CNN_v01.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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BR7CRsZXBNHQCxmGTuQ4m2I/gK/Yb97qNoBejLRipBVbxPjiXjz32WICL3EOaTj8QNYzupWMPHUogBbDNsPW4jrxhc2IjYuMywos0sRPRM506wFvySqcYCAtw1zbTgGkgOQ2UGmZCDBNkh6wFs/HbwWYCXq7zRqzm8PkJ4EOYuk4b4BeYREcybL/YtiA8zYVrX1xnPTCQ+7FT8bdpVwiEsaYLndnAOdoDAmu2mQZMA9nXQKlhIPFEyFDWk8KW5f3BvngbvikkAHYXNnDYPgTPiJNCIGAHQgTQWcTZcdiDrN8HhhKfxJbELqZzCn7sLrvsEnQQxoYmLcgJsNo2vwaKlgjA6aFRpaARsMU5iQ2A+1TiIwLoieTYLgoowWacM5whemIRMMGxwBCA3aLXOo4E+eB6jAM3zNk5hgRkqASuBydBIRwwAuykxYYDR3AH4f9EG2kQJMIYoNcB74ORQMXBYKEHFr0U3AYRQACJ4Y04QDivpEFvBHpwYeAQoI8lAho1ahTkBX1QmelphXPneskSXI8lAgisITwLVg8HjfM4bQTcWKyZZ5EWwyl9mzOEYPkaN24cABA6JRBGLzqcThxThh8BDLCA9OijDNCrHwcRQGA0AAwmRhnlAn3Q0wJGkhEa9Ljlu0EkQCzwPPLG9+TdcFhx5AEiRhHYZhooBg2UKmZiWOy6664BEQg+QvZB/BJkYxQR5CX47wuOVYYIAGMIooFR4DV4hPEDMQn2chxnD2wlb/wPyQuJ6doI7gF/6T0LIUnAkjYFxxLCFGwj2A/eQsYSHKNXBUE48NcRAWAiPZVxaMFUgmTgdjLtZjGUf3sH04DTAPYcHTzoWYkDQl1w9c1dE947+yRMBDgnBVyBVKN+YpcRmMYmo+66OkYAGlvswQcfDOow9Q97DLvM2YlgE/eCF9gq2C/OPsOBcvYd9g7vgO0Y21klnG/ttw/r3LUQAYyW2nPPPYMAP84YzyW/dJiApCC4BfkB4QieYD9h59HZAiIDmzhZIgD9oAewmgWMn3vuuYDw4L3BS/TDSAjIAuw/RgJjl+NAcg4bHPyEEIDcRR/kFcG2q169etAZBqykHXTv4t7X9qYB00B8DZQaZoLr+NqMHsL2ApOJCUBgMiqK0V1c47AdzKdtqF+/fmDbEaAH0yAtsdWwNcElt/mIAPxRpm3DnwZbaZtoU9worfBsBi4925sGTAPZ1UCpYSCdL7CPDzrooKATXDLaJQZHZ1rsQzqLYNdiC9I5A5KTjiXYvxyDNOA8C6Hjp2LHEYMD+5gNBKzDFnRbGDPdcdvH10DREgEUIBpkAikEPXBUktl8RAAFkEYdx4OGnYaXhp5gMo4JjTpOCEBAMJqgNM4SQX2uxeFwexxGnD8cOQgDHBQKN+k6p87lFaeEIBDC/4k2iAgCV0xZQwUl0IMTRoWDtYNBi53ChgrF0ELygb54B/KBccI9BJ3oheUcTYJIdevW/b1XG44dzigBca7BWQ4TAbw3+cApBDBwZHHeeA5GDABA7wV6MfAs3+YcbYbYQz5gbDmdkh4OL441eud7sOc3egBUyB/vh45wVDHK6EmGw8zzIYwgQHgX8sX1lAdGETg9GBHg+zp2POoaKFXMJLi01VZbBUEtgnCM9qFnFUYJ51yQyxccqwwRgJGDQUPQH4IRnKKtAM8wcDgOQcl1tCUQnmAaeAX2gbf0+KVXGW0R5CxYC/EJ4cpx8u9wnUAkzipTikDAQnA4IgBSm0AZ7Rp5IO1Ewc+ol3nLv2lA0wBEHJ0AsJnoIJFMPXD2SZgIwJbAdsS2w+YhLeo59hYBHewsN2SaZx544IHBM3GyOE5dpP66+g5RR9AJR4pRjOAC5xH3DN6J+7BtsB3Z8zvZzYd17n5sK+w1MJLp0LCPnT2H7piuCJuS3lwEu3g2+XRTO2JHErhPhwhAZ7y3wzVn1/H+6JaRU/QMwx7EjuU4ecN+5jedZLDp+AboFCJg5513DnqP8d58L/ed3Pva3jRgGoivgVLDTHAC35rerdiJ+MLYZviRxBxoB+hE57ARX5Jeqoy8xEcH20kDHMTv3W+//YIRoE7L4aCWa1/AfZ5FBxDXJoB7tAsQCkYEOA3a3jSQWw2UGgZChII5TG+GTZZoA6forAI+4tti42KXYR9ip+KbYjdCFEAYgI9gHJ3YnB3HMexKOs3Q2eO+++77/bFhzPz9hP0TVwNFSwTgeMGQQwQQfKbwUQgTbRoRwH0EYgi4UFCZ5oeemTT+9P6nhzz3UaDp7c95GnycNZwMCifOCY4L+cIxouc7BZme+lQkpoCgItFTvjLDWAgEEfwhuE/QH4eGSkba9IQiX+TVbRABTAuE00RA3m1UNN6DgBHsHM4WAS8cuGrVqgVpERxnuDeBdSqr28JEAAYPwXVGQOy4446BgYT+eGeENDFsAAeCU77NGUIwhuiNgJXb+EaQK/SqABgwztjzm+Ox3x7AgRzAUOP5fFuGNdG7zX1Hly7AzvBKjDe+vREBTjO2LzYNlCpmguM1a9YMHCjwmsAfmI1zBkkLXoMfvuBYZYgAsJOh4mARI5jASjYCevQg5ji9+h0ZAcEJjjPtHEF88kYvMvAejOeYG3FGkI72ybUvDm/BbdIEAzHEHBEAptIeECCzzTRQyhqgHjE1FjYBtgp2VKLN2SfUWTc1ELiBXUhveHqbgyuQeEzjgMOzww47BDaZC9Rjv2GjIdgkjBaiXjO6ldFD2GH0uIfoY8QSQj2m5zvH6A3vMCRRfuOd92GduwcigM4uOGwEu9xG0As7kimVsH8dscl5iGamPkI/dOagQ0qyRAB6xBHkuZ06dfrdtgarwSzXmQYnERsSfMN2xeZ0uIcusTV32mmnQG/YfegKzGfkB2mjY9tMA6aB1DVQipgJHmNvge+M0gTfsbfwURkpwIh/yEfsNHxL2gWwL7bDmyOFaWvActfWkBadUuisBla69gU7jbYjHCcgfZ5tREDqZdfuMA1kQgOlhoHgHh1ZiN3FruXk0yVxQnr305mDjmpgmuug4joiYh/SOQ77DJsPmwwbkymIiElizzEtONfQCRs73W1hzHTHbR9fA0VLBGDgEzShQabQUWAJriTawo0pjgUFlOlkGNJMwIXADeQC8uc//zmYx5SCSi9OCjrT1eCccD2BGNgy5kbFQcHBoqEnLzwLJ4QenwR3SJsgPoRDbM+uRHnmPI4SFYpnuN6sOJAEmhAqKY4RAW3yxntxD/+zACakhFvRm/SYQoLeaZAEBNRjiYA//elPQU85eoQRZCfIH48IwAiCiKEXPgt50POBnhNOmJICx41pKXCWfZszhBhJAViEDSECYkwFxJ5RCrG/Y9MEWBg6ThqMUiBvzLeI84oTizPrNnTHdBt8RwgBIwKcZmxfbBooNcx03w8igLmo6aELhvs2sJsewgSXICExUtiYegIHD+xOdY0ADEfaEdIk2O+whz3tCMfpwcp1bDyXdoUAIFhJUBBDDAylRxhtFfhG0IuRaQS96GHssJY9z6MdAAPBfEcEgIe0c4wosM00UMoaoJ4RgGEaB4Ir1Ctspnibs0+oR44IwI6jXoMxjLjBjqC3PHUQLMGWoo67aW1Iw43GxC7EwcIuhBRgHRDIWmwxrscxwqaiLpMuPUUJjGOPOWyKl99455IhAgg4QUhix7oNW48pLOkcQ55pU1xgi7wzGhT9YIvi6IFZpAGmMdrJbfS0hTRA/+jObdh8jJSCKME2xW5kD7mCg0nPMew0bLmGDRuqtiZ4SMAN247rscHBf0gY7DvbTAOmgdQ1UOqY6TQGhhMPwJ8F58BkbLJwbMFdH+4453zzcFArtn3BX8XujN186cdeY/+bBkwD2dNAqWEgndUIzjMKKnaUuk/D2IN0gob0xO6jk4yzq/kfDMM+ZApxfFtIVjqL0OEEPCX2ig9LDBGbjfUA6ATjtjBmuuO2j6+BoiUCCIrjTFDYcAgI5uJ4JNrCjSmFE2fFOR4w+Tgc9BSHaICdjw0i4fQQFIflJ8BCkJnRALBfOGw4b2xcR9oMlyZfBKAJuOPQkWcqWDLEhXsf3pdn4nxtu+22wfQ9VBZ6xCNMFURvfHq+4ljhlFEB0yECqHywcASjeCem04nVbdiwIajFe6IDHDQW4UMvOMixQuArXiDOGUKM8kBweN0GqQEg4GQz/yzfjHwBGhznvNvowYtzD7iwmAgGFc4g4BQOBkKIcL8bgsS7sfA0YGVrBDiN2r4YNFBqmOm+WbJEAMF4gkgE7sAqR34SnGKYI8GsVIkAppKDBAaLIKsJpLERoKK3PschkrmOjZ7DkLpgL+fohcaUQMzXDZaC6VzD0HOICXqOEZiLxVneg/RwTjHMYokACFQjAgJV258S1oALaGNDEfDGeXF106cWZ5/EEgEEm7H5cGKwzQjyM6UDdgULTe6xxx4BnoAhBO/BYJ6DvQTGvPPOO0GHDOwdOl5g82DLcC32LfcRtGf0K5gAltCzPRwk8uXZdzxZIgA7CpvZbeQfcpHON/S+x9ZypAS4AtEIbjF1D7jjSA9GF3DObeQfonP33XcvRwSQFjYY58E07Dz0yjdiBALHGQmLvQauQxqEsQ/8Y1QodimkihEBTuu2Nw2kr4FSx0ynOTAQnAZX8BHBQYjbcGzBXR/2lytDBDgspA2wzTRgGsitBkoNA/EhwRpiZ9i9BO+dX6xpHmxjSm/ikdirECfOPgQH6QyCfUjHaTCUjiPEXrF/8a2ZqQTblBk/6MTLiIAwEUDnEDeKSsuDHauogaIlApyzRIFgPnkKEg4ZQ+wIstBY4whQEAnsuyBMuLHGeSE40rFjx8C5YAgyvcMIutODiF5ejgjAMSOwQpAFB4gKQcEnoEMPpAMOOCBw6ij4OEE4La6nGekxLRCBa4I3PId8JrtBKhDwgZiA+CAwRU9RKgvC/xxzCxWRJ9JPhwhwUyEBAASheCYkBu/EO4cNG5wtngVbRy9VrsUZQw9udAS6w1jiXt/mHG1GMMAM0uuCY6TNtBoYXByHQQRE2LMQMoDj5qrle+J8UiY4zjfkGzB6gIAbUyThfFM2IEtw1hlVQlANkoBjfCfAit4efGvX482XbztuGoiCBkoNM903SZYIIDDHcG8C/vRWBUvAbXCGBdwIxqdKBBDIA0tIk1FZYChtCO0SeEm7A/ZwHRuGJm0VxhGkJ9Nk0F6A7wQP2VxbQJCOaYBoF8gnOEW7B9kK1pJ/8JpnEpjDkDMiIFCh/SlxDVBPcGrocY+NRycG5kOlhxL2DHYdNgsdGrAZ6Qjh7JNYIoCgOD1C6YzBHpKODh6QAdRv5qaHWKR+cxzHCvIAO4Pn8AwC4vT2J8iPvUFd5nkuLeow91PfISqxebF/KrOlSwTQhoCT2EbYTXScQWfYaNh8BOsZ0el6yWK3gXvgKp1LwC70gE6x52rXrh0E9MEucBGM491ID/sRG5oeaTiOkAFgGaQI3wtcdSOc+F58U/TKd0V35JXjjgjArotnf1ZGn3avaaDYNVBqmInfCBa5hYHBLYdTHAPnIG/x/SEewTQwGpyKDdSH/WUwCWwK927V2pfYMoXfyrNor+ggQhq2mQZMA7nTQKlhIJiHDcc0tEx5hl3Hb3xMZ3Nh04KH+K34tdhfTAUODrK+FOex5+iwQSdocJMR7WAmOAmZSkcRpr7kOmxE/G/ijo0bNy5HBNBJjjgee2xY25LTQNESAe716XmFo0HhIKhCb3EaTHrcExwm8IEDQiAY5yDcWNPYM1UPzh3DVejNjnPHnK0EjZnDzxEB3I/TQyPMwo+OvaIHOYEanB3ICJwRnBXSJR3ywugCnCacPYwFHEUqGYUe5wfhf9/Gc2HGCPwg5BEnMlbID8EqjBP3vukQAVQ+8oxhQhCMNAlI4bDimHEcpwq94GQ5w4b3JWiFgwYZAGBQWSFi0DvsX+xCxuF3dYYQQ+UZWcBcYQTy3XoLkD2ABkCDE80eAOE4AMV1PINgHYQB34/84lX2NMcAACAASURBVFCSDg45usMAI08E0Jjjkd58rA+A048zih5xNPlWMJMcxyDEIbfNNBB1DZQKZrrvlCwRQPAc8hBMYbFJCEV6pIIX9BwGl1IlAgjq0SZB0oI9tBWMDqOdAafBS85zHRtYSgAfXK1Tp07wXO6lvXFBLIxRcJ+p3WhzyBO4B9aC2+QZ3CKoSFtkRIArCbY3DWzUAPWJeoVjwkhNhj5jw4CPjAilTtH7nOPYCs4+iSUCsOOYPpF6SIcM7DzuJ1gP+cYITewOHCScHDcylPRwnrBDsNUIqrteUgTUyRc2Cx1GeAa2DUF2RmjSq4qOKOAANim2I3t+J7ulSwSQPo4dOiHPECnkE5sb+5Ce+nQeQXfYsxAY2L0QJRznvdERNnnz5s2levXqARHg3gXMJR2cSrAMcobgF/anm44JkgQHkzlksbvBO65F0BPP4xsYEZBsabDrTAPJaaCUMBN7EBxj9CVBKrCYdgEMZzQ6+EWwinPYbOHYgtNopogA2gFwkzgF+Es7gQ/Ms20zDZgGcqOBUsJANIptyTTkxNnAPDCI+Cb2FphEB1riZwTn6YBMrJLOG3R+w25jykvsOTrdgl3YgdiM+Kb4qdiQ+NykQZrgLLE/bHLWdoodEUDsFVKCuB0kAp1SeB5xO9v8Gih6IoACgGNAAJ6gCnPIMz8rPe/pnQ6LxVzJOAj0tgw31gSUKUg09jhZbj57N/cf85PGEgEEhFnIgh6iXIvg5MDS49BhKMCW4cwQwKHHOvmhdyfpU+gZzux6F0AU4Ogg/O/bCKDTa4xAOBUONg3nKVZwCAkOUWGphASAKkMEEEAn4OSC6IAA0yBRWTUiAMDAcaOS4qTx7rw334E8MX0TQXXf5hxtHEwcShxp9O90DJsIMQGA4OThaOI4EujDEYe0gQDg27EAIODDO3AteUO/OOW8B8OLuJbyATtJLz2+CYE40geUIJe4jnTRO9/eNtNA1DVQKpjpvlOyRAB4QvtAwA3MAbfBEowZ9mBqqkQA7QuBM4L9YCBYyLzg7DF+OM55rmNzwSvWgmGE2fbbbx+MDgDLwTJ3DbiPEUa7R88L2hZwys2pDZFJwA2i24iAQG32xzRQTgO09Th1BHuwv+jFhM3Cmh3UUZwWOjXgwBBwcfZJLBGArYDzAzZgB4IZrOHENZB5TNcYSwSQFh0rSB+MwT6iwwFzQuNQUV8hB7BJeD51mjS5HhuT49gh4AU2DXYreWfP72S3yhABdIhAHzwTnfHe5A+bGBscspP3QL9ci23FCCdnM/O+2MZgFGQn+OzscHAPm4/3Rj/gGbbd0KFDAz1DppAmxAqdTXiewz7yAFmLjc1oUoelNiIg2VJh15kG4muglDCTuABtA1iOz+iwmD2Yg90F6QjuYo+FYwtOk5kiAgj44+/SDpAH/FdGvkNE2GYaMA3kRgOlhIFoFGwD44hbYrNhl2KXNWvWLLCXsWc5jv1Kx1vsM2KyxEOJA2IPO/vQdWpz9iG+qbOJidliD4O1+NzYzayzFUsE0AGaeK6L83It5+n4YZtfA0VPBPDqTPNC70fYJZwT2HKEnpwPPvhgEJx2wRYKHo07zgqMOs4CTgi9s+jFBZNFQYMBo5cThZQeXzg+OCEE+akQBONhrXDMmIeewD9Bbgo4AWXSY9gyhZRr6LFO3mDHcGLcRr4ZBoPwv2/jubBh9AYjAMQzwhsBPnrh05uMgD2GAxUEY4V3gTxwG/qgZwNpcg/gRiAMB4p3wwhCN4AADjD5JjhORYRwoKKjF3pjualzuJaerRhE6J2RGu47oFscZtLybegXJ5heeRg3vAPPRHcsEEe6zugiDZ6HXiB5cCD5dugb8oMgGCSG2xyYQWpASPCNuRbd8EyezfsivA/fj3fgGp6P0xmbnkvX9qaBKGqgFDDTfRdww2G4hpvuOod1kI1cD3bRDtDzgJFl4JKb0g3nDkIULIFwdBuYSnAe/AB/wROwlWAYuExvCnCKPb85znmui924n7RpOyBdwXaHs+46gpDcD06SHvkFqzDAGHGAUcZ3dj1YMNRoozhmm2nANFCmAWwd6jW9PLEZqXPYB9gero5i1zn7hHpEjyjqH3UXuwBswL7DJsRm4D6CNvzvAvwQedhjjDLANuJaZxeCOdhn2KLUW37zfPJBfrgeWwW7Bixgw16jcwO2I3t+J7thR2HnubzxHrEbtjE2MrYyNnPsBg7h7IF/2L3oDEwDd7ATuT4Wz1w+saF4X+xl9AMu817sSZOOOuAnthzvjX5IFxzEfgVPXT7BMIaaY4uD1aTLPdiJYDW4yEa62Khcg81q2Bf7Je1/00B6GigFzATvIV2xpWKxGJwB68Fd4gLOLgvHFpxmwRzNXw7bilr74tJgT9uA3w6+gWfkAwynTbDNNGAayK0GSgEDYzWKXYqthh2H3UZnW2efEXsF/1wcDfuQOCU2q7MPXfwz1j7kerDR2ZH4sNhwxDmxPbkn1r/G1gbvXLyPjnnY4ozess2vgZIgAvyvb2dMA6YB04BpwDRgGjANmAZMA6YB04BpwDRgGjANmAZMA6YB04BpwDRQ3BowIqC4v6+9nWnANGAaMA2YBkwDpgHTgGnANGAaMA2YBkwDpgHTgGnANGAaMA2UuAaMCCjxAmCvbxowDZgGTAOmAdOAacA0YBowDZgGTAOmAdOAacA0YBowDZgGTAPFrQEjAor7+9rbmQZMA6YB04BpwDRgGjANmAZMA6YB04BpwDRgGjANmAZMA6YB00CJa8CIgBIvAPb6pgHTgGnANGAaMA2YBkwDpgHTgGnANGAaMA2YBkwDpgHTgGnANFDcGjAioLi/r72dacA0YBowDZgGTAOmAdOAacA0YBowDZgGTAOmAdOAacA0YBowDZS4BlIiAn755RdZtmyZzJgxQ6ZPny7fffedzJ8/X1atWlXiarTXNw2YBkpBA4aBpfCV7R1NA6YBnwYMA32aseOmAdNAKWjAMLAUvrK9o2nANODTgGGgTzN23DQQLQ2kRATMnDlTnn76abn44oulefPmct5558mIESPk448/jtZbW25NA6YB00AaGjAMTENpdotpwDRQNBowDCyaT2kvYhowDaShAcPANJRmt5gGTANFowHDwKL5lPYiJa6BpImA3377TV588UW58sorZdCgQXLLLbfITTfdJJdeeqlMmjRJli5dKr/++mtS6nz99dflhhtuMDEdWBlIsQxQ5+677z5ZtGhRUnXNLsqcBgwD84PZN95wo2gyZMgQGTZsmCrUkxtvvDEv+DJ06FC5aehNqnCOfGsyePDgvOQ3am2xYWDmMC3VlAwDc4uBYBjl3Ydzt912m4wePVqVO+64I+ioM+L2EaLKiBEyauQoVUaOHCm33367kL4mnFPT5FkjRgj3xxOu0YR7yLfvnfA7ooZX2cgvPpRt+dFAJjBw4cKFctddd1lZTtH/CdelwNa66aYAI8HJsPhw0x2/edjNosrNN8vNWRKeHc6n+z1k8BArEwnKBG0i7cDbb7+dHwCwp4phYG7twDDuxf6mPvgk9jrtf3xOzRflWCJsDTArDT+XtLW82LHky1SmMTApImDDhg3B9D8Y7ieffLJMnTo1CPwzPVDbtm2FwvTZZ5/J6tWrk4JIPvgf/vAHE9OBlYEUy8A222wjxx57rEybNi2pumYXZUYDhoG5x+s//vGPsummm8pmm22mCnWhVq1aqtSoUSO4J9ftDPmtVq2abLfddqpwbuutt1alSpUqsskmmxgmJsBEw8DMYFqqqRgG5h4DwT6wzIdze+65pxxxxBGqHHroobLffvvJ3nvvrUqTJk2kadOmqhxwwAGy1157SaNGjVThnC/dffbZR/bff38hDU04x7M14frDDjtMfR/ec+eddzZ8/MMfAkc61fpr11deA5nCwP/+979B/cm1fVJMz8PWql69utSsWVOV7bff3oub4GmdOnW8suOOO0q68uc//1nq1q2rCud4NnnTBBuwmL5RNt6FNnGHHXYIiOrK12hLIVUNGAbm3g701SN8ZOrD5ptvrgoY6bsXXxO88fmjYCu2p098Pi7Ht91228APxt8NC88zP7dyZSjTGJgUEbBmzRr56quvAhaWaYEgANavXy/z5s0Lev3ADD3yyCOyZMmSpDDFiIDKFQJfxbbjxa/X2rVrS4sWLeTbb79Nqq7ZRZnRgGFg7usWxsKf/vQnqVq1qir16tWTww8/XBWCTflwqrbcckupX7++N0jWsGFD2X333VXBedxiiy28hpvhe1kZNAzMDKalmophYPoYiMPmk3j1GgwDy3w4161bN5kwYYIqY8aMkX79+gWjeBnJG5Zrr7026PVFz6+wXH/99ULal112mSqcC6fnfvfu3VsGDhwYjBxm9HBYOMezNeG5Y8eOlbvvvluV008/3fDRiIBUoStj12cKA7NJBIAz2E6acC4e3nDOh1PxjidKMxvnt9pqK4EIBR81Oeigg7y4Cal49NFHyzHHHKMKna2OP/74tOTEE0+UU045RRXOQXQefPDBqmADZkNXxZQm3x07ety4cRmr15ZQ8hqIAgYWYnnPBn6C8QTWCdprgj/q0wXkAWTnLrvsogqdQDRc5RidOPbdd1+v0FGEOqrJbrvtZn5ugs5uvm/mjmcaA5MiAlauXBkMw2KoXK9evWTWrFkBahD4f/TRR4Nhy6NGjQoWDg7DCaMEvvzyS3n++ed/d1bOOOMMb+F0L2r79J1O013x6o4eJa1atQoW6g7XNfudPQ0YBua+TmHkQALQA1wTnMATTjhBFXrDcm+usZAGunHjxnLggQeqQm9ZzTjimBlIyZUxw8Ds4Vy8lA0DkyufGuak6wSCYWCZD+cIpj/77LOqPP7443Lrrbd6h40zwtdHIhCMHzBggFx99dWqcM43HB1SAX8AIkKTO0b9/5RFyvRAPJd8sxaZJue3Pj/nmK59z3wfozOVbbnXQLoY+OOPPwYj6ZlGlzoH4UU7lo1yBNZoJIA7luiZ8bDKdy5Rmtk4TycRAlLgoyZHH3W0Fzf/8pe/BIF61jrU5LRTTxNIx3Tk7LPOlpYtW6py1llnBeQCJIQmu+66a1bKRDb0n680+e7Y2NQj23KvgShgYL7KZrzn+rCT4/Hui3cu0Qj0P231J2/adDpjhCUd6jQh0H/IIYfocvAhclCzg6RZs2aqQBbg62rSoEEDiUdQxHtfO1fmg2QaA5MiAn766Sd5+eWXgznzrrvuOsGoYVu2bJk899xzwXGM/7lz51ZAJe6dMmVKMJSVACbCkGL7oOk7laa70tWdBcEqQExODhgG5r7OGRGQe51HoW0xDMwJ5FV4iGFg+vUxXSfQiIDyhIARAWVl0IiACvCUkwPpYiCd4e69917p2LFj4AMzxS5TJmSjvQVrXNBf2yd6Zjys8p1LlGY2zhsRkH57lI3vkas0Mx0EywlwFNFDooCBuSqLqTzHh50cTyWd2GuNCDAMzAS0ZJ0IYAohgAPy4Lvvvgvk8ssvT7vgx1YC+780K0Epf3cLgmUC9lJPI13jhycZBqaHU0YEpKe3YsdHw8DU8SsTdxgGpl8f03UCjQgwIkDDcyMCMoFoqaeRLgYyMn7RokXyww8/BD4wnePoGal928oeA2s0AsAdS5R+PKzynUuUZjbOGxGQfnuUje+RqzSNCEgdtzJ5RxQwMFdlMZXn+LCT46mkE3utEQGGgZmo20kRAekOBfJl0NYIKM3CGwtg9n96ZcCCYD5Uye5xw0B/ecXBZPEaTZjShwUpjzzySFXOOecc6dqlqyrdu3cPpqW4pt81osnw4cPlvvvuU4U5phm91q9vP1WY2uKG62/IuAwZMkRGjx4td915lypMmUG+NWHqvf79+0vfq/uq0qlTJznvvPNUOfPMM4NhmKxPECvM/1hs6w4YBmYX63ypGwb6MTCRPRPPCYznCLKwZM+ePQVc0eSpJ5+SadOmqcI85G+//ba88cYbqrz77rvy0UcfqfL+++/LSy+9FEzpybSeYXl5ysvyr3/9SxWe995778kH73+gyjvvvCNvvvmmKpz74osvgjXJWJcsLNcPut6mVrM1AnwQlfXjmcLAbK8RkC7eJLovHlb5MJBg1ZZbbKkK81vvsccesmejPVVh6onjjj1OldNOO0369u3rnaKMadP69OnjFaY989la2GHYj5owrRPrIt487GZVwGliHJpwL7antj4Kx7B5217UVhVsP+xo3/oC9faop84TvtWWW6UdaPR903weNyIg6zAX9wFRwMB8lM9E2JlunljHRMMhjoEnw4YNC9ZvveWWWyrsmb5xwLUDVGHtJqaHHH3HaFXGjB4jY8eM1WXs2GAtpzvH3imakObIESNVIU+sc8l6KZqAY+nqqlTuyzQGJkUEuMVBCFTYYsHpO4OlUkjtPbNXRiwIFtdGydpJw0B/mYYAYG58TVj8jAUnr7nmGlUe+esj8tlnn6mCszxjxoxgTRrWpQnLksVLhO+iyapVq4Kp61jHRhN66GVj++2332Tt2rVqnsjnyhUrZenSpbosWSqLFy0Oeg7SezAsH/37I/n73/+uysSJE+WCCy6osEAdjiPOdjFhsmFgNkpu4jQNA/0YmKh+ud642j5ecI2FxZlXfOrUqarMnDkz8YdL44oNGzYIDj+9/zQBP3/99VdVGAEHDvq2devWCfisCWWMZ/u2Rx951DtvNw4zxHOib1EM521EgK+EZPd4pjAwm0QA5TtRUCrd8+nUHUgAbY0njtWpU0eYr5+58zXp0qWLN9BPEAsy8sMPP1TlpX+8JA8//LBXwFWfTJ48WZ566ilVWJOF5/rITAjUJ598UpWnn3paXnzhRfnHP/6hypSXpgTTMDMVc1iw/SAoevTooQpBNW3hTxYRTee7Feo9mQ6CZRcxii/1qGBgLssveIptB+mpSTwbj3zGw+NB1w0SbCZNsMHStrVWr5EN6zcE95NGWLDxfL4q08KvWLEisBGxE8OCzcg1mnz//feCv6p1huMYOJbLbxfFZ2UaA5MiAjDMf/7554A9Ym5DehnxgadPny5t27aVwYMHy6effirJBldsRED6DmUUC63lOXPf24Jg+TGsDAP9ZdiIgI1lEmPKiAB/WclEW2AYuLG85fI/w8D0y7VGALhj8ZxEIwLKl3AjAsrKoBEB5ctFrn5lCgONCNgmIAeMCChPCBgRkLiNzXQQLFfYUSzPiQoGZsLXSDYNIwLKkwFGBCTGsWTLlnZdpjEwKSIAAKPyv/DCC3LFFVcIQ0oYHcDwuA4dOshDDz0U9LqEnUpmMyIgu4VEKzh2rDh0bkGwZBAmO9cYBup1yIiAjeXNiAC9jGSy/TEM3Fjecv2fYWB65dsF/bW9EQEbRwfYiIDkypcRAblGvo3PywQGGhFgRIA2KsCIgMT4l+kg2Maabf8lq4EoYGAmfY5EaRkRYERAojKSyfOZxsCkiQAAgoWOGPLWrl27YAqCVq1ayW233RbMMZosgHCdEQGJG7tMFhpLq3j0bUGwVJAm89caBlasS0YEbCxnRgRULB+Zbn8MAzeWt3z8ZxiYehnXCAB3zIgAIwJSxUgjAvKBfBufWVkMNCLAiAAjAlJvR8HJTAfBNtZq+y8VDRQ6BqbaplbmeiMCjAioTPlJ9d5MY2BKRMAvv/wSTAnEHE9MC/Tdd9/J/Pnzg7k+UwEQIwLSawBTLSx2ffHp2YJgqSBN5q81DKxYp4wI2FjOjAioWD4y3Q4ZBm4sb/n4zzAw9TLugv7a3ogAIwJSxUgjAvKBfBufWVkMNCLAiAAjAlJvR8HJTAfBNtZq+y8VDRQ6BqbaplbmeiMCjAioTPlJ9d5MY2BKREAqIBHvWiMC0msAUy0sdn3x6dmCYPGQJTrnigkDjQjYWO6MCMg+5hoGbixvUf6vmDAwka2lEQDumBEBRgQkKj/h80YERBn5RIwIMCLAiID0bMVMB8GijSTRzX22MTDcZmbztxEBRgRks3yF0840BhoR8If0GqPwh7HfpsdclAELgkXX8InNeRSDYFtvvbXUrl27gtSrV0+aNGmiyjHHHBOsKTNy5EjR5LnnnpOvvvpKla+//lpmzpwps2fPVmXx4sXBIvYsZB+WlStXytKlS4O1a5YsWVJhv2rVqmDdG+a69MlvG34TTYLr12+QDYqwTs7q1asr5Mflj0WUtPy4Y7yTTz7//HN5+eWXVfnb3/4mV111lXTq1KmctGnTRvbZZx9h0VFNtt12W8kFbmXyGYaBsUgS3f+jiIHplmPnKLrgf+w+HhGwxx57yOOPPy7//ve/VZkze46sX79eFbCIXnvxZN26daIJC57HW/ANfAXnNGGef+73PRfsJW1NSBd89W3oomXLlqqceOKJUq1atcjhWTplyogAXwmJxvFsB8HAlGyIVlY33XTToN5tt912ogntNTimCbbJ2WefLdgpmnTv3l2uu+46VVin8Nlnn5VXXnlFlaeffloefvhhr7C2oU8ee+yxYBpkpkIOC+lOmTJF/vnPf6pCnrhfk8mTJ8vf//73Cmm6ZzzzzDPy/PPPq8K9AwcOlF69eqly7rnnysEHH1xB9tprL6lbt64q2PNbbbWV0JlHE9op7Zvn81img2DRQIziy2W2MTDXZTTWpgv/H8/GAz+32WYbwRfT5Nprr5Uff/xRlXnz5gWztGi2FMfwO9esXqPKqp9XBb7oooWLRBNme/E9l3MrVqwIZoPBngsLvvfcuXNV4bszpfw111yjypFHHhm37cr1dy3E52UaA40IMCKg4Br6Qqx4hZInC4IVh0EUxSDYYYcdJr17964g9913n7z//vuqfPzxx0Ew32cULFy40B8YX7xEFixY4BWMEZ9wH8F1DBJNMJLCxkvs7yCYtWatrFWE6whYaUK6GFXx8u0753Sh5ZdjvM+iRYtUIc0ZM2YE0/UxZZ+TL774QiAJfA5vixYtIof/hoGGgYXSHmc7H40aNZKpU6eqdR4sWLJkqSxfvlwV8GLWrFkBLoANYZkzZ44XP8HVBfP92Aue+whangmByxzCPgnnxf3G8YSY8G30ou3Zs6cqF154odSoUSNyeJZOGTIiwFdConE8n0GwRARBquUR8u3kk08W1gzUhM4J/fr1U6V///4yePBgGTp0qCoEiyADNLniiiuCTibcr8mYMWO8JMCkSZMEu/Xee+9VhfNaIJ9jjz76qHDeZ1NNmDBBeLYmd9xxh9x+++1BIIxgWFjuvPPOIF/kLSykO3z4cBk2bJgqI0aMEO4PC8chTTSh8whEQa1atVSpWrVqweFppoNg0UCM4stlPjEwVYxL9noftsa7nw52Rx11lDRv3lwV7J3Ro0erMn78eKEzHcSkJkzhrnVY4xj2IQRkGGfcbwjNt99+WxX8etdpQ+tIh93nuxd/9NRTT5UDDzxQFUhLyBGfxCNV0LP2DeLpP4rnMo2BRgQYEVBwDX0UK2au8mxBsOIwiKJIBJz4lxPl1ltvrSCvvvpq0Cth2bJlFfYExgns+HqtEmjXAuocW/HTClm8aLEQINdk/jx/j4V5c+fJksVxiIDlP3mf+/PKn2X1Kr23Kz1gyZsv+EbAniCaj/igB0c88oL7NT1yjHzR01aTX9b+Ir+u+1XoBRwr6P+jjz6St956S5X27dtHDv8NAw0Dc9Xe5vs5BGk+/PBDL1aBQzh0moCZ3377rTCyShPW+vIF8yEJwDDwSpPZs2YL92sCCfnNN98E64jhiIblm+nfBOe5JiwQCIwk8G3/fPmfwcgnAlhhActq1qwZOTxLp4wZEeArIdE4ns8gmBYoccfSKYuMAoAACI9GdL8J9hP81mTE7SNk1MhRMmqULgMGDJDOnTur0rVr16BnfJ8+fUQTguy+EQEE8iEBJk6cqMpDDz4UBPwJ+oflr3/9a3DvPffcI5qMHTtWfVfen0A+pIdGXHAMfdx9992q3HXXXUJQP0weuN/3Trw3GG3AiINYgbzgfTXh3gMOOECwqTShp3I6ZSKb92Q6CBYNxCi+XOYTA7NZPlNNu3r16gEBcP7554sm3bp1C0YCMRooLGAJ9fqJJ55QBR0zXa0mdFgj6A8mafLwpIfVkVaMwHr33XeD0Qa+UokN6BupBYF6+OGHy2677aYKnTm00UkcgxwwIiDz66QYEWBEQME19KkCaSldb0EwX9MTreNGBJRNZ2FEwMZRDfTqNyIg8RRzhoHRwjpfbqOIgbm2NYwIKF96jAgow0cjAsqXi6j9ymcQzAX9tX06+GZEQHlCwIiAxDZcOuXM3WNEQNTQTs9vPjHQlaVC2BsRUJ4QMCIgMX5mGgONCDAiwIiACJUBC4LpRkXUjkYxCGYjAspGCdiIgMSGSjYNbMPAqKGdnt8oYmA2y7WWthEB5cuOEQFGBJQvEdH8lc8gmEYAuGMaBiU6ZkSAEQGJykgmz2c6CBZNBIl+rvOJgZksj5VNy4gAIwJSLUOZxkAjAiIUBE61sNj1+Q1YZUP/FgSLvgHEG0QxCGZEgBEB2cC0VNM0DDQMTLXMRPV6IwLKl3UjAowIKF8iovkrn0EwF/TX9ungpBEBRgSkU27SvSfTQbBoIkj0c51PDEy37GXjPiMCjAhItVxlGgONCDAiwEYERKgMWBAs+gYQb2BEgE0NFF4rwKYGSo64NQw0DEzVcI7q9UYElC/rRgQYEVC+RETzVz6DYBoB4I6lg5NGBBgRkE65SfeeTAfBookg0c91PjEw3bKXjfuMCDAiINVylWkMNCIgQkHgVAuLXZ9cYClKerIgWPQNIN7AiAAjAowISA+fDQMNA6PUZlcmr0YElC/rRgQYEVC+RETzVz6DYC7or+3TwSojAowISKfcpHtPpoNg0USQ6Oc6nxiYbtnLxn1GBBgRkGq5yjQGGhFgRICNCIhQGbAgWPQNIN6gEIkA17g0bdpUNBk4YKC8/fbbFWTaV9Nk0aJFqixevFiWLVsmcEjrUgAAIABJREFUy5ctV2XJkiXqfaS3cOFCmTdvnsyb6xHOeWT+vPmycMHCIA3SCQvpL1m8RJclS4R8LV2yVJXFixaXpU36IaFXvy9PHCdfYQLA/V4wf4EsWqjrkfyiS1+efMe5b+bMmTJjxgxV7r33XmlzQRtVTj31VMFITdVIyfb1hoGGgdkuY4WSfuPGjeWTTz6RX375RZWVK1YGuAA2hAXMmzVrlvzwww+qzJ49W+bOneuVH3/8UX6co0uQ7owf5Ic05Pvvv5fvvv1OFdLlXX3bm2++KUMGD1GlZ8+eUqtWrYLDq2yUJewH26KrgSgGwbAP99lnHzmo2UHl5JhjjpHOnTtLr169VOnXt58MHDjQKwMGDBCf9O7VW7p06ZKW9O3bV4YOGarK8OHDZdKkSTJ58mRVHnnkEZn00CRVHnzwQRk/frzcPe5uVUaPHi2333a7KsOGDQt01KN7D9FkyJAhMvGeiaqMGzdOOD9o0CBVbr31VrnzzjsryIQJE+SB+x9QhYWN+XYXXXSRKvvtt1/B4anzU3gv26KrgShiYDba8mrVqskpp5wi5513niodOnSQKy6/QpVu3brJBRdcIK1atVJl/N3jZfr06ap88cUX8uKLL8pTTz2lytS3pspXX32lCjbc2rVrvYXvpZdekiuvuFKViy++WHbbbTfZdtttVaF+b7LJJl7JxjeIWpqZxkAjAiIUBI5aYbX8ptfjNZ7eLAjmbXsidaIQiYBEPbuef+55VccsnusLfhNomjNnjhB0yrSQNoF3VeYvCAiEeIGueOeCIBiBMEXivQ/ngoD/fH/A3wX+tb1PjxwnL14dzpotM3+YWSHgxz1r1qyR9evLRmCE95A4zz//vCp33XWX7LLLLgXnCBoGqtUwcgcLEQPjtb35OLf33nsLDrNvW7VqVVwiFYzxYQrnVOxcsCAgK8EaAvOqzJwVEIyQjGHhenBQw06OzZo5S3AkNeG+eETA+++/LwTaNOnfv7/UqVOn4PAqG+XGiABfjYjG8WwHwbTe/u5YuuWxRo0a0rp1a+natWs56dGjh1D3fMH+q666SrhGk+7duwdBfoLRmkAChJ/nfnM9QaW2bduq0v6S9tKxY0dVevfuLY899lgQCCMYFpbHH39c7rvvPlUmTpwo2EYE0TUZNWqU3HbbbaoMHjxY2rVrpwbtCOZd2/9auf/++1WBCEDPqRIukBLkWZN7J94r99+nP498nHXWWQWHp5kOgkUDMYovl9nGwHRxLtf3QQScdNJJcu6556pyYZsLpX379qq0aNEiCKb78nzhhRfKP/7xD1VeeeUV+fjjj+Xzzz9XZfas2YJvrwl254YNG7yFcszoMVK/fn1V8Cm32GKLgsMVnw4L8XimMdCIACMCrEJGqAxYEMzb9kTqRCEGwYwI2Ng71hfE4rgRAZknOFMxtgwDIwV13swWIgamUg5zca0RAeWLjxEBNjVQ+RIRzV/ZDoK5oL+2Txe3jAgoIwaMCMiv/ZfpIFg0EST6uc42BqaLc7m+z4iA/OJJrr93Jp6XaQw0IiBCQeBMFCBLI9qgY0Gw6BtAvEEhBsGMCDAiIHZ0gI0IKA6sKdS3KEQMLDT7yIiA8qXXiAAjAsqXiGj+ynYQTCMA3LF0Mc6IACMC0i07mbwv00GwaCJI9HOdbQzMZJnLZlpGBEQ7JpfNsuFLO9MYaESAEQE2IiBCZcCIgOgbQLxBIQbBjAgwIsCIgOLAlyi8RSFioM/wztdxIwLKl2QjAowIKF8iovkr20EwF/TX9ulimREBRgSkW3YyeV+mg2DRRJDo5zrbGJjJMpfNtIwIMCIg1fKVaQw0IiBCQeBUC4tdX3wAY0RA9A0g3qAQg2BGBBgRYERAceBLFN6iEDGw0GwmIwLKl2QjAowIKF8iovkr20EwjQBwx9LFOCMCjAhIt+xk8r5MB8GiiSDRz3W2MTCTZS6baRkRUHxxumyWF9LONAYaEWBEgI0IiFAZMCIg+gYQb1CIQTAjAowIMCKgOPAlCm9RiBiYbQM+1fSNCChfko0IMCKgfImI5q9sB8Fc0F/bp4pB7nojAowIcGUhn/tMB8GiiSDRz3W2MTCfZTSVZxsRYERAKuWFazONgUYERCgInGphseuLD2CMCIi+AcQbFGIQbIcddpDOnTvLwIEDVXn11Vdlw4YNFWTFihUyb948VebOnRssrssCu5pwfv78+WnJggULZOHChapwjjyRvld+nCtzNfn/e+bNnSeaeNNz93l04XTke1933pe+W6hY0yPHZs2aVUFI6+eff5a1a9eq8s0338hrr72myv333y/16tWTTTfdVJVNNtkkLySyYaBhYDHZNtSjzTbbTJX99ttPvvzyS+8HX7VqlYp/Dhcdpmh7cAic1IRzYMrsWbNVAWtmzpypCue417fgOvd9//33qsyePVvWrFlToY1x7c4HH3wgrF2iCe3WjjvumBdMynV5xH6wLboayGYQTAv+xx6LV1bBos0331yV2rVry0UXXSSXX355Obnyyiulb9++cs0116jSp3cf6dmzpyo9evSQrl27SpcuXVIWbNUOHTpI+/btUxbeYdKkSfL000+rMnny5OA814TlwQcflPHjx8u4ceNUGTlypAwbNkwV6m2nTp3k4osvVgUMmzBhgipjx46V/v37S+/evVVB/4MGDaogt992u5oez2Hh4wceeEB4J03OP/982XbbbVXZZpttArswXnnKxrlMB8GiiyLRznk2MTAb5c6lGYul7n9wc6uttgoCtJTPsFStWlW23nprVerUqSPNmzeXVq1aqdKuXbvALwfvwtK6dWsBl7fccktVzjrrLLn77rtVAdc++ugj+eqrr1TBFlu2bJkq2Jdff/218A01Aad22WUXVfDfqlSpoubXvQe61ITz2Mo+n9RnR+fLV3VlJtP7TGOgEQFGBJSE45Tpipiv9CwIFm3jx+W+EIkAgr7PPPOMfPHFF6pgGKxcubKCLF682Bv0CQLtcYLxy5cvl3Xr1qUlBLiXLFmiCnkimKUFwDgWBNVnz5E5inBu0aJFsmTxElXIs6YHjnGO4LsvWE/avkB/vPtIj/Na0K4yx+LpaOrUqXLQQQcJI0U0wVDLBw4aBjoUifa+EDEwH+UZp6hmzZqqHHHEEYGT5vvSYI4P4zgOZvgC8pz3YQe4ENw350f5URHaAh8RwHHO++SHH36Q7777ThXujYev//73v+Xhhx9W5ZZbbpG6devmBZNyXW6MCPDViGgcz2YQjOAUgQ+fxCurBKsITGnSuHFjIeg/ePDgckLwmUA/AX1NOMd9mlxxxRUCGdC9e3dVXLCfgH9YLrvssoCQ0NLlGNefe+65qlxyySUyYsQIb4D80UcfldjRkbH/P/vss/LII49UIAgcYXDbbbfJVVddpUq/fv3kpptukltvvVUVMMwn3HfttdeqZIsjAYYMGSJhgQi4c+ydqhAk5F0gPjQZMGCAnHHGGaoce+yxAhkQrzy5QKm2j3dfvHOZDoJFAzGKL5fZxMB45YdzWnl0x+LdyzUEm8NkKbi55557SpMmTVTBjzryyCNVOe644wICoE2bNqIJ+Hj99derAs41bdpU6tevr8ruu+8uu+66qyo8l44V+LqaYP/5Omy88cYbAXnBiFVNaD+22GILVbB3d9ppJ9ltt91UIQ7RsGFDVTi3/fbbq/4oPip2NB0awwIR42sP+abxvnkhnss0BhoRYERA5CpBIVbMXOXJgmDFYRAVYhCsUaNG8tZbb3mDQwTdf/rppwqCEeELNnE8XqCKQFa625rVa2Tp0qWqkNd4QW7y5QtUcQ4iwZc2IyBWr16tCu9D8M2b9pw4RMCPcwNiwncvoxNcT19trxlzXBcvGMg92jflGL1FDjvssMDwwvgKCwZdrnAv9jmGgenWmMK6rxAxMLac5er/ratuHfTqomdXWI4++miZNm2a98OtXFGGN9RxTRLhcjwiIB5ugFHZIAIgCcDdeJj02GOPiSYE4HAwc/Xd8vkcIwK8VSISJ7IZBCOw4Qt6cDxeua1erbrsvPPOqhDkuvrqq+Xmm28uJzfeeGMQxCcwrwlB/l69eqlCIAuigGCXJp07dfb29u/YsWOQpi/ofmmHS6VFixaqMLKBgDu97DV59JFHZcqUKaq8+OKLQdCcALomw4cPFwgOTdAfBIT2TI6BYeFAvvuNngnM09tWE4KFkAVhgQgYM2aMKoxqgPR44oknVBk6ZKi0bdtWFQgCRgvEK08uuKrt490X71ymg2CRAIwizGQ2MTBe+eGcVh7dsXj3cg0kQDjIzfQ++++/vxx66KGqQJqdcsopqpx22mnCyJsLL7xQFTAyXKfdb0ZiHX744WowngB9rR1qeesnxAVTLdLxQhNsx+nTp6sCBjZo0MCbdjwd0qsfEoB4gyZ7N95bGAmrCWQ0JEPYF3W/a9eqHYwKZWRorFTbplrabWK8d8nXuUxjoBEBRgSkVZnzVQFK/bkWBCsOi6gQg2BGBJT1YjUioIzsMSKgOLCmUN+iEDEwH/aFEQEbRwcYEZDcdJZGBBQqqiWXr2wGwQhWGRFwlRgRUEYKGBGQXJ20q3KrgWxiYCI7zgX9tX28e40IKCMGjAhIzk6LV5Yqc86IAAvcW+C+hMuAEQG5NVay9bRCDIIZEWBEQGxPXCMCslX7LV00UIgYWBnjPN17jQgwIiDVsmNEQLQxNJtBMCMCyqblMSLAiIBoo0Rx5z6bGJioPdUIAHcs3r1GBBgREK985OqcEQElHATOVSGz5+SX7YunfyMCisM4KsQgmBEBRgQYEVAc+BKFtyhEDIzX9mbrnBEBRgSkWraMCIgCwvnzmM0gmBEBRgS4qUPY24gAfz20M/nTQDYxMFF76oL+2j7evUYEGBEQr3zk6lxeiQDmTWbOqG+++Ua+/fbbQFj0izk9U9nMASzcQHOuCrI9J70yYERAKkiT+WuLGQONCDAiwIiAzGNGsaVYzBiYD7vEiAAjAlItd0YE5BdVK4uB2QyCGRFgRIARAfnFh1J4eiFjYKL2VCMA3LF49xoRYERAvPKRq3N5JQImTZokJ510kpx55pnBAjznnHNOsNo1C82kshkRkF4QOFeFzJ5TuN/HiIBUkCbz1xYzBhoRYESAEQGZx4xiS7GYMTAfto8RAUYEpFrujAjIL6pWFgONCChbPNgWCy6/WLEtFpzY9850ECy/SBLdpxcyBiZqT13QX9vHu9eIACMC4pWPXJ3LNAamtFjwqFGj5IADDghWrB89erTcfffdMmHCBGEu4VQ2IwISN3a5KlD2nGh9CyMCUkGazF9bzBi45557yvvvvy8/Lf9Jl5/KFpGNDRbz/+JFi2Xu3LmqzJ8/X7hm5cqVqsz4foZ8+OGHqrz55pvy4IMPBu0MbU1Y3n33XVm1apUqK1asEJ7Nwr+aLFq0KMhX+F3c7+XLl4tPeN958+Z5hecumL9AFc757l2wYIEsW7bMmy964MybO08V9D9nzhyZMzskc+YEz5s/b75osmzpMlm9arUqn3/+hZxyyinSoEEDVWrWqJmX9WoMAzOPa6mkWMwYmA97qHat2rLvvvuqcvbZZwcjb33fB7yqUOcdBiSo+2CRT8AoFU/+P+3Zs2fLrFmzvMJ5n8ycOVPAfU04BzYvXbJUlU8++USeffZZVcaOHSu77LJLXjAp1+XGiABfjcjN8cpiYDaJAMqiFuByx+KV1Tq160iTJk1UOeqoo6Rfv34S29uc/ymLPXr0kM6dO6vStWtX6d69u1e41yekyVz/mnTs2DG47/LLLxdNOl7aUS644AJV2rZtK926dVPvIy3iGm9PfVuV119/XZ566imhA6QmI0eMlF69yoiO8B79UXbuuusuVYYNGybX9LtGl2uukWviyODBg+X222+vIMRqxt01TpXx48fLX//6V3ns0cdUGTlypPS9uq8qHTp0kO233z7neJvpIFhuEKP4nlLoGBgP59I9t/nmm0utWrWkbt265aR+/frSvHlzaXlOS1XOOOMMOfnkk72Cn9X8lOaqtDi7hVxw/gWqtGzZUo4++mg57LDDVNm78d6y0047qUIsd8iQIQIGaAIWXX3V1apceumlQZpVqlQRTbbeemupXq26KjVq1AjstD1230M02XvvvaVZs2aqHHjggUKcomHDhinJjnV2lKpVq6oCnmyyySY5x7F0yyD3ZRoDUyYCjjvuOPnggw+C4Eu60GZEQLSCz5UpsHZvZr+1BcHSRZ3M3IfxU6wY2LhxY/nPf/4ja9euVWXVz6vUIHUQoPYExgnq/Prrr17lQwL4nKKhQ4fKscceqzqlOKs4N+vXr1eFdyCY5QtGEUDzbRs2bJCff/5ZIBM0gViYNm2aKkyZt3DhwmC6PKbMCwv68AXf0GM8XZFn3/sQlGOaPjdln9t///33wfPIkya837p161T5+uuvpXXr1nLwwQergjGcD3w3DPSV3NwcL2YMzEd5xhk64YQTVMHhmjFjhvfDQlYSPNdk9qzZARmp1XuOQVb6SEmIRbDGF+wnbR8WJTo+a+Ys+eGHH1TheeCjL89ffP6FvPLKK6rce++9suuuu+YFk3JdbowI8FaJnJyoLAZmmwhItzzuvtvugX2LjRuW008/PQhEEzyKleuvv1569uwpBPw1IZh/2WWXqdKpU6eAIPARAV26dBEC/pqQJmlzjSaXdbxM2rdvrwpEAME5gneaEFCHdNQEm/WFF16Qp59+WhUC73369FGlf//+4jpRhju28HvwjYO9pAgExdVXXx2QMRAKYYGUgQwNy7hx4wJiA3IjLBMnTpSHHnpIHn74YVUmjJ8gkAGa9O3bV+rUqZNzvM10ECwngFGEDylWDIyHnVtttVUQiN5///0lVg4//HDBVgMHNWlzQZtgNhVmVAkLth+BfJ+fRcB+n332UYVzxx9/vJdgOPaYY+XQQw9VhWcec8wxgY+Nnx2WPRvtKdWrV1dl2223lZo1awZEIGRgWAi877zzzl7xkRMchwhAnz7xvQ/HDz7oYGnatGkFqV+vfoU8ujxvt912summm+Ycx+KVs0TnMo2BRgTY4sORqgCJKkixn7cgWH4tqsoaPy73hUiGGhFQ9nWMCCgjBowIcLXV9rEaKGYMzIf9YETARlLAiIDkOo4YERCLSLn/v7IYaERAGTFgRED5ka5GBCTGv0wHwXKPHsXxxGLFwHg2oBEBZcSAEQGJcSpeOarsuUxjYMpEAEMzBgwYELDOMMlTp04Npl5IBdoKMQhW2Q9j9+e3YpSK/o0ISAVpMn8txk+xYqARAWXlxYgAIwIyjxzFk2IxY2A+7AgjAowISLXcGRGQXzytLAYaEWBEgI0ISC9mkOkgWH6RJLpPL1YMjNcWGxFgREC88pGrc5nGwJSIAOZr/stf/iLMW8pCwW3atJFBgwbJv/71L1m9erUQQAlvTN3AlAZMp8DUBQhD3HKlMHtOeo2t6a0w9WZEQBhhcvu7mDHQiICysmREgBEBuUWVaD2tmDEwH3aPEQFGBKRa7owIyC9mpoqB+MdMC8iUWPjAU6ZMCdbdSfW7Z/t6mxqobKogmxqobKogmxoovzhTyE8vVgyMh7FGBBgREK985OpcXokA5iF97733grmRv/jiC3n++eeDOYRZVIIeDhg74Q0SAKMHw7VVq1aBMAdUrhRmzynMgLZ9l/S+ixEBYYTJ7e9ixkAjAsrKkhEBRgTkFlWi9bRixsB82CVGBBgRkGq5MyIgv5iZKgZ++eWXwhoWzHWPH8zCkdWqVSs4P9iIACMCYtcLMCIgvzhTyE8vVgyM1xYbEWBEQLzykatzeSUCWICRRRQJlPzyyy8yffp06dWrVzAq4MUXXxQWLgtvkAMYQZAGbqEaFunJlcLsOekFnE1vhak3IwLCCJPb38WMgUYElJUlIwKMCMgtqkTracWMgfmwe4wIMCIg1XJnREB+MTNVDGREPNPoTpo0KfCDWWAXWz7V757t640IMCLAiID8YktUnl6sGBgPY40IMCIgXvnI1bm8EQEER5wAVL/99pvMnTtXhg8fLkOGDJFHHnlElixZkhSGYcTmSmH2nMIMaNt3Se+7GBGQFMRk5SKHf+zZig0DIQI++eSTgOSF6A3LqlWrgmneGOUVK4sXL5Z58+apwnD4X3/91fs9Xn/9dbl+0PWqXHnllXLwwQdLo0aNVBkzZoxsWL9BlV/W/iILFiwIpqTDCQ8LeYbU1oR27LPPPpMPPvhAFfL8wgsvqPLaa68JPWUWLlyoCvrg2ZrMnz8/INf/97//iSaMuvv8889V4dyMGTOExTZjZfbs2YEefPlZsWKFrFtXFvgP77/55hu5+OKL5dhjj1Vlt912y0s7bhjorU5ZP1HsGJgPu6RBgwZy2mmnqdK1a9dgOhHaGk2WLVsWnGfKkbCAQ2CKr+6Dj5z3CfZ9GDfd7zlz5gjYko6AT+G8ut+cI1/gpCbg38svv6zKPffcI7vuumteMCnX5caIgKxDnfcBmcDAQl0joH79+nLKKaeownTA11xzjQwePLicMD0w0/1269ZNlS5dugiLAvsEjPPd27lz52AUBSMpwnLZZZcJ57t07qIK5y+99FJVsGvokNi8eVngP7xnlgOIG02w/5588kmZPHmyKiNGjJA+vfuogv6Ydmj06NGqQBD16NFDlSuuuEL69+8frNHIOo1hufnmm+XOO++sIKxFADZqwigVyKnY4H/s//fdd59oaxlwjO++44475hxvMx0E81Z0O+HVQDFjYLy23JW9pk2bSqwceeSR0q5duwCPwKSwtG7dWsVUsPbEE0+UQw89VA466CBVWJNwv/32U4Vz+GdM2a4J+WrWrJkqhxxyiBx33HFywgknqMIz69Sp45UddthBfFK3bl3ZfffdvbLHHnuIT/bdd99AH+gkLOSZ94nVfez/BxxwgDRp0qSC8KxatWqpUqNGDdlss81yjmPxylmic64c0rk+E1tSawRQ6desWSMEgtxaAAQNpk2bFjSy1157rRAAIaiQzGZEQHpB4ESFw84Xv14tCJYMwmT+mlLAQKZsY8o3LeDEMdoARn2FhaC2L5jEuXhEAMH8hg0bqoIhsc022wi9MDSBgJbfYGQqCs8kSKYFkzhGkPvDDz9U5emnnw6Mqr322ks0Ib84zJpgVL36yqtBQJ8Rc2Hh2eRNE67FYIT80KR9+/aBE4kjGZYHHnggIGLC5A3fDGLDFwxcuXKlsI6PJgT5aKvDRq37jUGWjzbHMDDz+JZMiqWAgfkozziABMM0IehGcDxM0rnf1OswxrjfzEUOLvswEGwGGzRZunRpQPZi02vCc9MhAbiH94Gk0ITz5CfcxrjfELTPPfecKgTBdtlll7xgUq7LjREBySBW5q/JFAYWKhFAYIkAuibgE4HsgQMHlhP8f2YGIFCtCYFtyABNsCUI8PsC9hwPEwCxv8lnp8s6qUJ+e/bsqQrPPfPMM73BOYiNu+66S5WxY8fKyJEjK9hgziYjQN7ryl6q9O7dO9AhAX1N+vTpo+aX9+BeCIrbbrtNFciFcePGVZCJEycK87n7JB4RgC38z3/+UxXSzQfxmukgWOZRorhTLHYMjNeWV6lSJQhEH3300RIrBNRPP/10Oeuss1KWU089NfD3CGJrgp8VDojH/iY47hOC+ZqfyrF99tknIEJbtGghmpx00kkCkaAJ/im2lo8oqFevnkpckJ/9998/yO9hhx0mmmAPc40mkASkTSc0TcCjnXfeuYLstNNOAjmhSe3atWXzzTePlN2YaQxMigggaIHT8OqrrwYsNg0kjSEMNA0qrDFGPUGIZDYjAoo/YB0PTO1c+t/fgmDJIEzmrykFDIQIwEH1bWvXrFUDNJUhAmhDqlcvG24Y3m+99day6aabehvoG66/wZfVILBN8EgLcnGMHvf07NKEoHq6vd0xsAhUaT36ObZo4SJvnjmPgQX5oQk9PsK9wNxvnEB60oY3yi1BvXSIAHr/3nLLLYGTj6MflsMOPcz7bbKJ8YaB4a+cm9+lgIHZLLe+tKlH4brlflP/CJyHCT73mzr/9ddfq/Ltt9/GJQKWLF4SYAP4EBZIVEZL0flHEzA/G0QAIw0cCRE76sz9/9mnn8kzzzyjCqSyEQG5wYJSfUqmMLBQiQDsj3g90iECnM3h9gS0CVIzglOTnj16qiQngXrIgQ4dOgidHDS5tENiIkAjLTjGKAONmOAYzyVo5xv90LVLV7n11ltVAZMJyN90002qDLh2gKoHdANBEIwW6NNHCPqHhWt85AXXkqdRo0apQkxG671Pr/+HHnrIK7EjAML/M6WzNirCTXNFZx1fu5at45kOgpUqlqX73sWOgfHKbdWqVQMfjcB/rBx//PEBloRHFrnfjD7Sgu0cYzQouKsFvjlGYPzwww9XhUA65329/hnlrwXMOUYnN5597rnnqnLG6WcEoxUYsRAWSBDSYESQJg0bNBRIZU3I61FHHSXHHHOMKoccfIiXRCBGgY2nBfQ5hm+o5YdzGkHAMcgMIwKSQAN6HzH1A0PLXGGmEW3Tpk3Ain/00UdJpLLxEiMC0g8ExwMpO1f8erUg2EYcyeV/pYCBRgSUEQNGBJSNEDAiIJcIU/jPKgUMzIcNZUTAxtEBRgQkZ8PaiID84GWmMNCIgLIRUEYElCcDjAhIjH9GBOQH+9xTix0D49mARgSUkQJGBCTGqXjlqLLnMo2BSY0IYFoIKj9DjOll5OT7778PehzRYyiVzYiA/BaiyhZCuz9/38+IgFSQJnPXlgIGGhFgREDsFEFGBGQOP4ohpVLAwHzYNkYEGBGQarkzIiA/iJopDDQiwIiA8GgAfhsRkNi3znQQLD9IEt2nFjsGxmuLjQgwIiBe+cjVuUxjYFJEQKYhy4iAxI1drgqUPSda38KIgEyjUX7SK0QMNCLAiAAjAvKDB6X41ELEwHzYQ0YEGBGxHIGYAAAgAElEQVSQarkzIiDaiGlEgBEBRgSk53tnOggWbSSJbu4LFQPjtcVGBBgREK985OpcpjHQiIA/pNcY5eqD23M2fp8//vGPokm2dKQ9K/aY77lcw5xjW2yxhSrx5j33pemOGxEQXcMnNuf5CoLFlt/w/ywehHHm2+ItFsxc1ZowHz9zSvo21pthHkNNWBRoyy239M5BGm+NgA3rNwgL4bpFJsP79957T5gCSBPmgGVRolq1aqmyww47SM2aNVVp0qSJ3H///eriaiy69vHHH8uMGTNUee2114K5HmvUqCGaMKeib/5e5qudO3duBTUT2E+4RsCv62W9IqTHfLTaonYcY55Hh0u53BsGVvjMkTyQLwzMZVlN5lnUIxbc1IT6x1z8bk2A8J61P1j4XBO3WLBvfZB4i/KCl+CGb40V0mS6UE3ADab48Um8xYK5h2eD3Zp88skn8re//U0VFvBkztdkdB71a4wIiCTk/Z7pQg2CsQilW58kvGcdANp9tzaA23OMoHb4evebhXeZr18TpgZibYCLL75YFc6lu5AwaxDwbE04x9zYvsU9ydeNN96YlvTt21e6d++uCvYbekGXmnCOUQGakO6IESOC9RlZDyAsLJauLXCMPfrYY4+p8uijj0p4XYDY388++6y88cYbqrD4MGsEhP2I2N/ZwNlMB8F+r5T2T041UKgYSPndZJNNVGHtNjDyhBNOKCes3xaeRz/2NzhzwQUXqHLOOecE8/yzkK4mzKmf7hoBrAPAnPqaNGrUKFjguFWrVqIJi6mffPLJquCLskaAb7HgBg0aqOsDsGZAojUCWC+BWIQmvA82nrYOQLxjLBbMQsKa4E/aGgE5rfplDzMHcGNwOxsNZTGmGQ+cAe1Y4yP2/3R1QRrpBvO5D1DSwJdjLIqabr4sCJYHwMrCI/OFgdQVyqcmBLG//PJL79tCBIQXlUz0m0UeCUb7tg8//FDGjRunynXXXRfUI19diUcEyG8ikAGxPdxj/ydo5FtECRIAx9QXdO/UqZO6sB1Oa9u2bQPnkkWYNDnvvPO897Zu3TpYMKphw4aiCYZR06ZNVcGQI/AX3nhnFv5kcU9NCLat+2WdKhA7TzzxRLA2EOsDhYUFsHzfJpvHDQPDXzmav/OFgdksm+mkjZNIsEaTJ598Mph+E+zVhDo9c+bGHvWx/xNwJ1A/f/58VQi4r127VhVwm8XLP/30U1VIm8WENVmxYkVACkMIaAKx8cMPP6gCEcBUo2HCw/3+4IMPZPz48aoMGjQoWCwunW8QtXuMCIgm5rlcF2oQjEUvhwwZosrgwYMDEkDrGECQ2icEvH2L9hIwZ61BLRDFMc61a9dOFWw0bC4WG9YEO00jH9wxnu2z8Tp37uwlIFiImPdxREd437FjRzn//PNVueiii4I8aeQEx6666iqVEIYkhpiYOHGiTJo0SRVwUVtImGD/q6++qsqUKVOE877FhCFdX3jhBVVYmHiPPfZQfQn8C3yNbOCqEQEORaK9L1QMpOxWqVJFFTqBsUjw6aefXk5YdPyII44ISAKIgrBAAjjiNLwHgwiOaz4fx/DLw+nF/o63WDABeV+HNkg8fDgfVkFQhN/T/YYIIZaFPjSpX7++dwFj8nvkkUcK6wxoAlng0wV4w/v4OuLVrl1bXUgYXx9daAI5QKfdbGBVttLMNAbaiAAbERCJCkBgnp70PokN/sf+n25FJA3AgR7JmsTr1c99gCRgqAm9fdPNlwXBom38uNznKwhGufWNVNl///3jEwGrdSJg2dJlQbCZgHNYCArFIwKmTZsm9DrShB5P1CNfXYlLBDhFe/bc60sXQwPjDMdXEww5HDZN6EmGQYjxpgkGHIakJvQM5h6maNJkz0Z7qngCxjRv3lymT59e4W0TEgErVnqDbvT65bv4HM8WLVp4dejTbSaOGwZW+MyRPJAvDMxEGcxkGue2PFftrUkvToIw1EONBOAYPfYJrGtCUJ1AvNZrn2M/Lf8pWPuL9b/CAm5DArz//vuqEMj3kQgE8uONGJgze45KApAm65KsXr06GEXGSLKwMJIr3BPW/SZYRgeMTH6bQk3LiIBIQt7vmS7UIBg9WxkRqQmjDgcOHKiOEOzXr5/45Ko+V6k93On1TjCeQFTLli1V4RydKzSBILjkkku8HSsI2NPzXxOeSwBfm56HYxABF154oSo8FzKhZ8+eqpAn3/vQ2YP8+O69+uqrhQ4wmgwdOjQYwUrgXpN7JtwTjBhg1ECsTH5ssrz11luqQBDQzviIgMcff1y1zbELGYHAqF2fPxHPR64MrmY6CPZ7pbR/cqqBQsVAyvPWW2+tCoFmguD0lo+VU089NQjWE9DX5KILL1LJVUhX8IZ7tFgRxxglEBv4j/3/kEMOCUYTaM90aW6//faiCcFxiADfSAXs0th3jP2fdgLfXEuXY+Tblyd6/McjAg444ACvLiAvSH+77bZTBd+9bt26FYT7GAGhCXqIN/NAZbAqW/dmGgONCDAiIBKOkxEBZaNILAiWU1slaw/LVxDMiICyT2pEQNkIgZVGBGStjlvC8TWQLwzMlnGebrpGBGwcHWBEQHKjhY0IiI8thX62UINgRgT0CQJzRgQ8FJADRgQUOpJEN3+FioFGBJRNYWREQHK2WLp2f2XuMyLAAveRCNxXppBr9xoRYERAdE2eijnPVxDMiICyb2FEgBEBFWulHcmlBvKFgZp9kc9jRgQYEZBq+TMiIJdIlflnFWoQzIgAIwJiRwcYEZD5um8plmmgUDHQiAAjAlK1x3J9vREBRgQYEaBMERQ7HVDs/+lWUNKgQdCmBeJYvGGP3GdTA5m5E08D+QqCGRFQ9lWMCDAiIF79tHPZ10C+MDBdmyBb9xkRYERAqmXLiIDs41M2n1CoQTAjAowIMCIgmzXf0nYaKFQMNCLAiIBU7bFcX29EgBEBRgQYEaAuDOoaWNsXvgbyFQQzIqCsbBgRYERA4aNEcecwXxiYa6M90fOMCDAiIFEZCZ83IiDa2FioQTAjAowIMCIg2tgSldwXKgYaEWBEQNjeKrTfRgQYEWBEgBEBRgRExdrx5DNfQTAjAso+iBEBRgR4qqYdzpEG8oWBhWbUGxFgRECqZdKIgByBVJYeU6hBMCMCjAgwIiBLld6SLaeBQsVAIwKMCEjVHsv19UYEGBGQVSIgdlod7f94BX6TTTaRqlWrSrVtqqmy0047SePGjVVhlXFWat9i8y1UIYCp5Sf2WLy8pXqOdHmmT3hXX5rbbLONHHvssXLmGWeqwsrlvnsTHbfFgsvZEpH9ka8gWJU/VZGaNWuqcsQRR8hXX33l1enq1atlyZIlFWT5suWyYsUKVX7++WfZsGGDN80ZM2bI6/96XZUH7n9AdtttN29dIZjv23766SeZMmWKPPH4E6q0atXKm261atXkxBNPlJYtW6pCvT7llFNUOf7442XfffcV6rgm4Nzuu+2uy+67yx577OGVnXfeWXass6MqxxxzjEybNq2COtavXy/Lli2TxYvLAv/hfbzFgpcuWSpvvPGGvPjCi6q0adPGq8NEOFaZ84aBFT5zJA/kCwMrU/aycW+7du3k1VdeVeWdd96RRYsWyaqfV6nCuVmzZqkyZ86c4F4Ns90x6rgm//vf/2TAgAHSsWNHVZ555hlZ/+t6VdauXRs8d/78+aLJj3N+lJkzZ6oyd+5c4X7aDE3envq2DB8+XJU+ffpI7dq184JJ2SgX8dI0IiCSkPd7pvMVBMNvqVKlimxddWtVTj31VLnllltUGTbsZrnxhhvl+uuvLyeUxZtuukluHnazKv3795cePXqowqK82GItzm6hyvmtz5eL212sy8UXS/v27b1yWcfLpEuXLqp07dpVLr/8crnyiitV6da1m3S8tKMqnTp1Krv3yivlSkU6dOggkLuanH/++dKtWze54oorVBk4cKAMv3W4KiNHjpT7779fHp70sCoTJkyQO+64o4I8+eST8uGHH6oydepUmTx5crAwcCwB4P7Hfn7uuedU4XkHH3ywYJdqUr1a9axgcaaDYL9XSvsnpxrIFwbGa1c5Bz5uv/32qjDl88knnyzntDinnJx22mnSpEkT2WvPvVS56MKLZNTIUaoMGjRI8N+4X5MDDjhAmjVtpkrTpk1l//33l/2b6IL/vO2226pSt27d4F1antNSNDnrzLPktFNPU+W4444Lpr/eYfsdRBOeu/fee3tlr710PaG/envUk5132lkV8sy3qVGjhip16tSRXXbepYLUq1dPgmfy3JBwjm+eTqwvUVnK1vlMY+Afclrz//9h5gDmfzXq2AB6+H+MRZ9wra9wb7755lKrVi2hsmpy6CGHyrnnnqsKgTWcOIgETSAIfM8ttOMA1aWXXir9+vVT5cgjjkz7XSwIlg/Eyvwz84WBGAU00poQ/J4+fbr3ZVevWq0GlH9a/pOsWrVKlTVr1sQlAub+OFc++ugjVZ544okgKO6r3/GIAIJjvXr1UrEGDIKQ9KWbaI0PdAepqQkO0S677Cq77qrLttW39T53s802kx122EF23HFHVSBwIBk1wSEjeBfeKkMEQKZ88skn8t5776mCo+zTYTaPGwaGv3I0f+cLA7NZNtNJm2DVp59+qgp1GvLOR7QuXLBQDagTaIcIWL58uaxcuVKVhQv9977yyivSsGFDb/2Oh72//vprQBYvWLBANAHzfeTFvHnzZN26dd4CDTF54403qtK9e/fA/kznG0TtHuqObdHVQL6CYNgY2BH4WpqcfdbZKgkQkAM33yLDbhoWBP0J/Dvh3NixY2XcuHGqQBz4AvIQjeecc46ceeaZqrRu3VouueQSr8QjAkgbokET8gNe+AiKeMd79uwpvXv3FohHTbCLfH7uBRdcEDwT21SToUOGqjpEt+PvHi/33XefPPDAA6oQmOc7hOX5556Xzz//XBUIAhYEdoH/8B4b/Pnnn1cFUqJ58+bSrFkzVbDTsoGrmQ6CRRdFop3zfGFgojJJRzDNt+MYNhFBf8jLWDn99NODcxohxjGIgHvuuUcVOjZAwB5++OGqHNTsIJUggDTYb7/9ZJ999vEG3Hequ1PQwZZOtmEB/wnok3dNTm1+akAUQHyEBeKC98Jf1YTn+jq14RsTJ/R1SCR+pqXpjvnu4/ifd/yzGtto2KChV0cNGjQIfOp01gNNVJaydT7TGGhEQImOMAgH/2N/+0gAjhsRkJjEMSIg2gZKLnKfryCYEQFlRKQRAYmnBjIiIBdIULrPyBcGZss4TzddIwI2jmgwIiCxfUk5MyIg2riZryCYEQFlxIARAWXEgBEB0caRKOc+XxiYyE4zIqCMGDAiYEuBHGCkQKIyk+vzRgSUaOA+0wUtNvAf/t+IgOScMd83MSIgyuZJbvKeryCYEQFGBMRODxRvaiAjAnKDBaX6lHxhoK/dztdxIwKMCEi17BkREG3UzFcQzIgAIwJiRwcYERBtHIly7vOFgYnaWiMCjAiIHR1gRECWUMYcwMoFmhMBWTLnw8H/2N9GBFTu+xgRkCXgKKJk84WBRgQYEWBEQBEBSYRfJV8YmIx9lMtrjAgwIiDV8mZEQISBT0TyFQQzIsCIACMCoo0dxZL7fGFgorbWiAAjAowIyAHKmANYuUBzIiBL5nxs4D/8vxEBlfs+RgTkAEQi/oh8YaARAUYEGBEQcfAokuznCwOTsY9yeY0RAUYEpFrejAiINgjmKwhmRIARAUYERBs7iiX3+cLARG2tEQFGBBgRkAOUMQewcoHmRECWzPlw8D/2txEBlfs+RgTkAEQi/oh8YaARAUYEGBEQcfAokuznCwOTsY9yeY0RAUYEpFrejAiINgjmKwhmRIARAUYERBs7iiX3+cLARG2tEQFGBBgRkAOUMQewcoHmRECWzPnYwH/4/3SJgC222EJYEZxVuDU5/vjjpV27dqq0bNlSGjVq5F2tHXBO5r0ydQ06YW4wDGdNqlSpIgRVNdn9/9j7Digpiu57/FBAcpAkUUWRIEmySJKcBAQk5yXtEnfJ6SMHyZklLBlEkiAiBgwoKkb8/kaC5LS75Azy/ufW/EZ6e1/17MTe2X1zzj3TUz2dqqtu17u3quuZZ6h79+40ePBgFpgd3tPzfPrpp9Vs9UePHg1ATZVD+CsH7OLArFmz0nPPPceiXr169Ndff2kv+dbNW2QUkZ3LeJf8rVu3WNy+fZv++ecfevjwIQtMDnno0CEW27ZtU+epqytWYsiJ4ycIAlv9+vVZgJ90+02XLh1VrFiRXnvtNS3AZVaoUaMGccBxMdEPB9ybcuXKUdWqVVlgXfHixVm8/vrr6t4hr424d+8eXb58mb1vuH/Xrl2jO3fusMB9/d///kffffcdix49emjzUJe3vkgXDtRW0aBaYRcH+qIM+nIfvXv3VvUMdc2MP/74Q9Vd1EUOFy5coBMnTrA4ffq0qt83b94kDmfPnqXffvuNxc6dO6lQoULa+v3fsf/VlrX79+9TbGwsXbxwkcXZM2fp5ImTLHBO4Czd5/PPPqfx48ezCAsLoxw5cmjP2Zf3zO59WT37dHkn6YknB+wSwZ544gnKlSsX5cuXj0WzZs1o8uTJWkyaNInMmDp1Ki1cuJCWLFnCAmUVbTEOiJNatmxJOC6HNm3aUJcuXbTo1q0b6YD2CXdMpIFzwRd9w/qywDod+vbtS4MGDaLw8HAWISEh1Lp1axbt27cnbD9w4EAWEydOpMWLF7NYunQprVy5klatWsUiMjJS3QfcCyPA5T///DOLb775hjZv3kxr1qxhgXXYngPOpUmTJoR4lgPKmD940tcTZSYeVkheZ2IXB7oqk5kyZVI6FrQsM14s/CI1bNiQWrRwdChzfjdo0EC1lxCbcACP6eo1+BP7fOWVV1iUK1uOSpQoweKll16iYsWKaZEnTx5Knz49i5w5c6oYtnHjxsQB11SnTh0WiFHz5s1L6PTKAXkAHYwD8hTHzp49uxbcPpGGbdDGw/YccE7PPvtsPCDuLlKkCAvoIbjnXEyONDwzXZWZQK/3NQemsIN6JABM3EaAp4U6Q4YMSvxC44ADGl/mRqTz95gxY5R43qFDB+JQsmTJgFZGmAAgB4hzHEqVKsWSJwi1efPmhEZoaGgoi/Lly3t8LSKC2cFYvj+mXRyIILB06dIsYMYdOXJEe7E6I+D69et09+5dLSAM6QCR+uTJRz1CjcuffPKJMgd1xqSVGPLnn39S7dq1qWDBgixg4Ol4DtsgyNm3bx8LCOPYPwcIa99//z1hEjYOI0eOJDTeOMCI2b17NxuwIZD76KOPaePGjSzee+89On/+fDzB78aNG8oIuHTpEnGAEaAzcSA8/v3333T48GEWCGh1eejPdOFAbRUNqhV2caA/y6Yn++7Xrx8dPXKUxfHjx5URAJ7kACMVdZQDjACYfEZj0LiMQHzLli0sZs+erYRC3fVYGQEPHjxwmI/RMRTDAEbA8b+Pszhz5oylEbD/i/1akbJ///4qONSdc1JKt3r2BRUJJNOTtUsES5MmDRUtWpTKlCnDAgI2YjEOY8eOVQbchAkTyAgIWRCho6KiWOC/PXs6euKbvyGad+rUiY35EAfCBIBZwAHb9unTh42xEHuFherFfIj84N3+/fqzQNvGyggYMGAAK+RD4MexcW4ccP3gKZ0RABNm+fLlLFasWEGrV69mBXsI+cj/ZcuWxcO7775LX3/9NYsvvvhC8b+uXfnOO+/Qjh07WKyKWkWdO3cmdELh8OKLL/qlfehrESyZUpDtl20XB7pqB0BwRtnlAB0KncuaNm0aB4g1EZdwHUORBj1sxowZLMCr0I2qV6/OAkaATsRGOvhcB5wTOq1ygKBet25dQtzPAeZErVq1WMC0gBCPDrocsO/8+fOzgEEIgwLnxgHbZsuWjQXWwVxAJxUO6EzM5QWMALOp4/yN84HBoDsm+MZVmQn0el9zoBgBKewX5QNdiHA88ygA429PzwfCOUhS1xsiIjyCEGBymDZtGkVERKjGGRpoZlSoUCGglRGjAGAAgHg4VK5UWdt4RcMWDT4YHxxA7J7mMYgTPWhkRIDt7RivTsAuEQzlp2zZsixatWplWa6sjAD04vQEV69cJYhZHD7//HPVGNMZAeP+O057D9CTtkqVKtoeB+nSptPWQTQaPvjgA5UXqGdmQFDXff4VwWJi2F74c+fOJRiBHJD/v/76azwx39mb99jRY3TgwAEWP/74I0VHRxNMGSMg9ENA5EwApF27ek17PBgBp06dYkVGCI8D+g/Q5qGn/JaQ7YQDdaUvuNLt4sCElLFA/gf1iBPykYae89HRMdr6e+6stRFg1bse5iKEJQ4QpBCs6fIhQUaAhgNhBOiuF+aF1Tnv37+fpkyZwgLiHIJT3TknpXQxAoKL68xna5cIBgEBYhZGPHJo27YtjRo1isXo0aO1RgDEa45HkIZe7mYDwPkbHaYg9kNQ5tCtazdWUIfIjm0h1kO09wQQ5HXo17efx0YAzoWL+5AG48LKRAC3QfDngM4pEPzXrl3LAiMFuO1gBHz11VcsPvvsM9q6dStt2rSJxZZ3ttD27dtZ4HjOER2ISc0oVrSYX7jY1yKYuW7K78DkgF0c6KodkCN7Dm0Pe3Sig8ZlNr4gmKOTHTrDcoCoDo2LA3gVRgA3ghxp0Is4U8KZxgnfzjTESpwJgDToWuh85hzVYP5u1LCRdkQ8RgBhe+5akQZhXTfqDOnoua8D2nBc51unHgcDwHnt5m9cNzdq/vlCz2vPB+eBa9GNULDSClyVJX+t9zUHihEgRkA8U8DTwitGgGM0gxgBgWlIBPNR7BLBxAhwDEe0eriLEeB4lYgYAcHMMIn/3O3iQE/bN/7aToyARyMaxAhIWMckMQISP79ZnaFdIpgYAY9MA50JgHQxAhzGgBgBVrVY1nmTA3ZxoKt2nBgBjtceiRHgWitwVZb8tV6MgGQq3Pu6QBlHAJiXPT2WGAFiBHjTMEhO29olgokR4PrhLkaAGAHJiYvsula7ONDT9o2/thMjQIwAd8uWGAF2sZZvjmuXCCZGgBgBxtEBMiLAN/VZ9uJ+DtjFga6etWIEiBFgHB1g1WnQVVny13oxAsQI8MlwO7P4b/ztaeEVI0CMAPebA8lzC7tEMDECxAgwviZIXg2UPPknMVy1XRzoafvGX9uJESBGgLtlS4yAxMBgnp+DXSKYGAFiBIgR4Hm9lS19lwN2caCrZ60YAWIEiBHgu3qu3ZMEgAkb/uuKsLxZbxT+zcue7leMADECtJVeVsTJAbs4UIwAMQLECIhTFeWHTTlgFwd62r7x13ZiBIgR4G7ZEiPAJtLy0WHtEsHECBAjQIwAH1Vi2Y1XOWAXB7p61ooRIEaAGAFeVe2EbSwBoBgB5gmDZbLghJUJCLkyWXDCeCYx/8suDhQjQIwAMQISMzMkn3OziwNdBYKBXi9GgBgB7pY5MQKCmyftEsHECBAjQIyA4OaOpHL2dnGgq2etGAFiBCQ7I+DatWv0ySefqNnmZ8yYQfPnz6eDBw/+yzX37t2j8+fP044dO2jp0qW0YMECioqKou+//57u3LlDDx8+/Pe/CV2QADBhoq8rwvJ0PUYA/Oc//9HCar+YERxiIgfM4I2JcvuG9WUxbNgwmjhhIosJEybQuHHjaNx/eYSFhVHrN1uzaNWqFTVt2pReb/J6XLz+OjVv3pzeeOMNFpjJvdZrtVjUrVtXCe6tW7cmDn379qUJ4yewwHWMGjWKRo4YyeLVV1/1+PVOYgQklGUS/r/kxIHPPPMM1axZk0XXrl3p77//1mbcrVu3KDYmNh6uX79OeE5wuHv3LmG7mzcd7503f9+4cYOwPQc8h4oWLRpvMnPnCKbq1avTf8f+lwUmfCtRogTlz5+fRfny5eNyhYE7WrRoQb1796b+/fqzGDp0KHtMnMucOXPo0KFDdPLESRaLFi2iBg0asMBxR44cSZMmTmKBZ/OaNWtYvPvuu3Tx4kVCfhtx+/Ztunz5Ml2KvcTiypUrhPLPAZMFx8bGv9/OMjBkyBCPeczqGeNqnXCgtop6vCI5caCr8hXo9YMHD1Z1F/WXQ0xMTDzOddbBs2fO0t/H/mbhauLdDz/8kAZHDGbRpUsXeuqpp7T1e8yYMfTg/gMWeA5Ycf6F8xfoyJEjLE6ePKn4S1eQD3x1gBCncIiIiKBcuXJpzznQ99WfxxMjQFdCPE8PJAfaJYKlT5+eXnnlFUJ8w6Fzp86EOI3D8OHDaezYsfFitMmTJ9Py5ctp9erVLGa8NYPQVuAQHh5OPXr0oG7duunRtRt1Y9C9e3fVTgvtE0osQkMJcSOH0NBQtW2vXr2IA84ppHsIi549exLOm7sepA0aNIhtN6r2ZP/+NGDAABo4YCCLSZMmUeTSSBbLli1T+atrA27YsIE2v705Hnbv3k2YC4DDxx9/TJs3b6YN6zew2LhxI7399tssVq5cqfKByz+klS1b1i9c7Ov3Y3vOGElvy+TAga6ey2hDlCpZigXKNHizUaNGcVCjRg3KkSMHpU+XnkXjRo1p1sxZLMaPH0/QsKBHcahcuTIVLVKUR9GiKkZGnMwBsVLaJ9OywPniWhB3cmjYoCHhujhUqlSJcubMSZkyZmKBfeti73z58lGePHlYDRHnCxE+c+bMLLJly0bQMAoVKqTF84WeJzOwDY7LAeeC/WbJkoXFk2me9AuPuSqHVut9zYEpzp07R+iNDbG0TJkyVLx4cVq4cOG/DIcg5IsvvlAPaxR+iBhNmjRR/zl+/Lhlo/3fnZgWxAiw1wiACfD444+zSJkypRLedIUQFRVkyAGEgWBMCfoQ9U0YPWo028BEo3P06NE0a9YsZURB8DIDjczt27ez2LJlC61atUqZWStWrPj3O2plFK1ft57QQOKweNFimjJlCovp06fT4sWLKTIykgXOZ9OmTc7oPxoAACAASURBVCzWrl1Lc+fOZYNVBLB16tTxmFhEBDORiQ9+JicOhFkH04wDzC0IMbqPMgIgDJtw4/oN1gSAIASzGI1LCM4c7ty+ozsceRMsp06dmgoWLEiFCxdmAdPDyBXGZZiSqGc6DrRKRwNl165d9Pvvv7NYFrmMOnbsyAKmJc4ZAhyH+vXrEwQ4DjDoL1y4EC8vHzx4oPLdfM+cvzE6AEYBBxgBuH+cwYM0cLZVXvhrnXBgvNvsdUJy4kB/lUtP94sOA2aD1PkbBinqanR0NIszp8/QsWPHWMAIgCmo+6xbu47q1avHokqVKgTRUHdN6OgAbuCAY/7zzz+qkxA6CpkREx1Df/75JwsY0Vbn/M0336i2FdpXZkCoFCNAd7cl3VUOBJIDvWnb6OpkQtIzZsyo6juEJw4hISEqhkMcZwZEbtR7c/tj4sSJ5BSpOTMAsRwEbg7QAiDKQ3jnAIOgc+fOLGBWQpTXCdF9evdR+8b+zUBHD+wb++CADm0dOjhe9Wr+RtsReYFOGxyGDR0WL++ceQkDYeDAgcoMgCFgBtqe6CzCYcmSJaoTJpfHSNvyzhZ6//3342HvB3vpo48+YrFnzx6C2I+Y1V3A/IGIyeUB0qq+WlX7/EhIWdX9x9cimCteSE7rkwMH6sqVM91K46pYsSLVrl2bEIsZUbVqVSVgp02bljhAM503bx6LqVOnqg60eNMDB3R4K1asmBacAeBMezr304T6wsGVEdCgfgOqVq0aiwoVKqi2lk6wz5UzFxUoUIAFDILcuXOr7dFeMwOiPJ5THCDWQ8xHnMyhQP4CrNjPGQDONNxvXAc6OXNInSq1X3jMWd48+fY1B6aASLNv3z4lsOJBAzHXaAR8+eWXSijFw/+dd95RowUgZuKBBUcYAYq7HzECxAgw9zgRIyBhZUJEMHfZxvX/kxMHihHgMAbECHAYOmIEuOaH5PCP5MSBnjS8/bmNGAGPTAExAhLWDpQRAb5n5UByoBgBDmNAjIC4ZoAYAa75z9cimO+ZJHj3mBw40FVbTowAxwgBMQIcxkCyMALQs+/MmTN0+PBh2rt3r+otYDQC0JMa7vj69evVf9BDaf/+/coJxpC7EydOuM16YgS4fti5Iitv1suIAMcIARkR4HbVTZIbJCcOFCNAjADnaAB8ixGQJCnN7YtKThzoTdvJH9uKESBGgLvlSowAtynO5QaB5EAxAsQIMI8GwG8xAlxrI2IEuKQyj/+QHDjQ1bNWjAAxAowjA5KFEeBkDLwa4PPPP1fDXYxGAIbf1qpVi7799ls1fPn+/ftKPMB7AfF+qb/++su5C/YbrybAKwZgNhw9elShX79+iW6ohStySErrxQgQI4CtrMk8MTlwoBgBYgSIEZDMic7i8pMDBya2tpwYAWIEuFsmxQiwIDEvV/mDA/FaRYyex+t0EQd/8MEH9Pzzzwc8DpZXAzleEySvBnK8KkheDeQlWSTRzZMyB7p61ooRIEaAGAH168d5NRDmD8BkFejB4HzvJ8wAGAGYdAjvQrb6wARAowcNV+f7r/C+K1eVUda7dsY9zSMxAsQIsKqzyXWdrvGTlDhQjAAxAsQISK4M5/q6kwMHetpu8td2YgSIEeBu2RIjwDWXefoPf3Dgr7/+qibUxQS3iIPRiQ6ivLv33dv/ixEgRoBxvgAxAjxliaS9XVLmQFccKkaAGAFiBJiMAEymiokhzII/0jFxhjndTI/oCYFGECZQXLp0qULjxo0D3gByVfmT03oxAsQIMNdT+U1qwlRuVFRS4kAxAsQIECNA2E6XA7oAMClxYGJr64kRIEaAu2VSjAAdg3mf7g8OxIh4vFJ3zZo1KgYeO3YsQXBy9757+38xAsQIECPAe45I6ntIyhzoikPFCBAjQIwAkxHA9YbF634QGCZkRABHmGjEuqqMst6/IwJSpkxJOsAo0OHZZ5+lmjVrsoDBM3ToUBo3bhwLTAg8fPhwFpiMeu6cuWo0Cl5NZQYa0Nu3b2exZcsWWrVqFa1YsSIOoqKi1NwWmOeCQ2RkJM2aNYvFnDlzaPHixf+aV04Ty/nt3DfmzjBj9erVhFdqzZw5k0WdOnU8Lv94SKFHEYYXy8e3OaBr/CQlDixatCi1aOF40Ju/8Y7SkydPajMVpq5RRHYu37hxg+7fu8/i7t27hAmorl65yuL27dtqpBlGm5nx22+/0auvvko5cuRgkTNnTtKhQIECVKVKFXrttddYYLLyrVu3sli0aBFVqlSJwHUcnnvuOSpUqBALHO/TTz+lY8eOsXj77bdp8ODBLNBbEMfLmjUri0aNGqmReBiNZwa4DK8eMH/wrL5y5Qpdir3E49IlZYCh7JuB0Xy4fxgByAF8bsdzWjjQfJd99zs5cKAdZdbqmCNHjiTwIIebN28qzkXd5gCBERPscjh9+jThnb+6z8qVKxW/gmPNePnllylt2rTa+o12HuYX4QDeAF+Y+dz5G9fx159/scCrU8A5us+BAwfY9hracUOGDKFcuXJpz9nqHgTbOjECdCXE+/RAcKBdcwRkypSJGjZsSG3btmXRs2dPigiPYIF2y4gRI2jUqFFxMH78eDXaATEaB8Ry0As4YNvQ0FDq0aMHi27dulGXLl1YdO3aVW3Tq2cv4oDX/2DfHFy9Gqhzp87UsWNHFjgn8N+okaNYYN2gQYO0GDhwIOmAOQLMsa/zN+JRxJ2ILzls3ryZ3nvvvXhAr3+j+G9cxjrEr4ifdeCOhbTly5fT1KlT1bwGOG8zoBP4g1dljgDvOc7VHpIyB7oqk3ny5KFy5cqxQFxYu3Ztql+vfhxUrVqVsmXLRiibHF5//XVCXMnhrbfeInBZ69atWVSvXp3QgY9DkSJFCDE93rLCAbES2nEcEFdjZNobb7zBon79+lStajUW6ByO2BvPEw5YhxicQ/78+Sl37tyqrYb2mhmIf9OnT88ic+bMhPibywukIX7Oly+fW0AeYb9G8d+4nDp1ar/wmKtyaLXe1xyYwkkIuopvniMAwgIa/wmdI8C5f+O3GAH+E/mdheexxx7TivlYpwMMgDRp0rBkhsKHhzsCVw5oIMIEQOOOA+47esJwmDhxohLdEZxy2LhxI+3YsYMFBD00QNFIMgINGzRysC0HiHLvvPMOC/wfoj9H3EhbsmQJQXzjgHWoN7Nnz2ZRr149j4lFRDAjk/h2OTlwYOnSpdmgCsEW6i+EJd0HohQnRsEI+OfBPywgCGH99WvXWWAd9ssBYvqkSZOof//+LBBMhYeHs4CxiPrNBUZIO3ToUDzhG/cfOHXqlBrBxnEG0sA3O3fuZLFv3z6KjYmlO3fusIA499NPP7HYtm2batRxDUmkoaHIGZpI2717txL8zfcOz2sYMVcuX2GB6+XEPKQ5Bb2HDx8SB/C483kTyG/hQPNd9t3v5MCBgSyrCTkW6pFTJDd/Q8iH4XrhwgUW4OsTJ06wOHv2rKURABFHZzoiuENHEd35w7TEvGEcwK1OExGivhkXL16kI4ePsMC1WBkBn332GdvJBO3OsNAwZRjrzjkppYsR4DvOM+8pEBxolxGA+g4TICwsjAXaWoMGDuKhEbcRCyJm07WXIBgjHuKATjY4F/CJDhDeOeD/ISEh1COkB4tevXqxJgCMARgBEN86d+7MomuXruwxcR4wS9ChbczoMSzQJsX+OeDYERERykiAYWAGxHRd3Dhv3jzV0c0Y4xqXEevCDDADb2P4+OOPWaDdiPuji3NhPuiA+BiGwNq1a1k0a9ZM+/zwhot9LYKZ67/81o+O96UeaBcHuip7EJLRyZkDOkzACKhbp24c4L/ONhPXwRad7swdRp2/oSFh3lSO45BWo0YNVlCHyP7MM89Q8eLFqUSJEiwgumMUGAeI8bgW1FMOMAlerfIqi7JlyyrjI126dMThqaeeshTjEcPpgHPVdUTGOryqHufNAfpG3rx5WcDg4YB8gBHA5RHSxAggUsJDp06dVCE+fPgwXb9+XQ1xdDr5aLi7+xEjwP9GgK4iIR0mgI4MsR4PWq5yI61BgwaExhsHZ+8AzgRAGoI13HsOEPyWLVum7ZWwadMm1gSAOWBlBEAk0zVQt27RC3rYJwja2RvD/I2Gk66BhHV4YGJUAQcxAtxljMD8XxcAogwlFQ58uczLKnhCAGUGBCmPjQCmRz9ELRgBEPnx3HAXeLagjoEbOEydMpWmT5/OAnXwyy+/pJ9//pnF+XPntYUKIx9++eUXVuSC8PXzTz8TRitwwEgdmAC6DwwRCHQc8FoqNOrQ8ODQrm07glnAAYEexDfzBwYN8h2jAji4MgJgJOg+/x1rz8g+MQJ0d8T79OTAgbq2j13pqEe6D/jTygg4e+asGsWFkVxmnDt3ztIIgOjE8QzSUqVKZdlORI9ZjHziAI6EkagzQ5URcOQIHWGAa7AyAj7d9ynB5OUA4S179uzatq1d99cfxxUjQFdjvE8PBAfaJYKh12qHDh0Ioz85DByg762OjhfcNhDFIUgjRuOwcsVKmj9/Pgv0hnVlBOgMAqcRYG7HOn97ZQR07ao1JrBfjIzgOAhp4YPClVkAw8CMPn36qNGgGJHKAZysG6WOmBKivVH8Ny6vW7uOzX90WrEyAhDn6u7PggULtDEwjADE1rrOdK1atvILF4sR4D3HudpDUuZAV89jiOcYTc4BPf9r1apFeKuDETAC0Itcp7uhLqBjGgeYqOBW3agoGAE6cRtmAGLGkiVLssB6rsc+0hBHQUxv2rQpC1wflwdIgxEAU5kbaYC0p7LpjYC8efMpQV5nBCAfdfcI6zBCAm8f4VCubDk+r/LkZU0AGANiBBClQI+j8+fPK7Fk3bp1hIIOQQiNdPT+xDAy9P7HAw6Ef/DgQcLDG8IuXsWC/7j7ESNAjACzGSBGQMLKhIhg7rKN6/8nJw4UI8BhDIgR4DAGxAhwzQ/J4R/JiQN1QYZd6WIEPDIFxAhIWDtQjADfs3IgOVCMAIcxIEZAXENAjADX/CdGgO+5z7nH5MCBrtp5YgQ4jAExAhwjKdAxxlWZCfR6X3NgCpgAeBi/+eabVKZMGeXyvPDCC+qdUeih/cUXXxB6KsJlx7sF0SMcTgycYryX1Kr3jpNczN9iBLh+2HlbsHTOpIwIePSaIBkRYK6ZyfN3cuJAMQLECDCODBAjIHlynvmqkxMHetu28vX2YgSIEeBumRIjwMxg3v8OJAeKESBGgIwI8EwH8bUI5j1zJJ09JAcOdPWsFSNAjADja4KShRGA1wl8+OGH6rUseM0CHGlM6oP3nO/fv1+9exRDjDFRK4aCwQDAUJbvvvtODf3F6x/c/YgR4NkD0BWBGdeLERClXjMkrwZyt3Ymv/8nJw4UI0CMADECkh/Hubri5MSBxnZSYlgWI0CMAHfLoRgBrhjN/fWB5EAxAsQIECPAMx1EjAD3uS2hWyQHDnT1rBUjQIyAZGcEJJQgfPk/MQI8ewC6IjDjejECxAjwZZ2Vffk2B+ziQDECxAgQI8C3dVn25lkO2MWBxnZSYlgWI0CMAHfLoRgBnnFOYtlKjAAxAsQI8EwHESMgsbCYd+dhFwe6etaKESBGgBgB3tXtBG0tAaBnD0BXBGZcL0aAGAEJqozyJ1tywC4OFCNAjAAxAmyp8nJQUw7YxYHGdlJiWBYjQIwAd8uhGAEmMgmyn3aJYDJZcCiFhoYSJhbv2rUrde7cmQXW6SYplsmCF6oJhGWy4CAjnUR2unZxoKtnrRgBYgSIERAAskhOAeBjjz1GVnBFSrr1Vvt0tU63T6SnS5dOzQherVo14tCxY0caPXo0i1GjRtHQoUNpyOAhLLDdpImTWOC1VFFRUYQJq3VYv249cVi7dq3aduWKlWQE9rd+/XrC64E4YAZ3zBPAYdOmTbRo0SKaN28eC6yLjIzksTSSlixeQosXL2bRpEkTSvVEKhZPPPGEKi+6eySTBQeAoAJwCLs4sFKlSjRo4CAW06ZNI7wGTve5eeMmXbx4MR5u3LhBDx8+ZIFXx92+fZtu3bzF4tq1a3Tp0iUWOJdvv/1WzVODuWrMwKvrvvryKxbY7vDhw3T8+HEWuI4b12+wgFCOSSuPHTvG4sSJE3Tm9BkWOGcMr71+/ToLXO/VK1dZ4HgLFy5Ur+fDK/rM2Lp1K/3+2+8sjh49ql7VRw+JjHjw4AHh/ly7eo0FrlWX/7gObK/7oAxbPWt0HOZtunCg7o4EV7pdHOht+fP19t4YAeAbHRdduHBBzeEFDuaA/E+ZMqUWqNu6a239Zmv1ulC8MtSMjz/6mGJjY+ne3XssoqOj6eiRoyxcTRb80Ucf0dAhQ1l069aNnnrqKe05664lGNNx7+QTvDlglwiG+gGRe8iQISwGDx5MEeERLAYNGkRhYWEU2ic0DhDzbd68mfbs2cNi8aLF7LFwDuHh4dSzZ08K6R7CQifGO9NR57t15YH9hoWGsejTp4+a97B7t+7EoUePHgTBnwOMBPTmHzVyFAvkE4wGDsi/4cOHE2JlDoiPZ86cyWL27Nnq9cyYu5ED2o5zZs+JB8Syn3/2OYu9e/eqGHb+vPnEQRf/Ih3xLe67mf+dv/GM8Ae3yoiA4OU945nbxYGuymTBggWperXqPKpXpxo1asRDhfIVKEOGDKTrgNumTRvasWMHizVr1iieadu2LXGADleoUCEWmM/1pZdeopIlSrKAqWEUtY3LuXPnptdee42aNG7Conbt2vTqq6+yKFu2rJpPNu2TaYlD1qxZKc/TeXjkyUN5LJAxQ0Ytb+D5Bc7HM4dDo0aN6Nlnno0HHA/b6oDzzZqFB/jGVZkJ9Hpfc2AKY8UM1HKwBoCc6OGqAOiIAenc/oxpVvvG/3RBnHPfVtvr1qHHCHpJoIHGAY2cgQMHshgwYAChgcU1gJCGBhDERg6zZs0iCPpotHBYvWq1agShF4IZkUsjafmy5bR8eVzAFFi7dp0yA2AImGE1fwDOZf78+YTGFwcYAStWrNAC82jo0LJFS2W4wHQxAxUc9093f0QECxRL+fc4dnEgGhVjxoxhgflfIB7pPhCUsd4MpOs+MAgwofydO3dYQBQ378/5G2LS/fv3dbu2TP/nwT9KiDf2fjcux8TEECbG4gCTwErM14n8SMd2OH8IdBywHtfEAaI78svTD2fGYJ83b95kTQl1vleuemwEjBs3zi/PIB33OdOFAz0tIYlrO7s40FmOEsu3N0YA+IvjGaSBx8C7HNcgbczoMdp2hqu8afp6U9X5Ap0tzNi2bRuBX3XHxToYlxxOnTqlnhe6kgqxsX///izat29PaL+6OveksF6MAF0JCY50u0Sw7NmzK+GdE6GRhhhNZxIgHoQwHhISEgcwD95//306cOAAC4jTEMI4tGvXTgk75n3++7t7iLZnPgShLl26sD36EcNCzIfwzgFiPq4FZgGHPr37sNthX/369VMiFPKKA2Jk7J8DuAv5jPrLYfLkyTRjxgwWiJEh9us6mGFuR04cQxz61Vdfsfj444+VqaAT/OfOmcvGv4iJcS7onLJr1y4Wbdu09QsX+1oECw7GSHpnaRcHunr+Q0iGQM6hZs2aShivUqUKGVGmTBllBOg0uQ4dOrAmKdoz0KXAV+ggygFifPHixbUoUaIE6ZA/X351XjApzMiZM6cyNBo2bEgcateqTVWrVmVRrlw5ZQSgLnLIkjkL5cqViwUMCAjzefPmZQGzQnePsB04cvXq1SxgEBcuXDgeEDOar9/5G8eDQYBnI4d0adNpz0d3nv5O9zUHihGQIuGv6TGK9M5lVzdcjIBHPSPECHCYA2IEJL1GjbtXZJcIJkbAFYIhIEaAY8QCjBhPRwSIEeBurZf/G3PALg501WYL9HoxAh6ZAmIEJCweQd2RT/DmgF0imBgBDmNAjACHMSBGQPBySLCfuV0c6Kp9J0aAwxgQI8BhDIgR4CemCdYA0Cn+G79dkYoYAWIEmEcGiBHgJ2IJot3axYFiBIgRYBzRIEZAEJFGEjtVuzjQVZst0OvFCBAjwN0yJ0ZAcJOhXSKYGAFiBBhHB4gRENw8EsxnbxcHunrWihEgRoBxZIAYAX5imWANAI0GgHPZFamIESBGgBgBfiKSIN6tXRwoRoAYAWIEBDFxJKFTt4sDXbXZAr1ejAAxAtwtc2IEBDcR2iWCiREgRoAYAcHNHUnl7O3iQFfPWjECxAgQIyAALBOsAaBT/Dd+uyIVMQLECBAjIACkEmSHsIsDxQgQI0CMgCAjiyR6unZxoKs2W6DXixEgRoC7ZU6MgOAmRbtEMDECxAgQIyC4uSOpnL1dHOjqWStGgBgBYgQEgGWCNQA0GgDOZVekIkaAGAFiBASAVILsEHZxoBgBYgSIERBkZJFET9cuDnTVZgv0ejECxAhwt8yJERDcpGiXCCZGgBgBYgQEN3cklbO3iwNdPWvFCBAjQIyAALBMsAaATvHf+O2KVPBfnRlg3I8ny7r9It3qvFKnTk2ZMmViUbBgQerRowcNHTqUxaBBg6hv374swsLCqFevXtSzZ08WmCx4xowZLObMmaNmAccM6hxWrFhBCxcuZLFo0SKKjIykZcuWxQG2WbduHW3YsIHFxo0badOmTSyw3fz582n27NkscEzsnwOE/6ioKFq1ahWLNm3aUJYsWVhgBnPMPK+7f5j9vGXLlnT06NEA1FQ5hL9ywC4OrFGjBo0fP57F4sWL6eLFi9pLvnHjBl24cCEebly/QQ8fPmRx//59NTlvbGwsccCkvdHR0Szw/zt37tC9e/fcBrbDpMC6iXBxzPPnz7NAHly+fJmuXr3qNrAd8ki3b6y/ffs2C5wz8uvBgwcssO7u3btaYL+3bt2Kh5s3b5IOuEZdHmEdzkX3mThxIqVJk4YFnjF4pul4zJt04UDdHQmudLs40Juy549tvTUCdFwDHoPpp6v7I0eO9Lh+Nm7cWLW50O4y45133lF8Dr7iAM5H+4WDTBYskwUHF4t5drZ2iWAQOBCjjR49mgVitCGDh7AIDw9XcV337t3JiMGDB9P7779PBw4cYIGYr3nz5iwQy3Tr1k3FnIg7zQgJCVHr8R8OXbp0oc6dO7PAvsJCw1hgsuA+ffpYAv/hgPh36JChhLzigBhZt+9+/frRqFGjaOzYsSwmT57MxsfIw1mzZqn4F+10DmiPDRw4MB6WL19OX3/9NYt9+/apmHnevHnEAXE5jssB8fj27dvVvcf9N6Ndu3YeP1+snrNPPvkkFS1alJYuXepZ5ZOtEkUO2MWBVmUL65599lmqVasWi5o1a1KVKlWocuXKcVC6dGlKnz690m6g35gBjkJd44D2EtajTcUBxytWrJgWL730EpUoUYJF/vz5CZoSh1y5chH0gIYNHcK/+bt27dpUrWo1FuXKlaOsWbMS6iKHLJmzEPbPIXfu3IQYTocMGTJoeSNfvnw0d+5cxTvgHjPA1y+++GI84Fi4PxyQN0899RTlyJGDRbp06bTn46os+Wu9rzkwhR2MYGcAaCW4++umebJfnKeV0O/NdTz//PPUoEEDFi1atFANxClTphAHNHI6dOjAomOHjtSpUye2UQaiQ+AJAZ0DGjZLlizRAoI8dz5Imz59umogmbeHMQATQCf2b968mUDCHLDdggULWBMA54LzhdjPAQYAtofRwAENcTRkODz33HMEEU1XZkQEs4OxfH9MuziwXr162jK9Zs0agkij++iMgGtXr2mFeojxBw8epM8++4zFn3/+qRXbsa2VqH727Fk6c+YMC6w7d+6cVpDHfiGUcbA6pk50c6Zb7RfHwnqcFwfsA+aHTpjHtqdPn2Zx4sQJQl7+/vvvcQCxDQbBP//8wwIiIe45B5gWVkYAgkM0NDmgsffEE09oeUzHbwlJFw7U1dDgSreLAxNSxgL5H1dGAOqmk1/M3+AUnZEKvjh+/DgdO3aMRf/+/T2unwiUp06dygLtLhxbxzm4niNHjrA4efKkMjp1JXnPnj2E8+bQvn17ypYtm8fXFMh77u2xUHfkE7w5YJcIBrEDHbZ0nUEQo0WER7CAyAxhvmvXrnEAgwAisE5sHjNmDJUvX54FRqhif5zgjjSI+RD7ddCZAEjHufbp3YcF9h0+KJwiIiJYQLDXdWiDyA/zY9iwYSwGDBigzBbEeWbguNgOZgAHiPkzZ85kgbgT4jtnAiANnffefPPNeIDA/8MPP7D4cv+XqjMbOr1xQBtv2rRpLLBf8LGubY974C3Pcdv7WgQLXhYJ7jO3iwO5MmVMK1SoECFO5gBxvEKFClS2bNk4KF68uBKZH3/8ceIAPjHHZs7fn376qdLM6tatSxzAndDsOBQuXJisjIACBQqwHX7RERgxmpURUKdOHapRvQYL5AE6s0Kr4gAjQCf0wxxAOw1GAgfUb+P9MC6/8MILykzRxcHgKuSHGTgXXac1CP14LnKmBdJgHhjPITEs+5oDxQh47DHVc9FfvRc9LTROI8DsLDp/e3O+LxV/idArnQMaXBMmTKC33nqLBYwAbjuktW3bVhkEHTt2JA6jRo5SLj6cfDMg4sMg0PX6R48InBcH9KKAaG9uIKGnGgT5t99+m8U7m9+hLVu2sICA76kRsHr1amUA6I7bN6wvlSpVigXcTBCWrtyICBbcjR/n2dslgjWo34Bt8CMIwEgcCNG6j84IQM9xXU91iNoIFHbv3s0CjUHjq2qMyzACIOijlyiHkydOKqELYpcZEMZhEmB7DhDUOBPAKNZz27lKg8BvJc5hPXctSEPjxmrbM6fPsIIehL7Dhw/TL7/8Qj/99FMcoLEJI0D3uXXzFmsCQKxzZQSgZwaMSw7ouZEqVSotj+n4LSHpwoG6uxlc6XZxYELKWCD/460RwJl4SIMYD174448/WEAQ9PQ6a9aoybbF0D5bsngJXTivNwJiY2JZEwDmgBgBMiIguFjMs7O1SwSD4IEe7VwchTQYARD22Wk+XgAAIABJREFUOUDgxkgAsyiPmNDKCMA+S5YsyaJSpUrKCAAXcYARYCX2W60L6R5CvXs/ejWtcRmCPMR83cj3/v36xxud4BytgP1YGgH9B1iaCDACkCccvDECYGo0a9YsHtCr39wudP7Ga4Iwqp0zAZA2a+Ys1uyFCQwjYO/evbR//34WXbt09fj5YvVc8rUI5lkNlq28zQG7ONCqbGHd84Wep/r167OAEQBhvkyZMnGATp0QjDkTAGkwUXUdMr788kvFcRDeOaD3PcwJDhDGYULoRgTACMicOTMLxFEujYAaNdR/8D8jvDUCYADozstK/ypSpAh9++23dO3aNRbgOi4v8jydhzUsYGKkTZtWGQEwRjhkSK8foeCqLPlrva85UIwAMQLiCPtiBDiMATECvH3My/ZWOWCXCCZGgMMYECPAUTrFCLCqpbLOnzlgFwf6q3Hu6X7FCHg0OkCMADEC/Mk5iWXfdolgYgQ4jAExAhwdRsQISCyMkPzOwy4OdNVOEyPA8aogNSLAZAA4zQAxAhLWTnNV1jxdL0ZACu9ugDev1PH0pnmynYwIiDsfgIwIcJR76Q2bNBpMdolgYgSIEWCsQWIEGHNDlgOZA3ZxoCftMX9uI0aAGAHuli/UHfkEbw7YJYKJESBGgHM0AL7FCAheDgn2M7eLA109a8UIECPAODJARgT4iWnsDADFCEhB8mogx6uC5NVAfqrgsluXOWAXB4oRIEaAsXCKEWDMDVkOZA7YxYGuAsFArxcjQIwAd8ucGAGBZCrfH8suEUyMADECxAjwfX2WPbqfA3ZxoKtnrRgBYgSIEeB+fXZ7CzsDQDECxAhwvrtfjAC3q65s4KMcsIsDxQgQI8BYhMUIMOaGLAcyB+ziQFeBYKDXixEgRoC7ZU6MgEAyle+PZZcIJkaAGAFiBPi+Psse3c8BuzjQ1bNWjAAxAsQIcL8+u72FnQGgGAFiBIgR4HaVlQ18nAN2caAYAWIEGIuyGAHG3JDlQOaAXRzoKhAM9HoxAsQIcLfMiREQSKby/bHsEsHECBAjQIwA39dn2aP7OWAXB7p61ooRIEaAGAHu12e3twjWAFBnIvznP/8hHbCNK+LRrdcdz5mu2w6zbr/44otUulRpFs2aNqOBAwayCA8PpxEjRtDoUaNZDBgwgLp168aie7fu1COkB/XowWP8+PG0csVKFsuWLaO5c+fSrFmzWGCOgOnTprPAugULFtCiRYviYMmSJbRmzRpat24dC0wIvPntzSzWr1+v9jln9hzisHjxYopaGcVi1apVhH07DQfz95gxY6jFGy1YYFb6jBkzasuMzBHgNt0kyg3s4sAmjZsQ6hqHLVu20KVLl7T5dfv2bbp8+XI8xMbGUnR0NIu//vqLUFcmT5rM4tNPP6W7d++yuHXrFl24cIHOnnWI9+bvM2fO0JnTGpw5Q5gQ+ML5CywuXrzInq+6jovRahts7wnUvi9GUzQD7O/smbMszp07R8jLy5cus8C1nDp5isUff/xBO3bsoA0bNsTB7t276cqVK9p76o0RAL596aWXWDz//POE55DuGeVNunCg9nYG1Qq7ONCbsuePbV0ZAeATHWeAY65cvsICXHPw4EH64osvWHTp0sXj+lmyZEnq2KEji2HDhtGJEyfo/v37LGJiYujo0aMsTp06pZ4FuoK8Z88e6t+/P4v27dtTtmzZPL4mf9xbf+1TjABdCQmOdH+KYIgPdfForly5CDHc5MmTWYwePZoGRwxmMXDgQOrevTuBN4wYNGgQvffee/TVl1+xQEz57LPPsgCPdO7cmfqG9WWBWBLrOeAccD4h3UNYYNtePXuxwGTBw4cPZ2NcxL641pCQEBa9evWiiIgIGjZ0GAvwU88ePVn06dOHBg8eTMOHDWcxYcIEmjVzFovZs2ertvTSJUuJQ1hoGFWrWi0epk2bRihvHL799luKiopSsS5iaDNwzOnTp7NYuGAh7du3j7795lsWPXv2pFSpUrF44oknyFNtxNcTZQYHYyS9s/QnB3rz3LUyAmrVqkWlSpWiYsWKxQHinQwZMlCa1GlYjBw5ko3pEOsdOnSI0HapVq0ai7Ivl1V6HjQ9M4oWLarOp0yZMsShYMGClDFDRhZ4FlSvXp0aNGjAAloU1nMoX748ZcqUiVI9kYpF5syZCXEah5w5cyqNK126dMQBnKG7f4ULFyZoBmYtwPkbGmKVV6rEQ8ECBSllypQsUqdOTTDIuXNFGu6r7nzsSvc1B6awg16CMQDUNa5QuB5//HEWWIcGmVO4N3/7qxChEjZr1ixOY83YcENDb+rUqSwmTpxIaNj169ePR99+1LdvXy202/Xrp44HYZ7DihUrCIL+lClTWMx4awbNmTOHBSo/GjALF8adYBgCJPaLhg6HtWvXKsEeor0ZWId96o6Jfa9cuZKFKyMA+xw6dCgLmCxPPfWUlnhATC1btlRBtB11V47pmxywiwObN2tOKNscdu7caSka37t3j27evBkPEOt1ws6BAwcUV7Rq1Yo44PVcug+EJAjj2L8n0JkTSI+JjiEIUjpAfPMHcB06cwHHg2h/7do1FrExsQSzgMOvv/5KCNoQyBsxf/58lXe6PPbGCICZVKFCBRYlSpQgNFb88YwTDtTdzeBKt4sD/VEmvdmnlREAzgVfQCDnAC67fv06C/DEBx98QDB4OYCPPT3vvHnzUsWKFVm0bt2a/vzzT9bchekL7j127BiL06dPixGQIoXL+yJGQHBxnfls/SmCIebUxaR4dkKYhzjMAVw0ZMgQFogLIY4bY0ksQzRH21FnOEKgzp49O4tChQpRx44dtfEkBHXOBEBa165dqWfPXtS7t6OXv/kbJoCuUxqMALST0DmNA/IIJgMHCNwwAnQxXP9+/bXHxTli37o8xrnoOsMhbkTntsjISBaIHYsXLx4PE8ZPUHwMTjbjxx9/JMSr5tjZ+XvunLkqLkdsbgZi4P379xP2wQE6Qfr06VmkTZtWaSOePH98LYKZ66b8DkwO+JMDPSlXzm2sjIDXXnuNIL6Dt4yA4A5hnBO2kTZp0iRtpqIt1KZNG7YthTYWBH6uXiMNcVbZsmUJwjyHZwo+oz0ncDLMh/r167OA6aEzJ8qVK6fEfN1zBnmBZw0HGAGowzqTELqpWSt1/obh8v777xM6GHKYN3ceey24p877a/6GKQkjIE+ePCxgpJi3sfu3rzlQjIAENLpx01EQuV4WYgTENQXECFipGlZWIwLECNA+E5PNCrtEMDECHCMXxAhwVDUxApIN5SS6C7WLA+1uxJuPL0bAI1NAjADXJgDKjxgBiY7O3Dohf4pgYgQ4jAExAhymgBgBblVN+XOAcsCfHGhuY7nzW4wAhzEgRoDDGBAjwE+EEIwBoBgB/zdCQEYEqFECMiLAT+SQTHZrFweKESBGgLGKiRFgzA1ZDmQO2MWB7gSFgfivGAFiBLhbzsQICCRT+f5Y/hTBxAgQI8DYg1iMAN/XX9mj9zngTw5093lq/L8YAWIEGEcHiBHgfV1n9xCMAaAYAWIEGF8TJEYAW7UlMYE5YBcHihEgRoCxiIoRYMwNWQ5kDtjFgcagLzEsixEgRoC75VCMgEAyle+P5U8RTIwAMQLECPB9nZU9+jYH/MmB7j5Pjf8XI0CMADECfFvX2b0FYwAoRoAYAWIEsNVZEj3IAbs4UIwAMQKMxVWMAGNuyHIgc8AuDjQGfYlhWYwAMQLcLYdiBASSqXx/LH+KYGIEiBEgRoDv66zs0bc54E8OdPd5avy/GAFiBCQ7IwATE37yySdqUlVMBoPJBQ8ePPhvjf/7779pz549ajKZefPmqVnr0Rt67969dPz4ccuJvf7diWkhGANAMQLECBAjwFSRk8jP5MSBYgSIEWCstmIEGHMj+S4nJw40Bn2JYVmMADEC3C2HYgT4nqsDyYH+FMHECBAjQIwA3/NDcthjUuFAd5+nxv+LESBGQLIzAs6dO0fTpk2jN95449/ZqTFjvPPz6aef0sCBA+mVV15RM0g3b96cgOnTp9P3339PN2/edP41wd9JyQhAowsTBusAA0EHI/mYl7GNbkZupGMm8qxZs7LADOZvvvkmde/enUV4eDiNHz+exdgxY9X9tpr0V7euf//+NGjQIC1QZlavXs1ixYoV9NZbb9GUKVNYwKSCEaUDyuyiRYviAIYV9hu1MorFmjVraMOGDSywDqbYnNlzWOBYy5cvZ7Fy5Upav349YcJgDgsWLKBx48axCA0NVTOYm8uD8zdmYW/ZsiUdPXo0wfVN/midA8mJA8HzujK/e/duunLlijaz7t+/T7dv346H8+fP059//sli//791KdPH/V8wbHNePvtty2PFxMTQ9i/DhcuXCAdoi86RP/oaP4b+9bh4sWL5A+ocz1/gS4wwPGQ/2iMc4iNjdXmw6//71fFmxEREWQEOBV5p/vg+a3Lg8uXLxPu+cN/HrKIjIykChUqsChRogQ9+eST5OQtX34LB+rupufpyYkDfVkWfbGvhBgBp0+dJg7gtuvXr7M4e/Ys7dq1S8v34GJPzz9XrlxUqlQpFogP/vjjD7p37x4L8A06GHHAZMHYTvd5//33qW/fvizatWun2sOeXlMwbSdGgK6EeJ4eSA70pxGA+DB16tQs8uXLR4j/EPNzABcNGTKEBWK7kJAQ6tq1axwg/d1336XPP/+chTOmyZEjh4ptjN+FChWiTp06kS6m7Nmjp1qP/5jRpUsX6tWrl2pfoo1pRs+ePdX54pzN6N27N40aNUpNuo26ZAbyyLyN8zeOOXjwYBo+fDgLxMHO/5q/sS32PWTwEBaIy2fNmsUCHdGWLFlCkUsjWSA/XnzxxXjAtf35x58sfvjhB1q1apXq6IkY2gwcc/r0t1ggBkb7/scff2SBfMicOTOLDBkyKM3EE85Fu7Jo0aK0dOlSzyu7bMnmQFLhQFflSqfJIf3555+n+vUdYrj5+7XXXqMiRYrQc889FwcFChSgLFmyEMo1h8mTJ7P5jcRjx44R2i6VK1dm8fLLL5PR2DMuI84qW7YslStXjgW0wLRp07LInj270nQbNGhAHGrXqq3WV6tWLd43jpcxY0Z64oknWGTKlIkQp3EA/6dJk4bdTrc/Zzrq/YcffqjyDPlmBjipSZMm8VC4cGFtWxf7zpkzJxnFf+MyrtNVeQr0el9zYAoIDvv27aPt27erh0yNGjXUw8BZamEEjBkzRj3wNm/eTEeOHFEAYUBE+Oeff5x/TfA3HkyeZhwqKsR3T2BV+V3tF+u5c3a1T26bhKShMQeh39hoMi5XrFiR2rZty6Jjx46qYaUT5RFIoSHEAY0pNMrwEOcAU8goNBmX0YCEwD1x4kQWaFRAmOeAhzqMAhAmh9mzZ6vyiYYQB2xvBoQqiPUrlq9gERUVxZoSMCsg5s+dO5dmzZzFAuvMjSbnb5ARrhH74LB27VratGkTCxgeefPmZcsayo2IYAmmmQT/Mdg4MCH8oftPmzZtaOfOnSzA9VevXqWHDx9qAb43A+INTGEOMBfAR/Xq1WOBuqD7QBCCcI79c4DQpTUIzl+wFPJ15oAz3RsTQGdMIB37VQYFTAoTIJDBCMA94ABhXifa//LLLyoQfv3118kIcP2JEyd0Waye4c5rNn/DeMAz/s7tOyzAw3gOcRAjQJvliXJFcuJAHTfalW5lBMCIA2ecPXOWBfhAZwScPHlStW/QVuEATvb0mhGI6DqiVKpUiX777TftMwQch3PjgLjCygjAs6tHjx4s0EECwbin1xRM2yGGko9vcyCQHOhPIwDCD0QeDhCKEach1uIALoLIzQHxJMR1cwczCNvbtm1TbxbA2wXMgBEA45ADBBoYCwMGDGABI6BDhw4sOnfuTGFhYWycitgV52o2D5y/cQ24xhEjRrBAnAvRngOOOXLkSEKnOQ64FrNZ4vwNY0DF0OERFMEA8TNiXQ6InxFbLlm8hAU6/0HQMmPo0KH0+2+/s/j+u+8tjYCZM2fSpImTWOCZAvMHZgIHHBdCJAcIbRDgPOFcX4tgvmWR4N5bUuFAq3IFzS5VqlSsUQoDFUJ/w4YNWcAIeOGFFyh//vxxAIMV/Gaue87f6JCl+6BDBEw8aK8c0NkK8RSHkiVLqg7cMAs44Lx0pnC2bNmoZs2a8URzp5Bet07deAaA0xQoX768amvp9g0DMHfu3CzwXELd12mn6dOn1+YjOqR/d/A7QizMAboangtmoNOKrkzgXHDvkFcckoURgEb3mTNn6PDhw+p1PwgOIGg6PxCH8HBC74GvvvrKmezVd1IyAnSFy9t0GAEQ/jlHDWkgJDRIOEDIRyODa8whDdt069aNBRoqEI84EwBpaAzq9jts2DCaNGkSTZ06lQV68+t60UNUsjIC0AiCsO8udMdDOkR69IbgACEfx0RDiAPWYcQAh4ULFtKyyGXaa8UoAZ0Yi9ECeMjoyo8YAV5RD7txsHGgrmwkJL1d23YEcZ4DGvVoCOqMAHrIZp/qqfrNN98QB5Rz9HaoXbs2C4y80X1wXyD0nzp1igXEMZ0RgHQrMd/VaAGrbV2tc2kEWIxQcGUEQJzncOjQIRV81qlTh4wAn+MVfroPhH6zAeD8jePcuHGDbt26xQIjrjgTAGlooCJgS0iZdPc/woG6u+l5enLiQHfLm7//nyAj4OxZgvFpRmxMrKURgHYOglAOdWrX8bh+InjS9TRDQAojQPeByanjdPA2yqLus/PdnfGESKcwiREOYgTock7SXeVAIDnQn0YAhBROdEdasWLFCAItZwIgzZURAGHc3MsdncG2bt1KH3/8MYvQPqGsIAShCMYERHKI4xxgBLRv354FRH3EqloToWdP1QkFHVHMwDFx3ohZOQwcMJDtKIfOczim1WgCbAthjwO4CucL84RDQowAtLs4wAiFuGfG4IjBio/ByWZ89913lkYAOqZNmDCBBcwKKyNg+LDhhBEfHBDjQoz15NkqRoArJvN8fVLhQKtyBQEaAjZ6pXMoWqQoawLAHIDuhhEDZsEYnTcRl+jEb9Qj3QdGAPQ47JtDpYqVWBMAxgDirNKlSyszoEyZMvG+cV6oZxzQkQPHM3YcMy7XrevaCODyD2lZMmfR5gWMAOibunuUMUNGbe98GBE//fSTts27ZcuWeM8nPK9eLvOy9niujIBMGTNpt9Vdg7/Tfc2BKZyFE+4KSB1DYcxGAEYEwDnHiAC8kgSNeAgWnowGwPHECEjhsmCJERB3ZIAYAY4yIyKYk7F8/x0sHOjNQ0aMAMcrf8QIcNQfMQJ8zyPBvMfkwIHe8Kc/thUj4JHRK0aA69gAZVBGBPiPZQPBgWIEOHqLihEQ1xAQI8A1//laBPMfkwTvnoOdA63aaWIEOIwBMQIcXCNGAFGCjAD0Aq9ataoattK6dWvlpONdnRiS7IkZIEaA64edGAFiBHAPMzEC/Ne4smr8JCYO5MpFQtPECBAjwFiDxAgw5oYsJwcOTChXBup/YgSIEeBuWRMjwH9cHQgOFCNAjAAZESAjAvzHYt7tOdg50Op5KkaAGAHG8iFGQAKMAEwCiWF/eGUJ3gmHiWwwpA5DjWEGYGSA1QevFfj111/VpGXOd7g3btzYZY94440yLqMSezI/ALbBtlaw2i+2M56Hv5fFCBAjgCtjYgRYsY1363SNn8TGgVy5SGiaGAFiBBhriRgBxtyQ5eTAgQnlykD9T4wAMQLcLWtiBPiPq/3BgXj9LiZWxasQEQePHTtWvUrC3fuekP/Lq4Ec8w1gjgDzK4Gcv+XVQI7XBMmrgfzHI8G852DnQCuehJYnrwZKpeZ4klcDpVDzFVjNESCvBiJSkwWCFPDuMPT+x/uR8W51NeFNRIR6TZAV4eF9oB988IEayop32AF4T6FVRbVah0psJdhbrbMyAVztF+utzsvX68QIECOAK1NiBFixjXfrdI0fiKWJiQO5cpHQNDECxAgw1hIxAoy5IcvJgQMTypWB+p8YAWIEuFvWxAjwH1f7gwPRGQ7zdeAd8YiB8f5lf01CKEaAGAHGeQJkjgD/cUVS3XOwc6DV8xRanhgBYgQ4y4iMCEjAiACI/w8ePFATSIL00MMfE87gXXatWrVScwZYkSG2hRmAHhGYXwDAZLbOm+DutyvBXoyAMJW/MlmwY2JhmSzYqnbKOmMO6Bo/iY0D3eVM4//FCBAjwFjmxQgw5oYsJwcONPJhYlgWI0CMAHfLoRgB/uNqf3Ag4ubo6Gg6fvy4ioHROQ6TTrp73xPyfzECxAgQI8B//JAc9hzsHGjFk2IEyKuBjOVDjIAEGAF3795VowKccwFgXgAMcRw1ahR16dKFMOO1ux80YnEjUCE5GG8St8xtk9A0bn9W5+Lcr247FKJ0adOxyJAhA+XMmVMNAUVPbjMKFSqknQ0cM4Bj2E69uvVYQNDr17cfj379lBkAw4VD37C+FBoayqJv3740ZMgQGjZsGIuRI0fSmNFjWOC+Tps2jWa8NYPF/PnzadmyZSwiIyNp0aJFtGghDwj6GFbLYfXq1Wqf2AeLpZEUyQDnsmLFChbYj/NVWHgdFofZs2YTB0xsvHjxYjUE2Pk6LOM3rmHLO1tYzJs3T80EnyN7DuKAMoMeRTDU5OPbHNA1fvzJgTpe8Vd6p46d6JNPPmHx9ddfq1e93b93nzg4nwHmXD9x/AR98cUXLHbs2KF4pm2btsRhy5Yt5t39+xuj0M6ePUsnT5xkAXP5wvkLPC5cUKPXMIJNBzVh8MVoMn/r/u9W+oWLdJHDxYvxjuc8fkx0DF25fIWuXrnKAs9eCPcc/vjjD+rWrRvVqV0nDkJCQpT48G+mmhawLxyXQ2xsrDrW7du3iQO487War7GoUKECpUuXzi9Ch4yKMt1EH/5MDhzoL271dL/Dhw1XnWXQYcYMvHoTAiLLJRcu0qVLl+j6tess0DZHe2LMmDEsqlWr5nH9xIjVNGnSsChdujT99utv2lJ57do1OnP6DIsLFy7Q/fv3tdtu376d8Azj8Prrr1PmzJk9viZP758d24kRoC0iXq8IBAf6c44A1IGCBQuyQFw5YsQImjFjBouxY8aq0f4Y8W/GgAEDVDyJGNEIxIpr166lbVu3sejcuTNly5qNxXPPPUc9evSgoUOGsujdqzch1uWA1/wgvsX8XRywbadOnVigrYR39OuOi/0Zr9G4jHxBR8hJEyexwLadO3VmgVcS4ZzNeev8jf0i7uTgjJ91MWu7du0od27H3AvGb5wPXgHEAe127A+vfuYwc8ZMmjRpEgucI7b/8ccfWaAslSxZkkXhwoXVs8MT7pTJgr2mOJc7CHYOtCpX6CyMtkvaJ9OyKFq0KDVs2JBFzZo1qUCBApQjR444QH2DNlP4hcIsUHd1H7TTwFPVqlZjUa5sOSpWtBiL4sWLU6lSpQhtLg4F8hcgGMMcoE0ifmvSuAkLjFqrXr06i/Lly1OWLFnUyAqMrjAjS+Ys8fROp/6JvEuVKpX2zS64NxkzZGSB5xf05/PnzrPYsX0HDRs6LB6qVKmibRf+awTkzUf5GGTKlInVqV1pw1Zl0Nt1vubAFBBbzp8/Tz///DOtW7dOTQqM9xceOXJEBSAopIcOHSK8JxtpP/zwgwouhg8fTlOmTKFz587pyrc2HY1YZCIqZMqUKeMAaVjnbUYFanuYANmzZ2eBgo8HYdmyZVnUq1eP2rdvzwKNJzQOBg8ezAIP9/79+7NAQ6NXr16EdyRyQKOGm6gIaTge7j8aJBwmTphIkyfHfW2P8/fUKVNp5syZNHv2bBYLFyzkhfrISDVsFo3J9evXs9i0cRNt3ryZxcaNG1VjJp4JsDSSlixeokR5CPNmLF2yVHs+S5YsUY0x3bWggTR9+nQWaGSjUaWMDZgbJqxcsZK9Rlz73DlzCQE6Xp/FAeVJjAAttXi0wi4ODBRHGY/TrWs3+uqrr1iA29EA5ERfpOkEmmNHj9HHH3/MAsINzEPOkETanvf3aO8Z7gvEfvSi4wAxCcKRDm4J9xaGQaD2A8EP+Q/xj8PtW7fV6/lgyJhx7Ngx9dqB2rVrkxF4FQHyTve5dfMWxcTEsIDIiJ6Md+7cYbF2zVrCfD8c0ICEEW4se75aFiNAdzc9T09OHOircuir/QwaOEi9YvPUqUc9453L4D/wAkw5DjAOIaxzQHsdbXSnyGT+rlSxksf1E+12BHMcEJjiVSi6D4wLGLwcwLW65wz2B+O4TZs2LBo0aEAI2nx1XxLzfsQI0JUuz9MDyYH+NAKeeuopevHFF1lUqlSJRo8ezXZsQmcndPIyit7GZbTXMD+gOSZFp0DES1FRUSxatWzFClEQpyCq9e7dW3UsxH7MCO0TytZ1cACMAMTAOCcOffr0IcTRHNAusoqvEQub+dL5G9cPcRyd3jhgNL5zLgLzNzpPhoWFsXE7rgUxN0RDDogjV61apUwXxMpmYN/ITzMG9B9An332GQuMTEGHOO54SEM8j2cIB6yHKPfTTz+xgFFSuXJlFnhGpE2b1iOu9rUI5jljJL0tkwoHWj27oTGiDKGjEgfoLjojoEaNGspsQzvDCGiAJUqUYHU+6H/oiKr7IHaDiaerK6VLlSYYZxyKFCmijov6xOGZZ55RcwFkzZo13nfevHkJxkajRo1Y1K1Tl3C9HNDRC4Yz1/5DGoyAPHnysIABAbEfnUk44P7o7h9G0e3Zs0dp0WjfmrFr1y5Wk0NMrNsnjACcE/KDQ+ZMmeNo1EbN2i692tccmAImACb+ffPNNwluCwrMCy+8QG+88YZ6QKB3MxoDEB+bNm1KzZs3V//FQwCvCIJA5O5HjACHMSBGwKMe/OjxL0aAGAHucokv/m8XB+oeTP5MFyNAP0IgUIK/7jhiBKTQNtaMdUKMAF+wXtx9JCcONJalxLAsRsAjU0CMgIRxoBgBcfnLF78CyYFiBDh6qYp+9DEqAAAgAElEQVQRELcznxgBrvnP1yKYL7gjqewjqXCgVbtOjACHKSBGgINrxAggSoGhyB9++KES/dHDecKECcr9hcMPtxevkXj77bdVz2k40njFCQTb77//Ps4rg9whQjECxAgw994XI8AxEkJGBLjDJL75r10caNVY8dc6MQLECDDWGhkRYMyN5LucnDjQX9zq6X7FCBAjwN2yI0aA77k6kBwoRoAYAdyIfjECxAjwPbMlfI9JhQOtnqdiBIgRYCwfYgQY5ghIOFV4/08xAsQIECOAfwWSGAHe80sw7AEcaHwYBWpZjAAxAoz1Q4wAY27IciBzwC4ODBTXJvQ4YgSIEZDQsuL8nxgBgWQq3x9LjAAxAsQIkFcD+Z5ZgmeP/uRA53OS+xYjQIwAY7kQI0CMAK/FOJkj4NF8ATJHgGPOAJkjIHgaI3adqV0imBgBYgQYy7wYAcbckOVA5oBdHGgMAhLDshgBYgS4Ww7FCAgkU/n+WP4UwWSOAMecATJHgGO+AJkjwPf1V/bofQ74kwOtnqdiBIgRYCwfYgSIESBGQHh4nImDZbJgx7wFMlmw9w962YM+B+wSwcQIECPAWCrFCDDmhiwHMgfs4kBjEJAYlsUIECPA3XIoRkAgmcr3x/KnCCZGgBgBxgmDxQjwff2VPXqfA/7kQKvnqRgBYgQYy4cYATYaAbgRqJAcjDfJvIz/czNNIw03FLNRYzIZDpilnpsl3JmWPn16ypAhA4uMGTMSB0xciJmsORQtWpSqVKnCzrqNmbibNWtGXbp0YdG9e3caOHAgRUQ4GjXm70GDBhE3tBBp/fr1o9A+oYQeERzwHwj+HIYOHUpjxowhBBocJk6cqOaQmDJlSrzvqVOnEnrDz5w5k8W8efNo8eLFLDD3xJo1a2jdunUs1q/nX6WDdMxZgTkGzK8bwm/sV4slSwmCPwecJ8537ty5LDCJtg7YBvNpcPtF2qpVq9S8G5h7w4wFCxbQa6+9pmaCxyz0ZpQuXVpN3H306FHvn8SyB9tywC4RrFu3bnTgqwMsfvzxR7p8+bKaAB6TwJtx//59Nr/++usv2r17N4vNmzfT2LFjtTy2d+9edp9IvHfvHp05c4aOHz/OAusuXLighW5SXrvToy9GE4voaLp06ZK6B7gPZty6dYv++ecfFseOHVPPETxXjMDz5e+//9bmMfYZGxPLAueC9Xfu3GEBrm7atCkLcFiGDBm9NtrNz3/8lsmCtbczqFbYxYFcmbIzbcCAAaqOop6acfLkScIk4rGxsSyuXLlC169dZwFOQHsMbToOaJt6et1oh6O9zaFkyZL066+/asvitWvX6OyZsyxcTRa8detWateuHYuGDRtSpkyZPL4mT/PCju1Qd+QTvDngTxHMlREwcuRIxQvgBjNGjRpFYWFhLBBXDhs2jEaMGBEHaN/Nnz9fG+8gzkXszSFfvnzUq1cvwjlxQPzapk0bFh07diRw5+CIwSxCQ0PZ2BptImd8zcXASEN8jX1zQCzunEuRi4PDw8OpU6dOLLp27Up9+/bVxu6u5ghArLthwwYW2Dfy0wzkwwcffMBi165dan7IhQsXEodZs2bR5MmPRv0blxEf7/9iP/30008sJk2apPQPPGfMKFOmjCoPnnCnTBYcvLxnPHN/cqBVuULbBWXIqfuZv4sVK0ZoS3CoXr065cqVK55GmD17dipXrhy9+uqrLFauXGm89DjLx44eo7Zt21LFihVZlCpViooUKcICGiM0IvyHQ8GCBSlLliws8uTJQzVr1qRGDRuxqFOnTpxY0hhXVqhQQbW1uPYf0jJnzkzYP4ecOXMqnVan4+L+6O4fdNb3d79P0Bw4QIfgNLt69epp94nzxT3F5MkccC26c02ZMiU99thj2n3rrsPbdF9zYIo4JTJAP4wBIDLRCFcZhMZEtmzZWKCAocK89NJLLPDwKV++PAtUwqpVq1K1atVYQNioW7duPLRq1Yp69OjBAg0ciPloGHBAg2PIkCEssA7bcg0RZxrW68Adz5mGxtzo0aO1gBGgA4wATCqtA9cwcqZNmzaN3nrrLRazZ8+mFStW0OrVq1lAzOcqONLQCMX6ZcuWxQHMgaioKC2WLlmqxH40aMxwNmx1JgLEfF2DDOYE1uuOvWXLFoIAysEprr3yyivEAQTcsmVLEiMgQGTlp8MYOdAV5/lyPQKgb77+hgUa9E7xFwKwGToj4IcfflBGHOqbGag/CCZ09f6rr77S5rAyAk6fiSeOOcUyGAEQyXRwJfjrtvM23eq42LdOeI+JiVHXotv+xo0b2rw6cuSI4oWXX36ZjHDFFTB7Ll+6zAIiI9bfvXuXxTvvvEMIxjk0adJENQZ9WXad+xIjQFsMgmqFXRzoLEeJ5RvtxJ9//pkFBPWLFy7GMwWdJiFE9Zs3brI4ffq0akvp2k1WwZGrvDG22c3LxYsXJwT5ug8mJDx96jSL8+fPKwNYt+2OHTu04h4ERwRtrs49KawXI0BXQoIj3Z8iGIyAwoULs0D8ACEbcRwHxKM9e/ZkASMAsRzac0agbYfyqIsZIZ5B3OGQI0cOCgkJoeHDh7Po3bs3awLAHEC7A4L90CFDWUBwR3uXA64RsbO5g53zNzrLcZ3okIZ8wLWOHzeeBeJsXBMHHBf71sXt48ePV/Es4k8z0IkMRujOnTtZ4DqRn2a0bt06TlxsjpMRd0Oo5IDObjA9OCBm//zzzwntfw6I92vVqsWiUqVKSoj1hIt9LYIFB2MkvbP0JwdalStXRgDE9fr167OARpg1a1ZKnTp1HMAIQKcoLhZC2rZt2+jhw4csELu1aNGCoE9ygMBv7hCa0N/58+dnOzCjUzPEb+idDeo3YFG7dm1LIwCdpiGEc8D+c+fOzQL8hA7bOhPBygh47rnnaPv27fS///2PxaeffkrofGgG4mBdmcB54FyRVxxwv1OlSsUC26L9q9u3v9J9zYG2GwHuZlT6dOkJgj8HuDnoMa0T+6u8ou+dD6G/QYMG1KhRIxbNmjZTQgsKlBE9QnqwQj4aVBDc0WBAI4nDmNFj2F4Q6BmBhhEaC2g0cIAZoGtMoHFkZTKMHDFS9dJFTw4OViYBGo86UQ/mAHoMoCcAhymTpxBGDXBAzxSIiBDQOaCHvbn3ivM3GiRoJJkbOM79ac2FJUu1vfphDHDmgvMY69auIwj6HEBC6LmhOy6I7JNPPmGxadMmeuONN5QphYeOGZUqVhIjIAm0icAH7nKfL/6vjIBvvqFvGECMsjQC7vEjAg4ePMj2JkIPI4xwQV3SiVE4D90HRgDELKfwb/72xghQPfItTARvzACdkI/0mOgYtmcvevzCCMB/dKMcXBkBzZs3J/TGNQJpaGjqPsoIYEYfQGiEYGdlBCAo1Y1o86coJ0aA7m4GV7pdHOgLHvXlPtCR5Pvvv2fxyy+/KD5wCv/mb2UE3LxJNxmAHzdu3MiOwMSIR/QE8+V1OPeFHnWujIBTp04RB1dGwLvvvksY1cYBXIfeb87zSMrfqDvyCd4c8KcI9lQ2vRGA2BhCNQRnDhDCdR3bIIAj9jML1DAFMJKAG3WENMTduroI0wJtUsTKHHr3SoARoBnxhPPlxHikOTvpOYV/83f/fv0JJgQHGAy4Xi6mRxryV5eHOK6nRgDiUcSOutG3Id1DCPlpRos3WsS7Z857iDY6DAAu5kYa2u1cOUEa4u7PPvuMNQFgDEyfNp3Qq5hD5cqVCW9g0JULq3Rfi2DByyLBfeb+5ECr8uOtEYDOBmYRG0bAm2++yRqoMP927dzFmgAwBw4fPqw0H65Hf0LSrEyB/Pnyxxu94HzrCTRUGAE608PKCMBzBPvhzF2kZczgPyMApsqhQ4dYwJhEG9GMNq3baLnGlRGQLWu2OKaP0QSCQSBGgIc85E0AKEaAwxgQI+DRsFYxAjysiLKZbTngDQdaNXJcrRMjwDFHgBgBjqIvRoBtFJDsD2wXB7riyECvFyPgkSkgRkAKbcBqLJeoO/IJ3hzwpwgmRoDjVWhiBDhMATECgpcnkvKZ+5MDjc9K87IYAY7Xn4sR4GhriRFg8xwB5gqakN9iBIgR4BwJ4PwWIyApNxeS5rXZJYKJESBGgLFGiRFgzA1ZDmQO2MWBCWlnBvI/YgSIEeBueRMjIJBM5ftj+VMEEyNAjADjqAAxAnxff2WP3ueAPznQ6nkqRoAYAcbyIUaAGAH/vgNLXg306DVB8mqgOep1JvJqIO8f9rIHPgfsEsHECBAjwFgixQgw5oYsBzIH7OJAYxCQGJbFCBAjwN1yKEZAIJnK98fypwgmRoAYAWIE+L7Oyh59mwP+5ECr56kYAWIEGMuHGAFiBIgRwMwTIEaAGAG+feTL3sw5YJcIJkaAGAHGsihGgDE3ZDmQOWAXBxqDgMSwLEaAGAHulkMxAgLJVL4/lj9FMDECxAgQI8D3dVb26Nsc8CcHWj1PxQgQI8BYPsQIECNAjAAxAtgJg2WyYN8+9GVvcXPALhFMjAAxAowlUYwAY27IciBzwC4ONAYBiWFZjAAxAtwth2IEBJKpfH8sf4pgYgSIESBGgO/rrOzRtzngTw60ep6KESBGgLF8iBGQCI0AzMKcN29eevbZZ1lghuxXKr/C4tVXXyXMdF2vbj0WTZs2pdatW7No06YNdejQgTp17MSic+fO1LVr13gIDQ2liPAIFoMHD6bhw4bTiOEjWIwcOZKset9jvQ6jRo2i0aNGsxgzZgyNGzeOJoyfwGLcf8fR2DFjWWBbK0ydMpXmzp2rxZw5c2jObB6zZs6iGW/NYDFr1ixavHgxRS6NZIF5ACZNnMRi2rRpFBkZSVEro+IiKopWr15Na1avYYFX/6jzxTkzgBkwf958Fssil9HaNWtZrFmzhhQ0x8WM5p999hmLzZs3U/PmzalKlSosKlaoSC1btqSjR4/69qksewtoDtglgoWEhNDBgwdZHDp0iGJjY+nmjZss7t27x+bR5599TuPHj2cxZcoUR92MiqIoBj/99BO7TyTieGfPnqUTJ06wwLro6GhrXIwmNTGw+Ts6mmKiY1i43KerY2K9+Xj/9zsmJoYuXbrEAnmPyTLPnTvH4vr169q8+uuvv6hOnTr0zDPPxEGtWrXoxx9/pKtXr7K4du0aXb92ncWNGzfozp07dPfuXRY7duygXj17sWjVqhVlyZIlQRNfGhtlCVl++umnhQO1JSF4VtjFgQkpY4H8D9qeO9/dyeLjjz9WXIB6qsXVa3SNAfjxww8/pO3btrN48803/VI/ixUrRgjydR/w2Lmz51iAe+/fv6/blLZv306dO3Vm8frrr1PmzJn9ck2BLA8JOZYYAdoiEhQr/CmC5cqVi0qXKs2iatWqFB4eTig/HBCzhvYJZYHt5s+fT8uWLYsDpA0ZMoQwOS8HxDLp0qZjkT9/fkIMrYtHw8LCCPzIoWPHjhQREUHDhw9nMXDgQOrdqzeLPn36UL++/ah/v/4scE66tg3WIS4fy3SiQ5pVHuJ6cF7hg8JZjB83nubOmcti0aJFtG3bNnrvvfdYhIWG0XPPPRcPiCexXw5on6Ndvn79ehY45pTJU1ggJkcsu3fvXhYTJkygxo0bs6hevTplyJDBI65+8sknqWjRooT4XT7BmwP+5ECrZyiMgHTp0lHGDBlZFC9enBo0aMAC/Ik2xhOPPxEHmHgXfDRgwAAWe/bsIXpILA4fPkxNmjRRZRrl2oyXXnqJSpUsxaNUKSpdurQWBQsWpEwZM7HAcwKaKeJGDrVr1Sa8Mp1DxYoVVf1FXnJA3c6VMxeL7NmzU/r06Sntk2lZQJjX3T/owhs2bKBvvv6GxSeffEK7du2Kh7Zt2mr3ieMhL/Lly8cia9aslCZ1GhbQqx977DHtvnXX4W26rzkwhR00YhUAooJWqlSJIGBwaNasmapwqHRmQMhv27Yt22hAQwK9Yfv3d0y2y33jAa0DKji3DdJ126gHfni4anihEWUGGjC6xgTyaPLkyYQHNQcI8lOn8oAwDmEdD2oOeLBbif26c0L6W2+9pQR7iPZmLFm8xCHiR0Yq8Q/ivBELFyxkzwfnCCFeCe/z56vGJhqXRuBadaYJGhzLly93iO9OET4B31ZGAM4J1zp9+nQW8+bOUw0R7MMMmASrV63Wns+unbto//79LLZs2UIo45UrV2ZRoXwFEcHsIC0fH9OKA719SFhtb9X79H//+x9BqIZQwwGCMPdBI4fjRqSB49auXUtbt25l8dtvv3G7VGkwAiCMnzr1qMeocRmCuaeivTIBYmLU9eKazfB0v662g9h/+fJlFliHazpz5gwLCIG6z++//06vvPIKZcuWLQ7Kly+vDEdjvhmXY2Ni6datWyxu37pNd+/wJgDKwnu73mMDf4gB7du3JzSgrMqip+vECNCVguBKt4sDPS13/tquUcNG8dpSzrbVxo0bFRfcvHmTOMAA0BmL4JIffviBDhw4wKJbt25+qZ+ujAAYzRcvOkZmmb9xLVZGwLat2xS3gF/MgOiUKVMmv1yTv+69p/tF3ZFP8OaAP0UwCBq6+AEd5RCH6uK/oUOGattyw4YNU/Ec2nNGIP5BWw+dTDhAbIL4w+HFF1+kQYMG0aRJk1hgv5wJgLROnTop0V3XWQ4d9CC8swgNoz69+1Dv3r1ZwARAW5kDTIQRI0Z4lIfQC5D/MDA4oAMfYncOCxYsUG1oTuhCGsyFMmXKxAPiSZ1AietYtWoVoQMaB8SyiIM5IEZGRzudiYCOii1atGAB4TFjxowecbWvRbDgZZHgPnN/cqDVczVlypRKxIagz6HESyWoYcOGLKpVq6a2efzxx8kICMngPp0piQ4dug86cdWvX58KFSrEomiRolSyZEkWpUqVopdffpnKli3LAsI5d41Iy507t3pOcEI/0qC/4nnBAc8XiP2cCYA0CP05cuRggecA2mmo/xxSp06t5QUYGytXrqR9+/ax+HDvh7R79+54aNe2nXafMAJg5KADOgdlBKRJQ2kY4FzFCNCVbBfpVgGgGAGOSXuRR2IEPDIDxAhwGANiBLgglyBZbcWBVo0Yb9eJEeAYQSBGgMNcESMgSAgjCZ6mXRzoLYf6ensxAh6ZAmIEpNAGrMZyh7ojn+DNAX+KYGIEOEbSixHgMAXECAhenkjKZ+5PDjQ+K83LYgQ4DBAxAhxtLTECEuGrgcQIECPAOBLAuSxGgBgBSalRZJcIJkaAGAEyIiApMUnwXotdHGgODO3+LUaAGAHulkExAoKX93Dm/hTBxAgQI8A4MkCMgODmiqR69v7kQKvnqRgBYgQYy4cYAWIExBkGafWKH3k1kMOgkFcDOV4VJK8GSqrNk8Bcl10imBgBYgSIERCYOi5Hsc4BuzjQGAQkhmUxAsQIcLccihFgzS2Jfa0/RTAxAsQIECMgsTOAnJ8/OdDqeSpGgBgBxvIhRoAYAWIEMJMDyxwBc9RcBjJHgDRW/JUDdolgYgSIESBGgL9qtezXnRywiwONQUBiWBYjQIwAd8uhGAHuME3i+68/RTAxAsQIECMg8dV5OaO4OeBPDrR6nooRIEaAsXyIESBGgBgBYgSoiYoxWbERMllw3Ie2/PJtDtglgokRIEaAGAG+rcuyN89ywC4ONAYBiWFZjAAxAtwth2IEeMY5iWUrf4pgYgSIESBGQGKp6XIeuhzwJwdaPU/FCBAjwFg+xAiwyQgYN26cmnE7bdq0anZpzDDtBGbfxgzaLVu2ZNG+fXvq3r07i65du1KHDh0I/+GA7fr27atFv3794hgD/fv3//c3tgsLC4sHpGM7DtgerxTSYfDgwTRypKPRYv4eNWoUIZ/Gjx/PYsKECaTDxIkTafr06YQe7RywfvTo0Vp4OiJg8eLFFBkZqQXWO9/5b/5esGABLV26VLvt3LlzCfMEcJg5cyatWbOGNm3a5BZWr16tjonjmrFkyRJ1rvPmzSMO+P+qVatYYL8b1m+gjRs3snjvvffo888/Z7F582Zq0qQJlS9fnkW5cuVUvTh69Kju+SrpQZADdolgISEh9N3B71gcOnSIYmJi6Pq16yzu3r3L5uyePXv+5UkjZ2J5xIgRqk6izHP4888/6eHDhyzu3btH58+fJ6NwbVw+d+4cRUc7hH13vxPrZMFnz56lM6fPsLh27Rqb/0j8448/qHr16oQJoIyoXLky7du3j/7++28WyLfbt29rcefOHdIB91P3bMNzOFu2bAma+NLYKEvI8tNPPy0cqC0JwbPCLg5MSBkL5H/q1KlDaMNwWLlyJZ0+fZpu3bzF4urVqxQbG8sC3Ilg+8cff2TRs2dPv9TPYsWKqePSQyION2/cpOiL0SwwWfCDBw+0hXjr1q3Uvl17Fo0aNaJMmTL55ZoCWR4SciwxArRFJChW+FMEe+aZZ6hmzZosGjduTIg7UX44DB8+nMLDw1kgJgUfmeOaZcuWqfgX8TcHtEvy58/PomTJkjRo0CBtLIu4uk2bNiw6duyorsUcOzt/R0REUN+wviwQy/fu3Zt69+LRq2cv6tmjJ4vQ0FDVrh07ZixxGDp0qLY9jPYSrleXx7gn3HMAaYiZt2zZQjt37mQxZMgQQnvPjBYtWmg1D5xrVFQUvb3pbRaI52fMmMECMTnibnN5cP6GdoF2IAeUw8yZM3vE1U8++SQVLVpUxexBUdnlJNkc8CcHWj1DYQRkzJiRsmTJwqJEiRKEzhkcqlWrptoY2IcROXPmVFzh5B7z9yeffMLmARL/+usvqlu3LoG3ORQpUoRKly7NAoZf2bJlCboQh0KFClHWrFlZII6qUqUK1apVi0Xt2rWpTu06LMAxGTJkoP/85z8soOciTzjkyJFD5TvqP4c0adJoeaFAgQK0fPly+uijj1h8+OGHtHfv3niAHqwrE04jIE+ePMQB+Ydz4pA6dWp67LHHtPvWHdPbdF9zYApt6fTjikmTJimRAIXY/NBq0KCBEnshxHKAwG2uZM7fQ/8/e9cBLUWxNvU3PkQRERMggiQxK1kFkYwkURExICA5ZxAQyUhGwUCQzEMJooIoQUBBMfAwPBUFQUAl52AA/P5Tfd/g3NlvNt3dOzu7tefU2Z24MzXdNd1fdejew7zcEezSACOgSZMmKrANrWXdgO2NGjUKAAo+2BYNUBFr1aqVChQ2EEhzm7cA2zTzAetQ2ECBzuLF+Y3gHLZrwDYYATAhNMBYGD9+vIqXXnpJUChERtWAAseMGTNUzJ49W9555x1BUFHD4sWLzXbs4wS2LV++XCC2diAAtmrVKldgu31/+28IDQJdzv+ylt977z0TYMM5nMB5IEbYRwO2QbA0wERAwblIkSIqbr75ZgbB4qhNmXVqFPgz+jKI5njo1do1a1V88cUXsnv3bjl86LAKBIS1z7vvvuta0Xj22WdN/nALRv36669y8uRJFQhQI5iFfTRgW6QGgLV/RowAmCVuwTesx3Y3BD1u7z4T9NuxfYdoQNDP7bN582ZBhc9ZYERhFjq2YcMGFeAVhosGGD8nTpxwBZ47Kp8akM4uv/zyuKRxGgFuqcBf673SwGh0M57HoBzsVi5F8A0m3h+//6ECmrBnzz8t6u2/oXUHDx6UQ4cOqejRo0dc8ieCNF9//bWcPnVaBYyA/fv2q8C1BjMCEAhr8GgDFag70Ajwlwak6tXGMwgGPXGrjyL4Dd1FHVwDGom5NTBDQHjBggWC974dqLuhLvvII4+ogEGHOo0G5FkExd0apqF+7HZeBJgR7NfqsViHbe3btVeBBnwtWrgH+2EEuJkEONY01Huun/RTgP92q7ejbh6s7o66utbYDevQS/3111+XhQsXqsCz00wTGCZu6QHxAsRZZkyfoQJGgNYQDutQ38f1IE1oQMM/t0aFeK45c+aM6v0T6yBYqmqQ1/cdTw0MVl4799xzTSAa6U8D6lC1atZSUb58eWMiIPhrB4LbiNlBWzWsXLlSbeyGRnDff/+9lC1bVtAIWgNiQaVLl44KMBFQX9KQP39+o8lVq1YVV1SpKlUVwECAEWDnwP4bRov2n9Y6GATgTMNFF13kqgswlNFo2M0MRext3bp1AYD+uaUJpAekA3sDOvtvGEZotK4B5gDMELdzx2t9rDXQEyNgyOAhxim65557BK2h7MBLHy8mvGA0IAitBaixDoUJOP14wWto+nTTgEC+Fdxv3KixPN3EPaCP/fBCdeKphk+5ntM6t9s3/s/NeEDBCsKi9ULAutatWqe1aECrBgdwHFp9wO3X0KN7D0ElUAOMgOf6PufKcUaMgOnT3FsPzJ071wTGnUH1cJYhsh9++KF89NFH6bDmozXy8dqP5eOPI8eaNWuMSeDmPMJgWLt2rQoci+u2Gwv238uWLlMNApgGMEuQL+DkarjpxptoBHhdgonB/3sVBIPOOfOJtfz555+bwLtb4AjBKO0TyghAfvj2229V7Nq5Sw1CIzCNIDSMCc0EwDpfGgEIgLm04EXgDq1/t2/frgIGjdsHPYRQCSxZsmQ61KlTxxSa8Gw1oIeFmxFjPYPjx4+LhiXvLlHfIXivoOBFI8DtaXE9GPBKA+NVOI/2vDffdLMawEF+RjALedutVw40ARqpAWYnehK4HYtgVrTXHOw4ywhAQF+DMQJcNDAcI0ALdmEdjQDqil8YiGcQDL2J3eqNCFCjpfaQIUMiBoZOfeuttwIaMKGejoZ3aIigoXat2qaXP3r6O/HAAw8YI8DZWM1aRt02mBEAEyGoEWDr1Y8AvIV2bduZGIFb/dsYAY56tVXPhhGAWINbHCKUEYDgO86hAfVvt2czatQo07v2zTffFA0wC9xiDW7rkU4Q0EcDNA0wApy9961lxGlQb3czJtAY0M3UQDpEMDDYe8RtW6yDYH7RjGS7znhqoFvawXoEftHKWwtCYx2MVIzKoAFGAALgzvPjOOQltxE8EC9y6/kOIwCBdc2UwDoYAXfddc5iEH8AACAASURBVJcrnI2p7cvonZk7d24ViC/hfpyafGa5ajVXgyAcI0BrXY91MAMQaNdMD6xDbwInv9Yyhr2DCanpH9Z9sOIDQYNGJ6Dz1jmc30gPqKu6XQ+NgDgpD42AtJ4FNAL+GTqHRkBa7wEaAXESnQQ7rVdBMBoBGZ8jIEM9AmgEuBbInAU0t2UUJDF0IIdHSzBRi/ByvNJAt3Tl1XoaAf+YozQCzgpLH5F3+PEvA/EMgtEISOvtbnoE2IL/lgmAbxoBaTEIGgH+1RC/X3k8NTBYWY5GQJoxQCMgraxFI8CjOQJoBNAIsMYRtL5pBNAI8HvBJpLr9yoIRiOARoC9ZwB7BESSa7lvLBnwSgODVRK92EYjgEZApOmORkAslSjzzxXPIBiNABoBbj0AnOtpBGR+3uc/pjEQTw0M9j6lEUAjwJ4+aATQCDjTlY5DA6UNFcShgdKGEeLQQCyuxJMBr4JgNAJoBNAIiGfO5rnDZcArDbRXAhLhN40AGgGRpkMaAeGqTGLuF88gGI0AGgHOgL/bMo2AxNSHVLiqeGpgsPcpjQAaAfb0QSNA5CyMW4UxgjFu8ZYtWwSTDm7atMn8xrijGKcZ+2CszwMHDpiJy7Ad3fIxnjEmdMT2SD7sEcAeAVZPAOubPQLYIyASDYnlvl5ooFdBMBoBNAJoBMRSPZLjXKmkgfZKQCL8phFAIyDSdEgjIPa6m5kaGM8gGI0AGgFugX/nehoBsdcRv57x9OnTJt6HXsKI7yHOByDOd/DgQRMDxD6I+WGdtc/WrVtNbBDbIvnEUwODvU9pBNAIsKcPGgEiZ8EEwCSFM2bMkCeeeEIwuWDNmjXl8ccfN7Mzf/XVV0YAjh49Km+88YY0bNjQbK9Xr55gQp8ffvjBTEYWiQDQCKARYBkA1jeNABoBkWhILPf1QgNpBKRNHszJgv8JgnGy4PDGx+YcAbFUv7RzpZIG2isBifCbRsA/Gsg5AsLTQBoB/tbAeAbBaATQCHAG/N2WaQTEXkf8ekbE+L788kvp3r27maAbcUBMmNujRw95++23jRnwxx9/yMaNG03sDzFA7IOYIGKDOD4SMyCeGhisXEcjgEaAPX3QCBA566+//hK0/F+8eLE8//zzZkbmsWPHyqBBg6RJkyYyffp0+fnnn2XZsmUyfPhwM9P9sGHDpHfv3tKpUyeZNWuWEYZIxG/gwIFyySWXSMGCBQWzWttxzz33mJm3x704TjSMHz/ezHKPme6dwDbMaI9ZpTWMGTNGRo8arWLUqFESDOBm0MBBAejXr5/06dNHRe9evaVbt26CSYs0dOrYSewTGNl/d+jQwRzTtWtXUdGlq3pO/A+EHLOXa9eLdeDg1VdeVTFhwgSZOnWqee549k7MfWOuvPPOOyoWvbNI3l38rrz7ro61a9bKN998owKG06effirrPlmnYtXKVfLekvdUfPDBB/Ldd9+Z3izo0WIBjjXS7rZt29zx8zbZpgDHWb1jLGfc/v3T5p9Mrxn0onEC+23YsEHWf7Fexbp16+Sjjz5S8frrr0vFihWlaNGiKm695VZOlBmJ2ISxrxca6JURgArBh6s/VPHZZ5+Zd8GRw0dEAwqB2mfJkiWuOgYd+uKLL2Trlq0q9uzZI8ePH1eBgiXeTTt/26kC2xA8jwb79u4TTPrrBnPOPXtlr4KMTBbs9n9Yj//89ddf5Zcdv6g4fPiwRr9ZhxY6PXv2lIZPNkyHdu3aCUxWvL81QKtOnTylAvkCz+bYsWMqli5dKv2e66eidevWcsUVV4Q18aW9UBbObxoBrskg6g2ppIHhpLHM3Ofaa6+Vu+++W8Vjjz0m3377rZr/kC8P7D8gO3fuVLFr1y6jb+jtq6F7t+5xyZ833HCD0fwTx0+IBrxbcN0aQhkB8+fPl8caPKbi/vvvl2zZssXlnjIzPYTzXzQCopY61wMzUwPjGQS78847pXmz5iratm1r6vaoy2tAvdmtbjhlyhTBO3/1qtXpgCBg3bp1Vf2CrlWqVElq16qt4uGHHzYxhc6dO4uGFs1bmEaJaJjoBMqyqO+i3KMB2zq076Cifbv2gjJKq5atVGBb2zZtVaBujvjHc32fU9Gtazdp07qNCpy3eXP92eCZIV6A2IuGESNGCBrOLViwQMWY0WOkY8eOAXjyiSelevXqKsDh0veXmgAwgsBOoGz/2muvqUCcYM6cOYKYgAbEYhAD0QAOcubMGZVW/+tf/zJ141dffdU1L3ND5AygfgFdQlxt5MiRJpY2evRok8eQN7/++mtZv369zJw508T+kAcQC4SmIG2uXLlSjhw5EvYfx1MDg71DEfjNkSOHXHnFlSruuOMOqVWzlop7771XLr744oB0e9VVV5m4m1t8ccWKFa51TcSDSpcuLTkvz6micOHCUqZ0GRU4rmTJkq64ocgNgvqShvz58wvup2qVqjqqVpVqVaupQJwWPJx99tkqEN/NlSuXClwL+LryyitVZL0oawC/1vPEsYi5TpwwUQVij4glOtGyRUvXc4YyhrJfml0uuugiFdCi//u//3M9t3Xdsf6OtQaeZQ35YwU+EexBgQgBW7iBzz77rMngeNH27dvXCD+GCMLwAhAJvBQXLVoUdubHjniQ5513njEDLr30UrEDiRMuNf5Xw/hx401gA8ENJ/CCfP/992X58uURA0ESHPvee2kts53fb7/1dsD/4f8RKMcLSQMEFUI5ZMgQFShIwG3VAL5hMGgcYF3fZ/ua54Fn4gSCb3g2mhmCdbNnzTb3ivt1AjysXr1aDVIjeI1goTOT2ZedBQn7Mlr/un3Q3Qy9S+znsv/GNb311lsq8Ly1gCKGtUJLR6RxFSdPme3YJ1L8+eefposcrtsJvAxhPliGhPMbFfv//Oc/KmCyoEJ72223qbjj9jtoBLgloijXe6GBXhkBTz31lKxatUoFjDjkIwTgNSDNax9opVYBwTpUaFDgQ1BKAwLgCP5oQHdUBPu147Buz+49UZkACLiHMgLAgxtwrBZYs9YFC/bjWDfjAv/322+/GTMAhoATwQrZOCdM3MGDB6cDCugw653vSmv5v9/8V9fGU6fk5F8n5fix42paQPqAAQuDXAMaCaCgF+sCEM6HwiACCDB6+YkNA6mkgfFIkxk556XZLpW8efOquO+++0zlW9NHrINR6dSJM8u//Co7tu+Q7du3q4A+Z+S63Y4tUqSIfPLJJ6YFITQ8AAcOmqEEUI9wAsEIpEW3z4L5CwICglaAEK0TaQS4Mcf1oRjITA2MZxDs9ttuPzP3nbMleMuWLU1dFPVDDZMnTTatexHcd+LNN9+UDz/80ORt5G8L8+bNk3LlykmBAgVUwAx46KGHVNSvX98ExhEg19CsWTPXe2natKlpJKeZAFjXtUtXE59AjCIA7TtIu7btTBATgUwnYBQEHPO/86BsgyCoW90c23HdGnDNeCYoh2tA48ABAwaoQCxh9uzZAjNUw8svv2xiK4iv2NGgQQOBOaQhVDkK6RSN0zTABMD1oHypAddrb9xo/w3NRjDW7R0SbH2sg2ChdCFVtqPRDxoVIA6Duog1VBAa0FatWtU0Fp40aZLJF9AOxADx/kY6QH0WcSfUX8L9xFMDg6WfM0aASyAaRgDKEhrcjIAzAeqJE2WigmVLl8mPP/6oAjG44sWLy+WXX66iUKFCUqpUKRUwAXBssWLFVOBYBN01XHfddVK2bFmpXLmyChgE1apVUwEjAMF+BME1ZLskm2oCwBwAV6gboqGYBgTd3Z4fjoO566znWsuIAaNRsBN4t7id85xzzpHs2bOr3OOZID6dNWtWFVmyZEkOIwDjIqIAhCAPTAAsA2jljBcZXkpwoRGcRFAHlW/sh8ADgsa1a9c2QfBwMz/2oxGQZgzQCPgn1dAISDMGaAT8kyYy65cXGkgjIM0YoBHwT48GGgHhDYtBIyD2yphKGuhWIfBqPY2AfwwBGgHhaSB7BPhbA+MZBKMRkNZDgEZAmiFAIyD2WpGMZ0TgH42AEYtBw0h8sA6NXdEzEcYgDDbE/BD7QwwQsUDEBBEbxFBBkTTOiacGBivL0QhIMwZoBKSVtWgEiJylCRqEAC254RYjg6O7RZkyZUxLAriGEAcIAFo1oyUAuhG5fdAqGy2gEeC0Ws2jpwEyIxwle28A/GaPgLQeAuwRkL57D3sEpPUQYI8AN6WJ7fp4ayBaGwQrrMRrG3sEhDdZsFtvAKxnj4C0HiPsERBbzUm0syWrBsZLW6M9L40AGgGRph0aAZmjlrHSQPTSQY9qqwc5enHD0I70uYezP40AGgHsEZA5+pDM/4IGwuiFjFEkMHcogv8YvQIxP8T+0HgYsUAMUYgW2eXLlze9Cdw4yUwNDKaTNAJoBNjTB40AxQhAxsYs4HD/EKwaN26c6ZqM7icY4sb+QfcLdE1xrrfvgxY+GDoCBVcYCwDmBKARMMSML6gNC4R1NAJoBGjDA9EIsKtLfH5nlgbaX0aZ9ZtGAI0Aa1ggfHNooPhoiN/PmswamFlaG+7/0AigERBuWrH2oxEQf4WNpQaiMRyG1Xj66adNHbhKlSqmIZz1PGP5TSOARgCNgPjrQzL/A3qIYvhPzO+GYbAQt8Nw4UhXiPl9//33Z24f+yIG6Fx/Zof//chMDQympzQCaATY0weNAIcRgIIPWkBYXYEw9i/GjUYG1gL+EAOMXRXMCICrCDMAbiC6DQEYe49zBNAIsL8okO44R8B/TM8ZzhFgTxmZ+zuzNBCTuNpfRpn1m0YAjQAaAZmrKX77t2TXwMzS2nD/h0YAjYBw04q1H42A+KpqrDUQPeMxlAbm4kMdGI3jChYsGJcyII0AGgE0AuKrD8l+dgwRhHgMxlbHnBeYCwL6hZb/WsAfMUDEAu0GgZOjzNRA6z2pfdMIoBFgTxc0AmxGAFw9jNeMSWgwCQ5mCcfEQBgKAYKAbj/I7NbQQOgWFM7QQE4xwDIKsSD/ggsukAsvvDAdMGlDiRIlTPcjdEFyonr16lLv4XoqMPFQ48aNTasLtLxwAoKGcfk1YKKTUaNGydgxY1WgNQfmSnACE6VoE+lY65z725eDTbSDbebcc16X1yMEenMsXLhQnVgXE+5iqKfly5arwMQlmBRq/rz5KqZMmSJjx45VMXz4cPPSwGTPGrp166Zyj+cB1xnHYHZvDY2eaiSP1n9UBSZ6woz1aN1qB15KO3bsMBPYYBIbJ8yEvps2y2YFmNgFPV5ggmnARMbrv1ivAhMqY0JWzBSvAZWAd95+RwWGz8KL9to816rARHxw5yMZi0/Lg1wXyEBma6D9ZZRZvxs2bCgrP1ipYt26dXJg/4GAya9h0gHWuJFO5vCeGDxosApMYIa8hEKkBrxz9u/brwLbgg3Rg22YLFOFy/9Z1xBqsmCzn8u5cV3gSQMmDMZ2V4SYLBiTde38TQda6Lh98L94X2CCeifQq88NMPr/Pv23Cpj4eN9rE0djHTR3xPARKrp06cLJgt0eVgKvTwUNzCytDfd/UAaGGaABk8CtWbPGTJqOidOdwKTpRjOgGy7YtXOXaECZC+VtDRi2E2X0cO/Bvh/KKGvXrlU13Wj9/v0BkwRbkwaHnCNgwQJ58oknVWDIUQwvar+WZP1NIyB+IpoZGhjP8bExMSTMAA3Qk4oVKwomgtTwwAMPmPHAMSa4E2hE0rVrV+n1TK90wATEmCgYk79qwGTBzZo209GsmbRo0UKt86EeiG3NmzdX0apVK+nevbs888wzKjAZOo7XgGvG5LUdO3RU0blTZzPZMOYZcAL/iaGd+vfrrwL7t2jeQgXuBRMGN31aR5fOXWRA/wEqhg4dKpMnT5YZ02eomDp1qtmOfexATKRwocIqUNfE6APDnh+mAhy61bvrP1LfTAD9YN0HRUOlSpWkVMlSKm655RbBpL/R6DMnC46f9uHMMEHREwBDAiGPYfJbxBswHDiGAXcODYQ6AmKDoYYGcl51PDUwWLqCEYBJYK+68ioVmCy4Ro0aKnDvF198cUC6zZMnj+HGOcG6tYxhlcCpBsRcMZF3zstzqkB5Chqq4a677kqbRNglnxUtWlRy586t4vrrrxdMfozeaRowQXS1qtVUYLLgbNmymTIiyolOYFuua3JFBY1f63nmzJnTpEkthot1mLh+48aNAUCM8Oyzz1aB9IByY47LcqjANpSHNWASYUyWbF1fZn3HWgPPzBFw8OBB+eSTT8wEwXjRIZCMdfhABBB8jNVkwSjExoMwPBDM4uw2w3ORwkWkQoUKKjA7dpMmTQQFBA1wQmfMmBEAzFKN4LkGZH5UiMCrBgSSv/76a1egK5Yrvv7G9TicE3M8uAHXgvGdNSxZssQUIpzBJGsZBRUUFjXgxQ+BdZo71jIyXDye+7XXXmsCXW+//bbYgXvBzPZuPHz88ceyfPlyFUuXLjVGCkwRDTBn0HNGw7Rp0wSGyWuvvaZi4oSJggCpBhQwIdCYOV1D3rx5aQQ4SxQxWk4GDQyVvxo+2VA1p2BYQRcQiEHAXwMKiNoHOjZhwgQVCE6jFZwV6HF+IyDvDG5Zy3t2p43H7xZUR7A+mFFgBf2177CMABczAcEs533Yl92u16wPYgTA0Ni9a7cguKfh2NFjGv1m3aFDh2Tx4sWm5Q5MZAvQoYEDB0qfPn1U4B3g9sHzDmYErF612pjnMNCdgOmLgESo9BjNdk4W7PbEMr4+FTQwmjTn1TGFChUy5ZPt27eLht9+/c1dPzGfCYzW/ftVDB0y1MzJhXm5nEDl8fzzz48q/xYuXNiMh+6mzbgmu17af4cyAlAWg5mtAZMY0gjIuAak+hkyQwO9CoKF0jHkebf682WXXSYISiGIawfyOwITqH9ruOfue0zAHnEFJ9BgAPXtZs2aqWjVspUgiKMBgXwEsXv16qWiXdt2AvNCA+r6CHLj/zV079bdnBvndwL/N2DAABMPQUzECdyjFkMIZ123rt0EjRI1IAaBBh2vvPKKCtRJtTgEOESZScPVV18tCK6h3q4BwS+r/u78RkNOBNdCpalYb491ECzV9c5+/+gJgNE7EO+BEQgzAPET64OGijDctcmCI22g6JUGnjECrkprGY96ih0YfhmjMmgoW7asagQgLoM6sJb/sA4Nb7V4G9YhhnT77bebfIi86MSNRW80AXsE7Z2AMQGDAIaAhltuvkUwKbAG6DbMGwT8I4VlBIBLDTACNL3Buly5cgliduBMAxrEuGkGNApGqlOTreU3Xn/DNDxE40M72rdrH2BWWOYFRqfB9WoNYsy6S7MHzGWLciYA0yLaBjNu9xjO+lhroDEC0AICGRtuP7oCwcVCYQgtAvGBMCB4jyAlAjsouCPAOnr0aDPMDyYCjuRDIyDNGKARkDZrdzgJP9g+NAIiyX3cV2MgWTQwWD7BNhoB4Q0NpJkH1joaAWmTBdMI0JTEv+tSRQNDaWQibacRkD4/0QhIKzOzR0D6dBGrpczSQK+CYKG0jUZAmjFAIyDNGKARECtl8cd50AAJjXowfDdGBsGoCGgMZH0Q64MBh9gfYoCIBSImiNgghqPCqAvhfrzSQBoBacYAjYC0ngw0AkTOggOIzIuuPeg2iJbxGBbIaq0MNwut0tG6etiwYWb4FnzDFcdQOzNnzjTdMMLN/NiPRgCNgFAF0ki20wiIJPdxXycDyaSBofINjQAaAfbeAewR4FSD1FxOJQ0MpZGJtJ1GQPr8SCOARkD6FBG7pczUQK+CYKG0jUYAjQB7zwAaAbHTl0Q/E3oVf/TRR2ZIHLR8b9CggWC4Z8QC0Wp9w4YNZoQLjIyB3jSIAVoxQeyHusSRI0fCvk2vNJBGAI0AqzcAvmkEiJyFsZ8xBjrGC0X3FIzBj2Ff6tata4CMjnGEMf4onL8nn3xSatasKfXq1TPj8mE8JowdFsmHRgCNgFAF0ki20wiIJPdxXycDyaSBofINjQAaATQCnArA5VTSwFAamUjbaQSkz5s0AmgEpE8RsVvKTA30KggWSttoBNAIoBEQO03x05nQIBhDK1euXFlgBNjjgBiOBe9exPsw9yLm5cBQQIgFIiaIOTExb5g1ikg49+2VBtIIoBFAIyB9Dj0L4wCjAIThf2AIYFylzZs3nwEMgGPHjglaS6Ab0JYtW2TTpk1m3oBffvnFHOs2dnT6v/pniUYAjYBQBdJIttMI+Cdv8VfkDCSTBobKNzQCaATQCIhcI5L9iFTSwFAamUjbaQSkz3k0AmgEpE8RsVvKTA30KggWSttoBNAIoBEQO03x05kQ48OQ4IgBIuBvjwNinjdsQ6wQQOwPc4ciFrh161YTG4QJgKHVwv14pYE0AmgE0AhIn0vPTBacfnV8l2gE0AgIVSCNZDuNgPjmV5499gzESwND5RsaATQCaATEPj/zjJEz4JUGhtLIRNpOIyB9uqIRQCMgfYrw55JXQbBQ2kYjgEYAjQB/aorfrtorDaQRQCOARkB6tUg6IwAz22fJkkVFvnz5pFSpUiowCzaGQ3rkkUdUdGjfQYYOHRqAsWPHmtnCMWO4E5MmTTJzKMyePVs0YFJmVGyCYeHChaIh2DELFiyQ+fPnuwLX8tprr6l49ZVXpX///mIPFtl/P/XUU1KhQgUVpUuXNjNvX3DBBaIBmS9UQTSa7VdffbUZr27cuHFixyuvvGK6rLlxMWvWLMEz0jBx4kR58cUX5YUXXlAxYsQIM68G5tZwYvDgwTJw4EAZMGCACkyqg4l4NLRq1Ury5MkjmBVcA0wPdMmDG8+PfxnwKgiGbpzLly9X8cknn8jhw4fl5MmTKtx6fv3nP/+RyZMnq5g7d65s375dDh08pGLfvn2ye/duV2CCXuyjAdv27NnjCmtyX7fvfXv3iYYz++/ZK3sV4Fr279/vCnOtoc69N82QOPNf/7sXcIEJuzRg/M3Tp06rwPieK5avCHhX/Pvf/xbo0bN9nlWRoTkCVq+WMaPHqOjevbtcddVVcdH7a665hhroX+k7c+VeaWA0ZQyvjoERsGLFCtmxY4cKdOffs3uPjj17jEahJ68GjOtbpEgRFSgrowwXzX1jErqVK1eaucdwfU5Asw8eOKgC7x+0TETrQg0o2zZs2FBF7dq15dJLL43qmqO5Ty+PQd7hx78MeBUEC5VmMV6yW/05W7Zskj9/foEm2XHdddeZYxBg01CuXDkzlAiGE3EC5YSWLVtKs2bNVKA+1K5dOxWYtLRbt26Cc2ho06aNNG7cWAWGOunSpYt6HM7Vs2dPM1wyhkx2AvVh1O0GDRykwron3JcG3JMbunbtaiZdRR3RiUGDBpn6Leq1GjA8y7JlywIA/vDcNOTNm1cQ+M+ePbuKrFmzqnV5vBtgGp199tmZrreoFxctWlReffVV/woAr1y80sCQRsAdd5h5EmrUqBHwDS27+OKLA9I88hFiRsuWLlOBuVbfe+89Fag733bbbSYf2k046zfSOv5XA4Zwuuuuu+SuMjpuvvlmgT5rgIaXL19eqlat6grMGasB8VKUtTS9xzq8KxCX04D6G2JciGVpwLFu7ylw0qRJE1fdhgZu3rQ5AJj8Gu82DdAyzIWC+3GDmz7iuHjFNd04wPpYa2BSGQEg6P/+7/9cgQd+0UUXqcALDw/7sssuU3HllVdKrly5AoAEDRFwAypUbsCL+frrr8904Ho0YcA63AcyqpaBsS5Hjhwm0yADOAEOkSlQOHBDsMQd7TZkblybU1RwL8E4DsUDzofnqyF37twBaUFLH9q6YPzmzJnTFLDc0jGDYMlRgvIqCPb444+rhREUUjBRFALKWgAG69yMABToMH+MhsWLF8vuXbvlxPETKtDdVAt6Yx2C4sEC/aG2BTMYzLldAmhnjtu121w7rt8O/K89gG//vWfP/8wJl3Nn5JoRzMM4nBqwDV16MbyfHTB3Ro0aJc/1fU4FAnZuHzxvDAsIA0LDmjVrZPz48SpQeYYmR6vpwY6jBro9MX+t90oDg6WtRNuGoPrq1atdNRJ6opmZWAezEoF1Le9iHYJJqEhqKFasmAnuRcNHgQIF5K233kqnQ3ZNgqlx+NBhFXj/BAPeMY0aNVJRp04dGgH+koCUvVqvgmCh8jPqbW51D9TtUIdGYzsn3OrVWI9AkrOxlLWMYHfr1q0FgXkN2IYgjgYYAQi0t2jRwhXNm7cQDTAJ+vbtaxpsodGWEwi6B4Np5NV/gAxQgGC+2zXhetu2basaGwjY41in8WAtgys0TtNMAKxDgze8K5zAcQj4aShZsqSpx+PZakBacKvLY32o9BSP7bEOgqWsCHl8415pYCgjAGUflCU0oBGsZgQgdjZ16lRZvWq1irffelswybIGzMN64403mtga4mtOIGCPRrZBUaq0lFaA8zrjYtZywYIF5d5775UqVaqowHyxmhmCdTgumBGA2CAagrkBdTgtNoZ1Gr+WjoAbNGbEZNUaYARs37Y9ADCM3d5RiFviP53xTGsZ9+kWF4ZpAd20ri+zvmOtgUlnBGTWg+D/pHVRJg+ZywODYB6XXmL0914FwR5/7HFZsmSJilBGwN+n9fEfEeRBy38NMBgQrLLGlnR+o6eAmxGA9aEC58G2nwnoB+lx4LZPsGsyJkKQnghu5zTHwSCI8tj9+/a7BskQ9EPPC4zXaceGDRtkzJgxgvSmYdXKVa4pOpQRsHbNWnn55ZdVoLcTjQBXarlBxKRHlh+Clx9gBHz44YfuPab2uPeYgiYg4K8Zh1g3aeIkqVixogpUOlFxiub5oHELWu5/9dVXKrZt22YMCpgUTsAEgKnp1uMKPZzcjABMbohKWzTX7LdjoOX8+JcBr4Jg8UjnCBZDKxBM0VDj/hqCYJcGBOBDGQFawAfrYASgJ8HTTz+tonmz5mqLfATjEXRHz3fLkHB+Dx0S2PvfGhEA+8IIwPEaQhkB+G9cuwYc26tXLxXI88GMgAXzF5jGPCjH24EegJv1TQAAIABJREFUoVqrXqxDy14EwuKRLuJ1zlgHwfyrIv6+cq80MJQRULxYcTNRMsoTTsAIQIDYmbbRqHTatGmmrIbymhML31yo9phHbwDkzxtuuME12IyAvdtIJqHWZ9QIwGTQGmAqBjUCLr5E0HhaA8wB1A0Ry9IQyghAY0ZNO7EORoDWe7ZHjx7qu0l7XznX4T6d5oy1jG00AqLUIbzQnBmJy8ErhOSH/CAN0AiIUnQS7DCvNJBGgPswRPYAPo0AMT1AgvUIoBGQYKLis8vxSgP9VJaiEZB+CDYaAWnlYOQdfvzLgFdBsHhoH42A9IYAjYD41tVpBPhX9+xX7pUG0ghIG5InnB4BmgmAdTQC0npu0Aiw5+gIf7MCGN8XZTwKezxnYjwzGgERik2C7u6VBtIIoBGAtGeBPQISVCBS4LK80kA/lWVoBNAI0NIr8g4//mXAqyCYlpYyuo5GAI2AjKahSI6nEeBf3bNfuVcaSCOARoCz1X+wZfYIsOfaGP5mBTAxgsqRvHy5b2I8MxoBMRQiD0/llQbSCKARgLRngUaAhyKQ4n/tlQb6qSxDI4BGgJZekXf48S8DXgXBtLSU0XU0AmgEZDQNRXI8jQD/6p79yr3SQBoBNAKCBf6d22gE2HNtDH+zApgYQeVIXr7cNzGeGY2AGAqRh6fySgNpBNAIQNqzQCPAQxFI8b/2SgP9VJahEUAjQEuvyDv8+JcBr4JgWlrK6DoaATQCMpqGIjmeRoB/dc9+5V5pII0AGgHOYH+wZRoB9lwbw9+sACZGUDmSly/3TYxnRiMghkLk4am80kAaATQCkPYs0AjwUARS/K+90kA/lWVoBNAI0NIr8g4//mXAqyCYlpYyuo5GAI2AjKahSI6nEeBf3bNfuVcaSCOARkCwwL9zG40Ae66N4W9WABMjqBzJy5f7JsYzoxEQQyHy8FReaWCDBg1k8aLFKlatWiX79u2TP37/Q8XJkydVxt577z3p1LGTiiFDhshPP/0kR48cVXHo4CHZv2+/ClxLhrF3n+xTsHfvXgmGPXv2iBtwnHZOrAt2TnPcvn2yf3/64JZ9Odjxhw8flt9//13FwYMHZcOGDbLuk3XpsHjxYmnTpo3guWuYP2+++kyx8vTp03L06FE5fOiwirVr18qECRNUIH1Dq+Lx3qAGuj4yX23wSgPjkSbjdc7rrrtOpk6dKqtXrVaxceNGQd7XcOjQIZN/MeG3hrlz50rjxo1V1K1bV1ABiua+8ufPL6+//rp88fkXKrZs2SLQMg245gMHDsiB/TpmzJghjz76qIrq1atLtmzZorrmaO7Ty2OQd/jxLwNeBcFCpdmzzz5bgkE7/oILLpACBQrIjTfeqAL5deiQoSr69+8vrVu3lqZNm6po2bKltG3TVgWOa9asmXocztehQwfp1auXK5555hl5pqcLsM0FOGe/fv1kQP8BKrp17SYtW7RU0apVK+nYsaN07txZRdcuXaVbt24q8L+jR4+WcS+OUzFr5ix5d/G7AcAxbtfzyCOPyGWXXeYrzaQR4F/ds1+5Vxp43nnnyZVXXim5c+dWUaJECalTu46K++67TxAodurg9ddfL2+++aZ8/fXXKt5++2158YUXVeBdXrx4cbk+//Uqit1ZTCpVrKSiYsWKZuLe8veWFw133H6Hek781w033CAVKlSQatWqqahRo4bUrlVbBXiAbpx//vkqUHa85uprdFxzjeE9T548oiHbJe5lOPwn3ietW7VWMfeNubJ3z94A9OndR3BeDZdccol5phdnvVg00Aiw59oY/mYFMDGCyk4x43LiPxcGwWIoRB6eyisNxEsUhRINK1askF27dpngEQLATvz5558qY5MmTpJSpUqpQEUDAWoT3EGAxwEYAVpAyKw7dFgOHjgYcIzzHFEt7z8QNCAfzIAwxkWQYL49sO/8jftBsEvFwUMmAOY8xlo+cfyE/P333yrAF57fwoUL02HixImmkFesWDHRMOHVCeozxcpTp07JkSNH1CAjAo8ff/yxTJ48WcWAAQNoBLgyyw1gwCsN9KKcE2lwzbpGvO+fffZZeeWVV1TAjEMe1QDz9fix43L8uI5lS5ed6RmEZ2EHKlpX5LwioMJrXVew73z58smsWbOMPkAjnNi8aXPUmj91ylR54IEHVKBSi0pdsGtLlm14Vvz4lwGvgmBI/8G0CK37zznnHBXYhmOdeShr1qxyzz33qMEkBJmaN28ugwYNUoF0jOC4mxHQvFlzadGihStgBLgBQf6RI0eqQAOVTp06mUYSaCgRCWAw9O3bV1DG0YBAPgwMDTAvunbtKt27d1fRpUsXYxTALHAC5x02bJgxAxDcdwLl8Dlz5gQA746hQ4eqwH8gKOp8pvblYOnFvl9m/aYR4F/ds1+5VxoIIyBXrlyCRhYaSpYsKbVq1VJRvnx51QgoVKiQLF++XLZv364CDbLc8iAMR5z3zjvvVIGgu1uZp06dOoIGEG7BfNTL0atUw8033yyVKlWS+++/X0WtmrUE59cAA+Lyyy+XCy+8UAUC9m5GC4L/efPmVbnH8wjWACV79uzy8MMPu2r+vHnz1LIwjFucVwMaj8DcwXtMw6XZLpUcOXKowPnwvsws7bP+J9YaeJY9Y2bWb7z8rRvid+IHn/mMEucZ0QjILJWK7/94pYE0Av5nRtAIMMYAjYD45nOe3Z0BrzTQi/JMtMEUGgHpzWMaAWllUeQdfvzLgFdBMGhfMC2iERDaFKAR8E+vES/epbEOgvlXRfx95V5pII2ANGOARkCaMUAjQIRGwFmJE+T14qXK//TX86cR4O/Cj3X1XgXBaATQCLD3DKARYOVIfmc2A15poBdlnmDBt2DXQyOARoCWPmgEZLZaxfb/vAqCIS0F0yIaATQCnHoTLL04982MZRoBsdUir87mlQbSCKARYO8ZQCOARgB7JtAI8VUaoBHgVbEltv/rVRCMRgCNABoBsc3LPFt0DHilgZkRrHD+R7TBFBoBNAKcaQnLNAKi05xEOcqrIBjSTjAtohFAI8CpN8HSi3PfzFimEZAoKpax6/BKA2kE0AigEZA+77JHAAPhvgqEZ0ZBI5H/g0ZAegHz65JXQTAaATQCaAT4VTWS67q90kAv3u/RBlNoBNAI0NIrjQB/a6FXQTCkpWBaRCOARoBTb4KlF+e+mbFMI8Df2mddvVcaSCOARgCNACsXpn3TCKARQCPAR2mARkB6AfPrkldBMBoBNAJoBPhVNZLrur3SwMwIVjj/I9pgCo0AGgHOtIRlGgH+1kKvgmBIO8G0iEYAjQCn3gRLL859M2OZRoC/tc+6eq80kEYAjQAaAVYuTPumEeCjIHBmvGT5H4k9ZwCNgPQC5tclr4JgDRo0kEWLFqn44IMPZNeuXXL0yFEVf/zxh/z9998BmDRpkpQuXVrFI488IuvXr5e9e/eqOHjwoBw9elTFkSNHZN++fepxbueLaP2evbJXwZ49eyQUIvof273jfvbv368C24L977FjxwK4t57HoUOHZOnSpTJv3rx0eOWVV6RSpUpy++23q8B2t8+pU6fk8OHDcvDAQRXr1q2T6dOnqxgyZIjkypUrLiYzNdDtiflrvVca6EUZJ1gwBdvcrunKK6+Uzp07y/Dhw1WsXLlS1U5LU6EZbli9erWMGjVKRbdu3eSqq65yvS6368X6fPnyyaxZs+STTz5RsXnzZqMr0BYnoGMHDqQP/tuXp0yZInXq1FFx3333ycUXXxzVNQe7n0TcRiPAX1rnvFqvgmBIy8G0CEZAMGh5AXkOec8tX7Zs2VL69++v4tlnn5XWrVtLs2bNVDRv3lxatGihAudt08Y9cP/MM8/I0KFDVQwYMEDat28vrVq1UoHzYruGTp06Sb9+/WTgwIEqunfvLrg2DTgvtLVnz54qunbtKji/Bpz3+eefV/UaOj5xwkT597//HQDMAzVixAgV+L9QOh8svWjpId7raAQ41cSfy15poGUEoJyioWTJklKzZk0V9957ryljOPNE4cKFZcWKFbJjxw4Vb731lvTt21cF8jrOe+edd6oIpq21a9eW6tWrS7Vq1VSUKVNGihYtquLWW2+VihUrqsfhfPfff7/KAbjBNeXMmVOyZMmi4vLLL5drr71WRd68eVXerWeRPXt213cUttWqVUuefPJJFbNnz1bLj9BrXK+GHDlymGd60UUXiQbMIYB9NMBQOOecczK9zBlrDaQRQCMg0xNxvAsKyXx+BsH8WehxXrVXQbAnnnjCBIwRNHbio48+kp07d8rhQ4dVnDhxQk6ePBkAGAH33HOPivr168uHH34oP/30kwoExU+fPq0CxsMvv/wiW7duVfHzzz/Ltm3bEgrbt22X7duDw+2acT9btmxReQJ/CIi5fRCsX7Bggbz22mvpgIpjqVKl5Prrr1cxduxYt1MKjAAE5vbv26/i66+/liVLlqiAwYACXzy0mBro+sh8tcErDYxHmgx1zmDBtWBGwCWXXCJVq1aVevXqqUDF5/jx466AmeqGTZs2ydq1a1XMnDnTVNZC3Ze2PX/+/DLn33Pk008/VQEtg664ATrnZpbiXYOKr4ayZctK1qxZ46I52n16uY5GgK+kLuBivQqChZNmnUEua9nt2Msuu0xQztMC31jXsWNHNeiNYDiC4giOuwXkcbybEdC2bVsTkB88eLBo6N27txpQR+CtQ4cO0rRpU2nSpImKdu3aSY8ePVT06tVL0NBh2LBhKmBAwNzQgPPi+L7P9lWBbQj4a8B5g/3vyy+9LDNmzAgAzFPopgYYNChPuT3bUOuttKF9hzo22u2xDoIFZE6uyBQGvNLA888/X/LkyaPWh1BPKl68uFSpUkXF3XffLSiTOctzN9xwg6nn7t69WzQgX6LurQFlu7vuukvsPbXtv2ES1KhRwxUI2LsBx+J+NMDwwHYE9TVUqFDBGAUwC5zA9UI3EJjXkDt3bilUqFBUuOKKK+SCCy5QgcA7DBM8Bw0jR440dWjUo+2A8QuzRgPKqyg3uv0ntmn3iHVICzQCopSLVKoARvuy43GJ3TLfq+fDIFiUopNgh3mlgXDSly9frgJBIRgBbgEaBJz++uuvAKCCgSCMBlQQV61aJT/++KMKBJndPn/++acJqqMFqQYElOwv+1j+djMfwlmPgL4bcLzbdeJ+EJyLhisEz+bOnSsTJkxIh8GDBpsC5nXXXScaxowe40a/MQLQYwM9FTSgIO+WlpAm8H/x0ElqoOsj89UGrzQwHmky1DmdFUf7MoIobsejEgKT1a3F19QpUwUGrQboNUwAZ6t7axmG5TfffKNi4cKFpqLsdl3B1hsjYM4c+eyzz1QENQIOHpID+92NgIkTJ7pyAZ5oBPhKAlL2Yr0KggXLt9FuQ0tJBLgQXNeAHk0I+Gvo0qWLIKCvBc2xrlXLVq5GAILqMADcWrv36d3HnBvndwLnhgnQqFEjFVbAHoF5J9CLAT0N3HppYX+3+0EPAxgUePdpwLZgBgTuF407NLw0/iW1d+a0adNk6tSpKgYNGpShnpuaAWCtizY9hTqORkByyKZXGggjAK3VCxQooAJB88qVK6tAANwK/iIAbAGt7tGQzq1HN8y4hx56SAVa9aNHvT34b/99b7l7XQP9bgaAtf6+8veZhmBoDOYE/rNcuXLGDIAh4ET5e8tL+fI6cCx6fGut5LHu2jzXSpEiRVyhBeStdVddeZVceOGFKtA6/+abb3blCkapVn+GEXDTTTepgGGBciPShYasF9EIiIvipFIFMNRLjdsZ8I8kDTAIFhdJyvSTeqWBNALStxRwC8qHE/B328fNBMB6GgEZ13tqYKbLVVz+0CsNjOR9G6t97YF/528aAbbeATQCXE0he1pE3uHHvwx4FQSzp6FY/aYRkH7YNhoBZ5uhPWKVvpznoRHgX92zX7lXGkgjIM0UoBGQZgzQCBDh0EAcGiisiofzZczljAe0ouGQQTB7UcK/v70KgtEIoBFg7xnAHgH+1RC/X7lXGhjNezejxziD//ZlGgE0AiJNXzQC/K1+XgXBIk1n4exPI4BGgNULwP4dTtqJZh8aAf7WPuvqvdJAGgE0Auy9A2gE0AhgEJxGiK/SAI0Aqxjh72+vgmA0AmgE0Ajwt3Yky9V7pYHRBB8yeow98O/8TSOARkCk6YtGgL9V0KsgWKTpLJz9aQTQCLAbANbvcNJONPvQCPC39llX75UG0gigEUAjwMqFad/sEcBAuK8C4dEUHJLpGBoB6QXMr0teBcFoBNAIoBHgV9VIruv2SgO9KA84g//2ZRoBNAIiTZM0AvythV4FwSJNZ+HsTyOARoAV/Ld/h5N2otmHRoC/tc+6eq80kEYAjQAaAVYuTPumEUAjgEaAj9IAjYD0AubXJa+CYDQCaATQCPCraiTXdXulgdEEHzJ6jD3w7/xNI4BGQKTpi0aAv7XQqyBYpOksnP1pBNAIsBsA1u9w0k40+9AI8Lf2WVfvlQbSCKARQCPAyoVp32edPn1ajh49KuvWrZPXXntNXnrpJRk3bpxMnjxZVq5cKTt27JBjx47JZ599JrNnz5ZRo0bJ+PHj5eWXXzYz1G/YsEH27duX/qwhllKpAhjNi47HeDP+vh94pxEQQlyi2JxKGlindh2ZMmWKivnz58svv/wiR48cVYH3wIkTJwIwdsxYufHGG1WUKFFCGjduLG3btlXRrVs3ea7vcyr69OkjHTt2lHbt2qlo3669tG8fP3Ro30EiRocO0iEY2ncIes1uPGF9MK66d+8uDz/8sNSsWTMdKleuLEWKFJF8+fKpGDlypPz5x58qfv/9dzl8+LAcOnhIxafrPpXp06arGDx4sOTKlSsuJjM1MAqRC3FIKmlgZr/nERhB8P+cc85REcwIsCqtBQsUFA3PP/+8HNh/QMeBA3LgwAE5eOCgCkxe/vXXX6t488035frrr48q/1577bWmHrFo0SLRgADAsaPHVODdg+t1u6fp06dLvXr1VFStWlUuueSSqK45s9NERv+PRkAIQYtw8x9//GHquigDTZgwwdSBUQ9esGCBfPXVV6bMs3XrVlmyZImp/77wwgumDox68Pvvvy/IS3/++WfY/+pVECxUurOCuG7f2vE5c+aUVq1aqWU4lO0G9B8ggwcNVtGvXz/p1KmTtGvbTkXr1q2lZYuWKrANZaKePXqqaNOmjTz11FMqmjRpItjuVn7s2rWrYNJfDc8++6wMGzZMRo0cpaJ3r97SqmUrFfhPlNWe6fmMii5duqhlXZSBwRPy/cABA1WMHzdeZs+aHYA5/54jb7z+hgrEc/Lmzau+l/C+wntLe+bWOrd0Yq239ovlN42AsGUmoh1TRQOtMlWB6wuIhuLFikvlSpVV3HPPPXL11VfLpZdemg7Fixc3MdT9+/eLhhHDR0jRokVVFC5cWPLkySOo12hAw61CBQtFBdT7cufOrQL/ibxvbxjm/J3vunyiAcdefPHFkuVfWVRky5ZNLr/88uDIcblcriBr1qxy7rnnqrjwwgvliiuuUHkCd4gZ/Lz15wC8/NLLUqtmLRXly5c3z9KtfJ4lSxbJnj27CpQ3cVws9S2cc8VaA8/666+/ZOfOnSbwjwJ27dq1pUaNGvLQQw+Zl+yyZctMcOjFF180he8777zTbK9bt640bdpUUGFAISiSD40ABrrDSezcJzCdMAgWidKEt28qaSBeev3791cBg3f79u1y/PhxFQjeHDlyJACDBg2Sq666SgUKA3ipMy8H5uXM4ASFXhQE8+fPrwIVWu2ZWuvQSMANK1askCFDhqiAGXLllVfG5blTA8PTtUj2SiUNzIx8Z/8PBEVQWXCr3IQKttjP5fzduVNn+fXXX1X89ttvsnfvXtNQB411nNi6ZasJciLQ6QQCoNEaATAABwwYYBoWoXGRE59/9rnAZNRw4vgJYzrCwNDw+pzXBUE8DaiToMLm5CgZl2kERKJuofeF4f3555+bwDDSEQx11INRx0XdFw3iUBdGw4S77rpLypUrJ9gPwDv0iy++MGWm0P+UtkciGwFuARHoFLTMmZ9Q9kNwe/To0SpgmqBsqQHHIJjv1niibZu20rJlSxUtWrSQZs2amWeE5+TE448/Lg8++KCKBg0amP9F4EhD7969VRMAxkDfvn1lxIgRMmbMGBU4FtemAYYJ0lDnzp1VtGndRp5++mkVON8zzzwjMCI0jHtxnLz++usBgJa/8847KtDws0CBAnLBBReoCBXkQppwg5ZWnGknmuVYB8HCzbPJvl+qaCDqRAhko3yjoVixYlKpUiUVZcuWNfkFx9tx7733yvr16+XQIVvvRtvvHj16BOhmNGmfxwSvR7uVh9944w1X0xfvCfRqc+P2Xxf+SzUBUNZMGiPg5MmTsmfPHpOIly9fLt9++6189913puXDAw88cKaQgxc2XnBogYQC0+bNm40BcPDgwYhaQkBMaQQET8xuCZLryRuDYLEvjqWSBtIISC0NoREQe71IxjOmkgZmdjmKRkB6M4BGQMbfQTQCYqvCML5/+ukngbmN3vE//PCD6SkzcOBAE0hetWqV6R2AAGzPnj0FgQXUgQE0pEPjCfSqCvdDIyDNGKARkN4QoBEQWhtpBISrMpHtlyoaSCMgdB7L7DJyrP6PRkBked7a2wwNhEIMWgvBEECF8NSpU7Jx40aBa46ue2gJMXz4cEHLT7TuiXQoIOvPrG8aAcmbEWOVoXkePY3QCLBUJHbfqMCligbSCNDzVbLqDY2A2OlEMp8plTQws/M6jQAaAbFOczQCYqvGGNYHrTl37dplesj9/fffgl5Sr776qqBB3MKFC2XGjBmmPozGcGvXrs3QBdAIoBGg9QqgERC6fE4jIEPS43pwqmggjYDQeSzW5ZXMOh+NANfsHXSDOlkwjIBNmzaZ7mkI/r/33numZ4C9RwCGA0KhCeOKodDk9sG50OUI3ZfR4gLAeHeZlTD4P8mb6VPx2dIIcFOa2K5PVg2kEZBaekgjILa6kEpnS1YNzOxyA40AGgGxTnM0AuKrxKjTolHc1KlT5bHHHjO9AaZNm2aGZMHwLOgRgLoshgyCgRCqNwDmVsIwXag34zjUqQsWLJhw9WBLq7ThgTg0UNqcARwa6J8ytNuwQG5pJRY6SCMgvtpnnT1ZNZBGwD/5Nxb5MZHOQSPAyr2RfatGALoIrVmzxoyRhUn/MFTQ2LFjpX79+oLJH++//34zNh+69aFQAzPA7QMTAIUeFFwxmSGAiSUTKfHwWpJXGJLt2dIIcFOa2K5PVg2kEZBaWkcjILa6kEpnS1YNzOwygRVc4xwBaYYAhwbK+DuIRkB8lRgmwO7du00v+MqVK8snn3xixljHhK0YJxrlKNSHMbnru+++a+bRCWYGYMjdSZMmmcZ1qANXqVIlISe2trSKRgDnCMC8AZwjIL46k8hnT1YNpBGQ8fJHZpehw/0/GgHRKUo6I8DqDrl69Woz2Vfr1q3NZMAYCmjlypWCFhEI/o8bN850kcQEQRgq6JtvvnH9d7SEQCEIk9WgmyWAiZjCfbDcL3kzLZ9t5M+WRoCr1MRkQ7JrII2AyPOcn3WKRkBMZCGlTpLsGpjZ+dkKrtEIoBEQq7RHIyB+koyeUJhoe+LEiWYSXMwLsHXrVtMgbv78+ab+i4Zxo0aNMkYAhs2FGYCeAW4f9Ij/6KOPZPr06aYOjFblKMvHKj3E6jyWVtEIoBFAI8AtNyf/+mTWQBoByVsHphEQnTalMwJ+//1308J/xIgRUq9ePZk9e7ZZhihgUuBjx46ZLpBo+YAeA/3795dGjRrJ3LlzI/p3FGJjVXDheZI3U/PZBj5bGgERSU3EOye7Bt59991msjtMeOcEKrbbt28XcKAB8ygcPXo0ABg+7qqrrlJx+eWXCwJgzMuBeTkzOAlV6EUQQ3umWHfs6DEzdwaeuwZMrIhnr6F9+/ZyxRVXxOW5UwMjlrWIDkh2DcyMfGf/j2DBNQTcMIyCff9Ifnfo0MEMN4IhR5zAsCX79+83ZXeU353A9u+//17F4sWL5aabbpIsWbKoOO+881yvOVeuXIJJVqe8NkXF559/bnoRoyexE0h76ImCoKoG1DWaNWumAi2ts2fP7npdkfCa6PvSCIhI0sLeGSYo5srDuw1z5MEE+Pjjj03dF+9A5CHMHYA6MPZDQ7iOHTsaQwD5KdyPX+cI0PIFynhPP/209OzRUwXKBzBONKDMibSMYYc19OrVK6CcapVbe/ToIeihAQ3U0KJFC3nqqadUNG/eXGDGaGUXrENsA9elYcCAAeZe0CBSA45FmtCAeQFwT3369FGBe0MvEw24X+jq0KFDVbzyyisyZ86cAMycMVOmTJmiAvd63XXXCcqJGkL1CMC7zW14IGzT0ktG13FooHBVJrr9kl0DYXDly5dPihQpoqJUqVJStWpVFWhIV6hQIZNnkG8slCtXTpYvW26GVMew6k60a8vh0DOa78M5vmXLlqZhOhqn24H3NLZpQM++nDlzmjiF1ljnoosukhw5cqjIli1byF5T4Vx3pPvEWgPPGAEo2Gzbts28nPBSxUt7y5YtZ+YAgBlg7/qIFhOLFi2SWrVqyYQJEyJSHLxcI71x7u9NIIm8JxbvDIJFJDUR7ZwKGohh2awh2pzfqLjgHQAeNKCrqDNwg+UhQ4aYoC8Cv07gBUojwDsNQeUOhVUUXjWgh5/2TLEOk4dZ730tPSBY4lahRSUchat4vD+ogRHJWkQ7p4IGxiNNhjpnvAImCIp/8cUXKhBsROMd5GEN6K2LoTs1fPnll2aS1Ntuu000wPh1u2cYARhSdNrUaSo2bNhg5hVDwMEJpD8EWt2wZMkSNUiGwBmCfnjfuF1XMq1HHYqf2DKAtIh88uabbwreXwjKIr3ZG8BhO/bDB/kHw+b269fPNJzDMLnhfhLZCHAL7LoZljAF8U62gmJMsW2IAAAgAElEQVTOb0y2jOC4Bky8jAaH6GmhASMJvP/++yoQf8AoBZMnT1aB0QdeeuklFQiao3fGzJkzVaA3yAsvvKBi/Pjx5n/djsUQUJpBgHUvv/yy+d9Zs2aJBnCA3iUacL9ImxpPWPf666+r94I5LerUqaOiQoUKZogqvJ/ckGi6GesgWLh5NhX2SwUNROOGO+64Q9AoTgOGbYNmacCw6CgP3XDDDelQunRpgVHnZtJVrFgxJcolXmtFpUqVBO8UJ9Aw7aGHHlJRrVo1yZs3r8DQ1oCYRqI1dIy1Bp4xAjB8D1wTFIDGjBkjqAigBQQ+KJzjNwID1geO14wZM0xQCRMqRfKhEeBdYMjrjMr/z9izZxAsEqWJbN9U0EAEg2vUqKGiVatWxghwYw2VYLwDnBg6ZGiAAWAZApdddhmNgLMyluczoplo/YKKeeHChVXgXe98ntYygnFW0ENLEzAC0LpNAwKUNAI01hJ7XSpoYEbyU7THugVZsD7ac+K4p5s8bcYvxxjmTqAMj2Cl2wfGrtbzC+vQUwAtosuUKaMiT548rtd9xgiYNs0EzBCsswPX5fr5W+T0qdOqcYH3z9L3l5rhWjAsqRNNmjQxFbmM8OmXY2kEuKagqDfAEEPQGS2+27Ztaxq6oYek9cF7EfVg1IfxQa85DPeDluzoGY/hg8L9JJMRAIMA5QwEJzRUrFDR9KxA7wonEDhDcNst2I9hij/99FMV4H7BggUBLeCtVvGzZ802MQrEKZyYNXOWzH1jrsybN0/F9GnTTdAegXsn0PARQXzrf5zfM6bPUI0JGBaIleA/cc8ali1dZnqgoBeKE7jfpUuXGnMKBpUTMAOc94nlDu07qAFPBEEREEVg1C+6h+uMdRAs3DybCvulggaihTfmOkXrfg33V79fDRgjkIxhze+8807TWxI9Ji2gFwF6GaHHj4Yypcv4Ko/5SQ/s13r3XXcHlAtRTmzSuIlptI6G607APEAPEbdg/9VXX22MbsTenECcw4uGjrHWwLNQqEFlAQV1OGBoVYOX1ebNmw127txpuhejgghY6/ESQ2EUXfPwQorkQyPAu8CQPdPwt/+eA42ASJQmvH1TSQNpBPgvz2dEp2kEhKcBqb5XKmlgRvJTtMfSCPjHDKARkPF3EI2A2Co2TO+NGzeaoQMeffRR07pz3bp1pr6LIbcwJBB6S3711Vfyww8/mPXr1683LcbRcwC9IlFXDvdDIyDNFKARkN4QoBEQWhtjHQQLN88m+36pooE0AkLnsWjLuV4fRyMgOpU6C61/EOBv166dadV5++23CxySunXrGgwbNsy0jEDBE60erC5mcMfatGljHGoMExTJh0ZA8mZEr4Ug2f+fRkAkShPevqmkgTQCUkt7aQSEpwGpvlcqaaAXZQQaATQCYpnuaATEVrFRh0WL6ltvvVUKFChgWlCj5SDqwU2bNjWtt9ESHD0FMKQi6sHY9sgjj8iLL75ohgiChob7oRFAI4A9AqIri9MICFdlItsvVTSQRkB0+S6W5Zd4nYtGQGR53tr7LLiAEICFCxeaLpEYF8/eFQ5d9jDpAiYPQ68Ba8w9DCOE8ftwLLpMRvKhEZC8GTFeGZznTUszNAIiUZrw9k0lDaQRkFraSyMgPA1I9b1SSQO9KEvQCKAREMt0RyMgtoqNianR2h9D5Q0fPtzUc616MMaSx7wWa9asMeOwwxBAPRhj0GPoFczRYR8yKJwroxFAI4BGQHRlcRoB4ShM5PukigbSCIgu38Wy/BKvc9EIiDzf44gzcwREd3h0R9EISN6MGK8MzvOmpRkaAdFpTqId5ZUG0ghILe2lEZBoOZ/XYzHglQZ6UZagEUAjIJbpjkaApSL+/KYRQCOARkB0ZXEaAf7UPOdVe6WBNAKiy3exLL/E61w0Apy5LLxlGgEeTuQYr8zA8yav0NEICE/YEn0vr4JgNAKSVxs03acRkOhKkLrX55UGavkk3utoBNAIiGUaoxHgb930KggWKg1CpzD5rxu04zlZ8Jx0EwdzsuD4lrFpBPhb+6yr90oDaQTEN39q74jMWkcjwMpdkX3TCKARwNnMfZQGaAREJnCJurdXQbC8efNK2bJlVTRo0EA+++wzM9wbhnxzYsf2HbJ169YA9OjRQ7Jfml3FJZdcIuecc46rxoSqeGK7WyEiWHAtFbdpPJ177rmSPXt2yZkzp4ouXbrIf7/5rwrMHbR582bZsmWLiimvTZGGDRuqqF27tlx66aWuz0671nDXUQMTVdUiuy6vNDDcdOaH/apXry7jxo1TgeE7165da4Y1wdAmTixZskSmTJmi4vnnn5c777xT8uTJoyLbJdlc83aOHDmMJnTu1Fk0zJo1SzZ+v1HFd999Z4Zo+XLDl6IB9/po/UdVgIts2dyvyw/PM9xrpBEQmdYk2t5eBcHCSV/Byk7a8dgf5Yzzzj1PRf78+aVcuXIqKlasKPXr15cnHn9CRYsWLaRL5y46unSR7t27m4mdUQZ1ok/vPtKvXz8VAwYMMENAjRwxUjRgEmPso2HgwIGCuRNHDB+hAhNHa8dhHY6FtuJ4DfjfIYOHqECeBx+NGzVW0eDRBlL3gboBKFWqlFx77bUqrr76ajnvvPNctVx73l6voxGQaGoW3fV4pYFZsmSRO+64QxA01lCpYiWpVbOWiipVqshNN90khQoWSoebb75Z7r//fnnwwQdVFL2hqK/ymNd5PNr/L1y4sPrc7rvvPilRooQKzIsLfbzm6mt0XHON5Loml4orrrjCvPuivd5oj4u1BtII8FEQONpEw+OSxwFlECy6QkeiHeVVEOyqq66S2267TQUCKZj3Zf369So+/fRTE1hCcMkOVE7QykIDXlhoMeamQdiGSqQGGAjYHqxiiu2pBHCiweLJyTO4wzbtGKxDUG3BggUq3nrrLVm6dKmsWLFCBdJwhQoVVKDyifTgvJ5YLFMDE03NorserzQwFmkwUc5x+223S6NGjVS0bt1axo8frwb6YQB07txZULHVcNddd0nWrFmjyr/I96VLlxYE+TQ888wzZk4yzEvmBLQIRgHGXtfQvl17KVOmjAq811DJT5RnE8/rQN7hx78MeBUEi2eajMe5Yey5BbELFiwo5cuXl6pVq6p46MGH1EYKaLwAzYQ+YvJnDe3atZP27dtHhTZt2kjLli1VoKzcrFkzMwE1JqF2ol69eqoeQ6PvueceQeAJuqzh/PPPTwnti3UQzL8q4u8r90oDkX4QuHcLDJcrW841D957772CXvVoUGcHzE6UP9B4QgOCzBnRx2B14IycN9mOveyyy6RAgQIByJcvn+t7JE+eayV37tySK1euiAE99sJIjbUG0gigEZAhgUo2IUn0+2EQzN+FH+vqvQqC0Qjwt3HgFtCnEWDlLH77hQGvNDDR3/GRXB+NgH9MARoBfsn5vE6vgmCRaEsi7Esj4B+jlkZAWoO+WAfBqEbeMOCVBtIISJ6Gsc53FI2A6PIyjQAaATQCfJQGaAREJ3SJdpRXQTAaATQC7GYCewQkmjKkzvV4pYHOyoOfl2kE0AhIHcVInjv1KgjmN62jEUAjwJlmaQQkhw56pYE0AmgE2HuZsUeACI0AHwWBnS9ELievoLk9WxoByVEI8ioIRiOARgCNgOTQEL/fhVca6PZu9eN6GgE0AvyuA6l4/V4FwfymcTQCaAQ40yyNgORQTK80kEZA8sbN2CMgOm2gEUAjgD0CfJQGaAREJ3SJdpRXQTAaATQCaAQkmhqk5vV4pYHOwIKfl2kE0AhITfXw9117FQTzm9bRCKAR4EyzNAL8rX3W1XulgTQCaASwR4CVC9O+aQT4KAjsfCFyOXkFze3Z0ghIL2B+XfIqCEYjgEYAjQC/qkZyXbdXGuj2bvXjehoBNAKSSxVS4268CoL5TeNoBNAIcKZZGgHJoZFeaSCNgOSNm7FHQHTa4IkR0K9fPzn33HPlwgsvlAsuuEAw2z1+I4MS7hyQK3durHSDdIT0BK6sdcn0jZnNH3jgAdm8eXN0OZ5HJQQDXmlgnjx55Pbbb1dRuXJlmTx5srzzzjsqFi5cKPPmzQtAw4YNBS9gDZdeeqlkyZLFNS9iW9asWVVcdNFF6Y5Fnra/J3BsKgF8BIOlc04NDHZM3bp15dVXX1UxceJEmTlzpsyePVtF586d5d5771VRsmRJkx6sa4rlNzUwISQswxfhlQbGMi1m5rmc+Rr/DS1/9NFHVTRu3FgGDhwoI0eOVNGiRQupWLGiijJlykSdf/EeKFWqlJQvX15Fu3btBNqiAVr0wgsvyJgxY1Q0bdpUSpcurQJcZM+ePanrFEgDqDsh7/DjXwa+/fZbueWWW0xdxdIQ1u8C63c5c+aU/Pnzn0G+fPkkb968ct1110nhwoWlXLlyqn5B12rVqiX169dX0aBBA2nUqJE0adJExdNPPy3QmmgA3X3qqadUoKz8xBNPuKJOnTqu93PXXXcJyj45cuRQcckll5wpZydzWoI5VKRIEXnppZf8KwC8cvFKA1EnhfaWKF5CBSbldisXlS1b1qQ9uybhd8GCBeW2226TO++8UwVaoFs6H4tvZ104FudMhnOgoSPeCwCeSYECBaRQoULm2/nMrGXrnYL3SqSAHiN+kdncxVoDPTEChg4dKnjB4wHhYSA4BWEvWrQoEYQDchU6fSAdIT2Bq2RMT7gvBN82btzIooSPGfBKA1EAuuOOO1QgeFu1alWpWbOmiho1aoiGu+++W2666SZX3Hjjja55EdvCgT1fh7N/qu4DzYuEKwTV8MzdUL16dXEDdKhEiRIqihUrZtJDPDSYGuhj4bNdulcaGI80mRnntOdr6/9Q8USASAN0uUKFClKpUiUVqNTGI//iXYD873ZuBO/c9AbrYUi7AZV0nFsDuMB/J3M5GXUm1J2Qd/jxLwPff/+9MbMQILLycjKnW+seI/2++eab5dZbbz0DLIMnBHpg/BUvXlxQbtUAMxKGpgboJfQRehIP4NzBoOk11qE85qabuFeU393K2ijzWvwmc1rCezB37twyYsQI/woAr1y80kDkH9SBtTIE1iGfBcuD0B0E/Z3AOd2MAGiYlTcj/XbWZe3lQHuej/S8ybg/3g/WcwFP119/vdFMrLO/R2L1G3rsxTOItQZ6YgSsWbPGiDgcXbjyeGEOHjxYbZXo1loxFdeTK73lqj0tIB0hPYEr+/pk+T127FjTanvv3r0sSviYAWpg6Lxsz7PJnq/t95rR38nOFTXQx8Jnu3RqIDUwo1qnHZ/M5WTUmRAAQ97hx78MoPyO3pd4l1lpOJnTrXWPGf1O9rJNRvmxjk/mtEQN9K/u2a+cGhhZ+c/K29TA8HijBtpzW/DfnhgB9ktC4n744Yflp59+sq/mb4UBcqWQ4liFdIT0BK74IQN+YID5OvRTYr4OzZG1B7mymOC3XxigBoZ+UszXoTnCHkxL4fHEvRKLAabb0M+DGhiaI+zBtBQeT9wrsRhgug39PKiBoTnCHkxL4fGEvWgEhM+V53syYYd+BBTJ0Bxxj8RigPk69PNgvg7NkbUHubKY4LdfGKAGhn5SzNehOcIeTEvh8cS9EosBptvQz4MaGJoj7MG0FB5P3CuxGGC6Df08qIGhOcIeTEvh8YS9aASEz5XnezJhh34EFMnQHHGPxGKA+Tr082C+Ds2RtQe5spjgt18YoAaGflLM16E5wh5MS+HxxL0SiwGm29DPgxoYmiPswbQUHk/cK7EYYLoN/TyogaE5wh5MS+HxhL08NwKmT58uTz/9tPz888/hX3WK7kmuQj94pCOkJ3DFDxnwAwPM16GfEvN1aI6sPciVxQS//cIANTD0k2K+Ds0R9mBaCo8n7pVYDDDdhn4e1MDQHGEPpqXweOJeicUA023o50ENDM0R9mBaCo8n7OW5EfDRRx/JpEmThJOfhn5o5Co0R0hHSE/gih8y4AcGmK9DPyXm69AcWXuQK4sJfvuFAWpg6CfFfB2aI+zBtBQeT9wrsRhgug39PKiBoTnCHkxL4fHEvRKLAabb0M+DGhiaI+zBtBQeT9jLcyPg119/lW+//VZOnDgR/lWn6J7kKvSDRzpCegJX/JABPzDAfB36KTFfh+bI2oNcWUzw2y8MUANDPynm69AcYQ+mpfB44l6JxQDTbejnQQ0MzRH2YFoKjyfulVgMMN2Gfh7UwNAcYQ+mpfB4wl6eGwHhXyr3JANkgAyQATJABsgAGSADZIAMkAEyQAbIABkgA2SADJABMkAGImWARkCkjHF/MkAGyAAZIANkgAyQATJABsgAGSADZIAMkAEyQAbIABkgAz5igEaAjx4WL5UMkAEyQAbIABkgA2SADJABMkAGyAAZIANkgAyQATJABshApAx4ZgScPn1ajh8/Ljt37pQff/zRYPv27XLgwAE5depUpPfh+/2PHDkiuH+Li23btsmff/5p7uvvv/82nICbrVu3yqZNm+Snn36SX375RX7//XfB9lT44D5Pnjwp+/fvly1btsjmzZsNF/i9e/duM88EuUqFlJAc90gNTP8cqYHp+dCWqIEaK1znVwaogemfHDUwPR/aEjVQY4Xr/MgA6ngHDx409TrU/VCv27Vrl6kb+/F+YnHN1MDQLFIDQ3PEPfzBADUw8DlRAwM5ca6hBjoZiX7ZMyMAJsD69etlyJAhUr16dalatap06tRJ5s2bZ8yA6G/Jn0euWLFCOnbsKFWqVJFKlSpJmzZtBGYAPjBGjh49Km+88YY0bNhQatasKfXq1ZNevXrJDz/8IH/88Yc/bzrCq4YJgBnTZ8yYIU888YTUqVPHcPH444/LuHHj5KuvviJXEXLK3b1jgBqYnntqYHo+tCVqoMYK1/mVAWpg+idHDUzPh7ZEDdRY4To/MoA63ptvvmnqddWqVTP1upEjR5q6sR/vJxbXTA0MzSI1MDRH3MMfDFADA58TNTCQE+caaqCTkeiXPTEC4ORgRudBgwZJ9+7dZeDAgTJixAjp0qWLdO7cWb7++ms5duxY9HflwyPXrVtnOOjWrZvUqlVLateubVq741bgDn7wwQcyfPhwadu2rQwbNkx69+5tjJNZs2bJxo0bfXjHkV/yX3/9ZVr+L168WJ5//nl54YUXZOzYsSYdNWnSRKZPny4///yzLFu2LOW5ipxdHpGZDFADA9mmBgZy4lxDDXQywmW/MkANDHxy1MBATpxrqIFORrjsRwagf6jLdOjQQZ599lkZOnRouroMeoAj2JFqH2pg6CdODQzNEfdIfAaogfozogbqvNjXUgPtbGTstydGALoCofU2egLADMDwQCdOnJCJEyfKQw89JAsXLpQdO3Zk7M58djSMkS+++EI2bNhgCoV2I+C3336Tfv36Sd++fWXOnDmmx8Tnn38uo0ePNoXIRYsW+exuo7tc9IxA4RjBfgwLhJ4QEINvvvnGmCcoTK9cuVJ69uyZ8lxFxzCPyiwGqIGBTFMDAzlxrqEGOhnhsl8ZoAYGPjlqYCAnzjXUQCcjXPYbA9aQaGj9j17gH330kanbYHgg9Hbu37+/qSOjXpxqH2pg6CdODQzNEfdIbAaoge7Phxrozo21hRpoMZHxb0+MAAT+lyxZIvXr15fJkyebYC5EAS250dIdAe5PP/0043fnozMgoH3o0CET5B4wYEC6HgEYN/Lhhx82pgl+IwCOIXLAFwyDV1991Ud3Gv2lwj1G5kcAARxgGcAcAc2aNRPw9u9//1vuv//+lOcqepZ5ZGYwQA0MZJkaGMiJcw010MkIl/3KADUw8MlRAwM5ca6hBjoZ4bLfGMDcbt9//73pBYDhXmEAoG4DTRw1apSpv6C3N+ZDS7UPNTD0E6cGhuaIeyQ2A9RA9+dDDXTnxtpCDbSYyPi3J0YAWnOjZXvTpk3NuPfWbSD4DxOgT58+snz5cmt1ynwj82MCYPSSsPcIwDwA5cuXN/MpYExdmCYIhP/nP/+RcuXKCVqVpOoHL5Mvv/zyjFHy7rvvSpkyZchVqiYIn9w3NVB/UNRAnZdga6mBwdjhtkRlgBqoPxlqoM5LsLXUwGDscFuiMYA539asWWPqKZgbbvv27eYSEfifPXu2DB482NSFMXFwKn6ogZE/dWpg5JzxCO8YoAYG554aGJwfbSs1UGMl9DpPjIBvv/1WpkyZIu3bt5e33nrrzFUioIvW7ZgrAGMnptrHLeOj5UjJkiVNodHOyXfffaeut++TzL9hiGzdutWYSZhAGRMGYwLq4sWLk6tkfvBJcG/UQP0hUgN1XtzWUgPdmOH6RGeAGqg/IWqgzovbWmqgGzNcn6gMHD58WN5//31TT0HDNwwFgc/Bgwfl7bffNusxdx6GhU3FDzUwsqdODYyML+7tPQPUwODPgBoYnB/nVmqgk5Hwl2kEhM9V3PeMNOPDIChVqlRA0DvuF5oAf4BMD/cPEwQ/9thjZvJg9ChBcEEzTVKZqwR4XLwEBwMMgjkI+d8iNVDnRVtLDdRY4Tq/MEAN1J8UNVDnRVtLDdRY4bpEZ4BBsOBPiBoYnB/7VmqgnQ3+9gsD1MDgT4oaGJwf+1ZqoJ2NyH97YgSwS7j+oNwyvjY0EMbJT9WhgTA22L59+2T+/PlmYuXOnTvLhx9+KHv27BFypactrk0sBqiB+vOgBuq8ONdSA52McNlvDFAD9SdGDdR5ca6lBjoZ4bJfGOCwGMGfFDUwOD/WVmqgxQS//cYANTD4E6MGBufH2koNtJiI/tsTIyDUJHGYLCnVJgvGI3TL+JwsOH0CR/fZTz75xEwQ3L17d1m4cKHpUou9yFV6rriUmAxQA/XnQg3UeXGupQY6GeGy3xigBupPjBqo8+JcSw10MsJlvzCA3szopTxkyBDhZMGBT40aGMiJtoYaqLHCdX5ggBoY/ClRA4PzY22lBlpMRP/tiRGA1uyYD6BatWpmYlxUCE+cOCETJ06Uhx56yAR2d+zYEf1d+fRIt4yP8SOfe+456du3r5lk+cCBA/L555+byaQ6dOgg77zzjk/vOPLLhvu3bNky6dq1q7Ru3drMDwAhOHXqlDkZuYqcUx6R+QxQA3XOqYE6L/a11EA7G/ztVwaogfqTowbqvNjXUgPtbPC33xjAUAbHjh2TkSNHSpUqVczEwajH/Pjjj/LEE09I//79TR0Z9eJU/FADQz91amBojrhH4jJADQz+bKiBwfnBVmpgaI7C2cMTIwAP75dffhFMhtStWzfzPXz4cMEQLx07dpSvvvrKFJLCuYFk2Qfj5c6YMcNwUb16dbnxxhulZ8+eZvgb9I7A5MnDhg2TNm3amO9evXpJp06dZObMmbJx48ZkoSHofUAYMXkWWtEUK1bMGElIMy+//LIBJt/65ptvZMmSJSnPVVAiudFzBqiBgY+AGhjIiXMNNdDJCJf9ygA1MPDJUQMDOXGuoQY6GeGyHxlAIGzRokXSvn17M8Qp6jWDBg2Sxo0by7Rp02T//v1y8uRJP95ahq6ZGhiaPmpgaI64R+IzQA3UnxE1UOfFvpYaaGcjY789MQJwycePH5cvvvhCBg8ebAK6VatWFbRuf+ONNwQt3lPts3TpUtPCHTyUKFFCbr/9dilbtqxg6Jv33nvPGCdz5syRJ598UmrWrCn16tWTZ555xpgAf/zxR0rQha5kEMjevXsbfmCYPPDAA1K3bl0DGCUwTdDDJNW5SokE4fObpAamf4DUwPR8aEvUQI0VrvMrA9TA9E+OGpieD22JGqixwnV+ZODnn382jb1Qr0Pd7+GHHxY0ikPdOFU/1MDQT54aGJoj7uEPBqiBgc+JGhjIiXMNNdDJSPTLnhkBcAJRCUQLb3SHBLZv325MAGuYl+hvy39HYgb1bdu2GR4wdiQC3mjpj54T2Ab3CwbJli1bZNOmTWYsfGxDZgCXqfDBfeJ+MfwP+EGawYSDFmAAoLstuUqF1OD/e6QGpn+G1MD0fGhL1ECNFa7zKwPUwPRPjhqYng9tiRqoscJ1fmQAw6NhSCDU61CfwRxnu3btMnVjP95PLK6ZGhiaRWpgaI64hz8YoAYGPidqYCAnzjXUQCcj0S97ZgREf8k8kgyQATJABsgAGSADZIAMkAEyQAbIABkgA2SADJABMkAGyAAZCJcBGgHhMsX9yAAZIANkgAyQATJABsgAGSADZIAMkAEyQAbIABkgA2SADPiQARoBPnxovGQyQAbIABkgA2SADJABMkAGyAAZIANkgAyQATJABsgAGSAD4TJAIyBcprgfGSADZIAMkAEyQAbIABkgA2SADJABMkAGyAAZIANkgAyQAR8yQCPAhw+Nl0wGyAAZIANkgAyQATJABsgAGSADZIAMkAEyQAbIABkgA2QgXAZoBITLFPcjA2SADJABMkAGyAAZIANkgAyQATJABsgAGSADZIAMkAEy4EMGaAT48KHxkskAGSADZIAMkAEyQAbIABkgA2SADJABMkAGyAAZIANkgAyEywCNgHCZ4n5kgAyQATJABsgAGSADZIAMkAEyQAbIABkgA2SADJABMkAGfMgAjQAfPjReMhkgA2SADJABMkAGyAAZIANkgAyQATJABsgAGSADZIAMkIFwGaAREC5T3I8MkAEyQAbIABkgA2SADJABMkAGyAAZIANkgAyQATJABsiADxmgEeDDh8ZLJgNkgAyQATJABsgAGSADZIAMkAEyQAbIABkgA2SADJABMhAuAzQCwmWK+5EBMkAGyAAZIANkgAyQATJABsgAGSADZIAMkAEyQAbIABnwIQM0Anz40HjJZIAMkAEyQAbIABkgA2SADJABMkAGyAAZIANkgAyQATJABsJlgEZAuExxPzJABsgAGSADZIAMkAEyQAbIABkgA2SADJABMkAGyAAZIAM+ZIBGgA8fGi+ZDJABMkAGyAAZIANkgAyQATJABsgAGSADZIAMkAEyQAbIQLgM0AgIlynuRwbIABkgA/Y6JzEAACAASURBVGSADJABMkAGyAAZIANkgAyQATJABsgAGSADZMCHDNAI8OFD4yWTATJABsgAGSADZIAMkAEyQAbIABkgA2SADJABMkAGyAAZCJcBGgHhMsX9yAAZIANkgAyQATJABsgAGSADZIAMkAEyQAbIABkgA2SADPiQARoBMX5ox44dk88++0zeffddWbZsmRw+fDjG/5C6pzt16pQcOXJElixZIuPHj5ddu3bJ33//HREhBw8elK+++komTpwoH3/8cUTHcmcyQAb+YYBa9w8XmfVrz549Rvvwfjl06JBAE/khA2Qg8RnYunWrLF++XBYuXCjfffdd4l8wr5AMkAEykMkMUCczmXD+HRkgAyoD1CKVFnUl4mmIqyG+hjib2+evv/6SX375RZYuXSqzZ8+W/fv3u+3K9ZnEAI2AMIn+/fffTYL9+eef5YcffjAVuY0bN8rmzZtNokbAHwl89+7dJlDTtm1b6d69u+zYsSPMf0iM3U6fPi0I8O3cuVM2bdok33//vQHuecuWLbJv3z5zn5EG4GNxd5aAdOjQQYoWLWoEB9cbyQfCPmPGDClZsqSMHDkykkO5LxlICQaodd5rnVtC++9//ys33nijtGvXTrZt2yZ//vmn265cTwbIQJwZsBon/Pbbb6YsaJWXfvzxR0FZY+/evQI9RTll5cqV0rNnT2nUqJHMnz8/zlcW+9PDhIT+wMSwgPtFOXH79u1y4MAB+eOPP2L/xzwjGSADvmaAOvm9/PTTT6ZeDY30ov7s6wTEiycDMWIg2bUI94cYHmKTVjkN399++21A+Q3rf/31Vzlx4oQpo2aEYsTTEFdDfA1lX7cP/uubb76RESNGSJs2bUw91m1f+3rEHqGhaACHWKD2sWIXKI+jPHr06FFBWRxlV37cGaAR4M5Nui1ITDNnzpSmTZtKpUqVpESJEnLPPffIgw8+KD169DAtvZD4/G4EQEDWrVsnQ4cOlZo1a0qZMmWkVKlSUr58eWnQoIFMnz7dtMR3y4jpSIvxAo2AGBPK05EBhQFqnfdapzwWs4pGgBszXE8GMp8B9FBcvXq1DBo0SOrUqWPKS6VLl5bKlSvLU089JRMmTDAVMFRQ/G4EoBfmTTfdJHfccYcUK1ZMihcvLrjXWrVqCRpnzJkzx5gCmf8U+I9kgAwkMgOprpOoRz/yyCPy/PPPm2AWDdNETq28tmRmINm1CMFvxPDq1q1rymkoqwG33npruvKbtf65554zZVQE6DPyCdcIQKMY/BdG9IikMdvUqVOlfv368s4775jG19q1onE2yqH9+/c3jW0+/PBDqVatmmmcre3PdWkM0AgIkRKOHz8uX375pYwdO9ZU7NDSH4H/fv36yZAhQ2TAgAEm0b300kuyfv16XxsByJxwDdGToVWrVuY+cX8DBw4UiAVas82dO9c4iDQCQiQcbiYDPmOAWpc4WueWdDLbCEDLNbZec3saXJ/KDKAV/KJFi6R9+/bSvHlz6dy5s/Tp08eUl1A27Nu3r2n1NG/ePNM6ye9GAMqAOXLkkIYNG0rv3r3PlAtRHgYH6OkwevRoMzQmGpQE+1i6Qm0JxhK3kQH/M0CdHGjiBC1btjQxhClTpphe9v5/srwDMuAvBlJBi9DoBKN3vPzyy6aBChqpABUqVEhXfrPWI7CORswZjemFawREkmJQPrRG/Qjn/B988IEpgyM+++abb5oh2gsWLGjil5H8b6rtSyMgyBNHIkQGGTVqlGn5j1Ze6PYC1wmZDRkHXVUwXvO4ceNk7dq1rkYA9sVwO9gfx6NLNVreYuggVJpOnjx55kow3APG2EL3GgzJg31xDEQM69H1B5kD14BuPegCZHVJRzdtjL8VzZAR6M0AUYBTCMMDBgiCgzgXutasWbPGrMNvXC/uCe4qrgv3Yl0nrhv7YLu9ood1uA/8D67ROgaiZZ3zDAkiplsP7g/7gQcci0AYClT2oYHAB4Zmsva1rgNc47x2pxPXxqGB7CzzNxkQk0+pde5aB41GCwboFjQbLRmgR9AlDBcHHbRruNXqAZxamo99cax9bH9Lx6FT0HGcE/pl6TjOa9dQzQiAzuKc0FG8Y6B3OK/VRRTXjPPh3NBSXBNajWAffHA8NBn6CeDesD/2xfiNbL1GhSAD6RlA3sEYp61btza9ALp06WLKf+i+jPyC8sinn34qs2bNEgR+sN7NCMC+yHNWeQj5FJqBYYVQ9rLyP/4T5THkcWtfaAV0A9qEbdgX+R7dolHWsfTEOifWR/uBEYBKFcq70AWr3IVyIsaGrVixoulFioYj0BxcL7QFvwFctzW0Jq7fGk4TfEF/oF+4TgB8gDNLU3FfOJfVPdy6L2gq9sW5rA+0Df9n5wh6Bo2182ntz28yQAbiwwB1cr/RQWgzRhRArwCMKoB6tvVBPR5aD/2DnkHT8Q0Ng65BZ+0fnAt6b5XpoP843nqHQP+cx9iP528ykIoMpKIW2Z+zs/xm3+b2O1R50j4XAAL16CU6adIkU/aFPkHLUJZF2c8qe1nlWNRDUR7EenzwjTIr4olWeRDHo3Ey9kMjm1tuucX0qsL8qyjfWeVe6/pR3n7iiSfMyCWYSxTl0rx585pyujVMEv4XZU5cB+r1uDar7o1z4v9Rn8Z2fKCl0FRsg8ba6+o4DtdqjzFa1+KnbxoBQZ4WEuaGDRtMBefRRx+VadOmmYSAFzcSCTIJEhQSDRIDEgu+0YXaOUcAEhu6BaIggCGFMNxO9erVTSsyTLJhnzDj/9k7D3ArqqvvG0VFsRfUqIgtUWwoxLyW195iQ+xGY6xRY4gCHxp7CFYsCGiUiEYhIljo7QIXLvXS66sUQbFhEo0mJibRmKzv+c3Nhrlz18w5556Zc2fOWed51j3nTtmzZ83e/1lr/dfem0Y/dOhQL8OKKXmYd+v444/3sq5gueh81AFniM7NkHTKY5j2WWed5WXy05ipXyEfjA86Mdcks8sFlCgHh4z7Yxu/2ca9wsAxX/UZZ5zhXf+4447z6v3ss896ASfXmagHejnhhBOkb9++3ugCiBWGTNJxOZ7y3IfzIB7o/JTNeRdddJE3MoP79RMBGEsswueORQ/UA13369fPIw9cuUYEOE3Yt2lggwYM6+qC52FYxwLwYCKjpbp16+bhO5gEVl533XVSXV1dD78wMDBgCIqBW2A+gTIChjiBvDP4gOMYGIwou/DCC73jjjnmGO/dQKYtwUOHtxyvEQEYNgTneEc9+OCD3vyL4DRBP4KRlMP0Hccee6yHpWRLTJ8+3cNzygR3n3zySbn33ns9DGXeRqaFO/vss73hlbxL7GMaMA3UaQDbhGBMz549vaQJ+g3TA2Efub6K8wAGYNe5gHYYETB+/Hi58cYbvemEsIfACmwXbDEcD+cocc358+d7I1EZ7oydA1ZgD4FN2KpcF3uIUQhk6INR2IbYj5TJ2gSF2oXuuQcdScrhemANNiv28cUXX+xdi3qCbQSz7rzzTg+DGCUBVmL3dejQwcMgnCpsMvDnyiuv9M6lziR7kLDBfq4DCQDOueHh6Ij7B1M5tqqqyqsmx4Jtd911lzcknGPQKXYx9qdfn+6+7Ns0YBqIXwOGk3WEKZpFF0xTwbsCjGSRTPch2PXCCy94frDDa7J3sdt4N/hJTs5ZuHCht74dGAquMwUIU9BBSvNe4DrOvnTXsG/TQCVroFKxyP/Mg/abf1/Yb+w7sOTVV1/1RoKCN357kjil+0AEtG3b1pstpUuXLp7NhX2K/4utTBIc5WHHkjyCr4o9iO3IBzsQm5XRpczAQlyQ61EG9jE2X4sWLWTvvfeWQw45xLMjSdKmLD48Y/7HJ8ce5zf12XLLLeXb3/72+mmSmOUEnxu7Fb+eadDBULD39NNP96a5hESgnpTJ/bsphsBYYorueGxv6urq4FUkg3+MCIh4aARIRowYIQceeKAX+KHxEPiP+nCORgTAXOGsMVyc4BCNEbKAxo6jVFNTs75YAkU4Nww1x3BgCA/nECwnCw3DABIAR4kOQsdxw3yYG4vRCVwPp8jNh8YQdjoH/4d9cJKYXwvigesT5Oc6OLI4YpTn/5A9yjEs+sF1uSeC8ddee63n1EFaUKb7AEQMLcfh4zgcQxy2K664wgtioQMcZzro0qVL5dFHH/VABD0QvOIa3CusY5AIoOOidzeVEeXSaTG6YAVxKHHSjQhwT8O+TQMbNGBYF411BPE6d+7sBdPBK4wHMBeMIQDPtGkOw8FJRodh6IBXOHQcC/4RZGebI3/BOhZOIpOB/eA832AexgYYieOHUcLHTwSQcYFwLnN0Y3wNGTLEM7g4nuAY12a7m8qDb+pw2223eRhLsNIF6wj+Q7ryPqK+ffr0kXnz5nn4v6Gl2C/TQGVrABuQfof9hm04fPjweiRgmHbCiACSGLBXsG/o//zGRqKfMrwb24UP9hbYQOIF/Rpbh36KnQQGQDxi33EdtrNGAXYWvx2uYK85O47ED+xChN+5PlzbPyLAfzw6wT6mzt/97ne9YdnYoNhbTCXEulrYYtSHupAMwjXBSUhQ7gmClbqiB3QLtmLXgU+QmkxLyflMw4QdzbFgLPcPnkKYYG9SHpgMjnEtVyb2KKSmI1b89bffpgHTQLwaMJysIwLAW3QxcuRID9Pwj3lnuA82HWvvkSgIpoN/JIwQFGOKXqYcBtcdvuHngqXYfA4HwUWS38Lw2V3Lvk0DlaiBSsUi/7OOst/8x/l/uyRb7CjwKGhPYmu6D0QAth/YhH/ppsfElmUb+Icth8+JvYaNh13obE++CeIT8GfUFDE/8BAbj9FUxEr32GMPLyYIPpI8wsgAbDrik9jH1JOkDzAVEvUnP/mJ7LTTTt60SNiByJgxYzzygcQZYrKsfUqCH9fDP6a+6ArbExKAmCSJdmArGOt0QGwWu5V7w07n3qhHFj9GBEQ8NRomD3jffff1GglslnOiwk4LIwLoUDTaJUuWeEEdgIlOhPND4J2gC+wTHwLrTM9D9hNDTwhgk20Go8b/nIvzyLk4RWR/EVCiEdIYcQj5nw9OJPeAceF3Kr2dgT/UkWwqWDEyCygf8oFOAOMFc8cxrp6UTYfjntxwG0d4kPWFswqT5j50ru22287LBoNwoJNxPHVDB9SP7AiGOZINB/CQxQZrxzaG9tDxIAH8RABBLwJW1MMNQUIP6KVdu3aec4oxxXFGBLinYd+mgQ0aMKyLxjqwGsNlv/3280hS8AZMBt8gATAQwC+wEewlS4AMAwwPFm5iG9hEZgTYiDMHdoLlYDrEJ6QrRhK4yDkYGATRyEBwwUDOOeiggzwDhyAaAUCCb4wmIPDP+4f3BXiJUURGLPXinQD+MUwTYwdji8Aa7xSwkqCZy7LFSQXPed/hfPJtH9OAaaBOAwRlIP1wTMg4ou86myhKR2AIWIFdQ2a++2DX0Hexhciip+9hd9H/cWrAZuxO8AFMITiEI0U9OJ5EDYLwYISzv7CTIBScvQhWgR04S86GJTEE2wvhd65PlCNJmeAEzhT2MsF46u2IgCOPPNIb5QQRATaBUWDf888/740+wrEjyQX8I+hPhusFF1zgJcIwMhScwh4F6yARwCzOxx7lvtAZ9wgmUhbZsoyGYhvH4SxyDmUbnuV60rbfNFC8BioZJ5mOArsNOw58AosIapFsAUbW1tauVzB+NXgOVmP7YaeRHEKgn4XZGT0Awck+glNu9Jgbhcr5BN3I1DUiYL1a7YdpYL0GKhWL1itAxAtuF4oP2JPEI7EnIR3xH7E50ScYhT3pPhAB2H4nnXSSZ79hl2JvEbBnG/gHDuYiArCpSWJhVCmYhw+KfUn5xAlJgMau9H+w84hdUkdiomCiP4CP7er/YINie2KLM2IBPKa+YDCB/ssvv9y7Z3DXXw7+NEmAlI8/j90OeUHiNHiMXrL4MSIg4qnR0BlecvDBB3tsvXOgIk7xAjHaiACcD5wWAkI4jZRFwyPITmCHxocBwD6uiQFAhgAdh+04TnQInBrOxaHCMSLzCYODDsl+xF2DenIeDiKOEt/8H/ahjnQQGDM6Do4YHY+OyfAcAmEYIgAB9aQsroW4EQN8Q1JAAtCZ/Q4vZe65555ekAznl+txPNkQOL0ABR0Sp41r4fDiEFN3rkWHZLQEmbJ+IoC6oKNgPSAV6Ohk4BL0ApSMCAh7+ra9kjVgWBeNdQTxyNIng4EgF3gDfoFVr7zyijd3Ic4Y2EgAjIAfxg+OIIYPGAXWgZ8EqnDy+A2Wg+lgl8NVygXPCXgR+CK7gkAYH0cEMK0cJAOkLfupH9jNNTCKCOyxHVyFxHVlcy3qCy5yPnjriABIV65H/SmHujmp5L5h924a8GsA5wS7DSIAuwg7I59PGBFA38Q2pM+BE/RRHA1GIB122GFekgN2Hf0STCFjCbsKjOBYbCP3DRFIUgnOEPYT+MNUkpTr7CNXV5wWbCskHwcmigigTLACmxV7GRuWd4ojAsAhiEnui/oi2GM4bozwxGbEvqOe4B+4hMNFthZ4BUZhU5JNyzmQL+Acx7r7Amc5jxFQTK1BBho64hhnG/ObetrHNGAaSFYDlYyTzZs3lwMOOMBLRAPfwET8fEZcYveB0+4DNoFdYDj45LCaZDh8XQJxJLIReAITsdHAYoelnEt2K9hYaKDP1cG+TQPlrIFKxSL/M81lv/mPdb9dYgn2JDE5knLRpd/ucscSqMde5TrYndh4HEdiGedjzxI4B6+iRgQQmCcJmmv77bUoIoDYJnFQbEOIB/DVH8CnTv4PAX5GnBLA5zwXY8QOpu5sZypd4pQuTgu2grFgLfeAvc66LiTRURY4TZ2z+DEiIOKp0QgIxvBCJ2jCCzrXhwYYJAJwPGhgNH7YfYaj8EIn8I2ztvPOO3tBJheop7MQdEJoZAyNobGShYYxQANkaDrTQBDkRsgyoKOwzWW/56qrtp+yaczUlWxT6kpQ65prrvGCTmSZkQWLE0YwDGOE6ZMI2JPtSjCMTkQmG0Eq2Dv3oTPCGJKpz324D04tgSnulXuEdcRxBHggJQAePjh8OIDMK+YnAjCk6NiMuKAeDBWnHgz3YX7YSy+91GMoCZQZEeC0bt+mgQ0aMKyLxjqCeOAJwXICU+7jjATeEWAwAT0yWMEoRg+A4WCRE7K5wEZIVuZdxNAhUIfxwRzb4C3BNLL2wUBGZjFMEpzl44gAhmCSXQYhwLsBowfDiw8BNsomUAmhDCHgrs83BC0BRd49vGscEcB1CNZhFNnHNGAa0DVAH8fWoH8R5GG0TT7BZY0I4DyykJiex9ku2JrYSmT/0885D/sRuwzbBkwhCYShyeAFfR0ygnphl4E/OCX0Z2w3+jwYQcY9dlJjP9QpKtCEfUzdqTNkBO8URwRQF7doMtfHZmMfmWa77babZ+txrsMpiFIwEqKF0QFgJIF9bGbsS2xFcJK5tblfMIsywTKX6QVeM9oBHZEIwnPimHyeVWN1ZOeZBkwDdRqoZJxkClx8Wje1L9iJP4sfjW0HprsgF7YbyRrEBhhJSmwAHCTJjwxUcAyfm6QTgmmUw3S3+LN8sPsog3dCFD5buzQNVKoGKhWL/M87zH4jroYNyshxbDBnh7l15Egqw+4M2pMkb4BJ7kP8EXsYW5OkFffBVsZmxffFhstFBHAdLbs+igigHm6qNHxanncUEcD9MooeLIWYdTFGF/+kvhC3EB/EKt3UQGCs39cmZktiIOWwzyXsuXvPyrcRARFPiuALwRkCOjhUjgmLOEUdEUBHw1GjIxLcwcFjGJ/LFNhiiy08h4jGjKMCmcCLn1ECZATgDLm5m93iQTRYjidrgA5GpirlYjzgIMFUUVYcH+pDZyHoxOIYlE/nwJEjiE/Qi6A/9WQEAR0InfEbp8x9woCIuVspg4AVAADJwdBuOpcfEMiYYAg8jqAjAjCm6Jg4w2SCuXqg38MPP1x23XVXbxuOKfdhRIB7GvZtGtigAcO6Ol2EYZ0WxOMMAlQE2HDAcPoIwmP4MLSQadDIkAAHnTBFBlPwMOUPo5swijA2wFQW9GVxJJw/AviQpoceeqgXcATP+TgigOAZeEt2MOQv9QAL+UBO8N4CQ1u2bOnhoLs+39SB4YyMTIB4dUQAeOuyKbyC7I9pwDTQQAMkQDA1GEEdbB2SJrDxcn2CGEJ/xaYkSE2mETYeWUjYhQgLnIEr2F7YOM7+IaDO8dh79GOwhmA52OCySbkWeIT9SHY8ZZO1BOFAXfNJagneT5j9xnHuXsja32effbygP7YWAtaBLW5UJsczCgKiANuVBeDAuSBGkcSBzUmAjPLBVhJDwC22Q2aCmdjmjJDFJibIzxSROGc8H6ZhY9o2kkEgDXDU4rKLg/qx/00DpoENGjCc3LBGAPjFWk8ky2HbkdwHuUsQn3cJxC0YBZ7ju4KF+NDYgCRs4AdD4uL7Qp6SYIedxwcsR9eQo0YEbGh/9ss04DRQqVjk7p/vMPuNoDlTKnbs2NGbkhzbE/yBbHTYgn1FPJL4H3YXCWrYdNhjzp4MC9SDXcT2iPER68tFBFCum0LIX/+w8qkj02ZiC5KMh6+MrRxFBBBjPPvssz0sxQcGi/mA0+gD8pXkPjctpSMCwFjakrOfwWDsSjAZ8RMj/rqn/bcRARFPyE2xgJMCu0+jYFvUh0BScEQAjZSAPc4LDYkgP5ntDDFhrnwcJxoRL3qcFIwDrsOwZgLfdFKGWpOBxlBpGjoOD8eSFcB5OIEEoBgdgONHgD4udor6cE0cSxbKhHmjwXMNAlEYJwT8uR+yrsiY5X4gJxpDBHAt7pUhkFwDB5CPC5qR0eqIAJw/HEp0i8NJAAwQoR6AF04y28m0MCIgquXavkrWgGFd3dMPw7pgEM+1lSARgBGBYwdGEvCHuIUEdgJW8T4gQEYQkCA8RgdGEoYWRhPvBv/UGGAumMjHEQEE+MA0MBZHkUwL6sKHb0ZpYVCR9UDmhrs+39SBjA3eDxhAfiLA4aRXkP0xDZgGGmgAjMDuIuj9ne98x3OYyDDK9QliCH0PG4XsI5wS7CZIQabUAUPIjPIHdnA+cHBwPsAPbBwSIAiMgwPYmHw4jrKxH6kXWIIdBnFAnQmU50NcBO8nzJHkON4f2MfYWpAj3BdY6CcCGAnqbDnsNrL4qTsLLoNX3I8fp/iN/YtDh73ryAZwi+1kqpHggdOKA0h5lMu9kanFtblXhqZjB0KEMA2lC6AF78/+Nw2YBuLTgOFkHRGARsEv8BBcBB/dFIzgJskXJH8wGpPAGjYeNhqBL94LJLhhFzpbEbuOmQrwZ/mAeWA9Nqf/fRHfk7SSTAPZ1kClYpH/qYXZb9hV4BC2IriDYHu5EUdh9iRJayRhYK9ic4UF6htDBGAHE+/zf1z5LByMbec+HIeNC4nKyAVsZPAWu5EkGjCRe/d/8KexCcHhoUOHrvediamSFAmWEvdlpBa2pCMC2A7Wgrl8GKVLbJZyGOnFvix+jAiIeGqAB8wRWUtk8uO0MFyGh09DoAPRWXBgcPLoSBoRwFQ9ZHySmcQ3DYuOQ8CHYcvMm++IALaTbU+wCMOB63ANsqLI9ifITxAHZ4brubJo/BACdEwyDgiIu+BRxC3W20VginNo/HQG6gIIwICxjXm+ICKc04VzScYVnYPhOBxP5yNbgXm+cDwbQwTg0GEUseI3jiv3S2eHfMDxA4AcEcB9o186LaQEziH3gc4AIJhLgmEuwAWAUD+cR4DFPqYB00Dd8GLDunCsCwbxXJsJEgFkumIMkPF1yimneIE6MId3CViKwcX7g2MI1oG3BPoxSngXEOQC9zGuCBAyjZBGBIDFYB3n8O7AEMHooVxwmHcS8yXiSGLEgKm8r1wwjf95V4HtRgS4p2nfpoHcGnBJGG7UDUkLBOGZ0hA7hb4ODmBD4dAwYoB+H8QQguIExyEB6edMD4btgm2HPYXN6QI72DP0VWwh+it2D32ZpAfsMUY/4pDgyIA34AhlcQzlMWoJmwfSkOsEnazcd90woww8ox7Uh3sDr8gqI1EDRxLblbq4EQF+IsDZleAXGWbYiTig1J99YBj3jM0HxnId7F3uC11yDFjHKAMytwiWoX/2Ux8wlnPQGXY2dUL8AbR87tmOMQ2YBhqnAcPJ+kQA7wN8TzAdn5n/sfVIACHzn6nhwHewj31MgwZBgH2HLwuOk30KhvPOIPEOG5IYANhKRqx7X+CH28c0YBqo00ClYpH/+YcRAf5jgr/D7EkS3BhZTnIFSSvYWS5QD8Zh97lPXEQAthsxVKYwwlZ0H65FQgz2MmsLuA92Itn+TFVJsh1Yiz3MB/8XfCXZhhgj9io2MbFXiAZG3BLDxOYEdx0RAMaCtdimlEfskbgjmMx5WcVdIwJcqwn5xglhOAuBFwLbzN/H8GpewgRryLbk5cx2AjMaEYAjQgCbaWsIztBxOB9HEieJ+QR52fPypzG6DC7KIwCPM0njZ6gzASOCR3QE6oXDiGPHNcjIYp4qssNguXCIcEoxFHCa+Ob/sA9OF/fDsGqmm6Bc7pH6kEnl5qtmH04s5ATzuBKEohOQfUWdISMACYZ2N4YIwIGl46ETAllcD2eaQBcdF8fPTwSgI8gBdEknpXOSeYGTCUFAvY0ICHvqtt00UKcBw7pwrAsG8VybCRIB4CvGBtjIegIYGYz6AhvJ9AJLGbnFKC+CVpC3GBFgF4YJ2wnucT7YxzBxjQgAGwl8gfEYSByLsYOBRp0wgpirke3M+cg0RGA5ApnNe4N3EO8EymCkGplmDifd/dm3acA0oGuA/sOUDmQiEZjBAaL/0tfnz5/v2SL0TWw3+lkQQ7BzsCXpd6z3QVY8tgvOC3PbEwRygR3OJ7jNuk3YfZQPnpDtTiAJ2xIyAucEfKFcyqEu2JuMNiCJBNsMW5Fgkxs14EhJ/S43bMWR3GuvvTxcAce4PrYptib1xQYjOtriuwAAIABJREFU2E4dnUMURgS4Uqkn+IcNia3I9BmuXPSFPQrOgWnoB0HH4Bg2IedgZ3J97GL04u6f39iGYCL1AjOz7Kw5ndm3aSBLGqh0nMRXB9MgLSFL8Y3JXAWrsdNYD4DkPd4lBM3AQDCeABNBLPCR7QTbwDSm2YA8Je6ALYevC663b9/ee1/gJ7uRV1lqJ1ZX00DSGqg0LPLrszFEABiFr+i3J7H5sCfBIPxGks6wJ5MmArCvGQmPvQs+MiqUeCvYSII1uIit5z4kovC8mUKXZG7sYWxflwTH6CtmUMH25n6wKzmfWC9xRhJrsLmxZR0RAMZyz9imzOzC9EnYvcWMtHX1bcpvIwJyaJ+gDk4IDgmZVwS+mfKhXbt2XgOjEeHEEOwho1YjAuhMBHdgmWC0mIOLxoMDyHQ7zPfsJwIoi8wqGjBGA8EgnDgaOg0SB5LOSNYp18dhpEyOhxVjO04RHQEHksA+deeb/8M+NHruk3oRPHfl8g25wPRIdBjKIOCFcUOZTMvD9XHImEMMwoJsfjJiG0MEUDaGDNciCIa+mTsXNo7FitGFIwJ4PrB4BLAIvPFc0Bn6ow7Mk019XIALxxRAsREBYa3AtleqBgzrvudhnoZ1wSCeayNBIgAdkn1CkB+sZnE3cAv8AsfBRN4DOIVgNO8GjA8yxMAtDA2uz8grHECyEsKIAIhjnEOyFsBKcBjDBsONevE+gkRl6jb2gc9gOfOF884C68F8IwLc07Rv00D+GnB9jwA8fZW+7eZXxUnCHmFOaDelZBBDsM/of9hQ9Hn6v7OhcGwg8fxEAHhB5jujMDkWwZ7E5oNsIDiO0wKJSJ/HbqI+4A7l4wwxhRhOGxn1OEbYhQi/c31wJJs3b+5Nh8QIBLCKOmCPkaACkUkQnjq4hJNcRAC2JMEs7DpGblJn6ov+GGmBo4WjiV391FNPedvQkbM3GUmBXYwzCOaRvEI9eBZ+HeFAUhakB3q3j2nANFAaDRhOtvfwCmwjfkDAH3IWWxEcxg7DbiMmAK6RcOiS/sAtyAPwjWPRJUkd4DsEND4/GEiCIe8FpqoDg4kR2Mc0YBqor4FKwyL/3TeGCMCWI3kC29GtX4V9BtaAQRAEzp5MmgiABIU0xd7E9iRGybRqYB/1wwYm8cV9nB/Ofnxw7EHifsRHmXGFuCFTjxP4xz4GS8FgfG6S6LC3GVXqJwLwnUk6AXOxQ7FTiZfi6zs9uOtn6duIgDyfFg4bTgaZ8WSa05hw8hjOBztFw+LlS0Y/QXoaBi9kAj288BlKQqfB6aMx02k4D1aJ3y7Az1AcWCzYfgwGjsUp5DyMAdgsHBkCTfzP9akH9eF4AkJuqA63RtYXTh7GA9/8H/ah7jR+sq785VI+16cMgksYJHxwvHA+GY7DtaknHYgMMYJbBNzJbnAfHGFGRlAGncZ9GJbDNdEX1+fDNWD8yHijs1M+ZXMPZJyxDgPPhM5OWTB9DA+irozOYPE8OjkBN/TOs+P+qDNggVNIRpl9TAOmgfoaMKxriHUEtHDYwCP/sESmACEQD66RmeWwEQMCrCY7C6xxuAQhSaAOvILsBMsJThG8Zx94zzfYyTYwDAG3+BA4A/vc+4J3C3UAN1lEFOwlcMY2jF7IAuqMkQQ+IwTUGDkFDpORxjuBe6NMh5P1W4T9ZxowDWgaoJ8xnQPZRdhI2B4INiJ2EUFuHA76eRBDsF3YTtY6o0PJEgUnsHPITiIhxNlL2C44JATy6f/gBDYfthF4Qp/H/sQWojzsHuxKjnH2IzYZeOA+1BubDuF3rg/2G84k0zYyyoiyqQd4Ql2xe3l3+D/gFnYu2OJwyb8fwoCh15Tt7EinP3RCAg0kADiFvQYuoieuzTd2HjY0GIrtjF3MNEiMxKAc6sezwC7GVkXf6N0+pgHTQOk0UMk46bASrMZ3BZ/BcvchEQP8A9fBanAbvMJGw54Dz5xfzDmMIsU2BHfBQLAOjCVhhPVWGK2Fvu1jGjANNNRAJWGR/+7D4m/+Y4K/8SGJRxJbC9qTYJDfnsQ+w9clvub8VcoDu4jtEeMj1ufsNGxC/5pN2IHYgGwHE7HV/B98X2xMbD7sZPxksJEMfQLznBe0P7Gb8WmxG8FW7EGOw6akHtSN/7HdwVIwGlsWW5H92Ip+IoDkYuxNdzyYje/MbCpZ/hgRkOWnZ3U3DZgGTAOmAdOAacA0YBowDZgGTAOmAdOAaaBsNUCSCcSoG1FAIgeEKZmtZLwyUt8lpJStEuzGTAOmgYrWABgHqUOiB0SAS/SIWyl+IoBEmHL8GBFQjk/V7sk0YBowDZgGTAOmAdOAacA0YBowDZgGTAOmgcxrgDVTyL4l65aMXUZz3X333d60Q0wpxMgz+5gGTAOmgXLWgJtC3GX0uxlC4r5nIwLi1qiVZxowDZgGTAOmAdOAacA0YBowDZgGTAOmAdOAacA0kJcGmBKYwD9zVbO+y+233+6tR9OtWzdvSjhbKDgvNdpBpgHTQIY1wIgAiFCIT0ZB+acbj/O2jAiIU5tWlmnANGAaMA2YBkwDpgHTgGnANGAaMA2YBkwDpgHTQN4aYJ2YK6+80lso3i2sfv3113trCkACMGWQfUwDpgHTgGmgeA2w9hbrArA4MWsClOPHpgYqx6dq92QaMA2YBkwDpgHTgGnANGAaMA2YBkwDpgHTQOY1QGCKRS5XrFjhLb7J1EAs2ulIAFsMPfOP2G7ANGAaSIkGIFZZk2XVqlXCgsXl+CmICGCIBCtI//a3v5U+ffpIv379vNWgWcXePqYB04BpoNw1YBhY7k/Y7s80YBqI0oBhYJR2bJ9pwDRQ7howDCz3J2z3ZxowDURpwDAwSju2zzSQHQ0URAQsXLjQW6H5wgsvlNNOO006dOggt956q4wfP16++uoryZeJhlVhTicT04G1gcLawFtvveVlg/zzn//MDsqUUU0NAwtrr9a/TV9xtwHDwKYFVMPA9PTpN998U6Ik7r4XR3nUlz6sCfviuEa5l1GumWlNi2z5X71YDMR+f/vtt62tWxzA2kCBbYB3xPLly+WTTz7Jv8PakbFrwDAwPjtQs4XcNkb+8K7QhCx19ocJ/SQp+zCqXK7LSKUwod5hwr1ElV3utl0+9xc3BuZNBLAwQ//+/aVjx44yePBgmTt3rkyaNMlbpObxxx+X999/3yMD8kEb5lk66KCDTEwH1gYKbAPt2rWTK664wiMD8ulrdkx8GjAMNMy291bTtwHDwPgwrdCSDAObvv37MejQQw+Vww8/XJW2bdvKwQcfnDobK4t19us8Db/Lda7aQvGoKY6PAwMJ6px77rmp65tpaNtWh3S9Y9L2PHh/HHXUUfL88883Rfe3a4qIYWB8fRQb7YgjjhD8Gk2OP/54OfPMM1U544wz5IQTTvDWCznuuOMafB955JFy2GGHqUI/aqx9yHmUi42pCf3zpJNOUuXkk08W6s2c+5oce+yxaplch2s2ts5pw7Fi6hM3BuZFBHz99deybt06efTRR+WCCy7wpgditWamBGKl+vvvv9+bIugvf/lLXiD5y1/+UjbaaCMT04G1gQLbwA477OCBKGyqfUqnAcNAw2t7Z6WjDRgGlg73/FcyDGya9v+tb31LwqRFixay8847q7L99ttLs2bNErGxwurjtkdh5dZbby3f/va3VWnZsqVsuummidQ5qk5Z24cPZZ/SayAuDCTrj0BA1tqd1bdp3gGm9w1632yzzaRVq1bSu3fv0gOAXVEMAze0xTj6JfYO9tBee+2lCsHyrl27qnLzzTfLOeecI6eeeqoqB7U5SLbbbjtVsMM22WSTRr2DsCu33XZb2XGHHVVp3769XHzRxapcdtllcuONN8rPbv6ZKhAfO+64oyrcS2PrHMezSksZcWNgXkQAQf/58+fLgw8+KDfddJNHAICHDM164YUXvO3PPPNM3gspGBEQL5CkpXFaPZJ/rrwwLrroIlmzZo2ZJCXUgGFg8m3b8MN0nE8bMAwsIfD5LmUYWPr+SWB944039pwfHKCg4DDtu+++quy5556Cw5BPnyr0GOoVrIv7n/qyP6xMgv1kdWmy//77S/PmzUOJj7AyK227EQE+YCrhz7gw0IiA0mNppWFEud7vFltsIW3atPHWqCxh17dL/VcDhoHxYhftGVKY4Lkmd955p9TU1KgyatQowRYIIwpOP/102X333VXZZZddvKQLl7xRyDd2ZRR5cckll3jTyPfq1avBN6MZ33jjDaHumvz0pp9K69atVeFeLFFkI4kbA/MiAr744guprq6Whx56SO644w758MMPPUj4/PPPZejQoR4R8PDDD8vHH3+cF1gaERAvkJTrC9/uq2E7sSBYXhAT+0GGgQ3bovVP00lTtAHDwNjhLa8CDQNL399xzowI2DAioinwJo3XNCIgL8iK/aC4MNCIgNJjaRr7sdWp8HYQdxAsdpAo8wINAwtvs1H93IiA+oSAEQG521fcGJgXEcCUP1VVVR4RcM8998hHH33kQR1EwMiRI73tTA/E9EHBz9///ndv4QeYn379+nnCUJaojmH7cjcE01Fl6siCYEGEKc3/hoGV2d8MZ9P33A0DS4N5wasYBpa+LxgRsIEEQBeGx3Vt0IiAIDqV5v/GYiA+8/Tp02XAgAGeD3zfffd5GZXWnkuPqabzbOs87iBYaZCjfK5iGBhv/zEiwIiAQt9JcWNgXkRAMQwgoDF+/Hhv+ApTmiA2N2K8QFJoI7Ljs6t/C4I1jUFlGJjdPmN4V17PzjDQMLBS+rQRAUYEaG3diIBsYeCbb74p/fv3l+uuu87zgZmyYZtttjFiq8A10rS+YNvKy77L9TzjDoI1DZJk96qN9YUNA/V+akSAEQG5MC+4P24MzIsIKGZOsG+++UYgA8iIYF5z5Oc//7kZQGYAWRtoRBuwIFjTGFCGgboRE3xB2f+mp6TbgGGgYWDSbSwt5RsRYESA1haNCMgWBjIynjX11q5d6/nAJMexHob2bG2b2VDWBsLbQNxBsKZBkuxetbG+sGGg3qaNCDAioFC8jxsD8yIC3CrhPXv2lAsuuEAWLVokgMF7770n3bp1kx49esjEiRO9gH8+8IYRW+iN2/E6iJheKksvFgTLB2HiP8YwsLL6meFqep+3YWD8+JZPiYaBpe8TEAEsjrbZppupssfue8gRhx+hyoEHHugtvJsEllGvKIm6JosYH33U0aq0bdtWttxyS/MPciSJGBGQD2LFf0xcGGhrBJQeS6MwyfZl53nEHQSLHyXKu8RKx0DWbArKVlttJYcddpgc9T9HqXLWmWfJj674kSrXXnutMFXcgw8+qMrAAQNlypQpqkyaNEmGDRsmr7/+uirPPvusMG27JtgQXbp0kVt+fosqJGx36tRJFc6jzEcefkSV3/zmN2p9qCfryo4bN86bbp4p54PCyDn0ockvfvELufzyy+XCCy5U5YTjT5CDDzq4gbRs2bKsbMq4MTAvIuA///mPkNn/3HPPyXnnnSdDhgyRuXPnesH/iy++WB577DGPFPjqq6/yQkAjArLz0jUDKV3PyoJgeUFM7AcZBqarHxguVe7zMAyMHd7yKtAwsPR9jmD75ptv7gX0mzdv3uB7v/32kxNOOEGV733ve9KiRYtGO0CNDfTnwub99t1PmBpFk2OOOUZwqnOVUen7jQjIC7JiPyguDDQioPRYWumYUS73H3cQLHaQKPMCKxkDsYmaNWvmJWeQoOFkl112kR//+MfSuXNnVZ5++ukGAW8XAJ88ebIsW7ZMVqxYocq8efOEYzSZOXOmvPPOO96MK8y6EpQ//OEP8qc//UmVP/7xj/Luu+/K6tWrVXl71duycuVKVZjZ5bPPPhOmidLknTXvyJw5c1Spra2VqVOnqsQGhMeihYu8e+K+gsL0UsOHD5dXXnlFFciDSy65pIFADpQL/nEfcWNgXkQAuEbnX7BggTz++OPeqIBTTz1Vzj33XG+aH9gdSACOyedjRIAZQeXUKUt5LxYEywdhkjnGMNBwq5R93a6ltzfDwGTwLZ9SDQP1NplUXzUioLT6Tuo5xl2uEQH5oFUyx8SBgUYEWL+OGxMqpby4g2DJoER5l1qpGGhEQB0xYERA076/4sbAvIkAYO3TTz+VhQsXygsvvCC9e/eWfv36eSwX8x4W8jEioGkbUaUYDOV4nxYEKwRp4j/WMNCwqxxxJUv3ZBgYP64VUqJhYOkw0IiA0uk6SxhoREAhiBX/scVioBEB1q+zhDdpqmvcQbD40aEySqxEDDQiwIiANGBh3BhYEBEQF7wZEWBGUBo6UxbrYEGwuFCoacsxDDQMzCL+pKHOhoFNi11xXd0wMDcGGhGQW0dpwKRS18GIgLhQqGnKMSLA+nWpMaNcrhd3EKxpEMCumkUMNCLAiIA04GjcGGhEQI5FudLw0K0OZjS6NmBBsPIwoCwIZn3a9Wn7LqwtGAYaBlZKnzEioDBsqJR2YURAtjEwi0GwSulbdp/pxty4g2DZRpLs1j6LGGhEgBEBaXg/xI2BRgQYEVBWi2ikoZMmWQcLgmXX8PHX3IiAdDsbSfZhK7u4Z28Y6EeS7P42DMzdD4wIyK2jSsRTIwKyi3vUPItBsErsZ3bP6cPfuINg2UaS7NY+ixhoRIARAWl4J8SNgUYEGBFgRECG2oAFwbJr+PhrbkGw9DkYaXjBWx1ytwvDQD+SZPe3YWDuto7judlmm8nmm2+uysEHHywdO3ZU5ZRTTpGddtpJmjdvrkqzZs0ibT+uHSZROLXxxhtLixYtZOutt1alffv2cvnll6ty9tlny7bbbhtZr6hrV8o+IwKyi3vUPItBsErpW3afud9LTamjuINg2UaS7NY+rRiIXRRmM2HT7LnnnrLPPvvUk8MPP1xuueUWue+++1Tp37+/TJgwQZXJkyfLokWLZNmyZarMnj1bqqurVZk+fbqsWLFC3nnnHVXef/99+fjjj1X58MMPZeXKlbJ8+XJV3nzzTbU+1JNz1q1bJ3/84x9VoU6zZs1SZebMmVJTUyNTJk9RZd68eWp9uObixYtl5MiR8uqrr6rSs2dPuemmmxrIaaedJvvuu68qrVq18p73JptsIppgAzcl3mnXjhsDjQjIUBBYaxC2Ld1GS9zPx4Jg2TV8/DW3IFhl9du4caCSyzMM9CNJdn8bBuaHgWHBeLafeeaZ0qdPH1W6/7K7HH300XLAAQeoAkmQBI5AAnz/+9+Xk046SZVu3brJkCFDVHn88cdljz32SKReSdxrU5VpREB2cY+apzUI1lTt2a6b37vA9LSRxB0EyzaSZLf2acXAnXfeWdq0aaPKMcccI927d29gbz377LMybNgwGT16tCrjx4+XSZMmqUKQf8qUKaHCeWEkwsSJE2Xq1Kkybdo0VSARFixYoAoBd4gEzteEOkFSaFIzpUZqa2tlzpw5qlCfsPt126snVYsq1dXqNakHuqqqqhL0qcmoUaO858CzyFf69u3r2cdbbbWVaEIiTtpwN24MNCLAiIDUNfK0dbo01ceCYNk1fPw1tyCYOT5pwpUs1cUw0I8k2f1tGFg8Bp7f8XwZOHCgKk8++aScfPLJ0rZtW1V23XXXRGw/suYgATp06KDKgw8+qGa34eQ9//zz0rp160TqlSWMy1VXIwKyi3vUPK1BsFztzvYXj9mmw+J0GHcQLNtIkt3apxUDd9ttNzniiCNUOf300+U3v/lNgyAzWeoEzsMC8gTOw7L6CYZPnDAxNNg/oWqCF/wmAK4JZECYEOAnYK8JmfkE111gPvgN+aAF29lGnbgnjUBgmwvYa/Xl3EkTdVKEOrB/3LhxqnjXnjAh9H65H61Oc+fMlaVLl6oyYsQIzz5mJKomzTdvnjp7NG4MNCLAiIDUNXIzlMINJQuCZdfw8dfcgmDhbdz6v+kmqg0YBvqRJLu/DQOL7+dGBBSvwyisSes+IwKyi3vUPK1BsLS2d6tXZeKc9tzjDoJlG0myW/u0YqARAXVBeiMC6ogBIwISwhhzAO2lrr3gbVvudmFBsIRAqcTFGgbmbuuGB6YjrQ0YBpYYrBK6nGFg8f3biIDidahhTNq3GRGQECiVqNi0BsHS3u6tfpWJd/7nbkRAiUAq4cukFQONCDAiwD8ywIiAhIDAHEB7mftf7PY7//ZgQbCEQKnExRoG5t/mDR9MV/42YBhYYrBK6HKGgcX3ayMCitehH1uy8tuIgIRAqUTFpjUIlpX2b/WsTNzjuRsRUCKQSvgyacVAIwKMCDAiIOHOT/HmAFbuS9wMuOKevQXBSgBQJbiEYWBx/cBwpHL1ZxhYAoAqwSUMA4vvw0YEFK/DLL5LjAgoAUAleIm0BsGy2BeszpWFgUYEJAhMJSw6rRhoRIARAUYElAAIzAGsrBe3GWrxPW8LgpUAoEpwCcPA+PqE4Utl6dIwsAQAVYJLGAYW32+NCCheh1l8fxgRUAKASvASaQ2CZbEvWJ0rCwONCEgQmEpYdFox0IgAIwKMCCgBEJgDWFkvbjPU4nveFgQrAUCV4BKGgfH1CcOXytKlYWAJAKoElzAMzK/fbrzxxhIm11xzjUybNk2V8ePHS69eveSB+x9QpVOnTnLxRRercs4558ghhxwi+++3vyqnnHKKXHfddarcfPPN8vDDD8uTvZ5U5cUXX5Thw4er0qdPH2nVqpUYpke3DSMCSgBQCV4irUEw63fR/c700/T6MSIgQWAqYdFpxcDjjjtO7rn7HlUeeughGTp0qFSNr6ovVVUyceJEmTRxki6TJkn1pGpVJk2qC7yHfk+c5JVN+WESdt2aKTUyY/oMVaZPn15nN06dJtMUqampkSmTp+gyZYpMnTpVtTuxR6dMmaLeq6eD6mqprq6WydWTVeFeJlRNaJRUVVWJJtzL7NrZqowdO1a6dOkiV191tSpt27ZNnT0aNwZuVMJ+v/5S5gA2/cvUDJpsPgMLgq2HkUz/MAzMZv8z3Gz652YYmGnoW195w8Dcfelb3/qWNGvWTDbddFNVunbtKu+9954qa9eulVWrVsnKlStVwWF77bXXVHnuuefksssukx/84AeqPPLwIzJ79mxV5syZI4sWLZLFixergpM3cOBAVSAQdt9999Q5XmnDfSMC1sNIJn+kNQiWtnZu9cn9jqg0HcUdBMskgJRBpdOKgddde53U1taqMmvWLFkfQPclYBBoHjdunIwZM0YViIPQQP+kSTJ58uRwqZ7sBc4JnmuSq1zqpgmBfO4n6l5nzpwpYYIewgRiQbum2wZRECYQBGH3BBFCoJ8kF03GjB4jI0aMaCA8G013bKPM0aNHy8iRI1XBDk4bxsaNgUYEbGQv2rQ1cqtPeJu0IFgZWEC2TkrqXqyGOeGYkzbdGAYaBqatTSZVHyMCsoNLSbUBrVwjArKNgWkNgmltzbYZBqWpDcQdBMs2kmS39mnFQCMC6kgQSIIwEoDtYSSAR5QYEZBojCNuDDQiwIiARBtsmgyIcqiLBcGya/j4a27ZsOZclQMeNcU9GAb6kSS7vw0Dc2OgEQG5ddQUGNTU1zQiILu4R83TGgRr6nZt1ze8y9UG4g6CZRtJslv7tGKgEQFGBPhHB9iIgIQwxhxAe9nnetnbfr2NWBAsIVAqcbGGgXr7tn5vesnVBgwDSwxWCV3OMDB3XzciILeOcuFFOe43IiAhUCpRsWkNgpVjX7F7Ki8MNSKgRCCV8GXSioFGBBgRYERAwp2f4s0BLK8XsxlapXueFgQrAUCV4BKGgaXrM4ZP5aVrw8ASAFQJLmEYmLtfGhGQW0eViO9GBJQAoBK8RFqDYJXYl+yes4WxRgQkCEwlLDqtGGhEgBEBRgSUAAjMAczWi9cMpfQ8LwuClQCgSnAJw8D09CnDt2w9C8PAEgBUCS5hGJi73xkRkFtHlYjfRgSUAKASvERag2CV2JfsnrOFsUYEJAhMJSw6rRhoRIARAUYElAAIzAHM1ovXDKX0PC8LgpUAoEpwCcPA9PQpw7dsPQvDwBIAVAkuYRiYu99BBGyyySbSrFkzVTp37ixr1qxRZfXq1bJixQpZvny5KjNmzBC/w+P//bvf/U5uvfVWueaaa1T59a9/LfPmzQuVhQsXSpiMGzdOXnzxRVUeeOAB2X333W3dqhxrlxkRUAKASvASaQ2CmS2UG5NNR02rIyMCEgSmEhbdVBiITbX55ptL8+bNVbnhhhuEhXI1YZHcadOmydSpU+vJlClTZPz48TJ27FhVJkyYINXV1aEyefJkCZOo89g3adKkUKFM6qZJTU2Ntxiwdp9uoWBsxDBBD2GCfig/TLT6uG1R9zRx4kSpqqrydI2+g4L+R48e3UA4LkxPlDlmzJgG57hyfvSjH3nthTYTlM0220xoT6V+J8SNgbZYcA6Du9QP2K7XtEZG2vVvQbASWioJXsqCYNbP0441aa2fYWCCwFTCog0D88NAHI0wwWldsGBBqIQF49nOefPnz1eFIH9tba3qDOMkcu6b//emKkuXLpU5c+Z451NGUN544w3p06ePKnfeeafstttuJXes0op1YfUyIqCEQJXApZoqCBbWnmx7flhsemp6PcUdBEuge1uReWigqTCQYO7ee+8tBxxwgCrdunWT6dOnq0Lgm4B1MGgfFbwmAM3xYUFxtrM/V8A/bH9YgNttDzuP7VF1yrXPBe61b84NkiXuf/ZpOnQ6pV6u7to3pEqYcLxWH7ZDIIQJZECY3HLLLbLvvvuqsueeewpkQKnfC3FjoBEBRgSUvBGXutOU0/UsCJaHhZGBQywI1vQORTnhQiXdi2FgBgAujyoaBhaPgddfd70XdCfwHpS5c+bK/Hl6oB8CYPGixbJs2TJV3nzzTW+UwbvvviuavP322+ooA0Yf4ODPnj3byzYjgy4or776qvTq1UuV22+/XXbddVezSXP4JUYE5AEwKT6kqYJglWQn2L0W/353nXnAAAAgAElEQVRJow7jDoKlGCbKumpNhYGMBIAEaNu2rSp33HFHZKZ7Y4L2BKddIDz4TWC8MWW6AL8WLM9nW/WkapkyWR8toAXTg9tc4F77rpkSTgRMrZnqXVc7z+khqv5hJADbOV8bpYCugqMH3P+QA1zP6TP4/f+6/j9p06aNKhAEEEulxsm4MdCIgBwGd6kfsF2vPI2XuJ6rBcHKwzayIJj187gwodLKMQw0DKy0Nh92v0YEVOZ7xIiAbGNgUwXBwnDEtlcmjmTxuccdBMs2kmS39k2FgUYE1E0lZERA3dRCRgSIGBFgREDJ2awsGh9pqbMFwbJr+PhrbkSAOV5pwZSs1cMw0I8k2f1tGFg8BhoRULwOs4Z/1NeIgOziHjVvqiBYFtu61bkyMS7suRsRkG3sc7VvKgw0IsCIADcagG8jAowIsCC4ESGZagMWBHNmRLa/LQhmzk2Yo2Pbo9uGYWC2sc/V3jAwup3ngwNGBBSvw3z0nLZjjAhwKJLN76YKgqWtHVt9KhO/innuRgRkE/OCtW4qDDQiwIgAIwLq98aCRgR8+umnsmrVKmF+0NWrV3uyZs0a+eyzz+qXmuM/cwDt5V+MIVDJ51oQLAe4JLzbMNCwq5LxJw33bhiYMMjlKN4wMD0YaERAep5FKbHRiIAcIJXw7mIxsKmCYKVso3atysSmpJ+7EQEJg1uexWcVA40IMCLAiID6nbwgImDAgAFy2mmnybnnnisdO3aU888/Xy666CJh8a9CPkYEmIGQtLFQruVbEKwQpIn/WMNAw65yxZas3JdhYPy4VkiJhoHpwUAjAtLzLEqJn0YEFIJY8R9bLAYaEVCZ/baUGFGu1zIiIH48a0yJWcVAIwKMCDAioH6PL4gI6NWrlxx++OHCqtq9e/eWZ555Rvr16yfz58+vX2qO/4wIMCOoXI2UpO/LgmA5wCXh3YaBhl1J93ErP7qNGQYmDHI5ijcMjG6fpey/11xzjcycMVOV2tpaWbJkifzfsv9TZenSpbJ48WJVOO+tt96S5cuXq7Js2TJZvGixKgsXLpS5c+fKnDlzVBkyZIg88sgjqnTp0kV22WWXTE3XWMrn7a5lREAOkEp4d7EYWE5EAIHZgw8+WL7X/nuq8L527da+0/PuyOqzMCIgYXDLs/isYuBOO+0k119/vRfHJJYZlEGDBsmypctUWbRokUyfPl1qamoayOTJk2VydYhMnixTpkwJFe9czldk4oSJMm7cuGgZO07GKTJ+3HipGl+lS1WVTJo0Saqrq8NlUrWwqHBQOG/ixInhMmGiTJgwIVqqJsgERSiX8sMkqr7oT9MzZWnX8rZNmODdv6Z7tmGrXnjhhaqQGL/NNtuU/P0WNwYWTASceOKJnnH/5Zdf5gkXDQ8zIsCMgawaIU1dbwuCNcSTUm7B+DEMNPxqahyo5OsbBpYS8RpeyzAwPfh31VVXNXBInZM6c+ZML5jPVJ6aEIxcsGBBqOD0hsn8efNl9uzZqkAAkBwUVvbgwYOlR48eqnTq1ElatmxZcscqa3hqREBDXCrllmIxsJyIgB122EEuueQSuemmm1SBIMha/7L65veO+9a3viVhkpQO4w6ClRI3yulaWcXAvffeW1566SWZNm2aKpAAH3zwgSrvvPOOZ/NABviFsghAhwWTIQiiAtha8NptI3t9xIgRjZJRo0bJmDFjVBk7dqwXqA8N6OcI5vuz6oO/x44ZK1w7TMLqxHaIiygSAL04Gzf4jf7VcyeGExccz3lO38Hv377wW7n33ntV4Z238847l/z9FjcGGhFgi+WWvBEnZSBUQrkWBGtaU6pY48fV3sjQ/ByNSujTdo+FtQXDQIciTfNtGFhYe02yfxsRkJ5nkeRzDpZtREDTYJ+7arEYaERAZfbbYD/O+v9hJADbk7q3uINgrk/bd2EayCoGGhFQRwwYEVA3osGIAJGCiYAjjjhC7rrrLunbt6/HqsGKffTRR6EI8ve//13efPNNjxliGiHknHPOSewlkdTLx8o1wy0NbcCCYKFQU5IdGD+GgYYFacCCSq2DYWBJoC70IoaB6cE/IwLS8yxKicdGBITCU0l2FIqB+Mj4ysyrjQ983333Ce+xUraZpK5lIwIqE4NoT0YElARuUnmRrGKgEQFGBPhHQRgRUCAR8OKLL8opp5wi5513nrdQ8A9/+ENvuATDKgj4//vf/24AWH/5y1+EYSMYriwsjBx00EFlYQAlZVhZuZVrWOV69hYEawAxJd1gGGh9M1cftf3JthHDwJJCXoOLGQYm274LwQ8jAtLzLAp5bsUea0RAA1gq6YZCMZBkuP79+8t1113n+cCnn356k8wtXGy70843IqAyMYi2YERASWEnVRfLKgYaEWBEgBEB9aGkoBEB7733nrAA2YoVK4TFwpj/ibkBH3zwQWGoI2RA8PPNN98IZAAZEWvWrPHk5z//uREBNiWRtYFGtAELggURprT/GwZWrtOjOcG2rfTtwTCwtJgXvJphYOnbfBjOGBGQnmcR9oyS2G5EQBCVSvt/oRiIb/zJJ5/I2rVrPR+Y5Lj999+/LHwgIwIqE4PANSMCSos7abpaVjHQiAAjAowIqI8kBREB//znP+Vvf/ubl/n/1VdfyapVq6Rz587eqAAWefjzn/9cv/SQ/zBikzCOrczKNUgq5dlbECwEVEq02TDQMKZSsCat92kYWCKwC7mMYWB6MNCIgPQ8i1LipREBIeBUos3FYqCtEVCZ/baUGFGKaxkRUCLASeFlsoqBRgQYEWBEQH1AyZsIYNofJxTxn//8R9atWyc9e/aUHj16yMsvvyx/+tOf6pce8p8RAWYElcJIKcdrWBAsBFRKsNnhn5sCzTDQcKwcMSbt92QYWAKwC7mEYWC6MO+KK66QqqoqVaZMmSJvvfWWrF69WhWmK1m4cKEqixYtksWLF4fK/PnzZfbs2arMmTNHFixYoJbL9V4Z9Io3RzrzpAflpptukp133tkShXKMFjUiIASgSrA5DgxsSiKgscHbjTfeWFiotUWLFvVk9913F3DoZz/7mSrHHHNMveOD57fYsoVsueWWqmy++ebSrFmzUKFOjb2ftNs5WahfU+jeFgsuAcjluESWMRAigHjlrFmzVMFmYgYTTd59912ZO3euzJw5s57MmDFDsLeYJj1MqqurpTHC6LHRo0eHyrhx47zp1zkuKNiGEyZMUIVgeE1NjUydOjVUpk2bJppwTtT9cp/+YHvw94SqCarN6uobPN79z3z+XLdmSo0q6J5jChHqGvbM2D5w4EB55JFHVOnSpYvssssuJbdX48bAvIgAOv0//vEP+fLLL9evBfD11197UwRde+21cuedd3oN/IsvvsgBH3W7jQhIlzOZBYPD6ljXZiwIlhfExH6QYaBhlmFQOtqAYWDs8JZXgYaB6Wj/fhw699xzvbnHmX88KIMHD/aIgPfff180efvtt70pPQlKFioQBTjEmsybN0+WLl0aWuZLL74knTp1UuXyyy8Xphrx32Pwd1MEn4J1aOr/jQjIC7JiPyguDGwqIoC+s8kmm4QKgfWwtk3w4ZBDDpEjjzyynpx44oleX/7FL34hmjB9cPAc///t27eXMNlvv/08YhByMCjgRPPmzWWzzTZThfsMuxfbnr53Wb7PJO4gWOwgUeYFZh0DwRSmNV++fLkqTHn06aefqvLxxx/LkiVLBBvHLyQ/EFSPCiiH7SNoPW7sOGFWFU0IjocRCATkqQ9JHZqwj6QNTbDhmOadmV00YSp3iA9N3nnnHe9cSBNNKBf7UhP2kSii2Y5s457CklucLiZXTxZN0JNGAkAeaIQG26bWTPXOcWRD8BuihfaiydNPPy2tWrUq+XsmbgzMiwj417/+5XUKFNy7d2/h5vv27SsPPfSQ3HDDDfL8888LnYfpgvL5GBGQ3Zdwvi9rOy6ZZ2xBsHwQJv5jDAOTac+GE6bXQtuAYWD8+JZPiYaB6eurZ515lmePY5MHZcCAAZ6T9sEHH4gmjBTQnMd8ti1etDjUkcPpZA2xsHJ++9vfCpn/mhA0NCIgdzszIiAfxIr/mLgwsKmJgLAs+ygigEx+AvgE/v1y1llneVME33XXXaLJlVdeWe94/7knnnCiHH/c8XLcccepcvBBB8tuu+2mSsuWLb1RBIwa0MSIgNw4UqjtlYbj4w6CxY8S5V1i1jGQtVnGjh2rBqkJXH/04Ufy2WefqfL73//es20IZPtl7tx5XgA7LNgftZ24KgSAFmhmG9nzkAyaMDIBQiNs1Ofyt5Z7RAGEQFB4BxHsZ90aTT784EOB+NCE0RKQAWHXXfvuWtXmxA6FWCBRJGw06vTp0xuMbHAjHSAC0FcYMVI9SScCGEFAuZpMmzpNJk2cpI6aYDQF1wt7fi+88IK0bt26MogAsv9pDL/5zW+kY8eOnnTo0EF++MMfymOPPeaxTYVAnxEB5fmCToORUO51sCBYIUgT37GGgYZZ5Y4tWbk/w8D4cK2QkgwD04eBRgR8q97UIFnBsGLraURAIcgV37FxYaARAf8lE4wIKHkQqVjsaerzjQiID88aU1LWMdCIgDpSwIiAOmLAiACRvEYEMBc2nf+TTz6pxwDBCMGQMWVQIR8jAtLnUDb1y92un1+bsCBYIUgT37GGgfm1T+vHpqek24BhYHy4VkhJhoHp69tGBBgRUEgftmOL00BcGGhEgBEBSdtJ5Vq+EQHFYVixZ2cdA40IMCLAPzLAiIA8iYBigSN4vhEB6XMoy9VoKLf7siBYEE2y+b9hoGFguWFTqe7HMDCbmBestWFg8RhoRIARAcF+Zf+nXwNGBBgRUCp7qdyuY0RA+vEtnxo2FQYaEWBEgBEB9XtoXiMC6p9S/H/mABbvAJbby93uJ782YUGw4vEnDSUYBubX3g0XTE/BNmAYmAYEK74OhoHF9+0zzzzTW6+LNbuC8tJLL3nz9GsLBbOtmDUCmN911qxZqrBwHmsEaIvIse3FF19U1wdgzQBbIyC/NkHfsU92NdBUQbBiFgveZptt5OSTT14/PbCbJviyyy6TO+64Q7p3767KT3/6Uzn//PNDhWmGWfRckxNOOEHatWunyuGHHy4HHHCAfOc731Fln3328RZyZDHHcpE999xTChXufa+99mqUcC3srTAJW78h1/Zdd93VWwtm++23F01YBDpo97n/jQjILu75a54kBm666abCmiaaHHrood588Mxoognz30ctFsz89gvmL6gnLHLb2MWCme+eBWqZj14TFrqdMWOGKrW1tYIewxY+Xrx4sWCPacJix1HnYh9qawewDb1xzbB1oFh8OOxcymWhYm0BY7ZxTywYHCbM1x+2RgDz+TPff1DQoT/47//NdaL0zz6vXMoOCGsE8P4BrzRhLR6HW3F+x42BRgRslJ/hHedDtLJM541tAxYE85sS2f1tQTDDgMZiQKWfZxiYXdzz19wwcAMGEqALk6j+ftppp8njjz+uynPPPectyhbmkLEwXliwnu1hTh7bIQHGjRunCk4ax1C+JixirC0UbETAhvYQ9czZZ0SAH0my9zvJIBg4wqK/moRhjH+7dh7bCO7efvvt0qtXr3rSu3dveeaZZ+TZZ59VhX1hwgLnTzzxhLfWIOsNBgVys1+/fqqwZiGBGBYf14T9Tz31VKMkuPB6Kf+PqjO6DhOei/YuiNIhukVPYcKz6dGjhyoQPywODQmkCfvuvvtuVbp16yYQSGEE0YEHHhgaQIs7CJY99CiPGieJgbvvvru3sDmLmwflwgsvFAL3TGuuybp16yIXuiWIPW/uvHoye/ZsIdgcDBS7/7GJwhacnTZtmmenhdli6AnyQRMC/SwYTBmasCjykCFDVHnttddkxIgRMnLkSFWoe1iyB4F0SIswG5A6afVlG3Wm3DByA9uRZ6AJCw1TNrrWhDqzuHJQ0L0/+O//DRHAvbgFiYPf6HD06NGq9O/fX44//nj57ne/q8pOO+0UimO5bLyo/XFjoBEBRgQk0lCjGrHty9/hC+rKgmDlYQRZEKzxfSDYJ+z/ytKlYaBhYLn1eX8gLvg76l5PO/U0efTRR1UhyLNkyRLBedLk7VWNJwJw4kaNGqUKThXOHJlfmgwcMNCIgCL9DiMCso2BSQbBwI+wYD7bo/Ak6tw99thDHnjgATXoTkA+TAYOHCiDBw9WZdCgQd55BFQ0GfzKYBVjwJ4xY8Z4gbewABsBnbAgV67to0bq2BaGebFtHzkqtM4E7YYNGyZDhw5V5fXXX5dXX321gXBeMLjl/7+qqkrChGDh888/rwpEM6RFnz59VHmq71MSRqBAWNx8881y7bXXqnLE4UeEttO4g2DZRpLs1j5JDNy79d5y4on/nX4s8H3llVcKIxpZ81QTAtDvvfeeKmvWrPHOhUjwC0QAOOQC/8HvKCKAoPSKFSuEsjVZtXJVaMIGgXUC2WEZ8uAFI0M1ISEDXA4jCgh+a8F2R3iAecOHD1elZkqNl/UPaRKUBQsWeCSARlywbeWKlfL555+r8vHHH3sjBqLqpWFZMUQA75mw9wXvrFNPPVUOO+wwVXbdZddQHIt6D+faFzcGGhFQpEGe64HZ/soKUiX9vC0Ill3Dx19zIwIMF5LGinIt3zDQjyTZ/W0YuAEDg8F///9R/diIgPojKaJ0VU77jAjILu5R8ySDYFHBfCMC9OzXYKDHiIA6csCIgGzjTJprnyQGGhFQ7ZEDRgTU4ZgRAdFIYESAEQGJMFbl5HSl6V4sCBYNaFnZa0GwDUGwNPUvq0v6n4thYFZQLrqehoEb+po/8B/8HYVJRgQYERDdy2xvGjWQZBDMiIDJXnaujQioGx1gIwLSiABWpyQx0IgAIwL8IwOMCIjGGyMCjAgwIiBDbcCCYNGAlpW9FgTbEASLCnTZPtNTsA0YBmYF5aLraRi4oW8Hg//+/4Pt3/+/EQFGBET3MtubRg0kGQQzIsCIAP8UQUYEpBEBrE5JYqARAUYEGBGQP8YYEZChILDfAbTfG5zoStKFBcHyB7c0H2lBsMrsv5WEVUndq2FgmpEt/7oZBm7AQH/gP/g7qh8ZEWBEQP49zo5MiwaSDIIZEWBEgBEBaenpVo8wDSSJgUYEGBFgREBYz2u43YgAIwJsRECG2oAFwRqCWBa3WBBsQxAsKtBl+0xPwTZgGJhFxGtYZ8PADX07GPz3/x9s//7/jQgwIqBhz7ItaddAkkEwIwKMCDAiIO0IYPVLEgONCDAiwIiA/DHGiIAMBYH9DqD93uBEV5IuLAiWP7il+UgLghXff/3BMu13GnFhu223k7322ksV+vamm25aEcTsJptsIpttupkq6IDnGfb8DAPTjGz5180wsHgM7HBuB3n++edVGTx4sCxbtkzeWfOOKqtWrZLlby3XZflyWbFihaxcsVKVOXPmyISqCaowH+uiRYu8a3P9oAwaNEjuuOMOVa6//nrZaaedQvt+GCZU2nb6jn2yq4Ekg2D0Bc0ectui+sqBBx4oF5x/gSpXX3219O/fX1579bV6MmTIEBk4cKAMGDBAlZdfflkGvzJYlVdeeUV+97vfeedTRlAIaI8YPkIVFvgdN26cVI2vUoV9Y8eM1WXsWBmbQ8aNHSeqjBvnXXf8uPGiyvjxwvoEjRHqrF5z7DivvmPGjJExo0NkzBj1XsHpydWTVZk0aZJX7ujRo0WT4cOGCwsGa8Kz4R0T+mwHvSKDXh6kyksvvSRPPPGE9Hykpyqnnnpq6Dtgiy22kDZt2ki/fv2yCwBW80QXTD/88MPlmmuuUeXOO++U5cuXy+eff67KunXrZM2aNaqsXLlS5s+fL7Nnz64ns2bN8tYlmTRxkmhSO6tWli5ZqsrixYtl7ty59crzlz9jxgyZWjNVFWwt+i3Tf2kCNtPXNAFr2a/1bbZRnodH/8U7/2+uSf8HvzUZNWqUTJkyRRUC9Lwrnu//vCpcZ8mSJaosXbpU3nrrLe/58QyDUltbq9YZnJs2dZoqNTU1wv6JEyeGy4SJMlER9HfttddKx44dVTnwgANDcSzqPZxrX9wYaESAEQGJNNRcDdn2Ny4IYEGw8rCgLAiWX/t3zmvY98YbbyxhkkaMgQQ47rjjVGnfvr1sueWWFYHHkAAtWrRQBSOHZxr2/AwDDQPD2kalbb/0kktl2LBhquBQEXBcvXq1KqtWrmrgSDnHChLg7bffVs+jvIULFwoOlCbTpk3znFqcW01eHfKqPPLII6p06dJFdtlll9C+X2nPN+x+jQjINgYmTQSEtZtc28/veH6DYLwLzkPgEeAJBtDZRkCE/Zq8MkgPFhFAIogMHvgz2P2/X3/9dRk6dKgq4B5kANfXhHr6s0KDvydMmCCNFQJHYVI9qS4bt7q68O+cAamIYBVBwalTpzYQ8Hj69OmqgN/oMOwdQsAvqDf3P7qjvmH3CQERbCvufwgNAo3Dhw9X5dJLLw19B8QdBMs2kmS39kli4EknniQ9evRQ5de//rWsXbtW/vrXv6oCEUDAXxOC0CRBEPj3C8F6+kFYMJlg/0cffaQKpAPnaRjGNvpmWLAerARzIVs1Abs1EoBt7KPcN954QxVwNwwXwGXKeOGFF1QB88POpb7YL7fffrsqTz/9dKgu0DF6DCNxIAo8shTC1CcE8TVb1W0DO8MEEiYMP8Ez7qVr166qHH3U0aE4lutdHLU/bgw0IsCIgEQaalQjtn35BUE1PVkQLLuGj7/mRgTk1wfCCAC3PYwEiAoka/2qVNuMCKh77kYE+NGgMn8bBuaHgVHYZERA8TqM0m9a99F37JNdDSQZBCumzRoRkB85EEYCeIFxIwK8oKgRAdnFp1LUPEkMNCKgjhgwIqCOEDAiILpHGxFgRIARARlqA0YERANaVvZaECy/AI4L+Id9GxGQnx6LCQ4kca4RAVlBquTqaRhYfN81IqB4HSaBb0mXaURAcrhUipKTDIIV0/aMCDAiwJ/JayMCSoEGlXmNJDHQiAAjAmxEQP64YkRAhoLAxRh4dm55OIxGBOQPbmk+0oJg+fXHMALAbTciID89pg3/jQhIMzqVpm6GgcX3XSMCitdh2rAxn/oYEVAajErqKkkGwfJpP2HHGBFgRIARAUn1eivXr4EkMdCIACMCjAjw97bo30YEGBFgIwIy1AaMCIgGtKzstSBYfgEcF/AP+zYiID89hjn+TbXdiICsIFVy9TQMLL7vGhFQvA6bCgOLua4RAcnhUilKTjIIVky7MiLAiAAjAkqBAHaNJDHQiAAjAowIyB9jjAjIUBC4GAPPzi0Ph9GIgPzBLc1HWhAsv/4YRgC47UYE5KfHtOG/EQFpRqfS1M0wsPi+a0RA8TpMGzbmUx8jAkqDUUldJckgWD7tJ+wYIwKMCDAiIKleb+X6NZAkBhoRYESAEQH+3hb924gAIwJsRECG2oARAdGAlpW9FgTLL4DjAv5h32FEAMeHObu5tjdr1kx22mkn2W233VTZfffdpVWrVqrssccesvPOO8uOO+6oSrt27eT8889X5dRTT5UddthBNt10U1W411x1b8z+MN0Wo8NNNtlEtttuO0+P6DIoLJp80EEHqbLffvvJFltsIWHPFv1fdNFFsmbNmqx0d6unogHDwPwwMKpP/+hHP5IJE/Tg1ZQpU+TNN9+UVatWqbJixQpvP8cE5a233vLOWf32atFk4cKFUlNTo8q0adNk9uzZMnfuXFXeeOMNefLJJ1X5xS9+IbvuumsiOBelx6ztMyJAAZQMbUoyCBbVlnmnY9+E2RgdO3aU559/XpWXXnpJRowYIWPHjK0no0aNksGDB8vLL9cFn4Lfrwx6xdvPMZq89tpr8vprr6sCVgwbOkyXYcNk5MiRMmrkKFUIBI0fN16X8eNl/PjxUjW+SpeqKqkKEfB24sSJkRK1mHDUPsoNw/Nc26urq1U8njp1qoDJmvCOYB0Anqsm7Bs3bpwq6I/6ht0PuvUH4/y/3TWHDx8umvBe23LLLVXZfvvt5eCDD5Z+/fplqMdbVYMaSBIDv3/k96VTp06q8O6cM2eurF69WpW3335btZewo1auXClLliyRxYsX15P58+cLfSkMFxYtWiTvv/++KpRL3wbLNAEfWfRXE7DWw8hhw8RP4Lnf7ON8TdhHPxw7dqwq9O8wzAETKFPDc7YNHTo0tO+D9Q8++KDcfffdqjz33HOh1wXDPvjgA/nkk09U4blo9wM2zpgxQ5Xp06d72AhOaoIeuB9NeCfSzq666ipV2rZtm4gti3/cpk2b2DDQiIAMBYGjDDzbV7xTnQUdGhEQNCey+b8FwdLbXwlaX3/99XLHHXeoghHTp08fVXr27Ck33nijahRgLHTv3l0GDBigyqOPPiqHHXaYFwgjGBaUrbbaKnajgmA7QfswaSwZAAlAUOHqq69WBR2++uqrqvTq1Uv2339/1QnEOYSEMSIgm7jnr7VhYPEY2LVrV/noo49UwfFcvny54HBrgkNLQF8THCqIglUrV6myYMECNegEOeCIgDlz5ogmOJdhff+JJ56QPffcM3acy4JtV0gdjQjwI0n2fucTBIsi6KP2RbUjSICWLVsKZLomHTp0kPvvv1+Vxx57TF5//XUZPWp0PSGAPOjlQWqgiuAVREBYf6c8AsEjR4xUxQtODx8hIxojI0aoZXrX+m/QLYpECAuAF7udwFKYULYWyMpnG7g6efJkVQhSasLxEB5h9wTJ44KKwW+eTVSwcMzoMaHnUpZGALhtnTt3liOOOCJUSKgxIiB7uOevcT4YGIVlUftatGjh4RxYF5QDDjhAbr31VvnVr36lCu167dq1qhCE/uKLL+TLL7+sJ59++qnMmjkrlBTDDiK5QhNIAkiyYP9y/9POe/TooUrv3r2ltra2QSKHS+xAx8uWLVOFfRAbYYkia99dq9qV2JvvvfeeVyZ11wSbEhtREwLvBPupuyZgjo/nfNQAACAASURBVKt/8Bv9RdUZPWvYOmvWLFUH6Gbp0qXePWh2MNuefvppueKKK1Q577zzpPVerdWkP5IB8Vej2mlj9xkRYIH7RBpWYxuknVe8M1+IDo0I8JsS2f1tQbDS9ptC+hgjASABwoL9ZAE4hyX4PWjQILnnnnuEAJ0mTz/1dKij179/f/n+97/vBcIIhgVl2222jR37IQIIDmgCOdBYIgAyBRKgW7duqrz42xfVbGGyiAkakPEF8aFJ69atjQjILvStr7lhYPEYSAb9n//8Z1XImoIIwNHRBAeOTDZNcIA4F6dLkwXzI4iAqdNkdu1slQTAUSM7ywvwKVmoffv29Yi+QvC6Eo81ImA9jGTyRz5BsKhgf9S+qP7ASAAIAN6hmkAE3Hvvvao89NBD3ruZQI1fsIHITA1LcIAI0DJS2QYR4GX1B8p05bMvDCvYHrS/Cvk/qlzIjnyC7405Jizo7kgAf+Z8Ib8J6IOtQZlcrZMDkAAcC4EQNvqB50DmsCYEKqlz2LnoUMuidduintVtt90mxx57rCpHH320fO973zMiIJPIt6HS+WBgFJY1dh+jri+77DK5+eabVcGHI9Ctybp16+Trr7/ecBP//fW3v/3NC8iHjY7BHgoLyBMsJzPf9YvgNz7jXXfdpQrkLLbau+++q8o777zjjZxm9HRQ2EeyCOSGJn/8wx9VuxJ7E+KD8jTbkG0E7MPuFxvwxRdflF//+teqgCdRozWwSzVChW3oWcMjRqhyniach31MYowm6Pj0009X5ZhjjvF81Ma2xcaeZ0SAEQGxB4Ma2xjtvOId+UJ1aERAg3dwJjdYEKz0fSffvmZEQB0xYERAJqElM5U2DCweA40IKF6H+b4X0nScEQGZgTm1ovkEwaKC/VH7otqpEQEbpqIxIsCIALVz2saSaCAfDIzCssbuMyKgjhQwIqCOGDAiQMSmBjJiwYiFDLUBIwJKYqMkfhELgqU3gGNEgBEBiQOAXUAMA4vHQCMCitdhYwMKTXmeEQHZBtB8gmBRwf6ofVHt0ogAIwL80wfZiIBs40iWa58PBkZhWWP3GRFgRIB/ZIARAUYEWBA8Q0HwxgJ/OZ1nRECWTZ8NdbcgWHoDOEYEGBGwoafar6Q0YBhYPAYaEVC8DrNoHxoRkBQqlabcfIJgUcH+qH1R7dmIACMCjAgoTR+3q0RrIB8MjMKyxu4zIsCIACMC6vfNjf71r3/JH/7wB2GYCIuDsWL2n/70p/VH/fvf/5Z//OMf8uGHH3pzQrGf+ag+++wzYV9jPuYAVqbz0ljgtvM2tBcjAhqDONHnGAZuaF/W1zYSIwKMCIhGjPLbaxiYTQw0IiCbz63Y96wRAfFjcCkxMJ8gWFSwP2pfVNsyIsCIACMC4seOcikxbRgYhWWN3WdEgBEBRgTUR6yNIAFYFJFVkY8//ng5++yz5ZVXXll/1D//+U+PIGCxiosvvljOOecc+fGPf+wtGvTXv/61UWSAEQGV6bw0FrjtvA3txYiA9dAU2w/DwA3ty/qaEQFu4WBbIyA2iEl9QYaB2cRAIwKy+dyKfc8aERA/pJYSA40IeM1bQNgWCx7nLbjrFhBm8eFCFgj2H8simcGFgvnfFguOHyvKtcS0YWCx70ntfCMCjAgwIqA+gm1E9v/AgQOle/fucv3118v3v/99efrpp9cfxSiB3/3ud9KlSxe5++67pWfPntKpUydhJeUpU6bIF198sf7YfH8YEVCZzosGyratsLZgREC+KJP/cYaBhbVBrc9GZaixTzsnyW0EsbfZZhvZfvvtVTnl5FPktttuUwXS+4knnpC+ffqqwvvxmWeeUaV3795CcK5L5y6q9OrVS954/Q1VXnrpJe89e8NPbhBN2rVrF7seeTYbb7xxqEQ9o1122UUOO/QwVU466SR5/PHH5aUXX1IFp3XpkqWqjB49Wo477jhp3bq1Km0ObCMXXXSRN0Ix/15uR0ZpwDCweAyM6iuN3QeObbvttoLzqgk2+bqP1qny/vvvy9KlS2XRwkWqLFiwQObPm6/KgvkLZMniJWr/pN/OmT1HaqbUqDJ16lSpra31juG4oEyfPr0uOFU9ucH3Cy+84PX5xuqrUs4zIiAKzRq3r5QYmIsIyGVPRdlUYMbmm22uytZbby0HHnigtG3bVpWOHTtK1y5dVbnvvvtk2LBhMnHixHoyfvx4LzGQBEJNXnvtNe88zg3K8OHDZeTIkTJq5ChdRo2SURHCuSNHNEJGjhTsjDAhIM99hYkXuB87TsYpwrljx4QI+8aOVc+jLAL7YXVav33UaBmtCHWaMGGCLlUTZIImYcf/d/uY0WMaPDP3DFlsmWtiy2nCvYwYPkKXESO857r+ngLPAhv85JNOVuWEE06QI488Uvr169e4jm5nhWogTRiY1Lt8iy22kEMOOUSO/N6RqlxwwQXS+dbOqtAuBw0aJMGFxmnr2Dxh9tScOXOkdlatLrW1Mnv27FCByAvDQLB45cqV8t7a91RhX5iftWTJEvFk8RLP1sPe88uyZcvkzTffVIX316JFi2ThwoWqUA7na8J5xI6D7xH3P2W+9957qqxZs0bmzZvn6Rp9B2XWrFkyc+bMBsLzefnll1V57rnnpGvXrnLTjTepctppp8l+++6nSqtWrWSzzTaL3S/P1fZpw23atIkNAzf6+9//LjxUlA8on3766fWIABrgrbfeKgQweABMCTR48GBvobdf/epXsm7dulBQCdthREA6nc5cjc/2N/1zMyIgDFUav90wsLh2nSuYHOXUJoUpvJz32GMP2WeffVR56KGH5NNPP1UFY2PAgAHSt29fVR55+BG59957VSEwB2nOO1OTno/09Ih3yPegYKgMHTpUcI41ufTSS0tucEQ9H0iAH/3oR6pw7ziH8+fPV2XZ0mWeAYuhGhSy2M477zxp3769Lu3aGxHQeLhTzzQMLA4Do/pJMfvAMZyN/fbbT5Vf3vdLbzpPpuwMCkk82PXY7Y2RuXPnSpjMnDHTc+Zw6IIytWaq5/CGObc4xWHl4luA2cXorBLONSJAhbGiNpYSA/MhAhpL0EMCEPDXpGXLll6yH8FUTS684EK54YYbVCFxghgBRJ5f6P/YK2T3azJ82PDQQBbxBS+YT0BfEQLeBHHCJCxAlms79+EF5MfVz8h3mflV46v0gDoB8qoJMn7c+HqZ/O48vik3rL5s94iC/xICwd8E3qPqrunIbeNcfz3c76j6sA/ywAXhgt/sDwY93f9cF6JEIwHYxrlh94L+qV8Y0dL9l93lBz/4gSqnnnqq14aNCCgK7tST04SBaXyPk1h2/vnny7XXXltPbr/9di8oHbTB3P/z5s4LxRMSJziOxA1NPl73sXzyySeq/PEPf5QPP/hQPY+yeM8QHNeEgDnY7Z8qzP970qRJoXWeNHGSTJs6rd57wP9OwN7USABHLjAV/dq1a1X54IMPvLgyseWgcF5NTU0obmCXric4HNGxZInnzxPs1+Sqq66SHXfcMVM2Z+xEAPP80/l///vfew0C8PWPCABsO3To4L0oaIxMFUSg5IEHHvCmCuJ3oR8jAtLpdKYReK1O9duKEQGFok3u4w0D67exQvucEQEbSAEjAuqIASMCcuNOmo4wDCwOAwvFzHyPNyIgnc8l3+eX1HFGBMSPnqXEQCMCNmT6u0C29m1EwAY9EVTXdOS2GREQPyZUWolpwsCk3p3FlGtEQN2IIyMCmtYujZ0IcED3+eefC8xUkAhgeD9rB5BZ9NVXX3lrAvztb3+TBx98UE488UQvm8+Vke+3EQFN24iKAUI7t2mfnREB+aJM4ccZBjaubRsRYERAcGSAEQGF408azjAMbBwGJmUXGRGQrueR1HMutFwjApJDy1JgoBEBGwLcLpCtfRsRsEFPRgTUjRCwEQHJYZ8rOQ0YWOg7sRTHGxFgREAp2lmua5ScCGAKBdYNYHEF9/nPf/4j2na33//9zTffyF/+8hf56KOPvJEEjCD4+c9/nqlhGLkeiu03Z61UbcCIAD+6xPs7zPjRsM4wcEOfNyLAiAAjAuLFoqYqzTBwA66V6p0edR0jAtL1PKKeVSn3GRGQHEImgYGMumdEPdMh4AMzJcr+++8f6gfnsqmi2ppNDVQ/eK5NT2NTA9VNt2RTAyWHI1kuOQ0YGIVxTbXPiAAjApqq7fmvmwoiAIAjOPY///M/9QgCDfggATB6MFxZ4A856KCDQg0g/83ab3OCrA3UbwNGBGgoE8+2Qowfw8AN7TKX08r+MEmqfxNAszUCNjyjJPRsawTEgztpKsUwMNk+U2g/NCIgXc+j0OeX1PFGBCSHmklgIIsu9u/fX6677jrPB2Ytvm222SbUD85lU0W1KyMCjAhw6wPwTbA/bM0CIwKSw5Esl5wGDIzCuKbaZ0SAEQFN1fb81y05EaBNDfTll196REA+UwORCYERBCvPegPIOeecE2oA+W/WfpsTZG2gfhswIiA58yrM+DEMrN8Gg30yl9MaRgKwPVhWXP8bERD9zOLQsxEByWFRU5VsGJh8vymk7xkRkK7nUcizS/JYIwKSQ8gkMJAR8SymOGDAAM8Hvu+++wRbPqyN5LKpws5juxEBRgQYEZAcPlRCyWnAwCiMa6p9RgQYEdBUbc9/3ZITAQTuzz33XHWxYLL7K32x4I033lg22WQTVZo1aybNmzcXHpomW265pbRo0SJUtt12W9luu+1U2WqrrbyyKT8olNmyZUvZbbfdVNlhhx2Ea2tCPXE+N910U1W4p7D7RRf+xmq/43dijQhIzgwLM34MA6PbcS6nNYoIiNoHnoA3YVgExh1wwAGqHHjggdK2bVs5/PDDVWGx+xUrVqgyd+5ceeqpp7x1cFgLJygEYe68805V7rjjDunSpYt0vrWzKpRFZqAmL774ogwbNix0QbhOnTqp94oOmGZgl112kZ122qmeYLjyfgDTwyRMv5tvvrmXtch7SBPWDrrllltUuffee2XSpEkyb948VVhzaMmSJaqQpXbWWWfJYYcdpgrPtLG2R3LoUR4lGwZGY12pbRr6IH37kEMOUeX++++X1atXq7Jy5Upvba+wPsj2+fPnq8I+cDBMZs6cKTU1NapMmzZNZs2aJbNrZ6tC2fR/TV5//XXZd999zY7cKLodGhGQHN6WAgOTXCPA+X/YR0FhlGT79u3l6KOPVuWCCy6Qm2++WRVsHqbUgdDwy5QpU2T48OHyxhtvqMI+zgsTbeoet41zWARXE/a548K+w67JduwMf8Dc/5tZDKqqqkLFZdpTRlDIvg+7LvuCx/v/jzqXe9TWUXDbONd/D+435bMvTLjPCVUTVOGcESNGqMJ1uQa60gQd8Ow14dwJEyZ4NiJ2YlB69OghZ555piqnnXaaN101fpl9ktFAGjCw1LZWPtdjFBdrVJx//vn15KabbpIZM2bI22+/rQp2VrCNu/+xl5h6PcyOe++992TdunWqfPjhh14MNuy6ixcvFmw1Tagv2D158mRVqqurhUWBNWEfa8pSd02wG5ctW6YKieHvvvuucF+afPDBB7Luo3WqcB71DqszdueiRYsayDPPPCNXXXWVKuedd54XY416/mFxiqhzktxXciKAlw/Ofq9evTyH/rPPPpPBgwcLGQ3du3f3GmehUIQRm6SSSlk2QZawYD1Bme9+97veVEhMhxQUAlUYZZocddRR3sLNjJ7Q5JhjjgkNCh155JHeMyMYo8kll1ziBcm4flBwOFu1aiW77767KtwTYKgJ5EQxZEBUQJF9STzXsA6e1PWKvQcjAgpFm/yPDzN+soaBxbTpYs5tTNuGVAwLRIOtUYQm7yWMAk1wfu6++25vPRrWpAkK+3r27KkK77Urr7xy/VR2bko7941B8bOf/UwVgvWdO3eWrl26qgJRwPtPE+qDg+QMxOA3hheGjiY4bBAQV199dT25+OKLBVIETNeEwEAY1hOQO/nkk+Xss89WhXsIcy5xDDE+58yZowqGpHNgg984eLzDtPqyDeLDiID8ca2QI8sFAxuDRWk8Z+uttxZG3pKMowkODvOOa4JjSbA9LJiPc6oF4922KDJg9uzZKg7Vw6aZs2SWIkuXLvUcQZy6oBCUipo7PY3PqCnqBPbaJxkNlAIDcxEBLgmCRIigYDNF+Sd77rmnGuQn+M971SUO0M+CQlDkrrvuUoXpgAn8+kkAfkMIYiOTxKAJAeGwoDp2QlSgmnNHjRylirMbwgLVnOuC4dp31HXZFybYPJQdJvkE7MPsprBruu2UHSaUqd0n28POcdtZlFkTdKwF8tmG3jk/TA8QQ4MGDVIFwpd3xYIFC1RhFLYW92DbD37wAyMCkoG+9aWmAQOLfa9G+bLga5hEYSvYS+yLBCu/tGvXzsO4xgTk8ZPoC7W1tapgp4UlTWGrEYgnoN8YCUvmYLsX6J86TaYpwj6C8fhxmmAfhhEBb731lnjB/hByg9FzH37woSoQB5wfVja60kgP/PUdd9xRFeK3PNew9kZ7CGsrbA87L8ntsRMBzOEPM9u3b18vCwAjAbbr2Wef9V74NK6BAwd6wQ0MBIIVKPXRRx/1GsIXX3yxHjzy/YERm6SSSln2lltsqTYuGh0Z+Yceeqga6PeyMo46Wsiq1OSUU06RSy+9VIKLMLr/Tz/t9AZBfBfUx3Ek8xQnUZObbrwp0lDcb7/9pHXr1qrstutu9QDQD4YAZFSHyvVcojpbFDjnKjdqf9TLIuq8ptpnREC+KJP/ceWGgcW06WLObUyfAC/CMtUZsUR7DwsIk9X/6aefqkI27JNPPin33HOPKnfecafcdtttqkAa4BCfccYZqoDLN954oyo//elP64iArl2lqyJcEzJAE+4Hxyss2wEDCCJek1WrVskjjzwi3bp1qyc33HCDHHHEEQKma7LPPvuoOA/+t2nTRjp27CiXX365Kr1791YNV2fQYgyGCYGBV199VZU+ffp4ozjCRrR95zvfMSIgf3jL68hyw8DGYFEaz2EkDjhE8oYmjCzSsqrYxmjdKCJgwfwFDTKnXDYV50URAXNmz4ns+5oz5rYRBMUR1ASnkv6dxmeRpjoZEZAXrBV0UCkxMEkiYJ+99/EIfEj8oPzv//6vF/zfa6+9RJMOHTp4SX4k+gUF35/AfRgREBYwJhBNjEET7ACC1y7QHfwmyOwC/tp3GAnAdkYRaJnq3rZx42XsmOhM+bBgfa7AOvUMqxf7uKewsrl/LZjvdMS5Lngf/KZM7dx86qvp1t1H2HMdMXxEHUETQk689tprXuyI+FFQ2EeSSFiA88leT6rEN2Q4RADrU9qIgILgLa+D04SBxb5vo3zZuGNNEKr03TAigHbu/KLgNyQAwXyC65rUzqpVR20S9KYPTZw4MRTn2BfmT7I9igiAAAhivfuf+mKrBZPV3P+MBg0L1uPHMpLh448/VgUiQLMN2YZdi47x7zXBbnV19H9fc/U1jbYpaUfEKTShHbG/2LZa6PmxEwG///3vvaD+ZZddJscee6zngAOyDBF87rnnPEeCYSsMCyQLD0aWbMkhQ4bIX//6V/nmm2/yAhj/QUYE1I0CONqIgHodKG5wzqdzRb0s8jm/1McYEeBHknh+lxsGFtOmizm3MX3BiIANpIARAXXEgBEB8eBaIaWUGwY2BovSeI4RAdFT5KTxmZWiTkYEFIJu+R1bSgw0IqCOGDAioP6UPUYE1E0XaURAfpgV91FpwsBi36NRvmzcsSYjAupP72VEQLJ2a+xEwNdffy10/nfeecebL5n5m9x8VZ988omwMPA//vEPj8Ehwwg2huG8ZCVCAvznP/8pGIuMCDAiQAP5uMFZu0ZwW9TLInhsGv43IqBguMl5QrlhYDFtuphzG9M/jAgwIiA4MsCIgJyQFfsB5YaBjcGiNJ5jRECyDlUan3k+dTIiIHYIlFJioBEBRgRoowKMCDAiIH5ky7/ENGFgPu/BqGOifNm4Y01GBBgRENUW494XOxGQP0TEd6QRAUYEaB0jbnDWrhHcFvWyCB6bhv+NCIgPh5qypCQxsJg2Xcy5jekfRgQYEWBEQFMiUdNdO0kMbAwWpfEcIwKMCNDapREBTYdbcVzZiAAjAowIsKmB4sCSrJaRCwO1914h26J82bhjTUYEGBFQSNss9lgjAjZKl2NgawRsWDTF1ghIvm0aEZBVs6d+vZMMgkUZQLleQMWcm6tsbb8RAUYEGBFQHxsq5b8kMVDDmixuMyIgeZsqi+3CiIBso2SuIBiBquAiwe5/bCbstLB2a2sEjPDm6Lc1AsZ5awbYGgHZxopyrX0uDAzDt3y3R/myRgRM9tYOsDUCctuXtCPeuZrQjqLexfm21UKPMyLAiABvAWFbLDh3B86nc0W9LPI5v9THGBFQHmZRkkGwYtp0Mec2pi8YEWBEgBEB5YFphd5FkhjYGCxK4zlGBMRj56Xx2RZTJyMCCkWbdB2fKwhmRMBYb/FNWyy4LphviwWfu37xYFssOF1Y1tja5MLAYt6PnBvlyxoRYERAvu2LdqSRAGwzIqCxvV9EmsoBjAIG9kU1jO222072br13AznxxBPl6quvVuWGG26Qe+65R3r06KHKww8/LE88/oQqLJbzzDPPyG/6/UaVBx94ULp07qLK3XffLYMGDfKyIkaMqMuO8H9TJ16mmrAY9I9//GO59tprVbn11lvl3nvuVaVTp05y8MEHyx577KEKIwaidBz1fHKdR4cMdtam6qRRdS12nxEBRQBPik5tKgwstv1p5xOsatWqVaPkhBNOkGuuvkaV9bh61dVytSL33XefvPDCC6o8/fTTAlaBwZp0+lkn6dKliyq33HKL/OQnP1HxD1y8+eabpWvXrqp069ZN7rrrLg/3wdmgUOfuv+yuCosFP/XUU9Lv2X6qjB41WhYtXKTK7NmzZdiwYTJkyJB6MmDAALn//vsb1MPV64477hDqrAnvEfTYv39/VYYPHy4zps9QZcqUKfL666/L4FcGqzL0jaHC/Wjy3HPPyVFHHSWtW7dWpc2BbeSiiy4S1iyyT3Y1UE4YqOFiHNt23HFHzx4Lwyr6+9p316qyevVqWbhwocybN0+VBQsWyOJFi1XhvLlz58qcOXNUAW9YEE6T2tpamTlzZqhQNuuQacIc2e3atZMddthBFTKh4tBr1sswIiC7uEfNcwXB8F02bbapKowMiPJXDzjgADmvw3mqnHrqqYIPsd2226lyxhlnSPfu3VV57LHHvAzzaVOniV941xOw9/uY/t+84+nXkTJmrIxVZNSoUTJi+AhVsD88GTZchisyauQotUyuQ5Y8ddbsj//P3neAWVGkXft9kj5BQUAFIyI5SxaUIIpiQEGCARREQFEUBAQV8RcFERBFEAUjQUlKmoFhyJMjMDMsIzmIEde0uu66u+77P6euzfT0vNV9+97bN8y893nOc++t7q6urq46XXVOVZcKwzYbIF2In8OG9RvY9KrrWL+ecKzdeTGTgYOTKYJr2rx5cwkYJoIu/+3yAdeH62GxYQNt2LCBzQMct3rValq6ZCmL1atXU25urqoHqAtWvDH3DbrjjjtY3HLLLdSxY0dauHBhbJNAGU+9Ewd69YwGd4JfdbDjVl2a8Gog1LOjR46yKMgvoIz0DBbp6emUkpJCycnJLNCW4tpZCMOxGNW/c8dOHjt3qu12I/+TdiURh5TkFEpLTWOB9G7dupW2JG5hgbYj1pPlgPVoT58+Td9//z0LrFl76tQpFidPnqRDhw7RwQMHWaBtibRZAY1Bd++cwu10yUDKitP5/NleqmYEOGUwt92fTNLtg/isYrHx30k0hgkA0d+KSZMmKQEGIowVeDDjYVdQUMDCWJQZHTYrUIGwvbCwkAUqqGqMoEFiAQgJnTFdBxAP2bvvvpvF8OHD1QMWRgIHdABRiTngWgcMGKAe0nhQWwGDQHdvggnHfS1fvjxVqFChGBCGbcHEHW3HihFQOlpPpUkEgwlw7bXXBoTnJj9H27dvZ5GYmEjLly+npUuXsnjm6Wfozjvv1KJPnz6kw5AHhtDjjz/OYuzYsUrMnzJlCrkF7isE/ZdffpnFjJdnEAxgDhDsJ06cyBoMMB7mzp1b4hljPHPA+fv371fiOARyA3iu2D1H8vLylOCHhpsVEArR6DLisn7DlNA1MNHpRHp1+QCDQnffly1bRjCIGjZsyKJ58+ZiBJQCGixNHOhVO6FWrVrKpHv11VeJA0QtdKw4oA25d+9e1QZF28wKdJpQ/zngOJgASvCH6O8G6KCmpWuNAMSla5uCz1D369WrxwIGgVd5HUvxihEQ2wToJIIpI6B8edWvQT/GDCcjoEWLFjRo0CAWGOiFgRu6sn599+vZQWsYzAb+wXPd+szfsWOHEs0hCrsFBGMlem/cqMR59JnNQHxGG4f7XrNmDekA4d3aNzb/t0urEvgt/Wp/j4UJcsakMMwK07fdeZ22mQ0W62/kG9rMbgDjAPfUnOfm3zAJzNdt/W2XXsz0RDuPA4wAO0N4/rz52nb9rbfeKkZAbNOfSr0TB+o4KhThOhPASQfUnRtGAOod1w5DGDRAiPYs0tLVYCqreO3vf7tBFxiopTMYEJ6UlKQFjmXT+6dxsWXLFmV+oO9pBfqOOjH/q6++op9++ol++eUXFt999532WMMIOHDgAHHYszv0RoDunkcyXIyAIF4NJEaAb2SYGAGxO91cjIBS0AKK4KwoLx5eYgQUGQZiBOxSQoEYAaWDp7y8CjECnNshYgQUNwTECPCVGTECvGQm7+N2EsHECPAZAhCaOQPACNOZAAgXI8DZFBAjwPu6Lmfgc8CJA73oqxpxihHgm30gRoBzG9woM9H0LUaAGAFqhoDMCCiqwDIjgH/QSmj05kBpEsHECBAjwDpKUIyA6OWeaElZaeJArzoJYgSIEcCVLTECooXFAkuHkwgmRoAYAdxoe+ssvy8asQAAIABJREFUAPN/mREQWF2UoyKTA04cyD33QhUmRoAYAaEqS5GIR4wAMQLECLCUATECIvMgl7MGngOlSQQTI0CMADECAueCsnpkaeJArzoDYgSIEcCVLTECYps1nUQwMQLECBAjgH/tp7waKLa5z0i9Ewdyz71QhYkRIEZAqMpSJOIRI8AiAru5CfJqIHk1kJvyEo37yquBjGZEbH+XJhFMjAAxAsQIiG0+ikTqSxMHetVWECNAjACubIkREAnGCt05nUQwMQLECBAjQIyA0DFO9MXkxIHccy9UYWIEiBEQqrIUiXjECBAjQGYEWMqAzAiIvoe8pMg+B0qTCCZGgBgBYgTY13fZWjIHShMHetUZECNAjACubIkRUJJPYinESQQTI0CMADECxAiIJU5zm1YnDuSee6EKEyNAjIBQlaVIxFPmjQCIvoFmPI7VEcDZZ59NFStWpEqVKrFo0aIF9evXrwSmTJlCK1euZPHJJ58QVtbesX0Hi+3bt5MO27ZtUytx413LHD799FP66KOPWKxatUrFq1stfMGCBfTII4+wGDduHL333nu0YsUKFhkZGYRVvzns2bOHHn/8cerfvz+Lzp0705VXXskCYmK1atXo3HPPZYF7o7vvuK/lypWj8uXLFwPCgikvuvNFMlxmBLhtbkTn/tEogqGuoA5VqFCBBeogHkBWNGnShDBdl8Ntt91Gffr0ob59+7J47rnnWJ4B/4Df3n77bZo/fz6L8ePH01133cUCXD1gwAAtHnzwQXriiSdYjB07lp555hlC2tzi+eefp5deeommT5/O4uWXX6YZM2awmDZtGk2cOJFwXRxmzpxJixcvZrF8+XJKTkqmzMzMEsjKyiIdwOdpaWkssK2goID279/PIiUlhczvqDX/xjMR14n84IDr0D2fcO+7detGDRo0YNGsWTP1fDl69Gh0Vm5JlV85EI0cGMlnO3fu2rVr07PPPsvyH3gxMTGRjh07xuLw4cOENllOjm8mqPUb2/Lz8lns3btXcQbHJ0ZYVmYWccD29PR0llPANTgmNzeXxbp166hHjx7UsGFDFjVr1tS2A7n8K61hYgT4RTFRu5OTCBYxI+D661X7BW0YK+bMmaP6olbTf8eOHRQfH0+ccO0UFhcXR3i3vQ44HpzAwVgweO2atcQB7RHEr4Nd2nTHGOF2x+K8XHqNMLtjnbaZ21jW37ZrBGxOpEQGXi4WvHr1alq2dBkLaCN5eXl08MBBFni23XnHnSzk1UBRS2uuEubEgV4+u3U6IMID0Yzq169PqEvok3AoyC9QbSK0i6xAmwh9KTukpqaSDrr+G8JxjK6f5RSOY9PT0lkgrVu3blXtT7RBrUD77vjx4yw+//xz+uGHH+jnn39mcfr0aTp16hSLkydP0sGDB+mzzz5jgfNyCyAPHTK0VLUby4QRgMoIYZ6DUVFRWUMJiFxXXXUVQdTi8NBDD9EHH3xQAhCq5s6dy2LWrFkEYX30Y6NZ3H333arTg46PFd27d6eOHTtSu3btWEBcGz16NIunnnpKCWg60ejjjz8mPKQ54AG9Zs0aLUAAukq4/y/7VadTJzrB0Fi2bBmLefPmKaEQ182hTp06thVZVxa8fJhEIm4xAly1NaJ252gUwWACQHiCKccBjZ2mTZuWwD333MOK3hDDZ8+eTUuWLCEI1RwgEusE+4EDB9K9995L9913H4v777+fhg4dygJC/8MPP0yjRo1iAcPyySefZAHOBodClOfw9NNPK6MAZoEVEO1wb1944QUWEPvtjAAcP2nSJBZjxoyhkSNHssB1GulG2g0gvtdff53efPNNFkuXLtV2WtHRRAdfBxjKurTCyBg+fDh7b3DP3n33XTpw4AALNCphBOhMYzyfYTaLERC19OZXwqKRAyPxTLc7J3gYoohOIIJZd+TIERboMEH8N4R76/fu3btpX8E+Fvn5+coIQPwc0MbLyc5hkZ2drY6xdniN/zgORgMHiFm9e/emtm3bskD7xy6/yso2MQL8opio3clJBEM/FwOZOKBfbCdWYdDaoEGDWNx+++1UtWpVbR264YYbCIMVOKCfiz6c1QjAYDYI5IbQbf0GdxkCOveNOs8NdkOYk8GgxPB162k9A6fzcmkxwnBenTnhT5qseWD+b5cubLMK/P7+R7oSEhJ4bEqgBA66/f8MRx4YeeL2GzrCypWrWOA6CwsL6fix4yzQtux9e28WvXr1og4dOtDChQujtn5LwpxzwIkDvXyW6/QiI9ztudE3Rl05dOgQi7y9edqBEU5GALYbbSfu28kICNRgUMZDSiqlMkCcENytzwLjP9qMaF9ywOCyL7/8kiD4c8AgY50RcOLECTUoDXFwQP5YTQn8Hzx4sPaZ58+9NsoF9+3P8aHep0wYAZwBYIQFYwTY3QzMBIDA0Lp1axYQjfDwsuKD9z8gCP4cpk6dqoQQCP4cunTpQvXqFZ/6bPyHCIJp4RdccAGLTp060QMPPMACo/0xegMmBQfMGMCMAw6YwYBrNDdczL8x6nTfvn0s8GBHBeZmCyCscH+hlgzR2IF4h04gB4wQs7t/ZWWbGAHODYxY2CMaRTDMBIDwZHCQ9RsjsTmBZvhDw1mRGeIzBF906HQzn2AEcMYfwmCO3nLLLYRZBRzAqTBoOYwYMUKZpHaj/iGccxg/bjw9NaFITDdEdeN70sRJBDOAA4wBXBPuLweMsrMzAiZPnszGi3OB12G6cIBhMmTIkBLCO8wQmBK6cy5auIg1hA2TGMawDrNmzmJnlSGdyH+IEVxaEfbOO++wI2cg7qOswAiA+cuhSWMxAmKB45zSGI0cGG3tiCuuuIIWLVrEdm7QwYGorjMCYLTBCOCEfKOjpmvL+WUEaGYaOBoBOTlqNChGhFqBZwUGuUDo4XDpJZdKO/Css9Szxal+yfbozQEnESySRoCurQAjAEKzIfYY345GwHq9ERAfF0+bNvKz3sEFG+PthWg7kRz9WLfitbG/nRGg0rQhroQWYGgDSJO5z2z+DcMiboP9LAW7a7LbpjUCNiVojRbksdY8SEhQ4qaRJ26/MWPDaEdav5FWDCiEsMfhrQVvEUwrDmIERC+vuUmZEwd62RbjBF0jLJDzoq8MIwCDLzhg0IOdYG8n1uM4XRsO4XbxQsx3ilt3vNOxdjMKMJNANyAYM1GhE37zzTcsvvrS3ghAuUH7lAPSzPHZoPsGBdVuNMoG9x1IeQn2GDEC/py6w90QpzC7zBcjwGcMiBFwVlCEYVfGQrFNjAA3TY3o3TcaRTAxAnzGgBgBRbPFdCYAwsUIiF5+iYWURSMHhuIZHco4xAgobgiIEeBrn6LuyCd2c8BJBBMjwGcOiBGw3u8ZAmIExC4flMWUO3FgKNtR1rjstELrvv78FyPAt+aAYQ6IEeCtjihGgBgBaoaAzAjwtqL5Q/6R2EeMgNLRZIpGEUyMADECrCO3xAgoHXwTjVcRjRwYiWe63TnFCBAjgCsfYgREI6P5nyYnEUyMADEC7Eb/c9vECPC//smekc8BJw7knnuhChMjwLcmgW42AMJlRkCRxhjq8hJsORYjQIwAMQLOKqqgwVaoWDtejIDIN2BCkYJoFMHECBAjQIyAUNRuicOfHIhGDoy29oAYAWIEcGVSjAB/GCZ693ESwcQIECOAE/vtwsQIiN76LikrmQNOHMg990IVFmphV2YEyIyAUJVNf+IRI0CMADECxAiQhTJLtitiKiQaRTAxAsQIECMgpmgkphMbjRzoTyM8nPuIESBGAFfexAiIaeojJxFMjAAxAuxEf26bGAGxzQllLfVOHMg990IVJkaAzAhwU5ZCXV7cnJvbV4wAMQLECBAjQIyAGG81RaMIJkaAGAFiBMQ4scRQ8qORA7lGdyTDxAgQI4Arf2IExBDRMUl1EsHECBAjgBP77cLECGAqmgRFbQ44cSD33AtVWKiFXZkRIDMCQlU2/YmnVBkB3AXbVVBjG3dcsGF45crzzz9PC99eyOK5556jBx98sAT69etHPW/syaJ79+7UqlUratasGQt08qpXr87i/PPPpypVqlDlcyqzuOyyy6hVy1Ys2rZtSzfddBPddtttLPr370/3338/iyFDhtCIESPo4YcfZvH666/T5oTNLLZv30779u1jV03HSuoFBQWUm5PLIikpid588021ACUWobRi6NCh1LFDRxbt2rWj5s2bU9OmTYsBYa1btybkhw7t2rYjFu3aEeJt3649j/btqUOH4p1U639deq+55hrCGg8ckE6Ui9q1a7No0KAB4f4dPXo0ah/wkjDnHHjhhRcIwnu1atVK1H/ce5Slrl26skD5adumLY+2bdWxurLXuXNn6tKlCwvw1R133EF39b2LRd++falvn5IYNmwYTZgwgcXTTz9Ns2bNotdee43FUxOeokGDBrEYPHgwgY+GDhnKAucdMXwEi5EjR9Jjjz1Go0ePZjHmiTH05JNPshg/fjyB7/E84DB9+nR6dfarLObMmUPz58+nBQsWsFi0aBF98P4HLN5//31655136J1FPMCPunxEOM4951V3ePXVV0mH2bNnE6512rRpLHBvRz82msWYMWNoypQpNPWFqSxgOKAjwCExMZH69OlD7du3ZwG+Fg505pho30OMAOdXHNatW5eWL19OWVlZLPLz87XtrcLCQsrNzWWPQ3x79+6l/X/ZzwJttczMTNK9QxbbdG25nJwcysjIoIx0HkhTQX4Bi61btyrOv6XXLcShfv36FGxbvzQcL0ZAtLObffqcRLBgjIAmTZpQv7v6sejZs6dqcyJ+Dt26dqPnJj/HYsaMGZSQkEApKb7RpMb3zp071YK2az5dQxw2rN9AmzZuYrFx40aKi4ujuA08NmzYQDieg50ojm04VsWN+ANAfFw8BYT4eIIorwOuWYv4jdrjdPEZ4chj3J8S2JRAmzb5jBXuG/vr+vSI2y6f1f3BPWKAsrBy5UoWiFM9uw4cpIMM5s6dy/I/ngkow+gfLVy40L6SydaozgEnDvTyOW1oibpvt+cO1gjA+/h1wMK7qj2FNhWD9PR0wj4cVPstNY3SOKSlkToWxzPAMQbHW79TU1J952OOQ1zGGgPcNeEa0L7UtT1RLqAjclAaYm4uoY3JAToix2XQGNze02jev9QbAZHKfIjIKIC6zyszXikh2EHExw2JVJq9OG/58uXpkksuoTp16rCAgfDuu++yWLZsmSIzdPQ4oOOJiswhLy9PdVg5okMYBLInnniCxaOPPkr33nsvDRgwoBjuueceGj58OGtowOh45OFH6JFH9EC8OkAAe/zxx7XQpRXhT459kiA2ckB6rrvuOmVgwMSwok3rNiKC6SppDIW/+OKLyuiD6YNGhBlt2rRR5QpiNAcI2BC6OaBco1zqyh+E98mTJ7PAuSAwwKTgMGnSJDZeGKQQZjkMHDiQHnjgAYKRxwFi/cSJE7XQGQwIx3VyeYAw5MOoUaO09RfHjh07lgXifumllwgdXw4Q+j/88EMWS5YsoVWrVpFukd3169ZrO4HopEEI27ZtG4ukXUlaYQ4NNRy3ZcuWYkAHGObDvHnzWOA+68oK7g3uGTifA/IZgj+HZ555RuUP8oLD1i1b2YYcGnfIB5xXZ2RDrBAjIIbITpNUMQKcjQCnDuaBzw4QBH8OaM/u3r1bW8/y8/LZ4xAXOmHo0Fk7gMb/zIxMto2Hdh/qMIwCXVsOaeI6eQiDqAg+0ZnDLVq0KFXt7UDb8GIEaEglRoKdRLBgjIAG9RvQrbfeyqJr166EQWbo53HAQBFduwiDIjZv3lxCNIL4sm7dOrLOJjT+Q8THcRwSNiUQ2kRr165lsW7tOhU34ufgJFIHYgCoYzbEseK2Mhc2xNHGeL2Yj2virtUIKyHUc+J9KMMcjAAjXdw3jBDdvUG4Xf6jDQwTm8OaNWsoOztbmQEwBKyYPWu2Evwh+lvRo0cPNUBEjIAYITtNMp04MNBnoz/HcSaoEQZzwJ84zPs4tdOgfekGVTiFox2ma0shnBPx/Q2zixfpMtp71m8I/BhMgjrMASYC2nI64JmhQ3Jysva8SIddfu3csZPlXjECNJXwz+Cz7Dd7szUaO4BiBPg6pWIEFBkDOhMA4WIEeMMNZSVWMQJ8xoAYAUUdSjECfKM8xAgoGywYje1Ac+cuGn47dTDFCHA2U6LhPoY6DWIExDZHOolgYgT4jAExApgR/m5NAjECYpssSmnqnTgw1M9Mc3yG6M99ixHgm3kgRkD0ti1lRoBH74cXI0CMAOvsADECSmkLJAouS4wAMQKs08PFCBAjIAqoKWxJECPAuaMhRkDx18bJjABfmREjIGw05cmJnEQwMQLECAjZzAExAjypwxJpcDngxIFm4T7UvzkDwAgTI0CMgFCXt1DHJ0aAGAGupy25KYQyI0BmBAT3eJej/ckBMQLECBAjgH/Po8wI8IdBYn8fMQLECLC+IkheDeRcJtCeFyMgtvnPSQQTI0CMADECSr4WCK8JklcDxTb3Gal34kA3upXbfQ3Rn/sWI0CMALflKdz7ixEgRoAYAZa1AjByX9YIMB6v8h0LOSBGgBgBYgSIERALXOVVGsUIcBZ9ZUaAzAjgOpliBHjFSuGJ10kEEyNAjAAxAsQICA8bReYsThzIPfdCFcYZAEaYGAFiBISqnHkVT8iNgL/97W+0fft2eu+992j27NlqQUEs/mB8jh8/rlahf/PNN+mNN96gt956SyExMZFOnDhBv//+u7Gr39/R2AGUVwP5OqUyI0BmBPhdkUvJjpHgQDECxAgQI0CMgGih0EhwYDS2A71quAcarxgBYgRwZUeMgNAzZzg50EkEEyNAjAAxAsQICD3L2ccYTRzIPfdCFWaI/ty3GAFiBISqnHkVT8iNgK+//ppeeeUVuuuuu6h169bUrFkzguhvfDBVd+zYsdS5c2fq2rUr9e3bV2HmzJmUm5tLf//7341d/f5GIxaVrVy5cgTh2YwKFSrQOeecQ5UrV2aBbcgEHSpVqkR2qFixInHAu0ex0vZf//pXFkhzjRo1SuC8885T6UW6OFSpUoV0OPfccwnH26Fq1arkFudXO58uuugiqlWrFouLL76YLr30UhZ16tShVq1aUdu2bVkMHz5clQ+UESveeecdiouLI5hEHDZv3qxMJa6BA1Hsk08+oZUrV7KASfX444+zwIyAYQ8OoyFDhhTDgw8+SGph39GjaXQAeMIy88D8H3XiySef1GLcuHGkw8SJE+mZZ55hgWvs1q0bm/fGPenfvz8dPXrU7/omO9rnQCQ4cNq0aVS9enVq2bIltW/fvhhuuOEGVTZmzJhBHKZMmULjx49nMWHCBHXss88+SxzAYzi3DtOnT6eXX36ZBc771FNPlQDW1hg0qLhgY/wfPHgwDR06lFAXOWCxYKSZA841adIkevrpp1ngGNRDDqibqGe6YydPnqxer4D8sOKll16iuXPnluA3g+8+/PBDWrVqFQtwGDjQKvIb/8GL27ZtYwFDfteuXZSUlMQiNTWVMjJ8jTTrN7aBV3FuM9auXUtI77vvvssC16m73ygjKEO6PHxu8nMl8s7IS7QpVqxYQevXr2exY8cOys3JZYG8QvnBNHAO1157LQkH2nOa262R4ECUFa8ayqUl3vr166u21LFjx4jDwYMHaf/+/Szw2p3du3erdjra6lbk5+dTYWEhCxyL9nBKSgoL8I+u/ubk5FBmZqaWq5Am6yuBjP/gP/D+Qw89xAJ9lNJyb4O5DtQd+YQ2B8LJgV4aAejDde/encU111xDl1xySYl+rNG3xXHmfo75N9p/aGOAF8xAe2XdunW0evVqFhs2bKDEzYksEB+243gd1q9bTyw0bQujzYF4zW0hN79x7Ib1GmzYQPHx8do2Hl5tiL6uDrhm7BM2bNykTSvaWkhHwqYEFsiHNWvWsEDb0shr7htt4eXLl7NAnFlZWZSXl8di1qxZdOONN7K4/vrrVX9p4cKFoSUAiY2iiQODeT46HcsZAEZYoEYAOAHtMQ579+4lLL4bCMC11v6e+b+Zi7nfGekZxELTlzTiRlpTU1JZYBvqLwaNc8B2tOV00PVxncKTk5Nt83Dnjp0s76I/6VQmYml7yI0AOIDolIPU3377bdV4sBoBaABADID4ceTIEQUQBkyAP/74wzV9Tp06VYnxNWvWVII1RGsDEKivvvrqYsKYWShDRwDiGYfmzZtTo0aNqGHDhiwaNGhA6Fhx6NSpE7366qu0ZMkSFhC0OGEdI7YgnHNAWmGgXHfddSwg+OJ9dxwgBt58881a9OrVi2655RYWMGvGjBmjRDQIaVYg/yGsc8ADGMLQ9GnTWbzwwgsEEY0DygiEbN0iu8jD3r17s8D1oJHarl07Fjju4YcfZoFzYoQ1xCczcB24VkOcsn4///zz9Nxzz2mB7TogH3BOHSAm6sRWY+bNvHnz1Awc8zeOu/XWW9nygnLUsWNHEcFcM479AZHgQJSBxo0bq7pirUsoN3Yi6qeffqq2Yx8OOjPNKRyN9g8++EDNDsMMMSsWLFhAr732WgmgDkB05wDDYtSoUTRy5EgW2KYz6SDmoz5wZgjCXn/99TMz1IyZasY3TMnVq1azHRh0QtD5gfDOAeY3GlQQszjYCVno4H/22Wd04MABFmgkHjp0iAW2QZjTCXsFBQWEBiUHpBfPLnSOzMD9RIcUz3gOEODwLOfw1Vdf0eeff04nT55kUZBfQGiYcYCAaNdQhCi5Z/ceFuiw43kAc54D2gdiBNhzmtutkeBAMQKcXw3UuFFjVY9+/vln4oC6CUFfB44rjDDUfVsjIC2drduo7+Cb7KxsFqj3dgB/gss4oAOJZwzaVxwgVMZSh82rtIoR4JbhnPcPJweajQAIT1YYopTu265cYeCZ0Z+2fsMkgJCKPgaHe+65h22nof2G9h2EY9RRMwwjAMIvBzzPt27ZymLLli2qLWYMlLB+Q1yL2xDHwzLowY3Qj30hcnMCNsKUKbF2Ha3jsG6drcGAa4DYrwOuSQe7NCFdyqCASaEBdz3K0NDsf+Z8GrMFbWWdwYN7jfPp8t2un4J4ITju2bOHBdr3KKccMCAVOoEYAc6c5naPSHGgHZ95sU3HqwgP1AhAPUDfjwPaXJxI72+YIc67/Ub/NSszi4dDOw3nSk9L55GebtvGw7EYnMYiJZWSk5LZwW54lqCfunXrVhboryNOXb7BCOB4N1gjwPp8Nv/3onw6xRlyI+Bf//oXffnll3T48GE18gjis9UIQIMcAise/qH4QJzFhUBYt45Mr1u3rhLPdSMarrvWJ4ZCELUChgFEAs4kQFiL5i3UjAfMerACIisMD07oQli/fv2odu3aJdCkcROCicABcWJEI0QNDrfffjv16dOHBcT8AQMG0MCBA1ncfffdhEYbB4y8hQgOoYwDRodyAiLCPv74YzVydNGiRcRhxssz2NG7GJ0L8wFiP9LGoeeNPalNmzYsIPigLFgbrsZ/PPx1YiKMAE5cN0a76gR5CJhcZ9MI04n8CEecupG0CDcbEtbfSJdZrDP/xkwbmB64Xg4wS0QECwULFcURCQ6E6QhOwojrOXPmFAMEd3QUdCPHMbrK2mHy9398XLy2EwEzGPV/6dKlLN579z1C2qxAmQV3csDMF4z615l4ox4ZpTUOMdIfMxQ4wxJhby14ixYvXswC14EGgS4P0ZiwjpA1/kOg/su+v2gFeQj2mJHDAaN28co8nXhuF47j8BzWGQWF+wu1gh8aRjBu8Po+M2CIoGGla0CeOnWqqCJYf/2X6I///EH/+c9/WMA84MQ8I0wnTiI8b28e2wFExxCdUxjhMPU54DkuHGi9WcH9jwQHihHghxHQuLEa1f/bb78RB9Rf3ahKp3AnIwDtfc7kU0ZAmn1H0M4IAL8aHGH9Bo+h/2FtMxn/b7zhRjECZLHg4MhOc3Q4OdDJCIDQYCdW2QkFFStUJMw254DZ4Lfddhvbb0Rf8oEHHiDM+uaAPh7amWYTAL+djAC0OXXCDowAtNN0o+M3xm/UCs06AdrfcE40N8JgBKA9zAHb0EbRncfOCMB1KnNDY2JAtNfNjEC4kT72e92fBoZldoWaTWEze4K7RiPs008+ZWe+YlDoJ6s/UbMmdPmw5tM1Wp3BMAJgCnNAH1qnAYkRoCGwEARHigPt+MyLbXbc6okRsCdwI0CN5ncYva/r3ykjwEHw17XVMjMytYI70qQMBk3cSI/1OWH8R98bbUjd6H+I/dwbRRCG50hARsB9wc0IMAv/1t9elE+nOENuBBjc8eOPP6obA8HaagRA4IGgA/KH+IHOx08//eTXbACICBjJBLPBEE4g8IgR0IvECCgyBcQI8I3kFSPAYKTwf4eTA2EAoMyLESBGgGEOiBHgGx0mRkD4uc84Yzg5EAa+U4O3rG/HrDEIJZwJgDAxApzNlNJYhmRGgMFYof/2ggNRV7/77js1SAD9YMzSw8x0lE2rsGD8txOr7Mq0GAHFX4+oE6pZMf1PwVyMAJ8JIkZA6PklFmIMNwfa8ZkX2+y4Ffzr9px4Mwh4hpsNgLC9YgScMQbECAi+zRoRIwCvaOjSpYtyaDHSG697gLP9yy+/OJoBMAHQ6EHDFaP4AIzGx3v6ZUaAzAgwZgiIESBGQKQbSHaNn1BzINZ7wGhnvE5LZgQ8ys4KkBkBxV8hJDMCfDMEZEaAd0wZTg5s2rSp6w6X2w5arO8vRkDx1z3KjABfJ1KMgNjiQLzuDzOxsfYF+sA33XSTWhsO/GQI/9ZvO7HKjtfECBAjwDyrQGYEeMcVpTVmL9qBdhxox2debLPjVjECfK92lBkBRYK99dls/u9F+XSKM+xGAF6DgHe9zZ8/Xy2gCNEKRgDeJQ8zADMD7D4YCQECgFtmvALljjvuUAv6ihEgRoAYAcXf6S0zAuzYxNttusaPFxwIQxVCGGZYjGqxAAAgAElEQVRaiREgRgBmBciMAJkR4C3DOcceTg7EjEinBm9Z3y5GgBgBXB0QI8CZywLdwwsOxIx4rJtjrOODtcfwmh7cW7OoYP5tJ1ZxZcIIEyNAjAAxAgKt/XIcciDcHGhwV7i+7bgVHOw2HTIjoPi6UPJqoCITwW1Z8mf/sBsBWBAYpIB3h2Fh4NOnT9P7779PGK0JQ8D2HcMaTpU1AnxrBsirgeTVQIY5ZnyLEaAhjTAE6xo/XnCgrBHgW/hb1ggoWohXjAAxAsJAc7anCCcHQsz0p9FblvcRI0CMAK78ixFgS2NBbQwHB8oaAb7FIGWNgOKmhawR4FszQNYICIrCgj443BzIPeO8DBMjoLhwz60TIDMCisR8s0Fv/e1lOdXFHXYjAOI/3vP/3//+V5ELRvgXFhaqRVaxmC3ed+j2I0aAGAHGTADjW14NJK8Gcssjod5f1/jxggPFCBAjwFgbwPgWI0CMgFBzmtv4wsmBYgQUdTR0DX4xAsQI4MqGGAFumc3//cPBgWIEiBHArV0gRoAYAf4zlXd7hpsDuWecl2FiBIgR4KZ8WcV/83838YRq37AbAb///jthRCzEMHywLgCmOE6ePJmGDh1Kx48fd81GL7zwglojoEaNGnThhRcWw+WXX07XXXcd3XjjjSx63dyLbrvtNha9e/emfv360YD+A1hg211972Jxzz33EN4DPmnSJBa41ltvubUE7rrrLrrvvvtYDB48WL0TcsSIEcRh5IiRNHKkBiNG0vCHhqvj8V5JKx544AG69957WQwaNIiGDRtGw4cPZ4FtQ4cMZYE033nnnWohY8xYsOKGG26gzp06s+jYsaNaABVrQHC46qqr6OLaF7O46KKL1Dszq1SuQhzqXFGH2rZpywLnRZnoc2efYsArqLD49c033eweN99MN998M6G8cbjlllvo1ltv1QLpuf2221kgnf379WeB4/DueFwvBwgDeMdoIAac64paxg7QNX684MBXXnmFrrzySoKZaq3bo0ePpnnz5ql3yuK9slYsWbyEli9fzmLFihW0Zs0aWrd2HQssxLoxfiOL+Ph4UlOKNceuXLlSTW3H9HYzMENs0aJFLN566y31GrkZL88gDnNfn0uLFi5i8d5779Enn3zCXgeuD+nF+jMcMNIsOTmZsDCRDmmpacQhPT2dcnJyKDc3l0VKcgolJiayQJo+/PBDeuedd1hg20fLPmKxevVqldbs7GzigFlDjzz8CAtwOp5/4DszwN94Vjw07CEWEyZMoOnTp7PAqCyU05mvzGSB1xtMGD+Bxfhx42nMmDH0xONPsHjsscdo1KhRLPAMatKkCV122WUswI/Cgd6Qczg5UIwAnxFg7lRYf+P1cfn5+fTvf/2bxZdffKkWosNidCWwd686tiC/gDhAjMSgHg779u0j8CC4jkN6WjplZWbxyMry8WdOLuUy2LNnD+Xl5bHAOT/44AN68803WaANHqrOVCzHI0aAN/yHWMPBgWYjgCuHVh5w879cuXKE1wNxOP/886lz587U88aeLNAHRnuOw9KlS1Vba8f2HWTG1q1bCQvvot3JAdvi4+JZQAy3W7R37dq16tXEeD0xB7QPA8WqVatIi5WrCO1dDjgGaeGuFWF4Jz8n8hth6nrXrVf7qff3m37jenXxGuFr16wlDsZ27pvbH2Fn9v10Da1hgGv9+OOPWaAPgu1ot3LAcYsXL2aBfgp0pOysbBbTpk2jbt26scB6le3atVOvmvaOBcp2zNHAgRwvhirMCyMA9RqvQeeANk9GegYLtHmcoDsWr+DJzMzUAiP9dXXMKVzNCEhLJ7T1SiA9XXtOpAfpKnHMn/GkpaX5+uQpqZTKYNfOXbRt6zYW27dvV4sO6/Jj586dlJCQUAKD7hsUVLvR7vkbqjLpJp6QGwF45c8333yjGuXLli1TiwKjg3/kyBH67rvvlNCPjgjek42w3bt30xtvvKEWuYRQ8PXXX7tmTBgB5cuXV8JvtWrVyAy8N7Fr167FxAyzsAEBdciQISwgfE+cOFGZFDAqrHj2mWdVurFApxUQRHA8BH8OEDSs8Tn9f+655wizH1588UUWaMxjHw7PPvssjRs3Tr2CCa9hsgLGgs6AgEiCh2inTp1YNGnchGrXrs3iggsuIDQk3RTKSO8LUq9SpYoqT+edd96Z73PPPZcqV65M55xzDgvuGPPxdr+rnleVqlbVw1ym3fxGms8++2xt/qN+iAjmmnJsD4gEB6KhCyO0devWJeooTFDUdx2/zJo568x6K8YrpYxvmAZGA53r5KDjwT0oEQZBHZ26bdu2sdi4cWNRx8HU6YO5gPViOKDzg3TojIvNCZu1DQqI4Xj2QJTigG179+7lsWcv7c7dzQr5EPhhAKBRwWHHjh3KREBHhQM6njqhCq/2wvMJdZTDA/c/QA8/7JsNYf1+6qmnVKcWJgYHCOSh5lq79wmD3/A8gEnrFjgO/AmO5YCGTIUKFVhUqlRJtQmqV69OHK6qe5VwoC2jud8YCQ4UI6DICMAzn0Pz5s1Vx1J3R7/66ivVJke73Ap0PgsKCljuBJ/u/8t+1gSAMXDGCNBwIDp5ug4oOp9IC87vFugQQ3zTCUgYRBFqDozF+MQI0NWIwMPDyYFORoBdmYQogT4PxxcIw3bd8Xi2wmRHu5PDtJem0WeffcYC7S20Da3tPLQLIYKZ30tv/g2xOWCxfqW+7Yg25UcffRQwYGwEAugkELI5kwBhuFYI+jqcEd9NbWgjDAZDoHnlxXHIY/OgH+tvu/z74P0P2MFBGDQEfodopxNAX5z6otKBoAVZgYGiYgQEznN2R8YKB+r4zd/wUBsBGOQKzuP6qQhDewjiuA66euAUDkFcDcjI4kf4K7FfM7CMG2xmDoMRANFeB921qPD0DG3ddromDOBDH5wDTAK0PXXnFiPArnbrt50FEwAL/w4cOFA1DNDxbtCgAWGkO0Y1vv7664QRqhA1MFK8b9++al+MWEWH4R//+Ic+ds0WMQJ8xoAYAc5T4/0hdjECNBVNgv3KgUhwoBgBvhkNYgQUmQJiBPiMATEC/KKtkO4UCQ4UI0CMAKtRgE6iGAHO7WIxAkJKfyqycHKgGAH+jeRfJUZARI0BMQJCzzPRHGOscKA/upDdPmIE8LPOxQjg216lfkbAzz//rEYfQvTHaEaMXsdI/7fffluNhsSISTTM4eIuWLBAjUSFC4xRleZXBrkhNzECxAiwI2m328QIcFP7ZF9rDkSCA8UIECNAZgTIjAArF0XqfyQ4UIwAMQLECOA7nk5tYDECQs+U4eRAMQLECDBmAZi/ZUaA7zUpMiMg9PzmT4yxwoFOz0en7WIEiBHgVEbM20u9EeAPOYR6HzECxAgwV7Jgf4sREOoaKvF5nQNiBIgRIEaAGAFe80w0xy9GgBgBYgSIERDNHOVV2sQIECPAbAAYv8UIECPAK86JtniD4cBQaEY6M8Du1Wq688qrgSyvPZJXA2lfz6crQ27CQ75GQCTIQYwAMQLcFHqnfcUIiEQtlnMGkwNiBIgRIEaAGAHBcEisHytGgBgBYgSIERDrPBZI+oMRwSBUoc8jawTIGgFerAtgjlNeDRRI7ZZj/MmBYDjQSRNy2q4zARAuRoBvtoCsEVDUNpMZAf7UaJf7iBEgRoATUbvZLkaAywoou0c8B8QIECNAjAAxAiJORBFMgBgBYgSIEVDU2XTT5pVXA0WQuEJw6mBEMDECfG1HWSzYv1kVZmHf7W8xAkJQ2SUKNgeC4UA3z0puXzEC5NVAXLnQhYkRwFbh4AJhBJQrV44qV65M5557bjFccMEFakV4rAzPoWfPnmrRYixcbEW/fv1o2LBhNHLkSBYjRoyg4cOHsxg6dCjh+D59+rAYNGgQG6fuXAiH0PPYY4+pxZax4LIVj456VO1jFYTwH2kdMmQI3X///SywePNtt93G4qabblJ52KpVK+KAaUzIZw7nn3++GmmiqxDRGA5SP+ecc6hKlSrFgPKFKTRYfJIDd4w1jkD+W8u0m/9IM65Hl88XX3yxWrj76NGjwVVCOTqiOfDSSy8R6lqzZs2oTZs2xXDttdfSgw8+qLgD/GEFFpWFEMBh6tSpavH3OXPmEAcs8r5w4UIWWAfm/fffpw8++IDFu+++yx6HY7BuTCBYvXo1bdiwgUVcXBzFx8drsX7desJUah1WrVpFOixbuozee+89FriexYsX05IlS1i89tprNHnyZBbjxo1TzyVwMIc77riDBg4cyALPLtwfXf4PGDCgGL+ZuQm8UaFCBfVcxbPVX4AfzzvvPBZVq1almjVrss8J7tlhDsNxOF4XN9KONHMAXyKuiy66iEX9+vWFAyPKXqE5uRgBPhEYdZVrnyCsdevWVFhYqM3wL7/8knJycljs3r2b9u3bR+hwc9i/fz8V7i9kUVBQQFi4NyUlhQW2ZWZmssjKyiKc2yrym/9jOwfECz7/+OOPWdx1113atpGuzVQaw8UI0FaJmNgQrAgWqJBVsWJFQv+vSZMmLJ555hkyLxhp/p2RkUFbt26lzZs3F0NCQgKhraZrx61fv57WrVunhfFaHO4bwrWuDYe1C1esWBEwdBzjFA5xHGlC25UDrgNtUw5GPqxdu5Z04PLByzBdOhCO67TLD+SFDugP6NqyiBPPFnP5Mv9G/6hrl64soAu1a9dO9UViorJLItkcCJYDg3muB8qfunPWq1dP8dtf9v2FOKCtA/7UAe2eQID40N7SITvLWfA31zvzb7Tv0tLSWCCtumsxwgO5HhyTnJxMO3bsYLFr1y6VHuMc1u+dO3cSnkdWQL/V3Tt/wsUIYKtwcIFoxOpGNRgGgVnkMP+GsFCtWjUWENachAts1wHH6+KuUaNGQILIhRdeSFpccKFtnDinDkgrhBYOyCMILBC6OaAxqBOKMN0U98afyhFN+9gRu1fbdFNzgwlHWu3yVYyA4LgnWo6GYI96iPpt5Qfc40aNGlHTpk1ZoCN3ySWXsLj00kvp8ssv1+LKK69UHUHEYQUaM40bN2Y7iOg4dujQgW644YYS6N27N919990sYGjMmDGD3njjDRYTJ06kwYMHs4Bg3qVLF+rYsSMLmJzIJw4NGzZU11e3bl3igHyoU6eOFtiuw2WXXUa1a9dmAfEazxCdAI5t4G4OtWrVUsZt+/bticM111xDMIk4oGOEMmEW443fumeIU3j16tXVddgZmbrrdDIRUMavuOIKFsh33FOdWNGyZUsxAqKFyIJIhxgBPiMAdQicwqFHjx508OBBbS5//vnnbEcNHTh05CD2HzhwgAUMBq7TirD8/HzV0dN1BNEB03Y+s7Np7969lJeXxyI3N1d7bHp6Bm3ftp22bNnCAs8Ku/ZRWdkmRoC2SsTEBrMIxokM/pRj7jinvhu2o92pMx4xuO7tt99mgQESiYmJBLHFDIg2CLcaBMb/bdu2FdvffOz27duVaLNx40bSwW4wCAyIQKEzLhBuZ15gm90gFVxHwqYEFps2bdJep3H92CdcgGCGe6fjW8Pk4fIY9wXbjfts/cb16PIY50QdOHz4MIvZs2fTdddex6Jz585iBMQEy9kn0syB/vBdKPex04ScOJRLB/rOKOu6gRVoD+naS2inWQVtN/918SI8WCPALh1It+7cOM7OCNC1KxEOI8D8jDD/NowAXdzYF9xihRgB9nXxLPvN3myVDmBg04E5ApKwspWXYgR4w0nhjtV4PRpEVquZB5EYQrNO7K96XlU1a8SuMaPbBpOqfPnyLDCiHKarTviFwQAR1opOnTrRjTfeyAIzrGB6cLMTEIZZUdzIeYR17dpVifE60b1a1WpqFDzSbQWuMZAGXST5FGmGKaS7762vbk0333wzi27dumnzSjeyHuEXXnCh1hiHEYCywI3aR1iVysVnYFkNe7tR/Zdfdjmh8cyhQYMG1Lx58xLlzCh3V199tRgB4SYsD84n7UBf2wV8z5mVCAMPHjp0SJv7J0+eZEfsY7QlOmMwAmAkcIARgBkDHLw2AtCJ5JCRnkFJu5K0HcEhDwwRI+Css9RsQG2hkA1RnwNmEQztFCsi1Q7peWNPNaN01qxZJb7nz5+vRN+kpCQyA+IMZgroxOSdO3ZqOQrxQLSxjuD0938wgrkhvHPfELk58VsZAHHxtGmjXqy3E8chltul2elYq9ge7H/kPYwaGDIccE919wLnxn3H8Ry2JOqPhXkEg/r48eMs5rw6hx3wgkEw6G/IjICopzjHBJo5MNx8p+sfIzyQfuMZI6CwUM3gRNvKjLy9eVrRHGK6neDutE0nxgdrBGRl2qfL7rxYX0An1iPcKyNg185dJUwAcNzgQcENILE+n83/w112cb5SsViwdADLlngdiYpSWs8pRoBj+yImdhAjwPfqIjECip4FYgT4jAExAmKCwoJOpLQDxQiwmgFiBBQ9D+zasKg78ondHDCLYGZRwfhtd++93CZGgG9mghgBPmNAjIDY5ZhoT7mZA73kNC5uMQKcXxkkRkBRW8x4LnPfXPnyOkyMgLOKbo7XmS3xS15HWxkQIyDamzf+pU+MADECrNwiRoAYAf6xR+nYS4wAX/tKZgQUzQ4QI8C/NrcYAbHNgWYRLFrEBbRHxAgQI8A8M0CMgNjmmWhOvZkDrX0hr/+LESBGgJsyxj2jjTA38YRqXzECxAiQqdFluAyIERDNTRv/0yZGgBgB1kaBGAFiBPjPILG/pxgBYgTIjAD/hH/rs0KMgNjmP7MIZggK5m/r/Q7XfzECxAgQIyC2uSVWUm/mwHDxm3EeMQLECDDKgj/f5mez9bc/x4d6HzECyrAIHOrCJPEF1gmLZL6JERArzRz7dIoRIEaAlUfECBAjwJ41StdWMQLECBAjILA2qBgBsc2FZhHMKizgv7VtEK7/YgSIESBGQGxzS6yk3syB4eI34zxiBIgRYJQFf765Z7QR5s/xod5HjAAxAiLWSAx1YZb43HcCxQiIlWaOfTrFCBAjwMp/YgSIEWDPGqVrqxgBYgSIEeC+DYjnhhgBsc2FZhHMEBTM39a2Qbj+ixEgRoAYAbHNLbGSejMHhovfjPOIESBGgFEW/Pk2P5utv/05PtT7iBEgRoAYAWW4DIgRECvNHPt0oiN/9tlnU4UKFahSxUrFULlyZapduzZdeumlLOrUqUP1rqrHo149ql+/PjWo34AFttWvx6NevXp05ZVX0pV1eFx++eV06SWXlkDz5s2pa5euLHr06EH9+vWju+++m0WXLl3467iqHuE6a9SoQdWqVuNRrRpV0wDv3caxNWvUdI+aNemiiy6iWrVqsUAd1N0bhF922WVaYPsll1wSEFq1akU33nAji+uvv546depEHTt0LIY2bdrQVVddRVdccQWLOlfUUfmMvLYCxyCtF9e+mMUlF19SoiwY5QN5gLLWsEFDFs2bNac2rduwaN26NeFaW7ZsyeLqq6+m/v3709GjR+0rmWyN6hwQI8AnAqOOtW/XnsXAgQNty/nJkycpJTmFRUZGBn322Wd0+PBhFti2b98+Fnl5+YTjU1NTWeBd/llZWSyys7Np7969lJ+XzyInJ4fS09JZ4JzYvjt3N4sRI0ZI+1eMgKjmNX8SF0kRzE6U6NatG/2/5/8fi9mzZ9PWrVspMyOzGJKTk2n16tX00UcfsVi3dh3hXfMcEN+uXbsoOSmZxfZt22lzwmYWCQkJtHHjRtoYzwPbExMTWWzevJk2bdpEmzbysIsX2xC3Dohbd16EY7vumtSxmxMpMQCoeBE3B00eYl/cl61btrJAeu2uUx27dasqF7iXZiBulY+4RxZs27aNCgsL1bMN7TgrXn/tdUKblkPXrl2pffv2tHDhQn+qmuwTpTkQSQ70wgiIi4tT7S20q6xAWwjtIhZZ2YpP0fbhoPg2s2gdJevgCbTDsrOyeWRnq/YU2lQc2PT8mc5ILRaM9iyeCRySkpJUezQtNY044BhwixX3339/UO1Gq/hv/m/3PPVqmxgBZVgE9qpQSbyBjcqKRL6JERClrRqXybITwTAyHCK0TlCGEHrjjTey6NmzJ918883Uq1cvFjf1vIluuOEGFt27d6cOHTpQ27ZtWcAIqFSpUglAQO7YsSMLxNWgQQOqW7cuCwj2unqEhy3yAmYJB5gAqA8cILgrIbphQ2roEo0bN6YWLVooMRqCtBW4Jt31IhyCvA7Yjk4MB9xX5KXOKECacI84oDz07duXBgwYUAy9e/dWYrouD5o0bkLNmjVj0bRpU0JeNGrUiAWOxT4cYA4hn7jrRNi1na9lrwPXBnMIZoAYAS5JJcZ2t+NAHSeUxnAYszq+fvjhh+nEiRPaO3vyxEmCEMcBncoDBw6UEFkM0QXb9EZAHqWnp1NKSgoLbLMzAvLy8ig/P58FOp+6eJFmCESHDh1iMXr0aO3zojSWDd01oe7IJ3ZzIJIimK5MIRztlvHjx7N48cUXaefOnbRnz55igFm4aNEiev3111ks/nAxrV+/ngXEeNT53NxcFuA1VtjevFkJ1PHx8QQBjgPEdqsgZPyHYA2BW5kBMARcQpcmhEM850wPI8zOJAhq2+Y/TQarEZDgyys7QV97PTbHIn+3JPIGD641YVMCe19wr7AddUBnUr8x9w3Vj0Ffxgr0X9BPESMgdvkPKY8kB3phBICL0KbiUJBfwArxEOfRHoK4z5kACMM2XVtLmQA6g+FPE0DHrQiPSiMgJYUg+OugazsiHMfgGWXFkAeGBNVuNAv/1t92z1OvtokRIEZAUAXaq4Ip8YbHTBAjILYbP0bq7UQwMQJ8dUmMgOIzB8QI8M0QkBkBBovE9rcdB5al9oQYAUVmgxgB/rUjxQiIbe6LpAhmx61iBPhnCmiFczECzpggYgTENkd5nfpIcqAYAT7jVYwA/9pbVvHf/N/ueerVNjECxAgQI6AMlwExArxunoQnfjsRTIwAMQK4WQFiBIgREB52Cs9Z7DjQqwZ0NMYrRoAYAW7LpRgB4eEor84SSRHMrqyJESBGQDGTQ2YEeEUBZT7eSHKgGAFiBNg9B63bzMK/9bd133D8FyOgDIvA4Shgcg7/HMJI5ZMYAaWj/WQngokRIEaAGAHyaqDSwXT6q7DjwEg9XyNxXjECxAhwW+7ECNDzSixsiaQIZlfWxAgQI0CMgFhgkNhPYyQ5UIwAMQLsnoPWbVbx3/zfum84/osRIEaAzAgow2VAjIDYbwDhCuxEMDECxAgQI0CMgNLBdPqrsOPAcDSmo+UcYgSIEeC2LIoRoOeVWNgSSRHMrqyJESBGgBgBscAgsZ/GSHKgGAFiBNg9B63bzMK/9bd133D8FyOgDIvA4Shgcg6ZERD7TYzovwI7EUyMADECxAgQIyD6WSy4FNpxYFlqh4gRIEaA2/IuRkBw3BPpoyMpgtmVNTECxAgQIyDS7FA2zh9JDhQjQIwAu+egdZtV/Df/t+4bjv9iBIgRIDMCynAZkBkBpaORZCeClStXjmrWrEm1atVi0bhxY7rmmmtYoCN37bXX0nXXXceiS5cu1LVrVxY4pl27dtSmTRsWderUofPOO68E6tWrR507d2bRoUMHQnobNGjAonbt2lSlShUW5557Lp1//vlUvXp1Fjj2yiuvZFG3bl113qZNmxKHJk2akA7Yv2XLltSqVSsWyB/kE4f27dsTrlkHbG/bti0LLIJ71VVX0RVXXMECawTo7mv37t2pd+/e1KdPn2K45ZZbCMc1bNiQBfKgWbNmWnB5Z4ThOMTNAfmHfNJda8eOHVU5RVm1AmUb59CluXnz5tS/f386evRo6SCDMnoVdhwYjsZ0tJwD5Rx1l8Ojjz5KJ0+e1JaQEydOUHJyMgssvHvw4EE6duwYiwMHDtC+fftY5OXlUXp6OqWkFIn05t/YlpWVxSInJ4dwfEFBAQssUGeOy/wbaUa6jhw5wuLxxx+X9u9ZZ6kZhdpCIRuiPgf8EcHMYoP5dzC8hXjOPvtsLdCOGzduHIupU6fSjh07aPfu3cWQmppKixcvpoULF7JYsmQJrV69msXatWtp546dLH+B13C+LVu2sEhMTKRNm/TCfeLmRNq6dasWCQkJhAVtWWCbDYqJ5Zs3k/k/0uUpNicSrs0KlYaEzbTZAlyHXT7ZXafTNpxTd604Z1xcHAscg2dEYWEhi9mzZ1OPHj1YdOvWjdCORnmTT+zmgD8cGAzX2R0bbUYA2j06ZGZmsu0so/1lt+Av2mK5uT7R3/qNbYDueMSPc+tgnJ/7xjEZ6Rks0HZMS0vTAu3BpKQkFngmmNuL1t/Yzh07ZMiQoNqN5uev9bddOfNqmxgBZVgE9qpQSbzRPQvAfH/ECIjdho855XYiGB40MAMwM4BDpUqVWOFcJ6ibwzHSHMI8B4jCEFl1wHZO0EejvGfPnizQmIcorjMXIAjrxG2YFtdffz3dcMMNLGBowPjQAYIy4ncLXA9EeZ0RgPzRmQhGHnLiuD9hENDtoEtT69atVT6ik2QGRHYI9o0aNWKB9OqMAKQXZofO1LALRx7iOnTXDKGfK4MIgxkCwwmNHQ6XX365GAFmMonR33YcaH7mlfbfqNPoqHB49tln6YsvvqD//ve/LJyMAAjqMBI4HDp0iDUBYA7k5+erjqmuw4ZOq64DiQ4n4kBHnwO2Wztwxn90KmFc4Jo5PPnkk0F16EpLWULdkU/s5oCTCIY2oJ1YFWg5hgmAARbVqlVjgbbD2LFjWaDMbdu2TYlHhoiEb4g+27dvZ8V6iPjLly+nefPmsXjzzTfpnXfeoffee4/FmjVraNeuXQFh+7btWpH6jHjNCOpKYHcQ883Cv/W3k3hut91OWEea1bEa80KJ/Rs30SYLNm7cSPHx8QEBx9rBKV6dEYA4YfLo7u3EiRO1/RC0KdHeFSMgdvkPKXfiwEA5zp/j7LgV3OtPHOZ9MBgOdQEDLzgU5Bdo2wL0q4QAACAASURBVEto89iJ4+BXM99af+vaYU7hRjxWg8D4j+1Ocei2K3MgM4uyGOB6cL06wFg22oNuv9Fe5QyVhx56yPU9Nd9fq/hv/m/eL1y/xQgQIyCoAh2ugirn8cZcECMgths/RuojJYJhdD0aLRzq16+vRmLrhGFdeOdOnalXr14sYBBAqNeJxuh46o7FaPY77riD7rzzTha9bu5FGAnPAfHCYLCONj/zv/O17AwGjIiDgYCOhk50b9a0mXa0OgR3iOvGqHnrN47V5SMMBpxXZ5q0atlKeyxEdxge1pkZCEMaAjUCcLzOaLELx3HosOmuFenhyiDCIPRXrFiRzI0t82/hQINFYvs7UhwYbW2TNq3b0PDhw1k8//zzQRkBmDXz+eefszh86HDARkBmRqa2k2gYAfv37ycOTkYAzI2vvvqKBUYrR9v9i0R6xAiIbe5zEsHwvNON3IeIhe2BlDvECROgRo0aLK7vfj2NGTOGBbgII+yt4g9EHzvxZsWKFfTqq6+ymDNnDr3xxhusSQDzYPWq1drZAk6jQ3ds31FspL5VsD9jBjCi/5ZEfhaCMTvBGpf5P8T6QEfg2xoBmxPV7AVt3Bs3saI9BEqdIO8UjmPtjACn43XbES/yUjdj48mxT9q2WTFQR4yA0s2BgfCbv8d4YQSgnnAmAMKi0QgAjxuiP/udkxu4AZGVrZ/FkJmlZgrojIC01LSAjQDEqUwIy2zV4Q8ND+h5aZQnc//T+tvYJ5zfYgSIERBUgQ5nYZVzhd4MEBEsths/RuojJYKJEfDnq2jECFAdLacZAWIEGDVWvkOdA5HiwGhrl4gRUPT6IXTixAhwbjeKERBqNgpvfGIE+IwBMQKKv4JIjADfq5zECAgvH0XibE4c6GU7TYyAbGXosgaA8SohMQLOaK1W8d/838tyqotbjAAxAs4UTl0hkXDnjlSs5pEYAZFosoT+nJESwcQIECPAPDtAjIDQ122J0b8ciBQHRtuzX4wAMQLclkkxAvzjmGjdy0kEg9AgMwJkRkCxWQsyI+DM4BWZERCtzOZ/upw40O0z0c3+YgSIEeCmvJiFf+tvN/GEal8xAsQIECOgDJcBMQL8b2hE856REsHECBAjQIyAaGaGspO2SHFgqBrjoYpHjAAxAtyWJTECYpsnnUQwMQJ8awrIq4FMCw+LESBGQGzTXrHUO3Gg22eim/3FCBAjwE15sYr/5v9u4gnVviE3ArAI2b///W/6/vvv1SJdWFzs8OHD6ve3335Lv/32m1qk7D//+Q/98MMPdPz4cbUd7x7FYl7/+Mc/1PZiNdzhj3QAS++I9VAVdImHLyNiBDiQSwCbyxIHihEgRoAYAQGQRCk/pCxxYLS1LcQIECPAbZkUIyD0hBxODnQSwcQIECOg2GwArGMgRoAYAaGnvTMx/vHHH0rvO3XqFEHfgw4IQOf78ccfCRog9oHmhzBjH2iC0Aaxzc3HiQPdPhPd7C9GgBgBbsqLWfi3/nYTT6j2DbkRABPgu+++o6VLl9LgwYPVooy33347DRo0iObPn0/5+fmKAH755RdatWoVPfDAA4TtAwYMoGeffVYtjvHPf/7TTf0nMQJ4kTdUhUTiKb35K0aAK6rxa+eyxIFiBIgRIEaAX7RQpnYqSxwYbe0jMQLECHBbJsUICD09h5MDnUQwMQLECBAjoJES/s3tVfzG6yzl1UCh5z9ofHl5eTRx4kQaOHCg0vl69+5NkyZNog0bNigzAFrfgQMHlPYHDRBaIDRBaIM43o0Z4MSBbp+JbvYXI0CMADflxSr+m/+7iSdU+4bcCPjXv/5FGPmPFa9feeUVeuONN2ju3Lk0bdo0GjZsGC1ZskQt3oXV3WfNmkWjR4+mmTNn0uTJk+nJJ5+kjz76SBGDG1oSI6D0CtWhKugSD19GxAhwwzT+7VuWOBAPEJgBHGrUqEEXXHABXXjhha5w2WWXUf169VlcddVVhO2XXnIpizp16rDHqfjq16eGDRuynQF0COrVq0dX1rmSBeK9/PLL7XHZ5XQ5A6S3du3aVKtWLRbIn5o1avKoWdOXhxdcSBdysMnbiy66SJ334toXEwekR3dvcOyll15a4noQptJbsybVZGB3v404kR9ugfPieF16kRauDCKsatWq6v3IumeAcKB/vOZmr7LEgbpyFanw9u3a06OPPsrixRdfVKPv/vjPH8QBC+umpqaywMK7GLV36vNTLI4cPkL7/7KfRUFBAeH4jPQMFpmZmZSdlc0iJyeH9u3bR/v372eRm5NLycnJLBDv559/Tt9+8y2L8ePHy6sxzzpLDaZyU79lX+ccCCcHOolgZqGB+23HVRUqVKAqVaqwuPLKKwl1CH19HWa+MpM4QBvYtGkT7dyxsxh27dxFqSmplJaaxmL79u3qOBxrRVxcnNIQlixeQhxWrlxJ69atY4Fjd+zYQUm7kngkJVFyUjKLXbt20bZt2wi6BoctW7bQlkQeEOgTEhIoYRMPaCnx8fH2iIuneA7x8UqLQRwccM1abIhTgi1E22JYv4HWr19viw3rNxCHuA1xfDrj4ovSsSGOsJ8bIH+QjyqfkdcWjBkzhhrUb8ACbX8xApz5zO0eP//8M4GXFixYQK+++irNmzePXnvtNcUX48aNI7QJdu/eTcuWLVPaHzRAaIHQBGfPnk07d+6kv/3tb36f1okD7Tgu2G3hNgIwoDorM4sF2jzp6emUlpbGAsfZLeqL9lZOdgDIyQk43uxsn5Fg+61pH6p2ZQbfrkR7E/mQklI0OMTN74yMDDYvhg8fHlS7kXsGG2HBlsVAjg+5EWC88gcdCrwWCI4fGkRoyMMNnDJliqrgTz/9ND3//PO0YsUKNQ0IhQ8kAcIGqbv5iBHAi7yBFAg5pmzlpYhgbpjGv32FA8tWHRLOjO37LRzoH6+52Us4MHJ14pqO19DYsWNZzJgxQxkBGK3M4eSJk6oTiY6kFWijHzt2jDDVn8PRI0epsLCQBTrp6OShk8pBdWqzspRZgI6dGU5GALYnJSWxwLnw2gHMUubw1ISngurQlRbulxkBbtjNv33DyYFeimAwAXSDGLp27Wo7cA+CFd4OwOHjjz9WgrxViIa4D7HGyj/G/30F+9QrhfEKESs+++wz2rx5Myv0wwBYvnw5vf/++yyQRojJMAM4wJgA13AAX0G4hEnBAeZAiRH5eD1PYqJKL66ZE+qVCRAXX1yItwjzOmMD4evX2Qv2dseuW7uO1q5d6xp2ccJAKGYqWK4l0G0oQ7jvujx+/PHHCYOIODRo0IBatWpFCxcu9K9iy15+5cDf//53+vrrr9VbPk6fPq1G9+PV4K+//jrdfPPNqry/++67SvOD9od6hVcCQRPE82jq1Kn01Vdf+XUu7OQlB9o95yHgwgjQLcaO7XbHc9swKA11/+DBgyww0wIitQ46EwDh4CoYMDpw/OZvmJPBYCv0+2MGcPtkZVNmRqZtXrgR/837Il7umkaMGOH6nprvsyH6c9/m/cL1O+RGAN6LiAbQ77//rkwA/AfQgUDmYUQSHsa33nqrmiWAEUYwC9BIx8PyjjvucE3IYgRErtMZroIq5/HmHosI5nc7w+8dhQO9KavCAZKvXpQB4UC/qc3vHYUDI1dXxQgoMgXECPCvHIoR4De1+b1jODnQSxFMjACfMSBGgH+mgBgBflNEqd4Rr/XBIGCsAYBBB/ggDG8Fue+++9TrfzAgGJoftD9ogNACoQniDSJ4VRB++/vxkgPt+h1iBBSZo5xoboTBTBAjwNce4wwAI8yurHm1LeRGAFdpQQRwsPr3768qONzvTp060csvv0xwDUEOIIA9e/YQRhhgGpHuA0cRU4ThAMPBBfBeMa8ySOL1ryMh+RSb+SQimI5pQhsuHBib9UN4rfTfN+HA0HKdLjbhwPDUJTECxAhw+9wSI0DHWqENDxUHfvnll2rUPEQ19IExux7PMbf33Z/9xQgQI8DNzAAxAkLLGaUlNgwQxqt+8EqwO++8U4n/WC8Amh+0Pwwehhb466+/0vTp06l79+5qNLzu+sPJgXY8KUaAGAF25YPbZoj+3De3v9dhnhsBqNiYvofFPyDYY8FgTElp166dMgLMlRzTijt06FAi3LwP3juGKWBouMJYAJo2bepJA8jrzJf4w9MxlnzW57OIYGZ28ea3cKC+/EndlLyJdBkQDvSG98yxCgeGr56LESBGgFtOFSPAzFbe/A4lB2IwHF6r8dBDD6k+8E033UTnnXeeJ/1gMQLECBAjwBtOKCuxYnYUFv/F6HCsBQDdDq8Lf+GFF5Tmh1d6GR/si0HC0ALN4cZ24zucHGj3PBUjQIwAu/LBbeMMACOM29/rME+NADR8MALCmAqExYTwfipUYE7wR6Xv2LGjrREAVxFmANxATBsC8P43rzNK4g9fR1byOnx5LSKY0azw5ls4MHxlWXhD8jqQMiAc6A33GbEKB4a3XooRIEaAWx4UI8BgK2++Q82BmBmPV2lgLT70gTE4rn79+p70g8UIECNAjABveKGsxIpXBOF9948++qhaGBjrg4C/MPKfE/xhBEALtDMCwsmBds9TMQLECLArH9w2Q/Tnvrn9vQ7zzAiAq/fXv/6VPv30U7VAMFYJT05OJiwaAkLAtB/zq4EwLcifVwNxxIlGrNcZJfGHtzMr+R2e/BYRjGOU0IQJB4anDAtXSD4HUwaEA0PDd1wswoHhr5uYav/8lOdZzJs3Ty2ei1dxcoCwiAXSOGA0H7Z/+cWXLCBIFu4vZIHRf3aLBXMLCBthOK6goEAtBoj3AFuBd8+ib8EBA4+wYOGPP/zI4ulJT6uF/rDYH4dgeCWWjhUjgGOv0ISFgwOd3o9doUIFOrfKuSxq1KhBTZo0oatbXc0Cgly3rt1YYJY/1v2b+/pcFpi1gDUBOaxevVq9HmTnjp1UDDt3qrqckpxCHMAHWISYAzSEXbt20fZt21kkbErwLaCLRXQtwEK1eG3x5oTNLLAYcEZ6Bo+MjGILnIN3iiEzi10kXXFcRialp6WTboHP1JRUltsMvsP12mLnLtrFANfjCOu9cfh/ZpHl7TtoB4cdO4rfa1N8OJZbaNkIw73BAsYcsLAq8hv3n8Ozzz5LjRo2YtG4cWO6+uqrXa9NGRp2KP2xwARF2wGvBBo1ahS98847yrxE+wOvAbe+GgivC4c26PRqIGvOOXGgl89jru1ghEHsdXtumLoo74cPH2YB7tNxkbGoOjiFA+rJ7tzdLHCf1MLA2TmU4xZ/LqSem5NLHBBfdlY2izNcmZlFWRyystjjEB+O1S2ajHDwqnkBYDe/kZdn0mbi9OEPDXd9T81lgDMAjDDzfuH67ZkR8OOPP6qbgwWCJ06cSHhvHMLwQYfBWC8Av0EIslhw+DuM4Spkcp7ovbciglmbE6H7LxwYveVeOEnujVEGhANDx3nWmIQDw1/PevbsSbNnz2bx/vvv06lTp9TaXOhwW4HXeHIdH4RBYDl58iR99dVXLI4dPaZm+2LGrxWORkBGprYzh3Oj44s4OKDjquvcYRsGH+HdxBwmPzuZypcvz6JcuXIUSCfe4JVY+hYjwMpcofsfDg50EsFgAtSuXZsFRCf0x4cOHcoCrx8aOXIkCyz62aJFC6pbty6LkSNGEgR/DtAEINJYF5CEOJ6UlKQVqbFNV99TU1OV4K7jMAg7umMhpsfHx9P69etZbEncohXkcR0wK628Z/zHa48xulkHYz/u+y/7/qLiRvwcsP6iDnv37mWFcU4sD0nY7j1KBFRi4p/CoN+/s3OUCGgYwNbvrVu2qoGlGFxqBUTTQ4cOqWcbnm9WzHh5BjVr1oxF8+bNqXXr1mIEhI7yzsSEmQB4e8eCBQvUAsEwA1BWjQ/WN+nduze7WDA4Cfqgvx8nDvTyeWyI/tx3IG0IcDJmeeH6ORTkF9AZwT893dVvcCMEfx2sfOzmv11dRzw6Xka4nZgPLgj02GCMACwQz+XzsGHDxAiwqZhnYRtGQGAV8AkTJqipQFgfAI0hvNYHHxADGp5Y4GjFihX0ww8/KBfqtddeozFjxqiFgG3OUWKTzAgIf2fTS1KVuMN3P0UEK0EnIQkQDgxfGRa+kLwOpgwIB4aE8kpEIhwYmXopRkDKGbFPjAD/yqAYASXoKyQB4eJAJxFMjADfCH0xAvgR8yExATAaX4yAkPBGaYnkm2++oTlz5ihdb8qUKQRDDIMPjE9cXBw98cQTBO0Pz2pogdAEoQ1i/QAMOvD348SBwfQRnI7lDAAjTIwA36uDxAgoaosZo/+5b6ey5sX2kM8IgAOIyoupPW3btqVevXoRXgv01ltvKSQmJqpRPQkJCTRz5kx67LHH1Dembj355JO0bNkyOnDggL91X+0nRkBRAfOikEicpTd/RQRzRTV+7SwcWHrri3Bh6bu3woF+0ZqrnYQDI1dPxAgQI8Dtc0qMAFf05tfO4eRAJxFMjAAxAkIm9mtev6PiFyPAL24oCzv99NNPypC/7bbbqEuXLnTvvffSrFmzlA6IdUMxWwWjtZcuXUpjx44laICGJoj98KoozOLz9+PEgW6fiW72N0R/7luMADECrGWJMwCMMOu+4fgfciMAiwNjehtWBsd712655Rbq06cP9e3bVwEVHVM88N5OOH/3338/4T2DAwYMoGeeeUaZAHhVkJuPGAGR63SGo5DKOby7vyKCuWEa//YVDvSuvAoXSN6GugwIB/rHa272Eg6MXD0VI0CMALccKUaAG3bzb99wcqCTCCZGgBgBYgTIq4H8Y67Q7IUBwVh3A+0RGAFmHXD48OGEBagx6Bevy4L2h1cBQQuEJrhy5Ur65ZdfzrxFxJ8UOXGg22eim/05A8AIEyNAjABrWTJEf+7bum84/ofcCMDCIGgA4fU/MATw3rYjR46cAQyAX3/9lTBaAtOAjh07phbDwHuwvvjiC3Us4nDzESMgcp3OcBRSOYd391dEMDdM49++woHelVfhAsnbUJcB4UD/eM3NXsKBkaunYgSIEeCWI8UIcMNu/u0bTg50EsHECBAjQIwAMQL8Y67Q7AWND68EhwYIwd+sA544cUJtg1YIQPuDBojFcbFOEbRBvEocr1bz9+PEgW6fiW72N0R/7luMADECrGWJMwCMMOu+4fgfciPA30obyv3ECIhcpzMchVTO4d39FREslEwUubiEA72rI8I/pTtvhQMjx1uhPLNwoK+eihEgRoDbZ5YYAaFkovDH5SSCiREgRoAYAWIEhJ+ZwndGJw50+0x0sz9nABhhYgSIEWAtS4boz31b9w3HfzECzirdIkc4CpGcI3bLkIhg4WuoeHkmEcFitw4Kf0b23gkHeslM4YtbONBXj2666Sa1+B4W4LPiww8/pFOnTqlZuZiZawVG4+HVnRwgJJ08eVKtAYZp/1ZgRB9mAXPYt28fYbG4zMxMHhmZlJGRwQJpyc/PV2uLIR4rsMhgSkqR+G/+7bRY8HPPPUeVKlViUbFiRQqkEx+LfC5GQPh4yoszQQRr1qwZlS9fni3LF110ETVo0IBFixYtqHfv3ur1vHhFrxvg/d+I99JLL2UxaNAgeuedd1iAi+Lj42nLli3FsHnzZhWOhUTdYuPGjbRt2zb1fnG8Y9yKnTt3kg44bt26dfTpp5+ywGtOsLYhh02bNhHOjesJNZAHODeH9evXqzQj3Rzw+pVwYs2aNSrvPvnkEwoEq1atUq+FwathrPjggw9o/vz5LN59911VhpKTk4kD1qmsX78+i4YNG6rXWC9cuNCLqilxhikHSqMRcOzoMeJQUFBAWPjcDhnpGcQB7ancnFwWaC+hnZadFQCys9WCz4iDg1eLBaNNqWs7IhzrUJjbhG5+p6amquMRhxnDhg2jYNp5nAFghAUTb6DHihEgRkBQBTrQgifHRVb8MvJfRLAwtVI8Po2IYNFRn4x6Jd+xcz+EAz0mpzBFLxzoq3MQ595++20Wy5cvV2I+FvLjADFfdQTRGbQAi/t9/vnnan0vvOLTChxrFemN/+i4onPIGQwIy8zI1HZq0dHLy8sjxMEB6UxKSmKBDu3p06fVooNYeNCKF198kWrWrMni/PPPp7PPPrtMtI/FCAgTSXl0GphvWJOvVq1a1KhRoxLo3r073XfffSzuuusuatOmDTVt2pTF5ZdfTqgLHKpWraqMhwoVKhCHVq1asedEWoYMGULjx4+nSZMmFQPeF/7CCy/Q1KlTWWD9wQkTJrDAsbNnz6a5c+eyWLx4MSuoQ2SHiA1z4r333mMxb948euWVV1hgoVPkMfKRQ8uWLZVRA7PGiubNm1Pr1q3Z4xAXtuHecmh9dWu1HftEC7jrN4e1bduWOGAf7hqNMJQlHXAs3kOPe8ChXr16BGOXA8ow7okYAR6RU5iiLU1GAMxVGKQnjp9ggXaVnQmANlNWZhaPrCyt0K/aYhiskREAMjNLtBmtbUhd+w/hdmJ+MNuCMQJ0psHQoUODahcaoj/3HQntQIwAMQKCKtCRKLRyztCJbCKChamV4vFpRAQLXZ0QfilbeSkc6DE5hSl64UBfve19e292BC5G5mKUJUb14929HI4e8cYIwIh+z4yALL0RgA6mnREw7aVphNHSHGrUqEHlypUrE+1jMQLCRFIenaawsFAJwZddehkrlsIcfPjhh1lg1D6E0Dp16rCoVq1awHWgQf0GdOutt7K48847CSMrrekaNWqUMgieeuop4vD46McJi41yePTRRwllefr06SwWLVqkBH+I/lZgNDpMAAjCHGbPmk1Tpkxh8dhjjxHE5urVq7M479zzCGILh8qVKxPyWHds9fOrsyaMYcxoj6tenWpUr0HgsXBCZ6wi/IKaF9AFF/DAdqRTdz04luNphNWuXZtgWAVShkMtgnlUxSVahxwolUbAiROE9RSssDMCMAtAmQCamZ12YnwwgjzMAzWTwDKA5IwZkJWtHQgSzHmdTIKgjIDkFHaGkRgB9pXxLPvN3myVDmDZEm5EqAvd/RYRzBtOCneswoGhqxPCL2UrL4UDw81W3pxPONBXb8UIKJodIEaAf1yOuiOf2M0BMQJ8xoAYAcWNCDECfOaWnZklRkDs8p455WIE+F4VJEZA8VdMihHg3AYMNQeKESAzDAIePSICnHOFDXUeiQhmbkrE7m8RwcJfd0JdFyW+yNxD4cDY5T1zyoUDffVHjAAxAtw+S8QIMDNJ7P0WI0CMAG4kvRgBYgTEHpsFlmIxAsQI4GYHiBHg3K8WI0CEexHuy3AZEBEssEZHtB0lIpjzw86tOCL7l408FQ6MNjYLLD3CgWIEWNcKkBkB/nG4GAGBcU60HCVGgBgBYgTIq4GihY8ikQ4xAsQIECPAv/aeVdsQI6AMi8DWwiD/A6tEsZxvIoJFoskS+nOKCFb26m4s8040pV04MPR8FIkYhQN9HCgzAmRGgFt+FSMgEowVunOKESBGgBgBYgSEjlFiLyYxAsQIECMgMB1EjAAxAmRGQBkuAyKCxV6Dh0uxiGCBPQDdCiayf+nLZ+FAjlFiL0w4UIwAmREQGD+LERB7fGdOsRgBYgSIESBGgJkTytpvMQLECBAjILD2nxgBZVgEFlErsEpTmvJNRLDS0VwSEUzqcmnipXBei3CgcGA4y5vX5+pzZx9avHgxizVr1tCJEyfo+79+z+Lw4cOUnZ3NYu/evfT555/T119/zeLo0aO0b98+Fvn5+ZSTk0PZWdks0IFLS01jkZ6eTrt37yacn0NmZiZZDQDjP67lu+++o19//ZXFzFdm0hVXXMECvFC+fPkyMVBGjIDY5sD9+/dTy5Yt6bzzzqNatWqVQIMGDaht27YsWrRoQRdeeCFhQVUOlSpVCrgO1KhRgxrUb8CiUaNG1Lp16xJpateuHXXq1Ik6d+7MokOHDtSmdRsWOLZbt27Uo0cPFrfffjsNHDiQRf/+/al37950+223s+jZsyd17dqVRfv27QlCPAQVDhUrVqTy5crzKF+ekMfccWfCKv0f/R8HzfnOHPd//0fn/N854cM559A5Dqh8TmXigOPM6bb+xvYqlavwqFKFqlatypZflGm7MozzNGnShBYuXBjbJFDGUx+LRsD//M//qDZGhfIVyIyGDRsS2mp5e/NYoF2TmprKAu/ExysRde04tMGwXQdOTD8Tlp5BaI/pgLZYoFDnSM8gLHYcSiCtyBNbaNqeqSmplJKcUgJDhw4N+JmI9j/uuw5e9w+4+EPNgbJYsBgLQVUQrpBKmHcip4hgpaP1JEaAd3VE+Kd0561woHBgaarj/e7qR8uXL2exYcMGOn78OJ0+fZrFoUOHtB3IYIyAgvwCys3J1caNjl9KSgoLGAQ52TmUm5vLAscawr/1G53h77//nn777TcWr815jdDp5lCnTh2CgFeayobuWsQIiG0OjKQIpitTEl66202l5f6GWgSLbSaJ3dRHkgP/93//l3SA4KurKzgG5a9y5crF0LhxYzWQY+fOncQhaVcSJScns4B4DbEfAy84oE2kMwEQfkb0z8go8dtJVNcZBAhX4j4Tp3E+u2OD2RbMedH25NqlDw59UHtPdffaHK4zAezKivn4UP8ONQeKESBGQFAVJNQFXOKzb4yKCBa7DR9zysUIsC/nwgOSP7oyIBxoZpLY/S0c6KvjYgQUrREgRoB/vC9GQOzyHlIeSRFM91yVcP/qnuRTZPMp1CJYbDNJ7KY+khyoMwEQbifuihFQZDYEI/bbHStGgDO/hpoDxQgQI0CMgBgqAyKCxW7Dx5xyEcGcH3bS4ZI84sqAcKCZSWL3t3Cgr36LESBGAMdzdmFiBMQu7yHlkRTB7MqVbJM2V7SXgVCLYLHNJLGb+khyoBgBvlfveCXI28XrtE2MAOdnUKg5UIyAGBKBo/0BLelzrsDB5pGIYLHb8DGnXEQw7+tKsHVNjo/OeyQcaGaS2P0tHOirX2IEiBHgrMBsNwAAIABJREFU9lkjRkDs8h5SHkkRzG1Zk/2jsx1UVu9LqEWw2GaS2E19JDlQjAAxAtzwp7wayAOekQ6gNCzcVELZt6i8iAjmASFFIErhwKIyLfVb8sJNGRAOjABheXBK4UBfvRcjQIwAN/yHfcUI8ICQwhhlJEUwt2VN9pf2WTSVATECwkhUHp4qkhwoRoAYAW44TYwAD4hAOoDSsHBTCWXfovIiIpgHhBSBKIUDi8q01G/JCzdlQDgwAoTlwSmFA331XowAMQLc8B/2FSPAA0IKY5SRFMHcljXZX9pn0VQGxAgII1F5eKpIcqAYAWIEuOE0MQI8IALpAErDwk0llH2LyouIYB4QUgSiFA4sKtNSvyUv3JQB4cAIEJYHpxQO9NX7fv360YoVK1jExcXR0aNH6ZtvvmFx8OBBwgK7HPbu3UunTp2ib77+hgXi3bdvH4uCggLKzc2lnOwcFhkZGZSSksIiNTVVpQfHc8CxycnJLHAd3377Lf30008sZs+eTY0aNWJRt25dqlixYplY80qMAA8IKYxRRlIEc/OslX2lbRZtZUCMgDASlYeniiQHht0ISEpi2ztoB6G9lJWVRTk5OSywLTMzUwu0p7Tv3U9Lp7Q0n+iv+05PSycW6emEuHXQnjM9XZ8eP7fpzml7rel/XmtqGqVZ8OCDDwbVLhQjwAMikA6gNC6irXERK+kREcwDQopAlMKBwoGxwjnRlk7hwAgQlgenFA70ceDAgQNpzZo1LDZt2kQHDhygkydPsigsLGTFdgjw+fn59OUXX9Lpb0+zOH78OO3fv58FDII9e/bQ7tzdLNAxtTMC0HnlzAmE4VhdpxTHffbZZ3T48GEWL730ErVo0YJFgwYNqFKlSkF1+KKN63TpESPAA0IKY5SRFMF0ZUrCpU0aC2VAjIAwEpWHp4okB4baCGjSpAktWbKEkpKKZjeaf0Pwt2svoU2Etg8HiN8wCwJCSqr2vEZ6UlNSiUVqqradhvZbMEaArv0XbLzIR27wyvDhw4NqF4oR4AERSAdQGhyx0OCIxjSKCOYBIUUgSuFA4cBo5JdYSJNwYAQIy4NTCgf6OPDugXfT2rVrWSQkJChhHKI9h8L9DkbAl1/S6dOnWZw4foI1AWAOoJOujIDdu2k3g2CNAF0nEh3hfQX7tOmaOnUqtWzZkkXDhg3FCPCgnkqUoc+BSIpgsfCMlzRK+1hXBsQICD0fRSLGSHJgLBkBaCsZon04v2E8eCXY28aLGQp+zhyw7of2IzcLdcSIEWIE2FTys2y2ebZJOoDykNc95CXcvmyICOYZLYU1YuFA+3IuPCD5oysDwoFhpSrPTiYc6KvjYgQUdfzECPCP91F35BO7ORBJEUz3XJVw/+qe5FNk80mMgNjlPXPKI8mBYgTwr3U0Gw1iBBTxnMwIMNfcEP2WDmBRAZNGheSFmzIgIliISCjC0QgHSr13U+9l36LyIhwYYfIK0emFA31lWowAMQLc8rsYASEioQhFE0kRzG1Zk/2L2h6SF5HPCzECIkRaIT5tJDlQjAAxAtxwuRgBIa78iE46gJF/mLqpBLJv9NwvEcE8IKQIRCkcGD11Svgttu6FcGAECMuDUwoH+uqdGAFiBLh9BokR4AEhhTHKSIpgbsua7B9b7aPSfr/ECAgjUXl4qkhyoBgBYgS44clSbwT897//pX//+9/0/fff07Fjx+jIkSNqkS78/vbbb+m33347sx0Llh08eFBtx34nTpygH3/8kX7//XdXdCEdQGlYuKmEsm9ReRERzBXV+LWzcGBR+ZK6JnkR7WVAONAvWnO1k3Bg5Oq9GAFiBLjlXDECXNGb485//PGH6uueOnWKjh49emah6i+++EL1cf/zn//Qr7/+Sl9//bXadujQIdVXRj8YYX//+98Jcfj7iaQI5rasyf6RezZI3pfMezEC/GUZd/uVJQ4UI0CMADfcWuqNAJgA3333HS1dupQGDx5Md955J91+++00aNAgmj9/PuXn5yuT4OOPP6ZRo0ZRjx496I477qC+ffsSVmLGImcwBNx8xAgo+XBzUyhl37KbfyKCuWEa//YVDiy79Um4NPbuvXCgf7zmZi/hwMjVAzECxAhw+xwSI8ANuznv+8svv1BeXh5NnDiRBg4cqPrAvXv3pkmTJtGGDRuUGZCdnU3Tp09X/d9evXqpPjD6wTNnzlQLFMIM8PcjRkDk+NZtXZP9o+teiRHgL8u4268scaAYAWIEuOH1Um8E/Otf/1Ij/zdu3EivvPIKvfHGGzR37lyaNm0aDRs2jJYsWUIHDhxQYY888gjdf//9NGvWLHrrrbfUtr1799Jf//pXV4wjRkB0PVjdVAjZN7L3TkQwV1Tj187CgZEt08Ipkv9uyoBwoF+05mon4cDI1cERI0bQ7tzdLNC+Pnz4MB07eozF/v37lQiZm5tb4hvCJmbxfvXlVywQb35ePgucNycnh7KzslmkpaXRrl27WCQlJRG2p6cXCfzm39iWlsojMzOTCgsL1cxjzD62Ys6cOdTj+h4sOnToQJUrVyY3XBKr+4oR4IreHHf++eefCeL8ggUL6NVXX6V58+bRa6+9RuPHj6dx48ZRQUEBoY8MY+Dee++lCRMmqD4w+sGJiYlqMJybmfFiBESOb2O1zku6fWVGjABHOgtoh7LEgUEbAedUpsomNG7cmJYsXsK2h9BOSk5KJvNCvNbfqk2EdhGD1JRUSk5OtkVKcgqxSEkhLPqrgzUd5v84r+44YyHh9LR0cgtco128TttwvO6cmRmZbHsVg9aD4c9SbwRgyuMPP/ygGjKY5vjPf/6T0Cnct28fYUTElClTVAFG4wjmwPvvv+9a+LeykhgB0ggKplKW5WNFBLOySfD/hQOFj8oyp8TatQsHBs951hiEAyPHgRAVT58+zeKb/9/emQddUZx7OJXFWCal/5j8k5i6epOU0UqMhqiAUjHKIiKKCEZiGTUkMa5gUKJF9AIGDZtsBRdEUUsBQQQVooDIvm/ZCJQgIopaEYFoBTWYvLd+7Z0v5ztfz5mZ7yxz5swzVW+dc/r0bE93/6a7357ut99umrJT9fNiU4eizwmgMDkCNM2JnAE+2/7X7aH7ygmwdu3a0M58NWyXLFnitZdeesnkDAhrvLoG60r/iDSdU53/um6fTZk8xXr37u21Ll262LHHHltWgy8rWogjoFjByvut0fya4kd5T2UxmCZjzJgxpnwlJ8DMmTNt8ODBbsCcOiPK2XAEpKe3WSnjXKc/j+AIKEd5wvfNkwaW4wg4+uij7ZhjjmlmJ598sj366KO2dOlSr6k+VNjJXvxd/7fWwupZCldnvs+5EIQVX0eS38ExWvNZ8jwrVpasO7pBJB6Hia5DA05Uhyy2vj/tW1a9sOEdAZobVo1AjWaQE0C/ZVojQCOVhg4davPnz3evP+II8D+YeGDDpVZ5gE6w8IpMa/9BAym/tSq/nKf8vIYGtlbpwvdDA8vPl60t2zgC/uMUwBEQLx/iCAjXstb8o45/DYD74IMP3Jp4OobC9Eb8j3/8Y5s1a5ZNmzYNR8Cn4uXP1moh+8E3Kg/gCGiNwkXvkycNxBHwydsHJTvkQwZrBPu0xgEQ7BMcw/uJIyDSaVFpDfyUTx5UGdJool69erm3AFQ51yu5WiPgmmuucVMDaU2BF1980a0voPllk2y8EcDDPuphz//+PEInWBKlaX1cNNCf/yiXcEk7D6CBrde1JHuigbUp6zgCcAQk1VQcAUmULHlcDY5777333FS5Wjdv8eLFzhmgqYG0ft4dd9xhU6ZMsaeeesq9PR8Moot7Jt4IqI22Ji1XxK//dKl0J1jcMpu3eI2sgTgCcAQk0fqGfyOgWNzkFXz11VddpUeLBmvB4Ndff9299iJHQKdOndyUQRoloUqRPDpabDhsk5ho7rF9+/Y1vep7yy23RHo8kiQScev/4U0aVSaN6AQLU5rKhaOBlcmrlHk4ViMPoIGV07qwI6GBtSu7OAJwBCTVSRwBYcpVfrjejtLCmZpea9CgQW5AnKbK1VQL9957r/Xo0cO6du1ql19+uV133XVuulxNYyVnQNh2+PBh107es2ePawe/8MIL9o1vfIN2MG8YkAcS5gEcAWEqU7nwRtdAHAE4ApLUuXLlCFDjT6PAgtchtXjw+vXrTZUYVXS2b9/u7OWXX3bzJWpUhJwBejMgbJMTQJUeVVz1hoHs1FNP5eGX8OGXJNMSt3aN+FqzphMsTGkqE44GNm7ZqXVZ5XzVyUtoYGW0LuwoaGB18m2YHuAIwBEQljfCwnEEhKlX+eGaIkhrBdx4441222232fTp010nvtYO0ELWagfv2LHDtY3vu+8+GzhwoFtYWOt5hG1a1Hvq1KnWt29f1wbu3LlzbtazCMvDhNf2OdMovHEEhKlM5cIbXQNxBOAISKKHuXEEyAO4f/9+mzNnjlsg+Fe/+pUbAREsnKSFRILXHxVXFZsHH3zQrrjiCtM0QWGbnAiK+9xzz9nkyZOd6U2DJIlAXCoM5IFP8gCdYGFKU344GojOoDP1nwfQwPK1LuwIaGDt8z+OABwBSZ87OALCFKy8cDlB9SbAuHHj3FS4auMGo/21jp7awXrLXTp56NAhW7Rokd155532s5/9zDTaP2zTG/F6e16D7NQOvueee0zPsaTpTvza6zPM64s5joAwlalMeB40EEcAjoAkup4bR8DBgwfdSstaIFgjHObNm2cK06ZKjyo/Eohg0+iH559/3tSpr4pNkk2V2CSJQNz6ehCTHumlB51gSZQmWVw0ML18jabAPm4eQAOT6VqS2Ghg7cvhTTfd5EYgaxRysentW3Uwvvbaa15TfK3n5TNNZ6J933j9Da/t2rXLNFe5z/70pz/Z5s2bXaeoOkaLbd26dRYs/Ob7XLd2nSmOz7TmWJht3LjRdu7c6aYn1RSlxaa2Rs+ePb3WsWPH3IyyxhGQRNXixdUoWHXYT5w40S0QLGeAylWwqf0bOAEUprfnVf6GDBniRvnLYRB3Y42A2uts3PoF8eo7bXAExFWZ5PHyooGVdgR861vfck7eZcuWmc80rdzqVau9tmrVKuck9i6c+/8L9mr/UrZyxUrzmY7tq58FYaXOqeOFnVP7RR07OIfvU/uWtJWrbJXPVq2yNWvW2No1a72mGWxUhyw2OerL0fVcOALU0a/FkDQySa9Dzpo1yzkBVOnRps8DBw640RCBtKiR8MADD7iFk2bMmBEEx/rEEVDfD9pyCgz7Vjdt6QSLJTGJI6GB1c236AJ8K5UH0MDE8hZrBzQwnTKq+cbHjx/vtccff9x1TurNWp/pjd3izvLg9969e92UJu++e8B8phHNWhDVZ/pPnaI6hs+0btgbb7zhNf0nx4WcED5TR7+mWPGZOla1/5tvvum1SZMmWZcuXbx2zjnn2Be/+MWyGnyV0qhqHwdHQCxJSxRJg9tGjx5t/fr1c2/FK3/qDYBg03e1g4N2sb5rsWBNkXvzzTe7chLEjfrEEZCO1la7XHL86qcrjoAodWn9/3nRQDkCPvOZz3hNnb5h5Vj7Kf994QtfaGannHKKm0LO1+mtMA2I2Lhho988HdeFHdnq3PYNqFCYBlS4c4Y4GfSfOs59pv/U0b98+XK/LVvudWoEjo5SToRS59W16Np1X2G2Yf0G89qGDW5QyuZNm81nGvyiwTPFpudzWJrGCW94R4A8gKp0a67DNm3a2IUXXmiaFkgVbtnChQvdqIjZs2e7qYA0WkKmURCqAOn71q1bEykPjoDqPyzjZG7iZC8d6ARLJDWxIqOB2SsHaFd+0wwNjCVriSKhgemVp/POO8/Vp1WnLjbVr9966y07cuSI1/QGhzrOfaaOfHVWqlPfZ+rYVLr7TKOd33nnHVPHgM/kgNBUoj7TfrrmsM58OQeKG2rBb72loP1851TYpImTTCP/fda2bVscAYlKPZEDAiof6tjo1q2bdejQwfr06WMjRoxwbWBN56M27pIlS2zatGkuTOVSA+EGDBhgw4YNs7lz5za9QR8cs9QnjoD09Ja6Y7bZ4wgopSyt/y8vGqhO3Wo4AjQg2tfhrjB1eBe/VVn4WwOrw0xOgbAOc3Wo6/jOGbD6k+l+Cr+vWe13AgT7lHIEqMN/6dKlXtN/2jfMGaC3H8JYyHmh+9mwYYPXnMMkxDmyaeMm27J5i23Z4jc573fv3t3Cbr31VhwBJaThU6rwaw7/QYMG2emnn24andSjRw+77LLLnA0fPtwWLFhg9957r/30pz+1Sy+91NnVV1/tFgDWvoWjJkqcq+kvHAHZfhBTkUov/egEa5KRin1BA9PLz2gJ7JPmATSwYtLXdCA0ML1yiCPgPyO4cATEy4e8EdAkXRX5IufTs88+a506dXKOgMI2sKYVUEe/Fvvt37+/9e7d27WBe/XqZZpKV4PkCt8UiHNBOALi5fOkdQPiNz5XHAFxFCZ5nLxoII6AT5wDUW8E4Aj4j5Y2/BsBmvdQjUCNHlKnvkbmqDIemEb2yFMokdBIniBcXhfto9eVC9cOiCM/OAL+k8GouMAiSR6gEyyOwiSLgwZSBpOUQeKmm1/QwGT6Fic2GphensYRgCMg6TMFR0AcVYsfR2/F6O0atX937NjR1M5Ve1ftXv2nN100TZbWAlC4PvVf4ATQ1GpxNxwB6elt0rJG/PpKKxwBcVUmWby8aCCOABwBSTW94R0ByaSiMrFxBNTXgzVpoSB+eulHJ1hlNCjto6CB6ZUh9Cvb7NHAtNWrMudHAz8phzgCcAQkfSbhCKiMBqV1FBwB2a6DJC2vxK9ceuMISEu1KnvetDQQRwCOgKR6jCOgsmXfHY0GYOUeikkzNPGzzZ5OsCoIUgqHRAOzXQ7R0fTSDw1MQbCqcEo08JMyhCMAR0DS5wmOgCoIUg0PmVYnWNJ8Rvz06jmw97PHEVBDoariqdLSQBwBOAKSaiuOgCoIAQ1A/wMuaeYkfv440glWBUFK4ZBoYP7KLnpdmTRHA1MQrCqcEg38pDzgCMARkPTZgCOgCoJUw0Om1QmWNJ8RvzJ1FjhWjiOOgBoKVRVPlZYG4gjAEZBUj3EEVEEIaABW7qGYNEMTP9vs6QSrgiClcEg0MNvlEB1NL/3QwBQEqwqnRAM/KUMn/teJJmeAzy677DKbNGmSzZw502tznppjzzzzjNeee+45W7Rokb344oteW7BggT0952mv6Xzjxo2zUaNGeW3MmDE2YfwErwX7jRw50nw2fvx4mzJ5itf031133WUDBgzw2kUXXWT/fdJ/e+2EE06wo446yvKgzTgCqiBINTxkWp1geSgb3GN6dbNasMcRUEOhquKp0tTAT3/60xZm6vQNy8f673Of+5wd9bmjmtnXv/51t5j84kWLzWdLliyxZUuXeW3p0qW2eLF/Px1Li/ZqYd8wW7t2ra1bu85v69bZuhDTfitXrrQVy1d4bfny5RZmK1assDVr1tjaNWu9pnNu2LDBa+vXrzedO2xf3aeuy2c670svvWQvLfGbOC58YWELu+qqq0LTNCytC8NxBFRBCGgANvaDurAA8b2yaU0nWBUEKYVDooGVLRfoTH54ooEpCFYVTokGRpfZY4891jp37my9evXy2g033GD33HOP1wb/z2AbNuw+u//++712+4Db7dprr/XalVdeae3atbMzzjjDa2edeZa1b9/ea23btrU2bdp499PxOnXsZDq+z7p27WrHHXdcWY22PDwLcARUQZBqeMg0O8HyUD64x+hnS1YZ4QiooVBV8VRpamCYE0DhpRwBYWVGgxA0AGLu3Lle02ANDczwmf6bNWuWd6CHBmQsmL8gtEN+5YqVtn7dem+He1hHfBCuDnnnCFixwtTBXmw6tq8zXmGrVq0y7R8cq/hz06ZNtmXLFq/pv1Kd/U2OETlHimzhwoWO75w5c8xnjzzyiGkwSbF1vbBrWXVKHAFVEAIagI37kA4TSsIrk+Z0glVBkFI4JBpYmfKAruSPIxqYgmBV4ZRoYHTZxREQzSiPzwAcAVUQpBoeMs1OsDyWF+65cXQUR0ANhaqKp0pTA3EEbHAd+TgC4ukijoAqCAENwHiZj8oLnIrzAJ1gVRCkFA6JBlK2i8s2v+PlCTQwBcGqwinRwOj8jiMgmlEedRNHQBUEqYaHTLMTLI/lhXtuHB3FEVBDoariqdLUQBwBOAKSPBNwBFRBCGgANs5DOUlhIm756U4nWBUEKYVDooHllwX0JJ8M0cAUBKsKp0QDo8svjoBoRnl8DuAIqIIg1fCQaXaC5bG8cM+No6M4AmooVFU8VZoaiCMAR0CSZwKOgCoIAQ3AxnkoJylMxC0/3ekEq4IgpXBINLD8soCe5JMhGpiCYFXhlGhgdPnFERDNKI/PARwBVRCkGh4yzU6wPJYX7rlxdBRHQA2FqoqnSlMDcQTgCEjyTMARUAUhoAHYOA/lJIWJuOWnO51gVRCkFA6JBpZfFtCTfDJEA1MQrCqcEg2MLr84AqIZ5fE5gCOgCoJUw0Om2QmWx/LCPTeOjuIIqKFQVfFUaWogjgAcAUmeCTgCqiAEgwcPts9+9rN29NFH2+c//3k76qij3HcJPBbOAFbhbIJ8ozyl/CRWQVgjfX7lK1+xHj162K5du6pQMjlkrQiggdFlubDcNnq5LrzXcr83Ois0sFYqVd3zoIHRGnj88cfb+eefb5dccomzi7tdbN27d2/6fe2119ptt93mtQG/GmC//vWv7a677vLaDb+8wa688kqv9ezZ09q2bWtnnHGG184880xr166d184++2z73ve+591Px/vhD39oOr7POnXqZF/60pfKqrs1cj1Z2q62k8oOW3YJbNu2zb7zne+4tkrwvG/kfBvcY7mfjV63KZdPsH8j56XjjjvOTj75ZJs4cWJ2BYArtzQ18JhjjrEwC8pQks8TTzzRhgwZYlOmTPHa1KlT7eGHHvaa/lNenjBhgtemTZtmTz75ZDObPn26zZgxw2bNmmVz5861Z555JrHNmzfPZs+e7Y6h4/hM//vsqaeeMu0fdt5nn33W5s+f7zX9p/19x1WY7uvxxx/32mOPPeb4Tv7fyeaz0aNH29ChQ52pjqQBE0oX1SuTpGe9x620Bn4qDT26//77XWX/m9/8pp100kl2wgknOGE/5ZRTDAtnAKtwNkG+UQVB+UmsgrBG+tR9/eAHP7AdO3akUXQ5Z4UIoIHRZbmw3DZ6uS6813K/NzorNLBCIpTyYdDAaA389re/bep0V6e8Pk899VT77ne/634r7JxzznH1AdUJfHbeeedZmHXo0MHat2/vNR27TZs2rkNfnfrFpv9KWXH8wt/B/egcxXbWWWeZ7rkcDWzkerLaTHKUqOywZZfA9u3bXd7/2te+1pTXGznfllOeC/dt9LpN4b2W872R85LywFe/+lUbOXJkdgWAK7c0NVD1qFKWtOyddtppbsBGly5dLMwu7HKh+UzxO3fuHGoXXnihXXTRRU2m+Oeee647n8K7devWais8btLvpc578cUXWynrdlG3pvvxnbdr167mM7Eoxatjx46OiwbPqG77/e9/39WLVa9MmqZB/FL5RP8F8Wr5WWkNTMURsGrVKifi8oJpRJMaM8OGDbPJkydjJRjAKjp/KB8pP4lVI+ansWPH2kMPPWTvvPMOVYkME0ADo8tyYflt9HJdeK/lfm90VmhghoWv4NLRQDSwXK3z7d/I9WS1mdQBprLDll0Cqr+rHq9nWZCHGznfBvdY7mej123K5RPs38h5CQ3Mru4VXjkamKz+F5RtNDAeNzSwsLSV/p6KI6DwkpS5e/XqZa+88kphMN89BGDlgVIUpHyk/CRWbBDIAgHKdXQqUa6jGQUxYBWQ4DMrBNDA6JSiXEczUgzyUjxOxKovAuTb6PRAA6MZKQZ5KR4nYtUXAfJtdHqggdGMFIO8FI+TYuEIiM8q9Zhk7OgkQCSjGRGjvghQrqPTg3IdzSiIAauABJ9ZIYAGRqcU5TqakWKQl+JxIlZ9ESDfRqcHGhjNSDHIS/E4Eau+CJBvo9MDDYxmpBjkpXicFAtHQHxWqcckY0cnASIZzYgY9UWAch2dHpTraEZBDFgFJPjMCgE0MDqlKNfRjBSDvBSPE7HqiwD5Njo90MBoRopBXorHiVj1RYB8G50eaGA0I8UgL8XjpFipOwK0CnTfvn1tz5498a86pzFhFZ3wykfKT2LFBoEsEKBcR6cS5TqaURADVgEJPrNCAA2MTinKdTQjxSAvxeNErPoiQL6NTg80MJqRYpCX4nEiVn0RIN9GpwcaGM1IMchL8TgpVuqOgJUrV9rUqVNZ/DRGmsEqGpIWoFF+Eis2CGSBAOU6OpUo19GMghiwCkjwmRUCaGB0SlGuoxkpBnkpHidi1RcB8m10eqCB0YwUg7wUjxOx6osA+TY6PdDAaEaKQV6Kx0mxUncE7Nu3z7Zt22aHDx+Of9U5jQmr6IRXPlJ+Eis2CGSBAOU6OpUo19GMghiwCkjwmRUCaGB0SlGuoxkpBnkpHidi1RcB8m10eqCB0YwUg7wUjxOx6osA+TY6PdDAaEaKQV6Kx0mxUncExL9UYkIAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIJCUAI6ApMSIDwEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhDIEIHUHAEfffSRvfbaa/b888/buHHjbOzYsTZr1izbvHmz/eMf/8gQwspc6vbt2939jxkzxh544AGbMWOGvfvuu+7g//rXv+zDDz+0TZs22bRp02zChAluRex58+bZ22+/bUeOHKnMRdT5UcTh/ffft3Xr1tnDDz9sEydOdCweeughW7p0qb3++usGqzpPRC6viQAa2ITCfUEDm/Pw/UIDfVQIyyoBNLB5yqGBzXn4fqGBPiqEZZGA2nhbt2517Tq1gydPnmyLFy92beNDd7dQAAAMK0lEQVQs3k8lrhkNjKaIBkYzIkY2CKCBLdMJDWzJpDgEDSwm0vrfqTkCDhw4YE8//bT179/fOnbsaJ07d7ZrrrnGRo8ebXv37s1N53aQdC+88IL94he/sA4dOthpp51ml1xyie3cudP9rcbyq6++6jq9u3fvbl27drVu3bq5+FoQY//+/cFhGvrzn//8p7311lumjv/evXs7RuJw+eWX2x133OEq0HIU7N69O/esGjojNMjNoYHNExINbM7D9wsN9FEhLKsE0MDmKYcGNufh+4UG+qgQlkUCW7ZscQO/1Ibp1KmTa9P069fPpANq9/373//O4m2Vdc1oYDQ+NDCaETGyQQANbJlOaGBLJsUhaGAxkdb/TsURoMrNrl277KqrrrLf/OY3tmLFCvvjH/9ov/vd7+xHP/qRLV++3NRAzNO2Y8cO0wj/uXPn2s0339zMEaBVwtX5PWTIEBs5cqRt2LDBZs+ebXfffbfdf//9tnr16lyg0psPf/vb39xbIy+++KJbFPivf/2re6ukR48eNnz4cNNDZdKkSblnlYsMkeGbRANbJh4a2JJJcQgaWEyE31klgAa2TDk0sCWT4hA0sJgIv7NIQCMap06damq7zJw507Xr1K654oorbNSoUW5AnJwBedvQwOgURwOjGRGj/gmggf40QgP9XApD0cBCGuV9T8URcOjQIVu1apWrAGlKIP2Wd0ed29dff717TVIdvHnaDh486EaySwAGDx7czBGgKZRuvPFG17mtNwA06l1vCzzxxBPuLQpNI5SHTQ8NTRulNyDkEJAQfPzxxyZmffr0cdyee+45u+6663LPKg/5Icv3iAa2TD00sCWT4hA0sJgIv7NKAA1smXJoYEsmxSFoYDERfmeNgNq7b775po0YMcJ69uzppgdS20Ztvdtvv93uvfde94bz3//+96zdWtnXiwZGI0QDoxkRo74JoIHh6YMGhrMJ/kEDAxLlf6biCNizZ4+bFugnP/mJ68wObkMj2/VWwNChQ23ZsmVBcG4+JYxvvPGG/fa3v23mCHj55Zfd1EnDhg1zb0qo81uVRs2Vf8EFF7j1FXIDqehGxUJOkb59+zpuGllz7rnnGqyKQPGzrgiggf7kQAP9XEqFooGl6PBfvRJAA/0pgwb6uZQKRQNL0eG/eiOg9pvWfFM75Ze//GXTmgB6+1vrnylcbzZrwFMeNzQweaqjgcmZsUd6BNDA0uzRwNJ8fP+igT4q0WGpOAK0EMZjjz1mN910k5sKJ7hMTeuiBWAHDhzopnsJwvPyGVbwxat9+/Z23333uRHweqVeGf7Pf/6ztWvXzjlP8sKo+D7fe+8993aJ1plQ5Vlvmpx11lmwKgbF77oigAb6kwMN9HMpFYoGlqLDf/VKAA30pwwa6OdSKhQNLEWH/+qNgPLrkiVLXDvlzjvvdAPAdI0aCaq189SW0bSvWhMtjxsamDzV0cDkzNgjPQJoYGn2aGBpPr5/0UAfleiwVBwB27Ztc9P/3HrrrfbMM880XeUf/vAHmzx5sg0YMMAWLFjQFJ6XL6UKftC5XchC0yedffbZrjJZGJ6H73KGiJfWk9AbJJo6SesraK2JM888swWTPLPKQ37I2j2igf4UQwP9XHyhaKCPCmFZIYAG+lMKDfRz8YWigT4qhNU7AU35s3DhQtdO0Tp5+/btc5csR8Czzz7rwjU9kKYPyuOGBsZPdTQwPiti1g8BNLB0WqCBpfkU/osGFtJI/j0VRwAjwfwJVargF78RoPmx/vKXv7g3BTSdUt62Dz74wF555RW3eHLv3r1t+vTp7rfv7Ym8s8pb3sjC/aKB/lRCA/1cfKFooI8KYVkhgAb6UwoN9HPxhaKBPiqE1TsBRsOWTiE0sDSfwn/RwEIafM8KATSwdEqhgaX5FP6LBhbSSP49FUcAc8P6Eyqs4IetEbB+/fpcrhGgjn0tqqWRNP369XNrJOzevds+/PBDg5U/bxFaXwTQQH96oIF+LsWhaGAxEX5njQAa6E8xNNDPpTgUDSwmwu+sEGB+7NIphQaW5hP8iwYGJPjMGgE0sHSKoYGl+QT/ooEBidZ/puIIOHTokK1cudJ69OjhOnH1ipAy/ezZs+3666930wZpKpe8bWEFX53emvpmyJAhjtv777/vFsh94oknTAsuazR8njZNKaAFtZRXxowZY5pSSg8VbbDKU07I7r2igf60QwP9XIpD0cBiIvzOGgE00J9iaKCfS3EoGlhMhN9ZIaAyrml/hg8fbj179rStW7e6NozaL7fffrub7nTx4sWmtnEeNzQwXqqjgfE4Eav+CKCBpdMEDSzNJ/gXDQxItP4zFUeAPDg7d+60Pn362KBBg1zntuZ21xQ3V1xxhZv3/d133239XWVwT1X4NNWNFrvt37+/nX/++bZo0SK3iJTCtXaCHAEjRoywDRs2OKfJ3Xff7eaS1D552JRvDh8+bI8++qhzIl1zzTX2yCOP2K5du5xpYS1VpKdMmZJ7VnnID1m+RzSwZeqhgS2ZFIeggcVE+J1VAmhgy5RDA1syKQ5BA4uJ8DtrBDSn8ccff2wPPvigXXrppfbkk0+6dp06/zXV6ciRI11b5qOPPsrarZV9vWhgNEI0MJoRMeqbABoYnj5oYDib4B80MCBR/mcqjgBd9oEDB2zOnDluapcLLrjAOnXqZFdffbXr6N67d68dOXKk/LvL0BHU6X/DDTc4B8BJJ51kxx9/vFsIeODAgTZ//ny3CO748ePt4osvtq5du1q3bt3s5z//ua1YscL279+foTtt/aVqHjB5/2655Rb78pe/bKeffrp17NjRLrvsMmcaXbN69Wq3dkLeWbWeMnvWigAa2Jw0Gtich+8XGuijQlhWCaCBzVMODWzOw/cLDfRRISxrBNQRtnnzZhs1apR7K0Btme7du7v2zfPPP29yAihO3jY0MDrF0cBoRsSofwJooD+N0EA/l8JQNLCQRnnfU3MEqJKjOWJ///vf27hx49wUQRoVsWnTpqZpXsq7tWztrQ5uTfEzduxYN8p/6NChzikiZ4kWBT548KBt3LjRTYkzYcIE94bA3Llz7e2333bTKmXrblt3tcGrZPPmzXOvzirfTJo0qckWLlxor776qnudNu+sWkeYvWpJAA1sThsNbM7D9wsN9FEhLKsE0MDmKYcGNufh+4UG+qgQlkUCGsS1ZcsW165T209vfqsdo7ZxXjc0MDrl0cBoRsTIBgE0sGU6oYEtmRSHoIHFRFr/OzVHQOsvmT0hAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCISwBHQFxSxIMABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIZJAAjoAMJhqXDAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCISwBHQFxSxIMABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIZJAAjoAMJhqXDAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCISwBHQFxSxIMABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIZJAAjoAMJhqXDAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCISwBHQFxSxIMABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIZJAAjoAMJhqXDAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCISwBHQFxSxIMABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIZJAAjoAMJhqXDAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCISwBHQFxSxIMABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIZJAAjoAMJhqXDAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCISwBHQFxSxIMABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIZJAAjoAMJhqXDAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCISwBHQFxSxIMABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIZJAAjoAMJhqXDAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCISwBHQFxSxIMABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIZJDA/wFsrVSYRUlMCAAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Link  https://keras.io/datasets/#fashion-mnist-database-of-fashion-articles\n",
    "\n",
    "Dataset of 60,000 28x28 grayscale images of 10 fashion categories, along with a test set of 10,000 images. The class labels are:\n",
    "\n",
    "| Label |\tDescription |\n",
    "--- | --- |\n",
    "| 0\t| T-shirt/top\n",
    "| 1\t| Trouser\n",
    "| 2\t| Pullover\n",
    "| 3\t| Dress\n",
    "| 4\t| Coat\n",
    "| 5\t| Sandal\n",
    "| 6\t| Shirt\n",
    "| 7\t| Sneaker\n",
    "| 8\t| Bag\n",
    "| 9\t| Ankle boot\n",
    "\n",
    "![](image.png \"Title\")\n",
    "\n",
    "### Usage:\n",
    "from keras.datasets import fashion_mnist\n",
    "\n",
    "> (x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()\n",
    "\n",
    "Returns:\n",
    "2 tuples:\n",
    "1. x_train, x_test: uint8 array of grayscale image data with shape (num_samples, 28, 28).\n",
    "2. y_train, y_test: uint8 array of labels (integers in range 0-9) with shape (num_samples,)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "(X_train_full, y_train_full), (X_test, y_test) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = [\"T-shirt/top\", \"Trouser\", \"Pullover\", \"Dress\", \"Coat\",\n",
    "               \"Sandal\", \"Shirt\", \"Sneaker\", \"Bag\", \"Ankle boot\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Reshape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_full = X_train_full.reshape((60000, 28, 28, 1))\n",
    "X_test = X_test.reshape((10000, 28, 28, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data normalization\n",
    "We then normalize the data dimensions so that they are of approximately the same scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_n = X_train_full / 255.\n",
    "X_test_n = X_test / 255."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the data into train/validation/test datasets\n",
    "In the earlier step of importing the date, we had 60,000 datasets for training and 10,000 test datasets. Now we further split the training data into train/validation. Here is how each type of dateset is used in deep learning:\n",
    "\n",
    "* __Training data__ — used for training the model\n",
    "* __Validation data__ — used for tuning the hyperparameters and evaluate the models\n",
    "* __Test data__ — used to test the model after the model has gone through initial vetting by the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_valid, X_train = X_train_n[:5000], X_train_n[5000:]\n",
    "y_valid, y_train = y_train_full[:5000], y_train_full[5000:]\n",
    "X_test = X_test_n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the model architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "attachments": {
    "CNN.jpg": {
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Architecture\n",
    "\n",
    "![](cnn.png \"Title\")"
   ]
  },
  {
   "source": [
    "We are going to use 32 different filters in the convolutional layer, hence size is 26x26x32"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.Sequential()\n",
    "model.add(keras.layers.Conv2D(filters = 32, kernel_size = (3, 3), strides=1, padding='valid', activation='relu', input_shape=(28, 28, 1)))\n",
    "model.add(keras.layers.MaxPooling2D((2, 2)))\n",
    "\n",
    "model.add(keras.layers.Flatten())\n",
    "model.add(keras.layers.Dense(300, activation=\"relu\"))\n",
    "model.add(keras.layers.Dense(100, activation=\"relu\"))\n",
    "model.add(keras.layers.Dense(10, activation=\"softmax\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model: \"sequential\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nconv2d (Conv2D)              (None, 26, 26, 32)        320       \n_________________________________________________________________\nmax_pooling2d (MaxPooling2D) (None, 13, 13, 32)        0         \n_________________________________________________________________\nflatten (Flatten)            (None, 5408)              0         \n_________________________________________________________________\ndense (Dense)                (None, 300)               1622700   \n_________________________________________________________________\ndense_1 (Dense)              (None, 100)               30100     \n_________________________________________________________________\ndense_2 (Dense)              (None, 10)                1010      \n=================================================================\nTotal params: 1,654,130\nTrainable params: 1,654,130\nNon-trainable params: 0\n_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "source": [
    "Here we see that we need to train 1622700 parameters, had we not used the max pooling layer, we would have to train 4000000 parameters. It has signinficantly reduced the number of parameters for us."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
    "              optimizer=\"sgd\",\n",
    "              metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 1/30\n",
      "860/860 [==============================] - 30s 34ms/step - loss: 0.8767 - accuracy: 0.6992 - val_loss: 0.7489 - val_accuracy: 0.7368\n",
      "Epoch 2/30\n",
      "860/860 [==============================] - 25s 30ms/step - loss: 0.5386 - accuracy: 0.8051 - val_loss: 0.4915 - val_accuracy: 0.8186\n",
      "Epoch 3/30\n",
      "860/860 [==============================] - 24s 28ms/step - loss: 0.4805 - accuracy: 0.8259 - val_loss: 0.9241 - val_accuracy: 0.7080\n",
      "Epoch 4/30\n",
      "860/860 [==============================] - 30s 34ms/step - loss: 0.4432 - accuracy: 0.8421 - val_loss: 0.4148 - val_accuracy: 0.8562\n",
      "Epoch 5/30\n",
      "860/860 [==============================] - 29s 33ms/step - loss: 0.4192 - accuracy: 0.8504 - val_loss: 0.4368 - val_accuracy: 0.8466\n",
      "Epoch 6/30\n",
      "860/860 [==============================] - 31s 36ms/step - loss: 0.3953 - accuracy: 0.8587 - val_loss: 0.3770 - val_accuracy: 0.8698\n",
      "Epoch 7/30\n",
      "860/860 [==============================] - 30s 35ms/step - loss: 0.3776 - accuracy: 0.8656 - val_loss: 0.4044 - val_accuracy: 0.8570\n",
      "Epoch 8/30\n",
      "860/860 [==============================] - 35s 41ms/step - loss: 0.3627 - accuracy: 0.8693 - val_loss: 0.5158 - val_accuracy: 0.8086\n",
      "Epoch 9/30\n",
      "860/860 [==============================] - 32s 38ms/step - loss: 0.3485 - accuracy: 0.8745 - val_loss: 0.3708 - val_accuracy: 0.8680\n",
      "Epoch 10/30\n",
      "860/860 [==============================] - 31s 36ms/step - loss: 0.3365 - accuracy: 0.8786 - val_loss: 0.3863 - val_accuracy: 0.8582\n",
      "Epoch 11/30\n",
      "860/860 [==============================] - 33s 39ms/step - loss: 0.3242 - accuracy: 0.8824 - val_loss: 0.3454 - val_accuracy: 0.8748\n",
      "Epoch 12/30\n",
      "860/860 [==============================] - 32s 38ms/step - loss: 0.3132 - accuracy: 0.8862 - val_loss: 0.3227 - val_accuracy: 0.8872\n",
      "Epoch 13/30\n",
      "860/860 [==============================] - 32s 37ms/step - loss: 0.3060 - accuracy: 0.8889 - val_loss: 0.3289 - val_accuracy: 0.8882\n",
      "Epoch 14/30\n",
      "860/860 [==============================] - 31s 37ms/step - loss: 0.2975 - accuracy: 0.8913 - val_loss: 0.3984 - val_accuracy: 0.8508\n",
      "Epoch 15/30\n",
      "860/860 [==============================] - 30s 35ms/step - loss: 0.2895 - accuracy: 0.8951 - val_loss: 0.3814 - val_accuracy: 0.8634\n",
      "Epoch 16/30\n",
      "860/860 [==============================] - 30s 35ms/step - loss: 0.2830 - accuracy: 0.8973 - val_loss: 0.2956 - val_accuracy: 0.8956\n",
      "Epoch 17/30\n",
      "860/860 [==============================] - 28s 33ms/step - loss: 0.2756 - accuracy: 0.8999 - val_loss: 0.3999 - val_accuracy: 0.8616\n",
      "Epoch 18/30\n",
      "860/860 [==============================] - 32s 37ms/step - loss: 0.2699 - accuracy: 0.9013 - val_loss: 0.3522 - val_accuracy: 0.8746\n",
      "Epoch 19/30\n",
      "860/860 [==============================] - 31s 37ms/step - loss: 0.2640 - accuracy: 0.9034 - val_loss: 0.3024 - val_accuracy: 0.8918\n",
      "Epoch 20/30\n",
      "860/860 [==============================] - 33s 38ms/step - loss: 0.2584 - accuracy: 0.9054 - val_loss: 0.3197 - val_accuracy: 0.8854\n",
      "Epoch 21/30\n",
      "860/860 [==============================] - 33s 38ms/step - loss: 0.2525 - accuracy: 0.9085 - val_loss: 0.2815 - val_accuracy: 0.9004\n",
      "Epoch 22/30\n",
      "860/860 [==============================] - 37s 42ms/step - loss: 0.2471 - accuracy: 0.9098 - val_loss: 0.2819 - val_accuracy: 0.8980\n",
      "Epoch 23/30\n",
      "860/860 [==============================] - 28s 33ms/step - loss: 0.2419 - accuracy: 0.9106 - val_loss: 0.2795 - val_accuracy: 0.9036\n",
      "Epoch 24/30\n",
      "860/860 [==============================] - 28s 32ms/step - loss: 0.2371 - accuracy: 0.9134 - val_loss: 0.2990 - val_accuracy: 0.8904\n",
      "Epoch 25/30\n",
      "860/860 [==============================] - 28s 33ms/step - loss: 0.2325 - accuracy: 0.9153 - val_loss: 0.2872 - val_accuracy: 0.8980\n",
      "Epoch 26/30\n",
      "860/860 [==============================] - 29s 34ms/step - loss: 0.2288 - accuracy: 0.9160 - val_loss: 0.2791 - val_accuracy: 0.9032\n",
      "Epoch 27/30\n",
      "860/860 [==============================] - 27s 32ms/step - loss: 0.2243 - accuracy: 0.9184 - val_loss: 0.2699 - val_accuracy: 0.9032\n",
      "Epoch 28/30\n",
      "860/860 [==============================] - 28s 32ms/step - loss: 0.2199 - accuracy: 0.9189 - val_loss: 0.3023 - val_accuracy: 0.8932\n",
      "Epoch 29/30\n",
      "860/860 [==============================] - 27s 32ms/step - loss: 0.2147 - accuracy: 0.9213 - val_loss: 0.2696 - val_accuracy: 0.9024\n",
      "Epoch 30/30\n",
      "860/860 [==============================] - 28s 32ms/step - loss: 0.2121 - accuracy: 0.9218 - val_loss: 0.2709 - val_accuracy: 0.9048\n"
     ]
    }
   ],
   "source": [
    "model_history = model.fit(X_train, y_train, epochs=30,batch_size= 64,\n",
    "                    validation_data=(X_valid, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "pd.DataFrame(model_history.history).plot(figsize=(8, 5))\n",
    "plt.grid(True)\n",
    "plt.gca().set_ylim(0, 1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "313/313 [==============================] - 2s 8ms/step - loss: 0.2821 - accuracy: 0.8997\n"
     ]
    }
   ],
   "source": [
    "ev = model.evaluate(X_test_n, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[0.2820536494255066, 0.8996999859809875]"
      ]
     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "source": [
    "ev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_new = X_test[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Python39\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\sequential.py:455: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([9, 2, 1], dtype=int64)"
      ]
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "y_pred = model.predict_classes(X_new)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([9, 2, 1], dtype=uint8)"
      ]
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "source": [
    "y_test[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "AxesImage(54,36;334.8x217.44)\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "print(plt.imshow(X_test[0].reshape((28,28))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<function tensorflow.python.keras.backend.clear_session()>"
      ]
     },
     "metadata": {},
     "execution_count": 21
    }
   ],
   "source": [
    "keras.backend.clear_session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "del model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pooling vs No Pooling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_a = keras.models.Sequential()\n",
    "model_a.add(keras.layers.Conv2D(filters = 32, kernel_size = (3, 3), strides=1, padding='valid', activation='relu', input_shape=(28, 28, 1)))\n",
    "model_a.add(keras.layers.MaxPooling2D((2, 2)))\n",
    "model_a.add(keras.layers.Flatten())\n",
    "model_a.add(keras.layers.Dense(300, activation=\"relu\"))\n",
    "model_a.add(keras.layers.Dense(100, activation=\"relu\"))\n",
    "model_a.add(keras.layers.Dense(10, activation=\"softmax\"))\n",
    "\n",
    "model_b = keras.models.Sequential()\n",
    "model_b.add(keras.layers.Conv2D(filters = 32, kernel_size = (3, 3), strides=1, padding='valid', activation='relu', input_shape=(28, 28, 1)))\n",
    "model_b.add(keras.layers.Flatten())\n",
    "model_b.add(keras.layers.Dense(300, activation=\"relu\"))\n",
    "model_b.add(keras.layers.Dense(100, activation=\"relu\"))\n",
    "model_b.add(keras.layers.Dense(10, activation=\"softmax\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model: \"sequential_1\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nconv2d_1 (Conv2D)            (None, 26, 26, 32)        320       \n_________________________________________________________________\nmax_pooling2d_1 (MaxPooling2 (None, 13, 13, 32)        0         \n_________________________________________________________________\nflatten_1 (Flatten)          (None, 5408)              0         \n_________________________________________________________________\ndense_3 (Dense)              (None, 300)               1622700   \n_________________________________________________________________\ndense_4 (Dense)              (None, 100)               30100     \n_________________________________________________________________\ndense_5 (Dense)              (None, 10)                1010      \n=================================================================\nTotal params: 1,654,130\nTrainable params: 1,654,130\nNon-trainable params: 0\n_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_a.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model: \"sequential_2\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nconv2d_2 (Conv2D)            (None, 26, 26, 32)        320       \n_________________________________________________________________\nflatten_2 (Flatten)          (None, 21632)             0         \n_________________________________________________________________\ndense_6 (Dense)              (None, 300)               6489900   \n_________________________________________________________________\ndense_7 (Dense)              (None, 100)               30100     \n_________________________________________________________________\ndense_8 (Dense)              (None, 10)                1010      \n=================================================================\nTotal params: 6,521,330\nTrainable params: 6,521,330\nNon-trainable params: 0\n_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_b.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_a.compile(loss=\"sparse_categorical_crossentropy\",\n",
    "              optimizer=\"sgd\",\n",
    "              metrics=[\"accuracy\"])\n",
    "\n",
    "model_b.compile(loss=\"sparse_categorical_crossentropy\",\n",
    "              optimizer=\"sgd\",\n",
    "              metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 1/3\n",
      "860/860 [==============================] - 30s 34ms/step - loss: 0.8466 - accuracy: 0.7083 - val_loss: 0.7893 - val_accuracy: 0.7126\n",
      "Epoch 2/3\n",
      "860/860 [==============================] - 29s 34ms/step - loss: 0.5359 - accuracy: 0.8062 - val_loss: 0.4955 - val_accuracy: 0.8084\n",
      "Epoch 3/3\n",
      "860/860 [==============================] - 30s 34ms/step - loss: 0.4792 - accuracy: 0.8267 - val_loss: 0.9630 - val_accuracy: 0.6964\n"
     ]
    }
   ],
   "source": [
    "model_history_a = model_a.fit(X_train, y_train, epochs=3,batch_size= 64,\n",
    "                    validation_data=(X_valid, y_valid))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 1/3\n",
      "860/860 [==============================] - 64s 73ms/step - loss: 0.7399 - accuracy: 0.7450 - val_loss: 0.6992 - val_accuracy: 0.7568\n",
      "Epoch 2/3\n",
      "860/860 [==============================] - 62s 72ms/step - loss: 0.4949 - accuracy: 0.8218 - val_loss: 0.4726 - val_accuracy: 0.8182\n",
      "Epoch 3/3\n",
      "860/860 [==============================] - 62s 72ms/step - loss: 0.4432 - accuracy: 0.8391 - val_loss: 1.0101 - val_accuracy: 0.6910\n"
     ]
    }
   ],
   "source": [
    "model_history_b = model_b.fit(X_train, y_train, epochs=3,batch_size= 64,\n",
    "                    validation_data=(X_valid, y_valid))"
   ]
  },
  {
   "source": [
    "The second model has a slightly better accuracy because it does not have the pooling layer which actually results in a bit of data loss. Although it saves a lot of computational time, but it also decreases a little accuracy. \n",
    "Not much, only a little."
   ],
   "cell_type": "markdown",
   "metadata": {}
  }
 ],
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