diff --git a/mup/CoordCheck.ipynb b/mup/CoordCheck.ipynb
deleted file mode 100644
index bf949108b..000000000
--- a/mup/CoordCheck.ipynb
+++ /dev/null
@@ -1,589 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-03T15:35:53.995109Z",
-     "start_time": "2022-02-03T15:35:52.763386Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from mup.coord_check import example_plot_coord_check\n",
-    "from itertools import product\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "sns.set()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note how the standard parametrization (SP) plots below (dark background) all feature some rising or falling curves (bad!), but the maximal update parametrization (μP) plots below (white background) in contrast all have flat curves (good!)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-03T15:41:25.334775Z",
-     "start_time": "2022-02-03T15:35:53.997040Z"
-    },
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Files already downloaded and verified\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|███████████████████████████████████████████| 35/35 [00:08<00:00,  4.22it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "coord check plot saved to coord_checks/sp_mlp_sgd_lr0.1_nseeds5_bn0_coord.png\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x288 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Files already downloaded and verified\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|███████████████████████████████████████████| 35/35 [00:10<00:00,  3.44it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "coord check plot saved to coord_checks/μp_mlp_sgd_lr0.1_nseeds5_bn0_coord.png\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x288 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Files already downloaded and verified\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|███████████████████████████████████████████| 35/35 [00:05<00:00,  6.47it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "coord check plot saved to coord_checks/sp_mlp_sgd_lr0.1_nseeds5_bn1_coord.png\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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G43A6ef6551FVNTda4suXXMSf/3QFXq+XRYcsRLPZ2Lzpc956622+d+l3cbvdnH3uWdy03PpdmjtvLoau8+mnn7Fxw0a++ndfoa6ulgWLFnD1X6/hu5d+h5LSEu658x5i0ViuHVu3buPxRx9nwcL5VFRUsGfPHtasWZtb4Qb2bUrIug/W8cHadcw5YDZ+v5/Nm7ewYtmNVFRWFKwSJIQQQnwRElgIIYQYNAcdfCDPPfsct958G9FolGBJkJmzZvKDn/yAQHDwVg7QNK1fx1v50EpWPrSy4LETTzqB7176nX1+Ta/Xw4frPmTlw48QaY9QWlrKkqOWcN4F5wJQXlHOr379S6675nqefvLH1DfU89W/+wq/+fVvcdjtgFU09Ne/vYyrrria//ejn1FRUc7FX7mYG5fduM/tGQyqqnLZ5b/imquu47Jf/BpQOOjgA/nmd75RMHVly+YthEPh3P31H6/nl/94We5+9n2eNXsm//5f/9bna5534bm8+867LL9hBV6Pl0u++mUOO/zQQf/e8l36o++z7Prl/P5//kA0GmXcuGYuu/yX+zyyYyA8Hg8P3PcAW7dswzRNGhob+Mdf/jxXO+KoY47C7XZzz933cdedd6OpGjW11RxyaGcR0vMvPI+y8jIe/tvDLLtuOQ6ng/q6Oo4+7ujcNj/40fe58i9X8S+X/ytOp5PjTjiORYcuYk9LC2BNH9q6dRv/85//SygUIhDwc/D8g/na1786oO/LbrfzysuvcNcddxOPx6moKGfugfO44MLzcLvdA3/DhBBCiDzKuvVr+zfxUQghhBD75P3Va/jFz3/J/13xx4LCjEIIIYQQYu9khIUQQggxSFY+/Ajjx4+nrLyUzzd9zvXX3MCUqVMkrBBCCCGEGAAJLIQQQohBsmvnLu6+8x7aWtsoLS1l7rwD+MrfXTLczRJCCCGEGJFkSogQQgghhBBCCCGKjjrcDRBCCCGEEEIIIYToSgILIYQQQgghhBBCFB0JLIQQQgghhBBCCFF0JLAQQgghhBBCCCFE0ZHAQgghhBBCCCGEEEVHAgshhBBCCCGEEEIUHQkshBBCCCGEEEIIUXQksBBCCCGEEEIIIUTRkcBCCCGEEEIIIYQQRUcCCyGEEEIIIYQQQhQdCSyEEEIIIYQQQghRdCSwEEIIIYQQQgghRNGRwEIIIYQQQgghhBBFRwILIYQQQgghhBBCFB0JLIQQQgghhBBCCFF0JLAQQgghhBBCCCFE0ZHAQgghhBBCCCGEEEVHAgshhBBCCCGEEEIUHQkshBBCCCGEEEIIUXQksBBCCCGEEEIIIUTRkcBCCCGEEEIIIYQQRUcCCyGEEEIIIYQQQhQdCSyEEEIIIYQQQghRdCSwEEIIIYQQQgghRNGRwEIIIYQQQgghhBBFRwILIYQQQgghhBBCFB0JLIQQQgghhBBCCFF0JLAQQgghhBBCCCFE0ZHAQgghhBBCCCGEEEVHAgshhBBCCCGEEEIUHQkshBBCCCGEEEIIUXQksBBCCCGEEEIIIUTRkcBCCCGEEEIIIYQQRUcCCyGEEEIIIYQQQhQdCSyEEEIIIYQQQghRdCSwEKIHX//qN1j1zrv7vN9f/u8KvvON7/Klk8/gqSee2g8tE0KI0Wkg/e6WzVv419/+OxeffwkXnnsx//yry9m8ect+aqEQQowuA+l3w6Ew//D//pGLzvsyF5xzIT/7yc9Zu+aD/dRCISSwEGJQjR8/nm9/71tMnDhhuJsihBCjXkdHBwsWzufKa6/gxluXM3nKZP7tt/8+3M0SQohRy+V28YMfX8pNt63g1jtv4ayzz+Bff/Nv6Lo+3E0To5QEFkJ08fv/+QO7du3mX3/zb5x75vncc9e9/d735FOXcsDcA7A7HPuxhUIIMboMtN+dMnUKx59wHH6/H5vNxpfOOI0tm7cQDof3c4uFEGJkG2i/63A4aGioR1VVTNNE1VQikQjt7e37ucVirLINdwOEKDY/+dmPWbtmLd//4feZO+8AAC4458Jetz/rnLM4+9yzhqp5Qggx6gxWv7tm9RpKS0sJBAL7ra1CCDEafNF+99Lv/pAtm7eQTqc5/oTjKCkp2d9NFmOUBBZC9MNtd9063E0QQogxZV/73d27d3PVldfwd9/42n5qkRBCjG770u/++a9/IplM8srLr5JOp/djq8RYJ1NChBBCCDGihUIh/vmXl7P05JNYcuTi4W6OEEKMCQ6HgyVHLuaeu+7lk42fDHdzxCglIyyE6ImiFNw998zze9307PPO4tzzztnfLRJCiNFtgP1upD3Cr395OQsWLeDc86UvFkKIfhuk8910Os327TsYP2H8oDZPCJDAQogelZSUsH37dsCa03fnvbf3a79UKoVpmpimSVrXSSaT2Gw2VFUGMwkhRF8G0u9Go1H++bLfMH3GdL7ytUv2cwuFEGJ0GUi/u27dhxi6zuQpkzEMg4cefJhQWxtTpk7Zz60VY5Wybv1ac7gbIUSxefWV17jmqmuJRaOce/65nHHW6f3a7xc//yXvr15T8Ni//ee/MHvO7P3QSiGEGD0G0u8+9eTT/On3/4fT6UTJ+6Twiqv+TGVV5X5srRBCjHwD6XffX/0+11x1HTu2b0fTbDSPa+aiL1/IrNkz93+DxZgkgYUQQgghhBBCCCGKjoxTF0IIIYQQQgghRNGRwEIIIYQQQgghhBBFRwILIYQQQgghhBBCFB0JLIQQQgghhBBCCFF0RuWypi6nm7SeHu5mCCHEF2bTbMQTseFuxl5JvyuEGC1GSr8L0vcKIUaHvvrdURdYuJxumhvGDXczhBBi0Hy2+dOiPnmWflcIMdoUe78L0vcKIUaX3vrdURdYZFPm1tYODGPfVmwtL/fR0hLZH83a76TtQ2+kthuk7cNhIO1WVYXSUm/Rf3om/e7IM1LbPlLbDdL24TCa+10YeN87Un+eIG0fDiO13SBtHw77o98ddYFFlmGY+3zinN1vpJK2D72R2m6Qtg+Hkdru/pJ+d2QZqW0fqe0GaftwGKnt3hcD6XtH8vsibR96I7XdIG0fDoPdbim6KYQQQgghhBBCiKIjgYUQQgghhBBCCCGKjgQWQgghhBBCCCGEKDqjtobFvjJNk44t20i1hK0HFCVzm91C6Xwsdz/vnqL0sW3n9kru8a7bd98292Dedp0PFz6XalcwotGCQ3RX+KBpmmCCiQmmiWnmPY6JaSq5baz7mW1y25qZp8zMPgpmZs6SmX0st2/2daznlNxTJnqrm7ZQPPNtKZm3Tsl7G7o+puS9xV0f63xfFBRMxcz8bJQux6KHx/rzmp3vYTpux0gmsj+V3Ota37eSfXcyb2rejWliKrmNrQcVMDP3zfydrA0xMXOvY5pmwW32Pc4cOvd+9/YcgAODyJ78gjid30Phr27XXySz8Cmz5+eU/LvZO2b2/TMpfGuy74GSt13+e1D4Yu16glhbtPP5ri3Mf8jsaZPCB8zuh+jHNl0e6OEgXR/yu7SeXqgo/eF//4RN07j0R98f7qYIIcSA6LpOe3vEOufodk5WfKTfFUKMdIZhEO2IDnq/K4FFRvvWXdz+P3eQTO5bVeg+S4oMoN6I2ddOezle/sVq57VpfhBReF98MYrS28WuEN35gy7O+9130bThDS7C4TB/+N0f2b5tOza7ndq6Wr536XcIBoMAPPS3hznwwLm89+7qYW2nEEIMhK7rPPXIs9x47a3E4wlW3HM1Xp93WNsk/a4QYjQzTZNnn3yR5VfeRCTSwYp7rsHnH7x+VwKLDFdZkAnzp7Jr085etjC7D5rIUbp9Dp17ps9wSenyVeaj9j726e14mqZi6GbByIPOAQKZY+YOnRk9oHR+nX28czulsClKdrueRiFk7quFI0Byzylq97bQOVrBZldJJ/XOPMYEMzcEg9yojuw7lAticl9TODIhbzRH5+ad2/f0XN4N3UIds8trZV5b01R03Sh4j3JfFv6f9bYq+U923ip523R9rMvgjfwNCrfv8ouRf1fpoV3ZtpP/fSmFmVifYUz3wSOdL5AbEVH4bI/HU/b+Wl0HcdjsGum00Ufj9vbfXfcN+hcC722fHg6S91BFfQWmbsAwBxaKonDm2Wcwe85sAJZdv5wVy27kBz+6lPUfrycRTzB/wcFy4iyEGFEMw+DpR59jxTW3snXzNqprqzjvy6fT50nVEJF+VwgxGpmmycvPvcYNV97Ipxs2UVFVzrkXnTbo3a4EFhkOt5OTvvcltm1p7XvDff4B7NsOAxs9o1BR4WP37kj3Y/QyvD9/WkTPm/bckIIL44Ive3m8H99QZaWfXbva97rdYDP7Mzyij00qK33s2pU3rWIfv+/hNFzv+WAYqW0vlnb7/f7cSTPA1KlTeGTlowC8+cZb7N61m5tX3MKGDRtZ8/4aZs6aOVxNFUKIvTJNk2cef4EV19zC5s+2UFVTyYV/dy6z5kxn1gGTcXtcw91E6XeFEKPO6y+/yfVX3Mj6DzdSWlbCORefwQEHzmLO3Cm43IPb70pgkUez23B6h/8P20A4PU4c7uRwN2NE6Veo0NdoF1VFUYs7mBCiL4Zh8MjKR1mwcAEA5194HgA7duzgztvu2ueT5vJy34DaUVnpH9B+xUDaPvRGartB2j6YTNPkiZXPcsXvb+CT9Zuorqnk69+7iAMOmkVdfQ0NzXW4B/mkeTAMdr8LA+t7i+3nuS+k7UNvpLYbpO2D7fWX3+ZP/30tq99ZS0lpkIv//mwOXjSPuvpqmsY34PG4B/01JbAQQogx6porr8XlcnHyqUsLHq+urh5Q4beWlgiGsW+FXYpl5MlASNuH3khtN0jbB4tpmrz4zCssv+pmPt24ibKKUi742jnMmDWNisoyGprr8Xg9RCIp3G7XPrdbVZUBh6/9Mdj9Lux731tMP899JW0feiO13SBtH0yrV63h+ituZPU7a/D5vZx+3qnMmz+HsvJSmsY14PP76OhI4/Ew6P2uBBZCCDEG3XDdMrZu3cpll/8KVZUVroUQxc00TV567lVWXH0LGz/+lNKyEi746jnMmD2VYGmQpnGN+AP7L2gYDNLvCiFGmg/WfMQNf1nB22+8i9vj5tSzl3LQwrkEgn6axjVSUhrc71PhJbAQQogx5qYVN7Nh/QZ+ffll2O324W6OEEL06ZUXXmP5Vbew/sONBEuDnHfJWcw8YDo+v5fG5gZKy0qKvnaU9LtCiJFk/Ycbuf6KFbzxyls4nE6Wnn488w85CI/PQ9O4BsoryoZs5TsJLIQQYgzZ9Nkm7rrjburr6/iHn/4csIYi/+KyfxrmlgkhRCfTNHn95bdYftUtfPTBxwSCAauo27xZON1OGprrh/SE+YuQflcIMVJ8uuEzbvjrTbzywmtoNhvHLT2aQ5Yswu1yUttQQ3Vt1ZCHrhJYCCHEGNLU3MSDK+8f7mYIIUSPTNPkjVfeZsXVt7BuzUf4Az7Ovuh0DjhoNg67nfrGOiprKrDZRs4prPS7Qohi9/mnm1l21c28+MzLKIrKkccdweJjDsPhdFJZVU5dYy0u1/AUMh45vb0QQgghhBiVTNPk7ddXsfyqW1i7eh0+v5czLziNgxbORVVUauqrqamrweGQ6RRCCDFYtny+jRXX3MJzT7yAYZgcduQijjlhCXang5LSII3NDXh9nmFtowQWQgghhBBiWBiGwTtvvseNV9/K+++uxeP1cPp5p7DgkINAUaiotD7ZK8YlSoUQYqTavnUnN157K8889hypVJqFh8/nhFOPwely4nK5GDehkUAwUBT1gSSwEEIIIYQQQ8owDN59azUrrrmV1e+swe1xc9rZSzlk8UJM0yBYEqShuR6fzzvcTRVCiFFj545d3HL9HTyx8hmSiSQHLZzH0tOPx+Nxo9k0GjMFNYtpJSMJLIQQQgghxJDQdZ3331nLimtv5d23VuNyuzj5jBM44ujD0HUdt8dN07gGAkF/UXyyJ4QQo8Hunbu5bfndPPbQU8SiMQ44aDannHkigZIAhq5T31hHVW1lUdYHKr4WCSGEEEKIUUVP66x+dw03X3cHq958F4fDyYmnHceRxx2BaZrY7TYmTh5HSVlJUX2yJ4QQI1nL7j3cedO9PPrgE0TaO5g5ZzqnnnUSFdUVJBNJKirKqK2vwelyDndTeyWBhRBCCCGE2C/0tM7a1eu46frbefu1VdjtNo47+RiOOXEJhmGiaiqNTfWUV46MJUqFEKLYmaZJ65427rntQR65/zFCbWGmTJ/EaWcvpb6pnlg0itfjZur0SXi8w1tQsz8ksBBCCCGEEIMqnU6zdvU6bl12F2+9+jaapnHMiUdy7NIjUVQVUzdoKOIhyEIIMdKYpklba5j77/wbK+97jD0trUyYNI6vfvtiJk6ZQKQ9ggJMmzmVYElguJvbb/IXQgghhBBCDIpUKsWH73/Mrcvv5I1X3kZVVZYcdwTHn3w0NpudVDJFdXU5dfU1OJyO4W6uEEKMeFZQEeKhex/h4XsfY9fO3TQ2N3DBV89h2qwpRNo7SMTjTJoygdLykTftTgILIYQQQgjxhSSTKT5c+xF3rLiH119+C4Ajjj6UE089FqfLSSwWJ1Dio76hDrfHPcytFUKIkc80TUJtIR558EkeuucRtm/dQW19Dd/8wdeYc+AsIpEOopEojU31VNVUotlG5rQ7CSyEEEIIIcSAJBNJPvzgY+666T5ee+lNDNPg0MULOem04/H6PESjMRwOBxOnjMfn9w13c4UQYsTLjqh44uGnefi+R9m8aSuV1RV87Ttf5uBF84hFY4Tb2qmuqxoVo9kksBBCCCGEEPskEU/w8YcbuPuW+3j1hTfRdZ1Fh8/npC8dT7AkQCTSgWmaTJs5mUAwIEuUCiHEF2QYBqG2MM8+/gJ/u/cRPtu4ibKKUr78jQtYeNjBpJIpQq0hSivKmDZz9Ixmk8BCCCGEEEL0SywWZ8NHG7n3tgd55fnXSaVSLDjsYJaefjzl5WW0t7eTTCZH7FxpIYQoNoZh0NYa4oVnXuHhex9lw0cbCZYGOf8rZ3PYkYswDZNQWxif38v02dMIBP3D3eRBJYGFEEIIIYToUywaY/2HG3ngrod56blXSSVTHLzoQJaecTxV1ZW0hyN0dHTQNK6RyqqKETtXWgghioVhGLTtaeOVF99g5X2PsW7NR/j8Ps6+8HSOOOZQNFWjvb0du93OlGkTKS0vHZWj2SSwEEIIIYQQPYpFY7z9xifcdN09vPTsK8TjCQ5aOJeTzziRmrpq2tsjtIfaqW2oobq2CrvdPtxNFkKIEc0wDHbt2M3jD7/AIw88zvur1uL2uPnSOSdz5PGLcTjsRMIRUBgTIbEEFkIIIYQQokAqleKzjZu459YHePGZV4hGY8w9eA4nn3EiDU11dEQ6aGsNUV1TSW19DU6Xc7ibLIQQI1441M6br77N4w89yZuvrsLhdLD09BM45sQluD1uOiJRYtEotfU11NTV4HCM/pBYAgshhBA5mzZ9zt8e+BuGbqAbOj/88Q9G5fBCIUTPDMNg5/Zd3HfH33j8oacIh9o5aMEcjj/1OJrGNRKLxWltaaW0ooypjbV4vJ7hbrIQQox48XicNe99yL233c/rL7+FzaZx3NKjOO7ko/H5fVbfu6eNiqoy6hvrcbtdw93kISOBhRBCjDHhcJg//O6PbN+2HZvdTm1dLd+79DsEg0Gamhr53qXfBeA///2/icfjuN2jo8q0EKJvobYwj/3tSR648yG2b9vJ+EnNfPMHX+PghbPYsb2N1j1t+PxeZsyZjj8gS5QOFgmKhRi70uk0mz7dzD233s+zj79AMpnisCMXcdHXzsDETjKZpLWlFV/Ax6wDpo/J5aElsBBCiDFGURTOPPsMZs+ZDcCy65ezYtmN/OBHlwLw3rureeKxJwgEAjidMsxbiNEuFo3xyotvcOeN9/Dxug1UVJXz9e9/lQMXHEA6rdPS0opuGEyZPomS0qBcTA+ABMVCiHymabJ7ZwsP3v0wKx94grY9bcyeO5Mzzj+V2voafH4Hn2/ahcNhZ/L0SZSWlYzZvlcCCyGEGGP8fn8urACYOnUKj6x8NHd/zgGzmXPAbK6+8ho+2fgJEydNHI5mCiH2s1QqxQer13HL9Xfy1uurcHvcnH3h6Sw+9nBURSHUFsJmszH3oGmgOGSJ0i9AgmIhRFYk0sHTjz7LPbc+wOZNW2lorucr37yQaTOn5JYwVRUf4yY0UV5ZhqaN3oKa/SGBhRBCjGGGYfDIykdZsHABAKvfW81LL74CpklaT9PU3DTMLRRCDDbDMPj8s83ccsOdPP/kS5imwTEnHsmJXzoOl8tJJBxB0dRc9fnqmhJ27Wof7maPaBIUCyGSiSRvv76KW5bdydr31lFSGuSSb17IwsMOBiAUCoNh0tBYx4xZ42kLxYe5xcVBAgshhBjDrrnyWlwuFyefuhSA2XNmF5xU74vy8oHNq6ys9A9ov2IgbR96I7XdUBxt371rDzdfdzv33vEw7aEIhy6ez/mXnEFFZRnhcDsmBrPnTaamthq7vfM0sRjaPhDF2O7BDooH0vcW4/vSX9L2oTdS2w3F0XZd1/nogw1c9acVPPfUy9jtds69+EucfPqxOJwOwqEIuq4zY8Z4auurc6suVVaOzBVABvs9l8BCCCHGqBuuW8bWrVu57PJfDcpQ75aWCIZh7tM+lZX+EfvJrbR96I3UdsPwtz3aEeWRB5/krpvvZdeO3UycMoHv/OjrNE9oIhLp4LPPtlPXUEtVTRUOh522tljRtH2gBtJuVVUGHL7212AGxbDvfe9I/XmCtH04jNR2w/C33TRNdu3YzS033MmTjzxNIp7ksCMXccqZJxEI+tm5M0Qqmaa6tpKGcdW4XC7C7UloTw572wdqf/S7ElgIIcQYdNOKm9mwfgO/vvwy7PaRmeALIfYulUrx6otvcNM1t7Hh40+oqqnkWz/8O+YcOItoR5RwW5iq2ipq6zo/1RP7z2AHxUKI4tQR6eD+Ox/ivtv/RuueNmbOmc6ZF5xGXUMtHZEO2va0UVFVQV1j7ZhaonQgJLAQQogxZtNnm7jrjrupr6/jH376cwCqq6v5xWX/NMwtE0IMFsMw+HDtR9zw15t4+/V38fq8nHfJWRxx1KHE43FCbWEqq8qpbZCT5aEiQbEQo18qleL5p17ipmtv4/PPtlDfWMeXv34B02dPJRaNsaellbKKMqZMr8Xr8wx3c0cECSyEEGKMaWpu4sGV9w93M4QQ+8nWzdtZftXNPPfUi2DC8accw4mnHotpmoRCYcoqypjaWIvHKyfLQ0WCYiFGN8MwWPPeB1z3lxW8v2otwZIAX/7GBSw6fD7JRJLWPa0EgkEmThmPz79/p52NNhJYCCGEEEKMAu3tEW5bfjd/u3sl0Y4oCw47mNPOXorX6yEWixEsCTJp2kR8Pu9wN3XMkaBYiNFry+fbuP6KFbz4zMtoNhunnHkix550FAChtjA+v5fps6bhD/hQFGWYWzvySGAhhBBCCDGCJZNJVt7/OLctv4vdO1uYMn0SZ17wJWpqq+joiKLZNKbPmkYgOPzV8oUQYrRoD7dz8/V38NC9j5KIJzhsySJOOetE3B43He0duD1ups6YTLAkIEHFFyCBhRBCCCHECGQYBq+++AY3/PVGPln/GdW1VXznx19n6szJRCNRAKbPmkIgKCfLQggxWFKpFH+75xFuW3YXe1pamTFnGmeefxpVNVVEIhHS6TSTp02kpKxEiusOAgkshBBCCCFGmPUfbuTqP93A26+vwh/wccFXz2HBYQcTi0bR07qcLAshxCAzTZNXX3ida/+8nM8++Zy6hlou/YdvM2X6JCLhCIl4nAmTxlFWXoqmacPd3FFDAgshhBBCiBFi187dXPeXG3nmsedQNZWTvnQ8x5ywhFQ6RTqVYsLk8XKyLIQQg2z9Rxu56g/X884b7xII+rn4789nwWEHEY1EiUVjNE9ooqKyHM0mfe9gk8BCCCGEEKLIRaNRbl9+N/fe/jfisTiLDp/P0jNOwG6zYWDSPF5OloUQYrC17NrDdX9ZzlOPPoemqZx8xgkcfeKRpJIpYtEYDU31VNZUYLPJZfX+Iu+sEEIIIUSR0nWdlfc/xk3X3U7Lrj1MmzmFM84/lZKSIAD1jXVysiyEEIMsHo9z2/K7uffWB4jF4iw6YgGnnHkimk0jmUhQ11BLVU0VDod9uJs66slfNyGEEEKIIvT6y29x9Z+u59MNm6itr+G7/+8bNI1vxDQMauqr5WRZCCEGmWEYPPbQUyy78qZcSHzmBV+ipCyInkpTXVNJTW01DqdjuJs6ZkhgIYQQQghRRDZ8/AlX/eF6q6Bm0M9Ff38es+fOxDB0KqsqqK2rxulyDnczhRBiVHnnjXf56++vY+PHn1BTV833fvpNmsc3kU6lKSsrobahBpfLNdzNHHMksBBCCCGEKAItu1u47s8reOrRZ9FsNk4+4wQOXbwQRVUpryiltqEWt1tOloUQYjB9/tlmrvz9dbz20pv4Az4u+rvzmHPQLNKpNMFSP/UNdbg97uFu5pglgYUQQgghxDCKx+Lcuvwu7rn1ARLxBIcuXsixS4/E4XRSVlFGQ2MtHq9nuJsphBCjSjgU5vq/3sQjDzyOqqic9KXjOOLow8A0Cfh91DfV4/VJ3zvcJLAQQgghhBgGhmHwyAOPs+yqW2htaWXG7GmccuaJlJQFCZYEaWiux+fzDnczhRBiVEmn09x1833ctvwuoh1RFhx2MCeedjwOh51AwE9Dcz3+gG+4mykyJLAQQgghhBhib7zyNlf+4To+27iJuoZazv/x13MnyY3NDQSC/uFuohBCjCqmafLsEy9wzf8tY+f2XUyePonTzzmF0rIgHp+XpnFW36soynA3VeSRwEIIIYQQYohsXP8pV/7+Ot5+fRXBkgAX/d25TJ81FZ/fR9P4BgLBgJwsCyHEIFvz3gdc8btr+HDtx1TVVPLNH36NceObcHncNI1roKQ0KH1vkZLAQgghhBBiP9u9aw//dflfeHLlM9jsVkHN+YceRCDop7HZOllWVXW4mymEEKPK9q07uPIP1/HiM6/g83s598tnMvvAmXjcbhqbGygtL5G+t8hJYCGEEEIIsZ+0t0e4Y8Xd3HfHQyQTCQ5ZvJAlxx5OaVkJjeMaKCsvRdO04W6mEEKMKpFIB8v+upzbb7wfFDhu6VEcumQhPr+PhuZ6yivKpO8dISSwEEIIkbN2zVqefOIpUqk0Xq+Xb3/3m8PdJCFGpHAozO0r7ubBux8hFo0x9+BZHH38kdQ01NDQWEdFZTmaTU6WhfS7QgymjkiUO268h/vu+BvRjigHLZzHcUuPorS8hIameiqrKqTvHWEksBBCiDEmHA7zh9/9ke3btmOz26mtq+V7l36HYDDIjJkzmDFzBgD/+tt/JxaL4XbL2uNC9Fdba4hbl93Fw/c+QjyeYNYBMzjyuCOYecAk/P5SKmsqsNnk9GuskX5XiP2ra1AxY840Tj/7RIKlZdQ31lFRXY7dbh/uZooBkL+YQggxxiiKwplnn8HsObMBWHb9clYsu5Ef/OjS3DZvvP4mjY0NctIsRD+17mnjluvvYOX9j5FIJJkzbyZLjjuChqY66hvqmD5zHG2h+HA3UwwT6XeF2D+6BRWzp3H0iUfS0FjLzDmTsDk8OBwSVIxkRRtYdHR0cN01N/DuO6u44cbrh7s5Qggxavj9/txJM8DUqVN4ZOWjuftPPfEUO3bs5Ctfu2Q4mifEiNKyq4Wbr7+DR//2JKlkkgMOms2SYw+nvqme+oZaKqrKsdls2B12QAKLsUr6XSEGV49BxQlLaGyup6a+mqqaKurry9i1q324myq+oCEJLPoaBtcbr9fLD398KZf94tdD0UQhhBiTDMPgkZWPsmDhAgDeeO0Nbr7xVuYvOJi//vlKLrrkwj776nzl5b4BtaGy0j+g/YqBtH3oFUu7d2zfxdV/WsGDdz9KKpVmwaHzOH7pUTSNr6dpfAOVmaAiX7G0fSBGatuLsd2D2e/CwPreYnxf+kvaPvSKqd3t4QjLr7mdW5fdQ0ckygEHzuSk046heWKjFVbUVOJwOnLbF1Pb99VIbftgt3tIAou+hsFt27aNv/75yoLt5x04jzPPPmMomiaEEGPaNVdei8vl4uRTlwIwf+F8li2cP6BjtbREMAxzn/aprPSP2E8/pO1DrxjavXP7Lm689jaeWPk0hq4zb/5cFh97GPUNtTQ01+dW/WhtjRXsVwxtH6iR2vaBtFtVlQGHr/01mP0u7HvfO1J/niBtHw7F0u5IpIM7b7y324iKhuY6GhrrczUqQuEEkACKp+0DMVLbvj/63SEJLPoaBldbW8u//Ptvh6IZQggh8txw3TK2bt3KZZf/StYgF2Ivtm3Zzo3X3sZTjz6LaRgctPBAFh99KPWZk+WSsqAskSf2SvpdIfZNJNLBXTfdy72359eosKZ+1DdIMc2xYMhrWHQdBteXq664ms2fb+Gvf76SM885k5qa6n6/jgxNHllGattHartB2j4ciqndN624mQ3rN/Dryy+TP/RC9GHL51u58ZrbeObx5wGTgxcdyBHHHEpdQx2NzfWUlAblwlP0i/S7QvRfj0HFCYtpHNdAfVMdlVWy4tJYMeQ/5a7D4Pry7e99i29/71sDeh0ZmjxyjNS2j9R2g7R9OBTT0ORNn23irjvupr6+jn/46c8BqK6u5heX/dOgv5YQI9Xnn21mxdW38tyTL6IoCgsOPYjDjjqEhqZ6GpvrCZYEUBRluJspRgjpd4Xon65BxfTZ0zjmxMU0jWu0liftoT6QGN2G9Kctw+CEEGL4NTU38eDK+4e7GUIUpc8+2cSKq2/lhadfRlUVFh2xgCOOPoT6xjoamuoJBP0SVIh9Jv2uEH2zalTcw323/41oNMb02VM55oQlNI1vor6xVoKKMWzIfuoyDE4IIYQQxeqT9Z+y7Kqbefm519BsGocuWcjhRx5CQ3MD9Y21+AM+CSqEEGKQdQ0qZsyexlEnLGbchCYamuopryhDs0l9oLFsSAILGQYnhBBCiGK0/sONLL/6Zl594Q1sdhtHHH0Ihx15CI1N9dQ31eHz798VI4QQYizqMag4cTHjxjfR0FxPebkEFcIyJIGFDIMTQgghRDH56IP1LL/qZl576U0cTgeLjzmMw446hMbmBuoaavD5vMPdRCGEGHV6G1ExfmIz9U111ogKWXFJ5JGJQEIIIYQYM9a9/xHLrrqZN199G6fTwVHHH8FhRx5K07h6autr8Hg9w91EIYQYdboHFVM56oQlTJg0jobmekrLSiSoED2SwEIIIYQQo96adz9g+VU38/Yb7+JyOzn6xCUcvmQRzROaqK6twu1xD3cThRBi1IlEOrjzpnu577YHc8U0j84EFY3NDZSUBSWoEH2SwEIIIYQQo9bqd9Zww5U38d7b7+P2uDh26VEcvmQRjeMbqamrwuVyDXcThRBi1OkxqDh+CROnjqehsV6CCtFvElgIIYQQYtRZ9eZ7LLvqZt5ftRaP181xJx/NoYsXMH7SOKqqK3G6nMPdRCGEGHW6BRWzpnL0iUuYOGU8jU31lJSVoKrqcDdTjCASWAghhBBi1HjrtXdYftUtrF29Dq/PwwmnHsthSxbSPKGJqupKHE7HcDdRCCFGnZ6CimNOXMKEKRNobK6npDQoQYUYEAkshBBCCDGimabJm6++zfKrbmHdmo/w+X0s/dLxLDpiPhMmjae8qhyHwz7czRRCiFGnazHNbFAxaepE6pvqJKgQX9gXCixSqRTf/db3ufaGqwerPUIIIYQQ/WKaJq+99CbLr7qZj9dtwB/0c/LpJ3DokoU0T2yiorIcu12CCiGEGGzdgopMMc3JUyfSOK6BYEkARVGGu5liFPjCIyx27tg5GO0QQgghhOgX0zR56blXufHqW9nw8ScESgKcetZJHLJ4AeMmNFNeWYbNJoNIhRBisOWCijv+RrSjs0bF5GmTaBpXTyAoQYUYXHv9a376KWf2+pxpmvILKYQQQoghYZomzz/1IiuuvpVPN26ipDTIaeeczKFHLKB5YhPl5WVoNqk6L4QQg623oGLqjMk0NNVJUCH2m70GFj6fjx/8+Ps0NjV2ey6VSvOD7/5wvzRMCCHE6GfoaUgnQdVAUUFOdkQPTNPkxWde4cZrb2Hjx59RWl7C6eedyqGZERWyPJ4Q/WfqeqbftVl9rvS7og/t4Qg3/PXGXoKKegJBvwQVYr/aa2AxcfJEwqF2amtruz2XSqUwTXO/NEwIIcToZ+o6pBKZE2bFCi6y/+REWmCt+nHt/y3n4w83UFFZxpnnn8ohixcxbkKjLI8nxACYRrbfTQIKaJoVXmT7XSGAXTt2c9ct9/Hog0/QEYlmgorFTJs5lYamevwBnwQVYkjsNbD4+69/rddPLex2+6gpuJkIRbjtf28k0h7Ne1TJuzFRyPuPMvMfqImZ205RyLtn7VFAydvX7OFxFHp8QskeL/8ppWBXTVNJp3WM7IammdvezHxtmiaYdH5d8Fj+c5nvLP/xHo6HaZLNq8zc13n7ZY7V/bXyHsNEVVVMw+z8tpTMO527xXrvM7fZvlFRlLznrG2VzA7Za5/O/fK2y98/c9vX6xYcM+/17XaNVErP/Dyz746S9152/jbk3r3cTd72XUK/zm2VvO3yfv5dfr4Fz+W9t9n3n7yfRZbNppFO63l7d/6u9v6nJ7td5mdV8Pua93XhZgUHzH2Z+T1Quv7Kd9ul+5OapqLrRrfvqbCdha/Tlx5DV7Pr3W4P9P18D5on1nPsN8/a63ZjjZFKWr8Imi3TYRiQTmeezTyuaaDIifRY88H7H3LN/y3jvbffJxD0c9rZSzn1zGMpKa+UqvNCDJRpkmoPgZEGu8t6zNBBz/S7qpoXXsiot7Ho/XfXcudN9/LK869jmiZzDpzBEUcdxrRZ02hoqsPn90pQIYZUn4HFu6vey329e3dLr9tVVVcNXouGSWtrmAefXoVhGMPdlCGVu3jPu7DPXfznfd1jWJDZpvPCP3OkvG06M5rObboeF+i8sCwIRrJPmQXXwl3DmMJtOw/WbZvcU50b52KTvNfvaduBDCRSCi7UlR4eV3rYjs73pofjdN1Hyduo+z49vWZhW6D3i+2+vueCffrcro/nenmBbo92ealuIUcPfzO7P1T4SH/+znbfpu+d9nbMUFuEwzviuLyuvb/4MOro6OC6a27g3XdWccON1+/fF0snad/wCSoK2J2Ydiem3QU2Z2cQaKRBT1nby4n0mPDphs+49s/LefXFN/B43Bx/yjEcffxiJk4Zz+SpjbS0dAx3E4UYuYw0HZs3oWFiKqrV5zpc1q1qA0zQk5CGwtEXqtXvilEpnU7zzOPPc/ct97P+w424XE4OWbyAQxcv4qAFM3B5AvgDvuFuphij+gws/vynv+z1AIqijIpRFjXj6vj9Xb/l/bfWFzze24Vj7qJP6bpVodw+PW6i9Hy+3cc5uNrLk8ESD+FwHFQFNdMmVVVQFNUKG1QFNTOSQFFV63GygQOFIw+6fCSePxKh4HGU3CiHzu9XzW3bU/qqdLvShrIyL3v25J2Adt1tL2FBtwvfHt/Uvi/Ke714NvPDjdzGmAYES9y0tUVRFGuUhKZ1/iHvfB+6HzP3Hpj5j2W/KNgy7/8Lg5Tc9mbncIbciIfCYT4ogJF5PHus0hIvrW3RztEn2ZOQ7O+zmteYvJ+Z9b1m7/TQZMDs14V+HwV7s6OXevqRKFBe5qNlT6TnfTMb7dt17L5d9A7oGllRqKzw0ZEojkA0HA7zh9/9ke3btmOz26mtq+V7l36HYDCI1+vlhz++lMt+8ev93xCbA09DM5FdLSjpBGo0BISs0Wl2B6bd1SXAMKzwIp0NMDJTRzRNTqRHge1bd3D9FSt45vEXsDvsHHnc4Rxz4pFMnj6JsvJSNE2TURViVBrSoFizE5g0lbZt2yGdQknFUZLW6GJT1aw+N9v3qqqMvhjlQm1hHrjrYR6862Fa97RRXlHGKWeeyPxDD6R5fBMVVRU0NVWya1f7cDdVjGF9BhbXLbtmqNpRFGqba7F5RmZ6WFnpH7GdSUWlH9M58tpeWenHNULf8/JKP8YIbXuwwk/SHHkXLd6gj2iRvOeKonDm2Wcwe85sAJZdv5wVy27kBz+6dMjbYvcFMKM6pqpZJ8aphHUCnUqgREOohKygzubMBBhOsDsBpXP6SO6TQHtneCEn0iPGnpZWVlx1C488+ASKAgsPn8/xJx/F1BlTqKyqkFU/xKhQNEExoNodmE4vuG3WBzN6OtPvxlGSMZSE9SGSqdk6g+Ps9BE9ZRXslNEXI9qnGz7jjpvu5ZnHnyeVTDFh8nhOOfNE5hw0i4amesrKS2VpaFE05DdRCCHGGL/fnwsrAKZOncIjKx8dxhZlqBo4PZhOjxVS5AcY6QRKLIQao/cAw0hZQ5mzx8oV75QAoxhF2iPccsOd3H/nQ6RTKeYePIcTTj6GmQfMoLKmQk6WxahSTEEx0Fm0TFHAZse02THdfusxPWUFF6k4SrwDNR6x+l3NgZmdPmJzgGGAHs98g6pVc0j63KJlGAavvfQmd910L+++/T42u40582Zx+FGHMG3GZGrqa/AHfDKSTRQdORsQQogxzDAMHln5KAsWLsg9dtUVV7P58y389c9XcuY5Z1JTU92vY5WX7/sINT2ZJBhwQXaKmap2mS7kz31l6jrpeBQ9FiUdi2LEQhCzntNcbjS3B5vbg+p0W9OXDB1r+pKJatNQbQ4UTUMZxJOxykr/3jcqUsPV9mg0xs3X3cWyq2+jIxJl7kGzOP2ckzho0Vxq66pwOB197i/v+fAYqW0vlnYXU1CsqJoVLug6ufmp1pzhTIDhwLQ5MAlaAUY60TkCIxZGjYW7BMcusNkLR19kX0NGXwy7WCzOIw88wb23PcC2LdvxB3wcc+KRHLZkIeMmjaOqugK3xz3czRSiVxJYCCHEGHbNldficrk4+dSluce+/b1v8e3vfWufj9XSEsEw9q1CbUWFjz2tmekdRrqHE+hMCeBciKGBzQ9+P3iN3Im0mYqTbm0h2dqSOZHOr4HhIDeFJHtc9YtPHxnJU/GGo+2pVIq/3fMIN19/B6HWEJOmTuCEU4/lwAVzqa2rxulyEgongESvx5D3fHiM1LYPpN2qqgwofN0XgxkUw8DC4sq6yszqbQambmCk05h6KvOYNfKis+aZN7efaRjo8RjpWAd6tAM9FoJYCBQFzeXG5vaiuT2o9s7gUVFBsdtQNbsVGn/B0RfFEkINxFC2fduWHdx0/Z3cd8dKOiJRGpvruOQb53LokgWMn9hERUUZdoe9X8eS93x4jNS2D3a7JbAQQogx6obrlrF161Yuu/xXwzYENDuqAjLDie1YwYJpgG6AmbamhkCmPqraGWSoKjjcmA53piCtUVgDo+CTQEfnFBLNKdNHhpCu6zyx8mmWX3ULu3bspnFcA+dcdDoLDzuY2oZa3O7iXjlHiME2mEEx7HtY3HuQk12dKbNCk6EXBr25/lEBzQd+nxUcZ0ZfmMk4emwXAKaigM1lTSGxOa3+NStbuHMABZNHangGQ9N20zRZ8+4H3HHTPbz6whuYpsn0WVM5/OhDmD13FvWNNfgDflRVpS0UB+JF0e79Rdo+9PZHUCyBhRBCjEE3rbiZDes38OvLL8Nu798nLENGyQwhVgEc1gm0aVgnz0bmtqdhzEpvAUYibygznQGGzVpKFZuzMxSRQnKDxjRNXnj6Za6/4kY2b9pCTV01X/7GBRy6ZCENjXV4vJ7hbqIQQ64YguI+KSpomQAZugfIejZAVjr/dak9pKTiVoiRjKN2WPP2ckuo2p1WgKGokFYGbcTbWJdKpXj28Re465b72fDRRlxuFwsPn8/iow9lyvTJVNVUSJ8rRiwJLIQQYozZ9Nkm7rrjburr6/iHn/4cgOrqan5x2T8Nc8t6oSigaJ2f0GWHLPc1jSQbevQVYMTbUePtncXk7JkAw3SAklnGT8ks4ycn0/vkzVff4do/L2P9hxspryzjnIvPYPExh9E0vhGfz7v3AwgxChV1UNybPgPk/BFwnX2v6fSC02v1rZkVSMitQtJ1CVUnps0BeqZ/z46+ULW8aYGiN6G2EA/c+TAP3r0ytyzpyWecwKFLFjFuQhNlFWU4+jntQ4hiJYGFEEKMMU3NTTy48v7hbsbA5U5i+5hGomcDB+icRtI1wOhSTC4eyQsw7NYUEpszU0wu80lobvpIZjlAUeCD9z/kmj/dwHvvrCEQDHDq2Us5+oTFjJ84Dn9gZC4bLsRgGHFBcW8KAuSuAYaeN1qNTNChYbp84PJZfaaRRkn2sISqasubtmfPHF+Wq+5NblnSx54nlUoxYfI4TjnrJA5eNI+GpnoCQX9xjuARYgAksBBCCDHy9fkpYGYqSU/TSDKFObsHGAkrwMCah2lq9s4pJJmT6VQESCatqSO5TwPH5gniJ+s/5dq/LOe1F9/E4/Vw/CnHcNzSo5g4ZTyBYOALF9kTYqQb8UFxb3ocAdfbFD5rxJrp9hcuoZqZPqIkO1ATEeswudDYYRVOzoQfetJhHXMMBhiGYfDai29w58338d7b72Oz2Zg9byaLjz2MOXNnUlldidcn0z7E6COBhRBCiNGnt2kkhg6m3vs0kh4DjEyIkSg8mU6kPSi6gqnZrZEeikpuOb8xEmJs3bydG668kWcffwGH08GSYw/n+FOPYcq0SZSUBuUTPiHGmr763twIjLwAQ7NbS6i6A5k+N5k36q0dNbt1pu5QwpaGuJnpY1VQbJ1fMzqnkMSiMR558AnuufUBtm/dgT/g4+gTl3DE0YcxedoEysvL9roctBAjmQQWQgghRr/8lUWw72UaSd5SqrkAI9jtZDrV0Y5qGLmXMFWbdfKt2TFt9sxcbFvmdUdXiNGyew8rrr6VRx98HEVRWXj4wZx42vHMmDWV0vISCSqEEJaufe/ewmNbpp5Qts/NhhepBEq8nXi8HQ2rBgY2h9XfanbQHHlT9rJ97cgehbFj207uvvV+Hn3gCaLRGHUNtZx94ekcduQiGprrCZYE0DRt7wcSYoSTwEIIIcTYtE/TSLJTSJy5k+mSoJu2Pe2gJ1HSKSvM0JMoqRjZy3VTUayCnplRGJ2jMUbmiXU41M5ty+/i/jseIp1OM/fg2Zx42nHMnjuT8soyOXkWQvSta4BhMwEzM32khyLKdldn3SHDwO9SiLSFrf42nURNxnKHNlXN6m9teSGG1ll3qHNFqeLta03T5P1313LnTffyyvOvoygwbeZUjjj6UA5aNI/q2iq8Xo9MsxNjigQWQgghBOz7NBLTtLbVPJiZ0bi51UjSKRQ9mTmpTqEkO1AyRTpNsOZx2+yg2jE1m1XYU7XlhRlqUYUYsWiMu2+9nztvupdYNM7MOdM44bTjmDd/DpVVFdhscjohhBiA7Ii27FKq2dFvRg+FPBUFzeXBdFtfZ5dRzYYXuRAjlR9iZPra/JEYNntnYKwUx1SSVCrFM48/z10338/Gjz/B5Xax6IgFHHXcEcyYPY2yilKcLuewtlGI4SJnGEIIIURPeptGYmT+QeEojOw+dA5rJvtspjo+6RRKdiRGOoVi5J1YZ+dza51TS7A5hjXESCZT/O2eldxy/R2E2sJMmT6JE049lvmHHEhVTeXIWZpRCDFyKGpngAGZ6XtmJjSmy0okCtic1hKpPYQY1r8ESrK3EMORF2IM/Yi3ttYQD9z1MA/e/TBte0KUV1rLki4+9ggmTGwiWBJEs8nINTG2SWAhhCgqhmGg6zrptI6eTpNO66RSKZKJJIlEgtYWL2ldxe124XQ55YJJDK3ciTTYfX6IZoYz50ZjGJ3TSgqCjMy+jszw5uzJsGF0n1KSiJI57c4ssZqdSpINMZydIzLyVzwZRHpa54mVT7P86lvYtWM3zROaOO+Sszhk8QJqaqulwJsQYugoqtWHqlpnv2uaQF6AnOtzFes2VwsjEzzva4iR62fzAoxB7Gc3rv+Uu266j6cff450Ks2EyeM47aylHLpkIbX1NXh9Xpn2IUSGBBZiWJimSTpzMdoR0UjEE9hsNkmRRzFd19HTeu7nnk6nSSVTJBJJkokEiXiSRDJJOpXCOuHIMlEUBVXV0DQVhTS7doWzH1vjcDjwBfwEAj7cHjdOlxOHQ0IMMURyBToz9/O7sGyIQXZaidEZalgbZPbJnCC7fJ0Rh54unFKSThaeXOdGY9hzVfaxuzpXKxngybVpmrzw9Mtcf8UKNm/aSl1DDZd880IWH30otQ01uFyufT6mEEIMqlz/lq1BlJGtQ5QLj7vUIsouT53tG01j7yGGZgdbpo+2u/JCjH0vnmwYBi8/9xp33WItS2q325gzbyZHHncEcw+eQ3llmfSxQvRAAgsxqPKDiNyn48kU8cwFaTKRIJlIkkylUEzrT0gw6CYUsv5AqIqKw2nD7nTgdDpwOp04nA5sNlvmn4Zms6FpqhR3KwKmaWZGQxT+zJNxazREInObSqbQMxdp2UsoE1AUBU3T0GxWGOF2u9B83j5f0+fzkEp1XojpaZ2O9gitLXvIlAjAZrcRCPjwB/y4PW5cLid2h10+rRBDKz80UPP6K7PLqIzsNJPsqiWZfU2b0yo4lw1FeqqNkYgU/DdVEGLYHWBzWdNK9hJimKbJW6+9w7V/Xs76DzdSUVXOeV85i6OOP4KGxnrcHvf+eY+EEGKwZOsQQWd4nN/fZoNjI6+vzS6rmh2JURBiJKzbrjUxciGGMy/E6H3K3s7tu3j2yRd46J5H2PL5NgJBP8eceCRHnbA4twS0fGAnRO8ksBD9YhhGwQVpOp0mnUr3HESgYGavHDOfjmualvvndDpxe9y5i8fSUg+K6si9jmEYpJNpErEEhhFC1/XM8Qr/CGiait3hwOlw4HRlwg2XA03TcsGGzWa9piyxt2/6+nknE8nM9IwkqWQK0zRQsvNGM/+nqtkQwvo5OJyO/fYz0Gwabpu74IJK13U6OmK07gmBaWJi/b74A378QR8ejweXywrDJMQQQ67rqIx8BSfXeSFG9lNCzWbVuHB6rRNsAD2Foqes2+yyq8koZM6xc6MxbNZ0EtPmRE85reMqKmve+4Br/28Zq1etpaQ0yOnnnsxxS4+mcVwjXp9naN4TIYTYH/L7267BcW5ERpdpJdnlqV2+zHQSo7N/7S3EyE4nsTvB5uazz7fz3FMv8+Kzr7Lh408AaBxXzzkXn8GSYw6nobken1+mfQjRHxJYjHF7DSLiCeKJRK/D9HNBhE3D6XLi8X6xk1tVVVFVtV8V561aBwaJZJJoLIahG+iG3q2IP5jY7HacDgcOpx2Hw4HD5cDpcFoX1TYr4LDZbGiaNuL/eJim2f2fYd0amT/QkXaVcKh9n6dlFARPDgdut6so3y9N03C7NdzuzqGVhmEQj8UJh9oxDB1Q0FQVn99HIOjH48tOJ9l/4YoQe9XbyTXkTTHpMiJDtVnL+dldVveXf4KdH2TEIyhEAAiHd7Bx006uX/Ewr776Lj6/l5NOO47jTj6GiVPG4fP7hvb7FkKIoZQ/rSRfwbSSzIgMlLwQw2vtY2ZDDCvAMJNxPvxwHS+++j4vvLKaz7fsAmDcuDpOPvlIZs6byWFHLkBzeGTEmhD7SAKLLjpHBlCUF2L91VMQkf1UvLcLU+v7tUYy7Osw/eGQDTfs9r3/Guu6jqEbxKJxIu0d6Lo1kgPo/KDTBFMBh92Ow+mwgo3M1BS7w54LNLLhRj7TNK3jmWCYRubvnYGJ2fm1CWbmOSPzaWnu6+x+hklaT4MBaUPHNEwMXccwDQzdeg3TMNEzz+mGgWkaGJnvxzTAMLPVszt/ntAZ4CgKBAJuQqEomAqqpuam2FgX+sX58/6iVFXF5Xbh6hJiJJJJtmzehm7oVl1ERcXn9xII+vH6vDhdTpz7cYSIEP3W17ztrlNMFANU1aptQeFKJVs3bWbF8vt46uk38HpcfOOSpZx0/KG4gyXY/UEUV+foCyGEGFMKppVkVynpaVoJpE2FVe9u4NmnX+XVl9+mdU8IVVWZOrWZrx+3iKMPP4C66lLrEICmt5NOJDDNJDjcYLNJPytEP0hgkWfXjt28/srqgulnnaGFYv0v8+EXipJ7Tslupyi5fZXsp9OZ7RSl8zFr27zbzP86t8+el3a+dtfHlbxPvxVFIdzmZeeOts4gIp3uHGnAwOsFjBbZi3E7fRdjNE0zN1KjoyNKONRuTUkxTFBMa7qLtSUlJV7CoTgmRt6UlfxtrPc8F4JlfhDWjYmiqNZ0ChTMTLCgKPn/8n6vAEVVc79r2X+apqEqNnAUPr43+dNwxjJVVXG5nLjy1jY3DINUMsX2rTvR9czwUEXB6/HgD/rx+b24XE6cLqfUURHFYW9TTDIn25+u/5Rblt/Fs0++jM2mcejhB3P66UcxY0oTHhuQTqC078Rs3wV2J6bdbX2aaHcN+XKqQghRNPL62HgyxWsvvslzT77IG6+8TbQjit1uY8KkcRx93OHMnTeDpuY6SkqDeL1udMzOUW5mCiXejhpvx1RUa/qI3W1NPcmvNSSEKCCBRZ60rqOqKsGSQMHj+aMu8r/udt/M3mQuac2CDXPPmWam2qRZeIyCI3d9rIc25G9v6AmiHQk0TcPjccunwQOkKIoV6LD3C9GSEjeGKfUxRhtVVa1RFXkhhmmapJIpdu/czfYt261YSgG3x50r7unMBB9SOEsUFUVh3ZqPuPn6O3jlhddxOOwsOOwgzjj3JMZPHE9piR8V05omlS02l0qgpGKoqThEWzFVDdPuwnR4wOkFLVNgTgghxoBwqJ0Xnn6JF55+hVVvvUcqmcLtdjF5+iRmzZ3B7LkzqG+so6S0BI/XnRm1mzetRLNh2ly4gy5CrRGUdBJS8Uy9oRhmx55M/Qs3ptMDDg9omoy+ECJDAot+yP/Eulinifj8HlLp4mzbaGUttSl/TMYCRVGsaULOzlEppmmSTqXZ09LKzu3WXFUTcLmcBIJ+fAGfNQXF5exXTRYhBpNpmqx6azU3XXsb7761GrfbxRFHH8rRJyxhxpxpTJnayJ490fwdrBNsmwvTkcI0AmCkO4t4JqKoiQ5r9IXNaQUYTh84XJ1L/AkhxCixc/sunn3iBV585mU+eP8jDMMgEPQzb/4cZh0wg2mzplBXV0ugJGCFFF37wB6mldi9XugwrBWgHG7rA0gjnQuJlXgYNR7uHH3h8GA6vTL6Qox5chadR0/ppGMJUs54puPpnLKRmdPROZUjb3qHokoHIopbR0eU7Vt2sH3rDrZv24GRTpHWydTjyFtRxaZh02y5FT4Kn+tcdcWW97WWLViaPZZWuPzsaA11FEXB7rBjdxROM0ql0rS2hti5Y3dm/o+J0+WkeVwNXn8Jdnvf05KGWzKZ5K9/uRKPx4OiKHzjW18f7iaJfWAYBq++8AY3XXcbH32wHp/fyzEnHcmRxy9m8tQJlJWX5qbIFcjO21Y1wJGrmm/qaSuYMA3QEygpK8BQYyGIhTIn1i7r5NvpBZuzz+VThRCiWH26cRPPPv48Lz77Kp+s/xSAiqpyDjtqEbMPmMHEqROorq4iWBooWO2uXxQFRdOsJVCxd4bEum4tq+rygaF3H30RaQHNYYXELi/Y3VYIIn2sGEMksMiT6ojTsbMVWzK/aCGg5C2pma1aSJepIIoCqpoJMKz7qpIpjJatK6AoqGperQtVRckM91Ly62LkQhI6O6S8oKRgWyEyTNMk1Bpi29ZMMJH3Lxxqz21nt9utETmpdKYwq1WUtet0p8GiZgp6dg0zCsISTcsLSfLCj7znbDYbdruNGbMn09DchLdI66/Y7VY7yWteOp1m25YdNE/wFkVgEQ6H+cPv/sj2bdux2e3U1tXyvUu/QzAY5JWXXmHWrFkce/wx3Lj8Jj7+aD2Tp0wa7iaLvdDTOk8//hy33nAXmz79nJLSIEvPOIEjjjqEcRObKa8o27eaK4oKmmqdGGdPrA0npkPHNPTMiXUCUoncMqrWibU9M/oiM31ElRNrIfZGguLhYZoma977gOeeeIGXn3+d7Vt3AFDfVMdxJx/NrLnTaWxupKqmgpKSIK7BXBmtp5BY1zOjLzyYpgFGKjP6Io4aD0Nu9EV2ip5HQmIxJkhgkZGMxHhx+SO0t0UwgwFcTgeKplmjJzQVRVVBVVC0zK2qZh7PBBWqFVQYma9RFAxFyW1rqkou5zBzRdDoUuhCyYQjGbmcROmybbZ6Y+b4igLtbto7EpmQRMm0J9MuRUXR1M6gQ1UKRo1YI0SUQR8tkk6nCbWGaMv+2xOirc36OrQnc9sWxuvzUFpeSnlFGeWVZdZt5uvS8tJ+rQIylui6TsuuPZlgYnveyImdxGPx3HZuj5vaumpmHTCDmrpqauqrqamrtt7bch+trdGC41ory6QzAYaOruevMqOj64UBR+5W77599rns4wXbp3XSmW113Sh4LplIEo3GCl83d5w0yUSKxx9+GkVRaJ7QxIzZU5k+axrjJzYXde0Im82GmV2VpggoisKZZ5/B7DmzAVh2/XJWLLuRH/zoUnbu3MXUaVMBqK6pZufOnRJYFLFkMsUjDzzO7SvuZuf2XVRWV3Dm+aex4LCDaJ7QtO9BRU8KTqzJBRim7gZn2gowssv7peJ5ReUUa/qIw22N0rA7ZVizGLMkKC4O6XSat159h+eeeonXXnyDttYQqqowftI4Tj17KTNnT6WmroaK6nJKSoJDtwSpooJNpWD0haFb4YTh7xYSq8koZgSwOfKm6Lllip4YleRKMGPTZ1v4nzvuzN3XVBWf04XH6cTndOKxu/A6HHgdTrxOJ16nC5/DicfhxONw9C9xzQYMeaFHt0BEVUFTugQkecFI/nOZx1AVkoaBHk1Z1YhRMLPNyV+hIvuFoqCoJqAWVpXPtI/McqGKmqnR0CUAAYVYPEYo1E6oLUwoFCbUFqatLUyozQoh2lrDRNoj3d4Cu91OSWmQYGmA5gmNBEsC6OkUW7fsYuP6T3nrtXc6lxvFurAKlAQKQoxcqFFZRmlZKTabBkammKmRWV5Ut5b/NA2r6JFpGJh69msT9Mz2urXCh2mYndsZJhhG7pj5z2X3Mw2TdrtGMpnOe/8Kv1C63M9OKcreUQo3KvgxAKTSOrtbW9m5p5Wde/awa4/19e7WVnS98z0K+LxUlZUxb9pUqspLqSovo6qsDJ/PW7CSDSiwo4PIzg50t51YPLOkbSYUU7q0T1NAU8CRa7+KomhWr2HPO67SeYxc+/NXLcjunrfiSd7Ona+p5O2f/57lPafrOi0tu3jr7Q/48KMNPPLAE6y8/3GcTieTJ49n2tSJTJ0ykYqKsp7/m+xxEEk/R5bs0wCU/CK51q3mKZ6pMX6/PxdWAEydOoVHVj4KQGVVJbt27gRg145dTJgwfljaKPoWi8a4/86HuPvW+2nbE6K+qY4LvnoOcw+eTdP4RsrLy/ZfiNfb9BEjjalboy9IJ3IBhtoRh45WTNVWOPpCkyX9xNghQfHwicVivPzca7zw1Mu89do7RKMx7HY7U6ZP4rilRzN15mQqKsuprK4gWBrEnbf8+bDorY+1O8GhW6Mv9KRVYygZQ42FIdZ19IXPmn4ySqflirFFAouMSTMn8V+//yUvPf06hqIQaY8QiXTQHukg0h5hR7iFSKSj4GI6S1UUvF4PPo8Hn9eD1+22vna78bldeFxufC4XXpcLr8OJimJd/BoGpm5mTvRMSOkYufuZi+PMhfPedI8GBiat67Qn4rTHY5nbOO2JWOdj8Rjt8Th6D++D1+nE73Ljd3uorqjG1ziOgNuN3+PB7/ES9HhwOZ3WtJjc1BgFm00jVTMJDBNd1wl3dNAWiVj/Ojpo64jQ1hrh4y07eDMW6zZ1we90EfR4KHF7Cbo9lLg9uduAy41tfyw9qUA8/4K424/I7PNuvngqye5Iu/WvI3MbidAW68h/OUo8Xip8fiY0TaTC56fC56fc68fVdYpB2IRwCx209Pqa7b0+U/xUYL5WyvzpBxObNIfPWnaxcfdONmzYxPvvrwOgxO1hQkUVEyqqGFdeics+/Eu4Kg4bTVMmF0wVKQaGYfDIykdZsHABAIccuogrr7iKTz/5DMMwmDxlcr+PVV7uG1AbKiv9A9qvGAx128OhdlZcczu333g/7eEIU6ZN4OKvnc2cA2fQPKGRyqryfhd53R9tt4JjA0NPY6bT6KkUZipJOhZFj0fR4x2QiICioDld2Dw+7IESNI8HVbP1K/yX35fhMVLbXiztlqB4aLW1hnj+qZd44emXWf3OGlKpFG6Pm+mzpjJj1jQmTZ9IaVkJVdUV+IP+4Q8p+pI/Rc+WGaVtODENHdPVOfpCScWtFZ6SUczI7szoC7cUSBYjngQWeabNnEx4Zwi/39dZlwIyX1ufqsdicSIdUdo7onR0RIlEokQ6ItZtpINIpIPd27cTaY+QSqV7fB2Px43P78Xv8+H3e/H5vPj8Pvw+L76AD5/Pj9/vxe/3YbfbcyMHsp/8dw0zTN3A7bYT60hYn+bmTzfJ3DdMk2g0Rqg9QjgSoT3SQTgSIRTpoD0SIRzpINzRQTQe79Zeu03D7/ES8HppKi3B7/UQ8HgIeLz43W78Xi8+lwubqhW8dq4tZudSrqZpYugGpDvvm5qKYZi5Oh1Bn4+g309ztm6HQi7gMEyT9liMtkiEUEeE1kiEUMS63dzextptmzHyAg0F8Pu8lAQClAb8lAQDlAYClJQEKAsGCAb92Ow2a8vs9BoU66oYBUXLTp3JDDdQVCtwAfx+N+3t3d+vntIJ0zQJh9vZubOFnTt3W/92WV9HIp3BhM2mUVFRTtOkJg6qKqeqsoKqqnLKy8qw2/KGY/f9cvkv3OM+Xq+Tjkii4ACFy/B2PXBe3Zaur1ew5G7eAXpqVw/LAPe0bbd6Gnntcbuyo0PABZQygbkomKZJS1uI9Z9+xvpPN/H+ps28/fmnKIpCQ201k5qbmDyumfq6arSePnHoVw2PHv7Q9/K3v2CEjQnG8GcmPbrmymtxuVycfOpSAJxOJz/6yQ8HdKyWloj13/I+qKz0s2vXyIzQhrLtu3e1cNuyu3jkwSdIxBNMnzWVJccezsQp42loqremftg0Wltj/TrekLXdVMF0WEv0ua0pJNmlU81UHH3PLhJ7dmEq2aVT3eDKfDLYw+gL+X0ZHiO17QNpt6oqAw5f+2swg2IYWFhcLEHOQPTW9i2fb+Oxh57mqcdeZM276zAMg5LSIIcftZDZc6czacp4SkoDVNdVUTJMIykG+323RgbrGOk0RjqFqesYiQR6vIN0NIqRKZCsaDY0jwe7N4A9EERzOK0R28PU7qEkbR96g91uCSzyuAIefHWV+AN+IG8qQPai2zDxmFCmG5imYV14Z0ZJGKZ1a2aHbZkmyUSSSEesc6RG9l9HZ7ix+fNtRDo6iMcTPbbJ6XTg9/syoYYVcvgyIYc/G3L4feiqypZwK+FQO6FQmHC43ZqyEQoTCrcTDrWj63rBsRUFfD4fwaCf8poKxgfGEQj4CQb8BAI+AoEAgYAfl8vReUGZX38j/0otfwpKwQVcXjFSTSmosUFmWdBgiYdwOAqonUFB1ykGeYerLHhcKXg9QzcItYVpadlDy+5W9uxupaVlD3taWtm8exerP1rfbcpJsCRAWUUZ5RWZOhoV5ZRXllJWWUZZeVmvNTRKSz0oXepA9L++hIuaumpmHziTmroaausy9SUqy4ZkVY2SEjf2tv5d2BSb0lJPt/obWRXAVA4ErCKEGzd8ygerP+SD1et49tU3eOaV13F7XEydMYXps6YyffZUKqsq+ny9fSpG2md4lERzFFeXe8N1y9i6dSuXXf6rUbuay0i3+fOt3HL97Tz96PPous7cg2ez+JjDaGiqLwgqitZep49ki8olUFJR1GSH9cmg5sB0uKyVRxwe+WRQjCqDGRTDvofFIzWAgu5tX//hBp594kVeef41Pt24ydqmuoIjjz+CaTOnUN9QSyDop6K6gmAwgNPlBCASSRGJpIa17YPOxAqJjUxI7PWAW7f613QcI9JOuj1MdPvmzuWpXT6w9z36YjT9vowkI7Xt+yMoLq6z5yKgKAqqljlxH+A5YK7uASblPYQepglml9AjGU8Rbm+nPRShvb3dmpLSESXS3kGko4NIJMquHbv5ZMNnRGOxvX4Y7HDYCQT8BPw+xjXWE5jlJ+D3Ewh4CQYCBPzW6I7OE928YCFXuyJTkDM7qkBRMsU78wp45q1skivimV3NJDdioW+eUg8JBu9iqdLjorKuqsfndF2nrTVEy649tOzeQ8uuPezZbX298eNPeeu1VT0GGtm6GWV5hUFrastY/9HnBatx7Ny+k3S6MxgKlgapqatm0eHzrcKXmX+BoH9YV3nZXyuCFBPNpjF56kQmT53IaWcvpSPSwbo1H/PB++tYu3odq958D4DKqgqmz57K9FlTmTpjcrcCW/u2bFnvT5lGcV1s3bTiZjas38CvL7+sKFYuEYXWf7SRm669jZefew1FgfmHHMRhRy2iuqaa+sZaKqsqijuo6E0PQ5tNR7ZwZ9qal50NMGJh1FgYU1EwbS5weEh5VKvGkFTFFyOUBMVfjGEYrHrzPZ578kVee+lNdmyzptI0Njew9PQTmDpjMlXVFfiCfiqrygkE/LmQYtTL9otqYfFO0+7GNFJW35lOdtYXyi5PrWp59YV8Ul9IFB0JLPaD7GgCoN+hhwcooTJ3PxdwZMMOg9z9dDpFe7iD9lA74VCYcKgdl9OGw+mmtDRAIFPVWNW07sulZjuz/GBhkFcHKWaapuUKePakr0Bjw0ef8Oar73SrY6IoChVV5dTUVTPrgOkFwcSQVZfuh2QiSTQWy/wB8xJqi1rFWQ2w2W04HHbsDvuoXS7X6/Ny0MK5HLRwLqZpsmP7zszoiw957cU3eP6pl1BVlfGTmpkxexrTZ02leULTqDyh3PTZJu66427q6+v4h5/+HIDq6mp+cdk/DXPLxOp31nDTdbfx1mursDvsHHH0oRyyeCFlFaXUN4zgoKInuZPrzHyp7Mm1oWPqadDTKOl459KpqRjtG1qskXo2B6bdiWlzW3OzNbuEGKLoSVA8MJH2CK+99AavPP8Gq958j9Y9baiqysQpEzhsySImT5tISWkQX9BPVXUFgYAfh7NI52EOpR6XTnVZfazRZYRbIoqa6MBkV7fVncbCh1yiuElgUaS6raiQx4YDl89bMIqgr2Hyov/2JdBQ0PH4/FRVV2J3FN+Jh2EYJOIJEokEmODxeWke14g/4KOpuYpt21pJJJIk40lrilJm6hBYwZiiqDgcdhwOx+i5QMpQFIWa2mpqaqs56vjFpNNpNn78KR+8b00feejeR/nbPY9YBbpmTmH67GlMnz2119+LkaapuYkHV94/3M0gHolx15X3096WKRucV6vEqoGT3dIsLH9SUDelsC6K2UOtlb62MXMvlnc//7Fur9d5cFVV0PW8uj0Fr1vY5uyd/G1MOmv5bA+18N7nG9kWasGh2ZhdP54pVY24wg42r3yPLYrC+4XN6vJ99f36XU84FQreInJ/bXqY6ZcLATI79bgCUuGunav+dD9YwSF73T6/kXnHt77s4eS54Pi5pYn62K4fD+f2z6+L1M9ApIfNVFXBNLo/1zUk7un97e/7WHCs3O69/Zx6ebyHKZmapqLrRi/Hz/5/5y9HwUzO/AN3+x3q2k6z53Of/DZ12a/ra+Q/X91cxZxzjut6tGEhQXH/GYbBho8+4aXnXuWNl99i/YcbSKd17A47c+ZOZ8qM45g4dTxer4dAMEBlVQX+gE9Cir3pYelU0+HBNNKgd1ndKdoG0TZMVSMS86GYNky721qeWqboiSEmgYUQ+yA/0CjGkEhP68RiMdKpNIqqUFIapHFcAz6ft+APuaIo2O126xMen5eyilLAOklIJpMk4knisTjt7VaQkWxPoqBgYhUGdTgc2Oy2UTP6wGazMWX6JKZMn8SXzjmZSHuEdWs+4oPVH7J29TrefuNdAKpqKnOjL6ZMn4SrmKuKjwB6NEp4yw6SSb2PixCly/1CuaV7+3iux/2zI826vK7S4+tlt+3tYq1zhy5NLrjo73oxbpomG7Zv55UPP2TLnj14nU6OmjWLAydOwJn59DX/YrPH96hLHZ/C11d6eV+s274+NCsooNv9Jm/D7E0PB+u6T2/b9nbsvIZ0Pcbe9HWsfm3bzw335XUKtu/2FvT8JhRmbfnb5IVoXTbOPd21xrHZvW09tadrbWQTMzvIs9fX7OlYBa9kmj2/Vg9fmNmQqtfvvXs7c091eTAVSzI9nsBeBFMCiiUoBmjf007b9j1A936yoK/LZX/dkrH8m9yGPS1L3vM2hdspikJ7OMLbb77Lay+9yfvvrqVlt9W+yuoKDltyCJOmTqCxqZ6yMh+mYqOishx/wJ8bqaKYkIonu30v3XV/sNdr756Wnu/ndj3R0zp6Wt/7hj30If3ta3rufnrpk3IP27DqyGmYNgcoHtBTmfAiQaqlxcoSAVNRrBFuNiemw4Xi8IDNnjeCO/9vqNLt92m0juIV+5cEFl2YhoFhGKPmQkyMfslkklg0hmGA3WGjvLKc0rISvF7PPo+MUFUVl8uFy+UiWBKgutZ6PJ1Ok4gnSSQSdHREaQ9ZdVYMw8xdINntdhxOR7+XVCxmPr+PgxcdyMGLDrQ+/d66IzP64kNefu41nn3iBVRNZeKk8Vb9i9nTaBrXIP3GPvJWlXPxf32THZ9uo6cTqn6d15jdT7Z7pJh72SBzUrW3k7283f0BN+3hWA+H7bvh6ZTOk0+9xN33PsbWrTspKw1y/rlLmTtnKk6ng6rqSkpK/PtvZJMJgYCbcHu28K6Su+3pE/TOr3tKP/LvqH08p/S8e8Fxe3ntvDslZR7aWqPkrshNrHnZhp75xFBHMdIo2Vs9nYlaM3uoNuufZse0OUFzWKtQoQ78E8N+/uzLy3y0tPawCHl2dar+vFR/29jLZj2NEOnPcO/yCh8tuyK9HFvpNYTq7YH8MSuZRux9d6X3DXq7mKuoChDuGNrCisUutHU311y2rH8LY+1nkWSMbeE9bG/fw85IG7ppoCkqVb4S5tVNotZfhs/phl3Qvusz1vLZcDdZDLJuI7DoxwcB+aFIwXPdQ/r9HpDsw+H3uSWKUtg39jdA63KIvb8Ohac53UYX7v0g+a9TVhXgxF98ZVDf+5F/ZTGI3G4XLreLSLgDw7TqFFg/QxNMqxinZtPQtOw/VS5Q+mCaJrpupcmGYVhf63rm5KgzaTeNJKG2KIqq4nQ6cDgd8r72wTRN4vEEiXgCTBO3x01Dcz2BQACP171fOmebzYbNZ8Pr81BWXpprRzKRJJHIjsaIWMVi27PLtJpoWmdtjJH6M1UUhdr6Gmrrazj6hCWkUmk2fLQxF2A8ePdKHrx7JV6fh2kzpzB9ljV9JPs+ib45SwK460fmJy7BSj/JfaiEHY3GePCuh7nv9r+xe1cL1bVVXPKNC5g6YzI2hz1XTHMoQj9fpZ/YCKw+7ir1o6V7eX+yq1iZmQAjM0/bNHQUPQnpFKqeyhSdS0K6w/q0UHNg2hyZwp4usDmsonWDXHTO7veixY29bzjE+vNfn83lQnWNvAt/p8cFElgU8FcEOPaMA2lvCfc84qfLg91Gt/Q62ip/FI3Z9RoIgFQ6zcZtO1i3aTMffb6VXaEwAGV+HwumT2ZqQx3ja6qwZ/rA7Aiy/GMp9DSKoK8RCXv5DR/CkVeKonR7rtfW5T3R+b3vLTTu/cH+nRr2lBhYma5Vvi1/6qFZEBYomYvr3K+JSaYOX/79vGP3ME2y837eyK3CoVpdplp2GWuW+QDD7PLT7284t68ZXr9qe2TfpH0OCDO/L5k3ba8jbAr+g+v+PmQe6OPV+ngi/3ex4Hvu+ZeqtCpAOp7APoijkCWwyFNSGmTOgbMAa9hWOp0mnbnV0zqJZIJkPHOBFk8Q64j1HmxoKprNNiqDjWzwoOsGRu7WAMXMTRuw/kNTcTitT92dTgdOpxOH04HdbkfTNGw2Dc1mo7LCx+ef76ajo4NwWzuRcMRaJtbMTD/I7DOWh5Hpuk4sGiedSqEoCoGSAA2Ndfj83mGrfq0oCk6XE6fLSSDop6rGKhqrp3USiQSJRJJoR5RwuJ2O9g70bLFSBRx2O3aHo9clY4uZ3W5j2swpTJs5hTPOO5VwqD0zfWQdH7z/IW+9tgqAmrqqXHgxedoknI6x+/s71oVaQ9x96/08dN9jhNvCNI5r4OvnfYUJk8ah2TTqm+qGLKgY1XLDkTOrkEAuwLAKzGVuTcMq6Gl0hhdKvB0VK8Axs0U9NQemPS/EUL7AKAwhioTqcLDgq19i187QwA6wDxde6XSaTzdu4qXnXuPt11fx8boNJJOp3Cpei09awsQpEygpDQAQCAaoqCzD5/fhcuXVo8h7zfIKHy27exiptD8M8n/u5eV+Wlra9/3APYxqGpB+hhtddVumsodwOPu1oqdQ9FRmSkky/6oA02bPBMQuTIcbbPbOfnU/da2VFX527c62vTj67/5ez4ylZU33Rs6OeqHZNDSbxt4uBXVdJ53qPdhIJJLEo7HcxVqPwYamZcKN4Qs2rBEQ1iiIbAihG3qP4zJtdjtOhwOv143D6bQKMzodmRDChs1m3fZ3OLPD6SAQ9BMI+qmtq8E0TRKJBPFYgvb2COHWMKG2cC6atTvtOEfJ1IO+pJIpawlb3UCz2ygvL6O0vASfz1vURTA1m4bH5sHj9VBaVkI9ViKbSqYyYV+c9nA7kXAHHZGO3H6qquJwOnCMsNEYgaCfBYcexIJDD8I0TbZu3s4H76/jg9Uf8uIzr/DM48+jaRoHL5rLr/7958PdXDGEdm7fye0r7uGJlU8T7YgxedpEvvz182loqkdVFeob6qioLpfVAvangir5GaZhfRKYG4VhZEKMzIl2OomiJ1FTcYhbnwCbqoapOawgw+ECu7tz6T8JMcQIY60ON/jnEYZhWLUoXl/FKy+8zvur1uaWHS0rL2XREQuYOnMyjc0N2G02NJuN8opSSstL8Pq8/Tqv0xwOlBHaZ2oOO4pt5LVdUVWUvZ2X5QUYZmaanmkaYKQ7+9V0EhIR1EQEOrr2q25wuEG1dauF8YXbLku0jnij+4pvCGSnh/Qn2NDTOqlUuvdgIxZD1wcv2DBNEyMTQuiGYRX7yUzJyI4cyk6PUjOjIZxOJ05XZjSEy1EwEiIbROzvkQ6KouTqKJSUBqGpHl3XiccSxGIxIuEIoVCYjvaO3PfgcDpxjvCpJKZpWqt6xBMYJrjcThoa6vCX+PF43CP6e1MUxQojnA78AR+VVRWA9d9FdlpJrCNGONROJBLJ/XcAJnaHA4fdjs2+/3/3vihFUahvrKW+sZZjTzqKVDLF+o828sHqD9GKN2MSg8g0TT7/dDO3Lb+L5558kUQiyay5Mzj+5KOprK6UoKIYKKr1RzZ7wZb3aWEuxDAzIUY6lfnEMDMSIxWDWMjK8jVbYYhhc4OmSYghxpRkIsnnm7bw4jOv5EZRJOIJNE1j0tQJHHbkIiZPm0SwJAAoeDwuq2DmKDi3EXm6hsPZpb4MIxMKpwvD4XQa9GSmsGd+v2rH1Oxgd3WuSqJpWHODpF8dqySwGCLZYGNvSy5lg410Ok0q1f9gwzSShEKxzpo1malcjkwhRJfHbQURmdUduo6E0Ir8akrTNLw+D16fh4rKcgBSqRTxWMKadhAKEw61594XTdNwOh3YHcU9lcQwDGLRGMlUCgWFQNBPXWMtPr8Xl2v0r0ChaRpujxu3x01JaZDahhoAkskUyUSCeDxBJByhvb2DUChs/V4bSRIJA6fLWdQ/WwC7w870WVOZPmsqppHc+w5ixNJ1nfUfbuTWZXfy6otvoKd1Dlo4l2OXHk2wJCBBRTHrYyoJdiNXB8M6+dZzIzGsACOOkoxClM6TbZsDbE5rOondtV/qYQgxXHRdpz0c4Z3X3+XVF99gzXsfsG3LdgBKykqYv+hAps+eStP4JmyampvGWlFZPqzTWMUQyxYT1lTQABx5ozDywuEe+9WY1a+SqZRhs2Nqzs4penZnpt+WfnWskMCiyAwk2EindUqCLlrbogUjITRNK/oLui8iuyynP+CjurYqVwQyHo8Tae8g1BYmFApjGla/ZrfbcDidw143IZVKZ1b10NE0G2XlJZRVlPZ7OORY4HDYcTjs+Py+XEBlGAaJRBK3U2HdB58RbgtjYuJyuUZEeCFGp3Q6zep31nD7irt5+/V3URSFRUfM55gTj8Tj9aAAdQ21VNZUSFAxkhR8Wpj5uWWmknQW8zQK5m3n5mwnoigJa7pb58m2g6gRgZSaKeqpDeqwZyH2l2yh7y2fb+WlZ1/lnTfe5eN1G4jH4qiqysTJ4zn9vFOYOn0yJWVBTFPBZteoqCijpKz4p7GKIdS1X82OwsiFGNlRGGZmKkk6N0VPSbSjJvLrDDkxbfbMKAw32GSK3mgmV0cjVNdgo7zSj8HYPhnOLwIZLAlS31iHrusk4lY9jHCo3ZpKEomQXfrI4XDk6m/sL1ZNDmslDTBxupzU1lcTLAng8XpkOGQ/qaqK2+2istIPqpNkMkV7qJ1dO3dLeCGGlGmaRDtivPfWKq6/8lbWvPsBNruNI487gqNPXGJNnUOCilFnb1NJTL2Pk+0I8Z0RNLoW9XRa87ZtDvnEUBSNdDpNONTOqjff4/WX3uSD9z9k86atgFW3ad78OcyYM50JE5tRNRVM8Pi8lFeWEgwGcHv2z4plYpTJjsJQ1Myq2F1GYZg66JlRGKYJRgol3VkPQ03FIJZZ7Ua1dY5uc7gzo9u0/q3kIYqeBBZiVNM0DY/XKgBZVmEtM5lOp4nH4kQ7YoRDYUKhdvS0NdxXVTunknyRIMEwDGKxOMlkEgXwBfyMq2vCH/DjHsRlfsYyh8NOeWUZ5ZVlEl6I/c4wDKIdUT76cAPPPfEi7721mk2fbsbtdnHiacex5NjDAet6trahhsrqShwOCSpGtb6mkhScbAOmgc+t0RFq71LUM7ObastU0Hdminq6pKjnF5CKJoi2hLAnE+CQKQh7Y01PjbN18zZefv413n1rNR+vW0+0I4aiKIyfNI7Tzl7K9FlTKasoRTcMVEWhtKyEsooyfD7vXkcGC9EvXUdh2Ckc3WbPrPjU5xS91tzotkjcA7rNCoZlKsmIJYGFGHNsNhs+vw+f30dVTWXBVJJoJEZbKER7OJIpuoa1KkomxOhLOp0m2hHDMAxUVaG0vJTyijK8Po98wrqfSXgh9gfDMOiIRPng/XU89+SLvPf2GrZu3gZAQ1MdF33tLOYtPIh0Ko0C1NRXU1VTJUHFWNbHybYj4CaSVPPmbRu5Twytk+0kSjIGsZ7qYUjxua70ZIpYS5hoS5jo7hDR3W1Ed4eItYRJx62aQU6/m4U/PhdtBC6hvb8lE0na2yOsenM1b7zyFuve/4jNm7ZgmiY+v4/Zc2cy84AZTJwyPjOKwsRut1NRVU6wJIjX65GpHmJo9DG6DSNtTdHLe0zRk7lRGKlQCC27tGp2dFu2xpBDVnsaKaQHF2Ne16kktQ01Vr2EeIJYLE57OEK4LUxbayi3hLPT4cDvd1jTTeIJTNPA4XBQU1dFsCSIx+su+kKmo5WEF+KL0HWdSHsHq1et4YWnXmb1qjW5ZfnGTWjijPNOZd78AygpDWK3Q6gtSm1DjQQVoneZk23N6bQKxnWbt51XDyNXQT/ZSz0MB6bNgWnLLz43ek+2Dd0g3tpOtCVkhRG7w1Yw0RIm2R4t2NbmceL0ewg0VuIMeHGV+KibUi99fQ8euOsR7r3jET7+YD2RSAeKotA8vpGlp5/AzDnTqKyuIJ3WUQCvz0N5ZTmBoB+X2yXvpxh+fY5u0zENh1UwGZNAwEW4td2aSqInUFKZ0W3dViVxYto9EgwPkJHWibW2E2sJY+4JoJSVDOrxJbAQogeqquZWrygrz5tKEk8Qi8YItbUTiyXQNJXm8Y34Az75Q16EJLwQ/ZGtev/266t46dlXWb1qDS279qAoCpOmTGDxMYcx9+A5eL0eYplaNGldZ/LUcaA6JagQ+6bbvG26fGJoYJpdi8+lrCVW00mUeDsq+cXnMiGG3QV2z4grPmcaJon2KLFMKBFtCRHdFSLWEiLWGskEPBbNYcMZ8OKtDFI6sQ5XiQ9n0IevugxXiRfVpqFqGqpNRVFVKiv97NrVPozfXfHZtmU7l/30P/F43Mw4YDqzDpjBlOmTsNls6LqOqioESoKUZ4qBS/8mRoSellU1TeweD3Rkp5J4rODXSPeyKkkrpqKAlten5moMjZw+dX8xDYN4qCM3ki1/VFs81JHrq10BDwt+eDbaIPYdElgI0U82mw2fz4bP56WyqkJOhEYYCS9EPj2tEw6Hee2lt3jl+dd4f9Va2lpDqKrK1BmTOeGUY5hz4GycTgfxeAIATVMZN6EJf9CfKwArfYAYFAWfGEJn8bmeinoCejIz7DmVKT4X7158zu7AtHmskRhF8IlhKhrPBBJhYrsz4cTuELHWMEZKz22naCrOgBdnwIO/PjNaotSHr7oUV2kAzW5DtamoNg1Filbvs9r6Gu5YeR3r1nySqTth4nA4clM9ZISoGBUyfapqt0ZPAAWhsNWf5k8lSWcKeqZQ0gkrGI5ngmFVK5ye53CP2pWeTNMk2R61+umWzn46ujtMvK0dUzdy26p2DYffgzPoJdBYiSvox1nipXFGE/og980SWAghxhwJL8amdDpNqC3My8+9xqsvvM6a9z6gPRzBZtOYPmsap529lFlzZ6BpGolk0prq5XRQ31iHP+CTonJiaPU57NlpFfU0DKvekmFkQoxMTYxs8Tna8uphODPDnjP1MLIn3INIT6Y6A4mWcOdoiT0h0rFkwffm8LtxBryUTW7ITeHwVATxVJVgdzqsQELTrPoJYlBV11Syc2eIisoy/AE/Lvl7J8aCHperzptKYjM6awyZBuhpa5Wn7BS9ZIzufarLKpRs2z996v6SDZBj2X46GyDvacdIpXPbKaqa66t9NaW5vtqbCZBtTnsmQLbl+uqK/fBhjgQWQogxTcKLTh0dHVx3zQ28+84qbrjx+uFuzqBIpVK0trTx4rOv8NqLb7B29TqiHTHsDjuzDpiRW54P0ySVTGHoBsGSIM2VZfj8XimYK4pLnyfcbkxTx9T13Al4QVHPRAdKImLt0m3YsydvadW++7pudSVawkR39VxXwu5x4Qx6CDZVW6Mmgl7c5QG8VWU4vC5rCodNQomhVlZewoxZU4e7GUIMv56mkuSWUdU7awzlViVJ9tKndi3o6ckU9By+URjpRDIzdcPqpzt2Z6ba5RUmBqwA2efGGfBQNrEOR8CLu9SHuzyIt7o0E0pkp9sNz+grCSyEECJjNIUX4XCYP/zuj2zfth2b3U5tXS3fu/Q7BIPBXvfxer388MeXctkvfj2ELR18yWSKlp27ef7pl3n95Tf54P2PSMQTuFxOZs+bxbz5c5g2Ywq6oZNO6xi6Tll5KWUVpfh8Pql8L0aWHlcmyS9Al51SYuTVw0h21sPIDntWNLDZMWxOkmmVRNwg0R4nEY6yORqn5fNdxFrCxFrbC+tKOO1WXYmqUsom1eMKenGWBvDVlOEKeFDtWsGnb0IIUbRyNYboZVUSd2GNoUwtDKtYcgI1llfQMzc9z4XpcFvLVavqoC6rqqfSxPdkAuRsOJGpBZTqiBdsa/e6cPo9BJuqcAS8uII+K0CuLsXh6QyQFU0tuvNcCSyEEKIHIz28UBSFM88+g9lzZgOw7PrlrFh2Iz/40aVs27aNv/75yoLt5x04jzPPPmM4mjookokkO7bt4rmnXuSNV97iw7Ufk0qm8Ho9HLjgAObNP4CJUyaSTqUwdJ20oVNVXUFJaYnM2RajT9dPDSFTC8Mg1REjEdGJh9IkwlESoQiJUAfxcNQKKCIJjLx5ygCqTcUZ8OAMeAk0VFjDgssCeCuDuMuDmboSxXmiW+xG48g2IUaVvdYYSvcyPS/ZOT2vI2+lJ81hTSPJTs/byygMQzeIt7XnRkvkj25LhDoKtrW5HDgCHny15Tj9HlwlXlyZANnp9+T6adWmjai+umgDi7Vr1vLkE0+RSqXxer18+7vfHO4mCSHGqJEYXvj9/lxYATB16hQeWfkoALW1tfzLv/92uJo2aBLxBNu2bufpx57nrVffYf2HG0indQJBP4sOn8+8+QcwbkITyWTSKqyFQV19DYHSAF6vp6h+XkJ8UaZpko4nSYQ6SIQ7iIc6SIQimduO3ONGWi/cUVGwux04vC6cJX789eU4fU7cPieeoAOv34HLa8/992KigGazlgK0aZhqErRMQDLGjeWRbUKMKX3WGHJnppLoXVZ6yi5X3Y6asEa2GaikDY1EwrT+xdMkIwkS7TE+aO8gtHWPVezS6BzVptqt1ZLc5QFKxtfiDHisUKK6FFeJL2+qnYaijo7znCEJLAbSgc+YOYMZM2cA8K+//XdisRhut3somiuEEL0aieGFYRg8svJRFixcsNdtr7riajZ/voW//vlKzjznTGpqqvv9OuXlvgG1r7LS3+9tY7E4n2z4jEcffJpXX3yLj9ZtwNANyitLOW7pkSw49ECax9UTTyQABY/XTU1tFSWlAdwe96D/XPal7cVmpLZ9pLYbBt520zRJxRJEWyPEWtsztxGie8J07Gkn1hYh1hZBT6YLd1TA6XXj9LvxlfspH1eF0+exTnCDXnwVQXyVAewuZ66WhKICpomhpzHT1km3kU5hpFKYySRGKomRSmGkEpjJvJoVioJqd6A6nGhOF5rbjebyoDmdKOrwfZo3lL8vY21kmxAiT5fpeelEkmS7FRQnwx3EQxES4SjJ9iiJ9ijJSJxkNFGwSlKWatNwB62+O1BfhsPvw1Xqx1NVgqc8gOawj7pQoi9DElh8kQ78jdffpLGxQcIKIUTRGSnhxTVXXovL5eLkU5fuddtvf+9bfPt73xrQ67S0RDDyPgXoj/4sDRqLxflk/ac88/gLvP36Kj7buAnTNKmsquDYE49k7sFzqK6rJplIgAkdcZ2KyioCQT8ulwuAjqhORzQyoO/ri7S9WI3Uto/UdkPfbc+OjIiHOzJTNKLEQxHrBDfUQbI92mMYYXc7sXtc2L0uSiuCOLwuHF43Dr/1iZun3I/mdFhV3DVrGdCuJ7cxE2KxNNDl+FmmQkVFKbt3tYPmANUAhwGG9ckheholc2saKfSOCOlIuHP3ghEZjtxqJVaRT3W/FqQbyO+LqioDDl/Hwsg2IcY6PZki0R4l3tZBIhwhEY5ZwUR7lGR7jGTECiPyV9vIUjQVu9uJze3AGfThqy7D7rbj8Dpx+x24gy68ATtOh4pC5/mUqajWyiSaCWoMEwMUl9WHmoyY1UkGakgCi4F24E898RQ7duzkK1+7ZCiaKYQQA7a38MLndQ5Lu264bhlbt27lsst/hTrI62LvT7FojI/WreeZx55n1Zvv8flnWwCora/hpC8dxwEHzaa8ooxkKoWCgtvtorGpHn/Qj8MhK3uI4WeaJkYqTSqWIB1NoO9sYcfnu61goi2SCyiS4Y7uYQRgczutAMLnxltVij0XRrhxl/pxlQWwux1WEKGpmdER++G/cUVBUTOfGhbUxOispm8VoMubw20YYCRR9EygoadR0onMUquZ3SFzAj70QcZQGKqRbTCw0W1jcaRSMRipbR+p7Yb+td00TVLxZMEItlgoQjwUJdYWIR7uIB6OkYj0EB4DqqZi97hweJx4SnyU1lfi8Llw+t04fR48pT68ZQEcfpc1oi1TRyLbbytY/ag1si2NoeuYehojN6ot8y/ZAYnOD18UTUO1O1GdTjSXG5vLjeb2oNrs++fvQT8N9u/LkNew6G8H/sZrb3Dzjbcyf8HB/PXPV3LRJRf2OYWkq6EYmlxspO1Db6S2G6Tt+119GdNmNJNMJAm1hdnT0kZNTQlO19AFFzetuJkN6zfw68svK/rlOU3TJBaNsfa9dTz75AusenM127ZsB6BxXAOnnXMyc+bNJFgSJJ1KoagKgZIA5RXW8qM2W9GWZBIjnGmYpBNJ0rEEqWiCdDxzG0uQyjyWisYzzyVzj6fjScwuxSuzbG5HbmSEpyJoLf/pc2P3u3GV+HCXBXF4nJkgojOQKCo9VdOHvCDDY9WOyRT7xNDB1DMBRiozr7uXICNbXb9rkKGOnDoZQzWyDfZ9dNtoHalU7EZq20dquw3doLzMy/YtLSTDURLhDqvQcDg7JSNKMhIj1WH1372NiLC5ndhdDmxeF+5Mf233OrF5nFaIHPBajzltqHZ7ZxDRJUg2gQSQSBqAQa+j2gBMlYryUnbvCoPNaY1ss+vgzvSleio3ss1IpSEeQwm1du6uaNbINpsDbE5Mu9M6Tnap1f1of4xsG/IzvP524PMXzmfZwvkDfp39NTS5WEnbh95IbTdI24ec6mTqjElWu9uTe98+u9sXGJq86bNN3HXH3dTX1/EPP/05ANXV1fzisn8a0PH2l0Q8wSvPv8azT77Ie2+tZueO3QBMmDSOsy74ErPnzcTj9aDraTTNRll5KaXlJfh8Xll+VOwTI62TjietMCEbKkQTJDOBQy6QiCVIxa3bdCxZuF59D1SbhuawozltaA47No8TZ4kPm9Nu3Xc5sDntlFQFSdvsuMsDODwua0UNVc2d2A731LFBkx9kdJWprG+aRmeQYeqZE3ArwMBI5YIMklGyMU02yMBmx9SsEMNaMtBZdEHGSB3ZJkQxM02TdCJFqiNmBQ3ROKmOBMmObOiQFxzHk+iJJHoiRTqR6nYsRVWwe1zY3E5cJT78teVWMOFxYnO7sLsd2DwuHF4nmtOOZrejOTKrIalDECQrCoqmdRb07HwTMqFwNgjOjmzTuwUZip5CiUdQ6Dxn7gwynGB3WEGG5gJNK+pRbUMaWEgHLoQQQ6OpuYkHV94/3M3o07bN2znrKz+hLWTNd68oKWXOlGnUVlTisDlIbGnnra2vYrfbsdltqKpm/T01yaz6kbmIybsPJtkvTczsiPVuz1lfm5DbltyGJnnHMzuPk39MAE1TSWdWXDDNLsc3zc7Zp12/7qk92W3yXqfP7Xo9rpnXnuxTZt77Y1FVpSDU7+liOftY7hml52173rdgz7zzIGUv97seo3AbTVXRdcP6ngwzd4uZ/doAA0zTyBRsNzJTFrDelD7Ox5RM1XfrRrFqPSiK9XX2OTXzT1Fy9SCy93t8T/LeB03r+T3v2qTCfUHJu/hXyPwH0G3frvt0voFK4S709p4rioKJ2fl6Zmdb7DaNVFovOEa334u8+7l2ktdOpYfHctvnMU0UJfvjst4vJTtqw6qIQeY/TGu73HukgGIFP6Zi/WzGTWni0PNPZCiNpJFtQgwX0zBIxZKkonGSHXFS2QAiez8vSM4Gx+lE76PWIBMeO+3YnA40lx2HL4gn4MbQNGwuB3a3C7vHCiI0hx3NbrOWZHbYrEDCnje9TtOKb1QbFK5Okt+8XJBhZlYnyQQZel6QoadzK5Yo8TBKPG93VbOm6GVHttldmZEdxRFkDFlgIR34F1Bwopk9WTYLnjNSSdDzhhZ1OVns8Ywi9+Xw/yIKIcaeVLgDl2lnXFktpe7A/2/vzoPkuOo7gH9fd8+1q5VWSKvDOixjSy60KxJKXIYcGCdApYqEOAn4CDjgYA5jY0Iw4ASKxOAyJhBho8sntoFQVIUkRSVQtuQQMKGCDyyvJIQt20jWfay058zu9JE/+np9zcyudnu6d7+fKmmme173/KZn9jevf/26BwVVA8aBs4cHvDbJO77SLlNohye68xhaJpQOY3e4pQX9dpFnDqZSp1EwlugyzeNp9jzOMvLrFu7rCC8jwne9x6V6CKJ3rbgb/9HQ91LgcfeMAJjBdTgFFkuelgpCctHHrSV5hSkrXMRJJmBvQwXwCg5Q4e1Ie/UAd6MkDAYIvyDLAmA4/xCML9y64aoQ3c7h1cQWpcKrCi0kv5/hpZK2W+w65OJc4joaxB2zfPT5gwW6YPwWQg/FP3doQXmu2/7Ai0ew8Z2/h1JnRziAGZGXkW1E08WyLBgTOvSqU2SojqM+6hQdvFEPNdSrE9DHat7pckbMqAePEFCLml14KBVQ6LBHQajlArRiAWq56N2qpQK0oga1XPQuLKxoznUhVBWLFndhaLQGRdOC1/uZTaPaAKmQgSbXGjKcQobpXDS5HhzZVh0KfB8GCxnOqSWFEiDSLWSkUrCY0wlcOuoX/Ha2pLum31Y6iuD12Nz23hGLKL0qgHot9rHmQj1u7wMoEOzhhnvIgP37Zwi1Ca13KkWS2N5VUo8u5jF3XuxsM9Is8L4EVmeFG3rt6yMWMD4Gb/grAH/7SIkjsI1C24cFJJrDVq+/EFu/eSue+dkuVMplLFgwDx2VCgpJF80MfIsmr9fvhMQ0arRc8yZyOQCdnUWMjk5EYkv+C04upMgTAvBzYOQwePyEfDXxVjphnfNKGB0ZD65HSmWWZcE0LJi6DqNuwNINGHUDhm7AqOsw6wb0ug6zbsLQdWeefd+sGzB1u61Z12HoZvIec4jbmVRU1e50SvcVVUWpUoAphH1kTDoqplWK0EpFKKUCVEXxhu3CGw0hjYxQRaSwFA2klTwc/o6LTARmdXdXcHawGvNwaMSBOyHFYBcWQh+y8Fdi3FO7bYQ7LiG4ntbeFWDhwk6cOTPmFX38z6e0HiF9p7nxus8rPZE9AqLR93lwUsTMsbtKJry+kjOSBqYOVZhQhQkFJkqVIsxSegfJ8jCyrTpaw+ZN38HAybPwCpoieOt9tIUIFPYCbWPmeQVCSF0g+TH5FgCU+OcXwm3nF2fdv5RKpYhaZGe3QW6VZ1jJbaJ5s8k6o00QySqhyUqliGpVOsVMruAi9GeQkDPjiqRxRcpQ9TCyDgvOKDXT3pG1DNO56KMFWPatZdrzVEVgolaHZdg53tJN56KQJkzd+TuMy2MAFNUZjaYqUBT/QpPyRSf9eap9v6DaI/GFgBA6hG5CMSeACfsz4f7SkVAUfwQcEBntJoRApVJAtZpUHIluy8bfDNEC72TaNy6NR2PpqBRRrda99zaydOhrIW462B2SZvhHDvxb55+Am08NaVo+AOH+XSp2QUMoEIpqn24iBC7qW4NVG/savNbJS6VgkYcEHiEXGryb8JsrFRrkgoRXeHCm5Y6FBcD9IHjFCWcdzvqEvA6pMuaesySs8OMmRocFFNMKFQ1idojjigrSpCVPRNpG2wfWnfScQHRHXZo3ZgxBjIwjuJHCxRog8B4ECkDOBo4diSJ15MOPR9Ypt2kwz1lmeABQ5AKF95qCry9pvhVYVrovH/7zlhP+tDPPcoa/AsI5lKj47eVl3VtF8Z7HPX84vDmiCTOu6GPF3I37Ao1ZV2QyoY0VM89RHzaB8Win35a8s9CwTfjxZk0aNYj9uwEsI/o72wSsfvXFKC1ZmurFSKdLu66nYlkWTN0I/qvrdpGg7s7TvcKC6dwaXhsdgwowMjhmn987UYcx7vybcP7V9Zb3ZpWCCkXT7OG0mgpV01ColFDSVHu4rTzstqhC0exzgb0jaOWifYSsUoSqqICqQFGEc40HNVBsWNzThdMDo87pGErs6RhZtbCnC3rerr/jWNjTBb2ck9i9/pGJV7yiAwNnx5svM4fUzgzihSf3oeZcYHDqI3WsyLxot03aGU/oS8QvHx1ZZDXqbyTGmNCmyUKtpL6k19Oo/eRTVXhv1J0bLeMltQ4/ZdL8yUgoscSMuLIQ262jWW1eRwm3fusLKFQq07ZOXlZdok9MANVhqXjg/KFJVXwhV/SBSFHBO8/SMgPz3XVOJUHYxSxp59TbcVXs6pbQoBU1jDs/s+OlscgOoTwvfoddxC3XZEf/XLuKtdHgaVjNyNFGdg4Do0OAaNHAfczdhtHHrHDbSNHHvi2VC6jV6tL7LEVnWaFtKd03ncJUuGDl3VrnvE2bOXPMHiGdR8MD+Yz97NkCsOiCqfRYZrVCQctlsQKwO/puEcD0bt3RBaHpet0vGji3lu4XEUxvNIIRWM7U9UhxotE5vK0QioBS0PwjWwX7QmL278J3+AUGqdjgFhjsi0g65wiX7ItJehceU/wrokeuASHdhxBTLjQUO8rQRhsMJSaSzvFWiyVAtH6h47lg4cql+Pz2m3H41weix4Aie5dJe5tW9HiG1KfxdoqbrSemsOHPjz+AERmlkLBY5Ek8jfOOPbpMuk6Oe40c078ujvyYZVmAdD0db8SCPM+5VQRQn9DtkQmG8zymad9KIxzc5SfLK+5619dRoKgCUAQUd557sV9nWvF+ycK+yKNQhX0tCFWB0OzRbR1dHZgwDKiqah8ilHK5d7Dd+7uLK1IFDvMDAEx5ZIl0oNey/HXYs8zAbkhwOdgH4JxVB4ttto5yAWMJn5cwSw7V36qtLRtTCZJ3UeILaXHr9j/3HZ0ljI2NOwcaQ8vELOqNxAs85k/Evr6k0Zzyw96oKvd6WMF9W1WxoKnC+9e9qAuKOr09dRYsXPoEhp57Hqo5uY6g/eaHj2zbhQQowjmKLrw2ltdO8b5U4+Z5F1SRz0lqoNLdgdrZMT+mtMWObmih6GGZ6JpfwfBwDYmFh0BRIqFEnfiiJ7s1wt/AyYrdHaieHUt8vPlqkurekLZXXEHDmR9XDJHaiEB7029jAeWyhlpNT8jD4ZlxybHRZzKYLP0cK4JtYhcNP1d0mXldJYwMyyNygMjWbrTx4z6Lk2IF7jb865Tel87ueRgxnS8T8kyM1jB2etDuqOn+EFTLMLyOm6kbfgfQcB5z7pumIc2XOn/eEFcrOt80YUnDXU23nTwsVlou2F6KcQodSpnbQfTOp5V/McIZPqtVivbFRp1TIryLgbmnS2iqN4xWnlYL9rBaUdCgFQp2e+fipUJTsGjRPJw5W/XP9W1UVDiHAgMRZU/3muWod07tF6hcgR1z+dYKTss7434+ti8MaJn+TrtlWU6eNZxbv42cdxVdwVjVzf/ud4XpfQeYhpTrdcNpYwbahL8n5PszRsD+ZSv3tAcnX6vFAgqaP0+49zXNPhXOGb2mFDSomjOKreRcy6FUsH+NqFyyR6+pWjBvuwWFuJxuVxlayuu5/HU2B2NPgVS8gGViYXcFZ4an98ACCxYuVUO5ZynGhsa8K6JacYWDuJEOrWjp/N2Yna7w6SUJTN2+8muTZlMQrYq23D5YaA21cUuyChRVA0RMJU7eaRWhZcMNE/axW2obebzJe+oUULRKJzB2rhu7xc/PFPYTGo3T6OzpQnUySTBDOyqVRV0YMbOTwFv9BFQWd2EkD188KaoODOHHX/jXmekkSp0z90iTN8/ttCkCQoR21p0jUJrm/wIEFPsUBQjhXahLKArKHUXUdcspJjg/cyafk+v880YraArUQsHueBZV6bQGxUk9ztGwmI6m/ZJiOprSY+55va2Y39OFcZWfR6K5pj42jp9+4zEMnx5yRgfYBVoERgaYzkBh05nvzvNHHGSBCIwaEFLxVZqWRx1oKkRRk0YZ2PlduLldkU5Hc0eLKcK5mKM7X3XaOKMR1GCBQWgaVKegrGrOz2A6bRb3zMPAmbHYgkKkYDyJggJRW8n7ywC0SgcwMr39CxYsXEJBqXsxRsdPOwWJUDL2dr7dcTlxR+KT9tCTjiY7/yUdTRbh2XEjEGxquQyM6ghc4LGRxB36ho0bzmrxQamZ3a44vwsYz+BPBzWhaFrmfvu9VfKODlG7lBbMw2ve8xYcf+God5FEu9OmOBegVp0Ba1InVMDuYKpOscHtiKoCiqo5w1/t4oHXERTCqTsrXgcRQjhFCL9YYA9ykzqNgN9xhFRAcOb39HTh1OkRZzKm0EBElDXCLkRYhuHkUhWaJvwL1AoRvFittzOt+I/JF7EVzk6/ElrWLe6K4Loj7eUddrmdXEyA/7wLFnRgeHTcOc3Bv8aNndMV/2CYvD5FzvMi8NxAuEDs5u/Q6IRpKCB0LOzCqN68HREFsWAhUbQCUKz4hYRYIvZuwgzpoZntvKrFEqDxPE0iyg9FVXDBJetRWb0s2kEE4juOQHIRIWVayf4ddyKivChUSnjzh9+JY97PR7ujtBCYDt+12zQ6gCWkdqF5cYvFrkskTPrze5Z04dSpkeiyRDRrsaclEYoCqNwkRERpUQsaip3TdyVpIiJqLM95l6PXiOae/I3DJyIiIiIiIqJZjwULIiIiIiIiIsocFiyIiIiIiIiIKHNYsCAiIiIiIiKizJm1V5hUJvF79NOxXBYw9vTlNW6AsbfDZOPO2+tk3s2XvMae17gBxt4Osz3vAlOLOY+v08XY05fXuAHG3g7TnXfFvv17rXMJKGvKpQrOX7mm3WEQEU2bA4d+g9p4td1hJGLeJaLZJut5F2DuJaLZJSnvzrqCBWAncN3Q2x0GEdE501Qt851mgHmXiGaPvORdgLmXiGaHRnl3Vp4SkpcvGSKiZnS93u4QWsK8S0SzRV7yLsDcS0SzQ6O8y4tuEhEREREREVHmsGBBRERERERERJnDggURERERERERZQ4LFkRERERERESUOSxYEBEREREREVHmsGBBRERERERERJnDggURERERERERZQ4LFkRERERERESUOVq7A8ii5379HHY8shMA8MQvnsSXbr8V5604r81RNTcwMIAt39iG7u4FqI5VceMnbkCpVGp3WC05fOgwHvzmw1i4sBtd87rwl9dc3e6QGhodHcW9d9+PXb98Bvc/dB8AYGJiAlu+sRUdHR0QQuCDH/rrNkcZLy72uHlZFBfn3j17sePRnajXdXR2duLDH72uzVFGxcV98ODL+MF//ACmYcIwDXz8EzdCCNHmSNuHeTd9zLvpYd5NH/Nuc8y76WPeTQ/zbvpmKu/O2YLF0NAQ/vmfNuHY0WPQCgUsP285rr/hI1iwYAHWXbwO6y5eh8HBQQwMDGQqeTeK+8BvDqCvrxfvuvxPcPe2e3D8+AmsXr2q3SF7GsX+5BNP4a2XXYo3XvIG3LP9Xux/fj8uWntRZuPt7OzExz9xAz53y+e99j//2c/R19eHP3jbZXjomw/j+ef2Y+269ryGycYeN69dJhv7+t71WN+7HgDwxX+8DdVqFZVKJfNxr169Ctff8FEAwO233YFardaWuNPEvJs+5t3sxs68m37czLvMu2lg3s1u7My76cc9HXl3zp4SIoTA5X/+p9h6zxbcteXrWL58GR584KFAmx/+54/w9ne8rU0RxmsU99p1a7HrmV348m13YHBwCCtXrmhztEGNYr/0srdg1zO7cN899+P06QGcOH6izdG29hmRnThxEkuWLgEALF22FCdOtO81TDb2LJlq7E/84kmsWrWybZ3PqcT97K5+fPWOr2H+/Pm5OTp0Lph308e8mx7m3fQx7zbHvJs+5t30MO+mrx15d84WLLq6urDh1Ru86YsvXoeTJ05607qu45lf7sLG121sR3iJGsW945GdePs73oZP33IzLrzwlXj6qafbFWasRrHPnz8fH/rIdbj2gx9ApVzGqgxUypt9RsJ6lvTgpJO0Tx4/iSVLemY8xiSTjT1LphL7zkd34vnnnsc173/fTIeXaCpxv/q3NuCTN/8NVFXBSy++NNMhth3zbvqYd9PDvJs+5t3mmHfTx7ybHubd9LUj787ZgoXMNE388L9+hNe/4fXevJ/+5HG86c2XQFGyu4nCcb/2dRvx2GM/xrbN27HvV/vaPsSskXDsp0+dxp2b7sKdm+7CmgvWZCKBy+I+I9s2b8ehlw9jy11bcezYcVzypjeiv3837rv7flSrVaxdt7aNEftaiT1pXru1EvsT//cEvvXQd3D2zFlsuWsrBgcH2xixrZW4+5/tx7Ytd2Pb5u2o1+tYff7qNkacPubd9DHvpod5N33Mu80x76aPeTc9zLvpSyvvin3791rTGXgebdu8HadPn8Zn//4zmU7YYXmNG8hf7HmLV8bY05fXuNOU122U17iB/MWet3hljD19eY07TXndRnmNG8hf7HmLV8bY05dW3PnZIjPk/nsfwJEjR3DzZz+Vqw9IXuMG8hd73uKVMfb05TXuNOV1G+U1biB/sectXhljT19e405TXrdRXuMG8hd73uKVMfb0pRl3frbKDHj4wW/hhf0v4O8+dwsKhUK7w2lZXuMG8hd73uKVMfb05TXuNOV1G+U1biB/sectXhljT19e405TXrdRXuMG8hd73uKVMfb0pR33nD0l5OCBg/jYR27EihXnoVgqAgCWLl2KWz732TZH1lhe4wbyF3ve4pUx9vTlNe405XUb5TVuIH+x5y1eGWNPX17jTlNet1Fe4wbyF3ve4pUx9vS1I+45W7AgIiIiIiIiouya06eEEBEREREREVE2sWBBRERERERERJnDggURERERERERZQ4LFkRERERERESUOSxYEBEREREREVHmsGBBRERERERERJnDggVRE+++/AocO3os9rGdj+7Ep/82+XeH+5/tx/vfe+1MhUZENCsx7xIRpYt5l7KKBQuiJr73/e9i2fJlLbX94z96F44cOTrDERERzW7Mu0RE6WLepaxiwYKIiIiIiIiIMocFC5qzdjyyE7d+4Yve9HXXfhhfvu0Ob/oD77sWL77wYqCKPDQ0hC/+w5fwnj+7Ep+86VM4Kg2d+8ynbgEAfPz6m/Duy6/AT//nce+xf/v+v+O9V16Da65+P3Y8snOmXxoRUSYx7xIRpYt5l/KOBQuas/o29GLvnr0wTRMDAwMwdAO/+tU+AMCxo8dQrdWw5oI1gWW2b7kbhUIBD377Adx408ew41E/Gd/+ldsAAF/fvAnf+/538bu//zsAgDNnzmBsdAwPPHwfbrjpemzbuh0jwyPpvEgiogxh3iUiShfzLuUdCxY0Zy1bvgyVSgUvvfgSdvfvwWs2/jYWLVqEQy8fwu7+PejtXQ9F8f9EDMPA//7s57j6vVehXC7j/DXn462XXdr0eTRNwxVXvQeapuG1r3stKuUKDh0+PJMvjYgok5h3iYjSxbxLeae1OwCidurd0If+Z3fj6NGj6NvQh87OTuzu34N9+/ahd0NvoO3Q4BAMw8DinsXevCVLlmAP9jZ8jq6uLqiq6k2XSkXUqrXpfSFERDnBvEtElC7mXcozjrCgOa1vQy929+/G3t170behF30b+rC7fzd29+9BX19foO38BfOhqipOnTzlzTt58mTaIRMR5RrzLhFRuph3Kc9YsKA5ra+vF/3P9mN8YgKLFy9Gb996PP3ULzE8NIxXXnhBoK2qqrjkTW/Ev3z7uxivjePgwZfx2I7/DrTpXtiN48fif8OaiIiYd4mI0sa8S3nGggXNaStWrkC5XEFv73oAQEdHB5YtW4pXrX9VYFib60MfvQ7Vag3vu/qv8PWv3YnL/vCtgcevvOoKbPrqnbjyL67C4z95PLI8EdFcx7xLRJQu5l3KM7Fv/16r3UEQEREREREREck4woKIiIiIiIiIMocFCyIiIiIiIiLKHBYsiIiIiIiIiChzWLAgIiIiIiIiosxhwYKIiIiIiIiIMocFCyIiIiIiIiLKHBYsiIiIiIiIiChzWLAgIiIiIiIiosxhwYKIiIiIiIiIMuf/AbK4t83F/OeDAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 1080x288 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Files already downloaded and verified\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|███████████████████████████████████████████| 35/35 [00:10<00:00,  3.44it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "coord check plot saved to coord_checks/μp_mlp_sgd_lr0.1_nseeds5_bn1_coord.png\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x288 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Files already downloaded and verified\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|███████████████████████████████████████████| 35/35 [00:05<00:00,  6.17it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "coord check plot saved to coord_checks/sp_mlp_adam_lr0.001_nseeds5_bn0_coord.png\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x288 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Files already downloaded and verified\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|███████████████████████████████████████████| 35/35 [00:10<00:00,  3.38it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "coord check plot saved to coord_checks/μp_mlp_adam_lr0.001_nseeds5_bn0_coord.png\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x288 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Files already downloaded and verified\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|███████████████████████████████████████████| 35/35 [00:05<00:00,  6.13it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "coord check plot saved to coord_checks/sp_mlp_adam_lr0.001_nseeds5_bn1_coord.png\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x288 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Files already downloaded and verified\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|███████████████████████████████████████████| 35/35 [00:10<00:00,  3.39it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "coord check plot saved to coord_checks/μp_mlp_adam_lr0.001_nseeds5_bn1_coord.png\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1080x288 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Files already downloaded and verified\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|███████████████████████████████████████████| 35/35 [00:14<00:00,  2.50it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "coord check plot saved to coord_checks/sp_cnn_sgd_lr0.1_nseeds5_bn0_coord.png\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x288 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Files already downloaded and verified\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|███████████████████████████████████████████| 35/35 [00:24<00:00,  1.40it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "coord check plot saved to coord_checks/μp_cnn_sgd_lr0.1_nseeds5_bn0_coord.png\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x288 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Files already downloaded and verified\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|███████████████████████████████████████████| 35/35 [00:14<00:00,  2.45it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "coord check plot saved to coord_checks/sp_cnn_sgd_lr0.1_nseeds5_bn1_coord.png\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x288 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Files already downloaded and verified\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|███████████████████████████████████████████| 35/35 [00:25<00:00,  1.39it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "coord check plot saved to coord_checks/μp_cnn_sgd_lr0.1_nseeds5_bn1_coord.png\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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+FDMzMzj++OPxx3/8x9izZw9+9Vd/dUmfcylTQl74whc6x51XXXUVlFK48cYbcfTRR+P5z3/+kq67HK677jo885nPxKMe9Sj0ej186UtfwjHHHIOJiQlMTk7iRS96EX7nd34HV155Jc466yx0Oh3cdddd2LdvH1796lfjvPPOw/nnn4/f+q3fwpVXXukcvn7rW99Co9HAr/zKr+Dnfu7n8IEPfABvetOb8IY3vAHdbhfvfve7nWNbAPjbv/1b3HfffTj77LOxfft2/Nu//Rseeugh5ztjqVNCbr/9dnDO8cQnPhGNRgN33HEH3vve9+J5z3teZaoSQRAEQSwHEiwIgiCIFeGZz3wmbr/9drz97W/HYx7zGLz3ve/Ftddei3e/+9344z/+42Xn+9rXvhannXYaPvWpT+HXf/3X0e12ceyxx+JpT3sa3vjGNw48L0kSXHnllXjd61636DVOPvlkvPSlL8Wf/umfDk03PT2Nj3/843jf+96Hfr+PE044Adddd51b2vO5z30u3vSmN+HWW2/F+973Ppxzzjn4zd/8TVx99dVuSstpp52G3/3d38XNN9+Mj33sY3j84x+Pt771rfiN3/iNJdyVleOYY47Bxz/+cdxwww345V/+ZWzZsgW/8iu/gte//vUuTafTwQ9/+EN0Oh0X9ulPfxof+tCH3PFb3/pWANpZ5m/91m8NveZVV12FD3zgA/jP//xPtwrIao7IN5tNfOxjH8N73vMeXHTRRQCApz71qfijP/qjJU01Wi5KKVx//fXO6eWZZ56Jj370o07Eeuc734mPf/zjuOWWW3DfffdhYmICp5xyirPUYYzhD/7gD/ChD30I73nPe/Dwww9jenoap556Ki699FIAenrQrbfeine84x148YtfjEc84hF4wxvegN/93d915ZiensYnP/lJ3HLLLZifn8cxxxyDK664ouI/Y1Q45/jYxz6G++67D0opHH/88bjkkkuc01mCIAiCOFyY8idZEgRBEMQyeMUrXoETTzxx2ctzbmY+9KEP4U/+5E+C5U8JgiAIgiCIxSELC4IgCIJYIdI0xR//8R/jGc94BtrtNr72ta/hYx/7WODTgiAIgiAIghgNEiwIgiAIYoVgjOFrX/saPv7xj2N+fh7HH388Lr/8clxyySXrXTSCIAiCIIixg6aEEARBEARBEARBEASx4Vh8/TaCIAiCIAiCIAiCIIg1hgQLgiAIgiAIgiAIgiA2HCRYEARBEARBEARBEASx4SDBgiAIgiAIgiAIgiCIDQcJFgRBEARBEARBEARBbDhIsCAIgiAIgiAIgiAIYsNBggVBEARBEARBEARBEBsOEiwIgiAIgiAIgiAIgthwkGBBEARBEARBEARBEMSGgwQLgiAIgiAIgiAIgiA2HCRYEARBEARBEARBEASx4SDBgiAIgiAIgiAIgiCIDQcJFgRBEARBEARBEARBbDhIsCAIgiAIgiAIgiAIYsNBggVBEARBEARBEARBEBsOEiwIgiAIgiAIgiAIgthwkGBBEARBEARBEARBEMSGgwQLgiAIgiAIgiAIgiA2HCRYEARBEARBEARBEASx4SDBgiAIgiAIgiAIgiCIDQcJFgRBEARBEARBEARBbDhIsCAIgiAIgiAIgiAIYsNBggVBEARBEARBEARBEBsOEiwIgiAIgiAIgiAIgthwkGBBEARBEARBEARBEMSGgwQLgiAIgiAIgiAIgiA2HCRYEARBEARBEARBEASx4SDBgiAIgiAIgiAIgiCIDQcJFgRBEARBEARBEARBbDhIsCAIgiAIgiAIgiAIYsNBggVBEARBEARBEARBEBsOEiwIgiAIgiAIgiAIgthwkGBBEARBEARBEARBEMSGgwQLgiAIgiAIgiAIgiA2HCRYEARBEARBEARBEASx4SDBgiBquPDCC/GVr3xlyef9zu/8Dp73vOfh1FNPxe23374KJSMIgticLKfe/eEPf4grrrgC5513Hp761KfikksuwT333LNKJSQIgthcLKfe3bdvH172spfh3HPPxdlnn42XvvSl+MY3vrFKJSQIEiwIYkU59dRTce211+IJT3jCeheFIAhi0zM7O4sLL7wQf/3Xf41/+qd/wumnn45f//VfX+9iEQRBbFomJiZw/fXX46tf/Sr+5V/+BZdddhmuuOIKZFm23kUjNikkWBBEiTe96U144IEHcPnll+Oss87CRz/60ZHPffnLX46nPe1paDQaq1hCgiCIzcVy690zzjgDL3nJS7B161bEcYyLL74YP/zhD7F///5VLjFBEMR4s9x6t9Fo4NGPfjQ451BKgXOOgwcP4uDBg6tcYuJIJVrvAhDERuN973sfvvGNb+Bd73oXzj//fADA2WefPTD9q1/9arz61a9eq+IRBEFsOlaq3r3jjjswMzODbdu2rVpZCYIgNgOHW+++8IUvxA9/+EOkaYqXvOQl2LFjx6qXmTgyIcGCIEbgjjvuWO8iEARBHFEstd596KGH8I53vANvectbVqlEBEEQm5ul1Luf//zn0ev18Dd/8zdI03QVS0Uc6dCUEIIgCIIgxpp9+/bhVa96Ff7rf/2v+Lmf+7n1Lg5BEMQRQaPRwM/93M/h1ltvxX/8x3+sd3GITQpZWBDECJx11lkD417zmtfg8ssvX8PSEARBbH5GrXcPHjyIV73qVbjwwgtxxRVXrFXxCIIgNh3Lbe9mWYYf//jHOPXUU1eraMQRDAkWBFHDzp078eMf/9gdf+tb3xrpvH6/D6UUlFLIsgy9Xg9xHINzMmYiCIIYxnLq3bm5OVxyySV48pOfjCuvvHI1i0cQBLHpWE69e+eddyLLMpxxxhmQUuKTn/wk9uzZgzPOOGM1i0ocwTCllFrvQhDERuNv//Zv8a53vQtzc3O44oorcMkll4x03ite8Qp8/etfD8I++clP4txzz12NYhIEQWwallPv/vf//t/xlre8Ba1WC4wxF/6FL3wBxx577GoWlyAIYuxZTr379a9/He9617vw4x//GHEc47GPfSxe97rX4ZxzzlmDEhNHIiRYEARBEARBEARBEASx4SA7dYIgCIIgCIIgCIIgNhwkWBAEQRAEQRAEQRAEseEgwYIgCIIgCIIgCIIgiA0HCRYEQRAEQRAEQRAEQWw4SLAgCIIgCIIgCIIgCGLDQYIFQRAEQRAEQRAEQRAbjmi9C7Ba7N8/DymXtmLrjh2T2Lt3bpVKtLpQ2deecS03QGVfD5ZTbs4Ztm2bWKUSrTxU744P41r2cS03QGVfD46EehdYet07rt8nQGVfD8a13ACVfT1YjXp30woWUqolN5zteeMKlX3tGddyA1T29WBcyz0qVO+OF+Na9nEtN0BlXw/GtdxLYTl17zjfFyr72jOu5Qao7OvBSpebpoQQBEEQBEEQBEEQBLHhIMGCIAiCIAiCIAiCIIgNBwkWBEEQBEEQBEEQBEFsOEiw8FBSQkm53sUgCII4YlBSQuU5lBrPeZoEQRDjhlKK2rsEQYwNm9bp5nJIFzro/eRhcBGBJTFYkoDHEZgQYJy0HYIgiJUmXeig9/AegAGMC13fCg4WRWCRAGMc4ByMM71lbEn5K6UAo4Uo2H1l4lA9LhJ76b1jFOd05yL0Oz0wzsCYLiPjDJzeFwRBbGCyhS56Dz2s69s4Bm8k4JEAhKmDl1jPrgZKKVN/KygJt9/v9JD1M3DBwJbxTiAIYvwgwcJHQTeOBYfsp1CdLhhQNKTXWMRQUgUNbN24VkWj2jagJdBf6CHrpWCCgwuqwAmCGA/68z0c2n0QPI4gJQCZa+/SSkLlAJQeBdTVn9INVM6hrEggOMAYFJipr5mnQfhWGwyAAmPMEyaYCdX75qCEgh+hlK6bZZ6js6WF+U6q61tmInUpwAUHjzgYF26fc6G3goML4QQOX+zwj4nNgRXNlFRQUulHjd7RxLpi6tIoAvIM+WwfmRfHo1i3eaMI3IrHpTavkioUFey+LIsNyll0SCn1ca4gpQSkhJQKKpeQSgJSh8tcBvWpgwHpnhYOHOy4Yy4ERBwhigV4FEEkEUQkwAQD58K0i0ncIIhxhgQLDymlVm1hGrxghUiQplALPcgsB7PNW87BkhjKjgpy3XDWjW1bQeshPFthw1XwgNKSsYlDWOE782hbuXqNZsa8RrUOT/e1cPBgx8QDXERFBZ5EiOIIPNINZyZMw1lQo5ggiPWl3+1j4cA8ksmW1zZlAGNgAgAEAAarCUBKvc0llEyR5xIyk5C5FhF0I1ghVwpKMSill/uTmXQNYZnlbptnebAt74fHEnmWe/WzRsQRokYMkUSIEm8bm8ZzoutjEQuIKArCo7iI55E3smka4tyK0CXBQxjrk0DkECXxY4M3zn3rlzzPkPcyZP0UeV9vs16GPM2Q91Nkfb19oBlj9lCnOFdpYV+59y5QEfiDdyuC93O5s+Wf597FpfOLrXTpbbib2lTKDwDiWCDNci2TmQeaM7tvnnHOYJ93MA7GtazGuE3DwLiJY2aQxZzHbBjnRV7mnHBrBBPzjFjBzZ1n82TmOQLD3q1tzC/0nEjIOTcdQn2s09pnj4Pz8NiFi9Kzarbce3aJ1SfPcvQWeuBx7p51LRooqDyHzOd1nZfmgNL1KwAoIYzFGwc4A7gWXsM2KRAKDV6YfYYZvGcSxbMpGEQkELHB9VdzSxuN3ORsf8dSot/JIWUXKh8w1YXEjSMGV0dDOZGMhOLxhgQLj4UD89hz70/AGwkAIw8wBmYaG341rAAtPsgcSgKcaSGBcQHEMXgUgcfGtJlzuNE7eD8YW3FHZt+EBWlGpLWljW5tBZ5BznXNXMWyWq1M5W1EjSj2Km9eNJRN44QYP4IGNpQZrFZIeynyNANgG7+saBQTxBqRpzl+8E/fxUPfvx8sEkZ4yJGbrfS2uScy+NvDwYq4IhLgkakLhQCPBeJGAj6hw3kkXBp/v9WKMXtwAXkvRZZmettPkfVSdBZ6er+vw0fx0cEYg2jEiJIIIrFbK3aU9uMIUSJMuEkbxxCJgIhjcK8zaS06tPChtw3kmNszBwAIO/MoBADoTrnsZ8jSHHmWIu/lyNMMMs30Z06L4zzNkWdGZEhz5GkOmWVma4SfNKsVidR6rzVvO1H6wHWozKF7JzP7Lnfv7Gr6Yt/LkzFwziBzGVpJWkEECIQPfehtnTgDJ4D4gs9mwoptVSGkEEPgRA4vndly7/gRJx+DU59/HnVUSnRnO9j3492IWo2ibVsWD3SgtjCOTFtYKUDlUFlu2gt9cBEBUaStMYTu/IOLkcqhlIJMc6SdPtJuD2m3j7TTR9btu30X3u0j6/TBGMCTGHErQdxsIGkliFsNxE2zbSXBPhfCXYvEjeH4Hf1C8AUyIyIHUyVrpkmGdZsJ84RhKaWzNpPK7ksjmAEKEiq37VUtoEECMGmkKvZhBiOUCYNSOr0dEAYAxbBvqom5+R6sVRHnDBAc3NQR3AhwnHMgYhCMA0Lo79bFF8Kqrtf934p9PwwI9wU6MJBwsjxIsPBRAOccjYnm8vMwjjtVnoLlfR0mIiA2lbkRA3QvsaYISnmjS/rPji5lvT6yXhGXe2niWM9DTCYaSNpNJBNNvW039H6roecnlq9nzPGyboo07znrkPKNYZyb0UEjasQCIolKDWLuXgxHOpVK3wgF1RE6NTRc5qqozD2zSkhTMUsbHqbRZpXmjWKN3mWR98J0C4cOdb1K1Ss8M3PwGSsahHbkSzBwxos0nHkjaTAjfKUK2qu4K5U7iSVHNN1D8/j2//oGsn6qRQBhLQiKrYg44maChhcepuWFiCC8c30xQhTHPBIQ9rlWDAr696R/q2baCLRYzbmeIgghjH8N83ybBszWbRM4cKCz6OdUSkFmubMScNYDri439XrpOO9lyHopuocWdJgTGheHR6Kw4GjEeuuJHnHE0VnoQ2ZZIQhlmRYS0lBMWA7CNvJjKwbp6ybtJrgNE1YEst9bMX1GmPcljwuBSEQCW3dMYm62C8WYflcZi8NCGND/goEB8w5TpXTMvYdLo8LWmNHGMC9JZQQ5PLbFqmN6uoVDBztDNYbq+cqLKJdBX1cpU1hjis9gOmc2bSCM+J0NAO4dVGQb5GHeSZOTDRw61NHvn8A6BchzL8x7HzmLUm/rnytLeQUWL+VzlH8MZ92ilCz8G5jOaJYX1z/0kwO6jdRMhtz1IxMeCSSH094F9PMjFWS/h2x2Hnmvj7SnxcwslciyHHk/Q9pLkfZS3dbsGhHCCBPDhGcuOOJWA1FTixCtrZNIGhEWZjvoHpzH7EP7kXb7Q+spEUdaxKiIGmZbiktaeuq37UzXiht+nbAEcUPJotNe/O58y7CSGODFBW1EeyzN78C2E00HX+/rd1uWZ8gWMmS9nv5uej3Xd8j6GfJuirSv+xR5mhnLNvse0gJ0ISrb8hTfvy2evSF+eTcNZfGhIk54Al+57QtWPd9ZGpkwXgo3om3ciJFLVYgrZhDcPk/u2fLC7LRZ7qX3B6D9Yx2mxRk3qMG963AWDFwPDHNlWT3LThIsVgi/QaobpabBmWZFxWAbommGPNUNxMyMTvkN2FGwJsdRI0aUxECeY2H3QfTnuwMr7qgRe2JGIxQ0SvsiiYKHzpoLZmmKtNeHymWhYALwG3rCNk49M2guotBi4zD8bASjgUFDS1Uq88GVfpjeduilEYzSTg/9Tg/pgn2x9pB2U6SdnlP+uVLodVOvPGUTY9S+iMojapVwex5QexzmWZNH+SU4qPVcIjA5rqtQg0p4mCDhmRzXmXxW8kdQScMfUTNhhTjC0WonSDMVdHKEsWZyHaDYms8LZzGkOz0RRKwraPuccsF1B0p4HV6aLrUmTOzYghdfexHu/fYP0Ng6uWbXdZ0fmGkjpvHpqggnDGa68+alZ7auUwpqfxPznb4xj9b+KuzIDTj3nnvdMWaMIWrohnf9bwPh725A2X1RO9xP3UhYMb3CCt4p+gtdZAcyJxAVQrSup5N2Q/9OvDAnOsSmXo+iQFTSvzvrk8NrFNWWX9dF7lwuQoHKiBj+aLo/ssUYx1FHb8Hu3bMD7413GbNbDvMb1dXwIK50biWvpVwHCjt3TELsnYNv6RjeJlbZZTVh1aQD0ngiyrBrDEzjhc3MTIX3vfa1EgbWvnqWe175xJo0qibNzpkpHDjUrcuQqMHWL6lps6bdvt72UqTdFFmvb7ZpJc0wKykecUSNGLGp/9rTE4gfsd2IBU0nIFhhwlpJiLjaTdm2rY39+xeCsDzNtDVGx1ppaEGkb4QRP7xzcF6XudsfWF7GGeJmosvjCRx11hxRI0HUiAFAi7yy64Q2nRnc89p9aK+Zvg0TyLytSc70FHWZSSMYeOJB3+tH2L6G3TfpdJ+imEo30IqkhJ7WGCNq6P5F0m6itU1b77Unm+j1sqAD7nfadbn9eqPo2BfJi/gij5K1gev4L3JuTT6Dzm1PNDA/16u0lZ3oU2mzV8P9qYN1AnBdHoPzU5W8fYHXhstc6v5GmptVLD1B2O77A5qluPVk+wkzuPC3X7Kig9gkWHgsHJzH3L5ZLMx3PdGhRoAIwkylkGYjqYk8EsZ0tzDnTSYbEPGkFh+8CtLuR8bcV1eK+tyy8yO/As/6KfoLPfTnu3q70NV/812kCz30FrqY33sI+3c9jHRAhc0jsaioYePjVuLK4xwrZRJZv+t+ZCHKKNL6s6jZOezfN+9+4HW+PaCsqZfNi5VGoooKX0G5kcmsb6xSen2jJPeROmuVopGf9lJtzt1LFzUz55HQ4k8zMaNvdR18rxNiOuG8bA42Qqe/tlODAeGDRAJU82q1EnQW+qWKszp6FYZXK+FQqKkPh1chu+ejdF5lHrk/H90Lc2aAue+Ua3Uo5lwXSnIhuIXOE/0RfGasApjwpxtwHP+443HUGY9ZtfKOK4sJQ1XfAUMaEDZPhNWxvYKrPZgeIYDZCm58RJhRDGseai2MihGQ8Hjb1jb27ZvTjuNypacI5lLLGlJq81V/RIoDijE91dCMjEuw4rcAFE7ppPRKHtwR9xli07j3PtkIgiEApste1Lt+g0cBTowuN6ZtA5w565fQMqIQ/epEh9X2rRE2mO3uxhEem1NtNLrLs1hZb7g1mR4zkmYCNttb72JsKLJ+in//X9/Ag3ffry1sfeuHXjr0vSpi4YSHqBljYptuu8bNxGzjID5u6GMRCZ2v7VTl0tQuTA9GRGa1kkgYvxi8rKINxVpwNbe0Rz5HSakHompEjfC4j+6Becx29yPt9Ia2ERez5jjYiDB7YCEQk91UQt/yrp+N1L7hgptphGZaYCNGszVhBjTtdMEYwogQdqDTitI81tZshR+Ssoiiy7Bt2wQOznb1oSqmXgTvHlSdWpfFmCLLwfFMWfHRf5PXpAUGCxwmTpfdE7iUJ2vacriP4t/vmvfeoDLUfi4f5coWXrz0+Zjv00iHbdvWxoEDCzWPgvLyQum70EipIGUevNtVXhY+ZGjhZtsypq1eFUKq6YP8zfGOY7YjT3MSLFaDQz/Zj/91w/8XNHp9uODGqsE6SouRbGmXwqLC8sGGWXGiEetRqmEvfDedROnfmQKYECNPJ7HYSqk9woilzKW2JpjXokbPiBpa5NDhvbkOZh8+gP5Ct161YwxJKzEiRhPJRANxu4mGb8lh9uN2EyISwcOu/SnkgUqqconUjho6Jd+IDE7l7wfqftbtO8V/sYo+aib6RdpMEDcSNLdMBGH2BVy2FLGO8aAUpqfbOHhwAbbgYSdphAquJl2Rh19RDqigXTJfbUbwkmd+Qi+bygiFV4lWbl2lgjc7ygsaOpRWV+mXjss9zFK8Fqf0vZieLhzM2opS2orTOF3UqzjYylOLG0VlXFTSdl9K3YCyQoiSEiqzHs1NuLSVfZhHlvWL69uK21ggFeco7LvnITzzcScibpBpskX2+jhwx3fAD8wimz2gHboJASUiKG6W2IsiNzWDc+Z1hr3paIyBR7wkGhZmloUzQq8TvwKISNf3i2HFlULEs07A9LxwBQbG9FbPoxU6TkRmKVctdACmljBqra5H4e0P8cLvvV+kqXeVktqMs2SR5JuD+ku10gomBDH+pJ0+fvjN7yPt9nWHuhGjMdky7R8rMiSe+BC79hEXhyFaMQYIBgaufbfBtAaUAnIFmXWgxyFs/cihnPUacw4+OwLozXcGdhDLDmWDY08wZZybNmpj5I/gfG50D8+aw7eStvuNyVYp3A5WWtHBHCdFnJ3q7Qvd/pYZu0DvE+iOsV/fO4fQwvggKZzqWvH+qKO3oDnMss2KDL5IX2lXltK5NmTpPGe14JW5nL8d7IIWwey5bkoMrJWBQmOyhUaqBwCsE2Fwb4AQ0KKZFTyM4+OqNQmCNndoQWLSlc6ppKs7BxjYJpmZmUIy4L6vFJV+b+VweHxNAHbunMK+khXU4UKChWFyZhrPeOWz8dAPHkBr22Tg5T1KosOrpEfFzgXyvxUpodI+ZLcHyaB/gJzrlUki7cxTMVOBz3VKI1rhfu0lBdc/5snWosVTSiHr9j2rjULo8K05Dj44j/58d+Bca5HEaBhRI2k3EHGGhdlOID4sNm/aWjlYk73GRBMTO7Z4Sr992SahONHUFT2AotNpR+srSq8qVltpWKuY2I2yH3X0NBp7rdM6IOzEe+qnKh0HooB3zrA8bOVr63kVzoEsz3u0FbX+oMpE6YocCohiLbA5JZfBVNalipZbcURX8ICt5E0YgxFQbJhX2cPfBLJ3IaTUhfmVuJ+P2d25cwrxnll3+xzK31XVeF9Oqiow4e5i8UPyL6YZofi+oLBz+yQW8uFC2pEIFwwRU1AL80Be/7tnSQyeJOBJDN5I9PLSDXMsEvAkAWskerWmVRq9PxysgALArHxSjxM2bKOz3zeCYb2wwSJhtnapbStwMDMlZXDdXzHxJwjiiKA1PYFfeOtL8YM7/hPNtZ6KZwcOrBVZrqC85oGCri9FZKcRM0QRBxfaCoDHAttmJiGaMRTnYIzr1y3TS1srVQw86JWhcj34kOtp2Fa4ta0LVVzVKwHgOreelawTQCKOxlRr6dYcvRTbt09gdiEd+p4KRq9LI9m2rNo6xIggTIFzYXzLxW6arHUibUWH1XISGvhq2IDsnJmCatG7bhCVZ6FyuPTvVdT4TDxcSLAwcM5x7KknoN/trelcasCvnIoKyjq/LA88MwYwpcCVBFPa3FwIgXZToJ/F0LKhQp4bc6AsM8vwBVeEP/JfFTnqKzTGmDFva2Bix5ZFP1eeZoW44U9P8fYX9s8hTvS86MZkq9bKoU58qHMgWtzLYhQx9LXBnEd7zjm4sXyJ4sKRaGDyv4ifjbgR186vHAdmZqbAxrSzkhirmHFjamYK3TG956sFbyQ44Weehod/9BB4I9HiYb8P2e9D9fqQ/RSyp49lrw/VT5EuHITs9eutejivFzXMvh9Wnla3EViOsCGzHOj1hgobMP4qCmGDIe81INO0sNjyrr2SVigEQWx+CsuuQozw2692jMM6p0yaemQ/ioWePmkd+Zmpl4NQSkEwBmSZ8S0EbUXAGBh0xz2KBFhT13V6ioldhpWbKQv+FLiySXsxZcUXPWRmLStzyL6dkjqwlMWnZkW7mhvLtKzbL6bfBecUAzR2yp2Ii9X7nL8fZwG3+VYpIYhBjGdvawwoixDSm39vK0wnGTDrhEzoTrn1gB9bZZSFJro1FdPEVAs9qQC7ZFoMMKYAFoPFuuJWQrgLaiVaBUsG5lkGmUojcvRrRQ5rXhYIG7xe5BBxhNZ0hNb0xNB7VedAqfaeykI5z1LrSKikjDMYZ3ER4iRxVjI84oU33Wg85+ISxGaHCQ7RakK0hnuuV0pBZZkTMaygobcpVL8PudBBduAg1ABLLxZH2jLDCBzciRlhGIujDdcYXAlhYz7vIz3UgSpXocxagFkT1sKkGuDa0spMsXHWHN6ftbAqW2H5+ZkPUXyGDXZ/g47EkP3QEEsN3VcKSBciyF4PhRWZf3/G494QRxZKqWBKpe1olwfTBOfgiUDUMm3YOHKOfe0KBCvhv4YxBh5F4AOm4ik7Da7b1StklMrKOHdtYivgcuPDyAkbI1J1gGiOvfa/brPqJbhVliNuN9DMC+f0gejgVmcgwZggypBgsQTKplnOpA2+vUJh0sYF174PGpFbocB2lpmZj72YmjwquiMeVb5RZUQMmWZ6PhszjTHGzPy1CKwVg0dNs3RfUWHb5ZcCiwUpa0UOles5ffatEL7MjNgxQORwwkleKPR1yrO+nwKiZRwHGX8STnE+zNVHCIIYDxhj2kFbPIL/CCkh+0bEKFltWCuO7OCsttqQNc7UGNMWGVbUSBLwhrbamD00gd5C2aFfOB1qWDgbEG6vW8uA8GBKVc1lyxfOI6Y7z1ZoMFvlhAd/ZpOd5pSDSQaoHHql5mJOcTG1TZkXgH4LMONvQwvlcC9KZpQSxZROW3JqCju1rBTeawDZ/AKsTxBzUZO38vbNsZsa5wJdnKqca8UKvRygTmc6QDY/3zRb6iU1GfSIrM3L7lvfIjDLd/abMXr93DkUtKupKPve9cIY51CcgTEBRGbJOOPLhTFhpujV3Df3/XEnilTFI4SWNXZLIskRQ97rQ+Xa4rQ8PQMI27LWP5uIzLKdxh9buLThxnhmbPuVDXD2p6QVcDOg09VDcAqw6z8pzvT0k8hYpFmHlJwH7WN9LQYGASzB+n2crVsJYj0hwcJDKYU8y9Gb71ZmswFW2TXLITZEsfybvy6ut0btRsA5KirVqHYJHdlPgW4PmRMalK6Y4wg81tYZnAuwRAA8XrQhM0jkUFIiT3PILEee5ZBpDilz5L0UvZhDSakFCM9fRLDEpCCrCIJYC3q9Hq655hpMTk6CMYa3ve1t612kw4JxDtFsAM3hjtWUUkCeF6KGNw3FWW90e8gOzUKZ5afn1+IDrBKHDudk33kpq9nnfrjx9m86y877v/WFw3gRB0+osNYY7k/b+OXNGJ1u3wgKheWikp5IYByb+svE2dUJXJjv/FQVcYs5bD4cVmxxTWfizrwldZm2fPG3Ng3jXofLhLs0xpGqYNpcR0ALImbFI+vw9uCBNjoH50v309xnd/+h76mfxt7TQd+V+y7MdxB8h973F1wzTBNcU4XX3Dc9hW0XPh0souauJZubx57/9Q9oAMAegShJwBoNiFYDrNmEaDUQtVuI2q2BlgzjihZWxGBBw1qk9bVFmpK6+jHyKhTTggasg0q7al+NoEEQxMpBNbghm5vH3r/5BzTBwNstiFYTfKKNqN2GmGhDtBsrujzLesMY096aa4QVa5WRp53Cy7ARKnhkzOjs8lMlqwzGGQQXWIrkTM7fCGJt2b9/P6666irs2rULSZLgpJNOwnXXXYft27fjS1/6Es455xy86EUvwvvf/3585zvfwemnn77eRV51GGNApEcRxfBZbNrarp9iqp3g0KFiOlsxRWBAp1cFqWqm3YW7akD4oPQ1J1fCbdEmJxqYm+1UOoV1Hcxqx7ImTbkz6Xc0bb5SQeXZkPNthxVBZ9j/LB3/I/nWBNaM2loqBB14BhYJMBZ5nXvbkfdEFO6FsTBPNy3GEwOKqTLeuaWpMsV5DFun2ziwf86YrcOIJNJ14K0FJ7P3wvhgUrkRWoy5u4vzw0pbmZXDirTL4eCyzloE/3uoE7sC4csTV8rWJN734M4x38nUji1uCgOhERNtHPesc7H3nvv11LpOF3m3h/TQQbPiQgGLI3AjYvBmE7zZ0O3jZhO81dCWvZsI3TYWA6faWUFDpSlyI2iYGGNYxkzbOCray1bQELxkPUwQxKhsrprmMBDtFnae+Xgc/PFDyLtdZHv2Qf1kN5yxr+AQ7TZEuwUx0TIiRgui3Rqo1I4rI1tl6EA9KiaMx3pjlcGMvwjXcCMIYsPAGMOll16Kc889FwBw44034qabbsL111+PBx54AGeeeSYA4Pjjj8f9999/RAgWS4ExBtZIkExNQAxf0GhD4Y9wN7e00BWrNXp6GHV+zam+wDG9pYWDh7qFBUahEgUbe1D447DCh7H2MFNSmNuHnraivDJ4+0yZf0p7ciqmvcCzEAm3zk8FmHb/EWuxn9npKWbKiipNUYG7lv2M2u+IX25rs18tvyqGhG0Cpdy0kNoldm0nzE5fkV6cOW5PNLDQ6YdiQY0ljJtaBAQCjk5X+D7xP0PxWcz9tdOEvLyZu588nA4DnV9xzz2LHMaxY+ck9s+lAx62IxPGGKZOPAYLmXZ8bLFCbN7tQna6kN0ecrudW0B/z/6K4MWiSAsXzSZEswHeakJYMaPZBB9Tx+SDcILGgHjbTlZZirzfN78vwFYkc715dA91wa24asRPbkQNK6DWinL+PkEcYWyumuQwYJxj+xNORtae0N7qbcU9v4B8YQH5fAf5QgfZwUPo/2R3cC5vNgoBw9uyZPEpFOPE8q0yIi1ixJG2UilZZRAEsbZs3brViRUA8KQnPQm33XYbAOCYY47BAw88AAC4//77ceqpp65LGYml4xrLbuS+6L270T8hwKIYUbsF0dOdj8Ma81vKiOFhjC7aM+OJNkRfFY13AHV+GMJOLoOfzHWWh+yz8ISB+0t5x7dnpjDPRlvhyI3E+vfMCwt1GlVsvTDlJ3JWK16+Vrjwjl02dujYiCtTW9vID3RGFAu8DhbsxvsuvG3gaNTbrlTbKW63wObrHe8SIVaI5Y0EmK6uBKeU0pYFnR5ktwvZ6RXixkIH6d46QUPUWmZYUYNFG8+p8eHg2smob99GzSZ4Jyt+f7nSvkTc7zH8yRt51OxbjZRVp3dZ/22+P5zg94hFl7re6NQ6Qy7XiUE9qLdZtwfZ79fWTeN8P440SLAYgF9xx9u3BnEqz5EvdIyIUYgZ6f6DQWXNhICYaOspJmWrjE3WWR9ulSG1c7dOt8YqI0I3VsjmO9VKlpOqTBCrjZQSt912Gy688EIAwHOf+1xce+21+N73voc8z3HGGWeMnNeOHUtfEro/O4/pqZaePz2Gv/FtWxeZP7KCKGWcEvvTCIBihJobD/px5LbM+ACyIrF/j4/ZPr1mZV9JWju3rXcRls3MzNR6F2HZTByz3iVYHuN8zzcSjDG9NHSSANPVe6oFjQyy23WWGXa6iex0dRs5D03SmBDgzjJDb31xYyOu0nS4BB1mYKC1xiCUtcJyllE5VOqFOZGjWObav5YCnNN7uGlxhT8c54+DwbW/ZZpBZVkoCgwTSc2OnuaHWoE0+BxQhWPkcjobZ6zJmLGYU8qzOjOWZPCt5ozFXCfrIT3U8YReVtwLZs8p/P+Aw7sHXn/EWIY5IZwEkDWFBItlwIRANDWJaCpsnCulIHt9Y5XRQT6/oJfW238A/YceDtLyVrPGKqOlzUU30cNezAesTpvRXqkzZLMLyOfmg8rEr2SdwsxKqrLv5IjM6GqpjNKZF4FMM6g8P6LvDaF55zvfiXa7jYsuuggA0Gw2ccMNNywrr7175yBLc6AXY9tUgkOdPtRc18w1L8z4XRsECBpPFWeM6/T8bts6gf0HVs71ZtkMv+zBHpyBBR7s43oP9gpACmDAkq7A+PoOGtdyA1T29WA55eacLUt8PdLRgkYMnsSItgwQNLLMs8zoBeJGtv+gbpf4cG7Ei0LUEFsn0V3oDTANU8GmNl5VQmwBB384VdkZPd4PajfQ6WeVtmvYnjXtXMGDOPvuq1iODS51fVGD9qAEckBm1rSjxsqDAXNZF72DHTO9LHwxB9PQ7HQuqPC97JU3tLSy90cZXz3WaX/uVnRReV7E5XkQVhx7YTLXPn7yHEpKzDKzEpNxJMyspbcQLgx+vF2NiXMwLrTFDBfm/avP5eazFVYfLBRAXH+kRgCxU+TcAELRlim3b5QVdoovb9gXu9gXX9zuSlxlZ0lxyne+nEv0m9pfy0q2zUiwWEEYY7oybTaAHeEIkMqMVcbCgidodJDuOxBaZcSRscJoa6sMs+Wt5ia0ytAVsWgk4Mnic0wrqnKee/NxqxWsq0ZM/WmXinOK8oCXwUAP+IeJKokGdt+Z47o4oFCqzWfzvKpbZbq4H9Io0xLW7Nc5yHPzoKHVaDNheC7toHdgwdwn/Rm5vTd2HXDPUVTtagDkn2TsufHGG3HvvffilltuWbdVeKJmA8nOHQDg/Z5LKzkoFTRYXIfe7BdtpMLxmZvfX9MIWA+Rrn61Cl+Qhfb9Y30BRZFrPOnGq6DfG0EQY4u/HHW0pV4QksZCw59ukne1sJEdnIXKMizUnjkeHHbZFxM6hLc8st++9Qb7QmHEa+d58f5Wtw+13w4lJWRZFDDHKpdA5bh4Z6s8rz+W+RCBaZF74UQH7qab8zgGa5hjwdFIInQ7Pa98OVS/r8uZ5U70WPK1o1D0KISQsvBhfPrZfSt82Pa2ENpRq9cGY9bnSWcOvYMLQZi2LjHtCet8uc4nUS5d/yAQFAJHzEa0sudYkcg/VouHl8WS/a0GdjzvWWAjLD0/KiRYrBEsEoi2TFYqaaWUdmjkWWXkCx309+6DetDrxDNWssrwxIwVfCA2MiuiKjtFWUFm8MSDYv6b0Ya9//CsOwonSV0hkR2aDwUCZ9ZWdEi0OZsWD5jyTNmckgBnwlY4VgMCp2vuA3uqNCvN3zaO3Ri4Z50W3iVb2Yg4Bm8kLj64N1kO6YlDtUKQvU9WJfasXgrnUTUmdUe4xctG4uabb8Zdd92FW2+9FUky2rz61SaY9z7iOcNWrHCNKvvyNuq/dC9YY6HhTa1g9je2RCut4trey9yOupg0nHO9HF6UaGHCjuzY3wv9JgjiiGSzLSe9XHgcgceTwNQAQSPLsKUV4+ChjveSqK832bD4ShBbPH5gElYOqD9XAVunW9i/bw6DVvdRMrQ0cBYHXnw1ruigqjSrOe/wVggCgP1LPaFisWA68nGkfaR4cbqjP/w46PAv4tDfH8gElF6d6eBC8T2U/efYc5wA4/8ZYSXLtQiT1aepCCF5DpUtQwjxxA/GOeY4Q27yCb7LxawploIdgPQFLcYCcYzHIkzDQtHM9pFsO39y+8pPwyPBYp1hjEG0mhCtZiVOZhny+Q5kScxI9+wLHlYWx5htN5FLO8nL35Qr0nKlGx6HJlzl2rkaF74P2ID9YofVlCNtJuineeUHU/wAysqw/4Mpq8KFFYVeb7v02bzPsJSuQVABGmU4W+gg73bdZ6mIB4KB2fl0wPDK1TN1QyZDhdq+iKSpFL3OlwqOcy9dcb5/bJcs22dLKfSILszWVZTl4wH7EBzgwpjbKYCVnEcZi45iio8qRA/Gjadsa2oXKvuVJe1MBbqoiVxdRe6mG9njyk6hxAxJo8qZeMdS5vo6Wb3p4ly6DUo0NoSl1N13341bbrkFj3zkI/Gyl70MgF4R5MMf/vA6l2zpLEfkALzfnS94lJaJtCNEkLme6qJkMUWDAWlHIO/2nXinnQpHEM1Im40KToIEQRAAaDnplYBHkXa8m65gh20N4VEEnqzPIKO/SlSlA+z7RirFKSnRbsR6Kotp8xVWBYVlQzC9YsR3XdURsGk3eoKDtRwGM9ssB1QG6fUz/Kmk4My0LU3fINLLWcMurS2tdbZtt5v2qbmebd8qN6hRTFAPBg7Lg4ZuekfR3l++EKLb+3ESIc1l2IaomQ4/TExgnEE5C/Ni6We3ipOd4uPdb/0tePfEpFFe342Z+2NXdWJuGXCGqe1TmFMr29YlwWIDw6MIfHqq4txISaXn/VkRY74DoXKo1Mz9G9KBCzt65p8qjnW3rghTxY6fqGa/OC6ih3Qk3Q8CyADILHdm0itK3Y+6pBwGP3ZW8olRJ4KY8IVOE/3ZhcoI7nJFhCVjK6C6eXdxBM6TqiJt4lvNBAtznbCCNKZxstdz+9Zkb0n3uyJ+8FDk8OOsCSP3XnQ8POacBdN7KiZyTMcqa9EitRlcMUqhPNO1PDSDs9+Fb/Lmd1rdiHnNC768GsMiSvpcs4Ed/+XCDSFYnHLKKfje97633sVYVxZbnq6OwIpCKUxsn0Qnni/McEmQIAhiALSc9JGHEwlMe8SK3EVvGXDLI6NYfrhCeaBxWHglujSQad99IqrJoz6f6a0TkAfmFxUXVJZDqazo2CsjJvgd3UHiQskBaNnfA1Dj8BLF/qB3sF6dqSoSVaZp+1O0Xf/IW3ba9lGcVbVZstaGmzahb3FdTMeGbs8KUer4G+HEtyD3ttumJ7D/4Hy9oOBZajNT7vLy14yZYUPFnPWDb/UMb6np0Bratmm8MtXdc7Nfvu/tmSnMr7DPIxIsxhDGmVttBDu3A1h5529riV/2sll1fcfRV4htZ7QmXMpirnidOZ1RmmWaDs1zkOnVXF3gskUEUaTzj0XYgS86+KJiQbLUe44RnxetEBuhJcuMMpyFx1leK37A7MtePwxfgjlbYAUiBOY5Q5ZmtULCYeN/fzXWPTyOa+Z9humswu1UbCN6bdm+BbS43ngT+G8BIJoJeNxb72IRBDEG0HLSmxtnoZDLwDeZtapgSYz2UdPoCN16HGaxqeyywn4av4PtbYNBQt+aVPnneRnJINIcl/Ir7ee9PmSa6Q4vZwA4IOAG9gZ2dn0xAaXjdRb4K0sqY2lWmovhW2+Gx0CtGKLg/EEoI4awJNKLMfj32LeWKAsKvoXHEEFhHNnQggXN6TvyKHcINgKBkxlPBNky2cSh+d6KiQiVa9pKTQe4rQKMgFBWXK1iXKiuFf8YZopG5qn8FVMvT5kN1F6ulzTTQnC9GjyyGaAcInIMCLdmcnESAb6JnD+NpGyCX3JM5bbeMo/KVubcqtTVUQPfJBHw1Wzz2QGnZNtRAPtCsSr2xM4pHFrctyxBEASxyVnJ5aSJtcV3aqiUhPNUxIV2KtqOtRNFa0nqtYuiVhO8MX4NgamZKXTHcJWg9eRwl68FjKUCjgw/hYux7oIFzek7crFzu/JeH7JvKvAVUylGzGgp12PQHV1ok/6o1QTr54V5WJaZjq5O7jq4bq1obQoHxlDoCkYcMEKCnQumnWd6q5ag2DLbsS4prlow8FVXBIqrLypM7JxCJzk02PzNdtKtc59aEzgA0A5Mg1VLAjM4Tzhxn90KIfqYxzEQx14ZTTkHiCLbtk5g//45e5OHCAxw17e3o7IElf2+GNMzS5g3alASHYauwY2a4xLN7VOYpRc+QRDEEc9KLicNYMnLsKbzC5je0kJU4z9tHNi2dWLVr6HsSLed5mvHLDiDSLTjcpHE2oLC+nMYgZmZlXdIuBaMa7kBKvt6sNLlXnfBgub0HRk4xzMyL6zTGPR63ZMtiP7w+f8jGfsPm2YwwhSEpU4ocKZa/pywwFwL0B1cvQ0633XOepgdqV992xIeCb1s4grnW7d0qyoJIsX8O9SbwjlxxDwopbmBufHCXJgl1s159BwAeSII/O8KGCowEARBEMRKsxrLSe/dO6cdA4/I1rbAwUMd8F6+ItdfS1ZjCnTgh8wNqujpHCzRy0zzKApXqEgBpBmwhMmeMzNT2D2GAxfjWm6Ayr4eLKfcnLOhwuu6CxY0p2/zMUycEK0meBwV6xUzhua2KUTZ+jsiXCpkqlVltecEAmSauNnozHcwf0g7rhScgwvzR6tqEASxydhIy0mrLIdEv8Zi0Lfa3Fx1sHW+rf1NAHaoyvmZiONCmChN5yAIYv1Yd8HCZyXn9C3VPA4A+rPzmJ5uI2o2lnzuRmAtTOTKKKX0yhJZDmNgDwYG0Yghmg1nLsei4RU/mTytPVT2tWdcy71aPHz/blxx2XXIsvpRPmu9xDgDN45MOWNOzLDH1rqG8yKO8+Jcxrg7tum5Z4Xj8rN5MA49u6qUzsuPMYZGI0Laz50qZ+u4cDnpcIqWqwWDJH64Z2nFUAn3r1PNkwVJ3bSq8jGAZiNGp5sWU6qAYEqY769NKYXCs7tyBmvaSMqfllWKd47bbBqXodsvX8ddu2yJZcLiRCBNw+clfLWwUhgrbfz7uMg5lbx9UbaaX/nLLZ/baMTo98fT9W6jEaHX88runvXiuPKGL4UxNy4x6P6yyn1dLH7gb8SEPuEpp+C0nzpr8AdbQzbSctKi0UC8c3uxUpmUkMaJdbCUupu/Wtxeb7blhhU7Kn4mbEXDOVgcQ7Ri8Diq9TNBEMTGY0MJFis5p2+p5nEAMN3kOLBvFizqFB7/x8RkfC1WCSleAFXLCR7H4EkSWE6gD6CfQtvNDeZIMnnaKFDZ157VMJEbdyLGcOKW7UjzHK6rajq/fsc53Pc6u7k9J4eCglRAPjSfogNdzE5Stde2XWnldaqJlYOVjupes0GXmJXP0aiBB9WIpX6P1ZmEg3OgZ2Rj8uUvfQXv/f8ej2Z7/X01bKTlpHkkIEYYnAuXcoZRFqXZLC52WA9dAKw7q0XEDiyp3W2nj+qySMBejwE8jiEaDT1111+5jSCIsWPDCBarMadvqUStBqJtWwFrMZBlkHkGVl4NgOt/zK5hu0IrQ2wkAnHCupew0zoaDW0654sTBEEQS2T7sTvxtk++FXd/ZxfCLl+pA2tGar3l6ovGr04O10q1Q2nO2a0q0gRpKye7Y7/GV+ZEpSTyXDfGMykh8xxTkw0cOtQNne1CuoupQDS3Aoj9xwrLAVZOr8IuttQfRwHG10v40XS+xv8LfAe4/uVVUZdDYWKygfmFvrG6UOCw690zKAYIWF8w+l5yY3HibrG1UjFOfpkdTedeGsbM+u9w6YqboOOVgvdd2ztexJc/x5YtLRw81Cnf1gq1wbLqK2kpQoOqHQSx6r0XMiDTqakmZme7S7jixsGWXTsiDD+gNB84aAso32oGQGmZRvOkBum9x93syCDaO1lvCjXTExgRfAEnPvY4RNGGaeqOHYezcpu/rKOyS8Q7saMQOAqxQ5XEjqL+LaoCZVY568FWNDyKwI0wwSNtMQGaUkgQm4oNUYtvlDl9PIoQtVtBmHKVrVGO7XKMWQaVSags05UtiqYWYyjWIua+erwxIXGCIIj1ojU9iYmdWyAiAREJRFEEJsw0DTPFI5gCYvxcbATG1doHGN+yj2u5ASr7ejCu5d4M+A6umVjaufVihxZjm9vbSESD/EwQxBHEugsWG2lOXx22wh1mRuY6/FYtznMgM1YaeQaZS28AxoxEmCkndlWDtapwSZwgCGIjMbf3APbtehBxI3GDrIrp0XxtJMH8IXjjT4GBR1yLHEKAC44oinSYEE78sKJHIHzwUAzZbEgpkXZT9Lo99Lt99Do9dDtd9BZ6Oqyjw9rtBAsLfWOFzZylYLBvt251HRNfmqPOPFE+SOufw7XlSpA/Y86vhn9OXb42bu+2CRw61IWIzXcfcUQi0seROY4i91yUy0PvNYLY+AwTO+KJNvjC+K1uQhDE8ll3wWIjzelbLowx57gHAMpN4MBKI7dWGlkhbKQZmLQOK82WMW2l4ZZoXHrDehRxghwOEQSxXvT3H8J9t38JI80uZ6X5zeZYMgYJhox5aVDauvQsTOMcc2prOC44YFco4RxMcHAz71nPgWZg3HSEOUc21cL8fC9wRummY3hhADxTemPYLBXyTKKfpuj3UqR9ve11++j3M6T9TIenqd7vZ0j7KdLU7Kf2L0fm7w9wYHqkwgOBSjtU9cOE2xpnrJ64JYQOE14YN88JN+dqix8TzxiEeTbcOaI4t9GIkKa5PyOmBKsJLw4CX6tBkmqaulkpg97zA6/nhcaJQK+XQUl/6hHcsTTtHKnCcPsnzVQNacPMtBJpfCP4aZVSzgeZdPnALG1t8oZJU8pTIbz2cY96BH7pjS+v/dwEQRDEeLDugsWRQGClMeCOF1NOlFkPOtd/WQZkOfI0hRlrdMIGzJQTxrhbrYPECYIgxoVOv4fP/8t/otvNzAoccH4RihU5SmGMmZl2of8E7rbMaBShbwVuj8GcZuHSmHgA4CjydSuNwNv3rqWUQpZJpLlEmuXmTyLNzTbLkeVyQFiOfAmOoQVniCOBOOKIhUAUccSRQCvmiFutIs7bRpFAYsNiobfmXM5Z4GNAz/2XrjMt3eof1iVA4RtDmRPsqoBudQ/PxYDtkBaOTot8XL4mnyJ4WFxxLE3nVJqOrVThvnL7ukMsFVy8Kp2jgvNzyFzHZ5V8io557TUlOWddKsHvsfz7HHQ84Pddf8yw8OBudGYX0Jpqr/OnJQiCIJYLCRYbBD3CN9ipUTHtRAEy19NM8mLaSd7vA4xDtEmcIAhiPEgmJ3HSGY/FQ7seBues6PhJqTuIZpsrpUdi/Tjb2ZTSdCxtvN8xla5Dakdd14IoEoiTCElsto0IraSJLXGEJBGIk1hvY5OmESOOIy0qJOYvjpEkMeJEIGlEEMZvR7EUqxarhSiWZuWiEFUYEEztcFMvAAAKUSTqrTFGvEXhraxzOlkNK+6/r2zonbIwUSQr9hUAITjy3JbbfBoripScdDJ3tv1vzQ8KZ6fB9UxedsWCwJlqkCb81IXoo5DnyjhnVciU3ubmORScIc2NzytVCBx2y0q3reKU0qSRXjhTRYSC9/mVuz1BbtVr+ElUKVxP4YECRMQhlTICorb8NAag5pmzSwGbZYGZ8pYY5k6M9J9TMOuI1Z8CZI49sQKeE95wSV8TV2Rh9os0zakJZO3xXKqeIAiC0JBgMSYU004AIKpMO5mamUKXHEsRBDFGtCZaeNEVv4jvfusHmNgyserXcybnUiHP80DwcPuyJITkoSDih09taaCfKkRxhCjWfhS44GBgTnBRslg1xGF60oxB+9uII0SRgIi109EojrQ/BjMlpZiGYKcgLGWKYL1ovWPnJHbvmVvurVw3ZkYt94BpDofLkpZbtFujA+zcOYnde2ZDwWCIiFaJGyRmlOOGnFc+d9Rr7Nwxhb1750Lxy9+v8RWyEQZMdpDTTYIgiLGHBAuCIAhi02Ct0aSEmeJglvNUEjKTZopA4dtHZno/kzlUXqRRea7nyOcKMs+hZA5kEjLVlm0qyyA5Qz/LwBQ3PhCY839hp7QI16lj1U5dWHK3xKeylgesmJOhzDKoxTQJf4qGN3fD8wdgskUwncMcR4Ijy6rLfC56b5f8hSw9ctgpkeDI8qWVu47BXekBMaymVBUrBnjmEtV8/LIH0ZW0rO70Ih3zLhtmNDzvmjw54zVx1XOSRPvfcFH2+bVWEd6x3ni+YoJ4eNY+zK2kVpxa/D5cPi7My5+zIK64ZmilkZ72SMRH76h+cIIgCGJsIMGCIAiCWDeyXh/9uQUIO9Uj1ystZWbam5ISWaaXk5bSCArWebHUJvdKSUAq5HkG5FqwgMmL5dq/D6SCVigApqQTMWCnikivU+/2C4d/dfTW9E5tcIYMpq//OHvIGs0MIjYA3//bb+JZV78CcWsk174EQRDEBoQEC4IgCGLdeODOH2D3Hd8DAwdUrldMUgAzKwDArijg+a1wQkIgLCy/DM5hn10pIjIrhQg9/YIJvYQqM0um8liARwLNdgNpmgGM14/+uhHfYhQanIGDQbLCgALBvnKD+UoxMGaG8e0otWelYUeTGRPGRF+BiwQs5uCRQBRHYEKXVwiuw0UEHgns2DmF/QcWln6vlis/LHMFWVY6cfv2CezbNw/j8hNwxhaFH4yyTwopPYsM3xmoN22jcCwK99z5eTmDFedsw6z8Bc/VhrQlye1O4SNDAZOTDczN9apWCvqDwrh81cccoSUFjAUBN1YFvhVG2eKC+ftl/xCFhYOU0vg58TLybhVjhSeLrVub2L9/3jgIN7cmzwFlTpFWOLSfN/dW9PDuKUyYBBSMBVRe3FP7W1Yo9mHrAph6wH1nqrj50q5E4r4RKCmx8/gZMLb5li8mCII4kiDBgiAIglgX9n3/ftz1ua+OlJYx5oQAxhmEEGCJmYIRGV8PQnfUeWR8QCQCIorA4wi8oX1D8CRGlMQQzQg8isCHLBktbQcMykwx0R0nvYKGxOREA7OHOrqvlSvXydIF5qWh/MC1IsAUOI902Y2/ikHiAo8EOBfaqaFdYtUKKt7xUnxbbJuZQtYcv7n9W2am0Ivi9S7GspgZY38KMzNT4GNY9nG+56vJwX2HsPuhvRBCIBICPNKibBRFum51U24IgiDWHxIsCIIgiHVh66OPweOf82TsufcnaGxpI4pj8CRC1DDbJNEdeiMqSDOFw4kHbmTV7kOP8pqRcWmXDVXW2YD2BaGkRDrfh57UocP18tDSHQPQ1hAsFARYxMGZFhla2yYgkwZYHHliycqLCwRBECvF7gd34+2/9o7Q8qgGt/qQtTxzK74UqxRxzpwzYObtc1t3WofBXtpyujAf4wuIeU6GGfPOYWi2EvR6WVDOiv+U4hCFNZK/2owf5/ts8X2teGFemupKNTAr2vg+Zur9zbTbCRYWesZZUZGNvz6PsgXwVhIyMS7c5q2kchdwlklm377xCt3cW3raOUqyOry1piqsz9w+gGYzxsJC3/OdZC2aVGDtpAJ/SebdLFVRDhXGl/0xSWdZVbJ+qwnzyxlaZoVxUcSRZ9J9t8z4zWGwlmfM+97D5dJh0rvvl9nnzfPdU/LjU/alwxZJ4zsoLjsubjZjdLsphuI1WdwxSmGj4Jv72QfIj/AHYLw4FaTVz/5jT38UnvLTT11iAYZDggVBEASxLnDOcdK5p6IvFUQUefMiOGSWo5d2vfeiAucCEKaBK/TyiIj0kp+cM22hIDhYJPSSi0JbWsA0ghl4ISSYPz2VohTOAPBCKBkEjd4SBDFuRIzjUdt2IjMrHgFwoq9ynU6/s+tNw4GZzmOnAEF3XPOac10HdlhcOZzYMLDSXp3BDSulLE9jq0wiZMGmfs8XkwaViwUJB6TzClPzfAVHqvr0KT+VKvf/vfSqJr3dUzXpi0vWXWldOHxbqjCHr//N1/DEp56BZnvlfAeRYEEQBEGsG1uP3YHjzj7V+Y8IxALOwTkARhYJBEEQK8G2R+zAaz/wW/iPb/0AUBIMHMqtxKLAwLwuFDMGasYSDTAWAF5f0PZkjaDhRotNDs7ywQkfcBkwxryOXeHkWCqJPFNgkMilRJ4rSJlDKYV2K8b8vHF5bEflXR4wlgfwOpIq6D0qpezHKVkgFGEwnznwpYLC4iA4SQX90uA6geWEVGi3G+gu9AFh/NCo4n5BmRF2VcwodPfSlIf5H9SO2iszaq8UlB3Nt5dluqyMcTcewK2fG1NWzrlJw4prepYLUECzFaPbSZ1QULVeMd+z92z4FieF6OFbuQTmJ57LHRae439Wi7f6VpGysFyAy4ZharKB2bme/RYQ4KsKdVHuoCRXKBUkUN5DoEr5+atrFc+J8jconhkvT6kwOZFgfqFfKVQlR2WMdsJC136goZ9rQGJWifO+DO8HbfceccIOiBVus5FgQRAEQawbUSNBa9vUeheDIAjiiOGkJz4S7aPGc7nXcbZsG9eyj2u5ASr7erAa5SbBgiAIgiAIgiCOEPq9PmYPzDqfEoW/isLHBDneXBmUUkh7KRbmFsDSHvbvX/D8GjDjUwGFY+myv4SKvwsV5O1FVOKD3QFLaY2SR8IlZg/Mh+U2ZbS+Svzy+p+NniFiJSDBwjB7YBYfufYPcGDfLKJIe5aPYr2EXRTH2pNybDwoxxEis2Sc9aocxfo4iqPiOIoQJRGiKEacREF6ERfniciG6zAyfSYIgiAIgiBWg/lDC7j33+9F3Ij1NI7AxLvY5RGHsCswCb0vIrO8s4mzq4o4sYNxMBE62ByXTqtdLlsa/x56CV6FNE3Rne+iM9/BwtwCOnMddGY76Mx30JnroLvQ1fELHfQWeuh19F+/00ev20Oe5YdVrrKAUXdcETkWiR8WXs4njiNIqbSPKLNKF7eOqK1DVOE5ShXe1jw3POKujyNM/8o+T7Z/5PZNHylO4iKt7T/56azfKlYj+pBYsuoopSBziTzL9V+ut1MTK7+SFwkWhqyfYt8D+3Bo/yyk0s6EpFdpSSl1uPFIr6Qszw5aUZxXZGYczJmKw4XDKuI2nBfz8jxHM252VzBVzHNUE+y7vSIfPzs7d62cp/NuW3++P7/NL4NNIjhHlusJh8VS7Z5XYQDOE7Hdh5fOmwtmPR/78w2Lc4yjp0o+vgMou1+cE3hQdnMZi2uG9WE4zy5w+jPsfnu3OzwO72G58h127H83xa4+5pwVKyisIst6VSxykp5n6U1WRd2zF5wQzOcsvHeHcx/DOJdb6beEgffU7vv5+XGPetyJuOjK/2f4hyMIgiCIVUZmGeKIoz3ZMv4B6l+8us0roaRCnubI+plrE2vHmyoQPNy+meZujxljusMqTIfVFzyiovNqO5zO2kMU1h823JWtLC4oBZUX5fXLmfa14LAwt4CF2QUnPnTnu1psWOg6oaHf7aPf66Pf7bvjLM1q74+FcYZGs4GklaDRaqDRbGBiywSSpj6OGzHiJMbERAMLC/2g3Wk/i+/zIIh37VaTpnTshBUV/rk4+1eTRsmadAPOT/upvt+5RJ7nul+Ue38y3F8rKoJJSUSJ40j77iitUFN+zoIwT3zxBRq36o35Y4w5wc4Jd1ag8coizNLlft42THC7rHlVCNq3pYW9e2chM+nuuy8M+GH23g+NM+fafZmZNH6Y/X79OLNfSZPXf89bd07jqlvfDCHEin3PJFgY4ijCo6Z3oMPaYYfJ7+wAoZ8RGHXJVhyAcRZkO7omzusYK6Ug/f1SWnjnSK+zHOTlnZfnEvbqZScwtoyuYBgtrt5ni6rGBekWiQ8uU05V38lnNfFVAcZfgqrcWS/HF8e2Mx/mYVYM8MWBQJAIy6UdUw27r9XPO8Air9YRUPV78J361MQNyKfuGnnJhc5qsTpXCMu+vOfVbgZ8fwOf1UU+05B8+7MdLMzOoz01MSwHgiAIglg1+rNzuP/Pv4RYSgQLJprlHp2A4R3DHNtRd8GZaTMxKLM0qbLprUUF4zoPziEVIAGk0G1k3Q5mXntXxwNMxzH9TwFQHKb5wwClMDHZwJ6fHESva8QFT2DoG4GhZ8NNXNpbfGnIRrOBRjNBo5kgaSaY2DGNRjNG0kiQNGIkSYykoUf94zhCkkRImjGSJEESR6Zloky7kUGYTrC2AtD7W6ZbmJ3t6XTS7xvoMmhRoRhA0+163daU/uCaHXAy7VTFrPPOoh1cOEMFwM1NZeZCTlNiLknZugLe8fR0G4cOdcI2cbmd5B1rIckM9maeuFESNXS81IPEuRkodmKIOT/Pi33pp9MOWYO83L4eWJa5BGMMWZaZ8lhhK4dMJTJ7bM9RqjjXDVD7g9ihEDYOWBFGBIJMaWlhuxSxt40FRyNJXFhhacW0yMIZuBAQUSHE2Gf9hMccB5lJEixWg8ntW3DFR16LXff8ZPSTBvU+AfcD8I99NdUql0F6FGHKX7fYXMuO+ruNKuKnp5s4eLBTVDr+dX2vuQCYVcBN3aZMKHPVbTF6rT04Fx1lxmzZvQ/L6u+DUsWIv10TucikqNu2bW3hwMFOZU6c/RB2fpyTFxiq1hql0WznedqGMl/08Cpk9zm9vODFsSI97L01cTt2TGLv3rnaz15/P2o6v3XPUG1Qzbk1lWXtNWrKsn3bBPbtm68tZx3e7R2cqU3LWJh2WIFYaTu0DMV937OnzplPnZpjgmxU7W922O94yHlWTKxk4/2GvO9o5qhpZJyq3DJK5kDW141bSzDax6oPIJl5EgRBLIter4e/+fY96JiRfqn8wTHbflXhMWrSBPFexxsoOteqrvWyOiSRQBLrv0YcYToWSKabSOJJNKIivOGlsds4GnXaSgZkGZAB6AA4ODx1IcRoOsv+dFpzWC9mMVIzzcEACPN32AQZcazvnSjwn3tphCYnwNmtRHisvGMjiEjv9yRtXtBtfGtpzzmDMFt7zDkv4oJwb3+I9dRqInbvRhStnFgBkGAR0Gg3sPXobetdjGUxrp5kgfEte3tLG/O9w5uXuF5sm5lCxla2MlkrprZPoTuGt33HmD7nq43MMiDrob455FvTlPfNxo0qecKGExxJBCEIgvARzRYmHjGD7oP7EEcC4KYL6HwYwEw1LgZp3DiEH+8cRBYDRW5Aiek87QCaraqLATUGbqwCivEiVqQzYdybumnDIsHRSCI0GhGajRiNRoQkjtwyisy9Dpj3OmBFeKmc5UEp/5ziVREOmKGIDuNQPd/l6zNosMNXd3yLTeWH2QHMMD8vuhpWCg/DlJ88PLdGbSpbHdeGlzdsQHh4Yphl+Z65uNKgmP0OS5cvstgM7/bB38cipywaWW+lvJS8qyeJVgNKSrAV9MlIgoVHr9PDob2HtMlLVMwr8ucqEQRBECsM4wBfgoBWeWsbC65qK67mXbpyIojMMkDmXnpUG1kEQRAbiPZkG698y0X4t29+HxNbBk9RHLSqhIkNNkFFWz7P84mg62RZstSVgFSQMofK9VRnKfX8eJXL4nzoUed2O8HCQl8LFJFAHgukcQRlHdgnkTF5N34yOAePhDNtr3aQvbrfzs2wyfzP579fVN3nVYPTm3QTkw3Mz/WG3NeNiS53Xx+UxJgQ7z1YKxb479lgZ5FBBFZsWZhs8PtX72/fMYl9I1lDL8cWaLBl79B8R7zUtm1t7N+/4J1X0/ZZCv75/pShwScMPh5y3raZrTjQWVnbKhIsPOYOzWPXf+5C1NC3pexEyFaEcaS91kZxhLgRBx6Uizk8JHAQBEGsCpW6dVDjaQksQwTJOhzo+0a+phCcAeB2SBFwU9zYkMYVQRDExmF4G7Y8TF7FOsTU/geU8y/gO+nUCQEwBi4SiESgmcRmhT2z0l4cBStOHH30NPbumw8ccK42K9Wen5yZQmcMLS3HtdwAELfb4PNjaJYLINkyBd7bGFNglkIyNQXWXdnnhQSLEnEjrlWcfW/EvW4PckG6Y+fwRoUVuF4K1SzPYyrdOIm1Z1jroMTbX8vKd71w91FJdOe6mJ+dR/fQIRzYv6D9VBgTQ7cMljPfs06gAOsxGkC4ZjU8Xw9DfA+4sEAoHHKeMl4JPIUfAFjWx4EDC8F65f6608PCCYLYYCxDBOEiAkTpNaodDJlRxJrROD9/YLi4EVh2kLhBEMT6UQgQoZND39GkTgiAwbVxozhCs9HUAkSsRYiltIGV56hRZTnS+S6y+S6YaWOBMbOvHQI6Z5EEQWwaSLAw5L0+dv/zt8AfPoB+EjvPxoxzs2UAE4DQS4jqStHGh2nBOSC4draTZ8izFL0F3Wa1AkfQEK6p3OMk1hW7+Stbb9gKfqk4Z0oonCo5x0j2z4sHECyFJKV0y0b5Hn973R4WDunlojpzHSzML6Azq9em9teo7i500ev00F3orriH3bIwUBY7/PhhcQPP5eFxnERQyqxTbteTFt765N4a00IIvZa0XXM6Kb7buKG/66SRIG7qpa/iJEbc0N6noySqFz6YJ4hwEkQ2Kr7YSRwBFBOoF8cJGsPEDWuXzEYQN8w+NdYJglgEpVSx5OEiAoSCcsuPDhUgfItjfwnSkugg8xyylyKd7yHPUqhMIk9T5L0UeaaXT0WWI08zfY4qrDIOuGkV3nQ+5jesmV4O1ayMAKFXM2BcT/dmQph4ASE4mAkTpv3Oua5rmRk4c8KIcXQooatiBX1ZKSUgpa7GzX1U7k9B5WYrFdSBgziwf0GndzcHzjF94KBfer4qlCw521fefpHefLFFu95aBNpreG17/7r+M2GvY60JlVL4yUSC+fl+8UyUpoXoQ+/do1To78MOLDL/Oyu9pzyhiUGZTM0z5FZBgXe+d8QZoMIVB207fn6qhdm5DphZ1YYJcw23MmDxXYPBiWCw7WmGsF1tFwHgAHMOYGxxubsXgfN+fxVCxr3wIs6GMy8dtRsLSLAwyH6K3sN7EXV6wAJcxeE67YeRtzMAZtrLq10qqrJslNnPAGSMQTG9nodupOofh/KXkeIcPIogoggPTzXQWUid11mYJXj0vEBAKekqt2L9jMK0mdlDpZCmGXrdPrq9FL1uD91uH91Oil7PhpltL9VxvVRfq+6zM4ZmI0KzkaDZiLC1GaO5pYWmdZgUxxARR27W8tX1pLn3YIUXaoQVs2+pLT3nQEWcX2HbfL3K2KZz1yri/LCKmGNeGnpN6j6yNEdm1iLO8hx5LpFn0szBXJmKRi8npF+8QhR/3B774Tat4IgEBxcCkXds4+I4CtdPDpxGhd9fLa4eZkFAzfsHdRGskqDm3AFm80IwpP0cEnb5KbvklG44FN6ZS56Y7VJV5vuzS1+5ZaqUdPm59MG2SK/zlUX+lXPCJbIA4OhjtuP1H37TsoRGYmUZPjd7afksltfQkb6lWE8sW9yAETZsI0m/T5T08iGBgyCOKOzgSZ7nWoBImsUg2QABggGQmemQW9Ehl1D9DHmWIcsk8n4feT9DnubI0lB0KLsLsh1zOxijpETa62sho9NH3u8j66XIexnyfuryVVKWLGXrVtQDvARec1f5QdV0lXCC2ASwYFMfWXNYTT98MMY2JZpTbZz/278CHq2czECChSFXEv++ex96nX7R+YtMp48z7bSH1ywfw/QxM2KEHuEGuJ3WAO3lmEOrdhwoKksjKDirBqvGuuNwf1AFKgHUuZNhSiHLJbr9rPSXotvPzbYclw0WHwAtMiQRmkmM6VaMo7e29XEjNsJErP+SCK1GjCQRhXoI3zNzaBHgptYAXiM8fAk51dkrXuHIqZTeT6fcawqVHf+jlt9Sg9KNAjOjFxLIpUQuldlK5Lnely7OhCktdORSITNWLDatDffPt/tZnqHfV8iMsp/nZj/Xa0znOSm0ANzyTtxOKzK/Yeb9lrl5LvU+XBxjDJFR5nnEa84N0xZ1QZHfzplp9Ls9NCda630rNhR5r4d0/wFtmbaU39mAtErJcsAix6XMbD0zKN7loTB7oIH+Qs8EqTCtNwpWqbuCdKo4vygAgnrH1WGlsJJwoerCy+KGadB3Io48s/eK+co6wkaJjXMJPIHTs+pwyQeIMG7krBRXJ5Z4eZTFzT33Jegs9Iu05fq9FF57XCcyDRKeRsl/xPyyRoxeL63eI/s5y/elJh3g3cO6ex2M7I14nWHXMNvZ+QPoH+pUr1fOp3RcSTnKuSOEsQHh5eNsgpq5dUxOt3HiycdB5RIyk1B5jjzLIDt9pIfmkfVTSCM6qDSDTE3b0H+sjT6qYB9H3caTDFBphrSTIu/1kXX7yLtafMh6VnjIINNC9BjU7vRx71RP9Ge2bekGrXnxcDArGHsDKsHgDAvCgt8HM4Mmrs3qf2idRAFgVnQBM+8N5mWnhePCspcjSQRSU++Gj225I8i88oefNbj/Xlj5t123xLy7H/49KvVmw3pdf55GM0KvmyGwAnEWHApQLKjzKu/Q8nsVMOnNQwTvdD2y6r0nmbMOcfn55+kUugzMZlGoY3EskKYZ3MPqvX+Vdxz2P/x8WPA+D5oEwbu3lC/CNOHukOfdS885Q+5+d6Wy1ZRjUIJKdPlz2v+1r7K6NlM5KDxXxAL9+S6a05PVxMuEanJDp5vh61//EeYOzRfTNlYRXhol55w5EzohWBAu7HQQXgr3RBSlFBYW+sYaoo+O+RvWWW21ErTaCdrtBnZsn0R7omGOm2hP6PB2O0F7ooGJdgPNZlypKwMrZPPSsB1DFtaBpQqoFDdm+D/g8gjqYr/tcoVV018aPa+6QD9IaQsUmStkeY4sk0izHBzQlaB3EfvqqLu2/urKFXnpauHGWfJU+w01H5aVX1hhAv8UERkh0Hv+Gfd+E8Jbi5pbfyjV76lK8bIL2yas/rjcuLB7dZdhDHGrhayZLFKGIwvZWcB9//gvkGmmA9wzVHqYHGr4D2SN6SyeZG0IOq/mX7lzGjReGTLOvE5CXUOlHDagkvIOFv866htEYZLhDTnnX38Uy5VhLOP8Smd/iflK/57XNoxRjRsmjKzh87+weJINyUIjwdQFz9BTdgnHwQf24r5//nfwiAGKhaIDh5kaoa1fs24PeaeHtNNH1ukj6/WRW/HBCA92aom2fljk4swM5Am9ekfUTCDMNFnRSBA1Y0TNBEm7iWiigaTdhGgmzix/aqqF2dnl175SelM0pLHYyAvrSuaXn4UdYS44WBQhSgR4HEHEEUQSQSQJeBzpAQ3BwUUEZlYc5JEAF3oVrJkxXd58sXI7a0PvnaKNjG1YYcHsTKQVoGCnugAwgw3ldHq6i+0UF+8QKXNY4QSe5asV6vX3C2yZbuLgwY7rozhBxvrCc30WZiwTi/aiC/ffr34cUHq3wk0LKdqJLEhjy+Ey9ON8kY0BO3dOYc8e774vKigM//GVx3MWO2+Q8LJY4h07JnGot7KOTkmwMGw7aht+5+Nvxr/feQ/aU20359xWwrlVgc1flmVu349bfN/mp/dzl2d4HRee5cikRN7386qeywVHo9VEa6KBiR1bsaPdRLPdQGuiiWa7heZEsd+aaCJpNYxpulGGpfKunSOXuau0F8xfJCMkjRhJM9H+FpLI+Gso/DcsGaWqy/ZsJAa0PRmA7dva2GfLXe4k+B3YRfJn8CpDeBW/rahVUSEV4SjCGYL0uvL31GIFcKUQA4gV0FQK27dPYO/euTBfIJwC5eK8l4cXV3ZeKm1N6K5fMtP0lWl/xBiAnT3DzJvLJfHKYF9CE1vbODTXgxAcSnAo68/F+JJRkQCEADiD4hzSs3QI/YBw59zV+S5ZRbbtnMSefRv0OV8nGOdItrQh09QPHSD6uH/u2B6pUDn10tWEsyJO+aNTTE/DC1oOwT6C3ykYQ6sZo9PLgo6s8vaZX0gWlj0UvpgTdJVpEDGF4LqVxk94NyrNDRbElK8NTE42MTff9T5jOcdquQf/RPw8qo2a6mlqQGyp4efnay6+ZUsbs3ZZQMa9NOXv3LvawIYWG/ahhrC8umL79gns2ze/vKy8EcP6aO8z2gp1gNihpAQYqz+n7jylsGVLE4cOdoL4ULcaIqyU8vICauIH5FmWzFVlpzafLTun0ZPSdU4Iza5/+R72/uePAaW86RaZbvPaaR+jWD3YgQEhEDcT7asriRE1IkRWfGg1EbcbSCZaiNsN8GgZbcUSbrqlnX5p/EjIXIepQb8X0yHkkRYcomZDCw5RBN4wAkSk/cYxIcAi4YQV7QdjddsJ44rvGN+yUX5xMzNTSMZQJAKAuJkgaozfQFdr6yTmVviek2BRA2PG9FtwRPF43KJt2yawf//8wPhADMly9Ho5gFx3dqHAGUfSSNCcMIJEM9Fqt3GyZD05rwbJ1BR4d1WyXlXiySnwVRhiXYvX4faZKeRi/CpBYHxHKHgUrbooMm6wRhM7z34y9v7ofrA4LjooCgCks+zxpABj+qmPi3AVdNCZ26pKmM5j6RRNd+Zy4VyiHdXlpgacxVDbwVrC1Yv8lonJKpudQ3O5eawj3c4+xKUw9626kamyQKW3yo8HwvTB1opgfn7Dzh1ESVJiEkIs4/tXylxHFUNkSrpP7oYoC0XYFNEXCLw0rJTWhYW/GZc/U8gXOmjyRaZbLe1DHca5dacPEkk6YHzb4V1rk7H/hw/iO1/4ehDGGMwqGxwiEUgi05GPI2Px0EDcShC1mkgmGojbTUSHYTHoHL87Z5W5ER1g/F2oquhgnlve76O7kOqyxgIiisFbAjyJECWxDotjsEj78XKCQyS05Yjg9C4miDFjPHrjRIAdTZd5BiWBPEsxp3LMH5jXgoRU4agxY4jiCI1mA612C0krQdzQy6tGUSFKEARBrDUijiBaLYAJFHaUBWULpGJAPRyZV96hqiQuXVT54ofttBnhw1n2yCLcduT8tEpBxBwyle4azOvuFdf1pZJh5RrQgB5gbqKK3SHnV60NbHi7nWDB9wVRc+rorFbjv/S9K6XLPW9WCLCdbuVZFJiOvP4eve9QyuI79L7Hwy15YVFTI2yUxJJuOgvet3OpSyJD8BxiRcu4aLkrFkjesRlR5lGEXHkmvkOfuxHkiMppK/Epq3mI5jjKcqvLtkcdg/Ne+Rzc/++7MHnUNkTtxrIGpIqpFdJZOygjNlin06zynRQWX0ww8FiAR1oQ4ZF2+MnsVItIaNGBczO9QoAJgaMeMY19++Zpmg9BHEGQYLHGBA42c7PqRCahVA6ZK8g8R9bX8wLTnnZ2lGfG27JdckrmgGLOxD7iHO3JJrJUIo713Dq9RFMELrQ3aMYZcsyjy4GeW6ZHuKWfrA8AGDWaGRM/JrSDI86FWTrTLFVl5ziaRhm3y7saszC7NoeSAPLczQvUpoZ54ehJSsi9B3DwgLYOWWzQpOr8RS0S7++W4krmjosO2JQmf3W3tHDokGdiwUovz6olYkHJrJCVG+be9VVpUNb2X1RNQ1YxXhpRs7sKfsen/2BLm/c6Z1DFH/fm6ulpQ7ZxYZZrsnP8UJj/VZZ2AorlnUyhw+WwgMIZq20j81K8SeNnyrh2DCYlLd+6SeBxDDHRBvjGFk3rOmATW9vYf2BhaJqNSjLdxrxaxylKfj3lRvvLcWUYosk2VGqfFX8ENtyv5FARfpgTQYprli0PSuKGqg8v74fCiASkPjeXqdbBXHmsQKDrz4pVh0mnSse+sOCmNZWFhiCt95ntvfLvxwiWEs3pNnoHO8BhP+UlqxMbtEo/nmS6hc6GcTazcdj+yKOx76H9EM0YMpPIZeYcv9tpFq4NAmutY8VX3fZgXEAk2m9D3Iz0qnVxBJEI7efBWjlwbenAItPWPMzpFVEckVhBEEcYJFgsAaXMespKQmZ6JMc5GZLKeFm2yz1lenlLMy8wy3LIfqZ9VUjPZNPbU0yBgyOKOEQkEMcxopijOdEAF20gzSHTHKqvPS/Lfo68p5eQ6u+fQ57l6FtfBqpoMBXrKhfhuk1W+EoA/LWX7UYV7Td/3zUs/LQEsQEJBphrRumDYyPElEcOg/a+n4YF5/pT6J0E5cVNHbUVT7rk52hZU2LzMUh8qH052Lee+WGZpbrdPrwwm977vSWTW4COU239QlTeTUGY72PHt2Rgxfu4cK5n48KedFUEKcrlBXjWPn6dwzBtBa7KfVHBJrySqrmwzXuwsOM2vi+WiiWFDfbqpJLlkj01npwCRp26OVKb4HAbDqp2t0zUmgC6pFiUEZF2Cil7GVisp1LwJAaPhZsKYtMwUUytKCwdOA0aEASxZpBg4dE9NI9DD+xBf98hqEyLD7DOLbMcMsuNF2HpHCNKCTN6rd+Z3DS6mHHyFxkTt2Y7QTTdhhAc6Od63eqeFh7yXgbZ72vHR2mONM3QM9ezzoSWIgoEHa5hnS3XdmGFZ1rOUCzlBOetlrEinRstLx/DpPfzDzqCfiOqGD1Pkghp6lkvlET3iga/mChfY0Jd134clHbUiyexQD81JrJlZ2Eot0mtNYQWpjyrSD3YFaQMolBkzAY0aE2+Na222k/HgCgSyNIcbiRygFFGMRxYF1ftIIT9h1JD309bd6/MaGelD1BqywvOkOfeclr+pQIfCKpSTqXCDOtWoyhb6ZSdixaf1QtXyuVVJ+pBAXO7DyKd76KxpQ2C2JDUCgB14kO5s2wq+8qfFSBYuC13mJfLgM71YVO2Ugt/1OG98Kej1N0zL63MMiDPMFBQqPjeMASWZoM+cyiOrDSMc1SsCAcmXvHLLxsexwAbQydZq8z0sTtw0gVnki8HgiDGAhIsPBb2zePAjx9Gc7KpRQfj8ZZzLUBErUSvhhFxcDCoNAeyVFtOmLWlZT8LvC2n/jrTNZ3aCsxb7kkIRA1hvC5H2vNyM0HUTIzzo4ZzfjS9beqwlnmyOAsMM3UFysxTzKVZ7lUVlhpQCCaOM7gpIzwRRqGPIRoReBIZ08HIOECKnCOkmaOng2V7Fn1/jtJoWiSPlRgZKDt/dMvhmntTtFWVJ2iocL1xz9pGuWWbEDR+9cpQXsNXugk3NjIIl56HeLvEk7ZMttY2CtNbWjh4sGMEKk+scg1o20Blwf1y7eVSB6QQrFCE18YVDfXydI/i2r7YpRP4QtjOmUns2T1XfG73PBb3QhW3NQh396I2qirODFpqUPlhteHwBBEdun3bBLpsY097IDYJw4QHW69kqek8+5gfHTe/Q84AmKlbgfjgdaxd2CYiNJdamQ64UoinpoDuCok1BHGYkFhBEMS4QIKFx8KDezEFBj6vrR2kWV+6b60dcttxX0x1sINL2i+EW2farNccJTFEM9bL1bQaWnhoN1d0uSdYXxlOgIARIcxyT/66yNZKBNBtVM7ARKRXAYsEooaZm+jWmtZihPW67Htgtv4wlkJkzA/HnXEyj5yZmUI0hittAEDcSBA1yusEbHymZ6bQ3+D3fHZ2Ftdffz2+8pWv4O///u/XuzhEZaqF2Q/igMLiwd+iEB58qwdzHLUngY45dzOLDxsFzzKRIAiCIIjRGf9e4gqx5z924Tuf/+dKeLDUUyQgWoleu9l03vVyTwmidgNJyyz1tEyPyz5loaEqQNhReX0cSYnurHGIpZhzpsk5B4zjTMG5FhpiLS7o/cjMWYyd+MC4ndLCi0YWQRBjxf79+3HVVVdh165dSJIEJ510Eq677jps37594DlTU1N4z3veg4svvnjtCrrZGcHaYTCssG6o8/MALHu6BY+iDe/olCAIgiAIggQLw/bHHo8n/cL5ePhHD6G9cxrJRBOimayg8GAFB73mtPbAbDwyA4XFgz02IzHCODnSq3ZwMK6dIOnlnrSnZR5x7Ni5Bc35nraCiKJQdOCsOCbxgSCOCBhjuPTSS3HuuecCAG688UbcdNNNuP7667Fr1y5cffXVQfqnP/3puPTSS9ejqKvDyI5/atLVnloNlHkGyHz51g5l0YGsHQiCIAiCIAJIsDBwzvGIJz4SC70Ura2TlXjrbBNSaR8BUjmHmDBWDspMsTAz9gEouCU/jYUDBHNTLKIkAhcRWKxFBy6EmW4hwEXkfAowZkQHbwnKsvCwfWYK+QY3NycIYu3YunWrEysA4ElPehJuu+02AMCJJ56IT3ziE+tUshqsfxwARUd/ELbuG5RmSCffd1BY56zQBYW+eWoPGANPEkBkxgLCxpPoQBAEQRAEsVKQYFEi72Xo7J+rtJkZY2ARd0s98Vj7XeBJpKeKxJFZ7iky4gMH41qIgBEatOAwPn4OCILYHEgpcdttt+HCCy9cNO073vEO3HPPPbj66qtx2WWX4YQTThj5Ojt2VMXexVBKYsdxRwEA2MAlgupEhcFxa2lJNnNMa82utdLMzEytdxGWxbiWG6CyrwfjWu7VROYZkHaN2GodbHtiKwmtBEFsIA5LsOj3+/jZn/1Z/O///b9XqjzrypZjtuHYp5wCJswa0269aePbgSpwgiDGkHe+851ot9u46KKLFk17zTXX4JprrlnWdfbunRvJKbHPzMwU9uybX9b11pvySkHjxLiWfVzLDVDZ14PllJtztizxdayQEshTLVbUTp9jgF0pjEQNgiDWmcO2sLj//vtXohwbgqTdRHvn1vUuBkEQxIpx44034t5778Utt9wyVivZEARBEKsIY4Md79q1waUCUOenByWHwGVRg6bEEQSxciwqWDz+8Y8fGKeUIqsDgiCIDcrNN9+Mu+66C7feeiuSJFnv4hAEQRDjQGBNUYO/5LKUqIoaKM73xYyyqEF9CIIgRmBRwWJ6ehrXX389HvOYx1Ti+v0+XvjCF65KwQiCIIjlc/fdd+OWW27BIx/5SLzsZS8DABx//PH48Ic/vM4lIwiCIMaayspGNQSiRlYKcxmFQkZ5n6aeEASBEQSLJz7xidi/fz9OPPHESly/39fLdhIEQRAbilNOOQXf+9731rsYBEEQxJHIUkQN5a0UVSdqcAZACxl5PzECCAdNPSGII4NFBYu3vOUtiKL6ZEmSbBqHmwRBEARBEARBrBGjCA4lUSPvdoB+18+kEDQ4JwsNgtiEDBUsvvrVr7r9hx56aGC64447buVKRBAEQRAEQRAEURI1eBQDIi3ifUEjH+BLwwkZRtQgHxoEMVYMFSze9ra3LZoBY4ysLAiCIAiCIAiCWFsWs9JwK55kg6ebME/QIOsMgthwDBUs/u7v/m6tykEQBEEQBEEQBLFyDFvxxIkZA1Y6YQxgorDMoBVOCGJdWNSHBUEQBEGsBmk/xf/4zP/CQw/theACQghwISAEg4giCMF1GOfggoNzfSwibsKECTNxkXD7QZyXjz6uxtES3QRBEEcYi4oZML4zciAfNNWEkXUGQawyJFgQBEEQ68KDP34Iv3fdHyLL8/UuCgA9xZHZLWNgYKbNyWri9L7CIAf4zOQ5MGaw53yTd+15xb9Fw+0hKweYcun2uILZFPvmWKEaX+x6++ZYFbt+jFtNrJK2fM1y2pr9Wpi7o8tIM/h70uGDc2VFokXD3dczrgurDSp78WHr73/5HtRGD7nHA7+Y+ue6fNnjjn8ErvvDawbmTxADGXmqiW+dYX8o3hKtniNQJXMtgGCRvAmCCCDBwkPlOZD19QHjZuurr1TBEARBrBQ7Z3bgF559IXY/sAdKKSgoKMW8fWU6znrfD5emAytdGt0ItOkklO4Y++cDpXy8DrKXt582jCv2oZRpgKoBfVBV6eBV0g3ohKuavUXzgnKhLltVTivdMQdz4cx7t5k3nmlsh11BVuncL3bs5z04j+AalfcrC/LjjLnvvo4iakiawTvDz1S130wlblAehUg0fjDGapaxLz1vtbHlwMWe+QFnDv4pVEJV8VNAOt/D3KE5TG6ZrC8kMZTKd14SI4eGD0hbFjQRHA7Ow5YlXWDIO92qPjuwbc5qdxdnOecZsUJJc1qmw0zZe/tzqPkOwE1Sm7EwjkB9ocOK5bzYd4WhPglxhEGChYfMMyDr6YoiqKQ9xdQppzCVhK1IzJaEDoIgiJFob2njN99/BR68b/9h5TNIMhh6jgobwaYv7/IKGuqqdI4J3rF9Anv3zhXpvCpeKVU/5sxsvDkc+F5Q9fHufBM/4Bp+8euusWPHBPbunTfx5XQ1woNLZDdscJw9Lr9GvbglTcHx8tk5M4k9u+cGpx2FdXoVz+ycwu49s+tz8cPkcMu+klOu/N9mVUQJmZmZwoGD3aFpjjhkjvn77oWanQcYhwKDMp1lpXTH2d3V4HsL7cnUgHB3avlgkWpaKRnWtUoBSnoCrFaiFuZjpJ2+EY5NuBU2vK2yypWb2uGlcVZeph713wfuPD89iusFefrnVcsAJ3DrsAXBkefGwsLvWrjbVDGLM8esum/7F8ye5/VNgnrWiiAo9k0+jIXHdVvGGHb/OEFnoe8+s3/vUN730/hxg9IPOrcmfJT98rndWCDNpCfElz6zEerrwitxo6Tlfj/Qu4afbsB9L4fPZwtID3ZQoWwxWGknLO24tn6ue+ZGyJMBkGmrmv4wIcGiDOMAF8PTBMNX0jR081IcsHyhw25J6CAIYnMjhECj3VjvYiyLrTunkCq+3sVYFtPbptDPxu/dEicxomQ8my5REiGKqexrSZzEAEiw8FFSorN7H7IF0xEqd9ZNZ7+2bx70x2s688aqDS4v5XXa/T//egqVzukQ1vzbLHf2rUBQCrP7bpqTa+8z195nznKiLIj7Qnm1M65K6QKLlNp9BPdUleMqxxjwHeh0Pf9elFli2MBO9qhhS+w85zmHzGVVOBm0hSeEjpB2NZlfk6usPPPNBiZ/6ulgYpH+9BIYv7fPRqA0CjXyaM2KCB1WfQuFDpllcPPiSOAgCGIMkFmGe//hThzaNwtwDsY5GGNgwuxzvQ/GwITQx5yH8eY8uP0BYUyf58KoniQI4ghE9lPM3n3faInLHXG/s+5GhHUaFoQxb7lQG1Y4pVScm3xsOmby8EbAWZGn/9duJeh006qQAFRHrm2ZAoEBgZDAgvTFvlJKx1U6q+afFWpcWH13wLbuGYDJiQbm5rsYnHroF1F/WiWbct+kroMfhrOglCURhAGTkw3MzfX8k5dIWVBAtS9VKVs4HdAN7Lrjmhvhnoki323b2ti/f0EHlafuFaaOKEUM+BxhfEXYkLI2vlYA8YU6KWvT6OelN7hMNWHOwmTgXL1h+ZS+e29TTqtqwiyTO7YgX2FRhwSLteSwhQ4AUIBUKAsd2QIHegtF/ry0DFMgdhAEQaw/6cE5/Ojv7kCerpPTTcZMG9o2jk2D2YQx05getP8jZ95rqGnA2sZtMELmjVLqNKrURlAlM2ivwWPTl+LLJstBfBDmNUj8Rqtr55UafMxPxoLzgjZvyXw0bDMy79Rq/sXprHJOkVTv/aByzweMQAa7qvQaLTWkau5/NY9R8jFpasL0R6jzA+GzyPv5MF/f4b1f4rmLln3RDJZ/7mEw9YjtOP4XnkUCpYdot3Hcc56BvffeD95ouk5eWSwAUH/fjBUAU9Js9SCcv63EDSiL+WVAMV7a1oXpbWuqhXSuC1XqHDoTEC+Mlad+oJS29Egzr1T6HqhQ0IBXV3n956L+KjKsfmYFoI8kljXhwalhOcppa3+G5TqrLl1dB7S+DiuTHpxFAl900o5EFauGwX53jHuilb1Z5X5IfX1dy5LrH/P9cUBEflg5zQrWDYuKRx7+M+gEBhmIGY3JBFkjtifUZTI4atTyrqyuAABob5nAnFzZOpcEi3EglBhrH3weRcUvUikMXIaJMYCJwGsxiRkEQawHyfQknvDCp2B+7wH93lba8kzZ0Qal93UHXUFJE2b2rYNMppQe2FDWUWdhluzyKYfJatii5+T6WJprZMrXoV13v7ZDXwz8eHW4q3ZLooHXoa/1HVH7SiiLC/b8MK0rF2w7pb7BU7QNqw3KalRZNPAzsptBIoB/LVlJU7lWrhD5N8DvNFR2fEqdjXIcK4ewILqWymn156zEm/Ww25SHlcEqtGjX6MoR60OmKUSSrEh5NgtRswEex2BRyWRbAdZhpD0s71T7uwyAMAe6HVr8FouOsxYLVLB1+04Eybz4enp79yPByvyuhqJK2xGTVyvioqLIZQom3Q1BaaeSPgirHFYq9+JcVklclK+2DvTCas5NGhF63b7+npTUfzLXwpTS7+hRvg/9uITixsjiB2PhMrIj9luC/tEoBNYPsnSst0zJ4LguPXPxsnK+vl+LP1jdOf3LGje63YPAzketaJ4kWGxG2JAfsa1scrsEkzupqARIzCAIYg1gUYSdZ50J9ZODqB9pGRhQGSWy4oWLs6KDazDA1Xm6Y20b0naEQ5ogb2TKih8ohBPdp9YN+snJJuZmfW/1XmNRwTOXNnm6elShOMk29MpDHSaNAhiz4dU0Lgqlz87Cq6D0uSYmm5if82eDH34dP5Lz09oko583MdHA/HzJNNm3FB7YkDf7QZt8cOPe5VN691WdoHppak2Ui6Pp6TYOHlxAhTpxaBgla5vRTqn7DQ06txq+ZbKJQ7OH6T1gHdoR247ehtmcmrplmODgSWwcBFZUVX1ofxzBb8QLM4eBs966jnZNWMUC36QrfgpeB9B2jKXuHLcaAt1epju4QYHL5QjjVG0cCz/bsLgRrrEY01vb2H+gpg7Y4DSnW+geWDD3vIagM24FDb3vOvfBVnrihwJkGqRbSfGjm8+DdfuFsDJIjPDCllNT6fLwsN/kWZqoIIwVz68/VcpPA2BqqoXZWc/p5pj0xbZMNXGov7J5Ui1+pDFIzHAN/QFihm/W5VcMNk+CIIhlwDgHiw7/VbQetdD2mSnku8dz1YfpmSn0x7DsW2emkI5huQGgPTOFeT6eZZ+cmUJnDO97a+cU5saw3KuNSBJEW6YAPn7dgObWNjpj2OnfsATTYxTqhEwlc+OfYYhA6kReDjBhTANYISyU81y0PJ74oRQgZWjhMaL40e8Y8Y2FgoErK7cCRyE0qJLwoCrnhUJDnbgdfh57b/1ProbeToBBJDHA07pMa9MPz3Cx+AGnhDuLJdRHK+hs0zJ+NRWxOgwcIULxY5MSgBUzvHS+mME5zARwEjIIgiAIgiAIYq0JOsqqFAaE7fiSv4nAwowhmdoCdPngzrcVDNy+ER2gjN89L20ddZYrgUgwPIdh4sfWrW0cqFsadCkE96302a0vFaBGE/Ctl0p/QOnzlax2GEM8OQV0yuZIw0QHNeRwkfMG3tyyz5W6cqhgw+MY6GZDrrd0SLAgFmdRMUMBMgeQA1nZMsOfeyYAlH6sBEEQBEEQBEGMxghWEUGnn5cFADa8bT+IodN/FiuvLavZ+uFlPw8VoYOVtsPKE4oEwTQ+vxxuUyfm+Hl5g7Tct7AIp28MEx5WlKH5saGHa0XUmgDmVtayjQQLDyUlVJYBfIkmMxsAmaa67AEs2CwpbsBDXjt/d1B6J2ZkehsUrxAz8n4C5Fkpr1X6oRMEQRAEQRDERmNRqwgLKxxQWj8kfmd62BSF9WC5QgfgiRio3hPrnwqyEB+UDIQImWW6j+FfnzEA3Pg/MhYlZeEhKPMGupdHKCRYeGTdHtIDB8GGeZMd9Myus8bRzXvI5noV8XF4sQrVsPqxVG1oQY3gUVI3lXMa5zu0gfPqb03IDh3ci363b84rrbttK2VTMTNuVjiBTaOVY+ZX3jDCil/B2HO8/XKa9V72rLIGcxExMFyV0wzb947TeQHZ7YYVs69ElxzJrfe9IQiCIAiCGFuU0s6bnS+IOjN8b+SeixqrCC/NkYLvXHUZVh3x5CTQpUHQcYcEizKcgyXx4ungdTD1QWlr/inbcQ/D6syiVCVNTb6w68CHaVKZIu/0oXxxQKnKsQ7DCGmWeY7zuD/IRK3KhnGdVGs9Mnj/EGeQ1mRtMQ/sS/LQvvocWu6JZY/4zC7dWC90BPGluXsVcaQsNpUFE3OcNWP0ellxbql8bEB4Zb8UxhZLP2C/8vnr0gKY788AyQQIgiAIgtiEBA4jy208207w/ESUHTcC1KleKWwbjXO6p5sAEiwMsrOAB758B2TfeGN1esIQUWIDseTFxoIOZLmTyMJj2/F0Dnl0fG0aL08smkZXIK1mjE43NRW8J/BYyiKQH+YFFQ5/fDHJE1CYf7oq7oONA3PxzC+FXxzvunEskKZ5eE/L93jQvR9wXOlsj5LnImF1HfhWK0HHClxA9eVasoippKkJq1iI1Pxe/DSqLj+TRg3JRzIGKUtOgGwZasLq0g0NW6Xf98P3/RgT5z1tRVbEIAiCIAhijQnaIwNECc6NdQQvpmuYNq92orjGZSaITQC1nA2MAXE7gWraW+KtE83cv6BTrxjAwCrL4fiVk2IMzM2PKqmndSO8w0Zq3Qi0KY9XromJBuY7qREDvHLWiQbwRreXgFKDrUPs1g9mpWklfrXuX7052UQ63/MCw89W/ex+HDNz0Py8eZEkHDb37qUub3gfPAsRAHpemyrOBYJVULbvmMT+/fMmulSIoJiD7jWr3V0kcEWU4p07J7Fnz1x9pC8ejJrhqJ385YgBJUFkx45J7N07oOzDrnMYZQwEksVEzPL55txtO7dgVg54TgiCIAiCWH8CMcK2Cb12lx28853KB30AgiBWGhIsDKzZxs5znoJ99z0IEccAjJ8F04nV+9KFVbYjeIuAFTdgBQ8ehBVb7qUZrRJMJpvoMm1nMXCkWxZLDsmiUJVusd9dZWXRwDNhY9bzMKBNruBbYvgii+eXwguzlh2trW1013tN7cU6s5UOqYLMMzBmX2iDlv3xzDoqLCdukIjh7VREndI+006ImPKsQ/xzh+oog57FsiXIIuVd7Pwhp0atJkTTW5e6cpsG3VM1OAoY8h0qFCtKq2BTf72SRZA5FK0YbGHjWWcRBEEQ68fs7Cyuv/56fOUrX8Hf//3fr3dxNj9LspJgwUCVjiZRgiDWGhIsPBjngOKQ/gg9oDscTFXD/B1T4VlhwwkdUPoUZUQNZcMkmJJG6FCLduuKVMzbwh33VRe8X3jB1R1pVunIaueUyjse8d6EH1rvS/NRg/tQk65uboWXbnYfA1elcwYxtL83emdwJV438/uNcQfzVXa9r+w+98ONGFVK68ePzKLWAlZsq0mvAJWlZvWWpbJIp38pWE3Gdez9Za2Kffu7sY2L+e4+sG6KpRWkPO2jpiyD4kbNcxH66ALJtpV5+AiCIIgNx/79+3HVVVdh165dSJIEJ510Eq677jps37594DlTU1N4z3veg4svvnjtCrrZCawjyqIEK9pdvpWEZx1NEMTGggQLD8YZmOCmYvOUVGMJYAJQnobAasL8kWjlTrXdflbtIDHbKdNCRtFRkyYs3GdmHyoHUwqq30fgKrQ0a2PgyHtQMbNS9CLpgvDwftmwwpKudL53GDU8J4qjcFgvk6r0UooaOaLVjNHp9FxHO/hOcll8nyN2bFXlJcqM+FEvcAwKd9shH4EJoUcPRipY8Wy6F78nLrCSwFBeT5spOSAPe3+WRr9TmQVUX+yRchvVsmMJ0t6ApHkPWrAgCIIgNiWMMVx66aU499xzAQA33ngjbrrpJlx//fXYtWsXrr766iD905/+dFx66aXrUVT9ksyXOHBRac8NTDTwcNHzR23jLebgkvNi+gZN3SCIsYYEC4+o2US8dXr0jtwaMsDo3BwobJ1u4cBBz5PPGFXGG2JKyDJIpltYYAueEDWAoCMvg30WHJut9AQqmYVi1Qjl0iJRnZBRWH5000PGSkF6S8x65fAsHpb6JLnrg4WNBMYBFhU+X5zVCYMzt7THQePCz4Nh69YJHBj2vGzQZ781maBPzrYIgiA2LVu3bnViBQA86UlPwm233QYAOPHEE/GJT3xinUoWwqIYSFoDYodYUtZNnRxqWVuO845VXdph5wEAg8wzLbQwBjBRDPCUHFwSBLF5IMFiM8AYLduzkgQrjKhSmJdM5t562gPwLVA4BzxvCGCDbS+Gix91wofSZakLt2EydVYg/R7TlkGBQ1YO8Ci09PAFBFhBIRQQQkHBTqda4rMY3PPSHQhWJAFUngPS+t9gxXkMQ7+KJTPSSNLQk0N8yy2CIAhiUyOlxG233YYLL7xw0bTveMc7cM899+Dqq6/GZZddhhNOOGFJ19qxY3LJ5Zs5euuSz1krVKXNFQobOye3LMt5/EZgZmZqvYuwLMa13ACVfT1Y6XKTYEEcWQyd12jxOuPW6VIwx1GnSaa2AF1e39l2nWxpwjzLBemlryPoKLPw2I4oILQCWqrwsW1rG/tX2qqlLPT4wsNQnxv+Z7N/8MSPUASJJyZLy4KNqlKoYLN48gFOOGsvPcQGyuzyJAF6KQiCIIjNzzvf+U60221cdNFFi6a95pprcM011yz7Wnv3zkHKUV9uujOxe/fssq+3nlDZ155xLTdAZV8PllNuztlQ4ZUEC2JzUPb67IcBKIbf2WDzweXObWRlcWEJ5S1bFDg/D/DEDhXuDzIlqJSDlcKXUq6asi3qcNMKDcbqgkNvA/Hh8Muo/W+M3/KgUasNzI3fi4cgCIJYGjfeeCPuvfde3HLLLeBj+L4iCILYSJBgQWxshlkvBDDPuVI4pSHoHG8UE8LliBxAvSATWHOoIo2/X0JmqXG25Qk5bguzXO0AMWclhBGCIAiC2ITcfPPNuOuuu3DrrbciSZL1Lg5BEMTYQ4IFsX6Yjrb2BZGjfgTfmx4QeHu2ccu0ihhX7OcFRhc6akSfqD1hplX4gskRdB8JgiAIYoW5++67ccstt+CRj3wkXvaylwEAjj/+eHz4wx9e55IRBEGMLyRYEKvPsKWnGAfjAuBxIUyURQjqRB8eNdYcPIo35Go4BEEQBDGunHLKKfje97633sUgCILYVJBgQawc5ekIAIoeMjcOLHnhQ8KIElF7ApgfwcEhQRAEQRAEQRAEccRAggWxNAJBojSFI1gT2/qSoOVWCYIgCIIgCIIgiKVDggVRz8BpHMa5pbOWEKg6YyQIgiAIgiAIgiCIw2PDrrU0OzuLt771rXjmM5+53kXZvFgxQkrt9DLPij8pocWJGIibQNICGm39l7R1WJRowYKTFQVBEARBEARBEASxsqyJhcX+/ftx1VVXYdeuXUiSBCeddBKuu+46bN++feA5U1NTeM973oOLL754LYq4uVnUWoIXviXAyVqCIAiCIAiCIAiCWHfWRLBgjOHSSy/FueeeCwC48cYbcdNNN+H666/Hrl27cPXVVwfpn/70p+PSSy9di6JtLipOL5neByucXnLhCRIkTBAEQRAEQRAEQRAbkzURLLZu3erECgB40pOehNtuuw0AcOKJJ+ITn/jEWhRjNOwUiTBw1JMXSWoFhEHBA+JHyENmKaAkOb0kCIIgCIIgCIIgNgVr7nRTSonbbrsNF1544aJp3/GOd+Cee+7B1VdfjcsuuwwnnHDCyNfZsWNy6WXLMmzbscWsxGk7+azYhR/E3b4LZt45qO4Oi2PlhKwm0SJxM1u2VtOOCTMzU+tdhGUxruUGqOzrwbiWmyAIgiAIgiDWgzUXLN75znei3W7joosuWjTtNddcg2uuuWZZ19m7dw5SjmoZoZmZmcLe/Z1lXW+9mZmZwu7ds+tdjGUxrmUf13IDVPb1YDnl5pwtS3wlCIIgCIIgiM3AmgoWN954I+69917ccsst4HzDLlBCEARBEARBEJuSLMuR5xKcM886mCAIYmOyZoLFzTffjLvuugu33norkiRZq8sSBEEQI3LHHXfg9ttvR7/fx5YtWyoOkQmCIIjxZs9P9uKi516KTqcLAOCca+GCcwjOwQU3YaV9ziFENVwIAS6Kc4UQEJzr/ISXhnNwwfQxN+eU8/Gu6841ae31JyebWFjou8/jCy6MMee2jRU73kzqMN7FeGkYD/Mr8q2G6yniZp+H1zFBbp+BYcuWJg4d6kJ5PvEBu6+073woMMagvFX99K4+QUFCqcKjnTJ+7bTPfQW4c4v87DVsnjpcAgqQSkFJBQWzVTp/JRWk0mna7QRzc10opXQeJl+3r8xVTLwtr90PymMWCHBlUyiu6c4v5V0Kky4PFZxXDgMUGo0YvV5m1hvQzznzngUGBmaEO/u9cs7NugTF98+D88w5/rELM88YY+BmkQO9RSkNM4s1crdvn1/GOBgDJqeamJvrYRj+I+Q9eqVnpxw2YAbCwOD6iEH5PP60R+Pkxz+2PrNlsiaCxd13341bbrkFj3zkI/Gyl70MAHD88cfjwx/+8FpcniAI4ohjOctJn3322Tj77LMBAFdccQXm5+cxMTGxVkUmCIIgVptM4ujmNNJownQ6i86n7QCrXAE5oPoSChK5UshKHWCdvuZ82ykuhanS+cSRC6vbq/H5V2f7wwI/g+VczFGN+0EV/qs8heXOd12qIkl9HoSm3Wjij/7nh9FoNlYszzURLE455RR873vfW4tLEQRBEDi85aT/7//9vzj55JNJrCAIgthkbD9mB17z1ovxH3d+H65nZ0f2zeiwssP/Xjigwl6lPY8BzI6ogwEcrmenbP5SBWvcudF86FF8mZt9qUUOKSWkklDSjr4r5FJvkyRCr5u6fJwBge2Mus9TXMslKIwNgv9ulFrWdELN6H8luNzBrRwX17DxjSRCP811MTkz981+DYXVht9ZL+wzSnHeOcyGmrDiuyxMOVx+jEEp8zWBgTH9JVoLBF1w5hcFcSyQplJbGriL+/twlgMAjLVJkWeddQorPUtF+VE8Z8z/bP79KVnQVMLshqHVjNHxnxcw93wWX4+1ECmKpMKHxX2/7jn2LWDMP6UUpN5xz6BSMkjvLEIAQIaWMCq4lnnW+yl8mPldBtjzmEJZ5il/jjCfGpGGlQPcCbZwpYtXZaVHnLBzsBXHMllzp5sEQRDE6rPc5aRvv/123H///bjyyivXopgEQRDEGsI5x0/9/Pk45bzTXNg4+bHYuXMSe/bMrXcxlsWR5DR8o0BlX3tWo9wkWBAEQWxyRl1O+v/8n/+D3/u938NP//RP4+qrr8brX//6oVNIyix3RZNxXu6Vyr72jGu5ASr7ejCu5V5txkmk8BnXchMEsXxIsCAIgtjkjLqc9LOe9Sw861nPWvZ1lruc9DiOIABU9vVgXMsNUNnXA1pOmiAIYvwhwYIgCGITQ8tJEwRBEARBEOMKCRYEQRCbFFpOmiAIgvBJ5zv46ic/j7TT10t4mmVEYZY2ZYKbrSiOBTPHAjwyccLGCXB7zAVYxMF5GG/3XbrIyz/i4CIqlhOtc8q4DjingTJcMtM5VDQeOoM4Y2GoZE2cUphNe+gcWAgcRvoOJ+E7qKyJ08Fe2sJLZbEMZ/kYpXzXGVV4liyO/dU3VCmN8xmpwuNh+XhhC1yid2B+9T7QKlJX9kW/x7r4wL8pq4Qtev5i12bhjszaQ4u4HEiwIAiC2ITQctIEQRBEGSYzNJtAJASUhOlQZ1A5oDK7SodZucB01vVqHabzLYvVDFa8bFx3rBnXq0L4x4wDjHF8nzNdJq9z6i9ZaRZYMFFqSJpS57cct1kpCx+VMOYF6TScc0gpi3ukN+FKGSUBwa4wUyyhsZofithI3DM9gVNe+UKwFbTqJcGCIAhiE0LLSRMEQRBloqkpnPqSZ2PPjx7SS3ZKaddadMuI2uOiAy/1co7SWgxIQGqHzjpMFlYFLkwLH3DhSsdZ0UMWVglKKRfujr0tfNFEysLggLFgcJeBwzc6gFlaM7BUQNgZZ14GA9PWdOIr17BRQ9Mi7Lv7y2r6Hfqgs2+CSlYI7nRvPc5yHvX5lvIoF0bV58uYKD5k8dGCILccqj8Qb+9RJa13j8rh4b/wWqW4yqA/8662MYxKVocaAUjVBy4969pzBmRUE5xMNCBzCUGCBUEQBEEQBEEQS6V11FFoshYAb2RcH3ipqqb6dftaRACgJKSbFqGFBQXoYymhbFqbRul4lVtRw4ggVjCx0yuU0vmasImJBubne4WQYFQGxk1HlbPic3AG12WyHWfuil7p0BaHyju2Cc1WKXOoiuCyJYE18/A6/VAKrWaCTrdfe8VFWYnOt//1snLY4M5usxmj28u8c8vqgr8TTmHx0dN+6qYsDJrGUPcFVdSQYBpMOXxqqonZ2V74gQ6XgdmooYdLvcjkZBNzc90l5LdUwWI5BVz8M27ZsRULYmV9ppFgQRAEQRAEQRBHIMG89GX4OfDPWKkuykARRSns3DmJPXvmVuhKI3DYQkGRwbadk8jWsuzLoeYZ2L5zErtHLPdG8ZVhmZqZQncMVzgCgC0zU+iNYdknZ6bQWeFyk2BBEARBEARBEMSGYJiIwqMITAiMI9bR6LjBON9wQgRxZDF+vxqCIAiCIAiCIAiCIDY9ZGHhMT+3gNmDs4jiCFEUQUQCfAyVUIIgCIIgCIIgCIIYd0iwMDyw60G84ZVvQZblQTjnHCISEIJDRBGiSEAIobdx5PatyBHF+k+HR4hiszVphBBemAk34ojLw50X6+uatasjIVxZOOfu2lwIdA9M4sCBBW1qxgBhvAoxwd2SUNxzTARoEy9uHBZZT72B1+XyGtDwvR+HcYPC/XMOB9+DtFSFp2m99Jb0wvWxtB6ql3iOcumMo6ch50xvaeLAwY6+v87XEDO+hgr30izwWGyO7frZ+rabPIr75PL07qv7Tvx4sEp65sKK65lv1x33Fuawb98Gn0c5AFv2YPktE1ddkkt/bv/7sekK30zle1rkWP5uB5/rP+c1YYwhz8O6hSAIgiAIgiCI4ZBgYWg1m3j88Y/E7Oy86xxL2+E1no+VUlCpguzlyJAhVd0iHMU5yqz3bMPLacZtKeIaP7/VkKGaBKs5o5SNt9RSsFb2qIUkiA3Otq3T+MPPfRBihT0nEwRBEARBEMRmhQQLQ3uyjWc+93zcf8+D3so4pYV/K+s9s6JD7a14BD8Mfrjescsz5VILIVJK/aeKkX4pc+SyiFMunYJUXnopkUsFwRkyM4LrVlayyyrBc7KslJfGEwRU6JVZKVWs1lQ5rzipZvlmVNaIHnIMaCsWez3fuiPYwrNKcIPozBvtZqVR7tAqwW55sCa2H+9ZNCgVXsvbMu84jiOkaXnUXLn7WF7HWFXug3/jqueoUmL/nNq8/Qzsylr+dbx8IsGR5xJrymF6H7dwwSBz6X9UR/EM+89yKQPlP7nBDQ9ClKqLr4poqkhczir4wRx9zE70FrpoT7VrPhVBEARBEARBEGVIsDA02g286HW/hAfv27/sPA7bHmDE060FiD1HQWH79jb27p2viiZ1ay6XO44mL9vZ9z+HlQOCbBjD0MKyIlt/2oMqXcded8fOSezdO1+Y8NupK/5xSbjwpzrozxymr52GMur9HTHhmi+ttYLM7Jwa27LvnJnEnt1e2VfIcfVqe8CemZnC7jFcnoogCIIgCIIg1gsSLDyEEGi0G+tdjGWxZdsUetl4Ljk0tWUS3d4aTP4Y8fYMmMBSQYjxdcrKBQfj4/m8cD6+ZScIgiAIgiAIYnTGs7dFEARBEARBEARBEMSmhiwsCIIgCIIgCOIIIc9zdLtdcMa1fy9ufHlxNraWowRBbF5IsCAIgiDWBaUUvvL3X8euXQ+j0UiQNBKzbaDZTJA0EzSSBEmziUYjRhRFekrQKvsbIQiC2Kzs27sfl1/0WuzfewAiiiCEQBQJty8ijjiKIWKBOIoQRRGiOEYcR4jiCLHZjxOznyTmOEaS6Ho8iWNEcYSkYeLiGEkj0WFJ7I6TJDF5RE4wGeiHjCCIIxYSLAz9g3P4h1v+HHmagXEOJnixFULvRyYs0r4LWCTABAcXwu0zIfSx0Ol5FIFzoc/10vFIgEdChwkdL+LInBeNbYWtzAoobtUR6yBUqvBYKSipt50I6B+aN8404ZyC+sd2ZY4wrprOHR/hFE5ZVbEqhrd0RZ5mkFlWOmuE+1aTpNbnxyhfwQjfE32Xm5v7f/wALv9/3jRyetuwDhrPUYTINoCTGLH5S/zGc8MIH0YQie02jkOhpNlAo5Gg0TDbZgONZgPNZgNJkkBEYhXvBkEQxOrTbDZw2pmn4vv/eS8YY8iyDHmWI89zZFmGXjdHls3q4zRDlufIs8zbL6+OdvgwxiAioet4ISDiSG8jYQQTI5xEAq1WA0oBURxBRDpdFMfm3eC9FyItouh4Lw+Xl4k3+caJea9EWmDR4kyxr8WWxKWPYi3wEASx+pBgYRARx7ZjtyDt9nQfT6riT+VQMoPs6ePcdL7DNLYDvkIFYtDiiFWbBQNjXG+5v8/BBce9EYfMZGlJU1Q6qzqcecKBF26XbQwEBxTLn/rn1O1jBT//4eIte2r34S97ysIlSyviR5De2/fifhAJZFnurSOrs/bvvQ0vCwblZWQx4Pvyl8v0v6Nq2g1078eJkfSQlRFNpo7diZNe/GwSYTyOP/E4/L9/8SH84//9FzBw5HmGNNWNZ7+hnKYZssz8mf00zZClqdnq485CB7OHZov0Ns7sB7+5ZcA5d43YKIqQNGIwxiE4d41tYURr3dAWxeilCY9MOBcCkYggIo7IjWxGiCIOIWyj2uRpGu1xFIF71xHCP7d0bOK5Odce2ziZd7Fv33ywhLO2XgHcktF29SXGwK2Izorlne2xNSe39eS4Cu4EcSTQnmjjt9/26/jOt78PJRWkkpC5hFQKSuaQUun9XEIqqetNxrz2B3PihsxzZJkWMfIsRy5zZKkROPJCBMkziTzPkJmtzCWyPNdbm08ukWcZ8lynCd8DuRNW5mbn0eunTmTReehr2/LIXCLP88Ou8xeDMQYuuBZXbD0cWK0YAcYIJM1mDKlQqY9FUK8LnWcpTJjB0uL94Z3nvytEXRn0u0aHReH1IrFIeTg6kzE6nS6gFKRUpu2pdH/J6wNI6fUhTH/C9Ze8tq9y50q3r/saXp5Smi5G9VrumqZ/4q7h8iiutXVrG4dm9fQnLrhz3C64HoTmnIObqVD2j5WOdbwI0jLOTR40jWotIMHCwJsJjj37sVjYdyDsCJofoV6is/jRoNJR1JWiFjDMD1DC21dQuXI/dvjh0vxoc/0jk2URRKIULqHyHDItfqSwLxTXp2aub+2WCC3tc5MOABivS8dcX425Dv3g/FzH3iZ0gkBdeq/xyxlULoP+PFDu0MN9D6oUBvs1eTt+v74a5okLS8mnJD4wJRFzFPcIJRjAwPV5xQ0o0tYZJtRnop+90oWYlySwc2CV04cUcgmM/N5XQ44Wy4t5SeoTMcZWvRGyJEYsSmM6gsxyiJiqXZ/Hn/ZYtCemoaAgpTSicA5I6Ia0qyd1nSmVhJQAlHR1ooR04rF0DR0rMEvAbPPcihu5ETR0o7efhmJIeWsFj7SfOpEkTTNwptDt9iGl0g1nKSGlRJal6Pf67lhK2yGwx6oIk7KaTsr1/lpWjEDAcEIH3DvA/cGGAWXBxF/amgsOpZSr8Vy+9h1VEqf9vIvy6JR++Yoq0isfvHJ6n6e8jLZ7rw04tufEEUeWyVDQKZfVewnUhfv3xx77QnxdOAvS19yfimBfPafZjNHvZ8V1yp+59p7432e5nADTDY9QAPPuybD7wrzrFeXx7pFJc9oZp+Dspz0VRMiW6Sk89vGPGSltuZPodzC1oIFKR9Z1XGs6sZV6z4ojUrr6XckcMjd1e67rb2XSbplqYP+BBd0el7atoLx2QfGsSGnq5UwizXMomWvRJMtdXJbmrv7WYkdu3hdGhMkLkSU3Ikye5U6QsWkK4SUP0tr9tN9HlvWRprl51+n6Ppf2XVbzPpDh/dpQbR8igDEWih3MDCpbgYOxiiBiBS/OSkKKCY/jSA+Meteou+6wY79jEbwL65PA68jVxrPqCZV8H/eEk/Ga11+2ogMX1HI2MBFh2xMfj+yh/V6nttphdtYGNtCJGvpYT38w+1J6I+e+BUbZusE0thUAW/nbBqvXUHd5mMa4P0rfbsXoLPSN+K3DGGOAUmEDhQFQDGAqeNlb1dxrr5i0dZ1o5aL1riy60/bzmX1mXiRVNaK4f3HMkaaHb2K4eMe4+jJbYm5BdBwLXW7/htX9OIMw7wZ7jeCiWJVaISyzDfMalS689jo1+TGg1YzR6aYD7k9d0BLiKkmXmG9dQi9Z0ojQ75Wns6w0y6xkh5zW3rYFqVi5ynuz0Gw2sH3ntjW7Xjg6g2IUKBidAaCKhvmg0aTt2yexd+9c0Ij8/9u7u9A4yjaM49duNmmTTd422ZSqbV6rRgNFgqnJqhgaEOOBCLUiFBEEQaFgaRBBo6DSYNoKllBoqXjgQVF60viBUKgnRqu1SWorEgiYSkkblcQ2sV+bavbjPdhkMrO72SR9k3lmkv/vJDuzz8xeO9m9Z/ae2SS70Tp1M3tM5lVuU/OnDk4TyUT6zORE+uB6YvIMYzwRV/zfuDU9EY8rGU9oIj55ZnPywDmRSE6e9UxaB+HJRPr2yuJCxWL/Oh433RhPWvuWlO3quqT9jJZ9+02OSWbul2xnvqxGfcbztLZ30r4u221rHzf9mIWFIeuDs2M75tj+c71/usxMvyYCCsy4/PTkDOtPpZQKTO3+p8cn40FNxBO25ZRx21qJrUQ6Hy9lG5y9fHpPbL9Kb/7bI+DIrMnfSTAQsH0gtb2eU/kfYyqv4zlk5s58vvbnaG1T2xaYx+P/d8N6HfqkVitXrhRujdeumFqzpkx//XUta76jrtje29YZd02/3qxxma9D27jp19j069de/61xuep55n4llW7uVJSHNXr5hnXlSkpyNOQlWU2gqc8ESevzQUrxZFypRCrdFIknNDHZMEnX9/T+YqppkohPNVkmr1hJJJRMpSb3DemfmY3y+OT+wTkvoaKiAv3zT3zGRmdAzsZl5jhnUzOjEZ3RsMzV5FZAtqv8Jsc7ruwLOtY9vd6gwqVFunZ1XNaJ38n9XGpyWzjmJ6ZPjFjjkimroTQ1L2lbRypzndZP+4mWqYaUc2zmstZrxX5iI+nYOaS3kWNKWffLGpVv2rZIwDnpeAzbIpN7B2Uf7DqXGhm+pPHYuErCJdmPd4toWNgEQyEFVyz+Ti2rQ5o57fiAn2t6cp5kHehFIiW6fPnG9AfZW965uL9TilSW6vKl67MPNLq/zH7wyspSXZopdyDrxv83TsrdDMk5bPZxFTPs8P0g73b3sHIfb/OlZCEPvisiZUok/fkd5pkO+hfDTB8k0j/s91kLOJezfYCujIQX5P1v4kxlJLLwtWumK9H+v5VmrzMSSTfn5jA0x5j8g2b/XTjvzz6Eyt2AkaTKSKkmElyuvRw4rhTyqDVryrSi2NxxQGbDOKvBYm9U2uqvvXY5z7Y7r3xycFwVpZzjHKftZhiTtcw8f7+z7euymqwZzS7nfdZCzvmZjeeM5pd9jDLnZzWSp+/PVXdzP/3Zr7zINS/nunItl3P9uRZNz6ysLNX1Gwt7YpGGhQH53tTzXtfkz1BxsYIrFvus8+IoKCpSoLDQdIx5CxYWKhDiLeQ2Lx+MAMi2kB8kSsIlKo4t/B/9c0NpWVjjN/35dZ//rCrTP/+aTjF/ERrFgOVWG/YlJcUqLvbnZ4zZeLnR5deTIiXhEt2ILWzdpe0MAAAAAAA8h4YFAAAAAADwHBoWAAAAAADAc2hYAAAAAAAAz1myfzEwGLy1P55yq8t5Adnd59fcEtlNmG9uvz1P6q6/+DW7X3NLZDdhqddd6dYy+/F5TiG7+/yaWyK7CQtddwMpE//bCwAAAAAAIA++EgIAAAAAADyHhgUAAAAAAPAcGhYAAAAAAMBzaFgAAAAAAADPoWEBAAAAAAA8h4YFAAAAAADwHBoWAAAAAADAc2hYAAAAAAAAz6FhAQAAAAAAPIeGBQAAAAAA8JyQ6QBedO7cOR0+fFiJREKJREJ79uxRIBAwHWvO3njjDYVCIbW3t5uOMmdDQ0N6+eWX1dDQoEgkopaWFtOR5uXgwYMaGxtTMBjUW2+9ZTrOnPzyyy86evSoJKmrq0uHDx/Whg0bzIaao5GREb377ruqrKzU9evXtWfPHq1cudJ0rDk5f/689u3bp8rKSq1atUqvvvqq6UieQN11H3XXfdRdM6i7uVF33UfddR9114yFrLvLtmExNjam119/XRcuXFBRUZHuvPNOtbW1qaKiQtXV1Wpra5Mk7dy5U7FYTOFw2HDitHy5JemTTz5RY2OjTp06ZThpttmyh8NhTUxMqKqqynDSbPmyf/PNNxoYGNDatWsViURMR3XIl7u2tla1tbUaHR3VyMiI54p3vuy//vqrotGoXnzxRb333nsaGhpSdXW16ciWfNm//fZbPf3003r88cfV3t6uvr4+3X///aYju4K66z7qrvuou2ZQd3Oj7rqPuus+6q4ZbtXdZfuVkEAgoJdeeknHjx/XV199paqqKn3wwQfW/adOndJrr72m8vJyFRcXG0zqlC93X1+fxsfHVVdXZzhlbvmyr1u3TkePHtXu3bvV1dWloaEhw2md8mUfGBhQdXW13nzzTV29elWnT582nHbabK9zSTpy5Ii2bdtmKOHM8mWvra3VyZMn1dLSotHRUd19992G0zrly75lyxb9+OOP2rt3r4aHh/X7778bTuse6q77qLvuo+6aQd3NjbrrPuqu+6i7ZrhVd5dtw2L16tV66KGHrOkHHnhAf/zxhzX98MMPa9++fQqFQurv7zcRMad8ubu6ujQ4OKiOjg6dOXPGU4VEyp996hLEQCCgiooK3bhxw0jGmeTLvm7dOpWXl0uSysvLPZV9ttf5xMSETp48qaamJhPx8sqXvbOzU9u2bdP+/fu1ceNGnThxwlTMnPJlLy8v19tvv63W1laFw2FPdcoXG3XXfdRd91F3zaDu5kbddR91133UXTPcqrvL9ishdslkUkeOHNFjjz0mSeru7tbx48eVSqUUj8d17733Gk6YW2buHTt2SEp/P+7QoUOqr683GS+vXNv8iy++UEFBgcLhsGpqagwnnFlm9ubmZu3atUt79+7VlStX9MILLxhOmFtmbkk6duyYnnjiCQWD3u5dZmbfvHmzOjo69MMPP2hkZERbt241nHBmmdmHh4e1f/9+SVJNTY3uuecek/GMoe66j7rrPuquGdTd3Ki77qPuuo+6a8Zi1t1AKpVKLUhKH9u1a5eGh4d14MABz7+Q7fyaWyK7CX7NLZF9KfLrdvFrbonsJvg1t0T2pciv28WvuSWym+DX3BLZZ7Lsr7B4//33NTg4qA8//NBXLwy/5pbIboJfc0tkX4r8ul38mlsiuwl+zS2RfSny63bxa26J7Cb4NbdE9nyWdcOio6NDfX19+uijj1RUVGQ6zpz5NbdEdhP8mlsi+1Lk1+3i19wS2U3wa26J7EuRX7eLX3NLZDfBr7klss9m2X4lZGBgQE899ZQ2bNhg/T/b9evX6+DBg4aT5efX3BLZTfBrbonsS5Fft4tfc0tkN8GvuSWyL0V+3S5+zS2R3QS/5pbIPhfLtmEBAAAAAAC8y19fkAEAAAAAAMsCDQsAAAAAAOA5NCwAAAAAAIDn0LAAAAAAAACeQ8MCAAAAAAB4Dg0LAAAAAADgOTQsgFnU1dXp4sWLOe/77LPP9Nxzz824bHd3tzZv3rxY0QBgSaLuAoC7qLvwKhoWwCzOnj2rqqqqOY2tqanR4ODgIicCgKWNugsA7qLuwqtoWAAAAAAAAM+hYYFlq7OzU9u3b7emm5ub1dLSYk03NTWpv7/f0UUeGxvT9u3btWnTJj377LO6cOGCNf7555+XJG3ZskV1dXU6duyYdd/HH3+sRx55RI2Njers7FzspwYAnkTdBQB3UXfhdzQssGxFo1GdPn1ayWRSIyMjisfjOnPmjCTp4sWLisViqqmpcSzT1tamFStW6Pvvv9fu3bsdxfjTTz+VJH355Zc6e/asnnzySUnSpUuXdO3aNX333Xdqb29XW1ubrly54tKzBADvoO4CgLuou/A7GhZYtqqqqhQOh9Xf36/e3l41NjZq7dq1+u2339TT06MHH3xQweD0WySRSOjrr7/Wzp07VVJSovvuu09bt26d9XFCoZBeeeUVFRYWqqmpSSUlJTp//vxiPjUA8CTqLgC4i7oLvwuZDgCY1NDQoJ6eHg0ODqqhoUFlZWXq7e3Vzz//rGg06hg7OjqqeDyu22+/3Zp3xx13zPoYq1evVig0/VYrLi5WLBZbuCcBAD5C3QUAd1F34WdcYYFlLRqNqru7Wz/99JOi0aii0ah6e3vV09OjhoYGx9iKigqFQiH9+eef1jz7bQDA7Ki7AOAu6i78jIYFlrWGhgZ1d3fr5s2buu2221RfX68TJ07o77//1saNGx1jCwoK1NzcrAMHDmh8fFznzp3T559/7hhTWVk54/+wBgBQdwHAbdRd+BkNCyxrd911l8LhsOrr6yVJpaWlWr9+vTZt2qSCgoKs8e+8845isZgeffRRtba26plnnnHcv2PHDrW2tqq+vt7xV5MBAGnUXQBwF3UXfhZIpVIp0yEAAAAAAADsuMICAAAAAAB4Dg0LAAAAAADgOTQsAAAAAACA59CwAAAAAAAAnkPDAgAAAAAAeA4NCwAAAAAA4Dk0LAAAAAAAgOfQsAAAAAAAAJ5DwwIAAAAAAHjO/wALXHAw45X17gAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 1080x288 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Files already downloaded and verified\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|███████████████████████████████████████████| 35/35 [00:14<00:00,  2.39it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "coord check plot saved to coord_checks/sp_cnn_adam_lr0.001_nseeds5_bn0_coord.png\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x288 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Files already downloaded and verified\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|███████████████████████████████████████████| 35/35 [00:25<00:00,  1.36it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "coord check plot saved to coord_checks/μp_cnn_adam_lr0.001_nseeds5_bn0_coord.png\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x288 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Files already downloaded and verified\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|███████████████████████████████████████████| 35/35 [00:14<00:00,  2.36it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "coord check plot saved to coord_checks/sp_cnn_adam_lr0.001_nseeds5_bn1_coord.png\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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rWLd2PRe854LMtHXr1g/ZzvEnLMy7A9zc3LzP/R9u+9MkZOq0RhwOB0uXLuPc8+xgj2marFi+gnPOGzn4M2v2TP78p78QDofxeu2K7vJlyzFNk1mpu/GzZs/kn3fcSfPu5kzThaadTXS0dzB7zqwRtz1avb297NmzJ9OB5u5du+nv66d2P5of7W+TkPr6OlavWpM3f/WqNSiKQn193X6U/sA1TGqgYVIDF1/6Pq77/fU88fgTnHfBuUyd1siz/34Wr9eTN2xsrqnTGnnm6WcoKSnOBDEGq2+oZ93a/O/64NcAHo+Hk09ZwsmnLOGyD36AT/zHFaxetZrFJy3e7yYhw9m6dSule+l0VwghxLFDAhZCCCH472/9D6efcRpTp00lGCxgz549/O2vt+Pz+5h/EB0kvuPUU3jn2e/kt7/6HTt37GTRiSdQUlJCa2sr/37qGfx+f96wprku/4+P8sJzL/Daa68zKydlfDBN0/jUZ67g+9f+YMSmKWk1tTU89eTTvPbq6zRMqufN19/i1Vdey1vm4kvfx89/+gumTZ/GohNPYO2adTz7zHNDtvPsM8+xcsUqSkqKefbfz7Fxwyb8fh9jaX+ahHi9Xs674Fz+9tfbKS4qoqKygnvvuY9YPM55F5ybWe43v/wtYPcTAXDGmafzr3/8i1/+/Nd87BOX098f4obrbuS000/NjNKx4LgFNE6dwq//9zd89vNXYlnwp+v+xIyZMzLZOmCPWhGNRDLD525N9Z1RVV2FxzNypdblcvG7X/+ez3z2U1gW3HjDTTRMasg0BxmN/W0ScskHLuGr13yNv9x4M+eefw6trW3ceMNNnHHm6XkZKodDc/Mennz8SRafdCKlpaV0dXWxZs1aGhunAHbGwoP3P8QPv/djPvaJy6muqaanu5eVK1ZSV1fLklOWcOFFF/L0k0/z0x/9jA9++DJKy0rp7Ojk7beWsujERcyaPZP3XXwRX//KN/nbrbdz1tnvZOfOJu6/74G8stx7930UlxQzZcpknC4XLzz/AqqqUl1jN0/Z3yYhd9z+D6bPmEZ1TQ3JRIKXX3qFp554miuv+syhO4BCCCEmLAlYCCGE4IRFx/P8c89zx+3/IBwOEywMMmfuHL70tS9RECw4qG1/9etfZv6CeTz5+FM89MDDmKZJRWUFJy5exEXve8+I6xUEC/jghz84ZISO4Sw8/jhOWHTCkCYZg513/rns2L6D//vN7zEMgxMXL+Ijl384r1PBk09Zwqc+cwX33nMft/31b8yaPZNPXPFxfvfr/8ss86GPfJD2tnZ+8sOfous6p51+Ku9574U8NyiwMd5d8elPous6v/+/PzIQGqBxaiM//PEPKE41FwFob89vYuLxePjhT3/IjdffyDe//p+4nC5OOfVkPn1lNvCkqirXfv873HjDn7n2298FFE5YdDyf/fyVec0q/vC7P+RlLXzlmq8B8JP/96O9jiRSVFzEueefw//7yc/p6upm1uxZfO2bXzngvjdGY/LkSXzne9/m73+7g0cefhSv18spp57Cpz7zycO2zzS320Vz8x7+9//9it7eXgoKAiw6cRFXpPbtdDr56c9/zO233cHvfvN7+nr7KAgWMH36NI5fZI8CU1RUyC9+9XP+duvt/OzHPyccDlNUVMTsubMzQ4pOnTaVr3/ra9x+6+3cd8/9TJkymU9f+Sl++qOfZcri9Xp54L4HaN69B8uyqK2r5b/+5z+HHdZ4NCLhCDdcdyM93T04nU5q62r41n99g1NOPeXgDpoQQoijgrJ+89r9bzgrhBBCCCGEEEIIcRipY10AIYQQQgghhBBCiMEkYCGEEEIIIYQQQohxRwIWQgghhBBCCCGEGHckYCGEEEIIIYQQQohxRwIWQgghhBBCCCGEGHckYCGEEEIIIYQQQohxRwIWQgghhBBCCCGEGHckYCGEEEIIIYQQQohxRwIWQgghhBBCCCGEGHckYCGEEEIIIYQQQohxRwIWQgghhBBCCCGEGHckYCGEEEIIIYQQQohxRwIWQgghhBBCCCGEGHckYCGEEEIIIYQQQohxRwIWQgghhBBCCCGEGHckYCGEEEIIIYQQQohxRwIWQgghhBBCCCGEGHckYCGEEEIIIYQQQohxRwIWQgghhBBCCCGEGHckYCGEEEIIIYQQQohxRwIWQgghhBBCCCGEGHckYCGEEEIIIYQQQohxRwIWQgghhBBCCCGEGHckYCGEEEIIIYQQQohxRwIWQgghhBBCCCGEGHckYCGEEEIIIYQQQohxRwIWQgghhBBCCCGEGHckYCGEEEIIIYQQQohxRwIWQgghhBBCCCGEGHckYCGEEEIIIYQQQohxRwIWQgghhBBCCCGEGHckYCGEEEIIIYQQQohxRwIWQgghhBBCCCGEGHckYCGEEEIIIYQQQohxRwIWQgghhBBCCCGEGHckYCGEEEIIIYQQQohxRwIWQgghhBBCCCGEGHckYCGEEEIIIYQQQohxRwIWQgzjM5+8kuXLVuz3en/4vz/y+Su/wPsuvIR/P/Xvw1AyIYQ4Oh3IeXf3rt38+Ic/5T8+/HE++sH/4Hvf+T67du0+TCUUQoijy4Gcd/t6+/jW1/+Lyz/0MT5y2Uf55tf+k7Vr1h2mEgohAQshDqnJkyfzuauvorFxylgXRQghjnoDAwMsPulErr/pj9x2x1+ZNn0aP/nhT8e6WEIIcdRye9x86avX8Ld/3Mod//o77//AJfz4Bz/BMIyxLpo4SknAQohBfv2/v6G9vYMf/+AnfPDSD3PPXfeOet0LL7qABcctwOF0HsYSCiHE0eVAz7vTZ0znnHPfTSAQQNd13nfJe9m9azd9fX2HucRCCDGxHeh51+l0Ultbg6qqWJaFqqmEQiH6+/sPc4nFsUof6wIIMd587ZtfZe2atXzxy1/kuIULAPjIZR8dcfn3X/Z+PvDB9x+p4gkhxFHnUJ1316xaQ1FREQUFBYetrEIIcTQ42PPuNV/4Mrt37SaZTHLOue+msLDwcBdZHKMkYCHEKPzjrjvGughCCHFM2d/zbkdHBzdcfyOfuvKKw1QiIYQ4uu3Peff31/2OeDzOq6+8RjKZPIylEsc6aRIihBBCiAmtt7eX7/3P97ngwvM548zTx7o4QghxTHA6nZxx5uncc9e9bNu6bayLI45SkmEhxHAUJe/lBy/98IiLfuBD7+eDH7rscJdICCGObgd43g31h/ju/3yfxUsW88EPy7lYCCFG7RBd7yaTSVpaWpk8ZfIhLZ4QIAELIYZVWFhIS0sLYLfp+9e9/xzVeolEAsuysCyLpGEQj8fRdR1VlWQmIYTYmwM574bDYb537Q+YNXsWn7ji44e5hEIIcXQ5kPPu+vUbMA2DadOnYZomDz/4CL09PUyfMf0wl1Ycq5T1m9daY10IIcab1159nRtvuIlIOMwHP/xBLnn/xaNa79v/+T+sXrUmb9pP/t+PmDd/3mEopRBCHD0O5Lz776ef4Xe//j9cLhdKzp3CP97we8rKyw5jaYUQYuI7kPPu6lWrufGGP9Pa0oKm6TRMauDyj32UufPmHP4Ci2OSBCyEEEIIIYQQQggx7kieuhBCCCGEEEIIIcYdCVgIIYQQQgghhBBi3JGAhRBCCCGEEEIIIcYdCVgIIYQQQgghhBBi3DkqhzV1uzwkjeRYF0MIIQ6arulEY5GxLsY+yXlXCHG0mCjnXZBzrxDi6LC38+5RF7Bwuzw01E4a62IIIcQhs2PX9nF98SznXSHE0Wa8n3dBzr1CiKPLSOfdoy5gkY4yd3cPYJr7N2JrSYmfzs7Q4SjWYSdlP/ImarlByj4WDqTcqqpQVOQb93fP5Lw78UzUsk/UcoOUfSwczeddOPBz70T9PEHKPhYmarlByj4WDsd596gLWKSZprXfF87p9SYqKfuRN1HLDVL2sTBRyz1act6dWCZq2SdquUHKPhYmarn3x4GceyfycZGyH3kTtdwgZR8Lh7rcR23AQgghhBBCiPFu7Zq1PP3Uv0kkkvh8Pj73hc+OdZGEEGLckICFEEIIIYQQh1FfXx+/+eVvadnTgu5wUFVdxdXXfJ5gMMjsObOZPWc2AD/+4U+JRCJ4PJ4xLrEQQowPMqypEEIIIYQQh5GiKFz6gUu4/qbr+P11v6OqqpJbb7ktb5k333iLurpaCVYIIUQOybDI8cZTb7FrewuWBZZpYpoWlmVhWhaWYWJZqdemiWVamJb9aFlW6rVlr5d6baVfp5e1yG7HzNlWZrv2cpjZ11Z6vfR2LQsy87Lb0R0ahmGhqgqqpqKqqX+5z1UFTdMy0zRdRVW1zDqKqqKpGqqmpNbV0LSc7Wiq/VrT7PU1FUVT7W2qKqqeWl/X0NL70lU0VUXTNRRNQ9O01H5Ty2saSjxGZ1sfRjKJYZiYhoGRNDGTBknDwEyaGIaBaZgYySSmYdrTTCO1vL1O+tHIvLYy65lm6p+R+jxyp+fMt0wrf/mc15nPMvWZqYqCYZooKCgK2P8DRU29RkFRUv+w59uvc5/nrpN6nlpGsf9nL5teJrVNMvPJ7sPecGbd3G0MXtbtdhCNJgCwLAAr85g7jSEPlv1fpmmaZU+3rJxpg7eTu33s7zBDn2c3n1lw0DZsTqdGPG7Y7wdl+Ef7UGaOqX0M08/JO9ZkPp+c5ck5vih5r7Of0eDpSv5+B2133okzmLxgJkIIIQ4/y7KIRmNYlpV3bh8rgUCAefPnZV7PmDGdxx59PPP630/9m9bWNj5xxcfHonhCCHHQOto7adm9i4rqmkN63pWARcr6t9bxq2//fsz2n1NNylZ6MpWw4V6nK0vp51b6P6ycyqeVUwG0rExVUAhxhD153zP86u5f4PK4xrooQghxVFu3egO//dkf6e7q4Za7rsfn9411kfKYpsljjz7O4pMWA/Dm629y+213cOLiRVz3++u5/OMfJRgMjnp7JSX+/S5DWVlgv9cZL6TsR95ELTdI2Q83y7JY+sZK/vaXu3j+6VcoKAzw8HN/J1Cw/+elkUjAImXmoll84T8/wZo3N6CqKoqqoCp2VoECKJqdoZC+s5rOOlBUBU21sxMUxV5GVVOZByqZ6Zqupe54a/Y6moKqaZltgT2kC5C6q56+o07qDq29jKLad9HVzHJ2gCMY9NDTE7HvdKcyM0ysTLaGZdrRjHQWh5m0MMwEZsLOSDCSBolkOjPBznSwDJNkMpXZYGazGAwjJxvBNDDMnEwTw8xmmqSnpTJJTCMnY8UyM1kkDl3DMO3sEAXFft+qkjmemUfN/kyUQVkkiqag5GSKZDJItNSx1nVUTUHXdTv7I/Uvnf2havZ8LfWZarqKomipaZq933SWiKqip6YVFfvo7gnnfY/Sx9nKyUSwMpEkO5wEqeCRRSZjJpNpYFqpRe3jlF4vk01jr509hiZg2ceWzDpWJjtESe3fNMxUeex5fr+HgYFo6vuWyhggmykAoKW+a/kZC2omgqZmMjdyMkPSB2Jwlkh6q+np6YBbeh+AomYWyt8f2WAdChQFvXR3D2SPmQVG6vtkf//taZZppmanjguAaWJagJn+vIz0JjLbS2c0WZnjj3280ysN6o09890n+xlmpufse8qMOjRNQwghxOHR093LDb/9C08/+ixen5fLLr+I3FtC48WN19+E2+3mwosuAODEk07klpNOPODtdXaG9qtX/rKyAO3t/Qe8v7EkZT/yJmq5Qcp+OEWjUZ5+5FnuvfMhdmzdicfr5qRTT+ScC8+gq2uAaGz05yRVVfYaeJWARY5zPvwuFp590lgX44CM9y/13kzUspeVBfBPwHLDxD3mMHHLPlHLLYQQ451hGNz3z4e49cY7iEYivOPMJZx1zumcePJ83F73WBcvz81/voXm5mau/f53MjeshBBiomjetYd7//kgTzz0b8IDYSqrK3jfBy/k+BMXUFVTyey5U4jFD+0+JWAhhBBCCCEmpOVvreR3P7+enduamDJtMu+59Fwap09hcuMkGiZXjKtA8d9uvZ0tm7fw3e9fi8PhGOviCCHEqJimyZuvvs09dzzI0jeWoygKs+fPZMlpi5k2YwpVNZWUlpXgcrsoCB76G3QSsBBCCCGEEBNKe2s71/36z7zw75cpCAa4/FMfYu6CWdRPrqO8smzcZS/s3LGTu+68m5qaar71jf8EoKKigm9f+99jXDIhhBheqD/EI/c/yQP/epjWPW0ECvycec5pnPSORVRUllNTV01hURBNP7xNniVgIcR+yozUYhh2Pwk5I0YIIYQQ4vCJx+L887Z7uPO2e4jH4px93pmceuYSKqrLqZ9cj8czvpqApNU31PPgo/ePdTGEEGKftmzaxj13PMCzT75APBanYXIdH/r4+5m7YBZllWVUVlXgD/iG1H9MwyTU1Y+RNA5pEEMCFinJgQirHn6GWCgy8kLpvkOUnOd5M/ax3r4n2pNTn/3g4RyHrJfzdKeq2Munh1jMrUTnDW2ZOyRm/tCOQ+fZnV9mO0JUhm4/Z8jMYfeR7rFRzS+PkjMt5nEyMBBNdViZ6oAy9/neplnZjkVHmjbsOjkdXqY7Kh152fTz/A9l23Afz76OLzkdTQ45ZtnhUAcf17zX6c+FQfsacZ30cmpm+f5hhjUd+r2yhv2e530vc1+kO61kpPlDngwaxnSE9XLmW0BHeljTI234P8hRG6gpw7dg9iEqjBBCHFssy+K1l97gj7+6iT27Wpgxexrvef/5lFeUMmlKAyVlxXLzQAghDlAymeTFZ17h3n88yNpV69EdOsedMI8lp51IXUMtldUVlJaX4HYPDQqbpkm4q5/NL6wkGY4w79LTJWBxWCgWulMBzzAHVxnxxT6W3ddkZZinORELLJTBa+5l+9mBKHIrltlKpz0KQnq0iNQKmSFQyVTk02MrpEdIsLIby1b0yVk3Zz9Dls0bIWNomQC6895bqtKermTnVuzTywz7PDuyxHDP7UWzI01kgyfp6epetpM7Uka6fDlH3coeguGOZ7aePmhezvHIPy5mznJkgyU5xzRziIcc+5zXuYEIK3/Z8OCg25DvlTJk+vB/Bvv+fmZHHxlB3j5G2mFWTFGGCfeNMpiQ++d1AA7mUjjSauJOTEOTdstCCLFfmnbs5o+//BNvvrqUouJCPvWFj9E4fTJl5aXUNdTidDnHuohCCDEhdXV288Bdj/DIvY/T3dVDUXEh57/vHE446TiKS4upqauisCiIrg8NG1imRaRvgO1vrGfrS6sJd/dT3lh1yMsoAYsUzemgYl498YEIKPuuNI1QMxtmwnAVqX1VewbVqvZaObSnOR068UQyf3+DKse5ldchCw2+yz7stMHr7+Pued6+B204J/CRCVDkvedM1CDnQRk0LzVtuHnKCNvKyWDIn0ZOFkTucoOXByXVLtbrcxEeiOW87XQQIi+6MOi5NfS4WsN9BoOmWZAao3SYZQdtZ/B+h8wHTVMxDJNDZn/+Tg5yw7pDJZkw97Hpg9zvQd2lG/7c4A74iMuwpkIIMWrhgTB/u+kf3H/XIxiGwXnvfTennHESHo+bKdMmUVhUONZFFEKICceyLNauWs/df7+fl597DcMwmDZrKhdddgHTZ06jpLSIyuoKAgX+YTPXLMsiFoqye9UWNr+wit7dHXgK/cx/3zuon1VzyLPdJGCRougOCqbPpLO5DZRUR00Hczd2uEpy7nQFQM0+z31NTpp/dsrwt7hT2y4q9dPeERpUiGGCBSPeiB7FHeqRghJ7XSdViR/SvCD7dkpKvHR2hvPnDXn/gw3NABh+68O93FsazN4/8Nw/wEBZgOgoesG1hg3ajMZwAaEDWHeYSSWlfjqGfF/Yj+/7oT0RDb+54fdRUuanvX2Yso9zwbLA8MdcCCFEHiNp8MwTL3DzdbfR1trO3AWzee8HL8Tn81JZXUFtffWwd/uEEEKMLB6L89Rjz3L/Px9i6+btuNwulpy2mJNOPYHyirJUs4/SEfsCsiyLeDhK+6ZmNr2wgvZNu3F4nEw/eyG1CxpRFAUjkUQ/yGbUg8nZPke0P0pHUzearqM5NVRNQ3Xq9qOmo+gqqq6iqCqKqqGoyrAV67FoQ6loWubO/3i0tyOiu72ozjHok+AIOfDvw2gyfQ6MqusoE/Ruv6KoE7Kd8kQssxBCHEmmabJp/RZu+O1fWLl0NaVlJXzuq5+mYXIdLrebxmmT8Af8o95ePBLDMq1Uv09CCHFsamlu5d5/PsgTDz5NKDRARWU5l3zoIuYfP5eCwgDVtVUUlxTtNRAcD8fo2tHKlldW07xqG4qiMOmkWUw6aSaa00GiP4TZ1IRpJvFUnnZIyy8BixzNq7bRumo7us+D5tTszkJUJTU0Vn7lUdFUVFVF0VQ0h47q0NCdOug6utOB6tDQdN1eTtdQVftR0VRUTR3XwYWDZaU6vLRMM/XPyjwmojESoSjxcJRkOEoiEqfd7WAgErdXznRUSV4fEvkdTqb3ZD9RVCUnWcVeNpMsoaiQc6GSvWixt5PpiDIzX80GoFIdjirp/Sigajnr9/XT3RnCTBqYhoGZNDGNJFbSTD03sAwDM2lgmalRRYz0Y2q+aWEZZuZYmYaVf8wMM3Usc6ZbZuYYY1rZ51b2ebbjUGCY13AASRt7c2gDqYM2Ony2jP0ZZzOXsgkyypDvT36/JanvSO4ymU5q7e+SHRTJXVbJdBJrd0Sb6htFVbOdz6amq5n5ucsq1Myup2je1MNxoIQQYsLrbO/i1hvv4MlH/g2WxUXvP593nLkEy7SorauhorocbRSB9vQdwO5dHXQaSQom1+DwSB8XQohji2VZvP3aMu6+4wHeem0pAHMWzOKU009iUmM9hYVBKmsqCRT49zoMdCISp6elgx2vrWfn0k0ko3Gq5kyi8dR5uAu8JGMJorua0Vr3oJomBTMno2qHtp4rAYuUjvU7efvvz4w4X8lUUJRUhoX9qKpKXvBC1bKPqq7Z/zQN1aHbz50OdJcD3eVEc6cenfY83amjOHQ0h46ma6lsDg1F1+ztaSqKph3UlyAbSBgUUDBMTNPETBok4wmSA1Hi4RjJSIxEOEoiEsOIxEnG4iRjSYxEEjOewEjaFXIzadqVcyNnm5kK9ME0ixAjyd6wTwdWsjPyWrfkdEwKZDKDRv2RDL4xlW4qNVKTqZw4w6G5pzU4gwnM3I5ISX3X0vu2zEHdt+Q3rcntBHVwkYdr9nSovrp7Vm7lzKm1OMbpkHtCCDEWIpEojz34FP+85S46O7o4btF8LvnQRTicDrw+L5MbG/D6vPvcjmmaxPojdO9qZ+fbG2letQ3dqXPe//zHEXgXQggxPoQHwjxy/xM88K9H2LO7BZ/Py1nnnsGiJQspKi6kvKqc8opSPF7PXreTiMXpb+9h19LNbH9jPdHeAYobKph6xgIKKoowkgbRrl7Utj1ovb1ofi/eqVMonlpDyDq0WdwSsEgpnl7L7HNOoHXjLhRNxUwYGEn7Drn9aNqV8VTF3jItzGSC5CGokCsKeXdxVVWx7/BqqaBIXiDEDoJoTgeaS0d3udA9TpoKPIS6QyRjCYxYgmQ8gZlIZt9H0g4mmDl38w9lUCH3PWSCOenyp4I5iqqgaZr9vjQdVVdQNQ2ny0E8nsxubHA58vr6HKGMOZkD2UlD+80Yrr/RvXZGmjtl0CSHrpI0TEjdjVcUBTTFzvJIZWoog+/Kp+7IK1r6bryaegQrfawy69jP0VT7uKnp7WLvM/NdSWUFoGW+M3Y2ADnbUe3MlNQ2i4v89PRFgeG7CslmtAyZk14g/2XeEsrgCQzZ2Ej73Mvr9HolxX46O4f2BWHHJvI7GR322zKoX9PsN8saYf4w3730iDqm3XGpmTTs80IylSmTsF9jGBgmYJqU1ZSgSptrIYQAIB5PsGrZam654XbWrdpAeWUZ13zzKuon12EkDRqm1FNaXrLXO38ApmES7Rugc0crTUs3sWfNdox4ktLGamaeNkea4wkhjgnbt+7k7r/fz7NPPE80GqOuoZaPfPIyZs6djt/vo7q2iqKSQhz7GK0uGU8w0NHHnnU72P7aOvpauvCVBjnuA6dTMqky1eFmGKW/F7W5GQwDd30N7rpq9MIgnrJiBg5xn21y9Zyiqir1i2YQMwyc3tQdUFXJVr5S6eKZimnO9PQ00zKxkgZGNGkHDOIJkokkZjojIZF6zGQlGJmmA+lAgv2YajYQNw9pdkK2ApuqxGoqWiaYkJ8domgqWroJi66haBqarqLqup0t4tBSmSG6nQKPgoWVOV52YEXNrKdoOppDQ9FVNF3PNJlBVSgpDdDVNZAp57DVzGEHtBiuY8lhAg3DvRjFMR183DOvU59JUaGX3v5oznCpOYGLdPAJUk1SUscdJdNEJa8PFCXVDCFnXmadw3CxVVoWwBpFh6Hjka80QNiaeE2qysoCtE/QYy6EEIeKYRjs3tnM32++k+eefglVVbnkQxdx2tnvIBKO4A/4qZ9ci9u992w0I2EQ6Q3Rsa2FXcs20bJ2B6ZhUjGzjkknzcZXWoBHk2CFEOLoZRgGLz37Kvf+4wFWr1iHpmksPHE+S047ieraSgqCBVTXVhAoCOwz+GskkoQ6++jc0sz2N9bTsXUPTp+bWeeeSPXcSaAoJCMxzFgcrbMNs7MTzefFO20KjpIiHMGA3afiYai3SMAih7fET2FDZaqJhJHKPgAMAzN9NzWTnQCmadgV4Nx+BFJ58qqmoXo0NI9dlbcp2ENTpiq4VnZ6prIPpKvs6Q9cIZUCb1oYiSRGMokVT2aaYxiJJE6nTtK0Uk1JtGyQwWF3FqqoWmrrw+TxpyrZaqoZiqppKLqeClBodjMVPdWsRVMzd/5VTYWcDJD080y7/lEqKAsQ0/Ye7RuPJnKlXwghhDiSLMuiu6uHR+97gvv/9TDdXT0sWrKQSz78XnSHTjKZZPrMRopKivZ6DZGMJwl399O5rZmdb2+mbWMTAJWzJzHppFl4gj4SkRjJaIyi6TXEmJgdTAshxEh6unt44K5HeOTeJ+js6CJYFOTCS85l4aIF+AM+yirLKK8oHVVzOiNpEO7qs5vTvbWRPWu2o2oaU94xl4ZFM9CcOslYgmQsgSsZJbl9O2YyaWdV1NtZFZrbfViz2SRgkcNbGKB0ev0BrWtl2tRbmKaVShlPPccONtjPAdNMTTIhtawJ2eep4ES6s0QzadgdLRpW9tG0sgEUCwI+F6FwDEXXUXW7I9B0h6DZvjZSGROqYgcXdC07XVImhRDAwMAAf77xZlYsW87Nt/1lrIsjhDgKhEIDvP3qMv5x691sWr+ZqppKvvrtq5k0pYGB0AAlJUXU1NfgdI588yIRjRPu6qd9y26alm2mffNuVFWlZkEjDSfOxOX3kIhEScZiBCoK8Rb6CVYUSmabEOKosW71Bu7++/289OyrJJNJps6YwkWXXUDj1Ml4vG6qaiopLi3e67k0zTQMwj0hepu72L1iC03LNmEmDKrnT2HKO+bi8rntfir6wzgcKq7uVhItbXZWxZwZOEqLcBQEjsiogxKwOERyR7HI7RPzSMX1Jd1cCDFafX19/OaXv6VlTwu6w0FVdRVXX/N5gsEgPp+PL3/1Gq799nfHuphCiAkuFo2xZeM27r7jfl567lUcDgcfuPxiTj/rHYTDEZLJJDPnziBYWDDiNuLhGAOdvbRvbqZp2SY6t7WgOXQaFs2kftF0HB5XKlARJ1BRhLfQjzpBh+0WQojBEokETz/6LPf/62E2b9iK0+nkpFMXseTUEykpKyZQYA9LWhDcd7MPSPX70ztAb2sXreub2PHmemL9EUobq5l2xgJ8JQV2J8ahMKqu4XOYRNetx0gkcdVV42moOSJZFbkkYCGEEMcYRVG49AOXMG/+PABu+ctfufWW2/jSV64Z45IJIY4GyWSS1uY2Hn/oKR657wl6e/pYcuqJXPyhi3C5nIT6Q1TXVVFVU4k+TGfE6aFJ+1q76djSzK5lW+huakN3O5l8yhzqj5+G5tTtph+xOIHKYrxBnwQqhBBHjT3Nrfz5j//g8Qefpr+vn9LyEi79yHuZd9wc3B4X5RVllFWU4fPvu9kH2F0bRPvC9Ld207m9he2vryfU3kNBZRFzL1xCUV05lmWRCEexLAt/kZ/E1m1EmltQvR58s6fjKi1GP0JZFbkkYCGEEMeYQCCQCVYAzJgxnccefXwMSySEOBqYpklnRxdvvbqU++58iC0bt1HbUMOV13ySyVMn0dfTh6KqzDluNn6/b9j1Y/0R+tq6ad+8m13LttC3pxOn183U0+dTu3AqqqaSiMSwYpYEKoQQRxXLslj6xgruvuN+3np1KZZlMnveLE496xTqGmpwupxU11ZSUloyqmYfAJZpEQ3ZgYreli52vLGeru2tuIM+5r5nCRUz61EUhWQsTjKWxFvsx5mI0v/G21iJRCqrojaVVeEak24EJGAhhBDHMNM0eezRx1l80uLMtBv++Cd2Ne3mut9fz6WXXUplZcWotlVS4j+gMpSVBQ5ovfFAyn7kTdRyw9Fd9p7uXtasXM9df3+Q559+Bbfbzac+/1Hedd7phCMR4tE4C46fQVVNBdqgAINpmIR7QnS3dNG8ZgdbXl9Pz54uPEEfCy86icmLpqMoCrFwFEXTKJpSia/Ib3f4PYJ4/wA9m3ZQMqVWAhpCiHEtEo7w6P1P8sBdj7C7qRmP18N57z2LBccvIBAM4A/4qKqpJFhYMOT8ORLLsogP2JlqAx29NC3bzJ61O9CdOtPOXEDdwmmouoaRSJKIxnB5PRSUFxJdu4G+Xc12VsWsqbjKSsYkqyKXBCyEEOIYduP1N+F2u7nwogsy0z539VV87uqr9ntbnZ2hbOfCozSR+9+Rsh95E7XccPSWPTwQpmn7Lp596kUef+hpQn0h3nHGEt572YV4vG62b9tDQbCASY1TcHk8dHWFM+umhybtb+umdUMTu5ZvIdzVj7fIz+zzTqRydgNY0L6nB1XXCJQV4gr6iFoK0Zzh0IeUaXsT/ctWo+oaUa8fzekc9XtVVeWAg69CCLE/dmzbyT13PMAzjz9PJBKlpq6Kj3zyMmbNm0FpaQEOp4fyqvJhM9L2JjYQpb+1m3DPAHvWbKNp6SYsy6L+hGlMXjIbh8eV109FUV059PXR+/zLWLE4rtpqPJNqcRQGUccoqyKXBCyEEOIYdfOfb6G5uZlrv/+dUXXUJIQQafFYnObdLSx/cyUP3/sY27bsoGFKPV/46meY1NhAf3+IcCjM5GmTKCsvzbvgTQ9NGmrvpmVdE7tWbCHaO4C/LMjci06mYnotlmmRiMRRNZVgdQnuAt9eMyoAjHiCvrdXENu1By3go2LhbJKKnNuEEOOHYRi88vzr3POPB1i1bA2aprLghPmc+s6Tqawqx+lyUllTwYwZ9fT1x/dr2/FwjP72HqK9Ido2N7Pj9XXEwzEqZtbReNp8vIX+TB9BWBaBiiLcXjehFauJNqWyKhbMxlVeih7wj2lWRS4JWAghxDHob7fezpbNW/ju96/F4RhdO0ghhDCSBm1t7Wxat4UnH3mG1158A6/Py+Wf/hCnnH4SRtKgq7ObkrJi6htqcbldmXUT0TgDnX2E2ntoWd/E7hWbiYWiFFQVM+OshZQ2VmMZJvGBKKpDI1hVjDvoG1VANdbWQe8byzAjUdz1NbhqqwjUV9ETNQ/n4RBCiFHp7enjgbsf4eF7HqOzvYuCwgIuuPhcFi5egM/no7AwQGVNBYECe7QPl9sFowxYJKJxQu09hHtD9O7uZOsrawh39VNYW8aCSxcQrCrJLGckEviKCvCXBUm0d9D18uuYsRiu2io8k+pwFAbRPO7DeSj2mwQshBDiGLNzx07uuvNuamqq+dY3/hOAiooKvn3tf49xyYQQ45VlWXR3drNtyw5ef+lNnnj4GQZCA5x+9ju46P3n4/V56e/tR9VUps+aSlFxYSarIh6OEersIdTeR8vaHexeuZVEJEZRfTlzLlhCUX05pmESD0XsQEV1Ke6gd1SBCss06V+1jvDGraguJ/55s3CWFqEHgzgL/CixidkMRwhxdFi/ZiN3//1+Xnr2FRKJJFOmTeY9l57P1JlTcDmdVFSVU1JWgucAggTJWIL+jl4iPf0MdPaz7dW19Oxqx1scYP7Fp1I2tRpFUex+KiJxXD43xXXlaJpC39KVRHfuRvV48M+fjatifGVV5JKAhRBCHGPqG+p58NH7x7oYQogJoq+3n7Wr1rN+zUYeufdxdmxronH6ZD708c9R11BLLBqju6uHyuoKauqqcDgcWJZFLBShv72Hgc4+mldvp3nVNox4gtIpVUxaMpvCmlKMpEFsIIKm6wRrSnEXjC5QAZDo66f3tbdJ9vbjrCjFM6keR1EQze8b8zbXQohjVyKR4JnHn+e+fz7Epg1bcDgdnHjKCSw5dTGl5cX4A36qqisIFgbR9P0PECTjSQY6+xjo6iM2EGXnm+tpXd+E0+ti5rtOoHr+FFRNxTRN4gMRVF2nuL4cV8BDrKWN7jdX2FkVNZV4JqfOm+7xlVWRSwIWQgghhBBiiHg8we6du+loa+P+ux7n9ZfeJFDg55NXXc7idyzCsix6untxuZ3MnjeTgmAA0zSJ9A7Q397NQGc/zau3sWf1dsykQfmMOiafNItARRFG0iAaCqPpOkU1pbgCow9UWJZFeMt2+lesRVEVfDOn4qwow1FYgLofHWwKIcSh1NbSzn13PsjjDz5NX28/pWUlXPyh9zB/4Ry8Xi9llWWUlZfi83sPaPtGwmCgq4+Bzl6MeJJdK7bQtGwziqIw+eTZNCyeie505PRTAYGKIrxFATAMet9YTnTnLlSP286qqCyzsyrGeT9mErAQQgghhBAZ6eYfWzZv57UX3+TpR58hPBDhrHNP58JLzsPj9RCJRImGo9TUV1FVXYmiQLirn/6OHkKdfTSv3ErL2p1gWVTObmDSSbPwlRRgJJJE+8Nojv0PVAAY0Ri9bywj3tqOHizAO20SjuKiCXHRLYQ4+liWxbI3VnDPPx7gjVfexrJMZs2dySlnLGFSYz1en5eqmgqKS4rQ9QOrehtJg3B3PwMdvZiGQeuGXWx7bR3JaJyquZNpPHUu7oAdBElE45iJJN6iAvxlBWgOneieVvreWoEZTWdVNOAsDqK6XPvY8/ggAQshhBBCCAFALBpjx7YmVq9Yx8P3PMqObU3Mnjed93/0EqprqzAMg+6ubvwBP3OPm43b5SLSEyLU0Uuoo5fdK7bSuqEJVVWomT+ZhsWz8AR9GIkksf4ImlOjqLYMd8CLou5fs41ocwu9b67ASiTwTK7DVVuNI1gw7jqIE0Ic/SLhCI89+BT33/kwu5ua8Xg9nP6uUznx5OMpLimkpLSYiqpyfAfRRM00TMI9/YTaejFNk+6mNra+tJpI7wAlkyqZesYCAuWFANl+KgIeCurKcXicmIkEPW8sI7ojnVUxC1dl+YQL8ErAQgghhBDiGGeaJh1tnWxct4l/P/48Lz37Kj6/lys+9x+cc+Fp9PREGAgNkEwmaZhcT3FxIdHeMO1N7fS19bB7+RbaN+9Gc+jUnzCdhhNn4PJ7UoGKMJpTp7C29IACFZZh0LdsNZFtO1E9bnyzZ+MsKcYRDIzLDuL218DAAH++8WZWLFvOzbf9ZayLI4TYi53bmrjnHw/w9GPPEY1Eqamr4oMfu4Q582fh8/uoqqmkuKQIp+vAm6eZhkmos4+2zbuwDJNQZz9bXlhJX0sX/rJCFl52BiWTKjPLJsJRNGe2nwpFUYjuaaPvreV2VkV1BZ4pk3AWF6IeRLnGigQshBBCCCGOYeGBMNu27OCNV97mkXsfp7urh1PfeTIXf+gifD4vRtKgu7ObwuIg1VWVGOE4nVv20NfaTdOyTXRtb0V3OZh88mzqTpiO0+PKBipcOoUHmFEBEO/uofe1tzFCYfuiu6EOvTiI5vFMqI41+/r6+M0vf0vLnhZ0h4Oq6iquvubzBINBfD4fX/7qNVz77e+OdTGFEMMwDINXn3+de//5ICuWrkbTVOYfP5eTT1tMbX0NRSVFVFSVEyjw71cTt8GS8SSR3hADnX0UBNzEQhG2vLSajs3NuAIeZp+/mKrZDSiqimVZJMJRQCFQVYy30N63mUjQt3wNke1NEzqrIpcELIQQQgghjkGGYdDa3MaqFWt45L4nWLNiHdW1VXz66k/QOH0ylmXR19cPuKmvr8WJQt/uTnqbO9m1bBM9uzpweF1MPX0+tcdNRXc5MOJ2HxW6S6ewrgy3/8ACFZZlMbB+M6E1G1B0Hd/s6bgqytALC1APsB34WFIUhUs/cAnz5s8D4Ja//JVbb7mNL33lmjEumRBiJL09fTx0z6M8dPdjdLR3UhAMcM57zuaEkxZSVFxIVXUFxWVFuA9yhI14JMZAVx/RngEUVcE0LNY9u4Ktb2xAdWg0njaP+hOmoznsc1+mn4riAvylQTSHnWkWbWmz+6qIRHFWVeBtnLhZFbkm3hlfCCGEEEIclFB/iI3rt/Dcky/w1KPPYlkWl3zoIs4+70w0XSMSiRIOhSkuLKCupJSutn5amtrZ+fZG+lu7cQU8TD97ITXzpqA59JxAhYOigwhUACQHwvS+vpREZzeOkiI8UyfhLCqc0MOVBgKBTLACYMaM6Tz26ONjWCIhxEg2rN3EPXc8wAv/fplEIsGUqZM496J3MXPudAqLglTVVFIQDKAdRJM00zSJhSIMdPQRj8RQNZVIX5jmVVtpWb8TyzCpOa6RKSfPwemzAyJGIkkiGsfl91BQX47DbQcizESS/uWrs1kV82biqqqY0FkVuSRgIYQQQghxjEgmk+xuauaNV5by0N2PsrupmbkLZvOhj7+f0vIS4vEEXXu60FGoKCzEpTppXr2T9S+uYqCjD0+hn1nnnkjVnAZUTUsFKgZwuF0U11fg8rsPKqgQ2bGLvqWrsEwTz9RJuKsrcRQVojodh/AojC3TNHns0cdZfNLizLQb/vgndjXt5rrfX8+ll11KZWXFqLdXUuLf7zKUlQX2e53xQsp+5E3UcsPoyx6PxXnsoWe445Z7WLd6I06XkyWnLeL0s5ZQ21BNVVUFFVVlBzwkaZqRSDLQE6K3rRsraeBxKnRtamPbWxvpbelGc+o0LGhkxmlzCZQFAbufilgoil7gpnh2He5AtklcqKmFpmdeJTkQITCphuK5U/GWl6K7x24EkEP9fZGAhRBCCCHEMaC3p5fVK9bx2P1P8tpLb1JQWMCV13yShScuIBlP0NrUQjwUoaKsFLfioGPdLlrW7STaO4CvNMjc9yyhfEYdqqqSjCeIh2M43E6K6ysPOlBhJhL0vb2SaFMzmt+Hd/oUnKXFR80dwlw3Xn8TbrebCy+6IDPtc1dfxeeuvuqAttfZGcI0rVEvX1YWoL29/4D2Ndak7EfeRC03jK7sbS3t3P+vh3n0/ifp7+untKyE91x6HscvPo7ikiKqaispLAqi6zrhiEE4cmDHIhGJE+7uJ9wTwrIswl19NK/eTtvGXZhJg4LKImaes4jKWfXoTgeBIi9dXQPZfioqCtEL/YRiBqFYyM6qWLHG7ozY7cI/dybOmkqiLh+x/jj0xw+onAfrQL4vqqrsNfAqAQshhBBCiKNYPBZn5/ZdPPfUCzxy3xP094U4412n8p73n4+OSsu23YR7Byjy+XFFVXY+s4pQRy8oCsV15Rx/0Ul4KktRFMUOVESjODxOShoqcPoOLlABEGvvpPf1pZiRKK66ajwNtTiKgmgH2S58PLr5z7fQ3NzMtd//zkF1zieEOHCWZbH8rZXcc8cDvP7yW1iWyYw501ly6vuYNnMK5RVllFeWHdSQpACWaREbiBLq6CEejmHEk3RsbWb3yq2Eu/rRnA6q506ien4jBRVFeevGIzFioQi+Qf1UAMRa2+l9c7ndV0VlOd6pk3CWFKE6J3ZfFSORgEUOy7KwTNN+oSgTtp2kEEIIIYRlWXR1dPP268u4/18Ps2HtJuom1XLVNVdQXlpCf3MnvZ29OCMWVleIna1bAQhWlzDj7OMpn16Ly++hqMhLe2svRiyBw+OiZNKhCVRYpkn/6vWEN2xBdbnwz5uFs6IUR8HRMVzpYH+79Xa2bN7Cd79/LQ7H0dPERYiJIjwQ5omH/819dz7E7p3NeLwe3nHGSSw+9USqayqpqqmkqKQI50E2QTOSBpG+AQY6ejHiSfrbe2hZu5O2TfYwpcHqEmafv5iK6XVozmx13EwaJGNxLMuisKiUssZApp8KYNisCldNFbrfe9RlouWSgEWKZZp0r9tEpL0bVNX+EVZV0DT7C5B6rqYe857rOoqiZL8o6R/w3KDHMI8TPSBiWVb6yfCPqedW7nPLsuebJpgmpmEQUeIkesL29Jx/mWXT20w/N017m4OXSQebctYZdhup19Yw27YGLTN4XSvnedypE48nRzo4+a8VZei0vdmfZQevk9pX7haUvMUsYsOVfaR9HsT0IUscgn0MaCqGYQ79m8p5ruROH+Fxr8sMmjaa7dlPlRG30dtbCsHS4d+nEEIcYpFIlG2btvHwvY/zzJMvoKoqF7//Qo4/bi5GOMbutzeR7AqT6BogAvjLC5l6+nwqZtbjCfoA+9ooEY0T7rVQVZXgpAqc3oMPVAAk+vrpfX0pyZ4+HOUl+BonoRcXoXkOzfbHm507dnLXnXdTU1PNt77xnwBUVFTw7Wv/e4xLJsTRr2n7Lu75xwM89eizRCNRqmsrueTDF7HwhPmUV5VRUVWBP+A76KynRCxOuCtEpKef2ECUji3NNK/eTqQnhO52UrugkZr5U/CXFWbWSZ9nTdNE03X85YW4/V4qaovzmlbE2jrsrIpw5JjIqsglAYsUMxqla8WabIbFgUgFNjLBjnQQY5jXmSCIoth3EdKPqpp9zHmOpqHmPFcGBVCMeAIzFrMrqqZpv4+cx8xzy8IyjOxjetqgZfKWz5mGaeZX6nOzUoaZN+y0QQYO/IgfOcNUUk1VYbgmo/u80Drc80exbCKhYhrmoEVHud2RlhvN9EHPh11jb+VQFDRdxUwY9uvhAh2Dg08jzR9u/cHBlr3MH/H5CPvoDfXhXlh0VN41FEKMH6Zp0tbSzovPvMJ9dz5E65425sybyblnnY4zatH++mbiXSGwwFPkp/aUOVTOrMdXUpBZPx6JYSYNVE3BHfBSObWKvnDikAQSLMsivGUH/SvXoCgK3hlTcddUoAcn5nClo1XfUM+Dj94/1sUQ4phhGAbPPvkSN9/wT1a8vQpN05izYBYnn3oiU2ZMoaamiuLSIlwH2TmlZVrEI1FCHX1E+8P0NnfSur6Jji27sUyLwtoyppwyh/LptZlhSS3LIhlLYCYMFE3BU+jHE/ThcDuHnGfNZJL+FWuJbN2RyqqYgbu2Gs13dGdV5Dp6fxn2k+b1UnfB2bTvaEHR9fzKfm6FPrfiPrhyv69lTRMrkRh2/sHqOwTHYFjpO8a5wZdUAEYZ/DwdSElnjwyzXN72Uo9er4twJJ698z3CHercacMtY6UyC4ZktYywTt7d8NxHy7K3lV4mJ3NBAfu1olAQdNPXG82vrO5vpX24ZYaZrwxeZqTtDJo27LEEiov9dHWFRlWuIVkDwy+03/OGfU+jWL+0LEDHgXT+dCBZKwe4j+EyS0pLA3T2Rg9/GYQQx6yBUJhVy9dw7x0P8PYbywkGC7js/POo8QQZeGsnA6aF5nFSs3AqNXOnECgvRFEUTMMkHo5iGSaKpuIJ+nAX+HB4nKiqitvvoT8yQkbhfjBjMXreWE68pQ09GMA7vRFnSdGEHq5UCDG+RKNRHrv/Sf51+320tbRTEAxw1nlncNI7TqSmrpqqmgoCBf6DGpIUwDQMon1hQh29DHSHaN+U7aTY4XFSd/x0auZPyQSDAYx4kkQ8joKCK+DBVx3A4XWNmNkxfFZF8VE1atJoSMAih6rr2bufqQyHvPTxVGVk2IpWbjp+rr1UZq3Bd3MHBTzyshsGNaXIBDtSy7hdOtG4ka1c5gYIUvtWBgcMBr3PvIp+6jhkKuzpeemmBnt577mV+r0en9Rzb6GPRG84O20UKft7a2qT3fwwFe0RKujK4Hl7WTb92lcaIKz350yaOBdbzgI/WuwIVOAPA1XTxn2WwnDfBM3lRFFiR7wsQoijn5E02LVjNw/+6xGefOwZIpEoJ0yfyXGldeg9FtGBfrx1xUw6bjpV0+rsIEXSIB6OYVkmmqbhLQrgDnjtO3zqof89i+5ppffN5VjxBO5JdXjqa3AUF6JKXw5CiEOgp7uXe//xAA/c9Sih/hC1DTVc8bmPMG3WdKprqygtK8Hj9Rz0fpKxBOGeEKGOXrp3ttK6vonObS1YlkVxfQVTT59P+dQaVN2+VjUNg2Q0jmWCw+OksLoUl8+T14nmYGYyye7n36R7zaZjNqsilwQscuheD47S0r2mkQ9rpLuqe1tvH9uz9laGYeYVFXnp7g5nJ4xwt37YL/neKuqMUJkfxXqDp41UofeVBQhrE2+4JEWd+P2QCCGEmLhM06SrpYMXn36FB+5+hKbmPVQECrlg3nGUFhbhKi/AWV5A/ZwplJXb1zeJcBTLBM2p4S8twO33orsdh+33zDIM+pavsdOZPW78C+bgLC85KocrFUIcec279vCPv97FU488SyKRYOac6Zz6zpOZOWc6x50wE9PU0fSDu9FlWRbxcIyBrl56dnfStnEXret2EgtFcHrdNCyeSfW8yXiLAvbypmkHhA0D1aHjLyvEHfCiu/YdoI21ttP71opUVkUZ3mmT7ayKYzi4KwGLHHkdZx7I+oewLPvLFQygxaXyLIQQQhztEtE4fR09rHpxKU88+gxvrV+Prqqc1jiHRQvn464MYgUclJSVUlFWgmopJMMxdLfdoZvL50F3Hb4gRVq8u4fe15ZihAZwVpXjbZyEo6gQ7SDbjAshxLrVG/j7zXfy2otvoqoqxy2ax6nvPIXJjQ3U1FdTWBSkvCKY13Hl/jINk2h/mL7WLto37aZ1fRNdO1oBKJlcyYyzj6e0sRpVU1P9UsRJxpOomoIn6McT9OPwDO2XYth9xeP0LV9DdMcuVLeL8sXzMYtLpMkcErAQQgghhBj3jIRBbCBMy/omdizfxMq3VvH8+pX0RsPMqK3n/HPPonhSJZFoFKdDp7qiHK/Xi9PlxFsYwOlzj+ru3qFgWRYD6zcTWrMBRdfxzp6Op7oC/SgdrlQIcWRYlsVrL77BP/56N2tWrsPtdnHaWadwyhknUV1bRW19NQXBgoOu4CfjSSK9ITq2NtOyZietG5qIh6O4/B4mnzyb6nlTMqMpGYkksYjdR5kr4KGgsgSn14Wqje4muGVZRHftoW/pKqxEAld1BZ7GBkqn19PVJ02JQQIWQgghhBDjUrr3+Y5tLexatpm2Tbvoauvi1e0b2NTRTGFBAZ/88IeZMWsqfT39DPT1U11dSXlNBf7iApxeN7rzyF7qGeEIPa8vJdHRhaO4EO/0KThKio/a4UqFEIdfIpHgyUee4V9/u5ddO3YTLCzggovPYdGS46moKqemrhp/4OAyESzLIhGJ09/Rze7lW2lZt4OeXR2gKJROqaJm/hRKplShqiqmYRAfiGCaFk6Pk2BVCS6/d6/9UgzHCEfofXsl8ZY2NJ8X7+xpuKoq0HxeNJcTkIAFSMBCCCGEEGJcScTi9OzuYOcbG2jdtItQWw+WZbGpv51XN60hYSR551mncvppS0jGE3S0dVJbX82k6ZPxFQb2+6L5UIns3E3f2yuxTBNP4yTc9dU4CoNH9XClQojDZyAU5v47H+K+Ox+iu6uHyqoKPvixS5m3cA6l5aVU11bi9/sOah+maRLrj9C2aRe7lm+hbWMTiUgcd4GXKe+YS/W8ybgDXizTJBFLYCUNVIeGrzSIu8CLw+Xc733awztvJ7RqHZZp4W6owTO5AUeRnC+HI0dECCGEEGKMGUmD/rZudryxnpZ1O+lt7gTAVxYkUeXjyddfZdfuPUyaVMdFF55DcUkhcSNJSU0ZU2c1EggGxqzsZiJB39sriTY1o/l9eKdPwVVRZvdoL1kVQoj91N7WwZ233cNjDzxFNBKlcfoULv7wRUybPoWyilIqqyvw+rwHtQ8jkSTU2ceON9azZ812eps7URSF0qnV1CxopKShAhQFI54kGgqjKAreoB9PoQ+H23XAoykl+/rpfXM5ia4e9GAAz7QpuCpK0TweOV+OQAIWQgghhBBjwDItBrr72fnWenav3EbPrnYs08JbHGDSybMh6OaBx5/ijdeX4na5uOSS81l8yiKSlonT62ZOYwPFpUVjepEba++k9/WlmJEortoqvFMm4SgOHtM92gshDszWTdu445Z/8cK/X8E0TeYvnMtpZ59MTV015ZXlVFaX43a7D2of8UiMto272P7GOto27CIZS+AJ+mg8bR7Vcyfj8nswEkni4RhYVqpfiiKcXveo+6UYjmWahNZtYmDdJhRNtbPQGmpxBgMoklWxV3J0hBBCCCGOoFgows6lG2laupmu7S2Yhok74KV+0QwqZ9aDW+fpJ5/jkb88TW9vP4sWH8clH3ovDrcDI2lQX1dLZXUF+hhe5FqmSWjNBgbWb0Z1ufDPm4WrugLd75PhSoUQo2ZZFsvfWsnfb/4Xy95cgcOhs+S0Eznl9JMoLSuhsqaC8ooynAfQ9CLNNE3C3SG2vbKGXSu20N/ajaKqlE+roXr+FIobKrBMe5SPaH8Yh9tJsLIYV8CD5jj482y8s4veN1dg9IdwlBThmToZZ1kxmlv69hkNCVgIIYQQQhxm8Wic7W+sY+dbG2nf3IyRSOL0uqhZ0EjFzHoC5YXEIjE2bdzMXfc8zIYNW6ioLOeKL3ycyVMbCPUP4A/4qJ9Uh8frGdP3kuwP0fPa2yR7+nCUleCb1oiztAj1ICoUQohji2EYPPfUi/zz1nvYumkbPp+X8977bhYtWUigwE9NbTWlFSU4DiJby0gkaV67na0vraZ1QxNGPIm3KMC0MxZQNXcSDo+LZDROfCCCqmr4SgpwB7w43IfmXGYmkvSvWkdky3YUpxPfjKm466vRA34ZMWk/jNuAxcDAAH++8WZWLFvOzbf9ZayLI4QQQgixX+KROHtWb6Np+WbaN+4iEY2juxxUzKyjYmY9BVXFWEkD07Lo6e7h4cee5qknnsdIGlz0/gs4+/wziYQjJOIJZsyeRmFRcEzvxlmWRXjrDvpXrLHbc0+fgqehVi6+hRCjFo1Eeejex7jnjgdob+2guLSID1x+MXMXzsbn9VJTV01JWfEBZZAZSYNEJErHtlbaNzbRvnEXPXu6UDWV8ul11CyYQrCmFDNhYMQTxAeieAr9eII+nJ4D75di2PfZ3ELf26swo1GcFWV4GyfhKClEO8gmLceiIxKw6Ovr4ze//C0te1rQHQ6qqqu4+prPEwwGR1zH5/Px5a9ew7Xf/u6RKKIQQgghxEGL9IbYtWILu1dspXPbHoyEgebQqZ5VR3FjDYXVpZimCZaFqqrohW6WLlvFbX/+B7t2NjNr7gw+9In34/N5iYYj1NXXUF5ZhqaPbUDAjMXoeWM58ZY29IIA3hmNOCtKJaVZCDEqPV093PX3+3n43scI9Q9Q11DLJz93OdNmNOL2uKltqKGouBBtP4KfRiJJfCBK2+Zm2jfvorupnb6WLox4EoCC8kKmn7WQqtkNqLpGMpYgMRDF6Uv3S+FCPcTBViMao3/ZKqK79qB63PjnzcRVVWEHdqW53AE5IgELRVG49AOXMG/+PABu+ctfufWW2/jSV65hz549XPf76/OWX3j8Qi79wCVHomhCCCGEEAfMsixC7b00LdtE86ptdDe1YZkWDo+LylkNlE6tIVhZTEGBm56eMJpTJ1Dox+Fz0dbWwR9/exMvP/sqPr+XT33hY8w7bg7hcJiCwgC19TUH3cHcodC3fTft/34VK57A3VCLZ0oDzsIC6ShOCLFPu3bu5o5b7uLZJ54nHk8wa95Mzjr3DKprKvD4vNTVV1NYXIi6j8q8ZVkYiSTR1BCk7Zt207Ornf62bsykCYCvpIDKmfUU1pVRVFtGRW0J7Xt6MJMmiqYQrCzG6fegOw/9ucuyLCI7dtG/fDVW0rA7IZ5cj6O4ENUpzeUOxhH5pQkEAplgBcCMGdN57NHHAaiqquJHP/3hkSiGEEKIfYjH41z3h+vxeu3hCK+86jNjXSQhxh3LtOje1U7T0o3sWbuDvj1dALiDPuqOn07p1Gp8xQVgWqBYOH0uSurLcZUn0Rw60WiU++96hH/89S56e/o47axTuOjS80maBhYwa+5MgoUFY/smsYcr7V+5jsjWHaheD/4F03BVV6J5Zfg9IcTerVmxjr/f8i/efOVtFAUWLTmeM951KsGiIIECP7X11RQEC0Y8l1iWhRFPEukdoGX9Tjq2NNOzu4NQew+WaQEQKC+kZkEjRbXlFNaUorkcGPEklmmCBYloHF9JAHeBD93lOGznreRA2B6qtL3THtp57uTs0M6SVXHQjnho3DRNHnv0cRaftHify97wxz+xq2k31/3+ei697FIqKytGvZ+SEv8Bla+sbOzGMT9YUvYjb6KWG6TsY2G8lHtvzfRefflV5s6dy7vOOZvb/vo3Nm3czLTpU8e6yEKMOSNp0LG1maalm2hZt5NwVz8A/rJCprxjLiVTqnD73ViApmq4g17cBXbnbaqm4S8OMNDay7pV67nu139m7ar11NRVceU1V1BWWYplWjRMqqOsonS/UqIPh8ydwpVrsWJx/PVVOBoacJQUynClQogRWZbFS8+9yp233sO61RtwuV2cde7pnHz6SXi8HgqCBdTUVREo8A8JHlimRTKeINTZR9v6nXRs3UNPcwcDHX0AKKpCQWUx9YtmUFRXRkFVCaqmYiaSWJa9bywLb6Efp8+N7tKprC6ioyN0+N6vaTKwcSuhtRvAAvekOjyTanEUytDOh9IRD1jceP1NuN1uLrzogn0u+7mrr+JzV191QPvp7AxhpqJvo1VWFqC9vf+A9jfWxmPZ7RMHWNgnEMvCfp15Yj8vLfXT0RlCQQFFwT5/KSiq/WhPVsbd3ZzxeMxHS8p+5B1IuVVVOeDg697srZleW1s7M2bOAKCisoK2tjYJWIhjViKWoHX9TpqWbaZtQxOxUAQUKKwppWZBI8UNFTg9dqqv7nLiCfpw+Tz2nbxBnbf19vRz3a9v4pF7HwcULvnwe3nH6ScRi8cpKSmipq76oIbtO1QSPb30vb2SRFcPms+LZ3ojVXOn0BdH7hQKIYYVj8d5/KGnufv2+9nd1ExBMMDFH3wPx5+0EF3XKCoporq2Er/fl1nHNE2MWJK+li5aNuykc1sLvXs6iXTbAQZV1whWl1B+Sh1FtWUEygvt9QwDUMC00D0OXMUFONxOdJc+pD+Kw1l3SPT00vvmcpI9feiFQbzTJuEsLbGzKsZZnWWiO6IBi5v/fAvNzc1c+/3v7LOdktg/lmllOvEaKTAwNHiQesTKzrdMLNN+xLLsoI9p31lKxuOY8STJRBIjnsRI2P+S8SRm0kg9JjETBsnUo5k0MQ0DM2lgJO1H+5+JkZquayqmoqCqKoqmoqoqqqaiaOlpGoqqoGoaqqag6pr9XFdzHnVUXUXTNRRdRdN1VF1Dc9jLag4NTddQHTpaerpDR9O01PZUSAdKlFSQBAXU1KOSPulln5umaR/zzIeQ94lknw2Om41quUErWblPreEXG7JKzudqpj93C8sy6VNMQp39WEZ6mpVaxsQys6+HfW5ZWKZpb3u46eagbZqmHflOrZO7DJaFZViYqeemaaXS+PK3S0453B4HsViSTFArFcxK/zgoqpKZlhfsygmGpZ8rigKqkvcZp5fNf82Q7aVfo6jZbQ3Zb3a+PrMG9LGvjMDem+mVlZfR3tYGQHtrO1OmTB6TMgoxVuIDUXav2squ5Vto39JMMhpH0VRKJlUy+ZQ5FNaV2cPsKRYuvwd3gQ+n1z1sm2jLsgj1D/DSc6/wz7/exa6de5h73Gw+cPnFOJ1OHC4H02Y14g8c+sDk/jLjcfpXrSeydQeKruOZUo+7oRZHsAB3UZD+CRgoFkIcXqG+EPf88wEeuvsxurt6qKgq5z8+/WFmzZuBqiiUlpdQWV2B1+fFNExiA1F6mztpXb+Tzu12gCLWHwFAczoorC2lZt4UCmtL8RYXZK5NARRNweX34vK60V0ONKc+JoEByzDoX72B8KatKLqGZ/oU3DWVdlaF9OtzWByxo/q3W29ny+YtfPf71x7UeLrjlWVawwQDBgUL0tOw7L89K2cdw8JMVxbNQZW/vEpjNpBgJU0SCTuI0O5x0Ns9gJE0UwGBJKZhZgIFVtLESCZz5hupQII93cyZng4kpKcZSWOYWvfoqJqaChzYAYRMEEHXcLmdqLqKQ9eIRROYhoFlmBiJJImoiWWmymSamIaJZZiZ93TIKYpd1nTQRFNTQZJ0EEWzAyiaipIKqDhdDhLxZOozZdDnPeh7MOz83CDSaOenK/H2tocPQuXMP4rYdyvtIEDmPaaOzUTgDni44HufQHeNr/Pf4GZ6J5+yhOv/eAPbt+3ANE2mTZ826m1JU7yJZaKW/XCUO9TZx5bXN7D1rQ20btqNkTDQXQ6qZ9ZRNaOW4voyOxCuqXgL/XiDPpxe14gjdxiGQUdbJw/c9TiP3P8U27bspKi4kK/812eZMXsqqqoxZVoDZeUlY34DxzJNutZuof31FRixOP66KopmN+KtLMWR01eFfF+EEGktza3cees9PPXoM0QiURqnT+HDn/gAdZNrUVEoqyyjrLwEHZXO7a1s3LiLrh2t9O7pJBGOAeDwuCisLaPoxDKC1cV4ggEs086eUBRwuJy4Am4cbpcdoBjjkZIAYm0d9L61AnMgjLOsBE96qFKP9OtzOB2RgMXOHTu56867qamp5lvf+E8AKioq+Pa1/30kdj9q8WicUEdPKqsgnV1g2nd/M4ED+y6xaaTvCJuDKoZDv6x2JTuJkUhlGSQS9vNEEiNpYCQMezzgpJ2pYCRSgYNEMrNc9nX2X7pH3P2l6ulsg9Sjnso6cOr28D4jBBi01HLZ9YZZTlVRUhkLqprKWkhVLnPv8tssFBQKgh56eyPYNU9lmMfUkU3f0UaxPwfDzNyRT2eYWIZpB39ME8sw7PlGer6RWcc0DPvzTAdBUsESc4TXucsZiQSWYRIPRezvSOZOupJzVz3nrn7qOKhKzp38wVkA+7jrnzcv7+5+anpuc5r0MjCobKnvp6Lg9TqIRpODtjM0A4GcMqfLmU5ztrMbBmcXgKKooCqo6XRoNV0WdVDGQja9WFHVvHKmt6Gp9mN6vqIqFBZ66ekJ532nM9+pnACPHRAk8/eZFxAiZ76592WHLD8oWJJZL7N8zryc7VXWl8E4/DEb3EzP5XLxla99+YC2JU3xJo6JWvZDVW7Lsuhv7aFp2UaaV22jZ3cHlmnh9LmpnD2JksmVBMoLUTUV3eVE8flwpJp6mKpCKGZALDxku/F4gt1Nu3no7sd48ZlX6Orspqy8lA99/P2ccdZienojuDx+qmurUDUHnZ0DB/1eDka8s5u+pStJ9vShBfwEZk7DWVlO3O8lETYgbKdmH0vfl8PVFE+Io8HGdZv5x1/v4uXnXsM0TRacMI93nX8mJWUlmKZJSTCIOpCk6+2tbN75Gn17ukjGEgC4Ah5KGioI1pYRrCzG5fdkLvVVXcPl9+Dy29kTumNos7qxZMYT9C1fQ3RHE6rLhW/2dFxVFTgK/DJa0hFwRI5wfUM9Dz56/5HY1UGJ9A7QvqUFRVNSAQQjE2hIBwwGBxLseekmEnZl1kikmkck7OYTlrF/gQXNqaM5cv45dXS3E1fAi+bQ0IeZHwh6icaNoYGITLBBywQW0hHAIen2gytp6ZR+e2m7spWpi9jBhmzzBDvAoKoqpDIQ8rIVMpkJ2SwFRVFRVCgtDaB19Kf2ndpXbmZBukmBZWbLlwl+pKabFiZ285XMeyJd/nSwiZwMhMzOMmXPBkfyAyVA5q5+7vRgoYfenkh29SEGbzO1r1EvP8y6ea9ztocdEMkUNtO8JTUpc9K3pxUWeunpjaKkAyNqqglDOoth8PRMkCEbOMgLZqT3lbff3GAM+c/JD1wMXnZvUeqysgCOY+TC+XCTZnriWGKaJt0722hauok9a3fQ39oNgLfIT90J0yluqMBfEkRR2WdTj8HCA2E2rN3EQ/c8xhuvvE0kHGHKtMl84PKLmTpjColEksKiILWTJuPzew/3W90nIxqjf+Vaojt2oTgceKZOxl1XhSNYIB3FCSHyWJbF6y+/xZ233s3KZavRdJ2TTz+JM84+BaeqE23rY6B1N/GuELtaezKZ0N4iP+XTawnWlFJQUYTD48pc6zk8LtwBDw6PE93pRHOMffbESCJNzfQvW4UZi+OqrsAzuR69KIjmdktWxREiIaGUUEcvT/7sHyTjiVEtryjKoMCChuZ02H+ABfqwQQfNoaM7dFSHngo6aEPm5wYURpLXT0DqMVjgprs7PKQZgF3Rti/UzJgJ8YQ9IVVJVDPNH1Q0VcsJLOQHHRRVyVZoU492RdYOOtiPB/ZH6/Z7cEWSB7TuwcoN1EC2/4/sc4vsHfucu/dYlBT70DpD5AYGMtGDdOU7Z1L2ZTbIkH6SqbTnPgw3LWfdgzlJlpUF0MdZ5VkcWUd7Mz0hAIyEQfvmXTQt20zL+p2ZztwCFUVMfsccSurKcRX60DQNT9CHO+DF4XEO6bhtOKZp0t/Xz5uvLuOJh55ixdurMU2ThYvmc9Z5Z1BRVU4ikaSgsICqmkomT64c86ClZZqEN28jtGYjlmHgrKrAO7kevVguvoUQ+UJ9IZ585BmeePgpNm/Yhtfr4V3nnsm8yY0kOwfofnETZiiWubnpLw1SPXcyweoS/OWFmQ6JVU3D5Xfj9HlwuJxoLn1C3CQxIlH63l5BbE8bms+Df+ZsXOWl6AE/yhiP5DRehXpCRHt7cQeDh3S7ErBI8QR9nHDJKbTv6sTld6M5HKkgRDbooDtTwQaHblfiD+KHfbigg5EwUmlTw9zhJ1VZVkBVsx1OKi4VTdMJlAVIOp1omp25kBdUGDHYIBcmB3Mc3AEvruhh6E9DiMNsojTTE+JAJKJxWtbuoGn5Zto27iI+EAVFoaiuzB7Zo64Ml9+TGdXD6XPjcDlHHXRPJpN0dnTx3FMv8uwTL7Bp/RZcLiennXUK7zzndNxeN2bSoLikiIqqcjxez2F+x6MTa+ugb+kqjP4QejCAZ0oDzvIydL9XRv8QQgB2/zurV6zlwbse5fWX3yQSjlJcGOSsE05karACpTfBwPJdoCgEKgopmlZPQVUx/rJCHC4HKAoOtwOX34PT60ZzOkaVpTaeWJZFeOsOQivXYpkm7vpa3PU1OIoK0NzusS7euNS2q40X7nuBpc8txev38I3rv4nbe+iO1cT6Bh1GmkNnxmnzcK7ZabepGoXhgg6ZjjGHCTpkUvlHCDqouoqm2aNcKIrdfEJR7NEy0hkM6WDDYIVlARKa3DEXQuzbRGmmJ8RoRfvD7F65ld0rt9KxpZlkLIGqaxQ3VFAyuZKi2nIcHsd+N/XI20c0SsvuVh5/6GlefOZlWprbCBYW8L7LLuSUM5ZkmhpWVJZRXlGGy+06TO92/xiRKH3LVxPbtQfF6cQ7vRFXTYU0/xhH4vE41/3herxeezjEK6/6zFgXSRxjOto6eOyBp/j348/RtGM3mqYytbqOaVPKqfYXoagqTo+Tomm1lNZXEKwsTnVErODwu3F7PegeJ7rTYY+8N0El+vrpfXMFya5u9IIAnqmTcZYW2VkVEtjNY1kW29Zs47l7n2PD2xvQdI0pc6dw+kUnozsObYhBAhaDmEmDeDg2NOiQ6XdgmKCDmh90SA+1ub9BByGEEELsm2VZDHT3s2vpJppXbaNrRyumYaK7nZROqaa4oZxgbSmOVBbF/jT1GLKf0ABbNm3liQf/zSsvvEF/Xz81dVV8/LMf5bhF84lFY4BFbV01JeUlOJ3jIwhgmSYDG7YQWrcJTBNXTRWeSbXS9nqM9PX18Ztf/paWPS3oDgdV1VVcfc3nCQaDvPryq8ydO5d3nXM2t/31b2zauJlp06eOdZHFUS4ej/Pmq0t55N4nWPrmchLxBKWFhZw6dQ6NheV43G4cZQHKplZQXFtJoCCAy+vG5fPg9LnRXXYG+tFwLrFMk4F1mwit3wSKimfqJNzVlTiKgqjO8TEk/XhhGAarX1nNc/c+R/PWZlxeF/NPnc/xZx5P/Yx66ieXH/IOpSVgkUN3qHiDHjvrQVXRdC0VYMgJNKhqXjOLtL3/raY6eLRMu1NI9jYS4yj/6ActlozGMBNJO6VVPbjmKkIIIcR4Y1kWXU3trHp6Gc2rt9Pb3AmWhSvgoWruZIrrywlWF+PwuA+oqUcuwzDo7elj5bI1PP3oM7z12jIS8QSz5s3gXee/k8bpkwmHIxjJJA2T6ygpK0YfRz3FR/e00b9sFcZAGL0oaDf/KC2R5h9jSFEULv3AJcybPw+AW/7yV2695Ta+9JVraGtrZ8bMGQBUVFbQ1tYmAQtxWJimye6mPTx872M8//RLtLd24HQ4mFZezYyiSsoCQZwlfpzlBVQ01lBWWcrUuZMYiJnoLn2/g74TQbyzm943l2P0h3CUFOFpbMBZXITmk/Nlrlg4xhtPv8GLD7xIb0cvBcUFLDl/CSe88wTK68ozTUAOR/8k4+fXdRzQVAWPDmgABpiGPeRhypBAw15H7xvFBdL+XkNZI+8wYsSI90Qy21VVFVIjhaBr9glGtTvUlICGEGK8WPXKapq2tdjDRQ9uYpcaOnrwCEb2Mqkhps30dDPTwfCw28h5nd2Glb9fy8yMKJQewtoyc55bFmZ6PcDp0EgmTZTUML5K6hybfq7mBLzVVGfGiqKgaGqmvyE1M8QxgGKP9pMapUdRc6Yp5PdJpOT3RZQ7RLKSs3x6Xmbkn9T0QMBNf390r5+NtZffnOxCo1lklEPdjrBYLBylc+seOre3EOm179o4fW4CZYX4S4LoBR7Cbo1EOERnUzyV4ZhzQyHzJOdYDZqZ7iTZMAz6+0OsX7uRFctXsWN7E4qiMGPmNI5ftIDCYJBoT4SNKzZRUlqCFvCye8tudm9pZtBusvtJPQYLPPT1RXLKkbtc/rTMMsNsJ7eT58H7MqMxwpu3kujsQXU6cFZX4PT40fuiqJHWnDIO2mbetOwy6ePS1+GjpzecGdo685gaPSrz/cz7l/5eDp6WHW0qMyR3avjr/G3nDA8+aPsTUSAQyAQrAGbMmM5jjz4OQFl5Ge1tbQC0t7YzZcrkMSmjOHoNDAzw0jOv8vhDT7NmxVoMw6SqqIQzGufSWFKBp8iPXhYgUF1CZV0FFXWV+IsLcLhdFFYUkDgKO2o3k0n6V64jsmU7itOBd+ZU3FXl6NJcLk9vZy8vP/wyrz3+GrFwjPK6cs7+0Nkcd9pxFJUX2X2XHGYSsBhE0TRU18RL/dHdbjS33QFkZshOw8RIRsG0SFpWqlVL6v+qiqLpdn8Zuo6ia9lghqpmLjBEPnvIVLvyYsTimIlE9ljlXJgJIfZt07KN/OI/fzvWxRiVdLVub3/e2fp9tuY9yqq6OBBbDu3mLMsinIjQE+0jloyjKgpBV4ACt59YS4RXH37t0O5QHJTckbTSz3ODUwpQWlrED2/7wbj7XTZNk8cefZzFJy0G4ORTlnD9H29g+7YdmKbJtOnT9mt7JSX+/S5DWVlgv9cZL6Tso5NMJtmwbgt33f4Azz75Mt1dPXhcLuZU1DOzrIbyshI8VUHcFQGqJlVTM7mGyvoKXD7PkH4ojrZj3rd9N7uff4PkQAR/fRVFM6fgKSvGGfCNq6yKsTzuTZt38fgdT/PG029jGiaNcyez+OwTWHjGAoIlQXR95GybQ11uCVgchTKVZxUUhv8ypSveZsKAWAwrk0mSCmkoip2VoWmounbUZWlkgjrpYU1Nu8mOZVlYhmkHJQzDHqrJNFMdrJooFliptz2QiJDojWQqJOl59t1MFRQV0kPC5gSD0qO15AU4JNghjkFTj5vGVV/7D9a8tSGbHaACpIdLTt11zdyRVYfcqc1mKCiouXd0U8M2p+/8qjnTFdXOQlNVJWdUJexlIPt3CmiKfQ61b4JnwhaggN/rpC8UwTRSmRmGaWdgGCaWkZOxYZp2FodpT0svb6bOMZZpYVr2OmbqXJNZPpUdYuY8YlkYOfOy+0hnheSub+8zfaLa3wDKRDwjDfse9zIxYRo09bSxtWsP4UQMr8PF3IpJ1BWWo6tDf0OHxqVGPqr7Ot5211gHsn5OUGzUiTAjLGjtq5zWkOzS3O3lH4b8Ze2ewCwsRcn+Ru5lL4qiYFkWigKWpdg/rNkN5XUtNmJYMGfZYp+Xgb4Q/uD4qmzdeP1NuN1uLrzoAgBcLhdf+dqXD3h7nZ0hTHP0f91lZYExH2L3QEnZ986yLHp6+njmsed4+rFn2bhuMwB1RWUsmr6ASeVVeCsLUYo8+EoLqJ1UQ9WkarzBAJquEYqZhGL5/Q8cTcfcjMXpW7qS6K49qG43/nmzcJYVE/MWkIgDh7jvhYMxFsfdsiw2rdjE8/c+z+YVm9EdOtMXTmfBaQuYdtw0vAEvpqrS3R0ecRsHUm5VVfYaeJWAxTFKURTQtJEvHDJZGkmMRAIsi6SVuvBIb0NNZWdoKorDgaJp2T400oGNIyBT1kzqeCoQkR6xxTDsi3zTBMPMpJIrWKkLPbvyk+5TNVMxyU1H1VQ7CyUnoOBwu1GHGdY0WwYTkgZmKp09ExzJ3Ku1L+Igp09XxU4dP5aCHelU+7zP0Mx9bmRS9zEtIiRJpNKqVVUBNZUdpKn5xwQm7DE5ViiKwvkfO5dF551yWLaf16Qht9PknBrWkKyI9OtBtcD8bdnPS0r9dHaEcnP08wz7/Rs8TRnyJLOIlWqHaCnZJjGg2OcShdQ5xS60gpI69WUDsXnNYAwTwzAwDRMzaRAMeujuGkj9XaW2hQnpAIdp2Wd7i1RAN39b6VqrZaYKmbNM3jk49SYsMzudVDOb9PRIb5j2pna6mrtJRBMoKgSKAxSWBfAHPegOHTXVp5TToRGPJfPKk/lsTSsVfDdzjhc57yXb3MdIGnT39fHK2g28tmETkXic+rJSzpizmDl1taiKfTwzQTDSbzl7Ps/9juR+vYar4A/3mWfO/vktMcifnP99GdKaZdD3Rxm6ytAmMbkz1WxzpPTK6d9ABfvGB6homoJpWSM20wBSQT47+Gc378i5BshpgqLkvc7ue3AzFfupkv+mhgkcZsqdXjdn+YLyQrw+L+PJzX++hebmZq79/ncOS1tvcWyKxxOsXr6Gh+97nDdffptwOILf7eGE2kZmlNdSVl+BUuRBL/RSXl1KTWM9JRUlOCZgVvmBsCyLyI5d9C9fg5VM4qqrxlNfjV4YRPN4jvnrxWQiyYoXV/DC/S/QsqMFj9/D8e88noVnLqRuah1u39h21HxQAYtEIsEXrvoiN938p0NVnnEjr+2zYWGYRqads2nad8hM076bZj/P3k0zDDN1F82+a2aaqTbSpomRe/css66JkUxiGQZGIomVtC8sjWQSK2lgGCamYdh345KGfdFp2hed9h06M9XuWUXTdTSHhqZpaA4dXddRHTp66rnm1NEdTlRnaprTgarrOBw6aqpdtabraKqKrtvZFZpmD7eqagqaqqfuSiqpC1ATM57K0khdt9rXEBYWip2doempR82+47mXLA0rdfcwW4FNX3gbkD7mSQMrYWAmkxiGgWXYfY0YqXWMnKBEun26kTrWhmF/Bmb64j1pkjRSxzRhkEwkMZJ2kMZIGKnnqcekgZE65oZhoAJJw7SPR84FV+4d4MzFm6Jk7hJD6k6wmrpzDKCoqOmLxPQ1WOqJqiooVnb76Tbuas5FopraoKoqQGp0Gk1FVRQUNdt5rKrZ+/R4nITDMTurxMw57pkKhomZTNeWzCF3atOj6KTv8GYrAtnPzK4Apdv7W2BY2c8YMneKMxf36frioApkfkUgRzYukbpUtV/YQQz7dfq7mg30pPsVyDleSvpOfCpYpCqo9u32TOAte/xSy2laNkCn2H83mcCWqtrHXLNHB1JVe6hix6xJoI2PYQ7Hi0QozNI7HyEZjQ+dOfi28TB3b/Nu7Y7+5uIhs+PI7/LADHOR0QujuzV/mCSSJt3dETq7IwyEEwAE/E5KKgspCrqzf1PJKBj5FWQXORXZ3AqwCoqWOhfYNeZBlVgwTJOdLR089fZKXl+3CcMwWTh9CucuXsDU2io70wXQdR2Hw2H/redUktPlGBIoTp9ncvaX6ZsitY7LpROLGzmB5vz3kQ4W5AWiM+8tGyiwLItYSwfxjm5QFJylhThLC9HcLlRdz2QWoaQziMiWVU0dl8x3YphmTpmAb3aSP+AhlO7z5ECuWRX28jc6XIRlcLAxXbThdj7y97iwrIjexPjpJPBvt97Ols1b+O73r8UhbeTFQTJNk/bWDh5/6Cmee+JFdu7YhaooTCoqZ2bDbCZPrsdVXoAScOEp8NEwrZ6K+mq8Ae8xVUFPDoTpfXM5ifZONL8P75wZOEqKcBT4UcZRZ8ljIRKK8PqTr/PSgy/R391PYVkhp773VBaeuZDymnKc7vER0DroT6mtte1QlGPMbV69he988YckDUPaHI+Skr64Ir/jt2wat5JdJvM8vzOu9Hp5dwLJVn7t19k7ZZnHzJ1RK1OtzdxoGzJPiLHnc7r48yN/xOl2j3VRxg1VsfAWuTCT+Rfuw90lzt4BzllmyHJDFxh+9j6Wg2Er+YOKMKzBCRvDLzR05pApe5swXCxn9BvKBJYzlW9yK+HknM/zl8mtYKcr0nnrDV5m0HqGYdLa1EvT5g5am3qxLIuCYi9z51dRN7UUb8CVaXKX2xElZJ+mmwwM/xaHvtf0FizLYtmardz16Cu8tWozbpeDC85axKXnLqGmsmToMTosTDy+TIkGPe6bZVnEeqOEWnoxEybeYi++ygI0hwbEwYzDMLG/QyE0Ti719vc3vberBWvSnHHRJn3njp3cdefd1NRU861v/CcAFRUVfPva/x7jkomJJjwQ5q3XlvHYg0+x/M0VxOMJCj0+ljTMYFbDJIobKlCCblSvTmVNFbVT6ygqLR7SL8XRzrIs2petpeONlWCBe3I97tpK9GDBMT+0c3dbNy8++CJvPvUm8WicqslVnHLBKSw4bQHB0iC6Y3wFcvZZmovfc+mI86xUeuDRoLyimBNmTaW/byBzF1tNXYCpqv1czblbY9/wGNSrtZJbgU9dWJG98FIU7HacpO8Ak3MnI/8uiEW2l2wUBSt9RyRVNiV1hz3dhlt36MRi8UwbZyPdnjn9L53ia2V718+9E27mpRGnsxPs1Nm8QIKJnTViZXvnN9O986e2k8lCgWwmSe68zGMqLVhRUDUFlZz3lXN3XE0dazV1bFQ157ky6Lk6+PhAXvv39HHN9ECuZo9zqj27qqXvvtt3yHObZKiqBpqCpuu43Q6isUQ29ThzvLLBlvS8TNAlNxMhtWwm2GKaedkEppUdHQFyAjiZdOR0ACdnm/aKedvNZjCkl1XQdRUjnfFA3lcxnVibvbtnT8w+5NyZy3tJTvUi985gag/5wwDvbRtK3gRFyVsS3aGRSCRz3lP20cpL2c4NbJGekJPxn04JSs0aLp3byjlGOR+O3aSInOe5jaXI2Un2RUVVCYloQgIWOTSfj/oLzqRt+568/iRUNf1pk3NnmZxKR+4XZuhv0NBJwwcf8qcPd5d35H0ABIMeensGteMcUnm2Bk0fbuERghHDbWuvNTYr7zs30iIAPr+LgVAsexwGR3YUZdD7zjsJZNdRcpYfvD52MLptSzPb3tzEnnVNGPEkTq+LmnmTKGuspqC6CF+hH1fAO6oOr0tL/XR0pNsYj/w+TdOkt6ePndt38eqLb/LyC6+zZ3crBQV+Lrr0XE5/5xLcbhd7+sO0tieprqmkpKwIh9Mx5LySPXbDfTjWMB/joPVTjwXpUUJGMnwUDoBkf4i+NZtJ9vSjedz4p1ThKA6i+j2gHf4Ly8Kgl56+kdosDy7vAVwXDrfKSMHI/VBUGqRrYGjTzbFQ31DPg4/eP9bFEBNUMplk985mHrnvcV789yu0tXWgqxqNJZXMrq5n0szJ6CU+LLeOL+inYfokKmoqMkNNHkvMRILItp2EN2/PDu3cOAlnURA94Ec5CodmHa1dm3bx/P3Ps+qVVQBMnj2Z4844jlknzsJf4B+3Qa19/sr5/X6+9NUvUldfN2ReIpHkS1/48mEp2JHm83v4zGVnkojEINUvA6qGqquppgx2kwY75Vuz+xfQNLtfg1R6OGqqnwMt29xB2dtFMOx7/igDQkWFPrp7Ru4oZmhb7vTTUUxPzbM7lTOwUs1XLMNupmEZdvMMK2mk5huQmm//MzOvyXk95GJQTffbkD2+mWOeTsHX1NQ0+9irqeOf/owyn4WeWien2cmhDq7t65jvS7atN9iBhPTr/Iq1QvZQ2dV/yw4IpPu0SAVU1NSj3ZQhncac7tcBe14quHM0daB0OOx1OMcDnFda6qeja+ROio5Fka4+Xv7zw8TDscx30/5uM0wwMfudTzfPUbTUo6qi6CqqpqHp2XOD5tBRdRVVd6A6NTSnbjdvc+pouobqcKC7HKgOPTXfiabbw43af1NAbjkGcRcF6E+OLq079zuVbhaVDjoaqfOimTSxEkm7yVoiYQcxDTPT74SVTDcPNFMde6b6pEg1Q7TSy5kWZDrwtLdtN0G0IBXQ9vncJBUFh9uF7nbg8Lpx+D04fW4cPje622nfYUmfW/ZTT3Mn215dQ9PSTUT7wqi6RumUKspn1FJcW4GvNIA74EN3O/br3Kw6nCh6bMT5iUSCzvYutmzcxivPv8arL75Bb08fVTWVfOzKj3DiySeAZREaGMCKm9RPnUxpeQn6EUgL9pQFCLF/5y4zkaB/1ToiW3egaBqeKQ24atJ3CF1H7KaRqySAao6P1OB9MU2Tjcs28vJDLxOPxvjMD6+0A1FCTDCWZdHf18/Lz73GEw88zdo16zEMkzJfAac1zmHu7BkEa8swvBqaU6OyoZraybUEi4LHZP8oid5+BjZtIbazGcsw0HxeShfOIukvwFFoZ1Uci0zTZMPbG3jh/hfYunorDpeDOSfN4bjTj6NxfiMe3/jvw2Ofv9CN0xrp6+2nqqpqyLxEIrH3C/sJRPO4mXTBGbRtb5mQw5ruy3DjzttP9/cLqgOHph1+OgBSVOSnpz8y7v9YhpMXdLCyHbtlgw527nV+0CEVDspUwHIzONRMAMIOtpANOuR0uDkRj9VEstfje4DHXtV1+dwGMZMmiVgCI2nkJAcMzgwau/JlE4GyGUNDmkLklDf3PaSmDMl4mGg/mZnMQTUbMFJT/RGp6cCQU0d16qAoDPSE6e3sJTZgBxX8JQHqFkyheFIFBZUlFFSX4C0MZPt7OEQi4QhtrR2sX72Bl557lbdeW0Y8FmfmnOl87MqPMHveTOKxOKG+flxuF43TJlNUVIi2l2HZxpJlWUS27aR/1TqseAJnRRnuSbU4iwrRfN5x0cRhvIkMRHj7mbd55eFX6GzpxOP3sOTdizANc98rH0Msy+L5B19i17YWe0JuRiO5WZfpV6nHdNZVKrs6PYf0Ugo505TMtMx2ldzlFXK3PlKGTbr/rtx9+QNuBgbimUByus+W3JGgMuunb1hltjnoBpaSvQ7OPd/nLqPkTcs9UulDouSvN2Rf2WUKCjz09to3LjK/Bbm/F6nX8XiC5t0tvP7SG6xds55QOIxD02gIltNYU0d5bQWK10WvahKLhigpKMEfLCAairFp5ZbU9vKz+zL1tUH7y8vkHWGdQMBNKJWRl9f0W1UzxzCbuZxtRpg7ipeSs246W5xB04fr0Dc73c50J/17lNOUMd7aQXTnLozePlAUnMWFOKvK0P1+EsECQgkFtS+M0hcmm8WZn4U9UpnIWWZo+XOmjUOJeIJlzy3jhftfoH13O74CH4vPWczCMxdSM6UGl+fQ9auW29H34Tjn7jNg8enPXIE2QuqMw+E4KjvcnCjSQ5NapkUyGsOMHabGq4f579AyDKxEYoQL+WyqfzYlOTctOvcXMJuToAyZRjZFObcCkV5w8LSc4EJ2+6n1U9OSEQ0znsgGHbSRgg45gQYJOgiR4Ssv5KyvvZ/Nr22ws6cg9Qeq5F85kzPihWF3pIthYFqprAPTtAeGSHdqbNhN4NJDgaY7RLbPl9hZBxY5zeTsIGN6WNB087jMfCsbnMx0TGulL8JzOkokXfTsRc+wQY7B09LP0xd3ORdi5GR3ZDqLVch0LKsopDqaTGWCQKZ5m6KSPdfkDgmrKjh0lUgogpEwUtkb9qOZm8Vh2s8tw8p0Fm1nfxiZY2SaFgnDJJE0SaYuUlRVwe3UcGgaajRO3+bd9G3ePeTzT5fFrnRkM2dUXUN1pDqQdtidRWsuJ5rTQaDYj6mqOPxeXAVeDE2lq7+X1Ws38srzr7N6+VoUVeHEJcfzrvPfSW1DDZFIlO6uHnw+L9NmTaVwnN99jHf10Ld0JcnuXjS/D8/MaThLi9ALClDHWbvi8aB1ZysvP/wyS59bSiKWoKymjNMvPp0Fpy9g5rxJhMLJsS7iuLJ19RZ+8z25dh+PLMsinIjQHwsRTtid3Lp1F+W+ErxOL6CwZXcrW3a3jm1BxbDSgZrBj/a87O99pr+nnDrMcM2m1cHT0sGh7M5yWnVmX1vYwYp4IollWaiqitOhQ2SAt557i7eee2tQ8C17cxXIu1GULcHQ5q3pUafJuWwDhZKyIq657uuH9Hd2r798K5avzDzv6OgccbnyivJDViCRL3vRnBqSM9V23kp9M1TdvrPlLPSjmWNwAXYIbhc6CnxoSXJD15mIZu7rQU8G3ehWBs8eedqgOEHuH37+jL1P85cFiHaEhi4jhBi1wtpyapfYQfG8O06ZfklyspUsUj+WZiabwcr+z17XTC9mnztTG0wNv0mqnx8js5/cwEV6fqa/FyO/01+74x4w7f8RCLjo74/lBQrSAYL0nSX7RE0qmAkouZlTZG8IZPrtSC2PmhpSMnWOUtW8c1XunZ/sBU92u9mbfpkxJjM3SAFKywK0tfZDur+j9OhKuX0dJe1p2SYr9ihWyaRB98429qzbSVdTO6Zhojt1CssK8AZ9uNxOe5SPVIDHTBiYRnoELNMOjuQ0d8nclUkkScZzgkP7+HkxLYst7S28sWMLu3u7cOk6ixsaWdTQSIHXi/nqFna+vhXNqePyuIn73DSt3k2L14XD58bp9+AKeHEV+HAFPDgL/OjOsQsImPE4/SvWEtnehOLQ8UydjKuq/Ig3/5gITMNk3VvrePmhl9myaguaptEwq4H5p8xn5qKZBIoD6A4dj89DKDwxmz8eLo3zpvKNH32OZS+uBqz8e0DDyLnqGnQH3sqrcOVnk+VFnFM3fNL93mW3YaWWVQZtN71RK2dbSmqCw6kTjyWyvwmQ029Y6vciHVyGvCGac0cwI9UPWyazDzLDNKd/V3L7DRv8W5R3nkrPz1uX1Ov08qQTVIYc3FAkzM7uVnb3thMzErh0B1NLqqkvrMDrcuctrw4zwl5mtpJbbR0su+Ohc/cyJX3/wLLPuWSOV845Oh3Yz5k3uH6bnje0NLk3JvOmZF7kHrLBTdmtoWvklDFfbhmGzhta5rw9WkOmDCn3oNINY/h9Dyp29oCn/lyyaylDypNt6WBhmCaxpEHcsK9xdFXF4dDQFBUL7BEQIftZ5WzLyi1b+u/BGmaZzKVYzvOc6zULiFpxIn1hfIX+vR6N/bHXX+bf/+4P+9yAoihHRZaFEU+w/bW19LV2p+702V+M3L4DRvNFG2K4M9PgPwUrZ7nUH3ruD0j6rlre3XmUvPS6LreDeMxIDVmWm1aVe0d/0Dx18DIMWS9v6Lgh2xy8fO7F8ei3G1Eh1jOQcwcz5y5nZshMK3+4zH3NNwcvZw4zLftoD9c5wvZG2G+TrpJImpn3k0lxVkdzrHKPQfqzHWmZ0X82uZ9Bpu1/+tcm81zBbPHQ15vu/C3/12BwSuCQ77E1aLnMtKHrDd93irWX+cNsY9C0iM9FaGDkNux7dQgCbAdKmVQBpUdqNIKJQ9M1nD7PWBfjgEzkvmA0Xd/vynnP7g62vbrW7peiP4zm0CltrKZiRh3FkyrwlRTgDnhRNS2V5ZLOfjFTmTFmNhiSClgYSSMbuEgNHZ3uiwPDIJFIYkTixMNRwqEQyXic1j2drNqylaWbNtIdChH0+njX3AXMr2vApWr2kOKmCam4lBlNEO+NjLr3iPT5XNWyGR/pTA/d5UB3u9A9ThxeNy6fG2eBF6fPgzMd+PB796vjMsuyCG/eRmjNBqxEEmd1Be76GpyFQWn+MUi4P8ybT73JK4++Qk97D96Al4VnLGT+qfOpn1GPN+Ad19kz44FlWcyf30htRcmg3+Dc39qcSmROhSjv998atF7Oc2vI73m6cmPlXwsM2kZepXbQNtLXAj6Pk4FwbkbxML/r1jAvh/z+W0MfhmzKGmZbe7muGXGWvR23WycyEMMwDEL9A7z4+nJeWrqanR3tKCg0FJdx/JQpLJw5mYKgx76sU1V0TcPh0O3O5C2GXLMyTHbgWF7vDGblfb6pabnfJyW3lgWkQ1hKtoKupN+nadfPUBTMVKZhbjQ+sw3FrsOpWk5dLn1tnJvFmHt9nVkmpy6TWjGTIZF3nQ0Kg7IZ7eLb+8i8h+zNguxG0osqmeXJWwbcbgexWDJnm6k3OChg1bynk7ff3sSWLc14nQ7mTq5kzpwGamvLcTj07EAPw0nvOy8souTNTw+yMfTP2rLno2SeY0GgLHhIm5vAPgIWf77lxkO6s/EsGYnSumozRiwxzFxl2Kd7mTTMxNwJ1l43lPsFH1ZecewXYfIjXEMib1bu80E/KOPnnHZ45Z5s0ieGzMmInBNNTtrWoGVyn8cZdPLNPdZkZ+b/1ud+NtmFM3/4uc85Bj+jo1jPKi+NH/v/7f15mCTZXd8Lf2KPyLWqsqqrq/dlumeml9FImhlJIyFAYjObjcw12zVczL2YxcJ4AQzX8oONL6/t175g85odZLHY2PhyMRixCAFGkgWjdfaZ3teqrq6uLffI2N4/TkRkZFZmbVNdS/f5PE9VxnIi4mRkxIlzvvFbvgZ1l/rMSySDaC03uPbcq1x/7jWqdxZBURg9NM7RZx5j/JEDFCdGcEorg2duRaRx3/ep1xoszC9Qv7fIHa/G6xcv8T/+5JM06k2OHjvMX/2mr+X8E4+jRBH1egO/EzA2Vma8MoqpG8KaIwjwOz5+q43fcHEbbfyWi992CToBgecTeh6hF/QKKLH7i9/xicJ27+BuHShqnLlKEwG8DctANQyM2MLDKuYwDJVg/h564GFXyhQfP40l3T9WMHNthk/87if43J9/Dr/jM3l4ki/661/Em77gTVQmK1i5re0cP8h4S8tc/a0/3ulqbJqlN7LxKl3rjRTarLFTQ1G4MnOXjz7/Gp+/dg3X9ylZDu8+9ThfcP5Rjh4aQ9e1tL+pqiITYPelVhT3UbXMQLv/M9uH7S7vTpPpDw8u2/uZ7TNDbALYHaukWkHmxVm6LuMWkZbLWA8q6U4zp747H3g+7bkarfkakR+imjrOeAFrpIBqxIH4M2JFdiAVywTohobvbSBTUHfsvnI5Q9ZtmtV3pusavj+47mEY8sqLN/nYn7zEjWtz2I7JF3zxGZ559jRjlVKabe2NV3HjX9gqOXjW1gY6lk/DGMPSOfbkfmHyu042d80qgz5YecOusX3/7hQlI1QMeWs9aD5ZvGKwnR0wi4l006yZVbTBbTMD+FS5H9Rgipme6Z4y8XfvUTOV2IIgMbsG0a6mJtNK3/aZBk4Z8pmpR888YJo6HS+T7WTQm4pB0v0KRX6IvN/XSKSZBYiIQsia3YlzGcbl4v1EiSo95HfpfqPVr7lEHR64bsgmme2GFxiyi1W220wnof9qX30X61UkN7gPQC/m6Tr8SSS7F9/1uPn5i1z7i9eYu3wbIiiMlzn+7Bn2nT5MeaqCU85jOtaWB8902y61Wp17d+eZvzfPlYvXuPjaZV57+QLzcwsoisITbz7Ll3zlezh5+jhhGNKoNQgJOXTqGJNTE9gbiASfZFkhDAiDKHWNSd1k4vnA8wn8gKDj0am38GptOo0WXquN3+rgtzsEiejhCZEk9IPUssR3PbxWR8RfGco8fOJKat2h6RqaKTLZGDlLWHEUHexyHquUxxkp4FTKmKUH06ogCAJe/ouX+fjvfpzrr15H0zWOnzvO+Xec59G3PkpxRLh9SDaGUSpy/EveRHN+qbdvlBTITmT7qn1vj9OXbz1dI2XljuIxqUr/9pnyK1Yp3Yd31jtYUTAMDW+1AWimk5P2GYbedut4Jvf32dZdXsyEUcTd2UV+7w+e43889yJ3FhfRFJVHDxzki952nnd+wVmcvJVaFGi6hmkaaNlA3X0D+fTFW2Z+5figv23uH3isv7xl6d03/UnHcsV37Vuw1nkbsj6KIjrVJq07i3TiTHxGycGuFDGKNqqmp0KFMniHvfsOw40NuocVzS7f2sfecAb8rp2Oz2f+8iIf/7OXWbhXY2Qsz5d/9Vt4yzOPUCjleuJeDN/nuhZuGkVVicJwS60DZUsfo9oOY089xb2L11F7Ojt9N6WidIO3RVF6EwjPPiVNryfSbuqgqKiaEvsf9zXkPdN9g+IkoFp8zJ7yPT7L4mKojBeYn6/31rXvOwx8O5QKDvG6rGDTPwCPIOMgjrAX6BNJ0nMS9ZTr7j/qM6eLKBRs6g0349agdr+vkiixGTcHlPT8kHV/SOkXHjJrBqq5PSWGrOtvFiPKJZvFpWbXB33d7eFajfe6V6xvt4mQEYnAhBAyNlpgYbE3/oaSFXA2xBto6AZtOujBm2FsrMDCwuDYISt+383WYcMM3kkURbhuh1azhWWpEISY0sBCsgsJw5C7r9/k6l+8wvSL1wg8H6vgcPCJk+x//DCjh/eRGy1i5uwtzdMeRRHNRotqtcq92Xmmb89y8dULXHj1Mhdfu0yn08EwDR47c5ov+6r38s4vfCuabuP7PtWlKihw4NAUE/vGMTeR4Stx+9iK7pAICtt1gwn9OD5HGBCGEaMlh7m7y7SuXKN97RZu26ODTtuHwAsIvJCg4xHGrjGhH+C7Hu3q2vE8kmwJSdaWHpGj4GCXRKwOe6SAM1bEHimgbkMq181QX67z3B8+xyd//5NUF6oUygXe+t638sQ7n+DwqcM4BeeBFGi2i0hRuHO9zuyVuZ6MP4qhddM+GxqaIYLf6qaBagjxTDW0dFpT4zfccYye5OVQ+tukQcgzmR4SVulfraBvgF2ZKDA3t1r8sMF93a1j7Z112i5XP3+RP/vv/4PPPv8SV+/OEEYRlUKJ97zlKd789icZ3T+Gautc8UFrqkxO7WN8okK+kF9/VTZyHlfbzRqD3JH75P6YHZeEnkfr6k1al68RNJoomoZ1aApz3zhaPo9WyIlYPkMSQQxjbM3rZfcyljnvtaUan/y9T/LJ3/8krXqLfYf28SXf+DaeeNcTVPZXtiTj1VbFSSrfh+tldz6tdgjNMFAsG8UwSTNwpFYApIqmaiqgG+Jhr+uouoqiaqBp8dumYYqokpncWjVLsywUY/UsIdslCG6U0kQRdxf7gQdBQMft0G67NBtN6tUG9XqdQsFiudpidLTMaGWUQjG/oTd728Wg390sFdE2GQZipzFyDmpjd0Z9D8OQdqtNs9liabHK8tIyge8DCvm8wfFHHsE0H7y0yZK9y9KtOa7+xavc+MwF3HoLzdSpHN/P5GNHGD8xRb5Swso7aMbWKW1BENCoN1lcWGLu7j2uXLjG669e4MIrl5i+NQNAZXyMd7z7Gc49eYbTj3fvm3xO5/bteTRN4/CxQ1QmxjCMrTU93SyKoqDoGiqDz5XSruM+/wJRvUF+cozx40cwRsvopWLq/hHGLixhHMcjijO3+J5He2GZ5nyN9mINt9rEa7r4boeg4wu3Fl9s12m0cWvNdYgcYnDZL3KI2BwOZiJylPOoRyZo+xFmwb5vQseti7f4+H//OC98/AUCP2Dq2BRPfclTPPHOJxibHNtyn+iHleXrs7z44U9t6T6z/vlZa4yuW62SvlxKY34p4qWbErs8JGmTFVVF0RRUVUPRFBRNzQgrOrat43Y2aOK/XjYjbMQWzp12h9p8las3bvPS9atcvDdDy+tgGyZPHDnOEydOcOzwAbAN0DW8RpucXeLwySOMT25OcN3rKIqCt1ylceEK7s1poiBAy+fInTqOMT6G5tjohTyKaW563CRSku7WEdDqKIrC3Vt3+dh/+xif+7PP4fs+xx47xpve/SbOvu0sxdHiQyPeSsEig6rrKIYZP8U1VFtH0cWfqmlp2koZAOvBJIoivI6H63Zot9vUq3Vq1TrtdjKyj9A0HcM0yBfyjFUKROg0m20WF64BYFoGlYkK5XKJfD63JYqnZPfi+z6tZpt6vc7yYpVatZ6+MbAsk1zOSS1wovA+pR2WSDZIY6HG9U+8yCt/+jy12UUUVWHk4ATH3v44+04forhvBLuQQ99CH9ROx6NRbzA/t8Ctm7d5/ZWLXHz1Eq+/colmo4mqqpw8fZyv+8av5fyTZ9h/YDJ1dWy3XZYWlyGKcJwKx04eoVIZ2xPtq7e0THv6Du7MXfyFJRTLJPf4KczxsYHZP1RdGxrnpnxwYuhxwiQtbRCnovVF7A23Wqd5b5nWQo32Uh2v2cZvewSul4ocQRDgtVzcepvoXnVd32tQYNJU+DANNMuIA5Sa6LaF4VgYeSGGmHkHo2BjFRwwdF7+i5f5xO9+gpsXb6IbOiefOMn5d57n1JtOSbeP+8Do8Sme/Y4v58pfvIaqa93sQP2fmaDkYnn3RV5PuugVQcz7s2wkrqtxeuSse3DqMrzSZXW3E0URfhDiBSG1dpuLczO8fvc2880aqqJworKP8weOcGJ8Ei0eN7Snl3r20QLm/+iFOAxDnNpZF+5gqqGn95CZs8W9U3SwSjmsch5ntIgzWkDfhS/K1iKKItq3pmleuoZ3bwEUBWNiDGv/JHqpgJZz0HK5hzaOT+AHXHnpCr/+h3/BC//zJTRd49G3PMqb3v0mTj95mlwxt9NV3HYezithGLqOl89hWhaWtXk1T7L7GWg1UasTJC4xCpiGgWGajIwOfxioqorj2DiOKON5Pndn5pi+NYOKQmmkxNj4KIViAVumpdvzdNwOrVaLWrXO4sIyzWYTBdFxtyyLYqnw0Kjdkr2B3/FYuHGXe5enWbg2y8KNWdrVJgCFiTLH33GG/WeOUp6qYJdyGPbWPftarTa1ao25u/Nceu0Sr718kQuvXOT61ZtEUUShWOD8m89y/skzPH7uUXJ50QlLLDB8z0NRRDt66PABCsU8hw6P7+rMLKHn0bkzR3tmls7de4StNgCqYzPy6HGisTGMcmnLs38MEzqK+8fg9Cr19X1CP4wtOnzCIBQix91lmvPLtJcbdOot1CjCbboEcWrb1Aok/gs8v3ewugau53N7qcr0YpVOEOCYBqenJjgwVsJGo/3cBV75nEhJq1kGuikGbpptYjgmumNj5iyMvIOZtzALDmYxh/4QvqXeDAfOncDX9dQLOI2RlcRYiaI43kqS+jlxLaUrQKQbRz0xtBIBoj9FqFgcxa6pEWGU3R7xsjBxI1aUOOVxmLpZRUEIQYBlG7RacYB8hSFWEaIN21gMK1YqJpn9R2FEs9qgNl+lPl+jUW9xfXGOC3O3ubl0jzCKmBwZ5Ysfewtnjh+jWMgTKQp+GOCFIYapM14sgoewnOp4It6NL+LjiPTP8f3U8Ymi9roFHDW2TOl3C9NtE8OxMQoWZs7BKguLKWesiFMZ2fY0zoHr0rh4lfbVm4TtNoppYB89hDU5juo4sduH/VC+GF6YXeDCZy9w4XMXuPTCJdyWS67o8NR7hZXZ8bPHMe2Ht32TgkWGhaVlXvz8q+iGCHRjOzZOziGfc7AcC9M0MAwDwzTkoGSP0G81UavWqFcbtNtuHO8zYzVRzL/h39UwdAyjkB7bbbtcvXgdRQXDMKhMjFEql8gXcui71H9YIoiiiHarTavVprpUZWlxmY7bIVJA13Qs22R0dGSnqymRpERhRO3uIveuTHPvyh0Wrs9Sm13sWv0UHAoTZfafOcqxJ45jjpVxRgpbFjwzDEOazRbVxSq3bk3zyguv8vrLF7nw6iVhIQEcOX6Yv/JXv4zzT57hyPHDaZvb6XRYXq4SBSGaoVOpjDFaGaFQyO9qS4ooivCrNdrTd+jcmcNbWBKDO1XBGCljHdiPPlJCdxwqU6NUPWVXvTVUdR21rzq5sRKjxw70LBuUwjcbmLQ/SKnX7uBWG7jLTdx6UwQqbbSZnbnH6xdvcuvOPFEUMV4ucGRilIlSHgVhURP6QTxgW78AkkWEwhLuBYVKibf93a+XLwv6sEs5Ro8f3Olq9BCGYVdhiMKV8cxjNaRSycRsyzLsJ1aG9+uGXRbJ8trcMjMvXWX29VvcuzqD73rca1S5uDTLxdnbtDsdCoU873znM7ztXU9z6PghVEPFbXfouB66rrH/4CRjlVFy+RyVSp67d5YJfT/NRiRSOgsRMPBi967Yzctvu/gtF7fWwmu28Ns+gduJMxv5wh0ssa7yk3svoNN0153VqDebUddKSneMVBgc2Vcm0HTMorDusEt5rFJu3e5hnflFGhcu407PQhiil4s4xw6hj42gOQ56PodiGg/Vfeq2XC6/eJmLn7/I6599nfmZeQAKIwVOnD3B4dOHedt7n8Qul6WVGVKw6CGKIgzToDxSEp0Qz6dRq7O0sCQa0gRFmHs7joOTd8jlnFTIMA1jV3euHmSCIMBtu7hu5w1ZTWwViehlx9YXvu8zN3uPmVt3QFEolYtUJsYoFPLYjv1QNdS7kSAIaLXaNOtNlpeWqS7V0mvHMA1sy0rfAEskuwG33mLu8jTzV++wcO0Oi7fm8OPU3Jqhkx8vc+D8CUoHxihPjVEYH8EqOBi2ydShMeYXGm+4DoEfUK83WJxf4sJrl4RI8cpFrly8hu/7WLbF4+ce5fyTZzj7pscpj5QBUlcPNxaPczmbQ4cOUBop4eTsXf1SIPQ83Nl7uDN36NydJ2y2AFBtC2tqH8ZIGS2OS6HalnD7MAyciTL1XWwdslFEQEUVBngOOQAHxgHwOh4vfPwFPv5nn2H6yjSGZfDYU49x7tlzPPLEIxRHiiiqkoodURqwNJ72QjrNNu5yA7dWx622YtcWl7Dj47u+sPzwhHtL5HffVEdBQKfewnoITaj3Gr33/PD738xZ6I3742LZabrMvHqNO69c5+6F27SWhDDiahGXlmZ55eY1FpaX0TWNs+cf4+1f8Axn3vQYpmXRbru0Wi0UV2FsfJTxfeMrrC5VVUUzdLRNDEBFUF8hTIR+kIqEIhtRgB+LGInYEXgBXqOFW2/RqbWEG1jHw+/4sXWHn6ZyDgORznmY2HFrlXqtcA8zNDTDEIFbFVACDzUK0QwVs1TE3jdKrlRGzxUwikX0woOZ5aifMAyZuTrDhc8JK4prr14jDEJ0Q2fq+BSPvuVRjjx2hGOPH0td4QYJxQ8rUrAYgqIoGKYQIZwB633fp91qU6/V8X1fvBmI1+mGjpOzyTkOuXwO0zKFdYZpyLfqW0CP1USrTa12f60mtgpd1ykU+6wvLl0HInTDoDI+xshoWVpfbBOe59FqtmnUGiwuLlGvNUhMUS3L2lXXzoNM0OkQtNvdLEFqN6/6isjyDzGBF7B4U7h23Lt2h8Ubd9PONArkRouMHdtPaf8Ypf2jIg5FKY+Vs9EtA83Ue2MlrJHpo6fD2jfdcTvUa3XmZu/x/Gdf4tWXL/D6Kxe5O3sPgMn9E3zhe5/l/Jse55HTJ9B1DYVYFFyuEng+KFAsFTl4ZIp8/JxMD9FqEfQfO37b3tYC/Goj/S6rvkUctm6D20RhSNBs0ZlboDO/iF+tCysKRUEr5rEO7Ucr5lBNC03XUCwLVVdBU8D3CGpicNUIWnjLrfXXZaN1T4pspPw6j19rLtOpZuref1+uSDEIS4s1nvuz5/n0nz9Ps95mtFLiXV/6Fs499RgTB8eFZQ8+4fLSiuNlU/MpOlglk2LZAsaG1yH5SvH3CoOQyuQYtd0Zo1myCwjDkHuXp5l5+Tqzr99g6fY8RBGqrmFNFJlpd3jh8kWu37xNFEUcO3GEL/vaL+Gpd7yVfDGH7/s06g0ajRbFYp6Tp05QGinel0DAIqjvSouo9ZBmMIpj3IR+AEHXtSvwA8JsXJuOJ+La1Jp4tRZaFNGst0Xq5o4QQwLfjwMDx/uN0z/7rkcUtQY3LdN1eG1mwHeLv18mJk4i7AjXFgPdEbFwzILIfCTSPOewig5mqbDt7i3robZU4+LnhAXFxc9fpFEVLwnG9o9x7h3nOHzqMCefOEllfwXTNmWfcxV236+7R9B1feigMghEsKuF5iJ3Z+cyN22EpmqpVYaTc7BsK7XOMAxdds77CPwA1+1aTdSqdRq1xo5ZTWwV/dYXgR8wP7fA7PQsKAqFUoHx8TEKxTxOzpHXxRtEpBd1aTXbVJdrwr0jDqaqqCq2bVEeKcnzvM0EzSZX/vgjXb/pYSRiRmZa6ZvvRqVXepclZfvLJR2DpEwilPQfY5V9zc9YtBpuN51zNoBc0vCnvt9Rz+B7YPn4L4wiWrU2i3drLM3VWL5Xp7rUin3CwbR1yqM5DhyYZGQsR2HExjS0uG4hij8P0/NwO8IF2v3HBpYy0/RPr8JSrclnL1zjs69f4/mLN2i6HXRN5ezxg3zVU+/mrY8dY6oy0t3g7u3hO2suwZ2buMB6kxZtV3I60cGPCHzxhj859yig6SqqHWcIUzyoLRLWFgmB1cbGb9yeZedorrNcFEXcnF7i0y/e5MKVe0DEI8fGeeo9j3P00Ki4Rquz+NXZVc/VVjFjWxTe9QXDbf8lDx21u0tMv3SVO6/dYP7KTGqVVtw3wtQTx7lVW+AvXnyJ1567iOt2GBkt82Vf/V7e8QXPMDm1T7i+NZosLS5hmiaHDh9kpDKSxjLbjaQZjHSNgSZRa1AZyzF7Z1mkaw5CCCMhdkRAHLjXv7dI5/ZtompVPNdsB8+w8QLwOsKSw3f91NIjdWvJih6xW5nvBXixpcdGNN0eaw9dQ9M1DNtA0bRuPJwkCLBjoTsioKmet2MBxMEs5DYtfviez7VXr3Hhcxd4/TOvc+f6HQDsnM3Bkwc5dPIQx88f5/Ajh7HztnT12ADyTN0HNE3ksrZYmYIrDEN832dxcZm5u8KHM4np8zDHzRhmNeG23fht0e60mtgqNF2jUBS5t8XguhMHpgvR9MSfu0wun9s1Kfx2M6ulF9V1Hcu2cPaQwPWgoto24295guWZe90BRXbwHs9Hgwb6A5Zl57NCQE9wOIaIB0O2XVEmw9BBdlb46PtUBiz3/JDl+SbLC02WFsSnF6ftUzWF4ojD4RMVSqMOpVEHK2ei6TpqkrlKy6Rt6/scttyyDNxOZsiYlI+nozDE8wOazTavXbrG869e4aUL17h+exaAkVKBp558jCceP8mZU0exLSttq+/5AZ4fQBRhGAb5goNt2+I5lh04DpheMazMilVALmfSbA4xBx80KF3PQFVRIIoI3A7+Ug2vWidotFKxSXVsjEIOLWcLP2tVRTUMFFNHUbV1xf9QFIV8waJRX0WaWa2uw87bWuXXctJfZZ2SmS8VbZYTC4sB90Kn4/HCX77GX/7p55m9PY9lGZx980nOv/UUR04exMlbItta/7ZD5lccYaPWMvHykckxWoNLSB4SOs02M6/e4M4r15i7eJvmopA9zbzN+MkDlKYqLPstPvYXz/H5j/0hiwtLmKbBm59+E2//gmc4/fgjKIpCu+2yuLiEqihM7JtgfN8Y+UL+oXjRoWoaurmy7xl6Hq2rN2hfvkZQb6LoGtahKczJfeg5By3voDk2St8L3jB2+yIUlhkkbmCJ20sYxoKxL6w9Gm3cukun0cCvt0VK57YrhI9O0E3rHLuDRWFEGFt7tOtdsX8jKEnaXS1xd4ljfBix+BFnQ2p6PrNzS0zfuced6Xv4foCqKozvr/Dku57g2JljnHrzoxRGCliODL6/WaRgsc2oqoppmmlO+SxvJG7GMLpBq+KozkSZ5aQd/4i++aSfTjI46O4rifqc3W9SNgmaFIahiAAdQhiFhFHUTZeVHi8iDOHObZ3bN++lVhOKQvzdTMoP4aBSURRs28K2heAVBAGLC4vMzt5FAQqFPJWJCsVSQVpfxCTpRRv1BsuLVarVGmEoxEDTMnrSi0p2D4qqUj55DNcpDR54rOfVSl+ZoS4Cb9B1II37lhEvRkoOS9VWjziQHeStQFEI/IDl6QXu3Zhl8cY9Fqfn0w40gFPOMXp4H4V9ZYrjZXKjBayCg5W3MWxTuHdk4yQNMM1fjcRCpFLJMz/f+87f933qtQa3p2f51P/8PK++fIELr12mXhMuGMdOHOFr3vcVnH3icQ4dOZC2PUEQMN9y8XzxprJcKcUWYoW0HVu1bmu1YZn1lYkiYZ9P72bawCiKCDse3r153JlZOvcWCGrifCiGgTk5jjEax6LQDVRDQ3UcVNMQ6c43cczRiSL+HvVHLk4UaQ+o+8LsAp/88Cd57iPP0W60GZ0c5dmvepZzz55j6tgUTn5nn1HD6i15cAn8gPmrd5h++Sp3X7/F8vQ9olC4eYwcHGfq3HFKUxVcAj7xyed47k/+gOvXbhBFcPrxR/iav/5XePPTb8J2bDqdDtWlGhBRHilx5NghSqXiQx+rzluu0rhwBffWNJEfoOVz5E6dwBgfRbMt1HwezTKHZvtQVRXV3LoXj2nQ39QCJEyFi9ERh/m5Or7v4TddEQy43sZvtOg0RFBTv91JY3qIbElxrJAgEOJHEOL7HlG7Q8fzWag3WWi0WGg0aXtC+HcMg8linko+x0jOQddUmG9Q/djLfOZjL/cEA04FEEO4vugZAUSkgjYxHIu7o3laboBm6nFAVEO4ysRunpphoFl6LKCYa7p67mWkYLGLWC1uRhRFBEEwNG7G+HiRpeWW6EyHwqQ4lSeiiO67kqhnO3FcSHI3Jf1zBYgy6ZzitSiIzm4iXIASf4jpZF9KpgOfPYaikL5lTDrOlpl/IK0mtgpN08gX8uQR10HH7XDj2k3CEHRDY2xshNE4ArU5QAF/EOlPL9qKg97tVHrR5HdpNJo0600ajabwa603RQDYeoPKeJn/9TuObVud9gqqrqM5gyIF7X7yE0Wa+uqDofq95W7WjhuzVGcWhP8woNsmpclRJk4dorivTGHfCFbOwSoI81TDMtEs/b5cy7rjoFo+btulWq3x8vOv8um//Byvv3yBa5dvEIYhTs7h7BOPce7Js5x94rE0Bg+IQIqtZoswCIQV2L5xRitl8oX8fY3Bs9m4JsmbO7/RpDM9S2fuHt7CMpEnRBa9VMQ5fgR9pISay6EqCoplojoWmmmiSMEzJYoiLj1/iU/890/w6qdfRVEUjpw+wtm3neXxZx5nZGKkJyaJRHI/EdmRlph5+Sp3XrvJ/LU7+G1hhVUYL3PozacYOThOcd8oncDjxVdf52O//ke8/MKrdDoe4xMVvurrvoK3v+tpKhOVNK1yq9XGdmyOnTzCyGj5ob+mwzDEvX2H5qWrePcWQFEwJypY+/ehFfNo+Rxaztm0oPtGSAMAD6A4UaStbPyZlKZt9n1uXrjJhecvcvH5S0xfnSYKIxEU80CFyf0VJveNUikXUYKQsOOJYKZJ2trE+qMvmLDfEQJIFEWbsgBZjexYi9hNRun5TKwzFdTYSlPVVBRNQ9VVISjpGoquoWkqiqGjaaoIJK13s8mouj5QTCmYW//7S8Fij6AoyqpxM3J5C7cT9QgFe+XNu21btFrB2gUlKIqCZVtYGeuLpdi9CCLy+TxjE6OUSrs/0j6I+odhJmBTPJ0s9z0f3/fxPR/PF2Z/Vy2FudmlnvSiI6PlLanPeoSHZHmzHs83xLzvDffINgyDU4+d2JI6SnYvnabLvaszzF+dYf7aLEu35ug02oAQ0woTI0ydOUphYoTipAiKaTomVsHBzNlopnHfA4cl2XDqywt8+Hf+jBc+9zKvv3yBhflFAA4cmuJLvvKLOffkGU48ciy1TErS/LptlzAC27E4eGiK4kiRXM7ZlW1NFIaEnQ6d+UUhUiwsEVSFwKToOkZlFGNsBL1URDF04erh2GiWlc5LurhNl8/+2Wf5xH//BHO357BzNuffcZ5zz57jxNkT5Mt5ackmue9EUYRbbwk3j1evc+/yNM0FcV8bOYvxE1OMHBqnNDWGlbNBU7g9e5ff+X9+m+c++RmWFpexbIunn30rb3/XM5w8fRxAuJAuLKHpOpNTE4xVxsjtsIXQThOGIUG1xp0rV5h/+RJh20UxDexjhzD3jaPZNlohLzIhPUD3/tLcEhc+d4HXPvMal1+8TDt+jlemKpx7+zmOPHqEk0+cZGR8BMux1h2LQgQ5jTIWIF3LEN/38WotOrUm7WoDr9FCVxSa1RaBF6fB9X3COH5IFEREYfIZp5YOxUvrKMpYtIexIBJby4dhCH7Qa2kP6fqt4NOqwnv+6bdvKhvOMKRg8YCQxM2QPFwk1hcJHbfDrRvTRMEtNE1jpDLCWGV0RRT+rSCKo7AHQZAKDWEYEoYBQbxcCA4BntfB94JUfPADEWGaKInfkrW+77aYihIrvVr8qYh0sCNjI2vWzet4PQLDVgkP+UKOfCFHLp9jcmqfmM/nyBVywhImn4vL5NPlpmkShfcnDdtW0mg0+MWf/2We/9zn+eVf+aWdrs6uJQxENPTZS9Nc+txl7l2ZYenWHPV71fRCtku59K1eYaJMYbyMbuoYBRs756A7Jrpp3HcTzlSgqNZ5/dWLfP7TL3Dx1UtcvnCVTsfDMA0ePXOKL/vq93LuyTNUxrtZGMIwpFFv0PE8FMS9d+DwFIViHtvefe56URQR+T5+sxVbUczjLS4RdeJUr8U89rHDGKNl1Jwj2pctcPV4kImiiLnbc3zk1/+Aj/3u/8RtuVSmKrzra9/FubefY/Lo5I67fUg2RhRFNG7fwb272H0rnQQVhoEBiIcFIe4JYtw/nY3Zk41Fs84XalEUEfoBnVaH6p15lm7dozqzQH12nns35oiCEEVTGT04ztSZo5QOjJMfLaBqKmbBod5q8kd/+j/58z/5ONcu30BRFB47d5qv+8av5cm3nse0TNy2y/LiEgBjlTHGTxyhUMihxecl8v3BMY3iz2jAsv5YSdkyzdDFW8q44vVvn1ke9c33r1/PumiN9SvKhyFh28VbqopYPvUmUdwn0nI21sFJkYZU11H8DlE7xG83uwF0N+N6OaTswNKruG8Oyxw1P23SGhb3KC7b6fhcvzLDpdducen1W8zPLQOQz9scOzrB0eOTHD85xUilhGHGbpl+A+408ABvje+yHvT4z3Gg7DiAg+MYtFoD9r5OV1Bx6ExIgHQ+a1WfrI8n42s29AORDabTIfCiNG1u4AcEfkjk+QRBlAZKTcYAURCRHysSeL4ULO4XN67dYnbmLkAc3FCeHolApLF1cdttWvFnoWBRHhvv9dHeYUzLTIWJMAypLteYn1uAKCJXyFMZH6VYLlKp5Ddl3eD5Hn48HYSJVUw3QFsSPDYKIxQ1IzioSvypouk6pmVu2Aqo43a4ef0W1y673J1dFAJDPRYi7qPwkMvnKRS6wsNeoVqt8hP/+ie5M3MH3TCYOjDF977/uymXh1uj5PN5/u7fez8f+JF/so013R10zT8DWtUG7aU6zfivvdSgVWvi1poip32jTaflptqaZuqU9o9x5KnTFCfKFCZGMXMWumVg5R3MvJjWtiETVCJQNGoNbt24zec/8yIXX73ExdevsLSwBEBlosIXfek7OfXYaU6feaTnuvY8X7h6hAGapjNWGWFsfPS+u3pslsj38Tsd/IVl3Dt38ReW8Ks10YHVNIzKCMbYKHq5hGIYYkBmm6i2dPUYhNt0uXXpFjcu3OD6a9e5efEm9aU6qqZy9LGjnH37WR5762OMjI9gWA+HC+KDRthqMf3xT+x0NVYEhg2CkEbdo15zaVRd8VlzaTU7PWO+Qsni8LFRxvblGR3Lo+kKihKhePcI7szywsUb/OmnXuK5Vy7j+QEHJ0b55i9/lnc/+Rjj5dit7e4tcehsfRqLcOPyfc1EtBszBSUxGkI/JPSjnoxIqqai2TqqrqBqEdSXCOpL7CWb6EFhjqMoYm6hwdUb81y5scDNmSWCIETTVI4cGOGJZx/hxJEK42N5EShaUSBswVwLn9WzQQ1kk8/9TuqCv+ILrL7hFphKKHRFlHSBGf8BMPzZqTki5tZWsvt6HzvE9as3+T++6e/3LDNNMx3Q5PIO+UKeXD6XGcg45PL57kAn75Ar5LFtGQV2p4miCM/zYpHBpd1q0267tNtt2i03NW0W8/G6VrdcVphot92hg19VVTl45AAnTx3nxKnjnDx9nLHK6DZ/28Goqko+n4PYAKPT6XDr5jTR9ZDb16+zvNyKr9OsGdjq1g2qqmKZJopt3XcT8KWFJS5fvMrli1e5cuEqN2/cFsGUMuiGTr4QCwqDhIfk/tzDwsNmURSF933913H+ifMAfPCX/gMf+uCv8H3f/35mZmb46Z/6mZ7yb37Lm3nf13/dTlT1vpCYQibqf+AHuPUmzcU6zaUa7aUGzeVGKkK4jbYQIpruwIe94VhYBRur4FCcHMXM2YzuK6PmHXKjRRFQOW9hFXJxcExdZEW4zyQCRbPeZG72Hi+98IoQKF67wu2b00RRhJOzefTMaR7/2i/l8XOPMjE5zuhojsXFpnD1aLu0m21QIizb5sDB/ZRGS7vS1SMMAkK3Q9Bq4c7cpTO/gL+4TBinKdbyOewjB0XAzHwu9t+Vrh6DCIOQu7fvcuP1G+LvtRvcvXU37SCXxkrsO7SPs287y1ve/QRjB/eRL0m3j72Olstx+Cu+mLkrt7sWFoMsBeLPgVYEyWf2TfhqVghJQPcgoNP2qC82qM03qC83aSy3aNTatBvdt+CKAk7BJj+SY9/hUfJFi1zBwimYWKaOHwfR1HUNVJXrt2f56Cc/xyc++wqL1QZ5x+LdT5/jnW89y4nDUwB4fsB8EKJqKrmcyFykG/rAzE0Ds/JkY7Otsr5/WXY8kC9YNBt9b/vXm/VnWBak/voN2q8iYtcJNzkPf2kZf1lYUIRxvA9UFb2QRyvk0PI5IeyqGqppUB4vUWt6vdY3g465onpD6r3W9xr0HVZb11cue87HxvIsLDRoVJtcePEyFz53gUsvXqVRFfJRaazEI0+c5NAjBznx+DFGJ0fJ5R1M2xw8ltuK8d069zFeKXBvfhUJbRPCRJo1bb37yt7XfcuHWQFV9pVYam1tXA4pWMQcPX6Yn/i5H+MvP/E8KMKXTZiMd03H796ZS03LV3t7q6pqRuQQg6ZcVtTIZ8zF8046sMrlH+5MBkn8gFRUyAgNbqtNKxUZkuVt3JZLK/5s9wkT/YPbYViWie3YWLaF7djYts3Y+JjI1OHY2LaFZdvYTnfedmwcR+el5y9w+cJVPvnnf8mffeRjAIyOjXDi1HFOnDrGydMnOHTkwK74XbPZaUZHcyjq7hm0B0HA9M2ZHoEi8ak3TIPjJ4/yZV/1Xk48coyjx6cIQ/WhER42S7FYTMUKgEcfPc3vf/gPAJiamuLHfvyf7VTVNk3ilxkGkTBBDEUk73atRXOhRmu5Rmu5SWu5gVtv0mm4dJqJENEeGNjKsEUMCavgUJwYwczbmDlbWEg4JlbOwnDs2HUjAkUREb0Ng337y9TbgbCeMLfHnSAMwzQjzsK9RS5fvMrF1y5x8fXLXLl4jY7bQVVVjj9ylK/8a1/OmfOPcvTEkZ42KAxD6vUGi4vLKECpXGLq4CTFUmFXuHpEUQRBIH5rPyDyfYJGG79WxfU61Gbm8JerEEagqhhjI9hHD6GPllF1HVAyrh4miq7JlwhAfanOjQtCnLj++nVuXbxFJx6smLbJ+IFxzr/zPPuP7OfgIweZODiBnbOxHIupA6PMyWwbDwzmSBnzwBYOKDKDldAPCPyAdr3J8vQCS9ML1OcWacwLizW33k00q6gKudEipQMT7B8rkCsXsEs57GIOVRWDWVVX0S0T07EwbIOJyTLLNZe7s/f4nd/7KH/20U9w68Y0qqpy5vyjvO+dT3H+TWfQdY1222W67aKoKuMHK0xMjFEorkxF2jO3kbZiAxmOAMbGiwT3BtxHq+xns21XFIai7Wy7dO7eo3NvAX+pSlCLB8Cqgl4uYU1NopeLqDlHBF40hAVa1k2uPFGks8fu//pSnVtXbjF98SbPf+Il7ty4A5Fo6yaPTPLEO0XK0anjUxTKBeycveuyv2i2hWrufnfifsxiEaW9tdeLFCwyHDiwn0r5BsViHkVTRURUTUNVFTRNR9EVNFVD1VTCIKDVdmm1WrSbLZrxgLrVbNJotGg1WzQbLZqNJtVqjZnpWZqNBq1me9U62LbVFTAyIod4Y9wVOZI3xvlCjlzOIQxtfD/2LYpN+oNA+BkFYZBOp/EF/G6ZMOjGHOh+Bt34BH3TSZkwjl3Qc0xfrM8eJ4inQz8u37dPz/NoNlq4bXd4SsIMSeBJJxEZYgGhNFIauNx2LGzb7l3n2EKksMxNv0EcHc1x/JFHADHgvn1zmssXrnIlHnR/5i8/Bwih4NjJo5w8LUSME48cI5fPbeqYDwqtZourl67FAsU1rl2+jhu/IR0ZLXPy9HHe+1e+iJOnjnPoyMGeh0jyZliyfsIw5Pc//Ac887Zn1iz7s//+57h18zY//VM/w/v+l/exf//kuo9TqRTWLjSAsdFcxg8ybjs6Pq1qg9p8jcZijcZCjXa1SavWwq23aNfbtOtiepA4aTgmTjGHU8oxdmAMp5RL04NaOQszZ4lAl4aGksRSQbh3GJaBnvzFUbG1ODK2qqk9Hcj7bU8VhiHNRotarc7CvSVu3Zzm1Rcv8OrLF3n95UupsLd/ah9f9CXPcv7Jxzn7xKMr2ph226XVbBNFEYamMTo2yiOnT1As5jF2KLuQCBwWEgUBgecRuB6dap3OUpVOvYlfb+LFf4Hb7bQZhRzlE0fITVYwy0VE8ioFzbHRcza6ZaHuso5nwsREcVuO43U8bly4xeWXr3L5pStceekq87PiWlFVlcrUGI8++QhTR/dz6NRBjj96lMJoAcs2Me3Bz8XtqvtWs1frfT9RNQ31DQj+XR93n1a1ydLNOZZn5qndXaSxUKO5UEsDDovjqeTGSoweniA3ViI3UsAu57GLdmrxpGoahm1ixO50KAqKphApStpnrLXafOYjH+e3fuPDvPT5VwiCgIOHp/jr3/xXeebZpyiVi3gdj0ajSdQOKY+UOXbq+K5JRapo6n2z8IqCoCtQ3JvHm1/EX6qlLnIoIhuSfewwerkorChUkRlCsy3UPWqBFoYhC3cWuH35Nrcu3WL66jR3rt2hviyEGUVVqExVeOKdT3Dk9BEOnT7EyPiIeOk1zIpCsuuQgkWGKBJ+93bBEVFVYxM23wvwOn432moUpjFLNBTydo68lUt7rknHV1FEyphE5BAdXeh0PNqui+u6QuRotYXA0WzRbrZpNpup2DG9uJz66gfB7vEaUxQFTReBPlVVFUE/dQ0tnlY1NQ0E2p1WMUwDW7PSdZqmUizmUFQ9Y80QCwsZawbLMlMRwjTNTJbWqPuRCSrT+9kXUCY2YoqCkHajnc6TiZYbhmEafAYiorAbiCZCROF1Gzb1ahtUBQWFYr7AW976BG9565tQVIXacpVrV25y7eoNrl2+zh/+7h8ThiGKorB/ah/HTh7l+CNCwBjfVxGphVDizjc95oe7IetLkpIpEagCPxAdlkSY8nx8TwTTDOJoxoEnYmEsLi0xffsOM7OzzN69y+KSCGikAOVSmUP79jNaGmG0UMLUDKIwonphnk+/MsdzidgWd1g0TaSvEu4qCmp8HSV5rTVNFWmZ0msvntYz12Q8LfwytXg7Dc1QxcA03We8jyH7EzE5uvf3bjOfT/j5n/kFbNvmq77mK9cs+13f+7f5ru/925s6zvx8nXAD6bmiKGL+latc+fwVOk03tYRIXDOS9J9ZNNNIXTPKB8dTEcLM2xiOECL0dMCVtMbiU9U1dENHs3R0U1hFdH9L8ZncZ0H853oReIO9Vicmilv+1jlrQbG0uMz83ALXrlzn0utXuHThCrdvJG4eDo+dPc1XxG4e4/sq6T7cDjRbNdqtdhwwEwqFPGPjYxRLRZyczeRkmbm5GkvLbWB1If2NIFJtC1EiCkIizyPoePj1OmG9Sdhq4zdbhK0WQbMNmeecomlo+Rz62ChW3kHNOYxOjlFvi+dxW1XpBIqIUK/rKKjQDKC5OwXN+3G9gDjHC3cWRNyJ169z47UbzFybSYW8fCnP+IFxjp87yeSRSQ6fPszIxAi2Y2PaZjqQ6wTQaXjQWBnk7X7V/X6zmXqrqrJp8XUz7OZgx1EYiWe659NarLNw6x61mXmqc0s0F2o0F2t4zW6UAFXXyFdKjB2dJDdWwinnMIoOum0SEbvoEaEaGpGh0VIjArw480GI31jGu+Ph+T4L9xaYnZnjzvQsszN3mZ2e5d7cPGEYUSjmefeXvJN3fMEzHD56KE1FuriwhO3YHD1+mJHRcppR7UFDBBgOCD2P0HXxFpbwFpbwl6v4yzWIzf61Qh778AH0kRJ6XryMjRQF1bJQbRPNEFYUewWv4zF7fVaIE5eFODF7YxbPFW2WoiiUK2XGD47z2FOPUTlQ4fS545iFPPlSfldaUUjWx965SreB6kKVVrVJ3aynb2xE37WbuxZiIUIl9U0TPlp0P+PyhBGRkjT4YiAXxulldEVDtxzylgMj3W51lkhYH6OqGooqAhK5nY74a7dpu3HchXYbTVPwvDAWD7qDp3SApqqxmJAM9MQyRVXEclUT08l6Ne7AayqqIrZTFDG4U1RFmOrFA3zoigGJIND1eeyKBr1BjZPtIkoFh2q1JYQgIIwiCDP7cQPcdgu32mI5iocfSny+sieu5yQqRFFIFCLMxuNUP2EUxn7tok5hJCLaimNGIsJtmg6I7nYZoSopYxo6ruulQkaqe2T9OqOIqfw4U+fH8R/3mV9aZH5xkXuLC3z6k5/lkx97DgDLtKiMjFAZGaUyMkq5WBID8x7XsSj+iv3RgUmPncSk6Fk4oLyuqbRbnVRwSD8DYWKfTieBCINwXdYv4vcLaXpt6m6TeqdJvdPCD+Mo04pK3sxxsLSPop2n6OQxDENci75Cp+biqR6qGvudayqGbmDm1PT4mq7idYI4l7X4DYNkPor6frMoLZdN77Te77JhFLpiRxL/I/6rHBjjb/6jb70/x12FX/7FDzI9Pc0HfvQf7zpBpT63zJ/83O9BBJqhp64Z5YPjWHknnTfzNkbOEm9DVDXjg9m96VVNRTM0IURYhsjAkREikrZrtxGGIe1Wm0ajyeL8EkuLy8zO3BUuHheuxtk8OqiayolHjvHV7/sKHj8n3Dyyv2cURbhuJ7ZUC9F0nUpljNFKOQ4ifX+tKJL0bIRCyAw9j8h18WoNwmaLsNkmaDYJWm3CVrvHhFwxTbS8gzW1Dz2fQ3UcNMcGXXw/JWlbVRWzVEAz/Ifa1aPVaHHz4k1uXrjJ9VdFYMxmTYg0uqFTmarw+NOPM3lkkgPHDzB1bAqn4GBaJvo2uS49rOzVYMdJ5iO/49NcqLJ0+x7VOwvU55ZpLFRpLtbx210rJ9XQsEcKFA9U0Is2es5CsVRCTcX3fdwwoB20UJo+RtDstse6jqop4IPqaigKNOoN7kzfZXbmLjPTd5i5PcvM7Tt4ne4gdHyiwoFD+3nr29/CuTed5ujx46iaSitNRao90KlIhXuHaFeDdht/uYa/uJwKFJGfZPJwsA5MYoyU0IoFFE0TL1ANA9Wx91Q2pEa1wczVGW5fvs3ty7eZvjbNvel7qWunYRqUJ8ocP3OcylSFiYMTTB6dpDRawsk7IiuXaUh3tgcEKVjEzN6c5ef/8QezMQe3jKygsWKalcuJhZJ0WhTqurhlhZT4U9NUYRWgKNkxe6ZMvEBJF5MOfZUh67LBdOI3/kldussz29Fbx8xHd3/Z2EDx/nRdo+P6XbEg6A48RW7hKF2eLEtSakaJyBCXyS7brTjoHLb3cXhyHy3fFYN6t8n8wiLTd2cB8cvkLYeilRcDezuPqWcGHEpGuEg/uudXLBgQDCme0TRNDK5VFUXrZvHQDLMrWGld0WDlX+wqpWt0Oh5zi/PcnbvH3bk55u7dI4jf7JVKJU4dP8HBQwc4cvQg+6b2YRiGEMBUIYYpKqAqqIra9VnVRJDPMIpoNlsEQcDo2Aj7D0xy9Ogkc3M1IUhlr5Os6JQIGWFI5EeEBEQBBJHIWZ1Ygfiej9fpEHghnU4ntRDxOp5Iw9rxCOJMKUEYEXqJS5SfZpQI4pzYqbCTXLPZazKKUHUVv7Ph2NJviF/90K9x+dJl/smPfuC+D1g3Q3HfCH/j//Md3Hh9GrvkpKmxskJE0lZoGcsI3dTRdF1cuxlrl71AVqBYWlhieanK8nKdyxeucOXiVS6+eomlRWGBtG//BM9+4dt4/NyjnH78EWynN76EyF7UJvADIqBYLrB/ap+IReHYW94p7VpLhCL/u+eLDnSzTdhs4DfaqaVE2GqlgTATVMdGy+cwJypoeQfNcVBtS7z1C+O0dJGIS6EYOqphiMwemjBbVlSV3ESRxkPU+QyCgNnrs2nWjhuv3+De9L10fXm8zNTxKfYd2sf+Y/s5/MhhiqNFLNvCsO5/2lxJL3sl2HGj2uDK8xdYvj1P/e4Szfkq7WoTr9Yi9DJWTrqKlrfQxnKY+RFUx0S1dYjfUgeKQqQpKJaKkbNwbBPdNIVbnWH0tEGtZovpW3eYvjXN7ZszTN+aYfrWHRr1bu6MUrnEgUP7efd7nuXAoSkOHD7A1IHJ1FIiiiJyjsbM9AIoIhXpxCMVCsXCrogVtlUk7h1hR1hQ+LUG/lIVf7mKt1xNUzWrtoW5r4I+WkYvFVE0MaxTVCUONGyKNnQXPx+jKGJhdoGZqzPCauLyNDPXZqguVNMyuWKOkYkRzr7tLOMHxtl/ZD+ThydxiiI4ppG4b+7i7yl5Y0jBImbfoX18/d/5a3z+uVcolgppblox6IinE9eC2FqgJ8dy1g0hcR3IlM8uT96+r1ze+3Y++ya4e6wB00RoqorvB71v45PpHiuIZN/xkv4Iz+lH73fqWb6iTHfbgev6y2XcMojEm1EUJX6rrqSWH0qcDlOJLUMUQ+tZpsQD23SbZB/x221FyexPUzLLuoPxdJtkmaL2zSvpNooaiwKxtU2xaFOvJ6bUSvo7KYrSc96EsUO8PGshEnUtRRJrmka9wa1bM9y+Nc3tWzPMzMwyU50DRDDPQ4cOcOjQAQ4enKIyMSZEn/hQWeMKJXEvUREpmRCWQcTfoVR2aNQ78Xnufk81+Z5K7MIE6fdN0pXO3b3H1cvXuHzlOlcvXePurOg8a5rG4WOH+KI3vzsNOFoeKa163w3D933q1QaKAkdOHGbf5DhOzgGgOFKg7d0nC4lN0HP/0rXkSExfhYACY5U8bXf7hLQb12/wm//5v3Lw4AF+8B/+EACTk5P8yAd+eNvqsB70nIEbdQhaYNoirkQSO0I3dXTD2LEYC1vBIIGi3e5w4+pNrl6+xsVXL3Hz+m0A8oWcyOZx/lEeP/colfGxnn1FURRnPXJBiTBNk/F945RHSuQLuS1LOyqskkIIhLVE5HmxMNEiaLYIm4ko0SZotoi8jAuBqqDlcsJH+sAkWj4n3uxZFkpscSgE7FiY0A1UQ18hTDysVOerqWvH9deuM315On3TbOUsxg+M8+S7n2T/USFOjE2NYedtYT0hU7HvOHsh2HFrucF/+5f/CbeWCX5paOgFG3v/CEbJRs876DkLzRTWuBERCgqaqaNbhhDDDD2N7ZMVJjzPZ3Z6ltu3YlEiFieSeDsg4rUdODTFm596ggOHpzh4eIqpg1MUS72uOGEY4rodlpaW075OqbSPE6ePUyqXMPfwsyEhyZ4S+T6h2xFZkNptYUFRreEvVVPxVzENjNERjLER9HIxdoUTfUnVtLrBMrXdaX3mez6zN2eZvjLN7Su3mb48zZ3rd3Bb8fdTFEqVEmP7xzj15CkmDk4wdXyKyv4Kdt7GMEV/QLp1PHzIp1uMoigcO3OUWzN3GZ8c39jG2RgJ9A/ys+4C/YP6/vXZgXx3QD9scJvsUfgni8FzxBpls4NbuhPJ2/rEZLpr/dFzlrpWIEmZzJv+rCXGRiiVhEvIUDLnNyvgiI5vV0DpHzhmzyGIc0JyProvcLtlVIQrygpDhUQcUQARBEpVVHJFhwBhJSCEANJ4Fl2hQ5yUVDyJrWbS85Y9n/H8yTMn0/Poez43rt/i2qXrXLl8jauXrvPiC68AkMs7HD95LE6neoxjx4+u219zvYErO26Ha1duiMwdF69y5eI1mg2xXaGY58Sp4zz7RW/n5KkTHDl+6A1n7XDbLs1WC0PXOXL8EGPjY7u+Q7Le+CKlcgF3G98MHzl6hN/58G9v2/E2ixv4zCwvYlsWxC9UEnFTtGERiiIEMd3Q0TXRSRbTGoauoet66oajxO5vao+QKZbFDUV8kK7bVtaFLVZzIUwXJI1PV7wmghBqrRrtpVYqBMatrgjI3G6ztFhjuVrDDwPm7i5w9epNLl++xuWL1/E8D1VVOXHyKF/9tV/K42dPcfjooVTABQhcF88LcF1hRaGoKuVyif3HDpIv5LEdSwiRSRvv+932N3tNZttskq8fd5ITiwnPI4wtJsJGU4gTrbYQJwbFl9BFfAlzfAwtn0PLO6iOjWKZKBmhX0EBTRUmyYYuOtiadl+Dz+0VOm6H25dvi6wdr13n5oWb6VtFVVUZ2z/GySdOsu/QPqaOT3Hw5EHyxXz6RnE3DkgkXbYr2DFsLOCxV7I5+vQp7kzfpXJoArNoofRZJ+imjmGZGI4ZZ0US7W7WrS4MQu7O3uPGpdvcvJ78TTNzeza1ctV0jYOHpnj83CmOHDvIoaMHOXLsIOMTYwOvX9/zabddOh0PiNA1jYlDFUbGRtKg83vdkmK8kif0fBHLp+UStF1818VdWKYzv0x7fhGvJqxOVEPHmajg7BvDGR9Fc6zUJVI1DIy8g25bom29z+3pRoPXNqpNbl66xY2Lt7j++g1uXLjJzPXZNLaOYQr3tdNvEm3c/qOTHD9zlPJYCcuxsLbQhW0vB97dq3Xf6npLwSKDoigEQUi73uqOarMD23i6Z6ybzCjZt9S9b7RVpfu2X1GUwQPcdDAbD5CTebr7HjS4TUSCkdE8S0vNbqc6Y8XQbwGSdNQT03kYHFQya+XRDTja7ayHoei89sSciINVElv2ptYE2XMVf4NESdCViHazLQYIGXElFReg1xoiOT+6mrHGSM5hcq7FedSSzr/SHVgqietKn7iAknVV6Y1bMojtyFZh2RaPnxNvW0H8RnfvzKUCwuULV3n5hVcBYaly+OghYd1w6gQnTx9nZLS8oeMtLSxx+eI1Ll+8wpWL17h5/Vb6cNl/YJI3P/2EEEhOHWff/okteZBEsdtHx3XJ5fOcOn2C8mh5z3dKJOvDb7bQWm0cQ4/btaS96iqHURSJN09tFzeKXXDidikIItEWxdZGKBFRrPJm470oiM6zrutC4NA1dF3DMIR7ia7r6JqIeaHpvXF8RLutoAJRGIAvXCK8OgStpgik3O5Qq9Wp1xuEUUSt1uDK9dtcunyD1y5epVoVEcsn91V49u1P8tjpEzxy8ogQaojb50YTP4rouC4dzyOKwLRMxkZLFMeFb3YSb4hGA6/REDE9krY123BGoMTTUX9jrEC1vkzjzgJRs4WfuHDEIkVPfAnLRM/n0KdK6PlcajGBrglhQvxAos3UVDAMVNMQWTpia4mHXZgA8byduT7L83/xauracefGndQfuzBSYPzAOI++9VGmjk5x6NFDjFRGMOOsHbI93HtsV7Bj2HjA41NfeJ47f/hJAtugo2oYsYWOpusocfsXAR3AdQOqdxe7bhzJ55A4E0+85TwHDu3n4OED7JucGPg2fGmpRRRFeB2PdtslDEQbbtkm5XKZif3CqtK2rbSf0XYj2m5zVwSATSydk78o7luTWC5Hmf5wbLEWRREjBYvFuSqR7+PX6qmLRxALFGgqxkiZ3CP70EfLaDmbKIjwiah3QlQ17AYZ1jRaHaDjAu7Qum4Fq53zKIpYmlti+uq0sJy4fJvpq9Ms31tOyzgFh7HJsa5Lx9H9HDhxgFwhh2EJq4nEfS0EWm5Iy92aYNC74XrZLHu17vcj2LEULDIUR4pMHZuiXC71vgEneVNOr0VCZhC809iOhdXePVlEhlk79Igp8We5nKO63NqUaPCwoSgKk1P7mJzax7Pvfhsg3EiuXLqWplT9+J9+kj/9wz8HYGx8lJOnTsQuGsc5ePhA6uMXBAHTN2fi1KJi24V7wmTTMA2OnTjCl33Ve9NUrPlCfku/SxiG1OsNAj9kbHyE/adPDMyPLnmwSWIXaG/QOkfsTLQ1ShihRCFKOh3FncYA/AA8DyUIIQ7C60chQRjRIUKNQIkiVOLP2NJj0FVZzUx7ns/1W3d48dpNXrx2mxt35wEoODbnjh7k/Dvfyvljh6hkTZ6Xq6xJuw3LVUKgPqxMolynZm7QE3MoKxTH00ud3kwQSXwJY6IiLCZyDmrOFsJ7GPa4Dqq6DnGMiYdRmIiiiHajTaPWoFFt0Kw2adaa1JfrNJYb1JfrNKtNGrUGzZpY16q3UksewzSoTFU4+/az7D+8n4OnDjJ5eBI7J7J27GUXKIlgNwc7BnBKOUYPTzJaGelZ3mq2mL72xuJMDCNx7+i4bmrxmy/kmDo4SaFYwMk599WicqDIQHY+7peGoWjzEtEhDCGMy8TLiS3/oqyFcdbdOwjTZ04UxtZsQcCC16F+5x5+tZ6aQOsjJZwTRzHGymiFfPd4KCi6jlawd42bR+AH3L11l+kr06lbx8zVGfHSEUCBcqUsXDredIrxg+McOnmIfYf3YVrdWBMSyUaRV00GTVcxLfE2Y69x37IebJKNCDm2Y9Jqb28wwgeJfCHP+SfPcv7Js4B4oNy8cVsE8btwlQuvXuRTn/wMICw2jp88imFoXHjtCm7sF1keLXPy1HHe8+VfyMnTJzh85OB98xH0fZ9GrYGiKOybmmBicgKnL5ig5OFgYW6Rf/MDP8HyUg01tgTod9PIxgYSq/o/u6Jo2v+ku594Ue+++8pFmXLdTmd69J5jpcvCkCAK8QMfL/TSTDgAuqqTMxwMzUBXdS7dWuLy7SV+++MvA4mGkAgLGeuvZDqxAiMTgJlB5RLXvJXLeraju12yH1VTCWNRuKvI9z1HlFiqSRWb9bXr9/N5pOsafpLuNlOX9LtlpaVUtMksonsuE4SlTiYrUig+A7+7rLs+iOPT9NLVhJTUHUmN3V6SIMOWY6CbJlpOZyFssDzd4sqd2yif/uxKN8zMvDJIhErW9bv7KN0z0F3WnV8RrDl7bfWdzOw1Y9smnY5wOUpcG4U1qdINlhyLY2rsppRNya1krEn7r+M09lJiGdT3nRKD1/7vM+z3zBZ509OPc+yxR9hOdnuwYwDP97kzM8uF1y6tHWfi6Sc4cEjEmThwaIpCcX3uJ4Ef0Hbd1ApDVRVK5RJTBybJFXI4djfFZNrGZgOmr3DfE/8Ct0PY6fRYNgwWGYhF6STLWexmGF8dURihhAFhIIJxR0GAkogMcQBtEctHpGVOAw7H4gNx5o5kPkndTLh6rCq9VMQ5ekgIFKWieBYFAaCgRApqLodqmcKKYhvFrigS1s71pTr1pTq1pVo63a43uPLqde7evEsQt7+aoTE2OcaxM8eoTFXYd2ifSJk8PoIRB8fejWKdZG8iBYv7QI8LBZlsARkVN8y6WGT+iETfMVV+M1YeiS93at2b+lhEEHosLfe7J4j1WpJmMU5xmvh6Sx5MNF3j2IkjHDtxhPd+xRcRRREL9xaEq8eFq1y5dBVdU3nbO5/m5OnjnDx9nLHK6H1X7t22S6vZwjANDh87RGVibNd25iTbQ/3uPaanZ/H8oDtAJzvQpmcABr0Dk8TSLS0b/1eTAXh2XXafxCJG7MYWRCKrTBglWYlCQoSrSZJtJvkMojBe3h2yWrpByS6Ttxxyho2qqBmhJT5a1C8HZAMeD1gadddlO+s9dU+3iAbsp9fgot9ERAQHzsx3V7AqazYTved5vfsaXH7l0uQZOIisG2Ry3sPkrWnq8kjXZDsjhK1Wx+w1iaLEgyylKyZkB98KK9rSMBCDnpYfEEUtqvPZZ3ym/n0XRG+RzLK+lf3levbav3zF1+1dHmWW9y0QV1p6XXaP0xO3K/0NBtWv7ztsA9YvG/zy7/0Mdm57RPG9EOx4bvYef+tvfE8aj0rTNPYf2MfJU8f5gi9+BwcO7WfqwH7GKiNxixz1XJuB6yZKMVnrBM8T7h2B7xMBpmFSLhcoT1awTVO4dyC2iRotvEYTL9lP7AqYHi+58eKGrCsGBCzOGzSXm6l4QCQCBKfZi1KRIZ73xXbJPkSAy2BNYWEFSewdTev+6VosLsSWZoPKaJqI46NrjE1VqNbaQuiIgDBCtUxUuyjiUGyx61cURbTqrR7xIREjaou17vJl8Rd4Ayy1FcgXc4zuG+PcO85Rmaqw/8h+Dp8+jFNwhDghrSYk9xl5hfXhui7LS1VEA512+YAIRVGFaEDchirZhrxXXFBVIRSoqipEAl3DUFThG62JQI1qKiSo6JoeZ2WIYzEksRqUbnwLkQYyG8BRTd9W7NtX4s6dJZFy0ffx/YAgCPA8j47bwet4uO0ObqdDu9VK0072o2nCZ1uT4sYDg6IoVCYqVCYqPPPsW4Htib8BZOJTdMgX8jzy2EnKIyXpjy0B4PCZR/jXP/kPePX513GKeaJ4EB0qCpGiiLiX6VvhlW9Xwyii3XZptdq0W+102o0/W8m6djst02p1lw1rBxMsS3SyC04ex7ZwHAfbscg5Dk7O5sDBCQ4dOtSTDUdYX4g39q7r4nVE592xTEbGRrAtC0PXIRJvIIM4FXMQBGLeW/k2PwiTMqIjHsbpe8MgSlP6RmHYm+o5ECJMFMRlM2mjwzDCMDS8xFIhOalRtHIwmV2QGdxHqy3rXx5P9wj1SX0yYkI4QMTvj6GkKNDpeAR+iJ+csyBJN7y6W6TaF5BVzCvxMjVdr2lqumygfrOmwJRd1hUEsi8cVhNrUslnzTLDlyndrku6cvVtemO+9O+nP25WaquhDFnXJxYq6b4SoYf0hKShVSK6AcN7atHtXyVm9N0TubJYMgCOgMpIccXe7id7IdhxebTM3/rOb2D25h1OnjzGxL4xtDhOWHrfKgp+tUYSDyd7chVFjPU7nQ6djkcYhSgoODmHidEShWIB2zaxND2TAtnDb7e7lgh+IiYEQmzIWir4AVHgd5f5QU97sqYjnSrEgRXigWmuXJYpR5/IkJbTNaL0ORSlYk02LlxWWY6i7LghtuaIxxNREIKiohUd4eahbzyoZBiENGvNHhGitlyjtlBLhYjEPa1RbaRxyLIoioKdt3HyDk7BYeLgBEcePYKTd8gVc+RLeYqjRUbGRyiPlzl4uMJyzcUwZbBfyc4gBYsMpVKRk6eOi06LrvUIAt2Uj8pQ0SAts0M3cyIysA4fwDAMhajh+wRBgO8H+L6P53q4rovrenQGiBuK0g3gpiriPGmaJjp5uibFDQnQjU8R+gGj46NMPXqCfEHGp5CspNP2aDdcWi2PZqtNqx2LCu02zbZLq+3SzCxL5pstF7fTWXXfqqqSsy0c2yJnW+Qsi/GJAo4lljm2Rc4ycSybnG3iOA452ybn2DiOFQtr4g16dmCWYNsG7bYHNReICPwAL367qKsqozkbe6yMaZpouir25Ufgx/EjkrfxmgK6Clam7e6/V7IDx75lg8QcEP3oMAjouB6NRotWs02r6dJstFCAdtONRW5hFh30uT8E8V/iIhGGYbdMGKaiSTodz4eREEmCRFjZQDDA9aAApqFjGRqOqWMbJpahx39az7Rt6JiGhqnvoP933D8gybCQuFrELjfpdFIutdZQerdXesv2lBu0berS0butMmh7IM04o3b3Jeqn4ORM2i0v3S4VEmMtMRvXq0cdSSykMt8l/VhjWTaOWFezHHCczHftfh+xbt/hCVxzfdmzHhZM0+CrvvY9PPc/PsNIZTRdHqWxf0IR4ycMUcJIWDD4AYHnEcbigRZFlHUNU9NQFVCjkHBpmWh+gZYf0OwTD/uth7JTqQVCRiyINA3VNImyYoIqxINC0aHpBqh67zaqphHFaemJMrGHoogoFmNi+QAlUlLbNFFe3J6JSA6gBiEEEXh+fE+osfuT6PcTB0BWVVWIJEkcn777O71fFIXivhLufDceSEIQBCL+zVKd6mKV+mKd6lKV6kKVxlKD6lKNRixCNGvNFZZhEeJ5Z+cs7LyDk7epHBzn0KlDOPkcuZIQJgrlIuXxEuWxMrplpNbXiqr0ugH3uEGCH4a4bRfX7axwCVMUcQ6V+FyK1Wp6P8p+n2QrkIJFBhH8Z/9OV2NbUFUV01Q3Jm4EQY/1RqfdwXVF+inX7dBuNAl8nxW95qy4kbE4keLGg4fn+TTqDRRg/8FJJibHsW0Zn0IymBsvv853fc8H6PjDY9hYuo5jmjimSc40GTVNDuQL6bxjGmLaMHEsE8eIl1sm5kaDlAVAwxN/1FjLYHhltxOyLWon/tsqwjDC9X1cL6DjiU83+exf7ot1Ha/XfWUtxLg6jsOgCAsENRbqk8/ESkFTFQxNQdU1NNVIlyWWC2qmnKp1LRk0VYmt95TYmqFvPrOsa+2goGsquq5iWXoaE6F3wBwPjtN00vQOmJNxdKajLVYpcdaopJySlkuXxalr+4WDdGDfIxiovctEdTB0Dc8fclWtEKSi3hUbZbXNNqEfGYaK5xnr2Dj7tvkNHnSwb0t6iPXg3XVRjpza+LEfYK69epkf+q5/ir9RlwjJpuh/Bt3XmHPz92/Xb4RUTCQTz6avHc0KHNnsfQNj4iiZeDgofbFxsutE+tQgCNOy2X2JumWP1RWZkun+50hv/KjkudAt0xuDp+85w5ByEX3lxIxl6Xixm05P/TMNvLJiPt5vahGVWRfRFYihazgVr8temen6hEHWllH3ek7FwQhOPn6ML/22tbMjbQQpWEjWJBU3WJ+4kVhsZK03suJGp9Oh2Wzhex6gQNRhaakFiPtoUFuuZCb6hfrU6iNbjq5ZroKysm+jkDmQMmRR3/qeZ46Cgkej3sE0ZVT3dtul1WhiWibHThxhtDIi41NI1mT/iaN8xzd8BY3lGsVCjpxtkXcs8vFnzrYGuw8l9+KgwWi2E6DEhZXuRkrP9n27TP91VySpnNM4Q+mhFQxDmPPqui4G3NlGIttQrYgDENFxfRoNl2arQ6vp0mp1aDY7tFpu/NkR69I/TwQ9HIKmqTi2ge0YOEWLEcfEcQwcO/mMp20DyzbFoJ9IiMd6IhBovZmZFGVFe5y2in1v6BXiQX3aSet2HPs7eyu3SzqymY6XMvDHwbIN3HY2w8mAwfGKh8jgAfTAccOa20YDJ9OZAW+Ss5NDdYRB263oMW6A+zImWoeIMuDeGrxgnULMqsXW3oei64RhgKJKN8SE8ckKTz3+CMvLdUDc4yFJ3LXMgDod1InzvAHZM+11pT2z7MBowL2nJLdPz20/4PdVFFQFgrDrgkFS5+ytmoljk3XfiOJ16TY9ZUmXJ7VPPT7otv/Zc5SWi7Jlh9E9CckgM20XSQakyQA461bVdx6G3FvruaOU/qlBp3iNb9Cd6HUhTM9I1Dfft112q+zv1T/fjZeTfCRKZW/g7H7ZLRpSh8xhVtS9f7uB9R+ybfaa6jta3zEHfPfsNdxfmwHbpdciu5s/f+5TPPt1X0i+tHXZBaVgIdlSEj/g9QxWE3FjbCzP3JzwSuxp7PsfKgy58Qes792mr9Vbs1y3aE/jkgxY4kWjozlu37pHtVpneXE5LanrOqZlYBgPtq9fFEU0G006bodCqcCjZ09TKhel5Yxk3Vg5h6/81r/OZ//8U2k6aSIFVJWOquBpqjAHVkSsHxSta64+NOXxWp27XqIoil3iAnzPIwhDsh1A0zFxHJt8IY/jiABjhmlgGDr7949wZ2aJZr1JrdakUW0Iv+HlRm/Ky7pIbdmst2jVW7gtd6BfcYJpm1iOhZ2zsYslivttYeqbs7EcC9OxsGwT07awHJHZSte1OKL8oAG3MPdVNQ1dFxZu4xMFlqtuGldJS1KTKsKkWh3gDpn8qRsZ/G2gDVxPezl+n/LSb0emrYnxAnP3hian3dVM3KfzPoitfG5WtrHee4XC2Ajf++PfzZ/+3sew8o5wVcjnKZSK5HM2tmWj6xoo0cA+WTbWRU//KoogjjWTpgGN41uEgBLG8UriGG5ows0CVUXREmFEyWTGEG+IMxWACEbKORFkPrlOMm97AaIgou26uC2XVqNNu9mm1WzjNtu0W2765zZd2m0Xt9XBbbl03A5u20uzmqyGYejo8Z9hiHZVN3QRr87QMdJ5XViGGRqabjBSdggjRMyQJE4OZCwQupJ52t+Mz2N/oNNeUUYITsn5ENMry4uifdvHMTii+DdLz2kU9ZzbZLsBlwTZAfYgcSI74F8pKHQH4it/ziirgayyT3rrFpEtlVomJOc3FdHjBalNQk+ZrliUNklKr8iUfVHSvzzdX+ZY3Rcp3TopfQdVMuVUNSP2pcJWdp/d+Ioo4rZTlCSOSkSkRIAan5sovhe7924aQ4q+aYRFZypkxuc9gm5CiUz5MMqUiSImJsdil6GtQwoWkh0jETds29qTbgMTE0V0MweI4HmuKx5+9VqD2nKN5eVq2qLqup5aYux1ESMMQ2q1OlEQUpkYY/LAJIXC1qmokocLs+BgjFcwRsvx2/atjwMk3Np8fM/H83xCPxBv8uKHrO1YOLaNUSiJmA/tDl7bo9NyWb67xExNCA6NWoNWvUWz1qRVb9FutnFb7tDjqpqaCg+WY1EaLTFxcEIIETkbK2dh2RambWLmTGzHxnRMtIwgkHQy0mDIuoau66hGPG/E81o3cKQaDwDS6QHizsREEVsO5FK2o11W7sO1vV1IX/QHh85yjWu/8Scc6QQw346XLhAgAlquGdRymwijiI7n0+74tL2AdsdL3eA6ftDz5/VMr+3qYugqpi5i2xi6Rk7XGMlZmKUcRrw8uz6dNzQMbbP3cQC1LRAssxZq/RZrirBUw1BS17SVbmx0y2amk7KDymmaImIRZdwg0ul05N4d8Ge3zw7c04G2mggB6Q6ENV52EJ8OzJOXFEAcGyPdV/KpdqeT+eS4ubxFq+X1vLzoEQmydUwViFjoGPQCLhqmww+3WhlWNFFYVlxPsVaXy5s0GxtxLM3KSYMqshXC/IB99BxWoTQxSsvObcGxukjBQiLZAjRdI6fnyOVzjMVBrIIgoON2hIhRFyJGdbmWvpHQNA0zflu7F6wSPM+nUaujqApTB/czvq+yJ4Umye5C0TRQ1PjlXCQixkeJC0aYCcDezRRBFAd2jLNiRFGEH/h4HR+vI7IiBV6A53ZwWx081xNv/vyI0AvxPQ/P9YQo0e4I4aHprvpmTTf1rtDgWFSmKhTLOVC1VHyw82K97Yh53ehGgFcUBVVXhftIIjok84aWumNkhYfsvBwsSiSSrUAzNMYOjeC7or3raVqy/u79E5kFw4zblN6CPcujKKLtejRbPk1XuLk1255wf3Pjz7YnXOHaHq328PbYNDVs08CydCzHoGQ5WKYu/iwD29LFOlPHtnRss7vMNPTNvf1dbZC63k0yg/GuOEDPc2K4cEC3zLCjKRucTz/6B9u96zRNIQgyVhsp/WYVw5avtGDO2FfEHwOEpkE+iRscc7vVOKzRJhh2qPtvjyfYq68UasvzaGee3FJXPClYSCT3CU3TcHIOTs5hdGwEEG96XbeD23Zp1BtUqzXqtXpqmqWqKqZlYu4iEaMdp4M0LZNjjxxlrDKKrsumQ7I1VBeq3Lpym1KxIBYk5o8JkbhvPNcTJr6JuNAWwoTf8fFdn8DzCbwA3/XpuB38VeI9GJaBk3ew8za5Uo7K/koqNiTuGKYtXC3snLB60HU9tXYwTAPDMpjcP8JyrZ2KEKoq3CuSyPH98xKJRLLTaLkcx772Pdz8/AUiVRUm3YpC4kUQKhAGwnQ8TMTjOPVy4nYQG6jRaYu4O+2mS7vZ7v41XJFGuhEvj10whrleWY5FruDg5B1GK2NMpeKvhZX+mTg5h/HJEvWGm1qwqnEq4sQlQ9c1tMx0No2xkgjAahzMceDQMxo8vs7OrRg4R2l9VpTLMDLisLTcis9g14qg30WhZ31aLrOcjBXCCga8rR/KOoQbBcbHi9y7lxk+b3rEPkCAWH3Byk36hY2eLFRRj8gRAWOjORYWm2vWbEteCWzoXK99xLFKnoUBWWVW380GLD2G7XI1UWwdVMYLzC8Ptz7dDHLUIZFsI6qq4jg2jmMzMlrmIOKtg+u6uG2XZr1FdVmIGMKXPkJVhSWGaZnbNuiJoohGvYnneRRLBU4fPyXjU0i2nCiKuPziVeauzLJsLorgvE3heyzcMoR1ROAFQ/dhWAZ2zsYpOBTLRZyck+aXt/M2dt5OO7ymJUQIcXCIFCFAJCKJZmgYhrjXDEuIEloc8yFJ4axmzIEnJoqY0q1CIpHsMXxUZlsRhplxGwB8z6edxn1o4TZcWk0Re6fdaNNqiOlkPhySacRyLJy8SKU5NlXByTvkCjmcgpOKwLYjBGLDNtA0TQjCkZK2y4YlLFBN20xjB2m6xr7JMouLzT0pBtvjRbRo7z0zVF1H0XbHkHGj43CzVERz96aFopHPozb3XjYfzbJQlK3MkSYFC4lkx1EUBdu2sW2b8kiZqUP74ywCwp2k2WxSW65Tq9ZEaiaxUWqJMTCLwiYJgoB6vRHHp6gweWCfjE8huW/cm77Hb/67/zed1wwNwzKwcjb5Qg5nfwWnIDq6uaL4S4WI2D1DURTCQATwDYMwE/mdtGejaiIQcCJCGKaBbuqpCCECUqp7quMrkUgkm8FzPT76W3/C5Zev47kezXozFSKGBQM2bTMVIEpjJSaPTKZtc7LczgmB2DANFFXpuuxtQhjW9OH9GidvU2+uHRhTIpE8OEjBQiLZhSiKgmWLgHzlkRJTB8TbaK/jiRSizRa1ao1qtY7v+fE2YJompmmu+rAfhNfxaDQaqKrK1IFJJvaNY9nW/fhqEknKxMEJfuhn/z6vff4yEwcnsGwrFQ2yIkQyrShK6pYREeG23FSAcEwHw4otkTR1hVWERCKRSKBVb/HpP/kcnY5HvpSnMFJg4uAETsHpihDxdCIQq5qatsVhEBKGQhxOTccjETTRtIQ1hG7pmJYphGGtK0ZIYVgikWwGKVg8IGxHSjbJzqIkVhWWSalcZHJqHwCdjocbixjVap16tYZX84TbnwJm/CZjUNyJdqtNq9XCsi2OnZTxKSTbz8T+CtMjcwR+QKveWvEWznbstPO7mnuGRCKRSNamVCnxf/7CD/DyZy/hFJweUTgMwtQ1I7FQ67Q7aIaGaZnCgsIQbhqJAJEEEN5Ka0+JRCLJIkcmu51uiHzSYDJpPuYICCGM8GoBtNugqqCo4nW7qgJqJmiP7Ng/iJimgWkaFEsF9u2fAMDzYhGj1aZWrVGr1qnX6uIaiEBVfBYXahRLRR47cZpiScankOwMhXKeo48fle4ZEolEsk2oqiKCgLdcDMPAcqzUWs0wjVSASARiKQxLJJKdRAoWO0HqY52IEZmUPlkxIlkPpEl5ISM8xAq4qqLqBqgdUSb0xaffHylYEWJGVtRIcynLh9GDhGEYGIZBoVhgYt84AL7v026LyN05R2fq0BHyha3NkyyRbBTTMimOFHe6GhKJRPLQUBor8eibH5XuchKJZE8gBYutZJ3WEHHhldunAgJdEWEjQkJ2+4F1Q9QnCFbmNia2yEhEjUTQyIoakj2NrusUCjqFQp6JiSJzMruBRCKRSCQPHYqiSLFCIpHsGXatYPHKy6/wxx/5KJ7nk8/n+a7v+c6dqUi/NUQiSLwBa4gdGfwnxxx27KT+YQSsJmhIdxOJRCKRSCQSiUQikdx/tkWwqFar/MS//knuzNxBNwymDkzxve//bsrl8tBtzpw9w5mzZwD45//sx2m1WjiOc1/rGQUBeG5m8J6kd7oP1hBbRRhC0MFvxpYTqr65eqxmnQGZc5K4m6Qbig9VAaS7iUQikUgkEolEIpFItoZtESwUReF9X/91nH/iPAAf/KX/wIc++Ct83/e/n5mZGX76p36mp/yb3/Jm3vf1XwfAp577NIcPH7rvYgVAGPgQdEDRdtYaYhBRLBb4HkrQQfE7YjoUykFzGTRiaUXVQdOJNB00gyieR9OFmLAZ7qe7iUQikUgkEolEIpFIJH1si2BRLBZTsQLg0UdP8/sf/gMApqam+LEf/2cDt/voRz7K7Oxdvu3bv3U7qilIglLuJFGYChP4nhAngg5KLASkooRuEup5Is2kWHSoVxsQeBD4KKGP4jbSbdJdqxqoXTEDTe8KGuomU1K9QXcTrw502l0xI7HWkO4mEolEIpFIJBKJRPLQsu0xLMIw5Pc//Ac887ZnVi33qb/8FL/2K/+Rp595ip/+qZ/hW771m1d1IemnUilsuG5Bx6VcclD17TktURQRBT6B2yZ0XYKO+Ay9TreQoqJZFmqujGbZqJaNZlooA0SVkVx+5f7DgMjzCL1O/JdMu0Ruo3cHqopqmPGf0TOtaPp9S2sVRREjZYcoiQkCmTAgIYqqie+rqaiqBoqKogohQ9msxcgWMTGxd7MbyLpvP3u13hKJRCKRSCQSyU6w7YLFz//ML2DbNl/1NV+5armn3/Y0H3zb05s+zvx8nTAcEHtiFcbKJsvV1uYtDVYjioSVhO+B3+laT0Rht4iqgW4SOWUi3QTdAFUnSISCCGhH0G6v2P3oSI7FpeYqFTBANcBC/EHswuELi4zAg9AXAkqzCaHf46wRoaRuJZFmZKw09M3HzcjWfbk1eOWwFLApirDISF1Nti+7yV7OtCHrvv1spt6qqmxKfJVIJBKJRCKRSB4EtlWw+OVf/CDT09N84Ef/MepOu13cT8JAiBKJK4fvQeClAkCEArpBZOaIdJNIN0Azt98VRVFBN4VIktYtmYhjZgQ+SuCL+ifznTZKJhDpfYubARtwNwlZV3YTma5VIpFIJBKJRCKRSPYE2yZY/OqHfo3Lly7zT370AxiGsV2Hvb9EkRjI+53up99ZaTWhmUSmI8QJzYwH8bt8oKwocYwLY4iYEQgrjFjM2HDcDE0XFh9vVKTZaHaTCLr+Jgo9gUDVXZrdpOd8RtkfYuWyzLXXtUpJyiYpeSM6tRDarTheSPYcZr53IjQp6b/ecgm75TxJJBKJRCKRSCSSB4ptESxuXL/Bb/7n/8rBgwf4wX/4QwBMTk7yIx/44e04/NaQWE0EXsZ6Ims1wUphQjfuj3vJTqNk3EMy2pMYF0epq4kS+BB66bTSaaNEvXEzIkWlXjVQI4VIUcX5UjVQVSKlOy0yt2xCRFhvdpMogGCIu0l/utZh2U2iPmknykz3rKcrLMRxOnpFiNj1ZYUwoQyZ7q1yb92Uno+k/kpyXpP9R9Hq9R14oKh3/+l5WU386KubFD92Fa+8/Ap//JGP4nk++Xye7/qe79zpKt0fBl7b0YDJIeXixaEn2rfuddt3TcvrWSKRSCQSieQNsS2CxZGjR/idD//2dhzqjRNFmQwdGWEiDLpFFC126XCItMSlw9j+zmkiDkQRoR93nBN2yu1BUYS4oGpEhpUu7vb/V8bNUDUI3PicR8EKC42efSSChqLGlhtabB2hxfNqevw1v/e63E2SOq90N+nUgXY2bkg0QEcYJiz0HzMrqiQWHsPKvnGUHvFnFQuV9bDCAmQ94seg87I+8SP0Or3XemJJcr9ZcYiNHDPCb+uirtt0P1arVX7iX/8kd2buoBsGUwem+N73f/eqwYvPnD3DmbNnAPjn/+zHabVa9z2ldBSFQhAWcytP66DfNysO9qwaIhqu+dsNuU/XwG+p4K2MKdTdJ31WTH0iXipuSKFDInlY2QmhOIoSV9qBazezx81tutHndwR+uwV+HJy+px2NP2VbKpE8cGx70M1dS+BTv34TtdXss5owiAybSDPiQJjmzllNZAQKIB6oG6BpGIUStJW4TPwgioL4gZR5GPQM/nbA7WFA3Ixcf8DQxOUkrr8SBvG8GNgo8bTieWJ+yKGijHjStdjoWnH0WHQk1hM9dV39gadkU+A+zA/FgVYTm2Qd4offaopB4sDjZBauGNT2B3AdsKxvOyXq337Qdpm6DtueiFbHAWdi264VRVF439d/XZpS+oO/9B/40Ad/he/7/vczMzPDT//Uz/SUf/Nb3sz7vv7rAPjUc5/m8OFD912sgNhKwW0OPy8rrIZWrFwxmS5Qsp/J4i06/1GEomnC2mzI+sxMt21O51fbeZ/Y0eOq1t/myA66RLJb2DNCse9Bpz9Qe7btGPQCZjU2IfoqKybWhXhp4bF2O5rZ9wr311joGNqeZraVbapEsuNIwSJBUVANkyhUiQxLiBM7YTWRJengpnEJFNBEPIjURSFZk7osxAu0AfshjIWM+C9V1xMBpE/E2KnvnricxJfniheuPTPJOYrFmTBAyUyL+dhFJXSF0DHgkBGklhqD3FFS6w1VI3FP6bVS6N/hoIHJoEFu9hv1D4YHr1M2vf/uulZnGaXj92w2YKb/Sw0sogwqM3B24/setG2zoaL6AT3fb4CgsB1Xb1qrrJtQT8enuyxa1cVm6ykWi6lYAfDoo6f5/Q//AQBTU1P82I//s4HbffQjH2V29i7f9u3fui31BLr31k7S05ZkxNJhbUsUUpsHNRvYN/5MxdE4Pk6UrtfoxsxZT/sBEEE4SFBjwBghcw1m3eHSTnm3cx4FsQgsO+USyZaxV4RiUVlEf3K3kQ2mnryoSvqsUUAnaqN0gritVeiNQ5Zx3033le440x8OBqyHbqOa/Yw/eiw/+6zlACkiSyT3j13YUu0QqkbuwGHadxd31oIiaaQBUERdNKPr4rCZhi/dToVsjMtsqtBEGMkKGgP3sQNWGauR/W7xz7a2wBF2BySZwQlRgJIMTMIOeGFPANX+fdaWNNQ0dW6vOLCLzlDm+3cfon5HybjerGUVMWClssb6/nVrWUMMXaX0L0DVdIJIEZJEj7jW2zkYuL6vExH1LOt/I9+/bIAQ0cMA8Siz3C45uMO8B+4zYRjy+x/+A5552zOrlvvUX36KX/uV/8jTzzzFT//Uz/At3/rNq74Z7GczKViDjku55KDqW/84isKQKAiIAp8w/owyn2EgUjlHvlg2FFVF1XQUTUMxTfGp6SiKQhQGYp/pZ0jke0ThoKxFvSiahqLGf5randeEYJqdV9TM+lXa4FQYy1gSRSvESwWvUadsZ+sXCuEbJRbAxXWuqGpGnE2EWrGPVLjdZiYmitt+zK1C1n372c567ymheDvo6Vsm/a6u6JvOJ2JwIlCssstOs7crO/CwWSE5FjWiFaJGVlDuEz6yz/me+GT91nKs2c5n+xxeU4FOe6XgMajPEW8jkUikYLHzJC4eSYOnal23k0FuClvJmgEpMw3zbnUx2ShZNxHWEDeArntKrzsKYYBuqLgdn5UPFoUo+8DpWzfoYRQNejhltxm4j9XWDdpPl3K/G84eYoUL0Ubof9uSfqwUGHpRBliuZM6r2nf9D3j7oufz4LY2V+83yM//zC9g2zZf9TVfuWq5p9/2NB9829ObPs78fJ0wXKvz1stY2WS52lqfUNwjOAbdju8wAXJoPBylx2Iq0nUwc0Pj46Aoyfu4HkaTa3HYk7RfBE4763F7miwLQ2HiHLqr1jvdbdq5TizC+jri6korj/5O+OggV7zMGRrs+oTYx6A3kD33wBBz6zVFv7WZmCgyN1fb1LY7jaz79rOZequqsinxtZ/tEoph42Jx6HVioXh9WfuiuO0Vgm9GoO2bDvuWD4+TIegRZU2jO51druldwVbTAAXCUIjDYdD9DMTzoGd50J0n9Im8uNw63FeS4OSKpnaFZVXNLE/m1e46LV6XEZajbN8hjBgpW31Ccrwu7VP0tsVK3Lb2CMmKKpZn+vFKXz98q8XkvSpYgqz7TrDV9ZaCxXbTH4dCVTMWFPdZoNgI/WLGXnYxeSOk7imC7GPEGcnR3qOD/l3PigFbdz5KBqjZdYMEhszb5N7OAH2CgrLyer8PWU1U3QBl+00sfvkXP8j09DQf+NF/jPpG0wjfB6IgiFMje4PdMDIi4eouXV23rcjQybpy9bh4qXGHdzvaI0UFrfecD+omDxRKs+1qFIsaGZGjuyxMLcKGnZ+e48TPmkZNQw0BRRECCJksSErvdGqt1LM8no/InMvEfWWQufUWCR2DztWqDFgfrbG+v/BqRYZY4Yl1vQMPv6XFQVpXeSYqQ+7R/rZn5coVk0PL78Vn8R5ku4Ri2LhYPFrUWF6skgz+lR4Lh5XzRKtbPXRjhsVWC5oBut0VSxUt4xq3zsxvEeDHf4TxnyfE1prbV1iL/wxhfrGmCcZKq2Klr81N+upCUA4g8jJl1z7XvVYeou00LIOOF2b6HH1tKkpXkM62E8lfpAxvBnroa18HubGkbUpf32bAb3LfBMuh5zFaZXbIU3TIrsbGciwsrNFXX7V9XVFw6OwqCzOr19/+SqG4ixQs7jcD41DoIhZF0ojtNbbaxUSyO3kjqR/XlRJ1tfnMZN9gJTGXTwc3cF8EhgeBX/3Qr3H50mX+yY9+AMNY35u0bcX3qF66gDbgeomyHVvNEFmHYhEiysaTyZr9bhdxWxcFcac+PfQWibNJ5x9tqKvbwGUrfL8TS5OwbzASio5s4MeWKknbHXfQN1DV1AlugNCxqhgSbxP1PAv6Be6k494VOjrVENr9wt+w9qa/bRm0frXt10AhPudDZbSeydB1u9kNhrKaQLKWONt//GHr+uobRd22sv9ajnEXXGi0+/bXX59eqzVlyPIVx16zXNTz0XtuVy/XcPNgjmx7X2tXC8W+R/XikHYXettVTSfSs5ZcWq9F13a/bIsikVmqJ5bPJo7dZ20Lw2+7gcuzonJqhZwRPbLCcqZs6LooSda5uN3dzJmLku/Q3/ZmhIkoM91tWzP9oqFl4hhLST9LVUSWM7/Td2/232/9M5nxwNATOUDwjZJ2a1jbtVobtLIuXtUDt73GQ3Qjz4DVRPBBbW7/vLLKukwZBZphDRpuZqvMi7hVXuwNbGvTdUO2G/Y7RhBHkB9yvqL0N0u+Wd3NgTm2pe2CFCy2mqGBMs0VgTIfODbpYhL6fpymcq2BbN+qFQdSej5WKqF7cLC6mmjwBhRnkQY3YPXzPUg4yIgH6eyA3z17nQ/7rZS++XRy9d9Jd/JQX93MVAI3rt/gN//zf+XgwQP84D/8IQAmJyf5kQ/88A7XLIOm4+w/SKPaEG/k+twwdoRVM78kKMLsVzdAdTPlwiEP9OTeij977okBg8TNfvfkHl3DqiMC8sNcq3q+S/Z51p1Wssv71ivZ7bZCDOl7E9lsaqhByLravxWrBnU0V+3Frtqh3eivVF/qNVbcSzSWd67uvWc++5xf67kCQVsBo7zxH+sNsOuFYk0nd+Aw9aVa3O523ct6RMOdoD+L16C2N6J3IJy0qasNGvvb3BXL2Nj3zorK2eoPKJpdtsIdt7+97XnpFwFxu5kROHottaO+9YlYEmXa3rjdXv+3W1H/6ryKOrRPObijuVt63M3q3m1327X7W/dVn2rDxlLJshWLuwvCtgpGGN8jW4MULLaCFXEo9DceKPNBYxUXE6NYEClZgd5OZt+t1BPkKBywPOrbLmmoe3ayibqn/3oW9vpnrrcj3L/jdQoGEF9LmeXKgIduTwyMPnEsU07PF6D1AAo8kpQjR4/wOx/+7Z2uxuooCmZ5lLrL9gU7XiFIJG1E5v5L3lilbXjGQiDTodcdB+oDgnX2BGkj0y5F3c72iuMnnVB6t6Wvbsns/RA8stuu0tFYrWVbtdUbJobEHe2VQshKMUTRVPFSTulvv7oz0YpFQzpcQ7bvLTbsXCrdy2a17TO/j+OYtFr9FhZD9j/0J9zIbzvkBQIbl18KBYt6I1v3eN9ZC5P0XEdkFgyoVuZ5n5zEdT+HNvaczI3lcWurBNTdYvaEUKwomKURolawvVlChra9PZWLXfeSdnel5YBRKEJryH7Tj6h3XdJn7G9zV/QTBwgkK8QPpW89PdfcyjKrsI72dlCtNrpeFBouiCR/Ss/6bttrmRptN3Mfrfn9lF79iIEzK7ZZd7u3gX3m8yaNZrft2lDbt+qgfY2N0mZulfYqW6v+Y0VQLFrUEvenod856l+5sg4KECXWitliSrJy9ToqZH7zta5/hXylgLvc77b1xpCCxWbYK3Eo9gi96UEHdSK3kDXMR3vW9cz2CySRcE0Y6AcYs0IwWKNB6Z+5j9eRqsWpcSWSB40eq4iMkNz/5kDRuqlFe94sKm/83nuj7dmWCR5K32e3WqHv98WC6fkCq86usjCzepXB/5DO+Xo65UMtQ3aKdfiyJ5glh2bUYrDAPXDnG6nIxooPJCMk9KFZDrT71w24Tvot8dKPIWLaeqzxehavJWj0Iizyts8HfE8IxfeLoW0v9PyO2bZ3iBi8Ibaq/9jfP8wKH9n1q4kf2cxxaWW6+4hSi+I3wMD7ZIM7SIW9PguRIbu1yjlay+tsd9fdDr3hBmv4PjO71hyHqLOB85X+ZAPEqhUFWaXMWkJIv0Czsu3TnZx4obtaGzrovhl6jawx7kgXvbE+kGZZoKzl/rgxpGCxHvoFCkUB1cjEoZACxZ5hqDK+cXQnD6Z0TZBItpV1dYqzHeH4bR0ZcXG3t9nbIHholg2aP2Cb/u36lqXTWaFk1coMWLZaRzBeN6SIcGcb5EI4jLU6lZspv84fJDvITqeHvA0b8GhaOdPfwe3fx1od5BU7X61gz6xRKMaCxcYEA8kDRH+bO9BVLmlz+60j4vW7+XoZGANrkwxpg7VcHlr9521Qe7vWvtdRuF9w6T/I0F1sYgCeLbNasRXrhhXebP985TWmOzlo9lsWrHX8Aes2KtxvwbWu5/LQkOMMkILFYIbGodAf/DgUEolEspOsKkhA9+1QpkOcWkhk1j/MrEPw0CwL9K19A7KCodYHa1kYDN9Oz+VF53PdA/AhZVbdbB373MQ1tsKUfQ+hyL7Pg01/u7uqu0Zv5otum/yQt7tZhrTBqq5vn/vjFrKX2y7VMEHbWvcEyfYjBYt+EpNaVZNxKCQSiWQ7CLNR31d7SzfMJFKyK1kl/sN6Fg1C1Y2eVNMSiWSThBFEibUSfZZpfdNSCJZIJDuIfOpnUHUDzJx085BIJJJtQtUNsHJIQUIikUi2B0XTZbsrkUj2DFKwyKCo6p401ZJIJJK9imx3JRKJZHuR7a5EItlLSIdEiUQikUgkEolEIpFIJLsOKVhIJBKJRCKRSCQSiUQi2XVIwUIikUgkEolEIpFIJBLJrkMKFhKJRCKRSCQSiUQikUh2HQ9s0E1V3Vy0481utxuQdd9+9mq9QdZ9J9hovffa95Tt7t5ir9Z9r9YbZN13gge93YXN1Xkvfs8EWfftZ6/WG2Tdd4KtbneV1y69Er2RCu02dN3g5NFHdroaEolEsmVcvn4J3/d2uhpDsS2Ho4eO7XQ1JBKJZMu4fusabbe109VYFdn2SiSSB4lh7e4DJ1iAEC0kEonkQWE3ixUJtuXgB/5OV0MikUjeMLqm73qxIkG2vRKJ5EFgtXb3gXQJ2Qude4lEInmQ2Cude4lEIlmLvdSPlG2vRCJ5EFit3ZVBNyUSiUQikUgkEolEIpHsOqRgIZFIJBKJRCKRSCQSiWTXIQULiUQikUgkEolEIpFIJLsOKVhIJBKJRCKRSCQSiUQi2XVIwUIikUgkEolEIpFIJBLJrkMKFhKJRCKRSCQSiUQikUh2HVKwkEgkEolEIpFIJBKJRLLrkIKFRCKRSCQSiUQikUgkkl2HvtMV2I3cuHGT3/1vv0sYhARhwN/9e9+Hoig7Xa118xP/5t+iaxrv//6/s9NVWTezs7P803/yY5w7d5bySJlv+ZvfvNNV2hC/8R//M9VqFVVV+d+/8zt2ujrr4sLrF/jjP/ooAJ967tP8X//ixzhw8MAO12p9LCws8NP/v59lZKRMq9ni+/7e+7Esa6ertS5u37rNh/7DrzI6OkKxUOR//bZv2ekq7Qpku7v9yHZ3+5Ht7s4g293ByHZ3+5Ht7vYjcP5umAAACPxJREFU292dYSvb3YdWsKhWq/zEv/5J7szcQTcMpg5M8b3v/27K5TJHjhzme9//PQD8ix//V7TbbRzH2eEaC1arN8B//93f4y1veZIXnn9xh2u6krXq7jgOnuexf//kDtd0JavV/VN/+SluXL9BpVKhPFLe6ar2sFq9Tz96mtOPnmZ5eZmFhYVd13ivVvfr165z7txZ/tr7/io//7O/wOzsXY4cObzTVU5Zre6f/tRneM97v5i3v+Nt/MLP/SKXLl7ikVOP7HSVtwXZ7m4/st3dfmS7uzPIdncwst3dfmS7u/3Idndn2K52V3nt0ivRFtd9T1Cr1bh29RrnnzgPwAd/6T9Qq9X4vu9/PwAvPP8iH/nDj+DkcnzX93wnqro7vGdWq/eli5d4/vMv8K53v5P/8p9+c9cpzqvVPYoiFEUhiiL+5Y//K779f//fmJzcPQ35anX/f37zt/A8j2/85m/gQx/8FZ5+5inOnD2zwzUWrHWdg1DLT548wdNve3qnqjmQ1eper9f5N//q/8a2bVRN4x/8wN/bNfcorF73arXKf/r130DXdebm7vHud7+LZ9/17A7XeHuQ7e72I9vd7Ue2uzuDbHcHI9vd7Ue2u9uPbHd3hu1qd3fPN95misVienIBHn30NHN359L5J950nn/wg38fTVO5euXqTlRxIKvV+9Of+gwz0zP82od+nVdffY2XX3p5p6o5kNXqnpggKopCOTZ72k2sVveJfRMUi0UASqUSzWZzR+o4iLWuc9/3+fznnuetT791J6q3KqvV/Y//6KN8+Vd8GT/0Iz/IyZMn+OxnPrtT1RzIanUvlUr87e/+Tr7j//hbOLbN4V2klN9vZLu7/ch2d/uR7e7OINvdwch2d/uR7e72I9vdnWG72t2H1iUkSxiG/P6H/4Bn3vYMAC++8CKf+PgnIYrwA58jR4/scA0H01/vb/zmbwCEf9x/+U+/ydlzZ3eyeqsy6Jz/yUf/DE1VcRyHY8eP7WwFV6G/7u949u387L//OX7pF36Zeq3O1/zVr97hGg6mv94AH/vzj/PsO9+xq9TaQfTX/amn38qvfOjX+PznnmdhYYH3fMkX73ANh9Nf9/l78/z6r/1HAI4dP/ZQdZyzyHZ3+5Ht7vYj292dQba7g5Ht7vYj293tR7a7O8P9bHcfWpeQLD/773+O+fl5fvgf/6NdfyFn2av1Bln3nWCv1htk3R9E9up52av1Bln3nWCv1htk3R9E9up52av1Bln3nWCv1htk3Yext87EfeCXf/GDTE9P84M//AN76sLYq/UGWfedYK/WG2TdH0T26nnZq/UGWfedYK/WG2TdH0T26nnZq/UGWfedYK/WG2TdV2NvnY0t5lc/9GtcvnSZ//MDP4JhGDtdnXWzV+sNsu47wV6tN8i6P4js1fOyV+sNsu47wV6tN8i6P4js1fOyV+sNsu47wV6tN8i6r8VD6xJy4/oN/s53fx8HDx7AtEwAJicn+ZEP/PAO12x19mq9QdZ9J9ir9QZZ9weRvXpe9mq9QdZ9J9ir9QZZ9weRvXpe9mq9QdZ9J9ir9QZZ9/Xw0AoWEolEIpFIJBKJRCKRSHYvD7VLiEQikUgkEolEIpFIJJLdiRQsJBKJRCKRSCQSiUQikew6pGAhkUgkEolEIpFIJBKJZNchBQuJRCKRSCQSiUQikUgkuw4pWEgkEolEIpFIJBKJRCLZdUjBQiKRSCQSiUQikUgkEsmuQwoWEska/I33fSN3Zu4MXPfRj3yUH/qHw3MNv/jCi3z73/yO+1U1iUQieSCR7a5EIpFsL7LdlexWpGAhkazBf/mt32D/1P51lf3ar/xrTE/P3OcaSSQSyYONbHclEolke5HtrmS3IgULiUQikUgkEolEIpFIJLsOKVhIHlr++I8+yo/96D9P57/zO76Lf/nj/yqd/1vf+h1cuXylR0WuVqv883/6f/ENf/2b+Aff/wPMZEzn/tEP/AgAf/d7v5+/8b5v5GP/4+Ppuv/3t36bv/lN38a3fcu388d/9NH7/dUkEolkVyLbXYlEItleZLsr2etIwULy0HLu/FleefkVwjBkYWGBwA949dXXALgzc4dWu82x48d6tvm5n/55DMPgQ7/+Qb7v+/8Of/yRbmP8L/6/Pw7Av/33P8l/+a3f4Au+8F0ALC4u0mw0+eCv/hLv//7v5Wd/5ueo1+rb8yUlEolkFyHbXYlEItleZLsr2etIwULy0LJ/aj+O43D1ylVeevFl3vzWJ6lUKty6eYuXXnyZs2fPoKrdWyQIAv7nJz7Jt/zNb8a2bY4eO8p73vvFax5H13W+8Zu/AV3Xeerpp3Bsh1u3b9/PryaRSCS7EtnuSiQSyfYi213JXkff6QpIJDvJ2fPnePGFl5iZmeHc+XPk83leevFlXnvtNc6eP9tTtrpcJQgCxifG02X79u3jZV5Z9RjFYhFN09J5yzJpt9pb+0UkEolkjyDbXYlEItleZLsr2ctICwvJQ82582d56cWXeOWlVzh3/iznzp/jpRdf4qUXX+bcuXM9ZUvlEpqmcW/uXrpsbm5uu6sskUgkexrZ7kokEsn2IttdyV5GChaSh5pz587y4gsv4nY6jI+Pc/bcGT77mc9Rq9Y4cfJ4T1lN03jHs2/nP/36b+C2XW7cuMmf/PGf9pQZGR1h9s7gHNYSiUQike2uRCKRbDey3ZXsZaRgIXmoOXjoILbtcPbsGQByuRz790/y+JnHe8zaEv7293wnrVabb/2W/41/+3//O977pe/pWf9N3/yN/OS/+Xd80//yzXz8zz++YnuJRCJ52JHtrkQikWwvst2V7GWU1y69Eu10JSQSiUQikUgkEolEIpFIskgLC4lEIpFIJBKJRCKRSCS7DilYSCQSiUQikUgkEolEItl1SMFCIpFIJBKJRCKRSCQSya5DChYSiUQikUgkEolEIpFIdh1SsJBIJBKJRCKRSCQSiUSy65CChUQikUgkEolEIpFIJJJdhxQsJBKJRCKRSCQSiUQikew6pGAhkUgkEolEIpFIJBKJZNchBQuJRCKRSCQSiUQikUgku47/P02oft/bfFr1AAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 1080x288 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Files already downloaded and verified\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|███████████████████████████████████████████| 35/35 [00:25<00:00,  1.35it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "coord check plot saved to coord_checks/μp_cnn_adam_lr0.001_nseeds5_bn1_coord.png\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x288 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for arch, opt, bn, mup in product(['mlp', 'cnn'], ['sgd', 'adam'], [False, True], [False, True]):\n",
-    "  example_plot_coord_check(arch, opt, batchnorm=bn, mup=mup, nseeds=5, download_cifar=True, legend=None,\n",
-    "                  plotdir='coord_checks/')\n",
-    "  plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "hide_input": false,
-  "interpreter": {
-   "hash": "6ec6a431dde256c1546881150f7c6b5e06acee342e18bc59d4693481ee2afc73"
-  },
-  "kernelspec": {
-   "display_name": "mutrfmr",
-   "language": "python",
-   "name": "mutrfmr"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.7"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/mup/LICENSE b/mup/LICENSE
deleted file mode 100644
index 9e841e7a2..000000000
--- a/mup/LICENSE
+++ /dev/null
@@ -1,21 +0,0 @@
-    MIT License
-
-    Copyright (c) Microsoft Corporation.
-
-    Permission is hereby granted, free of charge, to any person obtaining a copy
-    of this software and associated documentation files (the "Software"), to deal
-    in the Software without restriction, including without limitation the rights
-    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-    copies of the Software, and to permit persons to whom the Software is
-    furnished to do so, subject to the following conditions:
-
-    The above copyright notice and this permission notice shall be included in all
-    copies or substantial portions of the Software.
-
-    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-    SOFTWARE
diff --git a/mup/README.md b/mup/README.md
deleted file mode 100644
index e293a5294..000000000
--- a/mup/README.md
+++ /dev/null
@@ -1,303 +0,0 @@
-# Maximal Update Parametrization (μP) and Hyperparameter Transfer (μTransfer) 
-
-[Paper link](https://arxiv.org/abs/2203.03466)
-|
-[Blog link](https://www.microsoft.com/en-us/research/blog/%C2%B5transfer-a-technique-for-hyperparameter-tuning-of-enormous-neural-networks/)
-
-In [*Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer*](https://arxiv.org/abs/2203.03466), we show that optimal hyperparameters become stable across neural network sizes when we parametrize the model in [maximal update parametrization (μP)](http://arxiv.org/abs/2011.14522).
-This can be used to tune extremely large neural networks such as large pretrained transformers, as we have done in our work.
-More generally, μP reduces the fragility and uncertainty when transitioning from exploration to scaling up, which are not often talked about explicitly in the deep learning literature.
-
-![](figures/sp_vs_mup_dashed.png)
-<font size="1"> *Figure above: Training loss against learning rate on Transformers of varying `d_model` trained with Adam.*</font> 
-
-
-μP turns out to be the *unique* "natural" parametrization that has this hyperparameter stability property across width, as empirically verified in the gif below on MLPs trained with SGD. Here, across time, we interpolate between PyTorch default and μP's learning rate and initialization scalings (right), and we scale up the width-256 model (log2(width)=8) to width 2^13 = 8192 using this interpolated scaling rule (left).
-
-![](figures/parametrizations.gif)
-
-This repo contains the source code for the `mup` package, our tool that makes the implementation of μP in Pytorch models effortless and less error-prone.
-
-## Table of Contents
-
-
-  - [Installation](#installation)
-    - [Install From Source](#install-from-source)
-  - [Basic Usage](#basic-usage)
-  - [How `mup` Works Under the Hood](#how-mup-works-under-the-hood)
-  - [Current Limitations](#current-limitations)
-  - [Checking Correctness of Parametrization](#checking-correctness-of-parametrization)
-    - [Coord Check](#coord-check)
-    - [Making Your Own Coord Check Plots](#making-your-own-coord-check-plots)
-    - [Wider is Always Better](#wider-is-always-better)
-  - [Examples](#examples)
-  - [Running Tests](#running-tests)
-  - [The Basic Math](#the-basic-math)
-  - [Contributing](#contributing)
-  - [Trademarks](#trademarks)
-
-## Installation
-
-```
-pip install mup
-```
-
-### Install From Source
-
-Clone this repo, change to its directory, and do
-```
-pip install -r requirements.txt
-pip install -e .
-```
-
-## Basic Usage
-
-```Python
-from mup import MuReadout, make_base_shapes, set_base_shapes, MuSGD, MuAdam
-
-class MyModel(nn.Module):
-    def __init__(self, width, ...):
-        ...
-        ### In model definition, replace output layer with MuReadout
-        # readout = nn.Linear(width, d_out)
-        readout = MuReadout(width, d_out)
-        ### If tying weights with an input nn.Embedding layer, do
-        # readout = MuSharedReadout(input_layer.weight)
-        ...
-    def forward(self, ...):
-        ...
-        ### If using a transformer, make sure to use
-        ###   1/d instead of 1/sqrt(d) attention scaling
-        # attention_scores = query @ key.T / d**0.5
-        attention_scores = query @ key.T * 8 / d
-        ### We use 8/d instead of 1/d here to be backward compatible
-        ###   with 1/d**0.5 when d=64, a common head dimension.
-        ...
-
-### Instantiate a base model
-base_model = MyModel(width=1)
-### Optionally, use `torchdistx.deferred_init.deferred_init` to avoid instantiating the parameters
-### Simply install `torchdistx` and use
-# base_model = torchdistx.deferred_init.deferred_init(MyModel, width=1)
-### Instantiate a "delta" model that differs from the base model
-###   in all dimensions ("widths") that one wishes to scale.
-### Here it's simple, but e.g., in a Transformer, you may want to scale
-###   both nhead and dhead, so the delta model should differ in both.
-delta_model = MyModel(width=2) # Optionally use `torchdistx` to avoid instantiating
-
-### Instantiate the target model (the model you actually want to train).
-### This should be the same as the base model except 
-###   the widths could be potentially different.
-### In particular, base_model and model should have the same depth.
-model = MyModel(width=100)
-
-### Set base shapes
-### When `model` has same parameter shapes as `base_model`,
-###   `model` behaves exactly the same as `base_model`
-###   (which is in PyTorch's default parametrization).
-###   This provides backward compatibility at this particular model size.
-###   Otherwise, `model`'s init and LR are scaled by μP.
-### IMPORTANT: this should be called as soon as possible,
-###   before re-initialization and optimizer definition.
-set_base_shapes(model, base_model, delta=delta_model)
-
-### Alternatively, one can save the base model shapes in a file
-# make_base_shapes(base_model, delta_model, filename)
-### and later set base shapes directly from the filename
-# set_base_shapes(model, filename)
-### This is useful when one cannot fit both 
-###   base_model and model in memory at the same time
-
-### Replace your custom init, if any
-for param in model.parameters():
-    ### If initializing manually with fixed std or bounds,
-    ### then replace with same function from mup.init
-    # torch.nn.init.uniform_(param, -0.1, 0.1)
-    mup.init.uniform_(param, -0.1, 0.1)
-    ### Likewise, if using
-    ###   `xavier_uniform_, xavier_normal_, kaiming_uniform_, kaiming_normal_`
-    ### from `torch.nn.init`, replace with the same functions from `mup.init`
-
-### Use the optimizers from `mup.optim` instead of `torch.optim`
-# optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
-optimizer = MuSGD(model.parameters(), lr=0.1)
-
-### Then just train normally
-```
-
-Note the base and delta models *do not need to be trained* --- we are only extracting parameter shape information from them.
-Therefore, optionally, we can avoid instantiating these potentially large models by using the `deferred_init` function in `torchdistx`.
-After installing [`torchdistx`](https://github.com/pytorch/torchdistx), use `torchdistx.deferred_init.deferred_init(MyModel, **args)` instead of `MyModel(**args)`. See [this page](https://pytorch.org/torchdistx/latest/deferred_init.html) for more detail.
-In the MLP and Transformer examples (not `mutransformers`) we provided, you can activate this feature by passing `--deferred_init`.
-
-
-## How `mup` Works Under the Hood
-
-
-By invoking `set_base_shapes(model, ...)`, each parameter tensor `p` of `model` gets a `p.infshape` attribute that stores, for each of its dimensions, the corresponding base dimension and whether that dimension should be considered `infinite` (i.e. will be scaled up/down, e.g., `d_model` of a Transformer) or `finite` (i.e. will be fixed, e.g., vocabulary size).
-This information is used in the initializers and optimizers to automatically scale the parameters or learning rates to be compliant with μP.
-For example, the Adam learning rate of hidden weights `p` is calculated as  `globalLR / p.infshape.width_mult()`, where `p.infshape.width_mult()` essentially calculates `fan_in / base_fan_in`.
-
-
-## Current Limitations
-
-- `set_base_shapes(model, ...)` assumes that `model` has just been randomly initialized in the standard way and rescales its parameters using the base shape information so the model is in μP.
-- If you want data parallelism, please use `torch.nn.parallel.DistributedDataParallel` instead of `torch.nn.DataParallel`. This is because the latter removes the attributes the `mup` package adds to each parameter tensor of the model. Also, for performance, `pytorch` [recommends the former anyway](https://pytorch.org/docs/stable/notes/cuda.html#cuda-nn-ddp-instead).
-- We scale the learning rate according to μP explicitly by creating refined parameter groups from what is passed to the `mup` optimizer and by manipulating the `lr` attribute in those groups. This is compatible with PyTorch's learning rate schedulers. However, if you roll your own, make sure the scheduler sets the learning rate relative to what is currently in the refined parameter groups. The following is an example of what *not* to do and what is OK:
-```python
-optimizer = mup.MuAdam(model.parameters(), lr=1e-3)
-for pg in optimizer.param_groups:
-  # what NOT to do: setting learning rate absolutely
-  # pg['lr'] = 1e-3 * 2
-  # what is an OK alternative: setting it relatively
-  pg['lr'] *= 2
-```
-- By default, any parameter matrix that has 2 "infinite" dimensions (i.e. dimensions that are different from base dimensions) are considered by `mup` to have shape (fan_out, fan_in), i.e., in the forward pass, this matrix multiplies its input on the right. This is the case with all `nn.Linear` weights from pytorch. If you have a custom parameter, say `W`, that violates this convention, you can manually set `W.infshape.main_idx = 0; W.infshape.main = W.infshape[0]` to let `mup` know that its shape corresponds to (fan_in, fan_out). A similar discussion applies if you have a parameter *tensor* with many dimensions but exactly 2 "infinite" dimensions, for which the first is fan_in and the second is fan_out.
-- Currently, [`torch.save` does not save the `infshape` objects attached to each parameter tensor](https://github.com/pytorch/pytorch/issues/72129). Before this is fixed, you would have to set base shape manually after loading a model checkpoint like so:
-```python
-model = torch.load('my/model/path.pt')
-# Important: note the flag `rescale_params=False`!
-set_base_shapes(model, 'my/base/shape/path.bsh', rescale_params=False)
-```
-(`set_base_shapes` by default rescales the parameters of `model`, assuming it's freshly initialized by PyTorch, to be consistent with μP.
-The `rescale_params=False` flag turns off this behavior.)
-
-
-## Checking Correctness of Parametrization
-
-
-### Coord Check
-
-Just like gradient checking is a simple way of verifying the correctness of an autograd implementation, *coordinate checking* is a simple way to verify you have implemented μP correctly: calculate the average size (which we denote in the y-axis below by `l1`) of the coordinates of each activation vector in, and output of, the model, for a few steps of training and a few different widths.
-If implemented correctly, then we shall see this `l1` stable over many widths; otherwise, the `l1` can blow up or shrink to 0 with width.
-(We are essentially checking desideratum 1 described below.)
-(The `l1` calculates `x.abs().mean()` for each activation vector `x` and is just one measure of the "average size" of `x`'s entries; one can also use analogously defined `l2`, `l4`, etc, though they may exhibit greater fluctuation with random seeds.)
-
-For example, in the following, we plot `width` vs `l1` for 2 steps of training, where t=1 means at initialization, before any gradient update.
-Each curve corresponds to an (pre-)activation vector of a layer or the output of the network.
-The first set of 3 plots shows an MLP in standard parametrization (SP), trained by adam.
-We see after 1 step of update, activation/output `l1` are exploding with width.
-This means SP is "incorrect."
-![](coord_checks/sp_mlp_adam_lr0.001_nseeds5_bn0_coord.png)
-We now do the same for an MLP in maximal update parametrization (μP) (including using `mup.optim.MuAdam` instead of `torch.optim.Adam`).
-In contrast to the above, all curves stay horizontal, indicating that μP is implemented correctly.
-![](coord_checks/μp_mlp_adam_lr0.001_nseeds5_bn0_coord.png)
-We call this way of checking implementation correctness a *coord check*, short for "coordinate check."
-
-### Making Your Own Coord Check Plots
-We provide an easy way to implement this check via functions in the `mup.coord_check` module.
-The workflow typically looks like the following.
-
-```Python
-from mup.coord_check import get_coord_data, plot_coord_data
-# construct a dictionary of lazy μP models with differing widths
-def lazy_model(width):
-    # `set_base_shapes` returns the model
-    return lambda: set_base_shapes(MyMuModel(width), 'my/base/shape/path.bsh')
-    # Note: any custom initialization with `mup.init` would need to
-    # be done inside the lambda as well
-models = {64: lazy_model(64), ..., 1024: lazy_model(1024)}
-# make a dataloader with small batch size/seq len
-#   just for testing
-dataloader = ...
-# record data from the model activations over a few steps of training
-# this returns a pandas dataframe
-df = get_coord_data(models, dataloader)
-# This saves the coord check plots to filename.
-plot_coord_data(df, save_to=filename)
-# If you are in jupyter notebook, you can also do
-#   `plt.show()`
-# to show the plot
-```
-For example, the `mup.coord_check.example_plot_coord_check` function is implemented this way for toy MLP and CNN models.
-
-If you see the curves blow up or shrink to 0 with width after a few steps of training, then there's a bug in your μP implementation (did you forget to vary some dimension, like `d_ffn`, in the delta model?).
-If instead you see the curves converge to the right, then most likely your implementation is correct.
-However, there are two typical exceptions to this;
-the following can shrink to 0 at initialization in μP (at a 1/sqrt(width) rate):
-  - the network output
-  - the attention logits in a Transformer
-
-These are transient, and after a few steps their curves should be roughly flat.
-Nevertheless, to remove the discrepancy at init, we recommend
-   - initializing the output layer 
-   (should be a `MuReadout` instance) weights to be 0 via
-   the `readout_zero_init=True` option and
-   - initializing the query matrix in a Transformer to 0
-     (this has to be done manually). If symmetry-breaking is desired in the attention logits at init, initialize the (relative) position biases with nonzero variance.
-     
-#### Tips for Coord Check
-
-- Use a large learning rate (larger than you'd use for actual training). This would emphasize any potential exploding coordinates issue, which could be hidden by the initialization if the learning rate is too small.
-- If you reuse a module multiple times in the forward pass, then `mup.get_coord_data` will only record the statistics from the last usage. In this case, for testing purposes, one can wrap different usages with `nn.Identity` modules of different names to distinguish them.
-
-### Wider is Always Better
-
-![](figures/widerbetter.png)
-
-Another sign that μP has not been implemented correctly is if going wider does worse (on training loss) after some width, at some point during training.
-The figure above illustrates this in a collection of training curves: (left) the correct implementation should always see performance improve with width, at any point in training; (middle) if you used standard parametrization (SP), sometimes you may see performance improve with width up to some point and then suddenly it becomes worse with wider models; (right) or you may immediately see worsening performance even for narrow models.
-
-## Examples
-See the `MLP`, `Transformer`, and `ResNet` folders inside `examples/` as well as the tests in `mup/test` for examples.
-People familiar with [Huggingface Transformers](https://github.com/huggingface/transformers) may also find the `examples/mutransformers` submodule instructive (obtained via `git submodule update --init`), which is also available standalone at [https://github.com/microsoft/mutransformers](https://github.com/microsoft/mutransformers).
-
-## Native Integration With Huggingface
-
-Frustrated that your [Huggingface Transformer](https://github.com/huggingface/transformers) breaks when you scale up? Want to tune hyperparameters for your large mult-GPU [Huggingface Transformer](https://github.com/huggingface/transformers) on a single GPU, right out the box? If so, please upvote [this github issue](https://github.com/huggingface/transformers/issues/16157)!
-
-
-## Running Tests
-To run tests, do
-```bash
-python -m mup.test
-```
-
-
-## The Basic Math
-
-μP is designed so as to satisfy the following desiderata:
-
-> At any time during training
-> 1. Every (pre)activation vector in a network should have Θ(1)-sized coordinates
-> 2. Neural network output should be O(1).
-> 3. All parameters should be updated as much as possible (in terms of scaling in width) without leading to divergence
-
-It turns out these desiderata uniquely single out μP.
-To derive μP from them, one needs to carefully consider how the *coordinate size* of a vector Av, resulting from a square matrix A multiplying vector v, depends on those of A and v, when A and v are "correlated".
-Here you can think of A as weights and v as an activation vector.
-This in turn depends on what kind of matrix is A and what kind of vector is v.
-In the context of training a wide neural network, it turns out we only need to consider vectors that has approximately iid coordinates, and two kinds of matrices: 1) those that look like outer products of such vectors, and 2) random iid matrices.
-Those of type 1 cover things like weight gradients; those of type 2 cover things like weight initialization.
-Then, if A and v both have entry size Θ(1) and they are correlated in ways that arise naturally during training, then we have the following table.
-
-|                  | outer product A (type 1) | iid A  (type 2)    |
-|------------------|--------------------------|--------------------|
-| Entry size of Av | Θ(n)                     | Θ(sqrt(n))         |
-
-Given this table, one can then trace the forward and backward computation of a network to derive μP straightforwardly.
-
-See [our blog post](https://www.microsoft.com/en-us/research/blog/%C2%B5transfer-a-technique-for-hyperparameter-tuning-of-enormous-neural-networks/) for a gentle primer and [our paper](https://arxiv.org/abs/2203.03466) for details.
-
-
-## Contributing
-
-This project welcomes contributions and suggestions.  Most contributions require you to agree to a
-Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
-the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
-
-When you submit a pull request, a CLA bot will automatically determine whether you need to provide
-a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
-provided by the bot. You will only need to do this once across all repos using our CLA.
-
-This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
-For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
-contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
-
-## Trademarks
-
-This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft 
-trademarks or logos is subject to and must follow 
-[Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general).
-Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
-Any use of third-party trademarks or logos are subject to those third-party's policies.
diff --git a/mup/SECURITY.md b/mup/SECURITY.md
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-<!-- BEGIN MICROSOFT SECURITY.MD V0.0.5 BLOCK -->
-
-## Security
-
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-<!-- END MICROSOFT SECURITY.MD BLOCK -->
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-# Support
-
-## How to file issues and get help  
-
-This project uses GitHub Issues to track bugs and feature requests. Please search the existing 
-issues before filing new issues to avoid duplicates.  For new issues, file your bug or 
-feature request as a new Issue.
-
-For help and questions about using this project, please use Github Discussions in this repo.
-
-## Microsoft Support Policy  
-
-Support for this project is limited to the resources listed above.
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diff --git "a/mup/coord_checks/\316\274p_mlp_sgd_lr0.1_nseeds5_bn0_coord.png" "b/mup/coord_checks/\316\274p_mlp_sgd_lr0.1_nseeds5_bn0_coord.png"
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diff --git "a/mup/coord_checks/\316\274p_mlp_sgd_lr0.1_nseeds5_bn1_coord.png" "b/mup/coord_checks/\316\274p_mlp_sgd_lr0.1_nseeds5_bn1_coord.png"
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diff --git a/mup/examples/.gitignore b/mup/examples/.gitignore
deleted file mode 100644
index 65f15c4fb..000000000
--- a/mup/examples/.gitignore
+++ /dev/null
@@ -1 +0,0 @@
-dataset/*
\ No newline at end of file
diff --git a/mup/examples/MLP/README.md b/mup/examples/MLP/README.md
deleted file mode 100644
index 990d25591..000000000
--- a/mup/examples/MLP/README.md
+++ /dev/null
@@ -1,32 +0,0 @@
-# μP MLP
-This folder contains the source code for our experiment on MLP, which also serves as an example usage of `mup`.
-The script trains a series of MLPs with increasing hidden sizes from 64 to 8192.
-
-## Save Model Base Shapes
-To train a μP model, one needs to first specify the base shapes. To save base shapes info of the narrowest model, run,
-```
-python main.py --save_base_shapes width64.bsh
-```
-
-## Verify Implementation with Coordinate Check
-Before we scale up and start training, it is recommended to check the size of activation coordinates as model width increases. We have integrated such a test in this example using the helper functions in `mup`; you can simply run:
-
-```bash
-python main.py --load_base_shapes width64.bsh --coord_check
-```
-You should find the generated plots under `./coord_checks`, which show stable coordinate sizes under μP, e.g., 
-
-![](coord_checks/μp_mlp_sgd_coord.png)
-
-and growing sizes under SP, e.g.,
-
-![](coord_checks/sp_mlp_sgd_coord.png)
-
-
-## Start Training
-Having verified our implementation of μP, we can scale up our model and train using the same hyperparameters used for the small model and expect that the wider model performs better on the training data and that the optimal hyperparameters transfer.
-```
-python main.py --load_base_shapes width64.bsh
-```
-
-Note that if you do not specify `--load_base_shapes`, the script will default to training a SP model.
\ No newline at end of file
diff --git a/mup/examples/MLP/coord_checks/sp_mlp_sgd_coord.png b/mup/examples/MLP/coord_checks/sp_mlp_sgd_coord.png
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diff --git "a/mup/examples/MLP/coord_checks/\316\274p_mlp_sgd_coord.png" "b/mup/examples/MLP/coord_checks/\316\274p_mlp_sgd_coord.png"
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diff --git a/mup/examples/MLP/main.py b/mup/examples/MLP/main.py
deleted file mode 100644
index bab457e9c..000000000
--- a/mup/examples/MLP/main.py
+++ /dev/null
@@ -1,277 +0,0 @@
-import time
-import os
-import pandas as pd
-import numpy as np
-import torch.nn.functional as F
-from torchvision import datasets, transforms
-import torch
-from torch import nn
-import torch.optim as optim
-import argparse
-import math
-
-from mup.coord_check import get_coord_data, plot_coord_data
-from mup import MuSGD, get_shapes, set_base_shapes, make_base_shapes, MuReadout
-
-def coord_check(mup, lr, train_loader, nsteps, nseeds, args, plotdir='', legend=False):
-
-    def gen(w, standparam=False):
-        def f():
-            model = MLP(width=w, nonlin=torch.tanh, output_mult=args.output_mult, input_mult=args.input_mult).to(device)
-            if standparam:
-                set_base_shapes(model, None)
-            else:
-                assert args.load_base_shapes, 'load_base_shapes needs to be nonempty'
-                set_base_shapes(model, args.load_base_shapes)
-            return model
-        return f
-
-    widths = 2**np.arange(7, 14)
-    models = {w: gen(w, standparam=not mup) for w in widths}
-
-    df = get_coord_data(models, train_loader, mup=mup, lr=lr, optimizer='sgd', flatten_input=True, nseeds=nseeds, nsteps=nsteps, lossfn='nll')
-
-    prm = 'μP' if mup else 'SP'
-    return plot_coord_data(df, legend=legend,
-        save_to=os.path.join(plotdir, f'{prm.lower()}_mlp_sgd_coord.png'),
-        suptitle=f'{prm} MLP SGD lr={lr} nseeds={nseeds}',
-        face_color='xkcd:light grey' if not mup else None)
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='''
-    PyTorch MLP on CIFAR-10, with μP.
-
-    This is the script we use in the MLP experiment in our paper.
-
-    To train a μP model, one needs to first specify the base shapes. To save base shapes info, run, for example,
-
-        python main.py --save_base_shapes width64.bsh
-
-    To train using MuSGD, run
-
-        python main.py --load_base_shapes width64.bsh
-
-    To perform coord check, run
-
-        python main.py --load_base_shapes width64.bsh --coord_check
-
-    If you don't specify a base shape file, then you are using standard parametrization
-
-        python main.py
-
-    We provide below some optimal hyperparameters for different activation/loss function combos:
-        if nonlin == torch.relu and criterion == F.cross_entropy:
-            args.input_mult = 0.00390625
-            args.output_mult = 32
-        elif nonlin == torch.tanh and criterion == F.cross_entropy:
-            args.input_mult = 0.125
-            args.output_mult = 32
-        elif nonlin == torch.relu and criterion == MSE_label:
-            args.input_mult = 0.03125
-            args.output_mult = 32
-        elif nonlin == torch.tanh and criterion == MSE_label:
-            args.input_mult = 8
-            args.output_mult = 0.125
-    ''', formatter_class=argparse.RawTextHelpFormatter)
-    parser.add_argument('--save_base_shapes', type=str, default='',
-                        help='file location to save base shapes at')
-    parser.add_argument('--load_base_shapes', type=str, default='',
-                        help='file location to load base shapes from')
-    parser.add_argument('--seed', type=int, default=1)
-    parser.add_argument('--batch_size', type=int, default=64)
-    parser.add_argument('--epochs', type=int, default=20)
-    parser.add_argument('--momentum', type=float, default=0.9)
-    parser.add_argument('--lr', type=float, default=0.1)
-    parser.add_argument('--output_mult', type=float, default=1.0)
-    parser.add_argument('--input_mult', type=float, default=1.0)
-    parser.add_argument('--init_std', type=float, default=1.0)
-    parser.add_argument('--no_shuffle', action='store_true')
-    parser.add_argument('--log_interval', type=int, default=300)
-    parser.add_argument('--log_dir', type=str, default='.')
-    parser.add_argument('--data_dir', type=str, default='/tmp')
-    parser.add_argument('--coord_check', action='store_true',
-                        help='test μ parametrization is correctly implemented by collecting statistics on coordinate distributions for a few steps of training.')
-    parser.add_argument('--coord_check_nsteps', type=int, default=3,
-                        help='Do coord check with this many steps.')
-    parser.add_argument('--coord_check_nseeds', type=int, default=5,
-                        help='number of seeds for testing correctness of μ parametrization')
-    parser.add_argument('--deferred_init', action='store_true', help='Skip instantiating the base and delta models for mup. Requires torchdistx.')
-    
-    args = parser.parse_args()
-
-    torch.manual_seed(args.seed)
-
-    device = torch.device("cuda")
-
-    kwargs = {'num_workers': 1, 'pin_memory': True}
-
-    transform = transforms.Compose(
-        [transforms.ToTensor(),
-        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
-
-    trainset = datasets.CIFAR10(root=args.data_dir, train=True,
-                                            download=True, transform=transform)
-    train_loader = torch.utils.data.DataLoader(trainset, batch_size=args.batch_size,
-                                            shuffle=not args.no_shuffle, num_workers=2)
-
-    testset = datasets.CIFAR10(root=args.data_dir, train=False,
-                                        download=True, transform=transform)
-    test_loader = torch.utils.data.DataLoader(testset, batch_size=args.batch_size,
-                                            shuffle=False, num_workers=2)
-
-    classes = ('plane', 'car', 'bird', 'cat',
-            'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
-
-
-    class MLP(nn.Module):
-        def __init__(self, width=128, num_classes=10, nonlin=F.relu, output_mult=1.0, input_mult=1.0):
-            super(MLP, self).__init__()
-            self.nonlin = nonlin
-            self.input_mult = input_mult
-            self.output_mult = output_mult
-            self.fc_1 = nn.Linear(3072, width, bias=False)
-            self.fc_2 = nn.Linear(width, width, bias=False)
-            self.fc_3 = MuReadout(width, num_classes, bias=False, output_mult=args.output_mult)
-            self.reset_parameters()
-        
-        def reset_parameters(self):
-            nn.init.kaiming_normal_(self.fc_1.weight, a=1, mode='fan_in')
-            self.fc_1.weight.data /= self.input_mult**0.5
-            self.fc_1.weight.data *= args.init_std
-            nn.init.kaiming_normal_(self.fc_2.weight, a=1, mode='fan_in')
-            self.fc_2.weight.data *= args.init_std
-            nn.init.zeros_(self.fc_3.weight)
-
-        def forward(self, x):
-            out = self.nonlin(self.fc_1(x) * self.input_mult**0.5)
-            out = self.nonlin(self.fc_2(out))
-            return self.fc_3(out)
-
-
-    def train(args, model, device, train_loader, optimizer, epoch,
-            scheduler=None, criterion=F.cross_entropy):
-        model.train()
-        train_loss = 0
-        correct = 0
-        start_time = time.time()
-        for batch_idx, (data, target) in enumerate(train_loader):
-            data, target = data.to(device), target.to(device)
-            optimizer.zero_grad()
-            output = model(data.view(data.size(0), -1))
-            
-            loss = criterion(output, target)
-            loss.backward()
-            train_loss += loss.item() * data.shape[0]  # sum up batch loss
-            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability
-            correct += pred.eq(target.view_as(pred)).sum().item()
-            optimizer.step()
-            if batch_idx % args.log_interval == 0:
-                elapsed = time.time() - start_time
-                print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f} | ms/batch {:5.2f}'.format(
-                    epoch, batch_idx * len(data), len(train_loader.dataset),
-                    100. * batch_idx / len(train_loader), loss.item(),
-                    elapsed * 1000 / args.log_interval))
-                start_time = time.time()
-            if scheduler is not None:
-                scheduler.step()
-        train_loss /= len(train_loader.dataset)
-        train_acc = correct / len(train_loader.dataset)
-        print('\nTrain set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
-            train_loss, correct, len(train_loader.dataset),
-            100. * correct / len(train_loader.dataset)))
-        return train_loss, train_acc
-
-    def test(args, model, device, test_loader,
-            evalmode=True, criterion=F.cross_entropy):
-        if evalmode:
-            model.eval()
-        test_loss = 0
-        correct = 0
-        with torch.no_grad():
-            for data, target in test_loader:
-                data, target = data.to(device), target.to(device)
-                output = model(data.view(data.size(0), -1))
-                test_loss += criterion(output, target, reduction='sum').item()  # sum up batch loss
-                pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability
-                correct += pred.eq(target.view_as(pred)).sum().item()
-
-        test_loss /= len(test_loader.dataset)
-
-        print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
-            test_loss, correct, len(test_loader.dataset),
-            100. * correct / len(test_loader.dataset)))
-        return test_loss, correct / len(test_loader.dataset)
-
-
-    def MSE_label(output, target):
-        y_onehot = output.new_zeros(output.size(0), 10)
-        y_onehot.scatter_(1, target.unsqueeze(-1), 1)
-        y_onehot -= 1/10
-        return F.mse_loss(output, y_onehot)
-    
-    if args.coord_check:
-        print('testing parametrization')
-        import os
-        os.makedirs('coord_checks', exist_ok=True)
-        plotdir = 'coord_checks'
-        coord_check(mup=True, lr=args.lr, train_loader=train_loader, nsteps=args.coord_check_nsteps, nseeds=args.coord_check_nseeds, args=args, plotdir=plotdir, legend=False)
-        coord_check(mup=False, lr=args.lr, train_loader=train_loader, nsteps=args.coord_check_nsteps, nseeds=args.coord_check_nseeds, args=args, plotdir=plotdir, legend=False)
-        import sys; sys.exit()
-
-    logs = []
-    for nonlin in [torch.relu, torch.tanh]:
-        for criterion in [F.cross_entropy, MSE_label]:
-            
-            for width in [64, 128, 256, 512, 1024, 2048, 4096, 8192]:
-                # print(f'{nonlin.__name__}_{criterion.__name__}_{str(width)}')
-                if args.save_base_shapes:
-                    print(f'saving base shapes at {args.save_base_shapes}')
-                    if args.deferred_init:
-                        from torchdistx.deferred_init import deferred_init
-                        # We don't need to instantiate the base and delta models
-                        # Note: this only works with torch nightly since unsqueeze isn't supported for fake tensors in stable
-                        base_shapes = get_shapes(deferred_init(MLP, width=width, nonlin=nonlin, output_mult=args.output_mult, input_mult=args.input_mult))
-                        delta_shapes = get_shapes(
-                            # just need to change whatever dimension(s) we are scaling
-                            deferred_init(MLP, width=width+1, nonlin=nonlin, output_mult=args.output_mult, input_mult=args.input_mult)
-                        )
-                    else:
-                        base_shapes = get_shapes(MLP(width=width, nonlin=nonlin, output_mult=args.output_mult, input_mult=args.input_mult))
-                        delta_shapes = get_shapes(
-                            # just need to change whatever dimension(s) we are scaling
-                            MLP(width=width+1, nonlin=nonlin, output_mult=args.output_mult, input_mult=args.input_mult)
-                        )
-                    make_base_shapes(base_shapes, delta_shapes, savefile=args.save_base_shapes)
-                    print('done and exit')
-                    import sys; sys.exit()
-                mynet = MLP(width=width, nonlin=nonlin, output_mult=args.output_mult, input_mult=args.input_mult).to(device)
-                if args.load_base_shapes:
-                    print(f'loading base shapes from {args.load_base_shapes}')
-                    set_base_shapes(mynet, args.load_base_shapes)
-                    print('done')
-                else:
-                    print(f'using own shapes')
-                    set_base_shapes(mynet, None)
-                    print('done')
-                optimizer = MuSGD(mynet.parameters(), lr=args.lr, momentum=args.momentum)
-                for epoch in range(1, args.epochs+1):
-                    train_loss, train_acc, = train(args, mynet, device, train_loader, optimizer, epoch, criterion=criterion)
-                    test_loss, test_acc = test(args, mynet, device, test_loader)
-                    logs.append(dict(
-                        epoch=epoch,
-                        train_loss=train_loss,
-                        train_acc=train_acc,
-                        test_loss=test_loss,
-                        test_acc=test_acc,
-                        width=width,
-                        nonlin=nonlin.__name__,
-                        criterion='xent' if criterion.__name__=='cross_entropy' else 'mse',
-                    ))
-                    if math.isnan(train_loss):
-                        break
-    
-    with open(os.path.join(os.path.expanduser(args.log_dir), 'logs.tsv'), 'w') as f:
-        logdf = pd.DataFrame(logs)
-        print(os.path.join(os.path.expanduser(args.log_dir), 'logs.tsv'))
-        f.write(logdf.to_csv(sep='\t', float_format='%.4f'))
diff --git a/mup/examples/MLP/width64.bsh b/mup/examples/MLP/width64.bsh
deleted file mode 100644
index 93578cb70..000000000
--- a/mup/examples/MLP/width64.bsh
+++ /dev/null
@@ -1,12 +0,0 @@
-# This is a base shape file encoded in yaml
-# - `null` indicates a dimension is "finite", i.e. a non-"width" dimension
-# - a number indicates the base dimension of an "infinite" dimension, i.e. some notion of "width"
-fc_1.weight:
-- 64
-- null
-fc_2.weight:
-- 64
-- 64
-fc_3.weight:
-- null
-- 64
diff --git a/mup/examples/ResNet/CoordCheck.ipynb b/mup/examples/ResNet/CoordCheck.ipynb
deleted file mode 100644
index 1d262bc2b..000000000
--- a/mup/examples/ResNet/CoordCheck.ipynb
+++ /dev/null
@@ -1,215 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-03T19:12:47.878251Z",
-     "start_time": "2022-02-03T19:12:46.654097Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from main import coord_check\n",
-    "from itertools import product\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "sns.set()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-03T19:12:48.603481Z",
-     "start_time": "2022-02-03T19:12:48.600624Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.makedirs('coord_checks', exist_ok=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-03T19:13:10.807802Z",
-     "start_time": "2022-02-03T19:12:50.189538Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Files already downloaded and verified\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|█████████████████████████████████████████████| 4/4 [00:04<00:00,  1.02s/it]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "coord check plot saved to coord_checks/sp_resnet18_adam_coord.png\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x288 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Files already downloaded and verified\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|█████████████████████████████████████████████| 4/4 [00:01<00:00,  3.51it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "coord check plot saved to coord_checks/sp_resnet18_sgd_coord.png\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1080x288 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Files already downloaded and verified\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|█████████████████████████████████████████████| 4/4 [00:01<00:00,  2.91it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "coord check plot saved to coord_checks/μp_resnet18_adam_coord.png\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x288 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Files already downloaded and verified\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|█████████████████████████████████████████████| 4/4 [00:01<00:00,  3.47it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "coord check plot saved to coord_checks/μp_resnet18_sgd_coord.png\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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5ylcyPz/P61//ei677LLW2Bv/9t/+W2655RZ+8id/EoDv//7v57d/+7d56UtfyjOe8Qy+9KUv8aY3vYl/82/+TSsi5+///u8BOH36NKurq9X6aJePrbCyssLrXvc6HvOYx3DllVeytLTEX/3VX1XjpDzkIQ/hYQ97GP/u3/07fuqnfqoaDPMTn/gEnU6H7/u+79vU/fUDP/AD/NZv/RYve9nLuP3227nzzjvHZmJ597vfjfeeW265hfn5eT74wQ+ysrJS3Wfb6RKyG9dMEARBENZDHBaCIAjCrvKv/tW/4j3veQ8vfelLuf766/mFX/iF6iX0P//n/7yjn/VjP/ZjXHrppfz2b/82r3nNa+h2u1xzzTV8z/d8DwDz8/N88pOf5F3vehdnzpxhcXGRxz/+8Tz3uc/d9Gd0Oh3e+c538ku/9Ev8zM/8DKdOneLIkSPccsstPOIRjwBC14MXvvCFvP3tb+dVr3oVD3zgA3nnO9/JnXfeyROf+MTqWH/3d3/Hn/3Zn3H3u9+dv/iLvwDgiU98Iv1+n//6X/8rv/RLv0Sv1+N+97sfv/EbvzG1q0yv1+OLX/wiL3zhCzl58iSHDx/mkY98JP/+3//7avvb3/52Xv7yl/O93/u9XHHFFbzwhS/k537u5+h0OkCIPHnLW97Cz/7sz/K0pz2NI0eOcPvttzMcDrf8d9gpfuEXfoH/9J/+E8973vMYDAY88IEP5Dd/8zdbLf9f/vKXW2OQXH755fzmb/4mr371q3nSk57EoUOH+L7v+z6e//znt47d/Ds01z/72c9u+3yTJOHs2bO85CUv4fjx48zNzfHgBz+4+jsopXjb297GL//yL/Of/tN/4s4772RhYYGbbrqJH/7hHwY2d39ddtll/Mqv/AqvetWr+O7v/m6uueYaXvKSl/BDP/RD1bksLCzwm7/5m7z2ta9lOBxy1VVX8fM///OtWVe2ym5cM0EQBEFYD+U3ii8VBEEQhG3y9Kc/nauvvnpsykVh//nqV7/Kbbfdxtve9jZuu+22/T4dQRAEQRCEMSTCQhAEQRAuAt773vdy2WWXceWVV/K1r32N1772tdz97nfn4Q9/+H6fmiAIgiAIwkTEYSEIgiAIFwGnT5/mzW9+M3fccQcLCws84AEP4I1vfGM1QKggCIIgCMJBQ7qECIIgCIIgCIIgCIJw4NAbVxEEQRAEQRAEQRAEQdhbxGEhCIIgCIIgCIIgCMKBQxwWgiAIgiAIgiAIgiAcOMRhIQiCIAiCIAiCIAjCgUMcFoIgCIIgCIIgCIIgHDjEYSEIgiAIgiAIgiAIwoFDHBaCIAiCIAiCIAiCIBw4xGEhCIIgCIIgCIIgCMKBQxwWgiAIgiAIgiAIgiAcOMRhIQiCIAiCIAiCIAjCgUMcFoIgCIIgCIIgCIIgHDjEYSEIgiAIgiAIgiAIwoFDHBaCIAiCIAiCIAiCIBw4xGEhCIIgCIIgCIIgCMKBQxwWgiAIgiAIgiAIgiAcOMRhIQiCIAiCIAiCIAjCgUMcFoIgCIIgCIIgCIIgHDjEYSEIgiAIgiAIgiAIwoFDHBaCIAiCIAiCIAiCIBw4xGEhCIIgCIIgCIIgCMKBQxwWgiAIgiAIgiAIgiAcOMRhIQiCIAiCIAiCIAjCgUMcFoIgCIIgCIIgCIIgHDjEYSEIgiAIgiAIgiAIwoFDHBaCIAiCIAiCIAiCIBw4xGEhCIIgCIIgCIIgCMKBQxwWgiAIgiAIgiAIgiAcOMRhIQiCIAiCIAiCIAjCgUMcFoIgCIIgCIIgCIIgHDjEYSEIgiAIgiAIgiAIwoFDHBaCIAiCIAiCIAiCIBw4xGEhCIIgCIIgCIIgCMKBQxwWgiAIgiAIgiAIgiAcOMRhIQiCIAiCIAiCIAjCgUMcFoIgCIIgCIIgCIIgHDjEYSEIE7jtttv4wAc+sOX9Xvayl/Ed3/Ed3HTTTbznPe/ZhTMTBEG4MNmO7n7hC1/gOc95Dg95yEN40IMexO23387nP//5XTpDQRCEC4vt6O7Jkyd56lOfyoMf/GAe+MAH8pSnPIWPfexju3SGgiAOC0HYUW666SZe/vKXc+9733u/T0UQBOGCZ2lpidtuu433ve99vP/97+fmm2/muc997n6fliAIwgXL7Owsr3rVq/jgBz/I3/zN3/AjP/IjPOc5z6Eoiv0+NeECRRwWgjDCi170Ir72ta/x7Gc/m1tvvZVf+7Vf2/S+T3va03joQx9Kp9PZxTMUBEG4sNiu7t5yyy08+clP5vDhw6Rpyg/90A/xhS98gVOnTu3yGQuCIJzfbFd3O50O97znPdFa471Ha82ZM2c4c+bMLp+xcLGS7PcJCMJB47WvfS0f+9jHeMUrXsHDHvYwAB74wAdOrf+sZz2LZz3rWXt1eoIgCBccO6W7H/3oR1lcXOTIkSO7dq6CIAgXAuequ49//OP5whe+QJ7nPPnJT+bYsWO7fs7CxYk4LARhE3z0ox/d71MQBEG4qNiq7n7jG9/g537u53jxi1+8S2ckCIJwYbMV3f3DP/xDBoMBf/Znf0ae57t4VsLFjnQJEQRBEAThvObkyZM885nP5Ad+4Af4ru/6rv0+HUEQhIuCTqfDd33Xd/H2t7+df/iHf9jv0xEuUCTCQhA2wa233jp124/+6I/y7Gc/ew/PRhAE4cJns7p75swZnvnMZ3LbbbfxnOc8Z69OTxAE4YJju/ZuURR8+ctf5qabbtqtUxMuYsRhIQgTuOSSS/jyl79crX/iE5/Y1H7D4RDvPd57iqJgMBiQpilaSzCTIAjCemxHd5eXl7n99tt5wAMewE/91E/t5ukJgiBccGxHdz/5yU9SFAW33HILzjne8Y53cOLECW655ZbdPFXhIkZ57/1+n4QgHDT+/M//nFe84hUsLy/znOc8h9tvv31T+z396U/nIx/5SKvsHe94Bw9+8IN34zQFQRAuGLaju//jf/wPXvziF9Pr9VBKVeV/9Ed/xBVXXLGbpysIgnDesx3d/chHPsIrXvEKvvzlL5OmKTfccAM/8RM/wbd8y7fswRkLFyPisBAEQRAEQRAEQRAE4cAhceqCIAiCIAiCIAiCIBw4xGEhCIIgCIIgCIIgCMKBQxwWgiAIgiAIgiAIgiAcOMRhIQiCIAiCIAiCIAjCgUMcFoIgCIIgCIIgCIIgHDjEYSEIgiAIgiAIgiAIwoEj2e8T2C1OnVrBua3N2Hrs2Bx33bW8S2ckTEOu+94j13zv2c4111px5MjsLp3RziO6e/4g133vkWu+91wMugtb1165F/cHue57j1zzvWc3dPeCdVg457dsOJf7CXuPXPe9R6753nOhX3PR3fMLue57j1zzvediuObb0d6L4bocROS67z1yzfeenb7m0iVEEARBEARBEARBEIQDhzgsBEEQBEEQBEEQBEE4cBzoLiGDwYD/+B//I3NzcyileMlLXrLfpyQIgnBBI7orCIKwt4juCoIgTGffHRanTp3ip3/6p/nSl75ElmXc4x734Od//uc5evQof/qnf8q3fMu38K//9b/m9a9/PZ/+9Ke5+eabd+1c8mWPXQISUOWidu3jBGEi3nuct1g/pHDTF+vzDbZPKXdDTArDYTH2uRD6nHnafc88Hvz4tiq/19sadZpbx8/13LeNn8+EbeUx/PRtVx+5D9//Tb/IQeAg6W4xqrsGlMT+CfuA88X6mupyig11WXT3IOnuVYdv4gfu/UscBA6S7vr6ognCvrGR5oZlMF7m29o6dV8/RBtPkdvJ+gpNAdmwzuj2LdeZqmv12nTt20yd9TR963Xa32Vcb6fVufvCDfyb+7yJnWTfHRZKKX74h3+YBz/4wQC85jWv4XWvex2vetWr+NrXvsb97nc/AK688kq++tWv7pqA5yfh7/7LKvgRS1n72nlRGtMJwbg24+XNBeMnb28eY8r+aHGW7Bfeu004A9bfPs1wbQlrPMYksfW4c/wWikRn7UWF1OiUjsnw3gHhJlNKoZQCFApVHaPcVh91e9uqsrKOqmuPbUM17v3mtvK4m9s2fj7b3NaosNlt1kPfZay4hFWbsmpTZg/djYPCQdHd4hT87S+tgltHdxvauKu6awAjurtfeO931Unb1Ny91l2jUxKdXRS6u962Letudc5b091Vl7BiU9ZsysyhyzgoHBjdXYJPv3UVN1C19k3TS9Neb+lxqbUj25hUf9I2cUzvO87b4Iid5hCwg42dtKVTYYN6kxaPPafz1yqZausmOiPTXbpZh2H8HDX2gJ+gpUqNbRmrM6ZhjbJdqtOud451Rio3nwyTNHh6nZB6oO8Mqy7o7t0vuYKdZt8dFocPH67EG+D+978/v/M7vwPA5Zdfzte+9jUAvvrVr3LTTTft2nkkR+Daf9Ph1NfX8AV4S0gLVeUpGNkGbq0uG90+5vzYEn6ygT7JiG89ZHzcx8WHkAsPFeMh8WDCOsahEo/SDp+4sG4cXjvQDjzBePMej4tGlo/rvlr33lUtRM36+LIOcX8P3rX3JZQNimOsrmjSZJ4kmcWYLl75fTNcrc/P5VYCNhbR1HTp6UMTtncq47a5mNb+abtcd0aOn6JVMkGYaxYX5zl+fOmcv+f5zFgD0wbr3ntWCsvp4ZCTgyGn8yGnhwWnhzE/yDmd55waDlnOm62o4e/wpWKGJ9x9p7/F9jgoumsOw7VP26buTtl+4HXX+KC1+627+VFWVgxZOk+azmFMF4dbxyhdP7rANo3cibqbr9sqttu6a3RKqjv0kvlt6G46Xt7S3bBddHdjzkV3Tw2GnC5yTg3yWneHOWeGOSen6O6/5D2eeOVOf4vtcWB0tweX/18ZZ+4YQENXK41t2rP9tvY2t53zMHiqdngcVIdJ0Nmgpd7bWkt90OemTntvK22u61pqzXZknUsYDBVpMofWyVRHbe4GG0YOTG0Mq5bBuvsUbojzxQZXYIM/IbrWwilLN5nDqEnb1t+vaTObkW1p1GytzIbnKLq7Pbz3rFnL6WGwa0v79tSwTEPZ6WHQ4aIh7pcsdfj2S3f2fPbdYdHEOcfv/M7vcNtttwHwmMc8hpe//OV89rOfxVrLLbfcsuljHTs2t6XPHgyX+MgXfoFBdhbS2lj0vmkklsIVyysD0Y8Yhy48lC3gDMqm4Ay4BGVTlDNgU5RLUDZBuTQujbxNUb4sz9AuRRUpKs9QLkW7DO0ylE/RroPyYV37DO07nMuDxKkcr4Y4NcDpIU4NcXq8zOtB2BbLvBriqrIhVg8o9JBcD8j1kFwPGcb80AwY6iEDM8ThAY1Hg1copVFeARrlNQod2l98mRporcc6XhPkU5OoTlh0GvNzdFVtXBqVkhCMWENcVylapRidxLI61SrBkKAJeY0JZd7U6xiUMmgfBdRR3wdFzPtGGt9Fwj00WrZBPd+o58B5GMTFx7rNfUfr38kqYCYYhyM3g5+w4kc2+Ul1J+6yoXG63X22agRvH4Mm4xLgki3u2blMsfgdMzt1IjvGfurucLjER/7ltfSzM6K76+pura2+obFOD9bR3Vp7a90N+f3QXaPSUD5Nd1WCUevprqn098Drrm+cR2PfO73o7vYIunsMOLbFPTuLisXvvLB1F7auvXemf0d2j9PxJbv9gl2tly/lowsO5xzeeXyhogNDRUezDmU2rOPKdYO3IaXQIbUxdRpsEtZdgrIGCgOrSdTtJOp0WCj1+xwdJh7bsF1znCr1dFjZtu31tv76RrnVQ4q45Kph95ohQz1s27tK46PWBkotVeE7xVQBjOhtU4NDXpHqbu04VR2MOsSMLjU3qzQ3UaXmBudqkkzS4KC/uso3dFjVOmyiJofzbWtdpZ92gi5O0tIyT6m77fqVzsS89WBb+ho7JUzR6VOsAcnmNKw8zjplkzVyVEC3su+EskmH2+S5bvSdQrdzcN6HBY9z5XrcTsiHg6UoUo4AR+MxtIp3owp3qlYh2qJMu3fTLC7urO4qf4A6sv3cz/0cd9xxB7/8y7+M1ucmRHfdtbylOWDX+if42N/+LGv9pSAEKopEdMGGVE1Io8DEtArvVNHwa6XRKJxw/Pb69ONX4ZHN+qOf7RX4piGegDXR4E7iAyFplDdTXa3jDKooHyYGZQ3OquhlV9XDR1mNcvH4fmNv5wWJ8jSiYIl/vnq9fDY115vPK9UoU9tb30qdbjel38+rz25/l+nralqd9co3OP66IfhbOLfm8bz3DJyjby1rztK3lr51rNmQX7OWvgvp0Llay+PxPNDVmm5i6JmwdI0O+cTQjWW9xJDpaFhMOzcfDKNj1/bILxmu82XH0Vpt2RDdKvupu/3BXXz00z/LWv/seau75XmO6a5NgoaO6G7Q2lDe1t1gmI/qbmnoh1ZO0d0WorvTy/dZd0t9rXTX1dq7Wd2tNHdUd40hM5vU3Wt65IsXtu7C1rR3efWr/Nlf/cA5f+b6RN1VpqGZ8cVc6YbWmtg1Kr6Qx22lritlGscq9ytf8A3ad9E+RftOcDL7sCiXRsdydDb7FFyG9kGbVdRq7dKgzZXzOtrGUZexGu9MpdGl7ax3wGFyQTKqyaNaOKlMUf+WJ+koE8qmlTNeL8uSeuygzWhleY6bqLfh9p049ibqezy58wydZeAcw8YycI6htQx8WM8bOtEcfyLVmsxoOlrTMZpMGzKj6GhDp9oWylT1Rxs9D4f3lsNXddE3bi16ZyPdPTAOi9e85jV89rOf5Vd+5VfIsuycj7dVwxkuzrAh7z1Du8ZKfpKV4SmW81OsDO+K6amqfCU/xfLwFLlbm3icXrLAXHaUOXOMuWSReb3IrDnKrDrKjD5CTx+myyG6zKFc0go/nJ/rsrzSHxcqqIzL5rpXHueHWLtG4dawbo3CrlG4VQq7irWr5HY1rq+Q2xWKuOTFMoVdwSsHhFBpVGgxAIdXoWnMJD2SuKTZDEkyQ5rOji1JOkuWzZEmc6TpHFkyT5LM1C9EB5Tz9V7vW8vJMhRtOB6eVq6fHg4n9kbvaM3RLONIlnE4SzmcGhZSzYLxzBvPIWOZMzkzaohyA6wbYO0Aa/tVWrj2uo3rxci6tYMqD3Bk4Z488iH/eUvfd7cNZ9Hd/WGy7p5kJabLW9Dd2ewI8+YYc8mlLd3t6SPMqAW6amF93YXqpX73dHeFwi6fg+6W6dxE3U2TOdJkVnR3l+hbO0Frt667h7OMI+vo7qzKwQ2wrr9N3W1vA1g4dA23PfS/bOn7nm+6C1vX3iQ7zp13Hp/sFBhzHExwHsTyul7tfKidxQePoV1lOWprqbnLI7Zu0OBTDO3qxGN0kzlmk6PMJaW9ewmz5hizOti7M+owXXWILoeCo6TRpWZ+vsvScn+yrdso8wS9LWw/aK1bq7XXrgbtbehvYVcp3ApFEXXYruApAB91N2hs0NqwGJNh0h5p0iNJgt4maam7IZ8mM5XuVksyS5rNorQZd0QcQM5X3fXes1QULXu3snvzWnNPD3PO5PnEAIwZYzicpcHmTVMOp5pDiQq6mzjmdcG8zumpQbB5o34Wtl/pbWHXWtraWreNum6Ac8E5PD93dx79re/a0vfdSHcPRJeQN7zhDfzt3/4tb3/723dMvC9mgjG8WjsfRsW5zEdRLtxgwlEUM+kCs+kR5rKj3L17b2azI8ymR5nLjjKbHmE2O8JcepSZ9DBGb/9WumQxxR/vb2EPBXTicnjLn+e9C6JeLJPny+TFMsMyny+1ysv8anGCfK0u2+j80mS2YUg3jOp0nmxkvb19jsTMHNiH/W5gvQ/9k6P4nhoOOTkYcHKwxsnhIPaTKziTW/oTrGGNZ05b5nXOrB5yjRow011jhhVm/DI9v0zXn6FrT2P8MnZ1gF0Khi7RvB4Cd8VlI7TOMLqDMV2M6ZDE1OguWTpfl+tYHtevvnL3RnzfDqK7O0tbdxvOh1Hdjc7g3E3SPMVMcojZ7OgE3Q3pBam7o/q7C7pbO5VFdyHo7pkRp0Opu6eGA04Ow5g803RX4ZnXljmdMzequ6zQc0v7r7u6w1VX3ncHr9q5c1B098jCPSmGi/v2+TuF956BXaka2Kbau+vqbu38nUuPcPn8jQ2tPcJsZfMeZTY9TKK3/3c7tpjiNqW7Tb1d2PLneO8o7FpbX4u2jTssmtvO0s+/FvQ2XyIvVtioT1diZtbR2FGHcpmfj+lMdIBdXIyOC3EqjgFxajCMNu8wjs9TcKaw2Al/ggTPvCmC9qohl+h+pb09v0zPL9F1Z+jY02i7TLEcnAulMwGgAE7GZT2USqLWlrrba+juodY2o7vV+lVX3mcnL1s4l/2OsPinf/onvuu7votrrrmGbrcLhBGS3/KWt5zTcS+0lr4gystTPcPLDUFeyU9SuEkhkIrZ9HDlbKiM4CjGcy1RXkCrvfFnHeTrPgnvLXmxGkV+acy5MRwxukfTYorXvkaPi/7YA2C+td6sa0xvQ8N7u9fcexs9qwOKdVrBiqLPShFE93TuOFM4zhSKJas4axOWXcKyy1jxHVbpMim2LPNr9PxSEGCWovNhiZ5fYoblaluHVTS+IaxBOJvOgmC8ZhjTJam2tw3bxIzso5uGcTcay9m2H7Lbuea71dInurs5msZwiHqY3CIX9HeLuhu1VnR3c2yku7URvrR93U1mq0FIJ+tuW38Pvu56zhZw1iqWrGHJpZvU3aivLd0tdXiTuhv1tlzflu7qTtzWEd3dgK1q70H+/Xvv6RdLrUizsoFtUuSv9VvX3Z1sdNsKB/m6N9mUg3nEATLa2LcRSTJb6ezmdHd7EXXnes29d1g3jJrbjxEFg3rdDejn/ehoyCubt629weZd9R3yCbECylt6DYdDqbe92OjWtHtTBiGwZUx7R2zWlhN30va2k6HW6trm1dv8XeyG7u57hMW97nUvPvvZz+73aewLQZSXG2HB7XQzoqzQzKSHK0/wsYUrmUuPRYFuOiOOMLOHxvCFjFJhZP0snQcu3/L+zhXkxcpUkW+2NJYPiP7ql6s61k4OD2+e32SBryM8vnrnDGfPnqnDaUe6MlSCPGIc596xxhxrap41Nceqmq/WV2NZuW5VOnZuGsssq8yqPgtqhbub08zrgkOJ45DxHEoUC6nmSJrQTUqD9zKMvkdluAaBHXVGbF9YL0Yudt0Nzt9RvW04f7eou0cX7j4SfSa6u9PshO4Wxepkh0aVX6pbHreru6Xm7pTuuiG5s1N1d3R9K7o7nzgWpumuvhRjrh4xbEV3z4WLW3cdq/nZtr074oCoHRKnJs5eoTDMZoerBrZLZu5ROSNGoyGC7l58Lfg7hVK60jF6W9+/cjBPabibpL8rq1+v1otiZaMzDBF1E50a7Qi608uznDp1qtLWMYfDiO6WXR5yO2DFGpZ9NmLf1tq7RkiHagYYH2Sy61eZVWvMqFWuUKeZMznzpmDeBJt3IdEspIr5JCVNygiGyxuO217DodttOHK7F532Xlzfdg8Y9QxXkQ+jrXHDk6zkpydO5aYwzKYLlfBOE+W59Ci99JCI8nmG1gmdbIFOtvUwPygdHstjLY3Dlvi3WyD7/RPVPrbRBSiIXofcHGagD9PXh1lTx4IDQs+yonus6C4rSYcll7LmJ0vGnIHDqeayNOFwmnK00+FIp8uxTo8jWTesZylzyfrT/gnCdpjeIjcaBREM4sm6q0OLXNTdYzNXRweEGMMXAlonZNkhsuzQtvbfnO62Wx030t3CBM1t6+4Mq2qG5W3o7pFOh6NZh6PdHkeznuiusKs4b1nNT49F/o45gocnWcnP4LFjx9Aqadm0l81eNzEaYi47Qi85dODHqRECLQfzuTg8NoyoqzV5ZfVrG0bUeWDALENzjIE5ykAfZk0fqZwRK8yw6rssqw5rJsWbcd3sas9ColhINFelhiNxjIgjWZej3R7HOrMc6/ZYSFOSHRhQVwiIw2ITeO9YK5YaBu9oWPDJLXuGF2euHTOCS+N4Jj2YolxPdeOxcRqh1rQ4I1PkFNZjXchbG/axVtVlMV04PWCtv4YxYFJIjMLoMA60ilPnaFXmVZhOR6k4LlzMq0a+qlMf43zBe08Rl9w5CufJfUydq8pzB4WfJXc9Cr9IrhxF6smNI089RdfFep7Ch7Q8ztAV5AruXBlU89gX3hPHv6vIygEquymXx4Eqj2RpHDgt5I9mmYjyJvBxqihh83jvWSvONvomr9cdY/u6G9aPnfe66wmaupu6qyinM7s4dbfwkLuNdLese466m6XcLWrt0RHdPRJ1NxXdXRfR3a1jXREb2aYPSllq7mp+hknjHCQ6qxy9hzqXckU5JkSjG0bZCNdN5s4brQhaTNTboLWF9UFnLRQ22r026Ky1NJYwTeShb3j6/T5JAklKTD1JqkhMnEy6pas09LXOhy4B58d1a2K9p4iaWkT7tJV3UYsr3e1gfUbhj5LjKIzHak+ROvJu2M/60tZtHtsytDlDOyTXihNroavG2bwIutvEBd09nKUcSTMuqxwQcbDKLONIGjT4cJbSNdJYsR/s+xgWu8VW+/NZV/CXJ/6cL9z1VdaK5bDky/TtCv1iJY5rrvFxSFyPQmHITOjHmpkZMjNLamZJzQyp7pGaGRLdIzVdtOrUhuaIkVkboo2XfgfeK7wL8wh7D96pOL9wWa7iXO8KfMjjVbWu4jrVejh35eJ6tejJeRQ6rmuv0Wi0VzGNCyPbyvyG8/+sT66KsOiCQoUl17YqL3QxkrexTiwbyVtdhFHvaRjWlNMXhnW8qgx0fLmN8tEAhAeE9+XjoqS8JwJhfu1qGug4J3L427lGWfm3DynV9p3GKDBKtZZeaugqxWximEkMM8Ywk2hmEsOs0fQSTRKvhff1xEfh3JvrvjXns4+TJIU5sRv5WM9TT6NUrpcl3jfytA3N8f0a5zDlfPzYej1Xd3Ws0fNzCu0UOI2yBmVVnDoyTG+my3Kn0dW0kqFcl+XOhPWYKmcwzuDn+jzpCVsbrGsvptfbSbaqu84X/NWJv+DzJ77CWrHEWrHM6mZ118ySJbPjuqtnSMx2dVfF+6DWXeeBqJnjutvYdtHprp2otXXeBt1VvqWzF5vuaqVIou52U0Nvou7qKi+6u4HujuTX013tNMwOedJ3j3eTWY/zTXdha9prXc7/Ov4+vnDX14LeFkusFSusFcsMXJ9Sa5upVh06Zq7S3FJ3MzNTaW9ieqS6h1Jp43fWdq6OOlyDxnosvnLOeu+x8X5p7esb+usUyumw7hW4oJ+U91G57kO9sK4rvdWu1ljlam1VMTVeob2JeV2nGBKvw7Zz1NuxvwuOXOctHR5P8zEdLlRBYSyFsjhtKbSj0AUej9LR6lXxeqjaDkYRy8KzLOgOUNal6Y6KWusVzbusqautfKW19fpu6GwTEx06wc4NTp9ad3Vl484Yw2zU3Z4J66mO37/8Xg19q7/naFlb56p64QANnYs1RrQ4VmvpcVnW1OTyfOrjj+4/cl4j2lwfc1yfic9AZWtdJepvpb1OowoTf0c6Tqlea2+dj9prNWau4Hu+a2ftXYmwiPztqS/wzv8zQ9feFxONQOV1zGuS0kAcMxbbxqRHUXiN95pizNAMeRP3TdYxPHebMK2cx8cF5UE38nE9PKtCXsX5lZUO25Uu4jz3HhWnwFMxr2O5juU6zH6Fip9X4ClwDL0jSVLWVnJcoYOjptB4q8KPxSoyq+m4Dqrx4zHRMDF+89fK4ciVJdeOQtno5LDk2o6s19tzZaNh3qijLblyVVnQ/fhYL/PlNE/xateOcE/zMaCUR+HD9WpIno8qUv5zPv694sObxjHa6WhZeH7nI5tPjUdmHkgaM2ZBbHlQHhKfkPqE1BtSn5J4Q+pCWRLLEzear9PUJyTOYGKaxHQrvz2Px8b7IRgJBVZZrB5SJAVOW6x2WG1xynLkUgXcY+cv0nnM35/6Iv/lU1269j4t3S11cqu669AUfrLuVse82HRXgTZB/ybqrlU4qzbU3XrZO90d1+H90t3wPWgcYzu6OyEi/kCyXd0t1/dSd218YZumuwuLoruj/PPZr/H6fyyAS+MyBU+lm8ZrdB5f1tEYb+pybzBojPcYP8D4PLzgU9dJMBgf9jfekBD2S6ITIPXl55hK80tHQf0M2DmNdsqBDtN8eu3DNMu61F8XUl1qsgVdBA3WBM3WHqc8ytT6q6JeW+WDbnnHEMfAWwbe0vcWrROKvgPbfvlTVmPiC5+xBuPCes9lzBc6/l4M6RZ+Lw4fHR1t3S01dVI+1CmqfOkIKVSB0y48j4j6oFou46pcEa4HHrQu38irV+VYeyTf0N1Sh13juBumquEQaXsKzhvdXRcPCbrS16yydZNKgyv715V6nFSaG8pNVZ64Wpe38ruyylb6W9u8OYVewxqH1cGJNnvEAdfs6CUQh0Xk2uRePP7OGzddX6lSoErD0Fcv5bpcVLmu0BrMyPbJdcOxVDxe0/Ac3ac0UMeO1Vifum9ljbRNk83ivGfVWpbzgqUiZykvWCkKloqCpbxgOZYtF2FZyguWBwVLeU6+UVCPItyZSbso1aH1qZmmStHxCV3CD7hDMIoyr+OPVZP60MJtXHgYduPDQbsE5TKUDR55rAKr8MXWrokxvgrtM0kM8Wvmy7C/xLe3p3W+rFdub/+NxqnDxMdbMcoWC8ek1oyQP3J0jlMnl4Mnvfq20TClbN0sXwTKrXUIYlmnfmjV9SqcwhYhHNJZhSsURQGuUKGsgKIIdZwN22xcyvKiWq/z1m7tftW6fd3Lv83Ev1Xiqvyk7c0ybYhdCDSwcQve+TI6+F5ydXIdj7/zXpuuL7pb627Q1TykDd2t9LaV7o7udkjo+IQME5bSMPI6vqjuvu4a09TYcd01iW//9tNxLSjr76ruAkeOzHDq5Mqu6a5S4O3B1N31dFV0d2+5ZuYe/Ht1NStnXRU1FhZG0p2LICi1WpvwOzMm5pvrk7br5naPNn5y3bhuGuXaNLY1tqvqd77+i5r3nr5znM3zuBSczXPOjKyX+aVByE+LJEiUopcYDCp0AVGaRIcIrKn5qLthPZZ7HV8+TXwZDU6OyqkcnczYoLk4BUWKtxk+aq2zCpeH3/um/86qoaXpSDraxWWkTtrQ3rRh626kt6Wbw/lGxE0zUqfU3kpnR9en6y4wpr112bj+VuvN2iP7++gssYXCFSG1pSaX62Paq1oa3NbkOi2jCDfDRLs3hcS03zWCvrr2e0jjmTqqx1o3DYXO1M/fDd0Vh0Xk0AI85Qd7HD++Uhmk04xcpdb/kY2zgaG4j+TOVU6F4HwoWM7zkDbKKidELFspinXDuzpaM58mzCUJ80nK3Wd6zCdhfS4NZeX2y47NsXymT6oVaRTklnGsg/9vr/rree9x1reMtTJf5BPKCoXNm+uQ59BfGzX4Nu/FVPHBYEYeCLV4qIbx3X6IjIpU9eAw4T4GWJyfZ6Yfwv+KCcI4aqwWE8R0/XpbE1eUJzHj4pim0O2Nfn/fegnZyNCVbt4Hl/lDQXfvPL4ywYFw4epuEXV3aUR3R8tGnRDb0d2Q37rult0Y9lR3nZ+ssZvU3aKAfr9dfy90d9w5vZ7uarxfXzvPR90dN2y3eAMIe4eH+TRDd4sNXvg9xvixF/7S0TDmHJjiRNjIEbhXDJ3j5ERnw7jzocxPc/Rq4FCaMp+mHEoTrpzpcSg9xKE04VCaspCmHIrbDsV8V2suvfTQAXKghe/W1N0iH9Hd1rpq14np2kp7n81rbu3YMElwalT5NOhtlU9HnCUTHSW11jYpdbf1yZ4w1l5TT/Pg6J30vLFRg6vyiXV2yLGQQNZb37EwrfGz2eh5oSEOiwYLRzTDYr/PYut47xk4NxbZ0DKGJ0U8FAVrdnqslAJmG8buXJJwt26XuTRtGMCTnRBbGQxs8dg8x91BEfDwYC1b4Kf7D5ts7sXIOx8GZZomdC3je9wYtwUM+rBSCmsljpu/1sb4aEyukA/V1lvNTDToSw9sNHZ7vYaRXxrAqQv1phm2pSc3KVvNtnQqwgXChaG7tca2dbftAN6q7s5GjW3pblN7LzTdjS2i+6q7E5wk56q7pW7umu42tXUD3R2PVtjSqQgXAFkHHvNd3QP04rx1CudYKop1nQ2j63033d07nySVg+HSbofr5+dazoZDSSOfpswkJozBcwGgdbgnsg2Fd3Oa65yvdDLPa53NCyq7NpQr8gmOkv4aLC81NXjz0XfNiOcyzbI11lbVjjoWyny3aftW5W5Ma6dFOFyIjoXdQhwWBwjrPasjhm2r5a1hBI+2vI2NetsgUSq0sqUJc0nKJZ0O18zOtp0OE4zhmSTBXCCCfBBQGlIdvMUbs0lj3Efv+ETjm5bTo2qpy6HbS8iLnMR4kjQKsakFtel8KMW1bDERhAsJ5z2rha26tjWdvS0nREOXy/Lt6G5ZJrq7N+yL7jZb7Kborkl87XyYoLtlXnRXuNBx3rO8WedDEfIrxXSnb8+YOtIhS7lqtteIeIhRD0m9PpeK5u4kWoPOIM02mtF0s3rrJ0d/TEzHo0AAOj2YHYtKmOJYaDaqRdtYy8Qg+444LHaBspvFUhzfYaUM790g/HelKNb9+faMaRjACVfNzFTGcDMKYr5hJM+nCR2tz8vpj4SNabZKbq5ZMtxhi4sdjh8f7uapCcKe0tTdZmRD7XwY6fK2Rd0ttfeqmR7z6bzo7kWM6K4gTMZ7z5q10yMdigljP6wz7kOmdatrxWW97ojDIWk7ImS63wsOpYLDebtO58XF3nkdTSQExGExhVJ0R7tQVE6IZsRDLFsuLMsbhJ1pQrhv2Zo2n6Rc0QvjO8xOanmLxvBWw30FQRDON8pBzpbyfCSioT2Y7yQnxHq6q6AVxVDqbtMRMSniQXRXEISLmUHD+TA22GRROxya5dMizxKlWo6Fq2dnN3Q+dI00bQuCIA6LitPDIa/8q7/hK0srVcub3SDc91CaVsbuZd0u1420sLWdECHsbMZcOP3eBEEQzoUzed7S3eVi424Wpe7OJaGv8T2T2Zbutp0QoruCIAij/MOpM/z98ZMbDjw5mOIIVsB86WBIUu7W7XLD/CTHQ70+Y4xEnQmCsC3EYRFRSjGbJtwjju3QCvVtdLuYT4MTQsJ9BUEQzg0NLd1tdnkrdbdZJrorCIJwbnx9bY1nfeRjrbLZxFTOh6NZxjWzs2MOh2Z+VsbaEQRhDxGHRWQhTXnlQ26Vfk6CIAh7xLzoriAIwp5yea/Hu/6vh3Py1EqYmjNJSKTrmyAIBxhxWAiCIAiCIAjCxcDpVa5414e5bJiHKR2Mxic6zpur8YmB1IQ0M/jU4LMklHUSyBJ8J8FnBjoprptCN4FuBok4PgRB2HnEYSGcE845imKZPD9DPjxLnp+lyJfJi2WKfIWiWKEoVrF2DVusUdg+zvaxboCzA6wbkqYZ3qckpos2PRLTI0lnSJJZkmSONJ0nSefIsgXSdIEsWyDrHCFJZvb76wuCIOw5bd1dIs+XKPIl8mJpy7prTAdjZqbrbnqINDssuisIFwqFw68OUMM8zNHrPcoDeM61k0c9ApEK0zsoBTqkXuuQ1zo4NnTpKNGQmJAvHSVp6SgxkJZOEoPvpMF50knwvTQ4ULppdby9wLkhw+FSW3+LMl3FFqUGr2Ftv1qcHZBmnai7XRIz09DcedI06G6aLpB1DpGkC2TZEZKkuyffSxBwLixDB4UD66Cw6MLGfChTedxmHSqWK1uWeYobL4PLDu3oqYnD4gKmKAYU+VmGwzO1mJbOhEpUV6Nh2xZV64Y4l+PcEOcKvC9w3uK9xXuHx7PZOZSno1AoWPPheB629LSM9RUapTRKGbRK0DpB6xSts2CM6y4m6WJMjySZiQ+JOdJkjiSdJ8sOkaSHSNNDZJ0jZNkCWstPQxCErbNZ3Q2OhLWLTHdnSZN5knSeJJsnSxdEdwVhr7lkjt5rnjy5K55zMLTQz9GDAvo5DArUoEANw8KggNyihhaVx3zhIC9CWjiwtvEC48B5lIvbXAHegQfla007F2dJ21FCcJAAaPAKvPJ47XHK45XDaYtVNqYFVhUUZohVOYUekqshhR5Q6AHDMk365GZIbvpxfUChh2Ewpj2g1FytDEqVeptidIY2HYzpxqUXnSDBGZJm86TJfHCGZIfJsuCETtMFtHQFWp/yni1sTB0qt9VLOiMv66qsZx3K+nj/W7AeXLPMoZwH56Gwdd6Vv5XGuvW1Y7Eq9/E35MFRbQ/OxzIfy2F8vXz2j9gTOzUqTf6Xn4GXfvcOHS0g1sE+ML11bBmbr5CPtI5ZVzoTBjg3wNphNGhznC+CMessnoZRu1UjtKQyRlUwRjEobVDKkOhsXCB1B226JKaLSWYq4zRNZjHpbOUUCEIZPMdJMtcSycXFeY4fX4rX5SzDwRmG+WnyYbw+xXK4PvkyRbGCtbWxH4z8xjXxBdYOyIvVcF3OwcBXKECjlUbpJBrlacMo78bWyR6JmcGkvfB9k7lgmKeHYuvkAllsodRmRh4QgrAP7Izu5jiXR0dCcUB0t1cZqLulu9auBu1tthjuqe4maJ1N1d3EzMaIkJBm6YLoriBsB62hq6GbMn2i6O1Rac3wbNCW4ZmoM8vkg2WK4QpqbYDq5zDI0QOHzn1cVFisxhQa4wzGGrQzGGfQLgmpNxgfyrXXKK8bqUKjUF6j0EAGnPtLWkvplMI3okowMfLDGDAaYzSFdeBd1MrQou1xeB9eQj3hRdRXzpx2PvznGx9enoGa8n0ccCYuX2nVZWxNUapwmQ9fqywP36tMQ5muy9BV3XWv1GYeD+vMGDa5/uTiNQWzztUv7OU1a+UnH2Q/h5Ud/zrRGVeelaqdc/V6KPNaN9ZVcKo1y7TC6zpfRT6V5bq+d32VV/iYVt3IdL1edy1TLNz7CpZ3+HqIw2KEohjEVrGzDaP27LqtY+VLs7PDqoXM+6LRQhaEKUjMORi1QCkYdcuWQasUnfaiQTfJ29olSYIjwZjS6zoXDdl5kmSeLFsIrV77GHqmtY4G5mHgHjt67KIYkEdjfDg4Hf6+xRL5cImiCI6QIl+Jf9NGq6cbVi8q3hfkxaDd2rkjrZNmxBESXkhMdAIlpbc8LUO1D8XWyXnS9DBZJ1yz86l10jkHFPE3U7Yox+vshlibVy+HwTGX412Os2W+wPk8/MZcMfabC3kb8s7Wjr3m4uq887Y2IBqGRMiHhWrd1wZFlfo6HTUuQgmd7BC3PeoP9++iH2Am6m7VvWG57uJwIehuWjoTDl1cupufJR+cDrqbB2fI1nT37J7rrklmwt8rmQtRednCiO4eQutsR6/ZbnLx6S5k6SyPevQf7ds1P1+Y7NA92+jeuxwjcsvf6lrboeviveLyMQ3etgPTEF60upOduVqltTPXdFoanJiZGOE1Q5rOYpI50qR0bJbO3EPBmUsWIkmqqJJhiB4ZFDAsUEMb0jzmc4vKbWgZz20dUVLE1nDXaFF3DooCfA7O4xXoqZdChS+tzCYuznoC6Fv56tdQ6qZvlk/aZ6NyP7Z3s6Zvlah1TrV2i3jVdJJQ7adKp0npHFHtfacccqSs0R2p9RI/5eW9cjQ1Xt51/XLuzfgLvDcGqvLw0l6NC2NCPa8VPgn6ZU2I8qkjfWKUj7I4nePIccpG/R1G3R1G3S3wvvE7K/W4asAuIzMn6LAb0WOa+jtJd+107c2na296cud19/x5w9llTn3tkxx/8wcwXuP1Ck4PKXROoXOszilMjlU5uRlSmLjNDMK6HjJMBnhlUSZ4bpVOUMpgdI90U61jM9VLaVoaR+mhustCckhaiM6BJOmQJJfR6122o8ctH/LDYTTKh2fCQ75qvR3vTx5aJ4fxQR8MQWsHFMVqEJlzCvsO3m5dGuU6bYRqd6pQ7ayTMRwOG4LUMBh9W8CgKWK+nadsEZhkOELrYUlVtL9u6yYj56KaD0vU1FaFskVBk6J0MKQgtOIYn2B8RupStM9IXIrxCTNHr9jzr3fQOX3Hp7nzl/+SxJmW7tqovdN1N69Cclu6qwxKJzuou3PnlSPwoLG/uhujQuwatuiHdFd1V8VQ7fIeTEV3p7FLuquj7hqfYZwh8Rm9u91tz7/eQWd56Yu8709ehC2WMXp1+w7dRmQYDbtjqgaXzlzdjZFRTf2dxURHQlZp8MLejZkz14G5zo5HlYxSRradb7Qi8Yana8dWcZYiX6nH77B1N3NnB9HezWvHaHRU7n4kXnm/9eh2u/T7g+hU3c5Le1zWc5g6j7fjDVUVB0V/x86jHU2jVFnW1tywrse1VzWW0gZTmqPHrt/xUxdLLLL2lWPc6/QV53Q/+epGaITtND16emTQoTKExqgqXMwnzcGHhvj0NCRL+PQbYRCizITBh+JCJ4HM4DopdAxkqYzSvIeE1slDZNnODi4DcWCn+HAY5mdC+GRe94kvojPExhaP1sB6jVay0DrZaOloetmnCte0EEBdebxDC4eOeT1BwMyE1ARRw1Qv9sYnGJdiXMyXi0vQLiHxCcobjEswTqNdEg1Ug/IK7QzKKbSLoZ9OobyqUu0Ap1CesDhCH0FP7Afoq36DlCGDrtkPsAy93DiEcF39+JRh+dbt3w8XIv2vHOG6U3ffed0daz0Z0V3dbP2YoLvJaUiX8KmZrLuZgU4iurtP7JXu5nlwhlTjkJRRIRN1t9FV0+XkxcqIocv+6C5N7d2C7pKg3Lnoro+6q3ZAd2O6Ld3VorsjHD8zz9fOvhuFiu4vD8qCKtCqQOscYwqSJCdLCrLM0es4Zrua2RnD4fkORw716HZmSET3Lgr2JhLvFMNBGLw/LMsxsmeZIl8di8SzbhiiD7YaibdlDR59aU9QutTdhg5H+1Y3NJhqzJHSmd3Ix/GfSief1glKhcWYkGqVRIdMGuuERkil0zrCyIQoz1CWtcpKB46uupbuze91Nxxz4rCI3O2BV/E381dz19dXcGsFDCx6EELAksKSFDmJc2S2IPGW1FkSZzHekXqL8SFvnMXg0N6ReIfGY7xFe48uPIqQLwcaUr4clTk8fHfCAdd6jDf7OK1nvJvSgVJOa1Ua8HE9jthMWk5zFYx230nxqYZOGK15P0Zs3hOcC90AnWus+4mLqgbGcfVgOJWB1i5XrXJfl8XPyDzgUvDHUO5o41g0Bt2hNuga25WfbAx65+lmCYPVYRgoqBrYJw4CZJuGpA91Rs6xSicN8LOJwX12xtFcfvGN20TaPnzVSEZ+H9R5X4YFlr+h8jfTDCHU4QFW9/eL4YIxfLAZOti7t0RYjHLprVfyN7NXcdfXpuluQeLseai7jIWetnS37AM6VXdjOk13s3pKQdFd0d0LTncrrWXzutvsc93U3W8S3R3l6qNzPGX5g9jVITmGQhlyDLnSFJiqrFAGqw1WGQqlGSjDqjZ8Q3kK1adQQ6w2FNrgTT27h840aabJMk+3A724zM7AXA/me9DrTpCqSWMWTCjaVp1px9+xz9vcufuBhdw17n/q/EXKbkfiHTmScepUvucv7ftKczBOR3i+FD4OeFs6jqH1bIt1VfkscZNTNfJ8qx8J5bMU3HXADgdHicMiMjwD5n8rFu1cbIYNz0OC0wyVhZRYlgMDFe4BS30vWN+4L2K+ui/iqMVelevNNPRtg4KssXR0QVfFxRR0lKWnCrra0lGWTjTYy/50FGU/usZ0M9bVI8260jhy1Y1bGT87aLwDIwb8hFZPFQyRNaOYKUOpmgaXL/duGF7NB0Dz4dM0zHyjsPXAGN0+fqaTuBAfIw5IJ5TXrdUlDWNyNG05w3TdB7A1iE80QrXCl3Vi3sWXuDLvtMZpjdcapzS2LFMGpxVWaazWFNqE7UpHg0pT6LiuDXlZhiHXhoL4GU2734H3qrbz2+8CdQNfLC+vTViCIJeiX74oKe9D31Tv0bElUQ09Or4gu3/I+J4bdvxPeV5T6W6xs7rr4jN6I931gFOeUndTCjojutsxBd2m7hKWC1Z340FEd3eePdPdxsv9pnVXKZw2W9JdqzXFAddd+w8ZT7pxx/+U5zX2ywMu/codKHLiGwj1W4dHqfV/m5shyIQiiHd8K/eN/MSyOKVHcx3VKGsea6Njly+lB0tJ+sDclG31Vx9xZIyU+fW2s4W6I2WtS7/pupO2l99I1d+LukwxwZmpggkw/piot8EEn5MPcULhy9J+/MR100mZG4QpfFXrw0cO1nq2tR569ftI6/3E4338lr7xwd7XAWGNl3vfWm/sU9VvbCuj0Rp1muuqsa4az+tKD6dS2xxT88qvX09tsD+eM6YHL3vYeieyZcRhUXJqyH2+/i9oH1oNfNkC0GCaWTl2b4x5StXkes1jqgllrX1U/D0qgsmT4oFBWac0ymPVSiAU0YCp7BtUGlKtY3+lUufL1DtU9QCzaMKIxgobfxw2tCLh4o+mLA/1IE5fVU65Uy2ubhUqYn0KSgGbcMloX2/VSFS7WE2+Xuseo6mi5d+sLFOqYWer8Taq1gVubPN1WalrqhJSNV6vLHNNi1VVXRjGrk31UJ50Ds10g7xvPvCbN4Bm/E5db715ZTwjJ9vett4x1Hi5b64rjw+vqMCwta8f+8zGE0lNP9/p5zftFzhNATbHMEkYPuHmbe59gXJmyH2+9kW0t8AFprtxZO5t6a63aLWR7sbtoruiu6K7U8mNYfDdt2xz7wuUTgff/yamRclUY6IQ9Uh7YrMasQkWVW4vm2qVq8vwDRsypqpyIbe3qaJRVpfviNMEKH9fvvytKY2PadCr4Fgrt4WXcUMVwaN0KdyhbjX4YmMpoyqNqeq1Uq2q30uvm9JfHbZbv6sWbOrW7VYLeVlGo3V7pE6zrLrsvvHuGcvxI95AqnxVtxbh+t2URt1Wvt5/vb/CZsp3K8jEUc4HM+nzm+l4vh5nY1Q7J+3fSMde+uvjqql1m5897TgTto/Waa2399urQB6jZyh2+JjisIgkd1oymzeElPZ9NpXtiuq5i/FuH3P9G7s0smBym9EWP+ucj7CzxzkQTPoyqhRQqP/e44Z9ta5GTVbV2qvyylZGoT8w17B5HvVrRdt89bT7HrbPfpKpO7nVpXyEVMfy43Xab7dqZEvzuo5vB0XB/s0EcVBJjlsyOxTdbbB53d2BzzpgxzkQiO5WnP+6Czm9sbKLHWMTBu5KFG6KW3dk3Y3c+5X/rVGv/I20/ly+2iv8af1oAO3YPuMvZhZNcNYqFRy4iuioVa5uUFMOym0xvq5MgzOldIi44AyO5cEZl9eOl6o8HmMHfpi+dFJ6jUXtgMW8Duuc74ZPqqafdey3uvXP2/Tntg7kp+S3ctxR9YVwP5SKE7qNbuWsdhpfqX7pTAuLb0QVVfmqfDziKHyLkUgjz1jdoNkK73V0LWtUu2W7cQw1lldeVeepxupRf3bU8cLsvL0rDovI390z4bNX3ZtLBoquhY6DrEwdpHFJPCROYTzVoqODq/xTbpVqPO/m31v5Ut/wBrz2OBO2OQPOeJwK22yMois0WOWxOkhwoWHoPYWDoSI0wjlf3aThAV//VKi+RyyL30k3Q5CoPcBKlUZXMLJVDLcyxMhUXBWhanQor47t6s/XWmNjZEsZauZ987dQ+u1L2an98K2uu9TtBc7HwaSi3oXjxQxBusr4Mx9/vvHjq+9UXaP4V6Iqi3V9KacNcy3Gh5XfbXSftpE6euzyuL6MgmuZxOG49fGVCh5xDZiGtJXXuY6k95UTtvz7aa2xtnwYtA1F3xQ5XxqFzTpxe7leip5v5FF4H4XN0xY2396/FELV2q8+dtMQra/zJLN2tx4t50b5+x4A3V15YT5/+ey1KZ++6t5cOqK7mauX8013rYbce4YOchWHX9hT3fVo5fdHd1F4v03djd+9ukbxr1RyEHVXN8zITemuUlhXnsUO6m6jnuhuoPx9D4GO6G6Lvzvs+JUH9VjsQ8/CjINuEfIdB12r6FpP5qBbabEic57MKxIHqQ/abLzCuFqTy6Vq+KV9j4zbVutT/h1d8yfQXKIOu5h3MW8B20idiutaUehy3VMocFHzLR6ng4YDaB/GRTLOYbBoH8ZU0rFLoPYFhnKbI6EIYyfhSGjWK8dYsihNNSRPqblVPl6ZSoera0D1ql2uMyG/fuEoqpXd1i+kLZMTbeeWT6vc3tLo9r0wdd2vX3fsvmqdi45Oo/ph76vuR42U+ELfchbU20vHU7095hv7K6/xow6E+HRoOQUOmG5OnORWNfJluRrZ3rh3qkcTsNKxHNvhcxSHRWTZ38VvX3caRSdYoxjwYVTt0hxRXjfW49LqRxeM7bkiLLNlamEmPgx6NjwMehY6Nj4MbNs4bxroiQUzBO0VyU4Y6LVNFH5HpXGuq6+MN0HACwVWe3IFeew/nqPCelkW84UO64WKIc80z3Vnf5rNH0eVV+PbynJfjm/kfetFR5cvPoB29V81ieddbqvqEPav7oDyGLTuCLRX1fbSsK0e3mV0ZCNfpt4z2pW09UJTfrG2YG//ym5mpu+NaIrcpL/FaL5OY3Bpw/CoxheI9ZzyOOILogqBpk75yiBx1EaIw1dGSWWYqFC/mS/i9irVHovC6lCvaNTPNbEsvoTGbYX2FCr8DkI9TxGPXSgYGrBKkevwOypJzBrv5ugOXPULhyV/knddd3LXdLdXwMyFoLtKxXRcd4u4vpe6W6YXpO7G/S4W3S3XD4ruFuV9vUO6a8wa/110t405w5cXvsRX5ntAqbcG5Q065nWpv+UvzceW4NF0E0qTOJjLgxbPRF3uWU/PBdu460K+ZxVdR3Be+7LBUNUaHZ0lScNRoh2YqN2qscBmfqej2+v18EsxeGXq30/rd1Kv14uqNHxY3v+lc0RDEX/8WtdaWGl25Uyuy6HxnZiU+paTd3Q7zTIa12XUsdB0BjS1blq+2ncnnzDnRpDsEV1Uk1+mR/O1RtYdQCqNpPw7++pvX9qrLmpq6x5gXCtL55jDVprY1NBCh7o21rNKxXsmdsiLOlk2iIQG6VHNbOtsrZHj6zbeo6XWOj3t7zil3G+wHdCmz/+PI+v8xbaOOCwiV891UN3/Q+FKb1hpTUYvWvVznhTaGO/sOLryitfcUQp9ZZGWxncSj29aBropHxJKV/v5SQ8Jwq+g42C+YaDPlga6bRvoXRvFfyRaJIuin3pICtVqtTRRILsEz+RWqWaFL8W9/HE2vd+VmIfrWA6i1TQyFSOCPrKtTtW6otue4/1c2Nz+Vcttua5GRLGRrx56jD74JhmBdb5eXCVGVpWGnq8MvkL56Ejy0anko8HnqxfyXAejtF6PqfKVCBaxvHJM6dALfgej07eA37jKrhx7M587rU7ZF1xocvVsB9X7PxR2d3UXn1RG+U7r7kw0utfT3VJzRXe3w8HS3WAQbl93SyNxS7qrIDeiu1s/hgfR3TGOZQkq/ResDy2/qtJcNWUpibox+pv0NHQ1AUq9TcAZCgyDzHCaBI0J0zWOOUNq3fU+tGaHfKn/ivbfeOP7IrWemcIzYz2z1jNrFTPO144TCz1XRpW0o0k6DlILiVekNuhz4qKTpHKWqOh4UJWWTrw+O8wkravWGy/dlZOyoXsOX/UaGH3hbtaZ9NLd1MbyBdspVTstKR2Nqso7HTrxhKiW9gt0Mxq8clBSRi3W5aVz3ioY6vpFO5R7htpT4KMu+uaVGUlLtvL32YLu7diffTt6OOnD1zuhjb6Xn1Jngudq4r47r7visIj809IpbH63IDQqDDLlVTnYVIGnAHLwLsTdAi1BrwYfK43opsjWi2o9AOq0/tOOPxxa+djamGNYLp0gzRjm+JBQvmmc1814oW7dNqWqFstmyFJ8UYjhgGXLZdNIL6NFerHVsjLQmy2XvtFy6YLwm1jeDBssaY7mP2ZclqnywYvdEEjb9FYqau+kKlt0QkvMqDg2PY2VcakaL+RRHPNyiUbosBRIVebrsqEGZ6YJZp2vPMHKNbbXgdXtgX7KC+Ua+7Tr1+tBTMqhAJUqe8qVi2vkCXNIj6RaqTi3tAovcnFbqiAjBipX9es/YFgP30XFgvpuLx+wfp08IZy8dY3iug9/Y+VD6x/EEZeVj/vE+r68CvU83B7XGJfKxdRTWMLD3at4JWPqY+hlo89e4xtOWJ/0cjbpN14Aiwg1n1s6hR1O1t2guQdVd2sHx/q6m8R9z113Z1qRelN0149EjOyT7pYtRZvRXavqaJKNdLetweez7sa78jzQXa9CKrp74XA2X8PaHkF3LF4VoIYoivjXyOM9Fa51GeJe/l3CrL4NrY2DVU7UXrOe3ja1dry89eLvodbM0jFiUKRUIWpRc1XMW23op5pTlQ08qr+1czx82lY9gfXvpVqcJ/OO2cLRszBrXdBt55mxKkQlaShQjWgiFbVQkSvV0ErFUCsKpShiGqKHms+1rTLtmre/1WQtLdNSRyfr7KQyr0auU2OZqrvVPlM0N+ru6PhCqqXB9Tgt1V1VaqQK2gugYxo0No74oGodrbotUu8b6vu6vHoGlPduU5Ob8R+OJtX39+3v42NZuVaWtXW71ujQDb48WtDcMDu2D7e7J3aXj1exzPvm00JV6+O6W36zSb/f0d+uBS5hJxGHRcQwg2Y23Kajca4NPD46jpo/JEdoPXUNozsY25WjI/gN49+ybDmsWw19/bOIoqkmL6o0zGNR4+dRFpY/EE0wgrTSVV41brZKHnycec+D9b4yHkCTY1gac4yUUSLhwaC8QTc85op2SDc+eswbfcD8mFESDT0AVffWUw2RUy3BK/dpipqPBpOr897jlMN7N1K/NDLr+tXfcnR0ayy1iIbP1YDRikQpEg2JVvSUYl6HH1WqPAmeRHlS5UhwpFhSbzFYUgoSHxbjcozLUS4HPwSX48rU5nif422O8wXO5cFbrRQF5RRzYcmrNCFXJqQYCpWERRsKEnKlK/O5vn9GB++J90BTuKaK2CRjsZF35T23XgvO6DJyLuUD2k/7rEnnsV5+xBgaYZo5NY12/7/RfHnfDTZ5tIsHrbrnqLsOsOeF7tYmzF7obmytvFh0V0Giou5W2utIL2jdnVS2n7o7un20rFzbW931aojQ5l+Wc7S/tP5bTNPcscVVi5rqYC5Q5MHB7JuHHo2Sa4+hMrZUY6yUGlzu1r5LyuOHF86o4g1bV5X7Nl7UyvF37Oj39uV5RseIj3pL0lrXGIxKouYmMUKl1mqPZjnTlYY3I0bKbx5Oq37prpydleY1tDj2WzN4TENzR9Pw/6i7I/3ffNTdlsZWOuxadcNptp0AqDA+T6JU1OCoxQTtTZSL2htTH7TXxPE8El+EcT1cgfI5YXrvAmLe+wJfpr7AuQLnLS46Pi3BMZ5j4lTLipwwjXKhVKXHhTJxScIUy0rjVLz3mmP6jOilmlTerO+m7FuVt51fbUdY0+7QI8eYprWMbBv9/YxuHy1rlqhNBTxsVn9Hx7sYu1fwUQ92FnFYRO63YHls8hKGRY43l1EkdyNXlzDkCDkLDNQMQz/DwHfIfcrQGXJvKLzB+qSadWisU28D3zCPdRQThQMVDGzHAOsHoIbAAK+GeDUgDNc3iA+A8CMwKkWRBiElGKYuDgRTeo8tsX4rxLppjExABZ3LEk3XJHS1iamnZ1IyA4nyGBUGFiq9kx6F8x7rPYXzDJxlYB0DZ+lbSz/mB9a2L42PJvyUU0qVJtWKVGuSmE+0rspDmSbRKpbV+VAv7qt1MGKxJOXgSK7A+BxsKaBDlMvxdkBhcwo3pChycoYURcHQFRS+IHc2DKpXOIZeMfCe3KvYzzwI5lAlrGEolMbG1MW8JeRdTMOM8V2gR3zjCalRcQKWacK6Aa7cN/79XVtEx4Sy9RmsI+7j59RO2/mdDo8cH62/fHVseN0bD2UVH3fBXLCoOE2fUuWI4jkaiyYnvI6Ex6BmiPE5hiGGIYkuSCno6IIMS6ZyetrSVdBNFYlKSHVKqg2pykhNglYpRqcYnXDt3e67o9fhQuCWec9j05cyzIdjujvkEEM1y9DPMPRZWM5Rd0PLy2Z0dwj0ma67oZVvL3S3axI6Rk/R3drhUTjP0Dn61o7p7tDadpvOHuhuomN5Q3dN6SzYiu56S+HyMd0des/Ql2N7mPNEd2HUYD3/dDesbU53XdTYSbpbRJ3NUVF/w7Kx7na1p6fcBN1NSU3a0t1r7nafHb0OFwIPP9bhM+b1nPXzeHUMqw9TsECh5siZpaBL7jsMScmdofAaW2pueZB1NBcIwlS9gJezdBQ4VeB9jlNDFEMgb2huae/mYIITWZNgVIpWBu+TGJkTdNeVgyDGJzgobEt7oyPabxw5kShFJzF0TEJXJ3SNoqM1mfHBXtQOTYFiiCqj0jyV3ZtH/W3avgPr6DuLnTrlZ/3ZQWtrnU2izta6Wq+vV54qFSXMhQFAvUX5AlwenQUhj8vxboi1OYXNyV2OjTZuYaOdi6VwlsK78HzBk3soPJWtW0Sd7avgSCiiQ6HU2Vp3VdRcRWzWC1++HDF6qraVTp4RPatuxAmOAl8+s8O6bhxrsvN1UrpRWbm2szoL0xyx7UaDtu7WWtu2eUOqG5obFjuivcPYTJJjfI5mQKosicpJlSVTNmivLugqRzfRpCohMSak2pCplFQnle5efelNO35dxGERyXSXq+evY7m/FPq2+uM47sB6i/U51ubkbkjhBtEjOYIK+pwzR58j5OoyCn2MQh0hVwvkzDKkR+Ezcp+Re4P1GkuKc1n16Dcw/QFQts2ochT4MKWT0rFVUQ1x9Cl8n6FfDQ8ANQD6tTFWiXccZCl6i/EmiolGKU3fa/pFW/jbxvdG11PTM4aeMcylKZf1Mg6lPeaTlF6SkmlDR2kSbZif67K0tBq8q1E0vR1iXRDSgR0ycAUDWzCMxvfQO3LnyL1nzXsKH/37PppFvjSR4iRXKjzkwk+5Ed44cc6qJC4TpkNTbGHUtKZR1xAaFbc5HwYUVPW1BRVbAnSdNkW4sd7u369GlnMXUl+de/O7tA1TVaXjxmktkBalCgwW5fNKKE10CmhyEgYkDEnISemTuiGZ79OhT8aQjsrpKUuqHKlO0TpB6xSjs5h20CZD6w7GZGjTRes0tLrEKQ1C33sX1+O0aN6DL0LInLeE4bstOIv3IcU7vCtwFHhncd7GVoAyb3GrDu8t3ocWC+8dOT6uh3aPf/rKX3Ltbd9xTn+TC41Ud7h67jpWBmfjo/Y41rd1t/BDcrsd3T1Eziw5PXLf2WXdHTR0dxB1dxD6X2xbd5u6sHXdnW3qrknppZvQ3YYBO3Q5fZu3dDf3jmHU3b735FF3rQ/tqrbU33CFWrrrq/Tg6y6VYX2h6W6BiU6B3dTd4IaJjkLnW7obtLetu8rFKNWG3ta6a0O6bd39X9zztu88p7/JhUZmetx/8RrOrNyF4zjWfwPrC6zPo3OwT+EG5K7f3jHe2s5rhswx4Ah9FrD6Egp1hEItkDNHQY+coLmFT4KzgwTrU/Dh7jDrvsPXv98QZh+nNFXB4eXJ8Qwp/ADr+3g1iA6P0uYtYhfCuHiFJkWpMloiaK5r2Lc5mtxplu0ke3cDh3OkE/V3Nkk5lqXMJx3mkpSeCQ7oVBlmZxJWlldw1uLiM856S25Do9jQ2uAscKWd6xngWPFQeB+1NkbnVb/4UgVqjQ0zMm30zFBAFpcJm2CTujvaeOTrgA0Vnmeq1E5VOxO8H9HQ1kCuTa1ta+y5aO1odEAddwOjXU5UlS81tnYElJqrVQHRAVA7AuLiG05Yiqi3wRmb+gEZA1I/JPNrdPyAjCFdVZCp4IjVUXNLvdU6C3mToU0nrJsO2nRQOkGjoq0bv1PUWuWDJoapIqOdW677qLnR3vXO4X34TqXuuqi7vojaO6h11zX01/rQBOS948vf+Ag3PvKJW/77rIc4LCIKmCPFFQWFXaOwfQq7FsOvakZv66q/L4DSKJ2j9F2gTuOVjv2DffDCEv7Y4cFQtD7ceSiYYY0jDDjMgAUGLODUEQo1Hx4CKjg8LGl4CLjw2udtxqiYjv9ho/mlwCgfprtTLi4WpUMYn2WAZY3CrzL0qwzdElY5RkWlGji0YYBXA4uiGXjNoFCczpvez2Y/wa0JTR2eWO472evYGgY5blOjQ8A3PJOuKp/QSuTAq4Sqa0sZdl32lfQj18BrUPUgUqoMEZxg2HrWF9tJft32960N1so4bQplJZLl0idhjZQhHR1E0aiCjIJUW1JVkClLQkGmihBKbYJvOoRXaqoAy7Ifdfx/65HYcAR4H7tJuRh26GLod2mURrEsjVC8wzUMz6qHtG+3BJfBi7tBGXqY05ghxDf66UOjn76qyix1/32HatUNjzXD5anmMbt03ucrSinmVIrLC6ztk9s1lO1jRv7C56K7Dldp7ka6O4y6a9URcjVPsYu6q5VF69B1xV2wulv+1cp96pEONq+7GtXol97WXR3Kd1h3R/Mb6+5IhEBLdwek9Kfqbkqpv5N0t6G956i7wQFgK8PV+/y80V1Hc2yU9XW3rFvq7t3SBHETt7F2wMpdf89g7URVpqhdht1YNuoqQxmUSVE6RWkDagXUGl7dEV+Sg94GO9diySl8jiWvP4Tg8OizwIDD9FlgyAJD5rHqMIWaxzJDQY/4S4nRdAbnksYLbWDy+3TQo9A9JDiaTXQ2KxVeMr3K8Qwo/IDCr5GzgvX94PDQljH9qHQ2ib++0EGjOS5G3yv6heZUQ3/r7gCT9LfW0XbkaPO9o9y/dH43tLYxFs/4i3ddXo9L0xinxwVd90rHv7oJOlN1PWxqra71t6XLTedOmdZ/aIVqf5URJtm3taO26SCwlTOgjoIto7AGGAYkDDD0SenTVUO6Oiw9cmZTi3E5qSrCNzU6DrTdtGsbeQDVyJfnWjkDagds1diFwzuP8jY4X70Ptm7UYEftGHAjTtaW5sav7wFra13cLUpdr0ZsVHW+afc2B1itBl0lTIdexPXQQBG2X5ZYHr3D5yoOi0iC5ho7y6o9DCzE8Xk8zocbLRi9MV85H0LvquqfczgXBZsg1mFuABeFvNw23jswDHC2Ghb11TEzbnSk88p4iL+moe8yiEZ3eAAcYsB8bGGcxdLD+i6WDOszCpvglCGEZU3wrkaCPHmULx//tgobahplmgEqhvNZPcAxDCG9BoYqJdcJljjA3JjhDW3Dtd23ORjEUCqfwqGdC/240RhStE/Q0ajV5WPXl4/flNCrOYZxkxB6A+rWUreqTXcXrIennGKqGQ7bFNmmUdu8fqXgBuM2Yy14XlmjUy2rZAwwytVn6OPDyIceioVXMcBLYVVoSS5Do63SWGVqd4ZXFLGFVNv6KoQZAxqLr9tG6zouPK5i3TC/uG+se4yv99fxyqvyL1q2EOi2QJZl1TalokDGBWK+YcASes8GUS1HNGhMxUfTcTByV6n23ea3/Cf3U/Lt4vLvtVqc3MrBLwqMV1xTzETd9RN113kX9XaK7nqPK8o1i6OIuVpvd1d3D1ctjZvR3TBN3uZ1N+jGRrrbx+rhDusuDQcEgMc4O1F3tY/Okwm6Wxr2e6u7Ds1wy7qbxnSrulu5LkR3D5zurojujtExPR58ybdx9vSJaNMW2Pgi5YjOhtiyGmzf5jaHcxZnHdbnlYOi1mOLrdQ2MK67Ds+puIT7pKyHKgdjre+X2NRR9UIpfBJt3cPR0TzPkDly5hhG3S3o4nw3RNP5lGEYXYFg847fcM1OW+GN0aMrt1cdGarJ0X6IilF0IcIup1BDCpWTqwJrRqJjUdSRWuU3Y0xfW3nn47kmKFIMGuWD5oaGs3rmq3KMjabeUqmCrs6lPQ3t1nS2fY5lDEdp4w6oowum6WyptX1S1shYJaNPxhpd1oL+Kltdteo3XGl7+JsUxKhKpUO0ZKMbStkNsMBQOM1ZrzmJwVtqHW3o42S9HdfVSlO9r55mYbptV5WNbtO+1rhKb9WI9mqwcRaVoLk66C+qqu8a+5S/A4uvp5Wmkar2iCRBjxt6q9q6u3X9HdXb6d6oO93pzR5004jDokJhki4mmaEdrqoqT1tzPeRimaoFoN5V1XUrT1zwsOEdzoXWPudDKK51RXhoEPPY8YdE418wDsKNG27sPlZ9Hae+3ro5W6ZoeSrUN7EHcjL6HKZfRXccaoj/DAUzWNXF0sGFoCWcmmVTYXI2CEGKpxcHoAxLGAAt1QVKKYY29D0sMFgfzEznw6u+8/UY664ltCEtvYEbU3uf66M1xy4YNWwHMe1Xopuyhonialgj9SukrJCxGl8g6sbG6tagvO7RkRD79xUEQ9ZRG7Vl2o95i47XfiYYw/Esq3wUaT8xxHq9S+GZHJa9W4xK5PYW1b6jx8rV6OLrFwITH0pZ+XDxtZFvvCeZsGRV6kg9ZHEE8Mx7Mg9dH6es9GGqytTGMh+nSCOppsycO3TlHl7v8wOlNCbpYZLBOelutbod3SWWuaIRBVc6qe0mdPcbOPWNXdLdDpYujnSXdTf2UadcPyi6O4ytZpN0d5WMlViv8TFsTXdLPZ2mu029Fd3da931pN6P6W7Hx9lwiOl6ujt/xd5d7vME53KWT32Bwerp2DUqdtMguDLTSnvL6KUQTaaqaRPaGqwa+aY2ldFtztvg5PBFcEPHMls6O3zDEe0rVzV1Q190gqnyxazAcQKvTtR34+jLWLnu6/XyDh7SjVHMR2I6Rx4dzUNmKeg1bN4US0pOl8oJMek3HD9YAYmtX4ANIYo5wWKUhaixlnIsjjjeQ/XrKa/vZJ1YP8pp1KEQIsAMdQSuGYkES0YiFAxrpKyRslotWdTbVuRj1FvVWC2nvLaxK2ChNI62bVtqZx/Niiq1eCZE1ow4H8J67YiwlBEhm0f74ASpru+e6e80PZ3kMpimu9Pr1ndLW3s1rnaA+7ajRTd1F0fi6/U0Lon3pC7YDlnU3pQ4HTtRb6PWZtHmLW3hjlN0nSFVmvm5nbd3xWERSXSXa5NrWLbfaI1SG0attSF80pV9eYoYWllvd82yGObu4vp6XqhpKGVai1ZZCFlSSSvVI/U8IRza4mLojgv93lRse1SOgvDQGJIzoGCVglW1yqo6S5/Ps6Y9Q+UYKhcHNGt4B8vWlfgwyH1CzmEKjlD4w1gOYf08jjkcM3g/g6eL9aGFceDLVrdO42Uk0G7/LP2DoS9VmAkgjvhPTuirGAdpKgfIUwNgDVQfWA2LXgVW8KyCnjBaeOMUfOVqHA99I/7cW2VVi+Uh4EjcPmnfMr9VoYzhZFVa5kN7XrMshF/XdUe3jx5H6eb33WDx9UjdupWGWRDwMedbAczVeivsLtajKmuEq6vyRaye6jGE9odyp9oTBNYtfaF1r0xbqCn5HcKgMSoMllXO0WCgeghoHMY5tHfc2Jnj+Tt/Cuc1Rne4Nr2GZfv1A6q73R3TXRvc0WUMBCtRd9fUWdZ2THdncczifS/qbucC1t154PBFqrsQ+uVP1t1Q9+LR3SqovWrtrHX3+myWn9z5UzivSUyP+x57OCucwHtfdQ/yLo+aWutqtV5qc6W5eQhtd03tLVpaHGYJ2gpRW7VBqSxqsIm6ayrdJQ6m65WKLdRhWnqvQh/60AIdoqKLUoOxlQb3GdLnNGvcxZp2DJVnoDxDFadaptEKrtpdknIHuZqh4Ag5h3H+EAXzOD8X7d5eQ38zLBn4FOjEMRug7u7giB2hQDVntsqjvuYEre1HfS21dg1YBbUKagVYIYxVV1/vUtEhqPcozsOYVqpaP31r2wL4I1O2Tctvhcl6CUPUqIaqdj01cd8RzaVW1g311zd1c8I/X6e15urx7UpX5aXWtnS31FpUtCEa0wc3ZpRySrW+ZRWlwcRRvQJqJD1HNGHw7abmBr2l5XzW3mK8wzjLtdkMP74zH18hDotIfuLv+dKnfmNzf+DoRVSeaED4eJPGWzH+IJQi9vsKnuG+hqGCgVYMVMybUF4ozyBuH0bDtVA2zEev6iXMzRxa9/KmMauCKNX9Odshma2woMZy7jd0AZwIi69/9s1gOD2hXEGIP2UBjQVWMbqof2cN7215rWtProJGf+bgcdV4HwTXu+DV9UrjrcFxCM9hvDPRkx2MYBtfNMqWNofecuuX9mHaJuPLKafKvMX4gjClk48pcXucAsqHfu0JnsQTpunzYcToRBH6LJf97JSCOE0i6PCihKpaR1q975SKU3zpkfJo6CrFTLcLeUKmu3RNj8z06JlZOsksveQQ3XSebjJPJ50lTbqhlUUb0EnMh0ECN4X3YXFxsB9XoJyjHGCtLFfONurEMt9cD4tqbC+PoeK6t0VsObcUPuZ9GOnaOhvzRTBkfIF1oRW9wIV9CEaO9TYO5Bqimwp8KCfmCQO9WnIKPIWKM+SoYLwX+KovdhHDqY/JtKZj5Hd9li998tf3RXdLrW3qbq4cuehuS3fr01VBY9fRXY8JLxLr6K6LrW77r7uEFv2puhvSi0N3mxp7brprfRE1dpO668MsBOeiu2XEU1N3bdTeS+hvfK0uMuzpL/K5j74Ni61nzd0sURPqaFJVpVqFNGhS2d3LMVAq6i4MFAx0uZQ6DEPlo+1bkOu80t6h8hRRjwtCGH3Vz17V+WpsKWrtnarB56y/0THLV4HaPm3p7RT9rXSYUX1t27qqvNSNsSM8Yay0sr0cp/Eqaq0/EjW43l7awZX2onFRcydGKqzj4w9d0oLO6kpvJ2hu1OSk0uDYIdv7MP1pXC875yQxmkc37V3dcKoqjcZUY/pA6XLV1as/Kip0YxyKcD+G8pluD4qEruqQmR4d06OrZ+mms3TNDJ2kR9fM0tEz6CQd0dyY16axHvJq2nPLu5YdW+mnH7FvS31t6nFr30Y9P348a4tKb4PWFpXWFuU4Ms5SeEvhQrRT4WvNDXWaeuvC9piW86gF7bUUFLUOl/pLnPCgtJEIv8NkZedH3hCHRcQevjvvu/oS7rIr5PgYiTD6R/FVSFozbfbbbApkUyR3ytMFTDZQfW2khkBI6PgwErPxmgRVpQmGFE2KISUhUQmZSshURqoyMpWS6S4d3aFrZshMLxpXM+GHnszQNXN0sjm6yRyddI5udogk7YJJWG9k4sJZ7srPcnxwmhPDM5wYnoGO4+TSMn07pO+GE9M1F/IDO8RtoeVUAR2V0FMJPQxdZehi6HpNF03Xa3pe0Y1Lz0LPhbDSrvP0rKdrHV3r6VlHL7d0rWWmcHSKHGUtylpwBRTRILQF2CKIzLpn1kzX+WM3Aq+90pAkoBOcSRgkXQZJl37SYc10GaQd+rpD32QMTBZSndKPy0An9FXKcmKwRRFCx6qRhEN/POX7KL+K9t8A7+J2V9dzNvatdygXw8+8RcUBh7Sz8Xg27OPC2NVQ9xUM92w7hDjcz2W+7mtIVQ5lmCVlyHEs163jtI9PfAkpH4gmTtXYU4QHpI4vFfEhqUyKUp26TMcB8HR8edE6loUUXZbFB5g26KquQRmN14b5e16P9KZuYw9dzp9cvcgJu3wB626YE6SpuwlhCsbt6m4nmaWbztPJ5kjTmU3r7onBGY4PT++Z7nY3qbtdCzOb0N1e4egUBdoWe6C75Z20ed0tNfdi0t1pxx/V3TBlozkn3UXHwfK2pLvXie6O4GaO8eHLj3GHWyZXnhwXHAS46JitNTi8gNRO+KqVt3QI4HfZOdBggmOg0mCC7moUqVckPkY9eh31t9TgMBVjRhKmwVUZmc7oqA6p7tAxHTIdX2x1l47p0Ulm6CazdM0s3XSObjpHls4FDU7n0Gbyq1TuCu4anuX48DTHB6e5c3AK33GcWl6hb4cM3JC1Ed0dRN1ds0NyPyk2Yjopmm7U3S4maC0maDCKntdBbx10nWLGKbrO03XQc7XmzhSObuHoWUe3sPSKAmNt0FdbhBdn61HWQ1k+MZqm6Z7ZLLX2eqXApGAM3hgKnTFIOvTTLgPToZ90azu3tHl1GvIqpa+TsKwk2Lypu3GSVedQfojyfbQ/ifK2oclRf+OMGjouOBd1N46VFJ0MqlFedsvYLZu21e0Oxo5v8BitgwNd1anSKmimKp3xqmXPamXwUVdrHTaxngmOJG1QSuO1js6bmKqgt+X63DX33HHdFYdF5ENffD9/lP8koa9eGfpah2epuK5iPqRhKfuEJXGqRkMe553PSZQjowj9MZUjw9NVnq7SdJWmYzIy1aGje3RMN3j7dI9OEr2AySwdMxNaXtJ5uuksqZlBZZ1gpJo03CAHgMJZ7hqeaRnFJ6Jj4vjgDCeGpzmVLzM+rZCiq1O6JqOjM7omo6szeqbD4XSOjknpNsqrtMyPruuMTpnqdLoX9BxwwNo627335NbSzwsGRcFwmDMoQn6QWwaFZWAtg8KF1DoG1jNwnr7zIe9h4KDvFUMf0oEPwxv1vWagNj3PX4X2jp7LMYPwMCgnyPO67gUXjI6RHnJ72u/6PKd8g26ggHt/dYZXXbIfJ3Rw+fiXP8z/P38B0GEj3SX2uy11t5o3nCJq77nrbpaEfKW7yRydbJ5eOnegdfdkQ2OPRw0+MSy1d2u62zXZhrrbqTR6b3XXw7rt5eeiuwPn6Yvunr9M0d0bv9zjNaK7LT7xtQ/wW/qpoDqV3nqKlvaGLlB5w+4tUD7avL4cfybHYMlK7Q0jDxDmVHKkeFIUmTJkKo1O2uAQ6OhOcNCabnQMdMl0lyyZCRqcRgdBFm3f7BCpmUGn2ZYjsnYD5x2n8xW+tPZ1jg9OB5t3cJo7B0GDjw9OTdTdVCd0VDqmqwvpLJfpI+N6OzHtxLR9HLMNbdoMG8WGFs4zLCyDImeQFwzzkJZ6G7bFvHWV7g5drb0DF3R34GHgFUOvgvaiGBBmwLJbHL8CgvaaPAiDa+itaOu5ESJb6s4/Wqlq4hrl4Ia7Zvj5u+/sZ4rDIvKQ657A97p/5I7lIVb1cKTV8DTDaLgMHTH1YW5k68j9uU05k2pF1lhSregYRZrrVnmmFZlRZFqTaU+mB6R6GOrqsrys0ziW1mH7SLne4o/Vestdw7OVAVw7IaJhPDjDyXxpTJx7OuOSzmEWs8NcM3M3FjsLXJItsNg5zCVZyN/jboucOLF8Dldxa3jvKTwMrAsGqotGq3VBOK2nX+UbBm0s6zfFdp1jbK49UlH+DDXQMYqO0XS0opPU+UNGc6kJf89Qp5HXmq4Jf+MyX+7XNTqWh3wSYjZZXJzn+PGlLV2zqsXEl2ld5nzoC19vK/3k5Wj6k/cL2xr7Nbf7kWNPOoZvtOaM7Lflz574ObHOSFmYMjNcGzfps+N+VOcP977boU1f74uFb77ucTzZ/iPfWBpsqLu5C7+5Un/Pha3qbqo1He1J9YBMD1t6mmm9q7p7crgUjeHaEVxp7+A0p/KlseiHpu7e4/AB192mzloXHbbjulvuN1l36/x2dDczQR831F0dt22guy0dN+FeANHdndDd1udvQne/6bL5TV/vi4X73ONxfPOZv+XEmsNjKldD4cPMC7lX5N6TO6rfU/n3g80OthtQ1HqbxiXzIQoi84rUqeDUQJF6HQbzs5q0CPsk/aaG9kn1oKXfafO4UY+TxrZMK0yM9tkKK8Uax4dnuHNwihPDM9w5CA6J48PglDgxPEPh21eio1MWs8Msdg7zwMM3sdhZYLFzhMWovYvZYa6627EtacC5ULigi8MxLQ35oWtrZ7POsFE+bGjycPQ4zmE3/Tiux7fQilpPtaKThr9dxyjmdFs/Sxu21OFsRJOzpu42bORMaxIFl156aOI1995P1anKvhvT4I31rbQP91pbJz8P2sdlgvY7X2smjGvr5vert9946c7rrvLVxK8XFnfdtYzbolG7VWMCwh+saUgPrWfoXMuwHtiyjhupF7c7T94Qlfbx2sfaMYNdEcRdhbxRPgxm4wu8KrA+J/dhWM6B7zPwA7wKA7EFT7wlxXNIJRzShsM64wgpx3TKJSrjUpVxGR0Oq4SO96Tekvk69Cn05wr52dkOK6tD0GEQpdzDQJvQooVmoIKHtY+KZYqBV9X6AE3fR88s0Svrg5d24Km3+dqDu9WhoBTQ0SMCGwWya8JLTpnvtJZQv9sydMeN3o7RVf/pvWA79/p5Rby3WvlJZTGvptVf51hqvWNP2Hb0nldwYri1FgKtFceOzW3rEuwH56vuDl17yRuG2kHU3XllWNAph3Xa0t1FlXE3Oixg6MKO6G6puZN0t9LXcttu6q5uOwSm666q87rcJrq7JxxE3b32Ck7kF7buwta1dzP3YulkzBs6mDvXyNfpaFkxopt1HTehbPIxyrJzfUkJThPIVHSCqKiJKgxf6HyOpSD3A4Z+yMD3KarBMOshZme1YV4lLOiUBZVyRKcc1Vm0eTss+IRMeTqEWXFS50hxZM5Vs+XMzXU5vTxgoHWlrwOla1s26uvQN+3dWmOHUEcejGjscGR9K46lEg2Nl//azs10U1ujI6HS5O05EkR3d5H90OLG+tFrLudEf2u/3I10VxwWJXnOkc/9AyvHT4X12EeUhoHHyLra7rbR4663bcpxnffkpbApHRZtwqI0g5hf05pTqeGujuJkojmTac4mYVkxhlUT6obRfZMq1d6QuJBSDl+mEuw5hpylzpI5S+YcHR/yeTzfgU7oa4PdRqh1xxZ0naXjQtp1BR1r63wzreqGsmo/26zbrp95t63u8B5C+KJSIYS8zMfFj6xPWnxzP9j8ccp6ELY39ut0Egb9fExk1AYCNbpNTdu2jiE5URjX+ZwtnVMpogeRhQWWnvOcLe1yvhnOW9bdouDI5/6elTsvLN3ta83J1HBXpjiVak6nmrOpZskYVhLFqjH0p+iucQlmD3S3UJr+Lupuq2w93Y3bLhjdLT+TSbqbMugPzz/dXe8zDrruzs+x9GPP29Iu55vuwha111qOfPFzrNxxcqLejWnpetua22OqJu1XavuUY7a2N7YFp4kPUXeq7pQ9JOhvDuQNDe5rzZlUcyLTnE5V0N5EsVTavIlmoMO8MmFw5rAkzmB80F7lE8pBLq0y5Prcg9G196RRd7ejtdq7oJkNO7Vp146ud6Pmj2pyXa9eb2px4jd5D0U20lI/qoulnjbWN6XH0+zb0sadoM+V7nbT2t5dR/s29dI+Wn+jus315meso9UbaveU8zlQWjw3y9Lz/t2WdtlId6VLSEStrFD8yZ/T6Q/qH0b5AyiNj+b6hB+Pb9RlpG51TGPGjj/2eaPHnLLNKVhSQ46bAcdVv7Gscpw1jrPGXfTHwoW7GBbVLFfpGS7RsyzqORb1LItmlkUzwyVmjlndQRkz9pkWyJUORrunkerKC5z72HXGwxDa3Wm8j3lfda+Z7aao3EZvbqM1TUNXxXUVWtq6QEf5qqyr4vzuMP4DHv2xlw9BGH8AT9kX73HO0S9FYPQhu94xJnzeep8zZgxM+bypx3BxdGEYMyhG9/FGo0vjpvkAaS6NMj+ljp9QF0YeYusde/QzJtUfOXbr87dQt3VuG3zf5vrYZ2ziHMcersDh667knJuKLjDUygrF+/ZBd9c77ga666PunjAD7lR9Tqg+d25Bd++uZ4LeRu29xMxyqZnhmJljbpu6O0TVeus2r7vkNkR/baC7HQVdRHc31N3m50/SXV3OZsP5o7uTPn9a3a3q7jrneM66G7eJ7o6jlpcpfv+P6AzbUw57aGteIz/qfBvT2WnbGtq8oe07coxSo5VSZCp08VhSBct6wB1Rf4+rPneqVe5Ufe5klROqPzbNbo+ERTXDNWqWRTXDpSbYvJeaeRZ1l0UzR0en7Zfe5ncHCh27KaKC48SHkZWGXkUnNhSV3Rvs4KYtnEfdPTTbwQ7yhraqSmM7CrJGPmgtdOJsR5vSvwn6tCn988ExVEzS0qiLY46q9c6lqaeN9c2ey0RNX+f7jJY1171WGOe3p19xvaVf5f27znHW/YwpGrstG3i97VM+c1dt4Jgu3PNKdhqJsGhwySVze9qndz2sd5zOlzg+dQDLM9w1PIsbCbLt6LTVV3kxO8wlZf/lmJ8zPfYqFGszXJThWvuMXPO9ZzvX/Hxr6buQdPfE8Ew1dsSJxtgRorvCdpFrvvdcDLoLW9feS470OHHn2XFHwz4ysHk1o0Y7PVOt913byWKUrnR2sXOYSzuHuSQLaVk2a7oHRntFA3YJ7+uBdy1hYIU4AOTRo7OcPLUSZqwpF60a+Ua5Yt9/BxcCu6G7EmHRYK8ErTSKwyBqZ1oDWZYDWN6Vn8X6yUbxJdlh7rdw3biBnC0wlxwso1gQBGE99kqvwqjuy/WgwdEJXGrwicHpibqbqSTq7GFuWbiOxWxhTHtFdwVBOJ9QSQJpumefVw4gfOeIM+LE4Ax3Dk9xfHCGs8XK2H5H0jkWsyNc3buUbz58Q+WEWIxOicPpHHobs0dcsDRf3B1g6xf3UObjC31YlB2p73yoa8frq3J9glOg+VnhuI26zWPbdc5t5FxU67Pqc2mdh2uc2xT6nGVmK5ew5dhoprWTw6+zrekA8es5RhrbvB79zPH9/Gj5lHPw63xOdaz1HDfV/qpRb/q5teuEbc5sbUrezSAOix2mMopbERFtA3l9o3iBm0tnRLbAJZ3oORajWBAEYSLlFHPNiIiW7g7PcNfwzETdLTV2ku5eki0wn8yI7gqCcOGw5Fj75W/QW7K0OrxPyk9LG3kPOByFtxTekjdTCnIX1r3yzOGZI+UaFtHqUhJtSFRCqg2JDmmqE1KdkGjTmFmp8ZLU+vzVcA7rnN/076E2X39SXjV6G1X11fT6wKCTk60O2y/u5Yu/ba+XL+sTX9ynOQq2OrLxDuPLiUA0YFR4oa3KVCwnlqsqX9VPwVfbdbUtlLX3R6vpnxfzaJif77J0ts/IdBrg4/WqyondSJrrzTrj25r7tI41erzyWM1zcPU21Tym9416I6kf2WfK57S+3z7Qn1+Flx3a0WOKw2ILTDKKRx0SGxrFh+7JJWXLXCdESyyKUSxshdKDbgmdJquBrH05mHXwmocBsMH6se3F3ArJ0rD2jMJISFyzTLW3Qz35cumpbW1Xre1jxx/5LD/p+FPPa3Ldid8Dxr3RG36O/AYPGqXunoihwc1ItLK73F3Ds2NTzKUqqabzvHn+2thF43ArOuKQ6K6wWUZ114GK+nrB6y60tbR5DqK75x+pwtzUYXhiAMQ/RfmSU9LIW+cY2CEDl4fF5gxtzjCuD12B9w4VHQAK0GgyndLRCZnK6OkumUpDmUpIdUpS3bATPts3Csu8Y2p9NaFs2vepxmSYun2DdGLer789rrvEYvDjL+7VSzfhxTypX/S9mfLi3nAE+NEX/6ajwKh23dHPinmvx4/tzYRzq5wCE87lAP6Wk8U5iuMX5OgHm2OSM2OCc2bUaTJpH1Vu28A5c+S6eVbo7+jXEIdFg9PDZf5x+ctj89yXfZmnGcWl8+G+89dWBvIlMWxNjOLzgNJ73TIyqQxNZX3Y5oBi0nYqx0EwYv349sYxquNZxo3agjpsrphwjDJ/jgxZpXPuh7kgmW6EN/LVi4Ea2+6bdRv7DK4bwFPkqo9yZrjCPy1/pXIEHx9xSGyku/cZ0d3SOSy6e8DZKd0tWyFFd89rdk13rx3A98tVb9FVZN9/hDPHlyic5a7Y4Hbn4BQnhmfa3TYGp1m2a63dFYqj6TyXdMqxIhZY7ByJaYgIXkhn1+2qUcTlYkPGsBD2nFIvx36ObftoMy6dzbp99GIKx8VhsSt8/cwJ/vq3PkRvmOLj6OeXqYRrzBV0k2uZMR16cZlJYj7p0NVZ+8FZ0my9YDClznjZ1LC2SWFmk4613vaxcrW5z14vbZVt4hxH1j1gv76GOZmPG6otw5LJhuh626PxqRotXJNavXYzZKqeNUsFD3aZD7NmBe91OathpvA9wGjirIZ1yFwS9pt6PAMkG22vveVHL5nj5F3L7VaIkbxqthr48e1j+Qn11dj20bxf51jN8xipu955VPn2sdW0uuXfv6y/zvdTo8efeqzxc+lclyG0uePsSf73b72fmWGGJ8wIcZky3MNcQS+5lp7JmDFd0d3zUXfdOg6CC013dV33gtVdWEffmvkDprv3FN0d5Wy+wss/+J/53OmvcjJfwo+8hsyZXnA8dA5z7/lr4kCWC1wanRJHs0OkOzDVpyAIwmYRxYksFof4/q88FLOmqP9BS8f9SIoHH0Pq2novbIEBK3Q3Uc8ryimzx43I0ihsbk+JxqMOacOgDOVq3AhNgmE58XijRm8yaXs8Rhmed0BbePVigldm3TpyS+8sC4vzIC0rLS6x8/zAVx4mursP7Knu6tpBsKu6W4Ypi+4KEdHdcbTSHMnmeMDhG2JERDtComckIkUQhIOFOCwiybGMI2+8ZmdCtZotBjDB4F6nbKzO5o6lmtvX/Tw/XraZ/aZ89lhr0Cb3a5YdPjbD6aXV9Q3Vsq+cIAgXDOZIKrq7n7p7dnWKA0B0VxAuVOaSHq/45mdK1wRBEM4bxGGxGzT7V577wTZVa79aZXbic81iB3d8uHFFQRCEaYjubgnRXUEQBEEQzgdk8mJBEARBEARBEARBEA4cEmEhCIIg7A+rA4a/+QG6azl0Enwnw3dS6KZV3ndS6IyvH9RxCgRB2Ee8h8Ki8gIKi5+X8RgEQRDOd8RhIQiCIOwLCg/Woc+soIY5ajCEfo7y63d68BCdGNMdGtV6N4UsxXfHt2MkyFAQ9gTnIK8dCSq3kBeooojlFoqineYFqohpaz0eJ7f1/lXann92cHgOfuy79+lLC4IgCDuBOCwEQRCEfcHPdMl+5LGcaQ7+5n148RgMUYMcBjlqkLfX+/n49tU++tRSvT7y4jLx8xMTnRpZdGqUTpCs5RDxnSxEfWRpXb90fiRGoj2E8xPv246E5ot/3nAA5MUER8MEh0IZ2TDJseC2N4+tTw0+SSA1+DQJv7fU4LMEP9OF1EASt43WTQ2H7nXFDl80QRAEYa8Rh4Wwozjn8G6Iy1dwxSqu6IfUDnDFGt4OsEUfb/s4O8DbIctzPdb6oE0HbbqopFvltemE9WQGnXTRSQ9lumgtLaOCcEGiFGRJeCGZP4fjWIvqT3F4xLwa5CGio7Gultbq9WGx4cd4raY7ODrByTG1i0ssJ0vOyenR1t2gub7oY+3aOepuqb2iu3tK7NYw5kAoNuNYWCdCoRh1LNgNo5kmnp5SLQdC5SxIkxDJND8TnQcG0gSfGkhG0sq5UJaXx6nXd8IZqGVaU2EXccUAl6/ibB9brODzNaxdwxf9WDaiu7NdVvtgki7KdILmJr1ad9Me2vSC9qazKJ2J7goC4rA4L3HO4YsgkKVQunwtpHYNl/dxro8vBsFRYPt4l2NtH29znB0G49YN8S7HuRzvipD3Nqz7Au8t3ltcTL13YaFMPS7Oqxf+gd+rhsY4pWBYyn8apWKKQimDVhqlDKpKDUolaJ2gVILSCbpMdRofDinKpDHN0DqrHyymE8uicZ90UPFhY5Jo9CfhgYNO5UEjCPuFMfhZA7Pd7c+q4RwMC9RgiF8bwOoyrK3C6gr0+7DWh8EwOjiGqOEaetWiznh07jE56Dyo03p4PNYUWF1QmCHW5BR6SGGGIY35XA8o9JDcDCjMgFwPyU1YCjPEq12et2RTuqvRldZuVnfTqLOb1F2TRQdKF6M7KNPB6C5adUAZtFd19ID3KOfB+Vg2Uj5ShmuWu8Z2H17unWsdS3lXbZ/+maFOnhq6y/2pTofSkbCZ6KCJfx6jwwt/GYXQcCj42bTlWAjlplE/GV+f5HBIDWgtUUXCnuKKQbRv1yrHQGgIWx1zDATnQGn/DsPiBniXR5t3iHNFw+6N+Za9a/E43IjN65v2LuzgzFTr4MsZElTrny51V+mYmmrRyqD0BN3VaVtzdRr1NGiuNt2QN52gsSYLzhMTnSpJB53MoJJetHl7aC2vksLuI3fZFJwr8MUaNl/F2z42OgZ8sVYLZ3QGODsIzgE3wNk8rEdRbKdFyzHgfYF3NohlFMfKMYDD+XGXwG4JpPKjRqiqjM9KEJUJYtcwQFtlOm0IYkMIdRZEzwTDUusggKUBOjefcvbUmXAt3TBey+hQsXlwrth4DX1wrlROFl+E6xqvZfXAidfSeRseUhMfOLv80Bkx7lsmvtIj17dp3CdoPW7k63h91YSHTbjOnfp6J53x1tKGJ38tO8xweWVrX8dvJ6R3G/tso8Uv7riNXbZ+fm6b55cfupa9sW7OX7aiu94OQzTBuehuU3MrA3UbupvFZRIejE9IbBYWl5HaDONS0iIjdRmp7ZC49va0yOgO5mJ5ivZm4+unHTZxuMThEo9LwaUKl2l8qvGdJCxZSqdn6C+vhWtjCygKcOHahBdsC9bFF/Dwwu29Qznql3jvUaWIxkV5VaUKwKtQhkJ5Heqj4/ZYRqjTyhPqKp+jKMCvotHgVUgPKPGrhigETXixjy/4RWrQRuMTjU8MLtEwa/BJGpwDsdwnOjoJNM7EaIYJ25plJAZ0eZOeq+7auDS3A8O4jG3Y6mftoe7OXYNMiLd5NnIMlNGy0xwD7cawYbTXGo4Bb0PZVMdAqb+7aKNNdLy27V2NRqsUo3Wwv5RpOVqDDZa0bTHdQZvgcDWmG+2zbrR3u2idMT+fcebU6eBAqZ5hpb07xJU2b7yGlb07Yuu6RqNisHVdtI8He3cd172eDUd2vK5ajTpVkqlOldrOjde0fI/QGaq0b3WnYd9GGzedQZkuJumhY7SKc7O4Vpe0Ef2ZpEcjeuMn6dzofhN1beQ4Yzo2YR83+tnbOe6k85n0PUdWN7XP6GeNf3Z+6Gp2+mYTh0Vk9a7P8EfvfR4Fdnd+1BsJZBURUP6A4w9aB6eA0mmrRWq6h7RTOwZMFl9Q65BfY3pVyJlJZlBpb99DzhYX50n3OWTT2WEdNl2shgdyUTqk+vVDuViLD+VB3GeIc82olbzhxZ/sVGk/aErHSh4FtXxhaj9m9jR6RdgVMhK+5Qn/c79P40CxdvIf+KM/eC6F32/dNSid7a7uNru2bTLUt3xtHEBofR/t0tLPIQ5WGsb1yGEwRA9yzKBeVyt5o4tL/dY5V32SicvkGRW8VvWLt1agVXghHylDq1C3bIFXzXJdr6vyGKVTyOGVAywu/vPYuJTOpAKPxVE6+Ou8i9taeYLWOuq6db5OHQ6vys+1OOWq83F4nPZ4FQ3/Kh0vQ0W9Ppd72AN5XNbO4ThCRUrCg0R3WwzOfIE/+cPnkLvBAXIMNCMDtugYaDWEdWsNTnot7TXJDDqd2dfo18XFeZJ9tHdDo0C/HaFdrAXHSbR162iVYW37ujyUuTxEajcitKuo7Ng4MOaYWqcBcd+itIVdJcXwoCf8xY4eUxwWkXTmUi695BYGg9WqNbvdRSCLHr20FkTTod3/d8TDl/aiaM6gzbTmN+EgoE0GJsNk59JpfndxzoHLQ+tHsYbN1+JDpR8eQGUXoGZ3IBcfOK52rng7JM00w8HG/fPH2EYY8Ebh8NP22hbbClPem30uvep+2/icC5ukt8hll9yPfn9lHd0NBmrIdy9e3U3imADn2sVlkKOGOUePzHLy9CooXTsgKsfDiHNhHylfePaLUd31RR9brE3W3UaLqejubnzWdnT35m18zoWNzuaZn7+S4WD1nBwD1TgMpWOg0Ri2344BYTJaJ5DNYZiD3n6fzTiV3lbjgqzhY8RPFV1ZxEgfN2hEuDcifGze6gKUJIo8H4kYG9GsTenlBJ0b32/Cccb22/izxo47UWO389lb/6zJ12b9fS654j4bfu5WUX5iHMn5z113LePc1r7a4uI8x2Vwpj1HrvveI9d879nONddacezY3MYVDwiiu+cPct33Hrnme8/FoLuwde2Ve3F/kOu+98g133t2Q3fPyfU5HA551KMedS6HEARBEARBEARBEARBGOOcY7W++tWv7sR5CIIgCIIgCIIgCIIgVGw4hsU3fdM3Td3mvUfJ1FaCIAiCIAiCcPA5MWTtFz7MjPX4ThpmDpqJU9/Op/hDCe5Iij+a4I8mkG08Q5EgCMJusqHDYmFhgVe96lVcf/31Y9uGwyGPf/zjd+XEBEEQBEEQBEHYQbQCbVD9NdRgFXyBUtOnmvXegErwJoEkHXdyzKX4BXFyCIKwe2zosLjPfe7DqVOnuPrqq8e2DYfDyXO/CoIgCIIgCIJwsDia0nvNA9uD4q1Z9F056mSBOp2jl3LUUgHLOWqtQA0K1DBH5fnOOzmOJPhj4uQQBGE6GzosXvziF5Mkk6tlWcb//J8yv7UgCIIgCIIgnJf0DO5KA1duYZ/1nBz9Iizn4uTIEnw3xc8kMJPi5sXJIQgXK+s6LD74wQ9W+W984xtT69397nffuTPaL5wj//BJktN93LzBHUlgIYFU5pAWBEHYFUrdPbWGO5TgFhI4IrorCIJw4NlFJwd5jh6swtkdcnIcTvBHU3FyCBcXzoEDrA9pEVPvq7xyjdTGbRaI5VgP3qNimXKNbb5Zp9zXU9zbw2U7O8blug6Ll7zkJRseQCl1QURZ6H9co/jdT9IdKQ9iaEAneGMgSfBpEkLbOgZ6CX42wc0mMBcN7kPR4dHToMXwFgRBmIT+XJ/iXZ9YX3e1gXSy7vrZBD+X4A4ZWEhEdwVBEA4y4uQQ1qNwkHsYesgdqvCo3IcyG1JVhIXCh/oxr2yZxjIblkF6nE4/Dy/Z5Ut1M+8JL+LN9WYdyu2+yofykWPE7cr7uoxJ6eRtqvywaqiFKevN/aqy0fV4Lvs0L8bwf6fw8gft6DHXdVj8xV/8xY5+2EHG3TTL6lO+meJflukOLGqlgLUCNbCh715eQF6gBgPU2gp4i1J23WN6r4AEtMHrBBKDTxPIouHdNbE/XzC6/XwSRPKQgcMJJGJ0C4Jw4eJumGH1qQ+k+MLSdN0tdkF3e1FzRXcFQbgIOdu35M6T6vNgpr9zdXKcKdBnh7vj5OglMBudHIeC03xPnRy78IIfWtqbi4ut8HG9fHmPLe/hBd6jnGutl0v1Ak/cXr5YN/Nx2Y0XbAekm6wbfAPlSayTqtHyCWVx3asJ+ygVvQnNejEfL0K1jgoD5VYfFfeNeV+VNVLdOK4eKVMK9GjdeBxdr1d1NPEYCkzYx5f76UY9U9c/dN+jrLC+rbZVNhzD4mLh/xwf8p//uYdiBpdYOGLRlzjSxNFJPDOZZ74DCx3FkR5c0tNclikuzz29JYs+W8CSRa/ksGJRq1EQBxaGteGthwNYKoBiwx9mO7ojGt5ZEpZeAl1TR3fMJ7j5BH84wR9JoPf/tfemMbJ052Hec86pqq5eZr3b3O1bRHMRZdokxcWyaMkQQCMxhDiW7QSGkYCIqUSGZAtJHEGIg88hAdMhoFj+QQUCIcvyD4N/LAqyAiMykFibZXMRKUu0Ja7f99373Ttzl9mnt1rOyY9TVV3d07PdO/t9H6CmTi1dXV3T/dRbb51zSrLBgjAVa4uMOqNqbZPjovqcqrLvo2pzo6pw7K4WV68u54rXFK/Pv8dA+0w+8bnlPz5N+YVvxSia4941lkY48u5sAxabard3t3PYyo7g3Ryl9u8oel/vxt6tU707Z6Atp1RBmMoRvAsT1ZSfx7vvNtA5/Y97nvnGWsr/9X/3UGicykHnGGOJAkscOloRzEQw14CFpuZqU3GjZbjRMkTBBUhwwPEmOboZql8kOtLnS3L0A00ztef6An/6Z4Hqwry6QJ8s6+LCt7ygVqB1dbHrtC7mjS5+XTlt/NhpBUb76WJwWkFQrBconNHFuJgfKH+jIVC4YiAsXh8q5q91WNvq+Qtro0YX5eV0Oc/IzYrjwlxrQb1T32NAoquCd18J+cE/mfLGk4RuAr1UkaSaJDUMB5ptF/C4ypyNY8nB5BiTEzYt8Qy0i0B7PoaFWHO97YV/rWV8Rtta6Fr0euYD7q0MtZOjuimq5wNvhhkqyVFJcZdxMED18wM7LoKJu4wmABMUdxkNLvYJD9c0RW/NAW7G4OZHTVrkh3uOyCwMR1l0XZX9WCdFdjzx2XOVlmPrs+Wpz5BTZtMzy8Bo4jQfr8pW1SSrVY0DfzJl9zpjVdnc5DqT41q5Xr2tqtaGf8+qClytvGe1uN3b2F09bvf4rKrIDf9VBP/7B8/mzc8p71wM+PPvSXn98YR3M8NweATvxo54xlXenWvAYnMf725ksHlOvFvclRPvnjNyC4OadxPrHVt6N3WQ2pF3Mz9derd0bVkmL7ybnC/vjldFrs2/NN49/qrJF51X5wO+710p956mdFMYpook0/SHAf2+YcMZ1F7erRIcOaFxxKGlFUEngrkY5mOfWL7e0iy1A5oXJcEBp5Dk6OMAXd1VP8IFfjEc6QK/nD7sBX5xke/Ccp6GqJh/gWsemmtNeJKd9W4Iz4kkLAoCrfjRDy6MP+aphrWW1YFluZvztGdZHTjWB47tAXRTGBSB9mAYMOgbNvcQvsNVGe3AWKLQEYeadhQxewMW4haLseJaW7PUMlyJNXpalb1hDps5eiMrandk6G5WVKnOq8dQkebFXUYvS1zOQbU7fHziM8NogwuCWt8dPvCm5QNvOxP6vjtmfcLDzfuaHxeGvAg4h/YIQalDpfmeQanKRwkCX7XOFneAbJFJt7WMui2y6bVMuitvI/kM+4lUkXNgxr6fav+x2mNZtYlyvdEyV1+mJtYpT9bFbFeEzK5Ytwp11Wi+U0yUi2W1+XbKfIsqbsz519lingOsUqObdrXX56r49xTLy23Y8jWq3IYazauPi+p91fZQPLgS8N8d/K95oQi04uMfONi7K72cJ92ad4fQTWreTQyDwbN7dz5uceUw3k1y2MjRmxl6U7z7zJyGd21ZPqx3vQVeNO+C99V58G7p2OP07v3FgI/v/2954Qi14ie+b3FP76bW8qSX86hnedqzrA0cGzXvDjPv3WFiGAyiPb0LRWJZ52hT1FgOHa3Q0YlgNoaFhuJKq0hwtAyd6IJdGB8xyXHt2syex104AZIUl6RnvRdk1pFbSK0jtc63zCnG1bQr1nOO1Pp7lf6UVq7vy+X9x7Ilj7WO2unPV1SzYy14sNZhnRqbV1buGZVVVYFtVOFHVRXZXCF3V5YpT59lyrs8iSji+Cn/xw81jvUYSsLikGituFbcqTsMqbU87uasdHOe9h1rfcvGELaH0EsUg1SR5opeP6TXNWxgeDBlOw6LUznKWAKT0wgdzdDRLmS/GCuuLDW49l1NbrUNs41DyN5a33xlI0dtZqjtYugVQz+Dgb/D6IPuHNXv+7uPLj/EXUbtq1SrWpXqsg15XNxpLPvuaAekM32C9f4oKC1/mc8alFZV7Gyt85siPDqFoNT/kMtB19qp1bPpuqgWF+J0kUkvs+hGj2XNfbZcFxlz7bPkRcbbBXqUCQ8VeaDo5zn94YBektLtD+lmGd0sZSu37FjHtjakJiSLG+StFlkcY8OIHLVLYraUWG3+vgJzRRBc3YCrBdlluRir4hjtFeicJg5bCNcVQb2bKAOqCO9VWQ3T1ZoS1srKVTUeFeW/1fHKNdHtURnz7tWD1y+9+7if87g78u7OELp17w6e37uLNyKuH9m7vnbHdO/mMMiOybujDqJ3ebfl++4Q756gdwcJvTRlp/Bu1zq2xLu7OA3vvnRVvHtUQq241Qm4dcimNJm1rPYtj3qjG3obE4nlYVavsWx4zHRn1hMcQdE0sFkmOBq+BseVpuZakeCYjdT05LJwacmsYydxbCeW7WFGb7NPb3tAr5vQ76cMhpZBahlkkDhNpgOyRoRtNMijBrkJqkpnpWdHF+lqVPmt8GrVQoeRa+vTHjXhXT8eWff8JOLGvAtl9WbG3Ftz7eRYAVo7VOHm0r/11j63F47/84rJT4hQK27PBNyeOdwh7qcZK72cR11/R3Gt79gcwHYC/UQxTBVprtnpaXasYZXpiROLBZ2Nyb4Vuaq63mKtut7NWxHNlyf75z8Egxy1kaE2/Z1GdjL0jg+66fuEx1iV6iRFDfrgyjbk45vLYNdTAvbimYPSsrOYMigtgtEqKDXFdBmUGo0La0FpoCDSvqpcWMxr+LENlS83dPXag8isl+1WYtlJYCe1dBNHL3P0UuhnjkGGF65vjk+aQ2oVWa7Ic7C5v0hxiYahAgx6UooB+//Kk2Lg+YNHreyBweNY7cbisJf/Gn+t4NBK+el6LceyrFV5/VC7llAUNR4JjCLQ/t9jlCLQilA7Al2W/W8zMqPpYCzYURPj40PurJw8R/ZulrPSzXjcszztj7y7k/jE8tG8m6NNfrLe3cxQG3t4d1B0VCre3ZOp3k0dvdR7d5A5+vt41+aQV95VMNRM9a4phr14Qb0bakVoxLuXjUArbrQNN9qHu6FnrWN9mLLStTzp56z2HBvDUYKjnyqGmSLNNEli2LaGp3smOAr36hxT9DlXJZerBIfiatNws6OZj/aoPSccK9Y6+pnzrk0tOynsJN61/cyPB5mvLD4sxkkOWQ5prsitH2wxYItzj9PoMblGQGv3DoRUPW26wqkucYV3645ldMFe9yz4U1x5Ua5BKVtdoJde3c+1E615Kr9qtdu3esK3dQcHhVer6ZpzDd6tpvBsuX7p26Bw7WWIdyVhcU5ohppX5zSvzh1u/a1hxkrXV9lb7VvWB46t4k7imOyHhh1neHJgNjsnCkbtEevtwK81FdfbhhvNosOl2OCWDG6p6h/r8ORl7Y4MtZWjtjI6nQY7wwRXBKblXSsataA0KtrSnXIb730TCwMY7OT7BrilcJ1VxZ2wunAPX4Xb+oci4yu9ZmiXE7gc4zJClxEoS2ScH0JNHGkacUCzFdFsBrQjRTtUzESadqi4fb3DzkaPMEuJ33pA/OZDzBtvYVbX/fu1YvKX75C/eofslTu4uZkTOb6XDescic0Y2oyhTRnmxdhmvL11g0iUe65oBopX58Jz6d2rzeIioO7d2OBuHK93CTU2Op/e3UkcW4kbTyzs6V3lq9GekncDlxEW3g01NEJF3Ci9G9JshpV326FmJpr07kPiew+8d58W3m3G5K/cIX/lNtmrd8W7h0S8e7HQWnGlabjSNBzm+Q3WOrYSf1PvcTdnrWgauFVPcKSKLNfsJPsnl8drz1mi0Hc02glhJob5hvIJ5pbmZnuf5oGXhGEtqbBdJBQq12Y+iTt28yyHNFO+Ml6ufMU7q3wS15au9TUKjlKLy+FwvpddvG9ztMsIbEbgUgKXE7qMyKaEytLQ0AghjjRxHBC3QlqdmFanQbuh6YSK2UgzE2pu35zlyZNtVLePedM7N3j9LfTWDgB2tkP+yh2yl2+Tv3IH156SABHGyGw+8q3NGOTpmH/fES0xPy2R9Bwo56oelS4Vq6s7WHu0j3ZZM/E+m22rQHut77PZW4OR7JNMk2caZw3qEO3A6z1Kd6KR6K80FdeOKPrjOO4H1Vg46M7Z3gHu0QJ1S+6ztcqitEMp67Om2hEYP0QGIuNo2IymTWmmA1rJgPagS6e3w8zOFrP9LjPZkLl0QCdP0M0YNz+LnZ/Fzs/UyrO42Y5P0x6BvY652t7BvPGA4I23MG+8hd7p+c+1MEf2SpHAeOk2NI+3bdpJ45wjdTnDQqoDm9HPhnTzITvZgG42pJcn9PKEfp7Qtwn9UsC5D4QTmzO0OanNSV1Oai25s2TOkltHhh31T7fr96NoGs0v/5kfO9J+a624cuXidHEv3h0x6d31vmP9uL1bu4N4raV9B6PN0/fu1MTCnt49TGLhZLwbakfsvHfjdEg76dMeFt7dPqJ3Z9pgjtZvyN7e7fogeqp3b1fBNM1nqJlzhhzOuym9fHhi3m0Yxa/8mb91pP2+aN6Fo7v3snq3ZDuxRe05x2rfsjFwbA597bl+Mkou21yDNRN37UeU/lVFDY7Sv+2wSDDHPsHh/au51jITd7HHeZbjPtYEInV0U0svdeykjkEK/czXyq3XVEgyyKwiy6lqKzirsEVCAadR7uhNFWyZVFDW1z7QDq2dd23h29BQxLnQCKBhHC2b0coGtIYDOoMeM71tZrc3md3cYGFrg6bNqj1xYTDy7NzMqDw/i52bgeiwDyn1TD3mzqHWtyrnBm8+QA2GAOTXr3jnvnKb/O6tI7/fWTJK3qYMaonboU3pZkM/5EN6WeK9a1Pv3DxlYFOGec7QpqQuJ7Het6V7M2vJcOS2rBUI05wLEAeaz3/4eL0raecXgHo2+3sOsX5qLU97OSs9y5Oe9e3Ai+YpZXvENNP0hyH9ooPRaTgcucqwKsXqIblKyHWfnB6Z7pGqLqnyj9ZSLsIQoW2IJkK7EO0ClAvR+LImRDnj52PQTvtpzDPdObPK4shxyuJUBirH6RylctAZSuconaGV7xFbq4wgyAlMTqhzoiAnNDlxYGmGlthAaDRGaYxShGlO0EsI+kPC3pCwNyDoDmhs9wi7QwLnCK0iQBGgMZ0OpjODvjuLmbuDmZsln19ge36eoNk89Gd7HtxMh+w97yR7zzvBOfTT9SqQdv/x62R/8IcMDQxuLNK9c43+rav0r86SKEhsSuIyMluIzvlgsz4vd7YIOnOyYpzamhgLKaZlQOocmfOdENlqDBY3anNI2ZabqhO5qgOgimlV357nroni4HrfteOKw9rBc7yfcNE4qnezoqO7yrsDx0bfVd4d9eR/FO8m5Gp4Dr3r73Lu6V2Vo3VWeDcnMN69h/FukOaEvYRgUHi3W/NuLyGwdty77Q5mpvDu7G3M/NwZeLc97t3VdczrpXe/Qf4fvsbQOO/du9d3edc7NTu0dzNrSWzmg9L8cnvXiXdfSGYizUwU8faFw63fTzOWuzmPe75Z9nofNoeuaB4Iw1Tv6v9oGg6HVRm5SrEqJVdDMjUgV30yNcD3ZxBgXIRyAZrAe9YVRnKm8KxBFbXC9kqm7IXDYrE4ZbEqL+LcvHItKoNirFSO0im6iHeNzjDGuzcwGYHOaJicMMiJjCU0yrsWjdEKXZaznGCYEgwSwn5CsDMk6A+8f3sJgXV+cArjFLrVpN9uM/yuDmud7/Kx7+wMZnYW3WoRGEOAIdCGUPuyPuKNuX1RCrc4R7o4R/r+78FmGdmjR2Rv3sPef0D6H79K9rXfY2BgcGOB/s0rDK8vMlhok+JIC3+meXkxn5O4kV8zvG9zZ/20GyVcfQLAjpKv1vlEgLO+j37sqC+j0rns9i6oKYmD/Vz7rO492nkeIMuP37uSsHiBsdaynfV5mmyxlu6wluywkfbYTPtsZX12sjILl9J3GQOdk0aONHC+Z1qncBiwAcZ1CGyHwLYxrkVgWxjXJHAxxjYwNiJ0TZru6pHkW08sWJVjybCkWNXH6RRLjitODFalxTIfqPtyUgw+YWLVEEdadJSuasMhyYrhqCigXQzXI3y7u92fFjb9kANrxVDhagOMwsNiXLZtrr1l2b6umqfKj66wbpoIVXW3ytWOj7ulULcMUAbwfeAePLkHT57heByIm1KeFPHuz6+LvdbKz9dK7XrctlHat+lDY7QmVJqwGEcmINKGhg6IdUCoNdpm6HxIkCdoO0RnA0zeR2c9TNpDpdvodAedbBNkXUKX+2QUOZHNCXCEOmTxjjzSVDhN70YY2yB0cxffu3kxPAtH8a7lxLzr5z+vdwdcLO+6UdvrI3pX5SnGpgT5sObdATrrFt7dQafbU70bWEuEJdQhC7e/9yQOlHBBsdaykXV5MtxiNdliLe2ynvTYzHpspb7Wz07ma/r0bU6iLEnoyIKae9Fg9ci/roUp3WubmDLudRGhnSF2i2jGa9C5Iq3gVF74NC98m+DIsDrzY5XiSHEqrZIg3r0JlsQ7Vw1HzmWI047ydztupnK8j38dzx7n1mkWw2ID2KtG7rAYVmEHPzzce5MOh5rw8GF8jBrvQHO0xuRQcAe4U09WJ8AyPFk+Zu9O8y34E2Xdv1OcW7i2jOlV0S9G6VxdeVcRKEWgNIHWBEoTakNUjgvfRtpgnMK4DGUTjE3Q+RCTD1D5wDs366HTrndvso3JB4RYAmcr/wbkNHTE9RvvOc4DBUjC4sJirWU92+HpcJvVZIeNdIeNpMdm1merqGrZzQvp5hlDa0ly6x+b4/DVwiiHg8jRyqKV802cjWJGa2IT0NQB7SCkQ8ZMsszcYJvFnTUWNx5yZfMRV5Mh19IhUTzP5vW38+Daq3y7fZNvm2s8zCLWBwplLNYlKJ2gdYrWCUoN0AwJsGBzlMt9z/Qu9z3TO9/rpLJ5UbbFOsX8LEe58Zbe42HXSAZOG5wyOG1AG3JtRvNQUO+t3YKyzle9rPcoXHQ8ZwODM8U2jcZqBcbgtC67/SmE6ap26D5wLbtdc7XsadlVkP/jqjmjQLd8vf9EjtHjhcaX2+qN/EgpqscS7V579D7j98wmq5tOk60jsBAoRag0QRAQmpBQGxraVEFpw4TEOqRpQpo6ohmEtE2Dlglpm5g4iIh1QEOHNEwxLl7X0AFGPVum3TlLnmyRDTfIhhvkww2y4Xo1ne34cj5YJ0s2yZOt6RtShqAxXwwLmNYrVXk0v1gWz6NNk+vXZy91FdwXgf28u50N2Dkh7wb7eneFucEW8ztrXNlY5urmSuXdRjzPxvW38+DqK3y7c4vvmGs8zCPW+xpMfgjv2qJ33zPwLmr08JEDvatxRoM2WPHugd6NdUjLhDSD6JS9u0k2XN/bu6WXD+Xd+QnvztX8u4BpzKMD8e5lI7M5q8l2kXDYZj3tspF22Ui9g7tZQjdL6NmMQZ6RWkdSPvbR+U5yD06W+j4UjHIEGqKae1s6oKOgkw6ZGz5gvrfOlc3HXN16xNV0yI3hkNmow+D621m+9l08mFtiuTXLQwcPehkqcGT5oKhFVrxd6VKbVS4dxb05FPNVbZn3cuYf9WwLPx+EUlD4Fh1gtcEWzrXaHxeLAltc2Oe+hpXLAWux6NGjjRU4Y4qYV+O0xpqyXHTEXPh2zKVuZNRRbQE3ZkC//rgNd7mZuo/duBVrPvafu7YPqnyFLZ66tFfSYH/vqtK5zjs3NIYwjIhMSGQK3xYubZqQ2HjntkyDlon8OIh2u1aHxMV0qM0zO9cfB0ue7pANJp07Pj7Iu0oFmLgW37Zfqcqm5tzSyzqIT6TJmfRhUZJnLL71dbbXtkAb/1g4rX0b1SKQqspFYDRtme8NvTZd9pReI7EZ68mOv8OW7BSy9UHvTjqsBb1Z0QYpJ7H+7lruFNaV1SIPkq6jDHrLTthDrWmUQa8JaJuITtBgJoyZDZrMh03mwzaLUYer0SwLUYdI1/JaNketv4V+8i3042/68ZNvovqbZCgeN5o8uPISDxbv8rBzhYeNJss4VtIu3Xw4tncLYYsbjTlm4pg0zceUM/6tdFNK44qavrrD1QLrMvB2VZBtceUJonrsRrFuFW66fXRWy5YrXfyvda1sds+vjdG66F3f+OppqNG/1O31ntO/03sdu73WDwND6ExNlruD04YOffBam/bL63IdLY+7CY17y4RvvIV54wF6pwuAnZ8le/WObxP40m1oHa0d9uiiYvIiwi/L0y7ZcJO0DH6Hm8X0Jlniy1myST7cJB1ugyqPimJUlVmjw5lCwHOYcA4dzflyNFsMc9VYBe1a1n70fxpl72vjYp2335oh3e4d6bNftLbUR/duzuKDr7O9urmPdzXoYA/v+mXP7l1fq2E7HRzg3TLZcHTv+gddKBraPJ93N95CP/7WmHtVf4MMxZNGk7euvMSDxTssd67yoNFkRTlWki474t0L792o8G48Zf24l9B4c5mw6ExOb9e8+8qdUf8XJ+XdZJNssF55Nh1skSXew3nh3zTZrh2VPbwbzWKi+T29q6NZdND2/zPx7i6O5N48Z/GtPy68O+HOItE3cq2ukoqYWoyrJ2Leysdml3vrDLKEJ8kWT5Mt1pMd1tLuWNK3myV086JdfZHwTavYV+MO1Znj+E22SCsaxhDrgLYJaQfev3Nhk/mwxULUZiFscy2a5Wpjjk5Q/FZs5uPdMtYtvTvYIgeeRE0eXnmZB1fusty5ynLcZEUpVtIu6+n4dy7WITcas3QaMWl2mOpih/tfupp3cba6sYezRZLZVv6tkh55zbnOd3KJq6UC9jm8DrxHlcYpU3mWmnO9W8cd7MaW7z4/H/Fj7+nYaURhQGDNc8W68cSycKtb9H9ReLfvm0DkVxeK/i/ukL90CxrTavVN+TyulsRx42PwzUMAsqw/incHW6TJBulwq4h3t/wwLMbJDs5ZqhsAxfmu9K4uY9pwBlW4VkczmHAWFc5gohl0OIMyTf9aN+7bMiFfJn3qNQe/+/YM7eRozUIO8q4kLArU+lM6//RnIdu/HtQQx+NY8yRSrEaKtUizEWq2Aj9sB5puoOkZw0BrhtqQaEOmDLkyWGU4XMUWC+QYl2OwhDgiHA0HTQUtNG00M8owS8CcjphTEQs65oqJuRq0WNBNdBAWJ6ApwX11ciqW1S8EtAGbojfeRD/5NqqQdW/tHiuB4UHc5mFzhuXZGzxozbEcRjxy+Vi2MlCGpXiWpcYcN+N5luI5luI5bjZ8OTa+I5sT7fwpz1GbO+iNLfTGFmpzqyrrje2qk50SFzd8Bz8Ls9i5osOfmRjbMtAAlfVh2EUl3YlxD5XsVGWGO8W4i0p2UOnBP1ynDURtXNSCRhsXtf10WZ4cV8ta0Oj4cthk4ALWh5aNxLGeODaGlvXEsVmMN4aOVCmyzO4KRMEdHAzWpieXj7bnwDlc7nDOVrZ1lBcZqro7utfr3YFBycXiRsvw899/tCD4ogXOR/buxhqdX/xHkKX7rjfNu5uBZjM8Se86QuyEdxVtTOXdGR2yoCIWdJMrpsHVoL2Hd2uunfTu5DKXodffQD35DvqJD5D7q2+yHBgeNto8bHZ4OLfEw9YcD/fw7o3GLDcL3y7F875cePhUvVv4VtWcqze29vZuvaO1mQa2Fez2btJDDXcmvNsrXFz3bheV9g/cVae09+ehvNuCqDPu3eJ1fWvYSCzrQ8dGzbsb59G7ExWrL7N3rzUNn/3I5fYuHM29anOdzi/+n5Du791pbGt4FKvCxZq1SLMWehdvB5qdQNM1hr4JGBYuzpQhU4G/wD2gaZr/BuZocl/VvHBwDDRRtJWmg2ZWhcyqoPBvzFUdcyVocd20iYKwlgCflnjRu2822gS1fg+99jpq9TuoJ99mZ/0+K2HAcqPNcnOGB7PXWWnPsxyEPHY5Wc27GsW1xgxL8Rw3GnPcLMZLhXvnwiZKqVPw7nbl2fGYd594d36m6MB9BtuJavHuAJIuatgtxuNxrXfxyLuVg/ODv1fORJVbXaN0bgeiVjHdwTVahXvrfu5AOT9sYoGdtHBuPd4tXLyZODKlSdJ8SjLAjc/bq1yN904qOGsht7jc4qytHOt0UfukjHeZdsF/+ViINb/45472lKsLm7DY3t7mU5/6FL/7u7/Lb/7mbx759UcNnB93H/CJP/ws2zYkww85hgyNdcZXhTp0xyPFI3mwBLVkQ+wcTQctBx0LMxZmrWI+h/kcFhO4limuJY7ZzKGt9QLKa3eq8lq1sNz68jHgSEH1yFSPp42UB03Fw0bActziQdxmudHmYaPNVjjeW+5c5riVKm5miluZ4mZuuJUH3HIBV1yANkHx4OF6kF6cPEwA2tB55S7rneu4+cV9M/PTd9yhev2xgHhU3kJtd1G1r7gzeqzX4arn4bL34fiEnn5hcy/3MsFRngDqJ4My0TEsgvJkdHJIhwM2bMiGmWE9WGAtmGc9WBgvh/OsBQsM9e47adpZZtSANkOaOiUKnK9mTb19ddmSzxd8u7jRvQxdrKBQxbrFuFjHj33bZYpyuU1sCoMu9Lsw7EParzrdcwFYU3R86ob+rkale+vLzqF0gDERJogJTIwJYnRQHzeLwU9rFVC/eVq2ZRxrsTi1rbmq1q9/HevT016/a3tKja37PXdmmD1E4mrs/3bKgfNpe3e1t8zf/8OfZysf926OJj9h787lsHAOvbvcMDyM2zxstHgYd6Z6dzZz3N7lXcMtF3rv7rpjOhG0m4DOy3dYn7lx/N7d3EZt7ezt3TmfEC57fLfzs6fn3dr4MN7Nhn02bMi6mX0u77YK7zbOzLs9GPYmvOuwxh3CuwZjGoV3G5igeaG8++7bHeay8Qu1g7ho3oWjuXeYbvOr3/gMD/opQxUzJGSIYegMA6cYOsXQQWIdqYPUqZqLD6qiXnq46L/JWRo4ms7RtI5O4eG53A+LmR+uJ46rCVxJHIG1o5oAZc2AspzvbnZ2VLx3uwx0n5XY8rAJy42A5bjNcqPFw0aLlUabbjCe5J5LHbcSx80UbmaKm6niZqa5aQ3XnCHQo3i3qi044eDWS3fYnL2BvX7ryE8ZwjlUt4eqJSR8UqJIBG/tjKUbz8y7WVK5tEos15LMJDXfDncKL4/WJ+nRy2AtmGcj8H5dD+dZL5zr5/ll68E8udp9M8KQ0ynjXeMTuWXdrsqjOJRSVddK5TJdc2i5rp5wbuXpcn41D9+0p99F9bvev9kA8B2eutBhje9gGpvg8iG69C3WOxfr+6AIxr0bBA1M0MKU3g2bmLBFEDTRRY1MXfivfFBNNS4+o2b8vIMaPSdm39dU80bnqamvKcbvvD2L2znemm2nkrBYX1/np37qp7h37x5RFPHyyy/zyU9+ksXFxQNf+7GPfYxf+qVfOvJ7HjVw/vLqf+K1P/4d/L8wQ5GhydCk/pnrpAQkBC4lIqOpoWMCOiZiPmyyGHW4Fs1xvXmNxfgacTRPM1ogDucx+gQfiVM28K0H1fm0IHvUxri//ZBHG6+z0l1hJdnkoRuyHBiWG21WGk1SPZKocXDDGW7S4JYNuWmLwDjTLGWKTuZG7efq75NPnGzGllnIs9HJp6xO1OqQ33oJe+sl8lsvky/dgSiCNN2diNisZZDT8VoxttPCzc2OZ47rj6I7anB+QuTOsZ36O2/1O3NV7YhivDG07OxR8aehMiKdEpKgiw6MLAkpKQkZPZVjddGD0vn42MeKdl702jE6GeF8J3Cu7Axu1L1S1TlcsVwpRuvVZV6tP6U8sZ4uImY9tkxV8/06iu9enOdH3v3njvb5niNwvgje/f21P+Z//aPfYuTdHE26r3fbJmBGR8xHde9eLby7cPrerXw3PbmBzQrvLvN44zusdFdYTjZZdgMeBsGB3r1pA27ZgJu55nZmns+75fSYd9vkt14uvPsS+dLdkXc3t1HrtURE3cGT3m23xh4BOubdToujPnr5pLDOsbWHdzcTx3pi2Rj62mk76fTvcqQyGmPe9Z3dld7tq5z8BfcuTPiW+vzd3q3ms4eHJ/1cXkgw7t3RxYj37jsW5vhr3/MDR/t8F8y7cDT3/t7q1/jf/vgLqKlfzpx6DByQEyhLpByxVrSMoWMiZoMmC402i9EC1+Mr3Grd5Hq8wGzQPN6nOUzDuZpfJ+LeKrmRY9OUtc17PNp4g0fdFZbTLVbskOUwZKXR4mk0/hSghoMlQpZUzE0XsmRDblnDUm64mRtauSvcPophy/falVgZi8mLPiqyDDX0tb5cEJIv3fEx7+2XyW+9hGvPwDBBlwmIyYTw5jYqm4x32+OPXJ6bwZU1hM9RvOucY5AzimsT71/vWstm4dxyXjblq6xwRColVAmGBB/vpmQkDFVOovMiGZUC+aVzr3JuzLO+XDq4KDtX8+gU71ZjN+ZarUbLqnWLTjxHPi3XqSV0ymROMa9c9vbFBf7au/7skT7fuUhYbGxs8PWvf50Pf/jDAHz6059mc3OTT33qU9y7d4/XXnttbP2PfOQjfPzjHwdOL3AGuHKlzepqt5rObcog3WSQrNNPNugnawzSDfrJup+XbhTL1hmkG+Q2mbrdKJipguhmGVBHCzTD+SLAnqcZLRKH80RBB/WcgrHOsZbssNJbY2XtO6xs3OdRb43lrMey1qyH422qOs5xU0e+6trMTW7O3GCpaLZxrTHzXJ2+HLyzlsV0k+2v/AHB/dfRjx+ge77jFwegYlQeAg1wDSDAheHU2hFe0jMQnuCFygE45+hl1ALhUbXgpwPL2jBnPXFsJY6dlKnVb5WyGJWBSrEkZC7BqQSnUihkPCp7eTWUJVYpkRoSMiB0PYzdwdhtQtfz8xgQMSB0A0xVmbEebo7GozbG9cv9UXVi9linLPt+jXxbRa88jSrKqrhjrtSoA0Ll8NWXc4fKHbhChUoXdyqCom3seCjrO0VlbF45PbnM4YPkUU/RVZ/LterZozLV52PUyV8xo6ruR21+MW1r82ztmP0J+vzt/+y/P9L36XkC54vqXWuzmls3GKTesbu965ft7d1O4d3SufM0w5p/o/lq2XF5dz3tstJbY3n1OzzauM9Kf5XltM+yhvVw/G5Wu/DuzcK7S50bVdO5U/Futs32V36f4N4zeLdWS+28ebdeLXh96B9RuPoM3s3xvfAf5N2GSmmopPBu9wJ5V6GcKryLT2jt491R2DrNu1SvHVvmwCm1y7vl/k96t5rPuHf9/3i3dylOFft597tcn5/8zy+3d+Fo7s1szv+7/QdsbveZC5vMBREzytF2Kcr2GKTrDJKNCdduVP51bnoNs1GsOzfm3Diar+LfuIiFG+EMWh2xhsEUtrMBjwabrPRWebT2JivbD3k03GQ5T3lsDGkteaKc45qDJROz1Frg+uxtbnauV003FsLWc58HDuKKHrL1xa9i7r+OefwWamu1qo3mCFE2YuTdCNdojGLcWkK4rClBcLbPThjmrop1S/c+GeQ8HeSsFcmJ7RS6KWRu2rF16Mq76ZhznSq8WyUhMkKV01ApEUMC1yegS2B3Cv8OiehX3g3dgAYJoQFbPEJ0unN3+3dsPbXXa5i6ncl5o9Sq9zFOe/c6UBZw1W0unNYoVdSOZFr6VlWuhZFb68tGcW398zA+7Rj7vPWYeLT+eIzrXP0zF8vU6LPaWvll2+V//ov/w5G+Swd591S+6fPz85W8Ad773vfyuc99DoCXXnrpmQV9rPSH5P/i3xJvdKldsdAenUUL5sDNAi9XL1XOt4RKVUKPHn3dpae79HWPvur5surR0+ts6ges6B4DPb1trXaapm3Rsi2aeasoN/24mG7aJiaP2dQhj0LHgzBlxQx5GPR4GGYsR4qkJmntHNfTPreSlD+bKG6llltZh5v5HLfTmJm8HhhvgFsfTY71JDZxMnS1QlFWu47X6HWWjET3SIsh0X1S3WM1HY623QAXW99TnbZABtkQiiqALghxnRlcexbXmcG2Or49Yg8/vLVr56ry9NzctM+3e73EarbyJquuxRPbYS1vseHabNkm265Fz8b0XczQxbgp1dd9X8kjAUOKM6WME1AJDdWlQZeG6hG5ASH9Qsg9jOsRFiKO3IDQDioxByQEGEIVEBASqYBAhYQqINQBISGBCvw084TKEGjLoLtGmmyRpdtVAOIoA0A/6KCJCtuoqI02LVTY9POCGIIYZRrFEIEJfQc/LscWgy9ntfK0cerXt7V56RA37OOGQ1wy9M+lVhYXKPJAY43fT0u+6/3GmDw/nnjGXaGVQSkzNtZKc/vq8T/maT8uhHcHQ/J/8bvEGzvUPdJ6Ru/2dd23ftw/snebNG278m4zb/r5tjXm3YdhynLh3eUw4+EU715L+9welt513Mra3MznuZU2mM3rntg4Ye/2SXX3mb1LEGLP2LtPC++uH5t3U1BD71t2iJ/Tu6EKCI/bu2ELHbQr7xLEqGPzbnYo7zplsUaRh8/g3cnxibG3d29eefdJv/kYF8G7QZLxX31xm2RjB9xGMddN/ATnwc0Dr4xmFc4dquHIt8p7tYp5dZ++XmdLPxw5d8r/XzlFbJves3ndtcV07qdD22RLRzw1sBLlPsYNEpbDIcuhZScYT+zOpglLwz5vTzJ+IDHcTBvczGa4mc5yI2sQutHFGCxTPUfT7eHR4nNPzDhwHedyUt0nMb0i7u2T6B5r+ci7TgML4EJdeDdFZUNfI0PhO6vszOA6s96/nVkIQujihweTfp2yn3stmuLqkkGueWJbPLVtnuZtNmyLTdtk28bsuCZ912BoYxLXwO5xGVkmGaqxLpMQCaHqE9ElUltEbotQDQhcj4AeoaslefHOLaebZDQqz/oh0iGBDolUVJvfIVQLhCokwBAFOd2tJ6RJ8VSMdGeXcx2ACdHRjO/gN+z4cthGly4O296/QQsVRDilsTYb823lWJuNe9Zle3g3w/V7uO4OrtfFdfs4cnLlsA1D3giwkcGGutq+ddmYf8f34XmfR/tsKDRaB/7xvcqwtPjOY3+PU0/NWWv53Oc+xw/90A8duO4nPvEJvvOd7/Daa6/xoz/6o9y9e/fQ73PU7Phww/EvN9fp24zQ4QcgcBA6RwjV/ABHhCJwEOGKdfx6s6rJomtiuDpeFWsia5sr68Vu+vR0j57u09c9urpLX/fp6h6rQcI3zBZPTZdt1aCr2+yoNl3VYqDGq7KFNmMh77KYdfkz232WMsftLOAlO8tL7gZz5lViWv4OSdkkXEF1Jql2rz69z7Li8zjnyBmSsE3iuiRuh4RtUrvjy267GO+QM639vkJT9CpdDhPHS6FwzrenxVqce4jadrBd7Ez1qCbfC7Eq70yOHfPRvg8J6asGO6rJulpkQ11hQy2wzTw7ao4eMwxok9AmJSYnxn8bdjMu5R5GrRLRI1A7RGzTYJMG6zTYIFB9jOsTqtEdt8gVwa9LUIBxihBNgPbBcFEO0YTO+LIqxnQImSdAo5UeXby4KVGisvhnSfvOkEwQMzv/KmFzgai5SNRcJIzn/bi5SNRcIIzn0ebs7pqWuNzi7q9gv/kG+TfexL3xwFf/DAz61dvot7+CfsfLqDs38D3I59UJoTpR2FpwbUcnjNxlOFs72djxk8Do5FPfTjZ1vdFJaGJbxWuuzL7KtWtH64TouDi33t2Ef7m1dmTvhrjR+mPeZV/vWuXo6z490xvzbq8Yxr27w7aK6Wrv3B3V3te7H94ecDOz3M4C7tpZXj4V7+5Ufh337si95827XdVkbV/vtkhpHtm7IT1C1SVi65m8W3r2MN4NmCPEPLd3w3iBqCyfR+++tYL9xpvYb7yBLb1rCu++42X0219B3T26d0fePIp3J117OO8uzrx86b0LR3Nvb8Px2axHr2kr54bOEaC8d6t5pXshovAyitA1CIgJuULHwoKliIeLdV2hOqWwWIaqT8/06akePVPeyPPJjq7u8TRM+ZYesqZTNvSAnh7SVQldldInGXOKcTmzeY/FrMe7+z1upAOWMs2dPOZlt8A1c5uWuUmkYv8hqgrFqpZAm8igqCnr7OFhR+5jXXZI7Hbh2W0Su03qthkW81JX3PyceCOFqVzuda/riynexCeLHWBXoOt8guJRse9Ko8onYWnNuHP8KEPTVzEDFdNTDXqqWTl3S83TVXP0mKVPh0S1SF2LjBhHg70uDR1ZkfhN0GoHo57QoEuotgndFpHapMEaEetEyid7o6LmQ5n4Dd2QwCoCFIHThNW4cK5TBBj/uNDKwwEBM5Q1vOpHE4qmd/R3nXtyigZOQYOouUhn/nu8Y4uYd9K9JmyfeA2bg3BZjrv3kPwbb2K/8Sbu3kNfCy4M0N9113v3HS+jbl5H6en7usuXhWvzIrGRj83PsdbfNNy9vPRvNrGtyXV3L1/o3D127556p5uf+MQnePToEZ/5zGdOtJ3bUasmL/d7/MRX/h3D/Hg6U9MoQq0ItPbPRVeKUOvRtFYY5Z/hntmMzGUkNmNoEwY2oZ8PyV1OPf/XcjmdrE8n69GxA9q2T6xzmkGODiyZTsnpk2bbaPKiD46qCzuMUrSCDq1olnajXiW6aJJSVd9bIA5ncGmPbLBGNlgjHaySDVZJ+6t+3nCtKK/iplTJVjoiiK8QNq8QxIuE8RWCuF5e9NONOa5fnz9Sr8nOOfrdLfoP7tF79JDu00f0NtfYAbaCkNXmHE9nrrEez7Fl2nRVRD83JLkmswZc6AdC9mrDqVWCUQmBHnjxqi4Rm0SsEbnHRHaZJk9o0Bu741ZuLQo6NIIZGuGsH/Yq16eDmarjnJPmRHuqPmmSFHN/uXik1FuYx6sAuLhB9vLt6pFSbmF2d2ByhjzLMT+uzt/Oq3cfDfr8+O/9LoNz6l2nHG1berdPx/Z3eTfXGRld0mxnqne1gnYwU3h3vtZMpWgSWGum0ghmIOuTDVbHvDsqF94druHy3R0JTno3aCwW5Ss17y4SNOaf27u91Uf0NtbYZuTd1cq7LXZUg36uSXJzJt6NinG8y7szNMI58e5R2dO70YR358S7BaflXTiae9eTIX/va7/H6mBAZi2pdWNPGjouKv+qUZt3h8U6iyUnszmJS4uaUKNYt4GjaVOa2YBmPiC2Q2IGNHROGFishkwnWNvHuWHhXDvyr8sJdUAzbNEKO7TCDs1whk44S7sxSzucp9OYp9kY9XmklSYbbJANnpL2V0kHT2sx71PSwRrZ4CnZYIPdz3VQBPFCzbNXa969StBc9OPGPNdvLBzq++icY2BzulnGTr9H7/EK/acrdNef0t3eZMfmbJmItUaH9dYiW1GHrmrSIyTJDdYF4EIU4Wg8laxw7pBQ9YubbVuEbp3IPabBE5ruCS2eEivf9MKQ+kSvjiqXes/Ojfk1DmeJKgePnHui/UtN4UJ7d5hg7j303n39Lcyqr4lpmzH5K3fIX7ntvTs/e8Y7Os5JePdUExaf/vSn+frXv87P//zPE0WHezbts3LUwNnm0H/UYG2th9UOqyy5dljlsNqSKV8lPdOWXFlSlZMqPz8lJ1OWzDlSa8mcJbOWxFp6ecpWOmA7H/rnS2cJ/TxjkGckLmesLZKvTOM7LSncbVFkJxBMqVLuLq/JPiuEbyvpB8Uj/oKiHOIIla6eYRybJnHYohnO0IjaRGGLuDFDGMSEWhcnK+0vItTowiHU/kRmlKY52+D+k012spSdNGUny3w5G5W30oztBHYyxSDTuELGXsSRH1fB8O7AQJETMCBUPUK9Q8hWFQTHrNLmMS0e0+YxgRolYKJaUFsFu8FsJeKp84POqQXAz8qFFvgEqturnoUdvPEWemsHADs3Q/bK7ULqd3Ct5gFbOlnOKnA+19610F852Lt5Ua57N6MoH8q7Kf08PZJ3c2Vwx3zhNd27Ew52PtkR7OHdyIQ0TUwctse9G3UIw+aB3i2nx7xbOjc9Le8+IeYpbZ7Q4hEtnhCqUQLGJx5qSd3Crz4Ynq0tmxmVxbuniur2MG8W3n295t3ZDtkrd8hfvUP+8m1cu3Wm+/kieBeO2IfFAB78+5B+N/OVpAJAF9UitAPtcMbhtMWakY9zbf1YuSIW9rHvkJxN22PddlnP+6znPTbyfuXf1OaUvgWFUYYGhsgpQptj8hyTZ0WFBB/zpibyY+Uvj48npT2OckVSuX5jz2UYcoyzBGQEQKgUkTZEOqBhGsRBTBQ2CYOYRtgkCptEOiDQtST5VO8qonbE8tp25dluNhHzphk7maOXKvIy6VA61kUTSYiAae1ttEsJVI9I+doP3rlPiVmjqZ7Q4gktVmmwgVEJShnicK4W6/rEQ1xP7O6aN0dgTugpI8fMpfLu9g7mjQdV4lgXT+Kw87Nkr/pYN3vpNrR2P7XqNLnQCYuf/dmf5Stf+Qqf/exnaTZP/sLhqIHz8EFG+E8eoJ3FKv/M3NGgd5Xzyfn49p2Zhkw7Em0ZFL3WDrUl1ZahyXEBqFBhtCUmoZn16AzXmek/pZX30S5BGVCdOdTMIsxdhblruLmr2EBjlfMBfZE4yZQ/aaS2zyDdIMk2GSRbDJNtkmSbYdplmPZJ0j5JNiDLUzIVeD0r3wd0pgxZ0CQzDTIdkqmAFEOmtO8r2ikyV1StcgqLmRiCo91RcVB2tggaXFAIOaqJOcQU084FuKlV1Bxh0Q4udGvEao0mq8SsErNBQ60X1YLX6diUZqaJU0OcB8SuSdRcpNG+RjR/m3DxLo3m4lggHAWdY+kU6jxymQQ+hnOo9c0qGx28+QA19Amo/MbVKhud37156p0EnkXgfN69mzzMCP7JA7Q9nHctilxP864j05Boy1DlDHVOoq2fNjk2AL3Luxt0+k9p570p3r0Cc9d3eTdXtkqkeO8OGGQbJOkGw2SLQbJNku4wTLoM0x5p2md4BO9myhTPphr3bu4U+al5Nyy8G+7j3R6h2iRy68RqjZhVmjw92LvENBqLRJ3rRPO3CBdfohEvjiUmxLsXkLp33yi8Oyi8e/1KVfsiv3sTIvHuSXAU9+ZPM+LP3kel+VgHpVbBqAPTekd6+HUKB7vavKqjvWKeVaPt5cphtcNpB8qiyDBuiLYD/JNIUp8giUJoNKAZQ6uD6rQh1KhAoXzGABUoXAi5ceShw6rcN8NINxmmGwzzzcLD697Jmfdwkvb9rbjSrcqQakMWtMhMTKojMh0WMa8q3OtInSOzYJVPZ7jCu2XZqbB4gLap0s5jTPoWBU6Pkg1F4iFQEbrwr7MBuQuZloRQZERFwjdmnabyCYcG6zTUBg02iNnwjTysJR5CM9HEWUDDNWi0rhLNLhEtvkR47VUa7WtV4iE0zTNvCnGSXGbv6tV1H+u+8Rbm3kNUkvrf4dI179xX7pDfXTr1zlkvbMLim9/8Jj/8wz/MK6+8Qhz7rM+dO3f4uZ/7uRN7zyP3Vj/ImfmNTQZP+xQPnkZlDjKLSy02s5BZVObQ1hHkZfupZ8fCREBeJkMOEbjvWwarcqzOsdrijMVphwuV71Qt1OgwwIQBKghRYYDTCqcpMulgFeTKkSvIauMEy8AlDPLEj+2Qvsvo25S+zeg7R99ZBg6G1UOxIiwhthhPD4JHBOxUIo7V+qjMJu1gwGyYMxvmzEWGZtjZo9rvKFMcBW3fvtpa9NMVzIN7mOV7mAdvotefAr5zI3v9pn+sX/FYVTe/eK6qth4nl1bgk1iLXnkyqsb81goqtzijyW8v+UD61TvYpWu+Lahz/oeZOf/EksIBVTl3flk2Pj1Wzm21zmgbjtarM2z86faRdv95AucL493f3GTw5LJ412J1tr93gwAVHuzdXDvfOneqdxP6Lt3Du4qM8Bm8260SDVO9G2TMRnbMu6VnK//WHOyfvFJ69xHm4ZuYh1O8e22JvHisn3j3kmAteuVp4d37I+9qTX5nqUpg2JvXRo+7zSeduYd393LwXt59ZYaN915u78IR3ZtY5r68Q/9xD6xD5ZDlOcM0IUlTkiwjSzOyLMflDpc5jPV9DQTFOCyGwGmMVb5ihp12qX0y+MpwRcKkSLSMJVQU1WCL5946pbBaF95V2Mq/ilyD1X6cF9OZhrTwb6L8MFSWoXIMsAyUY6hgoBx9pRlozUAHDFVIqgMyTRE7Kz/WamzaqoxAbRCqTUI2aOiRd9tmwEyQMhs65iKYjRrE0QyNYNTsYhT3zhbObY/6EnIOtb468u7DN9FPVqonkuRXrvvHWd/23rVXro33p3GJeGG8m+foh48JXn+Aef0hevkJKgcXBNgbN8hvLZEv3cDNzRbBRuHT2qD8HZJx91bL3Linq2VAblHZ6LXR3TZbP3z1SLt/LhIWZ8GzPF7vkdnkqw/vsTLYZHmwwcpgk5XhJtvZeKdlc0GTpcYctxvz3A7muWlmuWk63Oj2WFxbIVh7iFpdRq8/QiUpFHeraF3HtW9im1fJwxmsibGZxQ2HkKTYJMWlDpcBmUblBm1DtA3QLkA7g7F+0McQtHsp1+9WKh9AqtH4MMH6rtomtelMKXKjyLQjL+6E5irHGosrHh+nTY7RlkA7IgMm0BhtMCbEGB/gBybEmBClFco/tc3HtNpV5bFxkdCur1sfU5bTAWZ1hWB1GfP4IcHTh6h0iMJCHGOXlnBLt3BLd7BLtyCOxrZ1UTk3At8rQTARmE4GrqOyrQLTQwW3WY7qJahBCknu5/nWmDgf0Tz3BXH10RT+QtUoXKAI3zbD1l+7caRtHFdb6tPiWbz7ONjiqw/eZHmwWTh3g+XBbu/OBk1uNua41ZjjTrBQeLfNjW6fxbVHBGsP9vHuErZ5bdy7SeHd4aR3NdpGJ+pdqzS5Pg/ezQk0hAaCyrtB4d3whL37iGD1IebJQ4Inde82cDeWsDdv45ZuY5dui3ePm4MSswd616EKT+/r3dyh0tK7qa91ccreDV7tsP1fLx1pGxfNu3A096Y249c3v8bXHj9gZei9u5WNP0GpaSJuNua4Ec8Wj7mfZSme40ZjjhtBTLz+FvrxN9FPvoV+/C3002+h0gE4hdMRbvFV7OKfwC5+F3bxu0hm7jLMLYPeBkl/h2F/h2TQIx32SYdDsiQhSzPyNPe1am0DRQNlI5RroGmiidFEaBf57nGtKfys0dYnTIwDnYNxrph2GAdBMdbOVfO0c0ULGP+Y4nJaFeXTVo0tOuN0tQQLyidVnC46ASmSLb7ixngZDRjly6acV5SNAiwq6aL72+jeFrq7gcqGKGVxgcHOzuLm5rELC9j5RV/zxfj3r7ZVlsfmjy93Ru2a7+r7Vn/dKQj9WLxrXfUI6OpivXZBv/fFfC0RkNniEdKHTQSUfmX6+lMSC2qye5VjwpnR98iX1ciz5bQZeTd+ZYbNH5g70ntIwuKQ3Out8mO//88AMEpzPZplKV7gWrjAlWiOxWiO+WCW2bCDznLS9WXSjUekm49Jt1dJuxsMMPRNRD9o0o/nGEQdBiYm0SFDZ0gcJFaROkNKRKYiMiLSYpypw7dz1M4RWUdoIbKOpnW0cbRgNDhH7KDpHLFzxBYaxTgqXl9uI8wdYe4IrCPI/WByh8kcOnPo4kehyx/Lc+CgyGz7ABvtnwFcPmLIny5G4zKTzq7yqFpiuQxVe257vVz0/lw9v32P11XbVrX3r5VBMfZUrFK4iuokU/UArRidVIoTTX1e1aTTqCrIL+erQu5O4XsC1rXlZeBfjsvXm9FyZUavqcpm9F4Lc002VruoCVmqbDRdCdmW8201v/66sbKdnLaj9e1oPLbuM32LduMU1Z2T0d0TPWVecQdGK991Vp7jsswP1nc9ZgODbUTYuIGNI5+hVsXry2qwE9NW61G5CDSqp585uHIn5Np7d3dQux8XLXA+qncf9Nf50a/+U2Dk3RvxAtcnvRu00bkd9+7WKmlvgyGGnonoBzH9eJ5B1GFoYoZ7ejcko3E83nWOthv3btM5mlO827DQsK5y737e1Vnh3nz0m9TnzLtjPlV1d15s77rKt8fkXY0P7BQszDfZeHoI79YC0X29a4vXXzTvKiDbz7vevcfh3cXbIdffd7m9C0dz79PhNv/jH/wLDDFXwnmuRLPMBTPMhTPMmDadoI0hILWKZNj3Me7OOll3k6S3TZoMSFRIoiKGQZNBY5Zh2GaoGwx1SOoMiVP+9QRkLsA9x917DUQGIq0INURGEenRdMMU87UiMhDq0fJyuqGL+cV2Ig2hGV+vnB8oRajwzk0VNnE+qZ06XAIuc9iiRuAo2e1rorjUQQbOt6GufuOuaN8XKEWW5v7C0jqw+N9mUcaWT2YC5YrfbZFEUdXYeeU5t2u+OuL8s65TUTq6HFfJjV0JktLNtQRMkUh3ZkrSpFZuNkP6O8nhEwE1H594IkArXHEOcWbkzDIZtWte4b9RjaGRV119ec2XlTuthf4QBkNcf4DLrfdmGGBbLfJWE9tqYoMAWzUHU9jiJO+cz3VTjJ0dlSfnzS0FvPzn0yMdi4O8e757qDpFmmqeV9TfZHPgk2BrO7A2dc2yY7BrxQDMFEOBdhkBafH4npTI5YTKEmlHI1TMGE3DBDSCgEYYEYcxcdSgYfS4UA00JsTa2EOwRp9MlrJ8LNAYdjzQIrW1O9h+un6XRqWjqpqj+RZVVP+Oo4BBP60k7aNVV0bJo7tBRVnZyfnW/2DsxLq2tg3rqg71qm3XlqvJ93MU65cni8vH0e457cYxUbUeNRY4jt0xRo+CzUBhw9o66InXqENs19+ZtmMC9/+lKmFfXJtUFyhMmZ5c11lUlqOyDJWl/jvSw58cwwCiwI+Li6Wx70Y+vr3J7Z9AJ+wXnljN8qr6m2xMePePdq1ZXnDUvDtbDAW+k+DkCN5tEEcxkdHjPj0H3rXs7of+yN6dts5e3q3789DedeLdZ+B4vevdKt6l9kbi3YOwtk2y9SMkFjaB7+xao3xUZMkV4ApGZYTtlKCdEZAQuJTA9QlcQpCtEZAw6xICEiIFUeCdG4UhcdCgEcXEUUwctYkbHZpxhygIaExLRBQeDjUEJ+TZAwkVxHCcJnqWu/3lheDYkPtxvsf8crCHmV8kV1wOJClqpwc7XegPUP1hcTIyOB3gwhhnGjgTgQ7876tsRjCZdMk5MIFS1mTZlVhxvlaCyorXTm7H1eZ7C+75Xn1VNtlUtf5a6n7To3nF2BY1B8pakHUfjr2+Pv+Q06P9gGeuYVKdY/dZRzm/+SKPWybuVQtoF8cqz/yQZahNi9roQ2C8cxsBRCHKqPHt1GpSjm27Nm/+1vGnwiRhUdDUKe8332QnXyNIN9H5NibvotXQi5mE0CUEDAldQqQccdQibs7SjDs0Gx1a8RzN1hxxc/QITxPOXL7ObMogJRyPBZ41Lpi7NsPWeagmexBloJ2XfWHcx6y8hXl4H7WxBk7hlMFdvUF+4w759dvY67dxM3NUtzGrAL0I5CcvEurBfhm02/HX4YoTjHU+m2mB3FWZzapswVUnkNr6zhHHEf00wxVVuspqXS5QqGLsjIJAV1W86tVsMX494MAAtUx8w0QgWX/dlO1Uv5op607/SR1HZKrwWgzARuhHT0ePk3preUo77NvYm9dH7bD34dq1Bk+eHO1O32UnVhnvN99ku+ZdnfcwarCHdyGOmsTNGZqNDs14ZuTdeJGgcK9492Aupncf+fbYy1O8e+UG+VLp3Vu4mfmL693CvS+ud1cJ3rjv+x26v4LKc+/d2zdG/V/cEu8+K1cair/7J9Z4ev8bmO5DTP8RavgU7XqgU9AJSicYUgKGPjFB4rudDGcImsXjOqtHKF8ljBcJmterxyfrC/IEiYuAUhQ1ZE/0XYpxVAzzftJZ9OoT3/dQ2R/G6mO/SCnfB9FLL1f9ELn5K2OimEy0HCqBUp8ul0+s73JwTk2fP7HdKAxI02zvi+yJ+dPm+fmuuug3E+vuu50yUTA23xXzxpMKu9c7aDv7v+ZgSu8eor+3yX6H9uHatYgnT3Y/ev15kCYhBcnyV/jW7/xPKAdRDqFpEjQWCNpLmLmXCa6+k2Dm9kjGwdk+MuYycW7a9T4P/W7RsZHvVM4s30elPkiyrQ75rZewRady+dIdOIXHnO3HpTjmp02aYt5aGT0+deWpv4HXiMheulV14OkW53efKZzj6rUZnj7dOdJbXrSqyUf1brr8H/jWb/8kAGEOoW4SxnXvvoNg5nb1fHvx7vFxKRzQ72Ie3q91LHev5t029uZLow49l+6Kdy8iaTbh3Se7vJu/ege7l3evdni62j3SW14078IR+7BY+Q/88e/8ZC07BYaQMJohaF73sW7rOkHzahHzXiFsXpVExDFwKRww6I28W8a7ib84HfeuxLsXljTF3F8uEhgPMI+KjrIbEdnLt6sbdlPj3Tz38e5a70hvKX1YHJY8Y379D9nMW7grr0AgUj4tLqVMznnP+JfymJ8maYJeW8V85x7m/j3M8iN0vwvkuEjjWg3/dAgsathH9bvo6zfZ+m/+9pHe5qIFzkf2rs2YX/tDtrIm9uqr4t1T5FI6YMK7+uE9zNoTYJp3d98NPGku5TE/TdIEtbZG8PqbmHv3MSsr6N5+3u2hr11n67/9ySO9zUXzLhzNvS5PMW/9P+xkDYLr7yJo3ZBExClxKR1gLXr1kX8CX5HEmPoEviKJ4eYWxLsXBWdhMECvrRK8fg/91luYR4/Q/R6Vd+PQV9SwmffucIBavMrWx/+XI72VJCyOgHypz4YX5rifo1oYL8wxPwxZiur3UP2uH/e6Rbk2PajP7/l21lNwACaEXKF8L1LYuIWbX6Dx/vey/p4PHmnXLlrgLN69OLwwx/0c1cJ4YY75YTjIu+Wyw3pXh2Br3m2U3v3TrP+pDx1p1y6ad+Ho7pXv4tnwwhz3/eLddsc/SrUe74bhie3KC3PMD8I5n/SdFuOW071uzctFeY80gdPG92OS4b3rDLbZwi4uEn/v+1h/9/uOtHvS6aYgnBeabfK3fTf5277bT0+phRF+6z8B56MWxoUkz2qBbi0YHguEJ6Sc7t2+2cVNXLONa7ZwM3PYG7dG081WUW5jm21cqwWNpm/f55zv/+L1oj3g/WWyf/sNOGLCQhCE56TZJn/bu8jf9i4/PaUWRuPbvpvX81AL40JS9+4u/x6Dd6/f9NOt9uG8W+t3KPvtr8MRExaCIDwnu+LdHP1kIt795n8EwGmNvX5rPN6dnRfvHkSWHZB4mOLePJu6Kac0rlVz65XrNecWQ225a7ZHyX1ri36HavHu//eH8N3vPdb/odSwqCFZuLNBjnuNU6qFcSGOuc3H77RNvQs3IeRk705+XCMeC3ar8kQQPCo3QR9TL1dpxtXFFk+3L/fj9cS7Fwc57jVOqRbGhTjmNkf1+3sHwmNOPoR3o8Z40DvVu3X/HqN3s4yrC5ffuyA1LC4KctxHqO6Ob7ZXenflPir1tahsZ7ZKXuS3X8LeuA3Bs9XCuBDH3Fpfk7ee7J0W79aTvvt5t5b0ta3OuGPriYfCzTTi40supBlXF5o83ZHHmgrC5eWy1sKwFjWYEgRPNrWoC3nY33NzLoxqQXALu3B1byGXQbA5Q92FASpuwBEDZ0EQToHLWgtjmnd3JSEusXcD8a4gnFdcu0P+9neTv/3dfkaeo58sj27YPbxH+I2v+XWNwd64PZbEcDPzZ7fz++EcJIO9Ew819+reDqrfg0EftccTl3Z5d/HaeNJ3MiFxnEnfZyEMUM0YjpiwOAhJWAjCeUb7Dovs9Zuk7/0zfl69FsbDe4Rf+z2ir/474JT6wig64dnV3GKiCrDud6Favo+Mg3DsrpudWzzgLlzrmTPtgiAIB7Knd0e1MMa9ewp9YTgLw8GEZ6c1vahP7+fdYOwu25h39/CveFcQhBPDGOzSHezSHdL3/1kA1M52LXH8JuHv/3uiL/8OAHZmrkpe5Ldext64dTIJ0jQ9IPEwpe8Ha6duymkzlnzIr9/aO/HQauHi9on273GRkISFIFw0jqMWRslkJvig2g8HdcJjzHi7t2s3dze5aI03vyA820deCYIgHMietTB8leZD1cIo2cu7e9V+OKx3yztw+3rXj8W7giCcd1xnhuwdf5LsHX/Sz8gz9OOyFsYbPnn89T/w65oAu3S7SGJ497rO7PgG83zCqxNJ32nuTffq7FeN+jNrtbELV3C3Xpri3nK64xPZ57E23gVAEhaCcNF5hloYw5kZ2js7B2SC9XgQfPXGrpoOk8EwochYEIQXgDHvftjPO6AWxnBm9jm9O9HkQrwrCMKLhAmwN+9ib94l/d7vB0BtbxXO9Y9UDb/yb4m+9FsA2NkFhvPztLe2Dm7yVu9vpz2DvbY0NfFgm53xzn6FU0ESFoJwGTmgFkZARnbj7j6Z4BZEx9gJjyAIwmVnWi2M1UeYB74WRuD28G7VBEO8KwiCcBTczCzZO99D9s73+BlZhn78YORdm5LfvDvR1G13zbMz7W9HOBD57wjCi8BELYyZazNsnvdekwVBEC4yWmOv3cRe87UwxLuCIAgnTBBgb72MvfUyKX9OvHtJkLosgiAIgiAIgiAIgiCcOyRhIQiCIAiCIAiCIAjCuUMSFoIgCIIgCIIgCIIgnDskYSEIgiAIgiAIgiAIwrlDEhaCIAiCIAiCIAiCIJw7JGEhCIIgCIIgCIIgCMK5QxIWgiAIgiAIgiAIgiCcOyRhIQiCIAiCIAiCIAjCuUMSFoIgCIIgCIIgCIIgnDskYSEIgiAIgiAIgiAIwrlDEhaCIAiCIAiCIAiCIJw7JGEhCIIgCIIgCIIgCMK5QxIWgiAIgiAIgiAIgiCcOyRhIQiCIAiCIAiCIAjCuUMSFoIgCIIgCIIgCIIgnDskYSEIgiAIgiAIgiAIwrlDEhaCIAiCIAiCIAiCIJw7JGEhCIIgCIIgCIIgCMK5QxIWgiAIgiAIgiAIgiCcOyRhIQiCIAiCIAiCIAjCuUMSFoIgCIIgCIIgCIIgnDskYSEIgiAIgiAIgiAIwrlDEhaCIAiCIAiCIAiCIJw7JGEhCIIgCIIgCIIgCMK5QxIWgiAIgiAIgiAIgiCcO4Kz3gHhfOCcBWfB5oAblZ3FOQfOlycHN2VetWzKtii2VX/dzlqLdGuI0hpQoHRtqE2jUHstUwrQxTamLKu2oVHTlqFAm+r9lVJn9r8QLg7OOarveFWuzYNqmcvjM9xT4TxyNt715aN7V4EyU72LUqhpy0q3KiPeFY4N8a7wrHiv7uPPqpwXrizLNSdbi/+uFeWDnFzb1s5qk3R7WPiu5tAxD49cqypP1mPbkWsrr+6Keyfj4t3bHvlZ3CvsT/W7KV1b9+yYf/0ylzWOfR8kYVHgnKP3+r8jffQIsDUJ7ZbWtIvuqdKz1m+rLNe25Sa2deTtuiIorZWd3b2Phw1yz5LBmb77Xqia/AvB14J6NbmsCtz1xImktmzKSUNNXBhMPVlNXBhMXjSofZZV+8L4srVWxLA3LP7/4L+nDlcJx1XzGJu3O0h0U4LEXYHjxDbclHlMCrEuv8Nse2Ibbte+T/s8o8/vJufts+1q3hEYXn0b8X/xT470msvOs3t3+sW8ePfwiHdPyru1dSa9224w7A5Oxrv7BZLP691DBqnn0ruLLxP/l//sSK+57Djn6L3xBdKVZZ4r3i1cu6cfi9e7Ka4+0I8299/xPfx6oK8nP0uVZBDvTkftdlktUTKecK678RndO5Ykr3t172XltGJy2eT5YNzha60Gw+6Q6b49XPzq9vLRWMJ03GHuiF4bT8TCuDunzau/x+S+7P25dnu9vv60z3U05wIM5m/T/JF/fuTX7YckLArs5j1WPv+3eZZ/zBhjPyw9EQCZiR+XHslAj2oAoMe3Uf3wdFiThpkQxO7tVj9qPblPeuJu2GR5cru1QZdCqW23Vt693bqodu8HSrO42GJtdXv0g7flj71+gip/sHa0XnUinL6sfN1oO7b2g6xfxEwsq9+hrNapbXNi2fhJuf7+tX1y+YRYxpdVd1XLE/S0z1deHDHtuJTr1O/MTtnPYt8T/2UtBF+eqMqynjpPVd/v+npMrD8+T6mJ96gC/Ml5MArwa9vRBghqJ7mJ9yvfo35SZOKzKMbeV7F7Xn0fdh2X+h3myW3vc0zUxD4svPQuus+glMuM23rAyi+fgXdLJz2Td/f3+YX17jTX1YOqvbxUc49494y8O+bNY/buxLaP17vTt3083vWDeHc3bvshK//ixzke7x7gsapmwKisprixfH3lWhMxXrOr5mQ1vr3Kj3t4UE1z6qTrdfGa8iK9cufu88auc8S0berxbZWvX1xoe++O+azmTCY9Zcc8Mu4iO74udsK7NSdNdfz4tscdOLFs1z49j3uLss1GiVdXf99x5x/0mdy0bdfeN3Gu9v/bP35Vk/PGnKynzlO7Ysf948ndntLj7z3Fo5NeG32GafNgqtenrj9+flCH+Qz7ngv8/i689I5j965y/ht16Vhd3cHao320hUaXp4+e7g7+qixhLcDdS3rCkbl2bYYnT7bPejdeGJxzXL8+K8f8lHmW77nWiitXOie0R8ePePfiIN49fa5e7fD06c5Z78YLxYvgXTi6excaPZ4+eiLePWXEu6ePHPPT5yS8KzUsagSzS5hh+6x3QxBOFGmrKJwnxLvCi4K4VzgvBLM3MMPWWe+GIAjCoZAUqSAIgiAIgiAIgiAI5w5JWAiCIAiCIAiCIAiCcO6QhIUgCIIgCIIgCIIgCOcOSVgIgiAIgiAIgiAIgnDuuLSdbmr9bJ1bPevrhOdDjvvpI8f89DnqMb9o/yPx7sVCjvvpI8f89Lns3oVn2+eL+DkvA3LcTx855qfPcXv30j7WVBAEQRAEQRAEQRCEi4s0CREEQRAEQRAEQRAE4dwhCQtBEARBEARBEARBEM4dkrAQBEEQBEEQBEEQBOHcIQkLQRAEQRAEQRAEQRDOHZKwEARBEARBEARBEATh3CEJC0EQBEEQBEEQBEEQzh2SsBAEQRAEQRAEQRAE4dwhCQtBEARBEARBEARBEM4dkrAQBEEQBEEQBEEQBOHcIQkLQRAEQRAEQRAEQRDOHcFZ78B558tf/jKf//znSZKE2dlZXnvttbPepUvP9vY2n/rUp/jd3/1dfvM3f/Osd+fSMhwO+ft//+/T6XRQSvH3/t7fO+tdeiGQ7/fBiHdPH/leng7i3bNBvt8HI949feR7eTqId8+G4/x+K+ecO6b9Ovesr6/zUz/1U9y7d48oinj55Zf55Cc/yeLi4qFe/7f+1t/iZ37mZ2i32ye8p5eH5znmH/vYx/ilX/qlk9/JS8x+x//Xfu3XSJKEv/JX/gr/6B/9Iz760Y/ynve856x3+VJwmO/9i/L9Fu+ePuLds0W8ezaId0eId08f8e7ZIt49G07Luy9UkxClFB//+Mf59V//dX7t136Nu3fv8jM/8zMA3Lt3j4997GNjwy/8wi9Ur/2N3/gN3va2t4m8j8jzHHPh+dnv+D98+JDbt28DcOfOHR48eHCWu3qp2O+4v2iId08f8e7ZIt49G8S7I8S7p49492wR754Np+XdF6pJyPz8PB/+8Ier6fe+97187nOfA+Cll17aM/vz+c9/ngcPHvB3/+7fPY3dvFQ86zEXjof9jv/Nmzd5+PAhAA8ePOBd73rXmezjZWS/4/6iId49fcS7Z4t492wQ744Q754+4t2zRbx7NpyWd1+oGhZ1rLV87nOf44d+6If2Xe/f/Jt/wz/+x/+YJ0+e8Nprr7G2tnZKe3j5OOwxB/jEJz7Bd77zHV577TXu379/Cnt3+Zk8/n/hL/wFvvjFL/IP/+E/pNvt8qf+1J864z28nEz73r+o32/x7ukj3j1bxLtng3h3hHj39BHvni3i3bPhJL37QvVhUecTn/gEjx494jOf+Qxav7B5m1NFjvnZIsf/bJDjPkKOxekjx/xskeN/NshxHyHH4vSRY362yPE/G07yuL9QTUJKPv3pT/Pmm2/y8z//8/JFPiXkmJ8tcvzPBjnuI+RYnD5yzM8WOf5ngxz3EXIsTh855meLHP+z4aSP+wuXsPjZn/1Zvva1r/HZz36WKIrOendeCOSYny1y/M8GOe4j5FicPnLMzxY5/meDHPcRcixOHznmZ4sc/7PhNI77C9Uk5Jvf/CY//MM/zCuvvEIcx4DvLfbnfu7nznjPLi9yzM8WOf5ngxz3EXIsTh855meLHP+zQY77CDkWp48c87NFjv/ZcFrH/YVKWAiCIAiCIAiCIAiCcDGQxj2CIAiCIAiCIAiCIJw7JGEhCIIgCIIgCIIgCMK5QxIWgiAIgiAIgiAIgiCcOyRhIQiCIAiCIAiCIAjCuUMSFoIgCIIgCIIgCIIgnDskYSEIgiAIgiAIgiAIwrlDEhaCcADve9/7uH///tRln//85/nrf/2v7/naL3zhC/zAD/zASe2aIAjCpUS8KwiCcLqId4XziiQsBOEAvvrVr3L37t1DrfvOd76TN99884T3SBAE4XIj3hUEQThdxLvCeUUSFoIgCIIgCIIgCIIgnDskYSG8sPzyL/8yP/ZjP1ZNf/SjH+Unf/Inq+kf/MEf5I/+6I/Gssjr6+v82I/9GO9///v5q3/1r3Lv3r1q/b/xN/4GAH/pL/0l3ve+9/Gv/tW/qpb94i/+It/3fd/HRz7yEX75l3/5pD+aIAjCuUS8KwiCcLqId4WLjiQshBeWD33oQ3z5y1/GWsvjx4/JsoyvfOUrANy/f59er8c73/nOsdd88pOfpNFo8Du/8zt86lOfGpPxP//n/xyAX/3VX+WrX/0qf/Ev/kUAnj59yvb2Nr/1W7/FP/gH/4BPfvKTbG5untKnFARBOD+IdwVBEE4X8a5w0ZGEhfDCcvfuXdrtNn/0R3/El770JT7ykY9w48YNvv3tb/PFL36R7/3e70Xr0U8kz3P+9b/+1/ydv/N3aLVavOMd7+Av/+W/fOD7BEHAj//4jxOGIT/4gz9Iq9Xi9ddfP8mPJgiCcC4R7wqCIJwu4l3hohOc9Q4IwlnywQ9+kC9+8Yu8+eabfPCDH2RmZoYvfelL/P7v/z4f+tCHxtZdW1sjyzJu3rxZzbt169aB7zE/P08QjH5qzWaTXq93fB9CEAThAiHeFQRBOF3Eu8JFRmpYCC80H/rQh/jCF77A7/3e7/GhD32ID33oQ3zpS1/ii1/8Ih/84AfH1l1cXCQIApaXl6t59bIgCIJwMOJdQRCE00W8K1xkJGEhvNB88IMf5Atf+AKDwYClpSU+8IEP8Nu//dtsbGzw7ne/e2xdYwwf/ehH+cxnPkO/3+db3/oWv/IrvzK2ztWrV/d8hrUgCIIg3hUEQThtxLvCRUYSFsILzauvvkq73eYDH/gAAJ1Ohzt37vD+978fY8yu9V977TV6vR7f//3fz0//9E/zIz/yI2PLf+InfoKf/umf5gMf+MBYr8mCIAiCR7wrCIJwuoh3hYuMcs65s94JQRAEQRAEQRAEQRCEOlLDQhAEQRAEQRAEQRCEc4ckLARBEARBEARBEARBOHdIwkIQBEEQBEEQBEEQhHOHJCwEQRAEQRAEQRAEQTh3SMJCEARBEARBEARBEIRzhyQsBEEQBEEQBEEQBEE4d0jCQhAEQRAEQRAEQRCEc4ckLARBEARBEARBEARBOHdIwkIQBEEQBEEQBEEQhHPH/w/kOksy5eXW+wAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 1080x288 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for mup, (lr, optimizer) in product([False, True], [(0.01, 'adam'), (0.01, 'sgd')]):\n",
-    "  coord_check(mup=mup,\n",
-    "              lr=lr, optimizer=optimizer, nsteps=3, arch='resnet18',\n",
-    "              base_shapes='resnet18.bsh', nseeds=1, plotdir='coord_checks', legend=False)\n",
-    "  plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "hide_input": false,
-  "kernelspec": {
-   "display_name": "mutrfmr",
-   "language": "python",
-   "name": "mutrfmr"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.7"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/mup/examples/ResNet/README.md b/mup/examples/ResNet/README.md
deleted file mode 100644
index 673ca7787..000000000
--- a/mup/examples/ResNet/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
-# μP ResNet
-This folder contains the source code for our experiment on ResNet on CIFAR10, which also serves as an example usage of `mup`.
-
-## Save Model Base Shapes
-To train a μP model, one needs to first specify the base shapes. To save base shapes info, run, for example,
-```
-python main.py --save_base_shapes resnet18.bsh --width_mult 1
-```
-
-## Verify Implementation with Coordinate Check
-Before we scale up and start training, it is recommended to check the size of activation coordinates as model width increases. We have integrated such a test in this example using the helper functions in `mup`; you can simply run:
-
-```bash
-# for SGD
-python main.py --load_base_shapes resnet18.bsh --optimizer sgd --lr 0.1 --coord_check
-# for Adam
-python main.py --load_base_shapes resnet18.bsh --optimizer adam --lr 0.001 --coord_check
-```
-You should find the generated plots under `./coord_checks`, which show stable coordinate sizes under μP, e.g., 
-
-![](coord_checks/μp_resnet18_adam_coord.png)
-
-and growing sizes under SP, e.g.,
-
-![](coord_checks/sp_resnet18_adam_coord.png)
-
-
-## Start Training
-Having verified our implementation of μP, we can scale up our model and train using the same hyperparameters used for the small model and expect that the wider model performs better on the training data and that the optimal hyperparameters transfer.
-```bash
-# for SGD
-python main.py --width_mult 2 --optimizer musgd
-# for Adam
-python main.py --width_mult 2 --optimizer muadam
-```
-
-Note that if you do not specify `--load_base_shapes`, the script will default to training a SP model.
\ No newline at end of file
diff --git a/mup/examples/ResNet/coord_checks/sp_resnet18_adam_coord.png b/mup/examples/ResNet/coord_checks/sp_resnet18_adam_coord.png
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index d130cbef1..000000000
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diff --git a/mup/examples/ResNet/coord_checks/sp_resnet18_sgd_coord.png b/mup/examples/ResNet/coord_checks/sp_resnet18_sgd_coord.png
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index 5099215d6..000000000
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diff --git "a/mup/examples/ResNet/coord_checks/\316\274p_resnet18_adam_coord.png" "b/mup/examples/ResNet/coord_checks/\316\274p_resnet18_adam_coord.png"
deleted file mode 100644
index ab8eac977..000000000
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diff --git "a/mup/examples/ResNet/coord_checks/\316\274p_resnet18_sgd_coord.png" "b/mup/examples/ResNet/coord_checks/\316\274p_resnet18_sgd_coord.png"
deleted file mode 100644
index b45782f17..000000000
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diff --git a/mup/examples/ResNet/main.py b/mup/examples/ResNet/main.py
deleted file mode 100644
index 508794ae7..000000000
--- a/mup/examples/ResNet/main.py
+++ /dev/null
@@ -1,268 +0,0 @@
-'''Train CIFAR10 with PyTorch.'''
-import argparse
-import os
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.optim as optim
-import torchvision
-import torchvision.transforms as transforms
-from mup.coord_check import get_coord_data, plot_coord_data
-from mup import MuAdam, MuSGD, get_shapes, make_base_shapes, set_base_shapes
-
-import resnet
-
-
-def coord_check(mup, lr, optimizer, nsteps, arch, base_shapes, nseeds, device='cuda', plotdir='', legend=False):
-
-    optimizer = optimizer.replace('mu', '')
-
-    def gen(w, standparam=False):
-        def f():
-            model = getattr(resnet, arch)(wm=w).to(device)
-            if standparam:
-                set_base_shapes(model, None)
-            else:
-                set_base_shapes(model, base_shapes)
-            return model
-        return f
-
-    transform_train = transforms.Compose([
-        transforms.RandomCrop(32, padding=4),
-        transforms.RandomHorizontalFlip(),
-        transforms.ToTensor(),
-        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
-    ])
-    trainset = torchvision.datasets.CIFAR10(
-        root='../dataset', train=True, download=True, transform=transform_train)
-    dataloader = torch.utils.data.DataLoader(
-        trainset, batch_size=1, shuffle=False)
-
-    widths = 2**np.arange(-2., 2)
-    models = {w: gen(w, standparam=not mup) for w in widths}
-    df = get_coord_data(models, dataloader, mup=mup, lr=lr, optimizer=optimizer, nseeds=nseeds, nsteps=nsteps)
-
-    prm = 'μP' if mup else 'SP'
-    plot_coord_data(df, legend=legend,
-        save_to=os.path.join(plotdir, f'{prm.lower()}_{arch}_{optimizer}_coord.png'),
-        suptitle=f'{prm} {arch} {optimizer} lr={lr} nseeds={nseeds}',
-        face_color='xkcd:light grey' if not mup else None)
-
-
-# Training
-def train(epoch, net):
-    from utils import progress_bar
-    print('\nEpoch: %d' % epoch)
-    net.train()
-    train_loss = 0
-    correct = 0
-    total = 0
-    for batch_idx, (inputs, targets) in enumerate(trainloader):
-        inputs, targets = inputs.to(device), targets.to(device)
-        optimizer.zero_grad()
-        outputs = net(inputs)
-        loss = criterion(outputs, targets)
-        loss.backward()
-        optimizer.step()
-
-        train_loss += loss.item()
-        _, predicted = outputs.max(1)
-        total += targets.size(0)
-        correct += predicted.eq(targets).sum().item()
-
-        progress_bar(batch_idx, len(trainloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
-                     % (train_loss/(batch_idx+1), 100.*correct/total, correct, total))
-
-
-def test(epoch, net):
-    from utils import progress_bar
-    global best_acc
-    net.eval()
-    test_loss = 0
-    correct = 0
-    total = 0
-    with torch.no_grad():
-        for batch_idx, (inputs, targets) in enumerate(testloader):
-            inputs, targets = inputs.to(device), targets.to(device)
-            outputs = net(inputs)
-            loss = criterion(outputs, targets)
-
-            test_loss += loss.item()
-            _, predicted = outputs.max(1)
-            total += targets.size(0)
-            correct += predicted.eq(targets).sum().item()
-
-            progress_bar(batch_idx, len(testloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
-                         % (test_loss/(batch_idx+1), 100.*correct/total, correct, total))
-
-    # Save checkpoint.
-    acc = 100.*correct/total
-    if acc > best_acc:
-        print('Saving..')
-        state = {
-            'net': net.state_dict(),
-            'acc': acc,
-            'epoch': epoch,
-        }
-        if not os.path.isdir('checkpoint'):
-            os.mkdir('checkpoint')
-        torch.save(state, './checkpoint/ckpt.pth')
-        best_acc = acc
-
-
-
-if __name__ == '__main__':
-    
-    parser = argparse.ArgumentParser(description=''
-    '''
-    PyTorch CIFAR10 Training, with μP.
-
-    To save base shapes info, run e.g.
-
-        python main.py --save_base_shapes resnet18.bsh --width_mult 1
-
-    To train using MuAdam (or MuSGD), run
-
-        python main.py --width_mult 2 --load_base_shapes resnet18.bsh --optimizer {muadam,musgd}
-
-    To test coords, run
-
-        python main.py --load_base_shapes resnet18.bsh --optimizer sgd --lr 0.1 --coord_check
-
-        python main.py --load_base_shapes resnet18.bsh --optimizer adam --lr 0.001 --coord_check
-
-    If you don't specify a base shape file, then you are using standard parametrization, e.g.
-
-        python main.py --width_mult 2 --optimizer {muadam,musgd}
-
-    Here muadam (resp. musgd) would have the same result as adam (resp. sgd).
-
-    Note that models of different depths need separate `.bsh` files.
-    ''', formatter_class=argparse.RawTextHelpFormatter)
-    parser.add_argument('--lr', default=0.1, type=float, help='learning rate')
-    parser.add_argument('--resume', '-r', action='store_true',
-                        help='resume from checkpoint')
-    parser.add_argument('--arch', type=str, default='resnet18')
-    parser.add_argument('--optimizer', default='musgd', choices=['sgd', 'adam', 'musgd', 'muadam'])
-    parser.add_argument('--epochs', type=int, default=150)
-    parser.add_argument('--width_mult', type=float, default=1)
-    parser.add_argument('--save_base_shapes', type=str, default='',
-                        help='file location to save base shapes at')
-    parser.add_argument('--load_base_shapes', type=str, default='',
-                        help='file location to load base shapes from')
-    parser.add_argument('--batch_size', type=int, default=128)
-    parser.add_argument('--test_batch_size', type=int, default=128)
-    parser.add_argument('--weight_decay', type=float, default=5e-4)
-    parser.add_argument('--num_workers', type=int, default=2)
-    parser.add_argument('--test_num_workers', type=int, default=2)
-    parser.add_argument('--momentum', type=float, default=0.9)
-    parser.add_argument('--coord_check', action='store_true',
-                        help='test μ parametrization is correctly implemented by collecting statistics on coordinate distributions for a few steps of training.')
-    parser.add_argument('--coord_check_nsteps', type=int, default=3,
-                        help='Do coord check with this many steps.')
-    parser.add_argument('--coord_check_nseeds', type=int, default=1,
-                        help='number of seeds for coord check')
-    parser.add_argument('--seed', type=int, default=1111,
-                        help='random seed')
-    args = parser.parse_args()
-
-    device = 'cuda' if torch.cuda.is_available() else 'cpu'
-    best_acc = 0  # best test accuracy
-    start_epoch = 0  # start from epoch 0 or last checkpoint epoch
-
-    # Set the random seed manually for reproducibility.
-    torch.manual_seed(args.seed)
-
-    # Data
-    if not args.save_base_shapes:
-        print('==> Preparing data..')
-        transform_train = transforms.Compose([
-            transforms.RandomCrop(32, padding=4),
-            transforms.RandomHorizontalFlip(),
-            transforms.ToTensor(),
-            transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
-        ])
-
-        transform_test = transforms.Compose([
-            transforms.ToTensor(),
-            transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
-        ])
-
-        trainset = torchvision.datasets.CIFAR10(
-            root='../dataset', train=True, download=True, transform=transform_train)
-        trainloader = torch.utils.data.DataLoader(
-            trainset, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers)
-
-        testset = torchvision.datasets.CIFAR10(
-            root='../dataset', train=False, download=True, transform=transform_test)
-        testloader = torch.utils.data.DataLoader(
-            testset, batch_size=args.test_batch_size, shuffle=False, num_workers=args.test_num_workers)
-
-        classes = ('plane', 'car', 'bird', 'cat', 'deer',
-                'dog', 'frog', 'horse', 'ship', 'truck')
-
-    if args.coord_check:
-        print('testing parametrization')
-        import os
-        os.makedirs('coord_checks', exist_ok=True)
-        plotdir = 'coord_checks'
-        coord_check(mup=True,
-            lr=args.lr, optimizer=args.optimizer, nsteps=args.coord_check_nsteps, arch=args.arch, base_shapes=args.load_base_shapes, nseeds=args.coord_check_nseeds, device=device, plotdir=plotdir, legend=False)
-        coord_check(mup=False,
-            lr=args.lr, optimizer=args.optimizer, nsteps=args.coord_check_nsteps, arch=args.arch, base_shapes=args.load_base_shapes, nseeds=args.coord_check_nseeds, device=device,plotdir=plotdir, legend=False)
-        import sys; sys.exit()
-
-
-    # Model
-    print('==> Building model..')
-    net = getattr(resnet, args.arch)(wm=args.width_mult)
-    if args.save_base_shapes:
-        print(f'saving base shapes at {args.save_base_shapes}')
-        base_shapes = get_shapes(net)
-        delta_shapes = get_shapes(getattr(resnet, args.arch)(wm=args.width_mult/2))
-        make_base_shapes(base_shapes, delta_shapes, savefile=args.save_base_shapes)
-        # save_shapes(net, args.save_base_shapes)
-        print('done and exit')
-        import sys; sys.exit()
-
-    net = net.to(device)
-
-    if args.load_base_shapes:
-        print(f'loading base shapes from {args.load_base_shapes}')
-        set_base_shapes(net, args.load_base_shapes)
-        print('done')
-    else:
-        print(f'using standard parametrization')
-        set_base_shapes(net, None)
-        print('done')
-
-    if args.resume:
-        # Load checkpoint.
-        print('==> Resuming from checkpoint..')
-        assert os.path.isdir('checkpoint'), 'Error: no checkpoint directory found!'
-        checkpoint = torch.load('./checkpoint/ckpt.pth')
-        net.load_state_dict(checkpoint['net'])
-        best_acc = checkpoint['acc']
-        start_epoch = checkpoint['epoch']
-
-    criterion = nn.CrossEntropyLoss()
-    if args.optimizer == 'musgd':
-        optimizer = MuSGD(net.parameters(), lr=args.lr,
-                            momentum=args.momentum,
-                            weight_decay=args.weight_decay)
-    elif args.optimizer == 'muadam':
-        optimizer = MuAdam(net.parameters(), lr=args.lr)
-    elif args.optimizer == 'sgd':
-        optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
-    elif args.optimizer == 'adam':
-        optimizer = optim.Adam(net.parameters(), lr=args.lr)
-    else:
-        raise ValueError()
-    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.epochs)
-
-
-    for epoch in range(start_epoch, start_epoch+args.epochs):
-        train(epoch, net)
-        test(epoch, net)
-        scheduler.step()
\ No newline at end of file
diff --git a/mup/examples/ResNet/resnet.py b/mup/examples/ResNet/resnet.py
deleted file mode 100644
index 648ceb350..000000000
--- a/mup/examples/ResNet/resnet.py
+++ /dev/null
@@ -1,153 +0,0 @@
-'''ResNet in PyTorch.
-Reference:
-[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
-    Deep Residual Learning for Image Recognition. arXiv:1512.03385
-'''
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.nn import init
-
-from mup import MuReadout
-
-class BasicBlock(nn.Module):
-    expansion = 1
-
-    def __init__(self, in_planes, planes, stride=1):
-        super(BasicBlock, self).__init__()
-        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride,
-                               padding=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(planes)
-        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1,
-                               padding=1, bias=False)
-        self.bn2 = nn.BatchNorm2d(planes)
-
-        self.shortcut = nn.Sequential()
-        if stride != 1 or in_planes != self.expansion*planes:
-            self.shortcut = nn.Sequential(
-                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1,
-                          stride=stride, bias=False),
-                nn.BatchNorm2d(self.expansion*planes))
-        
-        self.reset_parameters()
-
-    def reset_parameters(self) -> None:
-        layers = [self.conv1, self.conv2]
-        if len(self.shortcut) > 1:
-            layers.append(self.shortcut[0])
-        for layer in layers:
-            init.kaiming_normal_(layer.weight, a=1)
-            if layer.bias is not None:
-                init.zeros_(layer.bias)
-
-    def forward(self, x):
-        out = F.relu(self.bn1(self.conv1(x)))
-        out = self.bn2(self.conv2(out))
-        out += self.shortcut(x)
-        return F.relu(out)
-
-
-class Bottleneck(nn.Module):
-    expansion = 4
-
-    def __init__(self, in_planes, planes, stride=1):
-        super(Bottleneck, self).__init__()
-        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(planes)
-        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
-                               padding=1, bias=False)
-        self.bn2 = nn.BatchNorm2d(planes)
-        self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False)
-        self.bn3 = nn.BatchNorm2d(self.expansion*planes)
-
-        self.shortcut = nn.Sequential()
-        if stride != 1 or in_planes != self.expansion*planes:
-            self.shortcut = nn.Sequential(
-                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
-                nn.BatchNorm2d(self.expansion*planes)
-            )
-        
-            self.reset_parameters()
-
-    def reset_parameters(self) -> None:
-        layers = [self.conv1, self.conv2, self.conv3]
-        if len(self.shortcut) > 1:
-            layers.append(self.shortcut[0])
-        for layer in layers:
-            init.kaiming_normal_(layer.weight, a=1)
-            if layer.bias is not None:
-                init.zeros_(layer.bias)
-    
-    def forward(self, x):
-        out = F.relu(self.bn1(self.conv1(x)))
-        out = F.relu(self.bn2(self.conv2(out)))
-        out = self.bn3(self.conv3(out))
-        out += self.shortcut(x)
-        return F.relu(out)
-
-
-class ResNet(nn.Module):
-    # feat_scale lets us deal with CelebA, other non-32x32 datasets
-    def __init__(self, block, num_blocks, num_classes=10, feat_scale=1, wm=1):
-        super(ResNet, self).__init__()
-
-        base_widths = [64, 128, 256, 512]
-        widths = [int(w * wm) for w in base_widths]
-
-        self.in_planes = widths[0]
-        self.conv1 = nn.Conv2d(3, self.in_planes, kernel_size=3, stride=1,
-                                padding=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(self.in_planes)
-        self.layer1 = self._make_layer(block, widths[0], num_blocks[0], stride=1)
-        self.layer2 = self._make_layer(block, widths[1], num_blocks[1], stride=2)
-        self.layer3 = self._make_layer(block, widths[2], num_blocks[2], stride=2)
-        self.layer4 = self._make_layer(block, widths[3], num_blocks[3], stride=2)
-        ### This is the only μP related change ###
-        self.linear = MuReadout(feat_scale*widths[3]*block.expansion, num_classes, readout_zero_init=True)
-        ###########################################
-
-    def _make_layer(self, block, planes, num_blocks, stride):
-        strides = [stride] + [1]*(num_blocks-1)
-        layers = []
-        for stride in strides:
-            layers.append(block(self.in_planes, planes, stride=stride))
-            self.in_planes = planes * block.expansion
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        out = F.relu(self.bn1(self.conv1(x)))
-        out = self.layer1(out)
-        out = self.layer2(out)
-        out = self.layer3(out)
-        out = self.layer4(out)
-        out = F.avg_pool2d(out, 4)
-
-        pre_out = out.view(out.size(0), -1)
-        final = self.linear(pre_out)
-        return final
-
-def ResNet18(**kwargs):
-    return ResNet(BasicBlock, [2,2,2,2], **kwargs)
-
-def ResNet18Wide(**kwargs):
-    return ResNet(BasicBlock, [2,2,2,2], wm=5, **kwargs)
-
-def ResNet18Thin(**kwargs):
-    return ResNet(BasicBlock, [2,2,2,2], wm=.75, **kwargs)
-
-def ResNet34(**kwargs):
-    return ResNet(BasicBlock, [3,4,6,3], **kwargs)
-
-def ResNet50(**kwargs):
-    return ResNet(Bottleneck, [3,4,6,3], **kwargs)
-
-def ResNet101(**kwargs):
-    return ResNet(Bottleneck, [3,4,23,3], **kwargs)
-
-def ResNet152(**kwargs):
-    return ResNet(Bottleneck, [3,8,36,3], **kwargs)
-
-resnet50 = ResNet50
-resnet18 = ResNet18
-resnet101 = ResNet101
-resnet152 = ResNet152
-resnet18wide = ResNet18Wide
\ No newline at end of file
diff --git a/mup/examples/ResNet/resnet18.bsh b/mup/examples/ResNet/resnet18.bsh
deleted file mode 100644
index 650d58f16..000000000
--- a/mup/examples/ResNet/resnet18.bsh
+++ /dev/null
@@ -1,188 +0,0 @@
-# This is a base shape file encoded in yaml
-# - `null` indicates a dimension is "finite", i.e. a non-"width" dimension
-# - a number indicates the base dimension of an "infinite" dimension, i.e. some notion of "width"
-bn1.bias:
-- 64
-bn1.weight:
-- 64
-conv1.weight:
-- 64
-- null
-- null
-- null
-layer1.0.bn1.bias:
-- 64
-layer1.0.bn1.weight:
-- 64
-layer1.0.bn2.bias:
-- 64
-layer1.0.bn2.weight:
-- 64
-layer1.0.conv1.weight:
-- 64
-- 64
-- null
-- null
-layer1.0.conv2.weight:
-- 64
-- 64
-- null
-- null
-layer1.1.bn1.bias:
-- 64
-layer1.1.bn1.weight:
-- 64
-layer1.1.bn2.bias:
-- 64
-layer1.1.bn2.weight:
-- 64
-layer1.1.conv1.weight:
-- 64
-- 64
-- null
-- null
-layer1.1.conv2.weight:
-- 64
-- 64
-- null
-- null
-layer2.0.bn1.bias:
-- 128
-layer2.0.bn1.weight:
-- 128
-layer2.0.bn2.bias:
-- 128
-layer2.0.bn2.weight:
-- 128
-layer2.0.conv1.weight:
-- 128
-- 64
-- null
-- null
-layer2.0.conv2.weight:
-- 128
-- 128
-- null
-- null
-layer2.0.shortcut.0.weight:
-- 128
-- 64
-- null
-- null
-layer2.0.shortcut.1.bias:
-- 128
-layer2.0.shortcut.1.weight:
-- 128
-layer2.1.bn1.bias:
-- 128
-layer2.1.bn1.weight:
-- 128
-layer2.1.bn2.bias:
-- 128
-layer2.1.bn2.weight:
-- 128
-layer2.1.conv1.weight:
-- 128
-- 128
-- null
-- null
-layer2.1.conv2.weight:
-- 128
-- 128
-- null
-- null
-layer3.0.bn1.bias:
-- 256
-layer3.0.bn1.weight:
-- 256
-layer3.0.bn2.bias:
-- 256
-layer3.0.bn2.weight:
-- 256
-layer3.0.conv1.weight:
-- 256
-- 128
-- null
-- null
-layer3.0.conv2.weight:
-- 256
-- 256
-- null
-- null
-layer3.0.shortcut.0.weight:
-- 256
-- 128
-- null
-- null
-layer3.0.shortcut.1.bias:
-- 256
-layer3.0.shortcut.1.weight:
-- 256
-layer3.1.bn1.bias:
-- 256
-layer3.1.bn1.weight:
-- 256
-layer3.1.bn2.bias:
-- 256
-layer3.1.bn2.weight:
-- 256
-layer3.1.conv1.weight:
-- 256
-- 256
-- null
-- null
-layer3.1.conv2.weight:
-- 256
-- 256
-- null
-- null
-layer4.0.bn1.bias:
-- 512
-layer4.0.bn1.weight:
-- 512
-layer4.0.bn2.bias:
-- 512
-layer4.0.bn2.weight:
-- 512
-layer4.0.conv1.weight:
-- 512
-- 256
-- null
-- null
-layer4.0.conv2.weight:
-- 512
-- 512
-- null
-- null
-layer4.0.shortcut.0.weight:
-- 512
-- 256
-- null
-- null
-layer4.0.shortcut.1.bias:
-- 512
-layer4.0.shortcut.1.weight:
-- 512
-layer4.1.bn1.bias:
-- 512
-layer4.1.bn1.weight:
-- 512
-layer4.1.bn2.bias:
-- 512
-layer4.1.bn2.weight:
-- 512
-layer4.1.conv1.weight:
-- 512
-- 512
-- null
-- null
-layer4.1.conv2.weight:
-- 512
-- 512
-- null
-- null
-linear.bias:
-- null
-linear.weight:
-- null
-- 512
diff --git a/mup/examples/ResNet/utils.py b/mup/examples/ResNet/utils.py
deleted file mode 100644
index fdd13c0a4..000000000
--- a/mup/examples/ResNet/utils.py
+++ /dev/null
@@ -1,124 +0,0 @@
-'''Some helper functions for PyTorch, including:
-    - get_mean_and_std: calculate the mean and std value of dataset.
-    - msr_init: net parameter initialization.
-    - progress_bar: progress bar mimic xlua.progress.
-'''
-import os
-import sys
-import time
-import math
-
-import torch.nn as nn
-import torch.nn.init as init
-
-
-def get_mean_and_std(dataset):
-    '''Compute the mean and std value of dataset.'''
-    dataloader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=True, num_workers=2)
-    mean = torch.zeros(3)
-    std = torch.zeros(3)
-    print('==> Computing mean and std..')
-    for inputs, targets in dataloader:
-        for i in range(3):
-            mean[i] += inputs[:,i,:,:].mean()
-            std[i] += inputs[:,i,:,:].std()
-    mean.div_(len(dataset))
-    std.div_(len(dataset))
-    return mean, std
-
-def init_params(net):
-    '''Init layer parameters.'''
-    for m in net.modules():
-        if isinstance(m, nn.Conv2d):
-            init.kaiming_normal(m.weight, mode='fan_out')
-            if m.bias:
-                init.constant(m.bias, 0)
-        elif isinstance(m, nn.BatchNorm2d):
-            init.constant(m.weight, 1)
-            init.constant(m.bias, 0)
-        elif isinstance(m, nn.Linear):
-            init.normal(m.weight, std=1e-3)
-            if m.bias:
-                init.constant(m.bias, 0)
-
-
-_, term_width = os.popen('stty size', 'r').read().split()
-term_width = int(term_width)
-
-TOTAL_BAR_LENGTH = 65.
-last_time = time.time()
-begin_time = last_time
-def progress_bar(current, total, msg=None):
-    global last_time, begin_time
-    if current == 0:
-        begin_time = time.time()  # Reset for new bar.
-
-    cur_len = int(TOTAL_BAR_LENGTH*current/total)
-    rest_len = int(TOTAL_BAR_LENGTH - cur_len) - 1
-
-    sys.stdout.write(' [')
-    for i in range(cur_len):
-        sys.stdout.write('=')
-    sys.stdout.write('>')
-    for i in range(rest_len):
-        sys.stdout.write('.')
-    sys.stdout.write(']')
-
-    cur_time = time.time()
-    step_time = cur_time - last_time
-    last_time = cur_time
-    tot_time = cur_time - begin_time
-
-    L = []
-    L.append('  Step: %s' % format_time(step_time))
-    L.append(' | Tot: %s' % format_time(tot_time))
-    if msg:
-        L.append(' | ' + msg)
-
-    msg = ''.join(L)
-    sys.stdout.write(msg)
-    for i in range(term_width-int(TOTAL_BAR_LENGTH)-len(msg)-3):
-        sys.stdout.write(' ')
-
-    # Go back to the center of the bar.
-    for i in range(term_width-int(TOTAL_BAR_LENGTH/2)+2):
-        sys.stdout.write('\b')
-    sys.stdout.write(' %d/%d ' % (current+1, total))
-
-    if current < total-1:
-        sys.stdout.write('\r')
-    else:
-        sys.stdout.write('\n')
-    sys.stdout.flush()
-
-def format_time(seconds):
-    days = int(seconds / 3600/24)
-    seconds = seconds - days*3600*24
-    hours = int(seconds / 3600)
-    seconds = seconds - hours*3600
-    minutes = int(seconds / 60)
-    seconds = seconds - minutes*60
-    secondsf = int(seconds)
-    seconds = seconds - secondsf
-    millis = int(seconds*1000)
-
-    f = ''
-    i = 1
-    if days > 0:
-        f += str(days) + 'D'
-        i += 1
-    if hours > 0 and i <= 2:
-        f += str(hours) + 'h'
-        i += 1
-    if minutes > 0 and i <= 2:
-        f += str(minutes) + 'm'
-        i += 1
-    if secondsf > 0 and i <= 2:
-        f += str(secondsf) + 's'
-        i += 1
-    if millis > 0 and i <= 2:
-        f += str(millis) + 'ms'
-        i += 1
-    if f == '':
-        f = '0ms'
-    return f
\ No newline at end of file
diff --git a/mup/examples/Transformer/CoordCheck.ipynb b/mup/examples/Transformer/CoordCheck.ipynb
deleted file mode 100644
index f73ff94fd..000000000
--- a/mup/examples/Transformer/CoordCheck.ipynb
+++ /dev/null
@@ -1,201 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-03T19:14:09.410034Z",
-     "start_time": "2022-02-03T19:14:08.146448Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Failed to import apex. You can still train with --precision {float|double}.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from main import coord_check\n",
-    "from argparse import Namespace\n",
-    "from itertools import product\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "sns.set()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-03T19:14:09.414580Z",
-     "start_time": "2022-02-03T19:14:09.412102Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.makedirs('coord_checks', exist_ok=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-03T19:15:04.805896Z",
-     "start_time": "2022-02-03T19:14:09.415929Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|█████████████████████████████████████████████| 5/5 [00:03<00:00,  1.39it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "coord check plot saved to coord_checks/sp_trsfmr_adam_coord.png\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1080x288 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|█████████████████████████████████████████████| 7/7 [00:19<00:00,  2.73s/it]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "coord check plot saved to coord_checks/sp_trsfmr_sgd_coord.png\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1080x288 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|█████████████████████████████████████████████| 5/5 [00:03<00:00,  1.39it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "coord check plot saved to coord_checks/μp_trsfmr_adam_coord.png\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x288 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|█████████████████████████████████████████████| 7/7 [00:18<00:00,  2.71s/it]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "coord check plot saved to coord_checks/μp_trsfmr_sgd_coord.png\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x288 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for mup, (lr, optimizer) in product([False, True], [(0.01, 'adam'), (0.3, 'sgd')]):\n",
-    "  args = Namespace(nhead=2, ffn_ratio=1, nlayers=2, dropout=0,\n",
-    "                   tied=False, bias=False, init_var=1, bptt=35,\n",
-    "                  load_base_shapes='width256.bsh', device='cuda',\n",
-    "                  attn_mult=1, output_mult=1, precision='half')\n",
-    "  coord_check(mup=mup,\n",
-    "              lr=lr, optimizer=optimizer, batch_size=32, nsteps=3,\n",
-    "              nseeds=1, data_dir='./data/wikitext-2', args=args,\n",
-    "              plotdir='coord_checks', legend=False)\n",
-    "  plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "hide_input": false,
-  "kernelspec": {
-   "display_name": "mutrfmr",
-   "language": "python",
-   "name": "mutrfmr"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.7"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/mup/examples/Transformer/README.md b/mup/examples/Transformer/README.md
deleted file mode 100644
index 330cef821..000000000
--- a/mup/examples/Transformer/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
-# μP Transformer
-This folder contains the source code for our experiment on small Transformers, which also serves as an example usage of `mup`.
-
-## Save Model Base Shapes
-To train a μP model, one needs to first specify the base shapes. To save base shapes info, run, for example,
-```
-python main.py --d_model 256 --save_base_shapes width256.bsh
-```
-
-## Verify Implementation with Coordinate Check
-Before we scale up and start training, it is recommended to check the size of activation coordinates as model width increases. We have integrated such a test in this example using the helper functions in `mup`; you can simply run:
-
-```bash
-# for SGD
-python main.py --load_base_shapes width256.bsh --optimizer sgd --lr 0.5 --cuda --coord_check
-# for Adam
-python main.py --load_base_shapes width256.bsh --optimizer adam --lr 0.01 --cuda --coord_check
-```
-You should find the generated plots under `./coord_checks`, which show stable coordinate sizes under μP, e.g., 
-
-![](coord_checks/μp_trsfmr_adam_coord.png)
-
-and growing sizes under SP, e.g.,
-
-![](coord_checks/sp_trsfmr_adam_coord.png)
-
-
-## Start Training
-Having verified our implementation of μP, we can scale up our model and train using the same hyperparameters used for the small model and expect that the wider model performs better on the training data and that the optimal hyperparameters transfer.
-```bash
-# for SGD
-python main.py --d_model 4096 --load_base_shapes width256.bsh --optimizer musgd --lr 0.5 --cuda
-# for Adam
-python main.py --d_model 4096 --load_base_shapes width256.bsh --optimizer muadam --lr 0.01 --cuda
-```
-
-Note that if you do not specify `--load_base_shapes`, the script will default to training a SP model.
diff --git a/mup/examples/Transformer/_overrides.py b/mup/examples/Transformer/_overrides.py
deleted file mode 100644
index 4a7ef521e..000000000
--- a/mup/examples/Transformer/_overrides.py
+++ /dev/null
@@ -1,811 +0,0 @@
-"""
-Python implementation of __torch_function__
-
-While most of the torch API and handling for __torch_function__ happens
-at the C++ level, some of the torch API is written in Python so we need
-python-level handling for __torch_function__ overrides as well. The main
-developer-facing functionality in this file are handle_torch_function and
-has_torch_function. See torch/functional.py and test/test_overrides.py
-for usage examples.
-
-NOTE: heavily inspired by NumPy's ``__array_function__`` (see:
-https://github.com/pytorch/pytorch/issues/24015 and
-https://www.numpy.org/neps/nep-0018-array-function-protocol.html
-)
-
-If changing this file in a way that can affect ``__torch_function__`` overhead,
-please report the benchmarks in ``benchmarks/overrides_benchmark``. See the
-instructions in the ``README.md`` in that directory.
-"""
-
-import __future__
-
-import collections
-import torch
-import types
-
-def get_ignored_functions():
-    """Return public functions that cannot be overrided by __torch_function__
-
-    Returns
-    -------
-    A tuple of functions that are publicly available in the torch API but cannot
-    be overrided with __torch_function__. Mostly this is because none of the
-    arguments of these functions are tensors or tensor-likes.
-
-    """
-    return (
-        torch.typename,
-        torch.is_tensor,
-        torch.is_storage,
-        torch.set_default_tensor_type,
-        torch.set_rng_state,
-        torch.get_rng_state,
-        torch.manual_seed,
-        torch.initial_seed,
-        torch.seed,
-        torch.save,
-        torch.load,
-        torch.set_printoptions,
-        torch.fork,
-        torch.get_default_dtype,
-        torch.get_num_interop_threads,
-        torch.get_num_threads,
-        torch.import_ir_module,
-        torch.import_ir_module_from_buffer,
-        torch.is_anomaly_enabled,
-        torch.is_grad_enabled,
-        torch.merge_type_from_type_comment,
-        torch.parse_ir,
-        torch.parse_schema,
-        torch.parse_type_comment,
-        torch.set_anomaly_enabled,
-        torch.set_flush_denormal,
-        torch.set_num_interop_threads,
-        torch.set_num_threads,
-        torch.wait,
-        torch.as_tensor,
-        torch.from_numpy,
-        torch.get_device,
-        torch.tensor,
-        torch.default_generator,
-        torch.has_cuda,
-        torch.has_cudnn,
-        torch.has_lapack,
-        torch.cpp,
-        torch.device,
-        torch.dtype,
-        torch.finfo,
-        torch.has_mkl,
-        torch.has_mkldnn,
-        torch.has_openmp,
-        torch.iinfo,
-        torch.memory_format,
-        torch.qscheme,
-        torch.set_grad_enabled,
-        torch.no_grad,
-        torch.enable_grad,
-        torch.layout,
-        torch.align_tensors,
-        torch.arange,
-        torch.as_strided,
-        torch.bartlett_window,
-        torch.blackman_window,
-        torch.can_cast,
-        torch.cudnn_affine_grid_generator,
-        torch.cudnn_batch_norm,
-        torch.cudnn_convolution,
-        torch.cudnn_convolution_transpose,
-        torch.cudnn_grid_sampler,
-        torch.cudnn_is_acceptable,
-        torch.empty,
-        torch.empty_strided,
-        torch.eye,
-        torch.from_file,
-        torch.full,
-        torch.hamming_window,
-        torch.hann_window,
-        torch.linspace,
-        torch.logspace,
-        torch.mkldnn_adaptive_avg_pool2d,
-        torch.mkldnn_convolution,
-        torch.mkldnn_convolution_backward_weights,
-        torch.mkldnn_max_pool2d,
-        torch.ones,
-        torch.promote_types,
-        torch.rand,
-        torch.randn,
-        torch.randint,
-        torch.randperm,
-        torch.range,
-        torch.sparse_coo_tensor,
-        torch.zeros,
-        torch.nn.functional.assert_int_or_pair,
-        torch.nn.functional.boolean_dispatch,
-        torch.nn.functional.division,
-        torch.nn.functional.upsample,
-        torch.nn.functional.upsample_bilinear,
-        torch.nn.functional.upsample_nearest,
-        torch.nn.functional.has_torch_function,
-        torch.nn.functional.handle_torch_function,
-        torch.nn.functional.sigmoid,
-        torch.nn.functional.hardsigmoid,
-        torch.nn.functional.tanh,
-        torch.set_autocast_enabled,
-        torch.is_autocast_enabled,
-        torch.clear_autocast_cache,
-        torch.autocast_increment_nesting,
-        torch.autocast_decrement_nesting,
-        torch.nn.functional.hardswish,
-    )
-
-def get_testing_overrides():
-    """Return a dict containing dummy overrides for all overridable functions
-
-    Returns
-    -------
-    A dictionary that maps overridable functions in the PyTorch API to
-    lambda functions that have the same signature as the real function
-    and unconditionally return -1. These lambda functions are useful
-    for testing API coverage for a type that defines __torch_function__.
-
-    """
-    # Every function in the PyTorch API that can be overriden needs an entry
-    # in this dict.
-    #
-    # Optimally we would use inspect to get the function signature and define
-    # the lambda function procedurally but that is blocked by generating
-    # function signatures for native kernels that can be consumed by inspect.
-    # See Issue #28233.
-    return {
-        torch.abs: lambda input, out=None: -1,
-        torch.adaptive_avg_pool1d: lambda input, output_size: -1,
-        torch.adaptive_max_pool1d: lambda inputs, output_size: -1,
-        torch.acos: lambda input, out=None: -1,
-        torch.add: lambda input, other, out=None: -1,
-        torch.addbmm: lambda input, batch1, batch2, alpha=1, beta=1, out=None: -1,
-        torch.addcdiv: lambda input, tensor1, tensor2, value=1, out=None: -1,
-        torch.addcmul: lambda input, tensor1, tensor2, value=1, out=None: -1,
-        torch.addmm: lambda input, mat1, mat2, beta=1, alpha=1, out=None: -1,
-        torch.addmv: lambda input, mat, vec, beta=1, alpha=1, out=None: -1,
-        torch.addr: lambda input, vec1, vec2, beta=1, alpha=1, out=None: -1,
-        torch.affine_grid_generator: lambda theta, size, align_corners: -1,
-        torch.all: lambda input: -1,
-        torch.allclose: lambda input, other, trol=1e-05, atol=1e-08, equal_nan=False: -1,
-        torch.alpha_dropout: lambda input, p, train, inplace=False: -1,
-        torch.angle: lambda input, out=None: -1,
-        torch.any: lambda input, dim, keepdim=False, out=None: -1,
-        torch.argmax: lambda input: -1,
-        torch.argmin: lambda input: -1,
-        torch.argsort: lambda input: -1,
-        torch.asin: lambda input, out=None: -1,
-        torch.atan: lambda input, out=None: -1,
-        torch.atan2: lambda input, other, out=None: -1,
-        torch.avg_pool1d: lambda input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True: -1,
-        torch.baddbmm: lambda input, batch1, batch2, alpha=1, beta=1, out=None: -1,
-        torch.batch_norm: lambda input, weight, bias, running_mean, running_var, training, momentum, eps, cudnn_enabled: -1,
-        torch.batch_norm_backward_elemt: lambda grad_out, input, mean, invstd, weight, mean_dy, mean_dy_xmu: -1,
-        torch.batch_norm_backward_reduce: lambda grad_out, input, mean, invstd, weight, input_g, weight_g, bias_g: -1,
-        torch.batch_norm_elemt: lambda input, weight, bias, mean, invstd, eps: -1,
-        torch.batch_norm_gather_stats: lambda input, mean, invstd, running_mean, running_var, momentum, eps, count: -1,
-        torch.batch_norm_gather_stats_with_counts: lambda input, mean, invstd, running_mean, running_var, momentum, eps, count: -1,
-        torch.batch_norm_stats: lambda input, eps: -1,
-        torch.batch_norm_update_stats: lambda input, running_mean, running_var, momentum: -1,
-        torch.bernoulli: lambda input, generator=None, out=None: -1,
-        torch.bilinear: lambda input1, input2, weight, bias: -1,
-        torch.binary_cross_entropy_with_logits: (lambda input, target, weight=None, size_average=None, reduce=None,
-                                                 reduction='mean', pos_weight=None: -1),
-        torch.bincount: lambda input, weights=None, minlength=0: -1,
-        torch.bitwise_and: lambda input, other, out=None: -1,
-        torch.bitwise_not: lambda input, out=None: -1,
-        torch.bitwise_or: lambda input, other, out=None: -1,
-        torch.bitwise_xor: lambda input, other, out=None: -1,
-        torch.bmm: lambda input, mat2, out=None: -1,
-        torch.broadcast_tensors: lambda *tensors: -1,
-        torch.cartesian_prod: lambda *tensors: -1,
-        torch.cat: lambda tensors, dim=0, out=None: -1,
-        torch.cdist: lambda x1, c2, p=2, compute_mode=None: -1,
-        torch.ceil: lambda input, out=None: -1,
-        torch.celu: lambda input, alhpa=1., inplace=False: -1,
-        torch.chain_matmul: lambda *matrices: -1,
-        torch.cholesky: lambda input, upper=False, out=None: -1,
-        torch.cholesky_inverse: lambda input, upper=False, out=None: -1,
-        torch.cholesky_solve: lambda input1, input2, upper=False, out=None: -1,
-        torch.chunk: lambda input, chunks, dim=0: -1,
-        torch.clamp: lambda input, min, max, out=None: -1,
-        torch.clamp_min: lambda input, min, out=None: -1,
-        torch.clamp_max: lambda input, max, out=None: -1,
-        torch.clone: lambda input: -1,
-        torch.combinations: lambda input, r=2, with_replacement=False: -1,
-        torch.conj: lambda input, out=None: -1,
-        torch.constant_pad_nd: lambda input, pad, value=0: -1,
-        torch.conv1d: lambda input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1: -1,
-        torch.conv2d: lambda input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1: -1,
-        torch.conv3d: lambda input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1: -1,
-        torch.convolution: lambda input, weight, bias, stride, padding, dilation, transposed, output_adding, groups: -1,
-        torch.conv_tbc: lambda input, weight, bias, pad=0: -1,
-        torch.conv_transpose1d: lambda input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1: -1,
-        torch.conv_transpose2d: lambda input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1: -1,
-        torch.conv_transpose3d: lambda input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1: -1,
-        torch.cos: lambda input, out=None: -1,
-        torch.cosine_embedding_loss: lambda input1, input2, target, margin=0, size_average=None, reduce=None, reduction='mean': -1,
-        torch.cosh: lambda input, out=None: -1,
-        torch.cosine_similarity: lambda x1, x2, dim=1, eps=1e-8: -1,
-        torch.cross: lambda input, other, dim=-1, out=None: -1,
-        torch.ctc_loss: (lambda log_probs, targets, input_lengths, target_lengths, blank=0, reduction='mean',
-                         zero_infinity=False: -1),
-        torch.cummax: lambda input, dim, out=None: -1,
-        torch.cummin: lambda input, dim, out=None: -1,
-        torch.cumprod: lambda input, dim, out=None, dtype=None: -1,
-        torch.cumsum: lambda input, dim, out=None, dtype=None: -1,
-        torch.dequantize: lambda input: -1,
-        torch.det: lambda input: -1,
-        torch.detach: lambda input: -1,
-        torch.diag: lambda input, diagonal=0, out=None: -1,
-        torch.diag_embed: lambda input, diagonal=0, out=None: -1,
-        torch.diagflat: lambda input, offset=0: -1,
-        torch.diagonal: lambda input, offset=0, dim1=0, dim2=1: -1,
-        torch.digamma: lambda input, out=None: -1,
-        torch.dist: lambda input, other, p=2: -1,
-        torch.div: lambda input, other, out=None: -1,
-        torch.dot: lambda mat1, mat2: -1,
-        torch.dropout: lambda input, p, train, inplace=False: -1,
-        torch.dsmm: lambda input, mat2: -1,
-        torch.hsmm: lambda mat1, mat2: -1,
-        torch.eig: lambda input, eigenvectors=False, out=None: -1,
-        torch.einsum: lambda equation, *operands: -1,
-        torch.einsum: lambda equation, *operands: -1,
-        torch.embedding: (lambda input, weight, padding_idx=None, max_norm=None, norm_type=2.0, scale_grad_by_freq=False,
-                          sparse=False: -1),
-        torch.embedding_bag: (lambda input, weight, offsets, max_norm=None, norm_type=2, scale_grad_by_freq=False,
-                              mode='mean', sparse=False, per_sample_weights=None: -1),
-        torch.empty_like: lambda input, dtype=None, layout=None, device=None, requires_grad=False: -1,
-        torch.eq: lambda input, other, out=None: -1,
-        torch.equal: lambda input, other: -1,
-        torch.erf: lambda input, out=None: -1,
-        torch.erfc: lambda input, out=None: -1,
-        torch.erfinv: lambda input, out=None: -1,
-        torch.exp: lambda input, out=None: -1,
-        torch.expm1: lambda input, out=None: -1,
-        torch.fake_quantize_per_channel_affine: lambda input, scale, zero_point, axis, quant_min, quant_max: -1,
-        torch.fake_quantize_per_tensor_affine: lambda input, scale, zero_point, quant_min, quant_max: -1,
-        torch.fbgemm_linear_fp16_weight: lambda input, packed_weight, bias: -1,
-        torch.fbgemm_linear_fp16_weight_fp32_activation: lambda input, packed_weight, bias: -1,
-        torch.fbgemm_linear_int8_weight: lambda input, weight, packed, col_offsets, weight_scale, weight_zero_point, bias: -1,
-        torch.fbgemm_linear_int8_weight_fp32_activation: (lambda input, weight, packed, col_offsets, weight_scale,
-                                                          weight_zero_point, bias: -1),
-        torch.fbgemm_linear_quantize_weight: lambda input: -1,
-        torch.fbgemm_pack_gemm_matrix_fp16: lambda input: -1,
-        torch.fbgemm_pack_quantized_matrix: lambda input, K, N: -1,
-        torch.feature_alpha_dropout: lambda input, p, train: -1,
-        torch.feature_dropout: lambda input, p, train: -1,
-        torch.fft: lambda input, signal_ndim, normalized=False: -1,
-        torch.flatten: lambda input, start_dim=0, end_dim=-1: -1,
-        torch.flip: lambda input, dims: -1,
-        torch.frobenius_norm: lambda input, dim=None, keepdim=False, out=None: -1,
-        torch.floor: lambda input, out=None: -1,
-        torch.floor_divide: lambda input, other: -1,
-        torch.fmod: lambda input, other, out=None: -1,
-        torch.frac: lambda input, out=None: -1,
-        torch.full_like: lambda input, fill_value, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False: -1,
-        torch.functional.lu_unpack: lambda LU_data, LU_pivots, unpack_data=True, unpack_pivots=True: -1,
-        torch.gather: lambda input, dim, index, out=None, sparse_grad=False: -1,
-        torch.ge: lambda input, other, out=None: -1,
-        torch.geqrf: lambda input, out=None: -1,
-        torch.ger: lambda input, vec2, out=None: -1,
-        torch.grid_sampler: lambda input, grid, interpolation_mode, padding_mode, align_corners: -1,
-        torch.grid_sampler_2d: lambda input, grid, interpolation_mode, padding_mode, align_corners: -1,
-        torch.grid_sampler_3d: lambda input, grid, interpolation_mode, padding_mode, align_corners: -1,
-        torch.group_norm: lambda input, num_groups, weight=None, bias=None, eps=1e-05, cudnn_enabled=True: -1,
-        torch.gru: lambda input, hx, params, has_biases, num_layers, gropout, train, bidirectional, batch_first: -1,
-        torch.gru_cell: lambda input, hx, w_ih, w_hh, b_ih=None, b_hh=None: -1,
-        torch.gt: lambda input, other, out=None: -1,
-        torch.hardshrink: lambda input, lambd=0.5: -1,
-        torch.hinge_embedding_loss: lambda input, target, margin=1.0, size_average=None, reduce=None, reduction='mean': -1,
-        torch.histc: lambda input, bins=100, min=0, max=0, out=None: -1,
-        torch.hspmm: lambda mat1, mat2, out=None: -1,
-        torch.ifft: lambda input, signal_ndim, normalized=False: -1,
-        torch.copy_imag: lambda input, out=None: -1,
-        torch.imag: lambda input, out=None: -1,
-        torch.index_add: lambda input, dim, index, source: -1,
-        torch.index_copy: lambda input, dim, index, source: -1,
-        torch.index_put: lambda input, indices, values, accumulate=False: -1,
-        torch.index_select: lambda input, dim, index, out=None: -1,
-        torch.index_fill: lambda input, dim, index, value: -1,
-        torch.isfinite: lambda tensor: -1,
-        torch.isinf: lambda tensor: -1,
-        torch.instance_norm: (lambda input, running_mean, running_var, weight, bias, use_input_stats, momentum, eps,
-                              cudnn_enabled: -1),
-        torch.int_repr: lambda input: -1,
-        torch.inverse: lambda input, out=None: -1,
-        torch.irfft: lambda input, signal_ndim, normalized=False, onesided=True, signal_sizes=None: -1,
-        torch.is_complex: lambda input: -1,
-        torch.is_distributed: lambda input: -1,
-        torch.is_floating_point: lambda input: -1,
-        torch.is_nonzero: lambda input: -1,
-        torch.is_same_size: lambda input, other: -1,
-        torch.is_signed: lambda input: -1,
-        torch.isclose: lambda input, other, rtol=1e-05, atol=1e-08, equal_nan=False: -1,
-        torch.isnan: lambda input: -1,
-        torch.kl_div: lambda input, target, size_average=None, reduce=None, reduction='mean', log_target=False: -1,
-        torch.kthvalue: lambda input, k, dim=None, keepdim=False, out=None: -1,
-        torch.layer_norm: lambda input, normalized_shape, weight=None, bias=None, esp=1e-05, cudnn_enabled=True: -1,
-        torch.le: lambda input, other, out=None: -1,
-        torch.lerp: lambda input, end, weight, out=None: -1,
-        torch.lgamma: lambda input, out=None: -1,
-        torch.lobpcg: lambda input, k=None, B=None, X=None, n=None, iK=None, niter=None, tol=None, largest=None, method=None,
-        tracker=None, ortho_iparams=None, ortho_fparams=None, ortho_bparams=None: -1,
-        torch.log: lambda input, out=None: -1,
-        torch.log_softmax: lambda input, dim, dtype: -1,
-        torch.log10: lambda input, out=None: -1,
-        torch.log1p: lambda input, out=None: -1,
-        torch.log2: lambda input, out=None: -1,
-        torch.logdet: lambda input: -1,
-        torch.logical_and: lambda input, other, out=None: -1,
-        torch.logical_not: lambda input, out=None: -1,
-        torch.logical_or: lambda input, other, out=None: -1,
-        torch.logical_xor: lambda input, other, out=None: -1,
-        torch.logsumexp: lambda input, names, keepdim, out=None: -1,
-        torch.lstm: lambda data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional: -1,
-        torch.lstm_cell: lambda input, hx, w_ih, w_hh, b_ih=None, b_hh=None: -1,
-        torch.lstsq: lambda input, A, out=None: -1,
-        torch.lt: lambda input, other, out=None: -1,
-        torch.lu: lambda A, pivot=True, get_infos=False, out=None: -1,
-        torch.lu_solve: lambda input, LU_data, LU_pivots, out=None: -1,
-        torch.margin_ranking_loss: lambda input1, input2, target, margin=0, size_average=None, reduce=None, reduction='mean': -1,
-        torch.masked_fill: lambda input, mask, value: -1,
-        torch.masked_scatter: lambda input, mask, source: -1,
-        torch.masked_select: lambda input, mask, out=None: -1,
-        torch.matmul: lambda input, other, out=None: -1,
-        torch.matrix_power: lambda input, n: -1,
-        torch.matrix_rank: lambda input, tol=None, symmetric=False: -1,
-        torch.max: lambda input, out=None: -1,
-        torch.max_pool1d: lambda input, kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False: -1,
-        torch.max_pool2d: lambda input, kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False: -1,
-        torch.max_pool3d: lambda input, kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False: -1,
-        torch.max_pool1d_with_indices: (lambda input, kernel_size, stride=None, padding=0, dilation=1,
-                                        return_indices=False, ceil_mode=False: -1),
-        torch.mean: lambda input: -1,
-        torch.median: lambda input: -1,
-        torch.meshgrid: lambda *tensors, **kwargs: -1,
-        torch.min: lambda input, out=None: -1,
-        torch.miopen_batch_norm: (lambda input, weight, bias, running_mean, running_var, training,
-                                  exponential_average_factor, epsilon: -1),
-        torch.miopen_convolution: lambda input, weight, bias, padding, stride, dilation, groups, benchmark, deterministic: -1,
-        torch.miopen_convolution_transpose: (lambda input, weight, bias, padding, output_padding, stride, dilation,
-                                             groups, benchmark, deterministic: -1),
-        torch.miopen_depthwise_convolution: (lambda input, weight, bias, padding, stride, dilation, groups, benchmark,
-                                             deterministic: -1),
-        torch.miopen_rnn: (lambda input, weight, weight_stride0, hx, cx, mode, hidden_size, num_layers, batch_first,
-                           dropout, train, bidirectional, batch_sizes, dropout_state: -1),
-        torch.mm: lambda input, mat2, out=None: -1,
-        torch.mode: lambda input: -1,
-        torch.mul: lambda input, other, out=None: -1,
-        torch.multinomial: lambda input, num_samples, replacement=False, out=None: -1,
-        torch.mv: lambda input, vec, out=None: -1,
-        torch.mvlgamma: lambda input, p: -1,
-        torch.narrow: lambda input, dim, start, length: -1,
-        torch.native_batch_norm: lambda input, weight, bias, running_mean, running_var, training, momentum, eps: -1,
-        torch.native_layer_norm: lambda input, weight, bias, M, N, eps: -1,
-        torch.native_norm: lambda input, p=2: -1,
-        torch.ne: lambda input, other, out=None: -1,
-        torch.neg: lambda input, out=None: -1,
-        torch.nn.functional.adaptive_avg_pool2d: lambda input, output_size: -1,
-        torch.nn.functional.adaptive_avg_pool3d: lambda input, output_size: -1,
-        torch.nn.functional.adaptive_max_pool1d: lambda input, output_size, return_indices=False: -1,
-        torch.nn.functional.adaptive_max_pool1d_with_indices: lambda input, output_size, return_indices=False: -1,
-        torch.nn.functional.adaptive_max_pool2d: lambda input, output_size, return_indices=False: -1,
-        torch.nn.functional.adaptive_max_pool2d_with_indices: lambda input, output_size, return_indices=False: -1,
-        torch.nn.functional.adaptive_max_pool3d: lambda input, output_size, return_indices=False: -1,
-        torch.nn.functional.adaptive_max_pool3d_with_indices: lambda input, output_size, return_indices=False: -1,
-        torch.nn.functional.affine_grid: lambda theta, size, align_corners=None: -1,
-        torch.nn.functional.alpha_dropout: lambda input, p=0.5, training=False, inplace=False: -1,
-        torch.nn.functional.avg_pool2d: (lambda input, kernel_size, stride=None, padding=0, ceil_mode=False,
-                                         count_include_pad=True, divisor_override=None: -1),
-        torch.nn.functional.avg_pool3d: (lambda input, kernel_size, stride=None, padding=0, ceil_mode=False,
-                                         count_include_pad=True, divisor_override=None: -1),
-        torch.nn.functional.batch_norm: (lambda input, running_mean, running_var, weight=None, bias=None, training=False,
-                                         momentum=0.1, eps=1e-05: -1),
-        torch.nn.functional.bilinear: lambda input1, input2, weight, bias=None: -1,
-        torch.nn.functional.binary_cross_entropy: (lambda input, target, weight=None, size_average=None, reduce=None,
-                                                   reduction="mean": -1),
-        torch.nn.functional.binary_cross_entropy_with_logits: (lambda input, target, weight=None, size_average=None,
-                                                               reduce=None, reduction="mean", pos_weight=None: -1),
-        torch.nn.functional.celu: lambda input, alpha=1.0, inplace=False: -1,
-        torch.nn.functional.cosine_embedding_loss: (lambda input1, input2, target, margin=0, size_average=None,
-                                                    reduce=None, reduction='mean': -1),
-        torch.nn.functional.cross_entropy: (lambda input, target, weight=None, size_average=None, ignore_index=-100,
-                                            reduce=None, reduction="mean": -1),
-        torch.nn.functional.ctc_loss: (lambda log_probs, targets, input_lengths, target_lengths, blank=0,
-                                       reduction='mean', zero_infinity=False: -1),
-        torch.nn.functional.dropout: lambda input, p=0.5, training=True, inplace=False: -1,
-        torch.nn.functional.dropout2d: lambda input, p=0.5, training=True, inplace=False: -1,
-        torch.nn.functional.dropout3d: lambda input, p=0.5, training=True, inplace=False: -1,
-        torch.nn.functional.elu: lambda input, alpha=1.0, inplace=False: -1,
-        torch.nn.functional.embedding: (lambda input, weight, padding_idx=None, max_norm=None, norm_type=2.0,
-                                        scale_grad_by_freq=False, sparse=False: -1),
-        torch.nn.functional.embedding_bag: (lambda input, weight, offsets=None, max_norm=None, norm_type=2,
-                                            scale_grad_by_freq=False, mode='mean', sparse=False, per_sample_weights=None,
-                                            include_last_offset=False: -1),
-        torch.nn.functional.feature_alpha_dropout: lambda input, p=0.5, training=False, inplace=False: -1,
-        torch.nn.functional.fold: lambda input, output_size, kernel_size, dilation=1, padding=0, stride=1: -1,
-        torch.nn.functional.fractional_max_pool2d: (lambda input, kernel_size, output_size=None, output_ratio=None,
-                                                    return_indices=False, _random_samples=None: -1),
-        torch.nn.functional.fractional_max_pool2d_with_indices: (
-            lambda input, kernel_size, output_size=None, output_ratio=None, return_indices=False,
-            _random_samples=None: -1),
-        torch.nn.functional.fractional_max_pool3d: (lambda input, kernel_size, output_size=None, output_ratio=None,
-                                                    return_indices=False, _random_samples=None: -1),
-        torch.nn.functional.fractional_max_pool3d_with_indices: (
-            lambda input, kernel_size, output_size=None, output_ratio=None, return_indices=False,
-            _random_samples=None: -1),
-        torch.nn.functional.gelu: lambda input: -1,
-        torch.nn.functional.glu: lambda input, dim=-1: -1,
-        torch.nn.functional.grid_sample: lambda input, grid, mode='bilinear', padding_mode='zeros', align_corners=None: -1,
-        torch.nn.functional.group_norm: lambda input, num_groups, weight=None, bias=None, eps=1e-05: -1,
-        torch.nn.functional.gumbel_softmax: lambda logits, tau=1, hard=False, eps=1e-10, dim=-1: -1,
-        torch.nn.functional.hardshrink: lambda input, lambd=0.5: -1,
-        torch.nn.functional.hardtanh: lambda input, min_val=-1., max_val=1., inplace=False: -1,
-        torch.nn.functional.hinge_embedding_loss: (lambda input, target, margin=1.0, size_average=None, reduce=None,
-                                                   reduction='mean': -1),
-        torch.nn.functional.instance_norm: (lambda input, running_mean=None, running_var=None, weight=None, bias=None,
-                                            use_input_stats=True, momentum=0.1, eps=1e-05: -1),
-        torch.nn.functional.interpolate: (lambda input, size=None, scale_factor=None, mode='nearest', align_corners=None,
-                                          recompute_scale_factor=None: -1),
-        torch.nn.functional.kl_div: lambda input, target, size_average=None, reduce=None, reduction='mean', log_target=False: -1,
-        torch.nn.functional.l1_loss: lambda input, target, size_average=None, reduce=None, reduction='mean': -1,
-        torch.nn.functional.layer_norm: lambda input, normalized_shape, weight=None, bias=None, eps=1e-05: -1,
-        torch.nn.functional.leaky_relu: lambda input, negative_slope=0.01, inplace=False: -1,
-        torch.nn.functional.linear: lambda input, weight, bias=None: -1,
-        torch.nn.functional.local_response_norm: lambda input, size, alpha=0.0001, beta=0.75, k=1.0: -1,
-        torch.nn.functional.log_softmax: lambda input, dim=None, _stacklevel=3, dtype=None: -1,
-        torch.nn.functional.logsigmoid: lambda input: -1,
-        torch.nn.functional.lp_pool1d: lambda input, norm_type, kernel_size, stride=None, ceil_mode=False: -1,
-        torch.nn.functional.lp_pool2d: lambda input, norm_type, kernel_size, stride=None, ceil_mode=False: -1,
-        torch.nn.functional.margin_ranking_loss: (lambda input1, input2, target, margin=0, size_average=None,
-                                                  reduce=None, reduction='mean': -1),
-        torch.nn.functional.max_pool1d: (lambda input, kernel_size, stride=None, padding=0, dilation=1,
-                                         return_indices=False, ceil_mode=False: -1),
-        torch.nn.functional.max_pool1d_with_indices: (lambda input, kernel_size, stride=None, padding=0, dilation=1,
-                                                      return_indices=False, ceil_mode=False: -1),
-        torch.nn.functional.max_pool2d: (lambda input, kernel_size, stride=None, padding=0, dilation=1,
-                                         return_indices=False, ceil_mode=False: -1),
-        torch.nn.functional.max_pool2d_with_indices: (lambda input, kernel_size, stride=None, padding=0, dilation=1,
-                                                      return_indices=False, ceil_mode=False: -1),
-        torch.nn.functional.max_pool3d: (lambda input, kernel_size, stride=None, padding=0, dilation=1,
-                                         return_indices=False, ceil_mode=False: -1),
-        torch.nn.functional.max_pool3d_with_indices: (lambda input, kernel_size, stride=None, padding=0, dilation=1,
-                                                      return_indices=False, ceil_mode=False: -1),
-        torch.nn.functional.max_unpool1d: lambda input, indices, kernel_size, stride=None, padding=0, output_size=None: -1,
-        torch.nn.functional.max_unpool2d: lambda input, indices, kernel_size, stride=None, padding=0, output_size=None: -1,
-        torch.nn.functional.max_unpool3d: lambda input, indices, kernel_size, stride=None, padding=0, output_size=None: -1,
-        torch.nn.functional.mse_loss: lambda input, target, size_average=None, reduce=None, reduction='mean': -1,
-        torch.nn.functional.multi_head_attention_forward: (
-            lambda query, key, value, embed_dim_to_check, num_heads, in_proj_weight, in_proj_bias, bias_k, bias_v,
-            add_zero_attn, dropout_p, out_proj_weight, out_proj_bias, training=True, key_padding_mask=None,
-            need_weights=True, attn_mask=None, use_separate_proj_weight=False, q_proj_weight=None, k_proj_weight=None,
-            v_proj_weight=None, static_k=None, static_v=None: -1),
-        torch.nn.functional.multi_margin_loss: (lambda input, target, p=1, margin=1.0, weight=None, size_average=None,
-                                                reduce=None, reduction='mean': -1),
-        torch.nn.functional.multilabel_margin_loss: (lambda input, target, size_average=None, reduce=None,
-                                                     reduction='mean': -1),
-        torch.nn.functional.multilabel_soft_margin_loss: (lambda input, target, weight=None, size_average=None,
-                                                          reduce=None, reduction='mean': -1),
-        torch.nn.functional.nll_loss: (lambda input, target, weight=None, size_average=None, ignore_index=-100,
-                                       reduce=None, reduction='mean': -1),
-        torch.nn.functional.normalize: lambda input, p=2, dim=1, eps=1e-12, out=None: -1,
-        torch.nn.functional.one_hot: lambda tensor, num_classes=-1: -1,
-        torch.nn.functional.pad: lambda input, pad, mode='constant', value=0: -1,
-        torch.nn.functional.pairwise_distance: lambda x1, x2, p=2.0, eps=1e-06, keepdim=False: -1,
-        torch.nn.functional.poisson_nll_loss: (lambda input, target, log_input=True, full=False, size_average=None,
-                                               eps=1e-08, reduce=None, reduction='mean': -1),
-        torch.nn.functional.prelu: lambda input, weight: -1,
-        torch.nn.functional.relu: lambda input, inplace=False: -1,
-        torch.nn.functional.relu6: lambda input, inplace=False: -1,
-        torch.nn.functional.rrelu: lambda input, lower=0.125, upper=0.3333333333333333, training=False, inplace=False: -1,
-        torch.nn.functional.selu: lambda input, inplace=False: -1,
-        torch.nn.functional.smooth_l1_loss: lambda input, target, size_average=None, reduce=None, reduction='mean': -1,
-        torch.nn.functional.soft_margin_loss: lambda input, target, size_average=None, reduce=None, reduction='mean': -1,
-        torch.nn.functional.softmax: lambda input, dim=None, _stacklevel=3, dtype=None: -1,
-        torch.nn.functional.softmin: lambda input, dim=None, _stacklevel=3, dtype=None: -1,
-        torch.nn.functional.softplus: lambda input, beta=1, threshold=20: -1,
-        torch.nn.functional.softshrink: lambda input, lambd=0.5: -1,
-        torch.nn.functional.softsign: lambda input: -1,
-        torch.nn.functional.tanhshrink: lambda input: -1,
-        torch.nn.functional.threshold: lambda input, threshold, value, inplace=False: -1,
-        torch.nn.functional.triplet_margin_loss: (lambda anchor, positive, negative, margin=1.0, p=2, eps=1e-06,
-                                                  swap=False, size_average=None, reduce=None, reduction='mean': -1),
-        torch.nn.functional.unfold: lambda input, kernel_size, dilation=1, padding=0, stride=1: -1,
-        torch.nonzero: lambda input, as_tuple=False: -1,
-        torch.norm: lambda input, p='fro', dim=None, keepdim=False, out=None, dtype=None: -1,
-        torch.norm_except_dim: lambda v, pow=2, dim=0: -1,
-        torch.normal: lambda mean, std, out=None: -1,
-        torch.nuclear_norm: lambda input, p='fro', dim=None, keepdim=False, out=None, dtype=None: -1,
-        torch.numel: lambda input: -1,
-        torch.orgqr: lambda input1, input2: -1,
-        torch.ormqr: lambda input, input2, input3, left=True, transpose=False: -1,
-        torch.pairwise_distance: lambda x1, x2, p=2.0, eps=1e-06, keepdim=False: -1,
-        torch.pca_lowrank: lambda input, q=None, center=True, niter=2: -1,
-        torch.pdist: lambda input, p=2: -1,
-        torch.pinverse: lambda input, rcond=1e-15: -1,
-        torch.pixel_shuffle: lambda input, upscale_factor: -1,
-        torch.poisson: lambda input, generator=None: -1,
-        torch.poisson_nll_loss: lambda input, target, log_input, full, eps, reduction: -1,
-        torch.polygamma: lambda input, n, out=None: -1,
-        torch.prelu: lambda input, weight: -1,
-        torch.ones_like: lambda input, dtype=None, layout=None, device=None, requires_grad=False: -1,
-        torch.pow: lambda input, exponent, out=None: -1,
-        torch.prod: lambda input: -1,
-        torch.q_per_channel_axis: lambda input: -1,
-        torch.q_per_channel_scales: lambda input: -1,
-        torch.q_per_channel_zero_points: lambda input: -1,
-        torch.q_scale: lambda input: -1,
-        torch.q_zero_point: lambda input: -1,
-        torch.qr: lambda input, some=True, out=None: -1,
-        torch.quantize_per_channel: lambda input, scales, zero_points, axis, dtype: -1,
-        torch.quantize_per_tensor: lambda input, scale, zero_point, dtype: -1,
-        torch.quantized_batch_norm: lambda input, weight, bias, mean, var, eps, output_scale, output_zero_point: -1,
-        torch.quantized_gru: lambda data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional: -1,
-        torch.quantized_gru_cell: (lambda input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih,
-                                   col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh: -1),
-        torch.quantized_lstm: (lambda input, hx, params, has_biases, num_layers, dropout, train, bidirectional,
-                               batch_first, dtype=None, use_dynamic=False: -1),
-        torch.quantized_lstm_cell: (lambda input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih,
-                                    col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh: -1),
-        torch.quantized_max_pool2d: lambda input, kernel_size, stride, padding, dilation, ceil_mode=False: -1,
-        torch.quantized_rnn_relu_cell: (lambda input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih,
-                                        col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh: -1),
-        torch.quantized_rnn_tanh_cell: (lambda input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih,
-                                        col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh: -1),
-        torch.rand_like: lambda input, dtype=None, layout=None, device=None, requires_grad=False: -1,
-        torch.randint_like: lambda input, low, high, dtype=None, layout=torch.strided, device=None, requires_grad=False: -1,
-        torch.randn_like: lambda input, dtype=None, layout=None, device=None, requires_grad=False: -1,
-        torch.real: lambda input, out=None: -1,
-        torch.copy_real: lambda input, out=None: -1,
-        torch.reciprocal: lambda input, out=None: -1,
-        torch.relu: lambda input, inplace=False: -1,
-        torch.remainder: lambda input, other, out=None: -1,
-        torch.renorm: lambda input, p, dim, maxnorm, out=None: -1,
-        torch.repeat_interleave: lambda input, repeats, dim=None: -1,
-        torch.reshape: lambda input, shape: -1,
-        torch.result_type: lambda tensor1, tensor2: -1,
-        torch.rfft: lambda input, signal_ndim, normalized=False, onesided=True: -1,
-        torch.rnn_relu: lambda input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first: -1,
-        torch.rnn_relu_cell: lambda input, hx, w_ih, w_hh, b_ih=None, b_hh=None: -1,
-        torch.rnn_tanh: lambda input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first: -1,
-        torch.rnn_tanh_cell: lambda input, hx, w_ih, w_hh, b_ih=None, b_hh=None: -1,
-        torch.roll: lambda input, shifts, dims=None: -1,
-        torch.rot90: lambda input, k, dims: -1,
-        torch.round: lambda input, out=None: -1,
-        torch.rrelu: lambda input, lower=1. / 8, upper=1. / 3, training=False, inplace=False: -1,
-        torch.rsqrt: lambda input, out=None: -1,
-        torch.rsub: lambda input, other, alpha=1: -1,
-        torch.saddmm: lambda input, mat1, mat2, beta=1, alpha=1, out=None: -1,
-        torch.scalar_tensor: lambda s, dtype=None, layour=None, device=None, pin_memory=None: -1,
-        torch.scatter: lambda input, dim, index, src: -1,
-        torch.scatter_add: lambda input, dim, index, src: -1,
-        torch.select: lambda input, dim, index: -1,
-        torch.selu: lambda input, inplace=False: -1,
-        torch.sigmoid: lambda input, out=None: -1,
-        torch.sign: lambda input, out=None: -1,
-        torch.sin: lambda input, out=None: -1,
-        torch.sinh: lambda input, out=None: -1,
-        torch.slogdet: lambda input: -1,
-        torch.smm: lambda input, mat2: -1,
-        torch.spmm: lambda input, mat2: -1,
-        torch.softmax: lambda input, dim, dtype=None: -1,
-        torch.solve: lambda input, A, out=None: -1,
-        torch.sort: lambda input, dim=-1, descending=False, out=None: -1,
-        torch.split: lambda tensor, split_size_or_sections, dim=0: -1,
-        torch.split_with_sizes: lambda tensor, split_size_or_sections, dim=0: -1,
-        torch.sqrt: lambda input, out=None: -1,
-        torch.square: lambda input, out=None: -1,
-        torch.squeeze: lambda input, dim=None, out=None: -1,
-        torch.sspaddmm: lambda input, mat1, mat2, beta=1, alpha=1, out=None: -1,
-        torch.stack: lambda tensors, dim=0, out=None: -1,
-        torch.std: lambda input: -1,
-        torch.std_mean: lambda input: -1,
-        torch.stft: (lambda input, n_fft, hop_length=None, win_length=None, window=None, center=True,
-                     pad_mode='reflect', normalized=False, onesided=True: -1),
-        torch.sub: lambda input, other, out=None: -1,
-        torch.sum: lambda input: -1,
-        torch.svd: lambda input, some=True, compute_uv=True, out=None: -1,
-        torch.svd_lowrank: lambda input, q=6, niter=2, M=None: -1,
-        torch.symeig: lambda input, eigenvectors=False, upper=True, out=None: -1,
-        torch.t: lambda input: -1,
-        torch.take: lambda input, index: -1,
-        torch.tan: lambda input, out=None: -1,
-        torch.tanh: lambda input, out=None: -1,
-        torch.tensordot: lambda a, b, dims=2: -1,
-        torch.threshold: lambda input, threshold, value, inplace=False: -1,
-        torch.topk: lambda input, k, dim=-1, descending=False, out=None: -1,
-        torch.trace: lambda input: -1,
-        torch.transpose: lambda input, dim0, dim1: -1,
-        torch.trapz: lambda y, x, dim=-1: -1,
-        torch.triangular_solve: lambda input, A, upper=True, transpose=False, unitriangular=False: -1,
-        torch.tril: lambda input, diagonal=0, out=None: -1,
-        torch.tril_indices: lambda row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided: -1,
-        torch.triplet_margin_loss: (lambda anchor, positive, negative, margin=1.0, p=2, eps=1e-06, swap=False,
-                                    size_average=None, reduce=None, reduction='mean': -1),
-        torch.triu: lambda input, diagonal=0, out=None: -1,
-        torch.triu_indices: lambda row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided: -1,
-        torch.true_divide: lambda input, other: -1,
-        torch.trunc: lambda input, out=None: -1,
-        torch.unbind: lambda input, dim=0: -1,
-        torch.unique: lambda input, sorted=True, return_inverse=False, return_counts=False, dim=None: -1,
-        torch.unique_consecutive: lambda input, return_inverse=False, return_counts=False, dim=None: -1,
-        torch.unsqueeze: lambda input, dim, out=None: -1,
-        torch.var: lambda input: -1,
-        torch.var_mean: lambda input: -1,
-        torch.where: lambda condition, x, y: -1,
-        torch.zeros_like: lambda input, dtype=None, layout=None, device=None, requires_grad=False: -1,
-    }
-
-def _get_overloaded_args(relevant_args):
-    """Returns a list of arguments on which to call __torch_function__.
-
-    Checks arguments in relevant_args for __torch_function__ implementations,
-    storing references to the arguments and their types in overloaded_args and
-    overloaded_types in order of calling precedence. Only distinct types are
-    considered. If a type is a subclass of another type it will have higher
-    precedence, otherwise the precedence order is the same as the order of
-    arguments in relevant_args, that is, from left-to-right in the argument list.
-
-    The precedence-determining algorithm implemented in this function is
-    described in `NEP-0018`_.
-
-    See torch::append_overloaded_arg for the equivalent function in the C++
-    implementation.
-
-    Parameters
-    ----------
-    relevant_args : iterable of array-like
-        Iterable of array-like arguments to check for __torch_function__
-        methods.
-
-    Returns
-    -------
-    overloaded_types : collection of types
-        Types of arguments from relevant_args with __torch_function__ methods.
-    overloaded_args : list
-        Arguments from relevant_args on which to call __torch_function__
-        methods, in the order in which they should be called.
-
-    .. _NEP-0018:
-       https://numpy.org/neps/nep-0018-array-function-protocol.html
-
-    """
-    # Runtime is O(num_arguments * num_unique_types)
-    overloaded_types = []
-    overloaded_args = []
-    for arg in relevant_args:
-        arg_type = type(arg)
-        # We only collect arguments if they have a unique type, which ensures
-        # reasonable performance even with a long list of possibly overloaded
-        # arguments.
-        if (arg_type not in overloaded_types and hasattr(arg_type, '__torch_function__')):
-            # Create lists explicitly for the first type (usually the only one
-            # done) to avoid setting up the iterator for overloaded_args.
-            if overloaded_types:
-                overloaded_types.append(arg_type)
-                # By default, insert argument at the end, but if it is
-                # subclass of another argument, insert it before that argument.
-                # This ensures "subclasses before superclasses".
-                index = len(overloaded_args)
-                for i, old_arg in enumerate(overloaded_args):
-                    if issubclass(arg_type, type(old_arg)):
-                        index = i
-                        break
-                overloaded_args.insert(index, arg)
-            else:
-                overloaded_types = [arg_type]
-                overloaded_args = [arg]
-
-    return overloaded_args
-
-
-def handle_torch_function(
-        public_api, relevant_args, *args, **kwargs):
-    """Implement a function with checks for __torch_function__ overrides.
-
-    See torch::autograd::handle_torch_function for the equivalent of this
-    function in the C++ implementation.
-
-    Arguments
-    ---------
-    public_api : function
-        Function exposed by the public torch API originally called like
-        ``public_api(*args, **kwargs)`` on which arguments are now being
-        checked.
-    relevant_args : iterable
-        Iterable of arguments to check for __torch_function__ methods.
-    args : tuple
-        Arbitrary positional arguments originally passed into ``public_api``.
-    kwargs : tuple
-        Arbitrary keyword arguments originally passed into ``public_api``.
-
-    Returns
-    -------
-    Result from calling `implementation()` or an `__torch_function__`
-    method, as appropriate.
-
-    Raises
-    ------
-    TypeError : if no implementation is found.
-
-    """
-    # Check for __torch_function__ methods.
-    overloaded_args = _get_overloaded_args(relevant_args)
-    # overloaded_args already have unique types.
-    types = tuple(map(type, overloaded_args))
-
-    # Call overrides
-    for overloaded_arg in overloaded_args:
-        # Use `public_api` instead of `implementation` so __torch_function__
-        # implementations can do equality/identity comparisons.
-        result = overloaded_arg.__torch_function__(public_api, types, args, kwargs)
-
-        if result is not NotImplemented:
-            return result
-
-    func_name = '{}.{}'.format(public_api.__module__, public_api.__name__)
-    raise TypeError("no implementation found for '{}' on types that implement "
-                    '__torch_function__: {}'
-                    .format(func_name, list(map(type, overloaded_args))))
-
-def has_torch_function(relevant_args):
-    """Check for __torch_function__ implementations in the elements of an iterable
-
-    Arguments
-    ---------
-    relevant_args : iterable
-        Iterable or aguments to check for __torch_function__ methods.
-
-    Returns
-    -------
-    True if any of the elements of relevant_args have __torch_function__
-    implementations, False otherwise.
-    """
-    return any(hasattr(a, '__torch_function__') for a in relevant_args)
-
-def get_overridable_functions():
-    """List functions that are overridable via __torch_function__
-
-    Returns
-    -------
-    A dictionary that maps namespaces that contain overridable functions
-    to functions in that namespace that can be overrided.
-
-    """
-    overridable_funcs = collections.defaultdict(list)
-    tested_namespaces = [
-        (torch, torch.__all__ + dir(torch._C._VariableFunctions)),
-        (torch.functional, torch.functional.__all__),
-        (torch.nn.functional, dir(torch.nn.functional)),
-    ]
-    for namespace, ns_funcs in tested_namespaces:
-        for func_name in ns_funcs:
-            # ignore private functions or functions that are deleted in torch.__init__
-            if func_name.startswith('_') or func_name == 'unique_dim':
-                continue
-            # ignore in-place operators
-            if func_name.endswith('_'):
-                continue
-            # only consider objects with lowercase names
-            if not func_name.islower():
-                continue
-            func = getattr(namespace, func_name)
-            # ignore re-exported modules
-            if isinstance(func, types.ModuleType):
-                continue
-            # ignore __future__ imports
-            if isinstance(func, __future__._Feature):
-                continue
-            # cannot be overriden by __torch_function__
-            if func in get_ignored_functions():
-                msg = ("{}.{} is in the tuple returned by torch._overrides.get_ignored_functions "
-                       "but still has an explicit override")
-                assert func not in get_testing_overrides(), msg.format(namespace, func.__name__)
-                continue
-            overridable_funcs[namespace].append(func)
-    return overridable_funcs
diff --git a/mup/examples/Transformer/coord_checks/sp_trsfmr_adam_coord.png b/mup/examples/Transformer/coord_checks/sp_trsfmr_adam_coord.png
deleted file mode 100644
index 1fa9fac7b..000000000
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diff --git a/mup/examples/Transformer/coord_checks/sp_trsfmr_sgd_coord.png b/mup/examples/Transformer/coord_checks/sp_trsfmr_sgd_coord.png
deleted file mode 100644
index 796eb3bb9..000000000
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diff --git "a/mup/examples/Transformer/coord_checks/\316\274p_trsfmr_adam_coord.png" "b/mup/examples/Transformer/coord_checks/\316\274p_trsfmr_adam_coord.png"
deleted file mode 100644
index 4d9123b01..000000000
Binary files "a/mup/examples/Transformer/coord_checks/\316\274p_trsfmr_adam_coord.png" and /dev/null differ
diff --git "a/mup/examples/Transformer/coord_checks/\316\274p_trsfmr_sgd_coord.png" "b/mup/examples/Transformer/coord_checks/\316\274p_trsfmr_sgd_coord.png"
deleted file mode 100644
index 6bcb654d7..000000000
Binary files "a/mup/examples/Transformer/coord_checks/\316\274p_trsfmr_sgd_coord.png" and /dev/null differ
diff --git a/mup/examples/Transformer/data.py b/mup/examples/Transformer/data.py
deleted file mode 100644
index 5c4b96e18..000000000
--- a/mup/examples/Transformer/data.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import os
-from io import open
-import torch
-
-class Dictionary(object):
-    def __init__(self):
-        self.word2idx = {}
-        self.idx2word = []
-
-    def add_word(self, word):
-        if word not in self.word2idx:
-            self.idx2word.append(word)
-            self.word2idx[word] = len(self.idx2word) - 1
-        return self.word2idx[word]
-
-    def __len__(self):
-        return len(self.idx2word)
-
-
-class Corpus(object):
-    def __init__(self, path):
-        self.dictionary = Dictionary()
-        self.train = None
-        self.valid = None
-        self.test = None
-        if not self.load_cache(path):
-            self.train = self.tokenize(os.path.join(path, 'train.txt'))
-            self.valid = self.tokenize(os.path.join(path, 'valid.txt'))
-            self.test = self.tokenize(os.path.join(path, 'test.txt'))
-            self.save_cache(path)
-
-    def load_cache(self, path):
-        for cache in ['dict.pt', 'train.pt', 'valid.pt', 'test.pt']:
-            cache_path = os.path.join(path, cache)
-            if not os.path.exists(cache_path):
-                return False
-        self.dictionary = torch.load(os.path.join(path, 'dict.pt'))
-        self.train = torch.load(os.path.join(path, 'train.pt'))
-        self.valid = torch.load(os.path.join(path, 'valid.pt'))
-        self.test = torch.load(os.path.join(path, 'test.pt'))
-        return True
-
-    def save_cache(self, path):
-        torch.save(self.dictionary, os.path.join(path, 'dict.pt'))
-        torch.save(self.train, os.path.join(path, 'train.pt'))
-        torch.save(self.valid, os.path.join(path, 'valid.pt'))
-        torch.save(self.test, os.path.join(path, 'test.pt'))
-    
-    def tokenize(self, path):
-        """Tokenizes a text file."""
-        assert os.path.exists(path)
-        # Add words to the dictionary
-        with open(path, 'r', encoding="utf8") as f:
-            for line in f:
-                words = line.split() + ['<eos>']
-                for word in words:
-                    self.dictionary.add_word(word)
-
-        # Tokenize file content
-        with open(path, 'r', encoding="utf8") as f:
-            idss = []
-            for line in f:
-                words = line.split() + ['<eos>']
-                ids = []
-                for word in words:
-                    ids.append(self.dictionary.word2idx[word])
-                idss.append(torch.tensor(ids).type(torch.int64))
-            ids = torch.cat(idss)
-
-        return ids
diff --git a/mup/examples/Transformer/generate.py b/mup/examples/Transformer/generate.py
deleted file mode 100644
index 91764d9cf..000000000
--- a/mup/examples/Transformer/generate.py
+++ /dev/null
@@ -1,78 +0,0 @@
-###############################################################################
-# Language Modeling on Wikitext-2
-#
-# This file generates new sentences sampled from the language model
-#
-###############################################################################
-
-import argparse
-
-import torch
-
-import data
-
-parser = argparse.ArgumentParser(description='PyTorch Wikitext-2 Language Model')
-
-# Model parameters.
-parser.add_argument('--data', type=str, default='./data/wikitext-2',
-                    help='location of the data corpus')
-parser.add_argument('--checkpoint', type=str, default='./model.pt',
-                    help='model checkpoint to use')
-parser.add_argument('--outf', type=str, default='generated.txt',
-                    help='output file for generated text')
-parser.add_argument('--words', type=int, default='1000',
-                    help='number of words to generate')
-parser.add_argument('--seed', type=int, default=1111,
-                    help='random seed')
-parser.add_argument('--cuda', action='store_true',
-                    help='use CUDA')
-parser.add_argument('--temperature', type=float, default=1.0,
-                    help='temperature - higher will increase diversity')
-parser.add_argument('--log-interval', type=int, default=100,
-                    help='reporting interval')
-args = parser.parse_args()
-
-# Set the random seed manually for reproducibility.
-torch.manual_seed(args.seed)
-if torch.cuda.is_available():
-    if not args.cuda:
-        print("WARNING: You have a CUDA device, so you should probably run with --cuda")
-
-device = torch.device("cuda" if args.cuda else "cpu")
-
-if args.temperature < 1e-3:
-    parser.error("--temperature has to be greater or equal 1e-3")
-
-with open(args.checkpoint, 'rb') as f:
-    model = torch.load(f).to(device)
-model.eval()
-
-corpus = data.Corpus(args.data)
-ntokens = len(corpus.dictionary)
-
-is_transformer_model = hasattr(model, 'model_type') and model.model_type == 'Transformer'
-if not is_transformer_model:
-    hidden = model.init_hidden(1)
-input = torch.randint(ntokens, (1, 1), dtype=torch.long).to(device)
-
-with open(args.outf, 'w') as outf:
-    with torch.no_grad():  # no tracking history
-        for i in range(args.words):
-            if is_transformer_model:
-                output = model(input, False)
-                word_weights = output[-1].squeeze().div(args.temperature).exp().cpu()
-                word_idx = torch.multinomial(word_weights, 1)[0]
-                word_tensor = torch.Tensor([[word_idx]]).long().to(device)
-                input = torch.cat([input, word_tensor], 0)
-            else:
-                output, hidden = model(input, hidden)
-                word_weights = output.squeeze().div(args.temperature).exp().cpu()
-                word_idx = torch.multinomial(word_weights, 1)[0]
-                input.fill_(word_idx)
-
-            word = corpus.dictionary.idx2word[word_idx]
-
-            outf.write(word + ('\n' if i % 20 == 19 else ' '))
-
-            if i % args.log_interval == 0:
-                print('| Generated {}/{} words'.format(i, args.words))
diff --git a/mup/examples/Transformer/main.py b/mup/examples/Transformer/main.py
deleted file mode 100644
index 3c6dc803b..000000000
--- a/mup/examples/Transformer/main.py
+++ /dev/null
@@ -1,473 +0,0 @@
-# coding: utf-8
-import argparse
-import os
-import time
-
-import numpy as np
-import pandas as pd
-import torch
-import torch.nn as nn
-import torch.optim as optim
-try:
-    from apex import amp
-except:
-    print('Failed to import apex. You can still train with --precision {float|double}.')
-
-from mup.coord_check import get_coord_data, plot_coord_data
-from mup import MuAdam, MuSGD, get_shapes, make_base_shapes, set_base_shapes
-
-import data
-import model as mdl
-
-
-###############################################################################
-# Training code
-###############################################################################
-
-# get_batch subdivides the source data into chunks of length args.bptt.
-# If source is equal to the example output of the batchify function, with
-# a bptt-limit of 2, we'd get the following two Variables for i = 0:
-# ┌ a g m s ┐ ┌ b h n t ┐
-# └ b h n t ┘ └ c i o u ┘
-# Note that despite the name of the function, the subdivison of data is not
-# done along the batch dimension (i.e. dimension 1), since that was handled
-# by the batchify function. The chunks are along dimension 0, corresponding
-# to the seq_len dimension in the LSTM.
-
-def get_batch(source, i, bptt):
-    seq_len = min(bptt, len(source) - 1 - i)
-    data = source[i:i+seq_len]
-    target = source[i+1:i+1+seq_len].view(-1)
-    return data, target
-
-def batchloader(train_data, bptt):
-    for batch, i in enumerate(range(0, train_data.size(0) - 1, bptt)):
-        yield get_batch(train_data, i, bptt)
-
-def batchify(data, bsz, device):
-    # Work out how cleanly we can divide the dataset into bsz parts.
-    nbatch = data.size(0) // bsz
-    # Trim off any extra elements that wouldn't cleanly fit (remainders).
-    data = data.narrow(0, 0, nbatch * bsz)
-    # Evenly divide the data across the bsz batches.
-    data = data.view(bsz, -1).t().contiguous()
-    return data.to(device)
-    
-def setprec(t, precision):
-    if precision == 'half':
-        # do nothing since this is handled by AMP
-        return t
-    elif precision == 'float':
-        return t.float()
-    elif precision == 'double':
-        return t.double()
-    else:
-        raise ValueError(f'invalid precision string {args.precision}')
-
-def coord_check(mup, lr, optimizer, batch_size, nsteps, nseeds, data_dir, args, plotdir='', legend=False):
-
-    corpus = data.Corpus(data_dir)
-    ntokens = len(corpus.dictionary)
-
-    def gen(w, standparam=False):
-        import model as _model
-        def f():
-            model = _model.TransformerModel(args, ntokens, ninp=w, nhead=args.nhead, nhid=w*args.ffn_ratio, nlayers=args.nlayers, dropout=args.dropout,
-                                            tied=args.tied, bias=args.bias, encoder_var=args.init_var, 
-                                            decoder_var=args.init_var, standparam=standparam).to(args.device)
-            model = setprec(model, args.precision)
-            if standparam:
-                set_base_shapes(model, None)
-            else:
-                assert args.load_base_shapes, 'load_base_shapes needs to be nonempty'
-                set_base_shapes(model, args.load_base_shapes)
-            return model
-        return f
-
-    optimizer = optimizer.replace('mu', '')
-    widths = 2**np.arange(7, 14 if optimizer=='sgd' else 12)
-    models = {w: gen(w, standparam=not mup) for w in widths}
-
-    
-    train_data = batchify(corpus.train, batch_size, device=args.device)
-    df = get_coord_data(models, batchloader(train_data, args.bptt), mup=mup, lr=lr, optimizer=optimizer, flatten_output=True, nseeds=nseeds, nsteps=nsteps, lossfn='nll')
-
-    prm = 'μP' if mup else 'SP'
-    return plot_coord_data(df, legend=legend,
-        save_to=os.path.join(plotdir, f'{prm.lower()}_trsfmr_{optimizer}_coord.png'),
-        suptitle=f'{prm} Transformer {optimizer} lr={lr} nseeds={nseeds}',
-        face_color='xkcd:light grey' if not mup else None)
-
-
-if __name__ == '__main__':
-
-    parser = argparse.ArgumentParser(description=
-    '''
-    PyTorch Wikitext-2 Transformer Language Model, with μP.
-
-    To train a μP model, one needs to first specify the base shapes. To save base shapes info, run, for example,
-
-        python main.py --d_model 256 --save_base_shapes width256.bsh
-
-    To train using MuAdam, run
-
-        python main.py --d_model 256 --load_base_shapes width256.bsh --cuda --optimizer muadam
-
-    To perform coord check, run
-
-        python main.py --load_base_shapes width256.bsh --optimizer sgd --lr 0.5 --cuda --coord_check
-
-        python main.py --load_base_shapes width256.bsh --optimizer adam --lr 0.01 --cuda --coord_check
-
-    If you don't specify a base shape file, then you are using standard parametrization
-
-        python main.py --d_model 256 --cuda --optimizer muadam
-
-    Note that models of different depths need separate `.bsh` files.
-    ''', formatter_class=argparse.RawTextHelpFormatter)
-    parser.add_argument('--data', type=str, default='./data/wikitext-2',
-                        help='location of the data corpus')
-    parser.add_argument('--bias', action='store_true',
-                        help='use bias')
-    parser.add_argument('--save_base_shapes', type=str, default='',
-                        help='file location to save base shapes at')
-    parser.add_argument('--load_base_shapes', type=str, default='',
-                        help='file location to load base shapes from')
-    parser.add_argument('--d_model', type=int, default=256,
-                        help='width of the model')
-    parser.add_argument('--ffn_ratio', type=int, default=1,
-                        help='the ratio of d_ffn to d_model')
-    parser.add_argument('--nlayers', type=int, default=2,
-                        help='number of layers')
-    parser.add_argument('--nhead', type=int, default=2,
-                        help='the number of heads in the encoder/decoder of the transformer model')
-    parser.add_argument('--lr', type=float, default=0.001,
-                        help='initial learning rate')
-    parser.add_argument('--momentum', type=float, default=0,
-                        help='momentum')
-    parser.add_argument('--output_mult', type=float, default=1,
-                        help='output is multiplied by sqrt(output_mult/d_model)')
-    parser.add_argument('--input_mult', type=float, default=1,
-                        help='input is multiplied by sqrt(input_mult*d_model)')
-    parser.add_argument('--attn_mult', type=float, default=1,
-                        help='attn is multiplied by sqrt(attn_mult)/head_dim')
-    parser.add_argument('--optimizer', default='musgd', choices=['sgd', 'musgd', 'adam', 'muadam'])
-    parser.add_argument('--init_var', type=float, default=1,
-                        help='weights are initialized with variance init_var/ninp')
-    parser.add_argument('--clip', type=float, default=0.25,
-                        help='gradient clipping')
-    parser.add_argument('--epochs', type=int, default=40,
-                        help='upper epoch limit')
-    parser.add_argument('--batch_size', type=int, default=20, metavar='N',
-                        help='batch size')
-    parser.add_argument('--bptt', type=int, default=35,
-                        help='sequence length')
-    parser.add_argument('--dropout', type=float, default=0.2,
-                        help='dropout applied to layers (0 = no dropout)')
-    parser.add_argument('--tied', action='store_true',
-                        help='tie the word embedding and softmax weights')
-    parser.add_argument('--seed', type=int, default=1111,
-                        help='random seed')
-    parser.add_argument('--cuda', action='store_true',
-                        help='use CUDA')
-    parser.add_argument('--precision', type=str, default='float',
-                        help='float | double | half')
-    parser.add_argument('--log_interval', type=int, default=200, metavar='N',
-                        help='report interval')
-    parser.add_argument('--save_dir', type=str, default=None,
-                        help='path to save the final model')
-    parser.add_argument('--resume_dir', type=str, default=None,
-                        help='path to resume training')
-    parser.add_argument('--log_dir', type=str, default='.',
-                        help='path to save logs')
-    parser.add_argument('--coord_check', action='store_true',
-                        help='test μ parametrization is correctly implemented by collecting statistics on coordinate distributions for a few steps of training.')
-    parser.add_argument('--coord_check_nsteps', type=int, default=3,
-                        help='Do coord check with this many steps.')
-    parser.add_argument('--coord_check_nseeds', type=int, default=3,
-                        help='number of seeds for testing correctness of μ parametrization')
-    parser.add_argument('--deferred_init', action='store_true', help='Skip instantiating the base and delta models for mup. Requires torchdistx.')
-    
-    args = parser.parse_args()
-
-    print(args)
-
-    # Set the random seed manually for reproducibility.
-    torch.manual_seed(args.seed)
-    if torch.cuda.is_available():
-        if not args.cuda:
-            print("WARNING: You have a CUDA device, so you should probably run with --cuda")
-
-    device = args.device = torch.device("cuda" if args.cuda else "cpu")
-
-    ###############################################################################
-    # Load data
-    ###############################################################################
-
-    corpus = data.Corpus(args.data)
-
-    # Starting from sequential data, batchify arranges the dataset into columns.
-    # For instance, with the alphabet as the sequence and batch size 4, we'd get
-    # ┌ a g m s ┐
-    # │ b h n t │
-    # │ c i o u │
-    # │ d j p v │
-    # │ e k q w │
-    # └ f l r x ┘.
-    # These columns are treated as independent by the model, which means that the
-    # dependence of e. g. 'g' on 'f' can not be learned, but allows more efficient
-    # batch processing.
-
-    eval_batch_size = 10
-    train_data = batchify(corpus.train, args.batch_size, device)
-    val_data = batchify(corpus.valid, eval_batch_size, device)
-    test_data = batchify(corpus.test, eval_batch_size, device)
-
-    ###############################################################################
-    # Build the model
-    ###############################################################################
-
-
-    ntokens = len(corpus.dictionary)
-
-
-
-    def evaluate(data_source):
-        # Turn on evaluation mode which disables dropout.
-        model.eval()
-        total_loss = 0.
-        ntokens = len(corpus.dictionary)
-        with torch.no_grad():
-            for i in range(0, data_source.size(0) - 1, args.bptt):
-                data, targets = get_batch(data_source, i, args.bptt)
-                output = model(data)
-                output = output.view(-1, ntokens)
-                total_loss += len(data) * criterion(output, targets).item()
-        return total_loss / (len(data_source) - 1)
-
-
-    def train(optimizer, epoch):
-        # Turn on training mode which enables dropout.
-        model.train()
-        total_loss = 0.
-        epoch_loss = 0.
-        start_time = time.time()
-        ntokens = len(corpus.dictionary)
-        first_loss = None
-        for batch, i in enumerate(range(0, train_data.size(0) - 1, args.bptt)):
-            data, targets = get_batch(train_data, i, args.bptt)
-            # Starting each batch, we detach the hidden state from how it was previously produced.
-            # If we didn't, the model would try backpropagating all the way to start of the dataset.
-            
-            optimizer.zero_grad()
-            output = model(data)
-            output = output.view(-1, ntokens)
-            loss = criterion(output, targets)
-            if torch.isnan(loss):
-                exit(0)
-            if args.precision == 'half':
-                with amp.scale_loss(loss, optimizer) as scaled_loss:
-                    scaled_loss.backward()
-            else:
-                loss.backward()
-
-            if args.clip > 0:
-                # `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
-                if args.precision == 'half':
-                    torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.clip)
-                else:
-                    torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
-
-            optimizer.step()
-
-            total_loss += loss.item()
-            epoch_loss += len(data) * loss.item()
-
-            if batch % args.log_interval == 0 and batch > 0:
-                cur_loss = total_loss / args.log_interval
-                elapsed = time.time() - start_time
-                print('| epoch {:3d} | {:5d}/{:5d} batches | lr {:02.5f} | ms/batch {:5.2f} | '
-                        'loss {:5.2f} | ppl {:8.2f}'.format(
-                    epoch, batch, len(train_data) // args.bptt, lr,
-                    elapsed * 1000 / args.log_interval, cur_loss, np.exp(cur_loss)))
-                total_loss = 0
-                start_time = time.time()
-                if first_loss is None:
-                    first_loss = cur_loss
-
-        return epoch_loss / (len(train_data) - 1), first_loss
-        
-    if args.coord_check:
-        print('testing parametrization')
-        import os
-        os.makedirs('coord_checks', exist_ok=True)
-        plotdir = 'coord_checks'
-        coord_check(mup=True, lr=args.lr, optimizer=args.optimizer, batch_size=args.batch_size, nsteps=args.coord_check_nsteps, nseeds=args.coord_check_nseeds, data_dir=args.data, args=args, plotdir=plotdir, legend=False)
-        coord_check(mup=False, lr=args.lr, optimizer=args.optimizer, batch_size=args.batch_size, nsteps=args.coord_check_nsteps, nseeds=args.coord_check_nseeds, data_dir=args.data, args=args, plotdir=plotdir, legend=False)
-        import sys; sys.exit()
-
-
-    if args.save_base_shapes:
-        print(f'saving base shapes at {args.save_base_shapes}')
-        if args.deferred_init:
-            from torchdistx.deferred_init import deferred_init
-            # We don't need to instantiate the base and delta models
-            base_shapes = get_shapes(
-                deferred_init(mdl.TransformerModel, args, ntokens, ninp=args.d_model, nhead=args.nhead, nhid=args.d_model*args.ffn_ratio, nlayers=args.nlayers, dropout=args.dropout,
-                                        tied=args.tied, bias=args.bias, encoder_var=args.init_var, 
-                                        decoder_var=args.init_var, standparam=args.load_base_shapes=='')
-            )
-            delta_shapes = get_shapes(
-                # just need to change whatever dimension(s) we are scaling
-                deferred_init(mdl.TransformerModel, args, ntokens, ninp=args.d_model*2, nhead=args.nhead, nhid=args.d_model*args.ffn_ratio*2,
-                                        nlayers=args.nlayers, dropout=args.dropout,
-                                        tied=args.tied, bias=args.bias, encoder_var=args.init_var, 
-                                        decoder_var=args.init_var, standparam=args.load_base_shapes=='')
-            )
-        else:
-            base_shapes = get_shapes(
-                mdl.TransformerModel(args, ntokens, ninp=args.d_model, nhead=args.nhead, nhid=args.d_model*args.ffn_ratio, nlayers=args.nlayers, dropout=args.dropout,
-                                        tied=args.tied, bias=args.bias, encoder_var=args.init_var, 
-                                        decoder_var=args.init_var, standparam=args.load_base_shapes=='')
-            )
-            delta_shapes = get_shapes(
-                # just need to change whatever dimension(s) we are scaling
-                mdl.TransformerModel(args, ntokens, ninp=args.d_model*2, nhead=args.nhead, nhid=args.d_model*args.ffn_ratio*2,
-                                        nlayers=args.nlayers, dropout=args.dropout,
-                                        tied=args.tied, bias=args.bias, encoder_var=args.init_var, 
-                                        decoder_var=args.init_var, standparam=args.load_base_shapes=='')
-            )
-        make_base_shapes(base_shapes, delta_shapes, savefile=args.save_base_shapes)
-        print('done and exit')
-        import sys; sys.exit()
-    model = mdl.TransformerModel(args, ntokens, ninp=args.d_model, nhead=args.nhead, nhid=args.d_model*args.ffn_ratio, nlayers=args.nlayers, dropout=args.dropout,
-                                    tied=args.tied, bias=args.bias, encoder_var=args.init_var, 
-                                    decoder_var=args.init_var, standparam=args.load_base_shapes=='')
-    if args.load_base_shapes:
-        print(f'loading base shapes from {args.load_base_shapes}')
-        set_base_shapes(model, args.load_base_shapes)
-        print('done')
-    else:
-        print(f'using own shapes')
-        set_base_shapes(model, None)
-        print('done')
-
-    model = model.to(device)
-    model = setprec(model, args.precision)
-
-    criterion = nn.NLLLoss()
-
-    if args.save_dir is not None:
-        os.makedirs(args.save_dir, exist_ok=True)
-
-    # Loop over epochs.
-    lr = args.lr
-    best_val_loss = float('inf')
-
-    if args.optimizer == 'sgd':
-        optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
-    elif args.optimizer == 'musgd':
-        optimizer = MuSGD(model.parameters(), lr=args.lr, momentum=args.momentum)
-    elif args.optimizer == 'adam':
-        optimizer = optim.Adam(model.parameters(), lr=args.lr)
-    elif args.optimizer == 'muadam':
-        optimizer = MuAdam(model.parameters(), lr=args.lr)
-    else:
-        raise ValueError()
-
-    # half-precision black magic
-    if args.precision == 'half':
-        model, optimizer = amp.initialize(
-            model,
-            optimizer,
-            opt_level='O1',
-            min_loss_scale=0.0001,
-            verbosity=0
-            )
-
-    logs = []
-    start_epoch = 0
-    if args.resume_dir and os.path.exists(os.path.join(args.resume_dir, 'checkpoint_last.pt')):
-        checkpoint = torch.load(os.path.join(args.resume_dir, 'checkpoint_last.pt'))
-        model.load_state_dict(checkpoint['model'])
-        optimizer.load_state_dict(checkpoint['optimizer'])
-        if args.precision == 'half':
-            amp.load_state_dict(checkpoint['amp'])
-        start_epoch = checkpoint['epoch']
-        best_val_loss = checkpoint['best_val_loss']
-        logs = checkpoint['logs']
-
-    # At any point you can hit Ctrl + C to break out of training early.
-    try:
-        for epoch in range(start_epoch+1, args.epochs+1):
-            epoch_start_time = time.time()
-            train_loss, first_loss = train(optimizer, epoch)
-            # print(first_loss)
-            val_loss = evaluate(val_data)
-            print('-' * 89)
-            print('| end of epoch {:3d} | time: {:5.2f}s | valid loss {:5.2f} | '
-                    'valid ppl {:8.2f}'.format(epoch, (time.time() - epoch_start_time),
-                                            val_loss, np.exp(val_loss)))
-            print('-' * 89)
-            logs.append(dict(
-                epoch=epoch,
-                train_loss=train_loss,
-                val_loss=val_loss,
-                first_loss=first_loss
-            ))
-            # Save the model if the validation loss is the best we've seen so far.
-            if args.save_dir is not None:
-                if val_loss < best_val_loss:
-                    checkpoint = {
-                        'model': model.state_dict(),
-                        'optimizer': optimizer.state_dict(),
-                        'epoch': epoch,
-                        'best_val_loss': best_val_loss,
-                        'logs': logs
-                    }
-                    if args.precision == 'half':
-                        checkpoint['amp'] = amp.state_dict(),
-                    with open(os.path.join(args.save_dir, 'checkpoint_best.pt'), 'wb') as f:
-                        torch.save(checkpoint, f)
-                    best_val_loss = val_loss
-                else:
-                    checkpoint = {
-                        'model': model.state_dict(),
-                        'optimizer': optimizer.state_dict(),
-                        'epoch': epoch,
-                        'best_val_loss': best_val_loss,
-                        'logs': logs
-                    }
-                    if args.precision == 'half':
-                        checkpoint['amp'] = amp.state_dict()
-                with open(os.path.join(args.save_dir, 'checkpoint_last.pt'), 'wb') as f:
-                    torch.save(checkpoint, f)
-
-    except KeyboardInterrupt:
-        print('-' * 89)
-        print('Exiting from training early')
-
-    # Load the best saved model.
-    if args.save_dir is not None:
-        with open(os.path.join(args.save_dir, 'checkpoint_best.pt'), 'rb') as f:
-            checkpoint = torch.load(f)
-            model.load_state_dict(checkpoint['model'])
-            optimizer.load_state_dict(checkpoint['optimizer'])
-            if args.precision == 'half':
-                amp.load_state_dict(checkpoint['amp'][0])
-        # Run on test data.
-        test_loss = evaluate(test_data)
-        print('=' * 89)
-        print('| End of training | test loss {:5.2f} | test ppl {:8.2f}'.format(
-            test_loss, np.exp(test_loss)))
-        print('=' * 89)
-        logs.append(dict(
-            epoch='-1',
-            test_loss=test_loss
-        ))
-
-
-    with open(os.path.join(os.path.expanduser(args.log_dir), 'logs.tsv'), 'w') as f:
-        logdf = pd.DataFrame(logs)
-        print(os.path.join(os.path.expanduser(args.log_dir), 'logs.tsv'))
-        f.write(logdf.to_csv(sep='\t', float_format='%.4f'))
diff --git a/mup/examples/Transformer/model.py b/mup/examples/Transformer/model.py
deleted file mode 100644
index e8e7faed5..000000000
--- a/mup/examples/Transformer/model.py
+++ /dev/null
@@ -1,635 +0,0 @@
-import math
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from mup import MuReadout, MuSharedReadout
-
-'''
-The only things we modified from the original pytorch Transformer example are 
-1) replace the readout layer with MuReadout or MuSharedReadout, 
-2) use fan_in style initialization, 
-3) change attention scaling to 1/d instead of 1/sqrt(d), and
-4) zero initialization of query weights
-'''
-
-def init_method_normal(sigma):
-    """Init method based on N(0, sigma)."""
-    def init_(tensor):
-        return nn.init.normal_(tensor, mean=0.0, std=sigma)
-    return init_
-
-
-# Temporarily leave PositionalEncoding module here. Will be moved somewhere else.
-class PositionalEncoding(nn.Module):
-    r"""Inject some information about the relative or absolute position of the tokens
-        in the sequence. The positional encodings have the same dimension as
-        the embeddings, so that the two can be summed. Here, we use sine and cosine
-        functions of different frequencies.
-    .. math::
-        \text{PosEncoder}(pos, 2i) = sin(pos/10000^(2i/d_model))
-        \text{PosEncoder}(pos, 2i+1) = cos(pos/10000^(2i/d_model))
-        \text{where pos is the word position and i is the embed idx)
-    Args:
-        d_model: the embed dim (required).
-        dropout: the dropout value (default=0.1).
-        max_len: the max. length of the incoming sequence (default=5000).
-    Examples:
-        >>> pos_encoder = PositionalEncoding(d_model)
-    """
-
-    def __init__(self, d_model, dropout=0.1, max_len=5000):
-        super(PositionalEncoding, self).__init__()
-        self.dropout = nn.Dropout(p=dropout)
-
-        pe = torch.zeros(max_len, d_model)
-        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
-        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
-        pe[:, 0::2] = torch.sin(position * div_term)
-        pe[:, 1::2] = torch.cos(position * div_term)
-        pe = pe.unsqueeze(0).transpose(0, 1)
-        self.register_buffer('pe', pe)
-
-    def forward(self, x):
-        r"""Inputs of forward function
-        Args:
-            x: the sequence fed to the positional encoder model (required).
-        Shape:
-            x: [sequence length, batch size, embed dim]
-            output: [sequence length, batch size, embed dim]
-        Examples:
-            >>> output = pos_encoder(x)
-        """
-
-        x = x + self.pe[:x.size(0), :]
-        return self.dropout(x)
-
-class TransformerModel(nn.Module):
-    """Container module with an encoder, a recurrent or transformer module, and a decoder."""
-
-    def __init__(self, args, ntoken, ninp, nhead, nhid, nlayers, dropout=0.5, bias=True,
-                encoder_var=1, decoder_var=1, tied=False, standparam=False):
-        '''Note: `bias` only affects the bias of decoder'''
-        super(TransformerModel, self).__init__()
-        try:
-            from torch.nn import TransformerEncoder
-        except:
-            raise ImportError('TransformerEncoder module does not exist in PyTorch 1.1 or lower.')
-        self.model_type = 'Transformer'
-        self.encoder_var = encoder_var
-        self.decoder_var = decoder_var
-        self.ninp = ninp
-        self.nhid = nhid
-        self.bias = bias
-        self.tied = tied
-
-        self.src_mask = None
-        self.pos_encoder = PositionalEncoding(ninp, dropout)
-        encoder_layers = TransformerEncoderLayer(ninp, nhead, nhid, dropout, attn_mult=args.attn_mult, 
-                                                    bias=bias, encoder_var=encoder_var, nlayers=nlayers,
-                                                    standparam=standparam)
-        self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers)
-        self.standparam = standparam
-        self.encoder = nn.Embedding(ntoken, ninp)
-        self.ninp = ninp
-        if standparam:
-            self.decoder = nn.Linear(ninp, ntoken, bias=bias)
-            if tied:
-                with torch.no_grad():
-                    self.encoder.weight = self.decoder.weight
-        else:
-            if tied:
-                self.decoder = MuSharedReadout(self.encoder.weight, bias=bias, output_mult=args.output_mult)
-            else:
-                self.decoder = MuReadout(ninp, ntoken, bias=bias, output_mult=args.output_mult)
-        self.init_weights()
-
-    def _generate_square_subsequent_mask(self, sz):
-        mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
-        mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
-        return mask
-
-    def init_weights(self):
-        if not self.tied:
-            if self.bias:
-                self.decoder.bias.data.zero_()
-            self.decoder.weight.data.zero_()
-
-    def forward(self, src, has_mask=True):
-        if has_mask:
-            device = src.device
-            if self.src_mask is None or self.src_mask.size(0) != len(src):
-                mask = self._generate_square_subsequent_mask(len(src)).to(device)
-                self.src_mask = mask
-        else:
-            self.src_mask = None
-
-        src = self.encoder(src)
-        src = self.pos_encoder(src)     
-        output = self.transformer_encoder(src, self.src_mask)
-        output = self.decoder(output)
-        return F.log_softmax(output, dim=-1)
-
-from torch.nn import Module, Linear, Dropout, LayerNorm, Parameter
-from torch.nn.init import constant_
-
-def _get_activation_fn(activation):
-    if activation == "relu":
-        return F.relu
-    elif activation == "gelu":
-        return F.gelu
-
-    raise RuntimeError("activation should be relu/gelu, not {}".format(activation))
-
-class TransformerEncoderLayer(Module):
-    r"""TransformerEncoderLayer is made up of self-attn and feedforward network.
-    This standard encoder layer is based on the paper "Attention Is All You Need".
-    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,
-    Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in
-    Neural Information Processing Systems, pages 6000-6010. Users may modify or implement
-    in a different way during application.
-    Args:
-        d_model: the number of expected features in the input (required).
-        nhead: the number of heads in the multiheadattention models (required).
-        dim_feedforward: the dimension of the feedforward network model (default=2048).
-        dropout: the dropout value (default=0.1).
-        activation: the activation function of intermediate layer, relu or gelu (default=relu).
-    Examples::
-        >>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8)
-        >>> src = torch.rand(10, 32, 512)
-        >>> out = encoder_layer(src)
-    """
-
-    def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu",
-                    encoder_var=1, attn_mult=1, bias=True, nlayers=1, standparam=False):
-        super(TransformerEncoderLayer, self).__init__()
-        self.attn_mult = attn_mult
-        self.self_attn = MultiheadAttention(d_model, nhead, dropout=dropout,attn_mult=attn_mult,
-                                            bias=bias, add_bias_kv=bias, encoder_var=encoder_var,
-                                            standparam=standparam)
-        # Implementation of Feedforward model
-        self.linear1 = nn.Linear(d_model, dim_feedforward, bias=bias)
-        self.dropout = Dropout(dropout)
-        self.linear2 = nn.Linear(dim_feedforward, d_model, bias=bias)
-
-        self.norm1 = LayerNorm(d_model, elementwise_affine=True)
-        self.norm2 = LayerNorm(d_model, elementwise_affine=True)
-        self.dropout1 = Dropout(dropout)
-        self.dropout2 = Dropout(dropout)
-
-        self.activation = _get_activation_fn(activation)
-        self.init_method = init_method_normal((encoder_var / d_model)**0.5)
-        
-        self.nlayers = nlayers
-        self.d_model = d_model
-        self.reset_parameters()
-
-
-    def __setstate__(self, state):
-        if 'activation' not in state:
-            state['activation'] = F.relu
-        super(TransformerEncoderLayer, self).__setstate__(state)
-    
-    def reset_parameters(self):
-        self.init_method(self.linear1.weight)
-        self.init_method(self.linear2.weight)
-        if self.linear1.bias is not None:
-            constant_(self.linear1.bias, 0.)
-        if self.linear2.bias is not None:
-            constant_(self.linear2.bias, 0.)
-
-    def forward(self, src, src_mask=None, src_key_padding_mask=None):
-        # type: (Tensor, Optional[Tensor], Optional[Tensor]) -> Tensor
-        r"""Pass the input through the encoder layer.
-        Args:
-            src: the sequence to the encoder layer (required).
-            src_mask: the mask for the src sequence (optional).
-            src_key_padding_mask: the mask for the src keys per batch (optional).
-        Shape:
-            see the docs in Transformer class.
-        """
-        src1 = self.self_attn(src, src, src, attn_mask=src_mask,
-                              key_padding_mask=src_key_padding_mask)[0]
-
-        src = src + self.dropout1(src1)        
-        src = self.norm1(src)
-        src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
-        src = src + self.dropout2(src2)
-        src = self.norm2(src)
-        
-        return src
-
-class MultiheadAttention(Module):
-    r"""Allows the model to jointly attend to information
-    from different representation subspaces.
-    See reference: Attention Is All You Need
-    .. math::
-        \text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O
-        \text{where} head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)
-    Args:
-        embed_dim: total dimension of the model.
-        num_heads: parallel attention heads.
-        dropout: a Dropout layer on attn_output_weights. Default: 0.0.
-        bias: add bias as module parameter. Default: True.
-        add_bias_kv: add bias to the key and value sequences at dim=0.
-        add_zero_attn: add a new batch of zeros to the key and
-                       value sequences at dim=1.
-        kdim: total number of features in key. Default: None.
-        vdim: total number of features in key. Default: None.
-        Note: if kdim and vdim are None, they will be set to embed_dim such that
-        query, key, and value have the same number of features.
-    Examples::
-        >>> multihead_attn = nn.MultiheadAttention(embed_dim, num_heads)
-        >>> attn_output, attn_output_weights = multihead_attn(query, key, value)
-    """
-    __annotations__ = {
-        'bias_k': torch._jit_internal.Optional[torch.Tensor],
-        'bias_v': torch._jit_internal.Optional[torch.Tensor],
-    }
-    __constants__ = ['q_proj_weight', 'k_proj_weight', 'v_proj_weight', 'in_proj_weight']
-
-    def __init__(self, embed_dim, num_heads, dropout=0., bias=True, add_bias_kv=False,
-                 add_zero_attn=False, kdim=None, vdim=None, attn_mult=1, encoder_var=1, standparam=False):
-        super(MultiheadAttention, self).__init__()
-        self.embed_dim = embed_dim
-        self.attn_mult = attn_mult
-        self.kdim = kdim if kdim is not None else embed_dim
-        self.vdim = vdim if vdim is not None else embed_dim
-        self._qkv_same_embed_dim = self.kdim == embed_dim and self.vdim == embed_dim
-        self.standparam = standparam
-
-        self.num_heads = num_heads
-        self.dropout = dropout
-        self.head_dim = embed_dim // num_heads
-        assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
-
-        if self._qkv_same_embed_dim is False:
-            self.q_proj_weight = Parameter(torch.Tensor(embed_dim, embed_dim))
-            self.k_proj_weight = Parameter(torch.Tensor(embed_dim, self.kdim))
-            self.v_proj_weight = Parameter(torch.Tensor(embed_dim, self.vdim))
-            self.register_parameter('in_proj_weight', None)
-        else:
-            self.in_proj_weight = Parameter(torch.empty(3 * embed_dim, embed_dim))
-            self.register_parameter('q_proj_weight', None)
-            self.register_parameter('k_proj_weight', None)
-            self.register_parameter('v_proj_weight', None)
-
-        if bias:
-            self.in_proj_bias = Parameter(torch.empty(3 * embed_dim))
-        else:
-            self.register_parameter('in_proj_bias', None)
-        self.out_proj = Linear(embed_dim, embed_dim, bias=bias)
-
-        if add_bias_kv:
-            self.bias_k = Parameter(torch.empty(1, 1, embed_dim))
-            self.bias_v = Parameter(torch.empty(1, 1, embed_dim))
-        else:
-            self.bias_k = self.bias_v = None
-
-        self.add_zero_attn = add_zero_attn
-        
-        self.init_method = init_method_normal((encoder_var / embed_dim)**0.5)
-
-        self._reset_parameters()
-
-    def _reset_parameters(self):
-        if self._qkv_same_embed_dim:
-            self.init_method(self.in_proj_weight)
-            # zero initializing query head
-            constant_(self.in_proj_weight[:self.embed_dim], 0.)
-        else:
-            # zero initializing query head
-            constant_(self.q_proj_weight, 0.)
-            self.init_method(self.k_proj_weight)
-            self.init_method(self.v_proj_weight)
-
-        self.init_method(self.out_proj.weight)
-        if self.in_proj_bias is not None:
-            constant_(self.in_proj_bias, 0.)
-            constant_(self.out_proj.bias, 0.)
-        if self.bias_k is not None:
-            constant_(self.bias_k, 0.)
-        if self.bias_v is not None:
-            constant_(self.bias_v, 0.)
-
-    def __setstate__(self, state):
-        # Support loading old MultiheadAttention checkpoints generated by v1.1.0
-        if '_qkv_same_embed_dim' not in state:
-            state['_qkv_same_embed_dim'] = True
-
-        super(MultiheadAttention, self).__setstate__(state)
-
-    def forward(self, query, key, value, key_padding_mask=None,
-                need_weights=True, attn_mask=None):
-        # type: (Tensor, Tensor, Tensor, Optional[Tensor], bool, Optional[Tensor]) -> Tuple[Tensor, Optional[Tensor]]
-        r"""
-    Args:
-        query, key, value: map a query and a set of key-value pairs to an output.
-            See "Attention Is All You Need" for more details.
-        key_padding_mask: if provided, specified padding elements in the key will
-            be ignored by the attention. This is an binary mask. When the value is True,
-            the corresponding value on the attention layer will be filled with -inf.
-        need_weights: output attn_output_weights.
-        attn_mask: 2D or 3D mask that prevents attention to certain positions. This is an additive mask
-            (i.e. the values will be added to the attention layer). A 2D mask will be broadcasted for all
-            the batches while a 3D mask allows to specify a different mask for the entries of each batch.
-    Shape:
-        - Inputs:
-        - query: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is
-          the embedding dimension.
-        - key: :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is
-          the embedding dimension.
-        - value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is
-          the embedding dimension.
-        - key_padding_mask: :math:`(N, S)`, ByteTensor, where N is the batch size, S is the source sequence length.
-        - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length.
-          3D mask :math:`(N*num_heads, L, S)` where N is the batch size, L is the target sequence length,
-          S is the source sequence length.
-        - Outputs:
-        - attn_output: :math:`(L, N, E)` where L is the target sequence length, N is the batch size,
-          E is the embedding dimension.
-        - attn_output_weights: :math:`(N, L, S)` where N is the batch size,
-          L is the target sequence length, S is the source sequence length.
-        """
-        if not self._qkv_same_embed_dim:
-            #### swapping pytorch's attn with ours ####
-            return multi_head_attention_forward(
-                query, key, value, self.embed_dim, self.num_heads,
-                self.in_proj_weight, self.in_proj_bias,
-                self.bias_k, self.bias_v, self.add_zero_attn,
-                self.dropout, self.out_proj.weight, self.out_proj.bias,
-                training=self.training,
-                key_padding_mask=key_padding_mask, need_weights=need_weights,
-                attn_mask=attn_mask, use_separate_proj_weight=True,
-                q_proj_weight=self.q_proj_weight, k_proj_weight=self.k_proj_weight,
-                v_proj_weight=self.v_proj_weight, attn_mult=self.attn_mult,
-                standparam=self.standparam)
-        else:
-            #### swapping pytorch's attn with ours ####
-            return multi_head_attention_forward(
-                query, key, value, self.embed_dim, self.num_heads,
-                self.in_proj_weight, self.in_proj_bias,
-                self.bias_k, self.bias_v, self.add_zero_attn,
-                self.dropout, self.out_proj.weight, self.out_proj.bias,
-                training=self.training,
-                key_padding_mask=key_padding_mask, need_weights=need_weights,
-                attn_mask=attn_mask, attn_mult=self.attn_mult,
-                standparam=self.standparam)
-
-from _overrides import has_torch_function, handle_torch_function
-
-def multi_head_attention_forward(query,                           # type: Tensor
-                                 key,                             # type: Tensor
-                                 value,                           # type: Tensor
-                                 embed_dim_to_check,              # type: int
-                                 num_heads,                       # type: int
-                                 in_proj_weight,                  # type: Tensor
-                                 in_proj_bias,                    # type: Tensor
-                                 bias_k,                          # type: Optional[Tensor]
-                                 bias_v,                          # type: Optional[Tensor]
-                                 add_zero_attn,                   # type: bool
-                                 dropout_p,                       # type: float
-                                 out_proj_weight,                 # type: Tensor
-                                 out_proj_bias,                   # type: Tensor
-                                 training=True,                   # type: bool
-                                 key_padding_mask=None,           # type: Optional[Tensor]
-                                 need_weights=True,               # type: bool
-                                 attn_mask=None,                  # type: Optional[Tensor]
-                                 use_separate_proj_weight=False,  # type: bool
-                                 q_proj_weight=None,              # type: Optional[Tensor]
-                                 k_proj_weight=None,              # type: Optional[Tensor]
-                                 v_proj_weight=None,              # type: Optional[Tensor]
-                                 static_k=None,                   # type: Optional[Tensor]
-                                 static_v=None,                   # type: Optional[Tensor]
-                                 attn_mult=1,
-                                 standparam=False
-                                 ):
-    # type: (...) -> Tuple[Tensor, Optional[Tensor]]
-    r"""
-    Args:
-        query, key, value: map a query and a set of key-value pairs to an output.
-            See "Attention Is All You Need" for more details.
-        embed_dim_to_check: total dimension of the model.
-        num_heads: parallel attention heads.
-        in_proj_weight, in_proj_bias: input projection weight and bias.
-        bias_k, bias_v: bias of the key and value sequences to be added at dim=0.
-        add_zero_attn: add a new batch of zeros to the key and
-                       value sequences at dim=1.
-        dropout_p: probability of an element to be zeroed.
-        out_proj_weight, out_proj_bias: the output projection weight and bias.
-        training: apply dropout if is ``True``.
-        key_padding_mask: if provided, specified padding elements in the key will
-            be ignored by the attention. This is an binary mask. When the value is True,
-            the corresponding value on the attention layer will be filled with -inf.
-        need_weights: output attn_output_weights.
-        attn_mask: 2D or 3D mask that prevents attention to certain positions. This is an additive mask
-            (i.e. the values will be added to the attention layer). A 2D mask will be broadcasted for all
-            the batches while a 3D mask allows to specify a different mask for the entries of each batch.
-        use_separate_proj_weight: the function accept the proj. weights for query, key,
-            and value in different forms. If false, in_proj_weight will be used, which is
-            a combination of q_proj_weight, k_proj_weight, v_proj_weight.
-        q_proj_weight, k_proj_weight, v_proj_weight, in_proj_bias: input projection weight and bias.
-        static_k, static_v: static key and value used for attention operators.
-    Shape:
-        Inputs:
-        - query: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is
-          the embedding dimension.
-        - key: :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is
-          the embedding dimension.
-        - value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is
-          the embedding dimension.
-        - key_padding_mask: :math:`(N, S)`, ByteTensor, where N is the batch size, S is the source sequence length.
-        - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length.
-          3D mask :math:`(N*num_heads, L, S)` where N is the batch size, L is the target sequence length,
-          S is the source sequence length.
-        - static_k: :math:`(N*num_heads, S, E/num_heads)`, where S is the source sequence length,
-          N is the batch size, E is the embedding dimension. E/num_heads is the head dimension.
-        - static_v: :math:`(N*num_heads, S, E/num_heads)`, where S is the source sequence length,
-          N is the batch size, E is the embedding dimension. E/num_heads is the head dimension.
-        Outputs:
-        - attn_output: :math:`(L, N, E)` where L is the target sequence length, N is the batch size,
-          E is the embedding dimension.
-        - attn_output_weights: :math:`(N, L, S)` where N is the batch size,
-          L is the target sequence length, S is the source sequence length.
-    """
-    tgt_len, bsz, embed_dim = query.size()
-    assert embed_dim == embed_dim_to_check
-    assert key.size() == value.size()
-
-    head_dim = embed_dim // num_heads
-    assert head_dim * num_heads == embed_dim, "embed_dim must be divisible by num_heads"
-    ###### only thing changed from pytorch source ######
-    if standparam:
-        scaling = float(head_dim) ** -0.5 * math.sqrt(attn_mult)
-    else:
-        scaling = float(head_dim) ** -1 * math.sqrt(attn_mult)
-
-    if not use_separate_proj_weight:
-        if torch.equal(query, key) and torch.equal(key, value):
-            # self-attention
-            q, k, v = F.linear(query, in_proj_weight, in_proj_bias).chunk(3, dim=-1)
-
-        elif torch.equal(key, value):
-            # encoder-decoder attention
-            # This is inline in_proj function with in_proj_weight and in_proj_bias
-            _b = in_proj_bias
-            _start = 0
-            _end = embed_dim
-            _w = in_proj_weight[_start:_end, :]
-            if _b is not None:
-                _b = _b[_start:_end]
-            q = F.linear(query, _w, _b)
-
-            if key is None:
-                assert value is None
-                k = None
-                v = None
-            else:
-
-                # This is inline in_proj function with in_proj_weight and in_proj_bias
-                _b = in_proj_bias
-                _start = embed_dim
-                _end = None
-                _w = in_proj_weight[_start:, :]
-                if _b is not None:
-                    _b = _b[_start:]
-                k, v = F.linear(key, _w, _b).chunk(2, dim=-1)
-
-        else:
-            # This is inline in_proj function with in_proj_weight and in_proj_bias
-            _b = in_proj_bias
-            _start = 0
-            _end = embed_dim
-            _w = in_proj_weight[_start:_end, :]
-            if _b is not None:
-                _b = _b[_start:_end]
-            q = F.linear(query, _w, _b)
-
-            # This is inline in_proj function with in_proj_weight and in_proj_bias
-            _b = in_proj_bias
-            _start = embed_dim
-            _end = embed_dim * 2
-            _w = in_proj_weight[_start:_end, :]
-            if _b is not None:
-                _b = _b[_start:_end]
-            k = F.linear(key, _w, _b)
-
-            # This is inline in_proj function with in_proj_weight and in_proj_bias
-            _b = in_proj_bias
-            _start = embed_dim * 2
-            _end = None
-            _w = in_proj_weight[_start:, :]
-            if _b is not None:
-                _b = _b[_start:]
-            v = F.linear(value, _w, _b)
-    else:
-        q_proj_weight_non_opt = torch.jit._unwrap_optional(q_proj_weight)
-        len1, len2 = q_proj_weight_non_opt.size()
-        assert len1 == embed_dim and len2 == query.size(-1)
-
-        k_proj_weight_non_opt = torch.jit._unwrap_optional(k_proj_weight)
-        len1, len2 = k_proj_weight_non_opt.size()
-        assert len1 == embed_dim and len2 == key.size(-1)
-
-        v_proj_weight_non_opt = torch.jit._unwrap_optional(v_proj_weight)
-        len1, len2 = v_proj_weight_non_opt.size()
-        assert len1 == embed_dim and len2 == value.size(-1)
-
-        if in_proj_bias is not None:
-            q = F.linear(query, q_proj_weight_non_opt, in_proj_bias[0:embed_dim])
-            k = F.linear(key, k_proj_weight_non_opt, in_proj_bias[embed_dim:(embed_dim * 2)])
-            v = F.linear(value, v_proj_weight_non_opt, in_proj_bias[(embed_dim * 2):])
-        else:
-            q = F.linear(query, q_proj_weight_non_opt, in_proj_bias)
-            k = F.linear(key, k_proj_weight_non_opt, in_proj_bias)
-            v = F.linear(value, v_proj_weight_non_opt, in_proj_bias)
-
-    q = q * scaling
-
-    if attn_mask is not None:
-        if attn_mask.dim() == 2:
-            attn_mask = attn_mask.unsqueeze(0)
-            if list(attn_mask.size()) != [1, query.size(0), key.size(0)]:
-                raise RuntimeError('The size of the 2D attn_mask is not correct.')
-        elif attn_mask.dim() == 3:
-            if list(attn_mask.size()) != [bsz * num_heads, query.size(0), key.size(0)]:
-                raise RuntimeError('The size of the 3D attn_mask is not correct.')
-        else:
-            raise RuntimeError("attn_mask's dimension {} is not supported".format(attn_mask.dim()))
-        # attn_mask's dim is 3 now.
-
-    if bias_k is not None and bias_v is not None:
-        if static_k is None and static_v is None:
-            k = torch.cat([k, bias_k.repeat(1, bsz, 1)])
-            v = torch.cat([v, bias_v.repeat(1, bsz, 1)])
-            if attn_mask is not None:
-                attn_mask = F.pad(attn_mask, (0, 1))
-            if key_padding_mask is not None:
-                key_padding_mask = F.pad(key_padding_mask, (0, 1))
-        else:
-            assert static_k is None, "bias cannot be added to static key."
-            assert static_v is None, "bias cannot be added to static value."
-    else:
-        assert bias_k is None
-        assert bias_v is None
-
-    q = q.contiguous().view(tgt_len, bsz * num_heads, head_dim).transpose(0, 1)
-    if k is not None:
-        k = k.contiguous().view(-1, bsz * num_heads, head_dim).transpose(0, 1)
-    if v is not None:
-        v = v.contiguous().view(-1, bsz * num_heads, head_dim).transpose(0, 1)
-
-    if static_k is not None:
-        assert static_k.size(0) == bsz * num_heads
-        assert static_k.size(2) == head_dim
-        k = static_k
-
-    if static_v is not None:
-        assert static_v.size(0) == bsz * num_heads
-        assert static_v.size(2) == head_dim
-        v = static_v
-
-    src_len = k.size(1)
-
-    if key_padding_mask is not None:
-        assert key_padding_mask.size(0) == bsz
-        assert key_padding_mask.size(1) == src_len
-
-    if add_zero_attn:
-        src_len += 1
-        k = torch.cat([k, torch.zeros((k.size(0), 1) + k.size()[2:], dtype=k.dtype, device=k.device)], dim=1)
-        v = torch.cat([v, torch.zeros((v.size(0), 1) + v.size()[2:], dtype=v.dtype, device=v.device)], dim=1)
-        if attn_mask is not None:
-            attn_mask = F.pad(attn_mask, (0, 1))
-        if key_padding_mask is not None:
-            key_padding_mask = F.pad(key_padding_mask, (0, 1))
-
-    attn_output_weights = torch.bmm(q, k.transpose(1, 2))
-    assert list(attn_output_weights.size()) == [bsz * num_heads, tgt_len, src_len]
-
-    if attn_mask is not None:
-        attn_output_weights += attn_mask
-
-    if key_padding_mask is not None:
-        attn_output_weights = attn_output_weights.view(bsz, num_heads, tgt_len, src_len)
-        attn_output_weights = attn_output_weights.masked_fill(
-            key_padding_mask.unsqueeze(1).unsqueeze(2),
-            float('-inf'),
-        )
-        attn_output_weights = attn_output_weights.view(bsz * num_heads, tgt_len, src_len)
-
-    attn_output_weights = F.softmax(
-        attn_output_weights, dim=-1)
-    attn_output_weights = F.dropout(attn_output_weights, p=dropout_p, training=training)
-
-    attn_output = torch.bmm(attn_output_weights, v)
-    assert list(attn_output.size()) == [bsz * num_heads, tgt_len, head_dim]
-    attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
-    attn_output = F.linear(attn_output, out_proj_weight, out_proj_bias)
-
-    if need_weights:
-        # average attention weights over heads
-        attn_output_weights = attn_output_weights.view(bsz, num_heads, tgt_len, src_len)
-        return attn_output, attn_output_weights.sum(dim=1) / num_heads
-    else:
-        return attn_output, None
diff --git a/mup/examples/Transformer/width256.bsh b/mup/examples/Transformer/width256.bsh
deleted file mode 100644
index 81f2d8b66..000000000
--- a/mup/examples/Transformer/width256.bsh
+++ /dev/null
@@ -1,49 +0,0 @@
-# This is a base shape file encoded in yaml
-# - `null` indicates a dimension is "finite", i.e. a non-"width" dimension
-# - a number indicates the base dimension of an "infinite" dimension, i.e. some notion of "width"
-decoder.weight:
-- null
-- 256
-encoder.weight:
-- null
-- 256
-transformer_encoder.layers.0.linear1.weight:
-- 256
-- 256
-transformer_encoder.layers.0.linear2.weight:
-- 256
-- 256
-transformer_encoder.layers.0.norm1.bias:
-- 256
-transformer_encoder.layers.0.norm1.weight:
-- 256
-transformer_encoder.layers.0.norm2.bias:
-- 256
-transformer_encoder.layers.0.norm2.weight:
-- 256
-transformer_encoder.layers.0.self_attn.in_proj_weight:
-- 768
-- 256
-transformer_encoder.layers.0.self_attn.out_proj.weight:
-- 256
-- 256
-transformer_encoder.layers.1.linear1.weight:
-- 256
-- 256
-transformer_encoder.layers.1.linear2.weight:
-- 256
-- 256
-transformer_encoder.layers.1.norm1.bias:
-- 256
-transformer_encoder.layers.1.norm1.weight:
-- 256
-transformer_encoder.layers.1.norm2.bias:
-- 256
-transformer_encoder.layers.1.norm2.weight:
-- 256
-transformer_encoder.layers.1.self_attn.in_proj_weight:
-- 768
-- 256
-transformer_encoder.layers.1.self_attn.out_proj.weight:
-- 256
-- 256
diff --git a/mup/figures/parametrizations.gif b/mup/figures/parametrizations.gif
deleted file mode 100644
index 9e6312997..000000000
Binary files a/mup/figures/parametrizations.gif and /dev/null differ
diff --git a/mup/figures/sp_vs_mup_dashed.png b/mup/figures/sp_vs_mup_dashed.png
deleted file mode 100644
index 1c527687d..000000000
Binary files a/mup/figures/sp_vs_mup_dashed.png and /dev/null differ
diff --git a/mup/figures/widerbetter.png b/mup/figures/widerbetter.png
deleted file mode 100644
index 505a521d3..000000000
Binary files a/mup/figures/widerbetter.png and /dev/null differ
diff --git a/mup/figures/width_check.png b/mup/figures/width_check.png
deleted file mode 100644
index b0d5810eb..000000000
Binary files a/mup/figures/width_check.png and /dev/null differ
diff --git a/mup/requirements.txt b/mup/requirements.txt
deleted file mode 100644
index 5f28dfc0d..000000000
--- a/mup/requirements.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-numpy>=1.18.5
-pandas>=1.1.2
-seaborn>=0.11.2
-tqdm
-pyyaml
\ No newline at end of file
diff --git a/mup/setup.cfg b/mup/setup.cfg
deleted file mode 100644
index 5fd11e5a9..000000000
--- a/mup/setup.cfg
+++ /dev/null
@@ -1,3 +0,0 @@
-[metadata]
-description-file = README.md
-license_files=LICENSE
\ No newline at end of file
diff --git a/mup/setup.py b/mup/setup.py
deleted file mode 100644
index d65b24f2b..000000000
--- a/mup/setup.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import setuptools
-
-with open("README.md", "r", encoding="utf-8") as fh:
-    long_description = fh.read()
-
-setuptools.setup(
-    name="mup",
-    version="1.0.0",
-    author="Edward J Hu, Greg Yang",
-    author_email="edwardjhu@edwardjhu.com, gregyang@microsoft.com",
-    description="Maximal Update Parametrization",
-    long_description=long_description,
-    long_description_content_type="text/markdown",
-    url="https://github.com/microsoft/mup",
-    download_url="https://github.com/microsoft/mup/archive/refs/tags/v1.0.0.tar.gz",
-    install_requires=[
-        'numpy',
-        'pandas',
-        'seaborn',
-        'tqdm',
-        'pyyaml'
-      ],
-    packages=setuptools.find_packages(),
-    classifiers=[
-        "Programming Language :: Python :: 3",
-        "License :: OSI Approved :: MIT License",
-        "Operating System :: OS Independent",
-    ],
-    python_requires='>=3.6',
-)
\ No newline at end of file