diff --git a/src/CCU.ipynb b/src/CCU.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c301a9851ae76d2e363b1a211b804030f56017d5
--- /dev/null
+++ b/src/CCU.ipynb
@@ -0,0 +1,950 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"Feeding Dashboard data.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5386, 18)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "96948"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "39063"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum().sum()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5386, 18)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "39063 values are absent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot: >"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(df.isnull())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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epfVe3ifVfc4wvDBlIP5YtQ97R7yP0mIp3vxxBYQPOW7curbAjR/+xr6Ra3BgwiYYGArx5o8rYGgiVqdJuRuL48t2Y0e/5Tg46WMIBMBbP66AwEBQ434bApNX+sB60Rzkf/sDUifOQWlkNBy2fQwDG2ut6cWd2qH4z7NIn7sUadMWoDwtAw5fbYbQwR4AIDA2hlELH+Tv2Y+0iXOQ6bcWIndXOGxZ/wxL9XTMB78EB/+ZyPp6PxJGz4c0PAYu33wEoa2V1vQCYzFKE1OR+dlelGVka02TMGYhonuNV7+Spq0EABSculRv5XhSdq92h8faKUjachS3By5HUUg8Wh16D4Z22mOJRefmaLZjMdIPnsHtAcuQfeo6Wuzzg2lzV410OWcDccN3uvoVMfdzjc9th74I720LkX7kHG73X4q7I1Yj85eGUz9N578KjxmDcM/vW/yjapdfeES73KhSu3zllZXID47HC1XaZaGJETLOBSH6i2Na95FzIxx/t5mt8UrYfwbF8Wk6H/Twmj8cntMH4Y7fHlwa8h7Ki6XoenjFQ+uk8YgX0WrtRERs+RkXB6xCfnA8uh5aoVEnuXdiEbRoJ869tBTXxm0EBAK8eHgloCWetv9sFvJDdX/BVhvLob3gvGoG0r88hOjh76AkNBYe338AoZ32WGJgIoYsIRVpm79Habr2WFKakoXUzd8jesQiRI9chMKrt+G2612Ifdzqsyh1xmpoTzRaPQPpXxxC1LBFKAmNheej6iQxFakf11wnZl3bIOvHE4getRyxk95T3rjxwwcQVGqjG5rmbw+D9/SBCPTfh7ND30d5sRQ9Dz383Gny6ovwXfsWQrb8gr8HvovckAT0OrRC3Yd9IGb/WRz3nad+3V1/SP2Z0FSMngf9UJychbND1+D8iHUoKyxBr0P+EIiE9Vbe2mg2fzi8pg/ELb+9ODfkPZQVl6DnI+KJy4gX0XbtBIRt+QVnB6xGXnACehxaAXGVvm/sj2dxou1c9etepTp5IOCdnRpp7p8KqPMyPi7v+cPRdPpA3Pbbi4uqOulWixjbeu0EhG/5BRdUddJNS4y9tWgXzry0DFfHbQIEAnQ7vEIjxnrNHoKWK95A5Lbfcba3H/4ZswHp5+7Ua3krcxzRDT7rJiF2y8+48coKFAbHo/3hVTX+rrHq3Aytdy7E/YPncL3/CmScvAHf75arf9cAgPv8V9FkxmCE+X2LgCGrUV5cgg5HVqnr08jJBh3+9y4ksakIGLwat8ZvhFlzV7T6svr1lpCF23GpzSz1K+PkjfqpiCdQH/0VsbMNjJ1sELZuPy71Xo477+yAQ9/2aPv5nGdVLCJ6Snoz8CGXy7Fx40Z4enrCxMQE7dq1w08//QQAOH/+PAQCAc6cOYPOnTvD1NQU3bt3R3i45l2dx48fR5cuXWBsbAx7e3u89tpr6s9ycnIwadIk2NjYwNTUFIMHD0ZkZMUdOmvXrkX79u019rd161Z4eHio/54yZQpGjhyJTz/9FI0aNYKdnR3efvttlJaWAgD69OmD+Ph4LF68GAKBAIJKFxsvX76MXr16wcTEBK6urli4cCGKiorUn3t4eGD9+vWYNGkSLC0tMWvWLK31dP36dXTo0AHGxsbo3Lkzbt269XgV3QD06tYFC2dNRv/ePXSdlXplOXE0Cn45icLfTqM0JgFZH34BRYkUFiMHak0vC45AzuffoOjUeShkpdU+F4iNYNavF7I//wYlgXdRlngfuTt/RGliMizHDK/v4tSa+6zBiP78V6SfuonCkATcnf81xE42cBzcucZtPOYMReL+s0g+fAFFEckIXv4tyiUyuIzvAwAQWZigyZt9EbbmR2RfDkb+nVjcfWcnbF5oDqtO3gAA55HdURCSgOjPfkFxXBpyroYifP0BuE0dAKGZMQCgvFiKEP89SNp/FrL0vJqyU+9azBiEe1/8hqTTgcgNTcTVhTth6mQN10Gdat5m1mBEHTyHmCMXkR95H9f996FcIoXX+N4AAIcXfGDm6oCri3YjNywJuWFJuPrOLti184Rzz4q7xe5++gvCvjmF3LDEei9nbbnOGYaU/WeQevg8iiOSELF8N+QSGRqNf1lr+iazhiL7XBASt/+O4shkxH18BIV3Y+AybRAAoLygGHfeWI+M369CEn0f+TcjEblyDyzae0HsYq/eT9KuE8i/GQlpUibyAyKQsO0YLDv56PwHdE1emD4Il746hoi/biI9LBG/LdkBC0drtBhQ83FzaPJm3PnpIjIik5EWmoDfl+6CdRN7NGrrqU5z69A5JFwPQ15SJlLvxeHcp/+DlYs9rJs4PItiPTGLN19H4bE/UHT8NMpi45GzcSvkJVKYvTpIa/rs9zai8KffURoRjbL4RGR/uAUQCCDu0gEAoCgqQsZ8P0j+voCy+CTI7oUi95NtMGrVHEInx2dZtCdmM3kU8v93Cvm//gVZdALS126DokQKy1Ha2x3pvQhkfvotCv64oLXdAYDynDyUZ+aoX2Z9XoAs/j4kN57dRYTaajx7ONIO/I30I+cgiUhCjN8ulEukcBzfT2v6RjOGIufcLdzf8RskkclI3HwYRXdj4TxtsEY6uawMpRm56ld5XkX/DUIDeH4wDfHrf0TaD3+iJCYFkogkZB1vOHdPeswajChVu1wQkoDbqnbZ6SHtsqeqXU46fAGFEcm4p2qXm6jaZQCI230SMdt+R+5N7U/UKErLIcvIU79KcwrhNKgzkg5dqOsiPramMwcjYuuvSDt9EwWhCbi1YDuMnWzgPKjmOmk6eygSDpxFoqpO7vjtQblEBrdxfdRpEvafRfa/YZAkZiLvbhzCNh2FSRN7mLpqxlP3yf0hsjJD9PYT9VXEp2I/fSRyjpxG7k9/QxqViPvvfg25RAqbMa9oTS+5E4m0TfuQ938Xa4wlBWevo/B8AGRx9yGLvY/0LT9CXlwC0w7N67ModcZ+hrJOcn46A2lUIpJXb4dcIoXtQ+okdeM+5P3fpRrrJG7KWuT+fAbSyASUhMYhaflWGLk4wqStd30W5al4zxyEsK3HkHL6JvJCE3F94Q6YOFmj8UP6sM1mD0bsgXOIP3IRBRHJCPTbi3KJFB6qPuwD5RIppBl56ldZYcXd2JY+jSG2tUDwJz+hMDoF+RHJCNnyC4wdrWHaxL7qVz5T3jMHIVxVJ/mhiQhYsAPGTtZo/JB44jN7COIOnEP84QsoiEjGLb89KJdI4T6u9nXyQGlesUYauVT78fYseanqJFVVJ4GqOmn0kDrxnj0E8QfOIUFVJ7e11En8/rPIqhJjTSvFWEMrM7TwH4PABTuQ/Os/KI5PR35oIlL/DKzpa+uc25yhSN5/BimHz6MoIhlhqvaz8fi+WtO7zhqM7HNBSNh+HMWRyYj5+CgK7saiybSBldIMQdznvyDzVAAKQxIQPP9rGDnZwGFwFwCA/YCOUJSVIXzFXhRHp6AgKBphft/AcfiLMPHQfGq3NL9Io21uCMfLA/XRXykMS0Lg9M+R/mcgiuPTkHU5GOEbD8NxQEcIhHpzOZVIr+nNmbpx40b88MMP2LlzJ4KDg7F48WJMmDABFy5U/DhavXo1tmzZgoCAAIhEIkybNk392YkTJ/Daa69hyJAhuHXrFs6cOYMXXnhB/fmUKVMQEBCA33//HVevXoVCocCQIUPUgxa1de7cOURHR+PcuXP4/vvv8d133+G7774DAPzyyy9o0qQJPvjgA6SkpCAlRTlNSHR0NAYNGoTRo0fjzp07OHLkCC5fvoz58+dr7PvTTz9Fu3btcOvWLbz33nvVvruwsBDDhg1Dq1atcPPmTaxduxbLli17rPzTMyISQdyyGST/VupkKRSQ/BsIsW+rmrd7GKEQApEQiiqdE4VUBnGHNk+R2bpj4u4IYycbjacpygokyAuMgnXnZlq3ERgKYenriaxLlZ7AUCiQdfGuehvLdk1hYCTS2G9R1H1IEjPUaQyMRCivUjfyEhmEJkawate0ror41MzdHGDiZI3US/fU75UWSJB5Kxr2nXy0bmNgKIStrydSLwVXvKlQIPVSMOxVAz9CI0NAoYC80o/rcmkpFHIFHF5ouBcVBIYiWPg2Rc6lShdRFQrkXLwDyxqOGctOzZBzUfOia/a52zWmBwCRpSkUcjnKKl+wrPy5tTmcRvdC/o0IKMrKH78g9cza1QEWjjaIvVxxDEgLJEgOioZLR+3HjTZiC1MAgCS3UOvnhiZitBvTGzkJ6chLacDTO4lEMGrRDNLrmjFWej0Q4ra1i7ECYzEgEkGeX1BzGnMzKORyyAu111eDYiiCcWsfFF2tdEOEQoGiq7dg0r5lnX2H5fCXkf/L6brZXx0SGIpg7uuFvCqxJO/SHVh00h4bLDo300wPIPd8ECw6acZMq26t0eXuXnS49CWabpoFkY25+jPztk0hbmwHyOXw/fMTdA76Fi0PrK721IiuPGiXM6u0y7lP0C5nXrwLm4fE2UdxGtgJRjYWSDp8/on3URdM3R7USUU7XFYgQe6taNh01h5PBYZCWPl6amwDhQKZl+7VuI3QVAy3cb1RFJ8Gyf2KeGrezAXNloxC0ILtDXK+ZoGhCCZtvFF4JajiTYUChVeCYNqhRd18iYEBrIa9BAMTYxQHhtXNPuuRuk4u365480GddKy7PpbQwgwAUJ5bc7ukS2ZuDjBxskFapf5oWYEE2beiYfeQc8fa1xPplzTPnbRL92BXpd/rNqoHhgfvxCvnNqHNqrEQmhipPyuISoE0uwCe4/tAYCiEgbEhPN/sjfyIZBQnZtRtQR/Dg3iSXiWeZN+Khu2j6qRKPEm/dK/aNq6je2Bo8C70O/8xWlepkwfab5yCocG70OfkerhXGUzShQd1klGlTnJqEWMzqtRJRq1ibLo6xjr0bgOBgQAmjWzx8sVPMCBwGzrvXgjjxrZ1V8CHEBgKYeHbFNlV2s+ci3dhVUM5rDo1Q3blcgPIOncbVqr21tjdEWInG2RXasfLCyTID4xS79PAyBByWRmgUKjTyCXKGUSsu2rG7eYbp6NXyDfofOojNKp0M4OuPcv+isjSFGUFEvV02ETUsOnFGh9SqRQbNmzA33//jW7dugEAmjZtisuXL2PXrl3qpx8++ugj9O6tbMxXrFiBoUOHoqSkBMbGxvjoo48wbtw4rFu3Tr3fdu3aAQAiIyPx+++/48qVK+jeXbmWxIEDB+Dq6opjx45hzJgxtc6rjY0NvvrqKwiFQrRo0QJDhw7FmTNnMHPmTNja2kIoFMLCwgLOzs7qbTZu3Ii33npLve6Hj48PvvzyS/Tu3Rs7duyAsbHybvSXX34ZS5fWPM3KwYMHIZfLsWfPHhgbG6N169ZISkrC3Llza51/ejaENlYQiIQoz9JcK6A8KweGnk92MURRLEFJUDCsZ72FjNgElGflwGxwX4h9W6I0sfrc0bogdrAGAI11NwBAmpEHsaO11m2MbC1hIBJq3cbMx0W5X0dryKWl1ea/lmbmqb8z89wdeMwagkavdUfKb1chdrSG15LRyu2dtH+3Lhir6kGSka/xfklGPkwctU+XILa1gIFIiJIqdVSSmQdL70YAgMybUSgrlqLD6nEI2nQUgAAdVo+FgUgIkxrqviEwtLWAQMv/vywjD6aq//+qjByttaTPhVEN5TQQG6LpuxOQ/usVlFe5U67pu2/BZfogCE2NkRcQgbsTNj55YeqRuapsRZma5S7KzIO56hx4JIEAA9ZMRMKNcGREJGl81Glif/RfOR5GZsbIjLqPA29thLy04Q0APWBgrYqx2VVibHYORB61i7HWC2ZCnpmFkus1zC9vZAjr+TNR/OdZKIoa1tz72gitLVXtTq7G++VZuTB6wnanKvN+3WBgYY68X/+qk/3VJZE6luRqvF+akQcTb+2xxNDBGqVVYklpRi4MK8WSnHO3kPXHv5AmpMPYwxluK99EywPv4u6wVYBcDrG78k5K12VjEbv2O0gT09F49qto/csHuNVjAcpqGGR8Vmpql2W1aJelWtpl8xricm00ebMvMs7dRkmK9ml/nhWxqq3VVr4nqhPvxhrvuU95Ba3eexMiM2MURibj3zc2QKGKpwZGInTcsQAhHxyEJDkLpu4N72kyoY0ylpRl5mq8X5aZC7FXk6fat7i5O5r+9CkMxEaQF0uQMPcjSKMazhOoNamoE802py7qRE0gQKP3ZqLoRgikEQ1zCrQHfdiq50FJRh6Ma+iL1NSHlWbkw7LSuZPw6z8oTsqEJDUXVq1c0Xb1eFh4NcLV6VsBAGVFJbgw6kN037cYLRcrZ3UoiE3F5fEf6/TipfFD4onxI/r12raxqFQnib8o66QkNQdWrdzQ5t1xMPdqhGuqOgGAkI//h/TLwSiXSOHUxxftN06FyNQY0Xt0d4PCw2Ls09YJAHhM6Y/WqhhbEHkf/1SKsWZujhAYGMBn4Qjcfe8HlOUXo+WKN9D9yCqce9lfna6+GNbwu1b5u6ax1m2Uv2tyq6V/UI8Pa8cf/PbJuXwPPusmwm3ecCR+8weEpsbwVk33bORko94metMR5KiOF7s+vmi+aTqEZsZI+vbUkxa5zjyr/oqhrQV8Fo9C4v6GM+0zET2cXgx8REVFobi4GK+8ovmosEwmQ4cOHdR/+/r6qv/dqJHyYl96ejrc3NwQFBSEmTNnat1/aGgoRCIRunbtqn7Pzs4OzZs3R2ho6GPltXXr1hAKK6ZBadSoEe7effhaAbdv38adO3dw4MAB9XsKhQJyuRyxsbFo2VJ5J2bnzjU/wvegHL6+vuqBEgDqgaKHkUqlkEqlGu8ZSKUQixvu/LGkXcbqj2G/bhnc/j4MRVk5ZGGRKDp1DkYtn/wOzKfRaHQPtP6k4ry7+ZbuFnLNunAH4ev2o9XmGWj71duQy0oR/dkvsO3WEgq54tE7qCcer3XHC5srnk47P/HTevkeaXYBLs3+Ei9snIrm0wdAIVcg/thVZN2JhUL+372bRSASotU3SwABEOH3TbXPE7f/jpSDZ2HcxAEey8ag5bYFDWLwo83I7hi6Ybr670NTP3nqfQ5ePwWOzZrgu9c/qPbZvWNXEHvpLswdbdBt1hCM3r4Q+0avq/YUlb6wmDwOJq/0RcacpYC2KUiEQthvfB8QCJCz6Ytnn8EGymr0IBRduoHyGtYD0UdZv11R/7s4LAFFIfHodG07rLq3Rt7lu+q1cJK++BnZJ/4FAEQt/gqdA3fDbng3pP34bAeJGo/ugTaV2uUAHbbLlRk3soVD33a4NXPrM/9ul1E94PvJDPXf1ydsrtfvS/75MjIv3IXYyRpec4eh0+53cOXVtZBLS9Fi1TgURiYj+efL9ZqHhkoWk4zoYQthYGEKq8E90eSTxYgdv+K5GPyob40/mAPj5m6IHuP/6MTPiOuo7ui0uaIvcnni0/dFahK7/5z63/lhiShJy0Xvn1bDzN0RRfHpMDA2ROfPZiLzRgSuzf0aAqEBms0dih4/LsOZwe9BXvJs+iuuo3qgwycVdfJPPcaTuP1n1f9W1kkOev38rrpOACDs81/VafLuxUNoKobPvGHPdOCjyageaFepTv6t5xib9PMVZFy4B2Mna3jNHYouu9/BJVWMhYEBDIxEuPvu98i4oLxGEzB3Gwbd2QH7Hq2Rcb7hTdNZF4rCkxCycDt81k2C1+rxQLkcid+ehDQ9F6j0OzDu81/U/y68FwehqRju84brZOBDF/0VkbkJuhzwR0FEMiI/+anev4+I6oZeDHwUqqaQOHHiBFxcNEdmxWIxoqOjAQCGhhWLGj1YP0OuCuQmJiZPlQcDAwMoFJoXR7VNg1U5Dw/yIX/ERcXCwkLMnj0bCxcurPaZm1vFgn5mZmaPk+Va27hxo8aTMADw7vKFeN/vnXr5PlLOia4oK4fQzkbjfaGdDcqr3C32OMqSUpA6fSkEJsYwMDNFeWY2HDavRllSytNm+Ymkn7qJvEpze6sXWHOwUna0VMQOVsgPjte6D1l2PuRl5TBy0LwDSFxpH9L0XBiIDZWPpVZ66kNsbwVppTtk4nb9gbhdf0DsZIPSvEKYuDqg+btvojg+7SlL+uSS/gxE5q1o9d9CI2XYNnGwREmlOjJ2sEROsPY7/KTZBZCXlcO4Sh0Z21tBUukOl9QL9/B796UQ25pDXiZHaX4xRgV9hfgE3U0B8Cil2QVQaPn/N3KwgqxS/VQmS8/Vkt66WvoHgx7GTewRNHpdtac9Hnx/aXYBJDEpKI5MQregXbDs3Az5ARFPVa6nFfFXIJIrHTci1XFjZm+FwkrlNLO3QmqI9nOrskEfTIZPvw744Y31KEitftFaWiCBtECC7Lg0JN2KxPI7u9FiYGcE/3716QtTD+S5qhhrWyXG2tpAnvXwi/IWE8bAcvJ4pL+9HKVRWhZYFgpht/F9CJ2dkDFv2XPxtAcAlOfmq9oda433hXbWT9XuPCBq7AjTbu1xf2HDXOy9TB1LrDXeN3SwQmkNsaQ0IxeGVWKJoYN1jekBQJqQhtKsPBh7OiPv8l3I0pRpiyMqLtwqZGUoiU+D2OXZr5OTduqmxpobNbXLRrVol8UPaZcfV5NxfSDLKUDa6RqesKpHqadvIiewep1ULY/YwQp59+K07uNx6qSsQIKyAgmKYlORczMSg8K/hfPgLrh/7B/Y92wNy5ZuaDRMeTPWg98zA0N2I/KLY4hoABdiynOUsURkb63xvsjeGmUZTxdLFKVlkMUr+6wl96Jh4usDuymv4v67Xz/VfutbRZ1otjl1UScA0HjdbFi83AUxY1eiLLXhTDOZcjoQfwVW78OKHayq9GGtkFtDPKmpDyt2sETJQ9bby1Z9r7mnE4ri0+H2WneYujrg7LC16ul8rs37CiPCdqPxwE5I+u3fJyniY0s5fRPZGvFEe50o48nD60RbPCl5SIzNVvULzTyd1QMfVeUERqHlklEwMBIppz56BqrH2Io6qR5jn75OKsfY7JuRGBL+DRoN7ozkY1fV31cQkaxOL8sqgDS7AKYudk9RytopreF37aN/11hXSy9VnR8PfutW3YeRgxUKg+PUf6f9cgVpv1yBkYMVyotKoADgNmcYJDUcKwCQFxgFz6WvQ2AkguIZHS8PPOv+itDMGF0Or0RZoQSBU7c0yKmN/3Pk/D+g2tGLNT5atWoFsViMhIQEeHt7a7xcXWs3PYOvry/OnNH+uFrLli1RVlaGa9euqd/LyspCeHg4WrVSzgXu4OCA1NRUjcGPoKCgxy6LkZERyss1T+COHTsiJCSkWtm8vb1hZFR9ns6atGzZEnfu3EFJSYn6vX//fXQnb+XKlcjLy9N4+b8zp/aFosdXVgZpaASMu1Y8sQSBACZdO0B6J+Spd6+QlKA8MxsGFuYw6dYZxed1s4hqeVEJiuPS1K/C8CSUpOXArlfFmiNCcxNYdfRGbg0XkhWl5ci/E6uxDQQC2PVqo94m/3YM5LIyjTRmXo1g4uqgdb/StBzIS0rR6LUekCRlIv9ObB2V+PGVFZWgMC5N/cqLSIYkLRdOPVur04jMTWDfwQuZNyO17kNeWo7sO7FwrrQNBAI492yNTC2LykqzC1GaXwynHq1gbG+JpGe4oN/jUpSWoeBODKx7ta14UyCATa+2NQ4+5N+M0EwPwKa3r0b6B4Mepk2dcXvMepTl1GK6GQNlk2pgpPt7CmRFJciJT1O/MiKTUZCeA88eFceAkbkJXNp7ITlQ+3HzwKAPJqP5wM7YP/4j5NZiHmyBQACBQKBcN6ahKiuDLCxCvTA5APVC5dK7NcdYi4ljYTl9AjIWrkBpqJbjSzXoYejmgoy3l0Oel189TUNVWoaS4EiYvti+4j2BAKYvtock6PGebtXG6rUBKM/OQ9GF60+9r/qgKC1D4Z1oWPXUjCVWPX1RcFN7LCkIiIBVT1+N96xe8kXBzfAav8eokS1ENhaQpSkvdhbdiYa8RAYTr4obdwQiIcSujpAmPftB55raZftK7afI3ATWT9gu5zzhoHCT8b2RfPSSTi401LpOOnghJ0B7PFWUliPvTqzGNhAIYN+zdY3bKJMIIIBAfSEwYPrnuNDPHxf7r8DF/itwe+luAMA/I9Yhbt+fdVDap6coLYPkXhTMu7ereFMggHn3dii+VcfrcQgEEDTktkblQZ2Y9agULx7USWDN8aI2Gq+bDcsB3RD71mqUJunuRh1tyopKUBSXpn7lRyRDkpYDxyp9WNsOXsh6yLmTeydWYxsIBHDs2QZZNfR7AcC6jTsAoEQ1uCw0ESufYK58o6JcASgAgcGzuyRStU4KwpNRkpYDh17V6yT7UXXSq2qdtK5xGwCwav2gTmoebLNq7Q5ZTuEzG/QAal8nNrWIsQ5V6sShFjEWEKgvmmddV56P5l6N1GkMrc0gtrVAcVLmU5SydhSl5Si4EwPbar9r2iCvhnLk3YyATS/N9Tpte7dFnqq9LYlPhzQtR2OfQnMTWHb01rpPWUYeyoulcBrRDXKpDNkXan7KxaKNB0pzCp/5oAfwbPsrInMTvHB0FeSyMgRM+qRBLehORI+m+6szdcDCwgLLli3D4sWLIZfL0bNnT+Tl5eHKlSuwtLSEu7v7I/exZs0a9OvXD15eXhg3bhzKysrwxx9/wN/fHz4+PhgxYgRmzpyJXbt2wcLCAitWrICLiwtGjBgBAOjTpw8yMjKwefNmvP766zh16hROnjwJS0vLxyqLh4cHLl68iHHjxkEsFsPe3h7+/v548cUXMX/+fMyYMQNmZmYICQnBX3/9ha+++qrW+37zzTexevVqzJw5EytXrkRcXBw+/fTR0+aIxeJq01qVyuq/4a9JcbEECUkVa1Ik309DWEQ0rCwt0Mi54c11/KTyf/wZ9uv9IAuOgPReOCwnvAaBiTEKjikfPbb/0A/l6ZnI+XKvcgORCEZeymNdYGgIoaM9jJp7QV4sQZlqDQ+T7srp0ErjkyBybQzbxbNQGpeIgt8azkKz8btPwmvxayiKTYUkIR0+/m9AmpaD9JMB6jRdfnoXaX/cQMJeZb7jdp5A2y/nIi8oBnm3ouAxawiEpmIkH74AQHlnT9LBc2ixbiJKcwtRViBByw1TkXMjQuOJE495w5B57jYgV8BpyAtoumAEgmZtVf4wUjFr5gIDQxEMbcwgNDeBhepHREENd5LUh7BvT6HNOyNREJuGooR0+Pq9juK0XCSeqrgTtt+RlUg8FYCIfcppUsJ2n0S3rbORdTsWWbei0WLmIAhNxYhR1REANB37EvIikyHNKoB9Jx90/mACwnafQkF0xRNBpi52EFubwdTFDgKhAWxaK586K4hNQ1mx5pR4z0rizv9Dyy/fRkFQNApuRaHJrKEwMBUj5bBy2oMW2+ZDmpqN2I8OAgCSdp9A+2Pr0GTOMGT/HQjHkT1g0c4LEct2AVBedGy9ZynM23ri7oRNEBgYqO+kKs0thKK0DBYdvWHZ3ht518KUTwd5OMPTfywksanqHxoNzfU9p9BzwUhkx6YiNzEDfZa+joL0XIT9WXHcTDi4EmGnAxDwvfK4GfzhFLR5tTuOzPwM0qISmKnuiJLmF6NMWgprVwe0Ht4N0RfvoDi7AJaNbNFj7nCUlsgQdS5IF8WstYKDP8FujT9koRGQBYfBYvxoGJgYo+i4Mq7YrvVHeUYm8r7eAwCwmDQOVrMnI+vdDShLSYWB6ok8RbEECkmJcnqrj9fAsIUPMhevBoQG6jTyvAKg7Nn/KHxcOd//AueNyyC9F4mSu+GwnvQaDEyMkf+r8oKq86ZlKEvLQubn+5QbGIog9lLGAIGhCCJHe4hbNIW8WILShEpPEgoEsBz1CvKP/QU04IUg7+86Dp8vFqDwdjQKgyLRaOYwCE3FSD+snC7E+8sFkKVmI2GDcurRlG9PoPUvH6Dx7OHIORMI+xE9YN7OCzHLdwIADEyN4br0DWSduIrS9FwYezjD/b2JKIlNRe75IABAeaEEqT/+CddlYyG9nwlpUgZc5ir7lpnHdXNTQlVxu0/CW0u7nFapXX5B1S7Hq9rl2J0n4Ktql3NvRcFz1hCITMVIqtTmGDlYQexoDVNP5TonFi3dUFYoQUlyJkpzi9Tp7Hq1gam7ExIPVEzbomsx35yEz6KRKIpJRXFCOlr4j0FJWg5ST1XUyYv/W43UkzcQt1d5/sTsOoH2X8xF7m1lnTSdORhCUzESVHVi6uaIxiO6IePCHciy8mHcyBbeC0agvESG9DNBAIDiKnfeGtlaAAAKIpOrrWOmS5l7jqHJp4shuRsJye0I2E0dAQNTY+T89DcAwOXTJShLy0LaJ98DUMYPsber+t8iJzsYt/SEvLhE/YSH0/LJKDgfgNL7GTAwN4H1q31g9mJbxE15XzeFfEyZ3x5Dky2LIbkTpayTaZp10mTLYpSmZiHtkx8AVK8TQ+fqddL4g7mwHvES4md9BHmhRP2UTXlBMRRS2bMvZC1EfXMKLReNRGFsKooSMtDa/3VI0nJxv1If9qWjK5F8MgDRqj5sxK6T6PLFbOTcjkV2UDR8Zg6CyFSMONW5Y+buCLdR3ZFyJgiy7EJYtXJDu3UTkHE1FHmhyqfp0i/ehe9749Fh4xRE7f0TAoEAzRe8CnlZOTKuPP1NZU8j6ptTaLHoNRTFKOuklf8YlKTl4n6leNLzf6tw/2QAYlTxJHLXH+j8xRzk3I5Bzq1oeM8cDKGpMeIr1YnrqB5IPRMEWU4BrFq6oe0HE5FxNRT5qjpxfqUjjB0skR0YhfKSUjj2bovm74xA5I4Tz74Sqoj+5hSaVaqTlqo6SalUJ93/twopJwMQq6qTqF1/oOMXc5CrqhMvVZ1UjrEuI15E+oW76hjrs+BVyEtkSFPF2KKYVKScDEDbDychaNm3KCuUoNWqcSiIuo/MZ3ScJOw8gVZfzkN+UDTyb0XDTfW7NuXweQBAq21vQ5qajeiPDgEAEnefRMdja+A2Zxgy/w6E08jusGznhbBlFVP0Ju7+Ax6LX0NxbAokCenw8h8LWVoOMk7eUKdpMm0g8m5EoKyoBHa928L7/QmI+uigum2xH9ARRg7WyLsZCXmJDLa9feHxzkjEb/+/Z1IvtVEf/RWRuQm6HF0FoYkRbs/7GiJzE8BcOVuMLCtf4zoBETVMejHwAQDr16+Hg4MDNm7ciJiYGFhbW6Njx45YtWrVI6eSApQDF//73/+wfv16bNq0CZaWlnjppZfUn+/btw/vvPMOhg0bBplMhpdeegl//PGHeuqqli1bYvv27diwYQPWr1+P0aNHY9myZdi9e/djleODDz7A7Nmz4eXlBalUCoVCAV9fX1y4cAGrV69Gr169oFAo4OXlhbFjxz7Wvs3NzXH8+HHMmTMHHTp0QKtWrfDxxx9j9OjRj7UfXbsXFolpCyrmr928TVnHIwb3x0fv1ry4+/Om6PQFGNhYw2beZAjtbSANj0bavFWQZ+cCAETOjhoNrcjRDi5Hd6r/tp7yBqynvAHJjdtInbEMAGBgbgqbhdMhcrJHeV4Bis9cRva2vUADelQz9qvfITQVo82nMyGyNEXO9XAEjNukcWeFqbuT+sc+AKT+dhVGdpbw8RsDsaM18oPjETB+k8biZmHv/wCFXI72e5bAQCxC5rk7CPHfo/HdDv3aw2vRazAwMkRBSDwCJ3+KzLNBGmk6H1gBE7eK6Ud6nFXOJ3rKaVxdVsNDhXz9fxCZitF18zQYWZoi/UYEzr21WaOOzD0cIa5UR/G/X4PYzhLtlo+GsYMVcoLjce6tzSjJrLgj3dKrEdqvfANG1uYoSszAvS9/R9jukxrf7btsNLzGVsTGIX9tAAD8NfojpF99+rvCn0TGb//AyM4Snn5jYeRojcLgONwZ/5F60WFjF3uNcyU/IAKhc7+A54rxaLrqTUhiU3BvymYUhSl/CIob2cJ+UBcAQJdzmoPDQa+tQe4/IZBLZLAf2hUey9+A0FQMaXouss8GIf7zz3Vy11Nt/LPz/2BoKsbQjdNhbGmKhIAIHJz0scY6HDZuTjC1qThuOk9Urp01+eh7Gvv6beku3PnpIsqkpXB9oTlemDYIJlZmKMzMQ8L1MHw3ah2Ksxr20w6Sv84j19oKVrOnQGhnA1lENDIWroBcteC50NlR485Q89HDITAygv3mtRr7ydv9PfK/+QFCR3uY9O4BAHA+qLkeTPrsJZAG3q7fAtWBwpMXkWljBbuFE5XtTmgMkme9q17wXNTIUWPNI5GDHdx/3a7+23b667Cd/jqKr99B0mQ/9fum3TrAsLET8n5pGHek1yTr939gaGcFN79xMHSwRlFwLELe/BClmcpYIq4SSwoCwhE5byvc/MfDbeVbKIlNQdjUzSgOV01bJZfDtJU7HN/oA6GlKWRpOci9cBuJHx/SiBPxH/wARVk5fLYthIGxEQoDIxH8+lqU5xWhIYhRtcttK7XLNx7RLqeo2uVmfmNg5GiNguB4XK/SLrtPfgU+y19X/93t97UAgNsLdyD5SMUAieubfZF9PRxFURU3veha9FfHITIVw/fTGTC0NEX29XBcG69ZJ2YemnVy/7d/YWRnieZ+r0PsoOyrXBu/CTLV8VUuLYXti83RdNZgGFqZQZqRh6x/Q3F5+BrIMht2PK0q/8QlpNpawXHxBIjsbVASGoO4Ke+jXLXguVFjB41540WOtvA+sU39t8Os0XCYNRpF/95F7JsrlWnsrNBkyxKIHGwhLyhCSXgc4qa8j6LLQc+yaE8s78RliOys4LTkLXWdxE5Zo14E3rCxQ5V+vS18/vhS/bfDrFFwmDUKhf/eRez4VQAAu4lDAABND2uuLZa4bCtyf26YC++Gf/1/EJqK0emT6TC0NEXm9QhcfvPjaudO5T5s0u//QmxngVZ+r8PYwQp5wfG4/ObHkKrOC3lpGRx7tYH3DOWASPH9bCSfuIHQrcfU+yiISsGVyVvQauko9D2+FpArkHsvDpff3PzQ6aGehYivjkNoKkYHVTzJuh6BK1riSeU6Sf7tX4jtLNFKFU/yguNxZfwmjTpxeKkNvFSDRJL72bh/4jrCPj+m3oeirAxNpw5A2w8mQiAQoDA2FXfX7NdYL0VXolQxtp06xkbgai1irNjOEi0qxdh/K9eJVAa7F1ug6azBMFLF2Mx/w3Bp+FqNGBu4YAfafDABL+73A+RyZF4NxdXxm57ZE4fpqvazqd8bEDtaoyA4DkHjN6rbT2MXO431F/MCIhA8dxuarhgLr1XjUBybijtTPlH/rgGAeFU73uLTWRBZmiLvejhujduoUZ+WHbzRdPkYCM2MURR1H2HLv0HqT5fUn8tLy9Fk6gD4fDAJEAggiU1F5Ps/IrkBLfJdH/0VS19P2HTyAQD0ua65bt+5zgsgqcXT8ESkWwJF1YUpiGqhNFPLvOb/YUn9Zus6Cw1OeGr9z4P6vMkS6s1Yc51xKdfNUyIN2SXj2k9h+F8xxTFV11locCSFDX96l2ctM7d+1jp7nuXKeZxUVq5aE4MqeJoW6DoLDY5CweOkqvCSx5vF4L/AgJdRqhHqOgMNkLmi4dxg2FBIwRhb1ZC0w7rOwnNHFt9wp+NuqIzcO+o6CzqhF2t8EBERERERERERERERAXo01RURERERERERERER6TFFw103kBoWPvFBRERERERERERERER6gwMfRERERERERERERESkNzjwQUREREREREREREREeoMDH0REREREREREREREpDc48EFERERERERERERERHpDpOsMEBERERERERERERE9klyu6xzQc4JPfBARERERERERERERkd7gwAcREREREREREREREekNDnwQEREREREREREREZHe4MAHERERERERERERERHpDQ58EBERERERERERERGR3hDpOgNERERERERERERERI+iUMh1nQV6TvCJDyIiIiIiIiIiIiIi0hsc+CAiIiIiIiIiIiIiIr3BgQ8iIiIiIiIiIiIiItIbHPggIiIiIiIiIiIiIiK9wYEPIiIiIiIiIiIiIiLSGyJdZ4CIiIiIiIiIiIiI6JHkcl3ngJ4TfOKDiIiIiIiIiIiIiIj0Bgc+iIiIiIiIiIiIiIhIb3Dgg4iIiIiIiIiIiIiI9AYHPoiIiIiIiIiIiIiISG9w4IOIiIiIiIiIiIiIiPSGSNcZICIiIiIiIiIiIiJ6JIVc1zmg5wSf+CAiIiIiIiIiIiIiIr3BgQ8iIiIiIiIiIiIiItIbHPggIiIiIiIiIiIiIiK9wYEPIiIiIiIiIiIiIiLSGxz4ICIiIiIiIiIiIiIivSHSdQaIiIiIiIiIiIiIiB5JXq7rHNBzgk98EBERERERERERERGR3uDABxERERERERERERER6Q0OfBARERERERERERERkd7gwAcREREREREREREREekNDnwQEREREREREREREZHeEOk6A0REREREREREREREj6SQ6zoH9JzgwAc9kaR+s3WdhQalyZldus5CgxPSerWus9DguMqlus5CgxMtEus6Cw1O95JSXWehwUm9b6nrLDQ4yeUmus5Cg2PKH0DVGEKh6yw0KIYK1kdV5pbsm1QVmOqg6yw0OIwl1RUacPKMqjwVEl1nocFxtC3UdRYanNx89mGJ6Nlha01ERERERERERERERHqDAx9ERERERERERERERKQ3OPBBRERERERERERERER6gwMfRERERERERERERESkN7i4ORERERERERERERE1fHK5rnNAzwk+8UFERERERERERERERHqDAx9ERERERERERERERKQ3OPBBRERERERERERERER6gwMfRERERERERERERESkNzjwQUREREREREREREREekOk6wwQERERERERERERET2SQq7rHNBzgk98EBERERERERERERGR3uDABxERERERERERERER6Q0OfBARERERERERERERkd7gwAcREREREREREREREekNDnwQEREREREREREREZHeEOk6A0REREREREREREREjySX6zoH9JzgEx9ERERERERERERERKQ3OPBBRERERERERERERER6gwMfRERERERERERERESkNzjwQUREREREREREREREeoMDH0REREREREREREREpDdEus4AEREREREREREREdGjKBTlus4CPSf4xAcREREREREREREREekNDnwQEREREREREREREZHe4MAHERERERERERERERHpDQ58EBERERERERERERGR3uDABxERERERERERERER6Q2RrjNARERERERERERERPRICrmuc0DPCT7xQUREREREREREREREeoMDH0REREREREREREREpDc48EFERERERERERERERHqDAx9ERERERERERERERKQ3OPBBRERERERERERERER6Q6TrDBARERERERERERERPZJcrusc0HPiP/XEx9q1a9G+ffsn3j4uLg4CgQBBQUE1pjl//jwEAgFyc3Nrvd8+ffpg0aJFT5wvIiIiIiIiIiIiIiJS4hMfNZgyZQpyc3Nx7Ngx9Xuurq5ISUmBvb297jL2lDZu3IhffvkFYWFhMDExQffu3fHxxx+jefPmus4aLMa+CqvJYyC0t4UsIhpZm76G7F641rSGXu6wmTcZRi19YOjijKzN25F/4FeNNAJTE9i8PQVmL/eAga01ZGFRyNq8HbLgiGdRnGcqIOgu9h38CSFhUcjIysYXG99Dv5e66zpbdaaZ3+twnfAyDC3NkHMjHHf99qI4NvWh27hPfQVN5w2H2NEK+SEJCF71HfJuRQMADK3N0MxvDOx7t4WJiz1kWflIPRWAiE1HUVYgqbYvQxtz9Dq7CSaN7XDaZzrK8ovrpZy11WjqQLjOexVGDtYoDIlH9Oq9KLgVVWN6++EvwsNvHIxdHSCJTUXMh/uRc+aW+nP3ZWPgMKIHxC52kMvKUHgnBnEbD2ns0/WdUbDt3xHmrT2gKC3DP82n1GcR60SHZaPR7M2+MLI0RXpABK6u3If82LQa0zt1bY42c4fCvq0nTJ1tcGba50g4fVP9uUAkRCe/19Hk5fYwd3dAab4E9y/fQ8CGI5Ck5T6DEj2eJlMHwG3ecBg5Ko+TiFX7kK86B7RxHP4imvq/oT5OotYfQNaZIPXnDkNegMvk/rD0bQpDWwtce9kPhcHx6s9F1mZo6vcGbHv7wtjFHqVZ+cg4dQPRm46gXMt59aw5Th4M57kjYehgjeKQOCS89y2KgiJrTG8zrDtclo+HuIkjSmJTkLThB+SdDVR/3iX5V63bJa7/Hqk7j6n/turXCY0XvQHTlu6QS0tR8G8woqZvqrNy1YUWfq/D462+MLQ0Q9aNCNz234uiR8RYz6mvwGfeMIgdrJAXkoA7q79Hbg3HV7eDfnB6uT2uTfkMKacCND5zG/sSvGYPgXlTZ5QVSpB8/BrurPyuropWK15+Y9BkwssQWZoh90Y4Qv32PLKNcZ06AB7zhsPI0QqFIQkIrXJ+GYgN0WztBDiP7A4DsSGyzt1G6Iq9kGXkVduXoY05up39GMaN7XDWZ5q6jbHp3gpdfn2/WvrzbWZr3c+TaKIuhzJOhNUiTnir4kSxKk5kVooTgLI+XSb0U9dnmN+3GvUpsjZDiw3T4DCgIxRyBdJPXEP46u9QXixVp7Hr0w5N/cbAvHkTyEtKkfNvKCLW/oiSxAx1GufRPeHx9qsw9XRGWUExMs8GIXLdfpTmFNZJ3VSmi3rqeWMbTNwcNbaJ/PAg4rb9pv7b6dUX4fnOazBt2giyrHwk7j2N+O3H66bQ9UDd17dT9fU/rrmvbz5qMMyHvQJDbw8AgCwkEjlf7a0xfUPV0u91eFaKr7dqEV+bquKrsSq+3l79PXIqHW+9fnkXDt1baWwT8/3fCPLfW21fRjbm6HdmI0wa2+F4sxko1XEfFlC2Oe6qOsl+jDbHW9Xm5D+izXmxUpuTWqnNafvhJNh2aQ6LFk1QGJmM8/1X1Wm5nobv8tHwfrMvDC1NkREQgRsr9qHgIX1WAGg2pT9azh0KEwcr5IQkIODdH5AVFKP+3NjBCh3fGw/nl9rA0NwY+dGpuPfFb0j844bGfhr3a4+2i0fCuqUbyqWlSP83FBenba2PYtaa85RBaDxvBIwcrFEUEofY1XtQGFTzbx27Yd3g6j8exk0cIIlNQfyH+5Fbqc/mvXU+HMf21dgm59wthL75ofrvjtd3wNhVM+bGf7QfyV9p7+/pmtX44bCe9rry2kl4DDI+2g7pXe3x0cjbHbbzJ0Hc2huGLs7I2LgTeT9qlsv9r+9h6OJcbdvcg78j88Ov66UMT8Nh8mA4z3lN2a8PjUPie988vF8/tDsaL39T2a+PS0Hyhh+Qd7bid56BqTGarJoI64FdIbKxgDQhHel7/w8Z+0+r0zT/34ew6NZGY7/pP55CwsqddV9AIqoz/6knPp6WUCiEs7MzRKLnd7zowoULePvtt/Hvv//ir7/+QmlpKQYMGICioiKd5stsYG/YLZuN3F37cX/cXMjCY+C8YyMMbK21phcYi1GalIKcL/egLCNLaxr7tUtg0q0jMlZ/jOTXZ0Fy9SYa7doMoaNdPZZENySSEjT3borVS+fpOit1run84fCYMQj3/PbgypD3UFYsRdcjK2AgNqxxm0YjXkTLdRMRueVnXH5lFQqC49H18AoY2VsCAMTONhA7WSN03QFc7L0ct9/ZCYe+7eD7+Wyt+/P9fBYKQhLqpXyPy2FEd3itnYz4Lf9D4AB/FAXHo82h1TBUla0qy87N0HLHIqQeOoubr/gh8+R1tN7nB9MWruo0xdEpiFq1Bzf7LMXtEe+hJDEDbY+8B0O7in0aGImQefwqUn74s97LWBfazhuGltMG4OqKvfi/4WtQVizFgAP+ED7kuBGZipETkoCrq7/X/rmJEWzbeiDoi2P4fdB7ODtzK6yaNkL/fUvqqxhPzHFEN/ism4TYLT/jxisrUBgcj/aHV9V4nFh1bobWOxfi/sFzuN5/BTJO3oDvd8thVuk4EZqKkXctHFEfHtS6D7GzLcRONoha9yOu9V6GkHe2w65vO7T6fE69lPFx2L7aA65rpuL+Z0cQPGgpikPi0OzA+xDZWWlNb965Oby+XoLMQ2cQPHApck9fg/eeFTBp7qZOc6v9VI1X7OJtUMjlyPnjqjqNzZAX0fSLd5B59CzuDViC0JErkXXsYr2X93H4zB8Or+kDEeS3FxeGvIfy4hJ0P/zwGOsy4kW0WTsBYVt+wfkBq5EfnIDuhypibGVeswZDodC+H6/ZQ9ByxRuI3PY7zvT2w5UxG5B+7k5dFa1WPOa/CrcZgxDi9y2uDXkX5cVSdDyy8qHldxrRDc3XTUT0lp/w7ysrURAcj06HV2qUv/kHk+AwoBPuzNyKGyPXQexsg3Z7tceK1p/Pfmgbc7nbYpxvM1v9kmXmP3mBq5VjEmK2/Ixrr6xAQXA8Oj4iTrTduRDJB8/hmipOtKsSJzzmvwrXGYMR6vctrg9ZjfLiEnQ4skqjPttuXwDz5k1w842PEDThY9i82BItt8xSf27s5oB23y9DzuV7+PdlfwSO2wAjWwu027u0Ii9dmqPNtreRfPAs/um9FHdmfA6rDt5ouUV7O/481hMARG06ggttZqlfCXtOqT+ze7k92mxfgKTv/8LVPssQtmIP3GcPgeu0gXVeB3XBdEBv2C5V9fXHz4UsIgZO2zfCwMZaa3rjzu1QdOoc0mYuR+qkd1CWlgHnHZueq358M1V8veW3F+eGvIey4hL0rEV8bauKr2cHrEZecAJ6HFoBcZXjLfbHszjRdq76dW/9Ia376/jZLOSFJtZpuZ6G9/zhaDp9IG777cVFVZ10e0SdNB7xIlqvnYDwLb/ggqpOutXQ5jSdNRiooc0BgITD53H/93/roih1ptXbw9B82gBcX7EXp4cp+6x9D/o/tE7cX+2Kjmvewt3PfsUfA99FTkgC+h70h7hS3737l3Ng4dUIF6Z8hhMvr0TiHzfQc9cC2LRxV6dxHdIF3b+cg5gjF/HHK6vw54h1iPv1qravfGbsXu0Oj7VTkLTlKG4PXI6ikHi0OqT5u6Qyi87N0WzHYqQfPIPbA5Yh+9R1tNjnB9Pmrhrpcs4G4obvdPUrYu7n1faVsPmQRpqUPX/USxmflvmg3rD3n4Xs7QeQ+PrbkIbFoPHujyC01d6vfXDtJOuzvTVeO0l8YyFiXxqnfiVPXwEAKDp9qd7K8aRshveA6/vTcP/zwwgZvASSkDj47F9TY7/erFNzNP16KTIP/42QQUuQe+oavL5dAeNK/XrXNdNg2acjYhduxb0+C5C25zjcPpwFq1e6aOwr48CfCOowRf1K+kj770Yiajga7MCHXC7Hxo0b4enpCRMTE7Rr1w4//fQTgIrppM6cOYPOnTvD1NQU3bt3R3i45gj3pk2b4OTkBAsLC0yfPh0lJSW1+u61a9fi+++/x2+//QaBQACBQIDz589rnerqjz/+QLNmzWBiYoK+ffsiLi5OY19ZWVkYP348XFxcYGpqirZt2+LQIe0d09qQSqXw9/eHq6srxGIxvL29sWfPHvXnFy5cwAsvvACxWIxGjRphxYoVKCsrU39+6tQpTJkyBa1bt0a7du3w3XffISEhATdv3tT2dc+M5cTRKPjlJAp/O43SmARkffgFFCVSWIzU/uNNFhyBnM+/QdGp81DISqt9LhAbwaxfL2R//g1KAu+iLPE+cnf+iNLEZFiOGV7fxXnmenXrgoWzJqN/7x66zkqd85w1GFGf/4q0UzdREJKA2/O3Q+xkA6fBnWveZs5QJO4/i6TDF1AYkYy7y/egXCKD6/g+AIDCsCQETt+K9D8DURyfjqzLwQjfeASOAzpCINQMi26T+8PQ0gwxO07UZzFrzWX2MKQcOIO0w+dRHJGESL/dkEtkcB73stb0jWcORfa5ICRt/x2SyGTEbz6CwrsxaDx1kDpNxq+XkXvpLkoS0lEcnoSYNd9DZGkKs5YVncH4T44iefcJFIU2jAGgR2k1YxDufPEbEv4MRE5oIi6+sxMmTtZwG9ipxm2Sz91B4OafkFDljvQHSgsk+HP8x4g7fg350SnICIzGv+/+APt2TWHWuGFdiHGbMxTJ+88g5fB5FEUkI2z5tyiXyNB4fF+t6V1nDUb2uSAkbD+O4shkxHx8FAV3Y9Gk0gW01J8uIfazn5F98a7WfRSFJeLu9M+Q+WcgJPFpyLkcjOiNR2A/oFO18+pZc5r5KjIO/oXMo2dREpmE+BU7IZdIYT+un/b004ch7/wtpO48hpKoJCR/cgjF92LgOHWIOk1ZRq7Gy3rgCyj45x6kCao7NIUGcPtgOhI//B4ZP56GNOY+SiKTkHP8n2dR5FrzmjkI4VuPIfX0TeSHJuLmgh0wdrJGo0E1x1iv2UMQf+AcEg5fQEFEMoL89qBcIoX7uN4a6axau8N7zhDcWrSr2j4MrczQ0n8Mbi7YgaRf/0FxfDryQxOR+mdgtbT1yX3WYMR8/isyTt1EYUgC7s3/GmInGzg+pI3xmDMUSfvP4v7hCyiKSEaI+vzqAwAQWZjA5c2+iFjzI7IvB6PgTizuvbMTNi80h1Unb419NZn8CkSWZojf8X81fp8sMw+yjIpXjSNJj1v2OUORtP8M7qviRKiqHC41xAm3WYORdS4I8duPoygyGdEfH0X+3Vi4VYoTbrOGIPbzX5BxKgCFIQkIVtWnw2DlhQMzHxfY9+uAkCW7kB8Yhdzr4QhbtQ/OI7tD7GQDALD0bQqB0ABRG49AEp+GgruxiNtxHBZt3CEQCQEA1p19IElMR+K3p1CSkIHc6+FI+uFvWHXwqpO60XU9PVBeVKLxfy+v9FRMozG9kHEyAEk//A1JfDoy/76F2C9/g8f8V+u8DuqC1WP29TNXbULB0eOQhUejNC4RWes+AwQCGL/Q4Rnn/Ml5q+Jriiq+Bqjia+OHxFef2UMQd+Ac4lXx9VYN8bVcIoU0I0/9Kius/mSl5+T+MLQyReT2muPLs1a1zQmsRZvjXaXNuV1DnVg+pM0BgLvv/oDYfX+hKD69Tsv0tFrMGIR7X/yGpNOByA1NxNWFO2HqZA3XQTX3WVvMGoyog+cQc+Qi8iPv47r/PpRLpPAaX1En9p19ELH3T2QFxaAwIQP3vvgNpXlFsPX1BAAIhAbo/MFE3PrwECJ/PIuCmFTkR95HwvFr9V7mh2k8ezjSDvyN9CPnIIlIQozfLpRLpHAcr73P1mjGUOScu4X7O36DJDIZiZsPo+huLJynDdZIJ5eVoTQjV/0qz6t+42d5oUQjjVwirZamIbCeMgp5/zuFgl//RGl0AjLWfamMp6O0x1PpvQhkffotCk9e0HrtBADkOXkoz8xRv8x6d4Us4T4kN57tDSm14TRrBDIP/Yksdb9+B+QlD+vXD0fe+UCkqfr19z89qOzXT6no15t3ao6s/51DwdV7kCWlI/PAnygOiYNZex+NfcklUo3+v1xL7CWihqXBDnxs3LgRP/zwA3bu3Ing4GAsXrwYEyZMwIULF9RpVq9ejS1btiAgIAAikQjTpk1Tf3b06FGsXbsWGzZsQEBAABo1aoTt27fX6ruXLVuGN954A4MGDUJKSgpSUlLQvXv1aYMSExMxatQoDB8+HEFBQZgxYwZWrFihkaakpASdOnXCiRMncO/ePcyaNQsTJ07E9evXn6heJk2ahEOHDuHLL79EaGgodu3aBXNzcwBAcnIyhgwZgi5duuD27dvYsWMH9uzZgw8//LDG/eXlKadKsLW1faL81AmRCOKWzSD5t9LFDoUCkn8DIfZtVfN2DyMUQiASQiHVbNgVUhnEHdrUsBE1NCbujjB2skHmxXvq98oKJMgNjIZNZx+t2wgMhbDy9UTmpYptoFAg8+I9WNewDQAYWpqirEACRXnFIlnmzVzgs3QUghZsh6IBLJ4lMBTBwrcpci9W6oAqFMi9dAcWnZtp3cayUzPN9AByzt+GZQ3pBYYiNJrYH2V5RSgMideapqEzd3OAqZM17l+uOAZKCyTIvBUNx041HwNPwtDSBAq5HLIGMHXEAwJDISx8myL7UqUBCoUCORfvwqqGc8CqUzNkVzrPACDr3G1Y1XCc1JZIy3n1rAkMRTDz9UL+pdsVbyoUyL98B+adtE/zaNapuWZ6AHnng2DeSXt9iOytYNWvEzIO/V2xj7ZeMGpkD8gVaHV6C9oF7oHPj+9pPDWia6ZuyhibUSXG5tyKhu1DYqy1r6fGNlAokHHpnsY2QhMjdNrxNm6v/A5SLdMyOfRuA4GBACaNbNHv4icYGLgNXXYvhEnjZ9cfMXF3hNjJRmMwr6xAgrzAqBqPfeX55YmsKudX9sW7sFZtY9muKQyMRMiqtN/iqPuQJGZo7NesmQu8lo7CvQVfQyGveTCj25mP0fvODnQ6ugrWXZ7unNQsR/U4kf0UceJBfWZVqc/8wCh1+2vV2QeluYXIv10xHUv2xbtQyBWw7KgcFMq/EwPIFcqBJAMBRBYmaPT6S8p0ZeUAgNyASBg3tod9v/YAACMHKzgN64rMStM41gVd1dMDHgtGoHfot+j69ya4zxuuMYhsYGSIcqlMI728RAZjF3sYuzo8UXnrjUgEo5bNUHJNs69fcq32fX2BsRgQiSDPK6inTNatB/E1vUp8za5FfE2vEl/Tq8RXAHAd3QNDg3eh3/mP0XrVWAhNjDQ+t2jmgpZLXkPAgh1Q1NFg6dN6WJvzqH69tjbHpkqb03nH27hTQ5vTUJm7OcDEyRqpl6r3We1r6LMaGAph6+uJ1EvBFW8qFEi9FAz7SoPrmQGRcH/1RRhZmwECAdxHvAihsSHS/gkFANi29YBpY1so5AoM/vNDjLr1FfruXw6r5k3qp7C1IDAUwdzXC3mXNH/r5F26A4sa+mAWnZtppgeQez4IFlX6eFbdWqPL3b3ocOlLNN00CyIb82r7cpn/GroEfwffPz9B47kjAB3fuKOVoQjiVj7Vrp0UX70F4/ZPeO1Ey3dYDH8ZBb+cfnTaZ0xgKIJZWy/kVzlG8i/dhlnHh/XrNY+R/Au3NH4HFN4Mh/UrXWDorOyHWnRvA+OmjZF/MUhjO9vXXkK7Oz+g9d9fwGXFBBgYa8ZeImp4GuScTVKpFBs2bMDff/+Nbt26AQCaNm2Ky5cvY9euXZg1S/k4/EcffYTevZV3NaxYsQJDhw5FSUkJjI2NsXXrVkyfPh3Tp08HAHz44Yf4+++/a/XUh7m5OUxMTCCVSuHsXH2ewwd27NgBLy8vbNmyBQDQvHlz3L17Fx9//LE6jYuLC5YtW6b+e8GCBTh9+jSOHj2KF1544bHqJSIiAkePHsVff/2F/v37q+vlge3bt8PV1RVfffUVBAIBWrRogfv378Pf3x/vv/8+DAw0G265XI5FixahR48eaNNGd4MBQhsrCERClGflaLxfnpUDQ0/XGrZ6OEWxBCVBwbCe9RYyYhNQnpUDs8F9IfZtidLE+3WRbXoGjB2Uj6tW/QEjzciD2NFa6zZGtpYwEAm1bmPm01jrNoa2FvBe/BoS959Rv2dgJEKHnQsQtu4gSpKzYOruqHXbZ8nQ1gICkbDa3O6yjDxYebto3cbI0VpL+lwYVak/21c6ouXOxTAwMYIsLRd3xq5HWfbzcXGhKlNV2SQZmtPBSDLzYeKo/RHoJyEUG6LzqnGIOXYVpQ3obh9D1Tmg7TgxreEcUB4nudXSi5+ivgxtLeCxeBSS9//96MT1SKQ6b0ozNeujNCMXxl7azxtDB2uUVqmP0sxcGDrYaE1vP6Yv5IUS5JysmD5D7OYEAGi8dCwS1+2DNDEdzrNHoPlP63G319soz637dQgel7Hq/7dEa4zV/n8vtrWAgUiodRtz74rjq826ici+EYnU09qfKDVzc4TAwADNFo7A3fd+QGl+MVqueAPdj6zC2Zf9oSgtf5qi1YqRg7U675XJatHGVD2/lG2M8ngycrSGXFpabT0oWWYexKrvFBiJ4LtzISLWHUBJchZM3J2qfZc0LQchy75B3u0YGBgZosmEvuj86/u4NvhdFNyNe/wC16Icsoe0leIa4oSR6lh5UJ/a6uZBm2PkaF1tqi5FuRxluYXqOi9JyEDg2I/Q9ptFaPnJTBiIhMi9EY5bb1asjZN3Ixx3521D292LYCA2hIGhCBmnAxC2ovoaB09DV/UEAAnfnkLB3ViU5hbCunMzeK8eD7GTNSLW/AgAyDp/G80/mISUIxeQfTkYpp7OcJszTJkHJ2uN9VB07aF9fY/a9fVtFs1AeUaW5uBJA/Ygvmrrjxo/Ir5q28aiUnxN/OUfFCdloiQ1B1at3NDm3XEw92qEa9O3AlD2YbvsmI+7HxyEJDkLZg2gDwtA3a7UR508qs1pqIxr6LOWZNTcZ62pHS7JzIOldyP135dmb0PPnfMxJmQX5KVlKJPIcGH6VhTGKZ9MNVcdF75LR+Hm2gMoSsxAyzlD0P/n1Tjecxlkuc9+KmyR+rdOrsb7pRl5MKnht46yz1a9j2dYKZ7mnLuFrD/+hTQhHcYeznBb+SZaHngXd4etAlQ3t6Xs+QNFd2JQllsIiy7N4b7yLRg52SBu7Xd1WcSnJrS2VMbTzFyN98uzcmDU9MmunVRl3q87DCzMkf9rw5viWN2vr3KMlGXmwdhb+6CdoYM1yqrUV2lGnka/PuG93XD/eB7aBeyFvLQMkCsQ7/c1Cq+FqNNkHbsIWVI6StNyYNLSHU1WTYKxlwuiZ34M0gGF7m9MpedDgxz4iIqKQnFxMV555RWN92UyGTp0qHi82dfXV/3vRo2UjXx6ejrc3NwQGhqKOXM05xXv1q0bzp07V2f5DA0NRdeuXat9R2Xl5eXYsGEDjh49iuTkZMhkMkilUpiamj729wUFBUEoFKoHe7Tlp1u3bhAIBOr3evTogcLCQiQlJcHNTfMu07fffhv37t3D5cuXH/q9UqkUUqnmY55SuRxigwZ4B0QlGas/hv26ZXD7+zAUZeWQhUWi6NQ5GLWsm7slqe41Ht0DbT+Zof77xlub6/07ReYm6HLAD4URyYj45Gf1+81Xj0NhZDKSf374+aEvcq8E42a/5TC0tUCjCf3RavcS3BqyEqV1NJd8fWr6Wnd0/7jiib+/Jn1a798pEAnRZ+cCCAQCXH3GCzE/D4TmJmh3wB9FEUmI/eQnXWen3tmP64esXy9qPmVooGyLU778CTl/KAdEYpdsQ7uAb2E7rDsy9j/7H5NNRvVA+0+mq/++OqF+YqzzgI5w6Nka5/qvrDGNwMAABkYi3Hn3e2RcUN75HjB3Gwbf2QGHHq2Rfr7up1ZwHt0DrT6Zqf771lu6+6Hqs3o8CiOTkfKQNqY4OgXF0Snqv/MCImDi7gT32UNxb37DW2i0rhg5WKHllllIOXIRqb9egdDcBF5+Y+C7ZwkCxyifYjZr5oIWH05GzJafkXX+NsSONvBZ8xZafjIDIYu1T3PzvEnYVTHFZmFIAuSlZWj5yUxEfnQIClkZkn88AxN3J7T/0R8CQyHKCyRI+OYkzP3GPPQJoueR1dSxMBvYB6kzltU4TYuuuY7qgQ6V4us/9RRfASBu/1n1v/PDElGSloNeP78LM3dHFMWno/WqcSiIvI/En6/UWx5qo8moHmhXqU7+rcc2x75na5x/SJvTUHi81h0vbK7os56fWH991nZ+r8PI0hR/v7ER0uwCuA7qhF47F+Cv19YjNywJAlU/pfKC51cX78ZrN7+E27CuiKp0nD3vsn6rOBeKwxJQFBKPTte2w6p7a+RdVvZBUnYdr0gTGg+FrAxNN89G/Ib9UMjKqu1Tn1mOGojiSzdQnpGt66w8M45Th8K8Y3NETvkIsuR0mHdtDbePZkOWlo2Cy8o+aeaBir67JCwepWk5aH50PcTuzpDGp+oq60T0CA1y4KOwUHkX5IkTJ+DiojmyLxaLER0dDQAwNKxY8OvBxX55A5iOprJPPvkEX3zxBbZu3Yq2bdvCzMwMixYtgkwme/TGVZiYmNRZvubPn4//+7//w8WLF9GkycMfZ924cSPWrVun8d5CR08scq6beZTLc/KgKCuH0E7zTlqhnQ3KM3Nq2OrRypJSkDp9KQQmxjAwM0V5ZjYcNq9GWVLKozcmnUg7dRO5N6PUfz9Y1E/sYAVpeq76fbGDFfKD47TuQ5adD3lZOcQOmndJVd0HAAjNjPHC4RUoL5Tg5tTP1FNoAIBdz9awbOkG52HKwc0HMeaV0N2I2noMkTq4mFuaXQBFWTmMqpTNyMEKsiple0CWnqslvXW19PJiKUriUlESl4qCwEh0+edLOI9/GYnbjtVhCepHwp+ByLgVrf5baKRs2kwcLCGpVE4Te0tkBz/9GiUCkRB9dy6AeRM7nHpjY4N62gMASlXnwOMfJ9bV0kvTH3+6CKGZMdofXonywhLcnbpF47zShTLVeWNor1kf2p7qeKA0IxeGVerD0N4apRnV2yTzF1rCxLsJoudu0dxHujKtJCJJ/Z5CVgZpfBqMXHQzDU3q6Zs4F1g5xirPFWMtMTbvnvap7qTZBZCXlaufyKu8zYN9OPRsDTMPRwyN+FYjzQt7FiHrWhguj/oQJaq0BRHJ6s9lWQWQZhfAxKV+1szJOHUTV2toYyqfG0YOVigI1l5+WQ3nV+Xyy9JzYSA2VE71VumpDyN7K0hVx5xtz9awaOkGpyptTJ/QbxC79VdE19DG5N+KhnVX7VM5PI6aymGkpa18QFpDnJCp4sSDO3SrxhqxgxUKVG22LD232oLEAqEBRNbm6u91nTYQZfkSRK4/oE5z7+2v8FLQDlh18kHezUh4LByJ3OsRiN+uvFBVGJKA8uISdDn+AaI2Hqkx1j0uXdWTNnmBUTAwFMHE1UE9IBb14UFEbTikfMokKx+2vdoCACTxabUv5DPwNH19y0mvw2raOKTO9kdpZGx9ZvOppJy+iWwt8VXsYKWOdw/+flR81daHLXnIMZ2t6gOZeTqjKD4dDj1bwaqlG1yGKWcXeBBfhobsQvgXxxBa6Uaf+pR6+iZyaqiTx21zHlYn9qo2Z0gNbc6VUTVP+/ysJf0ZiMwa+qyV/4+NHSyRU0OftaZ22NjeChLVkw/m7o5oPm0A/q+PP/JU7WxuSAIcujZHsymv4PqKfZCkKb8vL7KiHZbLylAYnw6zemqHH6VM/VvHWuN9QwcrlNZwDij7bFr6eA85Z6QJaSjNyoOxp7N64KOqgsBIGBiKIHZ1REl0w5kxojw3XxlP7a013hfa2aDsKa6dPCBq7AiTbh2Q+s76p95XfVD366scIyJ7K3Xfu6rSjFyIqtSXoYOVul8vMDaCi/8ERM/YhLyzyqfGJKHxMG3tCec5I9UDH1UV3YoAAIg9OPBB1JA1yFv2W7VqBbFYjISEBHh7e2u8XF1r9/hey5Ytce2a5sJc//77bw2pqzMyMkJ5+cMv1rRs2bLaWh1Vv+PKlSsYMWIEJkyYgHbt2qFp06aIiIiodT4qa9u2LeRyucY6J1Xzc/XqVY15XK9cuQILCwv14IZCocD8+fPx66+/4uzZs/D09Hzk965cuRJ5eXkar7mOj96u1srKIA2NgHHXSosVCgQw6doB0jshNW9XSwpJCcozs2FgYQ6Tbp1RfL5hLSxLFcqLSlAcl6Z+FYYnoSQtB3a9KqZiE5mbwLqjF3ICIrXuQ1Fajrw7sbCvtA0EAtj1ao3cStuIzE3Q9ehKyGVluDHpU8irrAcTOO1zXHzZH5f6rcClfitwZ8luAMDVEesQv083j/0qSstQcCcG1qoLGwAAgQDWPduiIEB7XMm/GaGZHoD1S77IryG9moFAfVGwoSsrKkFBXJr6lRuRjOK0XDTq2VqdxtDcBPYdvJB+U/txU1sPBj0sPZ1wauwmSHN0P11RVYrSchTciVFfAAMACASw6dUGeTWcN3k3I2DTS3PKQ9vebZH3qOOkCqG5CTocXQ2FrAy3J22udl7pgqK0DEV3omHZs+IpUQgEsOzZFoU3w7VuU3QzXDM9AKuX2qHwZvX6cBjfH0W3oyAJidPcx51o5Xz7XhVTcQhEQohdHSFL0s3CqmVFJSiKS1O/CsKTUZKWA4deFeeKyNwENh28kP2QGJt7J1ZjGwgEcOjZWr1NxLbfcfblFTjXf6X6BQB33/8RgapFZ7OuK+ve3KtiWg5DazOIbS1QnJRZp+V+oLyoBJK4NPWrKDwJ0rQc2FY69oXmJrDq6F3jsa88v2I12iUIBLDt1Qa5qm3yb8dALivT2K+pVyOYuDqo93t72ue4+rIf/u3nj3/7+SN4ibJeboxYi8SHtDEWbdwhVV2seho1xQnbR8QJ2ypxwq5SnJDEp0OalgO7SvsUmpvAsqO3uv3NC4iEobU5LHwr+pE2PZXrveSrLpAKTcTVpi9QrxOkuoArNBFDUTWN6uanyk8+Py1d1ZM2Fm08oCiXV5sqDHIFpKk5UJSWw/m1Hsi9EY7SrAY2VWVZGWShEZoLk6sWKn9YX99yyhuwnjkBafNWQRbyZL+fnpXaxlfbWsRXxyrx1bFSfNXGqrU7AKAkTXkR79r0rTjTbwXO9l+Js/1XInDpNwCAiyM+QMy+v562qLX2OG3Oo/r12tqcB9tEbvsd515egfP9V6pfAHDv/R9rXOhcV8qKSlAYl6Z+5UUkQ5KWC6eemnVi38ELmTX0WeWl5ci+Ewvnnpp14tyzNTJVg/si1ZovVZ/+UpTL1U+kZt2JQ3mJDJaV2mGBSAgzVwcU1VM7/CiK0jIU3omGVU/NmGvV0xcFWvpgAFAQEAGran02XxTU0McDAKNGthDZWECWVvNAgVkbDyjKy6tNlapzpWWQhkTC5EXNeGr6YnuUBD39tRPL1wagPDsXRRd0u8h9TRSlZSi6Gw2Lav16XxQF1r5fb9mrvfp3gEAkhIGRYfX1kMrlgKDmS6amrZV9mZoGXIioYWiQT3xYWFhg2bJlWLx4MeRyOXr27Im8vDxcuXIFlpaWcHd3f+Q+3nnnHUyZMgWdO3dGjx49cODAAQQHB2usifEwHh4eOH36NMLDw2FnZwcrq+pzbM6ZMwdbtmzB8uXLMWPGDNy8eRPfffedRhofHx/89NNP+Oeff2BjY4PPPvsMaWlpaNXq8Ree8vDwwOTJkzFt2jR8+eWXaNeuHeLj45Geno433ngD8+bNw9atW7FgwQLMnz8f4eHhWLNmDZYsWaJe3+Ptt9/GwYMH8dtvv8HCwgKpqcqRaSsrqxqfKBGLxRCLxRrvZdXxNFf5P/4M+/V+kAVHQHovHJYTXoPAxBgFx5QLatl/6Ify9EzkfKmau1kkgpGX8jgQGBpC6GgPo+ZekBdLUKZaw8Oke2cAQGl8EkSujWG7eBZK4xJR8FvDW6TraRUXS5CQVHEnSvL9NIRFRMPK0gKNnBvGvL5PKnb3SfgsHomi2FRIEtLRzH8MpGk5SDsZoE7T9afVSP3jBuL3Ki8Wxe48gXZfzkVuUAzybkXBY9ZgiEzFSDysHDQUmZvghaMrITQRI2jeFhiamwDmyuNfmpUPyBUojte8MGlkawEAKIxIrjZv+7OUvOv/0PyLt1F4Oxr5t6LQZOZQGJiKkXpYOY1f823zIU3JRtyGgwCA+9+cgO+v6+AyZxiy/w6E48gesGjnhcjlyh+CBqZiuL0zClmnAyBLz4GhrSUaTx0IsbMtMo5fVX+v2MUeImtziF3sAaEBzFp7AAAksamQFz967aRnLeTbU2i3cCTyY9JQmJiODstfhyQtFwmV5n4eeGQlEk4GIPQ75UUAkakYlp4V8+ybuznAtrUbpDlFKLqfBYFIiJd3L4RdWw/8NXkLDIQGMHmwDk1uIeTPYE2C2krYeQKtvpyH/KBo5N+KhtusIRCaipFy+DwAoNW2tyFNzUb0R4cAAIm7T6LjsTVwmzMMmX8Hwmlkd1i280LYsm/U+xRZm8HYxR5iZ+Udu6aqubVl6bmQZeSpBz0MTIwQPO8riCqdVzLVeaUrad/8Ds/PF6LoTjSKbkXCaeYwGJgYI/OIcl0fzy8WojQlG0mb9ivT7/k/NP/pQzjNfhV5f9+E7YieMPX1QpzfDo39GpibwGZYdyR+8F2175QXSpC+/zRclo2D7H4mZMkZcJ4zEgCQ/X8NZwA++ptTaLboNRTGpKI4IQMt/cegJC0XKacqYmyP/63C/ZMBiFXF2Ohdf6DjF3OQczsGObei4TVzMISmxkhQxVhpRp7WxWUlyVkoTlCuO1AUk4qUkwHw/XASgpZ9i9JCiXJ6lqj7yLzy9D/cayt+90k0XfwailVtjLf/G5Cm5SC9UhvT6ad3kf7HDSTuVfYf4naeQJsv5yJf1cY8OL/uq8pfViBB8sFzaL5uIkpzC1FWIEHLDVOReyMCeaqLUlXvyDe0VT4FUVSpjXGbNRiShHQUhiVBaGwIl7dehm3PNrg5dkPdlH3nCbTWEifuq+JEa1WciFLFiYTdJ9H52Bq4zxmGjL8D4ayKEyGV4kTC7j/gufg1FMemQJKQDi//sZCm5SDjpHIalaLIZGSeuYVWW2Yj1O8bCEQitNg4FanH/oFUdQEq4+9AuM0egqZLRqunuvJeNQ6ShHQU3FPe8Z/x50202jILTSa/gqzzt2HkaIPm6ycjLzBSvZ+6oot6sursA6uOPsi+HIzyIgmsOjdTrufx0yWU5Snn3Te0tYDTsK7I/icEBmJDuIzvA6fhLyLgtbV1Wv66kvfjz3BY7wdpSARk98Jh+Zaqr6/ql9uv90NZeiZytyn7+pZTxsJm3iRkrNyIsvup6qdF5MUSKCQNr9+hTdQ3p9Bi0WsoiklFUUIGWqni6/1K8bWnKr7GqOJr5K4/0LlSfPVWxdd4VXwxc3eE66geSD0TBFlOAaxauqHtBxORcTUU+aGJAICiGvqwBZHJKNVhHxaoaHMe1Im2Nqf7/1YhpVKbE6Vqc3Ifs80prtTmAICZhxOEZsYQO1hBaGwES9WAUUFE0jNZV6omYd+eQpt3RqIgNg1FCenw9XsdxWm5SDxV0Wftd2QlEk8FIEI1cBW2+yS6bZ2NrNuxyLoVjRYzB0FoKkaMqk7yolKQH5OKrpunIfCDg5DmFKLJoE5o9FIbnJ+kfEK1rFCCyB/PwnfpaBTdz0JRUhZazR0KAEj4P91d9L6/6zh8vliAwtvRKAyKRKOZwyA0FSP9sHLqLe8vF0CWmo2EDcqnAlO+PYHWv3yAxrOHI+dMIOxH9IB5Oy/ELN8JADAwNYbr0jeQdeIqStNzYezhDPf3JqIkNhW554MAAOadmsGiow/yrtxDeWEJLDo3g+e6qcj4+SLK8579WiePkvvdL3DcuAzSexEouRsO60mqeKpak8Nx43KUp2ci6/N9yg0MRTDyUk57LjA0hMjJDkYtmkJRXILShEpPswgEsHhtAAqO/a286N9Ape3+DZ6fv4Pi21EoCoqE04zhGv16j63voDQ1C8nqfv1xNP/pIzjNGoG8MwGwHdFL2a/33w5A2WcvuHoPrqsnI6FEBmlSOixebAO71/sgcZ2yDsXuzrAd+RLyzt5EWU4BTFq6w3XNdBT8ew+SUO1PrBFRw9AgBz4AYP369XBwcMDGjRsRExMDa2trdOzYEatWrarVdFZjx45FdHQ0/Pz8UFJSgtGjR2Pu3Lk4fbp2F71nzpyJ8+fPo3PnzigsLMS5c+fg4eGhkcbNzQ0///wzFi9ejG3btuGFF17Ahg0bMG1axbyd7777LmJiYjBw4ECYmppi1qxZGDlyJPLynuzOgR07dmDVqlWYN28esrKy4ObmhlWrVgFQLqT+xx9/YPny5WjXrh1sbW0xffp0vPvuuxrbA0CfPn009rtv3z5MmTLlifJUF4pOX4CBjTVs5k2G0N4G0vBopM1bBXl2LgBA5OyocdFM5GgHl6M71X9bT3kD1lPegOTGbaTOUC4mb2BuCpuF0yFyskd5XgGKz1xG9ra9gI6nXakP98IiMW2Bv/rvzduUTyeMGNwfH727VFfZqhMxXx2HyFSMtp/OgKGlKXKuh+P6uE0ad5Kbujupf9QBQMpv/8LIzhLN/F6H2NEa+cHxuD5+k3ohUUtfD9h08gEA9L3+hcb3ne28AJJE3dzlVBsZv/0DQztLuPuNhZGDNQqD43Bv/Efqu5HELvYad3flB0QgbN4X8PAfD8+Vb0ISm4LgqZtRHKb8cawol8PU2wVOb/SBoa0FSnMKUBAUjaCR76M4vGKKHne/sXAe20f9d6cznwAAbo9ag7x/nt1Fytq6u/3/IDIVo/vmaTCyNEX6jQj8OWEzyisdNxbujhBXOm7s2zXF4J9Wq//uunYCACDy6EVcXrwbZs42cBvYCQAw8i/NC48nX/8IqVdD67NIjyX9t6swsrNEU783IHa0RkFwHILGb1SfA8Yuduq7owHl2gHBc7eh6Yqx8Fo1DsWxqbgz5RMUqY4TAHAY2Bmtvpyn/rvt7kUAgJhP/ofYT3+Cha8nrFTnVffrX2rk50rn+TpdaDf79ysQ2VrCZdk4GDrYoDg4FhETPkCZ6rwxauyg0cYUBoQjZv7ncPF7E038J6AkNgVR0zdBEq457YTdiJ6AQIDsY5e0fm/S+u+BsnI0/XIRDIyNUHgrAmFvvN+gfkRHfnUcQlMx2qtibNb1CPwzXjPGmnk4aZwryaoY29LvdYgdrJEXHI+r4zdB+phrAt1csANtP5iAF/f7AXI5Mq+G4ur4Tc90erS4r36H0FSMVp/OhMjSFLnXwxH4iDYmTXV+efmNUZ1f8Qis1MYAQPj7P0Ahl6P9niUwEIuQee4OQv33PFbeDAxFaL52IsTOtiiXSFEYmoCAMR8ip44GhirKUREnAqvECVSJE3fnboP3irHwVsWJ21XixIP6bPnpLHV93hq3UaM+787bhhYbp6HTT+9BIVcg/cQ1hK/ap/4853Iw7s7dBo+3X4X7/Fchl0iRGxCBwPEbIS9R7iflyAWIzE3gOm0gmq2diLL8ImRfDtaYHquu6KKe5NIyOI3sjqbLXoeBkSEkCemI3/UH4nf+n0beGo3tDZ+1EyEQALkBkQgYtQ75labRaUiK/7yAbBtr2MxV9vVlVfv6jRyBSnfbWr4xDAIjIzhuWaOxn9ydPyB354/PMutPLEIVXztUiq9XahFfxXaWaFUpvl6pFF/lpWVweKkNvGYOgshUDMn9bNw/cR1hnx971sV7IlGqfn07VZ1kX4/AVS11Ujnm3lfVSQtVneQHx+PfJ2hz2n82E/bdK24+7HtmIwDgzy4Lddr3D/la2WftWqnPeu4tzadmzT00+6zxv1+D2M4S7ZaPhrGDFXKC43Hurc0oUdWJoqwc5yd+gvarxqL390thaCZGQWwarr6zC/fP3lbvJ3D9IcjLy9H9y7kQGRsh81YUzozZAFme7gbIsn7/B4Z2VnDzGwdDB2sUBcci5M0PNX7rVO6zFQSEI3LeVrj5j4fbyrdQEpuCsKmbURyuirlyOUxbucPxjT4QWppClpaD3Au3kfjxIfXaHQpZKexH9ITr0rEQGIkgTUzH/d3Hcb/Suh8NSeGpCxDaWsF2wSSI7G0gDYvB/dmrUZ6VCwAwbOSg0S6JHOzg9kvFzTs208bAZtoYSK7fRvIUP/X7Jt06wLCxE/J/adg3iuYcvwKRnRUaLxuv7NeHxCJy4jp1v17sotmvL7oZjtj5n8HF7y24+E+ANPY+omdsQkmlfn30vE/RZMVEeG5brJx+MykDyR8fQMaPpwAop4Gz7OULpxnKm6dkKZnIPXkV9784+mwLT0SPTaCo9jwX0aPFtnvl0Yn+Q5qcaViPUTcEf7Ze/ehE/zEWgv/Wwni1ES0UPzrRf4xbme6nh2poLEWPvy6Wvksur7t1v/SFqaLh3p2oKwKwm08P59Pov7N4bW0FpupmHaaGTKjrDDRAhXU8A4I+8FQ0rHXvGgJHm4Y3La6u5eazD1tV56Rjus7Cc6fkxrNZr0qfGHcZress6ARbayIiIiIiIiIiIiIi0hv/2YEPc3PzGl+XLmmfsqK+Xbp06aH5IiIiIiIiIiIiIiKih2uwa3zUt6CgoBo/c3FxeXYZqaRz584PzRcRERERERERERERET3cf3bgw9vbW9dZqMbExKRB5ouIiIiIiIiIiIiI6Hnxn53qioiIiIiIiIiIiIiI9M9/9okPIiIiIiIiIiIiInqOKOS6zgE9J/jEBxERERERERERERER6Q0OfBARERERERERERERkd7gwAcREREREREREREREekNDnwQEREREREREREREZHe4MAHERERERERERERERHpDZGuM0BERERERERERERE9Ehyua5zQM8JPvFBRERERERERERERER6gwMfRERERERERERERESkNzjwQUREREREREREREREeoMDH0REREREREREREREpDc48EFERERERERERERERHpDpOsMEBERERERERERERE9kkKu6xzQc4JPfBARERERERERERERkd7gwAcREREREREREREREekNDnwQEREREREREREREZHe4MAHERERERERERERERHpDQ58EBERERERERERERGR3hDpOgNERERERERERERERI8kl+s6B/Sc4BMfRERERERERERERESkNzjwQUREREREREREREREeoMDH0REREREREREREREpDc48EFERERERERERERERHqDAx9ERERERERERERERKQ3RLrOABERERERERERERHRI8nlus4BPSf4xAcREREREREREREREekNDnwQEREREREREREREZHe4MAHERERERERERERERHpDQ58EBERERERERERERGR3uDABxERERERERERERER6Q2RrjNARERERERERERERPQoCkW5rrNAzwk+8UFERERERERERERERHqDAx9ERERERERERERERKQ3ONUVPZHwVDtdZ6FBCWm9WtdZaHAGBH+k6yw0OCaNe+k6Cw3OXoe+us5Cg2MrLtF1Fhqcu+UWus5Cg/OKd7Kus9DgxESxb1KVmVim6yw0KCdhqessNDilKTxvqjKHXNdZaHCyhEJdZ6HBKeEtpNVcMzDRdRYanK45us5Bw2NrIdF1FojoP4TNNRERERERERERERER6Q0OfBARERERERERERERkd7gVFdERERERERERERE1PDJOS0l1Q6f+CAiIiIiIiIiIiIiIr3BgQ8iIiIiIiIiIiIiItIbHPggIiIiIiIiIiIiIiK9wYEPIiIiIiIiIiIiIiLSGxz4ICIiIiIiIiIiIiIivSHSdQaIiIiIiIiIiIiIiB5JIdd1Dug5wSc+iIiIiIiIiIiIiIhIb3Dgg4iIiIiIiIiIiIiI9AYHPoiIiIiIiIiIiIiISG9w4IOIiIiIiIiIiIiIiPQGBz6IiIiIiIiIiIiIiEhviHSdASIiIiIiIiIiIiKiR5LLdZ0Dek7wiQ8iIiIiIiIiIiIiItIbHPggIiIiIiIiIiIiIiK9wYEPIiIiIiIiIiIiIiLSGxz4ICIiIiIiIiIiIiIivcGBDyIiIiIiIiIiIiIi0hsiXWeAiIiIiIiIiIiIiOiRFHJd54CeE3zig4iIiIiIiIiIiIiI9AYHPoiIiIiIiIiIiIiISG9w4IOIiIiIiIiIiIiIiPQGBz6IiIiIiIiIiIiIiEhvcOCDiIiIiIiIiIiIiIj0hkjXGSAiIiIiIiIiIiIieiS5XNc5oOcEn/ggIiIiIiIiIiIiIiK9wYEPIiIiIiIiIiIiIiLSGxz4ICIiIiIiIiIiIiIivcGBDyIiIiIiIiIiIiIi0hsc+CAiIiIiIiIiIiIiIr3BgQ8iIiIiIiIiIiIiavgUcr4e9/UEvv76a3h4eMDY2Bhdu3bF9evXa0zbp08fCASCaq+hQ4eq00yZMqXa54MGDXqivNWWqF73TkREREREREREREREz4UjR45gyZIl2LlzJ7p27YqtW7di4MCBCA8Ph6OjY7X0v/zyC2QymfrvrKwstGvXDmPGjNFIN2jQIOzbt0/9t1gsrr9CgE98PBaFQoFZs2bB1tYWAoEAQUFB9fI9ffr0waJFi+pl31XFxcXVa1mIiIiIiIiIiIiI6Pnw2WefYebMmZg6dSpatWqFnTt3wtTUFHv37tWa3tbWFs7OzurXX3/9BVNT02oDH2KxWCOdjY1NvZaDT3w8hlOnTuG7777D+fPn0bRpU9jb2+s6S88db78xaDLhZRhamiHnRjhC/PagODb1odu4TR0Az3nDYeRohYKQBISu2oe8W9Hqzw3Ehmi+dgIajewOA7EhMs/dRsiKvZBl5KnT2PZqAx//N2DR0hXlxVIkH7mIyI2HoSiXq/fRevMMWLbzhJmPCzL+CsStKVvqpxIeoZnf63CtVEd3/fY+so7cp76CpvOGQ+xohfyQBASv+k5dR4bWZmjmNwb2vdvCxMUesqx8pJ4KQMSmoygrkFTbl6GNOXqd3QSTxnY47TMdZfnF9VLO+hYQdBf7Dv6EkLAoZGRl44uN76HfS911na16tXbNMkyf9iasrS3xzz8BeHvBSkRFxdaY3sDAAGveX4o3x4+Cs7MD7t9Pww8//g8fbdiqNf3XX23C7FkTsWTpGny57dt6KkXdabdsNHze7AsjS1NkBETg35X7UBCbVmN6x67N0XruUNi19YSpsw3OTfsciadvaqRxG9wZzSb2g52vB8Q2Fjg+YBVyghPquyhPxH7SEDjOHglDBxtIQuOQ9P5uFN+OrDG99dDuaLT0LRg1cYQ07j7ub/wB+ecqyi+yt0LjlZNh+VIHCC3NUHgtGEnv74Y0LkXr/ry+fx+WfTshZsYG5P15rc7LV5/q49h5XpiOGgmz8WMhtLVFaXQ08j//EqWhYVrTmgwfCtNBAyBq6gkAKA2PQMGubzXSW63yh+kQzceXS65dR85S//orxFNwnjoILvNehZGDNYpC4hGzeg8Kb0XVmN5ueDe4+Y2DsasDJLEpiP9wP3LO3NKa1uvjWXCePAAx7+1Dyjcn1O8bN20Ej/cnwbJLcwiMRCgOiUfC5sPIuxJc5+WrL/aThsBx1msQOdhAEhqL5DU1xxtjH1c4L30Lpm28YOTqhOR13yJj7+/POMf1o9eS0Wg/vi/ElqZICojA6dX7kBNXc+zoNm84mg/qAluvRigrkSH5ZiTObTqC7BhlXDW2MkOvJaPh2astLF3sUJyVj8g/b+Lilp8g1dKH0zUfvzEafdh7tejnu6v6+WJVPz+4Sj/fdWI/NH6tByx9PWBoYYo/faZp9E1tu7fCi7++r3XfVwauQl5QTN0Urpa8VL91RJZmyL0RjtBa1IHr1AHwUP3WKVT91smv8lun2doJcFb91sk6dxuhVX7rDEg7XG2/d/6fvfsOb7J44AD+TZM0TfdeQAu0ZW8QZCiiTJWhKHsvFdmjbBFQkSUgKqAM2UMcgMiQPUQZpRRaoHQPutt0pmkzfn+kpE1JympJ6e/7eZ48D31z75u74+7ee997796P1iLxj8sAAPvWdeG3YBCsfD0hlEqQH5eC2B2nELPxr3JK+ZNrPLMvfAZ1gtjWCqnXQnF19hbklHGOBQC/EV1Q75N3IHWxQ0ZIDK7P34b0Ev+3Fi52aLZgENxfbwSxtQWywhMQvPYg4v66qgvT8781sK7honfcwK/24s53h8s3geWgxYy+qDuwE8ztLJF0NRT/zN2KrDLyyL1NXTT+WNsPsXJ3wMnRqxFdoh8iEAnRyv8DVH+zGWy8XFCQJceDi7dxbek+5CXJXkCKnl/baX3ReJC2fX1wLRSn5m6FrIz2tVrrumj18TtwbVwL1m4OODRmNcJP6PfNxJYSdJjdHz7dWkHqYI3M2BQEbj2OoJ2nKzo5T8V9RHd4ju9d1DeJQuS8zcgJLKNv8m5b1Jg1EBbVi/smstMBuu9910yAa/9OevtknLmBO4O+AABIqrug+tQPYdehEcQu9ihMykDKr+cRt/ZXaAqVFZPIcmA/+F04je4LoYsDFHcjkbRkPfKDQg2GNff1gsvkobBo6AtxdTckfbkRGdsO6oVxnjgYzhMH621TRMQisvtHFZYGosqioKAA169fx5w5c3TbzMzM0LlzZ1y+fPmJjrF582YMGDAAVlZWetvPnj0LV1dXODg44M0338QXX3wBJyenco1/SZzx8RTCw8Ph4eGBdu3awd3dHSIRx42eRq0JveA9pjtC/Dfh8tvzocpToNW+OTCTiI3u4967LeotGoqwVQfwT5c5yA6ORqu9c2DubKsLU2/xMLh2bYnAsWtwpc8iWLg7oPmWabrvbRp4odWuWUg9HYh/3pqNwHFr4dqtJerMH6QLIxCaQZVfgOhNx5B2/lbFZMATqD2hJ2qO6Y7b/ptx6e0FUOYp0Gbf7DLzyKP3q6i/aCjur/oVF7vMRXZwNNrsna3LI4m7AyRu9rizaBfOd5yJm5M3wKVTUzRZbfiE3WT1OGSHVM4buU9DLs9HXd/amDd9vKmj8kLMnDEeEz4dhfETZqNdh57IzcvDX3/uKnPaoP/MT/HRuGGYPGU+GjV5A3PmfYUZ0z/BhE9HPRK2d+/uaNOmBeLjDd/krmwajn8X9Ud1xX+zt+CvnguhzFOg865ZZdYlkaUEGSEx+G/etjLDJF+5h+tf7quIaJcb+54dUG3BKCSu2Yd770yD/E4kfHZ+DpGTncHwVi3roea6GUjbdxJ3356KzOP/odZPc2BRx0sXpvZPcyHxckfE6C9xt8dUFMQnw3f3YphJHy1jLqN7QaPRVFj6KlJFlZ2XgcWbnWA74RPkbN2G1NHjoAwLh+M3y2Fmb28wvKR5M8hPnkbaxKlI/ehTqJKS4fjNCpiVejAk/9//kNTrfd1H9vmSF5Cap+fcux1qfT4csat+QWBXf+QGR6HhnvkQl+hzlGTTqi7qrp+C5D2nENhlJtKPXkW9rf6wrFfjkbCOPVrDuqUfFAlpj3zXYMccCERmuP3BItzs6o/ckGjU3zEHYhf78k5ihbB/twM8549G4tq9uPfuVMjvRKH2jkVG2xuBVIKCmEQ8WLYdhcnpLzi2FefVj99FqxFdcWzuFmzrvRCFeQr03zELwjLaDq829XF9+9/Y3udz7B2yDGZiEQbsmAVxUbtq7eYAazd7nP5yNzZ1mY0jM35E7Y5N8PbysS8qWU+s9oReRX3YTfinqJ/f+jH9fI8S/fxLXeYgKzgarUv184VSc6ScCUT42j8MHiPj6j2cbPSR3idm5ynkRSe98EGPmhN6wavoWue/ojxo8Zg8cOvdFnUXDUX4qgP4t+hap2WpPKi7eBhcurZE0Ng1uNpnESTuDmha4lrnoduT1uNso490n+Sj13TfqfIUiN1yHFf7LMKl16YjYvXv8JvdD9WGvlW+mfAY9T99F3VGdcPV2Vvx97ufQZmnQKfdZV/rePV6Fc0XDsbtb37DsW7zIQuJQafdsyFxKs6jV7/9BLY+Hjg/YhX+enM24v66hvYbJ8GhkbfesYKW/4Lfm47XfUI3n6iwtD6rJuPfRYORXXFpzhYcKuqHdNtZdlsispQgPSQGl+cb7oeIpOZwalQTgWv+wMHuC3Bq3BrY+Xigs4FyVBm1+uRdNBvZFSfnbMGeXtr29f3H5InYUoKUkBicNpInANDxs8Go+UZTHJu8Htve9MeNzcfQafFw1O7SoiKS8UycerVDzc9HIG7VftzsNhO5IdFosGcBxE7G+yZ11k9F8u5TuNl1BtKPXdH2Terq900yTgfgapPRuk/oJ6t130n9qkFgJkC4/0YEvjEVkQu3wm1YV3jNGVT65yoNm7dfh+ucsUj9bjei+kyE4m4EamxeAqGj4b6ImVSCgtgEJK/aCmUZfRFFaBTutxus+8QMnFlRSSCqcAqFAllZWXofhUJhMGxqaipUKhXc3Nz0tru5uSExsewHOgDgypUruH37NsaMGaO3vXv37ti+fTtOnTqFZcuW4dy5c+jRowdUKtWzJ+wxOPDxhEaMGIGJEyciJiYGAoEANWvWhFqtxtKlS1GrVi1IpVI0bdoUBw4c0Nvv9u3b6NGjB6ytreHm5oahQ4ciNTVV931ubi6GDRsGa2treHh4YNWqJ59lMHfuXLRp0+aR7U2bNsXixYsBAGq1GosXL0b16tUhkUjQrFkzHDt27Blz4fl4j+uB8NW/I/nYdeSExODWhO8hcXOAa49WRvep+fE7iN15GvF7zyE3NB7BMzdBJS9AtYFvAABENlJUH9QJdxfuQPrFYGQFReLW5A1waF0Xdi19AQDufdohOyQG4d/8hryoJGRcvoN7S3bBa2RXCK0sAGgvBkJmbUbcztMoSM40Fp0KV2tcD4St/h1Jx64jOyQGNyf8AImbA9zKyKNaRXkUt/ccckLjcWvmZqjkBahRlEc5d+MQMHoNkk8EIC86GWkXg3Fv6T64dm0BgVC/CfAa3hliWytErD9i4JdeLq+1fQWTxg1H547tTR2VF2LSxDH4aulaHD58Ardu3cGIkZPh6emG3r27Gd2n7autcOjwcfx19BSio+Pw229H8PfJc3jllWZ64Tw93bF29RcYNnwCCivxUz4l1R/THUFrDyL2RABkd2JxcfIGWLrZw6tbS6P7PDgThMDlBxB77JrRMBG/XkLQmj+QcOF2RUS73LiO6Y20PSeQ/ssp5N+PReyc9VDLFXDq39lgeJdRPZF1LgDJG3+HIiwOCat2Q347Ai4jtC8ik9TyhFXLeoidtx55QWFQRMQjdu4GCCzM4dD7db1jSRvUguu43oiZua7C01kRKqrsvAysBnyIvMNHIP/rGJRR0chc8Q00+fmQvtvDYHjZ4i+R9/tBKMPCoYqJReaylYCZAJJWpW4YFBRCnZ6h+2iyc15Aap6e50c9kbTrJJL3noE8NA7h/j9CJVfAdcCbhsOPfRsZZwIR/8MhyO/HI2b5XuTeioTHSP38Mnd3RO0vRyP007XQKPU79SJHG0h9PBG/7g/k3YlGfmQior/YCaGlhcEBlMrIZUxvpO3VtjeK+7GIm/sD1HIFHPsZbm/kQWF48NXPkB2+AI2i8AXHtuK8Mro7Ln13EPf/DkDK3Vj8OW0DbFztUaer8bZj3/DluHXgAlLvxyP5Tgz+nL4RdtWd4d64JgAgNTQOv3/8LcJO3YAsJhnR/4Tg3Ipf4PtW80f6cKZWs6gPm6zrw37/1H3Y20X9/OpFfVgAiPrxKCLWHYLsuuGnmzWFKhSkZOo+hRk5cOveCnF7zpV3Eh/Le1wPRKz+HSlF1zq3n/BaJ27naTwoutYJKcoDzxLXOtUGdUJo0bVOdlAkbpe61nmoMCtXLy/UJepX9u0oJP7+D3LvxSE/NgUJv15E6pkgOLSpVyF5YUzdMd0RvPYPxB+/DtmdWPw7aT2kbvao3t14Pak7rgfCd59B5L7zyLofj6uztkApV6D2wI66MM6t/BC65QTSAyOQG5OC4LV/oDAzFw5NaukdS5mTj/yUTN1HJTd8w8eUGo7ujsBvDyLmRAAy7sTi3BRtP8S7jH5I3JkgXF9xANFG+iGF2XIcG7QMkX/+h8yIBKQEhOPy/O1waVobVp4V95RteWkxujuurDuIiL8DkHo3FsemboCVqz18ymhfo84G4Z+VBxB+3HjfzKOlH0IOXEDcv3eQFZeKW7vPIOVODNyb1q6IZDwTXd9kn7ZvEuG/Uds3GWh40NJjzDvIOHMDD9YfhPx+PGKL+ibuo/T7JuoCJQpTZLqPKjNX953sTCDCpn6PzHM3oYhJQsaJa3iw/hCc3n61QtP6PBxHvofM/ceQ+dvfKAiPReJn30Gdr4DdB10Nhs+/dR8py7cg+8h5aAqM90U0KhVUqRnFn4ysikoCUYVbunQp7Ozs9D5Lly6tkN/avHkzGjdujNatW+ttHzBgAHr16oXGjRujT58++PPPP3H16lWcPXu2QuIBcODjia1du1Y3gJCQkICrV69i6dKl2L59OzZs2IDg4GBMnToVQ4YMwblz2o62TCbDm2++iebNm+PatWs4duwYkpKS0K9fP91xZ86ciXPnzuHgwYM4ceIEzp49i4CAAGPR0DN48GBcuXIF4eHFU6GDg4MRFBSEQYMG6eK9atUqrFy5EkFBQejWrRt69eqF+/eNL3dSEaTerrBwc9CbTaHMliMzIAz2reoY3EcgFsK2SS2kXSgxA0OjQdr5W7p9bJvWhpm5SO+4uWEPII9N0YUxMxdBVerCWp1fAKHUHHaVqFPzMI9SzxffUFVmyyELCIdDKz+D+wjEQtg1qYXUkjdhNRqknr8NeyP7AIDY1hLKbLluqS8AsK5TDX7T30fgxB+gUauN7kuVT61aXvDwcMOp0xd127KysnHlyg282sb4BcHlf6/hzU4d4OenrQdNmjRA+3atcez4GV0YgUCAbVu/xapv1iMkxPBU4crG2ssFlm72SLhYXC8Ks+VIuREOl5bG60VVIRCLYNnYB9kXbxZv1GiQffEmLFvUNbiPVYu6+uEBZJ2/Aaui8AJz7RN1JW+iQKOBpkAJq1fqF/+2hTm8101H3PyNUKbIyidBL9D/ddkRiSCuUweKayWWgdBooLgWAPOGDZ/oEAKJBAKRCOos/YtC8+bN4Hr4N7js3gbb6VMgsDX8lKIpCcQiWDepDdn5oOKNGg0yL9yCTSvD9camZR398AAyzgbCpmS/RiCA33cTEf/DQcjvxT1yDGV6NvLux8Plw44ws5QAQjO4DeuKghQZcoJe7NPqz0Lb3vgi52Jg8UaNBjkXb8KqxYu9oWpK9jVcYO1qj6gSbYciW44HgeGo1uLJ2w4LG0sAgFyWazSMxNYSBTn6fThTK+7D6vfzZc/Qz089fwsORvZ5Em7dWsLcwQZxe88+8zGehdTbFRI3B6QbuNaxKyMPbAzkQfpjrnXyiq51Sh+3/tJReCPkR7Q59oVu4MQYm0Y1Yf9KHWRcDnnKlD47Ky8XSN0ckHiheBm/wmw50m6Ew9nIOdZMLIRjk1pILHWtk3Thtt4+qdfuw6vXqzC3twIEAnj1fhVCCzGS/7mjd7z6E3ri/dsb0P3El6j3yTuVbgDRpqgf8uBCqX5IYDhcy7kfYm4jhUatRkElX9bYzssFVq72iCnRvhZky5EYGA7P58yThOv3UbtLC1i5adeXr962PhxquSPahCtAlKTtm/gg80LpvkkQbFoabldsWtXRDw9AdjYQNi31+zJ2bRvilVtb0PzCt6j99TiIHKzLjIvQ1hJKWfazJaSiiUWwaOiL3H8Ci7dpNMj7JxDSZs/XFzH3rgafCztQ+9RmeKycCZGHy+N3ohdDrebnKT9z5sxBZmam3qfkUlYlOTs7QygUIilJf0nBpKQkuLu7l/lfk5ubi71792L06NGP/W98+BqJsDDjy/c9L67V9ITs7OxgY2MDoVAId3d3KBQKfPXVVzh58iTatm0LQPsfdvHiRWzcuBEdO3bEd999h+bNm+Orr77SHWfLli2oUaMGQkND4enpic2bN2Pnzp146y3tiP22bdtQvXr1J4pTw4YN0bRpU+zevRsLFiwAAOzatQtt2rSBr6/2CaCVK1di1qxZGDBgAABg2bJlOHPmDNasWYPvv/++3PLncSRFyzWUXIsWABQpmZC42hvcx9zRFmYiocF9rPyqaY/rag+1ovCR91AoUjN1v5l6Jgg1x70Nj/faIeHgZUhc7eEzra92fzfDv20KFi7aaZiKZ8gjQ/tY+Xka3EfsaAPfqe8hducp3TYzcxGab5iIu4t2Iz8+DZbers+REnrR3N20/19JSSl625OSU+Hubvz/ctny72Bra43gW+egUqkgFAqx4LNl2LPnd10Y/5mfQqlUYt13mysm8hVAWlRf8lP0b77mp2ZB6mp4unNVInS0hUAkRGGqTG+7MlUGCx/D5xeRi/0jAxXKFBlELtoLwfzwOBTEJcNz1lDEzvkB6jwFXMb0grmnM8Sujrp9qi8cjdxrd5H595VyTdOL8v9cdszs7CAQCaFOz9Dbrk7PgMjby8he+mzGfwRVaqre4InivyvIP3cBqoQECKt5wmbcGDiu/BppH0/QdsIrCbGjjbbelDqfFqTIYOdbzfA+rvYoLFVvClMyIS5xzq42oQ80SjUSNhlfRz+43yLU/3kWXg3bAag1KEzNRMjAL/WevqyshA6G25vCVBkkPobzrSqyKvo/z03VbztyU7Ng5fKEbYdAgM4LhyD26j2khj46SAYAUgdrtJ/YBzf2nDH4vakY6+cXPGMf1trv2ctO9UGdkHLmJvITXuwyauZFeVA6PU+SB2Vd65gbudYpKHGtAwBhX+9H+sXbUMkL4PRGE9T/ehREVhaI2aQ/0//1G9/D3Elbb8NXHED8rhdXlorPsfrpzU/JhIWRPJI42sBMJHx0n9Qs2PgWX+tc+uhbtN8wEX1DfoS6UAmlvAAXRq9BTol3QIRuPo6MW1EokOXAuVUdNJ3TH1JXe9xYtKt8ElgOpEX/p/JSbYk8JQvSJ21LnoBQIsYrcwcg/OBlFOZUvvcFlWRZlCd5pfIkLzULls+ZJ2c+247OX4/GuKvroCpUQqPW4OTszYi/cu+5jlteREV9kwIDfQ2psb6Ji/0jfZnCFJle3yTjzA2k/fUvFDHJsKjpDq85g1B/13zceneuwb6ZRU13eIzqgajF2587TRVBVNQXUabq92GVqTJY1n722bPym/eQMPsbFETGQeTiCKcJg+C9ewUi3/0E6tzKXW+IDJFIJGUuhV6Subk5WrZsiVOnTqFPnz4AtCsKnTp1ChMmTChz319++QUKhQJDhgx57O/ExcUhLS0NHh4eTxSvZ8GBj2cUFhaGvLw8dOnSRW97QUEBmjdvDgC4efMmzpw5A2vrR0fPw8PDIZfLUVBQoLdclaOjI+rWNfxkoSGDBw/Gli1bsGDBAmg0GuzZswfTpmnX6szKysKDBw/Qvr3+Uj/t27fHzZs3DR3OIIVC8ci6bwUaFcwFQqP7ePRtj4Yritcfvj542RP/XnlLOxeEe4t2osHyMWj83adQFxQi/Jvf4Ni2PjRq061B79m3PRqvKF7v7urg5RX+myJrKV7Z5Y+c0HiErvhVt73uvAHIuR+P+F8vlrE3VRYDB76H9d8X16levYc903E+/LAnBg54H0OGfYqQkFA0bdoQ36xchAcJSdix4xe0aN4YEyeMxittuj/+YCZU6712eHVZ8XtJTg9bacLYVFFKFSI++hpeyyegya3d0ChVyL54E5mnr0EgEAAAbLu0hnW7JrjXY6qJI/vkWHbKj9WQgZC+1QlpE6cCJZYMyD9VfFNNGREJZXgEXPfvhnnzZii4/mQzXF9WVk1qw3Ps27jZxb/McLWXjkVBaiYiei+AOr8AboPfQv3ts3Gz+ywUJsteTGTpqTTs0w7dvypuO/aPfP62o9uS4XCuUx07PzD8Dhxzayn6bZ2B1LB4XFz923P/3vPw7NsejUr086+ZsJ9fkoWHI1w6NcWNsWsq/Lfc+7ZHgxJ5cMPEeRBRokxk346C0FKCmuN7PjLwcbX35xBaWcCupR/85g1EXlQiEn//p0Li5P1eO7yyvPhpz3NDV1TI7wBAE/8PILa1xOl+X0GRno3q3Vuh/YaJOPneEmTejQUA3PvxqC687E4s1IVKvLJsFG4u3Qd1gWmWcvV5rx3af13clpwYXvH9EIFIiE7rJwICAf6Z83OF/97TqtenHd5aWpwnf4youDxpNqIr3Jv74uCoVciKS0W1NvXw5pLhyE3KQMzF4Mcf4CWVdvCS7t95d2OQGxKNlv/9ALt2DZF5UX+2i7m7I+rvno+0w5eRvOvki46qSeWeL14mTXEvCvKb9+Bz9mfY9HgNmQcq3/uBiMrbtGnTMHz4cLRq1QqtW7fGmjVrkJubi5EjRwIAhg0bhmrVqj2yXNbmzZvRp0+fR15YnpOTg0WLFqFv375wd3dHeHg4/P394evri27djC/R/rw48PGMcnK061MfOXIE1arpj7Y/HEHLyclBz549sWzZox1hDw+PcpnKM3DgQMyaNQsBAQGQy+WIjY1F//79n/u4JS1duhSLFi3S2zbYsiGGWDcyuk/ysevILLEW78MX1pm72EFR4iJe4mKHrOBog8coSM+CWqmCeaknOSQljqFIlsFMIobI1lLvSSiJsx0UJZ6MiNr4F6I2/gWJmwMKM3MgreGCuvMHIS9af9rWi5R07LreesUP80hiMI+iDB7jYR5Jysijh4RWFmi9dzZUOXJcH/mN3jrjTh0awra+F9zf1Q7CPbyR2eXOjwhb8wfur9B/dw2Z1uHDJ3Dlyg3d3xKJOQDAzc0FiYnJuu1urs4IvGm8075s6QIsX/Ed9u8/BAC4ffsuvL2qY5b/BOzY8Qs6dGgDV1dnRIYXP70vEomwYvlnmDRxDHzrVI51XmNPBCD1RvGSf2bm2lObhYst5CXqgYWzLTKCY1509F44VXoWNEoVxM72ettFzvYoTMkwuI92dkep8C72UJYIL78Vjns9psLMxhJmYhGU6Vmoc3AF8oK07ZhNu8aQeLujye3deseptXEWcq6EIKz//OdPXDlj2SmmzsyERqmCmaOD3nYzRweo08p+ctpqYD9YDx6E9CnToQwve3km1YMEqDJkEFWvVqkGPgrTs7X1ptT51NzFHgVGBh8Kk2WPvIBc7GKnG6ywbVMfYmc7tLq+Qfe9QCRErc+HwXPcO7j+ynjYdWgMxy4t8F/dEVAVPXUbMXsT7F9vCtd+byD+uz/KK4kVQpVhuL0ROz86i6wquf93AB6UaDuERW2HlbMtckuUFytnWySFPL7t6Lp4GHzfao6d/b5AduKj9c3cygL9t8+EIjcfv45bA7Wy4l4A+SSM9WFL9/PNn6Cf/yR92CdVfcAbKMjIRtLx648P/JxSjl3HZSP9+IJSeZD9HNc6BUaudcxLXeuUlhkQBp/pfSEwF0FT4qa+PEY7OzjnTizMXezgM+ODChv4iD8RgDSD51g75Jc8x7rYIcNIHinSs6FWqnQz43X7ONvqZoFYe7uizqhuOPKGP7JC4wEAspAYuLSpC78RXXBt9haDx04NCIOZWASrGi7IDk945nQ+j5gTAUg20JZInfX7IVIXW6SXQz9EIBLizQ0TYV3dCUf7La2Usz3C/w5AQok8EUm0eWJZqn21dLZFyhO0r8YIJWK09++Hw+PWIPJ0IAAg9W4sXBp4o+W4dyrFwIeyqG9iXkZfo7TCFNkjfRmxi32ZD1IoYpJQmJYJi1ruegMfYjcHNDywCNnX7iF85gaj+5uasqgvInLW78OKnO2hTCm/2X/q7FwURMXD3NvwyhpEVU3//v2RkpKCzz77DImJibp3Rj984XlMTAzMzPSXjLx37x4uXryIEyceHRwUCoUICgrCtm3bIJPJ4Onpia5du2LJkiVPPBPlWXDg4xk1aNAAEokEMTEx6Nixo8EwLVq0wK+//oqaNWtCJHo0q318fCAWi/Hff//By0u7jERGRgZCQ0ONHrO06tWro2PHjti1axfkcjm6dOkCV1ft0ja2trbw9PTEpUuX9I536dKlR14wU5Y5c+boZpE8dNa37LXaVLn5yMvN19uWn5QBp9ca6Tr/Qmsp7Fr4Imbb3waPoSlUISsoEk6vNULy0aLRdoEATq81QvSW4wCArJsRUBco4fRaIyQd0d6ctfLxgLSGC2TXHn0fgSJJexPP4732kMelIiso8jGprzhl5dHDi0SRtRT2LXwQXUYeZQZFwvm1RkjSy6OGiN5S3NCIrKVovW821Aolrg5bqb9OP4CAUathJjXX/W3fzAdN136My70XIS/KdINDZFhOTi5ycvSXQElISMKbnTrgZtFAh42NNVq3bo4NPxqfkmxpKYW61KwnlUqlO3nt3PUrTp2+oPf9X3/uwq7dv+LnbfvLIynlQpmbj+xSdSkvSQaPDg11N6vF1lK4NPdB6PZThg5RpWgKlci7FQ6b9k2QeeI/7UaBADbtmyB1m+HldnID7sGmfROkbD6s22bToRlyAx6d6q/OzoMagKSmByyb+CBhpXaJiKQffkXaHv22qv7JdYhfvAWZJyvn0lcsOyUolSgMDYWkZQsoLhQ9CSgQQNKyBXJ/+93oblaDBsB62GCkT/dH4b3HvwfIzMUZZna2UKWmlVfMy4WmUImcoAjYvdYY6ceuajcKBLDr0BgJW44a3Cf7eijsX2uMhJ+O6LbZv94U2UX9j5QD5x5ZZ7vBnvlIOXAeyXu1M2EennsfmYGqVkNgVrnWnjdE296Ewbp9U732xrp9E6RuO1L2zi+xgtx8FJRqO3KSZajZviGSi27EmVtL4dnMBwE7y247ui4ehjrdWmFX/y+RGZvyyPfm1lIM2OEPpUKJA6O/eeS9daZgrA/rXKKfr+3DPlk/P8lIP/9pVR/YEfH7L+g93FNRVLn5kJfKA0VSBhwNXOvElZEH2UV5kFIiDxxfa4SYUtc6jq81QnLRtY5l0bVOpoFrnYdsGnmjMCNHb9CjNIFAALOid3hVBGVuPnJK5ZE8KQPuHRpCVqKcODX3wf3thp8kVxeqkB4UCfcODRF/7PrDiMOtQyOE/qy91hFKi26WlGpHNSo1BGYCo/FzaOgNtUqN/NRMo2EqWmFuPgoN9EM8OzREekiJfkgzH9x9zn7Iw0EPu5pu+KvfV1DIcp7reBWlMDcfmaXyJDdZhhrtG+oGOsytpXBv5oObO549T4RiEYTmokfebalRl11uXiRt3yQcdh0aI/1YUV9aIIBdhyZI3Gqkb3ItFHYdmuj1Texeb4Ls68aX7zL3cITIwQYFScUPPJm7O6LhgUXICYpA2JTvAY3pVsp4rEIl8oPDYNW2KXJOXtZuEwhg2bYZMnYeLnvfpyCwtIB5DQ9kJZ8ut2MSVXYTJkwwurSVoReS161bFxoj7YVUKsXx48/Wx3seHPh4RjY2NpgxYwamTp0KtVqNDh06IDMzE5cuXYKtrS2GDx+OTz/9FD/99BMGDhwIf39/ODo6IiwsDHv37sWmTZtgbW2N0aNHY+bMmXBycoKrqyvmzZv3yIjZ4wwePBgLFy5EQUEBVq9erffdzJkzsXDhQvj4+KBZs2bYunUrAgMDsWvXk69lamgduLKWuTIm+sej8Jn6HnIjEyGPSYbfrH5QJGUUD2oAeOXAfCT9dVXX2Y/acASNv/0EmYERyLwRhprj3obQUoL4vdoXyCuz5YjbfQb1Fg1FoSwHymw56n81EhlXQ/VmnNQc/y5Sz9wE1Bq4vd0atSf2RuC4NXodZKs61WAmFkHsYAWhtRQ2Db0BwOhTWhUh8sej8JvaR5dHdWZ9CEVSRvEFIYA2B+Yh8a+ruoGNyA1H0PTbTyDT5VEPiCwliC3KI5G1FK33z4FQKkHg+FUQW0sBaykAQJGWBag1yItO1ouHuaMNACAnNP6RNYVfFnl5csTEPdD9Hf8gCXdDw2FnawOPMt578bL6dt0mzJ0zCffDIhAVFYtFn8/EgwdJOHiw+MRy4tg+/HHwKH5Y/zMA4M8jf2PO7EmIjY1HcMg9NGvWCFMmj8PP2/YCANLTM5Bear3/wkIlEhNTEBoajsrszqZjaDypD7IikpATm4xmMz9AXpIMMSWeAu2ybw5ijl7DvZ+1NyRElhLY1HLTfW/t5QKHhl4oyMhF7gPtTVpzeytYVXOCZdFLEO18tGtRypMzH1l/2pSSNx2E96rJyLsVhtzA+3Ad3RNmlhZI26+9seC9egoKEtOQsGwHACBly2H47f8SrmN7I/P0NTj0eg2WTXwQO7v4XVD277SDMi0LBQ9SIK3rjWqfj0Hm8f+QfSEQgHbWiKEnvAviU1AQm/zI9sqqosrOyyB37y+wnzcbhXdDUXjnDiz7fQCB1ALyI9qlUuzmz4E6JQXZGzcBAKwGD4DN6JGQLfoSqoRE3WwRjVwOjTwfAqkFrEcOR/6581CnpUNYrRpsx38EVXw8FFeumiydxjzYeBh+aycg52Y4cm6EwXPsOxBaSnSDFH7rJqIgIQ3RX2lnNT346S80+n0RPD/uiYyT1+HcpwOsm9bWPRWpzMiBMkP/xpJGqUJBsgzycO35Kft6KJSyXPh9OwGx3/wCdX4B3Id0hsTLFeknK/6p9fKQsukgvFZNQV5QGPJuhsJlVC+YWVog/RftDSmvb6agMDEdCcu1A/ECsQgWfto1twXmIojdHSFtUAuq3HwURJvmqevycHXzMbSb2AfpkUnIjE3G69M/QHayDKEniv8fB+6eg9Dj13C96EZ4ty9GoEGvtjgwdjUKcvN17wNRZOVBqSgsGvSYBbHUHIcmr4fERgqJjbYPl5eWZdIlW0uL+vEofA3080v2YVsX9fMfDmxEbjiCJkX9fNmNMNQa9zZElhLEFfVhAe2MCYmrPSyL2lib+l5Q5siRH5+KwhIvgXd6rREsvd0Qu8t0N6SifzyK2lPfQ15RHvgauNZpeWA+kv+6itgS1zqNvv0EWUX9eK+ia50HJa514nefQd1S1zqyEtc6Ll1bwNzFDpnX70OVXwinjk1Qe3IfRP3wp+53a4zsivz4VOTe17Y9Dm3ro+b4dx9ZCqui3dt0DA0n90F2ZCJyYlLQxP8DyJNkiDtWXE867ZuDuGPXcH+rtp7c+/EoXl3zEdJvRiLtRjjqju0OkaUEkUV5lBX2ANkRiXhl+WjcWLwLBRk5qN69Fdxfb4RzRUtYOrX0hXNzXyT9E4LCHDmcW/qhxaIhiP71IgozK9e1TvDmY2g2qQ+yIpOQHZuMljO0/ZDoEv2QHnvnIOrYNdwp0Q+xrVmiH1LDBY4NvKCQafshApEQb22cBKfGNfH38FUQCM107wxRyHKgLjTtLLLHCdh8DG0m9YEsKgmZMcloN+MD5CbLEF6ife27Zw7Cjl3DzaL2VWwpgX2JPLGt4QKXBl7Il+Ui+0EaCnLkiL18B6/NGwhlfiGy4lNRvU09NOjbAecWV573vmj7JhO1fZPA+/AY+25R30Tb1vl+OxEFiemI+Uob54RNR9Dwt8Xw/KgnMk4FwLl3e1g39UFEUd/EzNICNab3Q9qRyyhMlsGipju8FwxFfmQiZGcDARQNevy6GIq4FEQv3gaxk60uPqXfbVZZpG/9HR7LpkF++z7yg0LhMLw3zKQSZP6qLQ8ey6dDmZSGlFU/a3cQiyDx9dL9W+TmBEn92lDnylEYo+2LuMwajZzT/0H5IBkiVyc4TxoCjVqNrD/PvvgEEtEz48DHc1iyZAlcXFywdOlSREREwN7eHi1atMDcuXMBQDfbYtasWejatSsUCgW8vb3RvXt33eDGihUrdEti2djYYPr06cjMfLqbZx988AEmTJgAoVCoe+nMQ5MmTUJmZiamT5+O5ORkNGjQAIcOHYKfn1+55MHTiPzuEISWEjRaORYiW0tkXLmHawO+1pt9YOntprvpDgCJBy/D3MkWfv4fQuJqj6zgaFwb+LXeSwDvfrYdGrUazTZPg5lEhNQzQQiZpf8iZpe3msFnynswMxcjOyQaAcNXIrVoSutDrXbNhtTLRfd3+9PaJcqOuQ0oz2woU8R3hyGylKDxyjEQF+XRlcfkUcLBf2HuZIs6/h/o8uhKiTyybVITDi21/9+drqzV+73TrSZCHpv6AlL24t2+ex+jJs7S/b183Y8AgN49OuPL+dNNFa0Ks2LlD7CyssSGH5bD3t4Wly5dxTs9h+i9n6d2bW84Oxe/iHrylPlY9Lk/1n37FVxdnfDgQRJ+2rQTS75YbegnXirBP/wJkaUEbZePgrmtJZKvhuLkkOV6dcnG2xUWJeqSU9Pa6HZgnu7vVz7XvowrbP95/DNVW35qdG2B9qs/0oV5ff1EAMDNVb/h5jemXXO9JNnhixA52sJj2iCIXBwgD4lE+NBFUBY91Sj2dNZ7wi33+l1ETVoFjxlD4OE/FIqoB4gcuxT5ocVLCIhdHVFtwWiInO2gTM5A+q9nkPht5Zn5U14qquy8DPJPn0GWvR2sx4yA0NERhWHhSJ8+C+oM7QCo0M1V76WXln16Q2BuDocv9ZfDzN7yM3K2bINGpYbYxwfSHt1gZm0NVWoaCq5eQ/ZPW4BC0z+1XlrqwX8gcrKFl/8AmLvYIzc4CsEDv0RhUb2RVNOvN9nX7iF0/Fp4zxoA7zmDII9MwN2Ry5FXtJ78k1CmZyNk0Jfwmj0QjQ58DoFYiLx7sbgzYjnyQl7cgxfPQ/bnRYic7Eq0NxGIGPY5lEUvPDf3dNF70ETs5oi6R4v7I64fvQ/Xj95HzuVbCBswr/ThXxr/bvgTYksJeiwdBQtbS8ReC8X+Ycv1ZmjYe7lC6lDcdrQY2hkAMGS//lKAf07fiFsHLsC9UU1Ua+ELAPjkwjd6YX5oPwWZcZWnDxdR1M9vXKKff/WxfdjLRX3YD2Huao/sUn1YAPAe3gV+Mz/Q/d320OcAgJuT1iN+X/EASY1BnZB+5R5yw4ofennRooryoEFRHsiu3EPAY/IgqSgPfIqudbKDoxFQKg/uGbjWuVPiWkddqEKNkV1Rd/EwQCBAXmQi7n22A3E7SwwCmQngN28gpF4uUCvVkEclIXTJHsQZmWlRUe58rz3HvrJ8NMxtLZFyNRRnBy/TyyPrmm6QlMijmEP/QuJkg8YzP9Ati3V28DLkF73sWqNU4ezQ5Wg2dwA6bpsBkZUE2ZFJ+HfyRiSc1r7XUl2ghFfvtmg0/X2YmYuRG5uCez8ew90fDc+ENaWgon5I+2XafkjS1VAcH6LflpTuhzg3rY13filuP18t6oeE7j+PC9N+hJW7A7y7tQQAvPf3V3q/d+TDL5F4+U5FJum5XVv/J8RSCTovHQWJrSUeXAvFb0P188TOyxXSEnni1qQ2PtxfnCdvLNTmSfAv53FiurZv9teE79BhVn/0+PYTWNhbIysuFZeW/4Kgx8zUe5HSDv0DsZMdvPwHQOxij9zgSIQM+kKvb1LyHJt97R7uj18Dr1kD4TVnMPIf9k3uFfVN1GpYNvCGa783ILS1REFSBmTnbiJ22R7dDDG715tCWtsD0toeaHXjJ734/OPR98Uk/Cll/3UeQkdbuEwaCqGLAxR3IhA7+jOo0mQAALGHi14fVuzqiFoHv9P97TTmAziN+QB5/wUhZuhsbRh3Z3h+MwtCB1uo0jMhvx6M6A+nQpWR9ULTRkaUmq1FZIxAY2wOClEZXuRgwMtAhcoxHbYy6Rr8pamjUOlIPV8zdRQqnS0unUwdhUqnsTDb1FGodG6pbB4f6P9Ml1rxpo5CpRMR5vT4QP9nrCQFpo5CpXIUto8P9H+mab5pXupcmfHJwEelCZ9+tn9Vl1dJlkOqTLIq/wqNL1ybwsr3HhVTc7RhnpRWL7TyDcJWdvIja0wdhZeO9J0ppo6CSfDUREREREREREREREREVQYHPiqxCxcuwNra2uiHiIiIiIiIiIiIiIj0cSZvJdaqVSsEBgaaOhpERERERERERERERC8NDnxUYlKpFL6+vqaOBhERERERERERERHRS4MDH0RERERERERERERU+WnUpo4BvST4jg8iIiIiIiIiIiIiIqoyOPBBRERERERERERERERVBgc+iIiIiIiIiIiIiIioyuDABxERERERERERERERVRkc+CAiIiIiIiIiIiIioipDZOoIEBERERERERERERE9llpt6hjQS4IzPoiIiIiIiIiIiIiIqMrgwAcREREREREREREREVUZHPggIiIiIiIiIiIiIqIqgwMfRERERERERERERERUZXDgg4iIiIiIiIiIiIiIqgyRqSNARERERERERERERPRYGrWpY0AvCc74ICIiIiIiIiIiIiKiKoMDH0REREREREREREREVGVw4IOIiIiIiIiIiIiIiKoMDnwQEREREREREREREVGVwYEPIiIiIiIiIiIiIiKqMkSmjgARERERERERERER0WOp1aaOAb0kOOODiIiIiIiIiIiIiIiqDA58EBERERERERERERFRlcGBDyIiIiIiIiIiIiIiqjI48EFERERERERERERERFUGBz6IiIiIiIiIiIiIiKjKEJk6AkREREREREREREREj6VRmzoG9JLgjA8iIiIiIiIiIiIiIqoyOPBBRERERERERERERERVBgc+iIiIiIiIiIiIiIioyuDABxERERERERERERERVRkc+CAiIiIiIiIiIiIioipDZOoIEBERERERERERERE9llpt6hjQS4IzPoiIiIiIiIiIiIiIqMrgwAcREREREREREREREVUZHPggIiIiIiIiIiIiIqIqg+/4oGeSJmTRKamGWmHqKFQ6Us/XTB2FSkf+4IKpo1Dp/NvI39RRqHTC1NamjkKl00CYY+ooVDp7Y6qZOgqVTg2BxtRRqHRiNRamjkKlMtov1tRRqHQO3qth6ihUOjULC00dhUqnsUWmqaNQ6ShVfIa0NI/aLCelXQ/xMHUUKp3gPKmpo1Dp1DN1BIiqMJ6tiYiIiIiIiIiIiIioyuBj+0RERERERERERERU+anVpo4BvSQ444OIiIiIiIiIiIiIiKoMDnwQEREREREREREREVGVwYEPIiIiIiIiIiIiIiKqMjjwQUREREREREREREREVQYHPoiIiIiIiIiIiIiIqMoQmToCRERERERERERERESPpdGYOgb0kuCMDyIiIiIiIiIiIiIiqjI48EFERERERERERERERFUGBz6IiIiIiIiIiIiIiKjK4MAHERERERERERERERFVGRz4ICIiIiIiIiIiIiKiKkNk6ggQERERERERERERET2WWm3qGNBLgjM+iIiIiIiIiIiIiIioyuDABxERERERERERERERVRkc+CAiIiIiIiIiIiIioiqDAx9ERERERERERERERFRlcOCDiIiIiIiIiIiIiIiqDJGpI0BERERERERERERE9FhqtaljQC8JzvggIiIiIiIiIiIiIqIqgwMfRERERERERERERERUZXDgg4iIiIiIiIiIiIiIqgwOfBARERERERERERERUZXBgQ8iIiIiIiIiIiIiIqoyRKaOABERERERERERERHRY2nUpo4BvSQ444OIiIiIiIiIiIiIiKoMDnwQEREREREREREREVGVwYEPIiIiIiIiIiIiIiKqMjjwQUREREREREREREREVQYHPoiIiIiIiIiIiIiIqMoQmToCRERERERERERERESPpVabOgb0kuCMDyIiIiIiIiIiIiIiqjI48EFERERERERERERERFUGBz6IiIiIiIiIiIiIiKjKqJQDHxqNBuPGjYOjoyMEAgECAwMr5HfeeOMNTJkypUKOXVpUVFSFpgUARowYgT59+lTY8YmIiIiIiIiIiIiIKrtK+XLzY8eO4eeff8bZs2dRu3ZtODs7mzpKVEGazOwL30GdILa1RMq1UFydvRXZkUll7lNnRGfU/+QdSF3skBESg2vztyMtMEL3vbW3K1p8NgguretAaC7GgzNBuDZ/G/JTs3RhGk7qhWqdm8GhoTfUBUr8Uv+jCkvj0/AY2Q01xveCuYs9ckKiET5vC7JvhBkN79zzVdT0HwCLGi6QRyYi4oudyDh1Q/e994wP4dK7PSTVnKAuUCInKAJRS/foHbPG5Pfh2LkFrBvWhKZQiX/qjqjIJJabzxfOwOhRg2Bvb4t//rmGTyfOQVhYpNHwZmZmWPjZdAwa+D7c3V3w4EEStu/4BV9+tcZg+O+/+xofjRuKadMX4tt1myooFS/WtcBb2Lr7AELuhiElLR1rly7AW6+3M3W0yoXHyO6orld3NiOnzLrTFt66upOAyFJ1x2tGv0fqTvTSPci+cV8XRlrbA7U+GwbbV+pCYC5Cbkg0opfvReal4ApN69NoOLMvag3uBHNbK6ReDUXA7C3IeUwb6zOiC+qOfwcWLnaQhcTgxrxtyCjRxgKAY0tfNJ7dD44tfKBRaSALjsb5gV9DnV+oF87MXIS3jiyGfSNvnOg8F5nB0eWexiflMrwH3D9+D2IXe+TdiULsgp+QG3jfaHiHd9rBc+YgSKq7Ij8qAfFfbUfm6eu6780sLVB97lDYd2sDkYMNFDHJSN7yJ1J2HgcACO2t4Tl9IOxebwbzas4oTMuC7Ph/eLBiN1TZeRWe3ufRdlpfNB7UCRJbSzy4FopTc7dCFmW83FRrXRetPn4Hro1rwdrNAYfGrEb4iet6YcSWEnSY3R8+3VpB6mCNzNgUBG49jqCdpys6Oc+kQam6c+MJ606dorqTaaDudPx1HlzaNdDbJ3z7KdyYtUX3t2uHhmjo/wFs69eAKk+B6P0XcPvr/dCoKt8LHFlOikl794FlvwEwc3SEMjwc2evWQnnvrsGwFm+/C4uu3SCqWQsAoAy9h5zNP+mFF1hIYTV2HCTtO8DM1g6qxATk/fYr8v889ELSU55azuiLegM7wdzOEklXQ3Fx7lZklVGX3NvURZOP34Fz41qwcnfAidGrEX38utHwHZaORP2hb+Hywh24vfl4RSThiVQf2RU1x/eEuau2H3J37lZk3Qg3Gt6156vwndUPFjVckBeZiLAlu5B6KlAvjI//h6g25C2IbK0gu3oPd/03IS8yUfd9h6vrIPVy1dvn/he7EbXuoN4270/eRbWhb0Fa3QUF6dmI+/kEItf8/vyJLgeOQ9+B89j3IXJxQP6dSCR8vhHyoFCDYSV+XnCdOhjSRr4wr+6GhCU/Im2rfp1w/uRD2HZrC0nt6tDkFyAv4A4Sl/2Mgsj4F5GcCuE87G24jnsPIhcHyO9EIn7hj8i7abj/YuFXA+7TB8OykQ/Ma7ghftEmpGx5+dqN0qR9+sCqf3Ebm/XtWijvGm5jpe8UtbG1tG1sYeg95Gz6SS+825lzBvfN3rAeefv2ln8Cyomf/4eoMeRNiG2tkHH1Hm77b9ZrEwzxHtkVtcb3hMTVDtkhMQieuxWZJdqmGkPfgud77WHbpCbENpY44TcKyqxH+6kunZvDb3pf2NT3glpRgLTLdxAwYlW5p/F5VcQ5p8W09+HT61VYeTpCXaBC6q1IXF3+C1LKaOOJqPKolDM+wsPD4eHhgXbt2sHd3R0iUaUcn6Hn1ODTd1F3VFdcmb0Fx99dCGWeAp12z4KZRGx0H+9ebdBi4WDc+uZ3/NVtPjJCYtBp9yxInGwBAEKpBG/umQWNRoNTH36FE70XwcxciI7bpgMCge44ZuYixBy+gvvbTlV4Op+US+928Pl8OKJX/YKArrOQGxyNRnvmQexsazC8bas6qL9+ChL3nMb1Lv5IPXoFDbf6w7JeDV2YvPAEhM3djOtvTMfN3guQH5uCxvsWQOxUfEwzcxFSD19GwvYTFZ7G8jJzxnhM+HQUxk+YjXYdeiI3Lw9//bkLEonE6D7+Mz/FR+OGYfKU+WjU5A3MmfcVZkz/BBM+HfVI2N69u6NNmxaIj0+oyGS8cHJ5Pur61sa86eNNHZVy5dy7HWp/Phwxq37Bja7+yA2OQqM9843WHZtWdVFv/RQk7jmFgC4zkXb0KhqUqjvy8AcIn7sJAW9MQ1Dv+VDEJqPRvvl6dafBjjkQiMwQ9MEi7e+GRKPhjjkQu9hXdJKfSN1P34Xv6G4ImLUVp975DMo8BV7bM7vMNrZ6r1fR9PPBCFn1G/7uNh+ZITF4fc9sXRsLaAc9Xt89C4nnbuFUj89wsscChG05Aag1jxyvyYKBkCdlVEj6noZDz/ao8dkoPFi9FyE9pkEeEgW/nQshcrIzGN6qZV3U/n46UveeREj3aZAd+w8+m2bDoq6XLkyNhaNg+0YLRE5ag9tvTETS5sPw+mIc7Lq8AgAQuznC3M0RsUt+RvBbkxE19VvYvdEc3isnvJA0P6tWn7yLZiO74uScLdjTayEK8xR4f+csCMsoN2JLCVJCYnB6/jajYTp+Nhg132iKY5PXY9ub/rix+Rg6LR6O2l1aVEQynkvJunP6nc+gylOgwxPUnSZFdedkt/mQhcTgtVJ1BwAidp7G4SbjdZ9bS/bovrNr4IX2O2ci8UwQTnaZh38/XgePbi3QeN6ACkvrs2I5KSZ5oxOsP/4Uudu3If3jsVCGh8N+2UoI7O0Nhjdv2gyK06cgmz4FGRPHQ5WSAvvlK2FW4kEv608+hfkrrZG19EukjRyGvF8PwGbSZJi3fbkeVmg6/l00HNkVF+dswcGe2nLS4zHlRGQpQXpIDP4po5w8VLN7K7i28EVuYnp5RvupufVui7qLhiFi1a/4r8tsZAdHo8XeuUb7IXat6qDxhkmI330G/3WejZSjV9H055mwKtEPqTmhF2qM6YE7/ptw5e15UOXlo/m+uY+0Q2Ff78O5RuN0n5jNx/S+r/vlCFQb/CbuL9qJSx2mInDYcmQGGH8w5EWyfec1uM8dg+Rv9yC852Tk34lEzW2LITRybjaTSlAQk4ik5dtQmGz4/9yqdSOk7ziCiL4zEDVsAQRiEWpuXwKB1Pg1QmVm/24HeM4fjcS1e3Hv3amQ34lC7R2LjPZfBEV59GDZdqN59LKRdOoEm08+Rc62bUgbNxaF4eFwWG68jRU3a4b806eQMXUK0j8dD3VyChxW6LexKe+/p/fJXPY1NGo1FOcND4hUBrUn9ELNMd1x238T/nl7PlR5CrTeN6fMvolH77aot2gowlYdwKUuc5AVHI3We+fAvETbJJSaI+VMIMLX/mH0OO7vtEbT7z5F3J6zuPjmLFzuuRAPfrtUnskrFxV1zsmMSMCl+dvwa+c5OPz+YmTHpeLtXbNg4WhTEcmgJ6XR8PO0n/9TlW7gY8SIEZg4cSJiYmIgEAhQs2ZNqNVqLF26FLVq1YJUKkXTpk1x4MABvf1u376NHj16wNraGm5ubhg6dChSU1N13+fm5mLYsGGwtraGh4cHVq168tHpuXPnok2bNo9sb9q0KRYvXgwAUKvVWLx4MapXrw6JRIJmzZrh2LFjj+zzJFQqFUaPHq1Lb926dbF27dpHwkybNg329vZwcnKCv78/NKUK8htvvIGJEydiypQpcHBwgJubG3766Sfk5uZi5MiRsLGxga+vL44ePfpM8Xxe9cZ0x+21BxF3PACyO7G4PGkDLN3sUaN7S+P7jOuBsN1nELHvPLLuP8CVWVuhkivgM7AjAMCltR+sarjg8pQfIbsbB9ndOFyevBFOTWvBvUPxU5a3Vv6Guz8dg+xubIWn80lV++hdJOw6haS9Z5EXGof7/j9CLS+A+4A3DYb3HPsO0s8EIu6HQ5Dfj0f08n3IuRUBz5HddWFSfr8I2YVbyI9JRt69OEQs3AaRrSWs6hffvItesR/xPx5B7p2YCk9jeZk0cQy+WroWhw+fwK1bdzBi5GR4erqhd+9uRvdp+2orHDp8HH8dPYXo6Dj89tsR/H3yHF55pZleOE9Pd6xd/QWGDZ+AwkJlBafkxXqt7SuYNG44Ondsb+qolKtqH/VE4q6TSNp7BnmhcQjz/xFquQJuRupOtbFvI/1MIOJ1dWcvcm5FwnNkD10Yw3XHClb1vQEAIkcbWPp4InbdH8i7E438yEREfbETQksLvRsXpuQ3tjvurPkDD45fR+adWFyZtB5SN3tUK6ONrfNRD0TuOoOofeeRHRqP6/5boJIrULOojQWAZouG4v7m47j33WFkhcYjJzwBcYf/g7pAv764v9kUbh0b4+bi3RWWxiflNq43UvecQNr+08i/H4fo2euhzlfAecBbhsOP7onMswFI2vAH8sPi8GDlbuTdjoDriLd1Yaxb1kXaL2eQffk2CuKSkbrrBPJComDVzA8AkH8vBuHjliHz5FUoohOR/c8txC/bBfvOrwDCStf90mkxujuurDuIiL8DkHo3FsemboCVqz18uhovN1Fng/DPygMIP37NaBiPln4IOXABcf/eQVZcKm7tPoOUOzFwb1q7IpLxXHzHdsfdNX8goVTd8XyCuhNdVHcCDNQdAFDJFVCkZOo+yhy57rsavV9F5p0Y3Fn9O3KjkpB6+S5uLdkDnxFdILKyqLD0PguWk2KWH/SD/K8/kX/8KFTR0cheswoaRT6k3d82GD5r6ReQH/oDyvAwqGJjkL1qOSAwg3nz4rwTN2yI/BPHUXgzEOqkROQfOQxleDjE9eq/qGSVi0aju+PGtwcRfSIA6XdicXaKtq/v3c14OYk7E4RrKw4g6pjxcgIAlu4OaLtkGM5M/AHqQlV5R/2peH/8DuJ2nsKDvWeRGxqPOzM3QSUvQLWBnQyG9xrXA2lnAhH9w2Hk3o9H+LL9yLoVCa9R3UqEeRuRq39DyrFryAmJQfCE7yFxc4BLj1f0jqXKzUdBSqbuo85T6L6z8quG6sO7IHD4CqQcv478mBRkB0Ui/fytismIp+Q8ug8y9h2H7MBJKMJi8WD+91DLFXD4sIvB8PKg+0j6eisy/zwPTUGhwTDRIxdC9uspKO7HIP9uJOJmroZ5NVdIG/lWZFIqjMuY3kjbewLpv5yC4n4s4ub+ALVcAcd+nQ2GlweF4cFXP0N2+AI0CsN59LKx+rAf5Ef+RP6xojb2m1XQ5OdD2sNIG/vlF5AfLG5js1YWtbEtitsddUa63kfSvj0KAm9AlVB5H3qrOa4Hwlb/juRj15EdEoObRW2CW49WRvep9fE7iN15GnF7zyEnNB63i9qm6gPf0IWJ+vEoItYdguy64QFRgdAM9b8YjruLdyFm+0nkRiQgJzQeiYf+Le8kPreKOueE/3EZDy4GIzsmBRmh8fh30S6Y21rCscQ9FSKqvCrdlffatWt1AwgJCQm4evUqli5diu3bt2PDhg0IDg7G1KlTMWTIEJw7px2Rl8lkePPNN9G8eXNcu3YNx44dQ1JSEvr166c77syZM3Hu3DkcPHgQJ06cwNmzZxEQEPBEcRo8eDCuXLmC8PDiqWzBwcEICgrCoEGDdPFetWoVVq5ciaCgIHTr1g29evXC/fvGl9EwRq1Wo3r16vjll18QEhKCzz77DHPnzsX+/ft1YVatWoWff/4ZW7ZswcWLF5Geno7ff390yvK2bdvg7OyMK1euYOLEifjkk0/w4Ycfol27dggICEDXrl0xdOhQ5OW92GU3rL1cIHWzR+KF27pthdlypN4Ih3NLP4P7mImFcGxSC4kXSiwjo9Eg8UIwnFtqO7NCczGg0UBdojOsUhRCo9bApXXdiklMORCIRbBpUhuy80HFGzUayC4EwaZVHYP72Lasox8eQMbZm7A1El4gFsFjaGcoM3ORE2K65WaeV61aXvDwcMOp0xd127KysnHlyg282sZ4p+byv9fwZqcO8PPT3jxp0qQB2rdrjWPHz+jCCAQCbNv6LVZ9sx4hIYan2VPlYrzu3IJtK8N13sZg3Qk0WtcEYhHch3YpqjtRAABlejby7sfD9cOOMLOUAEIzuA/rioIUGXKCIgwe50Wy8nKB1M0BSSXaS2W2HOk3wuHUynAbKxAL4dCkFpJKtMvQaJB04TacitpliZMtnFr6QpGahU6HFqJn0A9447f5cGqtn3cSZ1u0XDEGVyauh6rETRhTEIhFsGrsg6wL+mUk68JNWLUwXEasWtbVDw8g69wNWLcsDp9z/R7su7wCsbsjAMCmXSNY1PZE1vlAo3ER2lpClZMHVMJliwDAzssFVq72iLlYXAYKsuVIDAyHp5Fz85NKuH4ftbu0gJWbAwCgetv6cKjljuhKcgPuoWetO/ZNaiG5jLrzkNf77dEzeAO6nPkajeb2h1BqrvvOzFwMdakbVar8Agil5nBoUqscUlc+WE5KEIkgqlMHBQEllsXQaFAQcB3iBg2f6BACiQQCkQjq7OIlWQuDgyFp2173hLK4WXMIq9dAwbWr5Rr9imTj5QJLN3vEl+rrpwSGw+05ywkEAnRa+zGCNhxBRqhplzASiIWwaVIb6RdKlFGNBunnb8HOSJth17IO0s/f1tuWduYm7Ir6IVJvV0jcHJBWotwrs+XICgiDfalj1pzYGx3vbEKbk1/De3xPCEoMrDt3bQl5dDJcurREh6vr0OHqOjT45iOI7K2eN9nPTSAWQdrIFzmXAos3ajTIuRQIy+b1yu13hDbatKoyc8rtmC+KQCyCZWNf5FwMLN6o0SDn4k1YtSi/PKrUHrax1w20sQ2fso3NyjL4vZmDAySvtkX+X3+VR4wrhNTbFRZuDkgt1SbIAsJgb/T6RQjbJrWQVqptSj1/Cw5G9jHEtkktSD2dALUa7U8uxZtB69Fq92xY16v+zOmpCBV6zinBTCxEvcGdoMjMRdpLfE+F6P9JpVtDys7ODjY2NhAKhXB3d4dCocBXX32FkydPom3btgCA2rVr4+LFi9i4cSM6duyI7777Ds2bN8dXX32lO86WLVtQo0YNhIaGwtPTE5s3b8bOnTvx1lvapzu3bduG6tWfrLFu2LAhmjZtit27d2PBggUAgF27dqFNmzbw9dXecF+5ciVmzZqFAQO0yxEsW7YMZ86cwZo1a/D9998/VR6IxWIsWrRI93etWrVw+fJl7N+/XzeYs2bNGsyZMwfvv/8+AGDDhg04fvzRdW2bNm2K+fPnAwDmzJmDr7/+Gs7Ozhg7diwA4LPPPsP69esRFBSEV1999ani+TwsXO0BAPIU/Q5IfkoWpK6Gp+5KHG1gJhIiPyVTf5/UTNj6egAAUq+HQZmnQPN5AxD49X4AAjSf1x9mIiGkRb9ZGYkdbSAQCVFQKm0FKZmw861mcB9zV3sD4WUwL5VOxy4tUH/DVJhJzVGQJENQ/yVQpmeXa/xfJHc37VrGSUkpetuTklPh7u5qaBcAwLLl38HW1hrBt85BpVJBKBRiwWfLsGdP8YCh/8xPoVQqse67zRUTeSp3xuuODNIy6k5hikxvW2FKpoG60xL1NkzRLq2QlIFb/Rfr1Z1b/Rahwc+z0C5sB6DWoCA1E7cHfgllZm65pO15PGxjFaXby5RMWBhZistoG5uSBRtfTwCAlbe2jjWY/j6CFu+GLDga3h++ho775+JEp1m6dyC8svZjROw4hYybkbCsbtr3dImKykjp/3NlaiYsfA33A8Qu9lCm6ocvTMmE2MVB93fMgh/hvWw8ml7bAnWhElBrEO3/PXL+CzEcDwcbeEzuh9RdlXdZQcuispGXqn9uzkvNgqWL4XPzkzrz2XZ0/no0xl1dB1WhEhq1Bidnb0b8lXvPddzyVp51R5GSBduiugMAMb//g7y4VMgTZbBrUAON5w2EjY8HLo9eAwBIOhsEv7HdUaNPW8Qe+hcWrvaoP03bz7NwM/zbpsByUszMzg4CoQjqDP0l/dQZGRDVeLInQa3Gfgx1Wqrejb3s79bCZtoMOO/7FRqlElCrkf3NShTeCirjSJWLtKicyEuVE3lKFqTPWU6ajn8XaqUawSZ8p8dD5o62MDPSh7fy8zS4j8TVHgWlzkkFKZkwL7oGMi/Ku9LHVJTqq8RsOobsW5EolOXAvlUd+M4bCImbPUIX7gAAWHq7wqK6M1x7vYrbE76HQGiGOouHoenmabjed8lzpPr5CR1sIRAJHznXKlNlkPiU0w1VgQDuC8Yi91owFKEv3w3Kh3lUWLo/kiqDxMdwH7eqKauNNfd6sjbW+qOPoUrVb2NLsujWHZq8POSfP//c8a0oEiNtQkFKJiRG7nE8bJtK92cUKZmw9nvy8mNZ1Pf3m/EB7izcgbzYFNT+5F28+ttnONduKgplpr/uASr2nAMAXm81w5s/TIBIao68ZBn+GrQMioyXb0CV6P9RpRv4KC0sLAx5eXno0kV/ymtBQQGaN28OALh58ybOnDkDa2vrR/YPDw+HXC5HQUGB3nJVjo6OqFv3yWcADB48GFu2bMGCBQug0WiwZ88eTJs2DQCQlZWFBw8eoH17/eVj2rdvj5s3bz7xb5T0/fffY8uWLYiJidHFv1mzZgCAzMxMJCQk6KVHJBKhVatWjyx31aRJE92/hUIhnJyc0LhxY902Nzc3AEBycrLRuCgUCigU+k/sFmpUEAuET5yemu+1Q+vlxe9SODt05RPv+zQU6dm48NG3aL10JOqO7gqNWoPoPy4jLSgSGnXlfMK2oskuBeP6WzMhdrSBx5DOaPDjNNx4ew4KUw0/9VLZDBz4HtZ/v0z3d6/ew57pOB9+2BMDB7yPIcM+RUhIKJo2bYhvVi7Cg4Qk7NjxC1o0b4yJE0bjlTbdH38w+r8gu3QbAUV1x31IZ9T/cRoCS9Qd36VjUZiaiaDeC6DKL4D74LfQcPts3Og+C4XJshcaV6/326Hl8tG6vy8MXVEhvyMw074rKWLnaUTt014gym5Hw7VDQ9Qc+AZuf7UPvqO7QWxtgTvfHizrUC8915HvwLpFXdwf8SUK4pNh3aYhvL78CAVJ6ci+qH9z0sxaCt/tC5B/PxYPvqk8L82s16cd3lpafG7+Y0TFnJsBoNmIrnBv7ouDo1YhKy4V1drUw5tLhiM3KQMxF4Mff4AKUqNU3blYQXUHACJ3Fs8wzLobi/wkGToemAcrb1fkRicj6dwtBC3ZjRbLRuGVdZ9AXVCIO6v/gMur9aAx8A6dF4XlpOJYDhgEi05vImP6ZKCwQLdd2ud9iOs3gGz+HKiSEmHeuCmsJ02BKi0VhQHGX/RtSj7vtcNrXxeXk2PDK6acODeuiUaju+H3HvMr5Pgvk5iNR3T/zgmJgbpQiforxuL+l3ugKVACZgIILcwRPOF75EVol/AJmboRr578GpY+HsgLr7zL+pQHj8WfwKKONyL6+Zs6KmQilgOL2tip+m1sSdIePZB/8qTR703Bs297NFoxVvf3tcHLyghdsQRm2llkYWv/QOKRKwCAoMnr0enGD3Dv+Spid5jmnakv6pzz0IN/7uC3bvNg4WiNeoM6ofP6Cfij5+fIT3s57qkQ/T+r9AMfOTnaUdQjR46gWjX9kemHLzLOyclBz549sWzZoycEDw8PhIU9/wvcBg4ciFmzZiEgIAByuRyxsbHo37//cx/XkL1792LGjBlYtWoV2rZtCxsbG6xYsQL//fffUx9LLNZ/kZNAINDbJih64be6jEGBpUuX6s1AAYD3rBujr00TI3s8Ku5EAFJvFC8VJjTXFj2piy3yS9wgtHCxRUaw4XdNKNKzoVaqYFFqxN7C2Q7yEk8yJJ67jUPtpkPiaA21Uo3CrDy8H/gdomNSSh+y0ihMz4ZGqYJ5qbSZu9ihwMgN1IJkmYHw9o+EV+cpkB+ViPyoRGQH3Mcr/3wL94FvInbdH+WYgopz+PAJXLlyQ/e3RKJdFsTNzQWJicUDdm6uzgi8afymyLKlC7B8xXfYv/8QAOD27bvw9qqOWf4TsGPHL+jQoQ1cXZ0RGX5Ft49IJMKK5Z9h0sQx8K3z4mZE0ZMzXnfsjQ4+FCTLHnkBudhAXStdd1r9sw5uA99C3LrfYd+hMRy7tMDluiOgKlqjP3z2Jji83hRu/d5A3Hd/lFMKn8yD4wFIC3i0jZW42JVqY+0gCzb81KPRNtbFFvnJ2jZWnqQ9VlappUWy7z+AZTUnAIBrhwZwaumHvtH6LwnsfGwJYn67hKuTNz59Ap+DsqiMlP4/FznboTDZ8IvXC1NkEDnrhxe72KEwRRteYGGOarOGIHzM18g8rb0JKb8TDcuGteD+cR+9gQ8zKwvU2bkQ6hw5wsZ8DY3StGvRlxT+dwASSpybRRJtubF0tkVuiXJj6WyLlJBnfw+UUCJGe/9+ODxuDSJPBwIAUu/GwqWBN1qOe8ekN7QTjgfg7wqqO5ISdceQ9KLfta7lhtxo7fns/sajuL/xKCzc7FGQmQurGi5oPG+A7ntTYDkxTp2ZCY1KCTMHB73tZg4OUKeX/WJh6Yf9YTlwEGQzp0MVUWKJRHNzWI8ei8yF81Hwn3YNdXlEBES+vrD8sD8yK+nAR8yJAPxmqK/vbAt5iXIidbFFmpG+/pNwb10XUmdbDPyv+P2HZiIh2nw2GI3GdMfetlOf+djPoiA9C2ojfXiFkX6IIlmmm9VRMnxBUXvxcDZI6esAiYsdsoOjjMYlMyAMZmIRpDVckBeeAEWSDOpCpW7QAwBy78cBACyqOZt04EOVkQWNUvXIuVbkbA9liuFz89Pw+Pxj2HZ6BREDZkOZmPbcxzOFh3kkLt0fcbaHstSMoaqqrDZW9Zg21rJff1gNGoSM6dOhjDC8DK24cROIvLyRuXiRwe9NJenYdb13bjx8gXnpdsXcxQ5ZRvomD9smySN9E+NtkyH5Sdr6mHMvTrdNXaCEPCYZUhPO6n5R55yHlHIFsqKSkBWVhOSAcPS7sBJ1B3TEze8PP/exiahiVbp3fJTWoEEDSCQSxMTEwNfXV+9To4b2BbItWrRAcHAwatas+UgYKysr+Pj4QCwW6w0cZGRkIDT0ydfwr169Ojp27Ihdu3Zh165d6NKlC1xdtdP+bG1t4enpiUuXLuntc+nSJTRo0MDQ4cp06dIltGvXDuPHj0fz5s3h6+ur934ROzs7eHh46KVHqVTiupHpm89rzpw5yMzM1Pv0sn6yNTV18cvNR05Uku6TGRoPeZIMbh2KjyOylsK5uQ9Srxt+L4q6UIX0oEi4l9gHAgHcOzREqoGXcSnSc1CYlQe39g1g4WyLuBNP9k4XU9AUKpEdFAH714pn40AggH2Hxsi+ZricZl0P1Q8PwP71JsgyEl7HTKDrPL0McnJyER4epfuEhIQiISEJb3bqoAtjY2ON1q2b49//jNcBS0sp1KWemFWpVDAreopl565f0bxlZ7R8pavuEx+fgFXfrMfb7w6umMTRcyur7mRdM7w0SraBuuPwelOjdU2nRN0xK1qXv/RT2Bq1Wvdk1IukzM1HblSS7pMVGg95UsYjbaxjcx+kXTPcxmoKVcgIioRrqTbWtUMjpBW1y3mxKZAnpMPGx0NvX+va7siLSwUA3Ji/HSfemoO/O8/F353n4uIQ7RP0/368Dre//qU8k/1ENIVK5N4Kh02HEoP1AgFsOzRBboDhMpJ7/R5sO+gP7tu+1gw517XhBSIhzMzFj8yyhEoNCIr//82spaiz+3NoCpUIG/llpXvRaGFuPjKjk3SftNB45CbLUKN9cRkwt5bCvZkPHhg5Nz8JoVgEobnokZmX2voieObjlgdjdcf1KeuO7DF1xxD7Rt4AgPyiAcWS8pNkUOcXokaftsiLT0XGrchnS2A5YDkpg1IJZWio3ovJIRDAvHkLFIYYH6ix7D8QVkOGQTbbH8pQ/XZIIBJBINa+s06Pic4vT6owN193UygrKgkZofHIS5KhWol6IbaWwqWZD5Keo5zc//USfu0yF791m6f75CamI2jDERwdvLw8kvJUNIUqZAdFwLFUP8TxtUbINNJmZF4PheNrjfS2OXVsjMyifog8OhmKpAw4lTim0FoK2xa+kBk5JgDYNKoJjUqNgqKZqbIr97QDId5uujCWtbXLb+UXnbNNRVOohPx2GKzbNS3eKBDAul1T5N24+1zH9vj8Y9h2bYvIIfNQGJf0nDE1HU2hEnm3wmDdvlQetW+C3IDny6OXxsM2tkWpNrZFCxQGl9HGDhgIq6HDIPN/tI0tSfr22yi8dxfKEvdaKgNVbj7yopJ0n5x7cchPyoBziXZDZC2FfQtfyIxcv2gKVcgKioRTybZGIIDTa42Q8bhrnhKybkZClV8AqxJLdwpEQkhrOENuwnbkRZ1zjBEIBBC+RPdUqiS1mp+n/fyfqvQzPmxsbDBjxgxMnToVarUaHTp0QGZmJi5dugRbW1sMHz4cn376KX766ScMHDgQ/v7+cHR0RFhYGPbu3YtNmzbB2toao0ePxsyZM+Hk5ARXV1fMmzdPd8PzSQ0ePBgLFy5EQUEBVq9erffdzJkzsXDhQvj4+KBZs2bYunUrAgMDsWvXrqdOs5+fH7Zv347jx4+jVq1a2LFjB65evYpatYpfbDl58mR8/fXX8PPzQ7169fDNN99AJpM99W89CYlEoptd89DTLHNlzN1Nx9Boch9kRyYhNyYZTfw/QF6SDLHHim9ev7VvDmKPXUPo1r+1+/x4FG3XfIS0m5FIuxGOemO7Q2gpQcTec7p9avd/HZn346FIy4ZzSz+0WjwEd388huwSTzVZVnOCxN4KltWcIBCawaGhdo3Q7MgkKE30It74jX+i7tpPkXMzHFk3wlB97Dsws5Qgca92aYy66yZAkZCOqK92AwAe/HQETX5fhGofv4v0kwFw7dMeNk19cH+m9mlqM0sJvCa/j7Tj11CQnAGxoy08R3aDxN0RKYcv635XUs0ZIntrSKo5A0IzWDWsCQCQRyZCnZf/YjPhCX27bhPmzpmE+2ERiIqKxaLPZ+LBgyQcPFi81vOJY/vwx8Gj+GH9zwCAP4/8jTmzJyE2Nh7BIffQrFkjTJk8Dj9v0y47k56egfR0/SfMCguVSExMQWho5eoMP6u8PDli4h7o/o5/kIS7oeGws7WBRxnvR6ns4jceRt21E5B9MxzZN8JQrajuJBXVnTrrJqIgIU1Xd+J/+quo7vRE+snrcOnTAdZNa+P+zA0AtHWnxuS+SD9+VVd3PEZ2h8TdEamH/wGgHXhUynJR99sJiPnmF6jzC+A+pDMsvFyRfrJyPI17/6djqD+lD7IjE5Ebk4JGsz6APEmG+BJt7Ov75yD+6DWEF7WxoRuPovXaj5BxMxLpgeHwG9sdIksJokq0sffWH0HDGX0hC46BLDgaNfu9BltfT1weq336Vh6fBnmJeChzte1ITlQy5AllP51XUZJ+PIhaqycj72YYcgPvw21MT5hJLZC6Tzs9v+aayShMTEP81zu14TcfRt0DX8JtXG9knroGx96vwbKJD6Jm/QAAUOfIkX35NmrMG46Y/AIo4pJh82ojOH3wBmIXbQVQPOhhJpUgfNLXMLOxhJmNJQBAmZZVaTufAZuPoc2kPpBFJSEzJhntZnyA3GQZwk8Ul5u+e+Yg7Ng13NymLTdiSwnsaxbfWLOt4QKXBl7Il+Ui+0EaCnLkiL18B6/NGwhlfiGy4lNRvU09NOjbAecWP30/qaKFFdWdnKK607Co7jx4TN15pYy6Y+XtCq/32yHhVCAK0nNg18ALTRcNQcrlO8i8E6s7bp1P3kHimSBArUa1d15BvQm98O9H3wImXOrKEJaTYnkH9sN21hwoQ++i8O5dWPb9AAILKeTHjwIAbGbNhTo1BbmbfwJQdENu+ChkfbUE6sREmDk4AgA0cjk0+XJo8vJQEHgD1uM+RrZCoV3qqmkzWHTphpz1T/feQFO7vfkYmk/qg8zIJGTHJqPVDG1fP/p4cTl5e+8cRB27hpCfteVEZCmBbYlyYlPDBY4NvKCQ5SL3QRoUshwoZPrrqqsLVZAny5AZYZoZDNEbjqDht+ORFRiOrBvh8Br3NoSWEjzYexYA0HDdp1AkpiPsyz0AgJgfj6LVHwvh/fG7SDkZAPc+7WDb1AchM37SHTPmx79Qa+p7yItMgDwmGT6z+kORlIGUo9oX3Nu18oNdCz+kXwyGKlcOu1Z1UHfxMCQcuKB711j6+VvIuhmBhms+xr0F2wAzAep9PRppZ2/qzQIxldTNf6D6yqmQ37oP+c1QOI3sDTNLC2QcOAkAqLZyGpRJaUhaoZ1BKhCLIPGtofu3yM0JFvVrQZ2Xj4JobXo8Fn8C+14dET3uC6hz8nQzSlTZedAoKs9SRk8qZdNBeK2agrygMOTdDIXLqF4ws7RA+i/a/ovXN1NQmJiOhOXbAWjzxcKvKI/MRRC7O0LaoBZUucV59LLJ/WU/7GbPQWHoXRTeuQvLD7RtbP4xbRtrO2cu1CkpyNlU3MZajxyFzC+XQGWgjX1IYGkJi45vIHv9Dy8+Uc8g6sej8J36HnIjEyGPSYbfrH5QJGUg6eg1XZjWB+Yj6a+riN6ivSaO3HAETb79BJmBEZDdCEOtcW9DZClBXIl+vbmLHSSu9rCspW13bep7QZkjR358KgpluVDmyBGz/ST8Zn6A/Pg0yONSUPvTngCAhEP/vsAceLyKOOeIpBI0m9QbMX9fR16SDBaONmgwvAss3R0Q+efTr8hCRC9epR/4AIAlS5bAxcUFS5cuRUREBOzt7dGiRQvMnTsXAHSzLWbNmoWuXbtCoVDA29sb3bt31w1urFixQrcklo2NDaZPn47MTOPLDxjywQcfYMKECRAKhejTp4/ed5MmTUJmZiamT5+O5ORkNGjQAIcOHYKfn99Tp/ejjz7CjRs30L9/fwgEAgwcOBDjx4/H0aNHdWGmT5+OhIQEDB8+HGZmZhg1ahTee++9p06TKYV8/ydElhK0WT4K5raWSL4aijODl0Nd4olY65qukDja6P6OPvQfJE62aDqzLyxc7JARHI0zg5cjv8T7Kmx9PNBsTj+Y21sjNzYFt789hLs/HtX77SYz+sKn/+u6v9/++ysAwN99v0Ty5TsVleQypRz8B2InW3j794e5iz1ygqNwe+CXKEzV/p9KqjnrPV2edS0Ud8evRc1ZA1FrziDIIxMQPHI58u5qb6BoVGpY+laDW783IHa0QWFGNrIDwxHY5zPklZiq6u3fH+7939D93fKU9unsm+8vROY/hl/Sa2orVv4AKytLbPhhOeztbXHp0lW803OI3rtoatf2hrOzo+7vyVPmY9Hn/lj37VdwdXXCgwdJ+GnTTiz5YrWhn6iSbt+9j1ETZ+n+Xr7uRwBA7x6d8eX86aaK1nNL1dWdAbq6E1yq7pS8yZx97R7ujV8L71kDULOo7oQYrDsdIXa0RWFGNnICw3GzzwJd3VGmZ+P2oC9Rc/ZAND7wOQRiIfLuxSJkxHLkhlSOF2jeK2pjW60YDbGtJVKvhOLCoGWl2lg3vTY27tC/kDjZoKH/B7qlfS4MWgZFiTb2/k/HYCYRo9miITB3sIIsOAbnBiw16VI8j5Nx+BJETnbwnDEQYhcH5IVE4v7QRVDqyoiL3o3l3Ov3EDnhG1TzH4xqs4ZAEfkA4WO+Rv694qny4eNXovrsoai1bipE9tZQxKUgftkupOw4BgCwauwD6xbad4k1vrRBLz5Br45DQVzlzK9r6/+EWCpB56WjILG1xINrofht6HKoSpQbOy9XSEuUG7cmtfHh/nm6v99YOAQAEPzLeZyYrm1n/prwHTrM6o8e334CC3trZMWl4tLyXxC00zRrQ5fl3vd/QmgpQcsSdediqbpjZaTuNCiqO5nB0bhYou6oC5Vwfa0RfMdoB0TyHqQj/shV3Fnzh95vu7/ZFPUm94bQXAxZSAz+GfkNEk8/2/viKhLLSTHF2TPIsbOH1YhRMHNwhDI8DLLZM6Epehmv0NUV0BSfg6Q9e0Ngbg67z/VfLp27bStyt/8MAMj6YjGsxoyD7dz5MLOxhSopETlbNkF++OV6d9LNH7TnodeWafv6SVdDcWyIfjmx9XaFRYly4tK0Nt79pbictP1cW05C95/HuWk/vrjIP4Wkg5dh7mQLH/9+kLjaIzs4CgEDl+peRGxRzUmvH5J5LRS3PlkH39n94Tt3APIiE3FzxArk3i0eBI367hCElhLUXzkOIltLyK7cw40BS3XtkFqhhFufdqg94wOYmYshj0lG9Ma/EL3hz+KIaTQIHLocdb8aiVYHP4cqT4HUU4EI/Xz7i8mYx8g6cgGJjnZwnToEImcH5N+JQNSIz6Aqepm3uaeLXr6JXB3he2Sd7m+XcX3hMq4vcv+9hchBcwAATkPeAQDU3vu13m/FzVwN2a+Vtx0xRvbnRYic7OAxbRBELg6Qh0QgYtjnupfCa/OouP8idnNE3aPFy8C5fvQ+XD96HzmXbyFswLzSh38pKM6cQbadPaxHjIKZo7aNzZg1U/fCc6Grq145seytbWPtF+m3sTk/b0Xutp91f1u8+RYgECD/9MtRLiKK2oTGK8dCZGuJjCv3cHXA13p9E0tvN5iXaE8TitqmOv4fwtzVHtnB0bgy8Gu9l6R7D+8Cv5kf6P5ue+hzAMDNSesRv087QHJ30S5olGo0/X48zCzMkRkQhv/6fqEbZK0sKuKco1GrYe/rgTofToaFgw3yM3KQcjMCh/t+gYxSS/8SUeUk0DyyTgPR4+3yHGLqKFQqNdSmmSVSmb2Z/o+po1DpyB9cMHUUKp1/G/GFk6UlCiSPD/R/ppawcl1YVQYXzKxNHYVKp0Yhu7SlxYor6RJRJjLYL/bxgf7PHLxXw9RRqHRqFlauZQkrAw8rnodLU6oq79JzpuJR++V5CPNFuR7i8fhA/2fixS/F89cv1Ni4naaOwktHvpX3EZ6WdOSLXxK0MuDZmoiIiIiIiIiIiIiIqgwOfAC4cOECrK2tjX7K28cff2z0tz7++ONy/z0iIiIiIiIiIiIiov8XnGMGoFWrVggMDHxhv7d48WLMmDHD4He2trYvLB5EREREREREREREL40S7/YhKgsHPgBIpVL4+vq+sN9zdXWFq6vrC/s9IiIiIiIiIiIiIqL/F1zqioiIiIiIiIiIiIiIqgwOfBARERERERERERERUZXBgQ8iIiIiIiIiIiIiIqoyOPBBRERERERERERERERVBl9uTkRERERERERERESVn0Zt6hjQS4IzPoiIiIiIiIiIiIiIqMrgwAcREREREREREREREVUZHPggIiIiIiIiIiIiIqIqgwMfRERERERERERERERUZXDgg4iIiIiIiIiIiIiIqgyRqSNARERERERERERERPQ4GrXG1FGglwRnfBARERERERERERERUZXBgQ8iIiIiIiIiIiIiIqoyOPBBRERERERERERERERVBgc+iIiIiIiIiIiIiIioyuDABxERERERERERERERVRkiU0eAiIiIiIiIiIiIiOix1GpTx4BeEpzxQUREREREREREREREVQYHPoiIiIiIiIiIiIiIqMrgwAcREREREREREREREVUZHPggIiIiIiIiIiIiIqIqgwMfRERERERERERERERUZYhMHQEiIiIiIiIiIiIiosfSqE0dA3pJcMYHERERERERERERERFVGRz4ICIiIiIiIiIiIiKiKoMDH0REREREREREREREVGVw4IOIiIiIiIiIiIiIiKoMDnwQEREREREREREREVGVITJ1BIiIiIiIiIiIiIiIHkutMXUM6CXBGR9ERERERERERERERFRlcOCDiIiIiIiIiIiIiIiqDA58EBERERERERERERFRlcGBDyIiIiIiIiIiIiIiqjI48EFERERERERERERERFWGyNQRoJdTNZXC1FGoVMJFElNHodLZ4tLJ1FGodP5t5G/qKFQ6r95ebuooVDotGw02dRQqnc9UtU0dhUpnRJs4U0eh0hHVsDF1FCqf/EJTx6BSufK7u6mjUOm83zrW1FGodAKvsJyUlpFnYeooVDpiM7Wpo1DpXA/xMHUUKh07gdLUUah0zNg1ofKgZhtMT4YzPoiIiIiIiIiIiIiIqMrgwAcREREREREREREREVUZHPggIiIiIiIiIiIiIqIqgwMfRERERERERERERERUZXDgg4iIiIiIiIiIiIiIqgyRqSNARERERERERERERPRYarWpY0AvCc74ICIiIiIiIiIiIiKiKoMDH0REREREREREREREVGVw4IOIiIiIiIiIiIiIiKoMDnwQEREREREREREREVGVwYEPIiIiIiIiIiIiIiKqMkSmjgARERERERERERER0WNpNKaOAb0kOOODiIiIiIiIiIiIiIiqDA58EBERERERERERERFRlcGBDyIiIiIiIiIiIiIiqjI48EFERERERERERERERFUGBz6IiIiIiIiIiIiIiKjKEJk6AkREREREREREREREj6VWmzoG9JLgjA8iIiIiIiIiIiIiIqoyOPBBRERERERERERERERVBgc+iIiIiIiIiIiIiIioyuDABxERERERERERERERVRkc+CAiIiIiIiIiIiIioipDZOoIEBERERERERERERE9llpj6hjQS4IzPoiIiIiIiIiIiIiIqMrgwAcREREREREREREREVUZHPggIiIiIiIiIiIiIqIqgwMfRERERERERERERERUZXDgg4iIiIiIiIiIiIiIqgyRqSNARERERERERERERPRYGrWpY0AvCc74ICIiIiIiIiIiIiKiKoMDH0REREREREREREREVGVw4IOIiIiIiIiIiIiIiKoMDnwQEREREREREREREVGVwYEPIiIiIiIiIiIiIiKqMjjw8RLRaDQYN24cHB0dIRAIYG9vjylTppg6WkREREREREREREQVT63h52k//6dEpo4APbljx47h559/xtmzZ1G7dm2YmZlBKpU+8f5nz57F6tWrceXKFWRlZcHPzw8zZ87E4MGDKzDWxnmO7Aav8b1g7mqPnJBo3J+7Bdk3woyGd+n5KmrNGgCLGi7Ii0xExJKdSD91AwAgEAlRa/YAOHZuAam3K5RZecg4fwsRX+xCQVKG7hiNts+CdcOaMHe2RWFmrjbMkp16YSqj5jP6os6gTjC3tUTytVBcnrMVWZFJRsO7tamLRp+8A+fGtWDp7oBTo1Yj5vh13fcCkRAt/T9A9TebwdrbBYVZcjy4eBvXvtoHeZLsBaTo+TWd0Rd+RXmSci0U/87Ziuwy8sS1TV00/OQdOBXlyZlRqxFbIk8AwKtHK9QZ+hacmtSExMEGh7vORUZwTEUn5al5jOyO6uN7wdxFW3fC521GThl1x7lnW3j7a+uOPDIBkV/sREZR3QEArxn94NK7PSTVnKAuUCInKALRS/cg+8Z9XRhpbQ/U+mwYbF+pC4G5CLkh0YhevheZl4IrNK0V7VrgLWzdfQAhd8OQkpaOtUsX4K3X25k6WhVmvP9Y9B3cCza2Ngi8GoQvZi1HTGRcmfu4urtgyvzx6PBmW1hILRAbFYcFU75AyM27EImEmDD7I7z2VjtU9/ZEdlYO/rtwDWu++AEpSakvKFVPp8HMvqg1uBPMba2QejUUN2ZvQU4ZbQcA+Izogjrj34GFix0yQ2JwY942ZARG6L7v+Os8uLRroLdP+PZTuDFri+5v1w4N0dD/A9jWrwFVngLR+y/g9tf7oVGpyzeBz0nSvQ8kvQfAzN4Rqqgw5G3+FqqwuwbDitu8Bov3h8DMoxoEQiFUCfFQHN6HgnN/GwxvOW4aJN16IW/Ld1AcOVCRyShX4g5vw/zN9yGwcYD6QSTyf90Idcz9x+4nav4apMP9UXjrX+Rv/tJgGMmH42Hevgfyf/8JhecOlXfUK4y4Y0+Yd/0AAlsHqOMikL/vB6ijQh+7n6hVR0jHzEFh4D/I37C4eHuz9hC//jaEXn4QWNsi94vxUMdFlHEk06o+siu8xvfU9WFD525F1o1wo+Fde76K2rP6FZ2HExG2ZBfSTgXqvnd5uzWqDe8M2ya1IXa0wX9v+iMnONro8Zrung3nt5rj5ogVSD16rTyTVm4kb/eB9P0BMHNwhDIyHHkb10J533BbYt72NUg/LGpLRCKoHsRB/sd+FJw5oQ0gFMJyyBiIW70KobsHNLm5KLx5HbnbNkKTnvYCU/V0TFFOWvz2GRzaN9TbFrftb9zz31SuaXtWL7oPa9euIZr8tsjgsW90n4WcQOP/Hy+K24ju8PykD8Qu9sgLiULk/E3IDTSeJ47vtkUN/4GQVHdFfmQCYr7cAdnpAL0wFr7V4DV/GGxfbQCBSAh5aBxCxy5HQXwqhPbWqDFjAOw6NoXE0xmF6VlIP3YFccv3QJWdV9HJfWJ+/h+ixpA3Iba1QsbVe7jtvxl5kYll7uM9sitqje8JiasdskNiEDx3KzJL1LkaQ9+C53vtYdukJsQ2ljjhNwrKLP002zauiXoLBsGumQ80KjUSj1zBnc+2Q5WnqJB0Pgn3kd1Rraje5IZEI+Ix9capZ1t4lag30aXqTUk+y8bBfXhXRCzYioSfjuh959C5BWpM+xCW9b2gURQi83II7o5cXq5pexq+/h+ieokyEfIEZcKrqEyYF5WJO6XKhJlEjLqfD4FHn3Ywk4iReuYmQmZvQUFKJgCgWv+OaPztJwaPfbrhOBSkZkHiao+6i4bCrmltWNZyQ/SmY7i7YHv5JZyIngtnfLxEwsPD4eHhgXbt2sHd3R2urq6wsbF54v3/+ecfNGnSBL/++iuCgoIwcuRIDBs2DH/++WcFxtowl97t4LtoOKJW/YJrXWYhJzgaTfbOg9jZ1mB421Z10GDDFCTsPo1rnf2RevQKGv3sD6t6NQAAZlIJrJvURvQ3B3Ct8ywEj1oJS19PNN4+S+84sku3ETLuG/zXfjKCR62EtKYbGm6eXuHpfR6Nx7+L+qO64vLsLfiz50Io8xToumsWhBKx0X1ElhJkhMTg8rxthr+XmsOxcU0Erv0Dh7ovwOmxa2BX2wOdt06rqGSUq4ZFefLf7C34qyhPOu+aBbMnyJP/jOTJwzDJV+7h+pf7KiLa5cK5dzvU/nw4Ylb9ghtd/ZEbHIVGe+YbrTs2reqi3vopSNxzCgFdZiLt6FU02OoPy6K6AwDy8AcIn7sJAW9MQ1Dv+VDEJqPRvvkQOxUfs8GOORCIzBD0wSLt74ZEo+GOORC72Fd0kiuUXJ6Pur61MW/6eFNHpcKNnDAEg0Z/iCX+yzH47dGQ58mxYe8amEvMje5jY2eDbYc3QqlUYvzgaXiv40Cs/PxbZMmyAQAWUgvUb1wXG1dvRf8uIzBt1BzU9PHCt9tNd1FUlrqfvgvf0d0QMGsrTr/zGVR5CnTYM7vMtqN6r1fR5PPBCFn1G052mw9ZSAxe2zMbEif9Ohex8zQONxmv+9xaskf3nV0DL7TfOROJZ4Jwsss8/PvxOnh0a4HG8wZUWFqfhbhdJ0hHjEf+/p+RNXMsVNHhsF6wAgJbe4PhNTnZyP91B7LnjEfWtNEoOHMUlp/OhqjZK48eu3UHCOs0gDotpYJTUb5EzTtA0mcMFMf2IG/lFKjiI2H58WIIrO3K3E/g6ApJ71FQht82fuzGr0JYsy7Ussp749YQUcvXIflgLBR/7kTeVxOgiouA5cQvIbB5TJ44uUHSdwyU9289+qXEAqqwYCh+3/Lod5WMa++28Fs0DJGrfsXVLrORExyNZnvnGj0P27Wqg4YbJuHB7jO40nk2Uo5eRZOfZ+r6sAAgtJQg8797CPti92N/v8ZHbwOV/KE98w6dYDXmU8j3bEPmlLFQRYbDZvFKCOzsDYZXZ2dDvn8nsmZ+CtnEUVCcPArrybMgbq5tSwQSCwh96kC+bztkU8Yie+kCmFWrAdv5X73AVD0dU5aT+B0ncaHRON0nbPGuck3bszJFHzbr6j3823iM3idh50nIo5MqxaCHU6/28F44EnHf7MetbjOQGxKF+rs/g8jJcHtq3aou/H6YhuQ9pxDUdTrSj11BnS2zIK3rpQsj8XZDwz++Qn5YHEI++AxBb01F/JpfoM4vBACYuzlC7OaA6MXbcPPNKQifsg72bzRH7VWfvpA0P4naE3qh5pjuuO2/Cf+8PR+qPAVa75tTZn/No3db1Fs0FGGrDuBSlznICo5G671zYF6ifAml5kg5E4jwtX8YPIbEzQGtf5mP3Mgk/NNjPq4OXArrutXR5FvTXSc4926HWp8PR+yqXxBYVG8aPqbe1F0/Bcl7TiGwy0ykH72KeqXqzUOOPVrDuqUfFAmP9kOc3mkDv3UTkbz3DALfmoGgXvOR8vuFck/fk6o1oRe8x3RHiP8mXC4qE60eUybcS5SJf7rMQXZwNFqVKhP1Fg+Da9eWCBy7Blf6LIKFuwOabym+J5Jw8B+cbvSR3ifldCDSL4WgIDULgHbwpCAtC+Grf0N2GQ8tEJFpcODjJTFixAhMnDgRMTExEAgEqFmzJt544w29pa4yMjIwbNgwODg4wNLSEj169MD9+8VPJM6dOxdLlixBu3bt4OPjg8mTJ6N79+747bffXnh6anz8LhJ2nkLi3rPIC41D6MwfoZYXwGPgmwbDVx/3DtLPBCL2h0PIux+PqGX7kHMrAtVGdQcAqLLzENRvCVIOXYY8/AGyrt/H/TmbYdPMB5JqzrrjxG08gqzr96GIS0XWtVDErPsDti39IBAJX0i6n0WDMd0RtPYgYk4EIONOLM5P3gCpmz28urU0uk/8mSAELD+AmGOGnwIszJbjxMBliDr8H7LCE5ASEI5/52+Hc9PasPJ0qqiklJv6RXkSeyIAsjuxuDh5AywfkycPzgQhcPkBxBrJEwCI+PUSgtb8gYQLxm9UmVq1j3oicddJJO09g7zQOIT5/wi1XAG3AYbrTrWxbyP9TCDifzgE+f14RC/fi5xbkfAc2UMXJuX3i5BduIX8mGTk3YtDxMJtENlawaq+NwBA5GgDSx9PxK77A3l3opEfmYioL3ZCaGmhd0H+Mnqt7SuYNG44Ondsb+qoVLghY/vjpzU/4+zxC7h/JxzzJi6Gi5sz3uz+utF9Rk0YgqT4JHw25UvcvhGC+JgEXD53BXHR8QCAnOxcfNR/Mk4cOoWo8BgEBQTjq7mr0LBpfbhXc3tRSXtivmO74+6aP5Bw/Doy78TiyqT1kLrZw7O78bajzkc9ELnrDKL3nUd2aDwC/LdAJVeg5sCOeuFUcgUUKZm6jzJHrvuuRu9XkXknBndW/47cqCSkXr6LW0v2wGdEF4isLCosvU/LoueHUJw8goIzx6COi0bexm8ART7M33rbYHhlcCAKr1yEOj4G6qQHUBz5FarocIjqNdYLJ3B0huWYychd+wU0KtWLSEq5MX+jDwovH4fyyimok2Kh+OUHaAoUELfpYnwngRmkQ6aj4OhuaNIMzyYS2DlC0vcj5O9YBaiVFRT7imHe+X0UXjoG5eW/oU6IgWL3OmgKFRC362Z8J4EZpKP8UXB4JzSpjz6dqfzvFAr+2g3lXcNPpFYmXh+/g/idp5Cw9yxyQ+Nxd+YmqOQF8BzYyWD4GuN6IP1MIGJ+OIy8+/GIWLYf2bciUX1UcX4lHriAyG9+Rfp5A4NCJVg39IbXx+/izpT15Zqm8mbRpx8Ux/+E4tRRqGKjkfvDKkCRD0kXI23J7UAU/HsBqrhoqBMfIP/wr1BFRUDUQNuWaPJykf3ZdBRcPAN1fCyU90KQu3EtRH71YObi+iKT9sRMWU5U8gIUpGTqPqoS5yNTMkUfVlOoRGGKTPdRZmTDqfsrSNp75oWk+XE8xvVE8u6/kbLvNOT34xA5ayPUcgVcjVwTe4x5F7IzN5Cw/iDyw+IRt2IPcm9Fwr1EntSYPRiy09cR88UO5N2OhCI6CRknrkKZpn2CXX4vBvfHroDs72tQRCch69JtxC7bBYcurQBh5bg9VHNcD4St/h3Jx64jOyQGNyd8D4mbA9x6tDK6T62P30HsztOI23sOOaHxuF1U56oPfEMXJurHo4hYdwiy64ZnS7h2bQGNUong2VuQG56AzMAIBPtvgkfPNrCsaZp+redHPZG06ySS956BPDQO4f4/QiVXwNVIvfEc+zYyStSbmOV7kXsrEh4lyggAmLs7ovaXoxH66VpolKX6ZkIz1FoyClGLdyBx+wnkRyRAHhqHtEOXKyqZj+U9rgfCi8pETkgMbhWVCdcyykTNojIRv/ccckPjEVxUJqoVlQmRjRTVB3XC3YU7kH4xGFlBkbg1eQMcWteFXUtfAIA6v1CvPdWo1XDq0Ahxu4vbEHlsCu7O34YHv1yAMqtytLdEVKxynNnosdauXYvFixejevXqSEhIwNWrVx8JM2LECFy7dg2HDh3C5cuXodFo8Pbbb6OwsNDocTMzM+Ho6FiRUX+EQCyCTZPayLgQVLxRo0HG+SDYtqpjcB/blnWQcT5Ib1v6mZtGwwOAyNYSGrUaysxcw9/bW8Ot72vIuhr66Mm+krD2coGlmz0eXCy+EV+YLUfqjXC4tvQr198S20qhUatRkFV5pjgb8jBPEkrlScqNcLiUc55UNg/rjuy8ft2RXbgF21Z1De5j07KOfngAGWcDYWOk7gjEIrgP7QJlZi5yQqIAAMr0bOTdj4frhx1hZikBhGZwH9YVBSky5ARV3qVIqFg1L0+4uDnj3/PF546c7FzcuhGCpq0aGd3vjW6vIfjmXaz86UucvX0E+/7ehr6De5X5W9Y21lCr1cjOzC63+JcHKy8XSN0ckHSheHk2ZbYc6TfC4dTKcNshEAth36QWkksOhmo0SLpwG06l2huv99ujZ/AGdDnzNRrN7Q+htHgmjZm5GGqF/rlYlV8AodQcDk1qlUPqyoFIBKFPXSiDSiwBqNGgMOg6RHUaGN+v5CEat4DQswaUITeLNwoEsJo0F/kH90IdG1W+ca5oQhHMqvtCFVoiPRoNVKGBMKtpuM0FAPNuA6DOyUThf4aX/IJAAIvB01Bw+jeoEyvfcoplEopg5uUH1Z0SAxQaDVR3bsCsdn2ju5m/MwjqbBkK/zn+AiJZcQRiIWya1Eb6hRI3njUaZJy/BTsj7YhdyzpIP6//QEXamZuwK6MPa4iZ1ByN1k/CvTnFS3BUSiIRRL51UHBTvy0pCLwOcd2GxvcreYgmLSCsVgPK4CCjYcwsraBRq6HJyXneGJc7U5YTAHB/vwNeC/kJbc6thM+8gTCTGp/Z+aKYqg9bmmO3VhA7WCNp7+lnSkd5EohFsGrig8xS18SZF4Jg3dJwnli3rKMfHkDmuRvF4QUCOLzVEvkRCai3ewFaBm1Foz+/hkP31mXGRWhrBVVOHlAJlt+UervCws0BqSUG+JTZcsgCwmBv9P9eCNsmtZBWqs6lnr8Fh6eoQ2bmIqgLVICmeFqdSl4AAHBoU+8pU/L8BGIRrA3Um8wLt2DzPPVGIIDfdxMR/8NByO89uuStdZPakHg6ARoNmv69Aq/c/AkNds8zOGvkRXhYJtJKlYnMZygTaedv6faxbVobdQiwrgABAABJREFUZuYivePmhj2APDbF6HGrffg6VHIFEv/8txxSRkQvAt/x8ZKws7ODjY0NhEIh3N3dH/n+/v37OHToEC5duoR27bTr0+/atQs1atTAH3/8gQ8//PCRffbv34+rV69i48aNZf62QqGAQqG/pmWBRgVzwbPNkhA72kAgEj5y0VaQkglLv2oG9zF3tTcQXgZzV3uD4c0kYtSePwTJv1965Cmn2vMHo9ro7hBaWiDzWihuDVn6TOl4ESyL0idPydLbLk/NgtS17CUlnoZQIkaruQMQ8cdlFFaSp8KMkRblSX6pPMkv5zypjIzXHRmkvsbrTmGKTG9bYUrmI3XHsUtL1NswBWZSCQqSMnCr/2Io04tvXN/qtwgNfp6FdmE7ALUGBamZuD3wS6MDi1S5OLtqZ3KlpaTrbU9LSYeTq/FZXtW9PNFv+HvYsXEvNq3dhobN6mPWF9NQWKjEof1/PRLeXGKOqfPH4+jvfyM3p3INoloUlXlFqfqTn5IJCyNLtkkcbWAmEiK/1D6KlCzY+nrq/o75/R/kxaVCniiDXYMaaDxvIGx8PHB59BoAQNLZIPiN7Y4afdoi9tC/sHC1R/1p72vj5Wb4t180gY0dBEIh1DL9MqLJzICwmpeRvQBYWsH+xwOAWAyo1cj7abXe4IlFn4GASgXFkV8rKuoVRmBlq82TbP33gGmyZRC6VTe4j7BWA4hf7YK8FZONHtf8rb6AWo3C84fLNb4vgsC6KE+yZHrbNdkyCN0N3xAR+jSEuH035H1ReZZReVZiR1uYGe3DehrcR9uHlT0SXvKUfZY6i4dDdi0UqWXMXK0MBLZ2EAhF0GSUqjeyDAiqG29LBJZWcPj5ACA2B9Qq5K5fg8JAI2kVm8NyxEcoOH8KGnnlOtcApi0nib9fQn5sKhRJ6bBu4A3f+YNg6eOJW6NWPdVxypsp+7AluQ96Cxlnb6IgId3g9y+SqChPHkljqvE8EbvYozC1VPiUTIiL8kTsbAehtRSeE95D7LLdiPlyB+w7NUedTf4I+eAzZP8bYjAe1ad8iOSdRgbrXzBJUZ/MUP2RGLn2Ny+qc6X7eIqUTFgbub9gSNrFYNRfNBS1xr+LqJ+OQmhpgbrzBwEwTX9NrCsjj9YbO2NlxEi9EZfIu2oT+kCjVCNh06N9eQCw8NLObqnxP/buO7ypqo8D+Ddt2jTpHmkLpYO2zNKyRfaQPQRfhgzZQ0CQIRsFEWUjAg6GgoMtKorKkCUgSIFSRoEuuvfeO3n/SEibNqWMtgnx+3mePA+5PffmnMu5557kd885C0YgfOW3yI9KgtP0QWj20yr4dXwXxem1G3CurE4UPEWd0LSPqbJOiOytICsoqrDOS0Fyhuozy6s3ujvifv5HNXUcEek+Bj70xIMHDyAUCtGuXTvVNltbWzRq1AgPHjyokP78+fOYOHEidu/eDS+vJz99tXbtWqxapb4o3HhJE0wwe7qntmqbQGiIprvnAwIgaNHuCn+P+vI3xB04B5N6UrgtGI4m22frTPDD/Y0O6LB+kur9X+M21fhnCoSG6LZjNgQCAa4u/bbGP+9Z1X+jA14tc07O1cI5+S9K/+ce/F5bCCMbczi+1RNNds2Hf/+lKFLOXeq5diqKkjNwZ/AHKMkvhOOY1+D1/RLc6rsYRYnp2s08VdD/f72xYmPpGkfvvLXguY5jYGCAgNsPsW3tDgDAw3tB8GzsjuHjhlQIfAiFhti062MIBAJ8vFj7a3w4/68DWm+YrHp/eezGGvussH2lw90zH0YhPyEdXY8uh6mrPXIiEpHw913cWX0ArdZPQtvtMyArLMKDLccgfbUx5DIdn6y/Knm5yFwwBQITMYTerSCe8A5kCXEoDvCHoXtDiAYMQ+bCqdrOZe0QiWHy1nzkH/4c8pxMjUkM6nnAqMvryN00t3bzpi0iMUwmLkT+vq2VnhOqml2f1rDu5AXf1xZXnfglJc/LRfocRVti1LwVJJNnoiQ+FsX3/NUTGhrCfPGHgECAnC8/1UZWdVrsD2dV/855EIXChDS0+mkFxK4OyIvQPPXey66qPuxjxnVsYN2tOR5M0+N6YyAAAKSd8kX8bsU6nrkB4TBr0xgO4/pUCHwYmonR+PvlyAuKQvRm7axxWHdoRzTbWNpPuDFmvVbyAQDZgdG48+5XaLJqLBotHwV5iQwRX59EQWL6y99fUzL1cUfdqf1xu9eiyhMp61H0Zz8h5Y9rAIDguV+g7a2dsB3UHgk/1GyQrM7QjvAqUyduarFOlGXVpgHMGtXDnVlfaDsrBEAu0/4INXo5MPDxH/T3339j0KBB2LJlC8aNG1dl+qVLl2L+fPVFr//1nPDcn1+UmgV5cQmMpepPMBlLLVFYyQ+ohYnpGtJbVUj/OOhhUs8O/kNXaZzTtig1C0WpWch7FIfc4Gi0998JizYNkXkj6LnLVF0iT/sh6VbpQnuGxopLVCy1QF6ZsortLJAa8OLTYwiEhui+YzbM6tni5Ii1OjnaI+q0H5LLnBMD5TkxKXdOTOwskFYN50SXVX7tWFUafChMTK+wALmRhmtNlluA/PB45IfHI8svGG2ubIfDqNcQvf0XWHXyhk2vVrjaaILqmgpd8jWsuzSHw4huiP78WDWVkKrLhVOXcdev9MutsXLhP1upDZITSxcwtJXaIPBe5W1fUmIyHgWFqW0LCw5HzwHqc5QLhYbYuOsT1KnniCnDZunEaI+4U374y69ieyqSWiK/bNshtUR6JQsRFqRmQVZcApNy15xIaoH8xMqnmklVfq5ZfQfkRCQCAIJ3nkDwzhMwcbBCYUYOTJ2l8F4+UvV3bZNnZUBeUgIDKxuUnfxRYGldYRSI+o5yyOIVa76UhIfAsJ4rTP43GtkB/hA28YHA0gqWO4+UHs/QEOLxMyAaOAyZM3Rrcffy5DmZinNibo2yX60E5laQZaZVSG9g5wgDWweIp3xQJrHixwOzzceQs2Y6DD28IDCzhOnK0kW8BYaGEA2eBOOuryPnoyk1VZxqIc9WnhMLq6c7J9I6MLBzhHhmmQdoHp+TL/5AzsopkCfH1XCuq09RaiZkz9WHtaqQvuAJbUh51p2aQezmgC7Be9W2+3zzHtL/fQC//3301MeqafLMDMhLiiGwtlbbLrCyhjytirYkTtmWhIXA0NkV4uFjkFU28GFoCPPFq2Bg74DM5fN0crQHoL16okmGn2ItA3F9R60GPrTVhy3LYWQPFKVlI/WUboyaKlaekwpltKs4+uexoqR0GNmVSy+1VJ3D4tQsyIqKkRekPn1RfnA0zF9Rn47QwNQEjQ98gJKcPAROXq+1qZ8TTt5UW3Pj8WLVivqfrtpuLLVEZiX9tULlNSeq0F9TP8bTiP35H8T+/A+MpZYoyckHoFg/JFcL10+Rqo5U/TuIap9KrpvHdcSiXRMY2Vmizc0dqr8LhIao/+E41J02ADfbzkRRouJ+nlumHskLi5Efkai2fmpNSTx5ExlPUSdET1Enyrc5ZetEQWI6DERGEFpI1EZ9iOwsUaDhGqw3pgcy74Yh805Yhb8Rke5i4ENPNGnSBMXFxbh27ZpqqquUlBQEBgaiadPSubkvXLiAgQMHYv369Zg2bdpTHVskEkEkEqlte95prgDFInNZdx7BqrM3kk8o55sXCGDd2Rsxe05q3CfzZhCsOnsjelfpE8bWXX3UghWPgx4Sd0f4/28VitOeYgimgWKZm8c/pmtbcU4+spQdrMdyE9JRp5OXKtBhZCaGXUsPPPz+rKZDPLXHQQ+L+g44MXwNCp7mfGnBk85JWplzIm3pgaAXPCe6ruy1k3Ky9Nqx6uSN2D0nNO6Tpbx2Ynf/odpm3aU5sqoK9BkIVJ3Mx3NDl3/SSS6TQWDApaJ0UW5OLnJz1H8QSkpIRrvObRAYEAwAMDWTwLtlUxz59udKj+PvexduHupTk7i6uyAuunRx4sdBD1f3epg8dBYy0nTjqe7inHwUl2s78hLSYN/JCxnKL0lCMzFsWnog9LszGo8hLypB+p0w2HfyQuxJ5fRNAgHsOzVD6N7TlX62VTPFoqr5CekV/vZ4m/OQ9siNSUbaXR358lRcjJLQQAi9W6HI97Jim0AAI5/WyD/xy5P3LUsgAISKNqPw79MoKrtmCADzDzag8OJfKDinuc3SKSXFkEWHwLCBD4rvKudyFghg2LA5ii79USG5LCEaOevUp3MyHjAWApEYBT/vgjw9GUXXz6Mk0F8tjXj6Ryi6cR5FvprroU4pKYYsMhiGjVug+LZykVOBAIaNW6DoQsWpu2TxUcj56G21bcavj4fARIyCIzsgT0uqjVxXG3lRCbLuPIJNZ28kn1D+eCoQwLpzM0Tv0bx+ScbNIFh3boaoMn1Ym67eyHiGB24ith1D7H71NQle/XsTglZ8h+TTNyvZS0uKi1EcEgQjn9Yo+rdMW9K8FfL/eJa2xAACI6PS94+DHnWdkLlsLuRZunGv0URb9UQTcy83AEBhYsXAZG3SVh+2LIeR3ZH44986s7ajvKgYOXdCYdnJB2knfRUbBQJYdPJBwreapyDKvhkEy87eiP/6d9U2yy7NkX0zsPSYt0Ng4qE+pZqJe10URJc+aGFoJkbjAysgLyxC4IS1kBdob9qekpx85Jbrr+UnpMGuczNklemvWbXyROR3mkcayItKkHknDLadmyGhzDVn27kZIiq55qryeIqkeqO6oaSgEMl/361ij+onLypG9p1HsOzsjdQy141lJ2/EVXHdxJW5bqzKXDdJR/+usE5M04PvI+noRSQeUoxgzr79CLL8Qog96iLL96HiY4WGEDlLURBd8/ftyuqEbZk6YWgmhuVT1onESupE5u1HkBUWK+rNH4pr0NSjDsTOUqSXa2cMJSI4vv4qgj45VJ1FJaJaoBu/9tILa9CgAQYPHoypU6di586dMDc3x5IlS+Dk5ITBgwcDUExvNXDgQMyZMwdDhw5FfLzihytjY+NaX+A8asfvaLLtHWT5hyLrVgjqTRsAA4kIccqbbePts1AQn4qwTw4AAKJ3/YEWx1ah3vSBSD3jB/shHWHe3ANBCxTrkwiEhvD65j2YedfH3bfWQWBgoHpqqig9G/KiYpi38oRFC09kXHuIooxsiN0cUX/xm8gLi3/hLxU16f7XJ9H83SHIfJSA7KhEtFw4DHkJ6Yg8VfpFt8/hpYg8cQMPvlXc+IUSESzqO6j+buYihY2XCwrScpATmwKB0BA9dr0LW283/DV+MwwMDSBWPg1RkJ4NWZFufCGozIOvT8K7zDlpsXAYcsudk17KcxJY5pyYlzsn1l4uKFSeEwAwtjKFqZMtJA6KpxQtPeoAAPISMyrM8a8tMTuPo9HWWci6rbh2nKYqrp0E5bXTcPtsFMalIHyN4tqJ2f0nfH5ZBafpg5B65iakQzrBrLk7ghcqnvIxkIjgPGcoUk9dR2FiGoxsLFBnYl+IHG2QfPwKAEXgsTg9B422zULkpz9Cll8Ix7d6wsTFHqlndOwHl2eUm5uHyOhY1fuY2AQ8DAqFpYU56jjaazFn1W/f7sOYNncCIh9FISYyDu8snoqkhGScO3lRlWb3j9tx9sTfOLTnKADgh12H8P3xXZjy7nic+u0svFs2xbCxg7FqwToAiqDH5q/XoIl3I8wauwAGBgawlSruJxnpmSguKq79gj5ByO6TaDJ3CLLD4pETmQSvxYr2VBXUANDlyFLEnLiB0L2KtiNo5wm03fo20m6HIdU/FA2m9oVQIkL4ob8BAKau9nD5XwfEnfVHYWo2LJu6oPmqt5B09QEyHkSpjttwxgDEn78DyGRwGtAWjWe9jn/f3gbo0NQJ+cd/hOnspSgJDURx8AOYDBwGiExQqAxSSGYvhSw1Gfn7FdNImrwxGsWhgZAlxAJCIxi1ehXGXXsjd9cWAMrRAdnqP07KS0ogS0uFLDYKL4PCC8dgMnoeSqJCIIsMglHXwRAYm6DomiJIYTJmHmQZKSj8/XuguKjiYuV5OZADpdtzsyDLLTf3vKwY8qw0yBNjar5A1aDwzM8wmbAAJRHBkIUHwqjHG4pzckURDDSZsACy9BQUHturOCex5Z7GfHxOym6XmMHAxh4CK8WaQwbKNVTkmWmQaxhJok2RO/5A020zkekfisxboXCZ1h+GEhHiDl0AADTd/g4K4lMR+slBAEDUrhNodWwlXKYPRPIZPzgM6QCL5h54uKB0OlahlSlMnOwgclT0PyTKNYQKE9NRmJShepWXH5OM/EjdCx7lHzsCs3lLURLyEMVBD2EyeJgi2HVG0ZaYzVsGWUoScr9XtiXDxqAkJBAlcTEQGBnDqE07iLr3Rs5XyimJDA1hvuQjGHo0RNZHSwADQwisFPcaeXYmUKxb9xpAO/VE7OoAh/91RMrZWyhKy4ZZUxc0+Ggc0q7cR/Z97Y+K1kYf9jGrTt4Quzogfr9uBZjjdh2Hx2ezkX07BNm3glFn6iAYSkRIUi6+7rH1XRTGpyBq7X5F+q9/R9OfVqPO268j7exN2A3uBFMfDzxaWPr0fuyXv6LBjvnI+vc+Mq7cg1X3lrDu1Qb3hylGIxqaidH44EoYiI0RNPszGJpJYGgmAQAUpWQCOjB9TPiuE/Cc9wZywuKRF5mIBotHoCAhrTSoAeCVo+8j4c/rqh+xw3b8AZ9tM5Dh/wjpt0JQf1p/CCUiRCv7a4BixIDI3goS5XdC8yYuKM7OQ35MMorSFesWuk7qg7TrgSjJKYBdV280XjEGDz85WGEdiNoSu/M4Gmydhezboci+FYK6UwfAUCJSBSkaKK+bCOV1E7v7TzT7ZRXqTh+EtDM3Yae8bkKVdaQ4LbvCA6Ly4hIUJqYjL1TxnagkOw/x35+Gy8I3URibgoLoJDjNfB0AkHz8am0VXU3ErhPw0FAnEsvUibbKOhGprBPhO/6At7JOZNwKgZuyHY5R1onirDxEHziPxqvGoig9G8VZeWiyZiLSrgepjTgBAMchHSAwNETs0Usa82fupXjoydBUBGNbC5h7uUJWVIycoJejb0ekzxj40CN79+7FnDlzMHDgQBQWFqJLly74888/YaR8Wuq7775Dbm4u1q5di7VrS9e06Nq1Ky5cuFCreU369QqMbS1Qf9GbMLa3QnZAOO6M+kS1cJeJk53aj0GZN4LwYMZW1F8yCu7LRiMvLA73JmxAzkPFDyeiOjaw69sWAND2vPoaEP5vrET6lfuQ5RXCbkA7uC0cAUOJCAWJ6Ug954+ILVsgL9S9L0yP3f3ydwglInTYMAnGFhIkXg/C6bc2oKTMkznmrvYQ2Zir3ts1d0e/o8tV79t9+BYAIPjIRVyetwumjtZw6dMaADDkrzVqn3di2CeIv1pxXRhdEqA8J+3LnJMzb22ArNw5MSlzTmybu6NPmXPSVnlOQo5cxJV5uwAAzr1boeOW0idTu3w1GwBwe/PPuP1p5U/F16bkX6/AyNYCrotGwliquHYCRn2ComTFtSNyslP7wpJ1IxCBM7fCdfFIuC1VXDv3J25ArvLakZfIIPF0gsOIrjCysUBRWhay/UNxe8gHyA1UDG8uTs3CvdGfwG3JKHgf/RACI0PkBkbh/oQNyLmveXjxy+Lew2BMml06Z/qG7Yq6MLhfT3zy/nvaylaN2Pv5PoglYqzYtATmFma45XsHM0bNQ2FBoSpNPTcnWNuUDgkP8H+AeZOWYM6yGXh7/kTERMZhwwef4c+fFT9w2teRonvfLgCAo+d+UPu8Sf+biRtXbtVCyZ5e4Be/w1AiQuuNk2FkIUGybxAuj16v1naYujmotafRv/0Lka05mi4aBhOpJTICInB59HoUKOcOlxUVw75zM3hOUQREcmNTEfPHdTz47JjaZzv2aI7GcwbD0NgI6fcjcWXip4g/d7tWyv20iq6cR56lFUxGTlRMeRUWguyPF0Geofjh2cDOAZCXCdSYiCGZNg8GNlLICwsgi4lEztZPUHTlfCWf8PIpvnUZBaaWEPUbA4GFNWQxj5C7cyXk2ekAAIG1FAZy3Qle1YbimxdRYG4J0aCxinMS/Qi529+HPCsdACCwsX/mcyJs3h7i8aVtrnjqMgBAwe/7UPj7vmrLe3VI/PUqjG0t4L5oBET2VsgKCIf/qLWqwISJk63avNMZN4IQMGM73Je8CY9lI5EbFo87Ezaq+rAAIO3TBk23zVS99941FwDwaOOPCNt0tHYKVo0KL59HrqUVxGMmwcDaBsWPQpC1ciHk6cq2RGoPubz0HAlMTGA6Yx4MbBVtSUl0JLI3f4zCy4q2xMBWCuNXOwEArLbvUfusjKVzKq4DogO0UU9kRcWw6eINl2n9YSARoSA2BUm/+yJsy3+3D/uYw+geyPB9iLyQWOiSlN/+gdDWAs4LR8FIaoXcgDA8HLO60nOSfSMQIe9sgfPi0XBeMgb5YXEImrQeeYGlga20k9cQtmQn6s76H9xWT0beo1gETd2genLf1Nsd5q0bAgBaXv1KLT+3Xnm7Vp7or8qjz3+DoUQE701TIbSQIM03ENdHrlPrr0lcHWBcpr8Wp7zmGi4aDmN7K2QFRMB31Dq1oLHr+F5osHCY6n373z4EANx+9yvEHFb8GG7Z0gMNFg6DoakJckJicXfh15X+2F0bkn+9AqGtBVyU102OhutGXu66CVJeN67K6+ZhmevmaYV/9APkJTI0+Hw2DEyMke0XjHvDPkRJRk61lu9phSnrRLMydeJGFXUiXlknGiwaDpG9FTIDInCjXJ14uOJ7yGUytPhmPgxEQiSfv4P7i7+p8Pn1RndHwp++lQbAOp4rXYfEsoUH6g7thLzIJPzddnZ1FJ+IXoBALv+PfVujanHBYbi2s6BTwoSiqhP9xzz/ZGj6q35JftWJ/mNevaf9RbB1TetmY7SdBZ2zQuCu7SzonNc68Amy8oTO5lUn+q/J1970JbrI9xfLqhP9x7R4Jb7qRP8x/r6O2s6CzjEW6PZocG0wMtD+yAhdk1pirO0s6BxLge4+YKktWXI+f11e3wROofWsctaO13YWXjqmS7/Tdha0gi0OEREREREREREREek+HZoumHQbV6UlIiIiIiIiIiIiIiK9wcAHERERERERERERERHpDQY+iIiIiIiIiIiIiIhIbzDwQUREREREREREREREeoOBDyIiIiIiIiIiIiIi0htCbWeAiIiIiIiIiIiIiKhKcpm2c0AvCY74ICIiIiIiIiIiIiIivcHABxERERERERERERER6Q0GPoiIiIiIiIiIiIiISG8w8EFERERERERERERERHqDgQ8iIiIiIiIiIiIiItIbQm1ngIiIiIiIiIiIiIioSjK5tnNALwmO+CAiIiIiIiIiIiIiIr3BwAcREREREREREREREekNBj6IiIiIiIiIiIiIiEhvMPBBRERERERERERERER6g4EPIiIiIiIiIiIiIiLSG0JtZ4CIiIiIiIiIiIiIqEoymbZzQC8JjvggIiIiIiIiIiIiIiK9wcAHERERERERERERERHpDQY+iIiIiIiIiIiIiIhIbzDwQUREREREREREREREeoOBDyIiIiIiIiIiIiIiAgB88cUXcHNzg4mJCdq1awdfX99K03777bcQCARqLxMTE7U0crkcK1asQJ06dSAWi9GzZ08EBwfXaBkY+CAiIiIiIiIiIiIi3SeT8/Wsr2d0+PBhzJ8/HytXroSfnx+aN2+OPn36IDExsdJ9LCwsEBcXp3pFRESo/X3Dhg3Ytm0bduzYgWvXrsHU1BR9+vRBfn7+M+fvaTHwQURERERERERERERE+PTTTzF16lRMnDgRTZs2xY4dOyCRSLBnz55K9xEIBHB0dFS9HBwcVH+Ty+X47LPP8P7772Pw4MHw8fHB999/j9jYWBw7dqzGysHABxERERERERERERHRf1xhYSFu3ryJnj17qrYZGBigZ8+euHr1aqX7ZWdnw9XVFc7Ozhg8eDACAgJUfwsLC0N8fLzaMS0tLdGuXbsnHvNFMfBBRERERERERERERKSHCgoKkJmZqfYqKCjQmDY5ORklJSVqIzYAwMHBAfHx8Rr3adSoEfbs2YNff/0V+/btg0wmQ4cOHRAdHQ0Aqv2e5ZjVgYEPIiIiIiIiIiIiIiI9tHbtWlhaWqq91q5dW23Hb9++PcaNG4cWLVqga9eu+PnnnyGVSrFz585q+4znIdTqpxMRERERERERERERUY1YunQp5s+fr7ZNJBJpTGtnZwdDQ0MkJCSobU9ISICjo+NTfZ6RkRFatmyJkJAQAFDtl5CQgDp16qgds0WLFk9bjGfGER9EREREREREREREpPvkMr6e8SUSiWBhYaH2qizwYWxsjNatW+Ps2bOqbTKZDGfPnkX79u2f6r+opKQEd+/eVQU56tevD0dHR7VjZmZm4tq1a099zOfBER9ERERERERERERERIT58+dj/PjxaNOmDV555RV89tlnyMnJwcSJEwEA48aNg5OTk2q6rI8++givvvoqPD09kZ6ejo0bNyIiIgJTpkwBAAgEAsydOxcff/wxGjRogPr16+ODDz5A3bp1MWTIkBorBwMfRERERERERERERESEN998E0lJSVixYgXi4+PRokULnDx5UrU4eWRkJAwMSieSSktLw9SpUxEfHw9ra2u0bt0aV65cQdOmTVVpFi1ahJycHEybNg3p6eno1KkTTp48CRMTkxorh0Aul8tr7Oikty44DNd2FnRKmFDz8LD/MkNtZ0AH1S/J13YWdM6r9zZoOws6p3WzMdrOgs5ZIXDXdhZ0zmsdYrSdBZ0jdDbXdhZ0T36RtnOgU3x/sdR2FnROi1fitZ0FnePv+3RzV/+XGAtKtJ0FnWNkINN2FnROaomxtrOgcywFxdrOgs7JkvP56/L6JhzSdhZeOjkfjNB2Fl46pquPaDsLWsE1PoiIiIiIiIiIiIiISG8w8EFERERERERERERERHqDgQ8iIiIiIiIiIiIiItIbnFyPnsslE87fWVYHzqFdgY2I61mUFyIz03YWdA7Xs6jo5r392s6Czvm32SJtZ0HnfHiTc9CXl3SjQNtZ0DmuAq57UpazMZ/5Ki/klrO2s6Bz6oHrWZSXKuDPBhVwpdQKsoUCbWdB50Tx2qmghNWEqoOMjTA9Hfb+iYiIiIiIiIiIiIhIbzDwQUREREREREREREREeoOBDyIiIiIiIiIiIiIi0hsMfBARERERERERERERkd5g4IOIiIiIiIiIiIiIiPSGUNsZICIiIiIiIiIiIiKqilwm03YW6CXBER9ERERERERERERERKQ3GPggIiIiIiIiIiIiIiK9wcAHERERERERERERERHpDQY+iIiIiIiIiIiIiIhIbzDwQUREREREREREREREekOo7QwQEREREREREREREVVJJtd2DuglwREfRERERERERERERESkNxj4ICIiIiIiIiIiIiIivcHABxERERERERERERER6Q0GPoiIiIiIiIiIiIiISG8w8EFERERERERERERERHpDqO0MEBERERERERERERFVSSbXdg7oJcERH0REREREREREREREpDcY+CAiIiIiIiIiIiIiIr3BwAcREREREREREREREekNBj6IiIiIiIiIiIiIiEhvMPBBRERERERERERERER6Q6jtDBARERERERERERERVUku03YO6CXBER9ERERERERERERERKQ3GPggIiIiIiIiIiIiIiK9wcAHERERERERERERERHpDQY+iIiIiIiIiIiIiIhIbzDwQUREREREREREREREekOo7QwQEREREREREREREVVJJtd2DuglwREfRERERERERERERESkNxj4ICIiIiIiIiIiIiIivcHABxERERERERERERER6Q0GPoiIiIiIiIiIiIiISG8w8EFERERERERERERERHqDgY9y5HI5pk2bBhsbGwgEAlhZWWHu3LnaztZz69at20udfyIiIiIiIiIiIiIAkMvkfD3j679KqO0M6JqTJ0/i22+/xYULF+Du7g4DAwOIxeKn3v/ChQvYsmULfH19kZmZiQYNGmDhwoUYM2ZMDeZa8bndu3dHWloarKysVNt//vlnGBkZ1ehnV7eu84ei5ajuMLEwRdSNIJxYvgep4QmVpu8483U07tsGth51UZxfiOibwTi77hBSHsWp0vRfMwn1OzWDuYM1CnPylWkOIiU0rtLjakO9ib3hMnMQjO2tkH0/AkHL9iLzVmil6e0HvQr3xSNg4ixFXlg8QlbvR8pZf9Xfpf1fgdP4nrDwcYeRjTmu9ViE7IAI1d+FVqZwXzQCNl19YOJkh6KUTCSdvI7QdYdRkpVXk0V9anbj+sP+7SEwkloj70E4olfsQu7t4ErTWw3ogDrvjYFxPXsUhMcidu33yDx/U/V3oZ0l6i4dD4suLWFoYYrsawGIXrELBeGa64LHdytg0b01Hk1Zg4zT16q9fM/La+FQ1B/THcYWpki+HgS/JXuQHVb5dQIAHhN6odHMATCRWiL9fiRuLf8Oaf6P1NLYtPaE95IRsGnlAXmJHOkBEbg4ah1k+UVq6QyMhXjtj49g1cwVp3suQ0aZeqUrZi6aiqFjXoe5hTn8r9/Bx4s3IDIs+on72DtKMff9mejUoz1MxCaICo/GB3M/xv3bDyEUGmLWkrfR+bUOqOdaF1mZ2bh26QY++/hLJCUk11Kpat4N/7vYe+Ao7j8MQVJKKrau/QCvdemg7WxVizoT+6LezNdhLFW0saHLv0H2rZBK09sNag/XRSOVbWwcwj7eh7Szt1R/d1kwAtLBHSFysoWssBjZdx4hYu1BZN1StFGWHbzg8/Mqjce+1Xcxsv0rb9+1qd+84Wg/qgfEFqYIuxGIH9//Bknh8ZWm7/hWL3Qa0xM29aQAgLjgaJza9jMeXPAHAEgsTdFv3nA06uwDayc75KRk4s7p6/jz0yPI15F7TVWGzR+F7qN6wtTCFEE3HmLP8p2Ir+S+Ud6gGf/DqCVjceKb4/jhoz2q7T1G9UKHwV3g1swdEnMJpniPQW5mbk0Vodr1nDcMbUd1h9jCFBE3gnDs/T1IeUI96TrzdTTr0xZSj7ooyi9EhF8wTq47iOQy/TUzqSX6Lx0Nz87eEJmaIOlRHM5/fgwBJ6/XRpFeWNv3hqLpqO4QWUoQdz0IF5ftRcYT+rB12jVCy7cHQOpTH6YO1jgxZQvCTt1US9Pj02loPLyL2rbIC3fw+9gNNVKG6tZmwVA0Vp6T+OtBuLRsLzKf0F+p064Rmk8fADvv+jB1tMapyVsQXu6clNV57UQ0Hfsarqz8AXe/OVUTRXghDRcNg/NbPWBkYYq064G4u2gPcsMqv04AwHViL7jPHASRvSUy70ciYNm3yFB+HzCyMkXDRcNh19UbYic7FKZkIv7kDQStO4JiDe2pkbUZOp9bB3FdW5xqMBnFOtDGaKsPa+XtBp/lI2Hdwh3yEhli/rwO/5X7UJJbUGNlfVrs11et5YKhaDi6O4wtJEi8EYSrS5/clji0a4RmMxRticTRGmcnbUFkmbZEIDRE60XDUK9HC5i5SlGUmYfYy/dwY81h5CWk10KJXtx/vX3VhPdhIiqPIz7KCQ0NRZ06ddChQwc4OjrC3t4e5ubmT73/lStX4OPjg59++gl37tzBxIkTMW7cOPz+++/PlZ/CwsLn2u8xGxubZ8q/tnWYPhCvTOiDP5ftxZ7BK1CUW4DRPyyBoajy4I1Lu8a4/v0Z7B2yEvvfWgcDI0OM/mEJjMQiVZq4u2E4vmAXvnptIQ6MWw+BABjzwxIIDAS1UaynYj+4PRqsGoewzT/heq8lyA6IQItDy2BkZ6ExvWWbhvDa8S5iD5yHb88lSDpxHT7fLoRpY2dVGkOJCBnXAhHy8QGNxxA52kDkYI2QVT/gWtcFuD/nS9h2b46mW6bXSBmfldWgTnD6YBLiPzuMwAHzkfcgDB77PoTQ1lJjetPWjeG2fQFSDp/Bw/7zkHHqGurvXgqThi6qNO67l0Hk4ohHkz/Bw37zUBiTCM8DH8GgTH15TDr5dcjluhcZb/TOQHhO7gO/xXtxdsAKFOcWoPPBJTB4wnVS7/VX0fzDMbi/+Wf81ed9ZNyPRJeDSyCyLa1fNq090eXAYsT/fRdn+63AmX4fIGTPaUDD0wE+H4xCXkJajZSvOkyc9RZGTx6O1Ys2YEz/ycjLzcOOQ5/BWGRc6T7mlub47vhOFBcXY+aY+Xij6yhs+nAbMtOzAAAmYhM08W6EnVv24s1eEzB/0lK4ebhg2/f61enNy8tHI093LH9vprazUq3sBneA+4fjEbn5R9zqvQg5AeFodvD9SttY8zaN0PiruYg/eBZ+vRYi5cR1NN27CJIybWxeaCxCl30Nv27zcWfw+yiISkSzw+/DSHldZV4PxL/eU9RecfvOIC8iQWeDHq9Nfx1dJvbFkeVfY8uQ91GYV4Dp3y+F8AntS3pcCo6vP4hNg5Zh0+vLEXwlAFN2LYBjg3oAAEsHa1g6WOPXNfuwrvdC7F/wFZp0bYFR63XjXlOVQdPfQJ8JA7Bn2U58MHgx8nMLsOSHFTB6wjl5zN3HE6+N6Y2I+2EV/mYsFuH237fw6xc/1US2a1SX6YPQYWIfHFu+B18O+QCFefmY9P2SJ9YT93ZNcPWHv/DlGyvwzdi1MBQaYtL36v21EZtnwM69Lr6fshmf9VmCgJPXMfqLOajj5VobxXohLWcMhM/E3vh72R78NGglivMKMHDf4if2YY3EIiQ/iMTF97974rEjzt/G3lbvqF5/zfq8urNfI5rPHIhmE3vj0tI9+GXQShTnFmBAFedEKBEh5X4kLldxTgDArW8b2LfyRE58anVmu9q4zxoEtyl9cW/RN/in/wcozi1Au8NP7q/VGfwqmqwai+DNP+Fyr2XICohAu0NLYKy8V4kcrSFysMKDVftxsetC3J6zA9LuzeGz5W2Nx/PZMg1Z9yNrpHzPQ1t9WBMHK3Q9vBTZ4Qk4O2AlLo3eAIuG9fDKVu3fh9ivr5r3zIFoMqk3ri7Zg9+VbUnv/VW3JWn3I3F1uea2RCg2ho23G/y3HsNvfT/AuamfwdK9DnrunV9TxahW//X2VRPeh4lIEwY+ypgwYQJmz56NyMhICAQCuLm5VZgqKi0tDePGjYO1tTUkEgn69euH4ODSp8+XLVuG1atXo0OHDvDw8MCcOXPQt29f/Pzzz0+dhyFDhuCTTz5B3bp10ahRIwDADz/8gDZt2sDc3ByOjo4YPXo0EhMTAQDh4eHo3r07AMDa2hoCgQATJkwAUHGqq6ryr22vTO6LS58fQ9BfN5H4MAq/zv8K5vZWaNy7daX7HBy/AXeOXkRScAwSHkTit/d2wqqeHep411eluXXwPCJ9HyIjOhnx98JxftOPsHSyg5Xy6VRd4DJ9AGL2nUXcoQvICYrBw4VfoySvEHVHddeY3nlaP6Se90fkl8eRGxyDR+uPIOtuGOpN6qNKE3/0EsI+/QmpF+9qPEbOwyjcnfwpkk/7IS8iAWmXAxC69jDsereGwFD7zYP9lMFIOXgaqT+eRX5wFKKWfgVZXgFs3+ypMb100iBk/u2HxJ2/oCAkGnGbDyDv3iNIJwwAAIjq14Vp68aIWv4Vcu+EoOBRDKKW7YDAxBjWg9Wf4hA3rQ/7aYMRuXB7jZfzWTWY2hcPPjuG2FM3kfEgCr7vfgWxgxWc+lZ+nTR8ux/C9p9H+OGLyAqKwc1Fe1CSVwC3UV1VaVqsGovgb04h8PPjyAyKQXZoHKKPX4OssFjtWI49msOhqzduf6Q5oKYL3pr6JnZ/9i0unLqE4AehWD77I0gd7NCjb5dK95k06y0kxCRgxdxPcO/WfcRExuHq376IjogBAGRn5eDtN+fg9G9nER4aiTt+AVizbDO8mjeBo5NDbRWtxnVu3xbvThuPnl07ajsr1crp7UGI338GCYfOIzcoGiGLdkGWVwCHkT00p5/aH6nn/RHz5W/IC45BxIZDyL4bhroT+6nSJP1yGemX7iI/MhG5gdF4tPI7CC1MYdpE8SOtvKgYRUnpqldxWhZs+7ZFwqHztVLm59F1Uj+c3v4L7v11E7EPI7Fv/hewdLCGd+82le4TcNYP9y/4Iyk8Hklhcfhj02EU5ObDrWUDAEBcUDT2zNiCgLN+SIlMQPDVAPyx6RCavdYKBjpwr6lK38kDcezzH3HzL19EPYzAV/O3wsreBm16t3vifiKJCd7ZOg9fL/4SORk5Ff5+cs/vOP7Vzwi5FVhTWa8xHSf1xfntx/Dgr5uIfxiFI/O/grmDFZo+oZ7sHb8efkcvIjE4BvEPInF0wQ5Y15PCqUx/zaV1Q1z97hSib4ciLSoR5z8/hvzMHDg1q1/pcXWFz+S+uLn9V4Sf9kPKwyicnbsDpg5WqN+n8ntz5IU78N14FGEnbzzx2CWFRchLylC9CjK0/9T+0/Ce3Bd+235FxGk/pD6Iwvm5OyBxsILbE85J1Pk7uL7xKMKrOCcSR2t0XD0O52Z/CVlRSXVnvVrUn9YPIVt+QcLJm8i6H4nbs76EyMEaDv0qv07qTx+AqH3nEH3ob2QHxeDuwm9QklcI51HdAADZD6PhN/kzJJ72Q25EIlIuByBw7WHY925Voe/uMr4njCxM8eirP2qymM9EW33YOr1aQlZcAr+l3yI7NA5ptx/h5uI9qDfwFZi6abcPx3591ZpO6Ys7W39F5Gk/pD2IwsU5OyB2sILLE9qSmPN34LfhKCIraUuKsvJwetR6hB+/hszQOCT5heLf97+HXXN3mNa1ramiVJv/evuqCe/DRKSJ7n/brEVbt27FRx99hHr16iEuLg7Xr1ccVj9hwgTcuHEDv/32G65evQq5XI7+/fujqKhIwxEVMjIyYGNj89T5OHv2LAIDA/HXX3+pRooUFRVh9erVuH37No4dO4bw8HBVcMPZ2Rk//aR4WjAwMBBxcXHYunWrxmM/T/5ri5WzFOb21gi7HKDaVpCVhxj/UDi1avDUxxGZSwAAeenZGv9uJBah+fCuSItMREZcyotlupoIjAxh7uOO1EtlAhRyOdIu3oVlG81lt2zdEKkX76ltSzl/G5ZtGr5QXoQWEhRn5UFeInuh47wogZEQEm8PZF2+XbpRLkfW5duQtGqkcR/TVo3U0wPIvHgLpsr0AmPF0x6ygjL1XS6HvLAYpm2blH62iTFct7+H6Pd3ojgpvXoKVE1MXaQQO1gj4VLpdVKclYfUW6GwraSuCIwMYe1THwmXytQXuRwJl+7BtrViH5GtBWxbe6IgORPdf1uJQXe+RLef34ftK+r1SWRngdYbp8B39lc6MTWAJk4udSF1sMO/F0vb8OysHNy9dR/N2zSrdL9ufToj4PZDbNr9CS7c+wOH//oOQ8e8/sTPMjM3g0wmQ1ZGVrXln6qfwEgIcx93pF+8U7pRLkf6pbuwaKO5PTFv3VA9PYC0C/4wr6SNFRgJ4Ti2F4ozcpB9P1xjGps+bWBkbYaEQ+eeqxw1zdbZHpb21gj6p/RelJ+Vhwj/ENRv9XT3FoGBAC0HtYdILEKYX1Cl6UzMJcjPzoNMy/eaqtg7O8Da3gb3ytxb8rJyEeofjAaV3Isem7h6Gm6du4F7/9x5YrqXjbWzPSzsrRHyT+k9pSArD1H+oXB5hv6aiYb+WuTNIPgMfBViS1MIBAL4DGoPocgIYf8+qL4C1AALFylMHawQVeY+W5iVhwT/UDg+wzmpjNOrTTDh1hcYdWEjuqyZAJGV2Qsfs6aZK89JTLlzkugfCofWL3hOBAL02Dodt3f8gbSgmBfMac0Qu9rDxMEayWX66sVZeUj3C4X1E/prlj71kVyuv5Z88R6sKtkHAIw09N3NGjqhwXv/g//sLyGX6UY7q80+rKGxkeIH/zIjuUvyFTMr2L3y5La8JrFfXzUzFykkDlaIvVxa3qKsPCTfCoX9i7Yl5RhZiCGXyVCoA1PCPcl/vX3VhPdhIqoM1/gow9LSEubm5jA0NISjo2OFvwcHB+O3337DP//8gw4dFPOd79+/H87Ozjh27BiGDx9eYZ8jR47g+vXr2Llz51Pnw9TUFF9//TWMjUunZJk0aZLq3+7u7ti2bRvatm2L7OxsmJmZqQIr9vb2amt8vGj+a5OZvRUAICc5Q217TnIGzKRWT3cQgQC9V45F5PVAJAWpz+XfemxP9Fw6CsamJkgOicX+MWt15gkGIxsLGAgNUZikXvbCpAxIGtTVuI+xvRUKy/0oX5iUAZG95mmgni4f5nCb9z/E7Dvz3MeoLoY2FhAIDVGUnK62vTg5HSYe9TTuI5RaVQhUFCelQyi1BgDkh0ajMDoRdRePRdTSLyHLLYB0yuswrmsHI/vS4GS9lZORc+MhMv7yrdYyVQcT5XVSUK6u5CdlwKSS60RkYw4DoSHyK+yTCXNPRf0ydbUHADR973+489EBpAdEwHV4Z3Q9sgynuy9WzTPcdut0PPrhLNJuh0FSz64aS1Z97OwVT2mlJKkPzU5JSoWtfeVPcNVzqYsR49/ADzsP4eut38GrRRMs/ng+ioqK8duRPyukNxYZY977M3Hil7+Qk63bX5D+64xszCHQ2MamQ+zppHEfY3srFJVrT4qSMmCsvAYfs+nVGo13zIWBWITChDTcffMjFKdqDoQ5jn4NaRduozBON6cNMFe2IVnlzlNWUobqb5Wp08gZ835eDaHICAW5+fjm7c1ICNH8hdnU2hx9Zv8PVw6erY5s1yhL5f93Rrm+SUZyOiyfcE7aD+oEt2bu+OD1hTWYO+0wlyr6Gdnl6kl2Uobqb1URCAQYuGIswq8HIqFMf+3ArG0Y9fm7WHF7N0qKilGUV4h9b29BSsST57rXNomyLuQlZ6ptz0vKhOQF+mWA4mnURyduIDMqEZauDmi3aAQG/rAQPw/+UKcXqnziOXnKelKZFjMHQlYswz0dnnPeRFnG8v21gqQMiMrdRx4zVn4f0LSPaSXfB4xszOE57w1E7SttTw2MhWi5YzYerjqA/JgUSJR9PG3TZh828XIAmn84Bg1nDEDw1ychlIjgs3wkAEDsoPmzawP79VWTKM9RXlK5tiQ5E+IXbF/LMhQZoc2ykXh07CqKsnV7/bH/evuqCe/DRFQZBj6ewYMHDyAUCtGuXenUBra2tmjUqBEePKj4JNr58+cxceJE7N69G15eXk/9Od7e3mpBDwC4efMmPvzwQ9y+fRtpaWmQKZ/ciYyMRNOmTWsk/48VFBSgoED9CZBieQmEAsOnLZJGzYZ0wIA1k1XvD07c+ELHA4B+qyfAvmE9fDvsowp/u3fsH4Rdugsze2u0n9YfQ798F3uHrkJJgfZHu+gCQzMxmu9fjJygaIRtPKrt7NSM4hI8ensdXDbMgs/dA5AXlyDr8m1knLsBgUCx3otFr1dg1sEHgf3maTmzCi7/64DWG0qvk0tjX/w60eTxejeP9p1D+OGLAID0exGw7+QFt1HdcG/NYXhO7gMjMxM82PZrjeThefX/X2+s2LhY9f6dtxY813EMDAwQcPshtq3dAQB4eC8Ino3dMXzckAqBD6HQEJt2fQyBQICPF+vXGh/0bNL/uQe/1xbCyMYcjm/1RJNd8+HffymKyn3xMq5jA+tuzfFg2qdaymlFrQd3xJtrpqre75y0/rmPlfgoFhv6L4aJuQQt+rfDmM0zse3NVRWCHyIzMabtXYz4kBic+Ez37jUdh3TB5DWlc75vmPjJMx/Dpo4txq2cjDVvfYgiPehjtBjcEUPK9Ne+m/Tibd7rqyfCoZEzdgxbpba91/zhEFtI8PXoT5CTlgWv3m0w6ot3sXP4R0gIjHrhz60uDYZ0QLd1pQ8l/TFhU419Vshv/6r+nfowGikPIvHWP1tQt31TxPwT8IQ9a5fnGx3Qpcw5OTG+Zs6JnbcbvCf3wU/93q+R4z+vukM7wnvjFNX762Nqvm8gNBOj7f5FyA6KQdDG0rWCGi0fiezgGMT8dLnG8/AkutSHzQyKge+cnWjx4Rh4L3sT8hIZQr45hfzE9FodEaNL50RX+/Xub3RAh/Wlbclf42qufX1MIDREtx2zIRAIcHXptzX+ec/qv96+asL7MGlar4hIEwY+asjff/+NQYMGYcuWLRg3btwz7Wtqaqr2PicnB3369EGfPn2wf/9+SKVSREZGok+fPi+8+PnTWLt2LVatUv9i2s2iGXpY+bzQcYP+8kPMrdLFXYXGiupoameJ7MR01XZTO0vE34+o8nh9PxqPBq+1xPcjViNLwyJcBVl5KMjKQ2p4AqJvBWPhnV1o3KcNAn67+kLlqA5FqZmQFZfAuNwTGsZSSxSWORdlFSamw7jck0DGUksUJGZoTP8khqYmaHFoKUqy83F34mbIi7U/EqYkNRPy4hIY2VmpbRfaWaEoSfPie4rRHeXSS61QXCZ93t1QBPabBwNzCQyMhChOzUTDXzci904IAMC8gzdEro7wuac+z239nYuR7XsfIW/Wbkcw9pQfUvxKrxND5XUikloiv0zdMJFaIj1A83VSkJoFWXGJ6unD0n0skK+sL3kJimNllhvSnBUcC4mTYpSEfaemsG3dAEMj1Bd/63lyNSJ//gfX5zz9yLbqdOHUZdz1u696b6xcwM5WaoPkxNLp7GylNgi8V/nUO0mJyXgUpL4AcVhwOHoOUF9nRyg0xMZdn6BOPUdMGTaLoz1eAkWpWZBrbGOtUPSENtaoXHtipKFNluUWID88Hvnh8cjyC0abK9vhMOo1RG//RS2dw8geKErLRuqpJ88hXJvunbmJCP8Q1XuhcjpAc6klMsuMdjGXWiKmivtwSVEJkpVP5UffC4OLjwe6TuqHI8u+VqURmZpgxndLUZCdh2/e3gyZDtxryrv5ly9CbpW2E4/PiaWdJdITS+8llnZWGhcsBwB3bw9YSq2w5o/Nqm2GQkM0btcUvcf3x7gGI3Rm6pmncf/MTUSVqSeP70NmUktklaknZlJLxD1Ff+31VRPQuEdL7BrxETLL9NdsXOzRYUIfbOm1EInBintR/INIuLVtjPbjeuHY8j3VVKIXF/6XHw77V7w3i+0skFumjRBLLZASUL0LS2dGJiEvJROWbg469YNLxGk/HL1V8+ekziuNILazwJhrpdP6GggN8eqKMfCe0hcH2mvnwZWEkzeRfrP0Onm8MLVIaomCMuUXSS2RGRCu8RiFyu8DonL3qvLHABR991cOLUFJdh5uTvxUre9u28kLFk1c4DhQ8bDb44d7ej3YhZDPjiG4lh5w0qU+LABE/XIFUb9cgcjOAsW5BYAcaPh2f+REJD53GZ+VLp0TXe3XR572Q5KmtkRqgbyybYmdBVKroX0VCA3RfcdsmNWzxckRa3VytMd/vX3VhPdhInpaDHw8gyZNmqC4uBjXrl1TTRWVkpKCwMBAtVEXFy5cwMCBA7F+/XpMmzbthT/34cOHSElJwbp16+Ds7AwAuHFD/YeTxyNESkoq/xHhafNf3tKlSzF//ny1bZubvXi5CnPyUZiTr7YtKzEN9Tt6IUH5xdnYTAynFh64WcXUS30/Go9Gfdrghzc/RnpUUpWfLRAIIBAIYKj8QUPb5EUlyLrzCDadvZF8Qvl/KxDAunMzRO/RPMw042YQrDs3Q9Su0ifRbbp6I+NG5T/samJoJkbLw8sgKyjC7XEb1Ne/0CJ5UTFy74bCvKMPMk5fU2wUCGDe0QfJ31WcdggAcvwCYd7RB0nfHFdtM+/UAjl+FReOlWXlQgZA5FYHEh8PxG3aDwBI+PInpBz8Sy1tkzPbEfPRHmScqf2pr4pz8lFc7jrJS0iDQycvZCi/EAnNxLBp6YHQ7zRfJ/KiEqTdCYN9Jy/Enryp2CgQwL5TM4TsPQ0AyI1KQl5cKsw96qjta+buiPjzirntb73/Pe6t+1H1N7GjNbocWoJ/p29HapkvcbUtNycXuTnqwYekhGS069wGgQHBAABTMwm8WzbFkW9/rvQ4/r534ebhorbN1d0FcdHxqvePgx6u7vUweegsZKRllj8M6SB5UTGy7jyCVWdvpJxUrv0iEMCqkzdi95zQuE/WzSBYdfZG7O7SRWGtuzRHVlVtrIFA9YNXWQ4juyPxx791IrD8WEFOPgrKtS8ZiWlo2KGZKtAhMhPDtYUnLu/7S9MhKiUwEKiCBo+PM+P7pSguLMbuKRtRrCP3mvLyc/KRnxOvti0tMRVeHX0QoVy7RWwmhkeLBjiz76TGY9z75w4W9Zqjtu3tTbMQGxqD41/98lIFPQBFfy2lXD3JTEyDRwcvVaBDZCaGcwsPXKuiv/b6qglo2qcNdo/8GGnR6v01I7EIACpMGyGTySAQ6NayhEU5+Sgqd05yEtJRr5MXUu4rfmAxMhPDoYUHAn6o3indTB1tYGJtpvbDji6o7Jw4lTsn9i08cP/75z8nQT/9g+jL6j80Ddi/CEE//YNA5ZPt2lCSk4/ccuXPT0iDbedmyCzTX7Nq5YGI7zS3p/KiEmTcCYNd52ZIKPN9wLazFyL2nFalE5qJ8crhJZAVFOP6uE0V+u5+k7bAQFw6e4BVCw803zodVwevQm547U0bp0t92LIKlCMy3UZ2RUlBIRLKrZlYk3TpnOhqv744Jx9Z5c5RbkI66nTyUgU6jMzEsGvpgYcv0JYApUEPi/oOODF8DQrSNK8Rqm3/9fZVE96HiehpMfDxDBo0aIDBgwdj6tSp2LlzJ8zNzbFkyRI4OTlh8ODBABTTWw0cOBBz5szB0KFDER+v+PJsbGz8TAucl+Xi4gJjY2Ns374d06dPx71797B69Wq1NK6urhAIBPj999/Rv39/iMVimJmpL7j0NPnXRCQSQSQSqW170WmuKuP7zUl0mj0EqWHxSI9KQrf3hiErMR0PT99UpXnrwFI8PHUDN5RfGvp9PAHNXu+Aw1M/RUFOPkwfz6mbmYvigiJYOUvhNag9Qi/eQW5qFizq2KDjjEEoyi9EyHn/GinH84jc8QeabpuJTP9QZN4Khcu0/jCUiBB36AIAoOn2d1AQn4rQTw4CAKJ2nUCrYyvhMn0gks/4wWFIB1g098DDBbtVxxRamcLEyQ4iR8UaFxLlvK+FiekoTMpQBD2OLIeB2BgBMz+H0EwMmIkVaVIytT58MPHrX+G6eQ5y74Ygxz8Y9pMHwUBigpQjii8CrlvmojA+BXHrfwAAJO05jgZHPoH91MHIOHcD1q93hsTHA1FLvlAd02pABxSnZKIwNgniRq5w+nAKMk5dQ9YlfwCKUSOaFjQvjElCYVTtPRH2JMG7T6LJ3CHICotHTmQSmi0ehryEdMScLL1OuhxZipgTNxC6V3GdBO08gVe2vo2022FI9Q9Fg6l9IZSIEH7ob9U+gV/9Aa8FQ5EeEIn0gAi4jegMC8+6uDpV8dRPXkwKyj4D9fiLW3Z4IvJ0bM2CfbsPY9rcCYh8FIWYyDi8s3gqkhKSce5kaad994/bcfbE3zi0R/Hk4w+7DuH747sw5d3xOPXbWXi3bIphYwdj1YJ1ABRBj81fr0ET70aYNXYBDAwMYCtVtOsZ6ZkoLiqu/YLWgNzcPERGx6rex8Qm4GFQKCwtzFHHUTfmCX8eMTuPo9HWWci6HYqsWyFwmjoABhIREg6dBwA03D4bhXEpCF+jGO0Vs/tP+PyyCk7TByH1zE1Ih3SCWXN3BC9UTIVmIBHBec5QpJ66jsLENBjZWKDOxL4QOdog+fgVtc+26uQNsasD4vdrf/2kqvy95wR6z34DSeHxSIlKRP/3RiAjIQ13T5c+cPHO/vdx59R1XPpeEZgfuGgkHlzwR1psCkSmJmg9uCM8X22KHePWAlD8KD7zh2UwNjHGD3O/gIm5GCbmintNdkqmzs+PfPKb3/HG7OGID4tDUlQChr83GumJqbjxOCgPYNmBVbhx6l+c/u4E8nPyER2k/nRhQW4BstOy1LZbSq1gJbWCg5vihynnRq7Iz8lDckwycjJ088eXx/7ZcxI9Zr+BlPB4pEYlodd7w5GVkI77ZerJ5P3LcP/UDVz9XvFD3ODVE9F8cAf8MHUzCnLyYKbsr+Ur+2tJobFIDovHG2sm4881B5CbloWmvdvAs1MzfD+p5qc6eVF3vjmJ1rOHICMsAZlRiXhlwTDkJKQj7FTpvfn1g0vx6OQN3FP2YYUSESzdHFR/N3eWwrapCwrSc5AdmwKhRIS28/6HR3/6IjcpAxauDmi/bCQywhMQ+fedWi/js7r7zUm0eldxTrKiEtFmwTDkJqQjvMw5GXhoKcJO3kDAt093TgrSs1GQrn59yIpKkJeYjoxHcbVTsKcUtusEGswbgpyweORFJqLh4uEoSEgrDWoAaHd0OeL/vK4KbITt+APNt81Auv8jZNwKgdu0fhBKRIhS9teEZmK8cmQpDMUi+M/cDKMyffcCZd89t9wIBmMbcwBAdlAMirW8aLO2+rAA4DGxF1JuBKM4Jx8OXbzhs2IU7n5yGEX/0XPyMvXr7399Es3fHYLMRwnIjkpEy4WKcxRZpi3pc3gpIk/cwIMybYlF/dK2xMxFChsvFxSk5SAnNgUCoSF67HoXtt5u+Gv8ZhgYGkD8+HeE9GydWQu0Mv/19lUT3oeJSBMGPp7R3r17MWfOHAwcOBCFhYXo0qUL/vzzTxgZKZ5q/O6775Cbm4u1a9di7dq1qv26du2KCxcuPNdnSqVSfPvtt1i2bBm2bduGVq1aYdOmTXj99ddVaZycnLBq1SosWbIEEydOxLhx4/Dtt98+c/617cqO32EkEWHA2skwsZAg8kYQDoxbr7YOh7WLAyTW5qr3bcb2AgCMP/KB2rF+fW8n7hy9iOKCIji/0givTOoLsaUpspMzEOn7EN/+bxVyU3Tnae3EX6/C2NYC7otGQGRvhayAcPiPWqtajNfEyVbtKdGMG0EImLEd7kvehMeykcgNi8edCRuR87B0DmxpnzZoum2m6r33rrkAgEcbf0TYpqMw96kPy9YNAAAdfLep5eefNrOQ/xSjZ2pS+vHLENpYoM780RBKrZF3PwyhY1ehWLnIrFFdO7VzknPzIcLf3Yw6C95CnUVjURAei7Cpa5Ff5ocmI3sbOH0wGUI7SxQnpiH1p/OI33ak1sv2IgK/+B1CiQhtNk6GkYUEyb5BuDR6vdoTf2ZuDhDZlF4n0b/9C5GtObwWDVMNn780er3qqTdA8cXLQGSEFqvegrG1KdIDIvH3yLW1OgVAddn7+T6IJWKs2LQE5hZmuOV7BzNGzUNhQen0gPXcnGBtUzpNQID/A8ybtARzls3A2/MnIiYyDhs++Ax//qz4McK+jhTd+3YBABw994Pa503630zcuHKrFkpW8+49DMak2aVrpmzYvgsAMLhfT3zy/nvaytYLS/71CoxsLeC6aCSMpVbIDghHwKhPUKRsT0ROdkCZ9iTrRiACZ26F6+KRcFs6Gnlhcbg/cQNylW2svEQGiacTHEZ0hZGNBYrSspDtH4rbQz5AbmC02mc7jO6BDN+HyAuJha47u+M3GItFeHPtVIgtJHh0PRA7xq9TG6Fh6+oA0zLti7mtJcZ8+g4spVbIy8pF7MNI7Bi3FoGX7wIAnJvVh1tLxb1mxcWtap+3qtNspEZr915TleM7foFIYoIpa2dAYmGKoBsPsG7carX1OxxcHGFubfFMx+05pg+Gzhuper/y6BoAwI73tuHi0fPVk/kacnHHcRiLRXhj7RSYWEgQcT0IezXUE0mZevKqsr827fAKtWP9uGAH/I5ehKy4BN9O3IC+i0di3NcLIDIVISUiAUff24HAC/61Uq4Xcesrxb2527pJMLaQIO56EH4fu0GtD2vhag9xmXNi7+OOIT8uV73vtPItAMDDHy/i3PxdkMtksG3ijEbDOkFkYYqchDREXbwL301HISvU/WD77S8V/fou6xXnJP56EP58q+I5MSlzTqTN3fF6mXPS4UPFOQk8chEX5u+qvcxXg0efH4dQIoL3pikwspAgzTcQviPXqfXXJK4OqsAEAMT9+i+MbS3QcNEwiOytkBkQAd9R61TfByx83GCt7Lt391VvT8+1mY28qORaKNnz02Yf1qalB7wWDIXQ1ARZIbG4uWgPIo9qdx0UgP36p3H3S8U56rBB0ZYkXg/C6XJtibmrvdo5smvujn5HS9uSdsq2JPjIRVyetwumjtZw6dMaADDkrzVqn3di2CeIv1r5Gqi64L/evmrC+zARaSKQy+W6/Zgd6aTVrmO0nQWd0iGfN73ybET5VSf6jwkpMqs60X/MavkjbWdB59y8t1/bWdA5/zZbpO0s6JyjJnx2pbwkeYG2s6BzXAVibWdBpziX6NaUWbrAkN8EK6in4096a0OuAa8dqlq2clF1KlXIU1JBCc9JBTOj9mk7Cy+drHcHajsLLx3zbb9rOwtawW/NRERERERERERERKT7XrI180h7+OhGLTMzM6v0denSJW1nj4iIiIiIiIiIiIjopcYRH7XM39+/0r85OTnVXkaIiIiIiIiIiIiIiPQQAx+1zNPTU9tZICIiIiIiIiIiIiLSW5zqioiIiIiIiIiIiIiI9AYDH0REREREREREREREpDc41RURERERERERERER6T6ZXNs5oJcER3wQEREREREREREREZHeYOCDiIiIiIiIiIiIiIj0BgMfRERERERERERERESkNxj4ICIiIiIiIiIiIiIivcHABxERERERERERERER6Q2htjNARERERERERERERFQlmVzbOaCXBEd8EBERERERERERERGR3mDgg4iIiIiIiIiIiIiI9AYDH0REREREREREREREpDcY+CAiIiIiIiIiIiIiIr3BwAcREREREREREREREekNobYzQERERERERERERERUFblcru0s0EuCIz6IiIiIiIiIiIiIiEhvMPBBRERERERERERERER6g4EPIiIiIiIiIiIiIiLSGwx8EBERERERERERERGR3mDgg4iIiIiIiIiIiIiI9IZQ2xkgIiIiIiIiIiIiIqqSTK7tHNBLgiM+iIiIiIiIiIiIiIhIbzDwQUREREREREREREREeoOBDyIiIiIiIiIiIiIi0hsMfBARERERERERERERkd5g4IOIiIiIiIiIiIiIiPSGUNsZICIiIiIiIiIiIiKqkkyu7RzQS4IjPoiIiIiIiIiIiIiISG8w8EFERERERERERERERHqDgQ8iIiIiIiIiIiIiItIbDHwQEREREREREREREZHeYOCDiIiIiIiIiIiIiIj0hlDbGSAiIiIiIiIiIiIiqopcJtd2FuglwREfRERERERERERERESkNzjig57LBPt4bWdBp8THWmg7Czrnbom5trOgc5oaZms7CzpnRYm7trOgc/5ttkjbWdA5r97boO0s6JwiryXazoLOyYOJtrOgc+6Y8BmnspoVFWg7CzonS86vg+XZGbKelJdZYqTtLOicQj5DWoGNTKbtLOicIgi0nQWdYwQ+qU9EtYd3ayIiIiIiIiIiIiIi0hsMfBARERERERERERERkd5g4IOIiIiIiIiIiIiIiPQGJ3UlIiIiIiIiIiIiIt0n41ox9HQ44oOIiIiIiIiIiIiIiPQGAx9ERERERERERERERKQ3GPggIiIiIiIiIiIiIiK9wcAHERERERERERERERHpDQY+iIiIiIiIiIiIiIhIbwi1nQEiIiIiIiIiIiIioirJtJ0BellwxAcREREREREREREREekNBj6IiIiIiIiIiIiIiEhvMPBBRERERERERERERER6g4EPIiIiIiIiIiIiIiLSGwx8EBERERERERERERGR3hBqOwNERERERERERERERFWRy+TazgK9JDjig4iIiIiIiIiIiIiI9AYDH0REREREREREREREpDcY+CAiIiIiIiIiIiIiIr3BwAcREREREREREREREekNBj6IiIiIiIiIiIiIiEhvCLWdASIiIiIiIiIiIiKiKsnk2s4BvSQ44oOIiIiIiIiIiIiIiPQGAx9ERERERERERERERKQ3GPggIiIiIiIiIiIiIiK9wcAHERERERERERERERHpDQY+iIiIiIiIiIiIiIhIbwi1nQEiIiIiIiIiIiIioirJtJ0BellwxAcREREREREREREREekNBj6IiIiIiIiIiIiIiEhvMPBBRERERERERERERER6g4EPIiIiIiIiIiIiIiLSGwx8EBERERERERERERGR3hBqOwNERERERERERERERFWRy+TazgK9JDjig4iIiIiIiIiIiIiI9AYDH0rdunXD3LlztZ2NSk2YMAFDhgzRdjaIiIiIiIiIiIiIiHQap7pS+vnnn2FkZKTtbCA8PBz169fHrVu30KJFC9X2rVu3Qi7X/6FcZsMHw/ytETC0tUFhcCjSN25H4f1AjWlNh/SHaf/eMPJwAwAUPgxCxhfflKY3NITljEkw6fgKhE51IM/OQb6vH9I//xqy5JRaKtGzsR/fD44zhsBIaoXc++GI/OBr5PgHV5reemAHOC0cBVE9e+SHxSF6zffIOOen+nvbmF807he1+jvE7zimem/5WmvUnTsCkiaukBUUIevfAIRMXldt5aoNzRcMRYPR3WFsIUHSjSD8u3QvssISKk1v364RvGYMgK13fUgcrXF+0hZEnbpZizl+ftLx/eA4/Q1FPXkQjqgPdj+5ngzogLoLRyvqSXgcYtZ8j4xzpWU1kJig3rKxsOrTDkJrcxREJiJxz+9I2ncKAGBoZYa6742CZZcWMHayQ1FKJtJPXUPsxgMoycqt8fI+raYLh6L+mO4wtjBF8vUg3FqyB9lPqAMA4DGhFxrOHAATqSUy7kfi1vLvkOb/SPX3rj8th7RDU7V9Qr8/i1uL96je23fygteiYbBo4oyS3AJEHLmEe+uOQF4iq94CPqM6E/ui3szXYSy1Qvb9CIQu/wbZt0IqTW83qD1cF42EibMUeWFxCPt4H9LO3lL93WXBCEgHd4TIyRaywmJk33mEiLUHkXVLUfcsO3jB5+dVGo99q+9iZPuHVm8Ba8kN/7vYe+Ao7j8MQVJKKrau/QCvdemg7WxVC6eJfeAycxCM7RV1JGjZHmTdqvz/STroVbgvflNZR+IRuno/UsrUEWn/V+A0vhfMfdxhZGMO3x4LkR0QoXYMY6klPFeOhXVXHwjNTJAbEovwz35B0h/Xaqycz6PhomFwfqsHjCxMkXY9EHcX7UFuWPwT93Gd2AvuMwdBZG+JzPuRCFj2LTKU59PIyhQNFw2HXVdviJ3sUJiSifiTNxC07giKs/LUjlPvzS6oP30ATN0dUZydh7jfriFg6d4aK+vz6jx/KFqM6g6RhQTRN4JwavlepIVX3ua2nzkIjfq2hY1HHRTnFyLmZjDOrzuM1EdxAAATS1N0nj8U9Tt7w8LJFrkpmQg+fRMXNx9FQblzpGvqTOwDZ7X2dg+yntjevgo3VXsbj0fl2lvXBcMrtLfhaw8+8Zi6QBvXTb03u6D5thkaj/2X19soTM6s3kI+A4cJfVGnTL8+/P2vkeNf+f+hzcD2qLeotF8f+ckPav36drE/a9wvcvV3iPvqVwBA3XeHwqpna0i86kNeWIybTcZWb6GeQb2JveFW5h7zcNleZD7hHmM/6FV4Lh4BE2cpcsPiEbJ6P5LP+qul8Vg0HE5vvQahhSnSrwfi4aKvVXXMukNTtPllpcZjX+uzDJn+oTBxlqLzjc8r/N23//vIuFl5X7qmaeueMyDhYIXj+r29DXHHrlZvAZ9RvYm9y/VPqq477sq6k6esOyll6o6if9ITFsr+ybUei9T6J0IrU7gvGgGbrj4wUX7XSTp5HaHrDqNEh+4/nouGo16ZenJ/0TdV1hOXib1Rf+YgGNtbIut+JB4s26uqJwBQb+xrqPtGR1j4uEFoLsGZBpNQnKn+/c597hBIe7aChZcrZEXFONtwco2U71nUdh0BgFY/r4B1Ry+1bdHf/YXARV9Xa9mIqHpxxIeSjY0NzM3NtZ2NSllaWsLKykrb2ahR4l7dYDV3OjK//h7xY6ejKDgU0u3rYWBtpTG9qHVz5J4+h8QZ7yFh0myUJCRB+vkGGErtAAACExMYN26AzG/2IWHsdCQv+hBCV2dIN6+uxVI9PZvXO8J55UTEfnoYAX3fQ+79cDTcvwJCW0uN6c3aNILHF/ORfPAsAvq8h/RT1+D5zRKIG7mo0txqMVHtFTZvO+QyGdL+LO3MWvd/Fe5b5yD5yDnc6z0fD4YsRcqxizVe3urkNXMgmkzqjWtL9uDPQStRnFuAnvsXw0BUeTBTKBEh7X4kri3/rhZz+uKsB3WE84pJiN1yCPf7zUfe/XA02Ley0npi2roR3L94D8mHzuB+3/lIP3kNHl8vgUmZeuK8chIsurVC2Luf4V632Uj45jhcPp4Gy15tAQBGDjYwdrBB1OpvEfDaHITP2wbLbi3humlWrZT5aTR6ZyA8J/eB3+K9ODdgBUpyC9Dp4JIn1oF6r78Knw/H4P7mn3Gmz/tIvx+JzgeXQGRroZbu0b5zOO4zU/W6u7r0S6JlUxd03LcQ8efv4Eyv5fh3+nbU6dMK3stH1lhZn4bd4A5w/3A8Ijf/iFu9FyEnIBzNDr4PIzsLjenN2zRC46/mIv7gWfj1WoiUE9fRdO8iSBo7q9LkhcYidNnX8Os2H3cGv4+CqEQ0O/w+jJTnK/N6IP71nqL2itt3BnkRCS9t0AMA8vLy0cjTHcvfm6ntrFQr+8Ht0WDVOIRvPorrvRYjOyACLQ4tr7SOWLRpCK8dcxB34Byu91yMpBPX4f3tQpiWqSOGEhHSrz1EyMf7K/3cpp/PgsSzLu6MW49r3RYg6U9fNNs9D2bN3Kq7iM/NfdYguE3pi3uLvsE//T9AcW4B2h1+cntSZ/CraLJqLII3/4TLvZYhKyAC7Q4tgbHyfIocrSFysMKDVftxsetC3J6zA9LuzeGz5W2149R/uz8aLX0Todt/xcUuC3Ft+BokXbhTo+V9Hq9OH4g2E3rj5LI9+G7wShTlFuDNHxbD8AnnyKVdE9z8/i98P+RDHHprPQyMhBj5w2IYiUUAADMHa5g5WOHcJwfwda8l+GPBLrh39UH/DVNrq1jPRTq4Azw+HI+IzT/Cr/di5AREoNnBJ19LTb6ai/iD53Cz1yIkn/CFV7n2Njc0DiHLvsHNbu/h9uAPkB+VBO/DH6jaW12kresm9terONNsutor8dxtpPxzX6tBD5vXO8Jl5UREf3oE9/osQO79cDQ+8OR+veeX85F08Czu9n4PaSd90XDPYrV+vV/zSWqv0HmfQy6TIfWPf1VpBMZCpB6/gsTvTtV4GZ/EYXB7NFo1Do82/4RrvZYgKyACrQ4tq/S6sGzTEN473kXMgfO41nMJkk5cR/Ny9xi3Wa/DeUo/PFj0NXz7L0dJbj5aHl6mqmPp1wPxd7Npaq/ofWeRG5GAzHL9kJvDVquly7z9CNqizXsOANx+9yu16yfhxI0aK+vTeNw/Cdv8E673WqLsnzy57njteBexB87DV1l3fDT0TzKuBSLk4wMajyFytIHIwRohq37Ata4LcH/Ol7Dt3hxNt0yvkTI+j/qzXofrlL64v+hrXO3/PkpyC9Dm8NIn1hPHwe3ReNVYhGw+iiu9liIrIAJtDi1V1RMAMBQbI+m8P0K3Hqv0OAZGQsQf/xeR3/1VnUV6btqoI4/F/HAGl5pNU71CPqq8z0tEuoGBD6WyU125ublhzZo1mDRpEszNzeHi4oJdu3ap0hYWFmLWrFmoU6cOTExM4OrqirVr16r+LhAI8NVXX6Ffv34Qi8Vwd3fH0aNHnyof9evXBwC0bNkSAoEA3bp1A1Bxqqtu3bph9uzZmDt3LqytreHg4IDdu3cjJycHEydOhLm5OTw9PXHixAm149+7dw/9+vWDmZkZHBwcMHbsWCQnJz/HGat+5qOHIfvYn8g5fgrFYRFIW/sZZPkFMH29r8b0qR+sRfbR31AUFIriiCikfrwZEAggatsSACDPyUHSrEXIO/M3iiOiUXjvAdI3bodx00YwdLCvzaI9FYepryPpwF9IPnIO+cHRiFiyA7K8AtiNfE1z+skDkXHhFuJ3HEN+SDRiNh5E7r1HsJ/YX5WmOCld7WXV5xVkXbmHgkjlE5mGBnD5aDKiPv4OST+cQsGjWOQHRyPt+JXaKHK1aTKlL+5s/RVRp/2Q/iAKl+fsgMTBCi59Wle6T+z5O/DfcBRRJ7XbuX9WDtMGI/ngaaSo6slXkOU/qZ4MQsYFPyQo60nspgOKejKhtJ6YtW6ElB/PI+vqPRRGJyJ5/2nk3g+HaYsGAID8wEiETluPjDPXURARj6wrdxGzfj+serYFDHXjNuI5tS8efnYMcaduIuNBFHzf/QpiByvU7Vt5HWj4dj+E7T+PiMMXkRUUA79Fe1CSVwC3UV3V0pXkFaAgKUP1Ks4uffLLefCryHgQiQdbfkFOeAKSrz7E3dUH4TGhF4SmJjVW3qo4vT0I8fvPIOHQeeQGRSNk0S7I8grgMLKH5vRT+yP1vD9ivvwNecExiNhwCNl3w1B3Yj9VmqRfLiP90l3kRyYiNzAaj1Z+B6GFKUybuAIA5EXFKEpKV72K07Jg27ctEg6dr5Uy15TO7dvi3Wnj0bNrR21npVo5Tx+I2H1nEXfoAnKDYhC4cDdkeYWoO6q75vTTFHUk8svjyA2OQdj6w8i6+wj1JpXeo+OPXkL4pz8h7eLdSj/Xom0jRH99Alm3QpEfkYjwLT+jOCMH5s3dq72Mz6v+tH4I2fILEk7eRNb9SNye9SVEDtZw6Nem8n2mD0DUvnOIPvQ3soNicHfhNyjJK4TzqG4AgOyH0fCb/BkST/shNyIRKZcDELj2MOx7t4JA2Y4KLU3RaMkI+M/+ErE/X0FuRCKy7kciUQdHI7ad3Bf/fP4rgv/yQ9LDKPw+fwfM7a3QsHflbe7h8Rtw9+glJAfHIPFBJH5/bycs69nB0dsNAJAcFI1fpm9DyNlbSI9MRMSV+/h744/wfK2l6hzpIqe3ByJu/1kkHLqA3KBoBC/aBVleIRwraW/rTh2A1PP+iFa1t4eRffcR6k4svZY0t7cSmDZx0XhMXaCt60aWX6R2j5bLZLDr5IWoA9q999SZNgiJB/5C8uFzyAuORtjinZDlFUA6SnO9cJwyEOnnbyHuq1+RHxKD6I0HkXs3DA5l7sNl77FFSemw7tMWmf+U6dcDiNl0GPG7f0fuwwhNH1NrXKcPQPS+s4g9dAE5QTF4sPBrlOQVwqmSe4zLtH5IOe+PiC+PIyc4BqHrjyDzbhhcJvUpk6Y/wrb8jKSTN5B9PxIBs76AyMEa0n6KB3XkRSUoTMpQvYrSsmHftw1iD16o8HmFqVlqaeXFJTVyHp6Gtq6dx4oyc9WuIVlBUU0Wt0ou0wcgRtk/yQmKwUNl3am8f9JPrX/yaP0RZN0NQ70ydSf+6CWEffoTUivpn+Q8jMLdyZ8i+bQf8iISkHY5AKFrD8Oud2uduf+4TuuH0C2/IPHkTWTfj8RdZf23f0I9cVPWk5hDfyMnKAYBquuwmypNxK4TCNv+GzJuVj4aLWTjUUTs/BPZD6Kqs0jPTRt15LGSvEK1tqMkW3dGBBGRZrrRiuugzZs3o02bNrh16xZmzpyJGTNmIDBQMYXStm3b8Ntvv+HIkSMIDAzE/v374ebmprb/Bx98gKFDh+L27dsYM2YMRo4ciQcPHlT5ub6+vgCAM2fOIC4uDj//rHlIMwB89913sLOzg6+vL2bPno0ZM2Zg+PDh6NChA/z8/NC7d2+MHTsWubmKoYrp6eno0aMHWrZsiRs3buDkyZNISEjAiBEjnvMsVSOhEMaNG6LAt3Q4N+RyFPj6QeTdtPL9yhCYiAChELLMrMrTmJlCLpNBlp39ojmuVgIjIUx9PJB56XbpRrkcmZfvwKx1I437mLZupJ4eQMYFf5i1bqgxvdDOEpavtUbSwTOlx/D2gHEdO0AmR9NTm9Hc7xs0+OEDtafLdJ2ZixQSByvEXb6n2laUlYekW6GQtm6gxZxVP4GREKbeHsi8VObpX7kcmZduw7TVk+qJ+tPCmX/fUqtX2TcDYdWrLYwcbQAA5h2awcS9LjIv+leaF0MLCUqycwEtT+cEAKYuUogdrJFwKUC1rTgrD6m3QmHbRnMdEBgZwsqnPhIvldYbyOVIuHQPtuXqjcv/OmJQwA70Or8OzZa9CUOxsepvBsZGFb4gluQXwlBsDGuf+tVQumcnMBLC3Mcd6RfV60n6pbuwaKO5npi3bqieHkDaBX+Yt9HcngiMhHAc2wvFGTnIvh+uMY1NnzYwsjZDwqFzz1UOqjkCI0OY+7gj9VKZL3dyOVIv3oVFJf/nlq0bVvgymHr+NiwqucYqk3k9EPZDOkBoZQoIBLAf0gEGJkZI/yeg6p1rgdjVHiYO1ki+WNo2FGflId0vFNZPaE8sfeojuVx7knzxHqyecH6MLCQozspTTYsn7eoNGAhg4miDrpc2ocetz9Fy1xyY1LWpnsJVEytnKczsrRBe5r5bkJWHWP9QOLV6+vpgYi4BAOSl51SaRmQhQWF2ntanDqxM5e3tnUrbTwuN7e3tSq89gZEQdcb2VLa32v0xuzLavG7KcxreBSV5BYj7XXvT55X269XrRcalOzCvpF9v1rphhf5aern+WllCO0tYvdYaSYfOVlu+q8uT7jGWlfzfKu4x99S2pZy/DUvldSF2tYfIwRopZe5DxVl5yPQLqbS+SPu0hpG1OWIPXajwtxbfL0LXgF1o89sqSJ/woFRN04Vrp9naieh1fxc6nlyNemV+ENeGyupO2gvUneclrKKtqU2P60n5+p/hFwKrSu8dhrDwqY+Ucucy5eLdSvd5GWi7jjj+rxM639+Ndn9vgsfyUTAo872QapmMr2d+/UdxjY9K9O/fHzNnKqa1WLx4MbZs2YLz58+jUaNGiIyMRIMGDdCpUycIBAK4urpW2H/48OGYMmUKAGD16tX466+/sH37dnz55ZdP/FypVAoAsLW1haOj4xPTNm/eHO+//z4AYOnSpVi3bh3s7OwwdapiSoAVK1bgq6++wp07d/Dqq6/i888/R8uWLbFmzRrVMfbs2QNnZ2cEBQWhYUPt3QANrCwhEBqiJDVNbXtJahqEbs6V7KXOavZUyJJTkO9byZORxkawmjUVuafPQZ6jO+sSAIDQxhwCoSGKkjPUthclpcPEw0njPkZSKxQlpaunT06HkdRaY3q74d0hy85D2onS4fAiFwcAQN333kTUqr0oiEqE49uD0ejoatzt/A5K0nUrQKSJ2N4KAJCfpD6dQX5yJsT2mqcTeFmp6km5//fi5AyYeNbTuI+R1ArFyerpi5Iy1OpJ5Ae74Lp+Jprf2ANZUTEgkyNi0RfIvnZfcz6szVFnzggk7z/9QuWpLibKOlCQpH795CdlwERqpXEfkY05DISGyC+3T0FSJiw866reR/5yBbnRyciLT4dlU2d4Lx8Fc486uDr5MwBAwoU7aDC1L5yHtEfUb//CxN4KTeb/T5EvB82fXdOMlPWksFzZCpPSIfbU3J4Y22toT5IyYKw8t4/Z9GqNxjvmwkAsQmFCGu6++RGKUzUHmx1Hv4a0C7dRGJf63GWhmmFkYwEDoSEKy/2fFyalQ9KgrsZ9FHWkfJ3KgKhcHanKvalb4LVrLroE7oWsqBiyvELcnbAJeU9YG6I2mUgV943y7UnBE8pqrDyfmvYxreR8GtmYw3PeG4jaV/qjpcTVHgIDA3jOGYyA979HcVYuGi0ZgXZHluFi98WQF2nvieSyTJXnIafcNEI5yZkwlT7lfVcgQM+VbyHqeiCSg6I1JhFbm6Hj7CG4dVB3R41V3t5mwPIJ7a2m9rlie9sKTXbMg4HYGIUJ6bjz5upK21tt0+Z1U57z6G6I/fkKZPnae2q9sv5aUXLl92EjqRWKNPTXyteLx6QjFP361D//1fh3bTJW3WMqXheV/d+K7K003JMyYKzsyxsr+3Plj1nwhHPkNLoHUs7fRkGZfkhJTj4CV3yP9OuBgEwO+4GvoPm3C3B7wiYkaWF0nbavncB1R5ByOQAleYWw6+aNZusmQmgqQvjX2pkqzegJdedJ/RNNdUf0At8DjWzM4Tbvf4jZd6bqxLVA9IT6X1U90bSPaQPN7dDLQJt1JP6Xf5AflYyChFSYNXWF5/ujIfGoi7uTNj/TcYiodjHwUQkfHx/VvwUCARwdHZGYmAhAMe1Ur1690KhRI/Tt2xcDBw5E79691fZv3759hff+/v41lkdDQ0PY2trC29tbtc3BQfGj9uN83759G+fPn4eZmVmFY4WGhlYa+CgoKEBBQYH6NpkMIgPdGTBkPn4kxL26I2n6e0Chhi86hoawW7sCEAiQtm5r7WdQB9iNfA0pv1yEvOzT6QYCAEDctqNIU35xCpu/Hc1vfA2bgR2QtE83ftguq/4bHfDq+kmq9+fGbdJibvSD/cQBMGvVCMETPkFhTCLM2nnB5ZO3UZiQiqzL6k8fGpiJ4fn9B8gPjkLsp4e0kl/n/3VA6w2li+pdHruxxj4rbF/pD26ZD6OQn5COrkeXw9TVHjkRiUj4+y7urD6AVusnoe32GZAVFuHBlmOQvtoYcpm8xvKlLen/3IPfawthZGMOx7d6osmu+fDvvxRF5X4ANa5jA+tuzfFg2qdayinpqvpL3oTQ0hS3hn2EopQs2PVrC6/d8+A3eAVytDCFQt2hHeG9cYrq/fUxG2r8M4VmYrTdvwjZQTEI2vhT6R8MBDAwFiJg+XdI/lvxJOOt6dvR8+4O2Hb0QrKW1vrwGtIBfdeU3nePTHzx+26f1eNh17Ae9g3TvO6asZkYI/YuQHJIDC5vqXz0sz5L/ycAN5XtbZ23eqLprvm4paG91Qadum7KsGrTAOaN6sF/1pMfNNMH0pE9kPzLJfV+PamI6tjAtntz3Jm6RW17UWoWInf+oXqf6R8KkYMNXGcOqpXAh65dOyFbflH9O/NeOIQSEdxnDtJa4EMXGJqJ0Xz/YuQERSNs49NNV17d6gztCK+Npetb3RyzXiv5IHWxP5QGDnMeRKEwIQ2tfloBsasD8iJ04wEeIqqIgY9KGBmpLxIlEAggkynGBrVq1QphYWE4ceIEzpw5gxEjRqBnz55PvY5HTeax7DaBQPGj9uN8Z2dnY9CgQVi/vuKNs06dOpV+ztq1a7Fq1Sq1bXPruGG+U/XNxy1LV8ytamijPlrB0MYaspQnPy1s/tZwWIwfhcR3FqIoRMPCdIaGsF27AoaODkiauUDnRnsAQHFqFuTFJTCyU3/qQNOojseKktJhVO5pdiM7KxQlpVVIa/ZKE4g96yF0hvrTCEWJirR5ZZ62lBcWoyAiAcZO0ucoSc2LOu2H5FulCxQaGCuaMROpBfIS01XbTewskBYQWdvZq1GqelLu/11oZ6n6vyyvKCkdQjv19EZSS1U9EZgYw2nxWwidsg4Z5xRf+PIeREDiVR+O04eoBT4MTE3QcN9KyLLzEDJlndbmQ4475Ye//ErrgKGyDoiklsgvWweklkgP0DwtSEFqFmTFJaon7R4TSS2Qn5ihcR8ASFV+rll9B+REKILKwTtPIHjnCZg4WKEwIwemzlJ4Lx+p+nttK1LWE+NyZTOWWqGozPkpqzBRQ3sitURhufSy3ALkh8cjPzweWX7BaHNlOxxGvYbo7b+opXMY2QNFadlIPfVyraHzX1GUmglZcYnqCdrHjKVWFf7PH1PUkfJ1yhIFlaTXROzqAOfJ/XCty3zkBCruO9n3I2D1amPUm9gXgYt2P0sxqkXCyZtILzOv9eNFQkXlyiaSWiIzIFzjMQqV51NUoT2peH4MTU3wyqElKMnOw82Jn6q1owUJirTZQTGlx07JQmFqFsT17J6jdNUj+C8/xN6q2Oaa2lkgp0z5TO0skHC/6vtu74/GwfO1ltg34mNkxVfs4xmbmuDN7xeiICcfP037DDItzr1flcrb24rt52OFieka2+eq2tu2V7bBcVQPRG0/Vo0leD66dN2U5TKmOzLuhiPzTtizF6oaVdZfU/TT0zXuU5SUDiMN/TVN9chc2a8Pma6bDxcUqu4xT3/PKEhM13BPskShsk/2+Gnt8teWSGqJLA11rO7IbihKy3qqYEaGXzBsu3pXma466Oq181i6XygavDcUBsZCyAqLn75g1aToCXXnyW2qVYX0BU/oz1fG0NQELQ4tRUl2Pu5O3Ky17zqJJ2+qrbnxuJ6Uv4YU9UTzd53KrkNN9eRlou06UlaGn+L/SFzfkYEPIh2mO4/sv2QsLCzw5ptvYvfu3Th8+DB++uknpKaWfnn791/1Ycf//vsvmjRpUuVxjY0VcwSWlFT/TbZVq1YICAiAm5sbPD091V6mpqaV7rd06VJkZGSovd6p41a9mSsuRuHDINXC5ABUC5UX3NU83Q4AmI99ExaT30LSu0tQ9CCoYgJl0MPIxQlJ7yyELEP7T8lpIi8qRs6dUFh0Kh3FA4EAFp28kX0zUOM+OTcD1dMDsOzSHNk3K54H6aieyLkdgrxyc/Hn3AmFLL8QJh6lw0IFQkOInO1RGK2dH22rUpyTj6zwBNUrIygGuQnpqNPJS5XGyEwMaUsPJN0M1mJOq5+8qBg5d0NhXqGe+CDH7+nriUXnFqp6JRAawsDYCHJ5udEJJTJAUHqLMDATo+GBDyEvKkbIxE+0+oRhcU4+csITVK/MoBjkJaTBvkwdEJqJYdPSAyk3NNcBeVEJ0u+Eqe0DgQD2nZoh5Qn1xqqZYmrDfOUPlGXlJ6RDll8E5yHtkRuTjLS72vnhRV5UjKw7j2DVucyXeIEAVp28kXlDcz3Juhmknh6AdZfmyLqhoV0ty0Cg+jJWlsPI7kj88W+tLhZKlZMXlSDrziNYd25WulEggHXnZsis5P8842YQbMrVEZuuPsis5BrTxECi6OOUHw0lL5GpRiDWtpKcfOSGJ6he2YHRyE9Ig22ZcyM0E8OqlQfSntCeZNwJg12582nb2QvpZfYRmonR7shSyAqLcX3cpgrrA6X5Kq5PU8/Sh1GMrExhbGOOvKik6ijucynMyUdaRILqlRwcg+zEdLh1LG0/jc3EqNvCAzF+T64PvT8ah4Z92uDAqDXI0FAmYzMxRu5bjJLCEhyd/ClKdPxp9ie1t5W1n5ka2lurLj6VXnsqlbS32qBL181jhhIR6rz+qtYXNQcq79dbdvJBViX9+uybQbAoVy8U/fqK6aWjXkP27RDkVrLGlrY9vsfYlLsubDo3Q0Yl9UFxj2mmts22qzcylNdFXkQiChLSYFvmmIZmYli08lSrL4/VHdUNsUcuPlU/xLyZmyrwXNN08dopy6KZKwrTsrUS9AAqrzvWVdQd63J1x6ZM3XlahmZitDyyHPLCYtwet0Gri7w/TT0xNBPDspUn0ispp7yoBJl3wtT2UdSTZpXu8zLQZh0pz9zLDQBQWMkDiESkGxj4eA6ffvopDh48iIcPHyIoKAg//vgjHB0dYWVlpUrz448/Ys+ePQgKCsLKlSvh6+uLWbNmVXlse3t7iMVi1cLjGRkvFoUu65133kFqaipGjRqF69evIzQ0FKdOncLEiROfGGgRiUSwsLBQe9XENFdZB47CbMgASAb0htDNBdZL5sJAbIKc44qhtjYfLoblO6XT25iPGwnL6ROQ+tEmFMfFw8DWGga21hCITRQJDA1ht34ljJs2RMoHawBDA1UaCHVvsFPC7t8gHd0LtsO7w8SzHlzXvQ0DsQmSDyuGVNbf+i7qLXmrNP03v8OiW0s4vP06TDycUHf+m5D4eCBx759qxzUwE8N6YAe1Rc0fk2XnIXHfKTgtGAmLLs1h4lEXrmvfBgCk/n6lBktbvR58fRLe7w5BvV6tYNW4HjpufRu5CemILPOUV6/DS9FoQi/Ve6FEBGsvF1h7KRZyN3ORwtrLBaZ1bWs9/88iYdevkI7qBdthynqydrpaPXH7bA6c1OrJcUU9mTZYWU9GKurJt4p6IsvOQ9bVe3BePh7m7ZvB2NketsN7wHZYN6SfVARwHwc9DCQmCF/wOQzMJRBKrSCUWgE6MuVdyO6TaDJ3COr0bgWLxs5ou3068hLSEXuytA50ObIUHhNL60DQzhOoP6Y7XId3hnmDumi1fiKEEhHCD/0NADB1tUeTeUNg5eMGST071OndCm23TUfS1QfIKDMlT8MZA2DR2BkWDZ3QZN4QNJ71Ovzf/x7Q4lRXMTuPw3FMT9iP6ApxAyd4rp8KA4kICYcUPwg13D4bbstGl6bf/Sesu7eA0/RBEHvWhcuCETBr7o7YvScAAAYSEVyXjoZ5qwYQ1bODmY87GmyZCZGjDZKPq7cVVp28IXZ1QPx+3ZgX+UXl5ubhYVAoHgYpnniPiU3Aw6BQxMXrZnD4aUXt+B11x7wGxxFdIWnghEYbpsBQIlItAttk+ztwXz6qNP2uP2HTvTmcpw+ExLMu6i8YDvPmHojec1KVRmhlCjMvV5g2VKw5JPGsCzMvV9VTebnBsch9FIfGG6fCvKWHYgTI9IGw6eqD5BPXa6/wVQjbdQIN5g2BfZ/WMG/ijOafz0BBQhoSTpSOYGp3dDlcJ5VOcxq24w84j+kOpxFdYNagLpptmAShRIQoZXsiNBPjlSNLYSgxwZ15O2FkJoZIaql4YlcZ9Ml5FI/4E9fh9fF4WLdpALPG9dB8+wxkB8ci5Z/KHwLRhuvfnESH2UPg2bMVpI3qYdCnbyMrMR1Bp0vb3FEHlqL1+NI2t8/HE+A1pCN+ffdLFObkw1RqCVOpJYSPn2Q1E2PkD4thJBbhz0W7ITIXq9IItBQYexoxO39HnTGvwUHZ3jZQtrfxyva20fZZau1t7O4/lO3tQIg968JVeS3F7lVcSwYSEdyWjlJrbxtumQGRow2Sjl/VShmfhraum8fqDmkPgaEhYo5erp0CVyFu13HYj+4Ju+HdYOLpBLd1b8NAIkLSoXMAAPet78J56RhV+vivf4dlt5ZwfPt1mHg6wem9N2Hq44EE5X34MUMzMWwGdUDSAc33WGMnO0i83CBysoPA0AASLzdIvNxgIDGpucJqELHjDziN6YE6I7rAtIETmpS7x3htfweeZe4xkbtOwLZ7c7gq7zHuC4bBorkHIvecKpPmT9Sf9wakfVrDrIkzmn3+DgoS0pBU7v5h07kZJK4OiNl/rkK+6ozoAsc3OkDiWRcSz7pwmzMETqO6I+qbkxXS1hZtXTv2vVvBeUx3mDWuB4mbA1zG94THnMEI/0a701xF7vgDdcf0gOOILpA0cEJjZd2JU9adptvfgYda/0RRd1xU/RNF3YkuU3dK+yeKtS3K908eBz0MJCI8mLcTQjMxjKWWir/ryP0nYtcJeJSp/z6fz0RBQhoSy9STtkffh8ukPqr34Tv+QL0xPVB3RBeYNqgLrw2TYSgRIUZZTwDFyAdzL1dI6iumSTdv4gJzL1cYWZU+GGviZAtzL1eYONlCYGgAcy9XmHu5wlAiqoWSV6SNOiJ2dYDbvP/B3Kc+TJylsOvTGk0/n4m0K/eR/RSjXYlIe3Tv19+XgLm5OTZs2IDg4GAYGhqibdu2+PPPP2FQ5gfAVatW4dChQ5g5cybq1KmDgwcPomnTplUeWygUYtu2bfjoo4+wYsUKdO7cGRcuXKiWfNetWxf//PMPFi9ejN69e6OgoACurq7o27evWt61Je+vC0i3soTl2xNgaGuNwqBQJL27BDLlgueGjvZAmafSzYYOgsDYGHYbPlQ7Tsau75C5+3sY2ttB3LUjAMDxgPr0GYlvz0eB3+2aLdAzSv3tHwhtLOC0YCSMpNbIDQhD0FsfoVi54LlxXanaD6nZNwLxaNYWOC0ajXqL30J+WBxCJq9DXqD6jdd2cCdAIEDqsUsaPzd69XdAcQnct82FgYkxsm8F4eGIFSjJyKm5wlazgC9/h1AiQvsNk2BsIUHi9SCceUv9SR1zV3uY2Jir3ts2d0efo8tV79t+qAgWhBy5iCvzdtVe5p9R2vF/ILS1RN0FoxT15H4YgseuUtUTkZN6Pcm5GYiwWZ/CadEYOC1+CwVhsQidsg75ZepJ6MxNqLdkLOpvnwehlRkKopMQs34/kn5QfAk09faAWatGAADvf3ao5efOq9N0YnRQ4Be/w1AiQuuNk2FkIUGybxAuj16vVgdM3RwgKlMHon/7FyJbczRdNAwmUktkBETg8uj1KFDOny4rKoZ952bwnNIXQokIubGpiPnjOh58dkztsx17NEfjOYNhaGyE9PuRuDLxU8Sf0277kvzrFRjZWsB10UgYS62QHRCOgFGfoEhVT+wA5TSIAJB1IxCBM7fCdfFIuC0djbywONyfuAG5DxUBHnmJDBJPJziM6AojGwsUpWUh2z8Ut4d8gNxA9YWJHUb3QIbvQ+SFxNZegWvQvYfBmDR7ser9hu2K9mFwv5745P33tJWtF5b461UY2VrAfdEIGNtbISsgHLdHrVEtYG7iZKfWlmTeCELAjG1wXzISHstGITcsDncnbETOw9IgoF2fNmi67R3V+2a75gEAwjb+iLBNP0JeXILbo9fC4/0xaP7DYhiamiA3LB4PZn+BlLO3aqnkVXv0+XEIJSJ4b5oCIwsJ0nwD4TtynVp7InF1gHGZ9iTu139hbGuBhouGQWRvhcyACPiOWqdafNPCxw3WrRsAALr7qq81dq7NbORFJQMAbs/6Ck1Xj0Xb/Ysgl8mRevUBfEet1bnRU//u+B1GEhH6rZ0EEwsJom4E4ci4DWojNKxc7CG2Lj1Hrcb2BAC8deR9tWP9/t5O3D16CY7N3ODUyhMAMOOS+hQ+X3aci4zo5JoqzgtJUrW3b6ra23vl2lt5uWvp4cytcFs8CvWV7W2Axva2G4xszFGUloUs/1D4D1lRob3VJdq8bgDAeXR3xP/pi+JM3ZjSNvW3f2Bka4F6C0fBSGqF3IAwPByzukx/Tf0+nH0jEKHvbEG9xaPhvGQM8sPiEDRpfYV+vY2yX59yTHOAp96CkZC+2UP13vsvxbV0f+gHyLoaUN3FrFTCr1dhbGsBj0UjIFLeY/xGrVX935o42aqVP+NGEO7O2A7PJW/Cc9lI5IbF43a5e0z457/BUCJCk03TILSQIN03ELdGrq3wZH7d0d2R7huI3Er6IfXnDYXY2Q6yYhlyQ2JwZ9pnSPz9Wg2chaejrWtHXlQC14m90fSjsYBAgJyweDxYsQ+R+yoGjGpTorLuuJepO/7l6o68XN0JmLEd7kvehIey7twpV3ekfdqg6baZqvfeu+YCAB5t/BFhm47C3Kc+LJXnq4PvNrX8/NNmFvK1OOrysTBl/W+2aSqEynpyo4p6Eq88lw0WDVfVkxtl6gkAuIzvBc+Fw1Tv2/32IQDg7rtfIeawIkDSYNEIOI3sqkrT8Zxi6nTfNz5C6pXafzBDG3VEVlQMmy7ecJnWHwYSEQpiU5D0uy/C/qPrkOkCfVxPk2qGQF5hfhN6UQKBAL/88guGDBmi7azUmKi2r2k7CzolPtZC21nQOQ9Q+fRp/1VNBdnazoLOCS9hPSnPQV6g7SzonFfv1fziny+bS15LtJ0FnZMHQ21nQefcMeE5KatjAdvX8rLkfA6uPDtD1pPyMkt0Y5o1XVLIyTMqMIGs6kT/MUXQjREjusQI/AmyvNcSDms7Cy+d1De6Vp2I1Nj88nfVifQQ79ZERERERERERERERKQ3GPioZWvWrIGZmZnGV79+/bSdPSIiIiIiIiIiIiKilxrHNteAJ80eNn36dIwYMULj38RicU1liYiIiIiIiIiIiIjoP4GBj1pmY2MDGxsbbWeDiIiIiIiIiIiIiEgvMfBBRERERERERERERLpPpu0M0MuCa3wQEREREREREREREZHeYOCDiIiIiIiIiIiIiIj0BgMfRERERERERERERESkNxj4ICIiIiIiIiIiIiIivcHABxERERERERERERER6Q2htjNARERERERERERERFQVuUzbOaCXBUd8EBERERERERERERGR3mDgg4iIiIiIiIiIiIiI9AYDH0REREREREREREREpDcY+CAiIiIiIiIiIiIiIr3BwAcREREREREREREREekNobYzQERERERERERERERUJZm2M0AvC474ICIiIiIiIiIiIiIivcHABxERERERERERERER6Q0GPoiIiIiIiIiIiIiISG8w8EFERERERERERERERHqDgQ8iIiIiIiIiIiIiItIbQm1ngIiIiIiIiIiIiIioKnKZtnNALwuO+CAiIiIiIiIiIiIiIr3BwAcREREREREREREREekNBj6IiIiIiIiIiIiIiEhvMPBBRERERERERERERER6g4EPIiIiIiIiIiIiIiLSG0JtZ4CIiIiIiIiIiIiIqEoybWeAXhYc8UFERERERERERERERHqDgQ8iIiIiIiIiIiIiItIbDHwQEREREREREREREZHeYOCDiIiIiIiIiIiIiIj0BgMfRERERERERERERESkN4TazgARERERERERERERUVXkMm3ngF4WHPFBRERERERERERERER6g4EPIiIiIiIiIiIiIiLSG5zqip5LXraRtrOgU2JKxNrOgs7p5Rmj7SzonEORTtrOgs6Z0C5a21nQOR/edNR2FnROkdcSbWdB53QOWKftLOic+H5TtZ0FnWMVZa3tLOiUht5J2s6CzokPsdB2FnROfK5E21nQOSIB5xQpT4ISbWdB59Srm67tLOicoBhbbWdB57TsEK/tLBDRfwhHfBARERERERERERERkd5g4IOIiIiIiIiIiIiIiPQGp7oiIiIiIiIiIiIiIp0n5wyM9JQ44oOIiIiIiIiIiIiIiPQGAx9ERERERERERERERKQ3GPggIiIiIiIiIiIiIiK9wcAHERERERERERERERHpDQY+iIiIiIiIiIiIiIhIbwi1nQEiIiIiIiIiIiIioqrIZdrOAb0sOOKDiIiIiIiIiIiIiIj0BgMfRERERERERERERESkNxj4ICIiIiIiIiIiIiIivcHABxERERERERERERER6Q0GPoiIiIiIiIiIiIiISG8ItZ0BIiIiIiIiIiIiIqIqyQXazgG9JDjig4iIiIiIiIiIiIiI9AYDH0REREREREREREREpDcY+CAiIiIiIiIiIiIiIr3BwAcREREREREREREREekNBj6IiIiIiIiIiIiIiEhvCLWdASIiIiIiIiIiIiKiqshl2s4BvSw44oOIiIiIiIiIiIiIiPQGAx9ERERERERERERERKQ3GPggIiIiIiIiIiIiIiK9wcAHEREREREREREREREBAL744gu4ubnBxMQE7dq1g6+vb6Vpd+/ejc6dO8Pa2hrW1tbo2bNnhfQTJkyAQCBQe/Xt27dGy8DABxERERERERERERER4fDhw5g/fz5WrlwJPz8/NG/eHH369EFiYqLG9BcuXMCoUaNw/vx5XL16Fc7OzujduzdiYmLU0vXt2xdxcXGq18GDB2u0HMIaPToRERERERERERERUTWQywTazoLe+/TTTzF16lRM/D979x0eRdGAAfy9lrv0XkgICQm9d+kg0osgvfcuIDWhCNKUIogKCipFEBAVAUUEpCtFek9IJyEhvdfr3x8Jl1y4BJAkF/K9v+fZ5+E2s3szy+7M7E0bNw4AsG3bNhw7dgw7d+7EwoULnwu/b98+vc/bt2/Hr7/+ijNnzmD06NG6/VKpFC4uLqUb+QI44oOIiIiIiIiIiIiIqAKSy+VIS0vT2+RyucGwCoUCN2/eROfOnXX7hEIhOnfujCtXrrzU92VlZUGpVMLOzk5v//nz5+Hk5ISaNWti2rRpSExM/O+Jegls+CAiIiIiIiIiIiIiqoDWrFkDa2trvW3NmjUGwyYkJECtVsPZ2Vlvv7OzM2JiYl7q+3x9feHq6qrXeNK9e3fs2bMHZ86cwbp163DhwgX06NEDarX6vyfsBTjVFRERERERERERERFRBbRo0SLMnTtXb59UKi2V71q7di0OHDiA8+fPQyaT6fYPHTpU9+/69eujQYMG8Pb2xvnz5/HOO++USlw44oOIiIiIiIiIiIiIqAKSSqWwsrLS24pq+HBwcIBIJEJsbKze/tjY2Beuz7FhwwasXbsWf/31Fxo0aFBsWC8vLzg4OCA4OPjVEvMK2PBBRERERERERERERPR/zsTEBE2bNsWZM2d0+zQaDc6cOYNWrVoVedz69euxatUqnDhxAs2aNXvh90RGRiIxMRGVKlUqkXgbwqmuiIiIiIiIiIiIiKjc02qMHYOKb+7cuRgzZgyaNWuGFi1a4PPPP0dmZibGjRsHABg9ejTc3Nx064SsW7cOy5Ytw/79++Hp6albC8TCwgIWFhbIyMjAihUrMGDAALi4uCAkJAQ+Pj6oVq0aunXrVmrpYMMHERERERERERERERFhyJAhiI+Px7JlyxATE4NGjRrhxIkTugXPIyIiIBTmTyS1detWKBQKDBw4UO88H330EZYvXw6RSIR79+5h9+7dSElJgaurK7p27YpVq1aV2lojABs+iIiIiIiIiIiIiIgoz4wZMzBjxgyDfzt//rze58ePHxd7LlNTU5w8ebKEYvbyuMaHATExMejSpQvMzc1hY2MDABAIBDhy5IhR40VERERERERERERERMXjiA8DNm3ahOjoaNy5cwfW1tbGjs5/5unpidmzZ2P27NnGjspLsx7eB3bjB0LkYAv5o1DEf/w1cu4HGgxrUs0D9jNHQVa3OiRuzohbsw0pe47ohal6ejckbs7PHZuy/yjiVn1VGkl4bbV8BsJzxNuQWJkj8Xog7vruRGZYTLHHVB3XBdWn94bU0RqpfhG4t2Q3Um6HGAzbar8PnDs1wtWxnyH6xA29v1UZ0h7eU3rCwssFqoxsRB29inuLvi+ppJUIs/79YD5sCER2dlCGhCBt05dQ+j8yGNa0Ty+Yde8KsVdVAIAyIBDp32zXC2+92BdmPbvrHZdz9RqS5/mWXiJKQau5A1B/+NuQWpnh6Y1AnFm8CymPY4sM79aiJppN7QWn+lVh4WyL3yduQshfN/XCSMykaLtwCLy7NYOprQVSn8Tjzq6TuLf3bGkn57VJu/eDtO9QCG3soH4cjKwdX0IdbPg+kbzVDrL+IyGs5AaBSAR1dBTkR3+C4sIpg+HNJs+FtNu7yNq5BfJjB0szGSWux5xBaDWsE0ytzBF2IwC/fLgD8Y+Lzl/ajOyCtiM6w66yIwAgOigSJ788BP/zdwAAZtbm6DFnEGq2awBbNwdkJqbh3l/X8ednPyMnPbsskvTS3MZ1Q5XpfWDiZIMMv3AELt6J9CLySQBw7NMSXr5DIHN3RHZYDEJW7UPimdv5f+/ZAm5jusCygRckdpa41mkBMh6G653DxNEa1T4aBdsODSC2kCEr+Ckef34Y8ceullo6y8KNO/exa/9B+D0KRnxiEr5YsxTvtG9t7GiVGotBfWE5cjBE9nZQBIUg5dPNUPgFGAxr3q8nzHt2hcTbEwCgeBSI1K925IcXiWA9bTxkbVpA7FYJ2oxM5Fy7hZQt26FJSCyjFL0al3Hd4Tb9XZg42iDTLxyhS3Yg43ZwkeHt+7RCFZ+hec9ONMJX70VygWenIO91k+EypitCl+5C9HfHAABWreui/qEVBsPf7e6LjDtFP7fGInu3H8wGDYXQzg6qkBBkfPUFVAGGyxxZj96QdekGkWdu3UQVFIDMnd/phRfY2MJi0hRImjaH0NwCyvt3kfHVF1BHRZVJekqC3aiecJzcH2JHW+T4h+Hp8m+QfTfIYFhp9SpwnjMCpvW9YVLZGU9XfofEXb/rhXGcNhBW3VpD6u0GbY4CmbceIWbd91CElo9rUnlcV3gWKGMeLd6FtGLKGKc+LVHNdzBk7o7ICotB8Kp9SDhzRy+Mt88guI18B2Irc6RcD8Ajn+3IKvRO4NC5MbzmDYBFbQ9o5AokX/HH3bEbdH+v+fFY2DSvCYta7sgMisK/7xi3fltpXDe45+UnGX7hCFmyE+nF5CcOfVrCU5efxCC0UH7iMX8QHPu2gdTNHhqFChn3QvF4zY/PndOucxNUmTsQ5nnXKfWKH/zGfVpq6XwVZZ3HAkDT619D5u6kF/bx6r2I2nKkRNJU0iwGvwvr0XnlcGAIktZvgeKh4XLY4r2eMO/dJb8c9g9CypYdeuFNO7WF5YDeMKldAyIbKzwdOgXKwPJXtjxT1vmLzN0RXnP7w65tPZg42kAem4TogxcR9vkhaJXq0kzqa5H26AdZv2fvfyHI3P4F1EFFvP+1bAfTAc/e/8RQR0ci57efobjwly6M6ZCxMGnbCUIHJ2hVKqhDApC1bzvUQf5llSQiKgEc8WFASEgImjZtiurVq8PJyenFB5QhrVYLlUpl7GiUCose7eHoOwmJX+1FxIAZkAeEwu27jyGyM9z4JJBJoXwSg4TPdkIVn2QwTMSgWQhpN0y3RY5fBABIP/FPqaXjdVSf0QfeE7rhjs9OXOi5FOqsHLQ+sBBCqaTIY9z6tkS95SPxaOMhnO+6BGkPI9D6x4UwcbB6Lqz35B7Qag2fx3tKT9ReOBhBm3/HmQ4+uDToE8Sdu1dSSSsRsk5vw2rGNGTs2o2ECZOhCg6B3WfrIcwbmVWYtHEjZJ8+i8SZc5Aw5X2oY+Ng99mnEDo46IXL+fcqYt/tr9tSlq8qg9SUnGbTeqPRuK44vWgnfnz3Iyiz5Oi/1xeiYu4biZkU8X4ROPvh7iLDdFg2Ap4dG+LEB1uxu5MPbu84gbdXjoFXlyalkYwSI2n9NkzHTkfOz98jbcEkqMNDYLH0UwisbAyG12akI+fXH5C+aDrS5k6A4txxmL2/EOJGzZ8/d4u2ENWoA01ifCmnouS9M/VdtB/XHT8v2Y5N/T6EIluOqXsWQVzMfZISnYij637Ehj6LseHdJQi6/BATv50Pl+qVAQDWzrawdrbFb5/sxdquC7Bv/lbU7tAIw9ZNLatkvRSnvq1QfcVoPN54ENe7+CLjYTgaHVgCiYF8EgCsmtVA3W0fIHr/WVzv7Iv449dR//sFMK/lrgsjMpMi5eojBK/eV+T31tkyA2bVXHFv9Dpc7Tgf8X9eQ73v5sCinmdJJ7FMZWfnoGY1LyyZN93YUSl1pl06wmb2VKRt34OYUVOhDAqB4+Z1ENraGAwvbdoQWX+dRdy0eYgdPxPq2Hg4blkPkWNuuSOQyWBSqzrSduxF7KipSPBZDrGHOxw3ls9yx6Fva1RdPgZPNv6CO119kPnwMer++GGRz45ls5qouXU24n48gztdFiDp+HXU2uUDswLPzjN2PVrAoml1yKP1G3zSrwfgWv2JelvM3tPICY8tl40e0g5vw2LK+8jcuxvJ0yZBFRoC6zUbICiibiJp2Ag5584gZcFspHwwHZr4eFiv3QChfX7dxHrFxxC6uCJt2RIkT5sIdWwsrNd9BshkZZSq12Pdqy0qLZmIuC9+RHDv2cjxD0PV3SshsjdcpxeaSqF4EoOYdbuhjDNcpzd/qx4SfziGkP4LEDZ6KQRiEaruWQmBaenNCf2ynPu2Qs0VoxG68Vdc7bIQ6Q/D0eTA4iKfE+tmNVB/2yxE7T+Hq50XIv74dTQsVMZ4zngX7hN7wN9nO671XAJ1Vg4a/7RY753AqVcL1NsyA09/PI9/O/ngep9liDl08bnvi/rxHGJ+u1LyCX9Fjn1bw3v5GIRv/AW3uvoi82E46v1YfFlce+tsxPx4Fje7+CDh+DXULZSfZIVEI3jxDtzsOA93+y5FzpN41P9pKST2+ed06PUWam6eidgD53Drnfm4++5SxB1+/joZgzHy2GfC1x3Qy2ejdx4v0bSVFLOuHWE3dypSvv0B0cOnQhEUCqev1hZZDsuaNkTmiXOInTwfMWNnQR0bB+ev10HkaK8LIzSVQX7nAVK+/K6MUvHfGSN/Ma/mCgiE8Jv/Ha50mIfAZXtQeUxnVFs8rEzS/F+YtHkbZuPeR/ZPu5E6bxJUj0NguWwDBNY2BsNr09ORfXAv0ha+j9Q54yE/exzmM30hKfD+p34aiczvvkDq7HFIXzwD6rgYWH60AQKrN7dzNNH/o1du+JDL5Zg1axacnJwgk8nQtm1bXL9+HTk5Oahbty4mT56sCxsSEgJLS0vs3LkTmZmZsLKywsGD+r1jjxw5AnNzc6SnpwMALl++jEaNGkEmk6FZs2Y4cuQIBAIB7ty5ozvmwYMH6NGjBywsLODs7IxRo0YhISFB9/eOHTti1qxZ8PHxgZ2dHVxcXLB8+fKXSp+npyd+/fVX7NmzBwKBAGPHjjUY7v79++jUqRNMTU1hb2+PyZMnIyMjQxc/oVCI+PjcH8aSkpIgFAoxdOhQ3fGrV69G27ZtXxif8+fPQyAQ4Pjx42jatCmkUikuXryIkJAQ9O3bF87OzrCwsEDz5s1x+vRpvWsQHh6OOXPmQCAQQCAQ6P528eJFtGvXDqampnB3d8esWbOQmZn5UtenNNmO6Y+0X04g7fApKEIiELd8M7Q5clj172YwvPxBIBI2bEf6nxegVSgNhlEnp0KdkKzbzDu2gCL8KbKvl68f9J/xntQdAZ8fQczJm0jzf4KbM7dC5myDSt2bFX3MlJ4I33cOEQcuID0wCnd8dkCdLYfH0A564azreqDa1J64Pfub584hsTZHbd9BuDlzKyIPX0ZWeBzS/J8g5q9bJZ7G12E+dBCyjh5D9p8noHocjtRPP4M2JwemvXsYDJ+y8mNkHf4NquAQqCOeIHXdBkAogLRZoR/uFUpokpJ1mzY9owxSU3KaTOiOa5t/Q+ipW0h49AQn5myDuZMNvLs2LfKYx+fv4fKGgwg5eaPIMJWaVoffwX8Q+a8/0iITcH//OcT7R8CloVdpJKPEyPoMgvz0MSjOnYAmMhxZ33wGyHNg8k5Pg+FVD+9Aee0iNFER0MQ+hfzYr1CHh0Bcq75eOIGdA8wmfoDML1ZDqy6/vZ2K0mF8D/y1+TAenLqJp48isHfuV7B2tkX9rkXnLw/P3ILf+TuIfxyD+LBoHNvwE+RZOfBsXB0AEB0YiZ3TNuHhmVtIjIhF0JWHOLbhAOq90wRCUfnpW+E+tTee7j2D6APnkRUYhYAF30GTrYDrsLcNh5/cE0nn7iDi66PICopC2LqfkH4/FJXH548Oizn4Dx5/9iuS/75f5PdaNa+JyO3HkX47BDnhcXi86RBUqZmwLOfP0Iu0a9UcsyaPQecObYwdlVJnOXwgMo78icyjJ6EKC0fyms+hyZHD/N3uBsMnLV2DjIO/QxkYAlX4EySt3ggIBJA2bwwA0GZmIn6GD7JPX4AqPBKKB/5I+XQzTOrUhMi5fHW2AQDXKX0Qu+804g6cQ3ZgJEJ8voU6Ww6noZ0Mh5/UE8nn7iDq69+RHRSFiPUHkHk/DJXG6ZfTJi528Pp4AgLf/wJalX5+qlWqoIxP0W2q5HTYdW+O2APnSi2dr8N0wGDkHP8D8pPHoY4IR8YXG6GV50DWzXCZk752NXKOHoE6JBjqJxFI/2w9IBBC0ji3zBa5VYakTl1kfPkZVIGPoI58gowvP4PARArZ2++UZdL+M4eJ/ZD800kkHzwDefATRC35GppsOewGdTEYPvteEGLW7ELqH/8UWad/PHY5Un49A3lQBHL8HyNywecwcXOCaf1qpZmUl+IxtRci957B0wPnkRkYBf8F26HOVsCtiDKmyuQeSDx3B+FfH0VmUBRC1v2MtPthqDK+W4EwPRG26RDiT9xAhl8EHs74ClJnWzj2yP1RTiASoubqsQhcuReRe04jKzQamYFRiP39X73vCljyPSJ3/YXs8KJHAZcVtym9Eb3vDGIPnEdWYCSCfL6FJlsBlyLzk15IOncHkXn5Sfj6n5BxPxSu4/Lz3/jDF5Hyz33kRMQhKyASoR/thtjKDOa1q+QGEAnhvWocwlb+gOg9p5AdGo2swEgk/G78hiDAOHnsM+qMbL28VpMlL/H0lQSrEQOQfvhPZP5+EsqwCCR9/Dm0OXJY9DVcDid8uAYZv+SVw4+fIHHlZ4BAAFmL/Pe/zGOnkfrdXmRfLV/vuoYYI39JPHcXfrO3IunCPWSHxyH+5E2Ef/0HnHq1KJM0/xeydwdDfuoPKM4ez33/27YRkOdAWtz739V/oIkMhybmKeR//Ar141CIa+e//yn+OQ3VvZvQxEZD/eQxsnZ9BaG5BUQe3mWVLCqGVivg9orb/6tX/lXCx8cHv/76K3bv3o1bt26hWrVq6NatG7KysrBv3z7s3r0bv/32G9RqNUaOHIkuXbpg/PjxMDc3x9ChQ7Fr1y698+3atQsDBw6EpaUl0tLS0KdPH9SvXx+3bt3CqlWr4OurPxw3JSUFnTp1QuPGjXHjxg2cOHECsbGxGDx4sF643bt3w9zcHFevXsX69euxcuVKnDpleNqSgq5fv47u3btj8ODBiI6OxhdffPFcmMzMTHTr1g22tra4fv06fvnlF5w+fVq34EvdunVhb2+PCxcuAAD++ecfvc8AcOHCBXTs2PGlrjkALFy4EGvXroW/vz8aNGiAjIwM9OzZE2fOnMHt27fRvXt39OnTBxEREQCAQ4cOoXLlyli5ciWio6MRHR0NILcxqnv37hgwYADu3buHn376CRcvXixysZoyIxFDVrc6Mq8UGKar1SLzym2YNqpdYt9h1acT0g6V/WI6L8OsihNkzraI//uBbp8qPRvJt0Ng16y6wWMEEhFsGlTVOwZaLeL/eaB3jMjUBE23vo+7i76HPD71ufM4dqgHgVAA00p2eOfvT9Ht1mY0/3YWTF3tSi6Br0sshqRGDchvFJiOSauF/MYtmNSt+1KnEEilEIjF0KSl6e03adwITkcPwXH/bljNmw2BleEeNOWRdRVHmDvZIOJi/j2gSM9GzJ0QuDY1fN+8rOibQfDq0gTmzrYAgMqtasO2qgvCi/mh1+jEYoi8a0J1T/8+Ud67CXGNOi93ivpNIHJ1h8rvbv5OgQDmsxYj57cD0Dx5XLJxLgP27k6wdrJF4KX8/7uc9GyE3wlG1SY1XuocAqEAjfu0gtRUirBbhqcgBACZpRlyMrKhUWteO94lQSARwbKBF5L+KXDfarVI+vs+rJoZTrt10xpIKnSfJ527C6si8uKipF0PgFO/1hDbmAMCAZz6tYZQJkHKpYevnA4yArEYJrVqQH6twA8jWi3k125BWv/l8hOBTAqIxdCkpRcdxsIcWo0Gmozy1egukIhh0cALKX8X6Cyi1SL1n/uwbFbT4DGWTWvohweQfP4OLAs+awIBqm+Ziaivf0N2QOQL42HXrRkkthaIO1AOp1kUiyGuUQOKW4XKnFs3IanzanUTbXpe3URiknsahULvnFqlEpJ69Q2coXwRSMQwrVcNGRcLlKFaLTIu3YFZE8P3zX8hsjQHAKhTin62ykJxZYx1EWVGbhnzQG9f4rm7sM57Tkw9nCB1tkVigXJIlZ6NtFvBsMk7p2WDqpC52gMaLd46vRbt721D4/0L9Xp1lycCiRiWBvKTlH/u6ecPBVgZzE/uFll2CyRiVBrVGarUTGT45U49adnAC1JXe2i1GjQ5tR5v3f0W9fYvNjhCoqwZO4+tPLMfWvjtQsNTn8Jt+rtAOeqwoiMWw6R2DeRc1S+Hc67egrTBq5bDaS8OXM4YK38xRGxlBlVy+aqn6IjFEHnXgPKugfe/mi9XFovrN4HIzR1KvyI6yIrFkHXtA01mOtSPy9/oUyIq2iut8ZGZmYmtW7fi+++/R48eub0KvvvuO5w6dQo7duzAggULsHr1akycOBFDhw5FeHg4/vjjD93xEydOROvWrREdHY1KlSohLi4Of/75p26kwv79+yEQCPDdd99BJpOhTp06iIqKwqRJk3Tn2LJlCxo3boxPPvlEt2/nzp1wd3dHYGAgatTIzdAbNGiAjz76CABQvXp1bNmyBWfOnEGXLoZ7Gj3j6OgIqVQKU1NTuLi4GAyzf/9+5OTkYM+ePTA3N9fFq0+fPli3bh2cnZ3Rvn17nD9/HgMHDsT58+cxbtw4bN++HY8ePYK3tzcuX74MHx+fl772K1eu1Iu7nZ0dGjZsqPu8atUqHD58GL///jtmzJgBOzs7iEQiWFpa6qVjzZo1GDFihG7dj+rVq+PLL79Ehw4dsHXrVsiMNIReZGMFgVgEdWKK3n51YgpMqpZMxdTinVYQWlog9fCLG8CMQeaUO2Qyp1DDhDw+FVInw8MppXaWEIpFBo+xqOaq+1xvxSgkXQ9CzMmbhU8BADCv4gSBUIgas/ri/tI9UKZlofbCwWj902Kc7eRbLubyFFpbQyAWQZOUrLdfk5QMsUeVlzqH5fQpUCck6DWeyK9eQ86Ff6COjobIzRWWkyfCbsNaJE6dAWjKxw+3xTFztAEAZCXoV+azEtJg5vh6w3DPLduDzmsnYPL1zVArVdBqtDi9cAeirhmeU7c8EFhaQyASQZOiP1WGNjUZIrdi7hMzc9h8exCQSACNBlnfbdJrPJH1Gwao1ZAf+7W0ol6qLPPuk/RCeUV6fKrub0WpVNMdcw6tglgqgTwrBzumbERssOE51c1tLdFtZn9c/vFMSUS7REjsrCAUi6CIT9Hbr4hPgVl1V4PHmDjZQFnoWiniUyF1snml734waRPqfjsb7QN2QaNUQZOtwP2xG5BdzPo7VH4IbXLLHXWhckedlAyx58vVTWxmToImIRE51wyXvzCRwGbGJGT9dRbazKzXjXKJkthZQiAWGXgWUmBdzc3wMU42UBZ61pTxqZAUeHbcZvSDVqVB9PY/XyoeTsPfQfL5u1BEG54CyZiE1tYQiMTQJBeqmyQnQ+L+cnUT84lToUlM0DWeqJ+EQx0bA/MJk5Hx+Ybcka0DBkHk5AS1nf0LzmZ8ItvcOr0qQf+aqBJSIPWuXDJfIhCg0tJJyLzuB3lgRMmc8z8y0ZUxz5cZ5kWUMVInGwNlUipM8ur7JnnlcuFzyuNTYZL3LJl65K5h6DV/IAI/2oPsJ/HwmNYbzQ4tw6XWs6FKMf5o/oKe5SeGrlNR+YmJk42B8Cm6a/CMXZcmqL1tDoSmJlDEpuDekFVQJeU2iMmq5I6k85g/GKEf7UbOk3hUntoHDX9djuttPoAqxXg/5Bozj43e/icy7odBlZwBq+Y14bF4OCROtni8vOgpcI1BVEw5LHnJcth21iSo4xPfiNEdhRkrfynM1NMZ7hO6I2jFD6+eiDKQ+/4nhja1UFmckgxJMe9/AjNz2Gw/mNvhQKNG5refQ3VXfzYESbNWsJi7DJDKoE1ORPry+dCmP9+RlIjKr1dq+AgJCYFSqUSbNvlTG0gkErRo0QL+/rkL/MybNw9HjhzBli1bcPz4cdjb51fQW7Rogbp162L37t1YuHAh9u7dCw8PD7Rv3x4AEBAQgAYNGuj9+N6ihf5wurt37+LcuXOwsLAwGL+CDR8FPWtoKQn+/v5o2LChrtEDANq0aQONRoOAgAA4OzujQ4cO+PbbbwHkju745JNPEBgYiPPnzyMpKem56/gizZrpT0WSkZGB5cuX49ixY4iOjoZKpUJ2drZuxEdR7t69i3v37mHfvvw5ybVaLTQaDcLCwlC79vOjK+RyOeRy/aGvCo0GJsJy2CukGNYDuiPzn+tQF7EeSFmr3L8NGn06Qff5ysj1pfI9Ll2bwLFtXZzrvKjIMAKhEEITMe59uBvxF3J7f9yYthk97m2FY5u6iDtfPqcGexXmI4fB9J23kThzDlBgGoWcM/lTZ6hCw6AKCYXTz/th0rgRFDfLXwW5Vr/WeGfNeN3nIwUWsCxpjcZ2hUvjavht/EakRSbA7a1a6LRqDDJjkxFxsYL1WM/OQtr8iRDITCGu3wSmY9+HJjYaqod3IPKqAWmvgUhbMOnF5yknmvZtgyGf5Mf3m/Hr/vO54kKfYn1PX8gszdCo51sYsXE6vhyy4rnGD6mFKSbv8kVMcBSOf/5mLfpeWqouHAKxtTluD1wJZWI6HHo0R93v5uBW32XI9H9i7OhRKbMcMxSmXd5G/NR5euWOjkgEhzXLAIEAyWufH2FcEZk38ILrpJ642+XlOv+YVLKDbceGCJj8WSnHzDhMhwyHtGMnpMz/AFDmjfBQq5G2Yiks5/nA4fAxaNUqKG/dhPzavxDg/3eagoJcV06FrGYVhAwy7kLdxiQQ5t4LYV8cRtyxawCAhx9sRfvbW+HcpxWifjhd3OEVSsqlh7j5zgJI7CxRaWRn1Pl2Lm73XARlQhqQ974a8fkhJBy7CgAImP0V3rr9DRz7tER0BbtOL5vHPv0mv3Nqln84NEoVvNdPRvgn+6BVVJz1RK3GDoVZt46InVxEOUwvJHWxRZMDixF39F9E7S2HIy9fgzY7C6lzc9//JA2awGzcdGhinkL18I4ujPL+7dwwVtaQdekNi/nLkeY7FdrUFKPFm4hezSs1fLyMuLg4BAYGQiQSISgoCN2768+9OHHiRHz11VdYuHAhdu3ahXHjxumtP/EiGRkZupEVhVWqVEn3b4lEf7FWgUAATRn23u7YsSNmz56NoKAg+Pn5oW3btnj06BHOnz+P5ORkNGvWDGZmZi99voKNLAAwf/58nDp1Chs2bEC1atVgamqKgQMHQlFwWLwBGRkZmDJlCmbNmvXc36pUMdwavmbNGqxYsUJv3wx7b8x0LLk5ddUpadCq1BDZ2+jtF9nbQF2ox9h/IXZ1glmrRng6q/wsHhpz8ibO3QrWfRZKcx9HmaM15HEpuv1SR2ukPgg3eA55Ujo0KjVkhXr2Swucw7FtXZh7OqFX4Ha9MC12zEbi1Ue42H81cvLCpgfm/5CpSEyHPCkdpm7lo3ehJjUVWpUaQjtbvf1CO1toEotvzDIfNhgWI4YjafY8qEJCiw2rfhoNdXIKxJXdymXDR8ipW4i+nT+8Vpx335g5WCGzwH1j5mCFeL//3gtSJJWgjc9gHJ38OcLO3gEAJDx6Asc6Hmg6uVe5bfjQpqdCq1ZDaGOHguOUBNa2z40C0T9QC01M7v2vfhwMUWUPyPoPR8bDOxDXbgCBtQ2sv/k5/3wiEUzHTIO090CkTRta1FmN5sHpmwi/k5+/iE1yy0RLR2ukFegFZulojSg/w/nLM2qlGgl5c4NHPghDlQbe6DC+B35enJ+nSM1lmLZ7EeQZ2dgxZSM0RcwnbQzKpDRoVGpdD7dnTBxtoCjwzBSkiEuBpFC+alIob34RUw9nuE/ogavt5yIzb6qJDL9w2LSshcrjuiPAp/wvqPn/TpOSW+6ICpU7opcodyxHDoLVmGGIe38BlMEGyh2RCPZrlkHk4oz46fPL3WgPAFAmpUOrUht4Fop+dpRxKZAUetYkjtZQ5oW3eqs2JA7WaHZzm+7vArEIVZePhuvkXrjZfLresU5DO0GZnIGkYtajMiZNaiq0ahWEtoXqJra20CQXf4+YDhwCs6HDkeo7D+ow/XtEFRSI5KkTITAzByRiaFNTYfPlVqiCyu+Iy2fUybl1erGD/jURO9hAFf/6dXrXFVNg2ak5QocsgirG8KLNZUmhK2NevsyQx6UYKJOsoYjL7UX8rLd27r78c0gdrZH+8HHuOWJz92cWmMpIq1AhOyIWssrlo+5e0LP8xNB1Kq4sfj788/mPJkuOnMcxyHkcg/RbQWh++Uu4DOuEJ5uPQBGXe89lBepfp5zwWEjdHF8/Ya+hPOSxz6TfCoRQIobM3QnZIU//c5pKmrqYclidWHx+YjVqEKzHDUXsVB8og8JKM5qlxlj5i26fsy2aHlqGlOuB8Jv37eskpVTlvv+pILAuVBbbvNr7n7CyB0wHjEB6gYYPyHNyw8REITPQD9Zf7YP0nV7IObTP8DmJqNx5pS773t7eMDExwaVLl3T7lEolrl+/jjp1cudYHD9+POrXr4/du3fD19dXNxLkmZEjRyI8PBxffvkl/Pz8MGbMGN3fatasifv37+uNLrh+/bre8U2aNMHDhw/h6emJatWq6W2FGwdKS+3atXH37l29BcEvXboEoVCImjVz5+OsX78+bG1tsXr1ajRq1AgWFhbo2LEjLly4gPPnz7/S+h6GXLp0CWPHjsV7772H+vXrw8XFBY8fP9YLY2JiAnWhBXibNGkCPz+/565dtWrVYGJiYvC7Fi1ahNTUVL1tin0JL8yqVCHnYRDMWjbK3ycQwKxlI2Tf8S/ysJdl/V5XqJNSkXnh2mufq6SoMnOQ+ThWt6UHRCEnNhmO7fLnoRRbmMK2sTeSbgQZPIdWqUbKvTC9YyAQwLFtXd0xgZt/x9lOC3Gu8yLdBgD3l/2AW3kLnSfmTV1k4V2g8dDGHFI7S2RFJpRouv8zlQrKwEBImxZYmFwggLRpEygeFv0jvPnwobAYMwpJ832gDCh6XYJnhI4OEFpbQZ1g/JdpQ5SZOUgNj9VtiYFRyIxLgXub/HvAxMIULo288fSm4fvmZYgkYohMxNAWajDWajS6XoblkkoFdUgAxPX17xNJg6ZQBfq9/HkEAkCcmycqLvyFtLkTkDZvom7TJMZD/vtPyFi1oIQTUDLkmTlICI/VbTFBkUiNS0aN1vV0YaQWpvBoVK3Y9ToMEQgFuoaUZ+eZ9sNiqJQqfDfxU6jk5atHnVapRvq9UNi2y087BALYtquHtBuG0556MxB27fTn0rfr0ABpReTFhgjN8ubp12j146PWAOX5GaJ8KhUUjwJ1C5MD0C1ULr9fdH5iOWoIrCaMRPyshVD6G7jH8ho9JFXcEP/+AmhSy+e841qlChn3QmFd8FkQCGDdtj7Sbxj+AT79ZiBsCj07Nu0bIj3vWYs/eAF3Os3Dnc7zdZs8OhFRX/8Ov6Grnzuf89C3Ef/LhSIX5zU6lQqqwECY5C1MDiC3zGncBEq/ousmpoOHwWzkaKQu9oEqsOjGDG1WJrSpqRC5uUFcoybkly+WZOxLhVapQvaDYJi3KTD6XiCAReuGyLr1eg03riumwKprK4SNWAJlZPmYMvBZGWNX6Dmxa1cPqUWUGbllTD29ffYd6iM17znJDo+DPDYZ9gXOKbIwhVWTakjJO2fa3VCocxQwKzC1rUAsgszdETnlpe5egFapQvq9UP38QSCATdv6uvyhsDSD+UmDIstuHaEAQmluPSXjbig0OQqYeRu6TvH/LTElpDzksc9Y1K0KrVoNRUI5m8JHpYLCP1BvYfLchcobQ36v6HLYasxgWE8cidgZi6AwVA6/IYyVvwC5Iz2aHl6G9HthePjB14BWvz5brqhUUIcEQtKgUFlcvwlUAS/fWU8gFOZOe1wcoQCCF4UhonLllRo+zM3NMW3aNCxYsAAnTpyAn58fJk2ahKysLEyYMAFfffUVrly5gt27d2PEiBHo168fRowYoTcKwdbWFv3798eCBQvQtWtXVK6cP9fr8OHDodFoMHnyZPj7++PkyZPYsCF3Gpdno0Lef/99JCUlYdiwYbh+/TpCQkJw8uRJjBs37rkf+UvLiBEjIJPJMGbMGDx48ADnzp3DzJkzMWrUKDg7O+vi2759e+zbt0/XyNGgQQPI5XKcOXMGHTp0eK04VK9eHYcOHcKdO3dw9+5d3bUryNPTE3///TeioqKQkJBbAfb19cXly5cxY8YM3LlzB0FBQfjtt9+KXdxcKpXCyspKbyuNaa6Sdx+C9aAesOrbGSZe7nD6aCaEpjKkHf4LAOCydj4c5ozLP0AihrSWF6S1vCCQiCF2coC0lhckVSrpn1gggFX/Lkg7cgooJ4vtFiXkuxOoMfs9uHRtAqta7mi6eRpyYlMQfSK/p2ObXxaj6viu+cd88yc8RrwN98HtYFHdFQ3XjYfITIaIAxcA5M7Xmf4oUm8DgOyoRGRF5Fb4M0NjEH38BhqsHg27ZtVhWasymn45DenBT5Fw6RV+LC5lmQd+gVmf3jDt3g1ijyqwmj8HAlMZso+dAABYf7gIllMm6sKbjxgKy4njkLrmU6ijYyC0s4XQzhYC09zp9ASmMlhOnwJJ3doQuTjDpGkT2K39GOqoKMivXTcYh/Lo1o4TeGtWP3h1aQL7mpXRbdMUZMalIOSv/DnlB/y4CA3H5K8TJDGTwrFOFTjWyR3pZeXuCMc6VWDpmttLUJGRjSdX/NFuyTBUblkbVu6OqDOwHeoMaIvgE+Wz5+0zOUd/gbRzb5h07AahWxWYTZ4DSGVQnD0OADCbuQiyEfnTQMneGw5xg6YQOleC0K0KpH0Gw6RDVyj+zl0PSJuRBs2TML1Nq1ZDk5wEzdM3Z7qiCzuPo+vM91Cvc1NUqumOkZ9NR2psMu7/lf//+f6+D9FudDfd594+Q+HdohbsKjuiUk139PYZimot6+Dmkdwf36QWppj+w2JITaX40ecbyCxNYeloDUtH63LVQPZk2x9wHfEOXAZ3gFl1N9RcPxEiMymeHjgPAKi9+X14LRmWH/7bP2H3dkO4T+0Ns2quqDp/ECwbeiNy5wldGLGNOSzqesC8Rm49xqyaKyzqeuh65WUFPUVWaDRqfToJlo29c0eATO0Nuw4NkHD8zclfDMnKysajwBA8CswdfRb1NBaPAkMQHVMyU4qWJ+n7D8KiXy+Y9eoKsWcV2C6cDaGpDJlHTwIA7Jb7wvr9/GkrLUcPhfXUsUhauQGq6BgI7W0htM8vdyASwWHdRzCpUwOJSz8BREJdGIhLfCD2a3v6zVG4jOgMx8EdYFrdDd7rJkFkJkXcgdxpIqtvngmPxcPzw3/3J2zebgTXqX1gWs0V7vMHw6KhF6J35ea/quQMZD16ordpVWoo4lKe62Vs3bY+ZB7OiN1Xvqeiyf71Z8h69oK0SzeIqnjAYtZcCGSmyDmZm2ZLn8UwH59f5pgOGQbzMeORvmEd1DExENjaQWBrB8hMdWFM2neEpEEjCF0qwaRVG1iv3QjF5YtQ3izf5e8zCduPwG5oN9j07wSpd2W4rp4OoZkMyQdz/y8rb5wD5wWjdeEFEjFktatCVrsqBBIxJC72kNWuChOP/Dq968ppsOnXEU9mb4AmIxtiBxuIHWwgkBruuFWWwrcdg9uITqg0uD3Mq7uhdqEypu7m91GtQBkT8e1x2L/dEB55ZYzX/IGwauiNiJ0nC4T5E1XnvAfHbk1hUdsd9ba8D3lsMuLzyg91RjYi95yG94JBsOvQAGbelVB7fW4dOPb3f3XnMfV0hkVdD0idbCCUmcCirgcs6npAIBGVwZXRF/XNH6g04h045+Un1ddNgtBMipi8/KTm5hnw1MtPjsH27UZwm9obptVc4ZFXFj/dlVsWC82k8Fw0DJZNqkNa2QEWDbxQY9M0SF3sEH/0CoDc6/R0zyl4LBgM2w4NYOrtimrrcp/HhLwwxmSMPNayaQ1UmtQLZnU8IK3iBMf+7eC5cizif/0H6tTytTYMAKTt+xWW7/WEee8uEFetArvFH0BgKkPG77n3gf1KX9jMyC+HrcYMgc20sUhcsQGqpwbKYQBCK0tIanhD4uUBAJB4ukNSwzu3LC5njJG/SF1s0ezwR8iJSkTg8h9gYm8FE0fr50aelCc5v/8MaZdeMHm7G4SVPWA2ZS4gM4X8TO6zYT5rMUxHFnj/6z8C4obNct//KntA9m7e+9+FvPVgpTKYjpgEUY06EDo6Q+RVA+YzfCG0c4Di8nkjpJAK02q4ver2/+qV37DWrl0LjUaDUaNGIT09Hc2aNcPJkycRGxuLBQsWYMeOHXB3z11o6uuvv0aDBg2wdOlSvampJkyYgP3792P8+PF657ayssLRo0cxbdo0NGrUCPXr18eyZcswfPhw3bofrq6uuHTpEnx9fdG1a1fI5XJ4eHige/fuEJbRmhNmZmY4efIkPvjgAzRv3hxmZmYYMGAAPvtMf/7hDh064MiRI7qGD6FQiPbt2+PYsWOvtL6HIZ999hnGjx+P1q1bw8HBAb6+vkhL0+8xuHLlSkyZMgXe3t6Qy+XQarVo0KABLly4gCVLlqBdu3bQarXw9vbGkCFDXis+JSHj+N9IsLWG/axREDnYQu4fiqjJH+oWPBdXctLrOSt2tIfH4a91n+0mDITdhIHIunYPkWPy5zU1a9UYEldnpB76q8zS8l8FbTkKkZkUjTZMhMTKDInXAnF52FpoCvSgNvd0htTOUvc56rd/YWJvhdo+AyF1tEHqw3BcGbYW8oRX60F6c+ZW1F85Ei33+gAaDRKu+OPKsLXlqpdlztlzSLOxhsXEsRDZ2UEZHIKkeb66RUVFzk56C5Kb9esLgYkJbD/Wn6otfef3yNi5G1q1BhJvb5j26AahhQXUCYlQXL+B9O92Asry1Wu9ODe2/gGJqRSd14yH1MoMT28E4tCo9VAXuG+sqzjBtMB949zAC4N+XqL73PGjkQCAh7/8jb/yhjL/OWML2voOQY8vp0FmY4G0yARcWv8L7u0tPwtXG6K8fA7Z1jaQDR2XO+VVWDAyVvvoFrwTOjjr91qSmcJs8hwI7RyhVcihiYpA5hcfQ3n5XBHf8GY6s+13mJhKMWTNJJhamSH0egC2jVmrN0LD3sMZ5gXuE0t7a4z47H1YO9ogOz0LTx9FYNvoNQi4mLsWkHu9qvBsXB0AsOxv/TUKVrSdiSQj96Z8Ju63K5DYW8HLZzBMnGyQ/vAx7g77RLegqMzNAShQvqTdCMTDaV/Ca+FQeC8ehqywaNwf+ykyH+U3dDl0a4Y6X76v+1zv2zkAgLBPf0HYhl+gValxd/gaeH84Ag1/8IXIXIassBj4z/wKiWdul1HKS8eDR0EYPzN/bv31m3PzjL49OuPjD+cZK1qlIvvUeaTYWMN6yliI7G2hCAxB/KyF0OQttCpycdLLTywG9IHAxAQO65frnSf1291I+24PRE4OMO2QWwd02a8/3VnclLmQ37pbugl6RQm/XYbY3gpVfIbCxNEGmQ8f4+Gwj6HM6xUsdXPQGxmYfiMAgdO/gIfvUHgsGo7ssGg8GrceWY9evZHYeXgnpF17hOzg8jPtiiHyC+cgsLGB+ZjxENraQRUSjNTFC6BNyStznJz03jZNe+fWTaw/0p9+NXPPLmT98D0AQGRnD9Mp7+dOmZWUiJxTJ5G1b0+Zpel1pR67CLG9NZznjoDYwRY5/qEIG/sRVAkpAACJq6Nenit2skP1P7/UfXac3B+Ok/sj49/7CBu2GABgP6onAMDrwBq973oy/3Ok/Grceknsb1dgYm8Fb5/BkOaVMbeGrdEtHixzs9ern6beCMT9aZtRbeEQVFs8FFlhMbhbqIx5vOV3iMykqL1hMsRWZki5FoDbQ9fovRMErdgLrUqNel+9D5HMBKm3gnFzwCqoCvx4XeezKbArMCq41dncNQX/aTYDOU/KtoyO/+0yJPZW8PAZAhNHG2Q8fIwHz+Un+mXxo+lfwNN3GKrm5ScPC+QnWrUGZtXc4Dy4IyR2llAmpyP9Tgju9FuGrAJTgIWt/AFatRo1t8yEUGaC9FvBuDdwhd51MhZj5LEahRKO/dqgyvzBEJiIIX8Sh6ff/IGn3xwt8fSVhKy/ziPZ1ho20/LK4YAQxM1YBE1SCgBA7KL//mc5KLccdtzwkd55Ur7Zg9RvcvNR0w6t4LAi//cCx7UfPhemvDBG/mLXoQHMvCrBzKsS2t/dphefU87G/93IEMWlcxBY2cB0aG5ZrA4LRvrKBfnvf476ZbFAJoP55DkQ2ue+/6mjIpD5+WooLuW9/2k0EFWuAsu3u0FgZQ1tehpUwY+QtmQW1E8eGyGFRPRfCbTash+z9sMPP2DOnDl4+vRpkdMrPbNv3z6MGzcOqampMDU1LTYslZ3A2t1fHOj/iF+ynbGjUO68VS3a2FEodw5EuBk7CuXO2OaRLw70f2b5TRdjR6Hc6Zv9f9xFpQjtHq41dhTKnZgek14c6P9MxJPy13vVmGrULx+NseVJTLCVsaNQ7sRkvfw6jP8vpAKWw4WJUI6n/jGSyq4pxo5CuRMYVf7W3DG2pq1jjB2Fcsfu8AVjR+GNE/lWJ2NH4Y1T+epZY0fBKMp0TH1WVhaio6Oxdu1aTJkyxWCjx549e+Dl5QU3NzfcvXsXvr6+GDx4MBs9iIiIiIiIiIiIiIjohcpmbqg869evR61ateDi4oJFixYZDBMTE4ORI0eidu3amDNnDgYNGoRvv/22xOKwb98+WFhYGNzq1q374hOUsKlTpxYZn6lTp5Z5fIiIiIiIiIiIiIiI3mRlOuJj+fLlWL58ebFhfHx84OPjU2yY1/Huu+/irbfeMvg3iURSat9blJUrV2L+/PkG/2ZlxaHnRERERERERERERESvokwbPsoDS0tLWFpavjhgGXFycoKTk5Oxo0FERERERERERERUrmk1AmNHgd4QZTrVFRERERERERERERERUWliwwcREREREREREREREVUYbPggIiIiIiIiIiIiIqIKgw0fRERERERERERERERUYbDhg4iIiIiIiIiIiIiIKgyxsSNARERERERERERERPQiWq2xY0BvCo74ICIiIiIiIiIiIiKiCoMNH0REREREREREREREVGGw4YOIiIiIiIiIiIiIiCoMNnwQEREREREREREREVGFwYYPIiIiIiIiIiIiIiKqMMTGjgARERERERERERER0YtoNQJjR4HeEBzxQUREREREREREREREFQYbPoiIiIiIiIiIiIiIqMJgwwcREREREREREREREVUYbPggIiIiIiIiIiIiIqIKgw0fRERERERERERERERUYYiNHQEiIiIiIiIiIiIiohfRagTGjgK9ITjig4iIiIiIiIiIiIiIKgw2fBARERERERERERERUYXBhg8iIiIiIiIiIiIiIqow2PBBREREREREREREREQVBhs+iIiIiIiIiIiIiIiowhAbOwJERERERERERERERC+i1Ro7BvSm4IgPIiIiIiIiIiIiIiKqMNjwQUREREREREREREREFQYbPoiIiIiIiIiIiIiIqMJgwwcREREREREREREREVUYbPggIiIiIiIiIiIiIqIKQ2zsCBARERERERERERERvYhWIzB2FOgNwREfRERERERERERERERUYbDhg4iIiIiIiIiIiIiIKgw2fBARERERERERERERUYXBhg8iIiIiIiIiIiIiIqowuLg5/ScJKebGjkK5YqbVGDsK5U5osL2xo1DuuAu0xo5CuSN2tzR2FMqd+BtyY0eh3MmGzNhRKHdiekwydhTKHZfj3xk7CuWO3ZYlxo5CuXJ5q4uxo1Du1K8Za+wolDvJgSxzCpMK1caOQrmTppYYOwrlTkCUg7GjUO7Ur848trCkAOaxhdkZOwJEFRgbPoiIiIiIiIiIiIio3NNqBcaOAr0hONUVERERERERERERERFVGGz4ICIiIiIiIiIiIiKiCoMNH0REREREREREREREVGGw4YOIiIiIiIiIiIiIiCoMNnwQEREREREREREREVGFITZ2BIiIiIiIiIiIiIiIXkSrMXYM6E3BER9ERERERERERERERFRhsOGDiIiIiIiIiIiIiIgqDDZ8EBERERERERERERFRhcGGDyIiIiIiIiIiIiIiqjDY8EFERERERERERERERBWG2NgRICIiIiIiIiIiIiJ6EY1WYOwo0BuCIz6IiIiIiIiIiIiIiKjCYMMHERERERERERERERFVGGz4ICIiIiIiIiIiIiKiCoMNH0REREREREREREREVGGw4YOIiIiIiIiIiIiIiCoMsbEjQERERERERERERET0IlqtwNhRoDcER3wQEREREREREREREVGFwYYPIiIiIiIiIiIiIiKqMNjwQUREREREREREREREFQYbPoiIiIiIiIiIiIiIqMJgwwcREREREREREREREVUYYmNHgIiIiIiIiIiIiIjoRbQagbGjQG8IjvggIiIiIiIiIiIiIqIKgw0fRERERERERERERERUYbDhg4iIiIiIiIiIiIiIKgw2fBARERERERERERERUYXBhg8iIiIiIiIiIiIiIqowxMaOABERERERERERERHRi2i1xo4BvSk44oOIiIiIiIiIiIiIiCoMNnwQEREREREREREREVGFwYYPIiIiIiIiIiIiIiKqMNjwQUREREREREREREREFQYbPoiIiIiIiIiIiIiIqMIQGzsC9P/LZWx3uE7vCxNHG2T6PUbYkh3IuBNcZHj73q3g7jsMssqOyA6LRvjqvUg5e0v392qfz4DTkLf1jkk+dxv+w1fr7bN9pwkqzx0Es9oe0MqVSP3XDwHj1pVs4l6Bt88gVB7ZCWIrc6RcD4C/zw5khcUUe4z7uK7wnN4HJk7WyPCLgP/iXUi7HaL7u1AqQY3lI+HSrzWEUgkSz92F/8KdUMSnPncuia0FWp1dB5mrPc5WHw9VWhYAwLZ1HTQ/vOy58OfrTTF4ntLiMq473Ka/m3efhCN0yQ5k3C7mPunTClV8hkLmnn+fJJ+5bTCs97rJcBnTFaFLdyH6u2O6/TKvSvBcNhpWzWtCYCJGll84ItYfQOqlhyWevv+qzoIBqDribZhYmSPheiBuL9yJjLDYYo/xHtsFNab3gszRGql+Ebi9ZDeS74Tq/t7h1yVwbF1H75iQPWdw23en7rNT27qo6zMQVrXdoc6SI/znf/Bg7c/QqjUlm8DXJGnbEyad+kNgaQvN0zDk/PoNNBFBLzxO3LgdTMf4QHn/X+Ts+NhgGOmg6TBp0wM5h7+D8sLvJR31UjVw7jC8PawzzK3MEXjjEXYu+QYxj6Nf6tg+0/pj2MJROL7jKH5YmX9PdBrWBa37todnPS+YWZphYv0RyMrLR8qjGj4D4T6yEyRW5ki+HoD7PjtfmOd6jOsCr+l9IHWyRppfBB4u/h6peXmuxMYcNXwGwaFDfZi6OUCRmIaYEzcQuPZnqNKz9c5TeUh7VJ3aC+ZeLlBlZCP696t4uGhXqaX1v7AY1BeWIwdDZG8HRVAIUj7dDIVfgMGw5v16wrxnV0i8PQEAikeBSP1qR354kQjW08ZD1qYFxG6VoM3IRM61W0jZsh2ahMQySlHZuHHnPnbtPwi/R8GIT0zCF2uW4p32rY0drVIjbt4FkjZ9ILCwhiYmAorj30MTFWI4bKP2kPabprdPq1Iga/UY3WdR7eaQNOsMYaWqEJhZInvbQmhiwks1Da+isq7eZYMMv3A8KlTvKsypT0tU8x0MmbsjssJiELxqHxLO3NEL4+0zCG4j39HV/x75bNfLi9pe3wzTKk56xwSt3o/Hm38DAJh5V0LtTyfBvIYbxJZmkMcmI+bQJYRuOAitSl1yif+PzN7rB/OhQyG0s4MyJBjpX3wJpf8jg2FNe/eCabduEHtVBQAoAwKR8d13euGtFy2EaY/uesfJr15D8gKf0kvEa6o0rhvc8+qwGX7hCFmyE+nF1GEd+rSEp64OG4PQQnVYj/mD4Ni3DaRu9tAoVMi4F4rHa37UO6f7B/1h17kJLOp6QqtU4XLNsaWZxFfmPLY7Kk3rB4mjDbL8HuPxh9uRWcz7n13vVqjsMwzSyk7ICYtGxMc/ILXA+x8AyKq5ocqHo2HZsg4EYhGyAyMRNGk9FFEJAACJow2qLB0Nq/YNIbIwRU7IU0R9cRDJf/5bqmk1xBh5CQA4dG4Mr3kDYFHbAxq5AslX/HF37Ibnvk9ia4GWZ9dD5mqPc9XH6d4Ly4Ix3okt6lRB1Vl9YduiFiR2lsh+Eo/IPacR8d1x3TmcejaH+9gusKzrCaFUjIyASIR8ehCJ5++VzoV4SWb9+8F82BCI7OygDAlB2qZi8tg+vWDWvateHpv+zXb9PHaxL8x66uexOVevIXmeb+klooRZD+sDm/EDIXKwgyIgFPEffw35fcN1WJNqHrCbMRrSutUgcXNB/JptSP3hsF4Yj1O7IXFzee7YlP2/I2H1V6WSBnp5Wo3A2FGgNwRHfJBR2L/bGp7LxyJy48+4220BMv3CUefHpZDYWxkMb9msJmpsnYO4/Wdwt+t8JJ24hlq7fGBW010vXPLZW7jeYIJuC5y2Se/vdr1aotrmWYj76Rzudp6H+32XIOHQP6WWzhfxnPEuqkzsDj+f7bja80Oos+Ro8tMiCKWSIo9x7tsKNVeMQsjGg/i3yyKkPwxH0wOLYOKQf+1qrhwNx65NcW/S57jebwWkLrZouHOuwfPV3TQF6X4RRX7fxVZzcL7eFN2mSEj77wl+RQ59W6Pq8jF4svEX3Onqg8yHj1H3xw8hcSj6Pqm5dTbifjyDO10WIOn49dz7pJb7c2HterSARdPqkEc//+NbnR8WQSAW4sHAFbjb1QeZfuGo/cMiSBxtSjqJ/0nN93uj2oRuuOW7C2d7LYM6S462Py4s9r6p/G5LNFg+An4bD+F0tw+R4heBdj8uhLTQMxe69yyONpiu2+6v+lH3N+s6VdBm7wLEnLuH012W4N+pm1GpWxPUXzK01NL6X4gbt4W030TIT/yIrA2zoY4Kg9nUlRBYWBd7nMDOCdK+46EKeVD0ueu3hMizJjQpb96Ptn2mvoduY3th5+JvsLSvL3Ky5Fj4wzJIirlvnvFqUA3vjOiKcL+w5/5mYirF3Qu38dtXv5ZGtEuU14w+8JzYHQ98duBSz6VQZcnx1k/FPzuV+rZE7RWjELTxV1zsshjpD8Px1oGFujxX6mILqbMN/Ffsw98dFuDuB9vg+HZDNNg0Re88Vaf0RM1FQxCy+Tf83X4Brg76BPFGfmkuzLRLR9jMnoq07XsQM2oqlEEhcNy8DkJbG4PhpU0bIuuvs4ibNg+x42dCHRsPxy3rIXJ0AAAIZDKY1KqOtB17ETtqKhJ8lkPs4Q7HjavKMFVlIzs7BzWreWHJvOnGjkqpE9VtCZNuo6A8/yuyv1kMTWw4ZCMXAuaGy2YA0OZkIWvD1Pxt0yy9vwskUqgjAqA4/WMRZzCe3HrXaIRu/BVXuyxE+sNwNDmwuMi6iHWzGqi/bRai9p/D1c4LEX/8Ohp+vwDmBeoinjPehfvEHvD32Y5rPZdAnZWDxj8tfi4vCl77Ey7Um6zbInac0P1Nq1Ij+ue/cWvwJ7jcZg4Cl+6G28hO8PYZVDoX4hXIOr0Ny/enI+P775EwcRJUwSGw3fAphDY2BsObNG6E7DNnkPTBHCROex/quDjYbtgAoYODXjj5v1cR16+/bktZsbIMUvPfOPZtDe/lYxC+8Rfc6uqLzIfhqPfjkiLvG6tmNVB762zE/HgWN7v4IOH4NdQtVIfNColG8OIduNlxHu72XYqcJ/Go/5P++5PQRIyEo1cQveevUk/jq7J7tw2qfDQOkZ/9jAfd5iPL7zFq7V8Gsb3h+plFs5qo9vVcxP94Bve7zkPyiWuosdMXpjWr6MJIPZxR58gnyA6OhP/AZbj/zhxEff4LNDlKXRjvL2dB5u2GwLFrcL/THCT9+S+qfzMPZvWqlnqaCzJWXuLUqwXqbZmBpz+ex7+dfHC9zzLEHLpo8DvrbJqKjGLeC0uLsd6JrRp6QZGQhvvvb8HlDvMR9vlhVF88FO7ju+nC2LaqjcQL93FrxFr822Uxki75ofEPPrCs51kq1+JlyDq9DasZ05CxazcSJkyGKjgEdp+tLzKPlTZuhOzTZ5E4cw4SprwPdWwc7D779Lk8Nuffq4h9t79uS1n+5tTXLLp3gIPvZCR9vQ9PBr4P+aNQuH77MUR2hvMXgUwKZWQ0Ej/bCVW84fe6J4NnIaz9UN0WNWEhACDzpPF+PyKiV8eGj3KiY8eOmDFjBmbMmAFra2s4ODhg6dKl0Gq1AAC5XI758+fDzc0N5ubmeOutt3D+/Hm9c1y8eBHt2rWDqakp3N3dMWvWLGRmZur+7unpiVWrVmHYsGEwNzeHm5sbvvrKOC3VrlP6IHbfacT9dA7ZgZEI9fkG6mw5nIa9YzB8pYm9kHzuNp5u/Q3ZQVF4sv4AMu+HwWV8D71wGoUKyvgU3aZOzU8/REJUXTke4at+QOyev5ATGo3swEgkHr1cmkktlsfkHgjddBjxJ24iwy8CD2Z8BamzLZx6NCvyGM+pvRC59yyeHriAzMAo+C3YDnW2Aq7DOgIAxJamcBv+NgI/+gFJFx8i/V4YHnywDbYtasK6aTW9c1Ue0wViK3OEb/2jyO9TJKRCEZ+/Ie+eLAu6++RA7n0S4vNt7n0ytJPh8JN6IvncHUR9/Tuyg6IQkXefVBqnf5+YuNjB6+MJCHz/i+d6RYrtLGHq7YqozUeQ5R+OnLAYhK/eC5GZzGADijFUm9Qdjz4/guiTN5Hq/wTXZm2FqbMNXLs3LfKYGlN6IGzfOYT/9DfSA6Nwy2cn1NlyeA7roBdOnS2HPD5Vt6ky8nusu/dtiVT/CPhvOozMx7FIuPII91f9CO+xXSA2l5Vael+VScd+UF45CdW1M9DEPoH8l6+hVcgheatL0QcJhDAdOQ+K4/uhTTQ8ckZgbQfpgCnI+WEjoFGVUuxLT/cJvXFkyy+4eeoanjwKx9a5X8DGyQ7Nur5V7HFSMxne/2IOtvt+jcyCeWqeEzv/wNGthxB823CPqvKk6uQeCN50GLEnbiLdLwJ3Z3wNqbMtnIvJc6tO7YUne88i8sAFZARG4f6CHVBnK+Cel+dmPIrErQmfI+6vW8gKj0PixYcIWPMTnLo2gUCUW80SW5uj5sLBuDPzazw9dBlZ4XFI94tA3MmbZZHsl2Y5fCAyjvyJzKMnoQoLR/Kaz6HJkcP83e4GwyctXYOMg79DGRgCVfgTJK3eCAgEkDZvDADQZmYifoYPsk9fgCo8EooH/kj5dDNM6tSEyNnJ4DnfVO1aNcesyWPQuUMbY0el1Ela9YLq1lmo7lyANj4Kij92QKtUQNK4YzFHaaHNSNVtyNQfOaq6dxHKC4egDr1fmlH/Tzym9kLk3jN4euA8MgOj4J9X73Ib9rbB8FUm90DiuTsI//ooMoOiELLuZ6TdD0OVAj+kVZncE2GbDiH+xA1k+EXgYV79z7FHc71zqTNz9Opgmiy57m/Z4XF4euA8MvzCkROZgPiTNxHz60XYvFWrdC7EKzAbPAhZfxxD9vETUIeHI23jZ9Dm5MC0V0+D4VNXfYzsI79BFRwMdUQE0tZ/CggFMGnaRC+cVqmEJilJt2kzMsoiOf+J25TeiN53BrEHziMrMBJBPt9Ck62AS5F12F5IOncHkXl12PD1PyHjfihcx+Xnv/GHLyLln/vIiYhDVkAkQj/aDbGVGcxr5zcEhH/6M6K+PYZM/7L/8fpFKk3ug7j9p5Dw01lkB0UizPcbaLLlcBxm+Jq4TOyNlHO3Eb31N+QERyHy0x+RdT8MzgXq9e4LRyD17E08Wf0Dsh6EQR4ei5S/rkOVmJ/HWDSrididfyLzTjDkEbF4+sVBqFKzYN7Au9TTXJAx8hKBSIiaq8cicOVeRO45jazQaGQGRiH29+dHu+S+F5rh8dajpXMBimGsd+KnP55HwIe7kXzFH9nhcYj+9SKiDpyHc6/8vDhg6R48/uoo0u6E5o66+eQAskKj4di1iaFolQnzoYOQdfQYsv88AdXjcKR+mpfH9u5hMHzKyo+Rdfg3qIJDoI54gtR1GwChANJmhdKgUEKTlKzbtOnlN48tzGZsf6T+cgLph/+CMiQC8Su+hDZHDsv+3QyGlz8IROKG7cg4fgFahdJgGE1yKtQJybrNvMNbUEQ8Rfb18tVxiYiKx4aPcmT37t0Qi8W4du0avvjiC3z22WfYvn07AGDGjBm4cuUKDhw4gHv37mHQoEHo3r07goJyp24JCQlB9+7dMWDAANy7dw8//fQTLl68iBkzZuh9x6effoqGDRvi9u3bWLhwIT744AOcOnWqTNMpkIhh0cAbqf8UKDC0WqT+cw+WTWsYPMayWQ398ABSzt+BZdOaevusW9VF8/s70fifL+G1djLEtha6v1nU94LU1R7QaNDgr0/R7M521N635LlRI2XF1MMJUmdbJP2d/5KvSs9G6q1gWDczfB0EEhEsG1RF4j8FfhjQapH0933Y5B1j1dALQhMxEgucNyv4KbKfxOud17yGG7zn9ceDmV9Bqym6MaPVmXXocG8rmv68GDbNDcerNOTeJ15I+bvwfXIfls1qGjzGsmkN/fAAks/fgWXB6ykQoPqWmYj6+jdkB0Q+dw5VUjqygqLgOKgDhGZSQCSE8+iuUMSnIONe6HPhy5p5FUeYOtsi9p/8abdU6dlIuh0C+2bVDR4jkIhg06Aq4v4pMJJBq0XsPw9g31T/mCr926DPw23ocm4t6i0eApGpie5vQhMJNHL9iqE6RwGRqQlsG5Rtr7kiicQQVq4GdeDd/H1aLdSBdyD0NHzfAIBJt6HQZKRCebWI/FAggGzEXCjOHoImpvz9mPAiTu7OsHWyw4OL+dclOz0LIXeCUL1J0dcFAMatmozbZ2/gwaU3u5Jv6uEEmbMtEv7Ofw5U6dlIuRUC22KeHesGVZFQ6NlJ+PsBbIo4BgAkVmZQpWfrpoBz7FAfEAogc7FDh382oNPtLWj87QeQudqVTOJKglgMk1o1IL9WYAoRrRbya7cgrV+n6OMKEMikgFgMTVp60WEszKHVaKApxz9YUjFEIghdq0Idqv9MqEMfQFi56GcCJjKYzv4SpnO2QDp0HgSOlUs/riUgt97lhSQD9S7rIvIA66Y1kPS3/sjBxHN3dXWwZ/W/xEL1v7Rbwc/lK54z+6KD/3a8dXotPKb30TWmGmLq6Qz7txsh+Yr/qyazZInFkNSoCcWNAg27Wi0UN29CUvcl8xKpFAKxGNpCeYlJo0Zw/O0wHPbugdXcORBYFT3KyJgEEjEsDdRhU/65p18nLcDKYB32LqyKfCcQo9KozlClZiLDr/xMC1cUgUQM8wbeSDP4/me4HmLRtIZ+eAApF27D4ll4gQA27zRFdmg0au5fiib3dqHuH2th272F3jEZNwJg924biGwsAIEAdn3bQCiTIO1y0SN8S5qx8hLLBlUhc7UHNFq8dXot2t/bhsb7F+qNGgFy3wu95g3Aw5lfAcW8F5YGY78TFya2MoMy+fmOPvlfLoDIwhTKlGLClCaxGJIaNSAvlMfKb9yCSd26L3WKZ3msJk1/JgeTxo3gdPQQHPfvhtW82eU2j32ORAxpnerI/le/Dpt15TZkjV6u3HmZ77Ds0wnph06WzPmIqMxwjY9yxN3dHZs2bYJAIEDNmjVx//59bNq0Cd26dcOuXbsQEREBV1dXAMD8+fNx4sQJ7Nq1C5988gnWrFmDESNGYPbs2QCA6tWr48svv0SHDh2wdetWyGS5vbHbtGmDhQtzh+jVqFEDly5dwqZNm9ClSzE9oUuY2M4SArEIivgUvf3K+FSYVnMzeIzE0QbKQutKKONTIHGy0X1OPncbiX/+C3lEHGSeLqiyaDhq7/sQ93svBjQaSD2cAQDu84cgbPn3kD+Jg+uUd1H30ErcbjMTqpSy/RHGJG/aJHmhdCniUyEtkC69Y+ysIBSLnltjQx6fCvPqudfOxMkGGrnyuTlZFQmpkOZ9p8BEjAbbZiFwxT7kRCXCNO/a6J0zNhl+879D6t1QCE0kqDzybTQ7vAxXe3yI9PuPXz3Br0iSd58U/n9XxKfAuqj7xMkGSgP3VcH7xG1GP2hVGkRv/7PI7344eAVqf++LlsE/ABotlAmp8Bv2sf4IIiOR5aWl8H2TE58KWRFTcUntLCEUi5Dz3H2TBqtqrrrPEYcvIysyAdkxKbCu4476S4bB0rsSrkz4HAAQe/4eqk/qDvd+rfDk938hc7JB7bn9c+PlbPi7y5rA3AoCkQia9GS9/dr0FIicDf/QJqpaB5KWXZD16QdFntfknQGARgPl32XfC64kWOfdN6kJ+vdAakIKrIuZwq1Vn7bwrOeFpe8uKMXYlQ2ZY+5Q98LPjvwl8lxDx5hXdzV4jMTOEtXmvIcne8/o9pl5OEEgFKLaB33x8MM9UKVnoebCwXjr58X4+21faJXGn49faGMNgVgEdZL+s6NOSobY8+U6CNjMnARNQiJyrhUxksVEApsZk5D111loM8vvOjBUNIGZFQRCUe6ojQK0makQOhh+JjQJ0VD89g00sRGA1AyS1r1gOmEFsr9eAG1aUllE+z8rqt6lKCYPkDrZPFfHVcSnwsQpNw96Vv8zVJczKZAXRWw/gfT7YVCmZMCmWQ1UWzIMUmcbBH70g95xzf9YCcv6VSGSmSByz2mErPv5P6S05Aitc/MSTbL+/606KRkmVaoUcZQ+y6lToE5IgPxmfl4iv3oNOX//DXV0NESubrCcPBG2n65D0rT3AU05W2dM967z/H1TVB3WxMnGQPgUvXsCAOy6NEHtbXMgNDWBIjYF94asgiqp6Mbm8kKsq9en6O1XJqQU//6XUCh8gedE4mANkYUpXGe8h8h1+/Hk4x9g/XZjVN/uA/+By5D+rx8AIGjKBlTbNg/N/PZAo1RBky1H0IR1kD8ufv2IkmSsvOTZO57X/IEI/GgPsp/Ew2NabzQ7tAyXWs+GKiUTAhMx6m/7AIEr9hb5XliajPlOXJh1sxpw6dsKt0esLzK+ntN7Q2wuQ+zvV4pJVenR5bGF6muapGSIPV4yj52el8feKJTHXvgnN491c4Xl5Imw27AWiVNnlLs8tjCRjVVuHbZQfqFOTIaJV8l0crV4pzWElhZIO1z+phEkouKx4aMcadmyJQSC/AV6WrVqhY0bN+L+/ftQq9WoUUO/Z4JcLoe9vT0A4O7du7h37x727dun+7tWq4VGo0FYWBhq166tO2dBrVq1wueff15svORyOeRyud4+hVYNE4HoldNYmhJ/u6T7d9ajCGT6haPp1a9h3bouUi/eh0CYe20jv/gVScdyh/cGz9mCZre+hX2fVoj9oXRHvrgMaIM6n07Sfb49wngLqldfMgwZQVGI/tXw/K5A7jzCWSH5ix6n3giEqYczPKb0woMZb+ZiXuYNvOA6qSfudil+IUyvNZOgSEhFaN+l0OQo4DziHdTesxB3u/tCGZdSNpHN496/NZqun6D7fHHUp6X2XWF7z+n+nfboCXJiU9Dh4BKYezghMzwOsRfu496q/Wiybjyab54GjUIJ/01H4NiyVrGjhso1qSlkI+ci56ct0GYaXr9GWNkbkvbvImvD7LKN22to0689JnwyVfd5/TjDC7UXx66SPUZ/NAGfjFwOpdzwEPDyzHVAG9T/dKLu8/ViXmJLitjCFM33+SAjMAqBnxZY80QogNBEjIdLdiPhQm7Pw9tTN6Pz/W2wb1MXCeVsrY//wnLMUJh2eRvxU+cBhqYMEIngsGYZIBAgee0XZR9BMhpNZBA0kUG6z/IngTCdsQHipu9Aee4XI8asfIv45pju3xl+EdAoVaj96SQEffwjtIr86RbvTf4CYgsZLOt6oPqykfCY3gfhX/1ujCiXCPMRwyF7pxOSZs0GFArd/pyzZ3X/VoWGQRUSAseffoRJo0ZQ3Lpl4EwVU8qlh7j5zgJI7CxRaWRn1Pl2Lm73XARlGa7BV27kvdsln7yGmO9yp+3NevgYls1qwWl0N13DR2Wf4RBbmcN/8EdQJaXDtnsLVNs2H37vLUH2ozdvFO+rePb+G/bFYcQduwYAePjBVrS/vRXOfVoh6ofTqL5kGDKDohBTzHthSSpP78QFWdSqjMa75yNk469IvGC4XubSvw285w/A7TEbynTdy5JkPnIYTN95G4kz5+jV13LO5L8H5uaxoXD6eT9MGjeC4ub/Tx5bFKv+3ZD1z3Wo48t3hw0ieh4bPt4AGRkZEIlEuHnzJkQi/cYGCwsLXZgpU6Zg1qxZzx1f5SV7VxVlzZo1WLFihd6+8ea1MMHyvw0bVCWlQ6tS63p3PCNxtC7yR2VlfAokjtaFwtsU+yO0PCIWysRUyKq6IPXifShic8NmBT7RhdEqVMgJj4XUzfG/JOWVxJ+4iSs3g3Wfny3WJnW0hqJAOkwcrZH+0PCQdUVSGjQqNUwKXQupozXkeedQxKVAKJVAbGWm18PFxMEa8rxeQ3Zt68KydhU4986d2/9Zg1tH/+8Q9vlhhHx60OD3p90Ogc1bxU+LU1KUefdJ4f93E0cbveuld0xcynMLkBe8r6zeqg2JgzWa3dym+7tALELV5aPhOrkXbjafDuu29WHXpQmu1hwLdd76FqELt8OmfUM4De6IqC1HSiqJLyX65C2cuhWi+ywyyc22pY7WyClwHWSO1kgp4r6RJ6VDo1Lrerw/I3W0Qk5cqsFjACAp73stqjojMzwOABD0zXEEfXMcMmcbKFIzYe7uiPpLhur+bmzazDRo1WoILW1RsG+SwNIGmrTk58ILHVwgtHeG6cSlBQLnPg8WG48g85OpEHnXhcDCGuYf7cwPIhJB2nc8TDq8i8yVEwuf1uhunrqG4NuBus9ik9z8xtrBGilx+dfB2sHG4ILlAOBV3xvWjjb45NhG3T6RWIRab9VB1zE9Mbr6YGjLcQ+w2BM3kVJEnisv8OxIHa2R9vCxwXM8y3OlxeS5z4jMZWhxYCHUGdm4Oe4zvfWD5HnlT0ZgVP65E9OhSEqHaWX9hSWNRZOSCq1KDZGdrd5+kZ0tNInFv+RZjhwEqzHDEPf+AiiDDUwJKBLBfs0yiFycET99Pkd7vMG0WWnQatQQWOg/EwJza2gzUl7uJBo1NNGPIbRzKfkIlrCi6l0mBvKAZ+RxKc/VcU0craHIK2+f9eA2KVT/kzpaI72IvAgAUm8FQygRw9TdUa9jivxpIuQAMgOjAKEQdTZMRvjWo2U+Xc0zmtTcvERoqz+Vn8jOFpqk4vMSs6FDYD58OJLmzoMqtPjpRdXR0dCkpEBU2Q0oZw0fSt27zvP3TVF1WEVcioHwz9d5NVly5DyOQc7jGKTfCkLzy1/CZVgnPNl8pARTUPJUunq9jd5+icPzo7WfUcanQOJQKHyBa6hKSodGqUJ2oP7UtdlBkbBskdvpT+rhDJfxPXGv4wfIznsHzPJ7DMu3asN5bA88XvjNa6ftZRgrL3lW/8gsML2vVqFCdkQsZJVzO1Data0Hi9pV4FTovbCD/3aEfX4YoZ+WbAN1eXonfsa8hhuaHvwQkXvPIGzTYYPf6dKvFepunIy7kz5/bgqysqTLYwvV14QvUV8zHzYYFiOGI2n2PKhCXpDHPo2GOjkF4spu5b7hQ52SlluHLZRfiOxtoUp4/v3vVYldnWDaqjFiPnhzFnv/f6DRCl4ciAhc46NcuXr1qt7nf//9F9WrV0fjxo2hVqsRFxeHatWq6W0uLrkvjU2aNIGfn99zf69WrRpMTEz0zln4O56NBinKokWLkJqaqreNsvjvP35rlSpk3AuBddv6+TsFAli3bYD0m4EGj0m/EQjrtg309lm3b4D0m0UvpmtSyQ5iW0soYnMLu8x7IdDkKGDqnT+cWiAWQeruBHlk/H9Oz8tSZ+Yg+3GsbssMiIQ8Nhl27erpwogsTGHdpBpSbxi+DlqlGun3wmBf4BgIBLBrVw8pecek3Q2FRqHSO6+ZdyWYujvqznt3/CZc6eSDf9/xxb/v+OLh3NxK//W+y/FkV9HDNy3reegq0KUt9z4JhXW7wvdJfaTfMPz/nn4zEDYFwwOwad8Q6Xnpjj94AXc6zcOdzvN1mzw6EVFf/w6/oasBAMK8NS2eG8Gg0UAgLPssU5WZg8zHsbotLTAK2bHJcGqbP4er2MIUdo29kXgjyOA5tEo1Uu6F6R0DgQBObesh8abhYwDApp4HACDHwP95TmwKNDlKuPdrhayoBCTfN/zjeZlTq6CJDIaoeoH8QiCAqEZDaB4/f99oYiORufZ9ZH06S7epHl6DOvg+sj6dBW1KApTXzyFr/Uy9MJqURCjOHkbWto/KMHEvLyczB7HhMbotKugJkuOSULdN/nUxtTCFd6PqCLpl+Hl6cOkefLp8gEU95uq2kLtBuHTkbyzqMbdcN3oAuXlu1uNY3ZYREImc2GS9/FNsYQqbJt5ILubZSb0XBodCea59u7pIKXCM2MIUb/28CBqFCtdHb3huLZzka7nX2LxaJd0+iY05TOwskf2k9Mufl6JSQfEoULcwOQDdQuXy+35FHmY5agisJoxE/KyFUPobKLvyGj0kVdwQ//4CaFLfzB6SlEethuZpGERV9Z8JkVddvVEdxRIIIHR2hzbj9X+MKG259a5Q2BWqi9i1q4fUIvKN1JuBenUwALDvUF9XB8sOj4M8Nhn2Bc4psjCFVZNqevlKYZb1PKFVa4rtZSwQCiGQiIxSX9FRqaAMDNBfmFwggEmTplA+LDovMR82FBajRyF5gQ9UAUXX758ROjpCYGUFTWJiScS6RGmVKqTfC9WvkwoEsGlbX1cnLSzNYB22AdKKCK8jFOh+OC7PtEoVMu+FwKqtfv0s9/3P8P93xs1AWBW6JtbtGyIjL7xWqULm3WCYeutPFSXzcoU8MrdDjtBUmhu2UJ1Fq9boRkOUBWPlJWl3Q6HOUcCswNS2ArEIMndH5EQmAADujv9M773QL++98Ebfj/BkV8mvZ1Ce3okBwLxmZTQ7tBRPf/obwWt+Mvh9Lu+1Rt3Pp+HetC+RcPr26yT/9alUUAYGQlooj5U2bQLFw4dFHmY+fCgsxoxC0nwfKANekK8AEDo6QGhtBXVC+ctjn6NUQe4XBNOW+nVYs5aNkHOn6HLnZVm91xXqpBRkXrj64sBEVO5wxEc5EhERgblz52LKlCm4desWNm/ejI0bN6JGjRoYMWIERo8ejY0bN6Jx48aIj4/HmTNn0KBBA/Tq1Qu+vr5o2bIlZsyYgYkTJ8Lc3Bx+fn44deoUtmzZovuOS5cuYf369ejXrx9OnTqFX375BceOHSsmVoBUKoVUKtXb97rTXD395iiqfzETGXdDkHEnCJUm9YbITIq4A7nD2Kt9OROKmCREfJI7dVf09mOoe2glXKf0QfKZW3Do2wYWDb0RuiC3577QTAb3eYOReOwKlHEpkHm6wGPpKOSExSDl/B0AgDojGzE//AX3+UMgf5oAeWQ83Kb1BQAkHL38Wun5r8K/PQ6vOe8hKywG2RFxqOY7GPLYZMQdv6EL0/Tgh4j78zqe7MyteD7edgz1vpyGtDuhSL0djCqTe0JkJsXTAxcA5C4GF7X/HGquGAVlSgZU6dmo/ck4pFwPRGpe75rs8Fi9eEjschcuywyM0vWIqTK5B7Ij4pDxKBIimQRuIzrBrm093BzySalfl2dy75MZuffJ7WC4TuqVd5/kDsWtvnkmFNGJCP9kf2747/5EvcMr4Dq1D5JP34RDv7awaOiFkLz7RJWcAVWy/louWpUairgUZIc8BZDbeKJKyUT1L2fgyWe/QJOjgMvIzpBWcULS6SLmrS9jwd+dQO3Z/ZARFoPMiHjU9R2I7NgUPD2RH7/2Py9C1PEbCNmVO4Vb4DfH0fyLKUi+G4akOyGoPqk7xGZSPM67b8w9nFClf2tEn7kDRVIGrOtUQcMVIxF/xR+p/vmjpGpM64WYc/cAjQZuvZqj1ox38e+UL43Ws9QQxfkjkA2fA/WTYGgiAiHp0BcCExmUV08DAGQj5kCTmgjFH3sAlfL5xcqzM6EF8vdnpUOTVWj+bI0K2vRkaOOi8KY4seMPvDdzEGLCohH/JBaD5g1HSlwSbvyVX4lfvH8Fbpz8F3/tPo6czBxEBupfG3mWHBnJ6Xr7rR1tYONoA2fP3B/13Wt6ICczGwlRCchMLV8LWId9exzV5/RDZl6eW8N3EOSxyYgtkOe+dXAJYv68jvCduY3AYduOoeGX05CSl+d6Tu4BsZkUT/KeHbGFKVr8vAgiUynuTN8IiYUpYGEKAJAnpgEaLTJDYxBz/Drqrh6D+/O+gzIjG7WWDEVG0FMkXnr9F7KSkr7/IOw/8oXCPxCKh49gOWwAhKYyZB7NLX/slvtCHZ+A1K92AAAsRw+F9ZQxSPzwE6iiYyC0z+19qM3KhjY7J3d6q3UfQVKrOhLmLAFEQl0YTWo6oFIZjsgbKCsrGxGRT3Wfo57G4lFgCKytLFHJxcmIMSt5yivHIH1vGjRPQ6GOCoakZQ8IJFIob+c+EybvTYM2LRnKMwcAAJIO/XOnu0qKhUBmBknrPhBYO0J5K39aDZiaQ2jtAIFl7v0hsK8EIQBtRspz64mUtfBtx1D3y+lIuxOCtNshBepd5wEAdTe/D3lMEoI//hEAEPHtcTQ78hE8pvZG/OlbcOnXGlYNveE3/zvdOSO+/RNV57yHrLBoZEfEwdt3COSxyYg/fh0AYN2sOqybVEfSxYdQZ2bDulkN1Fw5GtEH/4Eqb70xlwFtczuJ+D+BRqGEVUMvVFsyDLG/XdEbcWYMWT//AutFi6AMCIDS3x/mgwZCYCpD9p/HAQDWixdBnZCAjG9zr4n58GGwGD8OKatWQx0TA6Fd7mgRbXY2tNnZEJiawmLsGORc+BuapCSIXF1hOW0K1FFRkF+7brR0Fifqmz9Q84v3kXE3BGm3g1F5Ui8IzaSIyavD1tw8A/LoJDzW1WGPocHhFXCb2htJp2/BqV8bWDb0RtCC3B+hhWZSVPmgPxJP3oAiLhkSOyu4jusGqYsd4o/mrzUgdXOA2MYCUjcHQCSEeV1PAEB2WAw0WTllexEKif72KLw/n4nMu8HIuB0El0l9IDSTIj7v/c/ri1lQxiTiyZrc97+Y7X+g9q+r4DLlXaScuQn7vm1h3sAbYQvyR25Hf/0bqm2bi7R//ZB2+QFs3m4M2y7N4DcwdyRvTnAUckKfour6qYhYuRuq5HTYdn8L1u0bImB02b3TAMbJS9QZ2YjccxreCwYhJyoROZHx8Hz/XQBA7O+5HSILvxea2FkC0H8vLG3Geie2qFUZzX5dioRz9xC+7ZhuBIlWo4EyMbfu79K/Dep9OQ0BH+5G6s1gXRhNjgKq9OwyuT6FZR74BTZLFkL5KBBKf3+YDc7LY4+dAABYf7gImvh4pH+zHQBgPmIoLCeMQ8qKj6GOjtGNFsnNY3MgMJXBYlxeHpuYBJGbG6yml+88trCU7w/Bac18yB8EIud+AGxGvweBqQzpeWtyOK1ZAHVcAhI37co9QCKGiXfuzCgCiQRiZ3uY1PKCNisHyoj8+hwEAli+1xXpR04D6vLd6YuIDGPDRzkyevRoZGdno0WLFhCJRPjggw8wefJkAMCuXbuwevVqzJs3D1FRUXBwcEDLli3Ru3dvAECDBg1w4cIFLFmyBO3atYNWq4W3tzeGDBmi9x3z5s3DjRs3sGLFClhZWeGzzz5Dt27dyjytib9fhsTeGlV8hkLiaIPMh2HwG74ayryFd6VuDno/pKbfCEDQ9M9RxXcYqiwagZywaDwatx5ZAXk/yGo0MKvjAafBHSGyMoMiNhkpF+7iyTr9eZDDV+6BVqVG9c2zIJSZIONWEB4OXG60Rasfb/kdIjMp6myYBLGVGVKuBeDW0LV6vYXNPJx1FVAAiP3tCkzsreDtMwhSJxukPwzHrWFr9RZ3C1i2B1qNBo12zIVQKkbCuXvw993xSnETSsSouXwUpC52UGfLkeEfgRuDViO5DH+kS/jtMsT2VqjiMxQmjjbIfPgYD4d9rHefFOzBlX4jAIHTv4CH71B4LBqO7Gf3yaMnRX3Fc1RJ6fAb/jGqLByGegeXQyARISvgCfzHrkeWn+Hh1mUt4Ks/IDKToumnEyCxMkPCtUBcHL5O774x93SGtMB9E/n7v5DaW6KOz0DIHK2R+jAcF4evgzyv56hGqYJTu3qoNjG3QSTraRKijl2H/+dH9L7bpVND1PqgL0QmEqT4ReDyuM8Qc/ZumaT7ZaluX4Tc3BrSHiMgsLKFJioUWd98pJuGRWDrCKG2/DTUlJWj2w5DaibDxDXTYGZljsAb/lg7epXe+h3OVVxgaWv1SuftPKIbBswZqvv80cHcHxK2zfsSfx88V9RhRhG65SjEZlLU3zAREiszJF8LwLUX5LnRv/0LE3sr1PAZCKmTDdIehuNagTzXqoEnbJtWBwC8fU1/7YqzzWYi+0lej8oZW1Fn1Sg03+cDrUaLpCv+uDZsjdF/oCwo+9R5pNhYw3rKWIjsbaEIDEH8rIW6BTRFLk5AgWfHYkAfCExM4LB+ud55Ur/djbTv9kDk5ADTDm0AAC77v9MLEzdlLuS3ylfe8ToePArC+Jm+us/rN38LAOjbozM+/nCesaJVKtQP/4XC3AqStwfCxMIGmphw5OxdC2TmPhNCawdoCtwnApk5TPpMgsDCBtqcTGiehiFnx0fQxuc3HItrNoW03zTdZ9mgDwAAivMHoTxfYL0cI8ivdw3Oq3c9xq1ha3R5gMzNXm/h19Qbgbg/bTOqLRyCaouHIissBnfHforMAnWRZ/W/2hsm6+p/t4eu0eVFGrkKzv1aw2v+QAhNJMiOiEP4N38ifNsfunNoVWp4zugLM+9KgECAnCfxeLLzpN7aIMaSc/YchDY2sBw/DkI7OyiDg5E83wea5Ly8xNlZLy8x69sXAhMT2K5aqXeejF3fI2PX99Cq1RB7e8GmezcILSygSUiE/Pp1ZOzYCSjL5xpU8b9dhsTeCh4+Q2DiaIOMh4/x4Lk6bP41SLsRiEfTv4Cn7zBUzavDPixQh9WqNTCr5gbnwR0hsbOEMjkd6XdCcKffMmQVmMbIw2cIXIZ01H1ueiZ3bbi7/T9C6mXjNrQn/X4JEnsrVF4wDBJHG2Q9DMOjEaug0nv/y3+WMm4EIOT9TajsOxzuC3Pf/wLHr0N2QH7ni+QTV/F44TdwndEfnqsmIDv0KYImrUfGtUcAcp+TR6M+RpXFI1Fz92IIzWXICYtB6AebkXq2bKfvMUZeAgBBK/ZCq1Kj3lfvQyQzQeqtYNwcsErXiFoeGOud2Ll3S5g4WMN1UDu4Dmqn258dEY9/ms8EAFQe+Q6EEjFqr5uA2uvy112MOnABDz/YWirX40Vyzp5Dmo01LCaOhcjODsrgECTN8y2Qxzrp3Utm/fLy2I/1py9P3/k9MnbuhlatgcTbG6Y9cvNYdUIiFNdvIP278pvHFpZx4gJEdtawmzkaYgdbyB+F4umUJVAnpgAAJJUc9a6J2NEeVQ7l///Zjh8E2/GDkH3tLqLG5q8HatqqMSSuzkg7VPKjn4iobAi02v/DX3/KoY4dO6JRo0YvXGj8dXh6emL27NmYPXv2a5/rcqUBrx+hCiRDU/6HmJc1c0HF6clbUqIF0hcH+j/TbQinvSlsymH2SShsRI7M2FEodxpUKR/r6pQnLse/e3Gg/zOKLUuMHYVy5fJW9tYsrH7N2BcH+j8TGFj6a/+9aaTC8tNIX16kqfn+V5gWnHO/sPrVmccWlplk8uJA/2eq+bFh5VU98Opt7Ci8ceqF/vHiQBUQ1/ggIiIiIiIiIiIiIqIKg91KiYiIiIiIiIiIiKjc02o5woxeDhs+yonz58+X+nc8fvy41L+DiIiIiIiIiIiIiMiYONUVERERERERERERERFVGGz4ICIiIiIiIiIiIiKiCoMNH0REREREREREREREVGGw4YOIiIiIiIiIiIiIiCoMLm5OREREREREREREROWeVmvsGNCbgiM+iIiIiIiIiIiIiIiowmDDBxERERERERERERERVRhs+CAiIiIiIiIiIiIiogqDDR9ERERERERERERERFRhsOGDiIiIiIiIiIiIiIgqDLGxI0BERERERERERERE9CIarcDYUaA3BEd8EBERERERERERERFRhcGGDyIiIiIiIiIiIiIiqjDY8EFERERERERERERERBUGGz6IiIiIiIiIiIiIiKjCYMMHERERERERERERERFVGGJjR4CIiIiIiIiIiIiI6EW0WoGxo0BvCI74ICIiIiIiIiIiIiKiCoMNH0REREREREREREREVGGw4YOIiIiIiIiIiIiIiCoMNnwQEREREREREREREVGFwYYPIiIiIiIiIiIiIiKqMMTGjgARERERERERERER0YtotcaOAb0pOOKDiIiIiIiIiIiIiIgqDDZ8EBERERERERERERFRhcGGDyIiIiIiIiIiIiIiqjDY8EFERERERERERERERBUGGz6IiIiIiIiIiIiIiKjCEBs7AkREREREREREREREL6LRCowdBXpDcMQHERERERERERERERFVGGz4ICIiIiIiIiIiIiKiCoMNH0REREREREREREREVGGw4YOIiIiIiIiIiIiIiCoMNnwQEREREREREREREVGFITZ2BOjNlKKRGDsK5YoEWmNHodwxlyqMHYVy54lWZuwolD85SmPHoNzxEFgaOwrlzj0Z+2kUZvPE1thRKHfstiwxdhTKHZMZHxs7CuXKqe95jxTWuqOLsaNQ7igDWK8v7LZYauwolDtmrJo8x1+sMnYUyh2vJBNjR6Hc8U+yM3YUyp1qxo7AG0irFRg7CvSGYHFNREREREREREREREQVBhs+iIiIiIiIiIiIiIiowmDDBxERERERERERERERVRhs+CAiIiIiIiIiIiIiogqDDR9ERERERERERERERFRhiI0dASIiIiIiIiIiIiKiF9FoBcaOAr0hOOKDiIiIiIiIiIiIiIgqDDZ8EBERERERERERERFRhcGGDyIiIiIiIiIiIiIiqjDY8EFERERERERERERERBUGGz6IiIiIiIiIiIiIiKjCEBs7AkREREREREREREREL6I1dgTojcERH0REREREREREREREVGGw4YOIiIiIiIiIiIiIiCoMNnwQEREREREREREREVGFwYYPIiIiIiIiIiIiIiKqMNjwQUREREREREREREREFYbY2BEgIiIiIiIiIiIiInoRjVZg7CjQG4IjPoiIiIiIiIiIiIiIqMJgwwcREREREREREREREVUYbPggIiIiIiIiIiIiIqIKgw0fRERERERERERERERUYbDhg4iIiIiIiIiIiIiIKgyxsSNARERERERERERERPQiWq3A2FGgNwRHfBARERERERERERERUYXBhg8iIiIiIiIiIiIiIqow2PBBREREREREREREREQVBhs+iIiIiIiIiIiIiIiowmDDBxERERERERERERERVRhiY0eAiIiIiIiIiIiIiOhFNMaOAL0xOOKDiIiIiIiIiIiIiIgqDDZ8EBERERERERERERFRhcGGDyIiIiIiIiIiIiIiqjDY8EFERERERERERERERBUGGz6IiIiIiIiIiIiIiKjCYMNHBefp6YnPP//c2NEgIiIiIiIiIiIiei1aCLi94vb/SmzsCFDpun79OszNzY0djSJV9xkE95GdILEyR/L1ADzw2YGssJhij/EY1xVVp/eB1Mka6X4ReLh4F1Jvh+j+7j7qHbi+1wZWDTwhsTTDX9XHQ5WWpfu7Xes6aHl4mcFzX+q2GKl3QksmcYVUHtcVntP7wMTJBhl+4Xi0eBfSCsS7MKc+LVHNdzBk7o7ICotB8Kp9SDhzRy+Mt88guI18B2Irc6RcD8Ajn+16109sY45an4yHY9cm0Gq0iDt2FQFLvoc6S64LY9+xIbx8BsGiZmVocpRI/tcfgct/QM6TeF0YlwFt4fn+uzCr6gJVehYSzt5B0Iq9UCZnlNwFeg0Oo3vCafJ7EDvaIts/DFEffYusu0EGw8qqu8Nl3giY1fOGibszolZsR/zO38s4xiWv1dwBqD/8bUitzPD0RiDOLN6FlMexRYZ3a1ETzab2glP9qrBwtsXvEzch5K+bemEkZlK0XTgE3t2awdTWAqlP4nFn10nc23u2tJPz2iQd+sCk60AIrGyhiQxFzk9fQ/M48IXHiZt1gOnERVDeuYycbSvz9zdqA0n7nhBVqQ6BhRUyV0+HJrJ08orS1HnOQDQf9jZMrcwRfiMQRz7cicTHRee5Haa/i3rdmsPR2xXKHAXCbwXhxNofkRAarQtj4WiNnouGo1q7+pCayxAfGo1zW47g4YnrZZGk19Zu7gA0Gpb77ETeCMTJJbuQXMyz02p6H9Ts3hx23pWgylEg6mYQzq39CUl510RmbY52cwegarv6sHKzR1ZiGoL+uom/Nx6EPD27rJL1UlzGdYfb9Hdh4miDTL9whC7ZgYzbwUWGt+/TClV8hkLm7ojssGiEr96L5DO3DYb1XjcZLmO6InTpLkR/dwwAYNW6LuofWmEw/N3uvsi4U3SZaEzi5l0gadMHAgtraGIioDj+PTRRhuMqbtQe0n7T9PZpVQpkrR6j+yyq3RySZp0hrFQVAjNLZG9bCE1MeKmmwRhu3LmPXfsPwu9RMOITk/DFmqV4p31rY0erVHWdMxAthnWCqZU5Ht8IwOEPdyKhmDz27el9Ua9bczjl5bGPbwXi+NofEV8gj7Wr4oTeS0bCs1lNiE3ECLhwD78t/x4ZCallkaQSJW7RNf9Zio2A4tiuYp6lDpD2L/QsKRXIWjW6LKJaIiqP64oqBer+gS9R9/fKq/tn59X9EwvU/R17toDbmM6wauAFiZ0lrnbyQcZD/byj1qeTYNu+HqTOdlBn5iD1RgCCV+1HVvDT0krma2s5dwDqFajDnntBHda1RU00LVCHPTpxE0IN1GHbLBwCrwJ12Lu7TuL+G1CHBYCm8weg1rC3YWJthtjrgbi4eBfSwoq+Ji5v1USDqb3gUL8qzF1s8deETQg/qX9NmsztD+93W8Lc1Q4ahRoJ98Nwff0viC/mnizvus8ZhJZ5eW7YjQAc/HBHsXlu65Fd0HpEZ9hVdgQAxARF4q8vD+HR+TtlFOOSYz2sD2zGD4TIwQ6KgFDEf/w15PcDDIY1qeYBuxmjIa1bDRI3F8Sv2YbUHw7rhfE4tRsSN5fnjk3Z/zsSVn9VKmkoCTV9BqLKiNzfk5KuB+C+705kvuD3JM9xXeA9vQ+kjtZI84vAgyXfIyXvOZDYmKPmgkFw7FAfpm4OUCSmIfrEDQSs+xmqAvV4h7Z1UdN3MKxqu0OVJUfkz3/j0ZqfoFVrSjW9RPTqOOKjgnN0dISZmZmxo2GQ14x34TmxOx74bMflnh9CnSVHi58WQSiVFHlMpb6tUGvFKARvPIhLXRYh7WE4WhxYBBMHK10YkakJ4s/dQcgXRwyeI/l6AE7Xm6K3Rew9g6zw2FJr9HDu2wo1V4xG6MZfcbXLQqQ/DEeTA4shKRDvgqyb1UD9bbMQtf8crnZeiPjj19Hw+wUwr+WuC+M54124T+wBf5/tuNZzCdRZOWj802K961f/65mwqFkZNwd/jDsj18G2ZW3U3jhZ93dZFUc03D0fyRcf4N9Ovrg19BOY2Fmi4c55+XFpXhP1Nr+PqP1ncbnDPNybuAnWjauh9sYppXClXp1N77Zw/XACYr44gIDec5Dt/xheP6yA2N7aYHiBqRSKiBg8XbcHyrikMo5t6Wg2rTcajeuK04t24sd3P4IyS47+e30hKuZZkphJEe8XgbMf7i4yTIdlI+DZsSFOfLAVuzv54PaOE3h75Rh4dWlSGskoMeKm7SEdOAnyP/Yi65MZUEeGwmzmxxBYGr4nnhHYO0M6YCJUQfef/6NUBnXwQ8gP7yylWJe+9lP7oPW4bjiyZCe+7rcUiuwcjN+zEOJi7hOvt2rjyg+n8PV7y7Bj1BqIxCKM37MQElOpLszgjdPg4OWKPRM34vNuC28go2YAAQAASURBVPHwxHUM/+oDVKrrURbJei0tp/ZGs7FdcWLxTuzum/vsDPmh+Genylu1cXPPKezptxwHRq6DUCLG0B98ddfEwtkWFs42OPvxfmzvshDH5n8Lrw4N0HP9pLJK1ktx6NsaVZePwZONv+BOVx9kPnyMuj9+WGS5ZNmsJmpunY24H8/gTpcFSDp+HbV2+cCsQLn0jF2PFrBoWh3y6ES9/enXA3Ct/kS9LWbvaeSEx5bbRg9R3ZYw6TYKyvO/IvubxdDEhkM2ciFgbvg6AYA2JwtZG6bmb5tm6f1dIJFCHREAxekfSzv6RpWdnYOa1bywZN50Y0elTHSc2gdtxnXHoSU7sLnfUiiy5ZjwEnns5R/+wpb3luG7UZ9AJBZj4p5FuvxEYirFpB8WQ6vV4tvhq/H1wOUQmYgwdvt8CARvVs89Ub1WMOk+CsrzB5G9bRE0MeGQjV704mdp/ZT87bOZZRjj1+PUtxWqrxiNsI2/4nqXhch4GI5GL6j71902C0/3n8O1vLp/g0J1f5GZFKlXAxC8en+R35t2LxT+H2zDv+3m4s7QTwCBAI1/WgIIy+f90jSvDnt20U78lFeH7fcSddgEvwicL6YO227ZCHh0bIiTH2zFnk4+uLPjBDquHIOq5bwOCwANp/dG3XFdcXHRTvzWJ/ea9HjBNRGbSZHkF4HLxVyT1NBoXPpwN37tvAhH+69EemQCeu7zhczOsjSSUeo6TX0X7cZ1xy9LtuPzfh9CkS3HlD2Lis1zU6ITcWzdj/isz2JsencJgi4/xPhv58O5euUyjPnrs+jeAQ6+k5H09T48Gfg+5I9C4frtxxDZFfH+K5NCGRmNxM92QhWfaDDMk8GzENZ+qG6LmrAQAJB58p9SS8fr8p7RB1UndMc9nx34p+dSqLPkeOvAwmJ/T3Lt2xJ1lo9C4MZf8XfXxUh7GI63flyo+z1J5mILmbMN/Fbsw/mOC3D7g21wershGm7K/+3Dqk4VtNjni7hzd3Gh8yLcmvIlnLs2Re0Ph5V6mono1bHh4w3XsWNHzJgxAzNmzIC1tTUcHBywdOlSaLVaAM9PdSUQCLB161b06NEDpqam8PLywsGDB40Sd8/JPRC86TDiTtxEul8E7s74ClJnWzj3aFbkMVWn9sKTvWcReeACMgKj8GDBdqizFag8rKMuzONvjyN08+9IuWm416pWqYYiPlW3KZMz4Ny9GSJ/vFDSSdTxmNoLkXvP4OmB88gMjIJ/Xrzdhr1tMHyVyT2QeO4Owr8+isygKISs+xlp98NQZXy3AmF6ImzTIcSfuIEMvwg8zLt+jj2aAwDMq7vB4Z3G8Jv7DdJuBSPlWgAeLd4Fl36tIXW2BQBYNfCCQCRE8JqfkB0ei/T7YXi89Sgs63lAIBYBAGyaVUf2kzg82X4CORHxSLkWgMg9p2Hd2LvUrtercJzYF4kH/kLSL2cgD3qCyMVfQ5Mth93gzgbDZ98LxtNPvkfK0X+glSvLOLalo8mE7ri2+TeEnrqFhEdPcGLONpg72cC7a9Mij3l8/h4ubziIkJM3igxTqWl1+B38B5H/+iMtMgH3959DvH8EXBp6lUYySoxJ5/5QXjoB1ZVT0ERHQL5/M7RKOSStuxV9kEAI0/E+UBzdC23C872EVFfPQPHnfqgeGe7d/iZoM747zm0+Av9TNxHz6Al+nrsVls42qNO16Dx315h1uHXwb8QFRSHGPwIH52+DbWVHuNWvqgtTpWkNXNl9EpF3Q5D8JA7nthxBTlom3OpVLfK85UXzCd1xactvCDp1C/GPnuCPudtg6WSDGsU8Oz+NWY/7B/9BQlAU4vwj8Me8b2Bd2QEu9T0BAAmBkTg89UsEn7mNlIg4hF/2w4VPf0G1dxpDICo/1S7XKX0Qu+804g6cQ3ZgJEJ8voU6W47/sXff4U0WDxzAv2lWk7bpblll771kq4DKEgQRlKHsLSgyZcgSRVFQRBFRQHAwRMXJHgIiq6Vsuvdeado0zf790ZI2JS2rbUJ/38/zvI/m7b1v7o737r28996d34jetsNPHoCsE8FI2PQ7NGEJiF27G+prUag+vr9VOEk1L9R/byJCX98As8Fo9Tez3gB9mtKyGbJy4NXvCaTsPlFh6XxU4q7PwxB0HIbgf2BOS4Duz60w63UQt+tZxlFmmHOzLRvU1m/mG66egf6fX2CMtNHJWoU82fUJvDFlLJ59uru9o1Ipekzoj2Mbf8XNI4FIvh2LPXM2QeHviRZl1LFbx36AwH2nkBIWj6Rbsdg770t41vJFrcI6tm7HxvCs5Yu98zYjOSQOySFx2Dv3S9RqXR8NurWorKSVC3G352EIPA7D5cKy9Mc3BWWpfc/SDzKXXZYcWe1pzyPh+2NIKmz73y5s+9cope0fMKU/Mk8EI3bTH8gLS0Dkh3uRcy0KtYq1/ZP3nUbU+p+Rear0uiPxu2NQnruF/Lg05FyLQuQHe+BcyweyAL9yT2N5aFeiDXv4PtqwMSev4r/7aMPe2ncaCeduISc+HdcfkzYsALSc2A+XP/sNMYeDkHkrDidnb4bc3wN1+paeJ/EnruLSR/sQfbD0PInY/x8Sz9xATmwaskITcG7lD5Ao5PBqVrsiklHhnprQH0c2/oobRwKRdDsWP875Agp/T7Qso869eSwIt04GIz06GWlRSTjw8R7o8vJRt12jSoz5o/MYNxTZPx1Ezq+HoY+IRdrKz2DO18JtqO3fOtrrocj4+BvkHvgHZp3t37+mrGwY07Msm8vTnaGLTYTm4tWKTMojqT+5P0I//RUphwKRcysWl2dtgrO/J6r1K/0aqD/1ecT+cBxxhc+Tri7YCqNGh9ojegIAcm7H49KkT5FyJAh5ManI+PcGbn+wB/7Ptbe042sM7oqcW7EIW/8L8qJTkPHfLdx690fUHdcHQhfnykg6ET0Ax/kFTg9tx44dEIlEuHDhAjZs2ID169fjm2++KTX8O++8g5deeglXrlzB6NGjMWLECNy6dasSYwzI6vjB2d8T6cUa7oYcDZRB4fDo2NjmMQKxEIrW9ZBxulhj32xG+qlr8CzlmPvh37cDJJ5uiN998qHPURaBWAi31vWRWSLemaeuwb2j7UaWe4fGyDx13WpfxokrcC9Mp6yOH6T+nsgokX+qoHB4FJ7TvWMj6JW5UF0pGsWSeeoazCYzFO0bAih4KwwmM2qM7Ak4CSByk6H6sKcKwhU+sFJeCoNzDR/4PNMWACDxdYf/wM5IL2V6k8okEIsgb9UQuWeCi3aazcg9cwUu7ZvaLV6Vyb22L1z8PBB7puh60eVokBwcgRodHq0RnxQYhvrPtYdLYUdZra7N4FmvGmLK+MFtd0IRnGo3gvFWsevTbIbx1mU41W9W6mGS50fBlKOE/uyhSohk5fMM8IPCzxPh/xZdJ9ocDeKCI1C7/f1fJ85uBSMINcqiae5iA0PRemAXyNxdIBAI0HpQV4ikYkSdq9z7yoPyCPCFq58Hos9Y50licARqPlSeqEsNI1XIocvVOMzwd4FYBNfW9aE8VezHrNmM7NPX4Naxic1j3Do0tg4PIOtkMNyK338FAjT6fBYSNv0GTUj8PePh1bcjxJ6uSN3toFOPCIVwqlEPxshi92OzGcbI63CqVcY1InGGbPZnkL31OaQj5kLg+3i9SUoPzquwjg0rVsfmF9axdR6iPskrrGNFEjHMZjMMxR5U6bV6mE1m1HvCdll1SEIhnKrXgzHCui1sjLgGp1pltOElzpDN2QjZ3C8gHTnvsSlLpbX9sx6h7f8wnORSVB/RE5qYFOQnpj/0eSqKoow2bLX/xzYsALfavpD7eyDhdFGe6HM0SAuOgP8j5klxTmIhmo7uBW22Ghk3H7+pFu/UuaH/Fv175udoEBscjrrt76/MCJwEaDuoKyQyKaKD7j0drsMQiyBt3giac0FF+8xm5P13Gc5tm5fbd7gN6o2cXxz3d5G89p3nSUVlxZCjgfJyBDxLqWcFYiHcW9ezOgZmM9JPXy/1GAAQu8lhKNaOd5KKYcy37kAy5usglEng0cbxX/wi+n/DNT6qgICAAHzyyScQCARo0qQJrl27hk8++QSTJ9ueVmP48OGYNGkSAODdd9/FkSNHsHHjRmzatKnS4iz19QAA6NKs39zSpWVD6udh8xiJlwJOIiG0JY7RpmXDtVHNh45LrVG9kHbiCvKTKmbaozvxtpVWl0Y1bB4j9fOALk15V3iJX8HwVUkp+adNy4akMP8kfh7Qpaus/m42mmBQ5lryOD82DUGvvIdWX89Gs48mw0kkhPJiCC6P+sByTPbFEFybsRGttsyGk1QMJ7EIaYcu4fbb9p/yR+ipgEAkhD5dabVfn66EtMHDXxOPE3nhtZBX4t86L10FuW/ZUzvdy4llO/HsBxMx5eJGGPUGmE1mHH17KxIu2J4/1hEIXBUQCIUwqZRW+805Sgir3T0lDwAIG7SAuHtf5K1+vRJiaB9uhddCbok6Izct2/K3exEIBBi47DVEXwxBSmjRQ+0fZ36GkZ+/gWVXvoZRb4Beo8P3Uz9BRkzpc1E7ApfCelBdouyo01Vwud+yIxDg2eWvIu5iCNJDbT/ol3m6ovusIbi8y3FGNYi93ArqzrvuS0q4N7Rdd4r9PKAvcV/Sp2VDXOyeXXPmEJgNJiR98/d9xcNv1DPIOnkFugq6/z4qgVwBgZOw4E3zYszqbDj52L5/m9KToPvtK5hSYgGpHOJuz0M2cSU0m+bDrHLMdNKjK62OzUnLhlvhffpeBAIBXlg2BlEXb1vq2NjLYdDlaTHg7VE4uHY3IBBgwMKREIqEcCulveyIBPKCe7NZbaMs+dquc0wZidDt31xQlpzlEHcfCNnkVdB8Ps/hy5K4jLa/vJS2v6SUtr/U78HbcjXH9UHDZaMhcnGGOiwBl4e/B7PeeO8DK5lLGW3Y+74Pl+KfZTvR+4OJmFSsDXvs7a1IdOA2LADICvNEUyJPNGkqyB4xTwCg9jNt0XvTTIhkEuSlKvH3qA+hdZA1Gx+EojCfch6izq3eJABv/PIuRFIxdHn52D51HVLCEyoopuVP6FHw+9dY4vevMSMLkvq2f+s8KNdnusHJzRWqXw+Xy/kqwp260dazoYd6ntSwlLrZyw2N5ryI2O+OWfalnbiC+pP7o8aQbkj8/T84+3mg8ZyhhfHyfNgkEVEFYcdHFdClSxereX67du2KdevWwWi03cDt2rXrXZ+Dg4NLPb9Wq4VWq7XapzcbIRYI7zuONV7qjpYfFXXEXBr94X0fW5Gcq3vBt1cbXJ78qb2jYhcSX3c0WzcFSXtOIfnXfyF0laHBguFovXUOgoavBgC4NK6JpqvHInLdz8g4eQVSP080Wj4azT6ahJtvfWXnFPz/aTqkG55ZM8Hyef+4jyvsu9qO64Nq7RritwnroIpPR83OTdH73bFQp2Qh9syNCvveSiWVwXn8fOR/vwFmtere4R8TbQd3x5D3J1o+75iw9pHP+cK74+HfJACbh1kvTv3cnOGQKeT4ZtR7UGfloEWfjhj5xRv4avgqpITEPfL3lpcWQ7qh3/tFZWfv+EcvO33fHQufxrXw/bB3bf5d4irDy9vnIT08AWc++eWRv8+RubSujxqTB+DKcwvuK7ykuhc8e7ZByJT1FRyzymWKD4MpPszyWRsXCtnMjyHq8Az0J36yY8yoPLUb3B1D359k+by9HOrYIYV17JfDVlj2qTNz8P3rn2Lo6onoPq4vzCYzgn8/i/hrkTCbzI/8nY7MFBcGU1yxshQbCtmsdRB1fBb643vtGDPHl/zzaWT+cxVSf0/UnjEQLb+ejcBBy2Cy8xSvTYZ0Q+9ibdjfK7AN22ZcH1Rv1xC/T1iHnPh01OjcFL0K27BxDtSGbfBiNzz5QVGeHBxbcXkCAIlnb+GXvkvg7OWKpqN64dkvZ2L/oBXIz3DsNnD7wd0x/P2iZwnfTHj4ZwmpkYlYN2AhnN3kaDOgM0aum4EvXln5WHV+VDTF0L7IO30RxjTH6WSuObQ7Wn9UdN+98Oqj33fvReQqQ6fvFyA3NAEhH/9s2Z/2zzXcXPUDWq+diHafz4BJp0fY+l/h3bUZYHaM0d1EVIQdH3RPa9aswcqV1g+6RslbYLRry/s+R8rBQKs1N+4sOCXxdYc2VWnZL/F1h+qG7eG2ukwVTAYjpCXedpGWOMeDqDWiJ3RZOUg5FPhQx9+PO/GWlIh3ybQXp01VWkZ1FA+vSy14O+HOG2EF+4rOIfV1R86N6IIwqUqrRd8BQCB0gsjD1fK9ARP6wqDSIOzdHyxhrr/+OZ4K/hLuHRohOzAMdd8YAuWFUMRs+gMAkHszFsa8fDzxxyqEr9lj9f2VzZilgtlghNjHw2q/2McDhhJvzVUVEUeCkHS5aBFgkbSgGpf7KKAu9m8h91Eg7WbsQ3+PUCpG9wUv448pnyLqeDAAIP12HHyb10GHKc87bMeHOVcFs9EIJ4UHijc7BW4eMKmy7grv5FsdTj7VIJtRrI4r7Eh2/eIvqJdPgjk9qYJjXf5uHg1EXHBRnSuUFFwnrr7uyClWNlx93ZF0H1McvLByHJr2boctL6+CKrnoR5BXbT90G9cXnzw3H6lhBT8Yk2/Fou4TTdF1zHPYv8T+I8PuCDsShMRiZedOnriUKDsuPgqk3EfZ6bNqDBo+0w7fv7waOcl3/zCUuDjjlZ3zoVXn4+cpn8JkcJy3bfWZOQV15133JY9S63R9qhLiEvclsa879IXhFZ2bQezjjo6Bmy1/F4iEqLdiDGpMeR6BT1gvcO03ojf0WbnILGOOdnsz56lgNhkhcLXOJ4GLO8y5yvs7ickIU1I0nLyqlX8EyW5uHg1EbLE6ViQpaNeWrGPdfN2ReDP6nucbvHIcmvVujy9fXonsEvVJ2Olr+PDp2ZB7usFkNCJflYd3Ln6JK3/8Vy5pqQzmvIJ7s8DFRlnKUd7fSSxlyb/8I1jO9GW0/UurY3WltP21qQ++rokxRwNNjgaaqGRkB4bi6dBt8B3wBFJ+PfvA5ypPkUeCkFz8PlysDZtXzm3Ybgtexp9TPkV0iTZs+ynPO1THR+zhIPxio20i81FAUyxPZL4KZNx4+Dy5w6DRQhWdAlV0ClKDIvDy6Y/RZMTTuPLFH4987op0o0SdKyysc91s1LkJ92jXGvVGpBeOSo6/HoWA1g3w1IT++Glx6VOFOxKjsuD3r7DE71+htycM6Xf/1nlQohp+kHVth+Q3bb/UYy/JhwKRFXT386SSz4Kkvu7Ivh5t8xwP8jxJ6OKMzrvehiFXg4vj19+1bl3kV38j8qu/IfX3hD47F/IAXzRbOhLqmNSHTyQ9kCr+/geVI67xUQWcP3/e6vO5c+fQqFEjCIW2R2ScO3furs/NmpU+9/2iRYuQnZ1ttb3sUnp4W4zqfORFp1i23JB45KdkwefJos4TkasMHu0bQnnJ9hybZr0RqqtR8C52DAQCeD/ZElmlHHMvtUY+jYS9p++6kZUns96InKuR8HqyVdFOgQBeT7ZE9qUwm8dkB4bC60nrjiXvp1shuzCdmphUaFOy4F3snEJXGRTtG0JZeM7sS2EQe7jCrXXRPJOePVpC4CSAqrDRIJRJ73orwTIHfeHDX6FMCnPJMCZTYRAB7MmsNyDvWjhcu7cp2ikQwLV7a6iDbtsvYhVIr85HdkyKZcsITYA6VYmA7kWLnEpcZajWtgESA21fX/dDKBZBKBFZ/q3vMJtMEDjZ99+9TEYDTLFhEDZtW7RPIICwaVuYIu9ec8KUHAf1qqnIe2+GZTNcPQdj6BXkvTcD5qy0yot7OdKp85ERk2LZUsMSoErNsloMV+oqQ0DbBogNKvs6eWHlODTv2xHfjHoPWfHW+SGWSQHgrjePTSYTBALHamLo1PnIikmxbOlhCchNVaJuibJTo20DJNwjT/qsGoPGfTvix5HvIzvu7mtE4irDiO8XwqgzYt/E9TDa+S3bksx6A3KvRsK9xH3JvUcr5FyyPQ1ITmAoPIqHB+DxVBvkFN6X0vb9g+DecxH87DzLpk3KQMKm33FzxOq7zuc/ohfSfvqnQu+/j8xohCkxCsJ61u0OYf0WVqM6yiQQwMk/AObcR38YQY5DW6KOTQmLhyo1C426FV0rd+rYmHvUJ4NXjkPLvk9gy6jVd9WxxeVl5SBflYcGXVvAxVuBm0cr7qWdcmc0wpQUBWH9kmWpJUzx99mGt5QlZYVEsTyV1vb3vEfb37NE29+rWNv/oQkEAARwKnxQbE8l27CZZbRhk/9P2rB6db6lI0IVnYKs0ATkpShRs0dRnohdZfBt2wApj5AnpREIBBBK7X9t3ItWnY/0mBTLVlqdW7ttwwder0PgJLB0pDwW9AZob4ZB1qVd0T6BAPIubZEffPORT694sQ+MmUqo/zl/78CV6L6fJ7VrgKxS6lmz3ojsq1FWx0AggE+PFlbHiFxl6LJnEUx6Ay6O/bjM0XLalCyY8vWo+WI3aOLTkX016tETS0TliiM+qoDY2FjMmTMHU6dORVBQEDZu3Ih169aVGv6nn35Cx44d0aNHD/zwww+4cOECtm7dWmp4qVQKqVRqte9BprkqTfSWA2j41otQRyVDE5uKRgtfhjYlCykHit7+7LRvKVL+voiYbQULa0Vt/gutP5uO7OBIKC+Ho96UARDJpYjf/Y/lGImvO6R+HpDXK3gjzK1ZbRhyNchPSIe+2OKz3k+2hLyOP+J+qPhFVWM2/4UWn82AKjgCqssRqD1lAIRyKRILF1RvsfF1aJMzEf7eLgBA7JYD6Lh/OepMG4i0o0GoNqQbFG0a4Oa8ry3njN3yN+q99SLyopKgiU1Fg4WvQJuShbQDFwEA6rAEpB+7jObrpuLWgq8hEInQdM14JO8/C21KwQOYtKNBqD11AOrPecky1VXDxSOgiU1FzvWCm3ba4UA0XzcFtcY+h4yTVyDx80STd8ciOyjMch57SvvmN9ReNxt5V8ORdyUUvhNegJPcGZk/FczDWXv9bOiTM5G0dieAgkV9nRsVzH8qkIggruYFWfN6MKrzoYt5/N7sB4CgrQfR+Y0hUEanIDs2Fd3mDYM6VYmIw0UPRV7atQjhBy/hyo4jAACxXAqPukVvTSoCfOHbvDbylWrkJGZAl6tB3H+38OSSkTDk66FKSEetzk3R/KUe+GfVD3fFwZHojv4C53HzYIwJgyk6BOLeL0IgcYb+bME8tc7j5sGkzIBu/3bAoIcpscSbYRo1zID1frkrnLz8IPDwBgA4+RcssGpWZcFsYySJI/p320H0nvUiMqKTkRmXhufmDkdOihI3DxfVuRN/WIybhy7hv50FeTX43fFoM7gbvpu8Dlq1Bq6Fb0jlq/Jg0OqRFpGI9KhkvPj+RPz9/o/Iy8pB8z4d0bBHS+ycULHTNZSHi1sPotusIciMSkF2XCqemjsMOalKhBYrOyN/XITQQ5cQWFh2+q4eh+YvdMW+yZ9Ap863zEOuLcwTiasMI75bCLFMgt/f/BJSNxmkbjIAQF6GymGmp0n86g802jATuVcikHs5HDUmPw+hXIrU3QVrkTTaOAu6pAzEvP9jQfiv/0bLX1eixrRByDoaCJ8hPeDapj4i5heM8DBk5cJQYp5ws8EIXaoSmohEq/3uPVrBuY4/Un44WgkpfTT6//6C9MXpMCVGwpgQDnGX/hCIpdBfLmh3SF6cDrMqC/pjuwEA4qeHFkx3lZkCgbMc4m6DIHD3hT6o2BovMhc4uftA4FYwD7TAuzqcAJhzlXetJ/I4y8vTIDa+6N8+ITEFt0Mj4K5wQ/VqfnaMWcU4s+0Aes8agvToZGTGpaLP3OFQpWThRrE6dvIPS3Dj0EWcLaxjh7w7Ae0Gd8OOyeuQb6OOBYCOw59GangCcjNUqNO+MV5YPgZnth5AWuTj1WbRny1WluLDIe46AAKJFPqgwrI0dAbMqkzojxaWpZ5DYYoLhykzuaAsdR8EgYcv9IEV324vD7Gb/0JzG23/pMK2f/PCtn9EYds/bssBtN+/HLWnDUT60SD4F7b9bxdr+4s8XOBc0wfSagV1h7xwTnpdqhK6tGw41/GD/+BuyDx5BboMFZyre6POG4Nhytch/djlys2A+3R560F0KmzDqmJT0dVGG3ZoYRv2arE2rHuxNqx7gC98mteGtlgbNv6/W+hR2IbNSSiYrrXZSz1wysHbsABwfetBtHtjCLKjUpATl4qO84YhL0WJmGIzFAzYvQjRBy/h5rcFeSKSS6EoliduAb7wKswTdWIGRDIp2r4xGLFHApGXooSzlxuaj30O8mqeiPrTsR5w369T2w7guVkvWurcfnNfhiolC9eL1bnTfliK64cu4szOgmcJzy8YgVsng5GVmAFnF2e0H9wdDbo0x5Yxa+yVjIei/PYX+K2ZB+31UORfC4HHmBchkDkjp3BNDr8182FMTUfGJ9sLDhCLIGlQGwAgEIsh8veGpGl9mPPyoY8t1kYTCOD2Yh/k7D8KGB1/yqbIrw+g0ewhUEcmIy82FU0XDkd+ShaSDxZdA11+WoLkAxcRva0gbyK/+gttN0yH8krB86T6k/tDKJcitvB50p1OD6FMiouvr4PIVQaRa0E7XpuhsgwzaDBjIFKPX4HZbEb1AU+g4czBCJyygcMQiBwQOz6qgDFjxkCj0aBTp04QCoV48803MWXKlFLDr1y5Ert378aMGTNQvXp17Nq1C82bN6/EGBeI/Px3COVStPp4MkQKObIuhODiiA+setTldfwh8XKzfE767T9IvBVovGA4JH4eyLkRgwsjP7BaPLDO2OfQaP4wy+euv68AAFx540sk7CnqIAkY1QuZF0KgDrd+IFMRUgrj3WDBy5D6eSDnRjSCRq6xxNu5pjdQ7K2k7EuhuDZ9Ixq+/QoaLh6BvKhkXBn3EdS3i+bLjy7Mv2YfT4FIIYfyQgguj1hjlX/XZmxE0zUT0GHfOzCbzEj96zxCFm+3/D3rzA1cm74RdV9/AXVmvgCTRgvlpVAEjVwDU37BeZL2/AORqwwBE/qi8YrXYFCpkXnmhtX0WPak/PMMRN7uqD5nFES+ntDcjETkmBUwFC74Jqnha9UAEft7ocmBDZbPflOHwm/qUOT+dw3hI5ZUdvTLxaUv/4RYJsWzayZAqpAj8VIofnltrdVb5u61/SArVpb8W9fH8L1F6e25/FUAwI2fTuHw3C0AgL9nfo4eC19B/8+mw9nDFar4dPy79idc/b5ocTdHZAg8Ba2bO6SDXoNA4QlTfCTyNi61TKch8PKDk/nBGqWiNl0hGzvX8lk2eTEAQPvn99D9+X25xb0indr8ByQyKV5cMwnOCjliLoZi+9gPLA/XAMC7jj/kxa6TLq89BwCYsmeZ1bl+mrcZQftOwWQw4tvxa9Fv4QiM+WYepC5SZMSkYN/czQg5GVwp6XoU5zb/CbFciv5rJsBZIUfcpVDsHWNddjxq+0HmWZQn7V97FgDw6t6lVuf6c+5XuLbvNKq1rIua7RsCAKaftl6/YlP32ciOT6+o5DyQ9N/OQuStQO0FIyDx9YD6RjRujHwP+vSC+5K0po/V27I5l0IQOmMD6iwcgTqLRkETlYTb49ci7/aDr+PiP6o3VBduQ1MJ999HZbxxDjoXBcS9hkHi6gFTcgzyv/8AKFyk2cndB6Zi9YnA2QWSQZMhcPWAOV8NU2IU8rcuhzmtaO5wUZMOkA6ZbvnsPPxNAIDu5D7oTxbNIf24u347DBNmLbR8Xrux4N4yuP+zeG/p3NIOe2ydLKxjXyqsY6MvhmCrjTrWpVgd262wjp1Woo7dM+9LBO47BQDwrV8d/ReMgMzdFVnxaTj++X6c3vp3JaSofBmv/wedXAFx7+FFZem7ssqSKySDC8uSRg1TUiTyv15mVZYcWWph279+sbZ/cIm2v7lE2//G9I2o//YraFDY9r9aou3v27cjmn9WNG1gqy2zAQCRH/2EqI/3wZSvh0fnpqg9pT9E7q7QpSmhPHcblwa+A326Y67hEFjYhn2mWBt2/z3asH6t62NYsTbsU4Vt2Js/ncKRwjbsgZmfo/vCV9CvWBv27NqfcM3B27AAcGXTnxDJpXjywwmQKORIuRiKg69a54mijh+ci+WJb5v6GPhTUZ50XVGQJ6F7T+GfOVtgNpng0bA6Gg9/E86ebsjPykXalUj88dJqZIU+HmWqpOObf4dEJsXwNZMhU8gRdTEEW0rUuT4l6lxXb3eMWv86FL4e0OTkIel2LLaMWYPQM9fskYSHlnvwHwi93OE1awxEPp7Q3o5E4tQlMGYoAQDi6r5WzxZEvt6o/cuXls+eE4bDc8JwaC5cQcK4orXZZF3bQVzDH6pfDlVaWh5FxOd/QCSXovXHkyBWyJF5IQTnR1o/T3Kpa/08KfG3c5B4K9BkwTBIfT2guhGD8yM/gK6w/eveui48OzQCADxzfoPV9x19YhY0cQXteL/ebdHozSFwkoihuhmDi+M+RurxKxWdZCJ6CAKz+QGf/pBD6dmzJ9q2bYtPP/30vsILBAL8+uuvGDJkyCN979/+Ix7p+KpGDBajknydNfaOgsM5YVbcO9D/mUkDHOMhsCN5/6CXvaPgcBRmx5o2yxE8pc23dxQcTttpj9FUFZVEMvM9e0fBoSzp+Hi+4FCRlo9hG7akc18xT0q6/hhMh1TZ5I7/QnyluyUy2DsKDmeG/PEYGV6ZbmXyt05Jg5J32TsKj52T/sPtHYXHTs+Un+wdBbvgkwQiIiIiIiIiIiIiIqoyONUVERERERERERERETk8EwT2jgI9Jtjx8Zg7efLkA4XnzGZEREREREREREREVJVxqisiIiIiIiIiIiIiIqoy2PFBRERERERERERERERVBjs+iIiIiIiIiIiIiIioymDHBxERERERERERERERVRlc3JyIiIiIiIiIiIiIHJ4ZAntHgR4THPFBRERERERERERERERVBjs+iIiIiIiIiIiIiIioymDHBxERERERERERERERVRns+CAiIiIiIiIiIiIioiqDHR9ERERERERERERERFRliOwdASIiIiIiIiIiIiKiezHZOwL02OCIDyIiIiIiIiIiIiIiqjLY8UFERERERERERERERACAL774AnXr1oWzszM6d+6MCxculBn+p59+QtOmTeHs7IxWrVrh77//tvq72WzGsmXLUL16dchkMjz77LMICwuryCSw44OIiIiIiIiIiIiIiIA9e/Zgzpw5WL58OYKCgtCmTRv07dsXqampNsOfPXsWI0eOxMSJE3H58mUMGTIEQ4YMwfXr1y1h1q5di88++wybN2/G+fPn4eLigr59+yI/P7/C0sGODyIiIiIiIiIiIiIiwvr16zF58mSMHz8ezZs3x+bNmyGXy7Ft2zab4Tds2IB+/fph/vz5aNasGd599120b98en3/+OYCC0R6ffvopli5disGDB6N169bYuXMnEhMTsX///gpLBzs+iIiIiIiIiIiIiIiqIK1WC5VKZbVptVqbYXU6HQIDA/Hss89a9jk5OeHZZ5/Ff//9Z/OY//77zyo8APTt29cSPioqCsnJyVZh3N3d0blz51LPWR7Y8UFEREREREREREREDs8MAbcH3NasWQN3d3erbc2aNTbzNz09HUajEf7+/lb7/f39kZycbPOY5OTkMsPf+e+DnLM8iCrszEREREREREREREREZDeLFi3CnDlzrPZJpVI7xabysOODiIiIiIiIiIiIiKgKkkql993R4ePjA6FQiJSUFKv9KSkpqFatms1jqlWrVmb4O/9NSUlB9erVrcK0bdv2fpPxwDjVFRERERERERERERHR/zmJRIIOHTrg2LFjln0mkwnHjh1D165dbR7TtWtXq/AAcOTIEUv4evXqoVq1alZhVCoVzp8/X+o5ywNHfBAREREREREREREREebMmYOxY8eiY8eO6NSpEz799FOo1WqMHz8eADBmzBjUrFnTsk7Im2++iaeffhrr1q3D888/j927d+PSpUvYsmULAEAgEGD27NlYvXo1GjVqhHr16uGdd95BjRo1MGTIkApLBzs+iIiIiIiIiIiIiIgIr7zyCtLS0rBs2TIkJyejbdu2OHjwoGVx8tjYWDg5FU0k1a1bN/z4449YunQpFi9ejEaNGmH//v1o2bKlJcyCBQugVqsxZcoUKJVK9OjRAwcPHoSzs3OFpYMdH0RERERERERERETk8Ez2jsD/iZkzZ2LmzJk2/3by5Mm79g0fPhzDhw8v9XwCgQCrVq3CqlWryiuK98Q1PoiIiIiIiIiIiIiIqMpgxwcREREREREREREREVUZ7PggIiIiIiIiIiIiIqIqgx0fRERERERERERERERUZbDjg4iIiIiIiIiIiIiIqgyRvSNARERERERERERERHQvJntHgB4bHPFBRERERERERERERERVBjs+iIiIiIiIiIiIiIioymDHBxERERERERERERERVRns+CAiIiIiIiIiIiIioiqDHR9ERERERERERERERFRliOwdASIiIiIiIiIiIiKiezFDYO8o0GOCHR/0UIwCVjLFic1me0fB4RyAwt5RcDgTG8XZOwoO58Kv1ewdBYcTIOFgzJJa6rX2joLDadwqzd5RcDhnv2R9UtKRb5fYOwoO5b1L79k7Cg7ncAteIyWJ7R0BB9RHmmnvKDgcVw+2TUp6dWobe0fB4RxdyucmJXV7ItHeUSCi/yN8ukJERERERERERERERFUGOz6IiIiIiIiIiIiIiKjKYMcHERERERERERERERFVGez4ICIiIiIiIiIiIiKiKoOLmxMRERERERERERGRwzMJ7B0DelxwxAcREREREREREREREVUZ7PggIiIiIiIiIiIiIqIqgx0fRERERERERERERERUZbDjg4iIiIiIiIiIiIiIqgx2fBARERERERERERERUZUhsncEiIiIiIiIiIiIiIjuxQSBvaNAjwmO+CAiIiIiIiIiIiIioiqDHR9ERERERERERERERFRlsOODiIiIiIiIiIiIiIiqDHZ8EBERERERERERERFRlcGODyIiIiIiIiIiIiIiqjJE9o4AEREREREREREREdG9mO0dAXpscMQHERERERERERERERFVGez4ICIiIiIiIiIiIiKiKoMdH0REREREREREREREVGWw44OIiIiIiIiIiIiIiKoMdnwQEREREREREREREVGVIbJ3BIiIiIiIiIiIiIiI7sVk7wjQY4MjPoiIiIiIiIiIiIiIqMpgxwcREREREREREREREVUZ7PggIiIiIiIiIiIiIqIqgx0fRERERERERERERERUZbDjg4iIiIiIiIiIiIiIqgyRvSNARERERERERERERHQvJoHA3lGgxwRHfBARERERERERERERUZXBjg8iIiIiIiIiIiIiIqoy2PFBRERERERERERERERVBjs+iIiIiIiIiIiIiIioymDHBxERERERERERERERVRkie0eAiIiIiIiIiIiIiOhezPaOAD02OOKDiIiIiIiIiIiIiIiqDI74qKK+/fZbzJ49G0ql0t5RuacmC4ah9ujeECtckHkxBNcWboM6KrnMY+qOfw4NZgyC1NcdqpuxuL7kWygvR1j+3nrtRPg81QrO/p4w5OUj62Iobq3ehdzwxLvOJfZ0xdPHPoCshjcONJ4Igyqv3NNYmlrj+6DujEGQ+Hkg92YMbi/eDlWxdJTkN6gLGi58Gc4BvsiLSkb4uz8g/ViwVZgGC4aj5qvPQKRwgfJiCG4v+AZ5xfKzx8WNkNX2szombPWPiN74m+Wz/wtdUO/NFyGvXx26DBXith1CzKY/yifR5eTJOS+h7chekCrkiL8UikNLtiMrOqXU8F1nDEKTfk/Aq0F1GPJ1SAgMw4kP9iAzMgkA4OzugifnvIR6T7aCoqY38jJUCDsciFPr9kGbo6msZD0U2eAhkL88Ak5eXjBERCBn4wYYQm7bDOs8YCCc+/SFqG49AIAhNAS5W7+2Ci9wlsFl8hRIu/eAk8IdxuQk5P3yM/L//L1S0vMwao3vg9rFylLofZSl+oVlSVNYljKKlSXfAZ1Qc+yzULSuD7GXG873XoDcGzGlnq/Nj2/D55l2uDLuI6QfuFSeSStXT8x9Cc1H9oLUXY6ki6E4tXg7sssoN9U7N0G7qc/Dt3U9uPh74sCkTxB1KNAqTO/1U9B0+FNW+2JPXsWfr62tkDSUp+rj+yJgxguQ+BZcNxFLtiHncnip4X0GdUHdBSMs103k6u+Rdeyy5e915g2H7+DukNb0hklnQO7VSESv2VXmOR2N8wtDIB9eVJ/kflFGfdJ/IJyf6wvhnfokLATqbSXqEw9PuE6eCnGHJ+Dk4gr9tSvI/WIDjAkJlZKee3HE+7C8QXU0+2gyXBrXhMhNDm1KFpJ/+ReRH++D2WAsv8Q/oj5vDUOnkb0hU7gg+lIIfl26DenRpbffes0YjJZ9n4BfgxrQ5+sQHRSKAx/sQlrhfRgAvGr7YeCSV1G3YxOIJCKE/HMVv634Frnp2ZWRpAp3Kfgatv+4DzdvhyMtIxMb1ryDZ57qZu9olZvGC4Yh4NWCNn3WxRBcW7DN6tq3pc7451B/xiBI/Qra9DcWf4vswjIo9nBB4wXD4fN0K8hq+kCXoULywUsI/WAvDIVts1qvPIU2n023ee4jLaZCl64q30TeQ4MFw1Hr1d6W8n9rwdZ75kGApR5yR+7NWNwqUQ85ScVovOJVVBvSDU5SMTJOXMGtt7dBl1ZULpq8NxaeTzSBa9MA5IYl4Nwzb1t9h3OAL566tPGu7z4/YCmyA+13j/IcPRBek16CyNcT2ttRSF71JfKvhtoMK2lYG76zX4Nzi4aQ1PJH8ntfIevb32yGBQDvKcPhN388Mr/dj5T3tlRUEsqd6/DBcHv1ZQi9vaALi4Dyo43Q3QyxGdZlyAC4DOgDcYO6AADd7VBkf7G1KLxQCPfpE+DcvRNENavDnKtG/oUgKD//Bqb0jEpK0aPbHRSNHRcjkaHWorGfAgufaYFW1T1KDa/K1+Pz0yE4HpaM7Hw9qitkmN+7OZ6sX3Dv3XouHMfCkhGdkQupWIg2NTwx++mmqOvlWkkpejj2eG4i9nRF+y9mQtG8NsSertClq5B86BJuv78HhlzH+o0sHTAEsqEj4OTpBUNUBPK+2gBDmO02rKTrk5ANfxVO1WtCIBLBmBgPzf690J04XBBAKIT81UkQd+wCYbXqMKvV0F8JhHrHVzBnPj5lh4g44uOedDqdvaNgxdHi86gazByEehP74eqCrTg94B0Y87TovPttOEnFpR5TY3AXNF/xGkLX/YxTfRZDdSMGnXe9DYmPwhJGeTUKwbM348RTc3F+xBpAIECX3YsAJ8Fd52u7fgpUt2IrJH1l8R/cFU1WjkHkup9x/rm3kXMjBu13L4a4WDqKc+/YGK02v4GEH0/g/LNvI+3ARbT5dj5cmgZYwtSd+QICJvXHrQXf4MKAJTDm5aPdnsV35Wf4B3vwT8spli1260HL37x7t0XLTbMQv+MI/us5D7ff3oo6UwcgYELfismIh9Bl2kB0HNcHBxdvw47By6HP0+KV7xZCWMZ1U7tzMwTuPIKdQ1Zg96sfwkkswojvFkIskwIAXP094ervgePv/Yhvnnsbf83bgvpPt8aAtZMrK1kPRdqzF1ynvQ71zh3InDYZhogIeHz4MQQeHjbDS9q0hfb4MSjnzkbWrBkwpqXBY+3HcPLxsYRxnf46JE90gmrNe8gYPwZ5P++D2xtvQtLVMR/O+A3uikYrxyBq3c+4+NzbyL0Rg7b3KEstNr+BxB9P4EJhWWpdoiwJ5VJknw9B+Oof7/n9AVMHPBZjbdtNH4jW4/vgn8Xb8POg5TBotBj4fdnlRiyTIv1WLE4t3VHmuWNOXMH29q9btiMzPy/v6Jc738Hd0GDFWMSs+wlBfRZCfSMGLXctKfW6UXRsjGZfzkbyruMIfG4B0g9cQIvtCyAvdt3kRSQhfPFWBPaciyuD30F+XBpa7XkHYm/b53Q00qd7wXXq61B/vwNZ0yfDEBkB9zWl1yfiNm2Rf+IYlPNnQ/nmDJjS0uD+wcdw8i6qT9xXvgenajWgWrYEWdMnwZiSAvcP1wPOzpWUqtI56n3YbDAiae8pBL38Ps52fwuh7+xAzVd7o8GC4RWTEQ+h57RB6D6+H35ZshUbh7wDnUaLiTvfhqiM+qR+52Y4+91hfP7iMnz92vsQikSYtHOR5T4slkkx+bvFMJvN2DJqNTYNWwGhRIhx38yDQHB3++1xpNHko0nD+lgyd4a9o1Lu6s8chLqT+uH6gq34d8A7MORp0XlP2W366oO7oNnK1xC27meceW4xcm7EoPPuoja9tJonpP4euLXyB5x6ej6uvLkZvr3aoPUnUy3nSPztPxxtOc1qSz1+BRn/3qz0To+6M19A7Un9cHPBNzg/YCmMeVq037OozDwoqIdeQ8S6fTj33CLk3IhBh92LrH7XNFk1Br59OuDq5E9xcchKSKt5os22OXedK2HXSST/9l+Zcbw0bDVOtpxq2VRXoh4+wY/IbcBT8Fs8Gemf/4ioIbOQfysStbe9C6GXu83wTjIp9HFJSPt4OwypmWWe27lVI3iM6I/8W5EVEfUKI3uuJzxmT4Pqm51Ifm0a9GER8N34IZw8PWyGl3Zog7zDx5E6fS5SJsyCMSUNvp+vhdC34D4scHaGpGkjqLZ+j5TXpiF9wQqI6gTAd927lZiqR3PodiLWnbyFqd0aYdeYHmjs64YZP51HplprM7zeaMK0n84jUZWHj15oj/0Tn8ayvq3g51rU7giMy8Qr7epg56vdsXl4ZxhMJkz/6QI0OkNlJeuB2e25icmM5EOXcGHsxzjRfQ6C3/wSvk+2RKu1Eys6yQ9E0qMXXCa9Ds2uHciePRnGqAi4rfoYAncPm+FNOTnQ7P0eqvmvQzlrArRHD8D1zYUQt3sCACCQOkPYoDE0e3ZCOXsycta8A6eaAVAsfb8SU0VE5YEdHyX07NkTM2fOxOzZs+Hj44O+ffvi+vXr6N+/P1xdXeHv74/XXnsN6enplmP27duHVq1aQSaTwdvbG88++yzUajUAYNy4cRgyZAhWrlwJX19fKBQKTJs27b47MGzFBwDWr1+PVq1awcXFBQEBAZgxYwZyc3MBACdPnsT48eORnZ0NgUAAgUCAFStWAAC0Wi3mzZuHmjVrwsXFBZ07d8bJkyfLLwMfUP3J/RH66a9IORSInFuxuDxrE5z9PVGtX8fSj5n6PGJ/OI643f8gNzQBVxdshVGjQ+0RPS1hYr8/jsxzt6GJS0f2tWjc/mAvZLV8IA/wtTpXnbHPQuTugohNf1VUEktVZ9rziP/+GBJ3n4Q6NAG35n8Do0aHmiN72Qxfe0p/ZJwIRsymP6AOS0DEh3uhuhaF2sU6JGpPGYCoT35B2sFLyL0Zixszv4DU3xO+/Z+wOpdRnQ9dWrZlM+UVNRyrD38SaQcuIX7nUWhiUpF+9DKiPvsNdWe+UDEZ8RCemNgP/37+G8KOBCHtdhz+nLMZbn4eaNynQ6nH7Bm7Ftf2nUZ6WAJSb8Xiz7lfwb2WD6q1qgsASA+Nx6/TPkP4sctQxqYi5uxN/PPRT2j4TDsIhI5bVcqHvQzN338i/9ABGGNikPPpOpi1+ZD1G2AzvGrNamh+3w9DRDiMcbHIWbcWEDhB0q4o78QtWiD/8CHorwTDlJKM/L/+gCEiAuKmzSorWQ+k9rTnkfD9MSQVlqXbhWWpRillKWBKf2SeCEbspj+QF5aAyA/3IudaFGoVK0vJ+04jav3PyDx1rczvdm1RB7WnDcSt2V+Wa5oqQuuJ/RC48TdEHw5Cxu04HJu9GS7+HqjXt/RyE3vyKi58tA9RB8sexWLU6aFJy7Zs2uzKGzn3sGpOHYikH44hZfdJ5IXGI2zBFpg0OlQb0dtm+BqTn0fmiWDEb/odmrAExKzdg9xrkagxvp8lTNqvZ6A8fQ35sanIC4lH5PIdECnkcGlWu7KS9UhkL72M/AN/QnvoAIyxMcjdUFCfOPe1XZ/kfLAa+X/sh/FOfbK+oD4RF9Ynwpq1IG7eArmfrYch9DaM8XHI/Ww9BBIpnHs9U5lJs8lR78OamFQk7j6J3JsxyI9PR9qhQCT/fAYenZtWTEY8hB4T+uPYxl9x80ggkm/HYs+cTVD4e6JFn9Lbb1vHfoDAfaeQEhaPpFux2DvvS3jW8kWtVgUjhup2bAzPWr7YO28zkkPikBwSh71zv0St1vXRoFuLykpahXqy6xN4Y8pYPPt0d3tHpdzVm9If4Z/8ipSDgci5GYsrMzdB6u8J//6lXxP1pj2PuO+PI76wTX9tfkGbPmBkTwBA7u14BE38FKmHg5AXk4qMMzcQsmYP/Pq0t7TNTPl6aNOyLZvZZIJPjxaI+/FEZSTbSp0p/RH5ya9IOxiI3JuxuF5Y/v3KyIO6055H/PfHkbj7H6hDE3DT0obpCQAQuclQc1QvhC7/DplnbiDnahSuv7kZnp2awL1DQ8t5QpbsQNz2w9DEpJYZR31mjlXdY89RZN4TXoRyz0Fk/3wEuvA4JC/7HCaNFh7D+tgMn38tDKkfboPqr1Mw6fSlnlcgd0aNdQuQtPQzGFW5FRX9CuE2ahhy9/8N9R+HYIiKQdaaT2HK18LlhX42w2e+swa5+36HPjQChpg4ZK5eBwgEkD7RDgBgVquRNnMBNEf/gSEmHrrrt6D8aCMkzZtA6O9n85yO5rtLURjaOgBDWgWggY8blvZpBWexEPuvx9kMv/9aHFQaPT4Z0hHtanmhprscHQO80cSv6GH/puGdMLhlABr6uKGJnwKr+rdBkkqDmymOO7rQXs9N9NlqxOw4iuwrkdDEpyP9zA1Ef3sE3g7UJgEA5yEvQ3voT2iPHYAxLgbqTesAbT6kz9luwxquB0N37jSM8TEwJSci/4+fYYyOhKh5KwCAOU+NnGVzoTtzAqaEOBhCbkL91QaIGjWFk+/jUXaIqIDjPs2zox07dkAikeDff//FBx98gN69e6Ndu3a4dOkSDh48iJSUFLz88ssAgKSkJIwcORITJkzArVu3cPLkSQwdOhRmc9Hrv8eOHbP8bdeuXfjll1+wcuXKh4rP5s2bAQBOTk747LPPcOPGDezYsQPHjx/HggULAADdunXDp59+CoVCgaSkJCQlJWHevHkAgJkzZ+K///7D7t27cfXqVQwfPhz9+vVDWFhYeWXffZPX9oOzvyfST1237DPkaKC8HAHPjo1sHiMQC+Heup7VMTCbkX76eqnHCOVS1B7xNNQxKdAkFg1LdG1cE43nDEXwrE2A2VQ+ibpPArEQbq3rI/N0sYeqZjMyT12DeynpcO/QGJnF0w0g48QVuHdsDACQ1fGD1N8TGcUe1BpyNFAFhcOjxDnrzhqMp299g85HP0CdGYOsHuw7ScQwaq075kz5OjjX9IFziY4je/AI8IWrnweizxTlhTZHg8TgCNRsbzvvbHF2kwMANEp1qWGkCjl0uRqYjZV7fdw3kQiixo2hCyo29ZDZDF1QIMTN7+8hkUAqhUAkgimn6K1I/Y0bkHbtbhkFIm7bDsJaAdBduliu0S8PpZWlrEcoS/fLSSZByy/fQMgi6+kmHJGiti9c/D0Qd7oo3bocDVKCI1DtAcpNaWp2aYZxl7/AyJMf4an3x0Hq4dhTBQjEIri1rg/lqatFO81mKE9fhVsp14GiQ2Pr8ACyTl6BopTwArEI1V97FoZsNXJvlj5NmsMopT7RP0R9Yr5Tn4glBacp/rKH2QyzXg9xy1blFfOH4sj34ZJkdf3h3astsv679aDJrBBeAX5Q+Hki7N+ivMjP0SAuOAJ1HuI+nKcseDApkohhNpthKPZAU6/Vw2wyo94TTcop9lQRZHVKadMH3Ueb/nSJNv2p63eVl+LECjkMOaW3zWoOfwpGjRZJf55/uMQ8pDvlP7NE+c8OCi+1fVFQD9VDho16yKPwGEWb+nCSiKzqlbzwRGji0h643QIAbXfOR88bX+GJ31fAt4wXHyqcWATnFg2hPhtctM9shvpsMGTtHu2BarXlM5B78gLyip/7cSASQdK0MbQXgor2mc3QXgiCtFXz+zqFwFkKiEQwqXJKD+PqArPJBFOu43cK6Y0m3ErORuc6RSNJnQQCdK7jg6uJSpvHnAxPQesaHlhz9Dp6f3EEL23/B9+cC4fRVPrw7FxtwUgPd2dJuca/vNj7uUlxUn9PVHu+EzIcpE0CoKAN27AxdFdK/CYODoS4yf21YUWt20NYMwCGG1dLDeMkLyg75seg7BBREa7xYUOjRo2wdm3B3OSrV69Gu3bt8P77RUPatm3bhoCAAISGhiI3NxcGgwFDhw5FnTp1AACtWln/mJdIJNi2bRvkcjlatGiBVatWYf78+Xj33Xfh5HTvvqfi8blj9uzZlv+vW7cuVq9ejWnTpmHTpk2QSCRwd3eHQCBAtWrVLOFiY2Oxfft2xMbGokaNGgCAefPm4eDBg9i+fbtVGiuD1K9gGLO2xANDbVo2pH4eNo+ReCngJBLaPMa1YQ2rfXXGPYfm74yCyMW5YK7bl9+HWV/wVpOTRIT2X87CzVU/QpOQAXmdyu21v5OOkg9LdWnZcGlUw+YxUj8P6NKUd4WXFOajxNfDsq84bVo2JMXyM/abg8i5FgW9MhceHRuj4ZKRkPp7IHT5dwCAjJNX0GTVGCTt+QeZZ25AXq8aak8bWBAHfw/kx6U9bLLLhUthWtQlpi9Qp6vg4mt7aPxdBAI8u/xVxF0MQXpovM0gMk9XdJ81BJd3Vf4bg/fLyd0dAqEIpqwsq/2mrCyIAu7vDXOXydNgykiHLrCooZjz+Qa4zZkHnz0/w2wwACYTctZ/DP210huC9iIuoyzJSylLklLK0p066X41XjUWykuhSL/HaAhHIC+sHzQlyo0mTQX5A6a7pNiTVxF54BJUcalwr+OPzgtexsDv5uOXwStgLuNHpj2JvdwgKOW6cW9Y0+YxBddNyfBKq/oVALyea49mm9+Ck0wCXYoSV195F4bM0h9AOIqy6hPx/dYnkwrrk8LOE2NcDIwpyXCZOAW5n34Mc34+ZC8Nh9DPD0Yv73JPw4Nw5PvwHU/8uQpurepB6CxB/M6jiPhw70OktPy5Fd5rc0ukMyctG26FeXAvAoEALywbg6iLt5FSeB+OvRwGXZ4WA94ehYNrdwMCAQYsHAmhSAi3UtqF5Bicfcu3TV9aGRR7uaHhWy8i7vtjpcYlYFRPJP5yFqb80kcEVIQ75b9kenT3kQe26gyXRgX3IomfB0xa/V1rEOrSsyG9z/IGFIwyC1m2E8qLoTCbTPAf2Bltv52L4HHrkFZi7a7KIPJUQCASwphufc8xZighbRBQylH3pnj+KTi3aIjooW8+ahQrnZOHe0GeZJbIk8wsiOreX554zJoMU3oG8i+U8m8qEcNj5mTkHT4Os9rxR+dmaXQwms3wlkut9nvLpYjOtP3yWkJ2Hi7GajCgeQ18/lInxCnVeP/IdRiMJkzrfndnoclsxkfHb6JtTU809HWrkHQ8Kns+N7mj/ZezUK1vBwjlUiQfCsSVuY6zbo5AUdCGNZdow5qVWRDUKr0NK5C7wPPbfQUv6piMUH/5KfTBpfyuE0sgHzcVulPHYNY4ftn5f+Cgr6aSA2LHhw0dOhS9/XLlyhWcOHECrq53v70aERGBPn364JlnnkGrVq3Qt29f9OnTB8OGDYOnp6clXJs2bSCXyy2fu3btitzcXMTFxVk6S+43PnccPXoUa9aswe3bt6FSqWAwGJCfn4+8vDyr7yru2rVrMBqNaNzY+oav1Wrh7V36AwitVgut1noOTb3ZCLFAeM+4F1dzaHe0/miS5fOFVyt24duEn88g/Z9rkPp7oMH0geiw5U38+8IKmLR6NF08ArlhCUj4+UyFxsERxX5VNK1X7s1YmPQGNPtoMsLe2wWzzoCE745BVscfbb9bCIFYCGOOBrFfH4DrguF2eYjZYkg39Ht/guXz3vEfP/I5+747Fj6Na+H7Ybbnt5W4yvDy9nlID0/AmU9+eeTvc1TyEaPg3Ks3sua+CeiL3siWDRkKcbPmUC5dBGNKMiSt2sD1jdkwZqRDH1T5P44dkU/fDvDs0QIXnllo76jY1GhIN/T8oKjc/DXu0ctNacJ/P2f5/8zb8ci4FYtX//0ENbo2R8K/Nyrsex2V8t8bCHxmPsRebqj+6rNovmUOLg9YBH0lzzVf2WSvjIK0Z28o5xWrT4xGqFa+A7e5C+Dz618wGw3QBwVCe+EcBKgaazY8jHvdh++4OmUDRK7OcGtRB42WvYo6MwYh5ovfKz2+7QZ3x9D3i9pv2yc8evttyLvj4d8kAF8OW2HZp87Mwfevf4qhqyei+7i+MJvMCP79LOKvRTpsJ+r/qxovdUerYm36i6Mrtk0PACJXGZ74YQFyQxMQ+tHPNsN4dGwEtya1EDxzU4XHp9pL3dH8o6J14C6P/rDCv/NR6DNzEPPV35bPquBISP09UXfGILt0fFQEUTUf+C+dithxS2AuYyqsqspt7AjInuuFtGlzAVvpFwrhs2YZIBAg64MNlR/BSmIyA15yCd7p0xpCJwGaV3NHak4+dlyMtNnxsebIdYSn5+DbUV3tEFvbHOm5yR03lu1E6Lqf4VK/OpotGYEWK1/Dtbe3VWi8KppZkwflm5MgcJZB3KY95BNnwJicCMP1YOuAQiHcFq4ABAKoN623R1SJ6BGw48MGFxcXy//n5uZi0KBB+PDDuxuz1atXh1AoxJEjR3D27FkcPnwYGzduxJIlS3D+/HnUq1ev3OMDANHR0Rg4cCCmT5+O9957D15eXjhz5gwmTpwInU5XasdHbm4uhEIhAgMDIRRad1rY6ti5Y82aNXdNzTXCpQVGuT7YNBXJhwKRFRRu+XxnIS6przu0qUrLfqmvO7KvR9s8hy5TBZPBCGmJN/tLngMoGP5pyNFAHZWMrMAw9Av5BtX6P4HE/Wfh06MFFM1qo/rAzgBgWTSz780tCNuwH6Ef7XugtD2oO+mQlEiHxEY67tCmKi1vkxUPr0steIvjzluoBfuKziH1dUfOjehS45IdFA4nsQiyAF/kRSQBAMJX/4jw93cVvN2aoYLXkwX/1pqYlPtPZDkJOxKExMsRls9CSUG15eKjgLpYOl18FEi5ee9F6vusGoOGz7TD9y+vRk7y3QsjSlyc8crO+dCq8/HzlE9hsuPcx/diys6G2WiAU7GOVgBw8vSEKbPsRR9lw1+BfOQoKOfPhTGy2MKPEglcJ05G9vKl0J0veKCtiYyEqGFDyIe/gmwH6/jQl1GWdKWUJV0pZUmbev/TVXn2aAlZXX88Fbbdan/rrXOhPHcLQUNX3fe5KkL0kSDsCb673Mh8FMgrli8yXwUybty73DwIVWwaNBkquNf1d9iOD31mDswPdd2UDO9xV3hTnhb50cnIj05GTlAYnjj7GaqN7I24jfvLMQXlr8z6JOse9cmwVyAfMQrZC+fCGGW9kKwhLBRZ0yZBIHcBxCKYs7Ph8dmXMISFlHsaHoSj34cBQJuYAS0AdWgC4OSE5h9PQcyXfxQ81alEN48GIja4qP0mkhS031x93ZFTbASMm687Em9G3/N8g1eOQ7Pe7fHlyyuRXeI+HHb6Gj58ejbknm4wGY3IV+XhnYtf4sofZS/YTJUr5WAglIH316ZXlXLtP0ibXujijE6734YxV4PA8etLXZei9uheyL4WDdXVil+wO+1gIP4rJQ+Kl3+Jrztybtie7rC0eqh4HuhSlXCSiiFSyK1GfUh83KEtMQLtQWUHhcP7aftMO2jIUsFsMELoY33PEXp7wJBW9j2nNM4tG0Hk44l6+zda9glEQsifaAnPVwfhdovBgMlx3xE2KQvWXBF6lcgTL0+YMsrOE7dXh0MxdiRSX58PfbiNBd2FQnivWQZhNX+kzZj3WIz2AABPmQRCgQAZedYvYWbkaeHjIrV5jK+LFCInAYRORS9Y1PN2RbpaC73RBHGxqSXXHL2OU5Gp2DaiK/zdZBWTiIfgSM9N7rizjlJueCL0ylx0/30FQtf/UmqbqTKZVQVtWEGJNqzAwxPmstqwZjNMSQkAAGNUOIQBdSAbPho5xTs+hEK4LVwJJz9/qJa8xdEeRI8hrvFxD+3bt8eNGzdQt25dNGzY0Gq70yEhEAjQvXt3rFy5EpcvX4ZEIsGvv/5qOceVK1eg0Wgsn8+dOwdXV1cEBDzcMN7AwECYTCasW7cOXbp0QePGjZGYmGgVRiKRwGi0/lHQrl07GI1GpKam3pWW4lNilbRo0SJkZ2dbbcNd7m+e0eKM6nzkRadYttyQeOSnZMHnyZaWMCJXGTzaNUDWJdtrjpj1RmRfjbI6BgIBfHq0KPWYgiACCCCAk7Tg4d+liZ/gn2cW4tSzb+PUs29bhmqeHbwS0dsPP3DaHpRZb0TO1UhLh0JhJOH1ZEtkl5KO7MBQeBVPNwDvp1sh+1IogILFULUpWfAudk6hqwyK9g2hLCNv3FrWhdlogq7k28gmM7TJWTDrjaj2YncoL4ZAn1H507Xo1PnIikmxbOlhCchNVaJu96L5OiWuMtRo2wAJQWWvVdNn1Rg07tsRP458H9k2puySuMow4vuFMOqM2DdxPYxaB39bzGCAITTUamFyCASQtGsP/c3SHzjLXxkJl1fHQPn2AhhCrR8+CkQiCMRiwFziwZrJBMF9TM1X2UorS573KEueJcqSV7GydD9iPtuP870W4MIzCy0bAIQu24GbDrDQuV6dD1V0imXLCk2AOkWJWj2Kyo3YVQb/tg2QfI9y86BcqnnB2dPVqoPF0Zj1BuRcjYRHievGo0cr5JRyHagCQ63DA/B4qjVU97punASWH6wOrZT6RHyP+kT28kjIXx2D7MV31yfFmfPUMGdnQ1izJkSNm0B71r4jLh+L+3AxAicnCMRCu9TDWnU+MmJSLFtKWDxUqVlo1K0oL6SuMgS0bYCYe9Qng1eOQ8u+T2DLqNXIii996sy8rBzkq/LQoGsLuHgrcPOoY3W6/78rrU3vXbJN3/7B2/TeT7awKi8iVxk6710Ek86Ai2M+tnoDuTihXIrqL3SptEXNjep8aKJTLJs6JB7alCyrOkLoKoN7+4alti8K6qEoq3y7Uw8pC49RXYmESWewOq+8QXXIAnwfqN1ii1vLOtCmKB/pHA9Nb0D+jXC4dG1TtE8ggEu3ttBcvv1Qp8z7LxiRA6Yj6oWZlk1zNRSq308i6oWZDt3pAQAwGKC7HWpZmByAZaFy7bWbpR7m9torUEx8FWlvvA39LRvXRGGnh7h2TaS9Ph+m7MdnBKpY6IRm1dxxISbdss9kNuNCTAZa1/CweUybmp6IVebBVOy3TEyWGr4uUkunh9lsxpqj13E8LBlbXumCmh62Xxy1F0d6bmJTYadSmWEqk8EAQ3goxK1LtGHbtIc+5AFewhI4FfwOvuNOp0eNmlAtnVO0hh0RPVYcpKZyXK+//jq+/vprjBw5EgsWLICXlxfCw8Oxe/dufPPNN7h06RKOHTuGPn36wM/PD+fPn0daWhqaNWtmOYdOp8PEiROxdOlSREdHY/ny5Zg5c+Z9re9hS8OGDaHX67Fx40YMGjTIatHzO+rWrYvc3FwcO3bMMtVW48aNMXr0aIwZMwbr1q1Du3btkJaWhmPHjqF169Z4/vnnbX6fVCqFVGr9RsWDTnNVmsivD6DR7CFQRyYjLzYVTRcOR35KFpKLzZnf5aclSD5wEdHbCjokIr/6C203TIfySiSUl8NRf3J/COVSxO7+B0DB4l81BndF2j9XoctQwbm6FxrOGgxjvg6px4IBAHkxqVbxkHgVzOeZE5Zw1xy6FSVm819o8dkMqIIjoLocgdpTBkAolyJx90kAQIuNr0ObnInw93YBAGK3HEDH/ctRZ9pApB0NQrUh3aBo0wA3531tOWfslr9R760XkReVBE1sKhosfAXalCykHShYlNq9YyO4t2+EzDM3YFRr4N6xccF6HvtOw5BdME+q2MsN/gM7I/PsTThJxag5sif8B3XBpRdXVEq+3I+LWw+i26whyIxKQXZcKp6aOww5qUqEHi56MDLyx0UIPXQJgTuOAAD6rh6H5i90xb7Jn0CnzresB6JV5cGg1Rd0eny3EGKZBL+/+SWkbjJIC9/8yctQOew0G3n79kKxcBEMobehv30b8peGQeAsg+bQAQCA28LFMKWnQb214DqRjxgJl7EToHr/XZiSk+Hk6QUAMGs0MOdrYM7Lgy74MlynTEOOVlsw1VWbtnB+ri9yv/zCbuksS+zmv9DcRllKKixLzQvLUkRhWYrbcgDt9y9H7WkDkX40CP6FZel2sbIk8nCBc00fSKsVvDkkL5wLV5eqhC4t27KVlJ+QjvxY+66DU5qrWw+iw6whyI5KgSouFZ3mDYM6RYmoYlNcvLBrESIPXsL1wnIjkkvhXtff8ne3AF94N68NrVKN3MQMiORSPPHWUET+fQF5adlQ1PFH18UjkB2dgth/HG9NmOISvvoTTTa8jtwrEVBdDketyc/DSS5F8u6Ch2ZNNs6ENikT0e//CABI/PovtP51JWpOG4jMo0HwG9Idbm0aIGz+VwAAJ7kUtd8cioxDl6BLzYLYS4Ea4/tCWs0LaY/J2+qan/fCbcEi6ENvwxByG7IXC+qT/Dv1yYLC+mRbQVmRvTISLmMmQLXmXRiTkyEoVp8gv+CFD8lTPWFWKmFMTYGoXn24zpgF3dkz0Afaf20cR70PV3upB8x6A3JvxcGk00PRpj4aLhmJlN/+K/VN98p2ZtsB9J41BOnRyciMS0WfucOhSsnCjcNF/66Tf1iCG4cu4uzOgvbbkHcnoN3gbtgxeR3y1Rq4Ft6H8wvvwwDQcfjTSA1PQG6GCnXaN8YLy8fgzNYDSItMujsSj6G8PA1i44teVkpITMHt0Ai4K9xQvVrlrjdX3qK2HECjt4ZAHZUMTWwqGi8cDm1KFlIOFF0TnfctQfLfFxFT2KaP2vwX2nw2HcrgSGRfDkfdKf0hkksRV9imF7nK0GnvIghlUgTPWAexqwxwLWibaTNUVqOfagzpCoFQiIR99utUjdlyAPXfehF5hXnQcOHL0KZkIbVYHnTYtxSpf19E3LZDAIDozX+h5WfToSrMg6J6qCAPDDkaJPx4Ak1Wvga9MheGHA2avT8eyouhyC424kRW1x8iF2dI/NwhdJbArUXBdMq5ofEw642o8fJTMOkNUF2LBgD4P98JNUf2wo05X1VS7twtY9uvqLF2DvKvh0FzNRRe4wbDSSaF8ueCNkj1tXNhSMlA2rpvCw4QiyBtWDBfv0AsgtjfG9Jm9WFSa6CPTYJJrYE2zHp0jUmTD6NSddd+R5Xz4z54L18I3a1Q6G7chtvIl+Akc4b6j4LrxWvFQhjT0pH9xVYAgNuYEXCfOhYZS9+HISkZTt4F7VZzngZmTX7B9FYfLoe4aSOkv7UEEDpZwpiycwCDwXZEHMhrHevhnb+voHk1D7Ss7o4fLkVDozdgcMuCl0iX/hUMPzdnvPFUUwDAy23rYM/lGKw9dgMj29dFTJYaW8+FY2T7upZzvn/0Og7cSsSnL3aEi1iI9Nx8AICrVAxncfk85yhv9npu4vdMW0h93aEMjoBBnQ+3JgFovmwUMs/fhiYu/a542kv+/r1wfWsRjOG3YQi9DefBBW1Y7dGCNqzrW4thykhD3s6CNpvzsNEwhofAmJQAgVgCccfOkPbqA/WXhVNZCYVwe3sVhA0aI2fV24CTEAKPwnZuruqxKDtEVIAdH/dQo0YN/Pvvv1i4cCH69OkDrVaLOnXqoF+/fnBycoJCocCpU6fw6aefQqVSoU6dOli3bh369+9vOcczzzyDRo0a4amnnoJWq8XIkSOxYsWKh45TmzZtsH79enz44YdYtGgRnnrqKaxZswZjxoyxhOnWrRumTZuGV155BRkZGVi+fDlWrFiB7du3Y/Xq1Zg7dy4SEhLg4+ODLl26YODAgY+STQ8t4vM/IJJL0frjSRAr5Mi8EILzIz+wepvLpa6/pWMCABJ/OweJtwJNFgyD1NcDqhsxOD/yA+jSCx5CGrV6eHVpgvpT+kPs7gJtWjYyzt3CmUHLy3ybsrKl/PYfJN4KNFjwMqR+Hsi5EY2gkWssD1Oda3pbvZmUfSkU16ZvRMO3X0HDxSOQF5WMK+M+gvp2nCVM9Oe/QyiXotnHUyBSyKG8EILLI9ZY8tOkNcB/SDfUnzcMThIxNLGpiPnqb8Rs/tMqbtVfeRqNVrwGgQBQXgrDpaEroSo23ZS9ndv8J8RyKfqvmQBnhRxxl0Kxd8xaqxEaHrX9IPMsum7av/YsAODVvUutzvXn3K9wbd9pVGtZFzXbNwQATD9tPXfnpu6zkR3vOA274rQnTyDX3QMu4ybAydMLhohwKN+eb1ncTejnB5iLriPZoMEQSCRwX2G9vol6x3aod34LAFCtXgWXSVOgWLwUTm4KGFOSkbvtG2j++K3S0vUgUgvLUv1iZSm4RFkylyhLN6ZvRP23X0GDwrJ0tURZ8u3bEc0/m2H53GrLbABA5Ec/Ierjip0Kr6Jc/vJPiORS9PxgAiQKOZIuhuLP16zLjaKOH2TF6lu/1vUx5Kclls89lr8KALj90ykcn7MFZpMJ3s0C0GRYD0gVLlCnZCHu1DVc+HgfTDrH/kGQ9ttZiL0VqLPgFUh8PZB7IxrXR74HfeG9RFrTx6rDU3UpFLdnbEDdhSNRb9EoaKKScGP8WuQVXjdmownyhjXh/3JPiL3coM/KQU5wBIKHLENeSLxd0vigtP+cgMDDAy5ji+qT7MXzYVYW1CdOJeuTgYX1yfIS9cnO7cj77lsAgNDLG7KprxdOwZeB/COHkPfDzkpLU1kc9T5sNhhRd+ZgyBtUBwQC5MelIW7bIau1Qezt5OY/IJFJ8dKaSXBWyBF9MQRbx35g6cAAAO86/nApVp90e+05AMC0PcuszrVn3pcI3HcKAOBbvzr6LxgBmbsrsuLTcPzz/Ti99W9UFddvh2HCrKK1odZuLBhxPLj/s3hv6Vx7RatcRBa26VsVtumzLoTgwgjrNr28jnWbPqmwTd94wTBI/Qra9BdGfmApg4rWdeHZoREAoNcF6zUJjnecZfXQLWBULyT/faHSXmCy5U75b/7xZEv5D7pHHhTVQ8ML66EYBBXLAwAIWbYTZpMJbbfOgZNUhPQTV3Fr4Var726xfiq8uheNyO96vGCa5lMdZyG/cJRz/beGQhbgA5PBhLzwRFydsgEpf56vkLy4Hzl/n0KqlwK+b74Goa8ntLciETtxGYwZSgCAuIav1T1H7OeF+r9/bvnsPWkYvCcNg/r8VcS++nZlR79CaI6chNLDHe5Tx0Ho7QldaATS3ngbpsIFz4XV/KxGZbu+NAgCiQQ+a1dYnSd7yw6ovt4JoZ8PZE93BwBU+/FrqzCpU+dAG3SlYhNUDvo2rYGsPB2+/DcU6WotmvgpsGlYJ3gXTnWVlKOxTFsNANUUMmwa1gkfn7iJ4d+ehp+rM0Z1qIfxnRpYwvwUXDDN66Td56y+a2X/1pYOFUdjr+cmxnwdao/ujRYrXytotyRmIOnvCwjfWPlrjpVFd+YE8tw9IBtd2IaNDEfO8mJtWF8/mIvVJwJnZ7hMfwtO3r4w67Qwxscid91q6M4UvADl5O0LSZceAACPjdZrmWQvevPudUCIyGEJzOaS85lQeRo3bhyUSiX2799v76iUqz+qjbR3FByKs9nBh07bwSXnx2Bql0o2sWHcvQP9n7l2vfRp9v5fhUhYdkpqqdfeO9D/mWatUu8d6P9M8FXWJyUdkTneFIX29N6l9+wdBYdzuMWSewf6PyMGfx6XFKBwnJfHHIWrB9smJflMbXPvQP9nji6tGiMWy1O3JxLvHej/jPcf/9g7Co+dXTVG2zsKj52RiT/YOwp2wV9DRERERERERERERERUZbDjw45iY2Ph6upa6hYbG2vvKBIRERERERERERERPVa4xkcF+/bbb0v9W40aNRAcHFzm34mIiIiIiIiIiIiI6P6x48OORCIRGjZsaO9oEBERERERERERERFVGZzqioiIiIiIiIiIiIiIqgyO+CAiIiIiIiIiIiIih2eCwN5RoMcER3wQEREREREREREREVGVwY4PIiIiIiIiIiIiIiKqMtjxQUREREREREREREREVQY7PoiIiIiIiIiIiIiIqMpgxwcREREREREREREREVUZIntHgIiIiIiIiIiIiIjoXsz2jgA9Njjig4iIiIiIiIiIiIiIqgx2fBARERERERERERERUZXBjg8iIiIiIiIiIiIiIqoy2PFBRERERERERERERERVBjs+iIiIiIiIiIiIiIioyhDZOwJERERERERERERERPdiEtg7BvS44IgPIiIiIiIiIiIiIiKqMtjxQUREREREREREREREVQY7PoiIiIiIiIiIiIiIqMpgxwcREREREREREREREVUZ7PggIiIiIiIiIiIiIqIqQ2TvCBARERERERERERER3YvJ3hGgxwZHfBARERERERERERERUZXBjg8iIiIiIiIiIiIiIqoy2PFBRERERERERERERERVBjs+iIiIiIiIiIiIiIioymDHBxERERERERERERERVRkie0eAiIiIiIiIiIiIiOhezPaOAD02OOKDiIiIiIiIiIiIiIiqDHZ8EBERERERERERERFRlcGODyIiIiIiIiIiIiIiqjLY8UFERERERERERERERFUGOz6IiIiIiIiIiIiIiKjKENk7AkRERERERERERERE92IS2DsG9LjgiA8iIiIiIiIiIiIiIqoy2PFBRERERERERERERERVBqe6oodST55j7yg4FFeF1t5RcDj6JG97R8Hh/BYSYO8oOJyhneLsHQWHE36Z10lJOWY2V0pKDlfYOwoOp1WTFHtHweF061nN3lFwKIdbLLF3FBxOnxvv2TsKDie4zVx7R8Hh/KX1sncUHI5zsr1j4Hgy3+V9uKQeZqO9o+BwwgL5nKAk5ghRxeGIDyIiIiIiIiIiIiIiqjLY8UFERERERERERERERFUG544gIiIiIiIiIiIiIodnsncE6LHBER9ERERERERERERERFRlsOODiIiIiIiIiIiIiIiqDHZ8EBERERERERERERFRlcGODyIiIiIiIiIiIiIiqjLY8UFERERERERERERERFWGyN4RICIiIiIiIiIiIiK6F5O9I0CPDY74ICIiIiIiIiIiIiKiKoMdH0REREREREREREREVGWw44OIiIiIiIiIiIiIiKoMdnwQEREREREREREREVGVwY4PIiIiIiIiIiIiIiKqMkT2jgARERERERERERER0b2YBfaOAT0uOOKDiIiIiIiIiIiIiIiqDHZ8EBERERERERERERFRlcGODyIiIiIiIiIiIiIiqjLY8UFERERERERERERERFUGOz6IiIiIiIiIiIiIiKjKENk7AkRERERERERERERE92KydwToscERH0REREREREREREREVGWw44OIiIiIiIiIiIiIiKoMdnwQEREREREREREREVGVwY4PIiIiIiIiIiIiIiKqMtjxQUREREREREREREREVYbI3hEgIiIiIiIiIiIiIroXk70jQI8NjvggIiIiIiIiIiIiIqIqgx0fRERERERERERERERUZbDjg4iIiIiIiIiIiIiIqgx2fBARERERERERERERUZXBjg8iIiIiIiIiIiIiIqoyRPaOABERERERERERERHRvZjtHQF6bHDEBxERERERERERERERVRns+Kjixo0bhyFDhtg7GkRERERERERERERElYJTXVVxGzZsgNn8+AwC83rtefhMHgqRryfyb0UhacVX0FwNtRlW2qg2/N4aDVnLhpDU8kfSu1uQsf136/ON7g+v0QMgrukPANCGxSJ14y7k/hNY4WmpKG6vvAD3scMh9PaCLjQCGR9+Ad31EJthXYf2h+vA5yBuWBcAoLsZhqzPt5Ua3lE1WjAcAa/2hljhgqyLIbi+YCvyopLLPKbO+D6oN2MQpH7uyLkZixuLtyP7coTl7wGvPYMaL3aHonVdiN3kONxoAgyqPMvfvbo1R5dfl9k89799FyM7OLJ8EleOOsx7CU1H9oLEXY6Ui6E4s3g7VFEppYav1rkJWk97Hj6t6sGlmicOT/wEMYdKLxs91oxHs9eewX/Lv8P1rYcqIgnlRjpgCGRDR8DJ0wuGqAjkfbUBhrDbNsNKuj4J2fBX4VS9JgQiEYyJ8dDs3wvdicMFAYRCyF+dBHHHLhBWqw6zWg39lUCod3wFc2ZGJabq0XUsvEak7nIkXwzF6XtcI9U7N0GbYtfIoYmfILqMa+TJNePR/LVncHb5d7jmoNdI4wXDrOqTawu23Ud98hzqF9YnqpuxuLH4W0t9IvZwQeMFw+HzdCvIavpAl6FC8sFLCP1gLww5GgBArVeeQpvPpts895EWU6FLV5VvIh+B12sD4Dul6D6cuOIraK6E2QwrbVQb/m+NhqxVA0hq+SNx1dd33Yd9pw+Dom83SBvUhDlfB3XQbSR/+C10kQmVkZxyIX9xCFxGjICTlxf0EeHI2fAZ9Lds1yeygc9D1rcvRPXrAQD0IaHI/fprq/Dui96GrH8/q+O05y8ga/6CiktEBRN16gNx90EQuLrDlBIL3V/bYUqIsB227dOQDrUuD2a9DnnvjqmMqJYb1iXl41LwNWz/cR9u3g5HWkYmNqx5B8881c3e0SoXfmP7o9r0IRD7eiDvZjRi3/kG6mDb9SkAeA7shprzR0Jayw/5UUmIf38nso8HWf7+RMKvNo+Le3cHkjfvBwA03L4I8hb1IPZ2hyE7F6ozVxH/3k7oU7LKNW3lqfucl9BqVC9IFXIkXgrFkcXboYwuvW1Sq1MTPDHtefi3qgdXf0/sn/QJwg9bt03kPgo8tWgE6j7VClKFHPHnQ3Bs2Y4yz+tIOs19CS0K22tJF0NxcvF2ZJcR9xqdm6Dd1Ofh17oeXPw98dekTxBlo73m2bAGui0egRqdm8JJ5ITMsEQcmLIBuYmO357tOecltBvZC84KF8RdCsXfS7Yhs4w86T7jBTTt1xE+DWrAkK9DXGAYjn2wGxmRSTbDj9qxAA17tsGeyesRctixnhXUHN8XtWcMgsTPA7k3YxC6eBtyLtu+xwKA76AuqL/wFTgH+EITlYyId39AxrHLRX8f0Ak1xz4Ht9b1IfZyw4Xe85F7I+au8yg6NkKDRSOhaN8QZqMJudejETziPZjy9RWSzgfhP64fahSrX6OWfgN1cHip4b0GdkXAgqL6Nfa976AsVr8CgHPDmqi9dAwUXZpDIBJCExqP0MlroUtIh9DDFQHzRsD96TaQ1vCBPlOFzIMXEL92F4w5eaV8KxE5Ao74qOLc3d3h4eFh72jcF8XzT6La4klI/WwXIga9ifxbUai7YxWE3u42wzvJpNDFJiNl7Q7oUzNthtEnZSB57Q5EDJ6NiCGzkfvfFdT+aimkjWpXZFIqjLzP0/CaOxXKr75H4sjp0IVGwn/TGjh5etgM79yxDdQHTyBl8nwkj3kThpQ0VPvyAwj9vCs34o+g/swXUHdSP1xf8A3ODlgKY54WnfYsgpNUXOox1Qd3RdOVryF83T78+9wiqG7EoNPuRZD4KCxhhDIJ0k4EI2LDfpvnyLoYgqMtp1ptsd8fQ15MikN2erSZMRAtxvfBmUXb8Nug5dDnadH/+4UQlpFPIrkUmTdjcXbpjnuev26/jvBr3xDqZNtlzZFIevSCy6TXodm1A9mzJ8MYFQG3VR9D4O5hM7wpJweavd9DNf91KGdNgPboAbi+uRDidk8AAARSZwgbNIZmz04oZ09Gzpp34FQzAIql71diqh5dmxkD0XJ8H5xetA2/DloOQ54Wz9/HNZJxMxZnqsg1Un/moML6ZCv+HfAODHladN7z9j3qky5otvI1hK37GWeeW4ycGzHovPttS30ireYJqb8Hbq38Aaeeno8rb26Gb682aP3JVMs5En/7D0dbTrPaUo9fQca/Nx3qQaX78z1QfckkpG7YhfCBs5F/Kwr17nUfjktG8oel34ddOrdExnd/IWLofESNeQcCkRD1dq6CQCatyKSUG+feveD2+gzkfvst0idNhiE8Ap4ffwSnUtpWknZtoTl2DJlvvoWM6a/DmJoKz48/hpOPj1U47bnzSB0y1LIpV66qhNRUDGHLrpD0ew36k/ug2bwIpuQYOI9ZBLgoSj3GnJ+HvLVTi7b1syoxxo+OdUn50Wjy0aRhfSyZO8PeUSlXXi90R8Dy8Uhcvwc3+s1F3s1oNP5hGUSl1KeuHZugwRdzkL7rGG70nQvlofNouPVtyJoU/Wa53Ha81Rb11kaYTSZk/f2fJUzO2euImPYxrj01ExFT1sK5TjU03OK4naqdpg9Eu/F9cGTRNvzwQkH7ddg92iZiuRSpN2NxtIy2yZCv34J7bT/sn/gJdvZfClVCOl7+cRHEj8G9p/30gWgzvg9OLt6GnwYth16jxQv3aq/JpEi/FYt/ysgTRR0/vPTLO8gKT8SvL7+HXX0W4+KG/TBq7f8Q+166TRuITuP64q/F27F18DLo87QY/d3bZeZJnc5NcWnnUWwbshzfv/oBhGIhRn/3ts1roPPEfg77sqjf4K5otHIMotftw8XnFiL3Rgza7l4CsY/te6yiY2O02Pwmkn48jovPLkTagYto9e18uDQNsIQRyqVQnr+N8NU/lPq9io6N0HbXEmSevIJL/RbjUt9FiN92CGaT/fPJ+4XuqLN8POLX78W1vvOgvhmNZj+WXb822jQHqbuO4Wqfucg8eAGNty20ql+ldfzRYv/7yA+Px81hy3D1mbeQ8OlPlk4eib8XxP6eiFm1A1d6z0bE7I3w6NkO9de9XilpJqKHx46PKmLfvn1o1aoVZDIZvL298eyzz0KtVt811VXPnj0xc+ZMzJw5E+7u7vDx8cE777zjEDd6n4lDkLXnEJT7jkIbHofEpV/ApNHCc/hzNsNrroYh5YPtyP7zFMw62w22nOMXkHvyEnTRidBFJSJ13Xcw5eVD3q5JRSalwri/9hJyfjmA3N8OQR8Zi4zVG2DO18JtSF+b4dMXf4CcvX9AFxIBfXQcMlauBwQCOHdqV8kxf3h1p/RH+Ce/IvVgIHJuxuLKzC8g9feEf/+OpR5Tb9rziPv+OOJ3/4Pc0ARcn/8NjBodao3saQkTveUAIjf+DmWg7TdDzHojdGnZlk2flQv/fh0Rv+uf8k5iuWg5sR8uf/YbYg4HIfNWHE7O3gy5vwfq9O1Q6jHxJ67i0kf7EH3wUpnnllfzRNd3x+DErE0w6Y3lHfVy5zzkZWgP/QntsQMwxsVAvWkdoM2H9LkBNsMbrgdDd+40jPExMCUnIv+Pn2GMjoSoeSsAgDlPjZxlc6E7cwKmhDgYQm5C/dUGiBo1hZOvX2Um7ZG0mtgPQcWukROF10jdMq6RuBNXcfE+r5Hu747BcQe/RuoV1icplvpk0wPXJ9fmb4VRo0NAYX2SezseQRM/RerhIOTFpCLjzA2ErNkDvz7tIRAWNLNM+Xpo07Itm9lkgk+PFoj78URlJPu++UwquA9n7TsGbXgcEpZsgkmjhVcZ9+HkNduR/efpUu/D0eNWQPnzMWjDYpF/Kxrx8z+FpKYfZK0aVmRSyo385eHI+/MvaA4chDEmBqp162HOz4fsedv1Sfa770Gz/zcYwsNhjI2Fau1HgJMAkg7trcKZ9XqYMjMtmzk3tzKSUyHE3Z6HIfA4DJf/gTktAbo/voFZr4O4fc/SDzKbYc7NtmxQZ1dafMsD65Ly82TXJ/DGlLF49unu9o5KufKf/ALSfjyC9L3HkR8Wj5i3N8Ok0cJnxDO2w08ciOyTl5G8eT/yw+OR8NEu5F2PhN/4orrGkKa02jz6dkLO2evQxha99Z7y9R9QB4VCl5CG3EshSPr8F7i0bwyBSFjhaX4Y7Sf2w7mNvyHiSBDSb8fh77c2w9XPAw37lN42iTp5Ff9+vA/hh2y3TTzrVUONDo1wdMl2JF+NRFZkEo4s3g6RsxhNB3etqKSUmzYT++HSxt8QdTgIGbfjcHT2Zrj4e6B+Ge212JNXcf6jfYgso73WZcFwRB+/grPv70b6jRioYlIRfSQImgzH7zTtPLEfTn++H6FHApF6Ow7753wJNz8PNC3jOvlx7Fpc2XcKaWEJSLkVi9/mfgWPWj6o3qqeVTj/5nXQdfLz+H3+lopOxkMJmDYQid8fQ9Luk8gLTUDI/K9h0uhQY2Qv2+GnDEDmiWDEbvoDeWEJiPpwD3KuRaLWhKKRpsn7TiN6/c/IOnWt1O9ttGos4r45gJiNv0EdEo+8iCSk/v4fzDpDuafxQVWfMgipPx5B2p7j0ITFI2rhVzBptPAb2dt2+EkDoTxxGUlf/ob88ATEf7QL6mtRqDa+vyVMwNujoTweiNjV3yHvehS0MSnIOnwRhoyC9okmJBZhkz+C8sglaGNSoPr3OuI+/AGez3UEhHysSuTIWEKrgKSkJIwcORITJkzArVu3cPLkSQwdOrTUzowdO3ZAJBLhwoUL2LBhA9avX49vvvmmkmNtTSAWQdayIXL/DS7aaTYj999gyNs1LZ8vcXKC+8Cn4CRzRl6Q7SkqHJpIBEmzxsg/X2xIptmM/PNBkLZufl+nEDhLAZEIpuycCopk+ZLV8YOzvyfSizXKDDkaKIPC4dGxsc1jBGIhFK3rIeN0sYac2Yz0U9fgWcox98O/bwdIPN0Qv/vkQ5+jorjV9oXc3wMJp69b9ulzNEgLjoB/h0aPdnKBAL02TMPVzX8hK/QxmJpGJIKoYWPorhQbom42QxccCHGTFvd3itbtIawZAMONq6WGcZK7wGwyPTYPK91q+8KlxDWiy9EgtZyukd4bpuGKg18jRfVJUR4U1CcR8OxoOw8EYiHcW9dDerF8K6hPrsOjlGMAQKyQw5Cjgdlosvn3msOfglGjRdKf5x8uMRXAch8+c6Vo5537cPvye1lA6OYCADAqH4P7kEgEceMm0F0qUZ8EBkLc4j7vu1IpBCIRzCrr9EratoXvb7/C5/udUMx5CwJF6aMjHJpQCKfq9WCMsL7nGiOuwalWGfdciTNkczZCNvcLSEfOg8C3VsXHtZywLqF7EYhFcGndAKrT1vWp6sxVuHawXZ+6dGhiHR5A9slguHawXY5EPu5wf6YD0nYdLTUeQg9XeA99CrmXQmA2ON5LCe61feHq54GYM9Ztk6TgCNR4hLaJUFIwm7eh+EgGsxkGnQE1n3j43wKVQVHYXosr0V5LCY5AtfaP0F4TCFC3d1soo5LxwvcLMOHyFxj2+wrUK6MzxVF4BPjCzc8TkWduWPZpczRICI5ArQfIE6mbHACgURa13UXOEgz97HX8/c63UKc5Xge8QCyEW+v6yCzxuzbz1DUoSvld696hMTJLdGhknrgCRRn3mpLEPgq4d2gMfXo2Ovz5Lnpc34J2v66Aeyf7vzx6p37NPl3sd5rZjOzTpdevrh0aW4cHkP3P5aLwAgE8n+mA/MgkNP3xHXS4uh0t//wAnv06lRkXocIFxtw8oJR7NFUsk4Dbg27/r9jxUQUkJSXBYDBg6NChqFu3Llq1aoUZM2bA1dXVZviAgAB88sknaNKkCUaPHo1Zs2bhk08+qeRYWxN6KiAQCWFIV1rtN6QrIfL1fKRzS5vUQbNrP6HF7V9RY/UMxE5/D9rwuEc6pz0IPd0hEAlhzLCeo9eYkQWhz/3lkefsSTCmZVh3njgwqa8HAEBXoiGqS8uG1M/D5jESLwWcREJoSxyjLeOY+1FrVC+knbiC/CTHm8ZHVphPmhLTXGjSVJD52h7ye7/azBgIk8GEGw66XkNJAoU7BEIRzFnW5cSszILA06v04+Qu8Np7AF6/HoNi+QdQf/UZ9MGlvDUnlkA+bip0p47BrHk85nSVl3GNyB/xGmlbeI04+rovzoXpfJC64WHqE7GXGxq+9SLivj9WalwCRvVE4i9nHWKO5DuK7sPWZac87sMWAgGqvzMZ6os3oQ2NLZ9zViAn94L7rinLut43ZmbByav0+qQ4t2lTYUxPhzawqPNEe/4Cst9/H1lvzUHO5i2QtG0Dz48+BJwev2a5QK6AQCiEucSIDbM6GwI3D5vHmDISodu/GdofP4b2588BJwFkk1dBoLi/PLU31iV0LyIvNwhEQujTrf+99WlKiAvvxyWJfT2gT1Nah09XQlxK/eszvBdMuRpkHTh3199qLX4N7cN2of2N7yCp6YuwCWseKh0VzaUwL/JKtE3y0lVweYS2SWZEElTx6Xhq4SuQusvhJBai0/SBUNTwhssj/BaoDPLS8iRNBbnfw+eJ3EcBiasMHWYMRMzJq/h99IeIPBiIAVveRI0u5fSSYQVxLfw3U5coT7np2XAtpTzdRSBA3+WvIfZiCNJC4y27+y57FXGBoQg94lhretwhLrx36ErUDbo0JSSl3W/8PKB/gN/OtsjqFKyPWm/ecCR+fwzBI95HzrUotNu3DLJ61R4kCeXOUr/aqC8lZdWvJZ4z6dOyIS7ME7GPO4SuMtSY+SKUJy7j1siVyDx4Ho2/WQC3LrZfdBF5uaHW7OFI/f7II6aIiCoaFzevAtq0aYNnnnkGrVq1Qt++fdGnTx8MGzYMnp62G8pdunSBQFDU3de1a1esW7cORqMRQuHdw6C1Wi20Wq3VPp3ZCInAMYdMl6SLTEDEwDfg5CaHe/8eqPXRW4ga+fZj2fnxKNzHvwKXvj2RPGleqVOS2FuNl7qj5UeTLZ8vjf7QjrEp4lzdC7692uDy5E/tHRUAQIMXu+HJDyZYPh8c+3GFfI9Pq7poObEvfu2/tELO70jMmjwo35wEgbMM4jbtIZ84A8bkRBiuB1sHFArhtnAFIBBAvWm9PaJ6Xxq+2A1PFbtGDlTgNdJqYl/87IDXSI2XuqPVR5Msny+OXlvh3ylyleGJHxYgNzQBoR/9bDOMR8dGcGtSC8EzN1V4fBxNjVXT4NykNiKGL7R3VCqFy+hRcH6mNzLfmA3odJb9+cePW/7fEBkFQ0QEfPfsgqRtW+iCHo8XEx6FKS4MpriiBZ61saGQzVoHUcdnoT++144xs411CTkinxHPIOPXUzDbWJ8h+cv9SNt9DNKavqgx5xXU3/AGwsa8Z4dYWms2pBueW1PUNvllXMW0TUwGI36b+in6rp2MWde2wGQwIubMDUQeD7b6DewIGg/php7F2mt/VlCeCJwK0h11OAhXvjkIAEi/GYvqHRuh5avPIPGc48yG0HJINwx8f6Ll867xHz3yOQe8Ow5+jWth+7Ci9bQaP9sedbu1wJYBix/5/FVOYTlJ+O4okgpnOwi/Hg2vJ1ui+qheiHxvlx0jVwEKy0fWoQtI/vpPAEDejWi4dmwK/zF9kXPuplVwoasMTXcugSY0DvHr9lR6dInowbDjowoQCoU4cuQIzp49i8OHD2Pjxo1YsmQJzp8vn2Hva9aswcqVK632TfdohBme5TdU2JilgtlghMjHw2q/yMcDhrQs2wfdJ7PeAF1MEgAg/3oEZK0bwXvcC0hc+sUjnbeyGbOyYTYYIfS27tASenvCmF52HinGDIP7hBFInroQ+rCoiozmI0k5GGi15sadRUIlvu7Qpiot+yW+7lDdiLF5Dl2mCiaDEdISb4pJS5zjQdQa0RO6rBykHHKMt4FiDwfhl8sRls93hvTLfBTQFEujzFeBjBsP/1Z1tU5NIPNRYOT5DZZ9TiIhOi8bjZaT+mF317ce+twVxazKhtlogKBEx6/AwxPmrDJG65jNMCUVTNNkjAqHMKAOZMNHI6d4x4dQCLeFK+Hk5w/VkrccerRHzOEg7CvlGskrx2ukeuE1MrrENdJl2Wi0mtQPP9rxGimtPilZF0h93aG6EW3zHA9SnwhdnNFp99sw5moQOH59qdOK1B7dC9nXoqG66lh1cdF92LrslMd9GABqrJwKt95PIPKVRTAkZzzy+SqDKbvgvutUYrSY0MsTpsyyR//JR7wCl1GjkDlnLgyRkWWGNSYlwaRUQlirJvCYdXyY81QwG40QuFiXEYGLO8w5yvs7ickIU1I0nLz8yz+C5YB1CT0oQ2YOzAYjxD7W/962RnXcYWs0iNjHA3ob9a9rp2aQNayFiOnrbH9/Vg4MWTnQRiZCEx6Ptpe+gUuHJlAHhjxUespL+JEgJBVvm0gL2iZyHwXUxcqB3EeB1JuPNiow5Vo0dvZfAombDEKxCJrMHIz+bQWSHay8RB0JQkrw3e01eYn2mtxXgfRHaK9pMnNg1BuQGWY9JWlmWAJqPGH/6YuKCz0ShK+KXSeiwjxx8XFHbrE8cfVxR/JN278Hi+u3aiwaPdMOO15+FznJRffuut2aw6uOHxZe+9oq/PDNsxF74TZ2jrB/Z6G+8N5RciSDxNcDulJ+1+pSlRCXuNeU/C19L7rUgnpHHRJvtV8dlgDnmj73fZ6KYKlfbdSXJUfG3KFPU0Jc4jmT2Ncd+sI8MWTmwKQ3QBNqnd78sHi4dWpmtc/JxRlNf3wHRrUGIRM/dMhpBInI2uM3pp5sEggE6N69O1auXInLly9DIpHg119/tRm2ZIfIuXPn0KhRI5ujPQBg0aJFyM7OttomeTQo1/ib9QZorofDtVubop0CAVy7tUHe5XJ+A0UggEAiLt9zVgaDAbpbodYLkxcuVK69erPUwxTjXobH5FeRMmMxdDdDKyGiD8+ozkdedIplyw2JR35KFnyebGkJI3KVwaN9Qygv2U6LWW+E6moUvIsdA4EA3k+2RFYpx9xLrZFPI2HvaYdp2OjV+VBFp1i2rNAE5KUoUbNH0RoWYlcZfNs2QEpgWBlnKlvYz//i5+cW45e+SyybOjkTVzf/hQOV8MbrQzEYYAgPhbh1sTmLBQKI27SHPuRG6ceVJHCCQFysnrjT6VGjJlRL58Cc49gLQdq6RtQ2rhG/R7xGQn/+Fz89txj7+i6xbOrkTFzZ/Bf+svM1Ulp94n1XfdIAWZds54FZb0T21SirOqigPmkBZbFjRK4ydN67CCadARfHfAyTjTdwAUAol6L6C10cciHiO/dhl+6ti3beuQ8HPdrDshorp0LRpyuiRi+BPj7l3gc4CoMB+tAQ64XJBQJI2neA/kbp912XkSPgOuY1ZM1fAEPIvfPOydcXAoUCpozHo0PIitEIU1IUhPWty4iwfkuY4u/znisQwMk/AOZcZYVE8VGxLqEHZdYboL4aAUUP6/pU0aMVckvpfFAHhliHB+D+VBvkBt5djnxHPgv1lXBobkbfMy53Rjg4Sez/vqNenQ9lTIplywhNQG6qEnW6F7VNJK4yVG/bAImP0DYpTpejgSYzBx51/eHfuj7CDzvGS0x36NX5yI5OsWyZhe21WiXaa/5tGyA56OHzxKQ3IvVKJDzqV7fa71G/OnIS0h/6vBVBp85HVkyKZUsLS0BOahbqlbhOarZtgPh75Em/VWPRtG9HfDfyPSjj0qz+9u+Xf2Bz30X4qv9iywYAh1d97zALnZv1RuRcjYRniXuH55MtoSrld212YCi8nmxltc/r6dZQlXJ/siU/Ng3apEzIG9aw2i+vXx358fa9Xu7Ur+531a+tS61fcwND4V4iTwrq15Cic14Jh3MD6/Q6168BbXyq5bPQVYZmu5bDrDMgZNwamyPuiMjx2L8FRI/s/PnzOHbsGPr06QM/Pz+cP38eaWlpaNasGa5evXtx3tjYWMyZMwdTp05FUFAQNm7ciHXrbL8xBABSqRRSqdRqX0VMc5W+dT9qffwWNNfCoLkSCu/xg+Ekd0bWvoJF+2p+PAeGlAykfLQDQMHCVtKGAZb/F/l7w7lZPZjy8i0jPPznj0XOyUvQJ6bByVUGjxd6wqVLK0SPW1bu8a8M2d/9DN93F0B7MxS66yFQjH4RApkzcn4rmFvf590FMKSmQ7lxGwBAMe4VeM4Yg7RFa2BITLaMFjHlaWDW5NstHQ8iessBNHzrRaijkqGJTUWjhS9Dm5KFlANF6y902rcUKX9fRMy2gnyI2vwXWn82HdnBkVBeDke9KQMgkksRv/sfyzESX3dI/Twgr1fwhqlbs9ow5GqQn5AOvVJtCef9ZEvI6/gj7oeiqUkc0fWtB9HujSHIjkpBTlwqOs4bhrwUJWKKjVIZsHsRog9ews1vC+YiFcmlUNQtesPWLcAXXs1rQ6tUQ52YAa0yF1ql9eLdJr0RmlQlsiOTKidhDyF//164vrUIxvDbMITehvPgYRA4y6A9egAA4PrWYpgy0pC3s+ANL+dho2EMD4ExKQECsQTijp0h7dUH6i8Lp7ISCuH29ioIGzRGzqq3ASchBB4Fb4Cbc1WAwWCXdD6oa1sPor2NayS62DUycPciRB28hBvFrhH3EteId+E1kvsYXiNRWw6g0VtDLPVJ44XD76pPOu9bguS/LyJm2+GCYzb/hTafTYcyOBLZl8NRd0p/iORSxBXWJyJXGTrtXQShTIrgGesgdpUBrjIAgDZDBZjMlnPXGNIVAqEQCfvOVGKq71/6N/tRa91b0FwNL7gPT7C+D9da9xb0yRlI+WgngLvvw+Jqd9+Ha6yaDo/BTyFmynsw5WosIzuNOXkwa3V3R8LB5O39Ce6LFkEfEgL9rVtwGT4MApkzNH8X1CfuixfBmJ6O3C0F9YnLqJFwnTAeyndXw5icbFkLxKzRwKzRQCCTwXXcWOT/cwqmzEwIa9SA2/SpMCYkQHvhot3S+Sj0Z/+C9MXpMCVGwhgfDnHXARBIpNAHFZQRydAZMKsyoT+6GwAg7jkUprhwmDKTIXCWQ9x9EAQevtAHOvZ9tjjWJeUnL0+D2PhEy+eExBTcDo2Au8IN1av52TFmjybl699R75M3oL4aAfXlMPhPHggnmTPS9xSs2VJvwxvQJ2Ui/oPvC8Jv/RNN9q2G/9QXkH00EF6De0DeugGiF3xpdV4nVxk8B3ZD3Kpv7/pOl3aN4NKmIXIu3oJRqYa0bjXUnD8S+VFJpT4QtLegrQfR5Y0hyIpOQXZsKrrPG4bcVKVVB8XwXYsQfvASLu8oaJuI5VJ4FGubuAf4wrd5beQr1chJLOhAbvx8J2gycqBKTIdPkwD0XvEawg9dQkyxRcMd1ZWtB9Fx1hAoC9trnecNgzpFichi7bXBuxYh8uAlXCuWJ8Xba4oAX/gU5kluYZ5c/upv9P1iJhLP30bCf7dQ++nWqPdsO/z6sv1HNtzL+a0H8eSsIciMSoYyLg095w5DTqoSt4tdJ6/9uAi3D13CxcI86b96HFq90A17Jq+HVp1vWTdGq8qDQauHOi3b5oLm2Ynpd3WS2FPc5j/R7LPXkRMcCdXlcARMGQChXIrEwimomm18HdrkTMv0U3Fb/kb7/SsQMG0gMo4GwX9Id7i1aYDb84o6c0QeLnCu6QNptYI2yp0ODl2q0rK2Zsym31F//svIvRGN3OvRqPZKT8gb1sT1ifaf6jdpyx9o8Oks5F4JR+7lMFSfPAhCuRRpuwvaEQ02vAFdcgbi1vxQEP6bP9H853dRfeoLyDoWCJ/BPeDSugEi52+2nDNx029otHkOcs7dRPbZ6/Do1Q6ez3XEzWHvACic3mrXcjjJJAid9SmErnIIXeUAAH2GCjBxgXMiR8WOjypAoVDg1KlT+PTTT6FSqVCnTh2sW7cO/fv3x549d885OGbMGGg0GnTq1AlCoRBvvvkmpkyZYoeYW1P9dRrJXu7we+tViHw8kX8rEtHjlsFYuBCVpIav1Q1F5OeFhn9ttHz2nfISfKe8BPW5a4gataggjLc7aq2bA5GvF0w5auSHRCN63DKozwRXZtLKTd7hf5Dp6QHP6WMh9PGELiQCKTMWw5SpBACIqvsB5qIfxoqXB0IgkcBv3XKr8yg374Ry83eVGfWHFvn57xDKpWj18WSIFHJkXQjBxREfWL0FKa/jD4mXm+Vz0m//QeKtQOMFwyHx80DOjRhcGPmB1SLpdcY+h0bzh1k+d/19BQDgyhtfImFPUQdJwKheyLwQAnV40Y9yR3Rl058QyaV48sMJkCjkSLkYioOvroWxWD4p6vjBuVg++bapj4E/LbF87rriVQBA6N5T+GeOY7zp9DB0Z04gz90DstET4OTpBUNkOHKWz4dZWTBs28nXD2ZzUV0icHaGy/S34OTtC7NOC2N8LHLXrYbuTMGbtE7evpB06QEA8CjsVLwje9Gbd68D4qCubPoTYrkUTxVeI8kXQ/H3fVwjLxS7RroVXiMhe0/h5GN4jUR+/gdEcilafTwJ4sL65MI965NzhfXJMEj9PKAqUZ8oWteFZ4dGAIBeFzZYfd/xjrOgiSt6My5gVC8k/30BBpVjTpOW/dcZiLzd4T9ntOU+HDVuOQyF92FxDV+rh68iPy80+vszy2ffKUPhO2Uocs9dQ9TIgjcnvV8bAACov9t6cd24eZ9C+XPpizY7ivzjJ+Dk4QG3CePh5OUFfXg4suYtgCmroD4R+vtb3XflgwdDIJHA891VVufJ3f4t/sfefYc3VfVxAP+mTZrunbZQKIUWkL1Bpuy9FESmLEHgRZRVNjJEEFBBHIAKiLIUBFEURIYgKquUUUr33itdSdOkyftHS9q0KWW0TYjfj0+exybn3pxzOPfcm/u755zcvfugKSyE0KcBHAcOgJmtLdRp6VBcv47cr/cAyufzycHCe/+gwNoeot6vwsLWEeqkaOR/uwkoXvDczMEV6lJ1JLC0hcWIGRDYOkIjz4M6MQL5X66GJjW+oq8wOuxLqs69B6GY9lbJuj+bdxSdW0YM6osNKxcaKlvPLOPkFQid7eG5aCxEEifIAiMRMnEdVMULNFuU6U9zbwQjYu7H8PQbjzpLJiI/MhFh0zdBHqw7vZHLiG6AQICME5fLfadaroDT4M7wXDQOZlZiKFMykXXxFsK3b4WmwDgf0rj2xS8QWYnRf+M0iO2tEX8jBMcm6V6bOHq5warUseTRsgFe+77k2qTXu0XXJvd+uITTC4vaj42bI3qumgAbVwfkpUgReOwv/POJ/tkQjI3/F0XX9L02FdVJ4vUQ/FymThzq6daJW8sGeLnU9Vr34joJ+uESzhVfr0WcvoGLy/eg3f+Go8e615EZnojf3tyOxOvGPSMAAPy98xdYWIsxdON0WNpbI+ZGCA68/oFOnTh5ucPaqaROOkzqBwCY/P0qnX39tHAXbh+9VDMZrwIpP/0DkYs9GviNKf5dG4Xb497XLmBu6emq05dk3whB4OxP0GDpWPgsHwdZZCLuTtmCvAcl65u6DmiPpp/8T/t3891FU9NGbvkBkVt/AADE7f4V5mIRGq6bDJGTLXIDoxEwZj3k0YYfuZt+8gqELvaou3gcRBJHyAIj8WDCeiiL+1exp6vOfaPcG8EI+9/HqLtkPOounYD8yESETPtAp3/NPH0VkUt3ofbcV+C9fjrkEQkImbEZOdeKZh+xadEAdu2Kpnpv849uQPpWxzehiDOeYNl/BUNN9LgEGk2pXyJk8nr27InWrVtj27Ztz7Sfew2GVk2GTIStvaLyRP8x9xNdDJ0FoxMvYqy5rFfaxFae6D/m6K26hs6C0amjNI5p5oyJl3Vu5Yn+YyR1cwydBaNj19PD0FkwKhd3GdfCxsagf6DxP+1d0wJaPb8Bl+ryp7mNobNgdCx5F6WcDDNWSlnd8p/Phxqqk7W5cQZiDenFhB8NnYXnzsdeEw2dhefO/JjvDJ0Fg+AaH0REREREREREREREZDIY+CAiIiIiIiIiIiIiIpPBeVf+Yy5evGjoLBARERERERERERERVRuO+CAiIiIiIiIiIiIiIpPBER9EREREREREREREZPTUhs4APTc44oOIiIiIiIiIiIiIiEwGAx9ERERERERERERERGQyGPggIiIiIiIiIiIiIiKTwcAHERERERERERERERGZDAY+iIiIiIiIiIiIiIjIZAgNnQEiIiIiIiIiIiIiospoDJ0Bem5wxAcREREREREREREREZkMBj6IiIiIiIiIiIiIiMhkMPBBREREREREREREREQmg4EPIiIiIiIiIiIiIiIyGQx8EBERERERERERERGRyRAaOgNERERERERERERERJVRCwydA3pecMQHERERERERERERERGZDAY+iIiIiIiIiIiIiIjIZDDwQUREREREREREREREJoOBDyIiIiIiIiIiIiIiMhkMfBARERERERERERERkckQGjoDRERERERERERERESVURs6A/Tc4IgPIiIiIiIiIiIiIiIyGQx8EBERERERERERERGRyWDgg4iIiIiIiIiIiIiITAYDH0REREREREREREREZDIY+CAiIiIiIiIiIiIiIpMhNHQGiIiIiIiIiIiIiIgqozF0Bui5wREfRERERERERERERERkMhj4ICIiIiIiIiIiIiIik8HABxERERERERERERERmQwGPoiIiIiIiIiIiIiIyGQw8EFERERERERERERERCZDaOgMEBERERERERERERFVRg2NobNAzwmO+CAiIiIiIiIiIiIiIpPBwAcREREREREREREREZkMBj6IiIiIiIiIiIiIiMhkcI0PeioajcDQWTAq/kkSQ2fB6NhCbegsGB1vpdLQWTA6Adc8DJ0Fo1MHhYbOgtFxNVcYOgtGJ0lmbegsGJ3MEEtDZ8HoKIM5/3FpIkNnwAgFtFpo6CwYnda3PzR0FoyOW/+Zhs6C0bkXz99/ZeVYmhs6C0bHScxr2LJSFVaGzgIR/YdwxAcREREREREREREREZkMjvggIiIiIiIiIiIiIqPH+UXocXHEBxERERERERERERERmQwGPoiIiIiIiIiIiIiIyGQw8EFERERERERERERERCaDgQ8iIiIiIiIiIiIiIjIZDHwQEREREREREREREZHJEBo6A0REREREREREREREldEYOgP03OCIDyIiIiIiIiIiIiIiMhkMfBARERERERERERERkclg4IOIiIiIiIiIiIiIiEwGAx9ERERERERERERERGQyGPggIiIiIiIiIiIiIiKTITR0BoiIiIiIiIiIiIiIKqM2dAboucERH0RERERERERERERE9NgyMjIwYcIE2Nvbw9HREdOnT0dubu4j07/11lto3LgxrKys4OXlhXnz5iErK0snnUAgKPc6fPjwE+ePIz6IiIiIiIiIiIiIiOixTZgwAYmJiTh79iyUSiWmTp2KmTNn4uDBg3rTJyQkICEhAVu3bkXTpk0RHR2NWbNmISEhAUePHtVJu3fvXgwcOFD7t6Oj4xPnj4EPIiIiIiIiIiIiIiJ6LEFBQTh9+jSuX7+O9u3bAwB27NiBwYMHY+vWrahdu3a5bZo3b45jx45p//bx8cGGDRswceJEqFQqCIUloQpHR0d4eHg8Ux451RURERERERERERERkQlSKBTIzs7WeSkUimfa5z///ANHR0dt0AMA+vbtCzMzM1y9evWx95OVlQV7e3udoAcA/O9//4Orqys6duyIPXv2QKPRPHEeGfggIiIiIiIiIiIiIjJBGzduhIODg85r48aNz7TPpKQkuLm56bwnFArh7OyMpKSkx9pHWloa1q9fj5kzZ+q8v27dOnz//fc4e/YsRo0ahTlz5mDHjh1PnEdOdUVERERERERERERERk8tMHQOnj8rli3DggULdN4Ti8V60y5duhQffPDBI/cXFBT0zHnKzs7GkCFD0LRpU6xZs0bns1WrVmn/v02bNsjLy8OWLVswb968J/oOBj6IiIiIiIiIiIiIiEyQWCyuMNBR1sKFCzFlypRHpmnQoAE8PDyQkpKi875KpUJGRkala3Pk5ORg4MCBsLOzw/HjxyESiR6ZvlOnTli/fj0UCsVjlwNg4IOIiIiIiIiIiIiI6D9PIpFAIpFUmq5z586QSqW4efMm2rVrBwA4f/481Go1OnXqVOF22dnZGDBgAMRiMU6ePAlLS8tKvysgIABOTk5PFPQAGPggIiIiIiIiIiIiIqLH1KRJEwwcOBAzZszAzp07oVQqMXfuXIwdOxa1a9cGAMTHx6NPnz7Yv38/OnbsiOzsbPTv3x8ymQzfffeddqF1oCjgYm5ujp9//hnJycl48cUXYWlpibNnz+L999/HokWLnjiPDHwQEREREREREREREdFjO3DgAObOnYs+ffrAzMwMo0aNwieffKL9XKlUIjg4GDKZDADg7++Pq1evAgB8fX119hUZGQlvb2+IRCJ89tlnmD9/PjQaDXx9ffHRRx9hxowZT5w/Bj6IiIiIiIiIiIiIiOixOTs74+DBgxV+7u3tDY1Go/27Z8+eOn/rM3DgQAwcOLBK8sfABxEREREREREREREZPTUefeOc6CEzQ2eAiIiIiIiIiIiIiIioqjDwQUREREREREREREREJoOBDyIiIiIiIiIiIiIiMhkMfBARERERERERERERkclg4IOIiIiIiIiIiIiIiEyG0NAZICIiIiIiIiIiIiKqjMbQGaDnBkd8EBERERERERERERGRyWDgg4iIiIiIiIiIiIiITAYDH0REREREREREREREZDIY+HgGFy9ehEAggFQqrTDNvn374Ojo+ET79fb2xrZt254pb4/rccpARERERERERERERPS84OLmT6Bnz55o3bq1NijRpUsXJCYmwsHBwbAZMyHOkwZDMvMVCCVOyA+KRMKaXZDfDtWbVtzQC+7zJ8CqhQ8s6rgjYd2XSN97UieNZPZo2A/oArGPJzT5Bcjzf4CkD/ahICK+JorzVJr4jUb9Cb0gsrdB+vUQ3FqyB3mRSY/cpsHUfmg4ZygsJQ7Iuh+D2yu+QeatcO3n3X9cCUmXpjrbRHzzBwKW7Cm3LwsnW/Q5txFWtV3wc6M3oMyWVU3BnoCP36uoM7E3hPY2kF4PRpDf15BVUgd1p/aH95xhsHBzQO79GAQt34vsUnVgJhah0ZqJ8BjZBWZiEdIv3EbQ0j0oSM3SpumffLjcfu+8uR1JJ/4BADh2bIyGq8bDxrc2zK3EyI9LRey35xCz69cqKnl5dbTlckTu/Wg8KFOustyGvQjfJWNgWVcCWWQSwtYfQNq5AJ00Pn6vwnNiH239PvD7Sqd+u13fASsvN51tQt87iKgdP+m8V2/2UHhO6gOrOhIUZOQgbt/viNx2/NkL/RTqTO0Pr1L1FPIY9dSguJ7kxfWUXqqeJIM7wnNyX9i3bACRsx2u9vZDbmC0zj7a/rgaTl2b6bwX981ZBPt9VaVlexaN/Eaj7sTeENnbIPN6MO767an0WKo3tR8azBkGsZsDsu/HIHD5PmQV16XI0QaN/F6F60stYOXpioL0bCSdvoGQTd9DlSMvty+Rky26n98Eq9ouONNwOlQG6E8ecp8yELVmj4RI4gjZ/ShErfwKeQFhFaZ3HtoZdfzGQVzHDfmRiYjZ8C2yzvtrP++U8KPe7WLWf4PEL4qOldrzRsGxbztYN6sPTYEKN5tMqtpCPSFD9CcA4Nq3DRosHAXbJvWgVhQg858g3J6yVft54w1T4NihMWxfqIu80Hj822dJlZb7SdSaOgB15wyHhaSojsJX7EHOrYrbieuwF+HtN1bbl0S89x0yz93Sfl5v0auQjOgKsacL1AUq5N6JQNTGQzr7rPv2K3Du2xa2zbyhUarwd+Mp1VnEJ2aI/vWFLTPg1KM5xO7OKMzLR9aNYIStPwhZWEJ1FbNShro2abxhMpyKj4/c0Hj822epzndY1pWgx40d5b776uCVyLpZcdutam6TB8GjVB8bs+or5AXov4YHAKehXeC5uKSPjXt/v04f2yFe//VE7PpvkLTzBADAd+8yWDerD5GLA1RZucj+6w7iNuyHMjmzSstW024E3MXeg0dx/0EYUtMzsH3jKvTp0cXQ2aoWtmOGw+H1MTB3cUZBSDgyNn+KgsBg/WlfHgybof0g8vEGABQEhUL66dc66a16d4PdqKGwaNII5o72SBj7JpQhFfdXxsIQ12siJ1u0/nwu7Jt6QeRki4K0bCSfvoHg949AlVv+ms7Qui8YhdbjekFsb424GyE4s2IvMqOSK0zfec4wNB7YAc4+taDKL0D8zVBc2HQEGRGJAABLBxt0XzAK9bu3gL2nC2Tp2Qj9/SYufXgUCj3XtIbk+vpguL05EiKJE+RBUYhbvRuyCu6RAIDjkC6otXACLOq4QRGVgISN+5F94ab2c6GrA2ovmwz7Hm1gbm+D3KuBiFu9G4qoxJI0Ekd4rpgCu26tYWZrBUV4PJI+/QFZv/1TrWV9FGO9R/Dwe+pOGwCruhLkx6chYttxJP5wuQpKTUTPiiM+noGFhQU8PDwgEAgMnRWT4DCkG2qteAMp2w8hbOg7yA+KRP1v1sHcRX9gycxKjILYJCR98A2UKRl609h0ao70b08h/JXFiHx9FQRCc9Tfvw4CK3F1FuWpNZo7DD7TB+CW3x5cGLwKKlk+uh1eCjOxqMJtPEe8iBZrJuLBhz/ifP8VyAqMQddDSyF2tddJF/nteZxqMVv7urf+kN79tf1oJrKCYqu0XE/Ce+5weL0xEPf9vsLVwStRKFOg7ZFlj6wD9xGd0XjtJIR/eBT/9luGnMBotDu8DBal6qDxutch6d8Od2Zsw/WRayH2cEKrPQvK7evevC9wsfmb2lfKbze0nxXKFIjdcwbXR67Fle4LEfHxcTRcOgaek/pUbSXolOt1RHx4DFf7LUVOYDTaHl4OUZl/24cc2jdCi53zEH/wAq72XYrU366j1b7FsHmhrjaN99zhqPvGIAT5fYVrg1egUJaPNkeWl6vfsE1H8GfzmdpXzNendT5vvGEKPCf0Ruja73Cl23wEvL4ZWf41d4OlNLcRndFw7euI/PAYrvdbitzAaLSupJ6a7ZyHhIMXcK24nlqWqSdzazGyrgYj7L2Dj/zu+G//wOXmM7WvsHUHqrRsz6LB3GHwfmMg7vl9jSuDV0ElU6DTkUf3J7VGvIgmaych9MNj+KvfcuQERqPT4aXaY0ns4QSxuyOC1h7ApZcW4/bbOyHp1QotP35T7/5afjwTOfdjqqV8T8J5eFd4vTsVcR99j3sDFkF2PwovHFwNYQXnF9v2jeH7+QKkHjqHu/0XIvP0NTTaswRWjb20afxbTdN5hc//FBq1Ghmn/tWmEVgIkfHz30j55ky1l7EyhupP3IZ0RPNP5yLh0EX829sP14etRtKPf5X7vvhDF5D0k+F+TAOAZEQX+KyZjOgPf4B//yXIC4xG80MrKqwj+/aN0OSLd5B06Dxu9vND2m/X0GyvH6xL1ZEsPBFhy7/GzZ4LcXvEKuTHpqLFkVUQuZTs08xCiLSf/0Hi/t+rvYxPylD9a/adCAS9vRP/dl+AgLHvAwIB2hxZAZgZ5prb0Ncm8YcuVnp83Bj9ns71S/btyKcv8BNyHt4Vdd+dioSPjiBw4ELI7keh0YFH97E+ny1A2qFzCBywENIzV+H79VKdPvZW66k6r8j5O6BRq5H5a0k95Px9D+GztuJuj7kIn7kZlvU84Lvbr9rLW93k8nw09m2AFQvnGDor1cq6f084L5gF6e5vkTh+FgpCI+D22SaYOTnqTW/ZrhXyTl9A8sxFSJoyD4XJKXD//AOYS1y0acysLKEIuAfpJ1/WUCmenaGu1zRqDZJP38CN17fizy4LcPvtL+Daozmab5le7WV+Ui/OGor2U/rj9PI9+GbEu1DKFHjt2yUwf0QdeXVqgpv7z2L/yDU4PPEDmImEGPvtEoiK7wPYujvB1t0R5zccxFf9luLUot1o8FJLDN48o6aK9Vgch3WD56ppSNp2BMFDFkAeFAmf79ZU2L/atHsB3jsWIf3IH3gweD6yzlxF/S+XwbJRSf/a4MvlEHt5IGL6BjwYNB8F8SnwPbgOZqXukdT7+B2IG3gWpek/D9LT/6D+54th1ax+tZdZH0Ofhx91j6DO5H5ouGIswrcexd8vLUL4lh/QZNM0SPq3rdpKIB1qvp749V/FwMdjmjJlCv78809s374dAoEAAoEA+/btKzdN1L59++Dl5QVra2u8/PLLSE9P19lPeHg4RowYAXd3d9ja2qJDhw74448/nipP48ePx2uvvabznlKphKurK/bv3w8AUCgUmDdvHtzc3GBpaYlu3brh+vXrT/V91c31jZHIPHIGmUfPQREWi/gVn0MtV8D51X5608vvhCJp415k/XIZmgKl3jRRU9ZAeuwcFKExyA+KQtzibbDwdINVC9/qLMpT850xEMHbTiDxzE1kB8XixltfwNLdEbUHtq9wm4ZvDkbUgQuIPvwnckLiccvvaxTKFag39iWddIVyBRSpWdqXvid56k/uC5GDNUI//6XKy/a46s0chIiPjyP19E3k3o/BvbmfQezuBLdBFdeB96whiPvuPBIO/4m8kHjcX/wVCuUFqD2uJwBAaGcFz/G9EPLut8j4KxA5dyJx7+2dcOrYGA7tdNuCMjsPBalZ2pdaUdK2cu5FIen438gLjkN+bCoSj/2FtAt34NTpheqpi1lDEPfdOSQcvoi8kHgEFZfLc1wvvem9Zg5C+oUARH/+M/JC4xH+wffIvhsJr2kDSqUZjMiPf0Tq6RvIvR+DwOL6lQzqoLOvwrx83XqQKbSf2TT0RJ3J/RAweQtSz9xEfkwqcu5EIuPS3Wqph8p4zRqC+O/OIbG4nh5o//3111PdmYOQcSEAMZ//DFloPCI++B45dyNRp1Q9JR29jMiPjlVapkJ5gU49FRrRE3L1Zw5C2MfHkXz6JnLux+D23M8hdneC+yOOpfqzhiD2u/OIO/wnckPicXfx1yiUF6Bu8bGU+yAO/tO3IeV3f8iiU5D+VyCCNx6BW/+2EJjrXlJ4Te4Lkb0NIr44VZ3FfCy1Zg5DysGzSDtyHvLQOEQu2QW1XAHJuN5603u8MRTSC7eQ+MVPyA+LR9yWQ5DdjYT71EHaNMpUqc7LaUAHZF+5B0VMydOH8VuPIOnLXyB7EK3va2qUIfoTgbkZGr83BSHrvkPc/j8gi0hEXkg8kk/+q/NdwSv2IW7v75BHV/zkZk3wfHMoEg+cQ/Lhi5CFxCHUbzfU8gJ4jNXfTmrPGIKMCwGI+/wk5KHxiN58BLl3I1B76kBtmtTjf0F6+S7yY1IgC45DxLvfQGhvDZsmJTcgord8j/jdp5AXZPggYVmG6l8Tvj0H6b9ByI9NRc7dSERsOgLLOq6wqutW4TbVyZDXJsErvkHs3t8hj055ZB6VGTk65yONqrBKyv443GcMR+rBs0j7/jzyQ+MQvXQn1HIFXMfqfzDEffpQZF28haSdJ5AfFof4LYcguxcBt6mDtWlUqVKdl+OAjsj5W7ePTf7yZ+T5h6AgPhW5N4KR+OmPsGnbCAKhebWXuTp179wB82ZORt+Xuho6K9XKfsIo5Bz/FXknz0AZGYOMDdugyVfAdsRAvenTVm5E7g8noQwJhyoqFunrPgIEAlh2LLm5mHfqD2R9+R3kV/317sMYGep6TZWVh5hv/kDW7QjI49KQfjkQ0fvOwrmaftc8iw7TB+LKpz8h9Kw/Uh/E4pcFO2Hn5ohG/dtVuM2RyZtx9+hlpIXGIyUoBr8s3AWHOq7waOENAEgLicPxWZ8g7NwtSGNSEP33ffy55Qf49mlT7prWkNzeGIH0Q78j44dzyA+NReyyL6CWK+DyWl+96SXThiH7T3+k7DoORVgcEj88CPm9CEimDAEAiOvXhk27FxC74gvI7oRBERGP2OU7IbC0gNOIHtr92LR7Aan7TkF2OxQFMclI3vEDCrPzYG2g+yjGfI+g9qvdEbf/HJJ/+gfy6BQknfgHcd+eg/fc4dVSF0T0ZIynRzdy27dvR+fOnTFjxgwkJiYiMTERdevW1Ulz9epVTJ8+HXPnzkVAQAB69eqF9957TydNbm4uBg8ejHPnzuHWrVsYOHAghg0bhpiYJ/+xO2HCBPz888/Izc3VvnfmzBnIZDK8/PLLAAA/Pz8cO3YM33zzDfz9/eHr64sBAwYgI0P/CAlDEYiEsGrui9y/bpe8qdEg90oArNs2rrLvMbezAQAUSnOqbJ9VxdrLDZbuTki5dE/7nipHjoxb4XBu31DvNgKRORxb1tfZBhoNUi7fK7dN3VFdMSRwF/pc/ADNlr8GcysLnc/tGnmiyYKXceOtL6DRaKquYE/Aqp4bxO5OOjdDVDlyZPmHwaF9I73bCETmsGtZH+mXS91A0WiQcekuHIu3sW/VAGYWQqSX2q8sLAHy2NRy+22ycRp63t+NTqff014UVcSuuTccOzRC5j/3n7CklSsqVwNk6CmXQwXtwaFdI2SUbgsA0i/c1pbxYf2ml6nfbP8wOJbZp/dbI/BS0Ffo9Mcm1JszTOcHgGv/dpBHp0DSrx26Xd+Bbtd3oOlHb0LoaPOsxX5iFdVT5jPU05PweKUbut//Ep3+3AqfFeNgVua4MhSrekX9SVqZ/kTqHw6nR/QnDi3rI+2ybn+SduleufZRmsjeGqocOTSFJc+R2DbyRMOFryDgrc+hURv2+RKBSAiblj7Ivnyn5E2NBlmX78Cunf7zi227RrrpAUj/vAXbCtILXR3g2KcdUg+fq7J8VyVD9Sd2LevDsrYLoNag0x+b0OPOTrQ5uFTn6X9jIRAJYdeyAaSXdNuJ9PId2FXQN9i3a6SbHkDmxduwr/B8JUStSX2hyspD7n3DB8MqY+j+9SEzazFqje0JeXQy8hPSnno/T8sYrk0eR+v9i9EzcBc6nFwDyYCKbwZWtZI+VvcaPvuvOxX2mTbtGuumB5B1MQC27fSXW+jqAIc+7ZB6qOKHxcwdbeHySg/k3giu0aAPPSWhEBZNGiG/dIBCo0H+VX+IWzateLtSBJZiQCiEOju7mjJZ/Qx9vVaa2N0JHoM7IuOfoKcrTDVxrCuBrZsjov4qKa8iR46EgHB4tq24vGVZ2lkDAOTSvArTiO2tUZBbcR3VNIFICOsWPsgpc48k56/bFd4jsWnbWDc9gOxLt2BTnF5gUTRCovSNe2g00BSoYNOhifatvJsP4DSsG8wdbAGBAI7DukMgtkDOPzX/oJsxnIcfdY/AzEKIQoXug7jq/AI4tPF97gPxRKaAa3w8JgcHB1hYWMDa2hoeHh4AgAcPHuik2b59OwYOHAg/v6Ih1o0aNcLff/+N06dLpohp1aoVWrVqpf17/fr1OH78OE6ePIm5c+c+UZ4GDBgAGxsbHD9+HJMmFc0bfvDgQQwfPhx2dnbIy8vDF198gX379mHQoKKnVL/88kucPXsWX3/9NRYvXvzkFVFNzJ3sIRCaQ5WmOyevKk0KsU+dqvkSgQC1Vs1A3vX7UIQY31OVlm5Fw1UVpeaTfPj3w8/KEjvbwUxorncbO9/a2r9jf/wbsrg05CdlwqGpF5qvHAtbn1q4On0bgKKTdYcv5uLuuoOQx6fDpp5hnqi0kDgCKF8HBalZELs56t/G2R5mQnOdeTgf7sOmoWdRGjdHqBXKcusLFKRlQSwp2W/Ypu+R8dc9FMoL4NKzJZpsmgahjSVivtKd5qnHrc9g4VLUZsO3HEX8gQtPUdpHq6hcBalZsGlYW+82YjdHFKRKy6W3KG4/D+tXX11ZlKrfmK9OI+duJJTSXDi2bwTfFeMgdndEyLvfAgCs67nBso4r3Ia/iHtzP4PA3AyN1r2OVl8vwM1R65+h1E9O9Ih6sq6gniwqqCdxBcdZRZKOX0F+bBoUyRmwbVoPvivHw9qnNu5O+/CJ9lMdLCUV9yeVHUv6tqmozYmc7eA7/2XEfldyw9/MQog2O9/Cg7UHkR+fDmsD9ScPCZ3tIBCaQ1nm31yZJoWVr6febUQSRyjTyqQvc5yUJhnTC+pcOTJ+/Vfv54ZmqP7Eqp47AKDBotEIeXc/5LGpqDd7KNr/uBpXurwD1SNuPtQ0UXE70VdHDhW0k6K+pGx6abl24tyvLZrsnA8zKwsUJEtx57X1UGUY3wMYZRmyfwUAzyn94bt6AoQ2lsgLjcetVzdAo6z5G9qGvjapTGFePoJX74f0egg0ajXch3ZC630LETDlQ6SeuVn5Dp6Rto9N0y2rMlUKS59H9LF6+mSRxElvetdXi/rYzN/K97F1lk+C29TBMLe2RO7NYIRM3vB0BaEaZe7oAIHQHIUZur/9CjMyIfJ+vOC407wZKExNf65Gd5RlyOu1h1rvfAseA9rB3FqM5DM3cWfB7qcoSfWxKa6HvDTdAFdeWjZsJI95bhEI0PfdiYi9Hoy0kDi9SaycbNH1rZG4dajqf9c9LXNn++L+VarzvipNCssK7pEIJY5QlelfValSCIv71/zwOBTEpaD2kkmIXfY51DIFJG8Mh0VtV4jcnLXbRM3ZAu/PFqPl3QPQKFVQyxWInLERBdGPXlOjOhj6PFzZPYK0i3dQZ0IvpPx2HTl3ImHfqgE8J/SGmYUQImc7FKRIn77wRPTMGPioQkFBQdqRFg917txZJ/CRm5uLNWvW4NSpU0hMTIRKpYJcLn+qER9CoRBjxozBgQMHMGnSJOTl5eGnn37C4cNFiy+Fh4dDqVSia9eSYdIikQgdO3ZEUNDjP8mhUCigUCh03ivQFMJC8HxFr2uvmwXLxl4If9Vwi6aWVveVrmhTag7VvydurrbvivruvPb/sx/EIj85E92PrYRNPTfkRaeg2fKxyAlNQOyxK9WWB308RnVF0y0l86jemvBBjX5/WREflyxWnHMvCubWYnjPGVYu8HF9xBqY21jCoV1DNFwxDrKoJCQd/7ums1ttYnaVTE2Uez8GaqUKTbbMQOiGQ9AUqAAzAcwtLRA49zPIihcIvD9/F178YxOsfWpBFp5Y0a5NSsK3JT8e84JiUZCcibbHVsOqnnuNT9lTe1RXtNjyhvbv6xOqrz95SGhrhQ4H/JAbEo+QLce07zdeMRa5ofGIP1Z+HQdTJRnbG2nHL0Oj0D/t4n+VoHg9hsjtx5Fy6hoAIPDtL9Dj1hdwH9YZ8d8+3VSfzxvplUDc7LMYImc71JrYF013L8CtwcugTHt+n1KuCUnHLiPjzzsQuzvBa85QNP/yHdwctlr3KdVqYGzXJpVRZuQgetev2r+zAyIgdneC95xhNRL4qAmuY/sg/fglvX1s0hcnkHr4HMSeEtRe8BoabJ+H0NcZ/DB19lPGwnpATyTPXAhUMOWxMTKm67WHglbtR+jWY7DxqYUXlo9F07WTcG/pnmrPV0WajeyCge9P0/79/dStz7zPAesnw7VRHXw3Wv8DWha2VhizdxHSwuLxV6nfgyZJVYiINzfBa/NctLx7EBpVIXL+uo2s8zd01q6ttXA8zO1tEDpuFQozsuEwoBO8P1+M0NHLkR9cvaNWje08XNk9goiPjkHs5oBOv64HBAIUpGYh4cgl1H9rOGCgmTSIqAQDHzVs0aJFOHv2LLZu3QpfX19YWVlh9OjRKCgoeKr9TZgwAS+99BJSUlJw9uxZWFlZYeBA/fOiPq2NGzdi7dq1Ou/NcmiIOU5VNwVVYWY2NKpCCF11n/QSujpClZpZwVaPr/baN2HXuwMiXlsGVVJ65RvUgMQzN5FRajFoM3HR4SiWOCC/1FMBYokDsu7pv7hQZORArSqEuMzTLmX3UVbGrXAAgE19D+RFp0DSrSkcmnjBc2hHANBe9Ay5vwvB208gSM9FclVIPX0T/9wsXQcibf5LPxlhIXFATqD+OijIyIZaVQgLPXWgKN5HQYoUZmIRhPbWOk90WLg6QFHmiZjSsvzD4LNwFAQWwqIb/sXkMakAgNygWFhIHOCzaHSVBz4qKpdFqXKVpUiRap+IKZ2+IKXoSZeHT+FalKlfscQBOYFRFeYlyz8MZiIhrOpKIAtPhCJZCrVSpQ16AEBeaNHTU5aerjUa+FA+op4qerqmoIJ6UqRk6U3/uB4u7m5V36PGAx/Jp29CWsGxpCjzb51dwb/1wzanrz8p2+bMbSzR8fBSFObKcXPqRzrTirh0awb7Jl7wGNoJQEl/0i9oN8K2nUDolqNPW8ynosrIgUZVCFGZf3ORa/knjh9Spkohci2TvoI2ZdexCax86yBs1kdVk+FqYKj+RJFc9H5ecMnTlZoCFeQxybCsU7IYrTFQFreTJ+9LyqZ3LJdeLVMgPyoJ+VFJyPEPRYe/P4HHuN6I3XGiCktQ9QzdvxbmyCHPkUMemYSsmyF4KWQPJIM7ILmaHzQw9muTx5HlHwaXl1o80z4el7aPddUtq75RHQ8pU6UV9Mnlr/lti/vY8Nn6R1OqMnOgysyBIiIB8rA4tL7xFWzaNUbezeCnKg/VjEJp0To05s66v/3MnZ1QmP7o3372k16Fw9SxSJ7lB2VoZHVms8oZ0/XaQw/Xf8wLS4AyMxddfl6D0I9+rPD6oLqFnvVHQvFvVQAwtyj6jWzjao+8UnmycbVH8v3KHx7tv+51+PZpg+/GvIecpPLTfVvYWOK1/YuhyMvHsZnboDaiqfIKM7KL+1dHnfeFFfSXwMPRHWXSS3TvqcjvhiN40HyY2VnDTCSEKiMbjX7aAtmdorZpUc8DkqlDEdR3LvJDYou2CYqCbcdmkEwejNjlX1RdIfUw9vNw2XsE6nwlAt/ZhfuLviq65knORJ1JfaHKkaGAD7kQGRzX+HgCFhYWKCys+ETYpEkTXL16Vee9f//VHZJ95coVTJkyBS+//DJatGgBDw8PREVFPXWeunTpgrp16+LIkSM4cOAAXn31VYhERScGHx8fWFhY4MqVkqf4lUolrl+/jqZNH2/uVABYtmwZsrKydF5vOFbtolYapQrye2Gw6dqy5E2BALZdWkHm/2w/XGqvfRP2/TsjcsIKKOMMu3Bqaaq8fORFJWtfOcHxyE/OhKR7M20aoa0VnNv4IONGqN59aJSFkN6JhFupbSAQwK1bswq3AQCHZvUAAPnJRRdAV6dvw7k+S3G+7zKc77sM/gu/BABcGrEOEXvPPmtRK1SYlw95VLL2lRccB0VyJpy7N9emMbe1gkNbX2TdCNG7D42yEDl3IuFSahsIBHDu3hzS4m2yb0dAXaDS2a+1Ty1Y1ZVUuF8AsGteD8rMXJ2gR1kCgQBmxXOlVqWickXAuXupGxfF5cqq4N8262aIThkBwOWlFtoyyqNToEjOhEupfZrbWsG+rS+kj2gvds29oSlUay/cpNeCiwIhxdPYAIB1g6Kh9flxNTv/ekX15FRJPTmVqSfnUvX0tOyaeQMAClKePVj7pArz8iGLSta+coPjkJ+cqXNcCG2t4NjWB5mP6E+y7kTCtcyx5NK9mU77ENpaodP3y6AuUOH661vLPX3tP+1jXOq9BJf7LMXlPku1Uyb8M2Itovf+XoWlfjwapQp5d8Jh3033/OLQrSVyKrgxlnszBPbddW8aOvRohVw96SXj+iD3dhhk96OqMttVylD9SfbtCBTmF8C61NSLAqE5LOtKaryvqIxGqULOnQg4lqkjx24tkFNB35B9M0Q3PQDHHi2RXVlfYibQ/og3ZsbUv0IgAFA959uyjP3a5HHYNa+nDTxWt4r6WPtuLfT2mQCQdzNYNz0e9rHlyy0Z1xd5t8Mgf4w+9mGg3cyCz/YZPZUKBUEhOguTFy1U3gaKOxWvnWc/eQwc3piI5LnLUBD0jP2KARjT9Zo+D0dqPnwgzxAK8vKRGZ2sfaWFxiM3RQrvriW/dy1srVC7tQ/i/Sv+/QIUBT0aDWiPg+PeR1ZsarnPLWytMPa7JSgsKMTR6R+VW6fB0DRKFWR3w2FX5h6JXdeWFd4jyfMP1k0PwK5ba+TpSa/OkUGVkQ2xdy1Yt/RB1u9F97LMLMVF36/WHa2gKVQDZoJy+6lqxn4erugegUZVCEViBqDWwGNkZ6SevcURH9VIDQ1fT/j6r+JV4RPw9vbG1atXERUVBVtbW6jLLNg6b948dO3aFVu3bsWIESNw5swZnWmuAKBhw4b48ccfMWzYMAgEAqxatarcfp7U+PHjsXPnToSEhODChZI5KW1sbDB79mwsXrwYzs7O8PLywubNmyGTyTB9+vRH7FGXWCyGWCzWea86prlK++oE6nw4H/I7YZDfDoHLtBEws7ZE5tGiqTDqfDgfyqR0JG/ZD6BosS+xb13t/4s8XGDZpD7UsnwURBc9cV573Ww4juiB6JkboM6VQ1j8tERhjgwaxdONsqlOYV+exgvvvIy8iCTkxaSi6ZJXkZ8sRcLpG9o03X5YjoTfbiBiT9ENxNBdv6L99lnIvB2BzFvh8J0xCObWlog+/CcAwKaeG+q+0hVJ5wJQkJkDhyZeaLFuElL/CUJ2UNETHHnRKTr5sHC2AwDkhMZDWWbOy+oWvfs3NJj/MmSRSZDHpMB3yRgokjOR8ltJHbQ7uhIpv15H7J4zAIConafQ/JPZyA6IQNatMHjNHAxzazESiutAlSNH/MELaLx2EpTSXKhy5Gjy/lRIr4cgq/hpEkn/trCQOCDrZigK85VweaklGrw9ElGf/6L93rpT+yM/Pg15oQkAAKfOTeA9Z2i5qbCqrC52nkKzT+YgOyAc2bfCS5XrIgCg2Y7/QZGUgbANhwAAMbt/Q/sT76LerKFI/cMfHiO7wL6VD+4v+lK7z5jdv6L+/Jchi0yEPCYFPktegyI5E6m/XQcAOLRvCIe2DZHxVyAK8+RwaN8Ijde9jsSjl6HKKpqPP+PSXWTfjkCzbbMQvOobwEyAFzZNR/rF2zqjQGpKzM5TaKqnnhKL66lpcT2FF9dT7O7f0PbEu/CaNRRpf/jDvbieHpSqJ6GjDSw9XSH2KHoS8eGN24IUKQpSs2BVzx3ur3RF+rlbUGbmwrapFxquex2Zf99H7mM8fVYTInf/hobzRyKv+FhqtORVKJIzkVzqWOp0dAWSfr2O6OL+JHLnKbT6ZDakxceS98xBEFqLEVt8LAltrdDx+2UwtxIjYM6HENlaAbZWAABFejag1kBWQX+SGxJfbg7dmpK4+2f4bHsLebfDkHsrFB4zhsHMWozUw0XTADbYPg/KpHTEbjwAAEj66hc0ObYeHm8Oh/TcTbiM6Aablj6IXLxTZ7/mtlZwHtYFMWv36f1eC09XCB1tIfZ0hcDcDNbFwbH8yCSoZfnVVl59DNGfFObKEbf/D/gsfhX58enIj0uF9/+GAwCST5Y8GGLl7Q5zG0uI3RxhZmkB2+LgfF5IXI2u6RC/6xc03v4/5N4OR/atMNSZMQRm1mIkHS66tmq8Yy4UiRmIev8gACDhy1NoeXwtPGcNRcYf/nAb2RV2rXwQungXgKJFub3efgXpZ26gICUTImd71J46AGIPZ6T+/I/2e8Wl2gnMzWBT3E7kBmgnZRmif7Ws5wb3EV2QcfE2CtKzYVnLBfXmjYA6vwBp527VbAUUM9S1CVB0fAhtLGHh5gBzSwvYFR8fucXHR+0xPaBWqpB9NwoA4D6kIzzH9ULggl01VDtA8pcnUf/jeci7E468W6FwnzEUZlaWSDtSNCVk/e3zoEzMQNym74rSf/0LGh99D+5vDkfWHzfhPKIbrFv6IMpP9yliM1srOA3tgth1+8p9p02bhrBp5Yuc60EolOZB7O0Bz8XjkB+ZWGHA5Xkhk8kRE5eg/Ts+IRkPQsLhYG+HWh6GXTerKmUfOAbXtX4ouB8MRWAw7Me/AoGVJXJPFl1Xu6xbgsKUNEg//RoAYD/5NTjOnoy05RuhSkiCmUtRH6KRyaGRF/WVZvZ2MPdwg7mkaFThw/VCCtMzoK5kJImhGOp6TdKnNcQSB0gDwlGYlw+7xnXxwurxyLj6APJY43o44frXp9HlrZHIiExGVmwKeiwcjZwUKUJ+L5nOb9zBZQg5cwM3vyl6aG/Ae1PQdHhnHJ3xMQry8rXrgSiyZVAplEVBj2+XQGRlgZNvfwGxnRXEdkV1JEvPLnfT31BSvvoJ9T58G7K7YcgLCIXb9GEws7ZE+vdF90jqffwOCpLSkfhB0VqMqXt+RsPvN8Btxghknb8Bp+HdYd3SB7FLP9Pu03FIF6jSs1GQkAqrxvXgueYNZJ25ipzLAQCK1gHJj0yA18Y5iH9vL1TSHDj27wS77q0QMfW9Gq8DwLjvEVg3qAWHNj7I8g+D0NEG9WYNge0LdXFvXvWOjCGix8PAxxNYtGgRJk+ejKZNm0Iul2Pv3r06n7/44ov48ssv8e6772L16tXo27cvVq5cifXrS+aS/OijjzBt2jR06dIFrq6uWLJkCbKzn23424QJE7BhwwbUq1dPZz0PANi0aRPUajUmTZqEnJwctG/fHmfOnIGTk/7FAw0p69RfELo4wH3BBAhdnZAfFIHIKe9CVbyYl6i2BCh1ASJ0c0bDXz/R/i2Z+QokM19B7r93ETluOQDAZdJgAECDwxt1vit20TZIj5Vf3M3QQj79GebWYrTZ+gZE9tZIvxaCK+M26TyhY+PtDnHxjUQAiP/pX4hd7NHUbzTEEkdkBUbjyrhNUBQ/na9WqiDp0Rw+MwZCaC2GPCEDCaeu4cHHJ2q6eI8l6tOTMLcWo+nWGRDaW0N6LRj+Y3XrwLqeu/ZmKgAk//QPLFzs4eP3KsRujsgJjIb/uE06i5kFr94PjVqN1l8vgJlYiLQLdxC05Gvt52plIepO7Y/G614HBALIIpMQvPpbxJVaHwVmAjRcMQ5WXhKoVWrIo5IRsv4Q4vZXzzz1JeUaU1yuKPiP26gtl6WnC1AqcJp1IwR3Z++A79LX4Lt8LGSRSbg9ZQvyHsRq0zys3yZbZ2rr99bYjdr6VStUcB/ZBQ0WjYaZhQjymBRE7/oV0TtLLu6g0SBg0mY0fn8q2v+0BoUyBdLOBSBkzf5qqYfKpBTXU4NS9RRQpp40ZeopcPYONFj6GnyK6+lOmXqSDGiPpp/M0f7dYvc7AICILT8gcutRqJUqOPdoAa+Zg2FmLYYiIR2pv1xDpBHNCxzx6c8QWovRorg/ybwWjGuVHEuJP/0LCxd7NPIbDbGbI7IDo3Gt1LFk39IbTu0aAgB6Xduu833n279ldD+UH8o4eQUiF3vUWTwOIokjZIGReDBhPVTFi/GKPV11jqXcG8EI/9/HqLNkPOounYD8yESETPsA8mDdoJbziG6AQID0E/rXM6mzaCwkr/XW/t3ibNF0WPdHrULOP4FVXcxHMkR/AgCha7+DRlWI5p/9D+aWFsjyD8PNUeu1gVQAaPrRm3Au9SRn5/NFc55fbj8X+Xqe0KwuqT/9DZGLPer5vQYLiSNyA6Nwb9wG7aLNYk9XnRsh2TdC8GDOdngvGYf6y8ZDHpmIwKmbISuuI02hGta+nnAf0xMiZzsoM3OQExCOgJGrISs1/Vc9v9fg8VpP7d/tzm0BANx+5V1k/V3xk881wSD9a74Sjp1egNfMQRA62KIgVQrpvw9wY+gqg62LYqhrEwBo9tGbcO5aMlK78/miuc4vtX9Le3w0mP8KrOq6Qq1SQxaWgDsztyP5F91R6NUp4+QVCJ3t4bloLEQSJ8gCIxEycZ22j7Uocw2feyMYEXM/hqffeNRZMhH5kYkIm76pXB/rUtzHZpy4XO471XIFnAZ3hueicTCzEkOZkomsi7cQvn3rI0fqPg/uPQjFtLdK1iTcvKNo5OSIQX2xYeVCQ2Wrysl+v4hMJwc4zp4CcxcnFASHI2XuMqgzpAAAoYebznnJ7tVhEFhYQLL1XZ39SHftR9auomtQq5c6w3Wtn/YzyaaV5dIYG0Ndr6nzC+A1sTearptUdM2fkI6kX68h/JOTNVDqJ/Pvzl8gshZj0MZpsLS3RuyNEHz/+madERqOXm6wciqpo7aT+gIAJn6/UmdfvyzchbtHL8OjuTc82xbNYjH7su50pZ93fQdZRjIyVfrzXxA626PWgvEQSpwgvx+J8Elrtf2rqLarznk47+YDRM37ELUWTUQtv0lQRCUgcsZG5IeU9K8iN2d4rpoOoasDVCmZyDh2AUmffF/ypapCRExeh9pLX0eDPSthZmOJgqhERC/YjuwLhlk7ypjvEQjMBag3ewhsfGpDrSpE5pVAXBu6ukavYYmoYgKNhmOv6MndrT/M0FkwKqFyu8oT/cfYap5tJJMpEvyHhxdWhPMtlpfPWinH1Vxh6CwYnexC458qqaaJBTzvlKXUsD8prRDVP0XH88ZJyP61rNa39a8n8l+W0H+mobNgdO7FSwydBaNzx7LqZ4V43g3U5Bg6C0YnVWFl6CwYnf7Jhw2dhefOEu9xhs7Cc+eDqEOGzoJB8NcQERERERERERERERGZDAY+jNiBAwdga2ur99WsWbPKd0BERERERERERERE9B/DNT6M2PDhw9GpUye9n4lEnOKCiIiIiIiIiIiI/js4iTg9LgY+jJidnR3s7Lh2BBERERERERERERHR4+JUV0REREREREREREREZDIY+CAiIiIiIiIiIiIiIpPBwAcREREREREREREREZkMBj6IiIiIiIiIiIiIiMhkcHFzIiIiIiIiIiIiIjJ6akNngJ4bHPFBREREREREREREREQmg4EPIiIiIiIiIiIiIiIyGQx8EBERERERERERERGRyWDgg4iIiIiIiIiIiIiITAYDH0REREREREREREREZDKEhs4AEREREREREREREVFl1NAYOgv0nOCIDyIiIiIiIiIiIiIiMhkMfBARERERERERERERkclg4IOIiIiIiIiIiIiIiEwGAx9ERERERERERERERGQyGPggIiIiIiIiIiIiIiKTITR0BoiIiIiIiIiIiIiIKqMxdAboucERH0REREREREREREREZDIY+CAiIiIiIiIiIiIiIpPBwAcREREREREREREREZkMBj6IiIiIiIiIiIiIiMhkMPBBREREREREREREREQmQ2joDBARERERERERERERVUZt6AzQc4MjPoiIiIiIiIiIiIiIyGQw8EFERERERERERERERCaDgQ8iIiIiIiIiIiIiIjIZDHwQEREREREREREREZHJYOCDiIiIiIiIiIiIiIhMhtDQGSAiIiIiIiIiIiIiqowGGkNngZ4THPFBREREREREREREREQmg4EPIiIiIiIiIiIiIiIyGQx8EBERERERERERERGRyWDgg4iIiIiIiIiIiIiITAYDH0REREREREREREREZDKEhs4APZ+C8+0NnQWjIoLG0FkwOunm5obOgtFpYZll6CwYnUyZpaGzYHQyBDw1l5VdKDJ0FoyOWKA2dBaMjtis0NBZMDq3hGJDZ8Go9BdnGDoLRueUwtnQWTA6bv1nGjoLRqf277sNnQWjIzmw2dBZMDovxSUbOgtGJ+NCrqGzYHQyY3ltQs+Ov4TocXHEBxERERERERERERERmQwGPoiIiIiIiIiIiIiIyGQw8EFERERERERERERERCaDgQ8iIiIiIiIiIiIiIjIZDHwQEREREREREREREZHJEBo6A0RERERERERERERElVFDY+gs0HOCIz6IiIiIiIiIiIiIiMhkMPBBREREREREREREREQmg4EPIiIiIiIiIiIiIiIyGQx8EBERERERERERERGRyWDgg4iIiIiIiIiIiIiITIbQ0BkgIiIiIiIiIiIiIqqMxtAZoOcGR3wQEREREREREREREZHJYOCDiIiIiIiIiIiIiIhMBgMfRERERERERERERERkMhj4ICIiIiIiIiIiIiIik8HABxERERERERERERERmQyhoTNARERERERERERERFQZNTSGzgI9Jzjig4iIiIiIiIiIiIiITAYDH0REREREREREREREZDIY+CAiIiIiIiIiIiIiIpPBwAcREREREREREREREZkMBj6IiIiIiIiIiIiIiMhkCA2dASIiIiIiIiIiIiKiyqgNnQF6bnDEBxERERERERERERERmQwGPoiIiIiIiIiIiIiIyGQw8EFERERERERERERERCaDgQ8iIiIiIiIiIiIiIjIZDHwQEREREREREREREZHJEBo6A0REREREREREREREldFAY+gs0HOCIz6IiIiIiIiIiIiIiMhkMPBBREREREREREREREQmg4EPIiIiIiIiIiIiIiIyGQx8EBERERERERERERGRyWDgg4iIiIiIiIiIiIiITIZBAh8XL16EQCCAVCqtMM2+ffvg6Oj4RPv19vbGtm3bnilvj+txyvA8MJVyEBERERERERERkWlT8/XEr/8qYU18Sc+ePdG6dWttUKJLly5ITEyEg4NDTXz9cy8qKgr169fHrVu30Lp1a0Nnp0o1XTwK9Sf0goW9DdKuh+DW0j3IjUx+5DY+U/qh0ZwhsJQ4IOt+DG6t+AaZARHaz186tgKSLk11tgnffw63luzR/u3UqgFarHgNji3rAxogIyAcd9cfQtb9mKot4FN4wW806k3oBZG9DTKuh+D2kj3Ii0x65Db1p/aD75yhEEsckH0/BndWfAPprXC9aV886Af33q1xdcpHSDp9Q/t+i/deh3OHxrB7oQ5yQ+Nxse/yKi3Xs2ixeBR8xhfVSdqNEFx/jHbScEo/vDB7CKwkDsi8H4ObK79BRql2YilxQOtV4+HRozlEtpbIDk9E4PafEPfrdW2aYVe3wbauRGe/Ae8fRtCnP1dtAZ+R86QhcJ3xCoQSJ+QHRSJxzS7I74ToTStu6AW3+RNg1dwXFnXckbh+N9L3ntRJ4zr7VdgP6AxxgzrQ5BdA5h+EpA/2oSAyviaK81RqTR2IOnOGw0LiiNz70Qhf8TVyb4VVmN51WGfU8xsLy7oSyCMTEfned8g8d0v7udeiMZCM6AqxpwvUBSrk3olA9MZDyLkVCgBw6NIMLX9cq3fftwYuQW6A/uOvpjUr08f6P2Yf27i4j5Xq6WMBwLmdL1osHQPntj7QFGogDYzGpXGboM5XAgAcW3ij5YqxcGrdAJpCNeJ/vY6Ad79DoUxRbWUtq87U/vCeMwwWbkVt4sHyvciuoF8EALdhL8J3yRhY1pVAFpmEsPUHkHYuQCeNj9+r8JzYB0J7G0ivB+OB31eQFffPTl2aov3xd/Xu++qA5cgOCIdlXQm63/i03OfXBq9E1s3Qpy/sU6o1dQDq6hw3e5DzyOPmRXhrj5skRJQ5buoterXccRO18VC5fTr3bQuvBaNh06Qe1IoCZP1zH/enbqm2cj4J9ykDUWv2SIgkjpDdj0LUyq+QF1BxnTgP7Yw6fuMgruOG/MhExGz4Flnn/XXSWPp6wmvl67B7sSkEQnPIQ+IQOmMzCuLTAAAiiSO8Vr0O+x6tYG5rhfzwBMRvP4rMX/+t1rI+qxcXjELz8b0gtrdGwo0QXFi+F9KoivuX2h0bo92sIXBrUR+27k74+Y2PEfH7TZ00Imsxui59DQ0GtIeVky2yYlNxe+8Z3P3ufHUX55k4TRgK5zdGQShxguJBJJLWfYH8Cs7DFr5ekLwzCZbNis7DSRt2IXPfTxXu22Xmq3BbPBUZ+04gecPu6ipCtem6YBRalGonZytpJ3U6NkaHWUPgXtxOTrzxMcLKtBNrV3v0WDYW3j1aQGxvjbirwTi3+ptH7tcY2I4ZDofXx8DcxRkFIeHI2PwpCgKD9ad9eTBshvaDyMcbAFAQFArpp1/rpLfq3Q12o4bCokkjmDvaI2Hsm1CGGMf1R1W7EXAXew8exf0HYUhNz8D2javQp0cXQ2erWghbvgRhu/4QWNtDnRYH5cUjUCdH6U1r3qQzxP0n67ynUSkh/+ytkjdEYoi6vgzzBq0gsLKBJisdqtvnobp7uRpLUbVEXQZB9NJICOwcoU6MguLEV1DHVn7dJGzVDZYTF0J17yryv9mkfd92y3G96RW/fAPlnyeqKtvVyvbVEbCfVNyfhIYjc8uOCvsTm5GDYTOkPyy0/UkIpJ+X6U96dYPtqGGweKGoP0kcP9Oo+xPPqQPgVeo6P2T5HuQ84jpfMuxFNFjymvYaNnz9AaSXuoaVDO4Iz8n9YNeyAUTOdrjWezFyA6O1n1vWlaDLjc/07vvuGx8h9WfjvmYj+i8zyIgPCwsLeHh4QCAQGOLryUg0/t9Q+E4fAP8le3F+yGoUyhTodmgpzMSiCrepM/xFtFwzAfc//BF/DFgJ6f0YdD+0FGIXe510Ed+dx88t52hfd9cf0n5mbi1Gt4N+kMWn4/yQd3FxxFqocvPR/dASCITm1Vbex+E7dxgaTB+A2357cGnwKqhk+eh8+NF1UnvEi2i2ZiKCP/wRf/ZfgazAGHQ+tBQWrvbl0jaYOQjQVPz9MYcvIuGkcZ20m/xvKBpNG4DrS/fi7NDVUMkU6HXw0XXiNfxFtHl3Au599CNOF7eTXgd128mLn8yGvU8tXJryIX7tvRRxv95A113z4NS8ns6+7mz+AcdbzdG+Qr7+vdrK+jTsh3SHx/I3kPLJIYQPexv5QZHw/mYdzF30B5bNrMQoiElC8uZvoEzJ0JvGpmNzZHx7ChGjFiHq9VUQiITw3r8eAitxdRblqbmO6IIGayYj5sMfcKu/H/ICo9D80EqI9BwDAGDXvjFe+OIdJB06B/9+i5H+23U03esH6xfqatPIwxMQvvwr+PdcgDsjVkIRm4LmR1ZCVNyGsq8H498Wb+i8Er/7A/LoZKMJepTuY88NKTp2uj9GH9uquI89O2Alsu7HoEeZPta5nS96HFyCpD/v4tyg1fhj0CqE7fkdUBd1LpbujnjpyDLkRiXj3JB3cXn8Ztg3qoOO22dVe5kfch/RGY3Xvo6ID4/har+lyAmMRtvDyytsEw7tG6HFznmIP3gBV/suRepv19Fq32LYlGoT3nOHo+4bgxDk9xWuDV6BQlk+2hxZrq1P6fVg/Nl8ps4r7rtzkEUnI7tMm7g5er1OuuzbuoGlmiAZ0QU+ayYj+sMf4N9/CfICo9H80IoK68i+fSM0+eIdJB06j5v9/JD22zU0K3PcyMITEbb8a9zsuRC3R6xCfmwqWhxZpT1uAMB1SCc03vEWkg9fgH+fRbg9fBVSjv9V7eV9HM7Du8Lr3amI++h73BuwCLL7UXjh4GoIK+hPbds3hu/nC5B66Bzu9l+IzNPX0GjPElg19tKmEddzR9MT70MeFoeg0atxt898xG/7QRskBACfT+bB0scTIVM24m7v+cj49V803LUQ1s3rV3uZn1a72UPRemp/nF+2B0eGvwulTIGR3y2B+SP6F5G1GGn3Y3Bx5TcVpum+egLq9WyFM29/gf29/RDw9Wn0XDcZ9fu1rY5iVAm7wT3gtnwG0j49iMiRbyE/KAJee9bD3Lni87AyNhGpW/dCVcF5+CHLFg3hOHYQ8oNqvo+oCh1nD0Wbqf1xdtkeHChuJ6Mfo52k3I/BH49oJyO/nA8HLzecmP4x9g9aiez4NIw5uAwiI71OAQDr/j3hvGAWpLu/ReL4WSgIjYDbZ5tg5uSoN71lu1bIO30ByTMXIWnKPBQmp8D98w9gLnHRpjGzsoQi4B6kn3xZQ6UwHLk8H419G2DFwjmGzkq1Mm/YDqLuo6G8+gvyD70PTWocxCPfAqzsKtxGo5BD9qWf9iXfq/vwmkX30TCv1xQFZ/Yif/9aKAPOQdRzLMzrt6zu4lQJYauusBg2FQVnj0C2bSHUCVGwemM1BDaPfohW4CSBxdDJKIwILPdZ3rqpOq/8IzugUauhuvtPdRWjSln36wmn+bOQ9eV+JE6cBWVIONx2fPDI/kR25jySZy1E0tS3oEpOhdunm2EucdWmETzsT3YYf3/iNqIzGq59HVEfHsX1fkuQGxiN1ocffQ3bbOfbSDx4Htf7LkHqb9fRosx1vrm1GNKrDxD23gG9+8iPT8NfzWfovCI+OAJVrhwZpQIoRGR8qj3wMWXKFPz555/Yvn07BAIBBAIB9u3bV256pX379sHLywvW1tZ4+eWXkZ6errOf8PBwjBgxAu7u7rC1tUWHDh3wxx9/PFWexo8fj9dee03nPaVSCVdXV+zfvx8AoFAoMG/ePLi5ucHS0hLdunXD9evX9e2uUunp6Rg3bhw8PT1hbW2NFi1a4NChQzpp1Go1Nm/eDF9fX4jFYnh5eWHDhg0AgPr1i374tmnTBgKBAD179gRQNJLmnXfe0dnPyJEjMWXKFO3f3377Ldq3bw87Ozt4eHhg/PjxSElJeapyVDXfGQPxYNsJJJ65iaygWFyb9wWs3B1Re2C7Crdp9OYgRB64gOgjl5ATEg9/vz0olCvgPe4lnXSFcgUUqVnalypXrv3MvmFtiJ3tELjlKHLDE5EdEo/7H/4ISzdHWNdxLfuVNcpnxkAEbzuBpDM3kR0UC/+3voCluyNqDWxf4Ta+bw5G9IELiDn8J3JC4nHb72sUyhWoN1a3Tuyb1YPvrMG49c4uvfu5u3I/IveeRV60cbSPhxq/MRCB208g/sxNSINi8W9xO6nziHbSeOYghB+8gMgjl5AdGo/rS/ZAJVegQal24tq+IUL2/I6MgAjkxaQicPsJKLPy4NRS90aTKjcf+alZ2lehvOaeWH8crtNHIvPIGUiP/gFFWCwSVn4GtVwBp1f76U0vvxOK5E17kfXLJWgKlHrTRE99F9Jj56AIjUH+g0jELf4YFp5usGruW51FeWqebw5D0oE/kHz4AmQhcQjz2w21XAH3sb31p58xGBkXAhD/+UnIQ+MRvfkwcu9GovbUQdo0qcf/gvTyXeTHpEAWHIeId7+B0N4GNk2KAmMapQrKVKn2pcrMgcvADkg+fKFGyvw4Gs4YiKBtJ5BQpo/1fIw+Nqq4j72pp49tvXYSQr8+g+BPf0Z2SDxywxMR9/NVqAtUAIBa/dpArSqE/7J9yA1PRObtCNxcsgd1hnaEjbd7tZcbAOrNGoK4784h4fBF5IXEI2jxVyiUF8BzXC+96b1mDkL6hQBEf/4z8kLjEf7B98i+GwmvaQNKpRmMyI9/ROrpG8i9H4PAuZ9B7O4EyaAOAACNshAFqVnalzIzF24D2yPh0MVy31eQkaOTVqMqrJZ6eBTPN4ci8cA5JB++CFlIHEL9dkMtL4BHBcdN7RlDkHEhAHHa4+YIcu9GoPbUgdo0+o8ba9g0KQ4EmJvBZ/1URK77Fon7z0IekQhZSBzSThrHjYZaM4ch5eBZpB05D3loHCKX7IJaroBknP468XhjKKQXbiHxi5+QHxaPuC2HILsbCfdSfUndpROQdf4mYt/7FrJ7kVBEJ0P6+3Wo0rO0aWzbN0bynl+RFxAGRUwyErYfhSpLBpuWPtVe5qfVZvpAXNvxEyLO+iPtQSx+n78TNm6O8Olfcf8SffEO/tl6FOFnblSYpla7hgg6ehnx/wYhJy4N9w5eQGpQDDxaNaiOYlQJl2kvQ3rkNLKOnUVBWCySVn8KtVwBx9H99abPvxuKlA/2IPvUJagrOA8DgMDaErU/9EPiyk9QmJ1bXdmvVm2nD8S/O35CeHE7+XX+Tti6OcL3Ee0k8uIdXNl6FGEVtBOn+h6o3a4h/lixF0l3IpAZkYizy/dCaCnCCyM6V1dRnpn9hFHIOf4r8k6egTIyBhkbtkGTr4DtiIF606et3IjcH05CGRIOVVQs0td9BAgEsOxYEgTMO/UHsr78DvKr/nr3YUq6d+6AeTMno+9LXQ2dlWolbNsXqsArKLz/DzQZiSg4fxAalRLCZo8a3aIBZNmlXjk6n5rVagBV0L9Qx4dAk5OOwnt/QZMaBzMP72otS1UR9RgO5dWzUN04D01KHBQ/7oRGqYCwY5+KNxKYwXL8fBT8fhjqjPIjwTQ5Up2XsFlHFIbfg0ZPWmNkN2E0ck/8iryfz0AVGY2MjdugzlfAdrj+/iR91UbkHi3uT6JjkfHeh8X9SRttGtmvfyD7q2+Rf+2m3n0Yk7qzhiLhu3NIPHwRspB4BC/+Emp5AWpXcJ1fd2bRb7+Yz3+GLDQekR8cQc7dCNSZVlJfSUcvI+qjY8i8dFf/l6o1OtfuBalZkAzuiJST/9ToiHYienLVHvjYvn07OnfujBkzZiAxMRGJiYmoW7euTpqrV69i+vTpmDt3LgICAtCrVy+89957Omlyc3MxePBgnDt3Drdu3cLAgQMxbNgwxMQ8+dREEyZMwM8//4zc3JIfEWfOnIFMJsPLL78MAPDz88OxY8fwzTffwN/fH76+vhgwYAAyMh79ZJY++fn5aNeuHU6dOoV79+5h5syZmDRpEq5du6ZNs2zZMmzatAmrVq3C/fv3cfDgQbi7F90gepjujz/+QGJiIn788cfH/m6lUon169fj9u3bOHHiBKKionQCI4Zi4yWBlbsTki+XPIGhypEj41Y4XNo31LuNQGQOx5b1kXL5XsmbGg2SL9+DSzvdbbxe6YphgTvR78ImNF/+GsytLLSf5YQlQpGRg/rjekIgMoeZpQj1x7+E7JB4yGJTq7agT8Dayw2W7k5IvVRSPlWOHJm3wuH0iDpxaFlfZxtoNEi9fE9nG3MrC7T/4n+4s2wfFKlZevZknB62k6RS7USZI0f6rXC4ttNfJ2Yiczi3rI8kPe2k9DZpN0LhNfxFWDjaAAIBvEa8CHNLEVL+DtLZX5O5w/DKvZ0Y+PsGvDB7CATmBhkop5dAJIRVc1/kXgkoeVOjQe6VAFi3eaHKvsfczgYAUJhlfDdeBCIh7Fo2gPTSnZI3NRpIL9+FffvGerexa9dINz2AzIsBsGvfqMLv8JjUD6qsPOTej9KbxnlAe4icbJF82DimY3naPtapZX0kP6KPFbvYw6WdLxRp2eh18l0Mu/M5ev64Ei4dS+rO3EJUFATRlAwvK8wvAAC4dtT/b1KVBCJz2LVsgIzLpX64aDTIuHQXDhWU3aFdI2SU7kcBpF+4DYfiNmFVzw1idyekl/oxpMqRI9s/DI4V7FMyoB1ETnZIOHyx3Get9/vhpcDdaH9yLSQDKr4BWF0qPm7uVHgc2Os9bm7D/hHHTa1JfYuPm6KpAuxaNoC4tgs0GjXant2MTrd3o/nB5TqjRgxFIBLCpqUPsi/r1knW5Tuwa6e/3dq2a6SbHoD0z1uwfZheIIBjn3aQRySi8cFVaHtnL5r9sglOAzvqbJN7IxjOw7vC3NEWEAjgPKIrzCxFyP5bt00aC3svCWzcHBHzV0n+CnLkSAoIh0cF5+bHlXgzFA36tYWNuxMAoE7nJnCq74Hoim5EGJpICMtmvsj7O6DkPY0GeX8HwOoZz8Me785B7sVrkJXe93PEwUsCWzdHRJdpJ4kB4aj9DO3E3KJopmaVolTQSKOBqkAFzw76+yODEwph0aQR8ksHKDQa5F/1h7hl04q3K0VgKQaEQqizs6spk2RwZuYwc/OCOqb0bxEN1DFBMPN4RPBXJIbl1A2wnPY+LIbOhsC5ls7H6sQImDdoCYGNY9HX1GkEgZM7CqPvV30Zqpq5EGaePigMvV3ynkaDwtA7MK9X8TWlRb8x0ORmQXX9XKVfIbB1gHmTdlBde7qHamucUAiLF/T0J9f8YfGE/UlhVk7liY3Mo67zK7omLbrO172OyLhwG/YVXMM/DruW9WHXoj4SDhjHbz8iqli1r/Hh4OAACwsLWFtbw8PDAwDw4MEDnTTbt2/HwIED4efnBwBo1KgR/v77b5w+fVqbplWrVmjVqpX27/Xr1+P48eM4efIk5s6d+0R5GjBgAGxsbHD8+HFMmjQJAHDw4EEMHz4cdnZ2yMvLwxdffIF9+/Zh0KCip/a+/PJLnD17Fl9//TUWL178RN/n6emJRYsWaf9+6623cObMGXz//ffo2LEjcnJysH37dnz66aeYPLlojk4fHx9069YNACCRFK0x4OLioq3DxzVt2jTt/zdo0ACffPIJOnTogNzcXNja2j7WPhQKBRQK3Si2UlMIkeDpp4WydHMs2neZm/D5qVmwlDjq3UbsbAczoTnyy2yjSM2GvW9t7d8xx/+GLC4N8iQpHJrWRYsV42DnUwv/TN8GAFDl5ePPV95Dl73z0WR+UaArJzIJf437AJpCwy35I3YrGq5btk4UqVmwdNM/lPdhnejbxq5UnTRfOwkZ10ORdMb4n+Aozaq4nZT9N89PzdK2obIqaif5adk6dXLlzU/QdedbGHV/N9RKFVTyAlyevg25peaHDvn6DDLvRqFAmgvX9o3QatlrsHJzxK21+ofA1jRzJ3sIhOZQpUl13lelSSH2qVM1XyIQwGPVDOTdCIQiJLry9DVM5GwHgdAcBWX+vQtSpbDy9dS7jYWbI5SpUp33lKlZsCjTppz7tcMLO98pmh4sORN3X1sHVYb+Hwge4/sg8+JtFCQ+eXC8OlRlH5ufWnLs2NRzAwA0XfgK7qw7CGlgNOq92h0vfb8cv/dagtzIZKT8FYhWayag0ewhCP3qNITWYrRcMRYAYOWu/7urkoWzPcz0toks2DSsrXcbsZsjCsq0iYLULFgU970WxXVWdp8KPe3mIc/xvZF+4TYUpdpEYV4+glfvh/R6MKDWwG1oR7Tatwi3p2xFag32zxUfN1lweMRxo+84K3/ctEWTnfNhZmWBgmQp7ry2XnvcWHoVtZ96i8Yg4t1vkB+bijqzhqHVsTW43vVtqKSGC64Ki+ukXN+QVnFfIpI4Qlmm/y3dl4hcHWBua4Xac19G3AcHEbvhWzj0aoOGX/khaPRq5PxbdNMp9M2t8N25EO3v74daqYJarkDo9A+giHr0+l6GYlN8PMjSdG/AytKyYSN5tvX7/ly9H703Tccb13egUKmCRq3BuaVfI+Ga/nnLDU1YfB4uTMvUeb8wXQqxz9MH9OyH9IBlM19EvfL2s2bRYKqrnWSEJyI7Lg09lryG35d9DaVMgfZvDIJ9bRfYVNAfG5q5o0NRO8ko004yMiHyfrx24jRvBgpT0/8Tozv+qwRWthCYmUMj0z1mNLIcmDnrvwegyUxCwdn9UKfFQyC2gqhtP1iO8UP+d2uhyZUCAAr+PAKL3hNg9cYmaAoLAY0aBee+gzqh4vWrjIXAxg4Cc3NocnWvPzS5Upi56T83m3k3gbBDH8g+XvBY3yFs3wtQyKG6Z1zTPVekov5E/QT9ieNbM1CYlv5cjO4oS6S9zpfqvF+QKoV1Bdf5Rb/9yl/zip/hnFFrfG/kBcch+4b+9byIyHjUyOLmlQkKCtKOtHioc+fOOoGP3NxcrFmzBqdOnUJiYiJUKhXkcvlTjfgQCoUYM2YMDhw4gEmTJiEvLw8//fQTDh8+DKBoWi2lUomuXUuG0opEInTs2BFBQUEV7bZChYWFeP/99/H9998jPj4eBQUFUCgUsLa21pZfoVCgT59HDNd8Sjdv3sSaNWtw+/ZtZGZmQq0uurEfExODpk0f74mAjRs3Yu1a3UV8X7VpjjF2jz8vaN1XuqDd5unav/+aVH2LmEZ+VzLVTPaDWOQnS/HS0RWwqeeGvOgUmFmK0P6jGUi7HoKrsz+DwNwMjWYPQddvF+HcoFU6825XpzqvdEWrLSV18u/EzdXyPR7928K1WzNc7LusWvZfleq93AUdSrWTP6uxnbT0Gw2RvTXOj3kfiowc1BnYHl13voU/Xl6PrAexAIDg3b9p00uDYqFWqtDhg2m4vfGIdlofU1dr3WxYNqqHiDF+hs5KjZNeuQf/PoshcraDx8S+aLJ7AQIGL4OyzE0ci1rOcOrZCkEzPzJQTgGvMn3s5Wo6dgRmRWtzRXx3HlFHLgEApPei4datGbzH9cS9948gOyQe197ehdZrJqDF8tegKVQj7OszyE+RQqM2XHC5JolrOcOlVyvcmfGxzvvKjBzE7Dql/Ts7IBxid2fUmzOsRgMf1Ul6JRA3i4+bWhP7ounuBbj18LgxKxoxF7PtR6SdugoACH7nM3S6tQuSYS8i8dvn5GnLx1V8vGSeuYakL38BAMgCo2DX/gW4vT5AG/io4zceQnsbBI15F6qMHDgN7AjfnYtw/+UVkD948uvcqtZ4ZBf03ljyIM3JKVur7btaTemPWm18cXLah8iJS0PtTi+g1/rJyEvOROxf5edpN0VCD1e4r3wTMVNWVDglpTFqMrIL+pVqJz9WUztRqwrx05vbMGDzDLx1dzfUqkJE/xWIiPMBJrt+pP2UsbAe0BPJMxcCz1GboOqnTooEkiIBFC3jqEgMh+WkNRA27w7lvz8DAIStesGsVn0oTn4GdU4GzGs3hEWvcVDkZUEd++ARe38OiS1hOe5tKI5+UW7Kr4qIOvSB0v8SoPpvHFv2k8fCun8vpLzJ/uRpmVmK4P5KN0R9dMzQWflP0zxq8VqiUowi8PE4Fi1ahLNnz2Lr1q3w9fWFlZUVRo8ejYKCgqfa34QJE/DSSy8hJSUFZ8+ehZWVFQYO1D8n4rPasmULtm/fjm3btqFFixawsbHBO++8o827lZXVU+3XzMwMGo3uwa5Ulpy88vLyMGDAAAwYMAAHDhyARCJBTEwMBgwY8ET1tmzZMixYoPvExKlGM58or4ln/HHWv2Rx14fD1MUSB+SnSLXvW0ocIA3U/1S5IiMHalUhLMs8JSaW2CM/peLpmzKKv9e2vjvyolPg9XIXWNeV4PzQNdqpWK7O+RQjHuxG7QHtEPdTzTztkXTmJjL9S560MROX1ImiVJ2IJQ7IuvfoOhGXq5OSenXt1gw23m4YHPKVTpqOX7+D9KsPcOUV3WnlDCn+d3+k3yppJ2bF7cRSTzvJfMJ2Yulqr32S3baeGxpNG4BTPf2QHRIPAJDej4GkU2M0nNIPN5bu0bvvNP8wmImEsKkrQU544lOXs6oUZmZDoyqE0NVR532hqyNUqZn6N3oCtdbMgn2vDogYuxSqpPTKNzAAZUYONKpCWJT597aQOEJZqs2UVpAihajMqAeRxAEFZdKrZQrkRyUhPyoJOf6haP/3DriP64O4Hcd10rmP7Q1lZi4yHjFvfXVLOOOP9GrqYy1L9bHy5KJ9PTxuHsoJTYC1Z8mCq7HH/0bs8b8hdrWHSqYANECjNwfXyBpCBRnZUOttE7p9a2mKFKl2VEfp9AXF5X74VJlFmXYiljggJzCq3P5qj+0JZWbOYwUzsvxD4fJSi0rTVaWKj5vyx8FDBSlSvcdZZcdNh78/gce43ojdcQIFKUX9kiwkTpteU6BCfnQyxJ6SZy/YM1AV10m5vsG1/Aixh5SpUojK9L+l+xJVRg7UShXkpcoLAPLQONh1bAKgaPFzj2mDcafn25CHFAXdZfejYNepCdynDELUUv3rctWkiLP+SCp1bjYvvl6xdrWHrNS/v7WrPVLvP32gxlwsQhe/Mfhl5jZEnQ8AAKQ9iIWkaT20nTnEKAMfquLzsLmrk8775i6OUKU+3QhAy+YNIXR1Qv0TO7TvCYTmsO7QHE4Th+FBsxGAEQaRw876I7GCdpJXpp2kPEM7AYDku1HYP2gFLOysYC4SQp6Rgwk/rUHSnchn2m91KZQWreVk7lymnTg7oTD90ddr9pNehcPUsUie5QdlqHGWj6qGRp4LjboQAmvdBZoF1nbQ5D3mFGdqNdSpsRA4Fo2whLkIoi4joPhlJ9RRRdPOqdLiYSapA1HbflAYeeBDk5cDTWEhBLa61x8CW0docqTl0pu5eMDM2R2WU0st8F4cELXZdBSyLXOhSS8ZTWlWvwnM3OpA9d2H1ZL/6lBRf2Lm7ITC9Eefd+wmvgr7KeOQMmcxlGER1ZnNaqPUXuc76ryv75r0oaLffo//u6AybkNfhLmVGEk//PlU2xNRzaqRyeotLCxQWFjxwp1NmjTB1atXdd7791/dm89XrlzBlClT8PLLL6NFixbw8PBAVFTUU+epS5cuqFu3Lo4cOYIDBw7g1VdfhUgkAlA0zZSFhQWuXLmiTa9UKnH9+vXHHiVRNu8jRozAxIkT0apVKzRo0AAhISVD4ho2bAgrKyucO6d/DkoLi6L1KcrWoUQiQWJiyc3XwsJC3LtXMo/ugwcPkJ6ejk2bNqF79+544YUXnmphc7FYDHt7e53Xk05zpcrLR15UsvaVHRIPeXIm3Lo106YR2lrBuY0P0m+E6t2HRlkI6Z1InW0gEMCtW3Ok39S/DQA4Ni9ajDi/+IaduZW46Knj0kEjtQbQAAKzmlu/oWyd5ATHIz85E5LuunXi1MYHmY+ok6w7kTrbQCCApFsz7TahO07iQu+luNh3mfYFAPdWf1vhQueGosrLR25Usvb1sJ14lGknLm18kFbBv7laWYiMO5E620AggHu35tptzK3ExYl1A4eaQrX2iXZ9nJrVg7pQjfw041gnRaNUQX4vDLZdSqYBhEAA2y6tILv1bD9kaq2ZBfv+nRE5cQWUcca70J9GqULOnQg4di9141gggGO3Fsi+oX9qlJybIbrpATj1aIWcyoYqmwlgJhaVe9t9bC+k/PCnQRaofqiiPtb9CfvYzEr6WFlsKuSJGbDz0Z0/2raBB2RxaeX2qUjLRqFMgbojXkShogDJl6p/zQKNshA5dyLgXKZNOHdvjqwKyp51MwTO3ZvrvOfyUgtkFbcJeXQKFMmZcCm1T3NbK9i39YVUzz5rj+uJhO8vPVabsGvuDUXx+ammPOq4qeg4yNZz3Dj2aFn5EP9Sx03u7Qio8wtg7VMyFYFAaA7LuhLkxxlujS2gqE7y7oTDvlup0awCARy6tUTOTf19Se7NENiXqROHHq2QW5xeo1Qh73YYrHx0p16wbFAbirii6zGz4vNR2dFQlZ2PapIyLx9Z0cnaV0ZIPPJSpKjbtaSvsLC1gkdrHyQ94nqsMuYiIcwthOXrQm08dVGOUoX8wDDYdNY9D9t0aQ35U56HZf8EIGLwbEQOn6t9ye+EIPvkRUQOn2uUQQ+gqJ1Io5O1r/SQeOSmSFGvTDup1doHCc/QTkoryJFDnpEDR293uLdsgLDfjXTknEqFgqAQnYXJHy4srLhT8ToL9pPHwOGNiUieuwwFQZxOxeSpC6FOiYFZ3dLrAwlgVvcFqJMe8ya1QAAzF09o8op/q5ibQ2Au1P3dC0CjUWsDAkatUAV1fDjMfXXPzea+LVAYXf7crE6Jh2zr25B/vED7Krx/HYXh9yD/eAE0Ut1rVVHHviiMDYM6MaqaC1KFVCoUPAjRWZgcAgEsO7RBwSP6E7vXX4PDGxOR8tbS57o/eXid71T6ul0ggFP35hVekxZd5+terzm/1BLZFfwuqEyt8b2RduYGlOnP3xopRP9FNTLiw9vbG1evXkVUVBRsbW210y09NG/ePHTt2hVbt27FiBEjcObMGZ1proCi4MCPP/6IYcOGQSAQYNWqVeX286TGjx+PnTt3IiQkBBculEyPZGNjg9mzZ2Px4sVwdnaGl5cXNm/eDJlMhunTpz9ij/o1bNgQR48exd9//w0nJyd89NFHSE5O1gZRLC0tsWTJEvj5+cHCwgJdu3ZFamoqAgMDMX36dLi5ucHKygqnT59GnTp1YGlpCQcHB/Tu3RsLFizAqVOn4OPjg48++ghSqVT7vV5eXrCwsMCOHTswa9Ys3Lt3D+vXr3+mOqtKYV+eRpN3RiI3Mgl5MalotmQ05MlSJJwu+dHS4/tliP/tBsL3ngUAhOz6DR22v4nM25HICAhHwxkDIbQWI+pwUbTdpp4bvF7pgsRzASjIyIVDUy+0WjsRqf8EISuo6EnKlEt30XLVOLTZOAVhe36HQCBA47eGQ60qROoVwy7yFv7laTR652XkRRTVSZMlryI/WYrE0yVPknf5YTkSf7uByD2/AwDCdv2KtttnQXo7Apm3wuEzYxDMrS0RU1wnitQsvQuay+LTIYspudFk4+0OcxtLiCUOMLe0gH2zooBRTkgcNErD3dAN/uo0mr09EjmRSciNSUVLv6J2EleqnfQ6sgxxp28gtLidBO/+DS9uexMZtyORfiscjYvbSWRxnWSHJSAnIgkdNk/HrXUHUJCZizoD28OjR3P8+XrR1Awu7Xzh2sYXyX/fhzJXDtd2DdF27UREH/sLyixZzVdEBdK+PoE6W+dDfjcU8tshcJk6AmbWlsg8WjRljOfWBVAlpyN5yzcAihbwFfvW1f6/0N0Flk3qQy3LR0F0USC11rrZcBz+EqJnvgd1rkw7oqQwRwaN4ulG2VWn+F0/o/H2uci5HY6cW2HwnDEEZtZiJB8u6tcb7XgLBYnpiHr/YFH6L39Fy+Nr4TlrGDL+uAnJyG6wbdUAoYt3AgDMrMWo+/YoZJy5joKUTIic7VFr6kCIPZyR9vPfOt/t2K0FrOq5I+mA8U3RE1rcx+YU97HNi/vY+Er62I6P6GMBIPiLU2i2aBSkgTGQBkbDe0x32PvWxj8ztmvT+Ezth/QboVDl5cO9Rwu0XD0OdzccgTK7Zo6d6J2n0OyTOcgOCEf2rXB4zRwMc2uxdqHxZjv+B0VSBsI2HAIAxOz+De1PvIt6s4Yi9Q9/eIzsAvtWPri/6EvtPmN2/4r681+GLDIR8pgU+Cx5DYrkTKT+dl3nu527N4d1PXfE61nssNaYHtAoVci+GwUAcBvSEZ7jeuH+gpoPQsfv+gWNt/8PubfDkX0rDHWKj5uk4uOm8Y65UCRmaI+bhC9PFR83Q5Hxhz/cRnaFXSsfhC4uyruZtRheb7+C9DM3tMdN7akDIPZwRurP/wAACnPlSNh/FvUWj4EiIQ35cWmoM2c4ACCtOI0hJe7+GT7b3kLe7TDk3gqFx4xhMLMWI/Vw0b9lg+3zoExKR+zGonWekr76BU2OrYfHm8MhPXcTLiO6waalDyKL+xIASPz8J/juXIDsf+8j++97cOzVBk792uP+6FUAgPyweORHJKD+5lmIWfcNVJk5cBrYCQ49WiH49fdrvhIe062vT6PjvJGQRiUjOyYFnReNRl6KFOGlbjy/cmgZwk7fwJ1vivoXkbUYDt7u2s8d6krg2tQLCmkechLSUZArR9w/Qei2YhxU+UrkxKfBs9MLaDKqGy6tM461tfRJ33MctTcvQP69UMjvhMB5ygiYWYkhPVZU7lqbF0KVnI7UD/cVbSASQuzrBaDoPCxyd4G4SQOo8+RQxiRCnSeHIlR3ZJ5ano9CaXa5942d/9en8eK8kciMSkZWTAq6LhqN3BSpToDi1eJ2cqtUO3Es004kTb2QX9xOAKDRkI6Qp+cgOyENro3roveaSQg7cwPRl6s/uP60sg8cg+taPxTcD4YiMBj241+BwMoSuSeLfvO6rFuCwpQ0SD/9GgBgP/k1OM6ejLTlG6FKSIKZS9HT3RqZHBp5PgDAzN4O5h5uMJcUjbh8OL9/YXoG1JWMJHneyGRyxMQlaP+OT0jGg5BwONjboZaHmwFzVrVU/n/Aov8UqFOioU6KgrBNbwhEFlDdL7r+tOg/BZpcKZR/nwAACDsOhjopEhppKiC2gqhdfwjsnaEKLH6AsyAfhXEhsOj2CgpUSmhy0mHm2QjCJi9CeemogUr5ZJSXTkL82jyo48JRGBsKi+5DIbCw1C5cLh47D5qsDBT89h2gUkKdrDuiTJOfBwFQ7n2IrSBs2QWKn/fVTEGqUM6Bo3BZswQF90OgCHwAu/GjYGZlidyfzwAAXNYugSolDVmfFfUndpPHwvHNyUhb+T5UiY/Zn9Qz3v4kducvaPLJ/5ATEIHsW2GoW+Y6v0nxdX5E8XV+7O5f0fbEGtSdNRTpf/jDvfga9sGi3dp9Ch1tYOnpCrGHMwDAunh9w4IUqc4ad1be7nDs3AS3x2+sodIS0bOqkcDHokWLMHnyZDRt2hRyuRx79+7V+fzFF1/El19+iXfffRerV69G3759sXLlSp2b9B999BGmTZuGLl26wNXVFUuWLEF29mMO+azAhAkTsGHDBtSrV09nPQ8A2LRpE9RqNSZNmoScnBy0b98eZ86cgZOTUwV7q9jKlSsRERGBAQMGwNraGjNnzsTIkSORlVXSga5atQpCoRCrV69GQkICatWqhVmzZgEoWpPkk08+wbp167B69Wp0794dFy9exLRp03D79m28/vrrEAqFmD9/Pnr16qXdp0Qiwb59+7B8+XJ88sknaNu2LbZu3Yrhw4c/ZY1VreDPfoG5tRjttkyHyN4aaddC8Nf4D6BWlEzXZePtDrGznfbvuJP/Quxih6Z+o2EpcUBWYDT+Gv8BFMVz7quVKrh1bw7fN4pu1skSMhB/6jqCtp3Q7iMnLBFXJn+IpgtfQa+f1wBqDaT3ovDX+M06U8IYQtinP0NoLUarrW9AZG+NjGsh+GfcpnJ1YlGqThJ++hdiF3u84DcaYokjsgOj8e+4Tdo6eVytP5oB1y4lI5p6nSs6mf/eYR7kseWf5K4pQZ/9AqG1GB02T4eFvTVSr4fg4gTddmJbpp3EFLeTFotHa6fFujjhA+QX14lGVYiLkzaj9fKxeOmbRRDaiJETmYx/396FxPO3AQDqAhW8RnRG84WvwMxChLzYVATvPo0Hu3+t2QqoRPapy0hydoDb/IkQujohPygCUVNWo7B4wV2L2hKdp0OFbs7wPVUyfYZk5ihIZo5C3r93ETm+aDSQy8QhAIAGhzfpfFfc4o8hPaZ/ZJohpf30N0Qu9qjnNxYWEkfkBkYhcNwGKItH5og9XXXqIOdGMILnbEe9JWPhvWw85JGJuD91M2TFa7toCtWw9vWE+5iXIHK2hzIzB7kB4bg9chVkwbpT1riP742saw8gD0uAsQkuPnbal+pjL49/9LHzsI9tVtzHSgOjcblUHwsUBVTMxCK0XjsRFk42kAbG4M+xG3WmsXJu44Nmi0ZBaGOJnLAE3PTbg5ijf9VMwQEk//QPLFzs4eM3BmI3R+QERsF/3EbtDxdLTxedNpF1IwR3Z++A79LX4Lt8LGSRSbg9ZQvyitsEAER9ehLm1mI02ToTQntrSK8F49bYjTr1CQC1x/eC9FowZBW0ifrzR8GqrivUKjVkYfG4M3MbUn65qjdtdUrVHjevaY+be2WOG02pUXHZN0LwYM52eC8Zh/rFx02g3uOmJ0TOdlBm5iAnIBwBI1frHDeR676FprAQjT99C2aWFsjxD8Od0Wuhysqr2QrQI+PkFYhc7FFn8TiIJI6QBUbiwYT1UFXQl+TeCEb4/z5GnSXjUXfpBORHJiJk2geQB5fcXMk8fRVRS3eh9txX4L1+OuQRCQidsRm514pGA2hUhXgwaQO8lk9E42+Ww8zGEvmRSYh4eweyzhvvIsY3v/gFIisx+mycBrG9NRJuhODEpM0oLHU8OHi5wapU/+LWsgFGf79C+3ePdycCAO7/cAlnFxbdfPht7qfouuQ1DPxkNiwdbZEdl4a/N/+Au98Z37nnoZxfLyHF2R6StyfBXOIERVAEYqavRmG6FAAgqi0BNCXtRuTmjAYnP9X+7fLGaLi8MRp5V+8gZuLSms5+tbpW3E76F7eT+BshOFamnTiWaSceLRvgtVLtpFdxO7n3wyWcLm4nNm6O6LlqAmxcHZCXIkXgsb/wzye601AaG9nvF5Hp5ADH2VNg7uKEguBwpMxdBnWGFAAg9HDT6V/sXh0GgYUFJFvf1dmPdNd+ZO3aDwCweqkzXNeWrMEm2bSyXBpTce9BKKa9tUT79+YdRW1hxKC+2LByoaGyVeUKQ29CaWUH0YvDILC2hzotDooTO7TrVQjsnHVGbwgsbWDRZ2LR9FgKGdQpMcj/fgs0GSWzQih++woWXUfCYuA0CCytocnOgPLvn6C6e6nGy/c0VLevQGBjD4sBYyGwc4I6IRLyr9ZpFzw3c5RAXWZEy+MQtu4GQABVwOUqznH1k529CDMnBzjMKu5PQsKR8tZSqIsXPDf3cNO5hrMbVdyfbF6js5+s3d8ga3dxf9KjC1zWlPQnrhtXlUtjLFJ++gciF3s08BsDi+Lr/Nvj3tcuYG7p6aozs0P2jRAEzv4EDZaOhc/ycZBFJuJumet81wHt0fST/2n/br57PgAgcssPiNz6g/b9WuN7Q5GQgYyLd6q7mERURQSasotEED2Go7UmGDoLRkXEw6gcmbFOS2FALSyNY4osY5IpszR0FoxOkkBs6CwYHUe1ytBZMDpigXFOd2NIYjPDjU40Vv8KrQ2dBaPSX/x0626YslMKZ0Nnwei86ppUeaL/mNq/76480X+M8sBmQ2fB6KiNeHpcQ8m48GwP65qisFiXyhP9x/RO/t7QWXjuTPUeZegsPHf2Rh0zdBYM4rlZ3JyIiIiIiIiIiIiI/rv4CBg9rppbybkGHThwALa2tnpfzZo1q3wHT2jQoEEVft/77xvv/MxERERERERERERERKbGJEd8DB8+HJ06ddL7mUgkqvLv++qrryCXy/V+5uzM4eNERERERERERERERDXFJAMfdnZ2sLOzqzxhFfH09Kyx7yIiIiIiIiIiIiIiooqZ5FRXRERERERERERERET038TABxERERERERERERERmQyTnOqKiIiIiIiIiIiIiEyLWqMxdBboOcERH0REREREREREREREZDIY+CAiIiIiIiIiIiIiIpPBwAcREREREREREREREZkMBj6IiIiIiIiIiIiIiMhkMPBBREREREREREREREQmQ2joDBARERERERERERERVUZj6AzQc4MjPoiIiIiIiIiIiIiIyGQw8EFERERERERERERERCaDgQ8iIiIiIiIiIiIiIjIZDHwQEREREREREREREZHJYOCDiIiIiIiIiIiIiIhMhtDQGSAiIiIiIiIiIiIiqowaGkNngZ4THPFBREREREREREREREQmg4EPIiIiIiIiIiIiIiIyGQx8EBERERERERERERGRyWDgg4iIiIiIiIiIiIiITAYDH0REREREREREREREZDKEhs4AEREREREREREREVFlNNAYOgv0nOCIDyIiIiIiIiIiIiIiMhkMfBARERERERERERERkclg4IOIiIiIiIiIiIiIiEwGAx9ERERERERERERERGQyGPggIiIiIiIiIiIiIiKTITR0BoiIiIiIiIiIiIiIKqM2dAboucERH0REREREREREREREZDIY+CAiIiIiIiIiIiIiIpPBwAcREREREREREREREZkMBj6IiIiIiIiIiIiIiMhkMPBBREREREREREREREQmg4EPIiIiIiIiIiIiIiIyGUJDZ4CeT2YajaGzYFRyzRhDLCufVVKOqpCVUpbITG3oLBgfdq/lFPA5jXKsUWjoLBid7EKRobNgdKx56OiwdVQYOgtGxzLJ0DkwPvfiJYbOgtGRHNhs6CwYHdEEP0NnwejI5s8wdBaMTmKCg6GzYHTEAv7+o2en5o9mekz8OURERERERERERERERCaDgQ8iIiIiIiIiIiIiIjIZDHwQEREREREREREREZHJYOCDiIiIiIiIiIiIiIhMBgMfRERERERERERERERkMoSGzgARERERERERERERUWU00Bg6C/Sc4IgPIiIiIiIiIiIiIiIyGQx8EBERERERERERERGRyWDgg4iIiIiIiIiIiIiITAYDH0REREREREREREREZDIY+CAiIiIiIiIiIiIiIpMhNHQGiIiIiIiIiIiIiIgqozZ0Bui5wREfRERERERERERERERkMhj4ICIiIiIiIiIiIiIik8HABxERERERERERERERmQwGPoiIiIhbRLRtAAEAAElEQVSIiIiIiIiIyGQw8EFERERERERERERERCZDaOgMEBERERERERERERFVRqPRGDoL9JzgiA8iIiIiIiIiIiIiIjIZDHwQEREREREREREREZHJYOCDiIiIiIiIiIiIiIhMBgMfRERERERERERERERkMhj4ICIiIiIiIiIiIiIikyE0dAaIiIiIiIiIiIiIiCqjhsbQWaDnBEd8EBERERERERERERGRyWDgg4iIiIiIiIiIiIiITAYDH0REREREREREREREZDIY+CAiIiIiIiIiIiIiIpPBwAcREREREREREREREZkMoaEzQERERERERERERERUGbWhM0DPDY74ICIiIiIiIiIiIiIik8HABxERERERERERERERmQwGPoiIiIiIiIiIiIiIyGQw8EFERERERERERERERCaDgQ8iIiIiIiIiIiIiIjIZJh34uHjxIgQCAaRSaYVp9u3bB0dHxyfar7e3N7Zt2/ZMeXtcj1OGh56mLERERERERERERETPAw3/e+L//quEhs5AVerZsydat26tDUp06dIFiYmJcHBwMGzG6JGa+I1G/Qm9ILK3Qfr1ENxasgd5kUmP3KbB1H5oOGcoLCUOyLofg9srvkHmrXDt591/XAlJl6Y620R88wcCluzR/v1K0sFy+7325g7E/fTPM5bo2bRcPAq+43tBZG+N1BshuL50L3Iikx+5TaMpfdFk9hBYSRyQeT8GN1buR3pAhPZzS4kD2q4aB48ezSGytUR2eBLubf8Jsb9e19lP7T6t0WL+SDg28UKhQomUf4Nwadq26ijmM2u7aBQaj+sFCwdrJF8Pwd/L9yL7EfXk0akxWswaApcW9WHj4YQ/pn+M6DM3tZ8LhOZo7zcadXq3hp2XBAXZciT8dQ83Nh6BLFlaAyWqWq6vD4bbzJchlDhBHhSJ+Hd3Q3Y7VG9ay4Z14bFwAqyb+8Cirjvi136F1D0nazjHz8Z9ykDUnj0SIokjZPejELnyK+QFhFWY3nloZ9T1GwdxHTfkRyYiZsO3kJ7310lj6esJr5Wvw/7FphAIzSEPiUPIjM0oiE+DuaMt6i4aC4eXWkFc2xXKjGxknL6GuM2HUJgjq+7iPrZmi0eh/oResLC3Qdr1EPgv3YPcSvoTnyn90HjOEFhKHCC9H4NbK75BZqn+BACc2/mixdIxcG7rA02hBtLAaFwatwnqfKVOOjMLIfqcWgfH5vXwe9/lyAqMrvIyPqlGfqNRd2JviOxtkHk9GHf99kBWyTmn3tR+aDBnGMRuDsi+H4PA5fuQVXzOETnaoJHfq3B9qQWsPF1RkJ6NpNM3ELLpe6hy5Np9DEk+VG6//m9+gsQThjvneEwdCM85w2EhcUTe/WhErPgaubcqPm5chnWGl99YWNaVQB6ZiOj3vkPmuVt60/p8MBMek/sjYtVeJH55Svt+u+ufw7Kum07aqPe+Q/ynJ6qkTE+qztT+8J4zDBZujsi9H40Hy/ciu9T1RFluw16E75IxsKwrgSwyCWHrDyDtXIBOGh+/V+E5sQ+E9jaQXg/GA7+vyrUx175t0GDhKNg2qQe1ogCZ/wTh9pSt5b5P5GSLF89vhmVtF1xoOBWqbOPpX9otGoUXSp2H/3qM83DLWUPgWnwe/r3MeRgA2i54BT7DX4RNbWeoCwqRdjcS1zf/gNRH/JsYA9tXR8Bu4hiYuzijIDQc0i07UHA/WG9am5GDYTO4P0Q+3gCAggchyPrs65L05uZwmD0Nll07QuhZC5rcPORf84f006+gTkuvoRJVnY4LR6HZuF4QO1gj8XoILi7fi6yoittJ7U6N0ebNIXBrWR827k449cbHiCzTTgDAybc2uiwfi9qdXoCZ0AwZoQn4beZ25CYYVx0Z4pwjcrJF68/nwr6pF0ROtihIy0by6RsIfv8IVLnyR311jRO2fAnCdv0hsLaHOi0OyotHoE6O0pvWvElniPtP1nlPo1JC/tlbJW+IxBB1fRnmDVpBYGUDTVY6VLfPQ3X3cjWWwjBuBNzF3oNHcf9BGFLTM7B94yr06dHF0NmqFhZ9R0A8eAwEDs4ojA1H/v4dKIzQ38cK23eDeNh4mLt7AkJzqJPiofjtByiv/KFN4/DtOb3byg/tQsGv31dLGZ6F+5SBqFXqd07UY/zOqVPmd05WBb9z7Er9zgkt/p0DAOJ67vBaPQV2HV+AmYUI0gu3ELXyK6jSsqq1rI+L17BE9LhMesSHhYUFPDw8IBAIDJ0VqkCjucPgM30AbvntwYXBq6CS5aPb4aUwE4sq3MZzxItosWYiHnz4I873X4GswBh0PbQUYld7nXSR357HqRazta9768vfdLrx9k6dNAmnb1R5GZ9E0/8NReNp/XFt6R6cGfouVDIFeh1c8sj6qDe8E9q+OwF3PzqOXwesROb9GPQ6uARil5L66PLJLNj51MKfUz7Cqd7LEPvrdXTb9RacmtfTpqk7uAO6fDILEUcu4dd+y/H7iLWIOm7YIFBFWs4ZiqZT++PKsj04OayongZ8twTmj6gnobUYGfdj8M/Kb/R/bmUBl+beCNh2Aj8NXIVzM7fBwacW+u5ZUF3FqDaOQ7uh9srpSNp+GMFD50MeFIUG366F0EV/EFhgJUZBTBISPtgPZUpGDef22bkM74p6707F/9m77/AoigaO499LLrn0Rhq996aAKMWChSpgAaUIIoiiIhYEBMECKgqior6CiooFRMWGjSIgoCK9t0BII4T0nku9e/9ISHIhoSYkxN/HZ59H7mb3Zia7Mzs7OzMn3vyGfb2eIf1gKC2XPl9met06Nafp+08T89Va9vacSMLKrTT7ZArOzesVhjHVD6D1j6+SeewEBwc9z95bniLy7W8LH+w7BvjgEOBN2MzP2HPzkwQ/+S5eN11No3mPXZY0n4/mj91OkzG92DnlU9b2e57cjCyu/+rs5WudAdfR/sXhHJz3PWt6TSf5YDg3fPWsTXni07EJNyydwqkN+1jb53n+6DODY5+sBsuZb5G0mzEUc3RihaTvYjQa358GD/Zm/+SP+bvvDHIzsrj267PnSc2B19HypREcnfcdf902jdQDYVy77FkcC+ocU6A3pgAvDr20hI03TmLPEwvx69Gedm89fMax9kxYwB9txhVu0b9XXp3jO7ArDV+8n4h537K752TSD4TS+qvpOJSoS09z79Sc5gueJOartey+bRIJv2+jxaeTcWlR94ywPn0649axKVlRpT+ADHt9GVvbPli4RX3ye7mm7XwFDOxC85dGcnzed2y57VlSD4TRYdm0MvPAs1Mz2i6cQOTS9Wy59Vlif99G+8WTcC2WBw3GD6Dug304NHkRW/s+R15GJld/Pc3mHPPv15k2743n5Fd/8u/Nk9nW/3lOff9Xqb/Z6q1xpB0ML9+El4P2j95O6wd68tfUT/ip/wvkZGTR5zzr4X/KqIcBko9H8ff0z/ju1qn8fNdMUk/E0XfJFJx83CsiGeXC+bab8HpyHCmLPufUiHHkHA3G793XsfP2KjW8qWN7MlavI+aRiUSPfpy86Fj83puDvZ8vAAYnJxxbNCXl4y+JHjGOuMkvYqxfF795sy5jqspHh0dup/0DPflz2id82/8FcsxZDDjXeeJsIu5QOBvOcp541Pfn7u9nkHjsJD/c8wpf9ZzGtvk/kpeVU+Y+laGy6hyrxUr0yu1sH/kGG7o+zZ4nFuB7QxvazB1T4Wm+EPZNO+Jw/SBytvxC5levYo09gemOx8G57OvdmmUm46PJhZv502k23ztePwj7+q3IXvUpmZ+/RM7utTjcNAT7hu0qOjmXndmcSfMmjXhu4qOVHZUK5XDtTTgNG0fmD5+TNmMclvBgXCe/jsHDq9Tw1rRUslYsIW3m46RNG0v2xlU4j52MsW2nwjAp4wfZbBkfzsFqsZCzrep1kPkM6Ea9gnbO/l7PkHEwlBbnaOc0ef9pYr9ay76eE0kso53T6sdXMR87waFBz7OvRDvHztlEi69eAKuVQ4Nf4MDAaRgcjTT/bBpUgWdruocVkQtRbTo+Ro0axYYNG5g/fz4GgwGDwcDixYvPmCZq8eLF1KtXDxcXF+68807i420LtODgYAYOHEhAQABubm5cc801/PHHH1yMYcOGce+999p8lpOTg6+vL59//jkAWVlZTJgwAX9/f5ycnOjevTvbtm0r7XAXLDY2lk6dOnHnnXeSlZUFwIoVK2jatClOTk706NGDzz777Lyn0qoITcb25sjbPxK1agcphyLY/vgCnAK8qNW7U5n7NH24L6FL1hO2bAOpQZHsmvwxeeYs6g+50SZcnjmLrNjkwq20N5xykjNswlgqucHU4sHe7J//EydW7STpUASbJyzEJcCLur07lr3PQ304tnQ9x7/eSMrRk2yd8il55iwaDy3KD99OTQn6ZDXxu4+TFh7L/vk/kZOcjk+7hgAY7O3oNHMEu17+iqNfrCP1+ClSjp4k/OctFZ7mi9F6TG92v/MT4at3kngogg1P5udT/V5l59OJ9XvZMXc5YWV0buWkmlk57HVCftlC8vEoYncGs3n65/i1b4RrrRoVlZQK4ffgQOKXrSbh27VkHY3gxLT3sZiz8Lnn1lLDm/ce4+Sri0n6eRPWKvbQ4HzUfKg/MUvXEPv1OsxHTxAy5QMs5iz8h95cevgHbydp/S6iFvxE5rFITsz9ivR9IQQ+0KcwTN1nh5O0bgfhL39Bxv4QssKiSVy9jdz4/LeczEfCOTp2LklrtpMVFk3K3/uJeH0J3rd1AvuqUbU2HdubQ2//yMlVO0g+FMHWCQtwDvCi9lnKk2YP9yFkyXpCv95IalAkOyZ/Qp45iwbFypOrXhrB0Y9XceS9n0kJiiQtOIoTP2/Bkp1rc6zAm9sTcGNb9sw8c3RdZWn4UB+OvfUD0St3kHownD3j38cU4E1An7LrnIbj+hHx5TpOLNtAWlAk+yZ9TJ45m7pDbwIg7fAJdo55m5jVO8kIiyH+rwMcmf01/j07YChxLuSkVJ06p9bD/Yle8gcxy9ZjDjpB8OQPyTNn4T+k9Oum1ti+JK7fTeT7KzAfjSR8zjLS94VQs9h1A+AY6EOjV8YQ9Nh8rLl5pR4rL81MTmxS4WbJyCr39J2P+uP6ceLLtZxc9ifpQZEcmrSIPHM2tYf2KDV8vYf6EL9+N2Hv/0z60UiCX/+GlH0h1Bvdq1iYvoS89T2xK7eTdjCcA+P/hynAG78+1wD59W3zl0cRNPNLTnz+BxnHo0gPiiR6xb9n/F6d+2/D6OFC6IKfKyYDLkGbMb3Z9c5PhK3eScKhCP48z3p4+9zlhJ7lJZPgHzdz8q8DpIbHkhgUyb8vLcHRwwWflvXK3KeyuQ8bRNqPv5H+8ypyQ8JInP02lswsXAf0LjV8wozZpC1fQU5QMLlhESS8PA8MBkzXXA2ANT2d2PGTMf+xgdywE2TvP0TS3HdxbNUc+wD/Uo9ZVbUf05vt7/5EyOqdxB+O4I8nF+Ia4EWjs5wn4X/uZcvc5Rw/y3ly3eTBhK7bwz+vLiPuQBgpYTGErtmJOT6lIpJx0SqrzslNTif8sz9I3nMc84k44jcdIGzxGnyubXE5kn3ejB1uJffA3+Qd3Iw1IYrsdUux5uZgbH22UQtWyEgptqXafGtXsxG5h/7FEhmENTWevP1/YY09gV1ggwpNS2W4vss1THjofm69sVtlR6VCOfYZRPafv5GzaRWWk2GYP30ba1YWjjeUXsbmHd5D7o6/sZwMxxITRfbq77FEHMe+WZvCMNbkRJvNoWM38g7txhobdbmSdd5Ot3PiSrRz/Mpo5wSW0s7J2BdCQIl2TvK6HUQUa+ckFWvnuHdugamuH8effBfz4XDMh8M5/sS7uLZvjEf3tpcl3Weje1gRuRBV4+lMOZg/fz5dunRh7NixREVFERUVRd26tj24W7ZsYcyYMYwfP57du3fTo0cPXn75ZZswaWlp9O3bl7Vr17Jr1y569+5N//79CQ+/8Lfthg8fzs8//0xaWlrhZ6tWrSIjI4M777wTgMmTJ/Pdd9/x2WefsXPnTpo0aUKvXr1ISLi0t64jIiK4/vrradOmDcuXL8dkMhESEsKgQYO444472LNnDw8//DDPPffcJf3OpXCp549TgDcxG/cXfpabaiZhVzA+nZqWuo/BwR6vdg1t9sFqJWbT/jP2qXt3N/od+IBb/nyd1tPuxd7Z8YzjXTV7FP0OfMBNv8+i/tAbz/j+cnKr54dzgBenNhWlLSfVTNyuYHw7lp4fdg72+LRryKlNB4o+tFo5tekAvh2bFH4Ut/0o9Qdch6OXKxgM1B94HfZODkT/cwgAn7YNcKnlg9Vipc/ql7lr13v0+HISns3rVExiL4F7PT9cArw4WSKfYncH419GPl0sR3dnrBYL2VVoapFzMTgYcWnbhLS/dhd9aLWS9tceXDtUrQZveTA4GHFt15jkTXuLPrRaSd60F7eOzUvdx61jM9vwQPKGXUXhDQa8b+lI5vEoWiydQce9n9Lml9fw7t35rHGx93AlLy0D8iyXlKby4FrPD+cAb6KLlQ2ny9caZylfvds1JHqTbfkavWk/NQquLVMND2p0bEJWXAo9VrxA/73vc9P306nRuZnNsUy+HnSc+yBbH19AXhVpEDjXz69z4krUOUk7g/E+S554tmtIXIk8idu4H68y9gFw8HAhN9WMtcS50Gb2A9x28EO6rZxFnYKHWJXB4GDErV0jkjaWvG724d6p9OvGvWMz2/BA4p+7ce9U7G9vMND0vceJfP8nzEdOlPn7dR6/g84HP6X9mrnUfnRApXQWGhzscW/XiIRN+4o+tFpJ2LgPzzL+tp4dm5FQ/P4DiF+/B8+CPHCu748pwJv4jUXHzE01k7LzWOH54t6uIU61aoDFyrV/vMYNexdy9dJnbUaNALg2q02jiXdz4PH/lTqaqjKdrocjS6mHA8qxHrZzsKfF8B5kJacTf7Dyp8krldGIY4tmZG0tNoWI1UrW1p2Y2rYqe79iDE4mMBqxpKSWHcbNFavFgqVYm6Kq86jnh2uAFxHFzpPsVDPRu4MJ7HAJ54nBQIObryIp5BQDvpzM6F3/Y9CKF2l4ls6UylAV6pzTTAHeBPbtTMLmQxeXmIpgZ4+dfz0s4cXjZMUSfgi7wEZl7+dgwumBV3Aa/SqOtz+CwaemzdeWqOPYN2qHwdUr/2fqNMPgHUBe2MHyT4NUPHsj9g2akXvAtozNPbAT+ybnV8bat7oau5p1yDuyr9TvDR7eGNtfS/aGqvfm/ul2Tkop7Rz3s7RzUkq0c5JKtHO8bumI+XgUzZfOoMPeT2ldop1jcHQAK1iyi17QsWRlg8WKe+eW5ZfAi6B7WBG5UNVmjQ9PT08cHR1xcXEhMDAQgMOHD9uEmT9/Pr1792by5MkANGvWjH/++YeVK1cWhmnfvj3t27cv/PesWbP44YcfWLFiBePHj7+gOPXq1QtXV1d++OEHRowYAcDSpUsZMGAA7u7upKens2DBAhYvXkyfPvm9zR999BFr1qzh448/ZtKkSReeEcCRI0e47bbbuPPOO3n77bcLp/r64IMPaN68OXPnzgWgefPm7N+/n1deeeWifudSOfnnD8/MirWdJzIrNrnwu5JMPu7YGe1L3ce9Sa3Cf0d8/w8ZJ+LIPJWIZ6t6tJk+BLfGNdky5u3CMAdf/5aYvw6QZ84i4KZ2XDX7AYwuTgR/vKqcUnhhnPy9ADDH2r6tlhmbgvM58iOzRH5kxiXj0aSoIbDp4XfpvnA8gw9+gCUnl1xzNhvGvE1awRzLbvXz3yBsN/Eudry4hPSIWFqO68ut3z3Hz92fITspvbySecmc/bwAMMfZ5pM5NgVnv/Jbz8fe5MA104YQ/NNmcqrYfMhnY+/tgcFoT05cks3nOXFJmBrXrpxIVSCjj3t+emOTbD7PiUvCuUnp6XXw8zozf2KTcSi4Bh18PbF3c6bW+DuJeH0p4a98gVePq2m2aDIHBz1P6r9nNp6NPu7UeXIwMV+uKY9kXbLT5UnJsjIzNhmngmuopDLLk9iUwvLVtaCsaDXxLvbOXErSgTDqD76eG7+ZxuoeUwrXD7lm/jiOf7GWxD0huNTxLceUXTwnv7LrHFNBfpXk6ONRZp3j2rRWqfs4+LjT5Kk7ifjSdv7oI699Q/xfB8gzZ+N7U1vavPYARlcToYsuf53jUHjd2KYrOzYJz7KuG3+vM6+zYtcNQO3xd2DNtRC16Lcyfztq0W+k7QshNzENj2uaU3/aMBz8vQl9sexpbSrC6b9t9hl5UPbf1uTvRXaJPMiOTcaxoI52LLi2Sh4zKzYZx4J8cq4fAECjZwYR9MLnmCNiqf/I7XT6/nn+7vokuUnpGByNtF34BEEvfUlmZHzhPlVFRdfD9W65ipvfH4/R2ZGMmCR+G/Y6WYlV84G/nZcnBqM9eQm2U/rlJSRibHDmFBql8Xp8LJa4eDK3nrmOBQCODniNH0vG6nVY06+cFzFcCs6TjBLnSUZsCi5l3Nee13F9PXB0c6bjo7fz79zl/PPqMurd1J6+Hz7BD/e+ysl/D5/7IJdBZdc5AFctfJzAXh2xdzERvWoHe5/+8CJSUjEMzm4Y7OyxZtieH9aMVOx8Akvdx5p4iuw1n2OJi8Rgcsahw2043TOZzC9fwpqWBED2hq9xvHk4zg++hjUvD6wWstd+ieVk2XP/S9VlcPfEYG+PNdm2jLWmJGJX6yxlrLMrHu98DUYHsFgwfzaf3P2ll7EO1/fEmplBzvaqN81VebZzHEtp55x4fSkRr3yBZ4+rabpoMocK2jlpO4LIy8ik7nMjOfHal4CBus+NwGC0x8Hfu/wTegF0DysiF6radHycj0OHDhWOtDitS5cuNh0faWlpvPjii/z6669ERUWRm5uL2Wy+qBEfRqORe+65hyVLljBixAjS09P56aefWLZsGZA/rVZOTg7duhUNT3VwcKBz584cOnRxb+SYzWauv/56hg0bVrjI+2lHjhzhmmuusfmsc+ezv8EM+dNxnZ4q67Qcax4OBvsLilvdu7pxdbG5Zf+5b84F7X8hQr9cV/j/KYcjyIxO5PrvpuNa35/0sBgADr/1Q2GY5P1h2LuYaPro7Zet46PBnV3pPGd04b//HHHmoqblpf3kQTh6uPDHPbPJSkilbu+OXL/wcdbcOYukwycw2OV3jhVf8HzzUx9y5453qHf7tRwrlp+XW+M7u9LttaJ8Wn1/xeXTaQajPT0WPA4GA/9MXVzhvydVTMH1kLhqK6c++gWAjAOhuHVqQcDIXmd0fNi7OdPi8+cwB0VwYt7Xlz26APXu6krHOUXl66YRcyvkd06XFce/XEfo1xsBSNofhn/31jQYehP7X/2aJmN64eDmxKF3fqqQOJyvWnd3o+3cBwv/vW14xdU5pxndnLlmyWTSgiIJmvudzXfHitU5KftDMbqYaPRo/0rp+KgIru0aUWtsX/bcNvms4U5+8Evh/2ccCsOSk0vjOQ8R9uoSrCWmS6uOTl9DIfN/IObXrQAceGIBN+xaQED/LkR+8QdNnxtK+tFITn1X+rofl1vjO7tyfbF6eGUF18Mn/znE972ew8nHjRbDenDrgvH82P9FMqvYNEblwf3+ITjf1oPYcRMhu5Sp7+zt8Z39PBgMJL42//JH8AI0u6MrNxU7T34ZVTHnSeE1tHonexblt+HiDoZTs1NT2tx3S6V1fFS1Ogfg0IzPOfrGd7g2rkmLaUNo9dII9j/7SYXHq6JYToXAqRAArEBWVDBOI17E2OZ6cv7NnxLQ2L4HdjUbkrXif1hSE7Cv1RTHHkPJSk/GElE1OsXkMsjMIO25h8DJGWPrDjgPewRLTBR5h/ecEdTxht7k/LMWcq686X4vShntHPdOLfAvaOfkJqRw7OE3aDD7YQLH9AWLlfgfN5G+NxgslT+yvbzpHvbKZKFqjYiWqus/1fFxPp555hnWrFnDG2+8QZMmTXB2dmbQoEFkZ2df1PGGDx/OjTfeSExMDGvWrMHZ2ZnevUufj7I8mEwmbr31Vn755RcmTZpE7dqX/ob37Nmzeemll2w+u8e1Dfe6Xdj8jlGrdpCws+htGztT/uln8vMkMyap8HOTnyfJ+0uf0iArIRVLbh6mEm8UljxGSQm7ggFwbRhY2PFRUuLOY7R8+i7sHI1nzFVfEU6s3klcQbwA7B3z88PZz8MmLU5+HiQeKL3j7XR+OJXIDydfT8wFb0G41fen+eie/HLTFJKDIgFIOhiO37XNaTbqNrY++ynm6PzfSz4aWXgMS3YuaWExuNau3PUtwlfvJKa0fPL1wFwsn5z9PEgoI58uhMFoz80LH8etTg1+v2f2FTXaAyAvMQVrbh4Ovl42nzv4epFb4k2X6iA3ITU/vSVGMTj4nvlm9mk5sUln5o+fJzkF51NuQiqWnFzMQbbDnDOPnjhjeLedqxMtls4gL93MkTGvlzkfbEU7uWon8TvPvE5Klo1Ofp4kHTh7+XpGeeLnQWZMwdomBWVFSlCkTZjUoydxKSgr/Lu3okbHptwdZvv2060rZxH+/d9se+KDC0/gRYheuYOkHcXrnPzFZE1+nmSVqHNSDoSWeozshJQy65ysEnWOvasTnZc9S16amR0PvHnOcyFpZzBNJ9592eqc4nIKrxvbdDn6eZFdRl2aE5N05nVW7LrxuLYlDr6edNqxsPB7g9Gehi+OpNZD/dhxTemLr6buDMLOwYhTXX/MwScvOk0X6vTf1vGMPDjzb3taVkxS4aiO4uGzC66P02VO/mdFxzD5eZJacI5lFVxD6cWmUbBm52IOj8apTv415NO9DW4t6+F/+7UAhSN3bzy0iJC3f+D43G8vNLmXJHz1Tr4/z3o4vhzq4VxzFimh0aSERhOzM5h7Nr1B8yE3sud/VW+tE0tSMtbcPOx9bN+AtffxxhJ/9mlr3e8bjMf9Q4l5bBI5x46fGcDenhqzn8c+MIDYR5+p8qM9QtbsJHr3meeJi68HGcXOExc/D+Iu4TwxJ6SSl5NLwlHbeijhaCS1ril9mpPLoSrWOafXk0o/dpKcxDS6/vwiR9/8vswy7nKymtOwWvIwuNguRmxwcceafp6dnBYLltgIDF4Fa9/YO+DQdSBZvyzEEpo/XVhuXCR2fnVw6HAbWer4uOJYU5Ox5uVh8LQtYw0e3liTzlLGWq1YYvLvKbLDg7GvVQ9T/6FklOj4sG/WFvta9cj436xyj3t5OFs7p+QIhtPKaudkn6OdYy7RzknesIc9XR/F6OOONTePvJQMrt79MVnh0ZearEuie1gRuVDVakI6R0dH8vLKftDQsmVLtmyxXaz5339tF5P8+++/GTVqFHfeeSdt27YlMDCQ0NDQi45T165dqVu3Ll9//TVLlixh8ODBODjk3wg3btwYR0dH/v7778LwOTk5bNu2jVatzm/OypLs7Oz44osv6NixIz169ODkyaICuHnz5mzfbrtQ4PkspD516lSSk5NttrtcLzx+uemZpIdGF26pRyLJjE7E7/rWhWGMbs74XN2YhO1HSz2GNSePpL0h+BfbB4MB/+6ty9wHwLN1fQAyoxPPGiY7Me2yPYDKTc8kLTS6cEsOisQcnURAd9v88L26MXE7Sk+bJSePhL0hBHa3zY/A7q2JK2h8GQvWNrGWmCPcmmcpfOMjfm8oeZnZeDQumh7LYLTHta4f6SfiyiW9FysnPZPU0OjCLSkokozoJGoVS7ODmzN+VzUmpox8Ol+nOz08GwSwcshrZCVVzak1zsaak0vGvmO4dSuasg+DAbdu7UjfWf0afNacXNL3BuPZvV3RhwYDHt3bkbbjSKn7pO0IwvN6245bzxvaF4a35uSSvucYTo1tp5VwalSLrBNFHaf2bs60/OoFrNm5HBk1u1IXhi9ZvqYERWKOTjyjPPG5ujHxZylfE/eG4N+9ZPnahviCaysjIhZzVALujW3n1HZrFEhGQVmxa/rnrL5lKmtuncaaW6fx1335o0/+Hfcu+1+7fA9s89IzyQiNLtzSjpwgMzqRGtcXLW5pdHPGq0NjEs+SJ8l7Q/Attg8GAzWub01SsX2Mbs5c+81ULNm5bBv5xnktWu7R5vLWOcVZc3JJ23vc9jowGPDs3pbU7aVfN6k7gvAqcd143dCe1O1BAMQu38Dumyey+9ZnCresqHgi31/BwSEvl3ZIANxaN8Sal0d2XHKZYSqCNSeP1L3H8SmRBz7XtyG5jPMheUcQPsXPBaDGjW1JLsgDc1gMWdGJ1Ch2THs3Zzw6NCk8X1L2HCcvMxuXYtNzGoz2ONX1I7PgGtoz+k023zyZf2+Zwr+3TOHg0/mdhdsHvkDEp5d/hFBOemZhR0RKaDSJBfVw7VLq4ehLrIdLYzAYsC94iFzl5OaSfTiocGFyoHCh8qx9Za8p4D7iXjzG3EfshGfJORR0ZoCCTg+HerWJfWwSluSqP9olJz2T5NDowi0hKJL06CTqlDhPAq5qzKmdF3+eWHLyiNlzHK9GtvWQV6OapEZW3j1rVa9zTo+UOf3iWaWz5GGJCceubvH15wzY1W2B5VQpHYGlMRiwq1Eba3pB/WFvj8HeCNYSbR6rBQo6kOUKk5dLXmgQxla2Zayx9dXkHbuAdVsMBgwOZ9Yjjjf1Iff4ESzh53nOXWan2zkeJdo5nt3bkXqWdo7HebRznM/RzjktNyGVvJQMPLq1wcHXk8TV535+VJF0DysiF6qK3PmUjwYNGrBlyxZCQ0Nxc3PDUmIY3oQJE+jWrRtvvPEGAwcOZNWqVTbTXAE0bdqU77//nv79+2MwGJgxY8YZx7lQw4YNY+HChQQFBbF+/frCz11dXXnkkUeYNGkSPj4+1KtXjzlz5pCRkcGYMWPOcsSzs7e3Z8mSJQwdOpSbb76ZP//8k8DAQB5++GHefPNNpkyZwpgxY9i9ezeLFy8Git4mLI3JZMJkMtl8dqHTXJXl2EcrafHknaQfP0V6eCytpgwmMzqJkyuLOmi6fzuNk79v5/gnqwE4+sFvdJo/jsQ9x0ncFUyTsX2wd3EibNkGIH8O+rp3dePU2t1kJ6bi2bIebWeOIHbzIVIORQAQeFsHnPw8SNh5jLzMHPxvbEvzJwZydMGv5ZKui3V40UraPHEHqSHRpIfH0G7yIDKik4hYWTQn6S1fTyVi5XaCPs1fS+Dwh7/T5e2Hid8TQvyuYFqM7Y29i4njBfmRfCyKlOOnuHbOaHbOXEpWYhp1enek5g1t+HPkPABy08wc/WId7SbeTfrJeNJPxNPqkX4AhP+yharmwMcruWrCHaSERJMaEUPHZ/LzKWxVUT71WTaV0JXbObQ4P5+MLiY8GhTNk+5W1w+fVvXISkon/WQ8BqM9t3wwgRptG7Dm/nkY7O0K5yrPSkrDklM5b/JfjNhFP1Fv3pNk7D1Gxp4g/EYPwM7FiYRv8+d/rvfmk+ScSiBqzudA/iJxTk3z58k1OBpxCPTBuVVD8tIzyQ6LqrR0nK+oD3+m8duPk7bnGGm7jlJzbH/sXUzELsufoq3x/Alkn4onYvaS/PCLfqHVd7Oo+fAAEtfuwHdgd1zbNeb4pKK3fE6+/xNNFz5N6r8HSf5nP149rsb7tk4cHDQDKJje6qsXsHN2JOjxt7F3c8HezQWAnPiUKjEM/OhHK2n55B2khuSXr22mDMIcnURksfLkhm+mEvn7doILypOgD36n8/yHSdwTQsLuYJqO7Y3RxURoQXkCcGTBr7R+5m6SDoSTdCCMBvdcj0eTWmwemz8NizkynuLjpHLTMwFIC43BHHX2N6ArWsiHv9P0qTtIDzmFOTyGZlMGkxWdSPTvRXXOtcuf49Rv2wgrqHNCFv5K+3ceIWn3cZJ3HaPBQ30wupiIKMgTo5sznb+Zir2zid2PzsPBzRncnAHIik8BixX/nh0w+XmSuOMolswcfG9sS+MnBnL8/cqrc05+8DNN548nbU8wabuOUWtsP+xdTMQsy79Pafru42RHxRP26tL88B/9RpsfXqLWuP4k/rED3zu649a+EcEF101uYhq5JdZhsObmkR2TVPgWnHvHZrh1aEry3/vJSzPj0ak5DWaOIva7TeQlX/61pMIW/krrdx4lZXcwKbuCqfdQX+xdTJxc9icArd99jKxTCRx75SsAwj/8nU4/vkD9cbcT+8dOAu/oikf7xhx85qPCY4Z/+BsNn7qTjJAozOExNJ5yL1nRicT+nv+gIC/NzInP/6DxpMFkRsaTeSKWBo8NACB6Rf7LOOYw27cpHX3cAUgPiiQ3pWq89b//45VcPeEOkgvq4U6l1MN9C+rhg2XUw+4l6mGjs4mrJgwkfM0OMqKTcPJxp9X9t+ES6E1IFbwXOS116XJqvDCF7ENBZB84jPvQu7FzdiL95/xOKp8Xp5AXG0fy/z4GwH3kEDwfvp/46a+SG3UKuxr5bzJbM8xYzZn501u9/gIOLZoS99RzYG9XGMaSnAq5V850Gns+Xkmnx+8gqeA8ufaZQaRHJ3G82Hky8KupHF+5nX2f5Z8nDi4mPIudJx51/fBtVY/MpHTSTsYDsOuD3+j1v/Gc3HKYyM2HqHdjOxreejU/3FM56xaWpbLqHL9brsLk50nS7mDy0jNxb16XFs8PI2HLYcwRlftCU3G5O//AsecoLDFhWE6FYrz6ZgwOjuQe/AcAx56jsKYlkfPPjwAYO/fFcioEa1IsmJxx6NgTg4cPuQcKXiLMziTvRBCO3e8iOzcHa2o8drWbYWx5HTkbl1dSKitORoaZ8BNFLzlGnozmcFAwnh7u1Az0r8SYla/s35fj/NAU8kKCyDt+GMded2MwOZG9Mb+MdX54CpbEOLK+yS9jTf2H5oeNPonBwQFj+2tx6HYb5sUlpgt0csGh8w1kLl1Y8ierlNPtnPSCdk7g2P7YFWvnNJo/gZxi7ZxTi36h5XezCHx4AElrd1CjoJ0TUqydE/X+TzRZ+DQp/x4kpZR2DoDvvTeTefQEOfHJuHVsTv2ZYzj14S9kVoGRDbqHFZELUa06Pp555hnuv/9+WrVqhdls5tNPP7X5/rrrruOjjz7ihRde4Pnnn+fWW29l+vTpzJpVNLTxzTffZPTo0XTt2hVfX1+mTJlCSsqlvWU1fPhwXnnlFerXr2+zngfAa6+9hsViYcSIEaSmptKpUydWrVqFt/elLRplNBr56quvuPfeews7Pxo2bMjy5cuZOHEi8+fPp0uXLjz33HM88sgjZ3RsXC5B7/2MvYuJq994EAcPF+K3BvH30Nds3lxybRCAqaDRDxD507+YanjQavIgTH5eJB8I4++hr5FVsHiiJScXvxva0LjggZ35ZAInf93K4bd+LDyGNTeXRg/0pO3MERgMBtJCTrHvhS8J+bKoY6oyHPzfLxhdTFw7ZzSOHi7EbAti/fA5Nvnh1sDfJj/CVmzBVMOD9pPuxsnPk8QDYawfPofMgvyw5ubx54i5XDXtXm78bCIOriZSQ6LZ/MQHnFxXNNx356yvsOTl0fWdRzA6ORK36xhrB79KdnLVeMhS3N738/Op2+v5+RS9LYhV980hr1g+udf3x6lYPvm2b0S/b58r/Pd1L94HQNA3G9n09Ie4BnpTv1dHAO5c86rN7/06+BVObb64dXcqQ9Ivf2Gs4UnNp4dh9PPGfPA4x0e+SG7BQneOtfyg2AgghwAfmv9e1Bjwf/gu/B++i7TN+zg25LmSh69y4lf8jbGGB3UnDcXBz4uMAyEcHj6LnIK3b0y1fW06ItK2H+HYY29Rd8ow6j47nMyQKIJGv475SNHUG4krtxDy7AfUGn8XDWaNwXz8JEFj55C6NX/UjGvbRrh3bAbA1ZsX2MRnV+eHyToRW9HJPqcjBeVJp7ljcPBwIW5rEJuGvV6iPLEtX0+s+BdTDXdaTx5UOC3WpmGvF5avkN+hYmdy4KqX7sPR25WkA+FsGDK7zGkEq5Lj7/2M0cVE24I6J3HrEbYOsa1zXOoHFD5oBoj66V8ca3jQbPIgTP5epBwIY+vQ1woXsPZo1wDvjk0B6LHVtlG9rtPjmCPisObkUf+BnrSaOQIMBtJDTnHo+S8Jr8T1k+J++gdjDQ/qTR6Co58X6QdCOTD0FZvrxlrsukndfoSgR+dTf8oQ6k8dhjkkisMPzCHjcMR5/6YlOwe/O7pR75l7MDgayYqI4eQHv3Dyg8qZwij6p8041vCg8eR7MPl7kXoglJ1DZxf+bZ1q17ApO5K3B7HvkXdp8uy9NJk2hIyQU+wZNZf0YnkQ+t4K7F1MtHzjIYweLiRtPcKuIbNtzrGjL32JNTePNv97DHsnR5J3HmPH3bPIvYIaznsK6uHri9XDK0vUwx4l6mG/9o24vVg93KVYPbzh6Q+xWix4NalJs8FP4OTtTmZiGrF7jvPz3S+TWGJ6varEvOZPkrw88Xx4FPY1vMkOCiZ2wrNYChY8tw/0t3kD3e3u/hgcHfGd86LNcZI//IyUjz7H3t8X5xvz2wuBSz+yCRPz8NNk7TxzjvqqaueC/POkx2ujMXm4ELUtiJ9H2J4nnvX9cS52nvi3a8Sdxc6T61/IP08OfbuRtQWLcx9fuZ0/p31Cx8cGcMPMkSQGR/H7w/OJ2lbK6JlKVFl1jiUzm3r33UyrmSOwc3TAfDKeU79tJfidFZch1ecv7+gOcpzdcbiuPwYXDyxxJ8j68V3ISAXA4O5jc+0YnFxxvOW+/OmxsjKwxIST+c1crAlFL+lk/b4Ix2534Nh7NAYnF6wpCeT88xO5+zZe9vRVtP2HjzL68SmF/57zbv71MbDPrbwyfWJlRavc5Wz5E4O7J053j8Lg6U1eeDDpc5/FmpJfxtrVsC1jMTnhdP8E7Hz8sGZnYYmKwLxwNjlb/rQ5rkOXHoCB7M2V2/4/l4QVf+NQw4M6Jdo5uWdp5wQ/9hZ1ztHOCS3Rzjk6dg5pW4tmB3BuXIu6U4dj9HIjKyKWk+8s59SHVWPKSd3DisiFMFitVq0I8x/2yiuvsHDhQiIizr/QB/g+cFgFxejKZLarVrPGlYtMZckZOtpX/akqLrfMnGrV/14uIqzOlR2FKselCoygqWq8DP+RRTgvQIa1fEajViehpUzt8V/Wu2bVH8V4uf10qua5A/3HNMy+ckb5Xi49pnmeO9B/jMPwsy+E/F+U8dTYyo5ClXN4ra6dknItelBQUrdT1W9UWkXrW69vZUfhivNb+G+VHYVKoSdO/zHvv/8+11xzDTVq1ODvv/9m7ty5jB8/vrKjJSIiIiIiIiIiInJWeodfzpe6Wi/BkiVLcHNzK3Vr3br1uQ9wgfr06VPm77366qvnPgBw9OhRBg4cSKtWrZg1axYTJ07kxRdfLPe4ioiIiIiIiIiIiIhUBo34uAQDBgzg2muvLfU7hwqYWmDRokWYzeZSv/Px8TmvY7z11lu89dZb5RktEREREREREREREZEqQx0fl8Dd3R13d/dzBywntWvXvmy/JSIiIiIiIiIiIiJyJdJUVyIiIiIiIiIiIiIiUm2o40NERERERERERERERM5bQkICw4cPx8PDAy8vL8aMGUNaWtpZ97npppswGAw227hx42zChIeH069fP1xcXPD392fSpEnk5uZecPw01ZWIiIiIiIiIiIiIVHmWyo6AFBo+fDhRUVGsWbOGnJwcHnjgAR566CGWLl161v3Gjh3LzJkzC//t4uJS+P95eXn069ePwMBA/vnnH6Kiohg5ciQODg68+uqrFxQ/dXyIiIiIiIiIiIiIiMh5OXToECtXrmTbtm106tQJgHfffZe+ffvyxhtvUKtWrTL3dXFxITAwsNTvVq9ezcGDB/njjz8ICAjgqquuYtasWUyZMoUXX3wRR0fH846jproSEREREREREREREZHzsnnzZry8vAo7PQBuvfVW7Ozs2LJly1n3XbJkCb6+vrRp04apU6eSkZFhc9y2bdsSEBBQ+FmvXr1ISUnhwIEDFxRHjfgQEREREREREREREamGsrKyyMrKsvnMZDJhMpku+pinTp3C39/f5jOj0YiPjw+nTp0qc79hw4ZRv359atWqxd69e5kyZQpHjhzh+++/Lzxu8U4PoPDfZztuaTTiQ0RERERERERERESkGpo9ezaenp422+zZs0sN++yzz56x+HjJ7fDhwxcdl4ceeohevXrRtm1bhg8fzueff84PP/xAcHDwRR+zLBrxISIiIiIiIiIiIiJSDU2dOpWnn37a5rOyRntMnDiRUaNGnfV4jRo1IjAwkJiYGJvPc3NzSUhIKHP9jtJce+21ABw7dozGjRsTGBjI1q1bbcJER0cDXNBxQR0fIiIiIiIiIiIiInIFsGKt7ChccS5kWis/Pz/8/PzOGa5Lly4kJSWxY8cOOnbsCMC6deuwWCyFnRnnY/fu3QDUrFmz8LivvPIKMTExhVNprVmzBg8PD1q1anXexwVNdSUiIiIiIiIiIiIiIuepZcuW9O7dm7Fjx7J161b+/vtvxo8fz5AhQ6hVqxYAkZGRtGjRonAER3BwMLNmzWLHjh2EhoayYsUKRo4cyQ033EC7du0A6NmzJ61atWLEiBHs2bOHVatWMX36dB577LELXpNEHR8iIiIiIiIiIiIiInLelixZQosWLbjlllvo27cv3bt358MPPyz8PicnhyNHjpCRkQGAo6Mjf/zxBz179qRFixZMnDiRu+++m59//rlwH3t7e3755Rfs7e3p0qUL9913HyNHjmTmzJkXHD9NdSUiIiIiIiIiIiIiIufNx8eHpUuXlvl9gwYNsFqLpiarW7cuGzZsOOdx69evz2+//XbJ8dOIDxERERERERERERERqTbU8SEiIiIiIiIiIiIiItWGproSERERERERERERkSrPgvXcgUTQiA8REREREREREREREalG1PEhIiIiIiIiIiIiIiLVhjo+RERERERERERERESk2lDHh4iIiIiIiIiIiIiIVBvq+BARERERERERERERkWrDWNkREBERERERERERERE5F6vVWtlRkCuERnyIiIiIiIiIiIiIiEi1oY4PERERERERERERERGpNtTxISIiIiIiIiIiIiIi1YY6PkREREREREREREREpNpQx4eIiIiIiIiIiIiIiFQbxsqOgIiIiIiIiIiIiIjIuViwVnYU5AqhER8iIiIiIiIiIiIiIlJtqONDRERERERERERERESqDXV8iIiIiIiIiIiIiIhItaGODxERERERERERERERqTbU8SEiIiIiIiIiIiIiItWGsbIjICIiIiIiIiIiIiJyLlaslR0FuUJoxIeIiIiIiIiIiIiIiFQbGvEhF8W+siNQxTS0mis7ClXOFjvnyo5ClVOzUXJlR6HK2XGwZmVHocpJMxoqOwpVjo/FUtlRqHLq1Eqq7ChUOUcifSs7ClXOIWNuZUehSrnv4faVHYUqJ2FWdGVHocpJdVJLp6QbT+g8KSnjqbGVHYUqx+Wtjyo7ClWOtd0zlR2FKqdxs7jKjoKI/IdoxIeIiIiIiIiIiIiIiFQb6vgQEREREREREREREZFqQx0fIiIiIiIiIiIiIiJSbWiNDxERERERERERERGp8ixWa2VHQa4QGvEhIiIiIiIiIiIiIiLVhjo+RERERERERERERESk2lDHh4iIiIiIiIiIiIiIVBvq+BARERERERERERERkWpDHR8iIiIiIiIiIiIiIlJtGCs7AiIiIiIiIiIiIiIi52Kt7AjIFUMjPkREREREREREREREpNpQx4eIiIiIiIiIiIiIiFQb6vgQEREREREREREREZFqQx0fIiIiIiIiIiIiIiJSbajjQ0REREREREREREREqg1jZUdARERERERERERERORcLFgrOwpyhdCIDxERERERERERERERqTbU8SEiIiIiIiIiIiIiItWGOj5ERERERERERERERKTaUMeHiIiIiIiIiIiIiIhUG+r4EBERERERERERERGRasNY2REQERERERERERERETkXC9bKjoJcITTiQ0REREREREREREREqg11fIiIiIiIiIiIiIiISLWhjg8REREREREREREREak21PEhIiIiIiIiIiIiIiLVhjo+RERERERERERERESk2jBWdgRERERERERERERERM7FarVWdhTkCqERHyIiIiIiIiIiIiIiUm2o40NERERERERERERERKoNdXyIiIiIiIiIiIiIiEi1oY4PERERERERERERERGpNtTxISIiIiIiIiIiIiIi1YaxsiMgIiIiIiIiIiIiInIuFqyVHQW5QmjEh4iIiIiIiIiIiIiIVBtVuuPjzz//xGAwkJSUVGaYxYsX4+XldUHHbdCgAW+//fYlxe18nU8aRERERERERERERESkfFSpqa5uuukmrrrqqsJOia5duxIVFYWnp2flRkwqTIvJg6g/vAcOHq4kbAtiz5RPSA85ddZ9Gj5wG00evR2TnycpB8PZ+9xnJO0KLvy+/Zwx+N3QBqcAb3IzMknYFsTBl5eRduykzXHq3nsDjR/ui1ujQHLTzJz8eQt7py6uiGSet8BRvan16EAc/bxIPxhKyHMfk7b7WJnha9zehbpThuJUxw9zSBRhL39J0rqdhd83eXs8/vf2sNkncf0uDg17ufDfHbYuwKmuv02YsFe+JPK9H8opVRWjy9N303ZYD0weLpzcHsTaaZ+SFBpdZvjanZvTaVw//Ns2xC3AmxUPvkXw6h02YRxcTHR/9l4a9+qEs7cbyRGx7P50FXu/XFfRybkkznfcgeu9Q7Dz8SE3OJiUd+aTe/hw6WH73Y5Tz14YGzYEICfoCGmLPrIJH7B+Q6n7pi5cQMbXy8o/AeWk6eTB1L3vZhw8XEncdoT9kz8m4xzlSf0HetLw0f6Y/D1JPRjOgWmfklysPKk74hZq3dkNj3YNcHB3YXXT0eSmZNgcw6NtA1rMGIbnVY2x5lk49etWDj3/OXkZWRWSzktx9TN302xYDxw9XIjZHsTmqZ+SElL2dRNwbXPaPNIP37YNcQn0Zu3otwhfVXTdGIz2dJw8iDo3X4VbfT9yUsyc/Gs/21/9GnN00mVI0fmr80BP6j3aH0d/L9IOhhE07VNSiv2tS/Lvfx2NptyDU10/zCGnODZrCfFrdxd+79e3M7XvvxWPdo1w8HFny82TSTsQVvi90cuVRpPvwefGdjjV9iUnPoXYldsIfu1r8lLNFZnUS+J2zwA8R96DfQ0fsoOCSZjzHtkHjpQe9s6+uN5+Gw6NGwCQfegoSe99bBPe+ebuuN99O44tm2Hv5cHJIQ+TE1R2vleGxpMHU+e+mzF6uJK07QiHzqPsqPtATxo82h9Hf0/SDoZzqMT5ZGdyoNmL9xF4R1fsTA7Er9/DoWc/ITs2GQC3VvVoOGEg3p1b4ODjjjkilhOf/0H4R78XHsO/7zXUHXUb7q0bYGcyknbkBMFzlxP/596KyYhL1PupwVw39GacPVwJ2X6E5dM/Ji607Hzset9tdB1+Kz51/AA4dfQEq9/5nsN/7r5MMS4/y3aG8tm248SnZ9HM34Mpt7SmbU2vMsOnZObw3qYjrDt6iuTMHGp6ODPp5lZc3yj/fuzjf4+x9ugpQuPTMDnY076WN0/e2IIGPm6XKUXl56an7+bqoT1w8nAlYnsQvz33CQlnuV/r9ugAWvTuhG/jWuRmZhOx4yhrX1tG/PGoUsMP+2wyTW5qz9dj3+RIifu6qur6p+/mqqH597Antgex6rlPSTxLnnR5tD/Ne1+DT+Oa5GZmE7njKOtf+5qEgjxx8nTl+qfvpuH1bfGoXYOM+BSOrt7BxnnLyarC9Q2AQ9c+ONx4BwZ3LyxRoWT9uAhLxNFz7mds3x2n+yaSu38LmZ+9Vvi529zS2y9Zv3xGzoYfyyvaFcrx1oGY+t6DwdOHvIhgMj9/l7zjpdfDxk7dMfUfhn1AbTDaYzkVSdbv35Lz9x+FYTy/WFvqvuavPiD7t28qJA2VZfvufXy6dDkHDx8jNj6B+bNncMsNXSs7WpcsYFRvaj1yBw5+XmQcDCVk+iLSz/KMwOf2LtSdPBRTHX8yQ6IIf+ULm2cEAE5NalNv+kg8rmuFwWiPOegEQWPnkB0Zd8bxWnw5Ha+bO3Bk9Gskrtxa7ukrLy533oHrkPw2cU7wMVLnv0POoTLaxLf3w7lXL4yNCtrER4JI++ijM8Lb16+H+7iHcWzfHuztyQsNI3HG81hiYio8PSJSPqr0iA9HR0cCAwMxGAyVHRWpAE3G96fRmF7smfwJG/vOIDcjky7LnsXO5FDmPrUGXkfrF+/jyLzv2dDzOZIPhNPlq2dx9PUoDJO0N4RdT37A2hueYfOQ18BgoMuyZ8Gu6Dxq/HBfWj57D0ffXcG6Gyfzz+BXiVlfuQ8SagzoSoMXR3Fi3jfs6TWJ9INhtPpqBg41PEoN796pOc0WPEXM0rXs6fkMCSu30uLTybg0r2sTLnHdTra1G1O4BT3y1hnHCp/zlU2YqI9/q5A0lpdOj9zOVQ/05I+pn/DVgBfIycjiri+nYH+Wc8fBxUTswXDWTf+szDA3Pj+cBje1Z+UTC/js5sns+nglPWbeT6PbOlREMsqFqUcP3B95jLTPPiP+obHkBAfjPecNDGWMhHO46ioy160l8aknSXjsUSwxsXjPfQM7X9/CMLF33WmzJb/+GlaLhayNpXeIVAWNxg+gwYO92T95Ef/0nU5eRhadv5561vKk5sAutHhpBMfmLefv26aSciCMzsum2pQn9s6OxK7fTfD8H0s9hinAm87fTic9JJp/+kxn29DZuDWvQ7t3Hi3vJF6yto/eTsvRPdn87Cf80v8FcjOy6Lnk7NeN0cVE4sFwNj9X+nVjdHbEp20Dds//kRW9Z7Bu7Nt4NqrJrZ8+XVHJuCj+A7vQ9KWRhMz7jm23PUvagTCuWjYNB9/Sy1fPTs1ovXACJ5euZ+utzxL7+zbaLZ6Ea4ui8tXexUTyliMce3lpqccwBfpgCvDm2EtfsOXGZzj4xPvU6NGeVm+Nq5A0lgeXnjfh8/Q4kj78gqhh48g+ehz//72GnbdXqeGdOrYnfeV6oh96hlOjJpAXHUPA+69j71ejMIydsxNZu/eT9M5HlykVF6bB+AHUe7A3BycvYktB2dHhHGVHwMAuNH9pBMHzlvPvbVNJPRBGxxJlR/OZI/Hr2ZG9Y99m2x0vYQr0pv0nRdeFR/tGZMelsO+x9/jnxmcIefsHmk4bQt3RvQrDeHdpSfyGfewc/hr/3jaNhL8PcvUXk3Fv06BC8uJS3DxuANc/0Jtvn1vE23dMJ9ucxcOfT8V4lnxMiorn19e/4s3+03hrwHMc/ecAoz98hoCmdS5jzC/dqsMnmffnIR7u2pSvRnanmZ87j367hYT00ju/c/IsjPt2CydTMpg7oAM/jrmR53u1xd/NqTDMjogE7r26Pp/f142Fg68l12LhkW+3Ys7OvVzJKhddx91O51G9+HXap3w88HlyMrIY/sWzZ6136l/bgu2f/8End7zAl/e9hr2DPcO/eBYHZ9MZYa8d0xur9cqa4/u6cbfTaVRPVk77hM8G5t/D3vvF2eviete2ZMfna/j8jhdZdt/r2DkYGfLFlMI8cQvwxi3Ai3WvLGXRbc/y6zMf0ujGdvSdM/ZyJeuiGNt3w7H/A2Sv+ZqMtydiORmK84PPY3A9+0uPBm8/HG+/n7zjB874Ln3mAzZb5tfvYrVYyN23uaKSUa4crr0Jp2HjyPzhc9JmjMMSHozr5NcxeHiVGt6alkrWiiWkzXyctGljyd64CuexkzG27VQYJmX8IJst48M5WC0WcrZtukypunzM5kyaN2nEcxOr3n34xaoxoBv1X3iAE29+w75ez5B+MJSWS5/HWKP068StU3Oavv80MV+tZW/PiSSs3EqzT6bg3LxeYRhT/QBa//gqmcdOcHDQ8+y95Ski3/4WS2bOGccLHHv7FVHOOt3cA/fHHiVt8WLiHhxL7rFgvN+Yi10ZbWLHq6/CvHYtCU88Rfwjj5EXE4P3G7ZtYvtatajx3rvkhoWT8MSTxD8whrTPP4fs7MuUKhEpD1Wm42PUqFFs2LCB+fPnYzAYMBgMLF68+IxpohYvXky9evVwcXHhzjvvJD4+3uY4wcHBDBw4kICAANzc3Ljmmmv4448/uBjDhg3j3nvvtfksJycHX19fPv/8cwCysrKYMGEC/v7+ODk50b17d7Zt23ZRvxcWFkb//v3x9vbG1dWV1q1b89tv+Q+gT0+Z9euvv9KuXTucnJy47rrr2L9/v80xvvvuO1q3bo3JZKJBgwbMmzfP5vsGDRrw6quvMnr0aNzd3alXrx4ffvjhRcX3UjUe25sjb//IqVU7SDkUwc7HF+AU4EXN3p3K3KfJw30JW7Ke8GUbSA2KZM/kj8kzZ1F/yI2FYcK+XEf8v4cxR8SRvC+Uw699g0sdX1zq5r9R6ODpSospg9n5+AIif/iHjLAYUg5FcGr1zrJ+9rKo9XB/opf8QczX6zEHneD45A/IM2fhP/SWUsPXfLAfiet3cXLBT5iPRhIxZxnp+0IIHN3HJpwlO5ec2KTCLS85/Yxj5aWZbcJYzFXvTfXiOozpzdZ3f+L4mp3EHY5g5VMLcfX3onHPjmXuE/rnXv55YznBq7aXGaZmx6YcXL6JE/8eIuVEHPuWrif2UDiB7RtVRDLKhevgezD/+guZK38nLyyM1DfnYc3MxLlP31LDp7zyMuaffiQ3+Bh5EeGkvDEHDHY4dijKO0tigs1m6taN7N27yIsq/W3LqqDBQ3049tYPxKzcQerBcPaM/x+mAG8C+pRdnjQc14+IL9dxYtkG0oIi2T9pEXnmbOoMvakwTOiHv3P83RUk7Sj9rSr/nh2w5uZy4NlPSA+OInn3cQ5MXkTN/tfi0iCgvJN5SVo92Ju9838ifPVOEg9FsPGJhTgHeFGvV9nXTeT6veycs5zwlaVfNzmpZlYPfZ3Qn7eQEhxF7M5g/p3+Ob7tG+Faq0ap+1SGeuP6EfnlWqKW/Ul6UCSHC/7WtYb2KDV83Yf6kLB+N+Hv/0zG0UiOv/4NqftCqFPsofSp5ZsIefM7EjbuK/UY6Ycj2DfmTeJW78QcFk3iXwcInv01vj07YrCvMrdfNjyG303qD7+RvmIVOSHhJLzyNtbMLNwG9i41fNz02aR9u4KcoGByQyOIn/kmGAw4dS7qLE7/9Q+SP/oS85bKrWPLUv+hPhx/6wdiV+4g7WA4+wvKDv+zlB0NxvXjxJfrOLlsA+lBkRwsPJ9uAsDo7kztYT0IeuELEv46QOreEPY/sRDvzs3x7NgEgJNf/cmR6Z+RuPkQ5rAYor77i8hlfxLQ75rC3zky43NC//czKbuPkxFyimOvLiPjeBR+PateZ/wNo/uw5t0fOLBmB1GHw1n69P/wCPCmTc+y8/Hg2p0c+nM3caGniA2J4vc3viY7I5MGVze9jDG/dF9sD+GudnW5o21dGvu6M71nW5wc7Plxf0Sp4X/cF0GKOYe37ujE1XV8qO3pQqe6NWjuX9Rx9v7gzgxsU5cmvu409/dgZp/2RKWYORidfLmSVS6uHdObTe/9SNCaHcQcjuDHpxfg7u9Fi7Pcry29fw57lm8k9mgk0YfC+WniB3jV8aVm24Y24QJa1afL2H6smFQ57ZiLdc2Y3vz93k8cXbOT2MMR/PL0Qtz9vWh2ljz5+v457Fu+ibijkcQcCueXiR/gWceXwLYNAIgLOsEP497h2NpdJIXHEPbPQTbM/ZYmt1xdZesbAIcbBpCzZQ2529dhjTlB1vcLseZkYexcetsHAIMdTsOeInv1MiwJZ46SsaYm2WzG1p3JC96PtZSwVZFjn0Fk//kbOZtWYTkZhvnTt7FmZeF4Q+n1cN7hPeTu+BvLyXAsMVFkr/4eS8Rx7Ju1KQxjTU602Rw6diPv0G6ssVX3vv5iXd/lGiY8dD+33titsqNSbmo+1J+YpWuI/Xod5qMnCJnyARZzFv5Dby49/IO3k7R+F1ELfiLzWCQn5n6V/4zggaJnBHWfHU7Suh2Ev/wFGftDyAqLJnH1NnLjbesYl9YNqPnwQI4//b8KTWN5cLlnMBm//Ir595XkhYWRMu/N/DZxv9LbxMmzXsH840/kHjtGXng4KXPmgp0Bx45F91huYx8k698tpC38gNyjx8g7eZKsv//BomnsRa4oVeZOaP78+XTp0oWxY8cSFRVFVFQUdevavrm+ZcsWxowZw/jx49m9ezc9evTg5ZdftgmTlpZG3759Wbt2Lbt27aJ3797079+f8PDwC47T8OHD+fnnn0lLSyv8bNWqVWRkZHDnnXcCMHnyZL777js+++wzdu7cSZMmTejVqxcJCQkX/HuPPfYYWVlZbNy4kX379vH666/j5mY7pH3SpEnMmzePbdu24efnR//+/cnJye+Z37FjB/fccw9Dhgxh3759vPjii8yYMYPFixfbHGPevHl06tSJXbt28eijj/LII49w5Ejpw2criks9f5wCvIndWNRxk5tqJnFXMN6dSm/wGhzs8WzX0GYfrFZiN+0vcx97FxP1htxIelgM5pP5nWR+N7bBYGfAuaYPN2+cS8+d79Lpwwk41fIpvwReIIODEbd2jUneVGzUidVK8qa9uHdsVuo+7p2a2YYHkv7cjXvH5jafeXZpzTX7PuHqTe/Q6LWHMHqfOU1C7fF3cs2BxbRbPZdajwyEKtxI8qznh6u/F+F/FZ0H2almTu0OplbHS3tYErXjKI1u64BrgDcAdbq0xLthIGFlPNisdEYjxmbNyN5RbGoHq5XsnTtwaN36vA5hMJkwGI1YUlJK/d7O2xvTdV3I/K3qjgJyrp9fnsQV+zvlpppJ2nkMr06lXz8GB3s82jUkflOxv63VStzGfXiXsU9p7ByNWLLzoNibUHnm/LeAvK9tcYEpqThu9fxwCfDiZLHrJifVTNyuYPwv8bopycHDGavFQnaJKcEqi8HBHvd2jUgo8bdO3LgPzzLqDs+OzUjYaPtiQfz6PXhewLlRGqOHC7mpZqx5lks6ToUwGnFs2YzM4h0UViuZW3ZiatfqvA5hcDLBWcqTqsa5vj+mAG+bzqvcVDPJO4+V+bfOP5/OLDsSNu4rLG882jfCztFIfLHjZhw7iTki9qznkNHDhZzEM19OKPpxA/ZuzuQknSVMJfCp64+HvzdBfxelNzPVTPjuYzTocH7XjMHOwFX9u+DobCJ0Z1BFRbXc5eRZOHQqmWvrF70hamcwcG19X/aeTCp1nz+PRdOulhez/9jPzf9bw92fbmDRv8fIs5T9Rm1aVv5ID08nx3KNf0XyquuHu783x/8qeis/K9VM5O5g6nQ4/3rH5O4CgDmpqD1mdHLkrnce47cZi0mPvXI6g7zq+uHm70Vosbo4K9XMyd3B1L6APHEqzJOyywKThwvZaVW0vgGwN2JXuzF5R/cUfWa1knd0L/b1m5e5m+Nt92BNSyZ3W+nTNxVncPPEvmVHcrde3EuQl529EfsGzcg9YFsP5x7YiX2T86uH7VtdjV3NOuQdKb3tYvDwxtj+WrI3/F7q91K1GByMuJbxjMCtY+nXiVvHM58RJG/YVRTeYMD7lo5kHo+ixdIZdNz7KW1+eQ3v3p1t9rFzdqTJ/54i9LkPyYlNKs9klT+jEYdmzcneXqJNvGMHDq3P8x62oE1sTUkt+MCAqct15EZE4P3GHPx++gGfhe9j6t69AhIgF8Oq/y74v/+qKvN009PTE0dHR1xcXAgMDCQwMBB7e3ubMPPnz6d3795MnjyZZs2aMWHCBHr16mUTpn379jz88MO0adOGpk2bMmvWLBo3bsyKFSsuOE69evXC1dWVH34omit06dKlDBgwAHd3d9LT01mwYAFz586lT58+tGrVio8++ghnZ2c+/vjjC/698PBwunXrRtu2bWnUqBG33347N9xwg02YF154gdtuu422bdvy2WefER0dXRi/N998k1tuuYUZM2bQrFkzRo0axfjx45k7d67NMfr27cujjz5KkyZNmDJlCr6+vqxfv/6C43spTP75QzOzSjRWsmKTcfIvfdimyccdO6N9Gft42XzWYNSt9Av+hNuPf4r/zVfxzz2vYs3JA8C1nj8GOzuaThjIvue/YNuD83H0cqPr19MwONiec5eL0ccdg9Ge7BI3FTmxyTiUSNtpDn5e5JTIi5zYJJvwiet3cXTCOxwY/CJhr3yJR5dWtFwyHeyKLv2oj38jaNxbHBj0AtFfrqHOhLtoMGNkeSWt3Ln4eQGQEWf7YC0jLgUXv0tbD2j985+TcDSSh7a9y4Tgxdz5+WTWzfiMyK2Xt2PwfNl5emKwN2JJTLT53JKYiL3P+XXkuT08jry4ONvOk2KcevXGmpFB5saNlxzfimIqOCeyS1wP2bHJmMq4fhx9PMosT8rapzTxfx3A5O9Jw0dvx+Bgj9HTlebThwHgFHD+x6loLgVpMsfaXjfmuBScyyhzL4a9yYFO04Zw/MfN5KRVjXnFHQr+1qWdH45lnR/+XmeUx/nn08XnlYOPOw2euovIL6vmAxh7L08MRnvyEmzLk7yEROxreJ/XMbwnjCUvNr7Kju4oybGg7ChZDpxP2VHyfCpedjj6e2HJyjljPaDsuOTC8qokz07NCBzYhRNlzMUO0ODR2zG6OhG9ompN2eJRkKbUEnmSGpuMexnpPa1m87rMPrCYOUFfMviVB/n04XlEH4usoJiWv0RzNnlWKzVcbKdhquFiIq6Mqa4ikzP4I+gUFquV9+7uzENdmvLFtuN8tLn0dQ0sVitz1x3kqtreNPFzL/c0VBS3gushPc72vEiLS8btHOdFIYOBXi+MIHzbEWKDThR+3Ov5+4jYEUTQmitjTY/TXAvzxLYuTo9LwfV872ENBm594T4ith0hrlieFOfs7Ua3x+9g11eXt313IQyu7hjs7bGm2Z4f1rQkDO5epe5j16AlxmtuIfPb98/rN4ydekCWmdz9/15qdC8Lg7tnfp4k29bD1pREDF5nua93dsXjo1/w+HQVrhNfxfz5e+TuL/3acLi+J9bMDHK2V79prqqj088ISnY85MQlFd7DlOTg50VOXInwxZ4pOPh6Yu/mTK3xd5K0fheHhr5EwsotNFs0GffrijoJ6r84mrTtR0hcdXGzmVxOdp7597CWRNuXj/MSErE7zzax+7iHyYuLI6ugTWzn7Y2diwuuw4eRtWUriRMnkbXpL7xenolD+/blngYRqThVanHzczl06FDhSIvTunTpwsqVKwv/nZaWxosvvsivv/5KVFQUubm5mM3mixrxYTQaueeee1iyZAkjRowgPT2dn376iWXL8hf3DQ4OJicnh27dioZSOjg40LlzZw4dOnTBvzdhwgQeeeQRVq9eza233srdd99Nu3btzkjvaT4+PjRv3rzwtw4dOsTAgQNtwnfr1o23336bvLy8wo6k4sc0GAwEBgYSc5bFmbKyssjKsm285VjzcDCcfydBnbu60X7umMJ//3vfnPPe92Kc+O5vYjfsxynAi8aP9OOaD59g04AXsWTlgJ0ddo5G9k3/jNgN+W/DbH/kXXrvXYBvt9bEVtFFQy9G/E9/F/5/xuFw0g+G0XHL+3h2bU3yX/lpj/rg56Iwh8KwZufSaM7DhL36JdYqMJd0izu6csvs0YX//nHUGxX2W1eN6kng1U34afQ8Uk7EUfvaFtw8637SoxMJ/+vMeYSvdC5Dh+HU42YSn3oCckqfq9S5Tx8y//ijzO8rQ627u9FmbtG81duHv15pcUk7coK9ExbQ8qURNH9uKNY8C2GLVpIVk4T1LG/vVrRGd3al6+tF182akRV33ZxmMNpz08LHMRgMbJ66uMJ/70pi7+ZM+yVTSA86Qcjc5ZUdnQrhMWoILr1uIvqhiZB95hzRVUHg3d1oVazs2FWJZUdxbi3qcPVnzxA87zviN5R+DxJ4VzcaP3M3u+5/g+y4yh1R02FgNwa/WpSPi0ZffD7GHD/JvL5TcHJ3oX3faxk671H+d+9LV1Tnx4WyWMHHxZEZPdthb2egVaAnMamZfLbtOOO6nTlCZvaa/RyLS2XxsC6lHK3qaHNHV25/tehe/6sH5p4l9PnpO2sU/s3q8OmgmYWfNbu1Aw26tubDvtMu+fgVrfUdXen9alFd/M0Dl14X95p1P77N6vDloFmlfu/o5sw9nz5D3LFI/nrr+0v+vSrD5ITT0CfIWr4AMlLPaxeHa24hZ+dGyK2adVK5ycwg7bmHwMkZY+sOOA97BEtMFHmH95wR1PGG3uT8sxZyqnmeSNkK1j1NXLWVUx/9AkDGgVDcOrUgYGQvUv89iHfPa/Do1oZ9PZ+pzJheNq7Dh+F0y80kTHiyaP2OgnWGs/76m4xv8+/dc48dw6FNa1wGDiB5z5nXl4hUTVdUx8f5eOaZZ1izZg1vvPEGTZo0wdnZmUGDBpF9kQsQDR8+nBtvvJGYmBjWrFmDs7MzvXuXPsfmpXrwwQfp1asXv/76K6tXr2b27NnMmzePxx9/vFx/x8HBdvE8g8GAxVL2MOjZs2fz0ksv2Xx2r2sbhrq1Pe/fPLVqB4k7i+bItzPln3omP0+yYpIKPzf5eZK8P6zUY2QlpGLJzcNU4o0ok58nmcWOAflTVeSmmkkPOUXCjqP0PfIRNft0IvLHzYW/lxpU1KjOjk8lKyEVl9qVMyd9bkIq1ty8M97ccPDzJKdE2k7LiU3CoUReOPh5lRkeICs8mpz4ZJwaBhZ2fJSUuvModg5GTHX9yQw+eSHJqBDBa3YStSu48N/GgnPHxdeD9GJpdfH1IPbghXdwnmZvcqDb5Hv4+aG3CVm3G4C4wxH4tapPx4f6VcmOD0tyMta8XOy8bd/GtvP2Ju8c0+253HMvrsOGkThxIrnHj5caxqFtO4z16pM886VSv68s0St32Ky5cXoRYscS5YmjnycpB0ovT7ITUsosT7LOcg2V5uT3f3Py+79x9PMkLz0TyF8/JCOs8uaTDl+9k9hi1429Y/514+zngblY+px9PUg4cPHXzWkGoz09Fj6OW50arLxndpUZ7QGQU/C3dizxt3b08yS7jL91dsyZb9Lln18XPqWKvasTVy2bSl5aJvsemIc1N++Cj3E55CUlY83Nw97Htjyx9/EmLz6xjL3yeYwYjOcDQ4geN5mcoyEVGc1LErtyB5tLKTtMJc4FRz9PUs9RdpQ8n4qXHdkxSdiZHPKnNis26sPR15OsEm9tujarTcfl0znx5VpC3vqB0gTe0YXW8x5iz9i3z5iCrTIc+GMH4buL8tHeMT8f3f08SS2WPnc/TyIPlp6Pp+Xl5BFXUFae2B9C3XaNuWF0H76dtqj8I14BvJ0dsTcYiM+wfUEoPiMLX9czF+MG8HM1YbQzYF/w8AmgYQ034tKzyMmz4FBsutHZf+xn4/EYPhnShQB354pJRDkJWrOTD4rfrxXUO66+nqQVu77cfD05dY7zAqD3zPtpesvVfHbPLFJPFd3TNOjaCp/6/kzZ95FN+MELnyR862E+H/LKJaak/Bxds5OTpdTFriXuYV19PYg+j3vYnjNH0uSWq/nynpdt8uQ0R1cn7v18ElnpmXz30NtYqmh9A2BNT8Wal4fBzbYsNbh5YU1NOiO8XY1A7HwCcHqgWIdXwYNJ19eWkzF3PNb4U0XhG7bEzr8OuV/OK3moKsuampyfJ5629bDBwxtr0lnu661WLDH5bbbs8GDsa9XD1H8oGSU6PuybtcW+Vj0y/ld6p5lUPaefETiUfEbge+bI5NNyYpNw8C0RvtgzhdyEVCw5uZhLjBjLPHoC984tAfDo1hanBoFcc/gLmzDNPppE6pZDHBz0/EWnqSJYkvPvYe28bUd32Pt4YzlXm3hIfps44WnbNnH+MXPJDbOtr3LDwnBse/7PwUSk8lWZqa4AHB0dycsr+watZcuWbNmyxeazf/+1Hbr6999/M2rUKO68807atm1LYGAgoaGhFx2nrl27UrduXb7++muWLFnC4MGDCzsOGjdujKOjI3//XfRWfU5ODtu2baNVq/ObS7CkunXrMm7cOL7//nsmTpzIRx/Z3tQXT29iYiJBQUG0bJlfQbVs2dImLpCfH82aNTtj2rALMXXqVJKTk222Qa4Xlr7c9EzSQ6MLt9QjkWRGJ+J3fdE6BEY3Z7yvbkzi9tKH+ltz8kjeG2KzDwYDft1bl7lPfhADYCh8wBFfMG2RW+OahWEcvFwx+biTcSLugtJVXqw5uaTtDcaze7FK1GDAs3s7UneUPtd16vYgPLvbjgjyvKEdqTvKnpbJsaYPRm93sqPLfojl2qYB1rw8cuIu/AFfRchJzyQ5LLpwiw+KJD0mibrdis4DRzdnAq9qzMkdZZ8H52LvYMTe0Yi1RCeg1WLBUOzhRJWSm0tuUJDNwuQYDDh26EDOgbI7alyGDMV1xEiSJk8mN6js88W5b19yjhwmNzi4zDCVIS89k4zQ6MIt7cgJMqMT8b2+aCFHo5szXh2akLS99OvHmpNHyt4QahTbB4OBGte3IbGMfc4lOzaZvIwsag7sQl5WNnEbKm9tmNz0TFJDowu3pKBIMqKTqNm96LpxcHPG9+rGxFzCdQNFnR4eDQNYee9rZCWmnXuny8iak0fq3uP4XG9bvnpf34bkMuqO5B1BeBc/NwCfG9uSfIHnhr2bM1d/8xzW7Fz2jJyTP+qwqsrNJftQkM3C5PkLlV9N1t6DZe7mcf89eD54H9Hjp5J9qGqvzZCXnok5NLpwSz9ygqzoRHyK/a3t3Zzx7NCkzL91/vl0Ztnhc32bwvImZc9xLNm5Nsd1aVwT57p+Nsd1bV6HTt/P4OTXGzk2++tSfy/wzq60fvsR9j7yDnF/7LqU5JebrPRM4sKiC7fooydIiUmkadei9JrcnKl3VZMLXq/DYGco7Ei5EjjY29Ey0JOtYUX3jxarla1h8bSr5VXqPu1rexOelIGl2NpQYYnp+LmaCjs9rFYrs//Yz7qjp/jw3uuo7eVSoekoD9npmSSGRRdusUcjSY1JpGGJ+7XaVzXmxM6z1zu9Z95Pi16d+GLoKyRFxNp89/eCn1nYayof9JlWuAGsnvlllVvovGSexB2NJC0miQYl8qTWVY2JPEee9Jw5kma9OrF06Kskl8iT08cZ8uUU8rLzWD7mTfKqcn0DkJeLJTIY+ybF2jIGA/ZN2pIXdua9qSUmkow3nsD81tOFW97BbeQF78f81tNYk2zbcA6dbyUv4hiWqNAKTkg5ysslLzQIY6uriz4zGDC2vpq8Y2XXw2cwGDA4nFmOOt7Uh9zjR7CEl/7Ck1Q91pxc0vcG27b5DQY8urcjrYw2f9qOIDyvt30w73lD+8Lw1pxc0vccw6lxLZswTo1qkXUifxaQk+99z95bnmbvbRMLN4CwFz8l+Kn3yit55Sc3l5ygIzYLk+e3iTuSc6Dsa8d16BDcRo4gcdJkckuueZubS87hwxhLrDtsrFOXvFOV93KbiFy4KjXio0GDBmzZsoXQ0FDc3NzOGIUwYcIEunXrxhtvvMHAgQNZtWqVzTRXAE2bNuX777+nf//+GAwGZsyYcdbRDOdj2LBhLFy4kKCgIJu1MFxdXXnkkUeYNGkSPj4+1KtXjzlz5pCRkcGYMWPOcsTSPfnkk/Tp04dmzZqRmJjI+vXrCzs1Tps5cyY1atQgICCA5557Dl9fX+644w4AJk6cyDXXXMOsWbO499572bx5M++99x7vv39+86CWxWQyYTLZvrV2IdNclSX4o5U0e/JO0o+fIj08lpZTBpMZnUTUyu2FYbp+O42o37cT8slqAI598Bsd5o8jac9xEncF03hsH+xdnAhftgHIXzS99sDriNmwj+z4FJxq+tD08QFYMrOJXrsbgPTjp4j6fTttXx7J7mcWkZtmptW0IaQeO0nc3xdwU1nOTn7wM03nP07anmDSdh+l5tjbsXcxEbNsHQBN3nmc7FMJhL+6BICoRb/S+vuZ1Hq4P4lrd+I7sBtu7RtzfNJCAOxcnKg78R7if91MTkwSTg0CqT9jBJkhp0j6czeQv/iZe4emJP+9n7y0TNw7NaPhSw8Q+91G8pKr1uKpxe38eCXXTriDpNBoksNj6PrMINJjkgheXTSf7d1fTeXYyu3s+WwNAA4uJrwaBBR+71HXD79W9chMSif1ZDzZaWYiNh/i+ueGkpuZQ0pkHHWubUGru7uzYeaSy57G85X+7Td4PjuVnKDD5Bw6jMugQRicnMlcmb9oocfUaVhiY0lblN+J6jJkKG4PjCb5lVnknTpV+GaM1WzGmln0lr7BxQWnG28idcGllR+XS+iHv9PkqTtJDzmFOTyGplPuISs6kejfi8qTzsunE/3bNsI+WQVAyMJfaffOIyTvPk7SrmM0fKgvRhcTJwrKE8h/89vk74VLw/xzx71lPXLTzGRGxhUuMFx/dC8Stx0hLz0L3xvb0uL54Rx+5asz5vevbAcXraT9hDtIOR5NWkQMV08ahDk6ifBVRddNr6+nEv77dg4tzr9ujC4mPBoWXTdu9fzwaV2PrMR00k/GYzDac/OHE6jRtgFr7p+Hnb0dzgVvwmclpWHJqRpvm4Yv/JVW7zxKyu5gUnYFU++hvti7mIha9icArd59jKxTCQS/8hUAER/+TocfX6DeuNuJ+2MnAXd0xaN9Yw4/U/QygtHLFafavpgC89/MdGmS34DMjkkiOza5sNPDztmRA4++h9HNGdzy39jOjk/Jn++miklZ8h2+L00m++ARsg4cwWPYXRicnUhbkX+vVWPmFPJi4kh6L38dM4/778XrkfuJmzab3JOnsCtYC8SaYcZqzh/9ZOfhjn2gP/Z++SMqHRrkNyDz4hOwnGMkyeUQ9uHvNHrqTjIKyo4mBWVHTLGyo+Py6cT8to2IgrIjdOGvtHnnEVJ2Hyd517HC8+lkQdmRm2omcul6mr80gpykNHJTzbR89QGStgWRXDDixK1FHTp9N4O49XsJW/hr4QgSq8VCTnz+NC6Bd3WjzTuPcGT6ZyTvOFYYxpKZTW5q1RlVBbDxk9+57fE7iQs9RUJEDL0n3kNKdCL7Vxfl47gl09m/aht/fZ6fj/0mD+HQn7tJPBmPk6sTHQZ2o/F1rfhw5OzKSsZFGdGpITN+20OrQC/a1PRkyfZQzDm5DGyTf65P/3U3/u5OTLihBQD3XFWfr3eFMWftAYZ2aEBYYjof/3uMoR0aFB7z1T/28/uhk7x9ZydcHeyJS8u/ntxMDjhV0np0F2PLxyu5/vE7SAg5RVJELDdNHERqTBKHi92vjVg6lcOrtrOt4H6tz8ujaDugK1+PfZOs9MzCtS+yUjLIzcohPTa51AXNk0/GndFJUhVt+3glXR+/g4SQaJIjYrihIE+CiuXJ0KVTCVq1nR0FedLr5VG0GtCF5WPfIruUPHF0c2bIF1NwcHZkxRMLMLk7YyoYIZQRn1KpU2+eTc7GFZjunYDlRDB5EUdxvP52DI5OhQuXm4ZMwJqcQPbvX0JuDpZo21Ex1sx0DHDG55icMbbrStbPiy9PQspR9u/LcX5oCnkhQeQdP4xjr7sxmJzI3phfbjo/PAVLYhxZ3+TXw6b+Q/PDRp/E4OCAsf21OHS7DfPi+bYHdnLBofMNZC5deLmTdFllZJgJP1E0Y0HkyWgOBwXj6eFOzUD/SozZxYv68Gcav/04aXuOkbbrKDXH9sfexURswTOCxvMnkH0qnojZp58R/EKr72ZR8+EBJK7dge/A7ri2K3pGAHDy/Z9ouvBpUv89SPI/+/HqcTXet3Xi4KAZQP6okdIWNM+KjCMrouwp0itTxjff4jl1KjlHjpBz6BCugwdhcHbC/Ft+m9hz2lTy4uJI+zD/Xt512FDcRj9A0qyX89vEPsXaxOb8e6z0r5bh9eILZO/ZQ/au3Ziu7Yypa1cSnniyUtIoIhenSnV8PPPMM9x///20atUKs9nMp59+avP9ddddx0cffcQLL7zA888/z6233sr06dOZNatouOabb77J6NGj6dq1K76+vkyZMoWUlEubC3n48OG88sor1K9f32Y9D4DXXnsNi8XCiBEjSE1NpVOnTqxatQrvElPPnI+8vDwee+wxTpw4gYeHB7179+att9464/eeeOIJjh49ylVXXcXPP/+Mo6MjAB06dOCbb77h+eefZ9asWdSsWZOZM2cyatSoi057RTr23s8YXUy0f+NBHDxcSNgaxOahr9m8EevaIABHn6KFHE/+9C+mGh60mDwIk58XKQfC+Hfoa2QVzHdtycqmxnUtaPRQHxw9XcmKTSbu38Ns6v+izZzYOx9fQJuZ93Hdl5PBYiFu8yE2D32tUqcgiV/xDw41PKk3eQgOfl6kHwjh4LCXC0demGr72jwoS91+hKOPvk29KUOpN3U4mSFRHH5gDhlHIvIDWCy4tKqP/z03Ye/hQnZ0Ikkb9hDx+leFa3dYs3PwHdiduhPvxeBoJCsihpMf/szJYut+VEXbF/yCg7OJW2ePxuThwsntQXw/Yo7N222e9fxxLnbuBLRrxOBvniv8900v3AfAgW83snpi/huCv41/j+5T7qXPO4/g5OVGyok4/p7zLXu/LHux2cqWtX49qZ5euI0ajZ2PD7nBx0icMqlwwXN7f38o1vnrMnAgBkdHvF6yHeaetvhT0j9bXPhvp5tvAYOBzHVVN+3FHX9vBfYuJtq+MRajhwuJW4+wbYhteeJS37Y8ifppM441PGg2eTCO/l6kHghj69DXbBYtrn//bTSdNKjw311WvAjAngkLiPw6/yGn59WNaTppEPauTqQfO8m+SYs4ubzqLRq57/1fMLqY6DpnNI4eLsRsC2L1fbbXjXt9f0zF8si3fSP6LC+6bq59Mf+6OfrNRv566kNcA72p1yt/xNEda161+b3fB73Cqc0Xvt5VRYgp+Fs3mnwPJn8vUg+Esnvo7MK/tVPtGjajvZK3B3HgkXdp9Oy9NJ42hIyQU+wdNZf0wxGFYfx6daLVO48W/rvth08CcHzut4S8sRz3dg3x7NgUgK5b37GJz9+dxpNZBR/QZaz+k0RvT7weGYV9DW+yjwQTM34qloQkAIyBtuWJ++D+GBwd8XvjBZvjJH3wOckffA6A841d8H1pcuF3fq9NPyNMZQotKDtaFZQdSVuPsPMcZUd0wfnUePLggvMpjJ0lyo4jz3+O1WLhqo+fxs5kJG79Xg5N+bjw+4Dbr8PR15Nag6+n1uDrCz83h8ey6Zr8KU7r3HcLdg5GWr4+hpavF71QE7lsAweeWFAh+XGx1i1cgaOzicGzx+Ls4ULItiN8eP9r5BbLR9/6AbgWy0e3Gp4Me/MxPPy8MKdmEHU4nA9HziaojKk4q6peLWqRmJHNgr+DiEvPorm/B+8P6kyNgqmuolLNBaOP8wV6OPP+oM68sf4ggxdvwt/NiWEdG/JA58aFYb7dnf8g98FltiPbX+rTrrBD5Urwz8JfcHQxcfvsMTh5uBC+PYglI1+3qXe86wXg4l10Xlwz4jYA7v9mhs2xfpr4AXuWb7w8Ea9A/y78BQcXE31mj8bJw4WI7UF8M9K2Lvaq549zsTzpMOJWAO77ZrrNsX6Z+AH7lm8isE0DandoAsAjm960CfN+tydJrqQR7eeSu+dvDK4eOPYagsHdG8vJEMyLZhYueG7n5WczMup8Ga/qDhjI3V317sXOJWfLnxjcPXG6exQGT2/ywoNJn/ss1pT8+3q7Gv5QPE9MTjjdPwE7Hz+s2VlYoiIwL5xNzpY/bY7r0KUHYCB7c9Vd8L487D98lNGPTyn895x389t4A/vcyivTJ1ZWtC5J/Iq/MdbwoO6koTj4eZFxIITDw2eVeEZQdG+Wtv0Ixx57i7pThlH32fxnBEGjX8d8pKiDMHHlFkKe/YBa4++iwawxmI+fJGjsHFK3Hr7s6SsvmevWY+flhfvoB7Dz8SHn2DESn5lc1CYOCLC5dk63ib1nzbQ5Ttqni0n7dDEAWZv+ImXem7jeNxyPJyaQGx5B0vPPk7PvyrpPqa6sF1E/yH+Twaqz5Yrw559/0qNHDxITE/Hy8qrs6PBT4LDKjkKV4mfIOneg/5gtDlV7LurKMKxxxLkD/cfsOFjz3IH+Y6KNVeqdhCqhXnVfmPQiNKkdX9lRqHKORPpWdhSqnJWqim28Mr3WuQP9x8ydpSk7SnKgik5xWokev6fqjgSvLHmnLu3lyurI5a2Pzh3oP2ZHu//GAuEXokGTs6+78V8UuPHPyo7CFadTzevPHUhsbI+68l4IKA9Vao0PERERERERERERERGRS/Gf7vhYsmQJbm5upW6tW7c+9wEuUJ8+fcr8vVdfffXcBxARERERERERERERkbP6T8+nMWDAAK699tpSv3NwcCj331u0aBFmc+mLUfoULKZUlptuuklz2ImIiIiIiIiIiIiInMN/uuPD3d0dd3f3cwcsJ7Vr175svyUiIiIiIiIiIiIi8l/0n+74EBEREREREREREZErgwXNiCPn5z+9xoeIiIiIiIiIiIiIiFQv6vgQEREREREREREREZFqQx0fIiIiIiIiIiIiIiJSbajjQ0REREREREREREREqg11fIiIiIiIiIiIiIiISLVhrOwIiIiIiIiIiIiIiIici9VqrewoyBVCIz5ERERERERERERERKTaUMeHiIiIiIiIiIiIiIhUG+r4EBERERERERERERGRakMdHyIiIiIiIiIiIiIiUm2o40NERERERERERERERKoNY2VHQERERERERERERETkXCxYKzsKcoXQiA8REREREREREREREak21PEhIiIiIiIiIiIiIiLVhjo+RERERERERERERESk2lDHh4iIiIiIiIiIiIiIVBvq+BARERERERERERERkWrDWNkREBERERERERERERE5FyvWyo6CXCE04kNERERERERERERERKoNdXyIiIiIiIiIiIiIiEi1oY4PERERERERERERERGpNtTxISIiIiIiIiIiIiIi1YY6PkREREREREREREREpNowVnYERERERERERERERETOxWK1VnYU5AqhER8iIiIiIiIiIiIiIlJtqONDRERERERERERERESqDXV8iIiIiIiIiIiIiIhItaGODxERERERERERERERqTbU8SEiIiIiIiIiIiIiItWGsbIjICIiIiIiIiIiIiJyLlaslR0FuUJoxIeIiIiIiIiIiIiIiFQb6vgQEREREREREREREZFqQ1NdyUVxs+ZVdhSqFH+ftMqOQpVzbWJlx6Dq2XGwZmVHocrxNORWdhSqnAiDquaScjBUdhSqnKDIGpUdhSqnbdPoyo5CldMowbGyo1Cl/DFdZUlJ3XVPfwZvU1ZlR6HKSVivtk5JUSc9KzsKVY613TOVHYUqp+PeNyo7ClXOj21nVHYUqpzBlR0BkWpMIz5ERERERERERERERKTaUMeHiIiIiIiIiIiIiIhUG5pPQ0RERERERERERESqPIvVWtlRkCuERnyIiIiIiIiIiIiIiEi1oY4PERERERERERERERGpNtTxISIiIiIiIiIiIiIi1YY6PkREREREREREREREpNpQx4eIiIiIiIiIiIiIiFQbxsqOgIiIiIiIiIiIiIjIuVixVnYU5AqhER8iIiIiIiIiIiIiIlJtqONDRERERERERERERESqDXV8iIiIiIiIiIiIiIhItaGODxERERERERERERERqTbU8SEiIiIiIiIiIiIiItWGsbIjICIiIiIiIiIiIiJyLhartbKjIFcIjfgQEREREREREREREZFqQx0fIiIiIiIiIiIiIiJSbajjQ0REREREREREREREqg11fIiIiIiIiIiIiIiISLWhjg8REREREREREREREak2jJUdARERERERERERERGRc7FirewoyBVCIz5ERERERERERERERKTaUMeHiIiIiIiIiIiIiIhUG+r4EBERERERERERERGRakMdHyIiIiIiIiIiIiIiUm2o40NERERERERERERERKoNY2VHQERERERERERERETkXCxWa2VHQa4QGvEhIiIiIiIiIiIiIiLVhjo+RERERERERERERESk2lDHh4iIiIiIiIiIiIiIVBvq+BARERERERERERERkWpDHR8iIiIiIiIiIiIiIlJtGCs7AiIiIiIiIiIiIiIi52LFWtlRkCuERnyIiIiIiIiIiIiIiEi1oY4PERERERERERERERGpNtTxcQW56aabePLJJ8v1mIsXL8bLy6tcjykiIiIiIiIiIiIiUlm0xsd/3L333kvfvn0vy2/VeaAn9R7tj6O/F2kHwwia9ikpu4LLDO/f/zoaTbkHp7p+mENOcWzWEuLX7rYJ02jyYGrddwtGD1eStx3h8ORFmENOFX7v3rYhjWcMw+OqxpBnIebXLRx9/nPyMrIKw9wS/fUZv73/4flE//jPpSf6EnkO7Y/X6EHY+/qQfeQ4sa+8T9a+I6WGdWxSH5/xIzG1boJD7UBiZy8k+YsfbMLUX/MZDrUDz9g3aekK4l7+X4Wk4VIFjupNrUcH4ujnRfrBUEKe+5i03cfKDF/j9i7UnTIUpzp+mEOiCHv5S5LW7Sz8vsnb4/G/t4fNPonrd3Fo2MsAmOr4UeepwXh2b4ODnxc50YnEfreRE/O/w5qTWzGJvAhNJw+m7n034+DhSuK2I+yf/DEZxc790tR/oCcNH+2Pyd+T1IPhHJj2KcnFrsG6I26h1p3d8GjXAAd3F1Y3HU1uSsYZx/G79WqaTrwb95b1sGRlE7/5EDtHzSv3NF6IwAd6U/vRAQXnSRjHn/uYtF1nOU/6d6He5CEF5Uv+eZK4dlepYRu//hCB9/fk+IxPifroV5vvvG/tQN2nB+PSsh7WrBySNx/k8ANzyjVt5anTM3fTYmgPTJ4unNoWxKZpn5ISEl1m+JrXNqf9uH74tm2Ia6A3q8a8ReiqHWWGv372A7QacQv/vPAF+z5eVRFJuGRNJg+mTrFr5+B5XDv1Cq4dx4Jr51CJa6dOsWvH6O7CH6VcO42evAO/Wzvg0bo+lpxc1jYbUyHpuxB1HuhJg2L18uHzqJebFNTLGQX1clyJernx5MHULqiXkwrq5dP561TXj0ZP34VP9zY4+nmRFZ1A1PK/CHn7e6w5eRWZ1EvictcduA69F3sfH3KCg0l56x1yDh0uNaxz/3649O6JsVFDAHKOBJH6wSKb8J7TpuDSt7fNfplbtpI4cUrFJaKc6f4Emk8eRL3h+WVJwrYj7JvyCennKEsaPHAbjR/tj8nPk5SD4ex/bjFJxa65dnPG4HtDW5wCvMnNyCRxWxCHXv6KtGMnAXDwdqPD/8bj0aoeDt5uZMelcGrVdg6/+jW5aeYKTe+51H6gV4n7/E9IPUt54tf/OhpNubfwPj941hLii9XDfn07U/v+23Bv1wgHH3e23jyJtANhZxzHo1NTGk8dikeHJljzLKTtD2X3kFewZOZUSDovhO/Ivvg/fAcOft6YD4Vy4vkPydhztMzwXv26UnPicBzr+JMVepKTsz8nZX1RnWv09aTW1PvxuOFq7D1cSdtygBPPf0hWaFRRGD8vaj83CvfuV2Hn5kxWcCSn3vuW5N83V2haL5bb4IF4jLgH+xo+ZB8NJnHuu2QfKL0scb2jL679euLYuAEA2YeCSHr/Y5vwzj2643Z3fxxbNMPey4OoYQ+RE1T2eVgVBIzqTc1H7sDBz4uMg6GETl9E+lnaOj63d6HO5KGY6viTGRJF+CtfkFysrQPg1KQ29aaPxP26VhiM9piDTnB07ByyI+MAMNUPoN7zo3Dv3AI7RweS1u8idPoicuOSKzSt5ytgVG9qFcuTkPPIk7ol8iSpjDzxKJYnQcXypLgWX07H6+YOHBn9Gokrt5Z7+i6X7bv38enS5Rw8fIzY+ATmz57BLTd0rexoVajWk+6m4fAeOHq4ErctiJ3PfkLaWdo6AI1H3UbzR/vh5OdJ0sFwdj33GYm7jwPgUseXftvml7rf5rHzOfHLlXt+iPxXaMTHf5yzszP+/v4V/jv+A7vQ9KWRhMz7jm23PUvagTCuWjYNB1+PUsN7dmpG64UTOLl0PVtvfZbY37fRbvEkXFvULQxTf/wA6jzYh8OTF7G973PkZWRy9dfTsDM5AOAY4M3V307HHHKK7X2eY9fQ2bg2r0urdx494/cOTnifTW0eKtxif99WMRlxAdx634jvlIdIeH8JEYMeI+vwcWp9+Ar2Pp6lhjc4mcg5EUX8m5+QGxtfapiIeyYQcsOQwi1yzLMApK/aVGHpuBQ1BnSlwYujODHvG/b0mkT6wTBafTUDhxqlnzfunZrTbMFTxCxdy56ez5CwcistPp2MS/O6NuES1+1kW7sxhVvQI28VfufctDYGOwPBkz9g901PEfLCpwSM7Em9qcMqNK0XotH4ATR4sDf7Jy/in77TycvIovPXUwvP/dLUHNiFFi+N4Ni85fx921RSDoTRedlUHItdg/bOjsSu303w/B/LPE5gv860f+8xTnz1J3/dPIXN/V/g5Pd/l2fyLpjvwK40fPF+IuZ9y+6ek0k/EErrr6aXWb64d2pO8wVPEvPVWnbfNomE37flnyct6p4R1qdPZ9w6NiUr6sxrqka/a2n67uPELFvP7lueYe+A6cT+UDWvJYD2j95Omwd6smnqJ/zQ/wVyM7Lo9+UU7M9y3hhdTMQfDOev6Z+d8/gNenfCv0MT0k8llGe0y1XD8QOo/2BvDk5exOaCa6fTOa6dwGLXzj+3TSX1QBidLuLasXMwcurnfwn/bE15JumiBQzsQvOXRnJ83ndsue1ZUg+E0eEc9XLbhROIXLqeLQX1cvsS9XKD8QOo+2AfDk1exNZS6mXXJrXAYMfBZz5i840TCXr+c+rcfytNpg29LGm+GE4398Bj/COkffoZcWMeIvdYMD5vzsGujNGypquvwvzHOuIff4q4hx8jLzoGnzfnYufraxMu898tRA+4q3BLenHWZUhN+dD9CTQe35+GY3qzd/LHbOo7g7yMLK5d9uxZy5JaA6+j1YsjCJr3HRt7TiPlQBjXfvWsTVmStDeE3U8uZP0NE9kyZDYYDFy3bCrYGfIDWKycWrWdrfe/wfpuT7P7iQX4Xd+GtnMqtyP19H1+6LzlbLttSsF9/nNllicenZrReuETRC1dx7ZbpxD7+zbalihP7F1MJG05zLGXl5T5ux6dmnLVV8+R8OcetveexvZeUznxySqslspf8NSrf3dqzxjNqbe/5ki/pzEfCqHxly9irFH6deLasQUN3n2G+K//4HDfp0hetYWGH03FqVm9wjCNPpqGqV4gx8e8wuE+T5EdGUOTpTOxczYVhqn/1pOYGtXOD9NzAkkrN9Pw/Uk4t25Y4Wm+UC633YT3U+NI/uhzou4bR05QMP7vvo6dt1ep4Z06tidj1Tqix03k1AOPkxsdi/97c7D3KypfDc5OZO3eT9K7H12mVFwanwHdqPfCA5x48xv293qGjIOhtFj6fJnniVun5jR5/2liv1rLvp4TSVy5lWafTMG5edF5YqofQKsfX8V87ASHBj3PvlueIvLtbws7A+2cTbT46gWwWjk0+AUODJyGwdFI88+mgcFwWdJ9NjUGdKN+QZ7s6/UM6QdDaXmOPGn6/tPEfLWWvT0nklBGnrT+8VUyj53g4KDn2VsiT4oLHHs7VmvllyHlwWzOpHmTRjw38cznH9VR88dup8mYXuyc8ilr+z1PbkYW13919rq5zoDraP/icA7O+541vaaTfDCcG756FlPB84aMk/GsaPeozbZ/znJy0sxErdtzuZImIpdAHR9XmNzcXMaPH4+npye+vr7MmDGjsGJu0KABL7/8MiNHjsTNzY369euzYsUKYmNjGThwIG5ubrRr147t27cXHu9yTXVVb1w/Ir9cS9SyP0kPiuTwpEXkmbOpNbRHqeHrPtSHhPW7CX//ZzKORnL89W9I3RdCndG9ioXpS+hb3xO3cjtpB8M5MP5/OAZ449fnGgB8e3bAmpvLkWc/ISM4itTdwRye/BH+/a/DuUGAze/lpKSTHZtcuFmyKv8tMa9Rd5H87UpSf1hNTnA4sS+9gzUzC/e7epUaPmt/EPFvLCLt9w1Ys0uPvyUxmby4xMLN9cZryQ4/iXnb3opMykWr9XB/opf8QczX6zEHneD45A/IM2fhP/SWUsPXfLAfiet3cXLBT5iPRhIxZxnp+0IIHN3HJpwlO5ec2KTCLS85vfC7pPW7OfbU/0jesIes8GgSV2/n5IIV1Oh7XYWm9UI0eKgPx976gZiVO0g9GM6e8f/DFOBNQJ9OZe7TcFw/Ir5cx4llG0gLimR/wTVYZ+hNhWFCP/yd4++uIGlH6W9UGeztaPny/RyeuYTwz/8g/XgUaUGRnFrxb3kn8YIUnifL8s+T4Mkf5p8nQ24uPfzYviSu303k+yswH40kvOA8qfmA7XniGOhDo1fGEPTYfKy5Jd5Gt7ej4azRhM78glOfrybzeBTmoBPEr6iab1QCtB3Tm53v/ETY6p0kHIpg/ZMLcQnwokGvjmXuE7F+L9vmLid05fYywwC4BHrTbdZI1j3+PpYq/OZ+/Yf6EFxw7aQdDGdfwbXjf5Zrp0HBtRO5bAPpQZEcKLh2ahe7dsI+/J2Qd1eQXMa1A3Bs7nLCPviNtEMR5Zmki1Z/XD9OfLmWkwX18qHCdJVeL9d7qA/x63cT9v7PpB+NJPj1b0jZF0K9YvVyvYf6EvLW98QWq5dNxerl+PV7OPjkAhI27MUcFkPsqh2Evf8L/v06X5Y0XwzXIYPJ+PlXzL+tJDc0jOS5b2LNzMT59j6lhk+a+QoZP/xE7rFg8sIjSH79DbAzYOrUwTZgdg6WhMTCzZqadhlSUz50fwKNxvYh6O0fiF61g9RD4ex6/H2cArwJ7F12WdLo4X6EL1lHREE9vHfyx+SZs6k35KbCMOFfriPh38OYI+JI3hfK4de+wbmOLy51/QDISU4n7LM/SN5zHPOJOOL+OkDo4jXUuLZFRSf5rOqOu52TBff5GUGRHJn0EZaz3uf3tbnPD3n9a1L3HafO6KKRUKeWbyL0ze9I3LivzN9tOvN+Ihb9Tti7P5F+5AQZwVHErNiMNbvyR+j6PziQ+K9Wk/DtWjKPRhAxdQEWcxY17r211PB+o/uTsmEnMR/8QNaxE0TNW4p5/3H8RvUDwNSwFq4dWxDx3AIy9h4j63gkEdMWYnByxHvgDYXHce3YgtjFv5Kx5yjZ4dFEv/steSnpuLRtclnSfSHchw8i7cffSP95FbkhYSTMfhtLZhZuA3qXGj5+xmzSlq8gJyiY3LAIEl6eBwYDTp2vLgyT8dsfpCz6gsytZY9OrUpqPtSfmKVriPt6HeajJwiZ8gEWcxZ+Q0u/hw188HaS1u8iasFPZB6L5MTcr8jYF0JAsXvYus8OJ3ndDiJe/oKM/SFkhUWTtHobufH5ozncO7fAVNeP40++i/lwOObD4Rx/4l1c2zfGo3vby5LuszmdJ7El8sS/jDypWUqepO8LIbBEniSt20F4sTxJLJYnp7m0bkDNhwdy/OmqOdLwQl3f5RomPHQ/t97YrbKjclk0HdubQ2//yMlVO0g+FMHWCQtwDvCidu+y2zrNHu5DyJL1hH69kdSgSHZM/oQ8cxYNht6YH8BiJSs22War3acTJ1ZssZlFRC4/q9Wi7QK3/yp1fFxhPvvsM4xGI1u3bmX+/Pm8+eabLFq0qPD7t956i27durFr1y769evHiBEjGDlyJPfddx87d+6kcePGjBw58rK+xWBwsMe9XSMSNhVruFitJG7ch2enpqXu49mxGQkb99t8Fr9+D56dmgHgVN8fU4A3CcUaQ3mpZlJ2His8pp2jA5bsXCiWVos5GwCvEg3E5rPHcP3Bj+i08hVqFnuYVWkcjJhaNcX8b7EhulYrGZt34XRVq3L7Dff+N5P6fdWcjsbgYMStXWOSNxV76GG1krxpL+4dm5W6j3unZrbhgaQ/d+PesbnNZ55dWnPNvk+4etM7NHrtIYzebmeNi72HC7lJqReXkHLmXN8fpwBv4oqd+7mpZpJ2HsOrU+n5YnCwx6NdQ+JLXINxG/fhXcY+pfFo1xDnWjXAYqHbH7O5ee8COi19FrcWdS46PZcq/zxpRNLGkufJPtw7NS91H/eOzWzDA4l/7sa9eF4YDDR973Ei3/8J85ETZxzDrV0jTLVqgNVK+zVzuWbPR7Ra+lypo0aqAvd6frgGeBG5qahczU41E7M7mICOpZfD581g4Ob549iz8FcSgyIvMaYV5/S1E1/i2km+iGsnfuO+Mve5EpRVLydcQr3sXFAvl8zflJ3H8CrjmABGDxdyE6voQ3+jEYdmzcjaXuwBmtVK1vadOLZufV6HMJhMGIxGLCkpNp87Xn0V/j9/j9/Sz/CY+CQGj9LfjK9ydH+CS73T9XDR9ZCbaiZpVzDeZZzrBgd7PNs1tNkHq5W4TfvL3MfexUS9ITeSHhaN+WTpI2VMAd4E9utM/OZDF5+gS3S28sSjjHIyvzyx7dBIWL8Hj7OUFSU5+Hrg2bEZOXHJdPxlFt33f8jVP7yIZ+fS6/7LyeBgxKVtY1L/KvY2sNVK6l97cOlQevxcOzS3DQ+kbNyFa0F4g2P+G8s2L2dZrVizc3G9pmXhR+k7DuPdvzv2nm5gMODV/3oMJkdSN5fdgVQpjEYcWzQjc4ttWZK5dSeO7c6vLDE4mcBoJC+5atyjXyiDgxHXdo1JKbWtU/p54taxmW14IGnDLtxOhzcY8LqlI+bjUTRfOoMOez+l9S+v4d276AUDg6MDWMFSrCPakpUNFiv/Z+++w6I4+jiAfw8Ojt6bKEUQuxhLjN1EE7vGGDX23qLBrmgSjb2XmJjYS0w0thi7xjd2jRUFCwLSRem9c3fc+8fhyXFgSZQ9ue/neXgeb29377fj7szszs6MeZNaENKzNCnt/s/sBWlS8v4vvUSaWLdrhLzwWNTcPRuN7m5H3RJpAgB6xoao9tNkRH6zCdLEtDd5WFQOTF3tYexojfhLD1TLZJm5SLkTBtsXlM3W3lURf0m9bI6/dB+2ZdwfWXm7w7qeOyJ+P/8mwyeit4gNH+8YFxcXrFmzBjVq1MCAAQPg4+ODNWueD9PTuXNnjBkzBl5eXpgzZw4yMjLw/vvvo3fv3qhevTp8fX3x8OFDxMe/eJzDN8nAxgJ6Yn0UJKq/UVGQmA5DB6tStzF0sEJBiQpHQWI6JA7KLq4SeyvVsrL2mXr5PgwdrOA6rhtEBvoQW5qi2rfK4YoMHa1V24Qt3Yv7o77HnT6LkHjsOmosHYEqI0t/06i86FtZQCTWhzwpTW25PDkVYjvr0jd6TWbtmkPP3AwZf55+I/t708Q25hCJ9TXOA2liOgzKOG8M7K0gLXFOSBPT1NZPPXcHjyb8gAe95yJq0W+waFYbtXZ9C+iVnh0auTuh0vBOiPtVO4anedG5Lynreiq6BvNLbJP/gm1KY+KmHBbPa1ovhK35E7cGLocsPRtND86BgZXpK+/nTTIoOk9K/r8XJKaVmb8YOFhp3NCUPK8qf9UDClkhYrecKHUfRq7KXmMu0/og5vsDCBy0BLK0LNT9Yx7EVi9uSBOCSdF5k5uk/vA1NzEDJvalDx3wqt4b1xWFskLc19I5PZ4p69p50XVgWEb59brXjrYp67helI9IyiiXDYvKZcMXpG9Z16KxuyNcRnREzK9/v/YxlAc9S0uIxPooTElVW16Ykgo9W5tX2of5uDGQJyWpNZ7kX7+BtIVLkDJxKjLWb4Lhe/Vhs3JpmeWQNmH9BKq66OuUqa9TDrsN/QSdwrajc/gOOLStj2t9FmvMgdNwvQ86h+9A+4CfIcvMRcDUTf/toP6D5/X8NLXlLyqHDR0062svyn9KY+ymLIerTuuNp7+dgX/fxci8F4EGB+bAuKrmfDHlSd9GeZ1IS1wnsqQ0GNiXfp2I7a0gK5GGssQ0iIvWzwuLQUFMApx9B0Hf0hQiAzEcvuwJQ2c7GDg8z48ix62ASCyG971deC/0AFyXfImIUUtQEPXi+WfKm76VMn+Vl5K/6r9i/mrlMwrypOR3pndHSWJVHTZNbbk0KQ0GRWVqSQb2VhrnlbRYOWtgZwl9M2M4f/UZ0s/dQVC/eUg5dR1eW2bAvKmyQSnLLwTynDy4fDMYesaG0DOWwHXOUIjE+jBweDP5+L/1ojQxfM00MSglTdLO3cHDojSpXixNAMBt7nBk3QpG6l/CD3lNr8+o6P+7ZDmbl5gOozLOHYmNOfTE+sjT2CYDRg6l3x9V7fchMkKeIPlW2fM1EZF24eTm75imTZtCVGzszWbNmmHVqlWQy5U3RN7e3qrvHB2VNwT16tXTWJaQkAAnp1e7KcjPz0d+vno3vgKFHIYi/X93EOUkOzgGgRN+hte8wfD8ph8gL8TjLSeRn5AGFD7v5hW55qDq31n3I6FvIoHbuG6I2XJKgKjLj0XPDsi5dBPyRO0dj/9tSD78fD6KnKBoZAdGodH1n2HZvA7SL6u/DWfoZINau79F8tGrSNglzIM5589boO6KUarPtwYsEyQOABAVPZQLXXsIcceVE7ndnbgeH935GU7dmuLxr2cEi+1NMvX2gPOozgj4ZEbZKxWNtx7z/R9IPn4dAPBo0k94/85G2HZrhniBG8qqfdYcrZcOV30+OWTlW/kdu3ruqDeiA/7o9O1b2f9/UenzFqhT7NrxE/DaIU0SJ2s03PM1Eo5ew5PfzgodzlthOrAfjNt9hGSfyUCxN2vzzpxT/VsWHgFZWDgc9u2GYYP3UOB3u7Rd6RRtq59U7tkC3itGqj7fGLj8rf7ekz8uI+nCPUgcreD5ZVc02jQRV7rPVXvT/8GcnQhZ9QdMPSqh1jd9UWfeINybue2txqV1iu6Hnvz6N2L3nAcAhN6PhE2ruqjU/yOEL/pdwODeApkc4WOWwnX5V/C+txsKmRyZlwOQfvaW2r1hpan9oW9hikf9ZkOekgHLDh/A/efpeNTra+QFa04O/66yGNIXJu0/QsKYqWr5q84rqp+m/nUDcZuPAQByHkTCvHFNOAzugMxrgZClZCB0zEq4LxkDpxGdgUIFkg9dQvbdMLV75AqjjDQxa1wTjkVpYt3+fVi0qIt77acJGSm9BteezdGo2PxWlwateOu/qWdkANfPmuPhmkNv/beI6M1hw0cFY2DwfOKmZ5Xg0pYVvkalZsmSJZg3b57askEmtTHErO4rbS9NyUChTA7DEm8VG9pboiAhrdRtChI03+owtLdEfoKyNT6/6C2QkvswtLdE1oNI1ef4g1cQf/AKDO0tIc/OgwKA69iuyI1KKDPe9NuhqDq1F0SGYsHGCJanZUAhk0Pfzkptub6tNWRJqaVv9BrEzg4wbtYAcRO1dzJVWUomFDK5xnlgYG8JaRnnjTQxDQYlzjMDe6sy1weA/Oh4SJPTYVTVSa3hw8DRGnUOzEPmrWCETd/wbw/jP4s/5ac258azydmU10OaarmhvSUyHpR+Q1tQdA1KSqSNpMQ+XiYvXnnuZRUb+qmwQIbc6AQYV7Era7O3Slp0npT8fze0tyozf5EmaL5JV/y8svigFgzsLNHY7/n/u0isj6pzB8N5dBf4vT8O0gRlWuSEPE8LRYEMeVEJkFQWJi2Kizp9GwfuhKk+6xsqi3tjOwvkFEsXY3sLJD+I/te/U6lJDRjbWWDA9bWqZXpifTSdMwD1RnbE7maT//W+/6uEU35qc26Ude1IXuHaKVl+ve61o23KOq6SaVNcfhnlckFRuVxQRrkssbdEZrFyGVAOz9Po4Byk3QxBoIBvqr9MYXo6FDI59GzU34DVs7FGYfKLH8qb9usDswH9kTJpKmRh4S9cV/40FvLUNIirVNb6hg9drJ/E/eWH1NuaeUnJfEBib4n0+5Gl7uN1ymFZZi5kmbnIjohDqt8jdAzeAqdO7+PpoX9U6zwbZzwr9CmkaVlocWQuQlYfFCRfel7Pt1Jb/qJyuCBBs772ovyn9H0oz7fsEsNRZj96AiOBy2F5ivI6MShxnYjtrCBNLP06UfbuKLG+vRVkxdbPvReG4E6ToWduAj0DMWQpGah+eAVy7irPT0M3J9gP64qHH3+FvBDlfFK5DyNh1qQO7Id0xuOv17+5g/yP5GnK/FW/lPxV/pL81Xxgb1gM7YeEcdMhDX1x/qrNZKo6rJXacgM7zZ7Jz0gT0zTOK4Ni5a4sJROFUhlyQ9Svi9xHMWrDWKVfCEBA83EQ25hDIZNDnpGDBv5bkR9dfqNClOZFaVKyV9kzZaWJ9CVpklcsTSxa1IORuxPeD/pVbZ3qm6cj8/pDBPaa86+Pid6Op3/dRvJtzXsdib0l8oqVJUb2lkgro56fn5KJQpkcRiXKIyN7C+QlpGusX6XrBxAbSxB54NIbOAIiKi/a36ee1Fy/fl3t87Vr1+Dl5QV9/bfX+2LWrFlIT09X++tn+urjfyqkcmTeDYdNq2KTpYlEsG5VF+lldBFM9wuBdSv1hhWbNvWQfisEAJAXlYD8+FS1feqbGcOiYbVS91mQmA55Tj4cP22GwvwCpFwoe7JM87rukKZmCTsxolSG/MBHMG76fLI+iEQwafoe8vwD//PuLT5rD3lKGrIvXH/5ygJRSGXIuhsGy5bq541lS29k+oWUuk3mrRBYtvRWW2bZ2huZfsFl/o5hJRuIrc1REP/8xtLQyQZ1/5iPrLvhCJ30k9o8MeVNnp2HnMh41V9WcAzy4lNhV+z6EJsZw6phNaTdKj1dFFI5Mu5GwLb4NSUSwbZVXaSWsU1pMgIiIM8rgGk15+e7EevD2MUOuTFJr39wb4DyPAmHZauS50k9ZN4q/f890y8EVq3UJ2+0al0fmUVpkXjgAvzbToX/x9NUf/mxyXjy8xEE9l0IAMgKCEdhXgGMPdXTQuJij/yYxDd8lK9Pmp2HjMh41V9qyBNkx6ehcsvn8xIYmBnD4T1PxPv9+67aIX9cwf5PvsaBDt+o/rLjUhCw4TiOD3i7b0W/TFnXTvHrQN/MGJb/8topa5t3QVnlss1LymWbEuWybbFyObeoXLYtpVxOK7ZPiZM1Gv05B5l3I/Bg4s+C5q8vJZNBGhICSaNiE5OLRJA0aoiCBw/K3My0f1+YDRmElGkzIA1++XmiZ28HPUsLyJNKn8dBq+hg/eSVy+EGnkgt4/pRSOVIvxuhtg1EIti1rFPmNspVRBBBBD3JC95VK3qb+YXrvEXP8hPrEsdm3aouMsrIJ5X5iXo5bNPGGxmvMXRIXnQi8mNTYFKsTgIAJh6VkCdQneQZhVSGnHthMG9RrE4qEsG8hTdybpdeN8m+Hay+PgDzlu8hu5T1CzNzIEvJgMS9Eky8PZF+Wnm96BlJlL9fqJ6vKuSFqvNEa8hkKAgKUZuYHCIRjN5vgIK7Zecl5oO/gOXIgUjwmYmCh+9uOQwoz5Psu2GwaKl+nijvdUo/T7L8QmBR4tqxbF0fWUXrK6QyZAeEqtVPAcDIwxn5MZov/slSMiHPyIFFi7owsLNE6mlhh3l6liaWJdLEoqW36hhLyvILUb8PQOlpYvSCNHm67iDutpuCu59MVf0BQNTc7QibvO5NHR69QbLsPGRHxqv+MkKeIDc+FY7F7nXEZsawaeBZ5rBUCqkcqXcj4FBsG4hEcGhZF8ml3B9V7dcGT0/fRkHyuzmvEJGuYo+Pd0x0dDSmTJmCMWPG4Pbt2/jxxx+xatWqt/qbEokEEolEbdnrDnMVveE4av8wDhn+Yci4EwbX0Z2hbyJRdU2v/eN45MelIKyoW/rjTSfR8NB3cB3bFUl/34Zjj+awqO+JoGmbVft8vOkE3Cd/hpyIWORGJ8DT9wsUxKci8eTzCluV4R2QfjMEsuw82Laph2pzBiJ00W7IMnIAAHbtG8LQ3grpfo9QmFcAmzbecJ/YA1E/H/s3SfVGpe04CIcl05B/PwR594JhNfgziIyNkFk05rXDkumQJyQhec125QYGYhh6ugIARAYGEDvawrCmBxQ5eZBGP32+Y5EI5p+1R+ahvwG5dndnfrrxKLzW+iArIAxZ/o9QaVRX6JtIkLBHOSxKtR98UBCXgujFuwAAsVuOo87B+XAe0w2pZ27D7tMWMKvvifCiHht6JkZwmdoHycevQpqQBiN3J7jNHoS8iDiknfcHoGz0qPPHfOTHJCJq/i8wsH0+4ay2THQXuekkqk3+DNkRcciNToCXbx/kx6ci/uQt1TpNDnyL+BM3EbVNOe9CxIbj8P7hS6T7hyPtTiiqju4MsYkEMXsuqLYxtLeExMEKJlWVQ+KZ13KFLCsXeU+SIE3LhiwrF9E7/4bX9F7Ie5KM3JhEeIzvBgCIPXKtHFNAnfI8+Up5ntwJhfOoLkXniXI4Ga8ffVAQm4yoxbuV628+gbp/zoPz2G5I/dsPdj1awqy+h6pnjyw1S2OyZYVMjoKENOSGKa8leVYu4naehuv0L1DwNBn5MYmoPK47ACDp6NXyOvTXcm/rKTSc0APpEfHIfJyAxtN6ISc+DZF/PR8bu+ueWYg4dQsPdiiH6hKbSGDp7qj63tzFHra1XZGflo2sp8nIT8tCfpp6WhVK5chNSEN6eGz5HNhriNp0Ep6lXDsJxa6d94uuneiiaydyw3HUK7p20u+Ewr2o/HryGtcOABhVtoWBlRmMKttCpK8H8zpuAICciDjIc9SHkywPURuOo04p5fLTonK5TlG5HFpULkdvOonGh76D29iuSPz7NpyKyuXAYuVy9KYTqFqiXM4vVi5LnKzR+M/vkBuThJC5v8KwWP5acm4QbZG9Zz+svpkJaVAIpA8fwqRPL4iMjZB7XDkcpuW3s1CYmIjMjVsAAKYD+sJ8xDCkzVsEeWycqreIIjcXitw8iIyNYDZsCPIuXERhcgr0K1eGxbgxkD95gvwb78a44qyfAOGbT8JrUg9kh8chJzoBNX17Iy8+FXGnnuclTfd/g7iTNxG5TZku4RuP4721XyItQFkOe4zqBH0TCaKL8hITVwc4f9oMiRfuoiA5A0aVbFDN51PI8wqQcMYfAODQ7j1I7C2R5h8GWXYezGu4oPac/ki5HoTcx8I97H+84Rhq/TAemf7hyLgTCpcS+UmtovwkXFXPP4GGh+bCZWxXJP99G449WsC8vieCpj3vASa2MoVRZTtInJTzPTxr4ChISFPlF1E/H4HH9D7IehCJrPuRcPriQ5hUq4z7I1aX49GXLmHLYbitmoice6HI9n8EhxHdoGdihOR9yqFT3dZMQkFcMmKXKd8wT9x2FF77FsFh1KdIP3sL1t1bwcTbE49n/qTap1WX5pAlZ6DgaSKMa7ih8tyRSP/rOjIv+QNQzgOSF/EUrkvG4cnC7ZClZcKq/Qcwb1Uf4cMWlnsavEzmrgOwneuLgsAQ5D8Ignn/z6FnbISso8ry13aeL2QJSUj/aSsAwHxIX1iNGYKkbxdDFhsHPdui/DVHmb8CgJ6FOfSdHKBvbwsAMHBzAQDIk1NQmPzfe6W9abGbjsLzex9kB4Qi684jOI3qBj0TCRKL7nU81k6ANC4Zj5co73XithxDrT8WwGlMd6Sd8YPtpy1h6u2JiGK902N/PoxqG6Yg41ogMv65D6uPGsD6k8YI7DVbtY7dF22R9ygG0uR0mDWqAbf5IxC36Rjywp5CaM/SJKsoTSqN6gb9YmniuXYCCoqlSeyWY6j9xwJUGtMdqWf8YFeUJuHF0uTpz4fhtWEKMq8FIr2UNJEmppV6n5f/JAn5j8seKULb5eTkIjrm+f/pk6fxCAoJg6WFOSo5OQgY2dvxaPMp1JrUA5kRcciOTkRd317IjU/Dk1PP73Va75uFJydvIWy78l4nZONJNFk7BqkBEUjxD4PXqI4Qm0gQWayeDwCm7o6wb1oTlwa+/SG16NUUQotfniKtwoaPd8zgwYORm5uLJk2aQF9fHxMnTsTo0aOFDuulEg5fhaGtBTxm9IHEwQqZDyLh32+J6sbFqLItFMWG30q/FYIHX/4Ij5lfwPPrvsiJiMPdoSuQHfRYtU7UuiPQN5Gg5srREFuYIP1GMO70XaI2BrJFg2rwmN4b+qZGyA59iqDpmxFXrGtioVSOKsPaw2v+YEAkQm5EHB7N+RVPfhN+roKsUxegb2MJG5/BENtZIz8oHE/HfAN5choAwKCSvdo4rGJ7W7gefN6F3Xp4b1gP743cGwF4MvT5XAXGzRrAwNkRGQe1eyJiAEg+8g8MbC3hOqMvDOytkP0gAoH9F0KapDxvJJXtgGJvtWXeCsajcd/D1bcfXGcNQF5ELIKGLUdOcNF5U1gIk9pucOjzIfQtTFAQn4q0CwF4vOx3VQ8fy9b1YexRCcYeldD4zma1eP6p9Hn5HPhLhBed+/VWjoLYwgSpN4Jxs+9StXPfxM0Rhjbmqs+xRddg9Rm9YehghcwHUbjRb6naw0a3IZ/Aa3ov1edmR+YCAAImrMeTvcrKX9C8XVDIClH/p3HQMzJE+u1QXP98IWTp2W/3oF8g6fA/ENtawHVGXxjaWyH7QSQe9Fukdp4Uz18ybwUjZNxauPn2hdus/sh9dp4Uy19eReT8X6GQF8JrnQ/0jAyRdfsR7veaC7mAafEiAT8fg4GJBK2XDYehhQnibobgxMDlkBfPM90cYFTsvLGv74Hu+79RfW4+dyAAIHjfRZyfor1DFJUloujaqVvs2rn1kmsnruja8ZrRGxIHK2Q8iMKtEteO65BPUK3YtfPBkbkAgHvFrh2vGX1QuW8b1TotzirnHLnx2Xyk/PPf35R/XfFFx+VZrFy+XaJcRoly+d6XP6LazC9QrahcDihRLkcWpW+tonI5rUS5bNPGGyYelWDiUQmtA9SHEPyf4xflcNSvL+/sOWRYWcJs5FDo29hAGhqGlKm+KExVPkDTd3RQSyeTHp9CZGgI60XqQ4RmbtuBrG2/QCEvhIGnJ4w7dYCemRnkSckouHkLmZu3AdJ3Y5x61k+AsHVHITaRwHvlSBhYmCDlRjCu91PPS0zd1fOSp4evwdDWAjVm9ILEXpmXXO+3FAVFZZU8XwqbpjXgMboTDCxNkZ+YjuRrD3G523coSMpQrpNXANcBbVFn3iDoGRog92kyYk/cQOiPR8o3AUpIOHwVBkX1fMOi/CSg32LVBOZGJeprGbdC8ODLH+Axsy88v+6HnIhY3CuRn9h1aIzaP4xXfa67STl0YsSK/YhYuR8AELPpBPQlBvCaPwQG1mbIehAF/z4LkBsl7HA9AJB29DLENhaoNKU/xPbWyA2MQNigeZAV/X8bOKvXTbL9ghA5YRUqTRuISjMGIT/yKSJGLUFeyPPhKA0cbFB59giI7SwhS0hFyh/nEPfDvuc/KpMjfMh8OM8cDI9t30LP1AgFkbGImrIWGee0bwLwnP+dh561JSzHDoW+rTUKQsKQ4DMThUUTnus7Oaj1XjH/vBtEhoawXz5XbT/pm35B+qadAADj1s1hO/d5vmK3ZLbGOtok5cgVGNhaoMr0fjCwt0LOgwgEDVigOk+U9zrPz5OsW8EIG78GVXz7w2Wm8l4nZPgy5AY/P09ST11H5MyNcP6qJ9wXjEBu+FM8GrUcWTeCVOsYezrDZdYAiK3MkP84EU9/OIC4TUfL78BfIPnIFYhtLeBSIk2kL0iT0PFr4PKSNIkokSYho5Yjs1iaVET3gx5huI+v6vPyH5V19087fYxF304VKqy3JvinYxCbSNB4xQgYWJgg6UYILvVfplY2m7k7QlKsbI45cg0SW3PUmdFLNSzWpf7LkF9U7j5TtV8b5MamIP68+pygRKT9RAqFNo8xQNrqjJY+oBCKm22a0CFonYRUM6FD0DpphQYvX0nHWIoEHFJOS90XGwkdgtZxk/I8KUmfbzlpqOv17r6V+bZkpxgKHYJWeZhiI3QIWsdUIRc6BK1jLSn/XnjaztYh6+Ur6ZjYp5YvX0nHsGaiqdHdlUKHoHUO1Zv98pV0TO/YXUKH8M5xs/V++UqkJiq57CH/KzLO8UFERERERERERERERBUGGz6IiIiIiIiIiIiIiKjCYMMHERERERERERERERFVGJzcnIiIiIiIiIiIiIi0HqerplfFHh9ERERERERERERERFRhsOGDiIiIiIiIiIiIiIgqDDZ8EBERERERERERERFRhcGGDyIiIiIiIiIiIiIiqjDY8EFERERERERERERERBWGWOgAiIiIiIiIiIiIiIhephAKoUOgdwR7fBARERERERERERERUYXBhg8iIiIiIiIiIiIiIqow2PBBREREREREREREREQVBhs+iIiIiIiIiIiIiIiowmDDBxERERERERERERERVRhioQMgIiIiIiIiIiIiInoZhUIhdAj0jmCPDyIiIiIiIiIiIiIiqjDY8EFERERERERERERERBUGGz6IiIiIiIiIiIiIiKjCYMMHERERERERERERERFVGGz4ICIiIiIiIiIiIiKiCkMsdABERERERERERERERC9TqFAIHQK9I9jjg4iIiIiIiIiIiIiIKgw2fBARERERERERERERUYXBhg8iIiIiIiIiIiIiIqow2PBBREREREREREREREQVBhs+iIiIiIiIiIiIiIiowhALHQARERERERERERER0csooBA6BHpHsMcHERERERERERERERFVGGz4ICIiIiIiIiIiIiKiCoMNH0REREREREREREREVGGw4YOIiIiIiIiIiIiIiCoMNnwQEREREREREREREVGFIRY6ACIiIiIiIiIiIiKil1EoFEKHQO8I9vggIiIiIiIiIiIiIqIKgw0fRERERERERERERERUYbDhg4iIiIiIiIiIiIiIKgw2fBARERERERERERERUYXByc3pX8mHSOgQtEpahrHQIWgdG/NcoUPQOg9yeJ6UpCcVOgLtI2f2qsEAnLyupAbN44QOQeukBBsJHYLWeZhiI3QIWqX5+0+FDkHrPPKzFToErZOYz/paSamPJUKHoHUkokKhQ9A6ntWThA5B6xyqN1voELROj3sLhA6BiHQIGz6IiIiIiIiIiIiISOsV8sU4ekUc6oqIiIiIiIiIiIiIiCoMNnwQEREREREREREREVGFwYYPIiIiIiIiIiIiIiKqMNjwQURERERERERERERErywlJQUDBgyAhYUFrKysMGLECGRlZZW5fmRkJEQiUal/+/fvV61X2vd79ux57fg4uTkREREREREREREREb2yAQMGIDY2Fv/73/8glUoxbNgwjB49Grt37y51fRcXF8TGxqot27RpE1asWIFOnTqpLd++fTs6duyo+mxlZfXa8bHhg4iIiIiIiIiIiIi0nkKhEDoEAvDw4UOcOnUKN2/eROPGjQEAP/74Izp37oyVK1fC2dlZYxt9fX04OTmpLfvzzz/Rp08fmJmZqS23srLSWPd1cagrIiIiIiIiIiIiIiJ6JVevXoWVlZWq0QMAPv74Y+jp6eH69euvtA8/Pz/4+/tjxIgRGt+NHz8ednZ2aNKkCbZt2/avGrzY44OIiIiIiIiIiIiIqALKz89Hfn6+2jKJRAKJRPKv9xkXFwcHBwe1ZWKxGDY2NoiLi3ulfWzduhW1atVC8+bN1ZbPnz8fbdu2hYmJCU6fPo1x48YhKysLEyZMeK0Y2eODiIiIiIiIiIiIiKgCWrJkCSwtLdX+lixZUuq6M2fOLHMC8md/QUFB/zmm3Nxc7N69u9TeHrNnz0aLFi3QoEED+Pr6YsaMGVixYsVr/wZ7fBARERERERERERERVUCzZs3ClClT1JaV1dtj6tSpGDp06Av35+HhAScnJyQkJKgtl8lkSElJeaW5OQ4cOICcnBwMHjz4pet+8MEHWLBgAfLz81+rlwobPoiIiIiIiIiIiIiIKqDXGdbK3t4e9vb2L12vWbNmSEtLg5+fHxo1agQAOHv2LAoLC/HBBx+8dPutW7eie/fur/Rb/v7+sLa2fu2hudjwQURERERERERERERar/BfTHJNb16tWrXQsWNHjBo1Chs2bIBUKsVXX32Fvn37wtnZGQDw5MkTtGvXDjt37kSTJk1U24aGhuLixYs4ceKExn6PHj2K+Ph4NG3aFEZGRvjf//6HxYsXY9q0aa8dIxs+iIiIiIiIiIiIiIjole3atQtfffUV2rVrBz09PXz++ef44YcfVN9LpVIEBwcjJydHbbtt27ahSpUqaN++vcY+DQwM8NNPP2Hy5MlQKBSoVq0aVq9ejVGjRr12fGz4ICIiIiIiIiIiIiKiV2ZjY4Pdu3eX+b27uzsUpfTQWbx4MRYvXlzqNh07dkTHjh3fSHx6b2QvREREREREREREREREWoANH0REREREREREREREVGGw4YOIiIiIiIiIiIiIiCoMzvFBRERERERERERERFqvtDkjiErDHh9ERERERERERERERFRhsOGDiIiIiIiIiIiIiIgqDDZ8EBERERERERERERFRhcGGDyIiIiIiIiIiIiIiqjDY8EFERERERERERERERBWGWOgAiIiIiIiIiIiIiIhephAKoUOgdwR7fBARERERERERERERUYXBhg8iIiIiIiIiIiIiIqow2PBBREREREREREREREQVBhs+iIiIiIiIiIiIiIiowmDDBxERERERERERERERVRhioQMgIiIiIiIiIiIiInoZhUIhdAj0jmCPDyIiIiIiIiIiIiIiqjDY8EFERERERERERERERBUGGz6IiIiIiIiIiIiIiKjCYMMHERERERERERERERFVGGz4EJBCocDo0aNhY2MDkUgEf3//co9hx44dsLKyKvffJSIiIiIiIiIiIiJ6G8RCB6DLTp06hR07duD8+fPw8PCAnZ2d0CFpBa8ZveEysC0MLEyRejMY92dsRU5E3Au3cRvWHlXHdYPEwRKZgdF48PV2pN8JAwAYWJnCa0Zv2LXxhnFlOxQkZyD+1E2ELN0HWWZueRzSK7Mf0glOYz+Dgb0Vch5G4vHszcj2f1Tm+tZdmsN5en9IqjggLzIWTxbvRPpZP9X3eiZGqPL1IFh1+ABia3PkRycgYdsxJP72l2qdGvsXwrxZXbX9Jvx6CtGzNrz5A3wDrAZ0he2Iz6Fvb438oAjEL1iPvLshpa5rWM0V9hMHwahONRhUcUT8oo1I/eWw2jp2PgNg5zNAbVl++GNEdBzz1o7hbWk07XPU7PcRDC1NEH8zBJe/3o6MiPgy13f6oAa8x3aBXb2qMHWyxukRaxD1l5/aOg2n9IRn96YwdbZBYYEcSfcicHP5fiQWXV9CqjajN6oUyysCXyGvcC3KKwyL8oqHxfIKANCTGKDG3IGo1KM59CQGSDoXgMCZ21CQmA4AqPxFG9T74ctS9322zmgUJGVA4mCFGvMGwbK+B0yqOiJqyykEzd755g78P3h/6ueo3e8jSCxNEHszBBe/3o70yLLPkUof1ECDMV1g710Vpo7WODlyDSJKnCNtV49Gzd6t1ZZFn7+LY4OWv5Vj+LeqDGsP13HdYOhghazAKIR8vR0ZLziPHbo1hYdvHxi52CM3Ig6hC3Yh+Yy/6nv7zk1QecjHsPD2gIGNOa63nYGsB1Fq+2h4cA6sW9RRWxbzy/8QPGPLGz22N0nSqQeMevSFnpUN5JFhyN6yFvJHQaWua9C0FYw/Hwi9SpUh0hdDHhuDvMP7UHDhtGod4y+GwrBlW+jZOUAhk0EeFoycXVsgf/SwvA7pP7Ps1w1Ww3tB384GBcHhSFz0M/LvBZe6rmE1N9h8NRiSOtVgUNkJiUs2IP3XP9XWcfvfLzCo7KSxbdruI0ha+NNbOYb/qsaMXnAdoMxvU24G457vNmS/JL91H/YJPMd1g8TeEhmB0bj/zQ6kFaub1ZjeG/Zt6qnqZrGnbiF4mXrdzK5lHdTw7QOLWi6Q5eQjZt9FBC3ZC4W88K0e7+uSdO4B4559oWdtA1lEGHI2roWsjOvGsFkrGPcuum7EYsifxiD30D4UnCu6bvT1YTJwJAwaN4W+UyUosrMhDfBD9i8boUhJLsejej2OQzvC+cseyjpsYCQivt2CbP/QMte36doMLjP6KeuwEbGIXvQr0s7eVlvHqFpluH47GBZNa0Mk1kduSAxCRi1HwZMk6FuZwWVaX1i2qQ+Jsx2kKRlIOXUDMct/hzwz520fbpk8i+omYgtTpN0MxsNXqJu4DGsP96K6SVZR3SSjRN2k+tyBcCqqmySfC8DDYnUTAGgfv0djv3fHrEXcoatqv+MyvAOMXeyR9yQJ4d//idj9l97AUb+6ysM6lCiLtyHzBWWxfbem8PD9QlUWhy3YheQzd55/37kJKg/5BOZFZfGNttPVymIjF3s0v1V6vnpv5GokHr325g7uX3Ia1hGVx3WHob0VsgOjEP7NVmTdKfvase3WDK4z+halSSyiFv6G1GJpUpznstFwGtIe4bO3I3bzcdXyRjd/hpGLg9q6kQt/w5N1h97IMb1pJp/1gGnfvtCzsYE0LBSZa3+A9GHpeaxx1y4w7tABYo+qAABpcAiyNm/WWF/fzRXmY8fAsH59QF8f8sgopM6eg8KEhLd+PG9Knemfo+qAj2BoYYqkmyG4PXMbsl5w7wcAnkM/QY1xXWBkb4m0wGjc+eYXpPqHAwBMqtihy821pW53ddRaxBy78caPobzd8r+H7bsPIDAoFInJKVi7ZDbatW4udFj0LxQqFEKHQO8INny8JQUFBTA0NHzhOmFhYahUqRKaN//3Ga1CoYBcLodYrP5f+Sq/r408vuoO95EdETDhZ+RGJ6K6bx802TsLF1tNQ2G+tNRtKn3aDDXnDcKDGVuQdjsU7qM7o8meWbjQYoryIaSTNYwcrRE07zdkBT+BsYsd6i4fCYmjDe6MXFPOR1g2624t4DJnOKJmrUf2nRA4juwOr9++w/024yFLTtdY37RRDXj8NBUxS39F+t+3YNOjNTy3zERgp6nIC44GALh8NxzmLeohYsL3yH+cAIs278Ft0RgUxKcg/X83VftK3HUaT1buVn0uzM1/+wf8L5h3bg2HWaMQP2cdcgOCYDO0B1y2LkB4h9GQp2imkZ6xBAWPY5Fx6hIcZ40uc7/5IZGIHvrN8wVy+dsI/62qP64r6gxrjwuTNyLzcSIaTeuFTr/54kBbX8jLuHbEJhKkBEYjZO9FfLJlUqnrpIfH4sq3vyAzOgFiI0PUHdUJnXf5Ym/LqchLyXyLR/RiVb/qDreRHXFvws/IiU6El28fNN47C5dfkFc4lZJXNN4zC5eK8goAqDl/MOw/bgD/Ud9DlpGD2kuGocG2Kbje7TsAQOzhf5B41l9tv/V++BL6EkPVPvQkBihIzkDYmoNwH9P57SXCa2rwZVd4D2uPM1M2IjM6EU2m90LX33yxp13Z54iBsQRJD6PxcN9FdNo8qcx9R50LwNmpm1SfCwtK359QHD5tBq95gxE0Ywsybj+Cy+jOeG/P17jaYjKkRf9vxVk2ro46GyYgbNHvSPrfbTj1bAHvHdNx45OZyA56DADQN5Eg/XowEo5cQ63VZTeUPvn1b4Qv26f6LM8tePMH+IYYtvgIJsPGI3vDashCAmHUrTfM56xE+lcDoUhP01hfkZmJ3AO/Qf4kGpBJYdC4GUx9fKFIT4XUX1nGyJ/GIHvzWhTGP4XIUAJJt94w/24l0sf1hyJDM9/WNmYd28DOdzQS5v2IvLtBsBr0GZw3LUJ0lxGlljsiIwmkMbHI+usi7GaWfl487jMBIv3nna4NvdxReetSZP9Vvg8gX5XnV91QdURH3JmwHjnRiajp2xsf7JmJ862nl5nfOn/aFLXnDsI9361IvR0Kj1Gd8MHvM3Gu5VQUJGXAyMkaRo5WCJy3C5khMTCuYg/v5SNg5GQNv5HfAwAsaruiyS5fPFp7CHd8foZxJRvUWzYCIn09BM7bVY4p8GKGLT+C6cjxyP6p6Lrp3hvm81cibWzp101hZiZy9/0GeUw0FDIpDN9vBrOJvshMS4X0zk2IJEbQ96yO3L07IYsIhZ6ZOUxG+cDi28VIn6KdL2XYdm8Bt++GIWLmRmTdDoHTqK6otXsO/Fv5lFqHNWtcA14/T0H0kt+Q+r9bsPusNapv88W9DtORW1SHlbg5os6hxUjc8zdiVu6BPDMHJjVcUZinPOcMHW1g4GiNqPm/IDfkMSRV7FF16VgYOtrg0egV5Xr8z7h/1R2uIzviftF9TDXfPmi4dxb+eUHdxPHTZqgxbxACZ2xB+u1QuI3ujEZ7ZuFKsbpJjfmDYfdxA9wd9T2kGTmotWQY6m+bgptFdZNn7k9Yj6RidRRZxvMGoCpDPoHXN33xYOpmZPiHwbKBJ2qvGg1ZejYST6s3OL0tz8ri4BmbkX77EVxGd8F7e77BtRaTSi2LLRpXR50NExG+aDeS/ncbjj1bot6O6bj5ia9aWZx2PQjxR66i1uqxGvvIe5KEy3VHqS1zHvQxXMd3R0oZjQXlye7T5qg6dwjCfDch8/YjOI/qgjq/f4vbLSeUmibmjWugxvpJiFq8Cyn/84P9Z61Qc/sMBLSfgZyiNHnGplMTmDXyQn5s6Q2mUcv2IP63v1Wf5dna9ULgM0ZtP4L5+HHIWLUaBYEPYdq7F6xXrkDSgEEoTEvTWN+wwXvIPXMG0rUPoCgogGn/frBeuRJJQ4aiMCkJAKDv7AzbdT8i5/gJZG3bDkV2DsRV3YEC7a2jlVRjfFdUG9EBNyduRHZ0AurM6I1Wv8/EX21mlJnfVOneFPXnDsBt321IvhOG6qM6ovXvM3Gq5TTkJ2cg52kyjniPU9vGY2Bb1BjXBbFnA8rjsN663Nw81Kjmgc+6tMekrxcKHQ4RlQMOdfWGfPjhh/jqq68wadIk2NnZoUOHDrh//z46deoEMzMzODo6YtCgQUgqKmyHDh0KHx8fREdHQyQSwd3dHQBQWFiIJUuWoGrVqjA2Nkb9+vVx4MAB1e+cP38eIpEIJ0+eRKNGjSCRSHD58uVSfx8AVq9ejXr16sHU1BQuLi4YN24csrKyyj19XpX76E4IXfMnEk75ITMwGgFf/QSJozUcOzUuc5uqY7vg8W9nEbPnArJCnuD+9C2Q5xagSr8PAQBZQTG4PWINEk7fRk5UPJIvP0Dwkj1waN9Q7cGD0BxHf4qk308jed9Z5D2KQdTM9SjMy4dd33alrz+iG9LP30b8hkPIC43B05W7kXM/HA5Dnz9oNWtUA8n7zyHz6n0UxCQgaddp5ARGwvQ9L7V9FebmQ5aYpvorzNLOiq/NsM+Qvu8U0g/+DwVhjxE3Zx0K8/Jh2at9qevn3XuExOXbkHn8IhQveBCrkMshT0p9/peqeaOh7eqO6Ig7PxxG1OnbSHn4GOcnbYCJoxXcOjQqc5uYc3dxa8UBRJ66VeY6YYeu4unlB8iMTkRqyBNcm7cLhhYmsKnl+jYO45W5je6EsKK8IiswGveK8gqHF+QV7kV5xZM9F5Ad8gQPivKKykV5hdjcGFX6f4Sg735FyuUHyLgbgXsTN8C6SQ1YNqoGACjMk6IgMV31pygshG3LuojZfU71O7mPExH07S94uv8SZBnacy15j+gIvx8PI/L0bSQHPcaZSRtg6miFqi84R6LP38WNFQcQ8YJzBADkBVLkJqar/vLThXvbtjSuY7vgyW9nELvnPLJDniCo6P/eud9Hpa7vMroTUs75I/rno8h59AThy/Yh814EqgzvoFon7sAlRKz+AykX773wt+W5BWrnjFxL81cAMOreB/n/O4aCsydRGBOFnA2rgPw8SNqV3oAne+AP6fVLKIyJQmHcU+Qf+wPyyHCIa9VTrVNw6W/I7vqhMD4W8seRyNn+E/RMzaDv5lleh/WfWA3tifT9p5D552lIw6KROO8HKPLyYd6zQ6nr598PQfLKLcg6eaHMcqcwNV2tzDFt8wEKop8i9+bdt3ko/5rHqE4I+f5PxP/lh8yH0bjj8zOMHK3h1LHs/NZjTBdE7zqLx0V1s7sztkKeWwDXvh8CADKDYnBr5PeI/99t5EQlIPnKAwQt3QvHT57XzZw/bYbMh9F4tPogciLjkXz1IR4u2A33oe2hb2pUHof+Sox69EH+X8eQf+Yk5I+jkP1z0XXzSRnXzX1/FFy7BHnRdZN3tOi6qa28bhQ52cicMxUFl8+h8MljyIIDkb1xLcReNaFn71DqPoVWaXQ3JOz+HxL3nkXuoxhE+G5EYW4+HPq1LX39kV2Rdu4OYtcfRl7oE8Ss+B3Z9yLgNKyTah2XmQOQdtYP0Qt/Rc79CORHxSP19E1VQ0pucDQejVqBtP/dQn5UPDKu3MfjZbtg/UljQKD6vdvoTghf8ycSi+om91+xbhLz21k8LaqbBKrKpw8BKOsmlft/hJCiuknm3QjcL1E3eUaaka1W3hR/+OncuxVidp5B/OGryI1KQNyhq4j59Qzcv+r+VtKiNC5ju+JpUVmcE/IEwdM3o/CFZXFntbI4YtleZN4LR5XhHVXrxB24hMjVfyC1rLK4UKGWJgWJ6bDv3AQJR65CniP8i17OY7ohftffSNhzDrkhMQibsQny3Hw49C392nEe1Rmp5/zx5OcjyH30BNHL9yD7XgQqFbt2AMDQyQYei0YgZPxaKGSlv9Alz8qFNDFN9VeoBelRGpM+vZFz7DhyT56CPCoKGatWQ5GXB+Mupeex6QsWIffQYchCQyGPjkbG8hWAngiGjRqq1jEbNRL5164ja8NGyB6FQv70KfKv/FNqQ4q28hrVEQ+/P4Snf/kh/eFj3JiwHsaOVqjcsex6ffUxnRCx6xwi915EZsgT+M3YBnluPtz7tVGuUKhAfmK62l/lTo0Rc+S6Vlwvb0KrZu9jwugh+LhNC6FDIaJyoj1PfSuAX375BYaGhrhy5QqWLl2Ktm3bokGDBrh16xZOnTqF+Ph49OnTBwCwdu1azJ8/H1WqVEFsbCxu3lS+GblkyRLs3LkTGzZswIMHDzB58mQMHDgQFy5cUPutmTNnYunSpXj48CG8vb01fn/DBuUwRXp6evjhhx/w4MED/PLLLzh79ixmzJhRjqny6ozdHGDkaI2kYhVXWWYu0m6Hwqpx9VK3ERnow8K7KpIvFavsKhRIungP1mVsAwBiCxPIMnO1ZqgEkYEYpvU8kXGp2EMPhQIZlwJg2rBGqduYNqqhvj6AjAt3YNbo+fpZfsGw+uR9GDjZAADMm9eFkYczMi76q21n81lr1L+7E3X+XovKMwdCz0gLewsZiGFUpxqy//F/vkyhQM4//jB+r+Z/2rWhW2V4XvoVHme2otLK6RBXsv9vsZYzc1d7mDha4cml+6pl0sxcJPqHwbGR1wu2fD16BvqoOeAj5KdnIzkw6uUbvCXP8orkEnlF+r/IK5Iv3lNtY1HfA3qGYrX9Zoc+Re7jxDL3W7l3a8hz8xF3TPihEl7EwtUepo5WeFzsHCnIzEW8fxicGv73c6Ry01oYeucn9Du/Aq0XD4XEyuw/7/NNERnow9zbAykl/u9TL96DZePSj92yUXWkXLyvtiz5XAAsX1CulMWpZ0u0CtyMDy6shOc3/aBnrIX5KwCIxdD3rA5pQLGhzBQKSO/6QVyjTtnbFd9FvYbQr+wCaWAZD/DFYhi174bC7EzII4UfLu+lDMSQ1PZC7rVib0MrFMi5egdG79V+Y79h3q0tMg/+9fJ1BWDi+qxu9vx6kGXmIu1OGKzLuH5EBvqw9K6qtg0UCiRdul/mNgBgYG4CWdbzupmexADyPPXGI3leAfSNDWFVv+p/OKo3SCyGuFp1FJS4bgr8/WDwqteNt/K6kT0ou+FLz8QUisJCKLTw5SWRgRim3p5IL1GHTb90V61OWpxZo+rq6wNIL16HFYlg3a4R8sJjUXP3bDS6ux11jy2FdccmL4xF38IU8qwcQID6vbGbAySO1mqN4c/qJmWVHcrySbNukvKSuklOUd2k5H5rLRmODwM34YNTC1UNJ8/oGYo1encW5hXAskE1iMT6/+aQX0tZZXHKxXuwKCN9lGWxeoNGyrkAWLwgH3kZc++qMK9XFU93nf3X+3hTRAZimHl7IO1iyWvnHswbl37tmDeqrr4+gNTz/jAvnoYiEbzW+eDJz4eRGxxT5u9X8emBJoHbUf9/K1B5XHfBGgxfSCyGQfUaKLhVIo/184NBnVcrh0USCURiMRQZRT3VRSJImjWF7PFjWK9cDvvDf8Jmw8+QtGz5Fg7g7TB1tYexozXiLz1QLZNl5iLlThhsX1A2W3tXRfwl9bI5/tJ92JZxv2jl7Q7reu6I+P38mwyfiKhccairN8jLywvLlyvHNF+4cCEaNGiAxYsXq77ftm0bXFxcEBISgurVq8Pc3Bz6+vpwclKO85yfn4/Fixfj77//RrNmzQAAHh4euHz5MjZu3Ig2bdqo9jV//nx88sknZf7+M5MmTVL9293dHQsXLsTYsWPx888/v9FjfxMk9lYAoDZe7bPPEgerUrcxtLGAnlgf+SW2yU9Mh5lX5VK3MbAxh9fknnj825n/HPObIrYxh0isD2limtpyWVI6jKpVKXUbA3sryJLU15cmpsPA3lr1OXr2JrgtG4f6t7ahUCoDChWImvETsq4HqtZJPnQRBTEJkManwriWG6p8PRhGnpURNmrZGzu+N0FsbQGRWB+ypFS15bKkNJh4uPzr/eYGBCN25moURMRAbG8D26/6w233CkR0/RKFWtrluyTjomsnt0SX+NzEDBjbW/7n/bu2ew9tf/4KYmND5CSk4UT/ZchPFe7hS1l5Rf4r5BWlbWNalFdIHKxQmC9VGxoCAPKT0lW/WVKV/h8h9uAV1dAb2srkBeeIicN/O0eiz99F+MlbyHicAEs3R3wwow+6/jodBz+dC0Wh8GOvGpTxf1+QmA4TL+dStzF0sEJBifxYWRa9XlrF/XkFeY+TkB+fArPabqj2bX+YeDrj3vBVr7Wf8iAyt4RIXwxFunoeW5iWCoPKZffwEpmYwmrLAcDAECiUI3vT95AFqPcQMmjcDGZT5gASIyhSk5E5dxoUmdo/zJW+lbLckZcoa+XJqTD8D+VOcWbtmkPP3AwZf55++coCeHbOl1bP+ld1s2plXHM25vCa8hmif31eN0s8FwCPUZ3g3KM5nh65CiMHK1Sf0rMoLutS91PeRBZF102q+nWjSEuFqMqLrxvrHcWum/XfQ+pfRs86A0OYDB2DgotnoMjVrt50QNl1WGlSGoyrlVEXt7eCtLQ6bNE5ZWBnCX0zYzh/9RkeL9uN6EW/wuqjBqi+ZQYCe81B5rVAjX2KbcxRZVJvJPz2vzdxWK/NsKicLXnev8p9zIvqJoZl1E0KStRNQpfuQ8rl+5DnFsD2Q2/UWjocYlMjRG85BQBIOn8XVQZ8hISTN5F5NwIW9T1QeUBb6BmKYWBjjoKEtH9/8K/geVms/jsFiWkvLIulr5Ger6JS/7bIDo5Bxq3S5wcsTwaqa6fkMabBsqxrx8FK81ordu0AQOWvekAhK0TslhNl/nbslhPIuhcBWWoWLN6vAbev+8PAwRqRc3/518fzNuhZWkIk1kdhaoracnlKKgxdX633ufnYMZAnJSHfT9l4omdtDT0TE5gO6I+sLVuRuWETJB80gdXC+UiZOBnSAO0f0smo6P+7ZH6Tl5gOozLuWSQ25tAT6yNPY5sMmJdRNlft9yEyQp4g+VbZc44SEWk7Nny8QY0aPe9WGBAQgHPnzsHMTPOt17CwMFSvrvlmS2hoKHJycjQaNAoKCtCgQQO1ZY0ba3aZLv77z/z9999YsmQJgoKCkJGRAZlMhry8POTk5MDExOSVjis/Px/5+epdG6UKOQxE/+3tIOfPW6Duiudjrt4a8PYftIvNjPH+Ll9khjzBoxUHXr7BO85hWBeYNayBR0MXoeBJAsw+qAPXojk+Mi8r3xZK2vX8YUtuUBSk8amosW8BJG5OyI968WSMFUH2xecPGvKDI5EbEAzP8ztg3qkV0g9o54Moz8+ao9XS4arPp4asfKu/9/SfhzjY4RsY2ZihZv+P8PH6r3Co21zkJZfPkGCVPm+BOsXyCr9yyCtehVVjL5jVqIK7X2nfZMRePZrjw2LnyPGhb+8cCT3yvLdLSlAMkh9GY+CVNXBuVhtPrjx4wZYV39NiD3GzHz5GQXwqGv4xB8ZujsiNevHkk+8KRW4O0qeMhMjIGAbeDWEybBwK455C9sBftY703h3lOhaWMPqkK8ymzUWG79hS5z/QNRY9OyDn0k3IE1NevnI5qNyzBbxXjFR9vjFw+QvWfjPEZsZo8tsMZIU8QfDKP1TLEy/cQ+D8XfBePgIN1o1DYYEUj1b/CdtmtQCFdvTY/bcUuTlIm1h03dRvCJMR4yCPewrZfX/1FfX1Ye47FxCJkP3zaiFCFYaeCACQ+tcNxG0+BgDIeRAJs8Y14Ti4g0bDh76ZMWru/Aa5IY8Rs2pvuYTo9HkL1C5WN7kjcN0kfM1B1b8z70dC30QC93HdVA0f4av/gMTBEh+cWACIRChITMfTvRdR1ac7oCMTxOoZGcCxZ0tErv7j5Su/o0y9PeA8qjMCPnnxCA9PNx5T/TvnYRQKpTJ4Lh+NqMW7oCiQve0wy43pgP4watcWKRMmPZ+/Q6TMX/IvX0HOfuXzAFloKAzq1oHJp92RroUNH649m6PR8hGqz5cGvf15jPSMDOD6WXM8XHPorf8WEdHbxIaPN8jU1FT176ysLHTr1g3LlmlWgitVqlTq9s/m3jh+/DgqV1Z/y0MikZT5W2Uti4yMRNeuXfHll19i0aJFsLGxweXLlzFixAgUFBS8csPHkiVLMG/ePLVl/U3qYIBZ3Vfavizxp/yQ5heq+qwnMQAAGNpbIr/YW0eG9pbIeFD6sDoFKRkolMkhKfFWu6TEPgBA39QI7++ZBVlWLm4PW1XmeKdCkKVkQiGTw6DEGxpiO0tIE1JL3UaamAaxnfr6BvaWkCYq1xcZGaKy70CEjVyK9LPKN1xyH0bBpE5VOI3toWr4KCn7jvINKIm7djV8yFIzoJDJIbZTf8tTbGcF2Rt8YFSYmY2CyCcwdCv9zRdtEH36Ng7eeT5MjL6hMis3trNAbrHz3tjeAskPov/z78ly85ERGY+MyHgk3A5Dn0srUaNvGwT8dPQ/7/tVJJzyQ/or5BWSV8grDF+QV+QnpEFPYqAcCq/Ym5USO0vkl3i7DgCqDGiLjHsRyLgb8S+P7O2J/N9t7PUv/RzJeQvnSHEZ0YnITc6ApbujVjR8SMv4vze0tyzzDdeChDTV27vF189P+G+9FNJvK89j46pOWtfwochMh0Iug8hSPY/Vs7JGYdoL8liFAoVxTwAA8shQ6FVxg/HnA5BZrOED+XnKdeKeIDskEJY/7YKkXRfkHdSeCapLI09Tljv6JcpafVtrjd6H/4bY2QHGzRogbuKC/7yvNyXuLz+k3tbMb0vWqyT2lki/H1nqPl63bvbB7zMhy8rFzWGrNepm4RtPIHzjCUgcrSFNz4KJiz1qfdsP2VEJ//4g3yBFRtF1Y61+3YisrKFIfcl1E1t03USEQt/FDca9ByCzeMOHvj7MfedBz8ERGd9M1sreHkDZdVgDO82ec89IE9NgUFodtuj8kKVkolAqQ26I+jA9eY9iYN6kltoyPVMj1Nw9G/LsXASPWFZu9fvEU364WkrdRFKibDG0t0Tmf6ibFJRRNzEso27yTPrtUHhO/RwiQzEUBTIU5knxYNJGBE7boizP4lNRZdDHkGXmqCZRf5uel8VWassN7a1eWBYblFJ2l8xHXpVD16bQN5Ygbv+Fl69cDqSqa6fkMZadJtKENM1rrdi1Y/FBLRjYWaKx3wbV9yKxPqrOHQzn0V3g9776xNXPZN4OgZ6BGEYuDsgNe/qvj+lNK0xPh0Imh561jdpyfRtrFKa8+P7PpO8XMO3fHylTpkIWHl5inzLIotSvS1lUFAzr1Su5G63w9K/bSL6tWa+X2Fsir9i5YmRvibQy8pv8lEwUyuQwKnG+GdlbIK+U+m2Vrh9AbCxB5IFLb+AIiN48BXSj0Z7+Oy0cyLFiaNiwIR48eAB3d3dUq1ZN7a+0RgsAqF27NiQSCaKjozW2cXF5/SEV/Pz8UFhYiFWrVqFp06aoXr06nj59/YrMrFmzkJ6ervbXx7TWyzd8CXl2HnIi41V/WcExyItPhV2r5w0qYjNjWDWshrQyuiMrpHJk3I2AbbFtIBLBtlVdpBbbRmxmjCb7vkZhgQy3Bq9Qm+xPGyikMmTfC4N5S+/nC0UiWLT0Rvbt4FK3yfYLhkXx9QFYtHoPWX7K9UVifegZGkBR8i0ueSEgKvvSN6mjHDe7rAYXwUhlyHsQCtNm9Z8vE4lg0uw95PoHvbGfEZkYwdClEmQJ2vH2bWmk2XmqhoiMyHikhjxBTnwaKrd8Pp64gZkx7N/zRLzfm++aLBKJoF90g18eysoril/3+mbGsPyXecWzbTICwlFYIFNbx9SzEoxd7DX2q28igVP3pojZff7NHegbVNo5kh2fhiolzhHH9zwRd/vNniOmTjYwsjZTa2ARkkIqR+bdcNi0KnYzKxLBulVdpJfRdT/dLwTWrdQb923a1EP6fxwaw7yOOwCgQNvyVwCQySAPC4GBd7HeoyIRDOo1hCz41RuwRHp6gMFL8gc9EUQvW0cbSGXID3wE46bFet2KRDBp+h7y/DWH2nldFp+1hzwlDdkXrv/nfb0pr1w3a+CJ1DKuH4VUjvS7EWrbQCSCXcs6atuIzYzRdO8sFEpluDlk5QvrZvnxqSjMk6LyZ82RG5OEdG1pcJbJIAst5bqp3xDS17huINJTvyaeNXo4V0bGt1OgyCyfHpb/hkIqQ/bdMFiWUod9VictKcsvBJat1B8wWraur1pfIZUhOyAURp7qL6EYeTgjP+Z5o5e+mTFq/f4dFAUyBA9dAkU51u/l2XnIjYxX/WUHxyA/PhU2pdRNyio7lOWTZt3EppS6SfH9mhTVTV5UJpnXdYM0NUvj7X2FTI782BSgUAGnHs2Q+L875dLj41lZbF3iWK1b1S1z2Kl0vxD1shuATRtvZPzLYXcq9W+LpL9uQZqc+a+2f9MUUhmy7oarXwsiESxb1kPmrdKvnUy/EFiVSBOr1vWRWZSGiQcuwL/tVPh/PE31lx+bjCc/H0Fg34VlxmJWpyoUcjkKkrRsGEqZDNKQYLWJySESwbBhI0gflF0Om/brC7PBg5A6fQZkwSXSUiaDNCgI4hLPV8RVXCCP066XUp6RZechOzJe9ZcR8gS58alwLFavF5sZw6aBZ5nDUimkcqTejYBDsW0gEsGhZV0kl3K/WLVfGzw9fRsFWnK9EBH9W+zx8ZaMHz8emzdvRr9+/TBjxgzY2NggNDQUe/bswZYtW6CvrzlMlLm5OaZNm4bJkyejsLAQLVu2RHp6Oq5cuQILCwsMGTLktWKoVq0apFIpfvzxR3Tr1k1t0vPXIZFINHqc/NdhrsoSuekkqk3+DNkRcciNToCXbx/kx6ci/uTz4YiaHPgW8SduImqbciLQiA3H4f3Dl0j3D0fanVBUHd0ZYhMJYvYo3+YRmxnj/X1fQ9/YEAHjfoLYzBgwMwYAFCRnAFowBj0AxG86jKprJiInIBTZ/o/gOLIb9IyNkLRXOVSK+/cTIY1LxpOlvynX33oUNQ4sguPoT5F+5hZsPm0FE29PRPoq528pzMpF5tX7cPlmCKLzCpAfkwDzpnVh2+tDPJ63HQAgcXOCTY/WSD/rB1lqJoxrucHluxHIvHYfuQ+Fm7y6LCnb/0SlZVOQe/8R8u6GwHrIp9AzliD9D+V4zpWWT4UsPhmJq3YoNzAQQ1LNVfVvsaMtJLU8UJidC2l0LADA3ncEss5eh+xpAsQOtrCbMBCKwkJkHDtf/gf4H9zfegoNJvRAekQ8Mh8noPG0XsiJT0PUX88nA+y8ZxYiT91C4A5leolNJLBwd1R9b+5iD5varshPy0b202SIjSV4b8KniP6fH3Li02BkY47aQz6BiZM1Io4J+6AuatNJeJaSVyQUyyveL8oroovyisgNx1GvKK9IvxMK99GdoW8iwZOivEKWmYuY3edQc94gSNOyIMvMRa3Fw5B6M0StxwkAOPVoDpG+Pp6W8RaUeR03AIC+qQSGthYwr+OGQqkM2SFP3kZyvJK7W0+hkY/yHMl4nIAm03ohOz4NEcXOke6/z0L4qVu4/8vzc8SyxDliW3SOZD1NhthEgvcn90T4iRvISUyHhZsjmn3dF+mR8Yi+UPZEveUtesNx1P5hHDL8w5BxJwyuRf/3sXvOAwBq/zge+XEpCFv0OwDg8aaTaHjoO7iO7Yqkv2/DsUdzWNT3RNC0zap9iq1MYVTZDhIn5ZveJkXjIxckpKEgMR3Gbo5w7NkCyWfuQJqaBbParvCaPxip/wQiK/DN9rJ5U/KO7IPphFmQhQVB9igIRl17AUbGyD9zEgBgOuFrFKYkIvc3ZToY9RwAWViwsjeHgSEMG34AwzbtkbOxaEgeiRGMew1Cwc0rUKQmQ2RuCaPOn0HPxg4F/5wX6ChfT9qOg3BYMg3590OQdy8YVoM/g8jYCJlFc3I4LJkOeUISktcoy1UYiGHoqSx3RAYGEDvawrCmBxQ5eZBGF3v5RCSC+WftkXnob0EmYn4d4ZtPwmtSD2SHxyEnOgE1fXsjLz4Vcaee57dN93+DuJM3EblNmS7hG4/jvbVfIi1AWTfzGNUJ+iYSRBermzXdOwv6xhLcHL8KYjNjZf0MQH6xupnnuK5IOBsAhUKBSp3fR7WvPoXf6LVaU3cDgLxD+2A2eRbkoUGQhQTB6NNeEBkZI/9v5XVjNvlrFCYnImdn0XXTawDkocGQxz6ByMAQBo0/gOSj9sheX3Td6OvDfOZ86HtWR+b8mYCePkRWyredFVkZgEz7hqGJ3XQUnt/7ICsgFFl3HqHSqG7QN5EgcY9yAmnPtRNQEJeMx0uUvbxitxxD7T8WoNKY7kg94we7T1vC1NsT4dOf36c8/fkwvDZMQea1QKT/cx9WHzWA9SeNEdhrNoCi4a1+/w56xoYI8fke+mYm0DdT9maXJmcAheV/XUVtOgmPyZ8hp6huUq2UukmjA98i4cRNPC5WN6n7w5fIKKqbPCufnharmzzZfQ41StRN0orVTezbN4ShvSXS/R5BnieFbRtveEzsgcifnw9nZOJRCZYNPJF+OxRiK1O4je0Cs5ouuD9hfbmlz+MNx1Drh/HI9A9Hxp1QuKiO9TwAoFZRWRyuKotPoOGhuXAZ2xXJf9+GY48WMK/viaBpm1T7fF4WK6+RkmXxM8bujrBqVgsB/ZeU09G+mqcbj8Jr7VfICghD1p1QOI/qAn0TCRL2nAMAeP3og4LYZEQt3q1cf/MJ1P1zHpzHdkPq336w69ESZvU9EFZ07chSsyArMQ+fQiZHQUKaqieHeaPqMGvohfQr9yHPyoVF4xpwnz8UiX9cgjw9uxyP/tXk7NsPy1mzIA0OhvThQ5j27gWRsRFyTyjzWMuvZ0GelISsTco81rR/P5gNH4a0BQshj4uDnk1R/pmbC0Wucv7G7N/3wGrudygICEDBHX9IPmgCSfPmSJk4SZBj/DcebT6FWpN6IDMiDtnRiajr2wu58Wl4cup5vb71vll4cvIWwrYr6/UhG0+iydoxSA2IQIp/GLxGdYTYRILIPeq9oEzdHWHftCYuDXz7Q2qVt5ycXETHPK+PPXkaj6CQMFhamKOSk4OAkRHR28KGj7fE2dkZV65cga+vL9q3b4/8/Hy4ubmhY8eO0NMr+237BQsWwN7eHkuWLEF4eDisrKzQsGFDfP31168dQ/369bF69WosW7YMs2bNQuvWrbFkyRIMHjz4vxzaWxW+7gj0TSSot3IUxBYmSL0RjJt9l6q9BWji5ghDG3PV59jDV2Foa4HqM3rD0MEKmQ+icKPfUlVl18K7KqwbeQEAPryxVu33zjX2Qe7jxHI4spdLPXoFYltLOE/rBwN7a+QERuDRoHmQFb15I6lsr3ajn+0XjIivVqPyjAGo7DsQ+RFPETZyKfKCnz9QCxu3ElVmDkLVHydDbGWG/JhEPFm2C4m/Ksf7LSyQwaKVNxxHdoWesREKYpOQdvIqnq7dV74H/4oyT1yEvo0F7CcMgr69NfIfhuPxiDmQJ6cBAAwq2avd6Bo42KDq4XWqz7Yje8F2ZC/kXL+L6EEzles42cF5tS/0rS0gT0lHrt8DRPWeDHmq9r5dWZqAn49BbCJBq2XDYWhhgvibITg1cDnkxa4dCzcHGBW7duzre6Dr/m9Un5vNHQgACNl3ERembIKisBBW1Sqheu+JMLI2R15qFhIDwnH084VIFfABPgBEFOUVdYvlFbdeklfEFeUVXjN6Q+JghYwHUbhVLK8AgKA5O6EoLMR7W6dATyJG0rm7CPTdqvH7Vfp/hPgTNzQmG32mxdnnwxxavucJ589bIjc6ERfe93kTh/+v3FmvPEc+XKo8R2JvhuDYIM1zxLhYmjl4e6BHsXOk5XfKcyRo/0WcLTpHbGu5oEavlpBYmCI7PhWPL97DjZUHUKhFY0QnFP3fe8zoA4mDFTIfRMK/3xLV/71RZVsoiuUd6bdC8ODLH+Ex8wt4ft0XORFxuDt0BbKDHqvWse/QGLV/eD5kRL1NkwAA4Sv2I2LlARRKZbBpXQ+uoztDz0SC/KfJSDx2AxHFxmHXNgVXzkFkYQXjvsOhZ20DeUQoMudPV014rmfvoDa3gsjICKajJ0PP1h6KgnzIn0Qj+/uFKLiifGCDwkLoV3GF+UcdILKwhCIzA7LQIGR8MwHyx5ECHOHryzp1Afo2lrDxGQyxnTXyg8LxdMw3ZZY7YntbuB58/iDRenhvWA/vjdwbAXgy9PmY68bNGsDA2REZB/8qt2P5t8LWHYXYRALvlSNhYGGClBvBuN5PPb81dVfPb58evgZDWwvUmNELEntlfnu931LV28SW3u6qulm76+p1s7/f90Hu4yQAgEPb9+A1sQf0DA2QERiFm0NXIuGsdo2/XnD5HHIsrWA8QHndyMJDkfnddCjSnl83ipLXzZfFrpuYaGStWoiCy8rrRs/WHoZNWwIArH7cpvZb6bMmas4DogWSj1yB2NYCLtP7wcDeCjkPIhA0YAGkqjqsndp1knUrGKHj18DFtz9cZg5AXkQsQoYvQ26xOmzqqeuImLkRzl/1hPuCEcgNf4qQUcuReUPZy9e0ngfMGynnTGxwVf3h/Z0mY5AfU/71+8iiukntorpJ2o1g3H5J3SS+qHzyLKqbZD6Iwu0SdZPgUuomD4vVTQqlcrgMa48a8wcDIhFyIuIQPOdXxPx2VrWOSF8Ety+7wNTTGYUyOVKvPMCNrnOQV473QQmHr8KgqCw2LCqLA/otVk3ubVTZTu1eJ+NWCB58+QM8ZvaF59f9kBMRi3slymK7Do1R+4fxqs91N00GAESs2I+IlftVyyv1b4v8pylIOa89L2UAQNLhfyC2tYDrjL4wtLdC9oNIPOi3SO3aKV4/ybwVjJBxa+Hm2xdus/ojNyIWQcOWI6dYmrxMYYEU9j1awHVaH4gMxch/nICnG4/h6cbyGcL2deWdPQc9KyuYDx8GPRsbSENDkTptBgpTlXmsvqOjWq8lk08/hcjQENYL5qvtJ2v7DmRt3wEAyL90GRmrVsN04ABYTJwAWfRjpM2ZA+m9e+V2XP9V8E/Ken3jFSNgYGGCpBshuNR/mVp+Y+buCEmx/CbmyDVIbM1RZ0Yv1bBYl/ovQ36J4e6q9muD3NgUxJ9/d9LjVd0PeoThPr6qz8t/VDakftrpYyz6dqpQYRHRWyRSaIyDQ/RyJxz7Ch2CVnEwyBM6BK1jZlIgdAha51KOzctX0jEuUu15OK4twg35TkJJNQq0a3hCbdCgufbMwaQtUoKNhA5B6zxMYblTXPP3tWfsem3xyM9W6BC0TobcUOgQtI4Y2t0zTQgSEdOkJM/qSUKHoHUuPar88pV0TI972jO3mbYwsPMQOoR3jqmJu9AhvHOycyKFDkEQnOODiIiIiIiIiIiIiIgqDL5WSkRERERERERERERar5CDF9ErYo8PIiIiIiIiIiIiIiKqMNjwQUREREREREREREREFQYbPoiIiIiIiIiIiIiIqMJgwwcREREREREREREREVUYbPggIiIiIiIiIiIiIqIKQyx0AEREREREREREREREL6NQKIQOgd4R7PFBREREREREREREREQVBhs+iIiIiIiIiIiIiIiowmDDBxERERERERERERERVRhs+CAiIiIiIiIiIiIiogqDDR9ERERERERERERERFRhiIUOgIiIiIiIiIiIiIjoZRRQCB0CvSPY44OIiIiIiIiIiIiIiCoMNnwQEREREREREREREVGFwYYPIiIiIiIiIiIiIiKqMNjwQUREREREREREREREFQYbPoiIiIiIiIiIiIiIqMIQCx0AEREREREREREREdHLKBQKoUOgdwR7fBARERERERERERERUYXBhg8iIiIiIiIiIiIiIqow2PBBREREREREREREREQVBhs+iIiIiIiIiIiIiIiowmDDBxERERERERERERERVRhioQMgIiIiIiIiIiIiInoZhUIhdAj0jmCPDyIiIiIiIiIiIiIiqjDY8EFERERERERERERERBUGGz6IiIiIiIiIiIiIiKjCYMMHERERERERERERERFVGGz4ICIiIiIiIiIiIiKiCkMsdABERERERERERERERC+jEDoAemewxwcREREREREREREREVUYbPggIiIiIiIiIiIiIqIKgw0fRERERERERERERERUYbDhg4iIiIiIiIiIiIiIKgw2fBARERERERERERERUYUhUigUCqGDIPo38vPzsWTJEsyaNQsSiUTocLQC00QT00QT00QT00Qd00MT00QT00QT00QT00QT00QT00QT00QT00Qd00MT00QT00QT04RId7Dhg95ZGRkZsLS0RHp6OiwsLIQORyswTTQxTTQxTTQxTdQxPTQxTTQxTTQxTTQxTTQxTTQxTTQxTTQxTdQxPTQxTTQxTTQxTYh0B4e6IiIiIiIiIiIiIiKiCoMNH0REREREREREREREVGGw4YOIiIiIiIiIiIiIiCoMNnzQO0sikeC7777jZFTFME00MU00MU00MU3UMT00MU00MU00MU00MU00MU00MU00MU00MU3UMT00MU00MU00MU2IdAcnNyciIiIiIiIiIiIiogqDPT6IiIiIiIiIiIiIiKjCYMMHERERERERERERERFVGGz4ICIiIiIiIiIiIiKiCoMNH0REREREREREREREVGGw4YOIiIiIiIiIiIiIiCoMNnwQERER0WspKChAcHAwZDKZ0KEQUQWgUCigUCiEDoOIiIiIKhCx0AEQvczdu3dfeV1vb++3GIn2yc3NhZ+fH2xsbFC7dm217/Ly8rBv3z4MHjxYoOjKD8+Rl8vLy8Pdu3eRkJCAwsJCte+6d+8uUFTCOHHiBA4ePAgbGxsMHz4cNWvWVH2XmpqKzz//HGfPnhUwQuHI5XIcOnQIDx8+BADUqVMH3bt3h76+vsCRCS80NBRhYWFo3bo1jI2NoVAoIBKJhA6r3OXk5MDHxwe//PILACAkJAQeHh7w8fFB5cqVMXPmTIEjJKJ3ydatW7FmzRo8evQIAODl5YVJkyZh5MiRAkdWPjIyMl55XQsLi7cYifZgmrxYWekjEokgkUhgaGhYzhFph0ePHuHcuXOl3uvMmTNHoKiIiEhoIgVfrSEtp6enB5FI9EoPmeRyeTlFJbyQkBC0b98e0dHREIlEaNmyJfbs2YNKlSoBAOLj4+Hs7KwTaVL8HClN8fNHF9KjpFOnTmHw4MFISkrS+E7X0mT37t0YPHgwOnbsiPT0dNy6dQtbtmzBgAEDAOjWdVNSaGgounTpgpiYGNSoUQMAEBwcDBcXFxw/fhyenp4CRyiM5ORkfPHFFzh79ixEIhEePXoEDw8PDB8+HNbW1li1apXQIZariRMn4sqVK/j+++/RsWNH3L17Fx4eHjh8+DDmzp2LO3fuCB2iIM6cOYM1a9aoGg1r1aqFSZMm4eOPPxY4MuEEBwfjxx9/VEsTHx8fVf6ii8LCwrB9+3aEhYVh7dq1cHBwwMmTJ+Hq6oo6deoIHV65mzNnDlavXg0fHx80a9YMAHD16lWsW7cOkydPxvz58wWO8O17Vod9EV2rwzJNXuxl6VOlShUMHToU3333HfT0dGOAj82bN+PLL7+EnZ0dnJyc1NJHJBLh9u3bAkZXvmxsbBASEgI7OztYW1u/8FxJSUkpx8iE8bI0KE4X0oNIF7HHB2m9iIgI1b/v3LmDadOmYfr06Wo3SKtWrcLy5cuFClEQvr6+qFu3Lm7duoW0tDRMmjQJLVq0wPnz5+Hq6ip0eOWq+DlCmnx8fNC7d2/MmTMHjo6OQocjqBUrVmD16tWYMGECAGDfvn0YPnw48vLyMGLECIGjE9aECRPg4eGBq1evwsbGBoDyof/AgQMxYcIEHD9+XOAIhTF58mSIxWJER0ejVq1aquVffPEFpkyZonMNH4cOHcLevXvRtGlTtRvJOnXqICwsTMDIhPPzzz9j4sSJ6NWrFyZOnAgAuHbtGjp37ow1a9Zg/PjxAkdY/v744w/07dsXjRs3VtXXrl27hrp162LPnj34/PPPBY6w/F24cAGdOnVCixYtcPHiRSxatAgODg4ICAjA1q1bceDAAaFDLHfr16/H5s2b0a9fP9Wy7t27w9vbGz4+PjrR8HHu3DmhQ9A6TJMX27FjB7755hsMHToUTZo0AQDcuHEDv/zyC7799lskJiZi5cqVkEgk+PrrrwWOtnwsXLgQixYtgq+vr9ChCG7NmjUwNzcHAHz//ffCBqMFmAZEBAXRO+T9999XHD9+XGP58ePHFQ0bNhQgIuE4ODgo7t69q/pcWFioGDt2rMLV1VURFhamiIuLU+jp6QkYIWkLc3NzRWhoqNBhaAVTU1NFeHi42rKzZ88qzMzMFOvXr9fp68bExEQtT3nG399fYWpqKkBE2sHR0VHh7++vUCgUCjMzM0VYWJhCoVAowsLCdDJdjI2NVWlQPD38/f0VFhYWQoYmmMqVKyt+/PFHjeXr1q1TODs7CxCR8Dw8PBSzZ8/WWD5nzhyFh4eHABEJr2nTpopVq1YpFAr1a+f69euKypUrCxmaYCwtLRUhISEay4ODgxWWlpblHxDRO6Bt27aKvXv3aizfu3evom3btgqFQqHYuXOnokaNGuUdmmDMzc1VeSoREVFxutH3kSqMe/fuoWrVqhrLq1atisDAQAEiEk5ubi7E4uedtkQiEdavX49u3bqhTZs2CAkJETA6YYWFhcHHxwcff/wxPv74Y0yYMEFn30QGgF69euH8+fNCh6EVLCwsEB8fr7bso48+wrFjxzB9+nT8+OOPAkUmPIlEgszMTI3lWVlZOjteNABkZ2fDxMREY3lKSgokEokAEQmrcePGar1/nvX62LJli+rNfl2TlpaGjh07aixv37490tPTBYhIeLGxsaXOMTZw4EDExsYKEJHw7t27h88++0xjuYODQ6lDUeqCQYMGYf369RrLN23apBqCUtekpaVh1apVGDlyJEaOHIk1a9bobD5SXE5ODoKCgnD37l21P130zz//oEGDBhrLGzRogKtXrwIAWrZsiejo6PIOTTC9e/fG6dOnhQ5DayUkJOD+/fu8forJy8tDRkaG2h8RVUwc6oreKbVq1cKSJUuwZcsW1YO4goICLFmyRG0IEl1Qs2ZN3Lp1S+O4161bB0D3Jqx+5q+//kL37t3x3nvvoUWLFgCAK1euoE6dOjh69Cg++eQTgSMsf+vWrUPv3r1x6dIl1KtXDwYGBmrfPxv2SRc0adIEJ0+eRNOmTdWWt2nTBkePHkXXrl0Fikx4Xbt2xejRo7F161bV0AnXr1/H2LFjdTY/AYBWrVph586dWLBgAQDlg/7CwkIsX74cH330kcDRlb/FixejU6dOCAwMhEwmw9q1axEYGIh//vkHFy5cEDo8QXTv3h1//vknpk+frrb88OHDOpunfPjhh7h06RKqVaumtvzy5cto1aqVQFEJy8rKCrGxsRov8Ny5cweVK1cWKCrhbd26FadPn1aVy9evX0d0dDQGDx6MKVOmqNZbvXq1UCGWm1u3bqFDhw4wNjZWlcOrV6/GokWLcPr0aTRs2FDgCMtfYmIihg0bhpMnT5b6vS7O8eHi4oKtW7di6dKlasu3bt0KFxcXAMqhSq2trYUITxDVqlXD7Nmzce3aNZ2/1ynOz88PQ4YMwcOHDzXmwtTFOXKys7Ph6+uLffv2ITk5WeN7XUsPIl3Byc3pnXLjxg1069YNCoUC3t7eAIC7d+9CJBLh6NGjqpsEXbBkyRJcunQJJ06cKPX7cePGYcOGDSgsLCznyITVoEEDdOjQQeNmYObMmTh9+rROTW73zNatWzF27FgYGRnB1tZWY8K/8PBwAaMrXxcuXMA///yDWbNmlfr9uXPnsHPnTmzfvr2cIxNeWloahgwZgqNHj6puGGUyGbp3744dO3bA0tJS4AiFcf/+fbRr1w4NGzbE2bNn0b17dzx48AApKSm4cuWKTk76HhYWhqVLlyIgIABZWVlo2LAhfH19Ua9ePaFDE8TChQuxcuVKtGjRQm0+iytXrmDq1KmwsLBQrasrD182bNiAOXPmoE+fPqoH2teuXcP+/fsxb948ODs7q9bVlYbVadOm4fr169i/fz+qV6+O27dvIz4+HoMHD8bgwYPx3XffCR1iuXvVxmORSISzZ8++5WiE16pVK1SrVg2bN29W9eqWyWQYOXIkwsPDcfHiRYEjLH8DBgxAVFQUvv/+e3z44Yf4888/ER8fj4ULF2LVqlXo0qWL0CGWuyNHjqB3796oWbMm3n//fQDKRrOgoCAcOHAAXbt2xfr16/Ho0SOdaDAEUOqIEM/o2r1OcfXr14enpyd8fX3h6OioMcm3m5ubQJEJY/z48Th37hwWLFiAQYMG4aeffsKTJ0+wceNGLF26VGd7GhJVdGz4oHdOdnY2du3ahaCgIADKXiD9+/eHqampwJGRNjAyMsK9e/fg5eWltjwkJATe3t7Iy8sTKDLhODk5YcKECZg5cyb09DjCIb3Yo0eP1PLXkm9s66L09HSsW7dO7UH/+PHjUalSJaFDIy3wogcuxenSw5dXLWt06Y3TgoICjB8/Hjt27IBcLodYLIZcLkf//v2xY8cO6OvrCx0iCczY2Bh37txBzZo11ZYHBgaicePGyMnJESgy4VSqVAmHDx9GkyZNYGFhgVu3bqF69eo4cuQIli9fjsuXLwsdoiAiIyOxceNGBAcHAwBq1KiBMWPGwN3dXdjASKuYm5vjzp07rMsXcXV1xc6dO/Hhhx/CwsICt2/fRrVq1fDrr7/i999/L/OFUiJ6t3GoK3rnmJqaYvTo0UKHoZViYmIAAFWqVBE4EuHY29vD399fo+HD398fDg4OAkUlrIKCAnzxxRds9ChBLpfj0KFDePjwIQCgdu3a+PTTT3X+4ZOXl5fG9aPrLC0t8c033wgdhlbQ19dHbGysRn6anJwMBwcHnXmIXVxERITQIWgdXett+ioMDQ2xefNmzJkzB/fu3UNWVhYaNGjA/JZULCwsEB0drdHw8fjxY5ibmwsUlbCys7NV5Y21tTUSExNRvXp11KtXTyd7cQOAVCqFu7s7lixZovFdUlIS7OzsBIhKezx7r7dk7wZd1K5dOwQEBLDho0hKSgo8PDwAKPPblJQUAMo5cb788kshQyOit4gNH/ROOHLkyCutpyvDJRRXWFio6u6dlZUFQPl2x9SpU/HNN9/o3MPuUaNGYfTo0QgPD0fz5s0BKOf4WLZsmdpY0bpkyJAh2Lt3L77++muhQ9EaoaGh6NKlC2JiYlCjRg0AyuHjXFxccPz4cZ0ZvmjKlClYsGABTE1NX3p96MpwCaXJy8vD3bt3kZCQoPFAV9fKnbI6Cufn56vm3tJVBQUFiIiIgKenp2qYGqJn5s+fj2nTpsHFxUU1Dj8A5ObmYsWKFZgzZ46A0ZWfnj17YseOHbCwsEDPnj1fuO7BgwfLKSrt8MUXX2DEiBFYuXKlWh12+vTp6Nevn8DRCaNGjRoIDg6Gu7s76tevj40bN8Ld3R0bNmzQ2V6Xffv2xYEDBzQe7MfHx6Ndu3a4f/++QJEJa+fOnVixYgUePXoEAKhevTqmT5+OQYMGCRyZcLZs2YIhQ4bg/v37qFu3rsbcJ7pWh/Xw8EBERARcXV1Rs2ZN7Nu3D02aNMHRo0dhZWUldHhE9JbwrozeCT169HjpOro0XEJx33zzjWqCu2eTeV++fBlz585FXl4eFi1aJHCE5Wv27NkwNzfHqlWrVPM4ODs7Y+7cuToztnpJcrkcy5cvx19//QVvb2+NSq8uPtCeMGECPDw8cPXqVdjY2ABQvrE+cOBATJgwAcePHxc4wvJx584dSKVS1b9J06lTpzB48GAkJSVpfKdL5c4PP/wAQHnMW7ZsgZmZmeo7uVyOixcvarylrCtycnLg4+ODX375BYByaEUPDw/4+PigcuXKmDlzpsARlo8ffvgBo0ePhpGRkep8KYsulsfz5s3D2LFjYWJiorY8JycH8+bN05mGD0tLS9UDW12dO6osK1euhEgkwuDBgyGTyQAABgYG+PLLLzXmrtMVEydORGxsLADgu+++Q8eOHbFr1y4YGhpix44dwgYnkOjoaIwcORJbt25VLYuNjUXbtm1Rp04dASMTzurVqzF79mx89dVXavfDY8eORVJSEiZPnixwhMK4evUqrly5gpMnT2p8p0t12GeGDRuGgIAAtGnTBjNnzkS3bt2wbt06SKVSnbwfJtIVnOOD6B3n7OyMDRs2aLyxcfjwYYwbNw5PnjwRKDLhZWZmAoDODg/wzIsmD9WVCUNLMjU1xbVr1zQmYw4ICECLFi1UvaeIvLy80L59e8yZMweOjo5ChyOYZ/NYREVFoUqVKmpDwhkaGsLd3R3z58/HBx98IFSIgpk4cSKuXLmC77//Hh07dsTdu3fh4eGBw4cPY+7cuTrTqFi1alXcunULtra2nGi2FHp6eoiPj4e9vb3a8rNnz+KLL75AYmKiQJEJJzc3F4WFhap5+iIjI3Ho0CHUqlULHTp0EDg64eTk5CAsLAwA4OnpqdFYpstycnIQFBQEV1dXnR3SKTExEa1bt0anTp2wevVqPH36FB999BHq16+PPXv26Fxvf0BZ/sybNw+DBw9WW/7LL79g7ty5Ojskpbu7O7p27YrZs2frdB22LFFRUfDz80O1atXg7e0tdDhE9Jawxwe9M6RSKcaMGYPZs2e/8kSiuiAlJaXUt2xr1qypGrdSlyxcuBADBgxA1apVdb7B45lz584JHYLWkUgkqoax4rKysnR2uJ7hw4dj7dq1GtdNdnY2fHx8sG3bNoEiE1Z8fDymTJmi8zeMzx4afPTRRzh48CCsra0Fjkh7HDp0CHv37kXTpk3Vhh6pU6eO6uGlLij+YKn4v3V9vHVra2uIRCKIRCJUr15dLR3kcjmysrIwduxYASMUzqeffoqePXti7NixSEtLQ9OmTWFgYICkpCSsXr1a58Zc/+2339CzZ0+YmJhovJhBSiYmJmjYsKHQYQjK3t4ep0+fRsuWLQEAx44dQ8OGDbFr1y6dbPQAlD1eng0PV1zz5s1VPYZ0UXJyMiZPnqzzdVhA+SypY8eO2LBhg2puLTc3N7i5uQkcGRG9bbpZMtI7ycDAAH/88YfQYWid+vXrY926dRrL161bh/r16wsQkbD279+PatWqoXnz5vj5559LHZ6GqGvXrhg9ejSuX78OhUIBhUKBa9euYezYsTo33u0zv/zyC3JzczWW5+bmYufOnQJEpB169eqF8+fPCx2G1jh37hwbPUpITEzUmOwdUDYa6urDfgDYunUr6tatCyMjIxgZGaFu3brYsmWL0GGVu++//x6rV6+GQqHAvHnzsGbNGtXfhg0bcPnyZfz0009ChymI27dvo1WrVgCAAwcOwNHREVFRUdi5c+dLh0uriCZPngwHBwf0798fJ06c0LlhaEozfPjwF/7pKhcXF/zvf//Drl270KRJE/z+++9qPTF1TbVq1bBv3z6N5Xv37lU95NZFPXv25AtwRQwMDHD37l2hwyAiAbDHB71TevTogUOHDunsOJ2lWb58Obp06YK///4bzZo1A6Acz/Px48c4ceKEwNGVv4CAADx48AC7du3CypUrMWnSJHzyyScYMGAAevTooTPDBbxswtDidG3yUEA5Fv2QIUPQrFkz1ZwnMpkM3bt3x9q1awWOrnxlZGSoGn8yMzNhZGSk+k4ul+PEiROlPtTVFevWrUPv3r1x6dIl1KtXT2OOHF2cqyAmJgZHjhxBdHQ0CgoK1L7TxTGSGzdujOPHj8PHxwfA854NW7ZsUZXLumbOnDlYvXo1fHx81OomkydPRnR0NObPny9whOVnyJAhAJRDsTRv3lwjD9FlOTk5ql6Gp0+fRs+ePaGnp4emTZsiKipK4OjKX2xsLE6dOoXff/8dffr0gYmJCXr37o0BAwaU+ja7LkhNTVX7LJVKcf/+faSlpaFt27YCRVX+nvUcKyknJwdHjx6Fra2tapku9vifN28evvjiC1y8eFE1x8eVK1dw5syZUhtEdEX16tUxa9YsXL58mXVYAAMHDlTNjUpEuoNzfNA7ZeHChVi1ahXatWuHRo0aqcYEfkbXCu9nnjx5gp9//hlBQUEAgFq1amHcuHFwdnYWODLhXblyBbt378b+/fuRl5eHjIwMoUMqF8OGDVP9W6FQ4M8//4SlpSUaN24MAPDz80NaWhp69uyJ7du3CxWm4B49eqR23VSrVk3giMqfnp7eC99KF4lEmDdvHr755ptyjEp7bN26FWPHjoWRkRFsbW3V0koX5yo4c+YMunfvDg8PDwQFBaFu3bqIjIyEQqFAw4YNdXLOoMuXL6NTp04YOHAgduzYgTFjxiAwMBD//PMPLly4gEaNGgkdYrmzt7fHDz/8gH79+qkt//333+Hj46PzvTHz8vI0Gg0tLCwEikY43t7eGDlyJD777DPUrVsXp06dQrNmzeDn54cuXbogLi5O6BAFk5OTgz///BO7d+/G33//jSpVqujU0HkvUlhYiC+//BKenp6YMWOG0OGUi19++eWV133W2Kpr/Pz8sGbNGjx8+BCAsl4/depUNGjQQODIhMP5ttT5+Phg586d8PLyKvVZki6+vEOkC9jwQe8UFt70uvz9/fHbb79hz549SE5OLnUon4rO19cXKSkp2LBhg6obvFwux7hx42BhYYEVK1YIHCEJ6cKFC1AoFGjbti3++OMP2NjYqL4zNDSEm5ubTjeiOjk5YcKECZg5c6bOjp1dXJMmTdCpUyfMmzcP5ubmCAgIgIODAwYMGICOHTvq3Jj8z4SFhWHp0qUICAhAVlYWGjZsCF9fX50dp9/Kygo3b97UGGIkJCQETZo0QVpamjCBCSgnJwczZszAvn37kJycrPG9Lg5rdODAAfTv3x9yuRzt2rXD6dOnAQBLlizBxYsXcfLkSYEjFFZSUhL27NmDDRs24OHDhzp5jpQlODgYH374oU7P3/AyS5cuxdixY2FlZSV0KKQFdH2+LUA5T11ZRCKRTr68Q6QL2PBB9I7bvn07zMzM0Lt3b7Xl+/fvR05Ojk6+9RMREYHdu3dj9+7dCA4ORps2bdC/f3/06tULlpaWQodX7uzt7XH58mXUqFFDbXlwcDCaN29e6gOYimjKlClYsGABTE1NMWXKlBeuq4tv/ERFRcHFxYUP90uwsbHBzZs34enpKXQoWsHc3Bz+/v7w9PSEtbU1Ll++jDp16iAgIACffvopIiMjhQ6RtICPjw8MDAw08tJp06YhNzdXJ+e0GD9+PM6dO4cFCxZg0KBB+Omnn/DkyRNs3LgRS5cuxYABA4QOURBxcXGIjY1F/fr1VeXPjRs3YGFhgZo1awocXfl71tNj165dOHPmDFxcXNCvXz8MGDBAJ9OjLCdOnMCQIUOQmJgodChay8LCAv7+/vDw8BA6lHIhl8vx559/qnp81K5dG59++inEYt0e3X3r1q1Ys2YNHj16BADw8vLCpEmTMHLkSIEjK19yuRxXrlxBvXr1OFcdkY7R7VKA3lkFBQWIiIiAp6enzldmlixZgo0bN2osd3BwwOjRo3Wu4aNp06a4efMmvL29MWzYMPTr1w+VK1cWOixByWQyBAUFaTR8BAUFobCwUKCoyt+OHTvw9ddfw9TUFHfu3ClzPV19E8rNzQ2A8qFLaXM3eHt7CxGW4IYMGYK9e/fi66+/FjoUrWBqaqo6NypVqoSwsDDUqVMHAHR6+KKwsDBs374d4eHh+P777+Hg4ICTJ0/C1dVVlT66ZuvWrTh9+jSaNm0KALh+/Tqio6MxePBgtcZnXWloPnr0KHbu3IkPP/wQw4YNQ6tWrVCtWjW4ublh165dOtvw4eTkBCcnJ7VlTZo0ESgaYfXt2xfHjh2DiYkJ+vTpg9mzZ+vsPEHPlHxRRaFQIDY2FsePH9e5e5zXpUvvtz548ADdu3dHXFyc6n5n2bJlsLe3x9GjR1G3bl2BIxQG59t6Tl9fH+3bt8fDhw/Z8EGkY3T7iTG9c3JycuDj46Ma5zQkJAQeHh7w8fFB5cqVMXPmTIEjLH/R0dGlDgHm5uaG6OhoASISVrt27bBt2zbUrl1b6FC0xrBhwzBixAiEhYWpHiZcv34dS5cuVZsLpKJLS0tTNfRERUXh5s2bapNB6rrExEQMGzaszKFFdHWIDblcjuXLl+Ovv/6Ct7e3xsSQuvLQ9pmmTZvi8uXLqFWrFjp37oypU6fi3r17OHjwoOoBt665cOECOnXqhBYtWuDixYtYuHAhHBwcEBAQgK1bt+LAgQNCh1ju7t+/j4YNGwKAal4COzs72NnZ4f79+6r1dKmhOSUlRfXmtYWFhWoC4pYtW+rsEHGkTl9fH/v27UOHDh1UQ5PqupIvqujp6cHe3h6rVq3C8OHDBYqKtM3IkSNRp04d3Lp1S/VQOzU1FUOHDsXo0aPxzz//CByhMNavX4/NmzerzbfVvXt3eHt7w8fHR6caPgCgbt26CA8Pf+Hw6URU8bDhg94ps2bNQkBAAM6fP4+OHTuqln/88ceYO3euTjZ8ODg44O7du3B3d1dbHhAQoJMPdRctWvRK6+lS9++VK1fCyckJq1atUo2FXKlSJUyfPh1Tp04VOLryY21tjYiICDg4OCAyMlKneru8ikmTJiEtLQ3Xr1/Hhx9+iD///BPx8fFYuHAhVq1aJXR4grl3755qYsziD2wB3Xpo+8zq1auRlZUFAJg3bx6ysrKwd+9eeHl56Vwj0DMzZ87EwoULMWXKFJibm6uWt23bFuvWrRMwMuGcO3dO6BC0joeHByIiIuDq6oqaNWti3759aNKkCY4ePcox+AkAsGvXrldaPZ2ZhwAAP35JREFUr169ejhx4gRcXFzeckTCY15Cr8Lf31+t0QNQ1vsXLVqE999/X8DIhCWVStG4cWON5Y0aNYJMJhMgImEtXLgQ06ZNw4IFC0qd3NzCwkKgyIjobWLDB71TDh06hL1796Jp06ZqD5zq1KmjeqNQ1/Tr1w8TJkyAubk5WrduDUD59unEiRPRt29fgaPTXrrU/VtPTw8zZszAjBkzkJGRAUA3K3aff/452rRpg0qVKkEkEqFx48ZlvlEZHh5eztEJ7+zZszh8+DAaN24MPT09uLm54ZNPPoGFhQWWLFmCLl26CB2iIPjQ5Tm5XI6YmBjVsGempqbYsGGDwFEJ7969e9i9e7fGcgcHB50e/ovUDRs2DAEBAWjTpg1mzpyJbt26Yd26dZBKpTrbaEj/TmRkJKRSqdBhlIu2bdvi4MGDGo2DGRkZ6NGjBycjJgBA9erVER8frzG0ZEJCAqpVqyZQVMIbNGgQ1q9fr1HGbNq0SSeHV+zcuTMAZa+X4s+SFAoFRCKRzvZuJ6ro2PBB75TExEQ4ODhoLM/OztbJN28BYMGCBYiMjES7du1U850UFhZi8ODBWLx4scDRkbbRxQaPZzZt2oSePXsiNDQUEyZMwKhRo9TeztZ12dnZqvzV2toaiYmJqF69OurVq4fbt28LHB1pg+LjI/MN9eesrKwQGxurMXTCnTt3dH6OKXpu8uTJqn9//PHHCAoKgp+fH6pVq6azcygRvcz58+c15hwDgLy8PFy6dEmAiEgbLVmyBBMmTMDcuXNVw25eu3YN8+fPx7Jly1QvfgEV/16o+Lw4IpEIW7ZsKXO+LV3Dl5mIdBMbPuid0rhxYxw/fhw+Pj4Ang8zsmXLFp2d/M/Q0BB79+7FwoUL4e/vD2NjY9SrV081UTERABw4cAD79u0rddJqXXqo/WyIPD8/P0ycOJENH8XUqFEDwcHBcHd3R/369bFx40a4u7tjw4YNqFSpktDhlauePXtix44dsLCwQM+ePV+47sGDB8spKu3A8ZE19e3bF76+vti/fz9EIhEKCwtx5coVTJs2TScfLJAmqVSKjh07YsOGDfDy8gKgnIuNdTWi0t29e1f178DAQMTFxak+y+VynDp1ig3LL9GqVSsYGxsLHUa56Nq1KwCgT58+qucDz3r3d+vWTfVZF97qLzkvTqNGjQBozrf14MGDco9NaG3atBE6BCISABs+6J2yePFidOrUCYGBgZDJZFi7di0CAwPxzz//4MKFC0KHJygvLy/VzXRpdGlOC1L3ww8/4JtvvsHQoUNx+PBhDBs2DGFhYbh58ybGjx8vdHiC2L59u9AhaJ2JEyeq5oD57rvv0LFjR+zatQuGhobYsWOHsMGVM0tLS9WNs4WFhc72KCwNx0fWtHjxYowfPx4uLi6Qy+WoXbs2ZDIZBgwYgG+//Vbo8EgLGBgYqD3IJaIXe++99yASiSASidC2bVuN742NjfHjjz8KEJkwivdYeJln5fCJEyfeVjhah2/yP8e0eLFLly5h48aNCA8Px/79+1G5cmX8+uuvqFq1Klq2bCl0eET0FogUujTQPVUIYWFhWLp0KQICApCVlYWGDRvC19cX9erVEzo0rWZubo6AgAA2fBTRpYagmjVr4rvvvkO/fv3UzoM5c+YgJSVFZyffpRfLyclBUFAQXF1dYWdnJ3Q4pCX09PRU/+b4yOoeP36Me/fuITs7Gw0aNNDpccVJ0+TJkyGRSLB06VKhQ6F3nC7U6aOioqBQKODh4YEbN27A3t5e9Z2hoSEcHBzKnKetItLT03vpSxgsh4le7I8//sCgQYMwYMAA/PrrrwgMDISHhwfWrVuHEydO6FRjIZEuYY8Peud4enpi8+bNQodB7zhdavONjo5G8+bNASjfkMvMzASgnPCuadOmbPggSKVS1KxZE8eOHUOtWrUAACYmJmjYsKHAkQmPE6uq45uEpdu6dSvWrFmDR48eAVD2wpw0aRJGjhwpcGSkLWQyGbZt24a///671N5SnOCc6Llnw8AVFhYKHIl2YNn7YqdOnYKZmZnqjf2ffvoJmzdvRu3atfHTTz/B2tpa4AhJGyxcuBAbNmzA4MGDsWfPHtXyFi1aYOHChQJGRkRvExs+6J2ir6+P2NhYjQnOk5OT4eDgwDdc6JWdPHlSZ8YGdnJyQkpKCtzc3ODq6opr166hfv36iIiI0KkGICqbgYEB8vLyhA5DK3FiVXWvOj7yuHHjMH/+fJ3oLTRnzhysXr0aPj4+qvnGrl69ismTJyM6Ohrz588XOELSBvfv31c1JoeEhKh9x+H06HVs3LgRjo6OQodRLpYsWQJHR0cMHz5cbfm2bduQmJgIX19fgSIrX5yb4MWmT5+OZcuWAQDu3buHKVOmYOrUqTh37hymTJnCIW4JABAcHIzWrVtrLLe0tERaWlr5B0RE5YINH/ROKeshbX5+PgwNDcs5GtIWU6ZMeeV1n71RqUtjeLZt2xZHjhxBgwYNMGzYMEyePBkHDhzArVu3XjpxM+mO8ePHY9myZdiyZQvEYlYPOLHqf/Pbb79h2rRpOtHwsX79emzevBn9+vVTLevevTu8vb3h4+PDhg8C8OpvbMfExMDZ2VltWDmquH744YdXXnfChAkAgP79+7+tcLTOxo0bsXv3bo3lderUQd++fXWm4aOktLQ0bN26FQ8fPgSgTI/hw4fD0tJS4MiEERERgdq1awNQDmfUrVs3LF68GLdv30bnzp0Fjo60hZOTE0JDQ+Hu7q62/PLlyxV66EAiXccnG/ROeHZTIBKJsGXLFpiZmam+k8vluHjxImrWrClUeO+Eivw24Z07d9Q+3759GzKZDDVq1ACgfLNSX18fjRo1EiI8wW3atEk1VMD48eNha2uLf/75B927d8eYMWMEjo60xc2bN3HmzBmcPn0a9erV0xiG5eDBgwJFJgxOrPrf6FJvMqlUisaNG2ssb9SoEWQymQAR0busdu3aOjMHGQFr1qx5pfVEIpGq4UOXxMXFoVKlShrL7e3tERsbK0BEwrt16xY6dOgAY2NjNGnSBIDyxa5Fixbh9OnTOjlMqaGhIXJycgAAf//9NwYPHgwAsLGxea2J4aliGzVqFCZOnIht27ZBJBLh6dOnuHr1KqZNm4bZs2cLHR4RvSVs+KB3wrObAoVCgQ0bNqhNZmdoaAh3d3ds2LBBqPDeCRX5IVTxtyhXr14Nc3Nz/PLLL6rxXFNTUzFs2DC0atVKqBAFpaenp/bmaN++fdG3b1+N9XRpaBrSZGVlhc8//1zoMLTGs6HgOLEqvcygQYOwfv16jTkaNm3ahAEDBggUFb2rKnJ9jTRFREQIHYJWc3FxwZUrV1C1alW15VeuXIGzs7NAUQlr8uTJ6N69OzZv3qzqoSuTyTBy5EhMmjQJFy9eFDjC8teyZUtMmTIFLVq0wI0bN7B3714AypffqlSpInB0pC1mzpyJwsJCtGvXDjk5OWjdujUkEgmmTZsGHx8focMjordEpGDtmt4hH330EQ4ePMgJyv6Fy5cv4/3334dEIhE6lLeqcuXKOH36NOrUqaO2/P79+2jfvj2ePn0qUGTaz8LCgm+Z0ktduXIFjRs3rvB5yevq0qULtmzZUuqbqbrI3NwcAQEBOpGf+Pj4YOfOnXBxcUHTpk0BANevX0d0dDQGDx4MAwMD1bqcwJpeRpeuHaKXWb58OZYvX44VK1aoel+eOXMGM2bMwNSpUzFr1iyBIyx/xsbGuHPnjsZoB4GBgWjcuLGq54MuiY6Oxrhx4/D48WNMmDABI0aMAKBsJJLL5a81pBxVLHfv3kXdunXVXgIsKChAaGgosrKyULt2bbXRRIio4mGPD3qnvOr4yBUd57QoW0ZGBhITEzWWJyYmIjMzU4CI3h1sB6dX0alTJzaQleLixYvIzc0VOgwSQPFJq8PCwgAAdnZ2sLOzw/3791XrVeQhJ4nozYiJicGRI0cQHR2NgoICte90seF0+vTpSE5Oxrhx41TpYWRkBF9fX51s9ACULypFR0drNHw8fvwY5ubmAkUlLFdXVxw7dkxjecmh5JYuXYqxY8fCysqqnCIjoTVo0ACxsbFwcHCAh4cHbt68CVtbW9WcMERU8bHhg94pcrkcO3bswJkzZ5CQkKCat+CZs2fPChRZ+So5p0VZdPEhy2effYZhw4Zh1apVqnFvr1+/junTp3Mib6I3gA1kROr4UgYRvQlnzpxB9+7d4eHhgaCgINStWxeRkZFQKBQ6OW8DoLyXWbZsGWbPno2HDx/C2NgYXl5eOt3r9IsvvsCIESOwcuVKNG/eHICyN+706dPRr18/gaPTbosXL0afPn3Y8KFDrKysEBERAQcHB0RGRmo8PyKiio8NH/ROmThxInbs2IEuXbqgbt26OvlgH+BDlhfZsGEDpk2bhv79+0MqlQIAxGIxRowYgRUrVggcHRGRbhg4cCAsLCyEDoPonaOrdVsCZs2ahWnTpmHevHkwNzfHH3/8AQcHBwwYMAAdO3YUOjxBmZmZ4f333xc6DK2wcuVKiEQiDB48GDKZDABgYGCAL7/8EkuXLhU4Ou3Gl3d0z+eff442bdqgUqVKEIlEaNy4cZlz9IWHh5dzdERUHjjHB71T7OzssHPnTnTu3FnoUEjLZWdnq4Yc8fT0hKmpqcARaT+OK06vgudJ6XQlXVq3bo0PP/wQbdq0QYsWLWBkZCR0SEQViq7kJaTJ3Nwc/v7+8PT0hLW1NS5fvow6deogICAAn376KSIjI4UOURC3bt3Cvn37Sh3+6+DBgwJFJbycnBy1ex0TExOBI9J+zF9106lTpxAaGooJEyZg/vz5ZQ4JN3HixHKOjIjKA3t80DvF0NAQ1apVEzoMrcMbAk2xsbGIjY1F69atYWxsDIVCwbcoiYj+o/bt2+PixYtYvXo1ZDIZGjdurNYQwgcvRC/3+PFjAICLi4vGd4GBgXB2di7vkEgLmJqaqurxlSpVQlhYGOrUqQMASEpKEjI0wezZsweDBw9Ghw4dcPr0abRv3x4hISGIj4/HZ599JnR4gvjtt9/Qs2dPmJiYoF69ekKHQ6T1nvWY8/Pzw8SJE3V2LhwiXaUndABEr2Pq1KlYu3Ytu6kWs2fPHjRv3hwPHz7En3/+CalUigcPHuDs2bOwtLQUOrxyl5ycjHbt2qF69ero3LkzYmNjAQAjRozA1KlTBY5Ou3FoGnoVbEDUbd9++y1Onz6NtLQ0nDt3Dl27dsWtW7fQpUsX2NjYCB0ekdaSyWSYPXs2LC0t4e7uDnd3d1haWuLbb79VDc0JKBtDyhqGgyq2pk2b4vLlywCAzp07Y+rUqVi0aBGGDx+Opk2bChydMBYvXow1a9bg6NGjMDQ0xNq1axEUFIQ+ffrA1dVV6PAEMXnyZDg4OKB///44ceIE5HK50CERvRO2b98Oc3NzhIaG4q+//kJubi4ADoFGVNGxxwe9Uy5fvoxz5879v717D6sx3fsA/l2l0pHl0AiNii4qFWnIMYc9E/bsGO1yiCjZm41Mic1rMlMahk2mmD0IOZVmCDNMU6nICA0jlYQOKl7nGsyqiOr9w2Vta3LI7Ne612p9P9c111X38/zxvUy0nud3378ffvzxR9jZ2UFHR0fhuiaebnj2QDB79mwYGxsjMjISlpaW+Pvf/w4zMzPR8ZQuMDAQOjo6KC8vh42NjXx9/PjxCAoKwpo1awSmU57c3Nwm3+vg4AAA+Prrr99WHGpG+HDwYv/zP/+jUS/+S0pKkJeXh5ycHOTm5sLY2BhDhgwRHYtIZc2dOxf79u3DqlWr0L9/fwDAyZMn8dlnn6GiooK/gwkRERGQyWQAgNDQUMhkMnzzzTewtrZGRESE4HRiFBcX489//jOApyf/q6qqIJFIEBgYiOHDhyM0NFRwQuW7ceMGkpKSsHv3bnh5ecHAwACenp7w9vaWDzsnosYqKyvh6emJI0eOQCKRoLCwEFZWVpg+fTqkUqnGvCcg0jSc8UFqxdfX95XXY2JilJREdRgaGiI/Px8WFhZo27Ytjh49Cnt7exQUFGD48OHyEw+aokOHDkhOToajo6NCH9eSkhI4ODjIHyibOy0tLUgkkpe+pH52TSKRcKcY0WtcunQJ69atQ0FBAQDAxsYGc+fORffu3QUnU75JkyYhIyMDjx49wpAhQ+Dq6oqhQ4fCwcGBp4GIXqFVq1aIj4/HqFGjFNYTExMxceJE3L9/X1AyItXVuXNn/Pjjj7C3t4eDgwMWL16MiRMn4uTJkxg5cqTG/72prq7G/v37ERcXh9TUVHTu3Fk+94MaGz16NLZs2aKRmwMJ8PHxwe3bt7F582bY2NjI3xMkJycjKCgI+fn5oiMS0VvAEx+kVjSxsPE6UqkUv/32GwCgU6dOOH/+POzt7XHv3j1UV1cLTqd8VVVVL+wxX1lZCT09PQGJxLhy5YroCKRGEhMTsW/fPrRp0wZ+fn7o0aOH/Nqvv/4KDw8PpKenC0woTkJCAiZMmABnZ2f5Lu1Tp06hZ8+eiI+Ph4eHh+CEyhUfH4927drB398fw4cPx6BBgzjXg6gJ9PT0YGFh0Wjd0tISurq6yg9EpAaGDBmCw4cPw97eHp6enpg3bx7S09Nx+PBhjBgxQnQ84QwMDODm5oZff/0VZWVl8g0amuDBgwdNvvdZK9/ExMS3FYfUQEpKCpKTk9G5c2eFdWtra5SVlQlKRURvGwsfRGqODwSKBg8ejB07dmDZsmUAnp5sqK+vx6pVqzBs2DDB6ZSnS5cuoiOQmoiLi4OPjw9GjhwpP9mwefNmeHt7AwBqa2uRkZEhOKU4CxcuxOLFixEWFqaw/umnn2LhwoUaV/ioqKjATz/9hKNHj2Lx4sUoKChAr169MHToUAwdOhQffPCB6IhEKmnOnDlYtmwZYmJi5BsxHj16hM8//xxz5swRnI5EkUqlTT4tV1lZ+ZbTqJ7169fj4cOHAIAlS5ZAR0cHJ06cgIeHBz755BPB6cR5dtIjNjYWaWlpMDc3x8SJE7F3717R0ZSmdevWr/27w9Pt9DxukCTSTGx1RWrF0tLylR9wSkpKlJhGNVRWVuLhw4fo2LGj/AX/iRMnYG1tjU8++QRSqVR0RKU6f/48RowYAScnJ6Snp8Pd3R35+fmorKxEZmYmunbtKjqiEMXFxfjyyy/lO8FsbW0xb948jf3zoP/o3bs3fH19ERAQAAD49ttv4efnh8jISEyfPh23bt1Cx44dNfah0cDAALm5uejWrZvCemFhIRwdHTXyZN3zioqKEB4ejtjYWNTX12vszwnR63z00UdIS0uDnp4eHB0dAQA5OTmora1ttFFFE2fWaart27fLv66oqEB4eDjc3NwU5sAkJycjJCQEgYGBomKqvC+++AIzZ85E69atRUd56yZMmIBDhw7BwMAAXl5e8Pb2lv+8aJI32ZTj6ur6FpOQuhg9ejT69OmDZcuWwdjYGLm5uejSpQsmTJiA+vp6jSocEmkSFj5IrURGRip8//jxY2RnZyMpKQkLFizAokWLBCUjVXL//n2sW7cOubm5kMlkcHJywuzZszW2n2tycjLc3d3Rq1cvDBw4EACQmZmJnJwcHDx4EO+//77ghCSSkZER8vLyYGlpKV87cuQI3N3d8a9//QsfffSRRhc+Ro8eDU9Pz0YzpmJiYhAfH4/k5GRBycSoqKhARkYGjh49iqNHj+LChQto3bq1fN7HvHnzREckUkmvm1P3PLZ21UweHh4YNmxYoxNA69evR2pqKg4cOCAmmBowMTHBuXPnYGVlJTrKW+ft7Q1vb2+4ublBW1tbdBwitZGfn4/hw4dzgySRhmHhg5qFr776CmfOnNGYB8U/0tOUNFfv3r3h5uaGL774QmF90aJFSElJwdmzZwUlI1XQsWNH7Nu3Dy4uLgrrGRkZ+PDDDzFv3jysWLFCYwsfGzZswNKlS+Hl5SX/Mzp16hT27NmD0NBQdOzYUX6vu7u7qJhKo62tjXbt2mHw4MHyweb29vaiYxERqT0jIyOcO3eu0QnDoqIi9OrVCzKZTFAy1WdsbCwfVExP2dvbIzExEebm5qKjKMW9e/ewZcsW+el2Ozs7+Pn5oVWrVoKTkSp4/PgxRo4ciRUrVuDw4cPIycnhBkkiDcHCBzULJSUl6NWr1xsVBNSZlpZWk/sBa+LLyp9++gkbN25ESUkJ9uzZg06dOmHnzp2wtLTEoEGDRMdTupYtWyIvLw/W1tYK65cvX4aDg4O8dzJpprFjx8LR0RGhoaGNrh09ehQffvghampqNPLfEuDpv7dNoSk9pPPz82FnZyc6BpHaqampQUNDg7y/eFlZGfbv3w9bW1vOxiEAT+ezBQQEYP78+Qrra9asQVRUFIfvvgILH41p0p/JmTNn4ObmBn19ffTt2xcAcPr0adTU1CAlJQVOTk6CE5IqaN++vbwlOBFpDg43p2Zh7969aNOmjegYSnPkyBH516WlpVi0aBGmTZum0A94+/btWLFihaiIwiQkJGDKlCnw9vbG2bNn8ejRIwBP218tX74ciYmJghMqX/v27XHu3LlGH/LOnTsHU1NTQalIVQQGBuLEiRMvvDZ06FAcPHgQO3bsUHIq1VFfXy86gkp5VvS4c+cOLl26BADo3r072rdvLzIWkcobM2YMxo0bh5kzZ+LevXvo27cvdHV1cffuXURERGDWrFmiI5JgoaGh8Pf3x9GjR9GvXz8AQFZWFpKSkhAdHS04HZHqCgwMhLu7O6Kjo9GixdNXXE+ePIG/vz8+/vhjHDt2THBCUgWTJ0/Gli1bGnVBIKLmjSc+SK307t1b4aRDQ0MDbt68iTt37uDf//43/va3vwlMJ8aIESPg7++PiRMnKqzHxcVh06ZNOHr0qJhggvTu3RuBgYHw8fFR2OmUnZ2NUaNG4ebNm6IjKl1YWBjWrl2LRYsWYcCAAQCezvhYuXIlgoKCEBISIjghkXq5d++eRgxQfZGqqirMnTsXO3bskBeFtLW14ePjg3Xr1sl3sxORonbt2iEjIwN2dnbYvHkz1q1bh+zsbCQkJGDp0qXy9iyk2bKyshAVFSX/ebCxsUFAQIC8EEIvpkmnG5pKk/5M9PX1kZ2djR49eiisX7hwAc7OzqiurhaUjFTJs8+v1tbW6NOnDwwNDRWuR0RECEpGRG8TT3yQWhk7dqzC91paWmjfvj2GDh3a6IOOpjh58iQ2bNjQaN3Z2Rn+/v4CEol16dIlDBkypNF6q1atcO/ePeUHUgEhISEwNjbGmjVrsHjxYgBP5zp89tlnCAgIEJyOVEldXR0OHDggf+Fia2uLMWPGaPTwzJUrV8LCwgLjx48HAHh6eiIhIQFmZmZITEyEo6Oj4ITKFRQUhIyMDBw8eBADBw4EABw/flzenuXrr78WnJBINVVXV8PY2BgAkJKSgnHjxkFLSwsuLi5sYURy/fr1Q2xsrOgYRGrFxMQE5eXljd4HXL16Vf7vLtH58+flbc8uX76scK2pbcSJSP2w8EFq5dNPPxUdQeWYm5sjOjoaq1atUljfvHmzxgyze16HDh1QVFQECwsLhfXjx49rxI6nF5FIJAgMDERgYCB+++03AOBDADVSVFSEP//5z7h27Rq6d+8OAFixYgXMzc3xww8/oGvXroITirFhwwb5S6jDhw8jNTUVSUlJ+Pbbb7FgwQKkpKQITqhcCQkJ2Lt3L4YOHSpfGz16NPT19eHl5cXCB9FLdOvWDQcOHMBHH32E5ORkBAYGAgBu374NExMTwelIlAcPHsj//79uViF/Tl5u8ODB0NfXFx2DBBk/fjymT5+O1atXK5xuX7BgQaOuCKS5nm8XTkSag4UPUju/35FsZ2cHd3d3jd2RvHbtWnh4eODHH3+UH4P/+eefUVhYiISEBMHplG/GjBmYN28etm7dColEguvXr+PkyZMIDg7W2JZO4eHh8Pb2hqWlJQse9FIBAQGwsrLCyZMn5TOTKioqMHnyZAQEBOCHH34QnFCMmzdvyovIhw4dgpeXFz744ANYWFhoZOuR6upqvPPOO43WTU1N2UqC6BWWLl2KSZMmITAwEMOHD5fPZUtJSUHv3r0FpyNRpFIpbty4AVNTU7Ru3fqFu44bGhogkUhQV1cnIKHyva4A9LxnxSBNnOFH/7F69WpIJBL4+PjgyZMnAAAdHR3MmjWL8xyIiDQcZ3yQWikqKsLo0aPxv//7v/IdyZcuXdL4HclXr17F119/jYsXLwJ42g945syZGnPiIzc3Fz179oSWlhYA4PPPP8eKFSvkL+H09PQQHByMZcuWiYwpjKOjI86fP49+/fph8uTJ8PLyQrt27UTHIhVjaGiIU6dOwd7eXmE9JycHAwcOhEwmE5RMrI4dO2Lv3r0YMGAAunfvjvDwcHh6euLSpUt477333ugFTXMwYsQItG3bFjt27EDLli0BADU1NZg6dSoqKyuRmpoqOCGR6rp58yZu3LgBR0dH+WeWn3/+GSYmJhrbslXTZWRkYODAgWjRogW2b98Oc3PzRpu56uvrUV5ejqlTpwpKqVxaWlqvbTujacWgPyIuLg5jxoxpNMegOauurkZxcTEAoGvXrpw7RkRELHyQehk9ejQaGhoQGxvbaEeylpaWxu5I1nTa2try3XJWVlY4ffo0jI2NUVRUBJlMBltbWxgZGYmOKVR+fj5iY2MRHx+Pa9eu4f3334e3tzfGjh3LhwICALRp0waHDh2Stwh4JjMzE3/5y19QWVkpKJlYc+bMwaFDh2BtbY3s7GyUlpbCyMgI8fHxWLVqFc6ePSs6olKdP38ebm5uePTokXy+SU5ODlq2bInk5GTY2dkJTkik2oqKilBcXIwhQ4ZAX19f/gKX6PnPs8+rqKiAqampxrzkz8jIaPK9rq6ubzGJ6oiKimryvZo4v2/Xrl0YN24cn2mIiKgRFj5IrXBH8lPPn3DIzc195b0ODg5KSiVO27ZtkZiYiH79+kFLSwu3bt1C+/btRcdSWZmZmYiLi8OePXvw8OFDjduxTi/m4+ODs2fPYsuWLejbty8AICsrCzNmzECfPn2wbds2sQEFefz4MSIjI3H16lVMmzZN3pJm7dq1MDY2hr+/v+CEylddXY3Y2FiFU4be3t7sr070ChUVFfDy8sKRI0cgkUhQWFgIKysr+Pn5QSqVYs2aNaIjkmAv+wxbVlYGW1tbVFVVCUpGollaWjbpPolEgpKSkrecRvW0b98eNTU1cHd3x+TJk+Hm5qaxbbCJiEgRZ3yQWtHT05MPZ36eTCaDrq6ugERi9OrVCzdv3oSpqSl69eoFiUSCF9UwNeUIuIeHB1xdXWFmZgaJRAJnZ+eXftjVxIeB3zM0NIS+vj50dXVf+PeJNFNUVBSmTp2K/v37Q0dHBwDw5MkTuLu7IzIyUnA6cXR0dBAcHNxo/dlgYk1kYGCAGTNmiI5BpFYCAwOho6OD8vJy2NjYyNfHjx+PoKAgFj40WFBQEICnn9tDQkIUdq3X1dUhKysLvXr1EpROvHv37mHLli0K8x39/PzQqlUrwcmU58qVK6IjqLQbN24gKSkJu3fvhpeXFwwMDODp6Qlvb+9GJ5mJiEiz8MQHqRXuSH6qrKwM7777LiQSCcrKyl55b5cuXZSUSqykpCQUFRUhICAAYWFhLx3iPW/ePCUnUw1XrlxBXFwc4uLicOnSJbi6umLSpEn461//qlEPjvR6hYWFCjv5u3XrJjiReIWFhThy5Ahu376N+vp6hWtLly4VlEp5vv/++ybf6+7u/haTEKmvDh06IDk5GY6OjjA2NkZOTg6srKxQUlICBwcHjTm1TI0NGzYMwNMWT/3791fYzKWrqwsLCwsEBwfD2tpaVERhzpw5Azc3N+jr68uf/U6fPo2amhqkpKTAyclJcEJSNdXV1di/fz/i4uKQmpqKzp07y+d+EBGR5mHhg9TKvXv3MHXqVBw8eLDRjuRt27Zp5AvcY8eOYcCAAWjRQvEA15MnT3DixAkMGTJEUDIxfH19ERUV9dLChyZycXHB6dOn4eDgAG9vb0ycOBGdOnUSHYtILURHR2PWrFlo164dOnTooNCLXyKRaMSMj2dDmF9HU04ZEv0RxsbGOHv2LKytrRUKH89e7FZUVIiOSIL5+voiMjISJiYmoqOojMGDB6Nbt26Ijo6WP+s8efIE/v7+KCkpwbFjxwQnFOPatWv4/vvvUV5ejtraWoVrERERglKpjrt37yI+Ph4bNmxAQUEBP5sQEWkwFj5ILRUVFcmPO2v6jmQOQqTXWbJkCby9vWFrays6CqmYoKAgLFu2DIaGhvJWGy+jqQ/SXbp0wT/+8Q/885//FB2FiNTY6NGj0adPHyxbtgzGxsbIzc1Fly5dMGHCBNTX12Pv3r2iIxKpHH19fWRnZ6NHjx4K6xcuXICzszOqq6sFJRMnLS0N7u7usLKywsWLF9GzZ0+UlpaioaEBTk5OSE9PFx1RiGcnPWJjY5GWlgZzc3NMnDgR3t7ejX5+iIhIc7DwQaTmXjYI8fLly3B2dubgamoyExMTnDt3DlZWVqKjkJK0adMGly9fRrt27eStNl5EIpFo7IM0/148/TkpLCxE27Zt4efnh8jISJ6qI3pD+fn5GD58uPzFpLu7O/Lz81FZWYnMzEx07dpVdEQilfPOO+9g586d+OCDDxTWk5OT4ePjg1u3bglKJk7fvn0xatQohIaGyk+PmZqawtvbGyNHjsSsWbNER1S6CRMm4NChQzAwMICXlxe8vb3Rv39/0bGIiEgFcLg5qRUPDw/07du30c7bVatW4fTp09izZ4+gZMo3btw4AE9fSE6bNg16enrya3V1dcjNzeUwN3ojrINrnnv37slnVpSVleH06dNo27at4FSqxdPTEykpKZg5c6boKMLU1tbi/v37aNu2LbZv346VK1ey8EH0Bh4/foyAgAAcPHgQhw8fhrGxMWQyGcaNG4fZs2fDzMxMdEQilTR+/HhMnz4dq1evlj/XZGZmYsGCBZg4caLgdGIUFBRg9+7dAIAWLVqgpqYGRkZGCAsLw5gxYzSy8KGtrY1vv/0Wbm5u0NbWFh2HiIhUCAsfpFaOHTuGzz77rNH6qFGjsGbNGuUHEujZPJOGhgYYGxtDX19ffk1XVxcuLi6YMWOGqHhEpAakUimuXLkCU1NTlJaWNhrcTUC3bt0QEhKCU6dOwd7eXj5f6pmAgABByZSnf//+GDt2LPr06YOGhgYEBAQo/M553tatW5Wcjkj16ejoIDc3F1KpFEuWLBEdh0htrF69GhKJBD4+Pnjy5AmAp3+fZs2ahS+++EJwOjEMDQ3lcz3MzMxQXFwMOzs7AE9nW2ii2NjYJt1nb2+PxMREmJubv+VERESkKlj4ILUik8mgq6vbaF1HR0fjWjrFxMQAACwsLBAcHAxDQ8NX3p+ZmQlnZ2eFkyFEpNk8PDzg6uoKMzMzSCQSODs7v3SnXElJiZLTqYZNmzbByMgIGRkZyMjIULgmkUg0ovCxa9curF27FsXFxZBIJLh//z4ePnwoOhaRWpk8eTK2bNmisS9rif4IXV1dREZGYsWKFSguLgYAdO3aFQYGBoKTiePi4oLjx4/DxsYGo0ePxvz585GXl4d9+/bBxcVFdDyVVlpaisePH4uOQURESsQZH6RW+vbtiw8//BBLly5VWP/ss89w8OBB/PLLL4KSqT72qafXedYnmD8jmiUpKQlFRUUICAhAWFjYS1sYzZs3T8nJSBVZWlrizJkzbIlG9Ibmzp2LHTt2wNraGn369Gm0YSUiIkJQMiLVtWvXLowbN06jCx2/V1JSAplMBgcHB1RVVWH+/Pk4ceIErK2tERERgS5duoiOqLL4rENEpHlY+CC1cvDgQYwbNw6TJk3C8OHDAQBpaWnYvXs39uzZg7Fjx4oNqML4QY9eh8Uxzebr64uoqCjObgAQFBSEZcuWwdDQEEFBQS+9TyKRaFybxaZiOwkiRcOGDXvpNYlEgvT0dCWmIVIP7du3R01NDdzd3TF58mTOcKD/Cp+HiYg0D1tdkVr5y1/+ggMHDmD58uXYu3cv9PX14eDggNTUVLi6uoqOR6TWWAfXbM/a5xGQnZ0tb4WQnZ390vskEomyIqkdtpMgUnTkyBHREYjUzo0bN5CUlITdu3fDy8sLBgYG8PT0hLe3t3zYOREREdHL8MQHkYbgDhd6nePHj+O9997jHBgi+q/xdw4REf1/qq6uxv79+xEXF4fU1FR07txZPvejuZNKpU3ebFFZWfmW06gvfjYhItI8PPFBaqm2tha3b99GfX29wvq7774rKBGRanlVe57fe9ZXfNCgQW8rDhERERHRH2ZgYAA3Nzf8+uuvKCsrQ0FBgehISvPll1/Kv66oqEB4eDjc3NzQv39/AMDJkyeRnJyMkJAQQQmJiIhUEwsfpFYKCwvh5+eHEydOKKw3NDRAIpGgrq5OUDLVx5YsmuX37XnOnj2LJ0+eoHv37gCAy5cvQ1tbG3369BERj4iIiIjotZ6d9IiNjUVaWhrMzc0xceJE7N27V3Q0pZk6dar8aw8PD4SFhWHOnDnytYCAAKxfvx6pqakIDAwUEVEtbNy4Ee+8847oGEREpEQsfJBamTZtGlq0aIFDhw7BzMyML/PfALvaaZbne4lHRETA2NgY27dvh1QqBQD8+uuv8PX1xeDBg0VFJCIiIiJ6qQkTJuDQoUMwMDCAl5cXQkJC5KccNFVycjJWrlzZaH3kyJFYtGiRgERiREVFNfnegIAAAMCkSZPeVhwiIlJRnPFBasXQ0BC//PILevToIToKkdro1KkTUlJSYGdnp7B+/vx5fPDBB7h+/bqgZETUXLGPNhER/be8vb3h7e0NNzc3aGtri46jErp06YKAgADMnz9fYX3NmjWIiopCWVmZoGTKZWlp2aT7JBIJSkpK3nIaIiJSVTzxQWrF1tYWd+/eFR1DuN69ezf5tMvZs2ffchpSdQ8ePMCdO3card+5cwe//fabgERE1NyxnQQREf23YmNjm3Sfvb09EhMTYW5u/pYTiRcaGgp/f38cPXoU/fr1AwBkZWUhKSkJ0dHRgtMpz5UrV0RHICIiNcDCB6mVlStXYuHChVi+fDns7e2ho6OjcN3ExERQMuUaO3as/OuHDx/i3//+N2xtbeVHv0+dOoX8/Hz84x//EJSQVMlHH30EX19frFmzBn379gXw9AFpwYIFGDdunOB0RKRu0tLSkJaWhtu3b6O+vl7h2tatWwGwnQQRESlPaWkpHj9+LDqGUkybNg02NjaIiorCvn37AAA2NjY4fvy4vBBCRERET7HVFakVLS0t+dfPn3jQ5OHm/v7+MDMzw7JlyxTWP/30U1y9elX+Eoo0V3V1NYKDg7F161b5Q2GLFi0wffp0/Otf/4KhoaHghESkLkJDQxEWFgZnZ+cXztrav3+/oGRERKSp2F6Rrl27hu+//x7l5eWora1VuBYRESEoFRERicbCB6mVjIyMV153dXVVUhLV0apVK5w5cwbW1tYK64WFhXB2dsb9+/cFJSNVU1VVheLiYgBA165dWfAgojdmZmaGVatWYcqUKaKjEBERAWj+hY8HDx7IOxs8ePDglfdqSgeE56WlpcHd3R1WVla4ePEievbsidLSUjQ0NMDJyQnp6emiIxIRkSBar7+FSHW4urpCS0sL0dHRWLRoEbp16wZXV1eUl5dr7MA7fX19ZGZmNlrPzMxEy5YtBSQiVXXjxg3cuHED1tbWMDQ0BOveRPSmamtrMWDAANExiIiINIZUKsXt27cBAK1bt4ZUKm3037N1TbR48WIEBwcjLy8PLVu2REJCAq5evQpXV1d4enqKjkdERAJxxgeplYSEBEyZMgXe3t7Izs7Go0ePAAD379/H8uXLkZiYKDih8n388ceYNWsWzp49qzC/YevWrQgJCRGcjlRBRUUFvLy8cOTIEUgkEhQWFsLKygrTp0+HVCrFmjVrREckIjXh7++PuLg4/n4hIiJSkvT0dLRp0wYAEBMTA3Nz80ab/urr61FeXi4innAFBQXYvXs3gKftfGtqamBkZISwsDCMGTMGs2bNEpyQiIhEYeGD1Ep4eDg2bNgAHx8fxMfHy9cHDhyI8PBwgcnEWbRoEaysrBAZGYldu3YBeDrgLiYmBl5eXoLTkSoIDAyEjo4OysvLYWNjI18fP348goKCWPggoiZ7+PAhNm3ahNTUVDg4OEBHR0fhOvtoExER/f96vp2zn58fbty4AVNTU4V7Kioq8Kc//QlTp05VdjzhDA0N5XM9zMzMUFxcDDs7OwDA3bt3RUYjIiLBWPggtXLp0iUMGTKk0XqrVq1w79495QdSEV5eXixy0EulpKQgOTkZnTt3Vli3trZGWVmZoFREpI5yc3PRq1cvAMD58+cVrv1+0DkREZEybNy4Ee+8847oGErR0NDwwt+3MplMY9scu7i44Pjx47CxscHo0aMxf/585OXlYd++fXBxcREdj4iIBGLhg9RKhw4dUFRUBAsLC4X148ePN9thdk1VW1uL27dvo76+XmH93XffFZSIVEVVVRUMDAwarVdWVkJPT09AIiJSV0eOHBEdgYiImrGoqKgm3xsQEAAAmDRp0tuKozKCgoIAPN1kEBISovDZvq6uDllZWfKNCZomIiICMpkMABAaGgqZTIZvvvkG1tbWPIlKRKThWPggtTJjxgzMmzcPW7duhUQiwfXr13Hy5EkEBwdrbL/xwsJC+Pn54cSJEwrrz3YD1dXVCUpGqmLw4MHYsWMHli1bBuDpA1N9fT1WrVqFYcOGCU5HROqoqKgIxcXFGDJkCPT19V+6A5WIiOhNrF27tkn3SSQSeeFDE2RnZwN4+oyXl5cHXV1d+TVdXV04OjoiODhYVDyhnt8AaWhoiA0bNghMQ0REqkTS0NDQIDoEUVM1NDRg+fLlWLFiBaqrqwEAenp6CA4Olr/U1TQDBw5EixYtsGjRIpiZmTV68eTo6CgoGamK8+fPY8SIEXByckJ6ejrc3d2Rn5+PyspKZGZmomvXrqIjEpGaqKiogJeXF44cOQKJRILCwkJYWVnBz88PUqmUM4OIiIjeIl9fX0RGRsLExER0FCIiIpXHwgeppdraWhQVFUEmk8HW1hZGRkaiIwljaGiIX375BT169BAdhVTY/fv3sW7dOuTm5kImk8HJyQmzZ8+GmZmZ6GhEpEZ8fHxw+/ZtbN68GTY2NsjJyYGVlRWSk5MRFBSE/Px80RGJiIiomZNKpU0+aVpZWfmW0xARkapiqytSS7q6urC1tRUdQyXY2tri7t27omOQimvVqhU++eQT0TGISM2lpKQgOTkZnTt3Vli3trZGWVmZoFRERNRcXbt2Dd9//z3Ky8tRW1urcI3zGzTXl19+Kf+6oqIC4eHhcHNzQ//+/QEAJ0+eRHJyssa2wyYioqdY+CBScytXrsTChQuxfPly2NvbQ0dHR+E6j0ETAPz000/YuHEjSkpKsGfPHnTq1Ak7d+6EpaUlBg0aJDoeEamJqqoqhYGqz1RWVkJPT09AIiIiaq7S0tLg7u4OKysrXLx4ET179kRpaSkaGhrg5OQkOh4JNHXqVPnXHh4eCAsLw5w5c+RrAQEBWL9+PVJTUxEYGCgiIhERqQAt0QGI6L/zpz/9CadOncLw4cNhamoKqVQKqVSK1q1bQyqVio5HKiAhIQFubm7Q19fH2bNn8ejRIwBP218tX75ccDoiUieDBw/Gjh075N9LJBLU19dj1apVGDZsmMBkRETU3CxevBjBwcHIy8tDy5YtkZCQgKtXr8LV1RWenp6i45GKSE5OxsiRIxutjxw5EqmpqQISERGRquCJDyI1d+TIEdERSMWFh4djw4YN8PHxQXx8vHx94MCBCA8PF5iMiNTNqlWrMGLECJw5cwa1tbVYuHAh8vPzUVlZiczMTNHxiIioGSkoKMDu3bsBAC1atEBNTQ2MjIwQFhaGMWPGYNasWYITkipo27YtvvvuO8yfP19h/bvvvkPbtm0FpSIiIlXAwgeRmnN1dZW3MSouLsbevXsV2hgRXbp0CUOGDGm03qpVK9y7d0/5gYhIbfXs2ROXL1/G+vXrYWxsDJlMhnHjxmH27NkwMzMTHY+IiJoRQ0ND+VwPMzMzFBcXw87ODgA445DkQkND4e/vj6NHj6Jfv34AgKysLCQlJSE6OlpwOiIiEomFDyI1l5CQgClTpsDb2xvZ2dmN2hglJiYKTkiidejQAUVFRbCwsFBYP378OKysrMSEIiK11apVKyxZskR0DCIiauZcXFxw/Phx2NjYYPTo0Zg/fz7y8vKwb98+uLi4iI5HKmLatGmwsbFBVFQU9u3bBwCwsbHB8ePH5YUQIiLSTJKGhoYG0SGI6I/r3bs3AgMD4ePjA2NjY+Tk5MDKygrZ2dkYNWoUbt68KToiCbZixQrs2rULW7duxfvvv4/ExESUlZUhMDAQISEhmDt3ruiIRKQmcnNzX7gukUjQsmVLvPvuuxxyTkRE/y9KSkogk8ng4OCAqqoqzJ8/HydOnIC1tTUiIiLQpUsX0RGJiIhIhbHwQaTmDAwMcOHCBVhYWCgUPkpKSmBra4uHDx+KjkgC5ObmomfPntDS0gIAfP7551ixYgWqq6sBAHp6eggODsayZctExiQiNaOlpQWJRAIAePYR8tn3AKCjo4Px48dj48aNaNmypZCMRERE1Lw9ePAAJiYm8q9f5dl9RESkebREByCi/86zNka/xzZGmq13797y3sdWVlaYOXMmKisrcf78eZw6dQp37txh0YOI3tj+/fthbW2NTZs2IScnBzk5Odi0aRO6d++OuLg4bNmyBenp6fjkk09ERyUiIqJmSiqV4vbt2wCA1q1bQyqVNvrv2ToREWkuzvggUnMzZszAvHnzsHXrVkgkEly/fh0nT55EcHAwQkJCRMcjQVq3bo0rV67A1NQUpaWlqK+vh66uLmxtbUVHIyI19vnnnyMyMhJubm7yNXt7e3Tu3BkhISH4+eefYWhoiPnz52P16tUCkxIRkTqSSqUKJwlfpbKy8i2nIVWVnp6ONm3aAABiYmJgbm4ObW1thXvq6+tRXl4uIh4REakItroiUnMNDQ1Yvnw52xiRgr/97W/YsWMHzMzMUF5ejs6dOzd6GHimpKREyemISF3p6+sjOzsbPXr0UFi/ePEievfujZqaGpSWlsLW1lb+O4mIiKiptm/fLv+6oqIC4eHhcHNzQ//+/QEAJ0+eRHJyMkJCQhAYGCgqJqkQbW1t3LhxA6ampgrrFRUVMDU1RV1dnaBkREQkGgsfRM1EbW0tioqKIJPJYGtrCyMjI9GRSLCkpCQUFRUhICAAYWFhMDY2fuF98+bNU3IyIlJXvXv3hqOjIzZt2gRdXV0AwOPHjzFjxgzk5OQgOzsbmZmZmDx5Mq5cuSI4LRERqTMPDw8MGzYMc+bMUVhfv349UlNTceDAATHBSKVoaWnh1q1baN++vcJ6WVkZbG1tUVVVJSgZERGJxsIHEVEz5+vri6ioqJcWPoiImurEiRNwd3eHlpYWHBwcAAB5eXmoq6vDoUOH4OLigp07d+LmzZtYsGCB4LRERKTOjIyMcO7cOXTr1k1hvaioCL169YJMJhOUjFRBUFAQACAyMhIzZsyAgYGB/FpdXR2ysrKgra2NzMxMURGJiEgwzvggImrmYmJiREcgomZiwIABuHLlCmJjY3H58mUAgKenJyZNmiQvrk6ZMkVkRCIiaibatm2L7777DvPnz1dY/+6779C2bVtBqUhVZGdnA3ja+jkvL09+EhUAdHV14ejoiODgYFHxiIhIBfDEBxERERE1SVVVFQwNDUXHICIiDbBt2zb4+/tj1KhR6NevHwAgKysLSUlJiI6OxrRp08QGJJXg6+uLyMhImJiYiI5CREQqhoUPIiIiImoSIyMjeHl5wc/PD4MGDRIdh4iImrmsrCxERUWhoKAAAGBjY4OAgAB5IYSIiIjoZVj4ICIiIqImOXDgALZt24bExERYWFjAz88PPj4+6Nixo+hoRERERERERHIsfBARERHRG7lz5w527tyJbdu2oaCgAG5ubvDz84O7uztatOAIOSIi+mMePHggb1n04MGDV97L1kZERET0Kix8EBEREdEftm7dOixYsAC1tbVo164dZs6ciUWLFsHAwEB0NCIiUjPa2tq4ceMGTE1NoaWlBYlE0uiehoYGSCQS1NXVCUhIRERE6oJb8oiIiIjojdy6dQvbt2/Htm3bUFZWhr/+9a+YPn06rl27hpUrV+LUqVNISUkRHZOIiNRMeno62rRpAwCIiYmBubk5tLW1Fe6pr69HeXm5iHhERESkRnjig4iIiIiaZN++fYiJiUFycjJsbW3h7++PyZMno3Xr1vJ7iouLYWNjg9raWnFBiYhI7T1/+uN5FRUVMDU15YkPIiIieiWe+CAiIiKiJvH19cWECROQmZmJ995774X3dOzYEUuWLFFyMiIiam6etbT6PZlMhpYtWwpIREREROqEJz6IiIiIqEmqq6s5u4OIiN6qoKAgAEBkZCRmzJih8Hunrq4OWVlZ0NbWRmZmpqiIREREpAa0RAcgIiIiIvXw7bffvnD9yZMnWLx4sZLTEBFRc5SdnY3s7Gw0NDQgLy9P/n12djYuXrwIR0dHbNu2TXRMIiIiUnE88UFERERETWJiYgI3Nzds2rQJUqkUAHDp0iVMmjQJFRUVKC0tFRuQiIiaDV9fX0RGRsLExER0FCIiIlJDPPFBRERERE2SnZ2Na9euwd7eHocPH8ZXX30FJycn9OjRAzk5OaLjERFRMxITE8OiBxEREf1hPPFBRERERE1WX1+Pjz/+GF999RW0tbWxfft2TJw4UXQsIiIiIiIiIjme+CAiIiKiJvvhhx8QHx+P/v37o3Xr1tiyZQuuX78uOhYRERERERGRHAsfRERERNQkf//73+Hp6YmFCxfip59+Qm5uLnR1dWFvb//SwedEREREREREysZWV0RERETUJD179kRsbCwcHR0V1r/66iv885//hEwmE5SMiIiIiIiI6D9Y+CAiIiKiJnn06BFqamqwZcsWFBQUAADs7Ozg5+eHmzdvonv37oITEhEREREREbHwQURERERNdObMGbi5uUFfXx99+/YFAJw+fRo1NTVISUmBk5OT4IRERERERERELHwQERERURMNHjwY3bp1Q3R0NFq0aAEAePLkCfz9/VFSUoJjx44JTkhERERERETEwgcRERERNZG+vj6ys7PRo0cPhfULFy7A2dkZ1dXVgpIRERERERER/YeW6ABEREREpB5MTExQXl7eaP3q1aswNjYWkIiIiIiIiIioMRY+iIiIiKhJxo8fj+nTp+Obb77B1atXcfXqVcTHx8Pf3x8TJ04UHY+IiIiIiIgIANBCdAAiIiIiUg+rV6+GRCKBj48Pnjx5AgDQ0dHBrFmz8MUXXwhOR0RERERERPQUZ3wQERER0Ruprq5GcXExAKBr164wMDAQnIiIiIiIiIjoP1j4ICIiIiIiIiIiIiKiZoMzPoiIiIiIiIiIiIiIqNlg4YOIiIiIiIiIiIiIiJoNFj6IiIiIiIiIiIiIiKjZYOGDiIiIiIiIiIiIiIiaDRY+iIiIiIiIiIiIiIio2WDhg4iIiIiIiIiIiIiImg0WPoiIiIiIiIiIiIiIqNlg4YOIiIiIiIiIiIiIiJqN/wMkOAtOw4hPqwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 2000x2000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "corr_matrix = df.corr()\n",
+    "fig, ax = plt.subplots(figsize=(20,20))\n",
+    "sns.heatmap(corr_matrix, annot= True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of referral: 1600 (29.72%)\n",
+      "Number of non referral: 3784\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dataset_filtered = df[df['sip'] != 0]\n",
+    "sns.scatterplot(x='sip', y='referral', data=dataset_filtered)\n",
+    "plt.title('sip vs. Referral Status')\n",
+    "plt.xlabel('sip')\n",
+    "plt.ylabel('referral')\n",
+    "num_ref = dataset_filtered[dataset_filtered['referral'] == 1]['referral'].count()\n",
+    "num_non_ref = dataset_filtered[dataset_filtered['referral'] == 0]['referral'].count()\n",
+    "total_ref = num_ref + num_non_ref\n",
+    "ref_percentage = (num_ref / total_ref) * 100\n",
+    "non_spam_percentage = (num_non_ref / total_ref) * 100\n",
+    "print(f\"Number of referral: {num_ref} ({ref_percentage:.2f}%)\")\n",
+    "print(f\"Number of non referral: {num_non_ref}\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.drop(columns=\"sip\", inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 3000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "null_counts = df.drop(\"referral\", axis=1).isnull().sum()\n",
+    "null_counts = null_counts.sort_values(ascending=False)\n",
+    "fig, ax = plt.subplots(figsize=(30, 8))\n",
+    "ax.bar(range(len(null_counts)), null_counts)\n",
+    "ax.set_xticks(range(len(null_counts)))\n",
+    "ax.set_xticklabels(null_counts.index, rotation=90)\n",
+    "ax.set_xlabel(\"Features\")\n",
+    "ax.set_ylabel(\"Number of Nulls\")\n",
+    "ax.set_title(\"Count of Nulls in CCU Dataset Features\")\n",
+    "\n",
+    "# add labels for total count of nulls\n",
+    "rects = ax.patches\n",
+    "for rect in rects:\n",
+    "    height = rect.get_height()\n",
+    "    ax.annotate(f\"{height}\", xy=(rect.get_x() + rect.get_width() / 2, height),\n",
+    "                xytext=(0, 3), textcoords=\"offset points\", ha=\"center\", va=\"bottom\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.neural_network import MLPClassifier\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.metrics import accuracy_score, confusion_matrix, roc_curve, auc, precision_score, f1_score\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tf.config.experimental.set_memory_growth(tf.config.list_physical_devices('GPU')[0], True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df = df.dropna()\n",
+    "imputer = SimpleImputer(strategy='median')\n",
+    "\n",
+    "# Fit the imputer on the data\n",
+    "imputer.fit(df)\n",
+    "\n",
+    "# Transform the data with the fitted imputer\n",
+    "imputed_df = imputer.transform(df)\n",
+    "\n",
+    "# Convert the imputed data back to a DataFrame\n",
+    "imputed_df = pd.DataFrame(imputed_df, columns=df.columns)\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(imputed_df.iloc[:, :-1], imputed_df.iloc[:, -1], test_size=0.3, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 15 folds for each of 10 candidates, totalling 150 fits\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(256, 100, 10), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=500, mlpclassifier__momentum=0.9, mlpclassifier__solver=sgd; total time=   3.4s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(256, 100, 10), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=500, mlpclassifier__momentum=0.9, mlpclassifier__solver=sgd; total time=   3.5s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(256, 100, 10), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=500, mlpclassifier__momentum=0.9, mlpclassifier__solver=sgd; total time=   3.7s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(256, 100, 10), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=500, mlpclassifier__momentum=0.9, mlpclassifier__solver=sgd; total time=   3.7s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(256, 100, 10), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=500, mlpclassifier__momentum=0.9, mlpclassifier__solver=sgd; total time=   3.8s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(256, 100, 10), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=500, mlpclassifier__momentum=0.9, mlpclassifier__solver=sgd; total time=   3.8s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(256, 100, 10), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=500, mlpclassifier__momentum=0.9, mlpclassifier__solver=sgd; total time=   4.1s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(256, 100, 10), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=500, mlpclassifier__momentum=0.9, mlpclassifier__solver=sgd; total time=   6.0s\n",
+      "[CV] END mlpclassifier__activation=tanh, mlpclassifier__alpha=0.001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=sgd; total time=   0.2s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(256, 100, 10), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=500, mlpclassifier__momentum=0.9, mlpclassifier__solver=sgd; total time=   3.1s\n",
+      "[CV] END mlpclassifier__activation=tanh, mlpclassifier__alpha=0.001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=sgd; total time=   0.5s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(256, 100, 10), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=500, mlpclassifier__momentum=0.9, mlpclassifier__solver=sgd; total time=   3.0s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(256, 100, 10), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=500, mlpclassifier__momentum=0.9, mlpclassifier__solver=sgd; total time=   3.0s\n",
+      "[CV] END mlpclassifier__activation=tanh, mlpclassifier__alpha=0.001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=sgd; total time=   0.3s\n",
+      "[CV] END mlpclassifier__activation=tanh, mlpclassifier__alpha=0.001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=sgd; total time=   0.3s\n",
+      "[CV] END mlpclassifier__activation=tanh, mlpclassifier__alpha=0.001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=sgd; total time=   0.3s\n",
+      "[CV] END mlpclassifier__activation=tanh, mlpclassifier__alpha=0.001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=sgd; total time=   0.4s\n",
+      "[CV] END mlpclassifier__activation=tanh, mlpclassifier__alpha=0.001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=sgd; total time=   0.3s\n",
+      "[CV] END mlpclassifier__activation=tanh, mlpclassifier__alpha=0.001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=sgd; total time=   0.3s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(256, 100, 10), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=500, mlpclassifier__momentum=0.9, mlpclassifier__solver=sgd; total time=   3.8s\n",
+      "[CV] END mlpclassifier__activation=tanh, mlpclassifier__alpha=0.001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=sgd; total time=   0.2s\n",
+      "[CV] END mlpclassifier__activation=tanh, mlpclassifier__alpha=0.001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=sgd; total time=   0.2s\n",
+      "[CV] END mlpclassifier__activation=tanh, mlpclassifier__alpha=0.001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=sgd; total time=   0.3s\n",
+      "[CV] END mlpclassifier__activation=tanh, mlpclassifier__alpha=0.001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=sgd; total time=   0.2s\n",
+      "[CV] END mlpclassifier__activation=tanh, mlpclassifier__alpha=0.001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=sgd; total time=   0.4s\n",
+      "[CV] END mlpclassifier__activation=tanh, mlpclassifier__alpha=0.001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=sgd; total time=   0.3s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(256, 100, 10), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=500, mlpclassifier__momentum=0.9, mlpclassifier__solver=sgd; total time=   3.9s\n",
+      "[CV] END mlpclassifier__activation=tanh, mlpclassifier__alpha=0.001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=sgd; total time=   0.4s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:684: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:684: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=100, mlpclassifier__momentum=0.9, mlpclassifier__solver=sgd; total time=   0.7s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=100, mlpclassifier__momentum=0.9, mlpclassifier__solver=sgd; total time=   0.7s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:684: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:684: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:684: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:684: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=100, mlpclassifier__momentum=0.9, mlpclassifier__solver=sgd; total time=   0.7s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=100, mlpclassifier__momentum=0.9, mlpclassifier__solver=sgd; total time=   0.7s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=100, mlpclassifier__momentum=0.9, mlpclassifier__solver=sgd; total time=   0.7s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(256, 100, 10), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=500, mlpclassifier__momentum=0.9, mlpclassifier__solver=sgd; total time=   4.6s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=100, mlpclassifier__momentum=0.9, mlpclassifier__solver=sgd; total time=   0.7s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:684: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:684: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=100, mlpclassifier__momentum=0.9, mlpclassifier__solver=sgd; total time=   0.7s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(256, 100, 10), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=500, mlpclassifier__momentum=0.9, mlpclassifier__solver=sgd; total time=   4.6s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=100, mlpclassifier__momentum=0.9, mlpclassifier__solver=sgd; total time=   0.7s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:684: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:684: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:684: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:684: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:684: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=100, mlpclassifier__momentum=0.9, mlpclassifier__solver=sgd; total time=   0.7s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=100, mlpclassifier__momentum=0.9, mlpclassifier__solver=sgd; total time=   0.7s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=100, mlpclassifier__momentum=0.9, mlpclassifier__solver=sgd; total time=   0.7s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=100, mlpclassifier__momentum=0.9, mlpclassifier__solver=sgd; total time=   0.7s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=100, mlpclassifier__momentum=0.9, mlpclassifier__solver=sgd; total time=   0.7s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:684: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:684: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=100, mlpclassifier__momentum=0.9, mlpclassifier__solver=sgd; total time=   0.8s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=100, mlpclassifier__momentum=0.9, mlpclassifier__solver=sgd; total time=   0.8s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=256, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=lbfgs; total time=   4.6s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=256, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=lbfgs; total time=   4.6s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=256, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=lbfgs; total time=   4.9s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=256, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=lbfgs; total time=   5.8s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=256, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=lbfgs; total time=   6.2s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=256, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=lbfgs; total time=   7.2s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=256, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=lbfgs; total time=   4.1s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=256, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=lbfgs; total time=   4.9s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=256, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=lbfgs; total time=   9.6s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   0.4s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=256, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=lbfgs; total time=  10.3s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   0.6s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   0.4s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   0.6s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   0.6s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   0.5s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   0.6s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   0.9s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   0.6s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   0.8s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   0.4s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   0.7s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   0.6s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=256, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=lbfgs; total time=   7.6s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=256, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=invscaling, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.99, mlpclassifier__solver=sgd; total time=   0.2s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=256, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=invscaling, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.99, mlpclassifier__solver=sgd; total time=   0.2s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=256, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=invscaling, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.99, mlpclassifier__solver=sgd; total time=   0.2s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=256, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=invscaling, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.99, mlpclassifier__solver=sgd; total time=   0.1s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   0.6s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=256, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=invscaling, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.99, mlpclassifier__solver=sgd; total time=   0.1s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   0.6s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=256, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=invscaling, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.99, mlpclassifier__solver=sgd; total time=   0.1s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=256, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=invscaling, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.99, mlpclassifier__solver=sgd; total time=   0.1s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=256, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=invscaling, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.99, mlpclassifier__solver=sgd; total time=   0.2s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=256, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=lbfgs; total time=   7.4s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=256, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=invscaling, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.99, mlpclassifier__solver=sgd; total time=   0.2s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=256, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=invscaling, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.99, mlpclassifier__solver=sgd; total time=   0.2s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=256, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=invscaling, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.99, mlpclassifier__solver=sgd; total time=   0.2s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=256, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=invscaling, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.99, mlpclassifier__solver=sgd; total time=   0.2s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=256, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=invscaling, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.99, mlpclassifier__solver=sgd; total time=   0.1s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=256, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=invscaling, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.99, mlpclassifier__solver=sgd; total time=   0.2s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=256, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=invscaling, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.99, mlpclassifier__solver=sgd; total time=   0.4s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=256, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=lbfgs; total time=   7.4s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=256, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=lbfgs; total time=   5.1s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=256, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(50, 50), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=lbfgs; total time=   8.4s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(100, 50, 25), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=lbfgs; total time=  11.2s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(100, 50, 25), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=lbfgs; total time=  11.6s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(100, 50, 25), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=lbfgs; total time=  13.1s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(100, 50, 25), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=lbfgs; total time=  15.3s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(100, 50, 25), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=lbfgs; total time=  16.4s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(100, 50, 25), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=lbfgs; total time=  17.4s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(100, 50, 25), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=lbfgs; total time=  18.9s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(100, 50, 25), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=lbfgs; total time=  17.6s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(100, 50, 25), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=lbfgs; total time=  14.6s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(100, 50, 25), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=lbfgs; total time=  12.5s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(100, 50, 25), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=lbfgs; total time=  12.7s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(100, 50, 25), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=lbfgs; total time=  16.3s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   0.5s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   9.9s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:684: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   1.2s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(100, 50, 25), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=lbfgs; total time=  19.7s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   0.4s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   0.6s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:684: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   9.9s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(100, 50, 25), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=lbfgs; total time=  17.5s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   0.8s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=True, mlpclassifier__hidden_layer_sizes=(100, 50, 25), mlpclassifier__learning_rate=constant, mlpclassifier__learning_rate_init=0.01, mlpclassifier__max_iter=2000, mlpclassifier__momentum=0.99, mlpclassifier__solver=lbfgs; total time=  20.6s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   0.8s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   0.8s\n",
+      "[CV] END mlpclassifier__activation=tanh, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(256, 100, 10), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=1500, mlpclassifier__momentum=0.99, mlpclassifier__solver=adam; total time=   0.8s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:684: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:684: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   9.9s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   9.7s\n",
+      "[CV] END mlpclassifier__activation=tanh, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(256, 100, 10), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=1500, mlpclassifier__momentum=0.99, mlpclassifier__solver=adam; total time=   0.9s\n",
+      "[CV] END mlpclassifier__activation=tanh, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(256, 100, 10), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=1500, mlpclassifier__momentum=0.99, mlpclassifier__solver=adam; total time=   0.9s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=  10.1s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:684: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END mlpclassifier__activation=tanh, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(256, 100, 10), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=1500, mlpclassifier__momentum=0.99, mlpclassifier__solver=adam; total time=   0.8s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:684: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=  10.1s\n",
+      "[CV] END mlpclassifier__activation=tanh, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(256, 100, 10), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=1500, mlpclassifier__momentum=0.99, mlpclassifier__solver=adam; total time=   0.8s\n",
+      "[CV] END mlpclassifier__activation=tanh, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(256, 100, 10), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=1500, mlpclassifier__momentum=0.99, mlpclassifier__solver=adam; total time=   1.0s\n",
+      "[CV] END mlpclassifier__activation=tanh, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(256, 100, 10), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=1500, mlpclassifier__momentum=0.99, mlpclassifier__solver=adam; total time=   1.1s\n",
+      "[CV] END mlpclassifier__activation=tanh, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(256, 100, 10), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=1500, mlpclassifier__momentum=0.99, mlpclassifier__solver=adam; total time=   1.0s\n",
+      "[CV] END mlpclassifier__activation=tanh, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(256, 100, 10), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=1500, mlpclassifier__momentum=0.99, mlpclassifier__solver=adam; total time=   0.9s\n",
+      "[CV] END mlpclassifier__activation=tanh, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(256, 100, 10), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=1500, mlpclassifier__momentum=0.99, mlpclassifier__solver=adam; total time=   0.8s\n",
+      "[CV] END mlpclassifier__activation=tanh, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(256, 100, 10), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=1500, mlpclassifier__momentum=0.99, mlpclassifier__solver=adam; total time=   0.9s\n",
+      "[CV] END mlpclassifier__activation=tanh, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(256, 100, 10), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=1500, mlpclassifier__momentum=0.99, mlpclassifier__solver=adam; total time=   0.9s\n",
+      "[CV] END mlpclassifier__activation=tanh, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(256, 100, 10), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=1500, mlpclassifier__momentum=0.99, mlpclassifier__solver=adam; total time=   1.0s\n",
+      "[CV] END mlpclassifier__activation=tanh, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(256, 100, 10), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=1500, mlpclassifier__momentum=0.99, mlpclassifier__solver=adam; total time=   1.0s\n",
+      "[CV] END mlpclassifier__activation=tanh, mlpclassifier__alpha=0.0001, mlpclassifier__batch_size=64, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(256, 100, 10), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.1, mlpclassifier__max_iter=1500, mlpclassifier__momentum=0.99, mlpclassifier__solver=adam; total time=   1.0s\n",
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   9.7s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:684: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100, 50, 25), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=1500, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   2.3s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100, 50, 25), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=1500, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   2.9s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100, 50, 25), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=1500, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   3.1s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100, 50, 25), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=1500, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   2.6s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100, 50, 25), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=1500, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   3.2s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100, 50, 25), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=1500, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   2.9s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100, 50, 25), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=1500, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   3.4s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:684: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END mlpclassifier__activation=logistic, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100,), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=1000, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=  10.0s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100, 50, 25), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=1500, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   2.9s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100, 50, 25), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=1500, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   2.2s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100, 50, 25), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=1500, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   2.1s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100, 50, 25), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=1500, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   2.0s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100, 50, 25), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=1500, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   2.1s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100, 50, 25), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=1500, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   2.1s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100, 50, 25), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=1500, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   2.4s\n",
+      "[CV] END mlpclassifier__activation=relu, mlpclassifier__alpha=0.01, mlpclassifier__batch_size=128, mlpclassifier__early_stopping=False, mlpclassifier__hidden_layer_sizes=(100, 50, 25), mlpclassifier__learning_rate=adaptive, mlpclassifier__learning_rate_init=0.001, mlpclassifier__max_iter=1500, mlpclassifier__momentum=0.9, mlpclassifier__solver=adam; total time=   1.9s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:684: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=15,\n",
+       "                   estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;,\n",
+       "                                              StandardScaler()),\n",
+       "                                             (&#x27;mlpclassifier&#x27;,\n",
+       "                                              MLPClassifier())]),\n",
+       "                   n_jobs=-1,\n",
+       "                   param_distributions={&#x27;mlpclassifier__activation&#x27;: [&#x27;tanh&#x27;,\n",
+       "                                                                      &#x27;relu&#x27;,\n",
+       "                                                                      &#x27;logistic&#x27;],\n",
+       "                                        &#x27;mlpclassifier__alpha&#x27;: [0.0001, 0.001,\n",
+       "                                                                 0.01],\n",
+       "                                        &#x27;mlpclassifier__batch_size&#x27;: [64, 128,\n",
+       "                                                                      256],\n",
+       "                                        &#x27;mlpclassifier__early_stopping&#x27;: [True,\n",
+       "                                                                          False],\n",
+       "                                        &#x27;mlpclassifier__hidden_layer_sizes&#x27;: [(100,),\n",
+       "                                                                              (50,\n",
+       "                                                                               50),\n",
+       "                                                                              (100,\n",
+       "                                                                               50,\n",
+       "                                                                               25),\n",
+       "                                                                              (256,\n",
+       "                                                                               100,\n",
+       "                                                                               10)],\n",
+       "                                        &#x27;mlpclassifier__learning_rate&#x27;: [&#x27;constant&#x27;,\n",
+       "                                                                         &#x27;invscaling&#x27;,\n",
+       "                                                                         &#x27;adaptive&#x27;],\n",
+       "                                        &#x27;mlpclassifier__learning_rate_init&#x27;: [0.001,\n",
+       "                                                                              0.01,\n",
+       "                                                                              0.1],\n",
+       "                                        &#x27;mlpclassifier__max_iter&#x27;: [100, 500,\n",
+       "                                                                    1000, 1500,\n",
+       "                                                                    2000],\n",
+       "                                        &#x27;mlpclassifier__momentum&#x27;: [0.9, 0.95,\n",
+       "                                                                    0.99],\n",
+       "                                        &#x27;mlpclassifier__solver&#x27;: [&#x27;lbfgs&#x27;,\n",
+       "                                                                  &#x27;sgd&#x27;,\n",
+       "                                                                  &#x27;adam&#x27;]},\n",
+       "                   verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=15,\n",
+       "                   estimator=Pipeline(steps=[(&#x27;standardscaler&#x27;,\n",
+       "                                              StandardScaler()),\n",
+       "                                             (&#x27;mlpclassifier&#x27;,\n",
+       "                                              MLPClassifier())]),\n",
+       "                   n_jobs=-1,\n",
+       "                   param_distributions={&#x27;mlpclassifier__activation&#x27;: [&#x27;tanh&#x27;,\n",
+       "                                                                      &#x27;relu&#x27;,\n",
+       "                                                                      &#x27;logistic&#x27;],\n",
+       "                                        &#x27;mlpclassifier__alpha&#x27;: [0.0001, 0.001,\n",
+       "                                                                 0.01],\n",
+       "                                        &#x27;mlpclassifier__batch_size&#x27;: [64, 128,\n",
+       "                                                                      256],\n",
+       "                                        &#x27;mlpclassifier__early_stopping&#x27;: [True,\n",
+       "                                                                          False],\n",
+       "                                        &#x27;mlpclassifier__hidden_layer_sizes&#x27;: [(100,),\n",
+       "                                                                              (50,\n",
+       "                                                                               50),\n",
+       "                                                                              (100,\n",
+       "                                                                               50,\n",
+       "                                                                               25),\n",
+       "                                                                              (256,\n",
+       "                                                                               100,\n",
+       "                                                                               10)],\n",
+       "                                        &#x27;mlpclassifier__learning_rate&#x27;: [&#x27;constant&#x27;,\n",
+       "                                                                         &#x27;invscaling&#x27;,\n",
+       "                                                                         &#x27;adaptive&#x27;],\n",
+       "                                        &#x27;mlpclassifier__learning_rate_init&#x27;: [0.001,\n",
+       "                                                                              0.01,\n",
+       "                                                                              0.1],\n",
+       "                                        &#x27;mlpclassifier__max_iter&#x27;: [100, 500,\n",
+       "                                                                    1000, 1500,\n",
+       "                                                                    2000],\n",
+       "                                        &#x27;mlpclassifier__momentum&#x27;: [0.9, 0.95,\n",
+       "                                                                    0.99],\n",
+       "                                        &#x27;mlpclassifier__solver&#x27;: [&#x27;lbfgs&#x27;,\n",
+       "                                                                  &#x27;sgd&#x27;,\n",
+       "                                                                  &#x27;adam&#x27;]},\n",
+       "                   verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
+       "                (&#x27;mlpclassifier&#x27;, MLPClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomizedSearchCV(cv=15,\n",
+       "                   estimator=Pipeline(steps=[('standardscaler',\n",
+       "                                              StandardScaler()),\n",
+       "                                             ('mlpclassifier',\n",
+       "                                              MLPClassifier())]),\n",
+       "                   n_jobs=-1,\n",
+       "                   param_distributions={'mlpclassifier__activation': ['tanh',\n",
+       "                                                                      'relu',\n",
+       "                                                                      'logistic'],\n",
+       "                                        'mlpclassifier__alpha': [0.0001, 0.001,\n",
+       "                                                                 0.01],\n",
+       "                                        'mlpclassifier__batch_size': [64, 128,\n",
+       "                                                                      256],\n",
+       "                                        'mlpclassifier__early_stopping': [True,\n",
+       "                                                                          False],\n",
+       "                                        'mlpclassifier__hidden_layer_sizes': [(100,),\n",
+       "                                                                              (50,\n",
+       "                                                                               50),\n",
+       "                                                                              (100,\n",
+       "                                                                               50,\n",
+       "                                                                               25),\n",
+       "                                                                              (256,\n",
+       "                                                                               100,\n",
+       "                                                                               10)],\n",
+       "                                        'mlpclassifier__learning_rate': ['constant',\n",
+       "                                                                         'invscaling',\n",
+       "                                                                         'adaptive'],\n",
+       "                                        'mlpclassifier__learning_rate_init': [0.001,\n",
+       "                                                                              0.01,\n",
+       "                                                                              0.1],\n",
+       "                                        'mlpclassifier__max_iter': [100, 500,\n",
+       "                                                                    1000, 1500,\n",
+       "                                                                    2000],\n",
+       "                                        'mlpclassifier__momentum': [0.9, 0.95,\n",
+       "                                                                    0.99],\n",
+       "                                        'mlpclassifier__solver': ['lbfgs',\n",
+       "                                                                  'sgd',\n",
+       "                                                                  'adam']},\n",
+       "                   verbose=2)"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "scaler = StandardScaler()\n",
+    "clf = MLPClassifier()\n",
+    "\n",
+    "pipeline = make_pipeline(scaler, clf)\n",
+    "\n",
+    "param = {\n",
+    "    \"mlpclassifier__hidden_layer_sizes\": [(100,), (50,50), (100,50,25), (256, 100, 10)],\n",
+    "    \"mlpclassifier__activation\": [\"tanh\", \"relu\", 'logistic'],\n",
+    "    \"mlpclassifier__alpha\": [0.0001, 0.001, 0.01],\n",
+    "    \"mlpclassifier__batch_size\": [64, 128, 256],\n",
+    "    \"mlpclassifier__learning_rate\": [\"constant\", \"invscaling\", \"adaptive\"],\n",
+    "    \"mlpclassifier__learning_rate_init\": [0.001, 0.01, 0.1],\n",
+    "    \"mlpclassifier__momentum\": [0.9, 0.95, 0.99],\n",
+    "    \"mlpclassifier__solver\": [\"lbfgs\", \"sgd\", \"adam\"],\n",
+    "    \"mlpclassifier__early_stopping\": [True, False],\n",
+    "    \"mlpclassifier__max_iter\": [100, 500, 1000, 1500, 2000]\n",
+    "}\n",
+    "\n",
+    "# model = RandomizedSearchCV(pipeline, param_distributions=param, cv=15, n_jobs=-1, verbose = 2)\n",
+    "\n",
+    "model = RandomizedSearchCV(pipeline, param, cv=15, n_jobs=-1, verbose = 2)\n",
+    "model.fit(X_train, y_train)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best hyperparameters: {'mlpclassifier__solver': 'sgd', 'mlpclassifier__momentum': 0.9, 'mlpclassifier__max_iter': 100, 'mlpclassifier__learning_rate_init': 0.01, 'mlpclassifier__learning_rate': 'constant', 'mlpclassifier__hidden_layer_sizes': (100,), 'mlpclassifier__early_stopping': False, 'mlpclassifier__batch_size': 128, 'mlpclassifier__alpha': 0.01, 'mlpclassifier__activation': 'relu'}\n",
+      "Accuracy: 0.7889851485148515\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Print the best hyperparameters\n",
+    "print(\"Best hyperparameters:\", model.best_params_)\n",
+    "\n",
+    "# Evaluate the pipeline on the test data\n",
+    "accuracy = model.score(X_test, y_test)\n",
+    "print(f\"Accuracy: {accuracy}\")\n",
+    "\n",
+    "# predict probabilities for the test data\n",
+    "y_score = model.predict_proba(X_test)[:,1]\n",
+    "\n",
+    "# compute the ROC curve and AUC score\n",
+    "fpr, tpr, _ = roc_curve(y_test, y_score)\n",
+    "roc_auc = auc(fpr, tpr)\n",
+    "\n",
+    "# plot the ROC curve\n",
+    "plt.figure(figsize=(8,6))\n",
+    "plt.plot(fpr, tpr, label='ROC curve (AUC = %0.2f)' % roc_auc)\n",
+    "plt.plot([0, 1], [0, 1], 'k--')\n",
+    "plt.xlim([-0.01, 1.0])\n",
+    "plt.ylim([0.0, 1.01])\n",
+    "plt.xlabel('False Positive Rate')\n",
+    "plt.ylabel('True Positive Rate')\n",
+    "plt.title('Receiver Operating Characteristic')\n",
+    "plt.legend(loc=\"lower right\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Confusion Matrix:  [[1039   92]\n",
+      " [ 249  236]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(X_test)\n",
+    "# Confusion matrix \n",
+    "cm_grid = confusion_matrix(y_test, y_pred)\n",
+    "print(\"Confusion Matrix: \", cm_grid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0    3785\n",
+       "1.0    1601\n",
+       "Name: referral, dtype: int64"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# counts = df['referral'].value_counts()\n",
+    "\n",
+    "# colours = ['green', 'red']\n",
+    "# ax = counts.plot(kind='bar', color=colours)\n",
+    "# plt.xlabel('Class')\n",
+    "# plt.ylabel('Count')\n",
+    "\n",
+    "# # labels on top of each bar\n",
+    "# for i, v in enumerate(counts):\n",
+    "#     ax.text(i, v+20, str(v), ha='center')\n",
+    "\n",
+    "# plt.show()\n",
+    "\n",
+    "df['referral'].value_counts()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}