4-Lights Out problem with qRAM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learning Exercise II-B\n",
    "Let's solve a 4-Lights Out problem with qRAM.  \n",
    "\n",
    "When the initial board state lightsout4=\\[\\[Board 0\\],\\[Board 1\\],\\[Board 2\\],\\[Board 3\\]\\] is described by the following data, \n",
    "determine the _binary_ number of the solvable boards in $3$ switch operations.  (ex. Board 0 → 00, 1 → 01, 2 → 10, 3 → 11)\n",
    "\n",
    "Answer by creating a quantum circuit to solve the puzzle shown in the figure below. In the quantum circuit to be submitted, measure only the `solution` (2bit) that solves the puzzle.\n",
    "\n",
    "To submit your solution, create a function which takes \"lightsout4\" as an input and returns `QuantumCircuit`.  You can choose a function name you like. Make sure it works even if you input another dataset to \"lightsout4\".\n",
    "\n",
    " **In addition, please implement the quantum circuit within 28 qubits.**\n",
    "\n",
    "Please note that you can get the answer with the same endian as the one used in the description. You can also use the following function.\n",
    "```python\n",
    "qc = qc.reverse_bits()\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nBzRFyocx39Vj4x8hy2M4uUf/fUH/Gyhn/zTPe0TPMIZEPMD5IChfR1/9N4YsDFfmvUe77q/fncb8geFhdNbyGnpQi+ZmzEMeV743bMZeiNCci0rfmnDZbUKj26c2jLf+IvG8Xc6fFOzE4u1S+0eifkHiua194Hti3PC3IJeNyj8H3bt44458ETY7umrY90Ipjv2FqH898aLcyy9qLBxZyJj+25EqR3DyPf6WR2isrDwcPO9/nPz/tNggaxHriHYXMX/zumaOb5x0DSh9BRTxvcM4DaUjBISAEBACQkAIzIbA+9Hcb8AMxvKDXbQ+jwPXV4X+2O/fxw5Q3gfy8fTl70vt+KZDm/4nwWfw/qkO+fPAb0s5SIy296PBm0FvB/GwMcpY5gNRW+p6IIy6fEDN9tbA+y8Q7X4QxLcxss4fgNeW4sNXbsM6D5AuA2XZwSqL/K+C9+kWXer8C2ic9ItQija7DqP6bPGgPdrgYVVbOgPMqHd2UMo/aOBDeVu6CEy3cQDltv0xCmO+6fpREA9Y3Zbn/wPe1UA5ca5dh3l+qKb+M5MO9Xio+nHQK0Fcn1w70Q6v4a6gmBYx/mi/r7yIfcb+5u0PvgU2vw6KWLJM/8KD/JeBuL/bdL4A/rGgnPaBke211aOv46EnD4yyHn9o8B8gjqFtnbn+OZBPm+a13haB5ag1/FlcdJufIy4XTgnIr6Cd48r8jzrsrIJ/adKl/iM69E9NulxbMc37HhFt95W5zuP1svxB0CtA54L4VmuW8151CqgtzXuPcu17//R7nnag8DmQy5h/mws7cvrbuI+4dnYn3c2ah1H3hs3YCxEaxniMK3gvipjzBwfkkz4G4rx4ehQKUTeX6UN5r3hPi95B8I4D5fSzYEQ79I+xHss/UBvz/v7qFj3+YIb31L8Acb1nP8/7+zGgtrQIm239ZN5vghGvMZZ5vzgP9CYQy1HG8v1BbSlj+vttSoFH38e40e1/DWXyYro1KvtBrsOcY+JhLt/efAkoy7m2+DyS0yLW0SLmb57XnDFoq9N35XVN360kBISAEBACQkAILBiBa8N+DIpfN6K/WQ9cHw77Mahi+TGpz30tOnxouwDEAJI/fWfw+6sgTz+IQrb7VvBOc4Wa3wT5O0BZ98ykxyqD+6zH+jNA8cHrBNQZgGfdu4GX0zvBcL34QOh6PIi4POhQl+NlcBbTSagw+HdbzBmgngbKqevh6/egeIug/GCUc1Dr9vlwdPegewrK54Jc7vl1gk5X8Wap3T93Kfbw8wEGH374BkJOPADxsTGPb9TcL8n4IJXTDcGI7Xng0Ja6MP47KH8HyB8sT0T5WaBok2Wu35xGPVRzzca9SztcX1zjMd0elQ+DYp/Ui2kR44/2+8qL2GeL8AfPxkVEDC9D/T4tF8Y55rrLc/PTLbr7wIs2WR7l6/IhH9vw4Co+7B+P+gtB2Tbr54CmSfNcb4vAsm0NE0v662uFC6YPyL6TuNwr6IxbvDEUI8b/2dGQezDqefm5HfqPTvqPDHqLuEcE853Fk9OYeABG35bTt4PxSZBfI3Pet3NaxB6lX/N+11KHTw4y6vxpkufqLyf9fH/YrHngOEfdGzZjL2T8WM++6vFtSpVHv+RzF/NPgf8gULy/c9/x8Dbq3Rn1nNrutWxzKehXQVy/vwDiPtwBYvo/oGj3IOp/DqJfj+k0VBhjRt0nRoVQXoTNYL6z+DJI4vhY5n2J174H5Ilx4IdAUZf1iLnr/n3Se4ALOvKbJ/03tOg9NekQx71Jj/vt/KT3/qTD6jlJx69plnW0iPmb5zW3wDDEOha1fM/jOmAMrCQEhIAQEAJCQAgsGAEesnlAwpwBal9icBX19/UpB9npKD8HdBUotr8E9WuAYtqHStRh+TeiAso8cCMxMZj4Mii2eRHqu0BtiYelLwFFfQaXWT8Hd9T/NVBb4lgYtEebbYFl3wMh7b4g2eAY2oJe6rJPPgTGPs+lIKW2h68/STpezUEgbX8O1PYGJgPiD4Ji/3dFfVTifDHY83aXjmrQIf+nYIO27tCi966gw+uIiQ8c8c2Oj0ZhLT8SuY+Tedf8t2H8ty32nPXGZPevXBDyUQ/V+WHv1WgbD92CKfvDL/8LRrwWHnx7WsT43faofN77bBH+gLh+DRTxO2PEhT0z6f9di/6+pEP7fb6O48jz+ETwulJ+UKT9c7qUR/Dntd4WhWXbGv6pjmui74q+mLj83w7dUezoA9egfM2WBpzTuHa8fFGLLlmvArnOQZRPIrOmRdwj3HZf/qMQ+piY0zd2pW+DIOpm37qIPcqxxDnlXMR0OipxTJ9FnffQrhRtsd1tk+JmzQOHMeresFl7IUFkPxyPmN8jK4Q6D8+iLsufAt0w6MTi81GJ+t8fhbX8vqRD/XeDblDlOft2MLjfot0uH8K21wPF+8JXyUxpETZTF51VxjzxWhh3PbpD+8bgX5n0b92imzHlSxt96REQxjH8SYvye4POx1rkzqJv5TOD2+MP1PjDxZjmvY4WNX/zvOZ4/bl8AhgXgBwzz/8oK6ouBISAEBACQkAILAaBfGD3OyO6eQDkfsNmfjmIBz1tdD74F4JiQBrbssyHuJz2gRH13oG6v32QdVk/ExT1OabrUNCTrg/ZflBs98NJn+OP8o+g3jcO/gT+QGpzO9Rjig9x+YHwFlBkQOx9Ereuhw23yYODz4c2bHtzF9Y8P3ztA7/rOhhcev+eM2DuSr8Hgesx/7EuxcSPQTPb8WF40vTTaBD7/uVkgGsg4vnsJGf1daBo40ZkhnQOylHOg4S2lDF+A5S6MGb7+4Oi3XPJTOlfUY86cU1fFzKuH5ezzDf3+tKfQuj6zH87KC9i/MF8b3He++xM9Bavcx7+4K6wGdcSDx9HpftCIY7jxS0N9iWdUb7uPkn/C6jvAXWl4yC4DBTHwTU9aZrnelsUlnkNvx0X2Xeg9kDIIy4vnxSUqv/kZOchLXbyGo/93iTp06fHexN9iadF3SPcfl/+eAjjuJ/QpwzZy0D0r38DOhsU52IRexRd9B64Uv7voHgN9yCzJd0SvKj3nqSzmfPAofTdGyjfrL3AvmP6b1QcR96jTojCUKafOgRyXeYXgU4DdaV8b6ZfiYn76CAo2rwE9atHpVSmz4j6f57kbdWnpDYnJ6VF2ExdtFYZj8ZrYfmprZoD5t+nNvSRMWVMPxWFHeVngB/H8UMtenGd8ND6+i06zno+CtHeGS5Avoh1tKj5m9c1h8s/ongiOP8JinixzOc+JSEgBISAEBACS41A30HNvIHJgU1+C3BUf3zz8Xs6iIcDtwbxpt+WzgXzxW2CxHsV6jzs6Ep8eIvpL1H5YmS0lBl4MxCMadSB1ZOg3DeOL0D+vGgQ5dumel/19hDGh1I+VH22rwFkB0D/kHRGXcc/Q7/rOtr6e0WyH6sXxwrK10z1rmpeZ3kddrWL/HgQQX4MvFn/blDE8/VkppR590vys0Kd8/uhUO8rvgjCLozZjsF2TOPi5m2456Kf4OHGhS7syPmA+megR4PuAXomqCstevxd/ZI/6z5bhD94B8bFh81bgb4P9IugUekzSWF3qrdVR/k6zntM9HXfjIxUvgJ16sya5rneNgpL+mI+XHalvJc5v9OkV6ZG9Dsx0e53BcYLQpnFe6X6PVE/LvD+JZQ36h4RumyKb2lKpcCD5j8AXaNUj/j8YXDuD/ol0J+D4lwsYo+ii5HpuUmj64eJmf/81G4z5yENZazqRu2FOBiui5sFxkdRvjzUY/EOqOyMDJS5frIPjSo5tvp4FKJMm7sSjz80+FrieZW+/a5eQc6x/n6odxU/kAS3DPVF2Azme4t3SlLGik9PvFzNP1i4aVLImL4ryduqd0nMd6Y6q/8ReDwQ3wfKftFVnoIC47Q7g04HxTFwfPNcR4ucv3ldMy65M70Ekoz/08B7XGcLCYSAEBACQkAILAkCOUhc5GVfJxn/XKovovq/MPrUSuPYz4F0bpMf3s7PCh116v1WkN0ilNuK+YCvTYdB1C8EQX7bNIiOKN4scXhYcrvEa6uuJWa2k8T2Rycyz+ucm5guQ2V/ZKRynyypDlU/i1p8IMjrcEi5o8KHsU+AGHQznVGy5pMP+56I0QVeCXk+cOVhx7Oq/FuQRyzPC+1GFd87QuHzSR4PWJKotRof6qjw9latYSax+vVhVmdt0ePv7BiCWffZmcn4vPzBlbDLQwNSW+Iccr3cBnQWKD8w7gBvVPr4CIVvTfJx5p3r9imp3aTVea+3jcDyohEX+bUk353q41bfDcUvgOgvmPIPbbge95ikfPDe9zDQsZXHdfLsWmbGA/2Y/iVUoj8ie1H3iNBlU3wnSl8F8SCNiXEKD6SeCHob6PWg14HeB1oH9aVF7dG+Pil7KeivQVdjBelHQL8E4nr0tIrCI7yC/CrQP4Y6i5s5D2koY1UvGqE1r70Qu+H9nVh64vrpSvlQiH4tH/7Ftlx73xYYjFu/HOosZptcoy9MOrH6yFhB+cUgHlKyr76UY+bTofzG2mARNvvGEmX5+vnDhsuiQkv54sQ7JdXvnOp9c0rV3aDbhzaXoPz5UPci79E/5RXktwC9AcR18KcgvnzgPoX3SFJbytc86zpa5PzN65rbcCDvoaD7J+HvoP6UxFNVCAgBISAEhMBSIjAqwJsnKPmgKweP4/SVg/XYhgdyXwd9EcRDnHeAXgliIDtu4kFRX4oBHfUu6lMOsk+HMou3TPVY5XV8KTI6yhm/WQ5cH4U+SJOm/DCY2+dD1Sj3oNZ5n/FCR973FmdHE2NnnPI67GsbZRegwgccplNBp4E4Zj7o3Q/k6V0ofMUrIf8IynwAYFume9hn+bh7KLM4yYFrW1/R3KFYmaJ8vdTms6k+a3XR4+8a3zz22Ub4gxvgAh4Auh3oViCuQa4hrrtZ0idGNL5uknPtjkp5r43Sb5Mvcr0tCstRvuubbRc6BY8+81Ug99VcB7cG0bcw3bdk9nkRPsnnQcWZIKZ7lqz5jAeuvGfG+cu+fVH3iGYwobCGMn9g80zQrsBnmb6SxId5Huq8BvRyEA9gD4Jy2og9mvtknf7lXNBPs4J0dRDxfhkrNfE6eB/xxMOefM/czHnwcU2Sb9ReiGO6c6ygzDXflbLu87oUK/9bke8NOu8LZS9mm891QUf+4MSfdm/Fe/sibKZhdlbz9f9Fp+ZAcJ1B0UqM3WO6S6ygzLiqL90Wwj1BoWsNvAQ69IM/F3RZ/E7Qv4D+G/Q00AtAEV9Uh1K+5lnX0SLnb17XPARAqDw1lFn8IxD9s5IQEAJCQAgIASEABHZsIAo5wIoPd+MM481QukYPnQIZDyT4EPOroH8GTXLYCnX7i8fMu1LG6+IuxcTnYQUflj3d2Ast+VdaeG2s3Hd8cGvTj7ybxcoM5ZuOaJuD6D71toflPv1xZXmd5XU4rp0LkuIZtX5H5NEmH/y7UjxIvT6UuF6ZuGY9cZ3kvlzWln+jjTlH3nWTrXEO3lKT3uqix9/V+Tz22SL9AdfGi0CfAv096BdBfFDkoeEqaNb0yREG8rxnf9PW/DIw+ZbeLCn3O4/1tmgsed0blfhDxJi+O1TiD37eUPnRl9Dn3LryeUDBteSJhw0xbdQ9IvYZy89B5YGgPv/AtfKTIB5UfhZ0b1BOi9yjua9cz4cwj0gKj0z1rE/xZs9DGuLI6kbuBR/MXbxQ877DuTsn3fNTPVez/juzAuq5/9e26DhrJwp98Z/rjZNfWpUWYXOc/l0nYvRFMD/pgp78JkmW1020eRi6fW8h05THY262aw3Q1qNBTwWxnNMtwHg26EJQ9K1ZL46PslnW0aLnb17XnDFgnfeUGwbBZ1D+w1BXUQgIASEgBITA0iOQH0YWCcihZDzXk3hTqt8c0Wse87j4HQO78ZDkip5++h4wY7PdsYLyOG/FepPc1vmT5ryuvnSwT7hBsjxnuT7uMN4ExRig3602vH8y8PpUj9Usu2cVfldQej/K+eEjiI8o8oB2kSnjxYeDeaZFj79rrPPYZxmbefkDHhC+FfQI0K6uCwD/YhB/sPR7PTpdolG+bi017BuHq3Iur/TKlHnGdNb1thFYbuQafiNw3R+w9UNWPvjGrz6/oepcEHRZvFetx7dbycoHrht1j6jDac14cMUDkN8B0S/2JV4//euvJaW8nua1R1M3rVXu4Y8HyfegfFKt8228Hwmy/0E53x8o3grzEIY5sriRe8EHEw+/6H8+6IKUXxP10wPv0yhfFOptxXyYmg/yOJ/RJt+A5Vx2pVMgiD6NeHGNTkN+4LoIm13jz/ybgkFcPXVh73LPb+aFmn8h1DOmH4Es+ryg2hTv3pRK4Z2pHqvE/Akg3hueA2q7Z90cfPqfR4Fymvc62oj5m/WaMwZev7UXav4s5NnnJhVVhYAQEAJCQAgsFwLjPnzMA5UYUNHeteZhdINtfCr1d8NU76qelgR9B2rXTrpd1WzzE12KLfysexZ0eCA8KX1Xi+2txsrrLK/DccfLr3q+Nyj7Q969A49vTeYHsiC2N1cPBwYPbU8E8WuLns7zwhbJ89sqp26Rcc06jHnss0X4g+vgwnjwEh9iea2fBj0X9KsgHppx/JyLHwXlw7L4wx2Ip0qXpFbZ3ySxVbmWT2gTTMCb53rbKlhOcPkjVXlQHn0EfTcP7+iH4ry/EXUmHjxcbqXy4f4qHrgS8w8FHRa3yj2Ch1dPAd0BdEPQz4BeAqI/zmknGE8HxbW6iD2a++2rPy8Ij0H5wbX+QORXD7IXobwW6l7cKvPg49lq+ckYEA+sPH0Ahau8kvI7pfq+VG+rjjpw9TjA277GCx15vn9y3nmoPg29u/axCJsdwz+CnfH59BEaRzKOB+t7Ansd5beF+rejHH0ZfVhfOhbC+yUFxyaxh6ofQ+1nQfQr9DFfA8XEMfwliAezMc17HW3k/E17zfH6YzljM+6Be7ShshAQAkJACAiBbY3ARh645gf4cQ88ttIEfDwN5kap3lWND4DU+WKXIvg8aGEAOSplm/mgoq89g66Y+DC7XVNeZ9MeuBKfCwJIt0P5aqC7Bh7lbQ/NrsJDgngge1vU2T7uw/NceYvkn0jjuEGqd1X/AoKngn4edA/QVkvz2GeL8AffD6AixgdRfzToJiAeNj0D9CbQl0Ge8g8V4sOq60yaZ3+d/U2bPT64zprmud62CpazYpLbvzIw9qJ8JuiegcfD00tr/RDyfbXM7B6g64G+A+Qp2nPeVrxHfA6Dey7oYSAept8N9DzQOiim7w6VRezRYH5k8YXQiPeEH6kt+IOSmJ4fK6G8FechDG/Ti/nAs+9wLuv+54jRHwf5bYIOfRN/qBpTtvnGKGwpcz/GxPvQOIk/VOGab/Pti7A5zpiok68/3zfa7PwYmCcGAQ9HY0zM2CqmGDNFvpd5f4w/6OM8MdbydCoKdwc9EPRIUMb8MvB+B8R7HO+vMdG/MoaJKV/zrOtoEfM372uO1x/LV48VlCPuSaSqEBACQkAICIHlRGDHBl52PujKB2EbOJSpu8oPbw8a09KDk95/p3qsck6+NzI6yrnvT3TotbHzQ9y925RaeAxY+fbd80FPAt0HtNVTXmd5HU4y/guC8rEo8wCMD0KeXueFnvy8ILslyvcK9f0ovzXUt0Ixr6s43q7xfSsEfBPzN0B/B3oyaKuleeyzRfiDsxJQPLR+VuLlan7LaGdWmKKeH3J5eDkq8aF21jTP9bZVsJwVk9z+1WAcDky+3RUPXN8QZCxGv3UN1H8HxPXvKb8hTf5m3iN+Gv3z4IP3mleA2hKv/+0g6v5eUohvuy1ij6bueqsXQ3p+0LgHyjcA3T/weB0fDfVY3Mx5iOPYquXs+/oOXCfR5fV+Oyj60uwTqRNtck2OerPyf9gopJuEcleRb7++F8SDwW+CPgO6GcjTImy67VF5vH7q5h/+tbXPB5j/LynlA9ePJHmssr8/iAyU8zwxDnkz6JWgF4LuDmpLXwOTccsfJyHXQUz5mvvWHNuNWkeLmL95X3O8/lg+FxU+FzhdGIUqCwEhIASEgBAQAhuLwO+ju/VAPzmi+wcEXbbbN0J/GjFtxjHxEK0v8SEp6l+F+o37GkDGwPgQKLa7B+ox8YEsyl8VhS3l70z6V6DOt5ZiYhDoNuMbNtThgZjLPL8vBT1pB2R8KHR95k9M+q9Ncj7cd6VVCKKt/+hSrPyHJP3HjNB3MYPv2M84DwTeNud7wDgQ7PHBJ9o+NTdoqZ+R2nwp1HmQMipNgjFtZZzf1tLBv4IXr+M6QecUlPP6vUOQtxV5ABLtMfj3tIjxu+1R+bz32SL8wYdwERE7+sG+dF0IPwGKbdoO/vclnVG+7jZJ/0rUTwN1Jdr7HCiO45wu5R7+PNfborCcdA0fl3C5oOf6xxW9Jdj8aigT/+9LRm6d5HGOLoOMvj2nRd0jcj+5Tn91CcjHyEOsk7NSqp8V9Nku+tFF7FF233d/pTymB6Pi1+Pji/VHReVU3qx58GH03Ruos9l7IfdPv9WVeLDluB9AeXeXYuWfHfTZjodxOUWbF2ZhS30XeJeDfByMzfrGTBO/GfTZjjFNTIuwGe13ldnvfpBfC/MPgNr8CdiWHorPqM9vasS3U6m0DxR1eE/oSs+CIOqynOfp15MO45O+dGMIo03GvTHFOZ/HOlrE/M37muP1qywEhIAQEAJCQAhsUQR+EOOKQcyoh/GteOBKaP89Xcd5qJ9IQUu6BnhvBMXr/gLqOSA9P+lQ/ymgtsQ3NhlwR5tPblEc9UD4T8nGx1Hnmzdd6ekQxD4ZaF4nKb826fD6u1I+CFzEgevx6PwgyMfNA6FZEw9L3F7MPzymYQbXX+2w8dgxbEyCMc1lnN/W0seoh+pnok281jehfrUWO2Tx4ZFrI+qfTkFNixi/2x6VL2KfzdsfvAYXEbH77Z6LOgmy9yd9tn1zS5t9SW/UgStN8K2qOJZ/Q30vBS3pT8CLuiyf06I3Dmte621RWE66ho9L2MzjwDU/UDv29Hf5AIOYx0MC12X+HAo70iLuER1dDbH/FrU4xpejTj/Wlf4Ggqj/50lx3nuU5kfdX+MQuNd4sBTH6OUrwO+KH9zGZs0D+x91b9jsvcB4yrG8CuX4RirH7+lUFFyP+dtd0JO/OLU5I+lmm89L8q7qPyS7r0K9a33/EmT8oUMc+wNRz2kRNnMfuX5bMOK4vPzkrFjrt0T+pdSGh8k58eDabTG/W1ZAnfehfwRFPS/nefr+pPdZ1G8E6kr5hzS873nKcz6PdUTb856/eV6zX3tbflMw7x7ohm1K4gkBISAEhIAQEAIbgwAPv+JBzH+N6HarHrjy606HQB7cMf8E6CyQvzFxDMr3BH0KFPVYfjAop/PByHqsPw10elXmg8SZoE+Doi4fONoOv0Y9EJ6CdvFNC9r8CuiRIB4QeGIA9TxQ7JPlZ7lCyCd5+OIDRrS5iAPXe6U+/i6MddoiHxDiuL38ZxMYfFmHjVuNYWMSjGku4/y2lj5GPVTzYD0fEnP/3hXkD7js58dAXI+OCfN/AcW0iPFH+33lReyzefsDPqxG/L6JepvPuAv4cY/HNh9sAWEfeFHn2BadzOKb9IdTu/eizmvmfDOdDHoJKNr28jlUmCLNa70tCstJ1zD9qWPC/IIpMMlNbp5suv0uP/rCDn0+mHelRdwjuvqK/Hug4tfj+cvB44FNTNdF5S9BruN5fgN/3nuUY4h7b42MEemvIPfxxZwHLaPSZs0DxzXq3rCZe4ExZcSS5buDGH8xbonpQahE3WdEYUf5k6HNQZR5yBdTtvkLUdhTvidkcSwsvxJ07dDmNiifC8p6fx90YnERNqP9tvLPgpnHxzrvGb8FOg3EtAP0UNAXQVF/H+oeP6DYpPNRinrEhnuA6STQD4B4H4o6Xm6bJx6SHkj6n0b9TiCOLab7oXIZyO0x//GgkOf8GUHWVRy1jthu3vM3z2vuui7y8xr96z5lyYSAEBACQkAICIHFI/AadBEDmWv1dPmApLuvR3daEW3G8YxzCMG+/iK1cxs8HLkYxNx5Me8KlnOAGduw/PVKmc/A9hGgtjTOA+Hj0DDbZJ0PkDw4+3QtZ523g8+HnZwmefjigU2023VQ4H08JOk/xgU9+e+mNlxTsyYG6XHcXr7vBIYf1WLjs2O2nwRjmsw4v62ln1EP1WzyWJBfa8z3g8+xf6NF/nHwrg6KaRHjj/b7yovYZ+xvnv6Ab4xwv0eMWf4YiG9CvbqWszzWr4BO/iHMPvCizri+7mmpndv4Cvj/2yFznXMgnzbNY70tCstJ1/BxAMExYT6PA1fi+tFkl7b/kIKWxEODOAaWuWf3tOhG1rzvEdF2X5k/0MvjZf1SEP3KRaCDoKzzPPDa0jz3KO2Pc3+N47gDKnmsrN87KvWUN2seRt0bNnMv0McxBmrDlbwzA55/nPS64iZvcu2kz28S5PQnYMS+75gVeupt65vXcgnoC6Bo18svBT8fEILVpEXYbIy3FBjP+tiYc1/GOsvcr19q4fN+dn1QW3oSmNkOseE9h7FplHEfMv5wXts8QbzyU0HHdZl/FXQeiOv8v0FRxjLt7wJ5WsQ6ctvznr95XbOPry0/F8yImQ5c21ASTwgIASEgBITABiLwC+gr3pwf1NM3D8ei7r4e3WlFtBn7GPcQgv09GnR5ah9txfIh6P0miIdfbel8MF2fAeP/DXXn55xvC/CrT11p3AdCzgGD/Gy/q/5u6J7U0ekkD1/EIvaxiANXHmx4HzxcmGR+Oy7RHni+HOzSPg+4Rh1cRHs3QsXH5fmzo0JPeRKMaSbjPO2BK209FDTqkM2vhw9ft2GjlBYx/tRFZ3UR+8w7m6c/eDiM9h0kOMbM6YN+HJQf1vKBwj7oxHaT7IU/SG2jnVimj4vr4xzUZ0nzWG+LwHLSNXwcQIg40S/NI/0pjES7LJ/VYfiUFl2+aT9Omuc9Ypz+qLMb9AZQvr6+Oq+H7brSPPfouPfXOBbe2+P4L0K9KyaI7by8GfOwlQ9cicvbQRHTWOab8p5iLECdW7igI8/xJ/1rTtHmNyHsW3u57fFg/D9QHG9X+SroPRV0DKgvLcJmX3/vgzCO+Qao982H6zIG4YF2V+JB+sdBrt+V8+3wbwEdCrpt8wSxpafjc9z7Kvt8DijHdXHOqTOPdQQzlhYxf/O4Zh9fW34umHF+dODahpJ4QkAICAEhIAQ2EIFT0VcMeBhwdiW++RFv5Od1Kc7Ap03v4yDKkwTM7PbGoFeB2t5Io10exL0AdEdQX+Jbaz4OBqxMTwRdDHK+53yj8N9AfIDuS2+F0Nsc6FOE7FogjvOLIG+T8wshexio7w2LV4b2DIJPAHUlPmjyQcL7eVOXYuX/YNBlm0eN0Cc+MRB/+Qj9ScRctz5u5jyAmTTltyn4Bu84aRKMaS/jvK+lk3g9fIPkpBYdZxHXV4C61jz5TwB1PRwuYvzobqy0iH0WO74xKvPwB7R5J9CbQXGdxTLf6vkz0HVBTD8MivIXGnfwMauvuw9MvTH14f19Gnz+MI0p/vDmbwprps9Z1xs7nzeWk65hPrQfBDler+Gg5pDOgA23yZz3hq59x+4+Aor6jyRzzDSve8SY3Zka7zUPB2VfGa+B8cQ7QA8CjZPmtUcnub/6uH4FhTj2J7lggnyj52HUvWGz98KNgN3rQBFXlj8DiukiVFyHbzWuRmFL+ZeDPtv9XIvORUHH47YWtV4W7TK28rHFnPELY4tRh3pQGUqLsDnUASp7QdGnfaIqkP8sUIzt/Jo+Cj73wE7QqHQjKMT7tdtg/i7QD4GYbgOKsrZ5MsX6cVvkLwbF2DC2Z/lDoJ8AtaWLwHT9ea2j3M+852/Wa87ji3UeejsezBmTKAkBISAEhIAQEAIBgVFBZ1CdW5GHNTw8Y2LAdgqIB31HcyKONwJ9W815PZ8BfRjEA6hZ0o3R+AzQsaB3g2iTh2KLSteD4W8F3RLEB/hPVboYOQOqoyU9EQN9chjsvVB+U6irOBsCvuZvBzM3BHG9/xfok6BFrk+YX0ia5z5zbObhD26Aq+VX40m7QDw04wMhH/Y2I90End4KRLx4uMq3lf4HtOjkmM6y3rYalovGbFH2N+MewR8s8L50cxAPdbj+LwW9D/QF0KTJ19M89uikfc9LfzPmYV5jn7ed02DwZqCrgS4CMW6ZNfaCiQ1LPBTj+E8GfRnEQ1geUF4JmjYtwqaP5W4o/IdXkP8z6EdD/VtQ/g4Qfe5nQLxPfAk0aeK9hoeqfE7gfYa48B44azoBBnhP5f3sVBBjXfoTj2FQ3PQ07/k7Gq5500HXAISAEBACQkAIbAcE7oiL4MGd069th4vSNWwpBPgw/UmQr7H4YLClBqrBCAEhIASEgBAQAkLgKELgsRirx1fMzz6Kxq6hCgEhIASEgBAQAkJg2yPwWlyhB2vz+Gn1tgdMFzgRAnyb1dcX8wdM1FrKQkAICAEhIASEgBAQAm0I5K+Sn9mmJJ4QEAJCQAgIASEgBITA5iDAryPFA7Hv2ZxhqNdtisCrw/p69za9Rl2WEBACQkAICAEhIAQ2GoGPoUOP4fn7UI/f6AGoPyEgBISAEBACQkAICIF+BF4BsQds/J2EO/rVJRUCYyFwT2j5uuIfVOHbrkpCQAgIASEgBISAEBACsyFwEpoztvI4i/G7khAQAkJACAgBISAEhMAWQ4B/8IG/RN+Dtp/aYuPTcI4+BPi7W98F8jX1d0ffJWjEQkAICAEhIASEgBDYkgjcF6PyGIv5c7bkKDUoISAEhIAQEAJCQAgIgZWHAgMP3D6H8l5hIgRmQOBhaOvriX+lmH+xWEkICAEhIASEgBAQAkJgdgR+GyY8zmL+87OblAUhIASEgBAQAkJACGxPBHZu8mVdiP5vU+lE5Py9UB8AKQmBaRB4NhqdCvom6CEgriclISAEhIAQEAJCQAgIgdkR4A+yLwW9vdJLkH8FpCQEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEthwCq1tuRBqQEBACQkAICAEhIASEgBAQAkJACCwMgfVzLzzmsmte7e6HD+08duXgoXXraG1t5dAhlNZAKDBbAa8UkJkMH8yRDlFmhZLZZ2U5Z1fV9frKys7GHnmoDdXd9s6VXZSUFGw2XNrdsbK6trbjwCVf/8Zb7vSsOx10deVCQAgIASEgBLYCAs09aysMRmMQAkJACAgBISAEhIAQEAJCQAgsCoHHPe5xpx977LF3XsNh4c6ddtxnXXk55xT28Xbs2NEM1fVim0YYClGP42hLUYfyNr29e/euXHnlle95zGMe87E2G328r5964tUOf3Xtt3btWjlhbWV1Zf3wYdDqyurh9ZXDdsK6urLDeDtQX4cMWjiWXV+HLgtrh9EK7+7wqHYH2qAd2aurfJ8Hbck/DDHhYZkynpCyTB4um5qssrCTitA3GfnrsE8hx0RFpB20RwWzYdZW1teu+spJKyvvgfhrpqQPISAEhMAYCDz+8Y8/edeuXbc4TOdVE+peXInl6I8jn8qxHstuqI3nsnnlxxxzzOr+/fs/dvbZZ188iU1hMAla0+kOVtR07dVKCAgBISAEhIAQEAJCQAgIgaMTgdX1dTxr1gOto/EScBzXPCyPM348ON8V9LvQtXY8MOUhIYllP0AtB4c45Ktyz13X88hn/85n2WUsd6X4IN+lQ36bHuZuFYcFfwLxxAeuF3/h8mNPPHbP7VeuWrvmGl5dXV/DoSoOUQvhnBOHrSs4h1jHy688jOVhaOEN8nXKuX4gs8NTHI4S1lLmqFGqMpzSYqbK4fIqeJRRzw5UkR3igS7rpse8asC+zTDq7Iof1tLqPPxdueTY3cfrmZbYKQmBCRBY9rfc4c++Az76sfRr9NWeog+PZcqjH47lUTLKx7kfUG+aRNu7d+/+G7R96STthcEkaE2nq5vTdLiplRAQAkJACAgBISAEhIAQOGoRWH/Tp/d86dCuH/rieZ/fe/jfcNqGszB7ixKHYWs4eLO3DfH2Jc7BUF6zb5YbkyLy+GamHZyxzP+lTXlnFA+v1PGEcvM4a23Cm6Wu57bQZofxmhZmq2kR9HYe3rl68eGP7L/iyotecbO//p4rvbsR+TVwSHhzHhTyIZU5H6qZWPa6y90W6zGxjes733mse9kOKV2h8r3qOtm2P/xHPnWzLeqBdy23N0m+dw+sX3Xo0CEcgh46xLk7vHLYchZRx5I4DCIfL5uGQ1fyeAhLvNjOjlWRFwz56Qeqvj54QFp4ReYHpuXIGzWYbI7NYQdH4NZHtWxtrQ36YmK5sbe+ql8lUGDRpxCYCIFlf8sdHvBU0FkEjT6v+tPmYNT9r/vp6JejLtu7jpdpjzznMze/SoUJk9vwZrlOPseD8b7KdcbNhcG4SE2vpwPX6bFTSyEgBISAEBACQkAICAEhcFQicMmBQyfgbOsJu1Z3XmON36jE4Zm9scjDNlT98JV8isvBGwoU18PWw/WA0g7W6mEYz2rLaevqys7K42uILNrBGgt2Wkc9M2cfdqDGfqoudfgV8vKmI1V4CMfDuMIjn19tx5i/eMyhU18P7lgHrngw9VHZgWl9UD3i4di6CYexrPNB13N/GGceH4Bj2ZTDB3W9HfX8AdzbeE5+tOt8N1VlmIY1Dqi5HpePkx93aO/6gcOHcHCJ5qDhw1awJjpsLT0ODk45V+VglJLmcNRGClmdZ5OhXA5bvQ24rlfbNmvDjloH9qyPOie0pSQEhMD4CCz7W+54Q3WNvpT+Nt4HiGD205Qzua7nrsfcbeSc7ZzHcl/Kvp519uXJ5d4v+eCt40dn/LbDQNEbjMiFwQiA5iDWgescQJQJISAEhIAQEAJCQAgIASFwNCHwzfVdO3cdXDvl0OrKtQ7jK+U4e8M5KQ7e7O3W8js58T1vnsXxKbOckVpuh5zg+VuOODGDDt73RI63bKDOU0D+LlB7UdVk5EHmj4N4RdbfX11HG6YddqqLAgzg2dWO1g7j8LUczqEOJu2WA1189Z9lvoG7fnj3zh273Ry4/YkPviR/YOYDLIk8L3ueebRMXlvubShj2VN8MHaey5lT7jqeux5z1/E8ymYpX3HlgdUdq7tskoYPWwdvtvrcc37t1xxyjlDufLOVk89ZsjkHrqwbD1ybe+fZEarJ8mHran1Tlu3sAJf2qh2rV3v+Rq3bnwULtRUCy4iA3nIv/pU+Pfp1+lpS5MUy1wrrrud55vuaorwvUd7m+2ObUTai7qRl2ub1xGskz/m0l+XOcz3PM591Jsr7EuWbiUHf2GaV6cB1VgTVXggIASEgBISAEBACQkAIHG0I7L9iBb/0bQ2v+OCvz+No1L5CzpM1/OepJut2wDY4dG0O3iDjoaj9bk8ejOHa+QeX2Jh/LMlkzcEbgcHDlJ3eoQQ90/QDOBpFm3LeChmq1LC3betpmh/WlQNbSFHw89nV9VUeFU+U+HCXHyD9gTE+dMYyOxhVjzosj3qApE7bgybbke+pTcdlU+f7McV71vFuFMHHf85PmHPDmjyTlQmyeaUA/2109XB0cLAKLlRNhtwPQ/1Q1fVcXvjQq/aGD1tNq1ye2eS816v1/llFWUkICIHJEVj2t9zpz+lbPfcykezidcnYlsTkud8vjNnx4brM6ffb7hmuQxNt8g7TY7H9Oj1nXz7uLh4Nt8nY1sfqudvqG4zrMt8MDPrGNg+ZDlzngaJsCAEhIASEgBBYbgSW7g/vLPd06+q3AwLHHYuvlB/EV8p5gIY/jGQHbiiWw1bk9bDV/s6RH7zZ4RzfbAXxNM0OwoBG28GbHYrhAZSHZPWgzL9aXup4OCOQtGE6wwdqFLnMNM1G0Rk6eMND2qSJD3ZtD4zk+QNizKnvD4WRz369zrLrsMzEen5AzjpZ7u3MQP2gzji2YptRZc7//rUDUCtv+zYHqzxMx9zlP5TFuXCeIe5zDgvNfNg8goE3k51X5hY6HFCdQ59XsprDVpMZx9o2+uDjUL22hZx1+zdoG5oWA/oUAkJgJAJ6y33Yf9OX08+SYjnyCKr7/JxHGdvENI6fj/pdZZjil2kAAEAASURBVLeb7Tm/q10f36+DOvG6Y3m7Y9CHz6wyHbjOiqDaCwEhIASEwFIi8PjHP/7kXbt23QKHFM2zHuoNFrEc/5Jp5FM51mPZDbXxXDav/Jhjjlndv3//x84+++yLJ7W5xH94Z1KopC8EthQCfNheXcFXynnI6geqlve82WoHrjzzgtuzA1Vc0hEHb3jQrIetftjGC/e3VAuPR2ZsWw/iWKuedPgtx3K4Rj0mtrX2rNR+vR1Z4yT8JefmgZn6/rDJPJajzB9m++SuEw9HnUdbLFPmcvL8odllruey2J68eaYvYf737MLPyurcr9e3mpvf4wvMy6E7egXurlfmzT6H56MeitphaDOXZcQ22zaHKDVzDhnmkJZ8TVC7yOt6oE3yqj2Og4evxg3rzsWmrA8hIATGQ2DJ33L3e4H7ZPpbEv28lz3v8v2ZT+Cd53ZjHifG+ZHXVo56sdymOylPGEyK2OT6gyfDyduqxdZEQG8Zbc150aiEgBDYZgjggOI7EFQ9lgcVHlzxEj04y2XW48FrLI+SUc4+aHsRibYRdP0NbL90UvvL+od3JsVJ+kJgyyGAh+3Dx+Ir5TzFssM1fODQDb9elSdt/G95kVGHh4U8JKQyfB0vyA+9wC+HYuBWWTlEoxJkxoMMevb2I5luI5SHD1v9YI0K7A//2J4JbZtf2orypMl9qfvu6F8jj3Zd1taG8ujzvc68LVGXD8z5oZn1vuR99+lMKjsODfhHtzg3/lbzkYetGBflQ3NeeuL8N/PBQ1C7BOR1PljnnFkCz8rGK6zmsJU61hY6ruc2qFrLLObDVl9PlCkJASEwGQJ6y73gRf+a/X4bL+t4Pfpn5/lMRN9Ovej7WfZ61GPbaJN114t8t0f5rIm2fOyet/FclvM4Lpf5mOK1+ZhdnzK/tqjHtq4T7ZAX+Szndq6/VXIduG6VmZjDOJb9LaP1cy885rJrXu3uhw/tPHblIL4bx4Q/pnDI/ggEyijYH2/gX9a1AjKT4aP+9q9DlDHVupUry8r42BVlxtzZ2GMVtaG629q5ErZbsNlwaRfnKWtrOw5c8vVvvOVOz7rTQZpSEgJbGYFl3ne4wZ8KOovz44eunscAwMsegDAwYNl12d51vEwZec5nTt40yW1421wnn+PBuF7lOpPky/qHdybBSLpCYCsiYA/bhw/Y18fNvyAO8UNWnq9ZBbkdtiGq4plg62ErdMqxGj5rOX4FvBzA1UO15u3E2oZvMMIu2w8ftlY5x8GjOuqALNW3Iku56LmoaozM6Afp95hHcj8dc5fTaOTHTujXqRdTrlPHk8uYRz7lbst1nOdtc44xDQxnYU/9ipX9K7sP7ym4Iy7lG6z+FmtZB3gQBv7Dc06DwIyfdW5s/mwE4BsPQs+p7nrIG4SaN1vBqaMfPmwtc+5rw3SatVOs2HqY6so5KCUhIAT0lvvAp3M10L/T75Kyrx9VZ3v35zEn3318zr2N5y6PbXw85DG16RSJjX8qj+jXRjvLioFjuIi8OetZhPGNsrnMD/wR42V/y+jrp554tcNfXfstfKP3hDWEdCVwZPDHb8vxhBPO04JJHHQgclvHKxwM1tYRwK2zgLc6oF0CP8Su/BoV2XR01pYuDMHiOl+pYJkynpCyTB66oCarLOykIoPM+goGA1bTZZBJRYpojwpmw6ytrK9d9ZWTVlbeA/HXTEkfQmALI7DM+w5vqOJvzdBXlDdcWS7+Alu6Pliz7nJOo5c9dz3mDHLacrZzGct9if25Tep5/97G5Uln/dChQ6sYEz3W5GmJ//DO5GCphRDYOgjYw/ZOOjEEJXbYChcAN2CegF8v53+euDGE4tutDFZQtRCGbZDKIZkVG1nbYavFP/XArDmU6z1shX30xd7Yk7Vntfbv9shifxZ7sTJG4rcL8gMmm8UHTfpKkut57nrMKR+Voq913dyO9azHetRr03F70+Z7D+1dP0RAeb/CpA8OW1nHbCMbPmytc44OyxyWOXJuM682R2Xeht6AtoEO2jQH7NWetbA5h33yfFKNxxgZqWXdWb811qaKkhAQAuMhsOxvufu9wP0t/SzJ/f24OdGO/jrXu/x3W5tZ7gUI44s7Hm/6TUsYTADWlKrb4sB1mR/447wv+1tGF3/h8mNPPHbP7VeuWrvmGl5dXecv/ccDQyHGkojYGEDi5ddBUEmeyxBYWoDJQLMEe+Uv7iLoBdD2tgdLVVb+IACfQqALHluYl4OvY36IB7pk8wGDQSPIJAyia53BLCPawmeRh78rlxy7+/htsTd5+UrbG4Fl33cemMWgjDznc/Ypi3LnuZ7nmc86E+V9ifIcoGX9UTay/iT1Zf7DO5PgJF0hsBURQKxEB1JjJMZMGCUDHv63uIlVxE29h63wUTU2yoetHt9YLATTzaEcui1xEXgsMzFO8ljJ46rKM3mNr0zb25ieScf+cJ/M62PZfXCbn6ZR58dyl0/1B3fqejn7Z+e7DvPsx73u/Xgbt+V8tp3mIZvtDlx1YHXXrj2YfM4/gAaWnGvWORceKxveftCJdh7zts154dmsw07NYSsentLekXMOJvQgsdi5WSe2rgb2TKOZc1iyso2QIiUhIAQmQGDZ33If5f/pZ93X+n0g5y7PsLvPdj713H87L+tkOfWy/TYdtzdNLgymQW2yNtviUGfZH/ibKV/yt4z27oELugrvaeFp4dAhRGAIpA9bziLqOIA9DCKfzxIeSJYgk4ew4NWHjL7gkngz9PNgkLmFggwUPVhEPgguy5urFsBaGAmZt0HONGRvfVW/SqDAos+jAIFl3ncepHjOoCgGYqxnHqe0Td91KWeZyW1ZpePDdZkzCGsLxFyHJtrkHabHZi/rH94ZGyApCoEtjADjo9XDeD2QMRNjEn7wf80bv8K4htfhB29eJxf6lLUettKmRTn4tDKq9bA1vrE4+rDV+6jtadXtoTxpol+kj3U/OyqnvvvSWM79UuZ+NupHPcfU5VGWedQlz/nM3T7bxXK0M255Hb/D1WJXHLKW39+KieX/Ov9Dcw6jDeZx/lG2ZDxGtEh1Tfi8mtwPzOv8k1cO2FGoPFtHPq9+qE7Fuu5srGxHXh2DlVlXEgJCYCIE9JY7fAl8apv/d170v+TFOsFmncl9tVXwQX4bz+XMo//2clu7aMf78/axjvG5N47djCzThl9vzL1MufdDXqzTuMviOJ3fxosD8usmz8u0l9vFuvfnbWId45sKgzimeZe3xYHrMj/wxwWx7G8ZHYevRh04fAjBICI10PBhK1gTHbYWZC0QtG0LZ2rBY3GqHnCWHDzq1MDwyDaQWfA4aGvhaLVLrtuzPuBklITA0YLAsu87D0Y4XzEIiWUGAiTX7crdBvMYPLDuQQjLnrKO8/tyb5PtOb+vbadsif/wTicmEgiBowCBvYcO80s/cDCDw9b4w2f+EJr15tCsxjn2ODMUG8Fn1QMzXjZjGotzauwzHOfUWIiK4RCtHLxB5m1gnzxL1Z619FjLeEVsMdQEXyl3/9zlizOf/tGpjqh5GIy+lDqxznLmeXvmWTfy2M6T24nyyMN4HSlvMn5OvNG6zDsLmIKWw1YzaHNT9G1N8NjTeWCXOUQhzWtpayuirIs62rJOKIUd8AZrBizqGA/8ZM+QAa9BqNqjJSUhIATGR2DZ33KnryfR50W/H8tE0/2/546w+3f319Eve5m522Ce+S43pfDhes5yPe8r8n0czpskFwaToDWd7rY4cF32B36f+mV/y4jXv2N11zp/19jwYevgzVbGkDyMZYDob7ay3Plmq/nIElAytLPgvwZ25ZDUeTXwg6w8IHibtkARMtqouoMHiho6Vvs+r8qFwFZGYJn33e7duy1A86CIAQ+JwYuXPc/BW1edc+0ytxvzuBacH3lt5agXy2260/CW+Q/vTIOX2giBrYKAPWzvPAZugTELgiHET/aVcsQh9BX2nMhYhUdbNTYpsQ+vgG3sc3AgRq7xXH8QG9k1ox9IqGV6hYea9YGa9QEZ67W/0kfhNT/Yrv3SjsvN1gQf9LP5IZvN3f/mvM00MYop1ylr4/G+0MaPtnK5S5+2cB0F1txojDrfZOVl2Butnju+1awZj/MKPZtJ8Jh8TQzxqAOyVA9HrV55ZZ1A2hzU+1yC532h2By2smz9Ul4uN/YHrpIQEAJTILDsb7nT1zsRPvpUJr8HsOy8nFPGFP1zVznquQ7tedkMtdjyPqO8rR15094L/Pr9mr1Pr7Nv5+U8jmtUmXK/Xs9pz8td7b3PKG9rR960GLjtReTb4sB1mR/4hxbFsr9lhOtf27O+usrIEcEaA2n7NQL2+6jqWxrpsNWCTP5Y34I7oOlBHANCCwrhdD3Aa3gl6MNnCSaDfBBcUgqq9sxGfSvD5sza1PZkeP+1bDr6EAJHAwLadxaEeFDiOW/6Xh6Vx0DCdX3qYxDiwYXrU0Zyvrdh7jrOc73Ib2vn+pPky/qHdybBSLpCYKsiwN/hiscdi0OaH0RX39LEJohRmJoDL1Zq3GKHXqwjlRioPKzyIK3EQUU2OFhzvcIvNkusZW0YH4EsuQ3WawyVD+pqb7XBeJn/kRD3t6NyWqVO9MfkZR+a62065GU75G1K4nPDMbg2dg6M7fDV41GPhyHyA9USy1IZc1vXhK8D8mIM3cwh7Ng64PzVeS1zWOvgmYb3S/PWAp8eQ5Pjch9XsIfjkpWVy6txa68PISAExkaAe9n2PzJ7MMZWrs/L3Lu+r82e73tut7B3TYaP4s9R8L1LHeoyuS/g/q68Zl9XnvsC06cO2yNrfAHrlef2Gl0rTPbBewGT3wPow0le9zzqtN0LzEj4aLsXtPn9zGtrF8w2xdzOBRgbUZsoCYOJ4JpKeYIv4Exlf2Ma8YH/0Dr/wjI2M/6PedBmf3kV+tGZmFMwZ1Kcgcm4dOvyLU4Dm7HyXF740Kv2hh0DtUyzcU5H6BMp65eF6RLfMsL35vFHofjVMBjjX5zlHz9A2b4S5viU7w3BqWI4bYeNvAa7XozZrycENiXIgswwMNfY6FlA7A41OVtDwK6R+IGszPHRRsWn9jcNAnb9a3y9Ff/9WnnYyj+eBZ7/Pqoi48UPeGVsZQz85PVbDXlznSwjGTZRTk3Wq3yAWdH3Nm7X5MTTr99sFQQGN55BW5WEwFZGYNn3HQMvD8iYe6AWeZy/vjrbMMVAi8EU6y6j3AMs5l52uefUY8o6Lo98t1Fa2Bjdizlr7JyHNvSxxb/inlPvPY0/rm/OQWK+z6643iMGfo9+sfrCdM8ht/jeKm/8J9qYD6UPLji6PzZN0ytt8z3HtL0N9aa++rFhkqIQ2HoIIECyUND2ADYBtygDRNRtz/k+xcgHcU7dhzGWMf3Cb/YxbVoK+5T2uNdAzd63utusTVyP1bpPG32Pu8wvwF7TT207Zka/2Oeb3Qx1sr90n+o6nnfxo9x1PHeZ55nPupPrxBzjI4LTJT432HNCfVaglTjnhi0xrvMG8cCX1sNazkedA66RvE58vmm6zGG1ZfNa2uKzrAv0w3LzHNW0QaEZF3T8itmvl1FUEgJCYDIEPHbz52TPbU82ey7uXUiwd22n+r5H3uxt57X4Atu3LmcbDrW5j6DmPObWB+2all2U+ReWmnFhFCzP4APiPYB+NtatU3w4L/tml3fxXc68Tcd5nuf7TGy/yLJfH3OOJda9X+f5WJ3veRff5czbdJzn+WZhEMc57/K2eMPVvs64dgDYlK8HcbPaT+lbD9qghk3Z6Ux8wzI3GtzUzUmAV5wDp6Js8HjTp6yp13Kjz7Z0KrTLZLbMXTUOxkVFYbLPZX/LyK5/F95vrYet62tYCLYGeODKRVHXBfLONWBzVHGvc8UZ8jn1INNmjXYoQ25zzCqcfrMG2B6puQFxcp3nE239lTaDm8dgiZgBfQiBLYzAMu87f0uKwQEDBScPSsbNOb0eaPhUxzrLbQFI1HEbWc/HFu1mHZfhsLRxZc4bN+dB6zL+4Z1x8ZGeENiSCNg3FBA988SVMRPS0MsIIWYpcRBcRBPzoFxjmRzn0JF43GRxD2Md4/ETyWIf8qqNaoe2LdU+zIaPoekXGlXPY7LaauyMvplEv+V+mo297Ln7WPejXb7TO85yb+9y5lEnlrNOW9uo42XamMV382zdYuI6RzEWLfhijhxvzlgtl3VQ59N4kNV5tbF5PBxuK23rpNipV4O5pkU/YCnzX9bOYFxHjqHo+yKqtpQJASEwGgG95W5+nz6fPtf9brwHZB5BJc/9t8sdbK+36TjPdbxNtOVl78dz8nM7b8+c8mnvBbzeZccgYjnv8rY4cF3mB/68IPiWEXacBZH4KG8Z2asLiJfqoaP9FVJGVwiQLFSqwVAJhGixBFcW9DDQrTFMW6DUBF5Vr2lDMzXwsvCpLxgzXbZE8kCu1Cb6PA7aa/hrqxzTYTtsxRDsr66Sgf/2qwXKdQ89UNQ+y9hrlwz67LoHgZ0FkgUxs2fXxWv0UXpwSY61hQy2B9dfdWt/bOY/vfNA0toRSyUhcJQgsMz7LgYnHpzFnIGRB0eRz6n1usvzdOfAinoxCKN+1sly6mT7bTrUmyUt6x/emQUztRUCWwYBvo1ek8eJFoV4bMg4p8Y0HjcOxTUe59AGYyfmIc6xWBAsP0RrjQ1NzoZI1V45bCvxUNM/rZucekXGJvaVcg+8yBgzRT/svjLyopnsO3OdutlPt+lEm33l2NbLPsbYro0X5aPKjJM5awXvok1kS3wf8KaOzyvWA44ninLrOinroJnzas+4XEt1nUR75NnXLmsfJX4uXdihPW3EMUCvGUNVUyYEhMAUCPAtd+wtvpzEIwNLvq9Rib6gSge+wPYhucVXWGnonlF8gbnneh8pexv61ReYTdgxjzLkH6qPoXWXN+OC3WrP7jcDVTM37of7e+q7L3We5y6LftjLlMWy19vuBZGX27AdU+az3tYu8tiOddI0KV8nbTjPc/Jin7FM2ahxu05sl9tQhynzNwKD0vPiPrfFgesyP/DnpcFgeVnfMrpiZf/K7sN7cP1wOPb1Vd444KH5H4TXNuxGMvxrFIhguRk0NxQ48OLEwWc7Js+bMvoAr3Ft6JPl+AZzc0OxtpDbTaS2CTcUf2hwObtQEgJHCwLLvu8YPHhAEnMvU05iIi/WyXOZBxTkMZHfxivS8hmDEi+3tYt2vD9aiHzWMT4PX1kdOy3zH94ZGyQpCoGtigAethE5Nj+UN2/lD7UhHvKYx6KdGhM1cQ6vzeMkj41CnGOxUb1+xjoDGyU2KjFXsUGZjaH+8Ln0wcbg1njKx2ImyWNf/KLbmMm/ncCY2X01m3o5+skxTZqa++FRbdxPu16uOz/nbfbbeLldVx0vt63sJX4+AfnA3OaKrTEnhj2K5NW58Tm3OeUcMDXxcKnyM7a19mQGe34A6/Prw2G/rmfrwtsgr6ukyL1v2lUSAkJgIgRwpmf7mvmQ/we77N0x9yF3JW34fnRfUO8nHFTx57BnfdZdbPuZUqTqg/yHNWbriHGhXfIFRd87LqbG+fR7gfv+nNNGvh+M43PbdNp4eYxt94K2dm082sL4JwZBGORZmH99W/wOVz7w8+1FO1TrOWg78q3GstEbZ8JNbst0eCObUyD2trmL0znCmTD4qJu/cSZV3wITlKFRHEx1QPGgrbFHnSkT3zLiQSOBKAeM5SdVft08aPS/Pmv9VQfIrenXzWvgOHsPDimv12rXwPYcszvUas/QbTCgTSohWR8F++an1t4vxI1tUx7/Y+8h/A5b/AoBu3gAMDhsLTzeSIYPW+1Ky/XWcdrYbJzDa4CjtRHbNaIEHbtmftb5NGdvbcs1FP0yfuvJ27AvrJfyk/xqhddf5dNe//hISVMIzA+BZd53DMxycJZ5DJ5I5HvZ0c+BVQygWHZ55rO985h72e1GufNG6bXZ8Lbj5PbtCt54+B+3Iv6Ay37IRcdL/0YjyJnc11mlysxfGqPIawvzleY/qQdTds9gjmT1Uqw2ocl7cb2XN3LW+c/bs62PifperraUCYFlQoBb1fam74May3G/lD002CO2T6HHVPZQkZU9BKnboILtO+pxT5KR2tQ92exTa1v3aRhDbVn9R+iD4/M2xcMU1Qk+3X+ziZfdV2cz9Mc5tfHG0cn+Ntdpo832uLw8hq768S6wNVCvz+eQvpHEeQLPUl0Tpmm8Ml/Dc4gWtGdtyxyxLTTrOkDe9FH0rJdqj7rNoSztILFtMwZra9aaNVG09CkEhMA0CAydDTR7zvfuYL8O7UPo8Q1z7t3y/I8yeANfUGR+cEo199fxsLXwIGS/8Pt8Nva9bjLaBK/xJ8kXNGOgzowp3gPclPO8zrzND0d5V3mcdrPcC9gvfohocHWNYRTfr9dz6seytx/nWlw35uO022wM4njnVd4WB67L/MAfFwLfMoLTtHNGO3S0w2d4KgQnXLx87i1BDPZiDITIpzurAVBrcFx1itOrvcIG6/xsHCr0zOFSxdtUnrXyQIkyD6hrv2UMZs1UJ/0o14/f4YrrLg/8GII/+KM/P4C1a/C+0Ulx7OH6eV0YkyUfLytoY9d7xPXwms2qXbPdIKjPayzoHGEvH7ZS06/f8sLQpxDY8ggs+75jIBLJg4kYoDgv5l7mBMfgovhqcx5D/Kjn+tGGLxSXRX2XRV7UI5+2ZgrU7Ad69L0wxpsN/i/LH94hfkpC4GhEgG842g9HkA3FRti/5cE5xEbUqLFRiXNQN1dV4zaLlyoKHi/VWIt6TRuzgTZNH2jDMv/RXohPSzwZxhD78Da0x3YTJPfZbJLLboY+MVL0mc53ntc9jzZYJn9Uim3b9J2Xcx/DKPut8iv2l3kw9H2OoMl5A6bx8IPtB/PB60lzaHNDHtuW6/V4GNy6dlDyOaxzbZY4h9Thp8vrGNiWPEsmM2sDe3WcVUOZEBACEyBQ7gFoYIcEzOP+Yr3ZmYN9SF7d4809wXi1Y/f/4Hlq9jB51KXA97XxYr8oN23pO0DWBnxvU32B2SfP+caY7MPvAfStMXb3cp/PdZn3yHqkyGc567u8K2/Tz7xc77LVxxcGfejMLtsWv1LAvs64aw9eceVbrtiR2HTTHrRxQ1uqG7k4hOoEuNmH5MHhVJnpm05pkx2D22MfjSwEIqXz6T/5lhG+iGoYNG8Z2WFr4ZX+i/0SONW+7HoxpuICjVmu1VoA0HQ9dLR2nQETtCo2q4xtoFPsQOg22I7tkYiBlaq9wjXRVB/r+B2uNi6sA64BWwfor/+wFV1xnNYjPjk+JuNVRJpxhutpbiiDNuX62bbwaLOZ53rNNO2Hzd4X9XwMVmZdSQgcJQgs677j13CYYlDGwMfrnkcd8kY9INNG1sl12sy8tnbUyym3cznG5t7PWePliNjX9gAH/vKvJfrDO+OBIy0hsHUR4BuODJvtYZsPtUiDuA11i30QByH3VOI2yKxdig2pFGMj1hlLkRhlmR2UKo/iRp/ljtiQItcrZR4Guj1wOJYJE31xm4+OZqKvdP/KnCnKvOwyz92Wy1nPdsijPOqwTL0+O106tDd2Og4rgN8MI351rm2+UB3MFyp1vgqvXH/7HELmc2H22Lbw3J61pg74LJe1VXXYD3geI/NQh/1YquMblkPi8qqmTAgIgfERsHsA1bmP6j2g+ALyuC9txw3uAdyjJLZp9edFZvs2tbUdb/sVrRv/UPqhvcYX0Lb3Yfq0iX/WH2RDvoDKZYwsTZriPcD9rfPcx2ab7sMzn/Xcxm226UY7sdymS94oHfY9TfLr9T6YOy9fj9vvG0tuQ92uFO3E8jj6bTrTYtBma568bfGGKwHhA385VNuqB211sbkzaxwIB19cRfdynGDKl/0tIzpjEJ/7bdMBW/6OLtYNX8efkFKXiXNhcxCcOViDGwpbgqhDXSZ/oKh8sspDiJUae0f2Qb0609Wej6ux7X3QlJIQOBoQ4F7iniPxho//y7LvPChhzmAh1n3qnNcVdHTxvT3zNh3neb6pgYb9OpsyYs4914P7Nht/9XemUWUxgC7+030jJUjQM//MMtsja76BUe2ZpumZtZUdTb/Uxz/oNQ8MLFuCJffh7o/BL394p6ooEwJLgoDtq7LjShxj+4R7pO4h7q+amjin7quyJ12KPO4r6HD/2a+ton2zU/ZkE++4Pk3Ufiij3fKw72Pgbi6JPmBw2GojaGRVZWTmPpmK9J+x7o2zP2Xdfa23c13PcxvyMy/boU60yzpTble4i/oc4Ft8rs9X6c8OPps5xHjr3Jq0bQ4xl/aAWQ9szDp4ZV7RCnybObSt3GKTBo1HLv6hH0vWtrRp1gl5HAdN4P67ckLVVSYEhMD4CCz5W+70/U4ELfpiL7svzrnLM9iu18aPMi+7Hc9juy4d51PXy23to62usl+/3wejHS97Hzl3ebbtem38KPOy2/E8tuvScT51vdzWPtrarPK2eMPVwMONl0FCfuAvASNk9Qbe6LLAGzlv1rzts31NTbDnbahDXSbwSniAz8prAlBKsj3qGA82kj3acXssuj0rT/Oht4ww/zhcBd52+O65BWoAtOJfcK/z3swPuHUNcD6Z4rqwNUFdJl8DtS1ZzRqw4NI4Zm+oL9MzDhVag0sfgynoQwgcJQgs877zQIU3eb/Rx6Al8zil5HUFB67fpuM81/HlEW152fvxnPzcztszp3ymXymwhH94J+KnshA4GhE4fHDv+urOK0qcipimRCglHmK5iX1ZtlgKXIt94O9CDGQta6zVfNW0kdc2bo98JuTWB8sen3mbGktR3sTxKHp8VmKlEo9HOdU3O43ytRxf1mF9VBpHZ5SNI+SY+tVjQOged4iKNXCtcXDBdoAzZ6w9Hh7McTMfbXNo81vsNc9EGFQTQ/s6YD99Y6hrrSwg9L19nmaPmCIxhMBCEdBb7g28jJH74uRGEYWox3L2z9nHx7axHO2QP4utaHfaMvvPY+qyFfVmGXe0w75msdU11s3mb583XHHQVt6owl0Y/8vbrpg0ItzcwFG3mzQDg0J22wePyWQudx6CgybgjAdtLjd9NKY945UcnyVwAZ/locCCNk1unyjjH8fIQGTWtMxvGeHAmS9WlYcBZDx89fk5Yg0UPQscDf8KfNUv68Jmrie4rPNnbTh/ZQ7LfNY5NrOuV3Kyjggum/VT37K6vI5HmRDY6ggs8b7zg1VOkQcMzmPuPA8ePCDznO1YznXnM2dyecxzu6hnjXrauW604WP1tpPm9rVk+t/sc5t7KPzfkH8tPTT35KYt74fBf8Z7aL1HDrWh/2768P4Lr7n3N/fWMgZab/pgW+sPPPxTEgLLhMCOg19axQEpd0GNddM+rXvH94hhw3iFyVqxENrUOMi4xart+6Jb+rDW3HPc27Tv8RnL3oZl2q37nzWPsa2N71X3FXhInDS5j86+z/1s5rsf936o16ZDudto04nto26bLbfjbbIO+Xz2sW8VuNKE+Q5/tnE/iNxSnYsyX/ys82dzg2qdG5uPyisrqciaufQ5oo63qXNuOo28zKGtCx+Dy9C0mX+X2ZDwGLsDhW3zNMsLVRICm4EA9rd3a/sZNd9r4Nu+pEbl2V53X+A+hO1djty2JfZ82felLT5RJ5X+yvmIce1+YCW/J1Sfwya0yxZFbpwSu8GWuf8Z3nKnX3Uqloc/s99lPfrmLj8/rs4ovXH7m+U+4Nefr9WRyPxxxzTq2mifOqP0xu1vFgz8WheRb4+fCfKBHz+hLRsbE9dz0GYgcnOak8AGq46hbGRKsZV9ozd6YLszMcdhVpqHtOI4iiMY2Km2aLHaM445DJSaPtCfjYXjxxVcXitUniYt+1tGuH7+lN7XgEHoWKNS5ptzPAA3roESsw3kbTcUn29aaB5COG1cG0w+x5VHbu8a4A3Ep53jqmZoSkkIHBUILOm+4+9w5UN7PGTlfHmd5RykxKCC8rbUptPGy21zQEJ5W7s2HnUxbvdErI6dcAte2csTVwatbOU+lyyzCG71i6ZR/e+Q/6QftLbBBcIOeY3/rPYGNlBq+oAiy/zHPqs/Ln3Q8GAMPhZyvU28JxhfH0JgGRDA20383f+MfQZvOJZ95Hui2afEw/e2xznGs91V9x0ZqJu87LlqzXgm9H3KisdNzT72NvBH1U9Ym2qPdj01e5tOgoduE3yl3H//ttti3uUXo06bXva7Xo/2nBdtRV4sU4d1T24n61BOGfkger2J097de9evXD/AyTff6XNOQ+WABQWf82aOwMO8Nb43zyHrNpqS25Wwbn3QLso1mQ0rV91q11hV30Zm844+6/wbPPzgr1FHZgu42lQmBITABAjoLXf3oUOgRb9Lgftf5p5cx+tR5jzXcZnnzu/SI991vextnO+586k37YEjbbk92mFyu85nTp7Xo441wEeUOa/NTlvbrEedaM/7j3yXe1vKpsWAbReVtseBK9FZ0gf+vDD4zGuPzBacEpfiGJrAKDx0YmtBXiwMB8dY4EHPNBgEodA8+Fp5oGclBkceSFn/aMN6DcYaWdMv5D7Opi37YU9uCMUJEvyANWVuV+7XD3YJ1MI1s6d6/cSh9AtF46HWjIm8Im29fuuztvbrQRMLSJF5m4JFHhfapTEUfRpVEgJHBwLad3ZYaZMVD1tZjkEAFWLAMMnsjtMu99XVX5ctBCnmNicZF3WX+Q/vTIqV9IXAlkKAD9u7EIdg5/s3giyaqXEJ4xFzCl1xjsdGzSEbYx6Pc8qVNnVWTUabIRYz2xQGXnRF3qYJi9Da++DAeVpc/n4hjUyc3G/6g5sbcL7XPY96s+jQntvKdlinzOXed86px4dL5DZNWT6qfuDggdUdu/DUQGyBqacG38qj8SaGT2uCbTy+tTm0eQpzZPNrM17j3TLUpo+6dqgx6MMshXWCNj4WGww+cMi+ynm3N1x3rJwYxs8xKQkBITAeAvaWO/eV7SHsK99L2Lu2L2mm+mTu0WafYu/adrQ9XvqKsvJDFmjAHvXMz3gb9/HVXpHbJ+zDLm0yedtathH5+KjOn7bwHkCaIvkP39znMmeKvtd55LseyzlFPbaPul6OOt7edVmPci+73MfkfG/PnPcBPnNAx5GL4t6yMOiFZy7CbXPgqgf+lRW9ZcQHBr6jUR11dTnmj805o9Q4bui4w4ae/QEGbqm2G0oNLv3glGolUIQ99tHcPChBqjzz/bWPJrA0OUdkt4zBGKDXjMGk+hACRw8Cy7zvGODUIGcoQCOPKQZKDJJioOQyn2kPprzuus7P+q7XlXu7KM+8XI+6k5TNz8L38sl94O/c5wZ/C6ONvM1/stPkcxk+lhCy2DPv2fCCvrXlR9E3Twv/TJ/M+8LA/6Ne+yCP0sGnFfUhBJYCgeYNR1wt94jthBy31H1qgEBWdgtqaZ9S3sRGdV9Rd6Df3oe144e3CX3QnrUKY2j8Bw9bd4Loamc4cLUeaGtEoj+el78c0ZWJ+/qjLMpneqPnMC+r+G52nH0jkSnzgILHu5xVzgmSy9zH2nz5HNL30gc3eoOylRr/PLBT/HRci9Cs9mya+MFDVj7B8j6L/3bwamvFutKHEBACYyLQ3AO4V7mvwz5atrfco0/tg69Nj7yc4v3CfGyLjrdx3TY7ub8uXeqBMInTp9xXl6U2PfJy8rGSf7RgkK9hHvXyRDgPS5tsgw/89lDFua7zzRXnwVpzs6Y7cWcCPR5y2cpE4GiuBrzmZwN9waT1U9rSntkgD20IqvdhMtoEr3nTk336GJA3Y6DODIlvGdm1Ewu7nnIt8UGVI7aDw9p/GTtG13Y9tOcY+P7ltdj1oI3ZgL3Ko7rpQ3foeqHX1Gu/bre0KRg0Tp5jmSLxwNls+Ib3cXifNm4qEQPmSDZ2G129nogZ5M31m7Z9xLbWntxgzwPSiE9pTcxANo4wBrStiBU7bqs00qcQ2NIILPO+80NVTpAHFX4A2xZ4RL22Sc1taNPtZv3Ij+Ws5/VROrlvbzdOzj+8Y/cQ+rLgZ+kD6V3LPahYGtxz6AtxfdBv/Ce1G5/LlkiUkyizvNqrZfKoadr0ryiZTcp536pyHwu4A/9vvrZ4XxuX26SSkhBYAgT4hiPeEMJrkr6HykWXOKfsH49pyt6toPg+tT0HHveZ2UCbtK+shctYqW3cXvEB3LtuA4Vgr9nPUc5Dt3rYyhecVlE+kQamTOP6v6gXy7HbyGe5zfeS53qeRxssRx3WXY+5l3nQOtNhK/sh1fn3vJnDOg/sP/tTslzfDlVt3sFjGwqRbO6aHNwwr8PxcG1R14nVqj/3tckpByh4YCChyDdcmfPgddu8PoRrURICG4iA3QP43VjszbLvS+e+t51n2486TPT/LBtRUvZ62e+omx50uJ+DHjjGM1/CNtXvWN33vrWFsLZt2tCrgMcUfQF/2MJvafCHL7PcA4rlo+cz3wP8PuD50XMl04/0aMJgW9yiypudmDC/s2/Jg7ayoAaOA/XqXExCJ+JOpqhO9Um3xz7oORtHVp3UUN/Us/5RMGdIaeEhK4kOFSV/s5PwNs7UnB7kDQ+Krs/WJi/6tDEI1FDzfsHOP0mnqumzMGGyr7WyDa/HAjXm1Z49eBfLvG5LdezGNV6+ntLWrpntkbytIcN+GmxRZL2uvaJXr4T9UNX0WcY/H4ONz6xVzGob6isJgaMAgWXed3646gev8cHayx4Q5NzleYqp1ybz9q7vdddl7ryoQ37UoSzqsRx1vO0kOf/wzsqO483TrcJe9Ivm3dz3VX9ntqtPpV+s3rXx1/bw7m1MDo3qM2mv3Idgxf0ndd1HW7n42eJz0dZsWK+Nn23uZWSbHeQWxVc9ZUJgWRBY46biHuPmwTbwfVr3FfdnE7MM8SiBzHjIuZN9n4J3xD6lco3NSh+oc9+yPVLzNlW1R4tuz6zHfRoO3Vb4sM3D19p3sTbep/vGNm2XRX9JvVwnz3W97DrO95zytkR5bkO9yGedOq7HB2u3O8tDts1tnf/BHBY/O5hD4Is05JuH1olJbc59Pbj/9WeI0p4zidSsk9IPWe6nh+SuRybHWN9sLYetqDdvuaK8TKctBpg+hMCcENBb7gak+9OMavbDlNMPu7775NzO+d4+68e662QbWcf7Zu5+3/vxerYxSd37y23axrddMcjXPo/6tjhwXeYH/rgI+JbR6k78Qq4a6DI+sdDGgiIGSgPtIwLqJsDNbYoVC5ysPerVThNUsQl41DRtD6jdZg2oi6zqULcGUiXwsjDPgjDTo81J0xX7V1b3HFcdIDqv1+0BPt889iCPRY6/BJelR6tzzEzWloEgZJXXPCDwKm3skHsf1IEuLZXrqjZd7piwR2sb+kCxsQc7Nk7ylITA0YCA9p3NEoORrkAlT2PUGxXE5LaxHu2QP4utaHfi8pL+4Z2JcVIDIbBFEeDv/mdo0sQvR8Q0EA7xSoxTeB5LlYtjJNTEmxYDVV2PgzwuYsREm0jND99ZAc9sWKyUYikeuvmbrcxB9pbjlIGTP6hm35nrNkb07fqsx+T83M4fSF0e27DsfM8jr82W6/mDNXPqzZwq5h4vD8fDnA3OCVIzX6FeY1+btyofjofL6Jp42dcB2zX6sOd26rx7rGyXxw8etvJtNnuzFWU/bGVOsnalL30KASEwPgLc277fuI9Yj8+lg30KifkKl5d2po/92/gH16HdusdLXtubHOW6Zwf+vti1Ooruj3xMQ76Ab7ebL4Ae9z9/rcxB0ByS+1mayn44mo96kZ/LUa+tPKoP9/GxbbwHsD/KqOf8PIZJ67GvUeMbx3a011Ye1cdmYDDOdY2rM2WIMq75DdLjA3/dvNz0Qzd18HmRjSNhudnopl3q4FkyR8MggE6B8oE9OoDGAblDYrsaJJQ+htuYs6j9NWPwPmjcnA37m/2gjW8ZYdylN46rjpcjsrEbz6+njNOvsXGgsc0RzrNef+mh4liuwQIl2m8CKZSrXnHAaFtxgmToum2clWejMo9qWpN94KHf+iKmFWPv034/qmENGcfl8jBeImcJPP9Xxg6u6SMnJrVtsUFe4XPszRpo+gCz6QNWfQxuA+Iih8zsoO7joExJCGx1BJZ83zEIcGqbKg8SXMZ6DjayDnXH1RmlN25/MwVp/MM7a9W/VV+JmvlKXks5TEGdvs38q/tgSpGcZ/ccetIgrz7T/G30n3Y/YR9Vnz7XfCd5ZsL6LSV82riqnzVmGa+15j2nPsw3+ioIgSVBwOIS7ifbN3WP2P4CAL7n6t7kfmlipbrn0KLRs1iwaQNd/qOe79NGVtoUe2ah+odBG5aYbO9bAXUG9PVBu+So14PXeb7hGP0qu2Zq4xXJ4LNNp42XfX6u5/7on92O+2rmzl9bWxsMYuJSmSOPPQ1186UwVH1ziX3rfLif5fxwbm2wyFObxh9DXtYWClxHrNe2VsGaGLSt0rpODJfgn/mrIxpfTZ+Ng5Zy6LI9HmcND30IgQ1GwH2/x0nF71ffHfa17Xfu4eDPy14Gr/qF5jyAddvH0KC+733wUeu5ZyQ5/QPU6Qbsw/c9/9hjLZff5QwfMONb7u5jOTxPbbw2f+36nre1c1nOJ9Fl23gPiHWWZ7sXYJrsW2q0NEhtvO2MweDK51fizwSO/sQH/jXs4Lq5y+avG5TbtN64i6MgnzzuXJS58dmOqQYW3r7wih1zFNSt9qw128GBsNw4q2rP5E0f0Gn6KDaG5bRjvc32obeMKn6ckwqoZUDb1wA07GZAzcprXwNBXp19WQNlvtnW5pD2mzVgHFou64oFXwPQbl8DVIK+26MJvq5xXB2cSfUhBI4GBJZz3zHoyIGHByfOZ06e1zmbruMzG2XOcx2Xee78Lj3yXdfL3sb5njufeh7EsTxJav7oAhpxFZgnrC6s8W3VV5rd/8/e2+tIsix3nlXVXd3ngzwXe0e6BJWRKFxlBQIDrDDzSBc4wgVWIyXOC4yyj8AR5wUILIEFQVBYgQK5VBbg5Q4Igjskz2EvT3d1rf3M/R9paeWRGZH10ZUZ5t2R5m5f7m4RbmFh5Rkpf0dD993uK0E1GdPiOpq+ybu67MM+XI4PyYQ+dH92H6ygX3SieH+IN9lH/vAO3VcpC5ybBaa1pTilr5EpzmFCtn7hU+IUlMdOYH3NQbd/HnMZ0ddX1yx9neard+LraxmF3kfX8WCdGp2cWt/R6jsdlWgF8lqBR5ToB1GDfzwFt1Qu687tOBX5ZXjiIZ6Ok0WFXgzdP2I+szmg+UurdB/Z/GZXRy+cpw456+Jzjj2Zdk6kj9i7yfZzjICPOvI1fZBwzT4gkip27gmNSbY6xFeTcAFy/m/t+NrqVcoCZYHTLMC69vXMwrO61jkNxWnUfc0ava91b3ff0dY3TFayPzHUJKNnY3i6vibrI3Cf4qOxPijNF+AH7LA1/6V3uR/y123E48+RXL5n5DaaopzuByOI7EuVOKY1fY7k8pxzG/1RbjR3eMC/pA3oc2m5jITrNFsWal+5DuzC64sVlotPtLHLiADEpn2voMedZzNQ22VkdWwj5yiHCYscqtFkxp1zhMH07/Fbu/+TnXMQ1mSCA3B5k5KDRV46GbgH1N7V+g/m/84O022pDT/3Pj7T70V903BcG8c0lsH8p0Cy28RnovEGO+49hIiuG0qf4+wYxI9ybHBhq9LnXR+Xa4ENr7s3b1qGjkAgBgjxhh+DBPGNLobIJ13SI7nIIx3ipR3pqosedUlWkCCF99Aaz+SZRVsC+dGFm9tlP7wz+VQUy+d2X8l9hxHgtycfbWzcI7y4r3Qv7LLgpvtH98cZN+lzumvZyeBveVjvD/P+wzvqq7HWZ1lgAxZo681XFrGOlWld0dA6ZQ31MtF9veRYqq9h451iI9Y1B/J9jU0xacd5vObrNI2Bpj9oG4fHiAY92Wp4T7YC7Thxk+ecb5TvNM17/p32oZLl4M04+edDeqDlB0vkOISn3nU3ox1TmOn+IxicP8RNl/yffDL8/ZrY889+LZic8U8dx+skX0e0+/mf7jK0De/y/TqaeED6M4FV8M+eWLV7FOef5HpPtl6/NQQJGL8V/zajrVIWKAustgA+gBiyCfqa7HX5f/cDojt0rmldyz+4hugLuoeYfIt8A/ipD1vDGnP3BeLHvzlRyVaP2QzHmgcXd7nLf0nXE8Dou6VuCW4JD/ryvSC31Sd40eT/I1R/xiOrSvTRULqjoiW4JTzozEPObfULXrQ4d+i01Z/xPLkNNIZT4WWkdjb8wB9PfO0yMt+Lk8drJ4fdgjgFdjCY/7bluAv8Gs3bfZlGmuSnZAE8PVAcPlDohgKPlr2NqfVssnl8EIgkCSY5qpQFzsgCte5awMDN/lghDsh8o9gg8iiImNMt3pGe3N8cL3x2HJ/A3CA2/MM7cyYpfFngLCxALGQD5Q/1lCmJNopzJrpJiB/5EOegzR1JT9pND85gQ+zjPN5Hk9iLv4zP6Uq6kVQjNnpjSTcetP2wOk8x/LGIxNs3Vl9Z5A8lltvCA6HJn85B+KBRRroyTjrFL1mg6qMHS/iFzzqhLS72BUE/P5zDfj6VYDl6DpGhIz+HMYZ2bLiOrO0mMR71gazk+zWBKschPjrvnH8lW/u14NcB557jxIQ7/VYpC2zZAr4uWXfTc223RvfD0Te39d7Wqtb19GyM2J7MzhdAes273KMflV8WTm2fwyDOz3y5HeWkSzzQ5OvFF9uiCye/P4JRJ3JrS5TP41QbnZFPfQgnvtyOcpkHmuYnvtgWXbjR3OEBr35pv6ZyGQlXs2g98F9dbX2X0ZRw9iDOnLwCO7s+dn9F786/B3u+GHtStN1QGp2byNS2urU8YHQqbehgnQa91Zt0543J1s6PVAs4BQ3jiuyDBwgD/lDhGuujLPD6LVDr7vWfozzCHLTQZreugpjMv7S91R/eWWqf4isLvEYLeHykh8j+sNweqnsSrSdOFRc51uMrqxHbKA7SriYmGZKthDge3PSYjBio4QztsVHTo52UE50xhR3onlgNyVbf2USizf574vVq/Q5H+UIfoWxAw0qkUedBTg9zemCMfOIBJz7qFOkSBCce6Yo01eWT5yB80Oy9fToLqF5e2OH63g6fu6nw82FNNMyew+srfL1fI/ApSWNQuOkcdhzXTIt9m9wuJkdBuxpuuD6oMpa0m21vZ6udbxKsfs492Urbjkq4mhGqlAVWWqB2ubvB5HNlPfln2pFGXTTBSBe//HrkEU48QIp4qGdd8v3QVI9QdY3L7gXNoSKwsuS+58alvlAvniwrHkF4VRevYNQjPqCK5khb9QhVl/7H2EB9PjW8iIRrPfCHy2LDu4w84fzWwkACwR7YY5kp8FOwDw4eSn9AcBkCQytOc7q1O5x00FZw6fq6jNW9FoPLqY9GI+Bs4xI0PEI9uPRdGwSZtlvjuzB+xlSlLPBaLVDrrp0ZBR35PIGPQQV0BQWqZ5mIl7z0S1dsiyfryTxRbwxQwKuddSxt+8O0zfX6M47PYPCVkz+e/KNxyD8apL7zj7S7PJ13mvSB2j24m790Onyuwdt7Ms0z7+4D7m+NnySN+1ugtf0ryqbvkT+6YJqqlAXOygKEIazR3ZrcrauH3+AxWo99fN3a+vMySLa6Toh93VNd/hoB6ycnW303qymxXa6ebAVOCVj6oYfTinzlIenot+Eb+d3MA598NnWVuf7gFb988giKD9jH4adR+pdCnp/u7Ke98an3PcHqsiHZ2nTtn/fJ1lw3xtD8c+fsOJ336NvbNdGH2q8j99ecO1dkH/LLnFur7yVbDXfdz78nXf3823Vwa7wkjquUBcoC6yxQu9z37DXnmyOT/K5wo3sBtKhrjkc6RlD3Amij+0DESb9wI31LcXHcczKXboO5eZ+Kv4iEaz3w75/+Te8y8t3kvMm3Rd5TcN8DO4/nvG426w/oHgz2QF0P8rsA0WpT8EhQ2QLFxrere60/VOz14bKubXpI0UMIz/1Ktvpf5y3R6kkAAkiNcf/UVqss8DotUOtuOi8xQFIANBFDJfIF9INq5BvVj/WhwCnKKiAThAaf2g8GsQDRvCGM5u/wp91Xyt+5CsM3/xi4HWft7vPwllPip/vPJttl5KvR1OvTH85g7PriGCYfjgpzsyRyWpLVxqNEK5Cvq1YpC2zJAtMOR5u01pOvIVsbSsDR7utqt04bzk01SLZOa25ap7bmfD134/a13WKphpvoBEdzyda+o5Gkm97r6fET+I9d94kg+siRikO+VvwjnhFu1Ff0v6pHiAxHxNHvSJfGcwzy/PTVu1t5a7zvzhdP50u4Q+ew+c7pHCpha5onf67rikGJbn1Q9uJhT6oa0l8VAWzXA+c7J1uvb+28G/7GrofPeicGCquUBcoCyyww3QNYw7ZIZ9ZpX+HdP9Qu92jckQ8Gh+9fU6Ie+XnkVY9QvBEHr/VpZ/Dli8YTe96aDeLcc/0iEq4+qXrgdzN48GoLfKu7jHBtUwCvYNEDuhb0TW4oJEenBwjxdxoGbQ8ZbtopaGw6rCduSn5jsrqCRsLVXgdnLcLXKbhsOMP7QO1Df8mf/opvvCRcH/ngQJdVygIvZQG/nPeue+vZ29tZd6PAYhSAjB6+83kayWUetdfwIpODM9oKCu1rOFJ7ArSrwH2eifbH9+nhG21KyoRQcKKPrhXXxZVl/rI/nO98Lvqc5LTG1XDuc+kjjwEmPbgr6erJVsN7shVox2NMYOJVygLnZIHdDsf+k7N9DT9cc219t6iGNdlnKf645jwu2tFdpvOxhtsDva1U16E1DpcVPmKylR9HYr1y5GSrv88TvAnxNHPCr9TL95m0+8E5Px754FUZ4cHJL4sunKDkBeWXaaseIfo4Mg5+8J8+fcKypxW7BWB3/+jndYqVIfi5E+xd7Plzo3HOdd7le9GoUU30pgctuoZG8bDvbMUfe4xseqiHna3uq0m2ct7Bc82ccP5NukpZYNMW2N0DbBkr1sIiva51ioeIcb37cudzSlvrfb0jY9ym0D7dL0gWvdCcOumT/3A0DkHPxt0HvNQud/nrOf/NlCgjP55xuS05oPRTp+S2/Dw06tDjIXqm9T67cZFeX9BB0ZjUzvPJbWQyLrfFA5R+6pTc1hyh5XnCK3qm9T4fZQP6fOpyMQlXLDs5BS10g76s+43ejTctfms5vcn5mek0l3IH4RJTwNACB+Ps+vaCE9xH1ydn4jrlvPqYRoFFcyTWF47lkYk279OHbeOxceqhs43JCW38Vp0Cauo+X5OWTZgP8hQfe9fcAyk5WrfBxOetJtP1xTFMDhhVBM8WID31LiM/BxjZvtbqo/H5WI3xhHEySN+t2nFtbCZH23FBJuJ0Pl3edKBINjPbTH2YDLQ9uvicYB/cUGwF8rVW/2orq9F3uRq+vtaKZauciQVq3S0/UTmoWCo5ksvBTG6jO8rFAAVabCP7qNJ9njYZTf7e/af5OFO+d8/RfUV+EXl4KY5rMjnod6zorsP4vQ/8rf3rddRMY2Bu+Fu7x7Z7jnbHGRM75brvPfWHd+irSlngHC3gOxzf9x2OWk+Kc/qa3K25tkbjOt2tue4/pCPG08JhIHTyz3DUvO04mqxROyw+bHGxrU1iRY6YbGW99uQba5b1y05HewQzwroi/yj/J4gW0XJdPNAjD3yiCYKjiE8QuurRDzfu5puhc2Q6beGg576kYw3knMp3R386mTSew173M97P83RNhPP+UNYkdN774Py2gyIlWNKzgV8PPBv18+zJdfw47Y5vyVcfzZopF29ZoCxgFqhd7uPLYIlfjX4cLbktHFD+njol6o80+fYlEDkOeIHo/PjxkYmkNrwHYwzovWqec27DrLnGeUY89UhbMndkNO/ntAH9PEUhbLmIUg/8Oo0toPHQg8DHyvTQScMCabBTcBTpPRCCNtE9sJKeBvcCZJcHfaMcAABAAElEQVShj6aXLgioPKA2PUr4TmNAhT/4GocCKwJsAiegB9JWf8wuIw/eTVd/aNifT5+/deFBpYFpbH28DFFzpMocwPk15oggM/VhPMhT6L/LKLiUbAsuTdv08I8drM28SbYCObpdrValLHAeFqh19+A8zQUeMbBAKPPl9ogHXNaT2/BQwIs2CmTUn/HIizXBFZ/u89zBmZDfA+iYQ/7TPavj3CfjVd3PNd85+U/do+hb/lX6Jpngg+Hr157ryPc9xhSTOHxN1e83JheSNp7kwfee8MM7SFUpC5ytBfj7tFZ+Xz8tjrF1xqScZut0WocNx8reW3NRR6/PxoZoNp5p3bNO95JuPdnq8aHRbG3m1whcs/u1x0zA+xfY4YivVJHfVHsOjtyqcPLHagNjXfQIqYuP+o35NGvvBjY3kDk8oj6vdtI4J5xbxaFTPIu8XwMPz3vr3D5dhX3d2H278bsuBE3GcZ0TPVTDeVdivSXbjcYfw3pS1c9zf07wBLufd6MDux+3WpWyQFngFAvULne3Gn4Vvx79PAT5ZJk2tnUfiDjxRT3iE038kSf6efgOtSMNXboXmL7myNXRSoguxhTHhQqNV+piG17aESe+qEd8ook/8sR5wXeoHWnoeiobaHxPCblVXU6ZgoR+Q6etyy4+xHXcFDzStoDDpaJMxylZhqEmGT0IwqM+XLbr6YGFZIkp7Op9/kQbY2A6PdiZxssYp/n4TA2h+Vhb/D4HJ3Wc5tNkpvlMwZjRCZwQ8T6o279eBz3R9+ZvBAVTHkT1wImHYXYyfIPkKaX1bevOyzQuWv0awA4tKLSxOV+b2xQYGm46p7puAp9s4HpMLfOVvhaMetc7+8m2zJ+ulGz1JIAhmH9Purbdrjb/LtM1FSgLvHILbHvdxWBBAYVwanMChYsnUzjx5XaUyzzQFLCIL7ZFFy4GJ9BiW/2CP6Xg2tz/dd8rPwt+2tnafav7TPdx7bp54G9dl2tsOmlHvy1f7nzNB7d7TpPZu+fkZCtRj/+xr/2By98J6PcgwwM1fnRXKQtswQKKUbTGpnVqk+84X8eyBWsEPKXDtv5sLTqtkWaTrUHGtfDBmrSj7Wg1v+BxkeE84Wp4a7fkmzHbGm5JN+o6UHB64ISPXOsD5Vet46ms0SP/izBysU1d7Qipwyt+0eyVAtMYVlf6+XbfR51AdeifG23oa8N1Mp2Gfi1EfR5nex82Sj0T6Nyza5V6T6LuJ1vbeW5Jdp1zrgMT4Hq4tQvk4yNssNpoJVAWuCwLsK6VO3joC2yu5hNs5bXS697W2me9U3rb63LJk6xJOG7S5G5geja25TxtxsIXcB/o94DXtss93i8Um/uc+8fSe4H8OZAinz6C4o001aFRV7sP41lB2WC5ebmtXUhpD279em1OQYtfD2fBCXjAINdhfM1pGEwyke8cEm1b32Xk54iTaecaMJ0z3Rw4vzrHve7n2HEmMd0UjG9Pxq+QSR92boFju+6Mu+uNfE0fJOJKH1DcwWH1dmMxGjs1dGO5NeYX2KnBuKqUBZ7CAltfdwqUZMsYhEQaddEEIx158QhmnNpAivRQz7pi4KV6hKqrL3uHa3NgKFtTph9dMKHgX93t2f3Xi+Hxte6ZPeBuLvHBPRfmLsN11aTts/voHQ6+pm/y84aa6DjduWSrHtKnP/q1h3b/2vLTfRuLmVQpC5yBBViVu6+4T+uUNdfX7LTGHGc7GJnV7DplHfpKb3PXOu2yIPfXKQjWq0H/ozvQDq1Tq+9+LMn4POGqNWv98CRz4tNM9p+xTT36VLUFrdcHJdJUF4zM0ffSh/oRPkLRwc3Vo+41dfY2cFrMwuafqZl93T/TtlYj+rk2SmsT/1KXf9Z14LhGc6UmaynUSR84ftQXlJ/vGA8fTLa2c63XRyjJ7snWW9OFLOe/fLcZoUpZ4AQLsKb9QbUt+Oke4L7ASP2Z2jWP/LmJtZVvn67ifHe542Px2YeKfLr45L+zTNYluREfuOj3c1u+X33Bm+8JfNvh5Dg+DCqPO5CmquZyqTaYJvpElRNDlCfq/QnVeDDI+uhOYQoOe0DgQUPzI80Z4Bxod8eg5JoPaU+mu5DgdJBx5+OyQYdVW78mI37EOWJg8YyJNh8t45/GZhjG62Nzams7zto+zpnAymQUUE/2wSl33esCapMjaLZjL6DuXwV6kl1GPPR/zVyYpznMfg6mORhWuzHaOcQumo/VsBs8lP6w0K6Rhp3o3QZgd3am7tomm052d0b70DXgiVX9Jd/wPdnqwaPRPPFaX2v101AfZ2CBWnfTSVLgMSEGlRzIKGjJrFHXHE+WiW0FZeBGQVzESb9wUc+S+u5HF/o3k7svPbiz1RTvfLTVaXNvcf/d6uC86D4lH+18yDS/3aTtU3QEY7IVH+v3HsMriaNkq++QA48vNlh/7DIjVNmUBfq6YVm1NWRroa9F1mSMc1ifLKUprvK1ae2+JiGtig19XVp/Bq+HydaeTGNtau12SPuGP1CzvtnheMIuz+yPc5v5xCJfGXFz9eiDxQNOePnbERQfNNWBaguKJv1r4Te3X9//+NHe4WVm9A8/n82vOgp0vz72/HPH6TowiR77Gn+/ZnY4o+k6QinJDMXD+N1ed/87+WWdd7swiIs5x7oGLMl6w7NDT7a6nP+AVv34AaerSllgtQW0Prsfj2t37x7g696ozsditrUpXxCejZUD0X0h6pMvcOngC/weYD7dYzXWevYFxHTuB/AFrc7af6pd7jHmZl74VuEEM472qIhfeoARJxn5ftqqj2DEyeeDi3XxSPcpMI8xzxedGUd7VKIu8UScZOK4VR/BiEMfB7hYF490vybIJX3+pR742znc+i6jb5vjd2duNwAvSpzS0F/hOy3eAPyGAk+/iXAjOHRDMarfZBDZu3m4br+NuLz/kUw3FIJGHi64iQD7DUM4393hDxJGfMw7bBlTlbLAS1mg1t2jLK1AJCoBNwpMIk+uRz0x6FA9QvFGHPqsz3HklDtL7a3/8E4yRzXLAmdjgd0OR1v//uBs8csUIxlOHqHHRoQuiqXg83anQZr+8Ox81p5ipR4XeR8wWhthe4j2GIikG08k4HpCdXpnK3jheCDv7d1Dd8ed+I1y+UPrxYv8b8ZDHOGa1PgTPy4ZoOrZ98a2+MDN1SM/fZz6Qyn/8PHD9bfXX91zntvXiVts286WzUkxtF8T/Rwar9emP46ZDNcABZrTjaPLOA0cQn7erWLn2c+fnfcWDxstJ1g4555s3Z1zT7CEZGtLxJo+ron6Y5kZoUpZYJ0FdvcA1jELeX/tTmtbvgAWrX35cz1fI93r7Xmapb7TB+617nKXb84+O7axLHwZBz6WEY/0i08+nLbqEcLPEXGxLpr41LYdrupiNUQHJc4PXGxDH+HAxzLikX7xaT60VY8Qfo6Ii3XRxKf2Y2ygsT015BZ1/qUe+P0cbn6X0ZRwNueOV7f/1Dj0gDD9JS7cANz5Nx9jMnbDQab/pQ7RSabj4ADnhT66jIJL8P6DAa7IPniQIGj0HVcm6w8UhuMvdf3hQg8QHjSyKk/3lz6s+igLvJgFat25qQlIKNzwKWrnQCW3xSu5UVs4YOQbtRWMQKMOfzxEz7Q+rjYJhNcWi8k8wEYOX+kQ/2hHr1Ob/Kno4DBZl5mC++5vYZvdMYdm98FwWYkP8/hW/XEL3+u7I4xFD/U8tJuvbQ/sxut1U1cP7ViyykYscG87HK8+scPR1iFz7rENrSnO8Ydq0Z3L+Vhivm6nNW48rGWKZLzdZKa1T9PjIesWJZ5sNVnqSqyyTomFpnXKWm1rFp5r293YEq4dxw5HdjyeWOSvJZ79LHhwx/gyj/TI76JH9Qjh45jDRTo8sc3XSE8tnmyxH9wiKYILn/y1D9RsbLCd09059L66f3aszjnXgc63X0f9egCPUPTPioctNvbNBuxups659fNuEnvJVmu7j27nfu8a4Frg/FcpC5QFVlugdrnvmwzfeqws4UHHKN4HLz8f/Th4SvbvkSfW4aUd+dWG9piCnmNlCQ86ztUGx+a/ls5t7fxLPfD7Odz6LqNdwtmckP7ChmX0l7f+IGHL33AQdsEgrfaAYFj8TPc10wPCFFxKVvzWpnR908M/6L3g0trTX/KNSLLVk7D9YYEg0wPP/tDwHqVVygKv3wK17h6eo/xQ/pAD97Db+QQ9t4UD5sAm6o80BXFLIHIcCtbQeeouKcbYfCD+0/xb96HTw/vkP43uzPbhPL0F3Yp8r6DjzH9PMuZnaTX6TsZrfPDMbYeSOO5TwXnC1fDuc63t3y6wtj/Am2D/toEnezQRY6tSFrh0CxA3fnv9/p5Ypz1jtfU2JVttnfqawxCKqwzHsorJ1mndO1+X8bgIhLV97VKx9UaCVeuU2AdlHFb3pNr0RxHWponwpNJpnmgjfvI1u0u+wXf16fSvlMuPyrfO+WP4xCMZ63mviEd0+WOYVM8Q3og7VIdX/OLbG8Dahp8nzrLNDb9M8fNluOin+/mE0/2383mr+3MQXQYeCtePV+0jnnfzyX7eiXtJGPdz3OJg4+3nuJ174/G26eqvEWgJd5N7168Z5KuUBcoCqy1Qu9z3TSb/Lqz8uNqCx/iQg0d80iOfTVs46YQmuiA81MWf6+IDPuaPbxoDUGMWLo9T+GN8yJ2rDTTHp4IXcYuqB/5wOdi1PQVC/SHWAx6CHtg8mDIHoOC34wyzF1hNQZceno3vte8y8oTzu1vNmhntkgDTfIVLNlBw6QGlW2pnIyVsTfPOLs2ezim6y+KoMJZ9EFzqAX96Nxk40+P4/SDT/0JveN5N9Xn6qUhOUJWywOu1QK27dm5yYKEzlgOV2CYQoR1xkouBjPhEE3/kiUEXfIfakYYuBWqmz+8Q6mcdxOPukshoou33nF6PSRySNeYBbaDGY2V3T+oyjnMNTh8lACYZ/C0P7oKTvzWckjjW2XP98E4bYH2WBc7PAm2HY8t92UZHVqyvRZ+J4iK8giIrx0HdrVOt4Z2MdPT1K5kYF7H4R8nWaaej6Z+Sbn19w2/vbFWy1ZOx/u5O6w/IZFaW6EMRxR8KB5SvFS1C8QmnNjKSk6+FR/UIxQtO9Sgb8aO6+pZO2msLZ4nzPv2taea8u7/mOnB/Dn+X8+uDK8L+maxfG3yiB8V+3g3ig+N55wmUHczEw5xrfLgnXq3ez/3uj2GG4329dp7bH8pMzpOvXYbzXz+aZcaoUhZYZ4Ha5f7QXvhg+fN8H4BbOPFEfvnvSFMP0U/DNzrgHfl6eEd48Ufd6u8xkP40B8036hNOPJGfOiXSJBvHCd/ogHc0V3hHePFH3ervtcCLSLjWA3+4nDzYsYu8B0X7ydZdoEQM5CUG0j2g9kduAqoeRME3m2wNMq6TDwIqO77ILqPPLXK00GwKHncphIYTzcfL5HrQ2PgM2+fudM0PtdiDMtGbPlAtyDSIkAeXVnEbAE2WQNL/ut/r/WtyngyA1h8aWvBpMvW1VjNKlbOxQK27VadKQQhCClqiAoKKyBNpsR4DFfAKNkZQvJGmuoIYtWMfi+v+kO7usT1o44P9HtJ8Z/SfOdk6Peg7f+vx6D2n99f8rclMu6faA7nfh6Zka8f5w7w5ZoPx68mP/eGdxTYqxrLAa7SAx422Tm3BKpYZ/YGj4WztsLYVa46Sbh4rGQ9lb50ajuQaMaKSraxJdjg6XuvU+GKyta/b+BoBT7p53CReOltf8H25gBv55YzLfFmX/Kkg/VCHj0P4OZzwglEuyrKryWgPJ5InNtP2M+WxrqmI8bDiX51D2sbi/OG849t314RT2z2A6l48bO103luStZ93YmF2N3vy1YTlv6fka/DdJFvt/N9MPNb+yo4qZYGywHoLxHuAPImv+xa/xT+4odzX+8TnrR7vdarTmi9wn+FV+5jiNON7hbvcY9wtlwpu5PuZaeShnYvo4KPPpg2NQ/jYFg44h5+jfTrhxyMZj0rZQJZ4WkgYcxmlHvj7ecTx7f4qPwVCOD87nOqOtbXzg6//Rdo1mWPsznTVgy8BEwEW8AvtMmLO0wbRMG89IEwPAMyzB5J+L1Aw2eftNxjVZbPOjyWbPpdEk08b9OwNpX+t1QNOf4AwZh4yCECxF4EjkL/iVykLnJkFat21ACoGK6NTqMBNfARTowJePNAll3klHwM0eGJbAVvkzcEaD+32ovmTnI/vkOgDa/cQU9N9Jgq5NrwAzc+Sb5l2xRmftzsN0vRebOezNv9cXxvedJ/yh3ljUuBOMO8JHMPpQZwHePevAYfPVRLHHubbLqqOO/GHdxhqlbLAOVrA1xcD72tWsaLHgD3p1nBtbarOWvYV6Wu3r9O4jk2f07VObaG3r5GbHIuedalkqyfZrN1jo90OR3DGbPj2RxKThYdkq+9wtB568u3qEWt3zr9aT1OR/5wQVhnhoEf/O9eOfpl69skRN6JJvtPc1PS1pnx79e3Vh/u727fmJD/fmw80LTtFZms7n+A8Hu6EG64Jx3VeY3Af73Src945v/28t98vsLZ/u8uYOO+eWDWo5CrxsSdWbQx+Xts5V0L16l2je9t3unIdtMNfK3D9+faHN//iIzCtVcoCZYEVFrCV1Na97gHuuw0X/Dn3B5a1no19sYU/vOi+8SBWg7H7AhQ8+IMbG5DMJ7Q/tBjvg3tAW/vuH555lzs+dVQyPt8vMh0dEbfkfoAMfNHXo4Mj4yOP6urTxjaeBAwLShx3ZM/4S7ZBnPdT1bmsL6ZwiW0+0eZO0uxgZ7U9lJqjokFgbHBahh3nztMcphc5Wqc11KpkK8qmv171h1dwCpaVgHVnagSDT77LyANB5sMk2nz9NtLnNnyQ77bY8Xutq7DkQLBLs5RpdFxvWd0DUr+hGM7tYDp4qPf5W3166O928YDT9LgNOi924cYDrFIWOCcLbHzdEXjEQmAinGDG5eBF8uKnLZ6IEx9BlorqIxhx6OMAF+vikb61sH54Z63Fir8s8HosgPciNjLv0OIeAx4r4tcp5moIZby00Mr4WkzZ4kvonbfLTD8cCp5kmuIixUK0e7wz/ZAdsU//YwgP3+0PIdaPPWTvtft7O+G9IRGHfmKqRyRc8Yf4WeBcGdEzTn4VHfKrc1B9AeGJfKqLhr5cFw546q8y/9unf/rp5uq7P7+5vvmOfbJ+Gv3WYmeU04Y5qHSzTLc6Q13bb63dTAj4TIYffDWaH/0PYDdAXQOcJ38msNja8cY6nXfjI6Hy9o3tXDU+k7l+Z/D2TePxZEuT91dI2DVyw3Xy7ub649XHf/zFT7/4yahVygJlgZUWsFXkPt0X+sZ3uWefPjKlfPeIlnHy5Ycg+uTf5+rIRxr9gIt6+7cd/HTmcaxplw3WWGsZ7+Wkdgjy/MbfogJPLnLH78+kW0i01S4ju+jt9LcHhV6froH2cDAlnO26MMzeQ0VLUCNn1w16WEP9+tFDBTIN13nEx7XXk808WPiuVR4oPGi0ILQHmR5YEnj2v8zvHiKajCehLbi8+viIJwfGXaUs8JIW2Pi6IwiixCAFXGxDH+HAxzLikX7xKcCirXqE8HNEXKyLJj61T31orx/e0ZkpWBY4Lwv4DsfPd2+vLVnGzkWLTrw4tA//47LFN8TQHgd1nNON0yWIf3QPAIc7RMbjIqsq2WYxksc8xEMkXntSzRNrPcGqmMhhT7QKxy5Hj62Qsx2u+uEkErKfb+7f/vD1DxqWMRwu5vPs2dTmzA7bXvCD/YHVxy7/7fMQk8HcDqTJx+Nvo7za8sPR91JXyXW1Iz+8sd3HvHju6gv4zz/72Q8//+Hmj67ub97f3Vjcaf85PZbCvbq35Cn5Uy+mvT0w2mdHcknc3e3GDoFcaivGR90OSxNYxQ7ab0y7ZXHf2HHv18Fb6xo+6PC9sX6sb6pvjeazsg5pWxbWx2bX033n5zsZbw3/7ub9h6vPf/HBuKqUBcoCKyxQu9z3jSWfu4/db8m3R2zGydfDozow+m7qx3CioyfzZ720T43j0a9SNpAlng62++fT6ftymuxmvPVE29Z3Ge0SzhaUe7RmkZgnTGn364Mr9NBf7/puX2I8dvd6IdCzw8L7SZ+3wTujffRk6+irEvvJVossbdVNDw3TQ4bh+9eo/CGkXv7fbF+fr94Cte52p2hJkLKEB405eFMvCrDQw6E2dOoRP1eHV7LiURva2uLXgOWXeW7e4g/vrLVX8ZcFXosFPn3+8OnN9Vf/95vrm3/6bNtS2aHYIh9bzcRNrTHVlUzVH6NjklaSHhbhDG4sPWb/3TEQIxHvgGfnosU7vsPRYyDD2a5GYiX/YaQeF8HvOxjJ4rGTlQQsyVsSr+x2tKQt71++fscoP//9L978YvFfqs2//g87/sZ6JpLzRKt8Kb5XJdbB5XbEIQ8dX0oSVG1B8JKPuNgX+FiQoQgiD4/anffa+vvHXl8FfvkHv2RX6J+sEirmskBZ4GIssPVd7uZLb97YH4KUqJQPjydYfnspDh+tewAyUWfUhR/P7eTbpy7Bi188yMZ7CfOwd7jubmCT9OGK6du8DQ5b6PHUi0i41gN/uxC2vsvom9uv73/8qKcF4m/sYsG4wcn79HpLzjfsRJ+SrZI1cR44ONDQ9QnnSgnMlWz1hwnaJsfK0lfn7GFBX5nyhw/jm9o8dPC1uvDOqvYDWt+ZgiplgddvgVp3u3MUAyewCop2HK12jE9BlfikRw/jtIWTbmiiC8JDXfy5Lj4gweGjivytOUjfFYcy97fm78gZtK1KHRd9qtX7H8E89dH9rcvIc3e/bZFp87dK4uBb5W8Zv+Plb43XfS3+lrqNAX7zta3d+PTV1MbLoKuUBbZjgX/77X/3w1c//Ot/vrm//5o/lhC/sLOxBT/W9PrOHvZc5iGOfxjt3hYqy7vterSdi74IDe9IdwpGN6ypdp22RnvD/vZtTCRNnWZ+yhK0vuvRFjUbIVn+9wi+xSlw8FXzJuOuwXjQcfPm1vzA/Y9/85u/WbzD0Xzen5lf/EPze6awlVN9YHxQVx2N8q/4ceGFoy9w0IQDilf88vORDxxt9WH1a3vI/nNH1EdZoCxQFlhhgdrlfvV35kL/VPcCfKv8LmbM7WOmxY/j3wXhl48XPupUXX6dNkVjEB0Y7xviER2aFYT/lsrKUjZYabC17JeRcK1Em5/3re8y+oePH66/vf6K+L+/y9cetPWgjoX6Q31LBjSH5jE8tJ4M8FdPKPw22OjG688NShzggE2Gj5RsnR7+Z5OtSgaYY7TVx7vLlGwlCTAlBr5mUFXKAq/fArXu9s+RgiawCoQih3BASuSPAZZozmQfCtiEhzcf0OAT/lhd/FE3uFOKeS+P9JqvNA34TzD4UyVbHWd42r0OzS3hfE0GvyuZyYe7vzW8uUz3s+ySI74kMaNkKz6UtidXrR59Krvk8LkOoRnfE//wjmmsUhY4Kwv8+z/49/+fDfiPz2rQTzDYX/3qV/+XqeGoUhYoC5QFNmuBre9ytz9W/Zkdf2hxM5HnlOjUBdHRah6Fo3g64tbUeU7QH98Ewdl4p3EID4Kkqx1/NREXVsoGCw31CDYeOc6+1AN/OIVKDNrT7NZ2GbWE8709g9tfp3jU1wM95umJV3+Qb4/3O/vsPfx3WyLrrtdSAbIpben0h3+j8fDPDgxLvPqDfn/4bw/9PbkK3fD7rxHo7b6zddp1xe4rkgBVygJnYoFad+1EKYFKSwEaOA61I024KNc0tU/RaSlAE4TGMWoLB4x8ET9Hi0FcHMuSunk5T6T6fjf5SodQrJgfnTwbeArJ1s6Lb8V3Cw88tx/eaYOvz7JAWaAsUBYoC5QFygJlgdduge+///43NkaOzZaywfOf+otIuNYD/+5CscfX9sjqSULD20OsY/yhVg+z4NrD8ZRAvJRdRkqO2sRawtQmaji3S7RBt89kG0yYvtbqUsg0izZbugntQztbySDknVasKr66SvKVZGvfZdVeIwDNejUekqy7r7iaov7Lu85npCplgbOxQK27vaRqPG8xcQp+LgE7JxMTpfAcasdEak62xnauoxecjc09Hu01Zas/vLPGRsVbFigLlAXKAmWBskBZoCxQFigLbMsCF5Fw9VNWD/xuBiVSt7rLyBOrWKInVOeSrb7TKu5sTclW19OvKdT511rduCRbDWFHe32AJVZps5J4PYDvZDVpkq2eeCW52g4SrTr8Bx/4lV0Sr3zF1ZOvXYYdrvWjWWaMKudiAV8vDHbj6y4nU0fnj8Tm0nIouYqOmGA9VM80yUb9fBXJxoaXW122+sM7qw1VAmWBskBZoCxQFigLlAXKAmWBssBmLHAxCdd64L+6ql1Glrxk6XrOwJIaemcr+Y2eR5jeBziTbGV/F9dSSxy13MN+stVwlg99kGy1ZMX+awSsT97jSrLVd7makCdXre2JV7UNkmy1JOvN9I5Ba39lR5WywJlYoNZdO1FLkqmjpGzGKRGKVtVj0pR+OI7hREdP5s96acd3QSGztGz1h3eW2qf4ygJlgbJAWaAsUBYoC5QFygJlge1Z4IISrnbyNp5o2/ouI08439/dvrXM52f7JV1es9pSpixsS3SSTAXBddIJN6p7ohW2xtfoVrcde55cRZBEK8lTg/4aAer+2oC2U9VfFeA4w/uuVsP31waQbFVC9epdo3v71up2TLtgea3A9efbH978ixGqlAVevwW2vO4siXnzxn5OW4nKnDjl7IHL5RCOxCe7TZUQjTqjHAnU3AY3KuDFLx5k6UP6mYe9w/XhYEcKE26rP7yTzFDNskBZoCxQFigLlAXKAmWBskBZoCwwWeAiEq5bfuCfzqRVtr7L6N8+/dNPN1ff/fnN9c13fDnWk6b+FeeWfL0hF0Hyo+ckpjyIoa7vLCE6IeAzGcO5DlIQvLOVpClwSroavv/idcMbOyuK97eyi5Vk6ts3lmg1nMlcvzN4+6bxeKK18/IKAeO/QebdzfXHq4//+IuffvGTUauUBV69BTa+7v7OEpZ/aglS9ypKXuqk5bbwczAmW0m6UpR4VRI26lRdyVTalJhUFQ15EsORR/K9L4T/1hXUR1mgLFAWKAuUBcoCZYGyQFmgLFAWKAs8ygKkgs6+bPyBfzp/W99l9M8/+9kPP//h5o+u7m/e3918urqy/2xAvbq6u7q35Cn5Uy/kQb1inx3JRte7u7g77M5zqU3A+Mh92GG/+20VO2jbjrAry7O8sePed7q+ta7hgw7fG+vH+qb61mieC7EOaVsW1sdmidj7zn/HuAz/7ub9h6vPf/HBuKqUBV69Bba87mxH6J/Z8YeW1GRVT4lOnTQlPtU+BpVcjXwRt6ZOMlU7bwXB2Xgn9cKDIOlqx19NxKqUBcoCZYGyQFmgLFAWKAuUBcoCZYGywMkW8BTQydKvRPAv/+Av31mi7X+5uX/7/o4s25RoswFOWbY22CnRtnvmnOGB37jJijWhXd12LYJ/A97pgc9xlmgT7atAg9fa5Omu3tsHPJ3v7fv39sR79eHqp//z/7j+/d+vn0wy01QpC5QFygJlgbJAWaAsUBYoC5QFygJlgbJAWeBpLfDrX//6d96+fft79gd93ziAdmtPncQ6r55SiXhwsR3rc/zCPyV89+7d9Y8//vjX33///W/W6N26DbY+/zXXyqm8uxV1qoZXIPfLP/glX7/+k1cwlBpCWaAsUBYoC5QFygJlgbJAWaAsUBYoC5QFygJlgVdrAUu0/gf7dtOv+AZVf72Uj5VvRHFQYp12TLzG+jEadPqQXtpPWdB9e3v7X0znf12jd+s22Pr811wrp/JeRML11MmXXFmgLFAWKAuUBcoCZYGyQFmgLFAWKAuUBcoCZYEtWcBeffW7dvwn5qykqyCJUb0aS3UlZcFTFy/y4lEdGjjhgeBOKdIh2dwGz3hsXP9NPEuhyWzaBluf/9Lr5DF8lXB9jPVKtixQFigLlAXKAmWBskBZoCxQFjhbC9z/8V+++/uf/9Z//Pzpzfurj5/aV2vtRwb9lde8mswq/oYyw+lVZXe8moyP/oqyT9Aoh15ZFmnObF/R7WI0/Qu7oS1d/CLAVAJ9wqKX33q9u/nw3//5h//99/+3da8m2/r8J9tWpSywMQvYDtU7kqdKnFInmUmhTlGiVMlW8QqKD9iTng8gekSjfqjkZKr6l4zo6he84e7tNwqubUyrM7pbt8HW56/r6jnhdK9+zk5Kd1mgLFAWKAuUBcoCZYGyQFmgLPB6LFCJtnYu/vl3v/utz//j7n+1Vxf+9t2V7eqyZ/b7z7Yzy15r+NkzotdXN46zHV32y6f3n43LchH39muo91TubCeX/Ws/ikrywpIWhiYxYJ/td1QtDeA/rEoOAxoZUur82KolUeGkSeUNjKQNoFm5vzf9EBkTjFZu0AeD63Bt9gOxP/2//9PV1V8Y+Z+caeHH1ue/0EzFVha4SAvgp0iGKqEKBCc8k8504cQnmPG0KdAPFegxgTriPaZjJLMUh+48R3DCoyfThROfYMbTpkA/VKB/KRvQd56f5gOekunCiU8w42lToB8q0L/U/A+N6ylolXB9CiuWjrJAWaAsUBYoC5QFygJlgbLAGVmgEm3tZP3m//mX99+9/+p/vvrp7ud3tmv1/s6SqpZEbUf7qu2VJVHvbfMryViSob6RKsB76OwIMxyPlSRsyYa2Ov1YrdMsS2sJ1LZV9dpw0ODzhKqBTyR0aTsfsHOw+4wEqx2++Yw2nN4m+Xv139/ffrv62W7r88fUVcoCW7QASTQSXYKqY4s53BwNWQ6KIDqOFfEC8aGjpJt40DWiH+vjEF3zFKQvjXsOh74RDVmNVVC6Do1BvMCXtoHmIcgYNOY53CXN/9B5eSra6pvyU3VcesoCZYGyQFmgLFAWKAuUBcoCZYEvY4FKtDW7f/2VPeL+ZF9ItSTop0+eRb367LAnVi0B+9kOy7JaItUe+KekKziSsCQBkPO0qsGedDD1SqhCo5AgbbhGo03C1A9oVvekKsymx9Ih3geSFGRdZqTv/vqjM6382Pr8V5qr2MsCF2UBJdeYVE6wkXyLh3jnoHQAkYtllCjNPJF/ri6ZrE/4OblDeM0Hni3aYOvzP3RtPAWtEq5PYcXSURYoC5QFygJlgbJAWaAsUBY4IwtUoq2drG8+fX3/4fMnS2ZaFtOO/WSroVYlW5vOXeKUhGlPkhopJlvBtmRrT6ZOyVbJmIAnVjtdekjQtrTrpM/7SAkOuJaUrc9/iY2KpyxwiRa4vb31BCPJSxKWOmLSMeKwgZJzczDySG+E0Y7CR9yoHvlifcS7Frd1G2x9/muvl1P4K+F6itVKpixQFigLlAUmC/z617/+nbdv3/6efcXSHwMhWHuix7q9nH2IPyQjgahHuKeG7969u/7xxx//+vvvv//NEt01922e9yXXRvGUBV67BSrR1s7Qv/7bh+ub67f3vEt1P9m629lKLpZkLIlNv9Vxu7P67M7WnhT1RCjJUdr9Dqldrw3n+1Wd1na27pKt132nLHKewJ0StL3d9WlHrfSvve62Pv+19ir+ssClWYCkak6gjnCZR214VYRTmwSpCnwxYUpd7cgHf9RJW3wRL33QH1vQpbELjnCiZRjHJZrGFOemMYsfmuYW+ZAVT9QDLuKpZznxr4Ho0bgFRzjRMoxjEk39x/FpvOKHxiG8ZIDiEU58ET+SE/9rgbsn4tcyohPHUQ+99dC75WvgxGVTYmWBJ7GAJVr/g91cf8U77eJNlpugboqxTqcx8Rrrx2jQ6UN6aT9lQbf9tfe/mM7/ukRvzX2b533JtVE8ZYHXboFKtPUz9KP9btVX9/Y715YY8ISqffAaAU+qttcI5GQrrMNkK7kFzy9YAsKgpyEmnLU7rSVbd/SGN1nrGpn9ZOsumdF09gQuw+/8VKmfVLY+/5OMVkJlgcuwQIzbFV8TYwu/FGIN5JQUEwRPfQTBKZ6XrDPah2TAiwea8LlO28baOqKxomiOiFBXn8IvhchrHhGC17gzlIyg6FFG4wFHGfE0ymk20PzQscX5y3bPBS8m4VoPvfXQu+Vr4LkcROktCyyxgN34f9eO/wSvkq6CCjigqa4bOwEDdfFGHtWhKdCAnzq4U4r6l2xug2c81s9/E88xaLw1dzOSzqFgtK3ql3Tej10XRS8LnIUFKtHmp+mb91/f/3j3wertftSSrvbcbknX/Xe22r2H24+R9B5XT4XGd7bqcR/oxy45mpOsdld0ngfJVukwFdC8j67v2n4Zy/UabUq+UrdxwRdEwS4qW5//IiMVU1ngAi3Ahoce906xNjGb4rWlENMgF0tsKw6MdOqRR21i/VgU+ws3pwu6xaD7g5DQAbh1G2x9/gcujScjXUzCtR56t5nsiCthy9dAtEPVywIvbQG7Wd8RECnZFoMj6hQCJNFpqy4oPqCCvwyRE476oZIDMvUvGdHVL3jD3dvPpthr/JZndGvu2zzvuo4KlgXO2QKVaGtn7x/slQJfvbX9rbh++39/Zx+ebCXh2nBtt6vx2y1NOH+yV7LV8FPitCdFLdU64aZ3t5IWNZXG7e9fnbIDpod6S8o27PQDWdxG0Sk6Fe+vyeiVAnDAurZsff5r7VX8ZYFLsQAxNfEwB3VKhKJlfGzDMyrE15FGPcbcyGSeTIcn6pAM8KnK1m2w9fk/1XV0SM/FJFzrobceerd8DRxa5EUrC7yEBQiIuGmPArWIi3XGRRvZeGS8xp+DLuEFoY+CNdGBx3RE3qV1dDKPODdwwqMn04UTn2DG06ZAP1Sg19znLXTMfvOSRSkLXK4FKtHWzu03Bu7u7q5JcH72ZKslAux1Akqsxne2fmbLq/13j+yJ01aPydZWt3uA6CRHm0SXNWnH9WtLyVZ4esZ0SrZKB6y9TpWdrm0M/tnkelIW+pqy9fmvsVXxlgUuzQLERzF+ZX4xZoWuGAp8bMMrWk6egh/hkFGJcavqI7moR/2hI+Jp2/hO+ZuTz2HLNsCmW54/185zlotJuGIkXSzxggEnPDzQIl048QlmPG0K9EMFuhzGHN8xHXNyh/DozHMDJzyymS6c+AQznjYF+qEC/UvMPY6JMeR5al7xvMc68rTFJ5jx6gf6oQL9S9vh0PiKVhZ4agto/QiyBuIa05qKOMYw4hcvdOoUyXlj5kO8QNbfaA2KBxUj+ozqg2jNQZA+NN45HApHNGQ1RkHpOjQI8QJr7g9jbdkHGz7VeT90PopWFjgXC1SirZ2pf7368er281ftvan2w1kkWnfJVuPx5Cv+wzKe9t/vTJ78NJ9t5JZYtRpJVHdBhldyVJCuusykw3FdR98VC2o/2Wp0dKIbotebzLSztdMhn1K2Pv9TbFYyZYFLsAAxJoe+bcachFMdSBwVD3AUxZ2Ks4ixcl1x1xxe9KZx9xl1gRWf9IhTfKILvxRqvlu1wdbnv/Q6eQzfxSRcuVhYgIKqY5w53BwNWS1mQXQcK+IFsuhHC1886BrRj/Uxomt+gvSh8c7h0DOiIasxCkrXqG/hxAt8ybmrf6DmI8hYNPY5XJSL/NQ5KILS5ciZD/ECv5QdZoZW6LLAs1ogro+83lgP8RDvHGSgomlNafAjv5l5xHsISibrE/6QbKZprOCpo4Mj1iNOfCMYcXkseazwZh5wx4pksj7hj8lH+pbnHu1Q9bLAOVqgEm3trH396ev7T2RBLXa3zENIttImnodkdPvvkaFeI2DiU7JVNOOIyVZr7cuYrh5dTvqmH8jq+lzC9FHgnXbPOs7uLRA0hqnfPpbjjytI75Wtz3/PGNUoC2zMAsRxOpi6YsEY3wmXoUwV48m5OryiCaJP9Tld6jPSR3LgLGna3KuYF0LNX3NWn2qjRrgM1UWcx1wdXtEE0af6nC71GekjOXCn2GDr85ddnwteTMIVA8VFQZ2LjiPWIy7KSFYw0pCJJS8KaJkn8s/VJZP1CT8nN8LncaODYwtzj/YoO0RrVL0s8DIWuL29dV+DL5PvOdX/5DXMDKQ3wjgz4SNuVI98sT7iXYqrufuPjG3uvC+9PoqvLPCaLVCJtnZ2Pvz04frt26/s3Vy8noskq913HJJppW4IJTaV6DTRnGzd+0Er598lW/1JgmSrHV66PpcB0WniM86WbDU+L0kfuCmxC6fz7T+vNMHjn1uf/3ELFUdZ4DItYK/j84kp9lYMr7YgTKoDiaEPFfRkntxGPuNGcqN+spx4bGyHBybGALdug63PP1wKz1Y94e+gzzaWRynWQ29+yMcpyHkIRodBp6N2xMXFzwKnncvcwj/Et1Qm68jtLc892qLsEK1R9bLAy1sA3yifKx86womWYfStomkW0V+KD6i66IKSizzgRJccuFinfUpBB2MWRIfqguA0rwzhURFNbY2ZtviAqosuKLnIA050yYGLddqnFHRsde6n2KtkygKvxQIk2u4/249F2dfoH5tonJJ/KTGIZyPJeDDR6DLG6I/KJBrtnycQDdf15V2dzYam3ROgO//Z8Os/7+0dri2xyrtb23E82Wr9MDfvzj71qO84ZmElzG1ng0aLCVq3Efz91QLITjbwd7O2XrSzVX05to+hc6Blddn6/FcbrATKAhdiAcWcOY4TnmmqPhczzuGjiUY8wgkqTo1yL1HX/LZqg63P/7mvsYva4YqxWLDxopnDZR61teCRE446JToB+GiLn7rakQ858VCniC/ipa9xnPaJDo1ZcIQTLcM4HtE0kjgnjVX80DSnyIeseKIecBFPPcuJ/xSIPo1fcIQTLcM4NtE0jjhOjVv80DiElwxQPMKJL+JHcuIvWBZ47RaIa4U61zOH8Esh89RaiBC81l+GkhEUPcpoPOAoI55Gcd+vx2ahDkLNDaaa+3bO+8GLoohlgTOxAIk2T971JKPv7DQPeHhXp02uJ/nMY3vdp+u4lkx8ukRjN6S+Kdq981MlGqfTRILX523AYjnqvNNvmof6RwBeCmOxw2csnKGm5Klk4IGXYjjG7p8dR2K1JUubLff0wYM8Mkmfy3R9Rm7j8coJH4zf+uHNCZuc/wkmK5GywCVYgLg1xq7MSXFtjJ2Fg674XHWgCjTKiEc48UhGMbnowotPeLVFjxAdp3ydHh1bt8HW5x+vo+eoX1TCNToC6ixKDuGXQgwdFzYLWAtcDiFDyQiKTlt1jQccRfhcp21j7WEYreNFc4OTuvoSfilE/tzmzphVNE/aW7aD7FGwLPASFuDrKKw3fJp8D1DrcSlkrMjFEtvUo98UX+QBN+LT2KLMSBf0NQFbzX2b513XUcGywNlboBJtfgpJMHuelSSrJx4N2n8lNWFqdaDViNLtiMlR7Ujdw8EDL0XJ1i4Lakq2Tj+aZbqnfrt+5/PeEXmQ0I39OcMJH1uf/wkmK5GywNlbQPE5E1EsLZygaIqZc4wtvIyheDviMy7SJAfM+Dm5PAbaGn/Ut6Se54mMcILgYp+xDu3YuMUT5bIMPJSMf24b5DkyBuEEwcWxxzq0Y2MWT5TLMvBQMv655996fd7Pi3mlgB56OZFcHEDVMaEumGMQXuRiiW3q+UI4JiNdWS7qFY/gKQ/86Nva3GUv4JavgWiHqpcFXtoC2e/IFzEO0Y75XmRGPnGJ38w8uc04su4RD3xri+anOQuiR7Sa+/w9GHvlc4Pt8vlZwpNl0JPlRjzwVSkLbNUCJNrYyakdrYIeCfcdldRbYtBqnhi0dUvS0RKDFKd5kjDg5hKNUQZh9DmuQftsCUfDU592dVJHp9P908fgdCU0oZ1SfrQuGUNPhHry0edjuAc2aHxtLDbGPh8lSZtdfORtvBrblGw1GnO2sm9T5tb0NapzdL6AyeOa7Gd+lp/T+hcXWfex9fmvs1ZxlwUuxgJ6dp6LU5noKXHUKNYa4bIhc1/QR3IjHLw2D3lcmovK1m2w9fkvukgeyXQxO1z1YMtCHTkN8FrEIzp2FD3blEUdadTzQs88mT7SP+LJfS9pb3nu0T5lh2iNqpcFXtYC+MWRbxUOuvxoXKsapWjZl4If4SQHjL5U9ZFc1KP+JB/bNr5VARuymmeEqkOXfnCxTf+ixfEJP8JBU9F8aauOviwX2+pPMrFt46u5y7gFywKXbgESbe9I1FmxlX8o0eimMJ62W7MlBh2nBCCpUCUnJz7j2Es0ukRINFobXpcFqrTaXrJV/Ux9GI/JUlqisTcaav3n5ztTZ77Tk89dXH1ZsyVWmeNOdUy2th0sO7onZDWkbgMfb8ftkq2mrydglbRtfG4V67fZgl6bjFWmcRnP1AcMcJ1Ytj7/E81WYmWBS7GAYlZB5kVdsaXmqRhT7aVwiVzuC90juREO3jUb1uDPRXMXhL4lG2jeglubf74enqp9MQlXDMLi0wUSoerQOSjgYhucaPHBVPgRDppKdBCqoy/Lxbb6Q0fE07bxKYSiebSgS/OMUHXo6g9cbKNctDwO8CNcHBB0FdVHclGP+kMu4mnb+HYKQawo6NWcI1QduvoGF9t0I1oeE/gRLg4NuorqI7moR/0hF/G0bXw7hSCqlAVeqQVYSxzskgJShFMdyPUeD3CUvE7iWlAdKF5gxovuTOFDfEKJL649aOITXfzHoOZZc9/WeT92XRS9LHA2FqhEm58qv8XYbQboTwpTUtPanmQ1bE+22p2s40zUE8E909n5RsnWh4lTk+G21nfyTslWRtNxkvGk6oNxPRxD40fp+rL1+a+3WEmUBS7DAopjFZ9rVornY4xOjBzjZNEkM4qtoQmf+SU3ByUX6RmX25F3aX3rNtj6/JdeJ6fytSekU6VfkZwuFIYkB5FxLEgO8KprCrSjA8l10TMeeeGAqktvpAt3jG+kQ7IjmOcJT8Zpvpc292iPPOet2iHapOplgZeygNafID6HQltFuAhVhyf6vugnIz7yCR91qC/RIr9oERf5wKNr7V/INWdBjafm3iwue0SoejwXquucCDYtu+tD+Kgj80iX8IKSFRQeXWvPu2QLlgXO2QIk2toPJbVE45Tks0mNE4275CKJR5c6kmi87js4865Ol/aEZbfgINE43NXp/ZmMy5J47GPqak4B+rEwJTfRofFpjm22IdFptrMnCudru06tTmJUOU8bV9OxG99BGyBnMtw1m+0bdJ0MSHakz2CDaQzwnFi2Pv8TzVZiZYGztsAoTlUsm+MkTXQUe4mWZeCd44/4WJeuDI/x5L6z/Fx76zbY+vznrounxO+ehJ9S6xfSJQchqIU5upBEA6rOsONipa62oKamtmDUkXmy3kyXDuHRtfbBT3MW1Hi2MHfZDaj5C27VDtEmVS8LPLcFeP8PRf6GdcehttZj5BHNBWc+tH4jOftLaBk3kos6VM9ywtvY9Lgs1CysuW/zvM9eEEUoC5yZBSrRdnVlb1Zou1fJPlN64lc7WtmJ2lKmIdEJridAxTdMtoa7yZQkBSedSpw6zvTTv+OsPskalqSyy4Qx9IQzIi4jXY5Y/rH1+S+3VHGWBS7LAorPFZPH+Fl1xcoZip4tIr4RPtJUlx7BKDfHIzy8qo/ko665+tZtsPX5z10XT4m/mIRrPfTWQ++Wr4GndAqlqyxwigUUrAEJemJb+oSbC4rm8JIHjniEE1TwFeWes6551dy3dd6f85oq3WWBl7BAJdqalb+VsUlwaresJzOtTVLVE6sh0Uni05Of8CPckqNTgrTLersnZZVsNU5PnCKDrH22tvFRb3yOdfxEb73s7WxFwul9DKg6pWx9/qfYrGTKApdmAWJoxdHH5hb5Yl1yS+PwLJvb6FuqS30/BtL/aAwjnZEv1sW7dNxZNrfRt1SX+j4V0veo/5G+yBfr4l065iyb2+hbqkt9vzZ4MQlXDFsPvc0GXKjRFrrohBtdyPDM4SU/xyM5wS+5KDRHIOOJbc1DOI1XeME5vOjAEY9wgl/SDnGsVS8LvIQFWFdz6441oXUnHsaktZLrsT3iEQ6oOjJacxE30iW5DKXj1G8YoC/OU31HnMYmKB6gimiC4FWPUHXoNfedjbAHRfaJkHo+4MV+a887clXKAudsgUq09bP3rz/2BKr5B0OR9GyJTPu0xGr8ij8SLdlqNE/OmkxPwLo2lwWHrGuZ9Ln2nhyd+jBZ7aiNydaJfmBnq2uXvj5OH8Paj63Pf629ir8scEEWiDHRaFqKoUSjrZgTHPXMI7xkDvEc07W0P4vh1N1qSB86RsJ5fkvHdGxu9AXPMb6l/Z1qA809z1O2yPil4zk2r9cyf83zueBF/WgWD7V6mNeFoQdd2hmHUeMFI7qMrfaIRzjxSEYXlujCi094tUWPEB1rH/y2PPdou7JDtEbVywIvYwH5WXqTbxNOULQ5Hym8Rkxb/nIOl2Uin+rArEtyWT9tjT/KH6rn+cErnCC42FesQ9N4qFPyeEe4LOOCXVb1Q3J5DLQ51pQ8P2SFEwQX+4p1aHke5zJ3xl6lLHD2FiDR9tU3fe1bxs6ThTarvsvTd2WEZ9j9RKOt7dlEY7PMlDQkfanEoPpYkmg0NeaZDu7qZAyP3j3yjaWe70wRY/Lx2Xitjkek/zZ2a4BzPDiovY0cxXCTzIRretpO2Z0+l4Yn72xFP0fXByB5C85LH98+3Siid7ZVYOvzX2WsYi4LXJYFRvGfYjPFhYrd1MYC4pE1Ik048YgmKPwcH3jxqi4Z4QWFh++xCUd0qEiv+gGCUxs+8Ugm0oQTj2iCws/xgRev6pIRXlB4+E6xAXqkCx0U6RQeCE7tyOMC9hFpwo30jGQzHzxRn/qPeNElC+2U+SP3nOViEq754Q6jCScILp+seIJiHV7akX+EyzLwUDI+6xI966fNsabk+SErnCC42FesQ9N4qFPyeEe4LOOCXVb1Q3J5DLQ5Ti15rugRThBc7DfWoeU5naMdmEeVssBLWoDXebDGtM4yZCx5bee1NhrviGeEy7J5XUMfyY1w8Nr4Fz+61ty3ed65TqqUBc7eApVoS6fQ4lBlLv0uYDGpJ4obmyc+qXacJ0Z1t5iSrYHuyVFr992uLUlq90N0IDclW3ex74Nkq3GPk60oMV3qAxXXlnr+JgzYOdZ8bH3+a2xVvGWB87eAXsdHPBxj5xizx1hZfKOZRz7pkh7JRR7pEC/tSFdd9KhLsoIk2Xj2MB55ZJGOwq3bYOvzP3qBPAHDo/8o/ARjeBIVr/GhN09MjiPiRzjo5/7AH+dIfTTPEQ7eNXOHX2XL14BsULAs8KUtQMBDEcx1J9qHAie1l8IlciPfMpIb4RiHBW67p9+lAzM+zVkQ0ViXqrl+RZ+DS+Rq7vvWG9lshEPq1PO+32O1ygLnaAESbb3MJhqNY0Wi0e8EU6KxyXofDxKNreeTEo2my/9Oz33ntzWBE+C/mh50+fw0VrOJcpcGr3tCtdmgJUGnR3slW5mvbh/IMBS3Qa9PuIaHjl6VKXnacZyVvTHQhtnH1WjedkX2ceo2mq3PXyegYFlgwxYgfiQ+mouRZJrIJ/5jsSd8Ix7plB61I1R/ws3xwmeHu0TxroXqiz4Olcin8YDLJeqhPuKRjPSoHaH6E26OFz47Dg9eSgZQ/cRxD9h8HhqDILK5RD3URzySkR61I9S4hJvjhc+Ok+cv/U8NLybhGg2jB1xBaLEu3ngRCLcELpEbXVAjuRGOMZz64Kd5CqIr1mlT5vpt1PnPJXJfau5x1JqzILRYF++S+Yg3wiVyr8EOccxVLws8pwVYXxysjbjWVNeaEYzrQziNj3Y8Ip565hd9Do74My6353SN8DX3bZ730bVQuLLAWVmgEm3T6bpR0tSToi2Z6UQSsVOik+c4a4HTs6U92zmWdsc5HWGe++CF41CyFZ6Jjjb0m5QnVq0hGgTHBZp3bo9zN1Z5xFPd1uePaauUBcoC52EBPUOws1UHI6e+lbJ1G5zT/B9xa359l3M99NZDnd8UiwAAQABJREFU75avgde3ImtEW7GAkqrMV4lLrUXdELMtxJfxtLMMvHP8ER/rI73gjvHkvuf0CF9zb5aQXbdy3nX+C5YFzt0ClWi7uvr69uuWKu3J0SnRaSd39xoBJULB9bNOUhQZ2iRXKb2NXEvANuhU5zN+Y5t09LrTobh804u6lrCloiSroGFckX28MbI90fFGgVPK1ud/is1KpixwaRZQHJfnNcLHWDnWoyx40YDoibrUjjxRnnrmASd+JVdzG55TSxxf1DHCq1/4Yj3KgRcNqPmIR+3II5pg5gEv/qe2wWie9DfCawxxPNRjgUd8QM1FPGpHHtEEMw948T/1/NXnU8MTb81PPYzH66uH3mZDLYgtPvRu+Rp4/AoqDWWB0y0gf6M1KD+ERtV1c8xQ9Ny7+Eb4SFNdegSj3ByP8PCqPpKPunK95r57h2u0neqya4aiZ3uKb4SPNNWlRzDKzfEID6/qI/moq+plgUu0QCXa2ln98PGDZS3t3d0kRMMGqWnXacd5fhMeCjtiewJVydYp+doTp3YHbDtXA59h2s7VnqCd+qBt/Th96sPaVp9krKbxTclW29l6bQnX67dM4ebquza6VZ9bn/8qYxVzWeDCLUBcpIOpKk7K0448mRbbUT7XaRN/RfwhWfEp0QYEB9QR5U+to1MHOtRv1hd5Mi22o3yu035tNtC8NFbBOCfq4sv43I7yuU77tc0/j/+x7YtKuOrBF6PEhyfVdYIzFD0bU3wjfKSpLj2CUW6OR3h4VR/JR125rnlvMdkRbVF2iNaoelngy1gA/7XUh0W+WNfI5RPVnoNZNreRW6prro8lePod9T2SjXyxLt6l482yuY2+pbrU9ymQfkd9j3RFvlgX79LxZtncRt9SXeq7YFlgSxaoRFs425/NhZHQ7KW9s1UJzkaZdr5achROPn0nK3WSsj05Gr/27xrBS8aTt62fvWSry3Y9jEHJV3qSjOOMxzu3D14jwHtbeYdtT7wielLZ+vxPMloJlQUuxwKjeGmEG8Va2Qojucyj9hpeZGKyNbap393dAU4uo7GMcJdqg9FcR7hLnf/JF84RwYtJuMZ5chEsuRCQiXyxLn2ji0y0CLNsbsO7VFfUu7ZOv6O+R3oiX6yLd+l4s2xuo2+pLvX9WMgYRuMY6Y18sS7epWPPsrmNvqW61HfBssC5WIDrXcdozHk90I7rgXrmQc9SnmN8S/tTIDeawxwO3TpGPHleS8dybE70Bc8xvqX91dxHZ69wZYELt0Al2vwEew5TCc0Olej0Xaae9DRWdqL25GijtySpy/ddqihsO1NdtdeFM0qT9z6szg9k9UTt9ANanYbO9iNc8Lmk3WsMx0d/Z+s1iVZ7mrsm8Xrqj2ahkkN9dLil+dv0q5QFygILLBBjzgXsE8tIbhQfTwK9EuUUp46gcFn+OdpxTGv0j+TO0QajeSyxw0juHOe/ZK6R56ISrpwwHXGSqo9OaDzx1DMPskt5jvGh+xgP/Z3iMDTv0fjRmfFLx7JkvPAc41va3ylzZ34q9KNDuAihxbJ0XMfmh87XZIc4x6qXBV7CAqN1l9eE1p8g4xrxSJegeCQnfJ5X5oMeealnnkiXvrV+aKRD/UgnPBRB6iMe6RIUj+SERz6WzAct8lLPPJEuXTV3WaJgWWAbFqhEWzvPbfeqWaMnO/eSjZYQ9aLkqCdWu08Xf0/Emudtu1mR4TC641y2y3iCVTSjmmzr33Cd5pwua3TvwyBIPvrOVl4jQMJ1t8vVHutOeacAavs4tzp/M0GVskBZIFmAODGXJbglPOglLo0lt0UDr/h0DvYYd1+hFDwCLp1L5stthjDC5Tnntob+pWwwGvMS3BIe5pbnm9tfev7q/zHwEX8LfUy3zyPLic0nVydNeCA4tRmJeDSqSBNOPKIJCj/HB168qktGeEHh4ZMzob6koEN6xC99wgPBqQ2feCQTacKJRzRB4ef4wItXdckILyg8fGvnjowK+qRTOOkWHghObfjEI5lIE048ogkKP8cHXryqS0Z4QeHhe4wdkK9SFngpC7x5wy92tHXEtazrWNe1aM4U+NSOULLgpEt6oAkXZSIv9ahDdclFXVkHa45XkxjP4oCt5r7N856vnWqXBc7VAp5oM7999dn8K/sclSAk8SdPSLLQyu6HoKzuSUDDw+N8Ji2ZiOuyTd7kqHgfTd/UB7JG2qOLzwn2oUSj7+q0dvg6/amJRoYzFR+D6Z3myxg71XA+DJodp0Ros0GjU59kSKQae0tmNj2TzNRHs5tTXbbr0XkY2YB3tmYbaIdr52+9rfykf0Y8jS3MZQvzX2muYi8LXIoFFBsznxwvqy1anrNkxZfbUS7zQFOcLr7YFl04PRuPoPpF5pQS5fM41UZv5FM/wokvt6Nc5oGm+YkvtkUXbjR3eMCrX9prS5TNY1QbnZFPfQgnvtyOcpkHmuYmvtgWXbjnmj/9PFe5mIRrPfS2i1UXMReMLnbqukhVjzRwKpFPusQLTTjxC0Z81KG66FGXZAVZQGuTHZIFbvkaiHaoelngS1pAfuLYGEZ88hdRVj4DnPxIpMe6eEd6cn9zvPDZwTPy6pL7mFMw4gOXi8YIvub+0D6yl+w0smG29RwvfHacdN41joJlgbO1QCXa7NS1xKdcsTsDuZ2ebNTrBDjPLanaXYbbz5DAJBP5psRrdzWe3Jz4GUEvD5KtRoFIwplk6xuDvb6fhLYdricnXLc+fxm/YFlgexbI8ZNiJSwRadRFE4x08UOLvKqLVxB+6ZEsUEXJNdqqR6i69Ns7XPGUJ5U4JhTMjUt9RZ4sKx5BeFUXr2DUIz6giuZIW/UIVZf+U20Qx0NfW5s/c36ucjEJ12ggXXARN6qP+PLFhly84KiPeKRfvCOe3N8cL3x2nOQwch8aV4YjPnC5aIzgX/vc49hH84t01Ud8l2QHzbNgWaAs8NACWusxWOEPN2o/lLgcTM19F7hii62c98u5gmsmT2eBSrRhS0+GEnlb+A2YkqOWwPQ2IbLC5F73ZKrjjKPzGddUb3Skd/q0S9hfIxD19XRr67fpcznv3GokWHlXKy+D892tBvlyCa8VAJKEvbXja6ufULY+/xNMViJlgYuzQHzun5scMVPkm8sPLOGZ6wO84lTqissjjHWNQThkTi1x3HM6LtkGW5//3Dl/DP4iE66PMchLy8qZyEHQ3sqD35bnHq+zskO0RtXP3QJzN2oFQ3F+XPvi1zqIdOrCSz7zx7Z4so7ME/VG3wte7axjSVv9ZN7RuGruZBF25zfb7JzOex57tcsC52SBSrTZ2frRjq9JPOOX7L6kXaL9q/V+PrUPwpKkJFI9LduTrfA3j2ac+uo9tC4z0Wm7bNPR9FJvSV69zmHiQSmve1Cy1ROr9tobkq6WaFWy9fqtIXznKxp/m491ZevzX2et4i4LlAWCBRSvBZTH7nMxceSL9agnxuKqRyjeiEOX9eleOep9ibrGE/sCtxUbbH3+8byP6heZcJ27uMHnCyIuhkyTwYSXvPRHPLzSJbzkgVEm1qHJWUhObWhri3RnOfDSL5rGSzvTIg91yUu/+GNbPJIVzDzgJa+55rZkT4XqM8uPxkjf4tc4spzwks/8sS2erCPzQJfe57JDHkO1ywIvZQFd2/Q3tyagRT7acyXyjerH+ojrT31o3QmiFz61xbcWxvEdG9cS3VHfqH6sj5r7/nWm8yv4VOd9ybksnrLAq7JAJdra6fjW7lMkWUmGhmSr5zvh6IlX0Tw96nwkaU0GHh7ze+I072xtdPv0VIDJqA9kJd/1ocpx3rl99FcHeJL1TUq2kng1XNvpapDE6x0aVpatz3+luYq9LHCJFsixoto5xsxtbJFxuS0eYIxjR23FZtCowx8P0TOt9+nuFtlTCjooGqPaeT65jUzG5bZ4gNJPnZLbmiO0PE94Rc+03ufJNkCeovGoneeS28hkXG6LByj91Cm5rflBy3N8zvnT33OUi0y4ylDx5I1O+ohPuBGM+kb1Y31Ap0RZXVCC0OBTezSOJbjYx7FxrdUXdat+rI+XnHucj8YH7tgYo9xcPeob1Y/18aXsMDefwpcFntoCrAtd59Id14pwh9aKeEZyomW4hhdZ+dgINW57/1FWv6hdc9+P8Ubn5BLP+6KLo5jKAq/RApVoa2eFxPN7OzxOtwxoT4K6RwvJ1ubh7LMnR2+MryVRgW2Xql4ZYBhPrLrMtLM1J1ubjPQhc4NuhBiLkq39na17O1tJtlqC9ZonORKtDq1+yu1r6/M3s1UpC5QFdhZQPLzDPKzleC63kZCeHA8KD0+kxZgc2qE2chzwANH58eNHxJ6kxDHOKYQnjj+3kZOeyBfx1CPt0JzhjXTkOJ7DBho3fc6VPN/cRk564hwjnnqkxflBO9R+zvnT91MVbtcXV+JJ0+RGOF0A4hnBkdyID9waXvjnLiBoj3ngRz6W0bguce7H5rxFO0SbVL0s8JosMFqPS8Y3ksv+LLfRG+XmfC940ZaM5VSeOJY1OkZyea65jf4op/mNoHBrxrSWN45ljexILs81t9Ef5TS/ERRuzZiKtyxw9hYg0RZ2Vh5KNHpqMCQafRcnBthLNHp6cZho9ESi89sDmMsYb+gbuuc7c6JxtKtzlGhE94nl69uvSZ22nackWEmQUkKytSF2Y267WBuWeSARcexidS0h2drmi1541Qc6rE6S1/B7yVZ7SvP3s+qdrek1Akq2wnPNTtdbk/8OfevK1ue/zlrFXRa4TAsoXsqxlPCadWyLF1zEwyua6pEn1qVXcdgSCA86MrQ+3ZdL51qoOcSxo0N46Ytt8YKLeHhFUz3yxLr0Lpk7vPDluav9GBto/HHc9Cc8dUpsi3c0H9GQoR55Yh065UvPv43i6T8vMuG61EzxYlkqA99ILl5Q8OR2ljt0QYmGzHOV0RyW9DWSy3PNbfRGOc1vBIVbMpan4InjWqNvJJfnndvoj3Ka6wgKt2ZMxVsWeK0WGK2FJbglPMw5rqtRW3aBT2trDsJjx6MCNvUHXDqHzJfbc7ryUHNbYwE/N2fh4bGj5i6jFSwLXLgFKtHWTvCHjx+ub+5bCpQ0qO0bteRnS4gqcRoTzo6Tp/TEKvwm2JOokvGErfF5gtn54LG2AT+8jyZLmuBBwpldqzfaxWoSttN1Sq72na3evm08b0i6nlC2Pv8TTFYiZYFNWyDGqLEuoywNJXvceTQ+JU6FF6iYVfWoQzSN4zlhnHesq89Lt0Gcc6xvZf6a5xJ42p15ieZXxjO6EJbglvAw1byoclvmAC9nMAfhsUOhnERPhkvnkPlymwGMcHmoua2Bg5+bs/Dw2PFkc1ffc2MfzSfjcntOVx52bmss4DXfOQiPHc9iB42jYFngqS3AWtF6mYP0KVrsX7g5GOUiD3W1pS+3wfc15Sxz6w78qcsujkP9Z0jnwvlA+odwczDKRR7qaktfboOvuTdX+hznXXYvWBY4RwtUoi2ctc+WAzVXYV7Vd6BC2e2VajhoUzIVBkuY+j93McaD/JRYtbaVHd0a0HMfJGqt7CVb7ens2n8oyxKp/FCW101T/9Gs9voAY6JtydZd2/o84Tez6N923W57/m6E+igLbNsCS2Jg4szIF+vRehk/ik/hF9+xGA2+yKuYHai6fUO4Od44kJV19XFI7JJtsPX5Hzrvp9L4++hFlbiYtRiEU5sJCxcnL5z4cjvKZR5o8QIVPeqHLp5DTkX9Rtkl9Sin/oVTGz3CRZ3CiS+3o1zmgaZ5iS+2RRfuOeZOHyoaO+08VrVFk4ygZMWX21Eu80DTHMUX26IL99x2oL8qZYGXtICubfWp9UM70qiLJhjp4s9rTHLiFYRfeiQLVNFao616hKpL/ykBWxwL/cyNR31EniwrHkF4VRevYNQjPqCK5kZb9QhVl/6auyxXsCywEQuQaLNHVP9Q8s8TiMzfCJ5AFOw28USjUZ3PaFMi0eja5YlG6ZnoTY9rVl+GMofZk4pWjcnFWGf3Zt/VuZdoBM87Tr/uY3sEYLz3fVzMyefVbeBq3Ra9g15n+D7nPken9tcIeF36JlmTcJxLOktLtlrV54sNrM5uVoetrh/Gaq8RMFleI8CrFUi68g5XT77udBp2ddn6/FcbrATKAhdigRizMiXFhNRFy7gYh8KnIn7a4ok48Sn+pK36CEYc+jjAxbp4pPsUmMeIfuEEM472qIgfmngiTjJx3KqPYMShjwNcrItHutfCPD50CyeYcbRHRfzQxBNxkoljVn0EIw59HOBiXTzS/ZrgxSVcdVJl5HhyI426aIKRjrx4BDNObSBFeqhnXfEiUD1C1dVXPfRixdNKtv3ceZGt6UU8WVY8gvCqLl7BqEd8QBWdY9qqR6i69J9yDaivgmWBL2kBradDY9B1Lh5k4lqK+Fgf8Yg+gpFfayzCWNcYhBvpO4aruR+20KWe98OzLmpZ4HVboBJtdn5Iknrmsz1AYpP9JLS32on05GlPKCvB7PyQTY+ruG4/gAUq0PzVA/BY8d2wVEPC2d5n4AnU9gNZVreEspKqnmwlsUoy1p7gpne48jSnhLRVTypbn/9JRiuhssBlWECxsuJgZqV4TbQ5XLbAnFzki3G26hGigyPiYl008altz86xm1V1dFC2aoOtz3/VxbKS+eISrpp/PfTKEmPIooo2is4lSizhify5rsULPjpKtSNOYxAu61rbjmOfk92CHebmXviywGuyQPQVGlden8IfglFP9CWqRyjeiEO3+Y4WdR3q6AlpGkdUWXNvCYlok0P1aEOdT/hVj1C8EQfvS593+qxSFvjiFqhEWzsFJEXx/P3YJVtbYnW6K4ySrdq9ii3Rg0bfxTrW5zziS8lW37XqSVeTfZBsNc32w1nX/aDOrl9kHN5aNvbjJ3pfXxhPn3ubg+n2OWxk/ustVhJlgYuzgOKjQxNbwoO8nuuzLsVe6OFQGz7qET9Xh1ey4lEb2mMKeo6VJTzoOEcbLJnbEp5znf+xc38K/WITrqcYY3TxgGOxrClRT3Yi6BFOTiXjaL/0g18cM/1TtjL3Ntv2WXaI1qh6WWC9BeQvtZbUBgqH1twe4eZ44I26Rm35WWjytcjoED3Tep/rnD6dWEGWorGpneeR28hkXG6LByj91Cm5rblBy/ODV/RM633W3DFclbLAVixQibarb+xct0dskovUzA0Ok41GhwqLdrYq2aof2YLe667UeG2P6qQP3E3vwhz//msEeEXAbLK1J1Xh4enNk60kXw1/S7vjP1p9Zdn6/Feaq9jLAhdrAeLAWHJ8KdoxPuTgEZ/0KP6kLZx0QhNdEB7q4s918QFv7AcGn6JozNKVxyn8MT7kztEGx+Z16fPX/J4SXmTCVReKFojaQOEwYm6PcHM88EZdo7acALToIJDjED3Tep/7Hg8lCwqyFI1N7TyP3EYm43JbPEDpp07Jbc0NWp7fc82dvlQYO0XjUjvPKbeRybjcFg9Q+qlTcvtL26GNqj7LAl/GAqydYyWvr9xGXnry+hIenkjTulsCkeOQn0Lnx48nPLEyiFDi2AJ6rwpPHHduwyw9kS/iqUfakjkjo/nW3Fsgj52f4rxj2yplgXOxQCXa2pn65vbr+x8/2ldR/ZZlH0q2KpkKW68fTrZK1vgtqQqvp2i7PuFa1tZ49c5Wkqe97u+pzTtbSara5tW2s9V09yTrDa8R6MlWl/Mf0PqO0a4qW5//KmMVc1ngwi1AXKjYcy4ujTy5jnkkD01F8Slt8KMD2ig+hXeEF3/UDe6xhf40hy3aYOvzf+z1k+UvMuEaJ6nFEnG5nhdSbsMvPVyAsQgPLtK08JdA5DjkSND5FA9+cWxxzLEOTxx3bsMrPZEv4qlH2pI5I6P5Psfc0a+i8as9gnneuY2M9MS5Rjz1SHttdmB8VcoCz20B1gBrRetF/cW1AS62td4iTnJRj/hEE3/kWbPuIi+6aPMXctO37+jV4RGIDsYSx4OIxinx2IaXdsSJL+oRn2jijzxxPvAdakcaumrup593nZOCZYFzs0Al2toZ+4ePH66/vf7qHs/ffjTL/PhestXTpo6zaNCFWjLVqtrpanC6c5ieRlcCttPAI85HSrbO7mydkq3a2WpJVnt6uw7J1t0rBkz3CT8etvX5+wmtj7LAxi0Q48kYY87Fn5FnZDrRocWYkzY0DuFjWzjgHH6O9unTia9UYVBWtm6Drc+/XQVP/3mRCVcWJxdMvGgwHfhYYhteLerIQz3qEZ94pCPyREcB36F2pKGLdj3wTyGrzLwaYkvOSTwvKNH5ksLY1rmNOPFFPeITTfyRJ55X+A61Iw1dj70GNK6CZYHXboG4ZvK6Yuysh8gzNx/4dMAT11Ruiy/yqA6Nutpz/T0FPs6r5v7w3hztM2dvnUsgRedtBMUbaaq/5Hmfm0vhywJfwgKVaGtW952+9/eW87RnAfakkhjVCYk7W5Vs9R2rxhDf2So3hqzXB8lWdMZkq/34FYlX/1EsfgyLP/gB9XoA6L6zVclWa5Nsha7XCBgk4cpO12t2uJ5Qtj7/E0xWImWBi7OAYqk8sYzPMWumIx9xirWWQHiQjQe4iB/V1aeNTZ44T2NRO447CmT8pdogz1M2yPhLnb/m+9TwIhOua4zEBaOSLx7wXGCRR7wZwqcDGs5gDoov8qgOjbraruSZPuK8tjb3aNKyQ7RG1csCj7cAfiyuq5FG+RzxITMqWZfkMq/k5TtHEB4dyMvXCkebP3jZr5zubgy5oyNtdGlOc6yag/iQGZWsS3KZV/KjOcMLHh4dwkV8zf1x5z2fk2qXBc7BApVoC2fJw3ZLfpJylUv2ZGtLwGona3vdgCU3Sb5OfN5qbcd1mZ6g9VcJ+F3FPrSzldyoJVR9Z2tPtvprAUim0ibZaonU3WsEDOdtEqvUW9LV3+H6ru96fcxT3dbnbyatUhbYugXm4sxoF8WcETdXPxSXIpPjUMWpGZ/bko36id9N3j3t3HiW4Ldug63Pf8k1spbnMbfmtX29KD8LlgvmUNEFJb45B5J1SS7rlnxc/PDENjw6RMtOpB74T092xHOSz1ukqa5zeWnXgOZXsCzwkhbQOlKfcQ2KlnG0R0X80MQTcZKRf6Wt+ghGHPo4wMW6eKR7DcxjQ69wghlHe1TED008ESeZOF7VRzDi0McBLtbFI91rYB4beoUTzDjaoyJ+aOKJOMnE8ao+ghGHPg5wsS4e6S5YFtiUBSrR5qeblKk/Nbg9DDWTbPU9pHFnq/1Alsvh0uxwPd2mKPZXE8DgO1sNmoL2+gBLmKKMJzFeD+A7WU2aZCu4Kdlqwuxg7cf1rdVtJys/nLX7wawuww7XE19B7uM2NUoob23+TL1KWWDrFlDcdcgOxGSZL+NiXKV6jL1iDKa6YORTnfFAV1t18MIBbcMEqEcVdB8reb7wZxzjUVFdY9VcNY/YzjjJoGtEAy8e4GNtQB/HSp4r/BnHWFRU1zjjfI/hREfXS8xfY35KeHEJV052LJwY4QQzjvaoiB+aeCJOMrqIaKs+ghGHPg5wsS4e6V4D89jQK5xgxtEeFfFDE0/ESSaOV/URjDj0cYCLdfFI96kwj5M+hBPMONqjIn5o4ok4ycSxqz6CEYc+DnCxLh7pLlgWOBcLxDWiOpA1ozZzGeHyHEc8UQf8ca2oHiH8HBEX66KJT+1TghVkKXGu4GIb+ggHPpYRj/SLT/OgrXqE8HNEXKyLJj61a+6ycMGywDYsUIm2dp796cE3R5kv70lU3+naN0xN73SdSbbCiy1bwrI9i+wnWw03SrY+eI2AjYcfzSLh6rtcTciTq9b2pKvaBtnpaknWG9vt2nis/VWbz9rPrc9/rb2KvyxwCRaw2O/mzZs3U5Iux6zMEVwuh3DEmmweU8wZdUY54s7cBjcq4MUvHmTpQ/qZh73D9eFgRwoDzvRt2gZbn3+4FJ6tenEJ17gIVQdqMcqSI5xogiMe6RSPnAlt1SOEnyPiYl008aldD72y8HqIDSnxnIOLbegjHPhYRjzSLz6dT9qqRwg/R8TFumjiU/uUa0BjKlgW+JIW4Bo+VpbwoCOvW+nVGtK6URs69Yifq8MLLfKrDe2UgvyxsoQHHTX3h7bUecaG+VzF83iojm0lKz1qQ6tSFtiSBSrRdnX1rf37cH93+9a2l36+t2SnGWX31G6JTnNFnnMg2doJN6pD44IxBk/Qtkbb2Wp5UBc06MlT2rxGgESqvzag7VT13ayOMzyJ1vCOVhKpSqhevWt0b/tOV3jbccVrBa4/3/7w5l98BAxpadn6/JfaqfjKAhdogb+zWPNPLUHqAVeOO3P72PyJvZRsBVIUtwkfdapODEadg0KbIjoQeZ6NI4/ovS+E/9YF131s3QZbn/+6q+UE7otLuMoGWqhqj+ASHuS0mLMOORD0cKgNH/WIn6vDK1nxqA3tlIL8sbKEBx3nNvc47yVzXMJz7naINql6WeAlLKBgSH3NrbNjfMjBIz7pka+lLZz6gia6IDzUxZ/r4gMqQJS+tVBjlVwen/DH+JCrue8H3jpPOo+yJRCa6ILwURd/rosP+NjzHsdS9bLAuVigEm3tTP3bp3/66ebquz+/ub75jrcAegbVvw3Zkq+eiiAR0MPrnhOwINn+2zdYbyaE6eOnt/hWK+6Lg3e2kjQFklT1pKvhfVcqSQTwxsoTGYlWdrGSTH37xhKthiNB+87g7ZvG44nWzssrBIz/Bpl3N9cfrz7+4y9++sVPRl1Vtj7/VcYq5rLABVnAdoT+mR1/aHGSe7cOphnm9kSYqSiuiuSIW1MnBtbmI0FwNt5JvfAgiOPs+KuJuLCydRtsff4LL5NHsV1swvXYw6ysdowPRwOP+OR45DBoCyed0EQXhIe6+HNdfMDHPvhprBpPHp/wx/iQO7e5a27AY/MT7zG+c7eD5lmwLPCSFtC6oU/WWPZDwmn9RX7xRprGLl9JG77RAS37WPGN8OKPusGdWuhLY9c8oy7hxBP5qVMiTbJxfJpPhvCO5gjfCC/+qFv9nQLpR2PXPKMe4cQT+alTIk2ycXzwjQ54R3OEd4QXf9St/gqWBbZggUq0tbP8zz/72Q8//+Hmj67ub97f3djDvP1nA+rV1d3VvSVPp7cCkgd1vH12JBtd7+56JrbLkEttxfio22FPAFaxg7Z99fXKsrhv7Lj3na5vrWv4oMP3xvqxvqm+NZr14R3Stiysj80Ssfedn18+eGv4dzfvP1x9/osPcK8pW5//GlsVb1ngkizw/fff/8bmw7HZsnUbbH3+L3HhX2zCFePxkKUHNz3kRaMKJ57IT50SaZKND2fwjQ54Rw948I7w4o+61d8pkH40ds0z6hFOPJGfOiXSJBvHB9/ogHc0R3hHePFH3ervsZA+NQ/NOeoUTjyRnzol0iQbxwrf6IB3NF94R3jxR93qr2BZ4FwsoPXCeOMa0lrTPEQTjHLiiTqoa20IIssxagsHjHwRP0eLfz2PYzlWj3OI86q5N1+K/WQXwWizaF/RwcVzRhsah/CxLdzcuQU/Rzv1vDOmKmWBc7RAJdraWfvlH/ySXaF/co7n8CnGvPX5P4UNS0dZoCxQFigLlAVGFrjIhGt8gNNDG7h66N3OQ++Wr4HRQi9cWeClLCCfm/vL+Dl/HOWiTEykwXOoHRNq6OAAF/GjOnrhtbHtnCXIhSWON4pkfM19Z95sG+wWcZwnyhIYzyk6OMBF/KiOfnhPPe/IVykLnKMFKtF2jmetxlwWKAuUBcoCZYGywLlY4CITrvFhLZ6IjK+H3st96M3nWtdBxl/iNaC5FiwLfCkL5HU1GkdeiyMe4UiSUQ5B9HGMEmrCZ5p0Rr280sX4/Quc3unKj5r77r4yMh3nYmmJ5wWZURt9Or9z9Zc470vnVHxlgbJAWaAsUBYoC5QFygJlgbLANixwkQlXTl099B5+qN3CQ++Wr4FtuK+a5Wu1wBL/MlqfGacEG/NUPSbPRgm2EU4y6IGuturSr3Z8CT+0NQUdx0qeJ/wZp/lCUz2Om340XtUFI5/q6Mn8WS/tmjuWqlIWKAuUBcoCZYGyQFmgLFAWKAuUBR5ngYtNuPJgeazkB1z4M04PpNBUB+rBVvAYTnT01EPvzn7ZprQf88CPfVW2fA3IBgXLAi9hAVtrN2/sR0C0drMfZQzgcjmEwxew21Q+IuqMcqzz3J5b++DFLx5k6UP6mYe9y/PhYPPge9v01Nzv2q/HyIbRVPHcCH8Idy7nXXMpWBYoC5QFygJlgbJAWaAsUBYoC5QFRha4mIRrPfRuM9kRL+otXwPRDlUvC3wBC/ydJdH+1BKk/peunHjL7WPji0k3kq4UJV6VhI06VSeJSp2DQpsiOhB5EsORR/TeF8J/64LLPmru2zzvy66O4ioLlAXKAmWBskBZoCxQFigLlAU2aYGLSbja2auH3nro3fI1sEkHVpN+HRawHaF/ZscfWoLTM5xKdGp0uS38HFRyNdIjbk2dZKp23gqCi79ILzz9kXS1469i34fqNfdtnvdD10TRygJlgbJAWaAsUBYoC5QFXrcFfv3rX//O27dvf8+eK6avRlt7GnSs8w04lYgHF9uxPscv/FPCd+/eXf/4449//f333/9mjd6ywRprnca7u6JOk381UvXQWw+9W74GXs1CrIFs0gL95r7qBn8phqq5X23yvF/K9VvzKAuUBcoCZYGyQFlgmxa4/+O/fPf3P/+t//j505v3Vx8/taSjfQvs0yezB2+Lsoq/NIpXR7W3R13dOc0+gLD8/+y9bYytS3bf1d3nnHvPuXfmTjImwCSOg5VECQpIFhlehMAYRbwK+ACf+ACyBFJAAllYvMhISE5CFOADingJkpEiJCIQRiiCRED4kIwwKDJmBAISxYkJTuKJHWfi8czce4/nntPd/H+r6v/stauf/b67T/feq855dlWtt6r6712rqtZ+nt39Z6VcD2KXjbJenndZ1y8uFLxMMhHKTHXbenaRQlWJP1Gxe3VxeX199foXvvXxT3z5x778ZtHG5pICrX+nbrL4IW7k6E+5hRI3ZnCRcpl6Drzm8iYefNqwXerHTNh+8eLFfyyb//UudguDXdDaT3b6vO6n/ni06tBbh95z/gw8nplYPSkECoFCoBAoBAqBQqAQKAQKgUKgEHjMCHzruz/63M0vX/+Ibur8vH5s6+JWgcfbGwUbdcOn/qqKuq6f4gqa/qbChf4Owo2kFJa9vZUshWv9DQT9E0vRRP4orX7aS+UWVJQu9Bux+XUwyvCIkFKGpiYIa1Kl8AxByQcP+q3sw6RPLf6p/iAvubAR1i5urz/7xq++uPiqJL+pa+ukJ/C+W9ffh4KDrs4Zg5/Qc9lBWeiULYu+ZVyGB810cmj7JNuw7liHTn/Urz9imW1z6Zw9Bttita/cyQRc9wWg9AqBQqAQKAQKgUKgECgECoFCoBAoBAqBQqAQOBcEvvbz337/o/dfft/FZ9dfvNatq7fXCqoqiNquFoS8UBD1Vje/EowlGBpBw5TfwucXxUSL4KmCo0RDWxkkVeo8RWkVQG23ql6KBg+5CKgqe0tAl3rIkXcJ2Y/Aq+rx42XUkYw6wd+LX3j/xYc7x7V0h+o1fXfglDLBzOhCNKT2e6DUwVbLOo+x01XJ96DnnRx75lFel8Zgqtu3jvluF7pot3rS91J9ClQtu01eGGyD0mEyO38wD2uutAuBQqAQKAQKgUKgECgECoFCoBAoBB4LAuf+aPFjeR+qH4XAQyLw6qXCdp8pVKcg6Nu3itUpXncTOUXVFYC90QVdN5umoCs0grAEGtEjMKoUwdZWdkAVHokAaaOpHPItYBp3qsKTyQiqIiw7ug822mjWks6cvdvLnX5KgCacCGASDHVAlRya6ciNfNMs53ykUyfBX5fg5wDqnOwmG3M629KwPY4RmunYGfmmWc75SKdOgr8uwX+XGKzr26G8CrgeimDpFwKFQCFQCBQChUAhUAgUAk8UgXP+oxnnPPb8cT33R4szFlUuBM4FgQ/evrp9ffNWAVBFMe8EW0XaKdjaUFsETgmYEi5tgbYcbIUagVbdmkq6qyNiBFYXumFHQVlbtL1oY0MwD6255OCqc4J+lEmraKt4OeDo4KJthcEVL5YlJ+A4F3S0DCbm+CtMb0X2OJ3Tlvu9iobhOR667qtz21rXGcuSvwsM1vXtGLwKuB4DxbJRCBQChUAhUAgUAoVAIVAIPEEEdCfT2f7hkHMee/6onvujxRmLKhcC54LAp995fXl1+fyW31JddWcrsViCsQQ2+Y1W7mylvPLO1h4UjUAo4VbqQVOoFFsTrYdixWt3tiq4Kj4h1st+pyx6EYydArS93u35jlrbx/quKQcExyAiAcB8WXZVTtvmOYjo/swFSkcZy67LrTPaM32d7iqe+wyfMra4cjnTLDeXZ9rYp7HPyI4y0DYl64z2TN+k/9D8Crg+NOLVXiFQCBQChUAhUAgUAoVAIfBIENCh5Wz/aMY5jz1//OrR4oxGlQuBM0HgE/1qwMtb/QKoIpgRUNULPyMQQdX2MwJjsBXR2WArQdAIhCpkqjzuTZ1oLVAK1QFY81uwVbqzwdaQam9G2Oz6ULp8MFXeJ7148SKCigTuCNb52ifQOAYt6Y/t5jz30/RMmytnuVyek92VVhjsitju8hVw3R2z0igECoFCoBAoBAqBQqAQKAROAoFz/qMZ5zz2/OE990eLMxZVLgTOBYEP3n91+8n1aw33SnFVRS35T7BVQdfl32xtPAKp/uNZEQrNv9lKcJVEHtciODoGWSMcK5k7wVbbkAl4DspGAJefHzA/eLJPe+oauVmQdk0EWh0wdT5HM2/MkXUyz3UCpE7I5YApZdezHPLZJnXLZbrtwT80Yct9dz5HM2/Mc7/Mc5/y2Nxny8Pz2LIcupbJdqBlOuVRz/KPJa+A62N5J6ofhUAhUAgUAoVAIVAIFAKFwDtAgEMLhyQflMh9sMm0XKabWc7yI93Dgb8uwd90cNpkY539VTxsMo48NmimozfyTbOc85FOnQR/XYL/LsbuPtWjxUai8kLgfBD4un5S4OVz3d/ag62314peRrCVgGsLsra7XYWJ4oamhTdzsJXgp2OKPShKKNQ0/9YqNIKjku5/NKvjLDvYC3n/pmsEUUXFrmlTG8g2Hf+kAPpmd6tbZ/b7KFC2Lzd92xx9+/GcQ7dvH3PrODc/67g/0EhzMo0T/d8LBo8RO+eKgTG8j7wCrveBatksBAqBQqAQKATOAIH6gzNn8CbXEE8eAR+wnHPA8wFsFQ1Q5nj5cEjZclFY82JZcg6U+VBpNctQn+NbbpfcY3BOG5RJq2ireOi6j85tKwyueLEs+UOOfak7Z/5o8RIWVSkEzgSBDzTO6+vrS4KiNxFslW/VHa4OrObfbL3hltcIhEpJOWkKlFIhCBrhPuXmq95Do123BVHbyiAdB1ux1EOF6IaObWC7lymOwdYclIW/S9ITDuHn8bv4X1/229vmtGk/7vZznfLcmpVlbGOUc9+y3VHGPN2lPEFr2qa8MNiE0OH8CrgejmFZKAQKgUKgEDhvBHR7QOwqnywK2mb2re5uQ6g/OLMbXiVdCDxWBHywpH+UOQhy5XKmWW4uz7TxQDl3UBxl0N+UrDPaM32Tfuaf89iNQz1abCQqLwTOB4FPLz65eHHzsv2RKv3hLAKti2CrcIjgq+h3gq0REu2B1R5EjV2k6A6OOgfOKEtO+RQRdLCVO1hng619Z+rtNTLjna1Bo4H9Ul7fvA7knPXEa0qm05rr5o89YG3KPMrjejXKjHxsZhvU52Sg75sYB21weUw5Nw/7mZ7rYx/dl3F8yI39H2VGPrZG+3MybvMx5hVwfYzvyo59qjuMGmCFw44fnBIvBI6LwFkGHW//xP/38utvn/+Tf/V/+rlXN39Uz2Fpw6q7BWJTec3dAmwyVb8RiRdYQYRFuctCDV0U9P9ZEHSXVchHJcrtvivVsdulomS5oEPRxihok0boN7tiJrlnN88uv3bzpz/59Ds/+4d/83/4j34nlLd80abnbP/YzpYQlVgh8OgROOc/mnHOY88fzHq0OKNR5ULgPBB49fbV7Vs2hNw0sBRspc6TBrDE1/8IlPYbKCm3wKpK5nFfqsqRgqY6FevIVtR57TqX6YbMuTtbp1sBwq4CgtletwEp2k3bXWjbJoJ5c4FE0+A74Act12nDvDFwCH2OlvuVA4cuz+llO24PO5lOXf0TyrsnbHq8OXcZvtuFluu0Zt7YH+hztNxD+E4uz+llO24PvUynrv4tDEJ4BKkCro/gTTi0C3WHUUOwcDj0k1T6hcB+CJxz0PEXXr/9vPaL/8bzy2e/6po/NKCN6fQ4lqoOvkKH3Ta0KmijeNuDrTfabMQmgw1lbCqlFzkR08uLZ53GN/sU2YDG5pJNMDaVQkQvsb2F1mVp9womOiHHRrdvWkMOUR4fu/2r77397j8mkZ0CrvUHZ4RYpULgRBDgEOMDlvM5mnljng9B5hkaH6SoI5cPSZRdz3KWtQ1yy+W2bC/L7VrGhvvsfI5m3pjn/pjnPuQxua+Wh+cxZTl0LZPtQMt0yqOe5XfJz/3R4l2wKtlC4FQQeP3Z68vnz1/eskH1b7Wyh40NqzLf7RqBUgdONfgWWE2B03SXKhtS9qLWiZw9KHtOUvCpB6ftT+HDC5mm29qw/MIeYhMPLfa4TZvCTglfzaUYwpL/tw93br/r3I3Y/5KT7Mtz2f55lDHdeRhIL9mW7ZHbjkUtt8qO5VblhcEqZI5HP5mA6+2P/6n3fvGLn/v+m7fP3r9487ZNaR1m374VWJxZVYibfTjgRkFZ8PRCjgi8KLQsXjvJlOdd1vW4wyjJxN1DqW7bzy4S1Ik/UbGrLwyur69e/8K3Pv6JL//Yl98s2lhf0gSrO4wEUeGw/nNS3ELgvhA456Djr9w+f/b8zfWve3t58V03WlRu8OWKlsZvYXkD+1Z1NoRx94B8VeQR5IxNbcRN+YY3NqHkV7HnZPt2KRuxZHiDqoUC+Ui6RdZf6N9Kh3TFRpmkDJNsUW+8EaYuInZbQFcbTcpa+/TbXC+eXb2wOSxsndj8ecOGEmVoppvmjWvOLefcsrkOjfq6BH/TZnOTjXX2i1cInDoCnpeMc9UcNm9dDs/zMefQPUfH3DrOzc862MpzeE4GeZL63x1hq296PeexG5tzf7TYOFReCJwbAvryXxsojVr7x3bDgCr8Z+Pa957TXaoSm4Kdkmk7M72iTwqag6OdD818BW3RiWBrp8VNBKHb7ATf+1wHZeH3gK/bQq61535A2D3h/32h7XUmrwumjblbW7UeZTqyrjvHnsurbLnNzJ/Tg6bAccBi2W1zj99jdpuuY8e0MXcbeRyrysia5xx7Lq+y5TYzf04P2r4Y2PZ95FO87z6MP6TNb333R5+7+eXrH3n+/OLz15rK7UCrSc2BN46rOhDGIVffYmh23uqgygS/1UTm7h5On+ECmPzap/EtTzgAvXFM49i6afLfchxFRpeozYHEiZV6Y1F4hiDOoh9fuR0feziLMAkLewhEm2FNdzx99o1ffXHxVbG/qWurVHcYNZgKh60+LiVUCBwdgbMOOn7y6cXFixfX1wpkXr9VaFS/KtCCrXL3RDWpEwTV+sPeNV6UB42/BCtWbGqVxxoSeyV90w6dC6rk4fEaspRYS5SHTZXi3lfJ0xRybIihxuY5Fhl0Gq8FbMVVocmLd3t55+tEpDclNmNscJy7jN4q2ioeulwk53mzF4yZF8uSs2kbN27ZHuU5/ozZIhUCZ4PAOf/RjHMee/6A16PFGY0qFwJnhAB7Q/abymN/xF6SzaX+x44sx/CQJbHX1MU+M+IdQWQvOehQR5bU962h02ltrwpTdkI32UMmaOK6D9Q7zfbQjv5EYbcX/D/Je032kVyuO88y0DbtI70fDeP9ZU5npM3pZRsuj3qmq2+gtlMqDHaCay/hkwm4fu3nv/3+R++//L6Lz66/eK27jG45yOqw2y47DtF086u/sbEzcR4HU0643cHcxuRudwP5gGoej2te3rZbVX2ItVMif0tAl7ck5MhxJaLIfjge1Wkq6t3JtODvxS+8/+LDnd8XOwc7BnJoptMVaJlvmuWcj3TqJPjrEvxVDsB6m2xYbt8c++M4oZmO3ZFvmuWcj3TqJPjrEvx3jcO6/hWvEDg6AmccdOQPjbx+81Z+XQuH1hevJ/5rrw62xnLRN7D8hMB0JwGLg/6HV+kbSq8RQY31iLVD7xqXUltzVIh6rCzNRsio3uWmTbDqbYPa7bAadVoYpI0Nfi3kVrx4XYFN2T40lzPNcnN5po19mvOrowz6m5J1Rnumb9IvfiFwagjkuer5nHPmhudHpoOD6+aP2DDPMo/yOPdGmZGPzWyD+pwM9F0T/cc2l8eSc/Owm+m5PvbNfRjHhdzY71Fm5GNrtD8n4zb3zc/90eJ9cSu9QuCpIxA3qWlP6C//HSdZ2pcyyGE/Kq8ZNFjely7RlvaZwWn7U9GtE21wF2vQmr2gua2wHZSmg01K3i+nPoTAHi/4dfbuq/w7Js0bfbGbm/Pt5jmfkzHN+X34dre/Li8M1qFzOG/nwN7hTd6PhVcv9RH97O3btzrVvtXjm9z2cxM5RdXjriPNUpXjGxxoIQaNICybt0Zbnsh2IqKKTcJlTI5FtHAhOAouasoXB15t4ERrtxGF5dANnTl7t5db/5QArZGYJExU5y5n3khbxUOOi+TcTiaIK14sS46zmHMYlsHEHH+F6a3JHr9z2nPfV9EwPsdD1/11blvrOmRZ8neFw7r+Fa8QuA8Ezjno+Ol3Xmu2P1ekVW5+WleorLmzNQKuLBmsP6waStPm0esH6wY8/ZvWF9VFC2rQ4KILhaRX6JS6PeqxJsEL3V7vcm7Xek17+9f6gzPbY1WShcBjRoB9i/c5OXcZvvc40HKdcZnH3sdl0+doGYu8J3QZG6Neruc2Mh276p89XG5mZRlbHmfOXYbv9qDlOkbNG/sBfY6WOwLfyeU5vWzH7aGX6dTVv4VBCDukc3+0eAeoSrQQOA0EPtHW8D35DUYjzxHBV+8p87607x+9p5TXm2IhS3tQ68hWjoWwR136GYFoo+9Zke17VORaaqVpLysi+9+gTm10ffHiB7K+vZ/rw6ePfp06CV9rf2ua6dlfh3B/sTz5KGOaZayX5Vx2O86hj3rWJ4evc8gCwszcUC4MNgB0IPtkAq4f6K/svdaP513GgXcMtsqZKOC6fbC1oRqH1Ji7+uzaMYiVg60x9ZHh2xl4KjcHYx0Rw0l1vu10u1BtL9rQ5NwnZSdAmQnJlcuZRhvWGfPMGyd2dgLu5yhj+rrcOqM909fpruN5LMjksecybXBZdlVuG+Rjv8Z+z8lA25Rsd7Rn+ib94hcCjwGBsw46sll9/1YeRU5d/r19wac1iAcgtB6xJMVL8JDhCyk2RhDkW3gDp83j3fUj1pNYL9paEZtS6jw9EbrdRipPG1TJscG1HCLU2hqlitrvv3oTZfj7pjmfOkdb5W+zz7OM+5L9I3LULU/Z9SyHrmWyHWiZTnnUs3zlhcC5IMCc48J/ef6ZBgamef44Nz6eR+Qkz8lc9jwbZUx3HgbSS7Zle+S2Y1HLrbJjuTH3OM9x7CMWrAkt6NLew1jW4lzF2qGUz/LIkmI9amtL6DdqOw9Rtk6sR50pGvbiFX3KXg/hIEt+pw3koidNJuSk3O2FoW4vyvVSCBQCmxHQhlW7qinYGgp9nsU8jXmY5qMEcuyihyan+bqIhUjQc5N52edmm+uyF7Q+nz3/O62123m9vahN/ZKH6PbCTyxEJb198tqGhtcU05yb57UFOZfh5bLrczKZNuqgRxrp1Of0Mg096lz7pHGc2DDNObTcZi7D29Rvy2S9UQcZ0kh/CAxay/f3ejIBVw78V5e6y0iH3FV3ts4dfJmkswdfYd4msj684QT6xE7OQhJNxgda8ZZ1VO+OITYP8NlAYKPLLpxFnyTwdkx1h1EDrHDY8YNT4oXAsRA446Ajd/d+cvM6npKIYKt+CdVrDTHVKdgaj1Gw3qxYc7zZZI3o5fFuAFYQb1BjxbAOX/ixpqi5u2sOfeCNbutVXnPCBqxuZ4/lB+1pY+YyGyoub9S2zdH3Zizn0L0BG3PrODc/67g/0EhzMo0TY9kXBpuovBB4kggwT30xAOYNyfOXsmljDo+0am5lepYzHXsuh6EZW24z8+f0oMkXT+7N8utyj9tjdVuuo2vamNtu7v+qMrLmOceey6tsuc3Mn9ODtuvYbZP83B8tzlhUuRA4FwTYl7KHJA/HOQU1Ve/7x7aPhK9/QZNO7B27q+1y7DHHfea0L5XKGGxt7WG3px5TsU7YutOvu31o8hLcMfk3vO3rxxxzc/53UzOjT0d+jjbamfPrc3pzNGyp/zuDUBiM78Lx69PNLcc3/cAWdeC/fnurG1w14/lPzs8I8BdBVF46+Aat3WWkv8zcHYb6awfDR1XkmP4qhxOAxqXUHIkme6eZv3AwTcfOIg7CHIjtTsJm18eg2pq40S7E3ROTFEfhHAsuO4c2OhPXkXEyzfU8sS1H7rL5zq2XZaCZbz1ouUz90IS9wuFQFEu/ENgeAYKO3NJ5q59xCd9L0LHf4bnkeyPouOYOT3xh+Fn5IvvFHkykN9PGL3yv/A/ELhdPGciHhy/1WRtfG/62yammui772ZDvfs92sLlD+rq+7NPvf8f3dhcRbJUhrTksQfyGeHwJGOuRjPLb4nNrTm67l+eCrW3N8RgZjVLHZ37cospejJvXwFaFqQ3KoRm4qLZz8kYt+12XMea1ZFOO7LgW5Dplrx/IOmUZaGMd2qg3J4Mc6ZBgRbNQr4XA00Ngmz+asWoOrxvt3Fwb5yP6I21Ob66dUc8y6qu9nUkr83Me+xIofHHKegFyutY9WtzWnramsCIt1lTRpB6r1LS2NLloK9Zc6pLiQjbWo15Xu7bXuCHR5RKlr2GLc5t49F2pPVrcyvVaCBQCmxHgb9i0WIVkmf9KzLZpz93nm+dmCEhOEYeQW+wjpdP1oTUb47xVHRn24r2NkIAmHQJT9ifhG0Rv/HhVWf/6XGfOT32Q3qFpXOOwZ1q2ve36lHUob6M3t6bN6c3RaOPQPazH6xybuUydtKr9xl39uo3eu8Zgde/355xMwDUO/Nfc3hoftp5rlnLAFc0/AB3BgO5YTIspHBuDNqmnA3o4BIGLU6CsFPk0+YMSjsP8pYNsaDQd2ggZ9SU2Gsojha3B6XTWLhmTwROCnA/0SMNelhnrngTk/rCTUzcPncxz2XznyJHgZxnzM938phF97Gibsn0+jpn2RhrWTh2H7REryULgcATOPeioP9KooGsPNitf+gNZPdjKhlacWJuW1hzWhHgL9OoNahxG2/sSG042mHjFtEENbpdr60uz4k1zrCqxzjTdxQYVTnDDXthBbk+viy/Fz9rXOseueev8LXLWp5zTuDYgN6ZRZqwjP+rNyYx2q14InBsCeZ6O89hYWGacU+avoptPPidjmvOHnqMeFzl9yHX33TT30XTnq+jmk8/JmOb8ocee+xdfnLKGxdU5+XzU15QIsPQ1Y7G2yOeHCmtZ02XdWpyPvO6Il3SDSp31jNVQuuHpoXlN7H0QpQdoGy/qrGfdXrTrMsxKhUAhsBYBfc/S5mvc5qpy32d6DseeMixonvV53faZMUvXzvVpXko/6042k73ldtOcDp+gtjSv8RWTnfATqQ+2FX3d7QXfPvp+LIw+f85Hm+YWqecr0ymP8uavyufkR9pYX2VrHb0wWIfO4byT+UkBDvwvn1/q5iHNOP1vdxYRbOVqtHa3q0DTpDUtpqo3E0xmL9TTQXYx6T3JmfDNEbWJ36e7aEhLinMAAEAASURBVMGZDsa8PegEFbuxmchtUG46sXlBHjFduyTfYcQmjUnny45i25w2x0mb65TnNoJZxjZGOffN41plC/6+384UDka38kLg4REg6CgHwfxlEregY7+bs9Hwu/LHeLjwi+qjfa/r3bfiB8c7PLMfDb50Ik2+uutAVBP48tAJOZU6LXTsq0O2e3DbC4HdXhjf5Y2OmsoZfrwon8btL56iT/Bbm62PtKW65GNcfTxBDVrj9dEsNptdLg643d7mcbsNWUdfyetaVPZ8wZ/PrTOmwfc6AS3Xow/ik1atE9bNsqHQdcYy8qOtXM/2Mh076p/epUqFwPkhwNwc5yd1Up6zppnOHHI5Cv3F88zzMcuYZhnrZVsub6NnfXL05HubU8mMNeVzHnuGJd5KvZ3kAaDXKgm1tUJU1jjq/Ovltn51yIO2Ys1Nb8t4Pmrt9XZpQGsUND8tGOezO/2624d9Hy2myUqFwLkh8KEGHFMWN+75KULMx9hntnk9zXXm4DQPQWuc6023zddl3ZjRtIP1qQ1Vp7neeWRuI+SjlTv+Jqx3f4PKPmlcz7BhGmvJuEbB9/pFeUyjzpy+dbKdXDZ/zDfJ5DVz1F1X93iRcX9NG8djO+v6MurYpnVznu3kcpbJ5U0y+2KQ27iPcttJ3YflB7b5gdqLv66pRzhvdHGH0d1gq2atJiaHYOIAi4nayuEc6DcTPyb6YiGf6pOTsEwfqJwU9uIVB0U52lAZZ9Adx+Sw4HcHMzk4nIqdnYrbJiYFH0Aulz1R5urYzXzq1qec0/jBRW5Mo8xYR37Um5MZ7e5a91jPHYddcSv5QuAYCIRf7T8jQNAxvtTioQMq1LVxiXmvcniR7uuab6UHzVfCmw22Sm95U9bq+FYWsjhk4UPxtVzdXpQ6TdzoS2uDcvSk++rg7vzy6q2iyGncGnCsL/65Gn5CgLXI/XKbhPXoJ/3zxnPluNto0mZTOuij7jWj21s/7uAGjtFyfy+wk9cmeNsm/O64now0ry/ZR9s+vLwejGXzRzr6ppG7bLuZb9omuTkb1q28EDhlBDyHGSNzjmSa5y20cT7mOTPOL/Oco+9yzim7jgxpVd30rOOyee5/s7T51eNE0rqmnfrYR3SW1qpYn4QJuLD+5rWKNSloIktOp5CQa+uRyn09Qgtas9E+V5DQDSptpGBLSECTTlvXke7y2KTiMxYW3AflUx+QqVQIFALbIfDpJ9Oeuc3TPs/YW2rO5XmIwcX+OqSHuY6u/jFHPU9jrjd6xEP6vEUb+3n+95a7fxCv9yFsprkedfSDRnutn5B2Tfh4X+h6Dchlry1jnmVzu5bLNMrQM89l23Ge9VbJmG675HP62daqssdPTsp2XHZ7Y27+aNtyc/TMc9l2nGe9VTKmI+vynH629a7KJ3OH66cXn1y8uHmpSaopHMFWDrmaifxnQsadrrwhjRYTHXq4Br32chxicQDdIVBqEzpKvSxtyYeN4DP1VQ8H0+SmzYTbCKfSdZKDaZsTdKUX7Tb9XV/5gHmi5Nxl+P4QQst12jKPD6zLps/Rcv/8IYfmMjZGvVzPbWQ6NtS/vZHArsecc5fhu21ouU7b5o19gj5HQ8cJvpPLc3rZjttDL9Opq38LgxAqFQKPFAGCjm/5tMq/Nn/bgo72ty3oCN++rnnP+ITHp3zhU9f6UZlYHLCajXjF75O6/fDIyfeGf4Uffrv563EzCDtst/0G1a3S689eXz5/9p6mL2NQA7H+qCP8j82VzNAvVgnlpGkM0KJP0PoYOn8h7/6Gqmz0OhZjjKJjP9po5Wit00LL46b9YROMrHEJ2T1e8KW+ULdfg+Zk2pibD1ZOq8rwzXOOPZdX6bvNzJ/Tg7brnXG2WXkh8JQR8BNCnrNjztjm5tGmMY9zE/k52mhnbn7O6c3RsKX+LxzKaHyon/PYMxQ8WvyKNcULpteavM6Ewrj29LWry8VaZPS1rsG1SdTzutXWRhFTG9MaFzTp21Zfq8JGXv8k11rpdiZ5JCsVAoXAWgQ+0D2u+vnF2J96Lqka8zbNucU+kz1jm/Ozcx0dz8Gwp9bZd4ZNdJvtkOk+pvmELtPbXrSRfEDvnySX+jC1t3agm5msO+M6t0ory61ar7LMNnaQOcTWqjZ2odP+Nv12X237kH6P7R1iy/15bPniNPTYerZjf1691R9twWFEkDUHWxuNs5wP/4uJ2pboaaIzkSWHK5g2BNPkxkmgKZ5kwgav3kzAC13xuo43EKFlHfFwPAG8HVZvN+Tg75jYGI+b45HGh5cLustuZvxg5w0sZfNHOvqmkbtsu5lv2ia5ORvW3ZSPY0Z+pHnsp4zDJpyKXwgcGwGCjrozJr7jWgo6yp+1Oa8WwwfiJ/F0zU82n6k6PjBo8IO98KNRb77aPB/IoE6BSuxHG9K3TqeFxWhD8vCy7w0mfQhrUdv1JX5OIdYemeFu1n6FP+tjm9YD+uQGPO4FZVqDQiRtRqPfYEf/laLein3c8NoVOHS5hlUfd8I++kDd/eu2ds0IVpDwqaTsY6mPPtg08nUJO2OaWx9G2pzeaIf6qGcZ9dfImVR5IXBWCHguO2fwuWwwtp1rlne+jd7c/JzTm6PRzr5fnHiczrGVy9RJq9pt3NWv2+i9q7HzaHEkPGBfI6f1oa8trF7T+UhysdagpHUkeJ1GzbrT2oVEyIXktE5bLtY2tYPnb3J9DcAm9uiXUqzU3U7TbTruQ5Oq10KgENgNgT6PUIq5plnneSYSN0OEhOfw0lxvujFPzVceu8LuO6w7zeVprkMJavInvU6Lfd7fnevSUhvhgxBnD/p55Xsk/LKvOfXRb1PPfpryKIOdbWU2yW3bXjxRODeALWi04WtOfBzftn3aNDbaQmaT3LbtHYLB3LiPRTuZO1zjLqPnL/XDTfx2k2anJmE8GkOZ//0wHFPYh22hOC3qTFrq4Rg6vEELFyF7PZet5cnfdVDpvGhDZSyGTdmJNNiD5s0HsodsFtgQ+gq7/bCaN4qeLGMefdPL+GGfo0OznPNxEmQZl90mdRK6c3rQ9t0oY9cYeNxu13VkTBtzeCSPa1058yyPPZfhk3Kdstts3PvDwfYrLwQeCgGCjpoB4cemoKMX0e5fvXmLDZI71nndwwa1+djwpJokgx9NPnryxdJqNqWD70Wn++MwaBudBy02ahS6vd4alN0TPxtweyXfJVWNOWAgZ2y5TbGnPvc1ZW7NiQ6Yj81IbWz0c2WQOY8Tnb7WhTrjVJrGzZqjvoFu66OYLWYacru84F/Z5NjPjjm2TBt9oNuZ85/mOZ+TMc159rnWq7wQKAQ2I8Ac5WIucTnluet9DHmea55/1sn60Cxr+ihvvVW59TJ/pI31LLupfM5jn7Dh0eKXH/T3vq9fMPs6EsvDtB61tYz1w/ylNZe1L9aWts5gZmntCTuSCDkx+zrJpw4a2vFqfu9D2Ay+uMGLHkQ5WpSdPZexaK9eCoGzQ+BTzZz3dGnu6JS6mFcb5tnmvayQzPtrz+Wlud7mOZhPvmDbuW574TT0ckBEi7VjXD9WrVlZzjL+zGSeaZYxz7npq+SgW9Zl65ju3HTk9g04Ysv2sEOyXdPJobmeZUJBL5ln2pydOd1RDplsz+1nuvnWhbcvBujeVzrg43lfXdrfbvyGKxNZQVaCrf4tovXBVslLp015vaJPCtpiIQ8+NPPlEKDlw3JsNkK32Qm+HVY/7ML25sRtTbbFinIIbf+yzR1GtuaNM3n+cJqf8/zBNn1OZ6TN6Vk/56OeeeqbUTZpq7xw2AqmEioE7geBcw066jnM65c65PFzNfr9cJJ/v7X59ubVY30Iz6a6N4p4/O7t2maz18fAadq0xppDI9Jra451ICp5zeltxJrV159pQ5vk2uE2NPd+mQtWeK1hPfCGyDQayuuE+e6A63MyplnGOl5PzDfdcqa7bn7OsXHIF37ZVpULgaeEwDg36btpzIu5eeM5NTfOUWdO33rZTi6bP+abZOwLRr1VdY8Tvvtp2jgO21jXh1HHNq2b82wnl7NMLm+S2XXs2fZFPVq8BEdVCoFzQeCKmIa2km3/qLL3kewz4x88BPq+s+9bpxsiqHda7DkRnPat0vOeN+SwKDvd3mIv23XgsQd2G9YV/U7/wpC+YuFblj2/aXHswH7bPjT7bdPoguUojynL2VfbjvWyjPUtSz3zXTY/27Kuc9/0IBkjZ9bGvDDYCNHBAnt+PA9u934M4CB0ce6ND6k+cnwAqYeb8OSmdWRJMfnh659pIvNxXeioRB1ZEo5JWbx22uIg22WRsD1k0EfHfaDeaWEPGZLzVtv61ZtDciZkrtuIaZ6wpjtfRTeffE7GNOd2EFnvocoe47nj8FB4VzuFQCBA0FH+Lr7cioLcH45X//F7sfFSFj4v/CL+sfnRkOh+Dz8qD9b8oP2sfSKbt25vPujY9Wgv2mi5qAqGqpXuexe+eiEXvt/tiLxzij+a1bTim9Xez9kNqsegcUa7Umt9oqdKbDTJGYP7RP9VjTFAox40UUMOHfn9qV3k9S9km90oSzUsGduOCVTd20a2c7LPRdFrgGn2w+axNnh9cA4v013PeS5bzzquI0NaVTfdepbNdfc/DNVLIXBGCDBXfTHsPBdcznMIGdfNh5aT+ZlGOc8518ltxzk0J9syz7npyLlsnnU35R43OSnru2zbY27+2Ibl5uiZ57LtOM96q2RMR9blOf1sa7syq0dPrCHUWGd6imBIosVaFHIS8NqCbNdhfQtkIwATK1Pwoo2+RlFua3Rrua1vzR6mpvWMSqxzvY+pjdBBnfdxz0eLMV+pEDgnBF690E8ykvQa86zPKUhtrqvg/TMyTVryknaduU3q9dijh5zo0DrP5cmG6GEDPlKh3+wGybr0LPrlXNIY5YVftdKUv2zuO9T2fcGP4kM3+dEsZ3n74Nx2tkN5TsbytuN6zt2eaatkkdMVcFt219xt5b7P2chy7g+0MWU7lOdkrGM7rufc7Zm2ShY5XQdh4DaOmZ/WHa4EV/Vex6HfeSzMgszOAvSCpveCz4UuTa2gwZoOwJmGDLIkbya6LqR2WFaB9zfkmj29LtoKuaBAXTiYqV9NJ5h7vHjDmD+AefMInWQaZWj+4JsPneT6nIxplmkaGnqfaOabbjnTXTc/59g45A6jwiGjWeVC4AERIOjY0y5Bx3woC1+MDflS+0/bbL4af9v9aPfLC//ddOzD7efDd/e1d/Lj3b+3Nro9NdSCjnb2U8ubCzfXai5+DqX7fKnYt/d+qucTb9FnxgOn9yE2lOiGdFtPOhZTkFnstkFt9kJ7asO63aLHbXu9D2F9ojWdWLteq7xj4ptx+11UvcY4hzb6fK8V8FalOZk52qjvdSbT5/TmaOio33t8AHJrVS4EnjYCzKFxzq4aUZZbNfeyzDZ2kDnE1qo2tqHT7jb9dR9t85D+ju0dYsv92SuvR4v3gq2UCoGnjMDrN68vr55r38POx3tQFafYRqexS5320D0WEjreZ6IfuydJ9nyyEby2L2179LbnXfCbDhKLNtQebQ77VpHko/vLlbgRcBVBX7R8lPqP3Ckn72HjvKWBUmc/7vopj91je0oYHOH7AA/7Hee6w6r9fp76oYlN0HW8szXmpw+ZkvGhtU1+6XVem9x9mne5GF13MO2bG/iyEToq43CQ5Z/baBJdrskvdFSaDuTiSYcUB/5vt/K2r3MHW9PIvZnzJs4fUOe0Q3msm+5+mJ/zUW/UyfVRz7xsw311m7vkHjM6tmPaOeGwC2YlWwgcDQGCjvK764Ot+LrwsGkTlfwonem+0P44Nm7W6Rs7xOy/m7xs4n+XdFs74ZuRxy93H20d9wV7QSNovKP/DVXZDX3aCELz99EePKidF73q/ZzWD+rBF7fLoeXA83Rnq0hLOqDImKINMW0j+rPoA6bcB/KpDXSt03reRPd8zf7WJkxzndz+OdO2KW+j53Um25vTm6Oho89vAy4bqHIhcAYIMCd8zQ13nDPU83yjPMpgZ1uZTXLbtrfPgRPbvs5t7Hm88WixCNO6oPUhEmtF/FMtXKRq0FhrSFqbcZxR77Tgd57PR14HY123jl0u9rz+9bU+7rBzH8zDZtDUpnnRuI60CsC022kRqlQIFAJbIaA7BtoMbtLsOaPueSbyNNc8b5HwPEWO+YsXsI5ypmXQrdN5kJfmeuimNkJ3uQ/2HeqoBJnnurhtkDvaHXjF8AFpbv3C3Bx9XK/mmkXGcuTjGuN6lhntjDLwLe+1bqyPNnapz40V/Tm624Wfy9SdoJtH7vGY73qWMc/5KAPd8veBgds9Zn5Sd7he6MCvt3IKtgZQfSGP+emJHnmDcXIgorXosyQ7f2kzEc5HOmwkuJSas5B80GhBSbpR6jTK0x1ZUe78qV84LBSVaLebifqWL3WHUQOqcNjyA1NihcA9IMDfJ4x7A+0D7eNEbz5Ozq3zFpsy/CNur/HolsuTK5w2ahNl0Gk+NPtRbETdm0H72Kkd8d3P1D/0Jge/JUb6ru/iFYP3dnXXcUffWsvGJ5rua860fvR+hqT6HkhNfVeV8jhuj7GPOxCcaAudWHswuWciqOovtfKmzMFWaN5okXujRHPmuemsD82ypo/y1luVWy/zR9pYz7JVLgTOBQHmwTgXVs2/LGcZ45R5plnGPOemr5KDblmXrWO6c9OR8yGM8jYJG7ZjedsznRya68hZxjqZZ5plzHNu+io56JZ12TqmOzcduV3Hjg6JR4u/c6vHHPpakteFCHwidGd9E4012mtRX3PDhmjxhSc5a1DYhdYu1qNp3e7lWKMs2+2KtdBljfP6FzkYoawX7nRTdoxHi2myUiFwLgjEFOrzadqHep4xb2MOCw3mt+ex559y9M2jGP6AgpJ1W971w4bKtjHNaymEPc1zlLu/cZ+mud6/WLkk0ErwhmgW8/+NriOk7E/xsbmeza+iZxnKWW6uvKmNdX7e/h67yLk+9mHXeu7npv5tYzvbmytvauNdYLDNuLaVOamAqz5r4QjI80Sl7AV63eQO0GLyp80DxPHg2+2FO4g27Rh6u+iwUQi56ElzOHf6JZnubCYHE84Fo/slH3CdY4Vy/nBDW/fBhr8qbaM3toWtOb05GrJyFg00Knsmj985Zs4Rhz3hK7VCYCcEzjno+KGQIt4qJzttDmOzGW5crsybR/t6ibZgr3ih57UiQe41Zzi8huS6NQoTXae10evRF5W7XcToV/xervtlmWBu/5J9LD6dZBprgWnZ4irfj8yoM6dvW9lOLps/5ptk5tau0UbVC4FTRIAvrEmef54Lef6ZluVCaXjJcp5ztjPaz6qWtX3zbM/8bMsyzjls4n8ks7VHO+exGzfyerQ4o1HlQuB8EIhYBPs3bWbX7TNBpD3l27BZ3ssGtwVbY2+renhhYh1tbxgSokWt7z3jyxx7685b4lsOIn3sd7a2YKvq6S7Xi49oYf/k9Slb8PqTaV6LMm0sz+mNMq7vIouOg6o597p4fZ1+380N7JAXBjuAtYPoaQVcu6OIbVafvDE/Y7KqpJw6r+FcKEouDp2Uu0NYHJYbrdlomogtO5hwTUv2OPDG3bJ2ElO7UvZhOPdB/KkPNLBHYoPZN5lLB1wffO0cnOfJbZqb9aR13bKmj/KWW5VbL/NH2ljPsruUC4dd0CrZQuA4CJx70DHWCPl03bvZ1wdwFTV8f1pvoHo9YI3SetDWF+R7crD1znrU7MX6xaa0r3EOomKH9kixhlFgvUF26gtE1XsbTT40ox+Nu9urfW5ea2zBft1ryJibb3nnyM3xrJ/lKFuWfE4GepZBJ8u5PcvAr1QInCsCng+bxj8nl+eV9fO8ojwnM8rOyYzt2e4oS11Xc2w2vGU+trFKbU4O2pjcR+iPfezRdx4t5j1i4aDPw1oBqIvzU1+/WD360M3zmiPppTVv6RHkvuos1sRmZ6kNr6F0JtY3cfsaqm4C6hSA0SGIw1T7Tce+FqJWqRAoBLZAYNorMrE0j6j3eb20z0xzPSSp971s7DetM7O/neb6tL+Vf5jkaa/5FM/1yZ/kua7vBu8EW4lmcT3QvJ/z9Wp9Y5rTG9eFsY7RrJeDrPByHd2HSrlPu7Q5pzeOeaxjP+vlMcPL9YfEgLa3TREX3Fb4Mctxh1VMNG94liateJrQ3YWkzUKb3DGuPklnHYydgQQ9+WNzYZue4MgttZscCa3jYEKnbUDcbncvrf+2FcztXnzQRdofNB+C8wc0W7Ncprk86iC7Sj7Tc9m2xnyTzNj2qL+uXjisQ6d4hcD9IoB/tS+LTVX4Tfxeo0++E7mgqZB8ZuZPmzv7TMnxcwVhP3ykStTRJ/VDIX3whiv41OWTF37XPaQPfDHW+oflbh2NndLNG/2FV/rBmKKPqDe7YdV9hNqxaH1qY3B/m07rnw+lDZ9ur9uJMdtmb492Yn0BIXhxidr57T1AqPUhCsax00IvGPu/4N83+Xhbz3K5bP62a8GoO9axt60tt115IVAIPA0EPLc5cPmi5z6APY1R7NfL+xh7rFmxTnm9Akz619aWaZ2e1lXzF/KxfoWOaF6DsMC6NOV9fYq2VPZaTzuUSZ0XtaAhR090+WV4tPjSgZcwUC+FQCGwHQJp7yiFmHN9vnp/HfvWPjfbXI5JuNhnMtcHnYWceN0nhJ1oQ/qTfNalx/gBcpWY7FzMdYKtzxbl5btd7yekNben3Ia2jQzjsx+nTBrrjdroXtdW5ejqMqpWPTjfdiyj3FinI3O0sctj3QOAvmrspiOj6+gYuA/75vfz6dy3NwfocYdVJCBeWqxVj42BJmiawHEQlSzUpc2E3yIv6tTRV5omP1ohp7zLRV1ySDa5phPtoNztImE7y5sJhLoOxR2Sg6sOOOYPs8v+7I25+WNzlpujZ57LtuM8662SMR1Zl+f0s6115cJhHTrFKwTuD4FzDjpevfm6Hl5lWynfPqwLsSLY98da4XXBub2+6tOao7J1mtW2RvVya0nvJfKsOcjmTbB1oLOm9HWJGjpL/ey06E2cYCHsnvDbvua0R79O3T4fecqjjOm2t05mk61t2/OGzW1WXgicIwJzcxEc5ujj3JvDCxnLkWMn23I9y4x2Rhn4lve8HeujjW3quV9Zfo7u9pDL5awH3Txyj8MyrmcZ85yPMtAtf8yxY3c6v3i9SmtGW98kxHrDOsPZyGuP5aH1tWdan6jbTujGihNrV5SC1+xN5yPs0x9dTVflaEN5MPTiR4uf6/PUy+0uVx1tD3y0mGYrFQLngsBi3mkuadCea7HPpB7zvaPBfHa9z+uQg07yPA25mMFT3CN8RuiqnS5nf6FWupx0wq7numxOX6y0cvxuK7+Cw9zv+cUL6X1etD2T/SzqlOfyTAuB/rJK3vSsZxq5rzlbmYa/H32+fX/OLZN1dynn/uR+YsP1sWz75q/Ks16Woez6aMt18ofCILd5zPLp/KTAp59cXL78oL9pms2eyH0zEJHlPoEBsB1amdR9UoUD6NCGrj4A08ZB8raHKwo74pvWnQqWoOk1DE18tzE5E7FDVzaQtD3ZOTQCPvfBjc7MvOQPOOVxolLPMjMmgjTKHGJrVRu70unD2K9VNrLcIX3PdmjrEFur+lr0QuAxIkDQ8eLqQ7yo/CYOsfnJ8ImUIUHtfi8q+FdSaFFIOtn3NqvdT0YL4b/RmPwo9ru9OBRaB3pvv7e25G+Dh0j0C9FJCup26YMPL26vb2P/J4852QrvHr5dZvO4vR64TVrpZfcd0qQTNtpaAS1SH1/0NuPo8Xa5K8ujpDJWJhsitTYo6GJDu+dmFV83+j+vJ6bbH7quFu+sOZkHnzRnJ9NDaEYOerbn9jPdfLcBz5tXypUKgXNGIM+LPH9GTLLcyMv1LDdX3tTGuvnqeYtd5FzP7e9Szv3b1K9t7GZ7c+VNbTzk2NuazFoWK0w7L8XaopGKBjU4nTatI9S1HpkX6xngdNrs2jO1IT23oTyCtejGGiZeX8tiieaF9eoRPFpMFysVAk8agfgjDMw/Zq78p/eNfW7G2IZ9ZszymK9tbsacR9D+ofsCSAv/ICnmNrTQVYG6fUaf65NMCOrFwVYCq8/0XBqBEgVaHWy9fC4CvgD+vptYaWa/jCX7XMqZR9k855lveXhZ1mXLOkfedqxL7pTXMpdz7rLt6zdcQW6vlPuEgVX9cltZZtS1jHNkXbas82zHcuROHiN1l3Pusu0fgoHbPHZ+aHzv2P3Z354OvUzSWJw1aZsziNf02Kj4TG7zp4VeNE/+cBbo6fM60bCHrl677sKBNDr85qSa3sSf2pDFcCYStA0V24ZGPNryBX3HxKTwNaeaJw186vmD7g/pqLutzCa5bdvzpBn7sW3dGIzjtf5I37Zfm8aHfWQ2yW3b3qE4eLyVFwIPggBBRznI8JN4y/Bx+M1Wpg/NJzaJ6SDX5aKP1kl+cNIRr1lLflRy2A+Lc5vB0NFeDbvYJEUb3UanTb4aQ/sEHT+Vfe1vwr+H/dYv2iK138CTcdrzZrTLhYBpjKGPY+rTZAM7Id3GILkY+bS+iB/jkY1JhwElHTSMA2X3gXetH14tvkue/+AMevaD+Dr7W9PMX2Xfcvaj5LaTaS7bjtuxffPJcznbMs98fC5lyUwo2X7lhcA5IeA5kcc8R8vzLsvm8pxe5ufyLrLoeZ805vD2/cMhc32Yo53i2MEtVi88YPeCsYrYI/a1qp2F2voyrTusxJKz/ORFp/Wt2Y5Xr2Ve77ouvFijp/ZCelr7AvO0Xj30o8XRv3opBE4NAYVPpnnrudnnLUEi/8RH7Bljjks65Nqe0rrT/pb567ntfSb1mNfsPUOj+YuwJ/luD2jxHXGYWJrrYozBVjonGsHWC4KuXEdI+JlN/p01Icvkcu5CplOeW0uy/FjO8uM6R32kIW/aaGuXOn3NfZ/TPXUM5sZ8CO107nCdUNCHxDuFPrl9AEVk+ZuUPrFDTkxvDBDsTmc6lIazwHbjTQ5GdMqTA6E8OZDmVJBYbD7Ej3/Njl5jMxGSYUgO44PeOMwt09zk8ET1pCEfJ4hl3IxlXSe3jHnOTbes6+ZDH8ujjPmmo3OIs8CebWKLZNumk0NzPcuEgl4yz7Q5O3O6oxwy2Z7bz3TzrQvvEBzQr1QIPBgCBB15tEc+7LZvsPBzCz8atdhgsRELf9floo/2vdOmTPbM7+5wqqMQPOykNvDjg8/PfnnSsb9H223QcfZq8Q05DWyfXr14dfud29eh0HuUxq1xwIm+Ran3vdv3uL0BFbn1yeOKETYbqEzjbmVIyEfOS5TTuERqfNFSH5bGHb+HJcE9xk6TOY1+NfNyeU4u+z7L2i9Sz37T/Jxbds7O2N4qWeR09TcqW69yIVAIjAjMzbVRZq4+pzfO77GOnazn/dFc7vk91/axaLkvu9ic0xvHOtaxn/XmxowM9EPHHusFHnA622BZqa+VbR1ppLauyTfH2gKtrVuTA13SadRpveprfZzJvCZHnuWavbAMmct3u7Feq3zn0WLWMx4tfoVWpUKgENiIAHe4vq+LfTCTWv8pcd29OULUvs/k7yqED0DO/qLPa0jTPtNzXRY9/6ONrmN76MRTWdG4XvqNAP5iZenOVua/AqztN5tV8W83X2Pl4VP2z24d2q7+ONuxn8eeyzm3bKYhqzbDk1J+yOT+5DbPDYM89rF8OgFXDvzv6Qpf0RyCpvZicsfCD0WpOwtkp4+lD77JWXiDEd/UINt1I+fj3J1FPtRPDqYfoDf2oW9IwjjObo93JN9hlDdqeaLnibBuAmQ527Id62UZICFZlnLmu2x+toVsTjgNfodVMqC7cyocdoasFAqBoyBwzkHH129eX1690NbTvjzWF/nEvs7EhjRtLGP9AHWvOQ629vUoVqmui+y0QQ37XdvrS9Ak1NcjzE5rULYXfLiJz3rTg61sZNnUfhTtNrlTf/Xa5M0qddYQ1099/DW+QmAXBLyHyzrb0LaRwabno+2P9Uw3z3M1525PMni9oyTbzMa2oW0jg82xq2Pd7UI3L48ZPnW3J5n9xl6PFhvqyguBs0GA/fv1xZvYO956P8ro0z6zgaE9o/emaU/pvW+c3LvnWexDpSPaYl+L3V6PNnjpMuiqKEe2+GKFGwH8xUr+GQHubI0gq2IGyFB/oZzA8QEJH0qyC3XdvtWmxzr0kTbWLUNu+5RJY93+HR5l+Pkyf+T1NtsgUN4jYYPkPrk+jmesozPSxrplyG2fMmmse4zwxnEia/7I620ehAFtHjvtEd47dheOZ+9KziE+J3YIPjz2yR7o24FAY3KTHDil3mmZl51F2Ai5cB9yUP09FQ2dxm+0pW9uw8FEawuH5f4hzolX/w/9EVc+hHzYNqU5ufHDjo1sa27i5HYsO2dnbG+VLHK6Ng8gNzxTHtubEQnSnBy0Mbm/0J8SDuM4ql4I3AcCZx90vGY/Kbd103xH22yqTtXri/39Eq25Om9U86Y01hO7olg/2pqTN8GhjbvsctMTHNEG77R4bpcuhh3Iok93CqnMRpbg6ySL7u4p+8msPeczs++d87nom2592890y1kmt0s56+QyPG/YbM91eJUKgXNEwHOEsXtOmea6eSM+o9xYz3q2ZRl4noeWy3XzTfNcncuzTfS2TVlv7J/r2Mpytm2a5cZ61htl4Hlclst1802bGzMy0N0u9Z0TjxazBmj9iBwDnKuUceW1h+pivVIJHYix9nSdXg5Zrz3TeiUZrzfoWj9oYanRonG9+G43zknbPFr8ju50Y6yVCoGnhAD795fvvZgO3ot5rXnpecvs7nNzQdMog++53OetdaZ4i/j4AlKf5yFpfthVWxC9N1UQNQKp3IRFpCrmv+wEXU4gBVsvX6gu+pX8wg2P2B0pbeNL7cvd5FiHbjv231nW5cxb59+Rz3z0uKCR09abN29s9uDcfV9naBzzWEfXdvI4M51y5uUxwltXR4/rvjCg/WOkkwm4TndYxWS2Y2gQTYdQT25kpsnfHEHU7W7MTxsDO4m2mZC+TE82ejkcCJzQv+tg0GobDOeihCG98A2NsrhlvnX75F89uTyRqJ/jHUaFw8l/1M9jgGcedOTxKrZ69vFtrZCvn2gqsO5oXWk0Xqm3K6jeeLJWQCeJ1jQoN53Fhlf1LseXf91i0KhNfZHqpMOi4ztbyXXFHa4cZI+U7NMwN7f5cjNZzrS5PMvNlTe1MbfZ87rjHLvIuT7Xj6IVAqeOQJ5fjNVzh3Lmeb5kmcy3vOem7VjPss6zHeuSO+V56XLOXbb9ff5oRu4L7brPlDPPbWSZzLf8Uxo7fb6oR4sDhnopBM4OAX1XwwYyXrwP9R4UhvehkXd0+p6z7VUlI3nKKqkcr2Fv2stO/GYPsbZHVY44L/lGAJVb0LXRpztZe7A14iYEW6lzhyv72QN/SsS+Pft++jnn36GT7OdHGfNCKMm5bvncltexbfIsgy3qhzwlnPtFn3K/4Lm/Wc7lU8PA4zpmfsQj1jG7tbstvqHRTFWYW7rdWWBlOmTagUBDhoSzoByX1JXCWQRf9Z6HjSSHQwonYYdiB0Q9yt0uBqlLd9LBFbkvNNkdTHxrw11G+jbnI/T2TOMEsZk5ep48uWwdcujmkWMn23I9y2R9yqMMNMvbYYx1ZA5JuY/ZzhzdbSOXy1kPunnkHpNlXM8y5jkfZaBb/r5wcNuVFwIPgUD8ppMaapso+bPwm3g/01RYosExTTLZP1JDltT9ais3ncm3o9PlIuhoHeXYm/rSy6GN341go2TI+f1ZrYb7fuHl9cBttXWF1lvfo9+sD7qgTeOij72f07qATF8jGBcaIZOCrZO9wAUbzW7g0LFqbUQPwl6UYr2RdoxVFI85sFD9kMVHZu3Pon/9ZY4254ezDuU5vVHG9V1k0bG/HXN4+/6xHXQrFQKngoD3K+vGw7zLczmXs16mU951vmb5cc5SH2nIm5b7sW2ZPuY+z+md4ti5cYWxxvrDesOaRRrXHlaxWKPa2uT1t63TbU0zDVthBVvDWhfyUxs01GWwjVKsVyqwTnFTCgEYlce/UO7fcYxzlB8tPnAtUyuVCoGzQiD2jDGvNWw8wdx8NSJ9XjNNw08giw6pz/Uo2x7ywZdG0EIzRJjmMd+nYKvq2pPGXGfexx5d+fSbrVIQ/ZI6fP5YFkFXfrv5AVNeI3LZXcjrlmlzOXJ5zfLaNZdbLvMoZxvmzbV1bFoedy67Hfq1Tcr9R95jmMsfGwbbjA+Zk7nDNQYcT9PwJ7PaGzzd8dMnd8xpT345hDbH9do/Dz7kBsc6ymMKS4ZDLanJLcqNr1dkJr4KodvamRxMt9ccjKRxMLwLCrROm4poW7QDUv6QMwlyPZtdRc8ylLPcXHlTG56IWXecSPCQM33swz713N6mPm5jP9ubK29q413hsM3YSqYQOASB8Iuav5fxWL1y/Gr3s83/tfrkJ7vfjXr4TvnC7vsigJj58qy2Rx/tq72xw+9ObUW7+OKuE1656YQczpdDm67LYYMXG7s9Dmphlz64zb5WtDUgGGl9SNLjuK2PSl8/Qtv2OiaMzetWfHHYm2jY3sUqWuSFcaeNbASbHWzlC78HStl37tLknN7oc8c69rOe15e53P55lz6VbCFQCDQE8jwzJtB2nVfZjucp9lzOuWUzDVm16RWE6r0n9yM39NTGXo8W53evyoXAGSHAHpO9cd9Y4j1jn5n35Iaj702X9uQhj4BshOe9bH8AC1Li3Ymf0GTsyVXo+/IIrFLud646qBpfrPQALOXpi5Yud6yI1jZ+2/tMr21z/j+GPqx/1oOXk/XHdSzXkfGFLjwu0yhzh+s+T3fkvlDGpsc28lz3WCyHzlwabVlvlLV+HjMyuY6ML/PuC4Oxf4fW+ZieTIp5253DdNCNejuATtsvHIsdgJ1JOBBB0XmA0hxOg8e6Le/6YUNl28A99bIPy/TJ3w67T+HTkoPh0N++tZUszuSAn98YP9g07w8xZadVH3jzyef0Mj+Xd5FFL08g1z1pj3GHUeGQ350qFwL3j0D4umim+dvwpdTtWyl3vzv99rVIzc8OftTrduh2y4866Nj6Hz3t/WQt6D2PNYCy1xGgmPixZgxrVBr3hBXYdfymNYenNDBG6jrRxtgHhAguT8FW6UWgVXTy2LCqfA+/eze31mxD20ZGPb6zTq1ai6Cbt2r96TL+9GG+UiFwdgh4L+b54vo4J8c6QI20sW4ZctunTBrrnqfw8qGqz9OlfaRp5L3NyTWiv21Cl+S+uD6OY6yjM9LGumXIbZ8yaay/i7FHR+rR4oChXgqBs0Kg7y8jWBr7TPnBNXvT2X1mj5+EBw1dIchuarAX9aCJl2IhcRMEd62uDLbKMk+j9avd6S4T/Q7XyxcKoLx5u/fbhr/OyWsJNPNG2ui3rW956pbJNMuNfh66aTnPZexxQctly9j2PvnYR+yb5nykUZ9LlodnmUyzTu63y3N5pmGPC1ouW8a2H1N+WgFXJjgTRndYxTF01lm0CRV3q/bPSDv4ik49aNJW3hxKovXHangDg0+hO5W7d2RJJvMtB9EORui3YKuIvBNxl6vKe9xhRVO7JH/4d9FBdk6PCZTpY33U84SYy+cm46593EU+9/tQvXHcY/0x47DL2Eu2EJhHQL5Lfg4X52DrFFSE1oOD4VepK038WV+NrbAmuZYvbdTsUx9D0JH1QuPx7/VP44o1pY1iGsM0bml4DF5vAMVYRNnjp9LxMF95cKONxpvWLGTN93qjvWh7RMt/1VVC/PGRvvbEpvUDWtkvZd9t32ea61g2LbdimuXGetYbZeBlP25+tg/fMnPrDrLQ3W7WrXIhcM4IbDMnxjk31sHPdjwPjanp1DNv3TxFNvM9v6FRxuYx/nBI7httziVkcr/HOjq2k+UynXLm5bHBW1dHj+uYY2cd8Vrmc9HiTEWHNCY6RurlthbpNa9lPQBjOedNV6/STZaEU68OT54sPVrMTSkEZVi3eCqDdY16p7ffdWwtSKJSIVAIbECAbR/bSObipXxJzMk+NyPW0Ziar30vS338Ur/v0WMKp/06hjU7F3OdOvohqJc819cGWxWFJWaS534EXkV/IXumH3jDmiyFv7Y/Jh99+hwNvZzmZGzTcvbr1F3OOfJcmZbL5lnO9UNuWsMGKY8ZWq7Dn6NBz2lOxvYt5/FQdznnyHNlWi6bZznXD8HAfTt2zpJ1WkmflbYxYDarTL19fhbOAkanTYdS6nIgoZV1Og05p0nHDgYZtxG63Y4dVtdtm4nuYOLwKzmcTT/wRk45teU2D8nHiYKtbWjbyGCLD3hOY908TwjqecK47vYkt2zQBg7MbT+b2Ya2jQw2x26PdbcL3bx3gYP7UXkhcFQE8H0y6IPa5CeZzZMfDQ8bzTa+6vaP6Hvm23ciaT9rOVoxX3lYjDYod1/c7Ux9wPnGHZ6S4dtzAo0c0OLqhzd94bVv0DG+cAsHT39zn9paMAVb+xiD6jF0mjRjrIwnxpTGHXWooSO+20AH/WncXdJ8+sQdrBpzjJsxpnF7A9vuLMDY53nZK9mnWRm/6ZR5lM1znvnoWMb5SHOdnGQ7lEdb9rHwXM65y27rGI9j0ValQuCpIuC5kOcVYxnnVq4jSz3TPP5sx3LmWT7LeE5uk2cZbFE/5A+HYIO+5P6cy9jjPdFarcGr2BbROEPltaev5SEb64ywQhQ9pSYfpW7i6T5aHAOql0LgxBH4QL/d/MkbPd4UU1gvsc/UvPY+kvH38tJcN9971eQnYl/a96a2Z1q0g49xsJUvTno54iDenzuIqrx9saI+ESOhriDrlX+vmTr0+ANah9+x5jWJYa9K28igO653tud1CztcrsOnnOmrysha1zKuwzskYWdT2kYGG08Vg03j35XPR/SEUlv4/TkJ3+HPjBxCq+u108JxNGrQLN/ogqXrZDkcUCRvLtCf2uj+CoGQaw6LamzeaMCH/jgEi8DhF5reiXa3qxyH20Bxh5Q3iP6Am+Y65kzLpk2z3FjPeqMMvDzxzM/24VvGjmUud7tZd9dytuG+mOY6Nk3L9k2z3FjPeqMMPI/Rcrluvmlz40cGutulXqkQeAoInHPQEdceG1KvBawPKkM/JNgaNrHd7VHEXrRHJTa8bd2bDrxBQ1BSY7CVFV9LTPxFV9YcNqxTAFZ091/FfdM2vgsfmOXsS8c2t5EZdXLdvhbanL/NNPfBtGynyoVAITCPwKY5Os71eStt74Ss56zn4VxuucxzGR5l11e1dwz6SY6ddYt1oF+x2vg8Yx7grVt70vqHXKQZe7FGYhOBFIBpXwDqy0HWKtasMQATP4Ojnt3To8V0p1IhcC4IfF1/dPzDy5e3zPt2w4TmlveRgOBYSPcDkJCNeetYiPIWKxETXvAl0XWCBw2lNNeZw8RAVs71Kdjab4zowdXYu053tjZfEL7iFb07LGW/jiWvSaPVTXLoIWM52/HaRN00285rV5ajbPmxbDlyvmw8RnKfbWvsp+mb5NB7qhh4jMfKWcpOJkUwlMnMxFc2BUe74/CEjwFr4tspxMYCjSUHI31shBylhT3/HMH4MwJNw3LNXuiFIZX6NzjtTqPuYDjssmmIQ68E+St7ezqMcULkiZB5ngCtb3TurkOxjHPLYNO2nMNb1RY8O4NcNo3cZbd16B1GuV+0uapvbi/LjLqWcY6sy5Z1nu1YjtzJ46Tucs5dtv1DcXC7lRcCD4FAeBJ8KD6TlA5dJx10/ERjfV8XAExjb9XV4+58y3tDKxP5jt7mnfUaclon7qxRjYYaaeKzoV0VbE0b1rbuaIPGTgD6AY9jRQf2fMk+1CbsB13fJs927E/Rcznnls00ZLVe+F2hWqkQOEsEtpl/3g96j+U5NQI22rLenBy0cU7mOrZ8WRa+aZQP/cMhY3/HflL3GE5p7PVo8dw7XbRC4LQRiHl/e6vQhM73utgBtb2nxp3jJ506xVbSHn9p79v3q3eCrcCYg639xrPpqSs/gaW9aNzJCl+xkeWfEeh1B1uVR9CWnDtcj5TyGmBfn02blv1/LiM71qF5LaPsNWvMLWf6uL6NdMtn29AOTbTjMZBTz8k0y2R5y2aedXM/kZu7kN00bvQsY/ls2+09lvx0Aq4cel/hKHAT+pDISUTSQXZKwVMNZxIORbzuGKaDKsI+/MLrOhM/OZjpWBb21Hbo8tLsUse3LDsY1fm2Fr/ANzs92BqPsuJYCLwe8Fgn2v6AU16V+KBmOcrQxrSNzKiT69mmJ0LOc9l9MC3b2aec+75K/xxwWDX2ohcCR0PgjIOOr/Q41rUilQRXw4N6g+q1J9YH+WXWhViX2lqxWKPg6ULZ6w06fnOs0+1GI8GXnc5DdFqjUMzBVtYY1ptYc/SiVZ+7A2KtiTVIPOd7ftknC5Hsc+33Xbdvz3KWWUUbdZCzvVF3rOc1hDL8fJk/8nqbE/TuW+WFwLkg4Dnm8TJvTHM+0qjPJcvDs0ymWcfzkbrLc3mmYY8LWi5bxrZ3yce+Ydc05yON+lyyPDzLZJp1cn9dnsszDXtc0HLZMra9a16PFu+KWMkXAieCgPeZ2mBOsY3Yc2r/iItz/CTk2J9qm2TX12Mm3psGN3h9K6VyxELQyTeeaZ8ae1PiHtyZ2b/4bwFYybJ3jUv84FGXraVgq/Te6/taZA5M2Udnvw3ddZpw2XnWy10wH5r9s3N4XHN108izXKav4r19u/8fDqOfeSzuP7RzwgAcjp2O8PE8dpf2tPehPiQ4AiZ2dwhT4BST/fBrnj46i8MvOsjgIORUKGcHMx1ke7AVaduJ9qwf7Yal0A8HwwsOhmCqfMKdYGunxWOdceeRCPfwl6LV8trkSZWFoOWJl3mrytmOHQOyLufcspmGrNrknXgnyX3KjZ8jDnn8VS4ENiFwzkHH13oc6+X7L9p2VGtArAArg63is17Yw4W81hPqaUMbNqb1RvK2yxuB/rQGSbLbCx3Wm6Vga/9yj3Um39nKys/PCbB57XcTcHeAtn60cJS0zdoxbuDGOh2xndE3m45M5o3rybo6elzIkGPzGH9shz5VKgSeIgKeS3kuem6Yx7jmaON452SyDeQ9P3PZNM9LdDItl80j9wV/nz+agT7pHMfOuOvRYlCoVAicHwKxp2TY3gKu2JuylYy9auxDkUcTWruW9qYixd4VgYiFKJeBdsOZbzwTjb0oMRKCqwRf2Z9OwVYpq+zrkqeAtVeNwCt72gi+dh3ucD3wKS2vAbK4lEZ6XiMQHPkjLa9Z8NbV4WEvX9Ayfa7sNtW3tpBB2CPNjQUzI/2UMdgDto0qpxNwne6wYmbrs9adAbUcbI067iGcin7MvTuJQEoHXPj+yQBo00G3H34XzkRM2ug6tofOFbbDkF4cbI1DsLTlDybHEeXmOHwYDkdzQMCVCUDyxHB9nBhjHZ2RNtYtQ277lEljHWfglB0Dclzmj7zeZhuEDeyRY4fkfrk+jmmsozPSxrplyG2fMmmse5zwxrE+BA60W6kQuG8Ezj7oqFhdbDgBmrUictaHthQ0nvx/bGIbu61RosUa1HSmbVJfb5Bc+ZutLDKxBnV7saEVTevK0pd7rD1cBFd9Z+sUbFX7sbkVT/ntgXe44tPwl1w5jX4x1+1fM8262Y7lzLN8lrG/3SbPMtiifsgf23G/Ki8ETgEBz691Y9lGBv1x7tqm5yB2PAczL9NXlZG3rmVct61dc/Q3pW1ksPGUxl6PFm9614tfCJwmArFji72rfF8Poi7tTb137XvTkE/B1rbXFTWO/m3/txxsFc17U77kj/iHsLzzMwKisU+N4Kt0fKNAD8Au6jJAsFVB1qtJRvWXug5Mq3x2Nrut/0fH69y6HHtcyKwqjzzbznbZw0q/vQG5wzuWC4MdAdtC/GQCros7rLT58t1FANDLTPyW9DnsDqE5k07uziTTpsPx5GCsK52Q75/pbm86/ELOh9+4XV6H0HAwYoYjkUg/9OJA2i303YHwe4BHSEyYTWmcVGMdfdvBCeRkOrTMy5Mf3rr66Fiweew7jHI/6c9cQiaPYayjYztZLtMpZ966cSOb+Q+BA21WKgTuDYFzDjrGGiAf0dcKrwXhgaf1Q3yDjyv1nqjnXnucI7oy2Jp0wiYvrC+6Yp3RF32xpkCLgKvo3EEQdw9ImOBrXJR9IdwHotJ9J/tT2pnzt/jELLOqP/ad9r3Zr6KT65bNNJfhUXZ9VXtFLwTOAYFx7nl+jWPfJIceMpazHc8z6qbZdp6HWY6y5cey5cgP/cMh7qv7M/bP9E1y6D21sU/nIy1isRYx2H7eaWsTi02jkemd1VpGSSkFYBpN3OB1HZXbsUT1R/5ocRtQvRYCp4/AhxcfXry+vX7xXBvEm1vFIzQ9+4zV4NscjnnLvrMzrlxmTgMRPp653irt5gK2lCiyL42bzlRXHmX2ohFYVe7gatwYID4xkf6zAexVHVC9eE+2JBv1uNMV2XbFzwpc3rz4+Nm3oweyunda5e+zQfz6KDfSvCah5zI5evnaRDMfO+i57rLtu77P0x3YyAlbm9I4XuRHGn11cjn3n3bcb5edZzmXsTXKj3apHwMD9/tY+ckEXOMOq/f6Y51CR1MwNgkANQVOJ5q4sYHoMHozEZ+LNlcnvgO2ci7T5qM7mJA0v3+mwimFgxHXB1y+wQHpuNtVdoIuDwStB1vjx55Fv5IDuml/JrB3breMDyIfeK6coOeU654gmWbZbMdy5lk+y+QPPnLr6pmHLerHusMIe/Qr943+uM+USbnu8WVak2pOJJezjMu5rTw29NbVMw9bx8TBfa68ELh3BOwD2YjhbvCT3VfmR5AmzxQyvYaOUvjYrudD3NMIOsrX6N/kC2LcCQe4HZ+GjTaNDDitHx2Bhh1Y+PAacqrzL2E2rVH4ejavzqf1RjR/8x8bXq85ktfaE78bHmuQ7EZOQ4cnMMi+cM6ifa3ljNsoO9qy3pwctOxLxzq2fJmHvGn2u/XHCkGnUiHQ9keeo3NzzzTL5PlKmZR5xtTzlLrn35jDG+enZebols+2oe2baMt99zizLdMsk+UpkzLPurl/Hs+YIzs3RuTm6JbPtt3ernmsM2Gwa2rdGtceArCxfvW1PtYur1exRnWdWPP6yoYdirygzJpEzhkocpW5CYXzUQRg4HGxPrVr8eWg6vf8aLFarlQInAUC33n7zc+uLj76qavLq4+4P1ITXg6IobfgazwJzLxtbq3NY9giXeqJ3KuY2BCUNJmhhQ3sEPfQPL7q8Y+Y++xXY2+qfXDQJR5znViJ5Jnbz58psCo5ArTvKX/xrMnEvG/6ETeR/BU6711dvrl480tf+uxLn4m7U5JfvXr27NkUpLNvz0bsy7el4YuJZ9gnZ5vZFj59rEObS9Atbxl0acP2GYd+w1WA7JZk7+wx2A2x3aX5OJ9O0mcuHAAv4Sw0iafPbaOFAAu/R903CU1OVMlTDr6DAKpNdiZ+s4cZH6TD5/CCA4nNRCvHXUVBkx0cTbqzNQ7K/O5IOBY2G9I58LFOD21Tnie5J2vW8cTOtLkycr7g28HM5ZbLPJfhUXZ9rq37oBUO94Fq2Tw/BPCuZxp07GsKy00LhMqPU9EaEusJZVKnydPLUUom8pY1XivvFGzF2LTmsGGlrmsKtnYadNN6Tv0qNrfqpza02qlJaL+U/SgW8OemOR9p1OeS5W2HPNOsk9cKl+fyTKNNLmi5bBnbrrwQOFcE8lzzHIXG5TrYuOw862XszIfmeeYcHtdc3TTyLJfpq3j7/uGQPAb3+1zGzvsTq1KcfeSb8/nI5yGfn6hLJORTsLWdn0SNM1hw25pIcel8pLrWnynYyheFPRATa1g/K7Xgq5SntUufQwIsU12L3T09WgwelQqBU0fgW1/4wseZqYZuAAAq5ElEQVRf/Pjq913cXr1/faU9oP4z/fiDMvqLsIs/K8O0C7pe+08f4gaur/M+7jqmZohFpFUlTVF5cBV0sTdVUJDfc3ym6zbm+XM1jRx85J5JU21TfC6e2ogGqSsKG31TrOS2y1+HO3iumOv7ry9uvvoa6R3TX5aP/18UIKWFO+sc/t9rwTZ2WZMcbCUnec0yPdt0mTYoc5Hcpvnk6HP3ZpYxv7eF8s+Fgd1eCoPd8NpZus2dndUerwKL/XSDKFNHlz6+ffFX3ZsFhtDL8dH25iGmG3qiuhwbh4V8bDHSZgJTMT8wNB18VeabmXAurRxegg0GqLNhkNeIW+fxHmweyDn8Hpg8adeZ8QTNk3ZOfrRlvVHWjsFOZS5Hxhf6yHCZRrk7k8NBkH3senxjf133eCyHzlwabVlvlLX+3PiRzePNspl+bBzGPla9ELgXBLo/ZQadU9AxfveuAzqNu68ZOLL8ZR3rSmy/UrA16oDWdpWSjxWrWQxMVU/rTWsDw7Ie60tfZ1h7WFug+TDKI1uxviTatAaxFqHD1del/eOtdzaHanHywfZ1q2jQc7K/HfWyjH0sNJdzji5XpuWyeZZz/TE+ipTHXeVC4L4RYC7MpZE+7oNGPjYyLc8/eOvq8NDNF7RMnyu7TfVtfhAIrEm5v1lspJ/i2OvR4vyOV7kQOA8EftuP/jbuCv3KeYz27ij15dxP6vpd8vGxZoy+fqzftbBMYV0aU6btUmad8Z7UObT8haLptEkMRddPj+1vqhcGmxA6nH9aAVcOrBH5bPus5W9aOfjOHGQRnQ66kglM9RomdDj2vFHdvDuHXxhx+FWBw24/8E7f3E6H3n6o5QBMMFbot+Cr6VI84B1hEuaEkzDN+Uhb5Ugsjz3LZJrbmXMcpuU8l7HHBS2XLWPb++ZjP2nDNOcjjfpcsjw8y2SadXLfXZ7LMw17XNBy2TK2XXkh8NgROOeg4+2LV7cXb/V1v/xveOC+ZlBra4XePdGizht5J9gqLa8vrFF2RdaJevPti2Cr7PA0hJaMWGci2CrdvvawjsQfyGI96U9UtPWmrzEEWHVXUL5TiD8+EHcK0ccDkv3kOhPbyKCPr52TtY/MftPtjf40y+Qy8tSzvOu2VXkhcK4IrJp7GQ/my7bJc3ZdnufnqnKer9lWLnPolH5zmtt2MMmd69jP/dHi9BGoYiFQCJwJAj/8wz/8NQ2V62xTYXD/b/0B4b3779zOLXBoZf/XLw64HHRFDfrsQdbB1jjcoisd7KAWutDale2FjOWGYGvctRoHX+ndCbbKsu4oisdkpruL1J7vSOKxzjf73WbkzW/eLELLdfUoDpkjDXpOq/SyjDe40FzOOTa4Mi2XzbOc6/nbmtzetmXskPIYoeU6/Dka9JzmZGzfch4TdZdzjjxXpuWyeZZz/VAc3L/KC4H7RuCcg478fviHl+/fEgxtridWikWwNdaJRpuCraKxROT1ZvqDW9BjPZKO1yCRFsFWrSH5SQqCpxiLNUcvDrb2L/aizkrf15h2N2tbg6LM45jmvf1IlcMSfjYn/Nlc2iSHHjKWsx37Tuqm2T48850jQ9nyY9ly5ARqKhUChUDbH23Cgbk5zsGR5vmFLZfJPR+db6KZjx10XHfZ9l0/ZP+EjU1pHCfyI40+Ormc+0077q/LzrOcy9ga5Ue71Pcdez1a7Her8kKgECgECoFC4HgInEzAdXGHlQ5osVnSoS/2OtS1EfL+ad1vEvU7jzgu8ht6kdDTpSPtZC/q0ENQL3OHX5C9E2zth2EOyHEAlm4EX0Xvh96gv4mW935hQ7YpbSODjXEDabve5OXNYeZl+qoy8vC8mcxytnVIjr1NaRsZbDxlHDZhUPxC4FAEzjnoGGuPYoOE6ripKpYFn7OVQ2lfBMJRChoF1e2ivN4kfgu2NnuTjr/cUzDVd7PeCbYqaNh+RsDri2zw5R4BWNYe/WxNvrO1/eEB2eMOVwZzhIRfxWeS5nynaZbJ8vbJmecued2hjtzcBS+vKZvKls+2oVUqBM4JAc2ls/2jGec89vwZP/dHizMWVS4ECoFCoBAoBI6FwOkEXPVY5ydveKwTaPTiYOt0uBW5l/Phd7praAq2WlfyOgxHsDbZMy3a8eFX59Q4wPbAa/wswBhs5Q4iDskRYJVt6gqyXvG4Z77DKP6A1mF3GfmgqlYicSidS5vk0EPGcrbjgyl102wfnvnOkaFs+bFsOfJj3mHkfrtvY19N3ySH3lPGweOsvBC4LwTOPujo9UYLxHRXaqw38p+4Xz/d6jVoWlO0JmjtiWUr5FTnX7jsoE7rlpxQ+3JvDLayinNnZtDHL/NkK56kkEwEW/taRVl6/iuv7Yu/wz8d2Zfa39p3uk4rLjvPerkX5kPL6wR1eFym57pp5Kvoq3j5t7Fop1IhcCYInPMfzTjnsZ/Jx7uGWQgUAoVAIVAIvBsETibg+vV4rPPlLQfV9kezdNDsh9uAth9qfdcQtBZMVcE/K8DdSXHQhWa+A7CdB51zsA+/Ouc62NruOBJvZbDVh2EpcdhNwdYWiJVh3pFXug5MHDJ9iCWnnpNplsnyls086/ogSx25uQveeJi13Bzd8tk2tGMk2vU4POZs1zTLZHnKpMyzbu6rxzbmyM6NF7k5uuWzbbdXeSHw6BE446CjVocWNA0M9E6xTkDBhTjYGjTRqfcyPJaTJtd0Yg3qOtMaFuuN5FhvdC39QUYHW/kyL+5iFT++2FM9BVvbTwuw9kCXjH+zFRm+9KO+36/ZSLkl+0zXnY90+91VfOhZxz5xmzz7VmxwQcv0ubLbVN941yoVAmeFwDn/0YxzHvtZfchrsIVAIVAIFAKFwDtA4GQCru0Oq1udRRVc5KjrAy2g+q6iOEbF8XZxF9LS4be/A+ha1kEE6tARycHW+MMl/aDLYXd6nLMHV+Fz51G/o4jDrg++/NGSpbuOVI87jmhjz+TgIOo+sEJbdcDNMnNNmg+PQ2rO4XGZnuumjQfbTF/FO8YdRoVDvFX1Ugg8GALyMs0/nmHQkXWBQKo84iJ4GmtGrBixBhEnjQSdRLA1ZCgTS+2yPdgaf7AxSHrpfyArfqfVX+hF4JUAqto2jRXdd6/GWiNdAqzTzwj0+ntND9mr97p91q8DA66ycGetgTamvK6MvLGe1wx4c/W89qwqj+uNbWV7h/6xnbHvVS8EngoC5/xHM8557E/l81n9LAQKgUKgECgEnioCJxNwjTfAwVGdXqd7VESLQMBw+EU+wgM+/A6PdQY3eDqMklQmzgp96TdbdRBudxyJxZ1GPvByeOWQzGE3LtWDR11WloKt0usH4LjzSKL7plUH2ZG+KgCb2806+VCKzLp6Ptjmw2+mz5Wxi7z65ncF0l4p9z0bGOmnjkMee5ULgftEAPd4jkHHDy8+vHh9c/38UgvBFUHUDnLkeomnKvjSi0Aqnq3Tgh9VCI1nzxd5/6Is7mqdAq5tPYnfYyXIypoCj3WoB1iX8h5oNe1CwdXpyz/d0co6FHXJ3VzdPv/41cfuVh/F7tnoY+csjH4XmZHmNQaey3ndoB2uTTTzsTPKj3ap7/sHZ7BfqRAoBAqBQqAQKAQKgUKgECgECgEjwHHtZJKOku2w6zusVgRbFd7UyasfcCkPwdaw04O3sOOxTo6hHIpR1hVBVu4koh6HXhXiTlZpE2yFNgVbpdcPw77biMc3I/AqG+03XLsOj3Ue+EezxoOrLN9J2xyKrZQPpdDm6nOHX+QyfazbVrZ3zDuMCge/g5UXAveLwDkHHd/evH777PLlX3h2efXNG92WeumfEmehmL6oE/697GBq+51xLScpSKuVJd6oWG6krh/5butKlPs6QpkAq4KlV/xueKwtoj1/FmsPd7Pm9eYq+FLiTlYCsARoCbwip6DtFbT3WNxufvFLz7608z2u8vFn+8d24s2ql0KgECgECoFCoBAoBAqBQqAQKARmEDixgKtGSCCVk61/s5U7ioLWA6eAsCLYykFYR0/pIoSdrkMxgq0q6Nx6J9h652cEJB93H/XDLUFVId3udJWNqS46d7oqyHo10VR/qeuAtE0wdS4YOdIcCKUrLuegaQ6muuw8y7mMHfiuu2z7rh/rDiPsbUrjmJEfafTXyeU8Btpx3112nuVcxtYoP9qlfiwc3PfKC4H7QuCcg47f+fx3ffzy40//navb21e3rC1y67cKuupVzkTVKC+QV3wSkZC7EI8fHg9P1XWexTd3+AiEmu+5FY04adj00xNwWbQImgZPX3ApQHsLX4vUs8ilgeJz+sL1TGtN04klTjLYuHr2Qiq3n/zM137mNa3smOoPzuwIWIkXAoVAIVAIFAKFQCFQCBQChcDpI3AyAde4w+r2+sVzHShvdKAlPhpn0HgPFfjkrAmBA3FncGdRlOEhJwHkeqXd2crJOIKtyji4Ulce5Tj4tjtV485V+HG4FV8BVP9sAMFWB1TjLiPdcRR17jDi7iLV45FPflbg8ubFx8++Hd1RS1slBe/qDiMhVThs9XEpoULg6Aicc9Dxe3/0e39FgP740UF9IgbrD848kTequlkIFAKFQCFQCBQChUAhUAgUAg+KwMkEXL/z9pufXV189FNXl1cfXXFvECHLuDmoBV+vIpAqIrlSBF+joLLuLLqaCDClw91G2OCKxzaVkU9BV9HjrlQFT4MuUdAk0MojnART9YjnFTQCtO8pf6FHPqlHoLXL8hMCEYCV/HtXl28u3vzSlz770mfi7pLqDqOGVuGwy6emZAuBIyFw7kHHI8H4JM3UH5x5km9bdboQKAQKgUKgECgECoFCoBAoBO4ZgZMJuH7rC1/4+IsfX/0+PVP5/vWVfoZO/7kB9ULPbPJ4ZzytSZU4KDmvnciNrtfXPRIbvOuIpUYxbk9VSXFRPbCpgq64y1XWFcV9pise4Xz2XE0jBx+5Z9JU2xSfi6c2okHqisJG3xSIve3y1/RL9Peu3n99cfPVnR7rrDuMwFZv+du3P6nrd+lOV1DmjleyKY31ibGi4Mf8MzvTdinzMwX+iQDn0NTfybzpEPgtW10/PTGrUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIfAEEbh8gn1+F13++9XoP7dlw28l97Guv6jrJ3R9Vddnuipth8B3Sez36/Jn84+p/J9vp7pWCnv/mK7frus36/qSrq/r4n3673TxXt3qqlQIFALvHoHyuQ/3HtyXz50bwT8t4u/sjN+t/I/PCRWtECgEThKB8usP97bel1//6zQE3kf20Vzfo+uv6PppXX9G15/Uxb66UiFQCBQCIwK1BoyI3F/9vtaAv1Fd/kd0/VZdv0XXB7q+puvP6fpxXT+jq1IhsBcCPyQtgnH7XJ9K7z/T9Wt0VdqMAJM34/yfblbZKIGDJ/Cd7Y5l+H/DRkslUAgUAg+BQPnch0C5tXEfPneu979WxG/osu/9wTmhohUChcDJIlB+/eHe2mP79ffV9X9N1y/rsg+fy39FfL5Me6WrUiFQCBQCGYFaAzIa91s+9hrwUt39N3V9W9ec7zftfxT/N+iqVAjsjMAhDsIfQO6m/Ft2bvn8FI7tILirlbuO/T6sy/l2ppzE+X3masSPD4HyuQ/3nhzb5871/CMRf1JX9r8/OCdYtEKgEDhZBMqvP9xbe0y/zp1Sf1ZX9t+byn9e8r/x4YZbLRUChcATQKDWgId7k465BhBs/Sldm/y++d+S7Pc/3FAff0tXj7+LJ9NDNix/VBd5pYdB4Mtq5r/S9Sw1xzczfPvyB3ThPK51Of0mFf5XXX+TCZUXAoXAk0WgfO7jeOs+p27897r+jsfRnepFIVAIPGEEyq8//Jv3B9UkPx+Q019ShcdH/z1d/4Wun9WV0/eq8od05f135le5ECgECoF9EKg1YB/UDtP5MakTU8npT6vyn+hiDWCPf6PL6fMq/De6vseEyguBbRAYv5HhMXeC1fliU/GhLn7fiDtZ/1ldbEgc7Xf+e0SrtBqBY34j8z+rGeNO/qd0fffQNEGAv6Yry/1Hg0xVC4FC4GERKJ/7cHgf0+eOvf4dIvx5Xdm/uvyDo3DVC4FC4KQRKL/+cG/vsfz6v6gu22eTv9HFOeZ9XTm9p8q/pQt+lodWqRAoBAoBEKg14OE+B8daA/4pdTn7dAKr/6quZ8NQ/i7V/4KuLEugtlIhsDUCo4PY9gNE8PWP68ofvq+rfrl1y+cneCwH8fcMuP9F1b+4Ak4C5N9I8twKz7czlQqBQuDdIFA+9+FwP5bPzT3+gip8MZnXvrH8g1mhyoVAIXDyCJRff7i3+Fh+/X9Tl7Pv/r0bhvCjgzxfuFUqBAqBQgAEag14uM/BsdaAP6wu5zXgP1gzhO8XjyeHLf+pyh+skT8bFndoVro/BAiu/shgnlvhf+1Aq+rxEfiXBpNsEn9poLn6/6jw77uinGDrP5PqVSwECoGngUD53Hf/Pv0T6gKPGv3z774r1YNCoBA4AQTKr7+bN5G98N+Wmv45lTc9pfdvS+YvJx1+WuBXpXoVC4FCoBDYFYFaA3ZF7DjyrAH/cDLFH0Uc41qJfcGTxf9HIvDHE39Tqp9t8fnZjvzhBv6/qynumOSPhjjxWPvXXJnJuQP2N+r6m/v165X/rK7/q19/Rfk+6ddJ6bfq4lsPfo+J28G/rYvD8U/q+hld69JfL+YXusA3lOMAX+j6AV3/kK5PdP3fuv6Iru/omkvg8A/oYgKCw/+r6yd0/Z+6+FbkWOnvTYaw+1+m+lzxD4r4uxPjX1D5D6R6FQuBQuBpIFA+d/l9eiifS6s81fHfLjcfNfz7n9D1r8zwilQIFAKFwCYEyq8vI/QQfp0nxTgnOP1JFThwr0tvxfyqrnxjyfep/hVdlQqBQqAQ2BeBWgOWkXuINYCY0S/q+p7eNAFVYj3r0l8S87cnAdYC4leVCoGNCPyQJHx7NPm2PymA4fd18eHM+r8Bxor0d4uOU8nyY5mg5t++Qn8k87uyv1fXN3WNdsY6dv9xXavSV8SwDodqgrZMLNOc/7xoOeCp6sXndP27un5Zl+Vy/m3R/2Vdv2Xg81jqrunXSyHbZpO4TfqzEsp6OLNKhUAh8PAIlM9tmH9FmX3SY/a5/oT8mtRf9/v3i8Y6+DsH3g+qXqkQKATOB4Hy6+29/ooy+8fH7Nd/QP3kD2J9Rdef0/Wv69om/Q8S8vjIf2AbpZIpBAqBk0eg1oD2Fn9FmX3kY14D/IH8ggp8Afe3mrAm/zPieWzkfOFWqRDYCoFDHMQ/qBbyB++vqX410yoH0j80yGa9sfxGsr9HF3eYrkq/Q4yf0zXq/v/t3QmMJFUdx/ElHAIiLiAICuyCKJeCRETAAyGYiEKignIIuIoEOWKMBl2PEC+UiESIooBGA0S8oqIod0hxKWI4FDlWjhVBQFCQUy7R329r3vLnb3V3zUzvTB/fl/znvXr1qrrqUzWvet5UV/eaPrjDCquwrvNV9huwTuvaSvNK8h/hvQaRy3ryG7WpDLjupdcr63Pub9Frk36iRnG517VZiDYIINB3AfrcmrRSVvqkQe5zywkQB1z9JnLbMkM5A64BgyICYyhAv14f9ErZMPXrkzlV/cnJ+8L+PaMyNy9MRpC2CIyuANeA+thWykbxGrCz9st9ftm3R1TmGa5CILUTmGoH4btQ/XH9cuI5992mTenrqoztSvlW1Z+ruK3D/O+pvimtp0o/yqCsp+Re33mKnyr8MPymNner3gPAOVWqKOvpll8TFvSbrz80LPeY6i5XeBuebJhf1j+VAdd8vNr+V/64tB0HaJqEAAIzL5B/h09puQn0uXPmzEafWw6PP1HxHcXWpSLkDLgGDIoIjKEA/Xp90Ctl5T1ut/yacI7MZr8eNqNn8bC0bzf2XIIGCCAwLgJcA+ojXSnr1veXecN0DfBg64Npv/wJNxICrQVyB3G2ltyuIXZU3a6K/RUnK/wso/JL4/x6xQsVOb1DFbGdy5cp5qWGG2v6CkVu+8bUzpP+oze2u1fT3rac/J9n3ykb/yPh5T6QG2q6UsR1uux9vFCxUHG64h+KjyhK+rAKeRkPLq9UGij3H+mnKXI7T09lwPWotK59Nd0mfVSN4jYc0WYh2iCAQN8F6HNr0kpZ7JNcHsQ+t97a7j8ZcO3uw1wERl2Afr0+wpWyUenX4zm7libi3a3exxNjA8oIIDDWAlwD6sNfKRv2a8CW2oc9FB7z8ZhV3p9rVTdXQUKgtUDuIPJJ1Wb6ar3avIZX9J2kfsxAXIcHLldoaOsqP0LgR4rY/jpNx/YezMz/ZXi96rqlkzQzrrPpY/hVauP2Rypi8hd+OZy8Hfcr4no/7Rkd0jGqj21dnsqA61fTetwhtEl5QOATbRaiDQII9F2APrcmrZTlPnEQ+9w2J0DuXxe0WYg2CCAwMgL06/WhrJSNSr9eTs5VVPCn1uJ++ZNsLy0NyBFAYOwFuAbUp0ClLPaVLg/be/tLG/ah7NOZmre2goTApAT60UH47snVGl7Vd6eWE9T5w4peJ+m6avNoWm5PTZe0vQrxjlX/56FXeosaxO34QcMCVWrju22bnkdbFt01tb9b0yuXmQ35qqrznbhxO6Yy4Opl4jq8HW3S+9QoLvf5NgvRBgEE+i5An1uTVspinzSofW6bE4AB1zZKtEFgdAXo1+tjWykblX7de+QbPs5K++T9O1xBQgABBIoA14BaolI27NeAO9I+xP25UvMOVJAmBLoNloHUXwE/H3SR4pVptflxAMdrvj+S0y3do5knpAa+tbsk/1Hu/zZvrthdcZiiV7o9NfCdtL2S32B5YLdTelua4X17PNXFSf833G2mm/yR25i6DfLGdnm5NgZxecoIIDA4AvS5dX86E33u4Bx1tgQBBEZZgH59sPr15+lkO0PhvzVi8if1TowVlBFAAIE+CHANmP1rgD/JPFfxT4W/p8c3Acb0Wk2cqviGgrFGIfi/kqTJC/jxAH4zkZNPqucrVld4sHMHxZqKkl6iwiUKD7JeP1GZB1wvmKjvlbndJ0OjTUPZxScUN02Ep3NaVRUvV3igdifFLoqY2vyC3BwXaCjnweXfNrTJVeer4uhcOcnpvO1Ptlw+PlfWi/jb9UgIIDD7AvS59TEY1D539s8QtgABBIZNgH69PmLD2q+voc3/hSL/HfNz1R1U7xo/EUAAgY4CXANqmmG7Bvhu1vUUZZzEA7AeZD1JsY2ipMNVuFNxTKkY15wB16kd+au0WJs7MT36/23FXuFl/AblWEW5+/PVYZ6Lf0nTnSYXpxmbpek4uYEmdlNsrfBA8CaK9RX+BZlOuqXHwuuk+f6l65V8i/p0U+kAynra3uGa2z1UVkCOAAKzKkCfW/MPap87qycHL44AAkMpQL9eH7Zh7Nfna9PPUeS/PTzYurfiKQUJAQQQ6CbANaDWGcZrQBxr8QDslQp/ofzPFHsoSjpShW8qGFMpIuQdBfIzR07p2LJ5xhdU7ZMxxlYTTf+e6pefqO+VebA8Pqf1/oYFPLh6usJvfOJrtyn7FyanShVx2RfmBmn6rtTeHz3qlTwI7Ltzy+t4wHqy6XNaoCzv/ICWK1iYluP5Iy3haIZAnwXoc2vQSlnsywa1z21z+A9J+7KgzUK0QQCBkRGgX68PZaVsmPv1bbX996R98P6crGj7N4yakhBAYMwEuAbUB7xSNszXgG6n7bqa6TGpuH/7dFtgHOblj16Pwz7Pxj76Fuucyp2tT6cZbY+JP/4e71D1809j8mDrZYr9FR6c7ZT+phk/VBzVqUGX+se7zPOs/6T53bajNPUvqAdcp5M8iB2T7/BtkzZMjfhvTAJhEoEhEaDPrQ/UTPW5Q3JasJkIIDDEAvTr9cGbzX7dz2qtFC+uN2Xpz8+o5H+q5ff9SxtQQAABBKYpwDWgBpzNa0CvQ+h/xvk7fmLaJE6MY7nt4N442vRznz2oeV1aYTn5bkv1edAvzV46OW9pqS7cG6bXVvk8RXx+rGcvVnxX8RHFLooXKdZX7Ks4UxFTHMyN9ZMp+5cuprzNcV4pr67CC8rEFPMb0nJtXteL5HaL0nqYRACB4RCgz62PU+7Tmo5eP/rcpvVShwACCPRTgH691pytfv1Denn/reDvqijJ35FwoOLoUkGOAAIILCMBrgE17GxcA1bRS7cdN8zjMPOX0fkwNKttM0I+NDszZBv6zMT23qz8DWHb56t8a5juVMy/bPeFhn52Rryr048UOELR7VEIa4XlXVxWA675lzC97Jy2A855uThdvpCs1G1fCl1y7+/rwvwHVb4pTFNEAIHhFqDPbT5+/ehzm9dMLQIIILBsBejXm3373a/77tVvpZd6QNPvUlSpnkkEEEBgpgS4BjRL9+Ma4E9JH6rYWLGuws/n/rGiV/KNHDE9GifGsdx2pHocbfq5z3O1si3TCu+cmPaAa0zvjBNdyu9O8+LdmDuleV/RdLfBVjffLi3Tj+cw/T6tMz5EOc1aOvmmpaWpFzz4HAetX6XpdXqs7tWaHwed/fDn//ZYhtkIIDCYAvS59XGZqT53MM8CtgoBBEZJgH69Ppoz3a/vp5fNg62LVbeDolKQEEAAgZkQ4BpQK8/UNcCfhN5R4cFWp7ZjVNvUzZf+vGJpiQICXQSm85Bn3zn5fYUH72K8YuL13prqn9T0RhPzOmUv14ynFXF9bw6N/fiCOG+3MK+p6MHIW9Iy5zY0rFKbXl+C5UHmuB1PaHqeolPy+u5QxGWm8qVZXv+X0np6fdzptNT+Y14JCQEEZkWAPrdmr5TF/nCQ+9xeJ4rvkIr7sqDXAsxHAIGREqBfrw9npSz2hYPcr2+ubfXdSXF7/6Tp9RQkBBBAYDICXANqrUpZ7FMH9RqwRdrOhzXdq+/3AK2f5R33b56mSQj0FJhqB+E7K09XxJPO5cvTK16S2pyv6Xw7dlnE/925KLW/W9PxbuWz0/xPabpTWkMzrlXkbby4YYEqtevVQXgVV6dlfqVpPwekKX1ZlXk7pjrgulVa1781/famF1XdQYrYOfxL0538O6yCagQQ6KMAfW6NWSmLfeIg97m9Dv8haV8W9FqA+QggMFIC9Ov14ayUDUO/7htGrkrb6u9meImChAACCExWgGtALVYpG4ZrgLc23wh3gep8bWhKvoFvkSLum68hJARaCeQO4q9a6owO8SPV/1JxoyKecKXswbzyhVkqLklb62e+Y9V3nO6kWHFJizlzVlK+s+I2RVlXyfPjBb6Y2jyu6dxGVUseI1A+Ol/WVfI/ukFKlabLfOdt/vjfQe2eSct5ENb7XH5h/ebNbnHdpTzVAVet7v8Gu59U3QcVq3mmkgebFyry9vW6G9bLkhBAYNkJ0OfWtpWy0hcOQ5/b7YxgwLWbDvMQGH0B+vX6GFfKhqFf98dH43a6fJei098/neq31TIkBBBAgGtAfQ5UymLfOsjjKXulbfV2X6jYVFGSx6reo7hXEffLn47YTEFCoJVA7iDiyTSZsgf2du/wil9TfdO6PFj6N4Xzpvknqz6nl6niIUVu/2fVnaX4tcLlPD9OP6b5ZWBSxSWp0s/Ypk0H4QWPTcuVdTyg+vs7zCttpjPguq7W7TeHZV0l98CrB7TjXa1l3m9U3+kOXM0iIYDADAjQ59bIlbLSNzkf9D633urmn4ekfVnQ3IxaBBAYUQH69frAVsqGoV8/J21n3ObJlPetd5ufCCAw5gJcA+oToFIW+9BBf29/Qtresu3+PiI/YuaJDvMPVj0JgdYC/eggPMC5T49X9B+kfj5GOZG75b4jdqGi3CWq4nPSezWV79zstD6/5oGKUxSxzf6ajqnSRJzftoPwOj6blo3riWXvUxyEnc6Aq1a15Fkjl7d8bd+VvJYXIiGAwKwK0OfW/JWy2D8OQ5/b6cTx9S3uy4JODalHAIGRFKBfrw9rpSz2hYPar/uP6bidUy0z4Fofd34iMO4CXAPqM6BSFvvTQb0GlPN1JRV8g1/TjWpxP0rZj57ZuyxMjkBbgUPVsJxEvXKfjL671HelXqo4UeFHAXQaGNWs56SNNOW7UJvuUPVr+87TUxWvUfRK/hjPxYpO2+xHIxyn8DM3nPZUxLanLal99sf5Yf5TKq/47KxWpV3V6iJFfI1SXqx6Ozv5F7XU22+6yR3F8Yp8q3t5jQc07+OKlRUkBBCYfQH63PoYDGuf23QGvV+Vpc91vl9TI+oQQGBkBejX60M7DP263993umsp9uNtyv7bgoQAAghwDajPgWG4BjSdrdup8neKTv3+I5p3imKughQElgtlioMl4GMzX+Ev3nJ+n+J2hW/d9mDsZNIGauzHDDhWUNyguE7h58nORtpYL7q5YiOFB1f9Mf67FDORNtSLeLDa2+D/3v9ZsUjxmIKEAALjK7Ccdn2+gj53fM8B9hwBBEZLgH59tI4ne4MAAghMRoBrwGS02rVdU822UHgsZ3mFx3A8PrVY4cFYEgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAwqAL/A9TvZrvSGurMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('4lightsout_pr.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "lightsout4=[[1, 1, 1, 0, 0, 0, 1, 0, 0],[1, 0, 1, 0, 0, 0, 1, 1, 0],[1, 0, 1, 1, 1, 1, 0, 0, 1],[1, 0, 0, 0, 0, 0, 1, 0, 0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hints\n",
    "- Change the oracle of [qRAM data search](#qRAM-Data-search) to an appropriate one.\n",
    "- Data storing/writing in *QRAM operation* can be performed in any order. We can reduce the number of gates by taking into account the _hamming distance_ of the address and input data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "\n",
    "# Importing Qiskit\n",
    "from qiskit import IBMQ, BasicAer\n",
    "from qiskit.providers.ibmq import least_busy\n",
    "from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister, execute\n",
    "\n",
    "# Import basic plot tools\n",
    "from qiskit.tools.visualization import plot_histogram\n",
    "\n",
    "backend = BasicAer.get_backend('qasm_simulator')\n",
    "prob_of_ans = []\n",
    "def week2b_ans_func(lightout4):    \n",
    "    def initialize(qc, address, push, oracle, flag, lightout4):\n",
    "        qc.h(address)\n",
    "        qc.h(push)\n",
    "\n",
    "        qc.x(oracle)\n",
    "        qc.h(oracle)\n",
    "        \n",
    "        qc.x(flag)\n",
    "        qc.h(flag)        \n",
    "\n",
    "        qc.barrier()\n",
    "\n",
    "    def qram_c(lightout4):\n",
    "        address = QuantumRegister(2, name='address')\n",
    "        light = QuantumRegister(9, name='light')\n",
    "        qc = QuantumCircuit(address, light)\n",
    "\n",
    "        board = lightout4[0]\n",
    "\n",
    "        qc.x([address[0],address[1]])\n",
    "        for i in range(len(board)):\n",
    "            if board[i] == 1:\n",
    "                qc.ccx(address[0],address[1], light[i])\n",
    "        qc.x([address[0],address[1]])\n",
    "\n",
    "        # board 2\n",
    "        board = lightout4[1]\n",
    "\n",
    "        qc.x(address[0])\n",
    "        for i in range(len(board)):\n",
    "            if board[i] == 1:\n",
    "                qc.ccx(address[0],address[1], light[i])\n",
    "        qc.x(address[0])\n",
    "\n",
    "        # board 3\n",
    "        board = lightout4[2]\n",
    "\n",
    "        qc.x(address[1])\n",
    "        for i in range(len(board)):\n",
    "            if board[i] == 1:\n",
    "                qc.ccx(address[0],address[1], light[i])\n",
    "        qc.x(address[1])\n",
    "\n",
    "        # board 4\n",
    "        board = lightout4[3]\n",
    "\n",
    "        for i in range(len(board)):\n",
    "            if board[i] == 1:\n",
    "                qc.ccx(address[0],address[1], light[i])\n",
    "\n",
    "        return qc\n",
    "\n",
    "    def qram_rev_c(lightout4):\n",
    "        address = QuantumRegister(2, name='address')\n",
    "        light = QuantumRegister(9, name='light')\n",
    "        qc = QuantumCircuit(address, light)\n",
    "\n",
    "        # board 4\n",
    "        board = lightout4[3]\n",
    "\n",
    "        for i in range(len(board)):\n",
    "            if board[i] == 1:\n",
    "                qc.ccx(address[0],address[1], light[i])\n",
    "                \n",
    "        # board 3\n",
    "        board = lightout4[2]\n",
    "\n",
    "        qc.x(address[1])\n",
    "        for i in range(len(board)):\n",
    "            if board[i] == 1:\n",
    "                qc.ccx(address[0],address[1], light[i])\n",
    "        qc.x(address[1])\n",
    "        \n",
    "         # board 2\n",
    "        board = lightout4[1]\n",
    "\n",
    "        qc.x(address[0])\n",
    "        for i in range(len(board)):\n",
    "            if board[i] == 1:\n",
    "                qc.ccx(address[0],address[1], light[i])\n",
    "        qc.x(address[0])       \n",
    "        \n",
    "        # board 1\n",
    "        board = lightout4[0]\n",
    "\n",
    "        qc.x([address[0],address[1]])\n",
    "        for i in range(len(board)):\n",
    "            if board[i] == 1:\n",
    "                qc.ccx(address[0],address[1], light[i])\n",
    "        qc.x([address[0],address[1]])\n",
    "\n",
    "        return qc\n",
    "\n",
    "    def grover_oracle_c():\n",
    "        def entangle(qc, flip, light):\n",
    "            qc.cx(flip[0], light[0])\n",
    "            qc.cx(flip[0], light[1])\n",
    "            qc.cx(flip[0], light[3])\n",
    "\n",
    "            qc.cx(flip[1], light[0])\n",
    "            qc.cx(flip[1], light[1])\n",
    "            qc.cx(flip[1], light[2])\n",
    "            qc.cx(flip[1], light[4])\n",
    "\n",
    "            qc.cx(flip[2], light[1])\n",
    "            qc.cx(flip[2], light[2])\n",
    "            qc.cx(flip[2], light[5])\n",
    "\n",
    "            qc.cx(flip[3], light[0])\n",
    "            qc.cx(flip[3], light[3])\n",
    "            qc.cx(flip[3], light[4])\n",
    "            qc.cx(flip[3], light[6])\n",
    "\n",
    "            qc.cx(flip[4], light[1])\n",
    "            qc.cx(flip[4], light[3])\n",
    "            qc.cx(flip[4], light[4])\n",
    "            qc.cx(flip[4], light[5])\n",
    "            qc.cx(flip[4], light[7])\n",
    "\n",
    "            qc.cx(flip[5], light[2])\n",
    "            qc.cx(flip[5], light[4])\n",
    "            qc.cx(flip[5], light[5])\n",
    "            qc.cx(flip[5], light[8])\n",
    "\n",
    "            qc.cx(flip[6], light[3])\n",
    "            qc.cx(flip[6], light[6])\n",
    "            qc.cx(flip[6], light[7])\n",
    "\n",
    "            qc.cx(flip[7], light[4])\n",
    "            qc.cx(flip[7], light[6])\n",
    "            qc.cx(flip[7], light[7])\n",
    "            qc.cx(flip[7], light[8])\n",
    "\n",
    "            qc.cx(flip[8], light[5])\n",
    "            qc.cx(flip[8], light[7])\n",
    "            qc.cx(flip[8], light[8])\n",
    "    \n",
    "        light = QuantumRegister(9, name='light')\n",
    "        flip = QuantumRegister(9, name='flip')\n",
    "        oracle = QuantumRegister(1, name='oracle')\n",
    "\n",
    "        qc = QuantumCircuit(light, flip, oracle)\n",
    "\n",
    "        entangle(qc, flip, light)\n",
    "\n",
    "        qc.x(light)\n",
    "        qc.mct(light, oracle)\n",
    "        qc.x(light)\n",
    "\n",
    "        entangle(qc, flip, light)\n",
    "\n",
    "        return qc\n",
    "\n",
    "    def grover_diffusion_c():\n",
    "        flip = QuantumRegister(9, name='flip')\n",
    "        qc = QuantumCircuit(flip)\n",
    "\n",
    "        qc.h(flip)\n",
    "        qc.x(flip)\n",
    "        qc.h(flip[8])\n",
    "        qc.mct(flip[0:8], flip[8])\n",
    "        qc.h(flip[8])\n",
    "        qc.x(flip)\n",
    "        qc.h(flip)\n",
    "\n",
    "        return qc\n",
    "\n",
    "    def counter_c():\n",
    "        flip = QuantumRegister(9, name='flip')\n",
    "        auxiliary = QuantumRegister(4, name='auxiliary')\n",
    "        qc = QuantumCircuit(flip, auxiliary)\n",
    "\n",
    "        for i in range(len(flip)):\n",
    "            qc.mct([flip[i], auxiliary[0], auxiliary[1], auxiliary[2]], auxiliary[3], mode='noancilla')\n",
    "            qc.mct([flip[i], auxiliary[0], auxiliary[1]], auxiliary[2], mode='noancilla')\n",
    "            qc.ccx(flip[i], auxiliary[0], auxiliary[1])\n",
    "            qc.cx(flip[i], auxiliary[0])\n",
    "\n",
    "        return qc\n",
    "    \n",
    "    def counter_rev_c():\n",
    "        flip = QuantumRegister(9, name='flip')\n",
    "        auxiliary = QuantumRegister(4, name='auxiliary')\n",
    "        qc = QuantumCircuit(flip, auxiliary)\n",
    "        \n",
    "        for i in range(len(flip)):\n",
    "            qc.cx(flip[i],auxiliary[0])\n",
    "            qc.ccx(flip[i],auxiliary[0],auxiliary[1])\n",
    "            qc.mct([flip[i],auxiliary[0],auxiliary[1]],auxiliary[2],mode='noancilla')\n",
    "            qc.mct([flip[i],auxiliary[0],auxiliary[1],auxiliary[2]],auxiliary[3],mode='noancilla')\n",
    "        \n",
    "        return qc\n",
    "    \n",
    "    def counter_rev2_c():\n",
    "        flip = QuantumRegister(9, name='flip')\n",
    "        auxiliary = QuantumRegister(4, name='auxiliary')\n",
    "        qc = QuantumCircuit(flip, auxiliary)\n",
    "        \n",
    "        for i in reversed(range(len(flip))):\n",
    "            qc.mct([flip[i], auxiliary[0], auxiliary[1], auxiliary[2]], auxiliary[3], mode='noancilla')\n",
    "            qc.mct([flip[i], auxiliary[0], auxiliary[1]], auxiliary[2], mode='noancilla')\n",
    "            qc.ccx(flip[i], auxiliary[0], auxiliary[1])\n",
    "            qc.cx(flip[i], auxiliary[0])\n",
    "        \n",
    "        return qc\n",
    "\n",
    "    def inverts_phase_c():\n",
    "        auxiliary = QuantumRegister(4, name='auxiliary')\n",
    "        flag = QuantumRegister(1, name='flag')\n",
    "\n",
    "        qc = QuantumCircuit(auxiliary, flag)\n",
    "\n",
    "        qc.x(auxiliary[2])\n",
    "        qc.x(auxiliary[3])\n",
    "        qc.ccx(auxiliary[2], auxiliary[3], flag)\n",
    "        qc.x(auxiliary[2])\n",
    "        qc.x(auxiliary[3])\n",
    "\n",
    "        return qc\n",
    "\n",
    "    def diffusion_c():\n",
    "        address = QuantumRegister(2, name='address')\n",
    "        qc = QuantumCircuit(address)\n",
    "\n",
    "        qc.h(address[:2])\n",
    "        qc.x(address[:2])\n",
    "        qc.h(address[1])\n",
    "        qc.cx(address[0], address[1])\n",
    "        qc.h(address[1])\n",
    "        qc.x(address[:2])\n",
    "        qc.h(address[:2])\n",
    "\n",
    "        return qc\n",
    "\n",
    "    def measure(qc, address, cbits):\n",
    "        qc.measure(address,cbits)\n",
    "        qc = qc.reverse_bits()\n",
    "        return qc\n",
    "\n",
    "    def measure_orcle(ac, flip, cbits):\n",
    "        qc.measure(flip, cbits)\n",
    "        qc.reverse_bits()\n",
    "\n",
    "    def measure_aux(ac, aux, cbits):\n",
    "        qc.measure(aux, cbits)\n",
    "        qc.reverse_bits()\n",
    "\n",
    "        \n",
    "    ##### Build your cirucuit here\n",
    "    ####  In addition, please make sure your function can solve the problem with different inputs (lightout4). We will cross validate with different inputs.\n",
    "    \n",
    "    # 1. Create circuit - OK\n",
    "    address = QuantumRegister(2, name='address')\n",
    "    light = QuantumRegister(9, name='light')\n",
    "    flip = QuantumRegister(9, name='flip')\n",
    "    oracle = QuantumRegister(1, name='oracle')\n",
    "    auxiliary = QuantumRegister(4, name='auxiliary')\n",
    "    flag = QuantumRegister(1, name='flag')\n",
    "    cbits = ClassicalRegister(2, name='cbits')\n",
    "\n",
    "    qc = QuantumCircuit(address, light, flip, oracle, auxiliary, flag, cbits)\n",
    "    \n",
    "    #2. Initialize - OK\n",
    "    initialize(qc, address, flip, oracle, flag, lightout4)\n",
    "    \n",
    "    #3. QRAM - OK\n",
    "    qc.append(qram_c(lightout4), address[:] + light[:])\n",
    "    \n",
    "    #4. grover\n",
    "    for i in range(5):\n",
    "        qc.append(grover_oracle_c(), light[:] + flip[:] + oracle[:]) # OK\n",
    "        qc.append(grover_diffusion_c(), flip[:]) # OK\n",
    "    \n",
    "    #5. counter\n",
    "    qc.append(counter_c(), flip[:] + auxiliary[:]) # OK\n",
    "    qc.append(inverts_phase_c(), auxiliary[:] + flag[:])\n",
    "    qc.append(counter_rev_c(), flip[:] + auxiliary[:])# OK\n",
    "    \n",
    "    # measure_aux(qc, auxiliary, cbits) \n",
    "    \n",
    "    #6. grover inverse\n",
    "    for i in range(5):\n",
    "        qc.append(grover_diffusion_c(), flip[:]) # OK     \n",
    "        qc.append(grover_oracle_c().inverse(), light[:] + flip[:] + oracle[:]) # OK\n",
    "    \n",
    "    #7. QRAM - OK\n",
    "    qc.append(qram_c(lightout4), address[:] + light[:])\n",
    "    \n",
    "    #8. diffusion - OK\n",
    "    qc.append(diffusion_c(), address[:])\n",
    "    \n",
    "    #9. measure - OK\n",
    "    qc = measure(qc, address, cbits)\n",
    "    \n",
    "    return qc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running week2b_ans_func...\n",
      "Computing cost...\n",
      "Starting experiments. Please wait...\n",
      "You may monitor the job (id: 5fc39b4e1dde84001b4c8dd4) status and proceed to grading when it successfully completes.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'00': 1717, '01': 2886, '10': 1732, '11': 1665}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Submission code\n",
    "from qc_grader import prepare_ex2b, grade_ex2b, submit_ex2b\n",
    "\n",
    "# Execute your circuit with following prepare_ex2b() function.\n",
    "# The prepare_ex2b() function works like the execute() function with only QuantumCircuit as an argument.\n",
    "job  =  prepare_ex2b(week2b_ans_func)\n",
    "\n",
    "result = job.result()\n",
    "count = result.get_counts()\n",
    "original_problem_set_counts = count[0]\n",
    "\n",
    "original_problem_set_counts\n",
    "# The bit string with the highest number of observations is treated as the solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grading your answer. Please wait...\n",
      "\n",
      "Congratulations 🎉! Your answer is correct.\n",
      "Your score is 276314.\n",
      "Feel free to submit your answer.\n"
     ]
    }
   ],
   "source": [
    "# Check your answer by executing following code.\n",
    "# The quantum cost of the QuantumCircuit is obtained as the score. The quantum cost is related to rank only in the third week.\n",
    "grade_ex2b(job)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submitting your answer. Please wait...\n",
      "\n",
      "Success 🎉! Your answer has been submitted.\n",
      "There seems to be huge \"noise clusters\" interfering with Dr. Ryoko’s device. Can you please help?\n"
     ]
    }
   ],
   "source": [
    "# Submit your results by executing following code. You can submit as many times as you like during the period. \n",
    "submit_ex2b(job)"
   ]
  }
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