Iris_Dataset_Deep_Learning_Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This notebook contains Iris Dataset project to diffrentiate between 3 varieties of flowers.\n",
    "\n",
    "Attribute Information:\n",
    "\n",
    "1. sepal length in cm \n",
    "2. sepal width in cm \n",
    "3. petal length in cm \n",
    "4. petal width in cm \n",
    "5. class: \n",
    "-- Iris Setosa \n",
    "-- Iris Versicolour \n",
    "-- Iris Virginica\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Iris.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x177622ec748>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eDZSpMZk+0xDFBblkCILIKxKT0M/VD3Xj6mI16P1c/WJGrN7DPPerc/HigRdTrjm3gll0MhnjyKi+PRt1yH0Rq1Fpt10cfXPb42UnEArg0XcfxV2v3YXK1ZW467W78Oi7jxbMd0OyQxQS+ppbqyUcTskJn11b3+uyubBwz0KNzvXYPcJGWbQOCJH+7+fuh+ph1VoZHLsAaw6tsZRloc+YS6Y+vKOnQxNhD0VChushV71viOKEIucEQeQVZayI3vvdz90PXeEu9HP1i3mYW/wtiPAI5t02LxZBycaMcjMDPJnokNn7W4n4EomxEpXuCnVhX8s+LP76YlzmvAyXui9h9+ndGPP5MXGR80KPTJPsEIVAhEcQDAfR2dOpkcVnv/5swpnQANAV7kL94XrcPvh2AEB/T388vfNpbD62OXZN45lGQ32rOAJoHfRtRPpfH5V229yGfW3UiDLmloxbEid/Sn347B3aM4viAADkCPuj7z6K5bcvt7QeCEJN1oxzxti/A7gbQAvn/G+jzy0EcA+AbgCfAPge5/wCY2wIgEMADkdf/j7n/J+zdW8EQRQOwjTxHbPxxC1P4Jldz8TSzwBgfMN4hHgo9lo7s+Ph4Q/HvacyriTVDdHMAM+EcWSW8k5kHo/dgy/2/yJ+9u7PYt+ZqPkUkFwKvEjOAKQle4kg2SHyjWLE+Hv8qNlZE2eQLP3GUo3RJEKJiC/ftxwA8OqEV9ESaNFcM+KqEWBgQn3rsrngsrloHRBx6A32zp5OSw510VlkzaE1cYZ49bBqTdDAzAHgsrliMkmp6oRVsqnF/hPAt3TPbQXwt5zz4QA+BvC46u8+4ZxXRn/IMCeIPoJRpPK6y6/TdDe1msaYiW7WRk2I3DZ3xhp8UVpb9tCnJgZCAWxs2qgZj7axaaMwrdFj98SN7BMZ8kZy1t7dnvUO0iQ7RD5RjJhryq5JOctEr89XHVgVN2O6dmwtXv7oZSzavQg1Y2ri9C2tA8IKVidmiM4iK/avEDZqs0k2jex1hbuozILIGFnTZJzz7QDadM/9ifNY2Ot9AIOy9fkEQRQHRka30vSt8UwjPA4Pnt75dNzhTbTBZqKbdVe4K9aESDHm1GPbrB4KaQ5v7hEZzR67B3cPvVszp/zuoXcLjYiucJfQkNeP7DOSs4vBi9RBmihpFCOm6WJTygaJ3mA6GzgLp+TEnFvnYM+0PagbV4eGIw1Yvm853jj2Bu567S784E8/AABLRjjpXkLBqkPd6CzSFe6K2+/18qUe+6ruj0MOIyIV8llz/n0Av1c9vo4x1gjgEoAnOec7RC9ijD0M4GEAGDx4cNZvkiAyQSnLbbop5KI08Tm3zkFdYx2AqKF+oQlvHHsDEURQM6YGg8oHGX5WJmqG9SmXgHEKvRFmHd+LZcMuRrkVpSae7DiJTcc2aTo+b2zaKOxX4LF7UD2sOmHZgpGcXVN2TdxzVAebW4pRbosJxYhZdWAV5tw6B0+995Sm47oV/aY2mNw2N5ouNmHBngXYfGwz7MyOvdP2WqoTFlGsupfkNntY6U2iGNj6CQJ6mRHJ1wvffCE29lXZY+o+qMMzY5/J+r+NKD3yoqUYY78EEALwu+hTpwEM5pyPAPAzAGsYY5eJXss5X8k5r+KcVw0cODA3N0wQaVKqcpuJFHK9V3vJuCXY2LQRW5u3xlKKVx1YBQDYfGwzJr4+EQAMI9aJ0t+tRFQy0Qm4FOZRF6Pciozm5/c9j+ph1ZrI+YTrJwjlJ90oy8mOk3HPFdN3XgoUo9wWE4pD9WzgLJY1LoulnM+5dQ7qPqiDy+ay9D7Kmpq5dSYmbZgUawan9HhIVQcXq+4luc0/LpsrboKAHpF8nWg/ERv7qkwVaAm0GMocZXYQZuTcOGeMzYDcKO67nHMOAJzzIOf8XPTPeyE3ixuW63sjCCI5Uj0E6TcmoNfYLnOUYdqXpsXGqm1s2qjp4JvogGZWX2bVmWC1Rs2MQu/6nS+yfSgRGc0tgRb0hHs0qepmRoSVsgVRX4IFYxfgctflackNQRQ6igOrblwd5o2dh2A4iF/s+AXGvzoeLYEW+Hv8lte1YX8PuztlHUy6t2+T7T3GyAFsVV4zEdQgSpucprUzxr4F4DEAf8c596ueHwigjXMeZowNBXADgKZc3htBEMmTyiEoUcqhOv3M6/Cielg1dn22y3JndLNu1srYH01n+O2zsfQbSzWpbpnoiJ2JkWulRjbSTfVlFW6bO76789gFWHdknaZMYdTVo9L6LtR9CZQ0xnVH1mHGTTOogzRR8khMgsfuwemO05i/az4azzTGMp3cdjfautosrWvROlJGZKaqg410r7/HD6/DS+uyhElnj3HZXOgOd2ues0v2OCeuSL5aAi3wOXyW5FU4oUZwDiH6LiwavM78GzNWD+DrAAYAOAPg15C7s7sAnIte9j7n/J8ZY5MBzIGc6h4G8GvO+R8TfUZVVRXfs2dPFu6eKGFYvm+glOS2s6cTP377x5pNatTVo0w3mWRfk25Nu/69Rq4eGTeObe+0vRk/qIUjYbR1teGxHY/FDgm1Y2tjnV6TpCTkNhV5McPoINbP1Q9d4a6YzNiYDW1dbXG1hP3c/VKOpuVSlkSfnc1xbRmkJOSWENPZ04nVH63G7YNvjxnWbx1/C7cPvh3zd823tK6zsY5EeqF2bC0ajjRgxf4VVgw2ktsiJd09xopuDYQCON913tJ+YjRuM0t7R97llsgMWYucc86nCp5+0eDaBgAN2boXgiCyQyozv5ONtqsj6eoNLhgOIsIjSRkouYxmm0WE9E3I+gqZTje1EoHwOXyI8Ah8Dh9qxtTgmrJrcLLjJHwOn2Fau9UDWj4yI4q12RVReoiaJyrNPNXrWpTdojjPlNryTK8jh+SIrfeOng7U/7/6WOYMRSpLl1yUNLhsLkv7iZGudtvchpkdZc6yjN0nUbzQTk4QRMokap4lqv1Ktdmauk7riR1P4HzX+aRrtjJRS24VpeO7ukHMiv0r+nTdYyYa7amxehBTehlUuCvAGEOFuwJljjKhMWu1HtCwVtbmTunfYpVibXZFlB7q2vM906K9HBrrsPnY5pixoWQQKetp9UerNY9fOfRKfO+GNHVyIBTAo+8+irteuwuVqytR5ihLufM7UVyks8dY1f0Sk1DuLEd/T38wxtDf0x/lznKhA1ekqz12D+bcOkcj83NunQOP3UNN4ggAZJwTBJEm6qZZSmREMcJFG51SE5zsYUy90T1080N48n+fTNpAsdqJOxNk2hAtBdJ1juidPVb/jyM8gvPB85j1ziyMXD0Ss96ZhfPB84ad+q0Yv+rMCKXJXMORhrh56JmGml0RhYTEJHgdXpzvOo/5u+Zrpmy8cugVdIY6Nevp9sG347Edj8UeL9+3HA1HGlA3ri5jOlm/RtKZx04UF+nsMZl2fBrp6q5wFzY2bdTsHRubNsIf8lOTOAIAGecEQWQIvde51d8q3Oi6wl0pGcjqjW7o5UMtGyhmneGNOnFnAmVmqvqQYHUGcKmSjnNEFNUIRUKWDmLJHLqsGr/5yowgpw9RiDgkB579+rOaCPryfcvhc/g060mku1fsXwGvw5sxnaxfI6sOrIrTxTRFoTRJZ4+xqvuNIuzhSNiS81hiEu4eerdmtOfdQ++Gx+6hjCgCABnnBEFkCL0BdE3ZNYYbnZVRVaL3VzY6q5GQfI4sUdelKTNTzeqc+wqpfPeA2MB+9N1HYx1y1QcxAJpDUjLRZqvGb76M5FyWZhCEFZQ08jJHGapWV2lmlh+7cEyzTnIRxdavkbOBs0I90ZcdpaVMOnuMkWzqjW6Rs7cz1GnJeQxAGDlvvtis+WzKiOq7kGYiCCIjZDuVUF3j++KBFy1FQvJZn2u1Lo2whpGBrThB1I0D9Q6Zjp4Oy7JoNeMhX0ZyLkszCMIKytoU6fxtx7dhwdjedfLW8bfiaswznVEkWiPlzvKcZEwRxYuRTg9FQpr9xGgv8jl8lpzHLpsL1cOqNZHz6huqse34Ns17UkZU3yWnc84Jgihd9N2rlVRC9biRdIwXfffzM/4zeOa2Z3Cl90qcaD8Bh+SIe0069bmZGFelNhipK3B6WO2OLurgvubQGiwYuwCzdySeKuCyuVD3QZ2my37dB3V4ZuwzcdeqO0Kf7DgplMFksCpzJFdEIaGszVUHVmHOrXPw1HtPxdZZ9Q3VaGxpjK2nrnAX1h9Zb2l9KaSii2mNEMmiduoosiYxCY+89YhmPznRfkK4Fx27cEzzforzWJFVtRzqP8dtc6N6WDV2fbYrrrO7kv1V4GMziQxCxjlBEBlBP1ZNnUqYiY1FqfFVxuEA8mzQPQ/uwV2v3aWZZaoc5gCkNKaHxlUVHlbH9okcMiv2r8DMm2daksVAKICWQAsmbZgUe27U1aOEToBH33006zPbSeaIQke9Npc1LkPNmBoMKh+EUx2ncIX7CszePjs20/nVCa9i26fbMH/3/NjrlfWlXpvqmdC0Loh84bK54vaT5/c9H7cX1Y6tRcMR7URos7OGyHkkMtjPB8+T7PdB6NsliBJHNM4sG2Q7ldCoHqzpYhOA3oi4us786Z1Pp9QIyCwdnkac5A8lUq3U8Isi1UZy0hXuipNF0dqwmq6ezZnt1BCIKBSs7B9q3T/vtnkIhoP4xY5fYPyr49F8sdlScza3zS3sD2K0Lvw9fho5RaSFXrb1Y/+MSqJaAi3oCfdoasa3Nm9F9bDqtMqc9LXyXeEu2hP6KBQ5J4gSJtfRuGymEooip3NunYO6xjoA2vosdVpzBJFYJMdq9N7I8HLb3Ji5dabp/2Em0uGJeKxGqq1G2M3WRj9XP9SNq4PX4YW/xy/8Dq2m2VuFRqQRhUYy+4fyeObWmZo1odScKyUlRhlVonKU2dtn44VvviBeFw4PRq4eGXdPpH8JK4hk+7lxz1kriRq7AOuOrNNk8Y26ehS+PezbCfeNZKA9oe9CGosgSphSisbpI/NLxi3BxqaNsbm6igGm39A2H9uMia9PBADL0XuzKL3Z/2E+u8OXOlYPKlYbppmtDSsz0TPdEI5GpBGFRrL7h2hNVA+rRj93v4QZVUbr21AXX2iKuyfSv4RVRLKtH/sHyCVRevnt5+4njJJ3h7vj9g39eLVkZJH2hL4LGecEUcKUgudVvamp6xA557jzujvjUpwzsaGJDplzbp2DVQdWATAfw1UqzpBCI5nv1cooHbO1YeU7zHTXdBqRRhQaye4fRmvCJtmE61Gt2/09fsOZ0Ga6WH1PpH8Jq4hk22pJlE2yxcm5z+GLZXaZjVdLxllEe0LfhYxzgihhit3zahQJUVKc73rtLlSursRdr92FR999NGa8p7qhKYdFAJoRKDVjalDXWBeb3Wv0f1gKzpBCxeqIM6uYrQ2r32Gq83RF0Ig0otBIZf+wuib0uj0QCgjXN+cc/Vy9kcu6cXXY2LQxpovV90T6l7CKSLb9PX7Le4xezkWN40Tj1ZJxFtGe0Hehb5ggSph8e17TbUZnFAkxO4SluqHpD4uPvPUIAqEAOOfwOrw4Gzib8P+w2J0hhYwyz1zdEE45FOmx2sTK6CCWr+8wk8Y+QaRLKvuHVZ2v1+0V7orYCEOlyVbdB3Vw2V04Hzwf0+1eh9ew8RbpX8IqItl2Sk547B7NHqPsL4lk2kj2ROPV0mkSR3tC3yCr3zJj7N8ZYy2MsYOq5yoYY1sZY0eiv/tFn2eMsTrG2FHG2IeMsa9k894Ioi+QT89rJur/kq1DVA5hqWxoRo6ArnCX5f/DfDtDShmJSSh3lqO/pz8YY+jv6Y9yZ3nc92BV7tTzzNXGAACh0c7ALN1nrqYjEES2SXb/SEbn63V708Wm2AjDytWVmLRhEloCLWi60KTpzm52T6R/CauI5KjMWYbaXbUIhoMAgGA4iIW7F8JlcyWUaZGzt3ZsLbYd36a5LllnEe0nfZNsn9D/E8C3dM/9AsBbnPMbALwVfQwA4wHcEP15GMBvs3xvBNEnyKnnNRIBgh0AjyDQ40+7/s/ICBfVIaZ7CEsUjbfyf0hpaNnFyvdgWncalU0EOzTzzNXGAAA4JacmeuKUnMIIvR5qSEUUHSqdjWCH/FhFMvtHMjXfet0uGrGm1JYQq7QSAAAgAElEQVQr3dmVtWR0T6R/S5wEsposotFloj3hRPuJhDItcvamO16N9pO+S1Y1Fud8O4A23dMTAbwU/fNLAO5VPf8yl3kfwBWMsc9l8/4Ios+Tyc0uEgH8rUD9FODpganX/6nuSQITRjEBZPwQlqmUSEpDyyIW5NVU7qKyifop8IS6hQ4eAFh3ZJ0merLuyDp0hbsS3h41pCKyQoaNEs37qnQ26qfIj7v9KX1WMjpfH+VWRqzVjavDnmnRTJZonw+lO7uVtUT6twixIt9GspqptQDjzLfn9z2vuU4k0yJn77ZPt2l61yR7TqH9pO+SD611Fef8NABEf18Zff4aAJ+qrjsRfY4giGyQ6c2uxw+sewho3gFEQgicO5K8sau7J6PUY5fNlfFDGKVEFjgW5dXQyXLhrzHZRPMOSL+fhgqbG0tv+RX2PrgHS2/5FSpgg0tyonpYNebvmo+qV6owf9d8VA+rtiQH1JCKyDjZNEp0OhvNO+TH7Z+l9FnJTlTQO1jLneXwOrw433Ue83fNj43J1ETQaS2VFlblu6dTLKvRBq6ZQCSTPocvllGlIJJpo/OD0isllXMK7Sd9l0JyKYoK+njcRYw9zBjbwxjb09ramoPbIoj0KUi5NTqY9fhTez+nFzi+M/bQ898LsGBMTXLGrsDAF6WZZcNzTCmR8RSU3FqUV0Mny9vPaN/v+E5Idg98z1VCmlMh//79NEg9flS4+mHpuDpZDsbVocLVz3LfAmpIlX8KSm7TJdN6Wo1OZwOQH/e7NqXPStbBKXKwKnrYKIJeymuppOTWKoby3amNpjt9Yll1+jJ6O6Iu7FZkOhvnB9pP+i72PHzmGcbY5zjnp6Np64pL6gSAL6iuGwTglP7FnPOVAFYCQFVVVZzxThCFSEHKrdHBzOlN7n0iEXmD5RFg8Bh5cwUgHWxAxcD/g6XjnoPH4YuNujHdrEQG/jdrMHtnDRrPNGLEVSOyGs1WNmQAsd99mYKSW4vyKjEpZlx7HF4EevzwQILUflr72sFjgLMfC97PB6mzFb51DwHHd8I3eAxQ/SLgHQhI5gctxTiZvX12TuSVEFNQcpsumdLTIrr9Gp0NILouDqf0WWoDRemenlDnG7yP1+FFW1cb5u+aj8YzjX0ik6mk5NYqhvLtA16aIP958Bhgar1YVrs7AVd51m4vGZnO9PmB9pO+Sz6M8w0AZgD4/6K/16ue/xFjbC2A0QAuKunvBEFkCMWQdnplb7Rws/MDrjLr7+dvlT3d09cDE5cB638U21Cl4ffDZ/cCqk3LFN1hsdfAjxpaKR72iAJHLZfdfsDhjTeEjQwJvbxGIpD8Z7XG9QOrZQM7+hwGjwEmvwjsfUn7GcphT4nkAL2RnKlrE66LTBknBBHDTO4d3sTrxgyHN35d3Ps8sK0m/rMs7gmZMlBoLZUgIj3fYyDfbc1aHcxssmy+/kOtrDJb1m87X057WgN9l2yPUqsHsBPAjYyxE4yxhyAb5f/AGDsC4B+ijwFgE4AmAEcBrALww2zeG0H0OfS1Xe//GzD5BWDIWECyy7+rX5Q3TKuoU9J6/MD+PwB3LgCebJF/7/9Db1TdSnMh5bCouidp5Az47F5q8FOqWK05FMiGLK8ebfqjqDbx99MAZ7lsYP+qVf7tvhyonKJ9P+Wwl0akkhpSERnFTO7TqUVXDCXvAGDKGllnT1wG2F1AR0vqe0IGobVUQhjpeYdHLN/v6sqQAucAuwe4p06W1Xvq5Mfx1a8lBa2BvklWI+ec86kGf3W74FoO4JFs3g9B9GnUhjQAvDtP/j1ljRwVSSXyok5J67ogGzt6z3awE6gdIj82Sg9We9Sd5XIKm9OX2j0RxYVeLo0i1ZIky87UtarIiwfwn9VG/mZsEBvXDjegHGxcZbLMucrlQ16/a4Hzf+1Nj0w3o4QgMoVQ7r3W140IdcbT8Z3ALz4FLp2Us57Kr5bXRMWQaK1vmXwPVrJbCMIIM3nVyzeT5KaEahrrgVtm6t40AtjdUTkluSRKB5JggugriGq7ttfKBzkmyb+tbGr6sSeDx8jPb/21HHXRe7b/9IR5cyG9R33N/dFoO6zfE1G8JFNTK0laee0JxEfJ25p7ZVJBMa7j3utyoOxKgDH5t+tyWYb1GSWTX5AdAQSRD/RyL0np1aLrm3Cpo5LfXiFf03kOkGxyq95uf9bHWBEljpm86uUbkB37ah08cjpwbAegjLQMdwF7XgK6O0guiZKDTr0E0VdQahfViIwWM8xS4z9aL2+Wvv5RY2cgsOVx4GBD7+tFh8dsdiMmCp9k5FLvGBId+N59xiAN2IKxL0mywb/3ZW15xt6X5ecJolBIR5/r183WXwPQGTSSHXhvuazn2z8jHU2kRzLy6nADb83R6uCP1gPXjgY2zQbmXin/HjkDeH8FySVRcuSjIRxBEPlA1Pwn6RrzTnFq/AOvyCnB5/8qG04cAFh8apooPThRBIjSKUsbq3KpT8UdPEYuydCnoLd/Jqfi6tOArcqM0ytnlCiyDciGyt/9PP1/K0FkinT0eXendt0cbAAG/B9g9MOyY9U3QHa8Kmug37XZ6xhP9A2Skddgh6zHn1cZ80PGAiMe1JYh+QbIuloNySVRAtAJlyD6CuraRaUploXxUBpEs0a31wLuy4A5FcDSEXLzrR6/SSMj3cZp5lG32ixMjz7CSmluhY3NqS2HsDnjrxFlWDi9chMrtYxNXCbXISZbrqGgLtVQGDxGfp5kiigU0tHnSudr9bqpnAJ0t8vjq1xlWqPn7GHjNUHrgLBCMvLq9BnodZd8xlDOGuf/anB26CRdTRQ1ZJwTRF9ClMabDErERY2yGSqo68iMNmO18cwkYyM+lZT3VA16Ij/0+GWHjvrQpTh41IgyLM4eMZgQkEYKutHB0OlLrzM2HRaJTJOqPhelDb81Byj/vLzG9Hp++6J4Y37iMjm6TrqVsIpVee0JiPX62SPa64xKmCIhga4Okw4migZKaycIIjFKarnRrNGuC73XqlPXlc0Y6P0tSk9+YLW4Q3sqTY/S6WJM5B6r37Fo3vNHf5TrDhvSKNXQoz4YDrhRjhru/wNwc3VmOmObTS0giFzQ7RenDSsRcmaTje/1P5JltqNFjlpOrZfX1tnDsjF/sAFo3k66lcgsDi9QNUOrMye/COx9SXudqISJSXJTWb2unvI7YO13SQcTRQEZ5wRBmKM2LsqvBu6Yr637cpYDu1bJnmsrxpHIeP79NHmDVXdrBcQGWaKxVul0MSZyj9XvWFSzWDVDntOsry8HUh+v4/DInYEbfqA9yG35pfa6VDpjA+QsIvKPaC1NXCY7oapflK/RO6j2vASM+b9yM65IqPe99OuAeoQQ6SIaH2hzyHq5ebvKYH9BdiQ53fLrXGVyZFy4/5eRDiaKBjLOCYIwR29c8Agw7sne8VMODzDmh3LDLP1hTHRQS8Z4TqXpUSoGPZE/rH7HRvOe9dkZ6Uaq1d3aFcMkwq01NxRBziKikFB0sneA3FDRVRZ1ZPlkPa6sO33kcuIyOUPKTLdSlgiRLSS7LKMPrAbclwNdF+Xn9P1JlJ4hehk9+7H2OtLBRAFD2pIgCHP0xsXBBmD5KNUFTFxHZlT7bdZwS08qTY+sNqIjCgOj7xiIrxG0UrOoniig6VPQGX+tCKVb+/Nj5Br458cAW59MXaYyMcKQIFJF3e9APa987pXA2u8Ana1yVFGy9a4pZU1OWaOtSd/66/jac/U6oLGYhBlWe28Izw5no85Yh3yN5AAcvvg9QNQzZPILcgmUGqMzB0EUAGScEwRhjpFx0Xo4vjGW5iDYIT6omTXcEpFs06NMdKUncov+OwasN/WLm30umChwfKexfOkRyXv7Z3L5RioyRc4iIlfEGT9h7TpKZl65JMnyeumkPFP6o/XR2nOPbLSL1gFliRBGmDVq1cutoYM1YME5K2gmd3yXnBJv9cxBEHmGTqsEQZgjMi4mLgO2L9RGJfWbr5GR1OPPfIdtPel2pSfyi9Xot+jAZ2WigPr1+kiOkTGd6ng2chYRuUC4FnQO0mTnldvdgKtcN+bQLkfaReuAskQII8x0ul5uDR2sFpw8SjO5TbPl7JBNs4HBo2UDPZtnDoLIIFRzThCEOfpa31ZVp16gNyrZratNVzr/6mu/mC2+4dbkF+Ta9UxBTYmKG6vRb1GztWCneKIAs2lfa1YfK6rHTUd+RFMLCCKTiNaCs0y7jox0cib6cURUYzGT6RFC9A3MdHr9VK3ctjWnJ6c2p65prRe4drRALj2pNw4liCxCxjlBEIlRjItgO7DpXwWbZmf85rt9kXYcj7IhAvENt/a+LDcjyoThIjK6Jr8I+AbInnLagAsfJfotkjNXee9zojTarU8Cdy7UHs5c5fIoKPVBDNygi3p9r6FDTa2IYkG0Fs5+rF1HyrxytePKzHju8cuTNNTrcMhY2VHr8PY6QHu6gO723oke99QBFUPk9SqqCyb6HmY6XS+3yvxyK04evSMeXCyz3/mDrpmoR65jJz1PFCA5l0DG2I2MsX2qn0uMsZ8yxmoYYydVz9+Z63sjCCIBypxzdcqvEpXUpzQebJBTx/T1iQ53fMOt7bXp1yVGTOrVGh6SD6pmtctE4WAmZ2qM6sNtLnmSgDJRwHWZfBCzlDrpo6ZWRPEhWgsf/VF2TCrrSJlXPnGZnN47ZY2xMRKJmNeQG9WyH/gvYOkI4KUJABgZOoSMoU6XUu/xISrlMNLrDl1ZUk+A9DxRsJhGzhljk8z+nnP+arIfyDk/DKAy+v42ACcBvAbgewCe5ZwvSvY9CYLIEeoaRE1UMjpnVDSH2qlr9GU06sRKyppRuro6Wj59vXhzHjCM5psWC4nkTMFoDJvd3XuQU1LT9VFyw9RJQSSHmloRhY5oLVTNkEs0ptbLRktbM7Dll7LxYxQljESAUDQSHjSIdurXU7K17ETfw0ynW9HhgDhKblmv684X1LyQKGASpbXfE/19JYBbAbwdfTwOwLsAkjbOddwO4BPO+V8ZY2m+FUEQWUeSANfl8hiT2JxzVZq40RxqNUYGFZN6x/2IXmdWI6yutzSqqzx7WP4zbcCFTyI5U19nReZEBzFR6uTkF3ojOSKDxH1Zdv69BJEuZmvBVS7rz7IrgUkrE+vYYCfwx1ny9aLSJH10Mpu17ERpYKbTRXIL6OrBBWnoMzaknhKvZJqQzBIFiGm+Eef8e5zz7wHgAL7EOZ/MOZ8M4KYMff4UAPWqxz9ijH3IGPt3xli/DH0GQRCZxKwTupUu6XHdq+vlBi5r7hePzTJLV1fS0NTGl1LrHtddPpqUQ92DiwORLIm6q1uROVEH9/bPgAjXdvDd+7IcsUlm1B9BFArp6mbFyalEwg82yM0/lTWi6Orzf9WuJ6WWncYFEmYYyaCVUZqi0axGet1Z3ltON2WNnD1iFCQgmSUKEKvFQEM456dVj88AGJbOBzPGnAAmAPiv6FO/BXA95JT30wB+Y/C6hxljexhje1pbW9O5BYLIGSS30BpWPVHvOJMAsN4GLnqjW11T5jBJQ1PXWyoHynvqopvz7+Ta94/W0wacJAUlt2ZzchMhqnesflFuHqfvfZDMqD+Rs4DIOwUlt4WKSHYVJ6cSCQdkffr8GODliQCPNtt6Z67WgaXUsn97Ra/DlRprJQ3JbRR1JpxyJtBPHgCArgviOnZEgLXfkfeJtd+RI+563Ww04hIgnU7kHaua813G2BbG2D8yxmYAeAPAO2l+9ngAH3DOzwAA5/wM5zzMOY8AWAXgFtGLOOcrOedVnPOqgQMHpnkLBJEbSlpurRgoZoaVWe2XKF1djRIF13vBO1oAl0/O+XGWy53gacZ00hSU3IoObFYb+Djc2gjgnQuAcLccZVGjjPrTz8mtmhHv0EnHWUBklYKS20IjEi0dEsmuEokUZR+p09lFEfU3Hwee/RI1gkuDPiu3+jOE2eQBNY318v5+T50sh/fUyY/fe97aPmElYk86ncgDlrQn5/xHAFYA+DLkyPZKzvmP0/zsqVCltDPGPqf6u28DOJjm+xMEkW2sGihmhpXSIE6NUuObKF1diYIbecElyVo6J5E7Uo02p9PAJ9ghG+LqKHljvbaTtSJPdrexLKlJx1lAEPlA0dfq7upq2WU2eQ10tABvz1VlH0VTg9VpxOqI+sVP5ccA9fQgkkN0hgi2G0weeEGrryunyLXo6skc7svkDCg1VmWSdDpRIFiecx7tzJ5uAzgAAGPMC+AfAPyT6ukFjLFKyLGuZt3fEQRRiKg3MyC+G7q6u6qRYcV5fNOhicvkv1N3XlUOf7EZurqmRpLK+00NXQoTs6Z+iZwmRl3+RY3a9F19nb54Gfvy/YCvv3EzuUSyRN1+iWJD0ddGEy0cbjk9PbYmOoGui3Iq+/GdwNcekw2khh/0rqN7nwe21fS+DzXVIowQTVsRnSHeXyE7Tht0kwf+8rqcrTHgRjmT7q05coNDFp3i4SrrNexFUzhc5eb3RzqdKBASjVJrh2wsx/0VAM45T6l1LefcD6C/7rlpqbwXQRB5xGwzUxtidy407owK9Nb4Kpvu/j8AX7q7t9HQ6z+U31edrk6Hv+IjkTPHDJGBLWrUJnIAKH0H9DI25odiI9xoZJ8a6vZLFBv6mnK97LY1y/rVOzC+HwgAvDtP/j1ljbzuLn4qG/MdLXI006gzNkEYOWZ9A+PPENtrga/9i9ZxyiTg0B+BzbN7rxsyNl7fKv1FlDOD4kBitsT3SDqdKBASdWsv55xfJvgpT9UwJwiihBB1S1W81GpDbPtC45R0hze+xvfL98uGuVLbOLU+uZpxatRVmKQTmegJWGvUJkpNfH8FMNJCHTlgUqoR1sqUw0PdfoniQjE+jCZavDNX24xTtF6318qGuf+s7Cjb8sve9HdqBEcYYZQyblTW1hPQlqMp89Dj9K1Hp5cF/UXemiM/nwjq4E4UCJbT2gmCIOIw81I73L0HOyUl/c6FwMAb46OR6jmnbc3yZqq8pv0zAEzbtMWMdFKnieySTmRCceIkml9rZFDoIzHCWboGaZbrHpKj72u/q5OpAYlnrBNErjHK/FCMj3UP9daUVwwBLhzv1bmSXX5dZ6s8Ns0oRVg/m5ojcdow0XcxdMz6rM0llyRZ305ZE01fj+pt/ezzKWt6+4soDBkrLn/SYzRznXQ6kWNI4giCSB0zL7V6vBkgH/w2/WuvISba8DiXN2slTTIVzzU1dSlc0olMmDX9U6OXO0AciQHEEXLDQ2SZQKYC1GyQKCzMmnSq19CklYBvAPDfi4DnvtzrDFX6OKx7SDa2RRF2p08ubgR6S4xI9gkzzPSyFb0eiciGuHpEWnenYOSa11hmrUANZIkCgCLnBEGkjroLtoLipXaWxXvEJ0fT0Lr9AA/LG2ZPF9Dd3nvd1x6To5Su8tQ819TUpXARRj981r9fK03/1NFBs0hMT6dBhHyNOFp49mPt60mmiEIkUV8HZQ1FIrLxUjUDaN6uXSvKyLSzHwMfbYzv1XBzNbB8FGUlEdYx08tW9LpIrkWzz88ekTu7m/UXIYgCh4xzguhLWGl0lQxmTbr0KWLBdrn299zHwO1P9abCP7Ib+OMsbdOh5u3WmoSJoKYu+cGKbCnRj2yWHFhNTVQMEDVKmqW+I/XkF4G9L2mvJZkiCg2jOnG9I0ld+lN+dW96+6XTcg3515/orU2//Smtfle6s6szSFLV1UTfwuaUZa3ftcD5v8qPrWI2+1y913/0R7m/iLrT++QX5KAAQRQJZJwTRF8hUS12Koa7ukmXyEuteMSDHXK9bvMO4Ic7ZcNc2VD7XZvZSLfVyCmROazW+afTrT3TKM0M9U6cHj+w92WtTB//s3zg00cYSaaIQkFZg0EDuVYcSZEI0N2hXYcH/kvOeLpzgfxnHuntJaKuTe/uBDY+2psCD1AGCWGNnk5t539Alrmp9dZ6FYic7srsc7UjVRmRqdbfe1+myDlRVJBxThB9BTPDyOFNrYlaKk26BtyoNcaNxvokG5VUOxec5fKm7/RRU5dcYNXozkXJgVVHgVkzw+21vWOjADn998kWahREFC7KGiy7Mj6bSdHJytrwDhCvwwE3yn8+2CDX3Kp1KOfyT/tn2tdRBglhBbNMJSuInO4jZ8iO07gxrPdoS+0kO/B3P8/cv4UgsgydLAii1FHGipkZRqk2UUulSZdijCsos8zTGV+ib4K05n753qlZUW6wanQbNQXqFshZquPwrMqy3SU7ce6pkw3ve+rkx8o96e+Rmr8RhYZ6jShrUBk/qTTpVI83U9aGXgcD0b4Kh3sfq6dkOKJdsd//N+ORmARhhtnYVasoafGKvnaXA9dUakdkjpwuR9TjPocawhLFA50uCKKUURutrQYHsm5/ehFNK91N1V26dyzWGuMdLXJam9EscytGGnVozy9WjW6r3drNOk4nwqos9wSAXauAcJf8ONwlPwbSm3WbqlOBIJJBv0bamnvX4MEGOXL48kQArFeXKmtDNOd88gty8zeRzCv69d15WsN/yho5Ck+OKiIRSqaSWuaUTCUr9PjltPilI4A5FfJvmyt+WsxH6+VsvkTz0EkvEwUMpbUTRCmjNlq3LzROd8xUEzWjunV9k66eLuO0c/XnWU1Rpg7t+cVqnb/VZm3p1KYbynInANb7uU6vOH39734O2N2ppbBblVeCSBf9GnlnbnyZhn4NKmtDqRlX0oG7O2VdPOaHsvyrZV7fZO5gQ+889Cdb5Ig6yTdhBbtH2xDOnkSTNqOGcKJpMSOm6fS3J/uNSAkig5BUEkQpoz9UvTUHuHNhfIQ6mfnTRpHBRNFOdYTd6ZWj5YlShM0i4vr7sJouTWQeq+UNyrX6TAv9d5mOs8VIliMhrWwG241lJtVZt5TBQeQK/RpR9LtRBhKgXRsfrZfTgP1n5XUCJl6X/lZtVF5BSYMn+Sas4HADWx7XZipteVx+3gqitPiP/ihP0tDrertLex3pZaLIoMg5QZQy+ijiwQY5jVwfgbQa0TSLDGajE7eZkdapuo+vPRbftdXMuZDJcXKEjJVZtSJEMjXld6lncohkmUlyHwK1bL6/Qj7YNTyUWGaU+0wkN5TBQeQKUYaIuk5ctE5iayOatdTWLI9Oa//MfLqCqMncxGWyM0CRbx4hfUoY0+0XR7mtZueJGnhWTgHclyeOks/YQHqZKCrIOCeIUiaZsWJWjCszAzwbholRinJQNwpISU2eska+91ScC3SgzA8imUrWcNajl2UeiZfN7bXA1/7FWvq6odwMkGvXldeDZ6Y8hCASkerISEkCwICXJmjl1Gy6QiQkP77/ZdkYOntYNswPNsgGVlszsHwU6VPCmHRHnDrcvfXlSmf2t+YAk1YCLBp9d5XJGVH6/UTJ/CC9TBQJedOejLFmxtgBxtg+xtie6HMVjLGtjLEj0d/98nV/BFESJJNubAUzAzyZTtxWEaUo3/u8+D621yZORTZMb+ukJjH5wui79A3InNwadQru8VtLXzeSm+4Obap8JJReMzmCsEo6ut3ydAXVujnYAGz6OXDppJwO/9H6Xn38zlxKFybMSfcs0t3ZG3mfUyH/bv9Mfl5TEiUY2fbuM6SXiaIi367NcZzzSs55VfTxLwC8xTm/AcBb0ccEQaSDqn424vChoyeCCOfoCIYQifDk3svMAE+mbj2Ze1dv6PfUAdtq5EYwqTgCjA6lDm9yHcGJzGEkU5kcXZZup2BDY6ZMa7D/fhq4sxyRKfXgv2pFZEo9IhRFLGoikaiuTFVnZpNUeyNYdaTq101Hi1zPO3FZb7d2JYKuQOnCeaOgZRVIXV4BEx3OtA5SkSO2/TN5TGamnL0EkWUKLa19IoCvR//8EoB3ATyW7psO+cUb6b5FRml2fyfftxDHzdcNzvctpM2BGQfyfQsFTSTCca6zG7PqG7G7uQ2jhlSgbuoI9Pc5IUlMeL2/Jwyv0wZ/dxhehw2SWWqa1br1ZFE2dB6RUyeVFEujzvNmGKXJnz2cmRp5InnSTXe0gt0tNyBUdwp2lcvPW8FQbj7WXnd8J+Bw47urdllaY1YQrsMU34tIDrHOrITPZYfbXsTfg9U1p6QST1wGXDFYW6M+cRkg2aJ17iooXTgvyLIaxKz6fRpZ7e9zFa+cqhHpcGc58N7z2hR2UW36vc/Lf59KTxSCyAP5NM45gD8xxjiAFZzzlQCu4pyfBgDO+WnG2JV5vD+CKGiSPbT7e8KYVd+InU3nAAA7m85hVn0jVs2oQplLqwpMDXkzAzzVpmBWUBtISqTmnjqgYoh1R4DoUKo0NgIo6pMPsuXU0X+G63JAcsiRlrIrk/sMkdxMfhHY+5L2uujINitrzArJOtSIzCLWmfswf9LN8Lnsxfs9WF1zShOv574M/O1k4Gs/B769Aui6KKe4A6k5SYmM4+8OY1b9vjhZXTW9CmXuQovDpYBQh3vkEig1gXPpjWwjiAIgnzkdX+WcfwXAeACPMMa+ZuVFjLGHGWN7GGN7Wltbs3uHBJEhMi23yqF95kt7MOyXmzHzpT0419ltmsbmddqwu7lN89zu5jZ4nba4NDj1oTQU4TEjw98TTi81LR30afMdLYDLJ7v5rN6HKE1enZZJ49c05Ezf5kKm0vkMQb0kd18udwvWpVmeD4Q1L1XWWCoYrsPucGGnr+aZTMmtkc78QoUXs+ob0dldxP/3VtaDaPTapZOyYa7MO9//Bzm9ndKF0yYdufW6DPZ3V2q6pyDRy2xPID6FvbE+OhpQhc1uPUuKIAqAvGlQzvmp6O8WAK8BuAXAGcbY5wAg+rtF8LqVnPMqznnVwIEDc3nLBJEymZZbU+MZ4tozf3cYo4ZUaN5n1JAKHD/nVxn4QUQi3NSQzxuZam4XS5OHbNx3tFCTGAP6qr4V1m7qDoZBbkePvUx28DzZAtxTh4izHL/bo922RusESLQAACAASURBVA2pgL87bPBJ5hiuQ5dN55gLFq+RmAUyJbedwZBQZx5t6YjqQ3vMKVrw9b6pEKdz6+XUYrXOrJoh917ItbO2BElHbv1B8f7uD8Y78kpGVkV9bqpmAO7L5Mi6EmF3XU5ySRQVeZFWxpiPMVau/BnANwEcBLABwIzoZTMArM/H/RFEoWMaBTeIqnvsEuqmVmLM0P6wSwxjhvbHwvuGY/HWj1UG/j74u8OGhnyqRkbGyGSENdOd7ImSwGpWitNhw+yNzTgRKkMEDCdCZXhl33lMGT1Ys8bqpo6A1xHv1LJyQDZzqGkdc/vyvzZLEK/ThtrJwzXfZ+3k4Vj+ztGYka5kMiSbyVQ0aHRuuWzokM4sOCQGLLxveNz+LjHEOfLau3osyWrBG/GGe7gtP9l9BJEh8lWIchWA1xhjyj2s4Zy/yRjbDeAPjLGHABwHcF+e7o8gCgKjunLl0K7UlwFa41lYWz69Ck6bhPmTbsYXKrwIdIfx5OsHsGH/qdh7xNLgOFA3dURcravIyChqslkjTxQlVnsz+LvD+OxSN257dnfsuTFD+6O6ajBWzagy7QVhtZbc67AJ1mEl5r1xSPN+JZe+WgAo2UavN57AgurhuKafB8fP+bF462G0tgdRO3k4Fv3pcG8mw8t7MtZroKAhnVmwuB29+/unbX64HbJRKuqZkEhWi6bfBckjUYLkZdfgnDcB+LLg+XMAbs/9HRFE4WHWKdjjkPDc1Er8RNOZNWo8Mximwn53Ve8B8mDNHThzKai5TkmDK3PLzY4SGRkEkQkKqSO51ZIOj13Cc1Mq8ZO1vWvwuSmVcNkk2GzyodjIMLPqAJAkFrcOJcB03RLpo3S+7ud14t4RgzB73Ye46jIXfvr3w7D4gUqcPB/Awi2HsWH/KYwZ2h/+YLjwyoAIAsB5f7fmsdIzQf+cXlaTaSBLEERmoVwPgihQxHXl+9ByKYgbn3wTa/98HCumjcTH88Zj1YyqmEfbMCVdd4C8GOgWp8HFGq8zlLnskFj0NxnmRBZIpblhNrFa0hEIRbB213HUTLgJh+eOR82Em7B213EEQpGEn5FMTwf9OoRR+irt5hlD6Xz9SWsnXm88gZoJN+E391fCJjG0dQYxe92H2HTgdCzNXWIozDIgou8g2J7tkoSGvSc0z40aUoFP2/xxz+lltSD7zhBEH4G2c4IoUMw6BYciHIu3HcE/rd4Lf3dYYzwrqbD6ulf9AbL2zcOxNLjDc8dj/qSbUR6d30sQuSJRc8NcY7R+9CUdXqcNdW8fxR1LtuP6JzbhjiXbUff2UeHhVV+76TdoNOYPJv43u+02lLvstG6ziNL5evk7R3HviEGo2fAX3PjkZsxe9yEAYEH18JhD5vXGEwCDJZkhiGzhdtgwd+MhBKPOwWAogvX7TmKqrgfGb+7/Mq7wOhLKasH2nSGIPgDlphBEgWJUV360pSP2WOTJFqXCeh02dIXCWHjfcPzrf32I3c1taG2XU2P7+Zy5+QcRhIBCi9BIEkOF14GV00fC57KjMxgSptmb9X1IVLv5wowqzVocNaTCcvRbkhjK3Q7YbBIYAwaUu6jkJMMona+Vfhw1E27C31xZBn93CF3dYcxe96GmnMhtt8Ftt1EZEJE3/MEwzlwK4o4l22PPjRnaH3//xati8nu0pQML3vx/+M39X04oq+J+F+RwIohcQMY5QRQooprWhfcNx4I3D8euERkDQG8qLCBvsko976IthzUb9dyNh/Cb+ytx/RObAMibOdWUEbnEqpGbKyIRjjZ/T9yhtMLrQCAUiR1o5ekHiQ+votpNtyN+LS7achiLH6i0dI/q9U1rNfMona//9b/k9PXW9iAW3jccFV4nnn7jkMZY9zl7s5boOyFyhb5Ph1pmYz0wplZi7Z+PY/G2I7HXjRnaH4HuSKw/hZGsGjn5yeFEENmHdhCCKFDUNa1/c2UZTl0IwO2Q0NoehF1iljzZ6qhdzYSbhJ71RJF40XsWSvMuovgptAiNUSOkFdNG4p9W79XcYz9P4gi7KDPgaEuHcC3myyFB9KL0OhB1vj7v78aG/aewYf8p2CWGj+eNh8RI9xG5RZSN87uZo7HoNa3Dz8aASSMHYWdTW9IZOgA5AQkiX9BqI4gCRalpVXu97638PFZNr4LXZc0wVhsay985itrJw/FYw4dJR+IVima8ClE0FFqExijN3ueyWzLY9WtBlBmw5eDpuGkLz02phMdObWDyiaLfKrwOdAbj/75Wpys7gyGUux05vEOCEDsQRWntP/v7GzD91iEaJxP1pyCIwodOAgRRoIgaspy5FAQYLHdQVxsaG/afwqI/yZ71j+eNx4Lq4ZpIvJUmRoXWvIsoDQppMoBRIyR1hgmgNdjN1oKowdyU0YOxt7ktpU7v2ULftC5f3fLziaLf2oMh1O86HmuuZYvKo1pX1k4eTp2ribwgciCKpq9MGjkIbruEAeWuWH+KcreDHOkEUeBQ5JwgCpRMpPt2RrtCKx72DftPobU9iJoJN+GOJdu1kfhgWE53Y0BHEim6NF6FKCVE606p3VRjZLBbadDocUj4cf0+hFQGsF1i+NHtN2TvHxZFVJYCgDJi0Kvfylx23DtikCbLaOl3KrGgejg+f4UHR1s68HrjCXzvtutQ7qYYB5Fb9Ps6ADTsPYFpuii5yybBYZfgjuaxU2o6QRQHtFIJokDJRLqv12mLS2WvnTwci/4kp2cqkXhwIBCNGokO58qBnnMubN5F6Z1EqSBad267hHtHaGs3l0ypxO93xRvsVurGA92RvDTBMypL8Thswjr7vtYcUsma+KS1E1sOntb0+wCg6dJOkXMiX4j29SmjB+P9T85i6MByAPIotQ37TgodSNQ3hiAKm76z6xJEEZJqQxZl8wUHXm88ETtkdnSF8NJ7x7DpwGlNGrtRE6xVM6rgddhiB/rVD90iNPbpkEqUEvp11xEMadbR0ZYOfPDXNkwZPVhjsIsyW0QG8b89+BXUTa3ELFXNeS6a4Bmt89/NHE0ZMejNmqj/818tR859TkaGDZFT/N3hOH108rwfI6+t0Ex3eW5KJTwW9FFfzJIhiEKGjHOCKAHUnvCunjA6gyHMqt+Hqy5z4ed33BgbrzLrG3+Df/zqdfjR7TdoPOZm6er+7t4DvT6ipBxSvz92KMpclN5JlCYeu4QptwyOO/hWeJyJ56ELDOJ/fuUDvPiPVTlvgme0zpW53oUyzi5fKFkT3x87FB6HJH+3TjtOXgiAR8SR83OdQfT3uciwIXKG12mLcx4tmVKJkxf8+O2DX8FlHgcuBXqw85OzGHvDlSiz9e7NZo74vrTWCaKQoZVIAAAOHDue+CIVN183OEt3QiSL3hO+7Wd/h8dfPRDbfCMcmD/pZgzu7+01AhjTbMRms6bVB/rl7xzFz795o+ZQkM+xVwSRC/RjDY+2dGDtruP4x69el7Bbu5FB7I6uQyB3taBG61ySUFDj7PKJOmvC52Q41xnE7HUf4qrLXDE9evJ8AK99cALfv20oZtXvw6rpVbG50QSRbQI9EWEmz8hrK/B/X/lAOzZN5zOivjEEUfjQbkIQRY7eE/6FCq9m892w/xQ2HTiNj+eNj6Ww66N1HruE56ZUxqfE2SXNgX7D/lMABMY+RY2IEkY01tAuMTzyjRsSRqAMo9LBEMBYbiPnBk0m3XYb3HZbwYyzKyScNgn/Nm0kyt12HD/nx89+vw9nLgWx8L7huBjolg0bFxk2RO7wOmyYOvpa3TquxKYD2qy2RVsOY/EDlZrXmjniKXJOEIUBrUSCKHL0nvCjLR0GxkAYgZ6Qrs61Ev19LgR6xJHB7982VDZMVAf61vYgfC47wKn7K1Ga6BsmgUO4pix1a2fAwvuGx0pLlIgWYww/eGlPTus+EzWZTKW/RSnj7wnjn1/5AAPLXfjVXV8EAPzm/spYJ+yn3zgU060UOSdyhWgdSwDePHgGv97wUey6MUP7x8mmmSOeIIjCIOerkTH2BcbYO4yxQ4yxvzDGfhJ9voYxdpIxti/6c2eu740gihH9XObl7xyNm3daN7USEgNm1e/TzWXeJx/QXXJk8I4l23H9E5twx5LtqHv7KLwum+Yg8PG88Vg1o4qaxxAli1ImMvOlPRj2y82Y+dIehCMR1E2t1Kyp56ZWYsvB05rXKhEoNW6nDYu2HNbMNF+05TDcDlvCGenZoJBmyhc6iuNzw/5TePqNQ+jncyJaiYB5mw6htT0YSx2mOfFELtGvYwBx+74orV1doqPoo7W7jiMQiuThX0EQhIh8uHpDAP6Fc/4BY6wcwF7G2Nbo3z3LOV+Uh3siiKJFn6ra2h5EmcuOxfd/GVde5sanbX44bRLcRrVm0RnnRtH2Mrc95a7xBFFsWG3g5rFLmDr62rhu7R67hI5gSBN1P3MpiDuWbI99xpih/S1F3Yn8Iirpeeqe+Ai6yyHhXGcwrvs+OTGJXOJ2SJo5525HfPzNqETnR7ffoNFbVNZCEPkj56dszvlpAKejf25njB0CcE2u74Mgih116q3PZcOq6VXwumzoDIbwH/9zTLP5jhnaH6umVxka4BcD3cLUW4ky3Yg+RjIN3PSppR67hDZ/j6YW9MUZVXFr67kplVgrmpEeDAMMdEDOIWYzn0WOTx4BbNG/v8LrwMvvNccaw1EHbKLQMao57+gKJWxuSRBEbsjr0ZsxNgTACAB/jj71I8bYh4yxf2eM9TN4zcOMsT2MsT2tra05ulOCSI9My60+9fah/9yDQHSuuc9lR93bRzXX725ui9W+6tPeLga6UfvmYbhsstf98NzxmD/pZpS77HDbKZLXl+mL+lZfJgKI09WB+NTSQCgSi7or6epOuxS3tlx2CVNHD44rPQlHIpp0+nOd3QiHI5QunSRW5VZUwnCuszv2fxwr6Zkul/TMn3Qz5m06hNnrPsTJ8wG8/F4zJo0cBImBOmATaZMLfSs7nOJLdP7zf4/lpcyGIIh48macM8bKADQA+Cnn/BKA3wK4HkAl5Mj6b0Sv45yv5JxXcc6rBg4cmLP7JYh0yLTcqlNv9ZupkXEBBpS77FojwSah9s3DsZrKYCgCxoAB5S6Uux0xrznVU/ZN+qK+VaKlWsPZ2lgxUdT9k9ZO1O86jmC0pjMYiuCl95rhc9k1fRx8Ljv++ZUP4tZ0Z3fY0HgkxFiVWzM9qiBJckf99kAPXHYJix+oxG8f/Ao+f4UbEyqvgcsm4by/27JDhyCMyJW+deqchf19TqFDn5xLBJEf8pJvxRhzQDbMf8c5fxUAOOdnVH+/CsDGfNwbQRQDprNKuXhmsTIuyWaTwBjQv8yJ//ifY7Fayg37T6G1PYiV00ei3O2Iva9+jjqlvBGlTKKO5maIUka3HDyNKaMH4yf12u7ILpsEm032j5e57IhwLlzTPpc9Pl16ehWlv2cAqzOfla7tO5vOYcKXP485E28CYwzBUASLt34MIL4jf1+dE0/kB5dDQmdQ+xyPyM/re2Aosqzw7s+/TuPVCKKAyEe3dgbgRQCHOOeLVc9/TnXZtwEczPW9EUSxYJZ6a9ZdXZ2G63XacO+IQZoIYe3k4cKDaaLokgJF2IlSINWO5qKo+5TRg7G3uS2+O3KPNl1dacqoxnBcm8umi6YHaa2lgJUShkiEa4z4DftP4an1f8HJ8wHUbPgLNh04jdb2INwOCSunj6SJFkRe8HeH4zJ06ncdh1+XeeN1xTuklmz7OC7VnZxLBJE/8uES+yqAaQAOMMb2RZ97AsBUxlglAA6gGcA/5eHeCKIo0Dcq0kdqrHRX93eH8XrjCc1s89cbT+B7t12Hcnev385qdIki7ERfRxR19zgk/Lh+H0Iq41npjvzdVX+OrZV/e/ArqJtaqen4/dzUSqz9c3zjuOPn/Lpo+j6smj4SiDrdKJpuDTM9GolwdIXC6AyG4LBJmsjihv2n8DcDffjtg19BuduBoy0dmLvxEBY/UKkZbUUQuUJxtj/W0Ju9UTt5ODzRkY2ArCuOn/PHRcnPXArGymxIfxBE/slHt/b/ASBa8ZtyfS8EUaykk3qrYLSZx0XOE4xZi10nGEFFHYuJvobeMdYRDAnXj97Atj6urRLz3jik+Uw5mm7XGPvZcoyZdTcvNoz0KACc6+xGZzCEx189gNUP3YLaycM1unLSyEF4av1fYmVBY4b2R2cwpCkJIohcEeiJCJ3td/zt5zTXKVHyWQnKbAiCyB+0AgmiSEl39nigW7yZf/+2oShTRc4lKb6eUjRmzSzCHuG86A/yBJEK4uis2MC2Mq5NghzpUqM4y7Jdm16K2TF6PRqJcHR2hzCrvhGv/GA0dje34ZPWTmw5eDqmK09dCMDtkNDaHoRdYoaOTYLIFV6HDVNHD06YeXPmUhA+p12z76/ddVze9200O5UgCgEyzgmiwMhVZEqSgEkjByU0ut0OGxZtOazZzBdtOYzFD1Rqrus0iBBeCvSgau62kjjIE32DTK5BUXRWYgYGtqABk9549HeHhM6yi4FuzeuU2vRMRtNLOTtGncZe4XNhd3MbjrZ0YNSQCix/5yh+/s0bNZHzpd+pxILq4fj8FZ5ex+bYoShzkYFDZB+9jvLYpVgX9i9UePFpmx+XueyYNHKQJvNm4X3DEeEcdyzZHnsvpcyGIIjCoLh3U4IoMTIVmbJiXLjttthoNWUzF80293eHceZSULOZjxnaP86Q8DptcamftZOHo8xl1zSSK4WDPFG6yGswqIlA1U2tRH+fKy0DXR+dNesZYYZo3Za57Hj5vWbNdeLa9PTWn9X+E8WGoneVNPaaCTfFjHJFpy3eehjzJ92Mwf29aL0UBI8As9dRd3Yi94h01IppI+O6sP/2uyMw5voBcbpixxHtDHVRmRpBEPmDXLwEUUAk0xndCGXjTtTNWZIYyt0ODCh3CWebK1id+6yueVO6Ur/eeAKftHbGrimFgzxR2vi7w5hVv0+3BvelNbNaP8UAgOFEhUQI163Ljqmjr9Wt0Uos2fax5rXprj8r3c2LEUXvfqHCi93NbTGjvLU9GDPKFz9QCZvE8NO1+zB6/luo33WcurMTeUGko3wue5zj7Mf1+1DmsqN/mTM2PtVjl3DzoCs0ukKUMUcQRP4gNxlBFBCZiEypN25A3c25Ks4zbqVuXZIYKrwOrJw+Ej6XHZ3BkDASL9e8XauJBi68bzgWvHk4dg3NTiUKHdGoISVFPBXMsmGs9IwwyoLRvzad1HmrJJoSUawoeldJY1eavCmlPB3BEM61B2OR8jFD++PeEYPwH/9zDFNHX0uGOZFTRDpKkV19WVlXTyTWpLDc7UAkwi1lzBEEkT/ohEwQBYQSmRJ1RgeDpdpXM+Oiw8CwNiMS4Wjz9yRMtY+rrQ2GEY5ENE2TSuEgT5QOIsM34RpMsg49nTrtZMpcMpk6b0QmpkQUIsp3rk5jV+aX104ejkV/kh2MirEe6A7jidcOYMP+U9jZ1EalOkROEU1Q2XLwNJ6bWomfqFLdF943HPqlqWTe2GxSLPOmFNYwQZQSlMhCEAWEKIV84X3D8eTrB6Lp6d1x6el6lI1bjWJczHxpD852BhGOROQ02wTvBSSXaq8YCBJjKHPbUe52pJS6SxDZRjF8teUf3ZCYPJ1AvwYZQ9y1VtZPOtkw6ZS5qA3pTK4/zRp32UtiPSt6V53G/vG88Vg5fSRebzyBDftPYcP+U7hjyXY8+MKfcfJCIBZdp1IdIteIdNSkkYNgY9CUlS3achhugWyW4homiFKCXL15YEjXmqRf0+z+ThbuhCg09JGp4+f8WPDm4dhBUB1xM0p3NRp9djHQjZ1N5/CT+n2omXATajb8xVKzuXSMi3THvRFEtjCKaL/4j1XChmsv7mhKKfotinJZbcCUbpkLrT9riDICwAGbxITdrqlUh8grDHA7JGFTyMXbjsQuGzO0f8oZPwRB5A+KnBNEgaE+UP/94v+OGeaAam64QdQvEuGabs6H547H/Ek3w2WTUBs9UO5ubsPfXFlmOQpXqk2giL6NkeHrdtgEjRLtqHv7aNy1lhxUBpF4K+fjZNaevumclag+0YsomqiMkVw5fSQOzx2PBdXD4bL1zjdXGu9JDPT/TeQMt8OGuRsPIRiKAACCoQjCkQgmjRwk1DP6c0I4HCFdQRAFDLl6CaJAMax9jR7MzepY1TVlAPD0G4diRv6oIRU42tIBwJqB4bFLeG5KJX6ytreW7bkplfDYybdHFC9m66vMZddEnDuCIdNrzXA7bVj02uFYvfLRlg4s2nIYix+ojLtWNLvYSt14pkYwElo6gyGcuRTEpUAPLgZ6MHvdh7jqMldspFpnMIRfvX4QZy4F6f+byBmi8aY/+/sbMP3WIZpoutsh79H6c8KKaSPxT6v3kq4giAKFTtcE8f+z9+5hUlR3/v/79GWmu4eLoqi5EUSJiREFGYyYgBdyI/oliZMY9Qlm/RogERmNawajhPBTIsKamEB0o8ZkIzzgZoWNxpW4u4gBDcYBwWDwq9EBiQgKDo7T90ud3x811VNVXdVd3VM1fZn363n6abqmLqebz/mcc6rO+7xrlGIWZqWmu+afvkugpTlgeNKzvO1M3LNZfQqYX+iqCImsgoef32/Qsj38/H4k+u7aE1KPOLUI7N93YoFVmZPF1eKp/o70Kbc8gS/8bAvefj9VUO+sZsN0xzMYFSm9boMbFoykkEiTH8vbzsT6HW+i2a9OI/7JZepNle6YOmj//a63+HuTQcUqd13+qTH5m/F6jsbThs+d+7rR0hxgriCkhuGTc0JqlGIrI0eTNk/yUllACJ3+3HiO3mQWv312L57YfdCxv2mkyY+VT71m0LIFfALXzRjv1VcnxHPKXXm8yW/UeDb5fZBSIpoqXPfBeB3rNSDM9c7Jqu52T+ndsGAkRhRFIp7O4fc738RXz/4wRg1rQjCZzf9dKshLhQD+3mTwsMpdAQFE01nDfgGfD2t37Dds08+c02DsElJb8Mk5ITWM3aqqWoffrC/rSWSKriT9o0dfwhfO+IBxNdcST/+oOSeNil39Muu34+kcvrPmBVxw19M45ZYncMFdT+M7a15ALJ0ruYK71RoQVr7CA1rVnXXUVbRZDOGgH1+Z9GF0PPJX/PPvXsR78QyEAI4b1oR1z+83rAfC35sMJubc1WShQ3901wFc8akxhn7Cz6+YiCdfOmg4F2OXkNqi5gbnQogvCiFeEUK8JoS4udrlIaQW0RYqMtumnDQybJimpp8q+/e3o5bTa2MlFoYpZ/ovIfWO1fTySLP1wNnJ9FDNV9i4wFwQAIw3AOwsEB10mllH3UWbxfD64Rh+v/NNLJn1SfzksolIZRX8YtPf8X4ig69M+jB/b1IzWMln/vjS22hpChhlMZEmXPGpjzJ2CalhampauxDCD+AeAJ8D8CaATiHEY1LKPdUtGSG1hdWCMFPHHVew0Fs83T9V9p7Nr2F525lYuP6vhoXdfvPMXqx86jXbhWHKnf5LSD1jNb18/7txSxmJ0+mhZkszqwXcfvnNs7HyioloX7er6OJvVrCOuos2i+Geza/hps+fZsiZy9vOxPI/voJTR7fg/qsmo6U5wN+bVB3NFcIsnwFQIIthriCktqmpwTmAcwC8JqXsAgAhxMMAvgyAg3NCdGhPyvSd++VtZ+Ku/1Y1kNoTN/1UWW0K5pJZn8T4E4chlsriN8/szWvJi3k30y+ZDBWsppf/7H9fLRg4//yKiXj4L4V6TicruFvdAPjOmhfw4D+1VtxpZh11D00moOXMFV87Ex86Noz978bx0/95BYd7U/jhJaejpal/WjEh1cbsfa6t1m6GuYKQ2qbWauWHAPxD9/lNAJ/S7yCEmAtgLgCMGTNm8EpGyABwO27NT8qiySz+TbfQm/bEzWwX9diLb+FwbwoPfKsVLc2VezeTocFQzLdWFmtvv59CS3PAMHAOB3y44lMfxbau7rKfdBfzWPf1LbnMTnPlDDRu9Tc/n9h9EId7U1h5+UT4fQI/uWwi4umsOjDn00biIkMx3xJCCqk1zblVS2cQwUop75dStkopW0ePHj1IxSJkYHgRt+YFYf7vtHEFdkvFtKhcRIqUYijmW7s6Ewr4DfXN7/flb5AVszmzgnXPWwYat9rNz/uvmoxXf6wu4vfjJ15GxyN/xcGeBPx9ThiEuMmA4tYuHBmmhNQdtXZr/k0AH9F9/jCAt2z2JYT0YTdNrZgW1WpqPBeGIUOdcvTblU4PZd2rfXw+gZamAHoTGQDATy6biH90xy1X2iek2oQCfmSyCoD+G3xBn4+xSkgdUmuD804A44UQJwM4AOByAFdWt0iE1DeVDNwJGcp4rclk3asPtJX2/X5ffqV9/j+RWoSxSkjjUFODcyllVghxHYAnAfgB/FpK+bcqF4uQhoULwxBSHVj36gP+P5F6gbFKSGNQc7VXSvkEgCeqXQ5CCCGEEEIIIWSwqLUF4QghhBBCCCGEkCEHB+eEEEIIIYQQQkiV4eCcEEIIIYQQQgipMkJKWXqvGkUIcRjAGyV2Ox7AkUEozkBgGd3BSRmPSCm/OBiFscNh3A4V6iGuBhO736Oe4raR/k8b5btU63vUU9y6Tb3ETj2Uc7DLWKtxWw//V05olO8B1NZ3qXrcEneo68G5E4QQ26WUrdUuRzFYRneohzISI/w/M9IIv0cjfAeNRvkujfI96ol6+c3roZz1UMbBoFF+h0b5HkBjfRdSO3BaOyGEEEIIIYQQUmU4OCeEEEIIIYQQQqrMUBic31/tAjiAZXSHeigjMcL/MyON8Hs0wnfQaJTv0ijfo56ol9+8HspZD2UcDBrld2iU7wE01nchNULDa84JIYQQQgghhJBaZyg8OSeEEEIIIYQQQmoaDs4JIYQQQgghhJAqw8E5IYQQQgghhBBSZTg4J4QQQgghhBBCqgwH54QQQgghhBBCSJXh4JwQQgghLNtcJQAAIABJREFUhBBCCKkyHJwTQgghhBBCCCFVhoNzQgghhBBCCCGkynBwTgghhBBCCCGEVBkOzgkhhBBCCCGEkCrDwTkhhBBCCCGEEFJlODgnhBBCCCGEEEKqDAfnhBBCCCGEEEJIleHgnBBCCCGEEEIIqTIcnBNCCCGEEEIIIVWmrgfnX/ziFyUAvvgq51V1GLd8VfCqOoxbvip4VR3GLV8VvKoO45avCl6kQajrwfmRI0eqXQRCyoZxS+oRxi2pRxi3pB5h3BIydKnrwTkhhBBCCCGEENII1NzgXAjxPSHE34QQLwkh1gkhQtUuEyGEEEIIIYQQ4iU1NTgXQnwIQDuAVinlGQD8AC6vbqkIIYQQQgghhBBvqanBeR8BAGEhRABABMBbVS4PIYQQQgghhBDiKTU1OJdSHgBwF4D9AA4C6JFS/rd+HyHEXCHEdiHE9sOHD1ejmISUDeOW1COMW1KPMG5JPcK4JYQANTY4F0IcC+DLAE4G8EEALUKIb+r3kVLeL6VslVK2jh49uhrFJB6gSAWxTMzw3kgwbkk9writTRo9Xw4Uxu3QoNHqAeO2fmi02CO1RU0NzgF8FsBeKeVhKWUGwAYA51W5TMRjFKmgO9mNBU8twOTVk7HgqQXoTnYz2RFCiAnmS0JYD0j1YOwRr6m1wfl+AOcKISJCCAFgBoCXq1wm4jGJbAIdWzrQeagTWZlF56FOdGzpQCKbqHbRCCGkpmC+JIT1gFQPxh7xmpoanEsp/wLgEQAvANgNtXz3V7VQxHPCgTB2vr3TsG3n2zsRDoSrVCJCCKlNmC8JYT0g1YOxR7ympgbnACCl/JGU8uNSyjOklLOllKlql4l4SyKbwKQTJxm2TTpxEu9CEkKICeZLQlgPSPVg7BGvqbnBORl6hANhrJi+AlNOmoKACGDKSVOwYvoK3oUkhBATzJeEsB6Q6sHYI14TqHYBCPEJH0aFRmHVRasQDoSRyCYQDoThE7x3RAghepgvCWE9INWDsUe8hpFEPCOn5BBNR6FIBdF0FFkla2s94RM+tARb4BO+fLKjRQUhpBROLW3Ksb6hTQ4htYNdfdT3G7R3jXL6H4R4hV3smuMzp+SqXFJSS3BwTjwhp+TQnexG++Z2TF49GWteXoOjyaMlrSdoUUEIcYrTfFFOXqn1HFTr5SPETSqJd3P/o31zO44mj2L1ntWsM2TADLTdySrZgvjsTnZzgE7ycHBOPCGRTWDh1oV5q4kZY2YYPttZT9CighDiFKf5opy8Uus5qNbLR4ibVBLv5v5H56FOLNy6EDPGzGCdIQNmoO1OMpu0jE/GI9Hg4Jx4QiQYMVhNjBs5zpH1BC0qCCFOcZovyskrtZ6Dar18hLhJJfFu7n9ox4wbOc7xOQixY6Dtjl18RoIRbwpM6g4OzknZONFjxjNxg9VEV0+XI+sJWlQQQpziNF+Uk1dqPQfVevkIcZNy412RSkH/Qzumq6fL0TkIKUaxmNT3je32s4vPeCbuedlJfcDBOSkLp1qbcCCM5dOW560mNu3fZPhsZz1BiwpCiFOc5oty8krIHyrIVcunLUfIHxqsr1UU5kgylCgn3rX+yba3tlnW4U37N7HOkAFjF5NZJWvoG2eVrOV+oYB1G8N4JBpCSlntMlRMa2ur3L59e7WLMaSIZWJY8NQCdB7qzG+bctIUrLpoFVqCLYZ9c0oOiWwCkWAE8UwcoUAIqVyqpPWEIpX83z2wqBBunahSGLekAhi3NjjNF073i2ViWL1nNWaMmYFxI8ehq6cLm/ZvwuzTZxfkuGrhcY50E8YtGTDl1F2tf3LzlJtx8SkXY0TTiLL6H30wbklRzDHpEz7M3zS/oG98z4x7oEilIO7M/eNwIAy/zz/QYlU9bok70OeclEU5+i+/z49hTcMAIP8e8KkhV6yTq9milNqPEEKc5gun+4UDYdz34n24Z9c9+W0BEcDcM+e6VOKBwxxJhhLl1F2tf3Jn5524s/NOBEQAO2bvgE/4HPU/CHGCOSYVqVj2jZv9zfmbQPq4s+ofE6JRk7faSe1CvSMhpJFhjiOkPmHdJdWCsUfchINzUhbUOxJCGhnmOELqE9ZdUi0Ye8RNOK2dlIVP+DAqNAqrLlpVD3pHQggpC+Y4QuoT1l1SLRh7xE04OCdlQ70jIaSRYY4jpD5h3SXVgrFH3IK3dEjVceKbTgghA4F5hpD6hHWXVBPGHxlsODgnVcWpbzohhFQK8wwh9QnrLqkmjD9SDTg4J1UlkU2gY0sHOg91Iiuz6DzUiY4tHVzhkhDiGswzhNQnrLukmjD+SDXg4JxUhFvTfMrxTSeEkEoolmc4XZGQ2qVY3WW9JW5j7tuyj0qqAQfnpGzcnOZDb0hCiNfY5ZloJsrpioTUMHZ1983eN1lviatY9W2jmSj7qGTQ4eCclI2b03zoDUkI8RrLPDNtBda+vJbTFQmpYazq7tJPL8Uvdv2C9Za4ilXfdu3La7FiGvuoZHChlRopGzen+dAbkhDiNVZ5JuQP4b4X7zPsx+mKhNQWVnX39m23Y+Pejfl9WG+JG1j1be978T7MmTCHfVQyqHBwTspGm2bWeagTM0+eiTkT5mDcMeMQz8QRCUZKJi1FKvkEl8qloEiFSY8Q4ilmD9pYJpbPYxradEV9PiqWl/S5zI385fb5CKknzPEf8oeQzCURDoQhpcy/xo4cazhOq7f0liZ2OMmtiWwC886ahxljZmDcyHHo6unCpv2bkMwlq+ZfzjZhaML/YVI22jSz+RPno31SO5Y9vwytq1vRvrm9pPZLr+m5ZestOJo8Ss0nIWTQsZPUhPwhR2tquG2xQ8seMpSxi//Ve1Zj8urJaN/cjoOxg1jz8hq0jW/D/InzOc2YOMJpbg35Q2gb36b2ade0Ytnzy9A2vg0hf6imy00aDyGlrHYZKqa1tVVu37692sUYkihSQTwTR/vmdsOTpyknTcGqi1bZ3l2MZWJY8NQCdB7qxIZZG7Ds+WVlHe8CwqsTO4VxSyqAcesBVk8lEtlEPkdpWOUlfS4rtp9T3D5fjcC4JY6wi/8fnPMDXPrYpYbPy55fhpUXrkQkGPHqaSLjtoFwmltrLQdXUJ6qxy1xB05rJxXhEz5EgpGyted6Tc+4keNoUUEIqRrmqe6A8zU13LbYoWUPGcrYxf+4keMKPu98e2deQlfHN67IIFGtnD5Qaq08ZPDgtHbiCCtf80ps0PTHdPV00aKCEFJTOM1rbttA0laSDGXs4r+rp6vgM+sFKYfByulW/eTBKDdpPDg4JyWx072E/KGybdD0Os8Hdz+IpZ9eSosKQkjNEPKHsHzackNeWj5teYHu0G0bSNpKkqGMVfwvn7Ycm/Zvyn++7bzbsGn/JtYLUhZOc+tAcrAX+nC2CUMXas5JSYrpXpyuaqynyqu1V12Tw7glFcC4HSRimRhW71ldsGLv7NNnF0yh5WrtJWHcEsdoa9mEg2F0vdeF5w8+j3M+cA5OOeYUdXsgnF+9nf0EUg5Oc2ulOdgrvXqZ5al63BJ38ExzLoS4BMDtAD7adx0BQEopRxQ55jQA/67bNA7AYinlz7wqZ9VRFCATB5oiQDoOBCOAr7Y6Y8V0L1aazVLoj9HfAaR2jBBSbcKBMO578T7cs+ue/LaACGDumXML9vVJoEVRb3C3KBKQGFD3qJJ8Ssig4mGfRVvLZvLqycjKbH57QASwY/YOtX74WC+IDofx6DS3VpqDvdKHs00Ymng5CvwZgG8BOE5KOUJKObzYwBwApJSvSCknSiknApgMIA7gPz0sY3VRFCB+GFh3OXD7aPU9fljdXkNQ90IIGSo4znd1kr8JcY1BiHn2N4hjaigHM26Jm3g5OP8HgJdk5fPmZwB4XUr5hotlqi0yceCRa4B9WwElq74/co26vYag7oUQMlRwnO/qJH8T4hqDEPPsbxDH1FAOZtwSN/FMcy6EmAJ1WvufAKS07VLKnzo8/tcAXpBS/sK0fS6AuQAwZsyYyW+8Ucdjd6mod/uU/ulb8AWAHx4Gakxn2EBayKpochoqbkk1YNwOIo7yXR3l7yrCuG0kBinma6C/wbitB2osBw/VuCXu42XU/BjqtPQQgOG6V0mEEE0AZgH4D/PfpJT3SylbpZSto0ePdrG4VSAdB8ZMBc5oA67dBizuBuZ3AplktUtWgKZ70b87ZaD2ElrCc9OiYrBpqLglQ4ahGrdW+S6nZBFNR6FIBdF0FLlcBsr5CxGb/xcoi7vV9/MXqnmdVJWhGreeo/VZ9ExfCKSi6kApFYWi5Gzbaru+gHk7gIr7G/UM47ZMrOJxzFS1D62LyVqUGuWUnLE9UXLVLhKpIbzMeKOklJdKKX8kpfz/tJfDY2dCfWr+toflqz7BCPCN1cBnlwBPdABLTwD+0A6ke2symVTCQO0lFKmgN92Lo8mjrlpUEEKIU3JKFt3Jo2jf3I7JqyejfXM7utPvIz71Wix4YQUmr2nFghdWoHvK1VCCnMZIGpRgBPjag8DYaeoTygtuBSZfBTx8JXD7aCjP3Wvb3tv1BXJKznULKjJEMMfj2GlqnzrdO+g69HL6ulrMG9qTvrpACODt4Px/hRCfr/DYKwCsc7MwNYnPpyaU319bE5oZL0hkE+jY0oHOQ53Iyiw6D3WiY0uH40UyEtkEelI9WPTsoorPQQghAyGRTWLh1oWGHLRw60LkZM6Yl7YuRCJXezOfCHEFnw+IjAaueFidOnzud4D13873XxKnz0KHqZ5obXWxvsBA+ghkCGOOxyseVvvUVdChlxPHiWzCsj1hzBMNz6zUAMwH0CGESAHIwIGVGgAIISIAPgdgnodlqx2aWoD924zb9m9TbSEagIHaS4QDYXxo2Ic8sagghBAnRIIRyxw0vGl4wTbmJdLQ+HxA8zD1383DDP2X8HHji7bVVn+zq1usR8QR5niUSlX61OX0de1iPhJsjH4/GTiePTnvs07zSSnDTq3U+o6LSymPk1L2eFW2msJOMzMQ3aKiDLrexk5LNlB7iUQ2gQPRA/lzzDx5JjbM2oDts7cjnolz6hshxBll5EVzPotn4pZ5LJ6JY8OsDdg1exc2zNqAeWfN49MPMjTQ6tOid9Q1c85oQ+Ldv9u293Z9Aau6Ne+sefn2vR7XmCGDiDmvZ5Lu96kdUE5fN56JY95Z8wrajniDzJglA8f1wbkQ4gtCiK9ZbL9SCPE5t69X91hpZr72oLq9Eqrg+1hMazNQe4lwIIyRzSOx9NNLMX/ifLRPasey55ehdXVrXqfDhpsQUpQy8qJVPgOA5dOWG/LY3effjWQ2qeajNa1Y9vwytI1vQ8gfGuxvR8jgotWnh69U18p5ogOYsRjhd17FClM90dp7q77Abefdhm1vbTPUrfkT56NtfFtej0sNOrHFKq+ne1XduVt9aoeE/KGCNmL5tOWW7UEoEELb+LbCtiPAtoOouG6lJoR4DsD/kVIeNm0/CcB/SimnWh9ZPq2trXL79u1una56KIqqh2mKqHf3ghF1qk4lpKJqgtq3tX/b2GmqFkeb+uMysUwMC55agM5DnfltU06aglUXrUJLsGXA9hKKVJDKpZBTcmjf3G57HYdU3WqiYeKWDCaM24FQRl60y2f3zrgXWSWLSDCCeCYOIYAFTw04HzU6jNtGxK4+Xb4WSlMLErmkZXuvSAXxTBzhYBhd73Xhgd0PYOPejZg/cT6++Ylv5uuWC+38QGHc1gO2eX0dAOFOn9ohsUwMq/esxowxMzBu5Dh09XRh0/5NmH367IK4LdVnHgBVj1viDl5oziPmgTkASCkPCSHYY7HCrJkpRqmBfFNE1dec0QZMvwk4/jTgyKuAfgVhl24G6AfdxbQ2mh0KAEODXc5AXZFKXqdz85SbcfEpF2NE0wj0pnvR7G8uu+yEkPqj4ht9Wl7UY6NDDAfCOCF8AjbM2pDvZD24+0EEfUHklAwAQEAiHGixzHshfwjRdDQ/0AgHwvD7/BV/Z0Kqgl0/QVEASOCqR4EjrwBb7gJeWg8MPwkQAj4h0KJIQFq395FgBJNXT0ZWZvMytZNHnoxkVl1IUQiBE8InGIpCDTqxxC6vByNAxoG8yCLGFYGK2phwIIx9PfsM2/b17LOMW7s2JhwII5aJGa4NVFYeUt94MTgPCSECUsqsfqMQIgiA2XUgaFN4HrlGTUBjpqrTdSKj+wfX6bjqO3rWZcCj1/Xv1/Yg0NLnm1nqHE6K0jf1s2NLB2455xZMOnGS4S6gprXR3wXUH7Pz7Z2YdOIkrJi+AqNCo2yTjfk6K6avwMQTJuLGp2/Mn2P5tOUYFRrFDrDHTPjthLKP2f2t3R6UhAxFKskfebS1PfRPWDQdoumGaCqXQvvZ7Vj07KL8de74zB2IpqP43p++l9/2swt/VpD35p01D0eTR9GxtYP5idQvtn2N44H4EeP2L/8C+PA5wMe/BKy7Ir9d+cZqdCNXUF9bgi2YdOIkHB8+Hu2T2vF41+O4ZNwlWPznxfn9ln56KRQo2Lh3IwDr/gQhSMcK8/r0hUDsCLC+RB/XIsbtYtZJG2PVbiz99FKkcqmCAbrdvolsAgueWpDfdvcFdyOjZCpr80hd48X/7gYAD+ifkvf9+5d9fyOVkomXtogIRoBz56kDc/1+6/v2c3IOB+htI+7ffT9uO++2krrySixTzNc594Pn0oKCkCHIgCyXyljbQ1FyBdaNtzxzC3rSPYZta19eixXTjBraKz9xJTq2djA/kfrGrp+QjhVuf/Q64KxvFFjCJpLvWdZXRSpYMX0Frpt4HRb/eTFmjJmBxX9ebNhv0bOLcN3E6ypap4YMIYQf+Mq9xrz+qblqf7dUH9cixu1i1kn+VqRS0G4senaR5VoJdvv2pnsN23pSPbQZHKJ48eR8EYClAN4QQrwBVQPxEQAPAvihB9cbOjiZmunzAc3Di+/ngs2Efiq7dnf7lnNuwbhjxtlOvanEVs18nTun3UkLCkKGIAOyZdT74ZaQ84RtbG4+NOxDhm33vXgf5kyYg1UXrTJMOWR+InWPXV/DZJ2W3x4aWbA9PHKMbX0NB8IYFRqFnW/vxLiR4yz3+/DwD2P7N7cj2adf55NCUkAwBGy6DfjSij4J5yul+78aFjFeLGZLUU77ZLfv6PBowzbaCA9dXM92UsqslPJmqAPyfwLwLQBjpJQ3Sykzbl+vYbGy/XFiu6YdZ7efS9ZtZtuIjXs34o7n78hPPbNqSCuxVTMf8376fVs7FkJI4zJQW8b82h6i713Tz5rybMLGNu1A9EDBtmQ2ns93LcGWopZrhNQNmjzu2m3A4m71ffpC+76FxfZEz37b+uoTvnx97urpstzv/fT7mPM/cwCAA3OiYs7X6Rhw3MeM+/QectbHtegLF4vZUpTTPtnta25j9DbC5ZaH1Dde+pwnAAwHMAHA14UQVwkhrvLqeg2Fne1PMFx8aqZ23HO/VHVgVvu5ZN1WiUWaG8d0Huy0tKvgncTGYMJvJ5T1IkOHgdoyFmCTZ8OBUKEd1LQVGNk00rStMO+EA2HmJ1L/BMPA5KtUizTNKm3yVcDhV6z7Fk0tBf2KcOiYovVVq8+b9m8qkMUtn7YcnQc7OZ2d9GOVr4HCOHXax7XYr1TMFqOc9slu35HNxjZmZPNId9s8Uje4bqWWP7EQqwGcAmAXgFzfZimlbHfrGg1rNVHM9icYsV9pXX+cfrX2dAxoGta/nwertTtdRdKNY5r9zUhmk5Wuhlx1q4l6jdvBWBCu3GsMoQXnGLcYwGrtVhSzg3r9aSROmY5w8wgkUu8j/PoW4GOfR0JmEA62ING3oq7PV6gMyyk5JLIJrtauwritR+zqxpdWqKuzW/UtKlj5WqvPIX/IUGdCgVB+Ia0qPTVn3NYaVjG5YCfwh/bCOL3yd+rT9VJ9XBdXawfKa5+s9gUKr221rUh5qh63xB280JxrtAI4XXo1+m9kimnLtSmZQKHtmv64l9arL18A+OFh9TiNcqzbiqC3SHO6iqpbxwxrGmZ4J4Q0PpXkD1uK6Gp9669Gi6IajrQA+TzaIkLqtqbhtqf1+/zMT6S+sasbx59m37ew6Ff4gKL1VV+fzXUmYHHjiwxhrGLy2I/aWKmF+uOyWB+3gpgtRjntk92+TreRxsbLW5IvATjJw/M3HopOR1OJLtwlPXnRIkoFsUzM8O50m9sMxjUIIQ2ClV7RRj+bvfqPiN7yJpQfHUX0ljeRvfqPlnmUOYg0JHZ9iSOvGD+XqBOJbMKyftjVG9YnYotVTB59w6bPGytcs8khTmMwp+QQTUehSAXRdBQ5JWe5H2OaVILrg3MhxB+EEI8BOB7AHiHEk0KIx7SX29drGPR6mse/V2gP4UQX7pKe3LaIfT7DC55agMmrJ2PBUwvQm+51tK072e1qUrIqi9vXIEZ2791f9ouQmsBKr6hkLfNltimCo8eNRfvm6zF59WS0b74eR48bi2wwZDwlcxBpVKz6Em2/AvY8bvwcNGpf9XXilq234GjyaEH9yCk5y3pjt531iQCwjsnIsdZ9XiVbuGaTgwG605yuxWr75va+NqI9H8OVnI8QM65rzoUQ5xf7u5TyT25dq6E0OWY9zRltwIWLgFFjy9OFu6QntyKWiWHBUwvQeagzv+2/vvpfWLJtScltU06aglUXrXJtWo5VWRxeo+qanLqN2yUjKzimp6zdqTm3hXE7EOw0tBbaxGg2jvbN7QW5ZeWFKw3T1AeQg4YSjNt6Rd+XSEWB158GRp/ab1m153Fg6rWGacP6OrFh1gYse36ZZT2yq19W26tUnxi3tYhV/xYwbhM+YO1l1ms2lZBxOs3p0XS0VtuIqsctcQfXRT3a4FsIsVxKuVD/NyHEcgCuDc4bBkUp1NO8tB7Y86iq6XKqC/dwYA5YezNqPowzT56JORPmYNzIcRBCOPZm1C+KkcqloEglv/BFyB/Ke5yaF8IYkOcxIaShKVhsJxiG/MQsJC5fg0jzCMRT7yP84u/gDzQDGaMtTcTG5zwSjCDWtxCclp9OCJ+ADbM2YNzIcejq6cKDux9kDiL1jb4fAQBSqn2Q9VerTyQ1fAHg/JsMh+rbZTv/8kgwkq83pxxzCmKZGCKBCBLZBD77kc8aBjJs00lJzLpxqTjzObcgHAjb5nR97nejjXB1cVPScHgZCZ+z2DbTw+vVJ9p0y+59A9OL29mvlaG1KYWVN+OB6AHMO2se2ie1Y9nzy3DrM7cimo468mYsNgVu9Z7VRacDDdjzmBDSkFhNJYxnE+ie8FW0/+lGdRrin25E94SvIpeOF+RMO6/yaCZqOOfR1FF8f8r3sez5ZWhd04plzy9D+9ntSOVSVfrmhAwQq35E7DDQ+46j/om+XbbzL49momg/W+0vTF49Gddvvh4HYwex5uU1+NzYz+HmKTcb9mebTgA47+PartkUK3mJVC6Vj019Tk9kE4bcH81Y93HNbUQ0HcUNk28oOF86l+Z0d1IULzTn3xVC7AZwmhDir7rXXgB/dft6dU8mDjxyDbB5qb03eTnn2bdVvbu9b6v6OePeYnBW3owjm0fiyk9cicV/XozOQ524ZsI1WPv/1hb4lq6YVujNmMgm0LGlI3/comcXofNQJ7IyixljZmDh1oX5z52HOtGxpSPfULvueUwIaQj0eUXLHTmZK8gnC7cuREIoBTkz5AsWeJWvmLYCa19eW5CPYpmYYduiZxdBsVkYiJCax6ofsf7bQC7pqH+ib5cf3P0gln56qaEe3XbebUhn04a2vvNQJxb/eXG+zb/klEvYppNCnPZxhb9wzaav3KtuL4EilYLYXPTsIvSmew3b1r68FiumrSjZRvSke3DLM7cUnC+rZAvaKH3/lhAvvCrWAtgIYBmAm3Xbe6WU3R5cr77RprNr08W+tELVdGXiQLDF+bT0YvZrLuETPowKjcKqi1YV+DDqp7Jd9uJl6Orpwg/O+QHGjRyHvT17cWzo2IIpO8WmwNlNidOuZ1cWTgsiZGhjJXkZ3jTcehqi2RJt/zYE/EEcGzoWKy9cafAqv+/F+wqO/9CwDxVsC7u0ACchg45dP2LkR4ANc0r2T8ztciqXwqqLViHkD6Grpwsrd67Ess8ss6yLWps/vGk4dszewTadGHHaxw2GgE239cfqkVfUz5feX/ISdnLJ0eHRhm33vXgf5kyYU7KN0GSf5vPZTYvnjSii4YXmvAdAjxBivvlvQoiglDLj9jXrGs0eYt/Wfv/QC24Fzv2OurRDKupMO65N5dEvgqFNO7PSrJehT9e0MSF/KK+30TecsUwMk06chM5DnfmpbBv3bsTGvRsB9C+AERZhg8YGQMFxx4ePx5wJc5DIJfJ/09CmuLUEW6jXIaSRcJiPFCXXpyGPINHXIfL5jE9EEtkE5p01DzPGzMjr/KLpaMG2Tfs3IZ6OQsz/C8LHjUfi3b8jvOcx+NJxBJqHGXyX9TlOY9KJk3AgesBw7UknTkKi70mOvowQgvmK1D76/sgZbcD0m9QBTrJvYc97pxb0T5RgCIlsEiGdHldKCSklFKlAQGDO/8zJ1505E+ZY1qXeTC/mnTUP8Uwcw5qG5dt5vYaX9WYIk44D0xcCp19iXJTQ3MdNx6F8fBYSIz6IsBDq+8dnwZdJFiz+aW5jNFmGOTa7k90G3fim/ZuQzCVLthEHogcsz6dJp8zbzesuaYN1th1DDy//h18AcBjAqwD+3vfvvUKIF4QQkz28bn1htoe44FZg8lXAw1c6144rivrk3an9Whn6dE2/uXrPahyMHcxbR+g1MqWmsq2YvgIhf6hAY5NVsobj/mX6v+CGs29QE18maXkebSEN6nUKmfDbCWW9CKkJHOYjRbNa0nJQn32NeRp5yB9C2/g2g84v4AsUbGsb3wafL4AFL6zA5DWtWPDCCnRPuRpKsPDphZ2MZmTTyIKpjVklZyhjbzrKfEXqA60/csGtwIx0jiB8AAAgAElEQVTFwBMdwNITgN9dpX7++mpD/0R57Sl0J49i9ctrDP2D9s3tOBg7iNV7ViOejePu8+/O15NN+zcVyEZuO+82PPz/Hkbb+DaEAqplIdt5YiAYVmNPi8knOtTPZju/YAjdJ0/Fgr71RRb86UZ0nzwVisyVbGOa/c0FsXn3+XdDQBS0HSG/0VrTTvZpJ780b7/7grvzq7vr47033cs6MARx3Uotf2IhfgngP6WUT/Z9/jyALwL4HYCfSyk/NdBrNIzVhNmy5OEry7OB0GyChp3Qf6f76BvA8JOsp7Xb2QpZXENLFj845weWtiiaJUSxVde1dytLiXtm3JPfN55RLYy0a2lP0ceNHIcD0QMYHRmdXzVzAPYUVbea8CpuPbcho5VaNWnYuHWaj2LpKBZYWS1duBItJexr7Owdl0xdgov/82Lj+WzyiNVsHUhpeJLvEz7Mf+q6QbeWrGEaN24bFUUB0jb9kMvXGrbHbn4DC/50o23/QNu+6qJVkFLmpwCHAiEks0mEg2F0vdeFB3Y/gI17NxrsqKpsVci4rTUG2k6cfzda7hxT9NhoOoo1L68xzLAa3jQ8rxs3nM8iDi3bCFg/+TbvC8Ay3stpo1ADcUvcwQvNuUarlPI72gcp5X8LIe6QUt4ohGj28Lr1h9kKolztuF63/tL6vnMGVBu2Yvs7uIamwXGiAdeShV43o99mdXyzvzk/RUfT4WjXyspsfmp8QASwY/aOoueiXoeQOsRhPgrb6fQsFqWys3w0H2upGbfJI/ocl+8YCeRvDLQ0DYMiFcfXZr4iNYnWH7Gqk6bt4eYRRfsH2nb9VFxtKnAkGMHk1ZORlVnDMZG++sx2nhgYaDvRXLi+iPnYSDCC+168D/fsuie/bdfsXY7j0LKN0P1bv828r1XbUW4bRRoHL6e1dwshFgohPtr36gBwVAjhB8A5GXZomi89VpZqiqLeSZR97+XYsDm5Rt/5NQ2OnS2K09UlzdZnM0+eiUe/8igA9UmXdhfRybX055p58kxsmLUB22dvRzwT53QfQmodc+6ytb4x2TTZWJwlTKv12lk+Wh1rqRkfwIq5VmXU7CY3zNqAXbN3YcOsDZh31jyuzEtqE61+LnoHuHabqj0H1DqZiqq6XwDKGW2IZ2LYPns7ejO9lvVLa8u1WNc05IpUEM/EMe+seQXHxPvqM+1SiQFNc37tNmBxt/o+faHzdiLVazyfRRujxaQ+Vx9OHPYkDvV1IZaJIZVLDUobReoDLwfnVwL4MIDfA3gUwJi+bX4Al3l43frGrEG30o6bNZrP/RJo+5VzG7ZS19CdP/zYDVhx3u3YtH9ToT1aGTYneo3NxSdfjBvOvgFLti0x6GhC/hBWTF9R8lraueZPnJ/3V29d3Yp2TYM6RAfou/fuL+tFyKBjpS9XsqVzHvrqfYHF2fKCHGSl5xsVGlWgJVw+bTlGhUYVns+kJSwHqzKOCo2y1LubNYuEVB2tfj58Zb+ud8ZiVYP+5V+ofY3JV0H5+mp0f34J2jdfj9bVrXj4/z1sqSPftH+T7Vox7Zvb0Ta+DfMnzjfUSXM7T7tUAsCx5jwcCFm3E75gyTYmFChcryTsdz8OrdZTiGViuPuCuwuuM7J5JOvAEMQzzflg0LCanFIrF1tpb7QVVJuHlVx9veQ1TOdXzmhDYsYPERo5xnK1dsdfq+/pOGCtrdHbotmtDK8/l6ZRL1OTVnVNjmdxW64mvEw9ODXnVaUx4tZON3jl70qupAs4W60dKNT+AcDqPasLVmufPe7LQLLHuFr7udfar+/hAHMZIUQ1tbPVpjHidqhgVz8vewh44iZVNjd2GmJXrsOCzdcbYnr+xPn45ie+abCWSuaSBlcXq3pgtqPy6+pzFV1ZGLe1htO1klJRKK89hcQp0xFuHoFE6n2EX98C36kXAhBF2xi7GNWvjeRGHBZbTwHAQFZrr3rcEnfwTHMuhPgYgJsAjNVfR0p5kVfXbBjMGnTAOJg2a2/OaANO/z9AU4uawJwM0PXXCEaMA3XT+X0vrUfLnkeBHx7O68Uq6VRqGhtNWzPz5Jn5Bd/29uw1PEkSQkAIYXstn/DRK5KQesNONxgMAVqHo8jA2OfzGzTegH0H3qznM2sJAyKAuRPmwPeTj6v7AepTlek3Ob4J4KSMdlpC5ilSVaxu0NvVz1DfjdlrtwHHn4awEPmY1rfjyVwSQL+uvMXX33bbacg1jbm+zdew0/CSIYA5Pm1iUwmGkUj3IhxsQSITQzgYhm/91WhR1LUM8nn9h4dLtjHhQBgnhE8w2KY9uPtBw9pIbsRhsfUUrK7DOjD08PIW5H8A2AlgEYDv616kXMxTQbv39Ws0z2hTp53t+QPQ84/yLNiszr3uciDVW56GvUw0H+L8lPQ1rbjj+TsQLdNyiJo0QuoMp2tqOMSp3ZJtrugxyTvGTIWSSTqybHMK8xSpOezsC+3Wf+j5h8FaLfHeG5h04iTMPHmmoR0v1mbb1YM3e9+kTRQx4rBfqpy/EN3Jo1iw+fq+XH09upNHobT9xng+h21MKpdC+9nthmnt7We3I5VLufnt2CaQkng5OM9KKf9VSvm8lHKH9vLweo1LJg48co06nUfJApuX9nuaT/8+8Oh1wOmXqO/aPvu2qsdkSiQk87n3bQWeuw9oK60BrZRwIIwrP3ElFv95MToPdSIrs+g81ImedA86tnQYtnVs6bBNWNSkEVJnOFlTowwS2YSjnBGWwIrzbjfmik//GOHmY4xl+cq9SEBBx9aFxnNuXVhxx4l5itQcVu3+I9cAwl9YP9t+BfhDhv5FeNPtWHHe7bhu4nUF7bhdm21VD5Z+eil+sesXjtp7MoSw7Zca11ZKnPsddGztKMzV4y+sqI1RpIJFzy4ynG/Rs4tcv2HENoGUwksrtT8IIa4F8J8A8redpJTdxQ4SQhwD4FcAzgAgAfxfKeW2Ysc0PObpPC+tV6fnXLFOncq+X51qVrYFm9W5AWDLcmD6P6tanhIa0ErwCR+GBYcN2HLIJ3wYFRpl0KoPoiaNEFIuPh8QGe1abnFqt+QLhjHqDzdg1fSOfn35k4vh++ovgS+tUPPnkVeATbchfOn9jizbnMI8RWqOYvKSQMhUP8OAEAVSt1HCj1GX3leWzZS5Hty+7fa8XWqxY8kQw7ZfeqMhX4ebCvuRaq5uqaiNGSz7PrYJpBReDs6/1feun8ouAYwrcdzPAfxRSvk1IUQTAHce19Yz2lRQ/UIYvYcAiP6/HXmlcJ/pC00a9DCQTQEypw7q0zF1P/NxY6YCmUSh7r0C7PSg2rQe/YIYmt2Rftu8s+YhnonbLgxHTRoh9YUCiYQAwkDfu6x4CpdVHtGmB7Yosr9zJgR8vQfRcs+nAPTpEMdOK5xZdNzHkMjEMO+seQWLxyUy8byWvFx8Emp50PcuwaV7SPWw6lNoU3+bhxnbfiXXb9eq3//48Xkrta73uvDA7gewce/GvB1aJBCGL5MwDIz07TUAvJN4x1CsfN1lW96YlFrsWMMuPo/uB+7tn9qeuHm/df7PxAFzG6Pk1D5v8zA1nptaANM6IprksiD3exCT7LuSYnh2m0ZKebLFq+jAXAgxAsB0AA/2nSMtpXzPqzLWDcFwoVVa26/U7do00T2Pq1Yn2j4X3KraTGga9G33Asn3gfgRYN0VfTqeKxzbGFVCMT2o1bSekc0jDdvmT5yPtvFtaNe0n9SkEVLXKErOVT237fTAbNqoV0xHgctWG/PcZavVTqDJmicUCFtbnwUqtD6z0/eWWg+EEK8o1qfQo+SA2GHVQk3Xv1AuvBXdU67OW6kte34Z2ie1Y/7E+bjtvNuw5uU16I4egPLcvbaxzqm9Q4xy8qCd/ClyrGFbWPgsbdOySs7UxhyFkny/vz/88JVqXJvanZC/0EqNtpekGnhmpSaEiAC4EcAYKeVcIcR4AKdJKR8vcsxEAPcD2APgLAA7AFwvpYxZ7T9krCZSUXVwffol/dMv9zwOTO2z/NHuRgbDxjuDD1/Zf+fx2m2qbuwP7RXbGJVLMbsIbQVl81N1oN82okKrtFJU/XkVrdTsoZWaLQ0Rt7F0FAus6vSFKyt+Kl2QR6SAb+1lFpY76wAp+/OjEOoNStN+sSsfti5jpXnHqQVQY9IQcdtwlOpTaGgDmn1b1cVnp98EHH8aYtl4gZXalJOm4KcX/BR3/OUObNy7Ua0zZ3eg5b9uso31KlqllYJx6zbl5kGrp+xAwTYFfTHUt1q7T/gx/6nrCvP3OT9Ey88nGq99+VogNCK/qVSftQ6oetwSd/ByWvtvoA6uz+v7/CbUFdxtB+d95TkbwAIp5V+EED8HcDOAH2o7CCHmApgLAGPGjPGg2DVIU0TV2zz94/5tvgBw/k19/9bZommJpnmYUbNz/Gnq+wBsjMqllH7HblqP9u9GskobknFL6h634zZsV6cHMFOnII9IxWb9jZb+PBcaYbufbRkrzTt2+t5S64GQimG+LUGpPoWGvh/x0nr15Qsg/MPDlnVkeHB4XkO+8+2dCB83vmisc2qvkYaO23LzoJWlsP7ffe8++NDSNBwA0NI03N66cqTp99y/raC/O1iac0JK4eUtylOklCsAZABASplA6bs6bwJ4U0r5l77Pj0AdrOeRUt4vpWyVUraOHj3a7TLXJnb2Jum4encxFVU7mqlo/xQhs2XRkVeAo294apFmZqB2EU6PV6SCWCZmeK81hmTckrrH7bhNZOLWdbqUq0Q5OLVrs9kvkYlj3lnzsGHWBuyavQsbZm3AvLPmWectu/xbSXmIazDflsAuJs2xrGnNTfslMjHLetzV04WZJ8/EhlkbsH32dsTTvVDOX8hYd0hDx+0g5UHbNsbCNhOpqPHYPs25o9xPiId4OThPCyHCUJe+gRDiFOhWbbdCSnkIwD+EEH2PeTED6hT3oYuiqLpwzTrNoAsP22t4zJqdPY8D4WNtzuPNE5yBasqcHO/U53goMDa5tqwXIYNNOBC21Ai6+mTCqV2bzX6hgJ3usNl4vFMNpcv2cYQMGKuYbPuVqi3Xx3JTxFKbblWPl09bjn09+/p9z1e3ov1PN6J7ytVQzFp2MvQYpDwYDoQs2pgVhbaZbb9SZ1PpCPmbneV+QjzGS8355wAsAnA6gP8G8GkA/ySlfLrEcROhWqk1AegCcLWU8qjVvg2nybFC0+kMOyGv98LRN4DhJ6l3uItpeAo0OxartQdbXLNIs2KgmrJSx1egEaq6JseruB1783+Vtf++Oy8u7wLUnFeTholbRcn1aQQjSGTiap02rZrrwkWcrQpssZ+qp3Wgiy9HQ+m0PI1Hw8Rtw6GPyVRUHZjrp7lrsWxey6apBcgkoDx3LxKnz8pbE4YOv4rEqRd5sU5MNWDcesFg5MFUFMprTyFxynSEm0cgkXof4de3wPexzwO5dNHV2r1YE2WQqXrcEnfwTHMupfwfIcQLAM6FGjDXQx1wlzpuF4BWr8plh6JIxDM5RJr8iKdziAT98PlqIM41nY6SVfVegHrnb9E7Bd6jAIwaHr1mJxgxJkUJoHl4/3FFkmY5A2yrfQeiKdNr0vTn1N6pESKkvvD5/PmOjisdHovclYOChJCIAIgLiTAUCInCPGaha3Ssiy9HQ2mnn/SYmm3XapiG/830C8jqZ5hNv1FdIG7LXWpfQ4tl4etfy0Z7b4rA96flaNmsDuZbAMAXQMRGi872eHBoqNh1YH1mSVMEvvVXo0XJAuiPTfzwsLq+EmBYBE5PsdwfTUcRCUYQ77uh7Hf7hjJqeoFEMsh4+r8upXxXSvlfUsrHpZRHADzn5fUqRVEk3o2lMee32/GxWzdizm+3491YGorizayCsrDS6UxfCMSOAN37nGl4Sk2/LPL3cqaNeznF3O7cA9W1E0LqGIvclcvE0J08ivbN12Py6slo33w9jibfQ2+611FucqyLr3EteU23azVKw/9mWn3Zdi/Q8w+jtVTPm6r8bcZidWX2YrFsE/txm7oTz9ZGnWhkaj52y7FS0yz8SlifWTKAvGyX+6OZaN7St73P/jNXof2nHZRoEj2DfUumJm/hxTM5tK/biW1d7yKrSGzrehft63YinnG38lWElU7n3HnA+muAzUuN3uZ2Gp5MHHjkGnX6pZJV3x+5Rt1e4u+JbAIdWzrQeagTWZlF56FOdGzpsBz8lrNvudid2yd89Eodwkz47YSyXqTBsMhdCShYuHWhMVds7UBPqsdRbnKsi69xLXlNt2s1SsP/Zlp9Of0S4NHrjG3+o9f1b79wUfFYtol9n/Bh6aeXGurO0k8vhW/Qu5pDj5qP3VL9UD3pGLD+28Z9139b3V6KAeRl69y/AmtfXmtoOxZuXej6AyAv+8+k/vDSSs2KGrmFZyTS5Efnvm7Dts593Yg0uT9tpWx8PiAyWtV+adM29VPdAeBLK1QteiZurSEvNf2yyN/DgONpal5OMbc7d7O/Gc3+Zqy6aBWnAhEy1LDIXZGm4Za54kPDPlSwzSo3+Xx+jAqNwqoLVxbXxVvl5hrSktd0u1ajNPxvptWX40+zbvO17aPGqr01u1i2if2QAFZuW4kfnPMDjBs5Dl09XVj5wkrcMe0Or7/ZkKfmY7ccGZDZCljb14kkaAB52Sr3hwJh3PfifYb9dr69ExHXF7KjRJP04/rgXAixCtaDcAHgGLev5wbxdA5Txo7C6OHNmH/hqThldAuiKXXgG01lB1e3Y6f91usVNXuTfVv7vUe1xVv0CUg7F2T//hraNJ/mYf3TgLS/f3E5cNY3APRP89EvkKFNGzdryLUp5k72LZdS56ZXKiGNT4EmTwr4TLktnu61zBUHogcM58rnD0UW5FsfBFr6WjH13Sb/17CWXGvXtnW9m982ZewoxNM5DGuuvOlvKF2riWK/WSToN3zvcMCHRFaprd+h1IJbWlt/5JX+Nv+MNnWx2dEfV/sWi97p1/gWO6cp9hWpIJ6J453EO7j0sUvzl5xy0hTEM3EMq48FteoWu9hNZnJQJAxxCmDw63A6rkoyT79EvQl05BVVRqH1Q/Wkotb7ZlNALqPToUfUp+NmBpCXzWuiRNNRy/YknolDCOHaQyEv+8+k/vDiFv92ADssXtsBLPDgegMmEvTjl988Gx1fPA1PvnQQb72XxHfXvDD4uh03rXn053r8e8Ut1PTnm7kC+ORXgH+fDWyYi3A6gRXn3V5y2rgiFWSVbMGUNremmA/Ulo0QUt9YavJycSiXrTbktrD0YbnF1MSRTSMLp6orikW+zTnXRlYBp9rSSNCPlVdMwtRxxyHgE5g67jisvGJSvnPu5bXrFbvfLBzwGb73r7d21d7v4KT/oLX1ex5XJXEX3KpqzJ/o0GnP/6Gu3B47rM7Oc1AXtLq57a1tBXVv+bTlCAVCg/xjDD2sYveX3zwbsVTWFKcp9CYzgx+7wTAw+So11paeoL5PvkrdbqYpUrjvud8Fkj0mHfqR/hmkHhEOhC3bk5zMuaoPZx+X6PHMSm0wcNNqIprMYs5D27Fk1iex5LG/Ge4+Th13HB74VuuAnjY4wk1rHvO5zmhTdWSjxlrvr51PKmrS27cVuHYb8EQHlGEnIHF+h2qZ0rMf4ZYT4TNNRdIszY4PH485E+Zg3MhxOBA9gNGR0a4lF5dWsqz6Ix5aqdnjtS68jq3XGjZunWJrm3jej9GSOGp4wpL7zPVIKGlEgi2IZ2IIiwDEn1cZrJ/Cex6D74yvAat0CwCNnQZcvrY/B+q3W+XhKhDt62w7aaPcfspdzrX7qLu4tfrN4pmc4Xs/ecP06vUT7HDaf9Cv1p6OWcf6l1aoAyKHdUFfN2+ecjMuPuVijGgagXgmjlAghIDV083apu7iFiiMXUhgzkOF9XXZpRNwwV1PG7Z5Hrvl9G+t9l24T31oZD7+8rW2q6+7Ra7P/lNbrd3v82P+pvmuWwa60MetetwSd/BiWvsfUERbLqWc5fY13SDSrOp1Tj1hWPV0O25a85jP9dJ6YM+jqp2E1f7a+aTSf1yf/synZNHSZ+PWollSmND0MlmZxca9GwEAARHAjtk7Sn5tp+ht1TjNh5Chha0mr+UE4Ccf79/oC8B//k0YFmgGAAxrGq7mNQvrJ0z7Z+NFNF2j0zxcBcrRlvp8It/hdqPjXfO6Vhew+s3M37uq/QQ7nPYfzH2HYtpzh3VBXzfv7LwTd3bemW//uf7L4GGOXUVKyzj9yKhIwTbPY7ec/q3VvqGRlevQB4jf58/LMoY1qRIOL/Th7OMSDS+y5l0AflLkVZNoep3X3oliythRhr9pmjPPKWUBoSjqHUUlByTf73+X2nbF+bn059Mfr+nZgX5dWrFz9FHK0kyRCmKZmOGdEEKcYptjevYbdxwzFcgkjbktFYVy/kLE5v8FyuJu9f38hcDRNwqP1edA/fYasUjT2io9g9VGVfPa1cT8vavaT7DDrs039w3M7b7VMVrbr2l/r90GLO5W36cvNNQFTWu+ffZ2bJi1ATNPngmAlqa1gF19/Ud3vGCb57FbrE9qjsl0rHDfZI99fJux6tu6SCKbwLyz5mHDrA3YNXsXNszagHlnzWO8E9dwfXAupfxTsZfb13MLTa/z5EsHsbztTFd1eo4ppiU3+5M+90ujT6lZC1ZKl26lT0v1AEIAbb9S99/60+JadR3F9DL0bxy60OqMuEXYH7K2OAsdY8xR31gNpHsNuU0RAt1TrsaCF1Zg8ppWLHhhBbqnXA2lZbTx2LZfqU9ttByo3x6sDd2sF1ryerh2NTF/7ydfOoifXz6xtn4Hqzb/y79Q+wpa38Dc7r/+dGGsf/kXqiZdqwtFdMJa296+uR2tq1ux7PllaJ/UjvkT51MvWwNY19eJODYSHPzYte2Thgv7okq2cF9fwDovm5+8l+OnXiEhfwht49uw7PllaF2jxn3b+DaE/LXRRpD6xzPNuRBiPIBlAE4HkI9YKeU4t67htgZS0+uEgz7E0zm0NAcGfxVWOy25psHRtGDaezH9TjFdupWmZ8FO4A/twMcvAc68DAgfo97BlLJ/VfcilhR2ehlbregA9TkVUnVNzlDSnE84eUzZx3gJNeeVU23NOVJRKM/dW6gbn3qd+oREy3OQwLorDLktdv0uLHj+dkd6dZz7HXVAY14p+NzveK5tdEo1V0wv89oNE7fm712zq7Wn+1ZaP/IKsOUuo5sLYGz3r92mxvY531bb+1Qv0DRMfUq59xnglAuKas7t2vaVF65EJBip5yntDRu3VVutXS1MYZ80E7fWol/5O2NeFwL4+ybg5M+osZp4rz9G9Xm5HG17hdRYn1ZP1eOWuIOXq3T8BsCPANwN4EIAV6PGA0ev1xkeUhsVpzq9AQ/s9UkLUFX7+kRi9ie18ynV30X0+fqSX1I9oYDa+AZb+s/3xeX9A3HtHPu2An9c2HeOPo258JVMbHZ6Gfo3EkKKYtVpg1RvDupsc3xHXkVL34ybFqkAR14FAs1ARjedsKmlIDeGR45xrFfH+TcBW5YDT/+4cLurX7nyAbbbWvJyyuPFtWsR/e8RS2X7Nbmy/3sP85fXT/AcTU9+++j+VazPaAOmf1+tW6kocNXv1XrTtQUY+RFg+j+rAx/9MUB/23/Vo8aBvq6fYde21/nAvG7J5RTEM2r/M9ZnA+z3+yzra83U4aYIMPwk9UaRdjN0608L83owDKy/2jpGzefzeM0Q9mmJ13iZPcNSyk1Qn86/IaVcAuAiD69XNTR7mV9v7cKBo0nMfWhHeRYVTqbhmP1JnejBFUWdqh4/oj5Jun20+h4/rHZ6236j2qb97ir1b937PNFaltKjE0KGMFb5LxNTrZz0tjnJXuBztxmn2H5hmWqnY5Dn9BbksUTPfud6dTsdrpW2seKvXFuWZLVWnmpj/j3mPrQDB44m8xZqNf276LW9Z7T1WaV9X2eV9iZw+DW17V93hVqX7Nr+7n39dW3GYvV8uj4B2/baIZdT8G4sne9/zn1oB96NpZHL1Yh80K6fm0332/kVy+vJwrxumZedrLc0QBj3xGu8HJwnhRA+AH8XQlwnhPgqgBM8vF7ViGdyaF+3E1844wNYuP6v2Nb1LrKKxLaud9G+bifimRILbWTiwCPXqE+slaz6/sg16nYNsz+p9l5MD56JA/GjwO+vLTy38KvTgdZ/u/9vm5c61piXA/0bCSG2WOU/JWvMTfu2AomjwIa5xm2pXmC96djn7gPajHrFcOgY6xxk1qt/7UF7zXmTe9MVtTaj7LbCI2qtPNXG6vdYuP6v+MIZH6j930Wv7Z3+feDR64z149Hr1KnBpdr+r9yrbtcfd+EiQ5+AbXvtEM/kcP3Duwwxe/3Du2onVu36ubl0YR/VKq//5b7CvPyVewGfSStfar0lF2DcE6/xcj7LDQAiANoB3A71qfm3PLxe1dBsViq2V3EyDcfnAyKjganXqtN7zv2O2lm8fK29HrwpAhz7Uetza4sbme3WhA+4Yp167hIac6f4hA+jQqOw6qJVA/UoJ4Q0Gk5tc6xymdW2LcvVqbpXPJyfJu8LRjBKoDAHSRj2y+e7ltH9uTXVp+E1dwIHQK1ZktVaeaqN3e+htfE1/btofQUtrq3a//AxJdr+GPD499Tt+uNGjVUld319ArbttUNLc8AyZluqPW1dwy4Wrez6bPP6jep6S9r09023AZfeb9zPHP8u9WMNl2DcE4/xLJKklJ1SyiiA9wG0SykvlVI+59X1nKAoEtFUFopU33M5xfBZUaRhn3g6i2jS+Herc8ZS2YHZsDmdhqPpyXx+dQEM7V3Tg5uTTzquWgXpz31GGzC/bxELq+mbvYfUBeDszlkhmh5d/04IIZb5z8o2x5zL7LaNmapqFZuHGfKYZQ7ScmpBvhPqNqDvfWDLpeRyCnqTGShSojeZQbyvzdBj11aY2y27KdXma5QznXWoWqTZYfd7aG18LBU0UxUAACAASURBVJVFNlv57+05Wlzb9S0S7xVv+yHUz+bjLKQdbNtrg5hNTomlsgX7mmM3mx2E2C1m9ec0rx/dD9w7FbhtlPree8hVuVE5MO6Jl3gWTUKIViHEbgB/BbBbCPGiEGKyV9crhVlDpmnH9Bq73mQG78ZSmPPb7bjx33ehO5bGnIfsNXjaOX/zzF4sbzuzchs2r6bhBCNA5Nj+6WoTvg58dom6IrudjYrL0zdJDbNkZHkvQrzAKv9Z2eaEjy3cL2KxbaC502UrHistaFaRWHlFaSsup1rwgepNh6pFmh3hgK/AKk3fxv/mmb3ojqttf03qezXs7NX2PlO87Xdiy0ZqinDAXxCzP798IsIBYx3OZhV0x425ojue9n6AbtfPbWpxntfDx5burw6ClRohXuOlldpfAcyXUm7t+/wZAPdKKc906xrlWE1EU1nM+e12bOt6FwDw5A3TseSxv+U/A8DTN12AH2zYjW1d71r+feq44/DAt1rzq1vqzznrrA9i/oWn4pTRLUhkBrhau5vTcBQFyCYB2fcExGQxhK+vVrXnHk3frEGq7hhQM1ZqoStdL4MZWqm5RsPGLQCHq7W3ABAW+8Hd3OmyFU9vMoO5D+0oaEse/FYrFKDo6ujmdks7Vt8OFbvG/VdNxvBQ0FE5PbJnq8u4jaay+PXWLnzhjA/g1BOG5Vdrf/1wDPdsfg2PvfgWpo47DktmfRJf+NkWAOX/3oOGvm5p9SiTUKVt6bh921/Kls0lW6oape7itjeZwTN/P4yppxyPEeEg3k9ksO31I/jM+NGGmHQjV1SMXT/XMv/DWZtg7q8OgpVaDVP1uCXu4KUYpVcbmAOAlPIZIUSvh9crillDZqUP/8ioSH6bE/24/pyPvfgWHnvxLQR8Aq/+eCZ8QpRnUaFNQwPcTSA+n86eTSnU8ay/ut8qrUY8fAkhQwy7/KflJH1ustrPzdzpshWPnRY01OSHT6h9Kbu2wqkW3A296VCxSHNCpMmPlU+9hp/+798BAK/f8SWctmgjsroZC5oGXf+5ZvS9evR1S6tH5s9Wbb+VLRvgui0VcYeW5gAWrNtliFGtP2rer2radLs8b7fdaZugZxCs1AjxGi9FEs8LIe4TQlwghDhfCHEvgKeFEGcLIc728LqWmDVkVvrwf3TH89uc6McHotNzqiN0lUGwmCCEkLrG5TxZjhbUTDydQ/tFp+LJG6bj9Tu+hCdvmI72i04taGMGcg1SiFV/wer/4bV3+vWuDfl7s89QN8RSWcsYNcdkw+cKxixpALwcnE8E8DEAPwKwBMAnAJwH4CcA7vLwupaYNXVPvnSwQJ9zbCSY1wH+69Ov4V++Xlw/XqlOr2qesoNgMUEIIXWNy3kyErTWgjrRc4cDPlx+zhgseexvOG3RRix57G+4/JwxCAeMTfdArkEKMbftXYd7Lf8fug73NvbvzT5D3RAO+G1yhb9gPyfa9LqFMUsaAM8054NBuZocs6YuHPAhkVUMGjsA+X2SmRwUBYg022vwKtHpOdUReoJX2vb6oeqaHGrOqwc155Xjqea81nA5T+ZyCuJ9a5HEUllEgn74/aXPV05bUek1PKZu41bftsdSWVudbo393u4zNPsMdRe35eSKbFZBItufK8IBPwKBBvo/HZoxC9RA3BJ38HK19hOFEA8KITb2fT5dCHGNV9dzgqap0/Tgfr/P8NnnE4Z9Ik0BDAsZ/w70Ndp9NmsQ6o8YT/UN0DO5kk/AHXvKKoq6uIXse3djtUlb6yBCCGkQBpo7Xc6TQgiIPn259m8n0qZy/Mf9fh+Gh4LwCYHhoaDtQLEqkqo6JK/Bl8V1uqV+75qj3LrBPkNNYq7H5eSKQMCYK+p6YG4Vz4xZUud4GbH/BuBJAB/s+/wqgBs8vN6goCiqL6Rms3bjv+9Cd7y45ZoZR1p12kEQQkj51FjutJYxpdCbzJSUNrntP141SVWdov1eb/ckrf8f6k2nW2N1g1SGVT2OJq215HUXo+XAeCYNipeD8+OllL8DoACAlDILoLIeRQ0Rz+RwNJ7B9//jr9jW9S6+e8Gp+X9nFYltXe+ifd1OxDP2X9WRVj0TBx65RrWDULLq+yPXqNsJcYGxybVlvQipC2osd8YzObSv22lqI3bhaDxTst1w23/cuizF26uhjPZ7KVIWrEHzL18/M7/aft1QY3WDVIZVPf63Z/cWaMnrMkbLgfFMGhQvBc4xIcRxACQACCHOBdDj4fUGhUiTv2zLNTM+n8BxLU144Fut9lp12kEQQkj51FjutJtu+pFRkYJt5nbDUVvhQlmKtVdDGe33OmlkGP/8u11YMuuTOPWEYXjtnSjuevIV/PQbE6tdxPKosbpBKsOqHq986jVce+Gp9R+j5cB4Jg2Kl0/ObwTwGIBThBDPAngIwAIPr1dApdo6vaY8pyjoTWby7/FUzrHlmvn6uZxi0KpDqi+9nj1PLdhBeKF5J4SQSnGSk6qcO815P56ytkP7R7exPOVMV89m1fZIkarMKpt1lpvdnibf6Gi/12vvRDHu+BYAgJQSJ45oxk8vm4hYSu0jaO27Z1p+t9riWuhXkAFjV497U1mcOKIZQgAnjmjGFz55ImKprCFX5HIKcjmlYFtV16KoNL4Zz6RBcX1wLoSYIoQ4SUr5AoDzAdwCIAXgvwG86fb17KhUW6fXlP/6mS4cOJrEb57ZiwNHk5j70A4s+v1uDAv581Pc7CzXwgGf4fq/3tqF3lQ2r1X/2K0bMechVXtoWaZq20FQy0OqwO69+8t6kSGE05xUxdxp1e4A0tLi6PhhTQV2RiHTomJW54uns+iOpzH3oR342K0bMfehHeiOpx0N0N2eJt/ohPw+/PzyiXkrtSdfOoi33kviu2tewMcWqb/9gaNJbH31He+0/G62xdXuVxBXsKrHD1w1GZmsosbmrRvx3TUvYPJHRyHoE4Zckcjk8G7MmD/ejacdrYHhCQOJb8YzaVBct1ITQrwA4LNSym4hxHQAD0N9Yj4RwCeklF9z61rFrCYqtSuLprI40pvCDzbsxpJZn8SSx/6Wf9fONeusD+LWiz+BlqYAIs1+JNM5KNJouRbP5AzXf/KG6WgO+PCDDbudl6madhCpqJok923t3zZ2GnDFw+rql/VL1QVYtWKlVi6VWK+Va6VW7oC77PPTSq1iqm6lVk5OqlLutGp3nr7pAsu8/9PLzsL7yWx+CuqTLx3E1Z85GcNDwaLn27X4c/jumhcsbb30x9pRif3nAKjruO1NZvCbZ/bi6s+cjLkP7SjoCwDqb/+v3zzb8v/EFXtUt9vioWszVQ41H7fmeiyltLT7+9dvno2Jt/1Pfptd/lh26QRccNfThm2DYu870PhmPOupetwSd/Ci1vmllJoY5hsA7pdSrgewXgixq9TBQoh9AHqhLh6XlVK2VlKISrV1ek25pic368ofe/EtPLH7IF798UzVck2XvLREZr7+qScMy5fBcZk0Owhg8AfE1PIQQmqJcnJSlXKnVbujX6NEo3NfN04YEcLUO5/Ibwv4BK6bMb7k+UaEg7a2Xk7IW4QB3ne865yW5gBWPvUarpsx3rIvAKi/vd3/iStafrfb4mr2K4hrmOuxIqVtbOqxi1Una2B4wkDjm/FMGhBPBudCiEDf6uwzAMyt4HoXSimPDKQQmiZH/7T7xs99DFJK9KayaGkOWD41iKdzONKbyuvM9O+jhzdj4RdPw8hwEyLNfsRSWbQ0mfzP++5kxlLZ/DHzLzwViXQWR6JpyzIB6hMSyycYg3lXUH+tVFTV7ujvZmpaHiZAQoiZgeQqJ8dq+sJByElOny6b9/MJoP2iU/GFMz6QfyJ+JJoy5H1A1Ye+n8xg1+LPYUQ4iPcTGWx7/QhiqSyEEPnzQaLg2PcTGcvzxVJZR0/OSXG0/9Nw0Id4OodXbv8iYqksXl06E+8nM2i/6FT89H//nt9/ythRtv8n8XRu4DdAisV9MGKqN2Egk+BTxCFILJUtyD1PvnQQvcksnrxhen5bb5/lmjlWrdbASGb6ZoW6NctGyQHpmJqvU1GgqUWNV/Y1CTHgxeB8HYA/CSGOAEgA2AoAQohTMYirtWuanPZ1O3HiiGbc9IXTsGHHm/jKpA9j4fq/onNfN6aMHYWVV0zCcS1N+YQTCfpxbCSIf/n6mdiw400sbzsTv9/5JlZePhHCByQzCuY8tF13/EQc19IMAHg3lkb7up3o/P/Zu/f4KMp7f+CfZ/a+SbgkhIsoRuQmEkhI0IMKpegpWiu1RU+DRfBoUaklqEVoqz8PUltfoLUSrTe0LWCFVrAealvpqUIRpUog4SIiIKIIyC3hkr3PzvP7Y3c2e5nZnd3sZXb3+3698trs7MzsJPvd7/PM7jzf52AbGicOwnPTRqPDI+L1rV9i6mUDQmPVH3htR+iYHnhN/VhCY3FW3xH4JHHA2MB4Gntl+hvc6OcaPx+Y8hKw5geRz01jeUgSaFx4kehKrtK6rTy+MHq9NOckeZy3nMsVc7PKes9PG42GywdgzsrW0LIlU2tClz3Ly5ZOr4PT68ecVWHrNdTAwBjuWNYcsb+mqTVoDNufUWBY0lATs63NSOPGu0p+TVd+8DlurD0fb7TE9hmWNASqXze9sz903+0TY16TtI3lV417W+z7ZspLwNblwMZFme0vEN2xGQ1ouGyAQk4BFqz9KLTsuWmjsWRqTWSOaqiB3WzA2IEVEbnH4REjco9SHtRM8gOOE5F9yikvASW9spLXCcknaR9zDoSmTesH4B+cc0dw2RAApcFCcfG2/QxAOwK1zF/gnL+otq7WMTngwMzlzarjxaLH1UgSh1v0Q5IAmznw6TkDcLLDqzpmHEDM2MDNP5mI+/+0PfS84d+8A4Fjinss2Rz3rfRcEx4E/uPuwHMVzqfwOR+TU0xjzjONxpxnT9y47Uqu0tlYcq31SroyvrxvdyvuXpF4fOjYgRV4+bb6mG+vJInDJfpRYjHC4RFhMxpgNOoyN+s7bqPIr6lavRmg83Uqs5oiagXYTQa4RCkzY/mV4t7nVH7ffHMx8OzYzvv5XycmF/IqboFAfQSlMedKY8mfaqjBaacv4hv2268aCDBEXLWTsI+aDPdZYNUtsfHa8CpgLqVx4+mR87gl6ZGRAWec838rLNurcfMrOedHGGO9AfwfY2wP53yj/CBj7E4EL5UfMCB+x1wQWOCTa4aY8WKTR52He74+CIN6l8IVnPZMbkgFgcFu7vzXlFkFSJzjArMx7riy6H336W6NeF5R4li7/QgA4NNfflN9X+ENcbbGfSs918ZFwNfmAkyIadwlLsElumAz2kK3AqNkqiaZuCVELzTHbVdyVbxtPR0RHTaJAS6BwYbgLUv/lCNq9UqsRgHn3L7QCXFXxpcfeEw5/0ePD91ysA1WkwECC7RNcqdYEBjKgifjdCl7rFTzrfyaqtWbAQKvSZnVhIt/Fng95VoBAmMoDVbbT/tYfqVxtWrvm15DI+93sb9AbX32JBO3oihFfEBXYlHunyqNJe9VasHlv3w7tCw8hoH4Y9hTHoduKVWOV0tpZB8zix8kUWwTvdJdFHLOjwRvjwP4M4DLoh5/kXNezzmvr6ysjLsv+RK1L045I8aOTx51HuZ+Y2hoapvAlGbxp41weiPnN5fJ48qcHn/MvvcdixyzHk5tX16fv3NaiROfZG8OxyTmi5S4hDZ3G2a/Mxt1K+ow+53ZaHO3QeI0zZqaZOKWEL3QHLddmW9WbVvPuYjpdSTPmazkHTmXh3t6ag3aoqcfcnjROHFQxHpqeT16PKc8Rjl6vbMuX8wymoM8eanm2/B5zdXabnl5+H2HR0zbsWvmdSi/b05+Enm/C/0FauuzS2vciqIUM51iR3AseTi1seRKy6LzjNpc6innI7mOUbgBYwPLc4Bim+hZRi5rTxVjrASAwDk/F/z9/wAs5Jy/pbR+ost+5EvUKsssmPuNoaHxYxLnyU1phs75z895xKhx4oEx527RjzaHF6K/c9/yibrSuLXnp42G1y/FjOfpZfaCrZwauPRnxBTg6oeB//1R9secx3kuh8+B2e/MxpavtoSWjek7Bk9PfBolppL0Hlf65fyyH62Xq2X6MvVsyPSl8HRZe/bEjdt0jzmf8jKwdRmw4Reh1RxzWjH7w59nPO84vSLaHN6IPP/i9DrFS0ZfuLUOd63YmiCv18BkEDSNObebDZi5fGtEm5DyGE99yPmBJ3N5sNYx56s+/CJizHm53Zz9YQVeJ+A8Cbzxw4yNOc/ztr4rdB23Spew33/NYMV6FxaDgLvDck/T1BqYY5Zpq6nRpXzkcwPu07Fjzq09AJM1+f11UYHGds7jlqSH3k7OByLwbTkQuOT+Vc75L9TWTzjmnHMMefDvECUeutT84soSCAILLZcZBRaaGk11f2Fj0e0WA5yeQJVeQWCQOMf9f2zFk9+ridh35+XzJXB6/RFV4gHEVgRmPPBNkRT8JH7EFGD8A0Dl0OxWa4/zXBKXULeiDiLv/LbAyIzYeuvWfLgkKOfJi07O04dOzrMnYdymtVq7DXi0d2ceBCA93Ia6V+oznnfkXD5rwqDQmMzBfUpV2wynNzKHA7F5nfNA/ZPQJfFh64UvY4xlcw7ybMj5wSc7dje6WrvdZAi+JkbsP96BzZ+exNiLe4WGxFmNQm7G+3MJeP1OYNz9gUvZT34CfPYuUHNLoAp2GvoLed7Wd4Wu4za8byszCgyfPHptqJ/p8Ihod3jQv6cdLp+UMEdpmY2iS/mIS8CHS4HqmwFbD8B1Gtj5GnDZzMBl7VlWoLGd87gl6aGrCOScH+Ccjwr+XBrvxFyL8Mty1m4/gklPbcStL38YmuYMCJw8vzf/62h9+BsAgA63qHh5u5ykrMEx7JAL9Pj8kDiHwyPi2FlP6FJ22drtR7Bg7Udw+aTA2EB51yyYHIPjCUstwSnZoi/x3LUG+NsDndNKZLJIhjyuTR7/o/JcLtGF2j61Ectq+9TCJboyd2yEEH3TmD80bStPrxPGdeaLrOQdp9ePSZf2QZ9uFjAG9OlmgUvlEk+HRwzk7rAcLs8/HJHXFRgMAsqsJgiMocxqgsEgaN6WZI78GhgEAYwx/GP3VxGPb/viNCY9tRHTXvoAHBzy5/mSxNHhESHx4G2cYXJp4XUC574KFH9bWB64/fgvAFhq70EF1NbrU3gfViZfci5/38Z5YAYhjxh7mbbWPJPWfOR1BuJz8UXAIz0Dtx//JTPDNDWg2CZ6pquT83STp1MbO7ACRoFh7MCK0PQmTVNrcf81g/H/rr8EHIGqlEMelMefeyIaVvnynpnLgussa8Y5tw+nHJ7Qst9t+gxLGmqwbtdRLJoyUvE5lfYTM9ZdnjalahwgGAO3OptWwma0YfH4xRjTdwyMzIgxfcdg8fjFsBltuT40QkghUMiDNmuPrOQdq0FA3YXlmPXKNgx58O+Y9co2OLwinps2OiKva52+zO+XcEphvLrfT2Mb9U6OhTuXb8XQh/6OBWs/wtxvDMX91wzGkoYabNp3AqccXoiilLhtT7cs9BWordcnm9GAJQ01MfmIAbhrRSDP3LViKy7qVQpHcHhn1uJSjc76thTbRM90dVl7srRcrqZ2WY4kcTi8Ik7FmR5NHn+udcqc+68ZHJhSJfhc4ZewCwLTPEVPNqYL6qo8rnKZ86+i6LL29KHL2rMnU1MAqlLIgxJDxvOO2pRE8tjxbjYTzrp82PzpSYwb0jthZW61/b04va4YKq3nddyqvXYv3FqH17d9iQV/2R16LZXWS3naKa2y0FfI47a+K3Qdtx0eEe/uPY6xF/cK5SOzQcAdGqd2zHhcqtFZ37YAYzvncUvSIwfvzuySL8sBIqc3EQSGEosR9gTTowHKU+soTZnT9M7+0HQUZdbYKVXUpuiJmZpCadoUnRGYECqakcfFMwgheqWQBwUg43lHbUqi8hJLaPosoHPMear7K8lF55gkJd5rt+AvuyPup3XaKa2y0Fegtl5/7GYDZq9sjRhzrjQ9o9rUjhmPSzU669tSbBO9yuuPiLSINw4s0fRo4etFr3P8rDvpaSbSPjUFIYSQtFIbz6k4/ZBHxDm3DxIPzOahdKm62v5yMv0W0UySuOprpzSVGrXtJFuU+pJK0zNq6d8SQvSnoE/OE43xtpsM6Gk34fGbo8eI14QqWsrrhY9dv/+awbCZDQm3ixZvDDwhJHk7P/siqR9CErGbFMZzTq1BT7spJt+LEk84llxxfw3x2wqSW3LfYdO+E4qv3bpdR2NqD1DbTrJFqS9pNggxsdrDbkLT1BqKS0LyTEGPOdcyxjve9GjhwseuOzwi7ly+FZVlluA0aaU41OZE724W2M3xL1VM69QUJBU5/2fTmPMcWnAm10eQqryJ20Lg90sJpzkTgJgxnmpjyZX2ZzAU9GfjsryM2/C+w4IbhuPG2v7oZjPB4RGxad8JDKwsC02zt27XUdw+bmDYlGvUtheAnL9wCacKjupLcs7xu02fYdKIfhGx+YNxAyEBFJfFgV7YAlHQg97kMd6dc42X4qszLoBzSJyHElX4CXWpNfZfEp0E5fFlosSxdvsRANrHHwoCCzTiXmrECSFEjwwGAWXBk2f5RDu6wrHVbECfbhasu3d8qDP83Ib9imPJGWNgwTm3wn+PRh/eZp/S/1ypPgwAMDCs++gY3mhtCS0zCixUa0apvg0hmRBdT0niHAdOOiLWOXDSAWswrgkh+aOgWxCn14/GiYNwY+35mL9mB/p0s2DupKGYuXwrthxsw5iqcjRNrUVFiVm1AyRf3ta4siW0zQu31mFMVXnENybyOJ5EjbLS/hIdAyGEkNxRytsvzajH3ElD8cBrO0LLHr95JNxeP+xh7YDWnE9tQ/ap/c9LLAaMqSrHpEv74JvV/TDrlW0Rr7HEEfpgXmvbT0gmub1+xXzk8voxc1kz5RRC8khBX9YuF4O7a0VgipN1947HgrUfJTWthNKl8fdfMxgNlw/AnJWtSSc8zdOpkUzJeYtEl7XnTjamXqteVp2J58ibuC1ESnl7808m4v4/bY/N5dPrUBp2WbvWnF+gbYOu41b1fz69Hi6fHyYDw6xXtsU8/th3q3HNk/+ik53ClfMXM9l8qzbt3/O31mHUI/+IWJbnOYWoy3nckvQo6HenIDCUWjunOBnUuzTpaSWULm9remc/7pk4CEtn1Cd9+aHm6dQIIYToglLe7t3NqpzLozq9WnM+tQ3Zp/o/twQubQeD4uMDKuz45NHr4PIq16ghJNvUpvMrixqqSTmFEP0r+Io04VNO7D/ekbbpz1w+CaUWY2icWbzL4sOncnN6aDo1QggJF2/KSz1Qage0TlOkdQpNmmoz+xL9z9WmSDvr8mHaSx8ADHRiTnRBLVbPuUWsu3c8Pv3lN7Hu3vFonDiIcgohOlfwJ+fhU048t2G/wvRn8aeV6Mr0Z0pTufkliaa2IISQoERTXuqBUjvQU+M0RVrbEJpqM/vU/uc2o4BTDi9+t+kzLJoS2WdY0lCDzZ+epNeG6IrNqDwFJOccC9Z+hKEP/R0L1n6EhssGwGYs+K4/IXmtoMecy8Krsbp9YdOmabwcPdUKumrj2V6+rR4Sp6ktciTn/2gac547yY45z4ZiH3OeL2OtldoBAJraBq1tSAFWa8/5wSc7JZU8JZock+GzvTi9ImwmA9yiVAivDVGX8xc2lXwrihJcYueUjQbGFKd71FtuJWmT87gl6VEU787wKScipk3TmJyip6zQSm08m9VkgBCcSocSJCGkmOXLWGu1dkBL26C1DUm1rSGpU/qfh8fk2u1HsHb7kdB0qQJjKC2OOepJnjEaBZQZO6eAlDjPi9xKCIlUlK1/Nr6dkCQOhzcwBqiyzBL65P1QmxNunz/iQwJCCClW8rjfVKam7Cq1tqCIv+km6JyGddKIfqE57NftOgqHR0SJWb3GDCG5pPTNea5yKyEkdUX37szGXLLyc6z84HM0NdTA45ci5p5smloDq5E6cYQQIo/7jc7JmR7Pq9YWlNtNaHP6aF7yImY1CGi4bADmrOqcLnVJQw32Hz+H83uW0GtMdEcUJbQ5vZExO7UGz08bjbtf2ZbV3EoI6ZqiuzbL6fOjcWULNh84BVHi2HzgFBpXtsDpS1/1Svk5nvznPrjFwIl55PO1pvX5CCEkXwkCQ0WJGUtn1GPvL67D0hn1WTn5idcWaGkjstGWkNxwiX7MWdUa8drOWdWKi3qV0mtMdEkxZle2QhBY1nMrIaRriu6bc6XxjX26WQAOSJyn5dLE8Oc4r4eNxvwQQkgcuRhrrTbWXW2+YJqXvLCFD1FQi4FuNhO9xkQXoofUqMVsSXDKX4DqWBCSL4rvm/OoeU0njzoPcycNxczl6ZvGp6tzqxNCCMkstTmu1eYLdnpoXvJCFT2d31mXT3V+c3qNSa4pTT+plrccHjFHR0kISVXRnZxHz2t6/38OUbjsvGuXrXV1bnVCCCGZpTbHtYGxmJz9+M0jIQjatqfcnn+ihyi80XI4ds5omt+c6ITSkJpN+04oxqzNSLFKSL4pumtcwsc3ypempfvSxOjncPv8WDq9Pqm51QkhhGROdJ4OzV3OgCf+/AkWTL40VKn7iXWf4Mnv1WjannJ7/okeorDgL7vBGPDi9LpQ5WubyYBxQ3rTa0xyTmlIzeyVrdjz82sjY9ZogNFYdN/BEZL3ivJdK49vFBjL2KWJ4c9hNxtRag38XmqhaVgIIUQPwvO0nJudXj+OnfVg0lMbcfHP/oZJT23EsbMexTZBaXuSf5T6AW/tOgbGGATGUGY1wWgQ6DUmuqDWb3WLEsqsps6YpRNzQvJS0b9z6dJEQgghMmoTig+95iSfULwSUtiK7rL2aHRpIiHFY+dnXyS1fvVFAzJ0JESvqE0oPvSak3xC8UpIYSv6k3MgN9P4EEII0SdqE4oPVX7+YAAAIABJREFUveYkn1C8ElK4iv6ydkIIIYQQQgghJNfo5JwQQgghhBBCCMkxuhaGEJIxVe5Xk1r/oPWWDB0JIYQQQggh+kbfnBNCCCGEEEIIITlGJ+eEEEIIIYQQQkiOMc55ro8hZYyxEwA+T7BaLwAns3A4XUHHmB5ajvEk5/zabByMGo1xWyzyIa6ySe3/kU9xW0ivaaH8Lbn6O/IpbtMtX2InH44z28eo17jNh9dKi0L5OwB9/S05j1uSHnl9cq4FY6yZc16f6+OIh44xPfLhGEkkes0iFcL/oxD+Blmh/C2F8nfkk3z5n+fDcebDMWZDofwfCuXvAArrbyH6QZe1E0IIIYQQQgghOUYn54QQQgghhBBCSI4Vw8n5i7k+AA3oGNMjH46RRKLXLFIh/D8K4W+QFcrfUih/Rz7Jl/95PhxnPhxjNhTK/6FQ/g6gsP4WohMFP+acEEIIIYQQQgjRu2L45pwQQgghhBBCCNE1OjknhBBCCCGEEEJyjE7OCSGEEEIIIYSQHKOTc0IIIYQQQgghJMfo5JwQQgghhBBCCMkxOjknhBBCCCGEEEJyjE7OCSGEEEIIIYSQHKOTc0IIIYQQQgghJMfo5JwQQgghhBBCCMkxOjknhBBCCCGEEEJyjE7OCSGEEEIIIYSQHKOTc0IIIYQQQgghJMfo5JwQQgghhBBCCMkxOjknhBBCCCGEEEJyjE7OCSGEEEIIIYSQHMvrk/Nrr72WA6Af+knmJ+cobuknhZ+co7ilnxR+co7iln5S+Mk5ilv6SeGHFIisnpwzxgyMsRbG2JsKj93GGDvBGGsN/vwg0f5OnjyZmQMlJIMobkk+orgl+YjiluQjiltCipcxy883B8DHALqpPP5HzvmPsng8hBBCCCGEEEJIzmXtm3PG2PkArgfwUraekxBCCCGEEEIIyQfZvKz9KQDzAEhx1pnCGNvBGFvNGLtAaQXG2J2MsWbGWPOJEycycqCEpBvFLclHFLckH1HcknxEcUsIAbJ0cs4Y+xaA45zzrXFW+wuAKs75SAD/BLBMaSXO+Yuc83rOeX1lZWUGjpaQ9KO4JfmI4pbkI4pbko8obgkhQPa+Ob8SwGTG2EEAqwBMZIy9Er4C5/wU59wTvLsUQF2Wjo0QQgghhBBCCMmprJycc85/yjk/n3NeBaABwDuc82nh6zDG+oXdnYxA4biskiSODo8IiQdvJZqZgJBk0HuIEFKIKLeRQkGxTIi+ZbtaewTG2EIAzZzztQAaGWOTAYgA2gDcls1jkSSOUw4vGle2YMvBNoypKkfT1FpUlJghCCybh0JIXqL3ECGkEFFuI4WCYpkQ/cvqPOcAwDnfwDn/VvD3h4Mn5vK365dyzkdxzr/OOd+TzeNy+vxoXNmCzQdOQZQ4Nh84hcaVLXD6/Nk8DELyFr2HCCGFiHIbKRQUy4ToX9ZPzvXKbjZgy8G2iGVbDrbBbjbk6IgIyS/0HiKEFCLKbaRQUCwTon85vaxdT5xeP8ZUlWPzgVOhZWOqyuH0+lFqoX8TIYnQe4gUiupl1Umtv3PGzgwdCdEDym2kUFAsE6J/9M15kN1kQNPUWowdWAGjwDB2YAWaptbCbqJPEwnRgt5DhJBCRLmNFAqKZUL0jz4mCxIEhooSM5bOqIfdbIDT64fdZKACGYRoRO8hQkghotxGCgXFMiH6RyfnYQSBhS7roct7CEkevYcIIYWIchspFBTLhOgbXdZOCCGEEEIIIYTkGJ2cE0IIIYQQQgghOUYn54QQQgghhBBCSI7RyXmaSBJHh0eExIO3Es/1IRGSERTrhJBiQfmO5AuKVUIKA1WCSANJ4jjl8KJxZQu2HGzDmKpyNE2tRUWJmSpgkoJCsU4IKRaU70i+oFglpHDQN+dp4PT50biyBZsPnIIocWw+cAqNK1vg9PlzfWiEpBXFOiGkWFC+I/mCYpWQwkEn52lgNxuw5WBbxLItB9tgNxtydESEZAbFOiGkWFC+I/mCYpWQwkEn52ng9Poxpqo8YtmYqnI4vfSJJSksFOuEkGJB+Y7kC4pVQgoHnZyngd1kQNPUWowdWAGjwDB2YAWaptbCbqJPLElhoVgnhBQLynckX1CsElI4qCBcmpRYDFg6vR52iwFOjx92s4GKcJC8IkkcTl8gdp1eP+ym2BgWBIaKEjOWzqiPux4hhOS76Hzn8IiBvOejvEdyS6m9praZkMJAJ+ddpFYhk8b5kHySTKVXQWAotQRSh3xLCCGFSBAY7CYDTnVQJWyiD/Haa2qbCcl/dFl7F1GFTFIIKI4JIUQZ5UeiJxSPhBQ2OjnvIqqQSQoBxTEhhCij/Ej0hOKRkMKW1ZNzxpiBMdbCGHtT4TELY+yPjLH9jLEPGGNV2Ty2VFGFTFIIKI4JIUQZ5UeiJxSPhBS2bH9zPgfAxyqP3QGgnXM+CMCvASzK2lEhMIanwyNC4sFbiWvajipkkkKQqThO9X1FCCG5oJSzqJ0neqIWjzajQO0tIQUgaxUjGGPnA7gewC8A3K+wyrcBLAj+vhrAM4wxxjnPeHZJphhWNKpeTQpBJuK4K+8rQgjJtng5i9p5ohdK7bXNKKDN6aP2lpACkM1vzp8CMA+ApPJ4fwCHAIBzLgI4A6AiGwfW1eIacvVqgQVvKRGSPJTuOKaiNYSQfBIvZ1E7T/QkOh5dokTtLSEFIisn54yxbwE4zjnfGm81hWUx35ozxu5kjDUzxppPnDiRluOj4hok0zIRt3pH76v8V4xxS/JfqnFLOYvkUlfyLcUuIYUjW9+cXwlgMmPsIIBVACYyxl6JWudLABcAAGPMCKA7gLaodcA5f5FzXs85r6+srEzLwVFxDZJpmYhbvaP3Vf4rxrgl+S/VuKWcRXKpK/mWYpeQwpGVk3PO+U855+dzzqsANAB4h3M+LWq1tQBmBH+/KbhOVqpZULEXQtKP3leEkHxCOYvkK4pdQgpH1grCKWGMLQTQzDlfC+BlACsYY/sR+Ma8Id3PJ0kcTp8/pqBLeHENm0mA0+tHicVIRV8ICfL7JTh9gfeFwyPCbjLAYIj/2R4VSySE5JNk+gJq/QlCciGZ9jaV9pwQkj1ZPznnnG8AsCH4+8Nhy90Abs7U8yaqHC0IDHaTgapLExLF75dwyuHFnFWtoffFkoYaVJSYNZ2gl1oCaUa+JYQQvdLSF6CZKIgeaWlvu9KeE0Kyo2h6y+FVWAGEKlkunVEfSmJa1iGk2Dh9fsxZ1RrxvpizqhUvTq9DGTXmhKB6WXVS6++csTNDR0LSIVFfgPoKJF9Re06I/hXNO1FLJUuqdklIrBKLUfF9UUKdUEJIAUrUF6C+AslX1J4Ton9Fc3KupZIlVbskJJbDIyq+LxweMUdHRAghmZOoL0B9BZKvqD0nRP+K5qMyuZJl9Bix8EqW8jorP/gcU+rOR3ebGXaLAU6PCEniXR5LJkkcbtEPSUJwv4FiMoU6Rk3iElyiCzajLXQrMOXPg5JZl6SPKEpwiZ2FYWxGA4zGyP+73WTAkoaamDFqSlVgqUhSpHhxTTFPiD4p9ReenzYa4IBfksA5xx9mXo6zLh9KLUZ82e5Cr1IzwAGJc8p9WUL5NZJS+8s5jyj+ZjMa8Ny00Tjt9OGCcjsOtTnRw26iqu5J0hpfxRiHpOuK5uRcSyVLQWAot5sw/YoqdHhEzFzeHHYiX4OKEkvKja0kcZxz+3DOI+KB13akbb96JXEJbe42zNs4Dy3HWlDbpxaLxy9GubU8JjElsy5JH1GU0OaMLQxTbjdHnKAbDAIqSsx4cXpd3OquVCQpUry4BkAxT4hORfcX3D4/HB4RK98/iBtrz8f8NZ1t+KIpI/Hx0TOou7A8IpcWc+7LBsqvkZTb3xqYDAJmvbIttOy5aaPh80v46es7I9ZjjOJUK619VurbklQlHR2MsYsYY08yxl5njK2VfzJxcOkmV7IUWPBWodF0iRJOO3144LUd2HzgFESJB4u9tMLpS/2SNafPj/YM7FevXKIL8zbOw5avtkDkIrZ8tQXzNs6DS3R1aV2SPi6xszCMHI9zVrXCJcbGo8EgoMxqgsAYyqwmxaqu4UWSOuO7pSDjW4t4cU0xT4i+hfcXJA40rmzFpBH9MH9NZBs+f80OjL24V0wuLebclw2UXyMpt7+tOO30RSw77fShcWV0rBZmPzRTtMZXMcYhSY9Uvjl/A4E5yf8CQErv4eSe3WzABeX2tBd7ydR+9cpmtKHlWEvEspZjLbAZbV1al6RPugvDUJGkSInimmKekPwg57ZBvUsVc1w3m4lyX5ZRfo2k1v5eUG6PWFZM/dBM0dpnpb4tSVUq11W4OedNnPP1nPN/yT9pP7IccXr9ONTmTHuxl0ztV69cogu1fWojltX2qVX95lzruiR90l0YhookRYoX1xTzhOQPObftP96hmOPOunyU+7KM8msktfb3UJszYlkx9UMzRWt8FWMckvRI5eR8CWPsfxhjYxljo+WftB9ZjthNBvS0m/D4zSMxdmAFjALD2IEVaJqqXAAr1/vVK5vRhsXjF2NM3zEwMiPG9B2DxeMXq35zrnVdkj42Y6DQW3g8Lmmogc2YWjzKRZQi47u2IONbi3hxTTFPSP6Qc9u6XUexaEpkG75oykhs/vRkTC4t5tyXDZRfIym3vzXoYTdFLOthN6FpKsVqV2iNr2KMQ5IejHOe3AaMPQbgVgCfovOyds45n5jmY0uovr6eNzc3p7y9WmXp8KrqNrMApzdQ6bKrFVgztV+90mm19pz/k7sat+kUr1p7xPvD44cgAFZT/CrsVK09UhqrCef8n6inuM206mXVGd3/zhk7M7p/HSmYuI3OlaEcZzbA5ZNgMwpwiRLlvizKYLX2nL9wqcStUnvOGCKqtcsn4dHLlOrIEHU6rdae87gl6ZHK4NLvABjIOfem+2CyKVFlabvZmPbq05nar14JTECJqQQAQrfpWJekhyRxtLt8inEIICZGH795JJ5Y9wmOnfWoxqtcRAlA6LaYxYtrinlC8kO8mS0MgoBSS6CzXRo8waHclx2UXzv5/coxWlFiRpnVBAAos5qKpv+ZaVrjq9jikKRHKh/fbAfQI90Hkm1aKktnqvo0VbUmehAvDpUee+C1HZg1YRDFKyGkqCQzswUhueD0KcdodDtN/U9C9C+Vj3f7ANjDGNsCwCMv5JxPTttRZYGWytKZqj5NVa2JHiSKQ6XHBvUujVmPEEIKWbpntiAk3bTGKPU/CdG/VL45/x8ELm3/JYBfhf3kFS2VpTNVfZqqWhM9iBeHao/tP94RsR4hhBS6dM9sQUi6aY1R6n8Son/aq2MwNogxdmX49GnBKdQ4gC8zd4jpIUkcHR4REg/c2oxCwsrScvXL+68ZjHX3jsenv/wmXri1DjZj14o5FFJVa4lLcPgcEbckO6JjWpISF3cM30ZgUK3aqhSjj988Es9t2J/X8ZoMim1CCl+iPCpJHGaBpXVmC5IcysWJ2U0GLIlqz5cozAZUSP3PYkCxX5ySuSbrKQA/U1juDD52Q1qOKAPUCmCU201YOqNetbqqIDCU201ouHwA5qxsTVvxDEFgqCgxx33ufCBxCW3uNszbOA8tx1pQ26cWi8cvRrm1PJPVKAkSFzTUus3z00Zj6fR62C2xcRgRo8Fq7U9+ryZv4zUZFNuEFL5EeTT88bu+dhFeuLUOpdbYmS1I5lAu1s5sEPDYd6txQbkdh9qcMCtUYC+U/mcxoNgvXsm8ulWc8x3RCznnzQCq0nZEGaBWAMMlSii1GCGwQIVppeTkEiXMWdma9uIZclXreM+tdy7RhXkb52HLV1sgchFbvtqCeRvnwSW6cn1oBS+Voi5K29z9yjaAQTEOI2LUaoTdnN/xmgyKbUIKX6I8Gv74bb9rxshH/oHvL/0AjDE6Mc8SysXaOH1+zHplGyY8sQEX/+xvmPDEBsx6ZZtin6AQ+p/FgGK/eCXTuljjPGbr6oFkUlcKYFDxDHU2ow0tx1oilrUca4HNqOtwKAipxCXFsnYU24QUvkQ5kXJm7lEu1oaKFhYeiv3ilczJ+RbG2MzohYyxOwBsTd8hpV9XCmBQ8Qx1LtGF2j61Ectq+9TSp3pZkEpcUixrR7FNSOFLlBMpZ+Ye5WJtqGhh4aHYL17JnJzfC+C/GWMbGGO/Cv78C8APAMyJtyFjzMoY+5Axtp0x9hFj7BGFdW5jjJ1gjLUGf36Q3J+irisFMKh4hjqb0YbF4xdjTN8xMDIjxvQdg8XjF9OnelmQSlxSLGtHsU1I4UuUEyln5h7lYm3sJoNi0UKK1fxFsV+8GOeJKzxHbMDY1wGMCN79iHP+joZtGIASznkHY8wEYBOAOZzzf4etcxuAes75j7QeS319PW9ublZ9XJI4nD4/7GYD3D4/JAmKha/ibef0+mEzCnCJkur98H2pbWszCXB6/SixGAuqAIfEJbhEF2xGW+hW54Uqcv5PTxS3WkXHmpaYEkUJLjEQh3JRI/m+2+uHxDnsCWI0lefNRzqL7Zz/g9MVt/mgell1Rve/c8bOjO5fR3Qft2r5zO+X4PTF5kp5vUJry/UsB7k45y9oKvlWqX1nDBFxbDcZYFAoFKekWNp6PUsy9unFKRBJD0bhnK9njG0E0AeAkTE2ILj8izjbcAAdwbum4E9ynwokSa0Kq91sQGmcMThxq7cyBrvJoPo4gIjHGicOQsNlA7Dqwy9wY+35mL9mR9oqvuuFwASUmEoAIHRLskMu6gIgbkzL/H4JbU4v5qzqnHlgSUMNtn7ehnUfHcPcSUPxwGvxYzSVKvH5imKbkMKnlEf9fgmnHLG58vBpJypKrAXZlusZ5eLERDG2fX9u2mh4/VLEbENLGmpQUWJOeIJeTG29nqUr9rdu3drbaDS+hMCXq7r+Bq3ISAB2iaL4g7q6uuPywlS+OZ8N4H8AHAvuFAicf49MsJ0BgbHpgwD8hnM+P+rx2wA8BuAEgL0A7uOcH4q3z3ifLHZ4RMxc1ozNB06Flo0dWIGlM+rjnsgk2i7e4wAiHlt373gsWPsRFky+FAvWfpT0sZCMyHmrkqtvIM+5fbhz+daYOHxu2mgcO+vRFKOpvq9IlxVt3OYCfXOeNnkZt2q58oVb63DXitjllP8KTt7FrVLMbpg7AT99fWdMvL44vQ5lVlPc/VFbn5dU43b79u1r+/bte0llZeVZQRAy+uUo0U6SJHbixInuX3311e5Ro0ZNlpen8unJHABDOeeXcs6rgz9xT8wBgHPu55zXADgfwGWMsRFRq/wFgenaRgL4J4BlSvthjN3JGGtmjDWfOHFC9flSrbLaleqt0Y8N6l2KLQfbQrfJHgspHFrjNpPUqrl2s5k0xyhVLy4ueohbQpLV1bhVy5WlVuXllP9IOnQlbpVi9oJye8oV3KmtLzgj6MRcfwRB4JWVlWfQOVw8sDyFfR0CcCbVA+GcnwawAcC1UctPcc49wbtLAdSpbP8i57yec15fWVmp+jypVlntSvXW6Mf2H+/AmKry0G2yx0IKh9a4zSS1aq5nXT7NMUrVi4uLHuKWkGR1NW7VcmWHW3k55T+SDl2JW6WYPdTmTLmCO7X1BUegE3N9Cr4uEefjmk/OGWP3M8buB3AAwAbG2E/lZcHl8batZIz1CP5uA3ANgD1R6/QLuzsZwMdaj02JWpVVq0HAObcPEuc45/ZBFCV0eERInKPDI8JmFELb3VhzHjbMnYA/zLwcAoAOtwirUcCSqTW4/5rBWHfveOz/xXV4cXod7GYDBAY0Te2slnngxDksmVqDdbuOYtGUkfqo+CpJgKcD4MFbSUq8TQ5IXILD54i47cp6hUiSeETsSlJn3vX7pYg49/sl1WquHIGrPF64tQ4f/OxqfPrLb2LD3Al4ftroUIzK+7OZAvHf5ViOE4fRr6lf8hfta0wIyT7FXDm1BmYDi2jjA/mvBgIL5Ei1fJzX0thnoNyeOTajAc9NG40NcyeE2vBepeaY9npJQw1sRkNM/yBawc1UoLO+r1LftZj7syRSMgNHyoK3XwR/zMEfIHFxt34AlgXHnQsA/sQ5f5MxthBAM+d8LYBGxthkACKANgC3JXFsMQSBoaLEjKUz6kOVJq0GIaJghlywLbyARtPUWpTbTXj5tno4PCIaV7aiTzcL5k4aite3fokba8/HR0dO43uXDcAfFQq9PT9tNJZOr4fNLOCUw4tVH3yBSSP64bweVrw4vS63FV4lCXCeAFbfAXyxGRgwFrjpZcBeCQj6qQ8hcQlt7jbM2zgPLcdaUNunFovHL0a5tTyiSqXW9QpRvGItnHPFYkbldjPs5kAD3s1mwjm3CAMDZi7fGlrv8ZtH4sd/asWxsx40Ta0BEFscqXHiILxwax1KrSnGcpw4lBhiXtNF4xZhzb41eGH7C0X1GpP0yPT4cVJ4DAYBFSXmUJvd4Q6cdP/375vRp5sFj323GgMq7DjV4QE48K9PjqPuwvKYvkTeF89KY59Bqb2m3J5eXr+En76+s7Pdn1qDHhZjqM0/6/LBahQUC8NGF4lT6kPnbbV2nfV91fquJsGE+zbcV/D92fnz5/dds2ZNhSAIXBAEPPvss59PnDjRkY59f+1rXxu0Zs2az3r16pXXl3hofsU5549wzh8BsFv+PWxZ3G+5Oec7OOe1nPORnPMRnPOFweUPB0/MwTn/aXAc+yjO+dc553vi7VMLuQqrwAK3LtGPOatasfnAKYgSx6QR/SLubz5wCo0rW+ASJUgcaFwZeGzWhEF44LUdmDSiH+av2YGBlWW4d1Vr6H749ne/sg1ggMsXqJD55D/3YdJTGzHowb/jzuVb4fT6A8eUiwTncwaS08F3AUkM3K6+I7BcR1yiC/M2zsOWr7ZA5CK2fLUF8zbOg0t0pbReIXL6/Ghc2RITu06fH06fPyau56xqhUv0Y+byrahZ+H8Y+NO/4aszbswMFpCR13vgtR2YNWFQcH+tivt78p/7cNeKrXB4xNRiOU4cKr2m89+dj6sHXF10rzEhJHfkE5XvL/0AEueY9co2bD5wCm+0HsGEJzbg+0s/gMkgoHFVK8Ze3EuxL+H05XX/MK19BsrtmeUS/ZizMqrdX9kKj8RDbX7Nwv+DW5QU+wdKsRrdh87LE3NAd31ftb7rGc+Zgu/P/vOf/yxZt25dj507d+7eu3fv7vXr1+8dOHCgN137/9e//rU/30/MgdTGnP9U4zLdiS6YEa8IVngxjOjCbloKvemymIbZHvjUMNwXmwPLdcRmtKHlWEvEspZjLbAZbSmtV4jixZdaMSOt8T+od6nm/aUkThyqvaYDuw+MuF8MrzEhJLfk3NfNZlItphnv8bwvnpXGPgPl9szS2k6rxWrK7Xk+0FnfV+290L+0f8yyQns/HD582FReXi7abDYOAP369ROrqqp8/fv3r541a1b/6urqS6qrqy/ZtWuXBQCOHDlinDRp0sUjRoy4ZMSIEZf84x//KAGAM2fOCDfddFPVkCFDhg8ZMmT473//+x4A0L9//+qjR48aAeDZZ58tr66uvmTYsGHDb7nllgtFUYQoipgyZUrV4MGDLx0yZMjwRx55pHeu/hfxJDPm/DrG2NMA+jPGmsJ+fo/Apei6F10wI14RrPBiGNGF3bQUetNlMQ2vM3A5T7gBYwPLdcQlulDbpzZiWW2fWsVvzrWsV4jixZdaMSOt8b//eIfm/aUkThyqvaYHzhyIuF8MrzEhJLfk3HfW5VMtphnv8bwvnpXGPgPl9szS2k6rxWrK7Xk+0FnfV+29cLjjcMyyQns/3HjjjWePHDlirqqqGjFt2rQBf/3rX0vlx7p16+bfuXPnx3fdddfx2bNnXwAAd9111wX333//sV27dn385z//+dO77767CgB+8pOf9OvWrZt/7969u/fu3bv7+uuvPxf+PNu2bbOuXr26vLm5ec+ePXt2C4LAn3/++YrNmzfbjx49atq3b99He/fu3X3PPfecgg4l8835EQTmKXcHb+WftQAmpf/Q0i+6yMu6XUdjir7IBS/Ci2E8t2E/Hr95ZKiwW/Rtou11U0zDZA+Ms6kaBwjGwO1NLweW64jNaMPi8Ysxpu8YGJkRY/qOweLxixW/OdeyXiGKF19qhd9sxsTx//jNI/Hchv2a9pdyLMeJQ6XXdNG4RXj7i7eL7jUmhOSWnPs2f3pSMQdu/vQkFk0Zqfh4ztv7dEhjn4Fye2ZFt+9yjFqCvxvDbtPanucDnfV91fqu3S3dC74/2717d2nXrl27n3nmmc8rKyvFGTNmXNzU1FQBADNmzGgDgJkzZ7a1tLSUAsB7773Xbc6cOQOGDRs2/IYbbhjU0dFhaG9vFzZu3NjtvvvuOy7vt7KyMuKT0Lfeeqts165d9lGjRl0ybNiw4Zs2bep24MABy7BhwzyHDh2yzJgx44LVq1d369mzpy4/QWWcJ1dRlDFm4pz7MnQ8Samvr+fNzc1JbeP3S3D6/CixGOHwiLAZDXD7JcWCF5LE4fT5YTcb4PX54ZN453pRt0qF3sK3Vyqmofq4JAXGwpjtgU/2TPb0Fa1IYd8Sl+ASXbAZbaHbVAtUaN1XvPXCH/P4PZC4lMyx5XzQVCpxqyRefEXHud1kgMEgQBQluMTI+A+/b2AM1iT214WDV43D6NfeYrDALbphN9nh9DlhM1phEOJfgueX/HCJrrBtbDAIed35KJi4zTY9FoTbOWNnrg8hW/I+bqNzppxvbSYDXGH5N15fIq/F5Gob4HOl1D+Jzu1WgxVuvxtWg1UxX6ez75GknL9wqcStUvsuCCymn8A5j2nPGYtdryDiVyb5Aa8DsJQGqrWbS4A09wmSiVeldQHELOOcJ9OXUX3Btm/ffnDUqFEnu/o3ptvvfve7nitWrKj45JNPbG+//fYnw4YN83o8Hta3b9+R7e3t23v27Dnq0KFDO0pLSyNOVocPH37Jn/70pwMjRoxCVS2eAAAgAElEQVTwhC/v379/dXNz88e//e1vy48cOWL6zW9+E3k5AgKXxP/5z3/utnz58l4VFRXia6+9djDDf2ZC27dv7zVq1Kgq+X4yl7XvZIztALCVMbYj+icTB5sJBoOAMqsJAmMos5pgNAqqBS/kYhjgwDmPH3cu34qhD72FO5dvRZvDhxKzEQahc39q2ys9JlfbnrmsGUMe/DtmLmvGKYcXklxVcmUD8PPKwK3zRPqmfRCEQHJiwVsNJ+Zt7jbMfmc26lbUYfY7s9Hmbktpiodk9iUwASWmkohbpX3c8/Y9oct+wtcrBvHiKzrODQYBksTR7vLhzuVbMSRYoLDN6cXvNn0Wuu/w+gEOTfvr4sGrxmH4a24zWHHa3Y7G9Y2oW1GHxvWNaHe3Q5LUP+z0S360udsitmlzt8EfZxtCCImmmDMdPthNBrQ7fRF9gnZXYHneF8+KFp6rTXbAeTLl/kl0u24QDLAarIr5WpTEtPU9ioFSrLa7At+jRfcTottzxphKf7SApgN0ngRW3RKI21W3BO6ncTq1ZPvKSn3c6GWc84Lry2zfvt2yc+dOi3y/paXFdv7553sBYPny5eUA8PLLL/esra11AMBVV111dtGiRaFx4e+//74NACZMmHD2ySefDC0/ceJExCcW11577dk333yz5+HDh40AcOzYMcPevXvNR48eNfr9ftx2222nH3300cM7d+7U16XDQcn0sL8F4AYAbwV/vh/8+RuA1ek/NP2IVxk73fuE15EXVSVTGQeTjn0Vc4X2rlKKuTnBWQf0WmHYJbow7935ka/3u/Pjvt4u0YX5UdvMT7ANIYREi9f2p7tPkBcyUPVaLV+7RTe19UnoSkwWfDxnoVp7JvqmhdiXOXv2rGH69OkXXXzxxZcOGTJk+J49e2yLFi06AgAej4eNHDly2LPPPtunqanpEAC8+OKLh7Zt21YyZMiQ4RdffPGlzzzzTCUAPPbYY0dPnz5tGDx48KVDhw4d/re//a0s/Hnq6urcDz300OGrr756yJAhQ4ZPnDhxyKFDh0wHDx40XXXVVUOHDRs2/Pbbb79o4cKFX2b/v5CY5vKMnPPPAYAxdiXn/Mqwh37CGHsPwMJ0H5xeZKLyuto+maUkL6pKpjIOJh37KuYK7V2lFnNydXb5vp4qDNtMduXXO85YMbvKNnad1VYgqcnGZeo7P/siqfWrLxqQoSMhuaSWM9UqY+spd2ZEBqpex8vX1NZr15V+qi5nF0qnLFRrz0TftBD7MuPGjXO2tLQoTpU9d+7cE7/61a+Ohi/r16+f+Ne//vVA9Lrdu3eXXn/99YPRyw8fPhwaMzZz5sz2mTNntkevs3v37rjTf+tBKtemljDGrpLvMMauAFCSvkPSn0xUXlfbJ/c48qKqZKrfnHd1X8Vcob2r1GJOrs4u39dThWGXz6n8esf5xNupso0zR1efEELyk1rOVKuMrafcmREZqHodL19TW69dV/qpupxdKJ2yUK09E31T6ssUr1ROzu8A8BvG2EHG2EEAzwK4Pa1HlUaSxNHhESHx4G0KY2iUKmM/P200wBGxX7Xnil7u90sQGNA0tQY/vmYwtv3kSnz2yHi8OvMyMMGQXFVJSQoUt+DB2zSOoQG0VUSXuASHzxG69Ut+xftWgxWLxy3GPTX34M+T/4zW6a146utPwWqwaj4eeR+FXtFSFi9+Ix5z++AM/n7O7YPfHxsHgTiOqtI6tQbrdh3NToXhFGLVZrRh8bhFodf7npp78NTXn4LNZIfD2wGX6IqIP3mbRWHbjOk7BovHBWJEKT4lr1PxmKLjmsY6ElL45LzqlyQIQEzObJpaAwNj+MPMy7Fh7gTcWHNe4VRnVxKet5nQ2T+pvhmY3QLMWItAZyiQH5XyZrxcqpavDYIBL33jJWxq2ISWW1vw1+/8Fb+e8OuCbeu7Sql9b5qqrQq7LmcX0kqpXxG9zGTT3K+WJD8c3o5ArHo7VOvbRMe01WBNavYgURLREXyeDm8HRCl2Kjul98aicYsK8j1w+PDhnf369Svg+fySk3S19tCGjHULbn8mvYekXaJqlnLRtcaVLdhysA1jqsrRNLUWFSXmpIu1hFfGdvsCcz83rmwN7ff5aaPh9UsRy5qm1qLcbkKb0xc6hsaJg9Bw2QDMWdWK60b0xrSanhC854A3fhi4zGbAWOB7KwIJxFwSvxqqXDxu9R2d2970MmCvTF91dySunN7mbsO8jfPQcqwFd426C1MGT8H8d+cr3l88fjFqe9di3ruB9Wv71GLx+MUot5YnLOYmP9fqvatxzYBrcFGPi+DwOVBiLEmmEnfOq/RorcIaL34BxDz2+M0j8cS6T3DsrAdLGmpQUWKOKNwmSYET93anDxeU23GozYmedhNMRgFWU4YrtHYhVqVg5XWr0YZ2d3tE7Dx65aNo2taE467jEXEUXq29w9eBVz9+FS9sfwG1fWqxaNwirNm3JnR/8RU/R/k/FkA4dzR0TBJDRFwnE6cZkjdxm2l6vKw9aQty1mxmW17FrZxzV37wOW6sPR9vtHyJqZcNgMPrD+XMHnYTlr9/EE3v7A/m5BqUWIywGgusujWgnLf/a0XgpIeLMflcsvdCm6c9Im/+esKv4ZN8qrlU4hKcPif83I8ycxk6fB3gEsd9/7ovtP7CKxbizQNv4qYhN2UrB+f8hUw234qihA6viNNh7XsPuwmlZiOMxsT/r0SzC+mSUnx+bwXg9yr0NXolnGVAChaTnRfsr9b2qcXicYsCMRfWx4zu98ox3dPSE26/O2G1dlES0e5uD/WL5X5JT2tPGKNmokly5pm8q9ZOArpSrX1a8PZ+xtj9AH4A4I6w+7qTziIX4ZWxJQ40rmyN2G+70xezTKlwzKQR/TBnVWC9iReXQnC1B07MwwtV/PFWACxxRfUsFLkA1CunA7FFMK4ecHVEAYvo+1XdqzDv3dSKZsjP9ZvW3+A7a7+DmuU1uHf9vXD73Wn9e/UiXvwqPfbAazswa8KgUKG36Dh3+vy4+5VtmPDEBlz8s79hwhMbcPcr2yBxZL7CcBdiVRAMKDGXwi26YmLnofcewh3Vd8TEkUEwoNRcCpfowr3r78VvWn8TUVDl6gFXd8bf+/8PrvE/jjgmKjxISPGR8+qkEf0wf80OTBrRD42rWiNy5qxXtkUV0WwN5FC9n8ikQilv/+lWgDHFfK6UN894zsTNpS7Rhcb1jbhq1VUYtXwUjjmO4b5/3Rex/sPvP4yrB1xNOTgOl+jHrKj2fdYr2+AStfV3483+oltK8elsV+lruBLOVKS1AK1a/8Dtd6v2lcO5RbdqEcRocl9GYAJKzaX5PiUs0UhzQTh0jisvi7uWjmSqyIXSfi8ot2sqHDOod2no/nmVvQBUpF6oIgtFLhKJLoIxsPvApO4D2otmFFsxuETxG6+4mxx7yewvo9IQq2rF4QZ2Hxj6PToW1GJG3ia0XcXgiGOyBZdHb1eosUYI6cyRcjsd3l7L9F5EM63U8rath+JypRzdv7R/3FyaqA8hry8vpxysTK1IYXQ/oKAoxWfPC1Pua2gtQNvVvmghFnoj6ZXMtUF/AgDO+SNKPxk6vi7JVJELpf0eanNqKhyz/3hH6P6REyeB9s9TL1SRhSIXiUQXwThw5kBS9wHtRTOKrRhcvPhNVNxNjj2t+8u4NMSqWnG4A2cOhH5X+oQ73jah7U7tizimYos1QkhnjpTb6fD2Wqb3IppppZa3XacVlyvl6MMdh+Pm0kR9CHl9eTnlYGVqRQqj+wEFRSk+u9Cn1lqAtqv9Ayr0RhJJ5uT8E8bYR4yxpYyx2xhjQzJ2VGmSqSIXSvvtaTcpFOOojVl33a6jWNIQWO9X67+Ez9IDuPFZ7QXgwpnsyRWPy4DognFvf/F2RAGLRPeTKeimpThdIYkXv0qPPX7zSDy3YX+g0FtDbCGYnBZ9SUOsRheHG9N3DB698lG8vPNl1VhQiplF4xbh7S/e7oyhK34O28ZfRRxTscUaIaQzR67bdRSLpowM3UYU0WzIYhHNXFPK21NeAj7bBHz7mZh8rpQ3u1u6x82lifoQY/qOwcIrFuLtL96mHByHzWgI9S3DY9VmLNDYBJTj094z5b6GUh9jsUIBtq72D6xGq2KhN6tRe3HkfGS322vVHqutrR2Wqef9yU9+0jdT+86UpArCBU/Irwj7qQTwbwDvcc4XZ+QI49BSMEOSONyiH5IE2C0GOD3+0CVoXSl+obRfq1GAS+zcZ4nFCKfXD5tRgEuUQsvD73t9fliYCOb3BsbBeDoCheDUxpVIUmCcTaiohS1hkYvE+9CwTbzdRRWMsxqsEUUxEt1XK5oh79disMDr94KDw2qwhopjxNs2jpwPpEq2QJFanPr9Epy+QJw5PCIMjMFqNsDhEWEzGuD2d8aYT+Ix62W96Esa4s4viXCJ7lBxFKNghNlgji1UKImBZaYSeEQ3JEiwGe2heHKH9uGAVbDAaDDGHFO8Qog5kFdxm0lUEC6v5F3cyjnXZhICOTKsPXd4xM523GSAyxfIoeGFNwtGeL6W+yU+F2CyBnJleH/F6wg97jdZI3K0zWgDYyzU9ocXt7IarWG5OLCu2++O6COEL89iDtZ93IqiBJfYGZfySXj0Mi3F4PKa5A/EX3g8gkfFqD1woq5BdB/DZrTCoLCtUv8AgOY+gyiJEbFvNVohMKGrfQ5dF4Sz2+21Tqcz4np+URRhNGZ26IXS8+pNygXhAIBzvpdz/nvO+Z0Avg3gUQCXAtDlZe0yh8ePmcubMeTBv2Pm8macc/twyuHBzGXBZcuaccrhTXqatfD9/nbTAbQ5vfjdps9wuN2NO5dvDe27zekLnAQFC20YDEKo8IbVKIB5zgCrbgF+Xhm4dZ5UnmZKrky5siGw7sqGwLome+LicXH3caJLU7BFF4wzCIak7qudmLe52/D+4ffh8Dpw2nMas9+ZjfpX6tG4vhFt7rZcnyxlhVqRFkniaHP6QnF25/KtcHj9AAdKzEa0u3yYuawZKzYfxFm3GLFeh0eE5OfZL/oiCAkLssQjcQntntNoXN+IuhV1aFzfiA5f4PLS8DiSJBFt7nbMXj8HdSvqcM87P4LL5wK4BKvBgnZ3e9g+5qDdewZ+LsUcU7xCiISQwiTnXIMgoMxqgkEQYDMacPKcB3cu34qhD72FO5dvRZvDi037TqDN6UtpilZdi+4nyP0Skw1wnorsrzhOAP9+Hni0N6R/PxuVXxvR7mkHEPi2sd3T+dgrH7+iuK5cjVrOuXIRLMrBnURRQpvTG9Gutzm9cIv+mGVK06oWDEkKxGV4PHrOAo7oGD0ZOIlPtDuFPka757TiNKrR/QMgMMPL7Hdmo25FHWa/Mxtt7jbFbSUu4XTU85z2nMY57zlN22eDJPHyDo9YLXFe1+ERqyWJlyfeSps333yz7PLLLx9yww03XDR06NBLgc5v1T///HNTfX390GHDhg0fPHjwpW+99VZp9PbNzc3W6urqS4YNGzZ8yJAhw3fu3GkBgGeffbZcXn7LLbdcKIoifvjDH/b3eDzCsGHDhk+ePPkiAFiwYEGfwYMHXzp48OBLFy5c2BsAzp49K0yYMGHQ0KFDhw8ePPjSpUuX9gSAuXPn9hsxYsQlgwcPvnTq1KkXSmmerlpNMtXar2CMzWWMrWGMfQjgFwAMAKYB6J6pA+wqparW8Sqrp7pfuQq7XOVV876TqWKdjursWarw3lVyNcwx/cbgjPcMHnrvIaqcHUZrJfdv1/QPzQ4grzdnVavmCq56orWCeryKqy6VKqkuhSqphBACBL6NVMqjYy/ulfIMMLqm1k/wOmKXr/kBMPxbgCTCNXxybO4N5uhEM7tQu66dWjzKv4cvK7jYDKdWrX2NQox6HQl315VZWpLZVm3dM54zung/SBIvP+XwXDhzWbM5+CWj+ZTDc2E6T9B37NhR8vjjjx/+9NNPPwpf/tvf/rb86quvPrNnz57dH3/88UeXX355zMnJ008/XfnDH/7w2J49e3bv2LHj44suusi7bds26+rVq8ubm5v37NmzZ7cgCPz555+vePbZZw9bLBZpz549u9euXfvZu+++a3/11Vcrtm7d+nFzc/PHy5cvr3zvvfdsr7/+ere+ffv6Pvnkk9379u376Lvf/e5ZAHjggQeO79q16+N9+/Z95HK5hFWrVmXlfDeZawk2AdgG4EkAb3DO9XU2pyKZyurJVFyN3m+i6q6q+06minU6qrProMK7FnI1zG7mbigzlVHl7ChaK7l3s5kKpoKr1gqpNlOJSsXVktDv0Y9RlVRCiBq1Sthyfi24au1q/QRLqfLyXkMBALaKwXFzdLpmbil28eIxelk+tvWaJVOt3RLzBWyMrlRhT2ZbtXX7l/ZP6bnTzenz929c2SpsPnAKAOQpI4WlM+r7l1qMbQk212TkyJGOYcOGeaOX/8d//IfjrrvuqvL5fMJNN93UfsUVV8R8OjF27FjHE0880e/LL780NzQ0tFdXV3veeuutsl27dtlHjRp1CQC43W6hd+/eMdUQN2zYUPrNb37zdLdu3SQAuP7669vXr19fNnny5DMPPvjgBbNmzer/7W9/+8y1117bAQB///vfy5588sm+brdbOH36tHH48OEuABkfl5bMNULnAfglgNEA3mKMvc8Ye4Yx9n3G2MB4GzLGrIyxDxlj24NF5WIug2eMWRhjf2SM7WeMfcAYq0rmD1GTTGX1ZCquRu83UXVX1X0nU8U6HdXZdVDhXQu5GuZZ79mE1V6LkdZK7mddvoKp4Kq1QqrL51CpuOqgKqmEkKSpVcKW82vBVWtX6yd4OpSXn/wEAOA6tU81R2utyl7M7bpW8eIxelk+tvWaJVOt3dOBRLpShT2ZbdXWPdxxOKXnTje72WBW+fLHnLbnsNsVrw+/7rrrOjZu3PhJ//79vbfddttFzzzzTMXy5ct7DBs2bPiwYcOGb9y40X733Xe3/e///u9+m80mXXfddUPWrl1bxjlnN99886k9e/bs3rNnz+6DBw/uevLJJ49E71+tztrIkSM927Zt211dXe168MEH+8+dO7ef0+lkP/7xjy98/fXXP927d+/uadOmnXS73dkpepFMQbiIDRmzA7gdwL0ALuKcq350zBhjAEo45x2MMRMC38LP4Zz/O2ydHwIYyTm/mzHWAOA7nPPvxTsGLYVe/H4Jp5xezFnZii0H2zCmqhzPTxsNr19CY3DZ01NrcNXgys6CLyoFXsKLc7l9fjg8YmgfjRMHoeGyAVj14Re4sfZ8vNHyJSaN6IdBvUvh8IgoMQf2GVvgS4DgPBG4NOeLzYEkctPLgL2yc/yrXJjFZAuMndm6DBh+A9BrCODtAMylnQXkwteVi2SEF7qS/IF9rLkDKOsLTPhZ4JPGiHVji8xJDKGCLh6/Bxw8biEMLUXfJC7B4/dA4lLMvjx+Dxw+Bw6fO4yqblVwiA489N5DaDnWgrtG3YVbLrkFJcaSiOfUWCRO94VeouMsVHQwrICbJHGccnjRuLIlFNdNU2tRURLInefcPrQ7fRhQbkeHR8Tv3/sMTe/sx5iqcixpqEF5iRluUdJeEC5eMbfwgizBsd2BIkFRBdYkf7BImx0unxOCYIDFYAnGS6BIm/yYzWiDEFUUUa5DMG/jvIg4KDWVhgqqGH1uSCYrznkdOOM9g/6l/XG44zC6m7uj1FwKn+TDOe85zH93PlqOtaC2Ty0WjVuEcmtPGARjRJEXpdjUMuYxHftQoPu4zRYqCJdX8iZuo9tmuXCrXBiuxGLEF6eceOqfe3HsrAdLGmqw9fM21FeVo6LEkt36Hemk1GfwdACMAR0ngJ4DggXfSjsfbzsIaf//wTXkG7D1uBBObwdsJju8ohte7scZTyD3tnvaYTfaYTVaQx+ANq5vDOXvKYOnROTixeMXR6xvNQb6G1qLyAJpK+SZ8xczXtzKY87nrOrs1y5pqEGpxYjj5zy4oNyOQ21O9LCbUGo2horDZr0IbFco9TkAhYLIzsDl69bugPsMYLAAnnOBPq7cp57yMqSSCrj8nsi44JH7k0w2OEUX/NyPMnMZznnPwcAMsJvsiv3X6D5vu6c91D+R47mnpWdM7ALAOe+50PvkcMdhdLcErpa+b8N9EduXW8uTid+0FITr8IjVM5c1m+VvzgFg7MAKLJ1R7y21GHdqPZhocmG2N998s+xXv/pVn/Xr1++Pfmzv3r3miy66yGsymbBw4cLeBw8etPz2t789FL6f3bt3m4cNG+YVBAG33377BVVVVZ7rr7/+7He/+91B77///p7+/fuLx44dM5w5c8YwZMgQb7du3WpOnDix3WKx8E2bNtlvv/32qq1bt+7hnKOuru6S3//+9wf69+8v9u7dW7Tb7XzFihU9li1bVrFq1aqDQ4cOHfH555/v8Pv9rL6+/pIbbrihXemkv6uiC8Jpvt6FMdYdwFh0VmqvBbAfwF8AvBdvWx74BED+2MoU/In+VODbABYEf18N4BnGGOOpfnqAzqJZqz74AgsmXxpxoswYw9IZ9bAaBbQ5AoU1wpNcRYk54gRd6WTo+WmjsXR6fejEyWYUcPu4gbAaBTRcPiDiA4GmqbUot5vQ5vQpnFBVQpi6SuXEJ1iYRT55n/I7oG5GZOKRT+aBwLrNy4BR/wX874+i1ukVKJ6xdRnwnecAgzl23fHzgbrpgTE6wW2l761AG/xYvXc1bhp8E7ySN3SiXNunFr+e8Gv4JF/ESZNSoxueZCQu4Zz3HBw+h+q+rrngGvxn1X/iw68+xNjzxuLpiU8HEqC7Ha9+/Cq+NfBbePPAm/jWwG/h4fcf7kpC043wOOvTzYK5k4bigdd2xJyACwJDRYkZS2fUxzS6fr8Ej1/CT1/f2RnTU2twz8RBcHr92LTvBGavbI3ZX5yDiozBiJjjgYJAa34Q+LDn6oeBN34Ys54EHjixDouJR698FE3bmnDcdRyLxi3Cmn1r8ML2FwKv4bhFgdcw7ARdYALKreURcXDv+nsjTrJ7njoI4cg2+KqnYMHmBaHHfnnVL/HYpsdw3HUcTV9vQtPXm2KqsYaf/Pe29Ubj6MaI2NQSV+nYByEku6Lb9+gP2+evCc/BNbCbjbAYBFzSrzvM+VypXc7tSn2GKS8BX+0K9EW2/ynicelr89E25r8j8vnCKxbi41Mfo7Z3TUTuffTKR9G0uTPPP3v1szAbzDjnPYcSUwl+PeHXKDOXwSk64ff78aN3fhSR0490HMF5pefF7U+E/pyoD3ALNecyFhja9ty00ehmM+GsywerUcBZjxjR7j83bTTaXd7Ql0ia2/xcU+tzGMzAH2/tXPa9FYDojTkRh8kO3NAU+OKp/XNIJjvaPKdj4wIGCGH7k76/Gi7RFfPhvVxJPXR4KnFmNVixYOyC0Am3iZngEl2hD6Tk9XpYesDn90W8TxaPW4we1h54euLTOZ8hxm4yHG6aWnNh48pWISzvSXaT4XDirbtm3bp1ZU1NTX2NRiO32+3+P/zhD59Fr7NixYry1157rcJoNPLKykrfY489dqRPnz7+hx566PDVV/9/9t4/Porq3v9/ndndZHeTEAggKBaBFoFYhEiIRQ2K2PvR4o9quAoWbO/XH1x/JKJ+P0mxXkqBq5J7lQpqL1W8H0FBW6CW4rXe++FHoUqFaFBawB8gUBEQSAjJ/t6d8/ljdjazszO7M5v9nffz8chjszNnzpzdee37nDNnzutMvVgURdhsNr5s2bKjF198sf9HP/rRqTFjxlR+97vfdW/cuPHLO++888xll102BgBmz5596sorr/SsX7++z7x58y4UBAFWq5W/+OKLRwYMGBD60Y9+dKqysvKSCy+80D9u3LjExgUpwvDIOWPsFKRl094P/+3inBt+3oIxZgHwIYDvAHiBc96k2v9XANdzzr8Kvz8I4HLOue6dnkR3xLt8Qdz7ags07v6gNDwPp9MrOV6r0/z6rgkos9tM5ZUo7a/vmqB5Lq08Ivi6JMfUwzuk9w/sBP6rsfs9IK3jOPMN6f+1M4AfNGunmbFGcq48vKM7H3VajfxdD+9B/a5FmFczD8WWYizYuQC7T+yO7H/71rejtm24eQOe2vVUVJqJgydi+bXLI66WroALZzxn4uallY+8f17NPDy166nIa7xzqch6jRRPt0rtvDt3MhZs/Js5vUBf0ytmT8Cc1Sb1B8RqEOjWHBdjNaWRzsWA+q0NMddpXs083Lbxtqj/5X3LpyxDSZH2PLEufxcaNPJbNuU5MDGE+j89GvdcWvpwBVyo31Kvq70EukpZHjrktG4zCY2c5xV5oVt1nS3H3gU3X6IZgxfcfAn+1y+3R94njKG5ihzb9doMt68CfnNXzH7Xgx+g/qPmmNj27DXP4tFt8WPvsinLdGK39vbnpjyHh7c+bCiOKuNvorQJyGndatXxe+Z/H/e/9lHUtm3//zWYt2Gv+To/2+i1OW5aBixXPA5e3wr8oSFhOrkNG6OLmn9ByXPjI9u6Hv8KDRpaWzZlGUoVbRE9nS2YtADTfjctatvSa5biqjeuislP7zdQqtPmMUjKllITRV7hDoSGOIssRW5/yO+0WY4JAkvJfHMilqRHzjnnA3tyYs55CMB4xlhfAL9jjH2Xc/5XRRItUcXcOWCM3QfgPgAYOnRo3HMmMs0C9I011CYaRvJKlFbvXHHNZNSGFwNGxTd0k81ZEpm5yGnUaTWOdZQPRevJVowol6wF1EYWQ0qHmDZ6cVgdMcep89LKR94v78sXUxmjulVqx7S5YBg9nZXak9AfkNhEUK0pjXQOaBuxKTUl/y+/d8QxaXPanDrGbvqmb8pzJTJpSVZXqcgjlzATbwkiVzCrW7MGr985rzTqfd4awsmxXS92O/pq7tczfutT1Cdh7NWP3drbS/QMPk0YbeVLzDWqW606Xsv8NRXmx1lBr83R73elJv0AACAASURBVKLobXrmb6p0chtWSevJVjjKo79jp47W1IaxZgzdyorKNPPLdWNaQWBtsvlbTt/IKVDMLKX2B8bYRr0/o/lwzs8C2AbgetWurwB8K3wuK6Tl2WLu0nDOf805r+acVw8cGP9+QTzTLBk9Yw21iYaRvBKl1TtXXDMZteHF6U/1Dd3ktHpplGYuchp1Wo1jPR1HUTWoCoc6Dmmas6m3GTF68QQ9CfPSykfeL+/LF1MZo7pVase0uWAYPZ11eZPQHxDfRFBLUxrpPDpGbIc6DsX8L7/3xDFp0zd2c8HjO5fwXIlMWpLVVSryyCXMxFuCyBXM6taswesX33RFvc9bQ7hEbQbPWc39esZv5/yJY288U06t7S49g08TRlv5EnON6larjtcyf02F+XFW0GtztB+J3qZn/qZKJ7dhlVQNqoKnI/oJKbeO1tSGsWYM3Tr9nZr5kTEtEQ8zkxn+HcAzcf50YYwNDI+YgzHmAHAdgAOqZBsB/Dj8/3QAW3oy3xwAnDYLls2swqQR/WEVGCaN6I9lM6vgtFmi0jw3Y3xUmudmjI9KYzQvI2mN5hHB5pTm2gyrBQQrsG+TNBdMfj+sVtpvc3an3bcJuOX52DRFJd157XgW+OGLsWk18nfY+6J5cjM2H90Mp9WJxVcuxsTBE2FlVkwcPBHlxeVontwc2bb56GYsqV0SlaZ5cnPMyHl5cXncvFbuXam7f/PRzVh4xcLIa7xz5RNKjfxq2xf4t3+81JxeoK/pj462JZVfjAaVmisq6daLrCmNdA6rA80qTSy+cjFW7l2JiYMnYkntEmw+urn7GtYuiXsN7VZ7jMaW1C6B/eQBOD5+M+659PThsDrias+IrlKRB0EQmUVdN7/71+N4bsZ4vPvX41hSd2lMLH33r8fNxdBcJV6boe5l4Ms/S9tV+x37NsbE2IVXLMTu47tj4rI6zuvGbp3th84eStiekFHG30KOuVp1vFVgMdv6Om1YNnO8+To/2+i1OZz9orc5+0lzzKN0uxJwRKdzFPfV1oW9b1Q6O7PpalOJns7Ki8pjjrUwS0w6Pa0Xmk6J5Enard3USRi7FMCrACyQbgj8hnO+kDG2EEAL53wjY8wOYDUko7k2ADM454d0M4WxuWSx7uhSUFJus1sEeIIhU27tiVwv9dIazkPpVBnwdm/nISlwaTqxi0DQ250m6FU4Z7sAec6VMl/N/OxA0Bd1rGhzwidGL0modKFWu6n21K09ntu1vN9usRe+W7s/hBDnCbWpJhQS4Q50a9omMBTZ9N3fExLXrT0obSsulXQTCnQ7/haVRFYSiHFrZwKKrXZ4wq683qAHDluJ5NYOAYLNHnFQ9Si1xBkEmx3BUABeMRAxdrNb7bCGyyEGffDwYCQ/pTO8UadftfYEJvQ4D3Jr7zk05zyvyBvdGnFrd/mCcFgtue9+HS9e66UNx9tIeyESw52a28UiJzzhFTbc4RU2vEFPOJaHV94IesDAIu7rDqsDFsGCULguUMZuX8iH4vCqHertZtoXvcGtHYit4502CzhHVFvWYZW0abTdmlNourWL3W0NWZuAoW0iE2J1oXJrh82JIMQYDVqF2Me6tXQm8thjBa3zMiHmNyD/NnpIyuacE5lFPefcfMRibCRjbB1jbB9j7JD8F+8YzvknnPMqzvmlnPPvcs4XhrfP55xvDP/v5Zz/I+f8O5zzmkQdc6MIAkNpsRUCY5F5E2dcftz7agsu/tk7uPfVFrR7AigpktKU2W26nR91XvECnF5aQ3nITpVrZwCLBgLvPy91etyngbUzgcXnSSZcrlPRHXP3KWDN7cCSYcD2Z6TlJNbOlPJYO1PaD0gBiwlSQCoukzpP9j7SNptTOs59JvpY3zm4Ai48uPlB/OL9X6Dd2476LfWYsHoC6rfUo93XHgk6JbYSWAQLSmwlkfdalaPABDisjph0yv/j7bcIFpQWlUZe450rn5A1Ag64/CHct+pDXPyzd3Dfqg/R5g5AFBPfULNYBJTZbRFN28P6dhZZUWo3pmFVobp1U1yqWk3gjKTHDfdJmnzjTkk3b9wpaVYUw1lYUFJUCoEDJX4XHK/fDmHRQJSsmQGL6zRK3v8VhA33ocTdBmHN7cCigRD/8iLavG1RWmvztUPcMAfW1+pQ6ndB4ECp1QmrXI5FAyG8/o8o8bulcxWVRmkznj60tAdIN4Me3Pxgdxm8bRC55jKdCfVLEETuoa6bLRYBTpsFba5AVAxu9wSkDo7ZGJop1O2HtTOk96J2vIIQrvfdZ4C//AfQ8ffoGO46LW2X2x1d3wB/+Q8IiwehZM0MCK5TKLU6pTq/qBQWwSrF+XDMk5ehkutqALAI0vJUbd42NGxtwMTXJqJ+Sz3O+s5GpbcK1pj2hMPqQLsvuv2hjMfKWFvIMVddxzPG0O6J1SoAw+3WnELd5oAoaVGtzVAwepvcfo1qh5yR2gJqXajOEQJHu7cdDVsbMGH1BDRsbUC7tx0hMXYagJbOrII10haV9aunR2W7VfnbIAggic45gP8E8CsAQQBTAKyCNOKdF7gDITSsbcXOQ2cQFDl2HjqDhrWtcAdyaA5OwC0tIXF4hzQqWXkj4GmXlqiStx3eIaWR56hoHbP+Hv308c7tjj2XxystQ7H7xG7cPfZuPPHeE9h9YjeCPIjdJ3ajcXtj3szryhfyTqu1j8bXqNYxcrr190iaVeXhqbwZjTuaorX2/r/AM/mx6Py18jSidwN4gp6I9knvBNF7yIsYrCaZWCgfU3mjtFyaVmzWe59knE02rlI81iYvtWoGvzu2Tbv+HiDkj97mbk+6LSAvo6bUVtOOpl6vLSLzJGPB5+Ccbw6vQX4EwALG2A4AP09x2dKCGdf1rKHl0A7Ed8w26+oe79waDphKt8tCcKHOB/JOq0Y1p+fEqqFzPVdgR/+Rsfkno3cD5LsDMJF+hnnXmEp/OD3FIFJMXsRgNYlW14h3jF4Ml2Oz3vsk4myycZXisTZ5qVUzKFcbkpFXIVKi5+BuQKP54KKezzidziq3292qta+qqmp0a2ur2osso1x99dXfWb9+/ZcDBgwwdUfr0UcfvaC0tDS0cOHCk6kqSzIj517GmADgc8bYQ4yxWwGcl6oCpRszrutZQ8uhXc+V0u/WPyZe+njn1jiX0u2yEFyo84G806pRzek5sZ7+1LArsOfM59H5x3OU7yH57gBMEERy5EUMVpNMLEzk2n760/jvk4izycZVisfa5KVWzaBcGUZGXoVISaK2chzIRT3zBIPSyliZ6pgHAgHdfX/605++MNsxT3UZZEwbwjHGJgLYD6AvgEWQljxr5pz/JYky9ohkDIpEkeOMy4+Gta3YfbgNE4dVYNnMKvQvKcr+XBzZ0A0A/J3SozhHdwKTm4Cae6Vtbz0gbRs6KexeGV5uI+jtPqZsMPD9xYDAuvNQptczhpHL4OuQ5u289YCU1zWPQ+x3Edq87Wjc0YjzHOeh4bIGPPHeE2g92YqqQVVontyMCrtUMWiZX6jN3+T/7RbJ8IWDR/2fAgMtPbI+4cqMQVHOalVGFCH6OuDxnoWjfCg8ARccPjeE0oFA+xGIpQPhEYRYExb3qWht1q2U7mzbnBC95+DxhfNzfQNeXIY2bxuGlA5Bu68dTqtTMhjyd8LBBViKnJIng/I3o/59GDVH0vqIXESbtw2N2xsjel96zVJYBWtCnabInAjII92mm1w0hDM9cv70NFPp85i81m1exGA18pzzRHV/xAjO0W30du44YLGqYvPLwIergO1Lut9zQCwdEDb4LIE74ILD6oQ35I0xddMzuhK5CHfAjRAPoayoDJ3+TliYJTLnXJ1WGUeDYhCPbHskpv2hPK6HsTfrF9esbvNSq/GIMYSzA95OaYpnv4ukTrijn7S/41j3ttKBEMVgd5uk4ygc9r4QissT1vshMYQufxc6/B0YUjoEx7qOobyoHCVFJfCFfFFa4pzHGLoxxmI0B2i3iTU/cs/bC6kzhBPFCgRcQ1BUUgS/yw9byTEIQszy1maQR843bdpUtmjRovPPO++8wL59+5wHDx78m7zvyJEjtrq6uhFdXV2WUCjEli9ffuT666+PugNz6aWXjn7llVcOV1dXewGgpqZm1DPPPPP3cePGee++++6h+/fvd4RCIfazn/3s61mzZp1dtmxZ/3feeafc5/MJbrdbePPNN7/UOseQIUPGtrS07D///PODzz//fP9ly5YNYoxhzJgxnrfeeuvLzz77rOjHP/7xsDNnzlj79+8fXLVq1eGRI0f6lSPn77//vuP++++/yOPxCBdddJFvzZo1hwcOHBiqqakZVVNT0/XBBx+U/uAHPzj7i1/8ImqUXW0IZ/qxds75bgAIj543cM47ExySUwgCQ/+SIrz04+rccq/U6hTftAyoGBZ2WncC1mJg5tpY93X3KaDlVaD6x8BtL3d3ymPyKEncKREEoLgcsBQDd/4m0uERju5ExdVNWH7NL+EoKoUv5MPya5fHBCF1J6Z5cjP6FfdDV6ALroALT7z3RKRzv/HgRkwfOR1+0R/1v1anv1BNXeKRs1pVIDKgDSE07lrUfc2uWISK3/0zMGAk2ib+Exq3NcVeT+dAYOYb4YrXJZm6rJ0Jsex8tP2vhZH85oybg7qRdViwc0Ekj8VXLsayncvwjecbLKldgorda2HZvxG4Y7Xi9yG7u8JYQzUOAhNQYa+I6N0X8sEVcBlqHGr9Hnqrngki38iHGByDIEjxLRJfNW5Iyh34lleBcbdL88yV8fHON6VOu69LWmZq7HRg8mPhUUoR4qEdaBs+CY07umP7wisWYv+Z/Rh/3ng0KbYvqV2CCntFTAdd7tyo0zqsjqguhl4cfWHqC7qrZ/TG2JuXWtVD6wbTHaul+eV/aIi+qR9A1DbxjtVo46o2Se0SVIAnfFSYMYYAD0S1N5ZesxRnfWdjtGRjNjzyp+42wNKrlyLAAzE38QNiwJAOc0qzolgB96mLsO5uIfy9FmH6yovC7aYeddBlPvnkk5LW1ta/jR49OmoZqFdeeaVi6tSpHUuWLDkRDAbR2dkZ8+Hr6uraXn/99Yrq6uqvjxw5Yvvmm29stbW17oceemjIlClTzv32t789fPr0aUt1dfWYm2+++RwAfPTRR6WffPLJ3wYNGhT6+c9/PijeOVpaWuz//u//fv7OnTsPnH/++cGTJ09aAOCf//mfh955551n6uvrz/zyl7/sf//993/r//7f/3tQeexPfvKT4UuXLj06bdq0rrlz517Q1NR0wSuvvPJ3ADh79qxl9+7dn8IAybi1VzPG9gL4BMBextjHjLEJZvPJJmZc1zOG2oht72+B5VXAqzcDYJKjuuyuzoSwy7oQbeSy7m7A29ZthhGTh8HLLYSd3LkYZawhbP1XlKy9E4LfrelCHc+opcPXETGRkw3lpg6dCnfQHfM/Gb10k5NaVaB5zcOGbZpmbvL1VLqkcg78ZrZkPDj5MTS+97PIMVOHTo0xaHnivSdw99i7u81axt0uafTN2QBYtKN8iozilDoXuWjIkIiMiwgi/8n1GKyJ3uoaMvEM4NbdLcVkJkh/a26X2hELK4DOY8Cbs+H59uSY2D7//fmYeP5Ew4ZaRs239OKoyEVdR/beGnvzUqtaaNXb7nZgvdpI9m5pu9q8eIfq2hs0ddPSTYevQ1NLHf6O6HT+2HR6x+r9HnJGswHXEKy7W1DFBQEB15BUneLSSy91qTvmAPC9733PtXbt2gGPPvroBbt27XL069cvZpmJu+66q33jxo39AGDVqlX9brrppnYA2LZtW5+lS5eeP3r06MqrrrpqlM/nY1988UURANTW1p4bNGhQyMg53n333T433XRT+/nnnx8EAPm41tbWkvvuu68NAO6///62Dz/8MMrw4MyZM5bOzk7LtGnTugDg3nvvPfOXv/wlkmbmzJmGb2wkczvmFQAPcM6Hcc6HAXgQkoM70RN0jNhMG7kkawQXL2+DeekZtThtTgwpHRJjKDeifERku/J/9fG93egll9E15+k/Ut/MTX09FUYv6mP0zAdHlI+I/O8s7iPtMGM+1wOjOKOGRGRcRBBETpLIAC6B0ayjuI9mbOtTpL1dy1DLqPlWMnGUYm+eo1Vv67WP+10UtUlpXizTerIVDgOmblq60WuXDikdkjCdmTZtTmm2qKRIOy6UFKXqFE6nU3NtxxtuuKFr+/btnw4ZMsT/k5/8ZPjzzz/ff9WqVX1Hjx5dOXr06Mrt27c7hw8fHujbt2/wgw8+cGzYsKFi9uzZbYD0NM66deu+OHDgwL4DBw7sO378+N7LLrvMqz6f1jmUZeCcgzFmbs63AcrKynTWs4wlmc55J+d8h/yGc/5nAHn1aHtOomPEZtrIJVkjuHh5G8xLz6jFHXDjWNexGEO5Qx2HItuV/6uPL/S73fmMrjnPmc/1zdzU11Nh9KI+Rs988FDHocj/bt85aYcZ87keGMUZNSQi4yKCIHKSRAZwCYxmPb5zmrHtnF97u5ahllHzrWTiKMXePEer3tZrH7cfidqkNC+WqRpUBY/BpdTUx+q1S491HUuYzkybNqc063f5teOCK2akO9V89tlnRUOGDAk89thjp2fNmnX6o48+ct51111n5Q735MmT3QAwffr0tieffHJwZ2enpaamxgMAU6ZMOffMM88MEkWpD/zee+9p3tnQOody//XXX39u48aNFSdOnLAAgPxYe1VVlevll1/uBwArVqyoqK6ujpoL379//1CfPn1Cf/zjH0sBYOXKlf0nTZqkciw0RjKd812MsRWMsWsYY1czxl4EsI0xdhlj7LJkCkFAmhPm7Af88EVgWK00z2tYrTT/K94dP5tTSrNvE3DL892vZvJIlLfBvBxWB5onN2Pi4ImwMismDp6I5snNcFgdKC8ux+IrF2Pi4IlYuXclFl+5GJuPbobT6oz5X+t4IjfRvOZXLIJj+zNw7NuI5tolia9nUYlkMjSsFo7tz6D5ikWRYzYf3YwlqjwWX7kYK/euxMTBE6U5ih//Rl+bJjWc9GfW+FxG0xHpZe+XR03/EURBo2436MVHdfzctwmoexmOg9tjYvvCKxZi9/HdMfE6Mo9chcPqMJQ2mThKsTfP0aq3nf2kOebKbXUrpe2KbQ5739h2h44G1Wjppry4XFNL5UXl0emKYtPpHav3e8gZzdpKjmH6SlEVF0TYSo4lPrhnvPvuu2WVlZWXjBkzpvL3v/99v8bGRs3lyWbNmtX+9ttvV9xyyy2RR8Wffvrpr4PBIBs9enTlyJEjL3niiSc0H8NPdI7q6mrvY489dry2tnb0qFGjKh944IFvAcCvfvWro6tXrx5w8cUXV65du7b/iy+++Hd13v/5n//5ZVNT04UXX3xx5SeffOJ4+umnv07me0jGrX1rnN2cc35tMgVJhlxxD04Zsls7D0kdloDif7Wpi9LJUk5nC5tryYZxxaXRxljKvPUM4mIcMh0Qgz54WHwHddll0m6xRxws1W7t/pAfQTEIp80Jb9Ab49CeyK09Rc7XWZ+ElbO6jbn2xlzNRTEUdu11whNwwwEGIWwmFCxywhv0wBl29LVbHbAKVm0nVr8bKC6FGPDAwxC5zmr3X4EJklt7wA27YIPVYotf3iQ/V9zPbFCL5Naeesy6tWeis01u7bqQbnMNMdTdPvB1dcfFyPsSIODpjpPK+KlILxY5FW7t7kiMs1vtUfHabrVHPGnUcTAUrjuMOLubjaO9za09LzBTF8fotAQAV2nVCYDFpBOB6DaJ1QEBzNC5tTTJwWM0zcDIrZ1IGWq3dtOtRM75lDh/GeuYFySyEVtxGcAhOaWvnQksGgisnSG5V4pit5Pl2hnSvjW3hw2uGGDvI5nH2ft0m8EAkhO8+7Qiv5nd+cmo8107A6LvHNpCbtRvqceE1RNQv6Uebd42iLz7ONllsn5LPapfq0bD1ga0edtiAktXoAsNWxswYfUEPLTlIXiCHjDG4LQ5UWIrgUWwRP5XG70oz6FXDqIHaFz7GH3oHCe4T6NkzQwIiwZKr/4ugAOhIifave1o2PowJqyegIatD6Pd246QGNQ41xmgSDIwEopKItfeYXXgrO9sRDcNWxtw1ncWj+94XPo/0AkR0DY9kklkjpQESn1qGRKZTUcQBJF2xBDgOgW8cacUe9+4E3CdBg5uA84eld4vPi86/ivjp72PNJJm7wNBsMJhK0Gbtw0NWxtQ/Vo1Xtv/WjjmK+K19yw6/Z2adbdFsKC0qBQCE1BaVKrZMQeSi6MUe3MMM20MUZTaq0qd+s5J7QTlNvcZaXvUttMQwFAS1lVJUanUMTdwbpGLaPdF67cr0IWz3ug2SLu3HRw8RrtamjOjw5zSrCC0obhsL5jwIYrL9lLHPLMk49Y+iDG2kjH2Tvh9JWPs7tQXrZcTz2XarAO12gle7xiNfD3eswkdJI24TPbUiTKnnCwLkWRdzeMcF9eN1+C5tK670q2dNEAQBGEQvwtYf4/K8foeYPhV2q7tCeK/Oj5rra7RuKMRHb4Oqrt7O2baGHpu7VrHq9zaNfM0eG5dt3aV+3vTjiZ4g940fllEbyeZ2zL/B8C7AC4Iv/8MwNxUFYgIE89l2qwDtVEneI18dZ0vFfNgjLhM9tSJMqecLAuRZF3N4xzntJXouPGWGD6X3nVXurWTBgiCIAygWBkjwtGdgKNvUvFfHZ/1VtdQO1tT3O6FmGlj9MCtvSertphxa9dagYAgUkUynfMBnPPfANLTpJzzIIBQSktFxHeZNutAbdQJXiNfXedL1ah4KtLEI6ecLAuRZF3N4xznDrh03Hhdhs+ld92Vbu2kAYIgCAMoVsaIMHQS4DmbVPxXx2e91TXUztYUt3shZtoYPXBr78mqLWbc2rVWICCIVJFM59zFGOsPaVY0GGPfA9CR0lIR8V2mjTpQi6JUGdscgEPPCV4y7oIYAhiLyddh75vQQVLPZdJuscMVcEHkIgQmGHKiFLkYOUZ+jXcOuvueIsy4msu64qI0D1F9XJ2kK4dQpO/GqzpGvGM1XAIzdN2Vbu25rgE9PRMEQaQcZWz2dcXO51WsjIGx/wjUtwI/3ghwDvzwV7Hxnwn6eSE2PmutrtFc24zy4nLNujuT8ZFicZaxOYE7Vkuam98mvd6xWruNYXMCM9cCTYeBn7dLr2WDNdsoYulAuB7eA3F+m/SqlafB9o2uW3ttc0w7xm619+jrID0S8UjGrf0yAMsBfBfAXwEMBDCdc/5J6osXn4J0s1QSz9kykeulbL6x7m7p8Z3JTcAVD0iVcMSt3SmZbrS8Coy7XZpzVjYYuOZxoGJYxNFdZIndJtUuk3aLHe2+djRub0TryVZUDarC0muWwipYdfORTd+UxzRPbkaFvSLi+E5u7WnEiJOqWldDJ0kVrGCVdOXrBP6yAti+BJjchND37oeHiXAWlcHt74QDAiy2Eimv8LnEgBdtIbfh6y4wAcWW4p66n6edRHo2Cek2DLm15xWk20yhFZunrwScA1Vtg5C0cou/KzaOM0t4lReXNDf3zdnx80Js3a9eXcNhtYNpuLUDSGV8jP/VmI/FpNtUY1SfQLdx4fp7utPWvQyUDJBWJ5JXDLA50KZqZ+peV4NO8VrtTJGLMW7tVsGa/FeR2raBktS5tRMZJWm3dsbYRMbYYM75RwCuBvA4AB+A/wbwVaoLSiC+y3QiB2q1Aca2fwXWzADAwseUSculrLsbqLyx2wxm72+B5VXAqzdLaQVjbpPqNN6QN8ZY45Ftj8SkVZLI9C2nnCwLESOu5lrGKm/OhrSciRt440eS1sQgUHkjLG/MROlTQyH8oh9KnxoKy9qZUh6Kc3kYN3Xd5Q55rmuATAzTA61ZThAaGDXcEizSTXqtOM4E6Q9Mem/AvEsdn62CVeVibdWsuzMZHykW5wBmDOH0jAvlZdTCbRSPRjtT97oaXLVFS6tqTfekYw70Xj06nc4qvX1VVVWje5r/66+/Xv74448PNnuckXPfcccdF3344Yc9e1zCBGYUtgLAdeH/rwDwMwD1AMYD+DWA6aktGtEjjBhgyGkGjErODCwOyRi4kelbHpBIV8p9BnVVqNe9UD8XQRA5SE8Nt5RpkzUINUEm4yPF4hzAjKb0jAvlpYHD5Ot1zddyp4NgMAir1YrW1tYDPc3rRz/6UQc0plkHAgHYbDbd44yc+8033zySKE0qMTPsZOGcy+vc3QHg15zz9ZzzfwHwndQXjegRRgww5DSnP03ODCwOyRi4kelbHmDGqNCgrgr1uhfq5yIIIgfpqeGWMm2yBqEmyGR8pFicA5jRlJ5xoa8ralO+Xtd8KLfIxQpXwDVW5OKE8GtFqvLetGlT2eWXX37xTTfdNHzUqFGXAN2j6keOHLFVV1ePGj16dOXIkSMv+eMf/1iqPv7SSy8d3dLSEhnFrqmpGbVjxw7nsmXL+t91111DAaCurm7YPffcc+Hll19+8QMPPHDh119/bb3iiitGVlZWjrnzzjsvuuCCC8YeP37cqjz3pk2bympqakZdf/31I4YPH37JzTffPFwMe23U1NSM2r59uxMA1q1b16eysnLMqFGjKidNmnQxAGzdutVZVVU1esyYMZVVVVWjP/744+KefEemOueMMXmkfSqALYp9cUfgGWPfYoxtZYztZ4z9jTH2sEaaaxhjHYyxPeG/+SbKRqgxYoAhp9m3CbjleWNmYAZJxsCNTN/yADNGhfs2dZsPmTRhKYTrXqifqzcwzLvG1F+6GfvqWFN/RC/EjKlnorRm8kqSTMZHisU5gBlNKY0LI0azL0vbFeTrdc31cotcrGjztl1Uv6W+aMLqCajfUl/U5m27KJUd9E8++aTk3/7t344dPHjwb8rtr7zySsXUqVM7Dhw4sG///v1/u/zyy2Pu3tTV1bW9/vrrFYDUmf/mm29stbW1MekOU1as0wAAIABJREFUHjxof++99z576aWXvvrpT396wdVXX925b9++/bfddlv78ePHi7TKtX//fscLL7zw9y+++OJvR48eLf6f//mfqJsDX3/9tfWhhx4atmHDhoOffvrpvrfeeusgAIwbN867a9euA/v379/385///FhjY+OFPfl+zDzWvhbAnxhjpwF4AOwAAMbYd5DYrT0I4DHO+UeMsTIAHzLG/odzvk+Vbgfn/EYTZerdKA0uAl6Ah8Jmb2GzC+dAYOYb+gYYggA4BwDf+2fpuBlrwmYw7u6A6evSPF7kInwhH0QuRpnAeUPeyPt+xf2w/Nrlhg3cBCagwl5h6JgUmcP1CuJ+VwZNUiLImpG1IutDNncrLofnzrVw2ErgCZsBCfE0iMTXXRRD0jabM5ynA4JgSf4zZwgzeiYIgtDFSJwWhMR1vjIvZ39VHC/pTqvIS7Q5uuNv0AMHU8WwcH4hmx2eKCM4Byxx4rRWfLRb7GmJ2xSLc4B4+hRD0jxzpRZLBsbqEyyqTSrYnNrXlUMyPEzC/A2INUA2us2onnJdj56gZ0jj9kZh94ndACDPiReWX7t8SImtpC3B4Ya49NJLXaNHj/art3/ve99zzZkzZ1ggEBCmT5/efsUVV8Q8TnDXXXe1X3fddRcvXbr061WrVvW76aab2rXOcdttt7VbrVI3d9euXaVvvfXWFwAwffr0c3369NFcAnzs2LGub3/72wEAuOSSS9wHDx6M6sRv27atpKamplMu+6BBg0IA0NbWZrnjjjuGHz582M4Y44FAoEemkoaVwDn/VwCPAfg/AK7i3TbvAqS55/GOPR42kgPnvBPAfgBDkikwEUZ2vlw7A9hwn+S6vnYmsGigtM19SkoXzwBDFKXj3rgTWHye9Oo61d0xl/NX5imKELmITn8n2r3tqN9SjwmrJ2D1vtVo87ZF3tdvqUe7r920eZcR0zfZ6VJ5rjZvGy1FoUHc70qpIdU11s9QoZlFA8OaOQ3sfBHihjlo855B/daHpXNtbUCb7yzEImdSJizS6UJS+bc2dOfpbYMoasbVxJ85w5CJIUEQPcJMnE5keiXntfNFoOOr6DjuPh2dpyBALHKizdfeHX/VsTScX+jgFrR529EQTtcQjtOhOHEaiI6PDqsD7b72tMVtisU5gJY+ZWf2qDZFuP1q7yOltfcBwDR/BwJH9HXlMPR70WondPo7DW1r87ah09/ZI63msh4dVkeRzpx4zdHmZHA6nZpf1g033NC1ffv2T4cMGeL/yU9+Mvz555/vv2rVqr6jR4+uHD16dOX27dudw4cPD/Tt2zf4wQcfODZs2FAxe/ZszRsGpaWlkXMYXZmsuLg4ktBisSAYDEZ1sjnnYCy2393U1DTk6quv7vz888//9oc//OELv9/fowtq6mDO+V8457/jnLsU2z6TO95GYIwNA1AF4AON3ZMYYx8zxt5hjF1ipmy9DqXzZe2jwFsPGHPB1MtDfVycfZ6gBx2+Djzx3hMRt8mpQ6eiaUcTOa/mGHG/KzPuqTJax6y/B6i8EZ7Jj6Hx/X9J6XXxBD1oVOtqR1NC7wLSB0EQBUEycTpRXsoVWuLkmTCWhvPzjJgcU/83JYjTaihu91J0ndld0emM/g4MptPSW4evw9C2xu2N6PB1FKxWPUGPX2dOfMxId6r57LPPioYMGRJ47LHHTs+aNev0Rx995LzrrrvOHjhwYN+BAwf2TZ482Q0A06dPb3vyyScHd3Z2WmpqahJ+8TU1NV2rV6+uAIANGzb0OXfuXPzHL3WYMmWK64MPPig7cOBAEQCcPHnSAgDnzp2zXHjhhX4AWLFixYBk8laS0Vs1jLFSAOsBzOWcn1Pt/gjARZzzcZDWUX9LJ4/7GGMtjLGWU6dOpbfAuYzS+TJZt/V47plx9jmsDgwpHRLlNjmifAQ5r8YhW7qN+10l48ird8yAUXD0H5ny6+KwObXzjDP3MR/1katQvCXykYLSbSqd002u0JIwlobzcxb30UznNDFHneJ2genWKAad2Q3/Dgym09Kbul2rt631ZCuGlA6J2VYoWnVYHceaJzeLqjnxosPqOJbuc7/77rtllZWVl4wZM6by97//fb/GxsaTWulmzZrV/vbbb1fccssthh6zf/rpp7/esmVLn8rKyjFvv/12+cCBAwN9+/aN/2iPBhdccEFw2bJlh2+99dbvjBo1qvLWW28dAQBNTU0nFixYcOFll102OhQynW0MGeucM8ZskDrmr3PON6j3c87Pcc67wv//FwAbYyzm7gPn/Nec82rOefXAgQPTXu6cRel8mazbuhnnbcU+T9CDY13HotwmD3UcIufVOGRLt3G/q2QcefWOOf0pPGc+T/l18QTc2nnGGTXKR33kKhRviXykoHSbSud0kyu0JIyl4fzcvnOa6dwmRvcpbheYbo1i0Jnd8O/AYDotvanbtXrbqgZV4VjXsZhthaJVgQltFfaKI8uvXe7/cPaHWH7tcn+FveKIwIQezTd3u92tAHDjjTd2bt269QutffX19Wc+//zzv+3fv3/fhx9++KnWvHQA+Na3vhUMBoMfPvPMM8flbQ0NDWdWrVp1FADWr19/+J/+6Z8ic9ErKipC27dv/2zfvn37Z86c2VZRURF0OBw8XrlWrVp1tKGh4QwA7Nq161N51P72228/t3///n2ffvrpvvfff/9zALjuuutchw8f/utHH3104Lnnnvv62LFje3vyXWWkc86kB/RXAtjPOX9WJ83gcDowxmrCZTuTifLlHKIoBSYeftWaW6Z0vtzxLPDDF7VdMEVRCkq+znB+nd35mXHeVuxzWB0oLy7H4isXR9wmNx/djCW1S8h5NceI+10l48irdUyd5Pjv2P4Mmq9YlNLr4rA60KzWVe0Scv0nCKJ3EC9OG2krKNNwEbjr94C9Avjhr3q+kka4bI5D22Pq/yUJ4rQaitu9ADEEeM9JOvSek94XlUhtCHWbQuXMbri9YjCdlt7Ki8sNbWue3Izy4vKC1qrAhLYSW8legQkfhl9TYgSXLb744ouiSy+9tHLUqFGVc+fOHbpixYrD2S5TPJjRSfI9OgljV0Fyd98LQK49HgcwFAA45//BGHsIwP2QnN09AB7lnL8fL9/q6mre0tKStnJnBdmwZd3d0qM4QydJgcU5UN91Vc+tHQB8HVKH/K0HtPOL5wIbZ58Rt/Z0uk/2wI27Rw6KqSCjuhVFiL4OeLxn4SgfCk/HUTjsfSEUlye+/pr5hStXTzvQ7yKg/Qjg6AfYHIC1CGLACw/jKdVAvrq1p5jepdt4LChP+ynSvTza4aenmUpvdnm0vT/u0U37VEK6TQVacRpI3FbQak/c8jzw8W+Ay+8FmCV6hRaDjtapcGvX/Ji5E7dJt6lGNn5bf0+3FuteBkoGSG1Ut6JN4ewHyG2UqDwMtlcMpsuWW3sa0dXtxx9/fHjcuHGnM1kYwjgff/zxgHHjxg2T35tZSi1pOOd/RoJgxzl/HsDzmShPTqM0swC6zSxmvhE7B0d2vgSi59PI23xdUsD7Q4N+fso84uWv2ie7q8qU2KS7nCVCSdT7dCE7XGbiXHlNwA3hzdkoCV//EkC6k2zk+mvhdwG/md2tJzm/GWsAmx1CkRPy1UjVdREEC0qKpLLJrwmPIX0QOYzZznbn/qfTVBIiL9CK076uxG0FrfbE7x8CftAMvDlbSsuEuLE/YSwNl80CoDQcn0sNxmnT5yLyF6XxG9Bt/DZjjaRFdZsiUZs3XnvFYDo9vfVkWw4jiqLIBEFI/4gsYQpRFBm6B64BZNgQjjBAqs1f+l2UuvyI/COVegKMm7cQBEEQ6cNIbI9j4EntACKjxGs7UBs1E/z11KlT5eGOIJEjiKLITp06VQ7gr8rtGRk5J0wgm1ko7yLKZhZmO0B+N9D1TeryI/KPVOoJ6DZvUefn6wqvRUoQBEGkHSOxXS+NbAhH7QAiU8RrO1AbNe0Eg8F7Tpw48fKJEye+CxqYzSVEAH8NBoP3KDdS5zzXkM0s1PPITCxJEpWXs59kFqeec55MfkT+kUo9AWHzlpdj542pzVsIgtCFHlMneoyR2K6VRp5zTu0AIpPoth1S3EYhNJkwYcI3AG7OdjkIY1DnPM2IIoc7EIKzyAK3PwSnzQJB0HiqRGlgUVQGzFwbbe4Wz6RLD0GQTDUsxYr8XIA8P8bXFWswY8YcjMg4hvUkIwiSQdDMN6KNAxmk62/2GgsWycBlxhrprrasIZPGP6Ywa1pHEATRQ0zH2kwjCIBTHYtLomOjOv7LaSY9IJl4xourFHcLgpzRsW7bwRqt0bhGbyGpDRul9zS2PQgiS1CkTSOiyHHG5ce9r7bg4p+9g3tfbcEZlx+iyNUJJUfVtTOARQOBNbdLlSJHt2lXsgiCFPCKy8LGL2XSduX51s6QXN3V29yntJdmIbKCYT2pkc1ROAB/J7B2ZvLXWBQB9xngjTulPN64U3qfLp2ofxukS4Ig0kzSsTaTiCLgPq2KxadjY6Mc/5kgTT0SLFLnx31aP65S3C0IckrH8doOSo3qtXllt3fl8a5T0naCKDCoc55G3IEQGta2YuehMwiKHDsPnUHD2la4A6pgonRUFYPdrqsBd3oKpnU+d3tmy0CYxrCe9EiFznJBq6RLgiDSSI9jbSboSWxMdCzF3YIgp3TcU00p3d7l49ffI20niAKDHmtPI84iC3YfbovatvtwG5xFqsdwUu2onQit85Gre85jWE96pEJnuaBV0mVhkYF1ywnCDD2OtZmgJ7Ex0bEUdwuCnNJxTzVFK8UQvQgaOU8jbn8IE4dVRG2bOKwCbr/qrqXsqKpEdqtMB1rnaz+S2TIQpjGsJz1SobNc0CrpkiCINNLjWJsJehIbEx1LcbcgyCkd91RTsqu7+nhfV2rKRxA5BHXO04jTZsGymVWYNKI/rALDpBH9sWxmFZw21V1L2VF1WK1kjjGsNtatUhSlIMTDr2JI9d7EXDCt8zn7JS5DEohchCvginolksOwnvQworNM5AFAFENw+bskXfi7ICrnjSm1zoS06JIgCEKPHsfaTNCTWGxzAnesBupbgflt0usdq7uP1cubCbptDarrc4+c0nFP2w6y27vyeJ2VYuK2LwgiD2Cc55DBiUmqq6t5S0tLtosRl6Tc2tVulbI5i7zUxOQmYMJd0UtSTF8pOV4aNY/TOh+QUndWkYto87ahcXsjWk+2ompQFZonN6PCXgGBZe2+UNbtdnui2x47r6bCgbeHeYhiSNLFjqZuXdQukXQBFq31oZOkRqNg7fnqBflNXus2Ljn4WPsw75psF6FHHH56WraLIJO3us0Zl+t4JBuL1W0KrTaEKAIBlxR32w4D254EOk9otjVytK7vCVm/0KmKtzmjY1EMGw+3S9Mo249Ig0LF5SbarYnd2uO2Lwrf2T3ruiVSQ15GzXxCEBhKi60QWPhVLyjGc6tUG2lU3hhrjGHWrEXrfEYcM03gCXrQuL0Ru0/sRpAHsfvEbjRub4Qn6OlRvr0Zw3rSz6Dn17iHeXiCHjTuaIrWxY4mSRdapjFvzgbAUqZLgiCIRPQ41maCZGOxEXMuQQDAgFdvBpZXAXt/q9vWoLo+d8kZHQfcUl2+vApYWCG9vjnbZLvVIq04oFx5QEXc9gVB5AlkCJcPqI00BozKC7MWh9WB1pOtUdtaT7bCYXVkqURELuCwObV1IT+9kQfaJgiCyFuMmnMZTEd1PZGQDJkMJmxfEEQeQENQ+YDaSOP0p3lh1uIJelA1qCpqW9WgKrqD2cvxBNzaugi4yYiIIAgi3RiNswbTUV1PJCRDdXvc9gVB5AnUOc8H1EYa+zbFGmPkoEmWw+pA8+RmTBw8EVZmxcTBE9E8uZnupvdyHFYHmmuXROuidomkixQZzhEEQRA6GI2zBtNRXU8kJEN1e9z2BUHkCWQIly/EGL84gIAnZeZt6ULkIjxBDxxWR+Q1ywYxWZ842Kt0q4MohiQ92JzwBNySLuT5Y6kwrSs8Cle3ZAiXcsgQrhuKtzoYjbMG0+VgXd8TSLfpIEN1e9z2RWGTdd0SqYHmnOcLsvELoP+agwhMQIlNWupCfiUIQbCgpEjSrfyq2JkX2iYIgshbjMZZg+moricSkqG6PW77giDygLy9rUkQBEEQBEEQBEEQhQKNnBMEQRApY+yrY02l35umchAEQRAEQeQbNHJOEARBEARBEARBEFkmI51zxti3GGNbGWP7GWN/Y4w9rJGGMcaWMca+YIx9whi7LBNly3lEEfB1ATz8KorZLhGRz5CeCIIgChOK70QuQ/okCENkauQ8COAxzvkYAN8D8CBjrFKV5gYAI8N/9wH4VYbKlruIIuA+BaydASwaKL26T1FAI5KD9EQQBFGYUHwnchnSJ0EYJiNzzjnnxwEcD//fyRjbD2AIgH2KZLcAWMWltd3+whjryxg7P3xs7yTgBtbdDRzeIb0/vEN6P/MNcrEmzEN6IjLA3i+PZrsIBNH7oPhO5DKkT4IwTMbnnDPGhgGoAvCBatcQAH9XvP8qvE19/H2MsRbGWMupU6fSVczcoMgJHN0Zve3oTmk7kVfkhG5JT4RJckK3BGGSXqlbiu95T0HrlvRJEIbJaOecMVYKYD2AuZzzc+rdGofwmA2c/5pzXs05rx44cGA6ipk7+N3A0EnR24ZOkrYTeUVO6Jb0RJgkJ3RLECbplbql+J73FLRuSZ8EYZiMdc4ZYzZIHfPXOecbNJJ8BeBbivcXAvg6E2XLWWxOYPpKYFgtIFil1+krpe0EYRbSE0EQRGFC8Z3IZUifBGGYjMw5Z4wxACsB7OecP6uTbCOAhxhjbwC4HEBHr55vDgCCADgHSnNyipzSHUabU9pOEGYhPRFEr2DYT99O+zkOPz0t7ecgTEDxnchlSJ8EYZiMdM4BXAlgNoC9jLE94W2PAxgKAJzz/wDwXwB+AOALAG4A/5ShsuU2gtBtlkGmGURPIT0RBEEUJhTfiVyG9EkQhsiUW/ufoT2nXJmGA3gwE+UhCIIgCIIgCIIgiFwiUyPnBEEQBBHDMO8aU+kP2+9Ma/4EQRAEQRDZgiZ7EARBEARBEARBEESWoc45QRAEQRAEQRAEQWQZ6pwTBEEQBEEQBEEQRJZhkg9bfsIYOwXgSIJkAwCczkBxegKVMTUYKeNpzvn1mSiMHgZ121vIB11lEr3vI590W0jXtFA+S7Y+Rz7pNtXki3byoZyZLmOu6jYfrpURCuVzALn1WbKuWyI15HXn3AiMsRbOeXW2yxEPKmNqyIcyEtHQNYumEL6PQvgMMoXyWQrlc+QT+fKd50M586GMmaBQvodC+RxAYX0WInegx9oJgiAIgiAIgiAIIstQ55wgCIIgCIIgCIIgskxv6Jz/OtsFMACVMTXkQxmJaOiaRVMI30chfAaZQvkshfI58ol8+c7zoZz5UMZMUCjfQ6F8DqCwPguRIxT8nHOCIAiCIAiCIAiCyHV6w8g5QRAEQRAEQRAEQeQ01DknCIIgCIIgCIIgiCxDnXOCIAiCIAiCIAiCyDLUOScIgiAIgiAIgiCILEOdc4IgCIIgCIIgCILIMtQ5JwiCIAiCIAiCIIgsQ51zgiAIgiAIgiAIgsgy1DknCIIgCIIgCIIgiCxDnXOCIAiCIAiCIAiCyDLUOScIgiAIgiAIgiCILEOdc4IgCIIgCIIgCILIMtQ5JwiCIAiCIAiCIIgsQ51zgiAIgiAIgiAIgsgy1DknCIIgCIIgCIIgiCxDnXOCIAiCIAiCIAiCyDJ53Tm//vrrOQD6oz8zf1mHdEt/SfxlHdIt/SXxl3VIt/SXxF/WId3SXxJ/RIGQ153z06dPZ7sIBGEa0i2Rj5BuiXyEdEvkI6Rbgui95HXnnCAIgiAIgiAIgiAKAeqcEwRBEARBEARBEESWyanOOWNsFGNsj+LvHGNsbrbLRRAEQRAEQRAEQRDpxJrtAijhnH8KYDwAMMYsAI4B+F1WC0UQBEEQBEEQBEEQaSanRs5VTAVwkHN+JNsFIQiCIAiCIAiCIIh0ksud8xkA1ma7EETyiFyEK+CKeiWI3gT9BtIPfccEQRBEPkH1FhGPnOycM8aKANwM4Lca++5jjLUwxlpOnTqV+cIRhhC5iDZvG+q31GPC6gmo31KPNm9brw1ApNveRyH8BnJdt4XwHROpJ9d1SxBakG57B1RvEYnIyc45gBsAfMQ5P6newTn/Nee8mnNePXDgwCwUjTCCJ+hB4/ZG7D6xG0EexO4Tu9G4vRGeoCfbRcsKpNveRyH8BnJdt4XwHROpJ9d1SxBakG57B1RvEYnI1c75TNAj7XmNw+pA68nWqG2tJ1vhsDqyVCKCyCz0G0g/9B0TBEEQ+QTVW0Qicq5zzhhzAvg+gA3ZLguRPJ6gB1WDqqK2zRk3B+6Am+bYEL0CT9CDOePmYMPNG7Bn9h5suHkD5oybQ3fHU4hWnKkaVKX5HRfSHL9C+iwEQRD5htEYrJXOTL1F9E5yrnPOOXdzzvtzzjuyXRYieewWO5bULsHEwRNhZVY8OP5B1I2sQ8PWBppjQ/QKii3FqBtZh6d2PYXq16rx1K6nUDeyDsWW4mwXrWBwWB1ontwciTMTB09E8+TmmBGIQprjV0ifhSAIIt8wGoP10tktdkP1FtF7YZzzbJchaaqrq3lLS0u2i0Fo4Aq4sHrfakwdOhUjykegM9CJR7c9it0ndkfSTBw8EcuvXY4SW0kmi8YyeTItSLe9gy5/Fxq2NsRoftmUZSgtKjWbHelWB3kkwmF1RF4FFn3f2RVwoX5LfS7Enx6TZ5+FdEvkI6RbQhejMTheOmV9pVdvJUHWdUukhpwbOSdyHyOP8zisDqz4eAVu23gbxq8ejzJbGc2xIXoF8u/CaXNqat5pc2apZIWJwASU2EqiXtWYneOXy4+N03xFgiCI7BEvBivrjXjpjNRbRO/Fmu0CEPmF/JhO4/ZGtJ5sRdWgKjRPbkaFvSIquMhzauQ7hoc6DkW9B7rn2OTgaA9BJIXy97H0mqWamncH3MmMnBM9QB2PAP34YzTGZQszn4UwzrCfvm0q/eGnp6WpJARB5DJ6Mbgr0IW5W+dG6o1fTvklxWoiKbLf0iDyCqNLQKjngm4+ujlqDjrNsSEKEeXvY9PBTTGaX1K7hDSfBYzOTQdyf5kbM5+FIAiCSC2aMbi2GWv2r4mqN9bsX4PmWorVhHlo5JwwhdFHKgUmoMJeETW3xm6xx8y1yYWRKIJIFcrfx9O7nwYALL1mKcqKyuAOuOGwOmARLNksYq9EKx7pxZ9cf2zczGchCIIgUote+3bFxyui0q34eAXuHXsvxWrCNKQQwhRmloBQz6mxCBbdOTa5PMeTIIyi/n08vftpPLLtEXiCHpQWlUZ1zEnzmcXoHD9a5oYgCIIwg1/0a9Yb3pCX5pYTpiGVEKZIxyOVtDQQUSj0xqW9Co1cf2yctEMQBJE9tGKwK+DC0muW5my9QeQXtJQaYRqRi/CFfBC5mJJHdTK8NFDWl5og3eYfRpbrMpM2Cc2TbtOA3rUyc70zDS2lZg6juiVDOEJB3uiWSC09rb8BZLPeyLpuidSQG60NIu+Qg1MqRm5yfY4n0bsxO1KZjqW9iNQT77rm8jI3pB2CIIjUY7SujxeDc7XeIPILUg5hmlS7GdMcTyKXSYd7N2k+++S6K7sepB2CIIjUY7ROoBhMpBvqnBOmSfXITa7P8SR6N+kYqSTNZ598HYEm7RAEQaQeo3UCxWAi3dBSaoRp5LuGyvk28l3DZOY80tJARC6Tar0DpPlcIB3XNROQdgiCIFKP0TqBYjCRbkhJhGmUdw2nDZ+Gt299Gy//w8sAkNS8c7UBh91ihyfooSWmiKwi689hdeCXU36JB8c/GLlLvvSapVFp9DQab7m0XJ7XXIior4XdYtcd/cjWMndGz0vaIQiCSC16I+J2iz0mLvc0BhuN9bTkau8k50bOGWN9AbwM4LsAOID/j3O+M7ulIpTIdw1fmPoCXAEXGrc3ovVkK6oGVaF5cjMq7BWGA5VswCHnMWfcHNSNrEPTjqak8ySInqLWZdWgKjTXNuPesffCL/rhCrjwyLZH4mpUMw/SclbQuxb9ivvFjH4AyMp1I70QBEFkD60RcbvFjnZfe0rjstFYT3VC7yUXr+5zAP7IOR8NYByA/VkuT69H686dvNxQMoZKynzcAXdUHlOHTkXTjqa8M2ki0kc27hx7gh6s+2wd5tXMQ8usFsyrmYd1n6+DN+Q1rPt8NRwrRPSuhTfkjRn9MHPdtLSZrF5JLwRBELmFN+RNiyEstSGIeORU55wx1gfAZAArAYBz7uecn81uqXo38ZaWSMZQSZ2fwxadx4jyEXlp0kSkB7PLmKUKu8WOG0fciKd2PYXq16rx1K6ncOOIG2G32E2ZxpCWcwMz18JoWj1tdvo7k9Ir6YUgCCJ7aMX0dBnCUhuCiEdOdc4BjABwCsB/MsZaGWMvM8Zy15mnFyDfuRvgGIDf3PQbvPT9l+AOuOEL+ZJaTkJ9J/DQ2UNReRzqOBQ3T5p/07tIxZ1jM5qR03iCHsx/f37Ueee/Px+eoMew7mm5ldzBzLUwc31jnq74bB06fB1J6ZX0QhAEkT202htfdX5lOC4bbWt4gh7MGTcHG27egD2z92DDzRswZ9ycmDzdAbfmud0Bdw8/KZHr5Frn3ArgMgC/4pxXAXAB+KkyAWPsPsZYC2Os5dSpU9koY6/CYXXgPMd5aKhqiIwiLti5IKGhUrz8lHcCX9r7EhZesTCSx+ajm7GkdomuSVM2RlFTAek2OXp659iMZpRp1U90yOd12pyGl1EphOVWCkW3Zq6F3WKPiUFLapfAbrHHpNN6uuKC0gui0hnVayHoJVcoFN0SvQvSbXbRam+8uOdFQ3HZTFvDbrGjbmRdVN1RN7Iupo5xWB1R7eOJgydi4RVnQK61AAAgAElEQVQLqU7oBTDOebbLEIExNhjAXzjnw8LvawH8lHM+TSt9dXU1b2lpyWAJex+ugAtnPGewYOcC7D6xGzcMvwH3jr0XI/qOgC/oA2Ms8oi7keUkXAEX6rfUR5aquGH4Dfjf1f870umRDTi8IW9MnupjAWDi4IlYfu1yM0sfsR58HSmBdGuceNdcqQ+19uQVAAAY1ozyXBtu3oDNRzdj6tCpGFE+Aoc6DmHz0c2YXTkbJbaSmBUG9HRvNJ0BSLfo2fdp9FhXwIUv2r/AiL4jUGIrgSvgwqGzh/Cdft+J0kyXvwsNWxtitLX0mqW46o2rorYZjVEp1EuukDe6HfbTt03le/hpzWYJURjkjW4JYxiJrXrtjRemvpCwnWvmWE/QY6hd4gq4sHrfat12iAZZ1y2RGnKq1uecnwDwd8bYqPCmqQD2ZbFIvR6H1YELyy5E68lW3DD8hsgI+s92/AxnfWfx4OYHcdXaq3DPf99j6PFL9TJscy+bi6YdTVF5MMY0l6ig+Te9j3hLm+jdpVbewbZb7EnNNd51fFfcO9tGl1GhJa9SR0+fnDF6LYotxbig9AI8vPVhTFg9AQ9vfRgXlF6AYktxVDqnzamprbKisqRHv0kvBEEQqcVo3aH31FSRUJQwLmu1T89znBfptCvPa2bO+fSLp0e1Q6ZfPJ3avL2AXKz56wG8zhj7BMB4AE9muTy9Gtm9uGpQFe4de29kHu7dY+/GE+89YXpupXKpin+Z9C+m8qA5mb0PpV4+nP0hll+7HBX2irgOqsp5Y4k8DJQo9VVzfk3MqgFNO5rgDXkz8rmJWDLlXOsNerWvfTD62seLR2q9UiebIAgiOxitO7whL9Z/vj7KR2T95+sN1fta9cED4x/QPK/eXHJ1efTaP1SfFD45t84553wPgOpsl4PoRh697GfvF7nbZ8ZVXfk4kS/kizziIx9jJA9lOdZ9tg7XDb0Ow/sOj8x9JwoX+W41gMhrojvP133rOjx7zbPoU9QHv5zyS6zZvwYrPl4RWSdUXgpQ+YiarK/G7Y20akAOkqknZ/RGxJ02Z0x5ZL0o16AtthRHOvKcc3DO6WFDgiCILGFmpHrFxyvwwp4XItuszIr7Lr3P0DmWXrMUHb4ODCkdgmNdxzCkdIjuebXqDmpfEDJ0+4VIiHz3zhPovjNodERS+TjR4zseR7u3PfKIjxkXTLkc/Yr7YfrI6Xhy15OoXl2NuVvnot3XnhemcETqiDdq6Q/58f1h38ej2x7FhNUTMHfrXEwfOR0ts1qw/NrlsAk2PLj5wZjH25R3qb0hLz2lkWNk6skZMw65NsGGBZMWoGVWCxZMWgCn1Yl2bzsatjZgwuoJaNjagDZvG0JiKKVlJAiCIIyRqRVWAmIAC3YuiBgnt3vbMWfcnJj8/KI/pu6wCbaY/PLZBJnoGdQ5JwwRGVmsleb/rty7EouvXJxwbqXycSL1o/DP73keT171ZCSPB8c/iOXXLgcAhMQQuvxdkdFNeVkKT9CDxh3pf7SVyCzxliBRaqHL3wV3wA2H1YHnpjyHZ69+NkZ/QTEY81hy445ujTyy7RFd/cij9OScnXvE8x8wsnyNWkchMaSpO4fVoe3Wbo0+jy/kwyPbHsG0303D+NXjMe130xAQA5qPxCtjGC0BSRAEkRqMLF+WiRVWNJfW/HwdZlfOxtu3vo09s/fg7VvfxtJrlkLkYkzd8ci2R+AP+aPqqExN5SJyj5x7rJ3ITUQuot3XjnWfr8PjNY9jeN/h8Aa9Ma7Z6rkwyseJtB4VtjIrFkxagAtKL4DL70KHrwMbD27EjSNuxPz350vLuF3WgCfeewKtJ1vRMruFHjcuMOS7w+pHvCrsFeCco83bhqYdTZF9i69cjGU7l+EbzzdYUrsEu2fthi/ki+gv0WPJRvSjHEUvIOfsvEbrmtgtdrT72jW1o7xWITEUo6MltUvgsDrQsLUh5tgKewWWTVkGp80Jd8ANu9WOs76zMec5z3FeVBnLisp0tXfPf98Tt4wEQRCEceK1HZSx1Ux9Lo9oy4+ma41oayEvrTn//fndZalthifowYKdC6LKV2orjaknrvvWdej0d0bVUS//w8vU3u2lUMuAMIR8B++FPS/g1o23Yvyq8ajfUg+ge1TdE/RE7l6GxBBcAVfUY0LqR+HvHXsvGnc0YtrvpuHLji/R4e/AE+89galDp2L++/MxwDEAP738p1Gj7YfOGjf4IvKDeHeHPUFP1EjkAMcABMUgnqx9stusRWXUFe+xZKNGLAA5Z+ci6msSzxhQiVpH8oh2iIcMjUr4Qj7N8zww/oGodJ3+Tk19fdX5FY1+EARBpBAzI8tG6nNP0INdx3ehT3EfMMbQp7gPdh3fZShWe4KeiGGyXBZ30K1bPnU9Me3b02LqKLNTP4nCgVqbhCHiGWqo58Ws3rc68n7RzkWRx9/Vj8KP6DsialRdNs8YUT5CGjGvaogZiXpp70tYeMVCety4gIinLeUouLyU34KdCzBh9QQ8tesp3DjiRjisjqi5WA6rI0YjC69YCLvVjtf2vxbzyDLpJ38xavQTb9kz9bZiSzHavG1R88b1znNh2YVRWrIwi6a+XtzzYsIyEgRBEMZJtUlosaUY488bH/GreXTboxh/3viYZTS10Kpj9AzhnDZnzOPzWk9dvbjnRZpe10tJ62PtjLFLAQxTnodzviGd5yTSg3ynb/eJ3ZFtyjt48t1BAJg6dGrkDiAAiBCxYNICXFh2IXwhX+TRInkUU17yqthSjKpBVTjUcQgPjH8A89+fj3k186LO+86X72BE+YjII6ey+zsAuAIuevQ4D1Fr64bhN+Ch8Q8BkEbB54ybgxf2vBC1lB8A7D6xG/Pfn4/npjwXuSMtexZsOrQJ82rmYUT5CBzqOIRNhzZh6tCpeGHPC/h2+bejHlkmzeQv8eKS7OwPICrWKNN1+juj8qsaVBW1lBog6UweEVcf7w64Yx6VdFgdUfqyCBYMKx+GDTdviOhx89HNMWUkCIIgjGM0/htFK/Y37WjCsinLUFpUGvdYua0ydejUSJxv87bplq9fcb+oesIb9Mak/cbzDZxWJ02v64Wk7Qozxl4B8AqAOgA3hf9uTNf5iPQSzyhDffdSPbf8nS/fwS1v3RLJR36kSHn3cOXelSixlWDxlYux+ehmXFh2IVpPtmLX8V0xI1F1I+sidw5dARc5WeY5Sm1NGz4Ncy+bGxkdb9jagLqRdXhw/IO6y5vJlbB8x9xhdWD6xdPx1K6nUP1adWSE/aW9L+GG4TdgTP8xUaOi5Pafv5gx+tEyebMwS8yxWiMgJbYSXZM49aOSFsGC0qJSCExAaVEpioQi1I2si9Jj3cg6WgKSIAiiB6TauNXoMppa2K32mDgPAEuvWappYtrui17Vwx10Y+nVS2PqmGJLMU2v64Uwznl6MmZsH+e8Mi2Zh6muruYtLS3pPAWhQLkutPIOntxBlu/4bbh5A57a9VTUHcCJgydi+bXLY+5mqtdAl7cBQP2WesyrmYfNRzdH3Y3cfHQzZlfOjqQxch4FWV9xmHQbi6wDQPuaLpuyDIwxzX3zaubhto23RV17pa6+6vwKz+95Hu98+Y4pbeYYpFsd9OKSmpAYgifoiXpigjEWc6w74EbD1oYojfx5xp+x9sDamDg0a8yshCMq6vgI5I3mUkHe6HbYT982le/hp6clWyQi98kb3fZ2jMZ/I3T5u2Jiv9z+SBTn4x3LGIsqnyfo0a0TOOdRdZRFsJj5CFnXLZEa0nkLZidjLK2dcyKz6BlqqO9eHu44HFlyLdHdTGVe8qincimrEX1HYMXHK3DbxtswfvV43LbxNqz4eIXmiD1AcznzFeUSZnp3rrXuki++cjFW7l0ZozE5P0C6G37aczrG50CZP2kmf9GKS1rL66hHtC2CRfNYrVH2sqIyzTjktDkNLeNDmiMIgkg9qTRuNbqMplacjzfqrlXH6NUJ6jqK6J2kc875q5A66CcA+CDd0eGc80vTeE4iS8jLT1xQegHavdFLrrkCLpRYzQVNeekLvbmi8ihrKucbEdkn3hwyu8UOG+te5uSU5xTKisrwZO2TunfM1UuoxNMTaaYwMLq8jh4WwRKzlJqeLrsCXZi7dW7c86R6XiRBZIuxr441lX7vj/emqSQEkXq0Yr/eMpo9ifNUJxCJSOfI+SsAZgO4Ht3zzW9K4/mILOEJevDItkciS6I17WiKWnJt7ta58Ia8iTNSoZ6XLt/JXHrNUgCSs6bREXoiP4g3h8wT9OCRP0k6G796PJ798Fmc8ZzRzEd5h1vZcdfSE2mmsDCzvI5Rii3Fsbqpbcaa/WsSnifV8yIJgiCI9KB+wkpvGU1fyBc1mm632A3HeYEJePKqJ6PSPnnVkzSfnIiQzpHzo5zzjWnMn8gRlI/o6Jl2JdsQVY98ygFx7YG1uHHEjdh0aFOPRuiJ3EJ9vdUda/WyavPfnx9zNxtA3JFTvfyJwqCnj5GHxBDavG1o2tEU0c+S2iXoZ+8XpRu7xY4VH69IeB7SHEEQRH6iVZ+c5zgProArpo3Rr7ifoThfJBTByqyRpwCPdR2DlVlRJBRl6mMROU46O+cHGGNrAPwB0mPtAGgptawiikDADRQ5Ab8bsDkBIXEDMZHhhvIRneOu4z16XEfvXHLHPCSG0Li9EfNq5kWW1TrUcQj3jr0XI/qOgCcgGT5Rwzc3CIpBeINeOG1OeINeiBDhtDqla8sZBJs9RovK+eKyuZu8TN7vf/h7PL/nec1l1ZRLqSmX9lPuo0fGCh9P0BOzpM3mo5vhD/kRFIMJzXY8QQ/Wf74+aim+9Z+vx6wxs8BYt9+OX/QbjnVqTRMEQRDpQbMdyWGo/as+VmBCTH1SVlSWsI0RL857gh407mg0ZB5HbdneSTqvugNSp/wfQEupZR9RBNyngLUzgEUDpVf3KWl7vMPC8zf1lisTuYigGMTiKxfjwfEP/j/27jw+qvLeH/jnmS0zkwQkCKjYiBT11ooGAS0qFrX3WvcF24J1aa9LrQpab4UrUi+Xqi3ozwWr1oXbKrTgglortdarUqhyK2vFarUWkYoLSwIks2WW5/fHkzNz1pmTZCaz5PN+vfJKMnO25HzPM+ec73O+D4LeIG49/tYedeF0Wlc6k0Z7Zzva4m0I+dVdTC1Dr2VQf/LmTzBu0ThMf206h1OrEKlMCm1xNVzIrFWzsDuxG9NfnZ7bt4k2ZJ75Xt5YNMfEnNVzcP3R1+ct7JYvc1oonqn61XnrLEPaXHDIBUikEoaha7S2xSzkC+HMkWdahuIL+UKGuIkkI7ZD33CINCKi8nD8jE/sKXj+azdvJBnB1MOmGj4PhoaH9qp3Vr7icTw3IaCEF+dSyu/afP17ofmEEFuEEJuEEBuFEBxHoqcyGSDRAciu78kI8PRlwJZVQCalvj99mbqTmEeh5ze1583vWX8PLvyXCzFj1Qzcs/4e3HTMTVh70VrDyapTlUs369qT2IPZr8/G5t2bMWbYmGyGXp9BLdbzpdRz+me946k4Zq6aiTWfrcFloy/D7NdnG/fTGz9C7Ox7EDnjTmTWPWYbi7FUDE+//3Q2nm465iY8/4/nEUuq3hp6Y4aNMRTvMr8XS8VK8jwydY9dJXW30pk0Ojo7kJEZdHR22F5c6+Muu49XzcCezj2G12aummm732OpmKVNueWNWxBNRa1xk44ZYnPZ35f1qL4GERH1nuNnfHy3zflvxHCe7DivqZ3fHt3ueP7hhlaY1jx/e2c7z00IQAkuzoUQ84UQV9m8/gMhxDyXizlJStkipRxX5M3rH+yy5IF6YOtq43RbV6suPnkUen5T/35DoAEbPt+AFz98Eec/fz5u+tNNSKRz2apCdwLzDaM1vGE4Nny+AY9segTzJ6pM/K3H38qhsSqI+a6z/u6wUy2CoDeIaevno3XMFGT81n0W9AZts5hBXxBzj5tryFrOPW6uypx7g5hvGg5l/sR5CHmDHNaqzHrTc0F7FrxQ9tspKzG8YbjltbDf2v45zW/uprjh8w0YEhpiO8wjERH1PcfP+IHNxgm3rlbnxbrzZKd5h4aHGs5BfMJnKf6mDbnmdhvtzl/sPmP4edI/lSJzfiaAh21evxfAGSVYH5klo9YseesWoHmCcbrmCerZmzzyZSH1718x+gp83P6xYdruZrWd1hVNRrGtYxvGDBuDFz98EfF0HDeuvBH3rL8H7Z3tebeP+o75rvPezr3ZfbN5z2bb/bR5z2YVF6vndCuLGU/F8cLmFwx3s1/Y/ALi6Tg8yRia1vwC9x09A+suWov7jp6BpjW/gCcZKxjPVFq96bkQS8UsGXG77LdTVmJbxzbLa3aZjpjD/B+3f+xqmYwlIqLycGr/o8kO44TNE9R5se48Obb7I8e239wTK+gNWntNpdz1moqn7c9fPun4xLJufp70T6W4OJdSWtMgXa8Jm+ktkwL4gxBinRDiSvObQogrhRBrhRBrd+zYUYTNrQDmLugFngMvuIxA2JolX3E7cMFCYMREwONT3y9YqIpi5FFoGCDt/ZH7jMQDGx8w3A3UZ7VPO/g0vDT5pWwly0gygnQm7XooioF1A7PPsu9fv382Q3/7n2+33IGsxGGKajJuu+gLts06ZhZOO/g0AMDyfyzHvK4M9sJNCy21COYeNxePbHoEgKp+CiEsXZ3zPZt1waEXGJ8rPvQCtd8DYXj+OA/19x8Lz9wm9f2P84BAmMNadVOx47Y3PRfyxYJxHUHrEIsT52NgYKDlNbv1hnwhzD/OVDfjhNsxsG6gJW7sXmMslV8tt7dUuxi3veeUlQ75G6znvytuN8776u225wcPbHzAMJ3d586WPVtse2I5baPd+Qs/T0hTimrtUSHEIVLKv+tfFEIcAsDNLaDjpZSfCCGGAnhZCPE3KeVK7U0p5cPoysyPGzdOFnPDy0Lrgv70ZeqCunmCajTCQ1xVUrddxjVr1HK2rMpN0/4ZEGgEpi7tVrX2QsMAae9Hk1Fsj23Hgg0LslWOOzo7MGbYGOwb2hczxs1AZ6YT016dhg2fb8D3jvoeJh8y2TBcUb6hKBoDjQh4A7jv5PsQT8ezVZJf/PBFAMCcCXNwYOOBFVvhsubitovWTVk/pMjc4+YCAH665qcYGh6KBSctyFZrX3DyAoR9YXzc/jEWbFiAFz98URX1O3p6Njb0Q5/pRwLQaHeTHeMy0WGN/65eIh5/GE3w4r5jfoTQwGbE9mxFCF5VydXNrcN+pthxm29/FqpirmVEzPNGk1E0BBqyrwnhgd/rNwxT4/f64RVey2v66utZqQT83jrjtMKPBo/fEjfwN3CItApUq+0t1TbGbe/ps9JadfUXNr+Aiw+/CPX681/hUefFOvJLZ8EvjJ8dIV8I22PbDdONGTYGHZ0d+MmbP8mes9x6/K1IpBOuLqadzqsB8POEAJQmc34LgBeFEN8RQozu+vougOVd7+Ulpfyk6/t2AM8COKYE21g57LqguyjUlpXJAJ0dxmX8/Q/A5Eetdwl9QaCuQTVKdQ2uL/61YYD0383vh/1hzD9xPnbGduKbv/0mrnj5CgDA/BPn49qWaxFNRQ0Fwc4ceaa1aNPKGYin44Z1AaqQHKAuBLVq3Pq7mztjO7N3LO22j0rHrpvyLW/cgitHX4nx+43HmGFjskPbhb1BNGQASImBgYG4tuVabLx4I24+9mY8/4/nbbs6CwjcfsLthrvJtx5/KzwQznHpD1t7iUxeCPhDQGcHPH9+BPX3tqis+r0t8DxxsfvjjXqlNz0XQsKX7Ymhf84vJIz3mLUilWc8ewZaFrXgjGfPwA9W/ACxVAyJtBrVM5FOYMnfltg/SiEklvz9KeO0f38K8fgeS9x4UgnUZyQ8gPrO02kior5j6nkaggcXjDzLmJUeeRZCUhjPf31By3lCbNRJWPLeEkPb/8a2N2x7Yv36b782nLPMfn12t4qb2p2/FDrXpv6j6JlzKeWLQohzAdwIYFrXy38FMFlKuSnfvEKIegAeKWV718//BmBusbexoth1QXdRqA1ALmMe3je3jCMmA4d9HVj3OHD6HcC+h6qL94D7i/GeyHcnsCnYBCmloYv7AQ0HFOzeapeV1TKq+bL51HecuimP3Gdkdv94hMfYu6NxfyRPnYs5q+cYsu2b92zO9oTQisVd8fIVmD9xPuYeNxf71++PzXs2Y8H6Bbh94u12m6N4PKrniXaXPNEO/N9DwMp5KoN+zs+AnX8D3l6mpnd7vFGvFeqJk4/XF0DT+mex4Kt3IVw3ANHEXoT+8iS8x1xumM4uJoeGhkIIYch0zD1uru2wZ1oRwlveuMU4bf0Q44SN+wGd7b3r9URERD1j0/PUc8FCNL3zAu47egZCgw9BbNffEfrDHHjOf8g4r/k8oTOaHUbT3PbvE9zH8JkV9Abx0F+My2PxNiqmUnRrh5TybQCX9mDWYQCe7epq6APwaynl74u5bRWnM2LtgnviTHUnsK4h1/0cUNk9fZd0Let++ny1jIahwOl3Ak9eopa34jY134iJqgGq6+r6mclYl1WEk0l9tlvfRTWSjGBXbFe2S6q+eFy+7q36rCyAbEb1vpPvQ72/3nZd1Ldcd1PW9RCJXfNnzHj9ZsN+veWNW3DTMTdlL84NxeJWzcBNx9yElkUtAIDx+41HLBlFva4rc16RXepiXOuZ8ptr1TGjXZw3T1DHIUTRjgVy5tROFNQZhffd59Hw4o0AgAZAtW0tF+baNtjH5NUtV1vaklveuAULTroXDVIY2kJ9EULDtJPuQcPVq4F9DwN2vgcEm3I9loBcr6epSwCIorevRMU2+rHR5d4EIntuzlP1PU+BbBvsOf9R1MdbAQD1MgMMPkSdUwuPdXnaZ0ddA2KdHfZt/0kLso9O1fvrEUlGevx4FpEbJTtjEEIcKoR4WAjxByHEq9pXvnmklJullEd1fX1ZSnlbqbavImQy6oLh3AdyXWsm3QyMvQRYemFuGLTEHuvQaNEduaz7yjtVxuZrc4DgwPyZeLth1qI7elaEziVzQbeRA63F4+y6t3LYq8rnupuyrodIaPAhjtl2u2Jx5vfmT5yHUL4hS8wx/tvpwCm3qF4lgNqOfQ/LdXk/9wHghR/0ybFAvWD3uIJNUUu7mDyw8UCHYnL1lrbQsfBcoAH43Qzg1qHqu0eo7LmezfA8jCkiom5we55q1/O0cT/VNuvb6rGXAEIUXJ77oqMsLEulVZLMeZenAPwcwKMA0gWm7Z+SUeCJi7sy3vO7uqBHgKXfVq9dtUpdRCT22mdopvxaZf3eXqYuPrSMoEMxLNQ12N9pXPsY8JWrjJn6ImZ67Aq6mYvHbevYZnnGpjfFo6hvuO6m3BnNxmVs19/t92syinUXrzMUizO/F0vsRegfK+EZdXKu90j2TngISMYASGuM67PlWqb8RzvUUCr/OyeXRX/6MmMvE6ocNt0Q7doqu5iMORSTiyUjqDe1q7ELl9pPu/sjy7Q4awGw6ancyvXD8+inY0wREbnjkBG39EqSGdXT9PAz8/doWna5aqsLtMtui4725vEsIjdKGUkpKeWDUso3pZTrtK8Srq/6aHf93l6mst97PlbPhjfupy62tTt/dQMcsuH1uUzSwC/ksujn/Mw5u2S+03jEZOCobxoz9SXI9HiEByFfCPX++uxdR33xuLA/jDpvnWEe3p2sDq6KmOiynqGV/w/zj/uxqcDKPIRXPwg88z2EhRc7Yztz702Yg/Dz18Hz34NQ/9OD4Fn2XRXH+jvrqx8AIl2/+x3qOOx7WO540D5o7x+fuzDXpuPz55VL64ZYoKilOSbV8GrzLDFnaUu2rnae9v0/WKZF04iCw/MwpqhSbfpwa7e+iPqEYy0mU6+kQFhlxd30aBp0kM3yrMNw2hYdtempx+JtVEpFz5wLIZq6fvytEOJqqIrrCe19KWVrsddZtXTZRJz4w1x2b9Is9bN2l2/ne/bZ8GQsl0lKRnNZdKArE3+YyhDqi8Hp1wnk1tuHmR63dx15d7KG6LKenkAYTcl4br8mIwitfhCe19RTLE0yjftO+RFC+xyEWDKK0OoH4NFfQDdPUM+P6e+OH36muju+ZVWe4yWq4lrLtuYZco1Zztri6Yyi6cPVuO+rdyFUNyDXA2O/I4wTNk9Q0675hbGg0JpfwHPEBZZpsffTXFu78z0gIy3D8zCmiIi6wXyeCtj3Skq05z73tdecejS1fWRch0277PX40BQclB3+NZqMIuQLwuspZSdjIqtSXOWsA7AWqiDcjQDe6HpNe500WjZx0s3AkH9Rd/I2r1TZGP1dQ3M2fNLNwIVLAciusZml8XnMd36j7iAm9qrnbATUhUgmA/iDalgpbVn7HtajavEZmUEkGTF87w63dx15d7L3eruv1EKMw5UYelYY3mvPdTczT6fLenp8dahPp9UQVP56eP44LzfZ28tQf9849Z6U8BxzhXVYtEC9MW71cezYe6TemG11+QwzFVm+WCo0a09jOVAPz/AxqF96ETw/HqK+H3QsEB4ETNsA3NKqvl+wEKhrgOeP81B//7Fq2LT7j1XxaZclDw4AtIrv3iBQV+8QU6Ee/81ERP2K02ezuVeSU69Sc1s9+VGgYYixrf/WImT8IcvnidfjQ0OgAR7hQUOggRfmVBalGErtYAAQQgSllHH9e0KIPFWc+iGPRw2DNvZSdUfwxJlqGLS9nxrvGr69DNj3X9Qz5oF6IL4XiLYCz11tGsJn39zzmMm4GubniYtz00xdojLphmHWbKrFF8j05BvijBfPlaUo+8pmuJLskFGA9b1zHwBemasyiHZDS2XSqgv6ssvVPNessY/BHe8Bv7tRLePCJ9WNJW1YtMPPMs6jz5ZrWfazFqgPaac6Ci6fYaYiyhdLBf7vvYrlVEKNa3vWAtW9se0jINAIxFpVwUBtW877ee0OBnsAACAASURBVJ42MWKKlRAQ3Wmc39wO66fjkGtERIXZfTYLj7VXUttH9m11ot3Y1vvrVc85XVud+dYitCbaeB5LFamUEfiGy9f6t2QMWHYZ8NqtwLFXqi7mL//Imvkbd6nqnp6MArE2dWG+ZVVueKinL1PL8muFMtK5br/aNJmUuiBacRuw8g51QRMIGzPpLrKH+iHOUjKVHeIslor14T+O3CjKvtIXZ8mkVLHCRET1yOjsUAUF9XH23NXAxBt0cRk1Lq8zkuuKlkmp2NePWDBioor/lXfkChZmUmpebVi0lXcYj5F3XgC+uSh3Z/xffww0Di38t7l8hpmKxBxLWoyk4gUzy3ljuVBWWqaBp78L3DcGmNukvsfagGevMm7Ls1epWDDH47kPqNf1Ugn7vyUZM8ZUMuYwXdS6nUREZCU8wAW/MGa/w4OtGfbJC9UNfH1bH9lhaYNj8d08j6WKVYpnzvcDMBxASAgxBl0drwEMAMD+omZa4YtMCjj/kdzPQO5ZxmRU3fkDVOZ8kEOxDH8ol5W65DfWabRh1o6YrArO/eVJVQzuL0/qMukdxmfUbXCIs+pRlH2lL86ixc5vrs1lAc/5mbpg1jLWWvE17WfzIxJ1DcbYfHuZ+uCdukTF9473VOb97WXGgoX69b0yV32dfgcw5LBcTxF9FvPcB4BXrnfO4FPfcxr6prO9YGY5byw/dnb+rLT5MQhtvU7t6Ctzjc+SvzIXOP9h63psh1IzxbtjcSN+HBIRWTj1sILH+Bl//sOqB5Q+S16/L7BynnF5gw6ytMGhgc08j6WKVYoz1VMB3AngQAB3Afh/XV83AJhVgvVVN63wBZDrmguoC5MHJgCPnwNAqBPNZFR1f9e68uhp3S61u4P6ZWnie9RrWhG4w89U31fcBjzwFXWXcem3VaYnz3Oh2hBnetoQZ1RZirKv9DGqLyCoZQF/c616XdM8QcWf9nOnKUOoFWLTa/8MkFJN+85v1fJuaQVOv1PdPDKv76x71TTv/Db3jLs5O1kog099Tx9LmkmzXGWWHWN590eFs9J26237SD1KdPVqFWtXr1a/JzpUPD4wQbWJD0xQvyfareuZZPpIs4t3u3XbTUdERM49rDJpddN09nb1ff1ilRXXZ8kTe+3betNrsT1beR5LFavoF+dSyseklCcB+I6U8iTd19lSymeKvb6qpy98seoua3dK8zBoK25XRYfM031rkTEjaVcUy+NThTG04llOxeC0DLx+yArd8Goc4qx6FGVf6WPUKWb2PczYBXjVXc6PSATqVRyaC7YE6lXs6YdGefISlTk/YrJxff6wmmbsJWoep+xkvgw+9T27Qj/mApiAwzA3DrH8qouhy+zW27ifdRiesZeoec1t5zk/U/FpXo9dkThzvLPwIBGRe049rDzC2F4f9U1gULNxureetJ5fhAZZ2uBQcB+ex1LFKmUZwoOEEDeYXtsDYJ2UcmMJ11tdzIUvkvFc916tQBWgsjnoGqbn9zcB//rfuekSHaoqe+uWwkWx/OFcwSO7IadOnGnMwAOW4dU4xFn1KMq+0seoNmSfXcz8aId6DjedVN3NEh0qprXuxZmMmj8QBoL7qAKHdQ1d09UDHq/62Tw0ijbEoBbTWuxuWaWmnbo097q5MIw5g8/hrMrLrtCPy6KUtrEsBTztnxrX0TxBtaMyYyz0Z16vVoNDH2vLLldx+clbwDcfB0L7ALHdwId/sj7LblskjoUHiYh6pTOqzisOPzP3aFHdQOt56W+uVYkpvXd/Cxx9ien8IgzAY2iDPf4wmgR4HksVqZRROA7AVVDPnw8HcCWASQAeEULMKOF6q4++KFUgDNQ15ooJAbks9gs/UFnJju3AvS3AkqmqS0+gQV3crLjdmPHp2K6y7BK5Ylcer5r+goWqiJZ5iLavfN/6TDBgyUZxiLPqUZR9pcWo3zRU1KSbVbZx6YXAm4+oRyeWXqh6XCy9EIjsVF3RtGfItN4Yv7oASMVUbAYHqLgE8mfADcXi7sy9FwjbZycLZfCpPMxF+MwxlWd/WWLZF7TO+61F6hl2c88fwLhep3aurgFoPkb12vjxEPW9+Rg1FI/T8HyFCgqy8CARkTvmHnS/mwEM2N++vQ4OtPbCy6SM5yHRXWp6UxvM81iqVKXMnA8GcLSUsgMAhBD/BeBpACdCjXk+v4Trrh36Z28AlQ2yGyJKe05SX8hIGy7IKZMz4WrVCOrvMMqUMQOvYeaRAGsWMNGhPvy2rFKZxicvsc9ECk/e3hhZnTaZeS1D+aMdKja1YnHZ97ri0pCdjADCqzL4zFRWtt5klu3mhVRDSBaKNa32gWUYHpveG1ocM/tNRFRayZi1DdZqJtm11/pzWG9A3fwv1P4TVbBSnlk0A+jU/Z4EcJCUMgYgUcL11hZzJvHtZcD941XxLEDVwk90qIvsCxaqbPnPJ6pCcnX1amxfOx6POrnUhv3RLnCCA40Z+NHfUENWXPo8AGk/TBH1L+YsoBafoX3Uc2H6AluN+6lpAmFVWd387LibZ4MvWKh6e0iomO7Ybp9hNWxXo1o2M5XVoTeZZfO8dpXZtVoa+iKX+Z4td8qoM/tNRNQzeQoNG9j1oKtrVJ/3+qHULlio2uvgANUuBwcAvgBHx6CqV8rM+a8B/J8Q4jddv58FYIkQoh7AO/lmFEJ4AawFsE1KeWYJt7Hy2WUST5ypugsvMw89tK/7zI7dUBVTfqUaTS0Df96D6i5kgSGOqB/TPy+c6FDDrD13tXE4s84IMG9Ebhg0QN1ksuuNUSiLymd3qRC7Z9jt2szJC9Wz5foh0/7yJPCVq5wzNMEBff/3EBFVO6fh0ezOJ+3Oezt2qp6d+qHUzvs5kIobi3U69r5jz0+qHiU7q5VS/hjqOfPdUIXgrpJSzpVSRqSU3y4w+3UA3i3VtlUkpzuKWiZx0s3A1f+n7hh+5Sp1kqkNM9EwFEhEVFE4wPiMuROtu3zDUOCqVcAlz6k7j1pF947tajtcDHFE/UwmDcT3qlgVHvUBOWKiuih67mrrcGYykxv65C9PAifeqOJ5yq9yXeMz6Vz8J7suuu0ylHx2t/rZtXVuMypu5vX4raNZHHulsc3cskr9/sWvGp9rHHepuiFpnv/cB9TPRETUfU7Do9mdT9r1oAuEgGevMs7/7FXq3KHQvKw5Q1Wm1GcbGwB8oq1HCNEspdyabwYhxIEAzgBwG9TY6LWv0B3F8L7A2EtzWZ/Z23Pddo6YrLKVv7m2e9ntQFh1OT55trpgOuqbahmN+wGn3ZF7hofdg0gvk1ZFCJddboy3c34GNA5ziJcGdfGjZc4HfkHF89Jvq/dPnKmKv5iXyR4atceprfMG1HPi+fa/3bzfWgSkO63Le+9FY0a8boBDV/VGa08MAWPtjp3vqd/Pf7hP/1VERDXDqdir0/mkN6DqKw06SNVPqmt0ftxIj6NjUA0oWbQKIaYB+BzAywBeALC863sh9wCYAaD/PNzsdEcxFVeZoGTUmPXRhkA7YjJw+p3qorrQ3UhzdqkzAkyapeY9/MzcMjY9BcwfoYp8aQWT9LTuQVR73GQvOyO5Qi3meNvxN/t42flebtrfXNu1DF08H36m8zKptji1ddG2wvvfbt5om/3y/sX0NFSi3T42Ex3WnhidUfVozwMTgLlN6nv7Z2z3iIh6yul8UjvX0J97JCPqZu19Y1QbfN+YXKFiu/nN5y3sYUdVrpQRex2Aw6SUX5ZSHimlHC2lPDLfDEKIMwFsl1KuyzPNlUKItUKItTt27Cj2NpeH3R3Fxv1ywwH5Te+vvFNlh742RxVwK3Q30jyM1ZIp6kS2aURumCrbZbgf4ojyq/i4tYuR6A7rBbpTb4p9D1PDlpm7A+uHPdOmNS/DMf4YZ+VW9Lh1yp4MOsj6mnn/28076CD75Q3Y39hdHRng3Afti7+ZsVtk1av49pbIRk3HbaDeoQBn2HruYVeUc8Xt1nZZa8MLnbcQVZlSXpz/E+pZ8+44HsDZQogtAJYCOFkIsVg/gZTyYSnlOCnluCFDhhRnS8tNK2ChN2lWLiOkZco1by8D0in1PK/5PcCY3c5kgE6bZ8efuDh3J9NpGclYrnvQj3ao7+xq3CMVH7dunwdzuvsd3wOc95Cqe3DeQypepvxaPTKhDXumTWteRqEYprIpetzatXXNE1S3RfNrybgxI2IXe20f2S+vdYu1vfP4VVd1ff2DZMy6jfpukWz3qlLFt7dENmo6bpMx1eaa2+DOKLD2MePr8b3Wdr39MyAjrfMn9rLXHdWcUp5tbAawQghxkxDiBu0r3wxSypuklAdKKUcAmALgVSnlRSXcxspgl6nRstqAyjya7zgO2F+9b/eeluXRsqFOQwNpmfF3XnBeBrsH9Q9unwcLhFXBQH2sTH4UeOtJ1f3s7iOAe7s6yATqVS0Duzvd+nh/5wXrMpmprE1OWenwIONr31qU6zmkZURk2pr9rnPo3bPiduN6t64GGodai785xRjbPSKi4vGHVZtrboO18wT963WN1nPScx8E/HXG6cZeos499NjrjmpAKQvCbe36CnR9kRO7Ahb64YC0zONZC9RFu/597T2teFFnRBXg8nhy1dZPn28/tISWGZ9wtRoDWCsCxwIa/Y/b4UeScWDd48ZiWeseV8+Nm+cDcnfK9UNVTbjapmBLiAVc+gOnYj2A8TVIle3W4lHLfp//qDGe1j4GHHetcV4hVJZFT+uxwRgjIup7Tm1/Z0eu5hGgvrd9BGx62tjWb1yi2nrtPDXRoYrGvftb43o4bBrVgJJdnEsp/xsAhBD1UspID+ZfAWBFkTercmmZGkB9z2RUBkirQtyxXWWJpM377/xGvW+ucKxlQzevVJlJczVsfWYcyI3hy0at/9Eymuaq1+bMYiAM7Hrf+Nqu99UHqMdnnW/cpfbLNMe73XeqTXb7Xv9zXYPqyt64H3D16tzJ2aq7VPb7GVM75gvm2ry6BjWigLm9m/yoytB4vNb1EhFRedjVsVlxO3DqbdZzB31bHxxgPU92Om8hqjIluzgXQkwAsBBAA4BmIcRRAL4npby6VOusKYWGg3AzXERnVA1TddjXu7KddwD7HpqrUMysEWncDj+SjKuh+567OvdheO4DamSBH+2wzschTagnnOIsGS8cTx4vUD/EmGHRX5gTEVHfchpGM9Bo7bXX/pl6vWBbz2HTqDaVMoLvAXAqgF0AIKX8C4ATS7i+2mN+7hEwFkgC8j8X6Q8DX/me6jK04jbgga+o54Kf+LZ9ISTq39w8ZyvT6oJJX4DluatVtrKnyyQyc4ozmXYXTx6vyqwIj/rudGHuZvhAIiLqHaeiszJtXzfEF7S29XbtNc8xqAaV8plzSCn/KYTQv+RwBk8FOd11zFdF2ONRhTU4TBUVS77igj8e4i4uiQrJF2fF0pM2lYiIus+x6Gw94K8vnP1me039SEmHUhNCHAdACiECQogfAni3hOurTdqdwmTE3VBXdvNymCrS9DZT6DQU1s73VFw2DAUSEUCAmcj+pphZaKc4s2u3erpet8MHEhFR7+Rr091kv9leUz9SyovzqwBcA2A4gI8BtADg8+bdod0pXDJF3UnsTgZcm/f/fu48TBr1L/p40oaniu7o3kWU3VBY5/xMDel3xGT1nPBvp/d8+VSdihFbek5Drpnbrd6s1+3wgURE1Dv+oP0wrP6gu/nZXlM/Uspq7TsBfFv/mhDieqhn0ckN/Z3Cne+5G+rKdt6/2Q+1Rv2LPiaA3J3nqUvdV682F2BJdKgbQG8vU5W1zUOidHf5VJ2KEVt6rgsU9mK9bocPJKIeGf3Y6G5Nv+nSTSXaEiq7zqj9MKxfuSo3UlCh+dleUz/R11doN/Tx+qqb/k7hyju7lwHXz/v2MuCBCcCtQ1kwoz8r1p1nfRe0QIMaLm3ERPWByzvb/VMpshpuujr2Zr1us/NERNQ7dQ3AynnqXHRuk/q+cp77C2u219SPlLQgnA1ReBLK0t8pfHuZeu2sBUDTiMJDRvAuI5mVIib0Gc4kY67fKld705v1chgeIqK+odU/MrfViQ53mXO219SP9PXFuezj9VWnTEZd6PhDwOSFwLKu6pQd24G6evVfLHTiqd1lNFe2zHeXUVsvG77aoo+nby4CYm3AoIOAto+A8KDe33nWMpyZTPdjjmpDT9obPbu2ByjcHvV2vVrsAryBRERUKoF66/lHaFD3RuBge039RNEvzoUQ7bC/CBcAQsVeX80xDxdx4kxgyq/UkGjduWDu7l1GDlNRm/T7tXE/4GtzVME2/T4uFt7Z7r96s++d2h5vAHji4vztEWOOiKg6pDuN5x+Ti3j+QVRDin4GI6VslFIOsPlqlFL2daa++piHi1hxG7D027lumt056XTzzKbTejlMRW3Q79eJNwDPXlXafdydmKPa0tN979T2RNvcxSpjjoiosnVGVC9QfZu+7DL1OhEZlCJz3pTvfSlla7HXWVPKNVwEh6moTfr9yoJtVImc2p5BB1lfY6wSEVWfugb7dp7d04ksSpFiWAdgbdd389faEqyvtmgFjvS0Ake1uF4qLf1+1Ybj0+M+pnJzanvaPrK+xlglIqo+WkE4Pa0gHBEZlKJb+8FSypFd381fI4u9vppTruEiOExFbdLv11V3Aec+wH1MlcWp7QkPYqwSEdWCQD0w+VFjmz750e4VhCPqJ0r6DLgQYhCAQwAEtdeklCvzTB8EsBJAXde2PS2l/K9SbmPFKVeBI9v1hli9vdqZ92syDkxdoj4QuY+pEji1eYD9a4kOxisRUTXxeIH6IcCUX6uu7IkOdR7i8XKkICKTkkW/EOJyqAvtlwD8d9f3OQVmSwA4WUp5FIAWAF8XQnylVNtYscpV4Ei/Xn8YiO4ElkwBfjxEfY/uUI0oVRf9fg2EVeV/7mOqJHZtnvk1QMUn45WIqPp4vGpMc+FR37ULc7brRAalvOq7DsB4AB9JKU8CMAbAjnwzSEV7AMXf9cWx0cuB1dtrH/cxVRPGKxFRbWG7TmRRyovzuJQyDgBCiDop5d8AHFZoJiGEVwixEcB2AC9LKf9sev9KIcRaIcTaHTvyXutTb7B6e1FVZNxyH1MBFRW3jFdyqaLilsilfhm3bNeJLEp5cf6xEGIfAM8BeFkI8RsAnxSaSUqZllK2ADgQwDFCiCNM7z8spRwnpRw3ZMiQkmw4gdXbi6wi45b7mAqoqLhlvJJLFRW3RC71y7hlu05kUbKLcynleVLK3VLKOQB+BGAhgHO6Mf9uACsAfL0kG0j5sXp77eM+pmrCeCUiqi1s14ksSlatXQixSEp5MQBIKf+ovQbg4jzzDAGQlFLuFkKEAHwNwLxSbSPlUa6q8dR3uI+pmjBeicpq04dbuzX96IObS7QlVDPYrhNZlHIotS/rfxFCeAGMLTDP/gAe65rWA+BJKeULJdo+KsSjq5Ksfafawn1M1YTxSkRUW9iuExkU/eJcCHETgFkAQkKIvQBE11udAB7ON6+U8i2oqu5ERERERERE/UbR+41IKX8ipWwEcIeUcoCUsrHra7CU8qZir4+IiIiIiIio2pWyW/vNQoiLABwspfyxEOILAPaXUr5ZwnUSERERldSI/1zerem3/PSMEm0JERHVklJWXLgfwAQAF3b93tH1GhERERERERHplDJzfqyU8mghxAYAkFK2CSECJVwfERERERERUVUqZeY82VV1XQLZYdIyJVwfERERERERUVUq5cX5AgDPAhgqhLgNwJ8A3F7C9RERERERERFVpZJ1a5dS/koIsQ7AKVDDqZ0rpXy3VOsjIiIiIiIiqlalGOc8COAqAKMAbALwkJQyVez1EBEREREREdWKUmTOHwOQBLAKwGkAvgTg+hKsh4iIiIhqwOjHRnd7nk2XbirBlhARlU8pLs4Pl1KOBgAhxEIAHNeciIiIiIiIKI9SFIRLaj+wOzsRERERERFRYaXInB8lhNjb9bMAEOr6XQCQUsoBJVgnERERERERUdUq+sW5lNJb7GUSERERERER1bJSjnPebUKILwghXhNCvCuE+KsQ4rpybxMRERERERFRqZVsnPMeSgH4DynleiFEI4B1QoiXpZTvlHvDiIiIiIiIiEqlojLnUspPpZTru35uB/AugOHl3SoiIiIiIiKi0qqoi3M9IcQIAGMA/Lm8W6JkMhIdiRQysut7RpZlGURuuI01xiRR6RX7OONxS0Rm/Nwnqg2V1q0dACCEaACwDMD1Usq9pveuBHAlADQ3N/fJ9mQyErsinZi+ZAPWbGnF+BFNWDB1DAbXB+DxiD5bBlWvvoxbt7HGmKRCytHe1ppiH2c8bgtj3FI16k3c8nOfqHZUXOZcCOGHujD/lZTyGfP7UsqHpZTjpJTjhgwZ0ifbFE2mMX3JBqzevAupjMTqzbswfckGRJPpPl0GVa++jFu3scaYpELK0d7WmmIfZzxuC2PcUjXqTdzyc5+odlRU5lwIIQAsBPCulPKucm+PJhzwYs2WVsNra7a0IhxwP2pcMZZB5IbbWGNMEpVesY8zHrfkaM7Acm8BlQk/94lqR6Vlzo8HcDGAk4UQG7u+Ti/Wwrv7PE46k0F7PIloIo3xI5oM04wf0YRoZzcy5529XwaRG25jrTsxmU6rYyEjJdrjSaTTGdt181k26u/Mx0AxPj/0HI/bRJrHHlE/5bZdcGyP2H4QVYyKujiXUv5JSimklEdKKVu6vn5XjGVrz9lc8dhaHHrzi7jisbXYFem0NEDadP+zajO2tcVx5ePrsGz9P3HvlBZMGDkYPo/AhJGDce+UFoR87v99Yb8XC6aOMSxjwdQxCPt5t5KKy22shXweV3GdTmewK9KJKx9fh0NvfhFXPr4OuyKdlgt0t8cYUa2yOwbSmQzundq7zw89p+MWkDz2iPopt+2CbXs0tQXpTIbtB1GFEFJW78E3btw4uXbtWlfTdiRSuOKxtVi9eVf2tQkjB+ORS8ehoc5nmW7O2V/GnOf/itWbd+Gl60/ES29/ilOP2B+jhjbgg+0deOntT/HvE0ca5i0kk5GIJtMIB7yIdqYR9ntZgKPvlf0f3p247Sk3sdaRSOF/Vm0uGNft8SSufHyd5dh5+JKxaAz6Dctzc4xRj/SLuK12TsfAPVNasDua7NXnh34ddsft2S3DMenOFYb1VsCxVzVxO+I/l5d0O7b89IySLh9AxXVrH31w6Yvxbbp0UykWWzVxq+lOu2Buj/YJ+3H90o387K5+ZY9bKo6KypyXktNzNiG/x9CVR5tu1NCG7PSjhjZgwasf4P7XPsBne2IYvk8I15w8Ch4AHfGueeP5uwFpF0shvweRrvVEk2nemaSisOtOrt14kzL3cyqV654upcQpXxpqWM7mnRHLs2f1dT7bY6fe9KHNZ9moVtgdT25eC/k9tsfA4PqA4bXNOyOWzx63nwXhgBebd0Ysy/tCU9iyXh57RP1DOODFKV8aigP2CUII4IB9gjjlS0Nt2wVzezS4PuDq/Jjnq0R9o9/cEtOex9HfGZx+8ijsinTiuiUbs0NKPHTxWIwf0YQPtndkp/9gewemnzwKU49pRiKdwf/8aXP25xufeks3HEULBtfXWTKUWlfHJX/+COeOORAzl+nn4RAW1DvmoVGmnzwKU45pxnVLc3F975QWNIUDaI12Zl+3m+6ObxyJeDKNcCDXNEQSKcuxM35EEyKJlCFzbneMac/W8u47VQu7oYZ+ftHR6ExnMF33WbFgagsCXg+uWrw+d5xNbcH0k0fhrv/9e3Z5008ehdZIJ+Y8/9fsdPdd2GL57HH7WRBPpvHDUw8zfPbc8Y0jsbMjYZiOxx5R/5FKpjGkMYgrH19n+NxPmKqw27VH906xb7d62kYRUe/0i0/tTEbCI4AFU1uw5M9bceoR++OLQ+oRS6YN3XWHNNYhlkxjwdQWrN3SinumtOCJN7eioc6L7xx/MFojnbjpmU2Yf8GRiHSmcdMzmzCksQ5/vHESBoYCCAU8iHSmcl2Ju74LCExfsgFzzv4yZi57C6s378LZRx2Aa04ahab6ACKdKdQHfGzwqFu03hiQyA6NAgCnHrE/rtN1UTv1y8MQ8HkQT2WMr5umW715F2586i08csk4tMeTCPm8iKczqK/z4aGLxyKWTGPfhjr8szWKfcJ+yzPs6ln3FssFDOsqUDXRDzUEqOOiLZrEu5/uwYMXHY0BIT/2xpJY/Y+d+NL+Aw3TXbdkIx66eCxWb27NHgPfOf5gvP7BDsO8yXQG1y0xHnvTl2yw7UaaSmUQS6VRX+dDJJGCVwjc+NRbluP2oYvHYsLIwYYTabtjr7ePV/HxLKLyM7cLAsDSN7diztlfznZXX/rmVnz3hIOx4oeT8IWmMP7ZGkVTfQC/fP1Dy3TfOf5gS7v1vUXrXLVRRFRcNX+E6bMgXz9iGKYc04ylb27F1GOaMbixLtuV5+yjDsAP/+0wXL90I75+xDCcdsT+WPdRK751TDOuX7oRiy8/FvV1PgwbUIfhg0KQEhg2oA43n/6lbDb93DEH4rkNHxu+z1z2FhZffqyhq7y2LmbQqaf0ca3Fl0b/SMacsw7H6aP3x/cXr887nWbNllaEAl7c/+rfbbPq//HkRny+N4F7p7bYblfA68FPzh+dPREIePvNkzNUI+wezzhwnxDCAS++r8+Sd/VG0VuzpRUNQR8euXRc9uI16PNg7EFNhnl/dcWxtseeuRt6KpUx9HbRbngNG1BXcL12F812vQK689nT2/mJqPfs2oXFlx9j6Zk5b/KRCAe8uPKZTQWnq6/zGtsPPqZGVDY1feacyUhEOlOYvmQDhjTW4ZyW4bhu6Uacd/SBiHSmsXVXFONHNOHsow7A3HNyWe2T/2UYrlu6ESOHNGaLZHywvQP/bI3i+q8diq27otmfI51p3PjUWzjvaNXYnXrE/obv2rz6rvI3nqouzIc0s5SxUQAAIABJREFU1mH59IlYfPmxiCRSiKc4rBoZOQ1NFk2msXbLLjx40dEQAvjfG76Ks486AACyj2G8dP2JuOS4EYh2pjGksc7w+j9uPx3t8SSmnzzKsL7xI5rQHk8asuqpjMxm574/aRSGNNYhmkhDeIRlm65avB6T7lyBL876HSbduQJXLV6vsvtEFcju+Ip2pg3HyUvXn4hEOmM5Hq5buhGJdMYw3fSTRyFuivfOlHVe7bNHT3tURL89sVTaMu/0JRtx/dcOtZ23oc4HjxDqu83Fsr5XQG55G7L1Two9X5pvfiLqG3btQiSRxnMbPsacs7+M9249DXPO/jKe2/Axop1pw2vxZCZ7bqrNO3PZW9lHYLT2g8P/EpVPzWbOMxk1HnNjyI9hA+pww78ehgFBvyHz/R9PbsSCKS1IpDNoDPqzWe3hg0KWonD3v/YBfnTGlzC4sQ6LVm/BOS3DMSDkz2bQzfOY5503+Uj89ZPd2a6N2jYZs+ctCPrYRZCUfFkqu2zcHd84Eh4BbN7Rbsl6z5t8pO3raqgVYMGrH2Sna6jzoXGo3/au+aihDY69PninnaqJ0/E1KOS3HCd22ephA+oQ7UwZnt188KKjEUmkLI92mOe953/ftzwComW5Lnr0zYIZ9ubBYUMXdm3eQvIVRnWTEecxTlR+dkVa6+u8jplzfRvl1PPGUuC1a0hWc5vAx9SISq9mM+fRZBpt0SQ+3xPH9V87FDOXvYW98aQh8/353gTiKVXUTZ/V1rIa2msA8PxfPsGPl7+L9ngKXz10KB5/Ywva4ylDNn38iCZ8sjtm+D7nrMNx+3lHYN+GAE44ZAhiXRl7bZvMGRFmIEijz46/f9tpePCio/FxWwSRzhTiSWs27san3sKt547GCYcMsbw3c9lbaGkeZJv9u+S4EYY77f/YETHEvkbLqpvvzi/580eIJtO800495iZrW2xOWWC32errv3Yopi8xTrc7mrS8Zjfv53sTaOyq5fD+bafhoYvHYlckjvZ4yjBvRzzlcBymLBmyWGemcOY7zzHqJiPOY5yo/LQirXrRzrRtRjySSLvueaPn8QgMrg/gkUvH4f3bTsMjl47j4ytEfaRmM+fhgBcHDgqhLdKJYYPD6pm8Oh8GhPy44YmNuPn0L+GObxyJ/QeqjPf9r32ABVNaMLixDjc8sRHzJh+J5zZ8jHmTj8zeidzRngCkRPPgMBa8+gE+2BHJZtNveEJl4YUHuO9ClY38+cVHQ0pgdyyJG59Sz56HAz78x5Mbcde3WpiBoLzssuP3TmnBL/70Ia45+RD7+KnzZn82vzcgZJ8NHxD049DZL2az73e+9B5G7luPe6e0WJ45bwj4bO/Oh/weCAjeaaduK9dzzE5ZYKehA83Z6uauzxW9LzRZX7Ob98GLjsbeRMpQCfneKS1oCBg/ktdvbbUch/dOaYFXwJINS2cyhsrxtplvh2yY099s/jxiNo0qzejHRndr+hKNi96nQj6vpV1wOoYbgj7La+b26N4pLQj5rMewxyOyxd9YBI6o79Ts0RbtTCOVzmD60o2Yf8GRGD+iCf/YEcGAoA+f703gx8vfxcyvH4Zop7oD+fxfPslmzT/fm8Cdf3gP15w0CgfsE8x2RdeK7EQT6ew8APDjc4/IZuFnPP0W5l9wJGY8/RZ+ftFY7I6qCu/as+d1Pg8+35vAtrYYh52ivGLJtKWa+nVLN2LO2V82DPWnGT+iCXtjyezPdkOf2b7emcL7t52Gz/fEkZES/++bLfhgewfWfdSKhy8Zi/o6X/a9WCp3d17bppnL3sLDl4xFY9CfvdPOSs7kll119L6oCuw09F++oQO14yGSSNlO98/WqO28uzoShmrtHiEs1dqvW6oqves99McP8cvvjDOsN9T16JP+OPMI4LJfri34P9Rnw/THqNthEJ3m5zFO1Hfi6Qy27Y7ioYvHoiHoQ0fcvj3SnxPoX4skUpaRJyYeOhQNvprtTEtUVWr2SAz7vdlM4R0vvYd5k4/ES29/Cp9H4I5vHIkd7Ql89Y4V+MWfPsS9U1pww9cOwfBBIdzzv+9j3mT1/hkLVuHihW8imc4AEtkiO+GAyh5MGDkYv9v0KR57XS1j+D4qC39A1/fGkM+QSbn/tQ9QH/Dijm8ciWfXq6z8hJGD4fMITBg5mBkIMnC6Ez5qaEO2joE+fu6d0oLnNmzDcxu24d4pLZb3Qj6vZR6V9fbihic2Ii0lfvjUWzhs9ouY8/xfMW7EYNR3ZfImzn8Nx897DeGA/TZpz6tpd9q1ojIA+ry7MlWXcj3HrGWBLW1wwP44CQe8aAz64RECjUG/7XT1ATWcoHGZLfB7Pfj+4vU49OYX8f3F69EQdM5ymbfH7zOu1+fzWI6zoN/5f2g+/szzejzC+X9h83lkNz8R9Z2w34sDB9Xje4vW4dCbX8T3Fq1DyO+1/dzXfja3ZS1zX8bIm36HlrkvY9qSjey1SVRBajZF6/GI7PN6Wob7mpNGYVB9AJ2pDB65ZBzCdSoLHvR5MOXYZkPWXBsD8p+tUdSbTkDssgdBrwcdXXcutazm3lgSu6PJ7N1MbTtuPuNL+PcTRiIU8GQzIsxAkJnTnfAPtndkY+kn549G8+Aw9saSeG7DNsz57TsAVLEqfbbN7xGIp3LVXLXxTZ/b8DG+e8LBuOtbLYh3pvHIJWMRNsVjh247nDL2dj0+OOwSueE2a1tsjlnkpP1x8u8TR6KhLnc/O9ppnW7Jm1tx+cSRlmVKKQtm3bWMVk+y0k7/w454Ct9btK7g8ceMOFH1sDteY8k01n3UasmIH31Qk6Utu+S4EYblsdcmUWWp2cw5AEuGe87zf0VbNImg34uGYNed/6AP8VQG1y3ZiLteNmbNL3r0z6iv8yGY51kcLXsQT2fwy9c/zGbo500+Eqv/sRP7hP244xu57MqO9gQ8Qm2b1+PJZkSYgSCNVhzL6U641gNkR3sC9XU+yIxEMi3x0l8/z06nZb21bFswoOLr/LEHYs7zf81mx88feyC8HgGPEAjX+dBgE4/6rNqDKz4wxHO+DBuHXSI3upO1LTanLPLUY5sNx8nUY5st2+MV9scTAMsyvV6PMevucGyH/d4eZaXt/of3Tm3BL1//0PXxx4w4UfUKeES2Ro3WQ2fsiCbU+TyGNmrKMc2WbDp7bRJVFiFl5XQzFUL8D4AzAWyXUh5RaPpx48bJtWvX5p0mk5GIJtOO2YBMRgICOPTmF5HKSJx91AG45qRRGDW0AbFONZ+bk5SMlDj05hdx+uj9cc1Jo/DFIfXo6Bp3tjOVQUaiK1OfQjjAE58yKvs/Pl/cmrPN901twQmHDDE8bxpPZyzxXCjOARWjNzyxEd+fNCp7F/3BFR/grm+1wCPy/1v0y48n08hkuuI5T4ZNOyZSuq7sPo/A+7edVnB9ZFH2f5ib9ran3MRvX9GG4WyLJvGFpjD+2RrFoLBfXVzrPzt6cTwBQDqdQTSZzh7bYb8XXm/P75eb/4chvweHzf59uY+/qonbEf+5vKTbseWnZ5R0+QCAOQNLv45uGH1wc7k3wcJlQbiKjlu7Xmm/uuJYrHx/O45ubso+h75+aytOGDUEMVM7I4SomPaWioo7sUZUWh+WXwL4GYDHi7XAQtUmo8k0drYnDF3Pn//LJ5gwcjAeuXSc6wsfrVuhNj+A7DL0620I+ov1p1ENMhfH+v6vNmTjqLErdrSiLVoX3HCg67vfa3jW27LszjQ+35vAqfeszL42YeRgV93Z9MdRWFdROt985equTNWnkqoCR5NpXLV4vSFu7dry3hxPAFQ2vetiXDu2e3OTwvw/7HDoOs/jj6i62RXR3BtL4qE/fojVm3MX9BNGDsbYg5qy7Uuj7vyzUtpbIrKqqG7tUsqVAFoLTlhE4YA3WwTOXMQn7Pdm71Be8dhaHHrzi7jisbXYFem0FLYqZ9dMqh1ui2O5jUvDsvs4RnlMUDVyewwWO757ckzn/Tt4/BHVJLs2auM/2xwLwRJRden3t8y07IdTEbiORMrVMD8sqEPF4Dbb3JPhp/o6RnlMUDUq17BixR5SjsdflauwLupUOezaqBGDG7D0za2G4m9L39yqCllyiDSiqlJ1R6wQ4kohxFohxNodO3b0enladsGpCFx3hvlhQR1y4jZu3Wa7ejr8VF/HKI+J6lbs9rYalGtYsVIMKddfj7/+GLdU/XpzntA8OIwFr36AU+9ZiS/O+h1OvWclFrz6AYdII6pCVZc5l1I+DOBhQBXM6O3yCmUX+NwsFYPbuHWb7WJcUl8odntbDcqVceYxXTz9MW6p+vXmPCGaYPtBVCuqLnNeCvmyC3xuj/qam2wX45KodMqRceYxTURumdso/dDBbD+IqltF3U4TQiwBMAnAvkKIjwH8l5RyYTm3ic/tUSViXBLVFh7TRNRTbD+IakdFXZxLKaeWexvsVNIwP0QaxiVRbeExTUQ9xfaDqDawWzsRERERERFRmfHinIiIiIiIiKjMeHFOREREREREVGa8OCciIiIiIiIqM16cExEREREREZWZkFKWext6TAixA8BHBSbbF8DOPtic3uA2Foebbdwppfx6X2yME5dx219UQ1z1Jaf/RzXFbS3t01r5W8r1d1RT3BZbtcRONWxnX29jpcZtNewrN2rl7wAq628pe9xScVT1xbkbQoi1Uspx5d6OfLiNxVEN20hG3GdGtfD/qIW/QVMrf0ut/B3VpFr+59WwndWwjX2hVv4PtfJ3ALX1t1DlYLd2IiIiIiIiojLjxTkRERERERFRmfWHi/OHy70BLnAbi6MatpGMuM+MauH/UQt/g6ZW/pZa+TuqSbX8z6thO6thG/tCrfwfauXvAGrrb6EKUfPPnBMRERERERFVuv6QOSciIiIiIiKqaLw4JyIiIiIiIiozXpwTERERERERlRkvzomIiIiIiIjKjBfnRERERERERGXGi3MiIiIiIiKiMuPFOREREREREVGZ8eKciIiIiIiIqMx4cU5ERERERERUZrw4JyIiIiIiIiozXpwTERERERERlRkvzomIiIiIiIjKjBfnRERERERERGXGi3MiIiIiIiKiMuPFOREREREREVGZ+cq9Ab3x9a9/Xf7+978v92ZQdRHl3gDGLfUA45aqEeOWqlHVxO2I/1zereVu+ekZPd0kqnxlj1sqjqrOnO/cubPcm0DUbYxbqkaMW6pGjFuqRoxbov6rqi/OiYiIiIiIiGoBL86JiIiIiIiIyowX50RERERERERlxotzIiIiIiIiojLrk4tzIcQXhBCvCSHeFUL8VQhxnc00k4QQe4QQG7u+bumLbSMiIiIiIiIqt77KnKcA/IeU8ksAvgLgGiHE4TbTrZJStnR9ze2jbaMCMjKDSDJi+O7mvXJsD9WenuzvYsQI44x6Ip1Jo6OzAxmZQUdnB9KZtOt5GXPUHznFvfa7/pjicUFEta5PLs6llJ9KKdd3/dwO4F0Aw/ti3dQ7GZlBa7wV016dhrGLxmLaq9PQGm9FRmbyvleO7aHa05P9XYwYYZxRT6QzabTGWzH9tekYu2gspr82Ha3xVlcX6Iw56o+c4l47lha9swifRj7NHlM8Loio1vX5M+dCiBEAxgD4s83bE4QQfxFCvCiE+HKfbhjZiqVimLFyBtZ8tgYpmcKaz9ZgxsoZiKVied8rx/ZQ7enJ/i5GjDDOqCdiqRhmrpppiJuZq2a6ihvGHPVH+eJ+xsoZOKX5FNzyxi08Loio3+jTi3MhRAOAZQCul1LuNb29HsBBUsqjANwH4DmHZVwphFgrhFi7Y8eO0m4wIeQLYcPnGwyvbfh8A0K+UN73yrE9lYxx2zM92d/FiJFqjbNiY9x2T9gfto2bsD9ccF7GXPEwbquHU9xrx9LIgSP7zXHBuCUioA8vzoUQfqgL819JKZ8xvy+l3Cul7Oj6+XcA/EKIfW2me1hKOU5KOW7IkCEl3+7+LpaKYcywMYbXxgwbk82cO71Xju2pZIzbnunJ/i5GjFRrnBUb47Z7osmobdxEk9GC8zLmiodxWz2c4l47ljbv2dxvjgvGLREBfVetXQBYCOBdKeVdDtPs1zUdhBDHdG3brr7YPnIW8oUw/8T5GL/fePiED+P3G4/5J87PZs7N79096W4A6HXhFv38sVQs+7NHeBy3hypPbwtchXwh3D3pbiw/bzk2XrwRy89bjrsn3V0wc97bGCnGMqiy9DYW3cwf8oUwb+I8Q9zMmzgPQV/Q1byMOap23TnO0pk0BIRj3M8/cT5e2foK5h43l8cFEfUbQkpZ+pUIcQKAVQA2AdBa6lkAmgFASvlzIcS1AL4PVdk9BuAGKeUb+ZY7btw4uXbt2pJtNynaBXLIF8p+9wiP5b1EOoFIMoIZK2dgw+cbMGbYGMw/cT6agk3Z6d2urzXeihkrZ2BoaCimHz0ds1+fnV3m3ZPuhs/js90eF0T3/wPF1V/iVr8fexoPPVlGRmbQ3tmOPYk9GN4wHNs6tmFg3UA0Bhq7HYdOcV8GjNte6G0sdmf+dCaNWCqGsD+MaDKKoC+I3YndruatsJgrBsZtP9Ld46Q13oqZq2ZiaGgorm65Ggc2HmiIe+14CHqD2WOqj46LqonbEf+5vFvL3fLTM3q6SVT5yh63VBx9cnFeKvzQrSyRZATTXp2GNZ+tyb42fr/xuO/k+1Dvr+/Rcp45+xn85M2f9HqZOmVvvPpL3BYjHnqyjGLFYYVh3PZCb2OiN/PXaDy6xbjtR7oT6x2dHZj+2nTLtAtOWoCGQEOfbbODqolbXpyTTtnjloqjqm/JU2UpVkEj/XL6UzGYWlOuwmwsrEVmvY2J3szPeKT+ojux3pviiUREtYwX51Q0xSpopF9OfyoGU2vKVZiNhbXIrLcx0Zv5GY/UX3Qn1ntTPJGIqJbx4pyKplgFjfTLWbhpIW49/lYWg6lC5SrMxsJaZNbbmOjN/IxH6i+6E+tOxRN5XBBRf8dnzqmoMjKDRDqBjMxki7hoxY3cFnPRL0MrNKe9HvQGkUgnICGz72nTuSwUU/ZncvpT3PakwJV5nqA3iHg6nv3dIzyo89blXZ65KJfX483OU+etQzwVz74X8oXg9XhL8vebt6MX62LcOnAbY92JRbv9BsDymoQ0xFLQF4TP43O1vFLFXIVh3PYz+liPp+LZz2rt+NCOF7u2OOhTn+/mNt/8e08KwnXzs6hq4pbPnJNO2eOWioOZcyq6SDKCRe8swqeRT7H43cX4NPIppr82HWMXjcW0V6ehNd7qOLyKVu31mleuwQlLTsDlf7gcnenO7DLb4m3YndiNaa9Ow6xVs9AWb8O0V6e5Wjb1PY/woN5fb/iej7b/9fu0LdFmuDi65pVr8u7vjMygLdGWjbnpr01HW7wNs1bNUsuLt2Hxu4uz77XGW5HOpIv+t2vViPXbUap19Vd28eLUBriNRbv91hZvQ0eyw/BaR7IDu+O7LdOlMinLNlriMdHGdopqjj7WZ62alf2sHrtoLBa/uxht8TbDucDuxG6EfKHs8TZ+8XgsemeR7TG96J1FPf6c7047QURUbrw4p6KKpWKYsXIGTmk+Bbe8cUv2+5rP1iAlU1jz2RrMWDnD8XlLbX799HsSe7LLjKaimP36bKz5bA0uG31Z9mc3y6bKZ7f/tX2a771Cy5j9+mxcNvoyrPlsDWaumolTmk/Jvjdz1cySxEwsFcPMVTMN21GqdfVXbmOiu8s077cZq2ZgT2KPtV1aNcOyf+OpeMm3kagS6WPd/Pl8SvMp1uOq6zjQHx9205nb7O4ePzwGiaiaWPvfEfWCVq1Vq7Le3WrrdtVehzcMzy5Lmx9gJfdaVKjar5v97bQMffxoP2u/l6JCMKsRl14pKqE77bfhDcMNr2ntknk68/5ltXbqL/KNtOL0eW0+3pymM7fZpR71g4ioXJg5p6LSqrVqVda7W23drtrrto5t2WVpPwOs5F6L8lX7dVsJ2Gm6zXs2W37Wfi9FhWBWIy69UlRCd9pv2zq2GV7Tt0X66cz7l9Xaqb/IN9KK0+e1+Xhzms7cZpd61A8ionLhxTl1S0ZmEElGDN/1tGqtr2x9Bbcdfxte2foK5h4311X11ozMwCM8lmqvA+sGZpcZ9oWz1dtZyb325Kv2G/KFcPeku7H8vOXYePFGLD9vOe7+6t0IeoOGWLRbxq3H34qFmxZmKwK/svWVklcIZjXi0utOdehCbZd+meb9Nn/ifAysG2htlybOt+zfoC/Y4210y+3fQtSX8o208srWV6zH1Ynz4fV48ei/PYrl5y3HGQefYTuduc12c/zojw278wqeKxBRpWK1dnJNK6oyY+UMbPh8A8YMG4P5J85HU7DJUFxJq7beme7EnsQeHNBwACLJCBoDjY5VUvXLHhoaiqtbrsaBjQdmpweQrdrKau21zamqrl383X7C7bhn3T3YHttuiEXzMvQV3vuqWntGZhBNRpGWaTQGGtHe2Q6v8CLsD3e70jAYt47cVGF223Zp09rtt5AvZKkYnZEZV9XaezJqQb6/1+3fUgEYt/2MPtb1n8921dpTmRR+sOIHhjiWUuLlLS/jhANPyJ4DdLdau90xcveku+Hz+FitndXaa1nZ45aKgxfn5FokGcG0V6dhzWdrsq+N32887jv5PtT763s8bU+m74WyN16M255xipGbjrkJ5z9/fqnipceKHNOM214oZdvV1yp9+0wYt2SrUHuu/d6TuC7CMVI1ccuLc9Ipe9xScVTcbXaqXN0pqtLdAiws2EKFuCn0VknxwpiuHKVsu/papW8fkRuF2nPt957ENY8RIqpmvDgn17pTVKW7BVhYsIUKcVPorZLihTFdOUrZdvW1St8+IjcKtefa7z2Jax4jRFTNeHFOrnWnsFF3iyCVomgS1ZZChd4qLV4Y05WjlG1XX6v07SNywy6Oe1L4ze2yeYwQUbXgM+fkilbkxVyQzVywrbuv6Qu9OBV3K2YxJVTAMzmM255LZ9KIpWLZAlw+jw8Bb8ASF/kKwuUrLlSMWHMqiNTL2GXc9pI5dvIVAkxlUpZCbx7hcRUbRW6vbPXFOoqEcUtZ+rg1F4jT/66Pa62tdhvf+nMV7XjvwTFSNXHbH585H/3Y6G5Nv+nSTSXakopT9ril4qjIT3OqLFrl02mvTsPNf7oZuxO7Me3VaRi7aCymvToN7Z3t2fe111rjrQCAen89PMKTLcKin27RO4sMv1/zyjXZbmfafPp165fNoYP6n4zMoC3RhumvTcfYRWMx/bXp6Eh2AMjFizadOWba4m2YtWqWJeb08VSMWDMvwy6mqe/ZxU5bos1236YyKbTFjdNGOiOuYqOv2iutTdV/J6pk5mNj+mvT0RZvw+J3F2d//yzyGRa/uxit8VYsfncxxi0e161jSL+OcYvHYfpr09Eab63km1dERBZsraigWCqGGStnYM1na3DZ6Msw+/XZWPPZGqRkCms+W4M9iT3Z97XXZqycYXm+S7+clEzhlOZTMHPVzLzzmedxWjbVPrexYDfd7Ndn47LRl+WNuWLEGuO1MnVnv8RTcUuM7OnsWRvH/U+k2B0bM1fNxCnNp2R/v+WNW7JttP51t8cQjz8iqgXWAVmJTPSVT0cOHGmpgjq8YbiryqjmCqp2yzLPx6qrpHEbC26qujsto7exxnitTN3ZL2F/uGhtXL71EPUnbquza+cFPanazuOPiGoBM+dUkL7y6eY9my1VULd1bHNVGdVcQdVuWeb5WHWVNG5jIV8V4HwxV4xYY7xWpu7sl2gyWrQ2Lt96iPoTt9XZtTa6J1XbefwRUS3ok4tzIcQXhBCvCSHeFUL8VQhxnc00QgixQAjxgRDiLSHE0X2xbVSYvvLpwk0LcevxtxqqoDYFmzB/4nxc03INnj37WWy8ZCPuOekeBL3B7DIyMgOv8GL+xFwF1Ve2voJ5E+dh/H7jccbBZ2D5ecvx6L89mp3evG5z1dWMzCCSjBi+U98z74dYKlaSfWKOhWtarsE9J92DkC+Ejs4OpDNp2+n0Vd31MWeOp2JU+M23jeb/B+O3+3r6P+vOvg36grj7q3dj+XnLsfHijVh+3nLVxtnMH/QGDdsT9Ab7pEo0Y4cqhT4GtXZY+x5LxQzxefeku/NWZ5973NxsG+2marv5ONAff07nFEREla5PqrULIfYHsL+Ucr0QohHAOgDnSinf0U1zOoBpAE4HcCyAe6WUx+ZbLquw9h2nCtSJdAKRZAQbPt+AlqEtmLlqJjZ8vgFjho3B/BPnoynYBABo72xHJBnB8/94Hl9r/hoO3udgRJIRhH1hJDNJRJIRzFg5wzKvU7V2QBWXc5onj7JXs6yluNUK8Oj3w63H34oF6xdge2y7233SrfVplXjb4m2YsSq33nkT56Ep2ASvx1sR1drttlF/THQzfvt93NrFmtv40i4Y9nTuwfCG4djWsQ0DAwPREGiwVGx3Ws+gukGGuAl6g2hLtBWcrtjFqHrzfyiDfh+3tcwuFuceNxcvbH4BFxxyAVIyhVl/mmWI03p/Peq8dbbV2rXq7Pna6Hzr1o6/zkxn3nMKF6omblmtvTBWa6dq0yef5FLKT6WU67t+bgfwLoDhpsnOAfC4VP4PwD5dF/VUAfRVgUO+kKGa+oyVMzBi4Ii8hbb2JPZg9uuzcf/G+3He8+eh5fEWXP/a9dkL/XxFXOwqE7PwS2XIV3ytFPtEi4FYKoYZq6zFhZxiRju5q/fXw+vxOla6LkYVbG3eeDpu2cZiFp/rb3rzP4ulYvjBH3+AM549Ay2LWnDGs2fgB3/8ge28TuuJp+OG2Iin466mK/YFM2OHKoVdLGpF3aKpKGb9aZYlTjMyA4/woCHQAJ/Hh4ZAQ/Z3rW3O10bnW7d2/BU6pyAiqmR9XhBOCDECwBgAfza9NRzAP3W/f9z12qem+a8EcCUANDc3l2ozySWtAEuh4m6FCiq+oig8AAAgAElEQVR1t4hLtRV+qdW4dVN8rRT7xK5o14bPNyDsDxd9XT1VKEarIX4rKW57c8x3J156W3iw1Puw2tq+cqikuK1lvSm+Wap1V1P7asa4JSKgjwvCCSEaACwDcL2Ucq/5bZtZLH3upZQPSynHSSnHDRkypBSbSd2gFWApVGgrX0GlnhRxqbbCL7Uat4WK/JRqn9gV7RozbAyiyWjR19VT+WK0WuK3kuK2N/+z7sRLbwsPlnofVkvslFMlxW0ty9f+uy2iWOx1V1P7asa4JSKgDy/OhRB+qAvzX0kpn7GZ5GMAX9D9fiCAT/pi26jntEJLr2x9BXOPm+tYaGtg3UBLIbneFOIqRvEu6r18xddKuU9CvpClsNu8ifMqav/ni1HGb/f15n/WnXhxu55y7UPGDlUKu1jUirqF/397Zx4uR1Xm/8/bffsufbOQQFCGLYDLGAEDBBQ1CI4zKoKobAmjooM/FIXgOAriOAzuEhyRoIBAGBElAQER0VEZAWE0QAIEwiIziGFXspHk7re7398fVXVvdXVVd3Xf3vv9PE8/VXXqnFOn6nzPqTpdVd/qSvP1t369Zjq1/tUwjHalXoZwAlwNbFbVT0fEeQ9wOpOGcMtU9ZBi+ZrRS30oZZKV0xyj2dGJ+SijLb+RnH99sXVR5Si1vSI03DCj3XRbzHytJ9lTYPgTNOCqNP/R7CjZXHZKeVdKXOO4YvHKNJ8z3cKEA3SpOs/kMnm66+1yvhwRDOtKhL/ZVY36rSWN2m4FmG7bHL8BZrBtjmZHyZEj3ZVv+FauXqP0XsX+NUjL6NYM4UpjhnBGq1Gvd87fAnwIWCcia92wLwB7AKjqZcAvcQbmTwJDwEfrVDajCHGdgcOcUf3/UntGch79qf6i+Qf/4fbH27lvZ5YcuIQv/v6LRdMY9cEz7YHJegXoTfayeWRznoO/31E9DnHc4NOpNNO6p9Vk3+KWKcoJOOrYlFpnFJLTXKg7evC4Z3IZtoxsyf9yxMKlpJIp/vnOf47l3hy3bhpVh6Ydo1nwzu3FXNuzuWzsthekVH9r/Wvnse7PzzS6CIZRU+rl1v4/qiqqur+qznd/v1TVy9yBOa5L+6dUdR9V3U9V7a/uJiCOM/BUXZTjpPXHO2W/U/ji779oTqxNznBmuMDB3++oHjePerrBV1om01/tiXvcRzIjhV+OuPssto5utTozjBpQzLV9qm3P+lvDMDqNiu+ci8gMf3pV3VyVEhlNRRxn4Km4B1fijFzKGd5oDqrhqN4oN/hKymT6qy1xj3uU7nadtmtBmNWZYUydYv30VNue9beGYXQaZd85F5GPi8hfgYeB+92f3eVuU+K4nk7FGbUSZ+RizvBG81ANR/VGucFXUibTX22Je9yjdPf8wPMl0xqGUT7F+umptj3rbw3D6DQqeaz9s8DrVXWuqu7l/vaudsGMmORyMDoA6k5zuapm73c9fc9e7+EX7/8FV/7DlSQkwXBmmMHxQXqTvSxdmO+MeuHhFzrF0xyD44PkNDcxn81lGRofmjD3inJV9QxdgttYvm55nvP7p+Z/iu8c8R36uvomtmU0nqBDtldP6VSawbEBhjPDeZrw6m5wfHBiXZg+zl94PnvN3IvbjruNi99+cWi9+3VXqSaCeRQrk3ke1J64Dsy9XYX90dKFS5nZM7MgbU+yh4GxAXKaY2BsgGwuCzjvrfvDM7lMqKZyuSyDbrzBsQFybnrDaHeyvjYiSOh1wvTu6Vz4tgsL2t6yI5ahqnntK+w8kM1lI/tbzxDOzvmGYbQbZbu1i8ivgA+oasM/KNzxLqy5HAxtgBtOgWdWwR6HwnHLIT0HEtWzE/Dc1D3Tt537duZzB3+O0ezohCnbx9/wcU563UlMS03Li+sZuFx4+IWM58a54X9v4LhXH4eITKTfuW9nPjn/k+w2fbcJV1WA7WPbGRwfjNxGTnP0JnvZMrKFs+4ubc7l0nA3y07Sreeu3dfVV1BPfmO38xeez43/dyPff+j7BesufNuFDGeHmZOew+DYINf+8drQeF69A7EN26IoZUR34eEX0pXoqqdbdsfrNqc5to9tZ+voVnadtivPDzzPzJ6ZTO+eXvD1iGC82b2zyWq2IC0K//y7SaOq8xeez6zeWQWGct5n15bcsSRPUylJ5aVf6hoeJur01YAWoON1245kcxk2h5guJhNJspot6Htn9cyacGkfy46xfWx7Qft6YeAF/mba30yEf/wNH+fYVx/L2XefXXCNkMllKjaYi0nL6LYT3do5b2aZ8bfWphzNR8N1a1SHSgbnBwD/CdwLjHrhqrqkukUrTcefdEcHYMUiWH/3ZNjchbB4JfRU1716cHyQM24/g9V/Wc1N772JnmQP5606j9V/WT0R5+BXHszFb78YYCKuxy/e/wvOW3Ue5xxyDj3JHoDI9P2pfgbHB9k0vKlonGC5ouIEaHjn1Ym6HRwb4Iw7lhTU0zmHnMMHbvlA3nzUOoBv3PeNonlE6a+EJgrLG6Gr4Lbq6ALc8bqN29bD4nn9TzDteYeex3t++p68sGVHLGNJiFYvPPxC3rryrSXTX3zEMvrr+PWAJqfjdduODIwNhLaRbx/+bT5z52eKttGotBcdcRFn3nHmRPhN770ptL+Pap9V7o9bRrc2OI8T3wbnRmtRiSHc94HbgXWAPUvUSLrTzh1zP8+scsKrTNCQDShq0hJct+u0XfOMvEql7+vqm0gTFSdYrqg4RuPpizDp8mspqI2wdXHN4aaqiWY0out0KjGP9IjqS8KMqqIM5aZ3T4+Vvq8Mw0PDaEWi2siM7hkl22hU2v5Uf154lPFrVHrrjw3DaBcqeQYoo6qfUdX/VNWrvV/VS2aUZmzIeZTdzx6HOuFVJmjI9vzA85EmLWEGLl58L22x9N72SsUJlisqjtF4hiNMuvzGbt581LooI8CgOVw1NNGMRnSdTiXmkR5RfUmYUVWUodz2se2x0g+XYXhoGK1IVBvZNratZBuNSjs4PpgXHtXfR6W3/tgwjHahksH5HSJyqojsIiKzvV/VS2aUJpV23jGfuxASXc70uOVOeJXxmzEtX7ec/lR/nimb35wpzLhpZs9Mlh62lN8+81vSXemi6b3tzeyZWTROsFxmztW89HX1sdRnDnfwKw/mq2/5KsvXLZ8wefvtM78tui5oBBiMV0x/5WoiLI+wbRn1I269Fut/gmlnducbVZ2/8Hx6u3rzjAy98KQkS6Zf6r6bbhjtTF9IG/nym7/M6hdXF4SHnbPD2tdTLz+VF/7bZ34bmZed8w3DaGcqeef8zyHB2gjHdnuXDMcUbnzIeZR9bMgZmCcS0eFT2ZTrnu6ZuuQ0h6Khplj+uH6Tt+HMML3JXkazo4gIOc1Fmmp5RnTF4kRtq4gxTMPfyam3bss8PrUrh2cOl0ozPD5EIpGkJ9kzoQnPMMhz8ffW9SR7GMmMkE6lGcmM5GnOHy9Pf4Ft9XX1lW3SFTxuUduqEx2n2zA8g8F0Ks2QW6/JkHot1v/4w1Q1NL9MLjOhuaHxIXq7eie+UJGXp5t+Kjprc0y3LU7U+SObyzDsayPe+t6uXkYzI06biOgrg+24t8u5JgieB4LLXl51OKe1jG7tnfM48e2dc6O1KPudc1XdqxYFMSokkZg0f/OmNXJxT0iC/lS/8/mT8YECR1b/P9deXCDPpMWbTyfy7+6HGbkkJJGXZ5TZS9S2jHDX8Ro428YikUhOGGX5DbMm6i4RrZdpbvy076mQsHgA5HIkhjbS7+q/v0L9l9KwUV9ymmPL6JZYWi5VdxNhMqktb0ouR9fQJqa5+pnm009Y+jBNG0Y7UOz8kUx0TbadrjQMbZhoM11um+lPz4GQ80wykSxod10J53I0eB4IOy/YOd8wjHYm9pWqiHxQRD4UEv7/ROSk6hbLmBLjQ87AfP3dkMs40xtOccKrwHBmmLPuOovVf1lNRjOs/stqzrrrLHvnqwnpyLqqsf6NxlA3LZt+DAMoo81ZmzEMw6ga5dw6+xfg5pDw69x1RrNQYxd3c0hvHTqyrur4FQOjftRNy6YfwwDKaHPWZgzDMKpGOYPzpKpuDwaq6jYgVb0iGVOmxi7u5pDeOnRkXdXxKwZG/aiblk0/hgGU0easzRiGYVSNcgbnKREpeLlHRKYD3dUrkkEuB6MDoO40V+bn5CNd3Puck+Xodjfv7eXnTXUd0nOaY3B8MG9qVI+mdbbNZWFkm6PDkW3OcmTcMttDlb5iYNpsLsrS8lT60FSa3InXMHjmWnLnbnamJ14TXz9h255qn24YDSCyzanka9nf5+53PJzxIJx8C6Blad36XMMwjPIM4ZYDN4jIaaq6HkBE5gLfc9cZ1aAaZm6JhBN/8UqfW3sfjG5zBuQ3f3JKRnEJSTC7dzYXv/3iKbmlNpNZWbtSrbqqKrksDG6AGz82qcNjr4T+ORB0uq6kPYTqv7yvFZg2m4/YWp5iH5oT2EyWs+77Sn7dS4x/s8O2feI1kB2rukGnYdSahMJsklx8yL/RN3MPhrc+Q58mSfzhu3DX+flaTs+Bk66Hse0Vad36XMMwDIfYPZ6qfgv4GfA7EdkkIpuA3wG3quoFtSpgx1EtYxXPxV3c6fgwDG1xBuZVMG3x3FL903LpSLOyBlCNuqoqY4POwNyvwxs/5oQHqbQ9BPVf5iDItNmcxNLyFPvQKdV92LaHtphZltGajA+RuO5D9F80n8SXZzvT6z8E844q1HIi4dxNr1Dr1ucahmE4lPUpNVW9DLhMRKbhfCO94B10Y4rUylilOw2z9mwq05aONCsznMFymA57Qj5F1SCjIdNmCzNFzUyp7sO23WT9rmHEJqot7fTa/GVPy1Noe9bnGoZhOJR9C01EeoD3AmeIyLner/pF61BqZawyNgRbnm4q05aONCsznPcUw3Q4OlAYt0FGQ6bNFmaKmplS3Ydtu8n6XcOITVRb2vhE/rKn5Sm0PetzDcMwHCp5vvVnwDFABhj0/SIRkatE5CUReSRi/eEislVE1rq/zh3sV8nMKjTf9Cx43yXVz7tCmtaszKgt3f3OO+Z+HR57pRMepFbtoQSmzRZmipqZUt2HbTs9qyEaNowpE6bnY6+Ex24N1/IU2p71uYZhGA6iquUlEHlEVfctM81hwADww7C0InI48FlVPaqcfBcsWKBr1qwpJ0lzkss679t29+dPe6ZNmrmND+ebu2VGQbOTcVPpQBz3ZDg+lJ8uO+6k05wvbX/NjIlympswbgozcAqu7032MpIdqaV5mVQzs0poSd3mcgEt+QzWiq2LyCOXzTCcG6Uv1c/w+KBTz4mu0PxyqV5HC17cRDeJZArGR3xtoIwyxSxvKe3WmbbRbS6XdeszzfD4kFv3ydIJncRla82Ll0MLt4uE5pfLZfI158X1+uXRAffPpJD0EC+sM8zg2ka3bU9U28plnOUJ3adDl3Pd/QxnhujtSjOcGSZdQftuoj63ZXQ79/O/KCvf9d98T6VFah7Om1lm/K21KUfz0XDdGtWhkl7vDyKyXzkJVPUuYHMF22p/POfqey6Drc/CypPgqzs708ENzoB6aCOsWARfmQOrLnE+PTW0EVYsdsMudeJ6cVYsgtGtjmtwXtg2yAzD0KbJtCsWO/Fq8Gkfz331jNvP4KBrDuKM289g88jmvM+j+I2d+rr62DK6pWh8owF4DtR+LXmaKbYuIo/cfVeweWwrZ9xxplPPd5zJ5pEt5HLZgvxyT97O5pEt+XHHtpF78vb8NhC3THHLSxMa6bUBuVzW6RPuWOLW5xKnjRf7lN5k4th1FzQEzKHh2x3dGpJfhsTgRvqvXUziK3Oc6ch2GNzo9MtfmTPZP48PFqaHQjPCKRoUGkZNiWxbmXzd33NZSDvYSO5Pd7J54HmuefzHvDj4Ikv87Wx0S+xzuPW5hmEYZQzORWSdiDwMvBV4QESeEJGHfeFT5VAReUhE/ktEXl+F/FoDz7l63lHws9MLXU7HBvPdT+cdBcMB1/V5RxW6X4c5BA9tqapjeynKdV81t9YmpZj7dVxnbF+84TecyFl3n51fz3ef7dRzIL/hfQ4Lj7vnG6N1XI3yGjVhODMcXfelmELdRW535OWQPneosD8d3gI3BrZ948cm501LRisT1baCbSHsWuPGjzn99Krz+Ls9/o5z/3CuncMNwzCmQDlu7WU9cl4mDwB7quqAiBwJ3Ay8OiyiiJwKnAqwxx571LBIdcJzrt7ptdEO1v5wzyU1GBbHIXjWnoVpveUaOAeX677azm6tLa3bUg68cfTky6OvZ0Z4PacK84uO2195mcw5OzbV1m1fKl287osxFSfoqO3ODOxTWJ8L0Y7rvTMLw0xLDael+9tGENW2wq4/QuJ5/fTeM/du23N4PTDdGoYB5X3n/GlVfRr4qjfvD5tKIVR1m6oOuPO/BFIislNE3MtVdYGqLpgzZ85UNtsceM7VG5+IdrD2h298otD9NyxtmEPwlqfr6hxcrvtqO7u1trRuiznwxnXn9cUbHt0WXs/jhflFxx2srEwNcn9vVaqt2+Hxoei6L8VUnKCjtrv1mcL8wr4mENVvjmwtDDMtNZyW7m8bQVTbCrv+CInn9dNPbX2qbc/h9cB0axgGVGYI94CqHuhbTgLrVHVeiXRzgVsjDOFeCfxVVVVEDgFuwLmTXrRwbWH04r3Tdf8P4Q0nOI+2P7PKOQGeeA0ke2BsO6y5GuYdDTu9Gka2O2E3f9KJe9jZcNCHncfNpr8S/v6r0DvDiXPDKfn5AYz60nrhia5wY62p7Jr7zvlZd53Fg399kANecQBLD1vK7N7Zoe+S5TTH9rHtbB3dyq7TduX5geeZ2TOT6d3Tq/nuWcMNM5pWt5GGQO77iH4tHbcc0u7FQ9Q6v4Z8eeRedzSb9/sAZ9199oQuLnzbhXQlkq5Z1yB9/3cHiRs/Su7Y/2TzXofmxV268HxmP/8QiTmvCddxKu20qRt9ZTp2OfTv5OxfLgPXfShk3XAzG3W1hW69d84L6rN3dmnTqAgd5tI7MRw0kVQKTAU3j2wJbHcpsyVJwq+F45ZDekenjx3e4twx3/I09M9x8gtqqjvt+B4U035n0xa6bSvC+nkobFuLVzj96cjWyWuLd3wJZuwCm9fDnV+H7X9x2mCim83ZYW546haO2vsozv3DubHP+U1iABekZXRrhnBx4pdnCLff1WXZarHu5HVlxa8hDdetUR1iD85F5BzgC0Af4N0aEGAMuFxVzymSdgVwOLAT8Ffg34EUgKpeJiKnA6fhfJ5tGPiMqv6hVJla/qTrXWw+fS/stdAdULtuwOMjk4Pr1x0N846ZHMA/dD0csBh6d8h3a8+MQnZ0cvA9/ZVw+Bdg9txJV3aAjM/l2r+dGlxglnPyLXcwXyEN77yaUrfFBuDeAL2abu2ZMYZ1nL5UP6OZEQbHB0MGbLNIjI+4bu0jkwN3SZHo6s53aw/q+LCz4Y2nQs8Mxwjx3svhrvMn96t7OqR6nbZyz/fz1zXn4KptdFtNt/Zcqo/No1sK+wwCg+4TryEHDI+8TN/MPRje+gx9vTs4XwgY2DA5CE/Pgp7pMLipcCDeM90ZjPjjhqaf2Yz6aRRto9u2oNQfrV7byozByMvOoPx1R8P8kybfS/enyync9kXY/hdyJ17DcDJFr3uuT6fSRc/5dTrfV0rL6NYG53Hi2+DcaC0quXP+jWID8XrS8ifd0QHHFXX93ZNhcxfC4pXOvLfuk6vgl2fBkUudaVh877MmAy/Bz5dExymnDGHxa8jg+CBn3H4Gq/+yeiLs4FcezMVvv5h+74+FqdPwzqspddtAHQyObeeMO84srPcjLqK/e3q8TKLKf8IP4foPl25jwXV11n4MTLchRPYZh/wb/RfNn4x4xoPh/eLRy+DiA/LDFl3ruFAH4554DZw/t3T65tRPozDdNhNx+/mRbfltwLsGCaY7cilccmh0PkWo0/m+UlpGtzY4jxPfBudGaxHbEE5EvEfZf+Kbn0BVH6haqTqFuEZbnglLlGmcF787HW1cFGVSNAWTpWrTzoZwTU8DddCX6o82fYtLVPn7djBzuDYmss8IGr1F9YueSaY/LMwQLsr8LSy96cdoVuL28zGN4CYMaqPyKYKd7w3DMMIp59mh/3B/3wPuBS4HrnDnl1W/aB1AXOMqz4QlyjTOMyAaGyrf8K2JDLLa2RCu6WmgDobHB6NN3+ISVf7hl80cro2J7DOCRm9R/eKWpwvDwgzhoszfwtKbfoxmJW6fF9MIjo1PFM+nCHa+NwzDCKcct/YjVPUI4GngQNdR8iDgAODJWhWwrUmlnfe25i503l2cu9BZTqXz1939bXjfJfDYrXDMd8Pje/mlZzlxo+KUU4Y609fVx9LDlnLwKw+mS7o4+JUHs/SwpfZPej1ooA76uvpYuvD8/HpfeH559R5W/vddAut+Et0emkj7RmVE9hm9O+TXa3pWeF2nZxWGdafh2Cvzw4+9cnK+VHrTj9GsxO3zuvvz28Bjt4a3icdurVj7dr43DMMIp5J3zteq6vxSYfWgLd4l8wyOUn2OaZtn8Nbd7xq35SYNrzw8Eyy/yZuXR2YEkMI4QYOiXM6NG8gzuJ1UetKkropO7pGHo/burQ1/J6dpdRvb2C07qYnRgcn4PdNcU8Lx/HWJ0m/P5HIZ1ySs3zEJI0Ei1euWo89xUu92tShJx8ytwJguUK5ElxPPbxxXqZld42lv3RZoqh/CTOJC6isnFPYZqoX5oZM69bQJhWGJLsfRvyBuItzlujX00yjaW7fNShwDT68P9a4Twvpzr+2MDYaE+5Yr1L65tUdj75wXwd45j6LhujWqQyW94OMicqWIHC4ibxORK4DHq12wjiGRcE5qQxvhnstg67OT02tPdAyIrn6v48I+th2uPWEybHwIUMd9ddUlk2m+ubvzeZ/BDdED89Gtzgl3aONknr/7ljO48sJXXerkufIk+Mocx0hmaIOTvlaHQxL0p/rzpkadSCScCy1xp1ED88ENk5q45zLns2UrT4L7rnAe/fXWrTzJWZfLFN9uLkdiaBP91y4mcdOp9A9tJnHtCZOaG3T1/ZU5jq6HNsJNp+brMZdzwv3bHtsOinMR2TM9fL/i7LNRW4KaWnmSs5zLBuK5TtMrFuX1Rwklv89QCrUwus1xYM/bxibnk2lheh0KxB3a5JQhqBXTj9FsRLSTifN2IuEMzAd91xzBNnDPZZPL3jVJMHxoE3RPTft2vjcMwyikkp7wo8CjwJnAp4HH3DCjUrxPlMw7yvnOuTddf7dzobj+bhja4sTxh91wijOQ9qcNrh8PeQdsfMjJz/vkmpdm3lHOt3298HLyNDqDsUHn8zp+zXjL+x2fv2793c5yqfcQPf2vvxsWfiZfk14e846aXL75k048vx79eZhWW4ugpiZ0E/AciFvHYfGGtjifRsvbxilOfxemV9OS0arEaSdjQ5P9avAcH+xvvWuSYLi1CcMwjJoQ263dQ1VHgAvdn1ENPAfVYq7sUW7DnqtqKSf34PY8l+GgI6s/bTl5Gp1BMRffKGf0Up/W8TsIx3UF9pbNeb31iXJHD+omrtN0WLxquLWbloxWIE47KXXdEHUtMAV3dsMwDCMese+ci8j17nSdiDwc/NWuiB2A56BazJU9ym3Yc1Ut5eQe3N6Wpwvz3PhEfng5eRqdQTEX3yhn9NGB4nmGfZkgmEfQFdhbNuf11ifKHT2om7h1HBavGm7tpiWjFYjTTkpdN4T1t1N0ZzcMwzDiUc5j7We606OAo0N+RqV4DqqeG3uYK3uU23B3/2Ta910a7cKayzknZM0574ilZznv4R73n3DGg3DuJpi5uxPeOyOeO7zReXT3wwnXuJrZDL2z4VhXl+t+Eu7om0w5uhvZVvgeMTh6OtHNc6fXlHYFft8lzhcMzHm9PQg6Q3t13h34zn1kHfc62vI0luotjJeeNanTiW0sh75ZIdv16fHczc70xGtMS0ZrUKovzOWc8BOvcfrvYNwwF/be2U6//9itzutLZzwIJ98CaE09aAzDMDqR2G7tIvJp4PfAg6pawuGpPrSVC2uUa7vfCRXCHVg95/XsqPNu5aw9nTtC6VnQ47paDm1w3hF7ZpXzj/eJ10BXn2Pgdf/V8IYTnHfLXnc07HeCYwzX0193t/Y60HA3y5bWrWc2FNSSJCfd0DUDvTMdbUkX3HMp3HW+E/fYK6F/Tr4TdzDPw86GN33c+fOoLLf2lnFer4T21m3Fbu2usdWNH5vU47FXQs8M2P6X/L4w0QUDGwL94/RwV/agxo9bDuk57aSnetHeum1WovpCr69dczXMXww3nwbTXwnv+BLM2MXps3umO74zvTNh24vw3//utKXjljvtanRbJ7SNltGtubXHiW9u7UZrUc4757sBFwF/6z7G/gecwfoqVd1ci8J1FJ7rLzh3rv1T/7uX3rw/LJFw7hpd9yHHqMVj7kJYvNKZ9wxiwJle9yFYdK1jinTk0klTmCOXwvUR+ZR6d9hof/xmQzCppcUrnQH0ysWF2jlyKdz5tUmzoUXXTmo7LM87vwbr78rX3MR0+mS6oB79bci02lokkoX9Xmi8QB2PbJs0k4NJjZ14DVx8wGS6uQvh6GWFYYtXFm53dKBQ4zecYn2g0TpE9YVeX3vkUmdg7ml84Wfg6o+H993rfuIs33CK03db2zAMw6gpsQfnqvpZABHpBhYAbwb+CbhCRF5W1Xm1KaIRi1ImMHHN5MwEzihGJToLmghVavRlGEGizNt6ZxaGhZm/RRlmmh6NdiRoPusR1xjOzBINwzBqTiXPIfUBM4CZ7u8F4N5qFsqogGImMFHrwkxhzATOKEYlOguaCFVq9GUYQaLM20a2FoaFmb9FGWaaHo12JGg+6xHXGM7MEg3DMGpOOW7tl4vI74HrgENxHms/XlUXqKp957zRFDOBiVrnN4YfRYAAACAASURBVJPzjN/u/rZjuGXGWkYY5eosaC5UltGXac4oQZSZnDfv11N6VjyNmR6NdiVoPutp/LFbSxtx+q8ZrG0YhmHUjHIM4X4F7AQ8gjMwXwU8onEzqAEdafTip8D0xW+cFTCR84zmgsZuQSO6nmkwPgKabUczOGgCw4ym0m0lJmoF5l1pRzPeVLOT5nCecVvZRl9tobVq0nm6DdMEWmgcByFhEpKW+BozPVaLztNtLaiGHkuazgavH0KuJ/zXDO3dNlpGt2YIFye+GcIZrUXsHlVV3wUcDHzLDfoXYLWI/EZEvlSLwhlF8FxXVyyCr8xxpkMbnROl+MxgvDhf3RlWngSDG/JPpp5xjGfIJAnnpJvqd/JbeZIv/w322ZR2IlRDJeo4l8vXxcqTHLfsVZc4y9ee4Fy4Ka6uuiZ11TsjfGAOkzr0tNt+F3tGOYRpc3Sro7U87W1w4gc1FqancjRmejSahUr66WJ5eNcCW5+Fey7zXRMkA5oPLgeuGaxtGIZh1ISyelV1eAT4JfBfOG7t+zD5DXSjXvgdrnOZSdfU8aHy4kwlf6O1qaSOw9Lc+DGYd5TpxKgeYTob2uJ8XSKovbHBRpfWMGpHNc7FYXn87HSn37b+2jAMo6mI7dYuIktwHNrfAozjfkYNuApommc6OoY4jsJTcR02x+L2p5I6jkoTdPU1nRhTIUxns/aM/uqEYbQr1TgXF+u3rb82DMNoKsq5cz4XuAE4RFX3VtUPqeolqvqQqtqzzvUmjqPwVFyHzbG4/amkjuM6sptOjKkQprMtT0d/dcIw2pVqnIuL9dvWXxuGYTQVse+cA1/1ZkRkdnClqm4ullhErgKOAl5S1X1D1gtwEXAkMAR8RFUfKKN87YPfcCXSnK0Xjl3uPOb5zCrnBBt0TU31OXHuvxrmHQ07vQbGBpzwUtuVhJPfDUXyN1oDf71mxiA75r4zKOXXcSoNJ17jPGI8a09nwNQ3C+67wnm//LCz4U0fd7Y1OtDppkKdRdw6DTV6o9DcMqizaXMK+7xjr5zUWqXmb4bRzHgO62H9dJg5Z9CAEwEUTr4FNq+HO78O2//iuLU/dL2TlwhoztqKYRhGE1DO4Px+HJunMDdABfYukf4HwHeBH0asfzfwavf3RuBSd9pZeMYtN5wC018Jf3curF0JbzjBeUfsmVXOAOigD8P9P4QjL5gcdHf7zFk8465n7nXi3vix/BN7ek7+Cdi/XS/eidfA4hU+5207abcc/np93dEw75h8LZxQQR1nx+DnS/L19ObT4bB/cQ27/jF/8HT/D+Gu833a28nRZvBiM6hJo3UI6z/K6WeyY4VpE935Ojt2OfTvCIuuzR+MDG0qTJvshus+ZPoyWp9EwtHu4pWFXy4Y3JDfnwf72xOugVxI2+qeBl29cMA/Otu49kRrK4ZhGE1COW7te7mPs+8V8is1MEdV7wKK3V0/Bvihazp3D7CDiOwSt3xtg9+4ZeFn4OZPOqYtPzt90sxl3lHOCfnOr8Elb4Ivz3YGROPDhfnMeZUTt5SZTJhhzHUfAsRcWVsZf73ud3yhFq7/EKjGr+MocyLNOfoLM+wKmsWNDZrZYLsR17QqyugtLO3wloCWTnEGJn5n9vGR8LRDW0xfRvsQ5pA+NljYnwf72+GItpXLwg+Pcb6AYH2xYRhGU1HOnfMJRGQWzh3uXi/MHXxPhV2BZ33Lz7lhLwa2fSpwKsAee+wxxU02IX7jFs+sxZt6BJch2gwuTtzgdovFMyqiYbr112vfDlM31CqlkzhmcT3TTGstQmzdxu0/yjF6m7VnYVhQq1HbDUtr+uoY2v46AaL7UX9/W8xEsZzrA6MudIRuDcMoSdm3QkXkY8BdwK+BL7nT86pQlqjH5fMDVC9X1QWqumDOnDlV2GyT4Tdu8cxavKlHcBmizeDixA1ut1g8oyIaplt/vQ6/PHVDrWI6iWsWNzpgWmsRYus2bv9RjtHblqcLw4JajdpuWFrTV8fQ9tcJEN2P+vvbYiaK5VwfGHWhI3RrGEZJKnlO+UzgYOBpVT0COADYUIWyPAfs7lveDXihCvm2Fp75y9yFcPe34X2XwGO3OuYtcxc6pluP3eq8W+Ytz10YYgbn5hNMGxY3uN1i8YzWwl+v635SqJtjr3RNgyrIL6iTsHXHXulo0B+3u9+01m7E7T/C4qVnhaftm1Vaq1HbTc8yfRntTXd/eH/u72/7ItqW1wfHvT4wDMMw6oaoFtycLp5AZLWqHiwia4E3quqoiKxV1fkx0s4Fbo1wa38PcDqOW/sbgWWqekix/BYsWKBr1qwpq/wtQSy39ggX7LB8Un2BtGW4KLffe+ZhT2jUlbrrNsqt3XPzTSQrzy+ok4J15tZeJZpft1V1a3cNr/KcqCO0Gjc/01cjaH7dtjJx3drD2kK51wedRcvodu7nf1FWvuu/+Z5Ki9Q8nDezzPhby4q+39X7lRV/3cnryopfQxquW6M6VPLO+XMisgNwM3CbiGwhxh1uEVkBHA7sJCLPAf8OpABU9TLglzgD8ydxPqX20QrK1h545i+Q/+5X7wxn6q0LTovlE0xbKn457yEbzY2/XlO9zg8mNTGV/II6CVsXFte01n7ErdOoeGFhnkaLabWc/AyjnUgkC9tIWJsp1QfHuT4wDMMw6kLZg3NVfb87e56I3AHMBP4rRrrFJdYr8Klyy2MYhmEYhmEYhmEYrU4lhnDXePOq+jtVvQW4qqqlMgzDMAzDMAzDMIwOopIXi17vXxCRJHBQdYpjGIZhGIZhGIZhGJ1H7MfaReQc4AtAn4hsY9J4YAy4vAZlMwzDMAzDMAzDaBta2HTOqAOx75yr6jdUdTpwgarOUNXp7m9HVT2nhmU0DMMwDMMwDMMwjLamksfa/1VEPigi/wYgIruLSNFPnhmGYRiGYRiGYRiGEU0lg/PvAYcCJ7nLA26YYRiGYRiGYRiGYRgVUMl3zt+oqgeKyIMAqrpFRLqrXC7DMAzDMAzDMAzD6BgquXM+7jq0K4CIzAFyVS2VYRiGYRiGYRiGYXQQlQzOlwE/BXYWka8B/wN8vaqlMgzDMAzDMAzDMIwOouzH2lX1xyJyP/B3OJ9Te5+qPl71khmGYRiGYRiGYRhGh1DOd857gU8ArwLWAd9X1UytCmYYhmEYhmEYhmEYnUI5j7VfDSzAGZi/G/hWTUpkGIZhGIZhGIZhGB1GOY+1z1PV/QBEZDlwX22KZBiGYRiGYRiGYRidRTl3zse9GXucvXxyOWVgNENO3WlOG10kowMxHRpGc2BtsTWxejMMwzBqSTl3zt8gItvceQH63GUBVFVnVL10bUIup2waHGPJigdZvX4zB8+dzbLFB7BjfzeJhDS6eEaHYDo0jObA2mJrYvVmGIZh1JrYd85VNamqM9zfdFXt8s3bwLwIQ+NZlqx4kFVPbSKTU1Y9tYklKx5kaDzb6KIZHYTp0DCaA2uLrYnVm2EYhlFrKvnOuVEm6e4kq9dvzgtbvX4z6e5kg0pkdCKmQ8NoDqwttiZWb4ZhGEatscF5HRgay3Lw3Nl5YQfPnc3QmP3bbtQP06FhNAfWFlsTqzfDMAyj1tRtcC4i7xKRJ0TkSRH5fMj6j4jIBhFZ6/4+Vq+y1Zp0KsmyxQdw6N470pUQDt17R5YtPoB0yv5tN+qH6dAwmgNri62J1ZthGIZRa8oxhKsYEUkC3wP+HngOWC0it6jqY4Go16nq6fUoU7nkcsrQeJZ0d5KhsSzpVDK2AUwiIezY380VJy+oKL1hVIMwHfZ1JSrWtWEYhcQ5V9g5oTUJ1tvgaMapv3GrP8MwDKM61GVwDhwCPKmqTwGIyErgGCA4OG9KquHQmkgI03qcw+1NDaPe+HWYTiXNedgwqkg55wo7J7QmiYQ4feeA9Z2GYcB+V+/X6CIYbUa9HmvfFXjWt/ycGxbkWBF5WERuEJHd61O00phDq9GOmK4No7pYm+oMrJ4NwzCMWlGvwXnYX8kaWP45MFdV9wf+G7g6NCORU0VkjYis2bBhQ5WLGY45tBpTpRG6LYXp2ihFM+q2mbE21RzUWrdWz0YtsP7WMAyo3+D8OcB/J3w34AV/BFXdpKqj7uIVwEFhGanq5aq6QFUXzJkzpyaFDWIOrcZUaYRuS2G6NkrRjLptZqxNNQe11q3Vs1ELrL81DAPq9875auDVIrIX8DywCDjJH0FEdlHVF93F9wKPV7sQpYx6cjllJJMll4N0T5Kh0Sx9qQQJgWWL57NkxVpeMaOHT7/jNeyxY5qh0SzZbI7hTC6Wqc9UTOUMoxr4NYjCFR8+iI0DY+w+O82zm4eYlU6FOg9nMjmGM1n6e7oYHM3Q15Wkq6vwvz3TuNHJeG7ewXeRe5MJto+MT7SfdCpJMhnvv/GwNgUUhKk68SrZRjl0ShsP7mdvMjHRB6oqP/rYITy7eZjv/Pf/8tdto1z2wQNBIafa1sfFaG3mfv4XZcVf/8331KgkLufNrG3+htGC1GVwrqoZETkd+DWQBK5S1UdF5MvAGlW9BVgiIu8FMsBm4CPVLEMpo55cTtk+Ms720Qyf+8nDrF6/mSVvfxWLDtmDM1c6g/LvLJpPQmDJirUF60uZwlTDVM4wpkJQg55+z7lp3YQmL1o8n2mq+N9EyWRybB4ay9P5RYvmMzvdnTdAN40bnU6YC3tvMhHafnbs7y45eA5rU5d98EDGsrmJ85DTzuaTSiY47UcPlL2NcuiUNh7cz4sXz+egPWfn1eH5x+7PLWuf51/f8zq6kglGsznO9B3/djwuhmEYRu2p23fOVfWXqvoaVd1HVb/mhp3rDsxR1XNU9fWq+gZVPUJV/1jN7ZcycBkaz7JlaJzP/eThiTjv3HcXzly5llVPbeLmtS/w8tA4S1asDV1fyhTGDGSMRhPUYJh+z1yxtkCTw5lsYbyVaxnOZIvmbxo3OhHPhT0hzjSq/cRpF2FtakvgPOS0s7W8PDRe0TbKoVPaeHA/D91np4I6PPvGh3nnvruwZMVaBDizoE7a77gYhmEYtadug/NGU8rAJd2dZPfZ6bw4r9p5WlnLwTzL2b5h1JqgBqP02x/4rFN/T1eseKZxwygkbvsJI6xNBc9TXn67z05XtI1y6JQ2HtzPGX2p0P32+tCo9e12XAzDMIza0zGD81IGLkNjWZ7dPJQX58mXBspaDuZZzvYNo9YENRil38HRTF7Y4GgmVjzTuGEUErf9hBHWpoLnKS+/ZzcPVbSNcuiUNh7cz23D46H77fWhUevb7bgYRknOm1nezzCMAjpmcO4Z9Ry69450JYRD996RZYsPmDDXSaeSzEqnuOD4/Sfi/PqRF7lo0fzYy8E8y9m+YdSaoAbD9HvRovkFmuzrSobG6+tKFs3fNG4YTruI086i0gbb1Kx0imWLg+ed+eyQTlW0jXL3pRPaeHA/V/1pY0Ednn/s/vz6kRc5/9j9Q9e343ExDMMwao+oBj833josWLBA16xZEzt+mBt7unvSUdVbnwBy6vz6uhMMjTkOrUNjWfq6Ennu7MHloENrNpubcNAdcf9Fz2n49o260PCDXa5u/eQ5CI9mSSSgN1WovShHZb8ePdf1OC7scd3ag/nXyjG6A2lp3Zai2RzAq12esPYjQqy2ElaWXE4rzm+qlHlsWla3Xp15+zlxXL39Dkz7upKMZON9ucVoehpecXF1W677ermU7dbejHfDz9taVvT9rt6vRgWpnHUnr4sTreG6NapDvT6l1jQMjmYjnWYTCaEnmWD7aIYBn2u754a7Y3+PY/bjXvBMc9/nCy57ZLM5Ng1OuvSW4+5uGEHCnJIvOH5/vvXrJ/jrttEJLQGhjsqz0yk2D43nhV+0aD4r73uGZbc/WdThuasrwXR3MD69NxVZvmD+pm+jFM3mAF7t8uRyypbh/HZxxYcPYmgsG8vB3TOYA+cck82Gfz1hx/7uibYZ1UarQbA87Yh3jFfe9wzvO2A3zr5x8lrg/GP35+YHn2PRIXvk9Z0TGhFp2+NiGIZh1J6OuqUVx2l2aDzLywHXds8Nt1zn1aHxfJfectzdDSNImH4/95OHOe3wV+VpqZjOg+FnrlzLO/fdpSoOz53i5GxUl2bTTbXLE5ZfJqdTcnCvNK0RD+8Yv3PfXTj7xodDXdqDfaf1dYbRnOx39X5l/Qyj0XTU37txnGb7e7pId4e765brvBp06S3H3d0wgkTp91U7T5uY97QU5Q5dLL0/XjXLZ/o2itFsuql2ecLyi3L3jtP2puL+bsTDO8ZR52wvPNh3Wl9nGIZhTJXOunMew2l2cDQT6YZbrvNq0KW3HHd3wwgSpd8nXxqYmB8ay0bGi3KN9tL741WzfKZvoxjNpptqlycsvyh37zhtbyru70Y8vGMcdc72woN9p/V1hmEYxlRpa0O4XE4Zy2TJ+XZxPJtjJJOjv7uLvu4Eo+O5POO3vlSSAfed85vuf4537rsL+8zpZyyTc/J0445nchOmceme5ITZmz9OJpNj60iGlfc9M5HPwGiG0UyO6e5dDjOHqzsNP8DlGBQFjaRyOeUTP3pgwsPgI2/Zi2m9XWwfydCVEPq6HS0OjmVYssLvbTCfWeluBkczbBkaZ/fZaZ7dPMRO07rJKkzv7WJgJEN3UuhOJSdMpiYMjkYzJETo7U4yNp5lPKcFxlPh7+rOp7+nK9S0ziiLhh+0WhnCNfKd8zBzM4BNg6OF7aevO68tplNJVCkwZkskJC/Pvq4E2WyOUV+b6UkIL49mONO3jYsWz2d2yDYg3+itryvJwFiGl33teId0iuk9XQXvq5dj3lYjU76m121wv3uTCTI55zphNJOjvztJJqfM6EsxMJKhv6fQCO65l4fZaVo3IM753Pq6VqfhFWeGcNVjv732aHQRpowZwnUWbfscXC6nDI1lyOTUOdGO57jp/uf4xzftyXg2x1X/8xSLD9mD0awT7jd9uXjxfI547c4seuMerLz3GRYfsgeSYCKPk988F4UJ07hXzOjhi0e9DpiM4+W9YfsIi964B2euWMsrZvTwb0fNI5mAofEMI+O5SNM5w8hkQoyfFs9n+ckL6Ekl2DQ4xsevuT/PHO5ff/oEe+/Uz4ffPJdvfGC/iYv3VDLByHiG0WyOc25aV9QQ7r/WPMvWofEC88ILjt+fJ9dvZ94uMyONrHbs7+aKkxdMuMlnczlO+cGapjD6MpqTRELydVOngU3UnwKz+lKkkomC9jMwluE094+xg+fOZvnJCxgYzeS1hUs/eCDj2VzBwD6VTOSlvWix85kz/za6kwlGMllO/eH9efmNZXMFg/ieZCKvHS9bPB8RibV/Ye2v2Uz56kVwvz3T1udfHmLXWWkeeHozB+05O6+OPUO49x2w24QxXG9KCgz+OuH4GYZhGNWnbR9rHxrPkskpLw+NMzCS5XM/cUxcto9kJuYHxybD/aYvp/34Qf6ybZQzV6ydiOfPQyHPNO60w1/FwEh+HC/vdHeKM1esnYi3fSSTF3eqpnNG+zKcCTF+WrGWrKpzIbhibag53Dv33YXTfvQAh3/rTvb5wi85/Ft3ctqPHkCRgjRhhnDHzN811Lzwcz95mAP3mF3UjMpzck6IgMAnfvRA0xh9Gc2LXzfTerrqMqCJMn4bzmRD28/LQ+MlTd1eHhpnyYqg6efagrRnrljLxoGxgm348/byK2izK9ayJZBf2LmjHGO7ZjPlqxfB/fb6vX3mTOfMFWs5dJ+dCurYM4TzG8P196TM7NUwDMOoCm1759wxZklOfFImaJwVnA+avuw+Ox1q+PKqnach4nyqxksTFicsb388b31w2QxlDI9Sxk+lzOHC0sUxhJvRl8rTt3/dtN74ZlTNZvRlGH6i9BnVTnafnc4LC2sL3nmjVNqosBl9+Z9AKye/YLsqp/11alsN7rd3vvbqNsq4z28IV0wz7X78DMMwjOrTvnfOx7JsGx7n2c1DEwZvT740EDkfNH0JxvMve/l6acLihOXtj1ct0zmjfSlm/FTM3C3KxCiuIdy24fHIPAZG4ptRNZvRl2H4Kdc48dnNQ3lhYW0hql8Ppo0K2zY8XnF+wXZVTvvr1LYa3G+v3/PqNsq4z28IV0wz7X78DKMVWPfnZ8r6GUajadvBeTqVpCsh7JBOMa03yQXH78+vH3mR/u7w+fOP3Z9D996RroRw6N47MiudYtni+RPx/HkIsEM6xQXHO2kuvfNJpvUmI7fj5e2P58X1b3OZ+x6iYQD0dSW5aNH8PI1ctGg+fV3J0HUXHL8/l975JL9+5MXQdIKGhv/6kRfzln+29vnQPC44fn8eeGZzaB5huk2nkixbfEBA4weYxo2mIEqfoe1u8Xx2SKfywroSUhBvB/e8EezXg2nD8rto0fy8vL38LlpcWJZZgbRh7aqc9tepbTW4316/96cN27lo0XxW/WljQR2ff+zked2LPzg6XhCvE46fYRiGUX06yq3dc1EfHc/lxUsIec7rQ6MZ0t3OY7pD41n6UpNpwtzavTy9fPLK4HOC7+/pYmQ8C5q/3tza60rDD3A57sFBZ/S+riRdXc5/akEn96Trpu7F89b58xgZy5LV/Pz8eaQSlbu1l9oXczCeEg0/aLVya28kUfoMtq2+LmeQFScs6NaeTiXJ5TRWWpF8Z/Ywt/Z0KolI4TbC2lWUG31YWnNrz+87J6apJMP+499d6NY+ks3R15VgOJOzvq49aHjFmVt742hGd3dza+8s2vadc3BMhnq7C3exNyUTDq3v2vcVvNs1dfG7T/e5A45p7ru06Z78wUey21n23F5X3Pv0hHur3/k9z7VVZGLQH2Rab1tXhRGTKNfk/m4m/BM8uroSTHcH6v51/d1dpfXdNenHML3L+QzayHiOU8PcmkWY5su/t7uLXnc+WKYgntEXMDE1jGYhTJ/ZbOFXEq748EEFbtwXLZrP7HT3ZDvytYVgnomEhLbV0LBkvLA47Sq4f6Vc2TuxrSYSQjqVZNNA4XGZnU6xeWic57YMsusO6dD67+pKMM2tx2luPXXS8TMMwzCqS9s+1l4Mv0PrMfN3Leo+HTcvv3ur3/ndXFuNcqiGa3IcfQ9nKnd2Nox2Zmi88CsJYc7sYe2o2bF2Hk6x47JkxYOOe3sb1L9hGIbR/HTk4Nzv0BrlxhrmPl0sr6B7azA/c2014lAN1+RK9N2pbs2GESTMeXuq54lmwdp5OKWc+8v5SoVhGIZhTIWOHJz7HVqj3FjD3KeL5RV0bw3mZ66tRhyq4Zpcib471a3ZMIKEOW9P9TzRLFg7D6eUc385X6kwDMMwjKlQt8G5iLxLRJ4QkSdF5PMh63tE5Dp3/b0iMrdWZfE7tP5s7fOx3aeL5eV3bw06v5trqxGXargmx9G3Z0hVze0aRjuQThW6tYc5s4e1o2bH2nk4xY7LssUHTLi3t3r9G4ZhGM1PXdzaRSQJ/C/w98BzwGpgsao+5ovzSWB/Vf2EiCwC3q+qJxbLdyruwcUcsYu5TxfLqy+VyHNx7e/pMtfW5qPhFVGOe3Cl+onr+F7t7Ro1o+EV0Y5u7VFks7kCh3TVQnf1sHbU7NS5nbeMbqOOixfem0y0Rf0bsWgZ3Zpbe/Uxt3aj0dTrhalDgCdV9SkAEVkJHAM85otzDHCeO38D8F0REa3Rvwd+Z9py3KdL5TW9N5E3NddWo1yq4Zpcib471a3ZMIIkk4lw1/QQd/VWw9p5OFHHJe/83gb1bxiGYTQ39frbd1fgWd/yc25YaBxVzQBbgR2DGYnIqSKyRkTWbNiwoUbFNYzqYro1WhHTrdGKmG6NVsR0axgG1O/OedijFsE74nHioKqXA5eD89jP1ItmGLXHdGu0IqZboxUx3RqtSDPqttzH5tf3lo7T7Kz78zNlxW/Gx+CN1qZed86fA3b3Le8GvBAVR0S6gJnAZgzDMAzDMAzDMAyjzanX4Hw18GoR2UtEuoFFwC2BOLcAJ7vzxwG31+p9c8MwDMMwDMMwDMNoJuryWLuqZkTkdODXQBK4SlUfFZEvA2tU9RZgOXCNiDyJc8d8UT3KZhiGYRiGYRiGYRiNpi6fUqsVIrIBeLpEtJ2AjXUozlSwMlaHOGXcqKrvqkdhooip206hFXRVT6KORyvptp3qtF32pVH70Uq6rTatop1WKGe9y9isum2FuopDu+wHNNe+NFy3RnVo6cF5HERkjaouaHQ5imFlrA6tUEYjH6uzfNrheLTDPni0y760y360Eq1yzFuhnK1QxnrQLsehXfYD2mtfjOahXu+cG4ZhGIZhGIZhGIYRgQ3ODcMwDMMwDMMwDKPBdMLg/PJGFyAGVsbq0AplNPKxOsunHY5HO+yDR7vsS7vsRyvRKse8FcrZCmWsB+1yHNplP6C99sVoEtr+nXPDMAzDMAzDMAzDaHY64c65YRiGYRiGYRiGYTQ1bTs4F5F3icgTIvKkiHy+0eXxEJH1IrJORNaKyBo3bLaI3CYi/+dOZzWgXFeJyEsi8ogvLLRc4rDMPbYPi8iBDSrfeSLyvHss14rIkb5157jle0JE3lnr8hnlIyI7iMgNIvJHEXlcRA5tdJkaiYj8s4g8KiKPiMgKEeltdJniIiK7i8gdbj0+KiJnNrpMlSIivSJyn4g85O7LlxpdpqkiIkkReVBEbm10WdqdVmoLraALO0807/VsuYRdx7UqrdTOjdajLQfnIpIEvge8G5gHLBaReY0tVR5HqOp83+cXPg/8VlVfDfzWXa43PwCC30eMKte7gVe7v1OBSxtUPoAL3WM5X1V/CeDW9SLg9W6aS1xNGM3FRcCvVPVvgTcAjze4PA1DRHYFlgALVHVfIImj4VYhA/yLqr4OeBPwqSbrc8thFHi7qr4BmA+8S0Te1OAyTZUz6eD2VWdaqS20gi46+jzRAtez5fADwq/jWpFWaudGi9GWg3PgEOBJVX1KVceAlcAxDS5TMY4Brnbnrwbe4xUTLgAADoxJREFUV+8CqOpdwOZAcFS5jgF+qA73ADuIyC4NKF8UxwArVXVUVf8MPImjCaNJEJEZwGHAcgBVHVPVlxtbqobTBfSJSBeQBl5ocHlio6ovquoD7vx2nAvoXRtbqspw+7UBdzHl/lrWnEVEdgPeA1zZ6LJ0Aq3SFlpBF3aeAFrvejaSMq/jmppWaedGa9Kug/NdgWd9y8/RPI1Ggd+IyP0icqob9gpVfRGcBg/s3LDS5RNVrmY6vqe7j9Zf5XsdoJnKZ4SzN7AB+E/3scorRaS/0YVqFKr6PPAt4BngRWCrqv6msaWqDBGZCxwA3NvYklSO+7jvWuAl4DZVbdl9Ab4DnAXkGl2QTqPJ20Ir6MLOE3Y90/Q0eTs3WpB2HZxLSFiz3Pl4i6oeiPOI0qdE5LBGF6gCmuX4Xgrsg/Po6YvAf7jhzVI+I5ou4EDgUlU9ABikMa9zNAXuH0vHAHsBfwP0i8gHG1uq8hGRacCNwKdVdVujy1MpqppV1fnAbsAhIrJvo8tUCSJyFPCSqt7f6LJ0Gs3cFlpIF3aesOuZpqaZ27nRurTr4Pw5YHff8m40ySOiqvqCO30J+CnOI0t/9R4Ld6cvNa6EeUSVqymOr6r+1b2IzgFXMPnoelOUzyjKc8BzvjuSN+BchHUq7wD+rKobVHUcuAl4c4PLVBYiksK5SPmxqt7U6PJUA/cR2jtp3fck3wK8V0TW4zwO+3YR+VFji9T+tEBbaBVd2HnCrmealhZo50aL0q6D89XAq0VkLxHpxjFWuqXBZUJE+kVkujcP/APwCE7ZTnajnQz8rDElLCCqXLcAH3Zd29+E8wjui/UuXOA99/fjHEuvfItEpEdE9sIxrruv3uUzolHVvwDPishr3aC/Ax5rYJEazTPAm0QkLSKCczxaxvjILfNy4HFV/XajyzMVRGSOiOzgzvfh/HHyx8aWqjJU9RxV3U1V5+KcB29X1ZZ7IqOVaIW20Cq6sPME0KTXs51OK7Rzo3XpanQBaoGqZkTkdODXOK7HV6nqow0uFsArgJ86bZou4FpV/ZWIrAauF5FTcC7Sj693wURkBXA4sJOIPAf8O/DNiHL9EjgSx2htCPhog8p3uIjMx3nEaz3wcQBVfVRErsc5iWeAT6lqttZlNMrmDODH7gXHU9RBR82Kqt4rIjcAD+Bo9kHg8saWqizeAnwIWOe+qw3wBe8LCi3GLsDVrktyArheVZv2U1NG09FObaEZ6OjzRBNfz5ZN2HWcqi5vbKkqxtq5UTNE1V5dMQzDMAzDMAzDMIxG0q6PtRuGYRiGYRiGYRhGy2CDc8MwDMMwDMMwDMNoMDY4NwzDMAzDMAzDMIwGY4NzwzAMwzAMwzAMw2gwNjg3DMMwDMMwDMMwjAZjg/M2QUQGIsJ/ICLH1bs8RnMjIv8qIo+KyMMislZE3ljFvA8XkVvd+Y+IyHerlXfItnYQkU+GbTskbkpEviki/ycij4jIfSLy7lqVzagt9dCwOGwUkVlu+C4ioiLyVl/cDSKyo4h8QkQ+HJLXXBF5xJ2fLyJH+tadJyKfjSjDK0VkpYj8SUQeE5FfishrqrWPRn0Rkayr00dE5Cciki4R/wsx810vIju586HXAdXC7c//JmzbIXHfLSJrRORxEfmjiHyrlmUzakut9SsiF4rIp33hvxaRK33L/yEinxGRv3E/OxqW150isiC4fX8fHJHus65GHxGRh8L6ccOoJzY4N4wOQ0QOBY4CDlTV/YF3AM82tlQVswPwyZKxHL6C8w3rfVV1X+BoYHqtCmbUjnppWJ1vjd4LHOoGvRnnG/RvdsvxWmCjqm5S1ctU9YclspwPHFkiDiIiwE+BO1V1H1WdB3wBeEVle2I0AcOqOt/te8aAT5SIH2twU2c+AvxNqUgisi/wXeCDqvo6YF+cb5QbrUut9fsHJvvVBLAT8Hrf+jcDv1fVF1Q1zg2nuH8OfAL4e+AQd98OA6ScghtGtbHBeZvh3un5rnun5RfAzo0uk9F07IIzoBgFUNWNqvqCiBwkIr8Tkfvdf613gYl/o78jIn9w/1k+xA0/xA170J2+Nm4BROQfRGSViDzg/gs/zQ1fLyJfcsPXicjfuuFzROQ2N/z7IvK0e8fmm8A+7j/6F7jZTxORG9x/wn/stok08P+AM3z7/VdVvd7Nf0BEznf3/b/dfbtTRJ4SkfdW46AbVaWeGv497kWjO/02+YP1P7h5TdwFd8vxkIisAj7lhnUDXwZOdPV6opvHPJ/WlrhhRwDjqnqZVwhVXauqd4tzV/93InK9iPyvOE+D/KM4T4KsE5F9pn54jRpzN/AqABH5oFt3a92+LSki3wT63LAfu/FudnX9qIicGndDbt95o4isdn9vccPPE5GrQrSHiPyb23/eJiIrxLmzeBywAPixW64+N/oZwf4aOAv4mqr+EUBVM6p6iZv3D0TkUhG5w93u29xyPC4iP5jKQTXqRi306+9nXw88AmwXkVki0gO8DnhQ8p9E6hPn6aKHReQ6oM8NL9g+kBSRK9zt/8an3y8An1TVbQCqulVVr3bzWS8iXxfnWmWNiBzonlf+JM6g3jBqg6rarw1+wIA7/QBwG5DE+Yf7ZeC4RpfPfs3zA6YBa4H/BS4B3gakcAYZc9w4JwJXufN3Ale484cBj7jzM4Aud/4dwI3u/OHAre78R4DvBra/E3AX0O8unw2c686vxxlAg3NH/Ep3/rvAOe78uwB185nrlce37a3Abjh/Pq4C3grsDzxY5Jgo8G53/qfAb9xj8gZgbaPrzH4N1fDhwO3u/N3utte4y1cA/+TOnwd81p1/GHibO3+Bb3t57cFN8wegx9XzJnc/lgAXRuz74Tj9+i5uuueBL7nrzgS+0+j6sV9ovXnn6C7gZ8BpOAOOnwMpd90lwIf98X3pZ7vTPpyBy47u8npgp7A0bti1wFvd+T2Ax0tob4Hbtvpwniz6P5+u7wQW+PJeT3h//QDwhojj8ANgJc7dyWOAbcB+OP31/cD8RteV/Rqm3/WuRj+Oc2f+KzhPGr0FuMuNM5fJ/vQzTPbx+wMZT5/+7btpMp62gOuBD7r63lJkn9cDp7nzF+L069OBOcBLja4T+7Xvrwuj3TgMWKGqWeAFEbm90QUymgtVHRCRg4CFOHforgO+ivPo4W0iAs6fOy/6kq1w094lIjNEZAeck9TVIvJqnMFtKmYR3gTMA37vbqsbZxDtcZM7vR/nzyZwBtjvd8vwKxHZUiT/+1T1OQARWYtzYn64RJnGgF+58+uAUVUdF5F1bnqjiaizhu8DDhCRfpyL0AH3jt+rcO70/Ic/sojMBHZQ1d+5QdcAxbwNfqHOEwCjIvIS8R5dX62qL7rb+xPOn0ngaPeIGOmN+tPn9kfg/MmzHDgVOAhY7Wq2D3gpIv0SEXm/O7878GqcAXUp3oHzdIa3PENEvNd5wrT3VuBnqjoMICI/L5F/WH9dip+rqrr9619VdZ27rUdx+tu1xRIbDaEe+vXunntPKO3qzm/FfUIpwGHAMgBVfVhEip3n/6yqXvnvx9GZ4PT7xbjFna4Dpqnqdpw7+iMisoOqvlwivWGUjQ3O25NSnY3R4bh/3twJ3OleIH0KeFRVD41KErL8FeAOVX2/iMx184uDALep6uKI9aPuNMtkH1XOO2CjvnkvjyeBPURkuntyDTKuqt4+5rw8VDUnItZPNiH10rCqDonIk8A/4dwRBLgH547OzsATgSRxLvj8hOn1UaDYe5X+NDnfcg47rzcrw6o63x8gzojmalU9p1hCETkcZ5B9qKvHO4HemNtNuOmGA3lCuPbKfd82rL9+FGfQ9lCJNH7tesum3+akHvr13jvfD+fu+rPAv+A8XXFVRPZx+9qg1vtUdZuIDIrI3qoa5YlgWjXqjr1z3n7cBSxy3/vZBbuLYgQQkde6dwo95gOPA3PEMdrynM39ZiwnuuFvBbaq6lZgJs4jteA8rhuXe4C3uHceEZG0lHah/h/gBDf+PwCz3PDtxDB1U9UhnH/6l4nz7q/nvP3BMsptNAkN0PDvgU8z+YTHKpxHyO/x/akDgHsnZatMOrr/o291LL0CtwM9IvL/vAAROVhE3hYjrdE6/BY4TkR2BhCR2SKyp7tuXES8Jzlm4jx+OyTOe91vKmMbvwFO9xZEZH6RuOD0tUeLSK84XiDv8a2Lq98LgC94/bqIJETkM2WU2WgNqq3f3+MYfW5W1ayqbsYxfT2U/KfrPO7C7V/FMSHc37fOv/1ifAP4nojMcPOZEfFOvGHUDRuctx8/xXlHbB1wKfC74tGNDmQazqO8j7mPgc0DzsW5U3e+iDyE81jhm31ptojIH4DLgFPcsKXAN0Tk9ziPEEfxERF5zvvhvOP4EWCFu/17gL8tkh7gS8A/iMgDOI8IvwhsV9VNOI/HPyKThnBRfBHYADwmjqHMze6y0XrUW8O/B/Zm8gLxARxfg7BHLQE+inPBtwrw37G8A+cRY78hXAHugP/9wN+75kOP4rwj/EKRMhothqo+htMv/cbV8W04XgIAlwMPi2No9Sugy43zFZw+M4y0v691B8RLgAXimGY9RgmXbVVdjfMo70M4j6yvwXmsGJz3xS+TfEO4sDwexvkza4WIPI5zF3SXqPhGa1ID/a7D8T+4JxC2VVU3hsS/FMcA9mEcE8L7fOv82y/GpTj98mr3uuB3wFCJNIZRUyTwp79hGEYe7iNon1XVNQ0sQw+QVdWMe2f00uAjdoYRRTNo2DBaBRGZ5norpHHuTp6qqg+USmcYhmFMHXtfwjCMVmAP4Hpxvn86hvNZNMMwDKP6XC4i83DeC77aBuaGYRj1w+6cG4ZhGIZhGIZhGEaDsXfODcMwDMMwDMMwDKPB2ODcMAzDMAzDMAzDMBqMDc4NwzAMwzAMwzAMo8HY4NwwDMMwDMMwDMMwGowNzg3DMAzDMAzDMAyjwdjg3DAMwzAMwzAMwzAazP8H7wPgl6qa/Z4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x177622ecb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.pairplot(df,hue ='Species')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 6 columns):\n",
      "Id               150 non-null int64\n",
      "SepalLengthCm    150 non-null float64\n",
      "SepalWidthCm     150 non-null float64\n",
      "PetalLengthCm    150 non-null float64\n",
      "PetalWidthCm     150 non-null float64\n",
      "Species          150 non-null object\n",
      "dtypes: float64(4), int64(1), object(1)\n",
      "memory usage: 7.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>43.445368</td>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>38.250000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>112.750000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "count  150.000000     150.000000    150.000000     150.000000    150.000000\n",
       "mean    75.500000       5.843333      3.054000       3.758667      1.198667\n",
       "std     43.445368       0.828066      0.433594       1.764420      0.763161\n",
       "min      1.000000       4.300000      2.000000       1.000000      0.100000\n",
       "25%     38.250000       5.100000      2.800000       1.600000      0.300000\n",
       "50%     75.500000       5.800000      3.000000       4.350000      1.300000\n",
       "75%    112.750000       6.400000      3.300000       5.100000      1.800000\n",
       "max    150.000000       7.900000      4.400000       6.900000      2.500000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X =df.drop('Species',axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "0   1            5.1           3.5            1.4           0.2\n",
       "1   2            4.9           3.0            1.4           0.2\n",
       "2   3            4.7           3.2            1.3           0.2\n",
       "3   4            4.6           3.1            1.5           0.2\n",
       "4   5            5.0           3.6            1.4           0.2"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_names = df['Species'].unique()\n",
    "target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Iris-setosa': 0, 'Iris-versicolor': 1, 'Iris-virginica': 2}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# converting Species text values to numerical values\n",
    "\n",
    "target_dict = {n:i for i , n in enumerate(target_names)}\n",
    "target_dict\n",
    "\n",
    "# each flower type is now expressed using numbers 0 or 1 or 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: Species, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y =df['Species'].map(target_dict)\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Creating dummy columns\n",
    "\n",
    "from keras.utils.np_utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_cat = to_categorical(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [1., 0., 0.],\n",
       "       [1., 0., 0.],\n",
       "       [1., 0., 0.],\n",
       "       [1., 0., 0.],\n",
       "       [1., 0., 0.],\n",
       "       [1., 0., 0.],\n",
       "       [1., 0., 0.],\n",
       "       [1., 0., 0.],\n",
       "       [1., 0., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_cat[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X.values, y_cat,test_size=0.2,random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.optimizers import SGD, Adam # SGD is stochastic gradient decent(Optimizers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(3,input_shape=(5,),activation ='softmax'))\n",
    "model.compile(Adam(lr=0.1),loss = 'categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 108 samples, validate on 12 samples\n",
      "Epoch 1/40\n",
      "108/108 [==============================] - 0s 2ms/step - loss: 7.9566 - acc: 0.3241 - val_loss: 4.0982 - val_acc: 0.7500\n",
      "Epoch 2/40\n",
      "108/108 [==============================] - 0s 74us/step - loss: 6.4786 - acc: 0.4722 - val_loss: 4.4395 - val_acc: 0.6667\n",
      "Epoch 3/40\n",
      "108/108 [==============================] - 0s 111us/step - loss: 6.2487 - acc: 0.4907 - val_loss: 4.3707 - val_acc: 0.5000\n",
      "Epoch 4/40\n",
      "108/108 [==============================] - 0s 74us/step - loss: 5.9981 - acc: 0.5556 - val_loss: 4.0386 - val_acc: 0.7500\n",
      "Epoch 5/40\n",
      "108/108 [==============================] - 0s 129us/step - loss: 5.8539 - acc: 0.6296 - val_loss: 4.0569 - val_acc: 0.7500\n",
      "Epoch 6/40\n",
      "108/108 [==============================] - 0s 65us/step - loss: 5.8865 - acc: 0.6111 - val_loss: 4.0368 - val_acc: 0.7500\n",
      "Epoch 7/40\n",
      "108/108 [==============================] - 0s 83us/step - loss: 5.8450 - acc: 0.6389 - val_loss: 4.0449 - val_acc: 0.7500\n",
      "Epoch 8/40\n",
      "108/108 [==============================] - 0s 120us/step - loss: 5.8598 - acc: 0.6204 - val_loss: 4.0348 - val_acc: 0.7500\n",
      "Epoch 9/40\n",
      "108/108 [==============================] - 0s 102us/step - loss: 5.8362 - acc: 0.6389 - val_loss: 4.0322 - val_acc: 0.7500\n",
      "Epoch 10/40\n",
      "108/108 [==============================] - 0s 96us/step - loss: 5.8333 - acc: 0.6389 - val_loss: 4.0318 - val_acc: 0.7500\n",
      "Epoch 11/40\n",
      "108/108 [==============================] - 0s 105us/step - loss: 5.8303 - acc: 0.6389 - val_loss: 4.0314 - val_acc: 0.7500\n",
      "Epoch 12/40\n",
      "108/108 [==============================] - 0s 153us/step - loss: 5.8279 - acc: 0.6389 - val_loss: 4.0314 - val_acc: 0.7500\n",
      "Epoch 13/40\n",
      "108/108 [==============================] - 0s 126us/step - loss: 5.8266 - acc: 0.6389 - val_loss: 4.0309 - val_acc: 0.7500\n",
      "Epoch 14/40\n",
      "108/108 [==============================] - 0s 151us/step - loss: 5.8264 - acc: 0.6389 - val_loss: 4.0309 - val_acc: 0.7500\n",
      "Epoch 15/40\n",
      "108/108 [==============================] - 0s 120us/step - loss: 5.8265 - acc: 0.6389 - val_loss: 4.0307 - val_acc: 0.7500\n",
      "Epoch 16/40\n",
      "108/108 [==============================] - 0s 166us/step - loss: 5.8254 - acc: 0.6389 - val_loss: 4.0307 - val_acc: 0.7500\n",
      "Epoch 17/40\n",
      "108/108 [==============================] - 0s 102us/step - loss: 5.8252 - acc: 0.6389 - val_loss: 4.0308 - val_acc: 0.7500\n",
      "Epoch 18/40\n",
      "108/108 [==============================] - 0s 85us/step - loss: 5.8252 - acc: 0.6389 - val_loss: 4.0308 - val_acc: 0.7500\n",
      "Epoch 19/40\n",
      "108/108 [==============================] - 0s 124us/step - loss: 5.8248 - acc: 0.6389 - val_loss: 4.0306 - val_acc: 0.7500\n",
      "Epoch 20/40\n",
      "108/108 [==============================] - 0s 288us/step - loss: 5.8247 - acc: 0.6389 - val_loss: 4.0305 - val_acc: 0.7500\n",
      "Epoch 21/40\n",
      "108/108 [==============================] - 0s 175us/step - loss: 5.8245 - acc: 0.6389 - val_loss: 4.0305 - val_acc: 0.7500\n",
      "Epoch 22/40\n",
      "108/108 [==============================] - 0s 157us/step - loss: 5.8243 - acc: 0.6389 - val_loss: 4.0305 - val_acc: 0.7500\n",
      "Epoch 23/40\n",
      "108/108 [==============================] - 0s 196us/step - loss: 5.8241 - acc: 0.6389 - val_loss: 4.0305 - val_acc: 0.7500\n",
      "Epoch 24/40\n",
      "108/108 [==============================] - 0s 94us/step - loss: 5.8240 - acc: 0.6389 - val_loss: 4.0304 - val_acc: 0.7500\n",
      "Epoch 25/40\n",
      "108/108 [==============================] - 0s 124us/step - loss: 5.8239 - acc: 0.6389 - val_loss: 4.0304 - val_acc: 0.7500\n",
      "Epoch 26/40\n",
      "108/108 [==============================] - 0s 157us/step - loss: 5.8238 - acc: 0.6389 - val_loss: 4.0304 - val_acc: 0.7500\n",
      "Epoch 27/40\n",
      "108/108 [==============================] - 0s 148us/step - loss: 5.8237 - acc: 0.6389 - val_loss: 4.0303 - val_acc: 0.7500\n",
      "Epoch 28/40\n",
      "108/108 [==============================] - 0s 158us/step - loss: 5.8236 - acc: 0.6389 - val_loss: 4.0303 - val_acc: 0.7500\n",
      "Epoch 29/40\n",
      "108/108 [==============================] - 0s 248us/step - loss: 5.8236 - acc: 0.6389 - val_loss: 4.0303 - val_acc: 0.7500\n",
      "Epoch 30/40\n",
      "108/108 [==============================] - 0s 162us/step - loss: 5.8234 - acc: 0.6389 - val_loss: 4.0303 - val_acc: 0.7500\n",
      "Epoch 31/40\n",
      "108/108 [==============================] - 0s 166us/step - loss: 5.8234 - acc: 0.6389 - val_loss: 4.0303 - val_acc: 0.7500\n",
      "Epoch 32/40\n",
      "108/108 [==============================] - 0s 157us/step - loss: 5.8233 - acc: 0.6389 - val_loss: 4.0303 - val_acc: 0.7500\n",
      "Epoch 33/40\n",
      "108/108 [==============================] - 0s 139us/step - loss: 5.8232 - acc: 0.6389 - val_loss: 4.0303 - val_acc: 0.7500\n",
      "Epoch 34/40\n",
      "108/108 [==============================] - 0s 176us/step - loss: 5.8232 - acc: 0.6389 - val_loss: 4.0303 - val_acc: 0.7500\n",
      "Epoch 35/40\n",
      "108/108 [==============================] - 0s 242us/step - loss: 5.8231 - acc: 0.6389 - val_loss: 4.0302 - val_acc: 0.7500\n",
      "Epoch 36/40\n",
      "108/108 [==============================] - 0s 130us/step - loss: 5.8230 - acc: 0.6389 - val_loss: 4.0302 - val_acc: 0.7500\n",
      "Epoch 37/40\n",
      "108/108 [==============================] - 0s 148us/step - loss: 5.8229 - acc: 0.6389 - val_loss: 4.0302 - val_acc: 0.7500\n",
      "Epoch 38/40\n",
      "108/108 [==============================] - 0s 203us/step - loss: 5.8229 - acc: 0.6389 - val_loss: 4.0302 - val_acc: 0.7500\n",
      "Epoch 39/40\n",
      "108/108 [==============================] - 0s 111us/step - loss: 5.8229 - acc: 0.6389 - val_loss: 4.0302 - val_acc: 0.7500\n",
      "Epoch 40/40\n",
      "108/108 [==============================] - 0s 74us/step - loss: 5.8228 - acc: 0.6389 - val_loss: 4.0302 - val_acc: 0.7500\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x17761e9f588>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train, epochs=40,validation_split=0.1)\n",
    "\n",
    "#validation_split=0.1 takes only the 10% of training data to perform calculation of accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred= model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.9470842e-01, 5.2916221e-03, 3.2693638e-08],\n",
       "       [9.9791998e-01, 2.0800720e-03, 2.1295379e-09],\n",
       "       [8.5929745e-01, 1.4066911e-01, 3.3350956e-05],\n",
       "       [2.2852602e-07, 1.8388275e-02, 9.8161143e-01],\n",
       "       [2.1042822e-01, 7.8936666e-01, 2.0512272e-04]], dtype=float32)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_class =np.argmax(y_test , axis=1)\n",
    "y_pred_class = np.argmax(y_pred, axis=1)\n",
    "\n",
    "# for each row find the maximum value "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00        10\n",
      "          1       1.00      0.67      0.80        12\n",
      "          2       0.67      1.00      0.80         8\n",
      "\n",
      "avg / total       0.91      0.87      0.87        30\n",
      "\n",
      "[[10  0  0]\n",
      " [ 0  8  4]\n",
      " [ 0  0  8]]\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test_class,y_pred_class))\n",
    "print(confusion_matrix(y_test_class,y_pred_class))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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