{"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"accelerator":"GPU","gpuClass":"standard","kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":3004,"databundleVersionId":861823,"sourceType":"competition"},{"sourceId":242592,"sourceType":"datasetVersion","datasetId":102285}],"dockerImageVersionId":30646,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Building a CNN on the MNIST dataset\n\nThis session, we will be building our own CNN! We will be training it on the MNIST handwritten digit datset. The goal is to build a CNN that will be able to take in an image as input and output which digit it thinks it is (0-9).\n\nHere is a picture of what the MNIST dataset looks like:\n\n![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAloAAAHECAIAAABuvRMTAAAMaWlDQ1BJQ0MgUHJvZmlsZQAASImVVwdUU8kanluSkJCEEoiAlNCbINKLlBBaBAGpgo2QBBJKjAlBxY4uKrh2EcWKrooouhZAFhWxl0Wx98WCirIuFhRF5U1IQJd95bz/nJn58s0/f8vMvXcA0OrmSaU5qDYAuZI8WVx4MGtsSiqL9BwggAx0YO/E48ul7NjYKAClf/y7fLgJ9aBcc1La+uf8fxVdgVDOBwAZD3G6QM7PhbgRAHwDXyrLA4Co5C2n5kmVeC7EejIYIMSrlThThXcpcboKN/TpJMRxIL4CgAaVx5NlAkC/D3lWPj8T2qF/gdhFIhBLANAaBnEAX8QTQKyMfVhu7mQlLoPYDupLIYbxAO/0H2xm/s1++oB9Hi9zAKvy6hONELFcmsOb/n+W5n9Lbo6i34cNbFSRLCJOmT+s4e3syZFKTIW4Q5IeHaOsNcTdYoGq7gCgFJEiIlGljxrz5RxYP8CE2EXAC4mE2BjiMElOdJSaT88Qh3EhhrsFnSbO4yZAbADxIqE8NF6ts0U2OU7tC63JkHHYav4cT9bnV+nroSI7ka22/1Yk5KrtY/QCUUIyxBSIrfLFSdEQ0yF2lmfHR6p1RhaIONH9OjJFnDJ+K4jjhJLwYJV9LD9DFhan1i/Olffni20RibnRanwgT5QQoaoPdorP64sf5oJdEUrYif12hPKxUf25CIQhoarcsRdCSWK82k63NC84TrUWp0hzYtX6uIUwJ1zJW0DsLs+PV6/Fk/Lg5lTZxzOkebEJqjjxgizeqFhVPPhyEAU4IASwgAK2dDAZZAFxc0dtB/ylmgkDPCADmUAInNRM/4rkvhkJ7ONBAfgTIiGQD6wL7psVgnzIfx1gVb0TyOibze9bkQ2eQZwLIkEO/K3oWyUZ8JYEnkJG/A/vPNj4MN4c2JTz/57vZ78zbMhEqRlFv0eWVr8mMZQYQowghhHtcSM8APfDo2AfBJsr7o379OfxXZ/wjNBCeEy4QWgl3JkkLpQNinI0aIX2w9S1SP+xFrgNtOmBB+P+0Dq0jDNxI+CEu0M/bDwQevaALEcdt7IqrEG2/5bBD/+GWo/sQkbJQ8hBZLvBK+kOdI8BK8pa/1gfVazpA/XmDMwM9s/5ofoCOEYO1sQWYQexs9gJ7DzWgNUCFnYcq8MuYUeVeGB3Pe3bXf3e4vriyYZ2xP/wx1P7VFZS7lLl0u7yRTWXJ5yWpzx4nMnS6TJxpiiPxYZvByGLK+E7D2O5uri6AaB816geX++Yfe8QhHnhO1eYD4C/fW9vb8N3LsoHgEN18Pi3f+fs4LOPbgbAuUV8hSxfxeHKjgCfElrwpBkCU2AJ7GA+rsAT+IEgEApGgRiQAFLARFhlEdznMjAVzATzQBEoAcvBGrAebAbbwC6wFxwAtaABnABnwEVwBdwA9+DuaQOvQCf4AHoQBCEhNISBGCJmiDXiiLgi3kgAEopEIXFICpKGZCISRIHMROYjJchKZD2yFalEfkWOICeQ80gLcgd5hLQjb5HPKIZSUT3UBLVBh6PeKBuNRBPQCWgmOgUtQBegS9EytALdg9agJ9CL6A20FX2FdmEA08SYmDnmhHljHCwGS8UyMBk2GyvGSrEKrBqrh//zNawV68A+4UScgbNwJ7iDI/BEnI9PwWfjS/D1+C68Bj+FX8Mf4Z34NwKNYExwJPgSuISxhEzCVEIRoZSwg3CYcBqepTbCByKRyCTaEr3gWUwhZhFnEJcQNxL3ERuJLcQnxC4SiWRIciT5k2JIPFIeqYi0jrSHdJx0ldRG6tbQ1DDTcNUI00jVkGgUapRq7NY4pnFV47lGD1mbbE32JceQBeTp5GXk7eR68mVyG7mHokOxpfhTEihZlHmUMko15TTlPuWdpqamhaaP5hhNseZczTLN/ZrnNB9pfqLqUh2oHOp4qoK6lLqT2ki9Q31Ho9FsaEG0VFoebSmtknaS9pDWTWfQnelcuoA+h15Or6Ffpb/WImtZa7G1JmoVaJVqHdS6rNWhTda20eZo87Rna5drH9G+pd2lw9AZoROjk6uzRGe3znmdF7okXRvdUF2B7gLdbbondZ8wMIYlg8PgM+YztjNOM9r0iHq2ely9LL0Svb16zXqd+rr67vpJ+tP0y/WP6rcyMaYNk8vMYS5jHmDeZH4eYjKEPUQ4ZPGQ6iFXh3w0GGoQZCA0KDbYZ3DD4LMhyzDUMNtwhWGt4QMj3MjBaIzRVKNNRqeNOobqDfUbyh9aPPTA0LvGqLGDcZzxDONtxpeMu0xMTcJNpCbrTE6adJgyTYNMs0xXmx4zbTdjmAWYic1Wmx03e8nSZ7FZOawy1ilWp7mxeYS5wnyrebN5j4WtRaJFocU+iweWFEtvywzL1ZZNlp1WZlajrWZaVVndtSZbe1uLrNdan7X+aGNrk2yz0KbW5oWtgS3XtsC2yva+Hc0u0G6KXYXddXuivbd9tv1G+ysOqIOHg8ih3OGyI+ro6Sh23OjYMowwzGeYZFjFsFtOVCe2U75TldMjZ6ZzlHOhc63z6+FWw1OHrxh+dvg3Fw+XHJftLvdG6I4YNaJwRP2It64OrnzXctfrbjS3MLc5bnVub9wd3YXum9xvezA8Rnss9Gjy+Orp5SnzrPZs97LySvPa4HXLW8871nuJ9zkfgk+wzxyfBp9Pvp6+eb4HfP/yc/LL9tvt92Kk7UjhyO0jn/hb+PP8t/q3BrAC0gK2BLQGmgfyAisCHwdZBgmCdgQ9Z9uzs9h72K+DXYJlwYeDP3J8ObM4jSFYSHhIcUhzqG5oYuj60IdhFmGZYVVhneEe4TPCGyMIEZERKyJucU24fG4lt3OU16hZo05FUiPjI9dHPo5yiJJF1Y9GR48avWr0/WjraEl0bQyI4casinkQaxs7Jfa3McQxsWPKxzyLGxE3M+5sPCN+Uvzu+A8JwQnLEu4l2iUqEpuStJLGJ1UmfUwOSV6Z3Dp2+NhZYy+mGKWIU+pSSalJqTtSu8aFjlszrm28x/ii8Tcn2E6YNuH8RKOJOROPTtKaxJt0MI2Qlpy2O+0LL4ZXwetK56ZvSO/kc/hr+a8EQYLVgnahv3Cl8HmGf8bKjBeZ/pmrMttFgaJSUYeYI14vfpMVkbU562N2TPbO7N6c5Jx9uRq5ablHJLqSbMmpyaaTp01ukTpKi6StU3ynrJnSKYuU7ZAj8gnyujw9+FF/SWGn+EnxKD8gvzy/e2rS1IPTdKZJpl2a7jB98fTnBWEFv8zAZ/BnNM00nzlv5qNZ7FlbZyOz02c3zbGcs2BO29zwubvmUeZlz/u90KVwZeH7+cnz6xeYLJi74MlP4T9VFdGLZEW3Fvot3LwIXyRe1LzYbfG6xd+KBcUXSlxKSku+LOEvufDziJ/Lfu5dmrG0eZnnsk3Licsly2+uCFyxa6XOyoKVT1aNXlWzmrW6ePX7NZPWnC91L928lrJWsba1LKqsbp3VuuXrvqwXrb9RHly+b4PxhsUbPm4UbLy6KWhT9WaTzSWbP28Rb7m9NXxrTYVNRek24rb8bc+2J20/+4v3L5U7jHaU7Pi6U7KzdVfcrlOVXpWVu413L6tCqxRV7XvG77myN2RvXbVT9dZ9zH0l+8F+xf6Xv6b9evNA5IGmg94Hqw9ZH9pwmHG4uAapmV7TWSuqba1LqWs5MupIU71f/eHfnH/b2WDeUH5U/+iyY5RjC471Hi843tUobew4kXniSdOkpnsnx568fmrMqebTkafPnQk7c/Is++zxc/7nGs77nj9ywftC7UXPizWXPC4d/t3j98PNns01l70u113xuVLfMrLl2NXAqyeuhVw7c517/eKN6BstNxNv3r41/lbrbcHtF3dy7ry5m3+3597c+4T7xQ+0H5Q+NH5Y8Yf9H/taPVuPPgp5dOlx/ON7T/hPXj2VP/3StuAZ7Vnpc7PnlS9cXzS0h7VfeTnuZdsr6auejqI/df7c8Nru9aG/gv661Dm2s+2N7E3v2yXvDN/tfO/+vqkrtuvhh9wPPR+Luw27d33y/nT2c/Ln5z1Tv5C+lH21/1r/LfLb/d7c3l4pT8br+xTAYEMzMgB4uxMAWgoADHhvo4xT3QX7BFHdX/sQ+E9YdV/sE08AquGg/IznNAKwHzYb2GhBACg/4ROCAOrmNtDUIs9wc1XZosKbEKG7t/edCQCkegC+ynp7ezb29n7dDoO9A0DjFNUdVClEeGfY4q9ENwwEc8EgUd1Pf8hx8AiUEbiDweO/AEDDjo+GAX8EAAAAOGVYSWZNTQAqAAAACAABh2kABAAAAAEAAAAaAAAAAAACoAIABAAAAAEAAAJaoAMABAAAAAEAAAHEAAAAAMoKiNoAAEAASURBVHgB7N1n0CZZWTfwd0EBAwpmEWEGUDGhgpheQBgkSg6S2V2UWKQCy1BW+YGCoiiQD4QSUWBh2QUlCYiIujtgACMiZoVdXjDnnJX39zz/2WvOdPfd3Xffz+zOzNP9oef0Ode50rnSOd33M+d9/OMf/z/rtWpg1cCqgVUDqwYOtwaudbjFX6VfNbBqYNXAqoFVA3saWNPhagerBlYNrBpYNbBqYE2Hqw2sGlg1sGpg1cCqgXV3uNrAqoFVA6sGVg2sGqCB9bB0NYNVA6sGVg2sGlg1sKbD1QZWDawaWDWwamDVwLo7XG1g1cCqgVUDqwZWDdDAelh60gz+93//14MfYv7nf/7nyd61tWpg1cBsDXCfwWs2ghXw6tbA//zP/xRJa9c+Vv8haZxH/kMi6riY0cN5550XMKlRz7Wvfe3xWevoqoFVA6sGzl4NiHJi3bWudS1ZUPRLxNM+nKFvTYenWPJ//dd/ef7ET/xEVsImWEklyFPg1odVA6sGVg2c/RoQ38S6inJpJ0Ge/cJtLcGaDk+o7N///d+vd73refi3f/u3T/qkT0rvoTWLre1onbBqYNXA2akBe4D//u//TtDTthk4O+U4AK7XdHhSiWziEz7hE/Lctk9CrK1VA6sGVg2ccxrwtYTQ5zDskO8OT0T/c259txboL/7iLy699FLTnBuoj+wRWcZ1r3tdG8Stca0TVg0cVg3UsVtHAbyp07M+ngkaEN8e8pCHfMEXfEGYyfLlPdGZwN7VzMOaDk8o/Jd+6Ze+7/u+Txb07DUys7BB1N7k3lfzOq3kVg2cFRrYlPZWPzozl8+mUC683/3uZ2voskzuh/a8dE2HJ6z00z7t0xRKDtBVRo4OGASzMLa68ZnpxitXZ6YGuM8gY/GmwaG18xrUgNL/Uz7lUxyDhQcx0KWmOZxflq7pcO+HhkxBCmQT//iP/xg78EpZImxLXf7MUFI9xedbALMAA9DIqITqcVN0mO8AnXwclnS64IdHfZeNLA6NFoAGmDzOJxfI61znOvnxJeREaHECCNqSNFNaWqUNQ7T6H//xH7TRV2kmjt9LuhYs2PSkaoFfexCynXUg7VZMCNvHcGUViE/YQXKtZgYB5nQi6gIZAwjd61//+v/0T/+U6bHVUkgAlHo5/DC3FhT8MgspJC2tz/qsz/r8z//8O93pTje84Q2Re8Mb3vBbv/VbmGSf2zpCWVc1IgWGQ1FDD+Yhz1BrtIi6oo3Tei8lF5+1xFkj1Evby1Q9wj9VoNLXbakojfDW6YQ2i8hWMeYC4CqGR+ieq0NrOpxYWfbBr/btZK/BaMr4tGtyOpldOaFGOitwFPCcRnxpz0gbKibGXiEHwBVbKkYDvIxiuEpMKSkq0eoRdNxzAS49kLp9TFtn2AgGs4J/23umVyhM9Emy0a6sk/4s1rYktoIvDdes0naYKf1UP672VnF/HXOvuVs1IAS/j+kEtkyPbpMLo4H0AEocDGO1XsVDNbZiI8DmtrTwpu751E/91Dvf+c4XXHDBV33VV/3N3/zN7/zO7/z6r/86+JREW1Hp85YeEllrsjCMWn0NzMRUUMFMNBAOt6K7LXBptSamJ36EEzzgWXHpXhwW8OJGPKLEhId+Qg6V9BfysFQqrVHwBbM2aGBNh7PMgAGVMZnAjFyxKlZeo9rlHmkAi0+20+eQNHFwSplyEQW2x81V/ECOYscB5lAMTDaFnA0GLq2zgk6G4t5GRSWjfC+0tIlvIn5MCUB5Jm6jh1DZ6k60DnxIlyoACLhouWcD1IE/HY/t6hSHFfKSntOfe+Dx7NHVTp/JnikmdoApHM4YXtoAEotrXfQAsHzhqoPB44KlwUn0by7GrPjNbnazW93qVo95zGPQes973nP8+PH3v//9oZjte5/uSE/pJ1IjZ33BEypyRYexhDIzAAWpXUhGCO04VCTCVbDhIc5idJ/fvQNk7U36X8BDS870rEWYiWasSxruoV6seqzOBaTP4SlrOpxY3NgZIMbEhlwaCQHcPj3urFN/Ig7guG5QGwIwQaY3XHSN1HQN/ai7kChOuFklrcCDDMPuPdxjHaElfpnofuzYsZvf/ObaIuz/279Ijfq//Mu/aLhkID/ZxJVYDK/T5r/+679O+IvHhhMYALdCjTHRjCWCkA5R0+F0EHe7293uRje6ESrh6vLLLw/FayoXRrRIFz5jAFF+R2qdUXIj5dxmzQ1meMoMtMsGNPyI1rrAS0XOUW984xt/7GMf+4d/+AfsZa6haswl38BlWXVYd+Sg+sqv/MoHPOABn/d5n/cbv/EbEuE73/nOj3zkIwXQTJ3VhDCabLX3yZ/8yb77+JzP+ZwjR46Qy9fgf/VXf/W3f/u3f/mXf5nNMWtkIeV9uwg4i8t930SltIFnbQfF7PO2t73t537u52LmT//0T//oj/7oN3/zN2finANGLVnKUI+ukC51VYNxGo2dAIDclMxyn0Pr8MCs6XB6raumY1igk95ucIMbcEjmlUgNRvQRblhhbA4kgFjhYrMLKvc9+92/oPVvsaQhBycOauPNKKLtxGkJGwjTQ4KwvPpJT3rSAx/4QD1/8id/8od/+Ie//du//a//+q+Qo5j8R15T9GRrIjD93u/93hVXXCFIOS7DSYN7p2Ykchz3pV/6pY9+9KMf9KAHQYf6u971LvcPfOADIuNOBOZNxkZURD/aUbi7tZaSDdFDpA5AOuGuidrBMI/gABTMoQuPtOd13ad/+qcrBf7gD/4gu2SjyYUmU85nfuZn2rdRndNLJhrqCdxGl/FTIrAEdoKHb/mWb7nLXe6iWPnd3/3dn/qpn/qVX/kVmEntGpBhRld/olz4f/evu9/97p/xGZ+B0K/+6q9+8IMflGyuvPJKVkfblAN3UoVG8TmD4BKQUiadZ36M4eu+7uu+93u/98u+7Mvo533vex+F4BZAXHUJpd6c6AcDKPJ9j1ZTw0Jr+0AmpgjAoosSMUUAB+iVPabO7o41Hc5avxRfzI5tpUR95jOfeZOb3EQUYHlC/+///u//7M/+7C/+4i+qWEUoYEyT2SXcZPosSqcCwRMrr+6EQk6VRvBnFLBL25Q02tHCMN4w1xUYW8BgkALVuSqAW9ziFoQijouzibkekwhBisXu9pDCkx2b/QHNFA9YgjmMjfPQjoacWQk3eMDJl3zJl3DvMPAVX/EVT37yk1/+8pc7nePqtqft9ANvF/+Rqx7FGrQwSTNh1QLp9FiQC8Qv/guPHgiLrl2Id3VMUbHy6le/2tYcQAjJ0NoU9aEPfUj/3//935tYiwtDtYvK/IbpZYF4UJ34gsbn2baGEiEDsBYYYDkgNbAxH3kgI2MEd9dJzEc+8pFHjx7ldx5vetOb+njnm7/5m20NGYB6SCPKj87NCpJtSW8LH1XQZ+hyhAjuMQ6ih8/SybaYN8FXVot+UAmkXHjLW97yi77oi25zm9twE8GK5hUKz3nOc1S0lFMK2WX1N3F1tvcf2PKc7YoY4Z9NuwDEkhjZve9973vc4x7qU8ZniOnzTL6qcvX7RSb4x3/8xzZSkoEprpjsCImRIdONsl1I3OUA+4Cv/uqvdmTEvhFCRSdnY+sAJAwMyAo5PhrBPDgEQxiGDVokHPXoTALm9vCHXKJtUiCWsGeXAKdQ9eVf/uX6f+EXfgFvRQVCMBGnOuc0EKVk0wFLMI7g7HJQsSXC2Bd+4RfaNPiCUQiWEuYgPBAYXNEDVYvItl+f/dmfjSWd4pRtMf1j2JmhwhzPYX4Xuq3eYo1oUfgXf/EX3+c+96EEm3I5701vehPquMKSTokKjLn//M//jLry5ed+7ucslh4X7YWlLPq27JnlwoYi6Vu/9Vudx370ox9997vfjRM202JbkAtNh9y9fIfUVhwVohnCP527NDigZGxz7CKdMi7Uo6iWk9PUxgPM4UrDFpmHMg/McxNvVV2WgztU3joQTpgcDTiSUetwTPoRgmxMWYWkaDRU+MVb3vIWmlExlE6K2wPh5NxAsqbDiXUU3RLLuD1LsvNz7vTwhz+cTzJ3QzxBxDly5Ihdi4rsvve9rym2iRdffLHP6pipRLKjDzBcOPkVHnI45iUNf3MI8xM/8RN8DBuJGpKQeMErpDFRGG+oT0h46nC8BTY4yfXhD3/YOaSqH7Y4s6zvUWBVDfA3IZg3mkV8fAKDDwM2bcmXHrFtiBLKFU+lOfFEihJQrEfOlxoKkTve8Y7uOIHWqZQDNFJXKJxAunQ4QYRElCMA0YZ3Zorxr/mar7H6OqnO/ljOdk7AQrCKeReCCZoaQbIVC/25LMFC0DPbowdnyI94xCPkg1/+5V/G2Nd//dd77+sLT2thaSj/z//8zy+77DIZGiqRsaVeyNvOyTa1M0u0mJwXzGQkuLBrFTIX0aSEWMUkwhaAGmmpeqJAnTIf5WvErbTBOBgk/td+7dfSv6qIDaBLqB39rqiPNBDCSQw7dz0qJGkplSI26N/FVJZVqIPUo1s5T+lJZC+GmQE7VKYzRfZAe0ox+tFWOhtik50XCsvWfZCfc6NzTYcT68js+Lw7hxSAxBGJkJElP3nkgdyAlzI7bdGZG/jcXGEuHToz/Mmf/Mn4zASl3jCfhzkOz8F8syfg8iu0VJqykShgDypdYUYBjjFZQT6+//3v70WFZIm6bZPoABU8lRq1E196NE+U5IG0pbjkkku8nBNScfJ3f/d3JvpWgpgAyKtNM/ITbCpi2ZHUj33sY4H5fMOO4a1vfauXOqgAc49EfaLjPZkLJ+9FSA4Q1uXab/zGbzQRGxTirRWFiPUExzZtlIzLiLbcJi6jruGibeLf4Q53kG+oOpUBNqy7UXRtBWgDvFWTLJ/3vOeVgLElYAsiUaxoj4P9L2ktq7dT3uymToLZuYVCTVlwt7vdDWlgFo7RogVGG4xc5RDb2T6WrCkYDEfDxWTbCMN6QIZnjQBoWA5HtbanyP38z//8T//0T7M32MyC3BVDqrkt5vG2KYWnIPXgIQxAjgrmjeq3H3rGM57hsORtb3vbi1/8YiZhi6y/5p6mRgSMN0VX3JCfKprZA6KpI5XFdszL+ClLJriLPcOT387f8573dEzN2rmkAwkOyBl5wXvf+16dyhRLwzJZDm2YiJ8wSY2u06STsxftmg6n147dsEL3GD3LU23xRkapbT6LZ3Maqj8NwErmW9/61vwhx3fxmWlKp0LEXhHlD6xfgmHuTmX1iD463fXHyj3KzaKeFCUcyw3cErCULEN4eWCzAn2cAfOnkjr5FGEjLzApUGSBlrDw68+mxxAfw5IGqbFEFaIAosI0rkDmgE4jekMjEp0kNqMVTgAilOnYsEf0PkyIsRCGuLo70mKiDUpOaMFnrvsMOhMgIQ0VtKRDVzqUj62IfCP0sAGCYwwDEg/N44cNWPpv+IZvsASGIElAhGSCXm/Y+mYuJC5mRttOCNgAzWPAVsDW0C4NObFYksYYNLSBNHh6sz+wRzREEGZjXaAaZyZEAQceQg13s9QlzuVkX3tQRuKVoSuLHvZBBjkq6Zl/zxTTgyETWS9TxH/MngFgD2k2zxTpQb9tmW845Wb988ntAsnNrX6MDYfqUbbBBvBGA4zT6qsSuJIl2JYQDFFFzFgbWsIyMCUgC7QphNO2TyJUGSMk5nhlwzKdo8qR4OkwK+6eRTeFRbW63ZaxcxJ+TYfTy1rRPKDii3ckDmeEP+bFA9kow+KlfEBDBNQjWDge4agcOD3TlE6FgNxVJosNiUeM437uqLN1RAGIhtzGxQfwwwFyXCZO2ac6r3Om6qURzhNV42CnUjv5ZDSkA8bVXRnW0x74tGcv4iDSXiPxf0EBYxKDTqiC0N11ksyWrVoFyhRZIP+zP/szcYF6kSMXuhZFKHRwRBXU4r4LxT6DEIq5ooyYqypXdhBQjLMpQVTb3kttLlk6yPWdfTKi4Chw4zkihLc+8vGeqJGY5EXx6NGjNqa2ntKhiRIhuo7o7dWcnulBhaIcZVOIDbqJ8qV3rr7D1Lm/FFusRewwyowUelD3iS8b0w+tEOw7GsbJGkvt6KZdZjwuZo22s8qEYNMmFzDZTqnHBliaxOC8RH1G4Y5MvdpnHl5h0hVmCudpauAnHGKA/fvYR5WgTQS6wifNOKqh/1LLfE5Kb4ycX2ciw7vXve71uMc9jtRwcknb/de97nU/8zM/IyxYehbCGRmq1+omIi1Z4gSwq6hju9prgwbWdDhhBjH0ALEzDXHnoosueupTnyrbpV+IBMZXY7uJCDxBQzqUothrtiwTxE4dDjZE4bHbU/JDhUo2Q+xeDFL4J0bgwWskMVo44ADmmghSFem1gWMTGZQn8CisusrNTqV54glkwNz3HGj/MeKb6FF7v3vPtTAg7igFBGLBUSDQKQyJwtiDITAUYsoJArP/yZSwEZZC193pk80QcoTCQ1TkfBhunVJ45rpn+myaXcCajopTUBXG+eef79RaHBRoFOMKDtWGVdajRFAJEdzdWlg7MctCZEXcIRlXfpf8/nNCIR2SSxHgpZGULPqjSEDbAudjsrJVhl/gQ9GmBGP2Sd4tATNXSnYZhTLK0SitDtLVCaG57ajVt9yk81Unw+MRP/ZjP8YaYc7qB3iBmEUFe0W39E8J+pF2V5L6bkhGpGF0HVwrhjCWQtByOMZoS7fCfLCNZFyscjoL/U3f9E0MQ1tCohnZWgnim3N3PGNvmU5oANswUK8lFgp8PcAU41OMkB681FB1UQXX8/qQld7+9rcH7/W/75u8VUnxWjxk7sFq42zHtqbDLVYwQUHV6cSJLarRhDwvbJi+ISd1PDNuwB9i/YzP0IIStUIA/xEBYXY4ZisAIY7lQsmAmzn+AgBYpECIc/LMuBwGIps60VlZ4pShMLZJ7EwH4wLjXq4Y8TMxJAzhJ5CykdSrGo2/cVqqAAZh8ARsE92RflRcprs0gp9O7EUkYCWCTtMlAFlfDEIRpJ6aNYJ8ZChICgA2mL2WU/5LRWIQ5avHsSEbSQYgow1LQ+dKE0zau1i7YglMVFdoZzbIRf9WWUM0FHZVP6jAj5y0hxOE3vjGN8qCjFMycG7mZ3mydbaDUV3uIRoBYxIjbJQeWDUwjxZX3eMS+pmivak3djZkbKxMPSIvEzbMUGZrcjqFe96nDFVtOJg9fvy4TM8HCUUVdmYYwyQzkKpVY5LEiFwHMoRJeDDgDMCRgDxE+aQO584MvKn9tV/7NQwDW6wNylTQuLN2JaDPaCkfaacRltsn3H7X6IUI2WlAuXDXu95VpeIIwXKwT1ZBUSkOYqIWcTEzB6K3MxPJmg4n1oXdJIIkGIHm/wK93zmJRM6LbBQ0dMoEInKsLbYrGkqcTLYCygSxZtgUqGKyHF6ykdIqqjJ9HxByA2cgIF1YYvGOy/iD0GADEWQ8BFfiFGy5kjgbUqc0OwHIGOTuSGeINlxhTKc2YcFI1QIBJvWA54foauSxj9bQggvdILTbFhDVvzJieCMX2W2YXNmLkzd8RoQF5DpTEHIQJxfStpUV5p7//Ofbb8GvEMGYBm2YRQ/ZxNC/yyhmANBDWOpgnnxEEQw8oiGpRcPkHoZnn+SyNTT63Oc+V75UqHlECzPhx1y86cGDS9ulkf5J6gEgQrIdPdiN+XhVQx0g60jJYNAiLCqQuxfaLFA9zmxAEkiNLCUqkq7sTl50aZ4ICDF7H5E97GEPQwgk5asdJWkFQfQ2k+ICML5vIRwCfdu3fZu3htq2p/DQA7bjp7ZlyxY9/JAoDRHGy0Kal+10kl258/rXvz4nE7yeMYgV3ub6xthHbWZRlNNyC+TFisd2xdMO5vUeDazpcNoSmDXTccU/3Rk3E+dp3goIi94VCU8Q6VehaxQ8bwG/LBzAJsC5nHtwgGQ42Pi/ylcIEPTBBLkcoGpWF3ODgsQht+QkjlDkZnMxhr05/ASytFPRDRJX+mWgNCRCtbm7WQiJU5wwUwDrrCmFcGYjc9vpaRNNZExwJLvoQCgp2dclYpB1wVsgd6GOSdPDqgYqhEIIdSFGGBKgO8qkcLs3SyZESs8g2YBlgsR0DVylMVMDATPFROfeXhnmY3qGYUjSdSzx+Mc/3vszhBwYOBxTGMlbLZVI4R4GOqRbyM6Qx9JAFtQjPftgx4p7XerDaXtTYCzf0mtE7RpJCWZlop6truAJb5SMc2ePL3jBCxL9vS2TF6WfgGlHRUhoOLOhFgxsRXEBMBIOaS2394Xe1fE1SDBj3TmpV4bWohRCddXeilZmHT16VAliayjgIOEI1B9esPkTgmBTfYpC/i9fAOyEeYB56UtfamsoWCGdq9bC41Y8HAbg024u54YSxVZ2716Bg7ln0yYK8AR7FEMuIUkQJHUiIDdepgHWrNaOyTr5sSGAR4zTjw2bQm8jgjkwiCqQ+YNQxXnkP9Vi9me2Cy58Yi/OMOIJNdT6rYke3Y1GLg09LjwgxFGd3aUgRVogkJAcJxpFMTg7aSPMT95DCJhG8e+Rnp0HJvQTTQUAwIrYFjgetC0QmMrzJ6nMBAgPgFFUbgtDeiJgVGEVxESZySVRERkbDsrco7cQatszSe8r++PoUrVoyBoZg2Sg4QBNOHZMpwqxNfEm78d//MfpKskJfmDZI0KiP6kFD5ifw0lEKz4VPUeOHFF46Rdn2aFViK1CyDjdGR5OGAYmnXDS1RxCRSKN0IXNY+5QaXtJZig7UeQ0OIUdIeUjRyFgqMiByoEbQBhr74oevu+IiPFTOEczSsl0ojRxgMFVy/JJ0VFmi2pTu2bRPOuiWJbPv3zB4IjIotOzIaTtUP2pPJbgGB8z0mG9UeYv8Bf14KzHTaQPW/+aDidWvGwxfl5ezeucXfjdjxcGdgP5rMZociE/ZGoehWkhLO3WHLX5TGHrM5HMp9/rGedjyTTiGjY4P1vPXEj0IJqzSoe3IpEhd0QxDwMAE+VvPSGqp08xPWD6Q6FFEEgSWwvMowDNCR/84AcLkWKTLOjDCmVp4QnwsthUKoKkgyGbIaugDkCLWvTYsqig/d7RAkVMGMZVXXy2jVp3jfAACR5QsTRROHmtu5QQ0nRusYQkP/YSknBr++60yme9DswrJgZDS2tOO0spAYuAvqNBCD/MIHzCiRN5UTUgAuq3LxETIzj+s2oIabtCsRaxegY5QSLKJBE8qHstigHnsTKT0seik85co5SAGZrxYa1PS6wIGCnTPnIQ+UhnREYUciQKkv61dVpfMCjm8CNg4JMRTUmjJu7SiJ5DFwmPai956KEPfSjjV4ZK+cSncKM0c9lll735zW/2prM0jz1DC3iwOtCGNIpsiQ1ceuml3hoimvjgzvf9CNW2WPlF595nOztRGmI1ubAlXUvfdq7tNR3OtQFWVaAxepZ37Ngxvy1TrzFZXsriRaK9vdh+kehRUOAw6kRGHJuGJ9Fn3CLZvS2Fj1P87w2CIL9CCAM8ykRu4EIRTtFHHBSAFKqQh0rChzYeOIM0pm00GWKZZ8aZgwEqbMCJkGzkvb26VdvhoW8HbM5KV6e1gaI4i5OolFzqDwlSIJaVbSai7YzuzglColuCrKSLivRDvXSi03Io0u2SrVooSocWTnBUp9NeIuMuMVHqFWdZl9X3gpCRwEZGekgsNmSz4i8h2C2JiSzHEBj8AKMBImyrB9MplplpKImkQ1/5s3kbFAdxSJTyCe5vLzivphmbM+wZcmj5kpe8BPC2dIM2K2huyz/Dw0wQVqxnCaTWyXG4IcFd2xLdBE9vaiBoiy4Bmf397nc/CpePZUdzfa7iRNd7Td+2yEZ6IoUGnrUX6N+UyC7PSYEMTAHqRJTfsTo6x4BqGHssn6V5aWpdfEpqYhxfo9jYJODaTwNrOpwwg9Z802aarNDpvNcnTJNXeGTr4pGoAcbFIQUOncKlR2cm0iGLBOlxguT+cMwXLXWfVCf68G2FsMiLis0izOKvYIQBX11zCfFRzDIRXTjwE0I6tbNJnUN6E0yCS/jHQziBXFmqMvAoRtsMveMd7+CKm5AcYD/lYImqI2n4wZjiQDgWjPKpRcLQ7nQJLhR6MakuEeiREH0EIxoQJTEj9DggtUYCk7aw6K+0yEmWvo3LiwOTFbTi6v23v/3tjsuOHDmSHgzATwm48h5LuhIrVSTebsrExMe2u6tlY6ZCyBXBIWG9kNv4kl3uQcLHXPqxoRjiC05KfHnLXDFDTKowhekyiQXpEF3UXVC54z+CxMLTmVErbnNs6QnFDFyI5q3BTDEnwRCyylgimovyKdmLOtogJsZgsDrKEX+mzq99HFmHw8IcmHqc2SB14VFremOKhCMQiV8OVvwpgonPBoJQCcj8WAJtSJ/YXkZ3JnvnGNiaDmctKKsqOG026mWeV4Y8n7G69HBCDXbJZ5gjeD3io1IuIVIZC8BooRppiKfJnepNGDhAJrJytCQ/tu61hGTA7e1U7FP5iVFE8QAG8jDmHk/W0AlmmYdgoOZGIaigZUuUNMBdfTjge0vBekS0gxrCg/hOUVmRQosl+hez9EQDNTS/UctUjWBzAulSjlA7utIAEvKBxfJFD6Lg9VCFXOjtjnNCUVKEsvq4NcXV4pzPEgzmgseAXBs8Vta6I4E0M7MV86GN/5ZLbaRGkTvpxxQrtWzRTYxEkFCsdCgTyIV+QsD8SGRUTaA4e8xjHuO7RzywAVUafjCjVmMzrHS+mIGMdJFXj0asN6NpR43Ed1ZpS2oJYue0zWuSDLaluwm+GCCO5c7vbWiDfihBp4nyE7q+LfJbT7zFf7EaKVr+N1Hp97eSxn6gFXasL62iTlgwVhlFC5TS2ffPVOFQCgCccqcl6yNfezoaWNNhRyFzH33izAO9NWSdHM+uSLGsIRALAZKlGBFc3NU+0rmZ/Uq5BP+p9iDJBFD7LTYtDqIlurFpxaA0rBIXiPUk7siFymE45cLyvTzq8VYfb/mwhXsEYJDoZCecoioM0AKWBfOFISUIBKKAjQuerx734/8UIvXaftF2PB97LkMlZtruk9KNA8BAan/7w0ezgpHjAbtDSZG8lljot48XsIQkecLbHT/Fs0tONqK3IIfENU5ocJR0DMZcDWwgVGD6icxILIG8SBUeVUhWHAOOzjyasoyuuSFEnzIcqyOy5fZukj3r8WhT6CW6XAjGKytvjiVLbHjFqETgFC23xfZ4g5hI7ynrKnVp6MxjLa4GEtzQtpiMADiOTED53h8X2DitOaOUDDN5AStPHZPKwRbXctOGRKgOYIfeE3uZWqxGe1Z/3NlHGDCXUJkuyNhk/9AP/ZAvp/zwFGkTyQiGBWrTM5b0OMLVUAxJmfS/5sIRDbdDazpstbGxzb7LtbRZp0Mz0Z9FurzR4QOipIa05C2CfCBvZY9oIneVkKSuBEdk4u0b6V31jkdEc+InlXobIdzwLq7I6B3RCDfiLz/k/CKCOMgreGZwpoE3pbqjWheXAADDYs+Emex1V4eSSxz0pY96meNla1gyjkh3IEMEcTRkqyRGCFX52ohiw6EGzYfhaiygGwzBiaKQJypJhKR2SGWJEUp1IlVQO1r+nqqtoU9aaJvOzcr2KPqHCpjYvRUzqGThcodBaE5eRBGqIGctNo6swup4t4Qlhlq5sBjYljQSZHFnfjCzJad2yixWJ++Kzr6kBcDIHRXKQ5bDAYa6UK3gQFWq3opiAaNI/+6RmjhMnRTsLbo1ZLcad2DtJoLnjMePH3dcWXgOpEEKRBWmF154IYraKGIActnIWz1nwi5uTvxWZCy5gC1Yd1RcqGS5La44gzqF6GFXihJlEPzAvDhERWL201j6x2qUsIDugWjsrEOypsOJJWOIMWVwaXsUX8QdVbDNELuXn0Qr5giGh3j0coXJCpcO91mnz76dKMZzCsk4YUmO27NjDSSYdSxb0PeuzrcSNqBwwi8iA+CBGAsJ7JmoU7ZwdOMdEj/hPCXIOOnB0bCdqATAo59/+N2xTyekf1EPe3K2WGC0YvcgqoPqJA6dO5a0U7FRlqLCGNldRl1hJkuzgG6kzkTY4EmPd3Ik1TZkl2ChrbiF9ihP+B2eHEAnHgVuOSMY6h7G6nFOw5oCQ8JcBYfH9OisRvAAYIQ6ceuiCqtmRVwe59BqYdqJ0psgq85T98g3Eq2A+4QnPMEfZEHUn0yzJ7MW7F99kF/KAvuBH/iB+lFQi3m8HUtDvUwOvBLQH+q0H6VVFI3qlCNxpScI1Sv2xIokjoOTcSrzR7OOzgBsvPzI3ULQMKdDxaVszX5UJkY0dJmHC58RQXuB/oMhSPbQnXeeYIIZwUEuVHX5Zkf5hQFWYSOoXvFumzsoVX3Ehx8KMX2+pIcZck2Hc1efSbHFQLNv2aXsvlDwTzAgDUlU+rXj0inu9AQgU9p2IamGQMbTPLL7QOoRZJ3T2n16Xe/ITiSSluJv/EGVCtKjECxaqVh97S12Q4KNeGk42dZDIkj5M4cU8uwJxCbkYEbFLhZYGCgpTl8DIcjthPi8cKCNOg5J6gqrYAJ2UGwgIRLBJhqiQnx7I19aOjemB1FYWPRtLZbkJDxYQWCt9vBTapzPFaKCr/MAd1sE1Zi5MBe22AAwdMEwFVSsCxiQRi3ZArqmJ92qw4Ra8sLMhOBkbF4Z6mefKgBS2xArvLxXoxMRWZtC1CumBMl8eWkMMHFMJKNHSJQdNuXYIGAWwp28kd2elV/oyfk5ruaTm4SkySNHjvglA9HUf7jCg8WlVWbPGf0cXloisn6VaBY9jEGOSVLEnSdpdQCggsfaKX9pNf/XKQdXeNE50iTFHgC+IOwIEfaLgoPzJOxRyIJ17/BwSB7XdDix0IwpmY81A+WfbCs9TJwVAtDvnqGg4y2G4gN8Q1uuSmAKHvCx8hHyrfOECrQM3RSVuDMr7qEeFJW0BQIVK96wAdjrq8suu8xnjbaGoVXYMDNCdHwocc1+VO3plMyGjFw6eaa/FZK8G3LBw43Jm6Cmh8gew2S5qJ5IN066P5pYYy0iGuTCgWAEM0WJFxiDWbs/d2bPIGPw49llt+QdkujvJCDknKO+8pWvdGZuop5QCZ+56xnEOckPon7f5vtJAvqVvXQY+8FG5jI2qrAz8CYPLVSEQprPcgMrA5ik1QJAFULw5IMpDZ20KhH6u2gSpIQnKMuFihIJ0s8uvd6GhO35rgqrGGtxzmxbPjzjHDmyaJNdQeC8lLVDEjYIro2rdGpgiR6MSk5MIgsBD524uwxt4iFenHusHbyJvppxHuNHpTaIEOqEgWtzQH8tVg3kdEQ+1h9yHRJlDJvobupHmv7JyOmkN6egTE7+cxLjT9KoQhxZm5scjGFBgFdigwgig/4Eq0341/5WA2s6bLUx3GaOBtile4I4l9MuNwOQEMkE47Rs16iQwY2NihfO1tq4zFvinEE7THhDL6M3XcLjihKeKODsFGngSCDHW2wNvS/kLfD3ScTDN6Af7s4U+BFyWnvBBRc4JPTI3/y0zvc+Ph6xazEZA20sQD2zInKYKQBoh+nN6BUj4HHRdvDwf/gtgaBA8xYl5BbIu4k+VDQArbsDUkeCR44cIbJF8dEKnVsXYTF0wcRgNmGb308ir2mPHTsm4zqJFQQJqxbBDxKo04YNgc87fVnqBTbM8hDlFIlwbi2qZ04DBshd6jACOnnL5ox5ezfJBuQeiZAB2CT5nuVpT3ua39o6UGUVzvEohBK2JYqxjhVZ03BLZLpNSsZVciFmknERQtqRiX2kV/sXX3wxmyS46VmRSZGzXqaADNssHFGfjMn9VhyAHneXRUFC3WlFpN4gn0lokpMCCCGfbtmYukiq7kT33e9+t+UIGBgNbGdT6EDVIwuxkaWrNSOWMscbazoc18/eaFyRDygGGRxzZ15cxV2w0Igt6s+QTiHAKHfVI45IS954x7v0wJk7n8/caSYaCPhduIJfonUi6tG4O4Q4dBeRJUt3nKAVcsERV2/wzWoWBkQ5my2RiEMil8NbmwMBmiyQo4iTsGEWeOV8TvkAGDLFNsJ7Tac6e5ztK2QWE6cCwSP22TM96lGPEpfhsUBCgEToSM3WDUvYWIz/VGonn9Cl4ezD/LlkucejuGwzJEIRqtY063Jy5g4taiSRDZn048iOXE7n4CesHn+rwaZBosIVfujBwZ1SSdwUypGNEnC+gIXIi5aSjkoVQ5ZYynE4wcBwJQTLwcoyQxYCG6j7G/e0gYFlS4Dh8Jzp7uFc6SMJpRMbyYuUoAQ0xCm0acDbRL9Nog2WKVdZGgpM/Vqrs0kVcRkLCt7KQigL2vXanOmkWxPphAE7BoDcRi25kMvjmaL2ed9z890v1PGAAX/1ybsJbyggv/LKK3016m7vi1tU8CPzUb58aQdJRVaKhSiejJI9Ffzu/JzbGNZ0OLG+TDxBPG4gE+jhe654l/ksMj7GWxiiuCD6a3Na/VyUUcZh9LQhMg4/wUFvuGbBhh+7kwKJb7hzIZzgIW5QUwpy2wackHBOZYGGczP3lMlSsg/rE2p1ElBOcneJmKKS5IRPLFEF7YH0sYPPKwQUbCSIZPp8rng4/AT0eYXjQexZDp0wiIaCl82B8ys9ME9GwPl0CQgYaa9s67g4qyzmomgIAGE1Ajwf+Qgk6SRaW08H48KinR8LRNeKOKoVqfHjQI9CgsT3hzZt2SBSDmawtEAP8Fu1TMeDCCvToIViNuXSrUBMFV5rMUUKV/n5xMx+0fqa6BqRa9NQqU6Dncf8pB8fr6quVABZa9ONurN2d+KDNKRBXtWDd2n4lxuogvg6A1bO67FzYRjR6Cou5nUd6eQetsTmY2l2wz4UkgvVneHQxAUa7lDvPGJeD7TqD3UGEmwgLylJmuWOLBwNjIty9HBJn7myGf6VkqiDeX3sa2BNh32dnNIjnTA7Xe4MTmHuaN4JjJfYPhPgCYZ4CKvlQsIBcxQplOo2LpyE4XqpxnOA8dLyMcB6FkQK/uxK8sCbl0mSgZ5gQxFmaPmD0fiJoboAVHurBpzBHJ5DUQ+12B/YKYrOqVWlwLxkEkmV5474lLRKBFPMpQFbih/90R8FnM9QyRJtbMWPWZIu6sIcnxeRqTdciRGCl52KH2CoQg48QqErvgg6BMe51ceDisc23UcceNAZYXPfSq5NwKoxGc4uQfr34tbnlH5qTcPg2YDMREwaQNqiA8aPMiW6jQYWKBny0h7LsWT+HxV7QR+UOjlkYHDiQY9yxxKj6HTUXwhzHKIdU2lD9ibpBvtRdAWJu4uGWQ5h7YatQvQMhveRnQVKfjpbVfh9nvIIe34R6DwfEvB9vygGYkVEgx8Yl5cLHUEr6cwFZk2d93B87+9f85rXeCsRFcUlAcC/TNXFQ9uIsdGtS2JzOuqvAas2nEUD04M6C1Sg+I6Ur6mW6MEqUJSXizihLuK0ONf2Jg2s6XCTZk70M+6YuwjrzY2zCJGIP3hxouR0TphzGJ3igsCkVOeBUoKQxFiFj1e84hUSAHQcKT4Th1nmNntRYd8t+QDX9bbGCQmfgV8/itBqoDhYEi521EwUd+AX4EgnAyFBKPJixoZP3cpp1bA2xwnQFIJDlXsiFGDTMQlbEGJbj3s9as+8ku8T/dGN4EkJIqD4hbr9BGzoRmkzMU+CwS8HpFpHkbaZAfFr4sGSg5aGvYpDl7Ytt/iu5hD4InjyIjDrYlvgmFRCclCZvUW4WsYSi1Vz0G1Ml837bAQz8oSCQ81n4VQDfnohWdoq2ZTb96tCMsu6j+SeUtdgA9FauDRUGwI9wQ35sCWBnjkRP5lYnlaZIYor/QyVitSvXvFqcFt80oNrk73pD8PEwZVVVuDaj5pCmdiA1mUHjBM1h35g+AlCQ2BatQ+KNr+Ti8nx6mwkiEmrfjqlELcEKDIDn/Y4FcgJgVIJZjAktTXHCRiCzyd3yCHXdDhhADyKcTM+nuYFiZ1fco/TIQbnCzpBitmJDuw10Uoy4JBCJJ/xMyx7FAaKTBymPEcY3aVqQzQHQajwh+CHnDMLiA61bGExvzgYdfRChHiXfoKLtl7ayYiipLCIAcFXP0UR37YJGOkyhUsLEyCzk8uRly0dnBSVgNIhN/kIIfyKZftvmdgGVMyKnikWWuQo3J1OXJMI5wPIQIpxVYi9GvxRuHMzOSBcQZW1XibaICfynNdFJLWsgiA9s0N1BiWQDhvsTduo04gXvvCFDiQSlC0BrnCSK4wNktjUSZlRLFSW0seiEj/7Z2ZqHbNkBXWhu3Tocxv6x49+jWpvQj7Sj+FaOA086JH27I2I7yDUTgg/Ou3VMOBVpe2jjSOv9HKa1MCkTBfnBUM5Eok8V2j71MkYeemWYXtHTkak6S2Oz27VBLze/gxkrTicYCxBH+cuPVzYOQTb5uwxeAuNExqW5r0mtCP0aoDTYVIWxB4VqcItBPZCWgGRXyLtwslhmLumw4lVTkzhckK5OovRa3NCFulKNSoLSgySARdlsuIjQ/SSw+/0/c0qZhq7zD0YUB3xyRGeMovj8WqXtMTWxWU+n1nc3hssb24W/PZ5hG4xLy47tXvVq17FRZ0Ji3cUQmQbYj6pzVHB6BdZyJ6ApS0YKQ5whUMVLtcNTkRLRSMMdIboGQl6EJscgtkTuMDAKUJxfplS1MZMBawOhsWPdgzOCaR8wiInPInCApCzX2IGLYmQtlg4XEyoM5EgtGdz5ojCeWkRClEKoYcXvehFDI9hUG9SESZ32axADkNoaUQcmS97UI/ERAgJ7MnBsgjqeshOOabY0yxgIMyX9kgXHpDjiahThbpKvw2fc0vR30IwMMb/9Kc/HV07V19jhjFJBcLghCerE4SdO4SAcc6e8ytDDQUQumZJNsjJhY4imTEqIDFJWMjDJI3BwAU6mJc98ix7Yj9kwgAPYu22g2xbkpYI87c4kKNkzFCLYsUGHYc5FxGjOOCaC2cqf02HsxTF1vmb3KYc42Y8n/GxQmekcqFRPiAQ6OcYLjHLwb1yXqXGYRLuTUEsdw2GPov2qUBIZGJwCsHSsA0ipzUE1ssM+1GOwUtxuIzKqTRPPBGEmKgIBLzuZS97mdBgkyToSI2A0EJRA6S2Tzls3bgl9kQH/syNPYopIlfiBV3hE/wgxZHOCm1ihGgIs1rectgZ2DSo3HGYN3mQh8oItv4QSTHmaudG5xKhz+5VISSiDWIqSixERqEyJatseh/z4h44Lesll1xCWOlQWMxfY5GM7Y3UKBKhuGmTGrrYW0yrJkaQWiCYBf3EfRm3wKqRoZb0glwIWzCUsxT+NOQ8i8v1IOdi2iK+JbvyyivZlXzJGZVHznKcIkoJMoRNMxVBaHVa9jqYk9VsMS+88ELJxg7MdDAWmuzQUnV+WkrSWt+oJaiQwAM8lKYfVwzGaafFyuaew6oYABty3yRjsMEDmHQcSlYmjsLXRG2vb8iICguEhM1rOBYCTANZCLkweNb7HA2s6XBCSwm7rIrXCTc8StSzDTKNrbs04mCSBDfTZvSqM7nTlBilzgkys4crMMWxMeCUkrdzV67rRCXuJz/FhWYjngCEFkXXvtB7/w26vUi8ThtpajGKDZCkdiolK3NO9awghW1D+uVLWgJZ9CgnaqyeOQ1IUHQJiBRO+cKBdXE+Jkl7k4eoOBJCWcQ5aAsmYtajBlrwIOHtnTAkHuGcmKxC6u2LsEyulmK/TSKZgLD2RmK9R/q0MfWykBIsR8wDq2UnfSRndU+siM27WJoVofmkN205Q3kk3+jhC7TkjwhqcxDteCjxY8yDegAs3yh5Tef1KjkmJL1JikkzzF72RSjU4+B9VGFJP4YdHalWnXkqHJllOyVmM2IqAJg0cn7R4eTWWwlZEAZLHH7Yv7eYyiAXF3CChXRRhxkPfePsM7z20MCSSHSOKS624q//+ZMTNi4VSspG2b2zCL7hnY3/4cwRikPC1Lz6ow2Zj1Eq3hWPYrFdEQvO0MFu0YKT52OPo7rzFr7q5Zm/psj57Y1sEP3qyx/LEAIOarFKLW0oSW4TOPDAA3mpUVdUaguoIRwIzS7tNgu2jJWq287JNswmtnqOqvFjdVCHATOI5j6JsA8Av06cayAH/0UXXUTJxNEj/fhPyR2JS4dWBCEXeEMus1x9nIt7MBBBrAVVI5GPSkTAbAJiFRbiYOkuZvh0TIxiBzF3Vhkkv6A0ximBmRLzKEseRAKebkHSLdOiTNNN0YaQaUHFusLGCDOmWI5ECYTwZi/rMBm2nIukswxmfMnw49u9Jz7xif7ij8zqke1JkM4JJMXXvva1ybI4L6FQ1A7+6uw3RDD/SYvQgYFMAaNNtD7wOd+z7g6nl5hJxc7EHWWmnOdTcp6WkMR02L3gKwUqS3WqKHkRAHZvVHuaxmyIhN0WJ9IuB7OO0SRj2dqBoTNM1eIBpkMJIDySSMhw1yNGcKG8wNdTQiQW0BuYllVu7DGeZrSdUnNnNhLU4u104qJ5eoDcKoTbkIMwYDMxD4IVq6KkiCbMWWUbBX8vzfZXJyUUzCCGHTvJRXUhQTrik9d2B1ptkiZ2hwrg08rMjrLsMp1csZ+6a7hibCU+ewBp96xHjZv+mAHtgd+kn1gOZco3MV1eXwzLPZCYLovEu2uo04AnJABjBqqcLVeCBF88VKODxCP/sr4uL2ve8IY3OAPQyQKZupMJvJmrgWE2YAghUyLg7mbf5+fc7lnT4cT6xrDiQmpwJ2NOQc1hoPGczE88kicqZqVgN8ooMzpBad4w6y8rj93j0FRnREpFG9P4Q7a581BuB4UBgbjvchSiE2/Ukit4sUd8XOlM+gTT+j8AV9szhyHYCizI84iN0g9yOiFPsiz4+Y2WqwjlJNZJqVCoMPK9kvCUaNuKULPazvlEByGhShVCWAC5BxJjRKZ8V8S3QINIzoFOMmZ9S8mtUMSnKMopDehxeUx/gEfsATDMLlo1xWMmaqAb0pBE/9lKtgy0bVRYIFRhxj15MTAE0TDaTum3QzRLzN5YXSVUjGXFUdFO7o9+OmiNdnr6hNYeGljT4YQZxIzYE7ssk/LeSHXGQxLoy9pi94w+SJkmtzExcXmC0rxhjgEwboCfNiyqWDHg58aSojK2zmTmIZ6GIqYrESF3cxImcKLHVSqKErCnJ+5qLuCWYVLA0M6aZmIDROgiEfwIAdSZK+uyYepwN1TmtmM0D8+zn/1sJ2ZGvTFVmxOB2onQIdGZ2+JZ1kai4mAwIO0iKdLRoXur3mWEzvBZZMRhZLcKHuMRYVtbijKqn2Y0KCcAgHmrdck6bhIz6wjY1eqzVayhPHYWvYOzWNUoljLRI+A5RkKK8J9UakrFHA01d0ZDuj2JTQ9WTXF1eFsfBzWwpsNBtZzsjB0zaIbF0xifK4dyjIxxl/+YA8bVeo7pZplu1kmkO7S4B7oQukIRfp0cA11tvNnBGBKmy5d2IHhiKrnSIlHrXSVXOoEhCqatAAxlFIeFJ430L2CvLbRRhAEqOsl6FVcLMGcK9oq3auPfS2IA0TkZswphYDGtOROzlO6pMwiIGdTdTdfv8qgf21HCHLRnHUxEI6krzFsdF7XE5PgjVQCjCle9swCfsqyWdVB2K5u5USwYyJNmNEI0932V771THMSjMxggVMeEvRQ08OAhbGiDTHsQDxH0o8idtffnnbDzKo9wEjAVcJAHVdseRL52djSwpsOOQrqPDLGsqkw/sTimaQL/YZH8UCPzq8d0wDHWLupFz1BB7opP4i1cFUuw8kBg+kfcbFviCKE4iNAQ8UsbFUeQwKd7sVowOltUJc58ropK5hY2nIRcGtU/H3MgCVUNJOAptNQr7lhuMC6EihmPZi0mOsJkMFtWV8tYFkVnGhk6HQyM8HZ1DhGTKtoVIayLtVsIQ9aiVAQs7yzig8DAuBdAn/NUt+kvEwoSE+Gv5Q7RPob0xB+1gxBw+rOO9aiz3xPI3ImAIiR40NZwd8UaA0McPQFrMVe7A9/iX9utBk6E77ZrbXc0EKsq2zJa4S+dTDNGn6jEvvWD0XAZcu/g3OUROVcwVCMs5RFFjZbhXch1CPVRJftGdnT5HpiIjAc92nxVe5MewnYf82QPhB1JsxCCkaFoQDt49LgKZ+ex+tOoiAlJRKv+9hSasAAiMgBtVwdVHsEUWMvGIHC/s0yuhhDCmDvd6tSIREUo/bnrTKOmn+GN0lX4LI3Z7UVwuyVD+p0ZBoY2Oloqu9JvFjCNWtnMGrlHvS0AJEUiyPFZBhZI/OCqlgMYgBDlKfpdhVO7Y8A1lIbRmLTHUHRPo4XUU2DVj5w26n34glkbrQbW3WGrjbW9RAMJEOLCvqef2EXF53UmpiQctIFgCaVmDievGIeKK9T1iwsVs8yoNoCKqsG0gJ8iBIPpkUt7U7JBcS967V8hmonV3r1RPFRdAid+0NTAQHjQxnw6dyd6ujG0SxMRUNRpk3Szm93Mb++kw+PHj3tHXmc2p5ulQfxZWZpndfjEVaqlLEqslAXGPPoZaxDnLp3JuDCUcaJ+Fq37LrLvPndNh7vr8FBjKE/j8Ek8ggJXTCCoSK1k1q7H3VVWtBASkvIIbYIjHsSFSo3FW4JsOEx7W04qx5gIjzsNaGyKdEgXIWCAPebalvQgfKiHq5KdtuvFUqKhuYjWYg2iOqM6cVv8RM959JsWfwbBX41hS/4cj88pI9Qm/ReS09RIIoyl4VkulK39kN9nVv5QQKVqZmCNWqFOEz+lB2rBFSp4iHmcJornEto1HZ5Lq3kNyBI/RzgJqaJz5Sf1stDMJ0EK0xUgduSVqyMhvvB/4dKlBy27B4RQcRlKADod4QDmFjkGBiVqKwDwBx4TS7TEZTygklyox3Jog6ETnNSiDLJ6ZnZSbPRc2s7vPiVCP7us6H9NMU+rOMzLRYz580zPetazbn7zm/uTTP5Mo7/KFP5ZIw5rjU4ftxY6PkgzIYpW/4vT08fAWY15TYdn9fJd88wnHCfa4kajIlQ6K14LHK6D4pjPFyptmNF1+SNq/i6Pv2nu8pfqfAjqDznaRhSHNWvHRoVpDagSrPs4jcrT7lJRVFET+8ALeirCRrcCH0IuqGjDaHCGyYMlvYDbbaf0GdZjgwiPdEixlt5F+QdoWlsxaU1T5NG8Pxzjf4Py/94oy/xZjJiEJdCwFtBmXbbCvy0wPbiiN5rBmw2ra1s8hxN+TYeHc90PTGq+B1eSTQUv3zuol/3hR/V7EqQAIShUubo7eaiQyx4x/u+PYAmU/hyPP/bvP2DyPw/LiH5zAia/PBG5EqGKejFcPTMbhWcSQ4XCYPYoSGkXhpkUN4GJs0m3Al/+fko+NvF3Mv1VcaNZGkFZLD4oopuYOdh+uu0g1ENGf6jdn9L2R15o0pq6OmBX5yOWmCJ+rII/0u3/CqZwf5zB7jB/GS7MxE6uBv2nPFIUSoGI1l/kuTp1cvbSWtPh2bt2ZxDn8fNEXmz5U8X3ute97n73u19xxRX+lMZb3/rW/IWqBIWD4jvkkJZr/U9bd97/b4+EJD+TlxKkBwAyov+ZT3jK3+tJ8i4GFvCTBBwMps/BgA1MitoaohUeslcoNnZsQBj8/jqEv7vrDz3Tv78eR/OkDl3Fgb8q523WjrSuwem0J//5u8H+jDVzuvLKK+v96DXIVQzA3ZmEIszfVWchLn/jjVlm4xj28O86wIpwUGqW7y8qf+d3fqf3l/6/gZe+9KX+rjdOzgRdDTJ8RnWu6fCMWo6zj5lU6JVmxGWnRv7vIaHBf4YlTPiPdcCIC2AKbHc5YYYtuVAKlAv9d8T+E2Z/4FhWgF8aACMo+Jtq3uj403oed6eb865gthmFX+yDFieDyPUTPwCiIa4U7P6qqr+WMAi/rBM/3ldR+33ve9/b3va2CgL//7uDYgHRNv3IkSPSpL+tevnll+ePXi6jcg3OokPao2oy3uQmN/Hn8gniL2JnTTcp/2pg2ILaiN8EdpTdAABAAElEQVToRjdS//kPoZiHEtCfDvbnguk/Zxhhct/8D+xlwSbReJ+tKkdQCGr7+/LS4QH63Sa650b/mg4n1pETxpjYdN/rGBwHUHxxVG+qhDltYDYBLiGPtxjyX9ClTgTMWLW5kDgF0isQFzzt+X4RDXNIh3oxkNNIo8IEEhrpgRmkex0lZWLuWDrYfQm6YQkPRIiiBCz/vYZcqJOMOAQjLtDMAbolFVEmuVTl9n/+JL80QJN6aFIkshzkBWY/IR2ibjT6DM8go7p0zrzDo9CWb9D1rkgS0mMuVC5rTUb4ISc76hrgrY5ODanaf0rs/22XFP2ZNwe8uAJpInFGeAhdADGVetRDUpf/gvEpT3kK5aOFBwD+RyEsYcDp4vnnn++v2vqP9xDNIV4sx2i4jU76DGT5gLlga8GoVw9aaoIsdMwPFUea2ICNmDjhCGQ3l5jupkRRAFqELXXkarSMnF3570Qgl9pdYAzBhq47VPgJEvi167GEtSJtf0txvF0LVCIUPPWqQh72sIc5pdfpP5rw/7t98IMfDACuXAW8bSOc1z3Y6pEGCO5eJLBH1ajQiSNTkAHelu7hhF/T4cS6l0fFP8sDY38iL/u76U1v6j+RUB5qxDR5LCtkkV6eXXbZZf7vbH9fGyWxTAD131gfO3ZMeDp+/Lj/F15AzEF/QjwwJh62gi0+0DIaizcqJGmjJR4FwKNLOzEi+a8wt0gOpI23ZNmkcxqwEbn//e9PUprBmDvqaJXHHgjdIIGZqlUb+b9Ewokt0Qtf+ELpASfeX0oVvn2vddyRurxONNWM/23HyZiIgwfFh5QQ25DVSG3j4v8Z0CZ+sh261sJcjyp37eQ/u2f/YeF4LjTXmiLEMFxZdJ2SK/zC8YMf/GDmB63cAxUqPuWgBzrHFYqpD/wPyRRCY3qyHLnHzAY1E721a5cFpQcixMbwlhxz9OhRRYkfBTqttWGyKDZJLv/1BwYQLTGrMUhUZzEWIwdPcFt/VQjR/G8tYHASPKgb1UPz7h71m+iCR2ewuac/dgJy/mUirUJIisyKHtCVpB1Qs3yEkPZTSNd8zOOQEAJo7+oMzOhxoejCFTaYh+Xghji0OpnlERh53ccJraM0sKbDuWYQe+JRadgfcPsc3bg7j/IfVafYt/8rpEIGHxYrnZ/4oMN0v5pSSPriQ5EuZLBgQ47y4quZGDd2LzzVz/qZPmDWjxORiK07G1GS2wYJFmbxFq8uvCvy/29oA3bBkHuQdzDv8gihSJGMriH3OEXMiWUCJR7gz30XQu1c2KiCBuRCx0ECLtKSgbb/6NF/hUPtWKJ/erZHidJaDMvadCjrOAq+y13uIvpDYgXRslkRBOVFhDD20Y9+lPKJzxiimSxc1lS/DVPiqYIJTm2oxlUEMzASgYSNAUDi5NC5qK8ZnRgnP9HJhz70IbsT6RAk5BhQjWHbZcuorQKLMQCYvNByBQxyTLpoW8xldRAyfoaHuiwoN9iLM0gGQBseacabXUnRf1HkjmcWSxbXSFrKECZd1BXqaNmBqSB9JYSljMbgaQZC7JU4RgOTzjJ7/S1YwU82yJuJZI8S3LlwPt3Kvt///sYUJexJbPMBwnnpyrpLeKwu/JCR4LC561eoYS/mpKEW5BTL5J3P4TkDuabD6aVkcK09MTU2Z5MnBtnkOTFTnrNFviHsGlUUczlGzF45iTQpJnqD5RRFp18QS5/ghRLTubo4IngViVh/7pjTcA/CMv34uU6kfVkgHsmy0MqyQjOcAqL/0BhduTYxGpK4TXCaq7H7hT1iBg+cNKAHIRymk0IiAgG1aWl3okUORWjVGe94xzuILEZ4qyTmAlCaSANigVEJEt2IjJldZEeCVukcHm2EMCDj2ui/733vEw1jLU5Eta2sK+KXEkwR0YAZgiqxHku1OpGuc4/qinPYSKQmk4Ee+tCHukNliuTqOJQ5KbDwgDeXucjl0k4nYD3u9dihmEcwBRARSE2rjEomYHJOqv26gFUTh1xCcKRLvucmtk2M029d/OLl9a9/PcYqvZkySFRnhlCMyJIBeVFk6qQmLCqBMZTEUMopnHoKg05T3Ol5XNU1vdOguph6yBlVgNoXSvn4QcuXSlI1N89RUGf64seovaSL9vBA1epsl9rLMgFwpyV8RkYN1ZIQcVZ/QrVYbwsmrulwQmmMzAUoRunufMy+0Ldb3EC5mnckrND5mD0Zy7MtEAoFJuHYuejDH/5wlbuAJX0yU0ac4w5TxBQxy13EiUG35EIXaUMAPJYbCwE4UZI/+tGPdjLpURko9YpT4Lk9DqVbp3nPfOYzledCJPggdI8sE5LPG0aLIGCD390Vht0JixwYngkskPMQz4IKIQFCkJUOEcr/2ioXipX6MUN2/1s90nTYIjUUhbSdk21hyCm38l/RAxhOhBD16DfXPncUntB1oRj8uNKwdhohapbLdLs0PRpZWeuYEN9nAzaQwNyTAMA8/elPZ2DOJ51/ekTUKbEPStmerSrgMJC3p6wuZhOKIdG2+0T1BAMwqobQnf3bFjuITg62F5SfFIgk4gL2RvKlbYqJACQwDZaJSW3SYcOPEIgzxyQwnIUzERJTkgjxE43FAPAZfXqkZO2M6gdpVno6UwblHemMEqAiEUJsTLJx0mMrjCgvtvqOvtVGHouBEYRzhkrAaAx1qlBhCD4Kcdt9dF/xilek9opa3IG50x4mrY4VSSKfQ/Eww6zpcIvV5waMTDjwpvA2t7mNfMM0maOAKwteccUVqkK5h1cojQ1JBoxYiuIb2pyHcYeeIz41nRcq/nvC+tgP/owCQ6g4019DjJuJi0ewqZTvdKc7ybWoCMd4gDaOCgCf3AAA6uIjbIWkGkVicSNeajoPdEYqNIiM6OJfcKQQR2S2jLjinHSymFBnIvx7SrnqQEzl4QIj9lE1WnbJihUHhtRbZ4OtVjsIZz4SBDZv+572tKeZgqgAbYlJygw80kNCT8mrxwXYQpgetkMuUhRpyqx2v2GiThjgl4Qkm/vc5z6ObT2GAXWYbGR/nLkJgiKm0OmVYWSHpM3K6eyw0SFtFFF3EqGbvaBPpRx4kMuiZ3MsJccR9OOH+VkIIhtVLmCSoToOkSn9/IOixoVNVgMT1VlWL4NtyqFS9zB1/WEejAaKSOg0EUseFQdYcteJ/xIqj+7VM6eBBFQgk+qokf5Vos6Bbb+QVpD5v3ktQVZ5Ds45MFn0QOIhbCss/JrCUZAXExzfyQQX4/tIUxQwF5E9CkQClMc5tFaYNR1O2wBjao2S9Tv/TMTnG9xbcHzb296m5vVKI94iFrNUYUhaQoAjxT/juvKl/YpIzbc1zEKi9c8gCWc1pKGyloN5gtdFiUcgeYKvNlxem8EpOguRHDXxyMfftjJ8Nfg5iUZHomkVjEJA6FK8qxJ8+Edq2xE5w+GhnYo9E7d0cdRRNNsN1oogTag8kouGCS7suqyC75ikw/SHwI6yI2c1ZXphSHaHTY9cqDy3xC6RMYSS+y1QsaofvB5TdGYtdEYzOG8hg6TuYAB4BAODfb/9GT3LK3AKeX7c+drXvjYfNEbGQNqWsQGZCaSjCCaH/wAU8pFGoqrlM8XJJ8NDF0LS0QCEvmpxvec976ETpsj87FoYvx0MtBKSd9iOEDHgMAMGRgJGmA7mTaRRNEQEdMmrsvHxsENXdv7+979fOjSqPzoBQzokGCFI2UJGTHrGIcY4afAEnv430d3UH1plaYIAU7c7xyFW8UNGH5QqCMbl2oR/U380YBT/YTsH1M6chAIBwdKrUdTWSt4wg58YIaldqQk24V/7Ww0cZIRq8Z5LbUZGnFi5LMgTVKlcOgYqKKh2hQOeoFMZLgD5GZyykXPazJmunx+yZrlBKec7e7NcApNDLZElOwkhLwadSIooEq4oU6iFzYmrd0XOQuE0S+kNGz+UYpWHXELRmtObzBKA7CahdUUKbFQcCcyOd9i4pe3yQx7yEK9SxCMOKQYplhUK4q/zQz5JA3S4IBJtYg/dBE04W8yi4Xd8x3f44TkN06RR94hfytyEc04/5dtz23XRP+R4IJ24Y1eEDTt+tCylkLS/eieO9ZLPwnCoFDOJ0TotMZMY5AFMMJBassGDeoiqGSTSyg7HdNKSrBC1ZGtoig89VG86WRre1F6SFg5Rd4/eopxBuhgGiTGnEbKgD4iEY5Be0Cp3lFleCiLK1I3SOSUoR6y4WoRypD2FEQeBR4HiPANjesJAu2p96hEkd0FftmPeDF7oR7F0BQAhNo86nfBNHmoUFSJfcskldsyYDMU+lZk9rfWipSbw4wrqRR1vki7XVnd6xCd7mIl2Dhj9EzB6sOI+IX7Qgx6kEiIRGRkMcq1KyY4xmHlllgNAVWlzKB5amDUdTix9rBBQghevYFsKXtFBcuLkbI6NilDcQNxR/Lrbn+nk7SZmiqrWRtBrdh8U5MMz8Lw65JP/QHa4YfE8QacgIqvxQLkQfkQh95mMlCMq2QMlxoHEgEysAbmUyTfEJpgJInwnuvUJdej2HxOL9bcKwV7CrgBkv4IxqUgn3lC3VRW/6C3RIdroY57fA0OoRydkKa7SQ0BHtQKxDQoVCZ2qBDGxSAQsj227ADY1SJGLvAoOiV9JTlKdItHRo0edW4o4ErDIu7dV/8hH8JYgFZzReZRQ/HfIVS4EBrmrACIpm0HXn/uxVSImZlQbLMrW5PLLL0c9U2JaVh9LeLMoUiaiVMFOoIq9oUKHRWJTA06252OZRz7ykZKNWbLg29/+dvtR6yvF+v2GNMnqZCPbMukBFSlQKWYVbAp12qPrEZ2xYeNIPzIlTkZWIbwhx4ZVmdIwQ3IUzLRMTHwnI/Y4I9JORNzZAMwkZQlKAfhpLLt5AloF8BCWqjdJ3e+nbaSZumNbbkhqaoSQjb3rXe/ylsQUMcEq1FzMgCECNuIpNdRpBLLTybqyRrkTxO4caXJxBJ3OJGg1pXCokI5osKFouqVXnaiTOpjXx0ENrOlwUC0nOyv6x29Zv7MabVFGCpRpXN6mcHsVop4LLrjAZIbILkVDbZ5p3yZ8+PrRqwVWm2jFtQJ2kljTMpRReHSzb0HEV3wSLZY8yqmXXnqpraEvJwU7aEF6PSMdAhMrzRIyhEv3BNPEF2jjOQ216WbFzejBhH0GT3xWKgrYLYVo6OIQUbNwBVKnhmua0mYIpF3h3z3IgWujJe64NERhEQGkwCQlUz6AHUkXU1ApzHM8IDrbFQlz9mp6kLDiqhNHBa6cW0ZvrcIjwjg/RgsARdQZnpxEOmcPPt+Q75kBVDLTq171KrkQM0Z1gmRaUYtFYZ8wsE8pgSWYEszuwRzIIleSpgEhDAKxN1XOKnU6kyfda17zGlHYI5OzX5Epnd0B5gUchJ0jd+9735s1cgowVkQmwzzHUU+oVBgtVjvk+o94Bi8Zy0OMGXV3nHMiGLAtLclPyhEvEWRi76ox5liCunRKkIzTnljydn4jESK6IBeaSKtQQagcoRCk2RseFEA5JsV85UJrgVBWP7oF3JeueowSKsunUzs2Ewx6IJHv1WEWgsmxNKUAVV900UUyMXgrC4zfgUTddHeVAbZb8yuKa6OvgTUd9nVySg9v9MzaGBmr0mDxSt38akIQZMFSlPMTPsYDuU0sWL+iTDi2gXOypF6uGg2e4AzaU+g1D/GidMAm2ciIYpPAB6fk6qcUDo44pFHOgLSApUgHybtM5/8cVQThmYEJznhOQ2q6mYl9hsVZDMTVg5Z0fJXIycSmZFYrzjS9qyCwneltXChUFW5KIn9D+VGPepTvbC2TLYhQZQdTo1dhXf6vSKT+8LtGKrXXsSuyCxF32AlVWAJnhqI/w3jxi1/s66rs2hOPsu5oR6JBJgJZAoIMPMwkEvcvvPBCdKnF9dznPtfZu8NACseDVXBpuMyyLRC4barM1Z+zBBor5IMMdDrBpxRje2S3E/Wa/HWve53Pa+UnnDz1qU9ldZKTIwqXdZcISa0KZJzykG9fsUFdWJWuZC/1Cr8AUGvaIVqPOOdWlCkToJWlRIu2SUFXcq1zS/Wo7SNt+z+VnMHIAT63sRwOkB/xiEd4ma1IxYnP1mCjnCi5qMxpMCETLS4D8+cA7XfpnCf6o6B04rsBWuKbfMGSubCXO+SGwnD1bKLYGmopx6GL7EsiVZfTZktp6eG3L1dhoE6TSMCZfgCFh9r1L5B3E4fndv+aDqfXlzEBYmGJI1xdW/nJqxXpDF2YkIFYJIfhirYj7qzZEZbyzWkGw2WX8IDUcEEImOGOkA85Jh7HQIgH8mdeJ7SJyPw/05EWlAU+bxaxpBPmOKFdqXSohytCVVKM0N001DqzdlyO/8s9Tm9oI7tDgZJy3vzmN3NgkiIaeU2pWZtI9PsrKBiiQBe6IZ0eWoqK6Na5nLd63mIakiTsmaRkwJnVR75VT5ZDTqJP0tGwAC30ywpI0DY2tIV7bEiTapE3vvGNlICHzMVGxKGHOaSBZaK7dbctEIhvdatbCfQiIMxKIptRqJBmHuCRoG3M6LQ1lIckDP2Mlh3SCQaCMwwUiRF+YKNSOQkP0pj9qE+H4JGr5Ei5UG6w91Igis55EZA0BkwSwtvR/Q+h2Um2dOEQwyNEw5g7GO8dWDWzp9IoEwYXhNGJxG+7KdvlJToYs9iDlxqUkJNeJipTwkBF0JYJjfDQDllu5GwNrS9ypnNz21AUbc4iL5g0uDYlYwAYPuGJBbaab5FrU7LpACKyHuTkeD2yoAMJBbel1GOISvmdv0fqcEgDcnPdM90oGHShwo9rW2FNP5zXmg4n1p2RgWBPbMuVBnPnaUKhR5dH1uzONB0WKRXFHY2EY5YKQ+YKAYw1vpH+CfJXfVxnistcVEzhilKjAOExeI4dO6ZGdl4kJwlVDlKkZOW5b96ETlPilhrwjLjlCD9EyERskBqkHgwg+sAHPtAJrbCIFvH9dwpKAW20wBTOxXSjPdNd2i508YATbZ3aUhEehE5r4VHU9oHJTCUXh+MNhADYo9idw0zP9kkJZCQVqmxinvGMZ1C+jPhd3/Vdor+vPeWhTDS3GuOEjJKrhbHWT37yk4VjStZvT/Dyl7/cwSANYMOlM/FXGxUvjaQK5xbqJLlQyJY+5Qx6Kx6q0RIabIvFcr9SwMuq5EK0nI7qpwfHwj/4gz8YSws/MTaaoRaHdTzFqTLOCYUHW3bbxEFCnU7+koXWDzM8jlgIiDrkXlerD+RpDPzwD/8wVXNA0R8wQtEGhqVkpGkDXdUDJB0qMx/JQpkB1obWoTH87AGTKOpEPZKyQG3bR3GAFIYiiP5BcmWoIAlIWEnXMYOCg57dpXNGpR8GqlZnK4hjWiSNotyN4gQYch61YdtEdJCTw9y5psOJ1Y8lxdYZFiOzDVKh+yuRXoQwu3g+axZxlJ+qRYdpnARenpCJwGLusLl0hqrGppAUGKPgAXvk/xwMKo+PfexjRQGbMDnP+wwHpB5V0Ik4YEzEhsMrFT0MfCx4zDU0QjeMDd7DkqF4rAbSXg45DfNqB2kkREx5yJUcXETpjfY2CTtILp2IIpd2tOGx9AlnzdUpcAhYVkQCoHyrEAaKjQKebJSwRToNqwBbBbisviG8aTtIFAFt00VqqUsIc7xpIRL9S/wRfmqoXSOiWWhZ1i6BJimZseWjknBlv4i6tfZIPwj5zNLSCKkerYW4qTbS32rMY8QsxoKtvQvBpLAvoVhgWUf80IBkf/ToUdtBacZdKqqJArQsiBZxaMxc8M5IbKzZjOxlA4exErYmVgMJEmU634GNdEpA2TQwOGHwzJ423vSmN9FGGDCLUBigMZBIy084x4BVAKyTnWybFPETV4LKhYR0yM5DxahSTNIirOCgMhMlsK1e4YP+KBKDxFhJt6lROqE6+GFw+OzVrB91OC8lFBGIKQ3/yI/8iCGcQBV+IrgplBZL0ENLGQ3kJrprfzSwpsNpS2CjrD9xhHc5OPKFp7f0sV3GFwBVs42Rw0n2yvikLrM0XHF7roIYe9VjyOVRe5CDTr9wIJqII9yMezsCshFRm3MPoVDDplAg5nuJX3Dyf3TjhNwVQnySooN5kPpgZ01Mg6eJzrak6u6c6qAiOvBSrJKOvHgIKlMyaxDzSKdZeEbL7oTUyQd0SHDi1CgYoV9wpByvDKUlCQCYK7JnCUYIzRwKQsDUblE09MQSxCA5WM3uwFbQFxaVKcKuQzxxvDRPMzNVETDIJR4R1oYPUahoWOFFyagbDQ+10CDFZW+5UMcScrhykimRsMlaEbNck5xQu18N5chBLnEswbwth7lUbc/qo0ofzkArWEdGqvYIM3JSuIbkhwcbO95BBG0w1mWfheGbWXEQOEG6K25MNJ2kdI4H9sAfHc5ba6e4eowyDBijDcek+cpJjz0cB6FJCtw2F4ZFPDNpQoV56Uq2YwNsUvFB4UeOHIFflaw0pBxMGqI0Z0UqEnNNJMigwEbxDCBSgyEIJSiqrCMH10M6CyrIWE2ZmLw+qeVxbMAsOqco7p+sDJXpwgWzAYmZQbprZ6uBNR222hhuMywDvF1R6c2BYOeIhv2xbEYc/+Tw3FJGZK8sjyGW/ZV9V49Z5gbtMMkmTjFxkGKQGlyZyf9R18nVOSEP53hcVHnuhaIy2UZNIQlMsLA18QWgQIAKluKK+OE/m9xyEz/BkFGoNKIQ3ynwSW1Rhj+LEZQgKIAJWMQPOW6ZODVCpTNEUj1CvHdUymS1CGHV2hQSKhBaArGAUNIhTkRGH1vaP5mIB/2l+Q7yBY+wuYhD86YjjZPImJCHluLdovhxmCrBTlFqjBQhpz2iBKiit9xNoTSSSq7usous5pWkj0ToAS22YQqE0LIr0ZmuHNiKoUiLxcKl+sBBghM2MNCCdxX+cSX4qtMHopKi6OwFQWod2jYLUdfeUcAHPkAhOIGTcoIQM5KoC1FzvbSTSrEdnqPDEdIQhk8kAkwKGZGArQhwUjsLt+64Si4MvKWhMV5gC2uWnO0LODALjBCfUZe7C2+EQjQfx9kXfv/3f7/EQ2RswM8kAuP9H2fEBuW4SLRJZKMwGzWdiohA8xz88Y9/vN05cpkokQsC7tIk2YMZLYZBNBOdrzoVkIZhYyFKVXhg20R37W81sKbDVhsDbYbI2jLA3H0i4UMvqYiFqQ3dWaQ06WKFXilxPMD6Y/173rMfIzwWdlZb7ZFGfKD8BE5VISd85zvfyfe8JONdULljjKt7OSQwcX6QOuUDVbMAYTQUdYafkmiEen8o0/XLtc6p7njHO0o/PJbP80w5QMSxfxW2PAIL0ZaW0NZHWz2tqnWGnOmEleO9ORNZaF68MCr7qsGFgOB05/yGICGjRdEmewWXVv9FcaRBh+E8XJXsWdasr+kCnzaKASByKOJHJ2CGIYXjs74rNtSRtGXDrDwKrCDxAKdtB6tTduiRVxw5UnJWGbkgNAsVv0d0bpGzaz3UpR56y1veklwYiYpES7ckKknpkDFTsowi2uLfRlBCQiUw6h5fc8CMw/SEGWhhQ0toFrUJYi/rPS47lJvBAKCZotiyUe0YLVbpyspCQo38Kz1ZTUO2p3powMSijluElAXf8z3f4+MyUtAA4wSJaICL0PyGShdXSGPGLOfhXg87pLUdRAtRfOoHIBMTHz/YCLCJ6GJgE7nAG6VMqJRQ+es/yiCP1OsySl4fTiu+o0A98MNsOuqWRh3GTTxSjn48mKJ/E921v9XAmg5bbQy0y/E0HMeJ/qyNnbFCGUj68cNYJih2KIRlRG7vFEuUhKusP0gGsI92sWneW0j4ibZX6PIc3+YkZsPM7l1ik/2Kx7gNSO+KpCiz4OnQAVNoO0Pjj6HFV20KhQOBIH9tQIVuT+ao1nsy8To8jKPqjxZLqJTGaMB5ly8k1cgcW0gSC9QE2ldccQXNi0TSs7lmkZQSLJM/jgMDLSVS92lN9pTSwolwhoS2/gyR0ZXONOBkFVkyFuJRW2S0tZKWPAJOyA6GQR4AAANA0gCQVDqU4aAiu68ZpfnABAB1DdHQdlC5BpIGaIMRgnS2JhsBwHwyXGZ17kYjRcmSwi4CAmbVzicsrjYxsZezEGtRPdhGlwhG3bFtm4INKVNiiEQlO+VEtx1O8mjhoEIdP6aTmmi+DFKHeXQ8YNRG2fYXCZmSpLhSJGVDzFrYjHMUFsJPacDhDdIQDpKb7MSGhWgx0LZOMirX+D5Z8OweDYCUhLStGsYMIVG67ZMzVMhNUYJIhJYSBooylAWK6wFuMWSUiaKIlqEoFhhF0Yy1CAPtrLXd18CaDvs6OaUn/pMYJ8rwAXeOLez6s0xKbyf1bFdEFvKkQ7W5FyQyFixx5jROQTr7IRjCAxNPlWfv5cISNCw+l/jLi5yNiBd8BoeihorelPhGS9OU4Gw7x9umAOB4LnPtVLyTkAsFIImZ1zkmtXUQjMbxbBqFv/gMLVTEAsgdkyYumCvH2xzbB5OOwi2HbZOKhDZwwu1NsSJPetKTKMFcyWBbSYtDqMwNV9G8IYRaPqstDFkCYJhH2oUZ0z26AqZRyDc1ol6QEV8CY2+O6J3IiXcW9Pjx41SNN/j3V2PvoAxpcVn0pyiHY5CzT+WC6sQJJ81gD2/JcJtIRzSsQg4zMFLIAYFX8aAbZbIuMPKcHBnIiGbUYywTS9UpJ5mSR6OZUqrbxI9+MOAdw/iJLcO23N/+7d/u5yX5jMjhB2FJ/d3f/d1+n05YaY+8aJ1//vleajBRO2kfeOdYBXshGklH6PaH8EzPNCmvaJNOWeySoeEkoH4LFJ71aOcuG0XerGkfc3qiWIxpBDImR8/EAROE4T+CALagRl3ArJQVMYRP/OAQXacIXGYT0bW/o4E1HXYUMvDI7GL9ykAxiLGyM47ndZ20xCITBIF5j+0bMGmSCTJcs6DTrx1zH8A+owsG04MBA7AFYeILZmrIpg0PnNZezd4o3yyEQoBnUBsG4W8Rh1tK//lpMMY4nh2DMC1m5au/cBiWhnEN9SYEZCTSaUOOlj2ouMPV9ajx/cRN3gWjk7xCniVATib2c0BBQY+DXPnDxsX23b5kiOB0H8VGaUl1JhAtcqHuMfcgYgNUFCnwg6i7IVmEcvLZi8foECTkmdi511IKr/BDIthBKzOBZG9+amZZTS8MzmNtkX3f66RUKWCWXGgf6QsObxklRcuEPcyHeodiHiNpu2qiqheQDjxNl2OYkyVOjDZFZzHgMf0R36NKxSpYPsnAJrLOSwC4QrGllZ72XviJo/RRYpLRLtAXZPIi1/P+TK1AdgD2307smYq6QWWmx1tzhLyqlAvdZQXkinRLaH4bxfe+971eRmDDCprIJsmogSL1BpW2K8rBuQNe62WIFVnKwPTvQYhJF5uxfEydQ1lKxZ9CJGmYCFlNq4MEe4AcMISqYVZH7ZzCW3ZLAMDC4QHOPsW1p6+BNR32ddLtYYLs2x5FHRqjZ6OCssLTJokTOioBI9bwDTFamyG6B1Eco4t06pnRI1p2HGweK6JpuwoNeGwIB0kPKuWcDgWgpucR5pq4VcN2kLxijSipzUV5oHf78pBwWbFyK5wtS63UeHYO5oiMRNo0r87wOxYhSUoW9x1cK1CEQkPWwl7BFzS26YK4713d5RLnt4K4xrJVoGGkiRlVtwK2yq+2hnyMJewJWMKfzO2wTsyqFFiNES0FIW2Q1O7QJzlMDjw8jg3TzxIsgerHjpAdWhRnA1RhLmA/sLON9rbPLPDuNJA94iBdYpaKQl14tfey6cSwuJywG1Qw0Im7WcFWDAMwxBFw5ecZMpZgnWODmG4ZsLmuSVMEIOvIfxdffLGERBUkdQzDvIkp6KPoBSc2ZERmGa9RorEW+0jGmUNdUoQ0+EmiEaq9UywrolW0nEurTkiXo2BrBDKCa4B0oaUqZXvOJ3Jc2RpPizltCiyuzFXJqXsIqJNtUyBbAmMFkQsqjxQCMkcyVA2e7QlTykHaAAAVDHjrU1x7+hpY02FfJ90eVsW+nYo4fsnbcoZu26Hskn5Ui+pWoYSNskgGyv8TeiAyMYGji3TqGdECqbbwkc7qKRhe6h2bMCFDAMMG713m+YWzbXAqgT4/HvB9RF7XoeLYyn9x7gQPOeITfJm8oRWGIya359J2A0IeeTm2n/aL79KM/YE3Q0984hMtB4rinXzsUkfDIxZjTBKlCl+o25oIE60sc9o4wQbkgFEXhtyJhisNV4vEI/0ANkW5kLeqeHNm7hKwjBY8DEFbPW0jGiCsTsZDdlnfXhwDEgAB6zhaupKr/Ec/vhaRF5GOsZFdJvD3AZwQBrN+o+LpSEQOh6ibAtgdk+TCiU4NiwtPyzmwkqumBEBRqCKxdaN5bsJfIlH4yd3czGo7q21UAkMxOyqQ3lyyQMWBAxjLSnxX4NkebSNBtzE/NZMf27iQhsH09GtAW2wXuckGDWDGbowFys3s35+esCNXg+qHPAYDP0gRwKI7IpILa3OmP4s7SKtYwh4wG0SVn3f/5FJxEg2VIKdhj9qE1TBRT6i7k5cBBBuKqgc5e5Di2tnXwJoO+zrp9rAtNhp/YIWGmabyX5wVpDiGnlgkw3Ws4a4nkLmPuEGX2FXPyF3V3Ps3j8HWGQqYSMHuWb8YIT04KBM6y8dqCgzVbvFPtqGSh3yhIMxJulJOdKL+dRJFZJjBuKAylLA4ibYDUGJqQEXP5BKIIYdQbkDaDxjs/GQCmtcpSr7sZS9zJOVcSFQy0RL4wBWH2S7YTtkrbMsP6qTAHh4EnbyIkk7kGDEx+bWWFYdqESaBq3zabtOGAfC26e74hyTwicsdwesRjHbUKI7bGJE6Ud6yyoXygWQg01NF9uhIZzpCDslxaF/oFxHCYhZCf2SZXBfUwyeEqNuUEJZKtfW7Qgg/WSlaLYbJ6PKocPTbDL9WNBczDmztj/WHOtG0Tc8VhJvuWbWsBerykD9/oxJVhtoG2YBSDn0iJPdQjoV2yUaqNNWDbVk0iVaRaNvVOdko+4HfokDrbt/mLbXcHw5lZcxwB8r3yxbH1BpRICtCQm4za5CWSGKZDCHkois611PceqTePMYeKMRjVhYDANxtW03XDxg2psIm9Q8SXTs7GljTYUch3ceYIMsTbSUY+UbNK9I5jmD9HA9AnIGNKgkFykQNRglX2jHNLuqpZ7MykXG7gOeeeZ1RXNkgylhGsSFkON2SGjvul1kL3EMIg80uxLmcSBTnFIDEX0FHPhAIIvJkzB2UG35clYAaVCcciGs235Crl8VZ+Zh7YwAzqEt13irZCVkLU3SaIg04OMWnDaJwKdAU2kHSmzqFMBLhysraoqGODcx4FygzEdOFLvyUb9+MN788kQ6Tp2leqhaUsWQiKiDBwyksUuAg3RgMbRjNMjE2j2h5JA78+ebw2LFjhiwoVPYELipyOOxX/2Kx6Unk7mAw0DeGlgH4MQYydLPZwrzaQo+5agtiejPnMeJkuoll5MDonGtI1bZNjvskZm/v8j4v4phlORAaXxQit+aUVbDiea9GsQ5yMckgIbcF554SFXugB17J+BEKY8UnivDojLDpn3mP/rENA4qQsDGO5kWdH+CrUfRIyfaCXM8vgNmtdtHKglr9TeQYiSGWFpNrwZgNPOhCEhFqpYCVGtMAyfAAG7L0yjIhK8va4lzbgxpY0+GgWk52MrI4j3MPdukP5DM1TugLUrlHeSj2MVPW7M7cbRltaMz36IoRx7FPIp3XQtrVh4U2Q+4MPbbO9F2GwOtJg290psdPOp0zH80lZmRBQmxS/wpMKoD4eZx5sbDYCHvudI6EyGI3QC6nUhxbtFX8JrIjSvlG/eUB/p810gBsT3PJJZeo2f3xIEfZpiyQ2lpLM1lBjPm7rH68QWScUALeXJEUaQDulgOHWR2QLMHP8pzXlXorGm7KhSCDGcNZQeKEnEZOTZ/ylKcAU38AAGzUJRk4r/b5JY3JCqGY1S8boJzipN+ILJXnwqHNjRKQaN6TqQYonBfINOiGOjzwJ7vrAfO4xz3O0S7XsEnyft0P/rDXMeNYb5+HtqeWrMwpvJkr20lIeFMJFRth2KiJwe/eIiw8Ott2CzPSzpTgB0YDTkQY/3Oe8xw/EPTfehDTf7jGILFnsWqJM9GoWR2W+uRK/0UOTOZqFNulHJ3VzkLHDtOJSSWy03sXCynmiy6Aaq8NGljT4bQZMG6GqPB0QiWmCHnMSPHrFZFzKumQ6YNhgs7ouKhAAKlHl0Y8tkx5mt4URNAWZlbushnyLaVMjLrKNKeX5V1TKKfHEUWCyLZHoGU+Ydc+SXDMyaFOMuJEg8jF5DTqHsS+QHt4IBGOJRUk/Po4Xwc4eRNxJBsHpM7iKN9yhBy6goJMZtRvYGwaZKCEp8oKPWrDHYlBQYgfb+OkBEdP2vJrfw54dI0iapatg/yE+T7keA8MsSUSkcs6ivvKDrsQhmduDgD+P3v38mtdUhZ+PPwXjsz7TgxxItEYLwmmaUBULhKQSHeD3XZHIEo0qIkadWBivEQHRlGU0E23aUQhoESJYGiNt8Q4MXGsMOYv+A1+v8Hvc94vPL16X9fe5/TtPbsG69Sqeuq51XOpqrXO2ioEt1UyC/4V1Q4MResALbrExJuyN1t/EtmRm3q8KbY+do3wE9MtchgTcO2MbVhdLUQw/NRTT5kdai/mBgn4+oVmFAwsMatHYnm9Pq19GFCJopeMfLDeRhA/3iZzNJrN7Bv4YrfzFLOv0LzCHszgDcaBF5v/lxf/JR0e1z/fAySqCjdik5M6gU/4c3bE83UBYH88wX9fWD7nqOP/V757c2f34/bxjUrIHdeImLKUo0KZmwPkEsfFWwfhmA5CkdpWwAjHRCRVpENeF44bFHOCmrMvr/PBbEdO8+gKx3KAMIS6SWlLigG8xQlgWcRJnaAseRe710n5Aijydq/SSw0eW9qWmXftGKsXURGQWizAhUVrEXW8eWZ2eEP2AmK7bjqC9nTWA0giW4GZVrTYAPMT+Cx9iOloVKGKwQFm6tessHkrPA+M/c+fd3YYuXws9UJrjvCDK/MiHXrByjuftuM04MzWCbbJorpl4rwmM8vhN2hsS7Tr65moWRATrAvpvKxjdoi8Hs/NQuKHazAYy3QvE6ibMkkRtzF8s+TuM2yXdHhkQhm3eJdXMzLnIfxQXOD2mZerQGyp7uSE5fEEAVqjUcZykhfJNyCfiIA9IRhdUVK0sn2pN7aPSLiu2+mfXQLPtyG25bJbcgzoRXYUyUgDxBx/G8bW4d4BBVXYyCW3Ca8iDrokgtxyRKYpAWiZMBQimm+9IhFaGi/zxA5Ke5ooUE9zRzpbUpLC7JC8/THMWnThhwbop0+JeqexScetbdMZGXEMJswSYf/z59/d2hoKeUzR2ssUmBQhj4wYpgocYjvV7ZHstGbvZNlq2+Z6xZfZWxB4LogfnLh6WuaA1EmyR4aSNyWwkI9+9KPMw7LMLMTP9e1hyXRT03UknQrIuhqybF8iObUO5wYqtwqdQ0XAJdFt4FPJnQ2PJStUx1RW6gzVQ25zxEG0b+BcMrzRdWtvL+nwyNRPiAHHwx1MeZHBQliw4/+yoHgtXlgv+78oUUAoBFkUGIPTeLMRAYlxOQzIf3aHog9Crp3mFUyPiLe6G2YPbPxPhc2ZKG8bZGsiH8TJoEnkbd8bgFMrNGyIWRBqpQGVyRbacWUiVNCtvURY3Sx0eChpNSPrqS+nDDY5CSEGINZ4mYVuPaWThyBUJ68iBgGjpUnPZ+TCBOxKdmwQQYr1SksrMJJql/8QkqRRxAMBtUfXrVFLLa2XehuSCA4b4HcOLwV6pdN6yP7Y2ksFb85RbQ0pIS94+umnbVXbFzIPasGY6zbms1vCtoETlbMRXmdgejZfw4CWM+ztOjwsx7Jzi3LPF6yW7NqtljzxzUc2NLYcdamngUs6PGIJk9iYuygjElmYO6Fy68V6pi9eyBDW6V5ksGOATmgIKfurLnYU1o8QW9E9Nj3xWnJyYOXJlhW61ChCCUwYG4AVWI+DEMQuTdaXBpJLCGhYEeE4ioMQI9cGVJEFCbmwLsrEjDJBvxYwZRFgsTQ4AW+gPXobhhCW9SGhW2ek4oteeyN6HhKYAVxCiudh7yitJUAIM7bYRsvmTIkZyLXX1UCEJtkXlGN+ifY6dSxJuo6sSYeQLOghLquz/9BFJ5YFZodhcAEP0iRO7OlS0HWdebkOG4fHRuswzHV6D+Mf/Sfp3F6H4nljLQcpn3FauIeBdyjZcC0ZyXn47+9Rl3R4fH5ZD2dQijJeELBetjb3NMuyS5LwFSjGJxfOqpBXGDVR6QbdQ+wrFOInxlw1upULCWMbITChqF3RflzCFRCQl9EnEJNumBkEkbtBuqkufSLHq9MqVY9oKtVH4fgB7Irbs2NxA0fGUp1/fZMFNZruiN5T89XBdRQR1a5eezCjn6OV1EtqISz+VTSOaQWgC37nE+3ABu3Zwg6G7QpmbItlRE8xPR+1SfWWr2+E2hTSiTTpnSZHBY5JARieolJC3J6qhG0epoViw3aDOAf5eZV07ool2eiMI4Hz6O4cxR4YTFMAgLqmHjyz2Tnw0kgDl3R43AxYeXGWbXFv5i4g2g5yAMV4+0XxogdFrC14wLqKicswfZzeQQgUJ8QUEZg7H/CEIB4cozkh0YjoxI6DKFd1lgsdl/UKScKmkMZHK5ZSwiq8W0Ab7grhuDfZo+hamhR9SFq9hKELSioCnNo3EG4R3NsAIQyGV8FJDFgAGTNotStuJ4cVgLRo34t9f0eCjM3ANvLWZWg6KfIilFUglyrS0n4KJ/ekAUtA/2Zqn+qhlNeU/NcjirIgd2CBXrphJB1WIzCszkLqZKq7BmT800N2xe1G+wC8BJUYoAo8NCNjey8B9W0SqLOZJgJXjAGHY07b8JeW0cAlHY4qjldyORbmaEhcKOo1jPPPAT3jY4Jjf4Wq49jXQSC69P8CpUc1TvD+6q/+ysdTvNFjCR8z61CeADWvU+b2Bd/GL+PRsn4C9nug1LsxZPQ8Kh38ovAAx8x0zaglkwO8ptIKYIgaog7/hD8VBcOVcA5dwGan2KRr8sTRWLkhiLEj1MgyVPSivmRyCa9+I2UOBuQ8CL3WZFOImVmplPxow4wQEEt6W6w09kbY2Eay1Px270vTsj1fG9Px0rAxVPL9XGNbP1oG8lLZ0MAlHW4oZO1tPiAcMC/W3+2syLQUKKGray3eFXAoQh6gQCkyavE9TxtWp1gOS+Uqz2+krgmjK7BeQFZpYPIfaGpXGlaSK0vJDTSvAHYN4OUNkatkOwWIVbcUc6UEt2VB9ZFU4+jnFNwX2IsGXh4NXNLhmXrn506BhL/inaAA0Ubmm/OiSY1nEts1bHCKPoqTUlevXxaA2sbNjmQXgkvbORpouim/GYeCwt1KBq4ZgGu9ZiQauqprnwR5DvlX0hjWZSNCokyOBuxIlgKOpLOJfCWxf+HlooFNDVwF8Us5TwOiXg4/u4FuC4VixERDkeI8EvtGCa+VAXArPbtqcUVdWZ4lDuSlcn0N0K25Nr9KFapWMfUK/asvJ715QVevsddn4GXHQKJyP3GSrluCx5sUOEwuj3an8VK5aOCVpoFLOrzujHB7y2QZ0RUuoWHi3VQmRlyX2L3xkYA8/G4FWcFIiVDh6UZoXZBsaMAs07bGlK/i1kKkFvqf9irLrLAcpf6qLmyMvY2lUQLZvelKKL5gKVAKHIBXtbAX5m+JBi6HpedPtMjYGyV+ysA/5vuMmX9A9IUqJ5Y2DQKBgChMqLg9n8zWyAm4yx5UxKCuApOiV3h6kd6pWZK+VfU23PRcAmiK5bw+Y+aRLZ23MPIepn//8o8ZXvQNbOalsa9qvTE24hDcVV7M2HzIzctcTM67Nv4fidmP4K9qYS/M3xINXNLh+RMtMgp/3/3d3+2T9v4Ny6c6/PT27/zO7/gfRBnRGrkniyrn01gxUsRRUClSi01CVQH3RX2pbwVr9zNIapf8fP3A5z98vczP7/kPBBOhyyx43dfrl37SwduYpkZ7a5T7QylkJAiJrAxU2JsvyPu5Dz+44cM9fmbEF3MsCLhAAPeH1Bcp7m8NvLiR+j7WXf9N5bfE3vve9/pldmFRBPTNXF+x8i9Z0uFEgQLHTali0A7C8LcB9e+AIq+68ATgZkkPxdtcaZ2RYp0H+AHeN73pTTKBdh+u86VvH0rtf0C95evfNH26iKmYlO2Je1WrkbWTyLWtMFXQw+OPP96ajH58rSYt2UHe7OnIq1pvF+ZfyRq4pMPjs8PtFb6dezfAYr+9l1+h83kOb3JqsVn0qRoxAkxHl4acFwuiuGTuHhffeEyoXRgq9JSYERKRO7zFyfAQ9ZipMd7O4CqWXGELVblBi4pvd/mIvt9XU/Gaq28HIyEx+CwA5cyTJHyCx0MVV/Uwq5xRMIMQbbgqtOGKvaJwG7Lr4MfSMKyeyJD7MKyPWftJRefkpPOju3aBCtl9wMU3a2QIGVEXltyarJmXkBwQFsXkoh+0IDGWXAmicVANEji1KyEfkZeQ0zijzq5ExRVXJt1vHPqQN+MnpmNS/6ofLVI4O70+XRhCMgLGQNLVRc8U1YZ1TJSAKTBJ76F5/r9fThKfd8+XApdKVr+n+CtLxo86nQQQdSxpUZ/G9XRHNNHGcAOXtNh5ACFHnbCW5o7uTYRFuR9J9pEEnw0yHBvchIpguI4e1jP/qoO8pMMjU8ZuQIwJqtfCMZjdnTt3dGnkKq7ifgaqzhzrUj+jbI+NBzaNLuOGv4ipXlBwixCAclX18c94iPmc81SujI0H10iriEGud+/e/f7v/37ftvb06HWve50U6AvCcqENkw93+TKAk8OlQgxBHRKV85gZ5pMoJxeXbUp8S1MgsDKgijQTuRlyamU5nG7hNAV++/CNb3zjY489Jtw7EVXaFEKeoojsnEB4IrjGYlmkZ4K2OYFZb6VeUhirhbrUZdlERredGRIUDgBjlAlmiXbUe0YsXuLZqKOCDQVaqvCwwKExGFz5RqCTUjPSkMxy2NjAc/jWWEKh1fwGjKiKnTfSyKn7hqo6EhxQO5v05SAeauKMdU12A5dTeZj0Ri8MrTVVsJR0YVsq3Cw0cBqDNMo0bc/OBpXt23gOLXIKVK4gNapTERJEZjmMX0TigH6l3NoUwz5l7IUGJxYWapwCpCHYSKvb5G55yyUdHjGAzDH7A+q24ljyzW9+s5NSBsfCwqKLqXXbEPUqg+EIvRXd3AAhCNk0HxMR8r0cbzipApjDBK8l3jC5gs4myHh4YupG3c74Xe9613ve8x4/qcEnseR74nYJcpIv64Pkih6m/v3f/72fw43VUUW6Gt426R27b+Co14pESn700UfFZT9L9Oyzz/pyUJKeTWKDBXgo0MfJHAl8+MMf9gOTNsF+yeTzn/+8XGgLCB5AW1Wax9vkQtpLgYeZKb5TI1QgFRgKXq5+gNrP8Gr0kLJff6ReFIU8H0sjrC4Di7zJnn7AKDGwIdTZt+yKjB6Z+40Xu2QegRbZbUc8PmQMGAAAf5ycQcjA7bFkNNeOqTuO9oTCvNsGwU9XZQUf2X/uuef8tobFIh4Sf0zlDFUYkiz3pvH594cpAdrUrisSeRzOa9eoBW/1nqcHo2CDIea7xox2WZBCrAYcWVul0YbUSBVmp5X6b/zGb3iUO/Aq24o9g7H7bMglHR6f0DHiMW5jRBxR4C1veYvgmG1pYX9gAmOvwPKiWo5T2gVh7DCgX7jhVyHk6m7VMdBHRKPoqmWQsXtsKOMDGzgH8kClEI8TQRlCFctP2wJvEsqFfvQOcv5mOyjtdU4oLTlRlBoFbr19Sc6yvbCCFiQTMg6Q3tdFIsMVsQZ+VPwmn2whVdCGDOH9xpYIeF6uFfYhPNxOaQqiQoxEaPatAGyCFW+QNrbgWErTAl6LISOyukY8H6YFgHJAItHPlcBjhWHzLRPb92i3EPHaKlogPbFuL25bpmKzGCFXmgGAXNfDdLd78bBzYJKCF4X9EKNZxrODYr973G99jMgwbKNd0xLzQTI/FZbvSv+Oqb2248k95OzQdLjyPqzGcHvHdNIiLDz7xKn3wDUlyPHl1yC5fHaVjKOomV9aaipds4REOEBoo8v01RIJ9So4YQCeTLN2db5mqaTLqbVDGmBlQezBcPfuXcqhw6xFb2gHZyQu10s6PGIDY+JMRxkzEmEZHCfk9oUekIyPhwAbg5ugcITM/u5hIBC3kg1v52BcC0XkEO2pBn60aJeHuJ+iV5bSgkklR9V+KmPjxircTyQSl2VBmySe5liGp3mRUi788pe/zEU9SVI8R7Rc4LRve9vbnGT+yZ/8iR0b0rgaHlLXhpj79fF8TwPdqxgubXi3U1I0Nd/+7d8uXXmxxWM8XXTy/LBTamFuhLoClaM5EYfUTp96i5hCmgi0shDS0bbb0bOxWqBqCvZxAcwohThk8ZIOWRQ5gMIhR9dY2nbFBvWqEJwNeGLn3eZ//Md/9MU+UVs7PCE0MOoab6SQghEyM3sRT08TkzY+/vGPOx7vK+cRGg2cQZdQRICBegnO8hWHgQ899JClD4noASf0D8zeiJhueYGFGvNzWE0VdvBD2hAAzdE0Hq3QoSGu4wXwaEHUWHVd0LqyvRjGOZ6ZAS0JFOoOS44S2gkQqmgBoA1o6eGtb32rAypZkEGiYgnY8BJ2RI2iHMsm8QHDBmpJFmzvJHebGy/pcO3ss/VAM3rLYbGgunZGps4l2H12ls015IzdCWzjAJC47WqhJwPZDaDiPOSzn/2sX5gTfZBwaiQKSH5ihCNc8bSg7FmOkzSe7DZ+lphrOXq1tGwUKmK0TaHobH3KLVHHnocTfg7eD9V+7Wtfs0cUiZxY8lg/oc4z7WYeeOAB+ckGQq/IlURn82PgthQmRezg+bhVClg0o6IclXEnwHIi6FxQNgXf+73fq0LAoj/FbuDH20bYNRZMPIPXu5NcAEIYdTkDpOdv/dZvJYLhJBLpILHPViCxwjCtMqUuA6VPLzkLl0wicoCXet5J8dRGCBWY7Uish3Boc8bsGYAjceeTWMLPZI5T8QefilAhuxbSORq1xCkBWAeYaJZmH8z8zLhblk9XbEA6tEqwk37wwQf9H7CTW+ydx4ZRhHWlT/5uAaQSP66lwLnFJELBFwcsBM2jMyTrRT+dbe/ul7DAn1Su1P3NRZKBqNy5c8dE+/8uW3MttJ26dIF0SxvauzIVP1NKRc1I/AOjW/Cp9yR+7mPgSzo8YXJZD2gGJBRKSEJ8QU17RibK92sSbllbwGevjvOB+FNXoLUt83jArgtaLSye22OA0YsCnhmIDtKhNaNb8LokMN7on8ENibF9sfiALrg3waHy0lpnkuUeYio2BM8888wXv/hFbCAhSFGFx1rChyIqidTO+izqxSnPdfz+xqglrR4gva9rBk5FUIgfQ/i/djwjNAD7UO1rp+EZezUBr3kNDcsBFgF+5NLTKandFBRt9abw4QFaDLiFZKlzYMvbJXXwuqRDBiYdWnNAy6g8kLPTDVJ0E/ptCHSZZZDgTQdLsB6yWNFiS0TzUQGGAWwsCa2sj/jBQ1URcxmhR4Y9tJNyvvCFL9ibMoAlZsAkSj/L9vV1w4nGnlsfyIjWeXZaX/rSl/7rv/4LXQV+LmmtgB+qUHFFgoeaHSJQuNvRAK425NrHD8i6pHznkFaWZoFuQzg5G0wUVVg4Dlk7Z5Gu+IsVg9Whl634xT5C+9oxQLolt0hbjXl3yZmBDD2GFDOsHSdGcUAVA7WAFDesnmkmVNnkPqK3tv2SDo9M/XgOM8qSmB07kxIc2hR/oWCj7E/KcS4hDIUUPADmeEY4GLrDHxKKJbndgAqDFgKcksmOCHF7r3JwD/AyYkclkGjHj6do3NjOLBEG5/oKJ3cEakWjmgAAQABJREFU+vrXv97bQ640QC7S9S8EwsTf/u3fykDjnBwPk54kUYhk7EmPAOH8yiic2ElMaEhF6zkZyKUskJBU6CSySI2i50bknQg4o9ZXoNoA1mLbLesgZ8PtqSGRqQJYM0J8MOrxpkLeQeJWAXPAHoKHXw6w+XNrNu25vbDDwKBF0eIDBl2wCXO2INLzO97xDhomu1Fvf/vbHRuYAmPxo8y8DDNnV0LogM7ZgG9QyMp+RMVGzQ+qYIxNdpCLE9uR5RydRBHDlEC3rF0KtMGyL5SACQWPY3k/Z8bqZF8WCEw7jUk8GHBr3nV9/etfpyXFELpyjZ8zMrQlHcv3IMAaSDoknQIzVEhTQs/wKMcsWJTIgpIQFWlB16YQP9xTTnJ7UhkdUoiBDkVFHj5IRUhnflStl4xuWQjesBQVFmuINSgGevEKwuwhnZzEzP0NfEmHq+aXTbMh1sPNmKAVMa9jcDVqsUT94z/+Y8t2/1TgtigMXn0INDxU7DULzi4HZiow8LQ8WaNRIuAHP/jBhx9+WKozKt+QpUTJTN81/2T9GJMeGu42YAwYCJUSh0NuWRm6MVyXD9HJu14iJTuEMNNAyYCT809hqwxneOx1lZMESqt7B1zYsMqGxL+maXcLZvhc8nC0nhTpHxI5QDwySp2innrqKQdTYLQspTiKdgkAOVlKXZiE2arfHkWYsyn0v1yme6I/fUbOKEiIpsWQJQMAmhGVwJbkqs9ASpbM/LK0vOsBmEM/OR4/LEoJAxIMz1ZJuzjoAA2HFkzvfOc7iW8h0kwlAj0guo/uNicbLU1Wk2vf88QTT0jAtl9I2Kj92Z/92X//93+PyMYybxQV1JfyTh2qtLFBqNvI2ft+6EMfKrUYCJte/+LpVFZaYn4oNgWOH4CxMesDkHKkd6k++tGPOodo7kAa66q3edlJN/YQCh4b8JtxFm4ZJA0DsPiQmzV2TA2S6/EL7XBySaK5QkI56lYJpsNTcxO6k+iBRvOFf6hMOkLQUjsn0k57ujQGAwlVCywaFfBMQgv+nSH/9m//tofKpsnKmCGNmAdI38KuSzo8MumFj645npMox2WcoR2Y8TxfSPLUxHLVKdYSI3fK8mBQ0RUqQ9QPuGUDwXAnQxi9ll7i4BvqGi3D2T1XZPrAIgRSUdeil8/YjVmcTvY1UFkyuVE3vJYYRovbk9fS2zuEurgTGKeFsoIuD3UEZWtzuwQRKi8tBMMAUrs4ImDZy4IXTIVRC1WoaKDNxAYPR28TIQ4BS7cSNpFRbxUsN6deMEQ4oOqdtAqIowpoTbeQJyyKMgQnskbF8PQ8WlXRPnSnfQhtt0wXPptKJ4EOG7TbSWdXVIqr0JLIhEZIIx02KXoFRCmhFZLGMCfOUFlfgTCiSYoHOx4vRv3gD/4gR9DYiYjHqGJ9ctGGUQRRUuBSXnW9GNB7gI2GWGmZVpPL8gFbQkkqjhwoR8KDhx7IbjHkjeJ3v/vd7KocQGPSoVyIgXhY0lrys2xXH/bUISegDMTs7bFgdvjJwp2a2hZjjEnY7RHElNmjiw+sTgRQ0YV5SLDnIYKiiyY3yB29NSRXCpIeaF5iJhR+Zri3qGzQRSFqwYxFG0cDYLiixawZRQqn7pMOSUfGQXKpXNLhERvgHiyGhzNrAUiUsTrjeII7o2dnAJxGio8ski2Gjgnec8PdT2tg40Kc2XCZbCcHABRdhSEVDiblFHndGouEraF2MIoWQ1xzYzB41i6e9ioNrjQqsVd9+1qwwJ6i1wrdadWjjz7q7XYI8x9O9bGPfcwi3crAEZZNiSgscEgS9xj/RqRTh42XeolGnUPeuXNHzBJKcCW6wZ+G9W5zcqAl9oylBGtw0VC25v/YSy0QggngAJ59XZDo6kpd8FC+dC4emTLHZS1oUHRLyQDAp3x1Icw1HoZVLQCA1bWPNGyCrDcvsg1Ixkjix8DUxRgQUmeNIjVmjKUB09QQpM2Lq4ExvI/ovvZYzXIYPHIygZe5aMOt0G/bKvewfIRibMJ3gmMSdYzphc1Vu0r1fXTBEAROiyd2BSc8TqedNFh32vgamDLNvm2xDbE8wVa1y4L27jI09qK1pIKuxmXLsq5LSc8p2YxL/DSMnAMSB/7Sia60oZH7W/AxcrNmceAxodzTU0wTod3m3hvXqAA+dRayZ2OJCRtmpLrOYKEyy2mJQuz8UDERFge/+Iu/SBvSNqUZS+Q0SZkqpDOhyuEpWKrlltQv6fD4RDN9BsRJGJPo7zl2x/HsyWDt1mXOzaz+gLF48Oys3vwqGtM10TN/28kBK9drCGAk+J6sYwXKK8CLDkhYqHq1XaZxpMZFxQLe4rxI1Oa9gqNRhUWeA9UwM5WdpHMSRKvIXh6cuFoKwMPBHIs9++yzHd+JUKgLWBgovV352b3VgOEKPMh5kodP78I50uSudEhpAlYMHOZnJ5PEKexybzjFaFMDD72hqOAhHRqOjZ1IVjbCg5ajMAsCtOy2vdvSE+J4GPwmSxm0eNAVV67Vp3dnBS3tBnbMYEhGQkzKJ9fQCszVRHu0ZolGsYDt0v7hH/7BFhkSvZNK1c1dDKuvLA3BfKSdBLz2ta9l/5CbcbRKh3qzDfBLqzZwGDBE0Rs2DLhV38kJMYlsqWei2bxbecUhhzMJlpMUxsImN8hSLYbcSgx/8Rd/wdj8+yNlLqdjCC11OI1TiSUSoetBoFejPagrA4GR8/DgtAM/RHZLD0wCt6jLRijiB9swmERsOAhBkWbAD5WVFZLCQ1FyIS/j166woYUiAVG0DxZ/rFDhbH3m1gGpRWfmhCUVo6BKdlc43a5k45aAXdLhkYnOaBgfONZjwSUgZojcho3yB0c3gkKmlvtxBmBOSzJcSAwHz2QNCRUkgMO8zQRgCJWAbb+8ZMgZtLNsToKuxycyiiUhfxN0+JuQzTltFCzeDVS3UrZzxUachM3VreuBMgAWARak4gJgm1HJT7T1ToH0T0z82xFaHYeKpAq5cK6SdDEMzMJZMMWb/1TzDEMqBXZ2mEARZtFBgdPpEHmxTTmueFCC2afkeN55TfN1NVyycWKsvdUPVagrYFxHXaR2a5q0uFIRNgqOKkvInXSj1XDShR9k7XBqEfLcElN4JbijPHsjlsmitNigfOpTn6JqSGoZoobsJHqgMYquUEkPCEm9GDNrTIv5mUQKwRLDhj8SblFvrwZYyDY1WughWtkGtPtIAyYjcZyOgjHc4zfvg5DLKhB+XezcsbCllR2b5RoXkHtsxbzjKnECG3JRGT3sI6o9GDyjTiLH4/6xUmrRovBfrzV94hOfIDuhtPQInNQkwjMMnNTC0cIUBkcgtrN5ygGih7tmUetNY8fUZKe3Cp+y0PSalSkQBFCkBCJYDeDZ+2s0YNbMHXjsuaYTFcAgD5O+bb2XdHhkxtlQ9seM5KS7d+92KM+eOIMVIveQlqRD/sDsGBlnsIj2dhnHyBAzO0tpZio5FTJc85+dHMCvPUhgbNqJpYg8wKLe7//+70sqrb6xx/9FDWkSY2HGjFMjJ6VGlRvy9kGysxK3GKgi2diYCm0wSLeW3v6tjfuV18NAdgU5pF01xn+V2tWlUgFFZpUUHUAJYU4dDQnJGVdiUpHMRHDDaakrtQ/a4eQk/GSf2VEhnfXNnTt3BDhng14FEo6RSKvIqYBBgrAo6nKrYl50maN6y/1609I2SwCMUgAEgxMBkYAqkGhM8xrt0myLncvZtmrHJ1oShtkRrGPGFU4DdSnbFA+3GJIIkJg47/c7l449b409+eSTnhGA0QLAXADGZzZpIeU8Q50FciL76SRCMUGMOkAdNrGevdG2KZYO3YbZRMPsVU+JUFKkCimHf7FMT+mAqcf2Ev9hckE26bGnztnlldEbA8aMRYApaBaalKYbPEugH9NhIH+kHA8U7B2BhXnJz8p66iWvxzS+AGXHbGAmhIS59o5Mp/cadeGBqmmsGaEcmtSIAYVowFRoY41CVjJ5f4Bd0uHxecwDHUL6TTv7JIbOvDJHi1Du6uTEqYjjFNnCqxYqcqHYIXuxRfbH+Dgn/9EoffKoo7ZoVC6Uv7FmKQR+7SKjNWnP3mBGAmaBRpfUJWoLQ8glGEgJEgxIERy2oz4AAJjhce62NNNAjgenlM9L4QyhrkJGARF1tGIJDFQFa4sGUthjSa6ijDreRK7jc7AFkedDq4yw2HBrLiQDdJujGNhCcKShUaMuqoiKVT9tZBJomReLHsVcS069XkgJzioFZZtma3bpk3IAI6nLtfpODgJAtw0QiZSSPR4aYja9Pur1QsnAmYGCJQyjIj4KlzY0KhZeFh/zmDMRmqadpPc1YkAXEtaCrEtd7qFhWzGijURpDH6EFGvH97///c4wwUuHX/nKV/xXooFuQ6gy6lXfKIVy5uHoRRLSy06QaN6Jb5Pq8RivNC/2ajgB5kMQXuMMFeQbONffmiAMmFb4vciDqBln9h7okqJz8sQk7xgD/MzAyaqlHsMwa5bLgNfIe4A3UrfVM9GCD8hSnTBiK2wWzHLTim1Sx48JQpqjacHMmBxsygHNH+Dkvu+6pMPjU8zfxCYH8QxdOJAJ2Ldwzz2sWC3NWJslod2beGERZy+lwp2YXTEUPHNUByNIsd08REt2vM2EsTXyOmOZuKNXdXZslEMSYYK5C0YZOoRyoXQrKLtiLyZFZAGxlSyEhrseoBvR2EPOLVSYxA+J4BEWycvZNMIWnyANKTEn0aSicTxj8exkT6qAgaNiVaAsREZ3/ZXURUZSC47CBE4swxW7Fqvy64chzKQoohEKn+aObuU8gQk5UoiYHdlhRsxydkcV4iBhZX1aEqrkJwNJ3ZZFojqg/3QOOQGbBWpPpU00omK0/YfiXwvwg0/wTBSAyWJ+3nuiB0QdITBR1mLxhCVg6zUcJHJwqjuV5QJNNIlkBQ+odMUVJplicjFUCzJrnQcffJCiGIAFoi4nGRQCvgInpSXaNleTZSnTqIANVCEpqZ0E9oKl1Y+s43m2XMXUBxXtwe9amXaVscllY3UC6kVIOieCXIgc79OCfw8mrIDpBNuU2WQZqELAjoV6DdViiAV6xgybopdEwBJhm+6+FrTYtmNSz90dTgAjjqsdqsjjtMkUa2lm4TcRuMWzWVBvK6kFHrcKyDDso3ib2y/p8Pjssy3uLQZxPw6W5TEsTzUck2rx9OJ973ufhTDDHXNnc9xgvN2tusBdOkE1qx2AnXzkb8YaJcrbHeJEdPBynX/2wkkBCCEwiv+/FoAEKTwLfyKvsNX2CwwShsB5mGjYACcLSVWMMnYquffwnE5QnBaQYLBUJYr4EbA4sFUFvQlkWB1yM3ZNpYlwxZhPwVGsmCj6eKhpyWxqqDeeJ6CvQTswxqoXlFEhhQQjGorCtmX+AVSLpY99GEiCi9o2glQt95CR/iUP6Qp1ect8mQgwQqqpVBlCG5U0qXHmaHgIEkJHFA4tPSxEF1hKCMz5s5TscCLrYires/D4ytNELaZjSY7mU/5G+wZMt1Kag1mbY0OgJY59iekjvoK6dvmAaNYBXuYSvg2Bmf2YHY0WLtZApimjhXZkXFLcqA8MhesyliBWHrDRFW24tlLBg/qVSPdOld1iDANKA/UqzeYGlbk1FqEw+NSGtYVbRFkUNbJYt0rweAMJvytVmGimaLWEK5A+4GcDhxwAdA3pOrSWFVrCMGColmiJYKnN3lidrScMqNsB2wczdeYE3tjY0IsNt4q1aaTBuwWgl+oiejVnL/wXjiUzt7Z+SYerpt5SkW+LBUyWsbJvAe4zn/kMJ3Fiw/MlIWFOFyMbjPlJtzxKpABm2dg/IbFO6WGANyosOPcOoUD/6U9/Wqi1F7Hq9zKLDdAyqjL9HvvL2WjB1nMODgCPW8xskNh3G9uuSPMl1/xK8LXXhJYISOeB+5DIB/keZqgFGImkc8FaiIHQLX1SwnrGlrRiLJy2aPYulsPCkJyESUEBWsgNOQ9/Y6OIlkotrh4Me7PfutvOr499+PoJnXipRC912RaQHVgfWRbOnBZ48VWXtzD8GhQ+cbgU52g9HmIDfhQdTlCsBGxeFKRFQPmGkqUKmckQNulAQmh2VoklYLRxkkKMomQME8FuyWwyQnt68lL1CGK6LXEASJmeF7SHxgyGEQVmxtWXmjwq8k4AYkLlaFQ+lmJpAE4atkxE2jufZoGzULUCGJJJFQle407kAbsiATM8VG0ZZEdo1mzIyL4cCHPIie8A0+7NepT++bXtuFzFSSkQJ8AG7RLD1HNSvM3sGEVvrMhX92CWC/NrqxCLG6thJAwHP7YUFQox6VZjZuSKv3sJu2DCHhiGUU1EkzI8XCqXdHjEBhgTCHbDOpkU49PCxCVCQUEw8n0K3sjygLHm4NWDHOzGwsBt+K1g0Qsv+cDALCvjFXDyCoSkQG4mEIjCHpZISBAq/BYw+xYN7VfkGLcw+x8MCduQqOQVSxIH6kvgOMEGPARHTi/PjOg+JI0CVmQBhj0bGkdJ1hZu7RLkrXgDBu0+VDvbS7dCrYpcyM+pApOUI0zTBibdGtt1J5KjjZCAGVnwSQMQSsAOP7/2ta+Jv2KlwGRHqBC2QAPSqsXEGSueCme2dFA5YBSvdR0lvQGQdNBqt7227RCpKY2wlKBopwSzL4DaI4KUsG0T1W0sVJgQ4GQZ5Bu30z4VGmif4eSDFHCaNYGeyEVhIpcMZCPPMj02c45CRaS2iQQv/mIedfDNcjMyuhpaayqNtRSgAUHfIoM3EZZdtR5iDE4RmQFymDcdqBOzgUiop8yd5OIQjIE8iD5J6sC5N3Sa3BCCqcBDFjtgqwFF3fyKD1YMvA8P2Gvg8LBNGphlBwAMqPALyKHygMamU4pt6lmddGh3qIQEDMh6iWyymIG58CQCnwyDEYKE0xSYFGbTQL1xtc3MrW25pMMjU5/FMx2F5bkawCg9qxBc3Ao0fF4jyAO4mKNe9spAjWKIKow499seqB057hFadUNkQUemfAweSHTNcDyAgRAhmdKGALCELXyHnL8d5nB4AFk973VVkt2Vv3EwJGCLqxm4rOAwr45DOHHoVUCrB6MEUMnDo8R6z3BLcsGpiLaulhdYslagnOnCoa4zkBMEk6Q2fJTmllZxTvNyuW/yeWlQ1ANDNKE/5WDAcI348dqUUwQLIC8EfuADHxChhEuLfcF6qas1dTpnMGSxC7EllVPlJwFOiU8U4cEeclKgwwxpyS5WDpO8HeYDE6YxD2yEOkoavNAsxbaIAU8u4ktINE/V5UJWITk5OraJxCG1OFi2QbFT9PjNXPt3QHPEKpYqPUp9G6DhUrKCE09Jf/InfxIPmOwhrrWpXamMaJnCT2HIDFSMVehhG+20EAr/9GyuLUDtCIlmU2jDrR0YhWxgkH6YsVMiM6uCnG2lOSIvF9DSms/YjYFDVMUokAFgUgtOrG4JKLchEQzqwg6ERAasZN4UGzYzZSIogbFpwYAhYJilKcAYrmrXaHijLtc0cEmHRyyBabIblurKCtmWlMNDnIqoC0bCDWPVBYDxbaCbuMNeGZ84KDRY3QM2HNoN+OXtJJtsHfLce2BCHlHbUw9UuJBeodkpjQJ+GMjHZuzhSqO64hMn6pCLs4pwIzTgihQH8BQF+C0YcVNA56vWucJ6K26qSAPD5AFsO7twYv1OdmtzARo/QiRIwoZzKjuHH2g0WTCEJDAra1FejrEdkWC83aDXBtGDqwkxgg6KJCWvUcwDDHk9dsLbr/7qr7Z7E1s1HqC+3RW8uYAQKhSpNypamlzt5oUGsGpDYx9ALVI1hgXr5557DtqlQhrouk1uWuBk7U59ydsWRIshBOnkEEVp4Nd+7ddker6AK4+12jR7NCA78g4bGm9f40ovEWCA/zDdYWCjwhQVtudqh+RTLPZw9k92hw7hcYVbDy+0SEieL/CFiEbRqGZ2A+3clvPctlSlZ4WLmdmSR1cAUBHElcJZtW2cPKSXVVsD+ZJcxjCYm7sDUg9mkEbRni8AOBDmd7qIbO6Qs6YRQJr6YpEuAKyOIxjiH2+owmoJEu3Ys3RwSm9xZn2cgPwumxn2LhUauKTDI2bANBVmx6sZHBNn0KKwsKhdKGRVGvWWlsamN/CyY8OtzmyJxBF1kHnUBmS30LJydekk0wfMsbVjQF2FN7rCo2IN7uCo3KPX2pbd6wIASdedhHY25rQEhMEVDP8hIMFJShsxcMC3UdQrNuFNxXmdT3V4/qGd7Bbvjg11EU2LgtBOTvY1YsZwC+d+T9G/V3qo6RBvNgTxlgYO8LkPf0MwFoBbj+vsFSjZLoQI9j1CrVgJJuC5YswoHKKurliYk92EShiSk1H76O5rT95mJF2xOsBDfQaiwk4Q9RKNJ7UMwzrJ3s6UxdJySDzP2O0KYMIK9J5Jm00A+QIqmGFvHMH/w5kFXWYTFQcY5HV06dxSo6WPvaz/sWGTOAcQlaSovv5KLjxf6fTe9x/sU51JmnTWTiFSMiZNjXxA2K9+9auSpYkb/MRRPyy1gRTI4EmKSQUt10GyrGjnEf7rA1HZyC3DtmyaKaalMYwDdDGWZtDK8SGkWDrUoouqJUKHUo5ArPyyhLwSXYseSx87Qt9J8EIvNtAiBREspBiqn5cyKcO5XhT1TsulQgMXdRwxA7aY0RS4mR1L0ijEKEuTYoIWYpZygzGb65bhcksPPKRDpgyD9pxz4JcVXYZrgbb65EKNhtcYjLq3W73aOulQmMAwziMEDJ7wD/UluZ11kNrR5VGiWwIKFuIjcdwWOHaO5cBg2tNY4foXNCeldodcnVdbO9tXxbxrlZ149jXiChv26H7iwyLDFwkcbWEpDTSqkLEPw5r2MHTFOUJyjHToIShLEH9RFH/p39STgtQKnbsahUn2YCJw1ZbavlB+cghm776GgYGBSh05V5ibffOLK3qoQuF6U6ZGQ6jaFNSLJQCuJ2kbISQEd8LaA8FPcCexJOpZndNvR7LQ6kJLvJYemKJ1g4AuBfpGjHcgOxEBk+lCS5ak0Li+YB4GErE9OoeEW/mxJ3S5wE/91E9ZeGEMexjA8M/93M8BMGo9rZS8ZGlYTfP5VGD0bGPqwNZDEyTsyCV+KwAKCQye6ofVrtfsxKQ6HQovMpzh9O+qi4X/+Z//uTUZANTBEFMuZI1ktzS0GgAWMBgqUlesFazGGKpRuMoy1ZUmLrDL9ZIOj9sAw3Is41Uu0ZzBiWtaMnFXpibkCT2MTKQYdGAyzVo8ShEXGLQNZX5yIJcY0lhuXxwMGDkWDDOAriqc39kdb8QbAPCeLsg3Am4OFgMDv+SqruV12dsQ0VwodAzlihbHs+hWtyqPt+XwqSOtjv9yoRWrU1aNGHNqZ1kgUrslF20QagaurAgELVBMx927d+1RnMhhyUKYCArml+paiXbAwgCJgkOoXFF06NeWy37Lidxv/dZvWXTb/XhSSwp5jpiACU46JuGqRbvDNFnEZuXsV2maXBxC64ofgVJMtN0U9wViR/GsMQBM+m8HROmfTsRQZxJkGQFXVghljuS5KigyNpnm53/+5z2cI5rv7bE9loAfbHiVxjEdNjAgicqCDi1t3KWudIjuvfl5Pmqv5CQwsuMELYrV4lZCwh5mHE3TMw5JXbscYJrUCT4TWmUf0WESAEJKVFLdtgIhJ76XukHatDmfsGBiEjgMVRggyST20a0dfqP4GiXbjtMkMU0xbDyOX5tEyylEdZkFpNk/VTMt+0juabJACkTEpArRxlbVvyc6qaalqGwIdZilW9V7SYfHp5ubcTnPA5ipD9MwU/bERhmlKMB8oWCFnIGN6mKIrnqFAKZpG+TWi9rPPvusMAFel+uBXAIhB3DN51UCrnH8CiF1pCVCMdHqT7w2BCcODzsj2nbgwQntzlK86AqAF8le1vu8zlh7Iz9YSBA+JgRjTNEOXiGXDRA+1bm0+Ch92sDl2MKE9+M/97nPiZLYhtxYItDPTk40wuO6LQVJYeDwusxFqwEpFmktSsj3oT3aHoZhLM0jJ+1Zszvz9BalUEs0z2OkHE/LCGJFr6vJbaJhoAQrd6FZYhArTU3YjvKwAZCuqFqxGoCfhi3RpD0Jz8NClqYx5Zsm+zbFmzUsgeYpR6+xG2gP3xpCFQ7oiIYc5OaXtgVc2qB5SmgGQULFR0h6j8f/R14/Ci39i8W6RurR6lQO8MD1EIIc8NW8fjOja4FQY1Ger8kWFisU4kUSo7iDK6VFZQwJhsbuJDpM6h32pmIsPAwPWnXi8wsWbkFslh2TWjSzbXvipbwROqp5mBUMwG/Ph//IudKALqLZiUqH+Jf5TK5JUe9EaoBxhRaFmHebZh4ndjX7BAGmLKWLvcuVBi7p8IgZcHWGyBw9EWFG7IwVOqMXZcQFic14oZlRigISpzjFmrUza6t1sc9rFJbJlmmt3MFnlFfa3/9m6QG28FMvfnJLcYpLuNUl9Ih90jAGcLV07wM4pyskg19FlLG1tf2SDjFMag/5CSgOapcRPbmhFrREH2xwWr6qYt0gWNguWMDi0/P8p556ShIVtaEFr3Ho7qvgZ6Mr8bXjARV4AnBb+NuAv9lbFEU9p2Guvqfsv/LtSGS7xx9/HGMYMPso4gqHzKa0oc4S/uiP/ojGGIlIt0b2JedsJsFdzQINM0LBEQPSoTdKZALtTBEAIxQoPT9jpZCIjDiB4VRjMNYQ1utZL4kQ9UKNRtNtLwKnOkEUgjehurS7lZ5xZaBeYGeUsk7D43w5v7Ugat7BANaijklgyLmlDS1g1DOkdEgVZ/NjrAItVAzbP8ibfa4BIQu3O+R60TqDBLSQ984Rv4MnQqST5EQeD3HNO9LAJGDaVokQ4DRgiCmzI+drVuEMNZhtrtLGGXzer0Mu6fDIzDI4RqMwJmdx/v2In8sEYo2zMlfxTnxniE6NxEowTicEIItW5mipKClavCsoiYMFkRAadYT8rm4s1Zzz8H9hUfjDoUCAAVsQMTfOdyFY1QZbXuR8xgJTxvXfAnKbmCvxk91BmXWxx2nWwr3AQhXiAnclJjAAioFUYZXqjJRC7KVCmx5ipYB1mC0a2wAQBMkIT6yep8wNnEdvrWkUdL2nJ2w5N/bvBOxBbHI1y6IY5YtfhMKYZZANpf260zyxkmFodD1KaBvAdKMr1bE6acn21C7N6kTsY5PmPQ3YWNgeebfFZEFigmRiuxYA5qW91DbyAy1kIRR5SWTqbYWZnPnV3ih0ExYnJppaeAR4gZiu6jqAf18XeXVRl6s6SS2wPBrkZc6ciUwcCY9CWCPDQ4gPYo9JKEbpojHtOAxPtJb1WtZclxaYDcNs8UfhyMmFjuu9GSAdrsG2AVNAwK12yDkdNULFp3AbaTJa39D8jNVV7k8PtKSFpZluxxV2hOrcxBA4dSkzdinONN7yyvOaveWK2Cd+FsaMWI9QYs3lOSLb8poABxBxmCPn18XztesVCAREIU8YYosZOs+BiluCQYuBcqGlde5jYLt9OYrDSzlOxrhK7U5IpJzCwRJyG8/Oljwnng3HMxFshjiYMAStsxoVoVAacEgrERJWuAeMGWdlzhIJSyGilTd6eLVgaovsrXcVXQqc6SGd7ORkZ+NSIuSgwgluw5nUOwfeVCNaUBFZ7FNMt7VIj44ILpzRlamnRiyRznQ4LfTyvSGTivQW+NZz1byAV6Fk227/TuAWrd6tMClIOJT2YsUjjzxSLnTIiUkLEeeWIuZ6cktIFm6+PIqW2iVCaVg+Nu/aKd88Krhi/4K47Ui5n6dopAGolrO2xHy4Dm0AdI6Q/ahFgA+nqbN2yyypQgK2bfIGpjNq9mZ94LYMwdg6vQhPJj04D5Pe2UsJ2uFheKbSdFt2IJrsnI62zf55RjiOAD/+mYerkg8ue5FOIWRJsYDV5U6aJ7VViFlwWjCJedvYYEiWnZLe2sZLOjwy9QxOYT2urb4N4AC2gByeM2jUy0Z9AkY86pb9sWCF6/Ii/lMwGs88QnVdN2xW614ksT5Fy60QjBNJKEJRX4fseajGhpDUKvqsx0VDS3KhxzN8dBXuKkhZttsYCcf0YKzriCw6UJR3iARlqwfKoRkAEaMW8EWZ58nvqeFk2WOsJCQxaJdxBWKRcdvtl0NupG7qSYEoec2p1bfFEMw0L1VoEYPwRvPCIn602zJqIXXFWOVUZhLNXBgrM7E96cet6G8x9NBDD0kPkq5HyNKhUzWpwvH+Jz/5SblQO8awhPMm9yTqJMoGRNsnn3zSy41uWf5oG0uQu2KJfkwE5YDJ5smO7kkUA05LlOaWkaiQUTJm4fIxnFoijZyKdmwgijEnFh6YOZy3HInPgM8QP2ZgxoPZTBZCUbIXWR2VA5CErH3nsxKUMMpp+NFr4lCaSnpzJQX/0gKhrlThlt9BqAUVtzmRgyg7Qg9r+Rp3MCSiwMBTzgYPYdtovOW3l3R4xADGJ5kXs1MM0MgxhAnFbYHG0US4ls6QUTZqKDHQlpAqG10Dc7jClBVsOJl0Yumkzi2WeKyQZMdQjtF4GM9277hxvCGhJeTyGX8TX7gcik6lLMbv3LnjrTZpaVBxPHsmJ4SiMBeVLbx6Tl4shTyVgs+ZVQ7oIRFmyFDBgL0CP4ccV17ll5m2wQb+piopFjYSiUQZQFFSvdksnMWMuAm4FpUkNfBUfhrInKC1yCCy/Z8Tci3OD+1Q7QsxIEsxCcqxU/Hjfz5V6jiXDikKb7F3Emnpljk1kCxI4AQPRB5TCSEqo39x3G0pBNiB+d3HjCFpKbQwsyvU7UpleknCQFwBU9GFT0Xd2sj2iFU4pPEQnTvsI3FSe/NO26UfW0OPDO0OIaFh3yfylJQFUhF1nTG/8JBRUWm47R0BrXVowHLTQgfREiEY/JgCwjqdNtfyMfdUlwh7coGTZg3PG7kQwsi5XspSA5d0uNTGjvo4QLYVRPbKFVmklgmRHLW4r91AQXCCAuAcexlHljh30N7TBFUDIXR0KSsIBBo7wXNoY3leut0IWHvw7W5GIobrhkohUZs8XWKBB/uOTBWxmBIACMdCoR2hxbJNjA1KumozQSeDVqUuqEai3awsWgN2lYN9aQVam4Cnn34aLdQXgC9WNUHQKnKRSCWhkNTrtuRHKHNdvcQwPGlP9mk5WklFRinEF4L90Ka1iNkX+DzBMh1ImwWQVmais1/28OAQ/CAHGT/TcrRSJEUx5OBVFCKrEyRZtICJll2yDXEtYDQSP/ij5AYg+OhCBQO7ssay9SG14xB0x2zYv50roizfvpngjkkpAZLwQAtJyMM5hE6qmHczbvHH9ixHaFt+QlE6lAv1YmlJaz3yuIpbdXiseKQ3e32z5oGIRQ9np9tMjvioU4jn+hY9hC3/adSlPosY2JSl+Ou5um2Ql3R4ZMYLsmNP90zr+bUV42N5UDBfxeo1dMsYBGbZOP7JrEN+hIOtbuYOCftWnFhaNopxTiwlKo+IvEcjNDRoaG3h2NtAQILg39iERQU0t28/JDBpByDnKcRviExAUpwU+oVRA8lorEpR2KiwEcEodcCuK/UQIWNxSGqZgMjk9bIS0nG4V7Ab6piFNjYSxxVuLNGY3kSOVVwlJhiVVOHakJM4GjtUgcpuwLfQxGVH9LaDv/7rv+79UggpwcrAv6WLkjYNtZg402QKmoWT6JqdFE6WpUlkWnq1u8IJzJUepKVu0TW/GAZzEtEBzhTdIgetpd5nP/tZr5O88Y1vJC89aAcDgM07tPDtFacRIDsgqcsVGCabi65DYn0lA5OQbMr9r5H3d8w1WhKhx7cqqGQeZ5hiXEEIiYJbElGg/1RmLQoH16udSkmXmZlQAzMzwE0EYHti7Sl/OekaR96A5/ZSoYFLOlxlBmM6Ksu6wVlbRrzExSjdTiAAsOxVX5kDNkZl/TU6MhIaPDgp2PEBb6x4fbHbjYErb0kX5+CXknK2MIxE5f5ZAazBPwiHxGDbN3yGxI+BjbXv+chHPiIHS8kdiG1reB/OG2lPITED4VBvWrE9lWXvUpwz2Bicqd3JmFhv2/S7v/u73p7QC79DAofV9uW6JkTSs1u9yql076n8Gw/qlhiYotsirEqqGD10e3R+9zET8lEvMDlG4rEPcxThKd2nPvUpm0J0USSa9QGTcDrqCTdg2aL1ivqwBHh5q35SQQW855cPPvigExG2JwU6keZ0suBIivQ4y3r8Mdn8GtWtqRwMy6w2jQcqhB2WBiwNzO2lsqGBSzrcUMgr/TaDblOVzwscKgKHZ3Uign8ETIZgxsFe6YKdyJ+tj72CkEdAsqtM1DsR06sbnNSeFdlD2A42121NzD61TPhTmfpJApftDNkeXsu0g1RuahagWhINLVoyIiN3iig5EVABVqJakl7WT5J3HzB9tvPzFo/XeTwWQZrmHVRaecyiMLb3Ibm0v8I1cEmHr/AJ2mRP3BcUWvdZPNoZ8FJHplbH0mHRoTGC44SqTSz3xT0lpIfZBwiLNx4HXy2qSnALo3JDmon5qzS1eHp0kkQG7lTpToQ3bm9DBWa2jZNathd5Iz4AvTt5PknwDeAoeobnkNbuED+2hg5mvbDD7wDHmPYbJ73ByeX2xdPAJR2+eLp9UTAvE5506I0JLU5HbQotnJ0UtVO88cD0oghzPaQW7BKhuJ9OpAEh6XooX62jxyrE4o1TNTpJLa5nWMVGcA8VPLTtqqSyZf1GlIjuNttDDgkMxANIxZ6YEpZpUi8Mo5lrcoU0bFKgf1yhYcehdoT2qc6rkdDFFJfUr0nuMvxl0cAlHb4sar8uUa4uGXBL75R7145zXsWGF/6AorzIP5cR5LpUX2HjSTd7IMEIdzcV+15hgh5iRyBeTrTEELR2letnqfBANZUSA0KZlva6btzSBiH8UyfsVfb7ZhnVLB/XgecLukANwDUrV0K+5jWeUjuXVuZlGY14cx3bU0f9Bklfk/PL8PUauKTD9bp6RUB2MMjZJINcsZSg5SofLvxwUsUrgu+bZqLdQEFnGYxums4rHZ9Y3ERTglThNsPINuJ+cskZwhTrw7YTT41LcmdQOTxkSZd0S2AGP6Tvmf/VnhXMJKcl8HXq0CLkGIbJUbg6bJMUlxzeOOnrsH0Ze5IGLunwJHW9/MCcLf/ngTaIPF8UcJUmOerSLfG6zI4vP+s3ysHsBhI83Nd5pfZGuXvpkLEBJtHUzwLIvN9gUGZUS7uCHFEUo0vUZe+LJ3kZCC0znmFfsbV4VoelDeo3a//wdxCNk7KyijLM3Cy5DVkuty+BBq6OFC7l1aUBQR/DnFNKcFXcTmxSB8AzVWwXXO/LUhgiJsHFRLfE3Hhsdl8KviGUFMgA6EGKcr0Kzy88uKtlrhvD19wamzkBVvGPPf73rn/vkZnCYArWoFoPM7LMkCFB3vZ/KjP1wyF4XGX5ucZguGYFTqqAhH+xOhTZGwYq2lUicR/73TV1+AoffkmHqyYot88Z8orcdQa7nRaBySlKXdM4kNeveJU0f4McNk44VHJIXlogEDWKgxFVL2hen4eXHQNJyZiYKWFYIuYoRKNb01fjwJigs2MW5EqooB2cGUnX6J5NYnAuKy2DtETdVE69FtqgFr861CjU3SpBVl8iXFMnjoEww5awvpPi3xx/5Vd+xWcQols7gJF9DebDMMmC9BKs2+Sa9hqHtH9GlKv0LqdmgK9T4U1oQduj+uFtSI9VxMBozBD180iPbgf54DGtkR7NA16CqZ9Nd6jcqsolHR6Z7sxxjBJ0XpG71h4K7pGHWLB3lKc3sHGYI8RWdJdo8QAWctehUh2tcYmNmIK9NhP3sZMQn5gJTkwhw207SI2jHBOUDleofBMktafDZrxJKQi66jIpZuFsEpsk792HjQhZXbfqiu2aT2j+9E//9Pvf/35fufQf4kbgRJcKYeNzJ9rDjdkSPMhB4rMP0qHfUfIms8+mpIowAEgDhxFev3dJFLY47KCYcnyx5Uoj995wmQXE9YmGEx5KwIAyKh2pVVA0+3WlsYY0WVL1qZyQyJAoNpa88KBC5EineZCsHSSwjEQ9uq2HTiV9C+HPXLPcHk2NZRM5UyvCumVzWvJDFWbKZ0TGadSSvWq/KY2VaJHmD3aBSKgriOao45w5sKsuV/AquUfXm2LpFYUn8SkkwZs+9Sp61YdhYKOuaTxcaQhNKgNpUmoXDTtAS+GnIh+E25WxJYQyuWCQ8H1nHwzze08/9EM/5D8BfJ7Gf8L5lxv/kzoCpoHRwzb+fS2TZtgM2/YrIj6eeefOHR/G6xcWw8kp0u0NiryPJSRoI/2jroCsBYfS4ejKXOxDciPtSLMrhZ7xozJTwxJahfA7RR0A3k6lm6saSyh4XB0OwVOG0+KDAP7hik5QR6XolHKG1nA1LZfKTg1c0uFOtTzfyMFY5JXJf/MzVGNbWSoD5RU5A69grPkneBVFJcjnkV6jxs0wwPpRhDxPC18OM7gBKN3igavcY+cbiWHA7r8KMZdB2a3dkilQ6EEhcqo4Q/Zi8eBhHpDQbaiKv2DgV59sdAahjSEmt9mEPBFCbl/op0X8CqDPeHqe56T0x37sx1iFL5r6mqshDQSc+BtoV96mT/h9H9VXyogGMzuEXIHZrULqlQjPBkMuQmilB7ewuZKxfBO3N6h8+KOlMjKqKOjqcsUYuipFjCwh5dCPgRIVlsQH9e0C1ZDY6NUFJ4RjZuw5PDD7annwaMEw0UkjcsaOwWygvdxua+CSDrd18oKWTJBVCTGu2TQ7c8siXX1G2e/piBQW6eoWa8b7aKQffe3ziW7z4RfgPfeGV6BudM6DPbe8RSDgisoG4qVLNIQU+xxvY+yr8Za8lNB8VaciytFSqVedErbVtUZkY2Eu7kyEKgT7nObb3/520+Es0e8qFBPX4DwKE/5iLmAMFOacm/mhAxnRD+9pYZO+nGKb6Eeg7N78k3iY8XyUxAGADIbIvn9kQeYHNfvVlFRq4EtmWrmSiRsbVpEeaOAHfuAHvvKVrxDcthhLqeiAUGd0oZ6wiCrqKVYdS/3uRJ/U98ugNtC0JETUS3vKqUQNl/MyJJj9xlPzjhPFD1n4HFUGWdKFXzvZNWrJbEStM0ifyup9AH9Jh0cmkW1dGf69raFYIOhIeBkcU5MI/eafJbNf2HHVq0VI8t19AXF+DjSfOUJpdTdbVwRi63RXhg6/iIAf7aIVTHjgDG4dpOjNJ3Mksqwm9eoDzP/xfRV7/u//FQhsmOhEiNSlNBdnJEIDFQjFJnioVF0FKvZgIpiHH0b+5V/+ZT8+ZTHkB5O/du+XgW9Eic0aQpGDUwseEL179666iSamY1K533c1PeTzhXeP94qS+DQE/2fMPqIGwkBw2PzGHlo+gcTGwukKZuoqL2VBmvc5K/7whz/sSzF+5MS8mA6F/Sf4jfBDCeFZ6hD1MpZt+hve8AY/COwwme+LAP/8z//cD5zhpNWqgeo7mVniDAA5xXRzbSSYsV82dSpusQUYXXPxb//2b34WsbEmheDGorVBJRvYSffSuNTAJR0utbGjPmbqqYlP9/7oj/6oYMcWhQbGZ1Oo8DqGy2plSuarne0qfpPFZyyY5g36JMfI1i0VRV6rRd8RFgI0YsP2VBzEgzWjsCUf+NUbq1SuQjYAuvIllR3SvvqbyKhQuGJSBEoRxNZBfvLjcP/7v/+b+E2reVypB6gAu6ZnyIWYZZTR9W3f9m1eM7FFYAaOCoQtjTeoUdjQhbBQqGL5ZUv08MMPI40Z38/0U/VkFJf9sImwCCYeiJlazuCnsYazPaRtDRWJh50zsDS5VMUZJNYPSQOEQrer/THf5A74lIe4QyKP7OuRr4Ecg0GFThIcM9KhBTFO+pGZRx999K1vfat2EYDt/cu//IuvR9mvryERDPwKeZ03vOc97xF2/MojKkUeaNWFI0dQTYov2vN0S3BLIgUAPDAMw+tJ31rISzo8MvV5u0AgzPm129e//vXyHItsE8bmnEfZeYh9IiD36OUxTiJuKuIFAmdHom3mcj8OgAEb09e+9rVgxCZ2b7WuERtcCFcFrDt37nzpS1/6p3/6J6yCSZz72EMsSmzQbYysDEwZ/Xznd36nIOV9S78C6CTN3JkOSjtJCVRX+ZEf+RG7MRr2E3d+V8F0mHoTTbG2hn4VlnlAruVm0yGeMdz0jU3i5Hu+53sESmbmc31+aciORCiUCK2HBErtTCUrYsPlkm5XXjPdBkrzQjCEzgAtsCgEEl0qWCo7rkR7Nliy42Gmj81b9Fh62ifZjgMgqV7XnOVsWtsDo64dA+aXyL3Hq8IZH3jgActTvYwQSw5vaI8lSJBmig/61RFJaxvtdku6dbXAetOb3sSuCAgshEg3xOE8zBpZtTNz6dAO0nqIZfqtK0epjgdMUJO4TeXSsqGBSzrcUMjmLctj0FrZtAgrqbhVckjh1WPCr371qyKFTCn3cAxdls8+7ys/cQyx6YwwtMnH1j2c/bRhy8b4xI/f5kZXJuCfnFO4FBosTnGivci1hWyzgf/wfAPBE41E0rwUK9bbDVOCWKOLaLqgdWRHZIGJ+AKlRhiLHdVxCwOnnSi2SXL//cQ1XIEaZcYkitDKfwTEEk6kQFKLFDaF/v2gIYKFhAEV4NYoKWQf2YhivsCKqOIc7Gd/9mdNtFEf/OAHbcoFINjSqqSLIqmVxFSBwRV8133kjrZDGCpXUZ7s+PeD7ChiAHJns88884xZwIxUHUJ1bKsTh9EepbITICV0FeK/5Vu+RcB1C7hGyPGgaMFYFKeSwQwSMMnimt52Ej3QaNTkuQRE0cZL+rcEHPyxcQDPSV3MLAGNUodcQLAd9JjWHp332brxOFJ7uZewLRdYCK8xTTavfjTYLT4B0BhIpghbljaqSFEmFIAUKMWaYr7jlqMRnJepxAaW3CaIOIABde4p+PglcK9T+TVQa5dRV8ihSoeujb1c08AlHR6xBEEkT7C4EwWs79yydQYq93iCIiWwdf+SLEwobN0Dg//8z/90aMPhi0Fj60eIrehmylwFZhb/yU9+UqzhM6644gaos34A1owPPfSQByrgJUV+YqnIi3RxP8OVndQSdiKak8Y3v/nNlsDcWMrxUAqAoIMiVySjzxmjq/zHf/zHnMqC4epIw8MDc0KK2knxaCMkKTCu1IUA+LsljiwlOWEVXdjkCZsYEQqkQEBSavFrsX79ox2zFmMPp4ciSJyrq5CdPuUDU6+FBlQCcEULJyoYUJ9C4UcFXAMQHpzDTHzBVyw2NX6HltReIlXoBA9NsdnBjC6SqifOGkIbMIZDgq5CsWzJOgNChFwDxpJKV2DgddG/OpaK3epgXAesIWfoxxBIGuiKQ+cBLJCJMshYityA1Xida8KWSOChB2/w/viP/7gkZ72IB+sw7Xj493//d47g2NZPcloo08YTTzxhfWaV9hM/8RNM0S62Z7qxt49Vopk1S16/eGyUbZ+Dn0QgLBLMHj/MmKpVsj18skzscX9DHnnkkRIzDhEapTULXa+jlvts7CUdHp9QlgSIBTMshUlZhwoHjNVuyaJMLhSLJUUR3xM7B1bSoSjA2hqbFR6ntAICwuwerCMRV7fiFH6EHr6HQ438x7qVk6jjBGOKev6cB7rdLhPjoNIrrfJk5408kJvJ9zNEalFEIkMQ/a7v+i7BiE4gFwssikFKS4q37JxiOb4rjQ2GNRVDODwFEjO21UmEPatyjFG7FOWxrv0fhBgI0kCQObxVi5ji2SEAjU0KbE3iTjaGHAyGAJaB/Pu5bXfwxuIh/IDhBBZkfO5Ee3YjQtFCgkJERiKLsCoUK9NLhyYiNkZA2ogiDg13nfldz0lDDEfXSawZF4itC3FyxdNrXhOA23BGlL1lbBrBdKUZ9dSlcoaijEII5hENEtHfdHM9BolQ+FWGJfVrlthGC06Z78EHH/Ro8HWve5385/zTWpN3lJJlQY6gsHkTYRNpiJUTDBo5aVI3IwDcqg97gJNRO8VCYhQq8iK6+QLvtuoVeQBThXarIokZjKugxOng/I7v+A6PMIUjtmEI0uARghkJla5D+lK5pMMjNmDlNRGELf7P//wPO7MeFA05g5Bk0+B8X1AuWHzuc5/7whe+wEOyvGyO2eUDR4id0l1qwQbMDF3hWniAQ8DymFO2EDK4qONc+1pS4BxXI84+ankjbHzYtlIRc9v4WgcgwQN1AaAHCQk86paiWOoJh32zGM2NnfPYTHjLziseWoDtI7qvfWMIeZEgCH48UFEsREQi0+QJrjSMB1wpEAqOFgqAHSFazeAcq4VRIuB/5mibulEUCyZtAKA38Aa6QnVvgfF/BoPK1AEbNQO3kZ/Rgh+coy4HWw2YlHe9611yM606CZcOWWYcQj55OnvQUgQkzqmkMzOjaJgtWVLIPY6FtWsk4xh2GjMX2GOBaJksE0H5GEvtwYM0Vu9RU9zmNq3SQwgBEFZigJOp6IW2iYifbQzntcCshJl0DuG9vkQhH//4x73eacHHLPWSiI+kE4yxfz8X7M0mwA6NBAcLZWtHvBFhxG92NhgLmwyn3cs4Tj4pVn3YCB5dzyk90naG73GAB9jyNPyQY+CXfumXhII/+IM/cH6eMSRCY5f1Wm759ZIOjxhANsQEhQP+xsK8y2fjZTNkCeb8kDlanXkq8Hu/93te6xKRPcRmZ+VRFaZ5Rhg6wFbOlim7TjwSofgAfuTp973vffIE5p1h8ijtSw+UJHKtnVSKUyS1sLX/kFZhJoJ8I+Z6RmXnRzrxSKEZvSpahAlh+u7du/CLmDKWOAWbRYMls3BQCtlJ9HBjygTj8S3NWwJbgsiFgi/lmxfrX6fZjqEwIy44yBK1qYJyiC8GmSAi4zZCghGJlH1066JeBfAELBVaVWijc2kYwLgCI6wrVUdoRu2jsr4dqoBh7hWhDq7NLAElRQwHg7qKW5wo4M1O7FVZTxRkyxFIEEo6yAX0aMFfhbVQgroppvbv+77ve8c73sEYqEi28DKXY8NQwdmQzOwkZgY4hbuFhNU5HoA8Iyd4vbpM0wy5ZgXPsU0VZOTmcmG5nyXYe2lEF9EczZVl8gJ64EQ4cZZjZQASJ+lKJVPJ2OJQFxLVoxh8Dgueqs2jIfRsIrDhucmzzz5LyRagxnrdlCoAxAnlSMYeIgJOM65TidDlmgYu6XCVJbDynFk4tg1icAzdSFbFDcQjPm/px+D4hvbC0Etgc0hwAOR4CB/gP/KB1aKM6HDPAwY7s15mA6kk7dGwmCMRlktzP7LbYnooIuuoiAUowgaMn6PI94BZH8idd+7c4X6ebOGB3ihEXrRH5M+59CqNfxMIctyiApUsKLP6/IpzUdtThASCQq3tr1nAm/NM5PQ2XwZ6wd1/xHd4W6DBOV19k8Luv+mKgFRKOgNtdKwPiGmAxquT4ntHc7pGsSq6alFXoribxomtdI4Tg+R7S/6oOAe2dRBn3VJRM4suDYBnkCcS2Q2egcFfiTRQ7WmyK3VR/jvf+U5nBgrlY8D2yLyzJSszjCWCsRS1m9iK1sZig5hZF5tEDj9IhGAIrcC3CgRyFNOwKx5I7dQhS9OLAQUDjNbppXWbdbPJYnv+B4YnWihnUUNvlDBs61rWg7QcjJwrclyb1B29AkbUQbFbIrP2v/zLvxSjrJmwJDrh2ao9u4Vt5m4qw8ylckmHq2yA6YDj0p6a2JcIx9zPGT1/sGHymsYXv/hFuUfy4J8g+X9O0kDWzGRXUVoBxMp5RW6ZO7mKU1psy+QhzwwsmTHg0NILZh5swMptgPEKYMo+OvBwKpD8VlqFR0Xxyox/VLDGzFfBEA05t0rSqXB7yAVrgcABsuxoBW1FjOcOMC1m95He2Z4adWHm3e9+t3/A6uURVORCO0L/XeCoUNjl81758bkyL6ZjmwjGWgroVTCQaEUBrGqBdl/QbOKADVcEIX7OfskAAEAASURBVBEpDDHvkLvSDJjAXBFVoiUmKmAGw3UqkI8tibPOxCifYk2uWUYFADPDDzCqwC1VKxmkzN3wU3mAkEQJCEPh1eKGMqGqPZwgbQctR5544gmKonmnuDh0mvKBD3zAisSJYumZfujwOh4xs1Y6dLX7txNN2/E8MKeKvA+esNkMG6AKdXRpOKJ6FWOFBQu19773vexQuCCmQwvu4+iojAWm4eBjMufdR1d7XgBMMbMOBn74h3/YEtzZ9de//nXRhl+H6m/+5m+o3U+OeCXVdOCQNjCJYgCJEA8xfIDubeu6pMNVM54liW42QM4oWJiAayR7YvpeMJOBrAFZJ7eUKW1T3LJdUVs0afgqSiuAwoY0E1eKVmU4ecjO1fkMVj1IsGHFBm/BSb4HGAWj9nlCeMA75ySUwFfM5dI9nSqWRZebLfmllm6FYxtEiwO3ArFPZ9nBwIOTJfzKurgPFdEEVpkAFUq2Cv7EJz4hwlqei4OebtqO+EkHYKYGLTPlapliRxvPrkkNAz0QIWF3sjH6ASbNgBFZUFER4ISnGTuQugAr8APGALbt23biP7UxW4pzY+HHgylW/GuNiaAlqzSzxiDtSNzi05svMhAYb75sTNZKBjKYgIlGFdQoAeBEY5kgxdqOeKL2Mz/zM2bZgy5rRDtXmcOW6PHHH7eIgWqUZiy9JdRKTgIzChujc2mAkuGhZ1ZRutVY/rjBZWgmFA+2YlaZ1l7e7LU4luS86kLbGOsAwz/OM1TWYmokwueee85JqeUyJPQGjAtThUoTmlrcppalQoLhztTraARah5/+c4PI3JyGmbejcutUTwRmscs7sCcd4goJ1jIam0rklrQu9Us6PGIDjIkBlYGEXTbN7MQah6XMnZ2pC8RurdMdD3IMK2jRv3+DtXaTD5bpkBXyinGAsc4jfCy6xzO5hGZRJiT8xJrRWaK1uZDkWZ0leYe3yzBkyAGiIYdTlIGHdDQgzQussot6jCAtFI4sRsFZASAhictBSlceN9JGnrkMrwEcvgoEQhtyd+/etddE0VLDewFCzJe//OXSgBQoE9iPihfWzhBiW2T0Vp58aRMZ0VlG4BPMhk422BglExkkujgJgy5F6DFEwNWFPcXSAWYtwNw+9thjn/70pwVEBTDxwRewDD9MfYMZt+CNpXNUxNwYEAcZGP3QDNuzZfTE1KxJhwGL1PKEBOn9jpYOuE18ONWH0DROS5XwkIgeXClcxfzSBiW3vtFOavFaCOYLf/3Xf23Xzk3gx6EsZaficM9WiRl0rgvJqZYQP6M6FRioRQV1vZk6oiVpLem8gde8pp8QImHvKxTAaXEsDtCSB4SM0HG6AwwK12X3Jlf967/+q6sgkCaXbMAZ2hrVyUKZbtkP6RS0SMf4vSBjOdi/MAIDA77XmhwP8H2NsmOHxoxfBYyx+R08UYFZC8ymoJbLdTRwSYejit2VLJINMVM2baFtvyUW+zihzRMHYOW6RAd1B5UiETuzcPPyiIWhVTnP6eRK+5X5fzO57qZ3rBUS5Bi3kturaMQGn7FPtSR0KwdIQh6nHcO32Q95YU7uL/aRlwgldbTwP2PUC+u0VDsAvYKC4IglSPiqyGitkCZn7MqKWE8cmwyObfEhrIsvTz/9tGQPuRNU2w7RVqSQD1AsUkDu3Q1BWSSSjyfKrCQKjB7QJdSINvwTmeZlHe8NokhkhdolquIycozBdLAKrFpJ0B5s0EIYzvWcBImEiuArFUmHsotbGhD1WJ23TE39HCNjD7yoRz8CJVbd/umf/mlP7wzEQ9fQdqtluxAW58AIRQMoulrrENBpgXSoXa9GYOzcmonOrZ+u5LxHRWgWph0h4pMtSYdwFqYpeZvi4ZbsDUwTxCTQRYKG8aA9PlWOinaY0EZvyMtSeGCWJEXXYzzGKRQ8/PDDb3vb29iAgUzOk0LLAuHCbaowKVgNzwZytxtdwIJkYI4ZvJvq+w9oYQB1dElHpUgr7AEDFoWOYQQcayBgEEILCfgWCpFoUrYZuLTQwCUdHjcDBsSqGBMPZFsyDa9m3NaAYpNe0d+ZCYss7ujSYqdizS5iCkNiN/NVEMs3jlPdA5GT6JwK/8eYpbeFufDHE2QgrmhxmvPswbS3uSW/sCXxyO48XGrh4Q3YcKeEGlwFKX5LORzV3gUeuzQ488+N4TNwX0U4gJNEXuSx/xNSPZKBRCyWGyhcaNYo6Jsa4YDC5SE6EYlsjlGHORXtI7GvnWhNlgoMromAJXp2MGu5oNEtPbtihj3A5ngApFwoV+EK/xrNF7BTxR/eDGRXsghCSrfmxdbE/5bRifMJvbICnTuc8KQKG7Zr1goUwj4pig4JssHDxu1QrKI3gKQwleYCErGYgGBIijEV1KVD7QCsfmhGY6qzR8QDDjlCaLPeMNey/or6qNQopsUf5WmqdgvnII+H9ZiPQkaCYhGVY7DhRSHG4JbhmQvLtb/7u7/zZMHpKBOlmaUPqre/3EdonHo0o8UM+j8K1oWuNa4H4azacsQCyFMJqMBYFjTR9A/AcCyFBFfWplhNG0PCbS37mLmF7Zd0eGTS872xIdBsiFd4Z8T+Tyxg4kK/HMnVvWvD8hQBWl2M5i02BzzHAhmSsfJ7pvj8/2wdYeKF3YjGDySo41Di8X/BtqQCpRBsM+fL3Z4fCFg58AsRHLqzNeSxMHMnz+RldAcyJJ2TqBFhsEx40oIfKcHehVu6dXTMgcWIRm2PHST7KoVagcA5m1NH8Z1WCevdOfEXLbJbLPflYjGxQ1oqEpENiaXDMWgfae0xTEAKGeYpn2L9H4tG7KEFALCIA6Zeasez5GEWaDISGoMcVAdIb3QRlhRmh/gZAGwiHU7sSyQbuZmNOUZ2KOo82f4MgHxp0+DNC8Mt0cwO0ktZjnICGLmCqQoN23yoMG/FQWimiA2i6aXzDD7+NQIwQfZSZgpLTFSLyoaA62+xpMS5K97MgmsY4kd9YNZjPgCJeUsNAKYgihyNUE4FbM6IQ//mWhDwwQc27yiVlgDHIT3g54AdjjhxDh5OSY6SfeXDk3g+CLlEa5mLrnbIOyRwKuDRhk2kBTESFqP8oqU5oqbDk87JjhFCZSoHpL5tXZd0eGTGx83A5WBaFLauhe9xA2bKE9SFGxHKA3YRyjM8dcW7LdzDAhkkExwvVTlCe0+3gOKsRqcYxMo5qtDs2zFe6nOLlrcYBGIAge1Bs7t5YjeXUz72sY9xMPLyTwOGZ8ImCInUcy0xlwN7iMJL9ZKXD/eEL2IN2U14T6shVI0BOZVoXJ2wUoKNiHyj13sNliaW5PavXvEXCPCjLniJkmKKsXtwH2pufoModsNT8jPLoaV/dTy41UV28DFMdksBOUlY1EIzzcXSAA6R3+proEWG2IeQ6aABR8To3r17Fzg9+J+TXi/SCAB7UqC6Xjaj4LbbLfR7G9BVdJMCBnKxEJilAesklt9ILQSUKS3FwATvmvIFawDmJb2pNCpNVl9/bTrAkxFXsoLc4KgAe5HDsOlTBnI98n2Q5UK93AH/TSidM0XHJ0S2IPOtCc9HzDhhQQIbbxqR9+FftuM8nRPtkUcesfJDhYU78kEFTjz4zyIxhwvo+s3f/E0vDWAJG+KABRC1ZI3QgpRBjYoHmPVqV1GWdC/1SzpcZQMMqBK0Ok/L7bW4HcMSiJ0x6uIVDvckBss0SdHaje0CC3jgV5FfAEVaw3i7xamHFl7qE488LHRg6D8iBBq+JPadkQyM5TmCCybhrA5VXhr/GkeEqaAFxqGx8IRDewKuaLNiiMhliN5T+YHccK4uBNit2nx0NAphLNmgyDqOy+QJe3GqxrOgbKskhBlLEMycmgZwa2yqqC6gOAOgVcVCwQ7MXBNHDpD5JCfrHjzoJSZyYqXdEtKpETZ1qFzPKPFPFmcPuAoDcurERMgTO8+rqEKXUEhdZkFwlEHZKp3YJRCB3ma+QOJqebuTscR3NZxQio0RMYlMfAiNMtfOD6ASixmAXanlywgrYdvfmLuMKroGnpGuhuGsEQYtVoSYySQSQeNQ3ynUqY3IETklSL3WZA4nCSsXktSH0Cjf1pxmsEHbroagYggtxQzx19gh4CaF2YM3xRbTn//85y0H6bnZh188IbWz8T/8wz+UJp2dOrA19RobzjI9PndQ1NFOIusaxk5Vwn0Pf0mHR6aY2YFg0FnYQGvXkpVnoAxXAeDZiQpH9XKH6Mz4rKPFJm7DTzjqIDmjYjhmDEQdCWvDN7zhDZ7wqXhW4b+OhEXt2wyvpIVbw1v8NoR3TeJPXjBVMJNaaANFxUCLWSeWAjQn9JBDDAUfJICVbCzB0GqgZC+/SnW0ms4hl5PQ8uiUtu1CMCA52ZIKEEUl4iyxra8TLekMISD1+ma63WdBym0PJk23lExA22JBP8bcorskDRU9hHAUuJ4ZkHDadihCHoW4JS/GpENoJRtTTyEgmZ92Sci7phmenYQVg4AuTW4QhWfE3OhymzgYhjOhpEOn1qxCJtDe1Jgj+KkCM9Z/4rLzCUEcgHxsBeMBW//sETnX84zBwJjMAmm4ShY4/J+HfIZvV1DJC8qFHkzQbftCXfKcf3VgnNijMdQV7SlWYzZTfNhGXkuiLefCRLeedvLP+Nmz2XcFD78rhKggLbYY+Au/8AtYAoCcXlz5r1xqH/whdx011nW5poFLOjxiCZldQGwoM2KC2tm9WxVGye0HhrUJDU75RUmvD7gVIOwOWWemHGSmeYT8nu68SwiwEpcOpR+bUUvyJ598kudMAtuIEXuQvaCZUCJmfit6Cq+igMZlZE8JhhUNCRIhYGKfBxjCn7CoUVcrYgxTVPUX0Dt2c6XxexEQLWrHhnBs2aueEghrw+TY0OsG0qQUZRNpqU4bGGjseXpAa2Zf3csRQjyJ6ARpU6mOugV7+O0MSjY0JpBhRu4kHzXSJ1R4TtypHJP++f7ER9GeAHUdRBv9q+CK/nvExdgYht1qSxOZ0lGbKxijIAlvygnVND5P8l4Nq8Ot6SOgyCs0U7i3ORJfOw0Q36m1F8eM8+9xJsWLrKS2UGMPkNjcsFKE0A1nQm1QPHxr4LANkipkCIJPI5zhj9A+uQ5T2e7NdCUbz639sIlzCNNKZKSx5BG7ZYcW8hYNhs8YyPEBL/1oSSVua2laAVOvry3KiNYZ5lRU0QUmvZHddGBMsfLwL7Y+d6CX+IouOCmcJ2YVWalGJULAqlyuaeCSDo9bAqPJQDMj18ya6Y9h5d4ZaFmE8RmlRQxCQz1PYLsGaoGk6HmcgwUEQjCjC6GzMsekXhpUd1Dz1FNPCVXqIqYryAgtRh+vxlJyxbkxcZ6MbgftVIxKdrGA3xpoCGYcKmrJOQmufpyDF0LgZKhMT5xEFHKknRU7uQJgsygR0sBwC2Abw6DaV9kZtogT/PTKhfBHyxUnukQxJ5Md2IKnDdNRMGq4IWM5+xjYaDdECxJSbJlYix2wRgagxV7QqkjE9FTPJ0PtXYTsO3fuSOHSkqdHnZgt6S7rG+TmdqnGbMNVvrdJkmvlBq9pwNwso+5FEgZALZBbuGDAd4JsDSVjnJgXZp8ZjN6G1spKbGfhUCkpp/YYztjWCLhBFCpjyTg4zSk82Lao/dCHPuT8mVBs22u6tmVeEaB/QzqUjuKSE/iHjTGbDaJuE8c1YBXA0JpuLbQNv8RPOqUIgEkAIE2HZbffOPNkHSqNYLr6eK+xflSAOEvqRmkBts3JbW65pMPjs89uiqccgyFyvwwro1yGWl3QCX92Bs6LWLCxbgUCR/wqegf+bFs0EBKrb/+ZK+p52doaUFQScawEkUBUUWH0Z1MxfE0RJoClFh5IcGJaEAiRpHaMCQAzV0Hr9Fx4gAEkIBQR5B7xyAPUzpE8PBs9JL5ogoEJSQdwXqeL7PYKzgPESiIrFvW96ecp43IWmppTaQ0GKlWHnw0QVl0opHZro7e85S0io6gtEdqfiYMMw/s13rzvHO9UosGnvdimRhsRb0jJuB4BSHWUjA2EzIWs7HCCd9gccxaMyZc4sS9kn3GO4UF1Hj+NwgnSSuxB2xR3m5fp1X4SFUiGPUo2nFwwkIVWeRy/pgG58DOf+QxLsymkcK8scYF77FydHsfJqXTBD+nGWj1Yyljq2ZU+9thjzzzzDE0yeLMMgMEjquJ1aw8O8SYUiABmAbx2/JsgbmK+rM/MSz6ifZkao3W5XmnsooXDGmDiA8DQ+QY/z54yKRacUbrlBpbG9hCiM+exYBclHXQ4OOVC4clR1VVy3cG/spKzWXF7hZWX8g3Hg9xGSIIhnEWBM9xyJQ8DlhIQQrHQUPigBCyJwgByckMoasQfDOdVJF26RVdQfuCBB5xWQa7ReZ2QgbpbvQXE8/R8EmOCjl2ph2o4MdA6XRRD3ZLI1OOHfoaNs+dFRkHCFQmKtQmD2VNM9uZ0FGkUxVDUiW85Ihd6k6L3PxntGSuSmTsVyBFlZuzZw1rZVwiWdKVbJ3XEnMnlIx7let3RgsDCyEelnV5kGDhvUk5S7wDP2HQYe0vdBpD9D/MzfE1lHNNwxcKLlzmG8Qsq9t/E9DSuh/T0Kf24SlGskdTRXUNlA2ZpEtktAKrzCQu5TcLz36V222ybv+MKDD7lP/PulT0TIdqwf72eE3vTx7wAs2j2wjlL8CaOjNgUaE9LGN5g45bfXtLhEQMYv2LuAgpr84zKnszDGO8Z8g12WUrIyABYp3vjXyxgrHYG3jqzNcyIEQOfNVc5Qn6r21ht3tpg8f6fjEs4sXEYYlXO1ll5JCDfGvoiNkROsMCYlTJK9ge2Su1W9eZ+Ey6vzwqE1O5qbe7UzlaMt3ul1vkh50dRPjB3Zs019q5P9DAGwajniMAwZvY9XWMMeIiZmffDeHb2QkJe+rS6KqtpYQBO7UinRdDU0kLNlTFIz15yZh7OMOE8TwlGZXIqpIAHLZbvazv2RhYiUsVHPvIRTuHWM10AXvD5/+zdS89tWVXw8fc72LBbp6Mh2tGGImJSnhLiBYEAAmJxVQTUaOIFExvGSKKxoaT0lUspBYVUlZYQRESBpEBFNPFDFIkdP8TbeX/n/KnhOnvvZ1/Ws8+x6py9GuuZa64xx22O25xr7fVYrYrghliX+H5p73oYrsd0XNMMUmMSxZ4eIsen/gGYHp1HHobkzuCzHEq2R+rHf2oOyUkutNJVlCg76Jz5WXJpm33w5Zus/UiK22CEqlM6/PjHP05vUp3Upf6o+tGWpKU6RYnPtqkIVeFGcTqfnvA7K8nbpq4pwJXNcytLs6aI6Ss5+HQgofOSEZf6v6TDpTZ2tIsvTI2hOxicsteyzO8ZmBTnAaDBwzU8srZC8sUsbzGwVOFJ5e5dZ0YMNQBggHmLSzgdO0ge6jLK0hMVC0QvNViECTeemcVPOIsInQ/hu9Z9vkooRBNfHKQE6V9aEjJ443ByXmZgw7c3Obk9h1eeyxOivzNmcEUbAAJLJ9eS89Bg4qPlMLmkdhZoLONENJWQuANBLLmFn1NZYnvMqXQIJ4Qu5VptRJkl/OhiAzmVgZrA80KzIEUV31cnoVjFPAyY18CM389YrCgHfaLTi80qEktSzGBAesAblgRfv8OzXg+DsTjJYHALJrUcUu0d90NF5MEJIaKQ6wSq3xGAxh2Dj7iALXWFQU1DNO4s9zAtS20SeXVZ/oNM9cPgNSicnmk+CivobgwMQ1P53HPPqat8eEjmk/YsEJkWkb2gxPJjAD+qHzNi2eq16t7olqr5hf0Jj9XtmpoXC0dymb4Ncl1ezpd0eMAG8o2iAIe3NaFa5MmsX5Jjtfwnr7Y7YWGkEPM9eyUzvKzTbqGtKiW8y3KhhlE5W+cDHNx521jOwDG82qetJPd6vadWSx82Iq8u/t6J4MxXBVmcYICLyoU8UyL0biff66UPJIuk5D0XSxAiZ5mubEcUCZhNhIis3XxpYGzaLu/eQXxRBq0o4g0tS3Yv1Li15AGf9LCCE6OQ8HtKAdG+qMUKe3MQmUmgC6d8aQVjQdZ3Ynv1Ma4Mn1h/PHWjwmwInbuETZuAYq4XZ5i3x2l25HiHl0hFZ1nQ8tGH4myNyBMNNCo3wcPYzPFsDCRm8OCyc6LBrBSIT2dHVDRm4JGN/Kjhzuot2zCSEEFsCFl7yTqhQprL9/KwqreSV6dRSXokxSXYMDwNurLjzaewodSW2MQWJIwShdxFC7A4YEfalrVULYmqC3HC+6zaZU0AShYqUjrjXxHT8HM541KEl3T7kg4PTx/HY39FMdYvyakKBZqKQXet0uQnHymVKW1rsD+GaJvIutBOqT1DdRnDHePL1vPnw+TvhMAAl1AVioCex9i3sTSsVgUYn8WIO8fdrSu0oiuO2ClVSvNSoUEFIGJOJliKfxZWIKdGSxBbcxRrRlQe1sp8Hv7oFizW6flUJvFD+WQ3MN5wJVVYn2EgLW0YwEkk4ITHQUDxkezqIXko8aUf4ot0Gl5s8ZsQ9mkWwFNC9uY8DJxEGjA8zjA4tOEhEcw6EcUSWsTXaTliowKMkoingNmQPR4Aj22cykycOCcd0naDLcTRgjYmw6m9AvlyiLLDEhwJ82i1TSJ3SQSzF7ikKOs28rqFAdSxtI5oA42FnIqmod8h2z399NM2Pz2QVnaYZerFiYpEblOam31Fki0B2ZFTiD9sT1siZ5MiEmAM+yWGrGm+mEc5m10t5X3A2xddHDCArJOBMkpn3u5gkUyzd6A9tWJtdi/7LXz7G0KS3VQbFz3aYX+5EGJ8pggF4QHau27bAPEI4aGHHuKinFAN7tWGLBt4EQqfyA2tXWjO1odWhFCkE1uXwpxOFYCE3VrtbrgcKhTI81XopJYOxUSNVFE0oWrLqXXhaYWCxCBcYQBXNKCtEmIJTXcIM4MVLIWwgKvMsu4UBx9++GGrQ1vTSiLxWojsLVbL5TbEMraIamPpVLlilaohSedhCJWNOFNs5eTQH7nOLg2hCrWaB11cgBkY5YAtGD3xeTxXBmZyhuDNcAyYeu4Qxe6u0PCSh0FCkzTs0rRqe0sWOUtwnHt7xeF5ucSjKpUpyZv5EfBUBmaIRpzANmxIeB/5yEeQ4+y8DEV7tpTPwMw7SwjSEAeFQ4IfrKb/mzdvwgnexpXEyVkkVABTRi9lf5Dbl3R4YPazMxGNhfEHbsABFOa2qoQAvm2hJjlp8Ek7Y/KTCGhTQmDioryCgUISHsSgiuSpDjOjJGPP8Dkes0ZiEAIYKjWWtxp+N84ilMOaVX1AD9q0RDockl1Dp9QocqWKs/CASpMiNql/TY0XCtS/cpLogIECk1s1zkJ0J5LmkYyVRzSPIgYYDGYMKcAFNhO0E9WezplQUhNKWPSFTCsGenBLJ6IOl5DQSahwpTEc7sG//xYSjlCFLXgBdzkwe+us35ACrlzoMmY0YCgLnpoLw2k4lToruUiqGlB0RoIGwIxtLFnVf8xh4vDJUEmBisU307IDrAb19I7Hteq1ZOT4Lvm7Z3sxYGwWvoJu6sXhaC91OWPDXZnPjPsPmqRDhWJR0Q8eXWdtRzK6lSACEXfw+g+bkdft/Xr1AfOesOj0OOMYnTw4MJd0eGCui2W5h5LK743EfQtB+1T2SawFhTylutRos8L7BfaO7GCIVr0Nb+ABAifeZveCCHPvNQ342f2KsHIi2SvB04wi1IMNeRozfFKSzoe1U2COOu56Jbqjb3BvqqB8idbiQ1vV/I53vEP4sEGNgfJEFAsNR+NeA0gPtsdFT6+WCMoswSskCqMiY+c1eF8YAz8k4bmV9/7f/2sRkID1vwB7P/9NUhNKA2bfdMuIvI/TsbTuppMs81RdhCHrgtw6zB6MLRnObnVlfj0f5XqoyyWSik0ghW/ODj5ySJ/L1KGCEzY+7sibRqi4da4x/Q0xinJELVUL3riJ5C0pwilHilpL+EubBi7p8IAZZGcMq+d/DIuHCLg8hFXZgrdf75Uzhwjo2VVPF5ZIty11effUthAg7HqFxANLRSv34CS8l3+eiuos8EjzNJHC2oiWpCV5SFvAcgsJWrrlrLef4miDOQtdqwE5uMWBsx1sqrBxikRhEaFIx8NZiO5BgpygzAa8SMIqxErvNZipkX3P2GNujd6SzpB6XGovz12aiGPQvtRhiMnjrIEkpKyLwlORyxVKSKtMuhJKjcuMObvf9in4eBy07M0LTYKATNkUz+ykz43L6yj5SFQZAEI52rKBW0c8YJt7kohm/rcixnW0cbfHXtLhAQ1bfPANRikPAVVV+cWV1ya18zp38zqGOLbL5gAUCsdAD1A67ratIQ8qvOAq5fBPKcG4FW5/HLXDUKlFciKmCJLg3M8+kksqijdBBK5x2sN4D0GoTiyP/LgYA96G9xaPmPh7v/d71cJG4wfp+LkHni/EeMDjqZ7fmyJXaLZ5MCaxFCjLWfYc3zY2ZWZXY10a0z4e20sO0oSaVhqmBwf+e5OFnvXXmfK7u05ATp3l8Hd7oYqtz3zmM234qz7ZHttmeNjIvNFK+dNYR3f/qKQL5uBcAwBPFRqUg3ncamN4eE7G/UQfqLuXdHhgulk/k3IwKeYl6ml4VDB2Pw2ImFcWxuw6DmA//TYn9Fa3N+k9QlfreaIAB5ZOx3SeERTCzTgbeTUglRoV7B6x9OZtZNylqPzwLITRomqz4y1K/+DGRlBEbZrBX+TSQHfOZ6F7FZJkFyi3tweuGnJS/5jZ/+Jcn8TwXQKmhzBnUV026UPxOpM+eoYk27bx7oCcjVF+xTHDiwF5kVVHEUydkJxrmpbyQu5IzOFzpK4hUkU94IHH8ABoYM8tnBevNpA8sJeXdHhg6peWzYDGqvSzvAzLOetkXjUGqUvtMcrpX91oh9BGjQMS1rxBcTXmdQNLA5I02cULSFTr8rQX2ASOwUlXlHBGPRRuEPV8zh41zFOyaM80BXYPVCQsFigrmPCAdJ2jhGms0MO2IdXTeTBr1EMzy877pk3Jo4omN5GptMhO8GRfoWSo4ITH2TEYMi2mHmbWnj5VXdM2augWGc6i8+FhA9t2f2rBNsixii6XY5c920iWkA9g+5IOj510FpZXsCHtpVVBMXcHnR6Hy/Pa3LifhQjMPPC8+If/IxtV0HiQC+3fGmU7145laZIGhAl3HWnjSLQHwYhvCuCsTheYqCJyo5NJkBqFqoNoVwOU+5MaEomQyMuCYDXmBo69pUbnjhH2mvhfQsNZ1FKx9EDb9EPhSymyh43OJcBV7UE1YydBGsKWMjZzPc6oc4ntLhnbC3N+K6oMCZ1DWjuenQMeEQJzudEzYy8NGrikwwNmwHqydU4yJqhzCn+uwtSW/hnGW3Z3p38eoHTcbYRKMOOKJaTjRp8fSlBIFd6y+/CHP+xXmHaSbeRiEjEam+ByXtrowgxnIYCq0WoiRCt3b6v/2zulM3Hn5WEntvgxTeedF2iH3IhWz9yahv6UM0Pup8aIaaLpmaSmu4w1t8Y9TxUchtxWo7ZL+AWB+pHrRx0wA8gZx8DG7E+lexU8EnNrY97rj6tpj+D1z91pBJlog/nSSAOXdHjAEthNkYWhZ/T5wOS/6RyD4zlg6med2txpm4x+x4qwNb4Xzp3It8mdpQfDxExAjZinCm+1eKLpkANcjiqIj266ui3uGnl3cj56Q8vUpAQNtLRjwBmrA7kTz9k7R/bzzsug3WZ4bk1jG+bsPaPYaSBh9hnncjo2bPUsbIxiZ0sc2giNBjK5FeQGg8a04WFFmf2YE3vWvyQEIDamEqIK8PAAzjhPZWnJwzFjl/yAx9ItMW7X5dp68JMn1nkMzgcH5pIOD8x1RpMBOd+yrNubYNlWl/oHi3YOU+fSOusxJODGzsCXRCPmJyJQQm1RgHTuFqrqH/93qwAxA68vLJyooAjnxFzaRrTLeIiiZcQydF6f+gXDTGWNFiXNvh6z4EjnJoK6Bv6lq7oNEfIF0rEut5aWr535jWWSetm+e0qgdowtj2iZIEw6XLrrjG1M1r57/Ly0MF/S4YH5mhxWYmNtBrDsDKvBS5PSNmRGBeBB14TjjVsHyL/4bvMrSkgD3MkRjxRCtDnnbIWAslGaWerqOsLBs4wv2MCVMKRTIzbgLw2P8q9D8TJ2qQFmQNVmYTzCFOicFDiF4NJTlhheum0iE5bsDkqYvVOXhHI3RwCT7NO4ByIvnWJJbqZjOG+mhKYl2APevqTDAwbAxNl3RlYmq11JaDDzcr7lGS+8LSIEMzX251aOMc/5QIbEGfwB2i+m2+NFxHfE/EakqwIlPtknAxk44qeZs4g1CpzG6BMbQzFaA3MW0hckNDDhlTGkEPo36aVJhuHQNuM20u8DjWXV2dhtD/i21D5AQRXu+gREv8Two1s/zCB1eXHM8p4pgcc5kDMFqMfA0gVqO2P7nnH1kiB0SYeHp4k9ZV7suyggFy5dYmlq0LVtoiEcGMsolzT0gF/2vCTaRTd6mDyXt2O+7Jh30Y9LwK0Ie/3VEMCCxXwd4/oiz1xQMnIRhRYb5cKUP3NxfYoXDBsayIyLtqZbg7bLDbmJeWkuQAa2geEldDmGxK7G4P3qiYD+49KP3j58yp+Ff+1rX/N/oPy3GVInOD3cS0mRG4oYyE/xjIfbHH37aY4Z4ZiX1eFyai7pcKmNHe0MKGNiQGyrf+fkfznxBGbHnhzyBMiyJs/R8FkWHzN0+B0es+u7LRGAJ8iXVowQ48hIBOfJRi59zVxp7O5DDz3ks6W+aOWTrb5m7mt2VFQFSoFFxh0qXtVVwDWUtjETDlrVvh0Nvh2I9WNA/3mpr2L5vhrE5qmUYqUHU8DCfdXTx5J8y9ctX4fwY1BfqvNde2IHcx/Iz/L5bG7rg22+X9o/RHz44YflQj80Uhb45Dcj9NE4H+3rW6YJTlcz9u6pgvL52ngHipMIY4B3kKJisVt3j5mXHOZLOjwwZSw76wenwYw4/Fvf+lb/XVolyOxUiL7hpL9UAYyRsTm/vfNLed/M7Z/vCMr6HQfovVhv41/USxXOBOd4sqD+7/3e7xUXiO9Lqv7vo09pe8X0c5/7nOjg1xfKgmQypCXjuUT0v9wUGbii7XCaLJfaPrVM1SZl/q/buYhe8KSBAu5oXhn0yCOPvPnNb3Y2BT7h+x//8R+PPfaYdOi/r6gIX+p6y39Zfi7M9pi6/6Dig/4aDsamPmDheriGnxt98YtflA6rz3Kce6CEyj6ZDycOXonhXA+H2DA7pUONM+7W3APR7gGJSzo8oGSWxHqYl6MQIMorCbmHHqbG/gDAos3adLIz/uBDmq973eve9KY3iQuPP/74V77yFTDCB5jgDxB+kd0mFI74dgIqgX/4h3/Yfzz2j2/knjzNjw49MnFpmfhLv/RL/hP3Jz7xiaeeespYnmkB4TiXWEoQ/9FJtvOr/3FvU2BeKP81r3mNf8KFq49//OM+soyBexaPziXgixwPVZtTTNKt/QBfapULf+iHfoiFmw5LJWWiL1y7ax/Fh7D7d7UvcqH2sEeQ7jIqFu7fJL3lLW9hZoQlKZNzl+zUwkd8TNh6US1oiQxep9AxpcMeKme55XEm17BY97+L1SI8Aun+b7AVLUeQpH03yrfmfXHef+Q4C9H7A8klHR6YR+bOgBysHKgGY7IN6P+kWJpo+EfVhYBKMIsh+ZLziNeveMUr7KzaQXJ861vfsncECTBIoA3zAfK7bvO9asByTGfWj67/vsvQ/esW0QdMjIUD0UG2gjSvxjnHNlawsxCUCJ0tDSVImKW6pXTg1c4E93+OsOSuqLHurQqo0hgGrEctRDyt8c1u/3DOcxoRx0HSOOT8L3/5y/2jVGcfTfUfmEWEtuwwuVTCaONgIwbIDrk6AEIrAG1T3NsT0IIhnS8+WxbYIfefT9xiIanaueIJLQ3cHiS6DTDz3i3axpI2hShE/BtOxqaTfZp6BtC/l0KO8nE7AX0b87oeUjjMiOrwVa96FYXjh+yUgAGcyArf//3fL1nSjLmwechWsdEsDP87qWMbAGCK1cjgg3QJA9IzMJU6OxiJ/pzC5RJs4Nc1TL3llFLPf7dg23K/TRH4zbJNICtgzwgYuf/+JiGxT/NiEUl2LO0Xdg8/M3EjSxYVwjq1s4G8A1eYZA/f+Z3fiRMcUr6iBBv/9V//xRh84tWzDBvajj2kH8Bbl3R4eNLZnyO/4paizD/8wz9wdW7/5S9/WXklAA2WcXhG+epXv/o973kPt5GrpBCRYunV6xyVA4zD1+AMrB+VN7zhDdKADI29T37yk6KP8BQM/ofDdQ14hGNhqKT7vve9z38Gx4woADkfQ8vXq6lCBSouOH72Z3/2B37gB1SgXrSjK3FhXVCAuQAqwXByby28//3vlxGpWoQSMf0rCXQL+nKk3Vqh+caNG8oCVQsYHIbheNmXQ4YBnebRqtdhTuVFwRpOAM6ijNrIZsC//uu/Kr3FmjSGsY3wvUR+JEvlQgpEkRXN/ryQJxuxNOszyglMfeB/fVAXNuKtXGhs9nAk0f1gDNhhIWKTwGIIOf9UxCa5UEsVAjR+LFA4gks2wDyoAs6COMb26CHkgDXiuVH1aEs8KLKBkOiRloJEK6mN3S/C8XcRkgsVHG984xs/+MEPSjPmQu2LrrdmSM272Rup5SQbJ87myChcxbPzCvtv4oZP5Mib6ngiEsybkTNLD3Eo3CpQaGKZhtCDsgwDuPX8QuDyL39lRHwKDuFkMIP80rikwwM2IJAxwcJZjseYGBl7YvGWHexVv0ADxpHDQypAWxV5gig0e7Tgu2UKPSnBLfAODZhP9VgDc/UIeY3FrqAHNpKT0MP95AOR8fnnn2f6WE08jNXYebm8tadNUiKQRUKyLsS5JQi5bArJwcKTfUuRWkSwhylG44q7WhxguEAc53tIbN8qgnBaupICiSYVkZH4Eq1IJARLhw2kHBGK5psOl/QjFhQ+tpHv6TGW0pqg0R712iUjPk4EI/1ijaCMSahsjysFLNSoiPL973KM4WR4C49zjT3Ut2813SOIkCfwWabI/fbuBERqd9dBV6L2T//0T+NNZhLEM2BEz5gLcUg0tOwZvv71ryc4G7Av7YGZtQgzYIFVRWo1IRvD4JNixE/J28LqSfMUy+QIglCe4hJyCEV/Kx5FD2NgY6aGtVul2Zi1SQsmDDuRr+jEOUOiVbNP4fRMTLWg/y3zn//5n7ZA6JntSfm2BwiIT/LigYwryM2QUdEEiho0QPN2X1QbSiIsSYHcDUW6wq0ARRu8Q4nGI2KvEg2GwEgxhC4NGrikw8NmwLgd4Ip6TE2Y4wNiEKtq/BRxzJdRijvCpUSlXpM7ZUGWpxM8W4RttZPAg42sWYhRlfMKO5b6LQU4oV0a4Vja8D8luEScx2RElz2HhX8BAmYykt3+D7f/+te/bu/XKoTzy0nKggDwJgQbpDLVKVrZLkMxou6e6oHxbCC6Np/9r3mJX7Dj3t5U8ppSKYdCmgJEq9MFL/UKYGqRs1+Q49i/BZ2oO3dI+da7dkpNruAr/VMFichOQOHPdAtPshHqOLFzID5GEgZM3tbEHaXJkQwhwX4KZBKhzQBllnlHqCmwAkst8qLVSYu25557TsmCSXSzvSPJHQSDjTGIvOoeNYfK79lnn/WA3CpkCOFWwrAcsX9IfJOYkWBGG4k9xgAmAANjpmpM8uN3ihLblayLtlujgzfXmJEaKYph8LuDUhwPwNc8ifACnbP0bKAIIO9+4Qtf8N4QI5SPGQABNfJxbBAwQUihsUfeqzgx0JE1pjRtPWovTyswwwAEHD08jkGqRFUhahGHRGg6pECGAT/2NIBFiz5xaAavIv0A9l/S4YFJZ0MZNzgNRwMYNzubSyGARTozMmWjQMxR5SQuqkdmEqMlxbDBkJMcoH3FbWPh5HXSrcLcRhlObNcoz1XiTFw6dOub3/ymTCBtXIHmtG5oC3OoCPRCklxo7ZtTqVVzOZ7J30RknPBeoUrMyo0nSp5EGF2SOiyD7FO97GUvixMqlQ4xEDad1CJAgxSCZQiMoW5L08LF1JxEdAMYZhgcRIDfJCKkIBBzNSQbneIgJahFrB6kIo1HH33ULRmxKYCkAx6NDRIHL4kTDHHMuPdWZGX/69HGrKBsSWQrDCG8SRUWrDdv3iQ7TthGiSH+x2IPUtwPAI9ZtkSjZCQsjzzHtU+gYiBdCYMl0IB9cqxiRuB2awTZzwlIsphWKYT9GCVww8OnJHubExEyI6xRyudxbAMAC4RZp8QApv2Y/bIcc9fyS0FGZFKYdIWmUuPJJ58kIMvHhk5FUvkYD+aCCA7Ir6N5shgekvSgzd7ih060adsUU4LkZxY0WhGmYcNpkgIxo4FVGGJM43IsNXBJh0tt7GhPQceqHEGwKv1ZGxvVz2/dCpiplQ9ELsaqeLR1Zs2kfzCAZJdh2EF1b5eB9kVFhPe+9732SeyHcEu1KmdA96tf/aotnXe+850chk9iD5Wx/hiYy7107rhpSKiI47Ug0ccOzPDvbhoQAZGwIvRKrYKAmIIFdel0BHMH3iMu+LzNSYW52kKgQVea93xUCFaVQxBaOUBDVEJUg/87cv4jiGyC4HapJZI6IBd69EMrF/q1NWZ0FpIEX1mQZqwgTZCFu4hpwwo/Rhk+NFZMfcxYEvmRjzeWrYFkfU+ITbd1qrvivn1ar7RIllIyNkyTJUIqivRSomFmdQMzv/zLv9zjYaoQgk00NtgJnKUibTr5sR/7MbnEQkqZMgaZGpdq2eCEVqlOpyEaCDFs9Z+FESo2ZhVkqkx1gIzLMF75yld6n1lCUprQvP5KtA206y6RsDkPJ65YlyrHQ2JJNw6ZKEFs3np4b/1KdrMDbBQ+jRXUM0UY2LOzS8WoHQJ6kIAphyPQv0RoXQi/OoB+2BiuGmUg79gwQpAAVvBzHw+5pMMDk5v9AdJYgubGHE8/u3eLmTJNFiZFcV3RUHKyV2YRo3hXQYNZujf4JcIj20YhpyrneK0PnnnmGf6gJCwTcAN7JpzWsswDdmUs+OiOLEfSWoJFVw9sDnFNm7wioKBjQayhcBb1cCUc280rbGEAS4aAh2RDjUsSO9tIiHTk9b5GY2mYSq19EXUXct5uLG/XNiPSMNL8P/iYjIGdJHZ27uQTIQnGVIq5Dz30kJxkkToBXSmAVSFSQycG5CTU1030BlcUK8VaGHleaM2HrnWheVcDIeEw1/LBa1/7Wpo3EQxAqsYqtUBVEN8p1AahIy/NQkEZfmlJ1rdbm6SMTUNEhsoeI18wI3Sih4nGQ5D7NQM+YGqnSTnVoTBC6Bvf+Abx2w+0QwBMBmKT1sSUoxDEGyWYBau3IyXaD4YZ5DwpVIrBz6PVhcM/BuwN2LvGgAZhTRBLoBy3HBmJacoT99Na3kWCVTccHm22Z++HGViX06dyhztI/1jig2PnhiyrgcwAZhgc7joolr8syT3g7Us6PGwAWQ9bHFNmRsxUj0bjGaIel/xfDesHSaISU7OH+cQTT0gJWV7wK7xiuDSWHasNrQOEAEtDSwR3w8zoBQ5+okdEED058DjtIFnRIOxSZKJRC9+jExSxBKdIpAiwZhWadUpXHNWaQFyIIiQrSHs0JR3aAeb8im7RsLc2UEcXA6NPHIq8ynPrJFEpWgAoZ50SYpikUCVjD2akW8kJVwSE2eyDVKoL1qoQ+keR+GKoGWlqBglIxwo9vOtd72rx4ant5z//eXbVu5rISb0/93M/94EPfIBJmBoH/DIobWDAJkG6wuo60tvcEgotSrY8tS7xgLBYHAnJEl3FgdLQHiZ+LONMHB2mxqas9jZyPTAUzelNGx4OZfll6SP/IfqpT33KcJjzSqq2GpaHCKuT4A45aSfyFZ3kIpH3dY0lF92qNmg1QVD0wNiGjYOVmhfy8npDABiyWu23bOUFa1HTCC+eGXuCLu+aAp5lwcoeKk/TJ1YTcAbSYbdqaFMpPutcoY37dcglHR6e2fxtA24ZWbQZPe9VNvJYb3yIyDzhox/9qHQlUlSiFhbhYYjOO9FuUNl5ycqFP8Wv3RuJAaFCf/FIvOAkYjHnESM4KgB4YpgDjLfsRH5VJwFznvAki6CANBcVCyyVesVDGEIaD96tsKFkFxcDQidmxi2vorLdHzn9KEJLaY52ouIHY+P2gCnBohwhwJQAuLvhGWzbhHb2RKIzAPOoBseJQ0KyJEJIP1qIWplZwtrRpRCBUpLwLE0OiIFBspPQdI6KKFZk14/nFgSKG6/qWPBRrM0xWmVI5BWdbVp64dZ0eIYEnrYNRBdXtg3YAIbrGUKnNkZ1FA6hJ5SWfQgxQqvD9BCJTA5X9M8a8fBXf/VXFlX6Z8pygZm4bWZ4E0KmzxALcW6lxGH2LvWTWgP1bDvLF+K90cPwWCBFKQddmoht5Ct6iI+iM+p4w782qROBATB+z0dhBqA/1+sSjLE6ibOCtFEJSBYl18///M9bhppQJCwKVUWmmKrjEP5traIe3WlUaqxg5v4eckmHK+eXYbFRB4dkf5mXxyR+bKeMlas82/jSl74kYp5ruyZGOR5yHJ5viIlqQ+5X3NQj4jhcchXwpckG8pZtPzleeD6JdFFPgPPcyKWQpy3uCFjqd4tCSTEYtCQtT0zt5qFS+Due3ECiq+K2ARvzbT5TKTHdIrJzwOiSUcR0meBuuTRHsTQ4r9MQ+oUeKwNP7BQ6SEBupWhLwEM7ScJazTaaOGUFA/hUnYOHkHWhgk/Mk5GGPTPzVj26nkN7d8ZLNO4W9+2b2Z1WkVgeWa3C4J1D8+LRtbNlekuH60i9PVboh9n+ofm1DAKQpOal1It/XsAqGAkR6GT7Vc/9Nkm3YSOmXQeoaEYn5bB5FHO91AUVKszMXRTVozhUim1zvq4H2mQkZpK6rNOMI2cWZCl+5+D19nI9swgm+Bm1ggHSsQTvkdp2EmRYtWeZfvT8N3/zN+qz3H8F2suQDQ1c0uGGQk645H5MnDc6O9TmXiF5+9vfzlfFQQ+3BAuBW9rgNoBPQH01qBJYjBP7eP4///M/C0bwQ45o0cEK1e4iF3XL3hHGNpBt92wA7LzkkMKcsfYJLX9t3AkBlkFCj1uoKwh4rDbGsOQAIE2KgxgTI6AtfOzEf1WngGgH0vIIAOqo0K2qX6MYAadDu4ZIYUYCxskE0DqvonJ8PzzSvFTXEHmXqmVB25j97MFualt5KhV56FRtg0eCODWIb3JtwCo1Wor5MUNvTLAE6vU4zXLBpNOzp8jWYdp2rT1eMh00YGpoBrfhPF7SqyAx5qB/URgnaoLl2zqMBP9mHwz2LJepyOzLhXpiI36067mKUP3YpmGrQ4lQD3uDDRUk9DjCRkyKwpI0TGlIS4fnmnR0seGMYRRjbM4cX3Vo9rkDMBUbE+Wbahcw2BvBj5F30NYgF4rQQq7oEV40uLZ66+mnn7b7MvxQSB66geFyebwGLunweF3dAZnxZYviFJ/3+z+BieF6WvZP//RPfuvW6zP84Vy5EAdoSTMcntsLSeKd6OBSynFXBLRS6XVzjgR4Qs8d3J9+kSfbq+GQVsCe5KFFLqSREHdEZxTjwRlpTzeFJ4x58aHhDTmJOA1b9KiFLQ54O9mhtfIuBC9jDeQYsKUmfeIKFcsX5Qje9K+IRDv5RJHOBWX6V5d4e8WP6qwMLIsthuQ/vHnt0N4gsKuUz0j28ONupHFOfPUH5NIh8aH1MJg4Vsz08Mgjj+h3aZVg1cjkbBUyAKFZjFaNeYI1SwcUQU703CndMZ3wkIuY5gItYhpFdrUXG2AS1OJ5oXWMnEQWiUF6IIsDJICRfY8e+BfMRDbEO6t+3a++NLMWytSr7W4InckVpK1pTocETmBgEsdIdAxMtNJeE5QUyi/Find9lSDhsWjz2jP7xNXS4NfpH11EWbJNeAdLsBb3Ix8PYqiFpBjI94GNYo+R6AKzrYGzmcs26vu7pyiQubNIpahvpNnVYaMWB2ITzwyGHva4/alaYvTKf6OYfrthwpBYkOdzDJ3SoUtxQUDMgZdU1jGT16nxLUzLN8VWKdmBCtLkBYCucKmit7djcYA9QXw8dsnJMW3hgEplRE/O4Me8HyCKtuK+zai2AZsFROlBvLBWEwoLDbY0sYRVgeMYcsfAYCOtSlRWyVYGnlShjoR+HFo+qlHwsK38g/gNB4P58gHxbY7BT0YRVprv0kJBLrQNANKeuRDsUWXLEQnDBOHH0k3onHR4kPR+gPQZjDblO5QF9SsONOjf2sVKTpokCM2rY7iDWaAKAB0hIZrLq4hSMgBSmDsWRToJniUod5iTFRhVAGB7YBzhIbLDRLQeTZlXkTipH//INcRcNLNqPvsWspR9bAmYkbN2u5fkBRn/Q2WPsAOz3SAaDci4dstZPrV4IG1p2G65WzTQqBXGtk3uAe+5pMP1BsAhHVxO3PfoSOj3ipfi3WKITw5ezskx1jnDIFk2eGPPxgSFYj1PQIVvqCLlA8EIvNfq7KXgEOllXNBewQxXNxAJgU+mL9zLRhqiv+Rk08yZ4O1kes9CRhTHrZ8sHUTq6velIMe0qQ5aG49tO5NUaPiVX/kVSxOvKfkgDh4oJKEAkx1Rl8h5j8PySDAtth5D7iAMfcKMDaqQDj0nNhdSDtLGiomSAQZaPl6FbY/+cW4qDYTfGR5BULElztKDJbK3ZuzJ61EVqRK8uvwXf/EXdkqNUrKIztZk5DUpciGu0gwVAbhmxAwV5sVo884kyKs8Ymk0gDcrJD0mSOb2YU+6wj9rSQ+TtOBxzOVVWpJsoMUzwR3AendpQ4pmJAEZidkhNXUxuQ3Iqwid2o9zB0n5ml86etWzpaFXXm1lY9J0EBB1YJDjp8YKfuDhR2zArjgqjFku7JkoBsqFKif4zW+qOFWcC/xo4JIORxWnNZg4x2OFwoGY6OUF0cFbdv6FQp9KEc7A8FKBY0/4O43q7ZcYBTuHgUjwB1QcmOGcdqhkZeTEoLZrNjzwVhy6vf44lS54A1H527/9W5WpXVNt+UbYRcIhEnFISpCku5SxVAlc1CVgGMaBj6dOQDidYZDsDRQiLRFsTdulJKMes4CofExqc2F9jNXSyTSOp7gfEhuiT6HHa71/+Id/iLrtQc/qhC23zALGrMvb0EvbxxvAQCJhLIQmlw4lP2cJWJ1h/afssCI0FwqFgq/kwdLcsn3K6hQlstRt07i120Yo2Ab5fhmXd3eOggd7JkI15jMxKNK8/XOax4NJQZE2mES5EOe05IAZS861l4S22yY97WVCVAE5zDBoj1HhxFgskdralG24rFBL8G3M63piprEs2RaFSbc0VJqwCkyadEthi+asfbR9jLB7WKJJiTDronB2ZfXJvAsCy4HRXfZc2idp4JIOT1LX/wBzSFbuIY1tK09xOKFHGpzBu5Tchm8wzVzXmHGM/xm/tiUG2YBS+NsQ8yqjtCQNoCgiWxa85z3vEZuUxrKUXLUzHCy9+iQueCBB5EJZH7kWfEjoh7OolOB6xGh+a9vWosGenhApujlOoggYZsLaIPKTLwHXIerJCm4pRJIFD5gRAa1ci4amBg+YER9bSZ9Kdw88cvBL0kh4e0XDctAOHt5kI3vmtk+lK/PiTOSTZp9E4FlXwQ5yiY0GLI9kRHQlfktej+48KfSMitQNcQvPDBIbgRloeNTxrBHkHtGOv0W3Zhxj3quyPNJgfjJB6ZmFyIVsFRjSfGFIuxwqEltsT880MqQADMd8xmP2YSDXQLplInQqCJiEcgRj1OJ8FfIZe1IjTRoiF7J/e0I+lee3pxblPM66zWaMpSFTxGTuMFIbNZ0nESUXY6AN+rRdTCFUDUNqTD9THNd5Ev4L8FIDl3S41MbudjbHsjUyOAbK3O3L2bZ6xzveYXdIJS4yemsACrabgQ78Eq9OfpVr5cbLuwfbQox3uP3IzK9PheUSAABAAElEQVTc7EeJifYMYbNLJvcI/Sp0WcHCRWyaqHqb4K3nNBo56kFCGwDYFpUwXEOdTkbhhlpAphYOD4bTig4IScm4lbClQytm8WIZHTbwX3UJD2xE9jadPVJhiLZTL2xkEZtQVzuLhpBgL5bcwok5kiBX6PkqfvQnLEKCshiNnPzksIuFN8/2/ATt5s2bMhaR6QS3zgQx0CgNx1X4qXTsR8NUehYl/UMr11p//P3f/72wK9VZeAGGsHPISUoP8N/eX7y1wdgc6VmhfMOH1eXwJpoN4NCSXcWDPV+t82zPe2S9RDazgMMlnqXgeFteLtsMyWUA8VB7p/U2I4aoGNgD9qyiWjQvcR7Tjm0UYzvtGahHm7CkRsKDW6/PkBqY+TWKxznYQxzmekaN3urPFOGR3txq+hJwJ3tuKYDyHQygJfuOEvS41Knh2MaADRhS5vbdS8+GBi7pcEMhOy6zs9xjbnseZpvo3e9+twjox17WLh4bcEIA7I/DGMVLM9yiof5bNnvbavOE3HhwHtNg+sBsyUo2HuCLR2KlQpUfyn9CpHJVpzWccBDwMWgPwsiyFj2QezIq3pEUFbIQimaIQ9LEEcFhk5/sWyrV3eL8RY0Vaclw2nO20nrqqackRVuR8iu1WwqIBW6l0tIhNlzijULwZpqUCCv0fFAhkIPpjAcktEUu1Yk3qjxBtJ0LwIaBWQgs+GkfJBEAnNbZ1ppUauWh8LL0UWegSDMZGJELrDJ05EC2jx1RHJ5Kd9jL8lOyTpdUahtcyjcXkjQ2TDqbtFrFbTvYwNLJarrDwJENBmBxrADCKnldxvmRwwPDLeORP5JXW09KhrMcptyxB2BZ7G5pj+vZFlISUTs86OZ6MQADy3eGM1OEB1hmozFlazwsz0jIhYxKte2zvfZ+fv3Xf92bug7kbBTNQhk2VJwjZFLwMGzM9C2RX9obGrikww2FbF4yaBbJqrLjak+m5g0Re6QecUs83gL35pvNycxxuxbLnaDOWDWg1d4kdsQ1W5cDrDw8P7BWsPTxtoJDPBKG+JU9KwBcRcTUGG85Avc+EEnXLpxnkzhXIPNGQiXOyOuWwyU2HOKFrGCZIlgYAru7qXEfpTvvkRdCCtftzRFJTiiRDiVa4Zj4oh7ZdYLh83IGwU2ZmbKsNEQ0uRPlea4w5ljiIh32vE5l81Dd4FVYW2dWqEoHjDXdDQG5JzwtrYVQLk20Y4oJw8OjZ4CJjBklgudMlK8wEiuHSpof81uyfbCNSlMcM+Dp3CPq9G9yIUdRwrZHOvwMaayuo3uQsQ0AFNkDA2DzWMLtWOYG5P5LIsQwJGFwSb3tVVoR+o2p54VEphm0Mml33VIoiBUMz6EfKtaIMW0AbJLq4GyI0gEnhZerWIoBri3IEI1p2RmyD6RCsiFkilOv6Vb9aKPFCKcSghyGmYurqFz608AlHR6whMxrgNi6cthLIspD1m8F5pOh3u5jryyP23ASMNoFET7JqbiEfmYKwCVncGgP2pMafMzhEQIPKSxiUo9Q6KuVCHE5PCB6rlyIPa8MWIMqAniaCCjH5PD5P5cjDmZIB1i68paBWgEnfF4op5+JqicJW0YnHfzSntWhDEd7kEu0CJkOCJHGiaNnOeBpHoc+4GIH7ySKq4HNL/FtCxMWEowpU0RAnJugNBPy/VNPlgBoldIc5HVurmHQJqAGis6FPEPsJNswUA8pPkyTRWTmF3VIon7SGdqhCxu6DEzWQUjeJWk/KvUk24wwD9vagSGKEKlrnER0BXCE8kFMSg+sdAUeQ0bepgz/0EpFLT0VwdKhDY+ZJg126Dui+nmcmOAB6r//+7+rj1mgs6SFJfNibcckwPMIux1cg3pZeDlvJ7dNLlMHz6kJZUcKHrWmUXoMZw+sTgWMHJMAow5TpvOU7CQkO/FfOpcauKTDpTaubDM4ZifBgOD2nhd6wc+lpzh+YmiHxOMT6yfOoAYUJtgrhymcCUOc08sg7LW4FpncyflKqlfcyMQFnaJhS0OX4pSC0SBBCictyK7AcXK30GDjUZC1MCW1zIQiZ04n0BWjscT3iO/RJsZAeqImd8bzyVRvf4jLKISg5fxkxAk12pvS0FnNji79q5r1SNu0CtiyzCoWzyvonjqE+Liidk/1xCapyPZyKpopntQyPdtUwgPSASG9TVsjeHY1bT215V1v9MjBkGOgXbvGgtlDcZuHZQ8M2IhKiQ0Vr49BKPNZo3iDDJO2AWREEV+nW0EumVzivBttFGk7hcOvBnKwllMNL55TF8HLVabSk0LLMrLLi1SdToBpGIKuZ+RMkQEw+8oF5scIuSH3Z5+KFbOjQNTzsY99zC4CA4ZBDmO0V2VEANk5Qr5N6nmBMwE9JeVlbJ7gtka4IVbhF3zMhecUbID3+R6IxlXI78ZEvKRxXtLhgelj6wV6BseULZLs4PsKCQ+RGFi8ZxUWixxAD0gLFJfSUpeM2K6mRMgl2CUkRYqiXl53gIM7b3M8Y3nXmLjMxGG4PVqyMq9QS/Ix/VG5E8HKK2gd3JvrkpHjifjYoBMYMeCMB7HAkxV7qkpp0YHP+zGAKhgAwfe4/R62iGws0mA6B6z4bY+0qOQlUuG4gAieqpUFdN4mNuXsIbHu1jLcFxPhkX3pH3UGgFt0l7MMzGU2sJNoUwYglYKZ4WOKDSRpghfxTX3vUpE3qQNreBQH1U7SOzuHVYw1+9aFfhJu6j0stz/hWSYYZYdnpWAgyTLjv+FJvRP/eTupncJlQcZpIljsqfiJQJ+pGvNSl/znQal/IMr3YSZ4ONkeKkho6HRLBcz+pT3RAMxS82kGe9aF8pmlnjYVNXd7mAwAG4D5kYPCXUrJXEx6hoe9MYbCjnDk0bU9VXv1dkrUBFarxxDaw8ODc+uSDg/MNZvOrMHxB98ne/jhh+UADqA083NDn8uSHtSGDp7AN9zlk2IHp1Kks11nVpsjFRqcDxC+4jb3yEPcl10Q4rTMXfC1WYolmCVgPqAfJ6hfgem0bmiFOU/s/PtZTuh3BfYhebW8Sz+JpmL1Y6y3ve1tloaSE6ltE3F+AMXuSeEn0UY6GZ3hIb4eOKk6PPSgIQZ53w+HdXqUZcuolEk/J1E8EjjDwAz4mZQCX51CJBjUmybt+vfgHzwJa4hGSBqV7HoGUtstUVuionZqEQRbE0fXXUgauIf0nlsJFRWlng0Si34vUgrNSOhH1O49e/MEwfpYKYYcontwnvcWJctMVJHILI1VZJYnEUqZ5JVvDCcCk7YbRORykn4ICWg3shVeb/naL5WBgAWQwlOCs8lSEIP89Kc/bT+Jcc707a8RcUIiZh8qYiKNAfg9qVWFYJiwwLRBSsl2R1Sizjdu3LCj49Y6vztJb/cH8CUdHp7HjMyix394920IWYd1skuhRxKyIlESSn4ea/EQEUHmEyZYPHeSEtSDdvPFCw5gIOvUDyfC+z1hJ2ciLFqou8vKNXI2l/KxxMwTYs+tc+VCyC1wCcjh5UKLP78wsXfEJ8mCHJ/ElYZYYBuZZijB27Z+eQLgmg5ZhCImNtIbQhpFHFp1uIWKkEQ/bmkL1qhbKrkF0vkuHRNA4Ue6qbFpZsZrO+sP7CAPWCWdsyO5GmK4QyfRahcf3dWjX0NBwAaEXbKjrqd+kIZ0DtvxZ8xDklzaDN6h4e1iFm7SxV95yIzzAtStS8z4pEmEwnA8xXWQqNuxsERmACZdlaaxIg2QNAYYMI3ZeJRX5Hh5JQv3XJBdOTv4NcXqN90s376xakx8sGMpiZo+GCC0iFQr9Nib0gQEU4xKM7KfyfgxBQ5DhBcUNRB1aECVnaRnrDqwrd9EyJ2CjLEjlyGX4yoNXNLhVZr5dj8jY39WXRaF9kx4BbsXgNgir7P44IdsjrV5OigKsFeXXEX+4CQSkvPQYLjZbja93xNm1LKRD+Qb+qfB+tk9ZjCMhEu3sMollsNXt/mwKGBDxiMiAdF3QTk8/DGA6DBT266OlyzoR7gsNAOgtxVumcYaOHoTSkKIgXggsgiInEuEPLAUgOiE/hu1QvaoLIcj4TCtbmFsJIo95CrJNXDSO6XgZ6KhKgjuYSZ5NwCGh1Hg9DTjDK/fGNCDUJ7VacMWpDPIdLWBfM8l+JBomG5oBXciyHyCfr/z4x2eY7E9l6h4oBhCYPSzU5w9FNfdom3GiTpVszf6p4SZnVNxxrOJU1+qd2Hj1AT0dTSPSPz0k7Pbitc/M4uEjRA9srK6RPWcqgHYzKw+hiROwt+87Octa6F5Jge5S+/02vuBEH5HqNzCKiUoRwpT2PN8XYxiMGMz+2ld7l7S4QEbYHBFfwnAwgg0+2aaCkOPtf2PFT1Sjiwl/nIYDcUyV2SC3KncsCIMHWBr6zbPj0rRH9tAyp1bsGs67Iv6h7digfWxp6eWgOKvGtxilKQIOeOByLBzwnaQ+GQhoMR8RrcslIgFAsQoWSdOhO+iA35Q1xn1FWIvAxZCDnuSMgEbkBWIrxFyZ22JweuFttCtU9kAbZgO/StIHz+EyOZCGpaTsME4FW0yEwzDf7Yxl8cjp8MCLtnZucWNWCzN+3Y5MyAa0oiKxZC75X98gjSKBerRRst03G0lsENSYwZR62Pzzgywsc7ksE1j5LXbT2TObr1lC/ojH/mIz+QqDdMJoUAmpgaR9RjCMOyjRhobAFZ7orGkMKdEc7b6/M3f/E0/Q3z88cfJCK1DwxT4DSjDc6iK+J3d7CeffNI3HAw/frofcMhLOjxsANKbRMis+QNbt+bgHpKfUlFbCszuOQP/4Q8MNKQFa+2S02FK14BAGjk+jLpA7CwWDyfXQPztoUSA0GrPq4OeTSqZOZ56nONFV/ylJYGPTrzS5t8s2LKbIJgGVoenbf4LQ/rFi85IaGPS2R615amo1NRcXw/FR+r1zl5fDRX6bRgKN1YDKLq0bu7psoaCQPVgndRcbPN/rh4s0TmKDrJDa9EmVylHsIftCNH8OiMcDOzfbELC8s2smOu3dzoBeE7JJDT0e4IlWY506b85ms670TDFVOFA0dTjAVFOsY5WDCeavMKQ5Bvnr3/96ww+WlRK/EiAjzr9OFAfUx/ZYUuHJ7GURIa03JcR/YRD6fPhD39Yw5eA4HTJEyXs6iHie06BVQ8XK48gwdVJdB9M4Es6PDzvLIkD2AmxQcHbZUHloRDAvtk6A5UPhCSIxAvWqQ2eW44J3gNzbNWCtLjMSXCFh8OynQJBWJLaBO5HDhrebbMxpV/F4LENt7RdIyZKmd4XACxAF4jLi+Pbp5C9EpaqKdkspGdnZXJrMmtZM0UVKALAyZVYjr4Bj8PSkJheWtHwqNiL7Mp2bMh/Xux02EVAzqtGvtJiJVEUQ2RsYOLj0ZT3AZIaddWJUIg9oOoSj68slWi+Hp2pyOWp1MnSKFQoXNtjMNHWJoGao05lIlUgR+1eXxKO9ZMXrcidRf/7tHC7KhL6kSNpwu6H33O3TJYIznZEHDAzcrNJrtGnu4MHAOU49Mxc15MSnKc6nFFHNmieDlkaC3/sscf8VxPv9Jlidkhqs+9tUsgZoZeK1axPPPGE5zVje0dSuYBd0uEBG2Bt1oIqfT8YAMrTpBmW16Jwys8cXjZya5x/IsI1/fMAi7dv29G1OOPJApOVWZ4wbnkMhv0wMCcsWejEpYiP1hBCi/hCBg2oFShBLCgla2xoZj+tY+4KLh3oYgZXIoWwZdVialDXMxNxDMINmOKaTlQ6N4n+3bxVl9eJhSSfGvBKkQoAQMohpkOeoBnxSCM8zjU2qFz/Elo6lwvFxKKzRQzqpsDlEI15l4lzEl0ahioMhisKrZB4RNbVbrmqSMimhLwDfpPijOIyZ5xE9yRgakeR7WEAk5VKqrEs8CRUeHZAQuQahod/8MBfGwzIgOduPS41Eh8e7DkmYgzw/gb8RqV8NS482h5eWvl5r40Fek5pMcoZFSJMzqEo0SN3YrLh1LKfyuVuGrikwwOWkPl6GpFhMUcH8xoLK+aye4jqBCBSuDSk/gM0znHbbzx8RE1otjyyeOW9sI4jXZ9CXs3H5L90Iv1Ewi0ij6Ta6BYvUo6eGBiY6/ODRCw5ayNhv+7ZZ5/1vo8A7cGe6IBcE4GZma8jSQ/P4GPbbGqjZd1pK1LbK3y9UWK/VE6ifIRsn1otecmw/QNgjiU27TPqASqpyIrNUpjgHl7KhZ/5zGfwgG4qKm+5TIRbDB194LZRaRI5s+/dEHpocq0FIZvpCEyPUQ7Dh/rRNNcAklTmk/9MAcay/1MnPcJjtCNj60LYRhvBBJ+MNOOoh+DGOk+PhiHLUScJaXga1sAGGxORWoubdAZg9q0LdXL/gHUOeyfRepCBL+nwwOzzLuZeWGGRXG4cvoJRdGB/wPSzVGfAwYfaKI1lzwGSq27zPTHRQzubuigWCDqvwrc5iBKIyZ+d557sqGJ16a5IhAdt56XbY8YQnIRhxl6/MQjpVlt6tklbe7RtXlounEqOFHA6lgMTEHKT7nPVyNmj9mNnKzOE+pmHnWqZ2NnSkOBhWCpNz/JyiX9FWzS0HLRglZLtleHHexa2cK0PYItQCclEjFpOIpQUS1Ww/HJPNk8twVDCTH206j+J3GpgCYBBGi5PNH0rUC21RLowKAHtiOTpevSzdiY9Mm6oyKVbNAbbGMA65SNnYOTwABtm6NkLPg6PD/XgJ+TIgW+HRiOXNCNLofRfjis1YOYe8IPnOPwzVXvx1JRJaWTHGlWaGswxALcGTA9zHOBxIQA6HdMD8u4dmLQ68RqFrcKoFBrORXEpb6ItMS/vJu/0TOPseki3QgNOeL7LJUv1d15N+tb83YkWCdiQ0yCaw7M671XarNYQibS7W/DaHm7Iks/rt2/zeOvfhiCNBxkxnJmr9qhoHa2lCGR3wKOzhnPy1rk8NzCwdaSPH+WVyx/90R+1VeiVzkcffdTT0yXbx+MByZWGZ0gcczmSAtM5/YMf8LTP0mjuZiqjiworQn2DnMsx+GF1v73B7EmwACihTiJwOe0HqnFZHR422lljKQ9BV4h1bjDrGSyMj1G6OwDsae7evQYmHbNDglDrtnNRHHEgXLbDv+xJG9MzjaWWzsJVjhpai5UNnC1TOq8mvXPuYItcolkFIm1tugRmA7MqFaGwUeSK5w1Wr3kJJ+QYSEwpGUKxkrmyRkwWtbdVdCTdpVyjyQRBV4Ph1Qhy4EfkIwldB8w+ofrvQx/6kGTmDSavv1I7xlbgXI4aiRJwqcNRxZLEyL7svE47Ay7yhCe641ZL5KjrX06Ku+DrWUJe2js1cEmHO9WyvnN86V7GgvXsXkaeSQMikRlv0pcol/FRpBZPhSeV+zKwLuFPbZdroy7tQR7mgqa74uPQKjueSmIP/FK6wJCo0zke9gw/4y1+5xc+9g+J3FPt2TNcR2WmcsQZPLfn+VYd0K3pfzE0XoQsvRjUciQPl3R4pKKOBasSB33bWW6tC3Oeux0axns3GF26xxJm2b8x5HK5TgNUSsMz74Nk+luosYQzKt/qoSQ3BqYxaW+KM8xMohrGrtNg59tSEH/nqiW1XIfcwbHZtsSfyC575/ngwKsAmjV3w7wUVnt5eRWGS/9LTgOXdHjmKctPlt5yy3Xu/n7pMSSCyb3PLPYF3W0NTE5KH9VGOtu4kypaFy6z1PU1V2YqC9owtGFe3tWvIQs6UDT7G+xdh/ROVDuNkL05dt66DgMbY5N0CGFPOw1sQB5/OTyHyuX0HI/kHkNidSfFFz/nO9m+x52XdHhmhWd2jFIMgtplgePFY44vHk7OrPr/VXRmPMUKwY4mXaNnP7JRu5olqp25ZB370CIdQsgdevzqRo89Qy+d1gl5Bnku0oXdpS3V0zlZ3J1jnXQnjSLpwGMj0tNzZGNbLgOhavjy7rJ9JPJ7ADas3gNa9x+JSzo885wKRiKOozh4Zuyno3txOu3pcrzYR9BzkUhDXK7NDPDtkWHLwTIiAJlpGbuvI9uYWYbnt6d+nf3GN77Rjy7+5V/+5ZlnnvEbQTD4uW2V//PO13WIGjvCDp56Ok/nvWzQQORUISk8/Z+Fh225tnvOQuiC5H9RA5d0eGblL4MOtyw4npnGLnQI7eq+9Yah/glVFx/eqaUzdjKAlOxnD3664wcAPmTqd4F96Ku5uGqyVrARqlmG+pWFz1f6PkAvlXjfsvdoJGDZYvmC4gpa20O2zQkVnctkj0PsLXu28ZylZyqDsBFZIbLiCeJSqFyYCPivDfnSx8/C+RmRXGVaS6HOSO4+Q3VJh2eeUK7CD/04Wij0IIeL2rDyqQjHmSndie4qN+DDPMHd8Ydp3IngcnUtDaRVeqbwDMCv8uXCV7ziFW9/+9uffPJJ7z3avSxkbwTu6xCObsnGbx99xJLh+WysL8r6LIO7XrBEznHehMTCE5a8+C9DIOfnj2hJQg4NPR3XkfGYsXSegJl6Zo+HY8YuYXA7l5B0wMmXtTVISi6rT+TujWjDzzENTO4EO+/s7yRxH3Re0uGZJ5Hb+MGfbwn6D9o+FCJq+KKSn0PpLFOyV77Er7xVsa5az0X5ZKzn/5C7VA4Xg+rUU/9SyP1v+UN+vJNHxdkxhLSRm7ACoZ68cWLWkp/97RkCj58MU2MaNgpanRMEh4cEbCCAYax+YHjTbj9t+NzPxtxteJdIuNSmc+00D6FOSzT//8i/v/A10W9961t+BldodnckGpzXb8Dp8ws3btzw7+6sz37/93//+eefV4eRPTHpAWnHqbQMbEgYnEPYZwckP4e2Th+He+ihh973vvf5xr3v2Plqj4EYw4PjVLpLPTdWT8cyssce/BpjCcOw/rRtYLLX2D8Fc9dwJsdU9PiHIb5xYTZ9stVXh/zYlHoRGsxLATGzQuQlhmWbU7vEfy4fxRE8yFmUU0IOgmdDYkMb2IZf6Dkjk7HxUj9f0uGZZ5AJvupVr3rLW97iGxk+aMlqWar/heQ/PKjW5b/cciz1VPJ5GjvmJByjEMzc9VsW+BgHo9fvw76+6O079wDcsjaVla0YfNjT7llIdpLOQ2BwLAEMqYe/jReNLBoAwLtVZ2PJPvCl6iXOY9qGkw5aRxmlzwvk2+5GAlFHcrU9CD5+nAsQcYgNAMEcw8AGDCp6IoR6d2MGFStC/9HCfz7yvz78dwtJETmfgHdYGjZNMbaB9jqX0OLE1L/hDW+wQ+s/0/odOgFN/XC4Gv9tvd4SObaVd0R7+OGH+/yNHNyBkO+4+mIqs9f25NKq1HxpJ3VJ9Hg20vMSfjjRSUz8VGLSsFvhZ6KBOcdwGsh0mYpOPXUukS/bYExlxmYS/bvd7/iO7zDFqlsTSswf/MEf9KVWH8OTF+kZcvYwFKGK9BLn6nam23CaTLohgbQDP/pJhw0WqCgpcdKMu6oxX9BVkC1tHvxqlu7jgZd0eObJ5aj8p3+8wkVVl4pKT498xNKDHOlwXIVPMtZT7ZJb5qj5Px9wQIKKQ6jy9AgPIrK3Kr7ru76LDwhYiln/kcOnLEnra7+GLH1jWwUY0zmsbrS5JU+b7BIwHvRoG8UbCe7/HoSheA3JqcIOYynKcJxDTiK3tFHRIIsG6n5jIApTLEhaci7wlUHT2wgOp2Mp45Db00AIA0i3sqdqaCHRb+lgov0vcv8J3T/WAKZfYiA+hk2cS0yaOHRXq2KDN8hhxgzqDM/kWqWRF3WQt0W8RetUMYdKfJK3rOM/eBDwta99LWMjckYIOUJ4ELspHEuog9fTFDQLg/M6DcKawXn0gBYSjlTqUmNco1s66+mMup49LBkV5yBf9rKX+VdKKkvfQfVNdutv/+1Sv4fBMPhqtk7aDpvJjZNkv46YMxbyJrGe5pHmcciokOPv6m/vEjN+mnnlK1+pWMHJwPuuKU/06JoIOuPQeUhcGqOBSzocVZynISIwVkFZoGSd/IQTWhfaWskbGXfeuC5CCb4wFAFR8WjK0tNOjj0rlbsgxRUFI66iR9tBMMz8+I//uCqSA3/0ox/lzKrFnQLjDWMbvOWQbmlATigLX4Xz93zP9/zkT/6kqhk5/tkorzKqoJ977jlfjxSdrYrwKX5NMNpJd08ntOO9eODqvpqtCpbs/aMlz+doIxLooo492v76179eCQLeQQ/wYFKDCBDqLIrtIb19CwadBpaY8SNWft/3fZ9EKFAKl1bkboHpXJq0ZsKYwNQX3ahxJNomcXwPPDgRMTHwyCOPWLuQ2nc79ZPOGSqEhpbOjZk9SCtWm3T4bXuYcStRyyOE7DcItSaXsZkRu6a2JYRdR8UHiksGDpLbBoiB6V9WMzScbZj9yC1NhWYSHA+Gd9ZYwgzaaaQiFsVcYfDE97Of/Sw8vozvnyjR85ve9Kaf+ZmfUWv+2q/9Gp/yb3gpwYtLkci0Vpv6sLHRSIdsCXuQo+Lfi/oXY94itnK1LcQBaUDbLTA4J2amziwx+Tu/8zu+729e9AOAMPPYIPSAX17S4ZkNQPSXbHiL9Rl3ZbsCB0NslzJieek6wnCydWdJyAuESnX/iFVtWNrjGIWPzqg7ZGi02ju1ncWRJM6r0iHIYU+srx02dOGXfgQ+a5FWn/YGpUYAuZnhorP0LCvYSvrCF74gKeIhDBBCcpLgG0PgkXJUAAKB4IsNehieLcplnSoPDEjYXioRyKRJXFER8YVOANISVCdxEjDNGyigiE2weUhsorFhXe6s7AAmMJU/wNQjkdCJ7TV3DaerFaQ3hsQ/oaiXDdy8eZPtEZal6dkAdonndSIbxQysQsw4QhqeDvoX0A7VFcWiC7kcydLIq9+/lzLROoeNMarpObKxPdAUUC9TpHCLb5MLhkk72hoB4K52dZJi1D8IRK4M4ey4ijqe3Z1cbqx/GUYDnsXKJayRgHKPUkzip5C3ve1ttOF/m9iTpIrQ7sF/Fd2r+rP/Jq5SDAMM2xLw1a9+Ne9jY8hhmxI08i8FCmaYuhmhBOb3Uz/1U3ps2gNYZwZXcXg/9V/S4ZlnkyHyHL7hX+4xRJFC5cip/MMdJusYky3KnEreKIfYKhHaHOMPSkL7JMpzhbNbIgUSSLN7HsIfBEdRQ9rAmwavyNt3kjakfqxqQNWlEMP/rYEERKlITkKRr7rLS6tJwRiFtKcXyDkjR3YbSnn1IA/nMefcGyQM3Jtjy4V+V/fd3/3d4aTnRIac+BUEWLXNZeFi59AH+/0Cz061LG4UVOK1fo2m4xg2BgYtbaQV3UKMt0blXRp2yEwJiE+XYEAGr4G37gIj1Mg1mFc04DRBtgQER+XOV77yFSs2yX5DLpdBzmweTyutmlNiKqTEYinQazL+pSIjh4c40I44pHaJYiSm/3iKQY665lIjKSQAlZCNARUAYT0rZf/u0oD5pQ13bVbLWIzBRKjJvva1rwXTFGQ5Yd44L+eLPRM2MZtNbRlFArbR8prXvEY9armsCEMUFXbFF8hO6hWq3uCkyxTYhFK+Csx+tf1bz4nJgqu8T8NEcHAzhUmL2v/+7/+WtqVMPW2iikssZKhsaHj6H+TGJR2eefaZoEDMLbkiG21PickWVpZOUk/+eTwTXBRy2zWe38yiUGbaxgBSJ0/gDy2hvO/3pS99yeKVS2/D18NJ5lauqEd854diro0ylSlsxAw/4MDAcFrRh4woKtXFI7tJAtbf/d3fKQtWx4j8ljLtkXJvudAmLRICnDxnySW7C5HAsCQoWK32ewO8qd9xrnQQqoRLGVqwUBD4Z+IT+EbeYxrERE5gwoktUBSJ5hI/hpMd5v7xr/WiSiUtiVPyMRHAzHbfMeT2wDRT+PG4SKa3LLZTanfaFBjV3Wlg7FRLG9IG4tnaiP3YVHAJGz0DaMbJ5SCpM204b5DTk5EMzmMazXuQ2o6yAv+iebUge4BWUdJdXuAFH/DcjT0ANtHKQdTpPzw6NfYwQ66EIsLMVJNuIJsx+2xJmvSKqaT4gQ98AD+snb9//vOf98Ja++GRu/4ZPyYUw6TggLxeRci8MUlMTJLUkVDA/vRP/1T9xwLZHkeohKUExm8bI8GpC7x5dFyfw/sJwyUdnnk2VabepJAJeA7jU0jyEC7EfCdGCBzac3kSB/KQPSIVolgAZ/ZdPsi4dYZQLOAwFmfcCTO89N/+7d+++MUvLrdtt0mPhww2TkUoPxsQg0Rebu+WSCGviAhCpFiDAX4rGHFCawj7VHxPhrCaRNpSDBg2+OHg3ya9s4c4AkFjBYIf+ZEf8fxGIrQH69EgcayHkCapQCwq0S0erBikKDy8+93vBt+6gSCYx49bp7IxvCUpfpCDBE5i0q1i3AaA1bC3OnXKhZTWLNOPAKoKMSNpIGMYnKsbsInI5gUD8EuH4iANkHEEnAbgMZiTKIq21AuzNYdCig6JTw+QuEUoRgg5mNCOjEMxKz2JaEiWQzDvMJuel8sKTEuWAgaGnt1ynlEu9ePN5kEzlRRluHLMEvm0iTZtjfCPsGSEAU4bD3SuOABgs8RWgWUitdOS/Xmdk0qX2Fa08YNzLiCltS6s9sUS8SHMv5Td3IEF8nEWSHyTZeo/+MEPGkgPkFQruJVQYxgruLpfh1zS4cqZzbtyQgEx67dsEoIlKgGRmYoCfMOCjDXz1aHk1rSvajBZJPLDqIDUsNYRj5g4AK6iZzDoYeLRdeaZDj/w0GmJJnVJS54t8Y1BOGOnMU6ikYzIvf71r/eMRIjXiaj4AqE3C9ShEtIwKRupRq1ZvW4g5ViKafNhIczayMJlqBzfKKOAp0N1APF7d8Mi7Ld+67ekH/VBEVmME6fo3C0RQUPZbl1odqRnM2KO5FF1PeUk5h497OHQcAOlH6RlaKhI94//+I9YtR/lpXY7qFilFgAo0rzY5MCAgeAde/DvvzW1VImfjK973etMhKWJBWLRfKdciOpfkZkmsrfTziqYBK3iMwskeBKVienHrQy+s8tuMdEjeQAWHmPNLDxo2RexR2qrsFpQnUHDABgkeIwBM6o84S43UbRZ16Irk+GB+Fg9XgnLmZp8nya5ADNTXMJv15Txe5Cs+pSZ9ODqLAee0eVWsiDxyUtAYuqH39QoUGyQPPvss9/4xjd0chP9BCe+LVyXYFK7ZKnhoEkqhWcp3Vm4fakjuaTDlTOYb7A2JpXfskVP8n71V3/VVp5kIBp6xu79ZnYZwEmUoIUh4+a9vF0bCXZ848aNVh7aMgG0rN+5CMIbPa0UgrlH+6L6+Q9s/MF5Pxt5CEIa6Eo/Xll817veJc3nSNZ5Xi7wmw2vSxBNJII8nCUqaY/3yoWCpoUaN5YUMSxGHB+GhklSE5NLIySTiUEiAkHatQMm3wjNZOTkDj0o6rRNKk/QjOGQYNIZ85/73Oc8SQIGz0FtDBvLBs2Y/W9+85vW/R7GoEUPSHu6Zq32yU9+UsMLR3SoHLGK/ZM/+RMaI3tlU8wsER7ZJgjSbAnmUIl3CFEFS0OCjGBwYqYopKnUhr85Sj9HkgvMcEeo9CBtWtUWCAUQ5mDGzqNu1AzslstsoOF7+Ak+cQKzuLFJ+Iu/+IvsH37JzxE2M25GzALeDNR2i83IVX/8x39swRQbt7i5XYisnoJEDkltVv3pT3/aJtBv/MZv2BN65zvfyS/YqtIhgGue04MpxjOcVEE0OPEg03/sYx/78pe/rD83NBekdpdhA6MoSZqp6Pdo04vNRlEdpaVSs3lN9u6z4Rd1XGtCWRXzYm2MT+bwXM12ohAsS8kN3jjgiuxYsMj+TiJmFOQGslp2zKwh9xsj6RYtPg+zQhgtjsFhuIRsYf1nIaJm1JA/xIhtooWw7X49iYMcn0FX2SvE86uEkuosdi2A7EaiCwCTRsWhIfzQgRkLNf3uooVzxx6iOzmpE04DcUXDMr24Yy9a+PMykUgRzkJAMQ6Y1EtRttTsIsoWFIUfGVr2+uu//msVvWyaYlewVIBGC1ratiDDp9yPkMWBesjWmZ4ijndr1Q32MEFiLD6RdnePyFfdMiptE1wDJ96nsGRnYzbKyCLBOzMY0zeiaQPG8DqizSCW8A+nyEtYa9+wkSVuNZBAKzuPqFtDHc9sQE9SuHSrsfvPw7kS0+5L70VHlwWyecUfO8cYj/N6i/W6IXCaILdsz8hPladDcZ0zznDIp62h6FEb2UHxbq2qkb8wME+198t15F1KYzbkNRGcjtmjQi7erQ7zyrq9EPPCASGkVWeGIQuC9Hi1/myVFwxM1MGnqy4v50s6XGkDmRHfLu4IFuKgnRyO55a0IUIp2LnieM5JlDiAI7+FUBtyzyccKAp8elD3AKNdULRkC7t2EoaGuzFW+Ea6mIgZ/XtYalRnKdBnxjwXwQCfFPrlQu/FWGPBkKMm1OBE1y1EkYtJrohPh36de0jv1A9/5rSGwyzHc2k9lpteHvH2oDgoJobc2S3RUCL0hTyJvPVf3+IRpL761a/aU4IEG2jRbch30t3ZaWAzghYA+hF3rIDt3VkW0BWK+mlDQARpA9M2prClX+AOZ2N34t/fSXWjPQWBGuXmzZsMz3rUvNO5OaKrpgCq0bZRWBqT2E/lqrthcJe8MCvIFGHFX52EGrncRWscRH9gVQOAqT1B9ujfcAPpMFTaFMjUVRiKTsUNu2LnUtFzzz3HLDkabIoSjFkjGkgngCkfRcwv2Rs1urX6CAm0NO/s8bDSxFNq5iddnSsdjtKQsBVBZOWIpx72fuKcfjTonDXSCa8EaQtdZeAdbMDuQqKTKtJqE0QnZ9FDbNwf50s6XDmPzIttCUZMkEUK0N72tCjhkzB6v4OLykzazG4FDWZtmWUgEnDah3n/+99vsyi7L0yIBfZA5Cch3qaNJQh4t8AMRZe1mf4ELwBXeUK3iAYY5+pxW395kUWndEKoiewwBz8yQuvQifk6tfGf07o1jB3ZwEZ5FzY4YdAglLjjZ8g+tWPnEwmxwNlDSqtnrxoKi6I2ErKRjUSxUmT047AWc3F4JAMDBn9tK3IMSISyoDeMTI2gI0DLeQ7sEdZdUntmCRg5RmJstkEijZmLwX+wYRS6zYXa32JUIFaaeHRk6meiqcKWAG5BwslQMdBxkMQ2AHHiFv6UrOCDzQLRwsuazBD9qABIRSwWaSE4lqqN6CR7Bp+KnB3bFOtBogYYDMCswrD57xflXqdSGgIgOEdzSBLAZCMTgT3wyOHN9qCHefComZxxBWwwh//6Z7Ijhz2otOVjZnB9tGEgOPXSpAMJC0TlnSKYXdEn3RKHUAE0xO6RL8eqYjkvVQCWnj3jt28PEowh+BwLORer9wGeSzpcP4lMymE8J7Q0FJ7YH7vkhyzPFpYNQ+bI+HjLqeGPrbPXXNcOjF/7+dECbByAV+fY0MpVMrHKVL9NQosGni86FHp0RjdUYcPwYN4W3hCBjNsIarAJ63pcKi2ff/55qUVyqggwlqNWeCYmMCzlcjhErqMe8DrT2DbdPT1UaiC6eJDY7A55LmjTzKNB+2PqAGtET7MEaInwzW9+swap6US49CaRGGpVgfNUmoAp8NRJSW8WHx7aebPRYVKEP1qS8IgmD1mUuCyEWSgoWT71qU8hnewUQtJT6aacVEfDGvCL/mKiNv0U7omm3fY4QlFBV3umYI+er7oV3YqS9uusvDM2hREDiDpmWKMnfDdu3LChh3qreRNEJ+YO55Dod9Akm9G4iujSloLXo5pxcCspQafdb9lOP2PwmgltmxdsUIIqwR61jXGFAjNGHWmCpP899n8VP9v9kNSJAW1uiDSuUKecbfh1PRie6tPMOlhUepN0u2UtyB5YIANQJvoJkLeNqJfV4U0VSxV8QVjAw3joNa1inTgv8lGXdLhyggo9BjM7i0JLQ/5QaJAIhWD7pQyOb4DJCU+lxI05Mytn4r1UxqUh4QMSkoaw681Jt6wV2Hr+KWmJBfKEqIF6nr9kgD/gGas7+cnTeBHftsCyzMq34RdYY2kGFgddNkqj0Gk4X410dwOIwxl+TCM9Q4uWNyOsBS254JeKBF+BwAMSG1MaVPETP/ETcpXYBzPIP/uzPxM7CsQaRDYwwdMMNVY3HMPJMI+ucGMnSt41BTDgjbqU4ZRvC9FGrgoJcgtHjJm1AlOzMJZzDNGBmVHYcFAvq1B+0Yb1EJNgJzrbIdAgoB7Dqc7EhWdC4aA9phHb2YwVofAqE7sk4GBG0aLENqYXiZ3lS7TsbFu62dazNFedKBPpwUBE4STFfuq3Bf12RQg+5g2HExsaWSkBFUN2BZSkGnBKwx7Z2lo0LwwvKx2DhxaqDHU/A8u7KWF64s2leTG/ZtkufTsEOofWwK9uEJMI9IwKQZZKiwr39LKb+oP5mXEVIWaMctcolaJcaKeKhTBR+l8Ksmyv5vB+GnhJhytnszynKLM4sEcnI+Zm8hBXtBZhu1wFdjY32eJ4YrAJbc6GQ1V04xLcmFnDIxa41BAWPbgCzPq5gUpc6HeIQcpnjzD5AN+AJ2yTEnYyA0ywcKhDycKLjOVmwi6HJ6YNKJ0JRcAa4KGFUMMZJ/jMXQVNfOrRv0IPBF8iF+lskEo5dsx8NNLLMoKvX6F5jiguOPBsO9fPHjxMtUdUSoAhPLFRKMQ5zexUwlWdFGgPSsR59NFHRd70bzoQFXHEX4yZCDA9uaEBKZOFSMmmBl1aynKuInFVfzxnAMoUIc+848fR5oEVEmuUlmCQsXrOlAldhfPIfjjNuIOkaj4lUVGVblkm3SrIXv7yl6tLvEnETswCYd0So4kvQVrMef3KO72m5kiiwEyQQ4OMc86EzKODel2aCGYpGXt2yyD9ANQLJnyQ/VOasc6ANQA7YzhlHs/JBiQkHfptVHi3yxN9SjAvUj6rUw1vDFl9iWceRFL6jKgeeVfJpRhSBzvUAapAMrIuc8QS2Jipp3YfKJYOjVUcZ65xApVG59W83X8DL+nw8JwWjkUBBqcEGzPiqyyVt6uFrRL0u2t/RiGsKOOZvI6BrrM5yMUUpo+6KtuP/HyNBTaEbgWJ20f4Wbk1nFvg8SAG6feYgVeImDavVNPCEFQG4Wp/OIYEAGxEkxEFF0Ih6pIHen1OcMeMZRDGeCmEiIKnnKKMtoAoDkIFBlHMcEhgaVLj+CO0owohmDa8FAOttCQToGUpBsAthOjfuxU4FAv0IARD5DB2PF2QcRvpIhEluKRt4hBWVqBVudBrft4tlBFFQ0QtCq0gaQ9wyjRH+l0atUIJw7bhkIh90pLpEPKYpV95aptuc2068GbW5DBasopCDg/4p7FTNZASpmiAXBsqehB2WQIZrRQ9yVOd2KvUrxqzLncG1taCTskSP7iSU+12amNyvx0iPdzi3GXWlSDaNK8TSzTgmbqSFJi5Zh6+UKouSfNgHI3VADPt23c2T9Qb5vKKxIN/h4FI2/htTqUisiD38MMPy0YKIPBqUE9J/PLBNsbMslGOZJnOTaovXINMWB3TXiqKvAwJObkfD3IwH2cMKiFy4WfUwhTNvpduenoKIW91Dv/y/ALxy9//c0mHxxoBN2BzTMqR5TEpYcgTLBmRIer3ZEVZWgnMLrO5sdFjKb0Ax4VyJAZtkQfzzZs3hRL3YQ45AEcjuI0Db2C4sYPbCBNPP/30k08+yZHcAgAYS9ov0LnjLwAIIce/trwi5ViEiYMczyahRYA4a4km30g/YAQCsov4hUuKkp8sX7DhrsAnOPLh4fMOesddJOzwDJsFEFcvZolW0GDGovyxxx6zLBCmdWLJQDwcR2QTynBKEMIc2jSArsNmuGirRDBBlgJ+Wmo1gBAwHFoeUbsKqbtygMK8jJIUyOzR/yYTd17DT6ukdtC2vCgPSUj2S7O3eLCOlyG8TPvbv/3bWIUDafOCJcedKA9cBX/LyG6vrjwCEOux4SmdJEdSDCDkUqcnu36EZ3dUReiSGTAeh0pOzjARVslylUSCKpWCcRzg4M7bcYIr2LQtDT1WxwNaUOlnsd4vUygYR0tp/k4c+66osduc3aYL91HcyOsUa+oVPfRp+mQjDHiCgLoZMQpF67A///M/96jCpIMPTww7m4I9ymeukIw2gocBGzxIw/S5C4mNB88FfuEXfgE2l9kSZrQpllmCB8wdeKha7ZpekBQPyPmSDg9PNNMEJOI7j8kyd/U4bxER1KdcBZjAZ49OkbhEuscHlmDbbQOZNYosW/T3BqktQYs/kEzfoYGoQwMD4qOjW535krgsHuFQXBhn26a17OE/sAVsaxQPys9SPs+37uGQtsVERotgxb5UrQ3eIUBTiJJZA05eqhPzAgSWJkYsye1vp73OiVxgsggQcyVdFQkSbqFlfaZfLoSTCM4pZz+Jq+4uI2l4kLbUJrLgRbfommvRx13kBF88qEJETzqE1sJavsxyhgpZVnCVBiDRwAbSkJhupQaTo3+2Z3Ys3804m6H/GzduMBgBEW+OtDRsnNpgBiQVYb3KBKdHp7IgJGxDJrARygzc9dYSZujBreZCdYglziKvqKiYYusqgmDpVDZMNE6cpQEHAdWjJWPzbnaUJvihGTxsaP5IWhRr+ijQJjx3U2qYbhQ1eIFdWXRZndTugNMtJqEI8Bpzj0gNH9JNXJYP8ioeTNDcgh9kwOVCt3Clx16IRCjsZACD3BAw6OpxduncKMYQ2OC/NK7SwCUdXqWZzX526chGmRp/Ew5EH2WpS76nLLVTJwSwxbwxFOtsERIW7+D8zlzdu2Fo4QHa2Ah/sTUwbU5rgeIxhriDDVFbJesTax6nCUyDdlO8xfUSuWjuqY/QwOHtf4r1hJURhVpBjbD6tZ0h4NLcla8KFhQiBOAKRSE7tleoAjMFAmcHDLFnWSAIYgZRmYDUSnVbVSI1TvQDKyhjYwVdehPRPJhRbmsIiKjLcwRBwu6oGqW0hBz8KDpUHh4rUhQwd8sQpo8S8DNsJMJC5Uc1IYHBQXB6JrhnwxbE9iTsH+BK/rZF5mVXvwCxkwapZaJbuMX8OqJpb5iX4BU3EJpl6Y0NSABe37VBLRd6siv4GmLeHZTg0ClHesBGe1ThLmVSqfZRYm8BNa0ISbEWnV6iYeewofXMM89Yu5e9jKOrrdEHOpIXNhpjAy4lV+bNm0iEEF8gtfKO/rVB8k13nbO9SYRLneMEKrSvmgV3wTsDIFpchkGZpUE6ndpqC0kaFW1E7d9qIM1VFQdCBNsAaaaMwjDSAJa1Xcgv520NXNLhtk42e/INZsq8use8PELnh97p4nsARPzHH39cnc5X2Z8ekFmzBis/1TNzG2cGzT1YPw/0UZUnnnhik7/b1/ADA8wf7B152VoAkrc8T5IOeYhU3b5ZGXEnkjoni+SZin27T3Ym1cUwO0NLItFf2vMAyb4c0g7hwxknwgRUiaBHg8boJMx7SO+5VbyAHBKECoXigrRkTSD6W6oS03RAAgb8HmwHb4k+NCm3FfRJJJrLdrKjWbb57H0NUVI8gspEmyAPcqwnfAGATvSIR+oPRZJbZTKQSZF5HORhCRCGhBIQK0fsTNoJdwsJmtGwQJH/fM1VSWTdZvYz2oabCMcS7cH2wIcf5/D7Bac3mGQFmwef+MQnKETOQMLdzoBvk7r1rQaTQm9shiWYLIp1Nw24i/ODPCwB4sdANmBdaKOivUpJV1ngsaVULVE1hDFMe4lkTxtv7mIVn3I8M3OJSSWIdKjaICmcqKcQBQc9sw2hQE5yl0kAgIEqjCVpPIf5KtLdTS1gjDWqzslkGIDZE0HZF0VcWambC1ZBdnPh/XYfKvLSNZ04IHHANhiuon7pTwOXdHjAEpgao2Sawgrbyrj1eH3ATp3gWIEm6nmXTwSEbmnTvEJPZn2A0p23dw6REbPvO2FvXYF3i917dIFnEVmqEBPdkiNtUjUkz+RXMVbnxhlMRWU4iSOsE1ONr/SGWSj0Lp+ILEnADBiG/F/aA+kyjWlw2rLFBpVTLzFTZIdZ4rEGEomQ9jjHdyk93fRg1aOyp556qjc8UyBgRxN3EkWZnoxiHDHFOFII62UXG8UEt+BWj9O2CCX0kNpjJEWSuImQTClO2UBTvLuM8xrTPokf4hDEWHo2y/iR8jEgas9UumumpCt24hsF+LSXC3jEh+EkooBnSNT1KDs8/3OG9vnnb/2qRy2IPUY1MMBMDctx5iZKCvlD1YKfSqIssPapLKHrYN5Cv515TmdeZEHFIrU37yXa0cxJJKjRYdL/8i//Ep+YJJfth9ZYFM6/tCF3iztIyX72bmVmr4iw1spW7Twl6lAVQ/bzQFFpQ2GneuC59jxUou2sJA427Mf8wR/8gQSMH5d2RKoC2yDx02euwRrRomqbEzI0VkGy0v0MXO7SwCUdHjAD1hwEk8q+ObZ1g1hT4GOXXi7w0zeVI0iOWjrkA4YcwH7oNmw5CW8Bqz25dnto61QASMvTLZXwLEAI1joLWAAKGdsYpgfnERoROB5ViLN4UJ9yP0mR64rInqDAjEPLBdrgeG3W8WH8u8V7LUxj7CDp4WHZQBonDg36t0i1WUcoXKmLuT0SIqMFsXrZQlYSilDiJ8sS4cG23eaeeGkQROltV9AuqMgrVJl92jbjSgRxEza8yYUFKWsgKvLqk3AWIXeLiRormAl/FkUDkFsB2KvHhhwjBTb1hKVt3HqZSBZMP85DMTUelH0DwKgYCA95aVjiuf3s7NbP2wNAXVuebrgGk5AIbaJIXe5auvGU7tKDBomGt/oPnhvCBuQMW/fsnL0xPOmQGRA2BsJ/KvJYokNI4CRm/CAHVQdhHVkXARHCg+lgLTKi3XXb+OBVCfI0ZwGAZ0M0Jjhsi5mbA2DJSm2vR7Euk+tRBSMnIwxGmWhcJSO0GHCLmYF/61vfyv7bJKdnY2VKZrA/aGxz8iD3XNLhgdnPr3gIfwbK/qwbvFOuJFQYsjbFl+DL+BRlZcQwTtxn4trhOUBscTv/mYGGYwAqPgAq3wg8zMK0JQs/FCP8Gt3HWazbuLQIxY1xiFUYCBLmBak7mnlswsa526ReOrakqBL3qcwA5AD+CW2Htq/7f+hDH4JEj7tiIo+1ajlVCSPg6F+DmNKAdRvq//f2IS5XC3uFh/gORHuQgwEam7m4Q9S9F3BWVmuY3D/6oz8S4KxBhRvv1gp/dv/EQSmQcmBCIm0Q37umvmbn7RL6R307jK5jCRWoEg1mDRWJuegHHnQrTzMDCxT9znReJDWQLAxgr8RX3oSZaEiD0HCQEeZmszhOz6ZGfzC0wR3sq/tBnh08m3vCun1dmRsAjTUjK+YFIRio3VqcBrSpwnNxC1Y5QDrUI2dIEviMZz3HH1iaUSQykBlbEQ42UvMpUoNMdgtB1eHv/u7vMj8MeN0GBpsWspHZyZXSVec9zOBcVFHq+fifqTRclvV9NdgwANXMAj3Eqn4IkeZ0lSO4pW1v3qnVzAiigA+S3sPVg3Prkg4PzDWby7uCE8jEQdFQfGGdzgyuHi81LKvjwTveNT3HNLLggil4SHJI/SKdxZYw7U33dvDc5TaiDwcWhixinPmq7Ih/MUhhzlHLamBy0Z1sLLmddm4PXpRpFJeLKxziBGZ6iGdKoDF3EXJXv8rdYsLl4AnJijPMaHlwAnMIMemwYLJ3KjJKvSp0u5SJiSUaWEFIfvXevGWuDSgLEf9XiAhIq/2tF1FPQFQ0kChaAbBtLnjZLdC5lHcZj1awFEW0ZDVxn7FZHUp75tpLnl5vsSSVnl0qg5ilAkiaFE9LyatzYaqjYQ08sCgm52CBLYt1dpcGktel5aAdFL+vUECYss9+9rNecsHJ5IaGZDCR2D67q7NzGtPGgCn2k1MJQN3D9mQd+5NeNJu431oqEttoD/bMwKWbTKfh4wWhsgr0CxO/8LFTYinMPHjie9/7XvViq+HGZpB58QYPdZo1bpVFJUtgtEpwRxh0htAo61GOr/xlDBmY6ZBHbWJXCTFIwEvb2yB9uVxq4JIOl9rY0WaFeReTHPL+dAAAQABJREFUkmPYq9WhTQltLuoWb9ewLtTTimQHltO78n9E2ffEAhGfp3k8wNYFF1mZP6iRcVUbHfBDjSPxB7srqkW8hYeTD86BvGYjzKGFX7Hs1QYLKa4oDeOQljBMmf+fvXvrtSUrCz7+Jn4Br02MvUPChVdqMCrRAI2aCBEFgpwEupuDCBiNKBfGeCHeqSHiMRLAIBABE5AAMUHkwkRjjPHwAeDOb/G+vr+1/t1P155rHmrWWnvT3XvWRa1Ro57xnMZzGqNq1jrXM8MZ/qSTbjvkp1G47OWxChL2muiH1NhAzsBtwhLB7qgSW8BVrduJhU0AwgPqvWeoXazRTy65R/iThORR6dldAe6WeQiSDoKIhqgoL0hkTr3f9NRTT5l3ByapAjO94qgA8sNQm4dYSgMpYZv+MYCuAyoWxf6tjy3IxFwKh9mhv+Th3RMPEaRkZcSrXvUqNqA0sXSzpw0YKsBPi3T0mboZhDNIpMsEGrxMLnSoURiVhbtcq/hgZuABDPINws7Ysxro0rOK09RgQJ4mtWfb0qTUKDLwVrYKp1yOK1bBO8wXGanUrDkImKkQ0Co/S+M4gI2CNm1kUQRn54oSE0EVdkqggh8e5BQHdss1IDEpS52cJdcjCHxJhycmPZNihVKOg33bDBEfFeMMl3Eb76dO8k3efgLdmbeLZQZhQ6R7/PHHn3zySUmR88jEdYovnKGc7cz6ceXI2eyYeafUgfmwORvofCYvx8DDhmjq4u22Fu1qWq9waQsFyxfrCQDHsOy7RzrIBz8QsusUeuAU98uIqJBX4CAvHoosqJumbcLCptiXVAii5LciV4hYe4lKEGKDnnGlrYGc1ZhXeJiBN04lJwAw3FUuTDFCmzhoijUoU46xOhQ62aQFgU7asE8r/cjHQrCAWAyl9mJiagzbyvMMSVKOYMXs7Vm5UAay7h88GGCiOLRalRIc6gb8+C9X/RYQJCRC9sxmXA2GIw0D4YdQDuAFUjI9oMUG7Nt73+SmqtcjP0L35C0ssUZ6NulqFNMhLAgRtihoSa6SKfUrrVisaZIpAX/uc59jt80OEpCQToMBe3WLv2iQyPrbATMSxKGEhthQ7de9kq6tKfo0F4YAsEv/+7//+8oyem4UtFwm/NqX44gGLunwiHKubjEj0UQu0eaEHoR4cCjzifV6rCFYnqBgaXK37pcpwxlasVVwtyXCDYomdu2u+Ps//4cncAMHBxAgJuXIBGKWd0CEJAyHqrsgG3v7c+zNOfxIiwICFv8XIq0YnNEK7CyisQptApJdW1AQQaRA+CnKoUSQq9wSLlGhLlQKkTod55LGPAzm1zqPOGjJPfIiGxCwRL2koHwAwr1XeLxPKHcKc8i5i+GJdwHf5lwiDAOd0IPJ9dsev6wQ7NzVaTlilewZLZskr+Na9CvZHZupp/kmAgn4lTt+y2H1SXAJEnVTgIRNbKHcax3Y4xQeqnm9VolAS2YEMxOUtQ0J517GRnWBBQn5E088YY9UytGDAbYtFzZZ8KR5jdvIu5efQ52SXLfMgiWg96foRzr0WrKDTXZ4+QuTZOGJYgW7NapZIwgkcQ7GcACyKRkVOr7Ax+tNqKrCipAGmLqBLNCmsYU4VUNLvSzQLn0/L46llGD6Ru2HpLj0p4FLOjzDEsT0ajeWygkFAtFHRPD0Iq9g0w/aD1m/I6Y5D8fghyzegQe3nIsdfhYtF/IigYm3FDIAgAzgDMkPgyZvgkObV3M/XEUlV0S3nnP1E7xzBzxIVJ0UHYRgmL2MJwbRBqmlJcDAmhQMuDwswf47UBkFg4l2ABKnoLITayHo2Zi7VGoF5rf/KPqNmkI+upFL4fuxn9+bMvEj9lGCSwnDJqGyIDGbAog1HGC0A65TG2Pxdj79qxUGKzJcvvegzi8sbdLaFLUoUSu0faLhwA9d2ZCwb+xJqmLC3UlvSLNAeJLoCCdjMMSheYWOH91beqpI9Fj+yrU4kaEp4Urm6yrkSsJnplvPtI8Qus0tdmKiEWoi1E8e66oFMUxdMhZtEET1rGGd56yYkBRNRxZCOeo2fOrBCdVZTSp2/ZzXLY8GmRwA2CwKyW51HsOGwGYUDXt4qeywRyoc6cEMA3D3NqI9gmMv6fDEpJc8OLkKV9Wm+mPi3JJlSzYc3jMDG3QsPge4KxMsnGEulxZNRFshxiMTr9XxFoT4A6JgRBYHN8AGMLWkzTTPlhwA9ORpPKqodOdhYoKOBo3FSUSdVbIUiOgJXR+4HbcEdD8nF24IKBGqnWmD89vJtGgAY7XklU5RqRkxdjR5AP3+bqFqlBYDih47UQKTIIWiYYSFHKT0jNxMfeHeqHjeT+DMXvOYKSKKsckuQzQm3XJkFSgsZTfQcSbZq4QaXThDywg5ghDs8w79osbkUotAbK0mWdouVoFRF82gSFfuDl2KopY4GYOcu8tGEgUp6SLnxRzpoW1/i2NfIbBZmm8auCOd4WFY4rzzdikNaRku5NjDFfFFBlmQ7HRIalNGgfQjkvRgb8kMVUACQIIXT4x93/ve5/X1DN4UeNcUEuTAQGjes0Pe/ZWvfKXP8WhDAmyp8Iejh6Usz9/2JR2emDuWx3szeg3Vrq/0sj8GbdEgKMs97gJjdhObTiBdfRvaYJGWgDkJc/eSgnWqFartO6YvN3hoxNnynCKRZavHFbJCw5dhEaoC3GouVgEOznjGD96M5LTKW084lK6YX4XrfqBlUMM8tSuf5T8rdTtRHsaQzrYSBoQhh2ANLFrO+mEolN+P+NhVzMNc4mmxaCFCuowBTuOjohNFl0taMMgEd2gSo+FII8cGyIUHRwJiGJhbWKp/yaf+cw8IRwQSuXTQsJeMzILNOvixodO8WNkwOR5BafLlkhau8KNnpkZ7BFlC1nYLEmej5EJVoOcUMqK2Q87w8wMphxuCH83s4MHVTs+DuMSkAy06T/k4ZyQSdmUTkemQIKxIonIJ0pCYyZwMkREB6JQy1Ry8W8HnQYM6D9qU3zMaT7W9Q2R9zNqtNbVlUJjhBOnIGKo2tIfWgxD/hYTzkg5PzCYzLbI48z2HJZcxjJuROXJyzsBL28Q7gXHdbeTghL84G0XWL/kJRiyeY4g+alLRxw+MAGPVpSwIJiLADB/GcjnnwbmOl2NQ8QkCqwOHGSQ4MFr0Ixfa6dIZPwN2VgM2jg2tnERG762IxbaPhAyxxiEieHBCFcUCyJs4dJujs8iNisR6Wi1miS+QhzacpJ6Zgt8oMmroHzbOonsIGBUydhcVcdPlrEj0j26jiwGC68fnTE2sHiKxtx9aGEIFM1QOpO3OmYWIFnYb7i54k+USObeoTufoU9uoekaim6QzHsNJ6qm5iso2KUNS56kLfRbOGrRJMRZ7EA4SbTw4pueBNiKUKlIUwR2iQQFBUgRDA9aFAeOwaWpU7AHgL9o65TYPAs2vJ4UOGRFmeBQZdoZskntbShHs0YBRJUKYEYXWoREnsKWNB6qBFwzySzo8PZVjWIFm0Noa03aZ6Z9Gtw4iohNEEKqtnycIFg47t/r1dNQGxm2cdTZkyVj+to6FVVCjAU6IJWfDeKAc7CycYcNadsrkVUjvB4JBR5iREyy8o9HzPP9wyjufkCsUrFf8qzkvs7gszhoI2BDH/ShPX82QZcpJe3MLFu2U3FlPjSXMaWIrIFLCAE4mmJ5hINJzCWCY2UEyY480lnh2UNGtHuf0HJLgmyz98TkMhGEHZwN3zqna2brfeyi2Co1yKdDbDOcCkrHcgLp+5yiGZEluB+0DukRxKRTBHUPrZtafuzujGqKTK9l89nYMPyKygze5i9A17v/HLDVAOtxN5EFb46a5DkuXxl4NXNLhXrU8dzsnt3EPR27gXGyayxoPQYwIcchxRU5om+gv/uIv/FtE6VAsUBR72QQzNk5VsrfkKorlP291KpaRtmq3cW0TSfwVICJBPxoPTRW3lOv5NXym+4GyrbJRS9kIsUZ3MKeeTaqHKlNKh8tc+ED5eWjIScRuuZK9kGtHvzqhrrOD4M80zy71HpoUzztCl3T4PJuySXs5A+7zlskByZPzaAN70BKihSvx0VFe9FDHTo4tnaHuFjYqV8/iB/JBshRHWlU7e2oSAPyBLYG7tew5i/QF+IgGHo5WLZJYlFWg6seuqefE3ttUA6mxLJtadZURj7D6PLrFYnGbbq/WfdeFXZej8Lm7I5exjhxt59blcqUGLulwpaKeW2BcIq+IrTzhO8Ii0jjJb0uHelyKYnXGlUuNZc9mbq9Fv3oepnFkWRDAUkubKV4GPnwNSH6KJ/sKHlKqrjwXsFKUHSU/j8zjhzndiUU9fOmOUGS33c10c5ylGdcDps7lrWX7CInLrUMauKTDQ5p5jvbf9H8+4Cj6P3ymS3slQmeHHS1ZqgZuNcBgT/sOmSTyUthiBFp1FlMG5g7pLok+yu00fFMDo/Obt87qmYejENoJcCyHm+4qITkSgMuBX4I979pkcaTbzmPSIwuAaS8bh/qXMJf2cQ1c0uFx/TxH7xb9J8pf+dANJ8mdbvbfrUjjrsr2ay6efafAre7qrwHmyHpuL2OH+CcdJUAbgEuHtjM8Go69CC+dd6KBB63ezAmrZvl6Mq9mVvtqmq8/I9ytm2+p3Il0Dx8JeceYl7olbMzUuXP58Pl8YVO8pMPn5fyWXfayPg7jrvbStfbC37ITfp7cKnDI6dTGpPjlcJmrn5sLj/AG53KhvGwb5e5y7M7l8tal/dzUwEzo0tSnE8/sKlPv/HyfYvzvFWGnc+eSHhJ/JvEmwNy6NE5q4Nmvy58EvQA8FzRQFNjxgeUlf5hjGUoeHPN2RyEXqtDVkB2dPftxWRbsVmB3xQY9QEVw5Gp3mexRcddxVxQveJYaSLc3z0uYW7ZN68ysWXYMwvrLjl5XzvDm7vOxsXRV0hF2abpLPY90dWbwc567l8YGDVzS4QalXQ0ZY/V4fwJ9j/pvYtR/s3Nbz9JtYOA2nGfCAUKepkwQ2Ubi0KjiUbKMRPTQcx0NbPQGhLf+5mHPVPQbXiuNE7QmFI5o9JDg5dqZDkO03XJoj2YOCXWH/TQ/2NIPth2jq+7qGbDbNxJ8cOJhVKFRP1XEQz3TOZAb2DCWbn0gzdlRTkIlQhCOUwxyPTjB4XB7nAFT7Gi4WXZoN1a/sZHop0dN9w7dOtlk/UN3OVPhX3+GZPAYNfKiNf3J5Tw9IGsPfJeJMMzHBukIS6tdugvVtZqf3fMwXI/+5YyD17NDIiTOSQ0bosD0RH0ALo07C9OPrCqXG4DLdnbJZHUu+2+pKLaeq2TQxQhtVh6t8PMWnXdId9hGRTvMmBkS+hGNnzt8rwHOITGiaaSECQqjB7dw4gjYOeUEMJ0PotFzLCEGw8NzdPEpGM1vRk3WBPpbcmIK1BlkRFdj+SytW/Cj5Wh2Rg963NqsHMNJ4QMxMMBsxl0uKx5TgIHrqbjaJCB+vA2HKepc8fGPXLSSBR48QJs16nRXWw/qe3kbHtZTR4UUkDuMQsIBv4muPTOOGf2dAWsE2cDA6oFHp9pxDGMvP6maIN11CX/YtJMlVescloLRjxYw/FehNnxQubuX6KPZeUmHG+edtWXTGlAw1hwgdNlc0SF/yHw3ElsMG6/TF2kNJMKvIe7oz0mWLC1wbGkOKo3Ga/hGmg+oWiL4/bvvZuEBdaog/hYa+8YMKmjhn8v42dGqu8UFGohPDZ07YPvo3LZPuEHL7CDnk1o+qeqbkz6p7PeX/vGCbOFAA1eYibfbkrwen43BiQHT4Zd5zNJv1fWbF5z0jz58wc7U+L9COv0fIodfsvsZQ7FyAyeT3pKI7Kj7HyO+Iuvfffg2QqowHZBjz6d0/csFSpgZ1G/UBtLNaQNZxeTgsMGv00f2fdsMdd/Z9+PUeMAGtoM/l7RRJHU0UAM2bQpEbrBpLwVMxoBjGEs0A0wjbWQY3b15Dn6JE0VSGJXJYWOY0XC3nkijBUzbx+7NDj5904e3MgMYwN+k+Cj3XNLhxtlndmzL0XgmuBdRBnfo7t4hJzuRzgFAZuuMnoc460FrnMfdDYXwIQZCOy5kf8z3Qt///vcLsj7P7Uuq4g5yy+hwCNX6/jRMuhG5sagkr0sKiaie6dQ/rK4ntxlygjKi3/d93/f617/+Xe96l06fjvO5HL8ip72ZuDGbzeRmIHK+3eq7LYKd5Oefh1BFH631qU8p+U1vepOG/0Lls7H+HxMefB5BLjRl0uGEzkG4spFd2YckF3Eee+wx1P13kR/4gR/wXyHv3bsHj7ko6PuCjP804v9d+N40nWSWo7GVFANbrqWadCQg1I4lAor1/i/uW97yFsD+JaTfLJJXfyzhVnvDFDRwjAoSsnfAnIniITD9IKOiHfOYzD7J7sB2wzVcBrNzhsQonfFsuCFl0BkyaJsOAKYVfAz4rI//nvihD33I/6FUnymV/CcQM+IuyHaSd4g+speXdHj21DMjY1hSI9minhIez2SF2hl9l2O1Z1M6MGBIux9yDPBDZ+7X3Xy1AHEAzcbuPBwtgvc/SKVDbQGIvP4ja6rYiH3fMLIUBdxEF5XRsJ4kJbVbXWq4xGesDozGgzuWEY3afWnaV1VpxidVrZZ8V8W/esD2cnbuhBnLwVe96lX+KfQP/uAPSkhIywEREunMi29+IiQgmiz9dsx80M4/Q5ALY4CW0uFZ/GTb5sVY39d+8skn/bsJDXqQm8Vc347pPxGCxAYN2BIEfz0tV0Vk8zVztJL6cl+ROEYhR0bZiC/4nD0NiPVWw9/61rf8y8C3ve1t1KIcwRL4iHZeSTEw+M1dx3IgVERwXpooeUHqpJyRt1F9p1DCpn8Ml90BL3Eu2xM9lsxDa3JNZQOdm8G2Q0e6sqOS6KmnnrJ2xwkmXdow8EFUEiF0rv6XvL3w2pd0eN6cjqkVlBkTWxyTGtsNKeOrwT1y3fOI7YNmxKzcssyulGiLH+FATFT3CXN8TLzw2Q7/2KFgdFcZEaHEzAO1SVRM5NKczfqAk+f/5B3Z9wlxRh+EjrBBi42UjCjRmoVYAtZ0gOlw6UCs8xlUzwed+cWSbTr5SfoxWXRi3WaO7E/Cei3Nfd/rOZ/UfSP8P9if//mf9594TQH9yHNiotDPHuhHDqYcvLEZ3/y0apQYrNJs4QLDKs24mwLvw3vqwoyQDsU3vvGN/tfEj/7oj1qkwgmzlahPqdktgDzqCBEfV8SHuBncZiRGNdHyLorWo/al/RckGZHlW4OijpB/B4GK3KNK+Mmf/En/hcauKeUwDBg2yDt+BANxOnBi5W1+HaabG6JoK1IpIB+rABACEzkDqRp1GmAVgG0bhHaM56bWTZxOGCh8jBl8n2x1V9stVLCUm2iQvSG2BNQEr371q1lgan/Ri17EPn1DuNwZ/pt0H82eSzo8Y94ZNOjOrI3pu2RkTNyOROsDtuj9AqYGDIwDTOczKB0G5T8q/Ve84hUCkGWHjCgOOlDkh6pjixLeKFs44+Qwpo13koX78UNtDYg4Knnzf9rILTcSuH/YkNAt/rY4IG8bfbRN8zih/BgwKag7sIQ3nQXl+7He/dVMMX78i9of/uEfRh23GKOQQg+qwAqId8WBMCe6CccQCpG+4yrI/s///I9/+GCFSjNFzPSQbaTDmNGPz2H+LK7YmNj69re/nTVSsgTAAv1Lzn/6p39qJzaizkSuDf80thElkYMlMP6XvexlVsZULR3CxjWIxh+//vWv0wPjty4kvqzp/+iqDv2XRBPBZnB7lqSAjaIoJLIrCmdpLv0DZP7o3L+t14mK/z6GAUqYfzg6gkNicUYJGkoHsrh1ZAp2nizKozgxikpJ5G7mRHw6ccaeIyWzPYwplRgJEs2CMsWWdWaJ/3P18MKGv6TDjfP74z/+45zQKwP8zSMZVSrfY4gqUM7gf5Nan7kM+zQ2ElsM43U/8zM/83M/93OckG9wP6bPrJ3FJmsFbQ9v+P9nP/tZ/yPbknExentzR4ScEDrOKffwQwtWMYI27tzHeHJ8I6Sh4PWihD0fhPwDvBe/+MWyIzasP/x3J+sS3u4rl86lzO0ynzlSPGIPinHPz5QpigNhyCqt8ogU8kcsCUaV6mdS2AMu88l/ECItvPrf1DSAFuWIxShqS5MA6pQw6Mp8MZhi8R6kK7ogkYYtO1idvEvz/oeJFRjDsAZFBbnwD6FpgHELDyvo7IJAa9LVgjbnX/rSl0qKXAAQ2yCstaCplw6pV9z/4he/yFS452te8xpcWTXKUlSRIe2iPnptCJFNseWgbeHXve51Zpm169Sjn1AqIZd4Ewr+9m//FiemgNQhRtekiBu/8zu/o2gQJfSD51lHJiJHAwAPVXtzzcaDBG959/nPf17JmzKhwiGVukw68PwCMy95yUuQcOksGihWWAj9IL1tCo7q6fl985IOT88fW2fH9pqYoy1KRsYDPSbhijr5p4atQt4YLpann6U6BGUV3NTjp4mdguAYqDPlloPsW8N/ATVOp5jIW5SEUrVnJwKllwiEYJ4wbgMy3zhF6r77MLhOFRpcTizQQF1W1vaQzP6P/nHI+8avuMB/njywdKut0y0kemvRiypCoeyLJRSFnmI9TgCYI2sUGcjZesVYYVEodMDG/xNB23DH0FrZGNUREzYYUqxzRkL54iNscqHVGAPwUqVbIJ2jsi0MxT9hS6U4YYr37t1D2rwT9nOf+1y7lAGg1doCaQ3DU++OklcKPmDo1kbd+kx1ogT8xje+IUDLx27hEEVnRsgvgHmibBbQLe4TP1kG582GGQcMj1ujczYgF4rv9j8dbABOIkvG2DD19JBZGqUQwY+HZNhQrcYJrtjJTXIne5pl9iYLvvWtb8WDasAopUZ8qgm8SoZhylejeGCBAZzj0Jk4psCOrrjBQgyUm/GWTtzVE6TGUmrI3YWB3Vrn/fRP//TLX/5yPeT1D9TEHKMMcWRUY1oKMoJLh9TC8AjOKfIL4chAZ2gbezmngUs6PGEJ/JYxsS2Fp1pYYci1RDrDWCqTcmZVbJ2bMVM9TJavekLDXi0TGf1J5z/BxOI295P8pFuHhOd7/3yPH3IDPVzUY3MLRx5l2YQNWzeoY3L8BLf52wLr2mZIIDSAH9qu4faKYiFPJJKoZILN61FMxkccooLPWIVcLBB8LQiEJORMgdJbkKIQOjeQh3t+I1jwfIf0gD0Nj5GsEkBKTuYIcpgdE0fWCn8NFz+aVFF7FKtTD/NQvIuJDrxhw7JVZHRrIAfJetJMK4Yba66pS2j2/qQVg7jvFVbv0CMHJ6mbccLGVdwW09cTPQKJGcHdGt3U/Nu//dvXvvY1YsIvG/EFFZsyhSNYDAH7zGc+Y7W0g+24Hc7sNFmkJpFcqNqTjZyZgQn97//+749//ONWpUrSD3zgA2hZlI+Y1C5ZWh062Iz0kDZ2OFlzmXHa53zD9UGf5FUH4EF8cCi/eLqsz8YYnksAtOEWYMNVjVIyPgUTeuO8n/zkJwFPiYmNma+s1Cjzrk2fSj2b0kKQHpfE5wIkdZelGZt1jWlRBZdBEQ8OABzzX//1X1Msunro1vlyjAYu6XBUsb/BvBiTLGh/0i4lIxP9GRNPEwXsiDIpRsnox2pZoWglN3jHXYDWL10pHvcTOLOXB/odm0ocUV5n5QdB/iAf2Bqymyoa8g1s8xzsYQBMfqLtGJ9ZTxweOB2G1OZXEpJwg66CFy2Vr61CdYA0EEvr8Q9XNZyHZzqUBd/73vfKNEh7GUT4c1YKKLFBir+ARQdqFzVUAyCFDDNlFtTROr1MQW94M1PETwMJsp7JIFF0pIphUuRCUSKUABCF2SxQgujsHLkZMo31pAvH0BbOQsggJQaap5BvfvObnhxX9QOOhHPGEJ8b6B7iECE8EA1OXDFFRB3YE+s90nv88cclIQCShJkCFs+HEN7sn6wGrbF0rtrz5o6VmW15DviVr3zFbxltPAr01kB4UPrYDESLTqQZdmh1zkpNjd0LrsEpGIDL8sFNood6ICQy0ViXsX//93/P8ilcbeoSq4gixBRj2zpMT1ToB1qaIYLpYBJGebib85IuoldW9cwONnkNb5SzDMq1eRmTxj+tEk0j0kaNbms7S/8FotiwO0JRH/vYx4QsCGNyRsXA5XxJhydsgGExQW6gMBTmsi1LIl4nANkOZVhMHBbu5/D0TiePVbnLnRrcRiV4gszq23yAhzswZhBP46gYkDMwqXIVF3gLz7RDZW1awBr0hZW5PLcx/lODP4tK/Jla8KP65rFcXWA6FzNB4IEkzM41SPfKV76SXM7WwX7sb/uXSysLLIlaF0Yr/UuTVgzSnuyoHhcELettGZlEocpdcWp4S4dzub4xA7EnzHWWmaxNLcrd1WlSLB2QYyGk01ng0y7SrScHkjaEY2gbCznpZH2rBCX/V7/61d4VzBTB0AZTMTCtoj4MnEX3EDDNF46hlWPEaz2YoW1R+Ed+5Ecs5VmCktHS3FM0LDWhhxDe7M8kRlfiOwv/iZ/4Cf0MjzF8+ctf5oOyCwYsy4T7f//3f+caCJV74KR/yZIS2IOFbFOQZm5SPNLThErwLUD/5V/+xd4PHth/2HamlUk4EgE/pLBcZsNWeHTFDqVDAOOe1BhwPLhMXRr2hL0upA7QQM4GjDRMsVaf2cMolqWlLmaAVUW8+gxal4ZQjlFL/DPwiOCP1K1LOjwx3cxLZLHC4HJA2RYH494f/ehH7YcU9xmlWwzXmYXx2/e9730qWVaok0VatZwgs/o2BuB0IOTAXmsjAcgbNHKhwlmAUI/bJvVumyCVhxhYw6jV1J4FXI7VhgQPnFl9qg05JQjQIk78PDtyXQs2gMk1DVU8zH5I4M0ggUAu9Ca96G9RWChxVwhAGg/OhosyyhG/NNcvBVqt+kW8GKqasXQwNRQiJpoaUazguI7Bp6HiMB2iHttosRAbWR5q6oEWMziUtj3WUsjTWJCJZmD6PIs0ho2CFnKx1YwnDoXYI6V2QiFtFlp2oILPCGm4qx3nZ9E9BIwNdM04tPfu3cOY9PwLv/ALVI2WYsWbXBZwonBLySE9jZR5CH+cp6uWd35Izrz1cz0LYhONB+5Jdhswik4NkKaDHhinsTjECT/VTzNMYjziEN1D/fTPs1CHXxDQRq5cSPaEIpG72hiDx6UDh7Rkh8PDCz8LYb2KOVYBAIfGQmJI8upspogAlbCjvGDDMiL+wXAEdYbcD88Oq4bUo3BXlinNq4qwqkA0Cq1gKCdCOxge8ctLOjxtAKyTVTkDZcSiqgchbCsj1slqCz18QO1mX5S5a7M81iwWZPqnKa2AyGf4WO6HLk9j9xKGJRROsGrx6jGS6nVWaddeeZVv7uSIB6Q5la0wK2BlOxKyPnklyInI68lNLDAkbp0F/Te/+c3WGWp/j4iEAF4tnBGzYAe4uIMZGISnllDu6nepJhAXhGmBwC3sgTQvaW89ewOJK2Od9QzPJl31Iw3Qg04HAGYgQGO40BOGqA+29Q38J2kN278eoTlbgBKzfQsmqo09uYGWIEcOJ/jRNnyz1Hv5VHJ94QtfsCK0LPZUz2IFGzRsfebNGu/1EF/IxkPMLJGs4SS2zbUGqSUSK55We+h+4hOf8I7S4DS5UWl/ErxZNhADjAcP5oib0JjpkFFS5gw/2eBWsH3729/m2tpWnDZgMIO9NBy38IQZP3pA2jKxRcE2lC+MRHlku8heK8fJMIQR7M1wGLRLWvzIM5rXvva1ftmJCjB0//iP/1i1Z3258/wlJBbBtnM9UbYM1YZK8AFpRvwi0+VYL4QnpX7UAC7p8PSMF4ByYBFf6c0llj7AECfWuMsBRAdxQXjSzxwBnCazDgJdnGApX+IwVoSPP/64nCQv8k8ls+0pz8lEAT7Z6g3u8bcwrKN2H1QDnfWmDaL1+7Z6VO6YQVS4uW/kmRdCjHCAkFBrf4mMwpA1kL2ppKZPd0mUUNqOiEzktXtsKWAiHnvsMZpRvojOQljRqnOEzuIuwYecS8zYAJCzBWuocG7eWYjX/SVj6xidxXSNGXgWUcDRDYP0YxlquSDewSzOEtPDISYHzFLALJDX/pjoCQBRXA2Gc0nvhSc1Ge0ZiryeGZNdslF+mSnrNuYn3DMPEyQltITai2dlJ3Iyiqlk+TKcpCIfwEwuB4U372VBkprfBNfAleGA2y9115CVdAeMCKhYa6LuiYBdB21KZuojHYrg4ddw2FZVHlnbqRKoyKS4ZYhcaGcbV7gFpoLBGz/FZIx1ZrQeuPzsz/4sDIUdsnBw5bjnIJML4YzJTIvCxR+5UPWgjTdFg8/UURfDAAksSGfexGZGxkvjkg5X2QCrcjAvJpunGcY9dLJpDZ6vh79Z0HhSJTmpRgHzUoarsYrMCqB8phAgTwgQIuMv//IvyxxiH6fyvNCPzzBWgMAbo1/6TM6wgtR+kIZDSBvISbr8GSi1eGvOA38lsCXR/sGre+F3iAIihUNik8mshBCFg/gaOHEEWQQBSXBnIsvNKmsbmOKCS8tlW8dWMIaIQeFZzc6zgMi5mAnVkP5NugCEIiXoASMLWsH00WoUm7XB0qTM5ZoGAWmDmZHOh8fsnnUJM2OTEUnEFKGiJSHbLqXtAemKQjAQCYKnqDUUj8NARatMjizwswThW4bwPM8eqcoDYykZ2KCKk3Q4nXsbIAPrTMmW+ExLTCcdclTdQJCRENkzRXpwN0nxSQMg4QGJqxm4l+6RThgYtsdvtG3nEx4U5f7UTg8AMJY99OqAVZ0NUlMGLdL0o1wwL4Hx0LhyCSD29GjDaVfDo2hv53rWCICziyS2352DT8PO4AmbthUlCkGZGD/AiC93/uEf/qG0TUtI2DSeyJBmDL8caeCSDk9YAgNiPWyOMWV57Ex0ZqM2hfSw9ZzQ0yNlox/8Kt7tV8DLQD23WG5aniC24nZOzlvwIw4K9yhKw1ZOnmiye6sEpp9vwFekGMSczS1sD8DcOtkwduIptbhEl8gEN1YPtNjjhNonse0AwIylBiajdk7uLOyC1xjOC3nRLYK4xb3NTglASW49YSKEJ5ELBpV1o+CB3MSdDAfAHOGPYW09BsaJS5HRNrUNMWEIZgrv8ZXFkwbbADOzAACeuQzn8jwa1qlt7MyUXIiEd1UsTVQ/7pp0icchBborVcDs/UkvdsqXqhPxlwVitRwQ9SW5k+3Ed26CYgZCjLFz/yfBylhb8eeMll+XqzmgTT9LcXSCOUkxgIZrj8ZSGn3aKrQ6DGBmRyM3nCEaYDDQBimAYOQwHn1EFcDi03ANRz0mXSa25WgvWmLmaLJjxgbSYf3H3uwT0LzHuuZIDhMrcOLn/3/0R3+ErkUzlkyHY5RjLMXqAdmhh0ohYbF6OntY4Nsa1pfls+aCIFeCXXucsyLJ+8bZNssvC4JPe2RpLJwnjf9pVh6lP5d0eGK2GRAzVRLasgDK5WyAiK2igELPrd40E48s1MRfmxsCcaWihytKM1nqSPg7Qf7AbfgtevyQw2qMt3MMDyT8ussTAru1eOZpYIxGOlfRGTKNaR9Af7B7BtaIDTHaAA6MKL91dhxEceBGvt1NDF+x+L//K4HZjLUsQEIAUn9YjIIBPPDAEBUU6knVGLA9ZaCqRYCQJ8QmyWk56khA3GFjyfIkUajQtSK0aWlHlxkEhor63aSbiLanlsNPtuOqKZs23WrrhFwJQjopBwmTLjhKQi7REunMiM0xqVFGtEARGS3XJMXounuuKVJvIjN1DODEWZRneLZJrX70mBeM6Zdp3Cqsx3xacnaclH0JMLJjQFaQ460+IYG85W8I3TUKD13iwVGnfp337t0zR+WkYPSHfElu2TY8PANfj1Goe2/ZBsyv/MqvKASt/KjUTgAVSYRcEjlqFwEglIoECus5KdAy3URUkJmdJbmddrKg5UBX0qJSlpZQyDFpNtA86sRkt+LZghIbYLAEs9cIbJNOCtyhdbnc0cAlHe4oZPeSh9um83I/zxdx2CXPlPmsgYQhZ49tGL14pDb0BIWZZqOABSaRiL0q0+7KIvEjJXvlWupl+ho45kJetffWpVtiZb7knDB5y65gZ14XZcbxNBAtCvC9IUH8CSJnUWjU4ImcYlz0EXlFGXWGNQE9iwV0Ky5QhfiCilENxA+pMeaw12SmQkv5poBmunRXY/Szl08w4dy5W01dXsEAxkQfO1SQg4xt4c8PACTFvRh2EN68hAT1YQ8S7UyI+IIvnSjFPJj043drFJOeRElXBBSR7d/aLrYuYYQBnJsL4w0/00BC4lcBfPCDH6RhCpePcWg9pGSRCbzcjxweKETcd8sQx00x1/SkT5XQ448/bkeaHkyl7MvjDM/UNUbPwXdZu+ThDIy3OszdEdKDM42BhMdRv8yEtPn1XNbimJKVXKYAflWyM0gpk549GgRmsnDLGMQK9iB6TC6Ef9he6geG2AOsyFDMuWTnMjrdgmR1sFHFFVvXwM7YI5chAoKJYC0guY9y3ARlrkekvtx6WucXRRzXgFgDgHF/+tOf1vDYhuXxig53+QBjFQ6sP9SDepyFbFbL93gvw9U4TmX93WKftakYwRtzWiQUp3ps0Dl4gh44+VulevhznnHC9URBGmvgcuw4IYUE4Izc0s/Xkwjz4IeECCKIHO/7kGKNhyhEMws9mBRxHNEtEEhUqI/sYoHhAoo4ImrbUZRE4yc9HOftStRn1tPgG6KHsNd3rhoqIVxJh+VIMFiyJSBLeZtJlGx2jhPauTsTek3z2R/7V07phN+PqYlpkSTrxGQ6d4aNhYjCFiUWiHZWrY104taQHVprLlGEECR7JqY4Kw089dRTMHMKT2S9ZU3D9mZlLLHeXgVtZ6Xh36AEAxuFujacvrzD0aQiJVEr74SNREoAPDJqdwBWQ/BQzMsizADbjdp7Nko/hHkQKlTHrqxQ7Td4f7vXUzkg0wJm08hd8gJAyIxIP0oQ+0nakECIKBhosecSQu16NIaitmNJF3UWRfNg4kFRbqtcPNHfQDgdBkLrUDQwS3eNxZKwIC4NrWsKl9NBDVxWhwdVMzeEAAHXrp0XtKQc5qX+UojpZGcszyMcKZD125cQsmVKIYPzgMlpc9dBeJsGosINl+bnHA8npV6F+Tve8Q6dGLCjyCGdc60luR3fW95a0zacLCMOP5R1BBr9hIUBe1Wma7DdhAmzc3yKYkTwc0PPUZThPvgiEFiCSwnElO0KBDopQZwy0INMhwD92PVbiJhRyFvce9fRNEWxUclyk4d6RsY4cVnPRBaLJGHaAt0ZFWZAdvHae4Pe/ZOQUsgh/If6422HB1akH0LMsDTvJQLQFv5wNTamDQYkpTFC8Vok1Wi4Ie4O/4cY2OlPap3ZktfE/MDDHqwtE2+ReF3INmCYbVcoCu/duyccSwCsIropcAftycv0QEC6NZV2JmHjYuZRVltKMVSGVci1u3SGRDFhagzPXI9QH5dpOOZxom1B7PevxGdmVFqJ4C4lq3uYqNmXDjmgxbG3oGkAFbzpB+wMc9g0Qg5AT/zXnn4wNExSOBkzivGsYRcKGIT1dNYDlbYGhjtoyQ6zBGne2ecS/tLeq4FLOtyrlmc7GStfcrA/Pyj+8Ic/7KUJCwLZjv0phzmYXRGJkP9zDBEcAKsVFxhl/uCc/zyLd2uLWTtQtDiwkSILOiwTEfVmDaxM306afy9gXSUocwlsLKlhe7xu2X+83ZAZqwGtPCQzlZbIiDEK0U8Vx7Edv4tW5IBB9YlPfIL21ON07rBr6imp1wrAmBecIG2x4rf2kqIdPAFL+rSYEAsAeL3z4x//uPrA1ETXQKMcQ+UQPzdh6Fyogp+2rRXmIylm3MNCNZPHt0IhlgTKoXgI/6H+oVvUQ3GZybTh11lW0BDxl6gozaFmwqfVoczNWgCAXIKtbOPBzBqrEPEz0FaBnoT5ZaHkRHCTTskW8dYi1vGeqFG793gxiQ1jVxK6CSadqz5tALZTSgr2hhx5mdly+qbtlnY2bzhu5TCzJrXIiHooDWOp7iZFYym/fg1SE58OvSDzxBNPEIdoeIBBVerAnrLg937v9yzH83HZC4fGuqunzpSQ/odVMPCMIC7nFkiZFeZXvvKVOlkyETTgLLeF1hCsdtYIAJPZA1aJqfOQsDfFf8R7LunwhAEwVibIOpmUbKdkc9gJYXBMmWmySysVNTujBOaylMCLMsTbhIObzEGbUwkNduS8ZacYFyw4jFWRwOEBg/WKzSsc+pKcty3ANyRsuLqJ9mSPUQR0BEl2OEnq4QTxdVKUuthLd+rZbSQGM1QO/COhQbd25Jy9v+C7JLLdu9/9br9NpoHkMjUClnyJPcHIfNG54YZ4D9DyxXlni2wNhyNsjEFI+ZKcDVLfXinN0EArMDux3rCwTWovl4UwjM250Njilx+QPfbYY9rWtWwPG5kc6XReTcb1ApES0kOz4Cx0+vmpzQPqUipJY0oWMGukTtjlGVGX1E5w2YWFS35sD379SiJo6UEpYO/E2kX6sSJXO4rLADC5gW6jkGbY5nQkRd38sgp0Q66tAXIaaU8pwC/8DEnJYu6kK8/ycQIsSKP2HmgBgDCcZkFOsjkBWK2jArD6h5wlWA1/6EMfskNjk0AxxPJloJl34pvKBEkDpgBOeHTWPxNXZ/xkALB5SPzbv/3bKjyPgb3AjCipkWOHQYYWqrlEnbpcckZrVu7P/jfoP4SP2vmSDk/MuGDHAxkxm+NU3F6gGSOeyFUIZnbtowJwGKI/uz9BZvVtrp5xczarQAfeVOgICZ3cRgjgP5aM3EkMtbEGHjMoNLDzaoLPAhLE0XUNPIgysk6BQxCkHNmRoqL47OBTLQhjbEjAqec6dl19lMs3uEVbKZCkZCSs9be4YArAUItpcqipLQJQE7/cFbIdchUwLC1l1x6ie7kLoFsgWYKNO09upATJWE4qKjEJMQhRbzM5JgcYKC25uxf5kc6YRFFqsSgRAaUW+IVakoZ/hhf6AetpFjTwZi68xGGP0fLF6hDA3J2xaxr0Rqsg7Yj4CiBsKjBx1mob/sGAK2tiswOM5pkB5WADANJLtc+Q4w3cmq8wjM2zq1CN1CHXP9LNLKsOGQkNONOh9GCBOLNzhKW5lVYVE+oA2w+MUCL0xESWinm3ZEr47R7bkGd7BYS0jRN81gOe3eoxg41dqmV01S2yJI4ixr6rYs42L18mEUhWIeGlCmBsUposArh0y3yZfdPkJ17SIcEBIx3yy/mIBi7p8Ihyrm4VCzLizhlWrqJdYzxfzOJOTF8PK3Rmo3doi5BD6AwnT6sh5spJPMGLBvwW27hq1QI4DnXWJoVRV7KdeSwH8nNoHYjW3xkJWLXPxP10qjZqdJVKIRTLnMn4kY98RLVhBaxgFxS8KSAKgCcOTuQk4djC3ZRZH/sv7QpqA3EIIJaALWVftncYNoNC55WEz7ygIejYqu3dGSRmirVFq29+85sWH/IueKgwD2BDLixyYRtv9h7f+c53WhpCa1ECbcWZhkSLhEMbxQRxdrhkGPC4hTc8iKExM6HZrZVHEw1YLeJ9FilQeeE3/lMD0T+KzliiZEqjKFOTHlZSuQmWGZBiMh8YbSRgdjRB5NXv3PySNHgKZComy/45ANza4ZSuGnWT3PQYCBW6GqmX+XkVgEQeiHibF5KAyWsbwNrL+pXTsY0KZatGQ+IfZPxolImHUJx3GaEZMpD1mETbPEoQXNmTYBVW6ixBqCEvPv360+zIytFiLazRsxLnegbzUL809mrgkg73qmV7p0dWVmZMk8eKPg62qL20/u3Yr0fm+SHJ0Dm5IpG3eJsGdRuD/EQNa1OLp40z3CEPIwJ/Q4uA3NLekcWBGlb0GYBbNhLWWVAgC1e36pXhPJqSC6lX7CAXHtztp34gNShBj4HaNHOu7NAWGeOfUOKdJ2dSrMlFTpqhc3c9x/V/yW0aq0VcilPGojtx8CwNNFlJLd5BYhFs8SHCKvatvLOrEm2RN/z6E5OkBjIDMRrbVEQPWAKwgaUrC77e8SMXcniwPIoNCB3u4hlpbCBtUjCPoqPOZDlLCYChjVtEHXpoQAayIGPY8lAIAxsSiOrHqg0S26Q+UqEwsrC2VLJ4ZQkBHDGGUAEACbmNXy9zUqPUwpusibFBLiIzM7mWBpDj9WzSwfL1x9udnBFyoBLPlnpcQOFVlYMTRsgyZWt0XXqVwY6RrWyb9liNhxl+Jyy9gJFc0uEdT67MZLOI03InrsUQEbhbc+R+PESwgL80IPx5lUYtzHttZ1kkKWM5sIoY9XH+adyhzNwSMxBiBmnRR2gQv8YV74oW/EQmAooEhFa6cumIxFLJCnnpUDizlCxpbWDDcFnEcAq3aenNSW/VexSnBy1npAXZKncU00PzgtwwdhZpQoGHHzbh23aZZ6KeB0sA6gBb3+IdfkoVncOPHJMzkEnISfMw1UTAA1IuKYmexY+BEJpWRYBpFWRJigFo038JRsKwGhOU0WKNVupGxRJyy8JiJfVEo3/zaEkqpSHa61SeT+tEBSpWEf7apgwYbj018DzPZOHHW1ef/vSnjYrVXOYQG6Xw7uIBBvPuEg8qHlOg3RyZIOyRUR6SBbGU8qs8YuwQlbP6oY0iWtCyBIcegjBCO+FEFnaAEV8/xqxT+2AFQvodxp5F9NEEvqTDO553zqBmZKZ8z5EVMsc7JMNLQ8g3HNr8wfuW73nPe8QjRD0j+eQnP+m1NLcALEnfuVfYKUIdWoddMrJzSMeS6G3aVeIwwA9t4tRI9mi5C0bbYYgKWgAVL6yQrFmDPIsNeKIli0gtfm/qKVE7b/QvrwjHQqRHOyoPD1DBwy8SFcd3GF5P2pRBErwgazUgIks25tcWsTn1eFKONMsgqzlKPNHFBoZlJi9YaeNT+gQPoSHr2VhCCrUW/diAzbawM7oiLxgqYurKIC+UtimCDWlDODYjI4gZSZlLtCfbBITN5qR9Qk+L/c7BVHpHjCAYsBaX6siIPbTCjxnFqM8WergrKRrudy/W7jDI0BAimqkcog4PAJAwOyMBkmHza4KrMyS/UbVLMur3YM8hS4V2mVMPEVrfnyZjidpRTF5sqM9+8Rd/0RvO3BDDNINhxaI2HgLTnolYT/TRhLykw7uf9zyTl4Za4w7NkT/kjUp11g9ztbD9NC6Bll3ET33qU/N+Y8ysCQTbFCFMCBByAxJFAWed27AdGZUgABDi5w5tZ96u0V2XGtKDs4jpUiajFo00cAT/zq3wUDU9C/eyi1xI4VAR1m6VLGitpmG1BAwbboXELOiBwbGD9uTlmAo1UqwHh5KiVZFAL/bJCt4NcUvF43UJL3Pat0SFwnUSVtKyT3jv3j1f5BGmmQF7AIk9zJ+kvheAJgVZ2OjQtgdmsIRPl3Qi/Vg6W415yYiqrZ9sKko/wUNIG9vsoYmmbc/nqMLOB7kIKC/agbD54Q0j4vMIsutxFw+0JDHjEwNSqceclu8t3UwQSFOzV8w6m8Qgne2v0KHvItE/PRDEcEgCpnm7Mswj+z+CdvOtlGA4ZsY2XDJLzJhx022T1l1TTGS68oazxDw2ucEIN3P7fB94SYd3PIPFYibIXhkoo9yx41vSgzMMnIFbCtOebfh4qfLcLXt3Vg9f+tKX2q0qAvKocapbUr85XCDAhhWJW/ZnrJMwRuSbkNt6liEgDJA7RqKlt9OAy6QWv4K/iWElJ0iAtDJ7+ctf7kxSlybU2zqCrMdRvVWrc2oUbdrAXlxpn0udCEZlNvQp6coxOGFOYp9U5PfgqFiYysQitZWfubYatka3nykLetWiLWIrS0zK2QDclVE28NPS0zMzm41+UGjbVmqUeHBIaklIlqIfdCVCi1csscB26clyrvhE6xhWUbHc8d4m5FFEyw4hYT0zUxboRMhCzQsv/teE9AADtXjE6OgXuhUo14ZzZZmG4P9pSjf+dBcAtNKtla6dBp1UQY3A4SEXPfevLSgEe1bh4MEwv6VN3kB/XgecyEW0RmYpN+uMVeTQxTCDVP34tU/r1CCNGmWeR/vRg76kwzuec8aXabLOrHBzVb6XM8mGD+RvXjAToawLxQXhkut6ncy74G2m6UE6l7hD/9zhSp4QZ0UE8cg6SSUuiG8OgjvI55IUCaKHhh0unbscMA06r1/kAmMWZuAS7GS7UdKPcO9XhhYleiwyxF+50NdYJCQaLs0011dsXQfKEX8D6WYqVBY31mEWiBKwdY+s4wVCt5BDGmNSI1ouWYW4WQXgMgYM9569UO5WaIexk+IPABNib0zLNqnDKtDP+FQ/GJNjMIMonbuUErz+amkocRpuVEjwNtjWN4ZVyEnEwPz2VMZ94oknaFUa9pqrrxSRCyF3sUEnShaHVMEO/TIHPzhHdOwfw7RxfF4AB0DA8h9ybNvaCye5s2UxVdioJB2P850HL7AYCGBorRf2COToYWCIoE1qtBQKNo1tZeOTEvwUxGV746KQsYE1ZDBcGoc0cEmHhzSzsZ+NFia4DYvMrzTyoo1IbwxDwgrAuzMek4iJimU+6ZUB/2pOlZoLTRhaOkP8LHtu4D6vAzl0bddYnnoF3/bdPEE5D9FhaPHLzZ0oQ4SdoKYHTP4PXpKmB5pv5XoY/f47sFGyXTJf/rTgEOKlFttQ4r6MKC5DLgA1+Ob8YsMtxrAf+9FeCYAgpINEaJMLhTkbg5ZHNgOUPsogvLmLB3xqjCqYmbGCo9RloE1LKRzCzG/y4lH6uzdRkQksO/z7MN8E924tlco90LIxKxILUBzavfTUEMMYSCG42kYxDio1zKNL8nqpVaUlLdkOpQSLM6WYW2gxPw18ykkWzdZzbLLfm+YFJiIrSl1HnBHPY0XSKu2xZzxIfmybsARnFZzOrow85D1bUiOqHIQcDzihmRratzywDa0DHrw5uoRfm3SmxnQoetinbSFMgkztWQLZNW7JxiMy/JION050BnpzMBtlmoxV/ciLeCzTPOJ+NzEc78nT7BT54IjXKzxJwoltMTtU3hrglkw/679ynWeWUIPzENsDcLyxHK6dz8vEKEoSQmEMHEdy7t3E2Rm15GR5a9lPV8baKtwWnsRZAwVE4V4y8PL6N77xjSWtad+c322JMIRhI0gNQdk6T76Rkq1W/eDS7HtZJrtayjv8aBgiHwiXAIaZvZpcjrrZpgSzDElf9sGS9GCpKgdDqxiSe7xPREWMUGgOw4hwiL2bhG72lAjrJ4jD1jEqdmLti0pFKkJeZo5wgh/JEpM90wUWD4N2ZN/pH4AaGJZ60XKpofqxAlOLICE9SzxuOZsCkDhh/HaSaUlthIfJQztoN18O2zCg6AiVhgWxTXJTYEucUCoAs2DelzxYK28m/QgOvKTDO5503uIBHm8UsGxtcVcGvS0c7+WsDOQRjt/eemVAreoByUc/+lElKqKcVo84wluC3IvkDjsFAum/tydQ5H5I36G857KKh6hTu7iAGTXBNlUINL7M6f/ZVn0Xc53PZelO4Aki9sk38qIwDSdOHPS/Fz+pAQPQoBDaSCHOe+EPdU7y0GDY3spReLExRQY2UEfCpDN7HLoc+EMIb9lv1Ss/qU5MroNoEJpfbGBAAyd40HZLflrm1JWkoQJpLNVpWHW9//3vh1BhZAp0snZJ1y2EyK7TXZfn6taQzQc2kJb/7I76Dycu5ePKkZjZjPlRHnhJh3c8+7xIvewxj9DgZw92dbx75jlTG/q3J9ajGpjtmGnbDfO80DMSMQJyAYJP5g84ybFvT/QIhsKfGBQMonjYln6OUDnrFs0XFo2iDZeCxbkxgiCQCKZCzLyYoPMsTu4QONKkEASLepBTda9UHCJE8MmFYDbEa5kPUQM7KMQBFa0i3a3R7UOYd3RxogLbEZl+sBEnTTdmNvADD6+BwdmRpHY+NCb7yn8YAKMHjIUcOtYAAEAASURBVCEOjjDkYmOHw7u9RAIDcMqCSpOQ63FcaeGZReT07/TcLTMvGGzfMfd+wWhwRxDWablmz9Av3ryB7R08LwR6G2IHbPOlaOhJofxqi0aKlQv9zznYlOp5b5mJV4iD/PPhuAFa4hRarRI2S3cnAwUpeGhDvJbJzMgGtCmTAmnSLlmilfs3YLv9kPi5iQd7Nzv1mA7cYnsGmiPHFC57R93s3Em36RYYtGNaGZvzktxNVHfSs5wCSYhEiOocMaMyfMqdZ9GdlJYCG4vK0+XAAlvyIqSBgQZq6wE8yjmL+lnASDhQpAcDmxE9ZyG5AC81cEmHS23cQVulZkPJDpsFnM9E2XCzm7ETU25DxooQTu902LHheKpUyLlrThhmHqJRFX8bWmvGSsMibHl3Qk+da4Y/CJhymF01z1TMhUduG2JEUQYqQjV9NEzPO2H3QfC/Fyfq+ptZ4qRq53puDikX1h8MWRw3IY/37NBF2mGIEKyBgeuOZ5PucWy3vxs/Q3Q5HW6R1IErxzZahoecAskIv0ao3GIA2tSY7PUPrRho+Dbq60dVfaKFmZSAPQwPt+tRXSBHA5d0OKq4mwajtIDzvMGOip/EyoXeQrTjdDfYr2tAqKQ6D/k1+KcnW3bPJtIVL8ZF74ruITzcbyIIZrRxcu4S5BDybf1FKxvI3gO0gPYuYnHtLGwFGmqkz5TpXOMsPHcFvEMaV4Vd50MkwJgRAMuccQj4UH9UlndNMcwTgpv9JcADbdMDikui2sTEz46KtrEByRRztZkT/Ox8qUlEGdX0p2rwDy0bLQmlDewtO5fiu7W8vLQPaeCSDg9pZns/r7A08WLL17/+dZYK0W3i0Q4fY/HKQ8j5qlyo4eCTSDtmyHLDZzrvtsHTkIZTo8CBDY27pbIemyAVdZvJ3jPEG8YEuPUYgiRFjTIKDEqQNHwuqjuBJ9dycgtwSXcIf5Yw9gCD49yNiggRnB4Qgo160zDqxxk4xNgt+2OpecGPS/7VasmlI/x422aKpLM9rqSDqsJOD9U5D2kkeOI4I8iljwNeXt5S3r3D7RKhHpXNku7F/Ch3XtLh3c8+R+U5uSvsudBdkckt81JuUJAaty8W6NRz7a0bv8t1FrcERBEzpHYY67LGWXjuBLgAIXtd8/J/rcst1jesVmmSRFiiSQI+nJ3nIxrYCa8njQrzYLIHaF3CsIPkCLm5RZMMCZ4lqqg71wh/5wEbDHfbMB1IoGt+m6DwT2bS2ZQB22CEDEbFMMhhk2hTnTbDjnREI6Q9etDjcoOeQ7j+zKoBY8mB+oNW+3rGnteQT5fAz2sZnlPMj7fwKDaamY57sF0O49DYxjZU43saYUZLPYsWzNC6dGjwXseVxzwT2SPa5VkMzH5vJDrDIFg4I4cZh7ZbLpd0daIokWs8uGOEwkBFg56ixhCNbf0OncdZSpxlImw2B1tIuhyFzN2T+JeQd96O+UG7c1k//veyPaM0VBI7Y7tM9oZLmTod9KM/rbrVXUgALHHepr2cAhSXqJDO6phfYDHpXAOwBq7mcjm89qyeQ+5MA2HTHswB69fpGDx6HHP54BokTb3ILRm4SfFK+K3R5ia2F3bPg41QL2zdnSUd2712nGedJzO9K+cpaktavHdSJmJL/HmF2LRhtVSYKM0QfDxwGvxT27kMPV46YtZ/ltKOA8M8Wh2Wks4GMk78IwLfvHbLy6X9aCxt4HM58DiVnbvQjmhuweMgmk78hFm7beo7F3mHmQ2X2UCz5uySRPAI9OdiayxhEzOR4XRApX8QbrC3Gbu+ge5IQagkxZuGw10sXTG3yF7rkT/XIEfSHcZIqmcp47K9A3y53NHAJR3uKORBXS6jQzSu/PKuPbNcODIgKljzEGe7SblQDjMwKxshKfQbIi8KNCIgikIPzBPyCKUn0ZwNdJn42ncoMlRh65wgtX0xy5e0fNXTJ9Z8wcunOj7wgQ/IkTv56VBMOaITQ0gxJUUaBp+e3U1YMEeQPHdu4X8bq+k5BTa5y+3KTIKRQO4u4GAenOB2R0zuTOg0UIz0UkyT9aD5eXCShpkIGmP50+juCLvT/6C5er7jv6TDhz2DWeqdm2keXrjXdhBMjHjFK17hB/t+AVmwECa84ONnkfPT3ZXyQ2isAzwRin0yjY/6+3qkBlp+DekdIl8M8TItgGLiMu6EZCXF42B4oMMlcvAooosTXxF75fXh+5b6LQ29f1uiwgNVGBiG41T23p25kxQtytN5pMGnmSkO9mL4DnaSOqVlIWQZcc7iynB4jCW4gRkGzaeQOsdOzsK8DXj2tDGG+tI8xmhHUgA7lrON6Hdw1CH+R0Ya+A6y9zwlfUmHD2nidqxzLsd8b8kH9ygE5OpitP8W65s473znO/1HOv831UquTCBj+fnBuemwmFIMRUgDTsuvN7zhDT444OuR8EuHvhol8fhWjv+GakgsDW+HfHiD7C1PaQ+JqGjDjzHfxvM/KHzQ1X+C9LtDnPjRhf1SiSrgDeRmCPWSq2yHnEv/7sBiFH4MyAf9rsZK1Lt/O08uB8l3sEEDUU9XmzlpKmnbvENi0s2I+oAqSE3VPpOUwiNxV3Z+nOEsoYrkJmRzp/8QwM0hz/2eHZNuXrD9cBT+3NfPWRxe0uFZ6roV8E0Dndh0K7zPDBabRGpUHAKTLCUlvOQlL5GrEJr4JVOKC88MOuMv/JX/GlaE9iFf97rXSYr+zYJQKEP48gCiPsHjN5f+/4CggxPARV48tJA6g+RhUDghdx9+56TTQ1j/bcB//8GVXxz+wR/8gW/m+SW+GA0GD40yZHg7TGTPncqCbkAo46oJfDy21OvXHf7FgQQMYNYre7B857pSFPpjEtqTJ9bzxcCoosP2g3/I7t8t3bt3T170W1u/uPU/qH0SiGGgCGw95m2Qw4/hRBur0G+iGV6zT1KNsYFttJ4Lo8joGE5IdEiowA7dHQyXBg1c0uFDMoPMMS+N5J27ZXbv5RGhWVbwrxAdUpR+mcm3bCyVvvWtb0lX8/rcWcJjWM5zhv+1r32tf0pu3SncwC/Z+K6jDGRX1v/D80/mXNqSBV8AEhBbRpxF8QgwNnbUiJA3iXwJyMfNLYut0vy3Af/3jtQ+WZD+sdpAwJtjtBAPGw37J4j+y48KoFzoa3zaSEvD/rmSNLzt+3BHpL79LZybMitXUpg1q1g4R5nr8ZtZGIis3vL/JdQfkqL/39IXBN31vSS0GB6bd2wgsZ4ZkCg6MwBs+P/AzmRsilmjDO2/T9gzxxK29T9ofs5ifgMwQRwG5vW1XWacpJueDcgf2SGXdPjwpl4+sLpir0gqV60e7nbThmOoza1UJCpB+UUvetH3fM/3REtclp88NfT1OKHB6u1csSF3EEF+ffzxx5944gl7knZcRTpnO2O+1IqKf7sjN+DB/8TxD3fkYKOidbf+uRNhUfEeqf8i8rKXvezlL3+5eCcX+o9XXqIR8SdwlJsBlw43x0R68Hjy1a9+tV1ZUvvkgv8zJwFgQKev4SAqHbaYPlfVDxSe+fkvK5KWpGjPXBZhihumRrJx+C9j/gWuXKg4gIq8Uqz/uaEWgZ+SZ1H+QIWCHDMmxV6F2fdPGT3StolNLnNtb8C/DnZYu5cnNsj7oPk/F7955ALOpHamapeEtSLPqi8Z8VyVgr+kww1KuxrC+JyzvGKrS+bINKuIu+VSuAHg0tF/rhdArR58M0WwqKq9wnjOgRCXLiUMCRHBnpU3SJzFAlSAxYx05X+32sLiLdvCH3LwQ+hhpHTbf14V/uRCaP2DeP8M3cucVmYikX8FLCT5Uh1yIxbxp337xuic+JIf9uzU+Uc8FivWvrKgfEzJ1DuxT6P6Q2O92lN1DKdwOU/096qqrC/z+ZfLPiFLq5A7f+Yzn/mpn/opr/BIh+KvgWqUFmHD823EL+oRGWPwkCW0wmLSxTAwtxxLWnSlQrKIt6rDktUShgEsZVzCayey3JbGUNGjrSry36ftlv/QD/2QNZl/eeg/t+AKCUauNLE3IA/BzEjWa3uH+pHLkRdvHlgqzizW8YMNBRmWaAMMDERWo1iyq1rav01Re5GPKmay6hlyzSZf4LwwdHe07dIxpg5Js9BE5Kp76R7qhM3Ym7Ng4oip6nW2+cH1VKJ/93d/x+xpXjmSyYV2xwwO0br0X9LhRhtg8WydfTO1sX5tEQFGRhxet+wfslpnYVSuEoyYsjMYKeRc8gWmnCS6RTT45SF7mFYt2tB2lxtbtYgF/hmsl2jOJTfwKJKX79kgJY6g4JZcKKl/4xvfsO60DWs9yg9lo+/+7u/+3u/9XinTU7QJPTA47sozRdheYCE+ZujTSsWyVVL0Ro8HeF7n0UAd2+impRFnW6OoZAnytre9Tcq3zBJe/+M//kNSHBsQhrxnKyjLiPQjKU4yGNk36AHpeDZ2bAxR/V2mZ5eoa7MT7ElaZkFtpFOGoAqXzMPOhAQGLImGsZtqWdqYu9QIiWyn3LFRTOe2Ir1LbG/AQplO4ESX+ABQsRURezcxb+tB3UGl0LLtdkftz+MkF5DpeaV5wQkYL5Thh2XaPqUH+yIrLSGd0HYqgk0KhLAsiAdowbBz2kjtLhm/Np1IvWY/2THpYBgrSS81Aycebg7n5h7e0zOp6YFLmlmOYKNYGfq5z31ONQzPtbbO/jcmSwYeqfYlHW6cbnaWrTdeCcwNOEPhgwU7bEmxVK8YyFLe75Ak9GTfnFZyUsoti7g1rBRbIUGRi/ITJHQqD5XGCnNJAg9uCYjAisjWSfYt8w184hzAGnIDA5WjDViBAAbCWnRaF/rHEdItSJid8VN+AkYJRunUcK6tcSdHqqABsZ74HpQKDaKAt1uVyQJ0RNHaEIaGw+GZdA60lB12COlZZKRGQT/ghBVw/W9Yt974xjcqQTyyQj1OxjYG+frGjB1+GmuWTURzjdBjjz3GtPRYv9KG8sUhf7M9zJs1kM6WrYJmCjy+ZAEPjOVgoDbBLYvf9a53CcEKjm9+85uf/vSnFQTerkLdBiwAkyJZsjorFeRcYmm9sEcgYesulggoCyoxpV7UE8eSl6krzjiIJSP/YpBkBE8DcUKWHTUOxTHUFA5sKJponVDxYk/oGTkSNoqxIVNam0Ki/gP27W9/+x//8R/tn6OoCJaooFUZxOHQWtPAPBsrSsBmCswmz1L5vf71r7c0h4Sx4QpyM2LezTi1iy30gPnhfw25Rxzmkg43GkBGxvM1uPrExFKOTo5nDcFqX/ziFwOrdmOybnEqQVPg3uYehkMSRcWv1zdEwNe85jV8I37wwFE9upMShGObmdZwve5o4DaBuaWlhkzgDIMwIe4IOl/60pcQGpz6HS7xUGRx1tN5wG7fEBrK6BTb41JPDUmNH0tDazK5wRSA2SbyTYb1OFTfCnNKIJRNKrKX7dxKZJdmVqEgRFqqiowO3A4bIAu1G5SQJg2cHFaagRxaq58nn3xSWaBhvoBRiAOf9i2t4cyXR8iszoYBDs0gTo4zk4nGPKKAbUe/+93vZnIQeiCnGGoJqCSyImeWZkTCMCmKJ2lYUL6rXEgiYmKGqqmUevmXrXupCAmiKTH/+Z//WSaQpy1VbWjbNgBpRhzShtmZidir/x1bRYjIOisFaFWK9XjSG1uIMrAsgdRcD0L2BlINasHqf5HSnjpYzqaEP//zP+eGS2fZy8BOZ0ZOajxgBjmLwpe+9KVeX+qLS+BJhEmzLPWa+so1s/DFL37RcAPHWnaQXy53NHBJhzsKWXtZmcnHGiDosLxqNJsnHlfYVHzzm98seroEwxWduRMYgaMUxTnX0nsGDhW+IdSycr7RqsiOjejDGTgGD+SQPEdFKTrwSaFQzW4UnrFhIDZys2ewnv7L3xSenFwtHLQ3Jryu4oxi7iooQFv4IFrSCUb1oHuazGoI2MhImfzfQkSMJp247PUZ6VBoIKMQGWPxsBr3MUBJVwlig1SV4x1d6pXtDMBP8mYYlG8FJiQxA/tmIzt+jmFfcY/UCHUmu5QjMtqdE2pNtyzFDKhFvYUxHJodac86VYlgK3vCMZ0AW0HwCiS2za91ieBO4TBLPN7Msi406UxOfoUfddONQ+mBKswCVo9noJU8BNaEwmyZLjE4rAulH0uxL3zhCxigB3lRSuB69I80xiRIpQA9EAGeIyyZKZqJFqEMr8fZdqunwtZefE3NgROPqOkBjCFsDwzbIDt+RAZe6VLClrYpHzaaOUvYAYaZJj0d5IAezyt8rU1hk2U5oClQ7CJqI0q2FhaYhAWiqU/SLHOwXRqHNLDWHw6Nf2T7WRgDlZbE3PyHdXIMOcOGvspUouI2OgHwXpCsU5RkuF/+8pf9C3vF9QbtISr6yGoCky/OKI0FaDFo/JzXaaMrGjr+8z//s0UAWm25cONzc6GxEAougi9BDOdmfsAgBuEn0mIrMfXTBhIdBvJkxwZJTw6hbRtH9GzDStwRF/7yL//STikNT0TDz7ZYcJPnazmuts4srZQX4pGIY6swZRLfrRklNql4BE3p0E8wTf2IMzDTs7JBpfEAPqGEZvuEqi5So25JhCW2IQGogeRjhZeJM0fOjuYIEsAwxJVOByvdy0ZjARjiEOKtitiYVbjKo1xoIN6YGWAAloZyIXII6TfWdGwwub38QKtfLuRiZP/+7/9+UpBaLvzTP/1THAJQJXhw4C4w0yRB/s3f/I3N4eyfSYRkL/5kSdV4brIgtBRjZr/6q7+qDNLPxqClZIUITeqRcVG3FoTfFEiTpp6uFCj6qReqMctDpG/2U2mq41yS8VNPPSW5oijdmoXqP78mEkzo3MEkVEJoNaFN3E20l569Grikw71qOd3JImUjsVgMUnqrPdXmgrKy9Jd+6Zd4BZcDw2RzD4HDKkH1mgVLVEzWVpLYeprYAiLf4GYI2QvytICvIsRbeJ2jHTwB0RuP8q4QUH6Cw11nDraMzgvcx5o8TWSRgzkYOPGCw5M9fuopiGAmz3e5xOjScTwSLeFPtimW/iUDSVoAsi0sQItTBrqVsCi6vA1RGIgTEnioVNy3QqJY/eju0KIQPZRgrm0MiIYj9aA6KdpegIYPMzYkmJmsoFHyw9jnP/95cyRWqlGY1gjeWLw5IMfhMINVx16KOoNPmbKLBO+zAy7tw6u03IWKSbAou6YC8Yc//GH7B3jADLQYADNsHKKyvj9tWwN5WRQhZYc85M0Rz7BLADZm5Eg5Q1YGDLM6CW8Mo9Qygh8hmpKdYZBg6NkXLUw6unrI/id/8ifO9IwBPTDDT151kjMf5NoUxTfdIr5OSnM+QnTvLdrDMGxKXnbuQE4PpxZ2TDfBRR6XpJaA27y1bM3HAZsdgjSDe0lcOkcDl3Q4qjivYXeOQ9oJkRWYo9gn2ykhbetLVOJgXlfBrkhnoDIib7GTI3hZY6F3bi40RH4VYRWqHmDYEuEtfKyqk9eJzvbxvOopHaoZnXNsA1F0V2NbbIInWtwbRYFA+iFXHs7rNCqBASQ7QkZpI9qxjfQzo+/7K/0LUpQgH5BLTBQX6BaTrV+jFXtY0n/f+E0XcKp7VAairXRYoNFpUkSfzuIObbjb/rnEMFMwNNPPXK5sQItWc420EEl28ZGNWR3aN8MYIxxsIBtC9pmFVFFwdBdvKWpG7TTA6EmoQrxNSBONXOaEKAx0LjnhR9SWNd2yXhGvGbzhkNxVOCYUVEK/bVKYicaJvLdCcPlPPsaeH8XixC1a+upXv2qZzvsAJztuV/Kj/uPRbMxLKx4Ya6tiJdf/+q//YmxcHk78pMBw8gidDvaAH7sXzICneKxAG+2jBLDyPDyzLskVFa5H5xbE3Nyr6fhh5F5j9vMqu6n4MYT56eQjGgjpXEnuEQe7pMNVBlD8mihmR8jbBKo/XudgbW4JAYKC+MITHLzRcxpxiudwV1lzSalQsuzZaecGOrl0MZ1xC0PCjd1Rb5B6r90lMB4CTEGKqEcUdrGsYJAuhGEMew4w2NPAYQh3KB6/bBQqxiLEG1XHIhH8BmJSUOCl3lzAFb8lviF4c0bU3WkcJ7T3bprHf3oTYjwxestb3mL7SL5RXnhe6KCNpgBdkOhSArqO0KaNQnM495Jbds5YnaRAWvqx8yYZi26xpJMGBKwikQZgVQtdmXfMNIlQgTRKz4YpoFIDk4iGBWiLFQwQX90jEyRXJJIu5rW7xBUkI13wc7m3MbpifnbL0TXLgqw9Q1vlFGJHRJokuEKQWcpG1oVyoc0Jy0cMkJ3B7EW+oRNFM96+vfm1Y0/DSEDF8Lgk73jrW9/qUtqgEy/7cAeQATQRx+kiAcAQJsTAfuM3fsNU0rPULhHKrx4Ya4fTjIB3Jmmzk5/i0GpSRoTKKy1f+9rXLB83pMPshHqhxY9L+qdhB9GkZzOrR32sCNAASdt0gtywlBkcl/pylwYu6fAMM2BVjNJGpVDIT5TDevIKWDiMM+sUQdSDfornRTv2qo52nEHmGrSwJbTlnCIsb+eWalXPD7xNyveKjwCw8Q//8A9+DM4HbMZyBlzV75aELXPwT0jEzX6EIFKcxZIIKBZ4PCPkGfjXf/3XCI17a8CvOPWQXwjAmLhDFbzXAb5IkSxn0VWhQ15Q1oDWFNimtjjwhr2CHX6dorN3awmLtDPS1CVvKcmtDMCMQoY6GJBzubKBB3HWtAo9hpsgZqA8IrvHxggpgCxVRS55AgnAOJnEkwbmciVRYFAR08Cksyj8sR/7MVvlLqUE5MAkY7nnpmjb5B2iDIAmP/vZz9KAV2l+7dd+jcJZkd05li9FmXcGhhMLaMZv49r6acOMH9dJyQZFwpKU1TFv+dgUsD3LOOsksjMDpZvfVmJGAxvEhzm1HFcFzCANITtC5FIE+Aq8tOoxMBcjNUmhAoMQ4JnQvNKGrazMOHFLb7aFlA701gQdF3DnLoSMihURFlEsuWRRilGPLSVFAHwBOYvyxgIWfyyXUUzq4/LuUHyULy/pcNXsZ5RAWT8jY5FMk6twCSZus9TPfoVF/m/vQsph/fYxREaGCyCjXEXpGSD4OQBadaAr4ng7wKdAFOBMnweqBDlkIZJ7eIKCpYYgKkZ47GEBIXfKYVjlnB51YF5D+nyG1Kq/+OF7lp5SO5/0CglCxUpEccIhbd5WDoOkAdpAqxCAyRqriC2ARJOuUBFqsUGuxx9/HDl1CbokQhRpCcklilKUiCAXWiLTld1jbz2IiTt5wqQ4io8LgqebkCsF5AZLYa9sOPf+lJE4fOyxxzCmIUPDbwGNqybLJDq26QGfxiJBA84Qsge1ArlIR+Fp2C2QN+WqcwNp+oTTASdjts+BricFZLRjzBgoP4sCgxnLJnYobQjWjboa/Eweqn2bM+UbjhP4VSQkknvMsnTYs3w2z2awoXrzBo16KL1FtOk2iroOsTG5k2gK0PZ++TKhpBl3IcmW6CQkpjhP1EMb3kG1TjVTuKUipshKQYI5RPRQfwxzWA2CQE52fkf/6i09bM8vXG0ViFElSxRxqw6A06gNk36ImRd8/yUdnphiRsZz2LEG29L2gIp1cgzeKEYzUMZnl1J08ORAwig7AnYL9g0+YFTumimLejzTO9Y2SK1K+YD+GJOTXEo8uCpz8BYU+SeHVCx7oqBKXQrpfVfYzk2H/N/Bq23N8bqkc4aZmGR/05ve1LNMDCgIFKctQPE5GrgZppeMHWqnhJSp5vA2uaezKJaM3ZWQ5EgAlIYl7WoCABpm5OMf/7ifhAMYTtCK+UNED/XPel24wcPv/u7vSg8CH8zSEgG9c2GOKIGRSB5WithIhHBODD1EYm//YMA2MSGxMBKp2WHJ3qhMFABgZ4dOkEntchtpoyBE1GFtZJ3EoiwNJR7T4S7MjNBd2raNDECt4DJBKIfmY2avaGd1MmwUJTlvx1gVUTIeFCXIWR1SCFpqEV9C8K6pFWrpM2cZ8Y8wA4a6wCMk5ciyZDfXytzWW0tuAYsA/I74hjjcZX5KIrs4Joi/iAkCAgB64EHL4WvaqZFi+Tg8fMpSlZiKPxUhiqouL5ShhW3AKKrArEed3dVzRNg1DDxSMJd0eGK6M6kCSubOB5i1FMVh+J4lCM+0kaKQtG2lQLZShJQX5X4aDpAnKN1/mx0XRtHiAHLbe9/7Xj/hgIdroY4lJTCKAr2VqO0p8EpF0YEPa3NI24n8BGIsaRS2Ck/3Uzt9Rdh8Hj8OSGBz1umSZhAVGnR6jSiRhSp4MTxuucEzCdIoZ/ty9PDkk096cAi5HpgBCAGWgCKgnxYIHFSHqL1Ey4XeBrSBZq1QoTCiGu6Yy5UN+IO0I60Mx49LocfqXPA1HX6gLS5blONBKGzjdOwHb9r0U89KosDANwXQuvQImZ4ZG9tQ3NCAtolIopDXZoRDboO8zV0IMe/SRrEtUzvzFiXqM6aIE/0OYVpoVjH4SaJnbEo04RtX68U8CcnewEi6iFoU2jRmdd6oZIpWS24pxVRsfhCJBzohMvFDmz9qH9EDKdIwhEi0AJVfzbVzeEwEnE1lNahRqUihoC5Ug3qJBmMYsIrNZgIIw7nnMOABNmjxxp39AFEbhwoyOg8nSHbOHrCn0/mIsOey8YKHv6TDE1OcMxdQMqwrv/+u71L/CkNyj04wKsepiAVojpffws4NNngCig5GL83Y7RR2vaoAD9ICELQynGrRW3P2puzd8RBbiNKhjRrRwVj7J8pbnGBPnDKEqxiu1FVpnhD7xu3EgQp1/pZaOBuHpAQcYtUluoaKmJJ0hTn96NzslsUmOOGxCiSaNA+hqEdqEUoDIVlHiJQOESJjCheSPF3DmEPnRMOEazZvCHq6g8joWhs5IEFdFiSyZYEAJFa6q5+WzJG7WAqpW/rxto00VcAAlbkQ72yQkohoErBAyQKRk5hZhSkeYZFrUk4Ltg8iVp1ZNQY0hHilgNxDTMZAscwJRZIyPwpXhNE8g7RXKR3iBNsZzD4K5/VBRXySsnw/3iW7rQLk4Mees7rHR+O8Vo206TALCDQFVDFqOUQ1SGdUbHKyLmgJyw2ZnwRvYL4QBmqhXgdCFoV+iOJFHis2HocczVi+wwDYma7GGBq+5kwKYzmvNTG3UgmpvAligWj4IGQSpkP/9EyjuVtD6xGHuaTD0wbAwpb2xDQty1SgoqFFABPkkDasvE3HIkPH7nkIf2DKeiruTlNaQCBqrGWNj0NaFCoA4RSMoGrV5WxLxHacpSFIa0fp0HMUTst7xSZlI3/AOVSFUQ3Me9NV4bwgtbYpwBV0yGUMnJC3b2brTAjAIfYkBrU5KrSU/wdvSDpZS+8aLs0bSBwkvGGvx4rcs0kv+FG7Q3giL36GEDEFMmBCNjTFhe7SIT04n8XGEhgzag5Tgx8KsS5kDCbI3p1+WrKxJlWjIjeIWThBGkV8bqab8mGAFjMiNcGtffFguUw5bgm+Om1UqMyYRFkZ8GaijU1dpHZJ+cIx2X/rt36L8bskqQfJFotSlGLl13/9163dTZOlFUswBXe7QGwqiSbr2w+QCBHlg8mLB9Ph13gt2jDsGKvQporjsz+6MrPUKP9xagUf51L0aBOHdTFseJyBOdieuTYRb3/72zkgy89P8cPXsHrNyJaJaLqTWgWAIhtrup3pwbzgWX8lr0bsLQUfoWLjcj6kgUs6PKSZZ/tZJAvjVCzPocEKvZ9pdcjzrRUs3QBoeILI9LUzXyh4RQb9LLrVLftgvF08lXKYPldk8QKQOhRaK6GPfexjHlvaL/KmjLdsvGLnrriJSZDoFn/5Kpa4hJDBde3giVOruXgaEE5jXcDpDJWGHGApIBfaHnQJxlm8EAXKhXqowsFLMbAMTCsZMBakhZfF7hve8AZLQ8sgO8Pea1AKmAhqwQyYZNRARY8FqyGCshmxeFpSpxmXnVeyMWAEERw9HbRThzfJwCz4RQFV0LO7QjMNaMtVZtD0WangDYZkAYNDDAzONY0wmNzERMKzMQtis2/bwFlysmjwYBV+yVKqQKW10eA/l6iBhkwmhpnUij9xHzni2DVFiPgqM+T0/NVf/ZWXTj1VFbVBWhu5u8PG8LOh0eRaq3kEbk/SSz3mmk5YPmPT4BpyIf0zRQync4QAaK+Zd5DwMA/rYKttlZZ5VHZ4o1vqVesofGkYJ1yezjmpylgoIC9XpRD2ZoL8LhkbkEAFkvsMM+sFx3CjYCCXykM54heHrKvcjBZukRYBijYxH4k0sIHueg5fSJCXdPj0bE6kmMaESybFtcC55RAd2KXViZ/WepqlbBSMciErNsE6V+ScwQ+es+zGKJ6WsxXuZTjmjhlURBnp0HsN3I8n+AcL/fwRGwYihGFtwA5tnc48U26Qq7jQWcwAJg7/R7rQ5lI1IBDzeXWxHEBqmBXUHhphzxBOaAgGagAQEYqt6aRbqe44P+KRbajWHDYJpUMHcjSME2oxPGw4xIy3DBxit1tmSoQSOzCAE4dR4J1j4zjpnbuGE8FANqBGsS6UEc1FOAPGicP+7W/+5m8KoOKXRdLggSHbmJ6dhunGNkGcQ4vcUvnghWNs2LRUgTE5T0mZor1KQ/QL4qgMWsO1lxzOrZONmXFcsUbT3RNZhuSHnuIyMZOOeq2l/LjIz5AIaKNehlApUv4GPY9VNBZCKmXD+GHqSEj8OFHuAGAJ7qpIzAhT0cnUDaG0EVCSqL3snLvLBkXBRoEkMrlW2x6Iynno8nSJn8HTPDCWplCTm+3HtFVgM9O+CP3TFQvEG1Tw8JqTdJc8LNtxDgOKPAh+c20bQI+ik6QABAdVaRbOAGaul2awxHlp79XAJR3uVcuznWyLCbJ7FmYt4kZmzQT5Cf/kJFyC9b/jHe9QSNob6a33sf4xzWeRrmgZxfeUwOXCkGijiB+JUDySHjiqlRn/nHjBATghSGedSHVLALUo7JdYbq1g4T6Q8ckiBR6skGwNyYiiD1AA9qnkQus2MZpnYhXb+JkhhfhYAiB24yTM9xFbXMBglNT72GOPWZah6zGVXUGBABQkACBxhofUIoVA+Z73vEf5bNZEDSwJ2WDMyEyKsXocBi6orWpKPzaoPSISeXFSnMpOSAohhWOGdEKVqfF4T3zUKYMiMAlmLzEsFcLKhbKCS2ISXI8h2mBcOhC1CrFGlHvYg3yAN3vIjNBAAHtJnNVJuohCriJReNmQ9xEGL+t6g1Qjrkhnukmt+PDzcA2/CrcyVi6YlA2cwIAutE0ZkeE3uXB6f8dKSCEILeXQqjRMXs5iCaWfqSjLaOYsSQOGITfHNtGsxkyxpOhbgJa8kDNFHgesCTXdZoTLS8mcSzUgXUHlv9ng3K1mMHE28JOJOiNHWIdalsht8Lh0CyEGpuHYQOIyZDRwSYejiv0NdqzkVIdyEs5vqyTL5i2MUnUsEarL7MsJE6KSIlFWYKb8xCE+Mtbx6v009vUayL29HpmVQ8U5dfIuPdYcHlEIBypTnikscjxoNJDLK+JT21gvpNlb8+yBY3sEIm3vo3m6DwNk8YMKnw63KrX8Etyx4RA1JEKPbUSiQglgehikIOVvVbYewDI6yO4e1w8kEhtaIpEpEOuJEHLCRkKgFK0sGqQfVbnNK4JbpP7Zn/1ZuZA26GGYSUVzub5B/+adGtHFtoURzLBpQ4KEtlsYq3gXKK2Q9OA8McsfhyjCEOawuXRQL5XCKSU4kDDdNTzGs1/tbUYqsi3pvSqpmhkU04cKJNM+q4FnqrbA9TsfBZ8Fn/35j3zkI3IwX4jVUWbAZkewVgooE5UCaeYsogEbGNsYiARrt1uuBISZNhzeLvHNF0kIe2gpmDIAjmk/gB5K5+upU3XAKJprk+vhAmVyGQYvGYsG7NB0KMh6ZszM+m2JQk2u8qiYlmjmaubuVzuJ8LyeGZDYGDxj80v/JSMmoQWmcRbyC/COBi7pcEchu5d2IQRZ0Z9fsX4FoHDG1pXhBR0JwCrBbol4JDWK+FyUKzJ9YDwK/MSLXeyHr/mbpy+2v6DigbBxBgeLh1PqdRjNUblBOSkYjhFp4RJLhnMeiyRRUqnbogrAYcr77xgCs6xDUp8CEH+FnnIwrtxSjFugOIN0ENld2sMD9hwWu57nCaxWk4pZP43AofDhbPh+qtePfMhbttMQrUyEBuTUS+FImAi6gtynqjxManNSkELCnmqPeZb4ZzqO0F3CL9tpOx4oVujXhjBUS4QgrRgAKFwowaU5BawB3nmJdtkOklp0DlhJlOAM4NoQvotVMDaFkV/dUYKsb/feFFMpsBCaiCWSJZWVbaziBH6KtRNg4iRCPynRCTlCy+xuRhzklbcwadK5hpnayc0rScNP2HQLG7OR6jwmV3GacZq0CWHprwKQfXFojSgjKkntW6gMzAWYlbQGDP+1m1aE4JHnGJ7SRwSQifM+qnAJ2FmJpuI0lkKuVPDM6820VIoiBciZzSF3stFAYHDmVpBoOMJMvXijapBgTiK8ABzRwCUdHlHO1S3/yN7BwZiaZYGVH/fg3p/5zGf4gHgnwXhXwnKEH1r6OJhmdslka2xwA7QqVLN1bgZbvBZz9fNV/SKF/vy22OFSv8zEY1Wy3vP0r0flBkPEUEEcqpCEcM0ZvDRjZ/iDH/ygtXJcGVjULt7JSRaOkqIMJFFhRmRUKUuEti6NtddkSAsd6ZmivJFopXiEgaK/YJf4EFoPyTH6saRgh1liVrajYkMPKotgSzG7eR7zmCBgQkZ8uourDdMxHJrQFK5hAURwMwVhE+1WDdNBRfQvW7R5PtFqycygnUZTY2wTZBR1Yd4egIlzSWkORYnk9Ja3vMUWgjD9qU99ytcGaFIuhIpyQKYxeG4pL1mkIpZDOksfBk9kYsKMSpzHJACJH2MqSKOAOUa0bQ2WTHxni37b4N6yRpREFm2W/pZlthlwQgmW7LyvwsiZuRpojs6lm+qQgNZEGE6TTI7sfmGsn7CmQL5kXdQCgBkAbu5cGgUMHgpxxPC5bNyER6JOjWmX8sfqbo669KzXwCUdntCVcDalvaDAG/8/e/fWs0tWFXz/efJ8kbXkXI1yoDYhzQIhioAICIIg4KYhuMNEY3ATRNQYDREhRtCmVRoQ2aiQQGJoG5VwoCcmHnjW/VHe97fWv3t0rWt/Xfe96M2qOqh71qwxx26O3ZxVV908gfEpe+3bCEDilCjfVl5rBfFukFbBcYka03+0wZ24nwio4OVORQR0xQWXnEHjjq/dfr+A+3FRdzkk/xewOKpI4cG+Wx77ixSAES08ncuMgaQmvqwv4oh01CIEkD1vd1fN7s1PmdJ6FA/xg3lRSZuKrPCwjUPUYdBjDaFBEAAk3akTwqJFIikWOQilQGnPx1/AQyIXmiAFOz3Yx/O1HS/+yQpWSzhELu0Ncoqd9mUNKk0DztKwlbeVQeGJNsxCEwR5bWJa5QPDoc7DDBRSYYMEJAx0i2JFDDwoqjkshduQsF3hvzxK/LYuqd1wJAw3NpaGHIRHqQPYOIxizzRvjkwESZU7CRJk1u6WSyxxilu3bjEGqpCxGN4SeAP54cvhHBjBvcUDuanXbwNG+rcaxkykmSXbwACGmYpDOXiBnaMFfxPXFFBpTjdrTQ2FTpoxL8yAEszR2IApawZzUpCHJT1wFyfuDobRSdTdRciCFQw9LyPPAZzrrX0aWNPhPs081c/uuRZbrwzkb/ych2jYmrNlCk5E1qlK9Yah9+iK7GDGiI/Q2HVbAILNXp8EDDkQqYh38fDclRPq5A8uuV98illYEnlt7/gdghUhGE6S22hjCWOOcyOFEICQqOQsAEmN3C+08OMQP/ayrAU14Ac24gdJhzpxiLQywtaWCK7TpQNvOw+pjj4J5fmQ9QEGOgK28JLgpT2SwmaZ7qfZcqeUjBC1YBs/qMQMBnZSOaszHaJoOqQlZQdtw6A//MQvMkqEDgxgZgAIS11Ut5Oo/vAUYcFYaam9iEDnFscyjT1SxmDGkbY76t0NnEQRPB5iYCmsHrcGZifpnZ2Q0L+jOZJySORdkubOkGwAA7xAv6SFN8ybGr7g2CfpTnLLTgMpCl37Lp6/KgJYl7kmOGOQDtV8YJC2RHagS88SsOcXrIuw2Dg3GY+dpD3IMUD5w9gSIH3O3dSOJUSBYVVZxhrLo03r4DmrgRnD4Q8JunpQqZ89KAexehbOFXhbA6sGt3VyV4/A6tmAJzRsUVyTpbgHc2SXsoKkqC3s8kz++dhjj6lYcxJY8iiQhZK78B67EIA8oeHVooA4KAapiy2wYGP3/BwChMqUCKHroQ6WZA4LI1W86hgY+HFXzHNRwMPhMS6euY+o1aqXZbzdw/14OKnJFQOFPPoxALm5rHKX0gpMAAwRKHFrm9RhQYNDo9LVM/SebtGDGKfU8IjIWF8hIYWbSBhix9VCwRLBuhCMo7QNAOkwj/hLEt1yvkAVkKMiCbEN23dmBxKiYSlsdKKhX33gkZtXPTFpFEGKjNr7DkxChedQ2ev2IqWXR+A0vJrMitC/FpGQ1D1k109Y8MXuEYrFwuZAS2eNfXT39WMGHvzQLXnbqxR8vcDSbrxOOfLmzZt2rS3cmYfFCvPr5/AW6xjDfCaxj8p2f7IgbaBtcGpUCsBDUg8p7P8rFlmCXXRKti3hfSJOylrc9dYYBtB1bGM+3DPqQtrwPBdd/elQ51KTS9EYeQZPaaiICfyXP5p02BxhO8zAxl16uC3GHaJUMfOogTSEnKtfgOiJ+gaG9fJ0Dazp8CldMaZa08jo5UJpwFLAOsBPj8EIc4KCQpUhupRgRATLHVHPI6uJdyw4hEvnqeeUM+dRV3pt3dqU0dsL8m6IwFci3MbggZn4aKnEZzCsQZAN0ljdHnhiDx5gRkIglgmw5xIzzvkq3sRBu5cODXHcBjIerJzs1iItVNm8BaxkFiYccXuUAU4ug6r6/cRbiUD/xQiq1i/oUJQGbJOJw7kUf9l2t8uNzqOcBJCFCMcWbd7ssD/MEmxXCnzYsDmsamEtMveNGzfwpk6StAQvDC+j5z5yVF0yAICW0E9GotGhDKRtftmbs0taHTwGao9QY4E6TdOAndUwENpW56bbDy14gfxncpm9u9Zqpt4CTrlGdqqQCJU7SkMqipklJ2dRb20nEcp5Mh88Mo2agANq40E96lmyJ4XmgkJ4gfUoKwXmoLSzyA0wtpcaW7YHpoZbJsvZECzxCw0aQ9pZP7PUad7PXaeGvzkdounTJcNwy5mYHtaqUINBl7FFK7WPLQ2StbFPA2s63KeZp/oLtRxMgS8nWRJ5SUTbvpCgIBB4hi9eC086RY0j6E6+zYWYu1gjCAqjGGDlvItf7cQhSjrcErNyhiLv+M/OUad3ho2AkllZEDOYzC21aUZsskqwZ2XfzJbmk08+iX9sY56Wyu6Wg4AdpIs3GBy57j5+aFhOdYAEY6DGdYm2j+jO/mHAvBDQitwaRebzkoUYjU96YCfWSQomIvt6EfEn8Scm8XcirxOMI0JSjoVvGCChPfnvssB6gOKBW1g1y4jacmCHEp4HeCqeXpYZhkVhd3GrgLOFa5Nfg/gBXDBTxXqhnLCyPquDBCf4aVNUFqRwmVKaZGaqRsWWxSi6tIcZpL/DRoJDpBOZv6BOLezk9nTe6cd/jWCuckYLCZ5IRfbPFQQ0IxypCRSatOcWGA3HVQjdV2PXdHhkusekhCSmxriZl0TYwf78eE7bgkAsULcuq/UjqA/ezohRz7HZumQg2rLyneOKnm6Nvxnr2Al8QSe06lADaYCMeTuuNMKm00LQIim6vNRyQTgoBGgYTkXbpIPf7q8HfhiSDg8O/V2OyPW4dNzrVDHcmgsv+nu5UdqzT+iDYRr2tGVHPChNZAXfklYkyWFL6Q5X63MXIYKjogTxihbtMQkKT1hqcWgDWCK/9nb2Ru2SsT0S1mi/xI/8rIltAOjHpLRnX0TFoz6wfLeNgWG34hBL2sQ5l7cCulEynPxKvV4jkvyEfvUWJVsRUgvGuIZ3az0sn/VotGBgeOfSPQs+8066kbFOU0kDQgQe0oDzWcgPAKPFGCCkAY6GoorBjAhTvUpGP9wBBrecr5H0Aa6e77fWdHhkBgsHbEsw4nhlKf4vK9gZY+tWPwIBi1SiHsF15u2cik3jgTVzKj5w1L3zyXzgTIKHwCONmfA7d6QQ5Nzq0A8RX6WQpRPSoX7ZQudEdj2NWkIu+YDHsexpSFQ2+r8DlyRNWJFOKpIG1AHqJHu5Vk6CtfBkjiR+hmHb0LZBDBMzjRl+gM+lsKMTQybtpS5gaf4Aqmu51axBlSPINx7LkdpeKGFJhDd+IRa3hlMSaeBc/8iife58GZKp07PHlh6X+rGTjGgzFieW4FjqzVWLwn5jinQ6cVfaHgzXood9SEzHiBmMHg1nxbH8ZJsE/6YP2LlK2EdUP7VElzaEI0boIYJ+nfxOJtYecmNIBxCut2hgTYdHzGCKLHA5G0NkbdKhQ0TQz/FaAYz9HUF6wu2iHkA48TCY0do5eiy+xsDvBL6gU5SHeTw/KjGp3WVodWISA/FAXXoAOBtOaUNdj/ZATv+yQdUBLMHCDAyGjhkyHE7P9TbuyHq7xp88IRdKBnZNI1Ttgo0JWGom0XB4noE7GQvMWDqEJMU604NzSnBO6sG5E9V1dUYa2xq4kuC92FmuwkY8kBoAtglbT5wDSJCLmYFTbrMHgyg12qTlhmnAE0oPC707o/JAF8USYRpG+mKiFwwccho4xLYn6J5uerDnWwHSFZxu3YspUytIvbZMrQspxHbCUKF8R3NxgVD325A1HR6fcbbF28Hlh4KUo0WAulg/9wMjWDD3XPE40mMQEKKSKYccdUurZTpZ4oh6o5zdul4fQH0bIXnr1OD/znjW44wBPfGjvYTs0tldh8aBA93lXQqByqjo3kFwBMNy+NXb9EwuszyCa2AyC9HfrCGEz6YvbUQ65o+yYchyFDE39HBUb0dJnAiQXSUISR0GyoXZvyTUZj65QphHuHRgEpjzUpYT6Ro12tNGxVNYbxVhIIQyn+0Z2CzE4U8/EQKgfzCcSPEysGgRNoohoQRraIzduHHDW6+W1BZwINPJZYQ2Ro2kqEi3CgVL9r/927/1iNdmVfw05HrpbrDxArtc0+GRCc2ChbkazgIEC8v9OKdLKFxO5xGMp90Wc1l8EcGIrJ/RLw19iWlnuMTbRv9yyFnt4qAhyYsf8urEoYZjSQiTca4/KjV0utQebN0dMbtcnlP79BjrmMtpAHO43MA8ANfVIOZSUtPUJQvRjgHiOPAZM52xp0fWlEvc3ccn9abbGDYKsHYGoB2e21rYpYfrEnPwDDPo4g3zxMRGq8NkwQnxYzIlAKAW/Sx2UJ3VMDZhKYrSnK3Cw4BE2sCSW9rxhqIGGEla1bhPw2excRS4WUAXOe0a9oo8Z/Us06+k+mqSQlaOvEaWKMcBocN/PG6nygLaROinIsykkHR1VJAVgAbWdHjEDNh3Fh+cdp6/HFZAzDOX/VdpL4nCw8SZNdPfhxOfDgDOAYO8Xk+AGc6EjSXnfSxFOrdMFuclcNhg2OjXszwaSyKNju4OZp16lreWw6+9jZOkKCjXNvWFoaR2jisyxliNYaa7c7lsUC8SEg+Y8IQzmKUC9QwzSwzX20YCA21ClgLxVlmmQeoRzSXmsY1JQwxMzAE4lzF4whAVw20JSiqxBC2Kw1JeGfzsoFxM+lxWEVoOwaQVoUWbx6h+inOPcnOeyA7bq6dzbWywxqlC0pXzhuUsuV3bo4G7Yv303leNXMgPzP3emfkW5mggX2JJ2mACG80Am865tdP9dDpWcxzVndVYejI1puplZ9ju6Pj2aZk8ziK0Ar+ANZB5EHB89rqErQjYwBY5G8XKCIFCcvLsEGn9Eys2hjxbl1bSn/vc5/yLAowV63ASq88WS88i3XV1eET5k8aWtsJcCrvs2/iKMj1Lkwqvno4jZNbbezSQ/ulZ3KHJKmKdAg2Fd1mUmZnag2ntvh81kNuynDGkeq7LWsYChxAtM0jG6XmnY6l0gSL4Zefafu5oYE2HJ81FAZdTDTTrz+j1MH2GroePLd0smOWoGb42TtQAxVIpHbYbZhS1q7s3As0Eo6X+TySxgr2ANcByHCNgl0xoeq7egI35wby0PXYrLEDu7jCw5sKra/ueYljT4RH1WpSw8qWhZ+LOy06mn1csO6ddpjxCab29SwMTUworhbOeXU0JIsoM2C4ca9/9q4FJRUsV7OxcApze3s6FGWoGCQ9aJctrJHo6eyvkWRpY0+ERdW0UdFm/8/R7cM3QXTqPb2g4xiWO0FhvH9NAoaT8B1adQeGO5ThBZ9X5UiFre6kBtuHyXuSkcXMkmCgqerQdyLFVx5KTtf2c1cCaDs+bGvad9cuChWOXY+7uOmB0nrWLywE4j9gKfeflyTtKvX1a5r9+95aGxB25cKLSqrZVA6OBWZmxn7EWBnPtLnk7+y2q5MjVGd1hYHhbG881Dazp8LwZYd8czJh5lMXKdcqOHEy8njAtOgcJGMDqDOcp+mloWqW92bJO/5Q5b9hrU7XDCAq/9jD3NCPr3+elBrIfhsFyCMBaHNdoJDBne/APWr8yRMulKFFnDAzA81KV9wHTazo8MskWeYyYC4ETlBm6rybKec4+2+07TF5x9ClhHwz0Iw2fEvb5RJ9v9r2MO373VEHa8COU1tu7NED/tNe6kM59icq/tfOlbD007+zzXX6G7NPh82uzXWjWvvtXA0zIzwkUrFQgP3nwnDndI42IBr4w7l9V+ycbvqrKPvsBfvn4HhFd0V6LBtZ0eESNSr+WJqzcf5Pxj0/ZuqToF8G+r+8/3UiE/gOchk8F+hcHvqPof9L6Bj/r7yNSFY9HyGzdXi50oOLSfFgahk165tstj4At8y7IKVe3UF6pYzDTBkSY2eBwI+W7CzImMY/tzvuYmLsaU38ATnsVIv4NrH+l9Ou//us+lh0P+v3LiN/8zd9UgvhwpU49SKPr7BKqJZ96CIJVVJzd1bPzgKegabhjGUDDYBQkDkjcTT8g2zbQOIB8J8WLO/EwWkLU4VPXfprtw2BuYWzYC+wCQtJJv+xm54ywT9gzQu/3ugU/daFLamAA9LiMkE48XEx6g9uERcIUN0eQO8wmfppTDWAOYFwVzwxGteRSCeVjMZgBuYH59EvkAIdhKVds+Adwfnr/gQ98wH/m8p9BH3744YzBeQm8kxzOs5/0SYekAOns3+bAoJ/y1YVSrDMAqqaKpoDyGxIhwvYCtuFHSe/k537rXNPh8Rlnajdu3HjpS1/Kyv2bTStCnuDwXywMZr4+5M2OJUtnH+193ete53+Q+v8+//Ef/yEjMsT89jilBQSzvu3ldw608OBm2aUG39AA5gyKxYMBOXaP6PIS2FWOQRUnUMVhON2tgWhujw3c4solFQma+fk+HhLEXajCRiiNAoT4/oY3vEE69C/uBDVuDzNXFxHgd0YueOzhITwa0Ia5iAAYGGCHnjp3spSYAAyvbSCE2rE3YhquVMIMrqB1Oclj4tFOEtfSWWKAKt6QfuUrX/nBD37w93//97/+9a+XnLBKing7l2hjU7gS8O1vf7v/xOsr3n/913/t/+rB5pYz5FKOoqSJRm6Un/7PpbsPPikyjJkOl6Nz+U+dikmbN5TDH5mN/8SpbMUJbn/jN37DP2Teh/+U/gxgIBkGLel0aCD94he/WHCQwHAFTL/zYT1kvQNjSG1nSIgg+Pif0kT2nW7fq9PJNVgjiZ588sn+u2RDqIjhQagCcKYQuXO4XRsHNLCmwwPKuX2LYbG8W7duMccHH3yQz7N4/YK7g+UxUA0Gx+xs4rllsQiMBzpbLPrXP7cRnXmgIhbD3Dj/KcZ/lHVm/b5wYW/QJkyRCMA4j1GOwjfe8sMzKe8GF1kgdMxthOKQyzmiFWkwfBXwiEAQviop3k7IAABAAElEQVSe9mn/QTINw8OTS9evBxVtFferX/1qS3OVvh46h8fhI40+60zDyg5FiX934N+yy456BGudArThdxi/zTnkxoZcJ/y1t89uNbPdconDpkObNqRA/3jvB37gB0KSfkyKfylg27xR1ebbyK+xZyokbNAMFanYvvu7v5sRpkkzUlJHVGM5g6ewAa2D7I21WU3J/mORZENMbZFagQgt0v7Znv93SPlINxCJhp9C6xSYKoyZiP7tqJzn/ygxOVMGgJHcvHkThy79K2Yz5RaXsXkjkWMyWz2F3BJmpyB0nummWGZA/7QEWEyoGg4JMMBL814idwuGkOgPTOT5ru/6Lv8Twz+YJKOCm20rDX22Ow3Tg4EmgrWbFN8NZ/CM0BQUHNxdc+FSz4fbazo8rJ//I6JJbCzSf9/mYP07clZra0445m8Olucs+Qn3GpKWQCk1CkmKRJsb/dO7I5Tuvp256+NgQh48krEvAvNtxaDPITJ0Dycg52bBAG7URKLL3P5uRp66KuyW5HSV53La/Bw/LrX5sMqATuSndqiEKndtJh9+vAdmyXBtdEWE7/me7xEOYKZe1CF3+Jewn/70p31dLxaFibe97W20hDcbYgoR3430WBFko6ZKeEqkp8v2uVw2UE86nRjrLBjpJ51ZEGc9H3rooYek6vCzim984xtik10yMQgw4xkkS+TX20adaCKjeacrtkdpSOvJMPBQKF+q90QeJkBTAirMnoDissNTg9e85jU+Uc3UJSHLLw/ONT7/+c9rINpYpNG6gPRODslFKMyQjqT8wobBj/7oj37f930f92RyiDIAbBguPdg0lh7w8LWvfc0/QmIPF/9f0n0i6M+0aICpe7ZNM9KSZZxF2yjwRCWMusiozvB0wL7IAw88QF7uQzSGxw2p112dDgZAJ0xOpvfpcI8PeAqpLSLxhgHaoLed+lw7lxpY0+FSG7vbbEtoc0/u8Q9di7MMXTpku6xW9AHAG525qChp0SAwSYesWdKyiPGWzW7s+3tzJA4ARJsPSLSyggUQWuK+alcxLuhjgMU7h4wPxJiGYz+F8+7AiQfuZ1gJQAN7eOOc9ICWs+pBbS5z01tO6z+n6/+f//mfj370o9x1H1X4ExkwGGOddYoFVCoDaUdImLMI+9M//dP+0R0eRAosyXww0M9rX/val7/85f7ZzV/+5V8Kgor0YugdfdxWCFTgk2UfP6O6GBN9VP2SrmAnB1gECEyyArWDRBdCNdN73vMeC9avfOUrF9RA+zg50I9osbgGnQvKFCj8OY9VUBHVOQ6gOnwLQkgYPwtXdvzZn/0Zd/Bf70lqaUIPlSw+/CscmyDe0WxehehOliicaGzsYx/7mJ1Js6MiYZkjo7aBvENV9MUvfhF7siZTMd39X1JDZnJ3kji9c/CgTgmWy2zA8vSJJ57wxAQPWE0DAFLITuTBYGzumlBrX5siIolOK0KFiP/ixKppnpcJMpRgiAOkUsBKnU3aLVemMMJHHnlEDZoxDNq1cUADazo8oJynbvFtqzH5j1MxLwFdZGeay5GirX8zxuIVZSybYzBNBiqAepIhdi+BT2mLa4I7yEK2YlMkEmLQhVDoUZLbnkKCS/z3f/83n1/Gndrjq6dQPAyDljTTRg02+Ko0L7hYKvE3dYAorNMlwUkNWJgWjh0yB3Hks4985COHqXQXwgKHsfxciJF+6EGYE9SsPGQ4gUbYJeYUAap+xYq1As1Qi8MocUFRAtijXMACR+GJZpbq2uaKLPEg7sCAeY32wcgo3ok7eDMQZGyYfdsDuLUvx1Two+fwgnib7rk95UJyYUMbk0iLxejiaiq2o/LuowuzW86wQeK/HNOwdHjjxg1FhokQ9MlrfjnIe9/7Xgs1PLAHpBt4jUYIYUnFmbwmmvE3jzhkIYyBTXIWCyOsWqxvLAf5KfiUtk/kw/0IOcBEl3QuU6/9IYtUC0SXFmoOzETxME530xJU0IbfmcEbTlKx5fHHH7ct5GD2NK8yrgoxltRImxedLFNxoHQjIzDv8ni7FfLwH2XjPgdY0+ERA2CRrOrv//7vBUcG2lGbmTKyzJdFOuCyOcMb5QOv3gAW0NkoyCNktm6XC2EQvt3kV/79Kff2eo7NE14nFniiYBf3f//3f70a8OUvf1mmVELaM7kXGyPCX/s2ZEEIe3KM5CT4Sjw8kJgyokttZzxzSMCd+bNCQbbYEnR3B8USXL7xxFT6VyBX8ou8FoUt+IqJxrtlFjw7+dKXviRtI2qgRYzkjSvc/td//RcwcQEbIMuIZbvd5O+Ep3iwHBf97YCRrmRjUhol/iqPyIsTh36ZUh0gNjnwU0rYR+Ja+hHFp6PV2w/+4A9K3jJTuxFkTNhoNR1n0YUZPOnoTWgmEcymUthleEo0+QZpbCiPrNVe9apXAUYIfAMZwJKHs6hvA0NFKIZkLngc9pBWedA8xlSuZsT6SWHk+ZmyIAw41wCPK8dV+DGWdLCRKxMirwYSKiTFEGtkpcqvdmXdSocBb0s0PU2lyxrwUyxxoKVbzwW+9a1vcXDYmkehAEzDuedLXvISBs8A1KNSsufHjJ9OKAFL97osGyme142zw/TzWtoLmBfyMnduwEyz6dyJX3UJIAtWIxfxZUEmayzb1QP+AtKGQFuWRQI2qU4mUPkyd1uI9kuliu/93u8FZudEuLdyFZ40pM+c8DK626O4FqJiPbSWSg5ex8eQFonIS0ZMCgrONINtPXgujXmO8uijj1rYbWPe7olzeGQg+d5SGxIIIadw7u2WQyyAPyqQYMMm0sc//nFb0+KCdyDpRKqWmcBgSbK0t0wz2xS3e5pZqxxbo7DRM67MKR7QJbhsZ1EOoe0sb1ohhBlMmm46wXBBcxvz9fYQDavOWMWD0IkHgVIAxYBbznoiSgkXUCdOAyuzbALbHSGm+Cs70qdnpWCsSBBCkewcQQ9aTeUFRPcNgVD9oTKzAy8HoMIp1KCWQbKFPGQW5AyHis3d7ARXMeauzgu4mlFUDadLOoETn1FRAipEvPxJfOtCU4CHZgeMIQHvkys8MQahS/BEIxHnsiltmzR3A1MW1ACJhIbC1EP0f/u3f6MEhfg73/lOlsBJjeVE9HOA7nprNLCmw1HFkcaEEvZXm71m5RqCDhvVz0AVaG33cwwrS4vFlnpHCOy6PUTzJeGGP4s+yn9lo0OkthKVEbmis0WMuthjA5FavB5WB7ce7UE7/UcbdmbQ9VzEqnTcjID5pBUYcsKl+ChhKNIpxEJBgBY9+epjjz3GUQ+nIjJOIBDcxQK0PDV0xl63nIHBQw8pf2QRLPQoyT0xsqeEAWtB6xVZHIYSNt7+8R//0VgH5p33CU5Rwq71t1RnNsUUk6hTuW3xQWrxTkPgg8dSiWY0pGTsYYMxOPYhv6A/bglF3kQmEYXrRxFjWMKwnKStHqIHDLjLZvSgOAPPom6sgZBn4caaaJUN22NsihWmKO4rdJRKVUtmXMUWw3iTGi+z/+Z3uI0TGvZYmlV4NswOSef46le/alrtASC0ZJW1UIJzGmtGAIRqMC8bmRmYOl06DDeELDp5tyUgJSBHz3qgZVfKU7cMZO1qVrZhVMN1GqsNeDCHf84jLELB6BE9vC/GozmUlBb/hoQKTjDaGmhRu7Yh6j++wLbpBzbHIB9ya2OnBtZ0uFMtd3VmysWdLI/1szx+KEYwNdBZKssTEaQBz/ayaXkix7gL4xUuoBURWL+FID8RAcVr6YE36rceso7RyRNUi1WFWB0nnMa5LIh38hMl2Dm0VcvZuJ+YKOzCqd3PoTBGLbRk01K4lI3oR+UuQIgg7u6jO0zmuiUqSxDP/4rpxmqgBYD+Bb4ltoY3O0jIUjaQVdaveMUrTIck4eUjuU0KkdfVCubL3B1IhyBv3rzplRyj7Muh21x/+9vf9rTM60tWSEIPHrQ9GzbjABjAPgGv2B+rxBypW6t1phn7w7K+3EM0q+TINYq6AsPhhNQT+UGxY+AtU0R8a/Q3velNfmJroSYdUjjbM1nKBblwGEDxYpMbSTEAiUvSkZT5sXYGRttI27O1H4ArYCTF51DMcvSHSqO7g3mEmsaMza9ZlJ5sEjZ1zw//8A97WwpR2BB1S8PSEFf21cGrk5QI9IyKsY6Qa8TA0NrZGN6gMmvsjSlyvfAQMMyYIX51BgDA5pp+1JFc0kAAZoEz6hwedlJcO0cDazocVexusLnMmrWNVTGyoJdlr+1E+/X+r7RfKApMLFJNJ2h6Bj7RYTeNM3txwvoNcrYAshaUq0QiRauSWRAXoZxxrmqWj0FqO+N/RDiT5u3fNgiCcp69QdjwIAXanPHmHlRUZD+Ho1oOcl3MWJMVm8juzXvRE8yBcOwWnFAVMrQ1dHJmnVHEvIZzkBsigHfLGQzN4NYUiFNeMXCgboLsIwlhhnvPQgUtZOxEBTO6MihB7MjBRjTAEMrx5UJxv8TslniUYmM+TjbYu+JlOONWG3uYoWGkbYuphHppiz1QtQ3zLKQ4DjLqWD2XjfCQjgKNhVDDFIv4ihWX7EFSVBh5odEa2u6xgmOoXECxsSYRoaROCjyYQdmIeVuvY4w2wCDK8tsrZqV8Uz7owXbU4xxaDahGlmFy2YhuPTMwJbAHO/APPvig6opKdWY/5kLBxMawx+Xbtt0peEayJDftoTU9GiGZJ9CYL/JQC9lRD9hYh7u8T3FMG54vuMX3UXR2d4l2be/TwJoO92nmqf6x4BpMPydkkYI1IP7DIdXmXju0d/QTP/ETfIO3sELbg5ZoCucjNM68zQ+HGYZuCWiZYu2FkCdGinRsOHvvxvs1cAdcZInUsn06cXjkEjgx4A0XCU+xbL9IcISwQ45x1xLZK502bbT1UwWFHMiFw+SyoS2uWVsLNDRMzxhwLqI9TfCpwt8t/ciN50sViEoMyhFrZcW7W5KHcEZXvZ53QHaQggskIixakAcsJkL1xje+EUV3ySsbWRqKTXEYA9oHkF/9VlRUXWahPCRDaHt/RCagB1aaitDCPPYMcewM04f5MXFQQQiM1C6d4VEWaJPdW0syooZkTNtWhygaMrSamsNUNu4arscZrdou+4mLM/UirYftadipVqaYZZNlvYgBU69h18RY2hjk2jOV07lsgIecopajdKLC92ViJS+iHA2VdKIQ8XYb8VFU/poCSXGJ85Q2MZdgmMSDzlTn7JgqHCSWUMeVyOPMCLkJo7WTbM+Wv6hXLJoVhcD0U8gS/9reqYE1He5UyzOdmWlloF6XzJRpMsdCjKDJH6QHm3LOLI87SRIO+3X9xIK9Tmh4BvVFLaiMCxvnDweW2L3D84zXv/71NkuFbPWyRYN4DWbD2TC/0XMKL/xKFLDU4/acnwd6cUDmI6nYgbHclUJUBo5yiahBIchR2gElTPTRwJ4hGhK892kNtErTiQQ8ohLNk10bmLszlhRzqRPD0h4YSrC3Jlu4q3C2dNY2XJ7eJ7iQh9uyuFHapDO5qMv0P/ZjP2Y4llDBDCSmIJ41XLrl2If8gn7kGoWBZJcLLQUw40xS1M24Vz2lQzxYuuHWwgLnmHRcQLQhMNAA/NB26Iec9uwbi7lvfetb3/GOd5gdpE0ZokOuUXN5Og8IAU6H2olPQNGfVUOoB0Wrf4c9ebMTn4bg1vSpxuya2DAHrBMwLcF5mBl38RzbgNmwM+QMRpqR+CmWF1iC2x1xC1qPJ372Z3+WO9gm9ajPXf1zJMJM3/QfboA30Dl+sOESV0RzEARdlzzdSwO4euCBB+wM0Q/z0O99Bcz40S3fXBPhYVUv767pcKmNHe0cI5t2m8tpq0OlAXWirPCiF72IizoE3JzHWk2MsGvkZWuZY1LpDuzndxUmcJV7OPMNnbxUCHCgyAF4kQqxzFEUGAc7n+YzIyC0+Snv8km92MjZMBAVscmGpDQpKeJBpUwPFAJ4dPgMukULe7DpgCrRDLeMsxi1N8X5Fb/u0rB8hoGWoYCxBNItGMIHuBwQRXt3JqIVjLgJGyap6zA/8LeINNE2weCcwgI5c+pcA1H5qaQYA/fijNsErOaQ0e1D+JEfWWxZC3/4FBmFPwmAdJZrAvSTTz5pW1jsplsi3GH5krxoIKEgoXANzFQo6KFeNYfFGZ0jYWHEAoOJ4eH8LLU0tnNEmRzztmGAikmnB3sVLi1JzZTfflCCnW1bheoDyqGQNGNbG56QLKU4wM9Iikp2oiDzyNDCVBaUaVS6tCoxMycGSdXEt2Psib4qIbbDv2wfoLhxK26xwa5Ma9UbF3PJBeDEj9fo+LhYJPjIizalwJsXGyrW6A6WAC14qst6N6islxsaWNPhhkJ2X2adwqJNSJbHB9TmnpxLhyzVmGKBzRluKfj6naKFGhPMl3Yjvag3TngFlygDQeNSSZ4P6xedxS/hQN1qeao9sSCaF7tosVhUmvgIoYUCTlDXqT71/WI7itrCk8WKdIhcAfSwxEuuYhghCdUhvwq4hqs5PJoloMJfUtQDchlw0S1VuyVuYkyAEC7pwXxJh0KY1Kgtc9Db6HCDN2MtdHx+QfoX72yxSvAmVL9ZRkJENulCv2gr4giIzpQAD0FwtRRnA/kFl2R0hJbJeUXoLW95i1AoFf3d3/0dm5QC2STN26Jw6YxVD3QT0ECNC1iClrZJimdI4hweklKCHk5h0u3YC806JSQpijXSLWtpiP4Ze5bsG6PMI4tSAcgQaiyvkrExqzEZkaTaNCBHoq4mkxTlCenZEFOZxdLhYWYANH0DZqLhYdWMUJuDe3XZpDMGl17nZlT0IxF6vwaH8QzP6OoUkYdcDBiih2dJey972ctU3vrNYJ0mhdptwNC8nvDjgVN4um/J7kEJPolsVD57Cg8rzJoOj9gA42Zw2Rx/8Dl8DiDQiKqCdabP4AIQFBiib0fxScFRRGCjvAt8AeUIsRNuQ4scx8jfnB0u0WL9xSlnOUBo8N6BUnGiEki3EOl8ArW7QEgRLcImuEA5OIHqpxY5WCaWJ0QiSxPH5KeYvwvp3RfDWPjdFHHQLdNQuEzmM2DinUNARCUNR71R2JjYpx9OPR5zQuISjAW9nW2MCaB6dh4UqNj3mpLgIhESwVMZ5DBTEtXjUkw043re/e536+mAEBV0l8rZSeX0TghhAy8ZWCh7POxhocLrc5/7HAZ+5md+BpMA/BbQ8y3rBm/P+o8Wfn6AyeoVOrnADukZUQp0Tr3xHD8KBXt0kpOFiAd4ZsfmrXnxWHEZhZcDG370TBaHgZGmTG0rM9WVxCMZmE3ph3RQaXMBApoOrif/ffjDH6YH25sypfKFYyZIeA5Qz3iA0VVsWxFKeJ4+NKEWhYok5uHSYZluOjhCeajXiPCTMyJEisjdEeiZ56n7eIgBdzHAm7y+Kx0ikR6gZfAmNOUQSmqkasBkNOP/8A//QC3YY4rO8Bh4gf73sffC7l/T4Unzy9nAsSrWL+5kYQyxwWyRY4ApOojgQq1g6swVjWLBxVB+oq3HEA02beBJHDwNNFGmgc41UOcAMYkBHCpgLR3GLd0KUuMCD4HccNhiRFlqceZxvUDA6yAkEemUscoFj210tm/WqEgTueE7z8PeeG9jbUzBL9rCr8cqRPy1NcTt02pqTMk4pIr0PNy6JU7Jpm45pEarQxIVoeB0xNLMCB60LXTsfclAv/Vbv0UoaPXDIE4ltXgkp8qvKgArEoszd+mkgBXagLVJgQoMsb1TCfs6cUucZsESWeJBSFyW9tiknOTpkbFs0krId0Qlcm9yYQ/P+ImTw/rfSTpDxXNGvlSUpOuZmeUL3f7Jn/yJX6RoW7N6nYROrJPIeyLFUTsetJ0RitYorUsJj+FZEQKDnE70a1ApJt11dtea3i2yc0lcyRNwQuUu+CW5DamzGdNHaW6R2rr//e9/P+lQ+cxnPuNhNuQIwXbjxo2f+qmfsm8Bp6WqTCl3WrK7dBeqmOcs2GO08rRV7FAENm2M1Z6GsQ4zbrgjYJwD0O/SgU+jwOATXXsGLlUMIg/T3UDYVOrUSG8BrOfRwJoORxV7G4zPPYbIDeyTCIusMBO0i8Lf7J2CAcDOvFbjUjmpQAYmUnAAAwUvSFghawZZmGDQe6meeWMiLJZspvEf/tAe0bVQKUwQk4xyvDdmnW0IC3zJInyIyBYHIrIwhLqf7duxIWyiaIyrny4c5NYEnoEhZE1ApfDIT2KxJ0NpFUs6FQpdQj5FQ/OiRy5UPuOqpZ7YYQjNOO9jxlgME9xAM+hSu1EoYqzkBCadyD2ioVpEarRdKV8GLHRGAjlDwKexfXR39qc6DJhoBoaiHkqASmq3bwYzBmwGMDw7w16plTZQNCQDiOedyA90Dqs1SAQPHkhq7WXj2rpQVu6HDW9+85stZSyk5MIy1gHMy1txWE9tNQSK2g4iLIFdpg1nd7tl6mnAoYd5GM4gXTpbrTbLAc+QJc5ppyXYAlM52RNSijEbxgMhy7H/ASw3p3wzTvMVJVjigPQTQo7AFBmtTmWcbf9lOhyiy8awSnwmZDaZfWVNaCFkitqY1HCJKJevyDMcUSlcbrYyNgssv9Q+VNIeyMOqGPj7p7GmwyNzzeAYTQ6pEv/d3/1dA9g3Y2WmXMUHMgQjvsFPGL3FhIZIYdvKWZEuWHAD/ePGnMpw5w0/P8LKCbdFYZ7AN7irCC4bIZT1L0df4AacB8NQcUKxz2aU/MS3xYIUAqcdSDuZlEMumfixxx7zfG68bhpLTo620xKf1+DnkMj0FG6XzPtKHpa45YAHQGciY8bh0vS5BOCcCIFpY9L5AANLAJDbQUQP/Lhy0IaYRTk6FUOyAi3Rv7Bl6g2PpZDE2wHS27cMCQkSCKlFGNs73/lOpM21WVB4WXx86lOfkgjl78GQjPisMf0nNowiQho2BBsYQNRqyds6zN63NP3STqcUqFi0QLE9QBsWKAKxIdE9V+SpIYbPwZMOl/1u6XSYMlnH2zQe6lup66cK2yQzVuMwJ+wEZuqCimJzam2yqPNu3boFM+kohJ3zNdao4fBbC6NcVqxQiCQkKxur3wSp6hSRZmc439eISZxQgglVdNoYl+Or5PRD6ExeHLJn1K2AbeYrzW1UOB566CHByrx89rOf5YkRAm/gbTUttqD38XB/9q/p8Mi8M518qS0vBReTYn9s3U8IHDYM+YyDFarWGSXbFS/4g5WipSSf8fzJ22gKvVwRBlQPu+URtrZuI8cVlbF44HsYk6vUhtxmm9B2zxa+zQ5D7vjRbY/ihPPS2hSeNo4cgpFIYbuMyDxf4AgRHRq+ifSEa3SRo08YyCK4GGS3kGLFO+mQ4AAgj8MAwIznG9j+qlECiktDwIsjhrjcx8VymoCF0BDwc0bOQc92dC0K7WEKmgj5cTqToAFL5G9/+9sFrzgMzz6iB/opgbYlQlkHP7AJzYKvysBOnU5bglaHLvEgZUIlk8UqeQ9IeoCoW5h3xjazN7nkUo7YFLVA9BEGc20RQ6Xwi/heIrODqhD02NJjPPD6D0x9gsTAsh3FBtafINjQcOjsaKx+hx7KpxYmQVcY9sxCURL8nBuy88wqTGgio06BcFpnE9DqkDiKYJ4OFRj4B9Ls0IMatGSJGfCGsz0YZGU18YFcCAZOLMVkvIXc1ojnDlAhEV1MunTXKGe8cQdcmRFn8cejZQ21KZa8Yqo0AUkcA8McOeedSrhvO9d0eGTqmTXX4gYM0aFhACPT7yw2MVY9HKNoKDDxRs8JBGv7JzKEW1KUsSCFKgP5W9aZUR7h4OTbgqB0iDT3EBZxYl1y8ujjgHyJRzngnyxoGAF7w0X6JxqntVFjadgGI3gw4370cJzSAsJwY5XJUo5k1h6aHptUvqAm8XuxglYdBpmpYkQa1iMYiRTCt11cYQKATpMoVEGyoLPZJCxWY949CJcQ+gHAoB8YM1DuwG9y/fiBQky9Uj2KEgZVzHDwwAbz9B9t0HkBUYhXbUCiYSVqj9pSQD7WiSVHuRBCM+UyzPG8IchRorh1AEOO3qjXppynpGQklxc3FF5UgZC5kDZkZd8oUAJ6B9ukSEUXSAqhNA8nDLGNB5w3v/F8h69nbImYeJOrGIYVqkfLBrIZKRlXF/CAShh8X1CGgxkzpObmplVNIPdIvXosi+3bW4MyAyrCJN5MgQUlDMwvzQCwbbtP4fhPz50HjCrEGdj0D8zc1QCAijKdqhXEWBIBTIqKxJLRD2TL0/Zp8DPIzSZ1LfGsbRpY0+ERM2BqkwKBssjsT79LVtV4risHOLy/IA6yRYbIJ4VjnuNhu71Tu2f9+sJYA/fZ9xGG9tyGUMHIUaVhtaTobNm603/2IDjePZGUw5OXHuA3DEWO57fYqOskF+eUj8HnigkL8gJ+QiiuifXyPVfXAzPdynBCEvVKBu2aJkOTgg3p0/vx4qMq4datW5hxSxRQl8jWCvaQ75R8hDUkMQMzdzA7BkC/gAVGTATsbV7hT/6mJRtcMrHIri5hRSaI3oxdItxJfbsT8zA4Sgl2GhibhORxncTjtUbWRRwDnYEtVV0b3W20p/eQFwYCmmX6NL/WHEqu2QBwV8KQe7zi5I0qv/SwHhKjY2mpriXR4XMaSFCUAssQliyy03nDnYlvOFoODaM6KNYle6Bw1CUGNsMZpQGlT94KQAN8Y11uHxFq0pmKVIcHb/CC1GmgyWXwHpF4iQaTemzaf+hDH1LxqFfaH85EnRmGiVPDyWelH9S3iepZslQ7yOytIfXHmzNWp0dbMnbIuNSuRuSPylNhh8aIgL3S4Yhv7D5mdnJ4P3Su6fDILPNMpsZu8hPQmTUjY6mOxmdkGSjjExn5oe1KSVE4BuP9Av4jWLTN2KisufYVz0K/ktwL2XCKlZIH6pgsTMRe5GqfS65YYJThRdv0oIcS5CSPLfm/4OgJlkMw0g8YgP7YuEDe1G4Z5Gs4YooPktkFMikwi32WKYoPd4lsJ0qABiNrCkOSkHDwtre9jU5u3LjhSRseHObFq3df+MIXxNmjqoh/ggzn2UOi1UkzAZBdFhSI9WNbHLQrIEN4lCsd2kgcJODP1f+YXyxBDoOlIc17cUYGEgrZgHMKR2ukK3TqHyRnUTccPJ6FdTsfdK4o8fkVSaJcaH6Ra3OSBli4JRS1U0XMnEuOkuV4WQd+oslJJkubyY3VwUlAycl0O3u9RRbsqWFTwAJZBS8w6THQTI02dnKVLEtFNZuQuKWfmNbHpFOQMTZceWorA0HriD3A2uG/XcLcqU4wqUd7J92xjblbz8Qfl5lN5+46YwljyGlrsHxeT3YFoo16OlG+eMqrYKJGwNlPw2sPxbWxpsMjNpAvZZQDyuyWbqm/S+alISQJgsKfeC1JiMs2TBSSttG8qG1pkrMBzigH7VUaCMm7vFRusC70EwuBCZ9wbhv9ZaRxa2CyUwjH1naIwmR0i08SzcKUK2o4kghMarxMXrQUttAi0eanHSHIkROS7GLJCjIf8d3FFWZMgZhozWoFCcBAkUukUDtboENlXQWDyDJha0P5eHYXw0ueAScI4ITVCNJlKUrm06ATt4z160/pUCRiD6OQDVqnXBpLD0M95XtpRbCzOiEaWmSEaqhgKeb1aBOHHpbinEIXjFHhlA7VInRuqWH9YdUlxEsPBWiQ2MCkM53IGY0SncHsozX8DG8ahpDLe2rwSGnyq51GpWSb/w0BZqPSXMuaVuFswAYAM8BkiVPdo1BoCGAMjBL2MaM/XWnEBoXrceiRd2GDn68pC27evIlPFZjJDT5zQgW84U1WKsKzu3riBPzOw91RSAAhWY6ik7BpBBN7gxAD7F+cEQTUJeZLraAcNDUgl/hBzqi1QQNrOjxiBiyG0Ys+NWS7rDCjNJilOhhZh342x1GFgK997WsAxAVFK4/lt6zTplYv/rFOaI+Q33U7Eu5gaTxQIvT8XA+P9bvsMBenMLZEs3G5vHWgHSFnGohzQR8Vwso64nIFAe/lhIrTpYuKjwcwH71FvWCcPZcyF8SkXqsiwiKKkCwodDp8miuR8abfEPDtEQmODk+SHBrugnQ3toeHUc6S/7kb8i4HoIDllhmnEJMrAEELOQ4dMqI6XXoGU6pAZYlq8B9uRDFygjKLEuZUWlZp1kDGhnOEWpKoPdIdJrRxlwhIC+vSDLXL64oJ+YmwbmX8ztrwa5gdpRg7lD+gyl82cG5fDm/wGKLisaanOpuuemR68R1pgsBPvfQgxNsRcSayA1HUYTYRXuTxUqUlUbYX8uZ6Y8a3ORldpeoAsFRmlXF9Fti634QqC7w6buOdRwQcA8iNeUwpEMCIuU1Xz867RItnesCbiaD5lDx0XSIEAHB305jOijPcxpszKhGC7cTZ2cntC69zTYdH5pTFMCA7cuUALmflN7bOmByMNRfSLnBYHFiseHDl99F2lozNXTUEssBY7RHau26zeKacTUdUDwbkJC+16vE4R7TiDMHswnFhH4SNzJeS1Kq3TSp1OuqipFxoa+tCGlvDEE3bSW3dadeUbj2OVVtICQULpOlBUKDbcFCvQGCUTVR59IknnvAcy0NN0yeKJcLRyLjFzqEOOqf8Rx55xAMbQVySxryU4CmOho0Bu+hxi9VDiPbcSwNkJJoFiu1EMU7xQTSYDapAIVQGtgfNhd1mgek6Y15STM/UPrS0oQZgU4SdU3tcSQOGdPcAbSqaSZHD/MDAPoefc7QelQNIJwlxwFCBp0waoJD0CQAttY7fM/jUrWXlZS62k8mMEEVKcERL6WOn1JnOm9mRYieSyzrJS3wMIKqk9hse5GR6kpJXpymgFsi5g6lRH3AQ1aEHNODBsEwHgBSVnl0enZTLGH7+jlrT4ZG5E84U+xIbI2NYjNKmDQdghZy20MMcuQFT4xLaSlo1u9pWWOTMnEd2NFYJL1WoMXOt/Fn/EQ7uvs2Us+bCEDbkIREBOQfn9F5JTw3xE6G7EVzDFS8KOTEtUBzWpmh5giLxiGI55zVQWtTL8BNZSlP183bJQCa2huDwtkNpgCro2aRQqYSHQ5lPZeAxqp86TNjCFc13XGM4gNC8YM/LljihEMzoFJ5UKn53oVAQo7HXxJ2rHDgL7unWlgOcVkse05pudogWzKHVPhf/PvjkchcJSpbhLI/sTJKOtRM5G3MLe0wdY7YKlAK+3tKa1XScwg8YRwjJaMnFy+xDcj0UIUSCD2ogZHIBIw25WVZ+6bQNoNyRBb1Bwws4mqw8i7N9Ap7YT/804MAb6iRlPOiacTvw2MZPqLRPxHkKWPOeFNpqa9UV0hokFYtoAD9QqQxsy6tFTIEK1StFGoZYVXvvSeRpIq6XvVNEeB7BrOnw+GQJvu973/u4JVAeWDGe9ee9+rNId5mgpaTA5BbvBcZRnY0qUXFa8HoAXBCOESoAZdaFBrx51sJX7ZupGcMc2HHxzoeAn4CYVytwPGkJDh4rhEkGIuZlEf8AIyFEEWlPkmQ7z//4f5WK7VObZmKBJIQNMUKkoGdrNf8AWUhVGouM9EPtjvA0ZQeInnULYxAigZwA5KAWtMwClqRta3e1gtgN8izMAcMMP84FONqA3PrYQtnjsQKiW4yhFAX4AhI7h+AWZoJoEETFgy6PoHzwpEMRQElaeeQBubkQfFn7WMIBkcMM1VAJLfx2ApDzQ0bp0LM6y52qLpBjD8BkTbREfFah5kDUkzM6gQfDztdy0C2iDti0HSYC8wRPutwtKZqp66ILD7r0LLbQLVWosbg8SaV81N1CkYXThinQo52dgPHSkx0LTpH9D1eGHJiXAbuvGms6PDLd7NuDn0984hNivWWflRAbYkk84ebNm8Kc8bxOD5PNGexsuNTPLVmh7axebxEitRllMYv/5EJHOLj7NupFECRYvNWGtdGP//iPCxYSgLpYtjAizAhdb2SEOeo5ki07b5zTg0uEpB97OADuZvmqV2QhqTP1aoh0HmKh0rantpcPBQLpkBJ0WjOJjxoCpU9ymA5TUyzQiHM8wXZVzu4en2bMghWb1YPFupUrxhwqJLzdunXLBAnZxpm4u0cfucIt5k06/oU8CNUiJQwjWwZREZgjiM6/jXT2TId3Ev2TDN5usLURY3MrTfIOPzLxKrXKz3Icb8BYIIJp5jDlmZfAqhq1KUrKUWT4/R+RIUTOmbAOi0W1jqwpPyEHMoOnEERBdnmY9Cl3w+bMnLBBaqmId+MhDaSEUyQ9hdwShi2hQjpKsAWtAFJaiTwW6PrdBUwJSPOOBOcjpsbrTupCc8Erlwi1aTuJNvrv88s1HR4xAFZuB8b/DWgLwt4If+CrkpBqlIHmDAxxcg8HZpQMTo2cUUqo4sgUkuOiE0qOMLG4ndfpgARFO0geodk55Btyrfd0sBQ4cx9CCwSXN5cOry382ZCxVhD05SRJSPqBnSq47uVk7h5J27AJdumqlZAoQL0IaUh4RKZwD05kQSKLU+oPaIpNhgCrPQpx6Rhl3k3zkitzUUCH09s6drBFKBkRaQ0xy8rGG1UmyLbtBXTjFgmE1GQwi8WOftk9AmrgXlKscYkkd4+ZWI+uO2xY8WETwq4AloRm8252yKswkiPl6YcffthPWTCGjRQOyT5+Ulo0a0Pr0rnIDrkJVVnqBBA/7g5OjKFSTwOBmfRwXtc53pyVWYKAxSiz9Aq3ULCczbgK+LpI00Da07Das8dAXgt0VRESPacXB1Rd3vWldurSyRe85uNMdTiMpXSr3eV1cfiCwbOmwyNTyQSrysVch5c2eWl+7sy8mFquqAG4hyi8wsHmJoi4BCaCc9Sl/xwhv3UbHsOR5iHyrs0T6ZAnyAoqRxziIRjnfTFoC+vZHUQTlH1tgLzk4oR+aqlu0L5eTxsRQus82iNpnVSqoToRBWhYyCYPzdMAGBiawUIkDt11S2NQnS3/1oBJtBkAI2n9hxDqCAmjOrEHYJY+W2iOdCgOZH2bhxD6IqW4nAbIiJB2R+ZxBNdptyGEGTmNVCedi7N/+Id/aLHrnygJ0BRr+05hBMAmin1sS8Ml+pnEZWdtaDc663GWbwgyYCZUp2N6pmG5RqVDBRsDtoH8KpeUb5YVNOpOjOFHoWNO4Yxi+rkKie2xTat+loM6B//kJz/pEYAHtCoSxkBLLEoVIgioAj0sd5gjCmmtjNVhzDxCeI/0s838865nTYdHpuyOA94uNtkQw2KReR2rYogGazizM2cAot6yJ+x6CpfOQTJukbp2MCeeG9IKlRvYglOV2ye0JWIByj/R4qiQY1j7AhIHOBm/0rBAsTosxzhjwGKFuiJ9XfGIxiytWlhTL3EIZQowEAmdLoElbHpupkYQkPUb1a0mcZAM5FUasOGBKjCpMrBCUqNIXeoGMfTRRx+lH/3ilAmKn9PJEQF+upV+LErkm14shCHBkyjMwE7HfBgSXThJFH60LEGI80d/9EesTthN/3I/SXkExiyOwcsT2cYpwm5PRMMRRTEOR2PJm0K0wWT2ekBCFXzTEduHZTzxLq1GwploJcJkDIN+gmufIvKJREmRCBqQ0zYlqz7tf9p8Snxnm8b2h2zSyJdsrBVhzCA0jdqjohN5uH/A1nR4fK4zJrY+DplVNXJpahnusmcDZpzz4oBV9GkBhCUloaTop+WeV6kK8xYMsPglt8eFPAYRXRQLjs7cUqLCgD1SpO2beZNwUtcSX2P14AqG5a1T2gkLMu11Hjyj0qXapzP8c8uopVoGySlsHIZJNHQJq+0BoX9S7/0mxYoE5heoX/rSlwpk8Cx5OIx2eXe4ffzxx5f9I93FmJfYttvLiI+Wwyqkt0aXwH7e4HKYnFGnCDujBuEMR246a0yPUTPRy87Alnc3MFx2OUzWGA5h27h1isgn8jACMh5DupT2HNtTsBPnaGbuxu3wPP1rY02HzzMbyB8sLlkzD/Hfo9SJ3K9QW+YYb+zB27VIuO1UIr7fMFgTWAN5l8GyGEsxUP2Obkli0uG1cPIcR0IJDqslOYNOLJq9Ey9y2ctVuOjB/32lkOf4fD1P2eNiOznf9tOdYGvnTg2s6XCnWp67ne2ySn75g/BahMWxHos2+VI4ToAqymsRZhZ2+ZvS2IrQvp+NO/sz9sesgQZGo82uOJn0PIxdC0vPKSQj2jRUBhUHXqTCqtnpaSLlTMn/nBJhZeZ5pIE17d2LyVrT4b3Q6j3EudyisdoQWDmG3OPsmMRTzzWGXVG+bDc4rQ69Vei9iaS14pGqy9NgygotT2UCvLm75PAe6uhZRZ2iaEODHkhNb9UlzY6puXir/FmV7C7ize9dXevFqoHnuQbWdPg8m0BhqK02qUVqLDlNihph3BKFy0PTeZXGRvjDw+S2FoJ4iA10EXLXuQeNpfAXQA44oECqHqnpivhpjNR0Nbc0Xth6OKCi9dY1aiAv20a44afbAGvPAQ2s6fCAcp6jt0TeAq70U86btOfSob9wfE99A6HwL6nIf7MGkgW98geMHi2SZE2bq0vg56h+L2Kr9D866TJMaUC7OkZjtHQRqefEoBFqg5sX6vxuiPmsX656vhdTsKbDe6HVe4tTmhFPJwXeXpQ9/a+UhvDE5em5YqMcPE64pKjtbmsgDenQhxO9+W2nFJ+GeNHG+QWcC0e3xKQBl6Mo7dnBHqXR1XTO2LWxamDVwLOrgTUdPrv6P5t6C4tlMC3+Wo5oyILF3Ir3ZVA+m9LdA6ByQCuUa0RFWw9m9Mx6SI9P6fu/u36XLS967/TDH/6wn2H4PcYLYFV0t1aeuWrH2HVactY2IzUsjmnMAUz/Nb7i9AwHa+t+0kBevy3xuOH2rbXnqAbWdHhERUW0gtoGqLjvcGvn3eKjhCEIbgy8ymXmHukx/RpzCX/ta6dO0jIxBqIybomWw9dJfKrGhyt9vtKHu6wIceI3Ul5/9eJPr1ZeRfwZi1aKxUDCxtLOuWjUQE7jcHqOxMBMwhselo2ej449bEzQPC8MbDnw9DZ+ki55pdjQRnSEgjBWDzN8Ol2QG7od6WwA+H8dPo/i00h+8+NDEH5PEpMHJuIs0geAiYwT2ljSGt5ie27pB5/NjPEcQL59K30S9qUvfSlL9iq1ryD5rRGc/dwWiSG3Pfyynsxv5hqSzIDUxHGZMSxnP0L1xNj23cuYecGPWtPhkSnO2gCx9TnPGNa/4QDMVw8PKW3M8BlyxUYIMcMrak9PnDB9d3XGxhXJHRgeudSS01oLfv/3f7//wetbOb6bJXz4jcE//dM/eQcVt1Ij4EYdQHvireIa4MSHX3s6dyJBmnLcwkacLBfZG0NAxmrRB+bDmYywOIkZqIx1RGUD82WXPndAgcbCiZaGXOhrsb5F4keN/rMH0nhWduAzyCUDJshdUjguY2A5CuYuNX7kR37kJ3/yJ31wQNtcS4dLyGtvk2KUPA1Ubs/o0yVa7HU5DOisX89y4AAcbUBIjX5o+8Y3vtEXtP246D//8z+pWoPa2UlJi4aX2eso2sMAmWh1D9lNva0F5x5GIGQ4injDAPaIJkmDMRB8P/U5TGK9OxpY0+Go4lAj11p6lJ7xrkYGMxHWXeboVpZ6OJgeon33vTyN0Q/1DU6m/+5xV73aQDuXIy8Cvkb2yle+8hWveIVgwSH9JNF3Nf3LJyGydDWjrsrNnfGcXyAIp/PhGFcYDZjGHHAc5icYgiAUwyHZyfzM7x3ct8sRDWFr1oU7R53eWYaLAbR8IM0/+vmlX/ol6zP/+V1OcunTXB/96Ed9UPSJJ55Aepn5ltN0OtEg08P2KMyYVh/q8zF3WvLlNl/N1bkNeY09qXd74vQg7W5mEFizgHrwLsFod3kuV/TpsOev/vCNUN8r91DAfyT++te//oUvfMFET+4Bdi7yffAEwTOEiWYezawPlr7uda970Yte5AsP9mPSuVtmgbv5QJVPc8jQIPXAnE72kVj7RwNrOhxV7G3kWt0eR5pG9nrbw54umUVPbRY8VjiNvTQuuoH0jBvqt/m4ExqEKgCcxOWAXbERxQ2EdkGhtT6QC/37cv/lEYBvsvhKmf+15reJ7l4lHO/jedjA1VEN00YwI8I0duIHPFlQ23DHAUGGh2ITSOxdVy6ErdyvIcZZKQrEt27d8i8ytLGn4WvOKL7qVa+ybvDRPp+ZtigHjzE7ZmXTC1YtaQme5aETLf/jxeNh6ZCpy4W+xgD/vZjoJellplnyhp+5pd+xnO65hKrsssR5SntU57Pp8p9Ps/o3MtTuX3yzf99x9d+U+ogoO7nGhwLDMBHI6NJjCP9UEl1Fpz0YpJmZ/27BANzV0K8Th+VC/IxmTpH0fob5fx/84AfvZ/lHdv+JSYnHgHhLZsf+5u40dHaISjq1WZvz+F53YegWbBpiWQCD5+JGlg2tAxVeKvyJiY7+V7ge5BwCkyN+Lia3MRC2jZ4uRUbvznhkKD5au1idSITSIa0SPFadd469uLNpghZXtU3KPj3Xb7IcdGgIRRl7OFIANhBykJTZwJ0MAwDsFuCQz+VO+HM74x9yUc/i4C1veYt/cml3msj+FbCI7Dct7mpbJvpv9fJf3LZ9GrnDwu5kaWYc8gD0OBiYjXGTLjH7orR/RuiT5VZObu3Ec72dqFB4OGNMytdw6MfbbRaf/t2tzi7v3H/qP1efy8+ojrD+r6f/36Lh6bjcQ+fOUqBdSnkoAzsX/wF4bLvrTMYbN2784i/+IgOwPWBmbcD4CqD/4uTr8GIXI/EvSP2XG8xgzyrWQBYyzG9ToSt7v/5dlFu0NADL9nS+4Bvr6vCkKc4ic6oGzOZYbgnAXZ6gv8jFCrV1unXAHE8ifzcQig7RmSlzD6YsEUpIMiJa/tuZQ2Dyf+kcMXA3gguvEj9VQOESG87yivcLPDK8efMmDYgIgsWXv/xlq5O0ZEPPPhK1gJ/hFzLx9DCo0rOOGnpG4U9Dbf4FaS5A2t8Ty+wuytybQHeuww++eSQC5JHYCW869I+2m3HwO4Ev68SDdQAFemQoEfqPmy4pnBkIhR4iQusuhq3Yfud3fkfUZgD+m7yFAuZN0wWrFggJtWTYpQNCbMiFYjTqiMrT+peQ97SNMceQmI1K2sCMW5RjUpo+83jFuaBnSAhoFhJfBSAJfeADH2D8NkWQY04yEG0AWPI2TF7WwHnmxM29smR31PcRP/KRj/jPFdajyqDElCAl5je/+c12CDzNlSw92qSNa+TkMv6fR6PWdHjSZLFIbi/iSDmtKmzccwwhlY1yuaIh4+M2MKrN+YZ37dRo7PUkGqcBIQGQZ1oQWAR4aPRDP/RDeMCeg9twA6HBzxuAiYOc4Xqdc7yLTjCDtBWhDRwKwZI0zFe/+c1v+v+omEymlBPb9Vz9XHjCQ/yYCNMhOhA25MNnl+D1YAmYN33oTY/Kel86HA6LRHYm+7+SKM6tZQNdB4WYempXsKsGRgNLyMvakFMjDXtq9ZrXvMbru+xQ5A0bumXKzKBO+UmB4nmef8ME8mJmlpq8I+XtMkjwZX6YwRKTQ8IZJIDLBDxr1JIl84gfxYHtek/yLI9yBMZgCrzMpQH5FRnLDNCdvOvNUiJ7WCsXWmD5D/XYMBH+3wgvOEucA8BwIlpSN8V2pG1ieRLh39yrO5f1n06Gxx/9o2l6YORc0qMKgQiSi2f/AG8vvFtrOjw+p4xJFuRmtkS8wqc6E4n84x7b9Ddu3FCRcT8my+BAVqnJggpzz29YrXR4jebILdESAkQiL3y/4Q1vUKHr0Y8rwmCATwoEPMfzDHnR3eNCng8hvpBLWHzta1/7wAMPYInTihE2cOyRWqHiB4x+Tgu9uHnB6mQfX+GEPxI2iBQrOsUIPdsi0w9u8SBD2Gb8+Z//eYU8PvfhN5v4F4A0YG4ruBXYziHA7vDyf6VDz5Zw4ucl11gJQd4U2xu3+GN7CDFLnTVwhVtnRHFO7TIEMO83KU3ExG2d7BTkaGdi2pK1LSHjmnQxV9Yx9ehO6XMUz7UAMHVE8SD0e4fLvyP25icPZWk+/qAU8KiPM7o0+3SF6FX0QKuG0wBsplhq9INa+FVjb3rTm2RE4lsyepMl+7y6jOwKEgI6y7L2Y/mXS4TwQPzEwZKGKZD7velq80OkUq+Ydwe2w3N1fl7YGNZ0eGR+RUCl1i//8i/zNxuSDMvygl/1xI6RyTdCMFv0NgEzlR05pEfcGmAyVoZ7gTk2qqDM7lk8H4NTTcrtX/3qV3uizg8Dc15KYjNN0Fcv/97v/Z5NFaEZb4ZDkm9cwI+xjcr3OCTqsouawH4RnYgLfO+v/uqvFKpgRGdnYImgPRyGoUv8FKfm7rKxlG54lgbkABNBIuWwQvihhx4yTdLbpz/9aQ+xEAVMaWUI8BqyCI1RiyBuXfjwww/70diS1kY7/s2gBu3hxBqrAKStP97IouHAknSLlpcsaMZhodDSZAPz4UuoAIyw6Q0DqOMECaTdRSjeqBc8NSrL9LM9/UYVMV//+tcb8sd//MfMZqnq5RQs2xu8dQuJVAozAAi9NqUacyklCNNiNPbMY2wjRIrsrSFG7ZQLfsADqQ1DQ4YrOoe5zmwpY2YA7Nze4Mtf/nJFoVvygR1C/xiZm6gGyM4YWAWGsRfCpRI2hD16OYY6DUM4/qOPPqrykI9xogYljoyIbUpL8NRyFP9OAHgw7xYNEByqLJM4CNGqfkdOwba9bpqkzu4GthPz2rmhgTUdbihk85LzFNoYorqbfWflakCV2pNPPmmRoZ/NqUMZnwwkNPNM20fWZ9lxXrGJ+tg1QuyexQMcDEKAXGhzRvRXALp0l3NiL3w40TBWlsKGpZsneV411MORQA6q4E8/G44ciYpr3E8RapUsI9KSAlwg+Nd//VdqESL1wOxMBId2Z2Ph4b0hiaUDPADAOYDYVvgL6xKPHpxYrwvKYpCXC/D27//+79LPneBwO2EYYqwz+Be/+MXipk1d3NogBenRmkJhH+kGQkWfDkFWvPNuvXaqxj9lAhjl3wG8XTHI0JSvXmES56ZDSOBcctUlYeU5ROkWCQD0MFujOlU8VmmYwSdIPODTWEYiVbzsZS8ToPGzjX9Ja7sddWgRNdaEOtC1OSHfaNuHUP34LY27wOjNkBoqSBaoQiplhmpIdOmcODIWCzEQAPzMaVjVCaxLMHA6g/GYQInDwglrJ4aqaUDDtPJZCRsqSuBBkWhgdjhsXLGBN5NCsZ6MKJ1NOrrktSIfeaN+MaFIhKRoABXkecSSiiKAKjzT5QvU3k4p4NHkxTzcJwPXdHhkogUdDq/M50Ws3JnDM0oWJuHZCxVqWSqvy82kQzWp4CX6SIe8usB6hMz+22PuQNTCKtD3ve99AgFy3Ftn1J25pTMnqUe8sEL9tV/7NaFB/c5j+RU+AWAezH6aO+4IfIbngRogRCJPTWyTioy0RBWf+cxnvEEjMhIZMzgMUnTWNjYkYccAsNhY9m/TbiCEVGEBCoBoMEvGqNM2bahOvvrVr/75n/+5EED5wgGAkBPfVqo3fbz4SiHWCn6Z98UvftGQNLZNUc+oXQOexx57rG/rWAfLN1iKeelZG0WK1WMgugoFOsGbXwTuRH64c0gvwbCKBOQ27anLJXIaDEy/hKTiIRomkfZksc0MnLMZD3dVLTRDZD078S9pLdtMxSW6ztoarM4OObQYILvdEeWF4EvnYPS0g8oRpGGaN2X0YAdlSMPjcAm+KXAmyDDG0QLQDyZIPQFggIZtCfzqr/6qnzoA/vznP2/2aYCZgccY6naJmaWxFGIgL3Y3JFIvciCv5aB/jwZYiGyk2KJti1Qic7pr2S3HPLbTdgxTCyUQzXS4iwERSQ+XtInNPlUG9opNd5pMb9ci7AsbyZoOj8wvK2RVAih/ZnwCEF/i84ZxNlGg8TwNpPLQhir3E3rEJvBM1nGExp7bDcwToGL0ntZYCXlUzr71YANRFi86AINm6SdYNUQyEByJIF7IiFWXCzQxpgAAJxdJREFUF7gHcuOQGnxPhLX32CvaEn+/PMPMxBrUgYkIExQgwWQhSWM0E/M71UCihHWXREjLPeDp+Vd+5VcUBxNwv/a1r81qD0zYQJoOadu60HBbZ37+4eGKUgZm2Aqg26RjFQwmtb2kIMSI+2p/Kz89bcdZL0ICTA/88CQUw6DzMY9t/Pt6RjkAUkuTBS0L9EU0T4tJ5G7LKUrGmM/BSPASD5v0yo91m8PSOWbwBkzdxmBi0nBol7T28RMb8QAeQvn4wQcflA4TmZiSMTCmRQ/BM7n3vOc9NjCQA6ZCYiFUneMsaYXZwG7hsymufyAB1AOhhsXuT//0T8NvQ4LszjZIow7A1HiSx1Ng86WCt7/97TTzF3/xF94FxT9+rjEXRhSf5lo+9h7ve9/7XlPAO+xAZPkzjycqfKTWaEjzWD/9uFyKUDXGE3/hF36hQtnPnPwjUvs0dIXD5fAl8rW9oYE1HW4oZPOSqTnswLjBsLJsHuWycthZIGBwDu7n8bVIJHryzwm1l7kBhHFzB/ftot7Gl8IcG5zBCqm75UIwABByiAgNAaktLHqc2WsggIFJWo096wwnbGQnoHjH4VXoeqxIJBjVsQdIVaOhpRYxjk4cxo4D82c8uKUzyO0oOYwRwV1C1WMuBDtbozKxzU84v/GNb1iIq1dsfpbsQeLKQKxKlsDe9a53CRZ2tpXt3n3AcHebx6G1bKCISXgwqSGXOGyMWwfQJzbalZIgJSdrAuk5MSERfL1I5fsgzGCJ86z27Ym8k36csaGsUQZ58OlzJIoMklKjEIyrPv1jd+7OiP8rBH/lK1+xRPAMiYDAmKh1AxtQD+EtiZbMEHB5uWy71V3Ia0BrRU4J2IDf1DN1smOJVqlF5eF1fxPUEGfM4AHdJjo8qLjVRNRwZlp6WBHRAN+m/TRvGgFYEcJvk/xb3/qWt5b++Z//WfXZXSUCDKaDcTIS8DbJ0bVU4i8pTefgXEp6WRtyxgAnPSsLbJmyN0tzb/8qiCncLRIBS95zSQ/8YKBznfRpZ0Jpoi4Rc2iYBZJafFDqMQD2IP4M6cuku99GrenwyIzz88Ixw2LZE3ANy1In57FXMYs3slcBlzMIncDEMgMLBEeILW4bkgPom0aJxLlciJmQywoCATCHgY5GuQTcUTYSPRdEzmgW71Ahnaz81re+1aaQbTEobJd5M0UtLFuEUaCkFtqwWKE3Q4ivAYn+dJijYjWAw6xQNZyGk0Um9l7oS17yEngkQgFRPhDloaINtJzBuxQsrAtv3bplGWGBghkRRLwG3NyJKWLZTtLDdjihRd1ZlPFLkhSOhKWJsCs9WI7AA62BlnESIa5s0O1EfmJnTEKItGoGCbLYeca2TpwzMLvTn/3sZyUkJoEr3EokFsrWzfRDXWixSTu3dGX5Ygi2w3wiG8AMcWZOOBFw7cipq9iScA+tjKgfOTxIk9Yolo/ugle7GCi9YSwkEY0BPc0XSIeHXvI9DpkTWfI7kMDcNdDcmdP3v//9vgAgw3384x83HfQAhrEZaP/cDq2sYMbbomc2ShbrQikTBqia2di4rjMGcKgQ4Qt83wR5yxSr1KI/EZwvI4fhxJ8GN4RZeWTrnvmpPwhOyfRPCdxQYZq8DST1xdQv4/l5OmpNh0cmjpNnhRkWaI4tynBCnuySnenJ4m2jCdacFoCB+gEEdoTM1u0hlx0XTXiaMIGfQcvQHdxDz3hLnS6NxYblixpZCWlfy3CQ3IbzbNE81BFRyUw0FIx4u4ZLvmeb7nOf+5wGGIKLYvqtIf7gD/6AQgSj1m0o2lBVJQgZwy0OjTqgIrdKrnBaFii9UXfQv0RoUWi1J/2IDiAdMQA/qW1seq1J2ia1NYScYYFo1w7RJgiSfTJD5RbI5UTgRH+VTWcblbKs/oDDBq2Z8ugoJPtI7OxHsX6TWJt+4BfvRHkR0IQibfVJds9rLQXmS6HgM1cAtI1zatGJDedEhnZJd6gsO5dt1GPDGTDbthyRmyGXCy2GpCXqhZyJWotbFFqcgdQjK8StPIeltOEW/INTGwlgGpY4ZGRIcLbvHfBYCCXItXaM5XUvc7IrU48KU0TUetFwajcpMrTSx+Hdrscff9wmualHAqG8UvtaDtiSKzshF21gWx3AKx0umxTkxpbOIk38BtaQ+Jk0PZPXs3C+bFIgRMtZFdJM4SEln0XrPgde0+FxAxBTxOKxLdafAzC4bM65HnYp68AoGOkpjjPlMejjxBYQ0HK2wi4MgqxQ7jVRDiAEQC4K8AGXBg17RrnliKsGqhnFiMIWxs7NhfCjDic9FHQEGpgtuSyD/OTcNpHwBwwAF5WK5EuxSYGsTpcU26OzZSeHWb7IFrSaaIdjBLkc4q9457fzooCYCy0Mdof8pMEiAB4Cqs0tg5xhph+ZwyLGGdsCtyWdECl5FJuKvwtl72gWy1B3j7Bxq02xzi4TGSrMANAPmDgOSsYzbcyoHQSOdUUaWmbQlrgehxxgn1CIJ5ft32yvc4UOkWVKScUbJXIM3hzaFm3eOjlG9q77ZJlryOncEUus0deiVQMAiImopaEShOAbyqF5WgpV2iNFaMfCNcglzata5ELiBAmsBgB2pcwiCwMmO3NyV79NezaGruFWRWxV/UczzNIevoxoCQvMQDwM6Ri44nn0wxi0s2qETD3M7BAzGiPLBeRmLLTa9mbU3DZj7X7rIRecvc5N7ezEno2jqBXpYfIC6vfVkDUdHp/uDHoDjiFOpNPI7hVukgFfdTBB6YdXg7zMHPntkCh8WwfIaiK7wOSQckSEUiNgHigicEvOIDrwk+K11Zg9JQP5kmNDkI1Lo4pEGkMdDO8S7xC1M+MtBuQkHknOGgX+MAOQoe3LSVrOLmHgn4pZnOATHtlLzKIZKnKpHwzkCbjBjEtsQO7Jn690emwmKbo0HCf9zAu3JsiSQk4SLqHSowGM5kHqERmFWusDcRlOjIHRODw1xg4/yxkkewNxDhUYJHSGkCCEQt1dx2A4sTF4hiJWkUsc2pB+LHfskcqIbgXWOaII6ZcsmcFv//Zv63QA8PscyL1RhSuXUDlH7jBvzVH24E2NCj5jSUrz+CE+DMixRnt3vADDye6ujUqP3llLVAwccqg3EXo0ZLIPfehDc3caMencwXLQkgI9ojPR1mGqItnRetGvTgkrVTgj7VVnBysNP640lgwMie0GDMtOpGkAk2nDXT0ZW2AZcFPvlpoPn6q0yDkD3mfkS0Lb7SzqtjHd+SwRy+cFciF/xwaKbJuGbcMgZwfFLKhBlQJ2g2BDOnPdxrz2bGhgTYcbCjn1cpwqU2OpRnIDMVTiEXlbD+kcyFNRH4RTBkoncKKljrZKQzpHzYFtTFlFWadaQgXmrlsyJVa5lrbD8J10Ck/uTpxKQF7Hxywv5Da3dNqwEpE9tJdsXOLEkwykwShgOS381o60YY/LIUCgK0bgYUMnB8IEWvbH/LbM23rWhWSxHURkaDm85IqWS1tkwp/VCTbQBSZRyZFf+tKXBAV37an2NMXdJksj0TSeO4eiYfSDPUfzayFOcLeIRkZrviYRQMybModOl9NWiwiaRXC1lOe7sKX8YE4RHEWjTASLsk0tEGsbDjOda+MKCQ/txGIzAj94AHijc+vUA/N7CgNQASMaEzKVZta6n4Uj6uksudRk9uHVOlbAHir78Z+VvVcrrQuzloafQmtgIjqXZCGpS2dtuY345MLV3ApYqqYBKQq3YEBSYFrCuZ7BeUoDcthiRsNw7mY/hnSULNoICLYBWhYD4Hc81PKRZlj+hhSnULyfYdZ0ePnsF4+MryFIcVEGqlJzqNQG9UBOz8UNfiXE8AQYhAaXY/GczaWclH/yDWBCBrdUO3Mkd40a+KM8wOMIXj5Tj3ujT1qCCtr+ta+01C6l10lu3brluY5waYhc5cGShA1Y7BYreamlsziFT8OFEhwe5UFA97zQyoYUmOHh3rNAlEQCAa6sRdyyNAynXz6g4q4AIVD6+ZcNUjwAQCtxhm5qPMrDdxKgrQgB1DHs2QRWZ6gGxFaTTvOEcrep6Uw0fJLUWU6iN3rWNukaDmnJnqFRid8oAEeNM3jqVWn5MZ+UY+7YucmVgVr2mVa5UJYCBmf8a+Cn77dpn3UsuYoBZ9uzXlAy72o72MypigcPJho/pH75y1+uGmMAnilYB+uHx0Bn+gmPgcv2iVw1llx5nGni7zJfwyU/OjE7VGSDRNtvP1Qw7lJ4U6ONjRPJDViszmShzv25gELELWzYWFZ8t70sF9qqUVHZwuGASkCcmIIL6A4D91VjTYeXTzcjY6b8JOcXJm7cuCEM8Y3JhUwWjONyMnePhKooiWguigROHFxFp6dlUgJf1SMI6rSLMmsjnQUsfnI34qeuACw9EImoyIJWBpALAaTzFoMPtahS3RUF9Pudu0h0R9b/T5DyVOlTn/oU0gCEKnun73rXu4x1CJ2oxD9ZtNHepyJj21Y1imLV+5/4xCekOqMkBhtl8ENCTJgFBQmS1KKhh2QSABh3yzHkGkKG76O4Uy3f4U7ikAuHZMGqQsTLtOaUBiQhK3KB3jyKdEsp9JhWPfqlBKt5bAuOUJFdGJVItDdkOawKdzMVyUa0NQvRVfDZgZeQzKPkZF3OBuxeop6BNS/mDqQFzQbRo5fQZucDCbN06Ocr9oGhxYyVX8bvFmFtIbAxdC0WH3nkEbsX+h0wTGOw7Wvs0wYjH5ehUnRZmoPLU4KEREzK8WhTTWya1IK0HWmzCR5j2eE+0jv7h/8wgMEGG/BKWnqGs4SHQ3f5C/e8efOm6TAp7F9WDslO/GvnUgNrOlxq47w2z5kBjJUnsD8BmnXqZ6z8+XQ/HFSnNJDmXVk50vwNOWdjBU2LCbewwW81JAmLKpfBjFfvJBTOOdfg595N8BKNhlGynVTng5944PxKUWsXIdtdEUoSErP8AoHTGo4Hodm6wVkIEyPAcNoww6ax1OQGV5zcs1KKtQrR8BMCaZgIpAZpXeisDczC9Od+7udgsyXoBQrvmvcKq8AaDG4HedQNXHbO3WexMWE3DrFHWIZkWk2fYkvWx7ZOSiPagIEEJmlRlBBpOsxXgVvyCLLCwkCjOh+VFK3sSgNw6kLdREiHWGJX5t2ehPll/HgAA79ZliR6zapRR2ktAZJrg09mg+0W+uhCSy4yMk5Tb3kq5SuYvOfcAwW3gIVqyUM9S3JH20QGw7NISrGyoARsLUg5tEpqu7VIeM/IXsg3vvENT0zloQwMz8oak3IB3Y0hzVrzuOTZRKBO5zzdHoAdY9PhIaI5WtPhUlGH22s6PKyfQ3ez1AyU3Vsdyg2CkSjMBAsiYAI4hOjMe2g5eKBxkDe6UCgiqApbUogU3EaNbCWBq7jd8K7DlFEBL9hx8paG4pH4wuU4vN0Y4c8WqB0q7xN6hqcsFf7Eo3/5l38RCvFm48hh0fCyl71M+PCQAz/WCriFnA8nxQE2OLnHJPSpYYfQQS6YsSH0O9OzXUTKt6cqLFoXitTYEBrCbyBazlFZtovmB6h/528VdjEpLwqg6VDwNYPWHEzLshsMJRDcXQcdOpspMtpDoxZvHgmILMEoKYE9GKLfGdidQc9UcifKWF6BDTzGFD2eTqGIDVPscR0mQ6WTYvFvur31gwcmxHJOJBRY7gMVVeCZvPrr1FN20YMf+emtb32rV3hQYZbf/OY3bV24BQyfBkICQ2hT1FzWeeA8upLkeAEqHMFZIiQg/5IgNRQr7F+Dkk0H/BpWZkhD7jLtHSC079awWsM5EWhYwyg60dlZQxVoK5VXqiA9oZCkwVDCUUfbx8B91b+mw8unm63nLRrWPd64856b9Yp9DOunzDeT5Zk5xuXEFiOhGmz5Bvzcg5eqkaUELhq4APrJT35SOhEl9QADzzccIsUC5TNNAMCKO3DyIisSctkQc0uP53aCsheFRCKbRd5zkYPFAs5vTeY3iFZvlCAcyFJSlLUjfiydxQ5LRlWzu1ARASFHinqGg7tbOOHe3tkZfQ48hNoWhZ5oCsdyAPakWwGR4L1PCBkYh0bkNEZ75Z67CT4nrjDcBFG+xOZNXfpX1vgGmH93Z3lEgRg1O4TScCkWE8ekeM3Sf39lDDrhkYcoym6515oEZbKnjRPlBM9azIIGhA4DEVUJsXkIJQYUTbQXbboVfrfEYo/QdF4Wi9GCCl3koj48Q+6WJCQ5+TqB2Sej3/DYI5WngdEbbRiVJc/Acxt3JL59Yszebe4LebxbpmfJcg8eaJ4R+iKusgB+E8faPTvgDnZQlWVJcS7p4I1N/I3hGyoFBsBMsXxbyu5izFN8dYPO7m5gWC+3NbCmw22dnNEzdsb9VMf8wQpJuFcRwyI25ZMDdgbqPaDFiG4Wp7Q5DHe1CJOZsKEHXZwIDUKSHSReUSgEyVUcYdg+Y1UQqX+CIOns0bmFotKbn1uueT5h10ggQJoTemnF2/9CtvirR5xyV4rCj7hprNAgV1neQS6C4Cc2lhJt81McNBwY5otuCQ6JEli6lTAw4xJ1K1fhQBsq2dEoqjAQBscIDgPMerYpPrs9aQNjNQRTpYwCS3qz/WVTWi2iPrDCJiPRQBITz/RJz14p9GEXOrFRoVPSgkHs/tjHPkb/UFHgWVIDzh6Qo0ZZB1GGDXMM+FW+58rMAwM4aWrYHj5lC1sIgPfVXodVPdONh47g4wfFW7du2ZYgLK5sCfS0GHvYQJGkcWhUytSA5zDR5V2jOnDy4IMP+l0TnEo6dq4oVP8puRLZ8whGaCxOFIJ2qumB5oWC3mJrjsb8llSOtvEQDCTjMjrTA0LaTaseNjDw5kgb83RylMoKQANrOrzcDFgnK8xpncUFWYflsUgNhssnwWS1l5O5e2T+zMozdDfxYHHmB2F+lic9IC0Z80mFuUWV9AMgf8DkKcyAhxZwoU1MUWnqdCmeWqZYLMp2fN4v/1SgNqmEgyeeeAKYDTqlsRQIwGGLSVAQESxNBCwRWYCAXOgcsQ5HKGICKKTiATPOjmhhQJxCiMhyrTxtaQg4KoRNXkhqDFEYpn1BA0twzkC6igS0M0Fz96wGJbMc2GrQrVWIvS9xVpGBkARgI1q+QasJpQrVia08AOVCxQGiNG94mvf6ScDbzGxoZgMgG2DGDlxBYgowI+9Ssp0AS0OTrtNAwLC5a/30N3/zN6qTejZwnnKJ1ggIniBtt2KDMTN4Onn3u9/N2JAoFyrU3MJhOUN/Zm/4YRtb8tPMBu+MB+r1Pq2H3zbkva3KxmREQyoxddr8wAxaHljSNuoKUz2WkqTwNW3VDDzaS0I726QDiS7mnZsabZ2RM0p/7lC/WzqDd4vHCQWcVMGELq4C2Elu7dzQwJoONxRy6mXBZUyW4TL3LmVBVshAdTpg3BeJTiW2gIsKQtFyx26VZ+a2jHyfs3UYb/HGinToy2QSz/iDIWEKyQLrXc3cDOclLTitDgUFw7mZBxIuLfs4nv3hcOqxMPWAUD62VhAcBYVqgt4LV1B76DUbmHfRO3iBGahaXgxjVga25t7xjnf4AYYVubsWTN63FKcAtzO8xDoaWHZe1p55b3izYLo13NIZLZeOw3reyYBRy345QKYX2mhY2yyoQlQbig+EzBGiUoVfm8hJtOEX6MoyE6efumREv0ywi2guMJMal/iPto1yAKNVw7NnBuZ3Nf9/e3dyJEmRBWBYFURBCG4cQAek4MaFGzYHFGnRmK/6L3sWlpGRlVsz01keh2gPX96++JbViLFgdSQs+yKb5FHIWqTqL1+++MVnOwHGTjL7EN10SO/QsSXiBTlKlPWxVnZeKEVpdV7o6pZUhFlNKajCHXqf4SDAiC+WZsrF1FmvO6vMGFLyJA1v+9jmZJaGPrFstxaFpiy//fab29QIoCmTRfRvp4CAn32g82jCuAJilGEhDZMM9ECtKT3GXU6hrIkKhAKzQ+tC985MpNCcys6iW5UnEljp8EQgN3xmuA1glArMVPS3c6jAOtVkiwWUG0Afd81F+Qn4CBCY5CcHFSak9se4nEpeYaZsbm6XjJ/s/SEgR0hA2HbAjp92OXwC1taoVOQt7RkuOyJAQTiWDpGEBt7LdS1PRSi3T/mkNUSHKGKlpiO8Z+shhSL/x3XRUHi1KJF95UKBRi50qOZCnbInOFsWzkK+r3IrTCjoHVUKxabKBJgM70BhoFHeAcQvjFIRzeLaW9rzt2ac1fk9H2OjAleZLFBEbQvEjq+KjHbz2IC7RTYtwRnabqKK/PXvDSCdWoNCpNIptVWaWRHsljVEYYO042Fb9LYTWQLiPaOU61HHvv7I9k7UyBDo8c7gJZsY9J9cWi4304LxehRne6JWfas0YiRwBLitY/3tfpan35aotwizFmf2aJMIOYgJKIEbbu5CMn4bY67gfJ2OzGnoFMAMeI8aEB08miorkC1ODXEEwJXke3gBqQ9vIliCqj+MsrJjVBZC+JTuWmn0YyEN7vGumq0EVjrcSuOGMrdhZMyXLcoQpsmiVT7T740Glg7j21N5d4HD8wHYZQibllZpFoXyEwLA1KpeFjSNlRGRN+43PvYh6jen/OqWggIviim/owJKtuPbQImJ4OimUiTytlYQKcyFm8YSAoe0dcknOWdOe2sujFRjiRrLHqlRfBEK/UFUGGGxLweLjDtrEeR5dG64sQrzWeWDb6F/AMIF2leZvb2qh9SjSc1NuKa/whsbXx+K8CkOEgUVC8ElIUFWwczAW0eI6klrCm5+WpQTi7Hz3ERMAL1jSla29YcMCQl88dejFVUwaqUFK1GB2wIRxltxbfsbjiPvDJhzMR68S70OCy2AIDXTsnVpWoCY2Fc58n+EAPqd/NHUltkzbyYnCaGBwK0LSR5G/FqeyoVQpyPTR7vT+iDbKJYQtAskaRqj0h9YzDL1X375RQ6W+KskIqD01EEcUOCSJqM6WzHbnrWvS1BcjyfaVOC8hpDPVrarfCSBJaYjyXxcnweyYzmpGTornC3BWkFhi7n0xxCv6NFE29qLG9gZc4fQj+65qKHcAC4Tc//jj0m6n/0NPMR4xhunMB2mwOumleP1aY5pK9IVFemn7VCBAA0ihSAoHvF/PS0dOKGTQvwKDdYujpfgHU8eLNcXjC2U8HyjzABw3T1S3i5S2JcTenJ7QQGpW04Nwc5wdD3eCz3BD6y3qBRw5G3rka210HwB1L4paLHQm04tCwREUwGKDrLNAJvS2RWujVJfk1HEbo5ipWLF7DILmUCk9Q56En50EjIzkGXt/rlIiWVPYEGG0SLG7S2GoVIasEgyHHZJ4lYXiHfwSRj2yBDxf/75Z7sCavyYx//oy+RqrX+d1RCIR+HWZwsHzQjAGn5NeV2lYX60YPnlhAKPsP/++++O6MiELwwuGyrM0nal4WYGyV+rz608p/8Uork3XZttcDezPVNbOmUJHtBIGBlcz9aozXPp0PrV0lACtnommT/++MPUhAr0B20IGESrcFYCKx2eFcvHlSx7bJctCtAcxkrFhQ5mavx45h0x6AJ67uqB0V0Gd2d4JseQCDmPx0BJSH6St7iryalI2hBNCkPzEQrU6laM08cnTj08SqLNFc2UbZcF1tsWmcwEu3Cpg7AoNBiSHMIYuvvCYmMhEhpMyd2uNEkXbhwayb7CvWjF7XUo5o7Ao3A+j1i+tR5HAxObsPhM3cpE5w3mlvFbUQBb3ATZY4Un5TuuE+wgom4qwDJ+iV2N/tAROx2pNNavP62cqKYw3UTBkMvh+CydDAyKKKFcUxBGJQEIyshgioKyTVrp2apRnpCD7RwKx7oFML2cBX5UGXeQ6oA7EMwAqN48DEfgu8ncxAsZEI20yV/Zg+Y7+DVwSEqb8pANZ5nGTWl7MGZ+dl9oBHYTRKcAJgHoHNtGLdSmAtwh+gOIsHQx8PcFGIeAymyehC2ImwfzaHDszaAEcMtHYUfGJQSO7ydVFqnmwdJn3neHuvdUfZ6alQ7v1zVzzPTbtJQJJKFcFFAuEegp3I9pM/LN0f/5x3029yb4J6QaxUT7ZpzfRNXmpOttfBh5E4+Ug3FNpAYfqGKQUeiPBShMh7XyMW9NW+8t9PDVcAWhgd4+ve8Ii0aFBWTJQJQRfRwWujTrESMEa5ScuL0h+iNyT2dyePA9YJFn3SYrWxAIVYiJWvX1eQTRQGBUf/31l7WCmGspEMtUT+/ecGFWpUcctCghbTdo7FXArglJ2ckAvIkqY4dfBfyyMVkBMTRiWuYWpVstsEOEPIGYOkxZFBiDTPxhGtjTgymV4RXrBX0bIc4FXCW18/Gfr38UCQEwMks9FXAaHOWG994Dv6aG/cOOEcnG3zki6tZn2JdvJB5JEXe6ZfDhglqNd66HwoBcgzE4ehruDYJ069SDkH/99VdwKIJmbQuZfJAJjEgyE2V7NELvzlAVCKTcGSXed8j/GoJfr89Kh3fqNOsXActDzNTEmQUzRD4QUIUSAIt8xDO3JALFMYDlCawcXlGAI3k6K7K1wkN4lEdPY3sHROVJzRa4cjRP3voK5v0cDqKiqgCnHkcge4IZg8pIQkz5yWfk9XmHHEAwynCPZCzfF3BhIWrA1euDDCsV8beyTzR4x51uz5J/AGGpQFByoeW4pYCwlerflPF1/hEx9bzynYgajv4+8SXk+d+LzLfcqLRSAQ1TOtCFcvwShXWM/9JIZsJvY2vSB3ls5koyphsWkr8aMs8wmIEkAQuukeQvAzhOY/9itJUr1B6+EIWsNEoG5jUFEoAOR8bSuAWogzEp1qrXH621CNYKC+5gGXFVUNNYrdfg2vbJ/sM70KRAl2Uci+KFwBMj349ZQ/REDLzElY+AifLxoyFyi2tbrkN21Rsc2x5///23S+Pu7MCCHTYvMVdwnC9Pmyp5HM9rCjVKQEaeGgW8fIh9S8lnLq90+K59FsZ2eTiLZ44+c/5Mc28i6vkAU9PkrT8nsXBhi+rZn3rlhms9grOHfLkGcITxNKEHtYIOu4dOCjRr9rsokQJeD6RAneA9W7nFiJf9kBMuxskbGMxwIa/KAVJNn8pTv0V6uYwXo8JCOzx/UKisXgfzkoAPiinch/csVaEjcFTVAUlQi4z0ApEOP/zwgwWT6x729DSdhXNUGamARHyfBE6/9uHNeJwMSQ8RoBtLQIBYyQxkJkd3hjDFrSgqN1c4wnuhPvnrsLUNkbfgC7smdugtR47MFU64uIBi32QsdEK/ph9//NEkwOLYNuyff/7Jy/CivjyEX5LP4MNu7NC8h3y5ZnhM8joreKggpCfDYTREZfwOyw2MHuVt/QmEPqeDIUM8pUt4lO4XU9IwGujdozMVS9Ka7JESfuwDFUZverkGOxkajgUyjE7AoThL5MtXrnT4rmIWwDL4s2+WwfKycsZxwQh0k5NkJt30F5s8yvxnO+oykG3Pa8qAw8UZTB6LjM4szZqd1tjGyWmxEFJ+cg3M1edKCdAvkRZ/lYs4EoNUpNynyOUIzf4ewzgbQ6/Ete0mugmOHtuh6sfAYEzRiFGfuqdyC+G55aOIOYQ9iK6JKWgWtd3nlAWlQ/neQjngmI1Thee62J74b83vHmM1sh1+nQiyMWyOlgUrn+RDAiqbGdwhB0NYqeEJEDR4gT3i94jO16hf6fBdj8yifSff2URJ8UjNWY99fMbEY01jnSC62GKSLldJkxMXgvYsdwUNqTbobIsJkX4djGwbpF0ltXkV/bAPAUcsrPo7JED4RjVVquxTLnSblyU4QmMSkpbjW2dLs5C9A9HJEOEphW7VmmmJhiojRjd26zMKT4A88XNLxhPBDqgWnZjy2KWUCNUw+3IhxjkUGuL9Wc412PeFb83vHmM1OG0V3idOY1aEmZpHdG1XA3zcKYhdpdvPmQvJc6XDjOptN4NZZA3Z32WbkIQMcV7CFtspUvBZhMpkgT75fEf2wD/w8gSbJHBZC7pgJvw5Pxd8OyooqQ8L/ys3foDF72BoakVoWcqmgp1M1/xsjfqrdX7h8OXLF6uZjjOfws/oMRP1ycbGzFqtVpnqn4L0ApCRwEmfIemk/tZP8IEq0PcLH+WEgFlij03J8lbI9/X/1vweUZU88UvvIxCdiz8EMhLQx5PEjqDt61t5G2g5PtFPed/zM9SsdPiuZSs86YTRF1m8GZZPhnLWDnhjHms3zFaG32OZXrnrMU47Y/PhTO0sqJsqmzVbjogR0qGxkWq7A4pIyiWUrVnLkTehWJ0vSEBIIliPPmwjtbZw8b95uH/vZ6CmJm51Ot0Rqp6l97azAPREA+zIqIaN+dQnwrxvDYsXWD7bFKJ9U+F7X39rDasW8U0vLAfBNLFgyZjCJmbVKIQrOTwL7xGd35rfI7zkEL8YpGvd8Esy+TXr0kGl1ii81d7MqpvNDxzwj4h5+fqVDt9VbJuxyFJMyfjY1gU3Y5RMkF36C2FueenJtgwp/534zwU4NxlZaa+psbKVohgBKeLDO9BgZOgnZEzrKtwngYRcYJo4RR2iiVzod342sW2WErvnsv3cRMDJtAbw8kE2QNeIiSpg/4WINrhOuHgWajOMThyybXNNDOZcuZIyXIwfJYT/LP86YWc+vzW/g+ikwLSGtZSuQ3Nxn8pjhA28Vf5+uYQ1DzgAkiSLOqHh83y+n8N/Hob3nGZtjqzdF28DnUllhbnffogafQpArAcEbhmcMaaMVc/qe58FdVMle0WbSCEZ29OAxWeRIhQ6IFsZSZ4h4yYsq/ORBIi3AJSQyVlBNNG/6KyVdlKNmpqOoF1fX6jyhtFjIMiZAYzqkVGl1rpdD/yOnqHbD3wWvzY2ZESPdSEx4tRKkc0rx77C8Ft5T8wTa741v0eksrea6FQB70TB5aUxZdJO3cjz6KPpCNTZetB++uknv2PetrKof0GkW4z/J+WVDt/TVZGFVqQQJtj7Q7OoA4tUYI57pWas+/q7awo34ZK8BYgTvCjxTHy8G9EaeCSBYhAjGb0rqFTDilhOPw9QnrnREag76inXqLPR6unGdgd5zxpCnhk2eSqUDLbAMRu/+6Ztt++9LL3xcVxglupPnH3L3Zvbf/3NybbyyrJpB4tiriAYcta6rgT1XXdb6fC7Vt8ifklgSWBJYEngORL4vKemz5HfgrIksCSwJLAk8BISWOnwJdS4mFgSWBJYElgSeEwCKx0+Jr81eklgSWBJYEngJSSw0uFLqHExsSSwJLAksCTwmARWOnxMfmv0ksCSwJLAksBLSGClw5dQ42JiSWBJYElgSeAxCax0+Jj81uglgSWBJYElgZeQwEqHL6HGxcSSwJLAksCSwGMSWOnwMfmt0UsCSwJLAksCLyGBlQ5fQo2LiSWBJYElgSWBxySw0uFj8lujlwSWBJYElgReQgL/BX96CuxMY3ayAAAAAElFTkSuQmCC)","metadata":{"id":"fw0fiIB6jmav"}},{"cell_type":"markdown","source":"# Importing Libraries","metadata":{"id":"XvQEo4zwbRbg"}},{"cell_type":"markdown","source":"We will be using **Pytorch**, one of the most popular coding libraries for building neural networks.","metadata":{"id":"RJHwy2xbkHYw"}},{"cell_type":"markdown","source":"Next, we will be downloading all the libraries that we need.\n\nA **library** is a collection of code that people have written and shared to be available for others. For example, the `import pandas as pd` line allows us to use all of the classes and functions of a library called `pandas` that let you manipulate tables of data. We can call functions from the library by using a shortcut called `pd`.","metadata":{"id":"mS_5Jf7C66qt"}},{"cell_type":"code","source":"# ALSO!! note: you can set the accelerator to GPU T4 x2 to make this notebook run faster\nimport os\nimport struct\nimport random\nfrom array import array\nimport numpy as np\nimport pandas as pd\nimport seaborn as sn\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom torch.utils.data import DataLoader, Dataset, random_split\nfrom torchvision import transforms\nfrom torchvision.datasets import MNIST\nimport matplotlib.pyplot as plt\n\nPATH_DATASETS = os.environ.get(\"PATH_DATASETS\", \".\")\n# BATCH_SIZE = 256 if torch.cuda.is_available() else 64","metadata":{"id":"ZYaWflQMUKop","execution":{"iopub.status.busy":"2024-02-14T21:40:46.308796Z","iopub.execute_input":"2024-02-14T21:40:46.309151Z","iopub.status.idle":"2024-02-14T21:40:46.315684Z","shell.execute_reply.started":"2024-02-14T21:40:46.309120Z","shell.execute_reply":"2024-02-14T21:40:46.314753Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Building our model\n\nNow that we have imported all the tools we need, we are ready to start coding up our neural network!","metadata":{"id":"s81Jd7yPbdYa"}},{"cell_type":"markdown","source":"Let us first define the building blocks of our model!","metadata":{"id":"m3fIwrDYCl7v"}},{"cell_type":"markdown","source":"Here are the hyparameters that can be altered:\n\n`use_conv`: can be set to True if you'd like to use convolutional + ReLU + pooling layers in your model\n\n`hidden_layer_units`: a list of integers that sets the number of neurons in each hidden layer of the fully-connected portion of the neural network","metadata":{"id":"diINW_JeFRwq"}},{"cell_type":"code","source":"def cnn_model(use_conv = False, hidden_layer_units = [], output_size=10):\n    layers_list = []\n    if use_conv: #if we are making a convolutional neural network:\n        layers_list += [\n        \n            # channels: result of each filter output\n            # in channels: number of input channels\n            # out_channels: number of output channels; 16 outputs means 16 filters were applied!\n            # 28 x 28 x 1 --> 28 x 28 x 16\n            nn.Conv2d(in_channels=1, out_channels=16, kernel_size=5, stride=1, padding=\"same\"), #after this, input stays same\n            nn.ReLU(),\n            \n            # halves height and width, keeps channels the same\n            nn.MaxPool2d(kernel_size=2, stride=2),\n            \n            # in_channels: 16 (remember we had 16 filter outputs earlier!)\n            # out_channels: 32 we apply 32 channels; filter are 5 x 5 x 16, and there are 32 of them.\n            # 14 x 14 x 16 --> 14 x 14 x 32\n            nn.Conv2d(in_channels=16, out_channels=32, kernel_size=5, stride=1, padding=\"same\"),\n            nn.ReLU(),\n            \n            # halves height and width, keeps channels the same (because stride 2, kernel 2)\n            # 7 x 7 x 32\n            nn.MaxPool2d(kernel_size=2, stride=2)\n        ]\n        \n        # makes this a 7 * 7 * 32 = 1568 flat input\n        layers_list.append(nn.Flatten()) #appending a flatten layer\n    \n    if use_conv:\n        dense_input_size = 1568 #compute correct output size layer by layer (explanation)\n    else:\n        dense_input_size = 28 * 28 #if we're not using CNNs, then input is the size of the image! (each input element is a pixel)\n        \n    for i, num_units in enumerate(hidden_layer_units):\n        if i == 0:\n            layers_list.append(nn.Linear(dense_input_size, num_units))\n        else:\n            prev_layer_units = layers_list[-2].out_features\n            layers_list.append(nn.Linear(prev_layer_units, num_units))\n\n        layers_list.append(nn.ReLU())\n        \n    if len(hidden_layer_units) != 0:\n        layers_list.append(nn.Linear(hidden_layer_units[-1], output_size))\n    else:\n        layers_list.append(nn.Linear(dense_input_size, output_size))\n    return nn.Sequential(*layers_list)\n","metadata":{"id":"yQ1kNRAXCO3L","execution":{"iopub.status.busy":"2024-02-14T23:11:34.894127Z","iopub.execute_input":"2024-02-14T23:11:34.894533Z","iopub.status.idle":"2024-02-14T23:11:34.905856Z","shell.execute_reply.started":"2024-02-14T23:11:34.894503Z","shell.execute_reply":"2024-02-14T23:11:34.904825Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Now that we have defined the model, let us train it!","metadata":{"id":"wYpxYUH9FMXJ"}},{"cell_type":"markdown","source":"First step, let us import our datasets.","metadata":{"id":"U-VAOq2UNErV"}},{"cell_type":"code","source":"class MnistDataset(Dataset):\n    \n    def __init__(self, images, labels, transform=transforms.ToTensor()):\n        super().__init__()\n        self.images = images\n        self.labels = labels\n        self.transform = transform\n        \n    def __len__(self):\n        return len(self.images)\n    \n    def __getitem__(self, idx):\n        # transform image at idx to tensor\n        return self.transform(self.images[idx]), self.labels[idx]","metadata":{"execution":{"iopub.status.busy":"2024-02-14T23:16:37.351621Z","iopub.execute_input":"2024-02-14T23:16:37.352488Z","iopub.status.idle":"2024-02-14T23:16:37.358415Z","shell.execute_reply.started":"2024-02-14T23:16:37.352456Z","shell.execute_reply":"2024-02-14T23:16:37.357476Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Load dataset; this code was taken from https://www.kaggle.com/code/fold10/mnist-image-classification-with-pytorch-lightning\nclass MnistDataloader(object):\n    def __init__(self, training_images_filepath,training_labels_filepath,\n                 test_images_filepath, test_labels_filepath):\n        self.training_images_filepath = training_images_filepath\n        self.training_labels_filepath = training_labels_filepath\n        self.test_images_filepath = test_images_filepath\n        self.test_labels_filepath = test_labels_filepath\n    \n    def read_images_labels(self, images_filepath, labels_filepath):        \n        labels = []\n        with open(labels_filepath, 'rb') as file:\n            magic, size = struct.unpack(\">II\", file.read(8))\n            if magic != 2049:\n                raise ValueError('Magic number mismatch, expected 2049, got {}'.format(magic))\n            labels = array(\"B\", file.read())        \n        \n        with open(images_filepath, 'rb') as file:\n            magic, size, rows, cols = struct.unpack(\">IIII\", file.read(16))\n            if magic != 2051:\n                raise ValueError('Magic number mismatch, expected 2051, got {}'.format(magic))\n            image_data = array(\"B\", file.read())        \n        images = []\n        for i in range(size):\n            img = np.array(image_data[i * rows * cols:(i + 1) * rows * cols])\n            img = img.reshape(28, 28)\n            images.append(img)\n        return images, labels\n    \n    def get_datasets(self):\n        return MnistDataset(self.train_images, self.train_labels, transforms.ToTensor()), MnistDataset(self.test_images, self.test_labels, transforms.ToTensor())\n    def load_data(self):\n        self.train_images, self.train_labels = self.read_images_labels(self.training_images_filepath, self.training_labels_filepath)\n        self.test_images, self.test_labels = self.read_images_labels(self.test_images_filepath, self.test_labels_filepath)\n        \n","metadata":{"execution":{"iopub.status.busy":"2024-02-14T23:16:39.629734Z","iopub.execute_input":"2024-02-14T23:16:39.630066Z","iopub.status.idle":"2024-02-14T23:16:39.641928Z","shell.execute_reply.started":"2024-02-14T23:16:39.630042Z","shell.execute_reply":"2024-02-14T23:16:39.640985Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Set locations of datasets\ninput_path = '/kaggle/input/mnist-dataset/'\ntraining_images_filepath = os.path.join(input_path, 'train-images-idx3-ubyte/train-images-idx3-ubyte')\ntraining_labels_filepath = os.path.join(input_path, 'train-labels-idx1-ubyte/train-labels-idx1-ubyte')\ntest_images_filepath = os.path.join(input_path, 't10k-images-idx3-ubyte/t10k-images-idx3-ubyte')\ntest_labels_filepath = os.path.join(input_path, 't10k-labels-idx1-ubyte/t10k-labels-idx1-ubyte')","metadata":{"execution":{"iopub.status.busy":"2024-02-14T23:16:40.648496Z","iopub.execute_input":"2024-02-14T23:16:40.648899Z","iopub.status.idle":"2024-02-14T23:16:40.654310Z","shell.execute_reply.started":"2024-02-14T23:16:40.648868Z","shell.execute_reply":"2024-02-14T23:16:40.653355Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Load MINST dataset\nmnist_dataloader = MnistDataloader(training_images_filepath, training_labels_filepath, test_images_filepath, test_labels_filepath)\nmnist_dataloader.load_data()","metadata":{"execution":{"iopub.status.busy":"2024-02-14T23:16:41.206173Z","iopub.execute_input":"2024-02-14T23:16:41.206554Z","iopub.status.idle":"2024-02-14T23:16:41.463419Z","shell.execute_reply.started":"2024-02-14T23:16:41.206522Z","shell.execute_reply":"2024-02-14T23:16:41.462599Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#import datasets; we can't use these because school WiFi blocks this :(\n# dataset = MNIST(PATH_DATASETS, train=True, download=True, transform=transforms.ToTensor())\n# mnist_train, mnist_test = random_split(dataset, [55000, 5000])\n\nmnist_train, mnist_test = mnist_dataloader.get_datasets() # our training and testing datasets!\ntrain_loader = DataLoader(mnist_train, batch_size=32)\ntest_loader = DataLoader(mnist_test, batch_size=32)\n\ndef show_images(images):\n    plt.figure(figsize=(10, 5))\n    for i in range(len(images)):\n        plt.subplot(4, 8, i + 1)\n        plt.imshow(images[i][0], cmap='gray')\n        plt.axis('off')\n    plt.show()\n\n# Get some random samples from the train_loader\nsample_images, _ = next(iter(train_loader))\n\n# Display the images\nshow_images(sample_images)\n","metadata":{"id":"YHrYk460G9Fl","outputId":"008ce5e5-4f95-47cd-82c7-15c52674a84c","execution":{"iopub.status.busy":"2024-02-14T23:23:01.282522Z","iopub.execute_input":"2024-02-14T23:23:01.282912Z","iopub.status.idle":"2024-02-14T23:23:02.141290Z","shell.execute_reply.started":"2024-02-14T23:23:01.282880Z","shell.execute_reply":"2024-02-14T23:23:02.140353Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let us now define our train_model function, that loops through all batches and computes the loss and does gradient descent for each step.","metadata":{"id":"Y-ViwRUwJ8KH"}},{"cell_type":"code","source":"def train_model(model, train_loader, optimizer, device):\n  model.train() # tell model you are training the model. informs layers (like Dropout!) which behave differently in training & testing.\n  for batch_idx, (data, target) in enumerate(train_loader):\n    data, target = data.to(device), target.to(device) #data is input, target is expected output. put data and target on same cpu / gpu\n    optimizer.zero_grad() #zero out our gradients before we do our forward pass!\n    # Note: we need to do this because PyTorch (the library we use!) defaults to adding all the current gradients with the previous ones.\n    #\n    # This accumulating behavior is convenient while training different networks (like RNNs!) or when we want to \n    # compute the gradient of the loss summed over multiple mini-batches. \n    \n    output = model(data) #get the predicted output from our model by passing data into our current model\n    loss = F.cross_entropy(output, target) #loss function; why do we use this one?\n    loss.backward() # Calculates all the gradients!\n    optimizer.step() # Does gradient descent; takes one step in direction of lowest loss.\n\ndef train_model(model, train_loader, optimizer, device, print_interval=300):\n    model.train()\n    correct = 0\n    total = 0\n    for batch_idx, (data, target) in enumerate(train_loader):\n        data, target = data.to(device), target.to(device)\n        optimizer.zero_grad()\n        output = model(data)\n        loss = F.cross_entropy(output, target)\n        loss.backward()\n        optimizer.step()\n\n        # Calculate training accuracy\n        _, predicted = output.max(1)\n        correct += predicted.eq(target).sum().item()\n        total += target.size(0)\n\n        if batch_idx % print_interval == 0:\n            print(f'Batch {batch_idx}: {loss:.4f} loss')\n\n    # Print final training accuracy\n    acc = 100. * correct / total\n    print(f'Training Accuracy: {acc:.2f}%')","metadata":{"id":"zwdDqUhVHQCF","execution":{"iopub.status.busy":"2024-02-14T23:16:56.166406Z","iopub.execute_input":"2024-02-14T23:16:56.166806Z","iopub.status.idle":"2024-02-14T23:16:56.177211Z","shell.execute_reply.started":"2024-02-14T23:16:56.166774Z","shell.execute_reply":"2024-02-14T23:16:56.176373Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Now that we have a function to train our model, let us have a function that tests our model!","metadata":{"id":"toDDT08DXfw9"}},{"cell_type":"code","source":"def test_model(model, test_loader, device):\n    model.eval() #let model know we are evaluating it! (testing)\n    test_loss = 0\n    correct = 0\n    with torch.no_grad(): #do not want to do gradient descent when evaluating model\n        for data, target in test_loader:\n            data, target = data.to(device), target.to(device)\n            output = model(data)\n            test_loss += F.cross_entropy(output, target, reduction='sum').item() #getting the test loss\n            pred = output.argmax(dim=1, keepdim=True)\n            correct += pred.eq(target.view_as(pred)).sum().item()\n    test_loss /= len(test_loader.dataset) #normalizing the test loss\n    print(f'\\nTest set: Average loss: {test_loss:.4f}, Accuracy: {correct}/{len(test_loader.dataset)} ({100. * correct / len(test_loader.dataset):.0f}%)\\n')","metadata":{"id":"_Jp7CPuNIIbU","execution":{"iopub.status.busy":"2024-02-14T23:16:57.804198Z","iopub.execute_input":"2024-02-14T23:16:57.804559Z","iopub.status.idle":"2024-02-14T23:16:57.811787Z","shell.execute_reply.started":"2024-02-14T23:16:57.804530Z","shell.execute_reply":"2024-02-14T23:16:57.810862Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Now, let us put it all together!\n\nLet us first take a look at a summary of our model!","metadata":{"id":"_tnBeA95MEl0"}},{"cell_type":"code","source":"torch.manual_seed(5)\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nmodel = cnn_model(use_conv=True, hidden_layer_units=[8]).to(device) #defining our model\n\nprint(model)\n# summary(model, ((1, 28, 28))) # 1, 28, 28 is shape of input data\noptimizer = torch.optim.Adam(model.parameters(), lr=0.000001) #Adam optimizer (adam is a fancier gradient descent technique)","metadata":{"id":"P9YnA_V7IM0r","outputId":"c127868f-478d-49d7-b720-2445b87332e8","execution":{"iopub.status.busy":"2024-02-14T23:17:00.007100Z","iopub.execute_input":"2024-02-14T23:17:00.007469Z","iopub.status.idle":"2024-02-14T23:17:00.017326Z","shell.execute_reply.started":"2024-02-14T23:17:00.007439Z","shell.execute_reply":"2024-02-14T23:17:00.016515Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Train the model\n\nFinally, let's train our neural network on the MNIST dataset of handwritten digits!\n\n`train_model` will train our `mnist_model` on training dataset and `test_model` will evaluate its performance using the validation dataset.\n\nThe `max_epochs` have been set to 10, so the model will undergo the training -> validation process 10 times.","metadata":{"id":"KbIIPKUSMIk1"}},{"cell_type":"code","source":"epochs = 10\nfor epoch in range(epochs): #for every epoch\n    print(f'Epoch {epoch+1}/{epochs}')\n    train_model(model, train_loader, optimizer, device) #train model, and test model\n    test_model(model, test_loader, device)","metadata":{"id":"9w1mHGLyMA9E","outputId":"cc98e0d1-5f0b-48df-fbee-4a7f2dcb9c56","execution":{"iopub.status.busy":"2024-02-14T23:17:02.182104Z","iopub.execute_input":"2024-02-14T23:17:02.182480Z","iopub.status.idle":"2024-02-14T23:18:13.301137Z","shell.execute_reply.started":"2024-02-14T23:17:02.182439Z","shell.execute_reply":"2024-02-14T23:18:13.300193Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The cell above will stop running once it has gone through all 10 epochs of training. What do you notice about the average loss and accuracies as you keep going through the epochs?","metadata":{"id":"rdvma2jyLApu"}},{"cell_type":"markdown","source":"# YOUR TASK\n\nWe've walked you through how to make a neural network on the MNIST dataset, it is not necessarily the *best* possible model that we can make.\n\nTry making some adjustments in order to get a higher validation accuracy and F1 score! For example, you can adjust the value of hyperparameters such as `use_conv` and `hidden_layer_units`.\n\nWhen you think you've built a better model, screenshot your results of the cells below and email them to ucla.acm.ai@gmail.com with 'YourFullName MNIST Results' as the subject line. The deadline is 11:59PM February 20th, 2024.\n\n**The student who gets the highest accuracy will be get a gift card!**","metadata":{"id":"bT01EvTJgPx8"}},{"cell_type":"code","source":"# Change these!\nlearning_rate = 0.000001\nhidden_layers = [8]\nadd_convolutional_layers = True\n\n\nyour_device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nyour_model = cnn_model(use_conv=add_convolutional_layers, hidden_layer_units=hidden_layers).to(your_device)\n# summary(your_model, ((1, 28, 28)))\nprint(model)\n\nyour_optimizer = torch.optim.Adam(your_model.parameters(), lr=learning_rate)\n\n\n# to try to improve accuracy -- you can try to train for longer!\nepochs = 10\nfor epoch in range(epochs): #for every epoch\n    print(f'Epoch {epoch+1}/{epochs}')\n    train_model(your_model, train_loader, your_optimizer, your_device) #train model, and test model\n    test_model(your_model, test_loader, device)","metadata":{"id":"QquDHVJoX-Je","outputId":"1c2bc64d-686a-43d6-eb6a-bac1e534c73a","execution":{"iopub.status.busy":"2024-02-14T23:19:48.316971Z","iopub.execute_input":"2024-02-14T23:19:48.317360Z","iopub.status.idle":"2024-02-14T23:20:57.825659Z","shell.execute_reply.started":"2024-02-14T23:19:48.317330Z","shell.execute_reply":"2024-02-14T23:20:57.824546Z"},"trusted":true},"execution_count":null,"outputs":[]}]}