqM//rFefzKrftxbG8n3vW6Q/jKTwahkGx3VmujG0lRQTIl4amBGZweW8bJ4QV84jdfkbGpP5IEdAua28ljKZjI2MYYexKNRrVaM3Ypivfe3ITOzs60+2UTREctFDa0+gey3W7Xgp0JIUgkEggEApiYmMgQnI2NjRn9CKvxQ/1yFzw1K3aKrZ3DcRwkSSpa7JTTqdxqVLFjZWXmStxYqpUrGo0W1XKhEnJZoERRxMDAAOrq6gr219LDMAxEScbkShxLkRS+f2YRwbgIG8dCJAqcPIsGtw3bW93oanTCsVZLZGQ+hPd+7hkohMDGsXh5fBVv/fQvsL/TgQn/GKaWIiAE8HlsCMcEiCIHnktnbk0uROBy8IgnRXAcA1FU1Mmk/5V1j43ZWdp6aGK5WT+p4gWO2bY5BY/pBSwyGNm4kBrEULE311q4WWoWFWOslNl2hcYxfSHfTjphZHLNMi6r+TjhmIDPfvMFJAQJ977uMP7iXTeCkeP4yi8mISvrezEMIMsEoihDJkBKVCBHU/j8D87g9iN9uPHQepHUPT2NYBgGgpiO9fFHUrhuT263l76x5rZt2zA0NASXy4VgMIjJyUmt91Q57pdy2cqFDYH0NXe73XC73Rmd3P1+f0Yn96amJtTX11s6j1qpCl0JNSl21CDkYmrnFBtbU0mncqthWRaRSARnzpwp221lpFzFnUwmcfbsWTQ3N2tZaNXELEBZrVK9c+dOdHR0lDSepAD/cz6GyIVJBOMixpZjaK13oN5thy8pQZAUvOpAK5rrbFgIC6h3pj/yPz8zD0kmqHPykGUJTh6YDaXrNTXVu9biQRmEogJ4joEoK7BxLKIJEbJCIMsEbY1ucAwwuxwBC0AB1q07xGwxNRMEuufYNeGhBqRmpKHrts0pePJsk/GcwWJTVgaWmUvGGHy82WS6iNbJ8A8Zdin0+TeKFbPrUGhf9aHJ4lHE98/p4OFy8PjOYxdx7+sOAwDecdtOuLxN+OojA2BZBnVOG1bDSQAEkpIufGm3c2DZtKD5zIMn8NCn3qKNub+vCR9923X4u++cghBXcGh7Mz7xjusLzkWFEAKfz6fF95m5X1QLRLXcL7XgxtrIORgFp9rJXXU1JhIJjI2NWdLJ/XLPxAJqUOyogchAcdlWPM8XFDuVdCq32nSnmiJHRkZw7bXXbmqRJjU2Zt++fZqPuNrkKlRYbpXqM3NxTIdE7N/mAMswOD8XQVJQ0Ohm0O51YDEiIJqUoCjA9dt92N6cjtHxumya9ZDjWLAcD5AUGCbd64qQdCCzslZlWZJkuGw8FCKAMICkKGjwONDgsmFuObKmRUzia7JSzo0Y/F7q+kmQuW+GUCHmAkh7aHzOLI7HRLzorU5Z0yzGipNLXOjmVIz7q2QYndgyuJJybZ/1VDnWmFyuqFz7qvMy2cfo8tJbn3Qp5wwAl50DIQT8WhaWeo/62G/cAEUh+OnJCQDAu165G1d1+vC33zmVdlMBkKT05zYUy+7l9obrd+B1123TKiqXgvE+aeZ+UYNu4/G4lm7d1NRkmSW5FsSO1bVtSkHfyb2rqwvDw8Oor6/P6OSuCs5SO7lf7tWTgRoUO2psTrE+R9WNlYtKOpWrC7NVH17VbSUIAvbu3btpQketKbS6uopjx45l+HpzbW+V4NMXKrxw4QIURamoUGEoIYNn09Y/jiFIJFKYSQiwc4DdzuOannq89VgXuhscaKxbv6neccCHFg+DpQgBIxMwkPHG4304MzIPnmXQXO/EQiAOj9OGhjo73DYWU0th7O3xgedYjM6FcGkhgj09DWiqdyIUSwGEgaQX3pqbyrDg5Qs6Zg2PswKWlcyXTL8mJgurmavL8FROIZU1fBnuK9M0dKthcvxt3KzI42cFGhvGLWYcbRO9+DIKnVzH1cf+pK9zvdeJpJguiPneu4+kt1r7fjpsPD757pvxu28+it//l8fwi5dn8LOXpnF8fyd+9tIUJIWg3m2Hx2XDDfs7TafLcSzcXOmCIZ/Q0Ltfenp6oCiK1ltqcHAQsixri3Ql6da1IHZqYQ4AtJjQ1tZWLcA8lUrB7/dndHJXxU+hTu5U7FSJXOnEZuRyY1kRD6OObYXYUbOttm3bBqfTuWmBXoIgYGBgAF6vt6jYmFxp8uWiZmO9+OKL6OrqQm9vb0Vjb2t2IiEoeGFoCWfG/UhJ6RYQQ4KEW6/pxI27GnGwc71E+2pMwFPnp7C8soqv/P5t+MnL85jzJ3Djvjb82k078DffeBw/OBOC123H3p5GvO5oNw5sa4IsK/jj/3gGPk86aPqanc1YCibx9T9+LT777ZP476dHkRJMRLdpTIzefKPfRv+cuhljEDhGwWPi0gIM7iqyLjKKysICio7J2egg5FLZsEwso5tMfc5o2cllDVItOYrpcXd2+/AX734Vzk+uIBIXcMd123HT4XStG+P38x+/ewpjc0H4PA4QAvzi5SnYeQ5JUUYoJmBnpw9/8hvFu6iKoZR7BMuyaGhoQENDg5ZurfYhVFta6ON9ih23FoRGLcwh1zwcDgc6OzvR2dmpFZQMBAIYHR1FMpmE1+vNaW2LRqNFubEeeeQR3HfffZBlGe95z3vwsY99LOP1xx9/HG9605uwY8cOAMAv//Iv48///M+L2rdSalLslIKZG8uqTuVW1NohhGB2dhZTU1NaT6mxsbGqFizMhdoKY/fu3WhraytqH9USY9UXOBgMYnl5GceOHaso4yuaFPGVpy7h2YtLGJwKIpJSoMgEHAvYORahcAIvX1zEH75mp3az9MdS+MqTF8GAoKOtFy8tCbjntt3oa1wv2f6GI234rdcdhqvOq3WGBtLZKjaeRTguwOO0IRQTsb+vEb2tXnz87ddjfD6E4ZlVLAUSmQJEEyY6l4T2UG+aMbMYqPE7RoFjkpIOFCl6crix8gYkk8zFuZArKt/iZLUQqkSEm+5bqotLfT2HVcls3wyX1ZrFTttH/WG1dp04BizDICURfPuxC/i7D70Gjd60JTaaEBBLiHCwmd/PC1N+uB08GIZBPCUiJchoaHRgd28TJEmBP5KEy1HarV9RCE4ML2M5lMBVnfU40NdoeL38ewTP82hpadHqnBktEMW2V6gFobGZbqxS5mHs5K5a2wKBAM6dOwdJkuDz+TA/P4+rr766KMuOLMv44Ac/iEcffRQ9PT3o7+/H3XffjQMHDmRsd/PNN+OHP/xhwX0//vGPHyCEnC//KmRy2YsdvRuLEIKpqSnMzc1V1KxSP3YlokS1LjEMk2FdqmbBQrNfWMZO4aX0Y7Gqfg8hBKOjo1hdXdU6MJfLciSFj317EOemgyAKQVJSwBDAbmMhChLCKQk8n07T/ZNvnMHn3tsPHhJ++twAbDYvdna1gAHApWScX4hliB2GYcAyyBA6ANDkdeKfP/gq/MVXn8e8P47DO5vxyd+8AQzDoL3RjVuv6cEzg7PqyWZOOMMroXMRZWgd1YKjiiLDvjATOEarTw53lWntnAKiJ+O1Am6XUuJwNjMbK0uU5NuumHGM15Upcl/ots1jKWLV1xksBhP40fMTePiFf8ddN1yFVxzqxhd+eBYMA3Q2uvHRt+wBAMyvRlHn5DG5kEJ7IwdZVsAAcNjVew8DZS0zy8YXtygTQvDp/zqNh09Oa9P/yJsP4y03bs/Yxirrr9ECYWyv0NDQoLm99IkmtSB2amEOQOmiS29tUzu5B4NBfOUrX8FHP/pRAEBjYyOeeuopXH/99aZxVidOnMCuXbuwc2e6xtI999yDhx56KEvsmGG279mzZ98EYGuLnVK+NKpwEAQB586dg8PhwPHjxy1R15WIEr3bymhdqkYfK3Vc401HFEUMDg5q16XUL6IVcxUEAWfOnIHP58OhQ4cwOjpa0Xg/OLuIqeUYfG47onEBspK+iacEWUsDlyQFEk8QiAk4eXEG9vgcent3IBUiOu1BYLwc+VyoB7c14zt/9gYAQDQh4rEz0xiYWMWlhRC++9SwTjforTj6RTHjSIbHakyOSbwOMQoU7X86q49qJVjbLytbyyhcdLFEucRK3tgcxvCnflyz7TcKvdsoz5yzditFBBn/VY+bw1Jkeo0NQidjzunn61x2JFISFEKgSDIABgoY/PC5Mfz81CXs7G4Ez7GYXo7gH39wEYy7BX/4+cchyQSrkSSiSREKARiOQyQhwu0UsRJKobvFg/NTfhzfmzu1XM/QbAiPnJpGozedBCBIMv7hoQG8/rpeOO3p+2y1Fnmz9gqhUAiBQADT09MAAJ/Ph6ampppIj64lsVPJPDiOQ3NzMz71qU/hU5/6FL72ta/hySefxAMPPIAPf/jD6OzsxB133IE3velNuOqqdLXs2dlZ9Pau90Pr6enBCy+8kDX2c889h2uuuQZdXV347Gc/i4MHD5ruC6B8t4wJNSl2SoHjOASDQbz44ovYtWuX1mnWqrFLFTuq22p6ejqndYnjuIJVhMtBrbWjfsgrSenWj1mJ2AkGgzh37hz27NmD1tZWJBKJisXTclSAx8ljOZxCSgEIGMgKAccQiEj/IHbaeTAgWAxEsbgwizffdh0E8Lh0bhmrMREsCwgywc0dmUHixcSLLQXj+PX/9wgGxlcgycr6Z0S1rGQsbnlEBNEVRVEFCqP71yxDS4vr0MNCv0iuBxmbuLUAg2uLQVEdy40LeaGMrILigZgexvy4uuuQaz755pJz3BL2Z0yey7cfY/bYIAjVp4j+tfUdmxtcmFoKwyxFPSFI4NcCieucNozNR/Hx/3gKLMugwWmD096A0bkgfB4XmhpsWA4mMDYfgd3GYTmcxIc/9zg+/OZr8eu37y14NqGYAI5Nu9MAwM5ziBIR8ZSkiZ2NKjinzzgC0j/mgsEglpaWEIlEMDg4iKamJjQ1NaGurm7DYyNrQXCp87Dandbf348/+IM/AABcunQJP//5zzE6OqqJHbP7pvH6Hz16FJcuXYLH48GPf/xjvPnNb8bIyEiue66lv5gua7FDCMHKygr8fj+uv/76ktwzxVCq2JEkCefOnQPLsnmtSxzHIZlMWjXNjHEVRQEhBDMzM5iZmSk7pds4ZqmoLsX5+XkcOXIEbrcbgDVtOLY3uTDe5MbkShwgBDYbA0VOC56mOhaignScgyChsdGGX77jBthtPJwA3nCgFcPLMUgKwc5mF9q9xbcJURSCr/38Iv7poTOYmA9BUbWMWRCytrjphE2WtSGH1Udd3DLie6BbB9Wb6Zr1SL+QGt1aQLaAyRefU4zw0Y+hUvJ7ahQEBbbV/VMWJVlu1IMZgrSLGcfMNZXxHFkflkn3oJLk7O+X28mnG3kybM63QCFpK2U8KaLd58RiREDjWgB9ai1jy+u2ocHjBM+yuLQUwf6+pnSSgKTgnx86g7e9aje4AtlXuzrrYeM4RBIC6hw2BGMp9LV54dNlN26WRUOfcRQOh7F3714EAoGslhZNTU0b0sTSyuzdWppHLBbLyGbetm0b3v3ud2ds09PTo1nbgHRZka6uroxt9GPcdddd+N3f/V2srKyY7gtgzrITwGUsdlKpFAYGBmCz2dDe3m650AFKEzv53FZGqunGEgQBQ0NDWpxQpR/4cuaqij6e59Hf358xh1K6nufipt1N+OnFFfga3YhEEkgERHCsumgw2N3mxmIoDqfNjvveci3stvWPeYOLR39fQ86x883vcz8YwJcfvYDZldia0DFxFeljb7T1LYfLKJcYWR8UmpVGP1aGgcBgLdAWR318z9r/So3PyRI+ueaJ/CKgmi4tSwKUc42hvxY5tjHNqlr7N8Oao3t9rbwAz65/1lhmzaoIoN5tx++97Xr87bdeQC5B7HXbsBSIIxAToBCCtkYXupqdGJoJIiEo6cKXJG3hBAB5TRipv7RZhkCWFQiSApeJ2FkNJ/Gz07Ow8yxefW03/u59N+CTD7yEhUAcB3ob8cl3HAOr67ZeK60EXC4XXC6XaUsLSZK0CsM+n68qHdJryY1lZeHcWCyWJVyM9Pf3Y2RkBBMTE+ju7saDDz6IBx54IGObhYUFtLe3g2EYnDhxAoqioLm5GT6fL2tfAN+37ARQo2Kn0JdG36ncZrOpKtByihE7eitKsUHR1QpQlmUZZ86cwfbt2y2pygyU3oZCLeDY19dnKvqsEHrPToSwt9MDlmPx9FwABADPsbDx6bidydUEWhrcuOlwF+ZTCoaXYtjTVpx1K58b67+eGkW9245FrtBNPV+si8m2DAGIKoaMFhhVpOgEkl6YMEi/pmV26dxSGdleBHnFlWnAci43jt6tVISQ2fRF0HgepVp5cr2ey4KTL7A7/VxboxvxlAyGAX77DYeRSCTx3LlZNHt4fOCNe7FrWzv+5SE7IslE1lw4lsWrrt2G0+MraLfzcNpYzK7G4XYriAsyFCV9fI5jsRJOwOt2QJIVNHqdCERSmPUnIMgKWAb4+++9jD+5pz9j/KGZIH7pkz9BLCkBIGjzufDI/349/uvjd+QVNVY0aLYSY4VhWZYzKgxXo6XF5ZKNVSrFVFDmeR73338/Xvva10KWZbz73e/GwYMH8fnPfx4A8P73vx/f+c538K//+q/geR4ulwsPPvggGIYx3ffMmTPnLDsB1KjYyYWiKBgdHUU4HNYaRUaj0aplNhUSJaoFg+O4kqwo1RA7c3NzCIVCOHDgADo7zQuGlUMp4mRhYQHj4+N5G5paIXaWIgJaPQ64bBx+8byiJa3YWUBkgR3dPnzwjekMAEFScHImbInY4VkGMiFo8jgRS4ggZsIjV0HA9QOkn8+oqmy2QOYKrlWtR1gTSbpjG3fLMAqYWGzyiR4V02uhdyvptyfFC6BqYtaGoeh9TRZnvYghObbJCDjONcY6n3rfrRiaCeCZgVmMLUTw+287jr96/2u0asMrK6uIJbKrHGMtEPrCVAAcy6De7YAkSfC6bLi0FEFvWwMcdh4syyAQSaK7uQ57+prwiv0dOLCtGW/4sx9pQofnWNz//UEc6GvGm29c71L+oX99BrGkmHajIZ0J9udfP4l/+1+3FCVeXhpbxf/57iD8kRSu3dGIP/u1w2jyVN+FVAiO4zSXFpC7pYWa4l6OUFMUpaq9BYvF6tiheDxe1A/5u+66C3fddVfGc+9///u1vz/0oQ/hQx/6UNH7WsllI3ZydSovVEG5EjiOQyqVMn1NdVtt3769oHnPbFyr3FiKouDChQsQRRHt7e0FqyGXSjHiRFEUDA8PI5FIoL+/P6/5tJSCkbnobnBg0h9Hi8eB7mYXLi1GwRMZICxYhsW29vUvJcumY3mKJV/MzntefxB/+92X1zO8VcwK/QEmAcdA1oKpz7RSBczaZqbjac+t/S/DrcUYBI66r9ESo4oVsj5GzvihYsSPblJmAsi4jX6crMPqXX7EcO6MudAgShEuKf0Uil3EjMfTiUrjxE31D5P5mm6OX/zxWUwvRWHj05W53/3pH+G/PvkW9LXXw+12o6/Pjc4WL2aWIkjJCghZH8vj5BGOJeGw27TPqigpsPEsBEmG22XTtn1N/zZ8+M1HtCmJigKnjdUWwpQo47EzsxliZ84fB8uu9yRkAEwsRoq6YnP+OP74ay+DYxn46ux4eSKAP//mGdz/3uNF7b+R5GppoS+yp1YYLlbA1EqAcjVidmgF5SpgVNSLi4sYHR017VRezZo1ZmOX47YyUkmHcj3xeBxnz55FZ2cn+vr6MDQ0ZHksUCGxozYSbW1txd69ewv+GrLCrP3qvc343lkJS5EUXnVNJ3709CgiKQYygF861o3rDnYgEBfh4FmEkxKOdHsxF0picDEGWSHY2eTG7laXll1inF+u8337rbsxtRTB33/vdA63T9ZgACHgOAayTNaWR527SdvGaOWBiZVI3VZ9sPYHu5aJpYqeDMsDySGQ1P11c9BT6JyMFBJAhcbJ2ITJsU2ez00uS05JnzUTgcaof+cSPGz2PrmObXj8woUF2HgGnc1eeF12hOMpPDMwg7729Zokf/7OV+JPv/AkZlbjmjBlGSCaktDAMdjXYcfZ6SgUQpAQCOwOB+YDSYQTIjxOHq0+N95+2z4AQDguYHYlBjvPIiUq6fw9kq7B09qQGe+4s8OLl0ZXADYdBE0AHN3Vkv/yrXFxNgyZENSv1alq8thx9lIAgqTAzm++CMhFvpYWs7OzUBRFS3HP11SzVgKUrXZjFVtBuZapSbGjou9U3t/fb6queZ6vqmVHL0rKdVsVGrccVAF48OBBrUCflRYjlXxiZzMaiQKA18njN67rwvDEFBbnw3jtm5ux7/B1cDt5uO08QkkJL89EEBNlHGh3o93rwBPjAXjsPDiWwZn5CDgW2NXizho7n+Vp8JIfn//RICRp7XqYuaz0lhSkb/bve+NhfPZbJwBCIGVdSpMgZ3U8w1iZFqLMlzL21XbTjWEWGK09ZXCh6cVS1jYmFCMqqhGknGU1K3pHrMcw6c+VyXw9165mz+UVXIZjrM1XlAmmliJoa3CB51jwBjHwuuM7Mbcaw8f/46m1+CtoGYCvumYbDu5sxZnZs4glBAgSQUOdgjqfHdGkjDdcvwO//6vH0Oh14vkLC/iDLzwDRSFw2njEU0ntM97mc+H3fvlwxnE//6GbcPcnf4rFYAIEwNXbm/DJX7/O/PwMNLhtUBSixeukJAUuOw9bwTi32iJXSwtjU82mpiZ4POvtaGopQNlqy85mNq22gpoVO2qga1dXV95O5VakMudCL0rUmjXluK2MVBK3orqMYrFYlgC0ymKkx2yuhBBMTExgZWWlqEaiVqNvInrzK47jhRdeQEv9+hwanDxu3bVuAbywGAXHMnDa0jehegePqWCqZLHz5MAcEoJkyMTSL5w6COC0MfjMO4/gTbcdwbODs3h6YDZzW2MrifQEcgsD0xghXS8l/bqqTiJfTI8+mFl7zUzAGawbWW6nIqhWoGqx42YFFBv3U2sVqdvmEi9r/zPTSLnmluViYzL2WQkncVVXA+44tj1riPnVGHiWBSGAjHVvXkom+PJPzqOp3oloUgRAEIzL6GtzQ5JT8K+uYnz4HNyeevz+v50GGAZuhw02jgXPMXjNkV70tXnx26/bj5SoICkk0ex1gGEYdDd78NTf3I3hmSDsNg77enwZmVf5OLKjCa/c14qnLy6DQboExJ/+6qGqBypXaw1QydXSYnp6Wmtp0dTUhFQqtSXFTrExO7VMTYodNT6nnE7lVqLGA01PT1fktjIbtxxRorqMWlpaTF1G1UhpN44piiIGBgbgdruLaiRqNYlEAmfOnCmpiaiNZaEvZSIqCrw5SuWrjUrNkJIxMCBZeiEz62ntSQboa/Oiw+fAA7+4iF951T5MLoQwtRxd81DpRQsx7G5YlPXZWNlBOToRpBdNum2J/rEOYnguQ/iYiB7Th3oRZLQsVTtQWX8tdO5Bs3PNN4apW8pwmIygbP1rDHILrhxWJ8P86pw83v+mI1rPKz1tPhfq6+zwRwXt7eJYBs9dmAcDoL7ODp5lICAdjM+yLDiew9X7duDw4X04Pz6HZEqEgwfCgojlqARBInji3CI+c/12/OUDL+GJwQUwAG460I7/967jcNg4uB08rr2qONeVHpZl8FdvvxbPj6zAH0lhX3cDdnVW3yKw0RYVs5YWfr8foVAI58+f17K8fD6fpSngxWJ17FAx2Vi1Tk2KHbfbjeuvv37TfZ+EEPj9/oJFAkulHFGiptvv379fyyYwUi03lirM1KDsSioyV4LqNjOL3cpHb6MT44EElqMCGAbgWRYH282D7cwsO4QQDA8P43Aniz09jRiY9K+t66pVB1mCh2cZzKzG8YF/OwmZsGAYYGElmh4vfSCsL676fQ1xNuq2qnhgAM0KYerC0S366kOj+NBbJ/QxQEQ3Vsa4qjurgHjJisMxjJFLdGkWKN2xMlxweouL2fma/Z13ouvHyRsLxJgMqbtueffF2vVkTOad6bq02zjs6zN3A7/ttn34z5+cw2pU1O3CgEE6uFhWFDR7HUik4uBYBrGkhN5WL379tnRZjr07ulHndkKSZaxEUhAkBSCAJKbwgX9+Ag6HHW2+dObRk+cW8OWfDeN3Xr8/93kVAcsyuHFva0VjlMpmuo/0LS1CoRB27typZXpNTU0BgFb1uaGhYUPmaXXskCiKNZFlVgk1KXYAbLrQCYfDWtHCQ4cOWTp2KSZdQgjGxsYQCAS0dPtcVMuNJYoiZmdncenSJcusW6Wgd5sVugZmOHgWt+5sxEJUACEEzW47PA7zz5dR7AiCgLNnz8Ln8+HWV/bje4cP4yOffxLff2EK2uqtr2JM1lLhAYiiDEFi0bIWADq3nHUww0IOk7gcw0KvWXl0FoWMbCSYGFR0i7aZ8FEXbv1rWeYrIFsAGSxHRWVqFXrNRMyUGmist2rpnzdul/FQf/2RfUxjTI52Pc3mZhRSZue0/mdcIPj5S1M4sqsNPz89jZNDi+hu8eDXbt2D+joH2ho9mF2NQ5CUdCFCQiBICo7tbsPkYgREIdje7sFvvf4w+tq8uOlQl9bI1mnn8TfvvREf/penEBcUEDBwO3h4PXUILUfBy/J6NXcFeHFoAe997V7TBTkhyJj3x9FQZ0ezrvJ4tV1IxVArsTKKooDnea1TO5DZ0mJkZAQOh0PL8qpmS4taKPJYS9Sk2Cn1TVIXKCveXEIIpqenMTs7i0OHDmF4eLjiMctFXWgbGhpw3XXXFTy/avXcmp+fR11dXUbn9o1CkiQMDAzA6XRqbjNBVnByKozpYBIeBw9eSl8XSSFgGZhmWdl5Fn2+wrFFerGjCt7du3ejrr4RX/v5MH508hJ+9tJMtlAxxPAwDIOuRhckeT14nmNZyMRgxciK0TEu0obnMkSP+rLO0qNtA51rSz8OkBWnw5gcL+sS5hBAGQs3s75t1nbGuB+j2ILhdZN4Jv1jo6vPFBNLlV5Mmu2WkfGWR2jl/S6mX2MZgGEZyBnTZzPmYOdZtDW68cDPL+DClB+nx1chKwQMAzx8YhJf+9jrYOdZNHudWAknISsEigJ4XDZ8/vfugKwouDAyiW0dDdjRZ15I9PiedmzvbMS0PwUQIC4oGJmLwMYB3FpxN0II4pEUmlwKTp48mVVzZnQ+gv/1hROIJiXIhOD9r92Dd9623hNpsxfWWhE7ZrEy+pYWQNoVr29p4fV6NbfXRrS0KJVaeH+toCbFTqmoFo1KF2JJkjA4OAie53H8+HEwDFO1tPZCBAIBnD9/XmugWQxWW3bi8TjGxsbgcrlw+PBhSz/w+i/QUjiFwdkwWAa4ts8Hnzv9qzQajeLs2bPYsWNHRqHE759bxkszEdg4Bg1ODmLYDvuYH0tRERzD4EiXB1eZBB8XgxrwPjc3h8nJSVx77bVgbQ7c85mf4+JMCMuBqFahNgOGgdfFo95tx56uBvzaq3bDHwjin74/CEFK9yliWUaLJ87tblQXQr3FCOuPdccrya0EGASZ+qdqndIfe+3vzMEMnhiTY+tFQvZkcs/NdHP9wlXAKpPvpVwp4BnxSka3odkcTYSa/jVj0DYDdDbVQSTASigJZe39149FALQ31oHnWAgMg1+cnkJXSz24tc/g8GwAJ4YW8N67rsYf/8fTaPI6kBRk2G0cvvHx12sWw0RbHdzO3C6GxWAC4wsRdPicmA2krTgKgJQMJAUJ0WRakO/pacQnfvMmeF22rJozf/aDZYSSBA0eJ2SF4POPDOO6Xc042OuricVQUZRNn4M6j0Kiy9jSIhKJaPd7q1paWG1tq4X3uFJqVuyUUnxOTT+vROzkyrbaaBMtIQSTk5NYWlrC0aNHS+r5ZWWA8vLyMoaHh9HX14dkMmnpB12dJ8dxmAkk8NcPjyKRkkFA8IMzi/iTN+yBGPWbVmOeDSVxcjoCn4sHxwLhlIJQisPIchw7m12QCfDiTAReJ482T3k+5uXlZTidTs2S9ejLMxiZD6POyWOVYaCYBN7aeRY+jxP/55034O5X7FgbZwVL/hB+NpiudrutvR6hqICUJCOeEtZT2DOsRLobpXER1bYz+0zqFu6MBd0gkPToLT9Gy4rZ/hmHM/k8GJ8iQIYFx+j2yjqW2XjMuossw2KVZx7FwBrEFFl/L2w8h719zRicXMkxL73Fx8xKxMBuY/CDz7wVDBjc/z8v43+eGkYgJoBlGLQ31SGaEBCJCxCktHiRFQUsw2pWyXRcTrr9yZ3XbYPLweOhZ8fgcvB452sOYFe3TztaoYXIYeNACEHKpOFoKC7h/vcfRUeTG/t7fbCvBe7ra86kBAkrDz4Mr4NBPB4HAEgicGFiCfu762vCqkII2fQ5qPMo5V7JMAzq6+tRX1+/YS0tSqXStbVWuPzPAJXVrdG7rTY6HsX4xVAznVwuF/r7+0v+YFtRv4cQgtHRUYRCIfT39yMWi2k3OKvQlwv40ZlFiDJBx5qLaSGUwDcfH8SNXTCtxrwSE8GzzNp6w8DJs1hQOHgdHBiGAc8AHAsEEmLJYkcQBIyMjIDneVx77bXae5MUZRAl7Yby1jkQCCfW9lh3WXU0uvF7b7kWv3TDdm08jmNxzyt78cn33AkA+Jtvn8SXHhmEx22H4JchqW8VySNQjQt83tR0IDOFWvtf5hi5xjYOZiaANHeZmbvNBGPKMmHMk54KrQ+5rCy5dzCMW2Af1RXIsJAIMDi5igyhlWsuOeZ6VVcTelrrcWHKjwvTAbjdTkQFBU1eBxw2DhzrQCwpQpQUJFIimutdOLq3AS+PrsBh4yBIMrwuO47sbgMA3Hx1N26+2rzBcKEFtsnrwK+8cgf+/ScXM6eZPmM47Byu2ZG7TpbDzqO7uQ7+qABPnQ2SrCApp+BEHCdPnoTdbkcqlUI8Hi+7zUKl1ILgUqnk/PO1tAiHw3C5XAVbWlhthdkK1ZOBLSJ2eJ4va5FX3VY2m83SbKti0Fs3ACAUCuHcuXMVZTpVatnRxwipLTkSiURVa/fEUjLsawXHFEVGPBpFqrEOR44cMf3Cuu0sWr02BONp03tSUlDHEfDauk0gKQQuW2nvZSgUwuDgIDo6OiCKYsax+3e3wmHnEEkI8NbZIYgyZFmGw8bi6u3N+MQ91+GG/e1ZYxqtk/e95QjCsRS+8OMBJEU1qFi1dKy5rrTYGDNXieE5ls3YlmOMrTEy/E65hVKuG2NOEWSwamTuZHCJ5XARWUlR8Tv59tHtyzDZp50R78OaC1AdTjuHGw92wx9J4l2feQSxpAiHjYOd57ASTqHeKaULk+7rxL13HoSNZ3HToW7YeA6fefBFvDi0gM7mOvzZO25Ak0k6upFiFrePvfVa1Dl4/J//OrO+H9Jp7If6zLM79Xzmncfwv75wArGkCFkheN9r9+KNt+wGAASDQQwNDWFsbAyJRAJerxfNzc1obGzcsLTrWhI7VlJOS4tqVE+mYqeKlOLGKseiYWWRwHJQ58yyrGZZuuaaayr6UFWSeq4u9rt370ZbW5v2fDVq9+hbMvTvaMC5uQiILGF6NYKYwiPCe3FhMYYDHdlWth2NLuxrrcOEP4FwUkYoKYHjOZxeiGNHkwKvg0dPgxPd9cUH+s3OzmJqagpHjhxBKpXCwsJCxusdjW58+SOvwie+fgqr4SRueGUX/vCXD2NPW/b8CCF4cnAeMytRdNZz6NB5Ie02Dn9x7yvwtZ9fgCiLql0IsqIWBtQH7ZoJBXVBYzPdQmDgcXGQBGktGDafu8oggLRAZZP3OF+cSk6MFiEmx9iGhUkfkKx55Mj640rcycX8ys2XHq7fpoCFyWXnUF/nwJtu2oVzk6sQJAVutXVCvRPhmIB3vvYAtrXX48037YbLkXkL/qvfurGIE8qkGLHDsgzue/PVEBWCz/73WSgE4DkG//DeV6ClobCg2t/TgP/52K2YWomj0WNHZ+P6B9vhcMDtduPqq6/W2iysrq5ienoahBDNDVPNtOutKnb0FNvSwmp311aosQPUsNgphVKagerdVsWKi2oEZ7EsC0EQcOHChYrbT+jHLFX0qddjbm4OR44cgdudGdhb7UKFr9zVhNmFZfz3mRUQmxM372xCb7MbPxlaRb2TR48hg4rnWLx6dxPmw0l84+VFtHjsYAURjIPDUlTCnbsb0eMz73tlRFGUjHYkPM9DEARTkX3tjmY89KevQXIt/ddh0ueHEII/++oJfO/ZCRCSLpn/a9e34ZMH1vsdcSwDO8+BEAFgAJZhIWsLv96CkDX4+t8MA4+TTweWrp1nNCEhWyisb28+ln7hNooPQ2xQKZ//LLdPETdes230x1Rv3oVcSjlfN8Y0rQvF7Dgn6F43zgnQGq7qM9TW9k9IwK4mD4Zmgnh6cBbhuACfxw6OZaEQAruNw4d/5SjcDussHqXcn/7wlw/jw790EEvBBFp9LjhKsIB6XDYc6G0AACRSEsYWo3DZObR7GG1x1bdZALLTrp1OpyZ+3G63ZffVK0HsGMnV0mJpaQnhcBgvv/yyaUuLUqFip4Yo1rJTjtvK6G6yCkVRcPr0aezYsQPd3ea++FIpVZhIkoTz58+DZVn09/ebnmM1xY4syzh//jwONgKp63dCwbqI4BgG8+FUltgB0gX73DYOCgGa6mwIiQw8dhYhQYECpiihk0qlcObMGbS2tmLfvn3ajSBfI1COZVBnz/05GJkL4XvPTcDl4MAyDERJxjefncdH3p6Cry5taVpdXcVv3NCMz/1sDqKspI+VMy5Hb+3R3ciJgnjKKO6NAbO6ffMJhHytKTS3Wq5U8hwUc1PNVU+o0P6FxlZde/raQxmCx8zlpXtetSIx6XAjRQEa6uyoc9nhcjowNh/WWZoYGNP2bRyDC1N+fPQLT8Fh4yDLChYCMjwOHg4bhw/cfY2lQgco/ceY3cahp7X8xWtmNY7f/fcTCMVFSArBTXua8JvXmSdSGNOu4/E4AoEAxsfHNZeXuiBX4vKqBbGz2fWG1JYWavueq666Cn6/H1NTU5orSnV5lZL4Qt1YVaaUL28xMTuq28qYxlwIVUhZKXZmZ2cRDodx6NAhtLdnx3qUSylurFgshrNnz6K3txc9Peb1OYDqiZ1EIoHBwUGt7cPo4DIWwik4+HTwskQIXLbcNy+XjQXDMBCl9A1GUtKVYeuK+KWquuz27t2r9bpRKcV9mjVuTADPrmfUcGuB1JG4iAa3HZOTk3jgsSEsCF788i174Q/HsBJKYnQmgHBCyrbKZKU0a7OEUlDAmIgHY3q0cT/9sXXHyhnfYqztky97K2t3Jv/jfPuZZa4Zp8Zw2YIq17Zmx2AYEIZFvdeG1x7txCOnZrEUFjL3Ixl/AGDAsCxEUQYYBoIow8azcNp5vOWmXXjjDTtxU44g40rY6LTg//PdQaxGhHTTT0LwxIVl7GluwaGDhfdV3TDd3d0ZbpiZmZmKXF61InZqIT1bXa9ytbQYHh5GKpXKSHHPJzRpgHINkc+NVY7byji2VQG6agNLWZbR1tZmeQGpYt1YCwsLGB8fL6r3WDXEzmxIwImpYRzYvRNd3R1gGAY37/DhO2eXsLxW5binwYG9bbnfK7edx207G/DzsSCSEoNUUsEN2xrQWiADa2ZmBtPT06YuO6CyxrK7uxtg51nEkiJcDh7RRDpz7PM/GoRdjiAQk/DwmVUQAIpC0FLvxCd+/Tr89t/+1LBmMuuLeq50a2P6dpZlxMRqorpe1h9kCxSzm3XO62GMYdGPq48/Mh6jzEVJlx5urlKM0ytm4dFbdHT7MQwYhkE0IeLbT0+l9ZNuOI5l0v3WdNem0etAKJ55H5JkAjvH4rX92zWhsxiIY3Ixgq7mOvRWYGFR2ehFdnIpBveahZNdC4pfDJdezNTMDRMIBLIqDRfj8qoFsWN1P6pK5mH8ca5vadHX1wdFUbQU90ItLYp1Yz3yyCO47777IMsy3vOe9+BjH/tYxuvf+MY38JnPfAYA4PF48K//+q+45pprAADbt2+H1+sFx3HgeR4nT56s6BqYsWXEjiAIWc+Loohz585VlG1lldiJx+M4c+YMuru70dvbi6GhoQ3pUK5H7Zgej8dN07rLGbMUCCF45OQQHp4U4fX6MD0ew3h4Hvcc7USLx453HOvAfFgAzzHoaXDAxq1/4SIpCbICeB0cuLV05hu2+dDrc+Lk+TFc1VOPQ72502cVRcHFixchimLez0I+N1auc/r8j8/ji48OAQS481gfXhxawOxKDHYbh3AshW/84iI4lkUkIaK90Q2OZbAaSmB4JoD7/uVJKPqeVFnF9/R/GAWPzoKjpk6nJ2U8Kd2EjSe89r9i0t/XTzr3tmb76I9RrtABMs8xX9p8PsFm8hrHMdBixJEOGjdaz/R6jWGAOocNYBjs76nHoS4bBqajGF5IgqzF/6iHUQiB08HjyK42PH5mBv/yg0GcGF6C15X+7v3xrx3FO169t5SrYHJqGyt29nXX44WRFTRwtnSxRIZBX3PhIOdC8DyfVWnY70/X24rH46ivr8/KPFKpBbFTjXCHcudR6Fqo9Xv0LS2MQvPpp5/GDTfcUJTYkWUZH/zgB/Hoo4+ip6cH/f39uPvuu3FAF6+4Y8cOPPHEE2hsbMTDDz+M973vfXjhhRe01x977LEsS7uV1KzYqdSNpaZyl+q2MmKF2FEtKQcPHtQC96y0GKnks0oU6pieb0wrxI4oijh79iyenubQXOeAz2OHw2nH2GoCl/wJ7Gxxw+Pgsbs18yOpEIJTMxGMryYABmhw8rihrx51dh42jkF3gxPBeqDbm/ujnEqlcPr0abS2taPvqh4ICgNXjntSqW6s7z4zjn/6/jnY+XSG1H8/O4lPvO0IfuWVO3D1+x+EnSNwu5wAGASiKYiSjKSsIJYUQUAgyOnKrzzHQZJlaGnomlXHaCHJmGz6BWPAsdEdVgwMq4tzUVd9gzhRxZXR6lT0Mbji55RLYDEM6hw8UpICSSZgWJNU8SLG41kGPM/il16xCzu7GvD33zm1dnkZiLL5NV//k4HLacNvvuYA/uBXj2J8fAwNvkacGvPjM986hemVGARRgaQQNLjt+Nafvh6Pn53Fn3zpeaxG0hWMU6KMFq8Dn/7WKezt9eHq7S1w5okHy8dGi50//ZVD+PAXT2J6NQ5FIbj7aDuu32l9Z3OXy4Xu7u6iXF614EKqBcEFlJd6brPZ0NbWpmXixuNxPP744/jUpz6FkZERdHd3o6WlBXfccYfpenrixAns2rULO3fuBADcc889eOihhzLEzo03rmca3nDDDZiZmSnn9MqmZsVOKejdWIQQTE1NYW5uruJUbnXsckWJmu2TSCSyLCnVcA/lwu/348KFC9i3bx+am3NbP8yw4gaidktnfV0YXFhGMiWgqU7C8d0tYBisLy4mzIZSGF1NoMXNQybA0HIcI6sJdNc7cKCtDvva3HkFSjAYxLlz59C9cw/OBhiEz6/CzjM41FaH/W3ZpvFSxc5PXkp/YW1rgdWipOCRl6ZxtIeDrEiw21iwbPrGY7fxWAql1tpNpEVFLCmBYRhwLAEUg+XCAmGQ97ms14zNLo2PdUHL5ZLPSlREYDLLcpBIuvAfmHTGW5arLEfgM8sATV6nJpDuOLYN/++9t6DOacP1+7vwd985iWcvLOoCkLWDr2WDpd83r9sGnufxhht2gFuzPtp4Dq+/YQ9edWQH7v+fMzgztoS+FhfeekM7/HOj+JtvjUGSZTBg1t5agmhKQlIi+M3PPga3g8c//s4rcfOh0n+Y5VroZYXgSz8fxeODi2jzOdHe4MQXHh2FICk42NuAb/7hzfC5S68y3lLvwNfuuxELgQScdg4kFUE4HAYABGMClsMpdDa64HFat7wUcnkpiqJVIrYyy6sUrI7t3Mx5uN1u3Hfffbjvvvvw13/913A6nZidncVv/uZvIhgM4uabb8YHPvAB7NmzB0A6DrW3t1fbv6enJ8NqY+SLX/wiXv/612uPGYbBnXfeCYZh8Du/8zt43/veV9H8zdgyYkeWZYiiiMHBQdjtdsuKBJYrdhKJBM6ePYu2traMbJ9Kxy0FfeuJY8eOaVH6G8n8/DwmJiawY89+fPpnM3BwDESWQTAh4emhVfTvakJXQ+7Ypaggg2fTX4bFiABBJnCzDBqcPAYXo/C5+JyuJzVW69ojR/CLSwlEBBH1Dh6CrOD0fBStHhta6zJv9qWKneZ651qdnDSSQsBKCYiJCF6xrxPPXVwAy8tICTIkhcBp55FIChmRM7JC4LDzYBkFqbU+WqYZUBkZVkBO0aE2m8w8MWSkWgP5BZARgyhJtzMgULQg3Vz7mLidVBFllmJvhs7apBCClL7Nhv6aaDV5tAlox71mZwv+6y/fBJ/H/LN209XdeGpwFieGlyAoOquVPg5p7V9RSmfD/e+vn8C3/vT1GWLD7bDho2+7DgDw/IUFfOQ/09aceFJCOnaegKzFdcdS6RgPB88hmZLwoX99Gk985u6cc8xFLrHzmf8exINPTYDnWLw4KiEYE8GxabfT6ckAfvUzT+Bnn3xNScdS4VgG3c3pmLflZBgMw+CHp2bxtz8YAsMANo7Fp3/jGhzZ0VjW+IUwuryGh4dBCCnK5VUtLmfLTj7i8TiOHz+OX/qlX8LHPvYxxONxPP300xktJMzumbkE52OPPYYvfvGLePrpp7XnnnnmGXR1dWFpaQmvec1rsG/fPtxyyy2WnQNgXri9JihFmXMch2QyiRdffBEdHR04ePCgZW92OaJkeXkZL730Evbs2YMdO3aYnku1xY4kSTh9+jSSyST6+/s3XOioMTLz8/Po7+9HWLIhJSq4qrUOPicHB89ClBW85eo2ePP8Aqx38pCU9CIXE9LWO489HbfDsSyCSSnLfacoCgYHBxEIBNDf3w/G5kBUkFBnSwsjB89BVghCieyg9lItbr/7hoNoqHMgkhARSQhQJBHPj4Xxxr8bxHJUxI276tHmc2Fvjw8t9U60Nrjg0Lkr1ILHKUlBSiaw8zzsPAsbl3az8BxnEARrCz/DpEWDvqJvQfSLNrMuFrSx9KLKuGtmXNGe7oZ0jRZ1v1z7aOOqx9Tdcth0HyqWZWDn2Wzdo+6fUQ/I7FyNcU6MdjiOZeDzOBATFPR/8AHc+cf/jZHZgMkYgMvOw8GnP1v6EXkbq8WJgQEESQHPsVgJJbRtjN/xqaUI3vdPT2A5lADHMFAIEE3KqFfFNZPex2ljsRhKYjUqYj6QxPvvfwqBaMp0frkwW2RPT/jxLw8PYSUiwB8VIKoCEeku7ATA4HQIPzpVvCtheD6EB5++hNnVzPYxiqJgOSrhsz8YgoNnUefgISsEH3/gLARpY6zXHMehpaUFV199Nfr7+9HV1YV4PI7BwUGcPHkSY2NjCAQCVbWm13KAciXE4/GMmB23240777xTc1kBaUvO9PS09nhmZsa0WO//Z+/Nw+S4y2v/z7eW3nv2VTMjjXbJkqzdxoaAwYDBgB0DYQuBCwECIfkREkKcey9kJyY33JB7SUIuAWLIBUJWQ2IChLAEDLZlWxrto232fe+9u6q+vz+qq6a7p3ume6ZHGvn6PI8fS6Pu6uqaqvqeet/zntPT08O73vUuHnnkkbwug/PalpYWHnjgAZ544omq7b+DG/+bWSOklIyNjTE3N8fBgwfXpM8phkpIiZSSS5cu0d/fz/Hjx13xVzGsZxsrEonwxBNP0NbWxt69e6/7BZhKpThx4gS6rnP48GF0XSfotX1xkJLmgEpXjcamWh/djcunk28Ke7ilJcBswiCTnWzZVOt1YyGCHjXvWDqkNxwOc+DAAVRVRRW2f086G4RoSgsLW+xciEorO5ubQ/zLb9/Dh356L/fsDTCbMJlYSBNJGjx5eZrH+2N85w/v43+82+5XW1IS8OqLkVHZdbkm4EEAadMi7NepDfn4Ly/fY7dM1BxCkfvf4l7nkI5lyIf9BXP+Pfc/Zzs5hMchU7mVJWETgaPb61jSfcwTJRd+TvGfWwg6msL8yftfwj/87v38+FNvobnOv5TkFG6/2PfKfgefrlIX8vGq27fz/vsP4/N5mJhL4NFUro7N89Y//DeS6aVE9w137aIu7MPv1dA1NRv7sAnTwv6u2aqREGBaFi/MTlcVO19OXp3CkhKvbpO5uqAHXVO5pauBuw938b/e91M01vhJGjbhdbbw3Z5RXv2Rr3P52gCJRGLJdouhsLIzHUnxC3/xk2x0iCSdsUik7XuYJWVePfCjXz7FheH5FT/jjX/yQ+7+ne/x639zkuf9t2/z83/xk7zPn1jIoCrCbef6PSqJtMlsbOngyHogl/A5La+tW7dy5MgRDh06RE1NDRMTE5w4cYJTp04xODhILBarqjfORhIoV3M/yhk9P378OJcuXeLatWuk02m+8pWvcN999+W9ZmBggNe+9rV88YtfdNtfzvYjkYj7529961vs37+/avvv4KZuYzltK03TCIfD6+IFUC7ZSaVS9PT0UF9f7+ZKrbTdZDJZrd104YSJXu9QUweORqbQw6ajzsdL9jTxnQuTpJImQgh+/kVbijoR50IIwcFNYXY3B4mmDE4MR4ik7VbPljovHTUerk7aBGV2dpZz586xd+9eN0gPwKspHGwP8eRQhNmEPSK7p8lfNCh0NT47ZnyO/fUxLtU0Ylqz5C7SUxGL//u9S5zpn6WrpYb+sXlUVSHg09FVhYV4mnBAJ+TX0TWFuWiK+pCPn76zmw8+cIj5aJpvPDlAzMoUz2xyhMJOe8g1uxP5XKHiOIjCaorzd4lXU7jj4Ha+9J+DLEGJCo+iCJfsFuJNL9nDz9y1OJH0w//1Fva846+RuTlfhUSqkAhJCxRBXcDj6mh+7Q1HyRgWf/v9Xnwe+1bn0zViyQz94xF2d+U/jGxqDPG1372PL//HBWLJDK+4bSt/9NWnEIhseocAIVGE4JXHu/mNNx61P7pIG6kmq4WR0m71zUQzZCyYihk8+KajPG9PK0PTcf7HP/S471Gy1Z6ZuMUz12aJzs+QTqepq6tzWzLFFrHCz+8dWcC0JHVBD/PxzCJJI9+xoCHkQUr4Se8U18ZjeHWFO/c04yvwqvrnJ4Z47OJ03s++dWqc/zgzxkv2t2FZFm11PkxLkjEtdFUhkTbxeRTqgze+hbSWKa9q7cP1xHpkY620lmiaxqc+9SnuueceTNPkne98J/v27ePTn/40AO9973v53d/9Xaanp/nFX/xF9z0nTpxgfHycBx54ALA7Em95y1t4xSteUbX9d/ex6lu8TnCM4bZv305LS8uyYqi1QFVVUqnly8qOALiYSd1y261mG8tpGxmGwR133HHd+tQOcmMnjhw5ssShUwjBG49v4vDmWnr7hmkOaTxvV/ljhj5dwad7uHtHA5GUkfX3sDg1GmMiKmB2mkQqxa5t+wjVLvUO2t7gp8mvMZ+yCOiCphI34Up8dpxRfkeA/tdPPcZSEYrkNz7/JB5dQQJBr4dfetUt3LarhUPbGnnFf/86V8fmMU0L07Toag7xzd9/FSG/zqf+5SzfPTNByhLYk0xLzxdVVfDoCsmUiaYqbsXL+ey8UM4lQmRZRLdTSCIKtT8KoaCPT3+9x148VxRS28TLctyGs8Ji53Nu3dbEr77+aN476sM+9nY1cHlkDsvK/T4UJzrC3i+RnW7zKQq/+ebj7O9uom98gYwpUbJaFcuSmNIqqYvpaArxoTccc/8+MBHNjqI7HyvY3VXPH737BW4VoxjZecG+do5sb+Kpy5PMxmzbhJqAzvRCkvf87//kG7/7St764p189lsXmVpIoSiLx1HVFBoamjh0qAPTNF0/lGvXrqFp2pIIgMLPrwt6MExJbdCuCsZSGQSCr/zaC3jrnz5GKmNRG9QJ+zQWEhn+96O9bptua0uIz//S8wjkZHY9dnGy6LH64bkJXrK/DSkl7XVePviq3fzJv14kbdjH+9Yt9fzMnzxG2K/z/71yJ7dtXzlwdLWohGjkTnlJKVlYWMjLl1qtseGztY0Vi8UIh1eetrv33nu5995783723ve+1/3zX/3VX/FXf/VXS963bds2Tp06tfYdXQE3/jdTAqUqI1JK+vv7OXfuHIcOHaKtrW1NRnArYTlS4gjiLl26xNGjRyvyCFhNjlUpJBIJnnzySYLBIMFgcF0uuOWOr2manD59moWFBY4fP17SilwIwe62EEe6QrSFlr8Y06bFVCzDXDK/3aCrgoasc+uJ4SgziRTXZpOciWj4mrsYTUieGY7mL5BZ1Pp1Ntd5SxIdZx/LaS+m02mefvppdF3n0KFDaJrGbbtbir5WSvB5NAJejUTaRKoqR3Y0oaoKX/zwy3jenjZ0XWXflgb+5tdfQjjg4ZGf9PPn/3oORbEniIIBD0JZ2sJSFFuDFPTr1Ic91AY16kP21F99yEdzXcBdlBc1MO63ZUXdz5KKCkwtJBmZiePPVkuWttiUIttdJFUeTcWrK9QEPHzgdUfcSkwufv+dzycc8BD069QGPXQ1h2ipD1C8tSVQENQGvHzwdUd48s/fwtvvse18u1trePNLdiOlJGOYSCTveuV+WuuXb586iKXMRaPk7Ef3ji7woc88tuz7NFXhc7/6En7n527Do6s0hL0EfbqbRdVzbYbW+gC/+9Zj9sh59vrye1Xqg15u22VXIVRVpaGhgR07dnD8+HFuueUWPB4PAwMDPPHEE5w7d45UKkUms2jqt6ejhlcf6ySVtlAUQY3fw2+98VaO72zm8798J81ZD6tE2kRXVUAS8Gr4PSqXxyL8w4/zK3YHS4iMB2cSfOCvn+FvHhslY8H9xzv4h197Pn/+rqO8eH8rT1yZIWNKxueT/OaXT3NlPFrWMV8NVltVEUJUreW1UdpY1SZd8Xi8LLKz0XFTVXactpXX663atNVKKEV20uk0p0+fJhgMcvz48YpPrrUklOdiamqKixcvcsstt1BfX8/k5OS6ZVkVO96OWWJnZ2fe6OFK21vuxjGfNPja+WnGo3a//1B7iLu31+UR4KGFFEiTyeERTCVA2KOTtqDZpzGXMJhPGjQGKs/aKUcYH4lE6OnpWZIQf3xHM6qquFoJsh44Hl3JGUaSxLIGiZpqJ6r/3994OVJK0um0+/nfOz2CaUo8mn3MvZqKoWuk0vaousTOODq0rZFfe90hHv72BR47N0YqbWFIEIpKNGUS9tsiYFugutx3c0hUbqNDlvTT0VUFPCoS6epBsgewhB/Q4seowk7cfvnBFu7YWVd0oXrBgQ6+9nv386OzIwR9OvfevpWf+/i3mJgfKb73AjY1BXndT+1c8vEffevtvORQF31jC2zfVMud+5YKJ0sh7NeJJDI5E2D2d//GiX7+IHE7Ib9echpKUxV++o6tfPRvnnY1WlJKrGyLCeD1L9jG8/a08Cf/fIbLo/Nsb6vhw68/SLjESHhhBIBj63D+/Pk875nfedMB7jm8idHZOLs21XBoq11VuXN3M//44RdxbnCe2qDO73z1tOv6bFeJYHw+Xyv0ludv4TPfvsyV8Zj7M79H5czgApqm8OO0wZXJNJ9+7yYaw14aw14e650m5NPQVCXbnk3z9LVZtreuT2u9Wi2klVpeuVleG9HY0NmP663ZuRlw05Cd3LZVW1vbdfvcYmTH2ZcdO3asOttqrW0sKaU7YXDs2DE3eqKaFSMHpcjO5OQkvb297Nu3j7q6urK3t1L15Ju9M/ROxVEVu+3w/atztIZ0DrQt3ijjsRijo+NsaW9lYTrpahLsD6jgy1UIxyDy4MGDS/rY+7bUc/fBdr53eoyMYZsFNoU0DAQZw8IwLby6yu17Wlfcx9a6/OqYaUnuvKWdyyOzROK2GHRrWw1ffvDlhPw6z7+lnS9/t5dP/OMzzMVNPLogmjSYiWbwqNhtMHJIWDHIwn/LEqAiep9dnfU8b28bX/vxFbyaSsowuTI8j+korguQOyH+znsP8F9etoeAmmF8fJxLly7h9/tpbGykoaHBnRzc2VnPzs7FqkJrXQCfrpLKmHmEpiag8zO3NdMf0bnrw48Aklff3s0fv/v5aKqdofZTBzr4qVXkUv3cS3fzJ/94yp0qEgI74NOSLmFfztBOUxU++ubD/N5XniGVMVEUwfNvaeX2nCpgZ1OIT7zreRXvmxCCmpoavF4vhw8fxrKsPO+ZWp+PrVsaaGjw5e1jV1OQriZ78bpzdzP//MQQmmpfa4oiOL6jccnnfPe37+bvfzLAD85Nsr0txD88PkzAqyKEQMfizHCM0bkkHQ32eRv0aszFM2jOLUPYBGm9sF5Eo7DlFYlEmJ6e5syZM1iW5eqp6ups0p47jn2jUO02lmmaG+J7rRUb9hs4F6ZjEjg6Oloyz2g9kUtKqrkva5nGcqpK4XCYY8eO5d1o1zOl3IHTvpuZmeH48eMl9UGmJfnhtVlOjUbRFcELttZxoD284j5em0kisPOjLGzvmpMjUQ60hdzfQXJigta2bgxVxasmWcjY4aDzSYOgrlJbMM6eNCzGIilMCQ1+jXp/ZVUfKSWXL192W3XFojZUReELH7yLL3z3Er3D8xzsbqBTG+f7IwH+/ZlhaoMe3vea/dzSWYemLM923v2KvTx6YpDphRQgCfl1fv9tx2mrD/DU5Uk0RXB8d4tb+dE1hfvv3MbvfOmEuwilMiYZUyKEktUuW2SVtsU/tIT+RlUFZnb0SggI+z186GeOcmRnM1vaapmNpji+q4Ufn+jh9/+5j7wOorO9rFwn7Nd5/r5NbGmrA6C5uRkpJfF4nJmZGTfSo66ujsbGRmpra90b96+9/jA/OjvCQiKNaVioqsIfvev5vPp5W/i1P/sWT/ZOZI+r4NEn+tnf3cC7XlFGOuUyeP9rDuDTNX7/K0+RMSzbRE/Cy450lay+FOJNL9rBnq56TvfN0FLr46WHO2yNTpXgEJnCqoRzTC9fvkwymXSDH+vr693F60P37WU2luF7Z8fRFMEvvXInL9q39AFOCMHP3LGFn7ljC70jEf7xCbvCljYsYhkTy8qv1v7yPTv4rb8/y2zUrkR2NQZ48b7ibd5q4HpUVRxyWVNT4xobzs3NMTU1xeXLlzFNk7q6OkKhEMFgsCL7lGqimmRnIzhTVwsbluzA0rbVciezUy2o9gnvkB3DMDhz5gy6rnP8+PE1n0yrrew4VaXCFkrudteT7DjTXsFgkKNHjy57vJ8cXOCJwQUaAx4sS/LNizPU+DSCK5AdTRUkE5Y9qSUlpoRI2sA0Tc6ePYuiKLzw9qPEMhbDC2kCRhRpWtSGPPh0hS21vjwykTIszk3E7CdXIZiMpdnZ6KexzMXKMAx6enoIBoMcOXJk2Yvf51F5zz173L8/9tgkv/XGw/zaA7dmR+cFwTKecJtqfPzrb7+C/zg1Qsa0eOG+Nldn8qIDxdswmirc2o1pSowsQVFVBem0sbIiWzsKghXNkL0ejb//768g4FV5+NvnMU3JW16ym1u3NfHa3/0Gl4bnyJgWHk3lF17cgt+r2a7Q2fermoIiFLdI9NLDXbzkUH67Uwjh6s26urowTZO5uTmmp6e5cuUKHo+HhoYGNjU28o2P3c83n7KDOe+9rZvW+gCGYXBuKGZ/Jc3+ZMOweOLixJrJjhCCd73yFu6/cyt//A8n6R+PcPueVn7pNQfc15SzIBza1sihbZW5l1e6n4VwEsY7Ozvzgh/7+/vdbKTGxkY+8fbDmNlroxwStq01yI7WEE/3zRLLTkZ6NPj30+P8l7u2AvBTe5v5s3ce4cTVWcI+jZceaCXoXb/lxrLsSqphSU70zRNJGmxrDrC9ef0ejjVNo6mpydVqXrx4ESEEfX19rqi3VMtrPVHtdfDZQng2LNmRUvL000+zZcuWstpWDnlYD7KTSqV44okn6O7uLmqUtNrtVkJ2pJQMDQ0xNDS0bFVpPdtYjj5g27ZtZf1OLk3FqfFqNvFQBJoiGJhNsq9uebJzsC3It66k3RgJjwIBVfDkk0+6QaoAYa/CnmaNUUMnkUiwra24HmA+mSFjWdRkBbWqCaORdFlkJxaLcerUqTVlrAkhCK/iRl8T8PDTd3SX/fqgT+f+Y2386zMTpDL28dVVhVTGdPfDHs7K1eHkejljzz9n/6wpgn1b6jm8owmPpvLH7/kp97Me+fFVLo/Mu20dw7T4zHdH2dVRz4XBWVRFoAhIm5KksciovnlymLlYioZwaZNLVVVpbGx0Tccc3YRToXh+d7ZCEV78/bXVehiYSaNlQ1WVbJuvWmiu9fPxd95Rte1dbxQGP6bTaTdnKhKJEAwGaWhooLGx0W2Jl4KmKvz+mw5w78f/E5+u4lGhNqDzue/1cc/BNtrr7VbW3o4a9nbUMDGf5AcXpuio93FwS926fD8pJRaC//lvVzk1FMl+Z/jFu7bw/B3FBdbVhqIoNDU1UV9f77a8ZmZmlrS8cquV64VnAzmpNjYs2RFCVCT8dcJAy0nyrgTj4+MsLCxwxx13VNW3phKy41Q0hBArCrPXq401Pj7O2NhYRf49IY/KbDyDPzuBYlgSv66iKNayAuV9bSGuziSYT5pYSEKKJLkww67Du/L8cxyU440jCoQk5dwKHE3SgQMHqKmp3sK5nvjFl2/h8I4WegZi/OuJftIZi2jSntRRVXsyZyaayh85F7nn0+JxNIGXHd3itspyMR9LY0npjiyriiCRNvnELzyft/zhtzAtiWlZLLjmffbr4imT//bw4/zFL72o7O9UGAjpVCh6L19jeC7DQkbjmcE4iYwknjEIe1W2tdXkVV/WEzfjk6/H46GtrY22Nnt0PBaLMT09zblz5zAMI0+LUux+kzIs6oMe/B77YdA2voSZaNolOwDfODXGh790BlPafcwX7mnkL955uOrHS0rJuZEYPcNRGoO63cY1LD77wyHuLBhuWC/kto9yW17d3d1LWl5OtbKhoeGGtrxWQjqdvu42JuuFDUt2wCYE5Y6U54aBVgOmaXLu3DksyyIYDFbdoK9c87pYLEZPTw9dXV10dnau+Pr18O9ZWFggmUxy2223VSRU+6ltdfztyXGmYmmkhJaQh/1tIVLxyLKErCWos681xJWZBOPzdqbV1uZmhtNe6ossLCsRvFqfjqakiaZNVAFpS7Krsfh4PCxqkqamppbVJFUTlmUxNzdHTU3Nmp76FEXhVcc6eNs9jXzwtbfykS88wY/OjRFLGgS8GqaU1AQ8HNjaiEdT6Lk2zUw0jabaIuq8lG8Jf/T3PbTVB3jLXTvyjvvz9rShCPs9albcenBziP3dTXzvj1/LqatThP0e7v2tRwv2UNA/HlnT96uvr2curfHBT/WwEE8zG0mjCEnAo5CxJFIIPvvBF5WtqVkrbkaykwshBKFQiFAoxJYtW/LaiFevXkXX9SUL86Z6H36PSixloAHJjIlH1+gqcET/zb89i2nZ8RqWlPzgwjRff3qM+45W1+keIJ4xXWNGAI8qiCQNTAnadfj1rGRsmNvySiaTzMzM3PCW10p4tkxiwQYnO5W42VZzkXcIRmdnJx0dHfzkJz9Z+U0Vopyb4/j4OJcvX2b//v3U1taWtd1qVnZSqRSnTp1C0zR27NhRsSK/KejhbcfaGZ5PoQrBlnofHk0hs8I+qorgSHuA8wPjtHgFXV3N+HWFoYUUm+s8TMcNRrJGbNsbfFmzuNLb82oKt7QEGY+mMC1boFxXQqBsmiaJRIJkMsmRI0dJWpBKGfh1dUVRMSz6EVWy+KXTaU6dOoWqqly+fBld1902jt/vX/VC2tEY5HMffDHJtMlHvvgE33xqEE1VmE+YPHN1GhAkUwYSQcYk68Fj5U1lSeCDn/kJ/ZNR/usbDrvb3tVZx19+4C5+83M/Zi6W5qf2t/Fzx+2FriHs48UHbWKuCIFVcA23Naz95vmBv3yMqfkkimK35kxpV6gCHjug9PuP97B3U8DVpdTU1KwrIbmZyU4hCtuIzsJ87dq1PMfhP3rLPv7b355jbDZNfcjHx3/2IDU5dg/JtEkybaKr2daiEJhYXBhZWBeys6M5gKoIYikDn64ym8hwsDNc1nVbDVSilfH5fGzatIlNmzYtaXmZpplnbHgjvXui0eiGJDtCiAeA3yr48a3Aq6SU3yj2ng1NdipBtciOM1q8f//+G9a6sCyLS5cuEY1Gue222ypqzVXrODjRC3v27GFqamrVpo1hr8aelvzTbCVCFo/HeepkD3XBTXQ21y2+DxiaTzMRTRP2qlhScn4izhb/IskwLYklJZoi8hYgn6awpa50NQdsbcjJkyfRNI1de/YyOJ8kmbEQAjRFYXOdF08RAzywW3STsTSJjIUmBM0h3W3fLYdoNEpPTw/bt2+nrs4utxdqVGpra2lsbCwZF5CLYg8IPo/K//j5O/gfP38Hv/zpx/j6EwP4dA3LsrAjHXOVyiLvfw4++cgZvn9mjD9+5+0c6LZbiXfd2sGPP/l6wD7+J06cWLI/jTU+JueTbsK5riq8cP/aF7qrowt2YKcQ7t6blv17l0Lwoucdpq3Oy+zsLCMjI1y4cKEiXUolWC9D042C3IXZ0e7NzMyQmpnhIy/2EUtq7Ny2mbYCjZTPo+L3KCQyFroClmW3kw9sLu/BrVK01Hh58JXb+D8/GGQmluHYllp+4YXleX9VA6udglqu5XXlypWilbVSqLaEIRaL3ZDYoZUgpfwn4J+cvwsh3gP8LPDNUu951pAdR7OzWjhxC6lUquRo8fWAk7HV0NCw4uRPMay1spM7Xu/EPszMzFS1NaYoCrNpwb9fnsWwJDsa/Gxv9CGEcE0S999yC96oymzCIOhRSRr2dFY8YxLw2CnUKgLVlEQyEl1KRiMpBudTSKDGq7KjwW+b35UBJ/Jj3759nDt3jvmkbSTnTE4lMibT8Qzt4eKL5GQsTTJja5IMSzIWSdNVu3zS/MTEBJcvX+bAgQOEQiHSadtEsVCjMjc3lxcX4Dx1BwKBis+PZMZ0eUwsbbLIanIEyyW2eXFojjf/0X/w3T98Nc0rfDcHv/ya/Xzin3pIG6btchzy8KrbtlS0z8WwY1Mtp65N49EEPo9CIm23SlDgV+4/QFezfYNuaWmhpaXF1aXMzMy4uhTn6bmurm5Ngw03uo11YTzF33zuKSSSt75wK7ftWL+pLydk03EdzmQynDp1yhU7+/1+d2E2hc7O9jCnBhZIZ29JrzzQzN37Wvjnp0YZmI4zMpdiPpFhU52P99zVTWvt2kjo3vYQf/LGvVX4ppWjmsaGq215VdtQMB6Pb8jKTi6EELuAjwJ3SlksBNDGhiY7ldxA1qLZSSQS9PT00Nrayt69e2/YjcupplSSsVWItYye5452547Xl0OgpJSMR9OkDUlr2LNswOdcyuJ81ENn2EQVghPDEUCiRsaZnJx0TRIP1VhcmIgznTCo9anc0hKkbzbBbMLAmd42pJ2EHjNgfi5FSFdQFEEkZTIwn2J7w/LVHIfcjY2NcfToUdfQzrDssrsDTRHuKHexbSQzlhugqCl2Wyhd4phJKbl27RrT09McO3YMj8dTsjqgKIp7YwP7xudoKRKJxBLvlJVav2950Xa+f3qUdMYkU4y/CoFXE+40l7sf2ZgH05I8fWWKe450Fryt+DXzC/feQkudn28+PUhj2McvvWY/zbXL/07KwZ/+wh288aHvMB9L49FU7tge5r+88hDdrWH2FAR8Ovvn6FI2b96MaZrMzs66glGv1+uaGlbqn3Ujyc6Tl6f5+L/PIBT73Pvh+Un+/N3HK8qdWwt0Xcfr9bJ9+3Z8Pp/r7dPb28tfPjbD6UHTPTZeDe7Y1cR//fvznBqYZy6eIZmxqPFpXJuMc24kymfecZCQb0MvSyWxXl4/lbS81iMEdCOTHSGEDnwJ+JCUcmC5196cZ1URrLZ947iNOnELpbBeNzRnYerv72d8fLxoiGYlUBQlLyenXDixD8WE0CuRHUtKHjkzSc9IhJRhkc5YvGx3Ay/YVl/UW2MsmgEpCbhTWhY/Od/H0SbBsWPH3BuGR1U40GZfaM6x7673M5eMMp/NzKrxqjQHBDNToILrE+LXFBaSy5Nfy7I4d+4cUkqOHTuWd5MIaIJpS2JaEiEgZUoaAsVvIkIIVMX2+NAUm2xICWqR88U0TdevaSWfojMDc5y6NktTjZeXHmzn6aszfP47l5FS8s67d3BsXwPz8/NMT0/T19fnkh1N00qery++dROffM/z+NTXz/H01Rk7oDOHHNUFPXzp1+/igd//9iLhEYKw3+MmeFfilyKE4LXP38Zrn7+t7PeUgy0tYb7/8ddwZXQBnwYLE/0cOrS57Perqpr39FxowLdS0vhGwcPfv4ZpSWr89u8kkTZ5+HvXrhvZAfsepiiK65eURucrPQm+15fvdJ0y4Is/uEoalbBXY2IhhaZALGXQVudjIZ7hzHCE522/PqPi1cb1NjYs1vJyrEcckrLWNaucxPMbjN8Dzkopv7LSC581ZEfTtIoWecuy8hxxl1PAF0sWrgYURSGdTnP+/Hk8Hs+qMraKbbPSyo4zYl1KCL3SNi9MxDg5EsEr4PRIhJRpcW0yxn9enuE3XradcMGTmkdVsbK3wXQ6w8jIOJ2NNezdu9jeMC3Jxak4Y5E0iiLY1eRnU9hL0KNyrCPMQsp2Zq3zaSRiURRpb9H5PaVMi5C39CKVTCY5deoUbW1tbN68Oe93K4Rt/Nce9jAVyyCxJ8TqlnnibAl6GI2m7GqJhDq/tqS6lUwmOXnyZJ5XUCn8408G+G9/cxKJbfa2sz3MxeEF0oZN6L97epzP/393csfuZpekp1IpLl68yPj4OCMjI3lan1xx+b3HNnPvsc289RPf54fnxkhkwy49muBffusednXUcvYvfoYz/TP865ND/N0Pr5JMm3h1hQPdDTxvz/o54VYCr65yy+Z60uk05ybWtq1CA75ircNSmolq3xtGZhN8+9QoAC+7tY1NDaUrTcU+W67kFFliO//y9Cj/fGIEn67y8y/u5kiJANBCOIZ+YJOt9z98ipG5ZJ6TtqOrShoSiUHCytgPBYDEHjDIWJKx+SSpjIVXL/8+mPv5NxrXez8KW15TU1P09fUtaXnV19evSqe2UTU7AEKIu4DXAUfKef2GJjuVtrGSyWRZr00mk/T09NDY2MjRo0dX/BxN0zAMo+ojgY6oc+vWrVU1KyyX7OTmay1H+FYiO/MJ2zH38kQcCQQ9KhKYiKT54ZUZXllgE9/d4MODxdB0hMjCAo0NDdxZkBh+dTbBSCRNrVfFlHBuIk5AU10S0awt7qsQAq/MUBvUmYxlEAI8ikJ3iXbJ3NwcZ8+eZc+ePe7ESS4cclvv18uOlfDpCl01PtKWhSrEEqLjfObevXuLegU5nwv27+UjXzrp+thIKXn66gyKgEC2qhJPGfzKXz3Jfbd38dYXbWNLcxCv10tNTQ2BQICmpiYWFhaYnp6mv7/fTc9ubGx0F+xP/+Kd/LcvPsVPLk7QXh/gD99+jF0dNtmtCXi4c28bd+5t48W3tvP0lSk6GoO8/vlbbW3MBkK1yUZh6zCVSrnVs1gsRk1NjUsidV2vqkC5byLGGz7xQ2Ipuyr5qW9c4isfvJPtbcVTp9/6wq18/8yoG8aqKoKfe9HWij/3a0+N8NAjF+2YFik51T/H/3n3UW7prOFHvdP86OIUDSEPr7utg/pg6QDMs8MLTC6kqfHpTIiUS3icI/RTe1s4PxpjNpYm7DNYSBhoimRgOoYlFT729Uv80aNXefsLOnj3C7dwZiTC1ck4jUEPd+6od32dCj9/o5CdGw1d1wmHw+zevTuv5XX27NlVTXnF4/ENSXaEEPXA54G3SCnL8rLY0GSnEpTbxpqenubChQslF7piWA+jvpGREWKxGIcPHy57P8pBuQ7KmUyGnp4eQqHQknytYttcrmrWEvIgJaQM2+ciY0nCXg1VCKKppfvi11W6xTRpw+DYvm10NQSXZFlNxTIEdbs0rgl7EmshZVDnX3rK2jdaydZ6H21hD5Zlk49iI6dDQ0MMDg4u60JdieVBLjRVoBW5gQwPDzMwMFB2izJtWKQyljuyC/aCLrO/o4xhkTIkg9NxPvvvl/nbH/bz9f96F1taQu6+K4pCXV2dG9DqLNg956/wJ98c5PKUweamIH/8juP86XuWD6F86aEOXnqo8hDNZwu8Xm+eZsIhkYODg4D98BSJRNxpurXgU9/oJZo03NDMWDLD//5GL598x9Gir79jVxO/+uIGHh+zr8G337WV5+9urvhzv/LYIErWDRsgkjT412dGuTAS4X9+4xJmlrX8y9OjfPEXjxPOeQjIJZvObJyUktZaL6NzqezPobXWy4dftYvZeIb/9a2rDM8mCHo1hICTAwskMxamhEza5K++P8CZ/mkG5i0sBIoCt1+q48F7t+dp6ZzP3whp4xsB5RobXrlyZcWKJdhtrGKxRBsA7wVagL8o2O8/lFL+bbE3PGvIzkrTWI5R3PT0dJ4QtRxU07DQmfpKp9N5Cc/VQjnEzIl92L59e1mp7Sttc1ujn7t3NjA2n2R4JkHYq1HnU0lkLPZvyn8iTafTPNlzFiyLe+84WPIm5dNVFpIGzvS2JSk59u3snxDC1QEVwrIsLl68SCqVum4u1FJKkskkExMTHD9+vGyfIq+ucktXLecH55FYxLJtJmFJkhnT1dIEvBq6qhBNZnj4u1f56BtvLUnUvF4v7e3tvOdz5zk7kEYime6b46c/9h0+/dbNdLU309jYSCgUeu4peRkIIdxpJLAfGk6cOMHo6Ci9vb2EQiG3KrSatsFsNEUuR1eEYDaaLvl6KSUHNnl55/3HK/6sXKQyJrG0ScaSBDwqArtK9Jf/cRVdEQSy5Gsykua75ya57+hiJTqX7OzvDNNe52dwJo4iBE1hD+11Pl5zuI033NaRHUdXeegNt7jv/8pPhjk9HEMxyVoH2Nqwn/TH6apV0ZAoUuEnl6c5N9TM/q78Vvv10MrcLFhOoLzSlFexc7fcNta//du/8YEPfADTNHnXu97Fgw8+mPfvUko+8IEP8OijjxIIBPjrv/5rjhw5UtZ7i0FK+YfAH674wtzvX8mLrzeqNY3lpIQ7VYxKL4y1jrU7KJz6ctT01cRKbayRkRH6+voqin1YaZtCCF64vZ6jnWH+uWecE/3zeDSFNx7dxN6cvKqhqXm+emoUQ6vHJETtcITbu5ZqhAB2Nvp5eiTCfNJAAo0BjeZg8ZbSSpUYx7SvsbGRPXv2rHherbaykwunciaE4NChQxUTiL96/x38/J/9mBOXpwEIeu2Rdgn4PSqWZHGsXkI8vTIZn5hPcmFoAUUBIRRQFUwEKV8bXq9Gf39/0TbNcygNXdfRdd09r6LRqDveXtg2KOe+84rD7Tx5ZQbDtK83RRHcc6i0L1E1Wng/6p2ifzpBNGVAEuYVQUuNlweOd/D1Z8aWhIOmC6YSc/fBq6t86u238vB/DjA0k+DgllrecFvHsq3PrkY/inCGNQSmtPVjFhAK2vYKpmmQjGfoOXeR1ITqLso1NTXPkZ0cVDKNVTjlFY1GmZ6e5plnnuFXfuVXXImHQ0qW+8z3v//9fPvb36azs5Pjx49z3333ccsti4T2G9/4BpcuXeLSpUs8/vjjvO997+Pxxx8v673VwoYmO5WgVBvL0UqUSgkvB9V40nf8Y3Knvqod7QCl21iFlY1K3JCdRPmVEPRq/OzxDn72+NKWx8TEBH/fMw3eII1+D7NzSX7Ut0B7jZfNRTxbQh6V2ztriKRMFAXqfNqS8rWD5X4/4zPznL14ie7NW9jS3lzWwrBWsuOEh27bto1UKlX2+3IXjZZaHx+6/xbe/3+ecFsIHmztw7teup2/+tZlDNOyE6d1hQdu37zivvt0FVNKt3JgT1cJakJ+2tubaG9vz2vTDAwMuPqVjVz1udE+N87nCyEIh8OEw2G2bNmCYRjMzs66E58+n89tG5RqZ77+js3MxjJ8/rtXQcLP3dXNm19Q2peoGt/9j75+Ea8maA57iaUMLEvyludvZmtLkFcebOWRp0bQVYWMaeHVVJ63o3g+nYMav84vv3x72Z9/54567jvUylceHyFjSvt6D+g0hr30zyTd3NqWsId7f+oAAU0yOzvL8PAwFy5cwOv1YhgGyWSy6pXycrFRdEOrJX655253dzff+ta3+Na3vsVnP/tZfvM3f5NPf/rTvPzlL+flL385Bw4cyPuuTzzxBDt27GDbNnvi8k1vehOPPPJIHmF55JFHeNvb3oYQguc973nMzc0xOjpKX1/fiu+tFp61ZEdKSX9/P2NjY8vqM8rd9mrbWE77bGZmxvWPcbBeoZ2F23QE2c3NzWVVNgqxFu8eRwQ9PT2L5WsjnC2HOxk2U9FMUbIDdszDcn49Dkot8JcGRrk8MUdb1zZiqoeBuSRb6n0lSZMDRVFWTXYKw0OvXbu26gWppdaHYVlZnz/hjsG/955d1Pg9/O0P+/DqKr96315uL2PUuDbo4c0v6ObvHusnlTHRNYV9XbUc2ba4eOW2abZt2+amYw8MDBCNRgmHw+6C/VzVZxHFfr+aptHc3ExzczNSShKJBNPT0/T29pJKpairq6OxsTEvbFMIwXtetoP3vGxHWZ9bDbITTRioqq1x8+keIkkDX3Ya6oP37iTg1fjBhSnqAjq/8soddK7gXVUphBB86JXbue9wK98+O4WmCl6ws4G//MEgA7NJDMtCUQR+j4pPV/DoKq2trbS2tiKlZGJigoGBAS5cuEAmk1kxxHQ9sFGqS6ZpVhzrUww1NTW8/vWv5zvf+Q6f/OQnaWtr49vf/jYPPfQQZ86c4R/+4R/YuXMnYGsSc6dLOzs7efzxx/O2V+w1w8PDZb23WtjQZKeSi9iZmAK7hXDmzBm8Xi+33Xbbmk/C1baxckXAxTxV1qOyU7jN3NiH1QqhV0vKDMOgp6eHYDDI8eNHOXVijJQp8StZaxdpC5nXisL9k1Jy6dIlBmKwbXMXeja1O5a2dQkrfeZqKjsOuZ6YmMgjtWupEu3trOUNd9rkRAhQFPjvrz9AyKfznpfv5D0v31nxvv/BWw9zZHsjT12ZZmtriLfdtX3ZFkNhOvbCwoLrlgu4VZ9wuPi00HOwIYRwx9u7urowTdP1SMoN26zUGbsaZOeFe5t49OQYQlMwLYmuKRzfbhNgXVX4pZdv55cqqNSsFrvaQuzKtr3HF1IMzyXZ2rSYDRdNmvRPJ9ndtmhyJ4TA5/NRU1PD7t273RDTmZkZrl69WpYItxrYSGSnmlEojmans7OTd7zjHbzjHe9YshYUu98Us2co9ppy3lstbGiyUwmcRX5hYYEzZ86wdetW2turEza3msV+fn6eM2fOsGPHjpIi4PVqY1mWlecMXA2jwkq/v5P5lPt7ePHWOr7aM05EUZCoHGwOsLNp7U+JuReNQzBrampo39SOmruQ5/vnLbu9Sr6vZVmcPXsWIcQSTVilZKdw8fqdN93Kq491MDQdZ09HDbd01ZW9rWIQQvD6O7fw+jsrj2zIrfps3brVrfoMDg4SjUZJpVKMjY3R2Nh4Xas+N2M2lWMFkOuM7SzQiUTCDdtsaGhY9km9GmTnw6/ZgyXhe+cmqQlo/Pqrdi0RAV9veFQFS9ptW0F2GhGJp0h8eS7RKBVimmsb4BzXap6j1XYu3ij74Xj15KKQ1HV2drqTiWBPvBZaqZR6TTqdXvG91cKzhuyALUQ9e/YsBw8erKrFdaVtLGe8+dChQ8vux3q0sZx97enpQdO0qhkVFiNlacPi5GiUhaRBZ62X3c32E+n4+DhXrlzhwIED7oVybSrGR//pgi2CBI63mLzqpUsrE6uB++SXJVjbtm2jra2N0UiKqVgGr6ZgWLZnjb8Ms7JKCEoqleLkyZO0t7fT1dW1ZOEpd1uO5qPwtUIIbtvZxG1lHqpqiKvLRWHV5/HHH3dF+JBf9VlvPcNG0EusBYVhm04FLVc31dDQsORYVoPs+D0qv/P6fe7fn7o2x2e+e42GkId7D7a5Y/DXE/VBnbv3NPLv56cRWZvEo1tq2dK49OFouapKoQg3tzIppcwTOq/lPrlRKjvV3o9yprGOHz/OpUuXuHbtGh0dHXzlK1/hS1/6Ut5r7rvvPj71qU/xpje9iccff5za2lra29tpbm5e8b3VwrOC7BiG4U4/rDRWvBqoqlqWO7Npmm78QDn7sR6VnUQiQSwWY/PmzUtiH1aLYqTMsCRf7ZlgZD6FpgieHlrgzu5aWo0p5ufn3cwn57Xv/7+nmY1lsknV8KNBi96xqFu2Xiucik4uwWoNeVCFYD5lh4m2hjxlBYOWq9lxqnfLtQivJ/m4kRBCoKoqW7dudQMinUUlEokQCoXcdkK1zTmfbcj1SMrVTRU7ltVe3L729Ch/9K+9GIb9cPBPJ0b5q3cddnPfVoO0YXFpPIYiBDtaA2WH8/7iizdzS3uQK5MJOht8vGxvY1G9XbnHoLAymclkmJ2dZWxsjN7e3rIE5KWwUchOtSs78Xh8Rb2rpml86lOf4p577sE0Td75zneyb98+Pv3pTwPw3ve+l3vvvZdHH32UHTt2EAgE+PznP7/se9cDG5rslPPEEo1GOX36NF1dXUSj0XUpJZbjzuxkS3V2dtLZ2VnWvquqWtG0zkrInfqoFtGB4mRneD7F6EKKxoCdx5QxTL51eoi37PYscaX+z95pZmIZdFW4QttoBoZm42smO44APJ1Oc8cdd+QtpIoQtIQ8tIQqW1zLISjOJMFK4vfrTXYqbcGtF3RdzxOROmOtp0+fdp+oGxsbqampuemrMuuNwgqacyzPnDmDYRhuvMVaqxMAf/rNy6hC4POptg5tKs4PL07z0v2rm2Sdi2f4tb89z9i8PVW1rTnAH/3MXgLLRLk4UITgJXubeMkKIearnYTSdZ2WlhZaWlqKCsiduJW6uroVRb/P1jaWZVllCZ7vvfde7r333ryfvfe973X/LITgz/7sz8p+73pgQ5MdWH6xcDxjnKf5gYGBdRlDXamN5ZCMUtlSpVBN87rLly8zPz/P8ePHOXHixJq3mYti+2lJiYL9+8lkMkxPz+D1Btm+Y8eS4z8dS6MqAkuCKkAgMSW0htf2hG8YBqdPn8bv9xMIBMquGEgpMSxQFYo+KS5HGBzxczQaLcso8P+Vys5yKBxrdZ6oR0ZGuHDhgmtm1tjYuKqqz/9Lx7fwWM7Pz3P58mW3OuH3+91jWekYtpSSRNpyJ7Hsc9c2tVwtPvODAQZnEm5O3aXxGH/75AjveMHy2XCVwLKsNS/whQJyy7LyQnZXsmDYSJWdau3HjbZ0qDY2PNkpBsuyOH/+PJlMJs8zxmk/rAfZKbb4OWGikUhkxTDRUttdaxvLad+Ew+Gycr5Wg2Jkpz3sJejVGJuLkUlE0QM1HOqsLVqi3toUoDHoYTaexrQkpgWbawQ7W1ZvB+BU0jZv3kxHRwePPfZYWe9LGRajkTSGaZ8nrSGNUMF0VimC4mihQqEQhw8fvi6ePZXiZiBXhU/UuZUKy7JWVfV5Nt2UK4Gqqvj9fvbs2YOUkng87kbiOGPYjY2NZWUhCSF44e5GfnBxCo9q69w0VeHI1rqS71npXBucTroVXecz+qcTFX/P5bBeIc319fV5Ibu5FgyFbsMbiexUs7LzbCI8Nx3Zicfj9PT00N7eviSt2qnAVFsTUIyUpFIpenp6qK+v58iRI6s6IdZKdpzJs3JjH1aLYmTHqwluq4nyRDSBf1MLWxuD3LGlpuj7j2yu5S23d/DVp0YwTUl3o5/XdqdWXdVyDBqXq6QZlsSwJLoi3PBAKSWjkTSWlPh0e8x2LJphi6bm5VAVIwwOueru7q5oyu9mIB83EoWVCsMwmJmZcas+wWDQ1VFUc6T2ZsPV8SgTCyl2tIVoCi8eh7xcKiEIBoMEg0E2b97sjmFPTU1x+fJlvF6vSyT9fn/Re9ZHX7uXP/qXXh6/PENd0MODr9lF1zK+Oist8ns3hbgwFsWryez+wt720q3r4bkkf/LvfQzOJtnc4OODd3ezqW75CtX1IBpO3IpjvOkQdCdg0+Px4PF4bjjpqUaVy8Gz7b614clO7mLhtIv27dvnBhzmYj0Ev852c9tYjnfNrl27aG6uPHQvd7urXfBHRkbo7++v+uRZMRQKdlPpNN8/cRpfIMTbXrSfgGflVs6bbuvg/kNtpAyLGr/GqZMnuTQVZyodx6ep7G8Nruh/43jZjI+Pu142UkrmkwYpNcBcIkOtTyOSMhiJpAGBAnTWeQnodsRCxpTuRJaqCNIOKcohO4XkznlSrrRN6Xz35yo75UPTtLyqTywWcxcVy7Kor693qz4b4Un6euB/PtrLl340gKYoSCR/+rZDPG+nLYhf7sm7cAw7kUgwMzPD5cuXSSaT1NbW0tDQQH19vVsdD3hUfvu1K4hkcrDSk/9/eX4n16bi9AzawdQv2FnPa4+2FX1tMmPx0a9dYj5pENBVrk0l+K2vX+ZTb75lWXPRcnUl1UIxgn716lUikQgnTpxwSWVDQ0NFnknVQDXbWOl0+oY5Uq8HNjzZAftkztVJlKrcVCvDqhAOKcl1ZV6rdw2Un1Cei9wg0eU0I+tVfpxfWODzP7zErAjhTXp46icjvPVIG80FImDDlGQsiU9bLGH7syGAAGOmn8nJJB5VxZJpRhZSvGJXA8ESxMk0Tc6ePYuqqnnj9KPRNNMxg7TmY2AhRW3SJJ428WoKqiIwLMnwfIrt2fwd52eaIrCkRACF91GHMOR6FRW6X5eLm5183EgIIQiFQoRCobz4hbGxMQa++122/fM/0/KtbxGKx2n0+7He/GbSv/zLyKz1/LMBPQPzfOlHA4Ctk8uYFr/6N6f40W+/2D23yr3O/X4/HR0ddHR0uJqUmZkZ+vv7URTFraBVEguyUiXD71H5+Ov3MBXNoAhoCOoltz06nySaWjT9DPs0FpIGo/MpuouMnJe7D+sNTdPcilpHR4dLKgs9k65X1ly17vvRaHTdH6SvJzY82Ukmk5w8eZKmpqYV20XVTCcvtt1Tp06h63pVXJmd7VZCdpLJJKdOnaKlpYW9e/cu+0RXzXKmg9HRUX54foA5tZGWkBchBJGUwaMXpnj7sUUjqKvTCXrGY1lBsmRbvY+mkJe20OKNbsLy4fcId6Q1kjIYmE+xt3npKemcA5s2bWLz5s3uzzOmxUw8Q0BXUKVJUFOYTWTQFcVtXWmKIGFILAs0VdAe9jASSWMYdgxDc0hfojMSQrjkSkq5Jq+icsmOlJKZmRm8Xu+abjDPZnLlxC+0PfMM/ve9D9JpRPZ61+JxrIcfRvvSl0h84QtY99xzg/e2NP799Dif++41JPCOF3Xz8oPFKx0Aw9n0cAe6qpBIm0RTBmGfvuqHmpU0KeXGgpTz+UIImssYRgh4VCxLYlkSRRFYlsS0JMEVfH5uNNkB+2HMOU6FpNLx9nHM83K9far9QFrN7T1Hdq4z+vv72bFjh+s0uhzWq42VSCSYn59n3759VXV3rGQaa2ZmhvPnz5cV++BUjKpFdizLIplMMjo6yqatO7l4Zd69qHyawkx8kWDOxDOcHIsR9ihMxQ2m4xmm4wbNoRTbG/zc0uJcPALbHzUHRdZop2WYG6Ca//J88aMQgLBvkqoiyJgWmhA4fMavK3TXeTEsiSJEXvvKgWmaXLlyhc7OTrZs2bKmG0g55MMR3CeTSaSUJJPJorlJ1fq8mxni6lX8b3sbIh5f8m+KYYBh4HvrWzn5hS8QOniQhoaGDVWK/+7ZCX79//ZgZMNdf+PLp1EUwUsPFNfc7WgLYUmJlNm2q2HRFPa4ovpqVXALNSmRSITp6Wk3FiS3fZj7edUkGq01Xl6xv4lvnJmypz2F4DW3Nq9IlErtg5SSy1MJ5hIGHbVeNtWun+ar1MNlrmcS4PpPjYyMcPHiRQKBgEt+NtJ5CuUZCt5M2PBkZ8+ePWUTmPUgO6Ojo1y7ds114qwmytnfXJ3K0aNHy7ogqunMnE6n6enpQQjB4cOH6ZtNIpm3HYkFRFIme1sX2X8kbbqj5ZGUScijkLGgxqtxdTbJ9gY/Xk2hScswZWggLExpoauCzoKb0eDgIMPDwyVbhroiCHpUYmkTKRRiaYOwV6MpoDEayZC2JKoQdNV684XsWdGylJK0aZE2JLG0gUQg0klGRkbo6Oigu7t7zcdvJfKRTqc5deoUTU1N7Nhhhz9KKZmbm3Nzkzwej6u9WGvr9GaH53//b1jB4FOxLPZ84xv07dvH+fPnMQzDXaxra2tvaBXgb388SMaUeLK904xh8ZUfD5YkOzvbwvzGfbt56JGLSKAuqPNn71iscK9Hu1oIQU1NDTU1NXnme7micUfoXO7nG6bFDy7NMhNLs7s1xIHO4llq77yzk8NdtYzOJ9lU5+NQidflohjZkVLy5WfG+dHVBZzde9uxNm4rMUSxVpRL+gr9p2KxGDMzMzc0xLQU4vH4c5WdjYrcMNC1wrIsLl68SDKZ5LbbbuOJJ56oynZzsRLZMQyDs2fPout6Ra2UapG+hYUFTp8+zc6dO7l8+TJCCLY2+Ll7Zz3fuzyHRLK53scrdy9WmvyanWljZRO6MxZ4VIEAkGBmF/6tPoMGr8qcqeLTdA62hQh7NSwpiSQzXL5yFcXKcPz48ZIXvRCCzbVexqNpxoE6r0p7rQ9NEQQ9Gqa0tTlFXVelZCpqEEkZRNIWHkUgUzGmZ+do6OjG9AYYXUhR69MJeFa/OC5Hdpx4ix07dtDS0kImk3Fvmrm5SblmZ+l0Oq/qU3hOPNsrO/pXv4pYgeyITIbAP/wDm//X/2Lz5s0YhsHc3Fye6aZDHq/303Rh6KrEbrUuh5+5vYtXHmxnLpamtc6X13a9HqPBhVYBzgJ97tw50uk0YFeei52PYE9GfvRrlzgzHHWvyff8VBevunWpUaEQgiOba4DySUkxojEwm+JH1xYI+1QUYVd4/+apMY52hd0WdzWxmkp6riYtd3putSGmqzVXLIXn2lgbGNVa5HO1MXv27Fm3m8lyC1MsFqOnp8f1kakE1ajsOIaNTr7X5cuX3X+7c0sdxztr3Mmm3OPTHNTZ2ejn0nTC7r8DHTUeFlImDQENf/aJVtMUdtSoNDcvEiVLSi6ML3BtZAK/L0RDYw1pC/zL3ENURbCpxsuwFaU1qLkLh6oIVEr/3iIpk3jGPlc0AQuRKGYqRkfHJiYWkhiWwIqbzMRMuhu9K+oGSqHU73hycpLe3l5uvfXWFRPD/X6/68xdOE58IxfuG4JotOLXaZpGU1MTTU1NrhdN4dN0KfJYbfz8i7v5ce80yey559NV3vWSlQXVIZ9GyLf0dn29fVAKF+i5uTmuXbuWN97uLNCOs/ipwQXOjkQJeOx7hWFKPvPDIV55oLnog0jPcITzY1Fq/Tp37axfMaqiGNmJpEwUsWgaqqsK8bRFImO5BofVRDXaeYXTc6lUyjU1dAI5nYegYkM61ZzEAlw/oWcLNjzZqeRC1jTNfdJYLZwx471795alE1oLSn035wn0wIED1NRUXnZdy0i7ZVn09vaSSCTyDBsdV2HnYtJVhWL3ICEEB9qCdDf4iKVNhuZTxDMW9T6NPS2LY5jF8qcGJma5PDTJpqZ6gsEgiYz9/p1NK5sPVhqTkDEkQkiktK32VU2lvrGVaNpCItCEhU9TSBsWUxGDYGPlN8hUxsLSwyykFfSUSTBbIerv72diYmLVRpTODTHX4t5ZuH0+n/v7v9GizXVBKASRSHmvK4JcL5quri5M02R2djaPPOZ60VQbh7vr+fx7j/HlxwaxJLz5zi4Oddetens32vTNcR7etWsXgEskc8fbx6JehFi836kKJAxJxpR4C1LMv3V+is89NowpbVXff1yc4fdes2PF0fPCc72j1oMiBImMiU9TWEiatIR19xqsNtbjevN6vXkhpo6OyjHfrK+vp6GhwW3NVnso5TnNzgbGWio7Ukr3CaWUNma9byyFsQ+rNUdczUg7LOpHGhoa2L17d953dS6mcgP3arwaNV6N9nBxUWAhORkZGeHq4DitbVsIBuz36KpCylxKYAxLMhFNE09beHWFtpBeMrzTMKWt6QECHgWPar8uZVpMRw2ikXl0rx+h6CQNE1MKVGFHW4B9w13NGZUxJQtJK7sNSTyVtS64fAGAY8eOrfnmWGhxbxgGAwMDTE9Pc+LECQKBwLPOkC/zhjegf+ELy7aypK6TedObytqeqqpu1QdwHYgLW4blOBCXi1u31HHrlrqqbOtGk53Cz3fOx87OTjezayE9iZFOM58Cr66StgT7O8JLCIyUki8+MYpXV9Cz1+ngbJJnBhd43gouzoXHoD6g84vP7+CzPxlhLmHQWeflF+7YtG7Har2zsQp1VI4NQ25rttrE5Dmys4Gx2tHzTCbD6dOnCQQCJRehSj0tKoUjBK6trV1z7MNq2lhOgncpo8Rqip5zt5dbSTq8/xYuz6YwLYkibJOxtgL/HiklQ/MpYlkvnWjKpN+woEi7KGNKhhdSmJZNWmYTsKnGQ8aUzEcTRGdnCNU1YqCiCUmdX0cAExGDDALDtDAsaPFV/rvImBZSgCIkAgnS5OKlQZrCoTVPeJWCpmnU1tYipWTbtm152grTNJ8V4ZvpX/5l9C9/eXmRsq6Tfv/7V7X9XPJYzIF4ownFSz2ApDIm/dMJQl6NTfXr195c7gEoV3v2P5qi/Mm/X2NqIcmuenhF6xznz5+nsbHR9Z+R2IJtJyTUPkclKWP5+06pfdjdEuDjr9mOKVfWRa0V17uS6tgwNDc3uxXekZERotEoTzzxRFHDyEoRi8WqPpRzI7HhyU4lN+XVVHacyIVt27bR1lba78LZ9nqc0M4+OELVtaLSNtbw8DADAwPLJnhXk+xIKcmgkE6mGX76aerr6jh06BBCCLYgGJpPYUlJU1BnU01+RcKwIJ6xCGQ1NKoiSGQspKIv2b+FlIFl4TompwyTiWiGhblZZuYjbO3qQNM0ZmMGiircXr4vZpK2LFRFoTmk0BCsvMKmCAHSJmGpVJqRsQnaWlvY0la/8purgEJtRWEMQygUcqs+1Y5XWU/IbdtIfOEL+N/2Nshk8io8UtdB10l84QtVMRYs1FA4LRonFdup+txIQXixB7DBmQTve7iHaNLEsCxedbCVB1+1NKB3vT6/GPa2h/g/P3fA/btlWW5bJtd/5tCmAM+MxPHpCmlToisKtywTL+Fsq9h9eSqW4d8uzBBJGRzcFOKOLetH8tdrbSgHToW3qakJy7LYsWPHEsNIh3SGw+Gyj0E8Hn+usrNRUamD8tDQEIODg2VFLjhkp9oOmOl0mrNnz1Y19qHcNpYzcZZKpVZM8K4W2TEtSe9UnL6YIB5L0NXcydbuVvcCbAl6aA7YT3nFE8kBpOvD4TgdK2JplouUzuvtf4smTeYj86jSJNzUjlA0hBDomsCU0vUz0RXwmzF2rCEKxKsJPKpCOmMxvzDL5s1dtNatPvi0XJQSRBfGMOSGb0op3apPJTfDGwXz5S8n9thjeP7sz9C/8hWIRjEDAay3vIX0+9+/bg7KuS0ap+ozPT3t5qY5x7DUA8N6oBjZ+Og/XmQmmsajqahC8OipCe7YXs9de5uq/vmrrWgoikJtba0bv+L4z9zTMUV8IUlfVKM15OE9L+ha4s5ezj7MJw3++HuDxDImuiI4Nx4nkjK5Z/f66DDXw8S1UjiEq9AwMp1OMzMzw9DQEJFIZEmIaSk818bawCi3jWWaJufPn8eyrBUX+dxtV9PDx4l9ME2To0ePVvXpuhxikkqlOHXqFM3NzWVNnFWL7IxGUozMRkhF5qkPhzE0PyORFF21i6V2IUTJOSpNETQFdCZjBkLYBKXBrzOtsGT/gh6V+aRJxrCYT2aYjqTwqRpNtTWkDclc0iDsUQl6FPy6Qjxtj8uHVJNMeu2/69mJQRamR+nu3kpbfWBdRl5Xg8JsH2ehcW6G5brn3kjIbdtIfeITpD7xCSKRCENDQ+zdW36m01rhVH3q6+tZWFhg165dTE9Pu8Jcxy+lvr5+XRfBYmSnbyrujrgLIcgYJn1VThpf7vNXg1z/mUMHbDI+MzPD9PhVTowuFePmohjZOTsWI5Y2qfPb9/aMafEfl2fXlezc6IGAUoTL4/HQ1tZGW1ub+6DjtLcdDyrn2Oa+PxaLVfQAPjMzwxvf+Eb6+vro7u7mq1/96hIj2MHBQd72trcxNjaGoii85z3v4QMf+AAAv/3bv81nPvMZV0Zx6tSpe6WUj67mWBTDhic71W5jOanpmzZtoqurq+ztV5PsOKPtra2tK44drwYr7aujz9m9e7crzFwJ1SA7UkquDY2RSBk0NjRgWRa6IohlKjuuLSEPAV0lkTExJQR0hRllaevOryu0hXUGp6NMz8UI+PzUBr0kDfs9qiJordHxqLYXT51fIoHJJKTW8F0ty+LcuXMAbGptxqNy3YjOanx2Co3Oct1zhRBuxaKSzKTriY3gK5RrD+AIc6enp7l27Rq6rueNY1fzGEoplyyyWxr99I5F8WgqUkp0VWHLMtlSa8F6LPJCCIKhEKMJFaW1gS01OlYyssQnqaGhwdVObYTz8kbvQzki6dwHnS1btiyZRjQMg+9+97vce++9FY+eP/TQQ9x99908+OCDPPTQQzz00EN8/OMfz3uNpml84hOf4MiRI0QiEY4ePcrLXvYybrnlFgA++MEP8qEPfch5edWIDtwEZKcSrNTGcrxNSqWmL4dqkR0n9sEZbZ+Zmam667OiKGRKCDid1t1y+pxiWMs4O9gGiT09PXi8NdQ3NkE6iWGYpE2LkL58VStjWJiAR8G9sfp1hel4hqQhWUgaxJQQaXPpohebm2ZqeIz2jq0YqEgECpJERrKpVseXnQgxLMlMzCBjSRKmjilXdwNPp9OcPHmSlpYWtmzZwrVr11a1nRuFwqkPpwQ+MDDgen04C831TJpeCTdqoSlW2ShmCjkzM8OVK1fccWynKrTWqk+xz//d1+7hF79ga3YsCa882MJde5aPmKnm5691e//YM8HfPDlGMmMR9CjUBXR+82Xd7N69KMZ1JuZSqZTrR5PrOryvLUjIqzKfNNAUQdqU3HfL+hyDjYLVTIQVTiNOTU3x+OOP89GPfpTe3l5M0+QNb3gDd99995IqTSEeeeQRvve97wHw9re/nbvuumsJ2XEiSQDC4TB79+5leHjYJTvriY1zt6oCSmlVqjHSvVayI6Wkr6+PiYmJvNH21Y6JLwdVVUmlUnk/c9pmmUyG2267bVVun6slO7FYjFOnTrF161Za29q4NJ1gKJkkY8GmoKfkeDrA5ak4IxHbOymoq+xrC2Z9MwxShkUga/YzKwTzKYljui+l5OrVq8zOzrJn934iGYEfiGUsDEsS9qnUZd0KpbSJjmmBV1VICkgrfjdfq1xEIhHXcdopxVZy3JzptLUsgNV2UC4sgS8sLDA9Pc3AwECegPd66lQ2EspZ7IuljTtVH8cl1zmGlRKHYp+/udHP3//SMfqnEwQ9Kp0N1a3q/EvPBH/z+AiGJbmzy8vrD1QvguG7l2b526fGSaRtU8BY2kIRGT79w2H+x0/vXGK3YJomTzzxhOs6rOu6W4n81Rd28q3eWRZSBgfbbYHysxnV0JQ2NTXxwQ9+kA9+8IPcf//9vPWtb+Wpp57ik5/8JAAvfelL+fCHP1z0eh8fH3eJTHt7OxMTE8t+Vl9fH8888wy33367+7NPfepTfOELX+DYsWN8/vOfr5dSzq7pC+Vgw5OdSi7+Yq+t1kj3WsiOYRicOXMGj8ezJPZhrRWTYigkUKlUipMnT9La2rrqsefVfn+nmpZrkLir0U/YijEzk2RXo7+0uWI0zfBCGp+moCiCWNri0mScA+0hTJkvYFbEYhSFaZqcPn0ar9fLkSNHkAhSC2niGQtVSHw+la46j/t+iV3Z8WR1DpoisJAkMxaqItBUseLo6sTEBJcvX+bWW2/NK/2We6wdobVpmu75oKpqxS2C9axwCCFcUem2bdvcpOxr164Rj8dJpVJMTU2tu07lZkaheDSZTLoLdSKRqHhkuBTZ8ukqu9uqLy798ZVZ/uIHg6hZd+JvX4ri1wXv66rO9p8cWHC/k6LYsTMZ056sKgZVVdE0jZ07dwJLj+fRmhoaOhtoaFg5buFmR7VF0olEghe/+MXcd999/M7v/A4zMzPcdddd/P3f//2S1/7BH/xBRduORqO87nWv45Of/KS7Lrzvfe/jIx/5CEIIPvKRjwB8Anjn2r+JjQ1PdtaCubk5zp49W9I7phKsdrF3Yh+2bNlS1LNgPcJLc/U1zjEoJy19pW1WUjFwKllTU1NLqmlCCHyaiirNZW9AsZSJEKBkiYZHE0SzwmG7jWVPdwkBplDwCItEIsGpU6dc/YQDv66QNEBT7AmtSMqiIZAVcWb3yankSARSaMwmTJfk1PmVorb1ud/z2LFjRauGy5FZKaVb0dE0zSW/pmm6/4F9/O0FYGXyc700LLlJ2ZZl8fjjj7vxAY5OxfGkWW8zzo3UxqoETsDwpk2b8qo+fX19ZWUjXe/v/qMrc1iWxJe1fhDAiaFk1bZf69MQihNyaj+IWFKyo9mutH7jwjQ/6Y/g1xVed2sze1ryKwyFx3NhYcFtw652BPtmQbWNDROJRJ6fVENDAz09PSVf39rayujoKO3t7YyOjpa0UclkMrzuda/jZ3/2Z3nta1+b934H7373u/nYxz52WxW+houbguxUWpqXUjI4OMjIyEjF2pRSWA0pGR8f58qVK+zfv79k7MN6kB1nmyulhleCSgTKpmly5swZdF13q2mxHJKiCFHW79SnK/YNz5IIRWBYkkDWMyfoUWkL6UzFM0gJATLIRIKnn77ELbfcktdfNi1JNC0JekS2rSSZixvoqiDosfen3q8wE7cwDIuMFEhp4deyr5eS+aSFV8vPAbMsi7Nnz6IoCkePHi1KRJYjJ7lER2SPiUNoNE3Dsiz3Nc45Ypqm+5pS5pc3As4+O8ntyWRyyXSSkz/1bKr6VJNYFlZ9HC3KtWvXSCQS1NTUuFofp+pzvclOrT9/yTAlVc2aeu3BFp4ZimBaMvuwI9jREuAXX9DJv5yb5l/OTePTBNMxyZ/+YIj/+tLNee9PGxZaduBAURTq6uqoq6tj27Ztrv5scHDQFd9Ww2tqIwjkofpkR0pZ0fbuu+8+Hn74YR588EEefvhh7r///qLb/Pmf/3n27t3Lr/7qr+b9m0OUAP7pn/4J4Mxa9r8QNwXZqQRSSnp6etA0bdnE7EqhqmpJ0W+xfbh06RKRSITjx48v20ettjOxg+npaQzDqNoxKFdblEgkOHnyJF1dXXR2dmJYFk8MzjMVzyAQNAd0jnaEy/rerSGdqViG2YSBEKArCruaF0lbrV9zb74nB2cZWVjg+PHjS4idlIvVG8uCSNoiY9g6nXhaoTmo4dNVGgKQNiVqRhK3FqtOirCJlgR3JN4Z3W9tbWXz5s3LLjjFvmcxolMIh8yoqoqu6y7pcdpdzp9VVc2r+myEm6/P58vTqTjTSVevXsXj8Ww4J+K1YL3IRm42klOlmJ6edo3iGhsbSaVS19ULZVtTABOYiWfwqAJdhbccqd44d0vYw0P37eCZoQgZ02JvW5CuOh9CCH50bR6fbvtXeYD5hMHJ4Sht2f357BOjDM2n8WoKP3ukhYMFZoTFRrALs6Ych/FK2scbYewcqkt2VkOiH3zwQd7whjfw2c9+ls2bN/N3f/d3gB0F9K53vYtHH32UH/3oR3zxi1/kwIEDHDp0CICPfexj3HvvvXz4wx/m5MmTCCHo7u4G+GBVvkwWzyqyE41GicfjbN68ma6uKjWRs1BVlWRy5XJtrkboyJEjK54w1a7sJJNJzp8/j6qqHDhwoGo34nLIiTNpljvtdnY8xljU1t1ICaPRNJemE3T5V96eoijsaw0QSVmYUhLyqHi0pR4bFy9eJBaLsXXr1qKLp6rYLbCUYZE2JIYp8Wq2t07alETTJvG0xULKQkgBloolpRtbYUjQs0+LYAuRe3p62L17N/UNjcQz9vfwasoSbU+x4+aQFeeGUu7vKLea41R9cjU+uQRoI6HYdFKx/KnrkTpebVyvykpulQJw9VKjo6PMzMwwNTW17lNy3++d4U+/N4CuKEjVbi+/93iQ7U3VjaOoD+i8ZNdSAuXRBLE0kLOe66pNNj/7xCgjC2lqvAoZS/KFE+P8xos9tJQwJCz0mnKypsbGxujt7cXv97tC52I5ibm4ke7Juag26ar03G5sbOQ73/nOkp9v2rSJRx+1p8hf8IIXlLw/ffGLXyz80WjZH14GbgqyU07LY2xsjKtXrxIOh/N6f9VCOaTE8a/ZuXNn2bEP1SQ7s7OznDt3jq1btzI+Pl7Vm/BylR0pJQMDA4yNjS0JUZ2KG2gCBAIlmxU1m8iwJbg03qHU59b6i1/AmUyGU6dOUV9fn6fPKYQQgqagxnzSZNow8KqCkE/JEg2IpSwiKQuPAkIVJNIChAICMhZ4VEFtdnLLESIfPHgQfyDIXMLEOTUTGZNan4qu5h/33O8ppXSNL9dyY8qt+jifYRgG4+PjeDwetwpZidbneiHXk6Ywf8rxUClnkXFwo8Mwb8RnO3qpWCzmtrWcKTmHXFbbG+nLJ8YQUhLwKARQiKVNLkxluGPn9fn+rz3QzF/+eISFpIGUUOfXuK0zxOULCkPzNtERQuBRBWnTYng+VZLsFKIwa8oJhHUmWJcLhN0I7slQ/crOsw03BdlZDk6QZDwe5/jx45w5cwbDMKqe97MSKVmtf021zPocjZJTTRodrSopLund4xjoSSk5duzYkotNVyFpSjKWfewsKQnqSsWC50JEo1F6enrYvn07ra2t9A8MkrLsYFCvpiwhHKoiaAho6KpgPrG4L1Y2agJAOAQCQGg0h3R3IXVG2aenp93WZCxtEx3nswxLkshY6DnHILe1tFLbai1w9EM1NTVOCTiv6mMYxpJ210ZAsfyp3EXGaS0Uc87dCLjRi4JjKpg7JZdOp13iE41G87Q+axlNtpx+cA5Ma6mp4XrhSGeYX7uri5PDUfy6wgu31eFXLXTVrtJmTIlHy0bIIAl7V7e8CSEIBoMEg0E2b95cNBDWIZN+v/9Z2cZKJpPPOjuJm5rsJJNJenp6aGpqYvfu3Qgh1kXwC6XJjmVZnD9/HsMwVuVfU8wTpxKYpum69Tr6nEwmsy7j7IXbdJyg29raSupWvKqCKuybopSgqYLuej9CmKveR2ec/dZbbyUcDttiRnxkpGQqaqAo0BbWl7S8AEIem2RFU/YUV4NfxZSSSNJCWhZCUTARYNnETgiBaZqcPXsWTdOWCJFzv7Gd0bwUDslZL6LjXAddXV2uwA/yqz4O8ckVO2/Eqk+hh8rs7KzrnOv3+11itFymz/XEja4qFXNQ9ng87pRcoTfSWqo+9x9s4S//c4iUYWFJ8GgKxzfpazp/MqbFI6enuDSVYHuTj9fd2oy6zPZ2NvnRVMFcwiBpWHiFhaoq/NyRFj53YpxM2sKSkuNdYbY3Vqe9VkjIHZNIR3zv9/vJZDIYhnFDjTarSXai0ehzZOdGoNgF6ehDCkeqKw0DLRfFyE45i/1qtlsunDHrwuiL9TAqLCQ75Yy0W1JiSsnmGh9J00Jgt40sCYpaeUWr1Dh7LG1ioaDKNF5dkDElswmD1vDS6p4QghqfRk3OfdCyLGr8CgtJC2FZaIpAxqeBTtejqL29nc2b8yc/PKpCHBPTyvr7SAgWVJQcU8H1Ijrz8/OcO3eOvXv3lnQFL9T65P636Gmy8YhPrrtrbmvh3LlzmKZJfX29m/R8o7ARyM5yn1/ojZTriB2NRivKQbt3XxOaIvjWuWn8usJbbmtHmR9a9fc3Lcl/f/Qa58ZjADzWN8+P+xb4n/cXT2iXUvL3p6f4wdV5O/gXeNP+OmoUhX1tIR68y8PwQpqwV2Vbg2/dfi+FJpEjIyOMjIxw8uRJVzje0NBw3eNVqnkuVhoVcTPgpiA7uSjlROyg3DDQSlFISpxSe+GYc6VYbRvLIXvFPn89Jrxyt1luy05gV3YkUKvrWFISz5h4NAVFkRXt43LVFdPCHRGHrMFgBV9fURRawzr1fokFYKSYtwzm5he4eHWATVv30FAXWnIz0VVBnV+1BcoSgprAqy/ul5SSQCDgErSmpiaam5tLeqZUivHxcfr6+jh06FDZU03FRM655McwDPc1G4n4FLYWHEHpyMgIs7OzKIrC2NjYmseIbzY4JLpcFE4k5eagAW7Vp5gPjRCCe25p4p5bFvP0zs1W9vm5uDqd4Px4DIEtdpaW5PJUgqeHIxztXGrVMTiX4gdX59zBgtl4hj9/YpLXdCnsk5LmkGfFhHSwSdZE1qSwJaivKbdOURSCwSD19fXs3LnTFY6vhkxWA9UiO5WGgN4MuKnITiaT4cyZM/h8viVOxA7Wu43lkK3JycmiZGu12y0XUkr6+/sZHx8v+fnr8TTh7Of58+dJpVJlteyEEOxuCnBuMkYsZYAQbK71EfKo7uJaDpLJJCdPnmTTpk1Lqitge/cAWNImPBlT0hCsPA7Dq9vHLS1Vkqk0lwfG6dq8Da/XQyxpIYt4iuiqoLbIcXCEyIFAgNtuu410Os3U1BRXr14lFotRV1dHU1MTDQ0NFZeenXNwdnaWI0eOrPomWkzkvFZDw+uFXEGpM5WUTCY5c+YMUsplF+1qYqNXdpZDYQ5aJpNxiU8kEiEcDrtTdKUIZLE2WrlIZiwkoDr7n+0Dz8SKP6wupEwUIciYMBG1yYolJd8dg80DC9yxpXbFz0xkLP729BSTsQwSaA95eMOBRrxFWt7lIrd9lGu0WYxM5o63V/u8qaZ+LB6PP1fZuVFwcoe2bdtGW1tbydetZxvLMAxOnTqF1+vl2LFjVbn5V0J2nOqGoiglyd56wTRNxsfH2bJlC3v27Cn7QhVCoCsKadOkRldoCXrcn5dzcTrtMic4tRh8ukKtbjGVsrCkoD6gUrNKozOHTGZMi+1dm/FoKknDbsel4iY+DbQVyImjj8ltW3k8njzPFEfweOXKFbxer9uuWalC42jEFEXh0KFDVT0HnGrOag0NbxSEEHg8Hrq7u+nu7iaTyTAzM5O3aK/X0/VGEChXa9HUdb1o1ef06dNA8arPWj5/R7Mfr6aQMiwU7Pa2rgr2thSvFm+q8SAELCQNLCkRCHRFoAvJY/2LZGcuYTAezRD0KHTUePL277GBCOPRNOGsA/RwJM1PBiO8aOvKRKkUSgmUi5FJpxp54cIFgsGge0w3igbNQTQafa6ycyMwMTHhClJXYpvr1cZKJBJEIpGSsQ+rRbktJ0ef09HRUXUPoZWwsLDA+fPnCYVCbNu2rez3ZUyLC5NxpJTUeFSSpuTiVIL9reUFHo6MjNDf31/WhJtPA78Zpauuo+z9K4Tj/Kxpmh1xoAjiGTuzyrDsMfTheZM6P9T4lCXfodyJq2K+M1NTU5w/f550Ok1DQwNNTU1LfGfS6TSnT5+mubk5T6O1HljJ0NC5xjbChFch4dB1ndbWVlpbW4s+XTti02ppKm7Wys5yKLZQ5xJIx33YaXuuBn5d5ffv3crvfbOPaNrEpym8/wUddNYXr5Y3BHTefVs7f/LDIaS0fXdaAiopw3AnIq9MJ/ibk5NIaZOnY51BXrOnwT1Gk7EMurJ47eoKTJWoJJWLcn12dF2npaWFlpYWpJTEYjFmZmY4d+4chmFQX19PQ0PDqvymqn0exGKx5yo7NwK1tbUcP368LKX7WqebisGJffD7/VUlOlBeZada+qDVYHR0lGvXrrFnzx6Gh4eX/HsiY3JxOk48bdEQ0NjREHCN9ZKGlRfx4NcE8YyJYcklo+G5kFJysbeX+TRs2nWIqKXhXSGBfK06JUeIvGnTJjo6Onj88Sey4/YWAsiYIJCkDcFMzCRtWDSFtLwn3NUKkf1+P11dXe4E0szMDOPj41y8eJFgMEhTUxOBQIALFy6wffv2Nee8rQblGBo6x2CjVX1yF+1cgW4sFsur+qxmkuZmbmNVgkIC6bgPLywscPr0aZdAVtqe2dMS5G/eegvxjIVPU1bUz+xrC/L793Tzif8cIpWxSGXFeffsakBKyVdPT2NKiYLt7nxiOMqtbUG6swSqo8ZD32wSn2Z/TsaCTTVrq/atxmdHCEEoFCIUCrnj7bOzs3nj7c55Wc5U1HNkZ2XcFGTH5/OVXa3RNI14PF6Vz5VS0tvbSzQa5fjx4zz55JNV2W4uliM7K4mx1xNO5EU0GuW2224jk8kseYLOmBZPDC2QNCW6AtdmDZIZi0PtYQD3xuVciKYlbTHiMhdlJpPhVE8PSk0rda2NmAhm4wZpw6Kj1lPygq40Py0Xzg3bmSyzE8gtan0KacPWAKnK4s1EUyBlStImeLXVOyIXg6qqeeZm0WiUwcFBzp8/TzAYJBKJ4PV6b2iQYaHWxzRNrl69it/vv2Fan3KPRaFAt3As21m0qyUiX2/cCLKV6z48OzvLnj17iEQiee0Z5ziWIxYXQhD0lE8W2sJefv2FXfy4f4G5SJQdIZPdzQEMSzIVyxDP2NoeCQR1hUhq8f76vK4w49EMV2dsN/zdTT6Od4YrPga5cEJ814LcyUOwNTO54+21tbWuV1IxYlXtXKznBMo3CJVczNUSKKfTadedt5zYh9XCSbkuRG6Y5mr1Oau9EWYyGXp6eqipqeHw4cOu10zhcZ1PmSQNy71R6YpkLJrGsCSaIvBrCq0hnbFI2jWk2VbvL/n0FovFOHXqFJu7txHXa/FlRYOaIohnLDKWxFOiIlRuZSdjShaSJpaU+HWFyMwk165d5dChQ+7F7Za4VUHYp7CQsMiY9pSXRwEUbI1B1jvIITrVXtSFECwsLBCLxXjBC16AoihMTU3R39/vmsU1Nzeva0TASnA0RLquc/DgwSxRXFr12Ygi58KxbGeSpq+vj3g8XjR4sxD/r1R2lvv8wvZMYeaU07KtNHNqObSEPNy/r4nxcdON8YmnTVJZiwtFsS0fomkzT7+nq4LX7WsgmrbPzZBnaTu6UpimWfUJQMdvqrOz09X4zczMcO3aNTRNc6s+DilfD7JzveUS642bguxUgmpodpzYh127dq17y6CYJ048HufUqVNumOZqt7ua8qrjTFwoBC9GJhyvC9dl2Pl59v9CCLbU+Wjw66RNm1yUeoKbmpri4sWLHDhwAF8gRN9sKs+9GGlHTpSC42ezHAxLMh2zQ0UFMDA2Q2J+tmRYqxCCsFdBVwRTMYOMBQgQEnRNoAgLw1j0qZFSkrXcQRFr03JIKbl8+TLxeJwjR464v8fcSY/5+Xmmpqbo6+vLezK8Xk9kDilubm52p+Sc77ySoaHz79VY/KolEs6dpCkM3sw1lgsEFjVnG4Fs3MjPL2xb5lZ9nMwpZ1rOacs6C3U1RLm5nx9NWzT4deaTBhnLFjDX+lT8ev49x76uq0cM1jsuolDjl0qlmJ6epq+vj1gsRk1NTdWv+ecqOzcB1lrZWc5DxllQqz0Bk3uzdhb93DDN1W630ovQyX06cOAA4XB+abcY2anzadT7NWbiBvGMyXzSQFXgX3unuGtrPWGv5pr4lYIz/TQxMcGxY8fwer1IKQl7VRaSJqqQGBLqimROFe7fSotexrTJiC4kIyMjKJpGR/eOZSd0hBD4PYJNmkY0ZZEyQBOSgEegsNi2sqQklrb9fSTgUSGgr24hcqp6wWCQW2+9teg2hBB5wZDJZJKpqSkuXbpEMpl0Dffq6+vXpZqSSCTo6elh69aty+bAlTI0zJ3w2ohVn8LgzWQyyczMDFevXiWRSLhZSc6+3yjcaLKz0udrmrZElFtoDLmapHEHuffjBr+GX1fQFQ1PdsrLoynU+dc3t+p669S8Xm/eZGckEmF0dJT5+XmeeuqpqtguOHq2ZxNuCrJTyS9staPnTsaTZVklPWQcIrUeJ7aUkmvXrjE1NeUu+muBs6/ljNo6uU8zMzMcO3asaEm2eGVHcHRTDWfGIpwcS6EqoAvBfNLkO1dm+Olblg9DlVJy5swZhBB5o/xCCFrDum0eZki8mqDGt7KnTzlP+KZpMjI4SF1dHTW1dZR7aqnZQNJSQuSkYTsoawpICWnT/nOl8TxO9ENnZ2dFYnifz+eGa1qWxezsLJOTk1y6dAmfz+dWfaqh+3Jcm2+55RZqa8sf2V3J0HAto+3rveD7fL4l1gFO2rhlWQQCATcr6XriRpMdKP/Y54pyt2zZUjRpvNI4kFy9jE9XeOvhZr58apJo2iLsUXnLoSY86voSkRspyndy0Zw2+tatW5dMzTlVoUrWlOcqOzcBVtPGcsa6nUiAUhdvJQSiEkgpq+7fU66GxTAMTp8+jd/vX+JMnItSx0RTBKYUC2rXGAAA4fJJREFUaMpi+KZiSWJpC8Oy0EpsL5VKEY/H6ejoYMuWLUu2rwhBnb/807Oc75uKRRgdGqeptc0W00qo95V/rJcTIptWbvsOlOzoayWIRCKcOXOGPXv2rNmV21k0nJiFqakpzp49i2ma7mh7bW1txQvlxMQE165dq8i1udQ+QvF210Y3NMxtKzQ2NjI2NgZAb28v6XTarfqsZoS4UmwEsrNaFCaNFxvFXikEtpBodNV6+fWf6iBlSrxq9eNZiqHaepm17EOxqbmZmZm8a7+hoWHFYN3nprFuAlTaxqpkrHs93Jnj8TixWIzu7m46OlbvEVOIUsLnws8+deoUmzdvLuuzLQmnRqOMRdM0+DUOtofwqIotGhYgLYlQbO2OUASlLiVHE+Xz+VbtF5PIWGQMC01V8OtixcrO2NgY165d48CtB1F0L5akaDp6Kaw0caUpdnXH+akFlLlpwA43vXLlCgcPHqxqAF9uzILzND09Pc3w8DDnz58nHA7T1NREY2PjiiS+v7+fqampNbk2l0I5o+2lqj430tjPEeg6VbXChGyfz+cSz/WYpryZyU4uCkexnapPbgisQy5zj2OxqooQwh0tvx7YCHYLxToOufqp3Eqac0ydc7OhoWHJg0ulbayZmRne+MY30tfXR3d3N1/96leLrqfd3d2Ew2FUVUXTNE6cOFHR+9eCm4LsVHIxl1vRWE3bqNpkx0nvdoLlqomVwkAdkrd///6yWhFSSi5lwiT6F9AUuDINQ/Np7tvbyJ5mP73TcRKGhchOKO1s8BW9ATi+PYcPH3at/SvFfMJgNmHaxEKaBH0qYa24QFlKyZUrV5ifny/bq6kQuY7IpW5qPs2u7hjZXfBqoJfxsCelZGBggKmpKY4ePbru+TmapuU9+S0sLDA1NZU3el2Y3yWl5OLFi5imyeHDh9f9xl6q6uMQIOfPjqHhRkJhQrYTXnrhwgUymUxZ1YpK8GwhO4UorPqUOo7rJSuoBBuF7KxUXSo8polEgunpaXp7e11vuqGhIV7+8pdXHBfx0EMPcffdd/Pggw/y0EMP8dBDD/Hxj3+86Gu/+93vuiP2q3n/anFTkJ1KUM6F77RufD5fRW2japGdXI3M8ePHOXHiRNVvWqVIX262ViXaoIRhMWV6afMqbhVlPJpmOp6hJeTh1bsbOTUWI5ExaQ972NmYX50o9O3RNK2sCapCWJZkPmG6JWopJbGUSVBb+n1N03R/z6uxD6jEKND2CpGLE2llfJZlWVy4cAEpJYcOHUYKBcOyJ7nWkE1YNnJHr7dv3+5OeeTmdzU0NDAyMkJtbS1bt269IQvrSlUfxwPqRiw6K123zgixYxhZWK2oVKNSDM9GspOL3OpkrgHfxMQEExMTzM/Pk0wm1616thI2Qhur0mEUIcSSc7O3t5d///d/52Mf+xipVIq//uu/5r777isrHuiRRx7he9/7HgBvf/vbueuuuyoiK2t9fzm4acjOWkzjcuGMVm/dupX29vaK3lsNslNMI+Nst5peKcXaWE62lqqqlXv3lDj0zo+9msptRZKKwf7OPT09hEIh17cHVud6bAGSReIhhCDbOMs7P5zwUKe9sBqUQ3SsbJQE2K2sckgO2Av06dOnaWhoYPPmLaRMgZHdfQH4NVhnXeUSFE55TE5OcuHCBfc8GRoaKiu/az1RWPVxTBe3b9++4bU+ufYAudWKak0m/b+CQgO+uro60um0W/WJeRqYEwHqwwGOdYQqMixcDTZKZWctXj+qqrJ3717+/M//HIA77riD+vp6PvrRj9Lb28vtt9/OK17xCh544IGi98Lx8XF3PW1vb2diYqLo5wghePnLX44Qgl/4hV/gPe95T0XvXwtuGrJTDYyNjXH16tWio9XlYK1kxzHN6+7uzpu0KUdfUykK21grJYevBL+uUK+kiaQsPJogY0oaAzqNgeXbLo4uqFim2GrIjirApwlShoWmCgzTFknrOW0sRxO0XHjocnBIk7O4l3paNC1JNJ31GgJUIOSVyxIew4JYPMmVK5fp6OyktaUFw8pOcmXfZklImRC4gffPeDxua5wOHKChoYFEIsHk5CTnz58nk8nkiZxv1I3emQrbv38/4XC4pNZnPYnPaiuyhdWKwskkZ7qrXBfi/1dhWRam5uNq3EcyaJ8Djw/HSBoZGJvj+1dnect2lY7W9SPpG4XsVLO6pGka7373u3nPe96DYRg8/vjjvOMd7+CjH/3oktf+wR/8Qdnb/dGPfsSmTZuYmJjgZS97GXv27OGFL3xh1fZ7OTxryU7uTciyLC5dukQsFitpIFcO1kJ2nNL1gQMHqKnJr4CspK9ZDXKJxOzsrDsqvFrRlxCC3Z4FtE1djEczNPg1buuscXOwimElXdBqyI4QgqaQxmzcJGVY+HRBQ0BzqyCjo6P09fWVFR6aCztnxyY5XsVi//79TE5OulMMjY2NNDU15WX/OC70WaNnDAvSBvhKnF4ZE2ajCSbGx+naugO/z0fWL9EOLnReKEsW0q4Lpqen3XPVGT/1+/1s3rzZbSM4RnFOPIDzpH29FubJyUmuXr2aNxWWW/XJJTvFDA2rtThVq/1cbDKp0IW4qanphsaEbETEDck3L0RJmnZFdCpuoAjwZgVzCVPy5BTE5m1dSm7YZrXIwXq4p1eKamqXHBd0B5qm8fznP5/e3t6S72ltbWV0dJT29nZGR0dLem85D7wtLS088MADPPHEE7zwhS8s+/1rwU1DdippY+W2hZzYh4aGhrwWymqwGrLjCGTn5uY4fvx40cVgPaa8nG0ODg4yPDzMkSNH1vxko6sKt3WGy7qoBgYGGB0dXTbTqxwjwGJQFZvw5EJKSTKZZHR0tGIhcjJjEUlZCCSmBUmgzh+gu7ub7u5uMpkM09PTDA4OEolE3JgGX00DQizeMAU2YTEtSBh2hUZTbPGyImByZp65+QU6OzrsJHHAkPbIumlBJrsNCQRu0JU5PDzMyMgIR44cKUlciuV3TU1N0dPTg5TSJYbrtTAPDQ0xNja27FSYc44WGho6ra6N3O4q9KPJZDLMzs663inhcNidTLqRuJFTcA4GYgqxjEVt9gljMmZgsjgVKQRE8NK4dS8qFnrajrK4cuVKxWGbGxnr4eJcybV733338fDDD/Pggw/y8MMPc//99y95TSwWw7IswuEwsViMb33rW26lqJz3rxU3DdmpBI6xYDQa5ezZs1WLfVBVlUwmU/brHa1KMBjkyJEjJW+o60F2hBAMDQ3h9/s5fvx4VS4EpwK13MLgZCWZpsmxY8eW/dzVCJSLwdFBAasitImMTXQE9gRVxsSOt8hWrXRdzwuPnJ+fZ3JyksHhMQJ1TQR8XkKhMIqmowqIZbJeO8Ku9iQyktGhPhR/Ha3tHViK4up80sZiG0xIEMoi4bmecEh5LBbLi6dYCbnjrVu3biWTyaxbfpezj/F4nMOHD1d0TjsiZ03T8gwNc52cV2NoeD2moYplTznkMh6P09fXR2NjI6FQ6LpWfTbCJJhh2Xo9B7pmX1OWXBwWmEsYfPPyPBaSrXVe7t65EyGEO4106dIlUqlUnj/SjRYcV4pqtrEcnWIlePDBB3nDG97AZz/7WTZv3szf/d3fATAyMsK73vUuHn30UcbHx3nggQcA+579lre8hVe84hXLvr+aeFaSHVVVGRoaYmJiouJ2xkrbdULnVkIlQujVtHOWQyqVYmBggGAwyIEDB6p2Q1ppP9PpNCdPnqSlpaWoUWCl2ysHyWSSZ555hq6uLhKJxConruxxeXuNcwTPxZEb0yClJBJPMRuJMz4+Rnxhhrq6WsINrQT9PoSiIKTF8OgEmu6job4OS9qBYhlJNgHergBZ2ERLV3FbW9cLlmVx9uxZvF5vyXiKcqHrupsv1TcV57e/doGRuct0hSRvO+Cle1PzqvK7HIdzXdfXfE7ntrt0XV9xtH058nO9F/xCcvn444/j8/kYGBhwyaVTrVjvcNiNoFVp9WToM+2gYE2BkK6RUS0shN3OElAf0AjodhX56myK7fVpttZ78fv9ef5I8/Pz7jSirut5WWgbHdUkO4lEouLrs7Gxke985ztLfr5p0yYeffRRALZt28apU6cqen81cdOQnXJvKKZpsrCwgGmaJWMfVotyKzDj4+NcuXKlbCF0NSs7jji3tbXVHe+uFpYjJ5FIhJ6enoqqaGslO3Nzc5w9e9bVIg0MDFT0fufp3qva1RgskEiEoGS6ei6EENQEfdQEfUADptnB9MwcC5EoY6PDeHQP6XSG+oYG6usbEIBpfwxKllxpqt3CMk37/2o2XFW/TmtIJpPh1KlTtLa2VjXlOJI0eNcXelhIGEggkhL85RnBxzuVivO7nAppY2MjW7Zsqdo+OliroeGNrG4oipJXdXTCS3N9kxobG/N8k8qBaUkMS+LVNg7RK4aQYvDG/fX850CclGFx52Y/B9sCjETsCvz3+hbwaQoSiBkW0bTFuck4m8J63ndTVTUvbDORSDAzM8Ply5dJJpOuDUN9ff2GrPpUs40VjUZvCoJXKW4aslMOnNgHr9fL1q1bq35SrkRKnKTqhYWFioTQ1ZrGGhkZob+/n8OHDxOJRIhEImveZi5KkRNnyu3gwYNo3gCWXH4iaaXtlQPnu65Wi5TriOzTBaoiSZt2+8mnCdQKjG6sbDSEECrNTY2EDYgmaxgbHScQCjE5amt9ahta8Hg1VFVDZJ88wR4x1yRkLHs7umoHia434vE4PT09bN++vSpt3lycHo5gmIutBMOS9M8k8de1cijHc8bJ73I8Zwqn35yssC1bttDa2lrVfSyGm9nQMNc3adu2baTTaTcdOx6Pu1Wf+vr6Zas+3+6d5UvPjGNasK3Rx6++sLNomO9GEOZalkVnrY+fPZi/OG9vsH9/vdNJBhfSxDMmKUOCEEzEMvxHX4SXb6speZ07Rq8dHR15WWjXrl1zqz7FnIdvFKopUH42RkXAs4js5KaFT0xMVF0DA8uTnUwmQ09PD+FwuGIDu7VOYzkOt4lEwhXnxuPxdZ3wcj7XcSfesvcQn3lmmvnkJH5d5WdubWZbw/I3gtUIlHPNCVfriCyldPPTnBuERxN4tPzXWNl2kirshcQRHpuWXXnx6VlNjoHbd/JpEJ2foq9/gN279xAMBFC62phPGMSTKWLRGIZhoGsKfq8X4Q+gqAIhIKTbJOd6rKFzc3OcP3+effv2LZkOrAZ8uoIsaMZZFnj1RTJR6DmTO/3W0NBAMBjk2rVr7N27t+rW8eXiehgaOooxiUBgoWT1Y2uFx+NxW4qWZblVn/7+/jyn50Ag4N6vzk/E+dIz4wjsc/HqTJJP/3iED794qV3FarQd1cZK1aUXbgnzxZ5p4hn7XPRrAk1VmE8aTCcMWoIrP5DmZqGBTcAdkXMikXB1ajey6lPNNtazMQQUbiKyU+qELhb7MD09fV3JjqPP2bZtG21tbVXbbjlw2hB1dXUcOnRoTYZ9KyF3m7nmiAcPHeZPfzRMLG0S9KikTYsvn5zgA8/vJOStnkDZ+cxAILAqIXK5jshSSuIZO7kc7MqLrkgWUsIVFgvAn60EacIWFksJEzPzTAwNcvDAfneaSUpQNY26sH25WVISiyeYnxpjOBbHHwhQEwrSWF+H0NaWdu98nkMzBEvJ0/j4uDuev16Os7d21rCtKUjvRIy0YeHTFV6xr5lwkQpBrudMd3c3hmHQ39/PhQsX8Hg8DA8Pk0qlysrvWk8UVn0WFhYYGRlhz549q57wkoCFgiNTt/9soVZZtaUoiqs1A/LcshOJBLW1tTQ2NnJlUmLmtK90RdI7lSi6zY2g2YHlJQ5JQxLyKGQsBY8iQAiiaYs67+r32+fzuVWfVCrFqVOnmJub49q1a2iaVpRErjeqWWV7juxsQOQuuLmxD+sx3VRqu2s1Kiy13XIQiUQ4ffo0O3bsWOJLsF5GhZZlkUgkOHnypBsgOpvIEM+Y+LNP7R5VIWVaTMbShLylqzuVELLCz6wUlUQ/pE3bQ0ez741kTEikbeM/NbsuWUDKsCs8ug6WtBgfH0cKhVtvPYimKiQNuzWl2Dmp9vsFgCAYCNC8fRuCxarGmTODWJbl5lOtZnRbSrImhY7DNHgUW4fkRIXMzMxw9OjRdRWwaorg0z93gL87MUL/dIJbO2t49a3leWdMTU0xPT3NnXfeicfjWZLf5VSEKtWhVBOzs7NcvHiRgwcPEgwG3XPL+c+pOKxEfKzs7N3it3Cmi1YmO2sZ/S50y3bEuTOjC1iWB8OwNSCGJWkuUf1Yb83ObMLg8kwSRQh2N/kIraK3a1gSTRGEPRrxbHafCYQ8Kg3+6pz/Xq+XHTt2AHbVZ2ZmxiWR5bYONxKi0ehzbayNhOWmnZzR82ojl5Q47ZRIJLImo0KwF/1KRtphUQR96623Fj0x18OoUFVV17V237597lNiIGvg5dxYLGn71SxX1XH2sRyyU4kpYrEbcCVEB7L6GxYrIoqwyY3zdyHsMXGwKzoZ02R0eAh/MEhDfSOqKkiZWaJD1jAwS3RMuRgHoTjEJxgkEAiyeUs3RibDzIwtMM319Cl3usbCJjrO/ltAxhJowuTixYvZHK5D1+WJ3KspvPV55Ud15JKxI0eOuN+3WH6XM4JeV1dHU1MTDQ0N162FMDExwbVr1zh06JBbGSvW7solP4ZhIIQoYWh4I8wGFqEoCvX19dTX17N1m2TgO31cnkqQztj92Vd2KMzMzFBXV5e37+vZxhqPZvjCySnSpn1/+M9+lXcebqbGV9nvuN6v4tUUu2amKCQyFnUelZduW94QtVwUto98Pt8SEjkzM5PXOnRatDe6BVgKz2l2bjByT4yVqimqqpJOp6u+Dw7ZcVpHtbW1qwqYLLXdclCuCHo92ljRaJSJiQmOHz+e1/7wagqv3tPI1y9MY1i21uUF3bU0B5d30y1nHysRIjvGk7m/j1wh8ooVHcMWKZtycTJKZEfDdcWumFhZUxwJeBTQrQT9Q2PUZ030AlkS4xAdIRaXMo+2WC3KhWFB2rRfJYWHpualnj6OMLKpqYnm5uaSx8J52HeJmQTDsjh71m51dnd3r6paZEnh1hwUIauuK5JS0tvbi2EYy5KxworE3NwcU1NTrkmcU/VZL+Ho0NAQ4+PjZRka5oqcSxkaIgRCEVgIt7qjUP69oNoLpqoIfvPubs6Nx4hnLLbWedAMO8Pr8uXL+Hw+d8FeT4Hyd68tYFjSzbWKpU0eH47ysu1LndgdGJbkyeEYfXMpNAXCHpVoxkJXBEFdYEmF7XVejm4K4ltmyqwSLNfKyyWRsNg6rFQwfr3xHNnZALAsi97eXuLx+LIL/Xq1sZwKzJNPPsn27durNh1S7v4WmhQud6OrZhvLSedOJpPs2LGjqM7jcEeYzjovk9EMtX6NjpqVtSfLVbScxc/5XZdzM3DIk3PzKSZELoWUIW0zQLKal2w7COzpKL8O0RQkDbAEeBVQjAXOnLNH32tqbNLtkgwWGxKLX8p2TJbYhEjL6nzSpi1QtskapCx7OizX0wfsVt7U1BQXLlxwNSyF+VSKWIyfEAIyRoZrV6/Q3t6+Kj2ZlHalyGLRIVpKgUr1CI9pmpw5c4ZQKMSuXbvKXsALhaPxeJypqSnOnz9POp0uenxWCyklV69eJRqNcujQoWWrSLndJSFWNjSUpokQCkJRUBRRtjh5vdpIqiI40J672PnyjvH09DQXL14kmUyiKAqzs7NVz0hLZKy8IFxFCBLp5e9nT43E6J1O4tUEY7EMgwsZe7JSCMJelbu3hmkNVVfzVYluqZCo5wrG12ITUBjvsFY8p9m5wUilUjz11FNlxT6sF9kZGxsjkUhw5513VpX5llPhcEJEy01rr1Yby4nbaGxspL29fdmLqjnoWbGak4tSESC5Kem5outyt1dp2wpsEqMI3HFwIcCr2hNXIrvtOr9d8UkaMB+JMjc7z4GDhwkFlpI/rwpJM0uYsiJmJ/DTWc082TgJxOKiZbfI5FKihD0O29XVRVd2dDs3nyoUCrlVDV3VMSxBMpliaKCfzvaWNU0z2QZt2d+TtP9eLQFtOp2mp6eHtra2VafTOwgEAuuS3yWl5Pz58wghVjRdNKXIU+AIKVHF4rFaztDQMg0sk7IMDS0p+crJCf7tvAff1Ys8sL+Je/euf3xEIBAgEAjQ1dXF9PQ0w8PDbu6fYx/Q2NiI17s2of2eZj+jV9MoQmYfPiS7m5YX0/fPp+2QYNNuowOQdSWPZywG59NVJzurnYIqJhifmZmhr6+PWCxWkTlktYXisVhsXbKpbjRuGrIjpWTr1q00NTWt+FpN09wn+mogN0g0GAxWvcS3EjmbnJykt7e3aIhoKVSjjVUogO7v769qa6zYPjpC5MKUdCklhrVouFdswcmNs6iE6JTcP8ESv6BERjI1O0ciFmVTRwemomDJRZLkQFdt00BH/wM2+XGeVqWEtJVta2UrJ4pwqgKLLs65AaFOWwyW5lNFIhGmpqY4efIkQgj8gQDz8/N2q7cK56tTKaomHF+s9fD5KZXf5Ti4lpvf5VSdHMfi5V5rT8FlSSvOfJXTWi3+ntUaGv7LuWkevThrE+i0yf99Zpwan8oLttaVf5CqgEAgwI4dO1z7gOnpac6dO4dpmtTX19PY2EhNTU3Fi/HtnUHSpsXTIzEURfCSrXXsbs5vTRY+KHk1e9LKyv5cCJBC2BYSUqKtg6SrWkTD6/Xm2QREIpE8c0gnC61YJEi1c7Geq+zcYPj9/rJ/odWs7DhPnnV1dRw+fJgf//jHVdluLkrtr5SSvr4+JicnS4aILrfNtRCTiYkJLl++nCeArrYOqHB7jhA5V/wM9nGYjpl2GVvY017NIRWlgGEIIdwnrUqJjk+zXZRz7596welmmhZjE9MIadLZ2Yki7FF00wKlyKmpCmf6CvdJM2NArposoIJXk6QMgYVEZltYaUsgpMx6sGSPV85kVeH3rqmpoaamhm3bttHX18fw8DCBQIAzp09TX19Pc3Pzik7FpWDLO20mJhG2D8waic/CwoLrfl1bW1qHUQ0URiw4ZntOfldtba0rcs59ina0eZVWnUTO/yupf5UyNMzV+ziv+3H/PKYlUYTddsqYFo8PRK4r2cnV7OTaB2zevBnDMJidnWVsbIze3l4CgYBb9SnnPqYIwYu6a3hRd+mHu0KicdumIN+5tuBea1JiR8EIgaZAaxmeOpViPcbvFUUpag6ZGwnitG91Xa+qxw7YrcrVThZvZNw0ZKcSVIvsLCwscPr0aXbu3LmuZb1iJMJ5otR1PW+svlxUkhKfC8e3aHp6mmPHjuXdmFYzNbYccr/38PAwAwMDRVPSoymLeNpyyUfatJhP2pk3ufvt8Xi4ePEira2tFXuyeDW7mrLookyeu6pDels376CutnmxClNEcFz0uwqwTEjnNackkTTU+sCn2a2rDIvTVKYlcipZdpXIsJaSsNxjcPnyZRKJBM973vNcwjs7O8vU1JTbanDaOeW0GoTA1uc4WiOsJVWsSjE1NcXly5c5ePDgDbGlzzXbKxSBa5pGU1MTNTU19Pb2snXr1oqufWEHjuRotlbf7luu6mNnPS1qzMDWpVxPLKcZ0jQtr7IWi8WYnp7mzJkzWJblVipqampWXX0tJBrtYQ+v3lXHwHyaJ0eiJDP2b8CjQVNAZTSWoT1cWQtzJVSbaBRD4fnqaH2GhoYACIfDeXYHa0U0Gn2usnOzoBqj584U0KFDh/J+8Y4RXjXZfCE5c8r7Tkjd9YJDsDweD0ePHl3yHatd2XEqMRcvXlxWiJwxpSvgBXsaKG0uLiKOEHn37t1uu2JgYMB16W1ubi5rUS10UXYQi8Xcdl5dQx2xjO0GLLG1OWXEaLlj4O7fsVscBosld8e/xyETFna1R+QQHFmiXmCaJmfPnsXv9+cFZeYKH3Odik+fPl22p48Q5OlO1oLh4WFGRkY4cuRIxdqZ9UChCDyZTDI8PMzJkyfRdZ3Z2VlUVS2rKiYEKFLaIu5sK7IaVTBYWvV5y+FWPvpvVzAsAZZFQFd59e469/pcT2uBqzNJvnZ+hvl4iu1hyeYtctl4FSEEoVCIUCjEli1byGQyzM7OMjIywsWLFwkGg+45WskDSrH7cJ1P48J0kpBXxe/JViUl2Sps9cf7r7exYm4kCNgPYcPDwyQSCZ544gnC4bCr9VmtHcpz01g3GJUwVlVVV63ZsSzLnTQotvg6xGS9yM7MzIxr45/byllvJJNJTp48uSzBqjbZkVIyOTnJpk2blhUie1RBVC726E0Lgh77tc6TrqNryPVkSSaTboxIqemllTA9Pc2lS5fYv3+/ewMIexb9ctQyKzuw2N5wPrnUrVdKMLOvlghMJNK0N6Agl+hnyhX5FjoVZzIZtzweiURKtnOqgdxppiNHjmzIMEWwrwPHXsHv97v5XU4rZqWqmFMJW06ns1YYhkFkqJffuKOJ/rQfAdyxOUydb/E+4tyjynVyLhdjkTR/+cQYpiXBsngqCcHzMzywr3xxtK7rtLS00NLS4uqppqen6enpAaChoYGmpqai+pRcjETSnMk0cPn8LJtrPRxpC6AqgoWUSVBXWEhZWc2OJG1JOqtc1YEb7yLt8Xioq6sjk8mwc+dOV+vjVH2cClolBqXPtbFuIqwmcwkWJ48aGhrYs2dP0ZPDISbVtK13hLUDAwOMjo4WbeWsJ8o17asm2YnH41y4cAG/38/OnTuXfW3Qq5AyJbGUhRDg1xXCPiWP6BT7Xfl8Ppe8mabJ9PS0O50TDodpampa9mlyaGiI0dFRDh8+nLe45U5tVYKABvNGfoXHk7Mdp4KSshZFripOhQf0rHbGkBI9+77cqlM54v1c6Lqel5hdqadPubAsi/Pnz6Oq6orTTDcSk5OTXL16lUOHDrnfuVh+15kzZ9z8rubm5qKtmPX6is49qquri7a2Ng4X/HsxQ8PlUtsrxbmJOBlT4tMVTEMipOSp4VhFZCcXuXoqh4DPzMwwOGiH5zrXaX19fd51OpsweGw4SUYq+KSkdyoJEo53BKn1qkzGJTVelVjaQArY1eCjvcqTWGCTyhvtkeMIlHO1e1u3bnWP5dDQEJFIhFAo5FZ9lquqPidQ3gBYrQ6lHMzPz3PmzBl27dq17GTIeo21JxIJ5ufnOXbs2HV96nW0MuWY9lWL7Djkatu2bUxNTa34eiEEDQGVWr8Kkuw4qsx7el0JqqrmPU3mRhA4kztNTU0EAgHXHTuZTFa1CqFrEEYSzxYdPSoUaiZ1BUxLYkh73FsAhhQIYU+TOL43mpTMzc3S23uJffv2r7nsXMrTZ62eNU6kS319PVu2bNmwRGdkZITh4WEOHz5cdCEoVhVzFpKFhYWyyPNa4VRfd+7cSWNjcXJRich5NVUfXVXyiJwlqeqUk67rtLa20tramjdlODg4iBDCbXeNJZSs2afdQvNpMDCf5nhHkFtbA/xkOEo8YxH0aGyp9bC/2b8u5161J6FWg1K6oWLHcnp6mtOnTwPkTcvlHptYLFZRZWdmZoY3vvGN9PX10d3dzVe/+tUlD80XL17kjW98o/v3q1ev8ru/+7v8yq/8Cr/927/NZz7zGXfd/djHPsa9995b0TEoBzcV2VkvOAv+4cOHV9R2VJvsJJNJTp06haqq7N+/v+oXZCnRmmPQWKpdVwzVIDtDQ0MMDQ1x9OhRTNNkYmKirPcJIdCE44hsuZMgqwkDNSUEwjVsq6lZ0u5KJpOYpkldXR379u2r+o3Mq9n/Le5P1rE5K0rWFIlPg6Rp76cJSCHd2AkH4+NjjE9Ose/QUXRNJ21JdCU/LXstp1Khp09uVSzP02eZhd0JSezq6irLG+pGwJl4nJubq4jYFi4k653f5VTw9uzZU1F7e7Wj7aVwZFOQ/7gyz3zSwMpOg71q9+o9nJZDbqUCcKeS+vr6GIpDWtTjybYMTQk+zT7Ofk3hRZvDxDMWqhBuZt964Ea3saA8kXSpqs/w8DAXLlxgdnaWvr4+XvOa15BKpSrS0z300EPcfffdPPjggzz00EM89NBDfPzjH897ze7duzl58qS7vx0dHTzwwAPuv3/wgx/kQx/6UPlfehX4f5rsOM7A6XS67AW/mmRnbm6Os2fPsmfPHi5evFh1olMsPgEWR2rr6+vZvXt32Z+7lnF2KWWeFkpVVeLxeEWVukqiH0q9fyENRvbXpykQ9kq33dXU1OS2MU3TdAV/6/nEbliQkYuuuaYl8Cm2EZ0lBUoOfTGyi8vk+Aiz01PsuuUAqrCrPo4Tc944vmVhCjuKwIPFau/5hVUx52n7mWeeKbmwO4vzrl27XPfdjQbnnDRNk4MHD6560coVja5HfpeTR3fgwIE1VfBKVX2c68rROS5naBj0qPzqCzbxWP8Co9Nz7G7wcLxz/fQdY9EMo9E0PlVhR4PPnUramTH5+vlJ5pKSZCSBFIIdQViIaoSDQRQhVhUcWimuxzRWOftQcYWugKxfunSJH/7wh7zpTW9ifHyc3/qt3+Lee+/l9ttvX/H7PfLII3zve98D4O1vfzt33XXXErKTi+985zts376dLVu2VLTPa8VNRXYqaWOVWugdOE+dzc3N7N27t6IFvxpkp5Jq0mrhkJPcC8EJUF1N3MVqKzuGYbhZYgcPHsybFCp3e4VC5NUgadhExym7GxZE03YoZyy2wPks8XRKsCu1u6oBA5FnFmhmQ0OFsA0IHQdZ0wKJZLDvCmYmzb59+zAR+S0FFIQlUZRstQjNzaxIoyAtA88aH0ILPX0KF/b6+nr8fj/Dw8Mls+s2AizL4syZMwQCgYoIfzmoZn7XzMwMvb29eTqiamGlqo9hGO5rcq+5sFflnl319PXNEwisT8sO4PJMkh8NRpHYrd0fDkZoDmh013m5tTXAT23SeWrUYEH48KqCCCY/vDZDXfoKoXCY1voamhrq15WMbITKjmVZa5psFEKwa9cufv/3fx8pJXfeeSeHDh3ic5/7HO9973u55ZZbeOCBB/LaULkYHx93K7ft7e0rVuu/8pWv8OY3vznvZ5/61Kf4whe+wLFjx/jEJz6xJsf3UripyE4lcEhJsWqNo8/ZvXt3xaLOtZIdZ9orlUqVXU1aLRzhs/MZzlTJrbfeuqpFaDVkJx6Pc/LkSbZt27Ykm6mc7a0m+qEUTCu/tWNakDEhEoszPx9j/62HCAcXF5TCJ/ZkMsnk5OSS6a66urpV71fhuyxph4g6fxbufdRiqL+fmtoG6hsaMHGiCezWle3UbBMd+9VL9yeDiqfMkMlyUbiwX7t2jb6+PnRd5+rVqxV5+lwvOOS7ubmZzZs3r+tnLZfflclk3MmjYlqo8fFx+vv7lwjk12s/oTKtz3plczk4MRpDz/o6zGWdkaNpi/NTSSwJnZokreg0BzUUIZCWynxKI1PTQFRYzC+kuTZwGp+Kq/Wp9oPlRiA71a4uqarK61//el7/+tcjpeT06dO89a1v5fd+7/eWvPYP/uAPKtp2Op3ma1/7Gn/4h3/o/ux973sfH/nIRxBC8JGPfIRf+7Vf43Of+9yav0chntVkxzCMJWRiaGiIwcHBVVdU1kJ2cnOmCqe91su/x3liW60Tcy4qzdtyxuj3799f1CV3JbJTTaIDdtsqaYJiT82SMcFIRohHI7S2tmGq6rKxCHa7q4vOzi4sq/LprsXvtfgZmpCkpUBkKzoWCkZu9dKSWJbBYH8/La2thEMhhLCJmkVW6yMAYU9uOdteHxn/8hgaGmJubo4777wTTdNW5emz3nAqups3b15VMOpaUZjflXsOBYNBmpubaWxsZHJykrGxMQ4fPrxuguflUI7WZz0GNXLhVGFThkQiXQNPryron0vR3mA/vThnkiHBlBJdgZDXQ1rXCdTsYVsYN7U9mUxSV1dHY2MjdXV1ayYJG6WNVa19KNyWkwfn2AIUQ2trK6Ojo7S3tzM6OrqsCec3vvENjhw5ktdVyP3zu9/9bl796lev8VsUx01Fdir12sm9GJ3xV8MwuO2221Z9cqyW7EQiEXp6ekpOexUmdlcDjuPxpUuXUFV1VU7MxfaxHAwODjI8PLzsGP1y21urPseBJSWJTHZqRIBPhZRll8WT8RgynWDTpnaEUNyU8+L7A2nTfi+AR1Fpbi5vusvdFwtSpsDIevR4VIktK7D1Oaa0oxiUnJZWJp2i7+pltmzuxB8KI7LmfnaMhH2jd46MhS3IlNheLybkMDdJ/tB79eD0/FOpFIcPH3bPsXI8fRobG6/bYhGPx91rcCPoiAq1UI4h5uOPP45hGHR1dZFIJNA07YZOseVWfZzrNR6PMzMzQ3Nzs+uq7vweq3UP21rn4dJsyn7oyebPeTV7CsurCYS0qFUhnrE1bqms+ag/68BpP9xIfD4/HR0ddHR0uG3F6elprl69isfjcas+q2kTPtsqO/F4vOKx8/vuu4+HH36YBx98kIcffpj777+/5Gu//OUvL2lhOUQJ4J/+6Z/Yv39/5TteBm4qslMJcsNAU6kUJ0+epLW1dc3jr4qiVGxYODY25vp3lDqRlmu7rRZOCbKrq6sq5fpyyI7Tpvv/2fvS8EjK89pTVb2pJbV2tbbRMqPZpNEymwHb4IkNJphFw26TGC54EuMbAr429oVwbRMnwXbi2E683xgCjlnCLKwzYTEXbBNjYIzWGY00M9rXXrS1eq+q7/6o/krdrW71VtVqQZ/n8TIzrarqUlV9p973vOdQ0fdaN2E0DZZSRIcQgiWPpM1hGMBLgBwtkM/x6Bs4jZKqBhSUlUtOzqL0cIy2K78IeIUVfx2fKJkD6jTxt7t0uUXwBREUt8CAZUQp+RwEfnFl5yKkLC6H04XNW7dDp9XIoaJS5Sb8QAOuPISHJrADPwAeXOBfRehUqPeIoohTp07BYDCsOU2YLk+faKBZXLt27cpIHRF1GZ6enkZRURG2bt2Kubm5mPld6QbLsnC73bIMoLCwcFWrSylDwwtq8qDhGIws+MCLBHqOldzUAVxgzoXodKPaAAz5ATcvEZ0CvQYIGG96eYKSnNDnT6S2ItVF+Xw+eRw7XouFTCE7Sh3D8vJywh2P++67DzfddBMefvhh1NbW4vDhwwAkO4dDhw7hxIkTAKRz/eqrr+LnP/95yM9/9atflQOM6+vrV/27Unjfkh1KHoInnqJ5UyQCjUYDr9cb12fpG+/y8jI+9KEPrfmQUnqkfXFxEXa7HY2NjYrpEmKRHb/fL4emRjNlDEakf1dCiEzBi9J/qCCZEMDh4TFy+o/YvHkzCotNcPGBz7BA7hrdPSHQHqKHzBCpbB7pRwwGw+qx7ZkZFFflgmEZaFgWnFYDgJFK9YGvyTGSvw4I4PX5IBKguLAA2sDbLMeQlRTtgPJYOp6giS2pmQUWBDqGQM8kfk1JNSAGEp0jcuxBOOjvu7y8HJs2bYp7+2p5+kQDdcFWQ+SrFGjlWaPRoLm5GQzDhKRgLy0trSKHSgrl44XT6URPT09IgCvLstBoNCGGhsHEJ1lDQw3L4ILqPFxQDXh5ESMLXjh8kpW4iydgeQKbaEC+XgOjlvpTEbj8BFoOMOlZ1BWu3bI3Go0wGo2y8ejCwgKsVqucI0erPtE0U5nQxlLS6yeZqIiSkhK89tprq/6+qqpKJjqAdK7tdvuqz/3Hf/xH4geaBDYU2Um0jTUzM4OlpaW4DPMS2W48pIQuBCaTCbt374557Eq6E9Ncr/LyckUzTtaahnM6neju7o4oRI4HSutzIsHjcWN+YQlNTc0wmaS3e5o5FWt3ss9NUFconmc3bVWUlJXD4WdABBF+nofX6YJGq4PTuYCi/BwYjUboWCkWwuFyg+U0yNFrQ0I/WYaADbSxNCKBByxCwydWqkZSSrmkCUoUYoDeUOUPzXkKPkXUHyrRoMxIiOXpQ3UsyWhXpqenMT4+njFZXJFAM+lMJhPq6+tXXfssy67K7wqOQSkqKpJdhtWsMjgcDvT19UWtjgW3u7Ra7arRdvr/1xptjwa9hkVlvg4T006IhGDey8Pv10HHslJQLSPZLjACkKth0FxugIZN7DnCcdyqHLm5uTmcPn0agiCEmPDRY1dbpB0PlCRc79dcLGCDkZ14IYoi7HY7WJaN2UpJFPGIdOl4dyILvxKVHUIIBgcH5VDNoaEhxYM7I8Fut+PMmTNoaWmRDcASgVpER8NK/+EFwOVchtPtRmVZMUx5K4tevLvScVQAKf2ZZYBEbDwYABzDgOc46DgOWr0eEAn8Ao+R0Qm4XMvIz8vF0tISCgsLUdewFWKAXBL551eOl7AAKwarcBhQcsKs/N8kIW0gVO3DgAtslC56O3fuVDy/LZqnTzJmfaOjo7Db7dizZ8+6W/pHA50MM5vNcYf+hsegJJrflQwWFhZw5syZhJLqlTY0HF2U9DuGQCnU55PuSYZIzuM0j0wkbMrPkWDH7E2bNoHneczPz2NmZkY+zyUlJfIzaz2hJNlJpo21UZCZT4AUQN84DQYDSktLFS8xxkpUt1gsOHfuXMIeI6kY9gErD02TySSHaqoVbREMJfK8lNDnUDh9Ihw+qd6RqwXydAxGJqbh5wXU1lTBmKTRDMNI2VaU7CQSAAoEBMkMAcgKjdBwQI65EiXllfALfpw93QcAsNls8Hq9KK+oQn5hETiWhYYJDZYUCBMUDMpQmiPlpAf2Ean1FB+CS1h0u9K2aEuotbVV9fycSJ4+1K/G7XajsLAQZWVlqyoahBB58qa9vX3dNRXR4PP50NXVhbq6uoQ9ryg4jgvJ73I6nbDZbHHld8WL4DZgsvd4LEPDeKo+IiBflgRSpTWHE0HYlUTzQj0HvYZJKrtuLWg0GpSVlaGsrEw+z3a7HR6PBydPnpQDN1M5z8lCSd1QtrKTIYh1EdHMpZ07d8Llcqmy0EcjEDTVeW5uDvv27Uu4ZJ7oWHcworWQlE4pDwZ1n/b7/UnneRFC5PNGR5JTwbJXhN21UtTw+AmWZoeg5wh2NDam/BBiGGmiK9mf1XE030qq1vAiC4YAPr8X09PTqG/cjqI8AwAiazRGhs6tYWYota1YhgTNWjEQAzobwkCe8Erkq7MgEAJUSWqLSdujLaF0+L5Egl6vD5mqmZ+fh81mk/UVVMB7/vx56HQ6VeJXlILb7UZ3d/eaOVeJgoqc8/LyFMvvslgsGBkZUbwNmIyhYVWeFhanHy6/CK9AIDAcWIZFVb4Oyz4ChiFgGQZV+ZLvjloIPs8WiwXt7e2Ym5vD1NQUBgYGkJubG1fgplKg0TlKINFcrI2EDUV2ooEQgvHxcUxNTckVBp/PB5/Pp/i+IhEInufR19cHg8GAvXv3JnXhJVuFob37SC2kVKtF0RAcN5GI+3QwCCHgeR579uyR826cTieKiooivq3HgwXPSk2CgMDH88gtNqOhPP6blwT8bggSr97EAhPwwgEj7YOHtOhZ7TZUVFRCp9MDEEOmuwBEnu4qK4PBVCxXiei4OYsVEx8iK21EuQUV13EC4CCAvkYTIsr5UXv37l13QSYg3Yfh+gqLxYK3334bHMehqqpKTs3ONMKzvLwstwEj+U8phVTzu6anpzExMaG610+8hobFehY7ivTos3uhYxnoiQgdp4GTF9BQpIMQGEk3qpiFFQkajWaVhUBw4Cat+mTitRiOZEbPNwo2PNkRBAGnT58GgBB9DjUVVBrBI+2AdHFQk7Lq6uqkt5so2SGEYHR0FLOzs9i3b1/EN+1UqkXRIAgC3n333aTiJoDV+hydThcydRKsP6Ama7ECJ+VjIyuSWr/fD41Gi5wEHtKEAE6/pAMAkeIacrVQvCQeOES43B74eQEVldVgOVaiJhH2FXG6a2oKrvNDqK7bjJycXOj1OrCsFD0hBrWdCAhEsGCJkHDbDYGQxYEzZ0AIiZgfJZ2qIHPMCEPxaoNhGGi1WthsNtkVfb09faJBqZyrRBEpv4u2BGnMBxU5cxyHiYkJWCwW7N69WzG9Ey8SeHkRBg0Lbo2bKrzqE/yfHA4oNWih1zBYdvig5Rj4BMCoY6FR5UaNjkjDGgzDID8/H/n5+auqa5R806rPephFxsLy8nK2spMJCGfFtBRcVVWFTZs2hfy7WnqV4MoOFeZGcwhOdruxIIpiCMGLVgGhpoJKwW63w+1248ILL1RFiBz+tr68vAyr1SoHTtKeeTQBnZaVRlR5QYBGqwXAIJHoHp8gER3qU8OLUp6W0vE/hBCMT06B1RlRWFwMlrrAMuGfA0TCQAQVKJNVAt6lpSXMWaewsLAIIgowV1SgqNQMnTb1W1sQBPT29spJyZHeSklgRJ0wgZoaWRlcTxfoc6CxsVGOf1nL04cS6HSPoWfSCHx4S5COXJ87d06+P1tbWxUjOmMLHrw55gABoGUZfGJzAUoi3FiEEJyf92BkQbL3qC/UY0uRQR5tN0IAHF6Iogie5yGIAMcRMEREoG6aNsQziRVeXXM4HLDb7ZiYmADDMHJcSDyC+3TA6XSiqqpqvQ9DFWwoshMMGkXQ1NQUMTQslpA4WdCK0cjIyJpVlWS2G8/xUrt7s9mM2traNW8QJdtYVIicm5ub1BtpohNXwW9IVJwa3sopKytDQUGBPBLvXbbDDwP0OUbpbZ8DcnXxP0AEEso32EC7SUlQP5Wc3HyUlxTLb7hErkoFHw8DAVLFhwT+rIO44vUTpd01OzMDhmFgMBiQm5eHHIM24XYcjTaprq6O+vAjAMCwcj7XytxY+kAnw4J9X4KxlqdPrGwqJUFzrjJxBJ4a7RUVFeH8+fNwOBwoLi7GwMCAIufI6RPwuzGHJEBmGfhFgv83tIjrm0tWaWsmlnw4P++BjpP2c37eAz3HolDPwe6SxPcFeg7Tc05oDTlgNRrUm1j52UK/jxI+XbGQqDA4WHDf0NAAn88XYhxpMpnk6lq8JDPeYOx44XK5sgLlTEFw+2atCSC12liiKMLj8WB5eXnNqkqi4DgupsZoaWkJvb29cQeYKtHGokJknuexb98+nDx5MuGbXAlHZL1eHzJua7fbMTk5if7+fuTn58Pn80Gr1aJ+azN8ojSNkafDmuXycHAM4AsiHSKQUEo4IdLPiKLkwROcZg6seC+VlpaiumYTfISRP0s1QsGg7Sh53JyEjoCHg7a7CJFaBkuOZcxMjcNmmUV+fr7sVxPrQUpjFeISzwbxm3S/l1Ln20Qmw9T09ImGiYkJzM7OZvQIPCEEAwMDIITI05x1dXVr5nfFS9psLj+WfYJ01RLJLJBoWLj9InLD/BusLj9YRmrHggFYhsGUw4cFlw4CkUg17/OhysiiqrwQBi0DHccqbmgYD1Id+dbpdHIFUhRF2WZhdHQ0xPPHaDRGfWYq7eDsdDqzmp1MAC2rU/+ctX7JarSx6Fg7x3GK53fEqsLEEzkRjlSnsfx+P7q6ulBSUiK3MRLdJhUi0+NRAsGtHJ/Ph87OTnAcB5fLhbOnu+R2l5ZLbExWx0nVHZ8IgEiGg/oEnmV+kZEiHxgC8Ay0LIGOk4iJ2+1GT08PGhoaUFBiho8wEMBAAAEnAhqWxGEAGJ+BDsMAWo5BSWE+Sgrz0bi5YWW6a2REHqON5MBLNSXxxypIbIdWdZg4jk8JKJEIHsnTx2q1JuXpEw3Dw8NYXFxEe3v7uuuFooFWG3U6HRrDphaj5Xd1d3cDAMrKK1BYUga9wQAdt5qwA8DQvJRSTsm/XyDg2BW/nGAwYOATJDsFWln1CZIbOMcAPr8PrEYLnSEHJsPK+VTT0HCt86bUM41l2ZAqrdfrlfO73G43CgoKUFJSImuqKJR2cM6OnmcIWJaV+5+xoHQbi461NzU1ob+/X7HtUkSrwlDPkKWlpZiRE+FIpY1FjREbGxtDHHLjJTvpcESmBKK+vl6+JlwuF2w2G06dOgVBEBJK2mYYSZ+TE6juJKJ3FAnAEwZswA+HQIp+0BACx9KifO0Y8wvhEyVRkLR5iShoIoiTORDwASdkSimS8c4Jbnc1NjZGnO4qKyuDz+fD8PBw3JoS6XBFgAT7NqsvUB4fH5fFs0pVYIJbDNEEvIlMCdKoGL/fj9bW1oz1+hFFUdZlRXJvDkZwa7mhoQEerx9LHh6LDidmZ+bhEDgYNQRtNUUw6leqPh6eIEfDwMMT2YW8IlezqurqEwiKDDpYXX74BallpeEYmHQaEBHw+bzS849lY0baKm1oGO3cqfV71ev1qKqqQlVVFURRlON/qO6MVn2on5pSWF5ezpKdTADLsrLoMBaUrOzQBG8lYyfCEel4eZ5Hb28vjEYj9uzZkzBhSLaNRcfZW1tbV73dx0N2kiU6vEDgEySvDL1mbV+lhYUFWbMVrNUwGo2ora1FbW2tnLRNe+LUhK64uHjNhxTDJNeSCSZITOCt1GazYej8OZlA+MSVzg9L9bwASATbY44lYIiUiA5IAmUlOGOk6a7BwUE4HA6UlZVhaWkJWq02LmJNfXhStGyOC4QQmXwEp6unDIalOwD9HvF4+kRzKaaVEq1Wi6ampowQnkaCIAjo7u5GaWlpcvl5nA65Rh0sXuC0Q4AgiiAiwaBtEk2aBZjLpIyzUqMG824eOVrpnhBEgoai1VVXXpTEy/UmIxa8AhgAuTrphc3DS9OVDCPR/cKc+Bf4eEfbE9X6KBnAuRZYlkVRUZGsTfV4PLDb7Th37hxcLhcIIbDb7SgsLEyZ+Lhcruw01kbDWjlO8SJYrxIeO6E0qw8nEXSkva6uLml1fDItp7GxMczMzKw5zr7WNpMlOm6/iOklQV788w0MynK5qP4f1NxuLUfX4KTt4ImTs2fPwmg0yq0cJQSj1NyPJpMTAEuL85gaG8XevXvlCgT9NlS/sCJ5iXytsgzkPCw1wLIslpaWYDAYsGfPHjidzrjaXekGvRdZlkVLS4tyBIIJDh9jAVFA+O8ikqeP1WpFb28vRFEMqR6KorhmzlWmgHpl0epBKuiaXgbHALpAkJtfyEVeZTEY/xIGBgbg8/pgMFTCJerAchzqi/TYVrr6pZEFgcsnxT2YZLEcgd/rgl6jB8dJarV8A4sSY/LP3ngMDeNpdykZwJkIDAaDTMQXFhYwOjqKubk5DA0NQafTyddqMi/mWc3OBxB06qmsrGzVQ4u2h5QkO8GVHaVG2hOpbtG3UVEUY46zRyM7qQiRLQ7pTU7DSSR1yUOQpycwale2Qd2WabhrIi09OnFSXFws271brdYV7UFA55Psjc4wgJ4l8IkMRAJYZ2ewvDi3qgKhYQh4AIKcW04CZoDpB7Uw0Gq1MoGIp91FJ+AUAcNihR5SF0ciqbwDpINq9QoKChQmELT9RstrRDoeEv2eCc5Moj4qtHrocDjA8zzKyspiTkquJ5SIqQAALUvA8wwEkUDDhlY2WY0OmypWqodzc3OYss5hcXEBeYIB09zqypjTK4IXifzCwzCAKPIw5hhRlqeFNhADYdAo1xZfq+oTLHam/x58L6vZxooXhBAYjUZs3boVgNTap5Van8+HwsJClJSUoLCwMK5jzWp2MghKVGxiYXFxEX19fVGnniiJUHKygm5TiawpingrO3TMuLS0NOZiEm2bqQiRCSHwi5JPDiD9jhkQCEG7oeaROp1OnhZJFtTu3Zibh7LqBvh4AW6nA+fOnYfb7ZLzhBIdtWUZQAsevX29yM/PR9POHauOk2EAA0cgiILsRqNllWlPJQKe59HT04OSkhLU1dVF/EykdlfwBFy8011RwXCQyQbDSf9L7+1AlYVWICorK1My7VQLtHpYXFyMzs5OVFRUQBAEnDx5cl09faKBDlls2bIlronOtcCxQI6GoLZQj9F5DzQc4Bel8fLK/BUtFY07Cc6VCs7vKikpQWFxKex+Y4jVgxSDwEGnZZFnSI9pYDRDw+CXONruUlocnAzCW2k5OTkhU6sLCwuw2Ww4d+4cDAaDXPWJtrbwPL8uUTDpwIYjO2pjampKnvKIVrpXY9KLYRgsLS1Bo9EknTUVjniOM5oQORoikR36JpSsEJlhGORoGXj8BBqWyE0EXWC0g5KxiooKbNq0KeHtRwIhwLJPKiBwLAdjXiEadhTCyAmYn5+TR20TWdRpNbCmpmbN1gDDSCGg6dC5RILH40FPT09Cb/aRzAzD211lZWWJLerytRIQSZFQ3ZLH60V3Vxc2b96MsrKy+LcbN6hGhxIuJtDGSgzRcq4iefooXhlLANRSYPv27RG9yeKFSCR9DQGgYYGL60zI0bAYX/LCoGFxQU0+TPrI90pwrlRtXR3cXh6LiwuYXvCC0RsCpaGAmI1hwBCgMj/97shA5HZXMAGiViHrWeFZq5UWPL5OCJGrPjTTsKioCCUlJSl5TB0+fBgPPvgg+vv78c4772Dfvn0RP/fSSy/hnnvugSAIOHToEO677z4Akn3EzTffjJGREdTX1+Ppp59O6dpcC+9rssMwTNwXIiEEg4ODcLlc2L9//5oLm9Jkhy7mDMMoqkeIVdmhsQyRhMjxbFPJiStzHocZhwAvL1U5yvNYyRI+kCOkZGAiEBBKBiIhAGksVhABRhv6Fhq8qK/1pk6Pc9u2bSguLlbsOJUGPc5UFrxo7a4zZ84k0O5iAi0jWaGNYKLj9fpwqu80du5sgsmkomCSCCsC5aDWWbxYXl5Gb29vRFPDSJ4+U1NT6O/vV83TJ9ZxNjc3J+V+TiESwMWvGAz4BSBHA1y4KR8XIv7fk18gsLsIRMJB0BdBy0jDCSBE9tgBEcH4HfB5DNCus8NweLvL4XBgYmICO3fuTEnknCrirS4xDAOj0Qij0Shfj/Pz87BYLHjsscfw+uuv49JLL024Srtr1y4cO3YMn//859c8xr/6q7/Cq6++ipqaGuzfvx/XXHMNmpqa8O1vfxuf+MQncN999+Hb3/42vv3tb+M73/lOQscQL97XZIeOn8e68GipvLCwMK4WiZJkx+FwyAZu586dU/SGZlk2YsuPGjNaLBbs378/IYEuJTtKj5ZrOAbVBZzkx8FINye119+1a5c6fWQqEA4uMAR9jfBF3e12w2q1ym/qdCKH53l1j1MhzM/PY2BgQPHjTKrdxTAAEQP/C8mbKPDLcLvdGB8bQ0tbW+DaDHx2DS1NSiDJ2TMkknMVj6cPjUJRelFfWlrCqVOnFMnj8guhBpgiJH8pjk2MJC54CHhBhJsX4PET8DwBw0pu3BwDcAwLLQcYRD5qftd6gb4wtLa2Ii8vT7XR9niQbCuN4zj5+bV161ZcfPHFeOGFFzA2NoaLLroIl112Ga644gpccMEFa25/586dMff1zjvvoLGxEZs3bwYAfPrTn8Zzzz2HpqYmPPfcc3jjjTcAALfddhsOHDiQJTsUiTwIqIvyWm9ODocDvb29cbdx6HaVIDuzs7M4f/482trakJeXh3PnzqW8zVgIztXat29fwjci/e6pOiJHAsMw8kN0fHwcMzMzqtnrswyg0wA+HvLLvF6ztrdOTk6OPNbO8zzsdjsGBgbgcDhgNpvhdruRk5OjyoM4nLMmesqpCV97e3vKWrC1EHe7y5gb6CIRatEDQIQtQABa23dDw3GQam5U08MARHlX9GSQSs5VNE+fc+fOwe12yxYJ8Xr6rAVKcNva2hSZqIsUCBIvzfELBLwo3WNOr4jpZS/EoInEHFEEp9FAJJLYv6qAQ56+ArXVodOU586dg16vlwmimtdzOGiFLNi1O5rImRIgqmVU0tCQQgntKMuy2Lt3L1pbW/HGG2/glVdewa9//Ws88sgjuPPOO3H99dfjG9/4RtLbn5ycDJEf1NTU4O233wYgPZcqKysBAJWVlbBYLCl9l7Ww4chOIohFSijZoAxdqe3GAp0qmp+fx/79+9OWfkunMMrLy1FXV5e0vmZ5eVm1BGnaTvT5fNizZ49qb3AMAxg1kihaEKV2ljaBZxDHcVheXoZOp8Mll1wih5aeP38eBoNBXtSVIGqEAAJY2a6PAQFHxLgJz+joKOx2e9rjCtZqd/GCgIbNjcjLy4NerwcDYHpqCpOTE2hrbw86TjqaE/i/DJt0JWbtg6VeO7G3rXTOVSRPH9piNhqNa3r6rAVqihhMcAkB/ES6lhgQaJn4ryNAukf8PFZM/Yjk/h0LLh+B3SXCJxBoWAZWl09qIzNSO5mAgGcY5GikAZT6Ig30QTdk8DQlsGIeevr06bRlnNEctljxJPGMttPPpHqsSoqk6di5yWTCddddh+uuuw6EEBw4cACHDx9e9fl/+Id/QEdHR8ztRkuHTzc+kGSHmpMtLi4mRTZSITs8z6Ovr0/2NUlXb5cKkbdu3Zq02JNOToyMjOCdd96ByWRCeXk5iouLFbnhqImiyWTCtm3bVL8hGEaKiEg0LDl4ZLutrQ0Mw8imX8E+LHSsnb6BJhs7IIKRJ7eoTzHAQhPDR5YSR7/fj/b29nUfkw1vd83NzWFqcgLLy8vwetwQRRHt7e3QykQn6FypqeVmNEGlMnbN6lEqOVeSHJoNECsChgirqiThnj50col6+lDiE8sRPDhOgxIymegETJ4IAD9YaBE/4aETWD4hMEnIEWhj3D+EEEw5eFidAUEvIeBFAo5lA9NX1HZA2r5ew0KnWfuAgs1DeZ7H3NxcSMZZaWlpQvldsRAv0QmHWoaGFEp6/SwvL6/6bgzD4De/+U1K262pqcH4+Lj854mJCXmAw2w2Y3p6GpWVlZieno67u5IMNhzZSWSx0Gg0q8JAU3UlBpInO263G11dXaitrU3rGC010ku0gkURrM8xGo1obm4GIQSLi4uwWCyKVDPohFBtbS0qKioS/vl0gYZ5Ui+VcIT7sPh8PvkN2+12y7ED8fpeSAsDEyg8SGEMBARijNVfEAT09fUhNzc3LcQxUXAaLcrMVSgtJ5iZnoFldgpGYw56e/tQXFqKkpJS5OblS4SCICAkZpSPoWfYFW8fIMCvVlePCCEYGRlZybnSaCG32Eh8wuZgogMAhNEAhI/q1h08ubTi6bOSkl1QUCAv6sEL3tTUFKamplbFacizZylySA0bWs3xCdJ/AMCgkf49GAIhsDp9sjO5X5DMN1nawgycAY4FjFoW5vzIZqJRj0ejWZXfFfyyQQXzeXl5Sd0HVPOkRCswnhiLRIiP0pUdNTSH+/fvx9mzZzE8PIzq6mo89dRTeOKJJwAA11xzDR577DHcd999eOyxx+KqFCWLDUd2EkE4KXE6nXKWEu0TKrHdeECztZqbm1FYWJj0vhMBIQRerxfDw8PYt29fUiQkOEmY3oSA9CAuLCyUv0sqJn1U6Llz5860nZtkQLO4EhmF1ul0skstnYCYnZ3FwMBAzImcl8/O49fnFiAQgu1lubilvRI6eSVhQeR6Tyio4L6iogI1NTXJf+GICPKAln1yxARbSwzAcNLC5HShqKQEFZUVYAgPMBrwAo9lxzLsNhs8Hjf0ej3y8/NhNOhV8iNae8lflXPFaUJICxgOENfWEhFgpaITstfQKbRoEMGA0eagtKIaZRWVgMhjaXERNptNzkui+WaLi4vYvXv3qkWQ1gbFQHq4RHxSO6E+AXD6V3Tmfh+QrwslPNQvi2UY+AWpgcbK1Ev6YKGBRVOZPuXqY3B+1+bNm+Hz+WC32zEyMhJCEIuLi+OqzC0uLqK/v18xzVMwUjE0pFhvsvPMM8/gr//6r2G1WnHllVeivb0dL7/8MqampnDo0CGcOHECGo0GP/rRj3D55ZdDEATccccdaG5uBgDcd999uOmmm/Dwww+jtrY2YrtMKTAxDPrWxwhkDQQLvmJheHgYBoMBlZWVcg+8paUlpdFLQIorcLvdsro8FiYmJjAxMRFTHPrWW2/hggsuUCyR99SpU7BYLPjYxz6WlFYjWUdkKri0Wq3weDxrjiLPzs5iZGQEra2tGWO8FgmUkKU6uksRPJFjt9tDjNdycnLQPe3E490WCILUbmBZBrurTLihVap6MRChYcRVwaDU82XLli3Ke9MwLOjiFOo0HFg6452WYlgIYOBcdkHDcTDmGqXRc+JfMRcMQBQJbJZpWK1WLC0tKWNmGHowABu2WARFRgTnXG3dulW6fhmNND1GQZjAd1/7cSkyGoSSHSbQyopBtgAIWPlZiS6I4ILamG63G2fOnMHS0hL0en3Ue84rsOBl2gNoIULLJf+YX/YBPFkR9vMCjU6RCI9RK32Drmk3PAKBIBIIgrS/Ah0HAgZajkFThR6sytVHGqhps9kwNzcHrVYrtwUjEZlgopPuZ1Own0+wfUh41ae3txdbtmxRhIj99re/xYkTJ/DjH/845W0pBEUviA1X2UlmGmt4eBg2my3hMeu1thtPZUcURSkbxudbla0VCXSsO1WyEyxEdjqdSW0jleiHYMFl+Cgy1fkUFRVhfHwc8/Pz2LNnT9pE2smAErJkJm+iIXwih4p36Vh7r78UfoEBFzjvokhw1uYMNLEAEnCZDgYtt0fyfFHgiCG3begzSBYLR5rRiQ6fzwuX248cY06Q6FaMuA2WZZU3MwwBkchNiEB5dUxFQ0ND6M8EI77iTKAVx618NO721wo5Wdl/0J8IwcTEBLRaLS6++GKIoijpoQKePvn5+VI1o6QUhNWBA5GPWYwQQJssCJFG00UihdbO+QT4BBFalqA4RwOL0w+/KP2ec7UcGJYDA6Akl1Wd6ACrAzWp6SONQwluMS8tLeHMmTPrQnTosUYyNKTrDh1tX+/KzkbChiM7iYBhGIyPj6OwsBB79+5VTKAZD9mhRoElJSXYsWN1bMBa203ljZX69mzbtg1lZWWwWq0JhYECoY7IqZ6z8FHkxcVFzM7Ooq+vDxqNBg0NDarHfyQLGoxKJ5nUJGTB4l2e5zHZOw1m0QdBJPK1Y9JrVnhGUIUAgDy6rEa5fTXo7yvxBcrpdKK3txdt7Xug1weqnHLaOFnx3qHbDqoWRZvu6u/vh8/nC6tmsACnXSEiAg9EFXSvrkrxPI/u7m6YzebVrUAiIiRANCgtfS2wICCED1RmpP3GcwaD216rxr4JwZkzZ8AwDJqbm+V7NpKnj8VqR1F5BXRaDXKNudBotZIGjCRuZUCh10itq4AfIHhREv0vengsuHmIgXubYxnkMgRa4oMpPx9+ImXImQwsKvLXxzMn3PSRmuzRCa+GhoZ1F/UDoe0urVYrkx6PxwO32y3F7fj9KRsaulyuLNnZiHC73RgeHkZeXh6ampoU3XYsspNoBEO8240Fi8UiL3j0ok1km0obBYaDung6HA5s2bIFxcXFsNlsioVxKglRFDE4OAhBENI+yWT3EGhzcpGjFeAXVpQdH6tkYbdakZNjRI6OBXQS+ZqamsLk5KRqnkQSgvQp8l+tTNHEE7NAW4G7du1CjkEn6Vyo9oVQoS+kthDDxCQR0cwMz549i+bW3eA0BDqtJkB8NIDgi+ubxg7KJIFjDwhVEqiMSDRu9efphB2R/xQcDkvAQAz8DQl8UpCnAg0GA7Zs2RLxfg2uIIoEcPMEbqcT9rk5AIBGw8HAIWlPHy0L5GoBl1/6chwDEFHEgpuX9w8AgiDCK3hRUFAAcz4HLcuAYQAtp35FJx5Qkz2O4zA/P4+dO3fKHmw02b60tBQmk2ndxf4sy8Ln88lVXIPBoIih4fs58RzYgGQnngttbm4O/f39qK2thdfrVfwY1iIQlHC0tLTEHcEQvN1EqzDAyqSIzWZbJUSONwxUbaIDrLzVB+tJ6KQJ1fkMDg6ql7AdJ+jEHm1fpHP/k0te/N93Z+EXpPRnDcfiTzabsK86H6YcHbweL+bsVpybnpLHTgkh2L17t/oeOnLLg/5vQJgcx4JPK08hrUAiRNH5BMhPAgitIEr6GI/bBefyMliGQU5uLojfC4Nhba+aaDlXEaFQRVKiL1ygQckC4CBCBAEBC6nVJf0vbfURiIEWW2FhIerr6+PaD8sAeo4FZzIhz2QCCIFzcQ5Wy2zSnj4+AbC7pNYVw0hH7xVWvol0aRAQMMgLjMszQMzR8vUANWDcvXu3HJpJp+Dm5uYwPj4Oh8Mh68aKi4vXpf3u9XrR1dUVEvkSzdCQ/v94DA2Xl5czOuomVWw4srMWCCEYHx+XU8Pdbjemp6cV308kskMIwfDwMOx2e9KTTyzLJlzZoUJk6oIZfjHHQ3ZS0efEi7m5OQwODqK5uTkiCVxL51NQUCA/XNS2iacj8Js2bUppYi9ZvHpuEX6BgA0EIooigdXJoyhHA0CE0aCFsboKVZUVOHXqFLxeL7RaLd59911Zc6CE8+5qRKq0xKf3mJ6exsTEhMqVp6CjYqR7NDcvD7kAREEAL/A4M3AGbrc7KpFeK+dKTVA7AcjNLYkqEBAIATEyK0/eSffq4Nnz2FTfiIJCEwQQcHH8HgBpZJyjJI0BDMVFKCkugiAQON1uzM3F7+lDCGBzErj9AWLGAESUpvP0HAufIIKIIggYsCwji5b1GUh06PNp9+7dq4ieVquF2WyG2WyWdWM2mw2jo6Ny1EdpaWnSPlqJIBLRCUYqhoZOpzOincb7Be8bskNLuoQQOTXc5/Mpnk4OrCY71NNEq9WmpA1KtI1FL/yKigrU1tZGvNFibTMdRGdyclL2/YjnjTFc50Nt4qmfT3l5OUpLSxVfOB0OB06dOpVwSCYhkmZBJAw0LFkzciIWvLy4Spjh4UMXMlp5KiwsxK5du+TA22Dn3dzcXDm0VLG3zyS0rCMjI7IIXVWiyrArU1VEBAS/pNkBActx0DEEbW1tUbO7tFotBgYGFMmPShy0DkJ/8eEnmtZIJG3GwNlz2FS/BUZjjmxUyAQ8tuNB+C3u5QGPyIHo8lBUkQdzVS00xA+73b6mpw8vEthcPvh4yfVJJAR6loVBw4IDoCEiBBBoNBxMBg55ehZFOSy4dUgwXwt2ux3nzp2L6/kUrBsLjvpIR34Xba9u3bo1rudTooaGWc3OBkC0RZ8GgSqNYALh8XjQ1dWF6urqkPyPVLcbC1SIvH37dpSWlkb93FqVnWB1vxqaFEKInPeT7GIX7E4MSG8fFotFcZ0PzTpqaWlBbm4uBBHwBGzxNYxklhaJBxICuAUGIpHK837CwMCJSFaKsLsqF5NLPvCBt28Nx2BP1Yrg2Ov1oru7e1XlKdx5lxqrdXZ2ynoEGjSZHKh4mF1Zh9cYNafeND6fD21tbSprniTfnpW2EgswAiB4EU4cImV3jY2NwWKxwGQyYW5uDhzHpWUChxKVtWTKwf9CF7ttO3fBaMwJEi4TkGSYKKSgd48oHQML6Xr3iSz0Gi0qKipQUVERMrI9NDQEnU4nEcT8Evh4JjBgIOmXfCJBDsNA9LvBMSzqy3KRo2UyspoDhBKdZF6eIkV90JatXq+XXzhSze/y+Xzo7OxEY2Nj0q2mWFWfs2fP4iMf+UhKx5nJ2HBkJ7zysLi4iL6+PuzYsWNVj52OnisNSkoWFhZw6tQp7Ny5U5FeZ7z6mkhC5FjHGox06HOCHXxbWloU20dubi4aGhrQ0NCgmM6HVp5om0UkgNPPQERAgyBKC4oxQoHEL0pER06BJoBPYJCjSU7Psb86Dz6B4L9HHWAY4JI6E9orpd8x1Txt27Ztzest3FjN4/GEjNgmrYciYhChiP79aJVVr9fLE0JRwWpWxMhB/jbxgZGExwHZ7wr5ImFj8VF+mmHkiRY6sh19ukv5e0QiOkHmhKvG72XPY3i9HnR1dmLr1q3Iy8+L8K1W/kaqNEq1HkBSAHEMiUjWfWKwEggy4REgOyqFjGxv3boVLpcbtvlFTM3aIeSUgGWliS6qz/HzPEAIco0GFOas/zRTNFDylizRCUfwCwegXH4XJbmNjY2xdWQJHCuwohN944030N/fj8bGRkW2n4nYcKaCgPTLJ4RgcnISY2NjUcdteZ7HH//4R1xwwQWKH8Mbb7wBvV6vqPfK8PAw9Hq9nBsSjmBdUHt7e1ztiXPnziE/P1+eKkkH0aG6l5qamqjfJRXMu3lYnVKuUEW+Bvl6Tm5PUPO5SDofkRD4AqZnukCR6fz583A6ndi1a5f8Ob8AOHnIHjeESAtAgW71guETGPhFRm5dSWO8JGmyEw0LCwvo7+/Hrl27Eha+ByP8PJlMJtmkT6l8s56eHpSUlKCurm7tD7PUiRiQ088Ff/w747T0hAfIDZ3oYgLEbO0qKc25amtrWyXupnlLapkZSjRGI9dkpL9jwIAP/P8VA0efx4nurk7s2LEDhYWFIAB4cIHkDDawJQFcwOiQFxnwhA0ZWdcw4qrATref4Py8F8s+EUYdhypTDjhWcjfO14gIX48FEVj2Ebj80ql2+XjMuXlpD4F7hGMY6FkReo0WpXkcSnLXZ6w8FijRaW9vT4uOjF5PNpsNi4uLced3+f1+dHZ2YvPmzWtW8FPB7373O9x///04fvz4uugU14Cii9OGJDsejwdnzpyB1+vFrl27oj58CCF466238OEPf1ixfRNCMDAwgImJCRw4cEDRCZixsTEwDBOxHSYIAk6dOgWNRoMdO3bE/WYQ7CKdDqJDje127NiRkO4lXsy7eJyf94XcBdtL9cjTrzxUg3U+c3NzUm6XuQKavDKAldowHEswPXQaep1uxRk3AL8IOH2SvT0tOhAApghkRyCAh19ZWEQAOpZAl4IrbTgsFguGh4fR1taWcjk8GNT3iLo4x112ZwJ+PyTUn4a+gcYt7tbogypFgdqC4Ed8j51AVUcW2wYyrmj1aY34Bjq9uLS0FEJy1/o8NTO02+2y825ZWRk80OJ3Y0tw+gTUFuhxYU1+XJqUcLJDDQZW/I2lv3MGRNPhJJcnDIRAgi0TqP5owINlpDYUIYx8rVICrmNXqsaEELw76cKSlwfLSFVMo5bDjvI85HAEOWGPNX9g6kogBLT47OUFuP0CPLwkUOYYBjqWBSN44FmYBbxLIe3T9R7ZprBarRgeHl6VHZYuBLeZ7XY7AMgi5+D8rnQQnT/84Q/40pe+hBdffFGFaJmUoegFs+HaWIQQdHZ2orCwMKZZn9I3Fw2BLCgoQE5OjuKjvtGmsagmqbKyMmG1PN1mOoTIFosFQ0NDqhrbWZzSYqAJLCh+gcDqEkLITkSdz5IXzqUliAIPvV4PvyDCVFKBusrVDxFNYGqEp2sDkR7+kU4ZxwB6ToRflJYcHQNoWeWIzvj4OCwWiyqmhsH5ZlJ7QkprP3XqFARBkBeqkABFVhO8AQBSmKXL5UJPT098I9vyzwYjkmXeWgg7x7TCE+PU0yR4nufR0tIS10tDNDPD7tMD+KOvGCJYMCyLeTcPp1/EZVsKY36TwAA56NQVAlNXwVgK+BJFTtpmQowlpXBYNjC1RSCQlXMhAtCGnRiXX4TDJ0DDSs8ChhC4/AL8vIAiXeg5EUTA7pZeAuj2OAYBnxwOWo5bqcyBwdaKfHCbCuQ2M9XtBYt318usz2KxYGRkZN2IDhA9v2t4eBhOpxMFBQUoKirC2NiYqkTn5MmT+F//63/h+eefz0Siozg2HNlhGAYtLS1pv1CdTqecOWQ2m2GxWBTfB50gC8bS0hJ6e3sjapLiASU7VLuklhB5dHQUdrsde/fuVfV3w0RYRmItLLm5uTCxRggiwPt9mJ+fg0ZrwOLyEs4uz6/SZTCMZJTmC/iFcKxEgKIhPAV6LdAqEYO1XWupuNvj8WD37t1pWRyMRiPq6upQV1cHv98vh0w6nU4UFRXBXFGBgqKSUGt/BnA4ltHX25tYbhjBiuAZBCC0vpEAURSFlQkshpXUtvTnWc2q6g7VEul0OjQ1NSVN+KmZoTOnBOzQIphAdYmIBIM2Fy6szIFRp4FGs3bFiEFw1IYYQl7oKHSkNjltq1JNWfi30DAEHqxUi6QfYqAloZVJmSjR0x6wN/IJkjMyBW1b0YKVSKT/aFkGvECCHNAZFOcAXOBajSTepdOCRqNRbgvG6+mTKiwWC0ZHR9eV6ESCTqdDZWUlKisrIYoi7HY7+vv7wbIsxsfH4Xa7o+Z3JYuuri7cddddOHbsWOx28/sEG47sANJNlIz5XrKgN2hra2tIKZlWSZRCuJh4dnYW58+fR3t7e1LTRoQQsCyLmZkZ5ObmqpIoLooizpw5AwBpWZTN+Ro47CvOwiwDlOXGvoz1HLDg9cOxsICi4hKwnBb5ZSY4F6P7+egVvjt4EfCLHMBIi7uWXa2jAFa8kwwGgzxanm5otdqQB/DCwgLsNju8fml6Iy83F8ZcI3i/gNOnTseo5gWmpeSeINV5EIREOMRIDl8FIgYitVlIHZ2gcxl2zmjOVSImfLHAMgzAAGxAd0SIVKvhfT6M26yYt1nk6lgkXZ8kq15N8KxWqyycDScChAC8XL9hIQRcejhADgYVhDCiA0AAA78A6ALXdI6GRaGBw7ybD1goEZgMGhi1LHyEgT7omMQA0SGA1B4L/JuGZSA6rdDrtcjLL0CenoFRF5nghU8LOp1O2Gzxe/qkitnZWYyNjcWtdVwviKKIkZER7NixA+Xl5WvmdyX7rO3r68Odd96Jw4cPv68FyeHYkGQnUSRLSmhv32q1rgoRVSLHKhx0GosQgqGhIczPz2P//v1J3ZxUn1NaWgqWZTE1NYUzZ84oatBH23pUjJqORbnAwGFbqR5WJw+GYVCeq0GuLvZNvzxvwfyiByUVNeA4FnlawKhlkRvFzycnJ0fWryghYCQE8BMp0VuqYRD4CQMu7G2bntPy8vKUrQyUAsuyKC4uRnFxMQikt3m32w2r1YapiXGUlpasnW8mp4pTMXGg6iLHRUiVjeQdiUUE2E4QVrZFz2nEnKsUUFegh1HDYdkvSJUPlsHuijwUFRWiqKgIFWUlsNlsCU13zczMYHx8XJ4QClY0AZKXkyDXgAKUhgE0EOTrKJosWyQMvAKBKEqi+vriXOiXvHD6BeTrOFSZ9ACzunaq5yQbBg1LuSWDHFbA0JlOVFdXo7IysYozwzDIy8uT3dNpFTHY00dJE9Hgc6q6y3gK4HkenZ2dqK2tlWOGouV3DQwMJOV43d/fj0OHDuHJJ5/E9u3b1fw6GYcNKVDmeT5uP5o//OEPcSWOh4MKgjmOw86dO1ex6JMnT6KlpUXREuz8/DympqbA8zy0Wm1CQuRgRBMiU0GqxWLB3NwccnJyZIO+RAkV1Whs3rw5ofyvdIO22Obm5tDa2ipfB2sRM/rmabVaYbPZwDCM/IaerJ+PSACvyIYsJAQM9Kwgtwc8Hg+6u7vR0NCA8vJy+ENEqCK0EFMyLFQODGZmZ2G1WLB5c4NMEj0eD4qLi+U3T4YGerIahD5KGEBMYOoqrkMKMhUEAsGfRBZNF1VuwgKThxwti6ayHMUqkG6/iJNTy3D6RWwqMGBnGXXRFcGQlapVPNNdk5OTmJmZQVtbGziNJuCbLLX5WIhgCIGfsPJIOb0UGBAY2JXnoV8A3ISTP0EIIIqBfC0iVXkAKaCCYyRvKGmSS9L66BkRHr8IryC1cIuNLASRgTPwK9MxPAb6pEU5cnZY8gj29Jmbm4NWq5VfOpKZep2ZmcHExATa29sznuhQcX885zS4Omaz2eLK7xocHMStt96KX/3qV2htbVXjayiN7DRWImTn3XffRVtbW0Jv53TRWUsQ3NnZie3btyvaR7XZbHKAaLK23fEKkenNYrFYYLPZwHGcbNAX66EyPz+PM2fOJKbRWAcEt9iSJY6AJBC3Wq2wWq1J+9QQIrnUIlDJkW47BgZOeiN3OBzo6+vDzp07UVhYCIEAfmjCfHRF6Bnl27fBW4x1hgghOH9+CBqdDrW19RL5CkxkCYIgL+iLi4vIz89HudmM0rLKsDHmGGSH5SAHjtLpLIaVJ+nW9uNZqXjQnCtdxRa8ObPSIjNqWfyP3WXQKNhyJbRVJ83PA0SM+qSONN3Fsix4npdawRwHPzisBH9KbU+REETaqgZiyKSVIAAuwsltJ5GQgC6H/vzKudMGxtU1jDRSLhDA45faxNrA6WEZoKqAg4ZlZPLY0NAg59upCamCKL10UK+aeO+96elpTE5OZjzREQQBnZ2dqKmpQUVFRVLb8Pslx2ubzQaHwwGTyQSHw4HGxkaUlpZieHgYn/nMZ/Doo49iz549Cn8D1ZAlO4mQnc5OyZ8i3reCtUwKg9HT04OGhoaUPE+CsbS0hO7ubuj1enzoQx9KahuEkKSFyHTCxGKxRJ/EgZSwPTExgdbWVkXHoJVGcKRCfX29Yi22ePx8okGq7kitLEmzI0DDrohRqXszAHiJ9FYfPIbMADAwyppkigDA6LAiZBUgqUIifFYU0d/fj+LSclRUVIY+iUjocQUv6LwIlJaWwWAwwJCTAw0LgEQhbSwHsNqVf2cYqUrDcmF/t3ZlKDjn6vEzLoQlbqDNbMTHGgrW3EY6QKfD5ufnodPpwPM8GrY2ITc/Hyy3QnYJoTRlpZ5D/6SDAC5M+yUIgB8MfCIDMZBAwgeExaJIIAZGxTUswLEMNIyIZZ80G7boDriqMwx0nBQLUZbHQkP8ipvbJYJwr5r8/HzZqya8Mj01NYXp6Wm0t7ernqeXCgRBQFdXF6qqqhTzuKH33iOPPILHH38cOp0ODocDDz30EG666aaMsQCIA1mykwjZSYSUTE9PY2RkJK7R6VOnTqG6uloR0e/MzAyGhoawY8cOjIyMJMy8lfbPoT10q9UKp9Mpv03Z7XbZgC+T35SoqWFtbW3Sb0rxINzPJx6dT/g01vT0NMbHx9HW1hbSEo1MdggMjLLxJyKjhRSvELQjwoMNU34IgoCenh6JPG7eGvoUYpiAuDj648Lj8WJhcRFWiwUul1Mm06sEqZw+SFwcOFkgYeSIkUhZFMK0uLiI/v7+AHnMw7/+YWrVZ+oK9ejYsb4JzzRSw+/3y9NhXl6EAMlskaGTalj5/SPEhlCq6mgD1T56NlisnEK/yMAjSDocLw/Mu/1Y9kjEVMMxKMvToVAvieeX/QDHECy4hMD1yUDLEvAEKNLxONPXFdO9O10ghMDhcMjVMRrIWVZWhoWFBczMzHwgiU44Jicn8Wd/9mf4+Mc/jsHBQQwNDeHiiy/GlVdeiQMHDmT0Cys+6D47QGL+OfHkTdGHzvLyMvbv3x/XQp5oaGe0/Z4/fx4LCwvYv3+/3IJKdBtKGwWGT+JQy3Oe51FaWoq5uTnFHHeVRng7SE0E+/kE63y6u7tlnU95eXkIcQ4M8ATcsEewsLCAPXv2rLrmtBDhCxpGlhxtlM95CyE69ABJ6DXk8/nQ3d2N6urqgCN2aCskHhgMelQYylFhLpdL7lSQWlpWhtKSUpgKTOA0YdumRoHBWGP3NOOsra1NquayGhg4Bh4h9Aeq89V3zQ2GNHdGxdgEDBFwpr8fHMfJRIcAYDVaECJ9TuJ5tOImQhBF+LxuGPR6MJwGbBDR8QRiS6jdjYETwbESeZFaYIDXL2DZIxkPMowkNJ93+WDO1YERAMYPAAwMWgZuPwFDCHgR4BgBZ051yw7OmQCGYWAymWAymUICOXt7e+FyuVBVVYWFhYV19fRZC4IgyFIJtYjOzMwMbr75Znzve9/DgQMHAEgt+d/97nc4ceIE5ufn8ZnPfEaVfWciNmRlJ9g3JhYGBgbkUmckUHv7/Px8NDY2xk0Wzp49i4KCgqTFuXQUVq/XY/v27XLPPpF4i3Q4Inu9XvT09KCyshLV1dUhjrtqJpAnA2pg1traqpqpYbxYS+cDSNelKIpraolEAghgA6Zw6oiTRdrCilLZofq1zZs3r2g0GFbSp8hPh9jRDFH3DwZ+vwiPxw1BEKA35Ky47dLrmfcG3JKx4twcYUx9ZmZGHi+Wr0dWC+uyF0/3zoLynZp8La5rVseoLRIkosPJxJUQAsvMDDyuJWzevHllgACSTkskK0PpKxshIESE3+eDy+WEc2kBhPehrKwUhrxCuAWaeS5tiwWBSSddM24/4OQZODw8Ft08OJaRCTcB0FopVRTdfmDZJ8WqsABYRoTg92FooBdNO3dktD4PkOI/LBYLWlpa5Bbq/Py87OmTKc8pSnTMZjOqq6tV2YfFYsH111+P73znO7j00ktV2UcakG1jJUJ2zp07B5PJFJGUuFwuWWyXKLseGhpCTk5OUqw8WlJ6IvEW6XBEdjgcOHXqVFRX3GCBM8MwKCsrQ3l5eVoSo8MxMTGBmZkZtLa2ZsQDLRjBOp/FxUUIgoDCwkLs3Llz3duBkmYneKKQAESK4/D6BMwvLKLAlI8cgzbsycMEORYnK5qmk1rStkhASOvzeWUiz/NeaBjAaMwFQ0lhhPYVdZpelXPFcADDQhQF2N08jFoOuQrnlsUCAaTWFCTtjdViQW6eCaZcXdh0npR5JZGy4DR0SmNEaAK0yc/7MT83j0XHMkylVdDqKUFc2Z6OFWHUECx6JXLj8gmwLPvBMpIeRxAJDFoG28tW7hc5fYNZ0T2lmseWDoyPj8Nms4VMXAKrp5YIIfLUklqePmuBtoPLyspUcy222+247rrr8M1vfhNXXHGFKvtIE7JkRxRF+P3xja5GC9e02+04c+YMdu3aJb9tJ4LR0VFwHJfwBUsF0NGS0n//+9/HJDupCJHjBa2SBItm1wJN1rZYLPD7/dE1GQqDOg273W40NzdnZGuNQh6DDrS94tX5qA2JOgSE04F5HY9XwOLSEoqKCqHV6iC1XniFnz4BMXJ4W4ohAZkOwez0OGZnZ+S4gXBDNRqO63A4oudcBQgPQJJIVk8dlOwQUYTFMovc3Fzk5ZvAYfX5JJD8mKRB8ZUTw4JItSEiYHTBB5tTmuIry9PCZNAFPhEKBgLytZIWhw2QSesyD6dPCBAeYEuxFjkRfKpoO7ilpQV5eXnKnxQFQYlOW1tbzOch1SPabDZVPH3WgiiK6O7uVpXozM/P47rrrsMDDzyAa665RpV9pBFZspMI2QkP1ySEYGxsTBawJeuTMzExAUEQErLapkLk9vb2qG2WWGRHEARV21aAdM4sFkvSVRKe52WB8/LyMoqKilBeXp6S62ckUC+knJychFqQ6wHqS0RHQYHofj7hOp90w2azQ2A4FBcWgpOrJAwY4lf26cNqQ71x5DZVwGBQnroisqGa1WrFwsIC8vLyUFZWhvn5eYiiGNELKxlQarGSxi6EN5SS2iYvAHMLS8gxGmDMMcr+x5HO58oElpRgLgRCUrSMiMklH2YcPLjA4fEiYM43wJSjDdkAAeByLGJ65AyqtjRBp9VCr9NCEKU8OaOGIEfHyBlzwaAC70xoB8fC2NiY7KGV6O8/3NNHp9PJJn1KV6dFUZQNWNUyC11cXMT111+PL3/5y7j++utV2UeakSU7iZCdqakpeL1eNDQ0yNk4hBA0Nzen9HCcnp6G2+3G5s2bY36WCpEXFxdXl9nDEI3spEOfI4qiHJLY1NSkyOJBM3EsFgsWFhZkM7XS0tKU3qSoaLaysjLjQ+wWA4GOsXyJlPDzSRUTExOw2ezY1bYHbMhCqAbZ0QUU21SES/+LrCI7waDmmNSZmBKfeDyiYoGsMkGUJs2SJTwEgM9P4BUINBoNOI00FcJGcMyhX5nCwzOYdfKYWvRAJIBBI5n/8SIj/278AkG+QYsK0+qpmjxOAAse9oUleIkOgiCA41jo4UNJUUHE59DCwgLOnDmzIvDOYIyOjmJhYSHuQNdYSMXTZy1QolNcXJy0f1osOBwO3HjjjfjCF77wfhIdZ8kOIWRVYGY0zM7OwuFwYNOmTeju7kZ5ebki0QYWiwWLi4vYunXrmp+jQmSDwYDt27fH3G8kskOJjiAIYFlWlYWPCrULCwvR0NCgyj6CvVdsNhv0ej3Ky8tRVlaWUAXJ6XSit7c3pEqSqaA5R62trQktHqn4+SQDGlGyvLws5XFx2hVjPwAgVCod5eeRxJOJlZb90BwrJlTsHMF8MDzniuYHWa1W+P1+mSRGc5JdC4TRhAyjMQBApPZRMnDzBGB1AU4XmKwjftCEKRYk4GosLdYcCDSM5HQ84waGbM6Q7bGMFMDJBcgOLxCU5mpRlGsIelgT5LIi9MHFHgIIhGDZsQRbkJlhcHbX3NycPMmW4SPJGBkZweLiomJEJxyJePqsBVEU0dvbi6KiItWIjtPpxE033YTbb78dt956qyr7WCdkyU4iZMdms2FqagoOhwPbt29XbHGkC9GOHTuifoYKkWtqauKuPvz+97/HRRddFBLxoLYQ2e12o6enB3V1dar60oSDtnCsVisAyALntUrn1L15I4gmqWi6ra0tpfDBZPx8Et3+mTNnQAjBzp075TFoGlVJqy2RWy4MwkM+E7pC6c8CQcJjmoS+mmDQnKuKioqIkyw8z8v3psPhSJgkiowmKK8rcBYIHxL9EC+cTiegN4Hj2KD7WXLKocSRECaQXr5SSeIC7a2zc17YnSvVNPoJfZCRjo5j0Gw2gGGk0XMmEAERz2MimCS63W65oltaWprRLeFgjVY6xsrDPX04jpMJtTw5GAGU6BQWFqqWLO52u3HzzTfj05/+NA4dOqTKPtYRWbKTCNk5f/48RkdHccEFFySdaxQJCwsLmJycRHNzc8R/jyVEjoa33noLF1xwAViWTQvRWVhYQH9/P5qampISaiuF4BYODU0sLy8PEThTA75Md2+mbUtqwKhkJUZpnY8gCOjr60NeXl7IGHRcxwIAjFYSFAOQqjJJEJ444fV60d3djfr6+rDpSmZFgBxESqgmg5JEg8Egk8RIWj0CgDDaFb2O/JfemDEa4aCTjG0f+mjIgkzJjpxQTiRJOPVKphUyDSPgvN0Pi9NHv6H8b/trjFjySALlQgMnV3mShcViwfDwMDZt2oT5+fmo2V2ZgHQTnUignj40D66oqAilpaUhnj6iKKKvrw8mkwn19fWqHIfH48Gf/dmf4eqrr8YXvvCFjCaoSSJLdgDpglsLdEpnbm4Oer0e7e3tiu7f4XBgeHg4YqBaIk7M4XjnnXekfJxAArqaQuSZmRmMjo4m3GJRG+Fv54WFhRBFEV6vF62trRn18A0H1YVptVps27ZN9QdQKjqfVNPAqb6FBBoy8pIs+hImB7FAc65WO/hSjQ09KIlsRUIwSSSEhIS7MoxUYSGMVt4sIPE4aQot/kchfYFobW2FJteE4ER2Qkf1iVQpEwhAmFCyw4JAy4hY8AFnZp0I9kMsz9VgW6ly4cM0Eby9vV2uPkbK7gpud60XhoaG4HQ6U9ZbKgmqSQz29CkpkdLuCwoK0NDQoMp+fT4fPvvZz+LSSy/F3Xff/X4kOkCW7EhYi+zQXCSj0YiamhoMDg5i9+7diu7f5XJhYGAgZLuUYC0tLcUUIkfDyZMn0dTUBK1WqxrRoeO6tOedyeSBaok8Hg8YhkFeXh7Ky8sz7o0TWDnWkpIS1crWayERnU94wnoyIGBAqE+ODElzo+SYenDO1arqI6MJeyQGND8x2k4+n09+O3e5XCguLkZl9Sbk5hUGekDyTBTYBITZc/MLcHp4lJaVQcOx4CDAz7CghIchAkSRanSk6AdpMitw7CDQQgDHAh5BcjKecXjhF0Tk6bUozdXCpCNxtaligeZHxXpWhWuilBLuxguqJ6P2Epm6sBNCsLy8jL6+PvA8D71eHzVjMBX4/X7cfvvtuOiii3Dvvfdm7PlQAFmyA0gPq0jH7nK50N3djbq6OlRVVcHn86Gnpwf79u1TdP9erxe9vb3ydoMJVrJv9IQQdHV1IS8vD5WVlaq8RdHKg0ajwbZt2zLmDSkS/H4/ent7UVxcLJMHh8MBi8UCu90OnU4nT+EkayGgFGiLRe08rnixls6Hntft27ejqKgo+X0AIAwrtbLkv5RIhlKTW8FVkoht6CTJTjDo27nOaALLaaDRaMAyLBiWBUMEcIjPwNRqs4GHFkWFRWA5Sl8INBBWZEkE8IjcypR94O8IIRAJA4EQEBFgGQINI1V9/AKBX5R+gAWBjgNMuvh0OdFA3Ybb2toSarNS4S4l1CaTSRbuqvHyQVvCXq9XjtXIVBBCZCuMLVu2qOLpw/M8/uIv/gItLS144IEHUj4fL730Eu655x4IgoBDhw7hvvvuW/WZN954A1/84hdl/7Tf/OY3Ke0zAWTJDhCZ7MzNzaG/vx/Nzc1yhosgCHj33Xdx4YUXKrr/4GgHj8eDzs5ObNq0KekxaKrP8Xq9sFgssFqtEARBXsyVMPaixM9sNqvm9aAUqGi6vr4eZrM54mdcLpfcwiGEyOdKSW1WPKBvc6mSB7UQrPOZmZEM+qhoPlUfFcmBOVh8LS3jSpAdamzZ3t4eXaNFoyuC58EiTHCFgwqwaWUFIBChBRgm4NAugBARbtcyXI4FlJaWyudKsl6UyAwdIZ+enobVNodtTS3ggjU6ALTgZWIiEsArcmCYlWeXX2DkZ5lPCBl6h5Yh8AZSyzlWMtARARToAW2S7yljY2Ow2+2r3IYThdrtLlop9/l8G4LonD59GgaDAVu2bFn178GePna7Xa76JOLpIwgCvvCFL2Dz5s3427/925TPhyAI2LZtG1599VXU1NRg//79ePLJJ9HU1CR/ZmFhAR/+8Ifx0ksvoba2FhaLJelKcBJQ9BeeWX2AFDA+Po6pqSns3bs35MFItS9KgwaBLiws4NSpU2hqakp6oQsWIuv1etTW1qK2thZ+vx9WqxXnz5+H2+2WRbvJjNTSBXkjjGsvLS3h1KlTMcM8jUYj6urqUFdXB5/PB6vVirNnz8Lj8aR0rhLB/Pw8BgYGsGvXrrgIKSEAH7CS4ViokncVDtr+83g8sFgs2Lt3LxwOBwYGBmSdT7LnigUgEiFoTD3QxkrxmKmWZM+ePWtPnBExwHNYSFqY2Pe6RFY4AKw8Zs4QmtrOgOM4iQQQQMMSeJxL8rkqKzejoqYOWq1UURLBwjo9gempKbS2tUNk2ZCqTTjk+TayMsAmEuk8CuLKz9C8c3+gykMVUUHDcUlheHhYbrOnWtVlGAYFBQUoKChAY2Oj3O7q7+9Pud1FiU5wInymghIdvV4f1XeNZVk5NHjr1q2ypw89VzTCItq5EkUR99xzD6qrq/Hggw8qcj7eeecdNDY2ysf86U9/Gs8991wI2XniiSdw3XXXyWPzaSQ6imPDkh0mYH8uiiLOnDkDnuexb9++VW8qat0kDMPA7/ejv78fe/bsSfpNJtgROfzho9VqUVVVhaqqKlmPMT4+DofDIVvnx5Pqa7fbMTg4uCGs3ym5S1TcrdPpUF1djerq6lXnqrCwUC4fK9m2m52dxejo6KrKg0gIlr0EIiEwalnoNCtjx8t+JqglAeRrieyGqyampqYwOTmJ3bt3Q6fToaCgADU1NavOVTKldiYguKUrcaqOwzTnavfu3XG2RuIjOStgIBEdskJKGA4s4SGSwOg5kUIedFpOroLxPA+HywuPx4v5gOMuy2ngFwja29vBshwEiBCDHImCW1iARHB0rACfyMk5VBqGBMTR0sEwQUdJIFVw/KKcogGOATQJXjNU9+JyuVTzpsnJycGmTZuwadMmud01OTmJ/v7+hNpdhBCcPXsWgiDIVgiZCkII+vv7odPpsGXLlriPNScnR36pDT9X+fn5yMnJgclkQllZGURRxJe//GWYTCZ861vfUux3Nzk5GVLhr6mpwdtvvx3ymcHBQfj9fhw4cAAOhwP33HPPhvXy2bBkB1hx0S0tLUV9fX3abgp6M/r9fnzkIx9JqledqCMyx3EoLy9HeXl5yATA4OAg8vPzZdFu+AI1MTGB6elp7NmzZ911LbEwPj6O2dlZ7N27NyVfmvBzRbUrZ8+eRW5urnyuUtnH6Ogo7HY79uzZE/L7F0SC0XkeHl4yixNFAQYtg5JcFkYdC7/IgA34oIhESqM26WKTA0IAn0iN6AAtG59IlRCC0dFRzM/PY8+ePauuj+BzFazzOX/+fNx+PgEqh1S73sE5V3QiUS0QJrRGTl2EOPBRv4ZGo4E2R4OJRT94mMB5fcjXSO10Gu5YWlYGnc4QqO6QiJU7lgH0rLBSAWIBt8DKE2zBlSENC+RrAZcgRU5wLGDUJqbXCW4H7dq1Ky3PSY1GE3Jd0XbXyMjImu0uQggGBwdBCMGOHTsynuicOXMGGo0mpbiaSOfqt7/9Lb75zW/CYDCgtLQUxcXF+MlPfqLoPRFJwhL+Hahc47XXXoPb7cZFF12ECy+8ENu2bVPsONKFDUt2HA4Hurq6sG3bNpSVlaVtv8FC5JycnLQQnXCwLIuSkhKUlJTIN4fFYsHQ0BAMBgPKy8tRWlqK4eFheL3eiItcJoE+4Hw+H/bs2aPoDc2yLIqLi1FcXCxPS1gsFoyNjUGj0cg6n3h9e+ix+v3+wNt86LEuekR4/NJDhLYlpIkaAcVGQKdj5YWKgUR4Yu9TWgzlzzJSIUPPrf3D9Fh5no+rbcEwjFxqD9b5dHd3y6n21EhNadBjFQQBra2tKi9yJOi/AUljtDq+IRx+gWDI5pN+r0QEIRw0OgM2bypCeUkhrFYr+np7IYriqikcnwC4BQYggI4DDNwKEWIAGDkRIgf4+MDnwIADQZ6OgGWBPBZAEtycEIKBgQEQQtatHRRvu8tkMmFwcBAA4nKbX0/Q88qyLLZu3arYsdJzdfXVV+PKK6/Efffdh/7+frhcLuzbtw8HDhzAVVddhYsvvjhlQ9GamhqMj4/Lf56YmFgVmF1TU4PS0lLk5uYiNzcXl1xyiWwBsdGwYQXKg4ODKCkpiastE+5KnCzcbje6urpQW1uL6urquBLKw6G2UaDT6cTMzAzGxsag1WpRW1uL8vLyjDXho6Z2ubm5CZWBlQDtm4eLwanvSqRjPXXqFIxGY9RjtSzzsC6LAXv+leqBlgM0LIOSfJ28yIlEIiy5MRYxgYS9+ROpMpHLiVHf8EVRxKlTp2AwGBQJSY3k56OUJopOCOr1+rQFukqp4hwIIxGdaKGcwZh3Cxid90PgebAcB5ZhIBKgtUoPjmEgCICPAIIgwjE3C6vVCqfTidLyChSaa6HVSGP6BAQGDZCjifx4lXU8TGoTV7TFotFoFF2QlQRt4VgsFthsNrkdlInWEhSU6DAMo5qXFiEEf//3f4/JyUn8+7//OziOg8fjwW9+8xu8+OKLmJ2dxdNPP53SPniex7Zt2/Daa6+huroa+/fvxxNPPBFilNvf34+77roLL7/8Mnw+Hz70oQ/hqaeewq5du1L9ivEgK1AGgM2bN0MQ4sur4TgOoiimVN2Yn5/H6dOnVwmRKWmJB+lwROY4Dna7HTt27EBRUREsFgtOnTolL+bl5eVpn1aKBq/Xi56eHlRVVUW0/lcbwX1z6rsSLAYPFlf6/X50d3ejoqJizYm7XB0LOyNKImQENBYBlsKygFFD5Dd8LUdgVOEOpH4/paWliuXx6PV6WbuihM6HQhAE9PT0oKioSDWn2UhgQNZsWRESOhXFMABDCHw+P1gW0sQVkVqJLKQW0xJPvzsHfXENmsorwTIi7Etu+P1+LDuWwHEc9HoDGGiRo4l8/zOMpMtJBZRA0umgTCQ6AOTqqt1uR2VlJcxmM2w2W8x213qBViABqEp0/umf/gmjo6P45S9/Kd9TBoMBl19+OS6//HJF9qPRaPCjH/0Il19+OQRBwB133IHm5mb87Gc/AwDceeed2LlzJ/70T/9UTpU/dOhQuoiO4tiwlR2e5+MmOydPnkRLS0vSmpXJyUmMjY2hvb095KZ7++23sXfv3rjeQAgh4HleVUdkOsVEiU4w6GSXxWJJ67RSNNDpsK1bt6KkpCTt+18LgiDIb5tLS0vIzc3F0tIStm7dGnUMPhjzLgEzDgH+wOWpCYzS1BRwMBlWhKnxnvbgNhYDqaqjZUjENla6/X7CIxkKi4pRUV0rtXg5JmK6NwUlkJWVletCdqOBEMArrhw5xxBwxI+enl6wJQ0gnPQcYRigIl+DinwNFryrvykLgkK9CA/PwM1LlRqe5+Hz+eFyLmN+ZkTWRCn5AkKjCvLz81Vz8FUKtPqk1WpXVfUiBbyuNbGUjmOlwmm19ESEEPzLv/wLOjs78cQTT6SkK3wfIOuzAyDghRGf2VdnZye2b9+esNaAsng6wRBOauIhUanqc+LF7OysHF8R63vSN3OLxZLwZJcSmJubw+DgYNzj2uuJpaUl9PT0oKCgAE6nEzk5ObImKtaDyOsXMe8WIRKgwMAiV5/8uaUCZRGSF28kgbLL5UJPT0+ESIX0QCSAV2Dg8Xrg9Xig0Woh+DzINxpWXZPRc67WHz6RAU9YsPJoOMHEyHnkG/UoKavEjFMELxDk6DiU5gA6DYM5rzwcLoMBQZFe+v07fIysuWLAIE8nQvAnH/URDaIoypWy9XDxTgTBI9uxqk/pNjOMdKx0FF6tCTFCCH7605/id7/7HQ4fPqxIyO8GR5bsAImRnd7eXtTX1yeUkk1bAXl5eVH73bFIVDqIDiEEIyMjmJ+fR0tLS8JvAuHZLmtNdimBqakpTExMoK2tLeOnw+x2O86ePSsTSCrapfoCOsmUiMBZLdCqXnNzM0wm07ocgwAWPAky1BMFeD0eDA2eDtH5aLXadSVlseAVWSlBnAFEQcCiwwE9x6LQlItlPyu3tWirK18rYsnHBCIgVsCBoEAvDaGLRBofJ4SBll1tN0BfQGw2GxYXF5NazAVBQHd3N8rKyjLeNDTYbTjhANo0Z3dRF2efz6cq0Xn44Yfx8ssv49ixYxn/bEwTsmQHSIzsnD59GpWVlXGb/lEhMo2ciIaenh40NDREJFHpIDqiKKK/vx8Mw2DHjh0pV2WCJ7vsdrs82VVWVpZyOZX6fDgcDrS0tGT0dBiw4kvT1tYW9Q3L4/HIrUFBEOT08WgCZ7UQTsrWCzxh4UfYaDsIdAwvL+ZTU1Ow2+0oLS1FVVVVStb5asEnMhAIC0EUsDA3j/yCAhh1HFgQOHk2ZJxcIECeVgREYIln5RF2BgQmjYhkvlqkxTzW1CDP87KmLJNagpEQHquQKtRud507d071uIrHHnsMzzzzDJ577rmM0SdlALJkB0iM7AwMDMg3QCxQIXJw5EQ0nDp1CtXV1as+lw4hMk2spiJUNfZBqxhWqzWlKkZwHtdGGCmlLrOJkDKag2OxWOB2u1FUVITy8nIUFhaq+n3p5F17e7sqZW/J8ZkBIZLAmkN0fx8/YcGDk59QkieQCD0jiZcWFhZw5swZ7Nq1C36/HxaLBfPz83H7+aQLhAAunwiHyw2j0QiDloOWEUGAqJUd+mdBXBE0K9URDp8apO2u/Pz8EPF8dXU1KisrldmpSqBTgrm5uVHdhlOB0u0uOrCgZgDp448/jieffBIvvvjiur6sZCCyZAeQbhq/P3YGDiBdsHl5eTHFpdGEyNFw5swZlJWVhQhsqRAZgGr6F6fTid7eXmzZsiVtHkM0aiB4TDueyS5KysrKyhSbDFIL1I2bVsqSfbhRgbPVapVbEuXl5YpXMcbGxmCz2dDa2qpSCKPU0hHIynnQMiJ0Ufx9/EQa4GaCbAZZiNAxojzp1tbWFkKWg/18bDab6n4+8cDpdKKnpwdNTc0BAf/Kv/kEBh5h5S9yOBHaNBam/H6/nGy/vLwMk8mExcVFNDQ0bAiik07hdLQKWbx5VNRxWk2ic/jwYTzyyCM4fvx4xusX1wFZsgMkRnboGGO08m4sIXI0nD17FgUFBbLzZTqEyHNzc3IWUyIaJCVBx7RjTXbRMM+GhoaME6GGg45AFxYWKurGTQiRp5XsdrsiVQwqlvR4PGhublaNVAsE8AjcqkqGkRMiVncEwsAPTo6LIGCggQDb7DTGx8ejtgQFAvhEThLwijwWbDOwzM7A5/PJVYx0TQ1S7dNa0SoiWcm2Ske2WTR4PB689957MBqNcLulKlQmVciCQYmOyWRKq8VAMBJpdw0PD2N5eVlVx+lnn30WP/nJT3D8+HEUFBSoso8NjizZARIjO+Pj4yCERKws0F63yWRK2NBsaGgIOTk5qKioSAvRoeLe1tbWdRfEUkSb7GJZFmfOnEFTU1PG38g0dqSmpkbVt+PgKgZtDdIqRrx9etoS1Gq1qvl8UAiEgUdYmUqi8VfRyA4gtbxESGGYHERMT4zBarVGrT5JY/UrpRECKfjSwIoQhBXDuVT9fOIBbbOtt/YpHni9XnR1daGxsVF2Ug+ukAEIMclcT4iiiN7eXhQUFKwb0QnHWu2uiYkJLC0tYdeuXaq9SBw/fhzf+973cPz48YwU6WcIsmQHkBYOn88X12enpqbg9XpXlU5dLpc8/prMIjc6OgqO41BZWan6xNW5c+fgcrmwa9eujBN0UtDJLprFVFpaisrKStUmu5QAbQmuh98PFTjTN00qcKYRA+FItwEfIYBL4MALkE0SNSyBSRvduXnlZ1dyrtYKnhQI4BW4kO2JBMjhhJCqCfXzUUvnQ0Xe4aGumQiPx4Ouri5s37496tCFz+eTry2PxxOSQJ4OewmKjTAKT6uvNpsNU1NTIITIlghqiIVfeeUVPPTQQzhx4kRcOtIPMLJkB0iM7FgsFiwuLmLr1q3y383NzaG/vx+7du1KuvIwPj4Oj8eDuro6sCyrCtGhcQpGozFtVvqpYGxMepNvaWmB2+1WZbJLKSwsLMjXwHq1BCmoFsNiscDpdMqLU2FhIViWlatP1dXVa04IKg0PD7h4yUOGAQEYAj0nOUFHA7XTF0Ux5qiuGKjsxNsqo9tXUudjsVgwMjKimshbSbjdbnR3d2PHjh0xBygowjVk+fn5stZQTY8aSnSKi4szXq8HSC+vCwsLaGxslM+X0tNdr7/+Or7xjW/gxIkTKbf2X3rpJdxzzz0QBAGHDh3CfffdF/Fz7777Li688EL853/+J2644YaU9plmZMkOkBjZoYK+HTt2AJACzyYmJlJ6iyOEwOFw4OzZs6o5ElPjterq6g0xTjowMACe59HU1LTq7XF5eTmkfbPe/jQWiwXDw8OrBLOZAFEU5YftwsICjEYjlpaWsH379rRrn1w8A6/AyFUWQqQ2U7Sk9kRzrggB/CIDP2HlpG8dK0LLxv/oCc7tSlTnMzU1hampKbS1tWUMCY8GKpxOxUspVdFuvKBVyNLS0oz3/AGklzTqVRb87FJyuut3v/sd7r//fhw/fjzldrkgCNi2bRteffVV1NTUYP/+/XjyySfR1NS06nOXXXYZDAYD7rjjjizZWQMZS3YA6SEXDxYXFzExMYGmpiYMDAzA4/Gk5PUiCEJI2ypct1JcXJzy2LHD4UBfXx+2b9+e8T1dQRDQ29sLk8mEhoaGmN852ckupTA+Pg6LxYLW1taMX+Cog3NhYSGWl5dhMBjkKkY6qhAenoFbANjA71QkUlRFXgSyk2ybjRBAhGS4xzIkJdEvz/Pyy00snc/4+DisViva2toyts1Ksby8jN7e3jWF08kgkmg3VUE4vQ7KysrWzJHLFIyPj8Nut8v5T9EQ3O5KlCi+9dZbuPfee/HCCy8ock7eeustPPjgg3j55ZcBAN/61rcAAPfff3/I537wgx9Aq9Xi3XffxVVXXfWBJjsbNgg0EXAcB7/fj/feew8FBQVoa2tL6kaONnFFKxXl5eXyW/n09DTOnDkjT2sVFxfH3Su3Wq04f/48Wltb111cGAu0+lRTUxN3e8VgMKwK4FSzQkYRPMW0e/futGoXksH8/DwGBgawe/du+Tqg7Zvu7u60jGnrORIw2QMQsMyLlNadSs4Vw0gRGGBSf7fSaDQwm80wm80hOp/z58+H6HwmJyextLSE9vb2jL8O6ISYGs+DnJwcbNq0CZs2bZKJYioBr9TFuby8fMMQHZvNhra2tpjXAcMwKCwsRGFhIRobG2Wi2N/fv2a76+TJk/jSl76E559/XrFzMjk5GVIxq6mpwdtvv73qM8888wz+3//7f3j33XcV2e9GxoYmOwzDIEZlCsDKqPSuXbuSDkeMd7ScZVmUlpaitLQUhBAsLCzAYrHg7NmzyMvLk3OVIj08CCGyd8revXszvupAwzxTsf3X6XSoqqpCVVXVqjRtJTO7qJmZwWBQdZxUKczOzmJ0dBS7d+8OsY7Pzc1Fbm4u6uvr5fbNwMCA3L4pLy+XzeaUAMMA+VoCfyDYScOuHrfO1JwrlmVRVFSEoqKikKiPP/zhDyCEoK6uDh6PJ6MnrxYXF9Hf34+2tjbVjzMSUaQvXvFUFCnRMZvNGd92ByQ5Q7xEJxIiEcXJyUk8/fTTeO211/CpT30KjY2NuPfee3Hs2DFFBdqR1r3we/6LX/wivvOd72R81TJd2NBtLJ/PF5PszM3N4fTp0+A4DhdddFFS+1HCEZlqfGiuUrhgVxTFEFFnpr9t0ukVtcI86WSXxWLBwsJCSpldG8nYEEiuzUYfthaLBcvLy2kLd6WC2UzNuQoGIUQ2jayvr5fbN16vV85WSpefTzyglb22trZ1jxAItkwAIJ8vGo0iCAK6urpQWVmZVgF9spicnMTs7KwqLUye5/Gb3/wGTz75JF599VXs2LEDn/70p3HVVVcpRnjiaWM1NDTI66PNZoPRaMT//b//FwcPHlTkGNKArGaHIhbZGR8fx+TkJFpbW9Hd3Z0U2VHLETlYsEunbcrKyjbExFU8uVFKIjyzi7Yj4pns8ng86O7u3hDGhjQ/jJqZJfsQjhTuqsb0DdWVrWf4aLxYSzidiM4nXbDb7Th37lxGCuhppdxqtcLtdqOwsBALCwvYtGnThqjoTE1NYWZmRlWtVn9/P26//XY89dRTMBqNOH78OF544QXY7XZcffXV+PrXv57S9nmex7Zt2/Daa6+huroa+/fvxxNPPIHm5uaIn/8f/+N/fOA1Oxua7Pj9foiiuOrvaZXE6/XKQuTf//73+PCHP5zQ9sOFyGqAev3k5+fD4/GAECLrf9b7bS4cNP3X6XSuq99PvJNdtM2WyJjueoEQgv7+frAsq2h+WPj0jU6nk4liKsnK1ICvpaUl43Vl1NSOCuhjfZbqfObm5tbFldhqtWJ4eHhDjMJ7vV6899570Ol08Pl8yMvLk4l1Jrbhp6amMD09jfb2dtWeX4ODg7j11lvxq1/9Cq2trSH/5nA40N3djY9+9KMp7+fEiRP44he/CEEQcMcdd+CBBx7Az372MwDAnXfeGfLZLNl5H5Id2rIoKCjAli1b5EUjEbKTrugH6vMS7DJMdRgWiwU8z4cYza0nqOZFp9Op7tybCKJNdnm9XgwODm6IxTjRabZU4HK55FYqISQpl91oOVeZCKojKSsrS3gEej1yu6jnz+7duzOSLASD53l0dXWhpqYGFRUVcqueEutkHMLVxPT0NKamplQlOsPDw/jMZz6DRx99FHv27FFlHx8gZMkORTjZcTqd6O7uxubNm1cJkeMlO5ToCIKgmlEgIN144+Pja0Y/hCdpqzmptBYogSwvL89ozwxaXh8bG4PT6ZRToDNJhxEOOsVUUVGR9ukV6rJrsVji1q1MT09jYmIibS3MVKB0Gniwn48aOp+ZmRmMj4+jvb0944mO3+9HV1cXamtrowYshzuEpzvnLBjpIDpjY2O4+eab8Ytf/AL79+9XZR8fMGTJDgXP8xAEAYDU46Zl9Uj6gXjIjhJC5FiguoylpaWEQkfV8PKJBy6XCz09PWlNWE8WhBA5qqKpqQkLCwuyDqOoqEg+X5ki/s4kPVG4bqWwsBDl5eUhAmfqS6NWyrqS8Pl86OrqUm1CTGmdD22vtLW1Zfy5jYfohIOeL5vNhqWlpbTqomZmZmQTWbXO7eTkJG688Ub8+Mc/xkc+8hFV9vEBRJbsUFCyMzY2Jj8oolVJfv/73+Oiiy6KSgzSQXQEQcDp06dTbgVRLx8ag5GMl088oG22jSBApQ7OgiCsmmZTcrJLKWSynkgURdkyYX5+Hrm5uSCEgBAS03gtE0Czo9KVd0bPFxWEJ5rbtZHMDSnRqaurS5pEUnM+2u7S6/WK6MgiYXZ2FmNjY9i9e7dqRGdmZgY33HADvve97+HAgQOq7OMDiizZofD5fDh16hT8fn9Mwezbb7+NvXv3Rrzg0yFEptlGFRUViraCgr185ubmYnr5xIvZ2VmMjIygtbU1I/rta4Hmh+Xl5WHz5s1r/g5TmexSCpREKu2GqwZEUURfXx9cLhcYhoFWq5UF4UovTEqAViLXi0SGJ9uzLLumzodWIjcCifT7/ejs7ERDQ4OiVV6n0ylPdxFC5HZXtEDceGGxWGSvKrWIjsViwfXXX4/vfOc7uPTSS1XZxwcYWbJDcfbsWfj9/pgLHAD88Y9/RHNzc0jlJ11CZPoWr/abZriXT05Ojkx84l3IaStobm4OLS0tGa8doCSyqqoqqbHXdGd2Wa1WDA0NbQhxLxWl5+TkyGJ/Gu5KFyYqoM8EETiNVMikSuRauV0jIyNwOBzYtWtXxhMd2hZUmuiEg+oUrVYrnE5n0n5RlOioqX+y2Wy4/vrr8c1vfhNXXHGFKvv4gCNLdiiCNTuxQMva9KGcLqJjs9lw7tw51cz31sLy8rJMfDQaTcw38o1mbEjf4hsbG1FaWpry9tTO7JqcnJTbrZlOImm+UXFxcVQjNCoIt1gsctRHWVmZIunQiWJxcRGnT5/O6IiVYJ2P3W6HRqORX4AyuX1Fic7mzZsVuc/iRbhfVG5urtweXOv+oaP7ak60zc/P47rrrsMDDzyAa665RpV9ZJElOzIEQZAN/2Kht7cXdXV1MJlMadHnAFIvfnZ2Fq2tres+uRL+Rh7u5cPzPHp7e1FYWIj6+vqMnV6ioIubWm/x4Qt5KpM3hBCMjIxgcXExpQDadIFOMdEYj3hABfQ0HVotHVkkZJLTcCwQQuSKdGVl5SqdTzrbqfHA5/Ohs7MTW7ZsSSvRCQchRK7C2my2qO3BdBCdxcVFXH/99fjyl7+M66+/XpV9ZAEgS3ZWIIoi/H5/XJ/t7+9HRUUFCgsL0zJxNTAwAL/fj6ampoxb3MK9fAoLC2G321FfX6/IiK7aoHk96VrcBEGQS+uJTnbRa0EURezYsSPjq2Ver1duVyQrQA035ov3jTwZUM+f9vb2jNQQBYNeCwBCjCMT1fmkC/RaaGxsTIvQOxF4PJ6QuA9qYjg7O4s9e/aoRnQcDgduuOEG/NVf/RU+/elPq7KPLGRkyQ5FImRncHAQhYWFKCoqAqBs9EMwaIWkoKBAdYM4JTA/P4/e3l7k5ORAEARZg6FkmKSSoK2g9aqWJTLZRcW9RqMxxOAyU0HbgkrmXIW/kSupi6JhqRvBaZg6ZGu12piRMMELebjOJ13XECU6W7duzfjMM0EQMDIygvHxceh0OnmsXen2oNPpxE033YTbb78dt956q2LbzSIqsmSHIl6yQ71tXC4X6uvrVXtbcrvd6OnpQV1dXdLp6ukE1RNRl2FawaBhkuny8okHSuVGKX1M0Sa7GIZBd3d3xhsxUqQr58rtdssVjGByTQMl48Xk5KScb5TpvjQ0lysnJyeuYYpgRPLzUbs96PV60dnZuSHCXYGVHDHauqJj7XNzc3I8SmlpaUrk2u124+abb8ZnPvMZfO5zn0v5mF966SXcc889EAQBhw4dwn333Rfy748//ji+853vAADy8vLw05/+FG1tbSnvd4MhS3Yo4iE7VIjs8/kwOzsLq9UKnucVF59SDcnOnTszzjclEiYmJmSxbKS34nR5+cQDURTR398PjuMUzY1SErQVQXVRLpcL5eXl2LJlS8ZPXa1XzlW4Q3hxcTHKyspikuuxsTHY7Xa0trZmBOldC7S6l5+fHzOXK55tUT+f4NwuJXU+1KNo+/btchU8kzE3N4ezZ89i9+7dEZ9jLpdLrpIJgiC70Ccy1u7xeHDLLbego6MDd955Z8rPH0EQsG3bNrz66quoqanB/v378eSTT6KpqUn+zO9//3vs3LkTRUVF+K//+i88+OCDePvtt1Pa7wZEluxQEELg8/nW/PdIE1d+vx9WqxWzs7Pw+XwoLS2F2WxO+O2SYiN50hBCcO7cObjdbjQ3N8e1WKjl5RMPeJ5HT08PSkpKUFtbm5FEJxhOpxO9vb2or6+X4xhEUcyoEe1gZMoovCAImJubg9VqxeLiIkwmk0yu6TVGCMHw8LBc3ct0/RPNPCsuLkZtba2i21ZD50OJTiYaXUYCJTrx6rUoubbZbFheXo5rrN3n8+HP//zPcdlll+Huu+9W5Pnz1ltv4cEHH8TLL78MAPjWt74FALj//vsjfn5+fh67du3C5ORkyvveYFD0YZ/Z9d8UsNbElVarlSdNeJ6XBa80f8psNselWaFTNvPz86qK4pSCIAiyb0pLS0vcNy7DMCgqKkJRUVGIl8/w8HBSXj7xwuv1oru7G7W1tRuiLUire7t27UJ+fj4AoLa2Vp7sOnv2bMqTXUqC5lxFeytOJ4JDI6nDrsViwfnz5+X24OLiIkRRTOjaXS/QANLy8nJVMs8YhkFeXh7y8vLQ0NAg63wGBgaS0vlsNKIzPz+PwcFB7N69O25hularRWVlJSorK0OqZIODg3KVrLi4WCb9fr8fd9xxBz72sY8pRnQAqQUb3NquqalZs2rz8MMPZ318FMD7kuwQQuSR9FhvfxqNRr4BqGZldHRU1qyYzeaIviG0D6/RaNDe3p7xb5k+nw89PT0pB04yDAOTyQSTyYTGxkbZy6ezszMuL594QY0YN5JugL5lhlf3dDqdTK7pNTY+Pr6umV1jY2Ow2WyqussmC4ZhUFhYiMLCQlngfPr0aXg8HuTl5WF8fDxjkrQjged5dHd3o7KyMu7R/VRhMBhQU1ODmpoaWedDr7FYLWi3243u7u4NQ3QWFhYwMDCQ0gQey7IoLi5GcXFxSJXsnnvuQV9fHz7xiU/g7Nmz+NCHPoR7771XUXIdqZsSbfuvv/46Hn74Ybz55puK7f+Dig3dxgKkt38KJY0CRVGUgzeXlpZCFiXaWikvL1e8PK0GaGtFKfO9aKBePhaLBQzDyLqoRBcl6puyHkaMySDZJPD1yOyiQm+n07khWkHUxdloNGLz5s0hjsQ8zyelwVATNDtq06ZNGVGNjKXzoURn586dKCgoWO/DjQmqL2tvb1et7To6OoqvfOUrclXx0ksvxTXXXIOLLrpIkfsy3jZWT08Prr32WvzXf/0Xtm3blvJ+NyCymp1g+Hw+OaRQLUdkuijNzs5ifn4efr8fmzZtQkNDQ8YvFjSHKbi1kg6Ee/nQB2ws8kLHiVtbWzNe2AtID0Yqlk2lQhJpskvp9mCw58/OnTszghysBap5oUaX4QiPFggWOK/Hfal20nqqCNf5AFL7aseOHRl5vOFYXFxEf3+/qkRHEATcfffdMJvNeOihh+D1evHaa6/h+eefx1tvvYXPf/7zuOuuu1LaB8/z2LZtG1577TVUV1dj//79eOKJJ9Dc3Cx/ZmxsDB//+Mfxy1/+Eh/+8IdT/VobFVmyEwyfzyeTHLWjH+bm5nDmzBnU1dXB4XBgfn4+IxK0o2FmZgZjY2PrThyoIDzYjTiSlw9traRKHNIB6oTr8/nQ1NSk6OIaPNmllDdNpJyrTAZtBZnN5rjaruHTgyaTSRWvlWjIFKfheOFyudDZ2Yny8nIsLy+vm59PvKBER00jUVEU8aUvfQm5ubn453/+51X3NDXLVGJK7cSJE/jiF78IQRBwxx134IEHHsDPfvYzAMCdd96JQ4cO4ejRo3JUi0ajwcmTJ1Pe7wZDluwEw+v1gud51aMf6Kh2a2ur3Cemb+Ozs7Ow2+3Izc2F2WxGSUnJui7WVDi9sLCAlpaWjCIOkbx8ysrK5AqQ0sRBDVC9lk6nw9atW1VfGIK9aZKZ7Ion5yqTQFtBNTU1STl6U4EzzaAyGAxylUwNIbbH40F3d/eGMOADpLZ2T09PSLV3Pfx84sXS0hJOnz6tOtG57777QAjBD3/4w3X/zlkAyJKdFUxOTuKb3/wmOjo68NGPflSVRZ2+wXs8njVHtamQcnZ2FjabDQaDAWazWZUppbUgiiLOnDkDABkfTyCKImw2GwYHB8HzvJzXlQkP2GgIHoVfD+KQaGZXMjlX6wnq3Lt582bF0rWDq2SpaMkigWpeNoovTSSiE450+PnEi3QRnW984xtYWlrCz3/+84x99nwAkSU7FH6/H7/+9a9x9OhR/OEPf8AFF1yAgwcP4uKLL1bkDU4QBPT19SE3Nzfh0n944rjZbEZZWZmqI750IaZv8JlWig6H3+9HT08PzGYzqqurQ7x8MrE96PP50N3dnXTFQWmEV8nCJ7uUyLlKJyhxUHMCL1hL5vf7ZbKYTDwKjdfYKOLe5eVl9Pb2oqWlJW7hf6SWKq0sqj0NR12929raVHO9J4Tg7//+7zE1NYVHHnkkY541WQDIkp3I4Hkev/nNb3D48GG8+eab2LNnDzo6OvDxj388qfFEj8eDnp4e1NTUpPxG7HK5ZGddlmXlCoaSwYW0lF5fXw+z2azYdtUCjdaI9AafDrFuMsdLWxWZFooIrJ7sMhqNWFpaws6dOzeEhoRWHNLpQM7zvCxwjkQW4znedAv/k0UyRCcSqJ8PJYtq6XzSRXT+8R//EefOncNjjz2WUe3+LABkyU5sCIKAN998E0ePHsXrr7+OXbt2oaOjA5deemlcN87S0hJOnTqFHTt2KF6a9ng88ng2IUQmPqm8JdEHw0aJqqDH29TUFPONOPzNUkkvHzWONxOwtLSEnp4eFBYWYnl5OSPI4lqg53c9iUMkGwCaqRT+tk+PN1XikC5QotPa2qqog7daOh+1jjcYhBD8y7/8Czo7O/HEE09k5H2RRZbsJARRFPGHP/wBR48exauvvoqtW7fi2muvxSc/+cmID6rp6Wl59FmtNwoKWlKfnZ2FIAhJ5XVR9+d0HK8SoOZ7yR6vUl4+8WJubg6Dg4Mb5vxSjyKacxWJLFL9RSaM9lPXaTUXtkRBK4s0qV2v18sE2+Px4NSpUxl1vGuBEjO1jzeSzicZgp0uovPTn/4Ub775Jp5++ul1dw/PIiqyZCdZiKKI9957D0eOHMFLL72Euro6dHR04IorrkB+fj6++c1vYmpqCj/+8Y/TzvRpjpLFYok7r2t8fByzs7NobW3dEDfs1NQUJicnEzbfi4ZIXj5K5k9Rz5+2tra0VZFSQTw5V6lOdikJSiTVFJ8qAepNMz09DbfbjU2bNqG6ujrjyW+6iE44wgk2ze2K9VJCW4NqVswIIXj44Yfx8ssv49ixYxvivv4AI0t2lABNIz58+DCOHz8On8+H8vJy/OIXv1h351Oa10XToMN9aeiEmNfrRVNTU8aL6ugo/OLiIlpaWlQ53ni9fOLF+Pg4LBYL2traNkQvn7o4t7e3x03UI012pXLOEgElZqlY/qcTtGLW1NQkV328Xm/G5JyFg04xZUJFMh6dTzqIDgA8+uijeO655/Dss89mNMHOAkCW7CiL+fl53HzzzWhra0NeXh5OnDiBgoICdHR04KqrrkJpaem6PsQiTdwsLy+joKAAjY2NGfWAjQQ6Cs8wDLZv356Wsc5IXj5UeBpPuOv58+fhcrk2RJwCoIwZI9VfRJvsUhIzMzMYHx9PiJitJ+x2O86dO7eKmIVrVuJJ0U4HqAFfJhCdcISfs8LCQuTn58vmp2oSnccffxxPPfUUXnjhhYw7L1lERJbsKIXz58/j5ptvxte+9jV0dHQAkBa7c+fO4ciRI3jhhRdgMBhw9dVXo6OjA2azeV3JhcfjwXvvvQeNRgNBEOQFqaioKCNJD7X7LygoQH19/bocY7iz7loiSlEU0d/fD47jsH379ow8p8FQK+cqXKyrpBvxxMQEZmdnN0zFzGq1Ynh4GO3t7Wu2XqlmxWKxYH5+Hnl5ebJ1Qjq/ZzqchpWCKIqYnZ3FwMAAtFqtfM7UENIfPnwYjzzyCI4fP74hROVZAMiSHWVACMEtt9yCr3zlK9izZ0/Uz4yOjuLo0aN45plnwLIsrrrqKhw8eBDV1dVpXQxpmCcdfQ7O64q1iK8HqCdNdXV1xpjZEULkRTw86gPAuhOzREBzrggh2LFjh2rHG+5GnMpk18jICObn59Ha2prxrVdA0myNjY0lXIEihMDhcMjWCVqtNi0ThDQkcyMQHUCy5Oju7pYDf5PR+cSDZ599Fj/5yU9w/PhxRaYpX3rpJdxzzz0QBAGHDh3CfffdF/LvhBDcc889OHHiBIxGIx599NGoa0wWayJLdtYDhBBMTU3h6NGjOHbsGLxer1zxUXtxnJ+fx5kzZ6KO5hJCsLCwIAeVrrchHzVby1RPGiDUy8dms8Hr9aK8vBxbt27N+NbKeuVcJTvZRStQLpcLzc3NGUHGY2F6ehqTk5Nob29PuTLjcrlkUTghRD5nSoqG05EGriSob1VTUxNMJtOqf1fKz+f48eP4/ve/j+PHjytiIyIIArZt24ZXX30VNTU12L9/P5588kk0NTXJnzlx4gR++MMf4sSJE3j77bdxzz334O2330553x9AZMnOeoMQgtnZWTzzzDM4duwYFhcXceWVV6Kjo0PxrKTp6WmMj4/HHeYZLa8rkl+IGqBl9Obm5g1htubxeNDV1YXq6mo5RZt6+ZSXl2fclFsm5VzFM9lFCMHg4CAEQdgQSeuAFEMzMzOjSquNTl1arVZ4PB6UlJSgvLw8JYEzFU+/X4hOOCLpfMrKymJWsV955RV861vfwokTJxR76Xrrrbfw4IMP4uWXXwYAfOtb3wIA3H///fJnPv/5z+PAgQP4zGc+AwDYvn073njjjYxwXd9gUPRhkflN8wwEwzCoqKjAF77wBXzhC1+AzWbDs88+i/vvvx9WqxV/+qd/ioMHD6b0cCeEYHh4GIuLi9izZ0/cD12GYVBQUICCgoKQcvrw8LDqeV10PxuljE49PYLNGDdv3ix7+XR3d4NhGLkFsd7fiQZkZkprMCcnB7W1taitrZUX8cHBwZAppfHxceh0ug1DdMbHx2Gz2dDe3q7Ky4FOp0N1dTWqq6shCALsdjvGx8eTNuXbqERn586dcREdAHLcjtlsDvHzOXv2bFQ/n9dffx3/8A//gOPHjytaXZ6cnMSmTZvkP9fU1Kyq2kT6zOTkZJbsrDOyZEcBlJaW4tChQzh06BDm5+fx/PPP4+/+7u8wNjaGT37ykzh48CBaWlrifoAFC2Xb2tqSLvszDAOTyQSTyYTGxkY5r+u9996DTqeTF3Elqhd0VHvPnj0Z3wYCVsr+kUZdc3JyUFdXh7q6Oni9XlgsFpw+fTpp40clQONAlAzIVBLBiziNYejp6YEoijCbzZifn1dlsktJjI6OYn5+PqV7LhFwHCdXEMMX8dzcXLkVHe1+mpubw9mzZ7F79+4NMb5Pr+FUssRYlkVxcTGKi4vl8GWr1YpHHnkEv/rVr/DJT34S27dvxw9/+EMcP35c8Uy4SJ2QcBIfz2eySD+ybSwVsbS0hBdffBHHjh3D2bNn8YlPfAIHDx7Enj17oj5MaThmaWkpamtrVbtJaF6XxWIJeegm+tCk02tut3vDjGoHV6ASeRtW2ssnXlAN1EZJ1qattpKSEtTU1Kg22aUkhoaGsLy8nBHXMF3EqcA5kjYq2jh8poK2i3fs2KFapM3w8DB+/vOf49ixYygtLcVVV12Fjo4O7N27V7HfabaNlVZkNTsbES6XCydOnMCRI0dw6tQpHDhwAAcPHsSHPvQh+aF/9uxZPPPMM7jtttvSGuYZnNcFIO4pCFEUcfr0aeh0OsW1SmphYmJC1mOkUoFKxcsnEWRCblQi4Hke3d3dqKioQHV1dci/KTnZpRSor5LH40Fzc3NGXsPB2ihBEJCbm4vFxUXs3bt3QxAdr9eLzs5O1cn6yZMncffdd+P5559HUVERXnnlFTz33HPo6urCRz7yEfzoRz9KmWDzPI9t27bhtddeQ3V1Nfbv348nnngCzc3N8meOHz+OH/3oR7JA+e6778Y777yT6tf7ICJLdjY6PB4PXnnlFRw+fBidnZ346Ec/ip07d+Jf//Vf8a//+q/4kz/5k3U7Ntq2sVgsctvGbDavMuHy+/3o7e2VK1CZDqqBcjgc2LVrl6JVhUhePmazOWVzufCcq0yHz+dDV1cX6urqYpL1TMjs2oji6ZmZGZw7dw65ubnwer0yyS4oKMjI408X0enq6sIXvvAFPPPMM9i8eXPIv/E8j87OTuzfv1+RfZ04cQJf/OIXIQgC7rjjDjzwwAP42c9+BgC48847QQjBXXfdhZdeeglGoxH//u//jn379imy7w8YsmTn/QSfz4eHHnoIP/3pT7Fp0ya0t7ejo6MDl1xyybprX6LldWk0GvT09MS1qGUCCCE4c+YMCCGqL2rh5nLJ2gDEk3OVSfB6vejq6sKWLVtQWlqa8M/T6oXFYlFtPDsY9JpgWRbbtm3LSKIQDpvNJkds6HQ6CIIgk+ylpSUUFBTIU0qZ0CKk18TWrVtRXFys2n76+vpw6NAhHDlyBNu2bVNtP1mkHVmy837Cj370Izz77LM4fPgw8vLy8Jvf/AZHjhzBm2++ib1796KjowN/8id/su7lajqWPTU1hYWFBZjNZtTW1qYlRykVCIKAvr4+5OXlYfPmzWk91mAvn0TaNsnkXK0n6ITNtm3bFFnUgkk2nexSUhtFCJHbrxshcgVYcXLevXt3xGuCem3RFmGyqeNKwefzobOzU3Wi09/fj9tvvx1PPfVUiNdNFu8LZMnO+wX33nsvLBYLfvGLX6yaiBIEAb/73e9w9OhRvP7662htbUVHRwcuvfTSdRuBpinVTU1NcLvdmJ2dhdPplL1CMq2UTsXe5eXlIaOg64Hgto3VapVddcO9fGjOVVtbW0a8nccCDXBsampSxJ02HEpndlFDRqPRmHbymywsFgtGRkaiEp1whLcIOY6TdXjpqBJSotPY2Kiqqejg4CBuvfVWPP7442hpaVFtP1msG7Jk5/2Ct956CxdeeGHMB64gCPjDH/6Ao0eP4te//jW2bduGa6+9Fp/85CfTpuWYmZnB2NgY2traQqpMtJQ+OzsrhyFmQl6Xx+NBT08P6uvrFR8/VQLUy8disYBhGJSVlcHtdsPn82XERFA8WFpawqlTp1RPqqZINbNLFMWQSJCNAIvFgtHR0ZSqfB6PR66UCYIgeyDl5eUpfo9S3daWLVtUJTrDw8P4zGc+g8ceewy7d+9WbT9ZrCuyZOeDDFEU8d577+Hw4cN4+eWXUV9fj46ODlxxxRVxm3QlApoPNj8/j5aWljXNDSPldSkh1E0UNEdso4xqezwe9Pb2wu12w2AwrJuXTyKgPkXrlaxNJ7ssFgvm5uZitgiDx+E3gqAeSD6bay3QdrTVaoXT6VR0itDv96OzsxObN29OSrcVL8bGxnDzzTfjF7/4hWKi4ywyElmysxYOHz6MBx98EP39/XjnnXeiquCjhbnNzc3h5ptvxsjICOrr6/H0009n7IJJ31QPHz6MEydOoLKyEh0dHbjyyisVOWYaNkmnVRIhLJFCN81ms+riycXFRZw+fXrDjGqLooi+vj4YjUZs2bIFPM+vi5dPIrDb7Th79mzGuPZGm+yivlGCIKC7uxvl5eWoqalZ78ONCzMzMxgfH8fu3btVS02nVVmr1YrFxcWUPJAo0WloaFDV9HJychI33ngjfvKTn+DDH/6wavvJIiOQJTtrob+/HyzL4vOf/zy++93vRiQ7a4W5ffWrX0VxcTHuu+8+fPvb38b8/Dy+853vrMM3SQxUdHnkyBE59O6aa67BVVddldTDR0lhb/BbuJp5XTabDefOndswcRV0EY42vh+uVykuLobZbF5XbRTVj9CJoExE8GSXKIrw+Xyorq5GQ0PDeh9aXJiZmZEF6moRnXBE8kAqKytDaWlpzN9zuojOzMwMbrjhBnz/+9/Hxz72MdX2k0XGIEt24sGBAweikp21XDCD3S6np6dx4MABDAwMpPXYUwUhBGfPnsWRI0fwwgsvICcnB9dccw2uueYamM3mmAulz+dDT08PKisrVxnDKXFsNK/LZrMpZiw3NTWFyclJtLW1ZewiHAyac1VTUxOXs6ooijLxoWPG6W4R0iTwVA0Z0wW/34/33nsPJpMJHo8HPp9PFtNnUqUsGEqmrScLWimzWq2w2Wxr5sPR67iurk5VbZzFYsF1112Hf/qnf8InPvEJ1faTRUYhGwSaKtYKc5udnZUXn8rKStlVeCOBYRhs27YNf/M3f4P7778fIyMjOHr0KD772c+C4zhcffXVOHjwIKqqqlY98B0OB06dOoXGxkZV+u6R8rpmZ2eTzusK1hTt2bNnQ0wwJZNzxbKs7D0T7OUzODiYtJdPIhgfH4fVasXu3bs3xDmmQtmGhgZ5EaaVstHRUUUmu5TG1NQUpqen1/0cMwyDvLw85OXloaGhQRY49/f3w+/3y61Vg8GArq4u1NbWqkp0bDYbbrzxRjz00ENZopNF0tiQZOfSSy/FzMzMqr//h3/4B3R0dMT8+Q9SUBvDMGhoaMC9996LL3/5y5icnMTRo0fxF3/xF/D5fLj66qvR0dGBuro6vPnmm/jKV76CEydOqOqNEQz6UN2yZQtcLhdmZ2fR1dUVV14XdcDleT5t4Y2pQomcq/AwROrlMzQ0pEoEw/DwMJaWltDe3r4hznE0g8Pw9Gw6RTgwMACTySQnjq8H0aBER6209VRgMBiwadMmbNq0CX6/H3a7HUNDQ7Db7SgqKoJWq4UoiqpcG/Pz87jxxhvx9a9/HX/6p3+q+Paz+OBgQ5KdX//61yn9fE1NDcbHx+U/T0xMoKqqCgBgNpsxPT0tt7EycWw5WTAMg5qaGtxzzz24++67MTs7i2PHjuHuu+/GzMwMnE4nfvCDH6ybINtoNKKhoQENDQ3yaHZvby8AyMSHCmKpX4rBYEBTU9OGIKtq5FwxDIOCggIUFBSgsbFRFuq+9957Ub184gUNefV6vWhpadkQRIcGTsYyOGRZFqWlpSgtLQ3RlJ0/fz7tmV2Tk5OYnZ3NSKITDq1Wi9LSUkxMTGDHjh3Q6XQyYVS6wri4uIgbb7wR//t//29cffXVChz9CuIZRBkfH8ett96KmZkZsCyLv/zLv8Q999yj6HFkkT58IDU7a4W5feUrX0FJSYksUJ6bm8M//uM/rsM3SB8efvhhPPzww7jxxhvxyiuvwGq14lOf+hSuueaajMgMCs/rKikpwdzcnOzivBFAc67SOapNk+2tVqusu4jXWI5O4hFCsGPHjnW/BuIBdXJOJVk71mSX0piYmIDFYtkwJpKCIKCrqwtVVVUhWrNwt3BqoZBISzoYDocDN9xwA+666y7cfPPNSn4FAIhrEGV6ehrT09PYs2cPHA4H9u7di2effTbr1Jw+ZAXKa+GZZ57BX//1X8NqtaKwsBDt7e14+eWXMTU1hUOHDuHEiRMAIoe5AdJY7U033YSxsTHU1tbi8OHDaWvppBuEEPzt3/4tent78atf/UoWH87NzeH555/HsWPHMD4+jk9+8pO49tprM8Lszul0orOzExqNRjbjKy8vT4upXbLIhJyrcGO5tbx8aJq9Xq/fMHEKtD24c+dORZ2c1czsGh8fh81mQ2tr64YmOpFABc6UaNPzFg/RdzqduOmmm3DHHXfgs5/9rFKHH4JkBlE6Ojpw11134bLLLlPlmLJYhSzZyUIZPPnkk3jrrbfw/e9/P+rDdnFxES+++CKOHTuGc+fO4dJLL8XBgwexe/futBMfuqBt3boVJSUlskGaxWKB2+3OSE8aOiWWSTlXfr8/qpcPIQS9vb0wmUwbZlR7eXkZvb29qnsrKZnZRYnORtGaUaJTWVkpt/zjhdfrlYlPrIk4t9uNm266Cbfccgs+97nPKfkVQlBYWIiFhQX5z0VFRZifn4/6+ZGREVxyySXo6+tTxbw1i4jIkp0slIEoimAYJu4H9fLyMv7rv/4LR44cwenTp/Enf/InOHjwIPbv36/6mymNJmhubo74sBEEATabLSSvy2w2w2QyrRvx2Qg5V+FePjzPo7y8fMMkgVMdVLoiKygieSCVlZXFNdk1NjaGubk5tLa2bhii093dDbPZnLIVRfh5czqdWF5exhVXXAFCCG655RYcPHgQn//851O+/tYaZLntttviJjvLy8v42Mc+hgceeADXXXddSseURULIkp0s1h9utxuvvPIKjhw5gs7OTlx88cU4ePAgLrroIsX9QWgAabx6F0EQ5AfqeuR1EUJw/vx5uFyujGj9xQPql2IymcDzPJaWllBYWCift0z8DouLi+jv70dLS8u6RmvQyS6LxSI7EUeb7BodHcXCwsKGEXwrSXTCIYoi/vjHP+KnP/0p3nnnHRgMBlx44YX4wQ9+oHr1JN42lt/vx1VXXYXLL78cX/rSl1Q9pixWIUt2MhnxqPwHBgZCRHdDQ0P45je/iS9+8Yt48MEH8W//9m+y/8pDDz2ET33qU2n9DonC6/Xitddew5EjR/DOO+/goosuQkdHBy6++OKUWzc0gDRZx97whUhtMz5CCM6cOQMAG0bYSz1pgkNTg718aNyH2l4+iYBmc2WaW/ZamV2Tk5NYXFzcMERHFEV0d3ejrKxM1ZgNv9+P2267DQ0NDcjNzcXLL7+M8vJyHDx4UDZCVRrxDKIQQnDbbbehuLgYP/jBDxQ/hixiIkt2MhmJxk0IgoDq6mq8/fbbqKurw4MPPoi8vDzce++9aTxq5eD3+/HGG2/g6NGj+N3vfof9+/ejo6MDBw4cSHiahbaBWltbFakWhed10TfwkpISRRYfmnOVm5ubcsRGukBHtakOKhLCJ23SPZodDlrpy5RsrmgInuyamJgAIQQNDQ0wm82qTHYpCUp0SktLQwxYlQbP8/jc5z6H3bt34/7775fvmXPnzuG5557Dr3/9a7zwwguKV4ujDaIED7K8+eabuPjii0PI6UZ4+XwfIUt2MhmJqvxfeeUV/O3f/i3++7//GwA2PNkJBs/zePPNN3HkyBG88cYbaG1txcGDB/GJT3xizbdx6u/i8XjQ3NysWgUmOK8rLy9PXsCTqVzEyrnKRCRjcEgX8NnZWdhstpS9fBKF3W7HuXPn0N7envGEgWJ4eBgOhwNbtmyRE8fpZFd5efm6pMavBVEU5YR4NYmOIAi488470djYiAcffHBDvBxkkVZkyU4mI1GV/x133IE9e/bgrrvuAiCRnUcffRQmkwn79u3DP//zP2ds6noiEAQBb731Fo4ePYpf//rX2LFjBw4ePIhPfvKTIXoLn8+HEydOYOfOnWkTyUbL6yorK4vrjTLRnKtMAJ1giib4jhepePkkCqvViuHh4YwOIQ3H0NAQnE7nKtIePNmVSZldlOgUFxerStoFQcDdd9+NiooKPPTQQ1mik0UkZMnOekMplb/P50NVVRVOnTol96VnZ2dRWloKhmHwta99DdPT03jkkUdU+R7rBSpMPHz4MF5++WVs3rwZHR0d+OhHP4rbbrsNl1xyCf7P//k/6/IADK9c0Lyu8vLyiC2bZHKu1ht0sk3pCaZEvHwSxezsrKzdypQR/lg4f/483G43mpub17yWo012pUtQTyGKInp7e1FYWIi6ujpV9/OlL30JeXl5+O53v7sh9EvhIITg4osvxgMPPIArrrgCAPD000/jkUcewUsvvbTOR/e+QZbsZDISaWM999xz+PGPf4xXXnkl4r+PjIzgqquuQl9fn5qHvK6gb5KPPfYYfvnLX2Lfvn244YYbcOWVVybtgqskqObCarWC4ziYzWaUlZVBr9fD6XSit7c3pZyrdIM6Oast7I3k5WM2m5GXl5fwAp4JSeCJgE7jeb3ehKNMEpnsUhJUb2YymVBfX6/qfu677z4AwL/+679uSKJD0dfXhxtvvBGdnZ0QBAHt7e146aWXsGXLlvU+tPcLsmQnk5FI3MSnP/1pXH755bj99tvlv6O5XADw/e9/H2+//TaeeuqptBz7emFsbAzXXXcdvvnNb6Kurg5HjhzBiy++iOLiYnR0dOCqq65SJYE9UdC8Llq58Hq9aG5uzohjiwc2mw3nz59Pu5MzrVwEeyCVl5ejoKAgJhHYaHEKVG/m8/lSzmxba7JLyeoWJTr5+fmqGkmKooivf/3rWF5exs9+9rMNTXQovvrVryI3NxdOpxP5+fn42te+tt6H9H5CluxkMuJR+QOS1mHTpk0YGhoKsbf/7Gc/i66uLjAMg/r6evz85z/fMDqQZNDX14c///M/x89//nNccMEF8t8TQnD27FkcOXIEzz//PIxGIzo6OnD11VfDbDava49/fn4e/f39MJvNWFhYgCiKGSs2pZidncXo6Oi6610EQZArF7G8fDZanAIlOn6/X/FMOUIIlpeXYbVa5cwuqitLRahNCEFfXx/y8vJUJTqEEPzd3/0dZmZm8PDDD2+I32c8cDqd2LNnD3Q6HU6ePLlhRPMbBFmyk8X7B9///vfxqU99Ctu3b4/6GUIIhoeHcfToUTz77LPQaDS4+uqrcfDgQVRWVqaV+NCcq+BpICo2nZ2dBc/zcoxApuR1TU1NYWpqKuPaQGt5+YyPj28oTxpKzgVBSIu/Eq0ypjLZRQjBqVOnYDQasXnzZtWOlRCC73znOzh//jwee+yxjLoGlcDXv/515OXl4atf/ep6H8r7DVmyk8UHF4QQTExM4OjRo3jmmWfA8zyuvvpqXHPNNairq1N1kYkn54rmdc3OzqasVVECY2NjsNvtGV8doV4+s7OzmJ6eBsMwaGxsRHl5ecYvjoQQDA4OghCC7du3p/33nMxkFyU6OTk5qmpMCCH4wQ9+gK6uLjzxxBMbRlyeCN5PdiEZhizZySILQHqQzszM4NixYzh27BiWl5dx1VVXoaOjA1u2bFF00RkdHZXzjOIlDcFaFZfLheLi4rTlddFqmMPh2FDVkXPnzsHr9aKurk62AqATcWVlZRk3ck4IkQcQ1oPohCOeyS5CiJxqr/R9EgxCCH7yk5/gv//7v/H0009n3O9OKWTJjmrIkp0soiOeuAoAqK+vR35+PjiOg0ajwcmTJxP6+UyE1WrFM888g2PHjsFut+NTn/oUrrnmmpTaCnSyho4QJ0saIuV1mc1mFBYWKr7Y0JYK1Y5sFKIzODgIURRX/b7S6eWTCCjRYRgmI4NTI012lZWVwWq1QqfTobGxUVWi84tf/AKvvvoqjh49+r7WsmTJjmrIkp0soiPeuIr6+nqcPHly1SRRonEXmYq5uTk8//zzOHr0KCYnJ/HJT34S1157bUKEhRCC/v5+sCyr6Fu7mnldah2zmqB5YizLxiQNanr5JAJ6zBzHYevWrRl/ngkhcp6Yz+eTheFqRX48+uijeO655/Dcc8+tOynNYsMiS3ayiI54fX6ikZ1E4y42AhYXF/Hiiy/i6NGjOH/+PC677DIcPHgQ7e3tUclFunKuwkW6qeR1iaIYosPI9AUYSK2l4vP5YLPZFPHySfSY+/v7odVqVa2OKIlgctbY2Cj7RwVHfqQ62UXx+OOP46mnnsILL7yg+HRiIpVnQRCwb98+VFdX48UXX1T0OLJIC7JkJ4voiDeuoqGhQe7jf/7zn8df/uVfJvTzGxXLy8s4ceIEjhw5gv7+fnz84x9HR0cH9u/fL2txFhYW8JOf/AR//ud/ntacK+qrMjs7i7m5uYTyugRBkN1v1TSFUxKUUObl5aU8DZSKl08iSJfeRUnEarcpMdlF8fTTT+PRRx/Fiy++qMo0YiKV5+9973s4efIklpaWsmRnYyJLdj7oUCKuYmpqClVVVbBYLLjsssvwwx/+EJdccsn7nuwEw+124+WXX8aRI0fQ1dWFSy65BAcOHMDf//3f4/bbb8fnP//5dTs2mtc1OzsbkjQeKa+L53l0d3fDbDajpqZmnY44MdBogoKCAsXJWSJePokgeIJpo6TaUy0UgLh0Ralkdj377LP46U9/ihdffDHEO0xJxFt5npiYwG233YYHHngA3/ve97JkZ2MiS3ayiI5k2lDBArv3YxsrHni9Xjz11FP4yle+gs2bN6OlpQUHDx7ERz/60XUfl42U10VjKwBsuBBSQRDQ09OD0tJSVVO1gbW9fBIZxU/XqLaSSHUkPpHMruPHj+P73/8+jh8/rupAQ7wvYzfccAPuv/9+OBwOfPe7382SnY0JRclOZhtYZJEwrrnmGjz22GO477778Nhjj6Gjo2PVZ5xOJ0RRRH5+PpxOJ1555RV8/etfj/vn348YHR3FD37wAzzzzDP40Ic+hNdffx1Hjx7FV7/6Vezfvx8HDx7EgQMH1mV8lmEY5OXlIS8vD1u2bJH1Fu+99x5cLhcqKipQXFyc9uNKBoIgoKurCxUVFaiurlZ9fyzLori4GMXFxbKXj8ViwdDQEIxGI8rKymKm21MtFNVvbQTQibxI023xQqPRwGw2w2w2y6L6mZkZDAwM4LXXXsOWLVtwzTXX4L//+7/x3e9+FydOnFCE6KxVuY4HL774IsrLy7F371688cYbKR9PFu8PZCs77zPEE1cxNDSEa6+9FoD09nbLLbfggQceWPPn38/o7OzE7bffjl/+8pdobW0N+Tee5/Hmm2/i8OHD+M1vfoO2tjYcPHgQn/jEJ9Z1ysTj8aCrqwu1tbXw+/2wWCwZNZYdCTzPo6urC9XV1etehaLxC7G8fNKVG6Uk1IytoNt//fXXcfjwYfz2t7+F2+3GN77xDdxyyy2qta8o4qk833///fiP//gPaDQaeDweLC0t4brrrsOvfvUrVY8tC8WRbWNlkYWSeOWVV9DY2BjzrV0QBLz11ls4cuQIXnvtNezcuRMHDx7EZZddltbRZ6fTiZ6eHuzcuTMkGd7r9cpBpTSvy2w2q5puHi/8fr9Mzsxm83ofzipE8vIpLS3FuXPnVE8CVxLUF8rn86lCdILx29/+Fn/zN3+D7373u3jzzTflFta1116Ljo4OVFRUKL7PRIKWAeCNN97ItrE2LrJkJ4ss1huiKOLkyZM4fPgwXnnlFbmkf8UVVyA/P1+1/TocDvT19WHXrl1r7sfn88nEh+Z1mc3mtPvR0GPp6upCQ0ODrDPKZHg8HrnVxXEcampq1sXLJxmcP38eHo8n5cT1WHjrrbdw77334sUXXwxpR46MjODZZ58FAHzxi19UfL/xBi1TZMnOhkaW7GSRRSZBFEV0d3fj8OHDeOmll1BdXY1rrrkGV155ZUjlJVUsLi7i9OnTaG1tTWjh9fv98oRNuvO6vF4vurq60NjYiJKSElX3pRREUURPTw+KiopQWVkpZ535fL6QkNdMm8YKdvpW89jeffdd3HPPPXj++efTas2QxQcOWbKTRWYjHuOv8fFx3HrrrZiZmQHLsvjLv/xL3HPPPQCk6bB/+7d/k6sADz30ED71qU+l/XskAzq1c+TIEbz44osoKSnBwYMHcdVVV6W02M/NzWFwcBBtbW0ptaV4npeN+FwulzxarEZeF9UVbdu2bcPovijRKS4uXrWQB587Nb18ksHQ0BBcLpfqRKezsxP/83/+Tzz77LMbRsOUxYZFluxkkdmIx/hrenoa09PT2LNnDxwOB/bu3Ytnn30WTU1N75usGTr6e+TIEbzwwgvIzc1FR0cHrr76apSXl8e9KFmtVgwNDaG9vV3RjKHwvK7i4mKUl5crktfldrvR3d2NHTt2KFrdUhOJjMSr5eWTDIaHh7G8vIxdu3apSnR6e3vxF3/xFzhy5Ai2bdum2n6yyCKALNnJIrORjFdPR0cH7rrrLlx22WXvG7ITDEIIhoaGcPToUTz77LPQ6XS4+uqr0dHRgcrKyqiL1MzMDMbHx9He3q6q3094Xlcqi7fT6URvby+amppgMplUOmJlQYlOWVlZwsaMSnn5JIORkREsLS1h165dqpKs06dP44477sB//ud/YufOnartJ4ssgpAlO1lkNhJ1YR4ZGcEll1yCvr4+mEwmPPjgg3j00UdhMpmwb98+/PM///OGSV6PB4QQTExM4MiRI3jmmWcgCAKuvvpqHDx4EJs2bZKJz49+9CPU1dXhiiuuWNMHRmmkkte1vLyM3t7emALqTIIgCOju7kZ5eXnKDtTBXj52ux1Go1Ge7FL6dzg6OorFxUXVic7g4CBuvfVWPP7442hpaVFtP1lkEYYs2cli/aFEZAUgLY4f+9jH8MADD+C6664DAMzOzqK0tBQMw+BrX/sapqen8cgjj6jyPdYbhBBMT0/j2LFjeOaZZ+B0OnHllVdidnYWXV1deOaZZ9Z1CihSXpfZbI5YtaCTYi0tLarkIqkBSnTMZrPiJofxevkkg9HRUSwsLKClpUVVojM0NIRbbrkFjz32GHbv3q3afrLIIgKyZCeLzEa8bSy/34+rrroKl19+Ob70pS9F3NbIyAiuuuoq9PX1qX3YGYHZ2Vl87nOfw+nTp1FSUoJPfepT6OjoSMruX2mEVy1ycnJgNptRWloKp9OJ/v5+tLa2Kp50rRaom3NlZSWqqqpU31+wlw/LsnLgZqIGkGNjY5ibm0Nra6uqRGd0dBSf/vSn8Ytf/AL79+9XbT9ZZBEFWbKTRWYjHuMvQghuu+02FBcX4wc/+EHIv01PT8sOu9///vfx9ttv46mnnkrX4a8bCCH48pe/DIfDgZ/97GdYXFzEc889h6NHj2JqagqXX345rr32WjQ1NaVdBBvpWGle18zMDLxeLzZv3ozq6up1zxKLB5ToVFVVrYubs8fjke0ABEGQiU+sKt74+DjsdrvqRGdychI33ngjfvrTn+Kiiy5SbT9ZZLEGsmQni8xGPMZfb775Ji6++OKQMjwdMf/sZz+Lrq4uMAyD+vp6/PznP1/3eAG1IQgC7rzzTphMJnz3u99dVcVZXFzECy+8gKNHj2J4eBiXXXYZOjo60N7evq7EZ25uDmfPnsXWrVuxuLgIq9UKjUYjx1asR5ZYLGRSbAWwOmk8mpfPxMQErFYr2traVP2dT09P44YbbsC//Mu/4JJLLlFtP1lkEQNZspNFFu83+Hw+HDlyBJ/5zGditqscDgdOnDiBI0eOYGBgAB//+MfR0dGB/fv3p5X42Gw2nD9/ftVIvNvtlt2bMy2vixKdmpoaVeIMUkU0Lx+HwwGbzYbW1lZVJ7xmZ2dx/fXX47vf/f/t3XlU1Nf5x/H3CNWCC4oLoJCgQRRRWSYGNSpqBRGVYap1a6vUeuISlzZNWqw9qVqT6EmriTax1ViXGjUCbihgDIpxQRAVgpBEKoIIyLC6AMo2vz8M8xMVxDgDzPi8zvGczMz3zjxgzpmP93vvff7O6NGjDfY5QjSChB0hxAPl5eVERUURFhZGYmIi3t7eqFQqhgwZYtAvRY1GQ0ZGBu7u7g3O3jx8u6a5+3W19P5cj6o9yycjI4M7d+5ga2uLjY2Nwc7yKSgo4Oc//zmrVq3Cz89P7+8vxDPSa9hp3hv/QuhZVFQUffr0wcnJidWrVz/2ularZfHixTg5OTFw4EAuXrzY6LEtkYWFBWq1mp07d3LhwgXGjRvHrl27GDJkCEuWLCEmJobKykq9fubNmzfJzMzEw8PjqbepfvrTn+Lg4IBSqcTNzQ1zc3O+/fZb4uPjSU9Pp7S0VK+11cfYgg6AmZkZlZWVmJmZMXz4cGxtbcnPzycuLo6UlBTy8/Oprq7Wy2cVFxfzi1/8guXLlxsk6BQVFeHj40Pv3r3x8fGpd3dmSUkJkydPpm/fvri4uBAbG6v3WsSLSWZ2hMmorq7G2dmZY8eOYW9vz6BBg9i9ezf9+vXTXRMREcGGDRuIiIggLi6OJUuWEBcX16ixxqSiooITJ04QFhbGmTNneO211wgMDMTb2/u51tHk5OSQm5urCy4/1qP9umoX6Bqi51Rt0Hn55Zfp1q2bXt/bkGp/1+7u7nVm6WqPA9BoNBQVFT33WT63bt1i0qRJvPPOO6jVan3+CDqNOVUdYNasWQwfPpw5c+ZQUVFBWVmZ0ZzALfRObmMJ8SSxsbEsX76co0ePAvDBBx8AsHTpUt01c+fOZeTIkUyfPh34/23yGRkZTx1rrKqqqjh16hQhISGcPHkSDw8PAgMDGT169DOto7lx4wYajQY3Nze93iIzZL+uyspKLl26ZDQd12vl5uaSk5PzWNB51POe5XPnzh0mT57MwoULmTp1qj5/hDoacxzF7du3cXNzIz09vdmPWRAtgl7/J2i6Y1mFMLDs7Ow6PY3s7e2Ji4t76jXZ2dmNGmuszM3NGTVqFKNGjaK6upqzZ88SGhrKihUr6NevHyqVCl9f3wbPx7l+/TqFhYV6Dzq19dna2mJra6vr15WVlfXc/boqKipITEw0uqBz8+ZNsrOz8fDweOrvWqFQ0L59e9q3b88rr7yiO8snKSnpqWf5lJaWMnXqVObOnWvQoAMPFj7X7nyzs7NDo9E8dk16ejpdu3blN7/5DUlJSSiVSj7++ONmPVRTmA4JO8JkPGmW8tEvyPquacxYU1C7/mP48OHU1NRw/vx5QkJCWLNmDU5OTgQEBODn51en1cM//vEPBg8ezJAhQwy+28vMzEy3e6u2X1dOTg7ffffdM/Xrqg06vXr1okuXLgatWZ/y8vK4cePGU2d06mNpaYmjoyOOjo66xeEpKSnU1NRw69YtbGxsGDBgAOXl5UybNo2ZM2fyq1/9Si+1N3SqemNUVVVx8eJFNmzYgJeXF0uWLGH16tX87W9/00t94sUmYUeYDHt7e7KysnSPb9y48djJuPVdU1FR8dSxpqZVq1Z4eXnh5eVFTU0NiYmJhIaG8tFHH+Hg4MDEiROJj48nKyuLRYsWNfl5Pq1ataJLly506dJF168rLy+PK1euNNivqzbovPLKK3Tu3LlJa34eeXl5XL9+HQ8PD7300apdHO7g4EBFRQXh4eGsWbOGgoIC2rZty/jx4wkKCnr+wn/w1Vdf1fuajY2N7rDQ3NzcJ66dsre3x97eHi8vLwAmT55sNBsFRMsnu7GEyRg0aBBpaWlcu3aNiooK9uzZQ0BAQJ1rAgIC2LFjB1qtlnPnzmFlZYWdnV2jxpqyVq1a4enpyfvvv8/FixdZtWoV27ZtIyYmBoA9e/ZQWFjYrPVZW1vj4uLC4MGD6dGjB0VFRcTFxZGcnKw7ifj+/ftcunTJ6IKORqPRa9B5VOvWrZk0aRKHDx+md+/euLu7c+XKFTw9PXnnnXeIjY2lpqZG759bKyAggO3btwOwfft2VCrVY9fY2tri4OCgW8sTHR1ttBsERMsjC5SFSYmIiOB3v/sd1dXVzJ49m2XLlvGvf/0LgHnz5qHValm4cCFRUVFYWlqydetWXn311XrHvohqamp0v4f169eTlpZGaGgo4eHhdOjQgYCAACZOnEjXrl2b/Vbfw/268vPzuX//Pg4ODjg6OjZpp/jnodFoyMzMxN3d3aCtNiorK3W7nd566y0UCgX37t3j2LFj7Nu3j7S0NE6dOmWQv9PGnKoOkJiYqNuJ1atXL7Zu3UqnTp30Xo8wCrIbSwhhGDU1Nbq2FR9++GGdLz6tVsvVq1cJCwvj4MGDtG7dmoCAAFQqFba2ts0afO7fv8/Fixd56aWXuHfvHgUFBbRp00a3M6ml9uvKz8/n2rVreHh4GLTGqqoqZs+ejVKpJDg4+Il/V1qtttnDqxAPkbAjhDCMkpIS/vvf/7Jw4cIGv/i0Wi3Xr18nLCyMAwcOUFNTw4QJE1Cr1djb2zfpl+a9e/dITEykT58+dWYBSktLdTM+LbFfV1MFnerqaubOnYuzszN//etfJdAIYyFhRwjRcmi1WnJzcwkLC2P//v2Ul5czfvx4VCoVvXr1MuiXa23Q6du3b4OHz5WXl5OXl0d+fv5Tt2Q3hYKCAtLT05/abuN5VVdXs2jRIuzs7Hj//fcl6AhjImFHiJYsKiqKJUuWUF1dzZw5cwgODq7z+ueff647PbZdu3Zs3LgRNzc3ABwdHWnfvj1mZmaYm5uTkJDQ5PU/L41Gw/79+wkLC6O4uBh/f38CAwNxdnbW65dtY4POk8bVzvg0R7+uwsJC/ve//zWq3cbzqKmp4fe//73ulmRT76YT4jlJ2BGipWpM24mzZ8/i4uJCp06diIyMZPny5boDDB0dHUlISDCqs2EaUlhYyMGDBwkLC+PmzZuMHTsWtVqNi4vLc335lpeXk5SUhIuLC1ZWVj/6fSoqKnQd2quqqnQzPoY6yK4pg86f/vQnFAoF69evl6AjjJGEHSFaqsa0rHhYcXEx/fv3Jzs7GzC9sPOwkpISwsPDCQsLIyMjAx8fHwIDA3Fzc3umL2N9BZ1HPdyv6/79+3Tp0kWv/bqKiopIS0trkqDz7rvvUlpaysaNGyXoCGMlXc+FeFRWVhY9e/akqKgIeBAievbsSWZmZpPWUV87ivps2bKFcePG6R4rFAp8fX1RKpVs2rTJoLU2tY4dO/LrX/+aAwcOcOrUKZRKJR999BFDhw7lz3/+M/Hx8U8966WsrIykpCT69eun16AD8JOf/ITu3bvj7u6OUqmkbdu2XLt2jbi4ONLS0rh169YTT9pujKKiIq5cuWLwNTparZZVq1ZRXFzMp59+KkFHiB8Yx0EUQjyFg4MD8+fPJzg4mE2bNhEcHMwbb7zByy+/3KR1PEvbiRMnTrBlyxZOnz6te+7MmTN0794djUaDj48Pffv2ZcSIEQart7m0b9+eadOmMW3aNMrKyoiMjGTz5s0sWrSIESNGEBgYyODBg+u0TEhJSSE9PR1vb286dOhg0Pqe1q/LxsYGKyurRs34FBcXc+XKFTw8PGjTpo3BatZqtaxZs4YbN26wfft2vfcwE8KYyW0sYTIqKytRKpXMnj2bzZs3c+nSpSbfZtzY21jffPMNarWayMhInJ2dn/hey5cvp127drz99tuGLboFqT3kLjQ0lISEBF5//XXUajVWVlbMnDmTzZs369oJNIfafl15eXncvn2bjh07YmNjQ8eOHZ84i1JSUsJ3332Hu7u7QXd+abVa1q1bxzfffMOuXbuM5kBFIRoga3aEqM/Ro0fx8/Pjyy+/xMfHp8k/v6qqCmdnZ6Kjo+nRoweDBg1i165duLq66q65fv06o0ePZseOHQwdOlT3fGlpKTU1NbRv357S0lJ8fHx499138fPza/KfoyWoqKjgxIkTfPbZZ0RHRzNu3DimTp3KiBEjWsRZOQ/36yopKaFDhw7Y2NhgbW1Nq1atmjTofPLJJ8TGxvLFF1+0iN+NEHqg17Aj8V+YlMjISOzs7Lh8+XKzhB1zc3P++c9/MnbsWF3bCVdX1zotK1auXElhYSELFizQjUlISCAvLw+1Wg08CE0zZsx4YYMOPOjn9NJLL5GWlsbx48cpKioiJCSEpUuX4unpSWBgIKNGjWq2s3Jq+3VZW1uj1WopKSlBo9GQlpZGmzZtKC0txdPT0+BBZ/PmzXz99deEhYVJ0BGiHjKzI0xGYmIiv/zlL4mMjGTYsGHExcVhZ2fX3GWJHyk1NZUZM2awa9euOlv3q6urOXPmDKGhoZw4cQJXV1dUKhU+Pj5YWlo2Y8UPlJSUkJKSgrW1Nbdu3cLS0pJu3brRpUsXvd5e0mq1bNu2jfDwcA4cOKD3UFVUVMTUqVPJyMjA0dGRvXv3PrFP1bp16/jss89QKBQMGDCArVu3NlsAFSZFbmMJ8SitVsvQoUNZuXIlPj4+bNiwgXPnzvH55583d2niR3rzzTdZtGgRffv2rfeampoa4uPjCQkJ4dixY/Tu3ZvAwEDGjh1Lu3btmrDaB27fvk1qaipubm5YWFig1Wq5e/cuGo1G7/26du7cyd69ezl06JBBQt4f//hHrK2tCQ4OZvXq1RQXF+sOw6yVnZ3NsGHDSE1NxcLCgilTpuDv709QUJDe6xEvHAk7Qjxq06ZNREdH88UXXwAP/vX/2muvsXbtWry9vZu5OtEUampquHTpEqGhoURFReHg4IBKpcLf31/v29Sf5NGg8yT66te1d+9etm3bxpEjRwx2AGKfPn2IiYnBzs6O3NxcRo4cyffff1/nmuzsbAYPHkxSUhIdOnQgMDCQxYsX4+vra5CaxAtFwo4QQjREq9Vy+fJlQkJCiIiIoGvXrqhUKiZMmIC1tbXeP+/OnTtcvnwZNze3Rs+yPNqvq3bG52m3gPbv38+///1vDh8+bNAt+B07dqSkpET3uFOnThQXFz923ccff8yyZcuwsLDA19dXZlOFvsihgkKIp4uKiqJPnz44OTmxevXqx16PiYnBysoKd3d33N3dWblyZaPHtnS160dWrlzJ+fPnWbt2Lfn5+UyaNAmVSsV//vMfNBrNjz4k8GE/JugAWFhY4OjoyKBBg3B1dUWr1ZKSksL58+fJzMykvLz8sTGHDx/mk08+4eDBg3oJOmPGjKF///6P/Tl48GCjxhcXF3Pw4EGuXbtGTk4OpaWl7Ny587nrEkLfZGZHCBPUmB5dMTEx/P3vf+fw4cPPPNZYabVarl69SmhoKIcOHaJNmzZMnDgRlUqFra3tM7eFuHv3LsnJyQwcOFBvt5Me7te1bds22rVrx7Rp08jNzWXNmjUcOXKEzp076+WzGtKY21ghISFERUWxZcsWAHbs2MG5c+f49NNPDV6fMHkysyOEaFh8fDxOTk706tWL1q1bM23atEb/a/15xrZ0CoUCJycngoODOXPmDFu3bkWr1RIUFISfnx8bNmwgKyurUTM+hgg68GDLvb29PZ6envzlL3+he/fuLF68mN/+9rcMHz6cnJwcvcxIPU1AQADbt28HYPv27ahUqseueemllzh37hxlZWVotVqio6NxcXExeG1CPCsJO0KYoMb26IqNjcXNzY1x48aRkpLyTGONnUKhwNHRkT/84Q98/fXX7NmzBwsLC+bNm8eYMWNYt24d6enpTwwWBQUFJCcnM2DAAIMtEAbo1q0bAwcOpLq6mvj4eJRKJStXrkSpVLJ06VIuXLhgsM8ODg7W7XA7duwYwcHBAOTk5ODv7w+Al5cXkydPxtPTkwEDBlBTU8Mbb7xhsJqE+LHkUEEhTFBjenR5enqSmZlJu3btiIiIIDAwkLS0tGfq72UqFAoFPXr0YPHixSxatAiNRsP+/ft56623KCkpwd/fH5VKhbOzM8nJycyaNYujR48afHv72bNnWbp0KYcPH6ZHjx44OTnp+okdPXqUI0eOoFQqDfLZnTt3Jjo6+rHnu3fvTkREhO7xihUrWLFihUFqEEJfJOwIYYLs7e3JysrSPb5x4wbdu3evc83DC1z9/f1ZsGABBQUFjRpryhQKBTY2NsybN4958+ZRWFjIgQMHWLZsGZmZmdy9e5f33nuPLl26GLSO8+fP8/bbb3Po0CF69OhR5zVLS0vUarXuxG0hRMNkgbIQJqgxPbpu3ryJjY0NCoWC+Ph4Jk+eTGZmpm6BckNjX0RXr15FrVYzZcoUzp8/z/Xr1xkzZgxqtZqBAwc+sRHoj3Xp0iUWLFjAgQMH6Nmzp97eVwgjIr2xhBANa0yPrtDQUDZu3Ii5uTkWFhbs2bMHhUJR79gXWXp6OpMnT2bHjh24u7sDDw4RPHLkCGvXruXKlSv87Gc/IzAwEKVS+VzBJzk5mfnz5xMWFiZBRwg9kZkdIYRowO3bt/H29mbLli14eno+8ZqysjIiIiLYt28fycnJeHt7ExgYiJeXF2ZmZo3+rNTUVGbPns3evXsbbJMhxAtATlAWQoimlJeXh42NTaOuvXfvHseOHSMkJIQLFy4wbNgw1Go1Q4cObbAR6Pfff8+sWbPYtWsX/fv311fpQhgrCTtCCGEMKioqOH78OKGhocTGxjJ48GBUKhUjRoyo0w8rPT2dGTNm1LlNJsQLTsKOEMK4REVFsWTJEqqrq5kzZ47uzJZaH374oa6nUlVVFd9++y35+flYW1vj6OhI+/btMTMzw9zcnISEhOb4EZ5bVVUVJ0+eJCQkhNOnT+Pp6YlKpcLJyYmZM2eyZcsWXn311eYuU4iWQsKOEMJ4PGv7ifDwcNatW8fx48cBcHR0JCEhweBbvZtSdXU1p0+fJiwsjJ07d3Lo0CGGDRvW3GUJ0ZJIuwghhPF41vYTu3fvZvr06U1YYdMzMzPD29ub9evXU1hYKEFHCAOTsCOEMKhnaT9RVlZGVFQUkyZN0j2nUCjw9fVFqVSyadMmg9fb1Ez9dGohWgI5Z0cIYVDP0n4iPDyc119/HWtra91zZ86coXv37mg0Gnx8fOjbty8jRowwWL1CCNMjMztCCIN6lvYTe/bseewWVu213bp1Q61WEx8fb7hijVxISAiurq60atWqwYXcUVFR9OnTBycnJ1avXt2EFQrRPCTsCCEMatCgQaSlpXHt2jUqKirYs2cPAQEBj11369YtTp48iUql0j1XWlrKnTt3dP/95Zdfyhk0Dejfvz/79u1rcOarurqaN998k8jISFJTU9m9ezepqalNWKUQTU9uYwkhDKoxrSsA9u/fj6+vL23bttWNzcvL0zW7rKqqYsaMGfj5+TX9D2EkXFxcnnrNwwvGAd2C8fp2xwlhCiTsCCEMzt/fH39//zrP1YacWkFBQQQFBdV5rlevXiQlJRm6vBfKkxaMx8XFNWNFQhiehB0hhDAiY8aM4ebNm489/95779W5BVifZ1kwLoSpkLAjhBBG5Kuvvnqu8c+yYFwIUyELlIUQL4zZs2fTrVu3ehc5a7VaFi9ejJOTEwMHDuTixYu610xlB1NjF4wLYUok7AghXhhBQUFERUXV+3pkZCRpaWmkpaWxadMm5s+fDxjPDqb9+/djb29PbGws48ePZ+zYsQDk5OTo1kw9vGDcxcWFKVOm4Orq2pxlC2Fw0htLCPFCycjIYMKECVy+fPmx1+bOncvIkSN1Z/306dOHmJgYMjIyWL58OUePHgXggw8+AGDp0qVNV7gQLxbpjSWEEIZQX2uLZ2l5IYRoeSTsCCHED+rbqSQ7mIQwbrIbSwghflDfTqWKigrZwSSEEZOZHSGE+EFAQAA7duxAq9Vy7tw5rKyssLOzkx1MQhg5mdkRQrwwpk+fTkxMDAUFBdjb27NixQoqKyuBByc6+/v7ExERgZOTE5aWlmzduhWov+WFEMI4yG4sIYQQQrQ0shtLCCGEEKKxJOwIIYQQwqRJ2BFCCCGESZOwI4QQQgiTJmFHCCGEECZNwo4QQgghTJqEHSGEEEKYNAk7QgghhDBpEnaEEEIIYdIk7AghhBDCpEnYEUIIIYRJk7AjhBBCCJMmYUcIIYQQJk3CjhBCCCFMmoQdIYQQQpg0CTtCCCGEMGnmT3ld0SRVCCGEEEIYiMzsCCGEEMKkSdgRQgghhEmTsCOEEEIIkyZhRwghhBAmTcKOEEIIIUyahB0hhBBCmLT/A5VjlM7L1egFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot egocentric 3D coords.\n",
    "\n",
    "zdata = gaze_egocentric[0][:,2]\n",
    "xdata = gaze_egocentric[0][:,0]\n",
    "ydata = gaze_egocentric[0][:,1]\n",
    "\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "ax = plt.axes(projection='3d')\n",
    "ax.scatter3D(xdata, ydata, zdata, c=zdata, cmap='Blues')\n",
    "ax.scatter3D(0, 0, 0, color=\"r\", s=100)\n",
    "\n",
    "ax.set_xlabel('X')\n",
    "ax.set_ylabel('Y')\n",
    "ax.set_zlabel('Z');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fp = vid_direc + participant_id + '/gaze_pixel_coords.csv'\n",
    "pd.DataFrame(gaze_pixel_coords[0],columns=['x','y']).to_csv(fp, index=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Pair each gaze sample with a video frame. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_gaze_samples = len(data[0]['Eye']['TimeStamp'])\n",
    "gaze_t_start = data[0]['Eye']['TimeStamp'][0]\n",
    "n_screenshots = len(screenshot_times[0])\n",
    "n_frames = 100\n",
    "plot_up_to = screenshot_times[0][n_frames]\n",
    "gaze_idx = np.where(np.asarray(data[0]['Eye']['TimeStamp'] < plot_up_to))[0]\n",
    "vid_idx = np.where(screenshot_times[0] < plot_up_to)[0]\n",
    "\n",
    "print('Number of gaze samples: ' + str(n_gaze_samples)) \n",
    "print('Number of frames: ' + str(n_screenshots)) \n",
    "\n",
    "## Visualize events.\n",
    "t_zero = np.minimum(np.asarray(data[0]['Eye']['TimeStamp'])[gaze_idx][0], np.asarray(screenshot_times[0])[vid_idx][0])\n",
    "print('T-zero: ' + str(t_zero))\n",
    "\n",
    "fig, ax = plt.subplots(2,1, figsize=(16,4), linewidth=6, sharex=True);\n",
    "ax[0].eventplot(np.asarray(data[0]['Eye']['TimeStamp'])[gaze_idx]-t_zero, \n",
    "                color=[0,0,0], linelengths=0.2, linewidths=1,);\n",
    "ax[1].eventplot(np.asarray(screenshot_times[0])[vid_idx]-t_zero, \n",
    "                color=[0,0,0], linelengths=0.2, linewidths=1,);\n",
    "ax[0].set_xlim(22,24)\n",
    "ax[1].set_xlim(22,24)\n",
    "ax[0].set_yticks([])\n",
    "ax[1].set_yticks([])\n",
    "ax[1].set_xlabel('Seconds', fontsize=20)\n",
    "ax[0].set_ylabel('Eye', fontsize=20)\n",
    "ax[1].set_ylabel('Video', fontsize=20);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Find indices for screenshot timestamps nearest each eye timestamp.\n",
    "def closest_argmin(A, B):\n",
    "    L = B.size\n",
    "    sidx_B = B.argsort()\n",
    "    sorted_B = B[sidx_B]\n",
    "    sorted_idx = np.searchsorted(sorted_B, A)\n",
    "    sorted_idx[sorted_idx==L] = L-1\n",
    "    mask = (sorted_idx > 0) & \\\n",
    "    ((np.abs(A - sorted_B[sorted_idx-1]) < np.abs(A - sorted_B[sorted_idx])) )\n",
    "    return sidx_B[sorted_idx-mask]\n",
    "closest_video = closest_argmin(np.asarray(data[0]['Eye']['TimeStamp']), np.asarray(screenshot_times[0]))\n",
    "\n",
    "## Grab 3D coordinates.\n",
    "gaze_3d = gaze_egocentric[0][closest_video,:]\n",
    "\n",
    "## Grab pixel position.\n",
    "gaze_pixels = gaze_pixel_coords[0][closest_video,:]\n",
    "\n",
    "## Trim the edges up to the maximum number of frames outside the edges.\n",
    "counts = np.bincount(closest_video)\n",
    "n_frames_start_edge = counts[0]\n",
    "n_frames_end_edge = counts[-1]\n",
    "print(n_frames_start_edge)\n",
    "print(n_frames_end_edge)\n",
    "first_three_counts = np.sort(np.bincount(closest_video))[::-1][0:3]\n",
    "max_frames = np.sort(np.bincount(closest_video))[::-1][2]\n",
    "gaze_pixels[0:n_frames_start_edge-max_frames, :] = np.nan\n",
    "gaze_pixels[-n_frames_end_edge+max_frames:, :] = np.nan\n",
    "\n",
    "## Grab gaze position, make dataframe and save.\n",
    "gaze_position = pd.DataFrame(np.hstack((np.expand_dims(data[0]['Eye']['TimeStamp'] - gaze_t_start, axis=1),\n",
    "                                       np.expand_dims(closest_video, axis=1),\n",
    "                                       np.expand_dims(np.round(gaze_pixels[:,0]), axis=1), \n",
    "                                       np.expand_dims(np.round(gaze_pixels[:,1]), axis=1), \n",
    "                                       np.expand_dims(gaze_from_camera_polar[0][:,0], axis=1),\n",
    "                                       np.expand_dims(gaze_from_camera_polar[0][:,1], axis=1))))\n",
    "\n",
    "gaze_position.columns=['timestamp', 'frame', 'x_pos_pixel', 'y_pos_pixel', 'r_polar', 'theta_polar']\n",
    "gaze_position['frame'] = gaze_position['frame'].values.astype(int)\n",
    "gaze_position['x_pos_pixel_360'] = np.round((gaze_position['r_polar'].values+np.pi)*(4096/np.pi))\n",
    "gaze_position['y_pos_pixel_360'] = np.round((-gaze_position['theta_polar'].values+np.pi/2)*(4096/np.pi))\n",
    "gaze_position['mu_latitude'] = np.rad2deg(gaze_position['theta_polar'].values)+90 \n",
    "gaze_position['beta_longitude'] = np.rad2deg(gaze_position['r_polar'].values)+180\n",
    "\n",
    "gaze_position = gaze_position.fillna(-999) # this avoids a known NaN related bug in to_csv\n",
    "fp = data_direc + participant_id + '_gaze_position.csv'\n",
    "# gaze_position.to_csv(fp, index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gaze_3d.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some sanity checks and visualizations. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Egocentric gaze position across all frames. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Plot.\n",
    "zoom = 1\n",
    "fig, ax = plt.subplots(figsize=(10.8*zoom,10*zoom), linewidth=6);\n",
    "ax.plot(gaze_pixels[:,0],gaze_pixels[:,1],'.')\n",
    "plt.xlim([0,1080])\n",
    "plt.ylim([0,1000])\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Histogram of gaze position over latitude and longitude of a 360 panorama."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_lat = np.rad2deg(np.unique(gaze_position['theta_polar'].values)[1:])+90\n",
    "all_long = np.rad2deg(np.unique(gaze_position['r_polar'].values)[1:])+180\n",
    "\n",
    "fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(10,12), linewidth=6);\n",
    "sns.distplot(all_lat, ax=ax[0]);\n",
    "ax[0].set_xlim(0,180)\n",
    "ax[0].set_xlabel('Latitude', fontsize=20)\n",
    "sns.distplot(all_long, ax=ax[1]);\n",
    "ax[1].set_xlim(0,360)\n",
    "ax[1].set_xlabel('Longitude', fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Egocentric eye position over the frame we loaded above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "zoom = 1\n",
    "fig, ax = plt.subplots(figsize=(10.8*zoom,10*zoom), linewidth=6);\n",
    "ax.imshow(frame)\n",
    "ax.plot(gaze_pixel_coords[0][frame_no,0],1000-gaze_pixel_coords[0][frame_no,1],'+',color='red', markersize=12)\n",
    "ax.axis('off');\n",
    "\n",
    "print(\"ambient luminance:\",visual_search_preproc.get_ambient_luminance(frame))\n",
    "print(\"focal luminance:\",visual_search_preproc.get_focal_luminance(frame,gaze_pixel_coords[0][frame_no,:],10))\n",
    "print(\"gaze coordinates:\",gaze_pixel_coords[0][frame_no,:])\n",
    "color_at_gaze = visual_search_preproc.get_color_at_gaze(frame,gaze_pixel_coords[0][frame_no,:],10)\n",
    "print(\"color at gaze:\",color_at_gaze/255)\n",
    "# visual_search_preproc.show_color(color_at_gaze)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Plot target object frame.\n",
    "\n",
    "Used to visualize what the target looked like to the participant. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grab indices for which we were in a trial. \n",
    "times_in_trials = [visual_search_preproc.get_times_in_trials(d,screen_times) for d,screen_times in zip(data,screenshot_times)]\n",
    "ind = sum([[i for i,tt in t] for t in times_in_trials[0]],[])\n",
    "\n",
    "# Segment frames into trials. \n",
    "frames_in_trial = visual_search_preproc.epoch_frames(ind)\n",
    "\n",
    "for t in np.arange(300):\n",
    "    trial = t+1\n",
    "    scene = data[0][\"Behavior\"][\"TrialOrder\"][trial-1][1]\n",
    "    target = visual_search_load.get_target_object(data_direc, scene)\n",
    "    \n",
    "    if target == 'Cylinder 1': break\n",
    "\n",
    "target_frame_no = frames_in_trial[trial-1][0]-10\n",
    "target_frame = visual_search_load.get_frame(video, target_frame_no) \n",
    "\n",
    "zoom = 1\n",
    "fig, ax = plt.subplots(figsize=(10.8*zoom,10*zoom), linewidth=6);\n",
    "ax.imshow(target_frame)\n",
    "# ax.plot(gaze_pixel_coords[0][target_frame_no,0],1000-gaze_pixel_coords[0][target_frame_no,1],'+',color='red', markersize=12)\n",
    "ax.axis('off');\n",
    "\n",
    "target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Scatter plot of all gaze coordinates for this participant in pixel space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot.\n",
    "zoom = 1\n",
    "fig, ax = plt.subplots(figsize=(10.8*zoom,10*zoom), linewidth=6);\n",
    "ax.plot(gaze_pixel_coords[0][ind,0],gaze_pixel_coords[0][ind,1],'.-')\n",
    "plt.xlim([0,1080])\n",
    "plt.ylim([0,1000])\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Scatter plot of single trial gaze coordinates for this participant in pixel space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grab and plot gaze trajectories for one trial. \n",
    "trial = 1\n",
    "frames_this_trial = np.array(frames_in_trial[trial-1])\n",
    "zoom = 1\n",
    "fig, ax = plt.subplots(figsize=(10.8*zoom,10*zoom), linewidth=6);\n",
    "ax.plot(gaze_pixel_coords[0][frames_this_trial,0],gaze_pixel_coords[0][frames_this_trial,1],'.-')\n",
    "plt.xlim([0,1080])\n",
    "plt.ylim([0,1000])\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([]);\n",
    "# Add a few frames to capture target rotation.\n",
    "n_prev_frames = 100\n",
    "frames_this_trial = np.concatenate((frames_this_trial[0:n_prev_frames]-n_prev_frames,frames_this_trial))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Full animation of gaze overlaid on frames for one trial. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grab number of frames. \n",
    "n_frames = len(frames_this_trial)\n",
    "\n",
    "# Prepare figure.    \n",
    "my_dpi = 96\n",
    "fig, ax = plt.subplots(figsize=(1080/my_dpi,1000/my_dpi), linewidth=6);\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "blank_frame = np.zeros((1000, 1080, 3))\n",
    "vid_canvas = ax.imshow(blank_frame)\n",
    "gaze_canvas, = ax.plot([], [], 'r+', lw=10, markersize=12)\n",
    "\n",
    "# Define initialization function.\n",
    "def init():\n",
    "    vid_canvas.set_data(blank_frame);\n",
    "    gaze_canvas.set_data([], [])\n",
    "    return vid_canvas, gaze_canvas,\n",
    "\n",
    "# Define animation function.\n",
    "def animate(i):\n",
    "    \n",
    "    # Get frame numnber.\n",
    "    frame_idx = frames_this_trial[i].astype(int)\n",
    "    frame = visual_search_load.get_frame(video, frame_idx)\n",
    "    \n",
    "    # Update frame. \n",
    "    vid_canvas.set_data(frame)\n",
    "    \n",
    "    # Update gaze.\n",
    "    gaze_canvas.set_data(gaze_pixel_coords[0][frame_idx,0],1000-gaze_pixel_coords[0][frame_idx,1])\n",
    "    \n",
    "    return vid_canvas, gaze_canvas\n",
    "\n",
    "# Call the animator. \n",
    "anim = animation.FuncAnimation(fig, animate, init_func=init, frames=n_frames, interval=0.03, blit=True)\n",
    "\n",
    "# Save animation\n",
    "# HTML(anim.to_html5_video())\n",
    "anim.save(participant_id + '_' + 'trial' + str(trial) + '_animation.mp4', fps=30, dpi=my_dpi, bitrate=1500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Gaze overlaid on equirectangular projections from this trial. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get location this trial.\n",
    "trial_name = data[0][\"Behavior\"][\"TrialOrder\"][trial-1][1]\n",
    "print('Trial in experiment: ' + str(trial))\n",
    "print(trial_name)\n",
    "\n",
    "# Load equirect for this trial.\n",
    "im = imageio.imread(equirect_direc + trial_name + '.png')\n",
    "\n",
    "# Get trial onset and offset. \n",
    "onset = data[0][\"Behavior\"][\"TrialStartTimes\"][trial-1]\n",
    "offset = data[0][\"Behavior\"][\"SearchOverTimes\"][trial-1]\n",
    "\n",
    "# Get gaze on this trial. \n",
    "gaze_time = np.array(data[0][\"Eye\"][\"TimeStamp\"])\n",
    "gaze_this_trial_idx = np.logical_and(gaze_time > onset, gaze_time < offset)\n",
    "gaze_this_trial = gaze_from_camera_polar[0][gaze_this_trial_idx]\n",
    "\n",
    "# Grab enough colors to plot this trial's gaze. \n",
    "n_samples, dummy = gaze_this_trial.shape\n",
    "print('Number of samples: ', n_samples)\n",
    "cm_subsection = np.linspace(0.0, 1.0, n_samples)\n",
    "colors = [cm.plasma(x) for x in cm_subsection]\n",
    "\n",
    "# Make plot without gaze. \n",
    "fig = plt.figure()\n",
    "fig.set_size_inches(16,8)\n",
    "ax = plt.Axes(fig, [0., 0., 1., 1.])\n",
    "fig.add_axes(ax)\n",
    "ax.imshow(im, extent=[-np.pi, np.pi, -np.pi/2, np.pi/2], aspect='auto')\n",
    "ax.set_axis_off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make plot with gaze. \n",
    "fig = plt.figure()\n",
    "fig.set_size_inches(16,8)\n",
    "ax = plt.Axes(fig, [0., 0., 1., 1.])\n",
    "fig.add_axes(ax)\n",
    "ax.imshow(im, extent=[-np.pi, np.pi, -np.pi/2, np.pi/2], aspect='auto')\n",
    "for i, color in enumerate(colors):\n",
    "\n",
    "    # Plot point. \n",
    "    ax.plot(gaze_this_trial[i,0],gaze_this_trial[i,1],'.',markersize=12,color=color);\n",
    "    \n",
    "    # Plot connecting line. \n",
    "    if i < n_samples:\n",
    "        ax.plot(gaze_this_trial[i:i+2,0],gaze_this_trial[i:i+2,1],'-',linewidth= 1 ,color=color);\n",
    "ax.set_axis_off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make plot with gaze in pixels to check the conversion. \n",
    "fig = plt.figure()\n",
    "fig.set_size_inches(16,8)\n",
    "ax = plt.Axes(fig, [0., 0., 1., 1.])\n",
    "fig.add_axes(ax)\n",
    "ax.imshow(im)\n",
    "for i, color in enumerate(colors):\n",
    "\n",
    "    # Plot point. \n",
    "    ax.plot((gaze_this_trial[i,0]+np.pi)*(4096/np.pi),(-gaze_this_trial[i,1]+np.pi/2)*(4096/np.pi),'.',markersize=12,color=color);\n",
    "    \n",
    "ax.set_axis_off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Histogram of number of frames per trial. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gather values in array.\n",
    "n_frames_per_trial = np.ones(300)*np.nan\n",
    "for i in np.arange(len(frames_in_trial)): n_frames_per_trial[i] = len(frames_in_trial[i])\n",
    "\n",
    "# Get total number of frames.     \n",
    "total_n_frames = np.sum(n_frames_per_trial)\n",
    "    \n",
    "fig, ax = plt.subplots(figsize=(20,8));\n",
    "plt.hist(n_frames_per_trial, bins=np.arange(250), color='gray');\n",
    "plt.xlabel('# frames per trial', fontsize=30)\n",
    "plt.ylabel('Count', fontsize=30)\n",
    "ax.set_title(participant_id + '\\n Total # frames: ' + str(total_n_frames.astype(int)) + '\\n Resolution: 1080 x 1000', fontsize=30)\n",
    "ax.set_xticks(np.arange(0,260,10))\n",
    "ax.set_xticklabels(np.arange(0,260,10), fontsize=20)\n",
    "ax.set_yticks(np.arange(0,30,5))\n",
    "ax.set_yticklabels(np.arange(0,30,5), fontsize=20)\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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