health-insurance-cross-sell-prediction-xgboost.ipynb

{
 "metadata": {
  "tags": ["hide-input"]
  "kernelspec": {
   "language": "python",
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "version": "3.6.4",
   "file_extension": ".py",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "name": "python",
   "mimetype": "text/x-python"
  }
 },
 "nbformat_minor": 4,
 "nbformat": 4,
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# HEALTH INSURANCE CROSS SELL PREDICTION"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "![1.jpg](attachment:544d8b88-bde4-4e2b-92a2-2542814988cc.jpg)"
   ],
   "metadata": {},
   "attachments": {
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J1s4gi0BsiqolG4VoDfhXSt+y81dVZIbwND5Vcv8AbvmhyT3CwYvl20EX4z9wngUSfriZxHBpeCK/JrNadJV+05Cq+3vG1Elwib0d8UMlJimlsLFDduRSK2XptLmS4bWJCgCAKwFwGJoQJbJmaxQ42N8WmQNKxHQXRxWAl9+Xyarvu286KPxhaAwXIGqeQTIbRpo8pIw1tE2DKpsR7chqbrqVQejPY/D/AKvdL8MPNHjLoBSG4PmJsZvmFCXyyQ4TaMRKaItOMXI1pLheHOAz6vAn5BVyQr1KkStr5/YaztqcNa1Lt2Nk2paKqxHQAyUhQHQFA7B2SsWUqrG9aWlAtLkUSTUOcMnV9yI5VXYWVT5tvKNrZyIpuPZD3W2qoZZqwHyhd1iQ1diBkwrE2/h2I5I8/cV698ieB/SKFU+8JNxCTrRJ2O7kRSbiXAIo9WPjhuFNkSgOwqBIvuFaX0FZibmSFlMl34pMRH7leScIsqK9ERdUdw4y4USwQqTdAWBYgJANgpAOt6+SyNTFuSMKY0ckDY0sUuaNcXZT++LbmvZk7jgxLDESWZusw8wKUzjyx4kMjkCYNgNbkMidHk8IRbTG0ynrL3H5leg/RvGB8SRMyjcPtvY80K/XqsuGGrMFMpiTU9qqC1Wky1chFUuIWfgzxFhXJLG4MrttXW9WFuqd09Au0izL1EdASJm9zAuEWCbGD8G7N1zuv7XLOAqjMk1YDXq3NKjES9Dza/NJKtqZPQSsS0YSIqK4ZRmwHSV3cM/WugZcSjPV+I7J4Wcd+ovIGQ9NvotCZJsozGIGki9DfyYpFaHZRH78F6bT1TDwqxdq78/6rt8Zi6jIOXCM1Nv3WCCoxBUR+cIiEkHGna2VFRnrjeSGroboCgLGMAEDUmbrOK8OCmLEtqctiuUNamjjU3Uqwks3C73tvPWzJmNYJlsg4zN1iJTGNLYCUyN8XFmjESWRPDW3xabFgp5mS+l915391u1/NTcWr89qvrLwJguZGWaBGOYiwq37EwcpinmTUG5DO0qTyzEGwDTa9ZSdkZvb63t6MdNp3iodJVcnqAoBF91QlUF2NVE6aiXJytkwSyzjhcfEEbYtBL1dYgai3gSXQXQ8o2tB+MSi2DN+KguOVasxclGLzEhrZ8efV5f8e9v+NvFPp6ya0ZV6K9ZpiZNEkR36EEXoZKVFIKQ/KsdPxXqlETjLFX8yQupc71HT3JSsTfoDNaXDrLsor02yB178m3KmX77aqUOmWFlsqutUBQFjKwNsCbBY9M9kUtu2SCXxJWI4lNwKCrCSiaZnNynoPZ+d2an2RutDdYiUwjS2AlMjfHxpwopDQnRbMgaicON4eboJixYsQPZzu/zX9VO2fPQ7HUKchrE+TjTQljWkGHmIWlKhmYVkwdqGz0g5X2AadW4ujZOY2Wt7KpbTqZ0/FsQxJ66yoA3CnqEqgLgXbMgw6WiKhU1l3JGRG/iShU+syQRSDcC5VSu0+7QY2TGJxZ7Z1pq4yTZdHyDTdEZyvQfni4X9VuA+Y+rpdJYOzElXWyJUxUMv245RtLt+AQlwSIS9DBSRHXU06DMUaHCVD6EcsTLuPYm/QF1Fd4nRM5hl8cd4I5OMws62s4hcR3LrCzjVwKAwDiRSWQSi0SLclucliAOKY1DjYCgLhVhJoAdJb7fl8N2lJw2Ljw5x4WUuo8sUJDISB8eaJKQ0J0ay+MRIZnIYHKj8a0Yw7GIHJ7j6F5N+izvvyJmN/h1iffxnw9jAfNPsgeD0crDsBkiqpcklAniJ1eqothZnZ6/t6dtLp3KotlFiT91FQASTHUCqAoCkvGIijsFyEzXnDyU1gpAkCbOvyaSWgWgqVUqJlKNm3ejP1WKbEJo62VZkCn08oZD0F83/AJu+yWs850aWIZLLYLTIxV1h+VKVDRBuOTbS9fgEZcEgEvgyTkRl1tMEx+aq/faZqulrFIo38TfoC6iu8WYTcm1A0+EZ+yy5DS8hhckqGnIysYohia0g8mAgwhFtSD6mIksw+kCsBQFwqwk0AitEyeyvfVhwJOQpg46OElnGmDRJZCQPjzRpSGqXRDT4xEhmchgcoLEtRyLEatyPpn7Eveb/AEcekvir1hsuGGob4KwhPUKYqJs4DMKYwdrcXpBivnhrSvugbNyOr1lZ1iN1VOZEGyTxRJoCgHKFrErIFQFgC8RZ6teg9q0soWWolBdGTod0pKQLYXMqFGHlUKbyor5ixQOEzebKVM3yt517u+f/AIR9T2cfQnGWjRMlUxik6ITdjPhSlSaJNx37aX78AjLgkAl8GSciNdSY2mRwjUduKwrCwXQcxN+gLqK7xUBcJuEKlGcNsrBtw4y4MlQFDCgGAGIFgGhIQaUi26hIntwWAKgKAoCgdB6gKADuQPQq04ZMZOaHuLYnJHxZo4SWQkD480aUhol0S0+MRIZnIYHKCRbUYdkNCxSZQc3C7ue+g/0H8k/T3o3jATYQHzD49SkZLBeHPGuwclgtX2IidW2dGx8nstdW1O1m1Dp2LY00HqWKADqjqBVAUBY5R+Is/WOQCzWq6iniJwXBkpGRNkoMwVKrVEynLQaPxSSrBmxDZKf8ROF/Unxx4z9ByTj75Egi0gwlgmUYxPhv3I70UpUmi7TDlCCD8AhLgkQl8GSciMEUNOsbDm6m6cRYm3MrA8TfoC6iu8VAUBQGRMXEfIIcAWNlQhkRUYoCgKATDiLctJKrPHQFAUBQFAUBQFAUAKVW9Jy+edNWvLWy3GpyB8WaOElkJA+PNElIaE6NZfGIkMzkMDlR+LaMTs2AMCUhmHcAttHR7d9w+ZPsP3r5pvmUs0v4SCJMuCygYyHTVfODWsOlFs3F6nWNnWI29S4kQ6SvFEigKAL1qlAqgKB0h4qy6ZiyYJZNvHYNwb+NJGyomRGUiywsyCozIdoDKQ0/QoTAsfnQ4D9ePNPnvs4pBfWUSj4JAFg2UYgkZkMu9GfClKk0XaYcoQQfgEJcEiEvgyTkRoaUnkGn6UwRschMoxQFA6AoFQFAXCLBtIm24XY0qBnEFiBQVYKyCLgKAsQeYeyC6AzBZkLg6AoCgKAoC4Q3ONJJmb7stuMLrSMbfatPsCeZCQPjzRxSGBOjGXxiJAw5A8Sw0azZHZjyMGUloHbA7AMVl6y9T8H++Xd/k3d5nFxopHlDHI1KMrFmipjNjPY2c0OvLWkayalwpNFKyS7QOgANnRqBVAUBSHzkR0/Wua8s1PnzwBF4hipYsls9DlgJdMomQ4bmNHkgIF/82Pmf7L8RYv08Th21zFKbUkEQCDBsFI8J9MhGXoz8UpUmiqW3aWyC64hLgkQl8GVZCoAiZxpV9dOtTbHLoLsDoCgKAoCgEBDFHXu5ShbLGsFZXWEzM66t6eIu1AN+n2DIoJLBnaRKVIFWspiT9l1ms2c1qtoR9HKBa4QjVedoCgKAoCgKCWptMZFf37Y8Vk8yuYoNmy+xJ1kJAqPMGlJak6NZfGIkDTkDRLDxrNgdmNIxBTmYfsCoCwNq2Xp11TxT7++hvj2vJik2SGLOkmWjSg9zWURbMxWs1ra1aN5VKyI1kHZt2iFAEm3FSVQFAqQDENZavdgNmy8ljE1FIMoXOg5JSZr7QNNq848h0hev2tL8zXmr7S8b4n0dIhNSaOyRQGb5PgyZEYqxCfTIRl6M/FKVJokaXyUPzqyEmCQURBLTCSrSzGu50o98TkPOCkWN6wXQFLQsyoDOqubpvO+Y7Dl3Nd7y/n21wutJVEzfr83BSU3JVpLfX8zAbJqL7gA7ijVYyspCZKzZmbWY0u/IHQ+i831TqSm6L0jD35uJe4mmgqgQg2Gsmv7AseZ7blZBQgyYkMSdZh8NHmjiktydGsPjEyB5yBYliI1mOOzFkBZWzQP2BUBQFAD3U+lPQvFH0Q+ivjkcUBa27rIrEmCLGqus9iY7X6+tqhvdVarjVIFEKAoAS7EUB0BQFgDMSSfrF6/s23il06+SirFyq+4ByutwkuNm2uJ0um+Ybzv9suWcV6BKFNuDoCgKAoB0GDIjl2IRKZCLPxiBUhVLZRSXyUPTqyEmDgAQMxkgc3sdN1fU6EosOyfoPXWNHWGG4q1PEeG7XgWlHc+Ll11S1WAoCgKAoCgKATkw+u3OebMqNL5+yrShZ0I1AUDsCoCgKAoAfEHUELe9z5Tv3YNF2vdtZtBsmMNfj9H2HP8AoyVgaNLaO+xJ5E3x8acyKQKJ0Uw+LKSzE0OUgTHtWQnCCA8WzI5lgKAoCgKAZnE9Q+m+AvoQ9D/J0a6xRGWjSw9zV0stlYfXa4tahG7q1H2rJOyDoCgCtXKcEpVIoCmzKNvGIsiJy2rgrJU+WBc1q5EWhygk5uNVGj+ZDzb9nePcj6PfCTROUBQFAUBQDgMmiimWIZKRGLORngqShRzDsd6pp4Iaj8XMyYGWa08hp66NbmLIXqKbg+F7/jXCdhwLStxhV5cdicAixqrvIsYuAqAuSlEmqlSwO4VUuF0m9zXcdRteBpdxml5qEogYkTRLWTgoxYC5hQo+viop9F1NEjdULovc2d7D3LnO1duZ+Fv/AF+DcSmR6VxcRYgH4oHgqHWIMWS25Wr+JZPoT7qJq7xrGPhwOVraQaTqkAugKAoDEBAJ9heu/Nj3P718vrBotCmipqVkCcUV9BrGpaTqVRRZMy6bfozowNsaRQKoCg7ZKysdw/WuxGe2i/HpRlY0kVKiXBGYdjDFP/NT5l+zfE2K9aE2peBHiBQFAXB2jm1fgwaVRQWxxMbKiBqrgblS0OtfIRKnnp2o5kHtvrn7jasp8xdv8ajz1vt3wXrOQ+f9jwfRV9zJo86pFdtJjEnMWOPW3MLomOhJIE4+MPokpdKnD5uEvUiVafC3FI4vv6m6Tw85d1EQ3Mk0k3CWkhlk6YxwmbiUAxkcVmdbk0hwL2wXiEt68fR55j7n5jdzx3P99RQ2dEAuuJAnRIuZJgWBNweJKVRMKExOY9jtGPo98wbDZ9Z2HfNfv+gaTdSNN1Sjsa7migdjR7s9m+UfrJ3T50l4U0FbVaTw2jitdrW0q291Tun4tiFNPUBQD+OaoVQFAXZUUjOGYTkZmpQmHYApBMXPK4QSq7L5suBfXzze5h7OMLsUDFlmxXVaHdzXPOh5nz7P55qe847pl3PQB6c7jpeJcsDUEzMKsAmI2JM5gMwY9tMvZe9uPPHU/In0nzPn62z2EdhJYwJzFLklkZ/BMAYWxNMOk0ySapJRUx9GeeRTVNTgPOUuWNalsiTz/P8A0/nuqcbPa5GEmzJNnWmjjbczaAhj0NjIgBTYZuNjnIGSskxPSZiQ4S+SYc9F+MdA03s4PPmopXb61lOqk85iHkkZg1A5cgiZNgbUEzkssHa5kdZJjyMHPXnmLXoyB0jqqg6V1jnOx78R0BSNIuEOGG/pG9N/En0M23lULJrMJY2Tk9frq1qG1xWOVwaSeCJNAUAgGVAqgLgWJwtHdNwXYjYKScazAKMODJjdOPeH3nD6ueOXM/fZCqsw02n5puubctWXKeap2Dj1jSAZzUoGJFqcjsWyXKWqa1iNyDuE0E4hxEEmAiYoktQGoR7Ueb914g+pueNmK1Jtpkox4CBHt2Rh9aRugHUOnFSykSUUNx87JVJ96lbhMt40FTChqvYuL3Pl3rXL9o4+LcWbJBJoGEQGASi8yxXXM1wgaiYrhx9yCwfo1JFG0Rpy8CcREve+bvc2IuuLwORNchx2bjpslgaocXOTYNJAkAEwEVJHiKKVBayqWeSKzXZ35mtsijUp+UrYEPR9bV/Wew6LrOyoO1kszI/Uj6W+FO+Nlwu7hbNy+r1haVqN/VOJEKzZ4ok0BQEgqwoHqC7kqxyCTBmYb0RnIRfiZKQUhyx70LyX5J9BPAThX1i1bOy/Ktnx7nWzxYyXASVEGHFFSGplJzbQV0CY0jsSlzSuELgKAXB3AwATASBNzbwAbA2pFuagt+etLmmrkZqUceTLZZHl1MqkU8Qh7Qyw+XbtSyXXjRvm3nrzjg5K6HHEcWKNcBO0U21vkPrzCd15IY6nnKdojQIJG20ciorYYKhNwkKZjHK8A5DFmuVzOYa+TtD0Cc/ckmBYP0Ov4xPhKfB52S3LKljComZEaynLkuxE2CcTNiUJmcZo9Daya/Zj2d1TF3EhbeLpdzJSyJysCX0JTdD7Eh9O7PnZz6pfU/wbnM7KT7N7HW9rUoXNWu5BpIxTJoCgKKOoFUDpB2bkF47hyBIhFkaj1di5IKwj4H536X+UziX2D5DfyGslY5N8rySxEZk6yIegSqRi3Cy1Q3siMNDiO8qS1SJUKzBWB2B3CUQGDyUJ0NE0tgEg21NxguOxVWMTaGrQzch9HjBaNq+sKPxzxPFo8l07IeIkPGV4m2ocjMKuoLhdEqrJhvtPDnYOE9I8jQOvZutORMSC00Npxq5AjYyGUJTQsGydrwrkMKoF5uNHFGHxtaRakmg4Shynr0ggHn8d55EUvJNUzzFnY31w7mCuAiFXImpxcRFYEkciO6mU+2HspqRnem6yxJBb2S8qxLxiS8UP7tRrPb7ccb+mvu/xu2TndJq60rEr6qcSYVmzxRJoCgDlUtQKoFkZWN4sy4VhuxWYahxMHVcm886h8+/CPoHvjrXDtvyqDKQxEmZvNFVuvM6g7hx9QdSN2OW2OrHaEidJcmecZ0hIVs6wrJOiufhucvVkIj2rQlwkTdeMWolo7PwmEdDKQYl+uQJeSmGpsjiSPejjjper52L5iz/SC6phkWbtLqikrtO3bcdhGUlx6UrGE6nIjF3Ihuzh6u3nz89B8P6147r+wR2BYZMuNZsfCVGax4iAQ5BrHIYElg7CYOxRCmI7Kg7id53qFG7LVliaU8SOxfBxyJGDbjxslyDwzpiYUks7Mn1k0KI+jkmDHCTHSc1+xZxqJYNpEREHIzotpnQ6WPbSxwvULQ8P9EtPxHeLmEmBxtZo0PP1Jut4c1H0I+kPHMDt6dC3qFVxLkLJeUBWMIV0nN1V1s0tjE1FGXTUF+FSjdrbRKT5AcB9L+uncfNJCRAMIfD2CMGJBMKHLR4jcu9ZeamE9B7mv+e9p2fMektFz/pbQZfc50Tp+oeTq8q2obIVRtEo70eJ0DEsdLQdVy3S7rlKr6pxPnOi+ftX1fjis6bGWmhjkEkQDrj9RrxHL8mxxrNJI2b8oc46/F6il4ns265X1tc4Xpyw55ve3xe25OQ3peZuQ2ddLYhNK+P+dbnujfd5Oquj9Pi4+lYWPb6QY0mo6+10nS9W5cqr7lKn6ZyJmeo8r1/Q4OiyHHCCyKoI41v57knPjvSSdVPLKmLuOETdlz0Try4xeVrwXaN9zf0xvMH7QWOB2rb5c65XSBgCbityCiESUGW4GizodFseZK/oXN2f3HLFB0/QkPSa2wu++mv0J8+N8afmZeNPDyItOILQZ4uZA4U5t1zQ3Oupei/auK7M1uFRfjKtN5soWJNAEKKyVCrGKNNm3SqTMtnE3jqTExkEWYUOcdbtqLtLFz4eTT72GbR4eaPLvQXohvuBSaTTu1PsZCmqnDcWYIkQrqMi0+wlRsEEQhqYyEOCNRC2rrb5AHuru2NP0O48sM32ryJz3pXynxvbY0Vn2nccs83Ymr7YmUvsLouBeoem4t35qOH7pcrmC6OwddlPDzW1UuOUJFSW3jaxsOf+dJm9R98tjW731mPu2ojGkB5LdAGWyamWBvmIkqCkOIqvq3nJiPQPlhQdx86sv2NuzVbytMdzzR9RlUym9dNh569T9H578CdFxD5kLrM6RAlBkcA6Vbf+q1tn6zVwp846Hsq9VEl5DksXYNwCCSYGWZyCDT7R0eR/Ge9epvXOCG5zBJqQKcZuaSrVoKeqbJXsDOaOL2tYMuqlvIi4AZNuXAoDOlsVAqgKAoAwgGEnEnCWMN5BGGViniSDJdiWMmx1kOO40dqs1kvZCMxEQi3LZyA3CjzMkM9BTDhJt8ZMYxSRiGoE8h6S7JDRaC6Fi1oTNJFEtoqOmuDFqfc+I+N9M8z4DsXsJ1/yd37r+LvBEYGbdSSkFY9ytSDpxEoNaFkwZFoCXg7hmNjSvztqDT/fBMr9xbDGKG+XrZgqQmgCrYGOCwB+OuP2yMmopeuls3qPR1P0zx2wfqTz351270O6jwf2o23ljYlpk/Aai02oodnLUKIgMQUMBzMEDCY9Kqfp7s6+PW9a6jyHTAGvigHxExM8w6SjstZBumn25R2jxk2VMHSRMi7FsIfp7EvaOX0Wt7euaX1Nm9Ds2eKJNAUBIs/PVB0BQFgCgBdhcWkITWaLoLRwLknghJmOsZLRRB4hTRaHbMzByI1Ww8bktXItgovHnhZLGASTZUNloslBWKkY+hwmVmhlm6gg3LHOQ8gogzJYPREg6Xjvc8UOjVau+M6j1zxXnfSU0i1ffO68bds7jyYQjvMXGEydNsrDTombLpBtYyWw9YkMIMj86mh0f31Tq/c2mxizrz+uliLMrkCsQxjpYAGYqg9inMAjASKkRnSJD+BO83ud9c9Guo8bbmNPtrkue1e1oMyGmoGBIAJgA8AHmXnvMru+rTNMbOOoDeMmxeK5G4JLBYWRIJRiGS2XSSaLGJAhHkD3m6UgxDtw8uloObGzGpgttTs7qnTfYyQ5Zs6AoB3TTVAqgKAoAsQNMriMhCKxg6RVCha04AGmVCXk0A+Qpg4l6gYmbU23yDZuBIEZZWGeKgZNoxkxOKUl4aRjyFyUqg2biCCDHrTZMleLY4TKpMNmmleX2C9x/K7wj7z88UvYXzDrhFCKk6H0n2nin6vO/fn/AN4aXiiC3DMOWCmlklwiyoNLZIMGxhSfzsc/q/vnnVm4dPjVQ6WhSwVoWIBiGYlwrAG4xh7ElACNeBDiFifJNkFkRSKnEULHoQOzPQJzCE6UqQkcWA18AKAlykwifXS6+zEdksrEZBkDnhcB6RDjNQOvmWBzynxEyMWBkmgMdCRkaYtw79NmlzZ+Y02vbepY3dRjIh3aXZD1AUBJs/PUCqAoCgCJAs0qMvJsoWeU8QQ90WbSUjOjJqKBOm1NXCeRCSdNF4OUBqskjIowsU+i4BJlQ6SmxIJxiGvIcJlqIYaPNcXYj1t86vIPsH58ZL1jGY07rK8496/dB+avU19yb5DvO36CRTV44eQ3OMzC3pmsTHUVtxT7XfWv5gdmWXOzMKWLlsUHn8cxkph41IFxHvzpaLT/AH2zK7dN/mVFslo0oZOTcBwwBzgoAtHUMnJzIEq4xUgslPkI4Gyo7hCsTJqTXn/lNx3LgN8CD0GaflTapICiLigb6OZeics9HNDmAcllwhTxo2joyIO0pZKNVLzqOTKU28STNQsDftgc6SJkbrrYTNqMzXsih1MDtqVo5UXOHms8XJF3CoBxUWCoVQFAWAJgF2XIu+3io8XjJxiGSjsSCsJ0ZLTZRO0KaOE9jhIibyTdoNq4SBpKx1BX0UASaMY8WIBdhQp9LhJqtDxW5T9N/lr4B91QsazVQSjjjI5e/WOR6Ht+pU3ZptwmwJ8SETQobzQpT1NH7vdi+VP06d7+GTyJMF2DCjTpOOBE9p9HkDob/wCdPQaT76psHcmhzmTpF4b4iaxiATiEOknZAMMqA2iMmjKRTEPkoT5CLHFS47pErE0oIX8OnMun+pHm31H477zmvr3z3fer+H33z+dh5RyBZ5z6Dubdl8bPYnh77UNVlBMlq4D5kMXhYB4khyjUS+ViJEzWnZE3ASMEowGunSxIay1Dz6iyl7NoNTr+1pR509zh5mdlyLqKgDcCxVCqAoCwD4gXZciz7eKjxfMpCIZKPFZFI6hkgsiD9Bj3SeRgkRN5Ju2ybungAajqYuJzIO2jEySogRZAp8n7Z8V899NfEP5v/TiKh3WLjdzfQabWQj0XleVfOtPrKSOZ6OC7chkcU+lS1RUtCfInG21I5n9+Xsn8lxUgKmQ8iUUjSxEth8zMFRB+dHQ6L775lfu/Q5DNxwhCmiZTNnAWjqHPldBFIbgSwRkASYMTIJSO8RbijZTCiJmKkwvB9A8RfM/pyM47qmm02KEuo9Ycxc+RW3zU7bs+h69pHRYn3J9d+KLaihVAIMAe+MiBFsDlhRp4lDdGWMR4kIGEjBOMB4TjIUVrrMZNqcw5sKi1UHtaRiqpxVCyUain7ulQDqssVQqgKAsA9ALsrjD6MVHi8ZOI8NfYxUorHdGSmbtk+Spg8Ttl5NLKEgPY5NnzxAMRTFSyuA9YMQ+SgBFoDXQ8Sfyc+Zv0K+MXP/o2kzAVcCbthdpmkD6Oth8ivBLI/T1BVxUSuidjavrrnG59x5hgWc63qfEeoHKmko5/oTe1PyE7FlY4LPbugzMexFSoj6NKExE/nT0Wj++6bXbr0OZxefKQHR0qPitb5hY2S1Tay0JQawayMyMRwZKbzYdIoji5cZZqwsE6u5f1fz98a+6d4XdByhcZ2cZjcT2BImttjOU9Sx0XjtlrBA9VfdPzqR12acNh6wB0kYmZJp0Y6zmh0tBSHskPm1oA0HGysZ0abeD5Gq2WKnwcTXsqh1UBtKVgKnFbFLkOTds4dAEa11UlUBQFGCpEYjriklGCjxfUViOC32rGZRhYuUi6EvEKZPB6yvBLbZ8yDKRzx2AJRwOkEqRv2jGPFcG/aJg6ahSfgJ8gfrW0tn+iXClnVYOFichwwX0pdA+OPnTmvWfntQ+oOuz4jzb2PnHbnrj85mkDyc/flxng31U4T8qfe05Kpf0F/ZH5DZs/mxU1FMOPydHSW30aWJjD86ai0P33TK7d1/mFXHSUKSOksWWZFhY6S3SFFIag89uwD9hbF9OLbj9mKOlMum52JsD4krm3zP620jxTu+UYaYamkjXsIoxUNub2o6c9I+T95dh4wMs4yqCdRDHvld1T9tY51tVt0jCSJsUvkG1M8FJKRlizbu+DVdIGTomQXsHP6qA3FQ0OlsbFlPODcxeOgF66QqFUBQFAEgRhhyKSEUo8H1E4bgt9mxKKx1i5jVJDxJs3CdtPYE03eJ8yGTwsAUjmKklmQItKGvJpAINmOeJzEm/nq+Pv1/wyJfuQlQPMgzmJCjTHvnpvlFJLDPeA+F+q/wBQm8+K3lX7S+funbHivplXnz04XLYT055C+z/ZSYXvp6K+QjtCBjyaSCKTGuk/ZUMYmfnQUWn+/ObXbt0GNp2QSgyBspOIBOMoZNZpBmIag89qgZGI6wWM3X14cMbIS5W5Zthqpp5Hs/P/AMF/RNlVXkRDrUnXxplJI2Ru+Zddey/GxcqoVZ1tGRGITF0WeMiy4MdbUbfKwVhrKK/bU1UaSkFI7g00LPEWr5YmbBpTmwaLUQSxqWZU6wj0p2g9QFAPGpCoOgKAsA/Iy7K4w+3isZOrIRHBr7OJrJxZA2XGpCXjJtJQdsz0hCQeS+jEOeGazMwjDS03WbtoNHRdkn7RjpYdJd+IHyf+o/hfJ+krvOPDgjBauxCyatffbYfJqfoy/hjkvpj9VnWPhp4S+y/nJuKNGwgJ6YWfk6SXvlL7mfRxr/GPp91LwgagvCpIu2CDJDpJvWSCRpf50FPqfv3mV26NBjc3JBOFJGSW7KMjHNjIRgai0VYuUxigycZ4a62oyl0SR0hLtC03jbutv4jnKnl317pLi3dQwkQ4HsgEtKb6q9PeP9yde4wSiSxdzRorS8YNi8SoUTiPjX4GQmk4D4izgv2XGyFpSGykR4W6hR1Jeusw8+mpS5pU6YDKqRpVF0s2DuIeTAoB4H1QdAUBYKfEom25FHizNld1T2Kti83daiUVQ2VHonnsY2cpDph7A2mT5EY7oh1lRwzkIwklNLN00TV46QshHSOlk5I/nx439f8A5qeCfaN29MlYyM3k0nT1lxHsS08/dOXvF/NXn/sDcU7EdBv8u4k9nfM7nbU+WPQuAnlU1b4zWv8AWv5+/RntPvnlfc/ZuAyuQyOlNUgEkNj5CXraw8OT+c7U6f79pVdufQYzIPlYkoVORRgtGULkFitJWM4MkoxaBaOoU8nNl0gy2OlMOEKTlxkHkvIM0DElcu+VPXGpuS96UMCQRvYc89R/XvjhhJqSbb8Qt6oPY15GI4xdapwE4zop1OQdKQJAizgkGjaIXi+RSE+Lfbu8gxW2YafSWWvYNHqI5MqQy6e6WMido3aAoAkmSqSqAoCg4+Ssmw5FpYpMS4N6l9quMmThJt4a5HsHHiA0U26bfoMpvJeIkjVxsSBZpYiWmgHjSmUksW3HsdLWYlZsR+Bc/Nl5i+2cpkNCaw9cMbTlBPb+TqzvGrn9aUXE+he6+T8Wj6TwiuvbnoX6x/PLDdT5i6Fy2+9YfEnrxdt/YOB17eJbyGXWmbuj6U9I+W92dW5CHksvW3mEOT+clU6L79JkDfF3jMyffx5Q+SdyBOOkW+qygQZA19GDYJNKGSU5kt9EcGusOkG2mN+X+n4B5YX/ACD6FObe5eI/HHuHnDkXaXxpGGN+9B5r3B7d+b3z39A8OeqGa9Fdh1/SejhqH0WSxUxYyfE6OM7k6TjqFSmHhGiRZhT1l5k43dSSMGeNl01nFzKt00fkVItypzSzQfoO0BQF1LVCqAoCgoklRmMuKzE2S1YGSS4OW1iSibbg5xmwU7Qpqtt2h6y2mpkSbUOcTYgUaWIlJpw3cc2yk3In7ShswO4hOHBqqt1/yvecvvP5EYb6Jt2nqmxk3zWOVgI/0TXHymudR87ma+uS05uQzOc+l+X8r+8OO+erFit5m9SfM3vfi30ChmU3jCBYyvZc+789teEh9pDctOC4cr85yn0X35Ta/etzlcnQQjTB8gqAKNAa6MDBGObB8lCD5oDH0rpN1FMStJGNJ+U7snzp8dd75B9LMJ6Y9k+e+2uQPnd9XZpieqUBabA0Z7p+QXsn0CP8WXoX5C2ktuo036QuZe+PezB+tmMiPdonaHxyzcE6QjJGSkvUE1M8FG9ZDRwrAyNbNG2VPRqm1VqI5KqBDtRmUfIlYm/QFAOEylQqgKAsbxVtwvGdik0WKJQBFMocqJiFlWXhj0awdeINotl43Ku4yyNsk0oc4mwBVlwRKRcB5BNq+3dwiDShkwOIZOWA0SSgn+KPOPo98wnAfuTAhs0ZCXS3ckwO1C86/QRuPlx8j/P/ALuNHq86JHrgr52+1nMPEzp2PzJ27whBdHiu4eRe6G2L6FcL7N9geK9ldY5G6Q6MiyPznKa++/OTF3vd5y7zD+C6wmFZYIthg4eID9g2L5ZAPGgOeJdkPIhjJCfH/Xeafla7F801Hk/Svw76ccK7Lh8m+f8A9h+lPPns1OYx5VfQf4T/AE1aLoHhp0Dxr5F9J8q2dKgE3Fe3/LfZv0zYT22/akum5I5alkulIJiZ8d82aDQbyjexSbGE5BlK6cMsKbFSp1UaeNzagQ7UZocUTKxU9QFALiOqFUBQFAEm3jMN+Kz28TFyBNpY5xFgHzSxz6LkHSFtHEOWjwdCQIg0oa6mwBdhYmUmlB7DNq7GuZEGlDJhKMhZkNkB8hTB1nQlN1b5T+AfoC8ysr7RUUlBAPV1X9Lm/wDkL8yOB+ybJ8el9X4e9hM/85txQjlqnGz1B5bezfhz3L42+5u7mbikDaPR+RdjeyPGL8AbGlfnKU9/9/D1Xvq8p7vsvYqmbycXA7QGizoE9YWzfTYw8aNo4WbJOWz0LbZX4ge1/JnW1llLIe+4Hz99ovjA7l8ne8fE31g788b/AFl8wPoN8MuiO6eYfdbnPsv4sPQnyFUkTKSmjOgM2F+muK9G/WXzP6HzqBfjHDUIyUJwXLaeAJMpQfU9aDdJJPmWgTmE6lzW5MafTx2dTiHahRL2SZWBv0BQFyiKhVAUBQBNDpaNIiktulCxB02bBacwDDaxjxYkTxCmbiXDZ04G4J60Y50lAC7CwkpFLN7ENotNwCSTEyzdxyyQGKkmG1hnUXIPo7/ilz/6OfMpxP7jayrdyiid6Y2Pi7zqzvqcdM0v179q/OsA85RBFDrd6lIcGfBvqb5Gbg8w/VnoZq7cILAh2R608Z7l65yARGl/nK0199+rtTva+o7yDIwzHyE4uGSaAd4qALx1hpCFCBBoxbxKMEUQ98hXW/mv5b7vy/RL7Gy3YPpV5R9JvjY7p8g+3PNv0Llme6Fwv6t+aXrf4C/Q159XvNObrfl9JW47f47e948MUk6AQiPd2+SPr39SRd2642OGapUWhOjZbD5JotEjIN82tkhjJ9wvXTh1pTUlcuqtNGptUHVTqpdUKTib9AUAqIqoVQFAUATQ8ZivxOc3YCiBBtQ51NECzKhb6bEHbamjqXrR3WGKiItGOdKgCzCxchGYD1hTJ1OQBJoxcgOGxkkNVkTaWJdbySCKVtlDQlL1L5beB/dLycxnvbeMTnWsGdd659f+dv1vd6+Cfnd5Q9Q8p8r7ftyPaTZJxG3wcEgavcLU1VDlAbR6TyHt32N4yFxZn5x9Pfffw5D37f5iz6iESUxkQ7AEmQLkndsiMJQ6YnIw9bMfIC7KtTtZ34kuufMvQ294Xcnvf/lvv+PQK/wi694UdInU7C9gPN/0++f/AMCfb1aY47MUTbl+FJvbHxim/qn5XUavSDw/9ke8/GX102b6a8t+7ff/ACxgh17CcZTojxEhs2EnmnrEpsuJi8ZKFYDZVZgo5rT6KOWFOKcp1EuXKfY3KAoAemIqFUBQFgCIcMNOxR9uxHQBNBjFosFFGzGOJsFPWwzcJ4hWJtNVLJoA1YsAXbcFOJVM3zQZOwcjWRbljVMrkvIlILZJtPhXRkATQY5ZYEkml/xh5d9FvmP4X9vZ25nvuM9X/l52Tb53nbl264u4n6G35YxdbcY7PmE4vM4Nu2SuyT6Q9D+ZemfRXm0ZGl/nJ0979+blZvq+zF5MshDNg+igZKOQyYdgCMZTIN5APCMdLU5YTo3n3W/G7xr7j4h9H/Onzr9y/Hn6+eS/S35Yeu/Mvl7S8lyUfW2a6v8AXNwH6u/HngMlt/n/AGry59OeE7NDMx3hnu5es3R+UxbC9d70+dv152HKiW3fN/Sz294ZQSp9DdYT2XbZoMpRkG9YDQxjIMjGsB6q+z4m1TfRudUCTplEu0LCgqgKAbCKqFUBQFAFCBllcWfRYhYAkl0apmwUSbdHOM2M3jZtVpXS6mpvAERQY9ZUAYamCHW8yIi260XFo1vkODlxXSXlSUg4w+bkingsQdNm0cKgCbShsktC57r+nqfpHcG28sOI77VcccpPPfCPSvNnmj01QKgVA6AsD7n9keI9idH5gNjS/wA5Gnvfv4VD35oszUhT+FJYSYlKcJRFjZTFkh/CWyfbyUbxEofMjMc5ZeK/iv6Pa2ym8WfrOwfc3yi3rrcx8RXoT43buzPWtf67KfYnwH6geFXQPIHn1DzHpb5E+t3jV9EPgCoxKp0exOQ9TfVxxD6L6S4x23kbyb7GTkNbY6zxrs72R4wuHXUKYxmV71p5uQSkMvGH2oaxkESh2Q56sxdOa0d/HLCnEu1CqVWOzoLoCgBDleqDoCgLAyCQYZTFJabEKAJIdGrRRtlGLAU/CWStyhTJ1p2280Wig2SbfGukmEHY8iPyItBL5Eli4midMNODna50h9UnmKwXZAh4ZGCjYZuDFBlY62CmlDcdkhkCftvMnFKGfFfB+v638zey0QKAoCgDujoO+vdXz6duxWMOw/OWqLz78112+b+nykm/hSWMmssSiMeQNmppsiUFQ6QnMw9SsfJj+cHmT1xwz539PH7eJ333vzH3L3/zPwBY805cseT7f8S/V+T+l/Fvd14Phb7x8oNfTud+6/zS/Qp4M/U7867dxFlj2JxHqT6i/Mn1M0LwP0hDqK6f2cTq70r5LW1mfcGb2DKYTILxLyDSkpMd40pqhOMkEolqLcp277s4qb6Ny6cQ5T5ErNMqg9QFADbasUB0BcCzbpFkG4hRWSqwFwZNoD1lkGy7M8O9FzJ1ZLbZYfIeaOR8TWTZdHuNYlHPR5YKYxdKSLMhg+VIcNRHmEqtWRJzJ1s40SYfFPpyCn7ambjNJcKRgyU0qUhckszbfodDOR/I9zyxqThPpTq/zJ9SsgKAoCge+u2ef+rfUfkxYODYlh+cfU3v37rg78vqSz6iMOQxfrLBRFh9hJF0GSiPC5LVwl6kakye787PE3u0rGkbV65yf0T9IeTAFgicMrbNuwOLZzRyKdSPKnxl7T+Zr6O/nj9BPHX1f8g/oz8PNe6PnGKh7Ac59X+4HzR+/G0LKhhkSd036E807J6Nzwe+agU6iutJEF8h1BBJPtu2zZpTaQojFshrlW3eOb1V/G5tOLXU0TWRP2N6gKAh+iqVAdJOkCzBlEWskiuQWZQ4yEWCzsGYCsINzI9XXEfsa1ZkOEusZDRaNIZmGshgrEWwlJogerJgGVFulBVmczlLybWciPCXGHsewdCMLblFGJYZ1GIhlkz2imruGcYZGqiuXAqkMVtasazvie/4l7BHTeuuC/SjnXzL6/VAoCgWCpPWHpnxfs7vfCJOiwGQpX5zVTofvylQd8XNJT5EYRMX02CvFWps+Y4sqn2O03mvXaTW3dDoL1Bheocc+KvaFyfm3XeZeg3pfyYCMzxCSoPSrUzYJLkST8oPD/snwy95/HMnzfsnInonxPzJ3DxvvjnPd/ZHxP8AXb0D85fQV4hyEa/m/qb7j8BKuWwt5KgWSgsjZSXgUmySMgnrYYtlhKInGeGqjNpC5xW38ZkVIlypVTGublG5cCgIxd1qqjUbNdgsTcJt30uq3tbXWYs8u7TpiDOBza26iO11kCsKpwS8kuNAySbkpJDF+KaRJGhm6ifUWk4yzneuHsJ6W5pwHp/m/Jdo1Fk9zq2s62MjSwMGzHqfiDVsjDNBLLplY+TYtEvbeuMo8j18hVmtg2eM3VosdNdNzPmDY/Pj136Bk/XLY8kkPNrLkLy96+oBCwg6m3fmHnPq3irQXb/EXZknp3uBH9wjIb/5zFVovvzm12+7WmycYgeV6Z8/vHPqK0OTwJUWvoSgXdPzvjD0AKrMT4H0E+hPlPJZmf0Tx3vAjI6e2ooOnOmcaHSAWI4YmTu5s4Al2XsO+EPnbpnzB/Tv88fcfm337J8T1SD6rhf0OeKvuSXq9HeLWi1Q9adg89evnqbzM+W+NXHyKQSiJFSkOQbZRYGT9pTFxtI2SjUge5DTDk1rtNGZtUNfqVCj3NzBcjFw6A1LrefqIO6RkgLRXT1ZopxBsNQ6XM5yo+CiO1c0FbQKM5HTXcXuatxGD1DYqbFPQrQW4w3sYpiituXKbQ+ZHPfVfz8+CfvxD8h6GPQ45KZDid/y/VW347rXWcT17oOehLrGArjDJozwlmvHSEGnzIxJeSTTcuCMa7fM68lX6ae5bb7Tx3VtwZn0XOMp1iGWdV6v+j/mD2ZN8Rp9D+cPP3UfC4bR85uaLtxtip6B9OcT6asItl+dHRab7759Ru+2zNLHnTyv3P8AH/46/QMtGaVYlevHWfCErVW+KXIfocscBCdG+0/3V+azsXYebXCluFRx8xghBsZlUaPTcdyeLb8J+k+QtSS8bo2zyPkTpfLUdmUvSPJvVfqd83vvT32x0rY7E7CK7dUUC7UP/R/kT0V9D8TflNGOlmQKQlhZMB0HWzjzYwRbkjHKRNxZZieGeh4Jd2NRabV1lUoWVM8er8SYu0VgKAjtlBUCqAuCcIeOVWjm8K209r8yuli7STlbOAWFdRlIq+1illAXbMmwyOfhFoszVcYvPTmvovyX8pfVfiXinv8AhtP0wNMx/K3W/H+lOxeKhXTvK61/yiaLimCnPQaQNiZWUXMZFoEK7uSJApOANuBglzAJTMDklFjKPOMkOeeluvvOn1W9LK9v1c9bfHDXWp4Toi/8Ly2z802ej3EdMh9Hkf6Xb3g9U/OdotL9+k+p3bpM5is/NziXvH5EfIf35PorukdByP6mPTfxi2o9kvkY8rfcvl7O9kFTD+0z3T+bPsnfecl461yrh8l8pCLzSveJ/ON1DwzpSxxGnLLKtTNVJKRG6Al1VvPoQ+U/6Mtlbg+juT99tVuZnE1Te5rqP1v4j676jz5ymWMdCxKetLFTmCDE9iKtI0E2bITJqE5ThKvnArGouk9o5DX6nu6IddVTxxvFB3acuDoDV+xxagVQFALMKkma0exauTpPW5tWXESQuTV1hGbqusDlVHcw+/pXDRv4zeiK6d5neXPfnl15N+s3PXCvZTRxznXsPmbmr0p82Njdv8KdYWWbmgcQYXgcjElMwMCNEwiAiC8/wrzQB+0gPeobTBIgNwpAiRMIAIBSbLKCG9PCVpDi30G9Q/JH1L7d9UfLr017589+F7Pm4S08MyOfwa7ivV5r1t6dQ/V/53Gb6Z96trm9tarnWah568b99/JD44/RH2Hf8B+7X19+cWxC7zd1j8/fxJ+mvSdbuvst92/mu656J5SUbfJsrDTYZJDnmvO4h8kXdfmQkiVib1GdAZxU2IZIV7ufL/8ARj4cfSX8+XffHfcvq7509/8Ao2/oeB8BqPRb1X5l2Tp84/bmhpsBRt8rCbj8qsLMzmsipaPoLwbuOz6NmapPVW8ct87ihe6sdttLW9UG0FI8nRaUKSqiFAWJhQHQFwFGlyOjvp1Amae1+UwOKkDkVfaRy2qbkqUUGhitvQ6Izes8qfLn0K82/G31EH5vuDGpt+cpGQ4c91/HTv3vHjje6I/MjiO23WJUptEnLBVgaZqbgkyGIGALzlD3zyBz2hDvWAY7EFZkFJCSiYQAQBtyPFmOkgkTdixF5Kc67n3l40+124NlxH2h9kfIRHRUPGUng8ytfDPVL1v70Me9fzxcx2D727PIbS2HPM22/Prk3vD5J/G36PJu9i/RjX+Vfop7z8npzJpPmM4B9evNfm/rUIH/ALJPeP5r+veh+SFikEG5AmXXv2ZHA0zknx+9v+YbdS6M6IqCaB0BdlPsB8/PuT59+ovmtoLq/mn2txvrD6Qea+2fGLxt7J9lPaXkNxOYLxnQVjUrMumo0uNT6wzCsBciMxlEerpEbuKaxnJ6XSRS8yVkTd68/wB9pW9o43eUxKZEp2KokXAoDXNjTrA1ALgLNuTij0Umr5umtllsWqxJ0SmJp41Y5izTgLnPU/GfyN9BeA/IH08l0Hf736LznaWuyvAfnT0NwttOJdj/AEs+EPpRLg+XTi/GQD3oUz3MpvIGiBgAmAyAQAwAUMuDyc+N43PTMnpcovorTX7VDGAebAkgEwEAKQixCgtqDaNq8lKbUyHx39j9ycw7V1X7T+QXs72LzD53ONalsPPf0m22x+DnEd1+8LSc/nfQ+cNjh8McH+hnygeL/wBIGIPAH9NXoT4ybBdy/wApXm37XOoS85MH7CffX5kup+q+P1kTSTFjH36kwxMTfa+LPqvyw58nZKjPcZZjaQolTTQF0o678jfXqJ33LubvSfzO+qLm/wBj5bkOqedvjn1h6ne6PFA1kSKPZxW4q7QnpOzaRW0pTtbZhJtWyejyeDbxa7pU0JltJo4he5nFCukObdL0HoaKK6HMEZ1dmoZofojoDNic5I1AKAVaXNKC8l1U/pPYZhSSm0ZN8F13hLh3pLhnyn7k4a80+19y9Ib3B0LkB/TQkWLVVJxGlmcleXO98I9Y807K+jvyG6w13Ovn+TN9wQz66kygaaAsBYCgKMU4LEjnMF+emF+lRPbnWPqvTE3iGbAYgZAlAEQeIGIJqDbg4bHXyt4/+lU68/8Aunaeo55yjS9Z5c7p4C9/vXfzqeSonntJgeLdbefeVfYKa9B54mI2p+benflh8c/oCDlJcsrbhnOQinn8FA0Kv6wfbP5zZxr+OLtPlmLONzKM0xPstXzRbnwj5F6vzhiD34WI6UPI3BUBkBuDh3v3YfBPYukfc3xg+mbmH2m0Ty/pmiuO9j799V+VmwdkVXOjdpTrMvHmLGM2dQerbULKrGL0OUU+ijNxQ4IRKqjTRG6pcA50Xz/c6K1WVjN7nHUhqiGSDojoCIXtRmDoCgMEOS3O6LYlXL0FtMjwT4e+mPCnj36U6Q89d+H1c7FRZuKkeuhGrCuI2FeuBiqE8tYouotNTcp6VrXRY7za9g+Ata7bl8q03PuhtNkXNnjXN3n5JNgzybSy1ceVIKbhc8sJZgK/PwJz0fN3rwx9WhNTc0LgJAXIliCwJ4BQGYFKPQcUuHvGv304h4n9G3Jia2uCM0/hzYNlivRn3v8AJX1J2/NvjeEz6K7OBMNPybbNxk/Ut1UZ0GaQS7NKnTQu3p04kWcQtvErrKNCbmdLZRGdCeNSJBHkxOdSnWbRiit5k0vMvl/1nzn2kvNz46dU4tAUBkCl/Pu5dU+F/u51/wC6/mZ3I36V5E85egpTNk9E+hfOWBNy6ktofa59Vm7ktaxEtFVymot41Pht5dVL6HSQ+/oEw1OsrsIBq8nZL/UvKupc67PnUa0FC5kS8ELuzHsCoCe0evUB0BQGKXJRXarYFRY6K1GD8RPnr94uRuN+1MmI6yUZKGBHRigKZK5KxIsQKAqEdNpRiItFaUQrFlVR2MJDwh9uJSK+FO0sXn1EFnU+uL3Pxh3NxifVgLCteT88JnIav0OK60lbVY63oXxNtpVW+eqVZdS0uKdyogMmFpvF+h+P896zjMqt0Zp/PvOu48Z/S54i+nXpBxL0zuf218wPKP0z4v8ASHn/AE7Svmj2DF+Cen/V7U5noHuHmHp/rnm3rPsPLZr0nzg9sY8rg6mOWVK1XEk8CzjNjWLxnTqLON2tVrg6Lya2vgnhC88xspFUmbeQFAXCcm0kYmg6lznbN7/ND9Dfs97a8lRPR5fzv8k+uu3Oy8cnXU+SpKalNRdxu0rlWpkhgrjFxCP11nH7KsxcYlFPdRO6ocAcuzWhiWky2BK6D5v0rQen5sFu6Um/a5olZNCiFAae1nPFAKAoBNhzY2S2e2K+45L6PgNKcZ9T+Jvxk/Sm0rl5GeUxK7sfJZUBVgLSjUI1VjOULKFTGLLjrpTgo8XTyfKljKWaqjxNN31ZmmijZGlCSiNSaadTlRhU3je4d2NCsep6iKPebWIlKo8GwkCzdGDgxM4NntP5E6LY6h3Y9GM9m+/8VmJDDq9manIhn4ng/wC6viF9I3lv6SBOY942RZ5pd+NpTLdB0/n9vCK++I2NVvbpXnjoLovn/bHe/LW5+m+eem+gcji+s5w4jXcoi2PMGi4f4b3/AMzI49W5hzMPUBSSulWujutMs2/a1F6K+iON7P4Q+fvvrvj1T5cw6dyfzr+eH0Y9LPa3iYDuOb2Umc12hgd3SOmbmX06oLpqaVUV9E5VLSq6bUevgN/jrtvz7J62B63EWbndgcp6/wAx7LnEJ0NEQkzKbXTMegKAkiLBQHQKgMEO7Cyu02bU3PNnR8Fr7D9E+V34x/qInPHelUE0EjJChlpKTcPKI9ig8DZWTHcyUrQicMx3QTS1LBh4hp00TpIUOKspOa1U+Wde26SjAJu6Wutng/Bz6D/J7YNpQ9F2Ntp16B1J5r9aelnj33ThbDN5dgVBN3Ri4a4EYh2HHVzptyWEHpSppWUBtFZxm4iDbyh83Ppv8A/fPxd9YNC+f/WUxsKDZ17hS1iwOcso1CfbSJj7QpcLjDnSDv3LWcxt3oPnzpPf+UuxO1+TPJv0n8L+WtN5fqS9dTuBPbCa1WxqvtPkrP5tHJL+LM30yyPbvo9h+1eOvIPreesvyLe4bkHzr6J9VfefhCN6DLtFvzOhuodb0bxq6ldSINo6aS1FuAdpWio8wpddELzGZE9N89rYJoMVZE3rfmHYOYtXzeBaCiJTZubjigYoCgIhe46jPADAwhHe29k9htqmtOV+p888pPm7+h75bOBfT/0P88Wy8myTBWCFkNtNs+EuULqanNPUFcTKzM0yNuTzcKdmlwVdiqI2kyWQktAyQAXaCxjEOJhKoD02dduU2wjsUFHryU1JW62WQie0srNuW2CUFKyW2sqLjElU0rB6Q0NDpiBTSbBKpGCksJWee4q+t/5yvcX51/YjmXz17DWhHkgs3E3VWKhrNC84TajkYheNS2+YmVjRSSxprriiqmXqiTTtPXfydz+hf59JF2PyV7NY36Hbtuulefj3MfnEsPHappsU30szHY/oNnetebvG3r3Wnnf0+acIDpst6MfQX54xDTZVtCe2BTXsA1OdWZ0E1pkwS3zsso9PFrnPohU4o9dA9BjM0J2VkNRrjYZJFD/XvKeucodS53FZlEQrZub7iz7NGKAlNPNUB0BQNsktp5To2xYdrz/rc7xP4s+v/wArvKfoPzjyX0/vfH9hxbKmzyJe5Z/Iucne+2UckaojdPWqmagbUClASor8ji5oaqucvFNY0uTbyNShGuCskZqGQqIwqpmUPWUZXDeJxXJLVMYpFzJ+bbxtq8Jp4w3cJVfimnIqhRHAGAXgZxqwrkLGp5B+of5t/oO8wfQptzLufJWG9Ec4Y7v70oisdizEfBg3k4LPKXMJiO4YCwYfhL9ip6JlcuOxKcNbU/XP2O/I36CbmZue/wBl512XH/mq0nia4TYTPTHI9r+jhr2Vyt5K9a6b88ekjslILR5H0497fP8AilxRIBueU2ig9/l12byYVIhN3TSinvo5Pq2rjUupdZDbzH4pc2DndjAtHiLt2HU/N+jc0dI5pF5dITq7ZN+eo6rIzoDWut5aoFWjryjGyMt6ZHqMmoeo6JxPX3HJ+8/JhzH3r5Ccx979Wc99RYJRmDzRGlbuPQetmhvuWGgbF6mZ2D9ym0QcE4maUzKkN5BOQUoTjghYyozpQyQnIlR1zFymDqqJNzRQbXIrkzc4ywGRFkZ00pxBcuk11CiJcFkbFzguEkEl0m35XJOcPUXw+9O8l6PnVZ3vn/G+i+buNeopFAzux6zJb/k8q6y79xHSfNeo8gc+75GbKyh9rZZtTskpTOGqlp9GfJoHsh7n+DHv53vwhrDY8s093rzxwPP5b861j5AxCbCZ6NZzsH0BPepee/J3sWCeXvWh58RjS4Hv/wCinzhg+jzKSp+ys7c62v8APOWbqe0x640tXN6LSxWzxbOYie5XUwTT5NFa9lZDfa51/P2yJHavGuvcfdO5rHJlCViTUbBV5DFGVAH6K0UMUlu5JZKRvLK6iOcd90eCPnj6ScKc79c+ESej5DCdu8+9rpNFcKppoqmC5BIGlElMkWxCOcxjUmwtJndPUG4AO2aTTODzdAUBQFA6BUBQFBVCXQXFrTJymFrqAoCgKAoJoFQFAUBQVdMqgu5CgFgWQcsb+aiiFtj+u7TyDy+18/JdT+uQFV9M8oQ6U5ty3f2O88+SHqbxBq30b5Q3l0TBaqnStw+U/WHZXl727tbOd6496r38Q4tISMyWsI1kjqX018fvup9FfGvTFrDIdJ5J5N9G8ufNpp/EWJJsJno5Rdn+h6L6y17yrqulPIHtJDM66f8ArDyB1X3fgsKt8pQstgUV3BNHQqs3E6pHYHfZ2X0erh9rj28xud5nVwLRZbJ1Oych0DX2uxTdLvYXIetcm9N5bFJufJwrDGWdSGqAoCBWuKzC7krInWS3N9YzYxbiX0B+RPx79+NLeb/QY8mWUuQSGobIDeRIGy3UkE0mhtII5Xjb+Lotl1+K3Ft/KO1e68l1zxD0Hzzz/wBOaR0ev1ro5mEw6WKAskXIUsUYoJyJV0qjNjlpNA12JCwOiVmaLkKJVAUaaMqCG6l0R0SlTazSq4conryGtX6Lm/d8ryhxjC8gbIqPS4yF9Kd2YTDznPeLfkc+kHyDg/RuMe+V3U+msouULav43zdx4RVMz3Z8U/SP3U8i/T7gDt3oxlKn0R2AowW6h5H+nf2R+b3s+VQdtMO+XPaPHXy5dW+cWJoscz0Pi9d973fSrphnUnhP6ER3kvad2/TT5M7Gav4Nc41Nc3ZOc1WtbrEuGbyf003XGmzc2pdpEbfIM5sXZGV2mvNPjkVo2lkN7rXZYxvGd7t4l23jLsfKYJoKArXvXSjIo2YKgJJmdEoF5EdAMVHvDK73bWJv/EvmnvP5lfnl+pGxoUaLFYwM7kdGeaiuoAEOum6aB99+SnG3on5o83zuTWJLtUjs3nXrDsfy59mH2U9Vg5jhWOopAJ9VG7jppUdy3GIpiUlOVdHaPvEIjyimFUMqALkKAoyWJGRNKxGs3o+T5Kx05tJcOOWkIVDSxMxm1Txx1vM9rW/mXnlHkrb2T9H7A4P79HXOA+P/AOoH53ewdXzH2MCNXz6/SriLmXSQD4h4HxF+pvin6d+tHmT685PLfMsMHAxnHqDWo0Z0qkjdpzp7P5f+i766+CPxE+svg/Fp9BSpPo3nuxfQwPVLuGXPfi/3/DuWdM6o+ifzJncW1g2kom7juxaG7gNvmHLN1OaeVr/R1ExqNXGJ2PHyGp5ndVB7/KIODZuV3OttXz3Cvmdscs7fyb03lWv7bIEq6Uq2/c0KqOjGpN7gczFBugTBpHQPPeo7qyWw4p7H54+Mz43fvE1nUdOu6V1HQGJi5DJIe1lw1VU8yet/zu809E8l9JRmPUQoWwRH06GvJYHFZV/3Z5Y+6U/5T9HM65OcV+pkp44lNlzEzXWdzGceOxepTCba8Z661qAXNN2n80ylQ3c492jcMHeOSjYVdg02V21qElQmWEzlEbt+WdW6Tzlx9YeZ9kZP05IOafRXx59S/J/nD1p8fvW4pHLCF+pkiNuB9gSoueAXm+D7RBeANdovpU+c36fzGY7ck5OcAZzo+p9FzTYFRuR89PSXcvi39cvoDxT5m97+NngZ13xHTqvQit6n7waX0Cq0NdeC/pQC4x13a307+Suyqu815tcLd97Y2av9aXuWIsaDYVFP1VrMxsGh2sVtMeHmQdoYvba62uPZkzt7Bb/Uu/57VbbegnC+88Sds4/q7V5cvWTM4jyjDCxFQB+Nb5g7oJVIqO5vHDdC2jmb7i7qHIfnC+bv6ZfPrz79CoJb2cdnyBVi+NekKJeppc+znWuSvS3weifU/nB6ireizCuF2999KkrlvycGPaEDmJVhy5D0HTPmb9AmwOXej3rSKaXgTjoKbBSZqWIlnAEayBaPtMzFOlSl5AZOGRCm5ikqxaO5lmqRkyKbQrXoJspVbFiM9CwRyFz3aXQvPPEuo8nTmk9OxCn+nfzoe7fy5e4+i4zrM3vZBS/DItf7Uv5DUcqv83AXucB8+AG5Ujmbzb9uPUXzt9a00po1ZyGddP8AONh1nQXL8L319A/CH6MO1+O9vbHzp8DfpH4swi3ovRSm639AC/Tjl9rSnhH6FxXl/WuhPpX8qtg1ttrzV5PNVhs3PaPXV7mX8W6mdU/rXTZue0OsiNrkUnVbBzms11p8smQ2pkd9q7Yc3vXP9w8h71xr1vk8EuMsYrZmTbqiml1HQGt9pjszF2ys2GkRe8sR1De2Ot+Eux8F438l/VXxc+U36V5VFoz1RAetVsftG49bqhz0/l/e5Xl33r+f7ZzsTtFxr508t68+gUZz370vlnZRI+QYH6ziV4cpPaWR9P5eZfv24ZJNoYunRigKAoCzYbJonj19RnYFYhYiyM1zGAO4FwVyVtuNyzbicBza926PsarJubm2/TyXFfjX0fPRqx8+Syy8VE836+581PfOEPZX5tOkHk/ScHvlbouldN5v1d9Rmy8NdQW1HpN8fJ04PWgx5eAuOeY+2vUPyD+hJ25mCEZtVNek1ST6BNlNczuW45F9030K/OpIJFP8/wB0/wAJfPF2rwP34vpnuxa9xdJi678EfTkbxDtU9+qfxy2fVXWvLuoxckbUoL7VGwyxKputhxLvVN9iNlZbXwm7x7Ge3s7Fa3WG6yODRbfxfS9Pbfml40v0N4D6P4f7ZxjW2rwhaG4qxIzJ1c3qAmGc12ZHeMMo5YtPbrzW12nS3XIvT+YxrO9C+Iz5Nft/aUGoyUZZt8rERIaqlk8LP8N98+VderPkp7KhTZt7qOvvZZOzfhrKY+gppvZZjisOcKhXnKVZ2V8/PvP1Rz/tt3DuyKSKAoCgKcFnBdmfSV0BQFAUBQFAUBQFAuMth5M5l3fnnu3E+tOgOd9zwQqyRRo1PsshG7Gn0Zf8fhtx5t6UxvpfiLe5Hl/2X8iOmJBe3BvdMxr7c8ZPzsSc51G+fvVLHLkkarA+VMFqjM676TfmV+ihm1ZMluDJNoMftI4LKPTLDuXpXmD7bPcf57Z+pvyU3Xnn43/QPyZ9FGOsfQPH9QLtJ0d4Z+gcX5Z1vob6UfKTYUfQa90VGocnZmcvdaaLLlWLSa007XN5QzzPaaI2mQZTTn+X1OvtNlslDZuP3+rNlzm6X+2OP9x486pymG2+bLw5uZKzUhc3LBWptHz3MHQKgEWZW/ch1LfOL0vAfZ+KPXaTw680/ULzO4J9H9JUPRtYYPuUJx3XJC30uTQNnyJ6A+Lu/uqfNn39dlfLsY+0tlPDsydo51rocDq1A+QBKfoFdT44CJyN5++w3ojyT6/XB0YsBcCwFAUBQFAUBQFAUBQFAUBQFAUgYV3g/WDfgKYF6/lr/wBKsTdolYOFErXjiD2bi1H4q3DF87Ga/wB6+SvpHzFu7snyO9VHW9CGfdoHqQZebRGIWPXZ0/l2Un2sfP5rzLmeFO9MPBf6qiK9aDtKhtJy0isM53re8V9i9d5i+kjrvy8h1xjWrlD8uXbfmD5idE819/yule4th3PN5mGfPr6VMOP9ym/1F+MWyj1WuL/MJLe2tndRqvRY81Hk7Ho9VqPYYTYuc2kKuciwkN7PxnQNV7rGKxqzbnP+maf6Jy5Zi09DPPXozh7snGNZ7PDGo8hViRkpSq3aAlmX0agFA6SabUvd+O6Vs/O2vHXU+Iu31YknZdB0HWmm5/evelWP7Dxm418DPCfdfvl0byV438n7V9IUdzgmzzfdMud2fU6Ua6GLg0BMzfnRJm9JwLjxSdp/uI7h5O8ksL68kuOvIrrKMnDsnNdGCaGLKaO8jN9RppOT0d3GL6jUW3KcxpYvo88uy+brJ8duqmUUtzGrKrSfalPP9Z86/hr3OEVd+wXpDzz2r1rhaqW0Fo13Nief/J+ueZvmD1Ht2ND+gn3D4X9ce7eT/mY51veDsN0L2EddU2mM3pm9TvFtwCTnNC1ab1fH/QnHdg+Nl5z6oex+eoBxv272phtx0zNxno1a5XbJ5bc7UHnyxsIrq+bcIdK89fNtrPA+nbPBXfe9EE9Y96X/AEgouPqzwJ9FY5yTsW7fqh8ndh0t9r28xSyndk53Qaw0OZetWuxKG21nr8vP87qoZdZBq8/sbG7HX+2yjZpG0MPtdb7rA4NS+6OD934z6pyeC3+ZLNzlHk0+0uBQGs7/ABWYdu0u0d1FmT0bhuqbNzGh4b7Bw8jNpF1Dd+B6RBdHQWOXsfLabXmsynytcZ9Ab2mRfL7mHc+pc1oPRsy1SGCsiT5jx5fqnIa185A7tXbfOyQ2RquVfYf6W8bYkiZ5TVQLR05aBLeBmPTIEzq7OL21W2SmZU91ArmmpRTmg1UJvcq6bspRXvQ+0qJtRaWKWufbPNT3P6L51/Ff0x+fry37T3HOg/SV6V8ael/ffNT9yN5g8N9H/MN5d9pa4qrv329NeKfp89s/PvJDXg/ierfJvxbsv2pSZHkZcQehmxxlAc6SCpTKrtVNSunmbH5UHC+6P014p75iv95cN9OjUPwCJPAwLXUW15XFNpyznrq/nriVznnhZC4g1Zg05I9AHeo+3N92rOXXxr55/T0dwnt0o+vvxf2bmd3rm958mtzbea12qNXiX1PP2RH1GqNNiNl5baQW8yQ2XE2vid/rLeYtsxH2/gOl6i6Hyu0W79FfOHpLg/tXFIBtcAUaUoy3kErEKA2hld9mHKNdycTbl7qxXTNkZq24463xKYwLnZtFpnzlhevfbRGqWKZGZyhq5Hhzldf8ivP+xfYLFkiIr3AE5PO1vWdvR5vcrQ5pKX8tCW/t46dw/wBS9HhLJUsaEyOwFwVA7gODS2QVlKUMJkMjFAWIOVliAm6azZJkKeNnWTPmM8ke7/ATz97DtVWu8FrIGvnFmuF2NZ71ehPIP1M+xfCzo2aBw2LP/PN5R6D9C6Cd9Ekg+B5cLz2lwDSZXq7Cueh4ifkGUnsjV4/7f+jcMRkRMUruH82JKDTMdtq4/FnR2dn+fXMV5uMc4gfGvQkCw/Su1+6cl7563iM1M6a8KfQeM836lv76R/LrZVFcwC6xtOWO1M9pNWanJrVdvPayy1to89PqDVw22xzSejY2P3GuttjaZa2Zi+ha023NbM23cnDO+cYdN4pDtFgyzMjJ1xR9a5uUD1drMdmBdkqYCMWX0Xiep7Kzeg4U6/wretJ0TcWM6fdyPZs7NigKAsHLiQoGvh45X2rzpzu0+l+O/wCrgkbAWNWPH4zSY3zkGPd3Uc++u/oPJm8uPiTNw1QFAUBQFAUBQFAUBQFAUBQFAUBQFAUA4ciRVKgtVZ+d/B/SHyjefPWHCdLphUpe37+i+vDqfmz169CeZHF5n5JPjEEO0075O5LS/DhyXvPsEt36EZUfquQElP8AAgl/M6wnmZ+m/Qc6Rwzp24pG8kJuCgKAoHRpUNSZFdSFWkefnzM+rWr+Xdy6z+jXyShfsr5+uZDHyQeYvoZztjNT9gnrDwv1hAlay1WCTcd27nNvqm7wr2pudmMXOpdLi9lZ3WwW8yA+ee2MTudU7zHpR2dw8/6Dp3onM8K6Z6M+dPTnBfYOLa+1GALpXkFXUpwDoCeUGwuDsRUAm1I3VjulbgoLnjzqvC9m1WznOZ3uTzNiFEdAUBQOgtKIrydwHYfmazWv8t6ybvNLk3M9aLGllo9ZVF783+Z9bOj8p2rc5yiRQRQFAUBQFAUBQFAUBQFAUBQFAUBQFAUBc4o8wxbWlW2YGHPgLM1jHXMFty6zhu7OmeSoZZCsydxS/Fayw+e7mvX/AJbMrsouSt9rMYZ6DDvSbjH0A29Z6FXuE9M9XzqdWsHB0UBQDR2FsDL2++sj126iTcO9bMh+M19xNhPSOFcX+gvJ7ttv48eRewOeqa6+xzs3k7rKulwHVYlqiy2fndfrjUYV/V3E6audZ2+Y2HntjDbjHMZkbZOR22sdnjVI6dqYfd6r33NsmLjvDhPori3rHFYXo8IVQ4olzMEsBQGtrXL5A6AogmzK3Bkeo7wz17xH1Lh8/rbnozHd5zafoCgKAoCgKADV7nhvzbvXGmdvvLtmb41kaxgcR+ihj2KIutZFN7R9V4/0xpcfcFQFAUBQFAUBQFAUBQFAUBQFAUBQFAUBQFhFuCoCgKAoCgKAoCgKAoCwcpl7g3G9K8leddb43hy/CVDnODpAQe+THtwTfdpVXoVsOZ+wnQudVJYxdFAUadcaTmukejYCdZvY60ssg3U1unK7rSmwxDhpe3slodT7HJ4tDdmG6bqDV85aOtbHor7XOsw6Cbfaud2+sNBhydHabDc0GqbzDbHzurgt1kmM1e0sTvdXbfCU65uHnu/0/wBA5natl+g/nn0Vwd1/iEQ2GHIk9i1LVadUKRQH/9oACAECAAEFAO0XXaKrtFV2iqcikC6NrbRa3FLdjHRBdYSVvMFFmWAOrHNFyPtx4tpiByOalqb6NnU0mzb2KvLiBaouvbfyq96pbSVaKJKck/L16KhSv+vg04f8i2kmmydtzJMKKAppaNpu/wBjqSpKtswkz9KQKt+nXJNSNe0tqh2u5NXIpbjocCgPUzFdF370o7Q26ov6pSFyYerDzCi6+yhC5bJLt6tsOHI5At0827IYi3EyY7iGxFXXXgIkJnRHAXmlAa9ZtrdRYARCUVyGyduutWr+90xUhSUsWsa23QU+QmBDJMX1q8adi3r0dCau61KW4FJWN2twGtz/AJ03uKlpK77HHSpa2y1DJMyMtKW1LQurOSZSj6v+rGOE+2h1rS9rYVAjqbRG6sarqtozOo0B1I1RnG/2RTrG5S4pS8kGdbmdUIZRo2hCa3sCupGrqxa6sWve3q97er3t6ve3qcvTxFyuK3RBlOsuRrpJKDc5Wq7nK2We6ulPucmjdJATY7s63E96d0lXh1Ue1Xl1NqN6fq7XZxy2Q7w6IvvTtXm7LW0m9ObVXp3bkN2Wo+8Olk3d4k3N1F895dpy7SlC3XBTR93cq4XdfZw7m/0vdVmrxdFqr3OTom5SVGVKcRcjd3BTN0W6qPPXGk+8Lo3Bx9iG+tDXcu1OkKLPu9uYqbm1shuS8+iOlfIegm8hS9U8iSwp7M58pDl6uqlrutxVSrjPVQmSiELcUptR2nca2k0XHWwLjLSpq9TGgMiuKTDza5wi3yTcAW+R5XTh5+02WOR2ApGc2qRUO6xdjM5EipL6w3bHnE20PuJqbNW5CZuD7Q91k1cpz70JN1eaR709VxvLpkOXt0OC9vazrw8bl747Sb48Si7LN697dFC8yFJZuZTNF4kAG8SatN3UhZvEjV68PhqzXR1q1i7yKu14f7Vm8yDGF0k1cLnJMtN1e2e6vVHujn7Abs7qq6ubXJKu8t1zWhtF3eIF1kUbtIr3V+vdX691f/pcIAnkFNvA6sYDpq0KnFpSmAoNLD/op7ciygCLUkf5FsH+k1cxpbYnrH0NXhP8QDopJ25B99B0WUblu/yvdBe1m3HVGpqbotmMoBgugC4LS4oLGm4KqQsKurp1ET1WjaJD0uGyl/MLXFbXyC3GTP5BvEkP5NeHQ7OnPLGprTQKO2nn9VPK3FpW0JcBJ9foBrSToUrCR1RXVApTm4KTuIbNbCK2Gm16V1FVvNBdB3aUXa4NBnKL5FVF5IvTSbRyxaUot+a49cWp7oMG2qC2/vV307JSdyQ2auKAmS44OoFjWe2DcNlISNWtPdddKZXpUdrSdpp9LYR3BHq9/atP/SvWrqdIrBAYQ7oLg8DLW/69xUaRrkCpH8lP/wAXXlGZAeVsZXqEL0BWk16V6V6V3s2u9m13s2u9m0uZMImy5IEKW4Vtz5oS5Pn6qnTlBuS+pwSZJruZAFolTURzKuejz8ro2+VKTa+4l1MflLisSJSGu9mVdJUhaUyZG0SZGt0fedlIlv7e7lJKZEjv+5eNGS/tjPPpHcyKekv7GZL3S7l408t0pMmQKMqVTzjrUqVlMGCHeT7U1U/kt2W/Mya7TC69IdplxaQhWoV6mnng3S5Lmi5L2mqlKVTf4t/mfv6VroUn19a9a9fofuT6U8pQouugtuqoOikuA/VX3oHbRQnWLfr3DXa+TMjgItnLUGSmRkUa829Lsgth981NW8XRIkKoPSdX33u4E6VoiZL1adf9x7y5ilS7pozIk9z3t10VKuBNtfkBZfl6vSJXStUiSIPcS6uD0hUdqRKDPXlVMef7hcmUaMmYKYcfE7ry6L8zRa19eK67o3IlgJlzgDNmiu/mV38yu/mV6VoK0r0paEqEwbaiH/MjKb6biUaEIFR3UhWtD1FhOkbcakhJatgQLSUoFXJTaYER1Co1Xah+I+9yJ79j7rAKRoJ30jflTx0DR1a2mnlbBIlxWxcM9tNsk5Bm025Pyp0iSpemm0U0AVLQnYBtrQUpQSFzkCn5JcIJI+n3CPxR+QOn1GuulaVpS3Qg67qP3V+JGoX6Gt6qbd20l9K/qfvQH0X6V9izOmxKtfIt/hOWnPbFPW7MhS1JQ3olKDUohUlJFR1oKlKSm6Ic1oqJqNp3240r+QtWiXVhJqShJYsoSbfsTV0bT27KE9ttTU9Ce5WhOq0I2RgPcW0jQoGiykPww2pLTaNOmilNorporporpor3GXXuUyvcpde5S6Nxlmpb760xFuBaJDyUmXINKkv6MuPBxMh/QSHyLO++iOZUoh2TI2xX3kQ+4dqe44qFEW4mP15FXR53b13iDIeFS1uLfbcUmlPuEl5zuus5RfWKZfcJ679PvyCEvuNtXLJ2LYm8ckJLT2V3Z9SprshfUUQVEkpBGxNJG0hWtJTrS1BCZEv+IcCjSTqPoD6oH8U6A/eta+9fat6q6houmlfyPoKUqt5NE6Ur1V9NaaXoWlJUiiPoDR9KICqUhIGmoCP5LQ4pNnvk61NWbkNldQ76zPbC1qeStzRt11Ki6tVxS+4mjKdpmS73XdK0MlzSK+6hxUh80+890bS66mCZDoqe+6plLr3QMhwGQ64uS689qqQ4RGeeNw7l0UZT+inFKeiSFJDcp7Tu36VLdrunq7p6u6erea3mt6q6iqKtan6hMIarbCUpP5EgBhX+brrQPpZPSPoSJDYSzbkpVbOimrkkJgw9e11NXVG5KFJIICRKQjq6BFJUkkBAnaIpwJ3RAnelO6rnerfAbyXP+5M29yrgSEqooVSEkKSNBoT/AEI++8IEqVqFr3U2f5UkaD70RWlN+iVJJKNQd4rcK3JrqCitNbk1uTTitUoSoVrpX+Kvyo0o7QhwKpt4gtO7hRGn0PrQFLH8dNPoDrRIrrBpTeQzoxx3P1Lq0XqBc6W50nEek01qKbP+f6fRpQCkqATIcQGLQtPYh1IqetK47ax0k7SJ23ulpSpSwAlop9xP3PoD/di019qVWorUVqK76XXfS676XXfS67+TrKlylpiyJQV3UjTuZOhkSiG3HwRJlV3UsCC++w338unp0lSIkyQ1G76ZU2ZJdjMypLbXeyakypLyEuydFPSgH331LL0hZ6shKu5dEnvJNdzIWXb3HtdXTlR0ofulwmz3Fb1mk/Y/Q0n7fVH3kyFpUtalmm/yUdAl4kg+n0R9tdKB1+h/rJ0pbgTSHSStQ13AlROjrxps6V/iy8pBZdK6P2oV/iv8T9E0v8FBRJBFBa01Dm3CLKs/JWot96jTlhb5px15AbfdDiXZBouyBSXpRKXZRDzkktW5b4idSRTzj5Q2/IFJmS0h+TKcdTPkmnJsrYiVKEvu5Wqpcsgrc6kZSwG3pQHcS6VIlUZUoV3Uuu6l/wBICdZeiW4fqranRKWtpCNI4SV6NEEM7Yf9r0pxIKY6R0fSpWnQSBt2pp1IAbJ3aA1IT/MAClfbQdeXJiwW73nFpiQ517n3aQ42gNtKOxBO40j7fQ/dP2/w+hcSmn1lTn0R+X3raBSNaHp9NTSdda1rdW6t1bq3Gtxr1rSlr/zNdKU6dUqJOpogEoA+ifyYeQACFAjSh9PvRApX3H0UjVSkgHamlJGgbQDFus6DJxnlKNcXWZsWchtA6yPy0FJ2lQ+0gEsWr1hfandCgJ21rSynVBTos/wjjdJbaGimgUkaOxfQN7CNiTSmjRbFdIV0hW92t7tb3q3vUHHqmre2xFu7tV6BTgrc5TW8HculKc2w1OdMKXSyvbHKwxqupK1lltayjcupCnA2hTm7c9UhTm/+dAOKq6ZPFtCr/mMm/F1W4BO0q/knbtpH5KpA/jQ+5pP2/wAKcVtQt3WidT9EfegdaCttdSg6RSVBQB0O8/RStK31vrfW+t4reaC6UgFRRrSm/wCSUbTRRrSU7fqhe2o7moIofU/ZX5D6KGhKdSUaAjWh9Fa6YnmTmPpxvI4uRNpUtBK1qDfULgcWA64tSIS1NR0rcUJLjqW21rVWrlPqd6jJWAvftZWRLQ6tNF9whQc60cEJDjgoPO0Xna6zldZyus5XpXpXp9NQDLGqYo/lqBQBNaEU1+QSTSwQmJ/b+1LUNsZQLGtSdOk2P4U//ab03elSAQuQ43EbzPkNLIn3OZdKipS224QQaH3d+6PyNI/Ch96H2+kl0BGv9CPvoa9a1o/RKtBvFKWNFuLpClGvSvSvT6en9GopZ9aJ/pFRyNQ4NuorXX6H7K/IfRVClfY/YfV1KFphPyLZKwnkETY8d1p9htQWqjoajAFpv8ZQOxj70/pqx9l/g0E920hKqLSQCVdVncUoTqEto0UhAoIST0U10U1ul1ul1ul1ul1rJ1kKf2xy4VaqpKpOilStGy9vCpWjipOkQv8AT1fp0PlMfrBrV+nw90kdcDV+pCn+mgyddZWuVX32OJkXIE+7NADen7tE0aNJ+7v3T+VI/GjQ+5+jh/jIWqm/6Efl9NaB0rUEFZQEPBQr1ps1rWta1rXrXrS3dlCQFV6GtdB/W2dElZH0R9hR+x/IUfsfsKV9v8PrIJCUE6LTuFg5EvFuax6+xbzHBk0ovkRQ6WEqkASFSeiku7QqTo8qRvaU8ApUnayHTLT1qKpBCnXOtHcWU/5xA7kUVSaBlVuk1uk/07EkzEAJjJBOxNIUQVn0Z9Xh6B37RvxpX4x/7FPhAiMnVunyOnqUDKcvjWaLd8iud5ddAJCEppJ/k19zRpP3d+6fypP40aH3o1Kc2qX/ACISB/Qj8q0/oWgGm0AHT6AkVuVW41uVW5Vb1VvVTg3UlABBAr7/APgJURSVoJQSQ39q0o/kKP2P2FK/H/D6rJVSWxp001sTVmyCdY3cWyePfYvTXti/8u3+Mj+ylCCkelP/AJMfZz8I5UqYCChX4lCi5GSoU0oaBSaUQf6DrWprVVaqoFdTVOBENa9/rSd2pCyGdwdCnDS99Q9/T9aeSvSKk9L+VSEOJjsalB1IuDxijKM+Rb1Spkie96U59zSfya+5o/dP3d/JP5Un8aND7n0okAT3E79Qf6Ufl/SpBJSgpO41qKHr9NK0rT6bhTp1Kfy/8JGm5v7N/b6H8hR+x+wpX4/4fU/f6fevQ1HkPRJGG8ki/txm1KZQV6Sw4WWtyQCakBZU3uQFb9rX8piUq0KHNq9xfYCylLazQS4AUuGglYOq61XW9Nb01vTW9NdROsxaVIiEBe9NI/IfdBG9H2cqN+NLICYx1YqToYTA0afeaiR8vzVd5RudcX9F/dVJ/JqjR+6fu7+SPypH40aH3NLcSBM/mtI0H9CPy/8AA0rStDXrXrXrXrWlL+6fv/4QJFMvuKKElND6H8hR+x+wpX2/w+p+9f4Jo/cfdMRYewnkBcMtFK0SlJ6KTqEj0eOlAglz8I3/ADja06KWnasHrR0qAbWkAOopTiDW9Nb01vTWx+tkitkitkitsrV9EoJjdXcQ5QEgA9yQ2Htw7gBXcbYgf2bXqcS9pHDvS0ep7uS1KuDFtYzDOH7tLTJQ1XUKlamh9tBqsDYPybOhNH7p+7v5I/KkfjRofc/j1iVr/mQABoK0FaCtBWn11/o0+g9a0raa2mtDW00BX2rStK0Naf8Agio4QkhaVV9vor7ij9j9hSvt/gPoft9D9h9dSK11GC5+barqpmRUJfrSRTok7kpfpXcbWQ8qWESzRbm6L16zB/iA8qgiSKLcmtj9bH62P1tTW1NbU1tTRUkGWsFEYp3bxQTqCnQNkbwjULToIg/hS/wj/wBirncYlth5Rlkq8SnxvJb9Up2mh9qX+A/Jv8jR+6futG4hGhpH40aFOK0pTWiv/CSkqpEdZS5FnmkW+6rpuyXJRZsLrYXYSstY91RJw9DIVj3RK7EVoTYtlPY89sNjugpy03RujEmtlbK0pDitf69PWmB9CNKV9xR+yvShSvt/h9D9voft/Ts0OLZ3It1WyXGuURadpV9/tSvVMY/8YnRKVOfxUdz0QAhpCdAkUoVoK2pramtq62rrautq62uEzG1bYqFbtqqTrSgohpKt4CiFhQqEFBvQmnt4TE3Fu6XNizRsryqRepYH0Ukk7VVtVQ+i/wAB+Tf5Gj9x9zR+iSNtGhTtBxRV/hQ+uw6dRJMaI5JpFmdeKMQeIiYo0yG7UwzSIzKKSEpoLIonU6CowG64L2NJVuToK0FA7aK1Gt1LZZcowIhD+PRJFTcRdKncZnM1JjKj0Dr/AEH6R6+1H1pX5Cv8FfcUr7H7fQ/b6Kr/AA/pQEEYlmb9mkwZzdyjuhe9vdSgdrG4TEpWR03acCuvECtEJcKdj1FDuuxytjlbHK1FaitRWooEazCCiL+WopJAIWnVBG5r7O/aJ+NO/hPucW0wMqy+VfnT/wCAr8B+Tf5Gj9x9zRr/AARpt9RX3NO0WdFkehBFAEktO6wbXJlOR8cUSzYLawFx4jaozDbZ/wDAjei7sT0mCf8AwtBRt0Fapdjty23bC4ipcOQwltbyioEfSP8Acg0Psv8AIfQihR+ygaANaUojStRSiNdRp/VqdcFy96BO3sugDSlHRMUDvG0p0V+Kv70T0DSk6b00Tr/Qetqesa0kVpIoqlAviUtMdt4HV+tzwre9TDUhxQhSxS4koCJGfWnspVXuX7JByDJZN7eHpRP/AICvw+xQpWm9VFaqK1CkqKh/h/gn0TQorUaJKidmraC9TNtdkLZxkMCPbGEFDSEJbJAUN1dL1AA/o3JorGm5VbzW81F0Uq7nVMYDaSkVvRW9Fb0UVp06hpJ1H9CgCkpSSWm1JdtcRwTceWqn4khgycos9sciSo0qPsOnaurIhvaqiOprsHNEwXDRgOijb36FvkGnIcklyJIAeYfbJUU0XBXVTTatwH9RSFDBMvmB9th5bSochVMx3kzO2mUY84UWSHGY6dEsOaFmRSm51dC4mhFuZrtLnXTFdMV0xXTFaJBl7dkbTd/l6JaooUA0dFbVkFCgIZPSkzWojGW5o9fnt6KP3/pcfUlSQ+sNw5bhRaJAQLOokWd4KNqWB7Qs0LUs0LYtNe2L0FtUaFvUC7DKaTFWTkOV27G7hbHHp8dvGnHkxLI1CLDKUFzcaDatdiqSCkfTeK3iuomg4k04lRpCVfRR2hDoIiHcq8EbGF/xcVrW1dbF0remkrIoLOiXtEodBO9Nb00CDRBI6ZrYa2KraqpDAca5KwuHY5WPWmGmyoiIRTcZGhYa0DDQroNUWWzXRb0DDVdBqlR2VDtGBSbdEUhyw2ldfrlsFSsWhrJx4xjLt6wHOs2rc4RvFBQP1CimuK8yW64tR0YVpMac9VODatRL0RRIbQnQJNH0r1r1r1rovV0Xq6L1dF6iy5rJaWEsJVrsWaSy7opp7RptZWGXgFNPbWiIsLN8qcvqkkJVvTWoptPULkWRUez3R5UfFphr2KKBEs9vQkiI0RKbSFrbWrVNapolIrVFbkVqk1/GtUV/CltsroRmAeVrQq4ZhjNjbxyzuLSCfuFpAUtJA+hUEnenRx4ghzWgQqvTR57YnunK7pyu5dpUha0tioagFXoHa399NadeUk907QcUs6afQkaJ/HqAV1EaNrTRWkUNCPoVAEgVzQsR7Lx5d4+Q4opWhStO3qJpTqQFSVg925Xdu13Ttd07XdOadyuu6XTb2tBaa6iaISoJS2krixlqk2yDIS/Y2Qh21y2ldq4KVohWutNociy8MzZORttBXdISs103NFa9WLSEOkdJ+lNPCum7XTdrpu13Dddw3XcN13DdCQ3Ul5C0xjtPVToCDRIFNrALfqFhsIzjL313BxDMelnYYjDz5asypCWbM3HDLKUKQdCh1wgIAP2rQGtqa2itoraitEVtRW1NbU0ECtia2itor/LRWW3hp/kJ6WiSnqK+nUNFWtD7/YKcIQhw7nCSlaiKS8E13CDRUVVprSiE11mq67Ndy3VvkIKrwf8ALD7aSqQ2okgkivWvX6I/H0rqKrqHRtwkdRQDTqRRUFFS0pp53cqsmsLOSwcWsgw63hagN5rqGuqqvX6a1pWgr/GtaCyK6h1DhJQokN05qAtw7idSr+VKhIcqRYmdrkBbZUkpNrub1nm4lf2b4hI0Uft/98WmvsKX/R2iq7RVdoquzVXZr1kx1oTGQrUsLoMrTRQo022UqLikJz/J+gwwh/emOSWIbGiGm2ihzbQWSEj+Q01QoJCnNa69dUUHRRd0rea6hrqGt5rqGupXUrqCupW80VEg47a1XNtwaFyt5rqmkK3Vp6lSQVkbSvapT2oKgQ5pole0pXqN/qtWtdJNdJNdJNW1A33Rve1JQAW9DW81vorNBw66mg4QOpXUredG3Dp1DQcUS0oAOL1I9a3kV1KW5rRGp9K9K1ouq1DijRcVXUNb9aK63muqquoquqaDg1S8E009SnPRTe4q0TWupNdTQvJCqXFSpb8Utpsd2l2Sba5cS6xC2vQp1cjAUhpZAju0phwV0V10V10V11U11k11k0t6uqomSpRTHUsLWpWiDqKT986yxNkQhSpq+kyKSqOmm1Nk+lelNj0T+VBzQLWAVqTopxzXe7W96uo9QW+a3v1vfre/W96mlrJCk6bkmtRWtBVJc0HUFb9aCvRtQqWosNKkr1bf1pboJKgSFgBbiSOpXVoLBrcmtQK3prcKtpUXJv8AZmelI++qa1Fa0sqpS3deo8aaWrTqCt5pDhADgpLmhbcFKUComuqBXVGhcVvB1GqaeWRSXHCUq1GorUVqK1TWqa1TWookUFCtwrcmt6tzLh06pNOOA0pbpUla9UE6OqAO9NLDa6Edlw4Xd2scuMaTHmta6ri019hS/wCjtzXbGu2VXauUI5FSGigND1CCQhtW4tqAzW/xrPZMCsV9zVUfjV8Ij8YQpJPEePaucXWyPT2GzG6uNkuMBlEl8FlfUL8gNUJx0EgrpctKFNqW/TdruLlKtN8BTa75Xs12I9qvhPtOQUbZeWqUh5sIVJKpDrkahMCqbeC/oRQUoVvUa3KoakJd2vXXRTOhFakVuWR667jThNblVuVTCHHS+iSzQkrbKXkLrcgVbHwl25BXRlxZa6MWWgPSg0sTkVHfC6Pqmh963+m9VNuK06itQpVNOaHXWnNdFqXubWrTQ161t9HPtoK9a9a3yAW2n3qbtNwVS7PdhXtN919ov1e0X8g2y9opxLkeu7RuS9pQmaV3pJ6pWfWm40l5UDHrhLda45hqI4wx81/thj5pPGlgQu9YC5HtXBuXXmTfikpcilJpEfUdqsV0FCukquma2GuuaQ8CA8muqK3CpCkFLRQCVJpv8nyRGw7GLtkk5iDFht7iioqjQNO6Ew1JSzJYjSW8+tVutdixOK/lGMsYRIdLXH9vSoYrj6ai2WzspehQUqZiR0UFJFEtqeS22UgMIMZaEU6lt6jChhJs1vfak4YxNXI4/YbRIweahS7NPiEq0ovsitU6b0VvOiP5OSXSsr/IutigtKqQw64I1ruEp04XeSI+BrLUXA2N/wCoQoqWrBDFQrNa69stWsm3W8IfiRG2praKgRWCpECH0zbreorttuDz2O2h8PYbZXA9x/DWXsASkP4TdE1Psl4gvKjSE1tebpLi9wWqg7oG3TS3FbXFKJbUQA6ElL7SykFdMWCZKpnBpDlQ8BiLpOHW2KY1otrRTAtehiQ0hgNJkDpEHoANdJL60RnKVb7aqlWi1O0vEMZcM7j+A44rANwXhN1QbJLVkeQ2zHIlvZYZjIQ+B1WkJrpJFKQDIQhvZyNiES645jC8nZtllDim29RRUmlEGifroa0NDUVuXSkrNKSrRDa62rNNLAJUFBsJTROtLQVVFTtAIIdB0aQotIbKqy9VwuV5bgtxGkNEgt7a7bVIZUmnmyFJRpW2gj/PS1qFtFNMo1KUnRadUMt/5GzQKTpWgAWy25U3GrdJU/gERdTcJuTFTrbOgOpWpCHW+lFHXepiyXCUqLga3ExcEhsCFj1uioXFaaXtSK0BptCivoKpbelNNajpqp1tRblp0jS0kmECkhlRa7c0saS/ofVzbRSKQyh55zHrc8JGI21xMjjZp5y4YdcLe3IYlRy07q5PHQK1qUY0WW7UHCZ0xEfA4sIRcftrB/wr1FHcSU6pDawQ4BTadzyWSQtHolsdQNii36BsCtorbTSf8soBGb45IxuRaX3J1qLSjS2Tv6K66IoMjuA2oUptW0tN9SK2nRtjUBnSiCP6NRWorUVqK1FEejZBr7Fv8qQob610qOoKSkja6R02P7Kkbw8GlJ26ICiKcWSkPqSeuqniN/0H95JG1wjRn70r8Gf7NOEAUKJHVoKGsq2Rpyr7hm234gu55jDtFlZipZYYYqSUhkKGtOf3NRRI0jkdYEEP/aMaP3eWQJKipiV6CKr1Q4ko1FOD/ivoD/m/SN6ymyAlwjRlQAUltYuFogSWl4JBUJUxtWSW/B2oMlm0wW0rkBpD8kgqlPAd49QmO13jtGa9XevUJbxLSlKEYhS//spH976D70ftGUOlTzLbyGWUMinDosHUVuAk0fsr+9Cpv8RS/wCjU1616161qaWSpLKdFKpvUqUFEDXcNdFhRqODpoaI9GfRnbTgOiAUj1p4At/b6SEhS9PoEjrhOoUNKZ9DqacG5DHozqadSFAk160of5+prXWkpKV5QiXKsNhZ9lxyTn1iiF/leE0TyvGcqy5xZLs+hxpwO/3dSa9aQAJCf5B/0EcaHaKkJGyUkdvI+zGqqbSdFJTtHrJCQQpPoEjraVpTCQH9tbRTaCDpTiNyUNADKWPbsgcbUGGgNsgHSevYtJ1H9KddWUrUqOypLmytlIb0e2GumaQ3tO00tBIioAZ2UtvcAnRO0042d6E1tNBvbKSkgFBIUB1ooJDLOo7cUWAK6NdGujW01tNbTW01tNEaBpQ1IJpvUK3Ck7d2orUUwRs1FEjRojpaillO0FOmqad0KPv9HdN30H95Om1dM/elfgz/AGqWRpqKW420BOivvfRJBXmGXsWNibmWQXKkOlKFhKj0/wCSH32KxvPbxjrFgyKHkcRABopTokjroI2uhKg2dKB9JBGyV/Yk/jH0QEugBTn8Qf8AiU6EK026/wCdqK1FMkdYEaaiku6HeaK1aRjq0WoqzIXq02AEvfjcj/JP41qK1FaitRSSN0UkuNadWtRSCOtqK1FAitRRI0jEFnUUogAH01FLP+ZqPoCO6okaK/vRRoGVo0K00pSa3Jrcmtya6bddNuum3XTbrpt0pCSGm/Utt0htsq6TdNtt67E04j0YAFBKDRQnRpCOlsTTqTTQRpsTTraFNhtFdNFPtIUrYmumig2jrhtrRTbdNNo16aacbQUMto6SmP8ALvF/iQ48jkickTcnus99F2uDTkbML0xWF8gLfOWZgxBiTZUq4PJQoV6UfsaKia9as1+lY7Ksstq8Wox2dEtI66I7JSuOyEtsoSAwyQ+w0ESUJ6MknZHBVSW2yVMN6BpKZIYj6FhjQBvqdNpVdJumW0dXoNUWGKbZSD0qLA2xG0BnpIp5tJabaQlDqBpe5TFva/3JxTQ8nYokHk3D6PJmLGkcmYmQOSsRNNcjYotcCQ0+lDaettTXSbpLSOr0W66LVJYaB6LVLZb0jtgN9FqlsNEBpsJ6LVKZQHOi3Rbb0UAJKG0KSppsBTf+ey2naW26Shs10WqSwyT20eu2j1oa0NaGtDWhpX8Q0pOp9aR+ROgQ7/IH0V+LX4o/En0ZI6VPkAN/enPRGletPKAVrWoof3voz96X6IZcaQzmHIfZCZc7hIdQ0VUtKkK9RRlaJQXVLQp4od0NaaCta/w9PopIWnizIpDM0SUONpc1ebP8XXSA05uCT/GQr+Ek6sSftHVoUeiioAFzWSCCCRoCN7f4uObajr3O0v7sabQQCp4ARfwpz+2n8VmuT1dLE8I49k5UlXDNrCMowO54xHaUhxVk4guN1ZXw60isjscvG5dgcDltaIDv0SR1voCNaWraIzien6USNPo64ErQsKB00UR3DRG1Wm1SgH2lfxLlByuqaD5Fd0a7o1qa1NamtTQJpwapaSAo/dsncRrSQApskj70wlOz7Ur8WPRnWpA1S36Gnhq3rWpp8BSy2gnpN0lCRJ0FaJppKN2gp1KS3mt9as1pfuAuC1hJU0opKkbj/hsGjMdRZLxAG5VAGh9q19AT9AAatUhUW/QW2uzS2nrIabKXGUbW20AJbb0kNI6clKehI+zO0BKQo9FulISJDaEmumigy33HSbpbbYDKEhzYiumg1HaQE7E0tI1iIR0eminWkFpDKNvIl9kYnj165SvFztNrRf71D/X77FXcsiyWVb2Wy0tGQ5beGE49e3zc5FzeVE5Mv5qwXZF1Z+iBo9WgNJbSDsRTqU7Y6AEbEUpCdAhGmxFPoSFhI1KEaKSA+22hSVNNgKbT1mm0bNiNEtNmiwikxkGu0artGq0rStK0+iyNrShqfWkeityaHqpsgBSwBHWNuqaWsUwr/L3Jp4jRHodyacXqnek0VpAWv+e5Nbk0lQ7oqTW5NNKSVbk056t8nrL94StFEglv7k101UUkVjEHvoDo6b/UU2luRuW2kLqy4zkV+dY4ezR6rpgGY2hhSn0KDialKBRY1j2VCgp1JSlLriQGiFUFAB91JRIWDHk/ZhPoggK3IpZHXaUnRSwAlwB3cmnCCGiC4CNNwppehCkkL01iEBrUU8sJbbWNOV4ZuWOlae4s18uePyTm9yaNzub93k/4WW9y7PKVl0mG4/LemyEtqlKxOKIdq1FOL2hpzVwLBJUkAOAkKBDpTpHIUjUU44lIQvWtU0+U7wPUqTosgvtkBK1J2qUnrNrG0uUhyt6qS4RXXrr1oK0TWia0TWgpaUlLSEFRCRSAkq2ppKBq2lJC0o0joRptRSkJpltPT2pp1CDSEpB2JpxsJAQgAoQacbTqW0Gum3QQkSihFBCBTSEbtiKWltCMllvzbrrX+LfoaIKS4BvwCTHiP5Pa1QMhkjbHvkJu3SOJMDGVSYcOJj8D3CYabmPLTyhxfbbhbAkgsMNvptbSGrYhtCne3YNPMtJpllrXoMVIjtbJDaUsSPxj/cNNKV2zFKabD7bLJBYZ0Qy0XugzS2WgGkI6nRaoMNatMNmuizS229IrTRa6DVPMNdJDDQTOtUG4xck4bfhyHkhDjn2H2+wNak1jtpN7uts4WsdqciNNMnamlNJISkJdQAVFpsgNICumjR1tG2MkJQUINLZaIQ2BXTbp5DYc0BJbb0WlIfbQhSFNNBK0juGUJ2KSKQE0QNEJST0266bdaGtDWhrQ1oaX/FLSxr6Gmwd2oFJUkqQUgOKBqOoEb0UpxIDLiS3vRTqkiklOu9FOOp03oorQKW6lSitFb0UFIL+5FKcSmmVoKt6KuaDJgT0FqdpR0B/x1Op1JdGirCndb5riLvZZei412x5N6sWL2Zmw4/O/Bv8AEKKaKia5GwG5N5ximFWhi4IQ3HaStJc3JpTiRTbiDW9FOuJ2SVBUaRTCiAkgK6rdLUkyELRTik6IWC7vbpxaCGinqhaK6jYpt5G0LRqXm9I6gUfxp5xAaDiFjVNT1JVb75GMW6/T0+i/x41ZcGQuS3XUML1d6jdb0VqC8j8ioCg8jd1EU4tBTGUko6iKW62Al1BHURTy0FYUnUut6KUkvtuNhKnGylSSXm/wVoaSdK6hrea3mt5rWta1rX6LSkhttskpSKRoVbEGglALaEEKbRoylISlCCHGkaNhIQEIIfQgJSlOvTbpxtGqEoNFts0tCQpCUKrpt0G2+v026daRo2lAPTbpbSFIy+E1CyYLVQOo1FelaUtPriad7GJvNS4zjbrTWPXWLbl25xl2BO/Bv8D9tPTm4P3PO8GwoWltbTZpLSer026cbRolKU0EIIeaRpI0SzJH8WAkBLbZV0WaUhAkIaZpTLOgab3paZUCyzTDSFPdFilNNAttINdNulIRuYSnZtRTqU9NqOjRTCAHmm1Ncs4VItjv2+n+NQ4/ePcZYj7GxKkxSIxbLwQyR0mqS22Xukii02R0WwttDZp1tsJiJT0uk1TzLWiW0JoNtaSEoDjTbagWmdFISJLbTRStlrarQOJ026JNFKa2pramtqa2p/qWoaNKTqSDSClJ6jdBSdW1pFKcRtZKdqXEALWgpbILYcRpIcTsQ4iuo3Tq0ikKSK6iKcICm1AUVpAS4C9vRS1JIbI3b0UXEgcjQGlxnElsJKemHUENxZTwYx+6P1+lTVN2iPYrHbsdUYc7JGulJvpCLDxjdod2xWd/bb/Ek1l797jWI327wLhYObY5i2rILNcLemdBU8H2TSnULAUFUFJAdUnbJI6UnXbHBrqJSruU0XAZCHEaKcb2hxG9Dje3qN0wpCXeo3S3GwWnEVuTS9NYzgKeoiluI2MOJ0W42addbBmtxZ8TkTEWrOG1b296a3pritDUrMUqKihlJLiEtKiO/wAN6KDyUvJfST1W6C0KUhaAXHWyIzidnVbp11vYFJ0DjekhxHUZcRtLjeilo7ltxsJU4gpcdR1m1pKdQBqmtU1uTW5Nbk0Ut1tbra3W1utjdONIKWG0FRSmtqa2ppCEUUIFbUU0hATsRWxGjaEBvYinUp2pSgEIQadQgp2pramnEp3NoSaW0k0hsdfpopxKAEJTu6aDTqG9j9oj3Fq6cb3qLfF23F4CX73h8VH7hdBSr5fpYcjmQUJCEoPSq/JFyxzJU/8AlHCs5n4hcrPmdkyeNHjsuHOsxtuA2e6cw8gu3ULj52ucwuGtEl9AZvF0jU1nWVNVxNmDWQW1KUUlCNHm0FEhKUtSKj+p6SCTGBHTAd6beim2wkIRSG0FPTbptCC702qWlsFpLevSbpTbe1lpPT2JpYbCG22hUDEps5CsJtMODKZYdd5I5Ci3h1qUW2e6oStTxZ/kZnjbEGVcZXH8NapWCXZsu2e4w3QyhQEdsudulJ6bdBlnXotClMtkMMtlvpNCi00aDLVdFqlsthaWGwCy1opCA4202U9JqlttBxDbYGxvTYium3XTbrpt10260NaGtDWhrQ06ralhQ3EGtDWhps6k61oaa/knQ0fQN+rehp0aNpSkp0Ip30SQa0NOEAtnQbhXUT1ypROpJR6q3CnV6CO6A3frPByGBkPG061F2OWlobCVDUFIIr/HUCsVWgoyhst4QUBTcN12Ij3u7trv7rl0ckOrdUxIfYfhWK18t49I44yRMmba59rWKwG/OY7kUR9EpsegeICZP9qTppGWikKJO8ilKHXr70NvUA9NDTR1dogGmQkilEAMrBZ1NSErKcUx+J2JIbRmdxTEhYrDdnozy0R7Pk30hRJM17j/AIWZwfGcTZ33NrTb1V0uLHkryvFl295hYLqjqKQsLNEgCMsdGlqCQlQNaGnFALCgQfs8dFp0CacWnqo9RodNK0rStK0rca3Gtxrca3U4kENJQFbhpqa1NNgA6mtTTHonU0dSGvRvU08dWkgBGpp31Tqa1NOjVTY3U4j+O3/NSCmgdS36HamnkGgkbNhFLSlSMtwJu6VLh3KCsHWm/wAT6Us/xtk4Wy7ckx0x8b12tpWEjclajbIcyllaZLaQFcO35FryDlOzXDEbpe8ol32EW/X/ADW08XX43OxjcinClSZP9qUNUxm9C0CCRqCkh1BChpQQnqfRoaO6CtBTSQE0tQ0bRojdVix9d0EUJajE765DnJkyMFtxatPkfjr1jz6oMNc+TwbwaziNkyPI5l8nYdZEsRm2to2GthpLTC03rEwX1oCSE0hKQaWncIySGwdQ6jclBLZB1DyTuQdoOmjo1dQQU6UtKOs2kAa6gnStTWprU1qaKa21trbW2nlBIbcG7UaaUfSm1p3aprVJpj+SdKI0DPq1pTw/yk/jpT38U6VpTx0UhSRSlpKQodf0oECm1JKt6KcUNE+rWlLGgB0N8sdtyGNk+Hy7PJCVNrI0K/s+lel4lLvWII1LZJAluuIC3j1HnFLUDqI0mRBk5Ev3fiRR2OL/ACrjPLU41fG3OulZ2okkdKUdExnDq0oVvTSiOqhSRW9NBxHU3premmlDq701uRTTiSnemleqmvVuzxlXadb7c3bGG9dJLwZZu/UnX20NNwoHlxaym8oYdW/whwLDsaM8yd22QsDtAv02PEZiNaGgoH6uR0rq8YwzITKYdiOtnUn0oqGjGvSQQErUkJPrSVDa6QSBRKdFf3WxojUU4v8Azm1AjTQaA0RWlaVpW41uNbjW41uNOpCkstp3FIFa0fWkJAOgrQVH/AmidQz6Na05/bT+OtOjcnWtaeAKm0pNLQnaAOtoK0FJQgHYingkBsaN6CnQNmgrQVJhsTE33BLc+9d7fLtsrXSikKCrklFoa9UpClrxfArPMs2TKsYuzze4pGgPrXFV5h3Dj+UsKvDn5UQnXibMFXq2Of25H9mZ+EUAlkA1tTSv7rYBramgkdbamtE00E9XRNbU0yEbdE0rQFhSUjFLK9b1k6n/AAyyW3BtllbduVybaU1XlPjUi72Hhbgc2dN5vcK32u/c3uX252PyTuePRf8A3WXmnPKq9KTxVznZ85krSUr+k1xDEflDmjHbdeMOyqFlUUiiE6MH/KQAUuhIRQSnR0AE/cpTo5p1dAUbU0saOt66aa1ppWp/o2mtpraa2mtpp5QSlpwBXqRoa0NNkE6VoajnVOhoj0a9WtDT2oaQoFOhp47U6VoaeISoK20XNQD/AJ/pWqabWFK6hpxQKGyC3qmnlJCCRW4UVjq7U1eMdtd7GQ4PfLQW17m5CipTW1tPHGPxru46y03HzzjpnHWSQAoHWrHdpdmmqKnJbn5fTiaM9IzJ0jZIILEz8Yp0LPpW8UrQvoO0LV6Nr/zNwpRKihRCty63LphR03ilkAsOpZOL5pjuRLUkIPoaz6UFNYJCKpGgUZcKBcEOq9OcLmFY5AjJbhlka9ua7fWowciPcOc5i8KBCh6Ac1+QkhqS0kIVwjKCo+/0UrQRyShKkpD7g6QGoCgA8v1bOo6gpehfC1VvFLdT1mlBSTqAa0rQ1oa0P9Sh6ND+R0+qfqyP40Kb9G6X6oT6Jpf2+jn31rWv/wCUGtfVPpWppR1Sj8f5U7+Pqa0NE6OnWtTSghysvwMT6vEV+2OWa3Lutxx6yxMetzyhpyjZZt6sll4sySXcuUcRt+MM1/j1E0o6qpfongm1oJc/CR/ZmfjE+7H20FL/ALrfqHUkpb9FAhVFINNIBcofdmtBSjoc6yxvGMfRc5TEjAvJa/WisQzfF86s2UTU3C6Y7GSzbE/iATWX3Bq32luyyMte5Y4ZtOI2HiDBbLnlzPjBhYpXjNg6aT4y4QoZbwDjWMW/C78jH2ebsmvt4tOv+YFpNcYvvR8mSdQ4CoMDQbgae/tUkfxdSdWjtrQUv0fR6q0FLA6yPt/h/ToqtFVoqtFVoqlHYGVgqP301r1pNepoa0yf4+tfam/VvQ0v0RrqNDTnonQ1/g6oA6itwoLT1t6aCk0haddwpakhDaklvcmnSAg6a+lKP+b/ABr+NIUC6k7Tecctl4ZxbD4GMsJGgfNJLZLriFHm7cbX/SEpcVxXaU2rHnP7cj+xM12RToWNNPSl/wB9saBX4j+4gAJcXtpt1YcH8qCaY9a9Kv13RYouW5JcsmuNRI7017jSwtYNYrHBM27Rmkxo9/z3GsVZsN+xy92LNbub3csGsSrfC5Zsib5gvBNxXa8+ZeU4h4EqY9Dyb/6ckOlscmZ9Hs9uQrVQIB4isEy839J2hShtjjVlCkhMlY6I+wWkB5QKxW9NFQMlH31FOLPXZVqkj00Nba21trbRjq17dVduqu3VXbqpxohLKADoK0FaCkJAOgrQVHSNugpSQQykdHQU6kdJIG3QU6kFO1NaCnwArQVoKAHV0FaCmyFnQU4AENAFvQU96J0FaCiB1ikClgbU/nqRQOtfiNxp3QoAFEJA5w1Fl/pxLH13y/wYbEJh/wDGT/Ymf24o9WDRNL9X2lHR5RAb9V/anNNG0/z10rWmPSlupbTzPl6Lk00XWmrVaJN5PFGDJjRkvqDdouTVuN05Wk2mJmlzyHMMkxJ+Zj2H2ZyK3cImY4VEYyyL3Vjsr02w5fbwlcBxG4tJArkz/wBOZflkPE4UjD/3KQvjuOl20cYNzJ+P2JmwREk7ToQx6MoP8XwC1roEn+L2moUdaXoJDZO7U0tKeqj0GvpWiq9a9a9aKka7kVuRW5FFadXnBo24gnWtDXrSFhSiSKCqiq3IJ0O70Y16O6nVaNJJKNFU6ranU0V6B9z10FKOlJXq9uTRX6srOvUFOOJ6bTien1BT7idu8V1BSnEh7qClLBAWneVV1AkB3eNxp1f8Op6+tc5f9H/p4WiKXdGXAlp5xJRIUCxM/CL92lhJLopStXm1gBakrDawF9cU48CG3Rv3Ct4FMrFcg3ddrxuTIfedUtOvC6Umcy2EhLgKWlK3SGmnittpumwAJH9iVOR1OIc1xnLcb5OhrsmcYTcU3XFFfkj78oTo8fHs7yeRk9ytOYdCE5lAbdwW1yjD6oJDqSC6KY/spcCUvOp6e4EJcGjridddK6gpS9ZDaxu6gpa1F1CklOo01rWtRWorUV0lV0jRRpXpR11cB2sfzUGQKQ1qFtAJaQAsMoI6DWlsT1Geg3UplCYsNnWF0G6nMpRFithTHSqe0WqSyjRbDWklva7Sk60kavdNdLZUaYZ3lba20LdbCG3W+mubEbp24QlJNxgV7lAo3GF1BcYBKpDCkh1rd1EEhJoK3EocFKBUlKSaKDXOOhtH1+9bdtcRQlIbb/ih8Aokf2pQGkVI1bACqVqXkkIpTre2W/0yJDig28ojqNNqEiPSCFloEVyjEVLxEsvuERXq4aCmrhrq0j7I+3rosUnXWR/YkmOHvHTDYSoXkla1Qsy4Cu8ibhCwpaMq5TxbBYnL3NSs9gLcccCEK3Y3bRIucVSFoS2BQ0FH7Nf2m/xd9WzrSSdrvrQ11pY/zmx/Ol6dRHqCk6BJrp10jXRNdE17gkV3oNGSTSngSlz1kv7W4cgBYcGnVNOPK0acJX3CwEySatbikR+5UKclasx5ShD7pdS5Klw4shYj9ysVcpgUyJa9BLXV1vMWAuRypg0Vdy8iMSiVcvJOUozfIbN30yOZeRnqXylyI6qRyHnctH7flVIyvLnVypmSTzsuerqrgwO6kk9O6adO66++Zi2lWVZY2prMsuYKOUeRG0wedORoTdt8iMvbcY8mHE1C55wG4vReRcNmp5eju3jGq1FW62yLs9bOJ8uurVt4DjRpVssdqssVwhNPLTtfILUmo6zSf4q6grKL0ixWsc3qSDzosCZzs4Kb59SB/v8As09z468Rzo7pH5wXpZpgudslxGJ0e88NW6dIPB0onCuP5WITUqcI00Ch6AbEk+uutSVAMYRiVwmZfb7PbLDB8pIjsqx8L8kY1i9v5Q8gLNEtN3v93v8AJJBGxNIQkK40iLnXVkJjUh4ElwCuqmmVjpIcSEuOJ2B1GnVTS3E7+siuqmg6kv8AVTXVTSljqNLABdToHEV1UUl5APct13LdaoopQaKEGlMoNLY0qUFhMT1WwwVpDGlLSkJZACwsaFQUmyAIY31ISlbduSlMHqVdNFQYASmKAki8lCETMxtUVN45mxu2SM5zaXe7wqZJkICkgtl5wQ8eyicm0cU5tcXGODby4LfwIA7/ALEWLQcPYrCZa4+bKjx2A+OLLLPB4XwhATx8jc/x42X18eMqjK4btNxSeA7I6JHjwUibwdcGFzeKMzhtz8au1rddhtMOWlm43CdZuP8Akr2djg+8GrTwVZGWbTidgsMhLi0UXFH6bE04nRqSNI8n7RtoASoEdQnll/ZjTrmldYU7ousY4si5HY77xKnHLPFaQqKptILCQU8dPPSsQ2GghOjada9KeShaXWHdOqVFiHLnOwONm5MT/a6NUXjyyx2xarUyIiQwz5AwTJwecgE7W9ri9K61dRVNqJPCcRSpzre9TaBrsTRQnSO2FNBsgKQdoQnTYmloG7pproUGh3JY0otjQuq6jG5YQwTXa0Y2ldI10jXSNd8K74V3wrvhSp9TJQWmC8nqMS1IQbgnVycFUxK3LE1OiZoNWySG2O8TT0sdK3zR7X3wqbP6kUXZqLEyrnDDcaj5H5K3mdIvmbZHkE5cxlwW2y3S9LsnBmaXdi0ePeIW5Nmw/HseSGUJG0mumdemqtiqCFitrtFLlFCzXTVWjlaOVo7RQpVBtQrYuuma6Z1IZKsl46xTNJGRcOZLj4wjlvI8KmY5lMPKmGHSlbU0hLThWn6yVaJkq/yJPqIuopOwAFOvMA/0V5BqPCdluowXK3Dx/Ek2rGM9hy7li6sAzOMhTbjKmPtxkvbihWmlrTUc6p3U4SG8XG68O4dbHnoFih2tIZ0rpHRTfopv1S365dbnrti0uApdxl8L8iwYMyO+wmnngyI8F0N8TW1VvsHVFNuarKyKLnpDcQWesmpDyeiFEp1NOLIWhWlJdTo24DclPbFGUdFrBfhpVohTiR1XKU4s1vXW9db11tYraxW1itrFOoj7Z6WwI39yKf4dVsFa2lCMygrSlgJUlrZYEoUz02qebZ6Nu7ZuzXK9WyJG5G8jsWxlOX+RGeZQ1JlzZ01U9Rcw7ifNMuXYPH/GMel47ZLPjbW1QcV9vSvWvX+sf06mjX+Gla0v8vvRDbieQ+J7VkDRZybDr7x3y7b7zSXVAx5qgpqVuoOpI+4fOiH06syiUotyipr1TSSd3M0cIx5dYhr+0ku7iXCUOqRW5SlXtnZcWK4sBOEk6h1OjVvbKoqUK1ebIjY8f9bV9/6D96cSFtZJDRa81xpTczGc+8eLJmabp4yZFaI1q4GjMSsuYiQLzjFvagWLoejaNVhgkPsFLUFouR+gafZAaba1roEVJ/g8WNtdGmWdLqtg7lRzopI6sRJFMjUJT6FomuhXb129e4s17izXuLNe4s0u4MkTp7ZEGSA4xPjBBukQE3JhQhznFLFxaTSri0UWWW8zHNzmgXjLVW9Ge+S2L2gZZy9m+bybfiUm6nJ0Mx14NgGQ8g3fAOAsTw5lkiO3t1KVKTSVFQrQVoK0H00FaCtBW1NaCvStBW0UABRArQUABWgrQVpTidVBOh0GrH/DjPOPLRmkTMMIyDEpGBcy3bH12LI8evsJC9KRIGrEhKg8N6VhSm5o0atmvbKbWKSTu5pO7GdATEnyrXNPLedisLvNwvuNZRcJlsx6PyrmMaHNmuy5DFcVD/yNsNOglm2lSYQc0Mt7/g8e/wCqpdS6PrrWgrQVIA6XNtmet+b8ITlzcDc11y9Yft7jKIrdzdXdM9iMOMMFxW2M91HA8UpkOlbNpWowf8ypy1tssLIQHUmpgZ7rfHFGYzTb+68ma3qqczo64syYbSlJbQtIRIQivcWE17pHr3SPXukev41oK0FaClqIE9R0gadSMglC2P5bdqbMdUU//ZdynH8VgcleYVjsb2Rcs8h8gmz8eXRSbPi0G3PAR0HMIEuBceD+T7dgmVwZsS5wwkkhAota0lOn/g6H6feh6fX1/qUjcemK6ddMUr0qfbGb4jkfiP2O6Yrltzw6RgPNOLZin+Km2l7CJX8kOoLU9ZBs7esLakUAjXnEH9c1onWhWDZRj9uxnM8tsEnHHkBptSCotJ21xGjdgvSTUhKRHtoBgbKnN/6ZaWkrttuX1I39PpSkhQ8lYRD/AI4TxIx5TGrLhJXliwzj+CRzPzNbY12etqY3P9tUlrRNkSPatulXRJMdmP8A8OhkpM4f8SseulMK3X5Q/kofxfX/AMZBSFJZQkBABrZrXb12wrthX+ZW5wVvdre7S1ukXHqkQ1PdSMqSELnS0qVdHlJkcm4rirWU+a/G1kRm3mvnGRpu2S5DmU7HuNXIcCz2e3WhQc/itKlFTLgN5sLN9hZJjrtguHF/kFc8IGIZriuZ21ag20l5spLzddVvXqt11W66rddVuuq3QdQaA1FGv4/+BqK9K1rWvT6LGteSHJmaYlPi878oRjD5VxfK28hx13F5vGHPU1BiSI8uIlWtNyHEGS31Da+omKXlmg8oHkyzy8gtb/E/JndnijkwAcW8lLo8P8jqpPE3IzKP9qeQ3lf7QcjVG4k5ITcMFtztmsO9dSCssWpTioG52pq3Rbra48iFjcta0/T0/p5+tbMzBvGi6ut3155DEUF01yzLXb8V4KtaJFxUHTWju22KeD/WkgvKfUiyqX7bq7V1U4IsZb5j7n6uCn+4K5Sq1fpsvIvRLxol4h5vWZb2VBDfUSAt2i88KLr9dWRXVkVtTW1NbU1tTS2krFwZSkQkauxWEFHNfknk2IXzK+eucsmjXpGUX1/2Sca9kn0m13VpbOT5yy3HzfN2yOS8pYKuUclNJ5RvoLHK81Av2eyLzHkW0yxhuR5VhEjG/LW4NwkeTPGj67ZzzxFNab5S4qdRAzrjq5qRecOcpt/HVlLNjcoQrfRhwaaYWK2LbH09KP8ASRWlaVpWlafRz0PPNk93ydvFIiQrELasRYUl6Fm+CZlhkfjTmDI+NJWL57jWXQE9QU2RTEtDZavsfbHmR5aUsLNyIBAY6ym2gzdlAEvsbha07ZYApXom0RFvJ0SKkpSY9mQBbv41PQk2u3J/4W3tPNrQVbAda00/p5QgC6YRwjek2XOX7717gQmufri0hnx/t6UxNEqrYmrY2kydo1kIT0MeH+m7QKvSEqi29tJjFhs1dWUplhlKR0EVsSMiLKDSmG9ryR39tQktNITpsABAraK2pramvdb7Xut9r3W+17rfaN1vZqdOuqkQJd26jVzvaE81YnMsmXFmOoG3RVV7ZEoWqKaNkacr9bNfrZpWJ7z+oCv1ACv1EGjiGgcxRQV+qPU9gxfKeOmUqXx3CXS+PoqE/pAQpOKSEhGOXVuhbcij0l/O2SzfeR2QnLOS0Ki8l8qwmP8Ad3mNksc58prV/vnyS0IvkVmQA8j8lbI8lZpCfJaTrF8j46i15C40sq8hsaDiPIDGClvnvDXWovOeDOuJ5o48I/3m48pvlTBJNZHK40zKa3xjZnT/ALWQybFxzilpl3FnH7lE5X4tteBoxe53nGrhxx5Bw7q20tfUzfk/GcErK/LACRM585buD9g8meZ7ZcbF573SKnC/OjBbk7Z+e8IyaZBy83KOLzdtYdyuyZKbpelA3G9aWm5XtlPe3s1JmXro2WZeexEm9UmNkF1bt+MtxGW4gaSGhXTRXTTXTTXTTSwEl5zbVzlxpcGZ3+G57Zp9zkPOTrwEcxXVdxyHiD3S22JFzvm03O96W6de+4M6+09Ovhass28otff5EqrpMvqose4X1LHumQVcLje1TDdMgJ9yv5pU+8JvYumQaG45AUvy53ewbhdwhu43sD3O/EG534UbpfjXuV9r3K+1qa1NamtTT+pTcnAmrYrqPQGFqb8iOOGc3wiZkt4tchWdzk0jPZurfIT6QORnhX+5UqhyTKJY5Ger/cddf7jrpPIbq6GcyTSc4kBSc/cSP9wF1/uC5QzltdJzKKpX7RFNIyqKkozCFocxgik5XBUf2WGaGSwwP2WEK/ZoVIyRhdIurUhQhwnKTAigt2K67ZtpcZWtOPtKbjWRynrfaFBdktij7DbK9htlIxy3Lr9Wt1fq8Crmi32dWNzpASm5v7bnc2VmQixS1xrXiDcrbghqMcKbk29ze1y3Y7Pcc3PFGJPljgO0zBL8cozcZ3gu4MiXxemJQwWMKiw8ls7Nn5k5mtD2OeUedW2ZbfLHDZzlh5qwHImMWuduuNsLRFTAEsYxqbUn1U0rRIUTRII+ilBNdVNa60v8r0kGMsJK+cba7bsy41uEe5Wx0LS1k7puWfYHCctqG21FHSNWM/5xUdXyehipIs59ayAgW5kntty6uRJmrKtdDRX/AOYtVEK9EydfcbapXTaPpuIBUa3Gt1bq7W81215rtrzXbXmnId1IuzcttNpXNS9FenBvkHPUYVauW8dlvZCGFOpMLWuySKZtSJFN4n1B+nK0cworP6KTX6I4A3ib6KOPy0JdtFwRXtdxNG13IULTczXs90pVsu7VFi9JJavVBq9107zSZU1om5ywld7ng++T6N8n0zklwEiLf3ot1Y5c4vk0nkHAF0nJsZUE5RgyT5CZhx+/YWswkIprMFKH7aa/ba/bDSMzcRX7u8a/eHxU3KkTk2vNxEZezVCg5kEB4mdYFqEjHjUNjGXhHhYyw/ZUboHkJPt1u5Afmxo4iZPkTCmOQcwjhvlfKW6Vy1dSl3lGM4Y/LNjRTXI2LvBzIcfl0zMZWt1+SgWPNZ+OOQfIzI7cqD5gQWYeC+QeI3GJaZbc+M2f4oB0J0oLSS68EpE1apPoa1NOehvL8lba/dd3kNC2Xvj/AJP/AFW2yfISIY9gUu9Zpao89F2bTeEoPvAFqF1D593NSBeOhjQuhtel5Jvgu3ZMi79vtu9XEXYS1i70BdxTMee7fSLtShdtrwn97b0XYoabvGiG7sa7O6KrsbrXY3Wuxutb1VvVW9ddRdPuL0uxTttBDj9vZQGue8rk5RfQ2u+WtUFTbwti9vtqhS7b6e3uKFksVqZim0Y4om14xV9g25mCgyCgmUoaSBXXnNUXJyh7hdE0bjdqTdLogpvMsAXmQSq9SaVdn10HkKPWggl6011bTW6zLpEOwElmyKKW7Ign2hQTOUKeehy1SLPjkxCsYsqT+v28D2G3V7DAFDHI7gViiacwx9ROFTDQwacoDBbglKsHmijg8vX9KnJP63dmii3XSPUQS+6sfrZ/KmE8/wAlqhS2lKhz1V2E6kxbkmkszkkmdrtlGnEKTTIfBZu+QRyznWZxy3yXLIj5xYpiX12eeiXBySI1Y+TuWcMm8bec+WWyRxbz3gPKsd3eHckkui2MrUqMgavVqaJ1Ev1O9evk3/CVFcUGlfbj0BedW8FvItSGluLqzKKZm41I9WMW9LO8tVXdSlQmvSN61c9fcHNdfWgpSco+9L/BpO+Zamm+g/ohJec1bdcNBS9Ny63Lro5HXQyOuhkVdHIqMXIli8C6sCyoujjjl+uNqTfsDjZA5cMRaxa7ZTY4Uac7DKVdqqlRKTCO5to6dI6dFWkhvqRk286e317eRXt+4CANTBUmuw1pcFW16ApNGHtIhb67H17BYrsNSYB07RIIt5r29RqbBW1GERZHZqrs1a9oqu0UD2rldoquzXXZqpcVxSXYUtujHligzKBQuYg9aZXVm11pldaZQemVHLqg2hsOWQhFk5e4zuea5MOF8lRWbY7k+FNLvExJ94lmlzEGiuMoqnW5pTDkJYcEFSjHiUI8M17bYlEWzHqNpxw0GmoYTlU2Khy+Y5d1YXhedXO+8P5tlcLHMplXZUBj9kXGgC9CUPtR+1yTNKCzkJPkqmYDGP8AFR9OPw+c6ipyA31BydxBhZSoWmLkKpBaykU+3k4ZxxvJDalQ8qVV3h5K3Dabybtw3kwFxbyAzFt5OK6eS01DvrmQlrJApxvJQhsXFMq1pyEsyG8jKelkGqGsi0DeSV08krp5JXQZroM04yiiyk0lrQX5kJFvukSGnKcvtfWVk1nMjLrlDnXPMIzb0dUca9siu2box9ChgadBkV27VLjpNdvXb129FjSuimtia6STXRFKjIXQiMCjFZoxGzQiNCu0artWte2j12rVdo1Um3tqje3tgC3MijbmTXt7Wnt7QPtzJo29o0Le0K9uaBNuZNLtbSqNqQFKtDWptTJCLWwALS0FqtLRAtLRAtLYKLSglOLOuKHMJtsO5coJlEcumPWZ5UcuirgqXT5ixwq6NqqDIi9zZLhxb7bli7EqXLkdJ2JIafWqI8G0WtDrftjVe2NgqtyEhVsS4RZ4xHj0tTWGNqWhy1Xq5rXE0lx2m1JcA0+h+zw9doryiZQw1G/BRATxnNT/ALgwpEj9macUts/ayp1lllzWQysR8TaKrKpBFX8H2+3tpVE7dmr40EXJtltTfbtUlsIyksNmnI7fTSgGbamAWHo/olnQtMN6dBuug3XQbrqZDXUyKt2QGtcg11v+mQO3Rtu9yXBHvzy0SgzLfajx34guTSZFveQndsTWxNKABNeuv8qOooOsgv3CEwTdodM2a4yIDchrc4mMEsqSsetaGtDWhrQ161oa0P01rWpKUdiEpI2JrYmtia2JrYmtia2JrYmtia2JotINdJGgaQK6LdFls10W9Ay2KTHbUWIaQUp3VJhMvB2E22C2NykqC8xyeHbnLRashyeXgvi9ll1TinjHxtFea8f+G2VW3w44MnKzfxCagZNl3GeccfmzZFIYdt8qDdWTFST2g07MV2SK9vb04FlhqFG1NMaJVa89m2uHZLtdrqv6z2JaWwbzpyLxncOS15vhj+B35lhMl7H/AB9XYbzazfU31LuRFJGQrFmbvzcsychqRIyDt8Xk3tq0LXkC6vqMg7C3u34xAchXV8TfkzG3MgLYTkaq1vqMoCsiVS28j6YcuaZ1ql3sMPSr9tEm/atv5Aa6uQV1cgrq5BXTXXTVXTXXTXSW3CMmQtLd5aDsfkS4XCBOOWZgKTn+YRq/frq8lWVoI/bWKaym3kKyCyKoXZg01cG1Hu06P3D0uN2e3sWC+X5dl41y0SZcbky3wclsWQmcuTLhvQb2lumZSXqBBoIWRsVR9K1FJQpVdJepaXXSXoEHVSiphSVJP86O8Vqqv50SsVuXW5dArNf5lfzr+dEqFblVo5RKwdyq3KppZRUYaobP+ZdElSUhLMYaA5pkardE4p4fufJd1wLjDF+ObegKVUZ3pASyCxlj9sXNlIu8m6sQX4vNnjylMSyXN/Eqts5F1hEaH0oDWikiuKMnYsl+bfhupRsWJLfRa45urDkVJBH0fKFILCzTDLgneQjSxyHZUJcu8MLUzZElV/Q2vbtXVmbWJJbXq+hfRxRtRsxbWKvqXBBtza+16a6v6Vic02vp9NVQkLXmS21pW+lSWkIWufYYoUw7Ea2KjoB2BNa1qK1Fdvl1dtl1dvl9dtl9CNlpF+iZKUs2i4XCs04XuMx4wAK7JSiLancLIy7Rx6Ga/Xodfr0LX2CPQtoYKm/4R7VMub+H+P0Zlu2WOzWeFsVQbUKSj1yrinDspOUcOXfDXmYZQgJUgOzJYpUi5AvvXYrLt2oN3dwFi8ChHu4pyNeClUW+JKUXkBbV63JjXogtXnXo3quleq6N6rpXquleqLd5A6943IN2UenddFquya614FIXdlVtugGl0o+6AWq03W7KtjKYDDCgtVwSFN5qhTWNol3VoWey3jLLhx/h0LCMfcaVq2kpKVgJSdfpoKKQoON615I8SQW4drucy3Q3LzdHK92uuqLvdUKayO5s1ZLvNuLsabcI9DJshi0nOsw6eKTgYNskB6FvFa6plCetCY+XFPZ5a5J52Tcmc1sqVru0aNk/t9mOSC4txswLYjZiRaY2TqfMXLk1Ji5Z0MajZKq1dtmYVe2MsTCiN5a3H/8AN9Xk5IJbf7aEa5fUcZI9lCk5ekyo+VrS0q6GTaYeWOsuWjMNq7TloIgZUmhFykV2mVV2mVUEq02qraqtqqcLiBk8QSk2GMGZUdlmazlGNv4vlfb0mETXZFNGGqhCUR2atVRSkSkbas1tVeblguC23GIri1L+v+P0U2xKYz3AfbJS4iU1IYCTtQA10gNWzSkNAoWzpvarc3T6o6KQ030zDQoLt6NwiMIHQjV0I1OJiN1/wmpTFB2RDRh2/VduhKpVubARDaTXaNGlwELoWkilWsClWsbbYDAK56zWOu93Vy/HI0Ll2c2ZCEeO2KxEpZ/tOepT+VI/D+i+QIl3sGZ4bKsl3RY0aeyJTXsbZEKzoCnWQ1HFvSmoqHGTbcDTMwfC3FDGMQyibKdFJ/BBppKumylXeeRv/wCQbGoJu8RY9rsy0+8sKCm9FVYUlMopUDJBMfDkk2HaqskSfaoTSlxO3OmRsqF2MXaO2NREKRnS46i5OaKWkIPuGPA9Bz8HRrS0KopUK0NaGhHyvTt8rot5UKIytNPnKdt7/Yim1ovQft37T0vJmNJ465IYzaDKU3lViCUZBY3aZm2p0FcbXc2qnkkVKZK3ONMIRjVobUdtOvoapMxpRCgRXpQWQJsSPPYzvD1WuY9Ybs4Rily3KxGapX6fLr9PlAfqxo4dCeBwS1GmcItTRuGILUwjEZoR+oSlkYKNP0uDp+m2+msPhNn9aU3X6+9X68/tGNzQVY5PNHF7utAxK8Gl41kaAvGcmJVYcgbPRv1Kj5EanC9RFKvsiPWErVJq5/jKSiTDjWJM97GbMxYMfHoKT95UpqMI74dH+P1WNU84WB6RPcjhKumK6aaYQlKbwjoTtaZWhNNZrZHeKMWjmHjlmnLts62XONc2EfjJauGxtvLDTLeXKkc9tXtvkOyFwXaPbb+YVltmRLvLUXI0Nn9sSLMco7lKctXU/wDamo+IRsq9iV+21kBykW+3nK+z6WYKVff2YTtuXIbS1mALLeSu5g8nLdXmcvLbbN4M2yxssLDsLOFJcgZsCIWZ0I+YCujmFdHMKDitOoqiN1FungoVkKFlnHm+pKgx9jXmlx/OynAm7Xkzbb8C9uB2KiPTdwisph5ChspyK4KpvKr4yeP7hIy+/R4wTHLW2mmEqXcXFGXDZVvDeiOmKDadOmNVN1l+PsXG0qcKi44loJksqIO6tDXrXrXrTjiWgmSwog6j+VfyrQ1toAivWtVVqqvWitKQu5Qkhp5QJe0rvmqcdZBQps0EK1nttSFptkJVWlpqI5cEasQFAoxKKmZkyhtofYnQXGW8wlru5D8BfTQh716iqTqR9OWY3XxBMNclKba0guswULDsKPU+62ebMXcrQAvILU0bBeG7hOZaEZoOVx9epHfN/iUghtv0Z/5zyTP/APYNlKBd2YZis2VP/mFCVAKQrSxJKpyGSmr6otsYGndij6AqskSEWu2tjtOmKyJP+ruNgjpirWCc7eQOo6kdJtw+4Y9JAjmSdi3CuiNaCBWwV0xQjZZoY2V6LYysUqPlxoxstAu7WTbLD7sX47GUFvILDkd8tV+tEqxXpXpSIwdLkJkF7HLfNS5glrJdwppFcBYaLbPQ6VIUrdSlLTSmEuOsMIAWP47VVsVW1VbVUtoOt8uMXSx543PuySbjcyFTLua7u8V3d4ru7xXd3im512QTcbmQqbeCe8vNd5ea7y813l5rvLzXeXmu8vNd5ea7y813F6crFWH925usqcl9NVzuyQLpdk0q53dVMTb2pcOZNU22q4mrKp4O3Ba1NXFLrlu8e0S38hJ1I+1ORm3AiC2gpbKaQk6VpXp9Mxj93jMiNcFSVW2csN45kBKccyVNJxbJnSrCL0ai4RISrjPj2ZLyAFW4nSuPQTf2lapmMzHWjZ8jUWbLkglc+xLhG5BsgKrtHby12Dj8PJl3hMLK9vZZZVli5MZCbflj1S7Dkqo+I22/u2J6w5QRfbHkabfb7Fk/a+w5XV7s2RImosOWBHsOV03ab8Mr9iyrc7Ysn6SLdNE20Wa9rYXYMmKTY8qSRZMooWLKa9hyyvYcsoE6an6uVkX9jG/+bif2QoBzkfgIXqSOCcnkquvB+bwTceKMtio/VbvS8cuaAq3yQeI7Rco9lGraUHUL/EfdGh+q+qAhTx+g1Nc2Y/Bdl+3Ww17bbK9vtle32yvb7ZXt9sr2+2V7fbK9vtle32yvb7ZXt9sr2+2V7fbK9vtle32yvb7ZXt9sr2+2U5BtyUuIbbO5VKitySiyW9QNjt1exW+kWKDqxZG0F237QpkR1ynylm3tDThqxOmItl9kj7fTaqiNKb/JW/RJkmhppUuN3qLvjMiPeW8cl7BargKas89xScXuKzF41yOUmzcQoXVjsNtx6IfvDhzLi/i+LOWNLJJKvw/+2P8A3/Ir/wDINgITebd/yuP+mRJICP8ADHgFy2/SrtJ6EXAWdMac/LJv+k2v/klflkX/AFaT+VMf+vHv7rv9lP8A1DH/AOwgjY6QTSfqLTc9PabnXtVyFG23AU5b5+l9hXBDdhhSi/Et9xLXttyKnLTdAnGoFyfWLfeE0/jcmRWL4sxNg3Diexz15LxpjdmuOC4kmfi70ZbLoURVoSh2436Ai2S0KOiCSqoiwhUlxDtKACbayuTcJXGEbI7Q/wCL2GCNZfHLFJqmvGHCtL14zYZGttr8asSkRUeMODkX7xrw6G4PF/CNqvGDBwmT424a1kn/ALYcHJR4v4MRaPG/D5M//wBsGD1L8YcJRCsPjXhk2B/7YMGrI/GvC7fbIvjFhLkf/wBr2DGs98ZcbjI7VtmQtlSm+KsBwjPrSrx1xtKR474yQnx4xtBa4HtqRyLh+IYfHf0W41BcuUmxeO2MIg2XELLi8TOIfQnBZIbKlU3CgXDGy2aUhIUAAUrUg35pdisjbhrftHH2Nv3Oxcn8SKtl7bxJgU1jsRpaLTbWg5DjJLTaipKXGkW3HJ9xWjiRUc2vFrbZmVNaFtKQJLL7rabdPXTNruXX8gokuPyFZVIRdo2Nz2W7TZrkMmRZrltNoutYza7o5KRZ7rrcbLcDGwm23d2xqs92ByW13VFrt9puvZm0XYG/2y5pu71nu+vtN1pu1zjma7Pd9ztpuvTEZ8T7Na7mtj2q+BPtt8pFqvBoWi66e03avabtQS7ptdpbLy6MN+nIT9ZKw403jTTj0iJBk9JuMUKfkxI4tEpMKWzfYtP3yGtvFpSLVF/YIBGeRlTVYPLdh2S/MJblKaCShx1inpkuaW4yigJlNqa3rGxVaqAWfTCGCbwckgKLuQQVsWB1i2S13mGtVzuMWZCt1xixoPu8OrrMjy4xvUNRN3iESJTDt+N4h0i9wk1bZDMWf7zDqRd4bsWzzY0GEbzCrIJybhb411jIYN5hpq4yxIvPkDxdEtMuO6mQ1Fvl6xyViXk9JaiRec8BeQ/zpgUes88jVTo92vE69SQkrPAfG0l26m4kFU5S6zBoSYO0pplOhbdCUKPq646lTanVlIKDdJ9xvDzYNOILqMQmIi2DLmrXfLa5i1zSf1u8N1LhPR0ohyJa7NxrepglYFDtFrt4TGgLdO5Szoo6lv7bVKSwy8lLIc6/kklYz+1g+6AKRVpcYGVMsPupca6AxXpqmpZabN8YJj4A0HMRcjSAcobeTabc092hjyCcijvi9Px31V20ioUN9zP3YsjqyEBttOnuOO9IsJQxsUhg0UM1o3WjVaNUMclafrkqv1uVX63KpzHJWmRWR+O3jtsnSZETHZHSuFtkwaW4s0R6SQrRC3UFx1xwf5gr+VA6G/QQsOEBblElK2JjrRF5kKS86Xj9qAJLiFKGM29piKphKj2yKEZGvbJrtkV2ya7ZNdsijFTp2jVdsijGRr2yKEZGpjI17ZFdqiu1RSElIMdKiIyBW3VL8ZLieSOHnLdIm2+UwstxtW24iS81GVRCRUa0Spp454RkyCyG2IhBrSprfWjXW1txgk6UlwFQJBamKbp2UXKc9Q592kk1YLYiSY8l2O11FODadT1CHIkd4JgRmlNTrgyLmuZco7cmUltuVKQ5dr1eYcXKOepeFyHfLy3NVxvyG/ylZBjMqmsYmF3nPHpjfItusMlNxbxScEQMcuLuQM4pNCFYpJWMYxV8y0YxKWbpiclTWIWK5OWJWLz1HIMYmtW+24rM7T9Wmir9jE1N1dxSYK/VptM2C5sZgvFpxW7iUgtizONzbRir6mf1yYlJx6XSMYkKoYm8R+ou1+ou0Ho2nWjUpxo0oNGnUtisj2bMbSnrwSylrJ+m7IK1agnRz1P9PbR3hkFsahy1+ifoj7/4aVodbfa1KRAaQwwfv/8ASqANTJ8ZhOb2S35DTvFcgOjCJKVowGQ6q34C0lzFbHbsfmRMljzWYTweSsAGnkFxN6tTjsVwdJ5ClGknVKD/AB11rQmtPVj0NpYZbj7QKR+X101ofcJdIKHtUh5JkykRGuY7xb7lmCJsZuvGlwLxdtTKK68YDO7cxNvTWPxQ5jkfo2m1KBypptot9NmsWQyZZbZ1usQOs4N0v1HpM1lDbQtFtbY7TpMVkaGRenW2DSm2xUYKj5840tS5sRxxpMdYuFihudAMrCCg126lUILxrsX67F+hahp7UmvaRXtIpy1IAv1qKUWGC8t+JaErbvkFUFxQII+xSDWwVsFbBWwVsFTo3VanWj1ejoZCtAdRSVgV1TQdRSHE7oMB51Voilhs/f8A+k1oH1W2y5UmzwpNHGrbS8Ss6z+l2bVvDrQ2W7DbW6ZjRGaCmgCrU60o6CW+ylNwsVvcedR27m8ACQlI7gUJIpgB5TVuSKg2YuCNCMWkfl9YbRkTMss9+t011zLmaBy9VJTl1XFvIpTSOK7dtPFFpJwjHp1tuntajS7Qsi+WucLkqNeE1Y7O6/bbbY1KyRmw6MvWVbYxmzrcmKx5NXO0LjMYZYO4xddkKVZFaFpttusqu19lc1vNoJvy7MoE2l4Uxbp0jNl2eclT1smpbQy+Ztlt89xkWSUUmwSKNjlJr2uZXtUyvaplDbp/GvT6O1kGvTsP/MRdeneNu6R0N39d16PQmey6r9u3H2rUe1UPaq/0qv8ASq/0qo3tO/G+06P/APon9Onevb+nJ9g1V7bonta/0av9G1/0av8ARaHse6xe101t2I/L6emlq7bqntNuWdDsGel0VbNMu6XR/wCGo9t1Jfbfto6NP7Niex/bz0enG6W61/8Aqhn+05trGNveq13XrXo4Pu/UF7dMk2+1W7b2h21kenvJ26fxqHs/3Be0603TpN6d7YdeinXZR316/X//2gAIAQMAAQUACJGmyRWyRWyRTbcjda25BqS2/smIf39kya7FmshgMG39ixSIbaHMmjKck9ixVtgMde9R2UXnptVjDaE3G4wWFTfb41WuAybm5AZ6iYLO7G4DPbwoLHbOwWOmqCwMKTb2C32LDdXy3Mdgm3saWdlDMqbp1FA7rRr7iW5G5CHguCf9O+wKHN07+wVaVFBVKmuLD9WkHuJCf80gaW7/AJRDxQXJn8ESepbC8vVDr1PW6UpCbNeXjHxbK1VbeNMzvN0s/hrkrti5T8fMwwGULLmCybHk7YkwL4hkR5JpDD6aed6SHHN5H96+PKbgN+qB97o8vpiSsmIrfIvwWqUlL4NmJ90mLcRctqF1YYEF2fJssUPe3s1j8Z1LggMmvb2Kt8Bj2v29mm4LaVMsPmyqjLVUyClMa/R3xjC+opSYLKqukBkRzAZqBBYTKy2OtV99vYqzxUMS5rRlSvb2KtTRjRjERIU9DZaBhMLx4MtCo0RhTspbyGrrJl9VLkkqSpwgoQqukzXSarpNUm0N6ezt17O3Xs7dN2Zsm3WhpsPW9hTcu2Rt/s7VGzN6Xyzs9l7ND0FmhlV+s8bufZoVQ7PFD91s0b3g2aCDY7XERc51obMv2durPZ2usuzw9ws8QnHbO3tTZ20OC0IIjWRg46izxVB6zxw5PtjCx7NHqHZ2++kWuP1fZ49WuzxtDbY5pFti7oFpaVb/AGRFKsqNbhZUFluzNgt29liRcmEpfSlBVDhSGV2/j/JL4/B8b89uLON+J+ZFrH/EdjcjxNwVpmyeOHH7MSNwHiTKovEWHREs4VjTCGcasbNC2Q0hUGMilIG4oBoworlew2dyncQxqWmRxXiDof4XxN43jx+4ykNzvFzjiY7J8N8LJvHh5IecuHivmcVN34IzWzO3zDb5am1W55JjMLRJusEPzfZmRVvsyTdJ1mjd77NHqy2lhM522Ml42eOasFojbxZ42ns0erfaIotns8ejZ2KXaIxtXs7AqZZme2nWVldjFkiqAsjFXOysGOLKxo1ZmUu3y0MuXb2VirXZY/cvWVjuTZmALdaIy4xtUYBdnYWW7JHGOGysas2dkOSLWwpq62NnqJs0fcmyRyPY2K9jj17HHr2OPQUrTcqtyq3KpAWowSpAkKOyXqVJ9BqTWQHS3eopJV1MgQpyR0SKhI0evDfUvHQFWhG24S2yZOxVWdKjSm1bwhWuNoIRsV1yFJSwsqxxkKQh06vS0EnprqAdk2Q0S/01Vamz0VNaKQ0Cq2pKIIJIJ1VcD/kRbXcJyMW4N5Lv7zXh3IuCbB4qceWli38aYLao7FutsYbPVaF6R4U59USwKXQgRGIamtKfjkV0DXS0pTelLb1CkaHZTbdNtmi3pSEEmPu2uwY8tE/HwgvWuUyosqQrpgUEppdosc5u+8PcdZCnKPE3AburIvDbLXWMj4W5UxVm2W+6Q7vJSVT1IIFn3Gc42suhpYqwNnqBvUdLSrelXt6goHcpFMgi1OkoVIVvYuiSjG0JKBqo1ctRFGopoq6l41919atYJkv69wvUptTZRGLSiromunpj/Q1ptn/MlJKUXVagtJUVpKgNVVqqtVVqqk45bdv65bK/XLZX65bKbxy27ollhNh+A0luVb2CtOOW4pRjluCpttiSI/63bqOPwmTe7dHfk+xwqjWm3tvXK0wHrr7HBq3W9mPPmQ7aqR2tqq0sMariWrd2tqqxRLSIwjWrrIh2dQRbrKiE5BtAUIVuBcbbVXQFMRm+u8yhK9rdR9raeg0oojMbimOzFteCZHk4xDxDzq9N4z4i4g1DxrjzD8ObdefUjpqJ0NEGlEJDFtk3Bu24k8w2iKhlJbo04DtcSa0NKCtzgVRB00rSmk60kAUSdWfu0nQgaVoSXIKHquWNO7n464q9CP6G3XGj135Kco42wPLEZL4W4NcYuX+JudWRSsPvWO3V5tppaGYbqbbEisNtRrTs7W0VbmIZhGyWlyl47bUpegWZq2rgWhSjbrMpMli1uRParS6RabUirlb7L24t1k0at1l6l2t9nNz7Cy1bYFlEh2BZO49ts66g2m1tR02O2uUMft9SbdbWbMiw25SE2O3pPZWtTUyy2JZXY7DqbFZzQxy0mv1i01+sWmv1i00kfxCCaDZrppplj+UJmn0jZKQVLQyKbSN95H8m9CEgb763vkGKomMwW3rizuufairS3sl3FCevtTVnT/FcZO9MZG7HWtEFAD7iApJAMPpCm0aEoVpUf+L743LLRJSCEwIkme/hPjRyXmxwvxNwSxtWeyWzHrerfpuUAQE04kVsT9PWrTj8i8qjWJiA0QU04hIpaQAsDVX3eGp0NEEl0GiDptNbTSAdaCSS22RTSDqVU0CVJQnYNut+sUWYbpbHoTba96Pq0tNeiwz+NXnHrFkkTNPELjDImM/8X+ScIjrhz7YlwNbkISoRGiICBoHfVsoSbdtFbU1dmE+2pSAoJTrdkANgVH0L+QISLgQmrWE9w+EiTqKtw3Ridqt6qdRvsIYJSlj+SWh0ZSRq8gahAoNa10DXQNdA0mzQNotMIULXCFe3Q6bt8TWHCiASWWgiSw2Vi2Q1J9qhAyrXDkAWiCml22G3V1jsuSxCjER7fFU/crfFRdOyjGoTDTc+XAiuPe2W+rVHZbdVGaSpLKN1sZCEKt8Z1wWuGk9u2lvt0apYQAphArpppttGgS0qsbwbKMxl4D4aXpSsO4qwrCkqUvcGxq6ogKcrWlfdwfTrstKxbDrjIcjQYtvbc9Eun+RSFU4nRK/yKQS4gEdJVKQdziDrsUa6NdGg0oHYukN+qG91D+IQjeWmyCPQJAJQyVJkW9iWzkGOSrM6CD9Wz6oWrRtxQCXFFRNJ0KBc5va5RxDx/mzGf+GD7FX7Crtik3RDcVUcJpcOMpluAw9AftsVCm7XFIetcJyKu2RQ57bHq6wWekLTDNN2mIld5jMvXQ2qEKtVqhGTItEISDaoIq3wIzMUWuIsm1wQXocdmyC3QwlNvhalhpCJTGpVESSmFHpNtiKr22JXtsSvbYlJS3tDSVUGE0WG6ajjWE04mpS1hMpSt6Wk7W2dVOR2ul0kUG0BV3QlUrYim0I601tHuexFQEhM2W22H9qatyUhxRAUtZ22c74igp15DSkkJWGf8ykqWCVq0UtKBheC5RnL3HfhxBjPYzjVhxeFIUVqCVGumrUfd1JUChQFK9FLBNLK2k4dhib4SnpNrWCpaCQ62NxRTiDtUgaqQdVpVt2qotp1cbSK2JrYmtgrpiumKQjWtUgBJVTafQAbabb9UDRKG9al2Jm9oyDH37JdH2ug5SSAUKSAhadEKG4+tI/GmUqpoNoTf8KxXLovJfhdYZETNeOM8weX0AoOKSqGUJ16CacbSlvY3XTRV0BSnpo0abR1LwEqum1FW5KQ+sNqk7E0xqmMyhG3agVO3JsyAClKdFJB6Uj0p37qSCdia2oraitqKTbrXtEK1pHaWwV21rpLUAUyIoqSiGQ5FYKkMWsISxaklRtyk9vaiHYttAuTEXuQzbKSxC60liB7mGbYaixYYlONW9bnRtlR48ZtRjJJEaGoQmmGGe1hoX0YBoMN9NLMCu2hE2jD5+Tu8W+G0luTjWI49hkFW80n7L/JP3P2dcLaQ4Vhz8NyQT6msSxuTNlNIaZQRqFtnVWujg9XPssEhaFFTgIUv8a0pYrQfT0r0rQfRKN1NNemlI/BtGtITtOmpYSU0tJUL/aG7vapMKbBfI0ND7t/Zuh+CPx/xYIBpr8QdKv+NWPK4/J3hUZkjK8GvGHPdslFJEdRWwgIDNu0DNs1uTNqIDFqFNMWwuXBqMq4Fm2Cmk25tSI1sC+jbKZatnax2rYUBm2VLRa3IIjWlNBm16pYjhmS03q4y1r0YtBu3V0LXXQtddC10lpG3pIrpIrpoptpKi2wipAKQ84UqLY2IQnVxlsNdNNFGhvCSqQW06x0hL1zSDeOmmraCJM0JL4SAbSoqce/iUISpTPoh4BQabToCdiYypTnFPh1lmds4jxrh3HsAqK6cQAS36LAAdA0pX2lf22/xfJFF31UsJTj9kevUm3wEQYoSBS/QuAbSkEOoBBQU0pKSCgGnGTS2tBoK2ilgVtFbRW1NbU1tTSUA0GtKQ2dNBTaApKGgB9y21qEpCQn8lJ1GZ4wZzLjDiVUfQNfZP3QTTZOqB6t/dKioIUdEk6p/JtZU3l/GuG8hWnlPw4zDFp8uOq3SFBSmHG9SGDrc2EdEtbRGA696Vq+AKtzaFyHkgP7U1b20qjhCUlKUlTqEFhTSCpLTYUhxQZkvLJdKtVk6hDaq6TddJuuk3SRC27IlaMClFukJ1MNhKqnR0BMhr+QTqlLepcaT0dmtJRoq+JSZCIsdQaRHS/cG2fdtoKojQCpLQDm2rM2C6pDailuIKbKNriPSOzuXxzwxl/KV24Y8W8E4saDmjcpOrg9KdOqlfi59nPUU69tLit4SnaH601VBgd4rHrexbYoXr9FJ1K29Uhqi1qp5FEEV0aWkJUtCTXb6ktgFxuuka6RrpGuka6RpCAKOhpFdIkstbaHqGmyFJSEikflXRaerN7G5b7vSvs19k/dv8mvyR92/wAmvRKPsj8k/k0vQJVqRXMPjVgPJ45V4XzLje4KaAWlCSJLbbiHGIRTGhwy9d2wZamkgwUJS9IZAd6aatzY7coG5DQKnWkJjrQNwQNUNN9vLQNzqE6qQNUorYK2CtgpNvRtEBArtCKEZVIbUgxFAVLcSUPpUVEbUocRuX6slKqQFBd8TukKa0MNgB+7MhV4CSmo3UFOuooEKq1HpuuMpUlMZCS2kIR2kp9XB3hrcsnTieI47hlokOqeWupX92nvyV9nfQLIrUCn0lSwDqafrpOFWFW4OttMpRQ+/wBF/joaIVuUjWnGTu6aqW2SotkDaqlA6ug1oqtFVoqtFVoqtDSEFRYaToGPTYU02jUJTt+qPypQBGR4+q429cR6K8aa+w+7f5N/dH3b/Jv7I+yPzT+SCKR96hKUhWS43Y8stnO/hVc7bHcgSbZc7nvaHbNGo0dpL91BExYJMcHWUlWuhqMpSIjO5SEJVucB6Swd6QdyEnoyQdXARStdQlZraa2mtppMhjb3DFdwxXcMV1kENSAC660aOiq6cY0GYAp0NBr0o6a3hxKJBUldRdOvch/q3pTAQpTjUErUiMmmNiT6GhtNYJxzf+RbvwP4t41xTBWE6AL0NL+0n+7Tv5VK+9O/YgH6L+7n42uDKl3eLHajMtUPv/Qfu79ilJGxNLQncUhI3UrcS6DXrXrXrSEFVdvXbiuiAWmtaA0AG5SW9B9Ufl9FPvNpy20du+DqG/sn7p/JH3R92/yb+yPsj8z90/gj8aY+2gNR31RXOcvGHDuSbfyTx1kXGt59KjEB+77RNplaEl9xLhpjaYjOnTRpuWEdur8kn+SS10JW3V3XU6ajSta1rWkNq2dNVdNVdNVAaAAk9JQoH0IBCEJ3L/s0r730FTyQQmL/AH7l/wBXpj7vf3KT6IWVgcK+OWa8zyeO+HsF4vtK2ttKbVt2GnEkFf2lf3ad/Kpf3p37fRfqqQ83GRiFnkCAhWoa+4+/9G0U4hJotpA2JpaElS0JCdqKLatS0TXQFdCuhSWtD9ChOqfSumrVttIrcdPqj8vopOtZFa+6tZaUikDQJ+6SdyPuj7t/k39kfZH5n7p/BH41HFAUgAiKW2HOTuJcN5dtfOvj3mfCOQxQFO3X/mxQGpUnQ0yB27KSlKPycCQwr8gNSEnpSB6u0v7o/oStOzemt6a3prqIpopUXUpTXrXrSSAVqCm9hpSdKvCgl9RBqN/euf8A1amPUu/nUVCHpnjl4i3rKmrDY7djdt9KXS/sfs8aX9pX92nfVVS/vTv2+m3VQgqkyLSyn28tEFCCmh9/6VJJooOnSVS0EKUjVPSTWw1sVWw1sNbDWw1sNdJVBpVNIIpDadpaOvSVXSVXSVXSVSW1A/WasqZyCKzFfR9h90fk3+SPu3+Tf2R9kfmfun8EfjUc/RB0oKGgUNMoxuzZZavI7xPufGLtw2Ou+lMlOrySR010yRo8UJcRpvWCIytNw01Tt6MnUlwUr7j6elelIQrZsVWxVbFUlCtEpUCtBNbVUSPoQQCRSxqL3/N1CFbYyVB65et0pmnyA5FYky1+PXiBZrPBaDjTWv0XTn4q/F2l/aV/dpz86l/enft9PWsbiqeTBSEA/eh9/wCo/anPy+h010rQ1oa0NaGtDSE6lLOo6On/AIchJU5ntu2sISU0kElH5N/kj7t/k39kfZH5n7p/BH40x9h9k/n/AIJ/EfeUhqRb/JnxOQiO+A2+36rIKfo361IQrqo/Jz+ytCtyUKCtCWpGurppQJKG1VsVWxVbFUlyPt6keupHoLj11GKaXHJc7M0pTJohOo2grLWwgUNgq4KaDnUjimHY5XKKFydEUlQScas8vK7148eKeOccwXILNKZARoPpoCXQNV079l/aV/dpz86l/enft9GhsctENUVDFaCtBWg/qUsJovt6BwH6LA26CtE1oKSgqJbIHpRFD76AUlZ03H6aitQf63PRJSFHI4hnRpLIbdbb0rQUn7I+7f5N/ZH2R+Z+4/BH4j7sfYfZP5j7D7fTeGB5Q+JTGUyJcKRaJ7hSR6CmVtpDqUKWkJ3ObRH3sgpcj7j0+nK11cA1StApCmjW9it7Fb2KQlWzaqtqq2mh9kfdaCK2/T/DQitacPpfR/xAb1ERG2a8P+MrFsXvuZ3/AMfPHKxcN2+NbQ0+AAFp3DpV0qUnYXfuunfsv7Sv7tLRqSnQS/vTv2pDOojRzInNNja2rbQX6JVu/rfpR0DSggSb7bIZkcr8fRqmc6YEzT3kDiyUr8i7KmpPkpEQjBvKG4Za+PI5b0hHkrH3R/I21qpjyNxpxcLnXBHDE5QwSdUTIbHcKQlxxCXEa/1LG4FBTTiQ4i6sFuZ9E/ij7t/k19kfZH5n7p/BH4p+7H2H2QdVttlVdKulXSrpU02pMjya8WrbyFBuFvuFqn1oSaH3dH/DL/IDUgatSCNXRRbJpKCP6EFvZq3Wrdat1qimlJBecQa3CgRSdNV7QhKwQ4pOl12rkqQRUdKUreTq7heG5Dnd74M8f8e4ds8eOlCQNB/Q793KXTv2X9pX92j9lfaX96cBI2qrVYrD4vVltpVRGgR+KPqp1tNFaAmffLRb6vPLWH2lF38i4Ol05yzOa5IznNJqJVwuc1ws6nojQMAHpGpZWiFw7r38RpJkoWoAodNHcQGAquh6RJVwhVGzfMYqbZznmkMWTyKhLbs/NWF3V6Dk+N3NRSUhCkuEjQ06sAOaisgjoDn0T+KPu3+TX4o+yPz/AMUn+CPxT92Pt/gj849KB10NaGtDWhooKq8pvGSDyPbbhEkWec2UkL9VI9FOesZf5g+qSjoyNu50itRWta1rWtIcVs6iq6iq6iq6iqZVrTn2pv8AKnlENtEkO/acT3VI++K4rfs2yDg7g/HeH8daSCQAP6nfycpdO/Zf2k/3aP2V9pf1PrR1SMfjbYI0AP2R+LdSHWITd85MwqwRLx5FtCZf+c86yvJbjJulwWlCkf8Agz9TD4e/6lEBU+yn12Jotp1CdK0/pIBpb01hnEOac5ivY/5Fw0vY/wAjYjlCUkOtuoJKk6pymIYcZQ0+iPxR92/yb9Eo+yPyppO6ko0DaSost+n+Df5x/wCtxlx17yt8Y7ZmcebDn22d96H3d17df5J/If2pAO5yh6/TQ1oa0NJl27b3luFd3bK7u20JlupqXbtVzLcR3MGu9t4r3CDSpkFQ7u3Aql20h6fDLnfQNcUtD+Z3zgrgrHeJrU20VUlGn9bv3cpX3XspwI0kNpLhQAHFoSVLTo4NxLadShFFKaIGsABuA0UuhSShGQciYliD+QeSSHV3zkHNMmRKa72rpdmoUfF4C7MDMQRu3f0BKjQbVrsFBsElnSrggphcOD/ULaylTiGSmtqaKFVsVRQdOmmummummummumml+lOJFjlIeR0oyFQ6x3knMMeRifkW3JcsHIuGZdEy6Qw9bUqbLStAesyhCrvAaKMnszRTlGK0MpxQAZVimqcsxYljLMSSG8kx50Rr/bFuxZkV9AbUaQw7uYZdAU5opCt39JGot0VUWR5ceMzt6TMVHiy0XCKkruEPomXbyBLt+vfRgiTc2KcuEQlE6CaROt4oTLea7u313dvpEcbO3FduKQykANoFIb1KykVuTRbOqEEKVtKOmqigirylKZFhxe85XdPG/wAebbw1Yo7KUkAD+nao1tUAu6WVipuZYjGqZy/g0ZbvOGDBUjnTH0g89WZRe5zspWeb7Maa5fsTwTyZYt45Oxne3nuKu0xlGPSKadiSAHGTOk5fjlniXHySh2s5JzFnOSg9VVNAGjroQdiFpu94c/k4lCgpsFISjUdA125pDQ1U1rTccqpuLsrpBIuLSjA4WUFz7IwrRUc6dsa6Bos6UWgR25oRzRaKaKUgKUElf8qnxTIYsk1MqKHU6JdBJbbUWXn4buLZO7eG7pkl17tdxvSyuTe1FuRKSFLeWdqzW1VbVUUKNBKxSVPIK7ncyhN2yVlTWbZzHMfmPkK3twfKLLLfc4fkvjspyDzBg0kQMnx26tr1SEAqBQUj6bGyfMHxkcx9aVRypxP/AA6gNwSNU7A1ISglxApQQCNK21trbSZStvdKrulV3SqDytG3yC48g0NDWorUUoitRS/UXRua/dPFbx0a42TFhIQNNKQQ4D/EP3u1RTdORcYtibnzoy05debMqkM37mLL5MyPJu8mjF6lC3Mihb45pEJLZSnQV6U5H6h7RdC3sKr26HoYWw9S7MJxu4zDZsguEu+PbQgFKtemqm0lNH1F2uaIEKwW11ptEegwCUR1LDccpKGda6BoRyo9maTHIIZJoMHWZEV2fDKNkrGWw82Y3oIhrsU05DCaMU12ldmk0/GCEra9TGUSqOQXG1azk+23RneW2/y9KS0tQwGOpM/J4yItxDaiOkabYJrtxQZ9DFSqhERr2aq7VQrtVamIo12blORVJC4qiZlkZmtxoot8tFohoEV2Zb1WfkbLIxt3Nj7Llg5XxW7rjuNOjQmkrCqmttTYHlLwK3xNk72nbL/IEApKOjJ03OHSnCNUEVqK1FaikNK2dJVdJVdJVBlw02wdSwgV0iBX3oo0FRQVPeKfjtbbbIjqecdGxCZ1/s8JN65Rg29F05KyeQmXdLjPU40p1TqCG7quQqXDx8RUN2xQCYZFdrXa12opUQmuzdrtK7XWjF0rtRXZrrsyKdiDp2Fgt2EsdR1yOoLMMV2ooRtK6Bp6Ei7Xs29plDcfSm4qlkRNAIgNJjkUGfRLGpSwAOhpXSSKDaacjpUxxQ+GLjiO19hMf+Paiu0rtBXZijFFLipFOshVdoBRhOU9FUgqia1e7S3Jh2OYubFZjnRDABQxrVunSbcJJdlONx/VMQLAiBNdCu1crtjo2wBQj+higntAa7RNdqmjCUqnLa4adgKSZlpRNptx2A+2wpRaYAropUWm0tVbsjyC2JxTk6ehFoyu3T3XAopyPE8bzaxc4cRTOGcoWQVJ/L/+N/7u0v7o/oRJTtS8gjqt0HE0H0ikvAlbg0LqaVpQJFKWkpUoJHiD45QcpeItbFZJldus6J2a3OQqXLenOKa1LjCl0WFJoo1GQS3rIzAx6TFlNR9KTHcSFxlqPaqFdsaEY121dtXbV2pNGMoV0FV0F10F0qOtSYzymreIRCzDSunIxSei5RZUKujhttsxCyzGYKI6tyY6jUdpSSWVahnQhomu3pMZIrtgR0AKU2AQkU4QlnjZW24YC1pBab0b2qNdFZrpEU4naCCC4kqJZNFhVdBzR9haj0HNe3C0XKGMfypCEEtx1KpMdSabZNFg0GV020UhLRVXbmugaDB3JiqKRH0pMOu0VXaKrtxXbEhSFAvMldKg6GdZ2J7MN1+1y0xxtbjGu0VSYqk020pIa75D1i5DyGwjHc7s17a5O4tx/mDEs8wy/wDH2YDQlLiQzIV6uKpZGqCK1FaitRSGVbOkoV010EL1CXKbCgVA0pqgV6mpCtrfi94/S+YcoN6seKM3/KZNzD6JUqhbZBowXElTO0raFGG46u/X+NYG8VxqfHkPo3qYh7lJgeht+le3kgQEUICdTb06C3oNCAjRVv0KoH8XImwiMnTtk10UgiOKeYTSmfVUYKrtUU8yEVfVryG9BtCWjGSVJhja3HCSI4puODSmW00EIJS2oVsVQaK6TbEOULO0alWttuLhDikVws13VjVBSpKIZaWhoaBpunWWykxUEiGilx0adsiu1QA/GSB2yDXaprMceF8smPBEm2sNJoMA0logtsbgiMnRMYaiMCO3roUlgbm0thKWmlHtm67ZFdsiu2Zoxm6cgtmjbwT2Bp63gifY4syNb579nmR0BxCYiVBMMaiEEpjxVLpUICm0FpMHMZ1uZ8leH8Y5xwd6FKgvf/xv/d2l/dH9CJCdvcJruE13SKRKAoSGjXXZoyEGtRqCCeM8X98yzKefcd4jgXXzQuC3n/MnJ2qPmhnahbfLy/rftvmBj6DiPLWA57SreUpyG426xW/G8duN6dTG/j2O8swltUt63Rg5keLN0M947bU5yDxskHlXjTejkDjcgZ1x2tUfI8YnnuIJG+Oovxg4pMYAduCHmQlwMahxjSu3BoxEmnI6QJbaWI+H2xbr7aFqLURSlNQ9KMWjHGqI+lduKXHWoNwXnAmyyEBVobpyAlulqtjNS7hjQiY9dmYTvBKrWjF2ZdqfrtYywLcopcjlFFKyFtvtHvWEltKV124NGKCHoiaMMgOMaFcDU2u3u2m+MRysojHREVe5qIkgQaRGAT26dO3FduKEXdTdvaUH37Ray7lmKN0c+4+RX+4fG1f7iccEozvjpym8rxJ0x7jaZoQ1HWCwtbpgtrGRYZbL3asbub8wIa6aZEu3W1nOPI3jrF6uvllMcE3y35LRIT5e8rkxPLzkJLeIeaVwRcfJnF7febCuS2sSdxLnqVaag/TUVqKQz/Do+haNLa2kBRptla67dQrpLH0jJQZed5BjuM2ie7InuoZVq42BW1FNfxE5RkIhSptuVwRz5mVvyTK+RsDxnMLv5e2B1x/yxz99ub5D8qT0zuSM3uK5GS5HKpyfcFDZvUW9qdTt6aSTqKRNmN1HyO/RjC5Tzu3Ks3kXyXbRjPlxenG7L5ZYNcHLPy7xtlrDQhutyYb6KcSWF6oUH2wEZm+Y9ns1sTa7ZGYFMxN5bhqVT78CGbryjxlZmbr5UcR28T/Muxtqn+X2ZOMzvKDlCSl3m/lK4G559msx0ZdlBr9gvjpF0vC3rJIS3FuVznxV++3xJRkmQIMTOsxh1H5r5Nhhnyf5fhVA8vM0j22B5mIDVk8qeN5ci0cycXX2PDu+Oz0mG0tL8ZCQ7GKi7GKEZmJtppuMHGWmCaLO2kJ1pba26kS4kVF95u4txaFO8sLAh6d5S5zIavHkRynNVN5Qz+4Km5LkktSZ9xcU6+86jpopLQUR1UBNxnt0xkuQRqZzrL46rV5B8l2pq3+VufMIheYqY7r/AJDcZ5ra895UXg2BZ1yVmubS247LSwsqDidSG9C+jpxGAUnjPNptlvuRW3imRPvtvxhDjzcVC9AaI0okfVH4UQDWxNaorVuiU0rbRQSemQdijQAFKWhImICV9M0wP8x9wJfUpAb4zTa8cw/IL/dMhl6iow6lEaFtR3JUkNKcCg2oAuEFGtGlnUa0FaF1W5wnUDSkrApb2xNm5MzqzQcW8suQrXJxryg4tzBVrnY/emXIqEJnTnbpyMliUk5Dm+J4g3f/AC+x9tq/+T3IF+cuua5pfp77iVNlY1ac0L6ytptYTWopSgaO2kLAMNz/ADozpQbk4SoOEq3ppJ0Z+hBrQ025spT6drF6ySyyrb5Dcj2N3E/MWagY/wCRfGOSuRZ9qu0G7WVm62rjKS/ItkaMX1X+6WHHGsp8osAwx3IPKbP78ZeV5VelGKvUx3lV2rtdq9olo1IaRSUDqPDaNKQNoKgUlKirpGumqukTUdshct09Rp0oOE5lKzmNPabjXXSm1gBZBX608jW2IH8ADr/9shKacCQVLAIUTWhrQ1oaR+H9JH10NfaiNadBCJiiuRoaj+kuUNZwTvQww407J3Kc0qMFtUfUo+5SVMpQW6R+Wh2qIB1Bpf2pKCaX+dBB0pY1RFTthD7yLbElSLLmeTYw7x75Z3xyfkXKlj4kl5x5Ach5dImXGdcXkhJUUKSnUUsEs7TSW1CggpRQSVUUlJ1pIJqINrrP965esxsaK1FD+x/Sv8BSyNW3C22IyETcSz7LcWuOKeW9xbuWNZziVlYzvymvz4v+XZFkrzUKMHYscqSmGjTtEV2iK7MUYgFdmjQxEgSYgDj/AKUfyo/UfemjouWlQuFRpT8J/Q76A1ojT6OndbEfiPufVEj7vfdf3R/Qj8f6B91favtRIo1qBR0NOK3rP3B0UoAr1pp5CacSnUEaodbCSdSlWhQ+2UKUk1qa3HbuNLJIX9qbcQKc06iToUuN7KCwgNkLi/em1BNOOorEHLY/k12jZDnmaYL8c3khyParN8Rt8esc74ns8gI5X8OfIHipT7RjTVf5aA60oNutAvOIU3prSCAH1IUdKaWgGMsB9o/51xJExJ/lqaT6I+mnppWlKOqfor1ToNY+vUkSA0vDbi1cuPGH0rUohVNONtmOzsYT+P8ASfQTAhLLytw9PoSCNBXpWutehqNol64ONrn6CvtRrQU0tKacIUrQU6f9MSBtAAIcPTk+pdJB1oH01rWtaR+P9H+Kvt9NK/wPr9FDQ/f6EkH6K9a2Cg2NS2NemKSgUE6Voa0OmhpQ0CvtQSo0v8q0NaimociSj2i6Q4rzZYXR9B4reEGS+Q54k4e4s4cx1iU5dLFaI+TXPEcQxy62qxQImP8AtfOvg3wLzVa+YuE8z4QyFAND7lJCQKQnUvo2GgkkxAQ7H0U/cR/xiUqCtKT+H0+41rWlfUjWukmorY697aBlxpUyM2wNj2oAWRo1sNtT+OtaGtDWhrQ0oEiaNI7n29Poft9B9GU7nJiSm40df6dwpwAWxB/iPU7T05B9XR6lRBSsn+hBO3U1qa1NamgognTTU1qa1OmpI0pRCQ+VByj6UolStK9RWpJ0FJKgTqK3UhZFa1uorVs3Gl6qCgvTaqm9NXQouJ1U7wD4l8sc9zuKvjZ4Uw+Fb+A+ErKy3gfF9yZyLxQ4V5Lb5T+NmbcYvih4T5ZyPyTiWO4ziMS3TIJZxqbIEtpV0Bi9427bIVlt9ysl0sipfPnDWNc/2LlvjrKOLc7aUKWr/LBNNLIqQ5vOpptXqwsh6N/F+f8A870xt2rpJIb1Nan6amvWlKUU6mgTRUpR9ajej1zXvn6Ck/mDqHkFaLFHkXFlPGmUrpHF+UajjLLa/wBs8qFHjPKtRxhmCqd4yy1pOQJW06snbqr6EkitTQJ1phSg7cSO/wBTRJrU1qa1P03Kpwk2xGu0fcK0ak6auqIpSlapJrca1rWkfj/QASVAgfQ/b7fRY3JfUVuCj60fQ6ih+f8AjqPp9xtpIrT6aHboa0NK+2hoagqStavCrwkncqIsOIw8QTbLWhVwRa8cst0YyWzrubd0nMXbEZLjeVe1CLeG4TcO8vpjxpdtuJhXZ4XJq8+yh+7S7Didunpn4/DujuQuJyb5V+CLtcYDaCStJDfrTY1p5O0+tNIJMcaPs/3p/wDz2o2DQ1tHT+n/ANwB0OtH7UAa9fpG/vXPVE7eqkj/ADEjQJrjBvr5Tl+aRsaS/wAyPR1Ypn9oy15TDiKvvJ8C1vW/muSubb71EyKFkMIpu7g/j9P8PoPvUc6PXBBTP1o/atRX3P0dOtsR+I+4SS1J+7yTStNU6V6V6V6Un8f6B9yfT6k6/Q+gPqdBR9KV6nQUPz/x0FA+o+1D7fQn+OgrQUa1FCvE7gmRzvyxabdGx+QpNwVeZdrVHuFzi2ti/XSbEZu7l9kxXbtnjOMc5Pw3rg3emYNvuMqe3FVkFwLVwu7t73XCyT31yIVs6gUxGGRXe5qR5BYjdc04VmsTIV0dWC196SvYXE6nXSmdNWSRIa061wOkxLiiQs69Uln71pWp20aWolPr9CSa0qOdHri4Vz9aRu6yFHbxzYI+W5JZuKLbbLs+mx2p6ROtxRDxPFo855htaE4/hdqfeusKI3DgWxIkcV2J2XkdpVZL3Wgo/bQVoKBJr1qP6vXNaVTaJIoE6fTU160+tCLaj8R9wshqSSVOk0v7oP01rWkfj/SfUfQDdRb/AIA0Bup1sorWj60T66ikNkqUNqtRQ+4+1JBP1IJQB6H0oggafT478CtvGXB82c9dLbeO/kvXyzuSqutpt4s11kWOQ0Zbhb83MhkYrzTj4hZdhmdxsdThz2VW1rC37/7jj3KHMGE8eWXJPkh8dHU4F5V8BcqBy8417fNur3tMiXcLvi3JzYPIykaIAotFVP6bdtNehaBU+3/F2cd7yGNKEcElohoUkbitspTSU606gp+gGpUjQEaVHGr1yb2TqZSS7s2jiC4ptOUMudSPkWO2vJoSsZTcodngsWyE+oLq+4hb8gckY2i+hiOxEjuvIjtZZcUXK/aj6H7UBrXT0TTKSp2e3sn0hG4uI2GkN7qUnaalNE29v8B6nYrpSfu79+mFFEcUI5rt67ekE7dTWprU1qaHoVKJArU6gqBU65tBNBShTji1nU0daOu7VVJdcB3FR1VWhr1o6mgSKH2ok7BrpRJ+kNtLs3hWyQbBwYJ93kNPwbzKhOWuVcbebXaHoKLzYpWPY5epEiy+ftgk5dyH4TZkM84lyLG7fdOOPHnlO3Xa2+ZHmdZuDMHy7Ncsz26hpIppyQg+E3nHcsLyKwZbbb5Yrvk1wh4Fm9ybvGZFxewKUaQ44CtxSq3KptRpkqD7SSp24fwkIkug92/RfdU1qqgpQpTrik6mg4pIdcWsamgTRdcNHUmOSHri6tydqaZcWl9T6yIF2m26XhfM8WZGbc6jak61FITWmqljQJH8sjuoslqzHm/ILpE3vBn6EnQE6gkV1lkUyohyetap2poLUmlOFdamkurQFKKj61LfULe3qUD0KVK6UrUl0eoWUlMhYoSnK7pdd0ukfj/QNCTrpQ++hNFvROtJG6nUBNa0fWlHQ60lrWiNqta/x1rWh60DpWtEHZrWtKBA1qACq4cSXeG/xJabxIEbHpOSz2sdx2Q1Ai2mzEWm8Wu02yzSesjy4dchc1cA3qZxP5ByG2ZOJ8VcoucVeS/Peby+SOXCND9FNNpR47+dV8sPEvMnljzS/gqXTJTrR9aFH7UnUGLqVxv7ly/5xv8AOkg9PQ1oa0OigQQgmnkFI9aAJIbrQ1GH+bN076mf7q2CkdKrP/k3DHpYmWhA1pJ2n+dOH+R+3KU+PFxsWyM2ZDKUA/f1o/b7UPU7AB61G9HrmgCZ60lG6lo2160lsmlJ0Ohqa3ut7Y/yxrr/APxySAXzqQ2SUsV24rtxXbikfj/QFKFKUrT6A7Sp0lIpKikurK6I0P2pRJV60yf5OElz1rUgjX6JJAGtetbllHrRoqVXrTL62H/B3k205jwwJ7kabbmr+i72u0XmPPs0aBHuqX7Ixka5YYPltbbi7zD5S2hWD5nYMjs90xvOsHlZXlhcfeC9Aqj9vTX44LTjz3G/nx5BxeZOQm9raPWvX6a161qQYepdh6KduX/ONqO7U0kkshRFbia1JpQ9UrKQ8orGlAaHqKFKUTTK1B+adJ2402v/ADet/FT5qO4USOIc5YucZpe76b1UfUuvNsN8wZQnIpMeFNCpKHg2vVJrcSKB9Zi1NtNKJMcavXU/8ZTa1JUs760FNrKQv+StBUgaW1H4j7gHpyfu79+oUlL9dcV1xXXFI/H67FUlsmunrRZKa/xHqVNaD7UkFRlIIpG4UEFVOoKF02hRUoELoNFQHodaQCQK1rQ7daSgqpadp1oDcfjE5xt2N5c1cX0XO4XCSmZOyIWy+XrlXjmz3vK/LjhLGL1fvPa4KuGZXvyH5zynyE4lyLL+HvD3NFZVxdw7bYk3yh8weG5nB/MyyCqkJ3nBYNpeyW05LdLXj+VcCuRzJxbJrbIXAnt1274AZVotBQaS2VGIlXXhfxeuX/NtghXrSf7OhrQ0NRRBJ2mletbTWhFKSRWhplALs1omTsVSUK3OIIDaVEobUBEelw5PE+WSru84Ni9prarXlhT8TDFIKg5cpLSmn3pKZrCW1kaUGjt9QfvSklxHT0qOlSXbkhSpGxVJSoK0NaGm2lKC0kK0NSAr21HogHUgHpSfu7qa6SlFMcmu3rt67ekk7dTWprU0HSKS+pJMpRpUrWidSklJVIKk6mkLKC+8pygpQpLyk0+4XV710h8pUpZWQSCiQUjXX6IUpIBNGtytuppLmynXC4da3FJsWT5DimR2L5C0X7G7ryv5ZZc5duJvK7NYo8ErOiLE8WOFsGiJ46wyBZjb3m2c5xuTkth4unO8PeTPGAMHy88i+DMO8h+N+YfD/nHhqZIbehv4NgDmZ3e28ZYXHgPsTsSRa5MS7RRBiKTLxuzzae44xJ0cvYg7jd1eWV/Rt4tqiuFT0X+5c9e7Qo7taSTsBIok6amgTpqTSlK0/lXrSlKNa0haklx7crWgSDJSpmPmnkbx9hLl08nMyvtYVZ7jCx3jDjN/H2Hmeq8GKLBA5fQHePOY77d8V46tHlvnEBrHvK7jqVb7Fylh+cNEnXuVdNS1E9RdBSincqmVErlvq6+5VBShRUdQVUh5SApZUdVVIUfbkrXtS4sKCllqSVauKXqH1JKZmg7uu7ru6T+P12qpLZNFrSi0R9PU0pshNAE06gp+gSVU4koXTbZUSNppPr9UgkD6f/b9Ffah6k6a4fyDk3Hl64B87OPeT8cnxHb3i6rdBXBadtsyN3xlWxuTep7DzAnY/wCbmPv4VlvEt1hX3y2x+6x49rdS5eLRL4R44ubnlZheMYjxlsCBdWWJlutfKuV+M3LFp5QxefEt96t9zbLgKeRccRkuMzYzkB3UUlBVUNB6zR9Zra1EtFCtKT+Gor0o6D6kemtaijpXpWopY0XUVcZp3n/nZ673vqOvDxdwhzKeRPJTkCTgeN8NXwZDxp9Llc4Nnh8ieUt6505u8kbhBhcMrUpKyok2y6XKzyuD+eIN3j9EutqCknQ0kaJ0pgJ6kxs96QPpoPp969BWtS9PbQPRI/kP7UkjVw6ktqUQwTXQNdA10DSPx+vUApCgaK0miUGigkpSQVEFGw0gbS+d42GkaJp8Fa9hppQClo3HYaShQOw1sNISoANmi2a2HTTQ0r7aCvxo+p0NWx9UN3xx+QPOMIY4jz3j3k7Bsf8A11MZFziyG8dfmT6skae235IceOZ7xn4Q3F8862lTkeJbX5MybiVlygL81eVbVj0ABLjC2Errzt4mg5TgfhHySzyJh+P4hDsU8JKSENLVzTj6LJkSkDc2QKi69VsbTLcBpxIKumqh6I0FaAVrrWlelH7aCtBR++n0WSVV5Ccqx8WtDzS1FpKEL8PseTbME8yb8u+5V4GZacr8ealTINvi+aXl3duU7/wBxlbeOMV8q+UkZVlJWVnWtabffju8N+Qs2E5Gme6Ngk0nXbTQ1XPXo6j1Sfp6V9q/x0FSUn20E6DUka9OSPVXqpCBQSkVsTWxNbE0n8f6GaU2RWlaVpRGg0rSlpOuhoD1WkhehpI0UPttpI9dK0NIT6AVpW07VA66GlA6VoSSCDSNSVtpXXGPNHI/ENx8WPPDi/lhi23Czi52mdkEu+IscoSbpjkIsYVha+P/ADWh31C5iZN6VefM7M8sxFhbjst5ptDQrKrBCyvHeFbrJ4K8iYais+v05dw1WW2RSA28BoYY1dR61PToV+g1rQ7BRNAaVr9FJKU1rSgQa9aPqrNcoiYfjOUZbd8svKjrUOE5cZfHloXhHGfJV9byzNvi1zKIMcmSGILHmv5tvZhJ8XOJ382y/wAkM/VhOFSEPO3kIXSSlX1DrqE8Jc4P4VMsWRW3KYIBH0SspL7Zdd27a+/1P2H0fVrbkeoA0Kf7Mo+qtQps60ka1pW2ttJ/H6mmdaKiaNamtaJOmta0oneTWppaiV6mk/kPtqaCiK1NamkKNA0TWp01NamiSa1Nf4qJ1pP1jSZcN/xn8+8o4tmYbylinM9uudivYnXK0Ftzk/j9MHmJm8sW+r9fLhjzPmt5XXrmbl3E1X0wgQoJ+w9D5dYG/Z/ISxqD0M/QelczYenF8hNRNQ6mpo2gvJVXrWp2CjrXroda/lSirT+VDdSio1/KvWkFsV5Icnx8yvqlqSW1FVcEY2/lPKHPuUysc41aW4mL8ZmWW6wc1ec3mfMyG7YHhN4zzL+L/FGJx7h3J3gHaeTLofixxYlr4ssTS55FeCuR8JIBB+sdtbj/AIc+JecTLLmOB3XCntPRCVKVJc6Lm8LNa/QIJQhW4VM1NtFJP8h6tyTSvVTYAoen9Cfx/oZooUK9TWhrQ0RpWhrQ0sEKINAHVaSF6GkglWmlaGgnU6VoaQk0K0rb/H0r0rT6D7keugpA1+oHoFaDibmbPOF77wl5y4T5CNzbYzJt/K0BsQmrs21iXyG+TbWMYZDjJt8nj/kyVkb0ZSlkEfTPuPMa5EgxGW4MdB9KP25peY/SgCaiAl5ojdNQVhaEgp0Kun/kCia/w1FE0r7a1rR9DqKQAo5vY52RY7yH448w8bQlKUS2PTwuxNSU+amVJKgrci0Xe8WGQ6VOr8FeIYCcwavMKK3+w2cgZFaBTt7szqZkrF7lbPLrwpFvlqSpKkp1rwr8GA3GZVHis8+x0kg01pvmtpWsJSC0ndTyQk6imtOnsS2fStgXFKE6pQjdtIblD1XqC0rUpSCNDWhrQ0j8P6EK0reANya3JrcmlK0SlwEbk06vbSVggLSCpaSdyaSoa9Qb9yaCxW5Nbk0mgdK1NH8XEnVpKiSQEl4ikrKqUrbXX9UK3V6mtDWlHWiAoNOux3PGPzyyHjxGPchcWct8e5/5BYbxXwJnmdXfkPOQ56cQXiFZ8oyLlzEbPA4izGbmhI0KgTSkAqSCDQGp5/uaw3qRUQ/5rdXBf8Qo61uJQKP9Cvt9D96P34wwCbl17l2GFNheQHx0YPnUfmLhDkLhG8cBYozhmAc+5Y5mXKqPxKgmuEMHlckZ7lOe2fhqw+O3mFmPM/IXlJzvmnB2If8AyKcvV/8AIpy/X/yJ8vmuA/NLkDlHK+f+B7Ln958KPHPCscydeqG9VCuUG0SccA9NCadB+gOlEk/QEitdTTP4n1UNdfXpyqX92fun7fVH4f0D7q10+q9CkDbQ+76CSBtoepV6Lpsa0pv+VD1+qEk19q1BopO1SfVseq0ago1LaNS4g69JWrSNK0FaUB6aVpRT/H/MTXD3M/I3DWUeSfk3kHPkkJ0LetdAKCYMcHx+3RZivyrQfUaiuVrwu65SRUMavtJKjMbFFrYpOhPS/wAkGkDcXG9qdRWtK/GkjUlr0OgNlstwyCdxtjlsxjFilJq8S4FvgeS8++8nv8q3R7AuMZrzsp3jTgHlnl9jNOOsvw6/+PfGTHGWHeTXJ6s0yLxgyqPi3MfmBZLblvAkN8yEf4/4+HY15PTEBVxFgybrP3J2EA1y/kMKzWHWmjqqUP4Cmkbi8jZ9G0CnAAofdsbWz6FKtCr1TJPq5pqyr1QNU7a21tpLzW3rNV1mq6zVdZqi4hVFRrU1uNaqrqUpZNLUoVcFhqQVlRjkmU86lMkqOrSlUp713GmySFLArrIVSFEhbigG3Ca3Eppr7uegce0LSzRJr1oE66mtTWprU0knVS0pG4mmXVMvKcWtW5VNKVQUabcCnPHLOrbf8k/pyy7px/HZ9xeuUxBNQT/xEcblzXCKJ1pAG5z0jCkkhRO4aCiBSgNvpQ01WogEHTgrA5FopHRbXc75FtKOceR41zaQ04pfM2F5Fyni+PeEWHuw+LsUw3izBOUOLML5A5T5Aj5JJxK78Pcu1i91fxLJcgiQuRuH5EZiBP0+nh3/APk7j7FJGWX1eZWnCwxyG+81d+RpkK3X++Sb/dFgaN/lKJLWlMqCTJUlR0pKymlHcdKWNkBP2SNVEaIkj1WAC2tIpLya6ya6ya6yaTFO3tTXamu1NBrSukSVMLFAVpXpRH0c9BdEbpSWv5NNlDz7WspLXo2g7lM6KDfogeriN1JY0LWu1SdwSjbQP8N1Nr0Luu1TZUWkEleqT1DTaSv6BOtKRtbSrcE/d1oqoJ2ltrcXW+mumfVUiW3Fa5a5oZm2748LVcosv+gAmue710LLt0LY1MEaSI+gVMb3DaQUD+Tg/wCGAoD12nbtVQQTS2NEdI0pBTTg0HF+Ot5DlcJTcOKX3DXOClpx5QSTt2lYALb7yKafdWHT1Vx0ayXoFvYt/ltwfnHGeVcC3uBlXDnMliTjnLdGvDyBKe5U49xCLiVpyHADOu7GMPxI2fZI4m5pSAFJ3U236yh/AfZtsrLrfTNaa1pWlehhaEUNSSClEknV37hoKKY9dsK7YV2wpMte3u1V3a67tVCYvVh0LD56YkSVJUH1kIdWStag113K6zmt3fCJPeOUiW6p6VJU3P7xyoUpTkqVJUh/vFVAkKecVMUFJmLJQvVLrqqDy2lBxvptqS6hJAJ+yG3HCzbLktJtd5K0WO9uCDj2QU9jeTAnHsrAZsV+ERuxXvaLNdgqTBuLR6byQl0ppaiaS42ust5BxDAbflvIuc8jTePuAhDf8eYsK33n6k6UwnWvIBxPvUOdAm0394rm19v7znFJoqoOp1U4Q2lC1DprBZjL2mM2SuOBTiHuktMjX+bdKUFJ4bmtRM1klKHGUq286DdjQ+6vyPqRruCtEgaJj/8ANoMh1PyIcq5tecj8A58iRxd5vWdUDnZbqVJ4q8c+XeVh4f8Ah3I4YuyENoKlpJ5BvTkK0pDxWSNErCqBIp1xQWKCiKUtS/rpSlhIfd2w0q1QlWiiolMjXc7996kkSFiuuuu4XXcLpMD+Pt+ldga7A03b1FUWEalQAUSYG1aI7e1DDWrkdsM9sg0YupvMA9x7c5TMEpfuEEOXbsKt9vPezYCu57DWrVEQlxVvIUYZbFgxe/X0Yp4e+QGXvY78V3O17k2f4kcebi2T4tPHO1Q7R8cvjbbJQ8VvFy2mN45+McUr4f4Qhr/204tRUDD8DtyHo2OsKJsIptNjdLNnsrwXj+HBKsH4oWuZxfwlcTL4M8fZSbz4leKuQNyPAPxUuSso+L3g+avkj4BsqzN+xfEfzdxlbcp8eeYsLXwu69aMs+lwudutLPIfmLwnxu9nvyMZRdWc65s5G5Df8d8imt5Q36mKhXWaHrNTSkmktLCuOcEuXIOV274z4RhP/GTY3mWfjLxwwf8A4vbLX/xfWapnxgY+7b0fF/jjSLz8YTCLRlWKT8Tv7Dqoz2O82z4MP/3ANA57ydAzSyhbWuoVTTjLqqFDXbDTuneRfJUfB+I8uynJsyunx3ZM6xdPMLg3P+Ub9wP4Q5zfOSMWx6xYrbHfwWpQVPkmLD5Ju7aLao7laEoS2pJZ9FymiJG00EqJIUK0VQCiNaeSdJDQNuQCEJB3anpyd2rqVE9MEpjJNCM1XbNV2zVJdd29V2uq5XVdppbhVDKzT7atkppRWUKShvqb7glTCf8AMoBZVeN/cf5lISovXJS27t1XatClrl3F1wTAt5VWaMX12jBsiukzj7wj5rzM+P3HMLBMFN4gQwq/tgv5PLCMu5e45xMZD5qeNVjgyPk14gguTvlJZdRdvk/5HVKlfJr5DFUn5JfIeaV/IH5IPH/3+eStD5AfJNkj5FfIJA/+RnyApn5KvIHbD+SnnNsw/k/zuonyivCrf8mHGsmFjXnd4yX9mxcn8WX9CMjya2oyLM7EqxZ15JeDt6k8l8+YPCTyt5u8xRJWW8ycmZypOiw42nb001x5lC8WymLKRLYiHV1gBTlzJTJ6+ldwK+PKFGV5CS3VypIaNMoc3c9ebq+GeQuIfPljljkVZZUtQWSFONTPMptMbyH3GmipS5Di0SQ0XFwiUOQH2nI9vtcS2O3m+2bH4mS+ZNos13HnS8Ky/wA1syvcC7ZTkt+JWvTwzvrdr59tCnlT34wNNAJpSlFJSCb+ALXy5MQ1H6xNIcSQtYFMKQXbi7se61NObldUV1BSCkxxIUa7kmpD49ube/gl0lW8hEl4gvOpNOLGp3mtHK0crRykxWNvasV2rFGKxTUVkqhRE09CJbkxdVqhtdNuC3uu8EhRhNilxWkVc2UuSjGYBRFaDtziNm7CGlVW+G41Let0yVN4n8UOXOTJ3EHxl4RbKwTiXjLjZtq4tuv5fm2H4bCzz5DOHcZrNfkW5ryCRl/NvKWZ04406yyzDQse2VvtgoLt1b7fR9tNf6ZQNtB1txP+m1pbTQ9tTRchEbrfW631/ptKFuWO6W0OMvMbm7jSZxD5q8Uc2J8hfGXC+VInLnEnIHD98y7CrBlcLkTxhu2OiVDlWt9zQppKi07xRfHL/hcP1dZBK7gkB/tSR2np8eQ057Sk9XIr3bsYszXlJwCK8xsux3Oed/GXJbJhvOb3k7wFGftd1gXu3O/835np3eRPamm4xS7IZLlw7JVRY62pnkben7BxPE8heTbe5lHIeX5rL1JGgpQBoIIooNcW39WJch2fJo0SDE8oODbndWSHWj9nHA2L/cYzsTlCSuTkSfuv/JStQUI4JeuTe+aYhCrfC3vuxVdz266jNqREZjlxPaGpMY+3CMvppjK1TEe6EuOvVUZWpimhENCKa7Su0pNvi7fb4te3xa9vjU1BjhUNrpiQ4+ESi9uDbBCEMhV6K+qEMCh0kKvrqH5AQyKb0S9c2i/eLZid3u03hrwQ5Mzp/jfwm4l48btrzNntzlxSV8p+XHEHGzvJfyCcl5kjI8tyPJn25EptImrAE5wlLm4Faa6iK3orqJrqJNb0UNDRNf4KUlNdRG0OJNdRFdQV1E11E11EU4pJIUlVR5TkY+NHnHd8JkuLwblbEvIDwsvWG0U3CFLzfi7Es6RyJwPmWEKO4OAEjxfvc1TouCmJEJrczefWZufNbna+PGYpHOjLZ6XlN6+PfpWvo4jqIQkIRxApI4qdOsvzXJHkH1n6jLeU9dFKE0uSDVueeTcPKNQXxTcP+YpP4/0Ildi9xzfbVmvD+dY+nGs84K+QHMuNrdgXnnxbnsy7eRy58DCHZkqx5RcHp98bdJVdXAhvvDUWWovXiS4qX1H6tzj/AF3nH+46kirdIdbjB55ddR+nQ4q0pceAS4+VIS4piUw5q6laSoua6vV1H66j9dR+kwH9vYP12D9GA/o1Af1h2+TUq0ylIk2mWFiA+QiC+F3q1SkAW9803Ad6mSW9vuE21xSrHx1kN5n8MeCOU51K4l8a+GeJorudWy1NY7dXZsblDlvG+KcY5r8zuReV0uzZj7qTtCkbqcbFBAB9KCimuoa6grqV1DXUrqGgSQ39JGpSXFIDbvr1da6poOE11K6tKWQUrUTuVSVLQ1wh5BZxwbfuKeYMJ5as/PXiVhXIjObYLmeA3haUG3cgcG4pm6c2wO/cfHhB+RbM7kpSbhA/5G8vNpmqUAOoivj1IPOTA/4TNcMtXIGMJ+Ovx7I8pOM8e4i5i8esFsXJXMcj4/PHbW1WqBj9oBJlebagnyB7hBqI+kv3ZaUy+s3UJ1szvIO1XLI+OrzFfh3CtTRUqt6q31upzYtvwsyNd+4Q8tLO/jPOrh6VeOuJZLZsihvB+eVptmGvOtSV6MNKvDramUw4hEeGtMi7xXFz/b11a4nTkFjqSHYRKIMFIjCAdfb5FCC0LN2L2rEJYKIMUsS4UPVyHE1XDZpUFzXsHq7B6uwepP41rRI0a01hkipLxSiS+CsHUGsh/wCnUyhTjtq47zTPrtw58at4k1xfw/xXw5BvHJEJtN4yydcmuvJNYpeY0y1+TnECuXMAu0CbarokbiG9KKNacb9elrXSrpCulXSNdOunXSrpihoA3ruogEPtglLVBPp9NT9FI3FLeh9PolOtYRmWY4tfuEPIqTm1u5F49xTkeyc6eOeY8MPhQ3S7fCvYyvxstsqXEaubcWGSmFkOouSnlKO5VfHj684Mf8noBQ9T5eePvMvIHO/jb44c04Vzcy4qcjtiKdQUS/OGSP8A3BamoJV1ryvdN0NWxJVc7g31X+UoIt+fUBqbtd5kBz9pu9ftN3r9pu9LyW6Oj42c5Xd4Hye2mZYM8h5BLRPwT0w3DWO5yTOXkxcOD6wS+TVyOsYetMf3r44VXLSrV/GRMVvk6GoA0jJ9S6rYEa+x7vRB1W0we3nJ0p9ZSpbx1Ssmgqt1bqTb42326PXtzFe3MU1bo6TFgR9JFta2SbbH3iztho2tio3EGdZuzg3xw8s5XXGvx2cQYW/ZcawnCYly5JWhqReLpMU3GW44yyoJXoKg3Z+1uWDIIOQQfIPxgw/lVWd8TZjxvcG3mnne0epyI/uVGdSemqumqumqumqumqumqthAoimz/L6LB27VVoutqq2qraqtFVtNbTWiq2qpsaHwZ4Hseb49d/HnimdEY4iz/BHoN5hZRYed/DF1ipsCVbJika0/bmHiwopTkLzabk4tnVCmyrxi5Xs/CWcQfOLxevNsPmR4yAf+8PxoKD5f+MSq/wDeH4vNk+YvjLHQPM3xjIu3mn4yW+D5AZtF5Q5K1RUV1KZF6cbE4yUA218G6TZTbTvkLhrUO6Cj/BN6Uh9SkkHQ1oa0NfHTlAtHOPyYYsJPEmB425lebMQo1picSsd7lnONwMW1tttuk22NV16bMZL6dI7oU9fnki5pbS8m0xkIkzmG3ZXZoq0QGlRlwWkqVBZUPb0psabcxtTbY+4PqZYuEwFTjwUekXK7IGuwRXYorsUUFK03KrcqtyqbJKoaiKkqIblLO/xb8Hsa5NxrDvFrhDDF2yBbbFH3A1uTSekpyTY8edccxawPUcMs4P6Za9HcGt66/QIBq04nGtclu8dJGXYvh+cQMt8DrbKuszwi5SYav/ijzVZ5Mvxw5mbq6cG8l2oucZ5c1TmBZQipNhu0RRt0hNe3SaTFcaKVNpGtNa/Vz8ta1rWj61trStK0rStw+nxi2SHL4a/QI9foEcjKeIY4j4Nzfhmb3DmnxlxHlO38icX5nxrdmnWn0/apdpgTRIwe7MKudovUBduW1+t6J1cdQGCtLFl7xun3kvPZQhtmx7ksNJmICr+vqJ3GoK1KlX+Qr3TqqqxPFN2u8pa7lyabBdMLvFybt0yTe5C0vSVO/wBCvt4x5ErFedfLvG2s28eOAeDn8Yxd6eEK8f4S3n+dbipb7i1pWFKKrpJIidRVWhalOX2Q576pazViUoTbssmXuqylXbOqVv6iqlSC3ircw7GZZLsh7/Luryw6JL29t900oqNblVuVW5VJtWMbfacYr2nGK9pxim7RixVbrJiihLs2Lhu42rGQ54L8l43mvDitQFKVqHVV1FAla66iyA4sErWAXFGkuK06igS4quoojqlBMpxNGVJNCY+mjOkqpTnVKIdvcC7NYnC/iOHyalcXcbzQ5wnxQ4mR478NyVTPFTgyauR4bcLOuv8Ahtw6ylXhXxrLSrwPxIlXgZiBpXgvYt03wV3GV4I5Eln/ANj3INO+EPICad8LeS26keHHLKBM8S+XYMYeNHMGh8a+YAHvG/mRJ4HyXyP8eEzfMzPICD5w5WByR5leQWXwbW1l1lunjlzjmOesZFi2K5bZ+dvDfIcQUVgDA+GeSeR6wXwVkTjbvFThnHWMi8LOAcway/4t8ZcGVfHHynbm8x8WOU8Ts83F0W9TFsx3q3BizToTtssDiRZcc1udssSmuysukWHZ0yL1bMfNxatdjcVk2acWYC9m/lRdbnIvOXZPk8txxbtaVpWlEpTW9FWbG7rkErjngXNZ2WuNx8gwWZbrGuMuz4y5XD2P2i141yq7a7ncXoNi6nZWPW4WvHlRRaMfIh22ytOXez4y7d/ZMPAtNnxVuVNs+MPSvYsP0ttnxlqL7JiaybLh5KrTjarJ7HiCC1Y8N6kq32gN3aHbOo3As5U1asfVQsmLGvYsQr2LEK9ixCglGm1FbUVtRTbTa1WdvppmLWWriElzwt5yHD/L0QNXBgQBRt9KghI6Daa6LVdFqui1XRRXRbrot10mq6LdLjjUQ1E9mujCVqYStBDVXaOCjEcIERw0IjgBhuarjLA2EUWxUlodjY9qYPv1gNNXOwLLeV8WasOYzeAWylRJrQ0NaO6h99DW5VAkGybJC0x29FstJTMcjlch+OwufMX2nVy8m5PZWqBdwn3PxMvN1hcYzOVLjb2pfkPMgJg+Wdg60HyExC6phcgYzMQzebbMF+xHB8rbznw58eMqXl3x14LeGsz+Nrlm1oyTxN5zxNrMMcv9sKXGCbPLaRMypCkXflDke3cdY9kGQ3DJ7ggaUPy+hUBTaHXVtWYyEs4tGA8e4EdGVOr2HiS5RrzgPIENy2TIqU7sVYbt2C528uS2vRSkpTuvI6MMO+lnUtb2SDS+hwVjyi5Puj7ap+5qrApCbay4k11RUpKv1ZgjayNXX0lLF1Gru0BbSEmlNNV0m66TddJugq1abrTW+01vtNMrtG+0rs9TTYg1P9lU5wZwhL54zzjrFZeF48q2FCvb00bY1oiwRn6Ti0EgYnAWoYSwa/RkUrA3jSsInpP6ZOo4dNQn9ZeNfrDgr9XfUXMYmgfrNxBONXElWO3FKTAlM0IUlVdlJow5IpVvcdr2FCq/X26VjyHW2rFIkYxeuAuaYk17grk7X/21c4MokcV+QONK8RI3K0K99kiuxTXtyqNsbr25hNdkk17dHNdg0mra12jvdej85W1aVqV2ySe3SKeclNVepM1dmvKgLt4XstucPPWu1yBcsAxG5Nv8A8aSlp4CwhgtcN2WKlHHs2IpOMXlilWy+s0u/XG1LXnDbyRmUKUzd7JxzlcfKPDHgrMKzH4v48J3lbw65qxV7yqjZYzn+oJb+w++tY5g+VZa7euBkYrh8SFGiR9Ego+/juWjnbibQ5XjzLQqx5/xaMrusbx4mIkZABZcLu8iHItKxaErabtCzexanovbWcCys2dLuSC0Kv3bWarKm0sTJjFoVN6FmqxRrP7YyxZtOhZ6mKtSMVaRaEoZXaOq+qzlm6rtXVSq07mzZ9Cq0Cupaa6lprqWmh9voT6M/lbdNJWgauhAd8EOGLXxhx1zJGdtVphgXJr2tyva10u0qNG0K0g8ZM5K0jh1o07xJJQhfHb8QpxNpIXi0dZTi5RQx15Kjj76qXZpyEuxb2lPaX801FyBNKbvSEh/KU0ZOWg9fKk13GV0JOQ0ZN8TXuV8ANwltKsM4phOSrU8hLWMuU9Cw+lDA49dfG1pZaxXaUYoaMWyUmFZlU9DsqQiBZlqTZLUqvYbVSMZtyqk4vBWn9YcSP1mUKVjksUjGZ66fwu8KF3wy9N2nIELRk/g5aJsvhFNskro2qcijap9e2SqetTpWIATQjbacQEAojKoWSBNS9gdiWiTxi0tUjFsrsqnMlyuItGb2VSM04h4Y5SgeQHwkcGZgPJnwT8jvF+bYbU1er9xlwPglpuiWIlukcsbTh41LFJ/LgT0zMg6eMLhXaH2kKcQlIVyGSjBb46l/GR6r/E5MNsBonW0f3L7/wBefJFY2U9TeHJNWPT2pn0S+kqEoEYigfwZ/vSNeldAeroQpr7ECtRWorUUJONgd1jdd1jddzjdCdjTa8ZgWS7IvFpjxEsYaxeDiHlPMxGbxj5FRPI3GOC5ar3g67eBXY6hMICkwUdS3uZXGydqOHmDAWlMy2eoggDsta9mkKUu0yHh7U8yFWuYS5ZJblfr8qkWd1k+3+q7S44PYpVewyq9hlV7BL19gl0q1PJS5ZZSzabRLTePbHKNuVuctaFj2WKmk21aKVDfNdouhCcINrZXTlkg7fYLUqlY3alE41bAFYvbpFPYdY2x+qWLQ4nYTQxe1JpeNQSk2N5o3SHOTbMn/wDUnjr5GWvhvh0/IFiihwn5Go5gju3vOQtyfmrlKRma6WjKKScnCUru9Fz+KyFpDUuil9BddgrKl2NSbhi+MX+PP4UYjyVvZviUvkXkrg97EPNPxVxaRnHjtkNou9ukKxkyuahZf0U+iKH38fe1TlZdxevGpu0Ls606lCBu5DUynBJbuPJsT0jGd6ZWO6332N63iVjwFonY4Hb9Oxz39x7G1Kx5/H1zV/q1tke44hussnGn7YxOxJsC44hUtqxS8TamYeEMycN3ypNk6d1uNkS6ifY1KYmY5SZeJGhIw2uvhldfDKEqPQfZNdVugpJpem7CkqcH6Zd7qrEeEcxkx4XH2aT5HjFhGTYbYPH26oayNUrdQkp1MkAypREe3Zna7bSM3sho5haxTGUx5csOa11CKbkKFNviluJXW9ui43Qea1WWFVsigI7cEvN0XmgQ60QHW9S8itjKqCWQLKpv9lcYa6gYaAVFZUOyZrsmAXYTFGGg0IaAOybBMJs0LOy/Qxps1+sor9ZTRxdBr9Vbp3FG9f1NQpWK7RMsYaPKWSR+M7BL4HTfr3w54j4NyEhXxd4zu4E8ULX4/qaguNx51zjxVyMmjdXIMkyXqzLhzA5IxHLM6bZjXlUeM1c4kqkMoWEW8hCoaVUuIylTkVspjw9Q9aGZafkKgiDzKJfYs3fjKzi8TGejK5YTrho/kzQ+/BY1yBWoHichCrWr7p/LlFrq8f3kuRcQblPmmn1E5M5066z+lnde6t+ccF+MhWthcbLrzxckDp1blJRj6dgH+VS3AMPQ2dGQ6HZEhaWbtNe6oluBTctZCnlGuuquuquuqg9iuhexeupjVFdgNdO0GsdVDEviKzGa5gtkaiW7BrhZbTkvIOd41fZnG94Q3yX3J07jWusTS3lCjbLO9IXEt+vaxqS200picnSRd4UZMnNrDCQ3zPgS3YmYWCePeIxBuQI9wr3CvcK9wr3ChcCK9w9fcKTcNSi4+nuIq1S2UX7vVCu+VXfKrvlV3qqMvWu6ruq7qu6pMzaUXMivda91r3WvdPVFySqu9apcttaZtya6vmPdUKwO3SnmEeKFxUeRwpJouMoOacgQbUcgzi1Y7Czrzww6wvZn5v8AMd8e/wDdp5ALU350eQ9qk4V5/OzI+FeQfHPIDOH5Y7FlwZ8C9tmM3uTEhqrsrdo5DY1ZjttvfKvicNhb21KnZD7bE/jiBcpvNsKzwcUSf+GpP34FLKcmdNpdrjblu38XwuPM8Y5DsUucITOS+S1syCw3lVkkYoynHHEsuY40cnOOrQJWN6WiZjXWyFePG/vLx0VjYx1x12PYkSSnHkVbEY67jpYsKAU44CpvHVYch7H0IZmY4HX2cbcj3mLYusmJY97cWyAKj2IV0Mfrt8frt8fruKEkChKRSZbWneRxWMstPP8AAPKNpxu68Mcg4/fMdRjGLzGV4Pizxs+KWGzPhaxRWqmyo0dxrQ1tNaUUa1LD++/3GBboea8j8QhMPJOCrg9xbMwWbPtqnS41IRQUD9Nf6NRWorqIFB9AruG6bjpGS9ygV3KK7hFdYUZKQe5TXXFdcUZCRRlIA71Fd6ihLbNGS2B3iaEpJCZTYHeM0u5tpTNuRVM8qpAlYdaFFKPGl8jlz3JoHKrq+2zzNy/YuI7BypzFnfJ91eQjputblJbAS/b0v0HVGJarnOsUjgLzE1bw/N5Vtj2u6JnQBIBrr11q6przT4uc5d4aveM5PYJjjT7IgyUKleVeGIgY4dQ3SfvwerbkJkBJmy0IY8ZlFzju+ekBtaC9d3UnFoylhtK1A5Q4AnrLBsrgL+Uubb6VKNYusdWa4e6Dyqsy0nHXXVbuso0t0/qDbidjKmy8882It4WOuHQFh40p+i8a6xrrGhZcLI9nwxutuAtUlWB0o4LrjMzFmkzLzarE94z+aTNjtk+1WtmT2DVdg3XYN12Dddi3XZiuzFdmK9uBq/XCy4va+Z/kcmxpWactck8g3EsMpUWo6atsty1TOLvNTmjjO4+PHljxh5ERFW4sU3BCyqElKjC9EwkqoQxTsJ0lcCUoG1S9Ta5gHZyyW7creIitHYj+piyjXZSq7SRXZyDXZSaXGkIGx8UtuQSWZpPQnUqNMVQhShXSn6GJLVS40hFBLpqbIcjFR/z/ACf/APTNp12eNo//ALbU28o8hX+2YXjvMnJt35Wz4K3U4kqSpsalGp6atdiq6aqSnRPhrz0iULTJeiw3rldCfcL7XuF+oXC/U5k6YjuQWnHslbunCnEuQw5HiPwwxcvIDi2JJmZRj9xx2+9JVJBrghUVvJQrDl1Jg4GlHjuiGjjnJFpbtrysbVMnOY01aETsUePfYpvyi6YyHBdMXNWa440p7IpmMtXsXTESMduOLrdlXHF25PueK6WufjTlgVcsXSTdMUpy54x+oouuK1HnYyt59FhEe+SMZadN2xhK27zi1e84tXu2KV7vile74pQfd067tEk/RZ0VhdriyjkzDLUOZcZtgk8LchWbNOKjkccV+xxK/ZItfscWv2SJX7HFr9ijGrVLE93Osyx/jvGfKLyuyPyFv+gbbP2cIAc+3+I9TYrhcbDcvDXzDkcrxjJDBMsLpl5CqKmxXVjChcGG6F1ZJ91aBNzaI7yMoJfD8/tlgpaUii8NesmusKbW0qtrFLZjLBiwAe1g12sGu1g12sGlpjhwNtGu3TS4jSqTbIxVyS2m2vG6uCvIecZlptgrxjY63LSrTIWv5KuTjFjtFJbbJo/jqBSiCqiK9a0rEbxNx3J+F8oi8q8eRLRGQn22Jp7bDqVAYbj4faIF1gotexPtcpyuafLfPI3k35J3Fybzp5b8aItt311ofjwz6XYH0lKJZ8aVpPF99/5GOpKpV2B/UmV6NoJKsp9XNNasQ0eyz/rdYx/cm/8AM1Y9Bj7uu6lj/wAmsI1TGRo6/JWYl8K3HxEBW3CbowkijEArtRXaig5hOnVwmurhFdXCKRIwVCsdRiEsXVjFo7N3uGANvfHbzxa824Ej5NAcpu/Wxyvc4dIlx3A2tpwqbCRFT3DlltsiGj5APJC58n5w2z0vo0kvyZ/Glzt9qOmv2+jHrVmv10xe5eLnO6uZOOFXyamhkNzSff7k5SbnLcpF1WhPuwKjdda93AAuwBakzV3pK5BQXXiEykpHeN13idBMXXeuV3rtCfJ297K176UaTJnqHWngrfkad1KbButwSfdbgQL/AHRpec5BbXHE3nEV15GizewRJchhHi9dZsbk2HkciOfInkiXyly8Ps39idBaOPb1kce62z2id/hrX+GmpB9fi15CZvVokPtwl+4MV3zAqTcme34vltIxHvI4qfPUtm9cAZez5mc93yFfeXc4xyFl+L5Ri95xG8p+3CqoMfI1S8EbqRdOPg3449gvju/Ei3Cfh0WZdr7jBxJl3EyhifjCFZNcsTDnueKVZrjjKn8mn4ym/uz8ZNYxLx3qy52Md2i5Ytpb5+MrsHuWLIHueL1KuWLM4W3dsUKGbrjKnXX8c7S+SsaQ97tiqVt3nEKN3xM0btiwr3fFq93xata1+rTaXV4tsipv8vexfCpx/wCK/k1m0c5zuK2kOvcZXFgOYdfW6FsyNgu/ssNSckubFQ87chO84+UT2D8XzLjMuk1WhC2Hlp4iwaxWiDyJm2HPWRTfp0xWwU0dKfBdb8TOYHOMeZ+9ZuQ+mhrStK0rT6bA3PEgadxXcCu4FdcUJGldwK7gV3AruKRI1Vl3O1iw9yV5VwIQwbky151jwyKKS3eoa693g0b3BRWTu227OhdkSPIVi1JxBhzR3xrT2maeRmdjFOFpktU1wfZuuKMVj5JdpV2xfH7dk0xq533pii2K6Y16YNFAA+P7NF4j5DZFksBUn9vhCnM2goqRncBaMfyuw2CArlKwBTvKNqKc3zHGVMZDJeumSFs6eXXHjTFsA0HGZP7A6vfUsgNeLxB4vvv/AE5j/m7t/wCk439hI/lk4CV1Yla3DOwlnJ3HtKxV0dxNe1lhw7LI8P19x7ctxz+E5APHLKE9GO2nrvuJbiZE+kvLXq4hWlBdJKTQQg1000MkxjT9kxiv2PGBRyLGTTGR4yVWe+WVZud2snRn3zGQ7w1y7ZuNeSsOzax5Xhvd29wBu3uUixMOiTiUJ1EnA7aW3OPIqz8kYt2MwTISaS4DSEMuGJcLvbWXmSVrHptVW1VNg6EgVNWpt7xoy3MeU+MfaOSjXs/JVez8l17PyXXs/Jdez8l17PyXXs/JdJsvIwUi1chBPtfIVe18hV7XyFXtfIVe18hV7XyFXtfIVe18hV7XyFXtfINc8We+49d35C3K4Ii3OXhycWz4p/Xc/QfYs/p2w5+RdrfnDNN2LkBw80xbxBiBIRJ4iTc5WVeej+TWziqUlKCPszt3MQoyxe79e7lWmh+mgP0V+PHM2Xbs2h4nmt0ijjLOlpb4dyR9UThefQ4RLha4EjBEbg62Jb8t5/GfDnFhCkoNeTcy2NcUJGjHC7sWPkgvGFGpF8wVKfHGfa53Hd+AMAZRiwmXe/44jEouS4uGE5NixVk+RYx1TkuIirNlGFGXluSYw3kq8owHXHMjwxT68jw5Ev8AZsFNW6/4m5jyciwptJybBTUjJcVThKMowbYzk2GF1/IbL216yKxdYZJjQWjJcOo5NglHJ8EpOTYJX7NgtetetaGtDUT+5jlXf/lrv/zCkKcj+LfyPXDhfHovyccVPMY18ifDF6RY/NzjqamD5O21L9g8hsbugiczYxKk/JZyLjeacggrCWydX3YzC4RdLjh1A+miiEKDjbgNLQ7s+MDlXHWIoynBq/acFr9qwWv2rBa/asFr9qwWv2rBa/asFr9qwWv2rBa/asFr9qwWv2rBa/asFr9qwWv2rBa/asFr9qwWv2rBa/asFrzEctl8jbia8Sc5gY1lScowwUnM8Xbo5xjhJzfHNMtzzHG7i7n2NuHn3MrXluRLaBc8R4tkjzflXvFocly/UD7fTcmvv9ACougxnaVrts10asV1wDkfFLlhCeSsOQJXMOGx3Lr5C4bbH7v5W8dWOPknyE8aY89yH8o+ZJj51n+Y8kX9z8b/AH+1Y9b/ACD5pgchXNegPGf8b/oakJJT4rgjiy/f8gGguTfHeriCP7Ef+5k/qtz7WL/q3JIP7e7+eKf3p4/45qrB/wCnpX9yrl/+OmP7TH95/wD5PIf7yv7jf0T99K0oZDj+n7Dj9DILAa9+sNRb3YOpY75ZALzkFqEe65FazIbv1gDJvmMa5bk2OosSr3iiqZyawsO55ypMgXnH/JzPcYXxf5Vcm3PHOVMwfm8osht2KFmsnt0a4RMWyZvIrUFao9aJArKAt+yYvAZt+PvjRNwuK7dbcC8hb1gGTMfKNzGZF8+TnmGI9/8AKFzLVl+UDmCfc5/ye8ywp3/yh8yVZPk85dvEpPyi8ykD5R+YkGH8nHMFwxr/AOUXmWnPlO5iiC8/JfzHAif/ACjcx1E+UnmFc2+fJrzHarn/APKNzHWPfJ1zFerk98oXMjbx+UXmUDDPkTznkFxubHmohXJ6GrmDyl8guNJqfkG5wW3/APIZzVS/kL5rUmd52ckyq4p595l5IdhhBRdrnFtUKZ57cnYhN5W5sznlm9ccS1yrMFqIbUTUa6SrNlHUpK1EIUSp1xLCLCTk+ZoJI2qXXMeT2+z3bgvzinz8MkeTudrNy5v5Du0i6Z9l94E65TZiULUEjrlu8cl2C21yL5KyrLZM45JzvOZncOEJV68UPMR8oN/x9JeyHH93jSGXONb+Cbci6Y53t3m2T9PbvNjbaRerAtWT3a0BwXywoqz3PHHpuY3e1LyNV7xoKxu7WEvPXey921e8ZNWm52UY+bvYU0L9iiKuF1ssjC0ZFjIbj5FjHUemwHIt8m2nrImWALTdcbbCshxlNHJ8ZFfs+M1+z4zXaGkxykJaIOlRDo5jxTpeEoMe7IBkLcaSmLCemqvFnfuVsGGXHUYy6s5XAN3mnFpLbvCaEWdnlK3tzsow+Sh3G1gCk6awbZAs0cSUpUZEQJccbcptIDrKglD7iSnkl26exKxWa867iE8tZBa5N2Y/W5dQrHKjTcgx+Xdcg/VZtWuxS4T/AOqTUpXic1aLZZZUPFDis4FWKTlN3u3PXK0fqs4G2WCZClXyxyrndBis7THLZJtEp7F5jjhxSaU2a1SrfjviNzA5MskhCkv3CxWvIIOceF9qeM/xV5Ihot/i3yXOdwLw3Ytptdjt1lt+oQ35J8sQUW561tuKVakJRx0tMaa3+DdXO0226L/xS9HSO5jJMhC5si2We32dpCSmnXjHbzNNxu2UYO9OxfJUZxZDQy/HnjCvFtmqkdGBHyLmXGsdMDmbJ8kvt0fkzLlnjTpsqnN7S0bihGquNWv/ADA9GKw/EW3Xiysq4pvp/wBNWtpyVeLjFOGiWw40w6lC8m0pboJsZAu/JCx+3rAUcTSC9cABLS4E1j7qf12U4Or1k1c3R/t0wQtuHtS+88DCyR4dZb46geTSnhr1E11U11UUMusmicssBoZVYK/aMeqJlGOhyxZVj5F5y2yJj3XMLJ3Fpm2O6NtwYjRaUGiqSVDcmioGtyajSHGC4tTpxW8Px1svF1KfyIBpcRp5tywxaixkRWv8Ueqpj7TDeS3VcxxP+TRkLSO7VXdqFd2qu7VXdqru1UZKq7lyu7VQlqru1V3aq7tVd2qu6VXdKru1V3Sq7tVd2qoV+uNrXwd5Hwb43FlofGhFaumluyHI+wtpkXy3wxzj5DG1B2c9JoaKrQVAmrgvQnA5Fb9ac9acSFF20x3hEs8ZuuzQ2kp2USE1meQQ2Ib0dqRSoDApmK20FNtKLMhyNU2VMuCUw2QmAtNllPRnlu5ViEnJ4Nn8dLRNl4n4CWnNYkD4rZdwTnfjLb/G3NRmNgp3MMeK/Ha6NDja9Xhhy3/tsUzrtlUVOIxcstgbby/G9coyW26nLsaqzZVjSpmYZZanMm/b8ZrHMtx9bz2W2NqSMvxmrDktik4+/ldgZX+3YzUnJLLKwZvL8daETM8bU/KyC3rjZHkcNLysnhBxGUWs1+02QUMsxyhl2NCv2/G61Gm5NbhW5NRHUpcx95Khdygx70W+4wEIeiBpAH+P9Df5UXpDFYjc350JP5Ugfw1+n+P2rLrkuRTweLi0hRWkJH/0Z+6QKjW159XFuWZrZX4nKclxLHJiOmvkpJTN5DV089m33IzPwhmC9KiKiFJ1Df5LOicNyWTDkx1BxlX21rSkeiTR0AlSwyq5xJb9yKiHT9/qFlICySCUghsgttUhpT6vH2zTrPxmltQHmEoKznUaPq0Tg8/sMQ9/fKsn3v5ndeqnEE9RSGkpSvKHU7NyNbGB7jn7qJOVOMlVYs0tL0xpZlt7UiwupGPvLQV7kVcXw5gKSkJiN7pDrgELI32+st1BcS4muo3QW3XUarqNV7lbdPc7aK9ytte5W2o1ztwcsF5goF6v8FMe9ZDDMjC7xFlw66Sa6aa6aa6aa6aaSgJOtNzOyVZrkFxmJvVQlYUA7pXV0rq6/QkJFxuiG6vs3u31fdSQqukmukmukmukmukmukmukmukmukmukmukmukmukmukmukmukmukmukmukmi0nUNim1vN1FvFwhqTlV7SUZxf0D97yCnM2vzoOUXdVSZkuUo9U0BoG/y2hVQoj612i9XBthhfXbCfXbQSnTcKfkdBLl6SaumTdJybJE2Qfv8AW5umNA4nyezXGOGcHUdmCig3ghqJMw+2OMcvTGG1cwXKuVcltV6spukHRy7wUt2CdbPYkXe3lV1yoqvd3yn/AMnxckStKb1CJye8QtibxDBsl7he4Z7dYcTKE3iFtxa7we4n3iF3PvELSxXWGcefvEMrN3g63K6Q42BN3mKagXiF15V2hdtkV4gdZd5ghxN5hV7vDr3mFXvMOveYdf51f51f51f51RO56mP9xpeP+XvH9/Fe/wCq33en9bHV69m/YeyZ911T2tf8DQ7Gv+BodnS+1q4e+6ZL3nWP3/8ApD9//Db/ACV+PHXuPTT79ont6Pt9f6dQ9vr/AE/RXSqR7xpffcqd16p+/wBP5dXNvdu6Z7jXDe498ke694fdawjr7f8AjtVe5aWbb+q0Nmt79y/2/je69VG/fd9P1GPu6bG/XJt2z11s2vunJH/q97fpi/V60zqd41rpjH/p9O7d6Vetf9uW/wAI/U67/V7LIup117uo1v0G6kaV/Gv41//aAAgBAQABBQBpn/K6NdGujTjOqePoqTK5EipTA7RCnuzaNdk1WVx0ItaYSCewSVclM9S/JgNkW+3NKn5lCCstTEGmBxP/ADhdYOt39vTWExgi8MwgquxQKwaIOg1D3J7CnIIbwFMJSQqKQnLYgbHYVZoOl0yGHpd+3XWFsqE1mEoHtBVuiAYw1G9DFGl1j7LK2wjaywjq39oN3LoisejhV1VHOvbqqDGJtDMZKWywipMcKsjURxQRAcK7sw03QnWkmBlWGol8k8x8W8fWDyv8yYXNvOnx++VvGWZcRNZrg0ik3fGH1Q5dsNmZjMPiRFCA/oiwmOBXbishbBtPZpNPwkhvKIQVM7Ruo8VAkZg0HMi7NNWWIRkNwh7rg5B/jhtvSrIn7Q2qS9akNjBLa102balDXt9WSPsxPs00YiQDGQ9x1It4CzbxreLchOJe3JNOW5AbzaKkxWoSNWIaRI5LjNqzP21CqwqClnJpcFKpqYCNMdhhNj7M69sxUiMz/tyYgCiwnawVoxvKkLXOYi6rMWu1rta7Wu1prHbb0v1y21+uW2v1y20vHbbph1ohxX8wgsSoa7BA6xx22qBxmBpkthhGGMet1Jx62786sUB+8iwW8VEsUETspsMFWRCwW/TD7JDayW42GG5dv12DWJ2GIi7t2CEa/XYIrEbJFZjNY9BSPYoQpVohqxEWCAqlY/bynI7NFlLFkhAWuyRfc71ZIxu3sUKsXssRExuzRNos0QGLbIicbTZ4igbREQLla4arOm0QtrVphdW+W2K7PFmiKNmtLMa4T79hlpF48guDrIZ3mZxnbYM/zXc0l+Y/Ir7tz8oualsXLyWz94XPnTNp7jnJWZzHDlGSOhE6UoM7Atu4vtGS8mY26+4KN2urZjZplVuMflnLGk2zmzJkGF5Gcxpt0Tye5TZVF8vc2jB3zStc+BaPKbiyabNy3w/kbUu44zfHBamCWoERD2TwIrt+9qhVabXEbvNwtcIzRaYRON2uExfF4/H6qsdZIxjH2WmUYo10/wBUZ0tuOst45+rsUcXYp2wss4M7jzG1rGmSbtj7QxcY+xo9jjJZyvHm3mm8Va1jYsyJOeY2zIycY3H0xTHGRf5uNsCWMbjaWHHI4s360xX6oxrIxxpGDLxpjaccZ0bt7YsWRWGI5MZx+2pUbDba9gttew26vYbdXsNupr+19T61hzBDmVE9msavNtL3FlWmQp3wghNFtJrODuvu2oaD3+UeuTbDWItn9kuTZN16CqxJJRdwgqoMKrD2y3EZbO3pE0ppP6YUppLYVV/QVV0BVrb2T7wP9T6C6xlpYmtNq2dNVQ20jHOnqOlVwaCrPJLMBjJufuGMObzjzrwx+ZfPMHlm6NXTljkzJRMU/OW3FXtb3ti8Xy0WVM/kh6QmVc7ldHIi1MIS8FmP6FtW0I/nTbgTRkKrrE04sbXVjc6V7VOHbIeOrVyuMN22Z84hUC6We7IaJRUgJdK0MlEGRNti7Ty9ynjKsZ8vOSLLUTzdwq93HGOYOLMubxxAfvNxQTcggg2Fsm6PAh50E1i6NGwypCemqrfH3Y72xrtzpIj/APkgxwaEYJq7oCsdLKAl5v8AyclbK20tKBitKMrOmt2TlgVibH/mCdH0lhtAFkSgWbaCktr1eja4K5H/AIlj0YYT7Bk8cd20wAosA129dAV0BXQFNY7Zel+uWWv1yy1+uWWv12yhWLWS2IXktojKjnHLD1GrDZVE47aKvdktyoZx+zihYLSVZRZLXMunsFqqNYLYmVebFancgOP2fXH7JbY15l47AVO9it1Y7ZoEe4jHoDa/Y7fWP2qEyw1YLeU/r8EUbHBOMixW9I9lgJF3tMJVexwat1ot6Zd2tMFy5+yRKstsiRJSLLCSPZodRrXCTZ73MwrF42deYfDWLjNPOXlG8M5LyNn2byozCGqQypLiUK0ZBAq4XWBZo+R8rpnMvvPS3obxSIxGoUmmiNzCk6BZ0adVoHVa9cV1xTjwIcd1LjqtHnVAuOaqdUmpEhLSEXSRFOM8ntFLbrchtYJrQ0+CQRpS0bh0A29jnJnIuFy8V81uRYErDvLjhnKl4dPxa+vKt8BSzaoJGPQYTKzZohHssSoFqjCzjHIwpWOsbXLXH/VVY1bUg2K0VcLHblWdWO2zRzG7etF/tNucT7LbajWS3GRltngPXz2O1CrDa7bFuz9ggrd/XIBq3WOE1bf1iAK/XbNT9qtoxQWS2KPsFopmJHFnvljtr0hvGbMlRx2z1+u2av1y0V+t2iv1u0UzHPS6BroGu3NKYOmKNkPZO2TEWx/mtsq2lo6ZA0vtVMk10DrmjShegDpEH/FX1H+v7FaY82r36Y2TN6RqwskzAj/M2qqwNFTLbatqm1bQg/qoZUR0yir79qgNlc66sqF22GrNH3uS32bc1yH5i8M4G9nHnVyvkTN5vl7yiXESEJUkKLSRtYHqhI3BKdANTm2f2nFBfclvWQzmDuTTB0qOtWgUSptWlMvJ0S6kpQ4nTqJrrV1qce/iXAaccTo8QS4QC88kh19OrqwQ4fXGc3uOKO4/kVryeEr7aA0v8qfP+Y6P4lI1V97DkmSYlNwnzH5OxcYB5c8PZzWLuMTYwbVolKkiCR7HpRHo8hScOSN7YjJ1ubW3HdhUAwdby2dhbppsh3M0EZGWiqseZPu0ppXddI1bWz2BbVr0KdZH6f0BRjjRhn/Sb2z/AJ6WBuLArt67eu3rt6Zg2Do9jj9djYK7CwUuBj4GMwrQl7I48VUVVvtBcRbrKAbfZ9L3BtjkMxLLSIdjUvKWLM/c+xsIqJAsXc3m125++e22yrBb7ei8SrfbTN9vtlWGDbkzk2627/bbbrYokBppFutupt1tI7C3+wdhbwV2+3qF5t0A17Tbat1rtjci6R7V32UZPgGFWvkH5CMdsMnPeaOTeVpaIrIDcZvchhJptAQG0BVNpApCAKbQk0B68i8xRLMnuXZa0nQR3tE9TUMqIDLhFNuk02smkr0pLx2tPHQPHXrN0qQgFcjUF5VKdVTjitX1qpxatJLp3lRIeWRTz6imxZHdMUuWI5fbcut2xNONDVaQA40FUo7qWkAqQNHCRTi1VKbQ+rj/AJo5S4sXxj58WCYcSyDAc8tsa3wmrT7XDoWmFT8SD+qNwLbSbbbd1yt9tVZEwLcFJg23deYVuNIt1tJattrDuXQre9fhAtiRjsO2Ju0mDb+57G21bYdtFv7G3V2Nsp6Jbv1NNvtpT7da6aajC13mFb1votlsCjbrbXttuNe2WuvbLXXtlrpltfSDa6DBoR0UthAGNNIDuR7O3UhBcCEEdMVem09h000lO1WVtD3YM6iK2Eyr6QL2UqJsAIu0lCu90FWJQTP2qSsEk2MKQhKdtfcqQo48oAKP2vv0zTkfBOL7dzP8gd0ucvK80zXPbu22oKjJIpH4NJJLaFbUpJDSSAhCtU0yQRyrysiOvqlao5FMkEMOCkuJIbUNG3ACw6ClDv8AJDmpS4na26mg6NesaW6dynfTqpFKcTq46k086nVbidHQSXFDR5QC3HE7X3glvHskuOM3TDsttuY2dxJpxJAUNQUKBd9Cf5JeBBcSol1Ck19hJbbcbxfLMqwS58R/IPdrMzhHJeBcj2xZDgdA/U0DVAbXrPQfYyhQCT/K8pJptB1bQepljavfumqseQoXaS2rudKto/08oUK2KNOoJxROiEFxGjQPtN4/5hv8tFGtqq6a66a66a6Ztlg6Ptlhr2yw17ZYKNrx/Sx26xocvkG1qYNusW9u32DT2/H6ucO0ORxbbFTdtsRXf4lufnog2RCW4Vl692g2hy5CBZ9LJDtLdzlQbSuR7faqtUa0tzDDtigIFrq1R7c2n222uH2y2JHa2s2M2+0km32es3vmFYjaua/kQhNO5RnmY8hXZDoKGPs39mqb+zH3bHo199KR9hXL3KTWMwG33HFp+7CwC08oUhxQLD/8mn0kUyshKHVatvkAO6hpzUBZ16gpbg3Bwalz0W7/ACcd9XV61uAp14pp147XXiVLcURJdIKniTg2YTMJyC0Xe35BbZSAE07+DwNJqR91flIFK9Kc+z33cRvTZsjyfDrlwd8hMpl/FstwDkDBuysughWUmbDtBtvY2AgW+xa3WJZBRhWBdNW6w9S/QbTIvCrTYwbRb7OxcHrXZFPC34+KhwbEmG7BsW7sbCaMSzJxxVtsqkm12PRmNAFrvEKzF5uBZdwg2ahCsxrsLNXYWauws1MtnpdM10zXTNLbVpjTf+dkSU9upCQ4htJSpgaXFKQxtTSAnfkCEKuZaTqwynuLo1reCgCrAkC6Sye59asv/OAAjQVZgNugFaUtP/l5QQhHkB598fcaL5R5t5L5pvTICQyhOiAKjpTogDc0BogDcyBTf2Z/NABpI9ORc4jYRZ5cuVcZgAFBSqbdUFR3jq2v1S4Kae0pp0KShQCW3P5dQig6dG3ymu5VXcqruDSpBA7lVKeCi47oVuapcc0p14UXaeWNzjgCZKySSdxWrTg7P1Wa9PgFK0gKcSnatCdVpSA6AVLSnc6lJDiRTtP+hdJCXVKIf9Rx3yhnnE934N8/MIzYsFqTHnJAsiEgr2Jq5NBxoRiKbjnqZMyfflNL1s7ahcHkpLxZ1MBoiE4wFHo6U61txdSdU9I0ylPtV5bSXm0AK01rYTWw1sNbDTMS39HtLfXa2+hFt9KiW/SxsQkuX5mEpkQrctYhW9AMS36TmIi2RFgGkRLeF31iAbmpqACy3A69yat/u3QgGrM1BTcpTNvMntoFWZiCJgZgBHSt9WpuCEbbfrst5rmLnHibgvDeevNfk/mt/tW1uNMCmk6lpO1KKjfZtvVSE7Q21rTX3b9Q23oW6kyY8CHmmVzczyHp6GtabP8AJt0oLTm6kK202rWkPKRTUrclLyQRIJKXNUh0iuuquuquuqlOqUN6qLmhW6k05IAMh86Lc1pw6Jcc9XXdEOOV9zTxUy7xTnS80xxSdadGgX91jWnk7VL/ACd+zg1Lzeink6lxGtOJp5vWljZTywU+PvmTyZwTL4l8h+Iue8ZTEiJV28IVcWrelhPtJpItQXfGIbt4Uxb0m1MQFTXotv6oZgAQWbeYfYW+u1gGn2oAxxtm3A9C30gRBbrqxblvNQbdu7KAK7O3V2kCu0gV2kCmSrpamvWvWj6iyrHVv6h0Wh/JX21qadIy1bShSiq/KAuS/wCSmARIuYPvOhqzel0k+knWrL/z3r0/WrP+KyEN+TPyK43gz2Q5hk2b39n7tfZv8Gvu19kVH+zR9R9mvxbB3NUj7IUAecs7jy3GRp/Q36K3CkOEFD5NNPGg7qEPABDySeqaQ+drbqlVuXW5dbl0VqFdU0p4avSEAuPjVxxRouEU676PuL3LcWUkk/V9BWjivLHcQyxt1DzLxFLpX3fIJX+Tv2X+T/8Acc/JX3cp2nvUOfebEQ6qx5RlWCX7xe+RmxZa428xJYn/APLoA1SP5Xwf6u4P5Wf/AKg8D1tDUD/liKAOq0j9cIABBpAPttzB7hkHUprbX8q0VWiqZFi6OlhrSw1/oNFNg0tabR1b0mAGU+yBQ9jrbYDU0WcNf6FSfYt189lNy/0KopsfXuSbKLmo2PW0+zCc/wCyFz/QqtAtBlk2PTWyaZxypxfxBiHlH59ZrzTJhmdMMYKaEc60x9mKb+7P3R9mqR9kfjG9ab/IV1EtM5FkiMdsMmXIvFwH9A+g+yVFNNvgBp7QtuhSUOaHq0HfRmSUDvTXeGu8NLlFQLytFuDct0Aqd1HUp9317kU67qr+l8BtfC2YDIcaX+Qp+gSSv7O/Zf5OfaV+R+zv5OU59nvs99n2UvImW6YwvxR89s44EmYJybxfzBhwYslIZsut6TbPdVNWcm2NWcTXk2sPdKymobVmEXZZ6CbNUhFp9iQiza9Oz0luB2FybsvWZRZwVN2Y107PXTs9dOz107PTP9n6knSyEda++rKR/IeqCABP/s0j8r9/1SmSQ/clK95q1Ei4Sv7tWb/ndxNeTXl3xt41WjmzyD5P58yq12x1FMtJAabFRgKY+zRISydS16Bokhuk+hR+Mb7t/kPtIml2fzblKLld2WwhP9A+m9VBaqS4rVLqqYfUEokaK7pNB4kJdFdVs1uNdRApTia3qouq1deUCp9WnWVTzqtyyQCSf6n06t8TZYvH8wUG1qUkJp5IIDSNXCQXfsv8nPtISCVJTtd/JynPs99nUJp9ZQS6TU9oqXwzz3yPwLlHjJ5c8d+StmR/cvX/AFZ1IKrUkCe+pzrH7wP+VP2H3fSDjoSE/RKx7bdDrIY+6vuP6GBHLO2PW2LW2LRTE0szUYu3pqOGUphb9Ie3bCqcmEWenB0SiCFX5MP3ICBTKYPXuSIZu22AKtTURVwlCGH9YNWhUFEvzI+QnFeIWL1kF5zS+sQktVFSpJYSVBA21HSaZGlN/gz92UEhs6U3SfWkfjG+7f5PvIjsm4JstilTHLpe/T+kfUUD67xSH0pCJCd3XpMlICJKaEga9ZNF/wBS8DXVTSnU7nHhSngB1RS1BR/rPqErXDmYDf05DjCyCXVD6O/k79l/k59pCgCpYId/JynPs99nXBT/APMqSU1IcTS2UqXjuRZJht+8LvPTFOaV3dtgzlNxBVqTEE54xi9tjVBMZMfdErWHThjewaxK3RCEFn2+5GH1mVxNSqJWsOtYlaxa1i0yD0dDWhrQ1odLH6O370YTpuFH7Tf7AoH+V+/6nTX9+f8A9YP3sx/1CX/zUiXCgMeZHyKTrxTqXLk+lpKW2RpTP9yL9z9mPwb+7f4M/dj8U/kikfZH4xvu3+WQPbmed8jFvtFtYDTP9I/rQdFbzXWIpLtdVNdRFdRFdRFdRFdQ064a3k/+HckaDgjJd8GOSUO/R38nfsv8nPtK/I/Z38nKc+z32dpf3d+0mh9nU7m4bsuBI8MfkLVdVuLQ4LP/ANQeP+dqKikdsPsPu9/6do0gj2y6adwwfVR9RrWhrQ1oaZVYujusVbrFW+xalVg0s7lk6t8XbAyhywhQVYq3WGpxtKmEmyABVi1vQtHe7rHTKrJ1rguzquRNk1tqrMmTlNyxDGLR5r+eU3m2RHgyS82kpNI/Jj82fyT+MZaqZOtN/gz92PxT+SKR9kfjG+6PvHfMzIuQr8vI8naTtZ/8DStUilyWEF25MNhua6sdzKrfNNFV3NBdyNKcmoIelaBc5ZDdzSO5lCkvuLruEa90zSXW1AKSf/AkI3t8Z3pVlzCG7035HoNTS/yd+y/yc+z4BKvu7+Uj0p0mnydHfsv7v/g96k/etBT6CXPBnz8fwh/Hbha5z7z1m6w9iVUM2ZMX/Qa0sNOm0CwaWDTSwUgxvb7kbMXmjYASqxGgqw1/oNf6DX+g0z/Z+nrRB0sbZL2QIJYS3/Mo0BHpMXpHUdxA/lfF6TqY/wCYn/8AWD98nzzEONrB5h+a2SeSN/YjLJSDt0+iPyZOimPun8Y9MfZv8GfvHVSfyb9aR9kfjG+8+UYcS5zl2bD46iuRWlEf1yEKUksLdUm3PMtSckxS0uXvyU4Axhd287fHm3VcPko4ZiyZvybYWhTnyZskePnnBkvOnjB5KeXF74Bytj5MXGxA+TbElNwvkq4eeVbvO7x5ubdo8nfH3IV23LsKu6TGWEIAB/q10CX0qoPPw5lonpl2pauolSdtL/J37L/J1zRTrupPrTv5Pfyp4U/9nfs4rQvL1S56k/f6FINPRwpHg9583Hhu4RblbbxGqID2v0f/APTla0k6W25eshvQE6GvQVqfqy9H6PWj11o9daPRei6Wd9kuX15pLKZEfeX2FV1I4E12OGOrHpLsbdfXY/uHXiimnYxfnvRjdOQeRsI4uxPy/wDMTMPI7IYURSCAjRQGv0T+TRBLBGqSCmPTBADf4M/eP90/k0aQRSFAiNWQSUtxuZZ7bWOxVNtqbfKzR/oYjSJJu2RY9YE5h5ocAYk5kPyYW5beV+fPP19Xk3OfNeYyrlKn3lxDCWxtNbCK2qpWqWvAYA/Gl8io/wD7KNamv8SAaWyhdQi9bZGPc4cxYu7i/nrz/Y3cc+TC2bMN80fHrLjZ8kxrIipKkHX6H7PhS6deU2OJrixP4+jPKKXVpFLUkqdI0WRq9+a0ElR0DhGrhFPetPkbXSDT3pThGjn3P3+vpRj6V4Lecc7hW6QZ9uukGKtgRQ7GFdaLTzsf9fDsUUXYmgWz2FweiddDsXXrRa60SutErrRK60SmR/k6VpWlEelkH+dkH9hP5ij9p39g0n8r9/1Cmf8AmOVORMQ4osXlh5g5l5MZhCiEK2jX+mP92fsz+Mem/s1+LX3j/dP5NfkEjaJCmqi3NlRuqlyLny7d0TcljpSqm9E0CDRpxJZZzzyQ4T45VnfyTWRhXJvmx5GZ5MuN+vOROFKUn/wHwTG8AP8A9YnyMaf7mH6+v9RbSpu7OZVGhcXedXOlutuGfJFjsqPhHknwhyE6vqLafU4lK1hxXBdyeXEYWjR5SST93fsRqHmiKUNDJpynvuo+kj+2r8pP4vU59z9/6X94V4N+c0ziybbpTE61fR//ANOfRP8A064/8wgak+n00NaGtDTEi39FMm2iu6tFd1aKMqzaWl+1Fy8SLf0kOWLUybQAZVn0mzLWGe+tVCdaiq8zLcJZmWw1l/IGD8eY75peW9+8l85h2xphnYG/62CQWDqWfxj0gDawo6NKO6P90+im17B1lE9f0eUlS7fubmZLOduWQR9QWhIUjPfIrh3itnkH5LluDO/IbmzkxTUSOwoISBFgIZKUhFH6AanpUmKVBMEkiBqRbRXtgo24Jb8BSUfGJ8iMTq8jpt26vbBTsEpPZqpcVaR0X6CFpH2+gURSFElUdvHcojqIpxX+fx/5Jc3cXDjj5JW+lx7zdxDyxH4Uxa/syGYL/VctskVIkQ4Im5xx7CTL5q4fiuXHyR4Gtz7vk349qL3kjwGpLnkdwWaR5BcHylMcw8RSj+6YJKS2tiSX4iy3IZSKKm1OFlBLrYQP6CkEvMIcR8dPnBExKQp61tr9yaFOzo3sIuTRoXGNqZDCos64wm3k3ZlR9yboXJqvc269zbr3NumSOjqK1Fag0op0sxHWvX9pA9a1FTdCxrSdN19Qnv8AIL3YcVsPmd5oZH5F5JGgqbO4ClHU/XQkaGpdxtdvZncycU2ZN28yvG/GkS/kJ8Y4Ae+SvgJldw+U7iKC618r/FKKT8s3EiTaPlg4Hkt2/wCUTxklotvyUeIElmyeZvi9fkWflrh+/IjswbmhOPTXI3I2UYpxO7yb8hmC2VHI3lTzfydIW8ZA6aRQ0H0lxExYmvokFVJjrVTEFSyi1eqbX6NW0pSi3EkW3ShbvT28Cn7artPANpQ+Mrz2iCTyB7Zsr280q2BSnLcE0u3/AMfal1JtxTTsJYSYzoogpppQ1uUNmXGighCgd1KTuDIkRpPx7+aeRcp8c8lfIT5dZHmF75754yZ1/LuQJhkNS7k2mxW5DTFkgNpctEVRFkjGl2Fiv10bnbGkiIi7W9tOU8hxxYufee8bFl86fJyxPY58g3JcmFYfku4+fVi/mf4+5c9aswxK/gakEitfqEgHwA81BmjAVqHlpTjy3EKTqmm1IFsujzYfZeQSXUV1UV1m66zddZumZ0To99Ervold9Eoz4elplxVuXZ9lDaJ8QEz4hrvYlS5kVtgzohpM2IFZNdrbAX5/+Z7nOeQRG1JXrRI1+9XG52yzsZR5TcI4m/l/yA47Hq9+dvNNwTG5V5wz+HleX5LlrqMagsratTgX7aqjbTSLapQ9qVXtRpVmaURZmBQtTYp6ytKpuyRoS4mS57bEfH75N8n3bh7yX5nyjyT5jVDUK7VdKbWk7FVpocTsDuSX7Jpzd3vDEJxS2rXpTdrVqza103bCA3bTSLarai2qKhbUmk2wAG1E0u2rRF8AU6/GX5tQzIzhdu1BtprsFU/b1Ubcsj241ItytFWzdT1pQC7awA5btofZcbVFguyaCiqkILhER3VFvBT8Z1nvTvOflrwzcuIedmbX6JtegbtgFe2+otqtBbFGk2tYPtjle1qNPWpWhtJ0cs6iHLOvVdmKqbjy7dIexlrOUswUhNnFws72EeSvN+EIxDz3u7bmF+TXD+brZejym/pCmS7dN8G/LiyeQWLyHI7Nh7uFp3UKkqYNvuUiCl9qVB1MqHQlQq7uFXdwq7uFTOnR9K9K9KUpOlmUnq3spDaFo1C0n6Tv7P2pO5Svkj81ouY3NLKCUp1q+5ZjGLsZd5fYNaHcu8ruWMgTe7rfr5Jet5AlW4IVjGAqvkXMb69k85No9EWikWgV7Omk2YEeyivZU17RS7RoVWn09rVXtSqetGlOWwpR498C3/GPCF21hCl2tJr20U9akhPtqaVbdFRrPJwjjOHaFIEW1q1j21Ipq3nVu3Um3ja3bwS3BUkNQCVe3Ghb1gItyjUqAvs/j0iqX8ZfmXBWrLTblmjbVUberVy3ElVtOirerc7biUe2qqTbTq5a1aP20gTLaSqG05brjkthNrusa3KCm7erRmB68Fcr5zwLmfNfLWc8958xbF17aqmLYvX2xyha1gItayE2pYPta69sXTtqdUPaJFGzuml2Zzc5Z3NrtlcNM2y42uU5i7XJEaNbvVm2jVq2gpTaUAYpnme4S/gvmNeopwvlzjvP2didcQyvIcDynxh8jMe8jeGRWopP/Tbp/fZI1URqCK1FaitRTMiF0e5hV3MKu5hUZUEC0yIpdvDrXSRJh7hKha9zEqbKi9Bt6O4v5FfNFrBrSlpSU5fzXgWGx8v8ls9yFN1NxvzqbMhJk2dBS9adgeh1g/FEzOU5flCMqQmwgOiyLpuyHRNl9fZlULSoULUoV7WqvZ6XaBqu0p09no2kinrV6KtLak23yF55x/i9Vo0Uu2HRVt0p2Co0q36DBsDkZ7mvLbzOQ5pHtiUmPbUUxbUGm7WgBEBFItwIRa0CkW1OjVuSFe3ortmxXSbQHUtKj/G5HTK+MfzOQlnLkoQQlhtRMFJLkEUYAIXbkbnbeNot6TT1sSS7a9Kk2wgSbaVLeswCjZf2DCYlvWkMwVGmrerczblimrYQWraaatmtMWw6i2HVNrGiLWABawD7caTbE6e2Uq1nQ2tWvtO6nLOCFWMKpdnSFMC443dbzjULlC0MwXQY1rKwm0DYizpIFiBDVnVGdwjnzkPEUYHzrg+eq8fef8n8ceSeN+TcO5awjqQqD7YgXKdHD7UqGomVDFCVDru4dd3Dru4dMrHR3itwrUGiU6WYo6t72hpKhuKRWgqakdHzZ8uLT454dyFzbabNdMx5LzXNR7aHFm2tg+2pr2tNPwEgSYaNOMuI7hyhfOTcut2RBi1bXUW4LV7YaRbwmhCb17Jqk29tde3tg9giuwbpcFALsEk+2OUuAtFORUmuyRT1s9F2zapy2aBy26U5bP5PQABxZY4+A8PvQRFgw7YCItqTTVtbRSIIJRARXYtttlLCQ2jeCxLNe1XSQU49OFLsaUJVZbS3H+MiMlz4vPMq1x15wbPAIbskVCjDSKciIJ7NFLgp1dgo2dkinYKSXYGoctwKZdr9Xrf64XLh2LLc0wuRh+XsQk01C/kxB/i3A1DUGmoGhjwRqIPqIIAREAHaJNdkik25KgLeE0YQINt9fbiKNuVr7Z6Lt385VqQtVjeuuKXnLcAtOX4rDhpCWoiQGYHoiChRNsCaNuTRtiCcD5dyTE2/jt85YPF2RxZsebHH/Trp/fZ+6vuP6GZqOj3qK71FCa3RnIq3PtOKnbOmuUylSpbehlIFee/kta/Gbx2468Rub/khj4h/2/LEemvgJ4At7bHwH+N6Ku/wHcPqXyH8B/PNgb5k8D/KbgRLsArb4+4uynlnM+YchxOz2mLY220eythPtrLQtuI3HIHrZ48cx3Fq3eJXkdOU34Z+ULylfHn5iQ4SvDzyeD07xJ8kYL9x4J5dtBudhdt0tq1trWq2GnbU8syrW2A7ZlNgIW3S7f1A7bRtct1O20BLdklT5Hk/Ei4bdUQi84zbNDGt6k0YThCYNwTTVmfNG1WtK2YqWyLZfVV7HdSkY885Qxq1qNs45dvaF8SXttr4sobL3xfeWXHs/IczvWBG0tN47B6RxuCpK8Xu6jJs16iJcXd2wLjDRTYhPUuAmnbcAHradr9s1D9pZJuFobVWf2d7MeMY1rFN20EsW3RLdtpu2JoRWmglSUrEaW4G7RdFqFkfCPYUOLtOEyrzItfj1y5c3Y/il5HSVHxA8oq/9oPlRUnxY8kYS5fj1zfBdvGKXfHUmIysqt7DypFn0Tx1k+R8X5by7xRjzFhjWrcnHMSyDKblxB8OHmJyY1x98A+Jtx8Z+C/w8sL3/wALPhGoXX4EvFdUPMvg3ueFx/jw8vha+XkyWuxnyGw8i4oSfcmjXfNUmW0ol9o11mqZbV0ShVdNVbV0UK0toWFzw50Vu7V/5hD6322eCPEDlDnPyDh2K0WZvRbdXhAVJKFglCiq6f5k9URqQx8kfxmcF5Fx7xZ4Yc+5lwHgfwB8oTn8N+BPxktybJ8THgnj6MY8VfHHC28dwTA4LUdEFujIlJLcmS1aRMmBS504ouyyovQoEmn+PuPru9yL4UeKOfXDNPiF8HcpYyn4DuI7kOQfgk8n8bb5W8A/KPil24YxIhOv2J2MreEr9vQpuTCIHhZgEC78wZFkVwznKIEBKnWLf06Zip22mxXO7TOPvBjyt5Ok4F8JPmTlDmJ/ALkjb+N/At4zQ2LL8Q/g5Y14p8dHhLjEtfjR4+WAweKuL4DdjxDDYUq/Y9jy4PxgobV8cngjjFlk+MsnEsRlMzeNuMLkzK8aPH+9wLt8fPhTkFXf4lfBi6M5T8Fnind2sy+AZh1/O/ge8psfTyJ8cPmNw8MowHLsPlOw06PwFmpFvUkP2pbo8erKMzLtukR32IJ0bgFADKQpmE45WL4RfMrn4t4E+TF0hQOHPDTHHMRvfwOxo/ED3/b05bJ498cfE+32C3YNgluT2doYptbzeOh6Ua68ypa1vQVQoDipGHYVMRlPj3wRk8e//Gf4N5JJyr4R/EDKH+Tf+39Yacw/4q/Mrx9yHx6+LZjn3yF4C8TOBvGLGnFEuQSpVv3+m804pXsqNq1ebvhnifkrxrwTcPk1g4Dgtz5FdgnputpaWK6a6QhQP0Zeb6PWbrrNV1Wq6yatro3Tn2w0udtUVpSO5Sgvvlxt15BpTyQLw+O56yTTa0qduTiUXDqCvMiw8teQvNGNYxi2C2ITkVDkh92Q80VhxANpdStjvVpAuChSp/8ApnuNOXFWy5ylJb90BqJcAuTcbqG7obsCYs3uFSJTbh9yIj8i8AcB8x47zV8JXi9yBXN3wr+U3GjWY8YZvxneJEHqolwFcH/HfbrfcHHODvB3yp54kcKfAPyTdpHFHw/eEfFr+GcT8Q8cR4F2WqEbmCfc0Avygq3GQ2KYltJVdZKi4Z7oq3z3DNvM1RifFLHMr46/DBpETxvExCB7qs008lVm3tVuapD6RCM1KA3PXpKlyY0K949huSp5U+Mzwj5bPM3wJ4qlvm74rvMXhhu/Yzc7FccYnT8JzLyvwOJifMymWmThfHeccm3fgj4RvKfP04b8TPgJ444p5k/9x9xngD3MXlTyl5AX6Dk/GtvdYzXjqdTePcQ5EeAPJTzI8O734F/91NheUS8G5QwnlHB0TimxC6uJoXN4VKuqkQ/dyaF0c1lXBSme/XTc5RXd5KUTUT3kueXXFGSeL2e2DInr3aPdXkVDuby4fvUivdpVLuL4syrm85TUpDTgfcVGmXlaHF3HeTK3V1UGus2K66K66KZaX0ekuukuukuukuraDrNQrpONrKnEqKdiqfSezcQolSFaXhtQlJbcNMNuJfuyVe4hCtIjA7yQk7+k7VoSoTHgrXYqrOhYjqQrQoUAtCvaQ2s0tte25oUWtqqgIV3V1bX7xsVVrQorcSqihWkNtYt3SpLDiK5P4c4u5axzyk+EnhG8WXBfA7OvkUtPBXxceI/jothluJFtyCZzyVLV011CQo29xtYrprpaVCzKSrc0lXUuiFbti6gIV315QoR/ib9Pjt8PG1q4AbaWEdNdNg+xbVVtVWxXti0K3BJFXEH20oVTba9boFCSlTqDyV4zcDc527yX+CHBLw5yl4dcz5vYPF34KONscf414U4p4Stcl0R4/LPgVcvm0atX/aQ/GWhqP/2pfxuQi3/2sHxzb7h/2nvxrXFMr/tFvjSK7P8A9q/8f1ni5R/2nPxu5hWHeKOZfAdz7JSE2kflUz/lfpK/tU1/dvfpckfeMNshwtreWNDB/wCT2p+jn/QfpH/6Vc/76PyND+hl7/J61dautXWq3OndNe/ylPerkraBOSC8+oxVywk94ki7vESjNWCiasu3B9XuHuDoqDLW5JlSVB7vV1a5Lqpq5LqVCYurRJWWFSnNO7cpUpftPduUqWsJuUhSWu6XUOWsS7nKULp3S6tslZcElYHdOVFlKNuckr1XJcCZMpzsfJ1jkC4+O/B0Kz8C+N3NnzL+FfFV+zL/ALgPCLVe+Pf+4B4Nn3Pg35K/DryAlqlrNQ5LpgOSXNBKc3SZLgs3fKTTc5SlXWW6F+4EVbpi1zr1JWYXxOHX47vDl0o8eUSF13LgrrKNoEldGSujLWq2qlr1MxzbcpTgtvcrpMlZFxlLDnduVFnuIfuk2QiZ5WO3PjTybjSXBNkTHESue1XmbwVxlHckcbSMIw9hu4N8XxnWbY5eH4fFl9edjcX2RKE8Z4+mpuC4dbU/Jrgljz7wUtk9SMQF0Tr7lUm46xPcBXuVTLlohNx3FqcerfJyhclzjuhTj1H5x7jv1VFnHtO+VXfqpyer2Hv1UZ6qakqNunz1pfVO1rviK79Vd+uu/XXfrplaujvVW9Vb1VvVVtUVKnIWGVqUFOBTgER00+l4Q1tOrrt3BV3DplFt0lDboduAe78tOmoCHkyJSXi/03atRcTMdLhVourQpzt/5qG1VKSr2jRVLCtl0CultVUQK7u6IX7rtVVuSrqbV0BuNvm296I6haRsKhMb/wCE87vkhwTw7b8lOffJPyHzKXZLLZ77fhgmPZXyVntodznKLjkX7T40fI75S+LOSeNHklxF5SccPMBJ6YFPIWbMprUttFK7qlQUUetrChOvW/svib//AF2eHWqvH1JVruVSUk2VRKa3mgP9IJUo6LIuaV+27V0hK9LmhXU2rpkDq3rUXV5jrmBoZs4KEqQw1LjYdBXbcfjcVR5LlrxOwWtOrLKZ+c4vb6uvLayu25PfsrdY41tj55gw6ymwy0Fu1D77jrJChD3GgqpiNGtBTQHVvu5NyPqbejc7ITpJ0FQ0boelbzTm79eSSokejLSjbLiy6JBQ5QQ4aDbldN6um9XTepm53To+53Svc7pXud0r3O6VAlynFTJBS2q5XEK9yuJpNymipU2X0BNl0qdKAu0uWiT3suhNl9SfMmJnd7KqFNlmTJmzOv3sqrdOlmY5cJgPuUurTcJKo4uUvT3KXSrhJ9q9xk0bnKSLhPktt+5Sqh3KV3NyuUpu4i4zVq8o/kY8ffD638+/OD5c5w5yF5L+U2cTbhkvMGOycN86fLLgq4cN/Ndj9hlfIb8m+B+P3jbnuY8hZ/PyXFMkuMTJbPbGIt6XiLMG8ZJHkRMqyjMsisF0wzL3bD4y+R2d+G2YcN82Y5zlxcLxcqXcJPtS7tcQWrtc1OXK4TEK90uNW+5z1zL5NlrtnxNf/rs8OpL6fHhNzuWvudxFCdMXaE3KcaNymAOSpHtwuVxSPdLgBcJ8wQvdJdIukurnc5bbnu0ymrlLU9erlKRdfdZdQLhJ76bcpRk+4y9cr+Tnw5wDyTHkNjaTd+eospB5Ex2Q9bOWuMLdT3kLiTSJ3LfGt2dd5Nslnb8XflI8ZPMvlqRNkItBnzQv3a4U/cZiYwu1w1F3n6ybpPDQu1wFN3a4Fy+XGc3cfdrgKhXSYt1+6TxI93uFRrnKXE92uNe63OlXCabAm5XMH3S5Cmn1Kt1wnS0PJudyr3K40LlcdPcrjXuVyr3K5Uy0vo9JddJddJddJdW5C90xpwtKac3Fl3Tou1LaV2/RcpTLul3bWZRZd1DLvUntOGd0Xat7LolyWXev0XagNLEtbCykxlirRGWI4aXp0naW2sWnYulNuFNzQtTWxdQ23O7ujbhuvyT/ACY27x7t+UX23ZTFn3pIxm+5LmV/xmXiWTy8I9ihDGuQbdZZ3FMnIIRxBq95LLxFdhyObi7mPwxiLAxVzFBfoScVYvOR3HFhjuQXfGI2IxXcC+CnyVs9iyiRDLRW0RZunTTejl1aKV9Ora1/xt8b2wPib9Pjt8Om1q8eQ0sVsXSfSzbFUpCtFpV7QUq1KDtuKT7ftVSEq3XhKursVTKVF6+JV7poagpPfSwe5SlW7y5wJI/7oTzy+W7xl8Eslh/9z/fH7l4EfLx4ufIDkTY/h5Vf9wp4rcG51hv/AHOUudffEvzJ4P8AOLhz/tq+LoVt89FtrdsnSXXRcqQw6YnQeotOCpaVBnWmTq9fkqTc167oAJceCuroahoUqHoqtFUUKOOkKrQ6MNq9suSF9b+STqTQ3Voqv5V/KmVno9RVdRVdRVFxWlveXvmvL6Snl7i8vTruVLeX25ecBU+5pdnliT13KQ84XZ7ihOLzgNveX3kp5fX67lWx5api3lg9ZdW1xQaRNkpT38unpb3tBlvGhKkaz5T6G+/cq3THFyfkA8wIPiRwi9HGVWKHKxFjFbflZVjtnvOVXHGLLjuQT7RZrbbxYca4txvLfEiPdosSLi0/L8os9nx3IZUHHMetUmz2FWIIaxm/RGI2J3/K5ibBYsrusXCsYts+T4kZpaeLfICHePeILkt8W5c+WFM3CWXLhLfBVOla22bIVLv0t82z4m/X47vDdxY8eBIcaPuEuhMkqtJmzBXfyxS58j2r3CTQuEk1cJj4hGfK1TPlaXObJ6veyaZmSepepslN19wk1CucszZVzliTm2bzcXsHJPhFi3KHyJX3A+K7RzjE8r+DcmzrAfEXxRwzluzznLrZME8QvCfxezLG/Izifkrkvxp464ixrJfDbxd438G83sV7fvGFiQvXruU+852nXcpMhxJmTHW2vcZNM3GSXb3cH0XHv5ShElPrW9Mkh3vpVRJT64XeyjXfStVzZPswuEsULpMAalOm33C5SkOi7TQTd51Ju8+hdZpr3SbXuk2mW3ej03a6btdN2i26RAacDk1tZaUw9qWH67d+pbTpjmO+aVHf0u7Dxldu/SGHw7PQ4qcY75MBl5MqSw8Xu3fq2RnUzFxX9wivmrey6GQy9oGXyZDD3saWXQEMPKVcGHen0VVARtlfMLyTfOb/ACywu24/3GNP46mRhmSsRp+K3vK59zseMZA9Lxqw2pE/4psAx3lvxfvW3jfkfgjIMtPI8HizInOUccxC2WXOON8Fj3nKMf8AHzOrbcLhxxzbx5ebfa8qVkljx2LAy9K8Kx/KeLULVxg606Lc427uYQ71Lg05qY7pNsYdEy/suC3/ABNj/wD52+HKVp8eClakhLxppLntPSdNLYeIXGe9pMd4UWHgLi072HSdoNuAXJLhd6blNJcD16Q4q69J2oEZ4zpcZ7ueV7LNn4bjSpLeY4hksrDMg4w8NfErhLyM5jmIdYxdlSMdx+7NY1N8ZfDbxW8NeTNqVSYdul3rlWyWd61YMIz+ojP1KZeTFLTwoIdJmMOlgsrFMtLLt9jue5dNxNQEPKefZe62xyoCHjC6bwra7XReVZTHfA6L1MtOC3XGO+XhGf1VFkUmM/r2z+nbSK7aRTL8ro9eVXXlV15VdeVVvffKprryWlvyiovytOtJqY9I7cvytS/L0uz0kSevJoPySu4Pvid15NQH5IkS35PX68mre4+4+qVKKhJlCrdIkFtuZLSlM6WS/Jf9kTMlhImSibhKkIR3sumZcxT3kPJunIPltGx6y2bJYhxnEssayKLh+Ve+5NbMvuGI5haM5u+JKxrNfiEzuzcQcY/KdxY5w/5M4Lk9/Z5h82PHnIMKuXGPjrLy/LMuzjjbh2xo8sb1c3uPfJTjPNrr5A8BtZBCzTE1Y/fbtiOK5BnuBW1/F8BdlyfbDPuCSLhcdbjNmCu9uFW6dO7y/S5a4PxOH/8A53+HTjo8eG5s9CfcrhTc6abV38+jPn6LnTfa/cJ9d/Oq4S5Yhd7cKM24aXCZOS93lwpuZce4vEyci6+4T6g3CcZsq4ThJmSJcqPeOD3VvgaDSuYx/lvcjWO24zZLsq9Ww/aBFXOm4xxHY8eukiXJdsiJT4Hev1MlP9r3ckUJ0wGZLkloTJQpmdM6t8lSPcRNlioU2Wpb8uT1RNliocyUuEJUkV3s0V3Uo2XvJST3800086bdcJckPJlyDS5MnVMmUD3cnTupVd1KpllfR6C66C66C66K6t7Tm+cyvpKYlbiyvQsO6TGH+26Eo0piTpeY7xl9vKoR5XUuLEgz+3lVBYkGRKZkl/t5VWxmT3JYla9CVVtYeDSY8naliVueZke0mPK1THlbrkxJKe3lVDbfbmc34dkcTlrJLNZ2MhzZ/DE3bOMps7N0yu95VcLtkFqzC8zsyx1EWb8dOP2+/eK/mKUeQngXLuWSy8m8uOA083fHPYIln4y488VuI/8Afe7MMR2GfIjxlwzn7G/GbkfJMlxbC/jVb57icEfH94pK5HfaWp91p/24xZaymHM3XGLJJ7OXVsYktzb8h4Q/ic//AF6eHbZV48CO7s7aXTceQLT28oUpiUQuPI9oLEqu3lEXFh/sehKrt5RFxjyC920uo8aV1bzHk+6dCXUCNJ76ZHl9z20yoUOU85cm1sXIq2h3KsZmkXbjyNVvyW1TpSa4ttSLplyW3tCFyLCYswV2sun4zvamO5QjO6zosks9rMpmNL61+jSjcu1l1b40rqvxZfW7aXUGPJ7Ltpmoiy6XGkizGLMrtpdNK/0+4xn1vNRZaSqPLNCNLrt5ddvLrt5dMuSej1JNdSTXUk0XZIFvflFcx+QlpUubuD8okPShUuTMEczJtKmTdLtJlold5NoTJ3UnSpiZveTat0uWuXLlTUv95Nq3yZanhOnlfeTatjslxnupqQJs4U9Nm+095NpEyduuEqY233k2oUyUJPyf8DZLx55c3axQXbbe1Ym/ZbvlFsexy53nKbjYJuGZxfsPuGNRmcd+MS62OP41/HFkll5f4xz2LmNnzDHOU4HH3C3kBZ7na+B/D25We5ePHNPHvNGV57AUhMnx8cYufPPkveLgGfi68Sr344cHMvz9rsyUi3GbOJTMnbrhKla95NqBImLk3p+S5A+Jv/8AXr4cqKfHVEueEd7cKakzDZ+9uJozLloZM/24zLkkmZctLjKlIg97c6VNuOlzkTEOCbcaZm3AO3iRNFz7i4VAfmGdLkz+57i4VbpM4SuWsRkM3A+ouEdLcWPdppkhSVpaadec41wh3GIkeBdpbaYV0gWkybjp3txqRJm9qJU6hMm6y5U1LJmTqamzerf5cxN0E2fUCXNU69NnF3vJ9Q5Uww+8nGu/uFOS5YsRuE/UXCeCyVrizHn0KEycD3k+u8uVKnXID3C517hc6ZZf6PRfrov10X66L5EBlwLnIc6akvbm23lq7aRUqNIUyIskV2klVXZh5cjtpFCNILk+O/3vbSKtzDwnS2HVO9lLq2xJAeTClBztZNWth5LJiyDXaSaeiyPaO0k0I0kG4x31N9rJqNDdU588fipMzLDrbZYa8TYiWWZi1gsd8veE4TxBy9n2LYH8Ynk/yRj+D/Dsq34/xZafCHxDwbw+8jcW4q8oPk548k8YeSPkoq5p+OrjTMIvImL8fZ7knhdn1r8i+Dsgt3OnmRbLnA8QfHKDxliszijirI7jjHNUCTULIbBLQ42t629u/QYfCriw8D2sk1b2H0yrxGfTb/ic1Hx7eHKVq8d0sSNvQfppp4WboyK6Mg0Y8gW9TT5PRkGri08YfbyqUxJ0urD5V20qmWJHWvEeR7p20qoTD/eymJHcliRUBl8SHYq3a5hxe24gyytuQ1tVV4vVtsMTxky1rPuc41gtVrEm/qbTapki6RLlBWxM6EjV+PIMLtZFJiyd06PIDHQkU3HkdS9x3vc+3kVbo8jrPR5AdEaTpCjPiEmLJNdtJpyNI9gMWTr2smo0V3sZ8Z8LLEjd0ZNBiSa7aTXbSa7aTTNxk9H3GRXuMivcZFe5SdIM2Qtc195LSpUoK7yUK72XT0uUELmSwTNmCrpLkpkGXKNCXL6k6ZKE3u5VQJskTJFwkh1E2UoW+TJVJVKkk9zJq2yX1MmVJITKkin5T/tJlSTXcyQbjKkpR3cmo0yUJWdY9Zc7xz/4XLPguaYz44fGxxTMsXlj8evGdxT8v2VyLlefkS8reXsgi8pZ5l+RWe7wpN54qz+Fj2Uc79DyY+Pzm1Nwyv4y+GbvknGuX4tydx3yNAe8UvHa53DgfgrETmNl4iw+0W1gHBjFlxJ0YtFD0XKcnhUxyZn8Q8UcgXHLIc6S+ilTZYNtmyu8v059y0/E4Sfj18O33R47JmzNvezaTLkm3GbLNImS9wlye1MyWSZksCbLkph97caXNuOl0lzW1+6TqanTlOXedMZuPus6ocucubc7o7BT5BfLr42cKuZt80flxyVe+I7TnGBcX+QPkJD5RYh3q7wE/tuTaPy5U8+KOVwsM51+RfPuXuKPFrjb5vvL7FW+JPm/8WsqsnEPmb46+QyEy57SZFwniL7hPNCZO1mTZyGjcbiabuFw33ifPRcDcLiahTZylvXG4h1NwuOjM6cqI1cLio+4XGlzp/tff3A130+mZEoxJsyclXf3AK7+4V7hcq9xuNe43GvcbjTLS+j0l10l10l10l1bml75ray0ppYV0l10nalMqDBadNKacAurSu56TlBpzqT2l990nKt7KjNksKDobcFWxtfXU0vXou1bWnA10XdOi7TzS/aei5QZXrcWCpHRdqMwoyJ8dXd8o8RYNzFi3lH8YnM3ijmVoy60Y1njWV5Ja8xTAyJOWy8RkYrl78XG8dy6ddLNjuU/FfnsHlHFvICxZJjvxlX7Fp2P5SoY/iuSXTkmfjV/+MjOb3lHk77hFjojJbuSsUy9PD3Kknjm+GBKt02CpYGuEZE7i+SzQp9vtnDUKO4Jl6YcTbfic9Pj28OkKV48pZc06TtIaX7b0XK6TootL7FbKwSysic0vtOk9SGHDVwZcW72S6ZiOBy8xVrn9m7Udroyvkt+QbJ+b88ajCPXxV8PPcm+XPzD82TONfFzji9RsiwL6OyY0ON435hl/lJ5u/LfyDa8H8JiktpB0q3T59ou/wAe3ypRczqS0VQ+2NCIsmdFcDSIbhpEJe+8sKNx7RaqhRFpcejq6vSXTDK+zQwsHou6rZd9p6LtdF2mGXDDnMub+ksqDTldFyui5XRcrouUy+70eu7XXdrru113hVvfcKpjrgaU+8V9w7XcO1Lfd7fuHaVIe0uzziZPcO0JDvUuD7ondw7VufeM6U86He4dq2POKeVId3dw7VsecLXcO6CS7TzzntHXepL7utwecSjuHqjSnBInSlpmmQDUBIkyfNT4f+KebZnkPifM/EPImU27KpcnLbKhiVl0LDGZuaXzFXZvhX5A5BxXz38sOMQbT4iZdjxU7kTGLOR+SMl467X4tOG7tl2YdpCkKS3tPlHhHv2FeO3Lea2eHK5yv+f2xClKSQdOJ8ukX3FjJeJgvyFS706/7b8TpKvj28OXXEePIfe29d6kPuG2l94UZLwBludiqQ6SZD1Tn3TE7h6kynRU+U71evJpmQ/1Lu8/7h1ZVfLV5rP8GcaNPmi6jX4VeKkY5wv8yPLMvMPI/wAR741e+DKddbYa8nvJiXnEv4t/Dz/2t+P/AMu3mJF8gubSdx0rSnG94+Pz5Wb9xk/arxCvtr6kjWe7I6SXpCabkvhy9SFJuBku1Aku9V59RdL7urD7piIferuHqLrxtKnXhXcPUw48YU9x/d1XwoOyDQcfrqP11H66j9MxldHtVV2qq7VVdqqrfGIVKjLU0YD2/s1ihCdVUqG4uOLe+aVb3wLtHKpIiLNCIvfPirVN7NdQWFtzpCd73aKq2MFL5jqKu1XVrjqDPZukCE9TsR72ns3qTEd3Tojq2+yepqG4mROjLVO7J6rdHcbk9o9t568XeIvJ/GfOX4rPI/xrjXbGJcrDZsHBWcHuV2tKcVsfJt0Zt3KHJ8nmf4nbrjkp7HnbfiT2KfHXhXFGfR48YQGcUu/fW0HUXC2xbxbcaYVg3KeMOsxr3uSo1xdkzWP5EiK8pEGKtMq9xVi1/E7/AB+Pfw3jrX47i3v7fb36RCdFvMB+lW9/aYTvY9g9XYPVLhOrh9g9XYPVNgul7sHqRCdSu5x1qm+QPNWJeOnEXNPKWVc6cpJG0CJInu+NuDWjx+8Zed8/uXK/NvgZlKZONKUhCfKTyV/ZX/iI8P3efecvlE8wUeLHA5Dynf6E7SjwL+RjLPEy48dcgYbyth85pamww4abjPb7ww4uf2z1Qo7wW7GeLhjP6x2HUxkR3te2fpTK02lLJUrtjTDX/BzmFbu3cKgw4K6Rrp1srZTMl3o9y7Xcu13Ltdy7VukOFU19wNKkSArunqMl4ibIf7dEiToX3zV2J7pUh1JEp7fPkOpm909UFxxyfJUUvd09VukumQmU9v7p6rZIdUz3EjQSJGrzz/tHcSaEiTrOfkBvuJNQnnlyrk68Z3Ufq2LdLqXH9Oo/TSEyIPm/8OPGnOkfk7grJfHu8xrxiqsbg5Rc37T44ctzZnjIjEZkqLjVnx+XaPjX+PjAfHXgfLbhiK75aLi5bZ7S0uNoryLwxVj5FtEpwzIyy41TiA4jiTNZGRY5FUtUm/8AWTA+J06/Ht4breT48hyVpvk0hb/thXKpS5W1a3fa1uStyXJVT3JHadSVTbkrW7Lkh/qSqacmdS4rd9x+UbzSX5G8ua61/j4AcSNc1eXfyTcqN8U+IrLfSa8GL2Ldyh5ReSickehWxc+TwX8w9q8WuM/K7y4zfy/5ZF2j6e7R6YmtPq+r6ht8ZPLrnL4+7p4Z+amBeavGDxc0bXK6l7S77ilUhIguP9y6uR1N8mopfXHQX0qDj9aqVaCtTdGW9o0SqJPC93+buSXK+/8AQywej0DXQNdA10FVbo690uOtTSor2/tXa7V2pcTWOIi9DGdFXZpwyTGdJEV3qT4zqpvau1b4zgmyorpc7V2rZFIfMRW7tXatkdxLKILykiA4C5FKrV2DtIguBU2IVNe3u1EhOIkTYi1y+weq3RXEPJhPEdg/TENxERTL2vkL4xcQ+UWG+UfxWcn+HDuHZW8q4eNeQ5JKvNiwyVdeQvhv8E2OYOSILZU/y/40ycSfQtKhiM92Ta2/tnWGQM4x+xYXPbmMpCG/pww3KdzyJHdEnIWHhbvihGz48/DNpbnjqIrmztXqRHcFsMZw0IrlOxl+3mG4aENYqZGcVF7JdIhKCrlGUt7snKYgqK/JLjPOOWOJuefFzk7xoykaAV8I3FUUo+b3lkSsqNQ7jcra6ytCR4zeHDNs8BByHhzTEfkbEHEjO8V0/fsUFR+Q8TbVbs/xic/Q3KX4u+Kzb6+ROCIfKWK/ENwjkPA2dqZKqRFc33aKp249q4ioDDndOxl9XtXaix3UsdF6u3fpTT3tao7ih2jtMRliHOjOFYiOlQhvV2r1dq7Xau12rtMhfR0XWi60XWi6txO+cT0lbt2qq1VU1R7fUmlFWl5URL1Naq33Ekz9VVbl7Z8pYL2qqtZV1yVa6qq1E9Ft5aEh9ai6pQtBeeTSHXFKmKV0S84KircVJmlwTeo7VsU6X0KdI/zaZW6uE4p0ELdBlMRpMHzV+JDDeZZWQYHz3wFznhPhvynzt5d8W8W4jw1x9CUC75I4Hfc7xTF+CeRL5P5B4+x/j2yoXqN6aVtUDp9D9uAbaQqG4puVkD61W/4ov/15eGhI8d9yq1NIUr20k0FGnCr27U1uUBNWrtNTSCd1yJ6+pqOvQ+S/Lo4oxack3Nvk/wAOcNylXIvFee8Vyfj14tVxJ4meb/KrnNXlOj8a8JPGa8+WnkD5nXTgvgvwt8B8QxOy8o/IRwviuQWA8Q2TX/Z6wKpHDuP6/Ht8fHE3mFz1z18Hlqwri3x/+KrlHAuN8DwlqwwQyoHHg5ZOQX/4BtR6lzURclLJNvJ7txSuoSdY5Pb6q01NEn2olWmppgq7OcTvBO4E1qa1NamtTTLH+T0K6FdCugat0de6ayrpKZXr0XKLDuktlZjhhwUphzS8MLMoMOadB0rnsud90XKhMud++wsP9FyrYy4Hyy5qmO6o2uK8loQpGiYUjV2JI9o7OTQhPkzoT6mzBeNQobwlToTypvYva2+M4l9mI4B2jlMRVphGGuuyXT8QmCGwlPkH4wcN+UuH+FvgXhfh40WUgwWEiY8wCFR1E8/sFu17Skf0EgDjPHl4/hkRAMq/MpMH4pBp8efhi2FeOnQG3o0hr/TCzrXRpxr/AE4s0WPSc0O16QpLQ3XFrV/o1c7nb8etnLfJtx5WzuoFmuF7uXkh4qNPeKXl/wAmw/HrxgLr89XEXjrzFzkzmmEZVx9lPxZeIA8XfHz5gPMhvyO5n8b789j3M3l3ZU3ng5tZW1/gft8HP/8AmW4yQnmXOTZYCk+g1riXBlZJkS0BxTbOi7o3/qJZVVvZUJTrf+b0VVHaIj9KugultK9sUydOgmmGU9nPYRuTHRuEZuugiu3RXbort0UzIHR64rriuuKL40t0j+c+QrpGQvXuF0ZCtJchfbh9yi85V9fcTIQ+4UsvK6t1kEXDruVb3XDPkSHe767lWt5zrl9zctxbgsnrH2itBTnraNTWtXJsMNbEVBQnu7klPe6CrYkdVIAFRQFQVNpSdqakJR2AUQGlq6kp9wK6rlWxxZmB1algFSuW+XMRzLLHAP6cXsn7HkKAWEQlq7u9LUYvxS//AK9fDNRHjsHFBIdVq2tXthWqgtWjjivbeqquqvSctXadRVIcVuuS1B8KWo+X3LabrdUApNmsl4yGV4lcG3O1Xi+qaKPN7xa5Y8xMMsnwc+Mca0cV8F49wviWPfH7xFy75M+WGPc0ZRwHl/BPN+G5Jj13m49fMptEXL+NtAsq9Ffevg6//wAzM7zKDh1hs3EmRZ3DuGDWiBOx7jaz5RMhWqJYLS265q265vuzi/cy65rb3XDLdcc63VcqM4ssdRddVylOr9qW6sp6i6ZdWIc51zfvc3B1wUHlV1V11V11V0xFd6Pau12rtdq7XaPCrfFc3zIzvRVEdChFdow16TIaxHTHKR0CTfoijIEcpS3HK3bnEUq5dAirdGWZ0iI4JfQVVsjrDxiuA9surPHcQ0YzmqYjhK4a/auyUK7Fwi7RHHW+xXUSG6mXPiOqmdiurdEdS6iE6QYK6jRnkwBEdFdq7T0Z4wUwV0zAWXZcJYd7M1boikyrguJZ4flX54CWjxj44y6x2FKysf0cCY6bjezb3yYsFxEq+xVog/FL/wDr18MmVHx07J7aIjuqIy020w3a7R2nIbxt4tz5Htz+k2C6qJ2T9Ihu7rjFdU9y3kV0w3CbdxvjjaTgOEmvD63wbVzlF/zsjnIQxkd9fZgW3HoaG7XybeZFqjYhjsTDsfubpTa5Tjsp7zE4N5T425C4IvEi9cN8pQRZOSVJ3EN18IT7cXzC5Bza45rfMS5plWGxT8wxq53DjzDLfaYEyEpbaYDyKRFd33VlxdyTDdVUCG6mU7CkdXsZFR4b6WBGcrs3qVFX7U5DeCe0dphlAhzYqlL7J7Xs3xQgvmuxkV2MiuxkUzIT0e5TXcpruBRko0tL6XHbnozHXOQVmUlVB4VdXAWOqmkup3ZC6nuesimHEmRd3UJvPWRVnkoTPmSkmX1UVZ1hcn+JJ0Ax5QLZ0pJTuJ0tX2oKKU3VSkqK0AwXoyJlw6Spinbe2bXPtSXhMtRBlWmo8y2OW5Ui3pAfhJp5xtdvQyHaYYKXrhGKXghs1ynzvxjwLbeZfJPlnyUu3jb4EwraOeokZiztoKB9Vq0HBjLEPFbHmuLZM7GfPdX55XZfFEdfjy8NP/8AHYfjSCBbD6/T5F/k95G4K5UPkx88rSj5OfPIGvjI+TPJPMS7rSQpA/lclpL/AC9ajccPH2/x8TATz3aUqGSXT/1bkjgkzJcqNbIPGUWfmN5y2eENzfSw3Ju9i4/JLyzyC9mXhBeDL4n8yLQ5ZubG1BQ4g8bOZ+c3/Cfw8yPxovwZApsaJud3VMzlBUy/cHjtDijTa177sUe5K01txImOOL6pcXUZTio/SSmurpSlk2lxatm9VMFZhz1LCkrXu3Gt663rreut66jQlljszSYSSewRS7ejbZYAS/e4QMZVvT1PbhQty6usE9D26k247sjgazfbzUWAQ/e7co3kW1zSzwFIuc23Ayzb164/AKZot6qFtcUYUm1WKJyH50+I3GbWUfN94rWxF++eTK34F9+bbzDuM28fLB503tDnm5555DTvkb5uzZF55j8lb6lvkfm+U7dnM7vNwcsNwcRHsMhKP1+Uak2K9Iai2XMGZJzvnpti28w+T9sj2/yT83rQ1jXm/wDI5i0mF8r/AMjVhYsPzieVsKBdfO059lPiV5//ABzcc1x35MeOHK1cy4+1dcOJQQATT0iPHayXnni/GVZN5YXt5nIuV+UMqg/G1mjdh5ajRXO7v0Z8QfijGnx5+GjG7x2ERRT2Llcv8nYlwVxFcfnPypdyPzlZ0aneY9yunnZ/87PJICfna5H14S8z7pwn5fH508/1x/51r+3e8Pyyx8k4nIgtPxb5wHBmyj433JKuFeIct4p5UiXZyPcX3ZL95t12hvXfkO5v5xkEGFDtMLMNF3C4yY7Vg8tMyZ4+4nv+bZTnV98FchREvnmhgeQZbcfEbxEvfM3I1gx6w4rZtK1NX28s2CyeKdteyznVEYqXNhLLZhP0iDK33WG8u4uQnyqDDfTKdhP9Xsn6iQ3RHNukEe3Lownk2lUB4j21RppnbDnMLK0xXtxjP0IrtGM5XbO12ztM6dLQVqaSsitxCcdO+RkCQmG66Q6pRI0q7D/ICkCuqyg5ckN3RLiCmM6kScgDfvm2saQDeZjinJ+g1tEiHbn878qeKMKPkJ8mqH7Jk3L/ADtygxFsVyuLkbBZTaxAxq3Jv3KPGWMRMh8xMCx1E/zgxuc1M8ybot+V5W5m8HPIvmxRHkXzsDM8lOY0LR5IcwqcRznzq4Bz/wA2AHyD5lShfkdzM05D8l+XUOjyvz1hTXmBkTScc8149vfxzzks13ex7mrjXOGo9h45yNvIsdvWBDgPAvkxw6wYt5VcgYEm/wDlpmN2j3nNMtyiUUg1tA+nEGZHj7lOFIjzHb0B2fxP/wAvjw8PBt8ewtWm5VfLNIXG8F22EpGwUjVKfDP4n7h5ecGeU/w7z/GfgRha10W0gyEAM/HsSfCxW5VR1EvT1FD4XomIN0qNc7WqsZ8eLHx9fupx7wvjXLnzxYbZs4X89ilI5q+annnPbXkmTZLmF56IB8T7yxZub89xO75bC4n4ztXFmHuJ0GutaCsrZaexjwbx/W4oWUVNd0Y6m+kfneTpcVrVut61d26tXULp1hOntupRpxwosSnyquoRXRSu33R0tyGZAJL3p1a6ldSupTNrd6Ptbte1u0LW7r7W5tx22uokZDDdMRy1ul72p6jaXtLzaZAjezuGlWRShl1tkPXMWhwCPaV9xfrS4b4LJqLBaA1eM/5BwfBJfMHyI4lh7fK3yCciZ1JybP8ANsxkHGjKeyHKcB48jZz5z8dxGVc8+SvIKpXCPkNl0azeJ7UZ1nxzxqKGOFrTb3FcZx10eMIppfHD7av0Bdc82N7FpL815LWIYyi44gMKGi8JbNKwZmv0dilYPENLwWCancb2uWw/wpZHqufAy3RCyPn3i1vgPzQsVwk8Lcscv8I1wx5M+OnmfH5U8cco40vMXJIgnFKkn6OIDiPErNf9xOFL4yBbficST8d/h4hK/HksCiykV8trjS/BcfbF8XyPOMl/9h/m2B8SXHPIXFHhd8jmBZjyb4XM+A3mkau9mu1gu8htXS+PRsHwr7dumGEJdubQEsMDWGyBL+Xa4zbL4T4j8hvmrhcPlDyE5y5rohraphBDSAhJr0rAL9+rZzgeVWbD7lD8wfGu53UubiPuTpXLOQi3WHwzsDUPintBU+L/AMMmHqUQVb77GIuIia1bIf8Axr8ZAfLCBUGMDF6TQPQTUiMDYRCU4fbnaTCeFsvNufMhiC4FGG5XZuV2Ttdk5XZOUy230um3XTboNt10mynGEbZOR6di6hJfUhIBCdL4rdF1ApSgBmygq86iohBmZUkuZXm/N/H2CVzD8iyIb/JPkZyVyBOlGTLfdZKzyl5McScSLt3I/lx5JyMX+NudcpWNeMHG2GNHjl9oK47lFDfHMsJd46lAOceyNP8Ab2RTvHsgqPHknRXHMk1554c7YMXdmBUbx1xU3/hlOAoIXgCNV4AjT9AbpWANauYA3SsAbKf9vBTnHoNf7fbK5F8V8Xz1jjnmvmDxhyphPFvkZYuEvkh5W8eZGQcM8QeRmIZpxtyBxHPs2VWm9K1rWvjSy9yRV+eZTD+JzUfHZ4kap8em5AUnrJr5WUp/9iI/H49yR5zetFOqlAqSkaDyRUpXkhI/s+Aa20eG25imXGOtkr6ffu4TUJ8Gd8uig54Qn6H7/wBCFltzje8MZtxlkNg/Ucq4E+RXk7jOD4u+WmAeV2e2bihjqeREuK9ydxFZVY9xvubq4qb7XqoQGZI62TKUbxqmrG4lFzkvoMkut6wH227ep5ogq9Xlj9TJCK69NrPs9+d/4llad3UTW+t1bq3UzaGuj7Q1XtDVC0NamztEY3aoyJGQwmDEdtLZeVZm1A2JvS+WlrtRZWl17G1rm1pS5fBYAquUucOOeII/P3yJXjKpeXctZ7nSsT4kzTNV5/itjxKdy/ybg3CuGY5H81/Nqdwb8ZnD/D45y5Yx3gSztebmOOP4RNsPIeLnGkkfrIo4wdTi+tHFEqo4qNVYw2CcZaNfqor5RsRUz4wpnq6fiJz1GwTxuw/yj48v94TiVueT+mwaOFQDSsDtyi5gVvpeAwNv6BAo8fwCV8fQdv8At7GrNOCsO5Bx3kPgvnnwRu3j/wCT3GvkNjOESfIHw7y/xf8AOjgDzmsvM/iK/LcnzcswKVa7jb73F8OcyfwzyJyGAW43xO//AK7PERKP/bx2qV12DQr5VylHgkkDp8ZcjZRw5yS580Hn8lHxteQnJvk54r+e3MuceP3iSj5nfkBrKsku2a5a/wD2PAtvq+G6YRVTMFXWyOEHL4bemrbA/wBT+UDjrN+Q/EWU27Dm/TQVoPqn1Ph5lSrtw55S2T2DnFKglPw7cU8j455IPIMKPO73LORoFmRAhIggrvNu/wAoQU6swWutlUNAvRhINWC3oN0kwECUYTZNvhB20G3q3e0OVKtemH+0qo2lWjMFkWbIrYlUhi0rK/aDp7Qa9nVXs5r2c0w+2WQ4g1vaoLa13s6YkltTuYOOIjFbZccUjQgaXxpsRFJSChO9zyJ5g444nm80fIFmeUO3W75lm9zxrgm/3ZeP8VYdjbzLqkp5PxabaM18vPHi9c58XfHT5gYd5SYH/t3KTXyi4Cmz4Kn0V8dNkg3vx1OAxVn/AG6YpXHLequOkgHjzQHj1zVfHDiiONlgnj0JT8kXHIm+F0SWpUXx8T7r42vj/K4Lx9eTcOHBkJH6UilYX6rwkGlYOnb+jpr9MSijhwI/SBTmIpbXIw5mSx5T/GNeVXfxv5J8oG4HJHCeK8myPGn5R874UvV94/4r5itHK/jDkmAXTHOVblYr3hmfY7yfiPxN7V/HX4nqSngR2SlSBITr8pLhc8FNxS2V7gdel8Wnm14o8C+HXyHeevh3zP4ao1KANA9/Y8HHw14eqmpAiyQt7LHQjIeu3VleDt8nyFN3nyzwKycb+Sv04xwjHMri/wCzeDV/sxg1f7MYNSeGcG3eNVmtuLxfJbijEchyqXwhgTsLx5ixbTwDyRf2bBgvC9rZvHJDkw6B/ReQuoCXHU6Mup62cvAZZ1xWNPJN4nvJ73qsmrG60LWtaFK6xqWs/oheSKLw0ithWPZOptMtl5veHEEBxFdZNdYV1hTNpi9JFqiAe1Q6Fqh6+0wSMUahR3stiQ34jkOzh4W+1EIt9qdRn+V8aYfaeTPPXgLCkcjfITy7lLd2uOVZ7esd4TQBZbBasebQSQr7tkAZphlpzW337GLniszyN8NIHLF48avlxyTAMi+TKbZc38X0PsIHxN5cw/hSgAjStKKda2JrYmtiaKAK2JrzBxGNnPihGuZjo8KHjN4LSFuo8Zcam4v4+lpJroN12zRpEJhZVbo+ntkelWmIujZoeisfirScXthrkObj3HGFM5ZyHw9e7P5v+Q1imu+TvE/NzXL/AA3isquL868hvjuvfjR5ccL+UfHPLXjTi/ITQs/Nfihkvw/znZvxpeKyGlcIJgxW2Uw4Wvk/wOjyJ8YL94M+Wthun/sw8raT4SeX4jp8LfKVxKfCzynRTnhD5fx0/wDsv8r6sPgf5Z5deOMOILVwzxV7fD1iRoCZOYx7eMi7O11j0W3ryG4R7Wm6/L5w0MH5noqSK4WReodz+qPy4Xk9vlfOEVcnEOM8VmZzyFbbPZ7LA8k7q3auNPE2xiflzkWA3SmbOtWQw4CW0w7Qaah2bq5rEhLycQIJGNW+H7zcIMDvfa4RqxWy3C2drZRXaWfWTBhuYMu2wQowIJENCE47klvhuSm7Zbgr2630Lbb67GDXYwa7GDTS09Lemt6a3ppaxpiS09zlbqRAedAkeSHmHduPsky3ya52ydidMm3uSqHuPYrpEWQ04zkeWsU1n2YMhnkrIkp/3RuVHle76o5ZmlF9ziPkNuETpr5S4b475osEz47+duI27p40/I/ZHuMMm+R/xnyofKL8nWPqd+Z/yJxqLG+em1tCB8+3CBTG+eDxMW7bvnE8IpkiJ80PghJTaPle8O7/AA4PyH+OF0Zvnl1wbmGDuXAtT/jqyiJKvEKBcYrmE81YJmeLM5nZ5FKyq3opWY2lAazeyCv3eyEM5nYnXE5LaFJ/ZLVScitaj7rAVXkpOhTuNvPvLXrFyy3yVPFDkyQQx5EZLCx3xf8ANTiPyUtmVcGZFiWa+I3yR2jkjIbv7Hmtn+JFCI/xwcp+ZnIPiBkFv+cz46v1nxg+WTwO8tL8wssYi7J9O5pySTgbjyirqL0yZa/bytesuUpqPmjyEy+oKgFKZebupXlG9NYwst5PdHHVXryx42435c4Zj8D3GLcce46xWzUhpLf9CPywuUYeV8hW33HEPDDxZlYBhiCgDykvMlK/HCwLteFLeWpLbqUnLZA7dTwUIjg7vkhaf3Jp9ITiT6TfZxSqf1kisdcK7L6Gt6afdSrjdLoSgyE6RHv/ACxlD2s5tbYV/lqotprYmtia2JpnHOPOl+uceV+uceV+uceUrG+PduLWLCGpGS2nE1QXsfwBT/mzx7Fwzm5MdtxAgITSYiADGb07VFdsiu1RQjJ1EHWlwwkiKDXYmu220IoXSrZuCIQbAYWgdu4K6LursRMipWI47ONw4S4eu8aV4n+MExM7wh8TJ9Xb45/Di6SLt8R/gbfJj3w2+DDjl4+F7xCXHtXwx+O7tS/h/YTIT8T2e22pHxb8+MKifHf5oYnIl+GfyZ2pKPHf5imi3xJ81EcxLL84UJ8Xv5w8fnTuX/mntUYeTnzLtVE80vl3t7OYeaHyi3Wx8/eWvm9y1Cv3lZyBiCnvN+/hPIfl/wCQ+Uw7VA54x2+fHR578t8vSOYMBxLkWyeNvltyfw3O+I12O98bfyKfGbhfnByjxP4W+NvjNc4vhZ4+RuWMb83fJLGbZhfyMXNT+H+cXjDlBxvmPxkzXE4EHjK4NrsGBJF+tuDybf7NgdSbBgrreVWPD1zfZ8DqJY8MVIyyy4mvIPYsKNckcvcDcDt8v/IHmmTz8hyjJ8znKXuAAFa1rWppIUo2K0XTJb1h3hbzxlNwl2xWNZZGt+JLwFVmwTf5OSYJ5S4mxfGIfBqsdwLaMewMHIbNhbrXsOBUzYcD6+a4/hknKRjXHgGMWHBGL3Jx7AhMGNcfkWOw4QxZv17AK/W+PqdsOFHCFYxx4KOM8faR7ZYhYMjseErmM45x+pScb4+SP1vj41+t8eV+tceV+tceU0hHS2IrYitiKWlNYI3rJzVLke0vuO955HcRI5d4+lTFRHu+UaTOoTk7hcWq9xar3FqvcGa9z0pFzboXJkn3CjcGq9xaFC5jUXOPou5MGhcWjXet13rdJmIUesquuK66BSZLOnVjUFMGpLjKY3HDzK8FVH3nIbizZIE/zXwS1zeOfJfBeTZ6EAM9vsc2HVA2lxrqKajlC1sgtBpwBba65GlOxcX5XkPP3ZIfmr8WfHiZGU5BekKiYym4sOHBUov1n46uFi+H4JHxlcuYxbrxPtfjxiV9bt3ghZbwbv8AGrniE37wm5esBuvBnI9qeVYJ8RUDIswsCsf8rudcbiRfkR5FegY9518a3eXZPITiLLGMtvNov03pIFW0bLhyAojL+eOZ7ZwrheV5NkWc3oM6UGgPqBrX+UkX3lPE7Gb3zTkk9HxQTrlO8ukrKhy9bWrRnnHsx248EbG9czuLuQZw/DNu48SE7nnA21mQAjJQahoSZvKB1zncEnCFA5TKKFT9W6xlTfsO5sV1EVIUg8XrW2DuChB2pxTMggzmWUlwNIFEJFbU1tTW1NNW3B+l7ZhFe2YRXtmEUu24UBgzGLNSc2bsBtzzGIiTyfyHx9xXh8LyRRy7zIb6jX31Fe+N6z8tmRic5vIAzu9kq5DfbJ5Xitj/AHft1Dlm2pI5YsVI5Zxra3yriiwjlLCHB/uViNN8h4w7TObWJw/uVlNJySCoDIIdN31hQF49Pd/VF11Jnpr3Yik3UuHGb68MY56f8mMzynAcx5wxezYx5j4zb4Wf8yzc7ufgBmPJLq13VZPuiq91VXuiq90VXuiq9zNM3NOua2w5BZrlwLcb5N408ecMwt5UtpxsmLohUYKyKcIE223Rx+X8QX/6z/IiWY2Rov2QpTZOY+VrCzB8vvI+DGT5j83PIkeV2XS1z+aWrkYeeYY8p+bxneHv03D7gXuJZpVceP8AJ4rEKfmmJt455bcx42nBfkEtDL0byx8beS7x5hZ8jN+aDQ+i1JbThXFXIXIcnyH8d714/ePF9yu+5K90U04AD8Rvbny8aTi/T8lLdbYOQ8V8w4hYeJJnLnHy7fx5BZvGfXZvFnMTVHxLR6Lh5by9OLhtLGILqFExPveSIONrzMx8P1w+Nin7FKgYc3OMbCdcdj4gLGzFwoAR8KJlRsbTx24zh5PTw5CWDZf1zMhiJmMjAerpg5oIw2tmG1sw2tmG00EBpW3UpBHSRSgCeOIqS/m8Vs2ySylEryx5Ddv145LzNzBeTYWXRpcZeSIWf2H1N/BprJFMVeOYJeMzR5C3moXPd3dNq5fut4tn+4d/J/3EvSic7nKK8zfdIyOGtaMixjROQ4QaXf8ACFl2fx3IQlPFywzZOJHpHsXEjRFn4pFIt3GCKNrwIUq3YssjG7M8ImKW0vYhZ+nMNquqAu0X51L/ABZGu10tvFN5xNTVl5MabYi8mJp1vk9CFP8AKDKTd+UQU3XlIn3jkpsnJM2Qf27IEFXIdwYCuUn0hvlCPoeU7TSeULKqkcmY+TeuQcdlzoOd2FMn4fCV/GX5XX61WbLP3SylH7NbF177FJF7YIF6YJ90Roi7o2+5oWRcimmcousdNu5ezK2NW/yAujKIvJ3FOSShh3EN9kXThLL4zdy9zsT4lxVBLzLlYtbIF7yTg3xA4fw29JahwFfJ2oL8R1KUDR+3xOHb5Wpkq2eRay4nGSFW+Z6xuPQByJf3ijC1yFALkK25w+rpdY1EeIm8oPf+eO4UKwh9RyedJW5PDiAMacQcZDiA4XEGpBbTxV1EbX1jpCSWsbzCcpVwYWgr3poOCuomuomuommmeMul0eMq6PGVdLjKlM8XlWCjAm3szfxdMH2nC57+f+BuC5LiXyF+ISvFW8+OHI7t74rVke0/sulDJqGTuGmpPC8nDnJKaTObAsl67em8mIX+1ik5akUMsSK/b6/a6RlO4fs4r9nAr9mTTWUoSf25uv2VqkZKNP2hW1OUlI/aSaTlW045kLbWSqyH1GQg1+w+hvqFFu9sJCshQQb2wqhd00xk8iNRza6kN5zeG6bz/IGx/uBklDPMjUlXLGVlpvlbKgVcs5KpS+Vb+8DyPdVNTuS5USZA5Etj0r4f3d/xl83cDxuVL9cPi7v9vu3kr4p2vxqvDdj4b2sWvh5CRbeJqEDjAqeY43SWrNjDiU45ZVqTjLRr9a2LFlkmmLLkDrisbzphNhznlDGpVq8pbvJhtscCckNWzxx5wv19v3xz+SmK8feEmd4ZyNhQZ4wLnyns4kPD5xv+VGviRct6fLxB412+UP6yH8VGttmACPxwWE8k35jjlOJqTxXtKOKKzFHHxa6fFdRW+Le85FRx0vMunxXWGo40RkkuNxh7gY3FhOPDjqPYRG4sUro8W1JGAr40RH4zUHI/Fym2U2BeO5ZFwRU2NB4z3mFxgB23FoIZ4rNdDiyuhxZTX9v6etOhW7ixLTi+Vi5Aj23lLim3Kv3KnFVoxP5mOZuPOSr9wJcXoWWqKljaRWlNLQ07dcVyG43heJZqyheLZSlAsl0ttlK3AOo9XUerqPV1Hq6ztLkPCu5eruXq7uRomY+CJ6xXuC6NzdFC6vCvd5FC7yBXvEirTfJDOWe+Sa98kGvfJFe+SKF9f09/fpN/kA/ssihkz5r9ldr9mdoZQrT9oVScpVp+0qpGVK2ftS6GVGk5UrSTkoee4ZwK4csyvjjEPivh7kH5COQpmTzPl18kbpf/ACK81OSfKa+DJVLVZk3q9ORMNmKOF8d8cRjjkHxkVa+ROIuGLg1JxqWzIusC824fsCG1fsSKGSAAZE0mkZOE01kTrgZvzrC/iF8wINjuOUxxxJdrvxViNo5GeeLcj5K3S54lr/KjXxWHTyvC/wCPPaty8V/6bMGsfBDrnuTK28fBf8HPx5AV/wAKCDUEa3HlvROf7k1x3tVltxcJufWIrFngrFusCreqn3P/AOpwolKySnetvGMzee9yhurUtalVu1rStK0pp/jzpdbjyuvx7XcceAOSOOgeJ2sTlv8AlfyRx3x3juY+YXj89fOefLHxPn8ZZDkmLuyOKsjZb5ncKTWlaUDtVGvmQW5IynLlh/IsncbduE6bGU6huoduuVwFq4s5Kvan/FfyDYh3LEsrszxISpzTXUVqK1FaitRWorUVqK1Fba21D0TlfpXpXpXp9PSvSvSvSvSvT66mgTSCrQkmty66jmkqY4mR4fcgNYq/4yWrPMU5Hva77EyBUl8nu5FcT8Oy721lV8wfjDGeWfmR4asWQcs+dnkJLa8bsq5I5c4rPnDylgC+EfkXmZbYeB/K7xr8kGM24wh39jImsnxG6i/XEhV9uO5GQ3BB/ZrjX7LPrwfyVNu8kkWuz8jcS4NllmteSROErXdL58p8LEoXikv86P2+KT25Hlr3nGorkDDMDzs3DG42KXBtkSXMT40xO2XrIZeCLwbqcc6rXxztzyTgrjRVxxUNzj5Nw5RewV3Od3Gprj9/j1GXXE8dqugd43TWMyuPkYuH+N+p3PG4p6TgJ4sbkccBKpXG+jbmNHGcymYALhHkccb+4440MjjmuvxxXX44rr8cU04jp9VNB1FdVs0641XDhimZ5u8ZXPPMH5f8fcXxjL3eC+GHEzOAOGZtW3gjCsfkO8eTgFce3UBWB5GArD8lCnMbvLDaocttJGotePSrqqxYfj1tRa7nAtElHmFxrx1EifJZg8CBk3lRxdzFY77ZMVyAX/jt+AhSFIOhrQ/TUA6GtDX2+gNbqab25RrrRVod9bxQ9RpWlaVpWlbVUerr/m0OpruTQV6bjW80twIRPm6yvAvIbRaLl4rZPjcDlW+Xe0XfM1Ag8LYRFyK9+WXmDxZ4mYF5N+WfMflbk/B3DvI3JGaYL4Z5HyVk/FPDHCPD9k5R8CeNpz6fFvM+LcLuuP5xxbk3gf8ALRcJ1xzrELRyDjdwt02zzv4k7RX8AdE1i2eX7iTOeGPng8aJuJcaeQvjL5CXnHszhWq3/MVAtt28aVHVVH7fFydPKVCgoM7Ac2IORQ9DJtpPRyx0qwLqAU9Kb6PI8lswy4NsB/bP5kf/AP7G6qa47WDl9w1TdC764YUOYqdBRkJ1fcT/ALQNyUgKkp0jvI/UM8kti4x5KS71ga6goLSr6tSeGul3XDNdzwyK7rhnRyTwsTxVN4zRI5QnYfIsvkh4n8M5vfRlMhKf22RQyyUml5fLFfuE6jl84hOXSEAZjI1OXuLD0uM67JzKNZIF15rdYkZ/zyq4Y3yh5RRL/IkeTFwuGL8a+VseHE4r8lLq+1hHNFj5Bg3t5iZJRHSpTEK1imkY+3SP1tNB+xqHWsNR59jZPvFkr3iyUm52RdNzLCq7G4WYFNwsVJnWFVd3ZKTLsYBm2MUJtjJD9lNB6yGku4/p1LBQk4+KQ/j6qD1hB7yy13VkNIXZ1nbaKyzLrTikO4vOXGZ4L4jjuW3Pxeye83/lKzORJHK7tsx545dyVivC+B+RvkTnHkzyt4o+Ct8z9Npm8W8Mxcx8r+xej+Ts+9TrZ5iOodxDyoeuauUuKeI+e8Y8pfEvMfH29/FN5w32U3yPikTJ7vEwrjyORjfHqqk4Hgbylca4W5XJ8LFcJYuD8mfIXbbaGsenZZYLtFxPFfNbwbznCck42zXWjXxVPWdnyvRO4d0E/hwtchOWF7JYvpNZufEYbvkvi1GFG4cRqSuVw+pOfT+LmIxvXD6U2+78QOTOVpPGKc+9x4crA53FLuTXK4cPqnd5w2Bikzi5WOi48P7DP4ZJel8X/wC06bjw4lPfcNqqM3iZxXNpfDzM9u7cK9RF24a0904ZpNz4aJ9y4br3LhumyNmorUVqKeIB4bP/ABXLS9LU8ttcvmLF5fG3KPuq6FyFC4uV7k7XuKaFycoXF6okt6Q5l2S2TCMeezi+X6PzFygxdbL4rfED5SeW2L4v8dfxScDSmrN8PUxWdfGF8W/O8ryp+NPy38K7NxzyjbXn8L5KFyT7qNRLfVXcSRQlyVKS9LWlyW+0oXFQPuKq9wXSbm8imbvIXdfcJle4SqFylpr3aZXu86heJdC9PChf5Ar3+VScgk7v2CTX7Q6KRlC9f2civ2kUMoSaTlCEn9tOt1li/t/r5CvFHEcsu07xlx/G73yW3AtuL8gDL26+VbnGc6nwc8X5XIF7znlmz4JaONuP/JXy05Mw74ifGXh+NExb4lsBRe+EPiE5SYz/AOHLIchh4tyByHxhkKrvhXMeHczceZN4u8w8O85W7lviV7MJbaxmkuk5xKFKzyUlPHmSOTLpJvqn3XlxdnIPgPlM7wn+MiBl2c/Hj813iM9hd2+nxgkDyh6re1og1lKQLrGIEiMD22UJI4+QDotQQjkyRrDQ6F1bUaXTmYH/AHJri46ZtdE6XZQVuwID9XUNDoqpAP8Askdem2Kir0w7kXX3Fn1eSTodaQPXbW2m5vGnT73jSu840pEzjPQTOMteL7jx8mVyrNxVu1SJnFJl/It4j3GT5IXLxo5IgtzeJeTLct/F8vg0+zcY47lNB8kdddWiKppidcZ3N3MXNmWw7Ja/jV+PjCuMOPPK75D83595L8PJWYZb5j8f8NcD2j5Ps+zfnTgvnrxA+QK+8Y3D5O/juxniC08TOJvFls94j7o+Z4yyn9+xXT/cqytE8lwtWeUoSA9yu+lX+712prl6civ94ZdWnlaSMme5RjuOjk9tFf7q6E8rvaf7rXA0jli6qWeTYxB5OYFHkxrQ8k2Vyv8AcKzUM+x00nPsVTSs9wc1DznAXnP27jBdG88XbESeLpQtMDBJbYx3HnU8a8l2Lx9icEJyDBc+yJd6Yy5VzbjKy2VdvJ7yYfNq4f418ZPF/lP5FvITyK8uuBfCbijmjlLm3kdzjHivA7j8KvzqYLdTcvFvy35bxiFlWFcQfMVxVhFzz3hnkryD4mtXkFxt8ZOdy+zcbcJ6TldJynUL6XGLbn6rsdJUnqIxHk3HuS/j2+PnjOZw94W+QXEuJ898Q8l8cZFw/wAhV8WL9nj+Vve8VqpqfxWE51Ms0jIo5UJDNy42AymbgDGBs3Ljin7hxqWM7lcdR4Srrxmo265cae58v3nBWOREXbjdQ42mcdysvulz45buqbrxrtxWZx07ipufHCwbrxrT91wL/Z4XbjYoRdeNCWp+OnFM7umEe4x53HZdTO4809w47pE/jrX3DjuvcOO6bH8NPp6indyRw/qbhzIn/RnvWZ54wLpE4ageTdsUmHzzY5wi8l8fylKkcT3dUvjPhu6l7xt4ruhuviDY31eVHH83xx4L4px5PGvEvx0+J0bz98+/ld5n5h5ezKz47mGLXrxT+LTyW8fMua4vusjyZ8Y/CrjjnjD/AJvfju5C8Q/K7478wzPEbHy7wRkfg75YylsO29mSgFcphI7xihIZruGa7hmu4ZruGa7hgU0ppqSJbQBltGu5aruWqL7RpElpBNwbI71uu+b079qvcGxWDcZ3jM4EXxccuFqyvH7hht1TLbFJnJrv0134pnNoloKeTIYrLsvYyK18NhKci5OSlrKvIDMX8W4L+PHCG+tzjmUtmNjfAbXxu/FT5AYDzpfOSfDzwh8tfK6Lxh46XjBPA+/cMTLtm3yvfD9wdyd4z/GjYPM/ClfM34+XLlXhDijNXZcKx29HFPn8rLbYhTeQtO03MmuluBmM04ZxhyZYbFB4x5VnGJwVypNPG3DnktMuNlxLkSNi7PFdwkL+cHxUi4+VDQ/GX/8A5PtvjRCt1X5I92j69aMlQRlrhf47SkpMlae35KUn20KGtuK/c+dkNjlNATt4k/8AXV6P+thegwdW7EG3SGz95KdvBvUJQ2s7ojYOFchpUi5sPEOpkGu4FIe9erXWpvJuMen+z8YV+0cYGk5PxdSsj4oKuM7zx0/J5Nexdi2LyHilMnka38Ncjce3ksYzkTsqM+GXHWy3ebixTOb5PHEXlDKY5ic4ZVHT5xcwL5VzLkaUbbB+H/j60Yz8engNi3nqcnk8q8A8+8o/JT8jXKPjvidr8rfkRx68fH58iGR+U/FeXc3ycgrkbA/kjttw+b2wJy2HjJjXqx+GnM3F/LfAwncJIr3ThCvdeEK914Qr3XhCvdeEK914Qr3XhCvdeEK904Qr3Tg+vdOD6904Pr3Tg+vdOD6904Pr3Tg+vdOD6904Pr3ThCvKG+QReIOR3+IjxxvOATsK904O1N34OFe8cG17zwYBk104bkXRTvEKjzrLwVqycO/+o8DkYrE5j+YDLeL43jb4eWxdj8fvEPi60+Rnnj8lmIZt5KeWnIPMnyH8Q2HgLmjxv4F4K5+8/PNrnIeFPyYeU/BfKmf8v8eGs9xj5VvIm78A4zP5Q8dOH7vIVaPJx1eP81KzDhBJPLvE8RxfkDhLAm+T9njB3ysQpM3yeu6lzfJq/rr42sf5m8pPJDj2/t320/4fNLkFjsfhAoEK+LF61RfKxGT8WoBy3jJtOc3C0XTJIqgiS3lfFKEZRk/HjfH6Ms4xKpGU8UhnkG/8cMwhlPFgNvyzjEXLl/IePGuQ05PxWK43yfjpWXXTKOMfdf2ri2sYyfj57FkZTxbt/auLKl5LgI4h/aOLdreT8Xhbd7sxxbN8jwhFxjZNxcXBkvFoH7NxdSMm4u1/ZuLxX7PxfTY/hp9XK4f/AOe5f/6HI/54Eg+fXxS3/nXPso+In5JMFt9/8T/PHj9u82LyOxFhPOxFW3mW0SkMcqWFac/ym0Zp5u8jKiPXnwEs9wPxE4v5ceVPjD5bcR+SHMMiN5dcSO3XmG5cJYbYeOvFrh6Rxdl3kt5XeQknH/GDyE8l/J7yp+ZCyz3fBvit0JHg7l71qygX60V79aa/YbRX7DZ6/YbPX7DZ6/YbPX7DZ6/YbPX7DZ6/YbPX7DZ6/YbPX7DZ6/YbPX7DZ6/YbPX7DZ6/YbPX7DZ6u0qJe7AEpTWPz2m4zNziISLvBdo3ODSrpA2yrrB63u8FNXW4mbeOINvv2ZXqC5lHyMKk3+T48S46OEvhFtcSZ8pXzVT+T+NL58Q/mj5r8h+OvkTdVc+cPYPwtackv2F8Y4Lza1x3zvl/FnHHzceXfm/j2cfAric+Bj02FLg8heZz8pjHcbzKyysSczLGkl/kLFIzj3MWLNIxDJbxyNdeI/jU+RvmlvxK+ABrJHeIuJOBPGDA2cndsV8uXM6sPi/Jp55teY/ITlfGR6+TKvxRV4/6gx/eb/tZV68aDXWR6s8m6+3tfnbv+p876f7oj7cU/wDri8/9ZFYJ/wCjWv7dTP8A8FNfiion/ovkT/qbH91H2pP30rSm8lwnp/smE1+yYTX7HhVHJcJB4tu+LSZXK2R2WJbHMrwcyjlODqo5LhenIF9xOPZkZVhKQ3kmCKc54sHDNzyjKfEL4/s7f+X/ABqT4ceffwQWu0cq/EtlhZVM+JiUeRfj14zyqxeBXyF8wNclXTyHuts4sZym3498fbLbeM41ztlXiXOzTIOX+EJ0rzL85vnGyJNg454jsrl4zXk7hLx+5g4bufwd+KjUjMPgS8XMUofBt4wVZfgo8Xbpd8i+Bvxdx69f/Bt4uViPwK+K2Rz3vgz8Y0uf/Br40VB+BfxhfwtHweeLjlf/AAceLlX/AOBbxXs1l/8Ag58Wqi/Bn4rvysu+BrxYxbI//g58Wqwz4F/FbLMhe+DfxYaf/wDg38WzUD4Q+ELBg+Y47PwrJI0pth7wx+Mbws8wMNZ/7eXxnKVf9vJ40gH/ALebxpNXf/tw+Ipc7zg+Lbxn8KsYajCOnjWc5EuOLfAjxZleL+N3iTwZ4rcW/ODjDFg8z/jY5Bg8X+fPy3eO0t2HxX5DJ5n+PXgpyy4PwpkNq8bsQuGUz/HzCrJ5XY7kfjzf/kS5Stdq8f8AxExKJ49eN1obvPW+B5rCInj1n/8A2+fIvl95J8a/9qF4t22Dbv8At8/jZ4Wrhz42Pj442hWbFcR4gyu68iYzaWfeZAuV+ySz2lrzG8vr/wALcL8w85cvc5XIIQiiSa+LeTDieUSsixIITkWI7crkwJ13Y/vpyLD9mUZFibfHSMkw8mRkOE9DkrIMSYhoyrCqt2T4iq58yZHi8Xkg5RhRPGGR4i7mdyyDEG7wclwusOveMPYeL9hYaOQ4cDPvuNN8MpyPCQ0jJcL1RcLcnFc6yLF27ixkeG9ROR4ZX7HhuiMkw7d+y4fX7Lh9NqGzcmtyK3Jpak7uGUBcjmplPtbgSJSGho3ElyazPGrtkNmPGuSt0jj3JG3OVsYuuX5WeMsp2/8AchfF7znzBaf+2QwO/wA34+/PfhbIeBPKH4bfJGz8JeS/yV+ETeeYrxPKc8XPD205Nkfx++KuP8hcg4fk/MV5v/lBw95EYrmXmH4n/H74cWTjPGfkY8lUeVvlR8TPBVx8iPK08e5WVOce5C4nkHG73k0j/bzKRVgwnJLdfcxxPI71kx48ywVgeM3uw3N3j7Jin/b7J6t2O3iLx3/tzkyK/wBu8lrJ8Uu90xf/AG+ycGBgGSInZ7iN9yDLTx3k4rjfFrxYMxkYDkj8k8f5QKxnGrxbcS+UrwBye3JcivRneHuYci4iybxl+Vm2ZTbLB5K8LZLb8r584mxG2+Ufynrw6zcp8l37k3NApKUfDv4EZHmGQ9mtalRVJHzz8BX3OfHm2Te1vOK5Nj/np4hePHhRNxnzfxyRZfKXn/y08guR/JDl/wAFOcbjJgcD4W7Zcp4U8Gc8X5OfLvzHD4C8VRIu1wkeF/jPlXDHi/xKxkvHOZN8v4gpN75LwG92vF8/dx6JfsinXdrI+TMWsruHck3TKc0uIbcuHyCLSnxlU+hyigk7CK+MxBT5KrUNEqG25AqfbSULjSgUZdIa/wBsw4g1JIEfkxaRbQ4kVbHE+689OE8sBwacSuI/erw8V3jcKwJY/Ru5/iVgm5OD/YTeChKxohY/TOQ3ALqwv/NCzoF0lY3bhW6m84xEN/vGI1+84jX7xiNLzjEa4syixzZXLl7xiJbXM6xNEnGchx/JJ7Vst1uplWlPq1rdW7SgsilDqIsONY5isP5yfDKbzJw1heZxG5vx7+Y2LeZWA5/4vxeG+dPJLiv5asU5vieT/wAvUi6eFHFHyZWLnLhzx9tuVZt8pXnpZ+P+PLtc1T5HxF+HifErxa9SkDSmyQTqTpWhr1oa6unVGlbfVz8tKSP5KH8gPWigEoa2H1pTYUEN7BNtlvulv+Qn4lclwpL9ulW+U1c7pal4l5Uc7YgnKfMLyDzVmfcLrdp0iahk/HZ8QmWcpSYVvg2uL6j6cicc4ny7x/5S8DZp4j+SPxgecsnxb5G5u8ZOGuaML5a8f/NHOvD+9co/MzwMmFbvmn8kbVl2C5RcMKvD3H/gPhPlR5E5V5E8rfCr4fyPJjyTnIMt4Mp2hlSa6SqQVMm4PJmR+wt4TiD1usN7XbIKnPKDx4i+SfDx+GqY2nKfi1tOHSHvj2xlB8JPDO0cEZUeQ8OWhPIWG6ZplGO3G8wr9ZGJdu5KxxiLfs9sMHARyDirhfzvFAzyHm9gYhuchYwoW3P8VXdOZM3x2JyT/uDilcZZvjE3MbjnuLN3H9/xasNzLGn8UVyBipV+/wCLVcMxxscMjkDFNieQcUFIvFjdxPOcsw6Jc4/IWKBwcg4rp/uDitI5BxXX/cHFa/3BxWkEbNU1qK1ApepPDKiH+bCDbHSRL4jbShskk/aidT/TJjsTIny3/HBdfCfkXjjlsWFrxe+YxufjWJ+QfizyLaHLfxVHyPNvJvw540tnmF8uF2yXD+TuS4pifC/8ZF/5Qu63OqtX3/8ApP8ACbkwYWnKrmhzyp+PLx08pm/LfgpfjZlUrlu2sBfLtvcPDjEXlvK/APwz8QeNLF7/AHFS2cikpXb54nt/T5ZfjMxn5BOJWHc2425G8G/kh5a8XL5w15/eHnLy2b9wjyALjyn4+YW7zX8qvjnw7Xkv5Xcic9Zx48cEcn+aXL/jB4/cJeKHEEZwOoc+/wBRQWlJMlrUrSspcQgpfZJ5hvVruWeIUy5XDKWxw0kDSIU955VNqV5NTmV9j40r3eOmbrDfG6FoIkEdvyUrW3airYU+689LSrlcqFcRKAzu7EG7elYIR+iqUN3VRVycT/sIpSShKvRtQOF8if8AVmCnqpUnTUUhSd25Nbk03nMYNnOY5r94j1+8R6czllI4nzhiZL5SvNuj2tzkCIiVhOSQ70z9dK0rStK0q5R3Zlt5x4Kul4T5e/FvmnHclOf3nALng/kjf00fIzL7o9k/kdPEiHkedcpXrxc+NSaZvHnEORIriHFb3h2PE6n/AOkKipLlthvUrHbWSrG47qblxxZbq1M8YOELiUeJ3j+0LFwlgWLqTh7AKcci6M2O3MhtKGgVkis3n44jG/Nz43/FPyft3PHAXPvi5lGK+Q6rE7E53y66x8g8j8tabyrnVi7SPF3wQ5n8s7lwz4C4TxDYuMPC929ucZ4Y1x1jazqfrPkphQeDM6wbI7OyviWS4X+IkFyVxDTTXENwZaxXxk2pxLxkFcrvcZYxx3+8W2oWcwO78r4N/unlDcsbyxqB4v5wzbfHHKs4iMcfrz+K6JGexuhyHmjEOAM/jEWrO4i7vzTmDFv5L/fY4rizNI8vNbpnzCLn/uAxWF5sxIw/97Y3fvzGlwy9hHChz+MG059GSlV7YGL5znkSPc2ORYXUTyNb9P8AcW30jkO37v8AcO31/uHb6b/D6uabOJNO+5W/6O9/zmBe6e/D7f15V7T+tcwf7I+7ZX+p6+Xf+yHZZx/tt36f1L9Ntv6/3vir+gbcS9o34P8A7UdHxk/2y/Sv/wDR+XP+zXveUf7f9ryh+u/rnM3+wnvOMfqf7BP/AFTveFv1f3XiD9Y7DD/9v+t40f7C+/n+8v7/AEFcje7do9t6nFHQ/dJvS74dvrw121R93bjqVh+7/bel7dp73/Yt33nadNcx/wDxkPwf/scma+2f4WX/AKzzt/8AlQ61xL/66u3/AFYa1gn/AKJGtetXT/8AAn8ditOlK1/V8+092jdPevZX8KTpXrXrX//aAAgBAgIGPwDu+pd31Lu+pd31LGPqUyiCUGRH4YFSH4SGIV+ZQeIrM3oWNuAp6aIBYq610gR40utP+HHQqxFLDSeord+qkJHTtC9x6lvHRDT2B1hUfqhbVRJ+lHrCGaOeS3MP1lRclmUcTmFcD/yR18FbmVziS1QjqWSPOqfMoFuJWvFrHWskVYx2iPBLmV3xvwV34io47Ss0ZDEuOtDHNAcatogZv1J+Cpj2dI6yjrKapWYotXgMdpRl+LpdIT/iqeHEQq9erdw+7gZEAhy2wB8TxKBs6dQSJx1BsFGnKqAXfEoTF9QEfrLHeNJuSQ9qon8TAwiJOXHIh4dxEtyogVRjyrSfojrWThSk2Suzyjq4GKun2yPWu71KpHaypY8fWs/Wt4Pl4RVuGxEI9S2oH9cdao4nuBMJF1uunIFhcxUg6CsuNitqxdSjt8M+oovB1U00mkxxV+NgMeooaoug0FvHH55Waq/VKtic/En1rt0QVU0UgJcao+JTEiwWNALdxpwbt/EhqoiUmXuFWHhAR8EYNt1HFSBoBnzZSApbFcdn+8ls/WKDxQwWMfUsaXqXufUvcjoXuR0L8mekL8mekL8mekL8mekL8qR0KQlRRMLclD9zKJ/BnNS/dDkr0fhTjUx52WNmVM/g5ZFV4xtyQasj0l0P3U/p6VVH4ctpPUrEC1LCOb/oVhaEq6gbUh449OCojwC4iF+WKoRNuQNQ61Frd8Ef3YrdZNuzSccqpNb7FEC3xdV5m3Il4TN6XX5YotbHQroi2J1VSekLC2IUv3c5qmPwp0sg9oX9Ct4i2PeBWFmShGVjMA7VbT8AvoK7VsR0owFA5K5HgH9OZe5KrD8OSG4x8aIFMkOdoRPgT6QiJgsCMyONNVuYAjjLKMadenNx80uBz4ZqlMBzF/XhipARX3YJHImLxJ2qQhezieQqRO8KkhykrG6n0lMbqbc6INzPpKJlUkeclBgAu8U3iTbnKJhWmPSUB+Jl0po3c+lCUb+YkMi6lOpezqSI+cXZAREmTSJfnUpGLGWaAlgENd9ThzlSqQuItKRIIOYO3PIqXg1tRGbbFMTdUgKJkMeIbeVP+EkfTH2q7pm3MQYHHD4lCELYkaI7eQcS/KSR125A1DaOPlVOHgHCI/TBe5K3YJUX+/jz4KQ8A58q9wVaT8AuH9a/LlYW5U6vgHuEesFflz61IeASt4yhb4kw9GBQa3LI/u+KvNNudUpkoj8OWUz+HOSt4RtywqTx9K/LlGJtziVTH4fYEP3cqxe2OEsFEGzJLL8kq0janV4besrGzPpRP4PYq0tBc1JHpJQAtnQH4QnoWFgeke1H9yPSPavyXV7V+S6l+S6vg4h1JoAIIOAiMEXxDK8AGdV/Uh8inEnBirjD+9PXwVfqnqVh9U9fBeY96PUVR+r8XBbhsdQ6wgw2I8y3WOVUSM2QJyCrS/8AK/8Ae4KvMq8tpn8S7pRiRkFRDfNTsVQntEx1oLSNqtANkCiWUjyKsZFkTUrAEKrSNQHNSpWtuZbdT4dCMaDQitMq56UTUqkgpzmsUERwakzfBAZd0rupm4M1mgXTBd5YyTEJwMU1OsQOdRlRuSPShC40zgc0bW9s50aQD6nEseJhihVo30BF27RYvzFXBixiYHaMkJjbGPUsQgG+eFTbDshZrd0sD9/FTw2oYK0i4xfhqjboPWOCXEQt4kZEw6jw3ZbajgpuNitf8SfXwScbVbltnxLYrEMMJ/Eo44Ms1VIDfdjrKOOHoUsdirEZaz1oOUMVjisvgflj9oL8sftBflj9oL8sftBY2/8AxRUv3ctySB6lhQkDykICNnqH1wpD8MftBMbUgfWiq+m2OFQ7RjgF2rcj0hSahLI7QqwoUNQ8QvyIvaRb0e1VNVEh4naOJWUY0CYgZuMV+XPSFWFSjLQx2hRELYmDcYX5U/aCoaqJBEsiQUGonLjCxoHpVrGVI9nlyUYimSByp40yBzhTqGk70+PlXuD0oxFIgHlVWIot2uNe7PSpPSctxqB8FsMnXufWoEUspBYUz0hDTAg84Kp1asGOk7UTXMRIbHD9CkBHtDaOrmKrmyJpueL9MERK7Oko6qpxQicQsTjw5rArN0CeEcOCHwTwjSVicFjmhggOEcGQWrEHkKE7feFQEBsTqDcxcI060Y3BfAywYcWDYKMLq301gz44PtbkT2UnImHAOI9Gap/dyLRGLfKmFMv+nKreWguJgtxIkR28aHYx51bylSOoA7Qh9wekIEW0iOcKpI0zq0vn6k1O0BHKyGuzAHOParyQoEy7ODjiWFnH1Jqlq3MQPjVyY2xMtRGY6UfuPWFUekQG4woRhbkxEiXcbTxL8v6wiJ0mD8Y+JUoijg2br3XrVtqgxfjQ+4LgcYXuP+IKpVFA69A+cOMrC39YRe2w+sFUekX1HrXZig1H1hflif7QX5Q/aC/LH7QX5Y/aC/LH7Q+CXdSACDFB5Yo86xV1/inqCDKfMVcsf7wrNVePSepWL/RWKq8bMqbDMcFv9YIcyDqlzofpsRdSA/5Xx8Nz/icB5iqZ5FkoPtkEdVYYcqnbyhVlUjtiAQVqtDKFL4lrryefGc/9yKyRdRQbjWSJK0suyMECfhNwhZLJZJtKfgKYogcLnJ1EcJ+AAMkSM0Z2t7UpOA+ks7catbepdSqWcZASfGRi+OO0spRIqUogODNgDsYY5qnWtLiM46hiCONYFwtctio6csUDsTbVUJHzVgUxyV8BsEOpYFESZXIKwVV+JA7XPXwPtdUebgtudDmUlMf+T/7yPOjzKpH9Y9a29KHAcF3V3V3V/L5L+XyX8ul0r+XS6UxsSFJ7Uj0ssaQ6UNNEn0j40xtX/tBflSPSFcNQw8Q7Qh+7esKQNts+kFcCFvqHiHaAi9ph9YKoPAwY/OHEreMYAgDjQ+79aqg0sON1R003YZuvcjpVDVSY6htd0I+Fs404pY86oy0YmTZjiQak5503g+sKTUsdHHsde79a923pVU+Cx18a/L+sIgW+LcYUBUiIhtqPizA4uVShbmOvYpSnWDk7MEZ1S81gU5THgcLlQ50SSpdrFNt4BwtwO+Cw4X4MwtichHiQZNwH4AElicfhYonbwciYVpDmUKELkmmJE48qjSuGEePlRlbSEqe0vtVIiAMccX/R0Puxlxp40H9KqHR83HHLk5V7sE86wpB+f5FeS8IfNxfkXuh0o/cjpVSRpgSJwx2dCI0Dp+RVHgBggBTBDnby8yY0gTz/ACJhSAL8eao/chmG35E3gjp+RW709uToGVEO3H8iI8H1/Ipnww/hDb+siBRHT8iP3EftfIqkjEYyPWgwQamD6fkXuB0/Ij9yOn5F7odK90Ole6HSu8s1ms12ipaUXYLEopyrji8Q9Q4J/VKuf8TgmdrHqVnKWen4zwVAFRPIs1bnlQAzZOrTTkZ/EUzoBSD4eGOvgwVwT9NHFVate4iBHDDE9AxQhu2RMI4bQ/oUTcyxH6YJyHKGCJKY/BKxOKAiVnjwhYfABHEu1/sC2aHF8J1isDgEB8I/AxyTjNDwTLSOIkKjSu5scn9qjRo3ETW4nRgcwpzJzHDcf2erhmDxoB8VUMjgyD5uevgOWapR5FkrQAYvigQpYKrp/wCSP2imRJyUmy1fHwBBH4H5eH2vkX5eH2vkX5eH2vkX5eH2vkWNvE/2vkR/dofa+RH7mPSvcjpWrwQ31vkTGjH7XyKZjSBeWPa29CA8GP2vkR+4jlx/IqsYUQQZue18i/Lx+18ilA0A5H0uTmVCiKILD6XyL8tH7Q9iqQlQAw+k/wAShAUAwH0vkX5cfa+RRj+HjgfpfIg1uPtfIn/DD7XyKh90HEnHa5DyLGiH+t8iH3I+18ilIUxq0tny8y90D6fkT+AH+t8irVLsRjAknE4P6AqlO0pU3yfFSvat1LQQQYP2efnRkzPwD/YFGI2OsTwugED/ALfPFFO+CYFOtKieABB/glDgKK5ExXYkyp3FK/qQ08RVC2uxHAMZ46jyqFWjIEaWzT+CG+t8ifwR9r5FXl4Y1FtvIn/Dj7XyL3A+18in+7jP6XyJ/Aj9v5FUBoxZvpfIuxRBDn5zfEsaI+18ikPBGX0vkUR4I+18iwt4/a+RQkbaPZP0vkQHgx+18iLW8T/a+RTmbeIkabd7Y/GywoR+18iINCDfW+RSeAz4+VYUwf7XyINbhuWXyL8vH7XyIvbR+18i/Lx+18i9xD7XyL3EPtfJ8HEBFhsRcrvppZLsnFVnOHiFByHUnkMlVP6yyRwxVM8iyVVs9JUMBkF3QjzjrRD4MsQrcj6fxLLFEp2+Z8a8a7qRhS4yqp3deU6ldshmFWncXFQ0z80kkdCkQMVntQxPBHhPwe8pMcPgMU4ii+S5E6wKxPDksll8LSQs2TZrDBYlOQjhwDFkBsWfwMVkEeFwWWOK7oTAMhIDEK3uKVeWmmXYEseQ8YRtd7xjbUojCRwEjzo1LWqJ0jkQrgPlp6kBsWOSn9ZYDBVNmCPOVkpHay5eCPGsFLmUtR+Z8afl4lLmUxyngGAQZkcS3Bs6Fs6F7odK90Ole6HSvdDpXuR0otTHT8ixpjpTafX8i7g6V7sdKrdkNrO1dwdKl92MuNEmAfUdqxiOlT7IyO3kVN4h2413R0qq0dnGodkYAbV3B0p4wDuNvKi0Blxo9gdKtxoGE+PkXdHSiNI6VXqV5Dsgty7VHwSY278eaxGKJdGKZmQ4Ink4Aj8Al8k7+hH4TALJZYJ8uDLgGCyWSyWSyWSyTkLvI4p34M0cfgA5hP8ABPwHdEv8AtmtN4PFotk7cyr17MwjWLPEyDhtrbQu3B5DlCYUT0hVTKDNLjTaB0qUTDMKIiM/RtT+GOn5FJqYduP5EHi55/kXcHT8ij92Gfj+RMKb+n5Eexhz/IpEQaOhvWsvX8iI0+v5FPs/OO3lQcYrCD/pzL3Xr+Rdz1/IsvX8iy9fyLL1/J8HMIkFFYlYBYhVR+t8QWRRcI/WPWmUub4lT5hwTbiKhxsOA8460ebgtf8AE+JGvc1IwpDEkkABStty3gFT6cCD68lI3twZyOZ40KYwiMkGPCOZDgjzcA+DIfCyT8D7ODj4CxxQ0hB/9jmP9iASslmsOE8I4D8FpjBC+3bXnSuxgJCWzqRj5muBSrgsC+B2Ak8Z61GvRnqo6QX5ONVZDIy+IcGKi/6YoKR2N8ajwekr0fGpKQkQ2j40xzRyU+PUetBBwsVkmZbFsXuo9K91HpXuo9K91HpQeEelFxH1ou3rWQ/T0Ls049KxpxZT7Me8UGpwbnKD04dJUtUBr1Hau7DpKkdEXbjKpkRjlxruw6SqnZDMcUGjEhht5F3Yo9mOY61hTh0pvDg/OhdVxHxYFxEHEk4fKpWh/LEYjb/uXZHZdBEfAHMhwR4B8DPaiH2o/AHwSs8EY7OE/DLpgFhlwY/DwzTCXAeE8B4T8EEZrFDpUbS7lrsohg2bDYo1rSQAlF9MjiCu5DpWMIv6VB4jVj1rswiyn2IsovEOyDQi3pQenFl2YR6UXhFvSpDQG0cfKnjAetY04splvnHrQwWER0r3Y6SvdxXdC7kV3I/BCLLHj4ANnBKJy1FMgvSevgk/EepUubgqF+0Qo8BD4uOtajgERTmPxLYYqVS9r6g+GDepMhxoI/AHMhwDgHP8BkXyWHwB8JtibZw4LPgzWfDisEzf7FgUAQcFiUeE8B4T8Fiu1nwFQq29ZgDjtwQJqxFcAOHAKfTgypjn6+Co+TINxJl6TwSUtR+Z8awO1FuJS49R60CShis0cfgZcGxbFkEcAi/BhwVCc9RWBDIOQpGWeo9azCkX2bFSL4aVmFUmcmdA8aKpaz3iByDl5lW3VCpquWOI7vSqta4JJJ28A4Aj8AcyHAOAc/A6MkE4y+CPhOE/+yCH+zbk4DwngPCf9ixCoXdvIipSLgP8Sp7supiG9IBpuGieaXN60JA9nILDJTL7EDLggwwcoDai7KQjlo+NOCsQGVQjLUetMVmsz0rP4GazWazQxUgEXKz4Aqw26yigjznr4JE8R6lS5hwVOZQHIql1cSAoxDl1XtNztog4JyPOEZVqhnV2k8MeCPOpfA9CHBHgHPwyCGlAH4I/9hCH+zdAEbUX4TwHhPwz8DxqFzOnU445qlurfFUG1kGjIvqMnwd8MsP9yFSHcIdVA+KDIKLoEZKXMpf4fxrPapY7FP6x60HKDss0e0s1ms13x+npXfC74XfCDSR7UUXlHoWY/T0rCcehe8j0Kp2o94oNOPQUXqR6FN5x1aj0Lvx6FImUcvWoaZRyXfj0FSgKg0nkUal/XjTpNmpWdOpKNnSyIcCT4Yjb6clUlRmQZZ8qkVnwnBDnR+B6EOCPAOfgKMU5TMslksll8HLhyWSyWSyWCwWKyWXDl/smQ1FMDin4DwHhPwChwH4GBUScwXB4iv4Vva4MbLMVJkyxPzdpbqUri2mJUCAx4/Qh2wsKg6FF5RITiUW5kXnFuYqQiQ+j412TE+gouIsp6s9R60Gkx512ZBZjoXeHQu8P09K7w/T0rvD9PSu6F3Qu6F3Qh2AiBFkcAu4E7p3U8M5HqCBdM+al9Y9fBLmPUqXMOCpcV6g1AYBThGTW8ThjsTu6OKd+AcBQ5/gu6d+CPAOdFEAbEZbeb/ZHFHFAimSnp22CxtSyGqkRFdsOVFsNilE4KMqdwZRLZhtnxHpWqJcIGIaQK0kOAM1IwLkLCjgvy5Q8WmQjJEGOX+yHCeApuA/AKH+wBfEKnYXeNqcDjko3NtIEEcEX4+A4qTD5nxlO21S7OxTIw7R60MEMFkisgu6F3Qu8u8u8u8gjwZJhJM6qP9M9QWEliVME46j1rvKTcSpxfYp3FzMBhkpinWItw+GxlycBwWSy4Shz/wCwHAOA8ylIlP8ABfamDusInStEH1OomVYqRqTBKIYMg0B0LCITABuZOQslEAYuo4IPmsuDADoWLdCxiOhdumCExt49CJjSESdrKXhVmHIiRMyVOJB1NjzrI/AHHwRf9MODBHgPCfgFDgCHwgZM4Kp21WZNsc1C4oTeBDodrBYnBHFSaXzPjXfXeLKox+cetDtINJe9KPbK75XfK75WazWazQxUsUVms1mqn1z8SKHMVLnPWeAqNzWqNIRwClDURRBbNP8ADCKHP8AfAisUEURyIluysliEwzTaMVpnDTT40HIZNUovN1GFvSYIzjFpH/YgjN1EOm/2WS11aT86qGjBqzYcT8qk4eXIjKNAkBMaBB5UxGPAP02cJ4S3AUMFlwHgzWaGPw3JxVnu+6qta1JiJJOAHGqU6UxKnLI5pgip/V+NHDapcyn9Y9aCGKzRw+B709C98egL356AvfnoCH3suhH74+kBEmr6l749AT+Kehe9l0Kf3xxPEnFzmiTXdkdNcg6j1r8wql5dV3hkOcqcRUItxkE3+xknQL4rNZrNY8DIDhzTlPIo+EXAKjS0HUTgomffT1KYMlpjFOM0XzT7Vh8DNdk4rNZrNOe8EDyDqQwLrErvLvLvBFpY8D/Bc5hOyaUAQiRTAmhOBbFEaC3Go0bvegp1DiwAPSztzFUq1GpqhMAg8YO1OtQGaZlkV3cEewUAIF07IHYiAMFhHFdqmSsYFYjFAZFZ/DMS+PSqW6L65BoFo03YafTt9KjKFcGB2/oUSKwZSiaj9j408aw0ok1nCkJT7TnrWEupDTMrComjP1rCqy/MBfmQslkslkhgUUXW1OE5VRn756gs8ESiXx1lVK1zUMaUYkn0B1+GtZS/hzuAc+c8qyR+EIxpSJQa3kgBQkFMzggDDFNp7I6kDozWAWMWQiYonQh2EQQsIqLvmvwd/GoTUwjpDtz5KNajSlOJKjOoRjih4Yzz50JCIcIknNDgY8JwKxXKjgtrJwfWs06LldnJAjJh1IF8SmJ7S7y7yHaRcoJtqxPDgiPgzpmI7QZW9xaRc18Zc5LLdbP4ngQ6QAhFsUDIJtOCwiF3VjFNpwTiKxhimMMEWhmjGrALuYruJ6OaxxByQNKmSeZaTbydP4ZHBhw4FlV3FeSa3ohqfHJ+vNS0nBSL/M+NMSpYnJTIdtR60BKRZBuDH4H5qS/NSX5qS/NS6EPvyi9UlF5FYViAnF0VjcyPoCqffEdooNdSy4lL96llxBVrmvdGNKEiT6FVs7a6P4KJwMcCeN+RE7OFor7sYqOA0lCpMjQEDLPbzrV4QJbamhQj6k0bcdC1eEy90F7pe7C90F7qK90F7oL3IXugu1BAiGK3PaWh++qBz0q1s6kHqzGsk4lyOrBBhwBBkOAgp8VER2rEYrArFPAcGazWkqGKY5lD6o6lyNwBjgu6FHALJbUxQbNYOuVHFZp+FjmsSrO5EXMKmJ5sVbXNvH94ptGXFgmJxQxK2pxmmAwWSyC/3LJZLI8ABOHCHDhP4YdGfgh0dVNsNgR8MB0ezhxpiMUYk4jgo39rUMa9OYkGyJiXAPJxhG2u5xG9BDVOMQwbjHJyKeLdj41hMhe/OXEFMv8AOPWg2BQauy/MHoReuehe+PQvfHoXvj0LNZrNZrNEApzwOE5VT6x6gsFI1ZAQAU/L9nP93+eQcMeUFudSpUZagdqJAJksI4raECMwgWwdADJGJmTFk+fBislksgslksuDJZLJZLELXINEYnmW5byOv8JTqgEscn4uJUa1KeqmYBn4lsTugGHAOBm2oOyJQQfasEDwYlZrYhimfBRPIOoJzkuzknf4GSy4MGZPgpYBYsuVOExzXZyWJR3ZcxHhHbtVXdlkHt5LHPgyWXwM/gsAskBgiisBwEsmIcIiWAKM4SxUhJZYKndWwaROLbRxKEtY8ZsRzIhFS5z1oIcB+B72Xq9i99L1exe+l6vYvfS9XsQ++n6vYj94TzsiNS78vUvfyPQsZlVSZHvFl7whVLHdd6fxJBEiDjEqVSvMzqS2nNZYISnEEnNA04AJ8HKkHd1F0CjIbUxw5V3utZBZBd0LururururururEBdwdJXdCwATEBl/EK1tGTDANt400Y9gZDiXdCyCyWS9KAJWaZGOpBBFgg8mR7aZ8FmVmVmsJM3rQPFGP7KkmZZcGAQGlYLLgxiE7IvHasAE2lYZogjgxATiIWTIlYrBZLurJbF3QslkFkFktiLxwQ2I49acyYLCTp3ZMswswiGDKTsiSGWCp3FOZ0OHGWD4+pQvLG+NSLYnBv040fvj6lIk7T1obEGuJDoX5qfqR/eZ+pfmJ+r2L8xP1exfmJ+r2cOaDIZo4lYDBBiViceCo/0/YpWlCX72zsMwql5WgTVnIyPOUJSIBXfj0hMJRbn4SyCwQHEisDisMlmsyu9L1rvSXeku9L1rvSWZXaKDyWEgsCODMLJZLALNHtBQqZOnBd1iMFgNiLkIBlswTssliViVmFmFmEdIcLtfRHUEQJOsTgu8swsECHRYllnJdqRWSyRwWSGG1Oi5AKxKYjHnWAUscECSHWYXZOCxJZOTiswswu8u8F3gu8F3gu8F3gu8FmEdJwWOJWWKJB9aLEshqdFxgpuVmExkG9CEHDlUN23N3GG6qgkTKRwEtg9KlWta8ZwAfAuieU9aCHAfge+l6vYvfS9XsWFaXq9i99L1exY1CsZkrskhB60vUvfS9XsRPjyYc3sW8TLeBp3pB8PadRYAgbW+Jb2u9771lKvSqgCcoS7YkHwHJkpD+Jf8GfrVQbwvJSjs0hseXErCpV6V+7XMw2T4oeC84NnyKVaVAkBASpEBAttRGxYJyFpNKfoBQ8KhN+ZYW5dEQ3bMjmKD7pqDlRP4Qg8TIgbukycbtmfQnq2MgOYoidGb8gUf3chzgh4lNiU7IIjgwOCzK75QUIN3lClHvBMSsC0VgVis8EGKYFd5aYgmSH3KarRnzsvdVPslP4VT7KjAU6glKTYxLc5Qizlh1BfdW5J5E9WjIDmRiaFR/qle4q/ZTaJA8oUisUMMODGTJxUR7Sz9a7xRGpZrDjWMkcdvwAsM1msA6Oq3lp5kBSoTfmWNAvzLsWUiv5dJuZfy+XQnG7Zj0Ffe2EgF+8W8weQFECjUHOCnZsE7YrNMSxT7E1KkSqVOUNECWJbIcbKZv7iUwctPZbnRGmr9r5E7VG+t8iYCo7Z6tvQFe3AvHjSpymAIOeyHAz9C3tab6r1KFrJ4Uo1AQJNkQTh6FIahmUztzINXn0r30+le+kveFd8rvlZlYlZrvLvIuQeDA8F0Y94RwVzvTz3CVS5pV5ilCQaHhudJYBjg3So0bW3hTgPohupAAqQG2R4C/EqYcZLRVpRI5ldb3owjGVEOw2qnfbvoTjV8TtmqDAiP6oLOOLjQFxVIioSqXs9IIeLYEcWWSGnd8MOU+1PGzhq5nxUNFtCOOwZrVGkHTGlAjmUyaEG0jYh9zHoTyoQfmVxEACPiEjDkTTpDrUj+Gg7cSjKVvHWRmyMpADiWqMg+SkKc+zsRp1LeciNoBI6U5CxqBAvgu8gxwUJviECEV3wuyXXYpyPMoULe1kakiwcFunIJq1OAPIXUZVKjSWmrU2O3GgaMAD+jr7+mJAZL7y0pk8yeNrS+yPYqmi2gJNgWTQtoA4YtyqmDHBz1sm8IMoy/DQfmTysqZP1VSp/gaWmQk/ZGxkROzh0exkRRs4xlzn41LVdGDfRX3FwZc6P4eEJcTn5V4N3YyMmcaQSG5+NDVQkCvdlYoYrB1mVntWexF5LtSwREZuUBGJJJwHGgJUZx5wQh4taUYciBleykeI4fFiok0QTyZetCP4aOPGECKEAeZHTAOqZ0DunDZmvdx6E5ox6B7FcFs9PVsR1U4ldq2gfQpRq2tMx5giZbup48/tVSpaQjTgfmg4DmX3c8VKNGjTJbaQPjW8fLltSapaECUh3TzHnVETAlUbEoiMIhU47MViy7MvWERt0/GpRkxBBB9OxVKO6raNLeNM6oTgNJieRuM7Mlu6hvKvOVzGJEiQXLSID+hlE1p9r0oYjSsCj8DJZLBZlZld9HtLvIBEOQFOMSwB4AxUictR606kdjKm0tiYzZbr3DOjI7nlLVVLZ8gPEypW9EDwYRADBgwyWC7WIQADpxgfQqAfEzXeL8i7x6VM6zkEDrl0pxMqqXPeWMj0qY1HIqmDI5daYTl0p9ZzA6UyxDFRgaMWlEnDDJHQQDsWFSEqYyZ3QjUpHE8qBlFiqNYDGW1CNOgSONAQty5QndARJ2KIkHR00xr/TJU9A7JJfoWRTMUQ5dvjTkuEzYrTjnxld5TBnsQc49nH0hUzsc9ZUeNwD6UBrZe8PSqAfARl8XCeNliOC5pziGBDciJlSBPMpR0APyMp1ra7IxIbYozhONWmfou4bjds12qEtKpQIzKhEYOAgYQJK9wQ6jOrBgcQ+a11xqmdgbBU6kaY1jI55ehNmFgEWTlGIy5cUTr9aAJL86ieIda1GcgsKkvSqvaOxZlHEo5utqbFROP6FYgkKj5x8r0vCriYFalAe8EizkbWd1Y3FUNXlAGT7DxLGZVLtnF13i3Ou8VICR7nxrvlFzgpHSHdYBB/09azWf+xJRDouhwVOf4uAlSA+kUFLFUuZNsQ8SmDMDAoFtiyRBycLALEK2L/P4ZfVCGKGO1Vvr/FwT5j1KnzDgg5+cOvhpj9U/FwYxDKvGdMPq6MFeXNvHVOnAyEXZ2yD7HyVSZ3eaFpb1NGL4yGeODoRq0gSvu6YCJYO460I7eClzy6uA4qX1B1rDJMvSUedSGzFcnZ/aCpc5/aKgRlqj1oR28FL6suscMvq/Hw3TciLnapYqcduo9aapAEKcZ24xBHqVSdkJCvDHGRPWt3+VbuJ/iMpOS3zT3V+LrdqkAezs5FEihHIKIjDABTjpeTvI5Of0yGQGCBGA513lms0MUe0mMkXH6fp0LUOJejgrf2erhPAVEbceDRVhqijGEWjwUTynqTjgk/0Pj4Cpc6CHAfgbFsWxbOAh1nw7FIDN0EDgykx+cetZqTnYqYHEs1KJ4lyYLNMTtHWsOC2x+fwyP6oQxKGJzVY/rrNTD7CqeOxZqDn5w4aR/VPBiVcSfaOpb0tLUyNxVomMQC2JVlG+j4VWNPtD4+c9KlCFzKUxxRJHSmjSnUj0dYKFP8ORH9GVKytriRuZAnGLDAOcTghoqAllRO0GXUsQslUl+qE5WCI4zwSGORQHE3WFS5z1qnHYJj4keJHNUzyFRzyRxKkf1fj4brnCzKzKkRlqPWs0QtUCcV5durC3e/uq3h1JaR3AAxfYzsu0S+nggx51htQPwg2ay7O1R7R7r+td4rvFVjqOzqXfK95JFpFd4pnUIgknHNd4rvFM5WEiqcjI5nqT7F3ypSJcmHxrMhEaypcblDtMh95Je8l0o/eS6V35LvyXfl8IuVnwcizVTEO/xLNZhSx+cetZo4qnjsWaOIQxWaz2jr4aGPz+GX1Qhihiq31+CfMepU+bgjj84dazQlULBQFGYMxEuNvRw1wThq+JTFrMG5AwfYVVjdX0jRnlHYH4kY1JPLjWBT46UPArGEuMZqvbxjC41zEtVQyeLBmDHLkKtbm2I8aIPiAYgSIx9AKLlHtKYf5oQxXeRIId0MVLHYUOcdYVLi1HrKgxx1x6wtIIRyVPmKGIyRxCPFp+PgzVzjtHUhiFmFKII7x61sRwChEtkoTrUYyqR7pOzmU8Q7IMeAcaHBms1ms0GKkCcGQ49Hx8Gar+jqWazRxWaOKix4+tZol+DNURynq4Zgn+7HXwHmUnGLlAodsLvj1I9oLvBd4LvBd1d1d1d1d1Foss1iF3V3VUaOUmWS7IUg3Z1JwMEcNip4bFkmIwQi3MskzbR1pmWSoEj5yyWSkW+aEHzQYKq0cpMu6pgjYVAiOYRqMBEZq5n+IBqQiWD5kfKiIEvtxUq38QrxB2CcmHMHXi095VhU4xOToE3daTY4yJRtvMVcGvKR0zyiBsBbbsdjjmpDdtYSqVHZtjAD9OlTrXdSRJO0v1qLZIcLHYgqV7bVJeDGQMoAkCf1gM/arS+kO3OALPkDzI9jrUwY9nSE5hj6USIYo4PigdHrKk0dh2oHbh1hUg3zj1lUyQ3bj1hYxwRaAdUxoaJiUOzj6VkuQBOIruq6DbQu6sY+tS05aiu6pPxKMQMFkqg07FEAYMuyMVK7u5iNGOJJyHKVqjvSmR6fYn/FRkfT7EfvetODIImVYj0L359agI1MyADjmclCtTgRCQDenJHkiNpWWHOV3VWBHFt5F3V3PWUTpPSV3etYR9aiHcYruesrAN6U2ldz1lUtIbPqXdR7KJH0PjTyiiRFTY4aj1oOu6u4F3B61jDrXu17tZLJZLLgLrNOOB1NvpIFFSb6RQUuZU/qjgJ2MonZwOcnHXwZKgD9PgzUvq8Nb6/wAXBN+I9SiakmjGKnu+whqJd5A5e1VKlWuSJE4cSclymAIHAQAnpzaS+9q6lHiXIsRhw4LApiMHVxum9uXpTiDTBPd05j0heJS7UTtUsPmjgIARDbUOZT+qUDzdYVLnPWqeOGuPWOAl1SOwCXxIFHFSx2IJlcPmSOAKR26igVMYYqOOzgnzIcyiQt5VhkA3SjKd9C1tQ51zGB24I6POVGVYDCOnM/aUb24IqWciwmBgVAAuSqV1c30Le1kH1SVSFv5loTmMnYDpdWNvVIk9UESGRAOzpVjPjpx6giNukcNb+z1cJx4MlEBtqzRWfBDjc8BxRG3R8azRxU/rFDBZcHdK7pXcK7h+Fj8BjkqjD5yLlMclLD5xWClzKnzcBGxlAPwZ7R18Gat3+knMcV3EQBhoXdC7qqnTtXdCl2RkrowuYxuxHswcapPgWG1GpKmRI8aIIwUgndcqIAZSrxiTEFn4n4+RZFR9SywHBy8BxRwxWeC3RceJop62JywKoGi3hGOHpUnGxPpWEVLs7UOypNHYnbEN1hUuc9apgDHUEQRgu6oEDYUXC7qmdGGkLu9acRxVwW2hd1DsqbR+cV3VINgoDSslUBGxRiBsX4q0Ilfyk0Sclc7q3tZxlGsGBji3RgF/D7OpWnCllGDvGPo4lGr4tYzGI7R9qp7k3lvKtPd9MkxpyZgTtyfpKpVMNUVTsKN9WNtEMIxyZGj4terMnugyJLBywB2Zq2sb66nL8KWjGWcHzHq2qhYUrqrRp2hAkWA1gcpGOGDjmVldRgdVShEk8fLw1vR1cGSkWwXdWAURsxXdWSHZXdVFo7T1LJHson9T41iE7KX1isl3etd1ZLELu9a7vX8PPgxK7wVTHHV7EXKwKli41Fd4IgHYoRPEu8EcRko4rMI8TjrXeCdwqOqXzl3gu8OkJn+Yu8Old4dKqsdvxLvjpCmx2K1YYUhi5TmJAbBmzTorArMOmJW+bX58Yg823qdVKYzCiYkgoxBxf9M1qOTI0t37nqziz6sGZP8AgtI5VUuK+5KsoRwLMjCtSNOqMwc0NWaEgD2ZAv6Vu5/+VH9kKXMgNQTOjjtKAdSA4kfR1hU+c9apkyx1DrRxXeCp47D8SOITA4lEE4su8OlZjpVfHaOpDFZqYEsBIp9Q6VLEKHaGSzU8diAJdTH/AC3lzKpSbJG43bU0ylhLLELVG3jI8uOPMpXlxTEapGzk5EEKtGX3bYjnRv7QkXRBYnYTxfoVUvLib15lyeMoW0ZgTqHSCcseNbqokjVG2iPUs1gcVVJzw6k2SJ1DpCOk4J3Zd4dKgQXzWYTah0rkXeHSqHaGZ6kEe0OkIgHHSsSi0x0hSx+cVmEMFksFiVtW1ZLJZLJZLEBZFAMh2V3QqpA+cj2U7KXZHeK7oRIGxUyRiy7oTadiiNOC7oWXZcJtITaVSeI7y7g6F3B0InT8xdwdC7g6FW7A7y7g6EJGIEXbiV+a89QhWkBzCRZciywR4CJDHiQZeYK10HtxCm/pkB8a3l4bG0qESptlpIdTkMJAKx0S7NanGXpIcqW8N5UpHc9ENKOMdR2MdvoULHdNEU6Dvxy6c0/jlGNUicZAgggEY4bVcb03FamG84AzmXJeIxIEfYvDkDrGY2j0FShMPEyiOUgnFWcYlx4cfRgApaovgsaQddmmMljTBXuYo6aYdkDpD4dYVLnPWqcmPeHsXapg+he5ioNTDMUXphE+GFJ6Y7vxr3UehYUx0Ku0BsXuwvdhTIgG1Fe7CkBTDNxKI0Bl7sKf3YyQemFXs72lqtqkWIBY9Kvt67vJFkHIi7lusqdNu6WQ4MOBnVvu4FpTkAqV1va7nUqO4iMAD6FTpUx2IwAHMu6FgA6rBuLqQBGCbQOgI6YgJjEFdwdCgIjBj1rGA6E5gFiF3B0KiRAO56lkj2B0BEiLHT8ZWMQUSKY6ApgDDUetZcOSxCyWSyWSyWSyWPB3gsliVUD46vYi8ggYyGCLkNq+Nd4I4qAJbBd4J9QyQLhl3ggNjhd4LvBUccNS7wXeCPbHd+Nd8LMKr2hn8QXeCqUKMh4sgwxV3SkRrjUkDzgsUNqwJXInOaclRcvgF5ujt8GB6JAq0viMaXZJGeGRPKqojkfkVhvO2iTcW0acQOPUWl0Ot1bttsIRoRJbaZASPrKieDBB9iuLq2vbe33ZWhrqSnMRkBLF4x2qkDv0XbkYDEBQpw7gGCMnGS7w6VmEcRmu8OlTD7CsD9HrCpc561TGzVHrRJOC746VTYjIo9sINMdKOqQBYLvjpQAmM1cdoZrvjpXfHSpxf5xQOoKQfYo47F3gp9oHBRaWxd4K6hKQIMCAH41fUSP7w+s8D7U6CKtriMCYxIUTOkDBgnkR3V3x0rvjpVYvxdSCdHELvBd4KJfDFd8LGQQ7QHpXeCoNIZnqQOoI9sdKIEh3fjTGY6Ue2OlTIGGooYrMLNZrMLMLMfCxiCu4m0DoTEBljEKZEB3kXgOhHshSaIzQJgOhEsyh2Rkh2AiRAZKIMQu4EzYOE2gLuhUsPnIvALuBSPhjuruBYBlUaAwK7gREYDV8exbzt7cHwy8w+byLkdJTYIYLlQRQK81QDEm3HWFvTcBwryhKcOeLlvSjRrgeLEl/l415bt7wfu1xKUZehiD1KhUoF6Jjh6EPSvTwPsW7rOnVIkKMBpBwlgMDxhQ3hOmY3Ehls+RYQClGUQzBe7CfSF3QUDoHQu7xoRAGzrCpcTnrKpcesdaYwC91HoVOIgG0lF6UT6E4pRCJMQSwQJoxdYUgFdAx2he5ihppRHoUxpDCRXux0I9gKI0hmXcHQp6aYBZU2gGZe7i6qQNMalR8w2gJs6haq+yRy08Q5+F24BbU8a0sABxrxt50x+KzG3A4o6c1iQ+KBEIle7j0Kt2cOz1LuptARaKY049CwphQePH1r3cehP4YyQ7AQ+6j0Km1MYE9ScwCP3QUgIhtHxpzTCLU4vzKQEcHXdXcCwiF3Qu4OgLuDoXcHQs1ms1mswsZBd4cDmQXfCn2h3vYi8gj2wpdod4oAyCwkFA6hkh2gpdp8FAvsXfCEtQZwsSF3lRcjvouQsJB1IEh9K7wQYhVWIzXeCJEg/Oqt1CiPFG0DE+nkWLPxIGRYBPTkCORA07WcgeIIfusog7SMFrr72oURxGUfidbzE94RnfSpSiGB7RIwbrxwVG6b73UceR8lOsIsJsR6VuO4j7ynVLHaHZWdG3uhO6t4NUG0E4jPPDiUW4GESTxDFbz/hdnKcpUJaSGB1cQyxVK9voznvSjUc6yTMF8sScBkqNtvWApyAAMmJb0BW97bbypSoVA4JkIn0gsR0KZjeUyGHzh7V2aoKIhIHmRIO1AagjLUGYqJfDDrCpAfSPWVSfbIdaPaDrvBU5OGYrvhHthSGsbEO2F3wrmRmGJwxXfCHbCn2h3yu8OkIlw3Og5DMu+FPtDJRecWA4+RBqg6VJ6gHpVS2riFShIYgjUOdiqm8bYAW77MlGewrNZqrSrREogOH5nQjGQBUvELnkXYwKi52LvBVyJBi3UgDIMu+FJpBF5hYTCg5/R13wj2wh2gh2wqbSGZ6ljMLvhS7XzPjQeYRAmHUw+0rvDpC7wWazCzCzCzC92F7sL3YXuwvdhOIALGATaAu6OhdwdAU+wMZcQ5FhAdAWMB0BS7A7x2LuDoR7Ay4goDQMuILuDoRAiMlEaQu4EBoGYR7I6F3QqXZHeReI6E4iOhF446fjXcHQg0B0Kr2QzruBTGkOyq0Kw7Eon1q9q17uFLdZk8TMs0eQbVou76VcAY6Rny5qMN1brmazl9eXIzZqNOxEKVJ8GiHUhc7w1ROxgOpfe15vzlCLkgJ44Kx3haY1KdXRMZ4Z9PsW7qsSfeN0MrW9pgTtJQAnAkgHY+HEMlGVpeUxV4iWUY/xChj+sFeX0N40Km9BpEKWoOdRZ+PDNVLypvFrORLU27JCqXQELff5HMJ8nOpWtxTlTvYSIkDhzFuI7E0LioOaRCHgX1UEccigIbxPSru1vZPvGnIZ7RtIUhpDuh2BlxBGOgZHYg0RmOsKmeU9apH9YJ9A6Am0joChFmwOxDsR6Aj93HoCkdAfDYsYR6Avdx6Aq7wGfFyL3cegIdgdARGkNqOxe7HQERoj0BRkwbmXdHQEeyFTEoRYjiCjUrUI06Eg42kg5HDAdJU7q9epOHofoZXBtKWmiXaLv6H2rePlyxpxMbeoYzkC5EhgQyp0+ILJMrOZOFYkf8JQoX1r4tKYYByGLu+GeAKnVtKkhGWIi3d5H2rVSDwJ2jFuPDA+pShUonSNvIONPUhEn0KuBTGzZyJzANzL3cegI/dBe7j0LuDoCh92F7uPQsacehe7C92FBqYcusaY6EfuwsIDL405gH5l7uPQFL7sZruBdwLuBdwdC92F7sL3Y+ExRQPBkqg2iXxBY8EiPpFZInkUCOJZKZI2KJA2cAJ4xwZKi/wBJF1mif1fjRY4LtZKq30lmpcbICRxVWyuQNUgQCfmvtHKqcrAzq0qZxbFxt9CNOtRaY5FEg5FMCjGWbrkRK3huypIaK4kwOLSGIIHGWbmK3cJd4Xch1KlCYw0BCNpUNMfqkhR07wqsP1ip3G8KsqtcDAyJLYhCMy4jko16NSUasCCG4wXUN6QpU6G/LemI1cGqEAYGXxK5p7sELm1gT2o4n08TI0r6gYzHJwW12Ik0pyAlxAKNzSL05gEdDocyJ5Cn5R1hU34z1lU4hu8E+pZqnzHgZTi2wLAYcFfnHVwYqRb5x61mFIvsUIgYhZICEXk6jX3haRqV5gd7EDDIcTetU6dOIEIhgOQZIwqVQDMEAbTzDa21XFUjsiEtnJgvMVKFkKFxO7lKQZiXc6jz8NK2tLeVW6nJoxiHkTxAKt5m82wid/XFOJoU27VEyY4jYWcKkY/Nx6cFCQyYJjkmqwjjtX4mxY0MyBlynDaq4Bxw9QRDrMItwHEKA5+tZrNMOCm/GepOimGbDrQ4Jh8XWH+0crEIMMODNVC2Or4uDFSYfOPAQVBsmWaqPxKLDZwBxtHBmqL/AEkXRbJFxiyOKYqq30lkjzIdn0rAqMJBwSB6FXvrCUYVIh9Ld72KQ3hYzpQEmcjPFsME5GC9K5EVY3xjqFKqJEZauRWMYU2FS48QDiEg7KgW+amZDYNqqA38YEAn2D0q4pTi2mTA8YWohGhVrNQrgRkHz4kN6eX7ioLWudRMT2ATixHKoUL+hDx4nvAYn0rDAImlJpq2tR2q1IaTx4etNOOKI04MUBscdYVMcp/aKhtxTOmUS+wrLLgnJsWCyWSuOcdXBkpYfOPWslKLYqJBZZI3YqRjCiRmH1cfxMedUqcQ0QMlhsVraiB1UjIvxvxcyo13BNeBI5HwZXlzUuRON1pkAARpaLMXzPBC1pP4ssIgAkk7Bhxr/vfzTaa9+E6qEDlCLBjIHa/Ej+IcwgT2Rlq9nEqdedMeNIOT0sPQC2CZlms1Ujcx1QIVe4sIgHiOR2t7DsU4GmRViWIOYP6ZLGPqU9IWSYMonZ8qdkWZY4oFlAvtKaWKKP0mHWgeCfZxcrD4eazWazWaYLJDtDgxKqg/S+Jd4LCQdTP6x4CVA8nBPmUeYcAP6w4aAP0kXKIcIh/mrvBPqCq9od5d4Kcol8Col9nADscJ0LXedtGdMYhuyx43C8SjAm0dg2T7AhSmGqHFtrco4ln2kUDHMF1KpdgGvQ0CJ4hl0kKmScgAsOJSjrWunWJK7Qx4KF3aTMbgTjjxYqNaoRUraAdWeLcYXhnvNwwp3B+4rHSHOAJ28yjVBBcA4KZJ2FDHi6wqZHGf2ioudqcyxXeCiH2FF5BZhSiZbAsJBZq5B2ELNYyCkdQ7x61miXwUW4lT3fRg0tQckfN2ty8S/DUYtFm/3IuFVnq7QCED3TJWltSg0IRDDi29a3Tf7JRb1KlRp0pSqSkAAASSSWYN8StfOXme1iN5TANCicREEDEg/GqljTfxakXDbAzONgbrUK9zQlK1gXkMcTsBI6TjxIUaFPTALJYHh4lOcSBcbJAeo8Y/QKVC5jpqDj28oO0FSCxRxUWy+VByjinQxGSpseNYo4hHH5vxoOs1PDaeDNZ/7LHgyHBiplsdXxBZBZI/WPAVAcnBU5lHm4ADxjhoE/SReIR7I6EeyO6sgu6FNoDPiXcHQpsB3SoDkWSGHzh1rJZIUq8AYHFjlgp39jTAuxFn5OL0KVK5pFyc2WSKu92EHVUII4nH+9Dk/TBRhTi8iVG43xaE3ciSfq7MObNVP4BTqQsoTMSJhi4LdCBGfBmt/blv6o1UXEI8a3hCI7MJkdB4XIUrO/qxFejEQgDnIRDDnwUnGxRLcXWFT5z+0UP02rEBd0KJbYUXiF3QqmAZgsgsgrjDaFkFkFJojvFZI4KAIdwqd5XESZhw2J9OGxEjg/EP96XA9qtznODOT8fKVTgThgt07zs9JlSrCJj86RIwERtKtvNnmu3jO+lEToUZZQBxBlxE5q8vLqYhTo0jL7IyHxK4uIxkaFORhFxjpBKqWe791x8GWb5lY7pi6aO6Yuv4TvOMLS/04GTNI8QdsVIEYPgdhHGOGpXnEmIzbPn9G1R3Pu2jG7q0YkVJDDw5bACHflU69tTlGcQNQlgx4hxjlQdHBR/TasQFJohZIdkZKmw2nqQBR7IX9kdZQfiWSmOUrPgwWf8AsWKwzQPDUHFL4hwyb6R4CVA8nBM8ijzcETyjhoA/SRxTakR+qs1mqj8a7ynInYVA8i7yjj84J+CEQcGKcIi8pswwbA9KvL2nOEt2QLhu83GVCZwBHSotk6lUrdwYllHe046rNsOfYV4UOzHkVxvS33hqpyqatEu88pIY4InY/Abi2raYSixByKvLifeqzMi3GTsR4beUJkUKHaYHA4bVM7GKgdmHWFT5z+0ovx/GnPBTHIUXWGSkBxDgb5qqY5ngxKOOGo8BkSre4l3IFz6FHdlrvanTu4DGJIBw9KMdYk23jTK3s9QI1Hb+m1grqqYYR0oS2KlT3haxqwhLVEEOBLj51HxJfdgADkAyHoW993W1UiQp6pGJy2AYceZVuX7UgSScyXKzx51mn/TqVO4oVpU60CCDEsQ3KFR8q+bK4p7wEQKNY4CYGw8Z2J4kEciclgrvyj5OuIVLWeqnUuYh+0MJRgdhGIdVqk5ynWqSMpSkXlInjPUr+kCHDdaxWBURsxWGSmgRxIBU+csnkeAiOegdaaWXBPnKw/2mPwZ/W9nDP6x4Yc3BMciHAOccNL63DLj0/HwBS5+CT8RUG4lkov8ASHXwZKAOZB4ZQqRBHEQ46Cjc7spjx8SQAAPRs9CFG6oyEweLL0q03bCmSKxAPEAc39Co7utItSiPWn2BW9vYUddyJ5YDBUYbzshT3cSNUtQJHIzurEWALGOPPwDgccBKvd6Sg8yG/T0KY5Cojm6wofWP7Sjz/HwZKHMUXWCL5sOtOmVcOcxw/wBorJTRrw0yqVZeE20Eg4+hXFzSuasakqhkDGUgXJfYclRtvNVl+JoxGNQHuxA4s8lT3t5drxNIQeYByO0EbCgaE3pibDpxVEiIBIx4/SgsFOpKo02wG0nIAKdl25eI5mQQCI85w4gF/wBwbrr1ZREO3rIwnmwYDBuRU9271uZwuZQ1gRz08bo/5jdf8KY7yun9HsWG8br1exWe8t3X90a5qiOYwOeBbB+RR3bfVj+DEX1kmRfiLh+sK5svK+9zb7op0tdaQEoynxwBwIw2gqbntaifl50BtW66fiyEZ3EQzljjtT5Yn2LBQlsTxyU+AcyHOUxKyUvqDrXoWSlzngCPwc1ms1ms0X+BkqmPzviHBkpfWKyRJyZQLbFkpk8SDLJY8YWSJVH6yzWaOPzRsKz9R9iz9RVTE58R4JHkKhzLP1FRIL9oLNZqnxMV3utd7rVUc3VwXUbigNc4sCAHBbMKpSoVp1qkpE65xGvHZhkAhxo+jrUTVpgsomnTAIVlcGOJm3whTJzw6VSgAdUw77VM8hUTtw6woH9Y/tKIHH8fBmoR5CjijipR5As1gqmGJKdZo/XKzVxf3IHgCEiH4wMlK9JbdpxEQXiDx8ELW2Dzqdn0HAqha7vpypVKtEmfaPaMmckcfErO2qzOic8SNj4qnQi5EVUjvu8NO8JeEcGMNp61/HLK71W5GHWq1K3mRawYRbbLIkdSlcTD15gPyDi9HWt8WlSpKJhAzDNiWyK3RRjSBNSEqR5AAS46E5GBCwTKx/8AVx6keJXW44QjK+r0ciconBwyBOaBdUd5VImG7baYkJDbOPzTyIBsEeZQPP1piVNigeRDHYqQB2nq4M1IA/MHWsl3VNh84rFYLL4O1bVtW1behFwVkslkslUw+d8QWSyClh84rJHDYqeGxZKphsUezsWSjh84LJZK3IHz0cAslLD5qyWQVQNlJlkplhkVAkbFkFFh84dayCyVMNgxWSyVT0dXAyDDgJIxcdayCxAW7I/+c3X8Ld4gXpxqAyHIFTo28QKQiAOhSwGRQHN1hUvrHrUef4+Gmf1ZIoMiTngsFltVV9hWA4P7R61qmQIqp5csqhBpTGsxOeGT8XyrwQWhxKvTtYkygMWX8a33b+HfwJEacwHIydeGMR8S/FTB8YHs4OX+JVf3YTlIYbZYcQZvWri+uoXE4VqgEIFyIA4aYvgArPyvCch2/FlJ8sG8M8g4lRq302oCTuxIBGRLKnQl5ktBKUhEdsYk5DnV/RoEVBXtuw2Lu7dKomsZ0LiF4RhgR22PqwVhWBcToxPqC4kzKx/9ZHqUrq4lGV04EaZzk/EqvmW+uSK1UdmJL6I5iI5ORSH4vaqUI19UeLjVKwtaMYUwxOkNqPGeM8qDo4KLcvWsgpYIDkQwGSpltp6uDIKRAx0fHwZqZbFysBwZ/BPYl0LuS6F3JdC7kuhYArES6EezLoWRC7p6F3JdCmBGT6uJd2XQu7LoKmRCXeOxYxl0FSIicuIql2ZZcS7p6CpkxLNxKLAs3Eu5LoUXjLMbE+mTcyyKoPE4S4k/a6CsAT6ExjJ9PEspfZKYRl0Kqz97iXdl0FTJEsjsUGjLLiXdl0FRwL6hsXdPQsj0KngcjsWEZdBXdl0FVAHfDZyIdmXQUSYy6ECIS6F3JdBReMsxs5UAISJ5kFuz/H9vwr+uY4CGahEgktxKRY5HYo4cXWFT+setRw2/Gsj0Luy+yVDAtpl8SOB6CVlIegohi/Mu5LoQGk5qtgc1tTsehZS7x2cquriiJeLkNmeCnUuqplVkXJ43R7Lre4MQeyc9i1NghhinJddumC3HiomlbwfmC0qv9SXUVUBnWfxCx1906u83JxLdm4917zqVt4WdsI1ZTwlKYxwBxZlfRlIzNOv4uTO8tTfE63BeRhpE7WnJndniMHRRVMV5iIhciQfB2BJQu5Q0UIyYRd2Y+xC30MIDTnnyo3EaMqtQnCILO+xWu+LqjKlVqB/DPzeLFOYnNd2XQUezLoKi/L1pmPQpPGXQgQC3MhgcuJUwxdzsWRWR6FLAtoHWsj0Luy6Cpdgs62usisitq2ratqyWXDmiUQ/BjmnVXj1fEgZB1Ls7FWlLMTKxjiq8oRaQCtqm0xXcU5wDKnJhlwUjE9klDUMF3FbgZGfBjmiM+z8a7hWTFViGLTIWucWhxnJT7YOB2qPbGXGmncwHOQoiN3TJ1DKQPOvztL7Q9q/OUvtBR/e6eljjqDJheUn+sEdNaJ9IUvvA2G3kQEagJPKnIwXYk55E5iUw6didgFgVusf+d7fgME8ww5VUr04tCUWdsCWydASjlhzogcRURzdYUH4z+0qZEWDrDgg2bFHUQEe2EDGqGOX6fpgnE+hAyJRPihiccV76PSENJBWP0j1q88PGccQgTA62xBQOnBb2jIdrSonkXQiiyBbFHmVcfqS/ZKnK7qyhRFSTkbO0UPN+760tMZ+GXGkyADuAGcY5qveC2kKFamGkxESeIHJ1aU7q511KQEADnGI7oYbGyUpxCNXfd1GdaRMYxiRKYID4xGI51+A3PWq07WEtQcMScvThgsTsXeW7yaZMBViTxYF0REdjTEAehciYcEcf0dBSByTbEMdihx48Jw+aOtZcEudZLu8GY4MlkmNrNYW01+Xl0LGnJYU5KT0Z9CL0Z58S9zL9PSvdT6E3gybmU2pSwkdiH3EmUh+HnkVWalI/eFY281OBozBIOxW0BbyJjHiX5Wp0FVI+BUB5lSAoSIAX5afQqUvAnqcZhuhD92n0LG2mPQqcq8S0DqOMQw5iUYXO/KcZjMcXQrinZ21arWiSIlo6DjnxsU+7dzwFQhiZHBuRvWpwt7a3g+RxwRMd4wHpLIylv+cDxRJARp3HmOuafFqPtX+oLj7RQhT39cmZ/WKgb2+upmL5VJR6WOKMfFugf8WftQNWvdAHjqz9qYXdy/8Aiz9q97df9WftTCvdCX+LP2oCG9bkQAA7xRhPftwJDZqKjOl5griQOGJz6VGEd+Sk3GShTa0nykF+pPvGxtp0Wyi7uo/itygx2sBkqUJXhogjHUMAWxBIPHhkjO13nCcBtB9qtd5WMdVpSqayXB7J24cGJU7ewialWIcgA4DjUK9Cy+4kWcuGxxwzVGtfbx8WkM4F2dviKhabtomNMFyP1mZ0TsCfScjsQwOzZyhU+c/tKmNmoJyCcNgXcn0K73oYSIowdsiXIDBfy8nnZE/wimg25qZDeldvcUSV/p+PStdPcsG5QMUP8lpdCeW7yB+qyst4xdq1KM8dmoOpWtcA0zm+Sr3VreypymX0jJON7S08S3hWrVJVYVRghGVIgIBkQESc0TtRVYE4mEh6lYWe99wzlu6dYieuLw0mWZ9CtN27ot6dKxhAaRDLLrW47mn3KU3lzZYLeVrve58GpI09JOUmBduZUaXkzeYrbwqwY6fmS/W5s8EbzetQTuTmXWxZIYI2hB8CABIQEItAgYKQ0S9AXcn0I9if2VEaZdC7suhS7E+hDsy6F3ZdCpdmWfFyLuz6Cu7LoUuxLuDZyruy6F3ZdCk0DmsRLoWEZdCxjLoWFOXQVjSl0Fe6n0L3U+he7CwgFjELJdluhSyWIQICIAWMQ7KoWyl8SDQCmNAyKuC2dQ9a7g9SqExAYEq0rCIeUV3Aqk2ZlSGkMycAMqVYh4ROzFMagi3GQOtVrW43rSpzEC8WEsCOjFTr7q3pdCz1ScGpIu56uRGdaIlj3iMTzlOJh+dfdUpTbNgT1OqUrTctSVObaSQQ/qU6dxu+FuIh3qS0g8gwOKBud7UaVTbGPaHStd/vuMqDZANjzr+bFC4sbeqb+ADSNQkE7SY5Y8SHiUBpVWUKI8MnBRhvW3lOILgRloY84zRnCzreIMR96cxiNnGhqohlGVGj922POpx8H77BlCtc3Ap3G1EUt6NPlKEqe+IN+nKqgpb1pyYFg21tp50KkLKlWJIDU5knnZhgpW29N3Tp1tIOliSxyOSkDR0yfaGPrxVHdljcGnOeWLDpU9zU9806m7qo7UdesscW5ObYmud4wpz5lZ1r/ewqXMSDIAsJMcm5lUud2bvpwqyjpJYHBfdy0jkwCd8eA4YonMgFRbbp6wqfOetQbvagnCAW8qBzMYj/AIgi6YlA8Stt8S3qKNWqZDQzsxbP9Odbx3vcb0FYU6Z0RAI7ROBJ5OJU5kYtwMcmW7PEYyjRjEcwduDEYrDNDAKUtIyT0gZRwOAy6EYnYvCtKMqk+KIfpOQ9Ko17je4p1pBzHT3eRfz4fZUY3FSdasJPqEtIPEGHtU40bCESYs7B+fJUrdyYxV9WhDV4MAcnzkFFwCFExoxjU4wGRfJZ8ESeNbwudPZ0BigVMHYVkiocePWmUickMOClzrLglFvmfGnZHsqQ5T18AeK7q7q7q7vqXd9S9xNe4mvcTXuJr3E/V7VL7uXQsIy6EGtakuYfKj+61egJhbVOgKv9zPCoQcEPuZ9CI8CpiDsVYGjNvEOxe6mqrUpd09SsY+BMtHiX5ep0KpTjSllxKM7inOIiOJ1UjW3lI1y/ZjHVIEcYfBVqO45y/hpk0ZSGksczp5Efx29J1LN3Axjj6CjGdw9XaCST60DundtWtE4PGJIfnWm/mLWxkMQ/a6Fa1bgyrVoAagX0yO1+dVY7p3bTpmbasARJsncHJNGnEegD4lishwbFsdO5XeWJdcqz4M12sVxLvetOQFkFqqUYykOOIOHpClUvbb8HXYtUgMXbDANgVc39pWjX3XB2lqaYHJEYqFnQJlQMmlGRebckTiTzI3o3jT/FsNUDJpRf9Vd5w6EXQMsz8CUWL6Ts+NRDF+z1hU+c9apkjOQyTaJdCDQm/Mr3+z1ovkqdvbRM68y0YjMnYAONMNzVgeWJC3du2/o6LuGtxzyJHqPqW8bKxt5VLioAIgB9vUqUJ7krOI5AE9SlTq4VIkgjaCMCCh+mxbsOiRHhRyGWaJ8GfQidMuhCbdkruy6FUOiWR2KyOksISdx9VlUr1Kcdcy5wG3NSFpCI1Ztt51gAm0hYhYoNmt9WNtTjK4nQkwOWAc58gUrGMe2LiUAOUyYK43jW3BM2NKn4hnHGOgB3BGeCE61GUIk7Q3BEmJLllGdWJEZZKF1OnhVJIIxLcvpXcl0KqBCWfFyL3cuhH7ufQoHTJsdnKsYS6FNoSduJRPhyy4l7uXQqIMJYni5E5iUGhLoUuySNOSJjQmfQj+7Ty4h7VPst2j1oEB0P3efoHyofu1Xo+Vfl6nR8q/L1Oj5V+Xn0fKvy8+j5V7qPQF7qPQF7qPQF7qPQF7kdAUtMW4Am8IMm8MK5kY4CqfiQ+7GXIptAPpPUrjXBx4hXuQq33I7h6it3TqwHcVSpGdKMo5meTKdnu7Rd7zYloYwBGw7XKjGFSFlbYiIpOC3K+1HeFzeVKlYl5OXdR0U3csAM3VGta7vNDd5xlOoCA3EMsVTvN4yld3pj2hIjRjxNipU902FOjSkMgHz51OZrGQOw7OZHBZrA4LFbODNZ8LfAz+DkOE0q9MTonMHJVd8+U7Pw9/0ImUNLaTPl9Dr8Rc3U6e8AXlFyIy5COJW9h5hqQtbvuxPzakiWAD7TgEJArAuEHOJQPB6OtRjtw9TKnIZgnrKpVGxNQBEBRfJ1fz2tHrQW4GLH8THFSHinPiC7UnCGAKBdg63hMnE15/tFDn+JWJ2CI6yiHUpNgAoyAyXayVabYgK0H/l/FFDmHwTwVYElpRILcRDHrW94Ue1CjvEgPmwkc1umVeGqnUt46okliCMiHZlUu923s7a8BMhAaRB2yZnZU6tzvGl4pm2eDPh6WVvV3pvEzo8UCHf2Kvu22H3VKWkPtbmW7LSiSaYpxljm8g5605ifUqjZasOhOQpyAxAQLMA/WtvqUwc2QAOz4uC3jnigH9YWR9SlEZsjn6kc8lMNtPWhghwZL/csupZdS/IVV/L6q/l9VfkKqw3dVPpUhHd1QHnT1bObciDbvq+j5USd31PUuxu2bq6MbOZj4hOWGxAS3ZV9SnGO7qoJBxZV/Ct5Sl4knwwzTm0l0Kt+KgKcYxJJkQABz5PyO6/hNlXlXuKUcDTJZ+LDB3zRhLeFSlZOQIQlKJI2O2eClWMpa3GokkF+fNRs7aoJVKXZk2wjMc/Kv4ZuetoEQDM8QO1W9zvSP47eoxlKXahqfAgHiQoW4ELf6Iwj0BPtRwRyWKyCyWSyWSyWS7oWSyCZlksl3Qsgshw5BZBOwTGIU/BeOrNlA1LcU95U3MJgAYn6TZ+lTp78pyq0KcwaFWAYRILguMm41S3d5ouJXW7pYCQLziXzkc2ZUbzcd3CdCQyBcvtDZ4JwCu0SyZ8k0TiQVGRpyGHWqfG56yrYN/fhE+FNuQKP3FU48SvCYSidMcDnmFiFbbwsoR/E0ZCUSQ4BHGNqwpW5H1B7Vu/ee84xF5UMtWkMMCwwW9L6wqRjdU6bgyDgYgfGqMJVrepMh9Rg5Va4qxAq1JGUmyeRcsNmOxR/TYrIaJFwMRkMSm8KfQPaqkDRmOzxKJNGZDnIJ/AqH0BXP3EwNGZGHpVrdSiY0vCzLMMBt5diEo5N8HJZKQyy61e1p23h0arSiQwEiMzhtxzVvGrVlOrSkA5JJA4ubkWeSgNJlISjlnmpXJhIRgHcqlClHUKt3gOMPyqjTNGQMYgNgwwTeDPJVQKM2EulAfh6j8w9qqRFGYw5PagfAqGJJxAGwrC3qNzD2qU5W9RmbIe1QkKM8uRETt5kehWvh21SMiepCUt31TNs8Fhu+o3oVScbWcYimC2e0rHd1RuNS/y6oqrUpAajhxYoFih9zI83+9dqxq9C/JVfUvydX1L8nV9S/J1fV8HCKlwBSOnbxKUm2HBXb/TPBUw2K8uN+73oW9DXLOQ14Z9k7FdWnlS2lfHGMZEmIBIzIDux2bV+Eut5VvBmX8Om8YvswBRr7x1PIu5xKjVqUXkAmp0xEK9uZUyaM6hk/Oo3W8aQNlX0wlLUxjiwIG0Y48St94WFcVbOqNUJDIg+xY4LvLvI/Bz+G3Bhks/hPwZrNEOjZ70EJWJGkgxBIBzIPG2Sur3ypSnOwBLhnOnkHGq1fdPj0JibzhUfFjnpOHGy/D1Jypb1jEk083ER2pcbDM4YKFWmXpyyKzZRiQmMgB8qEAMHPWVaFsDcBZBDBbwOzSP2hwZIrd9nf7zhSuYGTg7HJKvrTd+9KNevVaOmJLs4JOWxU4bQE4CirEcZHxrMdCq4fNKHZ2ruepbw7OHhlQgY4GI6QAQeViAoyzkQD0jhyWXBmgOUKy3hGOUSFvmiZsYTiw9irVQdiudXdMyfUFveccPuj1LdtYxfRV+NcjI9nBXOG1OAqg5FQwzlLr4NIDuVSwxbiWXqVnAjAFADJuCpEjKkOsoqWGxVh+uetBDBZBd31Lu+pd31Lu+pflqnq9q/K1PV7V+Uqer2r8pU9XtX5aqOj2qRFOfpZN4Mwg1vIjnHtRjLd8zLkCmBYzfHYryp5h3nRtYiRJE6kYniwBLq5t7GlO5vA+gxGqmdnalsVSz8u0BuqkXj4ke1KQ+lE/NPOhHfW/Li4vKp95OZzPQPUqJvqorVGfVLEly+PNsXi21pAXGRkzkrs5lOMUA2KubCqwqVYkCW0HjQpmhL7pwJsfSrHdO8ru4r+VYTEZDOVIGTyNMZgDPb0KG8PLW9I1bchyJSHiAcozXjSfwuNsEDrC7yfUOld4LvBd4LvBd4JtQWoHDhzTP/ALWWK3Du7ytONKnGGuZie1MTOlpcsc/Wnr3hqH9Zj1hC28+bqjT3rIsbmnFpkfNBbBhzK1uLLeJhQl27avAtKW0CZGSo+W/P503epqNaHcNLYahxaQxc8TYbVTvrWrrtpgNJwQSeUbUSBkmdwhIHFzz5q3pF8K8Svy8+fD2oH8NNvR7VXsbG0JualPCMmLsQdnpVUQ3TppPhgQPQif4YGWmO6yTzIGpudpcoLoH+FxHMEW3RqkM2jlz8Sw3YwVKNTdOq2ObgkelW276luTUjEAiIDAjPPlX5efq9qq/cSHZPF7VGQoSZztC/Ly6Qr8G3kxpnFxgqGm1lLsDaOJSjWixfLq9X+wu7qVMeLSBIPJxK8sDL93qwGGxwrmJlgxUxOjIEyOODM5b0tmroQraKlVgOMgu/oV3d1qBqTgMAM348UT+EqP6Pai9tLLjCumoywOTjpzWFvLPjCmZW8hhm4VGIozl2pZNxnlX5ap6vaiTSnHEZt7VSHhTy5PasaU/V7VaEwkHODt8SDUJ5cYXuJ9I9qmdJ1eGMNuZRl4MweVvaiNEvUqznHWetA4n0INbzPR7V+Vqer2r8vP1e1e5n6vavcz9XtXuZ+r2rJZLJZI8SkwxWJQzVz5f8mWh8aB7UqgjMxIwOGOZVa3ut8mkJO0qYMJAcWDKNffVarXqjaZTk/LiUR4Em5l7mXQoVqNKXixLjAoQjOTAIa7cH0YoCVi/oXa3WJHlB+RRMvL8D6Ze1DV5biJ8Yf4ypUam5A/MqplaThKeYAwVOruc1I04nLFjyHjVC082bhNTS4JpNEEbMJFnHoTStbuMjs1R9mxTneb6na1BJhGbkkcbxDITh5miYHLEqcbPzBTkYs71Iwz+sQgI77oGR2ePTf1SQ/wAxp/8AVgfWCmpX4lI7BMErGVRuQuiYTqvzFdirJuVREiDgnWKYf7LNZ8E1aASA00HL7cT600wDJdqA52X/AG7vGv4ljOrEUiXJjInDlZRrb4sfG3GW++pZwiQ7l3ZhmoWt5fy3l5MrEHSHNSnE4jGWAbaqF75d3jGvUn36TiM6b5CQJzPIgKsGnxLEZKDjEF+hDXUZHwq8NXOFay1RL0ZEMQcHCGoArREABSDZQI9acgLABb1G3VD9koYBSDbFWqRIwlInpKbSqpb5qiWXdW8Q392VbR/UHUqdSjF4ux4uccZHEo6s02z4W/7Q4/cSPQFZ2c6UiakxAMcsSHKnYRpnTEFzsPE3rf8A3o8S3Tu6PvTF+QD5Vf3kxiB6tidsEcFdnZqKOCqFvmlW4/Xn1rJBxtVM/pknIVgw70m9SjzLJVI7PCH7RWKlhsVfD+8l1oHSgw4Dgslksl/Jh0r+TDpX8mHSv5MOlMd0gf2gpat2xB+sCiRZDpQEN2AjnV9vveVlojvKXiRJODcQHsWFKKfwQsaQTiiG9C+7pxBXciu5FPoivdxWMIruRR+7ijppAogW4T1KJWsWva5kPEtYpoUyOaRXZEsOUoRhOQ5iV93e3A5qkkJUd73kZDiqz+IoGl5hvvTVmfjX3fmK7w45SPxof57cnnJKFG33qcC4MoRmekh/Wo1TvWE4gvp8GIccSadnH7AQNSxps22PsUacvL1nVqDMmU4yPxepNW8mUyf1ah9ic+VJg/2iu35YkPtDrUfxnlyoKW0wl2hygHAr+WXvpEUYfw2+d+KCJlY3fRD2rxKlzWoF8pRBPqKjGrvkxgdpgeoca7PmCk360Zj4l/qK3+zU9i+68w2uP1x1hA0t90/4tAadQMhFucgRR/D78o1DyyAHWu1vCho5JOqO8K1/Tq14F4xlKOkHjLnFlebv3nc2tWyrhpwnUhpIIZgCcMOJVd8+U/MNGe56xb8K8asoyOyJckxHHsyVbem5Lirb3dQgyIcAtl2clu/cnmiE4b4wpiqxarM90nZF8uJFxgwUhvm/arh2AHIfLAY4qrZbh3CZUjE6axlkdg08qlOrvudCkD2IwiAw5dOZ5SrO5p76jcxpA/d1IARkGZjLPlVa281+VKZu4kMaRkQeN8MPQpHf1hKxILAy1EHnYOFWv92b7tJU9OWvSQD9cBU7vd9GjWtp92UakSC2BbjTDdAJ4nxV/OlurXVnOLgHugAt0oj+FMXyX8qHSrgUd1vS1HpfFP8AwoKoTurYVTjDdOqHG6L7kf0qtZx3WKcJhtcjgH5BiVShVLzjEdIDdCYDBdpZfAwyTE9lbw3dMSqGrRkBpDkHLJV6lrPw7q3uuzIh9r5HBULynYCoalASJBzJYk+k7NikZbqYcbqlbzAFanRj2c2J2KYtt2+KZQD4tyuv5MG+si+5x0q6lDdL4o/5TipvukMyt42+6NUBOeL59pYbiH2gpRrbm0wPKFSENyDS30gj/kY+0Fu81N1CMhMsHd8EP8kH2gv5HH7QUqh3WPG0gM+x360/8EH2gj/kkWb6QVeUrMCprLjiLoCG7QR6UG3U/pX8lH2gv5MOkL+TDpX8mHSv5MOn4JdTxXYKixV5c2VIHfdjAyoOWDtkq277wab6jIwqAHDUDsKGJ6Vi/Su06wJXfPSu+eldqoQven9PSvers1VhWHSnNUIDxA694F3wh4lQMgBUH6elPrH6elPr/TpTSkP09K94P09KwmG/TlXfCbUGWEgn8QOsKgQiZiXMtZs7gnkgnp2NyZfUWqhS7GGYx9KgLy0qSBx7MHbnRhWqyhU4pBisK46UdNePSvzEW517+K99HpXvouvfwX5iKbV2zgCONRnTqtHjc4o6rgdKlrvADzo/jN7wjVfbNvjUalXfUDAfrOPWUP8ANKJ9IVvO23nSNxGY0gEOS+DK1i+IoQ9YBVeF9UiK1anAY8kVrFzCUyeMISobxNMc6qVLTfQqXAyiSwPHjijKdtE8xf4kY1rfHmRhGMoHkJHsVChuvzHf0bakdUacKsoxd32HBzmhXt99uwyqx8V+eRIKNXf24KU6hl2pUpGIkOMxY4qlR3hZXNvUkMZGn2QW4+rjVxXsd9gCnIRlr7OJD4KpCy3jRnUNaUgBMEkEvgmlUA9KrETcaVRIOT9ZWjUVEQwAC7Sbhx4RjsUHyNan6phO2WCu6shH7+t4ob6OGfLgt11LYyaNqIl8O0CH9CqniiepXGsmQ8YR9bLeG75sJU6UMjhjFwoufWjielXzE989aPa2qrie6VZ4/Pn+0sVVG0qgxx0jqWZW7scRM9RQY4NwTMSW8L4ysyykeRXDSw8SXWgNaGaDEo4rNd5d5e6j0le6j0le6j0le6j0lPOnEDnUx4YJXaoRAUfChE9KlV3nGmasj2YHHWTlFuXYt8eb7WiIbsvKxqSh/wAuZOMOYKM3wQ1EpwS6ANYQkeNAjeUE/wDEqbehY74gBzr+cR6UZDewIHKnjWMseNbelYErMlYusIy6F3Jo6YSTeFP1r3U17qaGuE9KaUJujhJ0cJMspetY6lRpY6SVu2c5E28a1MyxziJAn1OoRt6FIHSMwNgCGmtaxfjZPAUjEhwQ2KiL25t6ZP0mVAeWKNH/ALihW0zkO6YD5wA+dyoA3PrKxrHLjXvD0r3p6V709KDVT0r3x6V749KEK0slGhHLjRjKtpPOjKpcP6VqqUxKXKT7V+VHSUddsOkqhXt6DXEJgxxPeBDK3uCcZUaX/wAuJVnUndSjcypxEg+AGnZ6EZ2O+rlydsnHqQlQ81yhFsidiMY+coyjxFioipvenU+rh0qX4qzpVDxiQdaq26iOZAV91SbjKBM40+cZLxbe/pPxYKMYUoTgeQIChbEcwbqQrWt/dwrRLtqLD0KVSV6azj+8cgczKlab03XKV2Q0pRwjicwM1b2YuadGcjgashEEnY/GqV3Tr0qlOYd6chKPoKPOsU6IThCgaZbSZasGzZufagRwPyKEaIBPiwYH6wRYRZbmu64+/qUqgPE8Q4ZW8Lm2FSAgQGzYs79GCuI092y1mEgPSCAt11LltVzXBLYfO2LfMaEIaowgDjh3cPSyiBSi3Oj93D9PQrvRCHfO1e7CqvTHdKtpQpjTrn1pxSh0/IpCpTgIqi0IswTimHVjqhHGXxINCLMmFOLKqCB43hA+jUUYmMW5ERpizKu8e14kutDTSDcyH3YTEQXcifT8i93Dp+Re7h0/Ivdw6fkXeXeK7xXfKI1YKZbFNI4OosBkrxif4fbVGpHZIY4tsZb18s1aeqncQMhLBxUjjEOctRwKqWwotKnIxPOMD60NcWOxYDBBoOUYRDFNvASnPidA/wAMiY8pL9aaVg3MT7VUnu2iYD1lAxMgGTa5MtJJK7FJ/QFqnSOriTQtgY8y/KgFdu1DLG0i/MiRaxfmQe1i3MmlaxA5kTKhF+Yrt2IJX8qwQbdSEZbrYoSlYtLjWNqelPC3kDyEojTUHLqQ0b7u4RGAAIYKP4y8r1m2yPsZEyjM1G2k5J6cGRAIAWaz/TpR0VoxCc3ECnp3lMBD9+pphe08U0b+CadcE8/yp9Qbjf5UNMgTz/KmjCJ9KBlTAbnVrrfT4kX6QrT/AAqX/wAqKtJwkWMIfsokRnINki9tUI5isLOp0FFreoPQVqlGYPK6bVJdoEhHXCRPpQFMSHpKH4a8lGXKVHXdiURyFGN/axnLawUYV7CUZ5Eoi0uhTlLjKhKwuhcx1YRhIOOVyW6MVG7sd5XcKIypmdQxHodvaran/UKy/H2bMZEaZx2AjS0S21f5Lv6gL+JETRmfDkCQ7DW2r0OqlMwIMQ+Ibo40ZQkYy8SORY58YVtqLnQMVD6nx8GaL8ShH/zIH/iCZ15ZqbZVKo9QWgZcHlWByNYdYXmOETg0P2VAjNgpdpXLHOR4Kv1SrP60/wBoosVUEjtVuHw0jqWS3QNmqT9CGGxZKoQW+5j+0U5zU24ipk/8yX7RUXhsXZDI9srGRQ7RWZWZXu6H2j7F7uh9o+xe7ofaPsXu6H2j7EToofaKkKxpPyOfiRlCnR9LqMKlvQkeQn41c1K19OkZyOUQdvKoULe6lWjKAlqkAC52MpypUBEVZGXTifW6EYxBAwXcHqQEoJxDBCJCbFZFTpCLhMYsUzFPpLLAF1iCm9i1BFg5QJjnyLNYYkLtArJaogpi7piU4BYoYFwqdQQxJQOhZL/csvUE4GKxx6FkFksAPUjGMiJIHxpaVjWKfxj60fvCu+fWu+U2orvld8oa3lxqkw7WodatJEHCjS/+VFR31b3FKnbQjERBPaJAxfiX7vXoS9IVoLm2pTq1ZNhkBxrSaEQRyLGjHoC7duDI5+lEi3c8y8KpZT8Tmw6UJyswaZ5E8LYD0L3Q6FjSAHMp+LMCaeVUIjxiOYsh/DL6QPKSjT3hpqxGDmLoW04Tp1zl2Qz86p1PKfj06lOT06sCYiJ2Eyj1Kz3B/Uq+/Eb0hTjCNUYksGAmc/Sc0Z21uDZyqRY7TireUIUBEwGZL5KMb6FHwfDOMXd3GHMyxz4DzJ7OEDU1wbUcO8HJRIFv0leVvxsICt41XuEkNpHHtz4CvKf4eL1PHGHpC381rS/FNDUMdIGnYc1GP4aizbSUTGlQbnPsV1KjGiZxkQXODogwodJVUyhQ06TtKtzbU6Pha5s5P0sVjC3fnKnKrCjo5CqOmnRbSNqHYovzqwFSFLVqLY8iH3dF+de7o9JVV4UvHFEHPBtRHWvd0m51MmFIhjtUhKMRW1l+J3Kj4cKWnlKL06XSj91T6UCKVPpXu6XSu5S6V3KXSvlXyrB1kmJ9alJn9aka1WMRxnBCNHeVEsdkkY/xGl4Aj3nwfidU6tjcQq0xTAJiXDgrdlWPecusXCzWfUiB1LJO4TakTHJM3qWXqWXqWIDLYswnDcHaDrGjF+ZdmlEHkCxiOhd1PoCcwBXuI9CwgwXcVPUOygNJZuJOIEehYxL8yZi3MsAR6F3D0LGJPoTCJ6E4gehdwvzLGLjiZSfJRIAXdBKYgYrCPZTiGKA0govFRjpZ8l40ajacQOY5Kz3ebWJIoQBIdniBHa/EjIW4bNYbqiGwdlCvOkI6AwDbUakol9qedQOEIwpOONU5V60IU32lUKe8bmhLeAB1Fw3IjLc9ah+HOQgQyA1YdKhShN5kI1NDwXjSpYpvC/ToRHgsE4pp5RcqRFGLiJY7VcU4Fpiti2Zw2p6cnfFUba4v6krQEHQS8QqVai8oaRiMlESiQW9nCeZHiePWFmvKNSeGqpUbnYcEicgF5R8KXa8f4wvMxiScIP6IYKAk7sjiVvEB38Q5IvGSqkwLaSrMgnGdT9pd8n0qriVR1AksF3Vuowwjqk/QFFxiyyVRiW/Dx/aKJIU8NhVX/Fl+0VH2lYE9KxKxWR6VkelZHpXurP7J9q93Z/ZPtWNGz+yfavdWf2Ze1e5tAfqy9qqfiY28Q3zRL4yqg1RxGwMpkDM86MKNrWMTj3D+npRhVoVoyJ+gVOrPUDCUQARnqfH0MssFkskQODBNpTlNKbIA1g6AhVBmSwHGeJSvfw5EBsZSp3ExTqg5FGcLgEImJcf7Skw7QKBZZLJZLJZLJZLJZLJYxTNgsIrurGKbTgnATsoSwwYpuJduYiWUjCYk21Fw4XhT7NDj2I7t3cfGrTDeJEsInm28SjZ7ttp17iRZogkOeM7FRuPMNejaW8SDOmXlPTtAIwBOx+hXFXfNmbyEogREieyXzw4wjQ/7PpGMgYu5cOG40bejuetTrVIlpRmcDx4nJX9n5c8zxo7vgWEasJTlqHKDkeJVau/LCpTtRLCrTBqwIdgSY918CxyyUaV5IztjmDgW5lKVnINEYhEgJmWIWSIiGdb23R4ZJE/EB5GZvjWpuyVGTsVTtKZpmnHJ1G5ryo/htB7o7Tkgj0Nmsc+EytfDE9UW1AscQ+XqTfhrXol7Vu62u94UaEraUzHsykBqDDoKluCvfwuKsaerXGJiDi2RVC3kcKk4x+0QPjXl3fdvvqjKrTqRnp0EPtZ15iNvVo+JphrwIBIi2AOI5EJGpQlyEFGYnaR5DGRPWr2Ua9pq1kl4yz6cOZN4ln9mXtVYSnaEaT82XtVrSo0LYwE5sS797n6Fp02cTxtJuh1U1VLSQ4hGQ9LuqQFC2GAzB4k37pBuOMi/rW7ZTNrNpHCIkNnKcVBqVrHnEj8afx7If2J+1VCY21Sr4AyBAbUcWJdFqdpHnEj8an+8WLsfmT9qqCUaOvxJOwLPqPqURG1tyPSvyVv6/asLKh6/ag1pbev2r8vbev2r8va9B9q/L2vQfau6V3Su6V3SsYlVHo6g3IqumkzDiRja1jAPsHyL7neFUjnbqWmvTqTHGST8aiLmlLHYhKdGQITeHJR8fVEImnWkZJ4z7CYFPsUiEREEISs7WtUDY6YyI9LBW0xuKpUMZxMQQY4vhm23jULe58rzjanBoypyxbkJKnOpuG6p1mc9gyHTEEIQq6gHLiWHPntQjGkWK1AsFgn0llkmKzWATaVknZkHyQpuH500ThzrNYyXeWazWZWZWazXeWfUs1ms0xKz4AZyYOomIccak/GsEJzk0ZLUZBjyqVGjDVIjMKnWE6tKyM5SnMgtEenjOzNG33LQEryqRKrVkHnKQDBie6M8Bx4um1yGPGgxYqMtoUa1AkVhkVVvjH76cnJ5VOlf2Ua9CQYxkHHpBwVXzT5HshCr4cjUoRckg4+JEYuYj5o6EDXlUeJ0zEwYnVtcEYHkwVK9pgdoOmJxWawKxXgV8KNTAk4BjzqFS3rQ8GQBDEJosRyKVcxPZxUac68R2HDkBxsbjQIy4YDM64/tBOBghIjBlUqEdg2461u2Ew8TWh1hbiqEYaYn1BeaSBh2eoqJ0o9nFbxwJ+9KyVTAvpKs2jhrn+0V3Sp4EDaqXZ2Bd0rdQbAzPUoDTsWSqxmP7kZ8WoqXZwdVDkdJVQzHaNWT/aKiSy7wWY4c1mvztv8AZl7V+ct/sy9q/OW32Ze1fnLb7Mk34y2+zL2qRr3tAx5HCnQjUhqKjWrRpS1HYUdEMXbJY0+lEml6k8oBl7v1LGATikHWAKaCICjaWNGVS4lgIgYqhvXzFU13JiJikcgTjpn8ahQ3Vu+nbzbtGIDFYEIMyGrGO1Tp1d2xoQIfXTA16tnFgdqq1bcmvuuQ1CeYjj3TxFRi/abYsDipQpp9TokRWAITSuZRlyOiPx1RvSgRfVH9KLXtR/Sv5hURH42pjzo/v8+krHeVQelEDeNQsv5hU9a/mFRfn6i/P1F+fqIn8fUTG9qN6UH3jUDr+a1PWsN41Svz1T1r+YVQF/Nai/mtT1p/4pUWO9J+HE4qNE15TkONEsg6pzpy0yO0If5hOQA2q03ZaR116tQRBIwHKWxVHdVnAeJKnHxJAd6bBzzA4D5VHEOgmL4J9ixWTJiEHdx6uZS86bmtfvalT72m3Z1HOpz8fKqdrCmYyAWEsU3iFAmonFET5woeNY+DQylVI7I5z+mCjGl5ll4McBpkWYcSj+A8xVXJ43+PFTpVvMdSVExaQ0guDmHJ28a3FeRfT4MD6GD+1UKmpwQFtRIRFlVhGpqj3gSMw+A9SGi6tm+rL2rR+Koa2z0yxQt94TiZxoDLJ35Vu+NMtM1Yt0rdmm5o+H4Y04F2YbXW+6dvXoxuAIajKJIIYszHNkJC8tsdmiXtR1Xltl9CXtV4Kd1biYmX7JIfpWF3b/Zl7VUe8oM30Je1WkrO5oxomc21RkT3i7sePJN+OtefRL2qcrm9tjR2gRl8ZVLTdW4gQM4y+Ir81bN9SftW7zXuKBmZERaMg2HKVFru2ZvoS9q/N232Je1VacLmj+M8IB9J0tqJ432qUTeWzgt3Je1TlK6oMxyjIdakNVPxdcswc3KHg3NuI/VknN5R9Aki93D1+1Y3dLoJX5qj9kr8xR+zL2r8xR+zL2odkruld0rulECJ6FOVWdSPMWQlAyJB2klUxc2zkDlW99wXQ7dKpLTxs54NiYFl3k7ptSclcqo2FCLykS7bGzfiXj/hIyvZkHXLGUS2UTsHIpSMnkyLfBNrdUY1Lc/NkHHpVzd2VEi2lMyYZBy7DiHENiIJQwwQLY867ode7H6ehOwAWAi6yj6l3Y+pCUoxx5kJimDEpzRCwiyYwC7sV3Y+pB4xWEAUxpB0Pu4gHBHCDphIaTyoszbMUxiFhTHQuzBjzLull3SFjTwKlGOESjjgqjnJSVOkTghKXyLe+/K1sfGi8ISOTgOWwzD58qpfVCDoAcfAPg3+5b2gJ2tWMnfMauJVt33cABGR0EBux81+VELVEdpNIYc6erHsj0qdoMKEmJHGSmhUlGOwOhpqEqr5mqiWvZjg3MtyyftwgG+qMAo2FyYmlHJgx6eCSbbqHWFHsnJQwORVQ/8AkjrW7Sf+dHrW6P8ACHUFv4/qw6iqYGbLIrebD+9KOBVXD5p6lYEDDVU/bK7pVbAsqGHzR1Lulbigx0mqf2VT0jBl3SqoY+5HQ6kdKqYYsepVP8WX7RUM1iiwK7pWRXdXdQava/YPtXv7X7B9q9/a/YPtWFxa/YP/AIkXuLX7B/8AEp+Lc24j9U+1Pb16L/VKjpu7Yf2Cf/eV9e71pRvZ3/3gnGJHal3o8gGDLVOxnSHMh4l1GJ5SgBfU+TFdi/pkc6enWiQnjEmSfQyLDF1K+vqcRvW4YyGEmBxDHlDLS5YLlTSzWl8VqRCzWGanQuQDCQUKtt7urqbkTQr0m5QfajM3lHHkKIlvIA8i/m0v09Cc70JCedaoTxgpqtxcj+0F+auvthE/irnEfSBVOnYb3uIgbHHsUQd8XDtxj2Ltb5uOkexaqu+bknnCaW8Lon6wTfj7pvrD2KUo3lY/WIQEaxITiZUoxnnxoNKked3WE6Q9B9qAheUQOYrC/ok8xRjSvbduY+1B72lp/VBRlPeDpoB4caanTBkoeLbmYIy5dqIqbpk+1TuCJQBHd4lJCnEDUFC0m41Zc7rc1lSgBIUAZMAC5zfjKHAOJ1B5gk8SDBHHD4BCob/hTj+DjDRI7QRyISbAjgzVXlDBGhtFOBPOQODtcSpbgtbqX8THYlHSQHJ+lktzWxxkKEH52dUbqETIRl2hxx2j4xyqNW1nqptmzYoqUbKpTjPUGMg+3EYICN1bN9Q/+JaY3lrGTfQPtVb+K1aUo/hw2gEYvnicuRbt8KINXxogPlic/Qt0whf2naphuzLiHKvMFKN5a+JTEdR0nFwWbHZtdQ03toSNmk+1EC5tG+qfar/Tc2jioX7Jz6UdFzZv9U+1TFW7tACD8yXtVlUt7m1/Da6jPGT97n43X5qzb6p9qqGrdWmj6p5OVUx+KtGYfNl7VqF1aN9U+1brFa8tPG1nQ0Dmx5eJQjO7tNTfRPtT/irT7J9qreHdWv4rwAD2Szajy5rQLq01/Vl7VUe5tO6fmy9qnqrUPF8ST4Fn1F1H8PdWbc0n61+atG+qfavzNp9k+1Y3doP7J9qb8Va/ZPtX5i0+yfavzFp9k+1DBZLFYBYR6lUOlgpDlUWCj5h3HRffVnXhqLf3OOs+hRq6xKEg4w2IxuLeEo8gCJrbvqE8jpvwlxA+lAeLVj9Z8E9vveMeQp6t3GpDkVDdFegIUyQ8wPSSeYBW8Yg6RTiNmQAWJRZ3AU6QGATiJflUXDLNMs8FgHVfeFSrKNW2iTEBmk/GjhtQMywKaNQOnB+CDUkwKaNQOnGI+BksAsuDJZImYwRhCYNRY7UXbBe8CBNQOU0JBDsqDEOM0ZVQHKnGh3XVSTPgepSfPYt120g8ZVg4HE+PqUKT4QACCJWikO0cH4hycpRNYktl6epRBOO1OclkENQx4b+eRix9ahMXtOOGR2J628qXSEYjedNudObyEgp3U7zTKQiGaTdkNhgjprv0haWlIrd256UTGhWu6bnaHkFbUabkU4iL8wZ1gACoboNKPgyjKWrHVgjzqJ26h1oOMlD0qX+AOtbs8QkQNeAwzxkAtw0yX0xiH9AXmsAfQ6iouCj2T0LeOGBqlBo4IiMQcNq3dKR+fV/bKcqs21U+YdQ4Nxwbs+Ln/ZKpY/NW1VxIM1uP2iqhfF1VPFE9SqyI/vZ/tFRHIsFjwbVtW1D/ADGl9gf+JY7xpfY//UuzvCl9n5VheUz6PlRJu6fR8qn+IvqWj6r/ABoihe0jJ/oAKJhf0QPqA/Gt57rv7uBtK9KcJgRxIIPLhit7bnIkaVtXlCLjYCsYHoQ1UQ3KERO2i/MjrpRBPIETEgEcWC+6qlb537Vq6yKWjSdgxxHE+XMoyAzCchCUTijOUO0TmiwYoADJd1d1d0ruqdGpDVTlmDiCOZb6oU/Eo2NWoJUYxJEdJA7oGTHMcaPiVKkw2RJIWAkOZ1hc1h6SvzdbpK/N1ukr83W6SvzdbpKOupUmP1iSywEgeTBYXNYDnK/N1/tFfm6/2ivzdf7RX5uv9or83X+0V+br/aK/N1/tFfm6/wBor83X+0VpFxXkfrFTnfRJm+1EaRggberKBHEWfkKYiXSiDrkOXFdmdSPMSvzVdvrFRB1E8uPSu4W5VMzyU9IwAKqi1kY1GOIwKlUvhKcqVGo5liQTgMSiUFyImQeTJ4xKwjimbH4O+7YURUqStamkcchEmLcr5KsJynCpEsQ5wI2IEzm2zErVCMWP0lnB12runEcq+9voyHIgalXFUrmlCVWNtWhOR2AAg4lScJ1TqkdkUpc2LYJjgUI2l14VQzixI1DvBw3KjKPmCAidnh//AKkP8+jqY4+Hg3Liqkr3ePjvbj5uli+fpW7hEPLxYtzvgt1H8dTJNMaRpGGA5cV5iFO+pxugKet4v80sBjt28SH+YUn+qPaj/mNJvqj2reGjeNIzFUudIwPF8qI/jEIn6vyqqau/YECJ/u8vWrT8Nv2MaRnNhoy7RfpK/n4b6nyqpKtv0eHt7CpeHv4Mwzgv9QR+x8q3dCrv2Jqyk0extZ8OJQB3+MhnD5V/qCP2PlVSlDfoF34IxEBlqOHTisd/j7HyqoT5gDaT8z/9SnGW9QagqSc6MzqOKiaW/mH1Auzv0H+yF/PB9gL+fD7A9qw38PsBfz6P2PlX8+j9j5UMVn8CpzFS51BPIPF8eUK+8y7kjTlOqXNFgC52v8iq+FuSBMJGMgJZHNiG4sebJU6UPLlWZmP7sagOfiKuK1zu+rCNIEyBidQbkTCyrN9QomdCcQOMMjCMHbat4Xda3kKFappBYt2XfH05KMInAI8aKCPAE9OnqT1KbBYLSMytz72rW8pTHYIEXx2l+LlyX5b1L8uOhflB6l+UHqX5QepflB6l+UHqX5QepflB6l+UHqX5QepflB6l+UHqX5QepflB6l+UHqX5QepflB6l+UHqR02oBX3cGWaetFwiTRD8y9xHoC9yOhdm3BPMnhYAjmRa30jiZERzU4kuZBkKdSLni9K35f21gYzNQDL0pqsWCHDkmOa9C7EXKGuk0OPrWGXAbcSkBKnIOI6sw2xb0oQhOQFaWOlnx4lF7eWb5IfcnoWnwT0KIFGQJQqQpxEDtKrnzESGbRoPS/qUbTddHRTGZ+cec7UVK2tqRJ41CrUr66xiHYZEjL0ceCD/AKYKP149ajzIfVPUqn+AOsrdhOXjR615ex+aOoLzh/8AD/ZkgUVvcN/ekqOO341VjpfVEqxrcc6n7Z4Lj9NoVD6o6lLnW4v8X/3Socw4Kv8AgjrKkq31T1Kr/iy/aKihjsWB+CP82h9hfzaH2FjveH2F/Oaf2FhvmH2CqglveEhxCJR07whEvtj7FFt8U/slH/OIE/UKlKO94uB9ErfD73iKkbgv2czpHF8a7G+Yg/UKqzuL6jMkF3p586uJyq0NUa8x2qeosJEZl8OLFVa1ehbyryGP3ZY4bWb1MpU6G5oRphuMg+txxq/p0Zwp2NMzMaYGBkQzjlwDkvlsdVYH5siOgrAq1jcQ1UDJiCHGI2jDLnUrSmHg+oHkOQ9CBdZ8GJCxzHoRwVvQjAky2M7qjbb3pRNEwYCUcYgjZtB4lXmLiYmI4YK6pyvapnTqacuLbgu3czfmKnXo3c41Q+I5FbVat3V7cQcRx4rG5n0KwNC7qeHOpGJbjJbH0bECbmSkfxUsluewjdz8CraVJyDYExID8+OC/My6FN7meA4lvK2rXdQxpGLPl2g+G3BsXX5mSuKtO7mJgYFslC4rXNTUSc8822L8zJfi6F1U1AjLM4gNjhtVGcrqZkYg/pggJXEmVC23JdVBf1oy0NlqjHVjzgNgr21qSH4mhWlTlH5w0lnI2KcKRGsggc+xRo1b+pS8wW0PvY7CB8/HlwwWs3tXRx4MnF9Vb0J439YH0JqO8rhjxCPxoU7nfVWW9KmrRDskHSMdWl2xwxYuqkm4/kVtu+hTMritOMYgOS5OLBbuqX1KX44wiZsC2pg4PKDgV/DrLd8IxqEGUomQcjInHrVE06bUhSzxYnVLB9pZs04KIOaNWlGNO/o1MSTjMDk4uZd5HBOMkTE4rd9nct+MuACzNIZkl+IhmGxuVMTmnlkFK6pSFGrKUomTHUY4MxcYDHLNVN6xuqZpXU3EAO6zO7ZbS23HiQ1VR6I/Km8GM4NtHSgPwkB6E8KEBhxD412AX5FrqOaeQ2q0hCwqijWkAJ6SwBwd+IJ691GXMUKcbaJrvjPaU0TgnAxURRuBCXiR2Ptx6U43pBuLSUAN6wy+iVWlc3cagNAMwI254rdmuLg16Y6ZAP6HW56f8QDiAbDLAcuPpXm2lT3gAYeG5bN4v6/Uok7xiI8odEDfdPL6JW9x/GYBqhD6TjzKP+dQZ/olVpVN90idJbslWppb3hC3M6jDSS3aPozdY78pv9UqtI76gQ4wETxhUG31THZHzTxI/wCeU8/olbkhLe8JGUyx0lgwz9IwUX33DL6C/nlP7JVWkN60/HFCMtWktjIhmUid+U/slVH37TZj80qtA3cTMVZh2OLSOPpUTS3vTiPqldnfFM/2ZJpb4pj+zJY75h9kr+d0/slfzyn9kr+eU/slD7sdIXuh0hdwdIWEYo4D1e1T1QR0xxdR7EelE1ZRAfjXaqgnkZb6qyYRqXBI5YsGUtcwpwjmQQq9O7YGVaZHMZEj1IgTHrQubSWupq7o4j1qdpOJhV8WZY4OCpmOMZSJcc54l3c09Isc1GFeWI2laYh5IvaybmXagYniKGKxLlNxqlfGQEKMhgcyTjh0IGLxjhgeRVacZgSkFe3FWqDGdQkelGXijFTtoVRqLq2t5VBqhER6AvehWdKnUD07qNQ8wOKwqBEeIMv02rdW8RViKFG1qUzxvIxI6l7wKT1BiFvO4qVBoqmGn0DFe8iq1ATGqQVOhOoNQfrK74dGhQ4xh6RiqdOVQAxACB8R1ua8HuqVSRPIDAx+NXXnDynu81bWoTKuIDtA8fKFTrUydMukKnf7kvJUrgZ6S2oZseRW9n5p3VOrMFiaenEbH1etQMN4RDjI7OT0Ia94xL8Sr2PlalWoycjxZaQ+OBizkBhg+KlebwuZ1LiWJMi+a0RDyKo+bN+23h2dE/dxkDqM/pDYyIptodMWRkYkkfp1Iw4kDxoRGSMgEdNvIhdqgYhRm4cYqnU3hV1TpgCPMMEC+SlTAd1Z0gSJgMehVLbQRfM4lsBGXsPIiIQjI8joGdqdKBuKRDKNOhSJlJQq14aKJPq5FGdxI1JymMCVa0KUfuxAKRAbBOS4ThHnUNIc6x1qJMBs2he7GXGFLVEAeCOtbrIz/E0v24rc0qpEY6Y4uPohecvvgx8NuXsFRMBHTzj2omtIDDjW9wKgc1C2KBlViXPGiRIaSOMLd8YN4gnUwfHvlPpj0hVzKIAw2jjCotAd0bRxInSOkLcMTEYzO3kUBERbSNoXdj0hVwwBFuDmMtRCmwi3OqorTiDpO0cSrEHs+NP9oqINSPq9qDVAsa0fUsKsV76K99Fe+ih/+SVPUv8AUlT1exf6kqepf6kqepdrzFP0sqhjv+pItkEYnfE4h+P/AHKJ/wC4Zj9PSpE78qSkNmGPqX3lWUpPtXKiBMhOKi06yAveldouE8QxQrCIBHEiNoUUSM1GcSHQ1U4nDiTlhwAn0ciAiC5KFcx+8kxPOMjyMCU5kV3pdK70uld+XSu9LpXfl0rvy6V3pdK78m51nLpK70uld+XSu9LpXel0rvS6V3pdK78uld6XSjFzpRJlJ+dOJF+daSVUp1AJ0ZggxliCDgXCud7eX6eu3qSMvBiG0cgbPHFGjdW8qdQZg4MsagfauwASgZAakNDMmontcXyK03t5jBnaTacYgmJiHwEujaqFlQpRjQphgBxDBZLJSpGLhVasRiCgVENtWDOn0AlNoARLIIgDEqRuBgC459ip0KZaAwTSTgsUxqFk1WmJDlWuhHRIZMB7Fpp7wqCHFgvAnf1ARIEHA/EFCMbubAcnsXiTu5zizaSzPx4AY7OZRqbosqVa4ODVZ6B9oOvw2+t1WMqgZxCvIyx5NKOncWH6sifiVhVFlU3dWrVpNN3eFMu7frDDk2r/AFFU9Sw8x1Bhmqh/iE7iEqAD7AQc+dbvJBbx4bMu0MfRmtzxp+Yp1TpA42wGOK8yUI38hOno1SDOezt59rIE+Y5xPFjgpCfmOUjyv7VvUjfshKMyOdto5E0vMCq//kEi0SwfP1q3lDe86dAzmx/tLteZ59J9qqVh5lqS0tgCfaqR/wC5qjkDafav9TT6T7VumJ8yVDrkwLnBseNQ/wDyep3eMr/U8+k+1VaNvvmc6ngxkZOcjIj1KRl5nqCXOVU1+YpSwPUqtCG9TpjVmH5pHFRmPMDLDf3rKx34fWsd+kelY7/K/n8ulf6gl+noQ7UVmFhMLvj9PSnM4qoxHoR1Hao9oITozGlY5oIfCkLgjQylK3k8JY4YspcOCdDjTNigZVYMQDyh8VCAk6P/ALNyKfiWxnLSWDZuOPZz7M1Uu6m6BRvJBnBdxxZYqrUA+6zz+JHwqEn5QhKvTIUDKmdP6bEL6ruvx6wIMXGA/wB6ptaTpVNOMWwB5DtQaJTDLgYZqqY5AOpUh80o8aDlDsld0pieyskNodQIqxeWLbQgRIHFHm+AyLZrCmWXuSiRRKNa8Ph0Ri55MVvS7tLjXb1JjSeY48yGo+peWa0pNSlUrRAOBd3y4iMkHmFLth2Kr1JwjJ9u1UyIBwQt0TlXHvIjNecPvwH8Pbn2SoHxIZcaP3kMlvnVKPvSyP3sOlE06kOyCc2VhSnUjr11P2yveQ6VcaZxMsMucKifEg+kdS95DpW4AJxbXJ+IdlQacB2U0dMjyKrKUez+Hjh/aKlIUcDzKqI0/mnqVWJGIrTHRIqJ8N8EPugvdLCksKRXuyvdlD98qdIX5up0r85U6V+cqdK/OVT6VOUbmo/1k0682+s6j+9VAedeFCtKY4yXKL5oLH4O1GArimOMqZO9qZBPGpQFWMnRD4LNOCFsXeQabFCX8ZhEFsHUSbwVW/8AZmWOSlqpgoCVMALGm4TmgH5gu4WRIpqLURgyGi3HqR0UwCnYLJOFOFarGD4YqpXob3pAnEgo0xUjMcY2oOcUA4XeCdwtPjiPKU/8TpOeVCR3wCXyBUGqmb5l1L4EaJHZOHSo/gPFlbl2058mCGqndAcrhP4d23I6P3V561+Gvre4lRkdLSfEnZzlEz8qapEv2oklAHyhBz+ofarO0pWpt7OA7MBg2zAL81U6VNrqoAx24qoLfVM8uK0T3XUMfpAFbthUu6sdUhg+A9HGvM9I3s2hobH9X5FH99qPzomN5MluNb0ibqbazt/TBdm9qD0qem8mXBzP+5WdzK7mCZ1MAeKR9i7N5NudVSLuo4bI45hUSbupkMii95UbnW5KM7uegyO3iG1N+MqZca7G8KgPP8iqW53hP3UcdrOcD6cUY/xSq4/WKqH+K1MInaeJVHuDr8WeP9oqLbynEIE70q9JT/xWr0rDelXpK/mtb7RX83r/AGiv5vX+0UPgBSRUWUvGfU+CObvsWGXwz4z6eRSfxNWP6exY+Myx8VY+Kv731L+9ZH3rr+9Ufeup/htTcv8A/Iy7svv/ABP0+VS967r938TVysvv/Eb0L+8Zf3qL+Kv71mUffL7rW7qDOylwln1bFB28bYqf4jR4jYOyh4enXrjkzs6o6dHdD5ZssdLeheV/C06/4lB2bLHPkWGj1KPc9S3R4WnT4VTU3HsX+5S0d5V9Xf8Awu19L6h/Zf1qTadDehlu7we54h5v04l5t54fslU+ZYre2nLxD1I86xdmKsdP06n7ZWOar/ptVLmHUFtW4dHf1S6goPxBbFVbLwIv9oqeaqcWk9RVRv8Amz/aKig7rB0c1i62rav/2gAIAQMCBj8AH70elfmj0r80elfmj0r8yelR/eT0r8wj99tXfk/Os5dJW48ZP4J+cfpLOXSVTlTlISEhtVqKtQmPhjI8i70ukqkdUu8Np41f4yxlxlZy6VZtIsScz+qVcSMpOZHau/PpKHanjSO0qZ1SdztKDyl0lb/eUvdnag8pd47Sp9qWXGfahjL33GqbmT6R85CqNTgjarOeqTyiDmUO90lNEn0qb8SKrCJx8IqX7wcyok13W8eLWnBxUSanZJyVu52cFLDFGAjgAslI86qE8aOKuOYI8TqTxwZQB2FYUZEcgQFCjKU3y0y+IKnOVtVMyPmwkR1IC23ZXkf8OXsQI3FXb6k/YrHd1vuGuK1xVjASlEiMTIsDIthEbSqFLevmiytrt8Y6nDdbuq4t9O8bBhpqUAZOSHOAGDMyIh5RvQeWmfYnq+Xrkf2D7FONbclxHEfMOKINlViRxxPsRJoT0/VIQDMUQWVLnVGIGLKJ4whzqiIjADFAaFBx2nREJ6Su1XJVjqOPiBbw0SYeJLrX3uJUtcDhA7eRXBhOQGs4Os59JW+IwqHR+GkMSeRPqnjyrvz6flW82lJvGjtPEu9LpK1QnLVzlSe4PvUDOuSOdVTCpITbjXl0i4Pe40YzqSI5+RAmUn51aYy7vGh2pdKokCRlqDYlVRVrSNM0qeRywXfl0n2oTpTlqbjVU15ycHjXel0n2q78CchhxlSqVDLU/GU4lLpUJ6pd7jTPLpKpvKXeG1aYVCwhHb+qFICtgsap6V2q8ulYzkfSs5dKzl0rOXSh98Ml78L34XvwvzACBFwCsa4Ra5AWNYL3zLcIlX7PgnrX5kFRa5DuFb67ge6HVyrC4DKkY3A7w61fCpcB9SY3kP09CsBG7i2qWXMVXauM170KrOVfGIzUj+IGfGsLiL863wBcDSQyqRjXaL5KQNfBlThUuB4fiPiuzXDBUo+OGJxVCnO6jpjFYXGCGm42KoDcgl1+YCuav4gCQiQiTeR6VH9+h0reAFznNe/9aiPxDelWn7yHZB62Co6agJRkZsGUYRIMigfBljyH4lG3tdzXNSrMOIiBcjNwFOpT3LOkBg1WXhkvtAxcK5N9vGjS1fNkcubjVyfMe8wRho8I9L5ehSqVL2rIgZEqBG6JXJByODKJo+WaMI/rRBUD/A7UTAzFMA9KjGO6LZh+oPYvud2W0eaEUAKFED6oWqNKmJcYABRJJJ5VjEEcuKOq2pt9UexPV3ZRlzwCMKu5rb0wCkRua1EjxQHsUvG3DTnTOwRGPOqMbvyuKZfA+7f0h3VT8PE0Kchk7kelaqW/q7v9JCnubzJA2YjlWli+3jwUzaThWmCwi+kHHN+pUYXm6KhlMEjQ8wG6EYXm6q9OLZmJCcUZAcsSFAGJd0YzqaV78Lduit/eBXbXIfxC/SsbpEi5D6Cq4/EONRX5kOt8PcBvw8viWFwsLl1vP95Hvo9SxuR0rs3AKAp3A0asRyrGqB6VVasMlunxKw8KIw5CtXjBD77DnVq1bYh9+FA+OAx41V1XAJFOH7Kwrg+lB64GBVZ64zTmuGV3puA4j0qQ/EgF178NzqP7wH1HBY3IfiVMCttG0L3vzI/shE+KUxr+tYV179e+Xvl75DtFd4rvFd4rCXrQGp0Vi+aBfYmdbmxxEG9fBSx+cFbGX/LHUm9qpnbqHWr7B+18SyVqwyPxFVcdq951q5IcBipES2oPNb37WxVe1ipEyfBU4u5NQrSZK3D9p1Tx2LMpiCqhEcHWMSyuHie6i8VEALeBMT307FQ4nVt9VTnu2zrVzEYiEJE8wbMqxuR5cuLexqnCdWJj6ioT8weZoRn9CESJR4u1k6pRvpVryvHbUPZ6AHVO1s/LtAUY7DF8eNzih+HsKUJDIxiAQOcLAll2IuVhbkR9KEK8cObNUqVvS01AcdmCIROxd3rTaChgyxRGkldwrLBYjBEusk3EvvoupStwDA5cYUomJYciIJxXcTyt4S5wCq0N67loVTLJ4hUI3/lyk1MkjSNOfMQ6jX3Fc3FhcgvgQYdGfrVS58v79oX13Ae6EDA6eNyWVWvvrybdRpRkQDCJqAgbcONWtPeW7a1CQx7cSNmGeSvZCWJqHrRJkiz9wqsQSO0U74rfGP8A9PL4lkfWsIn1q9gYnTKQL7MEQ5IWZCET9PrTF1VDlgFucZalpTOVajViywUXJZ1VY4+FDqW1O+QVXnRDq7L7FLnW1QD4mX6BamVNnfUOPjRBliIR/ZCIc9KPaOaGJW1ZlZlZlR/z6llxSX8+pdEvav59S6Je1fz6l0S9q/nlI+iSGjetGX9mSLXlBvqyR1XlAY8UkD/HKXRJP/GKcuRpfGqFCvfwEYjAsV/O6XRJRqQ3tSlIEYNJW4nd6IimMU53piqT7yc6h1q+nHeZhLUv5v1qkKV0KkeMqrKpeETfEYr82fWrkRrA0mOO1Se8OfKjpvZaleSjenUR2lW/fS2riKIneljhtUaMb4iDvlyIteF/SoyhWJI51AiQOC2KJiWKmxBAWz9PSpASaJCJlislVhKvHVKQwfNC03Puq5nWlkYU5yHqiqUd63kbKkcfElmMMjHN9mWaM9+1J3vgkRlrLCXLDSxbnxQo+X9xUaMQGdtRw5ZOhSlP7sZAYAJ5ScrJDBPIsEY22E+NQncT1SUBEBwOJPgvQhhi6crJSwOaDgo4LJZcGYdFMckBsTBAIvEAnapVKL+1eFU7yxHwNVKpKMuQstFxPXT4pYjoKlLzBuWlKpINriNJH2WVap5evza3tUvGZk8Q/wBIHtOpfhtFazpkgzg58QD5wGJD5qdvvLddxRpCJac6c4xLZtKQAPoVWU6sYwMjiSw9aEpV3p8YxW8jSmTE0JD/AHKOu7lq5lhdyfmVzTnVahxoeFvEgoynvRwFS03pxT/jC/MjGV3gVZ0at+fBi+nDLHJao35CBlfOPSrV705cRQ/fD61Brwu/Kq0p3ZfwodS/Nn1ofvhGB41W/eznyrs3rH0q5lPeBkCEZQ3rpBOTL+b4elQpneBLyz/QKP8Am7A8ii2+MlEVL95aQ/QnldFHTeF+ZYbwI9C/mh9fsX8361/N+tfzfrUeyMk4AWIC7qwiP06EOyApMPUjgh2MFFxtVEAfNCOAVPAd4K1H/ljqWGSpmWPaHWr5hnJZFBgqx0hd0KsBxKXYGZQ7IW9QYBmKrPHamACpxjHEFYxxWSjhs4NUh2VOUckS6ZnLKNtZW9SrdS+ZGJJ6lTNKwjaUjifGJidL49IxChc+YNV7vOPzTjTfjx2g5YKFnuKyhaVYnCcA0ulDxJ6qm08Z4/SpREuyUQEDtPDgHKD02pDaqQpxxC0nYhggwR4BhgyyRw2qOCOC7q7qyWSxGCDjmQLZLBehAshqGCjWtodoBGqaJICE2Z/gFYjBHglY7/sKdxan5sognlaWcX5FXnumFS1qEHRAl6cZbMc2fPBQuN3eXZXm5RPSJ0AZANi+OxVaV/Z1KE9JBE4kI6YhkWirlxgo4cSk+Spdnau6F3Qt3SER7xMwQwVEtgyyVIfrBVSIj3VPqWICOAyVVht4LjbgpsWxXeKBIc6kMVHtbVHDFgj2fUiyLoMCtvrW31rb61H/ADIZLDeUWWG8Yr+ZRWO8QUP3p0RGo6OqoAg+8Qgf4iFSlPeQwAWG8oqMxfxkRIYK3EroQ+6GzkWO8g/MqWreYZx1q+07yiDqX8wCgI3QnDbyKpKd+Ik7F/MoqvTjXBh9JSa61YnrTPgrs6mBHrUpVLsQKeN8JLs1AY6mZuTN075oEHFRaYLjiyWaxkniXBRsvLm7J1qgIBLHAn1KhvDz1faaMmkaUOzID6L8fKgN1bgoSqgAa6sROeGXaI2IypT8Ms3ZwHQFiSSscl2cuAocHh1e9LADlRv78fuuceVeDQptEhS4MV2UeDkWYRx2oZLZwZJwAFmEONAnYmCZNsQGxMU5ZToVIDtbVrMR+HOXMnB4TzL0osgCcEyAIX4GVZ7Rm0nJlUob43NCOsF5QaMtuLgOCri78iX4qU4l9FSWY2gFs+LBT3dvjdFxbXMfpxLSGI1RLMYvtVaJmzj9MRkg5aHGpyN2H4lSjVuRTiD0ogbyDIE7yH6elUKMt5jTEkotvIOv5kPUraka/YZ9Sb+Ih1CQvwSCOL2qrCdfRHwqeJ5ljvGKDbyDsqp/iIzWO8Yq5lSuhNx0KUpXwBJy4ljvGKpwhcaoGeJQJ3mAh/mkc0Ixq6gAOpFl2qjLG5AWN/EL+ZxX8ziv5nFRwGSwAWIWC7MEOym1Fd5B81pgMUHjislEgbR1q3Jx+6C7oVJo7R1q/IiO8u4FBhgVPs4ruhXGGDOpARDOVLmVxInYqr4gFBhggx+d8XBiVHHYnkVGj5e3RVrxE9JLaYvzlWe+vPF6ZVo9oWtPuRLfPO1Cx3NuqlQgGxEQ5bLEB/Wi0ieB+AsnOXAUGRqRi8hkqW+d5wMRHIFU6VICMIhmCbapPkjisFjyoknFYZLLFZIucUMVms1nw54IAIsgTkg2XA0kxQdfhbmXZbMnJGzhAm2keyUaZLkcBfiTE4o4oHiQIyQ4GTGIy4kaHmLdlO4t2YjTF24nZx0q9uP6ZRNK8kTMUJzYSJLtqOADL8B5m3FOhRi4jMYwOnPtDAgITDF9oKp0TmFhFdwKk4wx6wu4FhEOraJ7rIDSodkZqeoP91T6l3V3AzYqsdO1d0Ku2AZEti67oUXy1KJ2sonlUHGOkcBRcLJd1d1d1R+/OSYVivervjoWE0AaukczrsXBP9n5UcUBKtLVzJxcz6AhE1CywryfmCjpuJGWoNgFQ8eoQPDH6fGveluZU/DqEnUNnKr4V6xEtXEvenoVOdOqWfHBTNWqdTr3h6FV01izcTnrUj4hGPF8qMatYvyBVacah0kbB8qkZ1SAU9KsdXMhqme9xDi43R8SqX5l93WJJ5FTsNw29SrflsIwJHEMvarbev8AUGt91GL+DBmJ2O/FtUN3+XN00remC5IAeRyclSlIY8EufgKcLkRWaJ5UBtQur2kPwwLjlQo0KYhTAyCIRwxUhtQLIIMESBgmIR4Rgsllw5cHIuQcACBGac5phmoumjJijSjTe8gOzLa6qUL4feg/APOiooIIPwHnWSFp5m3ZSvLYAgRnEdkHNiMcVvff39PLrwqUtVWNrUYU4AfMpNi20OhZb83fXo3upiZRaBP6p2laqkmXYmdSpjU8MTlknnUIlxIffFW4Nyeh0CLiRfkUAK5d+L5UZVpdkwA6AsKxZahWLqRNcuVjWPQrj788yP30tqxqkDmUaIuDp1cXIvzMiebDrQ/eD0fKoNULaRs5EwfoRefqHtXart6B7V2q56PlX5g9C9+ej5V789HyqOGxZLJZLuoMMUQsT60DtZMRggRHZwQMTjqCt9eQpjqWSpN9Ida3gT9Lgjq7qqMncqsJF1JjtWIUwMm+MIk5o4KIdQoW9GVW4mQBGOMiTkGD4lbv375rqzsfL1OYmIDsVZy+jLaQ3GrTdvlndMaYpjGpOI8WUjm88yHyxyQMi5TI9pFggWx4CgjzrA7EeytUslTlTifAic/SqdGLOAsgsES2KIbFEALtDpWDAo4sse8uMrJZIYLJZLILuhd0LLBDiThZIARDoexYBYZocaCIiEd60pkVaeGgbeVEEZLFYI86CAfBEPgivQgVmhihzoRkMlX3f5lsqUpBzCWkGUZEM4OYV9vv+nsKm8d3Sx8A9qUIgHUWPqZVra9omjc05aZRkGMZcRByKpyMSAx+JTYYOh2iytgBisHVMucCoxI2DqWZ6UBIOGVQDDFZlXLhwymBgHQGKi8cNR6gidKDRxdUwCe6OpM56UcUXksQu6u6u6o/ejLjWFdYVHWx0wko6qnqXZx9CwiAo44sF3l3jgFmobcQrYZfdhapXDKloqau2Otbw1VGlqyQAlgoOS3Nyqp2j0LMetXBJZgfUiZVM0DGs8lUOOXFyptWaENWap7o8qWk5kN4k5x0wgDkQfnKlvDfFgL/AM06QZ1JhoiqMRIQL4AthtWgwhHkgNMegYcIw2IoqI4CHwWl3TOvQm5UKRDwJAKhTpQYt0oYcDujiu8pB1KIjkiCu8mIdOBisvUiNCDQXdXdXdXdXdTsggBBOzBZrSI4ptLrLHgjwSp1j91LNTq21R7E9HCedBAKSK9CblXpQUedEMmZci/Hbv3RGy37HtGUBhWmMjUGDl9vKVGj5ttpU93Sj9zUpw1QIJwEj80tm+SqRfAE+lBmVJ5jJAePjtUGuMXUQz4fEm0+pCRwHMpSzdd31K4LbCpdnbxIDJCIcy18WxkeyehBo4qniX0jZyI4FuZE6Su4/oQ7PqWR6FkehZHoUcDlyrI+tYOsfjWSigBmsIlCRIXeQZYAqDg5hWo/8odSMWLKm0T3h1reEyC+tBgUDI4PtUgZYrAuq77YnqWWLpxEuqrZaVQjQtqk5VO6IxJMvqgZq184f1Bvq1nuWTEWwDSMWcExPax2q23T5esYU6EG7YGmcmy1HPBGUsSgjwDmRWKjisSiQVlwehHTEk7EZ1oNJtoQA2IcJWSOBzWTIl13kXknd1kjhtUc1kVkVkVkVkVkgi7Ps5FmEzINms8eGPBjkq34Sk9UAknNVqNYkzEuAoIIo8B5+CKHOiXwXo4DIYSVfc/mLdtK73ZU70KgcHDYcx6Crvzb/TUxnukh/wAHHGrSxxwzlFsgMQFdbru6NWF9Sk04ziQxB2OqEGZwnILqkQDmotl8iwCGG1Q7JyCyKuGzZAoYFRw2n4lLBRwOap4bB1I4FHBZLulZFd0rulR7JyXdK7pXdKwigHIC7MyTyrEFk84khPGJdDRxLMqOeY61bg4jwx1JwqeJbUOtXz56uCGrEOjrgdSBogghOSWOazWeCjuDyrbzurqrCQkYxOmlk5nI4YB8lu3em96cN5+YtI1RrAyp0nzEAcRJ8iiKcNNMZRGQGwBMRhwDhjwDgCx4SrcRm1qDiqdOlEAN0oofBKCLjFZLJOBjwHPNDArJZFZFOyyWSGHAwTDYhh2fgR4Z06Z7MsCp3lKJEScR8adFBDgPAefgjwBBDnQQdCrCnGRYhjxEMVebx3FSjZ+c5Pp0jszMsXLcqh5Z810J0d40iQJSBIqN84SZseVuZZqmX2ponD5FgUNZOfEgyzVdziyCGJUcTr1HoUlHPNU3OOkdSLBFYrBbVt6Ft6FHmHwWigcODHgnycEOcdatgP8AljqQBVPnHWr/AOvwRKlwRJyWpjo42VC53c9p5fpT+9qzj34uHEdXI+IVLdflPdRoERGupqMqspMNRNTvMS+D5YICOPKc0X4H2IcI5uAejgHCAp+JjMjAcqt94VY9uZPyIDaAih8IIltiyRwRWSLZLHrWXrWSy9axGHCcFpiFiVqZCOz4EeFxmr6YGMQiJZhFBBHmR4Dz8EeAIIc/CcNqjW8MeIMjtCuN2ea92U6lzMkwrgaasJnI6x2mH0XZVYb1pmr5cuJn8PUiHAAx7Ug+zaVTlnB80P02cAHDX+kQmOaCg2booBQb6I6keH0/Ajhs4ll6lkVkVkscl2Ss1inKlGORWSiTk4VsAMfDHUn+cqQ/WHWr/wCtwAPgpcFvYTkBXqBxEnPkHLyKHm3z6alv5cNTsW8g0q0XcAx2RYZq33Pue1jR3fRGmEYgBo7BgA/pWSHAVFBHgA5ODo4BwgnJUpiINLaqFGI7MQEQGZF0PhBkcVmEUXy4MGZbPhbFsRfJDBYZLMLMLMLMIFx8CpRHdkGKloBEiXRQUUeZHgPPwR4Aghzp+DTtJQ4LjdG/t107zd9SJjKnNsjg4Jdm5FeecPJFOrd+TzUBjb0wZToAsZPEY6QSS74BCqJsfonCXpGxbVpJKGngkCiIugAMVDV3tXxKToMMXVMaQ+kdSOCOfSsuDMrb0rb0qOGwLJZLJZIOECFks+ALNRb6Q61b6RlAdSGCgSMHCvZjIy4ApEnBUrWxsZ3N7UmIxhDGXawdbu/qL/U1ri6BhK3tsOxIkGIqA4558ihRgR+Hj3IBhGA4ogNhwjgKigjwejg6OAcOAxX3geCEI91keAfDPAeE4rD4QbNbXT4/7PBWlamO1IIg5vwRR5keA8/BHgCCHOggooocF5u65pRnaV++CAXG0Y5OMFcf1A8hWdMiNX7y0pDt+GXMpRiNvoVekaM6U4SMZQn3okZiXMosmOfBIbU7YIKH1ijgoltqgRlpHUi/BggslkslHE5LMrMrNd8rtEkehA0wR0IMSj2XCfSyDE6vQu6u0GxCoAgjsbeZd5Uown23yVYgYg4uu6gWw4lbbk3RYVbu9qTEfDgCZY8ZyDZuTsVt56802n43zbXEdEHBjRAL4xJzAzLY7EIGI8MsWAwf5FF81lwBZbFJRQR4PRwdHAOGjGQ75Ttnih6Fksll8M4rHBZLLgy4GWKY5rAcOCz/ANlgsSoQdjAKUZ5g7ECQskEeA8/BHgighzoIKPAOEiFtGfiDTJwDgVV/qH5Is4fxSVGMattTEYCqI4kgBhrO2WZVfdu8LadC+pSkDTkGlFi2L8SDOVgE0k8XZBoqBOer4l2pEKP3m3iUWOwdSLFYlF0HK7xXeK7xUeycgu6V3Su6VkgwQwKz4ChhwRw+cOtWzD+7HUnJZW20ald/X4N3+XfL+75176vMAaQ4i/zpcQHGqV/Upwr+cawEq9aUARAkY04Z4DLVnyKrdeIdczl80cwQcOUzrvLvIYocyko+lBHg1OiXQ9HAOBmdWkAHMZB1CIbJMhgjh8OSwCec4xiNsiyP4neFAMHI1h0Y19+w1DZsTW134uPzUfBsK8yOJYbmuG5vlVSpb7pmJxBYSwBbYTsHKryjdeWqFE0pyGFYycAlvmjMY8iua9TdcKRoTNPTGZlqB+diMGZdvdctPpX3m6Kx5gtFbdNzTD5kYdaiKtwYmTZ7F91v6hHnlih+A3vQqH64WuFWlKHGJP8A702pz8NnThVIGL9lVdUcHPCEeA8/BHgCCHOggggy7y7y7y7y1VZarZu6rvzl5TpxpecqMTKUIwEY3ERmOQiOWblXe7d52U6F5RmYyjMMQRycvBgOEOdqkgFA/qjqWSwWa73wI5ZBbFsWxZhAuGQYrvBM7lYpxjgsVHH5w61QZmFIJwoT09sKUonM4q33H5a3fUubqpMRlpiZCnE96UmyYYq2q06dOv5mr0watcgGcHGNMHYHXKgPghDmUlH0oI8JQ/Tj4AwWS7IV3cVodnTg/GhwRR4e1MBCZkBDj2IRu940oTIcAyAJ5Qqs/wCIxq1YZxjJy/NtVMbq3fMnHVrDczK5FlGFG2kCI/Si4bA8fEdiELrzDdSgC7GXyI1bu8q1KhDOZHZ6VqMMU3hhuZEiJHpKGBV2AM6ZHqW+BqPfl8a3jMvq8Y9ZWkNp5l3imMkcCmAPSv3a8rR5pFU6dvv65jCBcR1Fs8lH8QKcyOlQp76tZeL9KOSjQqXgoE7ahaPSjDd2/LWtN2aFQFaj3URGTsm4G2omOTLXqckcIR4Dz8EeABBBDgCCOCyWSyWS7Uciq/m3yhZwt/OFCmZVNEWjcB3eTYGpsDK53XvKnKlvCjMwnCQaQkCxBHOokengBJwQw+d6kedBo4uqbxx0jqWS7qyWS7q7q7qj2tgXeXeXeXeQcocA4MDsRcqP1h1qmNnhR4DzK18u+XN3VLi+qEatIwhEnOR6VQt7K3jU35VgDWrEdrUQ8og54EkIPmsB8IcyHMpKPp4DwlD4EpDYqVWrHty2phwBFSr3txCnRAzJAX4u833T06tIAzJ4lUG5LAVLAAdqTO+30KtuW1u40txwBMzB9Tt2QDkGOahWut7V5yiGDyyHIu1MyJ2n/Y3LfQPUt78WuXxreBA/vj1lMQskeysB8LFTlu2/qW919KJZWlpc7x8ajbMJCTnWI/GVOlv7dhhRIGmUGw4zJ9nEv8s3lA1BmCQG6UK1CrGVPjBB6lgiOMKNYyBMiuTgCK9CbgHAOJBx2VmmHAEPhxEpfu3zhxq987+TbOP/AHXbRlOVOnHCtEfNb/mHjOCr2V5azpVaZ0yEgxEhmCDxcMW4wjzqPOofVHUsvhx+5GS9yvcete49axtUHtSENNsV+VKDUO0sbcsg9vgyH3ByTG3OajKpRaWkD0I/d4Kx8u7h3ZOvvKvMACIJAD4mRGQHGVb16FCM/NNaANeqcTGRGNOOxobDyokpycfhgoIpimGaOHBp1h0WkFmi+ayWShADMhW8PohSMJOyNSfZp8ZwA9OSofxPfgqU5S7UYNLT0OQr2z8r7oyH3dWRz9BRjv7ec2ctGPZAH9nNQF3UlUpxLgE4Pxqlb6BGrUwGDOrurcdqrXLk7eRMyf4DgLEYcDLIq6IB7h6lvj68+sreBgDp8aXWU+3hyRfLhyWSyUgFC+Z6Nc6W5TmVKnpjKMxtxR/CVp0gc9JIf0hClY3sqtrHOEi/rOK/CeZLLwqUfnjMqVXdW8xGuCezMiOXOyhCNaMiC+BB6lE6g52OjiG5wgZ1Yj0hEfiYkjlCarWi+ToCW8iCyb+Klk/8WKAO9yyD72xHMomlvSJfjIRgLiJi/GMUDSrR6QsCCecKJ04c6EjHsoh1nh8FjkjOzpRnXkcpZHkxwW8P6p+S7bTvHUfHtIB9QjnKMRk2JcDFVbdpaoYSBBBB2gjMMXGKANFw6gfw5fV6lhSxQelgotSwYbER4OCxoLtUD0Ifcle6XuwvdhR7IyC7oXdCxiFgEGAZZLuolgnKDAcS2IPk4VuAAB4Y6lZ7h3FZmrvW5LU4gYlyz4bAc1Z1d8ClV893NL76cS/hxLE0weJ1kxWA+CwgSUTKJCkLzfdrQmPmzmAehS8TzBblvoyBRjU3tHDixUhS3gJSGxaqFETCk+65dSJG6arfWPsTfwip9s+xHVu8xJ4ySgSSI+pCNS6EedE/xWDoGnvSmfSFGVK6gYHIuFTtxVidOOoZcyjPeG9KUCBkTiqo8v7lFYgkAVAwls1PjzhXVA7xnbWdUvojkE9eqalU4mRLuTtKkFnsU5A5BUoXI1QpHsnL1IiOSBOSLrHassFkU7JgMEzI6o5oacFeOcPDl1LfshkKsx1q+M2c1pcu0rCPqWSyWIRGkrJZLEJyMExUm2oibGMcQOXkU/E7NzAs21hkg+aaJLpzEKFa3wqA7FK0qTOqnDEFXFClcmEIzIHM6JG95h+X5URU3vMx9PtQ/eCZY486eVUrGZXeK75XfKwmU8a0gf05UIQvJAgMtVHfVV+SRj8ajo31UDccjJRB3jCrEccQ6t7Xee7YStScS2xUaV1YThGbAyEQwfaTxIad6xiW2qE93b2pVah+aCH60Dx8yGKc8MpSi5MTH0SDFVP6k+SN1VKm6qsybwCPb1nHVGIziNp51phUhKoMwMweUbFEkDNHDBBhioPHFh1I9kI9lZBYBZLJZKPY2LuLuLuLJdqLphAA8idd1d1BgMAu6oiMcXHWt22O7t3yuN4VRCMaccZdos/oVh513/YSuPNN1QejTOPgROeBGe11XlV+8q1JuZHGQ5AeLgkYF2RlLIIxuLqMZcpUtV4JTHFiq9Pd+7dUAOzM7S2ZHIUIRrUaLF3gGJ5Djkpbu3fvmf4wBziSGK17xv6teucSZzJPoQdyPrFYUY9Lrt20D0LsUogc6D0Yepe4h6l7mH6ehOIRC93HpX3tCMjzphax6UDRGk86Jo7yqRi2yXUp1KlcmtEd51Up3stUIlgQ74KMOII4LYi6Kug33zYc6pX1Q/e1A5dah3kNR51hkgCzIgoJmWSBI9SKDK6i4fw5dS8xau9+Im3ocFXsiMPGl6inwdbFkhpLeld9Zpzmi2ARGxPh0pkQwUb5tNGeEhsx2qMp7cRzbODNOq08e1FiqsY8aGOCbgy4HKwd+DMLY62dKzCMYsyJUoVR2jt4kLHeTmhOemBQMIHTzlU57vvJ0ZRLuCVTNfecqsBxlQje2xMNpVKhVreDVIzOTo1aF3GrRORBRIyCLK5sN40/H3fUpyjKlIAxIIILDjbJS375esm8pbzqaxIh5U6pBPhEgYBtiBz7XsRwQOlQeIfSOpFuDJDDb8COGwLJZLJZLtFBiSVlhwg8FHRjW1DTH5xL5AbVbf1c8+bkMt+SpRFpTJBEI56pRL4+j0I1ZjTLFmwYE5BsgnlUAUhdXsB6VUtN1WhlM/OVSNKtpdeLc3MzI54oymSfSpQgCZN6VHdNnATuzDVKLgGMcnxXcBqHF2D9LP61gCu0O0FksvWsihpGPBkVgCsQWXdPSsiy7QLehTaJBZVZHiKuCeMo4Fl3ZLI9KwHBXsZ40qJBlxYqjSo+7iGTkdkIGIKDgqLvyLI4J1jkmmCyduAAZqsCMdB6lv4Edk3Fb1SKvZRGHinrQIBZZH1Luld0runpRzdOTimOSyK7qIIRwLqUakT2Q45wpicSJ0jpbawyQMx2U5GxOclIW1NiS+ClWrDtyxQIzQ+ltQwL8/Bku1miF3U7F1iCsj0rM9KJiMEcMFpWice2DnxFQsrlxR+bLZ6SgR3TtWDutUhitUYtJSp7t3madORBIcl+nJfhN7ScZGSFKFYCRQMImUc3GS3v5c8zWsalncUJRhIxB8OoRhMOCxHJirzy/WNSruypPXRrEHQRPER1ZDSMMUSMlHnUPqjqR4Rz/Aj2I5cSxjFd2K7sV7qBQ7EUGiOhZAokM6wCGAGClLYFT/qj59sH3fTqRFrRZxVIPZqF/mnkwVKFOjG3tIQEYwjlFuJEW8xKRGGKlomQDljsRq3OozPLh0LIsmEMONF45cyYwVCWgTvbioBRjHGWkljKQ2MVcX15DxN4VMDUOYj9EDiTEDJMKYTmGKcxw9Cy6l/uX+7hwCbTiu6u6F3QiDAMpUOMZKZ04FHDtJgF3cFjEdAV5fVo6Yvphyk5DpKq3V3TPj1JmRkcyCcA/Jkg0cFliogxR7AQOnFBxwNpRAzWxF4hDAKsC2MD1Fb6Jy/EVv2irvSdX3sj6yoggOsAn0hMYBe7zR7ARGlOI4+hd0J9GCJMFhAKcKlJxJWVU9ndVUdri1LXTLwOXMmEUxjghHTh6EWGPoXcQIB9SIMepZH1Lahhiu0MfQgCFqAGKy/ToWIw/TkWSJjFGJDru4J9Oalb14k0vW+zHnUN135Mqcvdz2AcUjxrs4jjbNNGLjjWSdkCxyVOva3EomJxxVWVeqatq2Rxw5FI1YSpVCMH2lXnl7fFtHVMSNOsIgzjJjp5w7bVvfylv6yFG5tJkAlwJxdomL54MSgGUBoc6R1IsFku6FltWSyWSjnkNiwJWZXyLi9CDkoPIrvIsSceAEHIKhv/AHtUNPybYVI+NrBEar46Q+eGCp7i3FYU6O6rWIp0YxiIgQjgGAyVKNvUlExPzT1sgK+qfOjLwmA24LtRwRB6kA2HMjSoxMqhGAGZVN4me9KpMKcOOQzw5HQ8wb9reJe1Q4hI6hTB2RBy9G1HZHmROld1AaUSIOgDSDovSQPhbVhSCxphdxgidGKyx5kHz5l8ibB+ZdrqR091kWWIKzPQESC63R5VoEmjEivM8ek909HKhRjACA2Jmw5kWHpUUW6ljB25EXgAmHUsliFpTyq4o/fBwriYqYiEj6ivMVSJaX4iq32yt7VapeQqHrTxkxQeTxWK7UQV2aYR4+ZF4v6E2hln6k6LFYnqWBV1GE2uaUxKPHhxKwnSqicYR0SOfajgRzgpiG9CwHqWKKjxo7AgAcll6ll6kH6kzAoAUwsFms13QsMAsEWij2fUu6HVW3uBpjLF8iDyFQ3JvE6qBfwp8n63KoSA7J5E56l2R6lIkYrsxxTSpkjmdRjSiYxHEFCgRIwG3aqnnDyzQkfOO7KWrsgGrWADyB2yZviQtr6hOlfRJ1wkCJRaTMQeZ1H6o6keEc/wI/dbOVe6617pe7DrGkF3AFxrCCJEQAhEDErcovLMVNzif3vEwD4nlyW5dx7h3FRFpUoE6KM4x0mOHbbvE7EfB8sRlEj50wfiUJbu8r2dOe0yBk49Sae4t36eSBHxrVvLy/TnQIypy0lzkXL4I22/dwVaUnPbFQEN0Kl/BN+wjKXzav3cn2tE4kDj2qFSFWM6csjEuPUry5v7k0zGDjT3idjDM4qXmLzXaiF5NvBj9GGybbDIZoQPdAWcRz4I6pxI5wnu72nTHKQiD5hswOWpFEVfO1lGY2eJEqU/+97M/wBuKjTPm+1fj1D1rV/3vY4/rhEUvOllI/4gC02nmCynLiFWLn0IiN7Sl9WQPoTRkSE4HpTGGA5F3PUiQME5xWll3Qsl3MVUuKuFOOZ4htKu9/zP7xOchHkiDgx4iMVqIcldxDsL3Y6FhEdCwCxpOvu7YgIQlECaE6pjp5E8boRPK3tWN3A+ke1E1960otxyA+NXGvzBQEfDk51AtgdjrfdvDtwrV6umX6ustL0rekrre9GlWM5HTIgEY4bdq+73tRP9oe1dm+pn0j2p41YmPIQUcCiIRxTyoPyoicTq5lrGMSsIBuZd1EaHTmAZDTAdCm4LFbzo06gO7q0njBu5L5x9JxRQAgx4kHplEaWTgodl/Qu6Ohd1d1MzIfvGkom73tRiP1pAfGiZ+YLIR5asUPE832MT/iBf62sP+pFN/wB72H/Uj7U0PPFgZ8WuPtX3XmOylHjFWPt2oyst4Uqg/VkD8aD14+gj2qX3UhS2FsERUg6uqMgY3YDwlHMSGI9aG6N8kUt8UYMHwE2ww6FpqR7aN3vO8hb2cRjOZAA6eJVKG6r0bzuwO7TIiCRxHHDlVI7k8sRoTDmXiz1vxMzM21TNlQsqdA5DQSR6XxQFT8HKPEaZbrQnX3du+eP/ACz/AOJWdrf+U6MLyrMUxUpy0w7RbGO0cYVn5u3Nu6gN7SqxncSokHVEsSNIxjyp6dOUYtgCMsE7YI4BZBBgsl3Qu6FHAZBZBciDLuri51gQsQsVaCTaDIPzK23V/T8UqdKvQga04yeYnpBk2LjF1K4uLic6vGST0P8AEs0CRsWSEhkjRqRBg+0YqErG4lSnE4GJII9K3f5V3zRlf7mu5CEZycyoyyaLYnDEurmp/UC/lOzoDVRhDtCRGTgAueTkV/Hy5uQzoUogQlUeJbYww6lcSst22cKMnEH1axhn3gHBywWipvqVIv3o6XPOvFuPMt1KoeKZHqDBEVt93M4ttnM/Gu3e1T/aPtRJlJzyn2oDXLpPTmhF8F3pdJ9qpyFSTiLZn2oeFd1Y80pDqKiaO+bmJGX3k/ao/h/MNdonB5E9fEpVr3fkbmnHIVI5c2lvWqs98bgtKlrHvEGUamnaYuW1cThVI3267q2hEPEzIlq5GiMOPFUDZ74hbXEj8+UYk4t87jUfArQnFsCJAv0LUaMh6OhRjXjpfjTwD8yBKnuyNEyrXfZAActtyxyVlZNjCmB6W+JRdj/vQ0wwCIjBzxI/jLqlSA+lIR6yrutf7/pkUe8ISjI4ZsA5l6FD8FO4uHzaJDcTuMVc0d0+TqlWmARCqZAOSMCxL4HkTWO76VGoTnIEj0KRo3FMVCC0ohiOXHBH8b5quJQPzcAB0AIVKvmS69FSQ6inn5guz/8AFn7VHxd7XEhy1Je1aRvGsYEHAykRkePBRlKX3kYsDz4q8qWu8K0DUGIjIgYcxRffFz/1JDqK1R35dg/40/8AxI1bfzJeRkCG+9kc+QlH8L5ouIHnBfpdUwd4+JCOcpRjjykAdSoVLnddpeVTmGlEjlLkKlDfHkN6pLSMJDSz5gEvhtXh70sKltb6SdURI9rYGYo3llv/AEUNTfeEQLjkLH05KMrHe1vV1ZaakJHoBK1htPOPUsHQPKpYrd+9LUyFON1HxGHeg2IVtc0JCVOrTjMNscO3oWBxQydkNIc8iGuBiDxhk99eQo24zlKQDAYnNTp195RurrIGEtX7LqvHd+5bmtT2F4xD80gDxKVO23PQo44TicQNmZPpX3G/altSEWMYRiQcc3IJf0jBGdx5lujM/rEdTLXdb7uZy5ZyPWVIzvah55FSFSoSFiT0lYE9JUdEyGXYuqg5pFH8Pvi4gOSpIdRUKlHzFdiYyPiSLdJQox81V/DHGx6wVb06txSriMhqlOJ1SD4jBhlgvE3p5ckbfT/dntPsz2Kje3s7rdnmWnVPgsH18WoxDAHlVjvj8P4u8b0R8KUsNPKdgcca17+3tOVpEGMIRk0REl2IjgfS5RnCHb6UWJXKnOSExhF1SrP95HEHiOwow3pvCdTc1dhVpzOqMthPadsMMGV9cWd8YU5kSERkHiCQPS6l+CvpSj+nIiKU3CwbpC7o+BHmHBisl3Su4hghmiXCBcOgdqjqWWBUKccHiF3gqVI5kqpDaCynJ8gt7ecvxVOXmIw028CcYE4GbfSAduRTqb0qmcoSLE4ktkXQcqQ4keJZnJTPIiBmmkMFERHDTwbs8ALZFTkQA5TEYJmwRcIzg+sYjnGR9Cp3O5t7VI3sZANOpIw07S2OI2KjHfsadzYRiHYvJ+R8FTtN7Vq1tvGWDzAEQcy+PQoV9w74oXAGyMscRgCFIVoEsQSFWoWUdVpu2mNQb50w3qUZV6WiEpYElsztdVDvbflAXEBjTjJ5KpbeV9y1ql1A9+oWiSOZ1rp1hZRAYeEWPI6qVd9b/r1rSThtcnbmdn5UTTkSOORc+lEl3QY7UILlRIZOWKwCjyJ2BPa6ijEHBh1BVoSl8wlF8l3VUPKPj+DgVEknHNU63l7fc6UHc4kNzMVa1Ke8Z3PhhjGZcFR/7u3LTNHDVKkHIHI+1AUt5fh6stlRh1Knd7vv6Va3qB4mMhI+kByPSFvCxlHtzpnTh85bwsbovXtKsqf2ShTpw7e1Srb833b29Js5SyHoQttx1Z391PIgAxfYRyK/tacKNGyqe7ljrhhyDM5BXI3zvy5qUpuwNWRBfkyHEQF2KpA/TrWNxIle+Kbx5adoxRNQuWAwDM20M2LcaGmO3NseQcXx+pYRYqTDM/FwNIKMef0o8SzCzWICiBxqNIHnQI2Kr/TvzXXFa0qRejVqH3ZgHAcvmzLellQI8CjWlAcR0lnHAORlgtijqzdDBBigDmw6kcAj2VgEMfgR5h/shwEqjIZaAslbEjB1cS2akQ+DqM4XMxBu650/p6FORbglMbUSs1MBEHaggiCVmoc3Bgpc/A/JwSHIpBBU6/haascRpOnpbNW9xujelSlOAyBLHi1DIqy3P5tsBUlOcacagjpZ8NUuPlW86+76sN5733oBV1gBoE4iDY4BVoXN8LWzfswpx0EDnCNe/u6lesczORkTzump04gnYAyeQZZqfMEEVEngYZrHgw40ONpfslHmHUFccX4c9YRfgl9YdXCOGnzcEudF4gxbIhwvx1MaanJgOgIX+5d71oVTIPHUTAs2GklmO0bVC2837pErWMXqShHQ0cnwf1LzB5zsbk1PKN1HWAZYiocZh8wxVWj5LtRa28sNchqLfKFWq763rVrCecTI6ceIOoSjSGrZycyjKZ7GQDPht5ut0GlhsWZdYld5YSRBOCLnmwB61GeoH1dLbTx7UyPPwDhPAFjlwQuLeZjVjkQqsyXnOZkTxklzwHhg30kEFE8g6vgDn+BHm+Fl8ALFMokjEDgiTsKlM948DlSkFkpOMWRWJwU4niXY4AOAc6jzcHaUyMnQKmPnEcEn7qJjs4GKhp4luW33nVjTsZXEfEmWAjHNydjMFvK28tbrub6nK5MKHgwlMaQwiS3dB2Eq13je7rst27skDIfiqgp1NIzLEvjsGZUN4b2/qFa0Lh8RCnKex8xLL0KnvDyh54sbwVaYlE1B4eBzDSJYq5uPMnkmdaxgcatmfxEBF8JSMAdI4+JGwuZeHd/RlgfXy5qpCTMAMvahyI4hAQ4NJQ08EdWxGcdkZdR9qI4ojqCuW/8A25Hr4W4QNnDTjxcJKdsUNJOpaamMjx9S877mvK2ihQpeJRD5zliR6FTpCRcsE/KR0ICe0qNR8Co/CJUyMZFZAB/iZbFkohlku6EXGCwUDypojFviWScBRWS7QwwTgYLJQH6yCGG1QDDIbOTgLFYoYBZLJZKPN/tQeThOHCQtq2rB1tUs8lgskMFkg/GofV4AwUufg9CzT0ratOEsjGEiDzEBitNXdF5CmT3pUag/91ClVjONRnaUTEt6eCJ5FX39vq7lu7yRRaUqrgVK4zMKMTiZHZJtKrbu8keT6Ej4MadW5lTjKvOcNpJBAL56WULSVIilTjLRHI7SHPE6nb040bWrUjVBk5LCJMcCdpGJwX8P3pvqVxcUpSab5xfAFsMFe3O8N4UroDsmnI6gXwYxyLcoW+d67opR3J5z8CXh1qMGjUqFyDIMQMcOyyufKfmiyqHw6bwuGOisHwMeUjEpyMEEC2HCODAIgjYeoqoNjDqCuP8A0561iMOB+UcIKyWSidnC2xlmVBic0RF3U6FGqY0Z94A586hJmAK9JXpVqzZBDgyWSyWSOCdkOdZcA4S/BEInZwYIcD8MW+kgEAoNkw6uApkMPgR5vgsn4M1iUMdixTqAO0cGCkSQ78XAGzWPAWGzgyUwNoWSyQjsfgAUHxwWSBfFS2qFAe9kHA5uXJULjyxuo23lqNOMK13Vj93Bz2ph8ZEDEADHjZWlfzhKpvrzJEDUZNC3BHztIcF+I4MqVla+UdzRjRxEYUqRIbHid3UrO68gWlzDVUOkUKfGNJxGzYr7dW9P6X07S4iCTWAjTnGGxjDZmy37V/ohvHVe25mRa1iDOWknClM95+KTKFh/VPdFxuzc24aw/iAqQMRUmSTCjHBjqiHJDhsHxVTc2493W1DdtuBCjGEIxAjEMGAC3hZWrCpnINtOKr2srXVEPiclvCxs7giXjOInGIj84AbHPFmja1ao8Y5xAbBb4qVLW3p2ssRgGMjmWxx41eWMZRlVHaEdPzeMOGZb+8kXPl6Md6eFCdrdTjF4TEX7EswHwIyL4hb+8oeabXwL60rypiBDHTHKXEx2Msc0ODDNBZqOKOnNpesFVCctMeoK5By/DnrUWyWSI5Rws62FZKAOQy4ceJl3lA8qPNwQxYund8T6iiI5qhY0QZVjhEDaeJAT3ZUieVuLnTRoyA5WTaR6kMYlDTGL/pxp40wehE1YCIYnFsgvwkCJTgSJNxjNuhR5+GI2B+DNF+CDcaw73COBlhwRcfOUeJBQBGOkdSKLBZoYrNZlZlR5vg4LH4A4GUC2UeDBSB4+CHOjwHgzUubhHDHm4AiKQBnjngt3/wBQfP8Au6cfL4rA0bSZ0+PEHCcifm8jF32Kh5e8vWNDd+54UBThRoxEREgYOQ2pV7Xem8Z6I5NJgeccSuLunTp/i5ZA4gjjX8LsqcDciOoscQBngyuxbWZqwrUBB3bTnjyq+sBMwlUqycjBmL/Iq84XdV7uQMsQA4DAsM/Sra1lOjOM4yk5xlgqlWhTaUurJWtMUtRuInazKtKgBS5SjvTeF7XlcGAi0KhhFhtYBU41bipVEsWMjmetQv50fEjGiaUYxi2BxxIzVnQstw3U/wASBTiTEwhFsQTPYMPSt0f1wttz06Na3iaV6KZ1SqPhTlkPSomQMSWwOYcOokjBZJwMUCskGRxxaXUVLmHUFcf+nKiHxWaJ2v8AAy4I83BlwwWBXeVM8RRH6xPSUVuygThqfoVKRtKlxXl82GLc+PWqcqnlu88InEiIYD7PrRtbLVC4AxjLMc6mZBhHaq1na2lSveQJGmPJ6D1KhQufK97GiT2jpdh9gdar3FKJGmjIEHAgnYRmMslfyckCpPA4gdo5cXMhxPwjhPBDnQJy+DhwwOx0EOdQP6o6uArND4Eeb4Q+AOH0LLgPBHn4MkQyHBLm4M1jx8IdZLBWO4KtGrLcFnUjUupxiTHQ2rTIgYPkvKe5PK+6hQ3HZWXgyFSPh04ygBGAGWJ2KxN72dYm52DDDFW9ad1qMjiHwz/RluevcmOGDnJ9gXj0rOmxwcRAPShOju86thAW6PK9zVhS/iMZTiZ7SA8oj9Ykq1uqVyIGLDNswrC7qVYi7idBnqbE5xzZzxK0rVKZlQnIDkf/AHLy7c7vsiB4pBk2ABGHrVC5u6cBSlIYjPHJbmuad3Kjb6yKgdgWHzuRfiL6pEaJYSkWAOWfKoCyI0yL5O/MraFjaGVyCGIwbFn9GfM681WW+LUVa53fWFKPePimHZmBtlE4hX1lvJ/xdKtOExIMXiSMRsyQAWaHaZ+tAyyRAChhxIy/Vl1FFh80dQV1x/hysDksTgjEZEjgzWl8HWZWapB8gVms0HOQbgiUSc+CiAc5BGUtk5dAKNncwIs4R7QAx9Ctd5bqqVIxpSciQZ9nOvxd3OhRnMhjU0sTkG1K6pXlxbeHTiDMEQGkHLVhg6lvvd26qML6tEPVh86LYbdLNxBVYVA8C2HKGI6CF+Mr0LeF5WmwlUIBlLii+1V61S5t6drSkISlIQEYk4AORmchtdVryzoUx+IYylHKZGR4tuxldXFa0pz/ABcyA593q+cGOGOLLeu6o1NdOjXkARkwOHDEbPgZqm+ToRBxWfDmjwwBOLoFDnUAMtI6k7orILL4Eeb/AGLBAiPAWQJi2HBgjwDDFF+DDhlhg3CC2D8GKi42LJBU/wCoN5Yylv3fV4MYDGdIAxjmOQOTgFQnTtTEGrGTSLdnblmRs2K3qxm1GDONqtK4rygKbHAs6s7y5uabUa0ZnVMOQzYY8uSo/gSfGDHEKAp2+oxX9IfMFCeipSupAniE5RiX5gX5GW7t4y3xKnKj4RJhIjX2QX5laXs5xnKNcT7RGJGLlXF/IzqeCYHTCJnIgsMNILNy5qjPdtjObxjKLjSdj97JtvG2CtKvnH+oFjZ3EtJjSJMqpbHGEXI9IVXdtffO8J3An3qQam2GQIfnVpuyy840KV3NtNKu8NRiAQNUgIg8TnPJUbuyuQKdOPaA7WOwiQ7JBHKjeWlmZvHVA5Pg4x2K5p3tExIt6hAOOOgv0ca85iB7I3lX/wDmSQ4+CJfAEFUwBi3BBGMc9J6ipSOWkdQVaqBnSIRPqTMpHTt4YybDgyUebgZDDFuCCJbDgpEZiS0ttPrKo1jlMgc+Cp1htxX4PecTpd4kEgxPGPjCq7r3hX/cagAnOIarLSWiJSchmGwBUN32spG3pREYvsAyRDK2r39aoJUJaqYiWAlxlR3fvqrGNmSJSjTBj4mg9nW5PE7hlStLWDUIBgOIDJVK9U/dwBkeYLfNWjD7r8TIvx8GCiTwMjhjwQiCzlejgxGCyYcGTpuCBOPaUeZADNQw+aOpHFFF1h1Idld1d1Rx2LNZrNZ8GfBmnBxQiTgyzWai5wAWad0S+KzWBxTrPgzWakAdnDpfB+GGPBu6jNzSnVaQ4wvIe5fw3ud3UvBbKInGM5avpOTmcdijRtrATpx2qpWhDRJn48lSlfXLMADpPF6VVtroxnCmBIaszpx+JHeMLAGo+Eox2Mdg5lYXFhufXrg8zI9oTcuMcsGwXkPdVrLRdV5XADbJCnKfXFupUrDeVxL8bu+l+HrCRaXiUXhiP7Oe0K4p1CZV4PoOpgCAS5GRy5l5ksKUBK6trmVtVgWkXpyYFtj5jnVHy15KlT//AIk7wjMRLCf4aGI1mJwB2AFjtCu99+bN/wB1ebzr1NUpznLPHCIfsx/VGC2nlOJUKlK7rU6kJAxMZyiQRkQysP6a/wBXt5C+8j39SNOFWoAalvMgRiZVC8jE5McBmpRsN3161PxTGgRCIhKmH0HUM4kZS2rzFvfe1jChCwta82wcxjAuDyt07F5n3taxMbevfVpgEMWlORQjsWZTEvFAus1AvkykRLECXUVINhpHUFXhDCIok+tZrNGJJZwsXThCBJ0jgYHBU9UicFms1jI9KdRIzR1SKzVERJxktUpfOkPWVQu7GtpuYSBBOIHo2qz3TvFjfFgZMAMcFCo7iQdBkSRgnkMHTIFlX3jLuwBPQhu/cdoKdCYMakjiSMn5EYSIIlIk8bngzQ4HdGLrNRK7R2DgwKBKzRYokrNRiD2dSiX2IHHNQxPdHUjiUcFkv9yzWazUeb4Oay4WGaduDkCBCy4CGWSxGKIZZcGSyUjyJmWSEjHsuslkoODkslutv+avJVvCHiVDu23i4DsfCir+1taWmpTM4F+PjCju0GEa0SYmWYZy0mLOW2Le1Pfu8p1YmtIDTHwjnyEqpu+ldkVoxLCciT6SVe2k6JelLQ0IOxOTDi6lXmaUqbzLA4OBtA4jsX9IA7VpbyMf7NSMon1Ff1B/p9dVdFhf1PxcMeyfEbVGHH3gWHKrvdkySMDI/qTBIxX9RN1XlUU9x399U5gaPbi/EZQB5yF558wX1Qmyq3kvCL/NiTENxYDYiAMOGV1SJ/GgNFbh3HW3DvTeHmO1pxtqULajKpGUaY0xBmDgSNvpXnOjvbyRU3PuTeFtKmZ3NVq0jIMNFNsW+diMEbmpVlUqTcmRDEk8nBl8AYbUcMWl1J+QdQVz/wCnPWOEnmWXBlgjgnIwVPDBlkmZPxLJRUuCjIjKSIIzkT61krWQwPiA9BVjWGXhx6kU+1MhzcFalWmBqwHpU5ioNRLsOVFskWGCyQLZ8AAWOayVMttThZJzkgwWSLhMOCBbB1HDYhhtUTyDq4SxQf4Eeb4OEig8is+BxmhESWadASOLLMp3RJOLrMoY4o4rMrArNZlTYkYLMrMqMdR0usys1FyclmVb1onGEnXlepCfibwpU40ar5CrAYAcXZCvo093mprqSJ0jIlXFGzpinSq0ITicdcTMkH0hsFfWG8941alsZ6o6ji/OFXjXgJSAOZY4c63huu0qSlVqUo1TCXbxObTHZYbBmjGnuypLDMBf0i3hdwamN6Bhzuw9C/p3/VuyiRSs7mhRuWw1QrAPKe0iIZW91a3JqfjLOlIF3idMMDHiBfFf/wCwRsqs6e+bS6hVoSjmGHbHGxiTltKnK5JNeNSUZHjMSQfSjwgPizrel/vTcbSpVTP8RKIam2Jm5GQbNXXlvyxOI8rbqkaMZwDCtOOE5BgBpJy6VCm5BAbgzWfBmUCDisTsl1FTiMgB1BXJ/wDtz1hMFmpY7Rw4LNMFAE4AYLNOChzIl8VTIOZUuClEn5yLnHUetAAjFU6hkwBU/L1zKIvKQ+6bbEZ6n28ywZ+F1OvVkBSjm6Nlu6ofwcSQTyxy6UJGZMQtOjELHPgiCcA/AGUCDmi6p86Ho4AByocXAQDhgiTwQHKghzqLHBhwFEOg5WazWajzfAyWIKYgrEcDBAscuDAKGepkx7qywKkCDwRGLui/ASAXbhmwyHCC2D8HIgNjcAHGVvX+l2+q8KVhdx8WnKQJeqIs0eItxIXNhSJs5knUQwbYcWOOxU6n46nSq1AIjtjU+wAAvx4KxO9t+QNLESAB9ZAVvWv980YwAOomYAL5O5Garzu9/wBrTqCBbRLxHIyj93qQpeVfKe97uzm/hypUJiEzsAlUiA+K8hXm+v6dfwyz3feCtCrIvUMD9KMXGrSePA8SnuzeLfia1p2DpAOqEXEiTiSCGz2rdtlvCoDvjc5nZVqZzjoJjEl9uD8i/q5uy5pxlZ19MZiQBHbeBJBwyXmXcpsJ0/LN1X8azrN93VjUGuWkjAESJDHFFlimcDnVpPfNzGFEEBpBwR6wrncflbeVSlua5o+HONKWiBjIMT2Wx2q9u/L28ia8yZ6Z4vI4kB8lVtN5bvqG6pli0SR0gMmnY1QeWJTypEI6gQVlwAMXK0AYtL9kqZ2GI6grn/056wn4JYbQsuB2WSyUcNnCHGxZKMjxo1CFkgWxRAdnRGwrSIlU7i0rShcQIYxJB5nCp7uu5k1tO0uekqUSGI4GZXNxQmYyNeMXGGBGSM+8do2qUadtJuUfIgatJgjFy2aYoEg7UxHBETchYAqBI2rW3EslE6eNZLJFwmAWSgZDF1FBs3UXGLDgLBOyDhbVtW1Rx2LNZrNZLBDUcFhFFOChE44LMosSogklgsJHpQclGRJdd49KEijJ80CpMGwT8EwCQ44Wc6X4Bg6B2cAIOLrc3mby3eGjvK0rxmC/0S59WC8uWG4vKN9vLzPC3jTrU6QGkVANssDjm7cis73dn9KaW57aqYmlXuZ6zFzhJog+tjxYq2v/ADd/UY29gZ4ixpaJY4HVMsdIGwqyvvN3mfeW+6NYRlP8RdVKbOxlhEsw2casrvd/lShOBlGP3lIVpY7QZuX5XW7rjdXlQyp0ZCQjojEE4bDkqdzHdcKVIs2Hd9SsDd75qU6NGWpo7QxBgccjtX9QPIdxIjdG+pi5t37IMnLsMjynmX9RrKNR43FrRuH4i8uyOTlUvLe8LGB8w04a6FxGINSjU0sDyxbMPgMnKqjfvlmdxuac/ubmiJThOGzUweMjxKVteWVxCrHMeHLD1L8PXpXFPc9MOa3hyEJHMCMiADxHFU7L+CxNWIbxSTqVK1sjr3HHEv8ANHGTxepQura7p1onMwOqPNht4wgZ0KZPLEKUa9jRY8UQPWyJnYRdWd3Y0/8AKqgLtsOxCTBuCMhmEZbWl+yUOYdQVy/dNA9Lo4ngZ8MFmsygHWazUA/BigScWWSdElyeXgDHFSva1anC1jmZFlUt61zOvejACmHjq4icwFR3Z5U3RQoV7yQhQlGrKU5EnbEjBvWty0d930q+8xRBq1ZBiZSxLj9V29C3X5lvL2M69eD+GMRGJxjJ9rhlKowAKxAfmRLOw4ld1J/dkVIyaWBkxbAFb/8AMm5Ljwt4WtPVGWfIxCsob1taV1obxJEmJmNpYDAtxKFzviqbS6OcMSMthzULncG9aMxIkCJLFxhksSoRJLYsiXXfKDyKzKAMjijTJOpZlHtFZrvFM5Tgsu8VEiW1R7RQJkWdA6zku8j2ii5PShiVtW1bVHm+BksSyxKz4MBigRxcGCB5BwciIPAByojZwEcM2Gzh9PDHm+PgARC3d5h8uXk43NCtEmm/YmB9IbQEfLHmyNpujzz4EaUJTY0a1XBjTBYRmTlyqhZWt74dGtb05eJEhnwfEYY7Vb0riEKsaVMAk4jAYni51cytakKsI0zoIxAI4uUKzp+FKYMcy8ekEBvSqlCrTj4Ap4DaGG35EaM6pFQPgNq/pv8A1b3XS+93ZcU4VSAxlTme1qI2DlwxW/t5btrQq0LzcVCpTMCCC4k4HGxLFTNtu4G68MxMwCJagG2jMFU7K+p0rqvKenTXEZAjawIOKuK19/TjdE7mQIM428CQW5mcZheUbHyzuO1sraN7MSjRpxg/YIGrSA3aL8+xEg4q8sLyUhaVqcoTbNiGw9JW/P6b78lOr5Pu6satvUqk4a5FxAna2atbo3NWkK0BKOvKQ447CF4lncicefFEZlXVlKQ8aIeHG4yZGwrj76mSDx5nh9Ev2SiY4Fh1BVJElzEhErJPsw4QeGB5PgsiDmODx7ucY29MGUjLAAAOSSt7eXfL9/L+FxkYxnSl2Ti2Y2qX4qoZy45Yk9Ks96CJla7qMZy+jEy7p58MFZfgro0ri9riIIkx0Ya/QBiV5D3wIv8AiN2UjrdwdERD1s/Dcbx3ne07ewox1TqVJCMIxGZkTkFZeRfI9zWl5A3HWnKpWiTouCHBBkO9TfGPoXmand3UKdWvSEYCRYykfmjjPIiCGkG6gnJxX4zdl7Uo3GDEEgAjLAKh5a857xjHfUg8a05NCRGUHPzpbAqdWmB4ZGBG18cExyWSCxKiTsRqN2W4MFy/AzQAG1DBRcYOhhgw4CyJZYus1ms1Hm+BknCAIXGiU7oMODHJRAzbgAkHCPAONGUcBwYFZ8EwDmOEB8OGHN8Z4HGaPB48ak41IdoSgWkCNsTsPErfyD/UatPeHkSbQ8QSAuKUAWHblm3FmQhv3yX5toXO7oxIkDLtUwS2mo5wOO3PZgt4WdvWgKlA4sRJ+YDJVvwjyjB8xxK4rTpSpCJIx+cFeULjeUokV5yDAYRMniMXyC85bpvLUTkLeqac3GpwHjIDjcZK2p7xgRfWO7JW5ic5GlMhscjyKdKpTasQZMc+0dQHr2K+p/hZUjbSi0jkTOLhuJuXNbxsrnzhTFWpcTqQDCIEJDuky2hUv6abmp/xDflxdCpdVYSFSnbgHITi4Ey3dfaqdSB7wHo5FpniFQ88W9Cp/Gt2zxMM9EsNROwQzW9v6cea4wrVd2MKFX+88LZIk4kkuMMlO4sbmoaMg2knAcrcfAPFi8QhvCMQLW5lswZ0dBBjsQBzRlEswPUU55OoKZAbBEt2VmiNmHA7IDZwZKA2Nw+jg5USTwVvK27YSnvi8puZRkB4dM4PIZnUXDbEalTGoSSfSUDODw2q93tOyMLm7rMah/vIQ7pHIHW7PLtlexiLOgTJ8dMw5kG2GUcAvLsDcCobCXg4bAztyNwXV/vO8hb2FCnKc6ky0YxiHJ+RS/pV/TC9nR8txeEq0ZfmawkxA046CAAxVrd7zhD+O3FITqzbT3ogtjy5lUvKO6bnXua0MxUY9k1ARgPqkexGUi8uGlVt5aZiQx2gcY4irTcXmMTq7uphhMyDjnfEqN9YaZWUwJAgguDxJtqHNwAIx048ac5/DgHxJQQDqIP0R1IojgAZZLJZKPN8EBO/wI83COYcMhy8A5+DP4E+bhEtjo4LJRwOSyKGCIZZFFhsPUhqBw51SvvIvmKtaUvEjKpS71KqIyBInA9kuzOQ6/hPmgW+5/O1WDVKcowjCvMBnpSIbH6L6uIEK+hbwM6M4gwJDAk5MWY+hXthUtadC109iUsjx+lXIvN4xeUjhASD8zq9jUqzmJwaQJLHDiOS3vZUKZjQuacrgfRJqTGoDYwOwKNK4tzO60gOzRLMIh8h+hV3bzsaVC0qASlIuQZRwA7L4seZbop7h3zK1jXDSNJ46uQEscuJVK97UlUuKuM5SJJkc3JO3lKaJw4N7+W940RUsbyhOnOJ2iUSFV3ATOjZR3nVtJU5AjUKhaGeJA1BlBhgwPTiseAGlF6lt28MSW2BVLYAicMCDmGwxHBp2kHqK9A6lNi7cGSlLYCOF1kslSlsIKyKyQcbFkVkjgt5b3vKsYzjA6HIDybBnzx2K83xvq8lWvKtSR1EAYOWAAyADYIq2sKQJqVZiOHKcfUvL1GtER3da24kI5MGeRJOfOV5p3/GRIurmZz2Ds4cWA2Lzl/Tu3LCjcisIu+JABOOOZVzc3VeFO3pAmU5ERjEAP2iWA9JV3/SP+m9/Onue2mPxdzAj7+pEkeGJDOA/VLF8UfN29Ime5t31NQBcDxxi77SXGDqpQ3fXFPfd0DTpyweILOQOMhwOcqvd1BOVWo5lIuXJ2vxko9g58SJjIFs2PCRSmQeRW27fMN1Ur7unJiJSLM2HR/vVLee6pRlblu6XZ9hZ2PIUAeAEHFapHFMmHwoE4YoBAqPMEQsuHNZrNR5vgjFMfgDhjj80cMufgHwp8o4QHwWfBicis0OE83DRuLK6lQuIlxOPfiRkYkENIcYVpur+ptxdb88sQIiKkhruKUcsNR7RHGSCt1+aP6b76pV7OtEO8gJQkz6KkQXiQcC+1bovL7eeOrwtNEuYmXaGt2bAKFOV0PHqQwMjjjm+K8qeebOMROhKVOuSe9CbYR45CQB5A6sZVrWc41jEAxi/e4zs5SVabxtKVGVrCq9c1JNGNERJkQeP4lHdXlK7px3BuSvpomJ+7qzBaTkYGL5YHDHahPzHUpyu5BwYFwHDgPybVgcEE68i+d7S1kd2XN3RNaQGdYSAAHGQACTmytrgDvUKZ9JiOEso75tKRlZbwLOPmzOJMuIIFEjNj1FegdSk2SbJZqQfBws1mgsFiVSD4AcGKGOzhq1K1aNOnCJkZSwAAVLce55zO6rSemRlkZjvGLEgg7CiBkseNbis6dN7a2rRqVDs0jFb+pW1eMDVtxb0AcMZggnmDITqe9xfnfFb+s953Ao297ZntyIEI6O0TMvgGBD7Tgt6f0t/pbfyhuAVDC8r5eJmCKZBOoDEbFuPyluSMDvLeNzGjGUyRGPiHGoTiQ2brdfluhfwheU4A15RxFSqQNU3wfBh6F/Ft5ec7kVTh4bkU4NkYh2f0Ik+f7z1+1RNT+oN4Kb455dKqeYfJ1WpvTykZHxZB5VKbB9c44tHY75ra448OGlTo2ka1aRaMSMSTgAG2nIK783eaN+XG7d2XMAaVmXBJIwM4ksBxcijQ3nVpzkZNEwL4DaXyfi4AFpITg4oLNZqUziAnGXBDn4I86BObIpiskMPgR5vgsM0/wA/FwjmHAFIcvBHnRjt+DM8QWazQLrvLN0H24rvIMcUQc1miBxFPwEvgmdUt8+TfMNa2pmpGVSkZE0piJBaUHY5Ld26fNW/YeX/OdtKmREdiF3IR0vAlg+LNnirTeP4qUpQjExJk7jjJfMhbsqQg4iRIy5SrC4luytUnT0AGMMCCRi/JmqH9K/Jm8fD82XpBuzHvULaQPZBHdnUcEHNnwUrqlEmT6nOJJdy/OqO7bjdpjPSRqGXZG3nCDA5IB8W4LLd/mG2kadCtGrTlAiM4ziQXEmyLAHkVOzpE+DCEYh82iGCbgKgK0Qa5qdk7RzIAIx2kHqKxyYdQU8nbFEbHQClLBn4QeGHNwjmHAXW8NxbvuPBurqJgKgLaHGbqtvbfu56s9xyqE07nSZRqB/nH5o4pF3Xb7yLheZ/OVzSegaRpQLY6gvJ/lO0rabqnA1q45+58a51WudzbwqW1edMwMoEiRjLAhwvGrVDKWDyJck7STtJXlvz9vqlM3Bq6LeJLBmc1DFsXYCPFioU7wjxMW5nKI1JtanEVcCrjc+8qEa2760TGpCTETBGUs35OVb0/qD/ReylcbqPbuLQF50QA8pU4gdoHGTDIKUJRImCxBwIOTMm0kyfIO/QFYf1c/q7YSFaOmrZWsi0SJjsyqwPfGk6o5MWKpW1vThClACIAAAAGAGCoVm5eAE5Ba44kJ9qwzQ4JxlkVpGXB4ex0XGKjzoAZMEUSsSgW+BHmHwQ5WazWazQKclZ8ALoYo4rNAg5JtizWazWaPNwgLEoMcEFiyHGs8Uzh1LjbgyRO3gaQcKNWhVlTqjKUSYyHMQxHoVn5G/qtSrb08hjTTjUBlO4pQGQd9U4ji73Kql55c39bz3b+HMhGMwakMMIkOZagcwcQt4+cq9QVqNChKnb05DSa1UAwiYuxJjMORmwfJb78474qSN5fTM5AkkA7AHOQGAZY5q6uN5XAp2ZBcnLk9aqfgd4ipvMxJjGMSQR9YYDmW8bi8wEZYBEIMnJxRJ4AArDdcZdnU5CHGieQ9RXoHUFPQeyx+NGT4vwSBOD8I4afD6BwYKmalM/hKMfEc5FiMFQsL+wo1raNIQ0ThGUGAbuyBHqV3v/wAgz/hXmOvMGVOmwpO7sx+lkGYB0dz+dNwVaNLWI0qoGuNQE6RJ44BzsK3LRyuLinGtOJOGMdZfYvMe9plxCQpBsgIEjBBAlWm5TaylueiYzrzbCAHaD87fEoeZLmvChG3iIW8Yx71QBoREYjAH5xwAfE4hXnlHzZZW1vRNCVSiaAkJyMXYEyJDsHXlzfvljdNnc/i6wpzNzGciMHHcIC/0xuR/q1f/ABr/AExuT7NX/wAa/wBM7k+zV/8AEt6eXPMG492UrKjYyrfcxqAyIkAYy1EggxJcKXnbyDZx3d5inISqW8GjQqSz1AHAHPDJV/MH9YaNK437VJ/DROmcIVBlqjjFicnXg0C1CA0xAwiAMBpGQDZMs8VveZgDKFvM5DBhwYI47eBxmseAsU54DzFEoNmg+bBFHgHwI8w+Fl8AB8eAIA5kJkApx2g8Dcq1bPgy5AsSs0JbHRRQdO6jji6PGE7FHjZZ8B4Y8qBpVDGYyIzHMrLf/ke/iWmPFoVe1SqQftfdl4kkO5Z+IrdNhcbvp2Hlyxi8beiZCEq0salSUcnMnYDAArU2KxUhIHSTisJEel1vGzY+HpcFS5+DLhMhmMVdQf7qjgOfb0FcqbjfqKLDBh1BTicMHUpbEApS2OOEFuGmeQ8ACGGDIhUrGwpPKUgDyA5n0BW9hHHegI1SLOYtiFiFWvd41vDtqfaf9Yd0HnKrUrjw69nTvqDDSMKQm5IObZOt773t4iGm2NO32AyYRAw5HVe+uJaq9arKRO1ziql7/T/cUbndlCoI1qk3iIA5kEsJMOJ1ceVd6bt8Pf4qiFOnJx4jltQGenlVvO7th/Hb6AqXBkwaJDxi/FEHoV1uHdxA3Nuyekl8Ksie8NmfQwXk67BHjTuNEgdsJBj1luVeZKly8J2R/EUpAAnMADHIMVUkdkyOjh38P/7VP9oLDNUt+XNq9nb1O+/zhjpTRbTs4LndlMie8rmnpMTshLOQ5eE4cAKHHwOdqwQWs90uEUOdCXIOpMUfhR7Wxd5d5d5d5dkrNZlZlZlDsrDBRxOYVIOzwCcSLKhEnAlXETmJLMrPapR1F1mUOBgcVPHBlIkunx6VEueAoFBhs40Io4rMrNZrNHFZrNB804kWUK4kdcXb0qU5SJkS5XeKz41mpwJbSHJOA6Vvvd+6riFc0IGEzEvpMSAX5fhbx3jI9rQRHjcjBlXva5+8nIv0rPaEPT1FTxbsj4lN1ihgptx8AYrFZBZBQw2Luhd0LPYtTE4soeZb2iTC6i4EtmzBamCo1bqoBTkdpZT8v+Xr4V7CZiZVKZLAxLsfTgp+IH1RxcPtdWnk3dt9TtrCNR5znnnsZWtHeG+Lirf64iRjLsvyOcjtW6/K25TQp2NrQ+8MRESqEdoymRjI7HOK/wD4mbwhcSu6AFOjAyIAEZCQnzbFva28pThHfUqRjDVtDEER/WOQ5VVubvyPvIsZSlI0ydpJL8W1bg39Wt5CvRvqeEsCBGY1OMw2IIV5aeJGVtfbpMycx7rX6iFfbsoSc0KkgT6T7OHf/wD/AMqfWFR3dUEo20oEmbYBmX/a9nuzXGhhKUGiJS2mWTk7ScVGVDco0sM6h9iqXE91CEeMTf0nLBXe8bmvOYnI6dRfSD80PkBsCGHAOfgD8aDHhfgEwNqDqIOTphkyKLrJDtrNZrNRw2cqy61l1r/eu6sIMncMs1nwA8fADyhWxLH7sZIsGKpVHyKrSIBJOxFsEIjaVJwhimBDhEs+KchVAGwjxohwmd1HJnbNZI4KDtiHzTuo4qWWC7qwZ2JWxM4Rm4OPSnOCzUJI4oDjRi44NIIfHaqlxXnCFCAJMpFhhs5TxKruDyncEXTkVJRLS5Yg/GvM95vKjKBrUTIasy5Bz2/BYKz3UK4gZzBOKPadNyhNz9RUiTnEdQUyI7EUMVMPt4AnCyWOAVM6swu8hihiMuNWlvdEG0jiYnaypWVuBClReICfXhzLchlJnm+G3NFhmUQAsNqEYTaOzjRNS5qMP1j6+REg4bFbxOXiRHrCnUjb0p1DRAiKunQZGI0g4ceC35/ULfu4adt5W3lvE1aXhSE4RcAaYgAAOe03KvLV5u86oSsadKUduNMRIPPkvPu7Y2XgCF9MCHFHUW4bihaQMp3W7ZxwD6e2Igtzqha1CK28dQJqtpwbEMrq8hXP3xMiwy5OflQHiQFKMQ5lE4MMyx9KuNzWt0KlpEtqGRO1kAMlmidSAGT8AZMeAniWY4NEji6ZkAyA2ssRwFzigyyWSyUeb4GJQGkYpwVmhimTvisJMovIkOOtW5B/uwsJMFTiZlnCvKQGEZYLvoQlJ1UiBgDwVXk4CIEmxXfUzLEssDioxqlicnRnKoNIGJ4kKlKQlTORGIKLoMMGTU4SJ5AUTDd9c/2JexSbdtwz/Qk3Uo6d0XJJ4qc/Yqvi7muojY9KefJgjo3BeEf4M/Yi3l69f/BqexGNxuW6jVzANKYPQyJ/hFz/ANOfsQfdlx/05exASsqo54S9iJqUpADjBWBRlJDRMHmRuvMFQm4Z9G3HL1r+D+XatcburSfwdLCQBcFwNWAxzVHfXnutqnLEU49rs7dTYgv6VeW+7qQhZxtxGIZuyAG+BiiSFRF9VELaGLyLDpKnCyvKVYxDnRISYcrJk3P1FFjiw6gpB3TnNYHFTMi0XWqMSQUCYlU5SoyETtIwKdwuzihI0jpG1sG5V2aRbmKiasTEcuCeJcKz8eoIUpAhycHRlq7BOB407FluVj84Dr4ChzIOtLZhanxVAf8AmR6wraFOBlLs4M+zbycuxbt/pBvGztYbkpAXYnGUSY46RE4ntMHY8avLWtda69vdSw2xGrskjZsZb8vo2soUbulTqa9JEZzY6yDkS+bZKcYF5gthxqE/Lvluc91nOvIGMYAnNy2ojiGK3l5q837wjd73lQ0U2BAgCXIAPERi+3JOzhaiFfws6whV8IjBnxBB9S1VpGUzUkcc806LLBeGeAMu0fgOo1Bm6iXxUS+1ejgKLSWfwI/vEcuNY3MVhcRX5iKwuIqP7xFY3MH50XuInoUXrxy41hWD86H38cuNYXEVEC5g7hUCLiPuwsLumqeu6ge0Mjyq/MbmIGravzUVD95gqp/ExZ1+aiq8JXEXx2qT3MTiUZCvE8i8Pde769WrItEQpTnqJ2AxiQqMLDyJc0qVQPGdWJjHHkzVlDzLvzdlraVCCZwMzOkGyIZiRkqFDzD55lcQIGvTHTjtIO1+I5K1t7m+valaEcT4hAJ2sAcFSuhuw3Aie7UqljzuVG2rf023dGrGIxBMnwzOYxQla+Rd3iX+GD8SjCz/AKd2lQEO8acWHIxCaH9NaBHJb0j1hSNn/Su0BLe8t6J6MME0P6ZbsA/9LTP/ALq//wAa7tb/ANJD/wACaH9Nd2vy2sB/7ie4/pnupv8A01En1xRNX+l27ZRbZa0H9UVM1P6bUBMn/wDbUW6lGV7/AEvozIfKjTj0sFUhc/0nt/CkGL04jDnAcKhT/wCwqVnKBJJgZAycNihGpbV6MCcdMyGHpLdKvx5K85XFtGpSMYmemUok7WD5cau/MO6P69fiIyLRpVXYCOIDMq1zu61tN4XdGJAkJ/ey2HQDk4G0qrHzF5FvqEIv2hTM4kDa8BJlWsN4WVSlWqw0x1YY8xx4MFUr7yvaVCnEOTOUY+uRCq2+8t//AIq7gAdFAa8SMA4cZ8uCq23kryzStKEsBUqSJm3GwwxDKpU8weYqxpSDaYksBxK43VWmZQq0Yh9pDbU21GL44j1FS5h1BTiNgdAghAkhbm8p7knA393VEMRqjDB3kMcG9atpb1891YbwlASlGnACIJDsHVSkfP1wRIN3Yj1jEK2tKXny4hVicThIHpX/APkSv9iPsWH9Ra7/AFY+xC2l58ryrP3j2Q3EwUYHzzXcD6RV/d+XvPY/iNKBkIVhqjIdBK3/AOX961Kcb/d9waUwMNWJAlH9XD1qFe3nprAhiqNnvS2jVFMMCc8MkI/wuLqwshGNGvRL5oaagJ50ZPgj4VUSIz5ODAKOCtwS0fEiXOAbUNq837x3D5ho0fMFKwJoyhVgZCppwDAmWPIv475v3qbzftWAM6jku+IAfHALzr5ar1wK9eMa0QdsIkBhxnavLe8fJ27fxFvRp1RUAd4mRi3S2xbsvf6k7rhYeUbKcZTgXE7gxL4bGOWKpbn3BuylabvphoxphgQAzkDbxngYFTrlC5Ja4m4B+Ra5ntoAEO3GiSyBIDc61OGWEggHCbgOKbWEe0HVMkjvKI5FHnQ42RYousZB+de8HSsao6V74dK98OlR+8OS75XfK96UHmelR7ZTmoVjMoEVCovULOqcYzOIC96VAGoe8FQ+8PuwveFUgahfUOsK/Eah7y94VHVVKrATLOnEpMq4NUicgwxVO1tbevXqSLCNOEpE8zBW13DyLfW+7pSBFS5ejGRBB5cD1LdG49++R912HmqnACrK2jGQkwYES0gxJ28ajSIMdP0cD6lI2dCrM8pJRF1ot4DbKQD9JCFfzT5/3faU4kExNaJJAxMeySxKldUt83O8K7tpo9qMQ2epw/Qq9Hd3lK4uKY7plFiTyjn2o093/wBKKNKpqwmahkG5jHb6lTPl7yVY07bQHEwCdXpGSqGnabrhTfAeDFwOfkTSvt30jyUgnh5ksYj/AA4ot5osvsBCVTzNZSH+GFpqb7sDL/CC/nFh/wBEKMfE3PMhv/poOedGtvCy3VK3GbUYAqnO58mWVSkM8B2uPZgpm/8A6VW1ZxgIz0MfREuqNTfP9PJ0r0vrhA6gOYsHw5FQqb0u7jc13UIiDOnpDnYZA4dCpXnk7+rNtqmzHxhOD8REjFjxoXUqVvvGww+8pQiMCM8H588VvTfe9rCF3Z2lIzq0KdGJqRgMTLScZsr64vP6Xbx/j4MhGpGzFvITGGqMhjEn6Q2I3X9PvKW8TQDnRXkSSNgjIgreW7dweUBuijF4+JVh4jgjOJkzHiIyKNTzH5vvLimS+nxJCD/VBZE1QJkkntY48eKOCyW7t4iXZM4wPMcFb3lINGrTjLpDok7AW6CiOQdQVUA4CkSmOa+RbpuqlPXKVvUmRLEPEMGGzBVKshiWGHIsi6B1ZLevkKn5I/GTtKdKfjeOY6/EGox0acGGDuV5W/p/T/pybOpvKv4fjC4M9B0mTmJiHBZs8HfJVY0LnxICRGGGkjOPo40WlgoHUWlHSRxgledBRAjH8QIsMAx5NpWZQDowEiiTULqlEVHeTKoas4jtSGJba21XFa2JeqXIMnD/AKr5DiU77fW8qVvbAO85M7cQzPoBW8rDdPlmpd2NGWmnW1tGphngMA+CD+Q6YP8AjS/8KNjuDcFPd4IY1CPEz4iQG51KpvPfdxVMy8gZnSXL91yF2pOvLdtUmR/EKMqURscAnHoVO3lUlGDthgVSiDIiPGXKZF05UxsdbuoCWIkUwQ42WOSiG2owBWIQZMZB13gqsndgnfanf1qAf5yi0hkgNQzUWIZh1IjUEXmHRaScVSveFe8K94VH98p5ca/OU+lfnKfSvzlPpQa8p9Ki1xA+lP4selEGrEIfvtPZg6iRe0nfjVsDXgImI2rC8pn0qEhdQkdQwGatxOvCI8MZnkRH4yn0qlKN1A9oZc6v9d3CPa2lNC7pPyyAUJmtDTyF+pGla0TVnKTDQCXPIqMLTy1c0d3kx+8qRMIMcu0Qre+/qrvOVetECYoUCzSGIjKplJ9pbBSp+UPKFvbTMNJqTjGpUMeLUQdvI6/h9G3qggAuINT9EsudVd5+ZPOFlZUKbPqqx1F8gzu+xVLHyxuu43nvKIL1I9mBIwzLg8b8Sq0vLtG03Zu5yI6YvU0bHP0uMqvLevnq/nGq/iU5VDoL/QEW0oU7mpWqVOOc5y/aJUnonRzlflgm8DrWFH1r3HrWNsFhbBYW/WsbbrX5YOvyoWFrEFaTQ7PEsLcetfl+tfl034YPy4ofha9WkQQ2mco9RCsvwnmitebspYGlXlrpmLMxidoGXErPc/m4nc/nGIFMVJAClUGZ7WEc/ml3Wicobt37Ug9re20IxhXfGJrCOAEhmeNHcnm2wqz3MS1C6EXp1f7YDDYpbq8ybqjc05QMXcAwEh3gRiWd1O/8l3MrrdYl7k4GMWfVxnmU7PeVvOhcxOImDE+tOC44KNeOdOQl0Ldd9XmDWERFhsAwDoh2wl+yVLmHUFVi+VMn0LUKkele8Drcw/8AtK3Up86v9/76vIW+6bQCdarMtGENspE4ABR1f1K3c3+JH2rzJ5h8q7yheblqUqEYVYF4yMabFiM2JX9PvMnmHeELXc9rea6tWRaMI6JBz6SrnT/UzdZpymZAxqhmPHyqw3vuut4m77mkKlOWyUTlIchGIPEqHP8AGvOYcfm4jrXvAg9SOHEpRicFhUD86tpGoCNYW9ruwJp71F1AwqAkEDxOobedW1Kpv6ZpAMdbzJH6rNp9LqM/Me9KlWlTJ8Majg+eD8SzLLEBMQGWDLYvKHmKPetbyBB2jUdOHSt27/3kSKU6XjTYOYwIB1NxAZqz3PaefrM39eqKUYmYHbJYAknDHBRr0yDRllIZS5Qdo5UVqlkvAo1BI7WU6Ei8acQw5UFAuHKCgIs7ow1AYJvEDIxNQMyqxFUM/wAaxqDpV08gQylITDYrGoHUSZjNBqw6VH70PzqH3obSNo4ljVHSF7yKxqxXv4rGqF70L3oUfu9nGV7v1le79ZXu/WUGgekqPhyb9OdEeIVjM9KBMC/OokQOfGrGnKZNOVF29KwpP6SoSFIAiQ2lWwmHamE3hesqlpDdobeUK/Mo6pauVULLdm7zWuaksIhyfQysam87Cpu/dFVia0sovt0nE4cS3d49CW8N60IRNSdQRMZTAxkAzsdjq23Xumzo29lSHdhCIcbAS2xCrWaNAYyOTAZlzgMNuSvrW43tK53lCJ00aI8SWrYZSiDER2Orrdnk+jDdW4nIiQSaxO0mQOljsGYU7jfG9rivXMiSZ1Jyx+qZafUpQr3Eqjlw7YbGwbBYRxQBTzKz4M1hLrWMutZrApkyxKfgzTsVtWa7yd8F3sUJ0iRVjjGQJBifpAhsQrDyv/VW2O9vKVSUaEbibmpQADAvl2W2haqVW08x/wBP7iB7EhGdSiZ/QkA4MNqvvN39Mag3j5OkJVPBj261AM5jLkjy7FUs73TTrt2qZHbHOOJS/iW7YQvJBvFgAJcj7GHMql1bW4v9zg9+kCWGx+YZqcJw0zBxicxyFTYOGxW9NxVJarUNIPmDycnGqkGxxG3iQqk5t1BXQc/lz1he9Lci99PpW6JvjG2qBQqyzkv6tgZ/w09DhDBMylASMXGYzUYuSw2r+nAybc9D9kKgRxrzyQe1+KHxr3ypvUwddiZEl70hWYlN4mYW/B9G5pj/APqK25hwD4Nve/8AJqwn9mQPxLcu84QjKrfbpjRAGTTptNvS3KvMu7rOpUpStL6YgQS8SJEgg5uFbeWPPVM703BRhGNOZ1GtBjtORDcmaq7v3dum+/ExtjUIYZgs3Mrq33LuqIvTHDxHZtuTF1DeF9WlK4rnWX+a/wA0cgW8rqrECoJmOGQALBRDuVbyGSwIVMjJ12S0l71e9LMqv3hzXfKutcyQQiYTaLr3pWo1iCEwqFRHiHNQ+/k+kI/vB6Uf3g9Kwrkr3pXvV71e9Uf81p5L+a01/NaaP+a01/NKaAG86bcyJG8odCL7xggP4rTdR1b1ps6sK0t5w0Sp4HkdHTvWngqfibzgYag4VoLa9hD7sOShH+M0RMlgNqt6FtCdxIkNGnEk55sB61Q3xv6kd2bmngak4vUJGfYOziKFxS8vxq75pg6a1eMZ45GQd21ZgbHXg25iKcBhGIwHIBkql7cagK3aAlmxxz4uRXXmPzB24DCnSEmlUnsA9OYVxu3dtzPdW64yMYilI05Shk0jHPDAqde4vKtSvIMZSkSSOIk4kKQAz4AxKdys/UFku6FkFksgsgsghiiDwBkxB0rU2CGCyWSyWSYO3OgFnijQjIiiS7Pg52sre63Fdzq+WakgLi1MiYyg/wDdx7sTm5Cjvn+n19Stt6GLV7OqczLv6oSwntyBVXfnkalCx/qRrJlExEYVCxdhgNJOxV9y+d9zVbXeQqGOMWhMA4SiY9kA5jFSomANE5xIBB9C/GW1KlZb4jDQJQiIxIzeQiMZPtOKu7O+3ZVlbEkeLpcGIOEg2w5rdF9WuDS3dM6CdWBnLuiURjjyhVRGoG1EvsOD+vZyqnzj9kK6iTj+HPWFjILvBbqY/wD08z1qhzLf3kvfxn/Bd40fCraCYz0EgnTIZFwEHud6D/48l5g8jeUjVluW2p0ZQNSRlM+JDVJyeVeRvJHmapOG4t4XRhWMSRIRFOcnBGIxiMlPRHedMgs0q8nI4wxyOxbp3DumMhu6ytoUKYOJEKcRGLnaWGJ2lUX4wvPcnyuR8a74VMCT4rGaxqBWbTHfC3xufc1sa+9J3EDGmO9JpuWfDAYlRtLqmYXNKRjKJ2SiSCPQeDNYAld2S2+tbegqpCZ7MokYvtDLy/aVaIjHd1SdGJ2kRIDnbs2rztbU7ICyv/DuNTACGssG522KEJEdolVt+bw3dO33RXsj4dQkNJ2IAAL4q0oQPanIDnCvKndNO0wbBi3WpynUaUiSeMuomFUHHjVuDUDEceSGm8DqE4bwiKYOXGjRjcRgYgEk8oXa31SHSn/itKphkHVYm8hTD7UdO9Kbq7/EX8ThsUvC3nCMHwBX8zgo+LfRM3+IIgb0p8yiZ7zhgVAyvYmWkdS/Mo/vK7F2EdO8YgL+YwX8xgv5jBRw2LJZI4LNBpsji/pR7SB4Nxf4J/a4KcYd5wrHd+4fL1zUrmMY6tB0Y7dWStt+/wBRt6QtreqH8MF6gHFyE7D6VG23TuSjUuojT49aIqVG4tUg6NC2hSjKOAEGiG5hgjTp1CInNEVKjgq1t9QFWnTEW5lUjRqzjvPdsjXpRBYVDtiejBX27d4286V9RqGM4SGmUWJzGxxlxhEZLNZrPYs13lmsF+ntXeK73qXe9S7yAUn4XfBZt8DPgfUgdSyWXBab18l1atPedtUjLVGR06YlyJQGBdl/Dv6p7i/hvmWUNdteeIPDq4hgRhpkXdlKn5y3ba3JqUxGnc0mlOJZhJht2nFVq15QneeVakx4FzShqMtRwE4jCLZZohiGPpHOEKG/bKlXtSGIMQSzZKp5g8k334O5hIzlSIcTmC8SMRp05crq2jvC2030YgTAOoOMCQduAdWg2EfErs//AG561ocrNbp/9LP41QWXB5l8x+UvIF7ebkqUaEadamI6JmNNpMTIFwcDhsXkDzJ5r8g3lluK2uyalappEYg05gO0icSWwGfIhXq09Esm4mWStw2Drz7TP/7kfGs1TlyqcGxEY+sLJWAA/vAr+RwMapA6Vvi21a3rTm7N3pEt6OBlopM3Gvex6AvejoC96OhCFSYMHDjmLr+oflm4m3hVKVWjB9khIzYdC8j773dH92vLYwqSGRMA4BPOVZGsTMGoAA/0sPjXlyO0W0Opbthpdpv1LedIy0mVNgOPBYus8FbY7OCnjtCqg/8AKh1LNO+LKrji/BdE5kKXPwRk7gy+XgiOVU+y40jH0Iosi5Zd5/SslkskP3uOXGsLuPSvzcV+bivzcUB+MCL3celfnIqjVN5Dw5h4l8CORN+Nh0rcFj5S3Bc3dc02eMJacS+MmYYbVSut+70o7sswRqjKoNZfiHIrDeHmi8uN87womMzCchGkZj5shnKPGAcVo3B5VtLWhAYCnAYNylzhzqrC1OmRiRlyMG5lI17yrN+NOxMjtJR40HIUK1GqRGJyBVGlW0mtFnBO1bw3tuPdcbT+oEoGXiwi0LicYtHxTk+AAZlUsfNW7pwugWcROjpRoUqglVGwHELuoPFOYrJZLJZLJZJ+DBSHCQ2KyKyK7pWRWS2rELJZLulEsvNvnXfFrRrmN0KMITzGkCUpAcRBYqdGt5OpRqMdMqJlTlE8YIK8XyRv65nugH8tXn4kREZsTi7D2qru3zBueFShINK2nHUJSAZ4g4u+TK98+f0uAqxrPOvYTHbpy4qVPN2yCuN3XttOle0pEThOJjKJGBcHEMUDIFSJjiSoxOUQAFdkywNvIelwU1OYfjQFSQEONlu3znve7lPdlCtprGA1yjGeDaRz9C3fcw/qTbWs5UomUC0SJEYuCc0ZH+rNrp+sPatf/wDFa3NM7XDLH+qtv9r5Vpn/AFUtseOT9ZQnL+ptCFI7SzH1oEf1Tt25x7Vfbyp/1Go3FenRkYwixkZbNIBzW/8AzhQuNFtdXM6kNeEjDUdAPK2xd+KpREgQ6qASBmIx6kfvAt3iMh7wLeMDIOax61b+bbWAAr9kgce0niXKjMlgFICWKIGSyWSyR3BKTDedlUpj62Delbl80Ti9Wz3gKeTkRJYnmXlzcUO9UrRkeSILueIHJWW7bQR8GjRhDDJxFj61bS8MmnTBJ4gdjq2t6MgI1CAVolVHSg96BI8qtqcagMdOaLzCpsXxGxVoCQ1eFDnyWo3QBdavxcZFslUP4gQxX56PSrvVdxIZSH46OaaV6OlQH4sCnrOOzJRe6i7cai10HfjUKZi7RGPoRdkdJA6V72I9Cc3IX5xfnF+cQ7RXeK7xXeKxKDEolyiz58a3V55/qLeVp7quYGdpRgZREo5ucgw4lRna+S7evGAyqxEweUu+alZbm3Xa29qdlOnGJA2AEDZkn4AK+NE5heJTt2iU2kxPInhXmAiPxVTpRIup9KY3VRRrUryowPKoUgeyNu3p41dbu8w7nhXjUB7ZA1gkM7q73h5J39StaFRiI1ozkQduMePldSna7wtq9QHCMXiSON5FuVUaEfLkriMoPqhVpsMcj2ndNU8nXQHOD1FQN95evIiQLNCUzh9UFfe7qvh/8Gr/AOFEfwy8/wClUHXFGFza3MJDjhL2LtSqA8RBWE5dLda+9qEcjuhGLv6lyIjhDDYslksuDP4RK3rOds9We8amLjbTGwp9FLpCY06ennCud5brjRp3dKBlFyANQBLZgYreW6RfC18zW1epSqUqkgZVKlORj2CMGLYOq19uW3p7u87TiT40vdyln2xHEvlzq43X5m3RUhGkS9WIJpkfSBbI8uKhUoz1UztQVQ1af3kokex+R8VOcbmEzj3QXb5E1awmbQxc1HDczZlb/wDDmZfvUHcGPzeXNAmIRgIDoVu8RiRsXcHQraMYDvBbp1RxnIgIDSGURpx5lYSbYEypgDukKvAFhoh+ytisHP8AeBX8Y5eJLrV/une15Rtt66SaRqEDtZ4DM+hVrKnVjMwPeGRbiRGssnJxOfwCv6eb5jX8MRvoQJywkcV5zt/whrX0aYq0oDOUm1RjHlOxU/OPmiwFPzBWiIUh86mB2u11YbVTiCzAPynat87xlMaYyZtr7Fu+1BwJRD5IOTmrWD7FmnKuQ+Hhw6lnghjsUzylZq8xLMpYlZnpVLEvrUOZU/rDrWA+ZH9kKTHBd/asZLNd4rvFd4of/kOziX+ofUv9Q+pf6h9SGrzDgomG/wAk8zovv4x9D/Ejp8xFn+it0+Xd1708a/3THw/D0gSjHIlswMFhmsZsiNXWg0utE6utAGWPpWeHpWEsfSu91ognrQaXWj2utAGXWj8qGiWKxrSbiWBHpDrGqh4lOJfjAR8SzonniETU3ZbnnhH2I+P5csJk8dGBPrCa48n7vkP8GA6gjE+TrMDkgB8SMq3lGhyN2eoITqeWJQk3za1SI/4SFUnTo1qdORwj4syByAnFPT8UD/EkpTtbu5jEuxEhIDlDnYiR51vgOLw6eHS6Mj53vh/8OmpD/v2owO2EQW5cM1H+E+cxOO0yiB1KRtfNlqa2wSBx6GX86sP+P2r+cWDt+v7V9xd2dU8WqQ+JPG3o/wBmT9DqdeO6DcTDdiBjqPM5ZY+RLp/rw9q/0Ddfbp+1YeRroemB+NXG7P8Atu6l5XuK/imlGLyEiACxiTmByAKFS68h78hAlgYwnLH0In/svf8AgNtGfsW8dy7l3JvLdm6biBhOYo1TUETnpJj2SRtGIQ3zu7d+8qe9BV8TxPBrGRnnqPZxc4l81Ly15x3FXhe0RheSpypioAO6QQAJcvpVfy35i3VC6oVwRqnjMP8Ar94dKvd6/wBMakbjc7moaGc7amMTpfvxjiXOPIhayifxNHszLZyzVOt5X3AKu7ySPFlJo4Fi/MqVx5y83U6ZAc0beOoHk1TYhU7ar5RjcVSHMq0hPVsdsQByDnV1b1PKMqFerLVqo1ZQAkAz6A0ThsVxe+Vf6i3FCYcxoVKcTDmM+9hyKB3Dvi13jUOJhRfUOQ6wA/pVGlvzyzeUfDJciGvWwyAgZKUN5SvbapEsRUt6kPXKIUBV31MVAXDxAPQrO3vN+TNGi8oPADE8qP8AmxMeNBt7YureM97YxAZN/GFDw96EnUOdXM6m9mqaIdSEf4u0dp4lCreea4Vr6lE1PDBAJ0hzGP6xyD7VvWr5O3V4FKdQ+FKbGenjkDgCTkyjfeYN5Vat3qcPItE8g2IGpImfH8DErvKlZbroeJcTBYcy3Jc3Eadn+GuqdR6hkHjEuWI5lQob0lGpTlawJlBzEmEAARxgN6Vcyq78k8KhDMwAfiT/AMZJKvLqzuTKpUxywLbVZ1LnePhyjIhlI/xlD/OVak72+ah/myBo7y1y4lXndeYTSrSpweOnk9Sb/uWXQhKh5glKo2TFVTc7/NOT5Mg/mQ9CvDa7+M+zxFGVXzIYzfJk58yF/qlRoDfRlb6ndmQbzIcNjKm3mST6hsPHzI+FvGRp6IseMaQpfvp6E895t6ENW+CPQu35hI9C/wBSHoX+pD0L/Uh6EMAsgsgsgu6FExIHMi8iUX41uuO8a8qe4t6k0q0gHETMsDLEAR4yqO8LWoJWVeAnTP6hDhd31IERCLxx5k0o9rnXcHSu4Oldxd39Old31ru+td1d3rRMR8ay9SyQLI4BAaURsQWayKfFHNZl1iVdEjDSpylhEEkudic79tBz1AFq/wC47BhsNaKNOp5otTVGfbDOM8XU6nl3flpVpQwkfEA0nYFUi4lpLOCCOcEbFyrHh/38H+9B8vSpQMAW4wCm8KHpA9iJ8Gn9kexGJow+yPYpCVrEjkjH2Kr/AA60/eW7LRAx5WCEvwM348VfG5s5igaUtRL5Ni631KIzvKn7RV1StaRNCNSocPrOVrp7tqTkCzGLetkZ3W5oGA2Yk9DLwrjcVemfpRgPaFEm5nAMHE4tn6S6hOhfQOriZAwqQmOUA9boU9/+Wd2X0f8AzaMCRyu2JRu5eTYUt4k9+hPwgOaICNPy35pubeiIsIVI62PJMkYcjKpd+Ut82t/a0zhRcRqSB2gY+lTud5+R7iUIYtTiZEgcQbFUKu89w3VqI0wCKlKUSC2TMmNzCMxsJIPQypRlBzKQA9JVd4tExiOgMqtWtIm/u3jSjEOX4yHwHKp7x3hI+LqLZ5Eo4hAnhDrRTplCMg0k0y5UZ6O3TtpkchduorAYrddGhqJt6HhzcN2sXbjGK3jZ1hHxTVJwyY5KEWxMx1hWOmAjIUScPqrd1zMvKVSp6pIkhBoh3Vi4xMcUMPUgIM7hXcji9OHUsvUogNpALquNoku6t6SEQ2lTk2L8EZHPWehgg4DfGqTZ6h1qIID6I/shHFHBZLGAXcXcXcQ7RyXeXeXeKDzKjqqle8JXZrNzrdflLy9vL8PQifvrn5sIghwDslxLy1/Ta/rzuL7d1nCnCtMvKtTAwqE7XyfkRhPMJiME4zCBqXfhk54Jo70B9DIR/jVOJ4sHQ0b7t25ZAL+dW32wuxvq0PJrCIF3byHH4kV+Zt3/AMSKMp1aJHJMFMQX5MV3Sh2ZOjoozI5k4t59CJ/Dz6E8rSoYgfRTGzqAfVKOmzqH+yU/4Sp9krG0m31Su1TlEcyc3ZB5l+c9RVSh+M7JidnIvM1KhdHxxCrThgc5RlEesqdtd3latZaiQDKT5niPQi9ndP8AqzqD41GtQpARIcaq2OOOOOaFCnc3dIVe0fw9QkFsO0z4rzHZeeDez3GaMtEromUvE/ULBo8QRYrLFP4Zb9ORPMEJ4k6lj8SxJXYkfSpSBwK1PipRBxRkxdPKDr7uDFdmth6MFvWEqzg29TZ+qVvqG38ZV/aKM5UBI/jJwdthJPxJq+6aJB/Vx6UadfcdLHM6QpVJ7jEZnaCR8aj+Gt6kQOM5Im03hoPESnt98R086cXWr0rjZaam7Z1IbSAvDnu+vRk+YVKlcyiYRDYgF+d3UaO+tw2N3TlFjGpThi+zAAq8FDynSt7usMDRaOknbHiQr+S/N8o3cZCQpV462IxETIMA5wfYFW3hfeXxXs4yDyoS1vyRAxJ4leWu9fLO8LLdFjPw4yr050wTHAnSRjySyKJGSKHBGluXdczCRH3kgdIBw1cwVbfe995eJviGAjAtFztI4lSApvMhyTy5jmGxPGICCvqVctShZSJb6y75ZbxtaONCFcdBVWtb3RpVpSxc4ehUJz3rHRGYJyyBBW8o20Tpt6BAfHZt6Fuk3Ui/iVMdmJxZSjGZJ2cSGqZGIVnAzIAGC95J0/ikFXcPFOkU4dSwqlCUKpMmVepOZxJWEyt5nxD3VP7w5lYTKpxEzoMyPUonxC6pDWe8FEmoX8OP7IUmkVjLrXfxWFQr3h6AveHoC94egIcJQwCiskX415duLi2p/wDce9pU6taMoxMoasQBJsQeQryv5ssYgXW7pRFQjDTQLeJqGcnjkMWKs7m3jqhcUY1YnjjIDHnxTTiQeZd0tzKTRPQsafaVe8hvOVCpDPSQ/rRlT8w3BI5I+xEUd71Zc7IGtdGRHEVEzqVDzEoCnOoJc6Gm4nhy/KtQvKgPOU34yfSnp3tQnnRhT3rVhzMfiXZ39cdEfYg+/K/RH2LDflcy4mj7EBR3m9PZqhE/EialzSkfqgdQXZ/DnnBWMLYjmK7e7bKR5dXtTnclifte1NLyrZSjzy9qNSr5ct49k90lyeLEst7WlTcVM151NQGOIdRFz5SeoNoAz9KHi+W60Zc0UTX3LcxHJpT0tzXJPLGJ+JGFPddzCnyRj8QQJo3nNpHsXZhdxPLEexYXFzEfVHsTxuTKP6wI+JYXNMS9K0/xCiCeMo/5nQHpX80t+lahvW3HPJvjQ8Pftq7fTHtQFK/tZ/24/wDiTyubYR+sD1FdmdIjk/3po+H60TGEDzOt51J2sdEaEyeYRK8wxI7P4yt0ipIKNxRtjKmd4Tx5jJMLWRH1T8aBlZTB+qT1OsLCp9lfe2cw3GFKRtixTSpMUdIK7R6liYE83yIipbRZ+L5F27MkkYELxLG5lT4gShVs94agMQpG+sjVjtO3qVOG9LUweWU4Fn9Ib0oDzh5U3LvChVDGMqNM1S4bNnfnV7vX+kG9K/lzzFIGdKhUapaymST29IlIAvgAVcXP9RvJtSr5cJPh31pE1becAWEiIapUn4qjFbp3Pd3Bt7e5mImqQWgDkT7FY1zam+vadIymSxhLDAtLDFXUN3WMLanqI0xADDiwV8SqEjnp4At61X7MrGQ/4gj2ivMUTlGUD0lGZGKDBeapxPdonqK3Hsq65H1rSVFuMLdxGbIhyzIc6u8P7qHUsJFUzIOXV5ifey61sW9S2LKf1ivYqAOfiHqChxMqX1ggH+ZH9kI/DH3k+he8n0L3k+he8n0IPKooyt7ioPQtMKk5c7KU5XM6fMAVuCrPyxRNnY0acBATl29AYE4YLzld3e64WNa2pwpChGZkJZjV2mzVG0uKY/H7tqSoVJbWMjKIP9khggRkjhigJRRJiNLHqW8Ibupyluw1Mc2zVKTiMmcjlUnrFOJujxsmEO0jMhooaYLToco6YBuZe6T+HihrjmiDFAU47V3T0LuSXcK7qxiQF4eg8/6cS7hZXEJROhl3cOZaYxcIwqU3JT+AH5guxSiIvhgMk0IwfbgFlF/qhdoRfmCPi0Yk8wRItYk8sQUJVd30n+qESLOmB9UJ/wAJT6AiZUoxGTCIQkbYSK/KF/rELG2q+ipIL7qdxD/4sigIXlyDyVJe1fc73uv+pL2reJlvW4lEUZuDORBAicDjkvMGH/11f/5s1uzd99c3EvEvKlQ06UYSMWkdhLtJA0937xDj/lw9q3lX8vXlWlUtZxEqdWnDVpJA1Njhj0gqUaUqEqQOB8OLkceSD+EXzeAQlKNoY8sI+xflbQy+rFAfw2xkeNh7F+9bis/QAfiR/wDxugZ8gHsQ8Py4RL9UhH/8Zq+icfagZ+WK+nkkPagLjyxdgc49qMf+17g84C8C58vVIY4EgBijcbmr16Vw7gg5cjZIUd5WP4u0A72ImBzZdSvtwf1TrWk93XUDrtrmlGRIyk0SC4GLcuK3/wCd/wD/AFho1oeSpzqVPAk5MauvVI0h82GekDIEbFQhf1a1LfVvRNKvTmAJRMQwLHFiro1bioZazkr6W7qszX1xGOTKmOLDhqyvZGNt+GnqIzzw9behEeNU9a80y3ZXM5CUHBzBco48SDlebPHm0PAPUtwCpUqEapYjHF1U0zq6+ZDt1c+JWU5VpiIi4G1kDE1n24JnrD0OroE1tPhQ2ciOmdXTyhUraFSoNRzIyV3G6uZkmrPLnR+/qaVvP8LcTwjiSqkbivV8RzkMF2q9R+ZUq1tXl4ImcxjkEIG4q+KOTBUv3irq1BEwNQw0RbA5aQiNFZ+YrGNUehDXKqPQu1Vq9C99V6F7+r6l7+r6kPvo9KwqBd4LAhBouowjfypE7ASFTFrcVaxPEXWuO5rmUSM9J9qha2vle8q3BwEY0ySTzLzPPzD5duN33dWtAA1YaDKIOXKAvO25RVEYxhCsBl4si0dTbSBg6c7EHl1I9pVTCXaYqraX5FK5JJLjEp433qQe9Ube3jrpnOWQTvwMck4I5lizruj1LIMgMEdQigfDg67MIg+hY6VkOhPgvm+pEdluYJzAJhCLLeRmB4YGRU2gGde7DrGmFjBwj2V2RisYepMI4cycQ9Sxp+pM7ArCfqXe9S7/AKljIdC73qRIliu9hzJyVyuq2+Ly18WjdQlRAIcAyiRqPM/St4V4bxlCnUqVKrjb4lQyAw4gWCHk/ee8runv2jqnOrAltJLxcvkAUdHne60fXK32bLeEr78bDSZTOqUeLHiBxCpwlDtRi3QpQlUjqGe1gvDhT1y2K8p7qtJ+KHZ1KtTq14Uwe6CR6lRpb03dUqzOBIDnnKp17miNJAxOzjCiYGIBzXZiWRkYldqm4RHhBARhi6PZY8yqmrbRnPSQHDrdVqbfw6Ut3vCLMGEjqYZZupClHVbn5oGBfk2FQ877os6dvVjTn48acRHxdWRm2Zjsw2q4iMtZOPOr4E5kKjPjHDdD/wC2l+0i4LrzgaAPiaoa+dypehBebIbfBPUVuGVNgdcs/rImUgXUMswt36SBT8Mczph1IAyLuroN/dw2ciOpn5lTmBEzBV3ri/308+crERC3rKm2rkXcDrERUYgYa0JCA/QqkdPzh1oBsRCOz9UKQwTsOjhyCyCyCH7lefbh/wCFdm0u/tw9i/LXf24exdijdj+3H/wpxdXMfSD8SowoVas+cj2KzOsx9D9SpCpd1ctlKRW673em8N021oKgD1asISc5DScSTxDF1d092bxtTShgZRkBFwNg2863KN2TgYVbaQqyiXdpYI9pZptQWjYjdXNlGdZtoX3W7qQHMvydPoQMKMY8yInNihKdyOXJTqXN/GMIhzlkFGlbb7pzqEtxN0qAtb+E6hDsCCmhVc+hHs5ruLuLuLuL3a7rruLuIAwTZMu8t405zHiSDhN7PgZeoJ2XdXdXdXdQLHgyKyPBksV8iIU6erALd1CMgR4w+JUzTpmXZHqVenWpmFSraSwOeBCxKAlUACNhu6BrVp4eIDhDjBGZ4lfb88y73p0LcAntyEcOIPmeQK6o+TtyV943sHjGoZCFOMshKUTjKO1hmFSnuqtQ3dKJJJpRLywZpO+G1CvHzrISGwxHsVtUuN9291S1MYThgR6GL+lW0PPW5jE4OaUtMGw2F8VRoeTd9Qnezj7qpIU5uA8gBLEtycShO8Ou2fEO4bnU69k2lskXCBnEYo9kOtUWVKeAAK/px52s7B5yrTt6lQZRiYmWJ5TgpQERpVehCOqlPAhVK8KVQVJklgt7WlsZi9pzYg5f71bja3DXlcGQo/hpvpzzw9efIgRd1x0exbyoW+6K1x+KnGUpCYi4iXIPKUd/W+7p21M1NOiUhI5cYVxcGOrw6cptxiIJb0svMW5YeVK9OdWnKGo1Ilsw7MvL0LmnVeRmcxkMf96i0a0X2g/EojwLwsRiJxY8uS3bTFC8bRqfXHLoWNte/bj7EALe8BcYmcfYrqXjXOnw4cX0eZP+9S5BKIb1bVTiI3cDqz1ROHQrwyu7g/fTww4zyLtfi5vxSiG5MlviMZXdOQbOUS/QMEHubk4bCPYiDC8J5Jx9ip6Kt1A+KcCQcGG0BR7VzLDYQPiVJ7a9bUP7yPHzKM/Hu4k044OMOyNrKTXlw3OPYvzdz6vYsLy4HR7FjeXHq9i/OXPqX5y69S/OXXqXeCxksZI9tkxqj1KlXjIkD9Ni3bbX1OnKILdpvjVpVsNz257GP3cZfEVA3fl6xqXEZ6hqoUyQRtxiqspbgttcy5aAHqAUbjd+6aVOuAwkIsWJfBEFAxDpzmnIxWSxCyWK0wjIvxAlyrmvvHetrbSiM68tLN6Qq347+p9lC9kTrFOeoehiQQiLf+rFDxo9rF4+tgCo1tzf1VsKxGGnxQZYbNLuOhdgmcXzGII40BKYBWBw/wBh3gsZLNG41dg0secHBe9CxrBP4wZe+HSE3ihD70L3o6V70dKxqhP4oXvPWu+vegekInxo9IXfXvR0hY1Yvzhe+j0qXbyCrNNy6soyxArj4lQbEkBRMjj+El1hOKgdSlZ1e2o778xGMt9XEpCjbagZTk+DxdwDm/rU7jzHfyhuyOrwLaJ+7pwkX2d4n9ZyMlpERtx2vzooxI2KOHaBdCzlEeFtwxVK+3ZvGta16cgROmTqGOWHH0K28neeC1SUoijdSIAwDaZvgNRzkVRu7WsJ2txESJgROBEsmkHBHKCqF2ZPKYxWBWaz4L3cm7xKpvq1q+PSpgO5iMwRi7jJXFnvrcl1Quqc5RlrpTiDIFiRqAcLXVpyiOMggKlRjIGUjkMSX5FvTzTayEqVatpmI46ZZl+Ll5VbuM4A+gjhr44fhpv0pjUVMmYOfEozBema5Y+hbyP/ANtV/Ykt5DUwMpdZXlYRI1mNVQcF1HUNoW69Jw8D4gsAWPIhrjtV2BifDh+yE+gqkSO06uxH/my2cpWMfUVvgkNLD5Fg+Scxx5iqbDDWePkUcVSBl84dagI93w4fshSOpHtetZlHBd3rWXqKy9RQ/dK7/wCIU5tq+n/EdaTY1ifrLs2lYc8l7ip0hCnaUqjHlAUN429rUE4Yu7/Eq1hcVq0BTiwy+MqlKzqCUZxBiRiJAh8PasFsWQWXUsFms0yB1+pXu+9/7zo2u67eBlOpUkIxAGO3M8gxW89zf0f3bCVKnOdMXVeIYth4tLN4n5rst4bx8z+brqQrZ0xOXhl3caXaI5kSbaJJJx28vSgfwgX47d0q1tdg+8p1ZRl6WVnCG9f4juCOE6dwTOchlhKTnAOyNjohuvzhQIiaFXTA1jpcyp8YfDHaiGYegodnEoxMMQhIBYRXajinhAssIF0Tol0JxCXQu4XRlMfeMsYOU9OBbiXuz617s9HyLGB9a7h9a92ej5E5pFe7KBECj2ZMu6U0qZTikXTGEljTKxpFN4ahGci7KcjtVlRH/MfqVDmCBGf4OfWFIxl2nPWr7zVv650bst4kkP2pEDKL4E8i3v5o3pU12pqShbwJwp0oyIgQNkjFn5eZFFmWIWGXDsTYK0/pD5qrz/EDSbOtgYiLDTSkTiAHwAVO3MTExD8/KOdPQBZdwtzFYwPQU+gtzFQoVYzq3J+bAOVGW+P6X0b+fHXoU3+0xKqW++v6GUBIdzwgI556mAfkUd6Uv6VStq0aMjT7WIkxaQG0A4+hf1E/p3cWoiRc1jEEMxMjOBIG1ixK3juXeMRGvb1DAM/cBIj6lmE6r1b2MjbxtajiJYuSw9AOfGF4t3a150hgRGTFyqV0bO4FCTsDMvjxh1S/AAi0NwWfPJbxlJ9Ioyds2IK3zSjRmCZyyZszkvLF5Vo1DbGFQAAhwRhio1I21Y0hmNQCjOna1oxBAxkDit2fcTbwRtWNtMelAQt5Z8auZVaVQz8KDszZbPQvy1V/QqcYUKgx5FePb1CfGltHGUPuZv6FvZqMhFCJt6hwHEsKE/UoHwJNrPE+XQog29RvQqUYUJvqGbcaifDm3hx4vohSe3m6Y0Zr3M/Um8CfqX5afq9q/LT9S/LT9S94V7wrE8AAUKlachLkkynRgXgRxqt/C68oCRxxdeQvNVKpKdatu6lKmdWBjOA1k8Z4imMVtWCyW1ZJgMU8R2VvXzb5mvY2+57SOqUiRifmwHLI4BXW7LS9qWvke0nKFK3gTDxGl36pB7Z4idmC0RyAZF0McX4QrTfO5d4VrXelGQlGrSmYSGOTjYrXyH5+uaMPNVuI0qNXSI/iBEMBnjUIDylmcUMA61MHKx0usdPqWM4+pYaSEwpxxXu4uiTTiykBAKdIAABYUwyeVEOmNMfp6F7qP6ehe7j0IahEL5vStMhFvQiDpf0INpXzfUti2KcIwDBYQC9z6ljS9RQelg/ErfwjpgQi0wVZxlkJKk3IhES7f4OeHpCMKcWIxPGy8uf0t3Nc6KFSlK5uW75zAAkMg8RgqZicCMFIqTppHFEg4LNPw7j37u+uYXttcQqA8ZicjxjkXlrzYTT/ABMqEaczBgDKIxwQMqQWNEL3IVaVGiNWk9Svd7ypP41xPRL50RA6SAdmIdARuKmHHJVTTuS0ImR5hiqPlrdd1P8A7b3ZeQtoRj3ZEkd8fODnHjC8+b6nCEbu4rUpTjGOmGqVKJlpjsBcYK1/qFZUiLK5+7uGOAqfMYfNcO/GnRV+/wD+1l+0mfBUI6nD5K1gJDWLguPQt6Pl+GqfsSV+AcdcusrymWw01usqIElDHDUCt3Yf3I+JMQsBtCvOPwqf7I4KfOFdf4sus8G+X4gvQslSw/vD1BDjxVEnPUOsKGB91D9kKQTyiEOyFkF/vWazQ/yi+/6sPYv5Rff9WHsX8ov/APqw9i/lF/8A9WHsQ1bpv/8Aqw9ij+F3NfH/AONEfEETX3HfAf48SiKu575/8SB/91bj8r0LgjePlqIszGf/AChhAv8AOJG1PXu4PzpoVwSh96E4qhk0agdOZhlChSiZTJ2Lxa1AwpaXJ2Ntdbx/pp5YrmHkXdBAkASPEqAsSeNpOzrJj18FvawpTnWqyYCIJPpbIcqut7XdGMaFKmJFpAsJZel8CM0dPdQCyQwxVnv3ct1KhvG1qCpTkCQ0o45jjyVnvne0ox8228Y07iL4yA7hA5cXI9KanSpEDjBTxoUH9PtXbhRA5j7UTPwQfSiCYkPs+VCTrEoB8AiQcSqs4b0MKZ2IE76r9I9iJ/jNcjnHsTG8rmXOPYvzdfpHsXZu6z8pHsX5o+tfm1+aK0060GB2un8WiD/a9qxq0v8Ai9qGmpRI9KJE6L7c/agJVQ44kNFR02op9RUBStZ1Kj4AEB/SVSO/bG41jYCD8Sanuy6iOcexWd7uqhXxm0nIZQNKLyYKe8KW7fEuY2khI+kMyq723gI04Rpy1g5GDEkY5O2a85eaK8j+Fld1KdGL4QpQkYiI5MH9KA2MinZzxKpc0LbTRhHVIkthycarWFTGpDP0rLFYLBYbUGwXmv8AprfXcv4pRmLijAuQaZwJByDMcFpqLIlZqudOGg9RVvQlhKFxX9dWRWMkLagGjMtI7WW+d5753VOHk6pdG8pViXhIw7QB4iSGZefN6WcibWpfEDBu5EQOGwOCy3t5cuREwuaZHaYtIA6TyY4PsCvdy76tPCu6VSQYEEM50kEZghmKKrw3lRnUtDay1RgRGR7WwnDPFNPc18B/ix9ioj+G7wjLHKcfYqdXd0CKBrljLGZw27FvIiL/ALvUccmkv6nW8ZW9pdyOuXzo44nkXlN7C6xFX5wwz5FTmLK7xD94exRH8OuiHHz4+xbsMt33T+D9KPsWG77r7UfYg27bnMfPj7Fd+Ju+6fw6fzh9HmTR3dc/aHsVPVu65Ha+nE+pXX7hde+l84cZ5Ew3fckjjkB8S3sBu65+1H2IA7vun+sPYuzu65b68fYqdSru+6ETU+lE7ByKJjYXTH9aPsVIDd1z3h8+PGoPu27fwofODd0KRNhdD+0PYmNldfaj7FjZXb/WHsWFjdt9YexfkLrpHsX5C66R7F+Quukez4IBUSJlS7QUsBmqn9N99aP4H5gtxGJJOoXNP3YiMu251bURbUZCmRhmhOlVmA+S7NSeHOjEam9KEiJMOdEV7arI+lQqilXpSG0Bz6wy8z7yp3dwN4zoaKIIYamIOWL4q63re15VLu5nKpORzJmTJj0ovgjTttPjlgNWWe3LrX8av7qFbeNSm8yGPgCIc6M+9kX4lvLdG4bh6lZxKLggESJcbcTxrEMs0cU7IwdjxrcMd4X3+S7wakaMi1MSGEZEcWOKF/bRahVGoNkxxw5OLk4clkslksuCVVs1mswswswu8FmFmFmswswgHX8LFjWut9ibGEO6I8ZOzpUDd+XZQfLtP1Eso7/3dHTTgWqxPzJE4DqzQ4kDqWY9aB1D1qjcV5DsbONMIBR8GqNUqjsFbQ2Fgr6oMzZnrC8+75jdeHdQspxpF/72QaIQuKg+/LmXLIkl/Wgiri73zvClb2lrU1RhIh6oHzQDm6p7v3xAW1esJ+CwAMgIv0Nmt43lM9gzIHMMFmV3lms0S+S8u0qlfRu+9pzpVTtZni3p41WhOeEDhxtsPpCbWGXvIup0hUixcZqdnK4BBnKYx+kXRAqgp6BBW9vM+87GnWv7SzqmEpSkBBoyxYFi3GV5j3mW/D17ypOGLvGcjInpKOGxbr862cSBGUaVQ4Y6idAPIMW60+x1UmMhZy/aWCouVQY/356lvTD/AOlq/sSV/wDXn1ryiTxVesqCiOVbsiC7UB8XBb0nzmFc03w8Gl+ys8VRqGW1XYfDxZ9ZRW+pA4lvUmfYhIqhISzqEqmH2KiNRAMx1qEYkMKUP2ApdtHBZLMLGQWElmh/+ND/AKh9i/00P+ofYv8ATY/6h9i/02G/xD7F2fLYf659ijo8s4f4h9iJl5WLctQn4lIT8rxJf/mH2LyX503buUUbmyvqUxPWTpGsAnLDAnFeVfM9lvGjUtb6wo1YSiQdWuIJb0umlWgV2Y0yfQnjZQIPIEPxFvCEjyBS8MRPoClqoxL8gXk3y1SrCFxXnKuYDB40tJY7CJPltTsGKPEvvKulVI7o35OMJhpgzOI4m2qVWrGGsl3iGJPLxoABZLJEELFWd1Qi1zQqCcZDvDSXYHMAtity74obxjGNC1jCTkmQlF4iJ/WLIjxcOVe+j0L34Xvwvfhe/C9+F78dC1SnHoTGcehd+PQu/HoXfj0Lvx6F349C78ehd+PQu/HoXfj0Lvx6Fu+63hbVLerdYmWXijblnF+NYyw51vOnugFzXGoR2nDEgZqJERkidLHmKxBP9krs0j9krwKjib5MUNILKwst8iUasxqY5EcYVmRkJBVrPc4mb2VnIgQLEsR6luSx3rvEQp3daBFOJY1dBLltuna6psM4goJpFgoVY73FCtHGMnIY8fOrS03pvQXcLcnw6jkyDhszjkidp4G4ZLyvd2N1OjcC/ogSgTEsZhw42HJWdzK8mDVowk0i5YxDOUJC4DFaru8mDyIGrczdASuCXRNSWKqVZVvuqbaizs+WGa8wxr3NOr5n3laStqNFwZGUwRKYjmNMS8iWYKMSckXW86F9WpiVSpTFOMtsgTlyh1AP2nLqtV3jbmrbC1k8AWJGrj58fUv9N1Ptn2Kj4vlmcoAlwJl+pUZbo3abeyNcmLyJPFit5aiWFvUJYtgIyLelX8peW4gmc/7w5ueReUTU8tR0yjVb7w8vIqQ/7bGX/MPsUQPLQzH94fYt2E+WQPuB/eHi5k9TyvInkqH2KkKXlaoKxkGeoTj0K5G8PLRP3VP55dtPMmPlOp6Kh9ip/h/KsxjtqH2K9/GeV3JrTZpn6XMmj5Sqkf4hW9/wXlWUJ/rVD7EBc+VZGs2LVD7F/pKq3+KVSMfLRjaGeUqhfJBvKsyeSofYqWnynX7w/vDx8yiY+WTo8OLPU2aRyKWryyB/8T5F2/L0ftn2Lt+Wv+M+xY+VpPyTPsX+lZ/9SXsWHleY/wDiFf6Zn/1JcOSyQUEVLnU6ILRnFidrch2Fbp8lf1HF7e7nsuxQqU9R8OgMI0226eNbnvrned7bWV3TM9UqZApgEgAl8XZV50f6lWtvGmQD+In4Rk+LxBJccfKrSnY/1E3fWnVYUxG4iTMnJudGhf3kJVNIk3iDI5FAW86M57Y+MHCpW85xgJHE6sl5as9x39KpKztZwJ1jEzYehiPSgDMu2xRxQp3VwIEjDEdT9PEp4HwGwPGgxXKuRTnqiIxBJcswGZfkUKsKkTRl3TEu/wCnWhgqldvuo5lec/6f78AEgIXFGWoNMSkdUQNmlnPPksa8Ole/h0r30PUvfQ9S99D1L30PUvfQ9S99D1L30PUvfQ9S99D1L30PUvfQ9S99D1L30PUvfQ9S99D1L30PUvfQ9S99D1LcG991XgnRtjKnpGPeLu+xmZlmt47j39Afwa8oxEZGTCFWMiXbbqDBAxvoNzhkBC8okehfnaAHoR/zKh9ke1Ql+LoepD99pB+Jlb2e7ZiVKyh4c5Au8s8tmapzEcIlb68yb3lEVPC8KBOx2fFf0y3Tuje1GRpUa0p0oyHZ1adJPOqJ1RLxGRfYhwYlZ8IAzULe5qU415YiIkHI5s+CWK3dvWvKH7vc0qgjKQjq0SEmBOWS8p70uZ0qdatu+hIjUJM9OJzwWkb1phtj/KhD+KUz/aCjbirCoTt1gepSrbwvaVKsziJqByEKdTeXiVJAERoEViHDgSZtKurH+n3liNCcwYi6rjERO2MGz5yrzzL5x31XvN515mRM5HSCdkYu0RzBB83VXeO8r2nb2lIEzlUkI/ZfMqy3TuWUhuqg4ZsJEHCR5dvIEBE4KqBkbKX7QWSgAMS/xLd4OYqS6yt7f+lq/wDy5K8wxFSR9ZXkxhlGsFDDZ8ajzhbu/wAAfEp8ysf8Qdanh/cU/wBngpc/xKvh/eT61Jb45vjR5uCh/iexQVD6w6wof4UP2Qpo/AyWSH+Rz+1FfyKf2orDcNT7UV/IKn2oof5FPpChp3JP7QUm3JUy+kPYpPuSrn9IKAO46mX0oo1JeXpSqcZkP9y8qW8dwS8GVqXGofTKx8sn7QVobfctaBjUi2mqQ2OxpYehbtq2076E5WcIkivMOAMB3lE+X9/bwt5vn4okH5dT4canfXm+4XV5IGD6gSJNtbI4rd115knKvXnpmZymWAkcXYsMXA5GVG4oyBt5RGkguCOQocSN5cWvi3VAaoEO404uAMz0qlc29PTSgfDI4zHP4F/QtzI1JxYiLuQcCMMcVuu0pwMYwhkXwJLkYl0CCt51jEzpxozOkYksHwVDzl5WncWu+KdTKM5REoiWMJxJbTIBiGyVEVN20vD1Y9phnzq38PctOFKpAEdoseZzj6F/KIfaPtVHdNXckIyqZTMi2Ox3z5Fc2VTdMBUpyIYzxYFnzyK/lVP7fyreNH+D06YtrKc8Zd+URmMcSgTuumP7XyoTnuumYDEjVsGe1b28w09004Rp3cKcYai4EwS+eWC/lVP7XyoVam5ITg+I1cfMVum9juqn4d3TMx2stJY7eVfymH2/lVrZy3DCXiyYHUS3KWOCuLD+GU5GDYiWGIfjX8qh9v5V/CjuOEZaSdZkW6XZVaY3VDsyI7/EW40/8Lh9tGx80bho09wUqg8WRJwBkxkC/Gc8lYV7IiVGtQhWBGI0zyYjMYKFWjUMasCCGwxGxSoWe7qVTcUpao3B1TER9CWksMNpQrRqWnhcbTbpdfmbH/j9qIFzZg8gn7VC4urSNSsBiSSIuq1xPdQt9zUzH7xy0tX0CcC21XNaoXua8tdUk4ynx+pVr+vWjC3oxlKUpFhgNpK3t5f8qVbb+DirLTqd5Y8YwbiK/j3mveMzvCFMQiIzJiI7AMTkqka1yZ1xNyCXIDDHmKDFF8VeW297zVY3UCKEZMBGRyEeVHsoMcExK8WrIRp8Z/TBXXmGMpyo2kpRidR049nAZceO1x6TqWmIeRVpZ7xszdW2kGMQT2JjM9kjkz4lu3ydOxu57xsLYw8Q1IhwHECxx0xDR9ClKmKYfjMviK/FHzFO3wA0U8I4bWLlztxRjvHzNdVYn9bT+yxWi43hWqBsNU5SbpJUXk7NzqVdzIM5cq6p1b+mKlIHUBIag3EOPkVKtuCxhM1ahhGcsJjB3bYPQqlXzDvitO2fs09XYHoDJpAPxsHTHNeNc0jUoxs5PEYE9oYYrHclRvrRVMx3LU1Yt2o5srG6tqEqdGc5YEg4ueJbz0lj+HqdAiXHpGCvz/A6gBnL58eMryif+3qraa3z48eajGfl+rIcsoqIpeXJxLj54+NbtbcdX3I+eF995dqy55BUalLcE4VBIENOLuriV5uOqaopU/nDHsr7zy1My5ZhUpU9wTAfZMK7lU3FVLVZ/Pj9Iog+WZv9cLexpbjqg8kxkgam4asidmoIxl5TkT9ce1UZQ3DVhaioezqDYAcajTn5bqSAH0gqQHlSZlqDdscahMborRgacC2oYdkYKXibprfaii+56v2wsdx1D/biv9P1T/aiv9O1ftxX+nqv24r/AE9V+3Fe+h0hN4sekL3kekLvx6Qh2o9IVMGpDpUsY5cYUmMM+NR7QMm2YrRQiW4yCy3Ja0w9WlRMebtElAkYKBFMAxkD0FWNeFItGiI9AZSqSpg0yt5WlwRClUlqHOPkKFeDSHgUw+Byc5rd9sc4BkGGxMRgQ3Tgp2274aaMpmRH60s1EktEcaH7xHFNTqPgnIRiBtWRZXMdzXEad5IM8nbSe87cYyVWppcmWJ5dqnEQxk3qLrcNOlSb8LTESvdlWFzKn2KVUSPqV5vSlTalMMAm8Mq8qVKTxqW0qY5yGdRanipx8MhwVvPclSkTWrV6MxzUwQetYUyQpAQIJ415ZsIUSKlnRnCR+tJwsaZIVKuaZMQVc3lOmRCTN6Ayxpl0atWkTBj1KtUjT70yekog08WXmHdE4EVbulpiRsLgv6GVL+nHm66EbizjGNtXmcZwyEJHijsU6ZwIPH0ZcauNz75tI1d23ETGYOLA7Ryq6ufJm/pUaspEinVMvCA2ACOLqcqW7Z16gLaYZkcYJ2Lwq256tsPpTy9St7vzdvgXLNLw4k6fqyfPHNWdlu6zp0KNOOnRTDQwyIHHxqQPErjyNuS4lPec8asokGAj9EnjdCU4gzYPz7VIRirmJIAlTAQBUlbXF9S1V6MhKB4pDI+hdopp1gCnjVBClQrEmwlHJVaO7KYjSmXPOi52qdYSAlEEur6tdl7eJOk8ZPFzKhvK1P7tKOmoDtic+jNDVWLciIN7pPKjG1vASpXl7dUoWoxJMwOsoQtaguKhybJ+Uq5sLSAhZGlItHHD0ZK/uJ0pGrKqXw2uvEnAgQm+I5FTJyIdOEMVVchvwZ/aCGmcW51CWuOD7Qt2hw3iyyPKVvU//a1v2JK6apJxUlsPGV5Ojqk4hV2FDRI+l1E6hmNoW7aniBvB4+ZF6kT6QrH7yI+8G0caqtUHuKe0fRWFSLc6pDxI4njVyfFHvZ7RxnlReY6R7VvlqgHpC97HLjXeHSParcipEfenaOIKDVYZcaovMd8bRx86g1SPuYbf1ApffQ6UXqx6V72PSF72PSveR6V7yPSveQ6UP/xal0fIsfK9J+b5Fj5Xp9HyL/S9Po+RAnyxS6PkUNPlal0KWnyzS+yFLV5Yp58Q9ip+H5fpwBHEE9G2jAcgTxiFpMQzLuBYwC92FqonSnqFzyoW5n92+AUDLaFHBMclpkMCECJyfnXhwJI4zmuRR4lVlM92D5qrRB7C006cVjTivdxXu4r3cV7uK93Fe7ig0Y9C7sehe7ivdxXu4r3cV7uK93Fe7ivdxXu4r3cV7uK93FeLYVRTragRIDEGOTHNUPKfnWrp3+wFOucBMZB9j8rqJpVYzgciCCPUsmdYGSlbTnI0z09K0l2HGia04sMySPjV15f8l3cKl5J6VZgD4eoMTGXI+YU53FU1KspmRnLtTJP6xxbkTsskKseNW0tYJMAePMOigpA5FPKUgOdA6iUwKAQJ41OxtiBWlFieJPVDlaQCAhi6fQHT20jGXIhTu6ErilxSkSEac/LdnOHFI/IjeWPlq1p1jExOjaD6FVqR3JbiUpEnUeP0Kpu65pW1GhI/MzHp4kKG9vMtS1tAO9TgKhbmJCFxuPzpffgj/wDUTtoiA5G1ZnYjUtv6pyEQWxt45+mS3pRv/MUt8+DZQMY+EIRebOJNI5HH5F/pen0BUhLytSYvsHsVnOO7KVrbicjEDAnE+plvERP9xUyOJ7JLenJbwfynTifElgwDYnYvKhPlenjGrs5+RRNbyrT6B7FDV5NpO42fIt2VI+UqP4c0Rgw4uZdryZTJ5h7FSqQ8nUxVjINhtfmV1WvfKFF/Cp7B9HDYnl5Oo9A9ipij5RogvxD2K7Fx5Rok+LNsBx8y/wBGU+hb4jb+UKII4x8iNOr5PpGfIB7F/oun0KiY+UaIoiqdg2AciEB5OpMOT5FRfydS742Dj5kJw8pUdBpwIyy0jkUgPJ9H7I9iL+TaPQPYu15Poj+yPYu15PpdA9i7Xk6l0fIsPJtHoX+jqPR8nBms1mgQVTeakQQcFNztUAT81AN/sIztok1AdiAuomNQZAocEVksAsM05yWi1pz7BzIIfZ6QtVaJBWKDf+yHnQ4lGdGqISBwk7EcxzULGjveNe0hF2nNmxGId8VT/G3ERWbFi6H38T6QmFaPSpj8RF+dXFtbb48K2qPqaTE8WRVV77xKpLmROL8uKYSBin4JEZoWu8CfAJYbWUao7pRTrNBsliin0k+hXlQUpaJVCRntU6UoESHGj8AtJgi2ITCsAmNWK95FSpWdLxKrZRGK8uwv7fROtEkDaG+kNhXZDcxXnc6tUZUKJBzGAAIfjfMIYhRIOOoda3GaNX+6HZB+L1oE05adu3BeY7mjRl4AuahybCRwwXlNqUm01dnOomUCMFEyntHWt2A1MPAHUF3la1fEGiMwSrmpSn2BSpj/AIVhMAqn2x3s1dHxR72fWVjLFb501WT63wXeVCjSqMfGPUFEGeLKgYzHfHWqb1MfCh+wFJ6iLTXeXeCzCzCzCH+XQWG7afQF/LafQF/LafQEG3ZT6B7VD/KqfqUm3VTU/wDKqefEFCjCyhTm2Y4MysysysysynHBCf4c1HOQVGrHc9QSbHBGcrGcDyhA5LMMti2LJEkA8ilEbmlLDNkALI0QOPaimKzKzKzKzKzKzKzKzKzKzKzKzKzKzKzKzKzKzKzKOJW1dmoQjKhXIJ4k/wCMk/OUwupdJX5o9JTSupdJR1XBPpKMqlcrGZKYcDHJRqW9CVSUTsxUKF1uesQNo2dKFSUDEtkVsWLepYyxWYWsUjM8QRMtzTIHIpiG5mHKMlKsaQhJH4FWuMxit4UPMnhQuPEaD5GOOLnI8aaNzQfnBTfirfpC/NW/SEa9ldW0agBL4Ow6VTtrfzhooRwjETYBP/3sW+uFdbwjfUru7M+3IlzLkRbdVN+ZUj/CKesSD8uK3TeC4pW4NIDtHB2yCA/jNueQEYsvMExaQMTcyDsMRF2XlECygDprY4Y4/EqfiWMDHmUSd2QxI2Ldf+Vw9wOoKevdkNOxW1EbrhpnMA9KuLeG7YEGlTP/AAppbshq5FTMt2UzDVkrvRu2A++n1nlUX3XF2/Tat9yG64EMNmXyoad2U2TjdkG/TlVvcDdsNRqn4lGUt2UzFlQ/yyA7YUf8vg3hQ/Yipf5bB0W3ZTX8th+npX8ugv5bFfy2K/lsVtW1bVtQ0KnrU+ZS51S/B5MM3+JUvGZtIy/2FLwW1vtyVH8N4OhtqH4zw9XI6+98PV/aWOj/AIkH0f8AEsdP/Evms36y+70eJsfUpeD4HhNyqH4vTq2aeJH/ANlP+1kz5bM1X/CeDnjrz5F2/A08iH4rT4v9pnX93/xI9xv7S+Z/xI9x/wC0j+F8PW3KpafD0qfjeGzYssWdtiPDD/lbVS1a/wAPpLaX0s+1sHUvB1PyP8Sl+N8TwfBlnqZ2wV5p8bT4hbvZPgg/j/8AEvM343X+Qnp1vnh3X2o+9d+VF/GZuVb98TV4vjxZ34sc+COvuuty99/xR0s7tow5W9Sp6fG1Ps1K71P4uo6nzfavKLZaKyhpzUHzcLdjs/gexYsrFv8AmR6wq3+BT/ZCOao56XV2z+9n1ldrNebHdtFNuJ8XR15twWz/APOPUFT5lR0/SCg/e8GH7EVNHLg2rFbFsX//2gAIAQEBBj8Ab7Svm0fCP4Ij0lfGMekr4xj0lfGMekcj8JXVDWrtdrJRmM+swju0y7InpKk+wDDpUkE9ZX7cYpR5NUvEY9FH5pEbQIASV2JKVEAzOl0ynxiYQBPjpz92G8BPvG5dk8FpPVFCpSe8/mK2pTMatIDQJl4Y+aScegDyRbpsN4XO3nKciKluRzjcjobKdd0ccMpzOpDQxxxPZgdnhxJB8k4s3Z/lnJYmQ+SWOnpi5DRIeu1EsTL5xXAmQj5pH3/vodT3adLltr0qAOBHcLImOMKUUmWpYE5z9I8CYnp9geeN7p0mR22nCWBPfq9yE9k5TxmJYyl7EZHI9PnijaAxXu5xZMs9dEMSMxAEtUuJmD4JCFYSw6T7fCNu6UBGuwU5Vpl2j3i8VY4+GPv88Uch/LN9P4Q64q1DEhQyBIwHhjJWf4PvxdJg9qy1beOGCynxYFMKn6IWvEg5EnM9M4zT5DG4AJAm4WwkyOKkpXIzxy1GMQTlkD0Z5jOPROHV7Xaiz4S1IfTPq1pwz4ARieJ+/OG+PbT7fhhaBIBTNOUjLAtgeSOHlMNAhJAp6s5nP1dcSHDgMeJ6ZRx8nvxfVSxDdFI9H6Rjx64ExKfjOEuuMPv9mLfoA1irqJqA7RGlMkkzxA9iNIaUogTKgCBLHiSBjCZNLJ1jECYGOM+11Qx3imm9LAUrU423IATJkVg4CMLhRAAEzVUtpEhiSSXJAShsubq283pXJWu60qSmRAIM3cCIv28twbvta7NZKdVXVN2Z9F6ujqU9lLNFbKFblTVPuuKCUhIJmY5C8xNn8lObFFt7kRuYXRar1ZlMVe72X0VLLzlup2Q4qhS23UhaEu9tWk4CLZypr0bh2JvXYO0qlq7UW+qBdkoK1o3R+pTUWi61Hd0VX3qa1OhoK70pBMoCE7w208ZAaTd6M4qA/wCFzginvdkeM8mq6lWTPIgBycXFhqtolB96jXoTVMEqDZJUdIXOQwgloMujH5soVLyKgBDKSZ9qWkFGHwpKyxhJKdSlPrRMcZiQ6pJJj0BPjn4eMegPv8cbZQT6VLWEpxkZOoxwOGUej0jCXDDpgnTORSfZHXFCoej9WUg0if4GJzGceh7HvwwQgYPNGXgWnrlnFw+TAxZxGM/kkTwnHofwIsck/wDeLGQ6dc8JxXYf43U5kj+WX0HohQ7sHxz9ieEMhTKFD1SrI1CYnoV2hPjFSrS2mdS+dJEyPlV8ccImG2jgZkJOA6Y3irSgd7tm4snsnJTSCSeHwYbSlsABCR2SoZJHQRHoH4yvyo3Ugonqft8jMnJwgHEnzR6A8g88GSeByA8+EMtqbBCNy0hBI4yVI9RiQAAJHFQI48OEFMzinAzURljhPgI2IgJQoJZvsvSkJ1qSQDjAmhH8KHfk0mbS+CujwzxMbXBbA0WVtMhxxBzJnB7AGHQPOIpyEjCoZ/vifxoup0hSe6oCJJkMKFjACYljE9A8GXh4jOLY4GwdK3c8c2ljiTFdNuemsqcTMYl44TnjnOB2BkJcMJeGN4hKNOq2MTOoify4lkYxQZdM1H2DAwQoniSrx5Ra5BH9Y6zCapT9WGIlxMT7sES65Tn1mUKm0Bgch1dZMW1CSUpFltYCUqIA/QKcyA4QueojUc1E8fDHonye/A7Pse/Ho+3549H2vPHo+1549H2vPDf+cNN82j/FKj8EdUf1ipv1So80f1ipv1So80f1ipv1So80EHcVKJg/4pUeaGy1d2anHAIYWieP4wBgJcqm2wEjtEKUPIMYcP17SIx9E0tSZeRJESTuSkJliBS1PjyTwg/5x0uR/wAVquj9xG2Em+00kWdCUzpartJ1zmJJMgY/rBRo6P0Wp9js5YQ3PcdHLvEf4rVfhD8WKVT18pmFJs9AhKFU1QVFCWwA4CEykuJJvtGocD6rVfkGLdK+UkxcqAyFLVTV+kIOn0MzKL4s3ylQpdeVLQaaoKm1FKewopEiRH7dpf1Sp80Wpxu8Uzqg+pISmneQTqQoZqAlnFwJvNMg+tvkgsuTSdZHaj9uUv5l2CUXinWoUVYAlLTgKwppQJmcJJGMKBvNMkBasSy5PM4E4x+3aX8y4fNG6e6uzD3fWdDbpDTnySO99NWGI6owvVKoCY+Zd4nwRq+t6XDH5lwezLCKZpV2Y7tO5i4Hu7cCdfqImzLMnHoif1zSiePzDh9mUKH13SzIw/R3MeqLKV3enaDFkp2kJLTig4A4oFQwwkfbiX1vS/mnPyYoym708u+bE0suZzHsxWE3diQcGJackDLIy9yMLzT/AJt2K2V3YxtdTOTbkwJpxx4Doj9s03pKyZcx7R8ET+uKcy4FlzHqzMXoJulOUrrqAqPdrk2Ug6QcAZq4RMXilEsJd04cfJGo3elXwl3LnR1iLRO60+HfFJKHDgVCcsMJR+1qXp+adyw/FhuV1pj2wfmnMwZyy4yhC3a+nYV6uwChTbhIkJA5DAiAE3OmJPAMuH3YbcFYys9zUgJDTgnqZUCQo4YCEKvG7tu2zvlrQ167cqSm7wonMIDzqSozzhbT++m6l9C1o7uitdVUtktqLa9LzRUlY1JwImFDETEXelttpv8Ae3KltlLLjLTNG0pTNQJYVBDknEdrKaQMYcRb9kaEgEUirhW6hP4Knk0oTh0xKms23KJtKpo0M1DqhhhrU45pcx4ShKW97/VzCnlud03RW7S0XBNQbLjOpKZGQ6BB9c5i3euUFSU1ShlsJWMCe+p0j0eIlLGJnce4zIEJc+tqhokTJCi2ytAUVdeXXHeL3TuDtJKCHLvWLJEsQdTqsMIOrcN6UFDtD6zq5EEYggOgEERJysqlAzmFVDpnM8e1OEuT1LCgqalKJKhiCQokGRhSm3HEKcHbKVGa8PhGeOEJbqVd6lDofQ24SQl9OnS7+7GgSPCUEoecSZkgpcUkz4YgiAG7pcG8P5OsqEeyHAeEBdPfrqlCQAUmvqjql1l3qgqevd5bQkTUaW6V4UcfwO+Imc/FA9W3pfqVciZVdY+pskHAAqWZnwwzRUXMOrcomXu8aSmmpH0pSFAltSnWnFmfSSYnV3hm4dCX6RpKRLiO4baM/YgJfsNgr5Smt5usSo4CfzbyU+xFpYrdh1/rVAw6lardc6dul+UKToQisUp1JBT6U5GeQhSbnU3SxFKEKIqqFyo7apakAsgh0pJzQSDnErdzDtjaypKVN3KnctclkFYAXVqQgjSn0vRBwnOKJ21bkstxSaCmbnQ3Gkqu1pklADbqjqMspTiQebJhoestEh1GEwMQsGRM8JmK1SqxmnJLXpBS5fJoEwECco/bNN+YqPZwMWl1u6U9QtuvZUGUMvJUqWrAKWAkYxVlV3pm1KqXypBpaklJLqjpUUpImIH89Ux/6pVfkQ3UC80ygmmqUlPq1UPTSROej4MPabxTK1PvKHyDqcFOKOSscJxjcqUT4lCgc+giecbhQ3cqch6y1aXFgEhlJaPaWDmmEfzrS+gnHRMS0jHKcYXmhPUGHQR4SRKNxMfWtEA49Q/KaHBp+UUZSlLwR+3aH4i/PBnfKHxoX7phLAulGofXlM53iQ4lM+0JSlmJ+xGN5oDjhg5nIy6YOq8UQmCD2XMBMY+KNnpN5o0hn63DRKXNNQhdUCpSc5aCJeOBO70M/wBy5j7BhxX1xQjSk/BcxmQJZCWcWJJvFGgN2tASSlxWqZz8UGV7ovzbkhFOTeqFUn2jp7twapOJMvHFfVOXijp9TdGnuVoWojTStCZ8MvJEk3mgUOnu3PMYt5F3oj23MEoWCfkl4CchFf8AzxQ/S3vguH+U8GcY32gH/JuRuoJvVEvVQUwUQhYCR6wDNU8SDl4Y/bFF8VZHsxheqL82v3JRa2vrei0C/Va9WhYSSqmA09OoS8hjC70XjDkoIN3oJEH4C+iKJvvkEItdA33iQNKtFGyjUBnJUpwtS7mwz2jgppauP4o64/b9GOo0tQfaTA/n+iOH/Nan8iP29Rfq1T+RH7fov1aq/Ij9v0X6tU/kR+36L9WqfyIb/wCLR/FH9hLpENq1ccpYe3BEzgnp6odkJ4jKAZewejwQZiWHQfNG2lZBFpbQcZYz48YxM/AcIEiQdSJHDPUJcIpCSSPqW3oHibAMYZeCLdlhc7ef/wAJRG4h/wCsl/xER70WkzH0kGX71UXQTH0x/wDjxmISrPRQ1k5TkdTKhn0YwoZHvF+wSM/FGYjdxkf2KjDiZukYdOUTB6sJcTMAz6okSZcZyl7AnDU5dndKlkYjD1NI8sej7PvwezKQzJy8hMWeXC2JQB0DvCfJHpJ8o80UmRHft9c5qEVsxm5h5PBwMcPLFdx/mqq4fueqMp9pXA/hHqjEGXHA+aL8OPr9uy4dk5dMoEpASGfT5IxI6TLOQ6Is6UpJJW6AEglRM54AZmBUVzjNCxIyeq3UU7fZE1dpxSZaeMOO3DfFtrq2nfLZttnDlyfLjekqSpyn1tITj6UyJ4QpO0dp3S7u9wyhFZW1DdHRJUkaVAoKe+cVLHASEVlPbW7HYG3zKnfomVv1TDYyAU/NCnFDMywhJvG8byoJKVaKOsftyFFIWEKWijcaBWAs4jPjCF1bz9VoTIGqfdqJE5n5Va8zDh1KkCJJnhnhKYwA4SgTMkzE+jxmXEmALrVtUzqUFXcqILpAEwAhKidR4CJWlhhtlKiEPOj5RQGIKk8DI+WFLqq15wlUykLKE44ySkSAwiU8/HgOnpiXV0cPLGOOJ9qPCBlLojxT9mUcc+qMDh4DHDyGMYwmOz7OME6zLo+/wQUkzChiP9yAEEpCNUx04dBnOC5S1bzBJmO7VJPjQQQcMIS1dqcrQMFVdOntACXaU1kTLOUd5bLg1UAAam1TafBIn80uSiBlhEp8D5JTlj1wenDolhLw9EBLjbbmEhqQlRHTJRExBXbq2roF6gsKoql+nUFJnoUC04khSQTI5iA7Zt53VDkg2EXCoXcabugZrQWqjWZq/CnOGRfLfZNxtNu63nS07TVrjU5gNBADKXEHEcDCndw7V3Jt9Bp0l65KFPWsqeQEoSnuKc9/JY+FLACKf6n3xZRWVYSGKCveFDUKWoaggB8pmuQOGcWp1hxt9pNwZSt1haXW0qJMkqWglIVjlFwM/wDHKkT4fPKw8UoBnkRCTMYsu4Y9Bh0Z/KLy/dEwMx4uggxf0mfy9nqm+oHulCcNplkhGeOQA6OqP93zRuInFQeoZSnl3iuEcfv8UHM4Hp80Ds4G70/4UsNcJw+F0HgJwDpOOHHj1RstiRBaRfQc8vWkHPpMDAzyn9/VDolgoe2ePvRZkgHsWxvLoKhHHLj70Uw/4dnp/uieqLhqHZ0UcpYf4q11YxgMOucW7D+UX0/3JcVwl/jb34X90jFCj4sJdWHCN0kIONBT4dP6SnGMBI+Myx96M1eIjzRatWJ/pBWccfoyQJ4SJwiQEzPoMES4HgYogc/qyi4H/mzXXC8E5npj0B7Pmj0QMumP92Mh7MZD2YyHsw1/nSx82j/uxf4I64/rSx/kxfnj+tLH+TF+eP60sf5MX54/rU0MOFrcI8faEN93uGmqTwnRKamfj4RpdutI2iWYacV7EzCydy06CSZj6vWfZKxEk7rZBGEzbFyAx46iJR/W6nOf/dqvyosKX9xsU6U21AQBQqe75E8HZ6vk59EdjczKh1USmvGRjCB/SNn00506yMFDqEU7xv7VP3dupG+7XTKKlBCBJacBgoR/WJof9WUAPYMUaxuFo93XUbv0ZWpYbfSrSMMCqUXp1e4GUuOV7i1NlgzZJQiTalSkop6RB/n9rP8A5srzRb3mL2y86h8aWu5KVKMiJBRGGB9iKt9W5qZpblQ8tbfqOsoJWZp1TxI6Y/rTT/5OEKd+vmXh6pUokim0EhbZBUQQZSPCFzvrSiVrVhT8CT0nAx+22/1ZPvRuNCLyD6zaUNavVx8l8oqayMdYJOXXACb22CBIzp5TPEkggzJj9utj/q8x7JhFIL0gTvaqgPdwNGNOlPdyB9LDOAk3tuY/8mB/toUfrpJmCAE0ww8OJi3FV3CQ1QpSP0aZWFLPaEjhlH7aT+qnzxTD66ST3zch6sBORynPCZip/npKFTTqSadJ0zAEpzlH7eT+qDzxUufWYeDtC+0ZNaNIVpJXLiMIl9cDMmfqy8iScs+MftlP6sse5F3p/rZIS/V0jvelkhLXdJJIVM4gjjwg1e5N92SyUqG+8LtydTT6kJzUyhR1PSH4M5xU0216247/ALk2pTLbNJRO2u36zICoVcKjBbSZzSUg6iMoYtu2rfZ9rUNMVFirYaU/d5ESIXUuFTerVjqSkTh2r3Tuq93RVTJTyVVztOyvSJAersLbaGeOGIzgpaRoGMsMQDmNRJMTAwPlnKXTAw4DiPPBmJYJ937iqq5VCKZlJkVLzJ06gJZ9qFUe32u4kopcrVJI1pn/ACaVYzHixhdRVPO1D61alOvOKcWT4VE4Dh0QETkknKQ/BwHlic+IPiiU8YGI4n2IBn95ECSsJYZQe37UDt8eqPS/gn8mPS/gn8mPT9jw8JQO1LDjIcTwMKGvCfSOmPT49XRBmocZ5dEAkjxY+1EwCoqmBp4YHHHohDzDy2nU4pcbVpUCOIlLohNJuFRS4dKGq5CBp0S0/pCRPE/hCEPsuIdacSFIcbUFJWkiYIKZjKBLHPLxRkfJCZDiYkYlx6YS4kBLrZC23ANLjbg9FaFCRSodIxg1u2d43y1vFaXTKtdqGu8R6K+4qC41rTwMoSjd1otG5aBGhDz7KDQXR+Xzj6ltqFN6y4cSQmR6IpKauu9dtC5VJUk0V9oX1ssuJUlJ13GlQql7pRVMLJAkOEIrtvbss1/pyypKnrU+KpCFKBwUlsLWmQ4kYyhxRutMkl1zskqBB1HCRbnPCP2rTEZjFc/YQBiIu4+s2HJ2moCdIWrSpSZaikJEwkGE/wCcTfoJyoW+j90Y/rEj9Rb88XlgXxtSX3aWb3qmjutK1HFAOlYPXKP6ysdX6Cn8qFf5yMYD/mCfyoTR/W7KgbmgmoLCkgkTIJQCUiU4SXNzMtic9X1fqlIGeS5wB/S5kgSwFrPD99G3WvrpptFObmoVTjJXrDz41BKB4MoMt0U6ev1BWHHDGCkbqpZ6cf5vOYkcB1yi0yv7DQRb206hSKX3klHHSQNHgj+sjP8Ak+GNO5GNXfNaZ0EhPWmQJwkCYq3H78xTurTTDufVFOFKUsISma5j0pT8cf1lp/1ExSVDe4GH1tqWQ16opGqaFD0xOREVK1bgaSpb7ipepoMiVzInrmZZYx/WFr9SQf8A6IIvLab6w4KymbZJ9ULfdaXdWqSSQucso7W42AOJ9RSfHguZjHdbE/8A2WT/AG0UdINxshlu7vuprBQqKStTKR3QZ1SBkc+iJHdrMuj6sVl4jnH9bWfHaz7PaikbFyZcQmhpUpd7pYC0pYQArTiRMDKFF2+07UycPVXVn3BE/wCk7APR6gr8qP6zU5/+lxP9tH9Z6X/Jyvyo/rNTf5NX54/rNTf5NX54/rNTf5NX54axV82jh+KI+F5I+F5B544+T3445HhPo64b7YzGYECYB7I65+WHJYYn4MoOM8R7I4wSAMB9/CNuS42luXX2oxn4hPywmWqetHD8YRSFOErTQj0eGhOfRKMTj1ZRTYz/AEmm4f8ADCLwSJK9eUccDghGJ6YBmnHGLZiD+kJ4+Hq64q5kE+tP4yx+cVkJRx+LCknjSVGY6Uq4QqXXx6DL3Y+/zRfSk4m1gIkJgK73snhkYxlqyVhLGfRKDl9/ihoYfts+P5MT4RPHyRMzxwxEWf8A9n/233KQjIPtA+JQ64rB0qTw6YzHliqUZEopHSnxgn2ZQKm4PNUVEEqXUV1U6himpGkJ1F6oWtSSGusA5Q7RsXZ/d90pyQaDbRafp1lSCEB24KmygTUFHTiOMV9o2tT2rZtnqVMLbq2GVVu4HA2laXEu1C1CkZbdSoTSlBmY9a3FerpeXNalj6wrX6lDevFQYZdWptlJOYSABGgeiCZCUhIejhEyMRlGQz6OqMRhqOfggYDjwHR4IGAyHAdEAAZkAAdZlhDlI08iuvKJA0DYJSypXwal70WykYy4wqruFepaHPRpUzDDIBmEhJJCiAMDCSZTJE/uJM/wYGOY6PxoGJzEAno8cCZM8B4+PGBnl1Z+WDnn1eeOPseePSV5ffj0leX349JWfT78Yknw4+7Cs/vPhjxz8glCj9+IwhITqzPV7sEEq4ynwlnxwnHHiOHGOMpZTMuPDKBpKqq2uOJNVQqOokSIK6cqPyLgHiMIrrU+HW1JmtskB5hQwW280O0hSDnPCD4PcjHGPF5/uHwDHxmD9/AxkPIII4TOHDOBcNtX27WWrZUVsrt9dUsNpUcdZZQsNKUOsQwxuKnot50aXiuoeqyaO7uNmZ0t1aEljvJ5qWkzHXDVLX3RexruSEVFDuVSUUiFFM0qZuTY7l1C1YZAziuuFHU01ZRVNpqVMVVI+1U07regFLqHWFLRoUJSgSIy+/hEvHhj9+UXz/jaT+MfuEcSISZj9opy65wAqRIEugTl1cMYHhHE9Pgjbk5fPXU5/wDDzESxxliPLAxVmOHXFvGGFKf454Sjh7PmhrL5xvp/DHVFaTL5ulGHR6siUTE5dQnFL6Wa/gn8BUPT4OujqnrVH+75orsvQT4ZavBB9yfmjIeUxTYDC+vHM5GkTP2I98wcsukxSCQ+g0o48GEQcE5nj1xlPxn3oyl4zGHtmMx8YxmPjGMx8Yw1/nSr5tH/AHY9+CPxY/rSr/Jjx9yP60n/ACW95o/rSr/Jj3mj+tJy4218f2phBZvblViMBQuIn41EQAqtcaTL0jTFUvEDCyrcoQZmaTb3TLHqRGG55cMbc/MyyPoEcYI/pQnI/wDdz/R+4iwF6/eqlFsbS3Oicc71GB19gEIB6DEl7kC+iVvfAH8CEj6+U52knSmhqAcCOPdxTrevfcu/VtKA36m+ZNBMkkkIzVnH9YP/AMBqD/aRTFN/mfWqaSfUagaj3ydI9ASmcIujzt0dacVVqJa9UKgg92kEBYwUI/bBEsPoquHiigdTdFOqbqE6W/V1JCzIyBMvHFUr62UJ1DytPqqlETWrCcsZR+13P1NXnhRFyU4TTPpl6upBTNCu1xExPKFfzvxUPoqxxnMxjeCB/wBFXF4Kbl3oVb0hQLCkaB3pOqeMxMSlCpXg4kmXqyjIzOE5YwQbuoTwl6srLpyhpv61V6v9aqk56sdXf6ACmWenrgj63ywxpVcPFEjePJSq6PBFsDl1UiVCkMypiQpGozUeicfthf6sqGVm7rOh1KgCwpIJGIBUcAJiHHVVy9bhBklGpJIEsCIdvO7t3W7b9uabW4ai5KWyHQ3ipNOlKFKedxlpGJJi4W7lFav6Q1HdrbZ3JfGlsW9BkQlTFuQpLz3aM+2rIdcVD29d1XC4UzrynUW2lqaiit1OCmQZZYaWhXchOGlRUISUoShP4KRICR6ekx04dHvxPASMsur34EujwcBHXI9fHoiUsJT9qOvycJZRM9An4x0zwgCYSOlWQHSTjIARW2LbZbrbuVLpn7m04TTW+XpFo4Fx8ywlgIXU1Dzr9Q+ouPvOqKluuHFSlkkzMzCSOgQkY5jgIEujGceMDxCJT9mXHPxQDMZ+HjABIwGOXRGChnPMQM8umDn8Y+aMQZfuo4wQJ+SJJn14Sjj5fehUyc+nr6JQPHwg+EcOqAQenhBGPwuPSRwjE4ZwSCMEnr4eeFA5YYT4YdXXCLtaHNLgkl6nUo9xUtFQ7xt1A7JBTkZYQmqpVJaqUpAq6Ikd4w5LtSE5lsnIxl7cHLAdHVPpwjAcYKsJy4iePlgg5e978CXRBPHOAOBBnHgE/YPCUArSkzwMwFGQlLE9EOq2VvK6WynebLT9vdWKy2vsYE07lJUBbfcr0jUEyOGEU1q5vWM7drHClle6LAhyosanSAlCnLaVKqKNCj6UyqWYnlDd22dv207ipXWkuk2xTT7jSVcHGNffI0mc5pmJYxdmjcniXXKbEUg7MlHMEg4x+03f1QZ+WP2u4CeBo+nwThTJuTqUt3FPyopCdRnlLgMYH87vYnL1I+Dh0wJ3Z6Ux/iZnw4ziwNuXV4IC7l3ShSmZUX+1qHwQJeOB/O73H/Ej0HrlCf53eOIw9SInj08IoR9ZOo00vpeqqWVds5ieEoM7s8fBRkSn44bP1s8ZOIMjSK4KB6Yq1ruTrSlJppoFKpcv0dA9KYBmPJEvrZ8DqoZ+2sRTH6zedl3nYVRlAPYV8IKUR5IqP51eHy7p+hkyms4TnjKMbu/4qGf9uIrgLk8uaACpVKUSGr4IBUCYP87PD/qR84j9rv8A6h/98ilQLk9oN6dPemkIUXPVxNIb1HshvrgE3Z6Z/Bozh4piP2tU/qP/AN8imSmoWpIo6cBZbkVAMIAJTwmIVquKkYnAUy1eaJ/Wz3io1ecx+1n/AB0ao/azw/6mqP2q7+pmP2q7+pmP2q7+pmGsP5NHA/gjqjh7XtiMfbT5oxPt+aMOg8T5obmSMRxHR1iANSvR6+jwQqcuP4XT4ImDjPx+1KMz7Hmiw4nC2oOfXxwjM+x5oSUzmFCWXEgdAinxJ/mmiE559nHPGJicvCIpCqcjWUgGRx79PQIusvR9bJJMiQdKfdgkZHEZcYoJ/wDOUe0YqTIafWXujLWqMh5IM/7g+B4dB6IUSBmoez1QBIYkD2YvMx/3cj+/wZ/CJIw6/B1/cbBH/fip49CRBn0n24PRIxaBwFB/bCOJ4ADEk9AHTFPfeYG5bftuhKlOsM1rqEV9ctgzNLSUS/lXXlcBLKKy28oLU3brWoOtN7lvDWq4rmSUv0NFoT6q4gjBSlKSRwhy87v3PedyVrqy6pVyq3HGUGeAZpU6aZhI6EpET7ImZZHrHCU4OWfuGEjw+2Yw8EHDj09QgdQE4E+g+3Hi6fB9yQ6B7HuRVbU23UTqygt3S5tEKTTBQkaWmUDJTyhgojKcTUBqJPa+EonipU5knjA+/MSEeIexhA6pQB1AR7PijAnL3Y48PFn1wJE+CMzgRPOBn9/jg5+x54EukffnHwfZ88HAcOnoHXGMgJ9Bic/YMHOU58BOfmgSnhB6QRhhPEeGB4/vwiYyE4wPT058IxJnLDMwrH2D0iFacVYS6MfD1Qi50Dym3AsB1oE93UNzmptaQQMsjwhi5UDyC7p0VtLP5alfHpIWjMJOYOREKIGY6uiMuM+HAQR0iDMcfcAzygeSCBAn1xgOEuGcCY6ePgEYwUZjUJg5eLCGrvtC/wBy27cWFhSKi2VCmQojMOMj5NxJGBBGUfUvN2yqvdDVLaD27LQlDVypQnAu1dAAU12kmZ06SemG7tsfdFv3BSOIDmhhxLdeyCAdNVQKPfsOpnIgiEkEiU+oics+ucKln9YIxzyMHxwMOI4jpjb4lj3lxn+fMTMsPBCfCPbihEhP1aeeGKzBw4dXVCMPho4j8IRW4fApvYp24yHsRTYD+U6PwDFQP+Hd8HpGJHhhFeOlACceOr2IkR7Xu4xkPYikEv8Av13xSpEzwgavAfKYPg6Ipv8AoVP/AHhMHwn3YPijAYeKMvajIexGQ9iMh7ENfzvWfNo/xH8Udcftes/UT+VH7XrP1E/lR+16z9RP5UfteqH/AFIj+2hHdXSrdM8AaMCfjClQO8r6hsSzTS6jj1SEK/nisOP/ADAn21CMbvV55/V/V+64R+16wdJ9Q4eWLW25cqltDNEhNOpFAVl5kk6XF9saSZZR+16z/JwE/BN2Eyu9WCFJInQAAmYkJhxWZhlb93XSuJo2kaDb1OakJkELBCzKYzBjtXxxY6EW5QPlU4DnFKpN4fUPWqYpR9XkKKg8mSSdZABMXFZvLjC1VE3G1UCnNKpAlIKVAy64H8/k4f8Ao1Y9tcUZRdXahzvgUoFEWklQnIFZUZRUFN2dZPfulYcoFLEytUwFBwAyPGP29/8As1X+EjsXR91XcvZUOlJGg4zKyZjohRF3WlOqcl0JnP40jgY/bUv/AKXqM/GHIugTcnniqhAdUmi0pbQHE6VDtnUZjLoif14vjIfV68J8D8pnGr69Vh029RHsOQ2g3R/ufrdShU+pGRf0A92W9eoIJ4wdV+UkzxH1eo/284/bxlMT/m9Qw8OuKa97z3hbtt2ykoQRWXBPcNushRxQFEuOuEDBKUkkxdNvcjLe1cUAqpBvm+UrrZZVNSXXbRbS4mbwEu7ecHZOOmHL3vncNx3NcnCP0i6PKfDaQsqSllk/IspRMy0pESEhICQAPQZR4j7cJ8XtwfF7sJ8Pux4x7MxHj9wR40+39zx+4PuP7Ysag9uGtbT61UJXpTaWF4hJUJ6ql5J9HgIU44SpxZLi1qJK1rURqUo4EknjAnh72MJExiAc+iZgyl/uzMAyGMh5YBMh0+5kOmJTTiAOOZ4fcBAHR5IAwyjgOGY4eEQD2ch9+cGWk4/fxjgPLHpe35oPaPs9AjOfhnL2ZQfRyP35wfR4e0OuB6Mumfv9UKlLp9j2YxkPDCgJcfe6Y7OknE+T/djIZff0dEHAZ9fSIxCcxn1DwwoSGJI8GPgEU13pFa6YnubhSapN1NMvsr7OKe8QkzSZRTXa2Od5RVjYW2TLWgykttcsAtCsDGZ/3Zj7h8IjI59HVBhPj9uD4YHg92Mfvwjx+4Y8BHtR4Pv44RTXraV+uO3rpSrS4zVW2oU0StBBHetJPdPJPEKBEW/bXPqn9ZoAgIHMCx0aFVdOmQS0i62lsJVWLBxWtvTpAwBMJvOx94Um5rIurZWqvtrGtTSz2lNP0xV37JQDjMSnhnH7WenhP9DPHxwJ3h4Yj/EyejoMWRtV0eRTJVXKZeFIoqdWXZuAifYCVHCJC9Pk8P0In21QP54ezH+I9fhij13epbHqw0lFuLmoa1YntCREAC+VfSf5okR5HcoRO+ViQFJM/qsjIiQn3hkCYqFVF2fZeKWO8bTRk6JMpSmXbxmkAxIXqpA6PUj7isoYcbvD7iwVSSqjIBBSZz7RMPKVeqgKU6tRT6lkSo4Dt5CJG9PgjD6B7faip0Xh9QIAP6DKQ1fusYUTeagGcsaI/lHIwP58q044kW4uYeDWmUUyEXmpUyLs6oPG2HWXVMBJa7nvBpSECeqZxgE3qtx/9UplPhL5X24/bdd47SmX9+imSK11SBSMBLnq8ipIaSAooBOkkcOEEru9UgzOAtil8envBH7cq/FaZe29H7aqj4bTP2no/bVX4rT/APfo/bNb/kkf4aP2zW/5JH+Hj9s1v+SR/h4axHzaP4o6ozH3+KMx9/ijMff4owl9/ihGoDMYn/cED9z4OHUYVhPPp6YlhnxxHkMEzTkckDzxaOyP2WwZkDr6emMh7EIw+GngOmGppH0Bk+XT7MYSA6JAxTZfSac5dDyIuUznVL4fijriUhhhkIouyPpCeHUeqKnE/Pu8fxz4ozgH/gaj+IYMwD2zGQ8gi74DG3t8B/dFcOuMBLwYQRASMP55UR1Hu8wPFDjzhQ2yykreqHVJbYZSATqeeXJtsEA+kROUVe2OXHc783oyFJdrKZaFbYtzoJSW1V4Q41XVDKwQ8yJSyBnBuu/tzVl1axNHZwtTdmtjWOimoqIENpbbBkNQJhISABKUhgMoyGQ4cTOE4DIcBGQ48B0wMOJ92MhkngISOE4TgMjw6hB8PuCPi+2YMwOHuwPB7kLWn5S91rRFrphpklS0mVW+ZnSy1nLNRkIfr699dXW1jpfqql3FbrzhmpWOQ4AZACMBKMz5ZwMZ/wC4YAxyHwuo9UYq4YYzxwjBQHjgdrGfue3AmcZZzxOPsQATLPj1wO2ePEx6Xsg+3A7QyHR0QZGfSQfNKOPs/lRx9n8qPRn1z94xgmWPTP2JCPeHngnUCZ8ZTwwxJ4wO0BPh4OiFELM5cDL78IHb6eM+jwwrt4yP3nojFXk96MFeyeqFdvo4npECS55dcjLrg4nM8Y94QjbVyrQLRcwr1ZLypIo7gpXY0qVglt/IjpMDCeB9yXknBwHD2oPZHDgOmMs/F7UKkBx4CefTCZgceEKwGY4DogYDOFYDL+1gj8b3I8Y9qMCRgePggAnp9yFA4gpx+/phF85f7luO3qsLSammpX1i13NqaSqnulu+YrGXAJGYBHScootuc2kU+wN0rHdI3CRPaN0WSEtpW62Cu21S+hwBJPRFPWU1S1UUtW0h+mqadSXWKhpxIUhxp1tRStKkkEeGNvAify1yAJH/AA5PsQMBxykDkejGMJz8JihJz9SEpD8ZXg6YwmP3sN5ma0/B/GHXFWRMTbpsQnoYQJxnPw4H3YYmRKaiRnMaTPhDuA9NZ/hmCQEyJPR0+CKrsj4OMvxspy6YJAzVlpnLCPRMMkAiV3Uf/FgEmAB73HhGY+/xRTAjKip+J/uKBBMpZ9PT4eqM+I6fPGCJ+L3o+b9geaMj5PejI+T3oyPk96Gv0175tH+LH8ER9Oe/Vj54wrXvHTS90x9Nd/Vz5zGNc+P+r4ezKEyq3FZSmzL+2ga6lxGHwWiYURXPDw00/wC2gD157HH6PwPj6oP6c9+rDj++i2A1DwDduabBFOTqSCoBWJwJlH0x8fjer4T/AAZThH6a96af8XHSOuGyat0H1JnAMgiWHGY4R9Ld8bEvdMUx9bdn6yxgGJz+VThPVxiuUat0FT6lKHc4pOkTSZnMRP1t7pwpiRn0zij0VTyl99gksFIJ0qkCZ5RUyq3Zl93DuJ46zhgY+mP/AKsfPGFU8fkagj5AjDuznnE/W3sVqzpyOJHEgTwgfpTp/wCRxPsyi5LTUuHXRAOJ7n5pKXFY9c5x9Ke8TE/JjEhVPzMgB6viSTgBjiTPKEV/MTdAttQ/X1LlusDDAqdx3aoabGliitSFh4JUpQ1Lc0oAM5xVWe1PvbH2C4pLbe2rc8Uv1zNM6tVNU3eqbIW9VLB1FMylJMsYU6lISpwhazjNSlGa1KOalKPHjAVgMxITMpeGAJ5SE/Dh0xnPADyT6z0wnwD2o8vtwDPPHLqPXAxnMD2onPIjp9zOB1A9XREulXmgKn78ieHCD4vdh+vrHkMUdI0p+oecUlCENoSScVEDUch0mKu5vrKaVLrrFuZE0hqibWe5KxMjvFIzl0xOeWOXv/dHj9owJZ4Dp4S92BhKQnn0GUdM+uBwkQM/B7cYTyl08Z5GBqPhyy8MAgjV0Z+3gYA6cMkwMOHTxGBjD+NH++96P9970f773ol7sZ+zBEuPT1+CBOQl1zz8UKAlL2ThjIwmU8+geeDPGeHRw6MYy6TPwRlwHH3oUJcc5zyx9yBxn4BkIJ6Z/cbcaKkOawpC0HSULSQpKkkfCBE4a9deC7xaEooLgmaQt5LSE9xWKSAMXkGSj0iCqfDKXQII8HuQIUOs+3CcZwrwj2oHhgjpw/gwRPry8UZ9B9sQRPKY8MZ+iCcs8AYOPwZZdU+mOnLq4y64UkoSsFBBSoBSSDMGcxDViqLi7vLl++5TNVm2r465U1Nqom5Jee23WFYdpKooxIUVAyAEosS9jbrbF2ojUiv2xcmgzfqJ1YC3g5S6w440lZ7LgTJYxj9oIStM5pUw8CDkZiUxB/nFvj/IvdHgi2lyrUqVGPmmXTMBROpQlhP3IB9ZeOMiO4dHCc8sYRJ94q1J0juXfSnhPs5TipW7VLaVoZGlLOsSDKJEK1cYl646cv5A/lQyBWOzmqXyBz0niSZQ9Ktd+dc/xc56zl2ol647hgPkDMgcT2s4qh646Zp/5ucCciO1jjBPr7v6vLHy8Y+mu/q5/KhDRqnCgXFRCyyfT0JJTLVlIxhWOk9BpzLyznKPpbn6ufyoYT3qikUrEld2qZT3SJGWMsIM6t1JmcBTk/20ftB8Y/8ANvB+NA/T3j4af34xrnf1c+ePprn6ufyo+mufq5/Kj6a5+rn8qGs/m0cPxRHHyRl7EcfJBnPyQkY5j24HWPcEKhPUkA9Wef3LfjlQs/xlx2SMcTkcYRjPtp6PwhCJkfQmTn4BBIxGGWWQimkJn1mn/vyIuWB+lL4fiiMjFGeAdGPCelWcVB6X3cen5RQ+5L/gKj2WjCf3av4yoyPki7/9AT7K4deWtpphhHe1VQ+6hinpmAe2+++4UttoSDxImYr9lclEUe8t00z1RSXPc60rXt+zOp7xlXqLqFJTcqqnUJ9nspWBjxit3VvC93LcW4rmta6y53WoXUvK7wqmG0LUptlKQZDSJ6eMJ8Bj96I/fK9uPGn24PhhPgEeI+2IT9/AwPAPajyRkcjwgfuvNA8fuwEz7SiAAMyThLxzhvZ1sqHSaNwqvGklLKngAEU2BHels5zwBgDrVj4vH/YDx+0YzHlECSgMspcYOpR8nvQnt8Pc6IHbOQ4QAROcscBjxMCWB8vuRmfi+9Amo5DgfNBkSfF5xGZ8nvRmfJ70Znye9GJl5MfB0x6R8nvQe1xPD3ozJIInIdImIMgRjPhl19EDtGWPRHpexPy4GMFYyPDycIwUfFLqgzUeHRGP3TLMAkeHhFHVOulNFXKTR3JJPZLbnZQ4sZTbWZ9IhLjaw4hxHeIUkghSFCaVCWYIMKxHD3IEHwn24RKFeEe1A8MeT2o8Q92B4PdMHw+7CvB7kK8HuRhjgPbjxeeFLCihQloWD6BE8QDnlFFfrPd6603a3u+s2+9WhxVPUsuoPZS6pIk4wqfbbUCFJwiz7G57VFFt/cjqWaKh30yj1awXh9au6p0XBqZRa6hWCSVKIUccJwzVUrrdTSVKO9patlaXKepaUAUuMOtlTbiCDmkkRbz/AOQp8mpfkMeL3RCf3SfbEVH/ABbP96THiEMDjNZ8WhWMP4H51z+NGRipzwQOBkTPKfThGXH3IyhE8D9ZL8fySenqgaTPpyPh4R70MSB+h0/95bhWHExkfvIjIxlHvR70e9DX6Y/823/J/ij8WPpj/wCb/wB5H0x/83/vI+mP/m/97B/S6jI/A/3sJ7l2pWZ4TCQPcgd8t9KZZp0HCXROFTrKqfEaMsfFEjWVEp/gEe5GNZUcfg+9FF3lTUtzomygaQqadSpEyBkY+l1R8DQ/JhP6XVDtJ/khLPp04Q3rq30q9Sa9FskSmJGZTH0ur8IZmPahj9Lqp+sMSmzKZDqZD0eJisLlRUIdLpU4nSFdopzmBhB/TKnP+5p/Jil7mpqHHC8nQhSAAo9ZkMBD2upqEKLrgUkImEq7wkgGWIEH9MqOoaPegdzUVC1Bl4maQlITp7ROU8OiDKsqT2jk0PyYn63Vfmh+TF/3pvzcqLHY2KHSHKkoTU3BxKiU0loptQdr6xa5JCEjM55yuO3tmm87G5U99JizsOpavG6aZbckrvroWFssEK1erpyVmYPe0yaGm1HuUpc1OOtlU9aiDgqeYPGEJngJAAkE9ePHOB9/wY8Xuwn90qE+D3I/fCB4/bMeI+3CP3vuQPv4mP3p/jCPF5vuFxZklAmTn8EYAcSYuF+rVloMMr9SRgXVPuTTTISni4V4w9cqxRVWPLW5VLJCtbillU9WZUJ49f8AZjwCCcejOUAEHPDHIGUHA8CccwD78T0nhx6cRAzl4YyPlgYcBx6vBBxlj0g+2I9L+L+THpDyp80ekPKnzRLUJ+FPuCD254Zas4OZ8f35QJA/GzhWHDp6c4y9n3oIkfhceuMAryiMvZ8A9z+yJ0qIVI9nOY4gkwKCpVO4WdIpnApU1uMBILTmOJAGEHw/cPhT7UCZnB8B9qB4YP38I8fnjye0fuOeFftmFeOB4R7UH9yfdg/uz7cFKgFAjFJEweEFVKlt+nOrVTPJCxjgZJUJEyyIkRFv2dvVu4bx5PgNsPWZ9TtVfNpaXR+l7dfWVO1FIltai5SrM1GRBEpRaN4cvdzs32z1DTQUmlUj6ytpImuhu9DrW7QVbRJwWADLAmPpdd42kEexCD6y+ZKTipIBnMSJGAEP6qh4K0NTCUgiXdJlI+CDqqH5+ACGS2+9rmoAGUj2ThD3y73zrnwAfhHzRMvvzOeAz8HCH5VD4BGOAIwPsYR9Jf8AiAR9IqPipMIHrD3dfWKzMpAVr7pM0kZSlH0l89HZEh4fBH0l74o88MjvVlHqrACuJT3SJHwkQrVVvBRJwCQR7UfSXyOtAPR1R9Je+KBGFS/8QeaPpL3xR54+kvfFHnj6S98UeeGv+LR/FH9gceB9qE6s5/flA6Je0BCvH7cEdfm+5Qf9AaP8NX3E/uhCP+gs/cp5f85pv78iLnj/AI2rgPwB1fcpAP7qD4wkmHv+MX/HP3B/xL0/zZiQGoqWoISJalqmQEpxEyYDF3q2r/vqvplLtWy7c4HaxpSkrDb95W0SLdSh0ATV2lcBCtzb7u9VX1qC83ZLGzVODb9gp3SpLVJQUqNDJaZbMysp1lXGErrXl1NRMKUtS9TYmcQgcAOEEyywyl19MCYyUBnwwhQ6JS9kGPF7sAj8JXtwOqY9iCegiMegeznA/c+7CfCIH38TH70/xhHi833KS1st94pQL9UoY900lPZChl2iPJDO36NwGitknH0pWNDlYUylJOfdIPWJxOUierGXDHj/AGfDyRw8kAY+XDDqlE+kcMIkZnL4XvcImZ+Un2MIlj5PfgSn8aUdrV5ZyiQBnw8Mf7kSUDPj9/gjsz45+xGfsCDnn0wM+nODn5Zz6so+F8aOPwhOfX0Rh0+eMf7JRkDpBOPgi1l5Sk0tyfTb6lEzoIfmlpSugJXKJpxByOU+g9cxAlB8MvbjI+UxLhL25wPDB+/hHj88Yjo9owcPZMOeFftmFeOB4R7UH9yfdhQl8I8euJJyw6jl04xjP43vQp1IkoJzAmSU5kmWMDc3Lq8O0Dzha+trLU9ux3+nbUkrpblSKJQVuASS8ka2yZjoKG7YtO2t/wBvpEO7g2PX1DZqWZSQa+0vFQ+sLe8sEpUAFDKUI/dpn8YRU/uGv7yiMRDBSMQVS+KRxh2Y/lF8PxjBnnPGKnp0jjhnPLh91GH/AHmvp/uKIwHt5H7jGf0On/vLcK8Jjx+b7nj/ALBrED5NHT+COuM/YPnjM+z54zPs+eDicj9+cJKdWY6fPA1CQ0/fxhXaVx/C6f3USBOM+JHRnifFGKlS6j70UUyvCiaCe18HWqfjjNyc/wALh5JQMXJ6ky7WGfHDLGGxMz9Ra6errjHvOsBUvbmJSinlr+kU8tZmJ98jownFerGfrKtUifS0iYwPRGPez6lD3YpShSvnMjj8E8eGEPBSlYOrGBEzJWE5mYmY+F+cHmgrKyltFPULWSZlKEtqKiRxkkT8UXDl3ydfZ3PzKfadTctxa2n7DtBJJCUtoTqNbdNWBRMBGPRF03FfrnWXm83urdrbpdbg85UVdVUuma1FxZKUIAEkoTJKQMBHYzmDORPaOc+rAQEk5y6sRMmCBLPj4ox6Z4eKCfwpAff44I6vdjxq9uPGfag5Y48fB0dUAHjIQnrT7pMJPTKB9/Ex+9P8YR4vNDjq/RQkq8MhgPGYvu4alJDndOLbcUQCVFOhCQr8FJMPVSlKdU4466tZJJM1ElR6sQIwy4f+C4+x54xHAcRwgSHVmDn4Iy+/ywARlhmPF7EGWHkM4GPH8Xzxw+MIwn4pH2Yxn5B544+QeeD4TxA9iBhl1iXCPYwIPj8EcPjDzRh0n2T/AOAl0w0pJ0pQ4lSXBMFpwKCm1pPSFgRba0qm8loU9SSQT6wykJJJ6VgTgSM4OeBn9/l+54h7ZgeGD9/CPH54kZ8PaMEY/f44c8K/bMK8cDwj2oP7k+7CsD6XV54w6jj4/DEzLPhCpzx1+z44K0yEzPMg+7OLbuTal6rNv3221TFTR3SgdW062tlYWG3tMu/pVkSW2qaVCKDl5zZLG2ua5kzargzpZ2/vNtlCcGFLWEUV9UoFXc5OJHZxhSl4OlDeoYiY0hIwJB9ERjnh6R0n24ZIAnNWGrV8Ezwh3BPzrnA/hHrgZZdfnipmZApGPT4pnKJzPXhl4ATHpKl4eEIxmj6wVnidXdp9yMyM8s58B4JxKa8umGMcPVKfPEy7lHlhU1CczwJ92M+jh4OuMx8Ux6XsK88cPJGY8kZjyQ1/xaP4o/sD4D7UNz6fc96B4AfYg4jjx6/uHwGKD/oDP8df3E/uh7cN/wDQWfuU3XU0/wDfkRc/+mL/AIiYPhPtxS9Hejy6TFV0d+7/ABjD9dcqymt9vpGXKisrqt1DFNTMNJUtx155whCEpSknExeeV3IKqXbbMv1qz7r36HCK+5N6e6qqHa7zJBboFhRQ69NC1maZaRitx1K0JUqZUtanHXl5qefcWVLW84cVGfGAhIAlIzExlhwgDo0wnx+0YP39H3EeEe5B8EeNXtx4z7UeL3TA8IhPgPuwj977kD7+Jj96f4wjxeaGqNAKnX3ETSk4hOqQw6ScvBFq2zSuBla0perkt4uBJRJhk4yAWZqUc8o7w+m7ipXHTPLy/wDhR9+WMcPZ88S7Ps+eO0dPRIHH249I/wALzR6XsHzR6XsHzR6XsHzR6XsHzQcpT6/PAy49PV1xLDHw/wDg9YGemfixMhFx26tKUrDibjTLKu06nSG325YYomDhGJmfDOWeGZ4Qrwe4PueIe2YHhg/fwjx+ePJ7R+454V+2YV44HhHtQf3J92FfuoHgHuwPD7kHwK9uBCx+Kfvxinq6Kofo6ykeRU0tXTPLZqaWobUFNP076FJcZebUJpUCCDFo5WfaBu4Fw10tq2jzFrVLJqgptLNNatyVGpSULKkgIfXpBUcwTDbjS0uNuoDra21BaHG3AFIcbUmYWhYxBGYhjwr/AIiod/4xf8YxmPLD+PwFe79z7+iEf+01f3lH3OHlEMZ/Q6fgT/Io6oV4TwjDHweKMjGR/sGv0So+ab+EPwR+NH0So+MPyo+iVHxh+VA/RKnyj8qD+iVOR+H5jCQ1TVIM8NS5j2xA76neUJD0VH3CIV+h1Jz+GBx8Jj6JUD9+PLnH0WoPVrGPsxQldNUrHqbRQdUtKNSpJMpTIiRpKkfv/fhP6LUntJlJfWOuGlLpH1E0DUilRGByEs8BKPodV+c85inApKoH1lmR7zAHvUywBMVne09StwPqCyFSx0zkQDI4Qf0Sqz/ug/KhpbdK+laFFSStfZmBLEgnpi87m3BX0lns9ppqm5XC4XCpNNSsMMAuvEuLISXAkYIE1HgIuHLvlY9X2HlEh4tvOrW5T3feNTTKKDUVTjZQqnsqnAVNNgguoM1jhCKiqWl5lGrumASgIWpPpmRkvPjxiUpCXlM/D9wffwhH3/BMeOB4IH708fPBPV7seNXtx4z7UeL3TA8IhPgPuwj977kD7+Jj96f4wg+AwVuq0s0qlKcUASlttlM5noi51YPeNJrn2GFAH5RphzQ04njpUBgJcIQkiUkylKXsf+CxWgfulJGWeZiRWCfxcR5ZyxgaQ46omWltJJHhETS2BMGUyZjqljjHzfsRPuhIzlP34wqG20DJBbBVLoBIxiRqMP8AikQUqenLj3acZieMgIxcx6kSH3yiTbiAf+ESQPYwia3GcMTIKA8GIiRbTMZ6Rh7MdtABHVL/AHZwB3a0yMipQGk9GmWMel7BiYV7B80SB9v/AMBgJyM5YZeOLQpbSltVNWihcCc0oqvky4BPGRM5Q/TnTpTMNEnFagrowyTBmJEzylgMePRGZg+GB4YP38I8fnjEDMe1B8Puw54V+2YTLCc5y44DOMzn09Rg4n0ek9MK/dQPAPdjxiBPoMHwn7mQ8kZkpmlWkK0iaceBBGIzzEUfKnnxc6y57QqHm6bae8H1d9X7ZfcIQzb7k6oldTalyCQtU1NjLCKGroXhW0lW0mopK+kcafoXmHmgttbT6CpLqFoVmkkTwh0KoKhSu8XqUFyE9WJHhiaqSoHQNeQ4DPhFQEMVKUkdoTSZjGYBJmAY+i1Ph7zCfgmY+jVX5wQgFl71b6wV8mVScDmgGYVPKUfRqrxOD3VR9HrPzifPDOjBr1VjQkntBHdJkDjmBB75p1ZmZ6D7sfRKonp7yX9sI+i1Q/5b/fR9Gqvzvvx9Fqvzn++j6LVfnP8AfR9Fqvzn++hr/i0fxR/YHHgfahB1cRw9+BmMBwPRCsenh1+GJz9j3/uUAllQtcc+0qJ5YQn90PbhtMsqBn2JHo+5TH/ylj++oi5/9MX/ABEwfCfbi6by31faPbu2rFSPVtxuNY4htIS22VIpqdKykVFZUrSENtJOpSlCKq1Wtu4WPlVb6l36k20XSzcLqtpwhq73thI06nBk0cNPGO8cSCTiEpJKUz+CnoEjABmOr7o8ftQg9A9wwPCPageCB+9jxe7HjV7ceM+1GmXVOfRj7sDwwOoH2/fhH733IH38TH70/wAYQ88ASvToR0BSsAo9STjF8uyVtsVL6HGUVLvZ1LWgpAbmQVHUrDwQFKM1rXNSjmSVEknxn/wXZngnGU/bGIgJUkuSJkk4+3xhyodpi3StALcdUUpaQJgTUuekQn6x3Jt+2lwFaE1l4oKdS0pOOhLz6TjPxwj635obcpS7qUga3KxS9JkrQqhbqAMcMZQs0u4qu8oSTJVtoqjty4pDzDZxhbFHtHmDWsoSNFWKCibp3DLtSV6wtQSD0icuELFBy2vjgn2HKlxCVKB4rKHBKR6BDimeVneNpQ4sFd6qG1nQhSwFJTMiZT0xzt+0VX8uds7YufKHc1nsFu2nQXS519sv9NdnX2RU3S41KPW6N9hLE9LQWlZOMso5f7etWwbJuin3xyg2RzSq6q53y5UD9tq93IrF1NmpG6dpxl6ht5pfklqKXF6u0JCP0rleyy5pBUmlr6h9oKHQtxZURPiYZVc9gbhS9lVU9IlpaNUz8y88UhSZSxMobRW7I5hUqnFpT3lPTW2pp2kky1OqFShYkOgQ0p6+XSyLW6UK+tLatxLaJiTjhpQtSEAT1YR3dp5o7beXKYTUKdozwlM1LbKQcekw0q2br2xXF9GtpNNe7a4tYxybFQVgnoInCXUoJZWkKbcbSFtqEpzC0KKTBkqeGUiOI/sz4IKFK0kgZjpyyznDNRTuKaeYdbfadRgttaCFBacDIplFjuQdD3r1FTumoSdQecKQl1yYmMF59cCZyBJ458OqBjOcK8MDwwfv4Rpll78dOAPAdIgnpPuw54V+2Y6NM/Hl5Mo8h8uEH9yPbhX7qMugexOJS49PvQPHB8J+7iAfCBEpBWIMpSlIznh0RYeWnNFyovfLF+tTSUV7U+47ctkpqDoDjbS0rNbaUvKBcbmC2JqTlKKe7Wevpbrabm2mst1yonUvUtbSPjvGahlxJKSlaVeLI/cqTP4JkJZYHj91H/tNf95T91gSn+h0395bhWHE/CPmjLrzJ9v7mQjP7rXyJ+bRxH4Ij5k+UR8yfKI+ZV5RB+QVkeMJ0tBPhIEdpM8PwpQr5I8fhdcfN/w4JLZP7+KGbc/0Jo4L/GWZRi0nxqnhA+RT6QkdWIx4Q38kf2e18PDh7OEY06D1lUzFNJlKZ1NPkrKbyPYivBaBKalaSdUtR0pxMumLxvje9yp7Nt6zMLeffdeSHal4JJYoaFlRCqisqVgJQgTzmcINCKNVg2Dbaxx3ae1GXluNO06ZtU95vQKiKm7rTqJChJsHCO8fSVuqxUoz1ZCQT+CkDIdEAYAdE8suuDLL7o+/hA8EvYgYjPpgSIOED97GJlh7seM+3HjPtR5faEDwx4j7c4RiPg8fBAH+4cTH70/xhCGPSU6sTTgJJTjMnhM4CLNZ0uL1vAVym0n5NASVJAWOKjqmJ5xrUJq+DjLSeOHEygDTIHI+Pol/ZLFMw6+WwC4Gk6igKwSSBjInjDqr9uCyWLuZhz64uVLQlKhho7t91Dilk4AAEkxVUyN2q3HcaNS0PUNjoqlwKdQDNhqsdQmlUvUJT1aQeMd5srllVPLaU61q3XVopWyhQkFKpaTUpxWE0q1SE4QLDU7f2nTgOpW1R2huvcdQ4JJSt+rJUFNTJSpOM4VVX7mZurQ4hCHrfaq921Wt4NpShC10lOQCvSMcZEwl68VlbdXkJCEO11VUVC0pHwQVuESiSEaQfCf4xMoyMYTHgwjIxUEgyFO8T4mnCfFH2xnB6K+YmzgJcf0uvwEciAZg/wD2pvJsy/5O7R9/XHvxP3YxAMspyw8cCYAl0ccs5ETyj1y3VNRQ1f8AzikfeYdl1KQ4JShD1m5l7tpywgJYpqi6v1lvCUrS5oco6hS0KSop4SMKN6ulm3SwVhaWq21sUjjQ/lEJdpQnWhw/hYjhDSd88taulWnsNubPfVVh+XpLq265QDalE4EEARStO7uf2xV1gaSzR3+icaWl9yQLNRUMhdOyEqPpaiIcG3dx2K+qaSlbrVqulHWvMoX6CnWWXVOISrhMRJSSk9Yl/Ya0J0GYGEzkTxl1YxNQK1SIkczqEs4t7LYcS5aap6kWlZJSO8V3o0T+AQRBCsfRmeoz6uECahx9yDiM+mBiM+kQTw96CeH+7A4YcZ9J6oxw9uF9ZVL2YViMjxjDHAZeGCZjIDPjOFYj0umJnDEe1GfHzx4jB/sVSB7U54dc5ZxTcsuadVWXbllc6pLdsuTpXUVOz6h492gslRJ+rVOkd4mckiZ4RR3O3LZrrdcadqsoa2jfS9SVdK+gONP07qZpcbWhQxEVPyJwTMkq8MhOPmvEV9Xkj6Oj40IPdBKfrFQkFZq7oTPkjFoEEZFcweg9UH9HR5TDJ7pAT6pT9k8B3Lch4hBBpmyZnHV4I+YR5Y+jtn99H0dvymPo7flMfR2/KY+jt+Uw1n82jp/BEcfZjj7McfZg55HphGHGB+5HtQrx+39w+AxQf9Bb/jL+4n92n24b/wCgM+0fuUv/AEqm/vyI3dv7fd1RZ9t2H1ipqXygu1FW6hpJat9up0yXV11SrsoQnpmcIfdQg2Pl9a6ladp7UZUsMtU6TJq5XNBJFRc35BaiZ6CZDKO+dmtw4qWo6lFX4xOZ9yJyx/sh4v4xhHhPtmPEPdgfvYHj9uPJHjT7ceX2hA8Mff0QDLGU/HKJkE5HhwgI1kOSkUECQmZznlFMhPa1d2AARpxM1YgGR0iHqRhYU3QtM0vZCkn5NPa1A5FSjHaE8cOkYCflEI4JEs+A6zGBB8ECHKl4dzTNAF6odIaYaSclOOrKUIB6zC2Ny8wrEivS13ybfb6j6yqFJw7KjRB9tpZByUZxUUvLXY9Tc1NqUym6bmccpKaoEz+k0bNKVOtpTgQHBNU4Nrt+9DtDb1Qo/WNDt63U1PWoaWPo9BdwfWmknCZVMynlOA7uC83e/rSpC0vXq4VNze1oRoQ4XKt14hxKBKY4RJIAAwAAl7H/AIGrAzNJVf8Am7kfa7P/APMfaAwPRVV0x5Y5Edf2TOTf96u33fe/s5ETAPX9+ZhFVsrct62puKhebuFBc7Hcaq31LjtMe99XdNO62l8PJTpAc1JmYt91VupV+p3m+5rrLe6ZhblNcKNfdVVJV1IDlS2ouJOpQOo5gQhjfnL64UFQdDaazarwr20L7ILtZ68tlxbSsZhImnrhql2zv+0vV7iElVDcCu1PJcUhKlMJFaGg442VSOmYJEJcaOpCwFJcaIWhaVCaVJcTqQUkcZxpCVEgz1CUsJ4dRxgagZgSxl4eBi+2heg0zXdVjWICw56JP4ydOcEakzknCY4AzhPaGR4j7g8MEdMx7BifXPPDo8MSPRB8B9oQPH7kH9zB8B9qPGD5IPhMeT2zHiHtx4jB8J9v+yHdmSSmSxmDM4zEpxZuUnNK4Ov8u7jUN0VivtS4p1W1KyoUhphhwqKli2uqI1E9hEo9do6lmtoaqmRU0lZTrS7T1LDydbbzLqey42tJwlP7qP8A2mv+8pj7+j7jH/Q6b+8Nwrwn+zana0H5NH8qv8EdcY2tEv8AjVx+yU/nXPPH7JT+dX54M7SnI5PLHngFFsDZnmHVqlj1mAHKIOiWSlke0YP8zifT6wvpx4xIWkCcpyeVMjOUzhjB/mhP51X5UUU7WFTo2yn5VWA1KkMMT78S+qgAJ4B1zPpPThhCZWoekP5VzpEJDlsQ4s0belffLADchpTgcwTH7Kb8HfOeeLjvPdxpbLt+wtisr69+oUNIa+UQzToUZv1bxTJtAmVExUooy/beX1oqn0bZ26lxxDNQorU2q/3RgLCXLhVoAkMQhIAHGGjqK3SnU4VJCRrJySkZJCcokMjI8cP7P7+EzAHQcPHHiHuwMvge3Al1+3EuAn7koHl8keX2oT4YK5E6TkOiUBQSQDiAf9yO00tXgOXxoJAUgy6gRn0CUMOFSlhpZcJUZySlJUJk8OzF4rnwkO1FfUKUEegAlxSUgfvQIAlmSfYEOKUCG0hRLh7DaUgymtagEgDpJioO7d7Wn19ltSxZrS79bVzroTNLDnqBdRSrcyBWcDDzHKvl20hlxCg3dt11roq2SQR3tPSUoLZlmAqc8occ3bzDvtQw+tQctlrfVaLUWSCG2l0dEW0PBtCpTViZTzhxbSNCnVFbpClnWs4qWorUpRUo4kzjCeHR09cocUCo63FLOrtGasZzVngYmM/vHR90DrjJX3+KAQFGfRIS8sZKEumRn7EASVn0J80ZK8iY+F5AfaBh8nIU1USCnE/o7nV1R9rtKZnVzR2sJccbhXyAlKOQ6/8A/k3k4ky0+klF2BGJnhKMjw4J9iMleRMYBWWcgBnHwvJEwDmOEcfiiO0JnwY+QGMU+354yHs+eMMPv65x1iUj7sVbzIKLVuh/v3kGemnvaEAOLB9FCalInwmTAEwCAZeXoPSIaekO/p194w8CoOsrlgptaVJUgjwwkbU39enKRLwe+qr6+q90CzgC0gVmtyma04AJMhBoebuxVlwqQli+bSeSWlDDW7caOpILZ4zbMuqGX9mb4sdRWPhITYq2oTQXcLImWxT1Bb71QxnonlFXdl291u1V9P3LVQoia1gq7SEektBIwUMDDiUocUQpSVaUKXJSTIomkEa55iMWKiQEye5XIA8SdMgMM4SKuuoaQKwSauraYCsJmWonKCqt5icv6QJKkqNXvKxUqQsZo1v1jae8B4Tn1QWXuaPL5LgzKN57fdSBxxbrlTlBpq3mzskkgErp71S1bYxyKqdbmIiaecGzUieSq1eXiEFTXNnaD+cy3XoTLCWOuRhP/wAT9p8f+8mT0QpDPM7Z88tTt6pKdAwn6bq0jjwjSxzN5fuE4SO77K2ZkYfO1LYMAUm9Nq12vBHqF8t9YFEjAJNM84lcx0GNdO4h1ChqSpp1twKEpg9gmWHTGtSHEoMpKUkgHHApmMR4IIKwMOJAOB6I0gnAlJJIkc8j1wfD0nj44w++fj/sZmCCJiRBSclA5ieYig+z7zirXDt+5ulnYu9K6qdWi0VGTW3Lwt0lLVA9MCneB7KzJWEo0iiW52UrQQ+otuIWkLbcQqXbbWgggjAgxIW5v45n48DjCHRQpDnr5GjWe79CRVlnpIESVbmZ4EHvCJdOYliIE7e1LjJ1M5cZdcobWmlIbNOyUp1kEJLSJDASmBBH1eVKBzKz5okm2tjwn3jH7OaP77/ex+zmv4XuCP2c15V+6mP2c38Y+aP2c18Y+aGsf5NHEfgiM/ZEZ+yI9L2RBx4HjCZHj0wjHh5oPg80e/GY8oiiExP1Fo/w1x7488JxHpJ49Y6DDYlh9XtcT1dcXfc25brR2Sw2Oier7pc619LLFNTspKlGazJbqpSSgTUpWEf0b2t6zbOUlmrVp29aQp1FXuKubJbO47qjspSwUAlltUwkKguLJLpOJVn4jEpHDDCXCJjo/sJjETkSASAegnIGMB1YmX3ygv3C626gbSQkqrKxmnQCoyALji0oBJwkTnC13HmJs9rQopUhq/W6pdSoTBSW6d9xc5johDl15k0KErcLSfVKC415CwOIo6d5YSQcDKUILO67nc0qGdJZLk0mfQS9TBUvEIQmjt+7K4TxcYp0JSNOM1B+mSoTgsUvLLmXdUJkRW0jloRTrnjIJfCHBLjMR2+UHNg4ZoqNtgcOC0lUT/0P83eP8vtg8MpaY1V+z9/Wd1KtLlLV/VS32j+P3SVoEx0GNVS5ui2rEpIqKH1knGWoeq0+kDwmFLuHMe5Wv8JD20745oJUlIBUzRuTEzmISq283NuBtQQUrr1OUAIWAUz9bSgpwOM5ShKrNzU5dXN1a0Nt0dPuyzeuLKwCCmnVVJWQJ48eqPWbbWUNypyoo76hq6apbCk5oKm3CNQisFOlsPPUjzNOFqCSl5xJCVLOJQnrlhFc5zL3LZ9qrpXFrcpLjVj6xfa1KPrFJQNpXU1TCwZhaUkEAyyMV1Dys25VbpuCClFNd7849Q2J1WGp5mnptFaloJJkFE6uoRUfXm7HbdbXSUtWewJdt9FSsEaSw0tpwOuNqT6WskmUOuOzcqHgC8+slTrqulalEqXKAJZdE4kIn7XtRSPrSQqrQHEas5ceg+5HHIRh7OECQOP4plj1xJQPH4BPDw4QMPBhLwYZmcAFIy/Al7eEeiJYS+T1ezOcS0yy/kpexPGASE5j+TED0fiT+8Rik/vUS8sjFWoJV9CrCJzz9Wclxj7W4JGHNfaM8f8A1nWzHsRyMKUg/wD9rfK34AJkl+8pGfCJ6MyBgge0M49A/mh54xThh8DHA+WcYJmJ8ETlPLCfGCNBxwl3cpx6A+IPPGQBx+BiZDHEZSjAEnE5HgPajEHKfo8ImQfIYIkR4R4YWy4jUlZCjhiHEkKQtCs0LSRgYbBJKxpEyZzllM5kyEH7kjl94huqonqijqWVa2KqmdcpqhhwZLaeaKVoWnOYIj/RfudlQ3lyutNMPr9sBI3Ht+brNAhxK1FabpTqRJxwYLA1HGN3UjHNG4WOy0247pQ0dpt9uoadFCxQ1lRTIp23WW23FLQhMlKJJVIcZwXrtze5iOL0lKhS7nulC0UkzI7ulfbRn1YQlVZv/fNWUmafWN1Xl0CeeC6oiRgsXGoqK+nU4XFM1brr6FOGZK1BSj2yTnmYDbdGw0CQFaEFKpYzx9IeGJilQFHNRU4FewRKPmOIyW7LLqXGDE/3z35cD9GVxyW8Oj8Yxrbpk8ZalEkeALUZHDoEFL9K2uYwJkSCcMwZxooK6uoEpA7s0da/TrbIIxQpCwUmXRGmm3pu6nQCJdxuW5tKMgZTKamfGKcW7mTuoqo8Kc1t1dr0pBwksVS3EuSSciDDjlTvGmvlKttCE0N0tdEoIWhU1LRUsth9JUnDOBUVOx9q3qsppO3CgZqLjTXJ6mmCuroQlzuHS0PSSoccoFLujYO6bO73wbdqqJ5h6lpU5LW+hxkv6GjnKapcDDVJSb3boqt1rvEU10o6qjGeCDVvMt0pXxlqnKGjt/c9gv6XWw4fqe60lcpqeOl1tl1TiFAZ4SETkZY8Dw4no/sdQJSoEEKSopUkpOoKSoYpUFAEEcYtXInmveGkbtpUer7B3PcVhtq+UFMxpRtqtq1q0pubSEg05X86E6QZmUE4iRkQcCk8UkTmCOiET/8ASSh/4kGM+PSJe3GY9iKeZB/Q6fiP7iiFYgdo/g+ePSHlSPaMZ+yPPH+554z9rzxn7XnjP2vPDX81M/No+GfwRH7JZ+OY/ZLPxzH7JZ+OYP8ANLWR+GPPACKBlozzHalCSqnbcwB0kexOYgztLKusK88S+p2sfx5Dyicfsdj8770Ueq2tPaqNtQxloBUrsdco/Y7OHS5L2pwn+Z2B2k/ys+PWIerK2moqampLWKmqra19LNPSUrYKnah9ZKQGWU58SYXy15fpXa+Ve2a1YceYdcbqd93RpJQbhXJBHd2imKldyziSSSSYLizNWHCWnDBKZSAAyA6PuHrJjDHrGIwzygVV5udus9MoKLb1yraelbd0YrDSnFjWU9EPUtfvBNZVtoKksWikduLbq8ZNetszYbJOEySBFU3svYdzq3UMgUz14rWm6ddTI9shkFXc6sZdGEOGz0di2u4oAqFCwuqSspSU6nEVJUlSpjA4Si53vdfMy82LZlrcc+tr1Rq9SfrH3pqNnsbNOUGruTwwkj5mc1ThFMLtfmNs0jKKa32youtVUuKYClrNTXvOK7yorqpSytxSjPUZCCumomm3ly1OkFTiyTMlSjMhROcEhBGnMnGWkyM+lI9uPRUZ/iGMiP3hn5YlJzDowlnKQzj+W8h88ZveRXnglaEkmUytpOo+EkEmMG0jwNpHtCMyOjD34IWw242r0wUDETx1fhQVW+nTQvBYW3U0ilsVLSsNXdvIUFJ1DCfCEptvMLf1rSkoUhu3btvFIgKAmDpbqJYSjm0eYd4rN10nLB5i7Wi91NUp/ddyo6qjddftNbUu/JuIZdpwGlqmdJM43DzW3ZTN0z9VT2+ybftrSwpu0besrBprfTybShs1aw4svKAkoqj0TL7+qMj9/igjSrDjKPRMYxRW8LDVKgOVlwdMh3dDTI7x5eo4JkBnFQqkmqgplqpKApGlCmGDo7yWQKyM+MAKSZYSw6uI44GD2CMshOWPCAdBMzxGIE81dMSLZTnmCQcPDhAkkg4ETBE8BInHODMKnIZA5wOyVdcvY8US0SPWPaHGBPVw+DGOrHLDhwx8EdlM8MJpmTh7EVau7JCaKtKppylSu9eMfa7ABw5s7Vn4PrauEckHAnBv7LvK5tXZPpKqLyocRwEYDgcgcfETMx6B/Nq88ZK9r2JYRiFGZEsNXA9EpR6JHH0SPZnHzafi/wC+g9mWfomWYwwIMSUlREpdMieMpCJaDOUp6ZES/wB2FAoM54yQenwwSEkAdCTM+LLCAlKSQZzmJnDwYCHUsp1vNtrcDKZ61JbGpRA6EjONREieESTn/uwJpJBIynx8UA92PIYv/wBXrU3bxtGo+sGg3rSvUrShQXOQWkqwngI3PYKqlDFuv/e7p26ot6VVVqrnyp6sWAJFS6tawfB1wewfIcPB1RiFDHCQJEvD0zjAK68PJx6YGCsx8E+cygTnOXRGAUfBIe4Y9FfDCYPHolHzavIfPGLa5/uZ+3OMWzLD0kiRPRKXRHzaPEnHxdcfNp9Li3jxzjFsTGPozEpSmMc4kpsACeSZZiWM4aqaNbjFTTKCmXUCRSRLsrxkpCuIOEoeuNipkU+5aWlceuljQoIZuKGAVv3KgCp66pea2/hcIKHEEOalpW24jSUFJKSnQcUrQoSIlMQt60XC62epJmqos9wqrc8SJfCp3ETChDdLbt6XG420Btt623nRcG6ltuUkO1T+upSSBiQYZY3zy/Q6yXBpq9uVZVNgBIlUIqZEOEzM0xSs0O5W7TX1QAXbb0kUS6RxWAZeqllNOMclTE4D9HU01bTqAKaijebqGFA5EOsqWgg9R+7R3GgqXqOut9UxW0VXTuKafpaumcS6w+y4ghSFtuJBBEoTtDeBprdza2lbWzWBbxaG8bVT6WU3iiYUf0muamPWCMZmcI1U6ahH1iod2eyJloHVjkR7Mfshs9RcSB5ZEx+yGvzsMKFO0lJpKchEgdI7lEkz4yg6rc2szznL3YwtLefBfnMD+a0fHTH7Kbz/AA0x+yW/zgj9kt/nBH7Jb/OCGsR82jiPwRGY8ojMeURmPKIOIyPEdHhgYzM8hj7UCcxgMwR7YgzP3ziQIP3KH/oTf8dX3EhAmrUDmAAAZqUVEgJSlImScAIr+RfKe6B/b9s7ql3xuu21ilNXivYEnbHb3WFBDtvpHJhxYJ1rmJyEBwkqOU1Ek6ZS4jgYCQCcJADE4Q5U7iv9rsrbbXen1+qbacW30tNait09Qxh+j2narhu59qYTXAigt61y7MtepxTaVceMOU9pqqLadCCtHcWyjbW+40pXabdqnhrmekYwqout7u1yceKlLbrq1+qYQomZ7incWptgGfwRAT3aQDwCRI5zmJERMNgDUdUgeoDECUXDcd2dVadl7fSl+93nsqUpWoJRa7Y2e1V3WpUZBtAUUpmoiQikoqehFk21ZkBiwWCnUDT0rSQZ1tSUTD9zrPSddVM8BGCMDx6euQiZRLOWB6cYICcOIkcfIJmPRj0Dnw/3MTHoH4yR/Gwj0FfHaj0P/FKj0M55o82Uej/4s+6IyH5ox6BPiSAfEcRE+7yAOKdWM5fB92FqUkaUpUs/JnAJBUTkcpRve7tvtIr+Ym1LvulsJQ2WW7e+wW6JlupbSmTgQiagpR0k4SgtzUSkkKnM9oHtdPGJ6QTjmDw8U8I9EeRXmhR0yIThIHLySjFJP71XuCDKfgkQBh4OEVVzWlsXrmKVUduaUnTU0VhplDvqxBJ1IarVTHSQISAjJKUgSnKQlnlNXREigywn4cyPFKBh08D+F+5lHiOEj0jqlMxxI4nHDDhhnA8HQqf8WDnl0HpHVEhMDCWBn7UAGZ6pezlHHyHzQJJ9jOMUGePv4ZiK7sylQVpwB/5q7H2xVSBS1zb2w0epw3W4KkPAmOSywmYV9mblU2MD6SFXlauPQ4IxRHoDyQez7fj9mMU+LxeCcejIHA8MPGDB7Kc+g5cPHKD2B4ZHoMfNpPHI+eMWxkeBnkPFCuweGS0DiOkx6HDiQo5jLSZR6AAxmROcsfFFJWpSr5B0FxKJ6lsrGl1szmCFoMo0U410Fc0mvonEiaO4fGsomMy0okHwRqSJ5TJxx4gcYHZxnwgSQZEYdGfghrfXLyqp6S+ppXqGpar0B6319C/gukrGNCgpsGakkDUkmYMVW/t/1jdRfl0VPaqVNOnRb7fbqYIKbfb2wJs04cBVjioqxyifeo+F8LrHQmPnEnx+9OPSGZ4/i+CPSHlHmgDWnLp/3sekk4y6f7WPSSPF0Y/g9MemPJ70emn4qfdTOOypHDDSkYz/AHPRGaP4P5MfA8ifyY+Bl0jzRgUZ9U/ajHQcDwHUMJJEoYudrqV0tfSOpeYeZJ1tqQZz1CUwRmDgRwitv1gt6KfeNrpTUbj260An63ZSZPX6xtGQeXxfZSCpOJGEa1JkqamylQIUlSRJSVJUAQoEY4YGPQEpACf+4IkUjhlM5z48IwSEgiRkBMyymZgnGGntt7qu1A2yAlNJ3yX6P0gv5h7WBOXDhDNJzBsjF0ZnJV0s02avRMSW9TrBQogZyMIcsF/pkVKsFWu4Ot0lwbPQWnFDvJ9UYE5ZzEseIMpGLBvPal1rLPuHbNei4WyupHVNOpWj5yncIkHaWoRNC21TQoHERRX+krKNvd9oqU0m9NuodHr1qrglDTdY5TKS2sUNxlqacSC2pUwDhH38YzHlEMf9Dp/7y3Cv3RjMZ9PgjMeURmPKIzHlEZjyiMx5RDX80UfzTfFz8EfjR+yKLyuflR+yKLyuflR+yKP/AMb+VB/miiOBz738qBot9OzI5theGPDUqAVMIcEvRWBLzwZ2qjOeJ7zp6lCJfVNIgHMoLuodYmojCPoiPZ88UU6RBnRt9PZ7SsBjOUBCaJBUogJTMieBJmZ4Siu5E8qqqmb3teqVyn3xuO21BcXtW1OgD6soqhpZSLxXNrUlZCpspBmJwohKghGpbryz2AVEqU488tUgtaiSVKImYKHK8366Bah9W2daF92lE0q7yrmprUF5gQ9RbeSzta3OgoUunbTU3BxGOCqlwfJFQz0iA/eqyoujoUpYVXPO1GhSjNWhKyUpmeAwiaAlsTySlQw4ZYYCMABI/gnic+mApIBImR2SOiMScCcAkjLhOYi73qurW9ubJ2u163ubdNYZU1OlCS6i0UKR26q93JKdDCE+iTqUJCLdZrHbk2HZVhSpqxWBtAHeOnsOXu7KAKqy8V4Gpa1TCQZCUBfdg8BNJJlLoOEehLo8HCO0knoGQzzwBmYwR/CPmEfMJ+OPNHzXimMOoY5R8yk+HSfbJj5hHxUR8wPjf76MWgMOkn2jHzY8J1YdeJlHoq9jzx80DhP0Jnx9cYtaRICegjjhBBTMESI0mRHEHqMVnJyzcxbrScv6wLYNpXTMVD9Lb3ZF61W64uD1mioXSPRQeyCZRPRj16iSJ5k8SfLEtAxnI44cfCNUAaP4JhSdHlGWM5YcIM0JGBxGonwyjbuz6QELvtxap6h6WlNLQNAv19U4rAISzStLMyQJiK0WamKdr7bUnbG38u6coLP+iKrEhJIJrHW1LMumJd2n0ZS7Qy4iWES7sZJyCic+M8owbTxwkr8KAS2DMZSOHHgY+bHV2ZeGeOMJ+TGXADo8MTDaMQPwxHoJxAzCvYzifdpPgB8s8DhHoj4p88SllhkfPBJGAHQYqjoBApKqYxxHqzmGB4x9tNJTKXOfba54/wDpS4SAlxEo5KI0EgfZu5XnCfwmbgTE9Esev78YkGx5DB7HsH8qMEHySn1zJ4R6HlkfdgzQAf3/AJ4Mm5jHp6D0qnEiyB1lSvcJjBEpCckgkE+PwQoBlBx/AzmR1RPupYfBSccZzzEAd2SMTinLGU8T1xPuyCFZSV0dBOcVjKWwq8bUIrKUpSAt6zvE+t0wSBrcWw4QueMhCTnMj4Jl06ZZcYxHAYAEYnLKXi6okNeXAdY68ImAR4sejPGNRbCtQnMieniSMeMGaSB1DL3jBk1OUwSZjiOAwg/JgHwEzEjkeBxgdjj1wPkkHrKFTj5tI6gFAHhPCMGhPwH2JmPm1ex+VE9Bn4DPpxxjBB8YI92D8nPqkfdg/o/HpH5UYshPUUhXjnMxg2kY5hJBHj64xbHH8Lz9UEaBhngT5SSIoL5Yqh233a3vJqKSrRMlCxmhaCZLZWnBSTgQTFfvnZtsRb9y2Zj1nf20GdOqoJHy25tvU47T1A4qanm0gls45QjUj0kIVLVqICgCAocPBAkOCZgCWPizy4x6APhBPHzwfkkZ8UGcegBjwB4eGG32Spl9pWpt9orQ62rCWlxBChIiKajqnUbmtbakpcZuBUKttpIlJioOJUE4CcIo2KhyxXoaQ5abyU0ylnImmfVJp5BOXGLVzE24j1+kSEUG6NvqeUKXce233EGronEA909UISNTCj6CsQRFg5g7NprfW7e3DRt1NLJxXrNG/oT6zb65KVHu6ukdmlQMjgDLGMbVS+EKcl7c4alTshIpWNKdJkB3SJDOeAhQVbaNeJxIcB9hUY2qkz/Cd6vxol9V0nxnfyo/ZVJn+E7+VH7KpPjO/lR+yqT4zv5Ufsqk+M7+VDWXzaOP4ojh5Y4eWMwPH74g4jI8erwwBNPlEDtDLpEKxHHjPj4Y9EeSMh5IosB9Cb4fjLg7e25cKV7m9vGhq6XbtI27TuObWt7zRbqd0XJheoslplSk0wWE6nCFCcpRcC5VVe7tx1lQ7crlUGuS4l+trVLeferLiQ+tdW46uaxpJE4cpay4robXqCl2y2BVOw+ttU2nKpaT3rzjaTKQKUGU9IMLVoBWs6lqKTNRAzJ6ZHxx6EieMo9H2I9EeVXmg4JHgniZ9MwYkU8D0yPgxMVtAzUotFisVI7eN17irCW7bY7IwoCoqX6lUmxUrHZZROa1kCUUOxNiUCrFys2gVtWu3iQf3VeEjuq7eW4FNnTVXO56Jo1T7lshKQJQVBASnCQyz8WAkIACAJ8cR7mUDAZDpPlyxjFM+oCXHpxnGKJeGRHtR6KPzafPHZSjD/g0xLQg/wDJpj0E/m0+ePQT+bT549AZYSSEz8QBjSEcTiZmQnl1CPmx7J9oRimWP4OHtcIkR7gPjljHCMJZ8Jg+WJHDjnwJ9yUFUpiZ+EeOXggY9PH34JkPETLoy4EwcPg9YPlOUcxec1ZTvfX12dTyy5eNlHp19zZ7y73imKh8q3R0iy2pSSClRijt4SC4yCuoUTip5ZK1zM8SVKmeuO0jV2ekjxyECTYGCeJOEdpsZGRmZZwNKMARgJwZoCZfhJmT5ZTlAWoNpEpailKcZdM5iASUSyBnOfkBnB7psuJBlNKSZeHs8Y+SpseJcmgCeWOnjAI7hrISDhI6ccMI+UWhRPBtYwIznOWcEVb3doX2TNxKZ9QOqeIMVgRUlS/VKsoHfpxUKV0gEajnKPtsL7GtXOfZukFYmC5uKuQQATxSZRyhbqHWm1t/Z15cANKdAKUpTXBGAnhpVA11bcvxHRPHPiMYC2nHZjEFThUmUjw4ziWcuMjj7MDsg9M0A+2Y9BP5sD2YJkJT6MIPZQesoB4HpEZNg9ISB7Uolhln5YVgnh7YEGaRiPgj285xq09J8IxwlwnGSMxn4PDFrqbo2V26oLturpEpCKetR3K3FJlIoQCZ+WL1Y32ltsU1X39tS4hSFLtVWkP0DoJHa1MuDETBHGBKfDicdOHRxicpjo1S4jxiDhLH8LHy8coGA9EcZ9HCCMOHwvDBw9kp6OEEy4yzPR08IGAOI4wOz5FEewMo9GXDjPyyxiRlLwzHxTGafzY88dlWHVMeQCYAifpfGn7UEaTj+6MHAfm5+zxjBM/B2B5ImUT/AHxj0B5fehXyc8enDxSBhOpoHA4GePUTIRb9x7dqPq672x4OU7oGtl5uY76kqWlAoepahuaVoUCCDFXzd5eULNJRUdQxTb+2RSpU7cNsXSoUSu+UjCCtx3bNe4Se8yZcVpMhAMk4gHLDDhhhxzjIDhhh0GeUAlOYmO0TPGAkJ1KVjIHLqlwlEigfGKR4I9AD9+YCgkIdTi28hxSXWyDMFCx2gQRDdvuxc3BZgdKqd90isZRwNNVKQozRwSrAx/Ry53x6t5SbsqmWL1b65wN1Oy746EN017p21Epbo1kyqtAmtAmDhI09XSvs1NJVstVFLVU6w4xU077SXmH2XEkhTbzagpJ4gw1/0Rj+9IhX7ox4/N/ZNfzXQfNt/De/BHXH7LoPjveeP2XQfHe88fsqiHWHHj7ZjGgpZdHa92cDu6dpk4eiCfBmYm80y8AMlkj2iIIbtdEZHMreE/ITEvqykxAOCnjIzxE5gx+zaXyvflxft2tXChtG+b5axZthMhaFXJF3qlFJulLRPalvMWtqayrQoBRAIM4vfNC085Wtp0jrjNHu7dW7KGruVbeLiR8vSsNNhhbKGdAPdMhKUJUJARTp319ommujT5dNU7tbb9TQ1KUKkWyk17r571ufangqA3cucHMioWtB0uUrVuZdWk/CUlxuUp9ELc/0t811TSU6SLTPFIx9DhKFr23zr3jRurLaEf0ht7VwaAwDqXGqRDaZqMtJSqYE4uDmxebXLnfD9Mp5dPanbTV7aqV6e0xTLq6qodZ7xYMioYJzjv8AmVyl3FTUCUvOPXvbdK5uXb9M0zNS3Ky8W4LYpEacZrEpZmC62O8bmpIWCdM0KKVpxx7KhI4CLTsPZ9Gl68XR5ZfrqghFp2/bmJP119v9UohFFabfTJLji1EBQSUjEwnkbycqHajl3Y7iX937zIKLjzP3tTNli4XtTidKk7XYfLiKClV2AyQtQKwFBSEyAl2QMAJ8AJTAwgBSkIOYC5BRPCQURmOEYMuggjtJYecEukKbbIIPGFtWW3Xe7VDCEu1FNbLXW1TrTJUEhx1IYAQ3qMpzlDLtt5R8xrl3zYUwKPaVyfU+mctTYQglQPSBClU/2dec9SlIUVJa2DcXAk6gJHtJOE+IhxLP2aeeJC3FMsrPL25JZC5gaS4VaUhHE8IVXr+z3v8ALDdOuqdaYtne1fdJbDq0opUu96t7SPQAKirAAmDP7NXPMNpKe1/o7uuk9JnKRjuqz7O/OijUtCFoRVbBuTWptU9LiQr0kqOAlnDrV15U7/tq2nNLwr9sXKmUysDFpybStKuqFUNxoK+31zc+9o6uhrKeoRw7ba2AozlnGkNrBVq0lbaxMjE+klOnSBMzgkICxMnVh2hP0geIMDu2kLGOsatJE/CJxJxotzElKljLKfQJGAqlk8J5Or0k5YTlwjTUt+rkYA6tSSOBy4xqSDplPVLsynMTnEidXQJSy4+KBgeOXi6oVKYwkRIZ5TGGGMUlBRJ72tuFXSW6iQUk95WV1Q3S0jekCatdQ6kSHTHLrkDZ+69R5K7Upnb+phXeMVHMDc4TcNxqWr+VfoKhRaAPoJEuEL1jUskkk5AnE4+5GABwPCXuZQDpEpDMk9PCXTEm2e9X8FIJQM+kiUSRSNImCCXFa5T4giQwgl+qSEgdrSkpIynpMYmrrFEkKQG1uIBGSsMPHGmjtQUJAd44QgYCeS8JiFOIQww0o9mbiAAJCSScpmcJK66nmoAlGkqIxE5FIPlgOO1rwSQROk70IWnEEmTaiqWWEHVU3Faxgo6asy8J7mQAhT1otG5b0yhZbUu3W6vq0h0Sm3rFPoC/DFclGyd4F42+v0pfsleSgponj3ighmXyYxJyj7dLFQw539Fz12K0AuaVApv1Se6KcCk68COmOWdwt+17zcael5CcubeurobVX1lOp9DNUtbXfsMuNlxsHFM5iYnCBd9u3ezoqDpYerbZcaQOKT6XcqdpwFFMsYTKor22dWlLgbqwnVLEJUWdOAM5QO5ulYl0yLZX3hBVwmhbaSoTgFFwZQRiAtCkgyIngqWB6OiAoOUNSr+5pUEqV1gKOQMBNVbdKZ9kspKwoSwJ06uPGNNS2+hX4PcqIBljMgHEQktutr1CaQCJ48NJkZiJhKSJZg+5LGFSEssiTxHGUHE4yxEwceB7OUImrDTjMSHhJl1xjplOXpT4dEso06QAqaSQZSmJGWGBMbG5jqeaeulkq39gbnQhQVVaqRlNXaLrUNJ7TVLU0Z7sKVhrQemErKZpKRhMkzz1HDI9EeL3RwlB6emXhEpQnPEDhx8Qgylw+ArrgEqBw6MhnlKYGEKShOodntDKRE/wc4HdU6yVSkVAoAngDMpyj5RKUIzUdeoNgZqVhgIUpT7MkgqKicAkSxMjOUKQ8/UhxKdZRSpdOlGElLSG1EAmBR2ak3Dd6oiZprdbqyrfCcp6G6fAeGUIYtXLDmPcHnsWmKfbNc687IT+TbSkFQAOMhAbpPs686KpU9ITTbEujxUoAEgHCZljBl9mTntPGQ/0d3XPhH/+MfPj/wDp1dI0Vv2eOdVIvVp01Owbk0dWenHMyh1qt5OcyKF1lOt1qr2lcWnWUadWt1OjsgJEyeiEKv8AZrpZ+9WW2lXC3VlOlxwZtoKmNJUDwhZQhakpxKu6eQlI69TaZShOlSQSnUJmQKMtQmMowQVJxkpIKgfAQDFBvDbS2F1VIV09xtNagP2zcFmqklq42S6060lp+krqdRSdQmhRCkkERbudnKWmqHuUG7K1TFdQKKnbhyy3YsJcrtpX4TUpFA5ULUaCoVJLjWlJ7UBJMiQDjOXlhjb21bDdtz3+p1IoLNYaCpudfVrTKSGaelbUogk4zOEUlzv1hsHKWw1TFO9T3Td1aiouilVaQtKHttMKRXtFlJGsKI05ZwV82OedfeKimp1LWrYtpVaWaipkfk+5r1PFthEsSTNUCtuW5ub+6UOtKacorrfLainbcJ199Tpp6ZtbZGQCiez1wUi38xUlSVJChfqfUkkEBQmiU0nEQqtt3MnnPS1D9cEIbeuVodpmWJKWpruU0oUVSkArV4ouu6uVXP8A9TTaaGouBs17stVcK640lMnvV0ClUobp3X1tg6SRivogfZv5g72fuFBW7bpaDZiN0E0NVZrrt1hSH7NSuVKWlKdumoBCFqWpIQEJMNTacSfVmZoLayB8mjCYGMEikbXj/Ksuy8fYiRoLeOvulA+zKPolGP3KFflR9Fp/ZHuxL1dgeCcfMteIx8y15Ya7P8mjin8EdcZS8Y88ZeyDGR9jzx6JgaTp6sOmJkz7M+EKBUB4dPSYw9yHnEoLikMVCkoCkgqWlhwoAMlfymngYv3Pj7Z7193Fbth3lR5RbGv1Q3VbfbSqrqe61UDiENLtVEhpDrQKCVLV25yhdLZbPa7LSKdU8qktFvpLdSF1eBcLFK0y2p0pABURqPEwCkESIylkSAePGKcH4NG0MOvHjODpHZ4YjoE+MMoIzebAxEhNaZ5dUVYKD87MkkT9AdZipoathitoK1AaqqGtYaqqOoZE5tv0z6XGHEKBxBSQeMb2+0By+Nu5O7t2jb3L7uZujpEp2dumkS82hxFXbGUpTQXcl0BhxrSgnBSeMMba+yNtelvjvNdbtFzs553y4Uu2kVluo6xtym5b7LdqklbW36JtXeXFekqeeEgqWEU7u+uce09r0SgPXbZZrJWXatb9CbdHdU1jVG4FY9pTI/c8IYf3dzA5objr2CV1DFBX0FptFSdASEerGleqO6BGrTrE1kmcsITT1PJag3I60WlCu3BV1dRVFbSUp1FTFQ2ntqTqVhKZwimb23yY2FbGaJCW2Gv6P0VUhISJBSvXG3ytw8SomLkug2FsujW1SFtK6Ta1ip1aNRc7tSmqFClp1gGSp4iG0M262sBsaW/V7dQsKaAmQlBZYQUJByAibb62xKR7s93PHj3YTOGl+t1KXTUvSIeWZgklUypU+uCr1qoKicy6s49UzhCh63VSkZAPOefHGLW6tZW45bkJWtatROlZUJ6icQoz6YPrVttlUVYqNTbaGoKj0qLrCypXWcYaZuewdkVyX30JeVUbUsTjrgJx1PGh73GXTFzp908ithVzT6UoeSxaU24OJcT2wfUFU8ioiZlKPV7Vy1qthFTehDmzrnUU7jcvhpXXKqwk8cUqiqHLnm/vOw16aZxdCN4N01+thdQCUormqKnonXCVK9JCkkAZQ8vYG+Nh8z32ypz1Z0nabJaOIaSu6lxSXUky9JSeM4WN38jt8VFOwgrqbttO0VG4bGwlKihff3Gh+RaAWOySJKGIh9qqZqKIsqW28zc6GppXKdxJ0lDofaRoUk4EGC8XnQoAlDSVJUw4hWIWOocDwjS8hbcsNS5JSZ4GRnIx3gmRpJBmFA+wMMIVIS1AnMcTkJzxir3/ALoC29h8kNp3zmpu1wtNutqaszAasTcnULbLju4H6ZKQO12piNy72uxaF13pfbnuW5JbKi0ipulU7UFpvWpa9DSFBIxkJRpAJJ09BJwEpECQ9uAFBUyDxSBhliRMmCZyACZmaJDwmWEMW+0225XWuqTppqK20T9bVPqlqk00w2pTh0ieE4DG1+R3MANKpRWouN4sFXZbSphRTp/nC4NtNd4oGYSAokdEd/uGh2fsy3FQ01FbfqK6VWkkFQetjKmH2XJHspJIJzIilqt9c/7QaOaRUWzb2yn6WrTj8qEVr1wfQ5JvBJKUyXjiIoblubmVzdvbjNQ6Ki3UlXabdbqqS1FtLqDSreDQaICgFAlWM5QPXOW9fuVsJCe7vt0ddmpMu2r1cs4iXTxhb9p+zfsNDryEMuP1n1lXuLQMh3VTWrZThxAEULdm5NcvqNunZSlpJ23bHi2lOSUl1lapeGcFFHy02EwleKtG0rBM48dVETgYYZpNl7QpWC6FOt022LEwlfWe6okzVPjG5Uf0a23pXYN0tK02C0JUWvqG5Eo1IpAuR0icf7RUJQiVP9pXbOpISAEhW8rqUowGEkow6o2bWXOy2ivqTZ6dVK9cLdRVj7YXbqNfZW+y4vQgnKcgThBTcdobSrJEKb9Z21ZHw2RirR3lEdOqfCEU9by52LUtIXrQhe07F2V9IKaIKi71105O7AqX3VUjKnUbbtbTndLSpK0NqaYQGtScCUyMLTffs5bGr0rnOaKtlRmCAorZqUK1JJnnC2qblauxKKHEpqLTVraeaLnoqQpTxBW1LszBHSDFbWbc3jzd2/V1iiqlpzfLbUWi36WwEop6X1EP92ViagpxeoGWENjZPPaloULcQhpnc+16i5P1C+6JcbL1veYaaKl4hSjKWGcVD2znthb4Lbi3Apu5tbbdUyJyWlmsfqCS4QCgAzIzlFArc3JLdV4YuCVFl3ZVKrdjLSkKkpNY5bgr1QpAn25THGF2/dO09ybZrUKKV0d9slbbKhC0ntp7p9kTKZcDKFhSkoUM0KIQ5ioSmhY1Aq8EaC2qaSUmcsB0EcIJKFCZwxRI9nKcT9GQJzGMh0SnG/eTtWhg/wBPdsVdXYFrKUqp9y7aadu1vSgqwcdr1NdyEjE6pCH6SoaLVVSVDtHVNKBCmaqmWWqhkjMFp1JEuEAFJBIMsRlPqnhhB7OrjgRh5TjDaRInCaZgkHr05CFaUmaQVET4CU55yzhq2bc2/ddxXWpKRTWu00FVXVtWkqCVGkYYbUagt5qAIwyhF13By5o+VFncQ68xfOdN4tvLSyPU1ONTtSmtv5bK2m2hqKQnXLhxiqf5vf7TT7LW2mbbSVlTdrVy9uf+lOtS9SAly12mos1Wy09W6kqSAUkBQzMUFXvH7cnMa8v0rzb1bTUNuXaKC5Na20hp5Bt63pK7ybiRIFI6ot1n2/zf5A7lvBqmaWjot/cxqS3Vt+q3AhxFMGHqulZqHQFA6BpJVhnCbvy85X8qrpailDlFV2qgs9+pjTuS0ClqJ1kmQmRE1EShKbRsnZ9vTKR9T21ZmFSBMgVt0aVkT4Tger2q1UrjYPdOMW+ip1oMhPStplCk+WH1tKSgmqTrW0hCVqGAxWlIUCcpziaXnx4X1n+3jGof/PrP9tOLYl8IWkCrxWhtwqWhaUoWouJUorCTLOFLeoaF9ShpWp6ipHFLGIkpTjJKhjxhbVZsvadY24rUtFXtqxVKCekIeoVpSfBKG2r5yf2BXIWh1pY/ovaqcqbWkJWlRpadrEg4HMcIfrLh9n3ardXUIKF1FEKylWkSklSEt1AbCkZiYlDirNdeZGy3HAkMMWi4Uf1eg94VfKMOtLnMEJmMSkRUVHLHnzT0TYLbrNv3dYKm4vOpUFFxhp+jUwllwmWkrBSBOLuabbOzudvJveNvVYuY21Gb9T07t+tbhP8AONlsr6Q5T7hsjig/RPGRLqAlQKcI3Xy+ouZ7Vo5Z7I03m/XBqmbG9PqRypdo3rMigfUU0t8ttY2aV9ZSUlxOsAAgQnbnKLYFmshCGkV+4aqlZrtyXd5kjTV1t1faVUIfWe0ruihMz0RrWtS1nHUpRWrOeapxclDFQS2BIGZSqYUB0ygDUB9+Xgj0h7AhpcwB68oTGIn3Z6zElSWDIFJkUqBwKVD0VBXQYqf6PWai21zZ2f324uXG6tuUtNZroi/U6m6lVvrKm3opXamlufq4QZr1JVIg4mNk0O9E3aodoaJ2irqi7U1Iq5OUlJXVdNQKq1aQ4pz6vbaxPaIlMkzMIG+xVpuGkd4BSuJGqQ1Yp7OcAhL3eHPVMY+AgRiny4xl7IEYjo4j3Putej82j+KOqPg/f4oyT9/ijJPl96Mm/ZgakDwjLPhMxg2hRlkoGXjkYUPUKMieB0yJ8oMTKQOrhjwnIwCGkOGeRyl5Bxhh90E943MAqKiAFSkDjPGMEjPCczh05dMeinNMpT6R0iGMB9Ea4nzRkn2fyYZAA+eaOAxwWnjIRVAp/lBjL8UdRjLMdA49Bjkx9k+12i42TkRuatY3Tzo31Z9fe320WPXV0+1qlZIRQ22tqW+5qEA944VhScotu09kbct209rWJpVNabDame6o6JGslwACZW4tcypSiVKOZjL7/GRBbTgQhap/uR0z6IUdEp4Ay49J8UAyBlwJJHjEXI6EiVPklIAyPRnCQaOkIlgdPa8JBAxMY0tP4kj3ZxTq9Wa+kOgiQkJTxGGBj6Mz8UfkwSmnY1JBMykYSB6hFslTsK10gWZpBAJzA6oxpWfijzRTAUzQm+gTkJ49GHXFY2KdpWjuhNSRM9mATSs4fiiUKSGG0aGlukp7JAQmZyzgkjEjj7IGOcKp1Bt1haQlxh9CX2lpwmlbTmpCh4RF1tnMjkxy93fRuv07L7FVYqW3LeZcSpa9dTbWqWqWoqz7cz4hFyuXLO67n5UXp1t8W23U7qLns+ifc7SEfVr069NIwozSA6TlPARX3LZKNvc6Nt0DS33HLC6m3bhmlJUlFPYqtXeuuLkQmShMyhVm3jtrcmzrvpUt6x7ltVTQvNBKhrUFuN9yptClYlKiBOEkpRqUCBokpB6DqM5Yxdry8EI3Z9rrmYLTY5hTdU1yr5fU5t26mUpA1qZrN0sslsghJIOBlOBT09E653Y09kpJASmapMo1PaUJEzJJkMYtzXLflzu+rtVeVFq+VNvds+2gE4HXea1ltOlSZkSBnIyi0VvOznBYtpU7iBUXDalhpHb9eXqdKUqeYaviVtUVK8iclEpUYNReNiXLmtUBba1p5g3JVZStOtiShSMUQpkpYWvHSvV0Qii2Byn5e7ToWlIUxT2vbFqS4yW0hLam6t2mcq0LSkACS4riWmmU06WSjuUhtJ71ZQQUiSZzxwjtiajkoiZ68SYxSPip88MuoAAcqV8BKWmRw6TGKUnE+L2MJwAEIB1D4KenplOGVFKD8mP5NAxwPREhSUxHSqcz5BFOFUlOAVgdk+DpEbhbFM0gfUm6McyALBc1SnLIykY/2lQUsJCvtPbQkgISQkq3bdgRqImRjGwqI6FFvatJUhQQkEj6spCQZAQFoYbdJCTJYASDLASE+iJGgpJHoGPiwi5OFCUd5VUSNIHZBkqPRHs+aPRHs+aEOEDTrU2EyGaUzzMiZiCUApUQQcfJMTAg65EhM0nHMCUzhFoeZeWhVQla6gAnS6UzklaQZLT0g4Sh5G5Nj7Mv4qEqQ+q7bbtda44hXppLz9K46nUBiUkGWEXKquHJi17Rv10Utyr3JsqoqLTdCtaNIU00pxygQGvgDuwEwl7kfzludnUlpFUu078tar67WIUglNK1caJbHdrcUZalJwh6prOWq9+WxLhNO9y8qmtxVjlMokGoqaNiS6Pu0gFYVMgGKq0Xqz3Gx3Whc7iutt1oX6OrpHsZtPoqGmyhWrDjM4CNqbvtbim6/ae47NfmdJKNYoKpp55hWmUw+zqSU5EGRi9VtmpEMbb39RWvmDt2oRpDNbT7moWrjXuN6ZI/R7m840oJmEqTKc4ShUgrSmRkBmcj1w1t7Y219xbsu1U6humtm2bVVXKseK16AUuNIDKGxI6lFcgRLOKO4cwH9t8mNu1jDFQKm9uJvW5kIcktbNXt9pxLlNUpT2SkrmFReuZ/2mNzf0g25tO1P3Tdu4+ZN9Z29y7oaWkkt6pSw0uneSrsyDYcW4ZylOU6/lT/ALKXk3y05fbVtfrtmqftGb82hTrqalpDaqEL5e7UcZFyZqKdaVuNVtW6tp9BQoJBiv3j9oz7QPObnvuStK21v3/dV6t1uap1KVpp6Sy22pp7axTtpVpA7uejAmUJVbOWdtZKQr9IFOyHAlwpK5BTBkFFIOc548TCW6vatPRNzSFOO0iHm58Rp7uWUTt1ksbFYmS0VDVM3QV7ToJKHaSoTpcQ42oTBSQQYo92fZQ+0rzK2LWUtY1W1+2rtfqzc20tyt06yUWq626+LuFOm3OAlKkNJQojjFm5Tf7Rjl1Q8nN7PPs0TXPPYdO5V8trm/UuFDK79YQVV22m2pfLVBWtoA6pDGNu8xeWO49pcxNg7toUXPbW8drXCnvNlvVC6Jt1FJW0pWk6gJlCpLScFAEQ456u2jvqnSpqXYASMZCWEyMI0+oUx6xq4x9Apv4Xmi266Fg6k1i5CckqQ4lIkZTyzjs2+nCuGqcvGZdED9BpMxwPTDR0NIM8QEDSMOB4zj+S+L70IB7szWkS0ZzUMB0GFoOMg0RLh2BLECAULkE4plnMHDE5yMbT+2f9lDZlVUbptNxesvOblfY2nqqh5qWDcNUZ1blAHFKTe2qh9cnkyQjBctWMWa7v0DtsdvVktV5dtT01PWt650TNa7bH1yBU7b3HS0oynNJgpTR0awD6Sx2jw4J6ouCzS0za2UtrbDY7KtSiClcwPSiX1fRj96T/AGpj6FQ/FPmhp/1akKjXKR3OmTQAaPawHpQErpaZtOGLY7UuOYEJd04oIIIzwM+gCEPYq1stuTJUT20JVjhwnBQpIz6D4OMYJBP39AgzSkY8QfMIxA8nvRgAPv8ABHDy+9HDy+9DXZ/k0cR+COuPR9keePR9keePR9keePR9keeAJGfgjLo6OuDIdPEdJ64yOY6vbjL2R54osDgwZ/nDAw4eD24y4p4jpEM4YeptcR1eCMEky6MYZKkqA75oZcdaYqsD857g6YGHR0dHhincLSe8DiCFlKdY0nCSpTkDDo0mfeqH8JXnj5tXkMEFBB7h6cwRLs+3BGk5jPDhGCfZHni54f4v/aqhOHDqjIwxh/jL3tmPRPsQoaTMoUBlnKLVhlQpn1eGMvaimkkn9IbOHRMRXq0kJKkSMsDJPDpjL2R54f7Jwo3wcvwB1wnDp6OqMva88XE6T2qmlIwzAQfaj0fZ9+CpMwSfSBxz6opbDzJ5fbW3nbKqjeNQzerPS1Dy1qUB3nrobRWJWEkyk4BG6d4chdx3rlddaK03C8ObYuCDftrlq30b1VXNWtgLbrKRamG1LTrWtKdMckrnS3q38qvs9ckdj23YO1al+1ouFw3LXpSle9L/AGy3oNOyl+73VlTgf7YClSJJBi3Vtm5e02/d121wP0+9eYLbN9vLby29Dwpm1ITRsUriiZNlCwkYThqho6amoKNgBLVHQUrNFStBIkkN09K200iQ6BGmSpKadOqRMtKFKIxPGFlKSdSiQJY4mftR6Ji6CWaaOXifMDs9PR1R6PsiKXsn6S71+1Bw8uHDrhPZPRhLjDOHwB0dCRHomKXD4YniOkY5xuIlJkLJunLGU7DdB7cf7Sjq+0/s6f8A+V12jZp0kpGx6MJPCZtVNMTw6IT2SMBKeGGkDjGXsjzxWiWJr6PD96qMvajL2oaGkz9aWfFozg4dHR0QZj4JiyYH5t3gegxl7UHs/BPR0jrinVI4UdOAeghIPSDDpZUttS0q7SRI4gAjIzECy81OVe0d2sKeQ8aqrtVPTXVLiHO8Dn1nSIYq1OIVinWogHhFdePs2cwbjsyvcNTXf0R3sBebVV1Li1PM0NBdEhP1VTLn3QK0q0A6p4RyJ+y0/t+yt/af5TVu4dnP01TeGam13PlK665uKjuLe5GwE1KKW41qtEkEJbSZKMtIs26PtH7zum/Lw47SVY2Ntot2jbVEFpBqLffK2T9TfGFD+5FqR4mEba5U8u9q7GtNCt1ulZslnpWKhDZcUrGucbXWrJJJmXCceuKqtqn0MMUrTlVVVNQ4lLbNNToU7UOLW4oJGlpBlM4mQjcfMP7W/M7m7yS+ydY92XaxfZR5M8qtw09grd4basVZ9W3jm7zaYfpq9Srxf7rSOtUlsqGklinbS6CQpM0rTzW+0pXKSueqv5gW4JAkAAlhmz0ukYdY6oKGuY3PxITL/wCrS3HOWOpVuUs4dMFtPMnn8JpTj/TO2SIxl2lWpftQTVcxftBKUZjWjftvbIHDst2dOU4bUjnB9pqkUHNaV0/MW2oCRMTSFrsqykSyxj1Wl59/aaqKZpR0oruYVpeUgfgoeTY0K0Dxwy5d+bf2iHFoKEO91zBt03WQRrQQqyaCtenNSVA8Y+z8j7P/ADE5n84P9nT9o7e9Lyl5y7S5h3xjcFX9nvmFfkn+jPMq11NHQsMUW2brUI9XqG0oaShahqJJBKUJ9Bb/AHiZyGptYC0KwJElJIIkZYx4/d+5a/8Ai6/+/J+6z9/T9xr/AI1v+MId/cM/xEx4vdEJcCWlEJIk62h1JGOBbcCkKzwmMIVIAGa5gICQDqM5SlIdUeHH2TFy/wCLY/vsZfcZ6rgr+9q+7Tf9EY/vaYV+6j7+v+ya7Cfm2/4oj0E+SPQT5I9BPkj0E+SBNKT4pcY9EZD3YPYTmfbjtITjjgDwl0kxMITMZYe9FKvSklxkrM5y9PgARHaQnxDx9JiWgTJTwwzE55QwNCfobWPi9qNJQ2ZcZS4DwwwAhAm+1PDoWDFV2U4OCUxP4IPtxLQjDDLzgw0SlHZMssvJLOHSlKPSOY8HXnHoI8kKOlABZe4HPRhB1Bs4jJOIMvD0Rkn4o88XIlKBJgkST1Kz6YT2Wjh+B7kxHoNfE9+GDoa+lPCWjDDxx6DXxPfhR0N4JUZBMuHHOLdICblIlZJxkccBHoNfE9+KYFCCC82MBKUzFW2lCJIKMSNWqYnjHoNfE9+KiaG/olRKSSMk8c49Fs+FHvx6LQ69B9wxXnQiaKmnAOnDtAkmQ6CIGCMvwYwSg/vZe10RbTpSFmme1ApwkHAEy8QjnDQcqrYi78xrtsm7WTa9uJKPWay9sKtjwbcC0d04mnqVSVOSMzOOU229+O2Dl/TbC5fbds+4Tc7jR01BQXejtzSrshVatTSaooqitS1pB15wna9s3Xc+Zd4WW2zV7FtVRdLFSvOLS0GKm6AoQl9RV6IEpYzikt2zvs4bi3HRPVZt79bc92UtleRWrqUMUoZp1UzwWw+klSlEgoOEItnMPk3vPZFwaudwtNX9XXBjcbFK4w4lqjWH6ZpsuprUK1KITJucjCLXtXmpbNtbpfrHKFvZ+/2XNsX12oac7s+ppqAqnq6Zxafk3NadYxAhK0erOtugONOMOJdacbUJoWh1sqbWlQIyOHGLqSlv0aTHhi+RjmYHZbzzl5R44HZb6MhkcD0cIpJJRP1hY6pAHDLOJFCNXEyOPR0wAEozAOHScOiGSEIMm0nLOQSIxQgHjgc+PDpinklHppGWUyOrON0Tbb/YG5iFSxErLcMvDKP9pScMftQ7OMhwnu67YRsoBKcNl0JBljhbGT4JQgaWx2EHBPEDPPrickfFirVpTMVdLwwx1cBLKPRQfCmfkxj0W/En34aVpRP1hSMsJBM5ywj5tonpKJ+7BkloYHJEuHhiyGSO024ThhgJZTj0W/ie/B7LeGUky93qhpOlHZpWFZZlaJ+SMkfFw8k4b7DeKgPQ4mYHXxhTfdMzShE+zwKeOEfY654bW2XW36uve7dxcot9u7fp1GpRtjcFndp7XW3taEnRbbPXud6FS7JlMyinSdCpPgTzmRMTn1w/JKMHFSw6z0zjnFS2R5NJe3+X24W7LUhsrLNyVSkUywkS1mfDjlHL1hxtDDx2XthFQyyy3TNprE2ijRXLDDKUNtKcfSpRkBiccY9auNS9TjSnUs3JxpJURNQDaFzzgobfvNa4TIIoqypdCsMBqKhmRBZ2/tu606FyCKqsrahbWgJwClLUgJJAmfDHe3G5oo2ppkxSvvvEjM6lLVgodXlhKX6m4Oql2z6ysJJ6QJ4CJBdbLiPWFefiIU9WXB6kRPN6tlIjoBOox9orl/sG9lW8Nw2XZSrNUzXWer1ts5i7VuYfpQRpFQmlpnRNJ1AGLQHmW1OU9BaqQlwAqWpm10yHHFGRmpS0k+CB+jsZ/ge8MY+Ya+KItwLDRChVrxQMC24EgDHAEHGPo7PjQPcIj6NT/mh+VCPkGs8ewOiJBhrp9GG/kGT8oj4I/CEOgsMegz8HHFIPHrg/IM/F6oPyDPD4PWYV8gzmr4I6VdUfMMfEP5UXD9HY+bayQQDNzjiZx8wx8XCPmKf4h/Khk9wwJXBQkEYfNHGRJxj5hjxp80o+Yp/iH8qGF6GxOlYOkJIA+SRgBPAQR3FOrE+kg+4oR9FpfiK/LjBinH/Jnzx8xT/m/fj5lj4h/Kj5lj4h/Kj5lj4h/Khr5Go+bR/Iufgjqj5mo/MOeaPmaj8w55o+ZqPzDnmj5mo/MOfkwB3bon+E2pHl1ARPSchliePATgjunzicQ0sjygRIM1E8f5FYEjnmnqj5p3xpI9sRRDuldlgj0FcHIxbc8SFeYwD3buY+ArpH4sMENqP6I1khWcsfJEy0v4ivNDB7tfz7WaFfhjqirk0ojvB8Bf4IifdufEV5oQO6XxOKFdHgh0d0v0j8BcfNL+IrzQQUKwZe+Av8A9XTBBQrMH5tfR4I9BfxF+aLl2FfRj8BfQrqhM0LwAyQvzR6C/iL80Mdhf0t/wCArq6o9Bz82v8AJhfYc9BX8mvo/cxauwv6CkegoyPQZDAx6C/iK80UvYX8+38BfT4IrF6FaVlASQlRmUiRyHAx6C/iK80VHYX9DqB6Cs9Iwyzj0HPza/NC0koaUggFNS63SqMxMFAqFtlaZcROLkw1dLW86mpY1ts3OgdU2UJIUl4N1CiysdCpGNYSXEyAmzJ7GZ/uRVhGRAPTgfIcotuC1foqkySNRmVpGQBMhPyQdjbaoWuZXPK40a36PaFveX9XbVpqhJTS3Hd1Yz9GmnU43TpJcfSMJQm987OYF2XYxUUV8tW06ivdtGy7NSPFNTSItlrYefDTSm0hKg6txekTUAcIcXX3yiZ9ZqWbu41ZUpuJQxWPB1Lqko7kKSET08ZCUKrrYxf910LNRQXOkq36v6iFUsKp63u10Wl1LbTbqS1kFLTjMRX33bGw9r7VarmrZd2KBimcqDRVb9K05UkuOPFKlPujUoYgE4QDSF0VNYzb66kdapU063HHqVp8+qPpS2S22tUgpKuyBnhFDaLvUVXMnlGsWtV42hvO41NQu2UTrDXrL22bypVQu1kFZcwCg4oY4GKvfPKbclLdaSVBS3yzvPaLxtm7H5ddqujC22l97KZQ4lOlaRMdEaShxUsyhKlD2BEw0/MY/NufkxSktO/SXezoUVAYymkCYwifcv8A5tfmhJDD5OpP8mvgfBDHyLyptD0ElXAZySZQfknvzL3uIlFPJp35xJmWnABI8SpIjc821yO390Ar0K0fsW4GYVlLCP8AaU//ADP7O/8Asuu0bLGheGyqHtaFaD/NbGAVKROOQhJ7p+fdpB+QdIyA4IPRHzT/AOrv/wCDisOh36VS4FtYUSJk6UlIURjAk1UH/kXPyRHzNR+Zc80Nq0qChVq7JSQoAol6JAM5wSGniDxDS+jwQfknhgc2l+aLGNC5ht2Y0LmMDmJYR827+ac/JhXybmXFtY6fxYbOhwzo2PgKPoplwThHzbn5tf5MNTS6D3iJ9hX4Q6U9EVGlDhOhvNCiMuoQgu0+stmbRWxrLZPpFBWhRQo9IkYpEhLuDw+AroJ6Ie+Tc9M/AV0k9EVFLVUxfpqhlxt9lxsqQ42UK1IUJYgxZbfTPuJLKaxtl56altpXVuFpKhx7pKtI4SEF+83atrStXeFDbpbCiohUlaZDT1SygCktdI2ofDLaVqJA0kzUD0RpBaaSkejNCAAMsJgCHO9urLi2llC2aYKqHdQOICUAzlxlDrVmtDzjcpNVlSXG+10lgNK1DHphVFWblo7DiAwEIRTPuvHJtJXgvDgTOEvXq41l3qZ6nFF7u2nQMQlbaSoKRG17XRbftzlDX7ytNNdGlK7keo6XnlqlpV35S6yiSCZcYCEJSlCXwlKQDJIQ2EpAGEhpHigYEmeQEz5BEu7e/NOS8umUWxWlRHd1xkkFSsXU/BE1R829+Zd9xJj5t78w9+RDR0OmfQ2onLiNM84+bfH/ACLnuIMNSQ9PvESmy6BPUJYluHQW3TJDOKW1qHoJ4pBETKHx/wAg7/gzBAS7wwU04kceJQkYeGFDu38FKyaWRxyITIiPQf8AzDv+Di5EJdwaZPabWkmTvAKQNR8EfNv/AJlz8iPmnvzLn5MML7t39orBHdq1fNn4Mpyj5p/8y57qY+bf/MufkRTy/wCaMZzB+bRmJTBEKOhZxOSSfcj5l382qPmXRjwbV7s4+Ze/Nq80fMPfmleaPmHvzSvNHzD35pXmhr9KT823/JJ/BEfSk/mkx9KT+aTH0pP5pMfSk/mkwO+d7z94lPHqiaUjVLOfh4QdFQlKeA0JVl1kCJLqhLqaTEy4kkGfoiXjwilWFpHe06XFJ0iSFKUZpHUIxcT4kCPnBOaZdgdIhgIcQJ0jRIKAcTiceEYuJn1IEMycHz7Q9AZax4YqkhxEkuaR2BhJIJHsx84PiCGwHEkEkSKM8Jy8kO/KJkFkHsCcwZGPnB8QeeCFKTp7l0jsjgnD2RBVrTI/iDzZYR6afiCLiSpM0U5UnsjAyVA7ach8AdEemn4ghlepOo1LwJ0jIHCPTT8RMKOpBkkmRQJHDIxQEKT8tSpc9AdnE4J6o+cHxR5op0koVrdQnFImJmUx1xUsBadLJSArSJq1DVlwhCEKK1rUlCEIb1LWpRCQlKQCTMkRXDfu5kbi30qmqUW7l1tVdJcdxKqFNK9XcubSV6LdQuvAJLiyZSOGEUjHLOk2xye2nerUbi3SUlGm/brY7xx1julXt4tNocDaQsFDeCjLhjbbhdOdXN+uNdaqe7NuV16rkpdfqV1CFqYmGkd20lICUpwSlQEIqrbzL3ztZy92ehuN3rndy3AC63LSov1DyHqp5Qe7xWARpAHTFjuu2vtO329U1RZ7c+uxXr/O/a9ZXIY7ioXUIqGmahFaFNEOo1gKkkxtixfassq6G27loqJdBzV2faFN0jFY8ketm/babW65R26mWofLIWpUsdMbd3DyH3jtXffM3nFY62n5V3K2ViLlbrXbFIDVw3pVstzUGbQH06WnAhaniBwMN7s3FuK/7gv266MJ3BdK8O1FbcbgEkVLztQQVpCnFHSZ4JkOEbbuz6XKalr7Ubb3tdVgts/UyhT98suOqKkuJwSAJgxYLveb7RIdu1uNGins7RrlNt2ohkOPKkiWsnHgDFgqHLbcr6bjR1Wt5t82wNi3vinZIxJ7TQmQZE8MIsFfRWC0obet9TT1QudGmqqnV0jvdU6y6VDspaEgJRs6tfvN2qnE0LtG0hulZ7umktaG6VpSadK0BLOAxHZwyjb14raOsYoKqlfYVda+pQGFvNuJIbqAqoLqShpPZ1JACRhGwudWyd6W2sszjCtt712NbK1yqZ3FQIqHXl0t8t3ZpVKDc+4fBLrZVhgSI2Zzc2BWUr+297WinuNOindRVG3V/dp+srPVKGKKu21WpC0qAOE4xdQPC2nzCcUzodSHlVC0rWGwQUpGQScBHYfSrwtpEJCnEETxGhOIhkNFCQWwTNCTMyHTOUuiPnG/zSIZStSFIKwCA2kTExxEboYJQlg7e3ONASJ6RZ6+QCgDkoiP9pR/8z+zZdf+dt2jZLRWCy3suiW2jSAUqNsYUSFce1Cf0ofNo/kkfgj3Iwqv/FIipdU8FPIqaUNr0AaQZzGkYHEx2qkH/kkwT6wMAT80nzQl4LQFuPEk6EkEykAAR2ZzzgDWnD8TzCDNSDgcCj3osrgKQVtOlQ04FWIGBlkI9NPxRChrTOWHZEMjUkaqRtUtIxJAnGaPiCGiXECbiPgD8IDoh+TiT8m3IaB+D7UZt/mxFKC6iRdAI0ZgpUM5YQ7JacFn4AgDUkzMpFI44e7A+yZujmOi1c/DeKG1UXL/ANTdcr7nU3JAfpqihKUoa9TIPbWSAmRMFtVA/NvsEiobIJR2ZggHDCFs2+uRaUueg4pr1h2QGMpSAnCjuC8XO6IJ1FDb62tRWdRloUNI6shCTT7feUtAEnahPeKURxm5qmT0xJu2HSJADQ2APIiWEOuVW22jUPiS6hBLToP90SpGkJcEsxDldZrld6UMMVFRVl6s79CaOnaU+7pQ5OakNoMumLryI5Icw7XzK3hsyzXvdG7GvUHhUbVFguAtjqK0uJSy3UOVKyEY6iAZQ26laFrdeWVLW2kzyEtMpDGElJZwGYZbGJGPAGQMfOt/m0+aLcpKmgpTdbqUGkg4OoAlhh7sfOo/NpPtiPnUfmUeaGiFtif/AASQZY9Aj51H5tJ9sQ2O9QZrSJd0jGahhPhOHEs6EI0MkJKQrEpEzM4xIuon/wAUg+zKCHFIWJf3NCZZ4zlCtC0BAUoAaE4ZyGUfOo/NI80XAuKQtSG2S2dATpV3hkTp9IRg63P/AItPmEfOI+ImGXOwHjcFpK9AI0huctMpeOPnW+j5oDjjmI+cb+InzQwtQ+UVSslRGA1FtM8JYYwe7cCcZ+iFe2Il60JdHdJ6IxqR+aRA/Sf/ABSIwqR+aRH0kfmkx9JH5pMNYfyaOn8EdUZewfNGXsHzRl7B80ZewfNA7CvimWcYIUcBkD1wewrM8Osx82ryR82vyGKLD/FUdOerwRghR8AMfNqGKeB6RDBl/ijfTnx4dMYIUR0gGUM/Jq+fZ4H8MRVkIUQXjiB+KmPm1+Qwx2Femrh+IqHuwr5xXD8aPm1+Qxikj5F3AgzHZPVHsZH8I+OOJ8CTFyxzpyMj0K80DSlasMZIVh7Bj5pz4ivNDIKFgiqdmCkggEmROGRj0FfFPmhQCFklJkAlRJw4YRa5JUdFEhK5JJ0qxwVhgY9BXxT5opjoXIPtknSrATzOGAitUELKVqb0KCVFKuyEmRAlnFz5P8nLvTvc1bnTVduve8aPu69jl48tsoVSNNa20L3AUOakkLCmTwMN7n3FVXneW77vW3D683Nea5xVxrlEJdDjq3A6pS33lrUqSgBkBFirPq20sGiq6+3MuLpkKdap0qSulb75ztrCB6IM8cY2zSqqrncKWhqK+kaQilV3dM1UIQWaYOoYTpbckdKdUWS9XGmeo7VSXOvtqrleKhtplDy1INNSMIccVUKGlKs0hKZYRR1L14p1fV1zqTVIt6U1xKK5pDdKlyTiO6OtpZGcbbvTlPWXKntvcUr7CXE0T3dvtN9ppSkOA6ZAmYE4ttFZdu2lun2vVP01PW3Cneqq9pu7uF5TTTzr7iBNSQShKUowGEFSHrpO33A0H6PSqQypdV20p7xDJ0YGUpiC3WUVUwm23EIbqq+tT3IZr2e/qS22l1S0ALOpUwMYdfrb7QufUFxSmmFvWisqE09cAqoZW33iSltSxqSccYf1JuF1dpLk02gqUaFbNK82VLmlJVpSXABPCceqUVgtTiKG7juausYNVW01G8wSaZT61hssreGPZmVRUIonKxxujurU2qFlS2KJhTIDcg02UspWsyB6YuFJUU9e2aK70t7dqa9byaZdK6yulWkqWvS2dbgVjKYOUXZNZu/blI1ZLpRVLltYU65enW60FhC6SiKG0VLbTnzi9elIjdv2XLxuG53JvdlRW7r2RS1lGUUdtrqKnSK2jo6kvL/aLSe8LQAAWOOJiSuyROYIMwZAyxikVMyNS78EnOcsuEZq/NqhPpZj4CsZwwJkzbGSScwOiM1fm1RT4qxWPgKHERucTM/6P7nHomRP1NcMJ5ZiP9pSOj7UGzgfFu67RsshJx2VQgYHE/VbIkMIT2T6CcgfwR1R6CvinzRVgzCjV0gCZGZwUcMOqPRV8UweyrI8D0eCGOyr5/oPVHonyGCdJyPAxZZA/NOcDhMH249E+Qxkcug9Iim7J+is8D+CI9FXkPmhrsq+db4H8IdUVB0nBtkTkejoljGR8nvRSYH54cD0GH8D84eBnA7KpzHAzzjYFRcaZFWzddl0W7LaG0APNVdvt6ksqX2VLWhDjZmcAZkRS8vd10e7OZ/Ny72uovTfLzlpbnL5V7ctqlLbobjvl+iQt3btHcqkaKdOlbzoBUEhInHdL+wHuBmwMVzYrbrUbt3Yusp7MXdDtxTSObUCHKoNdpLBcAKjLVhFx5dbFYv2xObVst1ReE7K3bSN0i7zbKIrFa7YH++U9WuW1AnUIWhCgMQJQVKIShCFOLWs6UtsoRrW4oqIk22gFRJwCQTkI3bys5X7F3T9oXeu0H37fcbjtd2optlO3qkcWiottLfLam4VNR3CW+2tLUkqMpGRiga319g7du3NpKdUm6XfbW5dx7jvVMyUam10dtqNr0KH1qIJOtQ0ATkYd5n8k73UP0TlNc7XuTad6aTRbz2ZcPV3mHLduazkh6hfdSSWlEaVpkQcZR/tSN2W2ncUjYW9UbQYJVoql024LpWPJTUtNtzqCV0xIcJEwkmUU50aR3q8ADIdoAcIy9vzR6J8ivNFtkhR+TrpySTL5ZGeHGMW1gdJSZe1E9J8QPmhmaVD96rr6oyV40q80NZ/OI+Cfwh1Q6koXPQzPsqOTaTnKD2VfEVw8UEAK+DmlQGZ4kQ52V+mckKIz6QIyPkPmi5EJMktMzJB/uhywxj0F/EX+THoL+Iv8mGFaVS+sliWlU/mjwlOPRX8RQ49Yg9lWR+CejwRTnD6KweP9zR1QeyT4vBBmhfxT5o9BfxFeaB2VfEX+THoq+KrzR6Kviq/Jj0VfFV+TDWXzTf8UdccPv8AHHD7/HHD7/HByyP35wBMZdHXHDhw8MHLM8OuMx5IzihxGNIg5fjke5GBHkjPin2xDOP+KNHxnOM4YmR8+1w/HEVQwwcEuqaQYInxMMTIPbPD8RUPYj51fD8aM4IJB+Rd/iTggGWPi8kZjyRcMvmJ5dRHuwAHJCQkNKMBLIdmPnf4KPyYS8pU1ir0FRCfRByykImlwgeBJ9yB8oc+hPT4IoxrPyrGqck4aVEY4cYzX7ENzUqRViMOA9+Nw7xpau3K37fWntv8urdcXQmndvlW2tpy61DYQVvUVjbWHnUp7Rwjf26N88wF7h3RW7mY3BV1VEwu63q51d6qnqq8XN0VKmimlp1LS2lKZhDYAi60tNRXS+VtNV0dUy9UrNsNGzp7uoV3SSrWh9WKgJynG4bdQWmwhlqstldStV7QrqplCUK9a7ouY92VkdoAEGNz2duqrNb9TRXVulaaUhj1emXofQxpRJpzRgJEYGNxUr/rFGhC6S4hVfWtqp9NOgglLAfVpUEqkVBPHGNxIrtxUYap0UVyW1b0KqK1aaQuNSbZdSlBQpypAUonsyjeW8GaC7XpOybnZrTVgLLSPW7rTqqKCuUloa0MNoaIWcgcIv8Ab02i209NSJRWyfUV93VUT3dtK1LVqWognPp6o3dYaL140SrUi9UlFa6Zfdu11K+iZS4w2VKSWVHvCVYJHCN1sVFLUtBq0fWZeerEKYS6w+zTpdUhb5cSQ24UzCchlON2U113NRoUzbBWUtFbE+uVdXWU7iAEtNvBtDgaRMkAzwi90bb10u1TUWzSzS1DYtyHltq1qVqbKxqaSCY3PbbDZLP61V2cimav2q6E1lM6ioW5SggJ1FlBSdSTKcxKNzWqiqnGUX6zlbVDZ6I09Mh6gcFS+nS02opV3CD2s9Kc4vVL3VYmnulApYqKioS220ph/vUpfQ46Hi46BNMgcMTFdaL5ui2UVDW2mudq+4U49cEVdqZNZS0zLZTpWXXUkKxkY5X7rse47nS19JvKxKdrFUSac0tAa5Ca1HeIKlLbepllDnQJxSXVsoKLjR01wZW0QtpxiqZQ8ytCpSUhTSgZ8ZwwUqkpVQuagE5DhKUol3p+Kj8mE/LdIHYRlLH4MM6XNM2+12UmZ6TMHGUH5U9Hoo4Yfgw2lS9SZgyKUcCJZJnxjcTWv5NW390TTJPwbNXqGMp+lH+0pPT9qDZxPj3ddo2QkESGzaE+P6sYPtwlSDJRbRjIH4PXPhGDv8FP5MVSi5NSaqm0nSmYnOcuzHziviJ/JjFw/ET+TDCu9GovkT0oymMMo+dHxW/yYl3wl+5b/JizkOgFbTpUdKMSAJfBlhHzn8BH5MKPecPwEdf4sMjvP8VaV6KcykT4cY+dPxUfkw18qfnEfBR+GPxYfAcw0N4aUEYJnxTHpJ+InzRSgqRIuifyaeg9EP8AaR84f5NJ4nqh65tpadfU4KZhBQgSdcT2V4j4M5xy1/2jF133VUXMfl/y4uHLO47JZtVK/ZL/AENRT1TdBdHqnvEu227USasnUlJDgAmBKcbs3TTcohu7nlv1T9caPaOzmrnv/c1ss1GFO3Cvuta0aRux22gYKpF1ACU9lBMbP5Sr5S3TcW5OZFjqNxbItFjptr387xtFLU1tuq3bVRsMJqGKqlrbe+y+xUdy82W1dk4RQ/aq5KcsbPtHmNRJulsZv22reuwildvLJavFNfrG8y27TX1taFocOkAkEqBmDFA9UpTquNtcbqwjAOM1rDlLVNqPAvMuqBlLONy1Wwvs6Udfvy5XXcG5qO22Tb69w7udt9GpyuvVzcuNxSqhtW27d3i3FKcWHO0qU8oquQ+3OTd4vHMdzYtu5oL2DtVO2NxXi6ctax11k7ot1HbwaOrZoVNfLUy3EvtgDszInvze3K7Z+3bG5zDbpWN3XyyWR/b933A/Z0P0dNS7mp3ENFFxtTNQUegFAqJJJAMfaIu/J/ct+c3R9oTmEjmnvt26vpFSpY71FvsSW5SqrXQqfe0kSJDhnjjFruLrbbTlUlh9SGidCFOthTiUFWOkLOE+EDE5jj70ZxbjPHu632HkgRnAIOIyMhhj1zEMlKiCrMySZiXGYj51XxG/NDY71UitAI0t4gqExlDqULUkaGsJIOOgY4gnKAe9PxUfkwoLXqAAwKUyxPUBCwHZAKMhpR+THzp+Kj8mLiVr1FDbRSSlOHykuAE84+dPxUfkx86fio/JhohfaNeolWlOI7vLLTI+CcTDvlQj8mDJacjm2g8PBDCzpmaZknsiUy2meHjghKkD/k0n3IPbR+abP9rHzjf5lvzR84j80jzR6bf5lHmMemj803+THpo/NN/kw18i982j+TV+COqPmXvzavNHzL35tXmj5l782rzQfkXsv7mrzQNTa04cUqHHrEYJJwGUj09EH5F3M/ya+nwR8y7+bV5o+Zd+IrzRRaW1qlSpB0pJkdasDIZiPmXfiK80fMu5j4CukdUMyZcP6I0MEKzGfCPmXfiK80MEsuy79qfYVgNY6pRVFLbih3gxSkqGCRPEYYQT3LuJn6CvNDKi04AFEmaFAYpIzlLjDpDLpBcUR2FZEz6I+Zd+IrzQVKQpOpl4DUlSZnRgBMYmD8ks5fAX0D8WPmnPza/yYuHyTkyxIdhWJkcBhiYHyLxwGTaujwRLuHhPiW1AeWUFPdq1etky4ynnAHdO4Dg2s+yARAAacGR7SFJ49KgAYopoWZMSEhPT2jMYdcfMv/mlfkw0VNrA1GZUkpl4cpRfOVVvr6B7YfJbYtemq1VjNOqj369Tt1NyQ4yTqLzlucaSgJBKgDhOLvT1+5qV1qqstQ223ZkuO16aklGhK2n22ptYSUEnVMRcqFFDW3qqq7bXUrK6ioctxbfB7BU2EqCtWkiRyOMXW20dhsNI/cLLVW6lFyo03J4VySFAGpeLZQ52DIyziptrSq1Llxoqyg7mjp1JQlYSQoIS2jCRTD1PU253uK2iqaOpqjcGWlpbKTMpSV98oocTMgJM5ShyirNzWpyjuFBVWypboHHV1haUguoUgraCfkn2UqUZ8I+1hsJa37vSXayWaQq2/V3WLjb7XWv07lMttLi0q1MjSQDOcoudBR7XspNci52Wpp9xti7tFEni893b4nTVLb7YXLgoyOEWm22y4Vlut94obrRPNWumDdK4FzBS0htpTZKRPBPCEWGmbWhi+XC4UbdyudyRTUS0Pd+WfrP1h1s0zTT6QSFCQwkIRY9yb12zSs0j13tt1qqCrcfKK1tp1CEs6mAhSXKgBIxkT1Q99U2ndO6qegcuFBVPCiFpt/ePsustOJuA1BxKT6QTwnDdZa7JtuhS2zWIV61XOV76g+wtkJSt1pKUJBUMJeOKCvatb1XRvofo6ms2wgKpKFq4tvUdQt5BHeqdbZWVABJzEJnSvOd1Xv0nrNRVoae7hTjlNrcaWoLS4ts4iU0kyi309y3HZrbTu3RVsqlKdVVVdPT1jhpH1BpKNCnm0uTxUACJmHKekuW4biLZel2+lX6r9VN1aG65NOHE16CtVOh5An3gSJTnLGOWzgTMOcvtoFCQvvSlH1FR92kuSBdkmU1y7WcU4La5+sO4aTPonLOUH5F3owQrhxy4wn5F3OXzauPHKGZJUfkxkkmXUZTkYJ0qxJ+CrzQ1NChNQEylQGJGJJGUbj7KpJ2/ujGRljZbgBjKUf7Skcf/ALaDZ3iP9LrtnGyVhCtB2bQpBkZE/VlOMDkTMQkhpw9lIwQo8PBHzDv5tX5MVfyTs/WqXDQZ5KnISmY+Zd8bavNHzTglj6CvNDHYXMPnDQuefgjFtz82v8mJ9254m1+6kRZAG1nS06kgJVME9IlhlHzbnxFeaFfJOYj8BXX1Qz8k79DZ+Aro8EfNO/m1/kw0O6d+cRjoV+EOqKiTTnzbWOhUsjPhHzbnxFeaKT5NfzyfgKlkerqh/wCTX84r4C+nwRVKpmHFOUVQ1WLGlY+SRgsjs4kRu9mpDoQ+/Q1zGtSi2tlTRZ+TngFJU3JQGUN7ms9PRC6eqKt7779Iw67UW9alFVI48pBcS1qMwARjG5ftXcr+TdLYedG56m4VlTca29XK7bYtdZdlrfuVVt3a1W6q32J+pqXVufo+hIWtR0zJhVd6tR09ffr5VXa5O01OzTmurKlMn6l5LKGwp1avSMuEWcE4+pNeCRE/CIulZTWa0VLt8tD1ivTlVSNPO3Gz1LbjT9veccbUoMOIdVMAic8Y3vzp+zpyqc2xzP36xXUlz3BftxXLcgtNJc6hdXcqTbLVc4oWeirH1zUwj5KQAAEorawsU7NTcqt2vrl07DbAqKyokXqhxLaUguu6RqOZIi4VNLSvKZp6GltSlraUnvakHvFinUpPyqAlwTKZicWW3OMlNVSs0yHgiagD3QMhIHKYnA+SXmOHXHzSotw7pXoVgIljMuoIGfGPmXPEJ+wCTAHcvZj+Sc9xM4YJaWc+BBGGRBkRLrjFh3xJKv4pMNDuHsXEDFtyQ7Qzwyh35BZ7DXkCB1xLuXfE2pXsicSDToBGJU2sCYxGJASJw58kv0jwHnj5l7xNqPsgRdPkXR8k1LUhSZ/KHLUAI+ZX5B7U4+Zf/NL80NKDS8a9YkUlJ+b6FSMYtL6MEknyCZjFl6WP8k5+TFONBmKVjDI/No4cDBk0s49A6uuD8kvyeaMGl/Fj0F59X5UYNK8g88fNK8gj5pXkENfpa/m0fDP4I64+lr+OfPH0tfxz54+lr+OfPH0tfxz54ALxVhxVOeMuIJnEwtCcBln7UKIqljE4BZlnB/S3PjmPpTn5wxRaX1I/RUk6FadRKszhiZRjVOfnDHZqnM0/yh/CEM6ahxINI3OSyBOWOAEsY+lOfnDDA9acIFQyZd4ZHtjPyxUpQ+UJCxghWkeiDOQ4x9Kc/OGETqFKGI0qWSk+KHZVSwNWQWQM+A4R9Kc/OGFldQpZSy6Ualk6VSGI65QZ1L2f4Z8EfSHvjmLkTUOmVPNM1nsmWYzkZQJVL2QzWYANQ6R+7MTDqtZqykY9oic5QB6w6MBhrIgTfdOIzUTFBpeWO8p9SscylRA8EfSHfjmG0hbjs1Yj0ilKUqJWZ5pbzPUI53cylrRU2+98271VC6orS22tmiqFWvvFNuONqBT6rpKSkhMpDCKetu14pFppbuKlynolKdUsKeUUIBWlOl6SxLLGKeoXSXe9LZuzyqikfcZoChFW6pSCpSe8CykPDCcsIq0UVmo0/wA6LcUa7u6x5CKgkDulOJ+Al2QUkCGnbRWXKlFLdCks0DWloGrCU93JLOGsu4zh21uIrGKuhvTTYVUVbQcbTXutqKSpS9JZ1VOM/ggxXWyr3FZ0ptd1NE+be45VklaEFwMa0BLiFJdlOeBnH2ktyJadudrsTWyLg5TvANl5FVWNWxaNaZzQBVapnh1RuC62SyW1qz7nudFv+yVlRTJdtVTa91dxUPUzQRJOikc7LgBB1L6IFyoqmotTFJW1T9KxY2PUre01JtemmbQhbICpEagZyMbO3hVet19Huix2LeVPcnFmmL7V3YRUVSO7aU2Fpt7gCFLkRqGMoqL5uOsbG1mqr60IpahS6u6OuupdapS6ElDTSSJrIJnlCKjddzp9u0ay27b7BZmG3rzdh2kmopbegIKhrTJbitI1KBEVlTs77P269wWBhbbdPc6qrr6J17UDpDjYpC0kqTj2SRjCNt35Ffyx3k6Ali0buS/Q01TVFZQKKhrXm0d8tbgkhSglJJlOLju7atKum3lbECtdoWHO7bvKGpOLY7pI0mqfT2kuJ+cwhNDV36wUNyFDbKxyjeq3Hn2qyvp2qh1hYbbWnvqRxzS4knBSSI21tmhrNwVFRvC47eszNci3LpqNy4XJVHRKrWqlE2wgVBKtScJARsXa77ilv7b2XteyOOlfeKdXbLLSUy3S4ZKUXO7nFOUVLrajUOArQoBZSJySVKCpjCJCvqSJDNSD7OiBOvqfSGGpHSPxIYCKp5ubcz3ZSmZ6TNJmY+n1Xx0fkQwF1lQtBUAUKUkpIMsxpEbkQah7uTYN0HuZjupJsteUp06ZyBE84/2lROf/ANtBs5RHh3ddvNGyEhxQSNnUCgkK7KVJtlOoKSkzAVOARX1eQOK0Sy60R+0Kr46PyIqlKq3lOCqptDpKdaAQZhJCQMQOiPptQrwqSejoSJR9Lf8AjDHw4ZQwr1t7V3+meoTlwGXCPplR8eXuQT62/MCeK55eKLQr1p4KW24pZCgCoyOeEfTHvjD8mD+mPZfhD8mGAKt4TpGjIK4kCZy4x9Mf/OHzQ2fXHiO8R8KZ9IT+Dwh8CreCe7bkAQB6PRpj6W/8YfkxSg1bxBdEwVAjI8NPXDwFW/IOHDUMMeoYQ9TVFQ67TvoLbzThCkOIV8EjTlFyvVsdZV3FHVPN0+spedKSXktJGmREhIY5xIgpIwUCJEKGBBHAgx4xFgWR8khVT3igCUpM04KlkSBhOKVFnrmqy5oomGmqUtrCm3kKSl3vElIyE5RSV62FU630ArbVwUBIy4yOf3KWjR6VS+2yDnp1kDV4gZwxehXVFxfZaSpDNS2kMt1ACV6hIlSkpMNulZbX60UKU2dGrThkOqUCbywR+OR7sfPq8Svfi2APL7aatSiFYkocATM5+jGD7g8CiIB9ZewI/lFeeGj3zmKiZzxMx05xg+6PAoj2oa/SXvnEfyivwh1w6O+X6DQwP/Bj2YkKh0DqWR7UKSah5U9OBWozkZ5dQhz5ZfpHiYwqXh4Fq88XLU+6rS01LUomU3JcemUfPOT6dRj6Q/h+OqGyp5wq9eWCSolUtGUzjKMHlgiWIUQcuqJesvfnFeeGFFZKjSskk4knu0zJOeMEB1Yx4EjojF9z45jB534xMD5ZzPiTHzy4+dX7MfOr9mGsFfNo+CfwRGSviGMlfEMZK+IYyV8UwJpMumR6fBHZBUZDDSZ+7BlSvqEzjokP4UoyV8UwdDa1noSkz9mKIop3nAKRGrSn0TqOBnkY+iVA/ee/EjTPpBKZqKMB2hicZwwW2nHZUbYUEJy4Tz6Y+i1HxPfhj9FqB8uziUYDtjHOKooYdcSXB2komJhImM4+i1HxPfhvVTvITiSpaNIGXGZh2VM8oaz2kpmD4MY+i1HxPfh3Uy8gdwuQWmWokCSRicTB/RajM/A6/DH0Wo+J78XLvG1tk0+CVJOo4HHDwQP0V84DEImD1jHKB+i1HxB0fuobT6s/q9dmU6BOUzjKeUH9Ffz/AAMurOB+iVGY+APyhFulTPnTTqCgEDsnWcDjgZR9FqPie/FMtxh5tCXFalKTJIHcu4HHjHMwPMIZQrfu7H06qxpkKp0X2rKltoW42VpKcpDGcU9wrN12g0l8oaG+BiiQ67XU+gIYVSlpQ7tbhLJkQZTimutpcvl0p7jQUV9qEXulYoi8y6E6aVtFKvUmamVBSjIyIljFrrdsbXorC1c7VSXqpo66odvTiqqaZrTV1EyhslvBpIAAMMX5lVXQG82y33hz6kpVIpl1TpKCsNMoc0aAyOgAiBe3KOurXbhRUtRV1lTUMJqn6xLWh11xLlQ2sT0iWAEot6X9zWHvlWO2v166dNQ7Um4updXUh51a1J79BkFDHSAMY+2FaHaqpqqWr5QWWpcqGVro301VM05UMKYebV3iSioYSoESwwjkVz4tNA3XX/l06OVG61ONN1PqqKQNUYv1wbcS4tSG3qFs9vV2VkxZX6HvKS2+rIpHRbmEUzKK2kW4zXqcbbEmzql4QZyjkzv+ytrqN3ctNgWlyrqHUaqys2/dGmqW7U+Mu8aauIaXjLS2VHCO/uK6Zm2bK24ituTmlIYW4w3MtymAtLtWoNgzxEXb7RvN+lTuBiuu1dSbCslwBdtlNTUVSG++XRrSGlN0SUFttuWiSirEmYbYpqOkpqanSltmmpqVlqnabR6CEtIQEJSkDARWNO0NLad90NJU1G2NzUVOhmr+sWW1PU9FWloINRT1TiNAJM2yrUnGLps3eTzx31yqvJsF1crnO8ray0UzaWW11BcSlx52kqgGm1KxLfgim5iHmFys2TthNXW09Qd535Fuu9IPWV1FW842V+sVTjrjpU1qSNQwGEbAsm3vtDVHNnfnLm7Ul+d21tuxMObfpU2ivRXj65uLlI0aagNQ2EIUFHvcsYdWhs9shXdpBISZCaEYAaU5AcBDCRTvKWl1aigImqSgZYcc41ClfAPSjHDxwn9FezHwMM/DDGlh1fyeaUzlln0R9Ff+J78M66Z9I1pOooGkSI4z643GSw73YsG6PldPycvqWvE5zyJj/aXDgPtP7NkOj/O27xsoieOzKEDCeItbAlOEhDbizpTglBnl4Y+iVH5v34qwph1KjV02lBQdapAz0gTnnGNJUfm/fgypKnI/yfvxTgU7yl9+SpAQdScfhDhH0Sp/N+/B/RagYHNv34s4Qw65JpYJQkGRIyOMfRKn8378GVJU5f3Pp8cMaad5ZFIyFBKZlBlkoT6o+iVH5v34STSvgFwTJbwT4SCcBnFQUUzziShshaETB7Iw8IlH0Op/N+/FJqpnmx3w7S0STORkJzOJMPypXyO8VIhGfllH0Sp/N+/DrCqWoSHaaoQZtyHzK5Cc85xcGHUKQ41W1KFpUJEFLygJjwRq065AnSMCqQyBxxMVFJc2xSFr5FyludMtSXADiptSkFCkT6DOAtKbYlQA093QuuHoSRpaVjhCKS2U9a4hRV+kimUxSNEA4K71CFHUBhIGB4PcikU4vSi3IXX6JA96tsFLaMQQElSpnwR3i21ISMZkSSZHJI4QkMNuOqTWL1BKZkTOBPQDH0SoPgR78fRKgdZbw9uLYNC5hFcVDQrDU6nAx6Kh4UkDywMBmOnp8EM6GXXcT6CSqQ8csI+h1P5v/fQ1+h1I+URiW8B2hicchDpRTPOJ7tmSkImn0BhMkR9DqfzfvwQad9uYA1KRIZ45E8Ic/RKgjWZEIEjwwx4x9DqfzfvxdAqneQe6a0pUiRWQ5knEgmMaOp+IPPA/RKj8378ND1d7X68r5MI7enu/SlPLCPolTw/k+rwwf0OpyP8AJ9XhhhCuysUzIKVYKBDaZgjhKCUNqXM/Bx6I+iPn9578fRKj4nvx9EqPix9FqPzY88fRaj82PPH0Wo/Njzw1+ku/Nt/gfgj8WPpLv8D8mPpLv8D8mPpLv8D8mD+ku5fifkwAahwiXQnp8Edl5QPSAmfHqg/pj+fSOk9US9Zcxz9HzRhVOjwaPyYo5VTqZ0qdRGkTOs4mQxMhH0t4+EiPpbuY4jpHVDATVOJBpGiQnSNRPEyGcfS3fKIZ/S3pd+0JTEvTEVaU1TqUh7SEpkAAEpOUuuPpbvlEMNrqXFIWuSkqOBEjn5IcCKp1Ke8WJAjSACJSEfS3fKIcUqqeVJhxUlEETSBIjCFJNW7gTjhjIylH0t7yxcVOVTqz6uZE6eyQlUiMIA9bePDEjh4AImKt32IbV605qNbpKsJymcPBH0t3yiB+lvZ5TEUGmqd+WZ1LOGYUQJYYR9Ld8ohKnalxTQwUgyIWlQKVoI6FJMczyKKjpbRfL69ctus/WVOFLsdSxT1FPUhlwslkvPvuTABBMzOLDX3C+2ejqKelVZ00pStyoV6opTxQz3CFoW6lKzMkgExYKw09/qKtCKi0vtLNPSUa6WiCFsFtaO8qAtwvK1GYlwlG2am07YpbXXUnrllrquqqHbuLg3RlHq7iGauYp+ysaiMSRFoq7ausZRS1FVTVX1W13DLTWCmG1JaGCFKWcMpiLVuN5lLlIm5V1rXVVlzpaSp79tKVttqpnnkVBQG8denSYsn1juC0MO0bj7NarUHCgrkqnbKkg6tQBxnIx9pi22t2tq11XKmr9cqqpoMto9We0tpZQlSi62rUZHOUfaB+ypcbYlqq3bt++7itVVUHv23rvbQv1enomVkts1D7SdZl0Q1tZmzPUNVtzcd3t9dSUVsSm5etKrVtVX1khpM3UNKYGlREgDiTH2RbRuZ2m/ovzG5bbn2RuQVpCWm7ldKRYtNYUTCVVAuRQmRBHakMZRzz2w6yK290G3BbnGKZtau+qbff2l1OlsqU4lIZRNUySBHL5yzuoVTN01Sy603MGmq2Hu5rG3UGZbV6w2ZfhZxy7vXLzflPtLatgr0O7mt+Kn7g1q11DyqcJDdb3jI7pKFnskzinUXJIQtBW67JASEgFbjnwW04TPAR9p3cFEfWbS4/V0DFwQkmleuCL7pdabd9FS09yTq6BFBZbZuu2bfuPdUdYzZ7ka5NxvCK6rFJRLtdLTILlX3j/YSEglRIIhneW/rpc3ecHNehpL1fKOoWO52rt5fy9nsFPTr1d068gh93V8qCvSoCUoCvrB8mU5aW5CYwkdM8DFO4HlB5Tq0KclipKBhOUhEzVueKQhP6W7mMJ4GGdFQ43NvHTITIlieBOMfS3fKIbKqt4hJ9EkFJmRmJRuZLlS6ptNg3OO7mNJH1NcMCJcJR/tL/AP5n9m//AGXXaNkgYS2bRKB4z+q2D7BhH6c/6Kcg2OA46Jx9PqPKn8mKtxVW8p1NXTBDhKdaEkGYSdMuMYXCpHgKfyYI+sanLiUH20xTgVTqVFxZW6kp1r7Q9LCJC41Ev3h9tMEm41J6QdEiJHCWmLMWqhxpSm16lolNXTMESxj9o1Hkb/JjG4VJwPFP5OEU6m6t5sro2Csp0zWrT6Sppzxj6fUeVP5MNg19QQXUgglEpEgEHs4zh8N1r7SQluSEaQn0RMyIOcftGp8qPyYpA9XPuILyQUq06SZGUwBmDD4TcKgJ7xQAmnCRyHZnKP2hUjrmnD+DCCquqHEyVqSopkoSIIMhOHtzUVOVUj61IuBbE+5dSoht9SANWlxMgpWQOcZ8DKUPPpt6Lg60grTSlDSlPkfASVpPaM4pkU2xE05W633jrwZCGmz84syw1AdWcD5NCZSwCQNJEjIESEgYQ0y24664oIbbbSVrWtRASlKQDMkmF19wl9bXFtsLYCQfUmZlSGQsGZdVPt8IUEMvOtyMgRJKcOGRip1IcpyX2FpKSJlKdYVMTMkzIgn1t3KeYj6U75Uxbld+oKLdbMgJmqTyQJ+ARjUueGScIH6U7mOjp8ENfpbomVYjSJ4DHACPpj3lHmhudW6RrRMEiRGoTBw4w+lioW22EMyCZSn3aSo444xjVu+DD78Ykat6RKBLs8VeCHP0t3BxQGWQURH0x72IuJVVOkpbbIJkSDrxIwwnH0x7yiPpbn3+OGXQ+sOm4KBc+EU92rs9Eo+lu+x4Yn626ZYywxlDThWSpTSCT4QOEokh1SfAEzz6xGFY8Pi/kx9LdPjTH0t3+D54n6455Y+mOeWPpjnlhr5B75tHwD+CI+Ye+IY+Ye+IY+Ye+IY+Ye/NqiZZeHhbPTPojBp04fgHr6oPyD2Z/k1dMSFO/wCNtXQfLlHzD35tfmiiCWXSfVkz+TVgQomRwwwjFh382o+0IAFO6TMfyahkR0iUMKSy6R6q2J92o4jAiYEfMPfm1+aGfkHvn2v5Nf4Y4yiqIZdILpIIbUQRpTjMDqj5h782vzRTTZcSO9HaLagB2ScSRLKHUpZeJ7xeIbVIjUcRhjH0d34ph0KZcHyDg9E5mWE8pwsmndkSr4J/C6o+Yd+IfNFwBacBLBABQoTJSrAYYmBJh34ivNHzDvxD5oaHcuavXplOkzA1GRl0R8w78QwCWHcwPQV0+CLeUsuHu2JL7CsCVGXCPmHfiGJONOpSZYhCv92Nn/aX2/a6ipu+zHKfZ+7wyy8Siw17oFsu1SWxJFLRPApecX2W0EHCLyh292tL9krbcqnbCXnnV+uult5xKmkOAJSifamEniYuiaW23291NrrqJ5lCaYobKqp1xD3yiB8mytLQIJmfDKNzCw8vkpr7HX2q5JqaysbcVS0dQHUVjwpX+770JVp1JSCqRmRKL7SWq2XUOU1fbqphO2LPVEKbQ5pq2VN0jDynVJ1AyQCCT04RUViOXm9buX6ykeZTdDS2MBptC0oqCu8LpVtaEKkUqSkgHGK//StzV5S7GW5W29+ppLvuOmrbjQUrDLqflGWqxLRWXlS7KSZ9Mo51bXtX2iq3mhftz7FrdvXLb9otxoLZQLSCSugrFttd5qeEknU5hjKLRfbDZl0lttm57LcLhVOXapeqKjbroqLZWUqmQoManRcO8IkAdGMbluW2G3bdtrmJR0nNCzXmibQH7su9pQuupGKpGrVSMOKAUhHpTj7NF5aqVv3+0LuBt1ZWuBip/R3zVszUpSVBVO6AZiRwHRArL66q41Nyp6m1bwpXH2H6t4vTZfeqVMqUA+8JOAqkpWZxi/be3dbrneuR+666oq7DcLeO/ValOVQU0+0yjWW1UadSXm1aS6DqTOQnSXOk5k7aaYrG0uNtVdaKaqRPNLtOtPetLQcwZGLny3+z365vXfN6bqLN9dWylfRTW1NQBTvt0zNSlpdQ/UIWQl0fJpSFEGYEbUtm7KtpFxvVyO5d/OLVqaeq1SVS0QW6lTinFKJL2okFU5Rt7dVz2PtO+3PbdZbrrty51Nponl0ztqeRUW0tvBsEtUrjYUlM5ahMicNU+5qH6uqBJBuFGFKpHCSe25TGamZk46SU9QhK6e82xxLqUlAFbThZCsptqWFpPURFN3SS58s6oBElEgicwAcpGPmXPimE/JOZ/gmGAGnFfJj0Uk4mXgET7h3H8Qw2lTDomRjoVhiJYyjc6lNOJSbDuaSlIUE42evAxIymY/2mAww+1Bs4eMbuu0bKkhcv6G0ACtJ0qKrbTpASrJRmeEJSaaowAB+RXLASzlH0eo/Mr/JitSWHgr1umKUltQUoD8FJEzKPo1R+ZX5okKapx/4Ffmhj5B6epyaQ2rUO0M0ynGFNU/mVj2xEhTVEzgJtK4+KLS2lh5SmW194EtqOicwAcOMfRaj80qD+i1OR/kVnh1AxRyYeVOjZHZbUopIAmFSGBEfRaj80rzQ0DTVA+URm0v8ACGeGEP6WHljQ32ktqUkzTwIEfRan8yvzRSA07yR3ySSptSQAAT6REof/AEeoPyqzg0sjMykQOiPo1R+Zc80InT1HoqzZX0dMpRUNP0y1NOKdS4h9o92tBUQQUqwIMK3U26xQ2h+oYpKmlPYRT1T2CFs4H5NyRKhwOUIfp1oqGXEhSXGVBxBBE80kgGMj44NbcaltpkcAtOsylOSPSOENWkNH6hpduXOpZQoSU9UoUjuqnHFKkDASlDlQikU6QmZmC+rDGaEmcjBRQ0VU3p1HtU6xl0iWUKNawpHaKCFtFCVolnJU5w62y0oNLKi1xBTxkeEj0wQGHjLCYbUR5YtxFO92U1aFDu1EhS3QpOABzEfR3/zTn5MD9HfzH8kvp8EM/IuE6j2QgqIw4yEo+jP/AJpUI/R3x204ltUh2hD+ll1Y0NdpDaiMWwMxH0d/82qMWHUgFBmpBAwM4d/R3vnF5NqI9IwP0d/805+TFz1MupJab0hSFJKu3iEgiZIEYUz/AOaX7YBEfR3/AM05+TDKe5c1C4KOjQvXLu1YlMpyxj6O/wDml8BL8GPo7/5pf5MMTSQe4bmkgzB0jA9YjBpxWfotrPX+DH0d/wDNKHtiPo735tUfRqj82fdj6M/+bMfRn/zZj6M/+bMM/KufNN8T+AOuPnXPKfPHzrnlPnj51zynzx8858Y/lQAp1xXhUenwmJpdWnDgr3QAYP6Q7mfhnpj6Q78c+fqj6S9+cV54oSKl7GmBV21DUdSsT0nCMKh7L8NXngEVLwxH8orpHXDOh91KTStmQWQJnMyHGPpL/idcHtKEM/pVRLv2RIvOEYrAyKiOMVIFQ8AHBIBagANIyE8I+k1H55z8qKdC33VpU4NQWtS554donoh0BxyQcUJBRGGo5Yx886Mvhnj7UOgvuEdy4ZFRImACPJB+XdzOTihx6iI+kP8A51z8qLgVPOEpYmklRMjoViOvCE/LvZcHFDgOgiPpD3jdXLyEyhtQeXqNbLVqM5TnLwRM1D35xY9owmVQ9nxcWfbJi2lL7oKmCVELI1HUZE9Jj59z4ximHfuELfQhU1EgpVOYxi/bK3fQMXva+6LXW2W+WyuaTU09Xb65tTDzbjLgKHAAoKAVMTAjdqr1zi2Rtfl3UV9WbIvdDjrN0rrM48qqQ1T0qUrYdVSNqkhpGvSlGIEXG1XX7Qu4OaVyTQLXdNm7OtQpaWp9XBKXE1bKKZxpTCiru5uEJJxzi5UnKT7Nr+4twVbDlK9Xcyb+l62ON0hKlhbDjtS0urBaxkkkpwh3bvJrYPKjlC+6zVt2VW09o0V7cerkKK21uIRSMEsPNoVrVpnqkSYq7Te967yS7WMXOktrVBWDalrcr2kqWHKilaUwyhgKbUnVJRRhp4xWjfe6KFitvlrqrSm63e4u3uoYXNVQiuU4ylSj3RCk6j2gFTnD1ouW5b/dV3Zioo3H6AD1NxmlS6oOurWrv1F1LXYGoSSRD4odtUFS/uK3Pbd+sbhUvuP2tp1ZcaudKgpWhNZTqZSQZTGICpRyF53UNK7W7t5PXJ3ltuuoDPfVLttbULealzAvNtVTjbLjQMxqVgY+ztcrlSlF3te+tzbacpKn5JVUtr19tpdSXAnuFuaEhSSNKZZwh243+2bfoK112nv1srH3a5itDaldy66aQLQ1WEdlCxPDAxU2hmssV+RqVT19hvtKw+ptwTSuVFXImcPRdbxAyMVFcrlvUPVFU6XlN0u4r02w3MgqQylpxLbLIOIEjKecM7P5bbetu0SrVXXW4NOKrLzb6GjbBdUbtUH17S9IIQnUEqWuXEwxRKZduPcJ7dVVahUVCpCbjp7xYSoyymYedp3nanbSnJvUSpuO2hCyE99THFS2NfpJ4DGGqykeZqqWoSFsvtFK0OAyyUJiY4jMQtxAIJVPUnsnISxBEiIT6pfLswECSUoq3dIGWAUpQyjUzuS4qJwm+tLwHiUJRUUt1qEfW9AtBWUTR61TLT2Hikk9tC8FeGGtDzgm2JkKlPyQf0l7M/yiunww1qfdUNQmFLURIcJGcbkbU4spVt/c+BOHZs9eU4TwkRH+0v8A/mg2b7O7bvGyWw4oIRs6hWhOrBKvqynVqSOB14wj9Je9Efyiujwx9Je/OK88VCy+4VpqacJUVHUmeqek5iPpVR4nnB7ShCf0qoz4vOH2CqUIV37urW8J6zPCUsc4+lVHiecHtKg/pVRkc3nDw6CoxaSl91JcadKylZSVkZFUpTMfS3vzioV+lVBmDOTqvPFKpDzyf0RlStK1DUqWapEajjxj59/4y/yoaPrFRIrTh3i/wh1mH9DzwToRglagBhwAMs4+ee+Mv8qKULffUC+iYUskHPgTKK6srLgaOhow9UVVXU1AYpKOnaBU4/UPuKS0ww2mc1KIAirs+0Lze+c276J96kqrfsjQuzW6pZOj9NvFStLD7C3JibGsyE+iLbtjlDtXa/Let3RcqSw7cYpkVG7rxU3K5uhmnbVTXVLdOKhOKgAVIwM4s55xcxK/eu8aezfX++91XR1qitjVdUMN11xFBQhQo7VQW/vO7ShBCeyZAZQvaG2aRxO2qGvD7t8L003t5g/Iro29AnSIn6erE5YRpo7lXUycJpZqHEJJAkDpCpDyRL67uUv+kK80d5WVD1SsKJBedUsgkCZAJljONsP1qSpncLFVt1BSpKC1U1qdTDnaIC0TRIgRvXmdyV3UnaO89hOW/cjlxdtlFeaV+yU1Qlm50NXbq+VO+1UoeSkYhSVEEERT/wBOrFy/5pNdy246qqp3to1NRTuaXAUC0iobRWBCimWlSDhlBe5nUO5eUt+p2ka7fcaVy+W+rfU2FK9SrrcH3VMBwkTWkESjTyr5r2O+XQvHVY6t82i7h5xGssUltuAYqqsAZFCSSBOUAJfeTl2ZqBHhBAxEW4+tuoUtFYV9qRUUuJCScMZCJevO+HWZ+5A/T3zj/dFflwyUVboJzIMycOJ6Yxrn/jke0YR+m1GKkz7aunww8huseSgJbklJkJlCTl1mMa2o+OR7RhU6x8+jmokZngTKFgVrwAUQADIeSB+mvZDj1RXFVW6VIQ0Uk5glzGXRlGNa+cTmeqPpr3lhtZq3dfrak6p46Q1l4JxL1x7iMDL2pRhWv/HV54aUahZUWmySZEkyGJOZnEk1bqc8pdMY1rx8kfTHR5I+mveUeaPpzvseaPpzvseaPpzvseaGsP5Jvp/BHVGXt+aMvb80Ze35oy9vzQMJYcfDEpcAPvwg4cT09J6owE+oA+aPm1eQxRkAA+rJ6iO0eqPRUrrkTHoKzTwMvSEMjOVK3jjw8UYIUesAwzNKh8uziQZfOJMVRl/Kdf4I6o9BXkimmgj5QZgzyOWEO9kT7xWMvxiZky6Iww8vmhwn+4OY48ZdUGSScTkD0+CPQV5IuM0KxpyBhmdCpQn5NWXQegR6Ch1kGUNiWPrs+OUz1R6BPigTQQJjMe9FB2B2Kcg4ZGeGQj5tfxTFLqRL9JbOIxwn1Q8dPY7JSTlkZ8OBMXDaG+7IzcrbWUtQ1S1rSO7u1mq3m1NouFrrRJxh5gq1aQZLIkZiP9I+0qnfXOrkyzV1FTc63bLYp9x2Ky1AfWmh3FQNEVNwaTr+Uepkr0aASMYaubm26K4rte4HlLZuNfVvUg9YcWEoq6d1S2xU0iXkqUDktGMKaoHaIVFHdalthVrtrNE+tt5agC09TjvgnunZyKimCiuo7k87S3UyFe8ujLTdU4ltR7+sUhKAE1GsJnMg4Q8msvVotnqt2S1TtuVX1g49SVR0hwKoy62htYWU6SdQEKapq67VKGLkhkLtiKdhhtD+lLiia4BZQQ4QJYS4xUM2zb1OXaC6JQy5VVD+t1C9Cg+UNqDadSHTMJ7OGEc9/sp38st0m+dnv3/bqUP90zTXC2oQqkYtjbgmqq7+S1KB1YTxjlzZ72m4M32w/aA3hbboKvU1XNvJuVyb0vqf0d2440nSNUhLjKHqrcFwt9pYrLgivYaNWLiRROuIcQk+pKeSlxLBPZynLGDdn7lXVjweZuDCrPopm1UdU2HWUuOVZQrUptYKxLBQlFNXUtVfapdPUUN3pkHdF1YYqaN0CoRTVFIhYaUy82ZKSUy6o5ts3atS9TV/LW23CyUq1KDrT1VeEVzrFKtZ/SEUtIC3JIHZBVCy/VNICB2kqdQF5S0hBVqJnD6ltlLD9M9TaXEzB70lIckoAYA4RWcvN211SzsbcFQqqsl7qAt+nsS3Vd7rWwma10bneFKgkzRKcN3ixKo922R1DbiLtt5YrqcJdQlae9SglxolKgTMYRorKOopVkTCahhbJI6taROcSlhhhKLbdEE+r98GK5vg9SPkIcSf3JII8EUb7BK6d+lZeaWkzSpLqQpJmMDMGJ+4c+PswziQNQGRE5yy6ZRuGZmP6P7o6eNmrxLyx/tLweH2oNnDxjd11jZEhMHZ9vE8eNupgeGUJkknsjIHo8EfNr8hipEsTVU+GPQrqjBJPgBMT0KHXI4QzIYlTk88ZkT4QcJeXzQQBiQenzRaBLFLTwIxwPkj0HPIYOpJ/fThpJ4UtP0n4Bn7Meh7MNCUu2jDE/CEPSnihBwE+jrzjJXxPfimedWhthh0PVLzh0Ns07YKnXVqUQAlCBMnhG6OTPKXcddZeTG0LvUWu4XKx1S2FcyLlTPEVb1VVtpZqFWSjqUd2KeelS2pkkQsNoCEqJKkoSQkkmZUesnONq3ysoqaqsHK+z1u7bkmoTrUzd1JKNt1DKCCmaKptRKjKWWZhzau37iLZufmvuCk2+hbVQW6qqstLVNVW6mGmGyh0pqKRyalAgInljG0LvSBJp6vb9tDa0LDiFer0rVMohwABRKm8T0/deqqyoZpaWnSp2oqahxLTDLSACtx1xZCUJSMSSY5P7G5XSp+XnKjcC+Ye/72ouPUl9tdgefpVMIdbDYDNbVOtoZaUTrxXkI5pUtzLaqjfLVu2LZ2DUIZefuV4rGltlhpQK6n1dLOtaUjBIJJENtT1d000zqGRLTaWypPUSmYjIeMA+3OLffrRX1tpvlqqW6y2Xi2VT1DcrfVtEFuppKunWhxt5uXZOI6pRa+TH2m743QbscVTWzZPMes7tqj3K5oDNNZ90vEIYt1yBQO7qey28T2u0ZRbHAlCkKafUlaVpUhSXlJWhaFJJSttQxBGBEegB5Rx6ZQOxxHEw0ADIE4AyA8WcTkeIlM44dJhB0ZLTPE9MPHSJFLfD/g09XVEwj2SIJKOjEmcs/bhw6R6R4e9GXt+aK/DNtnp/unggyTwyA6wOiPm1Z9BhvsL+mq4HLusY+bV5I+bV5DDOH8i37Q6oy6enp8EZe35o9H2fejL2CfcjL2/NGXt+aMvb80Nds/Nt9H4I6o9M+x5o9M+x5o9M+x5o9P2vNAmsnyeaMFSy6Og9UK+Un5Ok9UemfIPNGCz5B5oo5LP0VJOWPaI6OqPTPkHmjBZzTwH4Q6oZAWZeqNzyz6co9M+QeaGe2Zd+z0fhp6oqhrODsuHBI6o9M+QeaKYazIuAHLKRmMof7ZMnFdHBXlj0z5BDgKjLuHDkMxKUHtnM8B0+CPTPkHmi4nWezTzGAz0qlwhPbOXQOgdUemfY80NHUZmv0zkOCj1dMemfIPNA7ZzHR5ooJKI105KssSDnHpnyDzRSgqJBqUDJPEGH0AkITowkJYiZg9PgP+5DqKgB+nNFVJWw8hLrDiVI0qS40oFC0qSZGYxEXPmbyCt23uWHNlLrl0cs7zL6dk7ruLTiq0iooKaTNqrq6pSB3iWy2Z9rAxXbO5sbMqtgbwLilm0Is7NNSvPsJSHjabnTU6Ke7U4KQdbDikKSemcU9c+Lqh+42+jub7lY6+wwH9CdDqVPFI0KLU4t9xuVZbrYu5UdDci8lwPMrrg2FulYZClJ1KTgIoKpV8eudTW2aguDztnSpFOqpebPeN/LgHvElOPARZq6y7WdVT/wBG7UhVbca54VNXcKdLiKt5ZbBBIXIeCOWu7qOmpW2LdcbFJtKihDlFUPpo7vTPPkBT7QpXVLWJzmkQ27trVX2nePOCn3klwpLzNO3uW21tXUL+SAHqgdcJmRITiiq6y4W2jpKlr1Zpxt/1gIVSfJu6GGipQQCJnDPri1V7twq6z12iNKBStFppBtf6DrSXZay4W5yHHAxsq4be2Qtl07epbRe3NwV9aPrK40CUMJr6VNMoobQ/pICBgmcs4uH2jt4XBGwtjbJtVVt22WUUr1Let7bjS0k2+mYbdQ249YKGhX3inUTDiylJOcCoNLTLdOlwVHdIUVKUArvEKKSUgnERMSzmc4RuimTKr21UMl5TYk6aJ9elxZIE+7ZVnjxhFvsW57pb660HS2Wq11VPUUDxJZ72kWo07wbBKTNM+uKXbu4LdQqr6Fxt9q/Mr/TKtQBK23WiiSEqT0GUAqz4n7zwgylOWE4prfUOqVU2QCjXqM1FgTLCjPMBIg6XFAdGXsSljDI70+kM+gEdWGUbhm4SPqHdGHgs9wI4cCI/2mB4n7UGzifCd33WNjpSqQG0LcZYZm30+OXTCe2chwHR4I+cPkHmiomszFVTyMhxSonhHzh8g80E6zh1DzQwdSvSXwHSImFkeSfjj5w+x5os6tRBW08omQxOHnj5w+QeaDNZxlwT5obCVqH6LTk4Digzj5wQ2S4fTTMDL0hwlDyUuEAIRLKXtR8//BT+TDPJHYd57rmjzYoXEXV6melV7U2KsPs1lxCmpuUtZdlIUyzPSrDUMIM1uOEq1KcdOt1xSu0px1Wa3VqM1HiTOJrkEzxJBHvRzF5t3CgcpL1zC3IzZrItxYdNTtuyud4xXU0plDNTVKcSQSCCnKLLyuRV0qrNyx2uyXmG1H1qn3Peu7euCarHS3OhLUkkahLojbbDQUHNt1FbYarvFgqU406X2ltgfyZbc8R+448842yy0hTjrryw2002gTUtxaiEoQkYkkiH+X2w6pxnaLDn1fdLiwhxL+7bmqqLTNBQqbBcVRLqlJbSRLvDIHDOlvG6be1Rc2uaCaLcu9HHlEv2S3qZS5ZdtqUVlnVaaR4lZQACtZBynCOVGzbia3lvyUu9bQrqEakt3XfaB6re6luR7t6kt3dJaZV2sdZBxieQ+6mSlJUhaXELSpSFtuIOpDiFJIUlxChMEEEHKNu8kvtG3Spv3Lt55m2bc5i1Li3rrshIQGmk7keeGq42XVpT3iSXGRnMCLdfLHdaK82S70jNfaLvbKhqst9xoqhAcZqaWqYUtt1C0HgcIEnVe/5IY+VPGfk6JRi6fF0+SG/lFHto/jCHRM+g34Pm0+eOytSR0R2nFESxnjnMCXjhzE4qPRGCyIrhrM+7ZkcMD3vg4we2curzR84fIPNDRDhH6asZDLu/BHzhzlkPNHpnyDzQye9/kWzw/BT1R85wPR5owc+/yR859/kj5z7/ACR857X5MfOe1+THzntfkwz/AMU3xH4A6o98eaPfHmj3x5o98eaMVCJAp9noPVCvyV9J6olqT7PmiSSknoGon2opJEHTTJScFHEKJ4DrjL+CvzRM4AETJCpATGJwhk60j9FbGSuBOOUTC0/wvNDPaT8810/hjqiqUFJxdJ4/gp6o9JPs+aKYnEBwTIBkMDxlD4/4Rf8AGjP2R5odnP5hziOIEGXSTjjx6oz9gxccFdpjSMsSUKhPZUMOKTj1iQyicj8VXmhpGlU/XyZaVTlqInKU5RLSrD8RXmhPYWcQJBJHtiKOSF/JM6VDSZzJOGWYlGR+IrzRS6krAFQ0onSQAJkcQIqSlKjPRiBMej1Y8Y9FXxVQslKh+jvpxEpkgDDjE+7Xx4dHinH9EOcOzqbcNPTBS7HfSDT7h2vV926lqssV0aSmqp1NKdKu61d0tXpAwm7bFfv/ADw5N2puveqd5pqgrcu3Lap5T1vtu5rMZuPMUFPqJqWJtACZHCNtXS63KyW2kS/WUS30XBu4PqTRrUy2gUtKkuBxQThOU5xtR1F4r71XeuXZi8i3URa+r1POhdrYDlQpJGplKpjKcWdyksPr7FBVVDD7twdW2+l1wA0qVpaOnQvRlxI6IsK2WqW1U9muNRRONUtG0X1U92cSXAmoW2XVLBMwsHUDG2LjUOIcve1rnRbSU2Hiqp7u0MuUdvfqdai4F1tIsLmr2o+sK+ottE3aNwVdr7xNU1Uutt1oNS6qqpacqeZSFp7JOcUT1zu9bclWe61VFSosbSWAWLiF1q1LTXBCiG3mjM5zXIYRvCruu3qfcV62y+y3aWNz09NWuUlnqRrqX/UvlKQtprZaHFDUkkicoYpqNtuio6dtKGKSkQ0xSsoSJJQ1TtJS2hIHADhHdO6jUUriWnVHSAoEdgpTnIAROLjaa1CXKO50b9DUoUAUqaqUFBMjhNJkR0GKuwqecaZortUWyQOJbcdmy26MRJSZERSOO4ArKAcczgMsMYJSJAkyH3GW6p4NUVzHqrqiQEBxY0slwkySnXLE5QlxaNCXAFIOoEKSoTBBxmCDhDJ0qI1DEZDEZ4RuFRSoJNh3PJRlp7VnuAGPjj/aYj8H7UOzh5N3XSNirAOle07clJyGoUFOJDDPCE9lQwGYPR1CcZfwV/kw+k5qqmJCSvgoVPCU+MDA/FX+TBw4H4K/yYZGlUwpeHHMdUZS6iFT9gR/vVfkxZwmR0MvAkBREyB0COHxV/kx6JV4AfdEN9lQ/RacTIMgQkg8OEcPir/JhHZJkpJnIyzB4iHXZHSQlI65Cc8uuN5c296upTa9t21z6vpO8LTt43DUzbslmpzIlTlxrQGzIEJBmZARvLmxvircrNx7xur1dUd68t0UNElakW6108zoap6Cl0oCUAImCQMYImDjwilt1Gy5UVlxq6agpGGxNx6oq3kMoQgYzPbn4o5a7TcU0zb9h7DF3uVS4UmanqJV3uC3nMFFtt10gTMx0xzR5lXSSq7du9b3XLUlfeA09PVuW+gksk6x6lSNnoxwjd+0C/3ht1ezekslICmHK0Fl4FcpuJWGwR0QVuLQ2hIUpTji0ttoSkEqUpayEpSlImSTFZy52BcFjbzBVTbjvVMtTS7vVtKUHLfQPI7X1c2rBaxLXkMIb5vbto01XKrk3XUVYGKqk72i3BvdKS9aLWluoQWHqGzLaU5UIkpXeIRMSVOKu3bVuFMxzW5msVe3Ni0xCFO2qmeZWzd9zdySAW7SwsBsGQLihKekiHX6h9ypqKh9+qqql9anHqmqqnVvVNQ6tU1KceeWVEk5mPv6f7ApUEqSZgpVIpIPApMwQYpti72eu28ORFfUsIesztYqpuGxnHqhCH7tt9dWpRRbWGnC49SJMlBJ7sTkIse/eXt/oN1bS3DSIrbXeLW8moYdaUBraeSglVLV0y5odaWErbWCFAGGpSMpz6pxIJJ8AJ9oQiaFemn4Kvwh1Q6oJURpbEwkkfNpHRBk2v4qvNBmhQ9GU0qE8T0gQuTaj2jiEqI8oEH5NfxF/kxW6klJKGZBQUJycmZTAnIQewr0T8FXmj5pfxF/kw0FApPrqjJQIPzfQRxgiY4nDw+CMz5PehrtDBpHsAQZY55Ax6JH71XmgYHyHzRiQPDOPST5Y9JPlj0k+WGsR8038EfgiMx8URmPiiMx8URmPiiMZHxQZHThwmPdhUn3RMnDWSM+E5xmPiiMFlB6Udk+WKKTziJ0qSdCimZ1kTMszH0l/PLvD1QAX3iCUggrJBmRwhnE/RkcfBBSCJDpA4icM4j55r4I/DEVQBEg6ZYD8FMZj4oilKlqkHB2QZJOBzHGHyM+8X/GjMfFEPYj5hfwR0CFYjj8EdMZj4oi4zI7LGoYcdCoT+kPZYDWZDAYAcBH0h745hlXrD4V6+RMOGcgoyE85R9If/OGB+kP5j+UMMEVDwJSmZ7wz+Hxj6Q98cxTpW84tPfI7KlEjMcIfk440AQkBtRSCAJTMs8o+k1H50wsKdcX+ivEFatRSQkYpJyMfSajj/KnpMfSaj86YqmKtKK2nfSad6nrW26mneYcEnGXmXkqQ604MClQIPRF93x9nxy1cqead0c9frtvuILOxN3V6Coobcp2EfzJW1CVFKFMJS1rkVDThG6uVnOmz3/bG77S79fItwoCwzXUVrBp6yus1yfnQ3em1PJAU0pSe10xeLbTbfernGKyhvIqry+EOst0iV07jNOinUlBU6l6ZCgcRhF6oLQ3S22lpKJNwSzTUrSygMvJbQpby0LXOa85gxzf5aV7dfVUe47fRbntNWXO8bpNy2dTVGpK0LVNqgeowo9mc18I32EVVsYtqNe4a11dawKtD7NQhpCk0QV373aWSUDtSEblpK27VF9r2qKlqdv2ew0TzNwuu4KmuZpKWibNSVh0PsuEBAE9cpQrcfMiym7cyeZ9pp9wbtVeEoD22rH3Jqrft2n9X0Kp3KZlYVUDFRdEjlFfT7RpblS2yleXThu5LS893ra1zLah/I6JaZ4wxVJJ7srSl9GMlsqPaBGUxOY6IbcQZocQlaT0pUAR7Bg+L3YsW8KRgItu4XqJVY6hMtN4pXQlYUpOXeM6ZT64onlEaQ62rAY8CeqErlIKAIHRMT937ikKJAUJTSZKGIIKTwIIzhm31zyV3GxkUTmoDvHaUYUr6jms92Aknpim0OLSC63qTOSTMicwM43Ge9XoTYdzHu9Z7vCy18holKQOMf7TAnNX2oNnE+E7vukbGCVnSNpW0pQT2Qr6vphOUsDMwB6y9kP5RXRH0l784rzRUTeWVes05SsqJUnAz0mWGUD9Je/OHzQf0l7I/wAorzRSkOuBYccmsLIUqZOasyBB/SXvziujwQf0l70T/KEz6iCDFo0urbm07PQopBIwngM4+kvfnVQf0l7L+6q6RDMqh0A0rBwcIx04mPpL351UNj1h0p1pmC4SCNQnMQ8FPBDaQlRKlhDTbaW9Tq1lUkpQhImScABFVy92ZeX3eUnKy71dot7dK+r1Dc+6aNwsXe9PNIV3bjdBUtllqepJlqET6fucm9qVnefVNkvyt+XkIRrS/b9oIFyVRvavk0s1jiEoVqzB4xzSuNFUfV9du+na2BaKajCW3kMblf8AV6g0iB80zSUmCinEJMoQ0JqS2NKSrFZAOalGZKjPE9MXWxKkmm3BYKqpqHpIBaNqAeISpZA0aFTJzABMV3Lbl5XKG36daqfcm4qRxTbl0qG+w7a7a+mRTQoV866n5zIYQ1SoWppKyFP1aWy+mgomflqutfbTIqaZp0KxMhrKZmU423yf5LchqSq2tYGFu3C97ruqm7/ubcFS4TdL/Xi2ypguv0I0pE9CUhOQEVnNfftKzaS3RMWnbu2bbUPv2jb1ophqSxSJfUT6zUOKUt93ArJ6AI+H8URkvPDAeXCOyJeHjKfsf2GkJU44pTaENo7Ti1uOJbbSlOZ1LUBC6xFj3NcdpbzoKWuTsW/LqW9o1SqpwPO3CkQdH1fdkNpmSxIOau3Oc4c3zs6hrNvX7b1e3Yd97OuOh5+wXp6mFXThqrSdNXS1lMe8QoCaAZKxglDim8PgHTjLqHRDf6Q76aM3FEekM8Mod0vOoGlvBCykegmZlLMxIVL8utxXknKGwt9xSSTMKUVcDLAiFaah5ICjgHCAJE5YGPpL35xXmiuCnnFyaRKayopOv0kzGBgzqajo+cM+B6BA/SKj84YaK1rWfXVdpZKlYIyn0CJpzmOE8sYOKfiiGjhMtNnLpAgydcTOfoq0+1H0io/OmPnXD+6M4xx8Z8/9g12h8030/giPSHsx6Q9mPSHsx6Q9mOEYafLCvRw6+s9UfB+N70fB+N70UeQ00qQcTnqJjs6ZeEj24B7IkU8fxhDJCgP0ZA8Yz8UT7Jn1y9iUM+j881x/HHVFURpkXTLH8VPVHwfje9FOTpkHJnHoBMPkaZFxfH8aPg/G96HdRE+4XORPQIVplmRmenpyj4Pxvei4z09piQx4lKuqEz04pBGPl4iJmUuOPvw22CmYrFOcZSCj7MYFPl85E4EynqxGfXjDEpGTaZzMsZq6+uPg+X34YOAIdSfF5YfMwTqPVmTI9eEZj2PPC5ywpnU58SAIw0nPiBxPjj4HxveiomQJut93iDgBjPGMZdWI4ZEdB9mKnZPOPZ1t3LSGmqGrTeVNoY3Ft599BSKm03hpKatltLhC1MlRacUkagYum8LDaB9oLktcrfcbfXX55lxG4NlU2lb9G5uG00Ol6rM0hsVTWHSJw9b7MzQ2i27ktlVTvU1NRhzWw0suNBXrqV1CChxvEEAgTBEXyxvCrVRXijrWAKZlIYcDK1yDaEAIbQVoE5DETi5Wqqrbda6WueviK1ddXU7OlTLD7lIh5taw4NVSkSEof+0Xv1hN35WctK5ym2dSOsKp6fcfMWgeSh5bqHSRdLPZG3SsKQNKapATOYMKDqEvIdS8h9taRpW26ClYIOEpHojcG8KHdFqY20p9dXSMXdxLVep15fylIgImp8srXIFI9HEyjsqChwUMldaZ4yIhDa+16q73CV6pzBGpKFfjJHDogzzkPdios9cDrDqKyieBAUxWU/bYUlRB0pWoaVHoMNtXVh6lpqcD5VCkL71TWnSlCkEgpVLPohKRknDyYD2Pu07dO4oIVRPKqGwewttAmNQnjpVFPLJLiNWHQRG4ySNJsO5hl+FZq4D2TH+0uln/APbRbPSfFu+7eaNirwkdo29OPT6gwITIicgfY6YyT8aKgTE/Wqf2AqchGEvHh7cY6fLDI/4RRy6cvaicgfCSPYg5AkEZk5xbAJfJIcCvCoSw6cY4fG96MZYzyJMNdVM0OIxAl4oyT8YwjISViMeBngeiOY/Lvl5vNrl/uveVgqLHb92uMuPfVbVW2W6qSGlJcSiqpyptxSSFBCjIxcNob4t7V4p7PUGmG8tsIcue2K9JJUioNWwXk292o9JTbx1hU5wJZcD9znDzyuTfe9wqk2BthaqYBVPcCpFXdTTvzJVNgSWcMDKOTnJK21hH1DQVe/8AcVO2pWioF7ZVb7WzVSISpdMWVqSgjsznx+4X7ZXVVufUy7TLqKN5dO+aaoTofZDrRSsIeRgqRExAK5IaQFKWehKe0tRJ6gTH2rfteb9tKzddycn9zWHlPTVZWBSbVcfpEXPdDTXZStd4U2lLClAlCWiUkThpLlfVNqDbaNKrdU6ipKQFaS3rBy6YUlFbVqkcf5vqUgTkQVFSZiB+mPfqzv5MAeuuAjppHZ+1ExXPk/i0j0sZDLDGUNUrdwU2tyaUF+ncZb1YAJLipp1KOU5fcbbbQpxxxaW2mkCa3XFGSW0D4S1EYDjFBzG5jWuqXVa2qvam11oSRpCSUXS6sqBUsBSiW2iJEgKyEP7d3ZtytuFAWlro3mWwm40TwR8m/RLSJtODDsiQIGUc+NsX2tZftV/VabztdT6RTXF2iZQqkcar2EySqqZLSRq+FM9MEI6PDxx9iEfuk8DnMQ8qeBS1LP8AAA4Z5RJJ6Pg9A92cNTIlM8Oow7jh3i5YHpj4PlisyE20fjYhWMZif7mPg+UeaGxhP1xSiJTAHd9I8EYEZzxEvZMfB8vvQyMJ9y3x6hLhGGnjx96Dgn40DBPxo+D5fej4Pxvej4Pxvej4PxvehrP5tHH8UdccfL78cfL78cfL78cfL78Zny9fnjMzkIOJzPHrMZnyxmfLFD2j9ERxP4RjAnyxmcxx6xFOJnGiaOZ6IwJ8sM4nF9nj+OIrJE/Pnj+KmMz5YpZk4uDp6CIfGoglxeGP4RPgjM+WHcTjTuceoQcTmePXGZ8sXTE/RunqMCROQ4mJTI65w0Qoj9OInPhMzEYTlwOrzwNUwOHa9jCGMT80jiekxx8o80MHGfeown154Sh8z+GTmeBMcfZ/JhwmUvVX+J/Bw4iPLxJ4noJj/wDWirnm1UMgY56gZzxgeCeBPScc84n7vvwGqxlqopaphTT9O+228w+gzC232nUqbdbJ4KBEVvM37ONzs3KLmToNVc9sLtzX9Bt2PFanat8U7QSLLeqpHZQtoJaKvTGMU+3t7bQ3Js/c7d5TQMN11G7T0NzoK2s9URWUL0hSVFJWIxStM8DOKTkha2qO2Xa+36mvW4bgh/1w7e2i6Wq6pvFQaXWWUKpAptDjgCEPlIOco2xy42VSN0di2tbae20pbabaXVKaQn1mtf7ttCVVNbUanXFSBUtUzAaSTrVrIJPBKSVDpwEUTe3WxW19rrlVT1AVBLtXRraVqFKpakt9+26lPZPpjAYmcU9G9Y6iz0PftIrbjX90ymkpzg46hhSw484EjBKQZHMRt222FkHU44qvrV/PXKqRpC6t30dKlzyAAAAjHDASzg4+wYkThOcSH3b7eHKU/Ns0dLWKHwidTrbRmMcROKZMyZuonj+MJjLKNyJxkbDubj0WWvIOXVH+0r/+aLZ//wBl93jYsif6pW7In/mNPAEzLSniegRmfLD+J+k0/E9CoGJ8sZnymGcT84vieqMz5YJBOR4mLSZ+k26T1+GMz5TAxPHieiGsT9EZ4/iiMz5YTNRwV0njh7cVdRblof3VfUOUG36HvSlwB1otVdwWrtIabpGySkrlqXICK1N0Ddz+sFOu3BFc02+zWOPEl1VQwsKac1TM5gxW3LY6kbRvjiXH00idSrLXVGKgypgTFGp3IFMkJOchDze8tr3ShoWyAm8tsF+zFLii2y4q5shdGgqViEFQUoZCOUu37pSU9JfrvZn93bmAShlKqi6qdrKeqecOJdNsUkaiSAkCWEc3t7prHKq1I3JUbe22lThW3RWKxH1FmkpzIfJetNOL8KoH3NpcraNmra2w1U/XXMK9NUi3aez7XtwRVVKVVKh6q1X3AlDdO24flCoyBlHMiwcx7PSnlXS8vjsK37NpahVsVuF2ooRQWXb9uVTpDiah+oSlzsjBSCTG4rULQxeKC+bfrKpNFua3Wm909C3QPBxhllNZb1OsvIS8kKcSpJVokc45f7ooaK3bbc2zcK6jNDYLXbbRR3P65ASXbk1R0rblS4wlqTZ1DTBP1jcB1aWTLqmcTEzdLmP+TY9uP2pdsvgpphx/GQuLzyn3/u3mHYbDbOXN33giv2bX2m3Xj6zoK+kpWGS/WUNUwqkLVQVFOiZWBiBjD9z+zTzE5g703pYgpxW1eZ1Taq/6/tzbQccRaqy1U1vW1cmQk6UOlSXJSEotXPfmlZqO6buKX6hrlWwBU1litRStAu9chR/S60pSHAyg62kkagTDFdXNNvXOtpm3AlTYR9XMqQC1TpQQNLiE4KEhLKJpwxJwIGfgEba3Nbyqmrma5ujqu7IDVdR1v6I41Vpw7wNpc1oM8FgHqgJK9SwhvUoDSlRUkOYDMS1QjE4qSMz+EIdmTk0cz+AmD2jLwnoxhrE5nifwTDmJ9NXE/hGMz5Yq5E/No4n8KJTPlMZnywzifpi+J/ucZnM8T1xmfLDOfzLftCMzx4wcT5TAxPlPVGZzPE9MZnymMz5TGZ8phr0fm0fC/FHXHwfje/Hwfje/Hwfje/Hwfje/AxMv3Qln0CcZg4D3YOWZ49cYAHxj3YPZGR+Ej8qKHLCkQDjx1GMAD16kj2yIyGafhJ/CHQYYOGFG0M/BGAB/fJHtkGGJJmfWGcApH4Y/Gis7IkXzLtJ/BR0mPRHxkflRSTAHyg4g9PQTDuRIcXhMfhHpwj0R8ZH5ULOnDuHfhJ4gdcHADE/CSePUTEkgE9GpI9lRAi5JITqVTyHbTL0TmZyEDsoyH8q30fuoHZQervWh7awIaRoTqFeSR3rMpFWervNPsx82PzrH+FhIKQjHPvmcerBZikkUjumQkyebBVNROM1AGUZj883+VFOZiQdTP5VsmQOYAVOHVAiRXP51ucs8tU4nqH51v8qHdSh9GeEtaTmBLEEjjHpjEkemngTPj1x6Q/OJ88VqSsTcqGSntj4IxxyjEjxrQcPLGaevto88UiZpwQ7Oak/hTw4GChUpEDiD4/YheyOb22mrvTpQv6o3BRuKt+5tt1amy21cLPd6bu6lp2lKtSEKKm9WMpxvm7MX2o3/AMwt8VNPRXHmDekd5eztO0o7iyWlypc1PKqVUoR62pJSh51IVpgiacyMzCFiU0oeykfSbUOjrgSzAEsR0+UwoHHE/g9PRONvvAAk17rBEgMFpSSoSwmJQAfc9z+xM/v4e7FnbcSlDte2q4vN6lFSF1B7OCjgkoAinJlg6iWP4w6I3FinGxblGBJONmrxl44/2lw/B+1JtBPiG77t542KokY7St+BJ/5jT459EJynIZKxy68Iz/hJioliPWqcTmngFRn/AAkxiZfvkwzj/Kq4p+/hH++TBE88M0xakz9Ft0EzAnh19cZ/wkwMf4SYZM/8UZGY6Iz/AISYr73dqxihtlrYXV1tVUuIbZZaaSV9pRM1KXKSQASSYvW6KwqFEuqXSWKmKlaKOzMFSKZCEEyCqiXeqJGrGX3KG1WikfrrpcqhmioaWnR3jz9Q+sIQhKUieE5noAiy8pqa1Ul4uO6ub/Jep31WPoU+6zQ/0nol311lwtqeYp6KhbLBQnsuEyPpRzS3hQPt0dbtjYtTYNr1KW22GUXd+jFlsQLQJS02XNIShIVIkDLGFV9WVet16lV9YVYqVWVqjU1alfjKqHVExc6jlhs5zcFHZnjTV9dUV9NaKNNUlIcXSsVNbJuoqGm1hSkpwA4zwh3ZW7LLV2rdDbzdObMsd9UuPvOBpltlTKVJeL7qglBSDOeAMUd83dQt0PNvms1Q7o30twthdhoE0jdRaNrOOoWppTdoaUtxbiiFKU5JUtMoHKbZla4/yw5KXaqt5qGnVGk3HvlE2bnc9KSWaqltoAapliYBClDExsmoDjiKe43QWivCFqSXKW4oUyoq0Gaw27pXpOBKZGN0vlIc/o45S31GhDepK6R0NF8ur7fckOhJSDjqnKGlkSLjaFnwrSFSHgnA8ceON4f/AJktw/8AvW3wgtlSVhSSlSSEqBnmFiRBxgbZt75XdbohwXJ1LmpdLQkSSkKEyFvqBCuoSjUcVSGPjz8c/uU92r6ZarLZip9Ti0yZqq4S7hgH4XdL7ZlllClTSSTPhIDgPEMIT6PpJ4dfgh30fQR/ewOiJhAI6RL3YaJSBiej8E9GMOCSfnF/xvBHoD2Iq5pA+TQeH4fVOMk/f4o9D+LDY0j6Wv8AB/uY647SQkTzmPcMZp8p88Mgq/kUdHECPTPsdMoPbPsQO2fYj0z7EemfYj0z7EemfYhrtD5pvh+IPxY9IeT/AHsekPJ/vY9IeT/ewe2PJ70DtDx+HxR6Sch7fhg9oZn2/DHpjy+/B7Q+/wAcUXbH0RHR+GrGPSmOjhPy5wkTlNSRMZ5iKeSjjStYk9PggHVPPHxwzqUB8u1LD8cT8kVQCkgd4c8OA8EelPril7UvlU5eOHhr/lnMMctR9yMx5Idx/kHJdXgg48T4DjGlRw6BMRckHFJpySD4FeOOOE+J88Ye2fPDWJwricz0keKMzE8cCDmR7UUPwu9p0qlqVgcTxMej7cU+EvlUHM9Phh/D4Z4nzxx+Mrzw6ePqb5zP4AjDpPT0n7lTMTk+zLqmDOJZ4T4+eMvZPniiw+A9xP4cYGE4jPiPHBAMh2fFNIMekYSCoyLb397VEiZCZwGGU4CUgqUTIAZnzAcTDnLzblYzc6nZ1R6xf7nTvIdo2bipJAtVMpvUH3mCCHlTGhWEoGM/f8fD+xtVn+BU1TZfGU6dshx0AyMipCZQhhA0IYbQy2jEhtttIShCZ4gACKcT/lm/4wi/gnD6k3LwH/oavj/aX/8AzSbR/wDswu8bFA//ABRt/s0NP7sJyyGYHRHAeACKnH/HKb+Iv24GPsCDj7Ahkz/lFHIR8HyCDlgCchFoM8S06ThmYzHkEDH2B0Q1I/4pTnIZlJmYkMSfB7cN8trJVtrttpAqNwPNOkiqu6pKapm9HYcp6NhUiZ/OKywieqfsZeAnKHqKw2uvvFZTtJffp7dTO1TrTKlaUurS0lRCdXGP9I28LVVUVbRKfpdt2auYWxWMuhCe9vNQ2sTbbKFFDQIBUZmNFQ2HFFxLpCkhSUOJWl1Cygz7aXkhSTjIicbU5Q7Ovlp2Zsiqv/13zD3PeQ5UKapbQlL9mpLfaW1NP3KoqK5BmQoIaHaVPKPrDe3NDnBcbhRUinLpVWG5Wyz2yoLDZL3q1I5aahxrVhpkAfDFs5ecubBfGNuWl94U9TcKdyovV3fddUDdLxVIabFXcH0qE1SHZAAjbP2h94VO4L1deWtPbFOWV12nG1KvdNvKl0EqVLSl1AtZktYcUpK3ADIECOYe2fs+1NnoeZ1/sr9oslbealVDT0jFc2qnr3aeqSR3FeKVRDCsNKyDwjcW3t1ctd3tXexXWuorw8m0VtW27XMOuKq6oVbba0VKahaVOBwTCwZziyX23lLNZQXKlqGFOIkUKbqUNuhSVJ1NuIBUMRNJEX231jKKy3X7aTzjqZB9uoaNAmsK0JVMOFLjYUCciIUUIGgLWEDTp7IWpKexpSEYJykIOHufc3j0Dkjf/wD3pb4euVQpLlQ6pNPQUoUErqKh1ClJKcz3bSRqUeHjhG6rte6SifuhLzbbjL76i0pRKdCkyCWwDIDOKuid3UyFUj6qdZNuqJd63IOJ4+gYdt9s3Y2p9hovP67VUNANBWlQaUuQW4eHCLZabY2hikoqZDQ0oSHHngkB6ocWnFRdUmcjlB7Zy6YR2jitIx8Ih6SpfJtZdaAY9Iw0Co8fah3tH5xftx6ZisOo/No/jx6Rj0z5YaOr/HFHytxPUf8Adj0jDPaPzLfE8Upj0zx49cHtmMVHxR6Zj0z5Y9M+WPTPlhn5Wk+ab/lh+AOqPnaT88PNHztJ+eHmj52l/PDzR87S/nR5oE3aQ+B8Tz6JQPlKYYcXZcIPytL+eHSeqMXqNPWp8e9B/SqH8+nzxRA1FGmVKgAqeAB7ZMxMiYxiRq6Cc/8AnKB0dJhMqqg9NOVU2TnwAOJin/SaMSpWh2ngDgD15EwB61Qcf8abHHrM4px6zQmVQ0ZCqQontpwkDPGK9XrVGAahRCFvpBRNKeyoGUiIA9aoMAP8abHuxTFL9GuTgwRUJWcJ/gk4w+ou0iR3zmKnwMdSukZx9Jov1hMOHvqRU2FjsvpOY4wZv0QxOdQjp64+kUP6yjzxcJu0qtdOQNDyVAHSqRUROQg/LUf6wmMX6McPpCZ+zCE9/SzFYVT79Ok9rIKyJjGoo/1pvzwSl+jURiZVKD7XVFuSHaUFFOAZvpE5JzTPNOOcfP0n6wmGJO0pAcTMh9J48BnDx72lAK+LyQR4QY+fpPz6Yd+UpiPVXk9l4E4pABl0QdT1IMT/AC6ekx89S/n0+aKmblNMvNSk8mQwlifHHaqKQn8aoSD/ALkdl6kUegVCSfbikTqp5hLpPyqZYrmJGeME9/SDH/nCOEIHf0mKpYPoPscYI7+lAEvSfSk4CWRyj6TRfrTcJX39KoBDokh9CjihXR0RVXW6XS1W220TT1RVV1dWtsUrLLKCt1TjqpIRoRiZmLjsHkZdnKajIFPf+YgSWUut9oLpNrrdCAUuiaVvmaVJI04xd+YV4oHKOw7jradm0vVjjwrLi6ULeermUVPylQy8t0lTkz2szAnwA9keAdH9jdb2oIBtNO2zRlbndgv1WDq+OotNpA8CoJW6xM5nvAZnjiBjDB7xkydQTJeOBBkBLEmNwuFbRAsm5DJK5qM7PXASTLHOP9pf/wDNLtEf/wAX3aNhrK2glW0qAAahrBFDTjUpPwUzED5WkHhfSMJdJlHz9F+so88VCS4wVqq6Yp0uAowCwZq4Ri9SDwvpHtyiXf0Zn0VCCfIDOGU95TYOKx73DHoMT76l/OiD8tS5H+VEWpIdp5padBKnAEmYn2TxEfOUv573oE3qQS6X0jhljLGGyHaQSpWEyL6QSQkiYE5kGLnXWdNM/fa8fVdnT3s0M1VUNKqt3L5OlamqXEiFVF6YVf7rUrVUV1xr3HFLdqXld68W0pUAhvvFYcZRIWGlSSCJpL2fDNccz6C30rTNInblnLbZLigzpXqc04kgKJmekxcWfggIeSchIo0y8GEKp04pU+ypQUAoErkoiRnMQ8tIS2pSO7aDYAJUuYwSMwAceqKbv2w4p8FZ71IVOajiUnCLftzbdNRq3Rumo+r7eO4aLlGysEVFxlpk2lhE5KVhMdMUdpb7srYQXa+rACDWVrp1VFS8o4qcccOfRFc60qShRVDiFDqaUQQRFc8GGqyr9YrQBVaFrqQH3U9w7VPodWlDiCUlRnJMbv5k7n2Vb9u7U3/vG43OwIsFzpbtQWNqoI9Uoa9VI0w1RurW0e7GkajwxjZDlQ6X33rA3S1LxcDoW6UuNud4Z9kqCu0nrjfVn0IaXb903SnLDaQhKGy6l1soRhpaV3h0+Dqic5eKM/Y9+N71b6kNsU3I7cLtQ64sIbbZTdLfqWtapBIGXhMLefcCbZROPM2ykQJNtNk933x/CddCZknhFBaKq3Ct9Sa7pp1DwZOgKmkFJGMh5YrK+qtd5Q/WVLtS42zVNlkOOKxCEkGRMsYYvrbbjFXeaRp5LFS73lRT0zqQ6ltwSHdrIUNQMN926yCMDrUEDI8OMEl6jlli+lPtkQg99RmS0mQqEEyB4AGZMPKQtiWhoALdCFTDYnMSwjF6kSejvgfZ6RDRD1KqU8A6CcjkOMOdum9NWToPGPnKb86Iq5rppFtGPe4CSiccMI+epfzwj52m/O+9DSO8Y7z1xRxc+T093+F+EI+dpJT4vgDynCPnqL9Zb88MgvNagy3MhUxPSMjOREdl5jjmsD3YPytL+eEfO0v54eaPnKU/8sI+cpPz6Y+cpPz6Y+cpPz6Ya9L5tHD8UR8LyR8LyRkYPYMJSEnH2pjqgKInhOUx0eCFDQoSPAzjTpVjjPDwz9iMj7EWzA/QUfx49E+xCZJIxHR0iKfDD1RmeWcoyPseeKaQI/SWBw/uieuLsCD9NWch+CnGMj7HnilGkn5UADDjFSnSoTecnOX4R8sej7UKQB6TDvRwTOCRpzPVxjMeIxfBwTRtlI4AqCp+WOHVAy+/rhpJ7Xe3FQbAI7IQ1qPDIlJP3FHQtQIlghRzBE8ot823dItbJA0SAUXFAywzIESK0pPQpQSfISDFIlyrpGlLdSUJcqWm1LlirQFLBVpAxlD0qil7Szj6yxx/f8YKXLpa21DNDlzoW1jwpW8FCHp3izTNK/8A960BxKQBlURI3mzTSTP+drd0npqI/bVm/wArW7/tEVXd3a0Lm+1LRdKA5Z4+sSETVdbUgTAGu50EyTkBKoxJgK+sbeQOisp8eH90iiUh1lY0PSUh1pYI14SKVkZQVJa7zpUhOvHjMpmJiGptKEnE4FChiTlKUKW4yUJMgFKQUg4cCRI5wAEjoA9iUG979vbLFYWn00W2KGVRuK5nSUAUtFMFKdagCpUkpBmcBDO1LTT1Nu2+/WufU2x9upedqq1K1DQu9PtuzrXZJGoyDWAGMW7fHPNmluNcAxWWnl1SOoqbVRAIC2HNwPONhT9SlWlYaSJNLBzlGz2KSlaom6Z12hpqemAbp6ambQkIZabSAgJSkSnpmYkSTljgZ+wP7AS8gz6YuNwdqGqZVXWlaXah5DDRp209spU6pKTp04kRXsba3LZ9xP2l0sXZq0VzNau2vBam+7rA0Vd0sqQc+iKbP51HAfhCNxYn9hbn4DCVmr5T6BH+0sJzP2otoEnrO77vGxsf/qRt/wD5hT+eE+Ae1GBEVAJwNVTeWSow+5tj7J32VtiUu/ftCXdNBXXY3i2P3mkoHLynvrHt+1WSnepnLjXXChJeceDgRThA1Tygtn7P+yiUmRI5dVJE5AkA/WksDh4RD7n/ANr7sxZZp33+7HLmqKnu5aU53LcrpMuO6dKesxvbkTzz2K1sL7QnKulrK+vp7Uw7R2e722gqxb7s09bH1LqLPdrRXHunGlKIcHaHGDpEx4z7UDAjifJDUj/itOPGEkRUvoStTtteRVN6CJEHsL1DMjQTA65ezHj+/qjmhIZ7dtkvEtJz4Sl4ouKlGc2UgYzwEpS6vDjB/wCOpv4giit7Y1uEoJSTJKVKIkCoZdkTMPVlWoNU9BSOVDysAEtUzRcXLEAmSTLpi5c0b2nSmqW7btpUpTI0tmSpWp9QPorqSRKWMpw3bwFBbpQ6VAkDSlYmnyRVEZ/VTv8A5sqKtVurbdS0gqHgUVVuXVOF3v3C4oOpeQAFA4CWHTDPIm9Nbf8A6KW5u2bwVXW1PeXC61dV3ybemocUpS6RNChC/kpJKiskzGmLjRPureft25atMi4FFlh0fIhLZMm28MOmLk+imRT0t6tNrurbjZSRUVL6FoqnFfDCx3aJzwmcOMTJkAJkmQEpnzQscv8AZlVVWxt4MO7gur7VosSF9maW6+qID7gQZ6UAzEbw33uzcVPcN37osSdnMW6yBX1XabGt9FXcw/VOhL1bU1jzSAFJSEJSnAmBlh4YkMJE9P38Y2XsO0utOXW93qgVcG1Aq9Xt9K+3XrSVJnpcqW6dSCk/BJhaAAhCFlDSUnspQDJGmYwASIRmZk9HQY4+Qe7CBI4qSJdnGZGGBnDwKTPS34PQT1xhgIaIMiNWP70w4dRxcWf4Rj0jFYmZILaJzPDXGrsiXGcS1ZYZdHihog/46sDD/g/BGfEdEel7UMmZl3Lf8VMZn7zBxOfEDzRx8kZq8gj0vYHmjP2B5oz9geaGibvaR8mjOpAw0j8WP2taz1esDH+DAnc7bn/d0+aP2lbvE970H+crf+ePuAwiVfQKxGCX5n2U9cCdbRp7PwnQBl+5MK/nC3Zn+X6+pMdm5W4HrfPupjC6Wz9YH5MW7+cbaJUSM38CdRmRhkTH7TtnjeM/YTCf5ztfpD+WV05YiKTTcralIoGRoU+ASoYaxh6JGUYXS1eN9X5BinJudrIFQwZJfM8HAZYpE4uSxdbWnvarWEuVQbKQpKcFdkgKwj9rWLx3FOPX6HGKRarnZlgOglLVaHFnA4JToEzFYr6ytqZvrIm/iBPo09MGV0suBl2q5KT5NJlDk7laVfor4AbrAvEIOKjoEkzzMGd1suav8fSeJ46Ikm5WdR4JRWhSj1BKUFR8Ubhq71uPb1ppGrelx6rr7pS0lNTNthaluVLlQ40plCQMSRKUVq75z82Fdam3pcVVWva1c5fLmlbY1KYSxTt6FOqE9MlSUcIfZ2ZtfmPvF5hh9SHa2zjb/e1YUQ0wFuuuJ7h1OIXKYGBE4NJtn7OdFaXW3qt2luVz3MquQkqbKKZ9VOhtOpxM+0jI9MXA2Wj5V7btbwU3QUy9tqrLlSoW3pKnax2qCHXkLJUlQTIGWEB1jmTS2tSlKUF2SwNoA7BbwUEuoKUnED8MThby/tJc3SFqUUIafom2UFSlL0NIVRtrCElRkJyAhdRduc3OOvfUEtiofuDDCUtImUpm0UggFRxlxhpzcPPbdLSqRepoXPdr9OthUyNJTSqDgVPhAK/tCV6Vq0kqd31eGk4AAdrXIQ9XXfm09cq1akpcrXd/X5SnEpwTJTdUhBSBlIQh17m/c2XUpWjSxv2/oSUOJGsKIrgTOWHRAYTzfuOlsSSXd+X1ascTqWuu1K8cYc3609X9Or4farTC26Pm/cR3ikqUlW/9xttEpnIgNVo0kS4Zwy/Tc4lsVDDqHWXnuY26AlpxBBSsF2sWjUkjCYMJl9opLgQhCQlrfle4vSkBKcAJkyELYs32la6jZUStTLu+a1SirL5NL6FnTLgJCPV7P9qXc7LaVrdZbt26QtSHV+mpHrDBSVLPSSI9Zpuf2+d3ILKmPV7iq03dmiRiVPgNstlLxGAWomUsIS7WvbcvaW2whTl2203W9huau/eDb4AdUkdopGWUO027dkbCulx7xZbuFp29X2oNtK1AIW24p1txxExJQllD9+5g2G8VtyuroVcL65dzcKlmfomnpHuyyymeKG9OEMWqkue9rRvi607I3Bvjeuz3GrYhQkstUFwm6aW3suoIShCdfwjPVBHL7nryy3HUAycpafcVOzUtOKJKGnW6kM6HVywGcMXhp9qoNpqEVDblG8iqp1NvdhZL1OVt6Z5KJAgFBCgZ4gggywzGGcYAmXRCnqmop6ZlIUpTtQ+0y2lKAVLOtxSR2EiZhDVRuNq5PODUBZm1VzbeEwFvJ0t6icNIJIhKNpWGjtlTNWp+7K+smyCZBYSjusQBgMB0wq2X3e98qrWXFuItNPVu0VuYLqprQwxSqbUEcJEmYwMbl2NUP90OYFkS8whQAbNfYNdQFlxRmFP0zikkT7Rkc4p9D9OE982e06iahP4Ax1k9UbiUX6RSPqLcw0peT35CrNXAfJATmCceqP8AaWJ6PtR7QE+mW77vjGxV6gJ7Tt6ZakjKgpxiDiBAAcYScMXHkITl04zj6RQ/rSfNG9Oam/q9tjbG0adNzr00LiX6ys7pJSxb6FIkF11Y8sJbHATPAxVm0ckrWza+9UKJqpv7jj4YClBCnVhqSlqRInrnH+pezY/+vHf8FFL9ul7ZFsXuigtVut1Ds96oDtvpHLba1WduvarFoLvrS6VxeIAkSI0q5SWskZkXh2Rnj+B1wP8A4R2vMf8AfLvT+4jmp9rS37Dt9zvvNW13W3XDbaa00dtoBda1FeuoZLTYK6jv0lS1H5wnGD/8E7Fn/wCm3v8AAxa1bs5K0CNsiraF9ctt6dVcGLco6X6ikSpnQt1hJ1BJwUBKNq8wNp3Bu57b3bYbfe7VWoW2A7S1jCHktrQlRDb7GvQ4g4pUJQ5TVaUOU9S2pp1pRQQ4gzmDImXhjvbFeF21lwqU5TVIFQEEmYDa89OJzygT3NTSJBUdATIHEmassOmN+7wvNRYbjty9WigobG/bqwuXF5xKzq9cpikdwGwmSjliJQ9XqbLnfp090JDEkAYx9ZO07iUqqG1aQPRQnsoSBmTIYmF3Qutv0q1LaD7LjdU006RpCC4ytxCVg5pnqHRFt5ZWN5aad9xiv3ZWtFQDNnZUHjTJcR6K6laUpkSDI5RS2+kQ1S0dGy2wwynSlKG20hKRwmZDE5mKRbakrSGpEpIMu2DjIxU63B2rW4EhPbWommIAShE1KM+qOaG8KPfLe1t12S1VFVtWhar6YVtwvhqyaSiFuUVPEVKdUwtICpCK7de8bvWX3cd6cFTdbnXEKqql/u0hIXKSEpZQAhKUgJSBICN87cdqko+tqKhudPSKSkqddolKQ46lRGpACVyIGBOcbLvVgtorhR0VxoaxxJSh1BQqkdZbUpWkLCgVaBwkYUje1FU2zl9tQCp3CtK5Lu9XoDtPZqZxEygr/lFDAJi27e23a6WzWS1UjNFQ2+kZbaQ200kJSXS2kd86qUytU1GecdH3Ljd38U0bC1oTkFvKGllJ6u8In1RbrvWvMKfszNXuF1+odOp6oWHafuyVFSUtobqFaZ4QflG1TJkpLiVJkVYSPHAiG5PUyZTEnXktzkOGc47FTbxxOqqR7ohB9btpktBwqm5+kMuznDq01FGkaW5JdqEIXggDFMpicfSaAf8AWkS8WBhsmpojLVgmpQT6JyGmHP0mh9NWdU3+EfxY+k0P603+TFcDUUhKmUgFL6FJT2pzWoJ7Awzgg1NBL/pTfmgj1mg/XUiGkpqKT6Ys6vWEluRb4OHDV1RNVRQS/wCloAx8XGJesUGP/liPNDSC+yClltJ7aSMEgYKMpjoMdmoo+MtVQhPtgx9Jt+fGsb/JEYVNu/XG/MYxq7eP+tt+aPpdvP8A1tvzR9Kt/wCuN+aPpVv/AFxvzQ1h/Jo/ij7mcS64Vn7ftw3qx7Q6vahMvwR1w4JHA9Ms8eiJGWPUBj9y2ddC3/GMdqc+rohKjqkFoyxzUBlkc4pABlaKY+XECATgceHX4YpSCfpNOMsvlUDp64uoIJ/SycgPgJPjjAJlwmBOXXhFEFBPpnTICeqRI4CXHGKyZkBUujyKVABCZnAYDHwdMP1Vxq6K2UjVJUGoq7hUs0bDKA2ohSlPKSpQkDgkExU01LdGNx3FnUUtUalN2tRCzqDlyISGlAdCSY3Tt3Ze6LbsK5uW+opLbdrH3tx3NaLg82tNDdrbUFIC1Uzsj3cpK4yEMVPOvmPuvem4qZ+qQbrX36qULlSl9fq9Y+xQvs0oNUxJS2VIIQqYlhOJ0Ntqa1xyagWk61uSxKtbhTrMuswhV2uFttczrfpHqlX1kygpJEqRDS0uLUchrxgrfqrld2hgumUKWyUzSZTUtVwfUpShhkQnrhb6l7TpCy4G3BeK6kuzs8Oy1pUElQ+EZxqpdxt0rVPTghralradpwHFnsmWptTq1mcsTBFMd3Xh5RlpeZYoFFMpjBloAAEDLGO7tWxLitagFantwVC3JkGc206WpHwTg+r7LZYdcUFaqh3vZEmR1KKTrOGcFbNk28+0skpSzaXVFpKidKXHDUCagJTIGMD/ADdsnAYWx0ex6xhKFNVFbb7c/PUaU2kpSkKGGjVq7OPSYl9dW3tyABtyJDwmXCUJcbqqR9rA6m7YgpUM5cJgxL6us/GcrO6RPw9/Cu9tllJmMVWl1OGAkR30sDCO7tdiAKZn+anTmOnv8JCAqvsdjqafujLRb3WnA4JSGDxmmUFC9o25w446KinA8EnFBUoQmp2a80vsh12mvL1OheOBDbadSUz4EnGEVNbR7ktLiUKCnKWoVcUoPwUlt1wJcQQJzlDrzm8boGmlBt+n3BamkUqtclaEqab1zkMCDkYeSzZdoXxkSRUM2F5uy3NClCYXTvvkpSpPQUyOU4lY79XbYrkpKnaDdpnQNEjsIbujSChZKgACEkGYxilqKmgudJR3msp6GivFhVUO22vcfeSGan1m2PSCErIV3iiFIAmRFDc9ic0a6k2FVMNu0tmvW629z7WulvcbS8ovUZXUAhGYQXQsLMyIcoOfFho66qCFFF823bnrbQKcSDMP0yVPd2lKzLWkqSRjIQf6JUlptlDUSXT3Rgi5uuICiUraeV2GVgSmJT6YdqdwX+43Jbpmpt2oWKcAqmUtU6NLSJnoGMdGM/PAA+5sbeCqgUtPaL9QuV7xKgPq9x9DVW0dOYdbXKRwPGLZVUoApqwUdXTgAABipbQ+3kZeg4I3QqQ+TsG5yDLH9jXAYHhH+0p6/tQ7OPTnu67eWNjpH/4oW3q/7upp4QBMSA6OjxRmPJ70b9LeHrG7dqMrGQI711QMsjjAOE5CPfMLkMgZez0xt/ncxzxTshm/XbcFqO3Dshq8+pmxVxoe/wDrNV2pVP8ArRGrT3YCcsc45i88Xefre7WuX9pYurm3RsdFqVdEvV9LQCnRcE3WrNMoGrCtRQR2ZcYSV5lIJxniQCQBwGMZT8fvw6Jem2tBxOS0lJ6so5EqnI/0eqAJTHo1MgTLjjxgAknSTLE8c8c4bBOBOXiMSBMigTE8Mug9UaZDhwHsxTgyPyyDlPiOHGFJrnKSkWlxymIqHWkIdLhUhAQ44UJSt0EgCYKuEbivmwtx7ms1p3ZWC4XHZD9xTcNoUNe+txytulgoalt121VtYVzXpdU3PFKROLvuPcl8t+3bW0l+6X3cu5bgywtSGwXFqqKypUlxxtpPopTPoAJjcFg5d8lajmBtS0Pqprdu1/di7Am+Ka1ofrKWjNEt1FCtaQWVKxcQrVhlA1/ZcCjhLVzIWTPDLVQdEU1s5PbKsPJunqmamlvt0rLg7urcbaXED1ar2vcEppU2isZVqmVocBn0Rc9wbr3Fe9xXq81irhdLjeLlVVr9dWLM1VD4cc7rvDPgkAcAImM+nL2BG3UVCyhN7oa2wsECeupqwFtoOIASS3MnoEWzb9lpnaq5Vd8YZaQwjvVNJUlTbtQpExNtmYKsYo9t29Sn6lzTW3evcSlLtbcHUAuqITLS22rBKcZCBLpjx/cvbTzaXWzQvlSFiaTpbKh4wRG99zLpEllihprG1VE4pqn1NVXcoBxIDAJJjq6sMOMUpJJ1pKhh0Ye0Yl0Y5CEfu0y8o6oe/cNZyy7tMGREsOAhqcszwH4J6ocMx84vo/CMHPM8fei45/MJ+F+MeqMMPHP3IyMMkcbgsS/5I+aJHDxxOeWPDhjDLpOK6VhRx4llB8GcETBxPtwMOjj4OgGB2f4XvR6J+MfNH++j/fR/voa/nrb/AM2j/GHfwRH7a2/+sOx+2tv/AKw7A/nrb+f/ADh2FTve3h4al0e5CP5zsz/ayYqFz/hCAPWKJHZHaceknLwThwi72ITOSqh0keGQj9tbenPL1l3DPqj9tbeGB/xl2Xnizn62s7ZVbGVHvHVJCiVYqRM4t44R2r1YyeqoIw8ohITebGVFaJD1o4kKBkJcTKKZwXWztJFrp2lJecUhwKQBmmfonhAH13YyeqoJHT7sUv8APVkJ9ap8A+ST8qiUh4RF01XqyNK9ZOpDr6kLQdCMFpOKT1R+3rHP/pSgD4OiKFz66s7ul4Ets1KluEBJwCR0T8kV67xuu0PvpdecFBa3jWVy5TMkNABE+mZkIcpbXW0VvWltzQ440qtutUUpVNDdEJJZeIliDIGULVZ2Ktruy423X7gqXKt1TZUoILNAhSKelSEyKRiemHHL5uG4VaHllRpkPrp6VClGatDLSkgAnpnAdeq6dgETCyoPVCpCZC2vSy4zgV+4rnZqNLTaletXa50imX28i6m0N6qgasQ3IntQu37Qp7pud5IcAttE2zQWpWmZSimqGwuqQlKhrVJJ6DBVsvYdNYLVV6KdiocpTW1aO/SFJfcrn3ELZUJzSsJAA4Q4xvnmHVO0a3Q+qi+sKhwla594l7uQ0n5KcpTUDAVd7vcK6nCTJhk6klZnie+KxpnCEC3F5KDNCainZVjhioJIBhL9NaadlxGCVppkApnxAVMcI7VOxPp9XYCsOsJnH0RBl+Cw2R7WE4k1aRoHwgUpJ4zIBGMol9Wfwx54slQWSyi4GoaSlYE5MgHTr+FI5dUFbekEJnM9IEz4JRt+ukpIq7dTv6gmSZqSJyPXKMlfm/fESKl444ITmcwZgjCUTKlGWEu7QePgGU49Jf5pESUZ/wDItqmOmcoHYQrMGdO0JA9ZTxhTS6anXMggLaaSCRwJQARMQf0ClaBOqTStEj1SMEWW6O2dSkHWth5aQpUwUpUltSSQT5IpmWKur3hZaYBCKV9P1mhNO3NRQQoGoa7M8yQBH9E7yoWaquDppqva+5dY29VVC0ltxVOajV6jVETSlSSIq98fZl3pUXOzpKnd2/Z/3dVO3jatxStxJqK6yvPLceoaqsT2UVDMktgAFJh3Zd0pKTlbzvpKdSrtyz3a5T0PrryUzqXtr172lu80CHCW21Hu3lrEtHGKip2iiopqJ5o1DtKzNyhqkglRacYWO7Q4FTE0gKHGF22+0x2zdUkSarXQaKpCjgqmeUAtKlH4PCCFAg59RBEwUnilQxB4j7pSQDilQBExqQoLQenBQEcvb/UV7NTcaKmRZLsC8kvJuFrX3JQUqOv5gIkI3W539PP+j+55tB1JekbNcMO7zmM/BH+0nUP/APaDZpxz/rddo2MrvWUato20SUsBQ/m+nE1JzAMfSKU4DJ4QT6xSgDGffDAdOUb37p5p7RvTagWWlhYSdbuBIyJGMJ8A9mLNsvZ1kuG5t37kqTRbe23akNrul6rEoU4uloEPLaZW8ltJUQpSeyJw4P8A7VXm7gk/4nZeAP8A6yMbJ2XzQ2Ve9gbtot0b3qqnbm4ksN3Wnpqy9Leo6h5umdeZDdU12kSUcOuOemxuX22rpu/eG4dtUdJZNu2Vtp25XKpTe7Y8pqlQ86w0VIabUozUJJSYTp+zLzWJwSAbVQDtS0kdq5gZxdrBfbdU2m9WK41Npu9srUd3V2+50Tima2hqUArCaikeSULAJAUM4Xl6Pn6o5FBTzDRG36ggPOBGudVjpnnID2Y+nUHg9YTDZ9coj2sAKhJUSZgADiSTBbLzKVoQkKStwJUJp6Dw64+kU4/5URTfL0w+WR/LA8fYjmTX2i71FqvFLvjlhUW+5WmucpbjRPI3YkBymfYUhxDhBPUQDOYj6rt32g953OgbLaWm9wepXRdO200ppthh1ynSpDSUKGHSkHhCkc2ebe+N+U3rSqpu33m7uC2U7ijPTT0NJ6vTpZEsEEECUaQkDDSAEyEpSlhmJQAB4Z+5KNI97Do8v3do7hASfqm/2+okpJUCFPBhQ0ggzIew6DA3NeULXa2aBx96sDa3nqJlbXeuVTbLepSihJn2ZkDKLFY7Nzd2td7rf7k3aKGkoapxSm694lLbVwU8whFGnWkpKlGQVhGklOoAKISZzCvRUkz7SVDEHiPuos9OqVbeHChRSohTNG2R3q8M9ahph29mqZZe3Peair9XeeCUoaoj6m2ponMrDePQRH0uh/WURQfpNIkBpUlKfSArtZpPER2ayh65VSR5cIR+mUROtMgKpMyZjCHguqpGyEszS5UBKvm0jEHGMK2iI6RUp9qGZ1VIuZPZQ+lSpSJnIZgQ9+mUSflFiRqACO0cxjIiPp1D+spxHTlFxUmqpFgMJ1FL6VBA1HFR4DGPptCf+sJjCroZf9JT5oZlVUmkXFR19+kIxayCsiRE0XK0NjoefWTkMZoBGMftexD/AJV7zRTp71hUqSnGtC5tK+RR2kEiZScxCj67bW8T886oceMhGN3smeXevT4eaP2tZfzro9wx+1rL+dej9r2T869H7Xsn5x7zR+17J+ce80NdhPzaOH4oj0E+SPQT5I9BPkEK7KfGP92GtKQBqHoiXtAQkY+iOJ6B1w5NKc+IEGSU9eAln4IPYRkfg9UWPUlKpWinlMTlicpnCPRT5IHZT6SOH46YZTpQSmz0RVNOM1oBBnxwj0E+QRQzQj6bSYFI4Pt+3G4SUJVpuLhWqQ7KQ2ialqOAQOJOEOM11ypbhWhnUhijcC2qdSVFK1VtShCmGmgc+0CIrKCx1pFRT+ssU1Ht5CWQtwOkIXVXNopbLYSPxlxVreuBslFUOrdNHaXHWlLWv03KiqcWX33FnFWITPhAqKx56pfUsqW8+4XFkk9o9oqkTAx4y1KUlKQkjNajglI4kyAEVNtvd+F43MkJLG37CU1layVt94g1gSC13LgIktC8NQhLHKXl/V7F2q4v1R3cXyyVsuyBQ/VXyqp2Agls4IbQUnphN+5y72ve4biXU1DrFtrUqUl5R1rbdq3kudhCyZBtMjPHCEq2/suhNclKEG51VLTvVzoQBIuullIUrDEgCcENUPcpV8BptDaOgTCAkSHCFpFA8SUyB0nh19cAG3r8JTjx6MMCeETNAoDI4YQP0BWf4vQekyj6JU+Wm/IgfoLqsM191PM5aAkSj6AryJPuxhRL8bLPsyOMbPvLzS2m6e7VDBJQlI1VDZlgmeMhDiWyJLbUJiYI1JlhnLAxsa6FoKSi1s0i1lII1tagASQUhRSR5YB7lnL+5+9HzLXV8mk4Y9CRIeHGMWW5YT+TRLr+CTl0R8y3+a86ZR8y142xPxyTKc+iPmWuqaEy8XZnPwwR3LXiQmfimmUfNOS/d0f5ES7kmWfZp1qM8pqSAmQ4SjBiUxiUtIyPAyM+EP1ZYds24GmluW+7ULLTBarEHWy4+EIKnElQAPETil2lzebr63bNS86bBvQNuqVRrC5himr0r03FhtsjvGTq05ARZ74iup7XuKpRS1Vh3jtm6m2OouVEsu0lYzVUqkVNovdLUyWC5jqEiCIouXH2y7LVc0eTNMlqmoudlqoWazeW16cgsBW87TSBf1xZ7ehKXai7pc71SZzROKXmBywvtj3Jty+0rVbaNw7fcYraJ5ioaS+wnUQHqN9xtYV3bgSsTxEOsKt9zu1mZmUUT5K1FuQIXS1pStLUk/AIljHcMrdo7inB22V6CzVtrHpBIUAl4DpT5Iyicbr5fPuNttWqvZ3PQKcM1OGtm1VMN4ghRKJ4zyjdKkFClKsW5QDhPt2avGYE8EmP9pSOA+1Bs/8A+y66+eNpOyBLWyrYoT6fq2kEAnTPj2VdA6MJRw8io3+QAkHe+01GQlMqU6JyAxnKB4B7Ij7LhnlzJw6ibLcRh0EiDicz7ZjUc+BgpmUzyUMx1jrlAEyZSEziT4T1x9oLUoqP+mbmBLUSTI32qOE+uF/uR7ZjkYHJEHbtRpBAIn34nKeU49FPxU+eGpBIIdbPop4KBiqlpwaa4T+D1GOHkV54ohhJVUynJXFUsJzyjmtgklrffLVCVSxkN1JliRhKZhz90fa/s2XUiamXmX0gmU1MOodAnwmURt+9stONG8bbQ24ws6loecoe5WEk4K1HERfbMyl5ipsV/uDTbjyW265DqKpdShTzjKGwHW1PdkgCQlFt2rv200vMXbFHJhm5Pv8AqO6rdSyASkV6UOpujbPwUuALAEtRi5ct9jWncdn3HZ9r1W7K5d8pWUUBt9FUM07zLDrThK6gqeCk5gpGOMJXeKgVDZCgadoae1MaF68ZS6OMXmkt7iXbfaKSjtlGhvBDKqZkoqGUmQKyl8EFXExtC1ulp3u7S1Vd6lASCa4etkASPoKclPjKPgyx4RbZ6cGFSw6V8MOMTSEnqAy68oakkT7xEsOOodQiomgH5NjE6T/JJ6RHaCQeiXmwimKZTmsDAj4Bio+TQZvOT7PWerjBmlHkTh1ZiUouqiEhDjCEAiUtWrIgEiUHBE/AOnwzgyCM+g+eKDBJBvFThmMKfonKeMTCUnGUpfeI9FPsRSkHD1GlMpmWNO2cJGUEEDM9cZIz4jwdBjBLfiE/bMeij4vvx6KfIfyo9FPkP5UeinyH8qGv849vfNo/xn8Udcf1k29+s+/H9ZNvfrPvwP8AOTb2f/Offg/5ybeGH/OBDc77aXiCPozqlk+DCUAC60aOxm7q04dYGEOH+kW3k45KqZK8YkJRI7l2946vHyQZbm29kf8AGjn4ZxYwNwWVuVop0/LVBQVkT7SJ5tk8Y/rNt6eP+NavbyhP+cu3j20YesYmSgZCUzMgQ2tN9stKl2025PcVVSpD/YZ094AAR3a+EGW4bHICZPra5AcSSRkBxhVTdd1WW+3OmeZW3Z7LcUO1eoFTiVVHepQ2lgFuRIUVTIkDF0b22qopKeuqH1C3Whwp7tlUkoYqLmQ26vVo7U9WGE4qvrG41zFO5qV9X0Lz6QWRiUOJZkuokMTgZ9EMPUdtet1seSl5N1urK6dl1JXpUGmnNNSp2XaxRKXGFWOiddu16pCGbndGnNFIFpnqp002KStHTgrph7em+bg/S0oWqmt1BTNIdqrrcCy48iipgoghZDYBMjIHwQ7beW23q/lDyjqXSTuO7UjtpLtK2ruallu9LQK2vqH25r7kIDRSSCqGrrvJlfNre63RWVt23Kl6os4qTJQSxaHXXG5U6x2CTIymRFlNwtrlVU3lxVPQWe1tNUdM3TUzfZWtttCUBDROkYThhuq2VVIo1OpNU83U63W2ZTcU02UgLWJYDKLVvDbShW2a7slyjfkUKSpCtD1O6gCaHmF9lQOIMEFgaT6RE1EDqBHTHzB8qvNBIaOOJBBwPQOGUDUyDI5FBIPhHGPoaCegN+5OUfQm/IY7VG3P9wT7sYUaJ9HdkT6pzwj6Ej4vvw7eKe1pXU2zfG2VFxtqa2qV5x9NS4XM0ISlAJ6YOlAVIE54HjgMsRG02rxtz68ee3XuqhYdNWlsIpKBxosIW0pJkppL2gYykkRS2m/7Ze263XVLNIzcgpupo2HHzoaXUJElJaKyASMoS4ilYWlSUqQsNnStChqQpMsClSSCPDH0Nnj/ACR8XkMY0zY8DHtYxPuACc5NZnpz4wJMD80OPhPVB/Rx+aT0+GPmB+ZR54n3CcuLJ/tTKMGEjr7lWGI65wVJab0J9KbR1cPRBzzis2tu+wMXm0VTTyGmnm0hygdeQU+u25whSqOuQTMOIkZjGcVnMHleqt5l8l6yoXUXbb9Shyuq7BSzR3zd3pUHW0pwTDVSzNSTiRhFLZX323mnaYMrt1zaY+utsXEI1ra7p8BVytbZWUnvJlQwIip5ofZS3FR1dhu3a3hyV3JcalPKHf6ZqcdXb6VTj39At3OpxFWwgocWlKZaTFx2elk8uOc9kZUxu/k3vlTFs3lbKlhIRU3LbrNSsI3Pt1b5JafpVOLLcitKThCb1tV1KXmEKcQ/SJKFFWMgpTUltrEuiFW3dlreudBSuSVXsEprKJU9JWsEaKhIAyVLqg1trqWKqnTp7ySkoeZUsTCHmFHvErn0AjDOOXzgqFM27cFcdu3JqaUsuiuwpVu6yEfJO5HMTwjdDwududCbNuKTKCoPLSbLXgJCTLtyMf7Sr/5odn4f/vddo2qpx5rSrY9tT3QKu8Qfq+kktfDQZcOmADc7c0NKTNa1oGU5zUBMCJ/XlmMsZetEz6sDON/tqqKWoKt7bSCFUxUpEgt6YJUMVYQnAZDgOiNlc2NjO0FPvPYFzdvG23btTeu2lu4O07lIXq+hmgVaUsOqCQVAA4wFtbh5Nu+kVBWxNISBjwrJ5RtvmxzafsFVvS47p3hZ6yo21bvqq1OUdjuyqOhLVGpx3Q73I7ZniqOc3OHlu/babeuyNvUlysL13oRcrcmpdvVsonPWaJS20voVT1KxKYkTOEE7p5SEEJJH9BkYgjVxqsTIxunfG4HWXNwby3Bctz31dKjuaJd2uz5qKxVFT/4rTrdVMIBImYX+5Ecg1etUtNPbFU5+kFfaPrzrUk6R8Hu8Z9MYXS2+NxY/tYaIulrI7xvJxUz2hgJgYxUqFzt7SS2zJLi1BYkkTnLCU4wu9tH/ACi/cMW4qutuWlNaypSW1qC1JCsUo4ajHM7Z/L2y1e9t0XPeuwq2g27t9v1q81NLbdzIfr3GqdbjaVCkpyFrOrBAJ4RX2+raXTVtBWVFDWUzoCXaerpXF09TTupmZOMvtqSocCPu+9HHye/90eGLbRuNNJesFzrrQSFrc7xhpwLpnFFYklZbMiBgmUhhG72adSlN3lVNuJxC0oGl+uaQ24pCkkktKDWAPGFrcIQlCCpZJklKUzKlEngAMYum979s29WLZe5OS12+pt13Oj7ux3c1tdSuUzdHWIWpK1PMp1gGXZB4xUVTlztOikpampVNxekCnYce7RxOklEjFa+24y49et4LWNUzShh65BBQ1/wSWiSngeMUtGi60AbpaZhhpADiQlDTSEAASkBNOWUAC62wq1eiVKEz0GYkZxb1G521tw05UtrWdKe0JaeqO1drZLrcUPYkYandraQHWyflCcNYwGGcVCfrOgaUEU2pp1ZC0gtJOIykRjH7Wtf5xwfxUkRT/wA621UgshKXHCT2TlqSkTEVOq720TfcPzhBlqPATygyvFtz/uq+mLu0LnbFhptpYWlxUgVLl8rMT0jhBSq82VJHDvXZ+UDKUYXqzS4fKri3oN3tfeC81Ci53iu6IUyAUg56xKZjG+2b84v2BKQiX17Zcvw1y8GUUiTcKRRFBSpK0q7CiGGxqTx0nhCtN5treKsFrVP2BH7ds5x/CXH7bsw8Lix54/bln8Ti/NH7dtH51Xmj9u2j86rzR+3bR+dV5oam20Pk0fyTX4I/Fj5to4/3Jr8mPmmfiNfkQPkmcx8Bv8iDNtnI/Ba/JhspZRnmlCU+ylMAJKkiWQJl5JShfeNtz/GSgn2Ux2W28j6KUeLJMHspyPwU+aLD8mgk2emOKEk4qPSDKMEoGHBCfNDSdAVqdaGkIBJ7acJATinuO+Nw2+2AWGh7i2ocafu9TpbIT6vRNr746iJAqAEznFVZeXFFUbS2+ta20VqlMJvdU2EKaKvXEU5Wyy8CVFAJ4CeEOv1L9xu1S5oQ73YedmrEJDmKjgMsTIQX7y+ix0oUBNxnv6hQOZSzqExj0wmqpLeKqsQUFNVWnvnELQAFKZCjJrvDjxllAkPQI0gSwAlIJSBIRfHKpJDV0qnbnRvBBCHkVKtSwDORU2vAw2jbDdK7vTYVyTuzaDFbjSVlwpEpU9SOtawKkVDdOlPdfDJABEN8rL+qi5dfaE5bUrVk3NyyuzQtT11pLYDR/wBIbDTOJZWmnqFsdunUnU2sySpYE4PeCm1ZEyVPDhlOOWG5lMoTWP7rrLCl1tQKVU66I1KG9PBXeJnPoiUuBEujAiKdCkrXUWncFfTHUDo+WUXfkxPjxPTBkwoE4kFagkexMx8w1+eV5oMmUS/45XsYZRMMt58Xle9GDDRPR3yoMqFBHT35HuRP1JAzODyjn4BAJo04GfzqoJNETIE4KVLDrlHO+ubpywnbW3huRxa1KI0215PzYA+cHemR4AmGu2Zu0rLmMp/KNpX4cjFE9TlSzs/fl7bvClFOltO4/VzbCDq1anFMKEiOELM5FKSsHiCkagRxBBAMctb+tnv3bntG0vvvKccJcdDPclSZzmFJbHGJqpcegKcl5ZHGPoh+M5+TGDCh4FL8voQJtKEukr/Jg/Jnyq/Jj5o+VX5MaQyo+BSh/axIsLH74n2NIifdK8aiPbgoNO4ZcQrA+xD1K7RlymqkLaqqd5CHmKlh1BbcadadSptaFoUQZg4RV85vsiVVfsLmIwldzqdhUL66Cy7jrGG9bydtutqHc3G5lCUChLZbeUT2gMIXtrn79m3mvsa50rTVvqr1W7CuVVs28pSkIKVdylxVHVuFBUFN95MzJkMYtO8rC7fNg81doLXXbV3LaFv7d3/tZ8NFtyrste423WPWypQqTzDgcadRgoAyIsXKD7cxo7dartWUW3dh/aOpaVaNn7srXXvVKK38xaSlS8nZO46tWhtsgLZfUrvFlAM4aulL9WLXdaZLzFytrrD9JWtujsuBxorYqmlT9IEiKi67PFVRinJW0ppl1+gr20nUG0hs6UzIxSZgRblbqo2dq7jtdxpKu23unp1ot3rVLUIcbdq6JRCmQjQZlEweiFbr21ebZfm6/Ztxr6p221TTym3U7aqWqlVQwFl2lV36FTSsBQnH+0pUVpGr7UGzzmOO7rqekZiNrt96Jq2BbnQAAew3baTWrAnIphJmFDQJE6eCQciMZgwMG8x8BsezKcb6KBgN9bTBCQCAZPngOMNgjEpE+GIHRKJFPlM/ch0Jw7BA4Zgg+xG0uX/NvnhszZW9KTd2+a+v25eKqoaudHT3K9u1FC66yincBZqaaS0rBkZyzBjnjy05dc/Nlbo3nuvbNPQ2Gw2p6rfuFzrGbxbqz1elaVTISpZbplEkkACZMN6sDoRMYYEITMHrBjP2oV+4THIUKVIDbVSEDq9cWSM5HtGMDM49AwlhhKGMRMvNiWAGKwIqklcgGmcuPZzMo+cHlMWVnUR31yp0ZniST0SyirdQtQUzX1aRpWpKtC1OMOpSoEFBW0siYGE45x7N281XNWuzb2ui2Rcql6urVG6rTeXVv1b/AMq93jtepSCSZIIEzL7tweu7NW7UUjjaUhh8tpDbgwJTLMyj6Hcv1tXmj6Pcv11UfR7l+uqy8ED5C5frq43Jty0h5NEK2nuDDTzinnC482pNSvWRMgrI8EWbc13p61NbW2kUL1TR1KqVDxpnfk0vFHzi20GQBita7q6OJepKhsp9fWCoLaWkiQCpzBjkrb6JsNMW/ltta3NN6QFtN0lClpKF4J1qCRiSMTG5LtUBxTTFsqm1Bv0yaltTA4GQBXiY24zVNrdpmapVY8EEp0d2kutLXLEIDgE4SdU8ANXgmBhIwVKWEzMxPCZx6JY4RaTMKnbmyVavSJOZgZZ/hDzQycPnW+IPwxFxy+box6X/AJG1mJdcZD4w80UuQl3nwgZ/Jq6uEVMtMu+cl2hL0z1RiUA8fD5I3AQpJPqzAkBl8ocZyhS+zOZ4ifEeGMiP3x80W52ZM9x1bWc8qVKpY8MIxkD+DPh05QcsvwveigWCClVsolAzOINM2Zy8cKmoDtK6+MYKnj1DLxR6Q8vmj0h5YzHxhGY+MIzHxhDX8+235tH90/BH4sY32259Ln5Bj9vW34zn5ED+frb8Zz8iCfr+14T+EvzCG0i7258zHorcP9qYBVdaFkaZk/KK4HglMOBW4qKc8R3Lxl49MTG4aOWH8g/lLp0w8tvcNItDCdT6009SUMpM5KdUESQnDjFjrt180tj2CnFrbanW3ZkvJU0jvVldG0tdYDpOWjOFs7fud35i1q0uFlW2aNdNbtTZEvWK2tDTzIUsSkEGaTOHWthUlp2FRvd62VFtdzuAYdTomy+sJ7ioQJlKvgqg3Xc99uu6ty1aw39Z3epdqayoUpXyTI1HSlOMkpAwhut3K++4pbAUmgbUpAbdJSqbiiJEJThLOcFqzUVPRBfzim0TccVLErUsqUCeqUYmePVhl7EHwn24M8MeqEUldNqqpipygrGiA6w4oSUg/htOSEwYcoqtDgb1H1ap0kNPJxloWOzqAOWcW3nFyo3DU8p/tD7MU3cds70sLqrczdaymUVNJviKYtmpqCMCpU0qTgRFr5Cf7RLatbyu36l9i22TnOzQOJ2XvKnUpunpbjckNDuqJ+pUpIBaKiorBUAZxtjdG3Lta9wWOn3lT1dPe7JcKW625tm4UfcsqcraFbzNI44qSdKylQJkcYRrqGEgADUp5vE6ZfhHExzT2R62w7XU1/pdzN061hTrNrdpmqMuU6J4NKqc5YTiQ6PZ+8xw8scPLGMvLGQ8vvxkPL78ZDy+/GXsnzxl7J88faK2hUNpLd/5UbmoQoJ7bLhYS4moQRj3jIQSOuDTzUv1RbtEFKImU0TqqVKlZSUoNTMfaZHaLq978n3m0qdJCKanZuwqHmmyVBKQQUqIlMnGChIK1OBLaEgTKnHloZbQPxluOADrMcpLJcmO6rqPZdqTVNLQQtp5aFKLZ1CYkgg49MSLaT4UA+5HzKPzY80T7hHxB5oxYb44FsdHTLCD+js/EHj4dEfRWvzY80Scp0gdLaAFS6JkSAgpTTgpPFae2PAoDCClTZIOYx80dpgE9ajP3Ivu9a6hFWmzMNCjt01EXS6Vbgp7bbsCkg1dUtKRiM4sG7Nyb0dXvzcNr/pEiwopWP6P7MoblP1O0UtI4flrtQNhChVAgpcBMsoRW2/nFc6slc36O+01uutBUoKirQ5TvMjFJ9EzwGBhi2c+dj2Giv4cnbeYe0m27Xc26wpKNdehgpSimWpZK0p+TMgCIq7FuVVj3jszdDDlNS3K526nq7Re6RxtazQ3GhSkppXWWVYPJ0rJJkYulx5T0+5uev2YnZVe4/s/3O9P3XenL+m1hdXe+R96r1qVWUiGlakWZ1QaCUzBnOdFvXljuql3jt14N0t7tdwZ9S3XtK5LUtuose8duVKU1dmvdvfbW04CgNLUiaFEERVVdoZoFrdbKhTpSEP0y1CYcp3E4mfAKi637l5ddwW5intd7dqqINuvWy5sOWmuadbrKVYVTVCdLkzgCdMf7ROsqFhFRWfaQ2FVPo7hsSeqdy3F5wSl2RrWcOGUbTdVWU6SnlrSMrbKFJcaDttp0966Anuw2NQOBmRCA5uC1AhsATL/AAQJ4FswP84bTn0v5/m45lcpaPdNqtl1vNZaa7b15eceRbaO+0YcVStVq0o70IeThMAy4xW2ip5K7luqqCoepkXSyOUVda7i0y4ptFdSPh9Cu6qQnWEqAUAROP8AUPvr8xR/9phdYPs9739RHZNWsUKW5yyE6n0scuMdrkHvFZOZVR25RJH4ynioyBj5PkHvJCpEAopLc2ceGtNQCATDLlX9nre1O1VJWulcPqCzUpQflFspFT8oETGojATj/ULvr9Wo/wDtUUe2/wDRDf8Aa9Pc9FO9ujcLlLR2iyhagF1dapp12o7hhmaiUJURLKOWPK+n3DROp2hsy0UNTUuOvLp7hc3Kdt251tCoo1mnqapRI1SwAif13bJ+Fz8mKdKr1QE9+0E6A4rUouCQwThFUl68UrLncsktlt1RSCgEGYGOEft6jA/4l/8AJixFm8UbrjdzYcQ1oeQXCjV2Ekp9IjLwRcdV7pEn1+qJBbd7Ku9VME6ZGRjbvNOzNt1G3uZtu9TratolZRue2JSp/vlBMkoqaZQLc8QBLP7mJAnlMgTPQOkxW95baxq1XGmbIrXmyhhTrHoBuclalT6Mv7AeP2oq2yZJqLW4lKSZanEr1CU8SqR4RQvjSpVLdGpTlqSHUkHScTIkZRs/ZVB26zcN9oqJAWgqYQylwP1aqlSZaGRStLxwxi22i0XCmbtlqtlvoKSiBUXGW6amQ0WknSEFsLB04kyzi5sCoWtV5rGLQlpAyUpIf7R/uawMfBF5vFRWsUlPb7WKNJqUqKV1T6kqQUKQFFOhtJnhAK75bUpA+CKhR4/8GOMA/XtGqU/5F8y/gxZyu+UTSFW5vuvkniVCectOGcdrcVGJcCy+MZfuc4aCb/QqV3relPdP9o6xJPo5k4RcFuXSgplqbpPkny4HABStCagkESVKYiYvVsI4SU8ej8TOKSV0oXT8qQ20XO8X8mcE6gE5RU/z1bcH3ZibswdZwVJMpiJ/Xdsxxze4/vY3FpvFE9Kmpw8pCHQhhPeHtq1AFYPV1QR9f0M5nNqolmfxZxhuKg8bNR+TFA23d6Msp3JUuetLDiGSo0oSWwCNWsavHExfbUsaQJgunrz0GCPrm24gjHvQPH2Mot7YeaKE2uiSHUjsKSKZuS0zx0qGIhRXd6FgzODiiCMegAx/WC1nq1ufkxIXy1n9875o/blsH75z8mP29avjO/kR+3rV5XfyI/b1q8rv5EN4H5tHR+COuMj7HnjI+x54yPseeOOXQD0dcNYHPoA6IHZ+B0DoMO4SmegdXXFVy/5eUVAu/wBqK6a/Xm6TqaahfeaS603TUQRpcfQhQnqVIE4iPVbnv+vZYW2ELRY2WrGlaSqakvooQA+ojCZkZQq43qqqbrcFSCqy4POVdSoDAanXlLJwEEpSMZmQ0gAT6xnOPRE/CiEOslTTragtt1DiUrQtJmlaFAzSpJxBgab1XLkf8YqA+MOkKJnOBrrKN4AeiqiblL90F5x8qmkWqYmUs6RwnIT4x26Bh0jMAlrUZ/hAKIn4IkLHb5DKdVUEyxzm2MYAcsVIlQzUl91UzMSPaAEO0NbaGSD22VKSVqadHorbUZ6SIUUjwYYCfAYTwnDm1+Ze0rXuu0lt1unTcKedXb1PNLbW9a7glPrdufAcUUraUFJOIxEXKz/Zd+0fXW7lxeH1VP8Aok5trf3Hsa3uKcLykqacW4qt7Z7DpbDqSZmecUTj9T9mfc9Ghz1p63WHb1Iw5V01N210ZrDRocpw+gS1Caxwxi57x5a/ZDte5LpuK3oobrXWbcoqLabaKkOi2uW180zLb6ldtLiRPTIQ79f/AOz/AKWrRMqT63uGup0NtoIbcSEW9p7UQszB1YHqhNVvv7Fe/bXT0zjbV7qLSxVXhNvcOD5pGWmw/WMsqwSogTGcoP1t9mrnc0CJNOM7Lr29ShMqmH0BJGkiQGMMtXvkh9o+33ALIq+72RSmgp096oB0qXUod7hDMlLJAlIyhtFwO77IytYQqtu23Xqajankpx9Li0AKEO09VzSobV3SQoO1ttrVMuz4NrYSuauqNTn2gdo0f4tRbL7PyN0iznCa+0c9tiVlGpS0Je11lOdaSUqSpqpZadQZjiITVW/nDy9fplpSUvLuzdPNRGKO7fcQ4NHEyxjd9oXzQ5Z1FFetqX+idQ5ui2spW3UWqrQiWqpK1K7yUhKc4ugCtSFXq9lpSSNDjButV3DiCZakLaIUk8QY58cvK3ue73Nyvrd0UJqalDCXrztFbJpKanLi0pVUKarFKSlOKtJi3VfqFc9T0tbaq58sUzrhcpqOvpat/QpDakklpk4RYdxbfuwds9wtdD6ql1oUzrXcUrTTjL1MsJWw426g9kgER8gtx/8A4lIc/iEyiamqtIOWphQn5RE1l5A6VI0+2RB+VXw4DzxPvFy6ZYeXKEtmrQ2VGU1rRIfupKmPJC1CrbkhQTitPanxTIqwj6S38b3okKhOYGEzn4omKlEiT09PgiltSapvTct87JplJMwFK+u6ctmcsAleMbct9EFDudl0KFlCQtLhZdLKnElWaNYzEDUl7AZhpEvFJQMEK77Gcx3YA8mrVxiqs6S9dUUdK69Z6OtUrQ3UsIW4zThyZCGXT2ccpxVWFi4tWne1nrqm13rYV5eboLvRXG3Pracq9vFbocr7ct1k9lM1pOYIMNc9Ps1b5r+TfOhhxVRU3amUtexd+MlJFRYuae0Wi1Q7ip6hI7tupcBepNWpAJlFt5NfaBs1L9nP7UDDKKVraVxqHqnlpzWaaKm2dw8rt71QboLnT3jTqTRPlurbcJQEnTM3zb+6aBFJUO2DcTCnnG9KqdSbHcFqkVDUkq0SAMxOP9o820SUM/aV2OhsqlqKGtzXFKSojCZAE45bX9vc+3LdyovvIDYKL/Yt4VdJR22qdFMHK9Nqq6h5KmLpUhMwptOvSCMot143tzvodrXy5IKLZtalsO4t03K91DelLrNkRty13ByqDZImpSUpyxxiq2Zyy51We18xWdzVm1KTlvvunqNn73u9zplpbYNqsd3RT1VU1cFK+REgsjNIi7MOpLTzO4rcmoZVLU04KZ8FDgwkoGBJagDKUlSHo9RlHzihh/dD+VHzipC4II7fWkniYwUrj8I8fAY9Nfx1YezGxBrX+zrx8I/h0o4HDOCda8/wleeKg6nD8itPpEiS06TmegxbGkpMkWmhSPAWG5AeCOPsRTK9EmoZUCTLHvExcyoEqSmmQD1BpJPGXGMj7HnjbihgRd6b2dUXVsqKp3KtBAE8e+XlITnjG6dgcxr1aNtioofrKwX+4vMN1NkvlCO9oaplLqg8lorHbSiRcHZ4xW0l0vtLVUdDWOU9Pcbb3qk3amZeWgVrJW2kNipbSFgEYFUuE4P82tV7pxD1ehNSsKABSpIWNKSmWYgBKUpSkAJSAAEyEhIAAAAf2A8MWJ5K+7ncGWlKnpmh490sE9BCou9MGHqmpYZ9aoqZhOp56taV+jstoA1LW6VAASmSYPOff9AKbfl8dTbLLZ3Ftqd21Y6jQqqXUISVJ+s7g2pPbwLaOyMZxp1JCugGRHRxwlG2NvJUg0lT6zdnu3qd9YpwKdoqTPspDaiR0xUbjcS4he4LnUtUiiFJC6e2q7la5ESmpap4cDjAKiTgJ9Ofi6YOBlI9HAGNuTSf2Y2cx+EIA0kSPVFNMH6QweH90TFymJkMUI4H/FGj0x6JyA8k/DFGACCS5LEZ92rKcV0shVPdGPyhMSkcMOHDDojdpQdOi3058JL4HAdEE9JmcBHo+wmLbIEAborG1DDtK9USoK8AlE9MxqI4QexwPteCLbhh9TW09eNEwYUCD6RyPX1xilX8HzRgg+OXmjBIjL24y9k+eMvZPnhr/wCIa/m0f4qPwR+JH+sRf6qPyI/1iL/VR+RH+sRf6qPyIJ/0iOfqw/Jhv1beL9cZ4AMJTPxiAHb26ynT873eqWGctMsodJ5iuNqnigUDatPVPSSYvd1oq43iz747vcFuuYb0NesLbDVXaU6hi/QIp9ajxSscYIPpdkjgMMTOWJMYH2En3Y9JORGIRjPGfVHpIPVJE/YlHudmMv4sZfxYGMhPhpl4YmVyn+Kjzx6Q+KmJAzJ/FEZq8giRIBHUknyxIGfHAD28okccZ5JHtGcSSFDLKQOGWPVBCRnOc5ZHzxgtQGZAMur8LHCAe8cJAIE1oMgrMehMg9ZMBNQ03UBOKQ8204Ek/ghSCBDiq3bliq1Op0uqqbVROqdSU6ClwrbJWkowkeEKo7ryj5bXGjWQXKWs2jaXKdwpBA7xAZSFyByMaH/s8cnwOhjZltZA8GlOEEPcg+XjUzq/R7HStAHqARlCKiq5J2NKwkpSi2n1GnSkGZ+RQNAJPlhyvruVG4KWoc9JFp3xdbfTYTkE0zQ7pseCFrY2XveiTObIb3xdHVt5S1LJBWRLOLnVW5O/WKhNK47b6Y7iqnGmnmglwNFXeJUpLiG1JHQVCG79tXmFzKsbVakLpFUNzLNTT0rsg9SethZeIGkoMzjKHkWf7Z3P+wWsoKKSzsN0la3RI0yDXrS6ppbyJcSNXXAp7T9u/n/SUaZ6G0vqZ0qyMm2q4IE+qALP/tA+eDTWntB643NpzV1GnuSBpl04zj1jZn2++YtaXmlIqW9z1Vzr2+81jR6uiprqhLY0TmRIzhF22z9tSgrroyClun3DT1S7b3S/TUsj1hKHRwJQqO8p/tYcrqpaO0Gu7VJZE+yQu0pBwgIT9ovlL3aTI6qWnUjGQ7QNsMxCqan5p8on0tOrQmoqbJaVd8lCpIeSTbysocBBE5HGHm6s8p+YdM4w2pRo6G2W5u3uTVI96hqnddUsDESUBHrqeT+w9wqpnWC5Z7TV29qvrWu9Ql1DblSttptCUGazOYSDLGFPH7H1IEBQUEo3FZiUgk6UideCQkYeAR3Fy+xDTXKpC1TfG4UNDSDgkJp6stkjpnI8IVR7o+whTLtiLhbrg0G9yVBqW7hbahNTRVDCmnnFjuXkgqHoqyMWe87/APsPXbaFRZj6s7u6xXu6XS4Cjd0a6Q25eplTbr41AnBCiZQyzufk7zOt9RUJKmUqt7qu9SDIlPdhWmRzBxgFjlbzCLmoD9Kt9Y03LEmSkNlRVhKUV1k21YNw7KoLmHKRVbRWe9VF5dp3iAaalqDSq7taxgS2CvHCLLuew7M5r2vcljfZr7buCh2Xuemujda0vvk13rgt4deedUZrUqesHGYil5R8/OWG8bXumkoFJt3Mys2teLNaLyzSNgab8ispWGKa7PlSUJWiaVkYyh3b+99vUl4omKhmvtr/AHaqK72K60x1Ul329eWAK2y3Wkc7TT7JCkHKKfldz2auHOHklUWC8WnbPOFFWUczOWhFirm6VjmO06sI3TtahWlCU3BsrrllUnEyE4/2jtUw4mopnftKbGqGX2vRfYXuW4rbdQCAdDrZBHhjknv3nx9ra9cu+Tu2/s9cq6TafIjaVhdvO4a/clDQ1Y3Dd64OrZt9HS31tbSW3SVONpCpSnKKC48grNu6311qffXSXrdl5Vea5pTgZCn7W1WJfTZU1CmEqU0iYGnCLzz6c2peK7nRfN1/07reZNZuWtVuCm3eqoFabxaHE6DaaliqT2C0QNIykZQm2DelFuC0JqKOsqqDcFmpq6ruj9HTeqseuXmaa1BUntKUnNWMOM8weXFEaJxpHd1u2bk4quYenpcUaeqSGXUrnMCYlDFJfr1vXl9UKKmjV7gtVK9SPOIzWgUCn1IZX8CeJljCbbtznvY3lNVYcqXqwpoH0vFQAp10tU23pPgnnGqh5p0taACSaFunq0yniqdOHBIHM9MTc5gPpAAUSqg0JkRNJUSgEBQy6RG1S/vL1GkZtlYu216aZbrdeH3mUvBI0HBOkHEAwNG+EuiXperFPsSGOMOBW9UBBT2vkCMBKeMuMUblVvAU7gttGEMqYnqZDQDbySB/KgTj+uif1c+aKYp3ohakvtltv1cjUoKBSnLiYuq6rdqKV4lgrYLBVoHcIAM5cYw3q34fVveig3HvbmQPWaR9NfRbdttL6xfLmqnJ0ssUqQVNpdII1L0ggGRi8U/K62MbUtNwrax5u93AetX5+mqXFLb0MkBmgdShQBlMzj6x3hf7ruWsS4X0PXaufqg24TiUNqWG0pAwAkQIlIASkAMgOgRMfdyiQBJxMgJmQ4yEzFNt/b1vqL3faptT1NZ7env655pB0rfLKQVN0rZwU6qSEnAkQ1S3S12nlwzKnqW7jvGsAbeHeJUfU/q1byy+zhMKA9IQzbquoZr12C80NNVVbKCaerVQusNP1DKVdpTS1oJSDiR1xRqTvFxVO+9RVAfVRp0pU4ykpp0NNgFKWkmQJM8I1De6UrVpmfUnBJRwnLGK23Wu5C8W2x26jp6etLYbUp99kPVLRAHaSwqQB4zjldRXnc67O6tF9ulMtTPeOVP1rVpqX2QmRSEU6sAYw5hOZCf6Gk45dE8Iw5gOKMjgaMAZcSEgxZUv7vS023bW+4c7knvhPFREsB1R/XhMspdwZz8mUM6N7pKu+a0hTBkpQWNKThkTFe/c96KttY43SFyjFMlfdpFK2lBJI/lEJCh1GJf6QnDiZfoYwnwwGUUT1Jvk1dQhThaYcpdKVktqBx08AZxWT5grQo1TxUgUqVBCtapoBImdJMoB/wBIS8Rj+iJnPjOYwMbpFPvRVWh6gp0VDqqcJFI334+VSkDtEzyj/WG4J/8AkaMcc8uMf6wnf1T3oo6f+m7ibeL+++mu9WEzVGmCFMaZYSRj44l/pBc6T+iiXtcIP/xDcyM/0MZceEULbN7U9SJtlEhmq0oAdaRTNJbd0ETGtIBlCjUb4NKvUZp9W1yx6QmMeYij1epge2mJDmCs/wDVh+TGO/1/q3vCP9YS/wBVTH+sJf6qmP8AWEv9VTDfZT82jgPwRHop8gj0U+QR6KfIIyTKRngIZDYSCT+CPNB1AKm3hNA6B0RUdhPpHNMuPgh2kpGArdO21OXjbTiFFrvHmhrrKJxQnNFVTAgYYGH6VaFM1FO+4w8y6CHGnGlqadbUlWIKVpIic0+NWPux2inxEH34Bnh1yj0pHj2Z+Q8Y+c/g+9Hzn8H3o9MeT3owWmXDs8I7ax1yT5MI7LnazE0H34+dR5PejtOgq4yGHliaXEz/ABhw8MCbiZcZS/JgTcxljJPviMHAfCnh7IiWpA/e+9Hpt/F96PTb+JL3IklSCZywT0+ECPRR8UeaOHxh5oxIHUBMeUcYIUZ8PRPHPhGR8h80AhJ+L70VKkDtppqhSMPhBlwp/hRtqQAH1enXqkDr794rnqxnriYTPAD0gPLFXXqoqitXTtL0MUzbjzqnTkQhkKWptM8ZYCKq3Vu3tx+tUNS9SVXdbd3Y42l6nXpc0OotCmXUz+EhSkwaDb1NeFPMutM16aizXahXRF5BUHCzcKWlfqGUykVNoWAZTjTOSXBNOpJRPUmctCgFCXQQDCiD8IHBIEvHGafKPNEyoZSwI6o1BRylgrifBBUpRlpUBMSkSJDHVjIwU6zMgYznlwwPGANbmA6PfjFSzn6RkPdnD/dLCSpaUq9HESmOGMJdOlag9VCa20rACV5AEHhxhLSKZLygoHQ1TNqIOQOlKCSYpuZG+LbTIS0FL2vZrlSUz2lbiADdVU7jau7WJzTqEwUiWcJVooyTIDt0iQCeGkkSA8GEJYuN0t9BTF5C3UJXQlxYSpJVoXPUDJOGMNsVLdnfS2hLWuobZqHHAkAFTikapqVnF8Q3bbAv+YL4QlKKdClj6qquxoKtSipQlKWMf7RDQkIR/wDbDcvtKEiSUJ/pBXhKEjglIyEcplVMwt/kdsBtejSFBLNCgJkZgpkFzhOm6XanW4pALrJS4pOrMd28C0RM55wlqi3leEuuICkiqpaNLKZ4gKU0EqOPQYRUWDem13qQtTcZuaLimpUvVP5NTDiEJQEDGczOHQKez3UpcW20m21qvlSg9mZqlJSzrTiAokiFs3Ha1Uju0F1QQ6w7IJmCAW3FaiJZRoNur2dClBZ9VfbKVDBWpSUiciISizbk3DaGiNKmqC41dI0qaguakocAJnmYFFS7zfrWUpSmV2pqe4vaUJCQku1KFqlpwHRwjbtp3DsbbtypLG0thVcmtqmbgtt1QLgDbGlkpMuzhMQumu+3L/YT3Qc9dCmKi3ggfKB5Wjv2wg5HtTGcVDVq3vZu97sBxqqd9U7nvEa5uLqUNpWlIBCijUAYttXZbpbbvSfUlraU/baqnq2krTTIBQpTClaVdRkYxTLGRwxijkE4VlPPAT7TqABOXXF6nIyFGMQDiaZsjhwnCb9UsNV16uzztBty2LwTV1qEanXXAMmKNBC1TBCsuMV+492XWovN6uLgU9VVSirum0lRZpmEn5unYQqSRHDymOHXif7DU7UMsIGa3lhCPLD7KKlV0qmytsM0ElDvB8JTygWwkHyw8za6ens7LiVNhxBLtXoUJT76YCVHjIRXVFdWv1rzXJXmC20qpWXSgOroCUoKshrM/DDLk9S0BCxq7SdQkcjMSJi8dypa/Wii5nUmfdu1Hyi0A/CSFzI6o2tUvOF11urYaW4oTUpTOvSFHLUEqAgKWMMzkCQMZDrIi+uraDSn7yqjbQntSaS+3StzOOpUlCcco6OSJ0FqvlGZAFOpl9pJlP0vDGqQ48BC1pABSCrFIyGeHWI2+lITI2psjsj4SUK6D0xilMpdA80UgKUyNTTjLDF1MXTAH9Gt4xSCT+htZmUSkB4AItMwPnXMwP7ivLrivGkY1tVwGfeKPtCMQJ8cBnG9JAfsynngMflx4o9HHwCMj5B54oCQMd4VHATwoEnxQOzLAZJGX+7BAGYPAdEWxJSJiz0AyH/NGoX2U+krgOkx8DPqgYIP70R6Iy6BHop8gj0U+QR6KfIIa/ne5fNo4Mfgjrj9r3PyM+eP2tc/Iz54/a9z+Kz54mLtcMuPde4YZDF1qydQlqDfT1GB3lyeCdAyCVGUh0kQ7O51IM+CWvPFduetr66urkpeZsNpaShLl1uoaLjNO44JrZpcPlFgHSOEc47VuW3WvbO+7VuOovJslpLxtVz2tcHQi3Xq0reJ7wd4C3UIGKXATIAwe1PpwPukRisD96T/AG0CTg+IoezPCAmisFyuYlNT1PUUyGgAZSk44lc+qUYbMvKz0IqKdR8gcjHZl/bTORKmlLw46SFEKIgBzbG6pgAn+a6g8JqlpQQROJObd3UjSBqJs9WoD4rSjEvqTcaTKfylmrk4TlMTZExHytsvTY4Ttlamf/i4kW7gg8UqpapKh4QW8I7T7iD0LbqArjw7uJqujbOGSu91iWJJSUCWUSG5KNC+CHVLQrpyUnDCDLclslw+X4cOETbv9uczyfx8Q4wNF3ojPiXsIkLrRE5ACpSJnhmQImK6jkf/ACxk/wD0SJiupOOVS2faWeEBTdZTmeAAqEz8mqJ98kjpDiTP2cY+dH5zzTiRdHg1Gfgn44+dPxsI0h0YYekfcgNhxJ7ybfpEz1jSfDgY3jZraFGptl33Da7aQZaV0LKXWm25EFCnHHJDojatRtqzc19tV22qbdNFu+op90brt6b9X3OspXdv1dPR0b31cpW3qJpbaNU0Ph6ZxAigp9wt87LtUsz79xNxuLj41hIS0O9d7pSApJJOeMO0W41bwXcGXy2pdzq7eh2i7lPdO0rzLpDiHELTNU8zFzuuwHd2N1VLTL03Lb1xV67TIWvWEuptSitDC1DGfa6MI3Ta9/3HeNxDwfru93Ou51zdO4qonQNUVXXmTKS3MFCcSM41FQIJMswTI9UzgY9IDHrj5wez5ozn8eM5dfbj0vYX5oOM/EuCV5CZyM5gTwmOMP09M5KoUsONJOCVqA9EqJkABlDiailfSouuFK1hsIRrPaIWVGY4jphq6VbSbrcgUuH1hltTKHAJz0kHVoIw4QNK1JCBpSlI0oSkASATkEgcBBJInI/yZnx45wccJH0kqPGfGENUyjpU024rMdpYmcZ5Yw4w4E6Km3XhhwmeCV2iuUVT6Rpj/aKjDD7RewBhl/WGvy6o5YqDzjZPIDlilBbcUkpX6o+UkSV0zibO4r6wQCAae6VLCeMiEoWE4Qhq277vrSm0JQlyoqjVL7Jn6T+r8KGKCk5ltttMpIT69ZqWtdVMymp1UpeCO6uNXYLupISl2oepVUinnAAFO6GQpIK5TlljHd1+2rO6tMwtxuoeTMk8Boy6oK6yxLZLpJ00jqXEkkfCDgSQJwlNdaFs6iSt1TKHCknH0U9MMAVDNO6rsIKmV06T+I4qQSJdJ4xJi9stZYJqGQkfGOcocVRXNlylKgplRCVFaSBIFSFaSJceMaW7UKqn1aO9p1IcV1KU2kakBXkhFPZ6/cu3UtuCoWi11dVTNqfSQrvnkoWQ4o9eAEGnb3O3dGUOodfRdqNmqedCNIU2uocSXU94gSKvShlfMbZFWpTFSh1L20ahAbeQ0pLiA4itmppZUkAkTGMJrqfelwsF0vDNM67aLxSN04pVtIapwya9ahSKcWchqmYu9Paa925bT23R0tn28+hI9UcISHK6q1NrWy5UvPnQpaZTCR/YFbi0oQJArUZJE8pmXEwKbae1rlXTP0ypZXQ24Iw+W9dfSlpTSQZnTMnhF05u3jc1quG6md1be25RbZpmXDbmG72XEv1NdWOaVrepEpmgNgpWcDHfXW4LXJIQGacep00kYBXq7JSgrPFRxMcc58Jk9JMsTEur3YuArVOIo/8AQpvsrcaSFvJc7+3S0AyBOnphv9LvHoJ/xdnoHSYsN0taqp5F3oXmnzWIbQNdIQhAaSiZkQrtTil2RfaLcbtzpNwrurVRa26ZynNMVK0sqU+6lR0g9HVFd3VHvQVIoqoUepi3hHrhYWmn7wh4kNB0gk8BFhortUOMtV12Qat8DvHQS56wopEjNxRRnGwg9V3cMJG4U0q+4bDiyKxLdT3o1HSEuNyT0iPp9xAwzZQDLrlCkuXO56FDQohlOGrDDPpixJqLhcUBNqaS13VOgzCQlM1knAyAgSuleP8AkU9MumKUi6VuFSwcWkf3VPSYuS37lVtuligKkpQ2UgeqNBMiTPFIBgzuVzOMppYbAwwwJPTFuXT3C5qfQtxTSVsNlBPdK1TxylFcXLjdgRV1GuTCCNZUrUAAZ4Ewf50vGeH6OifjkrCN4hm5XVSfqyn70qYQClBqABpxzJgzuV3nL/myJ4GePa4xL6zu2f8AzdEh455Si3IFZXrt6901S2n2m21vmoFCAttSCdIQBPrEYVl8yH+KsEfxoK1V99Egc6Wnw4YDVFBodWWfqmgKFrGl0o9UZKSpAwSoiF+sV9ybMz9HaSvj+MRH7W3HOf8AzViWfTqgaLlf1dZpmfPAlcb4PDTte4sR+0b3+rNflR+0b3+rNflR+0b3+rNflQ3MJ+bRwH4IjACUuiXuRIgeQRl7XmiRmMOEoaJSg4jMT9yPQR6HAfijqh+SR6XQPNG6lUbik2bZ9luFuoJOTaqbglsisrUBC5DUslAIxknxxtvm7RuPIXsy+9xuRDAUU1WzbqtLN4aeaQlT9S8FvJ7nBSUKxIlMxT1zTqDS19MxXUhCgsqpqtAfY1FOBWltYCpYBQMdlaZYZjwjqiWtHiHv8I9NI8GGMvDnHeofKCkTKgtQ7PGWfCGqRFK7dDVUjdWl2srHGKdpCh2UUYZ1KUuYmvXlwiRsqeH/AHvW5dRDiQR4oeXXNVVHIj1f6uqC92Zy0PCoVMqJ+EDDT1Bcq5LqKjunqG6M06u7ZImH6R1IKlpUodsKMxmISpFewCJhaV0bDqDL/jEnDyRN1Vod61WajB6MwmZiZprGT0my0hOUs9MfKW3bDnWvb9Fq4/CkCTAXUbf2pUpnihdjokz/AHyBqHlgd5sTaSiOKbe0mcurRgIBe5f7a1SnrZp+6V04aBAH9BLQUyOTjqZT6JAYwpK9iW8GWSKqoTOeYMp4RJ3YNOmeBU1dK9Ch+5KVHSR0wsu7OuaEE9hVPuW5CQP4pOYgSs25WMf5G+ValgcJa16ZQChG+GlZ9m4vEDpGLuIjs1O/21niivbJzwl3z2R8EFTN55ktJ/FrbeQQP3bijE6Xd/MmnlxectTqUnOYASQR1GcBSOam8GSTiiotttmOjtJbIPkhKEc3bs0pTiU6qu2U+lMyO2S0GwEiN2N0/NJ8Kb31e9RetMgttSKdCHxpcmpDqkkjjGprmlbqlU5d1VWtxtMpSGpclzI8Eam9/bRWvUOy9b6gYY562RMg9GEVLtaxyWraqpeSDU1m0223apX90frCyoKWo5qVKcPLtDHKC2OVEi59TU1Mw48kGSUuONtIJbHRONVJdNiUqDiUs1TKSqRBHZnqMj4YUVf0Xq1nNbd1apwsjgEqMkjyCEn6q28sqVIhO4aAGXSn9JzEBQ25aF5f/VBRieQwmvMz4xpGw+8V/wABe6CqHh1UzrrZn4Zxjy7rjl83XNOHHqB48IHf8u7s0nE4vpmQDLCQMAPcv9yNpPw0slxOJEu0kQQ5tDc6SJg6aB5wDDj3bSumCHNrbsmD2gbNWlCeHZUllWqcuIEAr23uZOMsbNW8DL+5ZiD3tBuGnUM0KslaBLjNSmhPxR2jd0g8PquqChPwtyzMfPXUDiV295CfGpTYEK7y5rpiMu/YcSVCcsAG5yhLrV7pH0BhpE1FadCkCSkq1NiWMKCLrb1Om3XotpS8pS1K+qK1KQlOnFRWsADrj/aIE4FX2h+XpPhN/rZ4eExyuoq6401O8rkByteS26sJVpNE9MnDD0sJ5xobutCTjip8Jz8Ilx8UTRc6BRJkEoqWlTy6xwgL9dpsMpPsyw6iqJitp5Z/SWx7AWcYkKunJHHv0E+WZnGNSzhkA+knxCcCTqcPxsM+EfOA+FWHkHGOy6U/uVKHtSgIYulY22kYIRVvJSBngArAQ0xT3hRaaEh3oS4spwwU4sEkDr4Rou1mtN2WQUqqFo7t9QnkuSShUh1QlN92o3bX3Wi2/WtJmysnLvENBBTM/CxIhf1FvdVomMKR9AdpUrVlodfUlR9iHHLY9bdwUaQXEGgrGStbQM0KCFrSO84gAkzhVsvFBX0BQf8AH6WpaQJnGTjjYbMzxBzMYPtdUjPojsOIVIZA4y8HHGLDZrnX/VVvut0o6GsuehK/UWKp1LZqdKgQQ3qGYlFLV3WgTvy7NUr7qbjuBpuooFoUAtv1e2Y0gbKZSJBJEVlJbqCjt9GzWPNM0tCwimp2m21qSlLbTaQEJEssovc0jDmPsU8Dk8/0iJdQ6Pu3hX9z5M70A6D3lRQJOMvxYT2fgjj1eCNoKPwW64Z/hOJykBAXIaioifRpwwh39yY22BhK7J/va45a6cZr3XM8cbsvpBlBwBx93wR404YdI6o28ZD9lNzHgl1dUZD2PNFFgMaym6P7qjqi6dlP0e38B/zNnqjIS8Q9oRbBICZezxkO5WeABi4pJlOtqRh1OKyw6IALipgAHFOY8Ub7OsnTQWyRmmY1VSwsT6CAIUNagAOlPVEtZI/exY9JIA3hcctP/MVGJ6zPUeIlLxQvHh1xbkJOAsdpIwPwrdSq6PxoWFL+EcNJ6YmDPH8Ej2xAxGR4e9GfsGPS9g+aPS9g+aPS9g+aG/533d82ifydN+CPxY/a+7/zdN+TH7X3f+bpvyY/a+7/AIlN+THau+8BhwRTY/wYZ9QuO53TMS9ZDCR49KTAD1ZeAgoHzCm9eXAKTIw6unuW6kmc1d81ShM546SkYxudun5mbspnLkH2goUFCvuXa1SyEqWlQPdAqOOcWNh/dg3ptXmS3eWrZWVDHc1oqrUwo1LL6EnukNh1aAniHOqLJabhWOvXbZj1VtS5VD01uVVVSOqqEuKWolS1tsvpQqZMlJMH5dU/HkcThlnE+/V14kez1xPvz4M/bjSh9TijJKUa5JUomQSZiQnOHrdu2/vI3zSJUVIp6J9XqjgRrpqdmoSCh1PaAUMjOFlAmEqKUmXwdRAnPLDOJTn0iXHjkeELS26QUp1KAwCUHj0YmFE1Ch2lT6vYj51XsxLVrnme8KSDPICXQIyUOrvBHHxrB9nojHV5RBmVTHAFOXCY6YzX5Ux8L4yfFwjM/HHmg9sjEZLE5ceEfOL8ah5o+ec+OnzR2XXD++Th5ROJa3MpT1p8uUS71Z4+mno6h1RPvF/nE+SMVFYMphTiZEDpyMbrpvWD83a68pUEnSqqU6kqnLFS9Mp9UGVQRiejzGPpGPQSAfJKPnxKUvSETLqSekqJOPswDrQFZzCscevpwj5/PD0jLHPIwB3gJ+DNZwPTnHaUFDh8or2tRgdxVOsAf3F9YPRKWqUdm6ViJT9GsUDlLMGeEA/XFwWROWuucMic8Cox8nuC6thQEwm4OynhiBrwPgjs7lvCTxIuT0z4w5CT/SO7TkDNVe4ccDiFLIV44Q0q7BWgJAWUNa1acJqIBxMajc6Yg5BdHTOEfviieMazU2tTkpBTlso1y8RaAwiVQqzPA5j6qoUA8MUhuRmIUjudvkLA9Ky0KiBnIEomOjCFs1to2xcqZVOfkHLPRM/KOYpcK20ajollxh3vdobUbSm03rU41QU6VJSbbUTUlWiaVJGR6Y/2iS0BAQv7Q/L5aAViYSq/VqkjrAnnHK6+XN/Zu3mByX5MbUtt13PT3esf3Der+aulpKSjTakrS0m391JZcl2Uz6Ic27cd08nXLuhSQqgYqNxesJLiAsBSUkqRqSQTxHGLHt3e1n2vfX79QOXTvttX3cjfqTJeDVO28Khxsh16RV2ZgAp64Gva16SriG75cdAx4TdmRPrgpVaNx05l2C3cqpwJzl6bmMKmreLSQQZ0tYoKOH4xIlHyFy5gthJycqW54DDQcdUzlOEhnc3MemcUQkIU5RuiZxIPePJMgeqAU8xN6UKTpIQWaQrGE9RckvHHKUD1fm1udqcpO1lGzUkcJaUISkRhzluAByJtKMhPEgAEQFDnnMBXoLsTxI6NUkyJjSxzut2o8Knb75ScslADSYLFPzV2vUqQZKW5bamnGOOrWeyRBVS7+2K/pSSo1PrRHHFAbWDMDphp1nfu3X26dc+5t6a1KFaRKRDiiky4QLfvqy2DeDGlLKHK2mbKm2wO12gAonDA5wp223Gs2BuNxICacuF2xpfX8FCidTaCrhLxwza9k7Z/pbRPFC2tw22rQm0IZWfk3KqpVJlsSkVJ1ahPKKbfRb2xul9tk1VfYtq1T9ddbShttLoU/MBuoU0odrupylFuo9xXLdVPv7aTFXt/ddpSUNqpaigSUtOhp5oVCGXWU9jUJkgiKwi67wINdVGZDAJ+WXjLuyBMxeztusvdTXnmZy/SW7t3Yp0sKqKgOuTQhJCkpyzxg4+x7/3bki7uPNWxzkxvQPLpJGrS4mqoS2GwQUhKwTj0iEj1/dUpAE6KbhgfgcI2Y3tyqu1UhNPcF1C7mhpKfTSEJaDYHaCs58IT+7X7cO8ezllPqmMRG1lVinE0ovKPWlMDU6lnQsLU0DIFYGIjYanrtuYW1B3GaF1pLZqXXF3ErqUvo0aUpbcJCJcIJF33ljKR7loZ5/Bwjt3jeaRgSe7a6f3PExYTX1+5GWTa2jRGnDSX3GRLt1OpPpTlKB/Ou8ev5iXi+TmIo9F03epz1unKAosFJWHElII0AynKfTFzN0r9zNVhYoNaKIMop0pFI0EFIUgqmU59cftPeR8bGfHEIMW5VHcN0uVIW73aKkMqZI7perWAhKiZZdcVy3bjvBJ9cqO8Ce4CAorXqCRo9GeUH+c94ymZGdPlPAehG8PVq7dDlOu30iKxVQGVKaQajsKYkkAOTnnPhClG5bylLCXq+ch+JH7S3j5af8iLS2mu3GLWjdNcpLpDIrjVmjGppStOjudKpjCcSNz3aDPA6mCD0iejrhSfrTduI4KZn4vkwIoO4XWrpBaLcllb6UGpUyiiYS2p04AuFKROF+tP7jbVMz9VQwkZ8CoGJquW8QegrpiPJogfznu3y0/uIjG67tHjZ/Ij9q7s8rP5EftXdnlZ/Ij9q7s8rP5EN/uEfxRHH7n3nzxPq4w2CkFcxLUAJ+MQh591VPTJAK1IHeSTL8BElGHGbnvM0NQnBaHrTW4K49pRAV4ovz7/ADL2OKZLbdW9Vu7goKWnZSwJBS11L7elMyMTHJ/bWxd3Wrdru2KC8XS61NiqlVtto3bw4kBhurSn1Z5w912tKtWGKQI33tlpCUW1bNp3MwpCSlFRdbmlxu6Ka+D2e6TqzxgYywHT0dExKPS8WPnjh9/jhpx2YabdbW4pKStQQhaVLIQASshIyGJisuNosF1vNDcVIrLfW2mjeuDD9MppCZrVSocFPUJKTrbXJaeIib+zdyNIJ9J20VrSZ8O0toDCUKeVtq8obSCpSlUFUkJAzJ+TyEVF6uNK9b6apeZttEKhtbTtZUPJU8V06XAjvadlCD3ixMIMgcTE0qM5ZjjPPxGM1/f4ozX9/ij0lz6h70ek58X3o+cPsQAHl+KUfPL9iPnlx8+7LqUYxeWrqUZ+QHjHpK8g88emny+YxIKn+5PvzjBSh4yPZmY9Nf5w+aPTV4zOPTPl9+N0rC1FTtrsLak4TSlC6ooV1apmPnT4lS92PnT8YeePnD8YeXPOPnD8YeeMVOHrmD7oiWtfjIHtKgHUrDrHR4TGasMPSHnjErHWFD2o9Jzyp804nN7DoIMY978ZOfkjN3yjzQPnRgBPUPNH8qesLHmjEuz/AHQy8EZuy/dCM3p/uxAn3pwHwx0eCFqcClqSdM1EEyxkMshG+kWy5Uloa2fy/wBybnuDtS0XXnqWno3GlNUbYGhT51zkrCOeP2S6RZu9v+0NzMsW8Lhuuq0MXTbg2rXVVWqnoKVlC6RKatJxStRJ8cN2jau3dpr2Zybs/Lu3W7bO+GqhxJ3btN+paot00FzsYYrXW7q5MVDFR2ESSECWMI3dcOU32b6m/qW25U1pd3iyupQ0Anu3Qy8hOspEtRmcMZxar9zB2fy62i9ZqRVDS0+wn70+irYK+8b9eevKlLQ5TElKQ2Ep0nEEgSkFKnwGrPqHA4QlNEw443qkp3tlKMpkEJAMuqcINTVKkTJSQ2tJ4CYVIzGPjhpV+eRcVE63fWq1bSAVJGDaEqTORyBg2ittO3EuopCnVUU1Sl9DhX21C4aXC6txMxpEiAeqKqs2ddGrO62lblMxTXEvpUTPSks1KgFI4ejgDDzJdKC04tsLQRoc0qIC0SJBSoCYxguN0z1WwkFSnGQSQMzqBExLqghaXUqBkpK8CDLGaSmYyj0FdI68scoICViecujowGUS0L6Zaj7InEkJUkYYCUp8OnGJ94tOWZAJn0DjKEuJrQhSVBRSXAMRIiciCDHMz7Pm/FU9fTL3ZQ7xsVW+53zrO3NwIRaRSArStfq6rjTrJSVJSCBpmTFrvFgrn29t3uqaS1Rq1OW9NRUaSKN1RJS23VhY7tJ45Q39oLl7RIoHtw0zu3uYm2qNKGmnqq5DuqG/90jQ2hdFUq1OKGKkZRVpAEhVO4nj2lTOYznG4BIYcw9iE9fy9QOmPEPu3I8Ryc3ifJU0kAyOIn5ceiNq5YMV3HH51PUIH7tftw5kMJ44ZYxt8jGdzHjmhUvajlkBgS5uwjhlczKWM54wQSZmXg4e7E+tOc5ekI2oVEmdmQBMfjAeSMJT6ooOJ9epP783F2CiAfVraRjj9Da4xmB++w9uLaCQQE1BGOAIaVjnFzGsSFfU5H/hFcYkJmWE/vMcwAtOAt1q0k5avXF6uMidJHiggEASymOgdZjI+X3otfZ/+rWukSc/5uRiMOmJDUMTkeJ80GSiD058Z+zFvQlRSE2O0yEhhO3UxPsmHPlVHtKwMumBM9H35wMT5Yx/sG/kN/fNoyudol6I/wDJ4+Y3947naP8As8fM7+/ynaP+zxixzA/ypZ/+zwZscwCJf+k7POX6tnDJtLO7NWrD6yrKFwT/AOrNIEPu7yuNwtgSykpcWpKmhNIIK1AAAeOK5prmjtyn7t3FupudO06nLBaVLmDG9Nvsc5dgu3a4WpyltFN69Rqaqbqo/ozSV6ghta1DBREhDSU7i2wpx1vUtNNdqQqcViNWhBQk4cRwjalooainrKK87e3Suv7lwOKaVbWaZ+iClJmEJU4teecDSCJgGWGUsvL90Hoxgs2q+3i1U6jqUxbblW0LS1qlqWpuldbQVKOJOcaXd17mWmc9Ll8ubqJicppcqFjCFtL3HfVNKGlSDc6ySkkyIJ7ycjFNS1ldW1jFCXPU2aqpdfape+ADvcJcUoNhyXalmYGtaUdGo5gdAzMToKCqrJ4JLLK1IJ6NcpQhNs2bdqwrLYBaa0oAcVoSsrc0J0685ZQmvPLO8O0i0qWh2nWy8VJQSlXZQqfZKT4Ydp7xtm/Wp9gqDyK+21NPo0GRM3EBJThnkY0q7J4BQKSfADjnAx4ZAR73vxx8g88cfJ78cfIPPHHye/8Ac4+T344+QeeOPk9+Mx7PmMf7vmi/EE947bbTMTGKW1uhJl+LrMZf2HHyx7/9jxEZjx+9GY9nzxnh4T549L2THpdPFXnjOfjJ93pjj8b34lrI6MTwh0d+oAKIlqkR08ZxzvQ7ab3fq6+co9x2O00dnpF1ak1VUwoeu3BwDu6C10ycXXlEBIi33kbRpb8r1yqebt9k3PZ6ioeXUBxKadKy6lAcE549EjHM1+/7dqbL9eXG0JpWjW0Vaumcoq12pdRX+quL9V1BwJRP0lCUfJ6kD8HHTOZnl4YxcWAcJyJ9jiYRuXd3fN2SSHbdQJcVT1VcrGanVSBRTKSqfAnhFTuLdl4suyNoWmn+Xul3qWaGlaZabUpDLS3locrKhTbZKEJ1LWRxMDZ/JLadz5l1lXUJpKfd92bNo27rC9C10lMtYrnEoVPtlIBGUVv9Hr9Z+X7f9DlXb120WNu8rtam32HH6xqhr0rcuLjdOSnSMU6iqUhF53bu37SfNao3JTp9fsNXt3Ze3XrE5SuTU2K31ehUjvdctKAqaU+kMDG5reN3XXmNebNWCk7vf1nobbai3rHeqpK2wUdOwy+0lOCXSTqJTmRG5btzT5fUNKmxruzq6vZ6qhulZtlnZLi3nU1ylKVVOtNlSgMCvLOKZvldzKtdduB9CNe0Ly4mzblQ6oYsM0FYUKq1AgiaJzlONdApu13dpStL5RJp8nNt8IwzxChiPBD9nu7CmnWlfIvafkaho+i4yspkpBET7xOPUD7koJ7weDSBP2InrGY+DHpj4o/JjEzn+Kk/2s4p6QOmnue+dqu0VA6ggpWrZzi724y+n09DaHi4gplJScc5RY7ReUFTV325aapqopXCh623NmiZdo62jWokh2kfSCnVMGUjnF12g7XJuxpkCxbnpqhhdOGHkgIpax5JUPnXBNSkYTOBi5Ufd3wtBKq5irZepRSrad1KDLImVqWFggTlhKN22u0sbmpb1b+Zux2HvrbuUUwLNW+hwaWyVrSZGWWcEjKQ+4Yub13FwcoGuS+8ipq2qQmqU4aujAI7zsqATw6YH6PvVPZTIaqTASGGBxizmgqt00JtaKltxNU5SgPd+pKklvSlR7MsZw7aKKpdqqRLTL7a6kJ9ZSp0K7xK1okladScMJiAws6UOHQojAhOStJ4GRw64tF+eqtwPIo6tNU5TN1VMl1wJSQUJJb0j0+PRHLwPUW6mqQO7nFvk7SrrWgm4gu+slQDboWo9kgCQhIFPvMHT6frFIrHE+iQPJHaG9BIgnt0IAAOJ9iNsCrb3YGm7MgUvcqoU6mgpMi6Se27LjxjsJ3nljpct5Pni3qQjehUmspigKXQkag6giYEiQTnF0Xcm90irFNb0r9WNGlnQKRrRIKOqZGfXHobxlL+6UE/DOcotqKZrd3fKQ/ocfcoSyk92QUrQDrVNPRlFzUobxma+qKgldCEhXeqmE4+j0RixvM4/wB0oSfLG+e6p92Fs0NCHw8qjK9HrHZDMsAvUcScJQr9H3keyPh0IPDjlOJGj3ufA/a/NFs/R92JozvGsCAV0JqxUCgSVKUpMm+5KCJSxnHzG8yZmelygB/hTGMH9D3x4A/a+HXKKBaUXtTBs1tUjvO4L/dGhpygOFPYLiUSBlhOFpfpt2atRxZ9RAzP4RnHob1GIwDlB0jonGKd7HwLoPMI9De/x6DzR83vX85RR83vX85RR83vX85RQ320+gj4Q/BHXHpp+MIxWn4wj00/GHng9tOX4Q88Ux9eNK4HAUhCniCZj+5zTjFchumpbqyadI+WQFqACQMdYIOEXb68sdCw67UnKhpF4k9PdzhsvbP2++82pK21PWa1PaVJnpMnGSCQTnjBW9te0d8BpQW7Vb20jGeBQ2lQHsQ/WbbpKC11TyCj1lqmaS4lJJ1I1Aaktq+EAROEyudGqYGIbcPD91LGNTVZSvac0hKkGX75UFSaZpaQJ6hUIAPgnIwf0GSenvmlAeGUyYLqqVRCR2kt9pQ6cJYygqXTPISMypBl5YkfdgIQsUzawT36iOziBkZzwMNrqD9ZVYTJan9BbKvwkNkYJMsjFCpNKkU7TyVKpaZLKC4hCda0IRgCooSTjwBMVVddjbbLRW1tCLlX3apoWKenWRqpaeqc7xSWairWQhlBALiyAM4pqKqbttE6u3KvDdDUXqjpq/6jJCn76u2K1VbNpZLgC31pShJUATiIW/aqS3XeluE10Fzt10oK6iqQe0XmltOF9aClU5SIlCWq+20+vugGKmmCGKhpCsQpC25JPA4znD1TaKkV9M3qV3CwBVpTPJIBk6R1QUrSpC0mSkqBSpJBxBBlIjo+7KJTE+j7svudMe+IrH5K+UttK3OWEkLURLpkYxiWknwCPQV5I9BXkgHSceoxkfIYyjKMj5DHonyGPQMHBWfQT7kZK+KfNAwVmOB80ZRnKMx5Y9L2oK5FZSJkAEn2oeX3K5KWqSiQAeiUc/6u9XOhslGvkNvNhNVc61ujpn6h2nAZokuuYOVNSrBtAmVGLE5V3enS0b27Vd80HF04Qk1EwpxCQhISkiZ4RzcXartRXJNTcrM+hNI8pxWlFctappKBMJyMoJIIEycQRCL1ekpNktbmptpRkK+uSJttASkplomaz04Q3unea03W83JmppNi8vrRUJau24K1plSWXhoSsUdmpX5d88RIy0CRM4Xf+Y+4qj6nYW6LJs63O1FNtuy0ZVNltqj7wCqfS0kAuOgqEjKQJiwr2ZteprbXS1K1vXyplbNvNCmUhVQz9cVSU0aqpoH5lKi4o5CLcxXXO6UtNTbTeavKtsUVO5c7PRsFhVZcWKq5trtzlL3SdJW8jSElRllDOxrBRUwo95KqUVT1+3dT09NUVKaVffPP1NuepaC2OOoBUkNIQkrOABMXOo2pvTcibPV1TFXR7XbvFsvFopbypRfRU22sWEVdcUP6u8beU72j1RzF245cvre7XW17hpKFFfR/UrjtXdaV1tlms7zVTIfCyAVpPdmGmLvRXzZW7rY6HKWrQmrtFaXKdxJFVbLjTqb9bZStIIW2spPjix8mvtQVlI8mo9Vte2OaiWU0tQy9MMsUW7VIHdVSXJAIeCUGZ7RhTU6Vyt9WFVYrqy42604VoDtOUVLepDlM+CJKBIkYqrXcWDT1tE8tioZV8FaTLUk/CQsYg8QYymegGZ8kegfIYlpx6OPkj0Y2Xza2tb03a87Euq6h2zLc7r6629dQzS7jtLT2lXcVVZQN6WnJHSrwxb27zc7RbHKKiTTPbd3HUjZN7tdXSpQh62Fd7VT09T6upRT3yEqaXLUkyIiv5tctecewjerlRJtt72bR7229cnVFEktvPOUFUsLuDJUEr1FMpDCLLu967UjtJb3RZ7immuVGtpylqQpDVbUaXlFxujHaMpmeUXzeVqS63T3Xf+xmnVJam04oVDqmqjUiSUCqTj2sTBI8Hkw+4YvRBl/8GN44+Cqo4Gog9lGcuKRE5gSM5zw6znKHVAgpVSUwSQZgkBZI8QhoEAjvMj4uiWMM9GnHxkcfFHLIhQPb3WDKR/7xEBRMwBLo97jD+H8kviOiNliQH8wsqMiOAbw8cBQOfSfNFCokEJrKVWBzk+jDExeBMgIprWDljOgZXxHXGR9jzxaQnMqfzlkWVzljnhFy1EAevVc8cpOrOeUHtCQJlkY5hajim3UBBn0VGUss4KvaOeMumDgYtTnD+nFaCJ5k25Inn1QcPhHiOqD2cMeIi1nD9gWnCY/9H00OdmfaVxHSeqPR49I9wQMJeQe7HH2PPEh7Y8/3W52nmJPu0Tlc7VKekTl8hGNp5i+K5Wj3aeP2VzG/ylZv+zx+yuYx/wDplZ/8AI7Vo5kZcLnZ8vFTwx9TWreKV6hpVdK6gdSMfhBlpBMKoL1QXhyiqEBKxb6phh4JI4LdBTOXVCrjt62b9LjrocqPWb1RvIBIx0BlpOkTga3QVgY44pIyBBOoKE8Zjwx84IkXGfAR7clSjBbXuZcMThOPTZ9mNJdZlkQFEGXEDERIKSfGfy5ZRipHt/2xghSAtJwKVIBB6iCTCnUo0ajqKEpISDM4gYyBhdTWrboaRlKQX3FBCVKUoIQlClEBS1rISAMdREbTpHFVNFTbz3ONoUFxpGm6p6z16qZ2rFbekTLVto0NskKU8QApQBzi52Hdl3rq7mLb9/XSrZqeUt7cpbKzY9vXr+bG7rvWocdtVpulypqcB+nKVam3FDpjflI1beWdhtHMqv2rdd42PcbV83fXJueyKqnesFVR3ezVQtiA6mmQXkIQnVMgznG7+ZD1/wCWz933NX/6L71VK2ZuB+j/AKLVu3FVj1ipWXH/AFy3msVTzKmyESGA1SjaSEbX2XuT/RpSJoNnWyw3K4cu6CzU9RSro/WKty8VA+vn1U6i3pcDoAOrBUbR2jy/3fSfW9Req6s3nUc70OUDS7Y62u4u2Ll3UUzjX9Iq2mdUpimqQfV+5A1CYEVNxtVPuG0qprpV2ldBua0VNkuDlXRr0vOUDNVoVX0BUew+3NDicRnHeqbQh9aPlVJSEFZSdIUvpX0nOJF0JSZzUJEgdQ4mAXX3XDL0TpQMsJqBnE3rdT1ZP4S1DoGSFCU/bjWm2sNJyDSSV9qWcySqUuMCdGyiQGASFT8OUfR2+v5IH3YITSUygqXzlMhUsRgCrKPoVH+pt+ePoVH+ptxhQUeGAnSNAHDHxyhwqoKTWKRrEUrcvTVwPgggUFFLqpGj7cdu30ajISlSNjw+GJJt9EMs6VvxT8cfQKH9VagfzfRfqjZjGgoh/wBTbiXqND+pteaMKKgyn9Db4xjQ0J8FI2D7sS+r6PLH9Fbx9iP2dRyEv8Wb8US+raXw+qIl4YmLfSAZY0qJezlKB+g0QxGPqzWGPXOPo9L+qU3+DnH0em/VaeXstxL1alGWdLTcf+Tj5il/VKf/AAcJqPqilri62ptttLDCJrUNI1HupAYxVVncNsJqahx8MpCdLQUokISQBgmfQI56U257NS3ZFm5QbnvtobqkKU1S3O2Md8xUlsdlzuzwUCItVNeTan2fryooyyLLa6VCaYmoTo0M0rYE0oA1ZnjHNCguFvtS6Khep26FtFBSsJZCqh0q0qZQgrCjiZzjQq3UJSoGZ7tAwUJSngYvm77ypig2ptW1v3CqS13aUzbSe5ZQCpGt2ofKU4TWZ4Ti7cxN1vuKDz1RbNq2Nha1U1n2+alZorZQ0xmFVDzekrKQFurii3rzFtjz1gbqGHvqVTT9PR29Dh71he5qptSXFkNyUuiaAeThrwMdzR2yzXhlptNsfdqbeKWkoaZpIDVTYdvNJRT/AFgk/wAq4nvCcZwp+kuLdO9SuutUFe1UO0ta5QuICV0Nfb6NxtTtI4hISttc0KGYh2gprla1u1zT6lIXRoHdtUmquWKTv9QplgNSmgBRThPGH0rudG42+gNrXTUye9t7g0hupommnQmnquzn2SqZMU4YWimYXbUM1KLoumu7V0qXFSW5XuPNOOBtLXogaVBeeEVdrrGLG9da6mbLtCksutu1KlzS5SXppK6uzvkkKmhSW5tiacIqO8o7g9tWoW06UVSQ9X7faqtDlCqurGPkay3VWqbVYiSZyCpKMK+zNzTuztxdZpV1PLLcdwqu8faZpZB7aNQ68oKccdSoKp1EmQSUxb70zUGkeU0li7IQEBVSllUm1oJmEr0YEmAo2VFVpQAtdY+v1hxWntFSmnEoxJwlwiQ2dQdGoVtRPwy72CpFoRSqUBNdO+6lY6goqUJARNVTdKeQ9Bh1tXgOpxJzimYtddcbzuOuHe0G1aOnNxu1TTaihVe4xSjXSUKCCO9XIFQIEIdvPK76we7lvU7X0ttfqEIOKUOqeZL6NOUlGYhz1HlpcbKXqsqeTt65P7cbeWiel11NleoiXJkzWCCriTFiv9ub5yU7+17nR3m20zfN3eS7aipoapD4U/aH707RVqNCSkoeQtBQZESjblpv1DS22i5s8q7JWU6wzTPLsG5vqdCaK7tBvS07U0F0a1CWGYyjeHL7d1C5btx7J3HdNtXikWCS1VW2oUylZWPk1CrZCXklJKSHMCZfduyr5S19Xbk8l96l9m2vNMVJ/SqHTJx0KTLA5cTMwFKs/MBGtKDpRX21YSdIMgS3iAIXK17+HZn2q+3ageoBmQMLXt2nutNbRQ0fdovNQxUVanSlffLC6dKEJbngAROKIq1loVtOqpDZAcVSh1JqEtkyAcUyCBPCcBbdr5gBJEmx6/ZpifSPV45cl+170Xa6tnctRbksXCgRX0zjVxQmsRWqW0WXFOvGaNIEkwofVfMPSDj/ADjZgqeEpfo2WMEfVXMXQQQ5O5WczQfTlppgQSmNrCtsW8alo2VHqXcXGjacQyCkSqCW5F2QGIwiX9Gd8njL61oT1fgRSBO2d8pWqspG0arrQBIcceSG1EaCSlKvSlwi7i823ebtxFPbRUOWqvtrVEseoslotIqGVupIakDM+l4Y/ZHMT/KVn/7NFubtlo30Liou+ruVlytZpmyG1FZcDVOFyKBh1xckuWjf/e+vVPed3cbcEd4HFBRRqanpJyiX1RzCmP8A1na/8DG+jRW3fLdKza6J24ofr7ct15pdX3TKaVSWgEOJXirVhKMbPzBHUbjbJ9Q+ZlB/mbmF/lG2f4GLY4bXvP6l/plWpbb9foBcxX+oJKnFr7otGk0iQSO1OcEm0cwJajMC5W0mZ4/MiD/M/MOXGVytkyDIYfI9cW1aGb0KI2S2Fltx5k1Sac0NP3KXVhIQXkty1ECRML9etW+nHdRmqlrqBCCZ9C2iYkiz8xJz43O2AeyzGNn5hf5StZ/+gR+xuYX+UrX/AIGP2PzA8dytv+Aj9j7/AP8AKVt/wEfsff8A/lK2/wCAhGXoJ6OgRmPYjMRmPLGBGXVFPke0PZIPAwk6U4IGYB4dcPd42k44aRG/dm16kprbNuS6pUUTKC0/WOvUxTgAR3CwMBIywj0x8SM1ez547BMuMxx8ZjE4cZATl4jOPheQ+eJIUJcBIT68J9MZjxjzGNMgACCSJiQyyxzi47n3HV+pWm2NBx92Y1uuLJSxR04UQF1NU52EDiTG+92Luu1jebVQ7cqrDtHdFtuY29ti03JCLiqqW8t5DV3v9XQlMlIGimeOnEiLvtyysXym2zeH3N2q2C3Wd3dLsVy9cu24almdworY/VSUhhpaHe7x1SEotnNPm1f7L9lX7P49WTRcweYdUzs7ay6ep0VFtb2nt+odbuXM164N/JN91N5xawCsLMoZ+uKnnFz33dQEUN6se4L/ALf5HbevbtIpLlZXWLbO7aCtu92sy3QrvHG3QoszKZGUnNss/Y/o2C5UqcVbabmpbe5XXIQthuqpK4g0lRcGEPTZJAKCcciI+r+W/MLmbyB5i3miectQ3U7Qc8eWezlUDJQ8jeVz2RRWtO1UuJYms1DrgSojICcI3Pd7Taedf2fbw8XrHzD2jVI3tsC70ZUPVmmbzbdNdtKuqUylSrW0pBMp8Y25Wb7ave5kbLt5RZFVt3uCtx8p6+4tJpra3WNUlUimvW26BZSlDjiVLZZmlyahM0m1947p2dX8yKegNfX0m1XS3b6u31Cy5QVdBRPrXWJ1Ui0d5qJClzKZAyiQCif3Ps54RNKXJE8EnxQJpczwmDn4jwgoAcUsDUUhKiZYY5y4wFpQ6UqEwQhciPLADgcQSJyKVjDpGMCaljHoUPbIj01/wvyowU4fAF+4YwK+PwVk5dHRD+hLrhbYZbWltKtU1FRB0kg6cc47Wps56VhST7Jj0wfjZ+IyjBYGX4fDoj5w+Vfmj004firwgFapjH0UL8XGcTEx+9cPuxIFZy/untcI+H5HQfLLERm4PE570ozWeP8AK4/wYke9mMPhj2Ix7zxhcvJxjErxxyX5JTjNXjCh7ZgzUodcz5zGDhJ/fH3Y+eJ6u154YaUsr7pQWhOJmrLI9PRDi1NmaSrJIlnmMZiOcVBtXd1VsioPKLdtxudzoKVh+tr7RS06fX7K0KhLjLQr2SRrIJSQJRQbetb26bX6xcaxX1rUXCkqK3v2XXNbq9CEjU4oE4SAnHM1ygum5Ks2+pt9HW/W9VSP0tQ49VOIRUU6UNpqGlhXAqKYKHFqwwEpmUp8cCJGNm8lrFdFooqplO6N5UOkpU6dba7A0tYcxYUhKnCggBUhH+kjdloS1tazVdMqier3S044AsLNZaKYDVUVoeACSolGgHCKPbtpNHYaa3K0vvsvdwh6tWgo9drUtqCKm5PpHbKgcBKDyu+z5sDc+97/AFVWGKq50zDq7VQ0dWhbv17e7w82aOzWJE5KqFHSiEXD7af2nrpvLdNSaWnrtifZ87p+x7LvyUKVXWDenN+rRXba9Y75BQA20hSQCFYxSjbv2cKO+XSxtPGkuW8+cNr3PX3mnWpVFU1Va5Yqa2sVGpbhSjTpS3MJVOUNW9XKDcfJ5NaKqoXurlRzq2qy+q40zyEVlCqwXe3VlXfKumdWU+rsqUpGnThKLtvT7AvPW3c7KQ0jl3o+RG/aKs5c8/qOx0VL3lTcKPbtzUP6brffAS0KMoCtYkBlFZtzmRYbzsXc+3q5Vnu1j3BQv2irNQpJYqqG42+rSiopq1oKmZzGRmZTio2tudNI21WUaqCiuN0dFxcbL9Opttp9VQHAqzqB0rbEyic0ylFPU2XvqA2O+U1429caF156hbcpqluoQwxWkJ7xlGQBmS2rjG0N+0b7Qrr9aErudM2QU0l0pdFPWtYFQQS+JgdEaS4T0aZiWPRHpE5YrmZeIER6SfD8oP4M8IUoSOkFUhqJOkFRGJAxAi/b7U8tV83BX1FIaxYC3KC10ThZpaClWqammBpKilPwlGCalXfKc1KUopE1alZq4knj0wrukqSfg6sQCogSnPEzyjeX2tbPvXdVh5hbAtFt383sxnuv6L3nlhXVtRa7mmqpFgVCb+16qp71gK7oNqCQmYj7IV1pLp9X0r/KihqVD1kre+UrKktpJKJufJcZ4ZRsj7Tu2Gnn7Xeaan2JzJRTo7ukpLsgqesW432W0aHKi4uKXTvPLM+y2Pu3wnIcl94k8f8AG6KG5cEJngB8FMdRkD7XuwqWXcNDyDCGyctUMTB+BMSM845XCWT27JGWAH1iiYHUTBwPpHh4IcUQZBCshjlGzJJBH1Eg+iCc0ynASpI4S7IzwEWySZfzlQHLoqG/ai+/9HtPsW5gn7lsV0NVBB6+6XlF1ASZfWFXkMPnTCsDmeHXHM4KkT9Q2nP/ANqYwoSPkywEcfJ70WqQP9fK3h/5BE5HFWGEHo0xahL/AOp+0jP/ANXU3CHDL4SuvjGHT7sDDh9/GPvEcIzPkjM+SG57Z3pPQmf+cduz0if+Kx/Vnev/AOUdu/7LH9WN6f8A5R27/skY7X3ocf8A8Y7d/wBkjHau8z4Nx2z/ALGYp0UFh3JTLKhp9cvNLUic+IYpkYQn1+2Xd1pTY7DFclpwjoCyyUjxiHyraO/FKKu0pG6balBP4qVUGpPjjcnMHYT9xpdrcyg3uNDV7WitqKK7BpmkrrY0/TtMtltkMJWEywC884K2EisCSJlpozIy9CYVOFIf2xUuJSJh5HZCxjjpMynKFJqNt3HsntFDS1pHRilMsYUH7ZXMmRnrpXQEgcSoiQHXHpoHt+3OJBafa9mc4kDPwE+4YaCgSt4pJOMkBWSlqPotpHHIQ8UPOucseV96csVgQimeett85g+rLNTuitBnTVdHtUhTSQ4FILhBAnCGX6hi9PWRg0TF0dQhk7gvoBffutchIS0umpFAaWgnQJT0zMWD/aF/bUsa9zXberdzun2YuQt/T6n/AE8FkUlFfzF5nMuuNptHJ+zVI71JWALkEBtA0qiq2xt/ddJdqinp6+20tzt1DTUdls1ipEhVNtfl5swspse3NoU7xPqzzlOa8qbCw6JCORd35vKRurlm9zGqttbjsRub11RuhiqYelQ11zefqfUn2ldtaNQUVSAwwj7Wt5u32a9pXPkCvkftO2ct9hVlSXKTbW7aJihqrxuGmZUpKmap5tTikq1k6jKUo5iU1zs+8dlbZ3JzW3grZhrqSqo23tr3K9vP7fYpK5ErdcqJiiUChLwWO7IBh7lzzDRs++2HmDQN0m69p7ppPXdi8yrZVJFMum3nYHXHaSyVzrB9Uo7tQNUzdC8tLjqVSJi0/b9+xwxdR9nvdV3asXNXlzV1TdfePs+b1uYC1bY3a4ns3LatwUvTa65wd3VUziFTnFrTthyzWqtrWm3WLzdKBitet7gkaqiFUEiuHd4paSF6UCWEhFTSOOO1dTY6oWy6FxvulrrW2kOrUlRmlwOoUFBSezIwEvWK5OOCUympaAMurTLGAP6OXKQII/Smc5S/BnOUaKaw1LaUkKSVllxc+M1aZkYwSbK6STwDQHiAAlGk2RCkyydaYWcMs0K4xOksdpCZSlU2+meJxJBSSjsmP2Ft/r/milx/gxNVgsSicSBQNIE+sBMf1csZ/wCqI/JjcdXX7N2zcKVdvtKKNNWy6lDKkreDuhNM4y4CZZnOFOJ5d7FxOfc3bAjD/n8o7Ww9mJIxHYuWA4S/SeqJDaG0wOACK2Q8ffmJI2vtdoHMIZfVORmCFOKWoHwGBp21trASH6OsTH4+WrxwkOWHbyWtXbSzRNgyIl2VKTIGCTt+kCpYyZYlOXAd3KUdiy0zR6UNMTPlRAW7aUOCekpS1TJMv3RbAgj6hrZkicjRATJ4DupyjGxVuUvSo/8ABx8pYK84DJymHhnJuO3tyuUDMFJdpyD5EYQr/NC5BRMyr18JbOAEw2kSSDCkVe16xDYHZJqdfl0icokNu1CMDiXHTxlHeNl5iePc+ruOLSZT0glQmATAW/e1UigJBH1TVOdciUrAnDy7duxilIATNyyVRUrtACSlOGWEaU75owVTxVZakJGojj3hnHMXcDVZRb1r918vL/si2WxFQixUzVXfW0Mpr6yvqgtLbNMlJOgDUs4RQ7hYuW3Km4oqnqlFC0XKylaNWpS1IcqG1JK0o1y1CWOMb3evDdkYp92OUFyZforo2+4HqR9by2fUyA82hRIIJ4Q+844E0lO05UVDpEwinp0F59zDEhDSCZcQIudPRVzrit9bzrbfZqiqVpXSWVt50trS2ofNUdK2tSGwBNIAlFj2Tt91h9qwWli0tVKW+5W9WMtJTVXx1pPbaStQJSlXwsMBFNyc2feGdq7J2/Sv7v5u81rs0pdm5ebHtq0HcW47m6JMrubNGqVLT6gpbpSQDjFu+zV9kikp9m8nKSmLN9u5rCjmBzxuFA0aY7w5ubtYUzebVtu71qS7TWWkfp1KVpJwEjQhneNFSjcgVUN0IrxbdNG4kKYastmolti6u04V6VQ248tQkpZJi201BymsW4vtPnmGxRHmfVU/1Puhy00nMay3Cpsvyra/VGvqelcRUNLRIlUxIx9jzfn2TeQKdj2q08pL8rmmzy8tgr6Wv3VdbjR1lLd7xTUyRUoqaiiZUpb2kd6pRAJlB3PW3C90zWydxM/XlsdudZablb3myyaW42e9r03W0VtG+NTLRcFKl0BS0qAi07V3VerJtb7YVnZfZ5KfaIqWLdb6nmHXU1t71jlDzxaoEUlPXXWvUyGaG8BtoOPqScco3Tye5o2Ov2rzE5d7krNrbgsl6pFpfpblbH1MVVKWKtINTQPFJLToEljEEiKy2qrLWxeaBmru1kVTNd3WvXCko1qatcyFdmpfSlIAwxjmPyqutY6xcrAtF3pbdV6kKpgy/wCp3WmRrwbW1UCRSc1CMTiMuMp9Yj3lYxx9mHcAPkXejD5NXhnDKh/6QuEpCf8AjLmMxgc4E5k5Y4ceBgpUSCZTlwKVJV5JpEbqsT9QxS7huXJ21cqWbCwXKhdRWVd2r6O6VrwCS2zTWpp9LzmrDQqcfZw5bVwYFVtPlpZ7e76uoOMqCtdQ2ttxJKHErbdBBGBnG/OUW710gtW87BX2lLrrjQcoLg8yr1C4MFyYbqaKsCVpOYlG8eV27adym3Dsi+VtmrdaClFWzTvLRR19OoiTrFVTJSoKGBJMvuXhd9pK6tt6uSu8kqYt1QilqVOCrolTDzgKUJ04eGAr+i+98UoOG47dLFI6aQmCkbY3nMCeO4KImfhTTAGFJ2/RXCgtrdM2nuLnUt1dV3yu0pRebSlKkEQwUhRlVMd4EYLLHep9YCDkFFoEA8IQ25t/eYR2CNF/bkcRl2MAZeKOWaqm1buVTvq3SqjbYu6C9SrFxGtNQpQCXQsDCWUEG072I1HD61piDh0KCsod/mjfPzav+8bf0H/gY2i47bN3LQ/YmVNpFbSa0ASwWSAkqPVKE/zRvMCYxRX0ST4MUKHHoi2abVvaf1jRadVxoANXrCJaj3AwnF6auNk3HUViWLb3rtLcGWmFA0DJb0NqQVJk3IGfGJmx7zThk3daGWeJOthUW9intO8m3VN1BSt650SmxJtROpLbCFZZYxc0Ksm71KTX1KVKTcqUAkOEEpmOJgTsu9hh8G6W+RJzzpyY307SWreKQ3a6U3JD9xo1aaZNYgtFgttNzcW5gZzkIKhYt4aekXOkBxkMJpMo/Ym+MOi62/8A7MYtR+p91mgO9qxIbVcKQ1YfFAkqUXEoQ2pogiQlMQZ2LeQGo4pudGD4MULzg/zLvjL/ANK23q/8ni3KaoLyKY2O1ltDj7Knw0aCn0JcWJJU4BmQAJw4KiybncVqOLVdSoE59cY2Dd05/wDpKl6YH8w7uy/9J0vmj9g7v/ynTR+wN4f5SpPNH7A3f/lOl80fsDd/+U6XzQj9wn2h93jBxOXTKKWZ1dpMsSTn1Q3gfmk9PR4IfwHpdPvQeYVnaLz3L6uTcbkhInos1WpLNwq1doBKKdvGcjKAorqSlWgoUWxJSVYhUyjIpM4S7UM07zSxIioDOhQOYXgJe3CUG2W4BRAXpdbVIE5gEKmJdcO+s0Fve7StJ0NpITxmMBxg9xQW2mUsqJJaYGJmcJDMQRR1DCahSSU92UT8aUqyBEBdBuH1NSQZttBspUM5FLisFdYwjfPM1zcTlzFJbzYLHbalxmnFzvt/Um2UFEy6ysVHetrqe9ASJ6UE8Ibt9bWF641jNKiuZR8mzTXxNMty/V9McFdxWuv6lapkqBM8Y2Ny1vdK03yd5Z0q+afNyrWoJS9sjalSqtrKekLq00nrdxuFO0ypJmpTJWQMjG+9icmdp3XeV0oKR5m17B2K5qat/KbaaxadiWHadst476i2vSULff3IaW0vvNEkKJinsVPQ3m7c2+YFbR0W7F1lE/S3RFTdHfV7ftPblC+KddM03WvJaJRJxxzHATjl7Z+fHKu88rbTbN9bW53WncjdzFc3d/rVqnYb2XeWUl1dBdqdpzvqjXoS1IpJJIjn1fa23uMbMu3LWnpdr7ieJFvvO4Ki0ihqbXS1DU/0unU4SAQACJmLrtPnVsCwbs5ZBldDcaPdlDTVbjLbQDb9wpK1c6ylrKXSQ33Sp6+Eo5eb4+y43uXmNyW3iwvae0bVbWF33c23a8VbtTWbIv8AT0CdVRRPUABYqFtpIbIC1agCFcgPti7Gu+09i869lUXKzmXtff8AQSqqvlnvRCKPZV+btFQzXH+lu177UsqYr/gUjKUmUc5fsnbqqKioGwd1VDu0Lu8laGL7s+4Omu2td6JxSUB9NTankIdUklPeDxRQ3z6zo2O/aTRromkKS688h5biqiocEk94AvSSZmQHCNLi0pX2QQsyVjkZKkcQYHyrefTP+LOPnm/4XmjB5Hxx54+dR8ceePnW/jDzx8638YeePnW/jDzxi8geOftTipqE1Sf0hthBAUf5EqInNMsdUS71v4488fPtjwODzx8+j84PPHz6Pzg88fSR+dT54BFQk4zxcBPtwR6wn84nzx8+3+cH5US79Gf90Ev404+fR8dHnj6Qn46fPC7+/dbbtvbCXPV0190qErudTUAEH6uszShUXJhRB7bZGk4Yw/d7bzJtj4QoKbp6q1VNIKlAB7xCHHakKp1IlmtMhPIwbXd6m3vOKR3zFTb6v1qkeaJISEu6E/KCWIkPBEwtGI4k8ceAj5xHlMz/AAY+cSemapeyQJR6afziI7pdQ3rcl2Fd5IdYKEkTnAm8kSlIhdR7rcJt7K+9m+245iopSlsEgyWlMyTFGkJSNQSDISzWAR0kGcLWmSdHeJmVaTpmcMxhhkI5k32grxQXCj269T26qcJTprqktMoaZKpa3nWnFBIE8CcMI3tzLulupXKotNWDbdwqXAp8POJcXfXbcx3Su7qacPtpU4VIUAo6ZyMVDVBT95V1DaLbRltYQqrdaStunbWX1pTrqaxQSqSgVTkMTGyaJx3atBzR5/7epedn2hae5XVNBv25sVbrT+yuWd6pEJFXb9l0FbXNLqX1L0rWyAdSYu2+PtB7I33YuX7VVTbltT1xtdZS0PNC/wB0SX7bVN1oZZo6zbjLiPkyydKUJA0mcXz7SfLflDS81Nu8o7zSm7rRVfVTLpqFJpqK0bW1Aor6+3snvFNtpC0KQCrIiKDY1ubud/3nUcxbpX3CiriUbndqqu4U5r2UoqFssPKoO9Up5wSmlBIkI5GW+31tLX6tiptVc3TsoqUJetdammqhUIUhSHHKV0aAVTTjhMRv3fvJO22Xln9qhratXX1NFbSm3bZ5r0dJSv1NTT3m2ocYZptzPhJ7ioaCW0ykRMzi53q88lOYv9AdhXm2be3nf6+0XKgYTbWrm0KOp9bqEtiortr34s1dLUNrCVraQnWRqnyi/wBpnty3Vid77eraDlD9oq7LaoqdreyLIluk2nzVUWFBFXcbhTIDFwLZcUalagrECAlmotzAq6Pvgq6UIepbepaFCsddrkONv0TyXdHdnFOOIikfpGKO3bQ5oW+pr9NJUuVrNeVUGuuqw3SBa0B6+tqUhA1YZxo7qtdP4QYdSFY5yU2kjxiB3Ntubs5YIp1rzy9FMaW7DelqOIApHMfB2cYcRS7Pu7jbiVoQr1dwHtpUkEzSBxintDWz6+vWiorKhT6tdPhU1C3UokUKmUJVIk4ThUtmKpgkyIfrJE4y7PyeMHTY6SmCc1PVsyZfgo0DVPjFr5V7Y3eva229+3+12u90bXq66SrbqqtkPt6iVVbBqm29PyakpMsSI2xt+iqGaC3WbbW37OzSpdbpVNi12qkolr+SUsJDq2dQGomRxhty6X955GlJdQEqK9QAmEKWsiQIzOJzjZP2ndrM6qV12h5db8DhZ1MKcS45tqtYSkNuFdQ6lxDy1d4pR0iYiRwIzB6RnF3/APzNbx/85ooAPwUpBwwwSAMseEA9Qn5YeOM9DZ8emUIkSDqOIlPLrhqczgnEmfHCfgjlOB6XebwMvxW7igH2VCJkSAz8kuEPY/yaunojY2J/q+zwPQOqBokTMdPvRapgSNzoJn/rLYwx6I3CJkSp7Tl/7MpurHAxJJJE/Bj5BFrH/A1fhl3Ks/HF4xlO51mH/Kn3Y4YCOaU8D/R+gAA/9oImemcAHjl0YdPHhBP34xaFiZP9Pq6c8hO3tiQlLog5emenzxLpBy8E4tPSdvWfiP8A0dTeOHcpa1dJ4x0Y9HnlAGOQ4COP8HzROZ/gxmfImMz5Ew3PlvuEnQmZ+vWs9Ij/AFb7h/y61H+rXcJ/+nrQ9uJHltuAeG+teYR2+Xe4fB9eskezFOm2bFvFIskaVu3ttUjPiEpE4S5X7XuVU0WwdCLslJA8K1Kh9K9g3zWDiRfGpHHqje/L24bAvoo96bXu+331fXbUkitpXO7USQAAHUpHXG49pVo9Rrdp3+6bdq6WrX3dRTm21jzFOh1KghWoUqUYyE84m1WslIT2gH0gYnMiYmYSpqocQmWCkOKCfCClUsZQSzX1In8EPrHUTn1QgN3BzSSQkKEzl0kzM4SU1R1JHpIUUKEhmCJHOEpW467p4qeVj5ROPsxcjLsqsXab/wAyGN7XKjVgw67YKeoo6dCyD8ukPPBRQoFOE84bpnW1vVjodq6opTpZqJHQ6Gm/QQ2UAIIAlH2sed7jy7XuHnjzOs/LzbFBbqZf1tdrPsj1l3cG37Ncmi1UWunW4w3UVKUFDagMQrOPtD/b2+yRZ+SnN7ae4N93zk1zI25zdQljmX6pY67ubVT8lq9Sn7bZENUbYZCXGdNWmZngSLRyS+3N9jLcvIbn3X3Oie2HzDXtinedcrbdVtPWWptu9LFTMUFFc37h3a6dp1K3hpA1SJEbP5G7KYHOTm6rRbtlubmty3LXti1Wptlqq3VzkvDLi17sYcaBbp6GnDFR3qUrUrSmUDmxQ86Nu7lu9bcamrtHLW9Wh2r2Kal1ZFctyxtvN1SLOyqkLLZLpWFFBM5Qnl5uP1rlluS3VlyPNCy7cULPuK2XSgS7c7g/a9xOU71PX7er6QOvUyUM62kgNqJWCY3Uz9kzkpW8yt0tUlRbBu+622quD7lxWjTSnc1yvjPq1K2qrUhx+qYS1oZnpGEcpftc/a9528hrRU2u8VXI61bN5H7fUikorPc6NVfYlblXfKuof3DuWirW0M01Yy2louJAkkGPsI/a8vVNeaXe3MXl7eOTe76O/UjdFeKrc2zL5cqxvcV0CENFTt0sTDZSntKRID0ZQbW9SIU04h5hxUiFpdeY74KSrGSkqAI6xG37pu+y2k7029d9xbV3VUpp6VDb1faLrUU1JI90FajbmmlEnTirAdOr6ns65nJYphLhgNEfsKyeSl9vQY/YFj//AAX/AAUfsCx+Wl/wUfsCx+Wl/wAFH7Asflpf8FH7Asflpf8ABR+wbIP1U/8A0NMfsGx+Sm/JMY2CxeIUv+Bj9gWPyU3+Cj9gWPyU3+Cj9gWPyU3+Cj9gWPyU3+Cj9gWPyU3+Cj9gWPyU3+Cj9gWPyU3+Cj9gWPyU3+Cj9gWPyU3+Cj9gWPyUv+Ci1NbJU5bNtvUaEPU9veU3SetsoSJJLBShtZ1HUABOCikvV2pkFWtSGrhVJQSeKkd7oPjEV7m/6Zi6XtF5d7qquXdLqHKUNp0hLjwUtbSVYA4RIWK0YZYU+XDJMY2K1eSn/IjGxWk9GFOP7QxM2G0y6Z08h0/ycPmnsNuXTlDYTpNOEDDEDs9MTFhto6fmMJy6OiLTQ7ZttHSXC41zgLlOGy4WKVtLzh1IEwlIIn0ziiJym2f4Yi4P7yp2KizpsN0b01SG3GRVrUj1cqS6laSScsJzlG2dt7KoqWivm49/2y5XF6n7pAXa7M0/T9z3bOgqQVvgrngVAT6Y22tzt1NXXbjvAS8lDblM3XBAacT2tRadLU0rMtRJHCPshcltx2ty47a3fzksLm5GGp6Ta7I+7fql15WpMqVoW7WszSNKc4sv2cuT+97Ozu/mfzEvW3NvM3dRumyL3ZLPWpXtNW5XHH22ndpW9uhk1TslK30TJJCTFHs77cn2L+W/2i+X+ybNRba3Xun7PNttF12BYtqWZtFBRqo9rP0dTd3K1LKAt1DTiFJxxAEb5+019ldFbyr5X7p03DfOw3rHc6bbVLuClpqllqjrtkVZS/aNxXR5K20uIUpC3VJJAAlFwvdHvR37N/KTal4uNz2nt7YlleufNm6U+4bwyhVZuG6BDduuVQXFpKm2GwhhkzVOSp7M2X9oC5bZ3rslZuG39kc2DZFPXu1XS4LRdQxv8pUpm+qq35NpcpSyEKcSOmdlY5g0e4+cXM3eiqC77c2ddau4V9vtArAKiynbbVpVS/V9O4ohSE1KniQClYi/cqdgXLlT9mXkRzDtbm09z1m+Np0VPzMrqWi9Yurg2zRU7lLQ0CFOMISxcHdRC1EkiWH2ofsS8zLpcub1ptv2febu3mr3VVDL9sp97csrjV7rs4p2ghyk+tUPWkFDqCr1gmYUQcaZtxhbdSdTVwacCWnXFU57l9p9tIShKwtvtCUp8I+zZzNS+9a27Zu7bm3qmnpUtusu2py8sVdwdU6lYQhVRThbSmCn0VEnogP2qy25FG8fWGmXW6UOJQ6hLpUQGpBBUsyAyGEOerWWxmWIcUlnVNIAlMNp7IMa2LbZCQqQCGGu8A4FJCZkwU0ltd0khRLVGyUp6dCtM5Sgop6KoQ5PDVToSjjmQMiYSQw4BiJpBTI9OAAMoUltFSSSdISpXgJHGcbTXZqC6UnL/lddaLfPMPcFS3V/VyaS1Od/QWFqoGls193eGkJST2ZzEoKVKnU0C1U6wSSpTAP6MozxOlvsk9IjqjcrVz9Xcq7zvLaVustK4lpypduTlTUKbepW1gqnTobJUpOKRHax6TnM8cfDF2qrzbl3WhZ5L7y10jVSKValKqqMa0uHsTA/Cwgd5yyuKlFKJrG5mkhfZHaCQ2ZZwkscsrkSc57lYVLo+CDjFRW2W0P2OiNHR06ba/Upq1NOMoV3rnfoklfeqVPpinUpBcQmobU62M3GQtJdQkzElLbBAM8CYQBy13CpKQkzN9bJIEpzMsZkRywqqnYtycoq47rVaKKnuraX6NIuKQ+ioflNfeFM/FElct72E44/XSPPDs+Xl6loV/30no8MbOVV7GujrT1gZXSNJvKQplshPZWZ4wNHLy+g9LV8aSfD2kKEW0jl5fVqFxotPf3xooSo1CNJIQgKlPOL03dtiXOtrkM20PVTF27ht0KoGFNANq7QKGyEnplExy/vyOkN35jSfjNGUUAtuxr+1Wd2/wB247faZTaR3ap6h3QnMRcw7sPcanRXVAdUL9TaVOBxWtQ+RyJj+oO4/Ff6c+2yJRvo0Oxb4yxR22ldrtV7pu9qWnKpCO4KgzjpmFCc5QJbC3H/AJep+k/8CY/qBuQeC+059ksgmLU6dk3w2f8AprWJRSi904qE1fqCZuqd7rSWlDAJlgROeMGWwNxK7WRv1OOiePcHCATy83BLGf8AP9Nhgf8AySLctjbNcikNktamWFXJouNsmgpy22pwIAUpCSATITlCxW7HvjqtSpqavzCRnnpLcdrYO5NXVfqeX97gf5g7k/y9Tf4KP6gbk/y/Tf4KP6g7l/y5T/4KP6g7j8d+pv8AAx/UHcf+XqX/AAMIw+Anh1CMvY+74opf3SfbMJ/4oe0Iqv3XuwFJwIM0nrGXsxfOd32d7/ZNu773Kpiq3ZsTcLqrXtzcl4baS0u80d5C+6tFS8hsd6lbawvhjFrrrlsjl3cmL7SU9bbU2Df9NdHXKeo1FOs+rtobW0UlLgJGk4Zx6rdfs28zb33TjgVWbPt4v1rbabUEKcerUOtsshwqGjMKBzEPVW5+SfNbb7NI6WqlV12tVNspdQT3jfesesoUpOnETHVC0P2yvacpwgvpet1awpjvFlsKWl+mQUArSQDlMQtyseFKWz2mn1oZOmWC5rKRLj1cYbMlKQsApcS82pCkzwUkgyUCOIj7M9NSqrF0G3n3UXmqRTOVNHbBc2n6ltb7zKFMtBKEpWvUpICSCZRVsU9a9VU/cVyaVb6iUqQh5KCWOhh9RBA4dMcoHrQlTdze5z/aTWlxlamXVvoTfmmXFKTjrTSyCT+DFo5Z8m+c2/uXHLrc26Nh1+8NmWeur1bMu98cQu3U9VdbS0y82tx9yp7sKQElSl4njFDZeemy7PvZmjWy/bdx+pUSLnY6hzS4u6JWtdQuoqqPShbehSO0nKcXvnHtLcdZW01yTQPM31hssPqpzTBi6W9TbyqpLDrylrbcQRmqZGMOcwWay1sUbC2nfVaG4oq79RVT9xFKXLnbFNoRTW92teQA6lau8U4BpGMqHmbd6qqsVdvJVdUtvN0xpnK1hp5YLTbCVtNppKhBm2opVqSrCYi7McprNb+XtisNiqlprkUDFbc68UVA6UvuPW9ukbp3XXka1BwOqx8tXZ+fPNvmHzDodvb3oXrJYNwVleaO3Oq3c0ltdutqgyyhamh3TZUgr0ZHGPsVsVoW23Rfah5pO03eLU++KSn2XUBumUtybjISsHszkCDhFMl1RSl24alFZAAIplJkomXZIEo517GvbFwtFuXum6bjtdZWUNdR0Rq3brXUzrS6t9thhoVDDaSjE6lASiQu1G51CuQqXXgsyj9o0v64Pyo/aNF+vN/lx+0aH9fb/Lj9o0P6+3+XH7Rof19v8uP2jQ/r7f5cftGh/X2/y4/aND+vt/lx+0aH9fb/AC4/aND+vt/lx+0aH9fb/Lj9o0P6+3+XH7Rof19v8uP2jQ/r7f5cftGh/X2/y4/aND+vt/lx+0aH9fb/AC4/aND+vt/lx+0aL9eb/Li4U1DU09U7R91cVtMvJqFJZaJS4sBJUpJSkxgBC2qhSkCn1lCw4UANK7R1SImUqgE3BoAoC0k1YBKTik4rmJiNX1hTjPA1aTxP/CcI/aNN+tI91YhX840xwOHrjXR+7hcqynOf+No8k+9HRGNYwJnMVaPb7w9MMJ1BVPQtOJbUF60lyobIcUVYjUQAM8hFMBIqTpAlmfhYdPRFyUqqRTLbX3C23Hktu6kklU0zBljlHLGislPX3tNFS3t24otTFZc0th9xv1cVIo230NqBRgDJUNIvlDV2+/M7ItVLR1z9NUUTNQhnR+hKYfabJraVSl6pCZJEcn6irQw+mh2PzTrbd36QdFaNrVwQ/SBcpVLbJUE6e1JRkI2HzF5eXbcW1tybQve0L3tzc21qyro71Zappu9BFZQ1NMkraU13+IMwNUiIt++t6cwtxczdxU289wWm6v79p6iuqqq0pU2lthw1BpqR5xynUpIeU2syOM4f2tTbcXsLcTlUt6q2TaqGnNpvLT5D1bcHX6Fqlp6oPv6llJbVpUfSnFRtLeb1toazabBTbhue4fVllt1G8yoIZQ2im1OkrkoEq0pAxyis5dbLFRedjbK3Lcq68XS2vqbNFcLJXTqKi3VLzHeOMvXBSFI7WlSQJEiKW28v7RS7k3FUWx2gptw3xqjUxt8KWlt1KKRxt+uqnVlkTCXGw2MiJmOX2yKHnbzDs23d97Dvr+67LsZ6us9pUiqv1RbWLc45RocqKIPsJcQAh9ClBYPh5frupuBZqtt3tmvbujtY4axaeU10bq2q5Va4tyqUlwkOlwqOoHUZxzMp6NJYUjmZvf1VphIShNCm/wBX3KG9PYDOgYSwlHL1VDaLu+7T7zRfqimaoat55ZpqZJW8y+004Ay6USAwCVY4xti4OVzqKms29Z6p6mfWW3mVv0TLi0PFwjttkyMwDhiBElXBtcxPsaViRHUSZwUqr2UlsBatT7DZCRmopUrUlI4nIQpKXm6pLaUqWph9L2hKjJJUGQ5oSrgTKZj6m5cbA3pvm8pbSty1bdsVyq61CVEhD8vU0sKp1n0SF6irCUCs2z9mDc+yrW8+lNPdua7qNp0b1ItpDouDPfNvPGnAJEtOqcukyavP2p+bC3HqZFJUVPLjl7TJtymalDq1VFPU7sFWV11E6EBKghhKgCe1jFBy65XbY27sjbFEHGhQ29po1dc+slb71wq16qqtqXXJklxRxOEV9y298lSOvPAM1KfkVtmYK1oBTJIzGUoqt2cwN723au0La2uuu94vD9LRWqhouLhLpSXEAkBIRNRJEos+2dhu1NNyS5aVlUdvqfbXTVm8twqQaWq3HXsqVJNuQgfoSCJhKio4mPF7sbg6uS+7v/PKGG/3Cf4iYT4D/GMO4SmlBPk80J8PuQn9xL2I5L9Te8z5bpH39cO/8Wv+KY2JMz/zZYl49MDxe2ItX/tOg/8AOW43Dx+RtH/uqk+5bP8Aiqj+9qi7/wDtOs/vyvuc1P8A2LQ/+dtQPB7p+5Z//r/rf/d6YPhPuQf3Ji0//W9aP/d1NDv7pceMe2YHgHufc8kZRlCP8xnPQT/35Vfgj/go/qK5/l2p/wAFH9RXf8u1X+Cj+orn+Xan/BRI7DcIkf8Av6p/wUMKotoqo1ahJRvNQ4Rjwm3CRV7edrEaB2Bc3E4dGCZw/PYi9U5k/XL2PlTGkbFWDiZ/Wzxx4fAgn+hC8j/3s90H8SOXpqdouPMu7Xo3KVCbm6DTd448SJlPaxBhSRsaoUlWBldnGyRniQ2o59cNoHL1tSnahka6usZrUBSnEpCi3VUbqVSn44oqTcnJjZ1+da25aFpcq7LY20FDzIdbbKWrYjU2gkzTlOGKjfH2M+Vt/Wy7ULDrlI7TPsetFBqVU66Wpp0JLpbEwoFKQOyBMxzf5T8qKc7Z5bVFLYd67L2xWAVlFtuz7oozU01ptdUQlxygpS2S3NS1AGRMc/8Aa962var1vPde7uYLNDvOtatYult7pNbcB3d0qrdUXCkShLSmwWXEkNkAShK9bephdbblIBJk+ipKlJQRIuIQWyDLGOavLBtHc3zkHznN/aSt5tKn9o80rRXJutUpKCBOkqqkICgAkYzxjaO7t9bStd/5X7tqrhyp36bzZ7bXJ+pn6tpqyX2meuVJVIo6ymrahFQ1UJGsMpJBEHklstY7vd14TcNqXy3VKqm2PbOus7gzcWbkyhKX6dq1qJ71GBWJJM4v3ITYd4pt97h5X7Es9Tv3Z1ipx9Y2W230Cmul2avZRUoTf728/rNIrtLQojszwvHLprbm6918yNvUFBd6bY6K3uKdm7UrqH1UC7csAu19tqJO1Da5hCgonKNxcpbzuu17V5r0GwE7q29y/LxpLztWkB12q507xU03d2bpUd2y6UBbaGlEySRON3WXmWlyg2Z9nC1XbfHPOpvC6dliw7csK3k0aFVFYDT1P1zWNNpp0qJS4knMTjmT9pRnb1Nt2wbg3eLjs6gp7TR260Um1tjNpqLPUoNFR0jDH1kmmGsHA1CseEfYr5FVVG01fPqLdfP/AHCwwCpi3Vu8aiusdtbSvUpSX36RJWUns6TgMo2Bs9htFwuW8t52O22u2qKkN1j1bcqelDL74BDLBcV21kSSIHIzmpyZsdXYXtu2Sy3J3bws1lu1DfLNSstG8Wi+W+0tVbdYxVMzbdKlzImQZmdQ9Qb15y09Ip5blPTP7ioKlxmnUvUhhyo9Sa75SE4atInFhaa5k856xW4bMxf1O1F6t+qjDyigUTARSJBaBE5qBMf6w+cH+WqH/s0W62O8xecSG7jWMURdReqHWz6wsN98gerS1tzmM4uFmPMvnNVeoVDrAqPruhSXEoWpKVKBpUkLKRjhnH+sPnN/lug/7NFxpnuZPOtlFvsFzvA7q927U67Qoa7trGlIDZU5NXVCgzzD5thoS0d9eKUuSljqKGNOcf6xOa3+V6f/AAEbk3c9zH5vLNjv1ksyKNq8UaUrTdaZ+oW+srYzbLIA8cAjmBzebEpyN6oTjPqY6I/1ic3f8s0PR/xEbSvY5lc43jutq4n1VF6oEtUarYpIJRrp5qDyVTOOcf6wOcn+W7d/gIpmF8wucwS8+0yopvdu1JDjiUTTOnkSNU4rbAnmRzlq/VGKV8VJvVvTrFSkqCShVOClSZY8DH+sDnJ/lu3f9niisC+ZPOaj9cRUqFSm925XdmnZW9ij1YlSVBEsIfpxv/nNNl51nV9eW8z7pxSJz9WHpBMf6wuc3+XKD/s0cz978ueYXM53eWzdqIq7Pa9wXGkr7PfHq2pNKbXW0zVMlcqmYS2sGaVkTBE4uW2rqw8xWW+qqaZ9t5JQtt6nfU0+xMgBZpXQUKIwmmG1vzNMpK0vFKStSW1pIUsIHp6UGcuqDcaPnNzNsHM+hW4N1bAcvFMuoa7x1QZuNhUttCrjbHUymlAKmFdkzlOP9cfNWeAI9eYmCJzHocIMucfNYEf+W0/uoj/XJzWPSPXafHyNw/U0H2j9+W6hUr5KmdoEPLaSTIa3Qo61ZAnjFrVU/aT3pvrmfuFSKjb/AC7paBlp9FC24Uv3O/1WtX1VSIKSW0qAU+kGUsIS2jToTIJIzKU9kTMpkyGPXDaKJqprLzcn6WzWC20TKqmuud9uzworXR0rCAVLcU+sKPDSkzjbm4+anNjmDQ8wrzY7dcd10thq6Vm3Utyq6dD7zNFNlzFpKwCqci4CZCLbyo5V7TpaqwW6pqrhUXje1utG6N03y61oSay53q6XKhqi++44k6QkBCEmQE4oK232q22W0bo5ZbNYtNDa7NSWugqa+00VUu9PM01FTU1AtafWEuOFtOBIBxj7KnMC6VP1XZ7ZzNttBfa1waWmbVummrrI8w4pRSkBx+rQkhR0444RuhqzWx56o23unde3XnHW0lTNVclG4bUrUuKSUln1YrDWk5jDAxsje22FWu28wvspUP8Ao35vbatluoLXVtbeQpCLDvu4UdDS04qzcdBS+7pUpLhGpWMovX2iueu9FbEPMOps+2NnJu7Ly6hFlvNU0xRC22x1KjU3W93AtFgIAUlnXPDJvf32o9v33adqbrqPb9Nf7U+VM3t55tuqNOwGEoUzTVTCe9CHA6pBMtUsI3DuvlXtc8peSFDQUd5uHMm4oRclXK0bguDVOHk0FI0FL765LYLrxA0M6jLjG0d1cvrhSX3ltzTtdo+pqq21ztfa29zVTSCtyluTanWKmm3M64HqVAkWkKCT2sI5F/Y7Rte3XLnlzXu9Nv7mi9cqG21lZadg0riKpmyrdcpnK2w1pqadKxqcQVtJCgCVCXNjmVcu5t6+W32eeZO4FXStAp7dTXnflsq7RtFgtoKCawN1qUlCZqSlchFPcq1QTd7j9YP3PT2+6q3mamreUkrBUoqewTPEkiOY299zbNtu+6rcPMulXZ6/cdntrq2KG12Nukr6KmTdrdUlLTdyCkrQAJKM45v785b/AGj9ucmOW25rwNyUdiqNoi83eym6rU5V0lutVFUUdK9SipUUJdm20kSAAMUSucX2gudm+700GvXqzalwpdj0VZpRJxDdI2m5mmC3O1MKUeEWOoXym3lzRttI9qrqrmbvNe4nK9SlKIpK9xijtwcZXkAW0iQljnC7nyu+yJyQ2sbq2n1p+l2nS11VUJW0hJQ+/cvWnCAnApBCZzkM49Vsm0dtbc2842mmpmbHtyx0Xc0ylKKUt1NPb2q1TbKjORcUQBLIQn9LNctxpRSijAcIGmQ7xWCUE9cO3G3PP0Trjrq0rQshYQ6okhUjIkgw/V3e90bb0+8TS1FVOoqFKM1d00CpWoZ8AYum9eV9koXdzN7gsdkpLhuZH1haG2bq84y8+u0pcZW46hKZo7YAOYMJuXNDf24N2FpThpbbXVbjdltiX1Fx2kttqp+6pWaJC/m0rSsgDOcCSQmQkAMAB0AZSjExfnq+jVX0w5KbwDlKHC13hNZQymsGaQM8oa/zOPoJxNydPwU9CYH+ZysRh/OTmAmcPRwh6qttCbbSqZp0JpC6XihbaVJdX3hAJ7xRn1QySnWlNQypaJy1tNuBTjZMpjWgSgEbMXp0jD6ydyA7XwZ4xyjWvZ7jjT7e7vVWfrJ0eqablJztSJc7w9OUSOy1gYf96O4ZfiCHj/QpUghf/ezvROWCZzlGyFPbOXUNu7bZUygXV1BZTNPZPZGqYgy2GtJ/CN4d68sDMiLWkbLcBNzoRqN0dMp1LcjLQAZEg9cXxi47RcrqpLVpLlT9YrY1BVuYKE92kEAoRIdcow2O4B0fWrp8eCYtyGNmOMOlqpKXfrRwlMmlHLRLhF3DuzVuK+saqZN1ez71Uz6E+Mf1JXiON2dEj1dmOZL1LtY0jTVutyatn19x01ba6lBDWogFkJInMQZbJX4frZ4EY+Dogj+hS8CR+13T7bZiz1J2utVA7vusaTQ+vrGl5NANT5flqJkPRlKBLZCuM/52dmD8XGCTsdcgMvrZ3ET/AHMW1bdrcRTqsNocbZ71Ku7act1K4hvWrtK0JUBM5yhxL+111CtRE/XC3Mz6hGOyleH61c8vowP8zFf5VWP7Ux/Utf8AlVfmgf5mL/yq7/g4/qYr/Krn+Dj+piv8quf4OEYp9BPEdAj0k+UR6Q8o88ZjyiMCJy6QR7YhglSRiOI6YT2h6A+Ej2pw+NSM+kcJeeNWocD6SeI4ATPCCijp3Kt2WDTCSpeOAyBAxMbAp6JlKHrZtWhp69DipGnq0OP66ZUs1oCh1Riy0ZngvD2RDK/V0EIeZWZL+Ch1KlcOgRR3awqZdokbdtNA+FqKFJraNkNvDGQImIkthg6gQT3wliOGE42T9r/k5suu37WcurC9tnm1trbdvNZuZnblIz6xbdz0zDKS9crXaaRpSXwkKU0chjjupFXZapume5u7soamnuFK9SqdZcdUmobLNQ224UrZd0nCWJHTHN3ldebPRWWnXfzvXZ1sQ20lyitF+SK63opVoEn2KmmKipEyW1CFba5hVrNJyo577VquVO+62vqgzadvv3uoQixbnq0LbWha6S69y0lRkEBZOAxjdjVQ2trc20kOUlRV0TL9Q68xbmVu7X3VbzTBQqrc9QqSw6tvVqQuacQI5W86Nx816PnLzr5kWm5cneXNrsd2Y3RfuXd6og9Tbf2lbrCyEXCnpkkNPvOuABsoUCRiIu9z+p9xb1+0/wA/r+5WcxN32/b1ZemdvXu7oU9O61tK1UVbFFZKRWijSlYQh8awDFFzHsb+53N90N1VuNy8VNFcn7jV3F2o9ZuDlynTqqX2a9K1IcSoSWlUjHKb7ZvJWxXXl99pjks8isuFCm0vWquvm26MFzcNkp6auZaqamzXynQfV2ng4lCgAnECNz/aZ+zfzKOxbHzRO2uV32j9uOXVi2Ve7qqlvTK6m0VdhpEm5Kv1grULUumcQkGmbJi0W6/PU9islrsX9I+Yl7rWUttbc5a2FlNfX1NyLiymjevD7BdSgKLpQoSSco5n81aSt7rYbNS1tLlpT1Di02617M262qzWRdK0tJVT0lwNOH8EiZXOQMWJxqytXrZXKK2vbr3FcKFTimaS4MuD+jD1VXtEKpW3r2QSmQ14AyEFdQ1TqWVFRKXUgEkzVLGUBJZZxMjN0SAnjkI2sba0ju7NtemtVSXCADUNPrXpQQMQEnGJd0zh0qBPliz177LXcUVxpap4hYB0MOhagOsgRdLnQ06HaWtfW+05rAlrWolCknFK0Ty8EY07Pg1j25yi71NwbQhut2zd7WyUKmTVVgZ7lBwyJbIn1xqLDcyMtY6Y+Ya+OI3jtd1lIud73Ht66UUiC2aa2Uz7VRrPBWpwSESLLR44LA6vcj5hsYH+UHmyjl5bKVtHre3W70LglShIG4LT3AQeJITMwZsNnwLEUjq22kIbqWHVHUDJLTiXDlxITFxvVGhv1aqpaJhAJkrVToIWfASY+bY+P54tl2uIZRSUrVf3qkLBM3KNxtpAyxW4QB1mKp9LCEF6pfdSlasUpccUsAywmAYxbpxmJ65SjmLb30o9bv1ptFHbUoUD3j1NdE1L5M5FIaZTPpir+0DsSzt1llqO4XvqloSHX9uXJDeg3ZTDSZt2m5kycV8B3tLwM4W08gtOMq0uIVmCMM8iCMjkRFBuTbd4uFhudtqUVVDerQ+tivoHUYEEtKSp+kWiYW0cFCKeg5uW5F7qG6ZCnd37SZCKxwpDSD61t0YOvEKBWtKgSeEzFHcKTetFQtVzaHGWLuh2hrWwoy0VFKpClsuJIIKTjMQq51u8KOvbSjX3FqQ/UVakzAGhgthStRIkcoq7XylttBtatqWn6am3XuWoYr7qFaZKVbdvn5JNUsTUhxZ0oykTF+3febvd71cb9VrqrhdL7XPXC5Vr6zJbqnXVFLLagBpbbCUJSBhGpakoQhJUpayEpSkZqUSQABFr+1jzYsCrbtCwuqqOT2274wpi4Xm+NK0I38qkWns2u3ICk0DhPyi1qJEpGCtRKpykSRw4dQB4RPHowKfcjYnO7bT1M1duT26HLddAq1Jq6g7O3N6qq/ViqxKkPMeqGgbQ2MQC4SBhKKCoo3SlWugu9KtCiV071M4mtpXFLTIBVNWMoJIyOGEbA5zVjjbm4qvalt5Yc9W2kolaOYe2WFUNn3SG82Ke8LbR3DzoSsicjKN0bDvnOGy8q9rcyeWO57xuPYl3uDlotfNZnbNQiru9hpKysQijdt7zbQqUrUe87JTLCLFzOdutZe/sh/Zgp3WNjbUdtK2bNuvfFlSujqbhbFvjut1C2vMhFI8EhKFAhPTFffb/ALW3ZYtsbTZe25snaFwsd1om7HbUgIfrLhbRTimNxuZSHAtYK0JI0kAxvT7KnNba973Fys3xZK9NDcbtYblV2/Zpulvcaq266qepCintlV3hW2rWQ04pGkZRev8AZp86L1X3SzbpvK739kndVa62A5TUNQq9Mpa3FUPBsXjalQhLrNMNM2EyE8I5q7/5+cwnecG5bRe3rLct809wRdmnbBYnGqG12hlSkICLrUIp2m3WkJKk6pCcjFg+yXbEUlu5ic+rhQ8webtnYd79/a/LCwMtjZO0lONqU/Q1dS8GXUpUoKWkKCgYFs260uov90q/qm0ONtpfaqL9JqoFMinBDrusqDMhipasI5M7IuFPb6O8narG5b8EgU7i71ugou9aioaQ2FNv0i3+5UF9uaMcYt+4iKZ+hUldDdadp6Sn7dUFHeSC0pSVMLSlaZ8UxqcqKkDMSY1nyIM5xW22orKnRVNFAK6NxIS4O00sFQwUlwCUVNDTLbrmlBw0qlEpS06SQlZCpHTPMQipvFSh40xJQT3DCW0kzVI9kKSgEnOcFhh9u9PJQCUUDmppsyE0PugfJqJPXxhFubbpKC2Ko6t1qib+Wf8Akm1HWt89pRE54AARVvLqS8s1L4Ljr/en51Z0hSlEpCSTgIuZStCyd+7MwSdSgBU1GMhkIIl2uBx4CUE4RKYjcClEBKuTG7kgnAajV0J0gkS1SxlCASJhCR6Q4JR7EDtDDrHT4YV1iX8Kc/HAMxgZ4/7kSUsGTeUxgdOR6I5LHWmRb3npOpMjK6DCc5HERJLiVHoCgfdh6eA7tczlwjYRJTL+jDGPjGOMGRSZjhjKfgnFpwH7UoPwh/jSOkxuLSqY7izejIj9lU3HKO0D7OPglFrww7mr/CGAZX0xdlFJmblWew+sCXij0VexHNUAHUbda5D4WFSCTLiAI0yy8PTODgrOLLgT/wDEG4kATBSBQIGMT0q9L3IWJH0TFomQP82bDhOR/ZFF0HOHcT6auIlnGXHoPnj0VeIRkr2IGCvY6I9FXsR6KvYhA/0aWQnQnEvO49kY+lGPLSyfnnfyo/1aWT889+VH+rSx/nnvy4/1Y2NRl/dXZfx4YFLy/sVEgqE1tvulQxz0zE4Quq2fQ1ytAOhanW0gyymlzEQ8lHLOxyBzL7pJ8qo9Qp+WVpQw3p9brG1kopQQdOrWrEk5DjDn1VQ0luQ7p7xNI2G9cjIaz2j7MCcuifRh78TBBExj4iI9+MB7MYEjxwnViD3iFJVJaFpcQptaHW1hTbzS21EFC0qSZ5Q9QbYsFl25RVNSutqaSxW2ktVK/Wu4vVjlPRtNMmoeVipQSJnGKX7THL+3IreafJejVT3phmn/AE6+7HqHm3KquqKppCn6mssSUkN6gdNOpYnFLc6MLYoKxxxoFckMN1jT/dP0KHEqITUMvo1szkZaVDGNscieam6afZ/2luX1jTt3lXzBrlpRaOau2qeS6Ta25WatwU7m76NKO6aLhk+2JntRtHe3J3lls+x/aEsN/rt4nlvvTb77m093XdVR9V39201zq1Wu0/W7VSqqeeSmYcICeySY5m89eVu/UsWq/v06l8q9lV7VNtinZo0lhjbtNthdKKCtepdRm/32qoVJRAJio2cxykojuq2UdPcLlTjl3txO5qWjrlFFLX3EIuDryadxxY0rKfggmE8+ufl4c51WFO26yge5Qbu3W+j+kFU9OptFBQJRQsW2w22zVZDy1FTinkgtgCccyazl7tjb1l5x3HfDO5LpYNpWI0PKbY+6q5husd3DumzrcZtL9wpLe48zUuHSHacFRVON0fZZ5Hbot19uu4HbfR/aO5qbYbFts+8b5Q0rSV7I5eMU7jiLdtVp/Ua5aVq1uDQDKZhxplLT9bcELYtVvCFJYr6x5wNSpGUAqfNBNXcNiet+Q4xR1l+tCLXza51OUu8N/uKp0sV1DQFhtuy2Ra9IebQqkaRUOs4JS44RKYnASevLr8mES+/24Inhn7XXE5/f5Yz+/wAsZ/f5YzPs+eBjxHt+GBjkB4scur7k+OX34wPB7p64z+/ywPCPvzg+E/fnAx4j2/DHDye/Ez7vnic+vjnHDye/B6T58YInwlxyOcXK0XahpLnaLvSP2+6W2tYRUUdwoKpBbqKSqYdCkOsutqIIIMbj5xfZ4t9VuzZDbjlzvGxaBsv7m2rTvuKcql0FKkd5dLFREzBRNxpGGmUVNJUMPtVNK4pqopXmXGaumcQrSpFVSuJS8wtJzCkiG6qz3KutlS24FoqLfULp3AUnCamyJpmcoqkW/er1eqpUhwOXphu4uU6kSn3ClpQAhaUyIIOeBEM0943waBNO2thtyxUbNsedaUD2H3UBS1pTOYHTFTcbtc6+61dQvW/V3CqdqnXFYmZccJA8QAEISluorHnlBLFLQMqrKyqWohKWqSlpwt+qdJPooSoxt3nd9qe13TZvLGkq7fftr8qKxphu98xFMAVNBUbnac1OWrbw1hZYUNb4wMopbba6Gktdrt1LT0NttlAy3TUNBRUrSWqelpKZpKW2GWW0gAAS+7vXlZvilTWbS39tu6bavNOoyT3VwpltMPhciptVLUFKwpJChLAiN3/Z639QU9BXWStqrny6r3qv1NG8dtXJ01FA4la0hC6W7UwBpkpOC2SFYmLlR31Ct0cpOYNAja3M3Zla44yxfbTq7hVwYoFKUi37n2+XFFCpEgy0mREWHm9bK080+Qj1muNx2hva0tLrt38tL25bX2PqXdTbD6bi+W0VZbcOpCXABNPTtXkbyYuHLPZ+2dj36gv/AC35i7FYc2tftzWEOVD1xsd9cYU9WUPqdSptanShSnHQQrASimtO66Kt5nW+1SX/AEy3ZarNvCnqXJerMsLu9R9WO1ultSUobdSpUgDCbTVbguXJWwV1S244za6Og2iqspQhxdPVVZttPcXa+1KZd7LRUlOCcpR9mHYn2kNt7KtyNhXS1G1827Daqiu5q783Mq2v2x1dsetzgqbC2tTiisJ+f1EmQEounOfnRdqMrS99aciORDq2TuS/XxilKKHc2/KhtTtS/Zg44X1supSorSM5TjePM7el1dvW7t63JyvvdzqnnRS0tMySinZpmz8lQ2q0U4DVKykASSBiYpObt4sxufIXk5UNXe4XKoeTQ09fzCtVUh+07dpUOAuO3AutN17ukFK6clM5yhyocS0FuvOPKS2gIQFOqK1BCAJJTM5CJSE8ces4ZxgB5YyB8ca8pdGOPDCO4qbJbbw2lRUlutccbxIlIqQodnjEhy8262ZEHunwdUsiZgknoj6we2LZqdv1WoZ75haTUJLqSAlBACkoXxlDinNibaUlTi1jRpn2iVTJIEyZ49cXPlNR01m5bv19+sN7Ru2lpBc36cWZ9byqdFEFImqoC5TnICPkftAhxWODuzENCRP4QqTDFHe/tE0zVbUILjdGztFLtQG8kuLSKgBCFEcTC0s89a991JCU6tjpbZcxxUl31o9kDqxjevP57fR5i0li2jU7JOy37K3bKSqXuZaHhdXK3v1KQ7Rmm06NM1BWcNq/0X2hJKEkg1CjI6UzAIVjIwEjlhaSZgdl5RxUZACZ4mHqmhO3tvMJbbaVaE320I7l1Ak4paXq5DrThUDNBAIhl526bcfQhYKmndx2RDSx+Ov14hI6YZQOXVhr0oaB9cYr26hp/CetLrSVtuJVwKSQRlHK+4O7BtdU1cm93Gmtq1gM2/TdAVlklOa5YwVHlpZ5kzINQQOuUkiHSOWdkBCFY+srJy4AzEbNdc2Haqht/bVOtplb3Zp0g+gjsZGJnlpZlcJF8g/wQkxamzyysySq50KQpNQ4Cmb6O0MxNJxi9sVXL213CoSzai7VPvqQ4rVbmSkFKQBJCJAR/qxsniqHIt1M3y4tNKtbVSQ+3UO60yaUcJGeMXBpXLOyuFuuqmys1DvaKX1gqMzmqP8AVlYv1h73EmOYtUnl5aKdujt9vU621UOkVAcqEaUOYDsTEH/4Y2Tx1DmGA6xH+rKxfrD35MWi4f6P7Spg74r2RQesOdylwUKdTyVSBKzMTGWEY8sbJmP8Zc9qCTyxsmCcvWHMRPwxbah3a7DYdsFodFO08e6bbXbaVTTSDKeltsgeKHNXL+gqFhau09Ur6TwjT/oxsZAIke/XPOB/8MLH+srj/VjZPz7ntx/qxsY6/WV+aP8AVnZP1hcf6srH+sr88I7Q9BPHqEeknyxnGfsRh0dEMAkZjMy49Zgej6AyUno4Yw/iM+rqi9OpMlOqp0kiY9Ak4xiZmMIx/sqmiq6Zitoq+kqKGuoqtlFRSVtFWMOU9VSVLLiVIcYfZcKVAjIxceeHKOxObh+ypv67VVfuDa9A2UVuxt33Fzv3qNHdd5UNUDiyXaN1tshOjQqQi0X+z7hcdovXaRVqvTT1XQ1rlShSVNM1LyAxVUF7onUAF1OkagDMTMWrlr9qzbVNzf2pb6WipLTvQlNHzS2200hKElN6T2rq02kDBRCnOJiiq9k/agoLFTqcSq1bF5w2UWusaUspUhtx23MVzTyNQk26VhRAxi57+e339ju27kvFvp6e+bwSKoboqrSwlv1dC3gErcp6YJTpGiQIEVFbvb7SdVzNp6R1YTtDlHTTty3MkU7lUoUz9IkH01hUh0Rf+VvJG2W/kjyvuLb1Jdf6MVi3N4bwbUldIKnce7m0MVqk1jKu2wCpQHYTOH7zdK+oVTICvVKc98n151Sper0bJSqpS1LF2oKdSseJnG2/tifaQtTTPL6gfp79yZ2rWroy/uK5UNQ2qmuK7a876zRbcty0hbZdbS68+n0SnGFuFOlSyVmSdKRq7UkASASOAGAEH/7mcbapSooUoBalSSZSE8fgzhLrYYCkKCkplqbmCDpUhQ0rQcilUwrIxdL1fduDl/vypU7XOb72MhFtramqS2s9/dqBkIp7mhKf5MgTkBD1gsVZvze1pp1P6r5X7Drbat5pnTJbNNRMPoWheYIXlnGl8XakX6Xd1W3L206RwOn1JUhOJsOXN4yHybe373qV4B6jjOKKxXv/AEl7WtLlTTIqLnYNiXO7OvUzywF93S1VKyhxash2pFJi1b55ecstzXffFCunfd3hzktSl3lm6OAuO1+2rLVoVR2SncOpMkaiUYGUJdU6HJp0SI0oS2JAJQgDSkJAwAlKAXW0LSc0z0q6iD0wpwNlvSrSQTMeXp+5lPqPHqhi9bZTQ2L7SfLWjqKjlxu6oQgNXm0gKqa7ZV1dBQtBrXEI9SqSoerLSBgkmL3yq5i2yt25zQ2pcVWm82ivaXTVFfXUJFMh1pbYdpDd6hLM1BLqkPjtA4xXja26e/slybRb907F3Awm77YrQgpDibnt+twp6xKMA8lAKJ4KMU9wrHt1fZo5kXRtTj1Vtrutz8uFPFJVVPsNuK761JuKyVLaDJSCZ8Ibs9J9ob7MHM2lWG3V0++ma1h5D4ktDj3rNOxRNvapYFQJVlDit1/az5SbSNAV0lTZeWiaqrrEN0ydHqNHSuUHq6kpkEiSjhlFdcPs5bFG8+YaEuD/AEo82SH3LXVoUW1V1j2gpL9FTBTYmhY0qxyEbm3tvPedw3VuS/qdqKx66VK3Wm6RpS3A4lSVuU1ttNJTp0Nto0nS3pCSc7Pyd5XXBu2U/wBbUFy3rv2ppEU23rXt36wa9br3nq52nmyxThQYbABeXwEba5NcnDY2LNYWfW73fXbhbG7vu3cVWlC7lf7qpVQlSnHXZhlOJbakmcJWhaHEKE0rbWhxtUzmhxsqQsdYJgeD3f7GajLSRPLD2eH3CoKEj1jCO0Zz6CPcgArbQnNbjriG2m0DNa1uKSkJAi9VdHVsV9M2qnpBUsKDjC3WElK/V3cltpOExIRNOR6TLIeGOaoy/nGzKOOCpBYBJnwnAllIS4iXVnFINSQfW6USJAx79sSkfDH2hSKuuSkc2dyAIbrqtDaAHUyS2hL6UNpwyTITiu/TLkf0GsONwreFM70VGUci0qkf/hVtFWo4qVO1t4qUokrOGZxjkr6IHq+8zLCR/nQCeMo7JAGHEDGQ8MPYj5teJUOiNiY//UwxxHSBwJjh4yBFpxH7Vt+RBw9ab643CoSALFlzAB/ZdMBhnmIzHli1yIn3NXxA/kV9cXUzH7SreP8A5QvzRwjmriBO3WriP+cp8MHEDxjoHX0Rn7I88WWXDmBcMZYY0KZeCJj8KX3+WFfuTx8EWjM/5sWDIf8Aqei6od/dq4HpM+EY6c+PvxmnIfCHnjMeUQO0B++Hnj00/GHnj00/GHnhH+Y9kPYTiadB+CPx4/qRYfHRMKH8ImP6i7c/ydTeeP6i7c/ydTeeCobE22ZA4m3U/wCVDDa9r2CiBUBNi3NpIx6Qswl36qtb50A6HKFtQnLww+hO0rEoJV6X1cxj5cYrQ3aqa1vtrTrTRsNssuoIklZSjJSZY/8AgaymYrVW51+kdbRXoKAqjUpBlUguFKPkzjiYvdq3d9oLZF4sF4adpK+3bxuJdZrKeomHad23M+ssJbSkySpMlA4iRiu3r9lzmfsjerT5qHbnsL+k76lraed72tp7bQVVKKappXUAJkSg4Cc4rbDvCwXXl3d7bWM0D9JVU31pYHH+7+XUy48tty0a3gZdyVoAlLCHqO2Xm33A0yTUU1HbLmaiuW2kSmhVb3HdrT+DMg8IqL2obicudIhmxuBdSyoobIU+0pVOusNNgk4rBJ4Q5QXHdllaLaHO+aVcnW61qTZceBo6ZtbFQtvHAqxlKcWqw7BsVbvG63hxCaG7XSlRQ2V2r1lVIWLWl5LCnEKxQ46oEKxOEpUW/ftBb72hXbiVV01WraNdfn3LVSOJPZNxcbNQ0lylICVU6QUHTKZEUFNYftE8ubZTpVTNUdJa9yXC2qo0NaW2m6dptlDKO5bTpSJBMhLKF01/5lL5lPVq2nmrip1moRRltsIW2zUtuLU6hfHVLGCf/uUgykRw/wB2Fd6yheqYM8M/BLGJpY0q4EKMgekZwUd93Q4HTOYyx6cIFPcGrZcWArV3dwtlLViYmAZVDDkiAo4jpjXcOVXLCud0hsv1ey7M88UJJIRrNICEBSiQBgJ4QQzyc5Ssz9Io2PZ5qPSdVMcfBC1ba2ttCwKcQ22s2nb1voypDIk0ibdOCEtjIAiUBSqtM04hDbelKiMhgAADElyIIkQJj2YAFOlRGM1FRxl1mAlttCABKSU6cpATlKeAiWH3+P7l2oL/ALma25S1dIulqq5ivapbhQIUAS83NQdbwGcsROKa70v2nNvbe5k2Z0PM7xqlu2mtrmKdSk0VBf66gp3frBDCnQlL6wXGkp7JImDVWDf9FQ8y9v09zeTYuZXLG6VF6YuVuWsqaWxe3qemrLhSsU4SVsuto7tRUATHe2feVPSuNKDb1FuNVVY62kUQAtCGWEPocaP4RUMcwIdpO+uFVabnTo76vttXRqaU40oPoabKasPvgupmFAAdMVdwu9yZtQQpCUqvNexSJSkIS2lRLLr6iqSOAmTDzbNbdt5XCspV1CLZtinf9X0tEBxx7cLqkvtomqWlKCDwigvG8N58u+Q3LF22983V70vb9guV8UupKGqWk9SaeqK+uQhZUpboSkJSM5yNu2ps77QHJJlmnCG6irb3Jcam8XqpCUo9YuFWm3smrUJHQCdKAoyhh5/nXtyrqGe2lG2bgLg+pTJGlLbdRUMlTiwOynHVCNuU+4bvuZtFQXxWXhJacYcUNK2KZjW56uwFD0Jy1R4MP7Crq1KCE07C3dSvRBSMCr8UmQi4028UWSjvbFcpxn19hptt+3lpCWn0OrBmmaFCRMxhPOAzTPbTqnlAqDTCqdxzSnNWhM1aRxMFJrNnJUkkKSp2kmlQMiDjgQc4E67Zv56jHtmKxie0a9hNG89WsINI+lNG0NT7riEklLSARM9cJUjb/LfQoBST6lRZKxB9GWM4JG3+XIxJ+hUeZi9sbCsW13UCutr11t9opWG2VFSiWXqkMhKXFBGWomQj+pG3z+6ttKonwqJmYowjZFgKvW6bSE2+lRKT6DMLJBRIceEfaArbbsvdCLfc+a26KmncodsXt+1tn1kIDbFezROUjgd0lQIWQZiUVvebJ3uAqjq0j/NK+mZVTuJ04UWRnKOQ9C5s60rdpeVG0GHE11uKaxpxu2NJWzUoqQl9p9sjFKgCmOU1cNo2VxVY3u/Uy5RMFpnRcpfJJ+BrJmekwFnZlgGkaZChYT0YmU8Yd/zMsPzas6Nno8kbIeTtexPetbdaV3XqbUmNKgdAK5pIx4R29lWFzqVSMgeRJEWhCNl2FgrulCkuIpWypAVUIExMmRGY64vdL/Rq012lm1KFRUU7Tjh129k6CtfaIRkImdnWT9UZ80otrP8ARSx02pqqPfeqNEp0sqOSe1jFyb/ohYVlFfVo1+qNSVJ5faxE5mP6m2L9Tajma9/RWyMmkttrUGxSt6X++qikpUBgQgI4xq/ofZD1GkZ6JeCJHZlg8PqtPj7MWi5nblq0vb3r6U2/1VldOO7o0FL6G56A4o8c4w2hZDiZzoWBkBlIygqVs6xSw/xNrOY6Bxi3umjpmtVhs7vcJbk22HbZSLCEpAkEpCsB0Q4g2K0VPbPztEhZzP4WESOzdvKyx+raeecAf0L2/lwt1PH9TbB/k5iB/mbYOP8A3ez5o/qbYP8AJ7Mf1NsH+T2YRn6CenoEcfZjj7McfZhU5+ieno8kM/uh7YhP7ge1FR+76ujrhj6v0dzpd9b77V6v3Wj4ent69UtMuMCechPPoGU//AXj691fU/1e99Y6O81+q92e90d18pq09EV39G/6e/V/eL0976h6Wsz7r64+V7mc9Hwoe+rf6V6ZnvfW/qjXonjr9V+D0Sxg/wBPe6+s9CvU9fqP1nLUPVfWNPyfeavR1dqLz9Xf0o06qX1j1H6h+b0nV6n6v8vPufTl4sYuXqH9If6I+tM5/TfrL1VPfeqS/Se97uc9fZ1ZRS9z639WfUSJaf6J/WP1d3itfrXrn6f6/p9OfalFu+rf6SfOt936x6jqy7Gn1LtaZ56eGUU3rX9IPVJDV6r6hr9ES7n1j5L089ePTFN9af061+so73vP6Pdxo1DVp9T+V7uU5z7U8orf9GX116j9YM/XP1363q+s/Vk/QfXMPVe5lPu+xq6//wBB4Z9cvdwhz+lf9J/6YfVFLp+pPWPq7T3SvpPefoXfd3nqi4fVf9O+47hfc97/AEZ7qfet+lP5XTPpi5fWP1r9Q+svetfXf1Xp7uR7zutHyHdyn6HanlFf6j9ba/WHZepfUmj5xUu+9Zw7vp1Yyzi5/wBFf6QfXupXqveeofVXddyfWPqzuv0HvPVtWqWM8sYr/U/XvXPUndf1p9WeradXynq/1x+j+tasuPRFo+vf6Xev+ptd39Xf0X1fVk/5f6o7Hcd7LPGcUGv6+9X+R7n6y06p9nT3XffJ93pl6GEM/Wv9Ke67r/EPqHXr7sd33Pf9rPpxiy+uf6Rfrn6ypfqbvJ93676yiXr/ANVfJ6NcuqUOznq7wzn0auz1zlnPjHl9z+wY9W/Y0v07RPvO9me69Zlh6tl1as47U+80nVp9LRPGenHRPPhDOnVq+qrjKU/7menriv197q9dqp/Of3dfRHw5/ja+v8LCOZ89Hef6L9w9zr9L06bXo4zlnKGJesy7lqXzn4CYw9Znjlr++ccye819961Yp95q191J3PV2pZRwhWuWmXazy8WOcUX1d9RaP6YNTn9Rd/3Olfe+sd58t6Up95jB9Y+pO6+F3n1BKUKl3M5fyGjuvRHod18npl+DhKOTn/F7x/8Aegg+Ef2sOz/uapeCXVjGwP8A62k/xkxxizf+1rfn/wBKbi+f9Hs//u5iMMvv6MYtfT3VV059yqUXT/2jW/8AnC8vHHnjmx/7Ms3/AJ4uOvr8OPsxx9mLD/8AnAuOfT6kiDqy1Hon7PCFSnmM8+HRFv8A/rcsP/uah6Ydn+GfwemBOeYy0+5AlP2I+F7EYa5+KOMcY//Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Our client is an Insurance company that has provided Health Insurance to its customers now they need your help in building a model to predict whether the policyholders (customers) from the past year will also be interested in Vehicle Insurance provided by the company.\n",
    "\n",
    "An insurance policy is an arrangement by which a company undertakes to provide a guarantee of compensation for specified loss, damage, illness, or death in return for the payment of a specified premium. A premium is a sum of money that the customer needs to pay regularly to an insurance company for this guarantee.\n",
    "\n",
    "For example, you may pay a premium of Rs. 5000 each year for a health insurance cover of Rs. 200,000/- so that if God forbid, you fall ill and need to be hospitalized in that year, the insurance provider company will bear the cost of hospitalization, etc. for up to Rs. 200,000. Now if you are wondering how can the company bear such high hospitalization costs when it charges a premium of only Rs. 5000/-, that is where the concept of probabilities comes into the picture. For example, like you, there may be 100 customers who would be paying a premium of Rs. 5000 every year, but only a few of them (say 2-3) would get hospitalized that year, and not everyone. This way everyone shares the risk of everyone else.\n",
    "\n",
    "Just like medical insurance, there is vehicle insurance where every year customer needs to pay a premium of a certain amount to insurance provider company so that in case of unfortunate accident by the vehicle, the insurance provider company will provide compensation (called ‘sum assured’) to the customer.\n",
    "\n",
    "Building a model to predict whether a customer would be interested in Vehicle Insurance is extremely helpful for the company because it can then accordingly plan its communication strategy to reach out to those customers and optimize its business model and revenue.\n",
    "\n",
    "Now, in order to predict, whether the customer would be interested in Vehicle insurance, you have information about demographics (gender, age, region code type), Vehicles (Vehicle Age, Damage), Policy (Premium, sourcing channel), etc."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Data Description\n",
    "**id:** Unique ID for the customer\n",
    "\n",
    "**Gender:** Gender of the customer\n",
    "\n",
    "**Age:** Age of the customer\n",
    "\n",
    "**Driving_License\t0 :** Customer does not have DL, **1 :** Customer already has DL\n",
    "\n",
    "**Region_Code:** Unique code for the region of the customer\n",
    "\n",
    "**Previously_Insured\t1 :** Customer already has Vehicle Insurance, **0 :** Customer doesn't have Vehicle Insurance\n",
    "\n",
    "**Vehicle_Age:** Age of the Vehicle\n",
    "\n",
    "**Vehicle_Damage\t1 :** Customer got his/her vehicle damaged in the past. **0 :** Customer didn't get his/her vehicle damaged in the past.\n",
    "\n",
    "**Annual_Premium:**\tThe amount customer needs to pay as premium in the year\n",
    "\n",
    "**PolicySalesChannel:**\tAnonymized Code for the channel of outreaching to the customer ie. Different Agents, Over Mail, Over Phone, In Person, etc.\n",
    "\n",
    "**Vintage:**\tNumber of Days, Customer has been associated with the company\n",
    "\n",
    "**Response\t1 :** Customer is interested, **0 :** Customer is not interested"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Import Libraries"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "import missingno as msno\n",
    "from datetime import date\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler, LabelEncoder, StandardScaler, RobustScaler\n",
    "from xgboost import XGBClassifier, plot_importance\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "# import os\n",
    "# for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "#     for filename in filenames:\n",
    "#         print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ],
   "metadata": {
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:32.537887Z",
     "iopub.execute_input": "2022-01-18T22:51:32.538261Z",
     "iopub.status.idle": "2022-01-18T22:51:33.718934Z",
     "shell.execute_reply.started": "2022-01-18T22:51:32.538163Z",
     "shell.execute_reply": "2022-01-18T22:51:33.718073Z"
    },
    "trusted": true
   },
   "execution_count": 126,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Import Dataset"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "train=pd.read_csv('data/health-insurance-cross-sell-prediction/train.csv')\n",
    "test=pd.read_csv('data/health-insurance-cross-sell-prediction/test.csv')\n",
    "sub = pd.read_csv('data/health-insurance-cross-sell-prediction/sample_submission.csv')"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:33.720607Z",
     "iopub.execute_input": "2022-01-18T22:51:33.720857Z",
     "iopub.status.idle": "2022-01-18T22:51:34.731756Z",
     "shell.execute_reply.started": "2022-01-18T22:51:33.720827Z",
     "shell.execute_reply": "2022-01-18T22:51:34.730942Z"
    },
    "trusted": true
   },
   "execution_count": 127,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "train.head()"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:34.732873Z",
     "iopub.execute_input": "2022-01-18T22:51:34.733124Z",
     "iopub.status.idle": "2022-01-18T22:51:34.759117Z",
     "shell.execute_reply.started": "2022-01-18T22:51:34.733071Z",
     "shell.execute_reply": "2022-01-18T22:51:34.758244Z"
    },
    "trusted": true
   },
   "execution_count": 128,
   "outputs": [
    {
     "data": {
      "text/plain": "   id  Gender  Age  Driving_License  Region_Code  Previously_Insured  \\\n0   1    Male   44                1         28.0                   0   \n1   2    Male   76                1          3.0                   0   \n2   3    Male   47                1         28.0                   0   \n3   4    Male   21                1         11.0                   1   \n4   5  Female   29                1         41.0                   1   \n\n  Vehicle_Age Vehicle_Damage  Annual_Premium  Policy_Sales_Channel  Vintage  \\\n0   > 2 Years            Yes         40454.0                  26.0      217   \n1    1-2 Year             No         33536.0                  26.0      183   \n2   > 2 Years            Yes         38294.0                  26.0       27   \n3    < 1 Year             No         28619.0                 152.0      203   \n4    < 1 Year             No         27496.0                 152.0       39   \n\n   Response  \n0         1  \n1         0  \n2         1  \n3         0  \n4         0  ",
      "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>Gender</th>\n      <th>Age</th>\n      <th>Driving_License</th>\n      <th>Region_Code</th>\n      <th>Previously_Insured</th>\n      <th>Vehicle_Age</th>\n      <th>Vehicle_Damage</th>\n      <th>Annual_Premium</th>\n      <th>Policy_Sales_Channel</th>\n      <th>Vintage</th>\n      <th>Response</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>Male</td>\n      <td>44</td>\n      <td>1</td>\n      <td>28.0</td>\n      <td>0</td>\n      <td>&gt; 2 Years</td>\n      <td>Yes</td>\n      <td>40454.0</td>\n      <td>26.0</td>\n      <td>217</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>Male</td>\n      <td>76</td>\n      <td>1</td>\n      <td>3.0</td>\n      <td>0</td>\n      <td>1-2 Year</td>\n      <td>No</td>\n      <td>33536.0</td>\n      <td>26.0</td>\n      <td>183</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>Male</td>\n      <td>47</td>\n      <td>1</td>\n      <td>28.0</td>\n      <td>0</td>\n      <td>&gt; 2 Years</td>\n      <td>Yes</td>\n      <td>38294.0</td>\n      <td>26.0</td>\n      <td>27</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>Male</td>\n      <td>21</td>\n      <td>1</td>\n      <td>11.0</td>\n      <td>1</td>\n      <td>&lt; 1 Year</td>\n      <td>No</td>\n      <td>28619.0</td>\n      <td>152.0</td>\n      <td>203</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>Female</td>\n      <td>29</td>\n      <td>1</td>\n      <td>41.0</td>\n      <td>1</td>\n      <td>&lt; 1 Year</td>\n      <td>No</td>\n      <td>27496.0</td>\n      <td>152.0</td>\n      <td>39</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "train.shape"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:34.760384Z",
     "iopub.execute_input": "2022-01-18T22:51:34.760592Z",
     "iopub.status.idle": "2022-01-18T22:51:34.766847Z",
     "shell.execute_reply.started": "2022-01-18T22:51:34.760566Z",
     "shell.execute_reply": "2022-01-18T22:51:34.765939Z"
    },
    "trusted": true
   },
   "execution_count": 129,
   "outputs": [
    {
     "data": {
      "text/plain": "(381109, 12)"
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "train.isnull().sum()"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:34.769464Z",
     "iopub.execute_input": "2022-01-18T22:51:34.769708Z",
     "iopub.status.idle": "2022-01-18T22:51:34.910468Z",
     "shell.execute_reply.started": "2022-01-18T22:51:34.769679Z",
     "shell.execute_reply": "2022-01-18T22:51:34.909507Z"
    },
    "trusted": true
   },
   "execution_count": 130,
   "outputs": [
    {
     "data": {
      "text/plain": "id                      0\nGender                  0\nAge                     0\nDriving_License         0\nRegion_Code             0\nPreviously_Insured      0\nVehicle_Age             0\nVehicle_Damage          0\nAnnual_Premium          0\nPolicy_Sales_Channel    0\nVintage                 0\nResponse                0\ndtype: int64"
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "***There is no missing data.***"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "train.drop(\"id\",axis =1).quantile([0, 0.05, 0.50, 0.95, 0.99, 1]).T"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:34.911916Z",
     "iopub.execute_input": "2022-01-18T22:51:34.912162Z",
     "iopub.status.idle": "2022-01-18T22:51:35.041632Z",
     "shell.execute_reply.started": "2022-01-18T22:51:34.912135Z",
     "shell.execute_reply": "2022-01-18T22:51:35.040768Z"
    },
    "trusted": true
   },
   "execution_count": 131,
   "outputs": [
    {
     "data": {
      "text/plain": "                        0.00    0.05     0.50     0.95     0.99      1.00\nAge                     20.0    21.0     36.0     69.0     77.0      85.0\nDriving_License          0.0     1.0      1.0      1.0      1.0       1.0\nRegion_Code              0.0     5.0     28.0     47.0     50.0      52.0\nPreviously_Insured       0.0     0.0      0.0      1.0      1.0       1.0\nAnnual_Premium        2630.0  2630.0  31669.0  55176.0  72963.0  540165.0\nPolicy_Sales_Channel     1.0    26.0    133.0    160.0    160.0     163.0\nVintage                 10.0    24.0    154.0    285.0    297.0     299.0\nResponse                 0.0     0.0      0.0      1.0      1.0       1.0",
      "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>0.00</th>\n      <th>0.05</th>\n      <th>0.50</th>\n      <th>0.95</th>\n      <th>0.99</th>\n      <th>1.00</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Age</th>\n      <td>20.0</td>\n      <td>21.0</td>\n      <td>36.0</td>\n      <td>69.0</td>\n      <td>77.0</td>\n      <td>85.0</td>\n    </tr>\n    <tr>\n      <th>Driving_License</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>Region_Code</th>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>28.0</td>\n      <td>47.0</td>\n      <td>50.0</td>\n      <td>52.0</td>\n    </tr>\n    <tr>\n      <th>Previously_Insured</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>Annual_Premium</th>\n      <td>2630.0</td>\n      <td>2630.0</td>\n      <td>31669.0</td>\n      <td>55176.0</td>\n      <td>72963.0</td>\n      <td>540165.0</td>\n    </tr>\n    <tr>\n      <th>Policy_Sales_Channel</th>\n      <td>1.0</td>\n      <td>26.0</td>\n      <td>133.0</td>\n      <td>160.0</td>\n      <td>160.0</td>\n      <td>163.0</td>\n    </tr>\n    <tr>\n      <th>Vintage</th>\n      <td>10.0</td>\n      <td>24.0</td>\n      <td>154.0</td>\n      <td>285.0</td>\n      <td>297.0</td>\n      <td>299.0</td>\n    </tr>\n    <tr>\n      <th>Response</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Check Columns (Categorical or Numeric)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "def grab_col_names(dataframe, cat_th=10, car_th=20):\n",
    "    \"\"\"\n",
    "\n",
    "    It gives the names of categorical, numerical, and categorical but cardinal variables in the data set.\n",
    "    Note: Categorical variables with numerical appearance are also included in categorical variables.\n",
    "\n",
    "    Parameters\n",
    "    ------\n",
    "        dataframe: dataframe\n",
    "        cat_th: int, optional\n",
    "                the class threshold for numeric but categorical variables\n",
    "        car_th: int, optinal\n",
    "                the class threshold for categorical but cardinal variables\n",
    "\n",
    "    Returns\n",
    "    ------\n",
    "        cat_cols: list\n",
    "                Categorical Variables List\n",
    "        num_cols: list\n",
    "                Numeric Variables List\n",
    "        cat_but_car: list\n",
    "                Categorical but cardinal variables list\n",
    "\n",
    "    Examples\n",
    "    ------\n",
    "        import seaborn as sns\n",
    "        df = sns.load_dataset(\"iris\")\n",
    "        print(grab_col_names(df))\n",
    "\n",
    "\n",
    "    Notes\n",
    "    ------\n",
    "        cat_cols + num_cols + cat_but_car = total number of variables\n",
    "        num_but_cat is in cat_cols\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    # cat_cols, cat_but_car\n",
    "    cat_cols = [col for col in dataframe.columns if dataframe[col].dtypes == \"O\"]\n",
    "    num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and\n",
    "                   dataframe[col].dtypes != \"O\"]\n",
    "    cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and\n",
    "                   dataframe[col].dtypes == \"O\"]\n",
    "    cat_cols = cat_cols + num_but_cat\n",
    "    cat_cols = [col for col in cat_cols if col not in cat_but_car]\n",
    "\n",
    "    # num_cols\n",
    "    num_cols = [col for col in dataframe.columns if dataframe[col].dtypes != \"O\"]\n",
    "    num_cols = [col for col in num_cols if col not in num_but_cat]\n",
    "\n",
    "    print(f\"Observations: {dataframe.shape[0]}\")\n",
    "    print(f\"Variables: {dataframe.shape[1]}\")\n",
    "    print(f'cat_cols: {len(cat_cols)}')\n",
    "    print(f'num_cols: {len(num_cols)}')\n",
    "    print(f'cat_but_car: {len(cat_but_car)}')\n",
    "    print(f'num_but_cat: {len(num_but_cat)}')\n",
    "    return cat_cols, num_cols, cat_but_car\n",
    "\n",
    "cat_cols, num_cols, cat_but_car = grab_col_names(train)"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:35.042889Z",
     "iopub.execute_input": "2022-01-18T22:51:35.043233Z",
     "iopub.status.idle": "2022-01-18T22:51:35.318217Z",
     "shell.execute_reply.started": "2022-01-18T22:51:35.043187Z",
     "shell.execute_reply": "2022-01-18T22:51:35.317347Z"
    },
    "trusted": true
   },
   "execution_count": 132,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observations: 381109\n",
      "Variables: 12\n",
      "cat_cols: 6\n",
      "num_cols: 6\n",
      "cat_but_car: 0\n",
      "num_but_cat: 3\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "cat_cols"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:35.319464Z",
     "iopub.execute_input": "2022-01-18T22:51:35.319721Z",
     "iopub.status.idle": "2022-01-18T22:51:35.326494Z",
     "shell.execute_reply.started": "2022-01-18T22:51:35.319675Z",
     "shell.execute_reply": "2022-01-18T22:51:35.325543Z"
    },
    "trusted": true
   },
   "execution_count": 133,
   "outputs": [
    {
     "data": {
      "text/plain": "['Gender',\n 'Vehicle_Age',\n 'Vehicle_Damage',\n 'Driving_License',\n 'Previously_Insured',\n 'Response']"
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "num_cols = [col for col in num_cols if \"id\" not in col]\n",
    "num_cols = [col for col in num_cols if \"Policy_Sales_Channel\" not in col]"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:35.328024Z",
     "iopub.execute_input": "2022-01-18T22:51:35.32847Z",
     "iopub.status.idle": "2022-01-18T22:51:35.335903Z",
     "shell.execute_reply.started": "2022-01-18T22:51:35.328309Z",
     "shell.execute_reply": "2022-01-18T22:51:35.335132Z"
    },
    "trusted": true
   },
   "execution_count": 134,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "# SUMMARY CATEGORICAL COLUMNS\n",
    "def cat_summary(dataframe, col_name, plot=False):\n",
    "    print(pd.DataFrame({col_name: dataframe[col_name].value_counts(),\n",
    "                        \"Ratio\": 100 * dataframe[col_name].value_counts() / len(dataframe)}))\n",
    "    if plot:\n",
    "        sns.countplot(x=dataframe[col_name], data=dataframe)\n",
    "        plt.show()\n",
    "\n",
    "\n",
    "for i in cat_cols:\n",
    "    cat_summary(train, i, plot=True)"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:35.337033Z",
     "iopub.execute_input": "2022-01-18T22:51:35.33759Z",
     "iopub.status.idle": "2022-01-18T22:51:40.460835Z",
     "shell.execute_reply.started": "2022-01-18T22:51:35.337552Z",
     "shell.execute_reply": "2022-01-18T22:51:40.460171Z"
    },
    "trusted": true
   },
   "execution_count": 135,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Gender     Ratio\n",
      "Male    206089  54.07613\n",
      "Female  175020  45.92387\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Vehicle_Age      Ratio\n",
      "1-2 Year        200316  52.561341\n",
      "< 1 Year        164786  43.238549\n",
      "> 2 Years        16007   4.200111\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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K0ocffqiIiAgNHjxYJ0+evAKfBgAAcHVODU9ZWVnq3bu39u3bV6G2e/du3XnnnQoICDBfPj4+kqSFCxeqc+fO6tGjhxo2bKiEhAR9/fXX2r9/vyRpwYIFGjFihKKiotSyZUs9++yzWrRokSRp3759WrdunV566SWFhITooYce0v3336/3339fkrRmzRp5eXlp9OjRql+/vuLi4lS1alV9/vnnV+hTAQAArsyp4emHH35QixYttHTpUof2EydOKCcnR3Xq1Dlrv7S0NEVFRZnvg4KCFBwcrLS0NOXk5Ojw4cNq1qyZWY+MjNTBgwd15MgRpaWlKSgoSLVq1XKob9261dx3ZGSkbDabJMlms6lp06ZKTU29RGcNAACuZh7OPHjfvn3P2r57927ZbDa99dZb+uabb+Tr66vHHntMDzzwgCTpyJEjqlmzpkMfPz8/ZWdnKzc3V5Ic6v7+/pJk1s/WNycnR5KUm5ur22+/vUL9z7cVK+O/+QsArml818HVXOicrGw/p4anc9mzZ49sNpvq1aunf/zjH9q8ebNeeOEFVatWTffdd5+Ki4vl6enp0MfT01N2u13FxcXm+z/WpNML04uKis7ZV9J561b4+d1ouQ8AXE1q1Kjq7CEADq7EnHTJ8NSjRw+1bdtWvr6+kqSGDRtq7969Wrx4se677z55eXlVCDN2u10+Pj4OQcnLy8v8WZJ8fHzO2dfb21uSzlu34ujR4zIMy90Al+Tu7sYvSlSQn1+osrLy8294GTAncTYXMydttspd+HDJ8GSz2czgdEa9evW0ceNGSVJgYKDy8vIc6nl5eQoICFBgYKCk07ffzqxrOnMr70z9XH3/at9/vtVXGYYhwhOAax7fc3A1l3tOOv1RBWczbdo0xcTEOLRlZGSoXr16kqSwsDClpKSYtcOHD+vw4cMKCwtTYGCggoODHeopKSkKDg5WzZo1FR4eroMHDyo7O9uhHh4ebu5769atMv77yRuGoS1btigsLOwynS0AALiauGR4atu2rTZv3qw5c+Zo3759ev/997Vy5UoNGDBAktSnTx99/PHHWr58uTIyMjR69Gi1adNGtWvXNutTpkzRpk2btGnTJk2dOlX9+/eXJNWuXVutW7fWqFGjlJGRoeXLl2v16tX6+9//Lknq1KmTjh07pkmTJikrK0uTJk1SUVGROnfu7JwPAwAAuBSXvG3XpEkTTZs2TdOnT9e0adN06623aurUqYqIiJAkRUREaMKECZo+fbp+//13tWrVShMnTjT7Dxw4UEePHtWwYcPk7u6uXr16OVzJSkhIUFxcnHr37q2AgAC9/PLLatKkiSSpWrVqevvttzVu3DgtW7ZMDRo0UFJSkqpUqXJFPwMAAOCabIbB3erLJS+PBeO4dnh4nF6cezipt05l73T2cOBkN9zSSEFPLFN+fqFKS52zYPzMnHxsyWP6Ofdnp4wBriMkIETzHpl3UXPSZpP8/c+/YNwlb9sBAAC4KsITAACABYQnAAAACwhPAAAAFhCeAAAALCA8AQAAWEB4AgAAsIDwBAAAYAHhCQAAwALCEwAAgAWEJwAAAAsITwAAABYQngAAACwgPAEAAFhAeAIAALCA8AQAAGAB4QkAAMACwhMAAIAFhCcAAAALCE8AAAAWEJ4AAAAsIDwBAABYQHgCAACwgPAEAABgAeEJAADAAsITAACABYQnAAAACwhPAAAAFhCeAAAALCA8AQAAWEB4AgAAsMAlwpPdble3bt20adMmsy01NVWPPPKIIiIi1LFjRy1fvtyhz/33368GDRo4vH7++WdJkmEYmjJlilq2bKnmzZsrISFB5eXlZt/8/HwNHz5cERERateunT7++GOHfe/YsUMPPfSQwsLC9OCDD2rbtm2X8ewBAMDVxMPZAygpKdHIkSOVmZlptuXm5urxxx9Xnz599Morr2j79u2KjY1VQECA2rRpo7KyMu3du1cLFy5UnTp1zH41atSQJM2bN0+rV6/WjBkzVFpaqlGjRsnPz08DBw6UJMXGxqq4uFhLly5VWlqaxo4dq7p166pJkyY6efKknnjiCXXv3l2vvPKKFi9erMGDB2vt2rWqUqXKFf1sAACA63FqeMrKytLIkSNlGIZDe3Jysvz9/fXMM89IkurUqaNNmzbpk08+UZs2bXTgwAGdOnVKTZo0kZeXV4X9LliwQCNGjFBUVJQk6dlnn9W0adM0cOBA7du3T+vWrdNXX32lWrVqKSQkRKmpqXr//ffVpEkTrVmzRl5eXho9erRsNpvi4uL0zTff6PPPP1fPnj0v/4cCAABcmlNv2/3www9q0aKFli5d6tAeHR2tyZMnV9j+xIkTkk6HrqCgoLMGp5ycHB0+fFjNmjUz2yIjI3Xw4EEdOXJEaWlpCgoKUq1atRzqW7dulSSlpaUpMjJSNptNkmSz2dS0aVOlpqZe9PkCAICrn1OvPPXt2/es7bVq1XIIN0ePHtWnn36q4cOHS5J2796tG264QYMHD9a2bdtUt25djR49Wk2aNFFubq4kqWbNmmZ/f39/SVJ2drZyc3MdapLk5+ennJwcSadvGd5+++0V6n+8rQgAAK5fTl/zdD7FxcUaPny4/P399fDDD0uSfvnlF/3+++966KGHNGLECC1btkyPPvqo1qxZo+LiYkmSp6enuY8zP9vtdhUVFTnUztTtdrsknbduxX8vXgHANY3vOriaC52Tle3n0uGpsLBQQ4YM0d69e/X+++/Lx8dHkjRx4kQVFxerWrVqkqT4+Hht2bJFH3/8sf73f/9X0umgdOa23png4+PjIy8vrwpByG63y9vbW5LOW7fCz+9Gy30A4GpSo0ZVZw8BcHAl5qTLhqcTJ05o0KBB2rdvn+bPn+/wr+o8PDzM4CSdXpdUr1495eTkKDAwUNLp229nbv2duZUXEBCgwMBA5eXlORwrLy9PAQEBknTO+p9v9VXG0aPH9ae18MBVy93djV+UqCA/v1BlZeXn3/AyYE7ibC5mTtpslbvw4RLPefqz8vJyDRs2TAcOHNB7772nO+64w6Her18/zZgxw2H7Xbt2qV69egoMDFRwcLBSUlLMekpKioKDg1WzZk2Fh4fr4MGDys7OdqiHh4dLksLCwrR161bzXwAahqEtW7YoLCzM8nkYBi9e184LOBfmJFzN5Z5XLnnl6YMPPtCmTZv05ptvqnr16uaVoxtuuEG+vr5q166dZs6cqUaNGqlu3bpasGCBjh8/rgceeECS1KdPH02ZMkW33HKLJGnq1KkaMGCAJKl27dpq3bq1Ro0apbi4OP30009avXq1Fi5cKEnq1KmTpk6dqkmTJumRRx7RkiVLVFRUpM6dOzvhkwAAAK7GJcPTF198ofLycg0ePNihvXnz5nrvvfcUExOjkpISvfTSS8rLy1NYWJjmzZtn3sobOHCgjh49qmHDhsnd3V29evVSTEyMuZ+EhATFxcWpd+/eCggI0Msvv6wmTZpIkqpVq6a3335b48aN07Jly9SgQQMlJSXxgEwAACBJshl/fkIlLpm8PNY84drh4XF6fcnhpN46lb3T2cOBk91wSyMFPbFM+fmFKi11zpqnM3PysSWP6efcn50yBriOkIAQzXtk3kXNSZtN8ve/Stc8AQAAuCrCEwAAgAWEJwAAAAsITwAAABYQngAAACwgPAEAAFhAeAIAALCA8AQAAGAB4QkAAMACwhMAAIAFhCcAAAALCE8AAAAWEJ4AAAAsIDwBAABYcEHhqX///jp27FiF9t9++009e/a86EEBAAC4Ko/KbvjNN98oPT1dkrR582a99dZbqlKlisM2v/76qw4ePHhpRwgAAOBCKh2e6tatq3feeUeGYcgwDG3ZskU33HCDWbfZbKpSpYomTZp0WQYKAADgCiodnmrXrq0FCxZIkmJjYxUXF6dq1apdtoEBAAC4okqHpz+aPHmyJCk3N1elpaUyDMOhHhwcfPEjAwAAcEEXFJ6+++47vfDCCzp8+LAkyTAM2Ww28z937tx5SQcJAADgKi4oPE2YMEFNmjTRm2++ya07AABwXbmg8JSdna133nlHtWvXvtTjAQAAcGkX9JynqKgopaSkXOqxAAAAuLwLuvLUrFkzjR8/Xv/5z3902223OTyyQJKGDRt2SQYHAADgai54wfhdd92lo0eP6ujRow41m812SQYGAADgii4oPL333nuXehwAAABXhQsKTytXrvzLeo8ePS5ktwAAAC7vgsLT9OnTHd6XlZXp6NGj8vDwUJMmTQhPAADgmnVB4enf//53hbbCwkK9+OKLatCgwUUPCgAAwFVd0KMKzqZq1aoaPny45s2bd6l2CQAA4HIuWXiSpIyMDJWXl1/KXQIAALiUC7pt169fvwqPJCgsLNSuXbsUExNzKcYFAADgki7oylOLFi3UvHlzh1fHjh01e/ZsPfvss5b3Z7fb1a1bN23atMls279/v2JiYhQeHq4uXbpo/fr1Dn2+//57devWTWFhYerfv7/279/vUH/33XcVHR2tiIgIjRkzRkVFRWatpKREY8aMUVRUlFq3bq25c+c69D3fsQEAwPXrgq48/fEJ4idOnFBZWZluuummCxpASUmJRo4cqczMTLPNMAwNHTpUISEhWrFihZKTkzVs2DCtWbNGwcHBOnTokIYOHarhw4crOjpaM2fO1JAhQ7Rq1SrZbDZ98cUXmjFjhhITE+Xn56fY2FglJibqxRdflCQlJCRo27Ztmj9/vg4dOqTnnntOwcHB6tSp03mPDQAArm8XvOZp/vz5io6OVrNmzdSyZUu1atVKM2bMsLSPrKws9e7dW/v27XNo37hxo/bv368JEyaofv36Gjx4sMLDw7VixQpJ0vLly3XXXXdpwIABuuOOOzR58mQdPHhQP/zwgyRpwYIFevTRR9W2bVs1adJE48eP14oVK1RUVKSTJ09q+fLliouLU2hoqO677z4NGjRIixYtqtSxAQDA9e2CwtPMmTP11ltvaejQoVq5cqU+/PBDDRkyRIsWLVJSUlKl9/PDDz+oRYsWWrp0qUN7Wlqa7rzzTlWpUsVsi4yMVGpqqlmPiooyaz4+PgoNDVVqaqrKysr0008/OdTDw8N16tQpZWRkKCMjQ6WlpYqIiHDYd1pamsrLy897bAAAcH27oNt2y5Yt06RJk9SuXTuzrVGjRgoMDNSkSZP0xBNPVGo/ffv2PWt7bm6uatas6dDm5+en7Ozs89aPHTumkpISh7qHh4d8fX2VnZ0tNzc31ahRQ56enmbd399fJSUlKigoOO+xAQDA9e2CwtOJEydUp06dCu1169bVb7/9drFjUlFRkUO4kSRPT0/Z7fbz1ouLi833Z6sbhnHWmnR64fr5jm0FfyMZwPWA7zq4mgudk5Xtd0HhKSIiQnPnztWECRPk5nb6zl9ZWZnmzJmjJk2aXMguHXh5eamgoMChzW63y9vb26z/OczY7XZVr15dXl5e5vs/1318fFRWVnbWmiR5e3uf99hW+PndaLkPAFxNatSo6uwhAA6uxJy8oPAUGxurv//97/r+++8VGhoqSdq+fbvsdrveeeedix5UYGCgsrKyHNry8vLM22mBgYHKy8urUG/UqJF8fX3l5eWlvLw81a9fX5JUWlqqgoICBQQEyDAM5efnq7S0VB4ep08/NzdX3t7eql69+nmPbcXRo8dlGJa7AS7J3d2NX5SoID+/UGVlznk4MnMSZ3Mxc9Jmq9yFjwtaMF6/fn2NGTNGMTEx8vPzU3BwsI4cOaIJEyaoYcOGF7JLB2FhYdq+fbt5C06SUlJSFBYWZtZTUlLMWlFRkXbs2KGwsDC5ubmpcePGDvXU1FR5eHioYcOGatSokTw8PBwWgKekpKhx48Zyc3M777GtMAxevK6dF3AuzEm4mss9ry4oPL333nuKj4/XjTfeqPj4eMXGxqpfv3569tlntWzZsgvZpYPmzZsrKChIsbGxyszMVFJSktLT09WrVy9J0oMPPqgtW7YoKSlJmZmZio2NVa1atdSiRQtJpxeiz5kzR8nJyUpPT1d8fLx69+4tHx8f+fj4qEePHoqPj1d6erqSk5M1d+5c9e/fv1LHBgAA17cLCk/z5s3T1KlT9cADD5htzz33nBITEy09quBc3N3dNWvWLOXm5qpnz55atWqVZs6caT6kslatWnrjjTe0YsUK9erVSwUFBZo5c6b5J2O6du2qwYMH68UXX9SAAQPUpEkTjRo1ytx/bGysQkND9eijj2r8+PEaPny4OnToUKljAwCA65vNMKxf/IyIiNAHH3xgrik6Y8+ePerZsyfPRPqvvDzWPOHa4eFxen3J4aTeOpW909nDgZPdcEsjBT2xTPn5hSotdc6apzNz8rElj+nn3J+dMga4jpCAEM17ZN5FzUmbTfL3v0xrniIjI/XGG29U+Htxb731lsPDJwEAAK41F/Sv7c7cDmvdurX5vKd9+/bJ399fs2bNupTjAwAAcCkXFJ7+53/+R2vWrNG3336rvXv3ysPDQ3Xq1FHr1q3l7u5+qccIAADgMi4oPEmnn7p97733XsqxAAAAuLwLWvMEAABwvSI8AQAAWEB4AgAAsIDwBAAAYAHhCQAAwALCEwAAgAWEJwAAAAsITwAAABYQngAAACwgPAEAAFhAeAIAALCA8AQAAGAB4QkAAMACwhMAAIAFhCcAAAALCE8AAAAWEJ4AAAAsIDwBAABYQHgCAACwgPAEAABgAeEJAADAAsITAACABYQnAAAACwhPAAAAFhCeAAAALCA8AQAAWEB4AgAAsIDwBAAAYIHLhqcPP/xQDRo0qPBq2LChJOmpp56qUFu3bp3Z/91331V0dLQiIiI0ZswYFRUVmbWSkhKNGTNGUVFRat26tebOnetw7P379ysmJkbh4eHq0qWL1q9ff2VOGgAAuDwPZw/gXLp06aLo6GjzfWlpqR599FG1adNGkrR7924lJibq7rvvNre56aabJElffPGFZsyYocTERPn5+Sk2NlaJiYl68cUXJUkJCQnatm2b5s+fr0OHDum5555TcHCwOnXqJMMwNHToUIWEhGjFihVKTk7WsGHDtGbNGgUHB1+5DwAAALgklw1P3t7e8vb2Nt+//fbbMgxDzz77rOx2uw4cOKDGjRsrICCgQt8FCxbo0UcfVdu2bSVJ48eP18CBAzVq1CgZhqHly5dr9uzZCg0NVWhoqDIzM7Vo0SJ16tRJGzdu1P79+7VkyRJVqVJF9evX14YNG7RixQoNHz78ip0/AABwTS572+6PCgoKNHv2bI0cOVKenp7as2ePbDabateuXWHbsrIy/fTTT4qKijLbwsPDderUKWVkZCgjI0OlpaWKiIgw65GRkUpLS1N5ebnS0tJ05513qkqVKg711NTUy3qOAADg6nBVhKfFixerZs2a6tSpkyRpz549qlatmkaPHq3WrVurV69e+vrrryVJx44dU0lJiWrWrGn29/DwkK+vr7Kzs5Wbm6saNWrI09PTrPv7+6ukpEQFBQXKzc116CtJfn5+ys7OvgJnCgAAXJ3L3rY748xttkGDBplte/bsUXFxsVq3bq0nnnhCa9eu1VNPPaWlS5fK399fkhzC0Zn3drtdhmGctSZJdrtdRUVF5+xrlc1muQsAXHX4roOrudA5Wdl+Lh+efvrpJ+Xk5Khr165m25AhQ9SvXz9zgXjDhg21fft2LVu2TE8//bQkVQg7drtdPj4+KisrO2tNOr3OysvLSwUFBRXqf1x/VVl+fjda7gMAV5MaNao6ewiAgysxJ10+PH377beKiooyg5Ikubm5ObyXpHr16ikrK0u+vr7y8vJSXl6e6tevL+n0v9QrKChQQECADMNQfn6+SktL5eFx+vRzc3Pl7e2t6tWrKzAwUFlZWQ77zsvLq3ArrzKOHj0uw7DcDXBJ7u5u/KJEBfn5hSorK3fKsZmTOJuLmZM2W+UufLh8eEpPT1fTpk0d2p5//nnZbDZNnjzZbMvIyFBISIjc3NzUuHFjpaSkqEWLFpKk1NRUeXh4mM+I8vDwUGpqqrmoPCUlRY0bN5abm5vCwsKUlJSk4uJi82pTSkqKIiMjLY/dMER4AnDN43sOruZyz0mXXzCemZmp22+/3aGtXbt2+uSTT7Ry5Ur9+uuvmjFjhlJSUvSPf/xDktS3b1/NmTNHycnJSk9PV3x8vHr37i0fHx/5+PioR48eio+PV3p6upKTkzV37lz1799fktS8eXMFBQUpNjZWmZmZSkpKUnp6unr16nXFzx0AALgel7/ylJeXp+rVqzu0dejQQePGjdObb76pQ4cO6Y477tA777yjWrVqSZK6du2qgwcP6sUXX5TdbleHDh00atQos39sbKzi4+P16KOPqlq1aho+fLg6dOggSXJ3d9esWbMUFxennj176rbbbtPMmTN5QCYAAJAk2QyDC66XS14ea55w7fDwOL2+5HBSb53K3uns4cDJbrilkYKeWKb8/EKVljpnzdOZOfnYksf0c+7PThkDXEdIQIjmPTLvouakzSb5+59/zZPL37YDAABwJYQnAAAACwhPAAAAFhCeAAAALCA8AQAAWEB4AgAAsIDwBAAAYAHhCQAAwALCEwAAgAWEJwAAAAsITwAAABYQngAAACwgPAEAAFhAeAIAALCA8AQAAGAB4QkAAMACwhMAAIAFhCcAAAALCE8AAAAWEJ4AAAAsIDwBAABYQHgCAACwgPAEAABgAeEJAADAAsITAACABYQnAAAACwhPAAAAFhCeAAAALCA8AQAAWEB4AgAAsIDwBAAAYAHhCQAAwAKXDk9r165VgwYNHF4jRoyQJO3YsUMPPfSQwsLC9OCDD2rbtm0OfVevXq327dsrLCxMQ4cO1W+//WbWDMPQlClT1LJlSzVv3lwJCQkqLy836/n5+Ro+fLgiIiLUrl07ffzxx1fmhAEAgMtz6fCUlZWltm3bav369ebrpZde0smTJ/XEE08oKipKH374oSIiIjR48GCdPHlSkpSenq64uDgNGzZMS5cu1bFjxxQbG2vud968eVq9erVmzJih6dOn65NPPtG8efPMemxsrI4fP66lS5fqqaee0tixY5Wenn7Fzx8AALgelw5Pu3fvVkhIiAICAsxX9erVtWbNGnl5eWn06NGqX7++4uLiVLVqVX3++eeSpIULF6pz587q0aOHGjZsqISEBH399dfav3+/JGnBggUaMWKEoqKi1LJlSz377LNatGiRJGnfvn1at26dXnrpJYWEhOihhx7S/fffr/fff99pnwMAAHAdLh+e6tSpU6E9LS1NkZGRstlskiSbzaamTZsqNTXVrEdFRZnbBwUFKTg4WGlpacrJydHhw4fVrFkzsx4ZGamDBw/qyJEjSktLU1BQkGrVquVQ37p16+U5SQAAcFVx2fBkGIZ++eUXrV+/Xh07dlT79u01ZcoU2e125ebmqmbNmg7b+/n5KTs7W5J05MiRc9Zzc3MlyaHu7+8vSWb9bH1zcnIu+TkCAICrj4ezB3Auhw4dUlFRkTw9PfX666/rwIEDeumll1RcXGy2/5Gnp6fsdrskqbi4+Jz14uJi8/0fa5Jkt9vPu28r/nthDACuaXzXwdVc6JysbD+XDU+33nqrNm3apJtuukk2m02NGjVSeXm5Ro0apebNm1cIM3a7Xd7e3pIkLy+vs9Z9fHwcgpKXl5f5syT5+Pics++ZfVvh53ej5T4AcDWpUaOqs4cAOLgSc9Jlw5Mk+fr6OryvX7++SkpKFBAQoLy8PIdaXl6eebstMDDwrPWAgAAFBgZKknJzc811TWdu5Z2pn6uvVUePHpdhWO4GuCR3dzd+UaKC/PxClZWVn3/Dy4A5ibO5mDlps1XuwofLrnn69ttv1aJFCxUVFZltO3fulK+vr7mA2/hvMjEMQ1u2bFFYWJgkKSwsTCkpKWa/w4cP6/DhwwoLC1NgYKCCg4Md6ikpKQoODlbNmjUVHh6ugwcPmuunztTDw8Mtn4Nh8OJ17byAc2FOwtVc7nnlsuEpIiJCXl5eGjt2rPbs2aOvv/5aCQkJGjRokDp16qRjx45p0qRJysrK0qRJk1RUVKTOnTtLkvr06aOPP/5Yy5cvV0ZGhkaPHq02bdqodu3aZn3KlCnatGmTNm3apKlTp6p///6SpNq1a6t169YaNWqUMjIytHz5cq1evVp///vfnfZZAAAA1+Gyt+2qVaumOXPm6OWXX9aDDz6oqlWr6pFHHtGgQYNks9n09ttva9y4cVq2bJkaNGigpKQkValSRdLp4DVhwgRNnz5dv//+u1q1aqWJEyea+x44cKCOHj2qYcOGyd3dXb169VJMTIxZT0hIUFxcnHr37q2AgAC9/PLLatKkyZX+CAAAgAuyGQYXPy+XvDzWPOHa4eFxen3J4aTeOpW909nDgZPdcEsjBT2xTPn5hSotdc6apzNz8rElj+nn3J+dMga4jpCAEM17ZN5FzUmbTfL3v4rXPAEAALgiwhMAAIAFhCcAAAALCE8AAAAWEJ4AAAAsIDwBAABYQHgCAACwgPAEAABgAeEJAADAAsITAACABYQnAAAACwhPAAAAFhCeAAAALCA8AQAAWEB4AgAAsIDwBAAAYAHhCQAAwALCEwAAgAWEJwAAAAsITwAAABYQngAAACwgPAEAAFhAeAIAALCA8AQAAGAB4QkAAMACwhMAAIAFhCcAAAALCE8AAAAWEJ4AAAAsIDwBAABYQHgCAACwgPAEAABggUuHp5ycHI0YMULNmzdXdHS0Jk+erJKSEknSSy+9pAYNGji8Fi5caPZdvXq12rdvr7CwMA0dOlS//fabWTMMQ1OmTFHLli3VvHlzJSQkqLy83Kzn5+dr+PDhioiIULt27fTxxx9fuZMGAAAuzcPZAzgXwzA0YsQIVa9eXYsWLdLvv/+uMWPGyM3NTc8995x2796tkSNH6oEHHjD7VKtWTZKUnp6uuLg4jR8/Xg0bNtSkSZMUGxurt99+W5I0b948rV69WjNmzFBpaalGjRolPz8/DRw4UJIUGxur4uJiLV26VGlpaRo7dqzq1q2rJk2aXPkPAgAAuBSXDU979uxRamqqvvvuO/n7+0uSRowYoX/9619meBo4cKACAgIq9F24cKE6d+6sHj16SJISEhLUtm1b7d+/X7Vr19aCBQs0YsQIRUVFSZKeffZZTZs2TQMHDtS+ffu0bt06ffXVV6pVq5ZCQkKUmpqq999/n/AEAABc97ZdQECA3nnnHTM4nXHixAmdOHFCOTk5qlOnzln7pqWlmcFIkoKCghQcHKy0tDTl5OTo8OHDatasmVmPjIzUwYMHdeTIEaWlpSkoKEi1atVyqG/duvXSniAAALgquWx4ql69uqKjo8335eXlWrhwoVq2bKndu3fLZrPprbfe0j333KP7779fH330kbntkSNHVLNmTYf9+fn5KTs7W7m5uZLkUD8T0M7Uz9Y3Jyfnkp8jAAC4+rjsbbs/S0xM1I4dO/TBBx9o+/btstlsqlevnv7xj39o8+bNeuGFF1StWjXdd999Ki4ulqenp0N/T09P2e12FRcXm+//WJMku92uoqKic/a1ymaz3AUArjp818HVXOicrGy/qyI8JSYmav78+XrttdcUEhKiO+64Q23btpWvr68kqWHDhtq7d68WL16s++67T15eXhXCjt1ul4+Pj0NQ8vLyMn+WJB8fn3P29fb2tjxuP78bLfcBgKtJjRpVnT0EwMGVmJMuH54mTpyoxYsXKzExUR07dpQk2Ww2MzidUa9ePW3cuFGSFBgYqLy8PId6Xl6eAgICFBgYKEnKzc011zWduZV3pn6uvlYdPXpchmG5G+CS3N3d+EWJCvLzC1VWVn7+DS8D5iTO5mLmpM1WuQsfLrvmSZJmzJihJUuW6NVXX1XXrl3N9mnTpikmJsZh24yMDNWrV0+SFBYWppSUFLN2+PBhHT58WGFhYQoMDFRwcLBDPSUlRcHBwapZs6bCw8N18OBBZWdnO9TDw8Mtj98wePG6dl7AuTAn4Wou97xy2StPu3fv1qxZs/TEE08oMjLSvDokSW3btlVSUpLmzJmj++67T+vXr9fKlSu1YMECSVKfPn3Ur18/hYeHq3Hjxpo0aZLatGmj2rVrm/UpU6bolltukSRNnTpVAwYMkCTVrl1brVu31qhRoxQXF6effvpJq1evdngAJwAAuH65bHj66quvVFZWpjfffFNvvvmmQ23Xrl2aNm2apk+frmnTpunWW2/V1KlTFRERIUmKiIjQhAkTNH36dP3+++9q1aqVJk6caPYfOHCgjh49qmHDhsnd3V29evVyuJKVkJCguLg49e7dWwEBAXr55Zd5xhMAAJAk2QyDi5+XS14ea55w7fDwOL2+5HBSb53K3uns4cDJbrilkYKeWKb8/EKVljpnzdOZOfnYksf0c+7PThkDXEdIQIjmPTLvouakzSb5+1/la54AAABcDeEJAADAAsITAACABYQnAAAACwhPAAAAFhCeAAAALCA8AQAAWEB4AgAAsMBlnzB+vXNzs8nNzebsYcCFlJcbKi/nqasA4GyEJxfk5maTr28VubtzYRD/X1lZuQoKThKgAMDJCE8uyM3NJnd3N419/1v9cuR3Zw8HLqBuzZv0Ut9oubnZCE8A4GSEJxf2y5HflXHwN2cPAwAA/AH3hQAAACwgPAEAAFhAeAIAALCA8AQAAGAB4QkAAMACwhMAAIAFhCcAAAALCE8AAAAWEJ4AAAAsIDwBAABYQHgCAACwgPAEAABgAeEJAADAAsITAACABYQnAAAACwhPAAAAFhCeAAAALCA8AQAAWEB4AgAAsIDwBAAAYAHh6RxKSko0ZswYRUVFqXXr1po7d66zhwQAAFyAh7MH4KoSEhK0bds2zZ8/X4cOHdJzzz2n4OBgderUydlDAwAATkR4OouTJ09q+fLlmj17tkJDQxUaGqrMzEwtWrSI8AQAwHWO23ZnkZGRodLSUkVERJhtkZGRSktLU3l5uRNHBgAAnI3wdBa5ubmqUaOGPD09zTZ/f3+VlJSooKDAeQMDAABOx227sygqKnIITpLM93a7vdL7cXOTDOPCx9Ew+Gb5ePJfEaTb/KubP7s5+f/yeN7SSLYbfJw7CDjdDX51zJ+dPSdD/EPk7eHt3EHA6f7H93/Mny90TtpslduO38xn4eXlVSEknXnv7V35/4HefPONFzWOF3r/70X1x7WnRo2qzh6C/O4f7+whwIW4wpyMbR/r7CHAhVyJOcltu7MIDAxUfn6+SktLzbbc3Fx5e3urevXqf9ETAABc6whPZ9GoUSN5eHgoNTXVbEtJSVHjxo3l5uzr0wAAwKlIAmfh4+OjHj16KD4+Xunp6UpOTtbcuXPVv39/Zw8NAAA4mc0wLmZJ87WrqKhI8fHx+vLLL1WtWjUNHDhQMTExzh4WAABwMsITAACABdy2AwAAsIDwBAAAYAHhCQAAwALCEy6rnJwcjRgxQs2bN1d0dLQmT56skpKSCtt9++23atCggb766qsKtVWrVumuu+7Szz//fCWGjGuE3W5Xt27dtGnTpr/czjAMJSUlqV27dmratKkeffRRZWVlVdiupKRE9957r4YNG1ahduzYMd19992aOnXqJRs/cL45zPem8xCecNkYhqERI0aoqKhIixYt0muvvaZ169bp9ddfr7BtdHS0OnTooFdeecXh6e6FhYVKTEzUY489ppCQkCs4elzNSkpK9MwzzygzM/O82y5ZskRz587VCy+8oBUrVqhWrVp6/PHHVVRU5LCdl5eX4uLitHbtWn3//fcOtenTp6tq1aoaOnToJT0PXL8qM4f53nQewhMqLSUlRaNGjVJGRkaltt+zZ49SU1M1efJk3XHHHYqKitKIESO0evXqs24/ZswY5eXlae7cuWbbrFmz5OXlxS8lVFpWVpZ69+6tffv2VWr7jz76SAMGDFDbtm1Vt25dxcfHq6CgQFu2bKmwbbt27dS2bVtNmjTJ/AsEu3bt0uLFizVu3DhLf74J16/du3crLi5O33zzzVnrVuYw35vOQXhCpdWuXVtFRUXq2bOnBg4cqA0bNvzl9gEBAXrnnXfk7+/v0H7ixImzbh8UFKSnnnpKb7/9tnJzc/Xrr79q/vz55i8lwzA0c+ZMtW7dWlFRUXryySd16NAhs39WVpYGDhyoiIgINW7cWH379tXu3bslSZs2bVK7du00btw4RUZGKikpSYcOHdKAAQMUERGhu+++WxMnTtSpU6cu8lOCs/3www9q0aKFli5dWqntR48erfvvv998b7PZZBiGjh8/ftbt4+LidODAAS1ZskSS9PLLL6tjx46Kjo6WJP3444/q2bOnmjRpou7du+uLL74w+9rtdk2ePFnR0dEKDQ1Vu3btHMbZrl07JSYmqnXr1urRo4d4ksy1JSUlRU899ZS6d++u3Nxc1a1b96zbWZnDV/p7E/9lABb9+uuvxoQJE4zw8HDjgQceMD799FOjtLT0vP3KysqMhx9+2HjyySfPuU1JSYnRqVMn48UXXzSGDRtmPP3002ZtwYIFRseOHY2NGzcaWVlZxpgxY4yOHTsadrvdKCsrM+69917jhRdeMH799Vdj27ZtRu/evY3BgwcbhmEYGzduNEJCQoznn3/e2Lt3r3Hw4EHjySefNIYOHWrs3bvXSElJMVq1amUsXLjw4j8guIyQkBBj48aNlvq8//77RmhoqHHkyJFzbjNjxgzj7rvvNlatWmVERUUZubm5hmEYxpEjR4ymTZsa7733nrF3715j5cqVRnh4uLF582bDMAzjjTfeMDp06GBs3brV2LdvnzFt2jQjNDTU7N+2bVsjOjrayMjIMHbu3HmBZw1XUl5ebqxdu9Z4+OGHjcaNGxtjx441srKyKt2/MnP4Sn5v4jTCEy5YQUGBMXbsWCMkJMRYu3btebd/5ZVXjMaNGxu7du36y+2+//57o1GjRkazZs3MXyqGYRj33HOP8dVXX5nvS0tLjZYtWxpfffWVUVhYaMyePdsoLCw064sXLzbuvfdewzD+/5fAH7+0unfvbjz//POG3W43DMMwtm/fbuzfv79yJ4+rgtXwlJqaaoSFhRmvvvrqX25XUlJi3Hfffcadd95pLF682Gx/7bXXjGHDhjlsO3nyZLNt7dq1ZpA6s5+QkBCzrW3btkZiYmKlxwvXt2nTJiMkJMR45plnjKNHj1ruX9k5fKW+N3Gah7OvfOHqlJqaqrlz5+rf//63OnfurLvuuusvt09MTNT8+fP12muvnXcB4913363Q0FA1b97cvOVXWFio7OxsPf300w5/nLm4uFh79+5Vu3bt1KdPH61cuVLbtm3Tnj17tGPHjgq3DGvVqmX+PGjQII0ZM0Zr167VPffcoy5duujOO++0+lHgKjJo0CClpKSY77du3erw8+OPP6577rlH//f//t+/3I+np6diYmI0a9YsPfzww2b7nj17tG7dOkVERJhtp06dMm/PtG/fXt99951eeeUVc45KUllZmbn9rbfeenEnCZdy++23q0ePHvr0009VWFioAQMGqHnz5pf8OFfqexOnEZ5QaeXl5eYfSd6xY4d69uypzz77TLVr1/7LfhMnTtTixYuVmJiojh07VupY3t7eDotvz/xymTZtWoV1AjfddJMKCwvVq1cv1ahRQ+3atVO3bt20Z88eh0WU0ul/MXXG/fffr7vvvlvJycn6z3/+oxEjRujxxx/X008/Xakx4uozadIkFRcXV2jftGmTnnzySbVq1UpTp051+EVzLt7e3vL09JTNZjPbSktL1b17dz355JMO23p4nP6qfe2117R8+XL17NlTPXr00Lhx49SuXTuHbf84R3H1u/nmm/Wvf/1L//znPzVv3jwNHjxYdevW1YABA9SpUydzblwKV+J7E6cRnlBpP/74o8aOHas+ffpo5syZ8vPzO2+fGTNmaMmSJXr11VfVqVOnCz529erV5efnp9zcXLVp00bS6cW3zzzzjAYOHKiCggIdOXJEn3zyiflltH79+r9ccPvaa6+pc+fO6tOnj/r06aOkpCR99NFHhKdrWGBgYIW2n3/+WU899ZSio6P16quvXtQvs7p162rr1q267bbbzLa5c+fKbrfrySef1JIlSxQfH6/OnTtLkvk8qb+ap7g2BAUFacyYMRoyZIgWLVqkSZMmydvbW+3bt79sx7wc35s4jX9th0pr0KCB1q1bp6effrpSwWn37t2aNWuWHn/8cUVGRio3N9d8XYiYmBi9/vrr+ve//629e/dq7Nix2rJli+rVqydfX1+dPHlSycnJOnDggJYvX65FixY5PPvkz/bs2aMJEyYoIyNDmZmZ+vrrr7ltdx168cUXFRQUpNjYWOXn55tz9GxXqM6nb9++2rZtm1577TXt3btXn3zyiV599VUFBwdLknx9fbVu3Trt379fP/74o0aPHi1JfzlPcW3x9fXV0KFDtW7dOjVt2vSyH+9Sf2/iNK48odJuuukmS9t/9dVXKisr05tvvqk333zTobZr1y7Lxx84cKAKCwv14osv6sSJE7rrrrs0Z84c3XTTTYqIiNDQoUM1fvx4lZSUqEGDBnrxxRcVFxennJycs+4vPj5e48ePV79+/VRaWqo2bdooLi7O8rhw9crNzTXXPZ35f+ZnTJ48WT179rS0v1tvvVVvvfWWpkyZojlz5igwMFDPP/+8+SiEl19+WfHx8eratasCAwP10EMPyd3dXTt37tQ999xzSc4JV4c/32K7XC719yZOsxlcnwMAAKg0btsBAABYQHgCAACwgPAEAABgAeEJAADAAsITAACABYQnAAAACwhPAAAAFhCeAAAALCA8AXBJffv21ciRI89aW7VqlZo1a/aXf0aiXbt2+vDDD89aO3DggBo0aKADBw785Rgqu51V/fr1U3h4uE6cOHFJ9wvgyiA8AXBJXbt21ddff33WgPTZZ5+pQ4cO8vT0vKB9BwUFaf369QoKCrrYYVqWk5OjrVu36uabb9YXX3xxxY8P4OIRngC4pM6dO6uoqEgbNmxwaD9x4oTWr1+vbt26XfC+3d3dFRAQIHd394sdpmVr1qxRSEiI2rVrp5UrV17x4wO4eIQnAC7p5ptv1t13360vv/zSoT05OVm+vr5q0aKFlixZonbt2ikiIkL9+vWr8AenMzMz9cgjj6hx48bq0aOHdu7cKani7bijR4/qn//8p5o2bapWrVrp1Vdf1dn+7OexY8c0atQoNW3aVK1bt9bEiRNVXFxs6bxWr16tZs2aqW3bttq8eXOFW4Lbtm1T79691aRJEz3yyCOaNm2a+vXrZ9bXrl2rLl26KCwsTL169dIPP/xg6fgALh7hCYDL6tatm7766iuVlZWZbZ9//rm6dOmi//znP5oxY4ZeeOEFffTRR4qMjFT//v31+++/m9t+8MEHGjRokFatWqWbbrpJ48aNO+txhg4dqtzcXC1cuFCvv/66PvzwQy1atKjCdnFxcTp+/LgWL16sWbNm6aefftKECRMqfT779u3Ttm3b1LZtWzVv3lzVqlVzuPp0/PhxDRo0SKGhoVq5cqW6deumpKQks56RkaHnnntOTz31lFatWqX7779fjz/+uH799ddKjwHAxSM8AXBZ7du318mTJ7V582ZJp8PF+vXr1b17d73zzjsaPHiw2rZtqzp16uif//ynbr31Vq1atcrs36dPH7Vv315169ZVv379lJGRUeEYGRkZ2rp1q1555RXdeeedatasmeLj41W9enWH7fbt26fk5GQlJiaqQYMGatKkiSZOnKiPPvpIx48fr9T5rF69Wr6+vmrWrJluuOEGtWnTRh9//LFZX7NmjapUqaKxY8eqXr16+sc//qGOHTua9Tlz5qh3797q3r27brvtNvXv31/33HOPFi9ebOlzBXBxPJw9AAA4l2rVqqlNmzb68ssv1bJlSyUnJ6tWrVq66667tHv3biUmJurVV181ty8pKdHevXvN97Vr1zZ/vvHGG1VSUlLhGL/88ot8fX0dtm3fvr0kOdxS2717t8rLy3XPPfc49C8vL9evv/6qu+6667zn8+mnn6pNmzbmWqsOHTrok08+0Y8//qioqCjt2rVLoaGhDmuxwsPDtXbtWnMMn332mZYuXWrWT506pdatW5/32AAuHcITAJfWvXt3TZw4US+88II+++wzc6F4WVmZxowZo7vvvtth+2rVqpk/V2ZB+A033FCpcZSVlenGG2/UihUrKtQCAwPP2z8jI0NZWVnas2ePPvnkE4faypUrFRUVJXd39wprrf74vqysTI8//rh69OjhsI23t3elzgHApcFtOwAu7W9/+5tOnjypjRs3asOGDWZ4qlu3rrKzs3XbbbeZr7feekupqamW9n/bbbepoKBAhw8fNtsWLFigIUOGOGxXt25dHT9+XDabzTxecXGxEhIS/vJ5U2esWbNG1atX10cffaSVK1ear65du+qzzz5TcXGx7rjjDu3cuVPl5eVmv+3btzuM4cCBAw7nvHTpUn3zzTeWzhnAxSE8AXBpnp6euu+++/Svf/1LISEhqlOnjiTpscce0/z587Vy5Urt27dPiYmJ+uyzz1S/fn1L+7/jjjvUsmVLxcXFadeuXdq0aZOSkpLUqlUrh+3q16+v6OhoPfvss0pPT9f27dsVGxurkydPVlgfdTaffvqpunfvroYNGyokJMR8xcTE6MSJE0pOTlbXrl114sQJTZ48Wb/88ouWLVumNWvWmPuIiYnRmjVrtGDBAu3bt0/vvvuu3n33XfMzAXBlEJ4AuLxu3bpp586d6t69u9nWpUsXPf3005o+fbq6deumDRs26M0337ygIJGYmCgfHx89/PDDGjlypB5++GH17du3wnYJCQmqVauWYmJi9Nhjj6lu3boOa67OJTU1VQcOHFCvXr0q1Jo0aaLQ0FB99NFHqlq1qt566y1t3rxZ3bt310cffaTu3bubDwMNDw9XQkKC3n//fXXp0kXLli3T1KlT1axZM8vnDODC2YyzPcwEAHDF7d+/Xzk5OYqKijLbxo8fr6KiIr3yyitOHBmAP+LKEwC4iBMnTuixxx7T559/roMHD+rLL7/Uxx9/rE6dOjl7aAD+gCtPAHCRvvjiCz3//PPnrEdGRuqdd96p1L6WL1+u2bNn6/DhwwoODtagQYP00EMPXaqhArgECE8AcJEKCwuVl5d3zrq3t3elHmcA4OpAeAIAALCANU8AAAAWEJ4AAAAsIDwBAABYQHgCAACwgPAEAABgAeEJAADAAsITAACABYQnAAAAC/4fjJCCuB8u/IMAAAAASUVORK5CYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Vehicle_Damage      Ratio\n",
      "Yes          192413  50.487656\n",
      "No           188696  49.512344\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Driving_License      Ratio\n",
      "1           380297  99.786938\n",
      "0              812   0.213062\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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AAJhAeAIAADCB8AQAAGAC4QkAAMAEwhMAAIAJhCcAAAATCE8AAAAmEJ4AAABMIDwBAACYQHgCAAAwgfAEAABgAuEJAADABMITAACACYQnAAAAEwhPAAAAJhCeAAAATCA8AQAAmEB4AgAAMIHwBAAAYALhCQAAwATCEwAAgAmEJwAAABMITwAAACYQngAAAEzwaHgqLS1VYmKiunTpoh49emju3Lmqrq6WJM2aNUtRUVFu24oVK4y2GzduVJ8+fWSz2TR+/Hh98803Rs3lcmn+/Pnq1q2bunTporS0NNXV1Rn1iooKTZw4UXa7Xb1799b69evdxrVnzx4NGzZMNptNQ4YM0e7du6/wKwEAAH4pPBaeXC6XEhMT5XA4tHLlSj3//PPasmWLXnjhBUlSUVGRJk2apG3bthnbkCFDJEn5+flKSUnRhAkTtHr1ap04cUJJSUlG30uWLNHGjRuVkZGh9PR0bdiwQUuWLDHqSUlJOnnypFavXq3HHntMTz31lPLz8yVJZ86c0dixY5WQkKC1a9fKbrdr3LhxOnPmzM/34gAAAK/lsfC0f/9+5ebmau7cubrxxhuVkJCgxMREbdy4UdK58NSpUyeFh4cbW2BgoCRpxYoV6t+/vwYPHqyOHTsqLS1NW7du1eHDhyVJy5cvV2JiohISEtStWzdNnjxZK1eulCQdOnRIW7Zs0axZs9ShQwcNGzZMgwYN0qpVqyRJ7733nqxWq6ZOnar27dsrJSVFTZs21aZNmzzwKgEAAG/jsfAUHh6uRYsWKSwszO34qVOndOrUKZWWlqpt27YXbZuXl6eEhARjv2XLloqMjFReXp5KS0tVXFysW265xajHx8fryJEjOnbsmPLy8tSyZUu1bt3arf7pp58afcfHx8tisUiSLBaLbr75ZuXm5l6mOwcAAL9kfp66cLNmzdSjRw9jv66uTitWrFC3bt1UVFQki8WihQsX6sMPP1RwcLAefvhh3X333ZKkY8eOqUWLFm79hYaGqqSkRGVlZZLkVj8f0M7XL9a2tLRUklRWVqYbbrjhgvq+fftM3+N3+QsAAFxGV+r9taH9eiw8/dC8efO0Z88evfPOO/r8889lsVjUrl07PfDAA9q5c6eefvppBQUFqW/fvqqqqpK/v79be39/fzmdTlVVVRn7369JktPplMPhuGRbSfXWzQgNvdZ0GwAAcGkhIU09PQTvCE/z5s3TsmXL9Pzzz6tDhw668cYb1atXLwUHB0uSOnbsqAMHDujNN99U3759ZbVaLwgzTqdTgYGBbkHJarUaP0tSYGDgJdsGBARIUr11M44fPymXy3QzAFc5X18fr3iDALxRRcVp1dbW1X9iI1gsDXvw4fHwNHPmTL355puaN2+e7rjjDknn1hmdD07ntWvXTp988okkKSIiQuXl5W718vJyhYeHKyIiQtK56bfz65rOT+Wdr1+q7Y/1/cOpvoZwuUR4AgDgMvP0e6tHv+cpIyNDb731lp577jndeeedxvEXX3xRo0aNcju3oKBA7dq1kyTZbDbl5OQYteLiYhUXF8tmsykiIkKRkZFu9ZycHEVGRqpFixaKi4vTkSNHVFJS4laPi4sz+v7000/l+u7/GZfLpV27dslms13u2wcAAL9AHgtPRUVFeuWVV/TnP/9Z8fHxKisrM7ZevXpp586dev3113Xo0CGtWrVK69at0+jRoyVJ9913n9avX6+srCwVFBRo6tSp6tmzp9q0aWPU58+fr+zsbGVnZ2vBggUaOXKkJKlNmzbq3r27pkyZooKCAmVlZWnjxo26//77JUn9+vXTiRMnNHv2bBUWFmr27NlyOBzq37+/Z14oAADgVSwul2cefmVmZmrBggUXre3du1ebN29Wenq6Dhw4oFatWumJJ57Q7bffbpyzdu1apaen69tvv9Vtt92mmTNnKiQkRJJUW1urtLQ0rV27Vr6+vho6dKgmTZpkfP3A8ePHlZKSou3btys8PFxPPPGEBg4caPSdn5+vadOmqaioSFFRUZo+fbo6depk+h7Ly1nzBMA8P79za56KM4frbMkXnh4O4BWu+W20Wo59WxUVp1VTc+XWPIWF1b/myWPh6WpAeALQGIQn4ELeFJ74w8AAAAAmEJ4AAABMIDwBAACYQHgCAAAwgfAEAABgAuEJAADABMITAACACYQnAAAAEwhPAAAAJhCeAAAATCA8AQAAmEB4AgAAMIHwBAAAYALhCQAAwATCEwAAgAmEJwAAABMITwAAACYQngAAAEwgPAEAAJhAeAIAADCB8AQAAGAC4QkAAMAEwhMAAIAJhCcAAAATCE8AAAAmNCo8jRw5UidOnLjg+DfffKN77rnnJw8KAADAW/k19MQPP/xQ+fn5kqSdO3dq4cKFatKkids5Bw8e1JEjRy7vCAEAALxIg8PT9ddfr0WLFsnlcsnlcmnXrl265pprjLrFYlGTJk00e/bsKzJQAAAAb9Dg8NSmTRstX75ckpSUlKSUlBQFBQVdsYEBAAB4owaHp++bO3euJKmsrEw1NTVyuVxu9cjIyJ8+MgAAAC/UqPD00Ucf6emnn1ZxcbEkyeVyyWKxGP/7xRdfXNZBAgAAeItGhacZM2aoc+fOevXVV5m6AwAAV5VGhaeSkhItWrRIbdq0udzjAQAA8GqN+p6nhIQE5eTkXO6xAAAAeL1GhadbbrlF06dP11//+lc9//zzysjIcNsaqrS0VImJierSpYt69OihuXPnqrq6WpJ0+PBhjRo1SnFxcRowYIC2bdvm1nb79u0aOHCgbDabRo4cqcOHD7vVly5dqh49eshutys5OVkOh8OoVVdXKzk5WQkJCerevbsWL17s1ra+awMAgKtXo8LTRx99pJtuuknHjx/Xrl27lJ2dbWw7duxoUB8ul0uJiYlyOBxauXKlnn/+eW3ZskUvvPCCXC6Xxo8fr7CwMK1Zs0Z33XWXJkyYoKNHj0qSjh49qvHjx+uee+7RO++8o+bNm+vxxx83PvX3/vvvKyMjQzNmzNCyZcuUl5enefPmGddOS0vT7t27tWzZMk2bNk0ZGRnatGmTMa4fuzYAALi6NWrN0xtvvPGTL7x//37l5ubqo48+UlhYmCQpMTFRzz77rP7whz/o8OHDeuutt9SkSRO1b99eH3/8sdasWaOJEycqKytLN910k0aPHi3p3Fcn3HbbbdqxY4e6du2q5cuX66GHHlKvXr0kSdOnT9eYMWM0ZcoUuVwuZWVl6bXXXlNMTIxiYmK0b98+rVy5Uv369dMnn3zyo9cGAABXt0aFp3Xr1v1offDgwfX2ER4erkWLFhnB6bxTp04pLy9PnTp1cvvzL/Hx8crNzZUk5eXlKSEhwagFBgYqJiZGubm5SkhI0GeffaYJEyYY9bi4OJ09e1YFBQVyuVyqqamR3W5363vhwoWqq6ur99oAAODq1qjwlJ6e7rZfW1ur48ePy8/PT507d25QeGrWrJl69Ohh7NfV1WnFihXq1q2bysrK1KJFC7fzQ0NDVVJSIkk/Wj9x4oSqq6vd6n5+fgoODlZJSYl8fHwUEhIif39/ox4WFqbq6mpVVlbWe20zLBbTTQAAQD2u1PtrQ/ttVHj697//fcGx06dP65lnnlFUVFRjutS8efO0Z88evfPOO1q6dKlbuJEkf39/OZ1OSZLD4bhkvaqqyti/WN3lcl20JklOp/NH+zYrNPRa020AAMClhYQ09fQQGheeLqZp06aaOHGi7rvvPo0dO9ZU23nz5mnZsmV6/vnn1aFDB1mtVlVWVrqd43Q6FRAQIEmyWq0XhBmn06lmzZrJarUa+z+sBwYGqra29qI1SQoICKj32mYcP35SP/jLNQBQL19fH694gwC8UUXFadXW1l2Rvi2Whj34uGzhSZIKCgpUV2fuhmbOnKk333xT8+bN0x133CFJioiIUGFhodt55eXlxnRaRESEysvLL6hHR0crODhYVqtV5eXlat++vSSppqZGlZWVCg8Pl8vlUkVFhWpqauTnd+72y8rKFBAQoGbNmtV7bTNcLhGeAAC4zDz93tqo8PTggw/K8oOJwdOnT2vv3r0aNWpUg/vJyMjQW2+9peeee079+vUzjttsNmVmZqqqqsp44pOTk6P4+Hij/v0v6XQ4HNqzZ48mTJggHx8fxcbGKicnR127dpUk5ebmys/PTx07djx3035+xuLy833HxsbKx8en3msDAICrW6PC0/lQ8n3+/v6aPHmybr311gb1UVRUpFdeeUVjx45VfHy8ysrKjFqXLl3UsmVLJSUl6fHHH9eWLVuUn5+vuXPnSpKGDBmi119/XZmZmerVq5defvlltW7d2hjXiBEj9Mwzz6hDhw5q0aKFUlNTNXz4cAUGBko692nA1NRUzZkzR8eOHdPixYuNvuu7NgAAuLpZXK6f9vDr1KlTqq2t1W9+8xtT7TIzM7VgwYKL1vbu3auDBw8qJSVFeXl5uu6665ScnKzf//73xjlbt27VnDlzVFJSIrvdrpkzZ7r9rb3MzEwtXbpUTqdTt99+u6ZNm2ash3I4HEpNTdUHH3ygoKAgjRkzxu2JWX3XbqjyctY8ATDPz+/cmqfizOE6W/KFp4cDeIVrfhutlmPfVkXFadXUXLk1T2Fh9a95anR4WrZsmRYtWmSsPWrevLnuu+8+t+9XutoRngA0BuEJuJA3hadGTdu9/PLLWrFihf7yl7/Ibrerrq5Ou3btUkZGhvz9/U1/2g4AAOCXolHh6e2339bs2bPVu3dv41h0dLQiIiI0e/ZswhMAAPjVatQfBj516pTatm17wfHrr79e33zzzU8dEwAAgNdqVHiy2+1avHix23c61dbW6vXXX1fnzp0v2+AAAAC8TaOm7ZKSknT//fdr+/btiomJkSR9/vnncjqdWrRo0WUdIAAAgDdpVHhq3769kpOTVVlZqf3798tqtWrLli1KT083vogSAADg16hR03ZvvPGGUlNTde211yo1NVVJSUl68MEHNXnyZL399tuXe4wAAABeo1HhacmSJVqwYIHuvvtu49iTTz6pefPmKTMz87INDgAAwNs0KjxVVFTod7/73QXHr7/++gv+YC8AAMCvSaPCU3x8vF566SU5HA7jWHV1tRYuXCi73X7ZBgcAAOBtGrVg/JlnntHo0aPVvXt34/ueDh06pLCwML3yyiuXc3wAAABepVHh6Xe/+53ee+89/ec//9GBAwfk5+entm3bqnv37vL19b3cYwQAAPAajQpPkuTv768//vGPl3MsAAAAXq9Ra54AAACuVoQnAAAAEwhPAAAAJhCeAAAATCA8AQAAmEB4AgAAMIHwBAAAYALhCQAAwATCEwAAgAmEJwAAABMITwAAACYQngAAAEwgPAEAAJhAeAIAADCB8AQAAGAC4QkAAMAEwhMAAIAJhCcAAAATCE8AAAAmEJ4AAABMIDwBAACY4BXhyel0auDAgcrOzjaOzZo1S1FRUW7bihUrjPrGjRvVp08f2Ww2jR8/Xt98841Rc7lcmj9/vrp166YuXbooLS1NdXV1Rr2iokITJ06U3W5X7969tX79erfx7NmzR8OGDZPNZtOQIUO0e/fuK3j3AADgl8Tj4am6ulp/+9vftG/fPrfjRUVFmjRpkrZt22ZsQ4YMkSTl5+crJSVFEyZM0OrVq3XixAklJSUZbZcsWaKNGzcqIyND6enp2rBhg5YsWWLUk5KSdPLkSa1evVqPPfaYnnrqKeXn50uSzpw5o7FjxyohIUFr166V3W7XuHHjdObMmZ/h1QAAAN7Oo+GpsLBQw4cP16FDhy6oFRUVqVOnTgoPDze2wMBASdKKFSvUv39/DR48WB07dlRaWpq2bt2qw4cPS5KWL1+uxMREJSQkqFu3bpo8ebJWrlwpSTp06JC2bNmiWbNmqUOHDho2bJgGDRqkVatWSZLee+89Wa1WTZ06Ve3bt1dKSoqaNm2qTZs2/UyvCgAA8GYeDU87duxQ165dtXr1arfjp06dUmlpqdq2bXvRdnl5eUpISDD2W7ZsqcjISOXl5am0tFTFxcW65ZZbjHp8fLyOHDmiY8eOKS8vTy1btlTr1q3d6p9++qnRd3x8vCwWiyTJYrHo5ptvVm5u7mW6awAA8Evm58mLjxgx4qLHi4qKZLFYtHDhQn344YcKDg7Www8/rLvvvluSdOzYMbVo0cKtTWhoqEpKSlRWViZJbvWwsDBJMuoXa1taWipJKisr0w033HBB/YfTigAA4Ork0fB0Kfv375fFYlG7du30wAMPaOfOnXr66acVFBSkvn37qqqqSv7+/m5t/P395XQ6VVVVZex/vyadW5jucDgu2VZSvXUzvnt4BQAALqMr9f7a0H69MjwNHjxYvXr1UnBwsCSpY8eOOnDggN5880317dtXVqv1gjDjdDoVGBjoFpSsVqvxsyQFBgZesm1AQIAk1Vs3IzT0WtNtAADApYWENPX0ELwzPFksFiM4ndeuXTt98sknkqSIiAiVl5e71cvLyxUeHq6IiAhJ56bfzq9rOj+Vd75+qbY/1vcPp/oa4vjxk3K5TDcDcJXz9fXxijcIwBtVVJxWbW1d/Sc2gsXSsAcfHv+qgot58cUXNWrUKLdjBQUFateunSTJZrMpJyfHqBUXF6u4uFg2m00RERGKjIx0q+fk5CgyMlItWrRQXFycjhw5opKSErd6XFyc0fenn34q13epx+VyadeuXbLZbKbvw+ViY2NjM78B+HGe/v3zyvDUq1cv7dy5U6+//roOHTqkVatWad26dRo9erQk6b777tP69euVlZWlgoICTZ06VT179lSbNm2M+vz585Wdna3s7GwtWLBAI0eOlCS1adNG3bt315QpU1RQUKCsrCxt3LhR999/vySpX79+OnHihGbPnq3CwkLNnj1bDodD/fv398yLAQAAvIpXTtt17txZL774otLT0/Xiiy+qVatWWrBggex2uyTJbrdrxowZSk9P17fffqvbbrtNM2fONNqPGTNGx48f14QJE+Tr66uhQ4e6PclKS0tTSkqKhg8frvDwcM2ZM0edO3eWJAUFBekf//iHpk2bprfffltRUVHKzMxUkyZNftbXAAAAeCeLy8VD4iulvJw1TwDM8/M7t+apOHO4zpZ84enhAF7hmt9Gq+XYt1VRcVo1NVduzVNY2C90zRMAAIC3IjwBAACYQHgCAAAwgfAEAABgAuEJAADABMITAACACYQnAAAAEwhPAAAAJhCeAAAATCA8AQAAmEB4AgAAMIHwBAAAYALhCQAAwATCEwAAgAmEJwAAABMITwAAACYQngAAAEwgPAEAAJhAeAIAADCB8AQAAGAC4QkAAMAEwhMAAIAJhCcAAAATCE8AAAAmEJ4AAABMIDwBAACYQHgCAAAwgfAEAABgAuEJAADABMITAACACYQnAAAAEwhPAAAAJhCeAAAATCA8AQAAmEB4AgAAMMErwpPT6dTAgQOVnZ1tHDt8+LBGjRqluLg4DRgwQNu2bXNrs337dg0cOFA2m00jR47U4cOH3epLly5Vjx49ZLfblZycLIfDYdSqq6uVnJyshIQEde/eXYsXL3ZrW9+1AQDA1cvj4am6ulp/+9vftG/fPuOYy+XS+PHjFRYWpjVr1uiuu+7ShAkTdPToUUnS0aNHNX78eN1zzz1655131Lx5cz3++ONyuVySpPfff18ZGRmaMWOGli1bpry8PM2bN8/oPy0tTbt379ayZcs0bdo0ZWRkaNOmTQ26NgAAuLp5NDwVFhZq+PDhOnTokNvxTz75RIcPH9aMGTPUvn17jRs3TnFxcVqzZo0kKSsrSzfddJNGjx6tG2+8UXPnztWRI0e0Y8cOSdLy5cv10EMPqVevXurcubOmT5+uNWvWyOFw6MyZM8rKylJKSopiYmLUt29fPfLII1q5cmWDrg0AAK5uHg1PO3bsUNeuXbV69Wq343l5eerUqZOaNGliHIuPj1dubq5RT0hIMGqBgYGKiYlRbm6uamtr9dlnn7nV4+LidPbsWRUUFKigoEA1NTWy2+1ufefl5amurq7eawMAgKubnycvPmLEiIseLysrU4sWLdyOhYaGqqSkpN76iRMnVF1d7Vb38/NTcHCwSkpK5OPjo5CQEPn7+xv1sLAwVVdXq7Kyst5rm2GxmG4CAADqcaXeXxvar0fD06U4HA63cCNJ/v7+cjqd9darqqqM/YvVXS7XRWvSuYXr9V3bjNDQa023AQAAlxYS0tTTQ/DO8GS1WlVZWel2zOl0KiAgwKj/MMw4nU41a9ZMVqvV2P9hPTAwULW1tRetSVJAQEC91zbj+PGT+m4NOwA0mK+vj1e8QQDeqKLitGpr665I3xZLwx58eGV4ioiIUGFhodux8vJyYzotIiJC5eXlF9Sjo6MVHBwsq9Wq8vJytW/fXpJUU1OjyspKhYeHy+VyqaKiQjU1NfLzO3f7ZWVlCggIULNmzeq9thkulwhPAABcZp5+b/X4VxVcjM1m0+eff25MwUlSTk6ObDabUc/JyTFqDodDe/bskc1mk4+Pj2JjY93qubm58vPzU8eOHRUdHS0/Pz+3BeA5OTmKjY2Vj49PvdcGAABXN68MT126dFHLli2VlJSkffv2KTMzU/n5+Ro6dKgkaciQIdq1a5cyMzO1b98+JSUlqXXr1urataukcwvRX3/9dW3evFn5+flKTU3V8OHDFRgYqMDAQA0ePFipqanKz8/X5s2btXjxYo0cObJB1wYAAFc3rwxPvr6+euWVV1RWVqZ77rlH//znP/Xyyy8rMjJSktS6dWu99NJLWrNmjYYOHarKykq9/PLLsny3TP7OO+/UuHHj9Mwzz2j06NHq3LmzpkyZYvSflJSkmJgYPfTQQ5o+fbomTpyo22+/vUHXBgAAVzeLy+XpmcNfr/JyFowDMM/P79yC8eLM4Tpb8oWnhwN4hWt+G62WY99WRcVp1dRcuQXjYWH1Lxj3yidPAAAA3orwBAAAYALhCQAAwATCEwAAgAmEJwAAABMITwAAACYQngAAAEwgPAEAAJhAeAIAADCB8AQAAGAC4QkAAMAEwhMAAIAJhCcAAAATCE8AAAAmEJ4AAABMIDwBAACYQHgCAAAwgfAEAABgAuEJAADABMITAACACYQnAAAAEwhPAAAAJhCeAAAATCA8AQAAmEB4AgAAMIHwBAAAYALhCQAAwATCEwAAgAmEJwAAABMITwAAACYQngAAAEwgPAEAAJhAeAIAADCB8AQAAGCCV4enf/3rX4qKinLbEhMTJUl79uzRsGHDZLPZNGTIEO3evdut7caNG9WnTx/ZbDaNHz9e33zzjVFzuVyaP3++unXrpi5duigtLU11dXVGvaKiQhMnTpTdblfv3r21fv36n+eGAQCA1/Pq8FRYWKhevXpp27ZtxjZr1iydOXNGY8eOVUJCgtauXSu73a5x48bpzJkzkqT8/HylpKRowoQJWr16tU6cOKGkpCSj3yVLlmjjxo3KyMhQenq6NmzYoCVLlhj1pKQknTx5UqtXr9Zjjz2mp556Svn5+T/7/QMAAO/j1eGpqKhIHTp0UHh4uLE1a9ZM7733nqxWq6ZOnar27dsrJSVFTZs21aZNmyRJK1asUP/+/TV48GB17NhRaWlp2rp1qw4fPixJWr58uRITE5WQkKBu3bpp8uTJWrlypSTp0KFD2rJli2bNmqUOHTpo2LBhGjRokFatWuWx1wEAAHgPrw9Pbdu2veB4Xl6e4uPjZbFYJEkWi0U333yzcnNzjXpCQoJxfsuWLRUZGam8vDyVlpaquLhYt9xyi1GPj4/XkSNHdOzYMeXl5ally5Zq3bq1W/3TTz+9MjcJAAB+Ufw8PYBLcblc+uqrr7Rt2zb94x//UG1trfr166fExESVlZXphhtucDs/NDRU+/btkyQdO3ZMLVq0uKBeUlKisrIySXKrh4WFSZJRv1jb0tJS0/fwXbYDAACX0ZV6f21ov14bno4ePSqHwyF/f3+98MIL+vrrrzVr1ixVVVUZx7/P399fTqdTklRVVXXJelVVlbH//ZokOZ3Oevs2IzT0WtNtAADApYWENPX0ELw3PLVq1UrZ2dn6zW9+I4vFoujoaNXV1WnKlCnq0qXLBWHG6XQqICBAkmS1Wi9aDwwMdAtKVqvV+FmSAgMDL9n2fN9mHD9+Ui6X6WYArnK+vj5e8QYBeKOKitOqra2r/8RGsFga9uDDa8OTJAUHB7vtt2/fXtXV1QoPD1d5eblbrby83Jhui4iIuGg9PDxcERERkqSysjJjXdP5qbzz9Uu1NcvlEuEJAIDLzNPvrV67YPw///mPunbtKofDYRz74osvFBwcbCzgdn336rlcLu3atUs2m02SZLPZlJOTY7QrLi5WcXGxbDabIiIiFBkZ6VbPyclRZGSkWrRoobi4OB05ckQlJSVu9bi4uCt8xwAA4JfAa8OT3W6X1WrVU089pf3792vr1q1KS0vTI488on79+unEiROaPXu2CgsLNXv2bDkcDvXv31+SdN9992n9+vXKyspSQUGBpk6dqp49e6pNmzZGff78+crOzlZ2drYWLFigkSNHSpLatGmj7t27a8qUKSooKFBWVpY2btyo+++/32OvBQAA8B4Wl8vTD78ubd++fZozZ45yc3PVtGlT3XvvvRo/frwsFovy8/M1bdo0FRUVKSoqStOnT1enTp2MtmvXrlV6erq+/fZb3XbbbZo5c6ZCQkIkSbW1tUpLS9PatWvl6+uroUOHatKkScZXHxw/flwpKSnavn27wsPD9cQTT2jgwIGmx19ezponAOb5+Z1b81ScOVxnS77w9HAAr3DNb6PVcuzbqqg4rZqaK7fmKSys/jVPXh2efukITwAag/AEXMibwpPXTtsBAAB4I8ITAACACYQnAAAAEwhPAAAAJhCeAAAATCA8AQAAmEB4AgAAMIHwBAAAYALhCQAAwATCEwAAgAmEJwAAABMITwAAACYQngAAAEwgPAEAAJhAeAIAADCB8AQAAGAC4QkAAMAEwhMAAIAJhCcAAAATCE8AAAAmEJ4AAABMIDwBAACYQHgCAAAwgfAEAABgAuEJAADABMITAACACYQnAAAAEwhPAAAAJhCeAAAATCA8AQAAmEB4AgAAMIHwBAAAYALhCQAAwATCEwAAgAmEp0uorq5WcnKyEhIS1L17dy1evNjTQwIAAF7Az9MD8FZpaWnavXu3li1bpqNHj+rJJ59UZGSk+vXr5+mhAQAADyI8XcSZM2eUlZWl1157TTExMYqJidG+ffu0cuVKwhMAAFc5pu0uoqCgQDU1NbLb7cax+Ph45eXlqa6uzoMjAwAAnsaTp4soKytTSEiI/P39jWNhYWGqrq5WZWWlmjdv7sHR/f98fCzy8bF4ehiAV6mrc6muzuXpYQD4FSM8XYTD4XALTpKMfafT2eB+fHwk1xX6N9xiseg3v2kiX18eHgLfV1tbp2+/PSPXlfrl+xn5/zZalmsCPT0MwCtcE9rW+NnnCr31WRr4PILwdBFWq/WCkHR+PyAgoMH9NG9+7WUdF4D6+fr6qHnzIE8P47IIHTTd00MAvE5ISFNPD4E1TxcTERGhiooK1dTUGMfKysoUEBCgZs2aeXBkAADA0whPFxEdHS0/Pz/l5uYax3JychQbGyufK/WsEAAA/CKQBC4iMDBQgwcPVmpqqvLz87V582YtXrxYI0eO9PTQAACAh1lcv4ZVlVeAw+FQamqqPvjgAwUFBWnMmDEaNWqUp4cFAAA8jPAEAABgAtN2AAAAJhCeAAAATCA8AQAAmEB4An6C6upqJScnKyEhQd27d9fixYs9PSQAl5HT6dTAgQOVnZ3t6aHAi/AN48BPkJaWpt27d2vZsmU6evSonnzySUVGRqpfv36eHhqAn6i6ulqTJk3Svn37PD0UeBnCE9BIZ86cUVZWll577TXFxMQoJiZG+/bt08qVKwlPwC9cYWGhJk2a9Kv4G4m4/Ji2AxqpoKBANTU1stvtxrH4+Hjl5eWprq7OgyMD8FPt2LFDXbt21erVqz09FHghnjwBjVRWVqaQkBD5+/sbx8LCwlRdXa3Kyko1b97cg6MD8FOMGDHC00OAF+PJE9BIDofDLThJMvadTqcnhgQA+BkQnoBGslqtF4Sk8/sBAQGeGBIA4GdAeAIaKSIiQhUVFaqpqTGOlZWVKSAgQM2aNfPgyAAAVxLhCWik6Oho+fn5KTc31ziWk5Oj2NhY+fjwqwUAv1b8Cw80UmBgoAYPHqzU1FTl5+dr8+bNWrx4sUaOHOnpoQEAriA+bQf8BElJSUpNTdVDDz2koKAgTZw4UbfffrunhwUAuIIsLr4BDAAAoMGYtgMAADCB8AQAAGAC4QkAAMAEwhMAAIAJhCcAAAATCE8AAAAmEJ4AAABMIDwBAACYQHgC8LPq3bu3oqKiFBUVpY4dO8put+vee+/Vf/7zn0u2Wbt2rXr37l1v39nZ2YqKirqcw72kBx98UC+99JLHxwHg58efZwHws0tOTtaAAQNUV1enb7/9VuvWrdO4ceO0aNEi/f73v7/g/AEDBqhnz5719mu327Vt27YrMGJzvGUcAK4MwhOAn921116r8PBwSVJERISmTp2qsrIyzZ07Vxs2bLjg/ICAAAUEBNTbr7+/v9GvJ3nLOABcGUzbAfAK//u//6svv/xSBw8eVFRUlF588UV17dpVjz76qNu03fDhw5Wenu7W9t5779Urr7ziNl329ddfKyoqSh988IH69Omj2NhYjRs3TpWVlUa7bdu26U9/+pM6d+6sRx55RDNnztTf//73n3wvP5y2O3jwoMaMGSO73a6ePXtq+fLlRu3LL7/Ugw8+qM6dO+uOO+7QypUrjdpLL72kSZMmadq0abr55pt166236rXXXjPqBQUFuvfee2Wz2dSjRw9lZGQYNafTqVmzZqlr167q2rWrJk+e7HbvABqP8ATAK7Rv316SVFhYKEnasmWL3nzzTU2ePNntvAEDBuhf//qXsV9aWqrc3FzdeeedF+134cKFeu6557RixQp99tlnWrJkiSTp8OHDeuyxx9S/f3+tW7dOsbGxbsHlcqmurtbo0aPVtGlTvf3223rmmWf0/PPPa8uWLaqqqtKf//xnxcfH65///KeefPJJvfLKK1q3bp3R/v3335fVatW7776rMWPGaP78+frqq68kSVOnTlV0dLQ2btyo2bNna9GiRdq6dask6bnnntPu3bv12muvafny5Tp16pT+8pe/XPb7A65GhCcAXuHaa6+VJJ0+fVrSuSdR7dq10w033OB2Xv/+/VVYWKgDBw5Ikj744AN16tRJ11133UX7TUxMVOfOnWWz2fSnP/1Jn332mSQpKytLnTt31uOPP6527drpL3/5i2w222W/r23btumbb77RnDlzdOONN6p379566qmn5OPjow0bNig0NFR//etf1bZtW/Xu3VuPPvqo25Op4OBgPfnkk7ruuuv0yCOPKDg4WLt375YkHTlyRMHBwWrVqpX+8Ic/aMmSJerUqZMcDodWrFih6dOnq3PnzoqKilJaWpp27NihvXv3XvZ7BK42rHkC4BVOnTolSQoKCpIktWrV6qLnRUREKCEhQR988IHGjh2rDz74QAMGDLhkv98PVUFBQTp79qwkae/evYqNjXU7Ny4uTt9+++1Puo8f+uqrr3T99dcb9yVJQ4YMkSQ9++yzKigokN1uN2q1tbXy9fU19lu3bu2237RpU9XU1EiSxo0bp+eee06rV69Wz549dddddyk8PFxffvmlzp49q3vvvddtLHV1dTpw4ACfBAR+IsITAK9w/onIjTfeKEmyWq2XPHfAgAF65513NGTIEO3atUv/93//d8lzr7nmmose9/X1lcvlcjv2w/3Lwc/v0v/M1tTU6NZbb9UzzzxzyXMuNv7z4xw7dqz69++vzZs369///rceeughzZw5UzfddJMkadWqVWrSpIlb29DQ0MbcBoDvYdoOgFdYs2aNYmJi1KZNm3rPveOOO7R3715lZWUpNjb2kk+pfsyNN96ozz//3O3YD/cvh7Zt2+rgwYNyOBzGsWeffVazZs3S9ddfr6+++kqtW7fWddddp+uuu065ubl644036u23urpas2bNkr+/vx5++GG98cYbGj58uN5//321adNGvr6+qqysNPoNCgrS3Llzdfz48ct+j8DVhvAE4Gd38uRJlZWV6dixY9q7d69mz56t9957r8GfdGvevLm6du2qf/zjH+rfv3+jxjB8+HDl5uYqMzNTX331lRYuXKj//ve/slgsDe7j4MGD+vDDD922kpISt3O6d++usLAwPfPMMyoqKtL/+3//T2+99Za6d++uQYMGqaqqyqht3bpVs2fPbtDTIavVql27dmnmzJnav3+/PvvsM/33v/9Vp06dFBQUpGHDhik1NVXZ2dkqLCzU1KlTdfDgQbVu3dr0awXAHdN2AH52c+bM0Zw5c2SxWNS8eXN16tRJS5cuVUJCQoP7uPPOO7V9+/ZGh6dWrVopPT1dzz77rNLT03Xbbbfpj3/84yWn+S5mw4YNF3wv1axZs/S73/3O2Pfz89Mrr7yiGTNm6O6771ZYWJimTp1qfOnna6+9pjlz5mjw4MEKDg7W/fffr3HjxjXo+s8//7xmzJihoUOHys/PT/369dPjjz8uSfr73/+uZ599VomJiTp79qxuueUWZWZmuq2fAtA4FteVmOQHAC/35ZdfqqamRp06dTKOjR07VrGxsZo4caIHRwbA2zFtB+CqdOjQIT388MP66KOPdOTIEWVlZenjjz9W3759PT00AF6OJ08ArlqvvvqqVq9erePHj+v6669XYmKi+vTpo/Hjx2v79u2XbDd9+nQNGjToZxwpAG9CeAKAHzh27Jjbp+N+KDQ01O17mwBcXQhPAAAAJrDmCQAAwATCEwAAgAmEJwAAABMITwAAACYQngAAAEwgPAEAAJhAeAIAADCB8AQAAGDC/wcEyd/eik2wTQAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Previously_Insured      Ratio\n",
      "0              206481  54.178988\n",
      "1              174628  45.821012\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Response      Ratio\n",
      "0    334399  87.743664\n",
      "1     46710  12.256336\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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GjhypgwcP1vOVAAAAdwq3hSen06moqCiVl5crOTlZb7zxhnbu3Klly5ZJknJzczVjxgzt3r3beI0cOVKSlJWVpdjYWE2bNk0pKSk6d+6coqOjjXOvWbNGaWlpSkpKUmJiorZt26Y1a9YY9ejoaJ0/f14pKSl68cUX9corrygrK0uSVFZWpsmTJysyMlJbtmyR3W7XlClTVFZW9sNdHAAA4LHcFp6OHj2qjIwMLV68WD/5yU8UGRmpqKgopaWlSboSnrp06aLg4GDjZbVaJUkbNmzQkCFDNHz4cHXq1Enx8fHatWuX8vLyJEnr169XVFSUIiMj1atXL82cOVPJycmSpBMnTmjnzp1auHChOnTooNGjR2vYsGHauHGjJOmTTz6Rr6+vZs+erfbt2ys2NlaNGjXS9u3b3XCVAACAp3FbeAoODtbKlSsVFBTksv/ChQu6cOGCCgsL1bZt2+u2zczMVGRkpLHdokULhYaGKjMzU4WFhTp16pQefvhhox4REaH8/HydPn1amZmZatGihVq1auVSP3DggHHuiIgIWSwWSZLFYtFDDz2kjIyMOnrnAADgTua28NSkSRP16dPH2K6urtaGDRvUq1cv5ebmymKx6J133tFjjz2mYcOG6aOPPjKOPX36tJo3b+5yvsDAQBUUFKioqEiSXOpXA9rV+vXaFhYWStIN6wUFBXXwrgEAwJ3O290DuCohIUGHDx/Whx9+qEOHDslisahdu3Z65plntG/fPs2ZM0f+/v4aOHCgLl26JB8fH5f2Pj4+cjgcunTpkrH93ZokORwOlZeX37CtpBrrZvz75hUAN+DzB8CsW/294RHhKSEhQevWrdMbb7yhDh066Cc/+Yn69eunpk2bSpI6deqkY8eO6f3339fAgQPl6+t7TZhxOByyWq0uQcnX19f4WZKsVusN2/r5+UlSjXUzAgMbm24D4PYFBDRy9xAA3MXcHp4WLFig999/XwkJCXriiSckXVlndDU4XdWuXTt98cUXkqSQkBAVFxe71IuLixUcHKyQkBBJV6bfrq5rujqVd7V+o7Y3O/f3p/JuxZkz5+V0mm52Sxo08OIPBHADJSUXVVVVXfOBAPAdFsut3fhw63OekpKS9MEHH+j111/XU089Zex/8803NWHCBJdjs7Oz1a5dO0mSzWZTenq6UTt16pROnTolm82mkJAQhYaGutTT09MVGhqq5s2bKzw8XPn5+S5rmNLT0xUeHm6c+8CBA3L+O/U4nU7t379fNpvN9PtzOuvvBeDm6vPzx4sXr7v3dSvcFp5yc3O1fPly/epXv1JERISKioqMV79+/bRv3z6tWrVKJ06c0MaNG/Xxxx9r4sSJkqSxY8dq69atSk1NVXZ2tmbPnq2+ffuqdevWRn3JkiXas2eP9uzZo6VLl2r8+PGSpNatW6t3796aNWuWsrOzlZqaqrS0ND399NOSpMGDB+vcuXNatGiRjhw5okWLFqm8vFxDhgxxz4UCAAAexeJ03mrOqlsrVqzQ0qVLr1v7+uuvtWPHDiUmJurYsWNq2bKlXn75ZQ0aNMg4ZsuWLUpMTNS3336rRx99VAsWLFBAQIAkqaqqSvHx8dqyZYsaNGigUaNGacaMGcbjB86cOaPY2Fh99tlnCg4O1ssvv6yhQ4ca587KytLcuXOVm5urjh07at68eerSpYvp91hcXH/Tdt7eV6btnl6Wpuz8szU3AO4BnVo2U/Kvh6qk5KIqK5m2A2COxSIFBdU8bee28HQvIDwBPyzCE4DbcavhiS8GBgAAMIHwBAAAYALhCQAAwATCEwAAgAmEJwAAABMITwAAACYQngAAAEwgPAEAAJhAeAIAADCB8AQAAGAC4QkAAMAEwhMAAIAJhCcAAAATCE8AAAAmEJ4AAABMIDwBAACYQHgCAAAwgfAEAABgAuEJAADABMITAACACYQnAAAAEwhPAAAAJhCeAAAATCA8AQAAmEB4AgAAMIHwBAAAYALhCQAAwATCEwAAgAmEJwAAABMITwAAACYQngAAAEwgPAEAAJhAeAIAADCB8AQAAGAC4QkAAMAEwhMAAIAJhCcAAAATCE8AAAAmEJ4AAABMIDwBAACYQHgCAAAwwa3hqbCwUFFRUerRo4f69OmjxYsXq6KiQpKUl5enCRMmKDw8XE8++aR2797t0vazzz7T0KFDZbPZNH78eOXl5bnU165dqz59+shutysmJkbl5eVGraKiQjExMYqMjFTv3r21evVql7Y19Q0AAO5dbgtPTqdTUVFRKi8vV3Jyst544w3t3LlTy5Ytk9Pp1NSpUxUUFKTNmzfr5z//uaZNm6aTJ09Kkk6ePKmpU6dqxIgR+vDDD9WsWTO99NJLcjqdkqRPP/1USUlJmj9/vtatW6fMzEwlJCQYfcfHx+vgwYNat26d5s6dq6SkJG3fvt0Y1836BgAA9zZvd3V89OhRZWRk6B//+IeCgoIkSVFRUfrv//5vPfbYY8rLy9MHH3yghg0bqn379vr888+1efNmTZ8+XampqeratasmTpwoSVq8eLEeffRR7d27Vz179tT69ev13HPPqV+/fpKkefPmadKkSZo1a5acTqdSU1P17rvvKiwsTGFhYcrJyVFycrIGDx6sL7744qZ9AwCAe5vb7jwFBwdr5cqVRnC66sKFC8rMzFSXLl3UsGFDY39ERIQyMjIkSZmZmYqMjDRqVqtVYWFhysjIUFVVlb788kuXenh4uC5fvqzs7GxlZ2ersrJSdrvd5dyZmZmqrq6usW8AAHBvc9udpyZNmqhPnz7GdnV1tTZs2KBevXqpqKhIzZs3dzk+MDBQBQUFknTT+rlz51RRUeFS9/b2VtOmTVVQUCAvLy8FBATIx8fHqAcFBamiokKlpaU19m2GxWK6CYA6wucPgFm3+nvDbeHp+xISEnT48GF9+OGHWrt2rUu4kSQfHx85HA5JUnl5+Q3rly5dMravV3c6ndetSZLD4bjpuc0KDGxsug2A2xcQ0MjdQwBwF/OI8JSQkKB169bpjTfeUIcOHeTr66vS0lKXYxwOh/z8/CRJvr6+14QZh8OhJk2ayNfX19j+ft1qtaqqquq6NUny8/OrsW8zzpw5r3+vYa9zDRp48QcCuIGSkouqqqp29zAA3GEsllu78eH28LRgwQK9//77SkhI0BNPPCFJCgkJ0ZEjR1yOKy4uNqbTQkJCVFxcfE29c+fOatq0qXx9fVVcXKz27dtLkiorK1VaWqrg4GA5nU6VlJSosrJS3t5X3n5RUZH8/PzUpEmTGvs2w+lUvYUnADfHZw9AfXHrc56SkpL0wQcf6PXXX9dTTz1l7LfZbDp06JAxBSdJ6enpstlsRj09Pd2olZeX6/Dhw7LZbPLy8lK3bt1c6hkZGfL29lanTp3UuXNneXt7uywAT09PV7du3eTl5VVj3wAA4N7mtvCUm5ur5cuX61e/+pUiIiJUVFRkvHr06KEWLVooOjpaOTk5WrFihbKysjRq1ChJ0siRI7V//36tWLFCOTk5io6OVqtWrdSzZ09J0rhx47Rq1Srt2LFDWVlZiouL05gxY2S1WmW1WjV8+HDFxcUpKytLO3bs0OrVqzV+/HhJqrFvAABwb7M4ne65ub1ixQotXbr0urWvv/5ax48fV2xsrDIzM9WmTRvFxMTopz/9qXHMrl279Nprr6mgoEB2u10LFixQ69atXc6/du1aORwODRo0SHPnzjXWQ5WXlysuLk5/+tOf5O/vr0mTJmnChAlG25r6vlXFxfW35snb+8qap6eXpSk7/2z9dALcYTq1bKbkXw9VSclFVVay5gmAORaLFBRU85ont4WnewHhCfhhEZ4A3I5bDU98MTAAAIAJhCcAAAATCE8AAAAmEJ4AAABMqFV4Gj9+vM6dO3fN/rNnz2rEiBG3PSgAAABPdctPGP/b3/6mrKwsSdK+ffv0zjvvqGHDhi7HHD9+XPn5+XU7QgAAAA9yy+HpgQce0MqVK+V0OuV0OrV//37dd999Rt1isahhw4ZatGhRvQwUAADAE9xyeGrdurXWr18vSYqOjlZsbKz8/f3rbWAAAACeqFZfDLx48WJJV75Qt7KyUt9/zmZoaOjtjwwAAMAD1So8/eMf/9CcOXN06tQpSZLT6ZTFYjH+96uvvqrTQQIAAHiKWoWn+fPnq3v37vr973/P1B0AALin1Co8FRQUaOXKlS5fxAsAAHAvqNVzniIjI5Wenl7XYwEAAPB4tbrz9PDDD2vevHn661//qjZt2rg8skCSpk2bVieDAwAA8DS1XjDetWtXnTlzRmfOnHGpWSyWOhkYAACAJ6pVeHrvvffqehwAAAB3hFqFp48//vim9eHDh9fmtAAAAB6vVuEpMTHRZbuqqkpnzpyRt7e3unfvTngCAAB3rVqFp7/85S/X7Lt48aJeffVVdezY8bYHBQAA4Klq9aiC62nUqJGmT5+uNWvW1NUpAQAAPE6dhSdJys7OVnV1dV2eEgAAwKPUatru2WefveaRBBcvXtTXX3+tCRMm1MW4AAAAPFKtwlPPnj2v2efj46OZM2fqkUceue1BAQAAeKpahafvPkH8woULqqqq0v33319ngwIAAPBUtQpPkrRu3TqtXLlSxcXFkqRmzZpp7NixfDULAAC4q9UqPL399tvasGGD/uu//kt2u13V1dXav3+/kpKS5OPjo8mTJ9f1OAEAADxCrcLTpk2btGjRIvXv39/Y17lzZ4WEhGjRokWEJwAAcNeq1aMKLly4oLZt216z/4EHHtDZs2dvd0wAAAAeq1bhyW63a/Xq1S7PdKqqqtKqVavUvXv3OhscAACAp6nVtF10dLSefvppffbZZwoLC5MkHTp0SA6HQytXrqzTAQIAAHiSWoWn9u3bKyYmRqWlpTp69Kh8fX21c+dOJSYmqlOnTnU9RgAAAI9Rq2m79957T3FxcWrcuLHi4uIUHR2tZ599VjNnztSmTZvqeowAAAAeo1bhac2aNVq6dKn+8z//09j329/+VgkJCVqxYkWdDQ4AAMDT1Co8lZSU6Mc//vE1+x944AHjoZkAAAB3o1qFp4iICL311lsqLy839lVUVOidd96R3W6vs8EBAAB4mlotGH/11Vc1ceJE9e7d23je04kTJxQUFKTly5fX5fgAAAA8Sq3C049//GN98skn+vvf/65jx47J29tbbdu2Ve/evdWgQYO6HiMAAIDHqPUXA/v4+Ojxxx+vy7EAAAB4vFqteQIAALhXEZ4AAABMIDwBAACY4BHhyeFwaOjQodqzZ4+xb+HCherYsaPLa8OGDUY9LS1NAwYMkM1m09SpU3X27Fmj5nQ6tWTJEvXq1Us9evRQfHy8y5cYl5SUaPr06bLb7erfv7+2bt3qMp7Dhw9r9OjRstlsGjlypA4ePFiP7x4AANxJ3B6eKioq9Jvf/EY5OTku+3NzczVjxgzt3r3beI0cOVKSlJWVpdjYWE2bNk0pKSk6d+6coqOjjbZr1qxRWlqakpKSlJiYqG3btmnNmjVGPTo6WufPn1dKSopefPFFvfLKK8rKypIklZWVafLkyYqMjNSWLVtkt9s1ZcoUlZWV/QBXAwAAeDq3hqcjR45ozJgxOnHixDW13NxcdenSRcHBwcbLarVKkjZs2KAhQ4Zo+PDh6tSpk+Lj47Vr1y7l5eVJktavX6+oqChFRkaqV69emjlzppKTkyVdeR7Vzp07tXDhQnXo0EGjR4/WsGHDtHHjRknSJ598Il9fX82ePVvt27dXbGysGjVqpO3bt/9AVwUAAHgyt4anvXv3qmfPnkpJSXHZf+HCBRUWFhoP4Py+zMxMRUZGGtstWrRQaGioMjMzVVhYqFOnTunhhx826hEREcrPz9fp06eVmZmpFi1aqFWrVi71AwcOGOeOiIiQxWKRJFksFj300EPKyMioo3cNAADuZLV+zlNdGDdu3HX35+bmymKx6J133tHf/vY3NW3aVL/85S+NLyI+ffq0mjdv7tImMDBQBQUFKioqkiSXelBQkCQZ9eu1LSwslCQVFRXpwQcfvKb+/WnFW/Hv/AXADfj8ATDrVn9vuDU83cjRo0dlsVjUrl07PfPMM9q3b5/mzJkjf39/DRw4UJcuXZKPj49LGx8fHzkcDl26dMnY/m5NurIwvby8/IZtJdVYNyMwsLHpNgBuX0BAI3cPAcBdzCPD0/Dhw9WvXz81bdpUktSpUycdO3ZM77//vgYOHChfX99rwozD4ZDVanUJSr6+vsbPkmS1Wm/Y1s/PT5JqrJtx5sx5OZ2mm92SBg28+AMB3EBJyUVVVVXXfCAAfIfFcms3PjwyPFksFiM4XdWuXTt98cUXkqSQkBAVFxe71IuLixUcHKyQkBBJV6bfrq5rujqVd7V+o7Y3O/f3p/puhdOpegtPAG6Ozx6A+uL2RxVcz5tvvqkJEya47MvOzla7du0kSTabTenp6Ubt1KlTOnXqlGw2m0JCQhQaGupST09PV2hoqJo3b67w8HDl5+eroKDApR4eHm6c+8CBA3L++zev0+nU/v37ZbPZ6undAgCAO4lHhqd+/fpp3759WrVqlU6cOKGNGzfq448/1sSJEyVJY8eO1datW5Wamqrs7GzNnj1bffv2VevWrY36kiVLtGfPHu3Zs0dLly7V+PHjJUmtW7dW7969NWvWLGVnZys1NVVpaWl6+umnJUmDBw/WuXPntGjRIh05ckSLFi1SeXm5hgwZ4p6LAQAAPIpHTtt1795db775phITE/Xmm2+qZcuWWrp0qex2uyTJbrdr/vz5SkxM1LfffqtHH31UCxYsMNpPmjRJZ86c0bRp09SgQQONGjXK5U5WfHy8YmNjNWbMGAUHB+u1115T9+7dJUn+/v76wx/+oLlz52rTpk3q2LGjVqxYoYYNG/6g1wAAAHgmi9PJyoD6UlxcfwvGvb2vLBh/elmasvPP1twAuAd0atlMyb8eqpKSi6qsZME4AHMsFikoqOYF4x45bQcAAOCpCE8AAAAmEJ4AAABMIDwBAACYQHgCAAAwgfAEAABgAuEJAADABMITAACACYQnAAAAEwhPAAAAJhCeAAAATCA8AQAAmEB4AgAAMIHwBAAAYALhCQAAwATCEwAAgAmEJwAAABMITwAAACYQngAAAEwgPAEAAJhAeAIAADCB8AQAAGAC4QkAAMAEwhMAAIAJhCcAAAATCE8AAAAmEJ4AAABMIDwBAACYQHgCAAAwgfAEAABgAuEJAADABMITAACACYQnAAAAEwhPAAAAJhCeAAAATCA8AQAAmEB4AgAAMIHwBAAAYALhCQAAwATCEwAAgAkeEZ4cDoeGDh2qPXv2GPvy8vI0YcIEhYeH68knn9Tu3btd2nz22WcaOnSobDabxo8fr7y8PJf62rVr1adPH9ntdsXExKi8vNyoVVRUKCYmRpGRkerdu7dWr17t0ramvgEAwL3L7eGpoqJCv/nNb5STk2Psczqdmjp1qoKCgrR582b9/Oc/17Rp03Ty5ElJ0smTJzV16lSNGDFCH374oZo1a6aXXnpJTqdTkvTpp58qKSlJ8+fP17p165SZmamEhATj/PHx8Tp48KDWrVunuXPnKikpSdu3b7+lvgEAwL3NreHpyJEjGjNmjE6cOOGy/4svvlBeXp7mz5+v9u3ba8qUKQoPD9fmzZslSampqeratasmTpyon/zkJ1q8eLHy8/O1d+9eSdL69ev13HPPqV+/furevbvmzZunzZs3q7y8XGVlZUpNTVVsbKzCwsI0cOBAPf/880pOTr6lvgEAwL3NreFp79696tmzp1JSUlz2Z2ZmqkuXLmrYsKGxLyIiQhkZGUY9MjLSqFmtVoWFhSkjI0NVVVX68ssvXerh4eG6fPmysrOzlZ2drcrKStntdpdzZ2Zmqrq6usa+AQDAvc3bnZ2PGzfuuvuLiorUvHlzl32BgYEqKCiosX7u3DlVVFS41L29vdW0aVMVFBTIy8tLAQEB8vHxMepBQUGqqKhQaWlpjX2bYbGYbgKgjvD5A2DWrf7ecGt4upHy8nKXcCNJPj4+cjgcNdYvXbpkbF+v7nQ6r1uTrixcr6lvMwIDG5tuA+D2BQQ0cvcQANzFPDI8+fr6qrS01GWfw+GQn5+fUf9+mHE4HGrSpIl8fX2N7e/XrVarqqqqrluTJD8/vxr7NuPMmfP69xr2OteggRd/IIAbKCm5qKqqancPA8AdxmK5tRsfHhmeQkJCdOTIEZd9xcXFxnRaSEiIiouLr6l37txZTZs2la+vr4qLi9W+fXtJUmVlpUpLSxUcHCyn06mSkhJVVlbK2/vK2y8qKpKfn5+aNGlSY99mOJ2qt/AE4Ob47AGoL25/VMH12Gw2HTp0yJiCk6T09HTZbDajnp6ebtTKy8t1+PBh2Ww2eXl5qVu3bi71jIwMeXt7q1OnTurcubO8vb1dFoCnp6erW7du8vLyqrFvAABwb/PI8NSjRw+1aNFC0dHRysnJ0YoVK5SVlaVRo0ZJkkaOHKn9+/drxYoVysnJUXR0tFq1aqWePXtKurIQfdWqVdqxY4eysrIUFxenMWPGyGq1ymq1avjw4YqLi1NWVpZ27Nih1atXa/z48bfUNwAAuLd5ZHhq0KCBli9frqKiIo0YMUJ//OMf9fbbbys0NFSS1KpVK7311lvavHmzRo0apdLSUr399tuy/HuZ/FNPPaUpU6bo1Vdf1cSJE9W9e3fNmjXLOH90dLTCwsL03HPPad68eZo+fboGDRp0S30DAIB7m8XpZGVAfSkurr8F497eVxaMP70sTdn5Z+unE+AO06llMyX/eqhKSi6qspIF4wDMsVikoKCaF4x75J0nAAAAT0V4AgAAMIHwBAAAYALhCQAAwATCEwAAgAmEJwAAABMITwAAACYQngAAAEwgPAEAAJhAeAIAADCB8AQAAGAC4QkAAMAEwhMAAIAJhCcAAAATCE8AAAAmEJ4AAABMIDwBAACYQHgCAAAwgfAEAABgAuEJAADABMITAACACYQnAAAAEwhPAAAAJhCeAAAATCA8AQAAmEB4AgAAMIHwBAAAYALhCQAAwATCEwAAgAmEJwAAABMITwAAACYQngAAAEwgPAEAAJhAeAIAADCB8AQAAGAC4QkAAMAEwhMAAIAJhCcAAAATCE8AAAAmEJ4AAABMIDwBAACY4NHh6c9//rM6duzo8oqKipIkHT58WKNHj5bNZtPIkSN18OBBl7ZpaWkaMGCAbDabpk6dqrNnzxo1p9OpJUuWqFevXurRo4fi4+NVXV1t1EtKSjR9+nTZ7Xb1799fW7du/WHeMAAA8HgeHZ6OHDmifv36affu3cZr4cKFKisr0+TJkxUZGaktW7bIbrdrypQpKisrkyRlZWUpNjZW06ZNU0pKis6dO6fo6GjjvGvWrFFaWpqSkpKUmJiobdu2ac2aNUY9Ojpa58+fV0pKil588UW98sorysrK+sHfPwAA8DweHZ5yc3PVoUMHBQcHG68mTZrok08+ka+vr2bPnq327dsrNjZWjRo10vbt2yVJGzZs0JAhQzR8+HB16tRJ8fHx2rVrl/Ly8iRJ69evV1RUlCIjI9WrVy/NnDlTycnJkqQTJ05o586dWrhwoTp06KDRo0dr2LBh2rhxo9uuAwAA8BweH57atm17zf7MzExFRETIYrFIkiwWix566CFlZGQY9cjISOP4Fi1aKDQ0VJmZmSosLNSpU6f08MMPG/WIiAjl5+fr9OnTyszMVIsWLdSqVSuX+oEDB+rnTQIAgDuKt7sHcCNOp1P/+te/tHv3bv3hD39QVVWVBg8erKioKBUVFenBBx90OT4wMFA5OTmSpNOnT6t58+bX1AsKClRUVCRJLvWgoCBJMurXa1tYWGj6Pfw72wFwAz5/AMy61d8bHhueTp48qfLycvn4+GjZsmX65ptvtHDhQl26dMnY/10+Pj5yOBySpEuXLt2wfunSJWP7uzVJcjgcNZ7bjMDAxqbbALh9AQGN3D0EAHcxjw1PLVu21J49e3T//ffLYrGoc+fOqq6u1qxZs9SjR49rwozD4ZCfn58kydfX97p1q9XqEpR8fX2NnyXJarXesO3Vc5tx5sx5OZ2mm92SBg28+AMB3EBJyUVVVVXXfCAAfIfFcms3Pjw2PElS06ZNXbbbt2+viooKBQcHq7i42KVWXFxsTLeFhIRctx4cHKyQkBBJUlFRkbGu6epU3tX6jdqa5XSq3sITgJvjswegvnhsePr73/+umTNn6q9//ausVqsk6auvvlLTpk0VERGhd999V06nUxaLRU6nU/v379cLL7wgSbLZbEpPT9eIESMkSadOndKpU6dks9kUEhKi0NBQpaenG+EpPT1doaGhat68ucLDw5Wfn6+CggL96Ec/Murh4eE//EUAcM/y8rLIy4uFW8B3VVc7VV3t/v8y8tjwZLfb5evrq1deeUVTp05VXl6e4uPj9fzzz2vw4MFaunSpFi1apF/84hf64IMPVF5eriFDhkiSxo4dq2effVbh4eHq1q2bFi1apL59+6p169ZGfcmSJUY4Wrp0qSZOnChJat26tXr37q1Zs2YpNjZWX375pdLS0rRhwwb3XAgA9xwvL4sCmlrl1aCBu4cCeJTqqiqVlJa7PUB5bHjy9/fXqlWr9Nprr2nkyJFq1KiRfvGLX+j555+XxWLRH/7wB82dO1ebNm1Sx44dtWLFCjVs2FDSleA1f/58JSYm6ttvv9Wjjz6qBQsWGOeeNGmSzpw5o2nTpqlBgwYaNWqUJkyYYNTj4+MVGxurMWPGKDg4WK+99pq6d+/+Q18CAPcoLy+LvBo0UPGW3+ly8VF3DwfwCPcFtVPQiP8nLy+L28OTxelkZUB9KS6uvwXj3t5XFow/vSxN2flna24A3AM6tWym5F8PVUnJRVVW3rkLxq9+vk+tGKPLBV+5eziAR7jvR53VYvKmev18WyxSUFDNC8Y9+iGZAAAAnobwBAAAYALhCQAAwATCEwAAgAmEJwAAABMITwAAACYQngAAAEwgPAEAAJhAeAIAADCB8AQAAGAC4QkAAMAEwhMAAIAJhCcAAAATCE8AAAAmEJ4AAABMIDwBAACYQHgCAAAwgfAEAABgAuEJAADABMITAACACYQnAAAAEwhPAAAAJhCeAAAATCA8AQAAmEB4AgAAMIHwBAAAYALhCQAAwATCEwAAgAmEJwAAABMITwAAACYQngAAAEwgPAEAAJhAeAIAADCB8AQAAGAC4QkAAMAEwhMAAIAJhCcAAAATCE8AAAAmEJ4AAABMIDwBAACYQHi6gYqKCsXExCgyMlK9e/fW6tWr3T0kAADgAbzdPQBPFR8fr4MHD2rdunU6efKkfvvb3yo0NFSDBw9299AAAIAbEZ6uo6ysTKmpqXr33XcVFhamsLAw5eTkKDk5mfAEAMA9jmm768jOzlZlZaXsdruxLyIiQpmZmaqurnbjyAAAgLsRnq6jqKhIAQEB8vHxMfYFBQWpoqJCpaWl7hsYAABwO6btrqO8vNwlOEkyth0Oxy2fx8tLcjrrdGjX6BTaTFYf/m8EJKlNUBPjZ6+74D8NfX7UWZb7rO4eBuAR7gtsa/xcX59vi+XWjuOv7nX4+vpeE5Kubvv5+d3yeZo1a1yn47qeOWN+Wu99AHeagIBG7h5CnQgcNs/dQwA8jid8vu+C/zareyEhISopKVFlZaWxr6ioSH5+fmrSpMlNWgIAgLsd4ek6OnfuLG9vb2VkZBj70tPT1a1bN3ndDXMBAACg1kgC12G1WjV8+HDFxcUpKytLO3bs0OrVqzV+/Hh3Dw0AALiZxems7yXNd6by8nLFxcXpT3/6k/z9/TVp0iRNmDDB3cMCAABuRngCAAAwgWk7AAAAEwhPAAAAJhCeAAAATCA8AQAAmEB4Am5DRUWFYmJiFBkZqd69e2v16tXuHhKAOuRwODR06FDt2bPH3UOBB+HrWYDbEB8fr4MHD2rdunU6efKkfvvb3yo0NFSDBw9299AA3KaKigrNmDFDOTk57h4KPAzhCailsrIypaam6t1331VYWJjCwsKUk5Oj5ORkwhNwhzty5IhmzJghnuaD62HaDqil7OxsVVZWym63G/siIiKUmZmp6upqN44MwO3au3evevbsqZSUFHcPBR6IO09ALRUVFSkgIEA+Pj7GvqCgIFVUVKi0tFTNmjVz4+gA3I5x48a5ewjwYNx5AmqpvLzcJThJMrYdDoc7hgQA+AEQnoBa8vX1vSYkXd328/Nzx5AAAD8AwhNQSyEhISopKVFlZaWxr6ioSH5+fmrSpIkbRwYAqE+EJ6CWOnfuLG9vb2VkZBj70tPT1a1bN3l58dECgLsVv+GBWrJarRo+fLji4uKUlZWlHTt2aPXq1Ro/fry7hwYAqEf8azvgNkRHRysuLk7PPfec/P39NX36dA0aNMjdwwIA1COLkyeAAQAA3DKm7QAAAEwgPAEAAJhAeAIAADCB8AQAAGAC4QkAAMAEwhMAAIAJhCcAAAATeEgmgDtW//79lZ+fb2xbLBY1adJEERERevXVV9WiRQs3jg7A3YqHZAK4Y/Xv31/PPfecnnzySUlSdXW1jhw5orlz5yo0NFTr16938wgB3I248wTgjta4cWMFBwcb2yEhIYqKitKsWbN0/vx5NW7c2I2jA3A3Ys0TgLuOj4+PJMnLy0vnzp3TrFmz9NBDD6l3795asGCBLl26ZBz7+uuvq3fv3urevbueffZZ5eTkSJK2bNmisWPHasmSJbLb7erbt69SU1ONdtXV1Vq5cqUef/xxo+3XX39t1Dt27KitW7dq6NCh6tq1q8aNG6e8vLwa+5Wkf/7znxoxYoS6d++un/3sZ/r000/r7VoBMI/wBOCucuLECa1YsUJ9+vRRo0aNFBsbq/Pnz+v999/X8uXL9eWXX2r+/PmSpD//+c9KSUnRsmXLlJaWpqCgIEVHRxvn+vLLL/XVV18pJSVF06ZN07x587R7925J0ttvv63Vq1crJiZGH330kVq2bKnnn39eZWVlRvu33npLsbGx2rJli0pKSrRs2bIa+y0qKtKUKVM0YsQIbdu2Tc8//7x+97vf6Z///OcPdAUB1IRpOwB3tLlz52rBggWSpMrKSt133316/PHHFRMToxMnTmjHjh3au3evMX23YMECDR8+XNHR0crPz9d9992n0NBQhYaGas6cOTp69KhxbovFovj4eAUGBqpDhw7at2+fNm3apEcffVQbNmzQb37zGz3++OPGeQcOHKg//vGP+sUvfiFJ+uUvf6lHHnlEkjR27FglJydL0k37TU5O1k9/+lM988wzkqQ2bdroq6++0rp16xQZGfkDXFEANSE8AbijRUVFadCgQbp48aLeeust5efna8aMGQoICFBGRoaqq6v12GOPubSprq7W8ePH9dRTT2nDhg16/PHHFR4ergEDBmjUqFHGcW3atFFgYKCx3bVrV33wwQc6c+aMSktLZbPZjNp9992nrl27Kjc316X9Vf7+/rp8+bIk3bTfo0ePaufOnbLb7Ubby5cv64EHHqijKwbgdhGeANzRAgMDjZDy5ptvatSoUXrppZeUkpKiqqoqNW7cWJs3b76mXUhIiPz8/PQ///M/+sc//qGdO3dq1apV2rRpkz7++GNJkre366/IqqoqeXl5ydfX97pjqaqqUnV1tbF93333Xfe44ODgG/ZbWVmpn/3sZ3rhhRdc2nx/LADchzVPAO4aPj4+Wrhwob766iutXbtWDzzwgM6fPy+LxaI2bdqoTZs2unTpkuLj4+VwOPTXv/5Vqamp6tu3r+bNm6etW7fq2LFj+r//+z9J0vHjx3Xx4kXj/AcPHlSHDh3UuHFjBQUFKSMjw6hdvnxZhw4duqU7RDfr94EHHtDx48eN8bZp00b/+7//q23bttX59QJQO4QnAHeV7t27a9SoUVq+fLn8/f3Vp08fzZw5U1lZWTp06JCio6NVVlamJk2aqLq6WvHx8frzn/+sb775Rlu2bJHValXbtm0lSWVlZZo7d65yc3O1adMmbd++XePGjZMkTZgwQYmJifrLX/6i3NxczZkzRxUVFcYzp27mZv2OGzdOBw8e1BtvvKFjx45p27Ztev311xUaGlqflw2ACdwHBnDXefnll/Xpp58qISFB8fHxWrhwoSZMmCBvb2/16dNHr7zyiqQrD9mMiorS4sWLVVRUpHbt2mn58uW6//77JUktWrRQcHCwRo0apeDgYCUkJCgiIkKSNHHiRF24cEFz5szRhQsXZLfb9d5776lZs2Y1ju9m/d5///165513tGTJEq1atUohISH63e9+p2HDhtXfBQNgCk8YB4Dr2LJli5KSkvSXv/zF3UMB4GGYtgMAADCB8AQAAGAC03YAAAAmcOcJAADABMITAACACYQnAAAAEwhPAAAAJhCeAAAATCA8AQAAmEB4AgAAMIHwBAAAYALhCQAAwIT/D6ieyHocq5/VAAAAAElFTkSuQmCC\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# SUMMARY NUMERIC COLUMNS\n",
    "def num_summary(dataframe, numerical_col, plot=False):\n",
    "    quantiles = [0.05, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 0.95, 0.99]\n",
    "    print(dataframe[numerical_col].describe(quantiles).T)\n",
    "\n",
    "    if plot:\n",
    "        dataframe[numerical_col].hist(bins=50)\n",
    "        plt.xlabel(numerical_col)\n",
    "        plt.title(numerical_col)\n",
    "        plt.show()\n",
    "\n",
    "    print(\"#####################################\")\n",
    "\n",
    "for col in num_cols:\n",
    "    num_summary(train, col, plot=True)"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:40.462048Z",
     "iopub.execute_input": "2022-01-18T22:51:40.462421Z",
     "iopub.status.idle": "2022-01-18T22:51:41.846405Z",
     "shell.execute_reply.started": "2022-01-18T22:51:40.46239Z",
     "shell.execute_reply": "2022-01-18T22:51:41.845477Z"
    },
    "trusted": true
   },
   "execution_count": 136,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    381109.000000\n",
      "mean         38.822584\n",
      "std          15.511611\n",
      "min          20.000000\n",
      "5%           21.000000\n",
      "10%          22.000000\n",
      "20%          24.000000\n",
      "30%          25.000000\n",
      "40%          29.000000\n",
      "50%          36.000000\n",
      "60%          42.000000\n",
      "70%          47.000000\n",
      "80%          53.000000\n",
      "90%          62.000000\n",
      "95%          69.000000\n",
      "99%          77.000000\n",
      "max          85.000000\n",
      "Name: Age, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#####################################\n",
      "count    381109.000000\n",
      "mean         26.388807\n",
      "std          13.229888\n",
      "min           0.000000\n",
      "5%            5.000000\n",
      "10%           8.000000\n",
      "20%          11.000000\n",
      "30%          18.000000\n",
      "40%          28.000000\n",
      "50%          28.000000\n",
      "60%          28.000000\n",
      "70%          31.000000\n",
      "80%          39.000000\n",
      "90%          46.000000\n",
      "95%          47.000000\n",
      "99%          50.000000\n",
      "max          52.000000\n",
      "Name: Region_Code, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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udb0+vvxwWeAJDAxUQkKC07bk5GRzqikwMFDJyck3tDdq1EiVKlWSt7e3kpOTVbduXUlSdna2UlNTFRAQIMMwlJKSouzsbHl6XhtyUlKSfHx8VKFChdueO78MQ277hbqz79KMuroPtb25u60LtXUfauseVqiryz540OFw6ODBg+b0lCTFxMTI4XCY7TExMWZbRkaGDh06JIfDIbvdrpCQEKf22NhYeXp6qmHDhmrUqJE8PT2dFiHHxMQoJCREdrv9tucGAAClm8sCT2RkpKpVq6aoqCgdOXJEixYtUnx8vLp16yZJ6tq1q/bt26dFixbpyJEjioqKUo0aNdS0aVNJ1xZDL168WJ999pni4+M1YcIE9ejRQ76+vvL19VXnzp01YcIExcfH67PPPtOSJUvUu3fvfJ0bAACUbi4LPB4eHlqwYIGSkpLUpUsX/eMf/9D8+fMVFBQkSapRo4bmzp2rjRs3qlu3bkpNTdX8+fNl+3WJdfv27TVw4ECNGzdO/fr1U+PGjTVixAiz/6ioKAUHB6tPnz56/fXXNXToUD3xxBP5OjcAACjdbIZR0mflXCc52T2Llv39y7ul79KMurpPaaytp+e1Rcvt53ytg6fS8twnOKiCtg9rqZSUK8rOvvNFy6WttoWF2rpHca/r9fHlB18eCgAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALI/AAwAALM+lgef06dMaOHCgmjRpotatW2vZsmVm26FDh9S9e3c5HA517dpVBw4ccDp227Zteuyxx+RwODR48GBduHDBbDMMQzNmzFCzZs0UGRmp6Oho5ebmmu0pKSkaOnSowsLC1Lp1a23ZssWVlwUAAEo4lwael19+WWXKlNGmTZs0evRozZo1S59++qnS09M1YMAARUREaNOmTQoLC9PAgQOVnp4uSYqPj9eYMWM0ZMgQrV27VmlpaYqKijL7Xbp0qbZt26Z58+Zpzpw52rp1q5YuXWq2R0VF6dKlS1q7dq0GDRqksWPHKj4+3pWXBgAASjBPV3V08eJFxcbGatKkSapdu7Zq166tli1bavfu3bp48aK8vb01cuRI2Ww2jRkzRl999ZV27NihLl26aOXKlWrbtq06d+4sSYqOjlarVq2UmJiomjVrasWKFRo2bJgiIiIkScOHD9fs2bPVv39/nThxQl9++aU+//xz1ahRQ/Xr11dsbKw++OADNW7c2FWXBwAASjCX3eHx8fGRr6+vNm3apF9++UXHjh3Tvn371KhRI8XFxSk8PFw2m02SZLPZ1KRJE8XGxkqS4uLizDAjSdWqVVNQUJDi4uJ09uxZnT59Wg8++KDZHh4erpMnT+rcuXOKi4tTtWrVVKNGDaf2/fv3u+rSAABACeeyOzze3t4aN26cJk2apBUrVignJ0ddunRR9+7d9fnnn6tevXpO+1epUkVHjhyRJJ07d05Vq1a9of3MmTNKSkqSJKd2f39/STLb8zr27NmzBb6GX/OYS13v0x19l2bU1X2o7e3daW2orftQW/co7nUtyLhcFngk6ejRo2rVqpWee+45HTlyRJMmTdIf//hHZWRkyMvLy2lfLy8vZWVlSZKuXr160/arV6+aj3/bJklZWVm37bsgqlQpX+BjikPfpRl1dR9qmzc/v7J33Qe1dR9q6x5WqKvLAs/u3bu1YcMG7dy5Uz4+PgoJCdHZs2e1cOFC1axZ84YAkpWVJR8fH0nX7g7l1e7r6+sUbry9vc0/S5Kvr+9Nj73ed0GcP39JhlHgw27JZrv2RHFH36UZdXWf0lhbDw97voNMSsoV5eTk3n7HPJTG2hYWausexb2u18eXHy4LPAcOHFCtWrWcgsb999+vt99+WxEREUpOTnbaPzk52ZyKCgwMzLM9ICBAgYGBkqSkpCRznc71aa7r7Tc7tqAMQ277hbqz79KMuroPtb25u60LtXUfauseVqiryxYtV61aVT/99JPT3ZZjx46pRo0acjgc2r9/v4xfq2UYhvbt2yeHwyFJcjgciomJMY87ffq0Tp8+LYfDocDAQAUFBTm1x8TEKCgoSFWrVlVoaKhOnjypM2fOOLWHhoa66tIAAEAJ57LA07p1a91zzz0aO3asjh8/ri+++EJvv/22nn32WbVp00ZpaWmaMmWKEhISNGXKFGVkZKht27aSpGeeeUZbtmzR+vXrdfjwYY0cOVKPPvqoatasabbPmDFDe/bs0Z49ezRz5kz17t1bklSzZk21aNFCI0aM0OHDh7V+/Xpt27ZNvXr1ctWlAQCAEs5lU1rly5fXsmXLNGXKFHXr1k2VK1fWoEGD9Je//EU2m03vvPOOxo8fr3Xr1qlBgwZatGiRypQpI0kKCwvTxIkTNWfOHF28eFHNmzfXpEmTzL779++v8+fPa8iQIfLw8FC3bt3Ut29fsz06OlpjxoxRjx49FBAQoDfeeIPP4AEAACabYZT0WTnXSU52z6Jlf//ybum7NKOu7lMaa+vpeW3Rcvs5X+vgqbQ89wkOqqDtw1oqJeWKsrPvfNFyaattYaG27lHc63p9fPnBl4cCAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLI/AAAADLc2ngycrK0uuvv64HH3xQDz30kN58800ZhiFJOnTokLp37y6Hw6GuXbvqwIEDTsdu27ZNjz32mBwOhwYPHqwLFy6YbYZhaMaMGWrWrJkiIyMVHR2t3Nxcsz0lJUVDhw5VWFiYWrdurS1btrjysgAAQAnn0sAzefJk7dq1S4sXL9bMmTO1bt06rV27Vunp6RowYIAiIiK0adMmhYWFaeDAgUpPT5ckxcfHa8yYMRoyZIjWrl2rtLQ0RUVFmf0uXbpU27Zt07x58zRnzhxt3bpVS5cuNdujoqJ06dIlrV27VoMGDdLYsWMVHx/vyksDAAAlmKerOkpNTdXGjRu1dOlSNW7cWJLUr18/xcXFydPTU97e3ho5cqRsNpvGjBmjr776Sjt27FCXLl20cuVKtW3bVp07d5YkRUdHq1WrVkpMTFTNmjW1YsUKDRs2TBEREZKk4cOHa/bs2erfv79OnDihL7/8Up9//rlq1Kih+vXrKzY2Vh988IE5DgAAULq57A5PTEyMypUrp8jISHPbgAEDNHXqVMXFxSk8PFw2m02SZLPZ1KRJE8XGxkqS4uLizDAjSdWqVVNQUJDi4uJ09uxZnT59Wg8++KDZHh4erpMnT+rcuXOKi4tTtWrVVKNGDaf2/fv3u+rSAABACeeyOzyJiYmqXr26Nm/erLffflu//PKLunTpokGDBikpKUn16tVz2r9KlSo6cuSIJOncuXOqWrXqDe1nzpxRUlKSJDm1+/v7S5LZntexZ8+eLfA1/JrHXOp6n+7ouzSjru5DbW/vTmtDbd2H2rpHca9rQcblssCTnp6un376SWvWrNHUqVOVlJSkcePGydfXVxkZGfLy8nLa38vLS1lZWZKkq1ev3rT96tWr5uPftknXFknfru+CqFKlfIGPKQ59l2bU1X2obd78/MredR/U1n2orXtYoa4uCzyenp66fPmyZs6cqerVq0uSTp06pdWrV6tWrVo3BJCsrCz5+PhIkry9vfNs9/X1dQo33t7e5p8lydfX96bHXu+7IM6fv6Rf31TmMjbbtSeKO/ouzair+5TG2np42PMdZFJSrignJ/f2O+ahNNa2sFBb9yjudb0+vvxwWeAJCAiQt7e3GXYkqU6dOjp9+rQiIyOVnJzstH9ycrI5FRUYGJhne0BAgAIDAyVJSUlJ5jqd69Nc19tvdmxBGYbc9gt1Z9+lGXV1H2p7c3dbF2rrPtTWPaxQV5ctWnY4HMrMzNTx48fNbceOHVP16tXlcDi0f/9+8zN5DMPQvn375HA4zGNjYmLM406fPq3Tp0/L4XAoMDBQQUFBTu0xMTEKCgpS1apVFRoaqpMnT+rMmTNO7aGhoa66NAAAUMK5LPDce++9evTRRxUVFaXDhw/r66+/1qJFi/TMM8+oTZs2SktL05QpU5SQkKApU6YoIyNDbdu2lSQ988wz2rJli9avX6/Dhw9r5MiRevTRR1WzZk2zfcaMGdqzZ4/27NmjmTNnqnfv3pKkmjVrqkWLFhoxYoQOHz6s9evXa9u2berVq5erLg0AAJRwLpvSkqQZM2Zo0qRJeuaZZ+Tr66tevXrp2Weflc1m0zvvvKPx48dr3bp1atCggRYtWqQyZcpIksLCwjRx4kTNmTNHFy9eVPPmzTVp0iSz3/79++v8+fMaMmSIPDw81K1bN/Xt29dsj46O1pgxY9SjRw8FBATojTfe4DN4AACAyWYYJX1WznWSk92zaNnfv7xb+i7NqKv7lMbaenpeW7Tcfs7XOngqLc99goMqaPuwlkpJuaLs7DtftFzaaltYqK17FPe6Xh9ffvDloQAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPLcFngGDBigV1991Xx86NAhde/eXQ6HQ127dtWBAwec9t+2bZsee+wxORwODR48WBcuXDDbDMPQjBkz1KxZM0VGRio6Olq5ublme0pKioYOHaqwsDC1bt1aW7ZscddlAQCAEsgtgWf79u3auXOn+Tg9PV0DBgxQRESENm3apLCwMA0cOFDp6emSpPj4eI0ZM0ZDhgzR2rVrlZaWpqioKPP4pUuXatu2bZo3b57mzJmjrVu3aunSpWZ7VFSULl26pLVr12rQoEEaO3as4uPj3XFpAACgBHJ54ElNTVV0dLRCQkLMbR999JG8vb01cuRI1a1bV2PGjFHZsmW1Y8cOSdLKlSvVtm1bde7cWQ0bNlR0dLR27typxMRESdKKFSs0bNgwRUREqFmzZho+fLhWrVolSTpx4oS+/PJLTZ48WfXr11f37t3VsWNHffDBB66+NAAAUEK5PPBMmzZNnTp1Ur169cxtcXFxCg8Pl81mkyTZbDY1adJEsbGxZntERIS5f7Vq1RQUFKS4uDidPXtWp0+f1oMPPmi2h4eH6+TJkzp37pzi4uJUrVo11ahRw6l9//79rr40AABQQnm6srPdu3fru+++09atWzVhwgRze1JSklMAkqQqVaroyJEjkqRz586patWqN7SfOXNGSUlJkuTU7u/vL0lme17Hnj17tsDj/zWPudT1Pt3Rd2lGXd2H2t7endaG2roPtXWP4l7XgozLZYEnMzNT48eP17hx4+Tj4+PUlpGRIS8vL6dtXl5eysrKkiRdvXr1pu1Xr141H/+2TZKysrJu23dBVKlSvsDHFIe+SzPq6j7UNm9+fmXvug9q6z7U1j2sUFeXBZ558+bpgQceUMuWLW9o8/b2viGAZGVlmcHoZu2+vr5O4cbb29v8syT5+vretu+COH/+kgyjwIfdks127Ynijr5LM+rqPqWxth4e9nwHmZSUK8rJyb39jnkojbUtLNTWPYp7Xa+PLz9cFni2b9+u5ORkhYWFSfq/UPLxxx+rQ4cOSk5Odto/OTnZnIoKDAzMsz0gIECBgYGSrk2LXV+nc32a63r7zY4tKMOQ236h7uy7NKOu7kNtb+5u60Jt3YfauocV6uqyRcvvv/++tm7dqs2bN2vz5s1q3bq1Wrdurc2bN8vhcGj//v0yfq2WYRjat2+fHA6HJMnhcCgmJsbs6/Tp0zp9+rQcDocCAwMVFBTk1B4TE6OgoCBVrVpVoaGhOnnypM6cOePUHhoa6qpLAwAAJZzL7vBUr17d6XHZstduD9eqVUtVqlTRzJkzNWXKFD399NNas2aNMjIy1LZtW0nSM888o2effVahoaEKCQnRlClT9Oijj6pmzZpm+4wZM/SHP/xBkjRz5kz169dPklSzZk21aNFCI0aM0JgxY/T9999r27ZtWrlypasuDQAAlHAufZfWzZQrV07vvPOOxo8fr3Xr1qlBgwZatGiRypQpI0kKCwvTxIkTNWfOHF28eFHNmzfXpEmTzOP79++v8+fPa8iQIfLw8FC3bt3Ut29fsz06OlpjxoxRjx49FBAQoDfeeEONGzcujEsDAAAlgM0wSvqsnOskJ7tn0bK/f3m39F2aUVf3KY219fS8tmi5/ZyvdfBUWp77BAdV0PZhLZWSckXZ2Xe+aLm01bawUFv3KO51vT6+/ODLQwEAgOUReAAAgOUReAAAgOUReAAAgOUReAAAgOUReAAAgOUReAAAgOUReAAAgOUReAAAgOUReAAAgOUReAAAgOUReAAAgOUReAAAgOV5FvUAYG12u012u+2W++TmGsrNLYZfwwsAsAwCD9zGbrepYqUy8vS49Y3E7JxcXUxNJ/QAANyGwAO3sdtt8vSw66U1+5Vw7nKe+9SrWk6znw6T3W4j8AAA3IbAA7dLOHdZB0+lFfUwAAClGIuWAQCA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5RF4AACA5bk08Jw9e1bDhg1TZGSkWrZsqalTpyozM1OSlJiYqL59+yo0NFTt2rXTN99843Tsrl271KFDBzkcDvXu3VuJiYlO7cuWLVPLli0VFham0aNHKyMjw2zLzMzU6NGjFRERoRYtWmjJkiWuvCwAAFDCuSzwGIahYcOGKSMjQ6tWrdJbb72lL7/8UrNmzZJhGBo8eLD8/f21ceNGderUSUOGDNGpU6ckSadOndLgwYPVpUsXbdiwQZUrV9aLL74owzAkSR9//LHmzZuniRMnavny5YqLi9P06dPNc0dHR+vAgQNavny5xo8fr3nz5mnHjh2uujQAAFDCebqqo2PHjik2Nlb/+te/5O/vL0kaNmyYpk2bpocffliJiYlas2aNypQpo7p162r37t3auHGjhg4dqvXr1+uBBx5Qv379JElTp05V8+bNtXfvXjVt2lQrVqxQnz591KpVK0nS66+/rv79+2vEiBEyDEPr16/Xu+++q+DgYAUHB+vIkSNatWqV2rRp46rLAwAAJZjL7vAEBATovffeM8POdZcvX1ZcXJzuv/9+lSlTxtweHh6u2NhYSVJcXJwiIiLMNl9fXwUHBys2NlY5OTn6/vvvndpDQ0P1yy+/6PDhwzp8+LCys7MVFhbm1HdcXJxyc3NddXkAAKAEc9kdngoVKqhly5bm49zcXK1cuVLNmjVTUlKSqlat6rR/lSpVdObMGUm6ZXtaWpoyMzOd2j09PVWpUiWdOXNGdrtdfn5+8vLyMtv9/f2VmZmp1NRUVa5cOd/XYLMV6JIL1Kc7+raagtSIuroPtb29O60NtXUfausexb2uBRmXywLP702fPl2HDh3Shg0btGzZMqdAIkleXl7KysqSJGVkZNy0/erVq+bjvNoNw8izTZLZf35VqVK+QPsXl76twM+v7B0dR13dh9rm7U6fq79Fbd2H2rqHFerqlsAzffp0LV++XG+99Zbq168vb29vpaamOu2TlZUlHx8fSZK3t/cN4SQrK0sVKlSQt7e3+fj37b6+vsrJycmzTZLZf36dP39Jv66Tdhmb7doTxR19F3ceHvZ8vzikpFxRTk7+pyBLc13drTTW1p3P1d8qjbUtLFaprd1uU4WKZeTpcesVJ9k5uUq7mK7cXPdebHGv6/Xx5YfLA8+kSZO0evVqTZ8+XU8++aQkKTAwUAkJCU77JScnm9NUgYGBSk5OvqG9UaNGqlSpkry9vZWcnKy6detKkrKzs5WamqqAgAAZhqGUlBRlZ2fL0/Pa5SQlJcnHx0cVKlQo0NgNQ277hbqzb6u4k/pQV/ehtjd3t3Whtu5T0mtrs9nk6WHXS2v2K+Hc5Tz3qVe1nGY/HSabzWa+m9ndSnpdJRd/Ds+8efO0Zs0avfnmm2rfvr253eFw6ODBg+b0lCTFxMTI4XCY7TExMWZbRkaGDh06JIfDIbvdrpCQEKf22NhYeXp6qmHDhmrUqJE8PT3NBdDX+w4JCZHdzucqAgBKnoRzl3XwVFqePzcLQrg1lyWCo0ePasGCBXrhhRcUHh6upKQk8ycyMlLVqlVTVFSUjhw5okWLFik+Pl7dunWTJHXt2lX79u3TokWLdOTIEUVFRalGjRpq2rSpJKlnz55avHixPvvsM8XHx2vChAnq0aOHfH195evrq86dO2vChAmKj4/XZ599piVLlqh3796uujQAAFDCuWxK6/PPP1dOTo4WLlyohQsXOrX9+OOPWrBggcaMGaMuXbqoVq1amj9/voKCgiRJNWrU0Ny5c/XGG29o/vz5CgsL0/z582X7dfl1+/btdfLkSY0bN05ZWVl64oknNGLECLP/qKgoTZgwQX369FG5cuU0dOhQPfHEE666NABACWS322S33/ptPLm5htvXwaB4cFngGTBggAYMGHDT9lq1amnlypU3bX/kkUf0yCOP3FH/vr6+mjZtmqZNm5b/AQMALMtut6lipfwt/r2Y6v7Fvyh6bntbOgAARcVuz//iX7vdRuApBQg8AADLur74F+BtTAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPIIPAAAwPL4tnTATex2m+x22y33yc01lJtrFNKIAKD0IvAAbmC321SxUhl5etz6Jmp2Tq4upqYTegDAzQg8gBvY7TZ5etj10pr9Sjh3Oc996lUtp9lPh8lutxF4AMDNCDyAGyWcu6yDp9KKehgoRPmZyrTbbcrJIeQChYnAAwAukt+pzAoVyzCVCRQyAg8AuAhTmUDxReABABdjKhMofgg8AFAAHreYrrpVG4CiReABgHwIKOetnFxDFSr4FvVQANwBAg8A5EMFX0952G23XJ/zaIMAjXiyYSGPDEB+EHgAoAButT6nbkDZQh4NUDhuN11bEj41nsADAADyZLfblJNryM/v1mG+JHxqPIEHAADkyWaz3XYqt6R81AKBpwThyyhvjfoAgHtY4aMWCDwlBF9GeWvUB3Av/kOBko7AU0LwCa63Rn0A9+E/FLACAk8JY4Xbiu5EfQDX4z8UsAICDwAgX/gPBUoyAg8AlFCsqwHyj8ADACUQ62pghQ8DLEwEHgAogVhXU3rl93vdCLvOCDwAUIK5Yl3N7abG+Bb44iU/3+tG2L0RgQcASrH8To2h+GERecEQeJAnFkMCpUN+psb4FnhYAYEHN2AxJFD68C3wsDoCD27AYkgAd4p3Dt0a66WKDoEHN8X8MKyCFxn3451Dt8d6qaJF4AEsgDVXN8eLTOHgnUO3x3qpokXgAe5AcbpjwJqrW+NFpnAVxtvkpZId4AtzvRRTjP+HwIMS4Wb/AF7/y1wcA0ZhKew1VyX1xYhFuSUDAd41mGK8EYGnmChOdwyKm1v9A+jn5/oXqvz8LorjHYPCWHPFixHcjTdNuAZTjDci8BQDxe2OQXFTmFMSBfldlMY7BrwYobDwpgnXoI7/h8BTDLDGIH8KI2Dwu8gf/hEFUNIQeIqRknjH4FZTbQWZhnNVP646V0n8XQAAbo7AU0gK8wW9MOR3QVxh9VPczgXcLdb1Aa5F4HEzu92mnFzDLYtri1J+FsTlZ+rHVf0U5pgLGy98t2bF+rCur/gpqe9OxP8h8LiZzWYrkS+y+eWqqZ/CnEIqSdNVpf2F73YvMjabTeXK+1iuPqwlK154d6I1EHgKSUl6kUXhut10Z2G+8P02YOQ1LlfeLXFlmCmJwaA0ryUraVP8vDvRGgg8FsQna5YMBVlTVFjvUPvt/2LdOQ1bkDtX+QkzJSkYFMVasuISMEr6OjqrvjuxuDw/3M0ygSczM1Ovv/66PvnkE/n4+Khfv37q169fUQ+rUBXkkzUvX7oqw8g79FjpCV6cFcWaosK8m+Sqc5WkMJMfhfl7L24Bw9XXfqu7kPw7dnvF7fnhbpYJPNHR0Tpw4ICWL1+uU6dOadSoUQoKClKbNm2KemiFJj//mDxY20+vdQhWpUplCnl0uJnCeEEvzLtJxe3OVXFVGNdeXBfqu/M5ZrU3iLhTcX1+uIslAk96errWr1+vd999V8HBwQoODtaRI0e0atWqUhV4rrvdPyal6QmOa3g3XOlmtWBZnJ9jJXF6yGrPj5uxROA5fPiwsrOzFRYWZm4LDw/X22+/rdzcXNntxfNJVpRKyxMczng3HKzEVc+x2wWR/ASV0jY9lJfivn7UEoEnKSlJfn5+8vLyMrf5+/srMzNTqampqly5chGODgBQHLkypBTnu07uVlK+md0SgScjI8Mp7EgyH2dlZeW7H7tdusk63jtm+/Vdt8FBFeTr5ZHnPnUDyrEP+7AP+7BPIe8T9l+V5GG36e3/PapTFzPy3EeSQqpXVPeImvk6n7en/ab7eP16B6S4XH9h1jGooq/+9mjdXz+KwnUvtLZbfxak877Gzd6qU4L885//1OTJk/Wvf/3L3Hb06FG1a9dOe/bsUaVKlYpucAAAoMhZYnFLYGCgUlJSlJ2dbW5LSkqSj4+PKlSoUIQjAwAAxYElAk+jRo3k6emp2NhYc1tMTIxCQkJYsAwAAKwReHx9fdW5c2dNmDBB8fHx+uyzz7RkyRL17t27qIcGAACKAUus4ZGuLVyeMGGCPvnkE5UrV079+/dX3759i3pYAACgGLBM4AEAALgZS0xpAQAA3AqBBwAAWB6BBwAAWB6BBwAAWB6Bx40yMzM1evRoRUREqEWLFlqyZElRD6lEy8rKUocOHbRnzx5zW2Jiovr27avQ0FC1a9dO33zzTRGOsOQ5e/ashg0bpsjISLVs2VJTp05VZmamJGp7t3766Sf1799fYWFhevTRR/Xee++ZbdTWNQYMGKBXX33VfHzo0CF1795dDodDXbt21YEDB4pwdCXPp59+qgYNGjj9DBs2TJI1akvgcaPo6GgdOHBAy5cv1/jx4zVv3jzt2LGjqIdVImVmZuqVV17RkSNHzG2GYWjw4MHy9/fXxo0b1alTJw0ZMkSnTp0qwpGWHIZhaNiwYcrIyNCqVav01ltv6csvv9SsWbOo7V3Kzc3VgAED5Ofnpw8//FCvv/66Fi5cqK1bt1JbF9m+fbt27txpPk5PT9eAAQMUERGhTZs2KSwsTAMHDlR6enoRjrJkSUhIUKtWrfTNN9+YP5MnT7ZObQ24xZUrV4yQkBDj3//+t7lt/vz5xl//+tciHFXJdOTIEaNjx47GU089ZdSvX9+s6a5du4zQ0FDjypUr5r59+vQx5syZU1RDLVESEhKM+vXrG0lJSea2rVu3Gi1atKC2d+ns2bPGSy+9ZFy6dMncNnjwYGP8+PHU1gVSUlKMhx9+2OjatasxatQowzAMY/369Ubr1q2N3NxcwzAMIzc313j88ceNjRs3FuVQS5S///3vxsyZM2/YbpXacofHTQ4fPqzs7GyFhYWZ28LDwxUXF6fc3NwiHFnJs3fvXjVt2lRr16512h4XF6f7779fZcqUMbeFh4c7fcUIbi4gIEDvvfee/P39nbZfvnyZ2t6lqlWratasWSpXrpwMw1BMTIy+/fZbRUZGUlsXmDZtmjp16qR69eqZ2+Li4hQeHi7br1+fbbPZ1KRJE+paAEePHlXt2rVv2G6V2hJ43CQpKUl+fn7y8vIyt/n7+yszM1OpqalFN7ASqGfPnho9erR8fX2dticlJalq1apO26pUqaIzZ84U5vBKrAoVKqhly5bm49zcXK1cuVLNmjWjti7UunVr9ezZU2FhYXryySep7V3avXu3vvvuO7344otO26nr3TEMQ8ePH9c333yjJ598Uo899phmzJihrKwsy9TWs6gHYFUZGRlOYUeS+TgrK6sohmQ5N6sx9b0z06dP16FDh7RhwwYtW7aM2rrInDlzlJycrAkTJmjq1Kk8b+9CZmamxo8fr3HjxsnHx8epjbrenVOnTpk1nDVrln7++WdNnjxZV69etUxtCTxu4u3tfcOT4frj3/9FxZ3x9va+4W5ZVlYW9b0D06dP1/Lly/XWW2+pfv361NaFQkJCJF17sR4+fLi6du2qjIwMp32obf7MmzdPDzzwgNOdyetu9m8udc2f6tWra8+ePapYsaJsNpsaNWqk3NxcjRgxQpGRkZaoLYHHTQIDA5WSkqLs7Gx5el4rc1JSknx8fFShQoUiHp01BAYGKiEhwWlbcnLyDbdecWuTJk3S6tWrNX36dD355JOSqO3dSk5OVmxsrB577DFzW7169fTLL78oICBAx44du2F/ant727dvV3Jysrk28vqL8Mcff6wOHTooOTnZaX/qWjCVKlVyely3bl1lZmYqICDAErVlDY+bNGrUSJ6enk6LumJiYhQSEiK7nbK7gsPh0MGDB3X16lVzW0xMjBwORxGOqmSZN2+e1qxZozfffFPt27c3t1Pbu/Pzzz9ryJAhOnv2rLntwIEDqly5ssLDw6ntHXr//fe1detWbd68WZs3b1br1q3VunVrbd68WQ6HQ/v375fx6/dhG4ahffv2Udd8+vrrr9W0aVOnu48//PCDKlWqpPDwcEvUlldeN/H19VXnzp01YcIExcfH67PPPtOSJUvUu3fvoh6aZURGRqpatWqKiorSkSNHtGjRIsXHx6tbt25FPbQS4ejRo1qwYIFeeOEFhYeHKykpyfyhtncnJCREwcHBGj16tBISErRz505Nnz5df/vb36jtXahevbpq1apl/pQtW1Zly5ZVrVq11KZNG6WlpWnKlClKSEjQlClTlJGRobZt2xb1sEuEsLAweXt7a+zYsTp27Jh27typ6OhoPf/889apbVG+J97q0tPTjZEjRxqhoaFGixYtjKVLlxb1kEq8334Oj2EYxv/7f//P6NWrl/HAAw8Y7du3N/71r38V4ehKlnfeeceoX79+nj+GQW3v1pkzZ4zBgwcbTZo0MZo3b24sXLjQ/BwTausao0aNMj+HxzAMIy4uzujcubMREhJidOvWzTh48GARjq7k+c9//mP07dvXCA0NNZo3b27MnTvXfM5aobY2w/j1HhUAAIBFMaUFAAAsj8ADAAAsj8ADAAAsj8ADAAAsj8ADAAAsj8ADAAAsj8ADAAAsj+/SAnBXWrdurZMnT5qPbTabKlSooPDwcI0bN07VqlW7q/7nzp2rvXv36v3337/boebLxYsXtXDhQn3yySc6f/68goKC9Je//EW9e/e+46+FefXVVyVJ//M//+PKoQIoAAIPgLs2evRotWvXTpKUm5urhIQEjR8/XqNGjdKKFSvuqu9+/frp2WefdcUwbyslJUV/+ctfVLVqVU2ZMkU1atTQ999/r0mTJikxMVGvvfZaoYwDgOsReADctfLlyysgIMB8HBgYqGHDhmnEiBG6dOmSypcvf8d9ly1b1hVDzJeZM2fKy8tLixcvlre3tySpZs2a8vHx0Ysvvqi//vWvqlOnTqGNB4DrsIYHgFt4eXlJkux2u9LS0jRixAg1adJELVq00KRJk5y+LfzAgQPq0aOHGjdurKefflqzZ8827+rMnTvX6Q7P/v379cwzzyg0NFStW7fW6tWrzbZXX31VU6dO1csvvyyHw6FHHnlEmzdvztd4s7KytH37dvXq1csMO9e1atVKy5YtU/Xq1SVdm/Z67bXX9NBDDyk8PFwjRozQxYsXzf2/++47de7cWY0bN9ZLL73k9A3UkvTpp5+qXbt2cjgc6tatm/bu3ZuvMQK4cwQeAC534sQJLVq0SC1btlTZsmU1ZswYXbp0SatXr9aCBQv0/fffa+LEiZKkS5cu6fnnn1dwcLA2b96sDh06aNGiRXn2e/ToUfXp00cPPvigNm3apKFDh2ratGn69NNPzX1WrVql4OBgbdu2TU888YTGjx+vS5cu5WvM6enpCgkJuaHNZrOpWbNmZogbMmSIfvjhB7399ttaunSpjh49aq7TuXDhggYOHKiHHnpImzdvVr169bRjxw6zr8OHD2vUqFEaNGiQ/vGPf6hjx4564YUX9NNPP+W/wAAKjCktAHdt/PjxmjRpkiQpOztb99xzj/70pz9p9OjROnHihD777DPt3bvXnNqaNGmSOnfurKioKH300UcqU6aMxo4dKw8PD917773at2+fkpKSbjjPunXrdP/99+uVV16RJN177706evSo3nvvPT3++OOSpAYNGuiFF16QJL300ktasWKFjhw5oiZNmtzyGtLS0iTpttNvhw8f1t69e7Vjxw5zemv69Olq166djh07pt27d6ty5coaMWKEbDabhg4dqp07d5rHL168WD169NBTTz0lSerdu7e+/fZbrV692gxNAFyPwAPgrg0bNkxPPPGErly5orlz5+rkyZP6+9//Lj8/P8XGxio3N1cPP/yw0zG5ubn66aef9OOPPyo4OFgeHh5mW2hoqNNdm+uOHj2qxo0bO20LCwvTmjVrzMe1a9c2/1yuXDlJ10LY7VSqVEmSnKam8nLs2DFVqFDBaS1P3bp1VbFiRR07dkwJCQlq2LChbDab2R4SEmJOax09elT//Oc/tXbtWrP9l19+UYsWLW47RgB3jsAD4K5VqVJFtWrVkiTNnj1b3bp104svvqi1a9cqJydH5cuX18aNG284LjAwUB4eHjIMw2n77x9f9/u1NdK14JSTk2M+vueee27Y52b9/dZ//dd/qXz58jp48OANoUqSBg0apGeffdac1vq9nJwccxy/P98999xjBp6cnBy98MIL6ty5s9M+Pj4+tx0jgDvHGh4ALuXl5aXJkyfrhx9+0LJly1SnTh1dunRJNptNtWrVUq1atXT16lVFR0crKytL9913n3744Qfl5uaafRw8eDDPvuvUqaO4uDinbfv373fJO6c8PT3Vrl07rVq1SllZWU5tX3zxhb744gtVrVpVderUUVpamo4dO2a2JyQk6PLly6pTp47uu+8+HTp0yCmE/fDDD07X8PPPP5u1qFWrltauXauvvvrqrq8BwM0ReAC4XOPGjdWtWzctWLBA5cqVU8uWLTV8+HDFx8fr4MGDioqKUnp6uipUqKD27dvr8uXLmjp1qo4fP65169bpo48+yrPfnj176ocfftCbb76p48eP68MPP9QHH3ygXr16uWTcQ4cO1eXLl9W/f3/t3btXJ06c0Pr16/Xqq6+qd+/eqlevnurWrauHH35Yo0aNUnx8vOLj4zVq1Cg9+OCDql+/vtq3b6+MjAxNmTJFx44d03vvvaeYmBjzHH379tVHH32kFStW6MSJE1q2bJmWLVvmNBUHwPUIPADc4r//+791zz33aPr06YqOjlaNGjXUt29fPffcc6pTp47efPNNSdc+Z+ftt9/Wt99+q6eeekoffvihnnrqqTynjoKCgvTOO+/o66+/1lNPPaWFCxfq1VdfVdeuXV0y5oCAAK1evVo1a9bU8OHD1aFDBy1fvlzDhg1zWlA8bdo01axZU3379lX//v113333af78+ZKkihUr6r333tP333+vTp06adeuXerUqZN5bGhoqKKjo/XBBx+oXbt2WrdunWbOnKkHH3zQJdcAIG82Iz+T2wDgJomJiTp79qwiIiLMba+//royMjL4KgYALsMdHgBF6vLly3ruuee0Y8cOnTx5Up988om2bNmiNm3aFPXQAFgId3gAFLn169fr3Xff1enTpxUUFKTnn39e3bt3d+k5mjZtesNi5N/avn27goKCXHpOAMUHgQdAqZCYmOj0TrDfq169ujw9+aQOwKoIPAAAwPJYwwMAACyPwAMAACyPwAMAACyPwAMAACyPwAMAACyPwAMAACyPwAMAACyPwAMAACzv/wPzkeSQDn9CYAAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#####################################\n",
      "count    381109.000000\n",
      "mean      30564.389581\n",
      "std       17213.155057\n",
      "min        2630.000000\n",
      "5%         2630.000000\n",
      "10%        2630.000000\n",
      "20%       21583.600000\n",
      "30%       26238.000000\n",
      "40%       29082.000000\n",
      "50%       31669.000000\n",
      "60%       34406.000000\n",
      "70%       37548.000000\n",
      "80%       41711.000000\n",
      "90%       48431.000000\n",
      "95%       55176.000000\n",
      "99%       72963.000000\n",
      "max      540165.000000\n",
      "Name: Annual_Premium, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#####################################\n",
      "count    381109.000000\n",
      "mean        154.347397\n",
      "std          83.671304\n",
      "min          10.000000\n",
      "5%           24.000000\n",
      "10%          38.000000\n",
      "20%          68.000000\n",
      "30%          96.000000\n",
      "40%         125.000000\n",
      "50%         154.000000\n",
      "60%         183.000000\n",
      "70%         212.000000\n",
      "80%         241.000000\n",
      "90%         270.000000\n",
      "95%         285.000000\n",
      "99%         297.000000\n",
      "max         299.000000\n",
      "Name: Vintage, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#####################################\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "def target_summary_with_num(dataframe, target, numerical_col):\n",
    "    print(dataframe.groupby(target).agg({numerical_col: \"mean\"}), end=\"\\n\\n\\n\")"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:41.847939Z",
     "iopub.execute_input": "2022-01-18T22:51:41.848266Z",
     "iopub.status.idle": "2022-01-18T22:51:41.854584Z",
     "shell.execute_reply.started": "2022-01-18T22:51:41.848221Z",
     "shell.execute_reply": "2022-01-18T22:51:41.853711Z"
    },
    "trusted": true
   },
   "execution_count": 137,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "for col in num_cols:\n",
    "    target_summary_with_num(train, \"Response\", col)"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:41.855794Z",
     "iopub.execute_input": "2022-01-18T22:51:41.856019Z",
     "iopub.status.idle": "2022-01-18T22:51:41.908919Z",
     "shell.execute_reply.started": "2022-01-18T22:51:41.85599Z",
     "shell.execute_reply": "2022-01-18T22:51:41.908091Z"
    },
    "trusted": true
   },
   "execution_count": 138,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                Age\n",
      "Response           \n",
      "0         38.178227\n",
      "1         43.435560\n",
      "\n",
      "\n",
      "          Region_Code\n",
      "Response             \n",
      "0           26.336544\n",
      "1           26.762963\n",
      "\n",
      "\n",
      "          Annual_Premium\n",
      "Response                \n",
      "0           30419.160276\n",
      "1           31604.092742\n",
      "\n",
      "\n",
      "             Vintage\n",
      "Response            \n",
      "0         154.380243\n",
      "1         154.112246\n",
      "\n",
      "\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "sns.boxplot(x=train[\"Age\"])"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:41.912498Z",
     "iopub.execute_input": "2022-01-18T22:51:41.912748Z",
     "iopub.status.idle": "2022-01-18T22:51:42.099823Z",
     "shell.execute_reply.started": "2022-01-18T22:51:41.912718Z",
     "shell.execute_reply": "2022-01-18T22:51:42.09901Z"
    },
    "trusted": true
   },
   "execution_count": 139,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot: xlabel='Age'>"
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "sns.boxplot(x=train[\"Annual_Premium\"])"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:42.101152Z",
     "iopub.execute_input": "2022-01-18T22:51:42.101392Z",
     "iopub.status.idle": "2022-01-18T22:51:42.325185Z",
     "shell.execute_reply.started": "2022-01-18T22:51:42.101363Z",
     "shell.execute_reply": "2022-01-18T22:51:42.324429Z"
    },
    "trusted": true
   },
   "execution_count": 140,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot: xlabel='Annual_Premium'>"
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "sns.boxplot(x=train[\"Vintage\"])"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:42.326783Z",
     "iopub.execute_input": "2022-01-18T22:51:42.327347Z",
     "iopub.status.idle": "2022-01-18T22:51:42.511904Z",
     "shell.execute_reply.started": "2022-01-18T22:51:42.327303Z",
     "shell.execute_reply": "2022-01-18T22:51:42.510973Z"
    },
    "trusted": true
   },
   "execution_count": 141,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot: xlabel='Vintage'>"
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Gender-Response Visualization"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "# FIXME estava com erro\n",
    "#sns.countplot(train.Gender"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:42.513475Z",
     "iopub.execute_input": "2022-01-18T22:51:42.513916Z",
     "iopub.status.idle": "2022-01-18T22:51:43.136141Z",
     "shell.execute_reply.started": "2022-01-18T22:51:42.51388Z",
     "shell.execute_reply": "2022-01-18T22:51:43.135358Z"
    },
    "trusted": true
   },
   "execution_count": 142,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "df=train.groupby(['Gender','Response'])['id'].count().to_frame().rename(columns={'id':'count'}).reset_index()"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:43.137473Z",
     "iopub.execute_input": "2022-01-18T22:51:43.137733Z",
     "iopub.status.idle": "2022-01-18T22:51:43.191663Z",
     "shell.execute_reply.started": "2022-01-18T22:51:43.137701Z",
     "shell.execute_reply": "2022-01-18T22:51:43.190821Z"
    },
    "trusted": true
   },
   "execution_count": 143,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "g = sns.catplot(x=\"Gender\", y=\"count\",col=\"Response\",\n",
    "                data=df, kind=\"bar\",\n",
    "                height=4, aspect=.7);"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:43.193208Z",
     "iopub.execute_input": "2022-01-18T22:51:43.193455Z",
     "iopub.status.idle": "2022-01-18T22:51:43.583439Z",
     "shell.execute_reply.started": "2022-01-18T22:51:43.193425Z",
     "shell.execute_reply": "2022-01-18T22:51:43.582601Z"
    },
    "trusted": true
   },
   "execution_count": 144,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 560x400 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Driving license-Gender Visualization"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "df=train.groupby(['Gender'])['Driving_License'].count().to_frame().reset_index()\n",
    "df"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:43.584817Z",
     "iopub.execute_input": "2022-01-18T22:51:43.585317Z",
     "iopub.status.idle": "2022-01-18T22:51:43.633437Z",
     "shell.execute_reply.started": "2022-01-18T22:51:43.585271Z",
     "shell.execute_reply": "2022-01-18T22:51:43.632557Z"
    },
    "trusted": true
   },
   "execution_count": 145,
   "outputs": [
    {
     "data": {
      "text/plain": "   Gender  Driving_License\n0  Female           175020\n1    Male           206089",
      "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>Gender</th>\n      <th>Driving_License</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Female</td>\n      <td>175020</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Male</td>\n      <td>206089</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "sns.catplot(x=\"Gender\", y=\"Driving_License\",\n",
    "                data=df, kind=\"bar\");"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:43.634654Z",
     "iopub.execute_input": "2022-01-18T22:51:43.636247Z",
     "iopub.status.idle": "2022-01-18T22:51:43.900771Z",
     "shell.execute_reply.started": "2022-01-18T22:51:43.63621Z",
     "shell.execute_reply": "2022-01-18T22:51:43.900139Z"
    },
    "trusted": true
   },
   "execution_count": 146,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 500x500 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Response-Vehicle age Visualization"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "df=train.groupby(['Vehicle_Age','Response'])['id'].count().to_frame().rename(columns={'id':'count'}).reset_index()\n",
    "df"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:43.901984Z",
     "iopub.execute_input": "2022-01-18T22:51:43.902252Z",
     "iopub.status.idle": "2022-01-18T22:51:43.959992Z",
     "shell.execute_reply.started": "2022-01-18T22:51:43.902216Z",
     "shell.execute_reply": "2022-01-18T22:51:43.959186Z"
    },
    "trusted": true
   },
   "execution_count": 147,
   "outputs": [
    {
     "data": {
      "text/plain": "  Vehicle_Age  Response   count\n0    1-2 Year         0  165510\n1    1-2 Year         1   34806\n2    < 1 Year         0  157584\n3    < 1 Year         1    7202\n4   > 2 Years         0   11305\n5   > 2 Years         1    4702",
      "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>Vehicle_Age</th>\n      <th>Response</th>\n      <th>count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1-2 Year</td>\n      <td>0</td>\n      <td>165510</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1-2 Year</td>\n      <td>1</td>\n      <td>34806</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>&lt; 1 Year</td>\n      <td>0</td>\n      <td>157584</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>&lt; 1 Year</td>\n      <td>1</td>\n      <td>7202</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>&gt; 2 Years</td>\n      <td>0</td>\n      <td>11305</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>&gt; 2 Years</td>\n      <td>1</td>\n      <td>4702</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "g = sns.catplot(x=\"Vehicle_Age\", y=\"count\",col=\"Response\",\n",
    "                data=df, kind=\"bar\",\n",
    "                height=4, aspect=.7);"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:43.961406Z",
     "iopub.execute_input": "2022-01-18T22:51:43.963302Z",
     "iopub.status.idle": "2022-01-18T22:51:44.377587Z",
     "shell.execute_reply.started": "2022-01-18T22:51:43.96325Z",
     "shell.execute_reply": "2022-01-18T22:51:44.376975Z"
    },
    "trusted": true
   },
   "execution_count": 148,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 560x400 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Damage Vehicle-Response Visualization"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "df=train.groupby(['Vehicle_Damage','Response'])['id'].count().to_frame().rename(columns={'id':'count'}).reset_index()"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:44.379318Z",
     "iopub.execute_input": "2022-01-18T22:51:44.379938Z",
     "iopub.status.idle": "2022-01-18T22:51:44.432429Z",
     "shell.execute_reply.started": "2022-01-18T22:51:44.379892Z",
     "shell.execute_reply": "2022-01-18T22:51:44.431799Z"
    },
    "trusted": true
   },
   "execution_count": 149,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "g = sns.catplot(x=\"Vehicle_Damage\", y=\"count\",col=\"Response\",\n",
    "                data=df, kind=\"bar\",\n",
    "                height=4, aspect=.7);"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:44.434132Z",
     "iopub.execute_input": "2022-01-18T22:51:44.434691Z",
     "iopub.status.idle": "2022-01-18T22:51:44.820879Z",
     "shell.execute_reply.started": "2022-01-18T22:51:44.434647Z",
     "shell.execute_reply": "2022-01-18T22:51:44.819913Z"
    },
    "trusted": true
   },
   "execution_count": 150,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 560x400 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# DATA PREPROCESSING"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## OUTLIERS"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "def outlier_thresholds(dataframe, col_name, q1=0.05, q3=0.95):\n",
    "    quartile1 = dataframe[col_name].quantile(q1)\n",
    "    quartile3 = dataframe[col_name].quantile(q3)\n",
    "    interquantile_range = quartile3 - quartile1\n",
    "    up_limit = quartile3 + 3 * interquantile_range\n",
    "    low_limit = quartile1 - 3 * interquantile_range\n",
    "    return low_limit, up_limit\n",
    "\n",
    "\n",
    "def check_outlier(dataframe, col_name):\n",
    "    low_limit, up_limit = outlier_thresholds(dataframe, col_name)\n",
    "    if dataframe[(dataframe[col_name] > up_limit) | (dataframe[col_name] < low_limit)].any(axis=None):\n",
    "        return True\n",
    "    else:\n",
    "        return False"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:44.822271Z",
     "iopub.execute_input": "2022-01-18T22:51:44.82259Z",
     "iopub.status.idle": "2022-01-18T22:51:44.833195Z",
     "shell.execute_reply.started": "2022-01-18T22:51:44.822541Z",
     "shell.execute_reply": "2022-01-18T22:51:44.832191Z"
    },
    "trusted": true
   },
   "execution_count": 151,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "for col in num_cols:\n",
    "    print(f\"{col} : {check_outlier(train,col)}\")"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:44.834508Z",
     "iopub.execute_input": "2022-01-18T22:51:44.835628Z",
     "iopub.status.idle": "2022-01-18T22:51:44.904023Z",
     "shell.execute_reply.started": "2022-01-18T22:51:44.835579Z",
     "shell.execute_reply": "2022-01-18T22:51:44.903049Z"
    },
    "trusted": true
   },
   "execution_count": 152,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age : False\n",
      "Region_Code : False\n",
      "Annual_Premium : True\n",
      "Vintage : False\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "def grab_outliers(dataframe, col_name, index=False):\n",
    "    low, up = outlier_thresholds(dataframe, col_name)\n",
    "\n",
    "    if dataframe[((dataframe[col_name] < low) | (dataframe[col_name] > up))].shape[0] > 10:\n",
    "        print(dataframe[((dataframe[col_name] < low) | (dataframe[col_name] > up))].head())\n",
    "    else:\n",
    "        print(dataframe[((dataframe[col_name] < low) | (dataframe[col_name] > up))])\n",
    "\n",
    "    if index:\n",
    "        outlier_index = dataframe[((dataframe[col_name] < low) | (dataframe[col_name] > up))].index\n",
    "        return outlier_index"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:44.905477Z",
     "iopub.execute_input": "2022-01-18T22:51:44.905763Z",
     "iopub.status.idle": "2022-01-18T22:51:44.913761Z",
     "shell.execute_reply.started": "2022-01-18T22:51:44.905725Z",
     "shell.execute_reply": "2022-01-18T22:51:44.912801Z"
    },
    "trusted": true
   },
   "execution_count": 153,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "for col in num_cols:\n",
    "    col, grab_outliers(train, col)"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:44.915153Z",
     "iopub.execute_input": "2022-01-18T22:51:44.915553Z",
     "iopub.status.idle": "2022-01-18T22:51:44.993716Z",
     "shell.execute_reply.started": "2022-01-18T22:51:44.915506Z",
     "shell.execute_reply": "2022-01-18T22:51:44.992864Z"
    },
    "trusted": true
   },
   "execution_count": 154,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [id, Gender, Age, Driving_License, Region_Code, Previously_Insured, Vehicle_Age, Vehicle_Damage, Annual_Premium, Policy_Sales_Channel, Vintage, Response]\n",
      "Index: []\n",
      "Empty DataFrame\n",
      "Columns: [id, Gender, Age, Driving_License, Region_Code, Previously_Insured, Vehicle_Age, Vehicle_Damage, Annual_Premium, Policy_Sales_Channel, Vintage, Response]\n",
      "Index: []\n",
      "          id  Gender  Age  Driving_License  Region_Code  Previously_Insured  \\\n",
      "1412    1413  Female   41                1         28.0                   0   \n",
      "11319  11320  Female   50                1         46.0                   1   \n",
      "13426  13427  Female   47                1         28.0                   0   \n",
      "15024  15025  Female   32                1         28.0                   0   \n",
      "25532  25533    Male   50                1         28.0                   0   \n",
      "\n",
      "      Vehicle_Age Vehicle_Damage  Annual_Premium  Policy_Sales_Channel  \\\n",
      "1412     1-2 Year            Yes        267698.0                 124.0   \n",
      "11319    1-2 Year             No        508073.0                  26.0   \n",
      "13426    1-2 Year            Yes        301762.0                 124.0   \n",
      "15024    1-2 Year            Yes        315565.0                 155.0   \n",
      "25532    1-2 Year            Yes        229935.0                 122.0   \n",
      "\n",
      "       Vintage  Response  \n",
      "1412        63         1  \n",
      "11319      192         0  \n",
      "13426       22         0  \n",
      "15024      150         0  \n",
      "25532       64         1  \n",
      "Empty DataFrame\n",
      "Columns: [id, Gender, Age, Driving_License, Region_Code, Previously_Insured, Vehicle_Age, Vehicle_Damage, Annual_Premium, Policy_Sales_Channel, Vintage, Response]\n",
      "Index: []\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "def replace_with_thresholds(dataframe, variable):\n",
    "    low_limit, up_limit = outlier_thresholds(dataframe, variable)\n",
    "    dataframe.loc[(dataframe[variable] < low_limit), variable] = low_limit\n",
    "    dataframe.loc[(dataframe[variable] > up_limit), variable] = up_limit"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:44.994819Z",
     "iopub.execute_input": "2022-01-18T22:51:44.995147Z",
     "iopub.status.idle": "2022-01-18T22:51:45.001629Z",
     "shell.execute_reply.started": "2022-01-18T22:51:44.995083Z",
     "shell.execute_reply": "2022-01-18T22:51:45.00041Z"
    },
    "trusted": true
   },
   "execution_count": 155,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "for col in num_cols:\n",
    "    replace_with_thresholds(train, col)"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:45.002804Z",
     "iopub.execute_input": "2022-01-18T22:51:45.003132Z",
     "iopub.status.idle": "2022-01-18T22:51:45.074907Z",
     "shell.execute_reply.started": "2022-01-18T22:51:45.003067Z",
     "shell.execute_reply": "2022-01-18T22:51:45.074198Z"
    },
    "trusted": true
   },
   "execution_count": 156,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "# CHECK OUTLIERS AGAIN\n",
    "for col in num_cols:\n",
    "    print(f\"{col} : {check_outlier(train,col)}\")"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:45.076377Z",
     "iopub.execute_input": "2022-01-18T22:51:45.076901Z",
     "iopub.status.idle": "2022-01-18T22:51:45.140831Z",
     "shell.execute_reply.started": "2022-01-18T22:51:45.076855Z",
     "shell.execute_reply": "2022-01-18T22:51:45.139814Z"
    },
    "trusted": true
   },
   "execution_count": 157,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age : False\n",
      "Region_Code : False\n",
      "Annual_Premium : False\n",
      "Vintage : False\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "train.drop(\"id\",axis =1).quantile([0, 0.05, 0.50, 0.95, 0.99, 1]).T"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:45.142032Z",
     "iopub.execute_input": "2022-01-18T22:51:45.142283Z",
     "iopub.status.idle": "2022-01-18T22:51:45.261559Z",
     "shell.execute_reply.started": "2022-01-18T22:51:45.142254Z",
     "shell.execute_reply": "2022-01-18T22:51:45.260639Z"
    },
    "trusted": true
   },
   "execution_count": 158,
   "outputs": [
    {
     "data": {
      "text/plain": "                        0.00    0.05     0.50     0.95     0.99      1.00\nAge                     20.0    21.0     36.0     69.0     77.0      85.0\nDriving_License          0.0     1.0      1.0      1.0      1.0       1.0\nRegion_Code              0.0     5.0     28.0     47.0     50.0      52.0\nPreviously_Insured       0.0     0.0      0.0      1.0      1.0       1.0\nAnnual_Premium        2630.0  2630.0  31669.0  55176.0  72963.0  212814.0\nPolicy_Sales_Channel     1.0    26.0    133.0    160.0    160.0     163.0\nVintage                 10.0    24.0    154.0    285.0    297.0     299.0\nResponse                 0.0     0.0      0.0      1.0      1.0       1.0",
      "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>0.00</th>\n      <th>0.05</th>\n      <th>0.50</th>\n      <th>0.95</th>\n      <th>0.99</th>\n      <th>1.00</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Age</th>\n      <td>20.0</td>\n      <td>21.0</td>\n      <td>36.0</td>\n      <td>69.0</td>\n      <td>77.0</td>\n      <td>85.0</td>\n    </tr>\n    <tr>\n      <th>Driving_License</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>Region_Code</th>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>28.0</td>\n      <td>47.0</td>\n      <td>50.0</td>\n      <td>52.0</td>\n    </tr>\n    <tr>\n      <th>Previously_Insured</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>Annual_Premium</th>\n      <td>2630.0</td>\n      <td>2630.0</td>\n      <td>31669.0</td>\n      <td>55176.0</td>\n      <td>72963.0</td>\n      <td>212814.0</td>\n    </tr>\n    <tr>\n      <th>Policy_Sales_Channel</th>\n      <td>1.0</td>\n      <td>26.0</td>\n      <td>133.0</td>\n      <td>160.0</td>\n      <td>160.0</td>\n      <td>163.0</td>\n    </tr>\n    <tr>\n      <th>Vintage</th>\n      <td>10.0</td>\n      <td>24.0</td>\n      <td>154.0</td>\n      <td>285.0</td>\n      <td>297.0</td>\n      <td>299.0</td>\n    </tr>\n    <tr>\n      <th>Response</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## CORRELATION ANALYSIS"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "df.corrwith(train[\"Response\"]).sort_values(ascending=False)\n",
    "corr_df = train.corr()\n",
    "plt.figure(figsize=(12, 9))\n",
    "sns.heatmap(corr_df, annot=True, xticklabels=corr_df.columns, yticklabels=corr_df.columns)\n",
    "\n",
    "corr_df = corr_df.corr().unstack().sort_values().drop_duplicates()\n",
    "corr_df = pd.DataFrame(corr_df, columns=[\"corr\"])\n",
    "corr_df.index.names = ['1', '2']\n",
    "corr_df = corr_df.reset_index()\n",
    "corr_df.sort_values(by=\"corr\", ascending=True).head(30)\n",
    "\n",
    "high_corr = corr_df[(corr_df[\"corr\"] >= 0.70) | (corr_df[\"corr\"] <= -0.70)]\n",
    "high_corr"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:45.263019Z",
     "iopub.execute_input": "2022-01-18T22:51:45.263356Z",
     "iopub.status.idle": "2022-01-18T22:51:46.196319Z",
     "shell.execute_reply.started": "2022-01-18T22:51:45.26331Z",
     "shell.execute_reply": "2022-01-18T22:51:46.195654Z"
    },
    "trusted": true
   },
   "execution_count": 159,
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (4,) (381109,) ",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mValueError\u001B[0m                                Traceback (most recent call last)",
      "Cell \u001B[1;32mIn [159], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mdf\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcorrwith\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtrain\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mResponse\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241m.\u001B[39msort_values(ascending\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m)\n\u001B[0;32m      2\u001B[0m corr_df \u001B[38;5;241m=\u001B[39m train\u001B[38;5;241m.\u001B[39mcorr()\n\u001B[0;32m      3\u001B[0m plt\u001B[38;5;241m.\u001B[39mfigure(figsize\u001B[38;5;241m=\u001B[39m(\u001B[38;5;241m12\u001B[39m, \u001B[38;5;241m9\u001B[39m))\n",
      "File \u001B[1;32mC:\\Python310\\lib\\site-packages\\pandas\\core\\frame.py:10571\u001B[0m, in \u001B[0;36mDataFrame.corrwith\u001B[1;34m(self, other, axis, drop, method, numeric_only)\u001B[0m\n\u001B[0;32m  10569\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m method \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mpearson\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n\u001B[0;32m  10570\u001B[0m     \u001B[38;5;28;01mfor\u001B[39;00m i, r \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28menumerate\u001B[39m(ndf):\n\u001B[1;32m> 10571\u001B[0m         nonnull_mask \u001B[38;5;241m=\u001B[39m \u001B[38;5;241;43m~\u001B[39;49m\u001B[43mnp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43misnan\u001B[49m\u001B[43m(\u001B[49m\u001B[43mr\u001B[49m\u001B[43m)\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m&\u001B[39;49m\u001B[43m \u001B[49m\u001B[38;5;241;43m~\u001B[39;49m\u001B[43mnp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43misnan\u001B[49m\u001B[43m(\u001B[49m\u001B[43mk\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m  10572\u001B[0m         corrs[cols[i]] \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39mcorrcoef(r[nonnull_mask], k[nonnull_mask])[\n\u001B[0;32m  10573\u001B[0m             \u001B[38;5;241m0\u001B[39m, \u001B[38;5;241m1\u001B[39m\n\u001B[0;32m  10574\u001B[0m         ]\n\u001B[0;32m  10575\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n",
      "\u001B[1;31mValueError\u001B[0m: operands could not be broadcast together with shapes (4,) (381109,) "
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## FEATURE ENGINEERING"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "train['Gender'] = train['Gender'].map( {'Female': 0, 'Male': 1} ).astype(int)"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:46.197422Z",
     "iopub.execute_input": "2022-01-18T22:51:46.198163Z",
     "iopub.status.idle": "2022-01-18T22:51:46.2462Z",
     "shell.execute_reply.started": "2022-01-18T22:51:46.198127Z",
     "shell.execute_reply": "2022-01-18T22:51:46.24542Z"
    },
    "trusted": true
   },
   "execution_count": 160,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "train=pd.get_dummies(train,drop_first=True)"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:46.247169Z",
     "iopub.execute_input": "2022-01-18T22:51:46.247845Z",
     "iopub.status.idle": "2022-01-18T22:51:46.379189Z",
     "shell.execute_reply.started": "2022-01-18T22:51:46.247807Z",
     "shell.execute_reply": "2022-01-18T22:51:46.378331Z"
    },
    "trusted": true
   },
   "execution_count": 161,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "train"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:46.380332Z",
     "iopub.execute_input": "2022-01-18T22:51:46.380912Z",
     "iopub.status.idle": "2022-01-18T22:51:46.40131Z",
     "shell.execute_reply.started": "2022-01-18T22:51:46.380881Z",
     "shell.execute_reply": "2022-01-18T22:51:46.400672Z"
    },
    "trusted": true
   },
   "execution_count": 162,
   "outputs": [
    {
     "data": {
      "text/plain": "            id  Gender  Age  Driving_License  Region_Code  Previously_Insured  \\\n0            1       1   44                1         28.0                   0   \n1            2       1   76                1          3.0                   0   \n2            3       1   47                1         28.0                   0   \n3            4       1   21                1         11.0                   1   \n4            5       0   29                1         41.0                   1   \n...        ...     ...  ...              ...          ...                 ...   \n381104  381105       1   74                1         26.0                   1   \n381105  381106       1   30                1         37.0                   1   \n381106  381107       1   21                1         30.0                   1   \n381107  381108       0   68                1         14.0                   0   \n381108  381109       1   46                1         29.0                   0   \n\n        Annual_Premium  Policy_Sales_Channel  Vintage  Response  \\\n0              40454.0                  26.0      217         1   \n1              33536.0                  26.0      183         0   \n2              38294.0                  26.0       27         1   \n3              28619.0                 152.0      203         0   \n4              27496.0                 152.0       39         0   \n...                ...                   ...      ...       ...   \n381104         30170.0                  26.0       88         0   \n381105         40016.0                 152.0      131         0   \n381106         35118.0                 160.0      161         0   \n381107         44617.0                 124.0       74         0   \n381108         41777.0                  26.0      237         0   \n\n        Vehicle_Age_< 1 Year  Vehicle_Age_> 2 Years  Vehicle_Damage_Yes  \n0                          0                      1                   1  \n1                          0                      0                   0  \n2                          0                      1                   1  \n3                          1                      0                   0  \n4                          1                      0                   0  \n...                      ...                    ...                 ...  \n381104                     0                      0                   0  \n381105                     1                      0                   0  \n381106                     1                      0                   0  \n381107                     0                      1                   1  \n381108                     0                      0                   0  \n\n[381109 rows x 13 columns]",
      "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>Gender</th>\n      <th>Age</th>\n      <th>Driving_License</th>\n      <th>Region_Code</th>\n      <th>Previously_Insured</th>\n      <th>Annual_Premium</th>\n      <th>Policy_Sales_Channel</th>\n      <th>Vintage</th>\n      <th>Response</th>\n      <th>Vehicle_Age_&lt; 1 Year</th>\n      <th>Vehicle_Age_&gt; 2 Years</th>\n      <th>Vehicle_Damage_Yes</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>44</td>\n      <td>1</td>\n      <td>28.0</td>\n      <td>0</td>\n      <td>40454.0</td>\n      <td>26.0</td>\n      <td>217</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>1</td>\n      <td>76</td>\n      <td>1</td>\n      <td>3.0</td>\n      <td>0</td>\n      <td>33536.0</td>\n      <td>26.0</td>\n      <td>183</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>1</td>\n      <td>47</td>\n      <td>1</td>\n      <td>28.0</td>\n      <td>0</td>\n      <td>38294.0</td>\n      <td>26.0</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>1</td>\n      <td>21</td>\n      <td>1</td>\n      <td>11.0</td>\n      <td>1</td>\n      <td>28619.0</td>\n      <td>152.0</td>\n      <td>203</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>0</td>\n      <td>29</td>\n      <td>1</td>\n      <td>41.0</td>\n      <td>1</td>\n      <td>27496.0</td>\n      <td>152.0</td>\n      <td>39</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>381104</th>\n      <td>381105</td>\n      <td>1</td>\n      <td>74</td>\n      <td>1</td>\n      <td>26.0</td>\n      <td>1</td>\n      <td>30170.0</td>\n      <td>26.0</td>\n      <td>88</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>381105</th>\n      <td>381106</td>\n      <td>1</td>\n      <td>30</td>\n      <td>1</td>\n      <td>37.0</td>\n      <td>1</td>\n      <td>40016.0</td>\n      <td>152.0</td>\n      <td>131</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>381106</th>\n      <td>381107</td>\n      <td>1</td>\n      <td>21</td>\n      <td>1</td>\n      <td>30.0</td>\n      <td>1</td>\n      <td>35118.0</td>\n      <td>160.0</td>\n      <td>161</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>381107</th>\n      <td>381108</td>\n      <td>0</td>\n      <td>68</td>\n      <td>1</td>\n      <td>14.0</td>\n      <td>0</td>\n      <td>44617.0</td>\n      <td>124.0</td>\n      <td>74</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>381108</th>\n      <td>381109</td>\n      <td>1</td>\n      <td>46</td>\n      <td>1</td>\n      <td>29.0</td>\n      <td>0</td>\n      <td>41777.0</td>\n      <td>26.0</td>\n      <td>237</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>381109 rows × 13 columns</p>\n</div>"
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "train=train.rename(columns={\"Vehicle_Age_< 1 Year\": \"Vehicle_Age_lt_1_Year\", \"Vehicle_Age_> 2 Years\": \"Vehicle_Age_gt_2_Years\"})\n",
    "train['Vehicle_Age_lt_1_Year']=train['Vehicle_Age_lt_1_Year'].astype('int')\n",
    "train['Vehicle_Age_gt_2_Years']=train['Vehicle_Age_gt_2_Years'].astype('int')\n",
    "train['Vehicle_Damage_Yes']=train['Vehicle_Damage_Yes'].astype('int')"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:46.402462Z",
     "iopub.execute_input": "2022-01-18T22:51:46.402894Z",
     "iopub.status.idle": "2022-01-18T22:51:46.440806Z",
     "shell.execute_reply.started": "2022-01-18T22:51:46.402843Z",
     "shell.execute_reply": "2022-01-18T22:51:46.439849Z"
    },
    "trusted": true
   },
   "execution_count": 163,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "train[\"premium_age_ratio\"] = train[\"Annual_Premium\"]/train[\"Age\"]"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:46.441982Z",
     "iopub.execute_input": "2022-01-18T22:51:46.44222Z",
     "iopub.status.idle": "2022-01-18T22:51:46.447948Z",
     "shell.execute_reply.started": "2022-01-18T22:51:46.442191Z",
     "shell.execute_reply": "2022-01-18T22:51:46.44715Z"
    },
    "trusted": true
   },
   "execution_count": 164,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "train[\"premium_vintage_ratio\"] = train[\"Annual_Premium\"]/train[\"Vintage\"]"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:46.449001Z",
     "iopub.execute_input": "2022-01-18T22:51:46.449213Z",
     "iopub.status.idle": "2022-01-18T22:51:46.460417Z",
     "shell.execute_reply.started": "2022-01-18T22:51:46.449188Z",
     "shell.execute_reply": "2022-01-18T22:51:46.459693Z"
    },
    "trusted": true
   },
   "execution_count": 165,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "train"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:46.468091Z",
     "iopub.execute_input": "2022-01-18T22:51:46.468382Z",
     "iopub.status.idle": "2022-01-18T22:51:46.49385Z",
     "shell.execute_reply.started": "2022-01-18T22:51:46.468352Z",
     "shell.execute_reply": "2022-01-18T22:51:46.493239Z"
    },
    "trusted": true
   },
   "execution_count": 166,
   "outputs": [
    {
     "data": {
      "text/plain": "            id  Gender  Age  Driving_License  Region_Code  Previously_Insured  \\\n0            1       1   44                1         28.0                   0   \n1            2       1   76                1          3.0                   0   \n2            3       1   47                1         28.0                   0   \n3            4       1   21                1         11.0                   1   \n4            5       0   29                1         41.0                   1   \n...        ...     ...  ...              ...          ...                 ...   \n381104  381105       1   74                1         26.0                   1   \n381105  381106       1   30                1         37.0                   1   \n381106  381107       1   21                1         30.0                   1   \n381107  381108       0   68                1         14.0                   0   \n381108  381109       1   46                1         29.0                   0   \n\n        Annual_Premium  Policy_Sales_Channel  Vintage  Response  \\\n0              40454.0                  26.0      217         1   \n1              33536.0                  26.0      183         0   \n2              38294.0                  26.0       27         1   \n3              28619.0                 152.0      203         0   \n4              27496.0                 152.0       39         0   \n...                ...                   ...      ...       ...   \n381104         30170.0                  26.0       88         0   \n381105         40016.0                 152.0      131         0   \n381106         35118.0                 160.0      161         0   \n381107         44617.0                 124.0       74         0   \n381108         41777.0                  26.0      237         0   \n\n        Vehicle_Age_lt_1_Year  Vehicle_Age_gt_2_Years  Vehicle_Damage_Yes  \\\n0                           0                       1                   1   \n1                           0                       0                   0   \n2                           0                       1                   1   \n3                           1                       0                   0   \n4                           1                       0                   0   \n...                       ...                     ...                 ...   \n381104                      0                       0                   0   \n381105                      1                       0                   0   \n381106                      1                       0                   0   \n381107                      0                       1                   1   \n381108                      0                       0                   0   \n\n        premium_age_ratio  premium_vintage_ratio  \n0              919.409091             186.423963  \n1              441.263158             183.256831  \n2              814.765957            1418.296296  \n3             1362.809524             140.980296  \n4              948.137931             705.025641  \n...                   ...                    ...  \n381104         407.702703             342.840909  \n381105        1333.866667             305.465649  \n381106        1672.285714             218.124224  \n381107         656.132353             602.932432  \n381108         908.195652             176.274262  \n\n[381109 rows x 15 columns]",
      "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>Gender</th>\n      <th>Age</th>\n      <th>Driving_License</th>\n      <th>Region_Code</th>\n      <th>Previously_Insured</th>\n      <th>Annual_Premium</th>\n      <th>Policy_Sales_Channel</th>\n      <th>Vintage</th>\n      <th>Response</th>\n      <th>Vehicle_Age_lt_1_Year</th>\n      <th>Vehicle_Age_gt_2_Years</th>\n      <th>Vehicle_Damage_Yes</th>\n      <th>premium_age_ratio</th>\n      <th>premium_vintage_ratio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>44</td>\n      <td>1</td>\n      <td>28.0</td>\n      <td>0</td>\n      <td>40454.0</td>\n      <td>26.0</td>\n      <td>217</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>919.409091</td>\n      <td>186.423963</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>1</td>\n      <td>76</td>\n      <td>1</td>\n      <td>3.0</td>\n      <td>0</td>\n      <td>33536.0</td>\n      <td>26.0</td>\n      <td>183</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>441.263158</td>\n      <td>183.256831</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>1</td>\n      <td>47</td>\n      <td>1</td>\n      <td>28.0</td>\n      <td>0</td>\n      <td>38294.0</td>\n      <td>26.0</td>\n      <td>27</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>814.765957</td>\n      <td>1418.296296</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>1</td>\n      <td>21</td>\n      <td>1</td>\n      <td>11.0</td>\n      <td>1</td>\n      <td>28619.0</td>\n      <td>152.0</td>\n      <td>203</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1362.809524</td>\n      <td>140.980296</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>0</td>\n      <td>29</td>\n      <td>1</td>\n      <td>41.0</td>\n      <td>1</td>\n      <td>27496.0</td>\n      <td>152.0</td>\n      <td>39</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>948.137931</td>\n      <td>705.025641</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>381104</th>\n      <td>381105</td>\n      <td>1</td>\n      <td>74</td>\n      <td>1</td>\n      <td>26.0</td>\n      <td>1</td>\n      <td>30170.0</td>\n      <td>26.0</td>\n      <td>88</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>407.702703</td>\n      <td>342.840909</td>\n    </tr>\n    <tr>\n      <th>381105</th>\n      <td>381106</td>\n      <td>1</td>\n      <td>30</td>\n      <td>1</td>\n      <td>37.0</td>\n      <td>1</td>\n      <td>40016.0</td>\n      <td>152.0</td>\n      <td>131</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1333.866667</td>\n      <td>305.465649</td>\n    </tr>\n    <tr>\n      <th>381106</th>\n      <td>381107</td>\n      <td>1</td>\n      <td>21</td>\n      <td>1</td>\n      <td>30.0</td>\n      <td>1</td>\n      <td>35118.0</td>\n      <td>160.0</td>\n      <td>161</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1672.285714</td>\n      <td>218.124224</td>\n    </tr>\n    <tr>\n      <th>381107</th>\n      <td>381108</td>\n      <td>0</td>\n      <td>68</td>\n      <td>1</td>\n      <td>14.0</td>\n      <td>0</td>\n      <td>44617.0</td>\n      <td>124.0</td>\n      <td>74</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>656.132353</td>\n      <td>602.932432</td>\n    </tr>\n    <tr>\n      <th>381108</th>\n      <td>381109</td>\n      <td>1</td>\n      <td>46</td>\n      <td>1</td>\n      <td>29.0</td>\n      <td>0</td>\n      <td>41777.0</td>\n      <td>26.0</td>\n      <td>237</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>908.195652</td>\n      <td>176.274262</td>\n    </tr>\n  </tbody>\n</table>\n<p>381109 rows × 15 columns</p>\n</div>"
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "num_feat = ['Age','Vintage','premium_age_ratio','premium_vintage_ratio']\n",
    "ss = StandardScaler()\n",
    "train[num_feat] = ss.fit_transform(train[num_feat])"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:46.494933Z",
     "iopub.execute_input": "2022-01-18T22:51:46.49563Z",
     "iopub.status.idle": "2022-01-18T22:51:46.581367Z",
     "shell.execute_reply.started": "2022-01-18T22:51:46.495594Z",
     "shell.execute_reply": "2022-01-18T22:51:46.580403Z"
    },
    "trusted": true
   },
   "execution_count": 167,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "mm = MinMaxScaler()\n",
    "train[['Annual_Premium']] = mm.fit_transform(train[['Annual_Premium']])"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:46.582924Z",
     "iopub.execute_input": "2022-01-18T22:51:46.583421Z",
     "iopub.status.idle": "2022-01-18T22:51:46.605082Z",
     "shell.execute_reply.started": "2022-01-18T22:51:46.583387Z",
     "shell.execute_reply": "2022-01-18T22:51:46.60445Z"
    },
    "trusted": true
   },
   "execution_count": 168,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "train=train.drop('id',axis=1)"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:46.606301Z",
     "iopub.execute_input": "2022-01-18T22:51:46.606666Z",
     "iopub.status.idle": "2022-01-18T22:51:46.620146Z",
     "shell.execute_reply.started": "2022-01-18T22:51:46.606631Z",
     "shell.execute_reply": "2022-01-18T22:51:46.61953Z"
    },
    "trusted": true
   },
   "execution_count": 169,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "cat_feat = ['Gender', 'Driving_License', 'Previously_Insured', 'Vehicle_Age_lt_1_Year','Vehicle_Age_gt_2_Years','Vehicle_Damage_Yes']\n",
    "for column in cat_feat:\n",
    "    train[column] = train[column].astype('str')"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:46.620984Z",
     "iopub.execute_input": "2022-01-18T22:51:46.621563Z",
     "iopub.status.idle": "2022-01-18T22:51:49.517276Z",
     "shell.execute_reply.started": "2022-01-18T22:51:46.621524Z",
     "shell.execute_reply": "2022-01-18T22:51:49.516343Z"
    },
    "trusted": true
   },
   "execution_count": 170,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Applying the same processes to the Test Data"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "test['Gender'] = test['Gender'].map( {'Female': 0, 'Male': 1} ).astype(int)\n",
    "test=pd.get_dummies(test,drop_first=True)\n",
    "test=test.rename(columns={\"Vehicle_Age_< 1 Year\": \"Vehicle_Age_lt_1_Year\", \"Vehicle_Age_> 2 Years\": \"Vehicle_Age_gt_2_Years\"})\n",
    "test['Vehicle_Age_lt_1_Year']=test['Vehicle_Age_lt_1_Year'].astype('int')\n",
    "test['Vehicle_Age_gt_2_Years']=test['Vehicle_Age_gt_2_Years'].astype('int')\n",
    "test['Vehicle_Damage_Yes']=test['Vehicle_Damage_Yes'].astype('int')\n",
    "test[\"premium_age_ratio\"] = test[\"Annual_Premium\"]/test[\"Age\"]\n",
    "test[\"premium_vintage_ratio\"] = test[\"Annual_Premium\"]/test[\"Vintage\"]\n",
    "test=test.drop('id',axis=1)"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:49.518556Z",
     "iopub.execute_input": "2022-01-18T22:51:49.518804Z",
     "iopub.status.idle": "2022-01-18T22:51:49.605686Z",
     "shell.execute_reply.started": "2022-01-18T22:51:49.518774Z",
     "shell.execute_reply": "2022-01-18T22:51:49.604777Z"
    },
    "trusted": true
   },
   "execution_count": 171,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "ss = StandardScaler()\n",
    "test[num_feat] = ss.fit_transform(test[num_feat])\n",
    "\n",
    "\n",
    "mm = MinMaxScaler()\n",
    "test[['Annual_Premium']] = mm.fit_transform(test[['Annual_Premium']])"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:49.606983Z",
     "iopub.execute_input": "2022-01-18T22:51:49.607263Z",
     "iopub.status.idle": "2022-01-18T22:51:49.637177Z",
     "shell.execute_reply.started": "2022-01-18T22:51:49.607233Z",
     "shell.execute_reply": "2022-01-18T22:51:49.636178Z"
    },
    "trusted": true
   },
   "execution_count": 172,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "for column in cat_feat:\n",
    "    test[column] = test[column].astype('str')"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:49.638495Z",
     "iopub.execute_input": "2022-01-18T22:51:49.638729Z",
     "iopub.status.idle": "2022-01-18T22:51:50.601705Z",
     "shell.execute_reply.started": "2022-01-18T22:51:49.638701Z",
     "shell.execute_reply": "2022-01-18T22:51:50.600905Z"
    },
    "trusted": true
   },
   "execution_count": 173,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Train-Test Split"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "training_data, testing_data = train.drop('Response', axis=1),train['Response']"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:50.602926Z",
     "iopub.execute_input": "2022-01-18T22:51:50.603159Z",
     "iopub.status.idle": "2022-01-18T22:51:50.731064Z",
     "shell.execute_reply.started": "2022-01-18T22:51:50.603131Z",
     "shell.execute_reply": "2022-01-18T22:51:50.730266Z"
    },
    "trusted": true
   },
   "execution_count": 174,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(training_data,testing_data, test_size=0.2, random_state=42)"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:50.732202Z",
     "iopub.execute_input": "2022-01-18T22:51:50.732415Z",
     "iopub.status.idle": "2022-01-18T22:51:50.968955Z",
     "shell.execute_reply.started": "2022-01-18T22:51:50.732389Z",
     "shell.execute_reply": "2022-01-18T22:51:50.96813Z"
    },
    "trusted": true
   },
   "execution_count": 175,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "x_train"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:50.970213Z",
     "iopub.execute_input": "2022-01-18T22:51:50.970428Z",
     "iopub.status.idle": "2022-01-18T22:51:50.999975Z",
     "shell.execute_reply.started": "2022-01-18T22:51:50.970402Z",
     "shell.execute_reply": "2022-01-18T22:51:50.999193Z"
    },
    "trusted": true
   },
   "execution_count": 176,
   "outputs": [
    {
     "data": {
      "text/plain": "       Gender       Age Driving_License  Region_Code Previously_Insured  \\\n332803      0  0.011438               1         15.0                  0   \n116248      1 -0.053030               1         11.0                  0   \n255005      1 -1.084517               1         30.0                  1   \n317474      0 -1.020049               1         41.0                  1   \n344212      1  1.107392               1         48.0                  0   \n...       ...       ...             ...          ...                ...   \n259178      0 -0.955581               1         36.0                  1   \n365838      1  1.107392               1         35.0                  0   \n131932      0 -1.084517               1          2.0                  0   \n146867      1  0.333777               1         32.0                  1   \n121958      0 -0.762177               1         37.0                  0   \n\n        Annual_Premium  Policy_Sales_Channel   Vintage Vehicle_Age_lt_1_Year  \\\n332803        0.239200                  55.0  0.868311                     0   \n116248        0.097096                  26.0 -1.498095                     0   \n255005        0.203098                 152.0  0.139267                     1   \n317474        0.126090                 151.0  1.465888                     1   \n344212        0.000000                 154.0  0.007800                     0   \n...                ...                   ...       ...                   ...   \n259178        0.094893                 152.0  1.585403                     1   \n365838        0.183920                 124.0  1.716870                     0   \n131932        0.077204                 152.0 -0.936372                     1   \n146867        0.000000                 156.0 -1.235161                     0   \n121958        0.105198                 152.0 -0.326844                     1   \n\n       Vehicle_Age_gt_2_Years Vehicle_Damage_Yes  premium_age_ratio  \\\n332803                      0                  1           0.762207   \n116248                      0                  1          -0.505070   \n255005                      0                  0           1.950175   \n317474                      0                  0           0.610270   \n344212                      1                  1          -1.449741   \n...                       ...                ...                ...   \n259178                      0                  0           0.059675   \n365838                      0                  1          -0.283800   \n131932                      0                  1          -0.081339   \n146867                      0                  1          -1.428107   \n121958                      0                  1           0.018646   \n\n        premium_vintage_ratio  \n332803              -0.241018  \n116248               0.797477  \n255005              -0.167140  \n317474              -0.477626  \n344212              -0.640800  \n...                       ...  \n259178              -0.526672  \n365838              -0.415878  \n131932              -0.213171  \n146867              -0.576788  \n121958              -0.311790  \n\n[304887 rows x 13 columns]",
      "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>Gender</th>\n      <th>Age</th>\n      <th>Driving_License</th>\n      <th>Region_Code</th>\n      <th>Previously_Insured</th>\n      <th>Annual_Premium</th>\n      <th>Policy_Sales_Channel</th>\n      <th>Vintage</th>\n      <th>Vehicle_Age_lt_1_Year</th>\n      <th>Vehicle_Age_gt_2_Years</th>\n      <th>Vehicle_Damage_Yes</th>\n      <th>premium_age_ratio</th>\n      <th>premium_vintage_ratio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>332803</th>\n      <td>0</td>\n      <td>0.011438</td>\n      <td>1</td>\n      <td>15.0</td>\n      <td>0</td>\n      <td>0.239200</td>\n      <td>55.0</td>\n      <td>0.868311</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0.762207</td>\n      <td>-0.241018</td>\n    </tr>\n    <tr>\n      <th>116248</th>\n      <td>1</td>\n      <td>-0.053030</td>\n      <td>1</td>\n      <td>11.0</td>\n      <td>0</td>\n      <td>0.097096</td>\n      <td>26.0</td>\n      <td>-1.498095</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-0.505070</td>\n      <td>0.797477</td>\n    </tr>\n    <tr>\n      <th>255005</th>\n      <td>1</td>\n      <td>-1.084517</td>\n      <td>1</td>\n      <td>30.0</td>\n      <td>1</td>\n      <td>0.203098</td>\n      <td>152.0</td>\n      <td>0.139267</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1.950175</td>\n      <td>-0.167140</td>\n    </tr>\n    <tr>\n      <th>317474</th>\n      <td>0</td>\n      <td>-1.020049</td>\n      <td>1</td>\n      <td>41.0</td>\n      <td>1</td>\n      <td>0.126090</td>\n      <td>151.0</td>\n      <td>1.465888</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.610270</td>\n      <td>-0.477626</td>\n    </tr>\n    <tr>\n      <th>344212</th>\n      <td>1</td>\n      <td>1.107392</td>\n      <td>1</td>\n      <td>48.0</td>\n      <td>0</td>\n      <td>0.000000</td>\n      <td>154.0</td>\n      <td>0.007800</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>-1.449741</td>\n      <td>-0.640800</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>259178</th>\n      <td>0</td>\n      <td>-0.955581</td>\n      <td>1</td>\n      <td>36.0</td>\n      <td>1</td>\n      <td>0.094893</td>\n      <td>152.0</td>\n      <td>1.585403</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.059675</td>\n      <td>-0.526672</td>\n    </tr>\n    <tr>\n      <th>365838</th>\n      <td>1</td>\n      <td>1.107392</td>\n      <td>1</td>\n      <td>35.0</td>\n      <td>0</td>\n      <td>0.183920</td>\n      <td>124.0</td>\n      <td>1.716870</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-0.283800</td>\n      <td>-0.415878</td>\n    </tr>\n    <tr>\n      <th>131932</th>\n      <td>0</td>\n      <td>-1.084517</td>\n      <td>1</td>\n      <td>2.0</td>\n      <td>0</td>\n      <td>0.077204</td>\n      <td>152.0</td>\n      <td>-0.936372</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-0.081339</td>\n      <td>-0.213171</td>\n    </tr>\n    <tr>\n      <th>146867</th>\n      <td>1</td>\n      <td>0.333777</td>\n      <td>1</td>\n      <td>32.0</td>\n      <td>1</td>\n      <td>0.000000</td>\n      <td>156.0</td>\n      <td>-1.235161</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-1.428107</td>\n      <td>-0.576788</td>\n    </tr>\n    <tr>\n      <th>121958</th>\n      <td>0</td>\n      <td>-0.762177</td>\n      <td>1</td>\n      <td>37.0</td>\n      <td>0</td>\n      <td>0.105198</td>\n      <td>152.0</td>\n      <td>-0.326844</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0.018646</td>\n      <td>-0.311790</td>\n    </tr>\n  </tbody>\n</table>\n<p>304887 rows × 13 columns</p>\n</div>"
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "x_test"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:51.001268Z",
     "iopub.execute_input": "2022-01-18T22:51:51.002157Z",
     "iopub.status.idle": "2022-01-18T22:51:51.030912Z",
     "shell.execute_reply.started": "2022-01-18T22:51:51.002098Z",
     "shell.execute_reply": "2022-01-18T22:51:51.03017Z"
    },
    "trusted": true
   },
   "execution_count": 177,
   "outputs": [
    {
     "data": {
      "text/plain": "       Gender       Age Driving_License  Region_Code Previously_Insured  \\\n200222      0 -1.148985               1          3.0                  1   \n49766       1  1.042924               1         15.0                  0   \n172201      0  0.140374               1          3.0                  0   \n160713      0 -0.826645               1         11.0                  0   \n53272       1  0.785053               1         40.0                  0   \n...       ...       ...             ...          ...                ...   \n258403      1 -1.020049               1         15.0                  0   \n234155      0 -1.213453               1         15.0                  1   \n24476       0 -0.697709               1          8.0                  1   \n60423       0 -1.148985               1          3.0                  1   \n185839      1 -1.084517               1         39.0                  1   \n\n        Annual_Premium  Policy_Sales_Channel   Vintage Vehicle_Age_lt_1_Year  \\\n200222        0.084583                 160.0 -0.984179                     1   \n49766         0.165893                  26.0 -0.625632                     0   \n172201        0.000000                  26.0 -0.769051                     0   \n160713        0.000000                 151.0  1.358324                     1   \n53272         0.149279                 124.0  1.322470                     0   \n...                ...                   ...       ...                   ...   \n258403        0.098894                 152.0  0.210976                     1   \n234155        0.140781                 160.0  0.940020                     1   \n24476         0.223328                 152.0  0.605377                     1   \n60423         0.142599                 160.0 -1.199306                     1   \n185839        0.167292                 152.0 -0.745148                     1   \n\n       Vehicle_Age_gt_2_Years Vehicle_Damage_Yes  premium_age_ratio  \\\n200222                      0                  0           0.112347   \n49766                       0                  1          -0.377517   \n172201                      0                  1          -1.420720   \n160713                      0                  0          -1.358213   \n53272                       0                  1          -0.402848   \n...                       ...                ...                ...   \n258403                      0                  1           0.190511   \n234155                      0                  0           1.191956   \n24476                       0                  0           1.461115   \n60423                       0                  0           1.093108   \n185839                      0                  0           1.372375   \n\n        premium_vintage_ratio  \n200222              -0.147817  \n49766                0.007921  \n172201              -0.618129  \n160713              -0.654036  \n53272               -0.434790  \n...                       ...  \n258403              -0.420332  \n234155              -0.416366  \n24476               -0.224851  \n60423                0.444732  \n185839               0.087758  \n\n[76222 rows x 13 columns]",
      "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>Gender</th>\n      <th>Age</th>\n      <th>Driving_License</th>\n      <th>Region_Code</th>\n      <th>Previously_Insured</th>\n      <th>Annual_Premium</th>\n      <th>Policy_Sales_Channel</th>\n      <th>Vintage</th>\n      <th>Vehicle_Age_lt_1_Year</th>\n      <th>Vehicle_Age_gt_2_Years</th>\n      <th>Vehicle_Damage_Yes</th>\n      <th>premium_age_ratio</th>\n      <th>premium_vintage_ratio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>200222</th>\n      <td>0</td>\n      <td>-1.148985</td>\n      <td>1</td>\n      <td>3.0</td>\n      <td>1</td>\n      <td>0.084583</td>\n      <td>160.0</td>\n      <td>-0.984179</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.112347</td>\n      <td>-0.147817</td>\n    </tr>\n    <tr>\n      <th>49766</th>\n      <td>1</td>\n      <td>1.042924</td>\n      <td>1</td>\n      <td>15.0</td>\n      <td>0</td>\n      <td>0.165893</td>\n      <td>26.0</td>\n      <td>-0.625632</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-0.377517</td>\n      <td>0.007921</td>\n    </tr>\n    <tr>\n      <th>172201</th>\n      <td>0</td>\n      <td>0.140374</td>\n      <td>1</td>\n      <td>3.0</td>\n      <td>0</td>\n      <td>0.000000</td>\n      <td>26.0</td>\n      <td>-0.769051</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-1.420720</td>\n      <td>-0.618129</td>\n    </tr>\n    <tr>\n      <th>160713</th>\n      <td>0</td>\n      <td>-0.826645</td>\n      <td>1</td>\n      <td>11.0</td>\n      <td>0</td>\n      <td>0.000000</td>\n      <td>151.0</td>\n      <td>1.358324</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-1.358213</td>\n      <td>-0.654036</td>\n    </tr>\n    <tr>\n      <th>53272</th>\n      <td>1</td>\n      <td>0.785053</td>\n      <td>1</td>\n      <td>40.0</td>\n      <td>0</td>\n      <td>0.149279</td>\n      <td>124.0</td>\n      <td>1.322470</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-0.402848</td>\n      <td>-0.434790</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>258403</th>\n      <td>1</td>\n      <td>-1.020049</td>\n      <td>1</td>\n      <td>15.0</td>\n      <td>0</td>\n      <td>0.098894</td>\n      <td>152.0</td>\n      <td>0.210976</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0.190511</td>\n      <td>-0.420332</td>\n    </tr>\n    <tr>\n      <th>234155</th>\n      <td>0</td>\n      <td>-1.213453</td>\n      <td>1</td>\n      <td>15.0</td>\n      <td>1</td>\n      <td>0.140781</td>\n      <td>160.0</td>\n      <td>0.940020</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1.191956</td>\n      <td>-0.416366</td>\n    </tr>\n    <tr>\n      <th>24476</th>\n      <td>0</td>\n      <td>-0.697709</td>\n      <td>1</td>\n      <td>8.0</td>\n      <td>1</td>\n      <td>0.223328</td>\n      <td>152.0</td>\n      <td>0.605377</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1.461115</td>\n      <td>-0.224851</td>\n    </tr>\n    <tr>\n      <th>60423</th>\n      <td>0</td>\n      <td>-1.148985</td>\n      <td>1</td>\n      <td>3.0</td>\n      <td>1</td>\n      <td>0.142599</td>\n      <td>160.0</td>\n      <td>-1.199306</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1.093108</td>\n      <td>0.444732</td>\n    </tr>\n    <tr>\n      <th>185839</th>\n      <td>1</td>\n      <td>-1.084517</td>\n      <td>1</td>\n      <td>39.0</td>\n      <td>1</td>\n      <td>0.167292</td>\n      <td>152.0</td>\n      <td>-0.745148</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1.372375</td>\n      <td>0.087758</td>\n    </tr>\n  </tbody>\n</table>\n<p>76222 rows × 13 columns</p>\n</div>"
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "for column in cat_feat:\n",
    "    x_train[column] = x_train[column].astype('int')\n",
    "    x_test[column] = x_test[column].astype('int')"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:51.032145Z",
     "iopub.execute_input": "2022-01-18T22:51:51.032352Z",
     "iopub.status.idle": "2022-01-18T22:51:51.859289Z",
     "shell.execute_reply.started": "2022-01-18T22:51:51.032327Z",
     "shell.execute_reply": "2022-01-18T22:51:51.85831Z"
    },
    "trusted": true
   },
   "execution_count": 178,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "for column in num_feat:\n",
    "    x_train[column] = x_train[column].astype('int')\n",
    "    x_test[column] = x_test[column].astype('int')"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:51.860506Z",
     "iopub.execute_input": "2022-01-18T22:51:51.860837Z",
     "iopub.status.idle": "2022-01-18T22:51:51.88779Z",
     "shell.execute_reply.started": "2022-01-18T22:51:51.860801Z",
     "shell.execute_reply": "2022-01-18T22:51:51.88683Z"
    },
    "trusted": true
   },
   "execution_count": 179,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## XGBOOST"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "model_xgb = XGBClassifier()\n",
    "model_xgb.fit(x_train, y_train,eval_metric='mlogloss')\n",
    "\n",
    "pred_xgb = model_xgb.predict(x_test) \n",
    "predictions_xgb = [round(value) for value in pred_xgb]\n",
    "accuracy_xgb = accuracy_score(y_test, predictions_xgb)\n",
    "print(\"Accuracy: %.2f%%\" % (accuracy_xgb * 100.0))"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:51:51.889466Z",
     "iopub.execute_input": "2022-01-18T22:51:51.890382Z",
     "iopub.status.idle": "2022-01-18T22:52:05.319028Z",
     "shell.execute_reply.started": "2022-01-18T22:51:51.890334Z",
     "shell.execute_reply": "2022-01-18T22:52:05.317997Z"
    },
    "trusted": true
   },
   "execution_count": 180,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 87.49%\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "#params = {\n",
    "    #\"max_depth\" : range(2,10,1),\n",
    "    #\"n_estimators\" : range(60,220,40),\n",
    "    #\"learning_rate\" : [0.1,0.01,0.05]}"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:52:05.320441Z",
     "iopub.execute_input": "2022-01-18T22:52:05.320741Z",
     "iopub.status.idle": "2022-01-18T22:52:05.324688Z",
     "shell.execute_reply.started": "2022-01-18T22:52:05.320707Z",
     "shell.execute_reply": "2022-01-18T22:52:05.323797Z"
    },
    "trusted": true
   },
   "execution_count": 181,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "#grid_search_xgb = GridSearchCV(model_xgb,\n",
    "                               #param_grid = params,\n",
    "                               #scoring = 'roc_auc',\n",
    "                               #n_jobs = 10,\n",
    "                               #cv = 10,\n",
    "                               #verbose = True)"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:52:05.325995Z",
     "iopub.execute_input": "2022-01-18T22:52:05.326913Z",
     "iopub.status.idle": "2022-01-18T22:52:05.337555Z",
     "shell.execute_reply.started": "2022-01-18T22:52:05.326864Z",
     "shell.execute_reply": "2022-01-18T22:52:05.336692Z"
    },
    "trusted": true
   },
   "execution_count": 182,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "#grid_search_xgb.fit(x_train,y_train)"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:52:05.339919Z",
     "iopub.execute_input": "2022-01-18T22:52:05.34059Z",
     "iopub.status.idle": "2022-01-18T22:52:05.351703Z",
     "shell.execute_reply.started": "2022-01-18T22:52:05.340539Z",
     "shell.execute_reply": "2022-01-18T22:52:05.350893Z"
    },
    "trusted": true
   },
   "execution_count": 183,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "#grid_search_xgb.best_estimator_"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:52:05.352763Z",
     "iopub.execute_input": "2022-01-18T22:52:05.353042Z",
     "iopub.status.idle": "2022-01-18T22:52:05.362004Z",
     "shell.execute_reply.started": "2022-01-18T22:52:05.353011Z",
     "shell.execute_reply": "2022-01-18T22:52:05.360982Z"
    },
    "trusted": true
   },
   "execution_count": 184,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "model_xgb_tuned = XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
    "                                colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
    "                                gamma=0, gpu_id=-1, importance_type=None,\n",
    "                                interaction_constraints='', learning_rate=0.1, max_delta_step=0,\n",
    "                                max_depth=5, min_child_weight=1,eval_metric='mlogloss',\n",
    "                                monotone_constraints='()', n_estimators=180, n_jobs=12,\n",
    "                                num_parallel_tree=1, predictor='auto', random_state=0,\n",
    "                                reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
    "                                tree_method='exact', validate_parameters=1, verbosity=None)\n",
    "\n",
    "\n",
    "model_xgb_tuned.fit(x_train, y_train)\n",
    "pred_xgb_tuned = model_xgb_tuned.predict(x_test) \n",
    "predictions_xgb_tuned = [round(value) for value in pred_xgb_tuned]\n",
    "accuracy_xgb_tuned = accuracy_score(y_test, predictions_xgb_tuned)\n",
    "print(\"Accuracy: %.2f%%\" % (accuracy_xgb_tuned * 100.0))"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:52:05.363457Z",
     "iopub.execute_input": "2022-01-18T22:52:05.363714Z",
     "iopub.status.idle": "2022-01-18T22:52:27.394319Z",
     "shell.execute_reply.started": "2022-01-18T22:52:05.363675Z",
     "shell.execute_reply": "2022-01-18T22:52:27.393402Z"
    },
    "trusted": true
   },
   "execution_count": 185,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 87.50%\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "***Let use all values in the train set to estimate Response values in test data. Then we'll compare prediction Responses according to the Submission data. Because the sample_submission.csv has actual Responses values. We can see how our model is good.***"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "source": [
    "X_train, Y_train = train.drop(\"Response\",axis = 1), train[\"Response\"]"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:52:27.395886Z",
     "iopub.execute_input": "2022-01-18T22:52:27.396471Z",
     "iopub.status.idle": "2022-01-18T22:52:27.432678Z",
     "shell.execute_reply.started": "2022-01-18T22:52:27.396422Z",
     "shell.execute_reply": "2022-01-18T22:52:27.431737Z"
    },
    "trusted": true
   },
   "execution_count": 186,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "X_train"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:52:27.434271Z",
     "iopub.execute_input": "2022-01-18T22:52:27.434694Z",
     "iopub.status.idle": "2022-01-18T22:52:27.464133Z",
     "shell.execute_reply.started": "2022-01-18T22:52:27.434633Z",
     "shell.execute_reply": "2022-01-18T22:52:27.463252Z"
    },
    "trusted": true
   },
   "execution_count": 187,
   "outputs": [
    {
     "data": {
      "text/plain": "       Gender       Age Driving_License  Region_Code Previously_Insured  \\\n0           1  0.333777               1         28.0                  0   \n1           1  2.396751               1          3.0                  0   \n2           1  0.527181               1         28.0                  0   \n3           1 -1.148985               1         11.0                  1   \n4           0 -0.633242               1         41.0                  1   \n...       ...       ...             ...          ...                ...   \n381104      1  2.267815               1         26.0                  1   \n381105      1 -0.568774               1         37.0                  1   \n381106      1 -1.148985               1         30.0                  1   \n381107      0  1.881007               1         14.0                  0   \n381108      1  0.462713               1         29.0                  0   \n\n        Annual_Premium  Policy_Sales_Channel   Vintage Vehicle_Age_lt_1_Year  \\\n0             0.179957                  26.0  0.748795                     0   \n1             0.147043                  26.0  0.342443                     0   \n2             0.169680                  26.0 -1.521998                     0   \n3             0.123649                 152.0  0.581474                     1   \n4             0.118306                 152.0 -1.378580                     1   \n...                ...                   ...       ...                   ...   \n381104        0.131028                  26.0 -0.792954                     0   \n381105        0.177873                 152.0 -0.279037                     1   \n381106        0.154569                 160.0  0.079509                     1   \n381107        0.199763                 124.0 -0.960275                     0   \n381108        0.186251                  26.0  0.987826                     0   \n\n       Vehicle_Age_gt_2_Years Vehicle_Damage_Yes  premium_age_ratio  \\\n0                           1                  1           0.023841   \n1                           0                  0          -0.783760   \n2                           1                  1          -0.152904   \n3                           0                  0           0.772756   \n4                           0                  0           0.072365   \n...                       ...                ...                ...   \n381104                      0                  0          -0.840444   \n381105                      0                  0           0.723871   \n381106                      0                  0           1.295469   \n381107                      1                  1          -0.420840   \n381108                      0                  0           0.004901   \n\n        premium_vintage_ratio  \n0                   -0.327306  \n1                   -0.333165  \n2                    1.951662  \n3                   -0.411377  \n4                    0.632109  \n...                       ...  \n381104              -0.037934  \n381105              -0.107078  \n381106              -0.268660  \n381107               0.443236  \n381108              -0.346083  \n\n[381109 rows x 13 columns]",
      "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>Gender</th>\n      <th>Age</th>\n      <th>Driving_License</th>\n      <th>Region_Code</th>\n      <th>Previously_Insured</th>\n      <th>Annual_Premium</th>\n      <th>Policy_Sales_Channel</th>\n      <th>Vintage</th>\n      <th>Vehicle_Age_lt_1_Year</th>\n      <th>Vehicle_Age_gt_2_Years</th>\n      <th>Vehicle_Damage_Yes</th>\n      <th>premium_age_ratio</th>\n      <th>premium_vintage_ratio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0.333777</td>\n      <td>1</td>\n      <td>28.0</td>\n      <td>0</td>\n      <td>0.179957</td>\n      <td>26.0</td>\n      <td>0.748795</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.023841</td>\n      <td>-0.327306</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>2.396751</td>\n      <td>1</td>\n      <td>3.0</td>\n      <td>0</td>\n      <td>0.147043</td>\n      <td>26.0</td>\n      <td>0.342443</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-0.783760</td>\n      <td>-0.333165</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>0.527181</td>\n      <td>1</td>\n      <td>28.0</td>\n      <td>0</td>\n      <td>0.169680</td>\n      <td>26.0</td>\n      <td>-1.521998</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>-0.152904</td>\n      <td>1.951662</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>-1.148985</td>\n      <td>1</td>\n      <td>11.0</td>\n      <td>1</td>\n      <td>0.123649</td>\n      <td>152.0</td>\n      <td>0.581474</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.772756</td>\n      <td>-0.411377</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>-0.633242</td>\n      <td>1</td>\n      <td>41.0</td>\n      <td>1</td>\n      <td>0.118306</td>\n      <td>152.0</td>\n      <td>-1.378580</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.072365</td>\n      <td>0.632109</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>381104</th>\n      <td>1</td>\n      <td>2.267815</td>\n      <td>1</td>\n      <td>26.0</td>\n      <td>1</td>\n      <td>0.131028</td>\n      <td>26.0</td>\n      <td>-0.792954</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-0.840444</td>\n      <td>-0.037934</td>\n    </tr>\n    <tr>\n      <th>381105</th>\n      <td>1</td>\n      <td>-0.568774</td>\n      <td>1</td>\n      <td>37.0</td>\n      <td>1</td>\n      <td>0.177873</td>\n      <td>152.0</td>\n      <td>-0.279037</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.723871</td>\n      <td>-0.107078</td>\n    </tr>\n    <tr>\n      <th>381106</th>\n      <td>1</td>\n      <td>-1.148985</td>\n      <td>1</td>\n      <td>30.0</td>\n      <td>1</td>\n      <td>0.154569</td>\n      <td>160.0</td>\n      <td>0.079509</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1.295469</td>\n      <td>-0.268660</td>\n    </tr>\n    <tr>\n      <th>381107</th>\n      <td>0</td>\n      <td>1.881007</td>\n      <td>1</td>\n      <td>14.0</td>\n      <td>0</td>\n      <td>0.199763</td>\n      <td>124.0</td>\n      <td>-0.960275</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>-0.420840</td>\n      <td>0.443236</td>\n    </tr>\n    <tr>\n      <th>381108</th>\n      <td>1</td>\n      <td>0.462713</td>\n      <td>1</td>\n      <td>29.0</td>\n      <td>0</td>\n      <td>0.186251</td>\n      <td>26.0</td>\n      <td>0.987826</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.004901</td>\n      <td>-0.346083</td>\n    </tr>\n  </tbody>\n</table>\n<p>381109 rows × 13 columns</p>\n</div>"
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "Y_train"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:52:27.466875Z",
     "iopub.execute_input": "2022-01-18T22:52:27.467211Z",
     "iopub.status.idle": "2022-01-18T22:52:27.474481Z",
     "shell.execute_reply.started": "2022-01-18T22:52:27.467167Z",
     "shell.execute_reply": "2022-01-18T22:52:27.473609Z"
    },
    "trusted": true
   },
   "execution_count": 188,
   "outputs": [
    {
     "data": {
      "text/plain": "0         1\n1         0\n2         1\n3         0\n4         0\n         ..\n381104    0\n381105    0\n381106    0\n381107    0\n381108    0\nName: Response, Length: 381109, dtype: int64"
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "test"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:52:27.475863Z",
     "iopub.execute_input": "2022-01-18T22:52:27.476188Z",
     "iopub.status.idle": "2022-01-18T22:52:27.508053Z",
     "shell.execute_reply.started": "2022-01-18T22:52:27.476145Z",
     "shell.execute_reply": "2022-01-18T22:52:27.507183Z"
    },
    "trusted": true
   },
   "execution_count": 189,
   "outputs": [
    {
     "data": {
      "text/plain": "       Gender       Age Driving_License  Region_Code Previously_Insured  \\\n0           1 -0.890089               1         11.0                  1   \n1           1  0.079795               1         28.0                  0   \n2           1  0.532408               1         28.0                  0   \n3           1 -0.954748               1         27.0                  1   \n4           1 -0.760771               1         28.0                  1   \n...       ...       ...             ...          ...                ...   \n127032      0 -0.825430               1         37.0                  1   \n127033      0 -0.049523               1         28.0                  0   \n127034      1 -1.148725               1         46.0                  1   \n127035      1  2.084224               1         28.0                  1   \n127036      1  0.144454               1         29.0                  1   \n\n        Annual_Premium  Policy_Sales_Channel   Vintage Vehicle_Age_lt_1_Year  \\\n0             0.070633                 152.0 -1.211054                     1   \n1             0.066321                   7.0 -0.517782                     0   \n2             0.079717                 124.0  0.534079                     0   \n3             0.073978                 152.0  0.390643                     1   \n4             0.120293                 152.0  1.705469                     1   \n...                ...                   ...       ...                   ...   \n127032        0.060154                 152.0 -1.175195                     1   \n127033        0.055538                 122.0  0.127678                     0   \n127034        0.057885                 152.0 -0.960042                     1   \n127035        0.128341                  26.0  1.322974                     0   \n127036        0.053891                 124.0  0.916574                     0   \n\n       Vehicle_Age_gt_2_Years Vehicle_Damage_Yes  premium_age_ratio  \\\n0                           0                  0           0.883128   \n1                           0                  1          -0.104058   \n2                           0                  1          -0.090483   \n3                           0                  1           1.093308   \n4                           0                  0           2.155934   \n...                       ...                ...                ...   \n127032                      0                  0           0.472638   \n127033                      0                  1          -0.253276   \n127034                      0                  0           0.862461   \n127035                      0                  0          -0.034289   \n127036                      0                  0          -0.377839   \n\n        premium_vintage_ratio  \n0                    0.572045  \n1                   -0.109867  \n2                   -0.298989  \n3                   -0.301730  \n4                   -0.303173  \n...                       ...  \n127032               0.344136  \n127033              -0.349193  \n127034               0.071281  \n127035              -0.232813  \n127036              -0.446676  \n\n[127037 rows x 13 columns]",
      "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>Gender</th>\n      <th>Age</th>\n      <th>Driving_License</th>\n      <th>Region_Code</th>\n      <th>Previously_Insured</th>\n      <th>Annual_Premium</th>\n      <th>Policy_Sales_Channel</th>\n      <th>Vintage</th>\n      <th>Vehicle_Age_lt_1_Year</th>\n      <th>Vehicle_Age_gt_2_Years</th>\n      <th>Vehicle_Damage_Yes</th>\n      <th>premium_age_ratio</th>\n      <th>premium_vintage_ratio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>-0.890089</td>\n      <td>1</td>\n      <td>11.0</td>\n      <td>1</td>\n      <td>0.070633</td>\n      <td>152.0</td>\n      <td>-1.211054</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.883128</td>\n      <td>0.572045</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0.079795</td>\n      <td>1</td>\n      <td>28.0</td>\n      <td>0</td>\n      <td>0.066321</td>\n      <td>7.0</td>\n      <td>-0.517782</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-0.104058</td>\n      <td>-0.109867</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>0.532408</td>\n      <td>1</td>\n      <td>28.0</td>\n      <td>0</td>\n      <td>0.079717</td>\n      <td>124.0</td>\n      <td>0.534079</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-0.090483</td>\n      <td>-0.298989</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>-0.954748</td>\n      <td>1</td>\n      <td>27.0</td>\n      <td>1</td>\n      <td>0.073978</td>\n      <td>152.0</td>\n      <td>0.390643</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1.093308</td>\n      <td>-0.301730</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>-0.760771</td>\n      <td>1</td>\n      <td>28.0</td>\n      <td>1</td>\n      <td>0.120293</td>\n      <td>152.0</td>\n      <td>1.705469</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2.155934</td>\n      <td>-0.303173</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>127032</th>\n      <td>0</td>\n      <td>-0.825430</td>\n      <td>1</td>\n      <td>37.0</td>\n      <td>1</td>\n      <td>0.060154</td>\n      <td>152.0</td>\n      <td>-1.175195</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.472638</td>\n      <td>0.344136</td>\n    </tr>\n    <tr>\n      <th>127033</th>\n      <td>0</td>\n      <td>-0.049523</td>\n      <td>1</td>\n      <td>28.0</td>\n      <td>0</td>\n      <td>0.055538</td>\n      <td>122.0</td>\n      <td>0.127678</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-0.253276</td>\n      <td>-0.349193</td>\n    </tr>\n    <tr>\n      <th>127034</th>\n      <td>1</td>\n      <td>-1.148725</td>\n      <td>1</td>\n      <td>46.0</td>\n      <td>1</td>\n      <td>0.057885</td>\n      <td>152.0</td>\n      <td>-0.960042</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0.862461</td>\n      <td>0.071281</td>\n    </tr>\n    <tr>\n      <th>127035</th>\n      <td>1</td>\n      <td>2.084224</td>\n      <td>1</td>\n      <td>28.0</td>\n      <td>1</td>\n      <td>0.128341</td>\n      <td>26.0</td>\n      <td>1.322974</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-0.034289</td>\n      <td>-0.232813</td>\n    </tr>\n    <tr>\n      <th>127036</th>\n      <td>1</td>\n      <td>0.144454</td>\n      <td>1</td>\n      <td>29.0</td>\n      <td>1</td>\n      <td>0.053891</td>\n      <td>124.0</td>\n      <td>0.916574</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>-0.377839</td>\n      <td>-0.446676</td>\n    </tr>\n  </tbody>\n</table>\n<p>127037 rows × 13 columns</p>\n</div>"
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "for column in cat_feat:\n",
    "    X_train[column] = X_train[column].astype('int')\n",
    "    test[column] = test[column].astype('int')"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:52:27.510145Z",
     "iopub.execute_input": "2022-01-18T22:52:27.510732Z",
     "iopub.status.idle": "2022-01-18T22:52:28.077864Z",
     "shell.execute_reply.started": "2022-01-18T22:52:27.510684Z",
     "shell.execute_reply": "2022-01-18T22:52:28.077116Z"
    },
    "trusted": true
   },
   "execution_count": 190,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "for column in num_feat:\n",
    "    X_train[column] = X_train[column].astype('int')\n",
    "    test[column] = test[column].astype('int')"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:52:28.078944Z",
     "iopub.execute_input": "2022-01-18T22:52:28.079172Z",
     "iopub.status.idle": "2022-01-18T22:52:28.111815Z",
     "shell.execute_reply.started": "2022-01-18T22:52:28.079146Z",
     "shell.execute_reply": "2022-01-18T22:52:28.111107Z"
    },
    "trusted": true
   },
   "execution_count": 191,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "from xgboost import XGBClassifier, plot_importance\n",
    "\n",
    "# fit model no training data\n",
    "model2 = XGBClassifier(eval_metric='mlogloss')\n",
    "model2.fit(X_train, Y_train)\n",
    "\n",
    "y_pred1 = model2.predict(test)\n",
    "predictions2 = [round(value) for value in y_pred1]"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:52:28.112851Z",
     "iopub.execute_input": "2022-01-18T22:52:28.113059Z",
     "iopub.status.idle": "2022-01-18T22:52:44.760505Z",
     "shell.execute_reply.started": "2022-01-18T22:52:28.113034Z",
     "shell.execute_reply": "2022-01-18T22:52:44.759561Z"
    },
    "trusted": true
   },
   "execution_count": 192,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "test_2 = pd.DataFrame()"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:52:44.762004Z",
     "iopub.execute_input": "2022-01-18T22:52:44.762333Z",
     "iopub.status.idle": "2022-01-18T22:52:44.767491Z",
     "shell.execute_reply.started": "2022-01-18T22:52:44.762293Z",
     "shell.execute_reply": "2022-01-18T22:52:44.76666Z"
    },
    "trusted": true
   },
   "execution_count": 193,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "test_2['Response'] = sub['Response']\n",
    "test_2[\"Pred_Response\"] = predictions2"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:52:44.76913Z",
     "iopub.execute_input": "2022-01-18T22:52:44.769693Z",
     "iopub.status.idle": "2022-01-18T22:52:44.86326Z",
     "shell.execute_reply.started": "2022-01-18T22:52:44.769629Z",
     "shell.execute_reply": "2022-01-18T22:52:44.862345Z"
    },
    "trusted": true
   },
   "execution_count": 194,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "test_2"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:52:44.864586Z",
     "iopub.execute_input": "2022-01-18T22:52:44.864903Z",
     "iopub.status.idle": "2022-01-18T22:52:44.879557Z",
     "shell.execute_reply.started": "2022-01-18T22:52:44.864861Z",
     "shell.execute_reply": "2022-01-18T22:52:44.87895Z"
    },
    "trusted": true
   },
   "execution_count": 195,
   "outputs": [
    {
     "data": {
      "text/plain": "        Response  Pred_Response\n0              0              0\n1              0              0\n2              0              0\n3              0              0\n4              0              0\n...          ...            ...\n127032         0              0\n127033         0              0\n127034         0              0\n127035         0              0\n127036         0              0\n\n[127037 rows x 2 columns]",
      "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>Response</th>\n      <th>Pred_Response</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>127032</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>127033</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>127034</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>127035</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>127036</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>127037 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "accuracy_2 = accuracy_score(test_2['Response'].values, test_2['Pred_Response'].values)\n",
    "print(\"Accuracy: %.2f%%\" % (accuracy_2 * 100.0))"
   ],
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-01-18T22:52:44.880684Z",
     "iopub.execute_input": "2022-01-18T22:52:44.881239Z",
     "iopub.status.idle": "2022-01-18T22:52:44.899828Z",
     "shell.execute_reply.started": "2022-01-18T22:52:44.881193Z",
     "shell.execute_reply": "2022-01-18T22:52:44.899183Z"
    },
    "trusted": true
   },
   "execution_count": 196,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 99.68%\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ]
}