diff --git a/.vscode/settings.json b/.vscode/settings.json
index f32caed..7df429c 100644
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -14,11 +14,18 @@
   "python.linting.enabled": true,
   "python.linting.mypyEnabled": false,
   "python.linting.flake8Enabled": false,
-  "python.linting.pylintEnabled": true,
+  "python.linting.pylintEnabled": false,
   "python.formatting.provider": "black",
-  "python.formatting.autopep8Args": ["--max-line-length=80"],
+  "python.formatting.autopep8Args": [
+    "--max-line-length=80",
+    "--ignore",
+    "E402"
+  ],
   "python.formatting.blackArgs": ["--line-length", "80"],
   "python.linting.pydocstyleEnabled": true,
   "python.linting.pycodestyleEnabled": false,
-  "python.formatting.blackPath": "/opt/local/Library/Frameworks/Python.framework/Versions/3.8/bin/black"
+  "python.formatting.blackPath": "/opt/local/Library/Frameworks/Python.framework/Versions/3.8/bin/black",
+  "python.linting.banditEnabled": false,
+  "python.linting.prospectorEnabled": false,
+  "python.linting.pylintArgs": ["--disable=C0103"]
 }
diff --git a/Notebooks/Theory/Anisotropy.ipynb b/Notebooks/Theory/Anisotropy.ipynb
index 4cc1b4f..8fc124d 100644
--- a/Notebooks/Theory/Anisotropy.ipynb
+++ b/Notebooks/Theory/Anisotropy.ipynb
@@ -57,13 +57,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "<class 'module'>\n",
       "Deducing working path from GME package location = /Users/colinstark/Projects/GME\n"
      ]
     },
@@ -74,7 +75,7 @@
        " ['Derivation_sinbeta_eta0p5_ratio0p5'])"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -122,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -333,6 +334,13 @@
     "# Anisotropy"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": 6,
@@ -344,7 +352,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "function"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(gr.color)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -379,7 +407,7 @@
        "⎠   2⎦"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -405,16 +433,12 @@
     },
     {
      "data": {
-      "image/png": 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shoiIiKhuVKrqoIc3SkqAjAwpeHbxYvX6VtZy/ryUdtIbAwZ4Tn+XkSEFYbgemnNpacCqVcDKlcCmTVKKz4YQBCkQZl2XLja2unTuLJ0LDOR/DyIiIuqQ2tszyO4AEpRKZYRKpRJjY2MvKJVKcxO/JhE1IZlMJgYEBBTrdLqylJSUOFEUIysqKkyQ/n93saBC28eAmR1RFDcKgvAQgPchvUE9VFXs5QC4QxTFnS5ucw+kN6QJAOIg5e11eBlI+Yk/bbSOExEREbVBej3QvbtUnLFYpJlpZ89Ks8hOnKieUZaaKs0ws0pM9Px6118vBc1uvRW45x4plWNHn62UlQV88w2wbJk0o6w+AgOr15azlp49pSBZXdYQJCIiIuoo2uEzyNEABJlMpgsMDMxksIyo/VAqlebAwMDcrKyscAACpCA9A2YdhSiKnwiCsBPAY5AWzowCUA7gFIBfAHwoimKem+vLBEGYCOAvAGYCGAggANKb3A4AC0VR/KMpfwZ7Z/PPIjQnFCq5ymmRC3IIHf1JEREREbVKMpk0CykyEhg71vFcWZm0ppl1Pa3rrnN/r4sXpTW3AOD996WSkAD89a/AffdJ6191FBaLNJNs8WJgzRppfTJvxcRIwcnBg6UyaJCUQpEfJ4mIiKgtEkURZtGMCnNFrZJalNrUr92enkEOl8lkOgCCr69vE61sTEQtxdfXtzQrK0uQyWQ6i8UyHMCXLd2npsKAmROiKB4GcG8DrhcBfFNVWtTsX2cDB1yfFyA4BNCUcqXL4JqropR5f81NCTfB18fXZX/yDfkorSyFj9wHPgof+Mh9oJKrGNQjIiIiB1otMGyYVLyxalXtupMngaeeAv71L+Dee4HHHwfi4hq1m62KwQAsXQq89RZw+rR31/TvD1xxhVTGjpVSWhIRERF54i4QVWGuQLmp3OW5CnMFys3uz9fnGlevKcLFUjyFzfLv1F6eQYbKZDKVTCazqNXqyhbuCxE1MrVaXSGTySwymUxlsVhCW7o/TYkBsw5OhIhycznKzV4uENJAVzxyhduA2Ws7XsObO9+sVa+UKW0BNPutSq6qVVfrnJPzKrnK6TWezqkVats+g3hERERtxwE3A4hKS4EPPgA+/RR44AHgH/+QZk21F2Yz8OWXwPPPS+uUuePnB0ycCNx4ozRrLzi4efpIREREDWe2mKVnPKZyGE1Gj/vuzhtNxuo2Ls7VvFfNwJTLQBS1R1oAMplMZmnpjhBR0xAEwQJpbUVdS/elKTFgRs1KJXe/kEW5yXngrtJSicqKSpSgdczqTn0kFbEBsS7Pf/rnp/jl1C9QK9R1KhqFxqt2CpmCATsiIqI6WLQIeOIJ4NtvpVlWKSm121RWSoGzzz+XZp09+iigaOOflrdtA/7v/4AjR1y30WiAW24B7roLuPJKrjtGRERUX2aLGQaTAYZKAwwmA4wmIwyVVduqemf79u2sxRqosg9cuQpUWevMIpeNohbHKClROyUIQof4/7uNPwKgtsZjwKyZZro1lFqhdnv+WPYxrDm7psleXybI3AbdNEoNNAoNtEotNEoNtIqqrVLrUP+Xfn+BXqV3+TpGk9GWtpMBOiIiaut69gRefFEKhu3YIQXHli+XZmDZKy2VUjV++y2wZAkwcGBL9LZhSkqAZ54BPvzQdZv+/YF584Bbb5VmlhEREbUn1uBVWWWZQ/EmgOWwX4fAl8lSh4VBiYiIqNVhwKyd23bPNowaPcqrXM+Vlkqv80JXml20tbhv66Pwcdvf9hIwM5qMTfr6FtFi+7DfEFN7TnUbMHv696fx/t73IRNktkCcfdDNR+4DQ6EBKpkKX6/8Gnq13haccwjYKbXQKXXQqXROt3qVHlqlFnKZvEE/DxERkTcEQVqPa+xY4MIF4J13gI8/BsprfAw5cAAYMQJ4910pVWNbGTty5AgwfTpw9qzz81ddBcyfD1xzTdv5mYiIqH0xWUy1AlmlFaW16soqy1Ba6VhfZCjCuYvnUG4px3v/ew9Gi9HpfdrK84WOyjo41355DKVcaduveeyj8LEt16GSq6CSeW6vkqscr7E79uY19+zcg6veuaql/6mIiKgZMWDWAchlcmhkUrCjtftk0id4e8LbtsVa7dMLWPNh16yz5sb22L4e96kwVzjtZ0sHzBqLVql1e94akLOIFpRWlqK0stRl26SipAb3R61Quw2s2bY16mYOmOn291sURc6QIyIip2JipIDZ449LM88+/xwQ7RJNlJcDDz4I7NwJLF7c+tMVfv89cO+9QJmTMTX9+gFvvimtTUZEROSJKIooqyxDSUUJSitLUVJR4rKUVtidr6yucxbwKq0oRaWlsnE6WdQ4t2nvBAi2NdnVCrVtrXb7fft12231LtrZt7df690+AOYuEKWSq9rEgNm20EciImpcDJhRq+Kj8PE4C605iaJomx1nv7itp9SSd/a9E/3C+jnkH7cVc+26mrnK7UtTLpKrUbgPohpMhiZ7bWesP3OuIbdO193Z9063AbP7f70fSw8vhV6lh6+PL3xVvratQ11VvV6ld2jjrE4pVzb0xyUiolakc2fgv/8F7rsP+NvfgGPHHM8vXQqkpwMrVgC+vi3TR08++AB4+OHa9RoN8Oqr0jk5n/sQEbVLoijCYDKgqLzIVrwNbLkLgjXl99GOwpqxRa1QQ6PUOCyl4GzfWSCqZkCr5r674JeP3IfroBMREXmJATMiNwRBsAXxfH28fzp2ffz1uD7++ga/viiKqLRUugymOQu62S8wbM3PXlZZVn1ctS03lXsM+jQ05WNz0al0bs+XVpZKMwcN5XUOxrniI/fxKgDn5+MHf7W/tPXxr7Xvq/LlqDUiolZk5Ehg/34pZeHbbzue27ABuPZaaat3ndG4Rbz+utTnmoYOldZi6969+ftERESemSwmFJcXOwS6apbiimK3x9ZiES0t/eO0ej5yH9sSAtYAlstgltz9eW+DYEqZksEqIiKiNoIBM6JWTBAEW7oCPx+/Zn/9RZMX4d/X/Nsx4GYXjCssK8TBowdRLpYjuks0TKKpVnDOUGmQ0jlWlNbaNsZoRZVcBYXM/Z8yd6kk66sxA3B6lR7+Pv448/AZtzPldl7ciazSLKfBt9Y0M5OIqK1TqYC33gLGjAHuvhsotXsb2bMHuOkmYNUqwKeV/On97DPnwbL77pNmnbWWfhIRtTdmixmF5YUoMBZ4LK4CYW1lkGJz0Cg00Kl0tjWwNUoNdErp2Fp0Sh1UggoZlzKgElQY0GsA/LX+0Kl0Dtfbt7fuqxVqDlYkImrnvvnmG/+vv/46+PDhw7qcnBylTqczx8bGlk+ePDn/scceyw4KCmqU0SUmkwkHDhxQ79q1S7dv3z7toUOHdKdOndIYjUYZAEyfPj13+fLlqXW5Z15enuy5556L/PXXX4OysrKUQUFBpuuuuy7/1VdfTY+OjjY1Rr/JMwbMiMilUF0oQnWhLs8bDAasz1oPAJgwdgI0Gu/XyRNFEUaTsVYQzZof31mAzWFbtS8TZB5fq7Si8QNmjamkogSGSoPHtfHe3vU2lp9Y7vScj9wHgZpABKoDa29r1AWoAxzqtEotRzwSETlx003A5s3ApElAdnZ1/YYNUjDqq6+Alv7z+dtvwJw5tesXLACefbbl+0dE1JrVJeDlrBRXFLf0j9CsZIIMOqUOepUeOpXOlmGj5rF1rWnrvlapdQh+WYNa1gCXTqmDWqH2+juJwWDA+vVV30OH1u17KBERtU+FhYWy6dOnx23atCnAvr6iokKRn5+vOHjwoG7x4sVhX3/99bnx48c3+EHhpEmTuq1fvz7AY0MvZWRkyMeMGZOQnJxseziYmZmp/Oqrr8LWr18fsG3btlPx8fEVjfV65BoDZkTUIgRBkNJYKDUI0YY06Wv9d+p/kV2WjeLyYpRUlKC4ohjF5cW2ra3OVX15cZPMUrPnr/b3+AWxqNz1itbl5nJklGQgoySjzq+tlCk9BtuCNEEI1gQjWBuMYE0wQrQhCFAHcIQmEbV7Q4cC27YBY8cCOTnV9V9/LaVvfOihlutbSgpw112ApcYYyXfeAR59tEW6RETUIiyiBQXGAuQZ8pBblittDdLWVmfMq3W+wFjQ0l1vMtYU8q6CW3ql3uV5V9doFBoOtCMiolbHZDJhypQp3bZv3+4HAMHBwaYZM2Zk9+7d25iXlyf/8ccfg/bv36/PyMhQTZ8+PX7z5s0nExMTjQ15TbPZ7HDs7+9vDggIMJ0/f75e+T3mzp3bOTk5We3v72+eNWtWVq9evYwpKSmqJUuWhGVkZKhmzpzZZefOnacb0mfyDgNmRNTuxQbEIjYgtkH3sIgW2+LX9kG14opip3XWbVF5EQrLC6WtsRCF5YUoLi+ulY7Sm5SbheWFDfoZXKm0VCKrNAtZpVl1uk6AgEBNIEK0IbZgWoQuAp9N/axJ+klE1FISEoC1a4GrrgKK7SYTPPooMGIEkJjY/H2qqABuvx0oKHCsf/ZZBsuIqG2rMFcguzQb2WXZtq2zIJj9cb4hv1HSvbc0AQL0Kj38fPxsxdfHt/pYVV3n7+NvO+er8rWla7ceM2U7ERF1FO+8806INVjWrVs34+bNm0917tzZlsJw/vz52ffff3+nzz77LLyoqEh+//33x/7555+nGvKaQ4YMKe3Zs6dxyJAhZSNHjixNSEioeO+994IfeeSRLnW9l9FoFH777bcglUolbt68+eSgQYNswbwHHnggNzExsfeuXbt8U1JSlHFxcZUN6Td5xoAZEZEXZILM9gUUvg27lzX4Zg2gebtAdye/Tsgpy7FdZ7K0bPpiEaLtYYVVmC4Mn8F9wGzad9NwJOsIgjXBCNWFIkwXhjBtmLStUUJ1oVDJVU39oxAReTR4sJSC8aabqusqK4HZs4GkJECpbN7+vPWW9Lr2br9dSsVIRNSaGCoNDsEv+21WaZa0b1fvLqtCa6aQKRCgDoC/j78tDbr12Lr1V/vbglu+Kl+H4Jefjx90Kp1XKeeJiIjqw2g0Cu+//37wTz/9FHTy5ElNcXGxPCQkpPKqq64qeu2119JiY2MrASA3N1fepUuXfiUlJfKXX3754j/+8Y+6jbBuRiaTCW+++WaU9fiLL75IsQ+WWX300UeXtm/f7nfy5EnNvn379CtWrPCbPn16vT90vP7663VP8eRCRkaGory8XEhISDDYB8sAIC4urnLw4MElGzduDEhOTlYxYNb0GDAjImpm9sG3zujs9XXLb6tev8y6BlxheaEtgJZvyEe+Mb/21kldUz2ICNYEe2yTWpCKc/nncC7/nFf3DFAHIFQTCkWFAv4Kf6xavwpRflFOA2xBmiCmiSGiJjNtGvD3vwNvvFFdd+gQ8Oab0syu5pKcDLz0kmNdfDzw2Wdcs4yImp4oiigqL7KlA7eWSwWXsP/CfhSaCvHyVy8j15iL7NLsJk9t3lgUMkWtYJd17V9b0Evtj0B1jfMa6TzXBSYiotZs/fr1utmzZ3e9fPmyw6jkjIwM1bJly0K2bNnid+DAgePh4eHmt99+O7SkpEQeHBxsevTRR7Nd3bM1WLNmjW92drYSAIYOHVoyZsyYMmftFAoF5syZk/noo492AYBvv/02qCEBs8YUFhZmUigUYkpKivrIkSM+/fr1K7eeS0tLUxw6dEgHADExMQyWNQMGzIiI2iD7NeAi9BF1vt66wLnLIFuNYJs15U1OWQ7KKp1+9gAABGs9B8xyDbl16qt1UXWr3Qd3u2yrlCkRoY9ApG8kIvVVxbf2NkwXBoWMb4FEVHcvvQSsXg0cOVJdt2ABcO+9QGRk8/Rh3jzAaDfuUCYDvvkG8G3gDGgi6tiMJiMySzJrBcIySjKQUep4bDR5WPajBR8/qRVqBGuCEaQJktbh1QYjSB3keFy1Pm+QJsi2di8DXkRE1F4tXrw48MEHH4wzmUyCIAgYO3Zs4ZAhQ0qzsrKUv/zyS1BxcbE8PT1d9frrr4e/+uqr6YsWLQoDgDlz5mTo9fpWnfP4t99+87fuT5gwocBd25tuuqnw0ar89Vu2bPF317Y5abVa8ZprrilYu3Zt4Lhx4xJmzZqVnZCQYLxw4YLq888/D83JyVEmJiaW9OjRo6Kl+9oR8GkhEVEHJJfJbQ8N6spoMiK3TAqe5RpyHfYj9Z6fFueW1S1gVheVlkpcLLqIi0UX3bYTICBMF+YysBYXEIdBkYOarJ9E1HapVMB//yutXWapyqZrMAAvvgh88knTv/6OHVLAzt7//R8wdGjTvzYRtU1mixlZpVm4XHwZl4ou4XLRZVwuripV++nF6U22Xm59yQSZLYV3iDbEIchlC35VraNrf6xRalq660RERK3Gtm3btHPnzo0zmUyCSqUSv/rqq+Tbb7/d9qaflJSUNXLkyD5msxlbtmzx++ijjypycnKU/v7+5qeeeqpVzy4DgOPHj9ve+IcPH+56hDeAmJgYU0REREVGRoYqNzdXkZaWpoiKimrZ9U6qfPTRRxfHjBmjS0tLU7377rsOD9dCQ0Mrv/rqq9QW6lqHw4AZERHViVqhRrRfNKL9out8rSiKWHvXWluQLbtMWruiZskuy/ZqXbf6EiEiszQTmaWZOIiDtc4PiRqCpL8l1b7QzsGMgwjWBCPSN5Kz1Yg6mKFDgfvuAxYtqq5bvBh44gkpNWJTEUXguecc68LDuW4ZUUdmqDTUDoTVCIill6S3+Nq3gJTyMEQbglBtKEJ1odJWG2pbt9ahXheKQHUg5DJ5S3ebiIiaSFpax3suHRWFZn1DtlgsePjhh2MqKioEAFiwYMEF+2AZAAwdOtQ4evTowm3btvmnp6er3n///QgAuO+++zL9/f29fjCzbt06fVZWVqP8Nx0xYkRZfHy8V7Opzp07p7bu9+jRo9xdWwDo1KlTRUZGhgoAjhw5oo6Kiiqpf08bT1xcXGVSUtKJ+fPnR65fvz4gJydHGRgYaLr22msLXnvttbSYmJiW/zDXQXS4P0xERNRyBEHAuNhxHttZRAvyDHm2ANql/EvYtm8bCk2F8I3wRV55nkOArbFHRHf287y23FVfXoUCYwFkggyR+kh08uuEzv6d0clX2nYJ6GIrgepApvghamdeeAFYulSaXQYAZjPw7rvAhx823Wvu2AFs2+ZY99xzTMVI1F6JoohcQy7OF5zH+cLzuFB4wbZ/vvA8zhecr3Oq68bm7+OPCH0EwvXhCNOEwZhjhL/SHyP7j0R0QLRDECxAHcDPQ0REZBMdjQEt3YfmJorY15yv9/PPP/sdPnxYBwB9+/Yte+KJJ3KctevatWv5tm3bkJmZqQQAvV5vfuaZZ7Lq8lrPP/98dFJSkr7hvQYWLlyYGh8f79WHnOLiYtvomoiICI9BpcDAQFubvLy8VjUyJyoqyvTll19eBOA+bRI1KQbMiIio1ZEJMoRoQxCiDUHv0N4whBvge0F6IjxhwgRoNI6pdspN5cgsldbcSC9OR3pJevXWbj+zJBNm0ezx9Tv5dXJ7vqSixLaumkW02EZx77m8x2l7vUpvC57F+sc6BNNi/WMRog3hAySiNiYqSkqF+Oab1XWffw68/DIQVPdst155913H486dgfvvb5rXIqKmZ7aYkVacZgt+OWyrAmTu1o5tKj5yH4TrwxGpj0SEPkIKiOnCEenreByhj3BIf2gwGLB+/XoAwIQBtT+vERERUfNasmRJiHX/ySefTJfJZE7bBQYGOjwomTlzZnZISIjnhyetQFlZme2H0mq1HmfEqdVqW5uioqJWFTCj1oEBMyIiavN8FD6I8Y9BjH+M23Zmixk5ZTmOAbWqbUZJBtJL0pFWnIa4gDi397lUdKlO/SupKMHRrKM4mnXU6XmtUmsLnsUFxKF7UHd0D+qObkHd0DWwK9QKtdPriKhlzZsHvPMOYKoao2gwAJ9+Csyf3/ivdf488PPPjnWPPw74+DT+axFR4ymrLENKfgqS85ORnJeM5PxknMs/h+T8ZKQWpKLC3HxrtytkCkT5RiHaV0qtHe0bbdu3ruMaoY+Av48/B/IQERG1cWazGVu3bvUDAF9fX3PNVIz2LJbqOJNarbY899xzmXV9vb17956qV0eJWhkGzIiIqMOQy+QI14cjXB+OgRED632fnLIc+Mh9UG72mB7bK2WVZTiefRzHs4/XOidAQLRftBRAC+xWHUwL7IZuQd3g5+PXKH0gorrr1Am4/Xbgm2+q6/77X+CZZ4DGftb83/8Cdt9jodcD997buK9BRHUniiJyynJwNu+sLRBm3SbnJSO9JL1Z+uGr8kW0XzQ6+XVyCITZjv2iEaYLg0xwPrKciIiI2pfdu3drSkpK5AAwZMiQErVaLbpqK4rVp2bMmJEdGRnZZtbL0mq1FutMsbKyMpmnddeMRqPtw5Cfn1+bmEVHzYsBMyIiojoaEzMGhucMyCnLwaWiS7ZysegiLhZdxIXCC0gtSMWlokuwiF6vkeuUCNF2/y2pW2qdD9WGontQd/QI7oGEkARb6RbYDUq5skGvTUSePfywY8AsORnYvRsYObLxXkMUga+/dqybORPw92+81yAi98pN5Tibdxanck/hVM4paVu1n2/Mb9LXlgtyRPtFI9Y/FjH+MYj1j0VsQKztONovmgNoiIiozbl8GYdaug/tWVJSkta6P2DAALc5ns1mswAAKpVKfP755+s8u6wl+fr6mq0Bs4yMDIW/v7/b6fv5+fm2eEhQUBADZlQLA2ZERET1IAiCtIi9LhSDIgc5bVNprsSloks4X3geqQWptmI9vlh40as11dzJLstGdlk2dl3a5VAvF+R4a8JbeGTEIw26PxG5N2wY0KMHcPp0dd3SpY0bMNu1C0hJcaybNavx7k9EElEUkV6SXh0QswuMpRakNngQjCsahUYKhNkFweyDYtF+0VDI+NWdiIjal6gotJlZTG1RcnKybW2H7t27G121MxqNws8//xwEAJGRkRWxsbGV9Xm9devW6bOyshrlA8uIESPK4uPjvcpb3bVrV+Ply5dVAHD69Gmfnj17ur3u0qVLKut+v379XP67UMfFT91ERERNRClXIi4wDnGBztdEM1lMuFx02RZES8lPwbmCczibdxbJecnILK3/wC6zaEa4PtxtmwuFF7Dq9Cr0C+uHvmF9EagJrPfrEXVUggDcdRfwz39W133/PfD++4C8kZaQXrbM8bhnT2Dw4Ma5N1FHlV2ajWPZx3A06yiOZR3D0WxprdECY0GTvF6EPsKWTrlboLRGqXUbpgvjmmFERETUqPLy8mzfRtylKXzzzTdDrQEnuVzuMm2jJ88//3x0UlKSvr7X21u4cGFqfHx8rjdte/fubdi+fbsfAOzZs0c7ZcqUYldtL168qMjIyFABQFBQkCkqKopBW6qFATMiIqIWopAppNHjAbFOzxeXF9vWQDmbdxbJ+dL2bN5ZXCq6BBHuP8v2DO7p9vzW1K2Yu3qu7TjaNxp9w/raAmh9w/qid2hvaJSauv9wRB1IzYBZbq6UlnH06IbfWxSBVasc6/7yl8ZfI42ovSo0FtoCY0ezjtr2s0qzGvV1lDIlugR0cRoQ6xrYFTqVrlFfj4iIiMgd+3XJCgsLnS5impmZKX/77bcjrccNCZi1lBtuuKHw008/DQeA33//3X/BggUuRx6vWLHCltT+qquuKmyO/lHbw4AZERFRK+Xr44uBEQMxMGJgrXNGkxEp+SlIzk/GmdwzOJV7CidzTuJkzknbzLQewT3c3v9o1lGH48vFl3G5+DLWJa+z1ckEGboFdkO/8H4YFDEIiZGJGBQxCJG+kTVvR9RhxcUB/foBR45U161a1TgBs+PHgdRUx7obb2z4fYnaG1GU1vw8kHEABzMO4kDGARxIP4Dzhecb9XUi9ZHoGdITPYOrStV+l4AukMsaaVopERERUQN16tTJllpx586d+rlz5+bVbDN37tzOBQUFtviAdS2z+ti7d++p+l7bEDfccENxSEhIZU5OjnLv3r2+O3bs0I4ZM6bWmm0mkwmffPKJLQ3PnXfeWevfgwhgwIyIiKhNUivU6BXaC71Ce9U6V2AsQHJessfR7Eeyjrg9DwAW0YIzeWdwJu8MVpxYYauP0Ec4BNASIxPRJaALU0pRhzV5cu2A2WuvNfy+v/3meNy5M9C/f8PvS9SWmS1mnMo9JQXG0g/YgmS5Bq8y93ikVWrRI7iHQ1CsR3AP9AjuAT8fv0Z5DSIiIqKmNHz48FLr/vLly0Meeuih7NGjRxusdc8++2zE8uXLgwFAr9ebS0pK5BcuXPA5e/assnv37vVax6wlKBQKPPnkk+nPPPNMDADce++9cVu2bDkVHR3tkG5x7ty5nU6ePKkBgMTExJKbb765yNn9hg0b1tOaWnLhwoWp8+bNa5wPmNRmMGBGRETUzgSoAzA4yvMCRz2De+JS0SWczDmJSkvdPg9nlGRgzdk1WHN2jcPrDowYiMGRgzE8ejiGRQ9DjH8Mg2jUIUye7BggO3oUuHwZiI5u2H3XrnU8njSJ6RipYxFFEefyz2Hv5b1SSduLA+kHYDAZPF/sgY/cB71Ce0lpiEP7ok9YH/QJ7YPYgFjIBKeZi4iIiIjahBtvvLEoKiqqIi0tTVVeXi5ce+21CTfeeGNeVFRUxdatW/327dunB4ABAwaUTps2Lf/FF1/sZDKZhMmTJ8ffeuutuRMmTCgeO3ZsrZlardHjjz+evXLlyoCdO3f6nT17Vj1w4MDeM2bMyOndu7chLy9P8cMPPwTt379fDwC+vr7mTz/9tMEpCE6ePKn66KOPQuzrjh49qrXfnzdvXpT9+WuuuaZ46tSpLtdYo9aBATMiIqIO6p2J7wAAKs2VOJN3Bkcyj+Bo1lEcyZK25/LPeVwnzV6BsQBbUrdgS+oWW124LhzDOw3H8GipDI0eytH51C4NHw74+wOFhUCvXsDYsUBlA8dlVlRIa6HZmzixYfckau1yynKQdDkJey7vsQXJGjpzTCFToGdwT/QN64s+oX2kbVgfdAvsxjSKRERE1C4pFAp88cUX56ZNm9ajrKxMZjAYZN99951DgKdv375lq1evPltSUiJ74403ooxGo+zUqVOaBQsWdEpISDjbUn2vK6VSiVWrViXffPPNXTdv3uyfk5OjXLhwYa11JMLDwyuXLl2aPGTIEGNDX/Ps2bM+77//vsu1Kk6fPq05ffq0w4LwCoUCDJi1fgyYtXOKhQuBzZuB4GAgKEja2u/7+nKYMhFRB6eUK9E7tDd6h/bG7bjdVl9aUYrj2cdxJOsIDmUcwv6M/TiYcRAlFSVe3zuzNBMrT63EylMrAQACBCSEJGB4p+EYET0CY2LGoFdoL47kpzZPLgdWrwbi44HQ0Ma5559/Aga7STSCIAXiiNoLs8WMo1lHsePCDvxx8Q/subwH5/LPNeie/j7+tvU/B0UMwsCIgegV2gsquaqRek1ERETUNlx77bWlSUlJx1555ZWI7du3+2VnZytNJpMQGhpaOX78+MIPP/zwkp+fnwUAfvjhh7OPP/545+TkZI3FYsHQoUMbPp2/GQUGBlo2bdp09uuvvw5YunRp8OHDh7V5eXlKrVZrjomJKZ8yZUr+Y489lhMcHGxu6b5S68aAWTunWL4cWL7cTQOFYyDNVWCt5r5a3Xw/BBERtQidSoeh0UMxNHqorc4iWnA27ywOpB/A/vT9OJAhbb0d/S9CxImcEziRcwJfHPwCABCkCcKYmDEY03kMxsaORWJkIh9sUps0alTj3m/bNsfjvn2lj2NEbZWh0oCktCTsuLADOy7swM6LO1FYXljv+0X5RmFQxCCpRErBsbiAOKYCJiKiuqmsBPLygJwcIDdX2ubkwP/PP1u6Z0QN1rt374pvvvnmgqd2kydPLp48efLx5uhTU7rrrrsK7rrrroL6Xr93795T3rSbPHlysSiK++r7OtR6MWDW0ZlMQFaWVOpCq60dTAsJkUpoaPV25EhAr2+avhMRUbOTCTL0CO6BHsE9cHtfaTaaKIq4VHQJ+9P3Y3/6fuxN24s9l/Yg35jv1T3zDHkOs9A0Cg1GdBqBsTFjMaHbBIyOGd1kPw9Ra7Zjh+PxuHEt0w+i+iouL8b2C9uxNXUrtl/Yjj/T/qzzmplWodpQDIseZkvvmxiZiDBdWCP3mIiI2jyTqTr4VSMA5vK4oMDprThOiYio42HAjOqnrEwqly65b3fqFNCjh+vzv/8O7NsnBdfsA20hIUBAANNFEhG1AYIgoLN/Z3T274wbE24EIAXRzuadxZ7Le7Dn0h7subwHBzMOevWg1GAyYHPqZmxO3YyUghQGzKjD2ldjvGJjz2AjamzlpnLsurQLm1I2YWPKRuy9vBcmi6nO99EoNBgcNRjDooZheKfhGBY9DLH+sZw5RkTU0YiitECsdaB3drbnIJiL4BcREZE3GDBr50wTJwIqlTS6JjdXKnl50oib5hAS4v78r78C77/v/JxC4XzWmnU/LEwq4eFSCQxkgI2IqJUQBAHxwfGID47HXf3vAgAYTUYcyjiEPZf3YPel3dhxYQcuFl10e59rul7j9rzZYoZMkPEhKrU76elARoZj3eDBLdMXIlfMFjP2p++3Bch2XNgBg6nuy110DeyKMTFjMLrzaAyPHo4+YX2gkPGrKhFRu1RW5hgAs+47K9nZUrpEIiKiZsJvIe2c6dlna68OL4pAcXHtIJqn/YIC6VpvyeXSLDF3srPddN4kPSmq+bTIFYWidhDNul+zLjQUUCq9/lGIiKjh1Ao1hncajuGdhmPe8HkAgPMF57Hjwg5sv7Ad2y9sx/Fsx5Tp4+PGu73nhnMbMPPnmbg+/nrc0P0GXNvtWgSoA5rqRyBqNvv3Ox7r9UB8fMv0hchedmk21iWvw5qza7D27FrkGfLqdL1MkGFA+ABp7cqqEuUb1US9JSKiJldRIc3sqhnochUAKy1t6R675+9vG7xdJgjA7t0t3SMiImpGDJh1RIIA+PlJpUsX768zm6WgmaugmnX6u3WKPADIZO7v6S5gVlcmE5CWJhVvBAe7DqjZ70dEABpN4/WTiIhsYgNiERsQixn9ZwAAcspysPPiTmw/vx0Xii4g2i/a7fUbzm1AZmkmvjj4Bb44+AXkghyjY0bjhu434Ib4G9A3rC9nn1GbVDNgNmiQ549VRE3BIlqwL20fVp9ZjTVn12Dv5b0Q4f0gOqVMiRGdRuCK2CswOmY0RnUeBT8fvybsMRERNZjFIj3rsU55d1fy6jZwoln5+0vPfkJDpW1IiPNja3ajoCCHwdWZ27dzEVkiog6GATPynlwufZAIDm68e44cKX0YsQbasrOl6fnNwRrwO3HCc9vycim1pSuZmYBaLQUh+WCWiKjeQrQhmNpzKqb2nOpV+40pGx2OzaIZ285vw7bz2/DMxmfQya8Tbuh+A6YlTMPVcVfDR+HTFN0mcqqgADh0CBg2rO5jb2oGzJiOkZpTWWUZ1ievx88nf8bqM6uRXeb9IDcBAgZFDsL4uPEYHzceY2LGQKfSNWFviYjIayUlngNgGRnSM47mWsrDW3q94zId1udT9sf2AbEawS8iIiJvMGBGLevll2vXlZXVnq3mbJuVJX2Ia+oFXUNC3AfLAGDWLGDtWulpWEQEEBnpvoSEcJg4EVED5RnycDDjoNs2l4ouYdH+RVi0fxH8fPwwKX4SpveajondJ0Kv0jdPR6lDEUXgjjuApCQgJUWq270bGD68bvc57pidFAMGNE7/iFwpMBZg1elV+OnkT1hzZk2d1iLrGdxTCpB1HY8ru1yJIE1QE/aUiIgcWJfduHxZyrhjv605Q6w1pUNUqaTAlv3SGfbH1jrrVqtt6R4TEVEHwIAZtT5aLRATIxVvlJdLAbTMTKlYA2nWrf1+draUWqAuIiM9t7Gus2YwSE/HrE/IXJHLpQ+BkZFAdLRj6dSpet+P6WqIiFwJVAfi9MOnsebMGqw+uxqbUzaj3Fzusn1ReRGWHV2GZUeXQa1QY0K3Cbgp4SZM7TmVD3ep0QgCcPCg40eB06frFjCrqACSkx3revVqlO4ROcgsycRPJ3/CTyd/wqaUTTBZvJtNoFVqcU3Xa3B99+txQ/wNiPH38nM7ERHVTXm5FPSyD4JZ9+2DY60hECaTSYODawa6agbArMe+vszQQ0RErQ4DZtT2+fhIQaZOnTy3NZul/No1A2mu9svLpRljnqSn163PZnP1B9x9+1y38/V1HUyz7oeFcbYaEXVIgiCge1B3PDz8YTw8/GGUVZZhc8pmrD6zGr+d+Q3nC8+7vNZoMmLlqZVYeWollDIlJnafiDv73olrY65txp+A2qsePaQgmdWpU3W7PjlZ+qhgr2fPhveLCAAKjYVYcWIFlh1dho0pG2ERvRtM1jO4J67vfj0m9ZiEsTFjmeKWiKghRFEa0HvpUu2ZYfb71vXhW1JIiPRcxDroNzLS8di6BnxgoDQ4mIiIqA1jwIw6Frm8epp/377u24oiUFQkzRpzx2yWPug2heJi4ORJqbiiUEgfUp0F06wz9SIjpXZERO2YVqnFpB6TMKnHJHwgfoCTOSdt6cV2Xdrl8rpKSyV+Pf0rfj39K7RKLYboh2BswFhcab4SGtRx4SkiSMGtVauqj+2DZ96o+bYfEQEEBDS4W9SBlVvKsa9oH5b8tATrzq1zOxvXSibIMC52HG5KuAmT4iehW1C3ZugpEVE7IIrS0hEXL7ouly5JA3Rbil5fHfCKiHAMgNkfh4dzHTAiIupQ+ASdyBVBAPz9peKOXF69cK41P3h6uvOSlSV9eG5MJlP1h253fZw40fHpHRFROyYIAnqF9kKv0F54avRTSCtOwy8nf8FPJ3/C5tTNLtOOlVWWYVv+NmzL34aPP/wYt/W5DbMHzcaQqCEQmDKGvNSjh+NxQwNmnF1G9SGKIrad34bF+xZj+fHlMFg8r0nmI/expaud0nMKQrQhzdBTIqI2prTUMfh14ULtYFhJScv0LTBQGjwbFSVt7WeE2c8M03MtXyIiImcYMCNqDBoNEBcnFXdMJiloZh9Es+Ygv3y5Oh1DXl7j9s9slhbU9WTUKGkIu3VmWufO1fvR0d7dg4ioFYryjcKDQx/Eg0MfRL4hH6tOr8KKkyuw5swalzMt8ox5+GTfJ/hk3yfoF9YPswfNxox+MxCqC23m3lNb4yxgZrF4n0H5zBnH44SExukXdQyXiy7jy0NfYsmBJUjOT/bYXqfUYUrPKZieMB0Tu0+Er49vM/SSiKiVEkUgNxdITa1drAGx/Pzm75dG4xgIs9/aFw2zIxARETUEA2ZEzUmhqP4g647B4BhIsw+mWffT02svcOJOjIfF2IuLgV2uU5ZBEKQRaXFxQJcuQFwc5NHRCMnORll4OFBZyQ/nRNQmBGoCcfeAu3H3gLtRVF6En078hGVHl2HDuQ0wi87/rh7JOoLH1j2Gp39/GlN7TsXsQbNxXbfrIJdxnQaqreaMMINBeuv29FZslZrqeNyNmfDIgwpzBX499SuWHFyCtWfXelyXTCVX4fru1+POvndiSs8p0Cq1zdRTIqIWZl07zD4Qdv68435pafP1RyaTvmfXDIRFRjquZ+7vL30nJyIioibFgJkTgiD4AJgN4HYA/QHoAeQBSALwJYDloug6r54g5Wy6s+oegwDoAKQD2AbgA1EUk5r0B6C2T6MBuneXiitmszRbzT6QZh9Yu3RJSg1hNErtPT2lc5fSEZC+WFgXIP7jDwCACsBo6+mHHpLWTrMLqDnsR0ZyAWAianX8fPwwc+BMzBw4E1mlWfjm4DdYtGsRTpY6Xzuy0lKJ5SeWY/mJ5Yj1j8WDQx7EXxP/yrRl5CAiAtDpHJ+3pabWP2DWpUsjdYzanctFl/Hpvk+xaN8iZJZmum0rQMAVMVfgrgF3YXqv6QjUBDZTL4mImpE1IJaSUnuGmDUw5mmd8sYUHi5lbnFWYmKkDw1cb7xD4zNIIqLWhe/KNQiCEA1gFYCBNU6FA5hcVVYLgnCbKIq1hh0JgqAB8AOASTVOdakqMwRBeE4UxTcat+fU4cjl1fnIhw513kYUgZwcKXAWEeH+fhcuNKg7gtksfQE5f955A6USiI11DKJ17SoFBbt1k1JBEhG1oDBdGOYkzkHXnK7ILM/EpeBLWHp0KVILUp22P194Hs9sfAYvbHkBd/S9A/837P8wJGpI83aaWiVBkN7yjh+vrvP2bda6NKm92NjG6xu1faIoYseFHfgg6QOsOLHC5ZqMVgnBCRjhMwLjAsfhjkl3QMOMAETU1plM0GZkQJeRAfn589IbZ3KyVM6da771w4KCXAfDOneWZob5+DRPX6hN4jNIIqLWhwEzO4IgKOH4RnUWwBIAFwD0BDAHQCiAGwB8C+BGJ7dZjOo3qhMAFgHIBJBYdb0ewOuCIFwWRfHrJvlBiKwEAQgNlYon/foBX35ZvWixtTRWSorKSuDsWak4ExxcHTyz33bvLvWf6SeIqBmF+4Tj7lF3419X/wtbUrdgyYElWH5iOYwmY6225eZyfHnoS3x56EsMix6GecPm4bY+t0EpVzZ/x6nV6Ny5fgGztDQpaGbP0xKp1DEYKg345sg3eH/v+zicedhtW71Kjzv63IHZg2ZjQPAA/P77783USyKiRlJcLAW/rEEwa0AsORnq8+dxbV2WJ6gPuVx6M+/SRRq50qVL9Trf1qLTNW0fqF3jM0giotaJATNHs1D9RvUrgNtFUbTN1RcE4T0AGwAMADBVEIQJoiiutzt/LYC/VB1uAjBJFEXrk7VlgiAsAbAdQDCAdwVB+EUUxeIm/HmIvBcdDdxzT+16UQQKCqpnj1lTW6SkwHLuHCxnz0JhrP0Auc5yc6WyZ0/tc3p9dRBt7lzgqqsa/npERF6QCTJcHXc1ro67Gh8YP8B3R7/DZ/s/w/70/U7b7728F3f9dBee3fQsHhvxGO5LvA96lb6Ze02tQc30i94GzGpO1NZqgRBm/OzQ8g35+CjpI7y39z1klWa5bTsmZgxmD5yNW/vcavvbY2jO1GNERHVRVAScOQOcPi0V6wDL5GQpraILjTKUUqFwDIhZM6FYj6OjmSqRmtos8BkkEVGrw3d/R9OrthYAD9i/UQGAKIo5giA8AmBLVdXNANbbNXmyamsCcJ/dG5X1+hOCIDwMaWRIMIC/AXi7UX8CosYmCEBgoFQGDnQ4VW4wYP26dVAWF+PquDioMzKkgJpdUA2pqdXrqNVXSQlw8KBUbrvNfdvMTOlLVs+efMJIRI0qQB2AOUPm4IHBD2Dv5b34IOkDfH/se1SYK2q1vVB4AY+tewzjYschMTKxBXpLLa1mwMzTUqFWztYv4yTrjuli4UW8s/sdLNq3CKWVrmf7a5Va3N3/bswdOhf9wvs1Yw+JiLxQXi7NELMGxazl1Cnpu1tTUSikwJd9MMz+OCqKa2xTS+MzSCKiVogBM0fWhDfZoiimu2hjP/3FtqKEIAhBAK6pOlwrimKKi+v/B+BdAGEAbgXfrKitEwRU+vlBHDwYcLYmhihKX4RqBtJSUqSRgxcuABaL96/XrZv782vWAPfeK+0HBQE9ekjBM/vSrRugVnv/mkREdgRBwPBOwzG803C8NeEtLN6/GB//+TEuFV1yaHdN12sYLOvAOnd2PPZ2hlnNdjUDb9T+nck9g1e2v4Jvjnzjdn2y7kHdMXfoXMwaOAsB6oDm6yARUU0WizQypGZQ7PRp6ftfXb7v1UGlVgt5fDxk8fHSdzz70qkTA2LU2vEZJBFRK8SAmaPCqm2YIAh6URSdrRRrv+y6fY6AsQBkVfsbXb2AKIoWQRC2QnqjGi4IQqAoivkN6TRRqyYIQESEVEaOrH2+okL6EmWffsO6PXdOWvvMnqeA2alT1ft5ecDu3VKxJ5NJIwtrBtJ69ZL6yaH8ROSlMF0Ynh37LJ4e/TR+OfkL3tr1FnZd2gUAeGrUUy3cO2pJ9U3JmJbmeBwd3Tj9odbvXP45vLztZSw9tBRm0fXaPNd0vQaPj3gc13W/DjJB5rIdEVGjKy+XUiieOCEt1GndnjnT8KwirkRHOwbCunaFMToam86fR6WvLyZcdx00zgZuErV+fAZJRNQKMWDmaC+AYZBSYj8J4F9O2jxtt7/Obr+v3f5RD69jXQJeANAPwLY69ZKoPVGppFlgPXrUPmc2A5cuVQfTLl0CAgLc3+/0ac+vabFUz3Jbu9bxXGAg0Lt37RIdzUAaEbmkkClwc++bcXPvm/HHhT/ww/EfcG3Xa91eY6g0IN+YjyjfqGbqJTWnmjPMioqk4ufn/rr0GuOLIyMbt1/U+qQWpGLBtgX44uAXLgNlMkGG2/rchqdGPcWZq0TU9EpKgJMnHQNjJ05IgxrNrgP69SIIUrrEHj2A+Pjqtau7dZNSJzrJDCIaDKjM5zN/avP4DJKIqBViwMzRu5AW3dQDeF4QhAAAnwC4AKAbgKcA3F3VdguAZXbXdrHbT/XwOvarWMS6bEXU0cnl1Xnmx4/37hqTCVAqa89M81Z+PvDHH1Kx5+fnPJDWubM0Y42IqMromNEYHTPaY7tF+xbh7xv+jkeGP4L5Y+czpVo74yzQlZnJgBlVyyzJxItbX8Rn+z9zmXpRo9Bg9qDZeGLkE4gLjHPahoio3goKgGPHpKCYfWDM22nRdRERIQXEevasDo716AF07cp0+dRRvQs+gyRqFPn5+bJffvnFb9OmTX5HjhzRpqam+hQXF8vVarUYGhpaMXDgwNIZM2bkTZ8+vUjWBM/wvvnmG/+vv/46+PDhw7qcnBylTqczx8bGlk+ePDn/scceyw4KCvIqN3FeXp7sueeei/z111+DsrKylEFBQabrrrsu/9VXX02Pjo52naudGhUDZnZEUUwWBOF6AN8BiAbwSFWxVwHgUwDPiKLDENBQu/0cDy+VZ7cfXM/uEpEzv/wiBc1SU6X0jNYFpa2l5pNIbxUVOU/vqNNVB8/69gX695dKeDhnpBGRS4ZKA17/43WUm8vx753/xuIDi/H8uOfx4JAH4aPwaenuUSPQ6aRSWlpdl5kpPR90hwGz9q+ssgzv7HoHr//xOkoqnGVfArRKLR4e9jCeGPkEQnWhTtsQEXmtokKaMXbkiGO5eNHztXXh61sdELMPivXo4XnECFEHw2eQRI3jX//6V/jrr78eXV5eXushXGlpqVBaWqpOTU1V//zzz8GDBw8uWbZsWUp8fHxFY7x2YWGhbPr06XGbNm0KsK+vqKhQ5OfnKw4ePKhbvHhx2Ndff31u/PjxpS5uAwDIyMiQjxkzJiE5Odk2iiQzM1P51Vdfha1fvz5g27Ztpxqr3+QeA2Y1iKK4QxCEOwH8BOdvJAUATgGoGdXV2u17St5tf17rspULgiB08tAkzLpTXl4Og8FQ15cg8orRLk+9saly1tdXdLRUrr7asb6oCMLZs5CdOQPh9GkIZ85AVrUV6vP/SmkpkJQkFTtiSAgsfftC7NsXlqoi9uoFML9+vbTq3zVqV5rrd+2DPz9ARkmG7TjPkIfH1j2GhbsX4qVxL+HmhJshMOje5oWF+SAlpXoE48WL5TAYpMGFzn7XRBFIT1dDypgjCQoywmAQm6fD1KTMFjO+PfYtXtz+ItJK0py20Sg0eGDQA3h02KMI00kf6Rv6WZ7vodRc+LvWCogihAsXIBw9CtmxYxCOHZO2p09DMDXewHQxKgqWXr0g9uwJS8+eEBMSYOnRAwgLcz1osBGfS/B3jZpLeXm5/WGYp+dxoiheqsv928IzSKLW7vTp0z7WYFlYWFjlmDFjihITE8vCwsIqjUajbPfu3bqff/45uKysTLZv3z79VVdd1XPPnj0nGjpjy2QyYcqUKd22b9/uBwDBwcGmGTNmZPfu3duYl5cn//HHH4P279+vz8jIUE2fPj1+8+bNJxMTE13+/zp37tzOycnJan9/f/OsWbOyevXqZUxJSVEtWbIkLCMjQzVz5swuO3fu9GIdGmooQRT5BdxKEAQlgK8A3FFVtQnACkgLa8YBuAdA76pzWwDcIIqioeraTQCuqjonE938wwqCcDWqF+V8QRTFl+rYT6//o/373/9GD2drQxGRI4sF2uxs+F68WKsoGulLmCiToSQyEjn9+uHwnDmNck8iapu+Sf8GKzJXwAzn64D00vXC/Z3uR5yGKdjasr//fSxOnQqyHT/wwCFcf32qy/bFxUrcffcNDnWLFq1HWBgHP7V1J0tP4tNLnyLFkOL0vEpQYWLIRNwUdhMClYHN3DsiaovkBgP8U1Phn5ICv9RU+J0/D98LF6BspMCUKJOhNCwMxZ07o6RTJxR36oSSzp1R3KkTTFo+c6eO4fTp03j66ac9N6wiiqLXI97ayjNIb+3bt2+1QqHorVarA3r27Hm2KV6DyJkZM2bEnD9/3ueJJ57InDp1apFcLq/V5vTp06rrrrsuPjU1VQ0At9xyS+4PP/yQ2pDXffPNN0OefvrpWADo1q2bcfPmzac6d+7sEIS7//77O3322WfhADB48OCSP//885SzexmNRiEgIGCQKIrYvXv38UGDBtkeRKakpCgTExN7FxQUKM6dO3c4Li6unmvQNNypU6e6G43GApPJdHzw4ME3eL6ibeIMM0ffArilav9hURQ/sD8pCMJ/AHwE4H4AVwJYAuDOqtP2n0pVAByGodRgn2uJUymJWgOZDGXh4SgLD0fmkCHV9aIITU6O00CasqysTi8hWCzwvXwZhpAQz43NZmkNNyJql2ZEzsCVgVfi6/SvsatwV63zJ0pP4IlTT+D6kOtxZ8Sd0Cv0zd9JajB/f8ePg4WF7tNtFhTUXsMlMNDdR0pq7QoqC7A0fSk25m10el6AgKuCrsJfIv6CEJUXnw+IqENSFRXBPzkZ/ikp8D93DgHnzkGXng6hEQZAW+RylERHSwGxTp1Q3LmzFCSLioJFpWqE3hORC3wGSc3OaDQK77//fvBPP/0UdPLkSU1xcbE8JCSk8qqrrip67bXX0mJjYysBIDc3V96lS5d+JSUl8pdffvniP/7xj6yW7rsrb7/99uXw8HDnI1Gr9OjRo+Lbb789N2rUqN4AsGrVqsDi4uILvr6+Xq0tVpPJZMKbb74ZZT3+4osvUmoGywDgo48+urR9+3a/kydPavbt26dfsWKF3/Tp04tqtsvIyFCUl5cLCQkJBvtgGQDExcVVDh48uGTjxo0BycnJqpYMmHUUDJhVEQThClS/UX1Z840KAERRNAuC8BCAgQCGAbhDEIQFoigeA2C/AIEa7t+s7J+G1Gf4V2cP5/sD+A0ABg0ahNGjR9fjJYg8MxqN2LZtGwBg3LhxUHegxZpNoghTejpkJ09COHkSMmvak+PHIZS6TUuMwCuuwIQJE1w3MJuhjo2FGBMDy8CBtiL27Qt00NGcHfl3jZpXc/+u3Yt7sfvybszfPB970vY4nLPAgt9yfkNSWRJevuJlzOg7AzKh8Rcopqbzyy9K7N1bfezr2x0TJkhrrTv7Xdu+3fG/b0CAiEmTrmm2/lLjMVvM+O+h/+Jf2/6FgvICp22uir0Kr175KgaED2jSvvA9lJoLf9cagTWl4sGDkB06ZCtCmvM0rnVliYmB2KcPLH36VG979IBSpUIQgCCPd2gd+LtGzUWn09kfTgJwuDHu28aeQVI7sX79et3s2bO7Xr582WE0REZGhmrZsmUhW7Zs8Ttw4MDx8PBw89tvvx1aUlIiDw4ONj366KPZLdVnb3gKllmNHDnS0KVLF2NqaqraaDTKjh8/7jN8+PB6/T+xZs0a3+zsbCUADB06tGTMmDFOR9QrFArMmTMn89FHH+0CAN9++22Qs4BZWFiYSaFQiCkpKeojR4749OvXz/b/dFpamuLQoUM6AIiJiWGwrBkwYFZtut3+h64aVb1hvQ9gaVXVJADHIOUVtgoCUOjmtew/h9b5j46nnMiCIERY9318fKDhmknUDNRqdcf7XevWTSqTJlXXWSxASgpw+LBjSU6WFqcBoExMhNLdv9XJk0BeHoS8PMgOHqyul8mAXr2AQYOAxESpDBwI+Ps3yY/XWnXI3zVqEc31u3ZV96uwq9surDixAk9veBrn8s85nM8qy8IDax7At8e/xeKpi9E9qHuT94kaR3S043FengIaTe2P39bftZISx/rgYIF/79qgI5lHMHvlbPyZ9qfT831C++DNa9/ExO4Tm32tQr6HUnPh75oXRFH63mBdD3nfPuDgQaCgoOH3DggA+vWTSv/+0rZvX8j8/AAA7SmPBX/XqCn5+DhkB8iq6xplbrSZZ5DUPixevDjwwQcfjDOZTIIgCBg7dmzhkCFDSrOyspS//PJLUHFxsTw9PV31+uuvh7/66qvpixYtCgOAOXPmZOj1+naznpNer7fNKCstLa33aNTffvvN9iBuwoQJBe7a3nTTTYWPPvooAGDLli1OH+BptVrxmmuuKVi7dm3guHHjEmbNmpWdkJBgvHDhgurzzz8PzcnJUSYmJpb06NGDs0SbAQNm1eLt9o94aHvAbt+6uIj9onsxAJwvUCCxnyF23nPXiKjNkMmqA2k33VRdX1oKHDsmBc+uvNL9Pfbvd15vsUj3OHYM+Prr6vqePYGhQ4Fhw6QyYADAUZZEbYogCLi5982Y1GMS/v3Hv/HajtdgNDmun7j1/Fb0/7g/Fly9AI8MfwRyWXt63NU+hYU5Hmdmum+fk+N47E0GX2o9KswVeH3H61iwbQEqLbUHf/qqfPHilS/i4eEPQyHj1zCiDic9vTo4Zi15eQ27p1wuDagbOLA6QNavnzRio5kD8kRUJ3wGSc1m27Zt2rlz58aZTCZBpVKJX331VfLtt99uC7ImJSVljRw5so/ZbMaWLVv8Pvroo4qcnBylv7+/+amnnmo3QVaj0SikpqbaouDdu3evd+7748eP20ZqDB8+3O16LTExMaaIiIiKjIwMVW5uriItLU0RFRXlLH3jxTFjxujS0tJU7777bqT9udDQ0Mqvvvoqtb79pbrhN7Vq9p8m1QCMrhoCsM9vap32edyurg+ArW6u71O1FSGNDCGi9k6nqw5oeXLggOc29k6dkoo1iKZUSiNJra83dCiQkMA10YjaALVCjX9e8U/c3f9uPLruUaw8tdLhvMFkwBPrn8D3x77Hf6f+F33C+ri4E7UG4eGOx54CZrm5jsfBwY3bH2o6+9P3495f7sXhTOeZmu7seyf+M+E/iPKNcnqeiNqZ/Hzgzz8dg2OXLzfsnhqN9Bl/0KDq0revVE9EbQ2fQQJIK07rcM+lo3xrB0qaksViwcMPPxxTUVEhAMCCBQsu2AfLAGDo0KHG0aNHF27bts0/PT1d9f7770cAwH333Zfp7+/v9Rpf69at02dlZTXKf9MRI0aUxcfHN+psqkWLFgWVlJTIAaB3795lMTEx9f5vce7cOdso9R49engMvHXq1KkiIyNDBQBHjhxRR0VFldRsExcXV5mUlHRi/vz5kevXrw/IyclRBgYGmq699tqC1157La0h/aW66XB/mNywn1Y9BMAGN2372u1bR2fsAlAJQAngKkgLc9YiCIIcwBVVh4dEUSyoT2eJqB177jkpzeP+/VI5cEBK02jx8nNKZaWU0mXfPuDjj6U6vR4YMqQ6gDZiBNCpU9P9DETUIHGBcfjljl/w2+nfMHf1XJwvdBwMuufyHiQuSsSrV7+Kx0Y+xrXNWqmaM8yyPCyVXXOGGQNmrV+luRILti3AK9tfgVmsvXxCQkgCPrzhQ1wdd3UL9I6ImoXJBBw5AuzaBezcCezZA5w927B7BgU5BsYGDQJ69OAAOKL2g88gAUS/Hd20C7m2QuIL4r7mfL2ff/7Z7/DhwzoA6Nu3b9kTTzyR46xd165dy7dt24bMzEwlAOj1evMzzzzj4duLo+effz46KSlJ3/BeAwsXLkyNj4/P9dzSO2lpaYoXX3zR9hDs73//e3pD7ldcXGx7Q46IiPAYyAoMDLS1ycvLc/lmHhUVZfryyy8vArjYkP5RwzBgVm0jgPuq9ufBxZuVIC00MNeuaj0AiKJYIAjCRgATAUwVBCFGFMULTm5xB4DQqv3vG6PjRNTOBARIaRvtUzeWlkrpHA8cqA6kHT0qBce8UVICbNkiFavOnYFRo6rLgAHS7DQiajUm9ZiEcbHjMH/jfHyY5Li8QYW5Ak/+/iTWJq/Fl9O+5MyVVigoyPG4qEga+yBzEd+sOcOMKRlbt3P55zBjxQzsvrS71jm5IMfTo5/GC1e8AB+Fj5OriajNyssDdu+WgmM7dwJ790qf1esrJEQa0DZkCDB4sBQc69yZKRWJ2jc+g6RmsWTJEts3iieffDJd5uKLSGBgoMPIr5kzZ2aHhITUHg3WBhmNRmHq1Knd8vLyFABwzTXXFNxzzz0FDblnWVmZ7R9Sq9V6HN2uVqttbYqKijj6pZVjwKzaCgCpALoAmCIIwnOiKL5i36DqjeoNVI/OWC+K4iG7Ju9AerNSAVgmCMJEURSL7a7vDWBh1WExgM+a4OdwcKC0FLFGI6J9fCDnB26itkunA0aOlIpVRYU0mnXv3upy4oS0gLg3Ll4E/vc/qQBSOpdhw6oDaCNHcnoDUSvg6+OLD274ALf1uQ33rbwPZ/LOOJzfcG4D+n/cH4unLsa0hGkt00lyKjDQ8VgUgcLC2vVWnGHWdnx75FvMWTUHxRXFtc71C+uHz2/8HIOjBrdAz4ioUVks0udr6+yxXbukzA/15esrBcWGDq0usbEMjhF1PO3yGSS1LmazGVu3bvUDAF9fX3PNVIz2LHYZjdRqteW5557zkEy+tr17956qV0ebkNlsxu23395l3759egDo3Llz+bfffpvawt2iVo4BsyqiKFYIgjAT0qgOJYAFgiBMA7AM0lTpaAAzAFi/+WYD+FuNe6wXBGE5gJsBjAJwSBCEjyFNoxwE4EEAvlXNnxJF0ek02Mb0SGoqHtm9G0pBQKxajbiq0lWjse3HqdUIVioh8EM6UduiUklfuAcPBh58UKorKpJmn9kH0S56OZPbYAC2bpWKVUJCdQBtzBgpFQz/VhC1iHGx43BoziE8v/l5vL3rbYioDo7nGnJx0/9uwoNDHsQ7173DGS2thLPAWH6+64AZ1zBr/UoqSvDQbw9h6eGltc4pZAo8O+ZZPDfuOajkqhboHRE1WEWFtPbYtm1S2blTGulQHyqVNFvMPjjWs6fracZE1GG012eQ1Lrs3r1bY12za8iQISVqtdrl6GrRbuD1jBkzsiMjI9v8elkWiwV33XVX7MqVK4MAIDIysuL3338/HRoa2uCZc1qt1mKdKVZWVibztNab0Wi0vfn7+fm1i5l77RkDZnZEUdwmCMIkSG9QwZDyCA9x0vQ0gBtdTHe+B9Ib0gQAcQD+XfNlALwoiuKnjdZxL1SKIs4aDDhrMDg97yuXVwfQqoJpXdVqdKvaVzNXOlHb4OdXO51jRoa00PjevdJ2zx6goMC7+508KZUlS6Tj8HBg3Dhg/HjggQcaufNE5IlGqcF/JvwHN8TfgHt+ugeXiy87nP/4z4/xZ9qf+PG2HxHjH9NCvSQrnQ5QKKTlbazy8123Z8CsdTudexrT/zcdx7KP1TrXPag7vp3+LYZGD22BnhFRvZWVSZ+NrQGyXbukQWT10a1bdZaGYcOAfv2koBkRkRPt+Rmkty4/fvmQ51ZUX0lJSVrr/oABA8rctTWbzQIAqFQq8fnnn6/z7LLWxmKx4O6774757rvvQgAgPDy88vfffz/ds2fPisa4v6+vr9kaMMvIyFD4+/u7vW9+fr4tBhMUFMSAWSvHgFkNoij+LghCd0gjMSYD6AXpzScfwCEAywEsEUXR6f8IoiiWCYIwEcBfAMwEMBBAAIAcADsALBRF8Y8m/jHqrNhsxuHSUhx2kntdANDJxwfdNRp002ikrVptO/ZV8NeIqFWLiACmTJEKIKWWOXWqet2FnTu9Ty2TmQn88ANw/jwDZkQt6Oq4q3FoziE8sOoBLD+x3OFcUloSEj9NxLc3f4sJ3Sa0UA8JkCbkBgQ4plp0N16Ba5i1Xr+c/AX3/HwPisqLap2bOWAm3r/+ffj6+Dq5kohalaIi6bPvtm1SVoWkJO/XBLanVkszxqwBspEjgbCwxu8vEbVrHfUZpFWUb1Sbn8XUmiUnJ6ut+927dze6amc0GoWff/7ZNgsrNja2Hm+MwLp16/RZWVmN8pB4xIgRZfHx8fUKblksFtxzzz0x3377bSgAhIWFVW7YsOFUnz59yhujbwDQtWtX4+XLl1UAcPr0aR9PgbhLly7ZRtD069fP5X8Lah0Y6XBCFMUCAK9VlfpcLwL4pqq0KB9BQEP/GogALpaX42J5OTY7ecoTplRWB9I0GjwTEwMfppkgar1kMqBXL6n89a9SXW5u9doM1sXL3Y2uveIK1+esDh8G+vQBOEOVqEkEa4Pxw60/YMmBJXh4zcMwmKr/n8015GLi1xPx0lUv4bmxzzHtcgsKDHQMmLmaYWZd38xeQECTdYu8ZLaY8fzm5/HajtpfC/x8/PDp5E9xR987WqBnROSVsjJgxw5g40Zg0yYpdbnFbdYk52JipKCYNUA2YABnjxFRo2hPzyCpdcnLy7M9jHGXMvDNN98MtQZ/5HK5y7SNnjz//PPRSUlJ+vpeb2/hwoWp8fHxuZ5bOrIGy7755ptQAAgNDa3csGHDqb59+zZasAwAevfubdi+fbsfAOzZs0c7ZcqU2gsbV7l48aIiIyNDBQBBQUGmqCgGils7Bszaud9790aP4cORYjAgxWjEOaPRtp9iNOKC0YiGzgPNqqxEVmUldhUVQSOT4Z+xsW7bny0rg69CgTCum0bUegQHA5MnSwWQRtoeOlQdQNu+HUhLq24/bpz7+12+LD1ICAgArr4auOYaKY1jfDzXQCNqRIIg4K+Jf8Ww6GGY/v10nM07azsnQsTzm5/HsexjWDJ1CTRKTQv2tOOquV6Zq4BZWRlgrvGhzN+/afpE3ikuL8Ydy+/A6jOra53rH94fK25bgW5B3VqgZ0TkkskkzRrbuFEqO3dK65LVVf/+wNix0mfe0aOB6OjG7ysREVETsl+XrLCw0OnMhszMTPnbb78daT1uSMCspbkKlvXr169Rg2UAcMMNNxR++umn4QDw+++/+y9YsMBlGssVK1bYvtVdddVV9VwYlZoTA2YdQLhKhXCVCiOcPHUxWSy4WF5uC6DZB9bOGQzIqmN6im4ajccg2H2nTmFrYSH85HL00GrRU6NBD60WPaq28UzzSNTylEpgyBCpzJsnTX04d05KXbN1KzBmjPvrN26UtgUFwIoVUgGAzp2rg2fjx0vpIomowfqF98Off/sTs36ZhZ9P/uxw7ruj3yG1IBU/3/4zwvXhLdPBDqxmwMxVSsai2pn+4OfX6N0hL10svIjJyybjcObhWufu6n8XPp38KbRKrZMriahZiSJw9Gh1gGzrVqDY5SBv5+RyIDFRCpBdcYX0OTcoqGn6S0RE1Ew6depke6i7c+dO/dy5c/Nqtpk7d27ngoIC20NY61pm9bF3795T9b22McycOdMWLAsJCan8/fffT/Xv37/Rg2UAcMMNNxSHhIRU5uTkKPfu3eu7Y8cO7ZgxY2qtE2cymfDJJ5/YvoTfeeedtf4bUOvDqEQHp5DJEKfRIE7jfNR5scmEZIMBZw0GJBuN0raqXCwvR81hB93Uaqf3sXe6Ks1bkdmMP4uL8aeTLzRRKpVDEM0aVItTq6Fkukei5icI0kLm3boBs2d7br9hg/P6ixeBzz+XCiClbLzmGqlcdRWg0zVen4k6GH+1P1bctgJv7nwT8zfOh0Wszrqx+9JuDFs8DKvuXIV+4f1asJcdT820iq5mmDFg1nrsS9uHKcumIL0k3aFeIVPgnevewdyhc5klgaglZWcD69cDa9cCv/8urbFbFyoVMGxYdYBs1CjAl2sQEhFR+zJ8+PBS6/7y5ctDHnrooezRo0fb8vg/++yzEcuXLw8GAL1eby4pKZFfuHDB5+zZs8ru3bvXax2zljJz5szOX3/9tS1YtmHDhlMDBgyoV7Bs2LBhPa2pJRcuXJg6b968WqkhFQoFnnzyyfRnnnkmBgDuvffeuC1btpyKjo52SLc4d+7cTidPntQAQGJiYsnNN9/s5FsftTYMmJFbvgoFBvr6YqCTLxBGsxmp1iBa1XaAh4fdxSYT0r1IiZFWUYG0igpsqTEMWw6gq0aDnnbBtAStFr20WoQyxSNR63Hpknftjh2TysKF0sOLceOA66+XSkIC0zcS1ZEgCHh69NPoH94ft/1wG4orqgelXCi8gNFLRmPlnStxZZcrW66THYy3KRlrrl+mVnN5nJaw6vQq3P7j7SirdBwgGqINwU+3/4QxMR5mWBNR4zObpfV1164F1qwB/vxTmlnmLbkcGDpUym5w9dXSGmQuBowSUcdVYbHgrMGAE2VlOFFaihNlZUiyX4iWqI258cYbi6KioirS0tJU5eXlwrXXXptw44035kVFRVVs3brVb9++fXoAGDBgQOm0adPyX3zxxU4mk0mYPHly/K233po7YcKE4rFjx9aaNdXazJs3L+qrr74KA6Tvw3/729+yDh8+rDl8+LDbN/sRI0aUxcfH1yNvs+Txxx/PXrlyZcDOnTv9zp49qx44cGDvGTNm5PTu3duQl5en+OGHH4L279+vBwBfX1/zp59+er6+r0XNiwEzqje1XI4EnQ4JdZgRcqG8HD6CgPK6fMGxYwZwxmDAGYOh1rkghQK9tFr01unQqyqI1kunQ2cfH8j40J2oeW3aJAXNNm6UZptt3Aikp7u/pqJCarthA/DEE0CXLsDEiVLwbOTIZuk2UXsxsftE7PzrTkxZNgWpBam2+uKKYkz8eiK+u+U7TEuY1mL960i8DZjVnGHG2WXNb+mhpbj3l3thFh0Xk+sZ3BO//eU3rldG1JwyMoB166QA2fr1rv94utK3b3UK8Cuu4B9VIrIpMZlwsqxMCoxZS2kpzhoMMNdsbDI5uwVRm6BQKPDFF1+cmzZtWo+ysjKZwWCQfffddyH2bfr27Vu2evXqsyUlJbI33ngjymg0yk6dOqVZsGBBp4SEhLOu7t2a7N692zbLQxRFvPbaa14tPLpw4cLU+Pj4WrPHvKVUKrFq1arkm2++uevmzZv9c3JylAsXLoys2S48PLxy6dKlyUOGDDHW97WoeTFgRs2qj06H0nHjcNFoxGmDAafLynDaYMCpqu15o7FWmkdv5ZlM+KOoCH/UeOKklclss9B6VQXTemu16KbRML0jUVPq1AmYOVMqogicOFEdPNu82fP6EqmpwCefAJ98ArVKhVEJCchMTIQQGwsMGMDZZ0Qe9A3riz337cG076Zh16Vdtvpyczlu/v5mfDblM8we5EWKVWqQmkvIuvrTV3OGmZOlZ6kJvb/nfcxbO69W/VVdrsLy25YjUBPo5CoiajQWC7B/P/DLL8BvvwEHDtTt+piY6jTfV18NhHPNTqKOLruiwiEgZt2/WN4kSxoRtUrXXnttaVJS0rFXXnklYvv27X7Z2dlKk8kkhIaGVo4fP77www8/vOTn52cBgB9++OHs448/3jk5OVljsVgwdOjQ2rMVyEFgYKBl06ZNZ7/++uuApUuXBh8+fFibl5en1Gq15piYmPIpU6bkP/bYYznBwcG14vHUejFgRs1OLgjootGgi0aDCTUWUzaazUg2GnG6rMwWRLMG1bIr65c+t8xiwf6SEuwvKXGoVwgC4jUah9lofXU6JGi18GEgjahxCQLQu7dU5s2TRur9+acUQPv9d+CPP6R0O64ur6hA6OHDCD18GPjiCyA+Hpg6FbjxRmn2mYJvZ0TOhOnCsGnmJsxYMQMrTqyw1VtEC/668q/ILcvFU6OfasEetn96veNxjY8jNpxh1jJEUcTL217GC1teqHVu5oCZWDRlEVRy5sYkahJGo5SVYOVK4NdfgbQ076/VaqXA2PXXAxMmSOvscjAVUYeUX1mJY6WlOFpVjpSW4lhpKXI5O4wIANC7d++Kb7755oKndpMnTy6ePHny8eboU2Pau3fvqZa+31133VVw1113FTRmP6jl8AkjtSpquRx9dDr0cZLmMa+yEmdqzEqzlvqkeDSJom2EkT05gDe6dcMTnTvX98cgIk8UCmDECKn84x/S1IoNG6S0O2vXApcvu7/+zBngrbekEhwMTJokBc8mTKj9dJqog1Mr1Pj+lu8xZ9UcLD6w2OHc0xuehsliwvyx81uod+1ffQNmnGHW9ERRxPyN8/HGH2/UOvf4iMfxnwn/4fq4RI0tO1uaQbZypZRqsbTU+2v79JECZBMnAmPGAD4+TddPImp1ysxmnCgrw5GSEltw7GhpKS5X1HsJIqciVSrbwGql0Yh3G/XuRETU2jFgRm1GkFKJ4UolhtcYcm0WRaQajThuN8XeOt2+yM2MFVfMAKJU7kcSp5eX42BJCfrqdOjk48OHKUQN5e8P3HyzVEQROHKkemH3HTvc547PzQW++koqPj7SWhVTpwJTpgBRUc33MxC1YnKZHIumLEKoLhSv7XjN4dyn+z7F3GFz4efDKU1NgTPMWidRFPGPTf9wGixbcNUCPDv2WX6+I2osqanAjz9K6RZ37pTSL3rD1xe49lopQDZxIsABjUQdQqXFgjMGg8OMsaOlpUg2GOq9hEdNMgBxarVt2Q5rSdBqEaBU2tptz8xkwIyIqINhwIzaPLkgoJtGg24aDabY1YuiiHRrzmq7YNrx0lJkekjv2NfJDDd7v+fnY+bJkwAAP7kcfXU69KtK6dhXp0N/vR5Bdh+yiKgOBAHo318qTz8NFBWhfPVqpC9ZgvB9+6DJy3N9bXk5sHq1VObMAUaPBm65RQrE8SELdXCCIODV8a8iWBOMJ39/EgDQJaALNs/czGBZE2LArHX615Z/4dUdrzrUCRDwwQ0f4KGhD7VQr4jaDyE5GVi1SgqU7dvn/YXx8VLWgEmTpM9x/E5F1K5lVVTgUEkJDpWU4GBJCQ6XluJkWRkq65FFyBkfQUAPu4CYNUDWQ6OBWi5vlNcgIqL2hQEzarcEQUCUjw+ifHwwPtBxofb8yspas9GOlZbifHk5FIKAnlqt23sftUsdUmQ2Y2dREXbWeNLV2ccHA/R6DNTrMUCnw0C9Hl01Gsg4Wpmobvz8YLnxRhzSaABRxHWhoVCvWyel8jl0yP21f/whlccek9I/3nKLVGJjm6fvRK3QE6OegEquwtu738bmmZvRJaBLS3epXevaFXjkESlwptdLWWSdKSx0PPb1bfq+dVQvbX0JL217yaFOgIAlNy7BrIGzWqZTRO2AcPo0evzwAyJ37oQ6JcW7i2QyYNSo6uwACQlN20kiahFmUcSZsjIcKi3FQbsAWXojpVNUCgIStFr0rVrio69Ohz5aLeI0Gsj5DIaIiOqAATPqkAKVSozy98eoGguEFJtMSDYYoJLJ3F5/xItc+xfLy3GxvByrcnNtdXq5HP11uupAml6PfjodtBzZROQdQYCYmCiNOH7pJSnFz6+/Sil+tm51n7px926pPPkkMHSolO7R1ZNronbu4eEPY/ag2dCp3M+opoaLjwfefddzO65h1jze3/M+XtjyQq36xVMXM1hGVB8nTgDffw/8+CPUR4+ilzfX6HTAdddJAbJJk4DQ0KbuJRE1o2KTCUdKS21BsUNVM8cM3qZjdUMA0FWtRj+93pbhp69Ohx4aDZQenuMQERF5gwEzIju+CgUGejGkWyuTwU8ur/MaaSVOZqPJAMRrNLYAmnUbqVJx7QwiT7p0AR5+WCoFBVIQbOVKKSVjzafP9vLygKCg5uolUavEYFnrUlzseMyUjI3v+2Pf45G1j9Sq/3Typ5g9aHYL9Iiojbp8GfjuO+Cbb4ADB7y7JjgYmDYNmD4duPpqQK1u0i4SUfPIqajAvpIS7CsuxoGqANlZg6FR7h2tUtlmjFmXwOil00HHAcdERNSEGDAjqoflfftCFEVcLC+3LURrXYz2eGkpKuqQb9sC4JTBgFMGA/6XnW2rD1cqMdjXF4N9fZGo12Owry86+fgwiEbkSkAAcOedUikvBzZskNbN+PlnKZhm79ZbpbXSiIhaibIyx2MPy6lSHW04twF3rbgLIhw/o314w4e4f/D9LdQrojakoABYvlwKkm3ZAnjzfScsTAqQ3XILcMUVgIKPH4jasuyKCuwrLrYFyPYVF+NCeXmD76uRydCvahkLaxaevjodArmGIRERtQB+YiWqJ0EQEKNWI0atxg12ad0qLRaccpKbO7uysk73z6ysxOq8PKzOy7PVhVqDaFUBtMG+vujMIBpRbT4+UoqfSZOATz8FNm2Sgmc//STNLrvlFvfXl5UB990nBdZuuEG6H1EHcyb3DCotlegd2rulu9Ih1Mz2rNG0TD/ao/3p+3HT/25CpcXxs9jLV72Mh4Y+1EK9ImoDjEZg1Srg22+B334DvFhryBgYiLSRIxH9yCPwGT8e4EwQojapZnDsz+JiXGyE4FiESoWBdmu9D9Dr0UOr5TpjRETUajBgRtTIlDIZ+ur16KvXY0Z4OABAFEVkVFTYAmjWYNrpsjLUJYt3dmUl1ublYa1dEC1EqbQF0BKrgmmxajWDaERWKhUwcaJUPv4Y2LEDSEx0f83KlcCyZVIJCJACZ7NmASNHcmYadQhbUrdg+v+mw1/tjz337UGYLqylu9TucYZZ00grTsOUZVNQUlHiUP/QkIfw3NjnWqhXRK2YKAJJScDnn0ufgwoLPV8TEQHcdhvKp0zBuuJiQCZD5NixDJYRtRH5lZXYW1yMpKIi/FkVJLvUwOCYHEAvnc4WFLPOHgtTqRqn00RERE2EATOiZiAIAiJ9fBDp44Pr7WajlZnNOFpjMdxDpaUoqcPaaDmVlViXn491+fm2umCFAjMjIvBW9+6N+nMQtXlKJXDVVZ7bfftt9X5BAfDZZ1Lp2RO4917g7ruBqKgm6yZRS/rv/v9izm9zYLKYkG/Mx/T/TcfGezbCR8GZlk2pZsBMq22ZfrQnhkoDpn03DWnFaQ71t/a+Fe9d/x4HFxHZy8wEvv5aCpQdO+a5va8vcPPNwIwZ0mcruRwWgwFYv77p+0pE9VZhseBQSQn2FBVhT3Ex9hQV4UwD1xzTyGQYWDWI1zp7rI9WCzWD5kRE1AYxYEbUgrRyOYb5+WGYn5+tziKKSDYYHNIf7C8uRmEdgmi5JhPMdVhHjYjs5OYCa9Y4P3fqFPDMM8Czz0oz1u69F5gyhSkbqd0wWUz4bP9nMFlMtro/Lv6Bv/36N3w57UsGGJoQZ5g1LlEUMXvlbCSlJTnUj4sdh6U3LYVcxod4RKislD7zLFkipVw0mdy3V6mkdNczZkgpq5k7lqhVE0UR54xG7Ckqwt6qANmB4mKUN+BZgTU4NqRqiYjBej0StFooZLJG7DkREVHLYcCMqJWRCQLitVrEa7W4wy6lY7LBgP12i+vuKylBgZsvtfZBOGdSDQbcfOwYhvn5YbivL4b7+aGnVgsZH4ZSR+fjA3z4obSo/bZtzttYLMDq1VIJDpYeHP31r0D//s3bV6JGppAp8PMdP2PYZ8NwseiirX7p4aXoG9YXT49+ugV713ZlZ0tZzYqLgZISID4e8Pd3bFMzYMbn0A3z6vZX8d3R7xzqugZ2xfLblnO2JFFysrTG65dfAllZ7tsKAnDllcBf/iLNKAsMbJYuElHd5VdWSsGxqplje4uLkVPHtdTtae1mjjE4RkREHQUDZkRtgCAI6K7VortWi9vCpHVkRFFEitHoEEDbV1yM/Kog2nAPAbPdRUXYX1KC/SUl+KSqzk8ux1BfX4zw88Mof3+M9PNDoFLZlD8aUeuj1wP33y+Vixer0xOdOeO8fW4u8N57Uhk1CpgzR1rzTK1u3n4TNZIIfQRW/WUVRv13FEorS2318zfOx7DoYbiyy5Ut17k2asIE4ODB6uMvv5T+TFiJImeYNaZ1Z9fh+c3PO9T5+fjh1zt/RYg2pIV6RdTCTCZg1Srgk0+Ades8t4+Lk9ZvnTULiIlp6t4RUR2JooizBgP+KCzEzqIi/FFYiOM1P0zUgVomwyDOHCMiImLAjKitEgQBXTUadNVocKtdEC3VaMT+khJ09fCwfm9xca26IrMZGwsKsLGgwFbXW6vFKH9/jK4KosVrNEzJRR1H587A/PlSGsY//pACZ99/L00RcWbnTqk8+qiUrvGBB6SpJERtTP/w/lh28zLc+N2NECGl7bGIFtz+4+048MABRPlyDb+60Osdj2v+CamoAGpmXmbArH4uFl7EjBUzbL+3ACATZPju5u/QO7R3C/aMqIWkpQGLF0trsV665L6tRgPccgswezYwbhzAB+VErUa5xYL9xcX4o7AQfxQVYWdhIbIaMHusp0aD4X5+ttJfp4OS/88TERExYEbUngiCgDiNBnFe5HHaU1Tk1T2Pl5XheFkZFqenAwBClEqMqgqeDVGrUQ6AiY2o3RMEYMwYqSxcCPz4oxQ8c5WyMS8PeOstqVxzDfDgg9JaZ5yxSW3IlJ5T8NJVLznM1MkqzcLtP96OTfdsglLO32dvabWOxwaD47GzAeE1ryHPKs2VuP3H25FryHWof33867g+/voW6hVRCxBFYONG4OOPgV9+qR2Rr2nkSClIdtttgIcsFUTUPHIqKmwzx/4oLMSfDVh7LFSplAJjvr4Y5ueHob6+zCRDRETkAgNmRB3UG127YmdRkS23+aXycq+uy6msxMrcXKzMlR5GKfz80M1sxqbz5zEuOBhj/P0RrlI1ZdeJWpZeX52i6OxZKXC2ZAmQkeG8/YYNUomKAubOlVI9hjAlGLUNz459Frsu7cLqM6ttdTsu7MD8jfPxnwn/acGetS01J30bjY7HDJg1jmc2PINdl3Y51E1LmIYnRz3ZQj0iamYGg5RK+t13gePH3bcNDARmzpRmwyckNEv3iMi1S0YjthYWYmtBAbYXFuJkPdMrqmUyJOr1GO7nh2FVa5V3UauZJYaIiMhLDJgRdVBjAgIwJiDAdpxWXo69RUXYXVSEnUVFSCouhtFi8XgfkyDglEKBUxkZeK8qYJCg1WKcvz+uCAjAOH9/dOJaTtRede8OvPIK8K9/AStXSiO5N2503jYtDXjuOeDll4G77wYeeQTo06dZu0tUVzJBhqU3LUXip4k4X3jeVv/WrrdwXbfrcG23a1uwd21HzYnftQNmtR9iMSVj3fx2+je8vftth7q4gDh8fuPnfEhI7V96OvDhh9L6ZLm57tsOGybNfL/99tp/nIio2Zw3GrG1oABbCwqwpaAA52p+OPBSuFKJ0f7+GO3vj1F+fkj09YWKqRWJiIjqjQEzIgIARPn4YFpoKKaFhgIAKiwWHCgpwU67RYTTKyq8utfJsjKcLCvDoqo0jl3VaowLCMAV/v4YFxCAOI5wo/ZGqQRuvlkqp08DixZJM8/y8mq3NRqldUQ++wy49lppvbOJE7lOCLVaQZog/Hjbjxi9ZDQqzNXvA7N+mYXDcw4jWBvcgr1rG2qOG/GUklGhYAbXusguzcZfV/7VoU4lV+GHW39AgDqgZTpF1Bz27wfeeQf43/8Ad2sZabXAjBnAnDlAYmLz9Y+IAEhrjZ+zC5BtLSjAeS8zvNTUR6u1BchG+/ujK79bExERNSoGzIjIKZVMZlsA+DFIH/LPG422BYb/KCzEkdJSeJ6DBpwzGnEuIwNfVM1A6+Tj4zADradWyw/51H706AH85z/STLIff5Rmne3a5bzt779LpUcPacbZrFnMw0at0pCoIXjz2jfxyNpHbHVpxWm4f9X9+PHWH/k33ANPM8wMBsd/P/4Z8J4oinhg1QPILM10qH/nuncwOGpwC/WKqAmJIrBmDfDGG67XUrVKSJDSQd99N+Dv3zz9IyIAQIrBgI35+dhSNYPsspeDT+1pqr6Tj/bzw2h/f4zw8+PaY0RERE2MATMi8oogCOii0aCLRoMZ4eEAgMziYny6dStOKBTIDgvDntJSlHhaVBzApfJyfJuVhW+zsgBIaSSuCgzE1QEBGB8YiK5MD0PtgUYjPaC6+25g3z5g4ULgu++cjwA/fVp6oPWvf0mBs4cektYWIWpFHh72MFafWY11yetsdStOrMAXB7/AvYPubcGetX6eZpiVljoe823Qe18e+hI/nfzJoW5S/CQ8OOTBFuoRURMxmYAffgBefx04fNh92wkTgMcek7acwU7ULLIrKrCpoAAb8/OxIT8fKfVIsRigUNgGlo7198dAvR5K/j9MRETUrBgwI6J681MoMMBsxgCzGRN69YLSxwcHSkqwzW6x4gKTyeN9Misr8V1WFr6rCqB1UasxPiAA90ZGYjRHw1J7MHgw8NVX0mjwjz+WSk5O7XbZ2cA//iE9DJszR3rYFRXV/P0lckIQBHx+4+fo93E/5Bqq18iZt3Yexncdjxj/mBbsXetWM2BWew0zx2OuX+ad1IJUzFszz6EuRBuCxVMXc9YjtR9GI/DFF8CbbwLnzrlup1ZzjVSiZlRqNmN7QQE25OdjY0EBDpaU1PkewQqFtHRB1fIF/fR6yPn+RURE1KI4VIWIGo1CJsNQPz880bkzVvbrh9zRo3FwyBC81707bg4JQaiX6SNSjUb8NyMDJ2s+QSRq6yIjgZdeAi5eBP77X6BfP+ftSkqktI5xccDf/ibNQCNqBSJ9I7F46mKHupKKEjz424MQRbGFetX6eUrJWF7u+HCsZoCNahNFEXNWzUFxRbFD/aLJixChj2ihXhE1ouJi4N//lj4LPPig62BZZCSwYIH02WLRIgbLiJpIpcWCnYWFeDk1FVccOIDAHTtw/ZEjeOvSJa+DZaFKJW4JDcUH8fE4MmQIskaPxoq+ffFIp04Y6OvLYBkRUSP5448/NA888ECnXr169Q4MDBygUqkSw8LC+vft27fXPffcE/P5558HmrwY4F9fN998cxdBEAZby+OPP+7VSOi8vDzZgw8+GN2pU6d+KpUqMSIiov/MmTM7X758mZOemhH/sYmoycgEAQP0egzQ6/Fwp04QRRGnysqwtbAQ26oWO3aXy318QIDb+xeZTFDLZFAxTQW1NWo1MHs2cO+9wKZN0qjxdetqt6uoABYvloJrt90G/POfQO/ezd9fIjvTEqbh3oH34vODn9vqVp9ZjWVHl+Ev/f7Sgj1rvTylZCwvd9+ealt2dJlDelAAmDVwFm7qdVML9YiokRQXAx98IA2cyctz3S4hAfj734G//AVQqZqvf0QdyEWjEWvz8rA2Lw8b8vNR5MXyA/ZClUpcHRCAK6tmkSVw7W4ioiaVl5cnu//++2NWrFgRXHNAZ3Z2tjI7O1t57Ngx7dKlS0OnTJlyMCQkpG5/2L3w/fff+61YsSK4rtdlZGTIx4wZk5CcnGz7NpiZman86quvwtavXx+wbdu2U/Hx8XVfEJPqjAEzImo2giAgQadDgk6HB6KiIIoizhmN2JSfj00FBdiUn4+sqvWd4tRqdPGwiMvbFy/irUuXcHVAAK4LCsLEoCCuf0ZtiyAA48dL5cABKWXjDz8AFotjO1EE/vc/4PvvpTZPPdUy/SWq8taEt7Dm7BpklGTY6uatmYdru16LUF1oC/asdfKUkrHmsY9P0/anrcsty8Wjax91qIvyjcK7173bIv0hahSlpcCHH0qzynJzXbcbMgSYPx+YNo3rkxE1snKLBTsKC7E2Lw9rcnNxrI4ZT7QyGa4ICMA1gYG4JjAQfXU6yBggIyJqFpmZmfLx48f3OHbsmBYAwsLCKq+//vr8AQMGGAICAsxFRUWyM2fOqLds2eJ3/PhxbVP0IS8vT/bII4/EAoBGo7EYDAavP6zNnTu3c3Jystrf3988a9asrF69ehlTUlJUS5YsCcvIyFDNnDmzy86dO5l+qBkwYEZELUYQBHTTaNBNo8HfqgJoR0tLsTE/36vFjdfm5aHEbMbK3FysrHqw0F2jwXWBgZgYFIQrAwKgV/DPHLURgwYB330HvPKKNKr8889rTzsRRWDcuJbpH5GdQE0gPrrhI0z/frqtLteQi0fXPYpvpn/Tgj1rnTylZKw52ZoBM/eeWP8EssuyHeo+uP4D+Ku57im1QWVl0tqmb7whrWXqyvjxUqDs6qulATdE1CjOGQy2WWSb8vNRWnPgmhtyACP8/DC+KkA23M+P2U+IiFrILbfc0tUaLPvb3/6W+e67717WarXO1g24nJqaqvT392/02WX/93//1zkjI0MVERFRMXny5PzFixeHe3Od0WgUfvvttyCVSiVu3rz55KBBg2zfGB944IHcxMTE3rt27fJNSUlRxsXFVTZ2v8kR38mJqNUQBAH99Ho82rkz5kZHu22bW1mJpOLiWvVnDQZ8mJaGKUePIuiPP3D1wYP494ULOFRSwvV1qG3o1k16cJaaCjzzDODnV33u+uuB4cNbrGtE9m7qdRNu7nWzQ923R77F5pTNLdSj1stTSkajkWuYeWtzymZ8eehLh7qbEm5iKkZqeyoqgPfeA7p2BZ580nWw7MYbgb17gQ0bpKAZg2VEDVJhsWBDXh4ePXMGPffsQbc9ezD3zBn8mpvrVbCsn06HRzt1wqp+/ZA/Zgx2JCbixbg4jA0IYLCMiFo9o9EovPnmmyGjRo3qERQUNECpVCZGRkb2+8tf/hJ7/vx5pbVdbm6u3NfXd6AgCIMXLFgQ1pJ99sZ7770XvGPHDj8AmDFjRvaiRYsuuQiWAQC6dOlSqVQqXZ2ul5UrV/p+9913IQDw9ttvX/D19fV6BEZGRoaivLxc6Nq1q9E+WAYAcXFxlYMHDy4BgOTkZObhbgacekFEbdLeoiJ4euepFEVsLijA5oIC/P3cOUSqVJgUHIwpwcEYHxgInVzeLH0lqpeICOC114CnnwbeeQdYuBD4179auldEDj644QNsStmEfGO+rW7e2nk48MABKGT8mGnlaYYZ1zDzjsliwry18xzq/Hz88MENH7RQj4jqwWKR0iw/9xyQkuK63dSp0vv+oEHN1jWi9iqvshJr8vKwMicHa/Py6rQWWaBCgQmBgbg+OBgTAgMRyWngRNRGrV+/Xjd79uyuly9fdgi6ZGRkqJYtWxayZcsWvwMHDhwPDw83v/3226ElJSXy4OBg06OPPupmCnzr8O6770YAgFartbz33nuXmvv1i4uLZXPnzo0VRRGTJk3Kv/POOwuTkpJ03l4fFhZmUigUYkpKivrIkSM+/fr1s31DTEtLUxw6dEgHADExMZxd1gz4JIOI2qTrg4OROWoUfs/Px9q8PKzPy7Otf+ZKekUFFqenY3F6OnwEAVcHBmJycDAmBQcjlk8nqbUKDAReekkKnOn17tsajcBbbwFz5wIBAc3SPerYIvQReOXqV/DQ6odsdUezjuKjpI8wb/g8N1d2LJ5nmDke81mcc5/++SmOZh11qHtt/GuI8o1qoR4R1dGmTdL7+b59rttMmiQFyoYMabZuEbVHZ8rKpNT9OTn4o7AQ3obIBABDfX0xsWqN7GF+fpBzZicRtXGLFy8OfPDBB+NMJpMgCALGjh1bOGTIkNKsrCzlL7/8ElRcXCxPT09Xvf766+Gvvvpq+qJFi8IAYM6cORl6vb5Vp2tav369LiUlRQ0A11xzTUFQUJD3uXUbybx586IvXbrk4+/vb/74448v1PV6rVYrXnPNNQVr164NHDduXMKsWbOyExISjBcuXFB9/vnnoTk5OcrExMSSHj16VHi+GzUUA2ZE1GaFqVSYER6OGeHhsIgiDpWUYG1eHtbl5eGPoiKY3KRgLBdFrMnLw5q8PMw9cwb9dDpMDg7G5OBgDOeXImqNPAXLAODDD4F//AN4+23g2WelwBmDwdTE7h98PxbtX4SDGQdtdf/c/E/c0fcOhOlaffaOZlHzf0OuYVZ3uWW5eH7z8w51A8IH4IHBD7RQj4jq4NAh4O9/B9atc91m4kQpUMbUy0T1YrJYsKuoCCtzc/FrTg5O1Ryd4kaoUonrqgJkEwIDEapixisiaj+2bdumnTt3bpzJZBJUKpX41VdfJd9+++2F1vNJSUlZI0eO7GM2m7Flyxa/jz76qCInJ0fp7+9vfuqpp1r97LJNmzb5WveHDRtWCgBffvllwJIlS0KOHTumKyoqkgcEBJh69+5ddtNNN+U/9NBDuY2ZjvH333/XffXVV2EA8OKLL17s3LmzqT73+eijjy6OGTNGl5aWpnr33Xcj7c+FhoZWfvXVV6mN0F3yAgNmRNQuyAQBg3x9McjXF/NjY1FkMmFzQQHWVS3gnFLz6WQNR0pLcaS0FK9duIAQpRI3BAVhSnAwJgYFQa/gn0pqAwoKgFdekfbz8qT1UBYuBBYsAO66C+CaCtRE5DI53r/+fYz9fKytrrC8EM9ufBaLpy5uwZ61Hp5SMnINM89e2PKCQ+pPAFg4cSHkMqZXplYsI0MawPLFF4CrgVyjRgFvvAGMGdOsXSNqDyoBrC8owKoLF/BzTg5yPGQcsTfU1xeTg4NxQ1AQEn19IeOASSJqhywWCx5++OGYiooKAQAWLFhwwT5YBgBDhw41jh49unDbtm3+6enpqvfffz8CAO67775Mf39/r2drrVu3Tp+VldUoD9BGjBhRFh8f79Vsqv3799tSH4aHh1ded9113davXx9g3yY7O1u5detW/61bt/p/+OGH4StXrjybkJDQ4NlaZWVlwv3339/FYrFg5MiRxY888khufe8VFxdXmZSUdGL+/PmR69evD8jJyVEGBgaarr322oLXXnstLSYmpl6BOKo7PgUmonbJT6HAjSEhuDEkBKIo4rTBgN9yc7EqNxfbCgrcpuTIqazEV5mZ+CozEz6CgAlBQbgpJARTgoMRwtGG1Fq98QaQ7/gwGRcvAjNnSjPP3n0XGDmyRbpG7d+YmDGY0W8Gvjnyja3u84Of49ERj6JvWN8W7Fnr4CklI9cwc+949nF88ucnDnW39r4VV3S5ooV6RORBRYU0aOXll4HiYudtEhKktUpvvBHgg3oirxnMZvyal4ePNRrsVSpRduqUV9epZTJcExiIqVVZRbgWGVErkJbW8Z5LR0U1a9Dj559/9jt8+LAOAPr27Vv2xBNP5Dhr17Vr1/Jt27YhMzNTCQB6vd78zDPPZNXltZ5//vnopKQkL1LjeLZw4cLU+Ph4r4JPWVlZtulir7zySlRqaqpaqVSK06dPzx09enSJUqkUDx06pFm2bFloYWGh/MyZM5rx48f33L9///Hw8HDvF7V04vHHH49OTU1Vq9Vqy2effXa+IfcCgKioKNOXX355EcDFht6L6q/j/WEiog5HEAT01GrRU6vF4507o6CyEuvy87EqNxerc3ORZ3L9eaVcFPFrbi5+zc2FHMC4gADcFBKCaSEh6MwnmtSazJoFnD0L/Phj7XN790oj2GfMAF5/HejUqdm7R+3fv6/9N34++TNKK0sBABbRgvkb5+PXO39t4Z61vJozzCoqAIvF8dgen+E5+semf8AsVn+XVSvUePPaN1uwR0Ru/PYb8NhjwJkzzs9HRAAvvgjMng0wiwGRV4pNJqzOy8Py7Gyszs1FqcUCeDGQMUKlwuTgYEwNDsb4wEBo5ZyVTNSqREcPaOkuNDtRdLOQaeNbsmRJiHX/ySefTJe5yDwTGBjoEDiaOXNmdkhISIOCSc2lsLDQ9sc9NTVV7efnZ169evWp0aNHOwxTnD9/fubVV1/dMzk5WZ2WlqZ67LHHor/99ts6rzdmtXXrVu3ixYvDAeDJJ59M69OnT7mna6ht4Cd0IupwApRK3B4WhtvDwmAWRewuKsKqqlz3x8rKXF5nBrC5oACbCwow7+xZDPX1xU0hIbgpJAQJOp3L64iaRc+ewA8/SMGxv/8d2LKldptvvgF++kk6/+STgFbb7N2k9ivKNwpPjnoSL2590Va36vQqbDu/DeNix7Vgz1qes/EV9mkZa6ZoZMCs2p5Le/DTyZ8c6p4c+SRiA2JbqEdELpw6JQXK1qxxfl6vl95/H3sM4OdGIo+KTSb8kpOD77OzsT4vD+Vu1qe211+nw9SQEEwNDsZgplokog7MbDZj69atfgDg6+trrpmK0Z7FbjSfWq22PPfcc5l1fb29e/d6N+W3kYmi6PCH/sUXX7xYM1gGADExMaalS5eeGzVqVG8AWL58eUheXt6loKAgr9NOWhmNRuFvf/tbF7PZjN69e5e98MILdf73otaLC5oQUYcmFwSM9vfHa1274uiwYTg3fDje794d1wQGQuHhy1VScTGeTUlBr6Qk9N67Fy+kpOB4aWkz9ZzIhWHDgE2bgFWrgB49ap8vKwNeeAHo1UsKnnn58IHIG0+MfAKh2lCHur9v+DvEDv575mwQvP0yK+XlXMPMlWc3PetwHKwJxlOjn2qh3hA5YTAAzz8P9OvnOlg2a5Y04+wf/2CwjMgNg9mM5dnZuOXoUYTt3Im7T57Er7m5HoNlI/z88GbXrkgePhyHhg7Fy3FxGOrnx2AZEXVou3fv1pSUlMgBYMiQISVqtdrlH1P772szZszIjoyMbDPrZel0OttMOI1GY5kzZ06eq7YjR440DBgwoBQAKioqhA0bNtQrheQzzzwTeebMGY1cLsenn356XsGsAe0KA2ZERHbiNBr8X6dO+H3AAGSNGoWvEhJwU0gINC6mrVudKCvDS+fPo09SEvru3YuXU1Nx1s1sNaImJQjApEnAkSPA228D/v6121y4AEyfDkydCqSmNnsXqX3y9fHFP6/4p0Pd7ku7a80Q6micBczs0zByhplzG85twKaUTQ5188fMh5+PXwv1iKiGDRuA/v2BBQsco+BWw4cDe/YAn38upWIkoloqLRasyc3FPSdOIHznTtxy7BiW5+TAaHE94F8GoI/JhPsMBpweOBC7EhPxZEwMutbMgUxE1IElJSXZUsoMGDDA7QMqs9ksAIBKpRKff/75NjVbyt/f3xYw69Gjh8FdYBAArAEzADh79mydv3nt2rVL8+GHH0YAwH333Zc5ZswYPvxrZxj+JCJyIVCpxN0REbg7IgJlZjPW5+VhRU4Ofs3NRYGbdc+OlZXhn6mpkAsCno1lyihqQSqVlPrprruk0e+ffea4cBIgzUTbuBH45z+Bxx/3aj0IInfuH3w/3tn9Ds7ln7PV/WvLvzAtYRpkQsccq6VU1q6zf7Zecw0zzjCTRrnO3zjfoa6TXyc8NPShFuoRkZ3MTOCJJ6RUx85ERABvvCG9/3oYdEXUEVlEEdsLC7EsMxM/Zmcj1813KyuFIGB8QABuDg3FBL0eBzdvBgB04igTorbr8uVDLd2F9iw5Odn2raJ79+5GV+2MRqPw888/BwFAZGRkRWxsrJNRQJ6tW7dOn5WV1SixhhEjRpTFx8dXeG4p/Wy7du3yBaTUk57a2wfYioqK6ry45eLFi0NMJpMgk8mgVCrFp59+OtJZu507d+rt963tEhISjLNnz86v6+tS82HAzI4gCP8C8EI9Lv1SFMVZNe4lALgTwGwAgwDoAKQD2AbgA1EUkxrUWSJqVlq5HNNCQzEtNBSVFgu2FhTgp5wc/JyTg7SaTzqr3BYa6rSeqNmFhgKffAI8+CDw6KO11zczGID584GlS4GPPwbGdez1pqhhVHIVXrn6Fdy5/E5b3ZGsI1h5aiWmJUxruY61IOcpGavTRBmNjimj+OwPWHt2Lf5M+9Oh7oUrXoBGydkD1IJEEVi8GHj6aaCgoPZ5hUIaqPL884Cvb7N37//Zu++wps72D+DfJwkhAcLeGwUZirh3HXW0Vltt+3bvvfeuXb7d7a+2dteOt7W1rd3bVjvcWhUVnKCIIHtDgBAynt8fYeQkYSnJybg/18VFznNOkluNSc5zn/t+CHF2B1ta8ElFBVZVVqK0h3MoczLGMC8oCBeFh+PskBAEdVyBotFYLU1DiMuhOUgA0dEu0/bPFdXV1XUlgwICAnos23355ZfDSktL5QAglUpPupf+448/HrNz586TanFoafny5cdTUlJq+3NsVlZW66effgoAUKvVfSbAGhsbzf9e+kywWepsX2k0GvHmm2/2q4XAv//+q/r3339VADB79uwGSpg5N7rcbXAI3nQYY0oAPwNYBWA2gGAA3gASAVwJYBtj7CEHx0gIGSReEgnmBAfjrWHDcGLyZGwdPRp3x8YixmxGdIyfH5J9fHp5FOBIayvqbbXvIcResrJM65utWgVERFjvP3gQmDEDuOEGoLHH9YAJ6dOFwy9ERliGYOy/G/7rsWuZ9VVhptUK93l6woxzjmc3PSsYSwlOwdWjrhYnIEIAU/viOXOAG2+0nSybMgXYvRt46SVKlhFipqa9HW+UlGB8djaG79yJl06c6DVZxgDMCgzEimHDUDFlCn4dORJXRkZ2JcsIITQHSfrH/NyrsbHRZg6gsrJSumzZsq4KqVNJmIll8eLFjaxjzcr8/Hxlm+XViBZycnK6FpRNT0/vsfKOeC6qMBP6EsDefhwXCuAdmP7+1AD+z2L/BwAWdNw+BGAFgEoAYwDcDMAPwAuMsVLO+WenHjYhRCwSxjA5IACTAwLwytCh2NrYiNXV1cjsx4Ludxw5gn8aGrAwJASXR0TgrJAQeFPbHmJvjAGXXgqcdRawZImposwyifHBB8CaNcCKFabjCBkgCZNgyWlLcNl3l3WN7anYg1+P/IqFwxaKGJk4bM3xmc8VWl474emdUTcWbcSWE1sEYw9PexgyCZ26EBFwDrz3HvDAA0Bzs/X+wEBT+8Xrr6f2i4R0aDca8WttLVZWVuLX2lro+nHBzESVCpdEROCCsDBEe/qVI8RT0BwksavY2Nius4ytW7f63XbbbXWWx9x2221xDQ0NXV+yO9cyOxk7duzIO9n7noqhQ4fqxo8fr96xY4dKo9FI3n333eC7777bZnXatm3blJ0JM19fX+OcOXNsfLnr3UcffXTio48+OtHXcffee2/0q6++GgUA99xzT/myZcvKBvpcRBx01mmGc34YwOHejukoc16D7r+76znnB832zwVwacfm3wAWcM47s9VfMMY+ArAJQAiA1xhjP3LO1YP4xyCEiETCGKYFBmJaYGCfx5ZrtVhXXw8jgO9qavBdTQ2CZDJcGBaGyyMiMDUgAJ1XyBBiF4GBwFtvAVdfDdx8s+mqeHOlpcCCBcCVVwKvvgoEB4sRJXFhFw2/CEs3LEV+bX7X2NMbn8aClAUe9/7GmKlTm/kSLeZJMsuEmadfRG9ZXRbnH4fLR14uUjTEoxUVAdddZ1rr05ZLLjF9Rtqq2ibEw3DOka1W4+OKCnxRVYW6fqxLNtLXFxeFh+Pi8HAMUVLLXeJZaA6S2NvEiRNbOm9/++23obfeemv11KlTu3raPvroo5HffvttCAD4+fkZmpubpcXFxd5Hjx71Sk5Odql2SM8++2zp3Llz0wDgySefjBs/fnyr+Z8VAE6cOCG78sorh3RuX3vttZV+fn5WV3RMmDAhtbO15PLly4/feeed/WoNSdwHXQI3cDcDOKPj9mec868s9t/f8VsP0weZoLSTc34IwB0dmyEAbrBXoIQQ5/VlVRUsG0jX6/V4r7wcp+3diyH//ovHCwtxtLVVlPiIBxk/HtixA1i+HPCz0W585Upg+HDghx8cHhpxbVKJFEtOWyIY21G6A38X/i1SROKyTIJRwsy2naU7se7YOsHYQ1Mfglzq4WV3xLE4N1VZjxhhO1kWFwf8/jvw+eeULCMer0Gnw1ulpRi9axfG796Nt8rKek2WRcrluD8uDrnjxiFn/Hg8mpBAyTJCekZzkOSkLVq0qCk6OrodALRaLZs7d27aJZdcknDfffdFjRs3LvX555+PAYCsrKyW++67rxwA9Ho9W7hwYcrjjz8esWnTpt7XGXEic+bMabnlllsqAKCpqUk6a9as9EsuuSThzTffDHnvvfeCb7nllpjMzMwRR48eVQDA8OHDW1944YVycaMmzooSZgPAGIsC8GLHZi2Auy32BwOY07H5O+e8sIeHWg2gquP2BYMcJiHEBVTrdJD3UmFxvK0NzxQVIWXHDszYswcrKyrQYhjwWqSE9I9UCtx5J7B/PzB3rvX+igrg3HOBK66gtc3IgFyaeSmGBg0VjC3bvkykaMRl2WZRp+v+DGhvZ70e60le2vqSYDvCNwLXjr5WpGiIR6quBhYtAm66yXYLxhtuMH1ennGG9T5CPATnHFsaG3H1oUOI3rYNtx85gpyWlh6PV0gkuCQ8HGsyM3Fi0iS8PHQoMm1dqEUI6UJzkORUyWQyfPzxx8d8fHyMAKDRaCRffvll6LJly6Kzs7P9AGDEiBGtv/3229FLL720XqFQGAEgLy9P+cwzz8QWFxe71GV8b7/9duntt99eIZVKodPp2Jdffhl6xx13JN58881J7777bmRjY6MUAKZNm9b0zz//5Pv4+Ljcem3EMShhNjAvAuhcwflhzrllSeZp6P477aFvB8A5NwLY0LE5kTEWNKhR9qK9sh0HLjqAkuUlaNrVBKPOssaFEOIIzw0ZgoopU7Bi2DBMDwjo9diNjY246vBhRG3dihvz8rC9sVGweCshgyYhAfjjD9MaZv7+1vs/+wwYORLYuNHxsRGXJJPIcN/k+wRjvx35DYeqD4kUkXh6qzCzvBDfUyvMihqK8N2h7wRj906+F0ovqjwgDrJunelz7uefrffFxZk+I1essP0ZSYgHqNXp8OqJExi+cyem7dmDTyoroTH2PKdwWkAA3h82DBVTpuDzjAycGRICGa31R0h/ufwcJBHf3LlzW3bu3Hng0ksvrY6Li9MqFAqjTCbjUVFR7Zdffnn1li1b8qKjo/XDhg1r//rrr4+mpKRoJB3v0+PHj9f08fBO54033ijdtGnTwauuuqoqKSmpzdfX1+jt7c2joqLaFy5cWLd69eojmzZtOhIWFkZXpJMe0Rpm/cQYGwmgc/GEfQA+snHYCLPb+/t4yM6ewwxAJgCHzD42bm1E9VfVqP6qGgAg8ZHAf4I//Kf4I2BKAPwn+8Mr2ENnaQhxsCAvL9wQHY0boqNR1NaGzysr8WllJQ710IZRbTDg/fJyvF9ejgwfH1wbFYXLIyIQ4cmlCGTwMWZar+WMM0xX1//2m3B/cTEwcybw8MPAU095dikM6ZerRl2Fx/55DHWaOgQqAnHz2JsRrPS8NfEs/6u0t3ffppaMJm/ueBNG3j3x6if3w01jbxIxIuIxtFpgyRLglVds77/uOtO+Pi5yIsQddVaTvV1Whm+rq9Hex4V78d7euCYyEldGRlKrRUJOkrvMQRLnkJGR0b5q1arivo5buHCheuHChQf7Os7ZTZ48WTN58uQTJ3v/HTt25A1GHMuWLStbtmxZ2WA8FnEsSpj131KYPlgA4PGOKzQsJZrdPt7H45n/x004+bAGpnGLsJWWsdWIhvUNaFjf0DXmk+5jSqBNDUDAlAAohynBemkdRwg5dQkKBR5JSMDD8fHY29yMTyoq8FllJWp76P9/sLUV9xcU4OFjx7AwJAQ3R0djblAQJPR/lQyW2Fjgl1+ATz81tWs0b8XIOfD888Dataaqs7Q08eIkTs/HywdLZy4FAFw96mr4yT2zBZOtCjOptPt2b8d6gub2Zry/+33B2DWjrkGAghIUxM4OHwYuuQTYu9d6X3g48L//AWed5fCwCBFbq8GAzysr8WZpaa/tFgFAxhgWhYTghuhozAkKgpTOSQg5VW4xB0kIIa6IEmb9wBgbCuCcjs3DAH7q4dAws9s1fTxsndntkJMMbcCatjb1eUzroVa0HmpFxYcVAABZiMxUfdaRRFONV0GqkNo7VEI8EmMMo1UqjFap8OLQofi5pgYfVVTgj7o62PqGrOccP9TU4IeaGgxRKHBTdDSuiYxEGFX9kMHAGHDllcCMGcBVVwEbNgj3Z2cDY8YAb74JXHON6XhCbLh9wu1ihyA6WxVmnRfeU8IM+GTvJ2jUdifmGRjumHBHL/cgZBCsXm2qHrOVDJg/35Qsi4hwfFyEiKhAo8E7paX4sKICDT1cvNcpWanEDVFRuCoykrpeEDJI3GkOkhBCXBElzPrndnT3BV7Ge148yMfsdlsfj2m+36fHo2xgjMX2cUh45w2tVguNprvlbOi1ofBO8YZ6uxpt+X2FaKKv1aP251rU/mxql8y8GfzG+0E1VQX/af7wm+gHqR8l0DxRW1ubzdtk8CxQqbBApUKJVovPa2qwsroax7Ram8cea2vDQ8eO4fHCQpwbHIwbIiIw2c/PLSpE6bUmsvBw4JdfIFu+HLKlS8HMZ/c1GuC666D/+2/oXnsN8PUVLczBQK81Yi8ymTfMlw9ubdX1mDAzGtug0XjOWpVGbsTy7csFY2cOPROxPrGC77Hk5ND7mg3t7fB65BHI3nnHahf39obu2WdhuOUW04Ug9BrsN3qtuS4j51jX2Ij3KivxR0MDevsEkjOGxcHBuDY8HKepVKZzDYPBoe/X9FojjqIVnnuH9zUfxzkvGYSndao5SEII8TSUMOsDY8wPwDUdm5UAVvZyuPnfp+0Zbdv7B/rv0O8+rHv27EGL+RWTYQDOM/2wJgZpnhTSw1LIDssgPSIFa+97Yp1rOdSb1VBvVqPsxTJwCYdhqAGG4QboM/TQZ+gBz+y25NE2bqQW2PY2CsBIAAelUvwll2OLlxfabSTD2jnH6tparK6tRbzBgDPb2zGjvR2uncboRq81EY0YgYAXX8TYZcugKhGeC8pWrULrxo3Y9eCDUMfFiRTg4KLXGhlMGs1MAN3tBffty8PMmYDBABiNwvfyHTu2oLy82aHxiSlHnYMj9UcEY5MxGWvXrhUpIvdF72uAsroa415+Gcr8fKt9TXFxyL7vPjQlJgLr1jk+ODdCrzXX0Abgb7kcv8jlKJP2fhFspMGA+e3tmKXTwb+hAW3HjsEZ/pfQa43YU77ws+LXftzllK5WddI5SEII8Sj0Jtm3i9A9u/E+57y3DyHzS6rk6P0Dy9vsdnuPR9kR9+fQj9dDP14PLbSADpAWmhJonUk0SZ2kz8dhRgbZERlkR2Tw/sEbnHEYE4ym5NlwPQwZBvAgz7lKmhB7kgAYYTBghEaD6zUabJTL8btcjqIeTnCLpVKsUCqxUqHAzPZ2LGhvR5zRVnNHQvqnccgQrH/lFYz4+GMkrVkj2Od/4gSm338/cm++GSdmzRIpQkKck5eX8L3XYJAIfpuTyTzre9PaWmFiLF4Rj0y/TJGiIe4sbM8ejF22DN5qtdW+otmzkXvjjTB6e9u4JyHupZ4x/NZxHqGW9H7OP0anw4L2dozW69H37AAh5BS57RwkIYS4CkqY9e1cs9tf93Gs+aXACvT+YaUwuz3Q3gV9Xbo/Eh1XvowePRpTp04d4MObcM7RfqId6u1qNG9rhnqrGq37W9FrfwYAjDNIj0shPS6F92+mz2RFisLUwnG6P/yn+0MeTf3N3UFbW1vXFX3Tp0+HQqHo4x5ksJ0LYBnn+Le5GR9UVeHb2lpobXRsaGMMv3t743dvb8wJCMBtkZGYGxAAiYu0a6TXmhM6+2xov/sO8ltuATObeJRptRizfDlGNjSYWjS62MQjvdaIvbzwghxHzIqokpKGASi2mTCbNWsa4uM9I2lW3VqNHbk7BGN3Tb0LZ4w9Q6SI3A+9rwHgHLLXXoPsv/8Fs/iexBUK6F59FeFXXYU5IoXnLui15vwOtrZieUUFVtfUoL3HLm9AgFSKK8PCcGNEBIY64b8jvdaIo/gK280vAJBr56d0xjlIQgjxKJQw6wVjzAfA7I7NI5zzvj4YG8xuBwNo7OG4zv2dqgcSV189kRljkZ23vb29oexcIOMk+KT6IDA1ELjKtK1r0KFpSxMaNjagcWMj1LvU4Pq+J3TajrSh7Ugbqj82/VF90nwQOCsQgacHInBmIOShlEBzdQqF4pRea+TUzPLxwazwcCxvb8cnlZV4t6wMR3tYR+DPxkb82diIVKUSd8bG4sqICPjJXOfjgF5rTuSyy4CJE4ELLgD27hXskn31FWT33w+MGCFObIOAXmuOodVr4S1zrcTqybCcy+PcCwCg11tfuKBSKeApL72v9nwFnbF7ETeFTIGrx15N//fsxCPf19ragJtuAj791Hrf0KFg33wD+ahRDg/L3Xnka81Jcc7xV309Xikpwe91db0em+nri9tjYnBZRAR8+2jR6CzotUbsyVt48V/VIK1RZpOzzkESQoincZ0ZUnHMQ/dVGN/043jz5sbxAAp7Oda8SqxogHGJxivQCyELQhCyIAQAYGgxoGl7dwKtaXsTjG19t3trPdyK1sOtKHunDADgm+WLoFlBpgTa9EDIAuilScjJCJXLcV9cHO6JjcXf9fV4p6wMP9bUwGDj2DyNBrcdOYJHjx3D9VFRuD0mBol0skkGKjkZ2LYNuOce4N13u8c//NClk2XEvjjn+Of4P3gv+z1sLt6MY3cec/ukmZeXcFuvN/22VWFmeay74pzjg90fCMb+k/EfBCuDe7gHIQNUXg6cey7w77/W+xYvBv73PyAw0NFREeIQRs7xY00Nni0qQnZz7+tinhUcjPvi4jArMBDMRTpQEOKGaA6SEEKcAGUlemfeC+aHfhx/0Oz2cAAbejl2eMdvDuDAwMJyHlJfKYJmByFodhAAwKg1Qr1LbUqgbWpE4+ZGGNS2puqFWnJa0JLTgpLXSpDyTgpibo6xd+iEuDUJY5gTHIw5wcEoaWvD22VleK+sDHWdM7RmGg0GvFJSgldLSvCfsDA8GB+PsSqVCFETl6VQAO+8A0yfDtxwA3DrrcAll4gdFXFSze3NGLdiHPJq87rGfsz7ERcOv1DEqOxPblFM395umpC0VWFmeay72lS8SfA6AIAbxtwgUjTE7WRnA4sWAaWlwnHGgGeeAR55xHSbEDejNxqxuroazxcV4UBra4/HyRnDFRERuDcuDhnCtnOEEHHQHCQhhDgBSpj1bkrH73YAe/tx/DYAOgBeAGYBeNvWQYwxKYAZHZs5nPOGU4rSiUi8JQiYGoCAqQHAI4BRb0RLbgsaNjSgYb3px9DUewIt6PQgB0VLiGeIVSjw3JAheCwhAasqK7G8pMTmybMRwFfV1fiquhpbR4/G5IAA6wcjpDeXXAKMGwcMGSJ2JMSJ+cn9EOkXKUiUfJr7qdsnzC69FJgwwVQ9JpcD48cbUF/v2RVmH+/9WLCdGpKK0+JPEycY4l5++MH0n86yPbWfH7BqFXDOOaKERYg9aY1GrKyowIvFxShoa+vxuBCZDLfGxOC2mBhEeMoVGoS4BpqDJIQQJ0AJsx4wxhTovgJjH+e8va/7cM4bGGN/ATgTwDmMsXjOebGNQy8GENZx+6tBCdhJSWQSqMaooBqjQtw9cTDqjWje04yGvxtQ/089Gjc1wtja3cJRHiOHMqX3lnAlr5egcVMjguYFIWhuEJSJ1EKOkP7wkUpxQ3Q0ro+Kwt8NDXitpAS/1tbCchXCTF9fTPL3FyVG4gZSUsSOgLiAa0Zdgw1F3RfB/n70d1S1VCHcN1zEqOzr0kuF2xoNx9q1tivMPCFhptFp8M1BYbeha0dfS63AyKl76y3gjjsAbvENJykJ+OknahdM3I7GYMCK8nK8XFyM0vaepy2SlUrcFxuLKyMj4eMi65MR4iloDpIQQpwHJcx6lgag81vk3gHc71WYPqzkAL5gjJ3JOVd37mSMZQBY3rGpBvD+qYfqOiQyCfzH+8N/vD/iH4qHsd2Iph1NpgTa3/XwSfHpc6Kk+ttqNG5sRPU3pnVKlSlKBM0LQvC8YATODITMn17WhPSGMYbZQUGYHRSEo62teKO0FB9VVKDZYKr+fDAujiYsiX3l5QE+PkBcXN/HErd0Xvp5uOXXW6DRm6o/9EY9vtz/Je6ceKfIkTmeXm9dYSbzgK8yv+T/AnV711dkMDBclnmZiBERl2c0Ao8+Crz4ovW+mTOBr78GQkMdHhYh9qI1GvF+WRmeKy5GeS+JshG+vngkPh4XhoVBJrH+zCGEOAWagySEECfhAafjJ22o2e2q/t6Jc76WMfYtgPNhKqfOYYy9A+AEgNEAbgHQuTjQA5zzmkGK1yVJ5BIETgtE4LRAJD6RCG55JagFvVqPpq1NgjHNEQ00RzQoe6sMTMbgP8m/K4GmGqcCk9LEPyE9SfbxwfKUFCxNTMS7ZWX4oaYGF4X3XuFh4BwtBgP8PWFGlwy+qipg/nxAqwV+/hkYM0bsiIgIVN4qnJt+Lj7f93nX2Ke5n3pkwsyyJaOXl2csq7Rq3yrB9qykWYjxpzVsyUnSaoFrrwU+/9x637XXAu++6xmlm8Qj6IxGfFxRgaeLinBCq+3xuPEqFZYkJODskBBIPOGDhRDXRnOQhBDiJGi2s2fmZ+yNA7zvlTB9IM0DkATgJYv9HMBSzvl7Jx+ee+qrqqVhfQO4vuekGtdzNG5uROPmRhx/4jhkgTIEzQtCyIIQBJ8ZDHk49WgnxJZALy88nJCAh+Lj+/x/uLqqCrcfOYJ7YmNxR0wMAmkCivSXRmNaN6aw0LQ9fTrw5ZfAwoXixkVEccXIKwQJs11lu3Co+hDSw9JFjMrxLFsyesJbam1rLX478ptgjKrLyElTq4HFi4G//7be99RTwBNPeEYWmrg9vdGIVVVV+O/x4zjWyxplMwICsCQhAXOCgqhrBCGug+YgCSHESVA9fs9UZrcbBnJHznkrTCXRlwNYB6AapoU4ywF8DeA0zvnSwQnTs/hP8kfqR6kIvzgcspC+8736Bj2qv6rG4asOY2vEVmRPyEbhU4Vo2tEEbuy9mo0QT9TXSbWRczxTVIR6vR5PHD+OxO3b8WRhIep0OgdFSFzaY48B//7bvd3SAixaBPzvf+LFREQzZ8gcRPhGCMY+zf1UpGjEY9mS0RMSZl8f/Bo6Y/fnhkKmwPnp54sYEXFZdXXAnDnWyTKpFPjwQ+DJJylZRlwe5xxfVVVhxM6duPrw4R6TZXOCgrBp1CisHz0ac4ODKVlGiGuhOUhCCHESdqkwY4x9ZI/H7QXnnF83yA/4LIBnT+H+HMCqjh8ySORhckRdE4Woa6LAjRzNe5tRt7YO9Wvr0bi5EVzXexJMvVMN9U41ipYWwSvMC8FnBiP4rGAEzwuGV7AHzFARcoq+ra7GodbWru1GgwH/LSrCqyUluD0mBvfGxiJUTpWcpAdLlgDZ2cCGDd1jRqOpXVZtLXD//eLFRhxOJpHhsszLsGz7sq6xrw58hWdPf9ajJvlstWR0d1/s/0KwffawsxGgCBApGuKyKiuBefOA3FzhuK+vab2y+fPFiYuQQbShoQEPFBRgp1rd4zHTAwLwTFISTgsMdFxghJBBRXOQhBDiPOzVkvFqmEp+HYF1PNegJsyI82MSBtUYFVRjVEh4OAGGFgMaNjagfm096tbWofVga6/311XrUPlpJSo/rQQkgP9kf4ScFYLgs4Lhl+XnUZN1hPRXTyfraoMBzxcX4/WSEtwdG4v74+KoVSOxFhwM/PEHcMMNwKcWlUQPPADU1ADPP0/VAB7k0sxLBQmzgvoC5FTmYFTkKPGCcjBPa8lY2VyJTUWbBGOXZl4qUjTEZZ04Yaosy88XjoeFAWvWAGPHihMXIYPkYEsLHj52DD/X1vZ4zCR/fzydmIjZ1HqREEIIIWTQ2HsNM/rWRhxG6itFyPwQhMwPAQC0lbSh7vc61P1Wh/p19TA0G3q+sxFo2tKEpi1NKFxSCHmMHKFnhyJkUQiCZgVB4k3dSwkBgJeGDsXlERF4pqgI31RXW10Z0WI04tniYrxVVoaH4uJwR2wsfKVSUWIlTsrbG/jkEyA6GnjxReG+F180VZq9+66pnRZxe2OixiAxMBHHG453jX1z8Bu3TJjV1AAVFUB7O6DTAd7epq/JnlZh9sPhH8DNPj385H44M/lMESMiLqegAJg9GygqEo7HxAB//gmkpYkTFyGDoFyrxZPHj+PD8nIYezhmtJ8fnklKwnxqu0gIIYQQMujsnTC7Eab+ufYyDwAtWklsUsQqEH19NKKvj4ax3YjGzY2o/a0Wdb/VofVQ79Vn7aXtKHu3DGXvlkHqJ0XwmcEIOScEIQtCqHUj8Xgj/fzw1fDhONDSgmeLirC6qsrqhL5Br8cjhYV4raQEjyUk4IboaHhLKPFMOjAGvPACEBICPPigcN8HHwANDcDnn7t/5oCAMYb/pP8H/7ft/7rGvj74NZ6e9bTbTQK++Saw1Gz1iMWLvXD11YDB4FkVZt8e+lawvSBlARQyhUjREJdz7BgwcyZQUiIcHzLElCxLShIlLEJOVavBgJeKi/HyiRNoNdpOlaUolXh+yBCcFxrqdp+RhBBCCCHOwt4JsyrOeVHfh50cxliVvR6buBeJXIKg04MQdHoQ8H+AplCDujV1qP2tFg1/N8Co6en6PcDQbED1N9Wo/qYakAIB0wIQuigUoeeEQjlU6cA/BSHOZbivLz7PyMCTiYl4tqgIqyorrRJnlTod7jh6FC+fOIGnEhNxRUQEZJQ4I50eeMCUNLvhBtNaZp2++QbQ64HVqwFaE8/t/SdDmDDLr83H/qr9yIzIFDGqwWdZNGkwdP4WTnrK7P3tXER1mjr8c/wfwdj56eeLFA1xOcePA7NmWSfL0tNNybLoaFHCIuRUcM7xdXU17i8owAmt1uYxYV5eeCoxETdERcGLvkcTQgghhNgVfdsiHkmZpETMrTEY+ctITK2disw1mYi5IwaKIX1c4WwAGjc0ouDeAvyb/C92jNiBY48eQ9O/TeBGRy3bR4hzSfXxwcr0dBwYPx4XhIXZPKZYq8W1eXnI3LULP9XUwLQmMSEArr3WlCCzTIz98APwn/8APUweEfcxIWYC4vzjBGPfHPxGpGjsp6eEmdHIej3Onfyc9zP0Rn3XtkKmwPyU+SJGRFzGiRPA6acDxcXC8dGjgQ0bKFlGXFJuczNm7d2Liw4etJksU0okeCwhAUcnTsStMTGULCOEEEIIcQB7feO6BsC1AHbb6fE77TZ7LkJOilQpRciZIUh5PQUTj07E+EPjMeSFIfCf4t/nKnytB1pR/Hwxdk/ajW2x25B/Wz6adjQ5JnBCnEyary++Gj4c2WPHYn5wsM1jDre2YtH+/Tg9JwfZarWDIyRO69xzgTVrAB8f4fjPPwPnnw+0tYkTF3EIxphVldE3h9wvYWZZOabvyBtZVpi5c8LMsh3jvKHz4Cf3Eyka4jJKS02VZYWFwvFRo0yVZT1crEOIs6rT6XB7fj5G79qFDY2NVvslAG6IisLRiRPxdFIS/N259JgQQgghxMnYJWHGOf+k46ek76NP6XlOdD6XPZ+HeA7GGHzTfBH/UDzGbBmDKeVTkPphKkLOCYFE2ft/l/bydpS9XYa63+scFC0hzmmMSoXfRo7ExlGjMC0gwOYx6xsaMC47G1ccOoRiSoYQwFQ5sGYN4OsrHP/1V1NCjV4nbu0/Gf8RbB+sPohj9cdEisY+LBNhnV1ILSvM3HVeVKPTYN0x4dLG/0n/Tw9HE9Khqsr0+VBQIBzPzATWrQN6uECHEGdk5BwrysqQ8u+/eKuszKqVOQDMCgxEzrhxWJGaimhvb4fHSAghhLgivV6PnTt3Kl5//fWQq666Km7UqFFpSqVyNGNsLGNs7Pnnn58oxmMNxIQJE1I7n6M/P3l5eb2uX1FXVye55ZZbYmJjYzPlcvmYyMjIkVdddVVcaWmpm55xDh76CyKkF/IIOaKujULUtVEwtBpQ/2c9an6qQe3PtdBV6WzeJ+x8usqVEAA4LTAQG0eNwh91dVhSWIjdzc1Wx3xWWYlvqqtxT2wsHo6PpytoPd306cAffwBnngmYv15+/93UnvH77wEvL/HiI3YzOW4ywnzCUN1a3TX2a/6vuGPiHSJGNbh6bsko6fU4d7H++Hq06bsT3xImwYJhC0SMiDg9tRo46ywgP184npFhqiwLDRUnLkJOwv7mZtyUn4+tTba7kcR7e2NZcjLOCw0FY320OSGEEEKIwIIFC4auXbs20NkeSywVFRXSadOmpRUUFHStPVRZWem1cuXK8LVr1wZu3LgxLyUlpV3MGJ2ZQ2YmGWNRAOIBlHHOTzjiOQkZbFIfKULPCUXoOaHgBo6mHU2o/akWNT/WoPVQKwBAmaqET4ZPr49T/1c9dHU6hJwVAqmvm86KEdKBMYYzQ0IwLzgYX1RV4dFjx1BssUZDm9GI54uL8UF5OZ5OSsL1UVGQ0kSB55o6tTtpZt6289dfgSuuAFatct+MggeTMAnOSjkLn+R0Nw345cgvHpIwE46763UDvx35TbA9OXYygpVUHUR6oNWaqouzs4XjqanAX38B4eHixEXIALUaDHimqAgvnzgBvY01fBUSCR6Ki8OD8fHwoe83hBBCyEkxdJ5cdQgICDAEBgbqi4qKBlyuPZiPdbJWrlxZ0Ncx0dHR+p723XbbbXEFBQWKgIAAw9VXX12Vnp7eVlhYKP/oo4/CKyoq5FdddVXi1q1b83u6v6ez6yk5YywWwEcAZpuNbQJwDee8sMc7EuLkmJQhYHIAAiYHYMjzQ9ByuAU139bAK9SrzysCi18qRv3aekiUEoQsDEH4JeEInh8MqYJOkIj7kjCGyyIicF5oKF4vLcVzRUVosvgSUq3T4eb8fKwoK8MbKSmY0kM7R+IBpkwxtdqaNw8wvxJ79WrAzw94/32AkqpuZ+GwhYKE2frj69Hc3uw2a1z1lDAzGNy/woxzjt+OChNm85PnixQNcXoGA3D55abEmLnERNNYZKQoYREyUGvr6nBLfj6O9dBW+oKwMLw8dCgSFAqb+wkhhBDSP+PGjWtJTU1tGzduXOvkyZNb0tLS2l9//fWQu+66K1HMxzpZV1xxRcPJ3retrY39+uuvwXK5nP/zzz+HR48e3fVF5KabbqodM2ZMxrZt21SFhYVeSUlJttuneTi7JcwYY2EAtgCIBVDbcXsigOkANjLGxnLOq+z1/IQ4km+aL3yX+PZ5nK5Oh4a/GwAARo0R1V9Xo/rrakhVUoSeG4rwi8MRNCcIEi+7LC9IiOiUUikeio/HtZGRWHr8ON4tK4PB4pjdzc2YumcProyIwItDhiCS1m/wTBMnmtY0mzsXaG3tHv/wQ0ClApYto6SZm5k7ZC5kEhn0RtOFcu2Gdvx57E8sTlssbmCDxLJyTN9xPaAnrGGWX5tvtSYdtWMkNnEO3HEH8M03wvGwMGDtWiAmRpy4CBmAmvZ23HX0KD6vsj3dMVShwDvDhmEurcFHCCHEwdra2tgbb7wR8v333wcfPnxYqVarpaGhobpZs2Y1Pf/882UJCQk6AKitrZUmJiZmNjc3S59++ukTjz32mFPP4b/wwgsVzvhYYqioqJBptVqWlpamMU+WAUBSUpJu7NixzX/99VdgQUGBnBJmttlzVv5xAHEAygGM4pyfCyALQDGAaABP2PG5CXFKNT/VgOutW3EY1AZUrqzEvrP2YWvkVuTdlIf6f+rBDdbHEuIOwuRyvDlsGA5MmIBFISE2j1lZWYlhO3bglRMnoLPsWUY8w5QpwI8/AnKLtWxfew1YulSUkIj9BCgCMD1humDs1/xfRYpm8FlXmDHB756Ocwe/HhH+O0aropEVkSVSNMSpvfwy8M47wjE/P9MFFCkp4sREyAB8V12N4Tt32kyWeTGGxxISsG/8eEqWEUIIcbi1a9f6Jicnj3jwwQcTtm3bpqqvr5fp9XpWUVEh/+KLL0InT56cVllZKQWAZcuWhTU3N0tDQkL0d999d3Vfj02cR3h4uF4mk/HCwkLFvn37BFegl5WVyXJycnwBID4+npJlPbBnwmwxAA7gTc55GQBwzqsBvA2AAVhkx+cmxCnJI+UImhsE9DIZpq/To3xFOXJOz8G22G04ctcRNG5rBLfR854QV5fq44MfMjOxbuRIpPtYr/+nNhhwf0EBJuzbh1x3nEUmfZszx9SK0fLff+lSYMUKcWIidrMgRVh19OuRX93m86/nNczcP2FmuX7ZWcln9dnCmnig774DHnpIOCaXmy6cGDtWnJgI6adanQ6XHDyI8w8cQJXOev5pWkAA9o4bh6eTkqB0xzd6QgghTu2DDz4IWrBgQWppaamcMYbp06c33nvvvWWXX355tUqlMgBAeXm5/IUXXojQaDRsxYoV4QBw8803V/j5+bnHCZmH8PHx4XPmzGnQarVs+vTpaffcc0/0e++9F7xkyZLIcePGpdfU1HiNGTOmediwYe1ix+qs7Nn0pXMlZsu1yoo7fofZ8bkJcUohZ4Yg5MwQtNe0o+a7GlR9UYWGDQ2m1LIN7RXtKH29FKWvl0KRqEDE5RGIuCICPsOsEwuEuLI5wcHIGTcOb5aW4qnjx63WN8tra8MTfn6Y1d6OMTod4pRKkSIloli8GPj4Y+DKK03tujrdcoupPdcCau3mLhYOW4j71t7XtV3eXI79VfuRGZEpYlSDw1MTZq26Vmws2igYOyvlLJGiIU5r1y7TumXmGAM++ww4/XRxYiKkn76vrsbN+fk2E2WBMhleHjIE10ZFQUIXChBCCBHBxo0bfW677bYkvV7P5HI5X7lyZcFFF13U2Ll/586dVZMnTx5uMBiwfv16/7fffru9pqbGKyAgwPDAAw9QdZkIZs2alXzgwAGf+vp6mVKpNIaHh+vGjRvXfNlll9WdffbZ6r7u//bbb5+YNm2ab1lZmfy1116LMt8XFhamW7ly5XG7Be8G7JkwKwWQCCDdYjyt47dL9wMl5FTIQ+WIvjEa0TdGQ1umRfXX1aj6sgpN25t6vE/b8TYUPVOEomeKoJqgQuSVkQi7KAzyUHmP9yHElXhJJLgnLg6XhIfjkcJCfFxh/THxj1yO0bm5+L+hQ3FVZCRVKHiSyy8H1Grg1lu7x4xG4MILgQ0bgHHjxIuNDJphIcOQFJiEwobu663+PPanWybMOjvNWrZkdLc1zLYUb4HO2D2JLGVSzB4yW8SIiNM5cQI45xxAoxGOv/gicMEF4sRESD806HS4/cgRrOphrbJzQkLw7rBhiKL1eAkhxKYyrdbNvvn2LdrbW+/I5zMajbjjjjvi29vbGQA888wzxebJMgAYP35829SpUxs3btwYUF5eLn/jjTciAeD666+vDAgI6Pf6GH/88YdfVVXVoPybTpo0qTUlJcVjK6DWr18f0HlbrVZL1Wq1tKCgQLF69erQSZMmqb/88svCzvXmbElKStLt3Lnz0COPPBK1du3awJqaGq+goCD93LlzG55//vmy+Ph4h74OXY0935i+AfAAgNsYY6s450cYY0MB3ApTPc2PdnxuQlyGd7Q3Yu+KRexdsdAUalD9lSl51ry3ucf7qHeood6hxtG7jyL4rGBEXhmJkIUhkHjbs8sqIY4R6e2N/6Wl4caoKNxx5Aiym4X/F2r1elyTl4eVlZV4d9gwDLPRypG4qVtuAUpKgOee6x5rbTVVmG3fDiQliRcbGTSzk2bjgz0fdG3/Wfgn7pl8j4gRDY7+Vpi5W8Lsr8K/BNsTYibA39tfpGiI02lpAc4+GygvF45fdx1w//3ixERIP2xqaMDlhw6hWKu12hckk+H15GRcFhFBF3cRQkgvYrZt87hFbfnMmdmOfL4ffvjBPzc31xcARowY0XrffffV2DpuyJAh2o0bN6KystILAPz8/AwPP/yw7StCevD444/H7Ny50+/UowaWL19+PCUlpXYwHsuV+Pv7G6ZNm9Y0ZsyYlpiYGJ1UKuWlpaXy9evXqzZu3BjAOcf27dtVU6ZMSdu2bduh3hJf0dHR+k8++eQEgBMO/CO4BXuekj8DYCFMFWY5jLFDMFWXKQEcB/BfOz43IS5JmaRE/EPxiH8oHi2HW1C9uhqVX1RCk6exeTzXc9T+VIvan2ohC5Qh7KIwRF4RCf8p/nRyRlze5IAA/Dt2LFaUleHhY8es2jT+09CAkTt3YklCAh6Kj4dcQgljj/DMM6ZqhE8/7R6rqgLmzwe2bgWCg8WLjQyKOUPmCBJmG45vgM6gg5fUS8SoTp1lIkzfcWpjWWHmbi0Z/y78W7B9ehK11yMdOAeuvx7IyRGOz5oFvP22qSUjIU5GbzTiv0VFeLaoCLYuuT87JATvUVUZIYQQJ/HRRx+Fdt6+//77yyU9zJsEBQUJJlyuuuqq6tDQUIPNg4ldPPfccyXTpk1rVSgUthbuqdy4caPPxRdfPLS8vFxeVlYmv+KKKxI3bNhw1OGBegC7Jcw452rG2GkAlgG4EMBoADqYKs/u5px7XJaYkIHwTfOF75O+SHgiAc27m1HxaQWqvqiCrsp2xa2+QY/y98pR/l45FEMViLw6EpFXR0IRq3Bw5IQMHiljuCUmBvP8/HDlv/9iq1zYglTLOZ44fhxDlEpcFhEhUpTEoRgDPvgAKCsD/jKrXMnLAy66CFizxv1KdDyMZUKlRdeCf0v/xbT4aSJFNDg8scKsoa0B2eXCi2hnJc4SKRridJYvB778Ujg2bBjw7beAnFqOE+dzTKPBZYcOYXuTdRv9QJkMy5OTcQVVlRFCCHESBoMBGzZs8AcAlUplsGzFaM5o7L4MRKFQGJcsWVI50OfbsWNH3kkFSgAAc+bMaelt//Tp01t/+eWXI5MnT85ob29nGzduDNiwYYPPjBkzWh0Vo6ew6+X4nPM6zvnVAAIAxADw55xfyDkvs+fzEuJOGGNQjVUh5bUUTC6ZjMxfMxF+cTgkip7/+7YVtOH448exPWE7cs/KRdU3VTC297vtMCFOJ1oux4MaDZa0tCDOYhLt9MBAXBoeLlJkRBRyuWlCNdNiXas//6QWXm4gzDcMWRHC7ix/HvtTpGgGT38TZu5UYbaxaCOMvPv7h7fUG1PipogYEXEaGzZYv18HBAC//AIEBYkTEyG9WFVZiVG7dtlMls0MDETuuHG4ktbXJYQQ4kS2b9+ubG5ulgLAuHHjmnuoXAIAcN6967LLLquOioqiNa6c0JgxY9rOPffcriKkH374IVDEcNyWQ65h5ZzrAJT3eSAhpFcSLwlCzgpByFkh0DfqUf1tNSo/rUTD+gbbdzACdWvqULemDl6hXoi4IgJR10XBd7ivQ+MmZLCM1+tx18iReKGiAq+VlMBHKsWHqak0OeGJAgKA334DJkwQrn2zfLkpkXbddeLFRk7ZnCFzkFPZ3abtr8K/8NTMp8QLaBD0lDCzbMnoThVmlu0Yp8RNgdJLKVI0xGmUlAAXXtj9n6DTZ58BKSnixERID9oMBtxTUIB3y6yv+ZUxhqcTE/FAfDyk9F2UEEIGrHTy5Jy+jyIna+fOnV0LvmdlZfVahWToOCmRy+X88ccfH3B1GXGcmTNnqlevXh0KAHl5edRWzA7sckrOGOtcybuFc263fqeMMSkAXwDgnFtf6kWIG5MFyBB1bRSiro1CW1EbKldVovLTSrQetv0ZqKvRoeTVEpS8WgLVBBWirotC+MXhkPm70cwc8Qh+Uile6VhIvbCtDYlKmnz1WLGxwPffAzNmAFpt9/gttwCpqcA0127h58lmJ83GK9te6dreXrIdze3N8JMPyhrSorBOmJkmV925wuyvwr8E27R+GYFOB1xwgWntSXNPPAEsXChOTIT0oFCjwQUHDiC7udlqX7JSic/T0zHe39/GPQkhhPRHtLc3VTHZUUFBQVcyJTk5ua2n49ra2tgPP/wQDABRUVHtCQkJtteC6cMff/zhV1VVNSiTjJMmTWpNSUlpH4zHcjcRERFd/28aGxvd6OzRedhrprwBgBHAeQB+stNzAMBCAN8B4HBQtRwhzkiRoEDCowmIfyQe6p1qlH9YjqovqmBQ285Xq3eood6hxtG7jyLswjBE3xwN/4n+VKVDXMoYlQpjVKo+jzNwTlf9urOJE4EVK4Crruoe0+mA884Ddu0C4uPFi42ctNMSToNMIoPeaDoX0Bv12FG6w6UTLpaVY+6+hlmdpg77q/YLxlz5348MkscfB7ZvF46ddRbw5JPixENID36pqcEVhw+jQW89l3t1ZCTeSE6Gn7u8YRNCCHFLdXV1XcmUgICAHtdpefnll8NKS0vlACCVSnts29iXxx9/PGbnzp2DcoXj8uXLj6ekpNT2faTnMU9KBgQE2K1QyZPZcw0zR81OMgc+FyFOjTEG/wn+SH0vFVPKpyDt4zQETA/o8XijxojKTyqxZ/IeZI/JRsUnFQ6MlhD70xgMmJCdjddLSgQ9uYmbufJK67VwqqtNLb/a6aI0V+Qn98OYqDGCsU1Fm0SKZnD03JJR0utxrmrbiW2CbYVMgXHR40SKhjiFdeuAF18Ujg0ZYmrFKLHr0tqE9JuBczxy7BjO3r/fKlnmI5FgZVoa/peWRskyQgghTs98DqSxsdHml63KykrpsmXLojq3TyVhRhxj/fr1XVeOp6Sk9Fg5SE4efcsjxE1JfaWIvCoSkVdFovVIKyo+qkDFJxVoL7c9edy8txmNWxoReVWkgyMlxH4ePHYMu5ubsfvoUaypq8P/UlMR6e0tdljEHl54AThwAFizpnvs33+BBx8EXntNtLDIyZsWNw07Snd0bW8+sVnEaE5dTwkzo7H341zVlhNbBNsTYiZALpWLFA0RXWUlcMUVwjEvL+Drr4GgIHFiIsRCo16Piw8exO91dVb7UpVKfDN8OEb4uW5rYEIIIZ4lNja2q7Xi1q1b/W677TarD7jbbrstrqGhoSs/YLBcYHkAduzYkXey9yX9k5ub6/3999+HdG4vWrSoUcx43JW9L+VbwRg7Zq8fAO/ZOX5C3IJPig+GPD8Ek4onYcRPIxCyKASwMSEXfXO044MjxE5+q63Fm6WlXdu/19Uhc9cu/FRTI2JUxG6kUuCLL4DkZOH48uWmCVnicqbFC9eg23ZiW1eLRlfUc8LMPVsyWibMpsROESkSIjqj0dQ2t9Ji/fiXXgLGjLF9H0Ic7EhrKybt3m0zWXZhWBh2jh1LyTJCCCEuZeLEiS2dt7/99tvQLVu2CBaAf/TRRyO//fbbEADw8/MzAEBxcbH30aNHvRwbqXubMGFCKmNsLGNs7Ouvvx5i65hnnnkmfN26db69Pc6WLVuU8+fPH6bVahkATJ06ten0009v6e0+5OTY+5Q83M6PT2WihAyARCZB6NmhCD07FNoKLSo+rkD5e+VoO94G1QQVVGN6Xw+Kc07rnBGXsVutthqr0emwaP9+3BgVhVeTk+HjLqUcxCQgwJQcmzQJ0Gq7x6+7DsjKAoYNEy82MmCWCbMWXQtyKnIwNnqsSBGdGk9qyagz6ATVgQAwNX6qSNEQ0S1fDvzxh3BswQLgrrvEiYcQC+vq6nDhwYNWLRi9GMMrQ4fi9pgYOgcihBDichYtWtQUHR3dXlZWJtdqtWzu3LlpixYtqouOjm7fsGGDf3Z2th8AZGVltSxevLh+6dKlsXq9ni1cuDDlggsuqJ03b576tNNOaxX7z9Efhw8flr/99tuh5mP79+/3Mb995513CqoE5syZoz7nnHOsJo4G87H6a/369arHH388Li4uTjtt2rSmESNGaEJCQgxSqZSXlZV5rV+/3n/Dhg0Bxo72JNHR0e2fffbZ8ZN9PtI7eyXMikHJLEKcmnekNxIeTkD8A/GoW1sH5tX3SeCR249AW6pF7F2xCJwZaP8gCTkFjyUmYqK/P646fBjlFutYrSgvx9amJnwzfDhSfXx6eATikkaNAt58E7jhhu4xb29TZQMlzFxKmG8YUkNSkVfb3dljc/Fmt0uYWVaYuUPCbE/FHrTphe30p8RRhZlHOnwYeOQR4VhUFPC//wGUgCAi45zj9dJS3Hv0KCy64yLMywvfDR+OaYGBYoRGCCGEnDKZTIaPP/742OLFi4e1trZKNBqN5MsvvxQkgkaMGNH622+/HW1ubpa8+OKL0W1tbZK8vDzlM888E5uWlnZUrNgH6ujRo95vvPFGVE/78/Pzlfn5+YIKO5lMBltJrsF8rIE6ceKE9xdffBHW2zHTpk1r+vTTT48nJibqejuOnDy7JMw454n2eFxCyOBjUoaQ+TYrggV09TpU/K8CRo0RtT/WwnekL8JvDgdCANCSUMRJzQ0ORu64cbgxPx/fW7Ri3N/SgrG7dmFFaioujYgQKUJiF9ddB2zaBKxcCUyZAqxeDcTGih0VOQnT4qcJE2YnNuOuSa5ZlWLZarGzkMFymQB3aMm4pVjYjjE9NB3BymCRoiGi0etNrRjNK34ZAz77DAjrdR6AELszcI47jxzB22VlVvuyfH3xY2YmEhQKESIjhBBCBs/cuXNbdu7ceeDZZ5+N3LRpk391dbWXXq9nYWFhutmzZze+9dZbJf7+/kYA+Prrr4/ee++9cQUFBUqj0Yjx48drxI7fUyxfvrxk7dq1jTt27PA9cOCAsra21quhoUHW3t7O/Pz8DDExMe3jxo1rvvLKK+uoDaP9ucEpOSHEEco/KIdR033tZUtuCwpvLYRKpUL7vHZoM7RQpih7eQRCxBEql+Pb4cPxYXk57jp6FK1Gs9ex0YjLDh3ChoYGLE9OhsIdSjuIaUL27beB4cOBe+4BvKgFu6uaFj8NH+75sGt7U9Eml20PbF1hZvozuOMaZltLtgq2p8ZRO0aP9PLLwA5ha07cdRdw+unixENIh1aDAZcePIgfa2ut9p0fGopP0tPhS98JCSGEuImMjIz2VatWFfd13MKFC9ULFy486IiYBtvChQvVnPNsZ3ssANixY0deX8cMHz5cO3z4cC2Amr6OJfYn6fsQQoin45yj8rNKm/skagkU3yqwN30vDlx0AI1bGx0cHSF9Y4zh+uhoZI8dixG+1uuorigvx6Tdu3Gk1SXac5P+8PUFHnyQkmUuznIds8qWShQ1FokUzamxNfdqNFpXmLnDHO32ku2CbWrH6IH27QOefFI4NmwY8Nxz4sRDSIfq9nacvnevzWTZ0sREfDV8OCXLCCGEEEI8GCXMCCF9Yoxh9ObRSH4juecqMgNQ/VU19kzdg91TdqP6+2pwAy1lSJxLmq8v/h0zBtdFRlrty2lpwdjsbPxYQxf0EOIshgYNRYhS2DZ4Z+lOkaI5NbYTZgycCxNmEhf/dl7ZXImSphLB2MTYiSJFQ0Sh1wNXXw3ozJZVkEiATz4BlNSNgIinQKPBlD178K9auMSInDF8mZGBJxITIXHBCmZCCCGEEDJ4XPyUnBDiKDKVDLG3x2LC4QnI/DUTQfOCejy2aVsTDpx3ADvSd6DsvTIYNAYHRkpI73ykUnyQloZP0tLgYzEzrTYYsHj/fjxVWAgjp4QvIWJjjGFa/DRMiJmA28bfho8XfYyp8a7Z3q/nhFnfx7mS7HJh9xJfL1+khqSKFA0RxRtvALt3C8ceeACYNEmceAgBkNPcjCm7d+OoRrgcS6BMhnVZWbgoPFykyAghhBBCiDNxg1USCCGOxCQMIWeFIOSsENTtqcPOR3dC/o8cTGt9NabmiAb5N+ej8PFCxNwRg5hbY+AVQu3RiHO4MjIS41QqXHDgAA5atGJcWlSEPc3NWJmejgB3WFCIEBf2/UXfu+SaZZZsvZUYDNYJM1evMNtVtkuwPSZqDKQSF88Ckv47cQJ4/HHhWEYGsHSpOPEQAuDfpiacmZuLBr1eMB7v7Y01I0ciw0a7bkIIIYQQ4plc/JScECImZZoSbTe3oenDJsQ/Fw/vOG+bx+mqdTj+xHFsi9+GI3cegaZQY/M4Qhwtw9cXO8aOxeUREVb7fqqtxcTsbBxuaREhMmJ369cDV15pWkSKODV3SJYBntOS0TJhNjZqrEiREFHccQdg+bm5YgXgbfs7IiH29k99PWbv3WuVLMvy9cW2MWMoWUYIIYQQQgRc/JScEOIU/ICou6MwsWAi0j9Lh2+W7RNPY6sRpW+U4t/kf3HwsoNoOUCJCCI+X6kUK9PS8FpyMizns/M0GkzYvRs/07pm7kOtBm69FZg1C/j0U+Ctt8SOiHiInhJmRqMwYebq+UHLhNm46HEiRUIc7scfTT/mbrgBmOqabVSJ6/u1thZn7duHFouLY2YEBGDj6NGIpkQuIYQQQgixQAkzQsigkXhJEHFZBMbtGYeRa0ciaG4P65wZgarPq7BzxE7sP38/1LvVto8jxEEYY7grNhbrsrIQ6iVsG6o2GLBo/34sO3ECnNY1c32LFgHvvNO9/fDDwLFj4sVDPIYnrGFWpi5DeXO5YIwSZh6iuRm4/XbhWHg48MIL4sRDPN531dVYvH8/2iySZfODg7Fm5Ej4U8ttQgghhBBiAyXMCCGDjjGG4LnByFqbhbF7xiL8snBYle50qPmuBtljs5F7Vi4atzY6NlBCLMwKCsKusWMx2s9PMM4B3FdQgFvy86GjFn6uzXJtndZW4PrrqTUjsbv+Vpi5cktGy+oylVyFlJAUkaIhDvXCC0BJiXBs2TIgOFiceIhH+6mmBhcdPAi9xRUJ54eG4ocRI6B05SsTCCGEEEKIXbnwKTkhxBWoRqmQ8VkGJhVMQuw9sZD62T5BrVtThz1T92Dv6XtR/3c9VfIQ0SQoFNg8ejQuCw+32vdeeTkW7tuHRot1MIgLmTULuPlm4dg//wDvvy9OPMRj2CpmcLeEWXZZtmB7TNQYSJgL/4FI/xw/Dvzf/wnHZs8GLr1UlHCIZ/utthb/OXDAKll2VUQEvszIgNyV32QJIYQQQojd0bdFQohDKBIUSF6WjElFk5DwZAJkgbbboDT804Cc2TnYM2UPan+vpcQZEYWPVIpP09PxXFKS1b619fWYsns3jms0IkRGBsVLLwHx8cKxhx8GqqrEiYd4BFsFDQaDdUtGV17DLKcyR7A9NmqsSJEQh3rwQUCr7d6WSoHXX3ftFzNxSevq6nDe/v3QWbyx3hgVhY/S0iCjZBkhhBBCCOkDfWMkhDiUV7AXkp5KwqSiSUh6PgleoV42j2va3oR98/dhz7Q9qP+73sFREmJqLfpIQgK+ysiAwmKC5WBrKybu3o2dTU0iRUdOiUplXVHW0AA88IAo4ZD+MXIjjtYdxXeHvsNT659CcWOx2CENSM9rmLE+j3MV+6r2CbZHRowUKRLiMBs3Al9/LRy79VYgI0OceIjHWl9fj3P274fWIll2bWQk3hk2DBJK4BJCCCGEkH6ghBkhRBQyfxkSHk7ApOOTMPTVoZBHy20e17S1CTmzc7B31l40bG5wbJCEALggPBz/ZGUh3EuY3K3S6TBr716srasTKTJySubNs24XtnIlJJs2iRMP6VPam2lIeSMF5391PpZuWIrtJdvFDmlA+ruGmavO6Ta3N+NY/THBGCXM3JzRCNx9t3AsKAh46ikxoiEebI9ajXP270ebxXqkV0REYEVqKiXLCCGEEEJIv1HCjBAiKqmvFHF3x2FiwUSkvJMCRaLC5nEN6xuw97S9yDkjB007qKqHONakgAD8O2YMMnx8BOMtRiMW7NuHzysrRYqMnJL/+z/A318w5HX33WC0Rp1TGhI0RLCdU5HTw5HOqecKM+GYq3YM21+1X7AtYRKkh6WLFA1xiC++APbsEY79979AcLA48RCPdEyjwfzcXKgNBsH4xeHh+F9aGqSULCOEEEIIIQPgoqfkhBB3I1VIEXNzDCbkT0Dqh6k9Js7q19Zj98Td2Hf2Pqj3qB0cJfFkiUolto4ZgzlBQYJxPee47NAhLC8pESkyctKiooBnnhEMSQ4dwtCffxYpINIby2ql3KpckSI5ObYSZpxbt2R01YTZvkphO8ZhIcOgkNn+LCduoL0deOIJ4VhGBnDzzeLEQzxSZXs75uXkoFKnE4yfFxqKTylZRgghhBBCToKLnpLbH2MsjTH2OmPsMGOsueMnhzH2LGMsrI/7ejHGbmWMbWGMNTLGNIyxI4yxtxhjqY76MxDiiiReEkRdG4UJeRMw7N1h8I71tnlc7S+1yB6TjYOXHkTbiTYHR0k8VYBMhl8zM3FJeLjVvruPHsUjx46BW5aLEOd2663A6NGCodTVq6GgVptOJysiS7CdW+laCTPGgGefBZ57DnjhBeCZZ3QICNBaVZi56vwurV/mYT78EDgmbMGJF14AZDJx4iEep0mvx1m5uShoE54HzAwMxKr0dMhc9eoDQohHojlIQghxHvQt0gbG2D0AcgDcASAVgG/Hz0gAjwLYzxib1MN9QwBsAfAWgCkA/AEoACQDuBXAHsbYFfb+MxDi6iRyCaJvisaEIxOQvDwZXhFeNo+r/qYavJ0SFMRx5BIJPktPx50xMVb7Xiguxo35+TBQ0sx1SKXAu+8KshSytjakrVolYlDEFssETHFjMeo19SJFc3IefRR45BHgoYeAe+/Vw9+/3arCzFYlmiuwTGBmhmeKFAmxu9ZW4OmnhWOTJgELF4oTD/E4Bs5x8cGD2N3cLBjP8vXFDyNGQOGqb6SEEI9Ec5CEEOJcKGFmgTF2B4BlAOQAWgG8AeAKADcB+KfjsHAAvzHGIi3uKwXwPYDxHUP/wvQBdTmAdwHoACgBfMQYm2nPPwch7kKqkCL2zlhMOjYJQ14eAq9QYeIs+uZoKIcqRYqOeCoJY3gtORnPJyVZ7fugvBxXHz4MvcXC88SJTZgAXHedYCj+77/BLNfmIaJKDU2Fl0T4GWBZ1eSKjEbXT5hxzq3+LShh5sbefBMoLxeOPf+865ZHEpdzf0EB1lhUgicpFFgzciQCqMqREOJCaA6SEEKcDyXMzDDGEgG82LFZDWAi5/xOzvlnnPMVnPPTAbzesT8IwBKLh7gWwGkdtz8DMIVz/g7nfBXn/BYAcwC0AZABeJsxRn//hPST1EeK+PvjMfHYRCQ9mwRpgBRSlRQJjyeIHRrxUIwxPJyQgA9TU60+TD+rrMSlhw5BR0kz1/H004CfX9cm4xxeDz8Mq355RDRyqRwZYRmCMVdry2iL5UvMFbuIlTeXo04jnLymloxuqqnJ1HrR3Lx5wMyZooRDPM+KsjK8ZrFubKiXF/4YORJR3rZbuRNCiDOiOUhCCHFO9GYp9CRMV18AwIWc8/02jnkQpg8yALjQYt99Hb/rAdzGORfMlHLONwL4b8dmOoDFpxowIZ5GppIh4dEETCqYhOHfDoc8TN7r8UadkdaUInZ1bVQUvhsxAnKLK+u/rq7GBQcOQEtJM9cQGWnql2dGumkT8MMP4sRDbLJMwuRU5IgUyeCxrDBzxSKdg9UHBdu+Xr5ICKQLWtzSO+8A9RatUJ97TpxYiMf5p74etx05IhiTM4YfRoxAio+PSFERQshJozlIQghxQpQw68AY8wZwXsfmT5zz9baO45xrATwFYDmANzruB8bYKJh6DQPAKs55Uw9P9Q4AQ8ftC045cEI8lFeIF4LnBvd5XOGjhcg9IxctB1ocEBXxVItCQ/HjiBFQWJSG/Fhbi9stJnaIE7v7bhjj4oRjDzwAaLXixEOsWLb5O1RzSKRIBo/lNR2u2JIxryZPsJ0WmgYJXcTsfjQaYNky4djixcDYsaKEQzxLoUaD8w8cgN7iTfP91FRMDQgQKSpCCDk5NAdJCCHOi85ku50O0+KYAPBJbwdyzt/mnN/NOX+m48MLAGaYHfJXL/dtALC7Y/OMk4yVENIPrUdaUbK8BPXr6rEzayfyb8tHe0272GERN3VmSAh+zcyEj1nSLEYux8Px8SJGRQZEqYT+mWeEYwUFwPvvixMPsZIeli7YPlxzWKRIBg/nwpIyV2zJmFcrTJilhqb2cCRxaR98AFRVCccee0ycWIhHaTMYcP6BA6jX6wXjD8fH48rIyB7uRQghTo3mIAkhxEm54Cm53Ywyu7298wZjLJQxNo0xNrejv3BPRpjdtlVGba6zb00QY4xmUgmxk4IHCsB1HVehGoCyt8uwI2UHSpaXwKijNnlk8J0eFIQ/Ro6ESipFhJcX/ho1CkOVyr7vSJyG4T//QV2qxWT/M88ALVSl6gzSQtME27WaWtS01ogUzeCwbMnoigkzy8RlagglzNxOezvw0kvCsTPOoOoy4hB3HD2KPc3NgrHFoaF4NilJpIgIIeSUjTK7TXOQhBDiRFzwlNxuhnf81nLOyxhjQxljPwAoB7AJwFoAhYyxHYyxKTbun9jx2wiguI/nOmF2mxZ4IMQO2mvaod6lthrXN+hx9O6j2Jm5E7W/1YoQGXF30wID8WdWFv4aNQqptJ6G62EMB668UjhWWQm8+aY48RCBxMBEyKXCtStdqcosNxfYvRvYtQvYtYtBo5G6xRpmVhVmlDBzPytXAiUlwrElS8SJhXiUj8vL8UF5uWAsVanEyrQ0SFzxDZMQQkxoDpJ4pN9//92PMTZWKpWO3bZtm12vLl69enUAY2ysXC4fk5ub623P5yLuhRJm3WI7ftczxqYD2AtgEQCZxXHjAWxgjF1sMR7W8VvNOe+r51ud2e2Qk4iVENIHeagcE/MmIuHxBEgU1m91mjwN9i3Yh32L9kFzXCNChMSdTfD3x3BfX7HDICepbvhwVI4eLRx88UWgsVGcgEgXmUSGlOAUwZgrJcymTjUV5IwfD0yfrkBRkb/VGmauVmHWqmtFcaNwnsayEpC4OKMReOUV4dj06cBpp4kTD/EYuc3NuMViLVgfiQTfjhgBlczyNJ0QQlwKzUESj6PX63HnnXfGA8DixYtrJ0+ebNfJuIsuuqhxwoQJap1Ox2677TaqriT9Rt8yu6k6fvsA+A6AH4APAbwK4CiAGAA3AngApr+3jxljhznne83uBwBt/Xgu82MGXH7AGIvt45DwzhtarRYaDSUDiH20tbXZvO00JEDkI5EIujwIJx47gdqvrSvKan+qRf26ekQ/GI2ou6Mg8XaxmUoP4fSvNeI2Ol9fhy67DBF79nTvqK+H7oUXoH/iCZEiI51SglJwoPpA1/a+8n0u811HIlEAEFZEWFaY6XRaaDSu0zY4tzLXaizWJ9Zl/k08wal+hkrWroX3YWFiWnvffTDSvzGxMJjf1zRGIy7avx9tRuH74VtJSRgikdB7jIejcwPiKFqt1nwzvK/5OM55SW/7zbjMHCQhg+XNN98MOXLkiFIikeCpp54q7/sep27JkiXlixYtUm3evNn/xx9/VC1atMi6FRUhFihh1s2v43fnoptPcM6fNtt/DMDDjLFCAO8C8AbwEoB5Hfs7/y4Fn6Y9MD/mZP4NTvR9iMmePXvQQuuuEAfYuHGj2CH07jJAOkYKxYcKyI4K/9sZNUaULC1B0ftFaLuxDfpR+h4ehDgDp3+t9VMDYwjgHNRMyDk1JiejbPJkRG/b1jXGXnsN69PT0R4QIGJkxKvRS7C9JX8L1urXihTNwOj1ZwHojt8yWQYAOTl74O1d6cCoTs3m+s2C7TCvMGz+Z3MPRxOxncxn6OSnnuq+Gg9AU3w8/jEagbWu8f+OiONUv6+tUChw2FvYPeksrRZBu3eDXnnEnLucGxDnlJ+fb775az/u0t/TO1eagyTklGm1WvbSSy9FA8C8efPqMzMz+/PaPWXnnHOOevjw4a0HDhzwefLJJ2MWLVrkOu1JiGiolKKb+YfaPgDP2DqIc/4egH87NucyxuI6bnde4ia3vpcV82/+fZVOE0IGiSHdgJaXWtB6ZyuMAdZX70vLpPB9yhfKl5RgNZTGIPZzVCrFnX5++Nqb2mg7s8OXXgpu1h9P1taGoT/9JGJEBABivYUX9pa09fdCXvFJJML+i5xbJ81cbUmeUm2pYDtGESNSJMQeVMXFCN+7VzB2bOFC13uhEpeySybDbxbfkYYYDLiGKokIIe6D5iCJR/nwww+DysvL5QBwyy23VDvyua+77rpqAMjJyfFdu3YtrZ1B+kRXFnQzL8P6knPLFSUEvgYwseP2VABfAmju2Fb047nMjzmZXhJxfewfiY4rX0aPHo2pU6eexFMQ0re2trauK/qmT58OhaI/L38ncCagf1iPkqUlqHy/0rRMrhn5VjkUOQrEPhGLyFsjwaQ0KSQ2l32t2fBXYyOeys9Hs9GIzxUKTE5Px3Xh4X3fkTiE+Wtt9GWXwbB9O2SrVnXtT1m7FnFvvAEEBooUIQmtCMWrK1/t2q5qr8L006dDIXP+9wW5XFgdxzmzWsNs3LjRmDfPdVoyfvHzF0BF9/bklMmYN2dez3cgDncqn6Fet98u2OYhIUh9+mmkKu26RjtxUYPxfa1Kp8ONubmAvrvjg1IiwbcjR9LrjnRxp3MD4tx8hetSLwBg3Yv65LjSHCRxcdu2bVNOmTIlAwAuv/zy6k8//bRrAeJNmzb5TJ8+PR0Abr/99oo33nij62q4H3/8UbV48eJhAHD33XeXv/rqq2UnG8OKFSvCASAyMrJ94cKFfbZFzM/Pl6empmYCwMKFC+t+/vnnwqamJskbb7wR+u233wYXFhZ6azQaaWRkZPsZZ5zR8Nxzz5WHhIQYbD3W1VdfXffII4/Ea7Va9sYbb4TPmzev8GT/HMQziJowY4zJAcQDCAaghOmNuw5AEedc5+Bwmsxu91WemWd2u/My2oaO3/6MMRnnvLeebsFmtwecVe+rJzJjLLLztre3N5R0YkEcQKFQuNZrTQmkv5uO2BtjkX9LPtQ7hJ/XxhYjih8qRv039Uj9IBV+I/16eCDiaC73WjNTqtXiP3l50JqdD91VWIhoHx+cFxbWyz2JGBQKBWRPPAF8/jk6sxpMrYbyww+Bxx4TOTrPlRWTJdjm4DjRegIjI0aKFFH/SSx6O3BuSpqZUyq94UpvcUXqIsF2eni6y75He4IBfYbW1Zne/8ywm2+GMji4hzsQ0u1kv6/dU1CAKr3wVHrZ0KEYRa870gNXPjcgzs9bWO1aNYA1yvriMnOQxPXt3r27601y5MiRrf3dt3fv3q59WVlZgn0DkZOT452Tk+MLAPPnz2+QWJ4U2bBr166u587IyNCsW7fO99prr00qKSkR/KcsKiryXrFiRcQ///zjv3PnzsMBAdbtpIKCgoxTp05t/PvvvwPXrVsXVFtbW9xTco0QwMEtGRljKsbYhYyxjxhjBwG0wvTGvw3A3x2/8wBoGGMHGGMfdhyv6uVhB8tAssvmfVY7/w7zzbb76kVjXiFW1ONRhBC7U41RYcy2MRi2YhhkwdbXEKh3qpE9NhvHlhyDoY0+T8mpifH2xv8NHSoYMwK49OBBbGhoECUm0odhw4ALLhCOvfYa0Nxs83Bif35yP8SohF+1jtYdFSmagbHsYsc5s2rJ2I/zR6dyrP6YYDspKEmkSMigW7kSMG+B5+UF3HqrePEQt/dtdTW+r6kRjC0MCcFN0dEiRUQIIXZDc5DEYXJzc306b48ePVpQZbhv376uxNTYsWMF+/bv3+/T076B+PLLL4M6b8+cObPP6jIAyMnJ6YqrpqbG65xzzhlWXl4unzlzZuNdd91Vfv/995dNnjy5K/F85MgR5SuvvNLjVcjTp09XA6a11L799lv/no4jBHBQwowxNpkxtgqmhi1fALgKQGrH8zMbPxIAaQCu7ji+gjG2ijE2xY5h7jO7ndjHsRFmtztLVQ+ajQ3v4/6d++s556W9HkkIsTsmYYi+IRoT8iYg8rpIq/1cz1H8XDF2Ze1Cw8YGxwdI3MrtsbF4LCFBMKblHIv27cPBlpYe7kVE9eijwu3aWmDFCnFiIQCA5OBkwXZBXYFIkQyMdcIMVi0ZXWlpqJb2FlS1VAnGhgQNESkaMqg4B95/Xzh2/vkAJS6IndTpdLgtP18wFurlhQ9TU8Fc6Y2REEL6h+YgicMcOHBACQCMMYwfP16Q+Dp48KAPAHh7e/ORI0cKFgs9dOiQEgCUSqUxIyPDPHE7IH/99VdA5+0ZM2b0a9LDPFn30UcfhQ8dOrRt7969B/7555+jr732WtnLL79cvnXr1iO33HJLV3P4devWBdh+NGDq1Kldz/vHH3/0eBwhgJ1bMjLGZsG0cOWkziGz3e0AjsHUgrEWpnLkAAAhMJULJ6F78UolgIsBXMwY2w7gMc75P4Mc7gaz2/MBLOvl2Mlmtzs/5MzvPwvAb7buyBgLBDDaxn0IISKTh8qR9kEaIq+KRN4NedDkCS+g0eRrsHfGXkTdFIWhLw6FLICWgSQn57+Jiahsb8f75eVdY40GAxbu24ftY8YgXN6ftZuJw2RlAQsXAr/80j32f/9nqrSgNTNEMSRoCDYUdX+NsqxyclbWLRmZVUtGqdSBAZ2iwgbri6MTAxMdHwgZfFu3AgcPCsduvFGcWIhHuL+gAJU64aoMrycn03ciQoi78tg5SIPBgMrKSo+fTImIiNBLHfTFPz8/XwkAMTExWsuWhXl5eUoAGDJkiEYm6/5nMRgMKCgoUABAcnKy5mRj1ev1XcmvyMjI9oSEhH4twdSZrAOAlJQUzaZNm/KCgoKs2i0+/vjjle+8804kAJSUlPT4pWHq1KmtXl5eXKfTsd27d/v2dBwhgJ0SZoyxZACvAzijcwhAG4C1ANYD2A5gN+e8vZfH8AYwBqZk2/SOx1LA9EHxJ2PsDwB3cM4H5ZJizvkuxthRAMkAZjPGxnPOd9qIKwTAJR2bhzjn+zruf5AxdhgdlXGMsaWcc1v9mm4D0Pku89VgxE4IGVyBpwVi3N5xKH62GMUvFIPrhZf/l79XjtpfapH2YRqCz6D1FMjAMcbwdkoKqnU6/GDWeqiwrQ2L9+/H31lZULjSrLknWLJEmDArLwc++wy4/nrxYvJgQ4OErU2PNbhGwsxWhZnR4rTPlVoyWiYqI/0i4ePl08PRxKVYVpclJwMzZ4oSCnF/f9XX438VFYKxhSEhuDg8XKSICCHEvjx5DrKyslIWE2OxKLEHKi0tzYmOju5t7blBUV5eLquurvYCgLS0NMFV4SdOnJDV1dXJACA9Pd2yHaN3W1ubBDCtIXayz3/48GFvjUYjAYAhQ4a09XU8ALS2trLi4uKuK1M/+OCD47aSZQAQFRWl9/PzMzQ3N0u55ZWIZpRKJY+NjdUWFhYqioqKFK2trczHx4f3dDzxbPY6Jd8HU4KLAcgGcD2AcM75Ys75a5zz7b0lywCAc67lnG/jnL/KOT8XQFjH4+zseNwzISxhHgz/7fjNAHzOGIs339mRxPsUQGfv1Vct7t95RUgogE8YY4LMNmNsOoDHOjaPA/h2cMImhAw2qUKKpKeTMHb3WKgmWC+j2F7ajtwzc5F3cx70art/xyFuSCaRYFV6OsarhK+vbU1NuDYvD9yyTxsR16RJwOmnC8dee826nx5xCMu2f65aYQbAqsLMlRJmhfXCCrOkQFq/zC00NABfWcypXX+9a/ULJS6j3Wi0asWokkrxTkoKtWIkhLg7moMkdrdz586uSq0RI0YIEl+7du3qutItMzNTsG/37t097huIo0ePdr0ug4KCDP25T3Z2ttJgMB06YcIE9fTp01t7OtZoNEKr1UoAIDQ0tNfqtcDAQAMAcM4FcRFiyV6n5N4wlfrO5pxP4Jx/1MOVDv3GOW/peJyJAGbDVKnmfeqhCp7jUwDfdWwmA8hljP0fY+xSxtjdAPbCVCoNmP58H1g8xIcAdnTcPg/ATsbYHR33fwvAnzBVyXEAt/SVNCSEiM8v0w9jto5B8mvJkPhYv2WWv1duWttsQ4PjgyMuz0cqxU8jRiDeW/hx9kVVFZYePy5OUKRn998v3D5wAFi3TpxYPJxlwux4w3Hojc5/8YLl3K/RyGA0um7CzDJRSeuXuYnPPwc0ZvMiMhlw9dWihUPc25ulpcjTCOfhXhwyBLHU8pgQ4uZoDpI4wt69e7sSZllZWa297BN8GOfk5PS4byBqamq6utsFBQX164Rt9+7dXc89f/78xt6OPXbsmJdOp2MAEB8f3+s6a4GBgV3PX11d7fFtQUnP7HVKvoBzfrod1hkDAHDO/+Gcnw5goR0e/mIAKztuBwC4D8AqmK7kSOsYXwvgbG5x+T/n3NgR0+6OoZEwtaZcBeBWAF4AdABu5Jz/bofYCSF2wKQMsXfFYvyB8QiaG2S1v62wDXtn7cXRe47CoOnXBTOEdIn09sYvmZlQWbRgXFpUhNVVVSJFRWw64wwgLU049tprooTi6YYGC1sy6o16lDSViBRN/1mvYWZdpOhK3Vgt1zCjhJmbWLlSuL1oERARIU4sxK1VaLV4yuICoXEqFW6KjhYnIEIIcTyagyR2tW/fvq5KsbFjx1q2XexKTI0bN661p33jx4/vscKrL1qttuvqQJVK1a8Js9zc3K6Yp02b1msBTnZ2dtexlhV0lsyfv7W11YUuUySOZpdsKud8jT0e1xHPwznXAbiKMfYxgBsATAMQDqAWwAEA7wH4vuODydb9qxljEwHcCOBSABkA/ACUA/gHwCudPYcJIa5FmajEyD9Gouy9MhTcXwBji9nbAAdKXitB7ZpapK9Mh/8Ef/ECJS4n088PqzMysHDfPph/uFx7+DDSfXww0s9PtNiIGYkEuPtu4Oabu8fWrAEOHQLS00ULyxOFKEOgkqugbld3jR2rP4bEwETxguoH6zXMmFVLRlfqQEYVZm6ooAD491/h2DXXiBMLcXuPFhZCbRDOnb2RnAyJK70REkLIKfDEOciIiAh9aWlpjthxiC0iIsIh7TEOHTqkBACFQmHMyMjQ2toXHBysj4uLE8STl5enBIDw8HBdRETESV8Z7u3t3ZXobWpq6telgQcPHlQCprXfx48f32sSzLwSbtSoUb0m9tRqddfz+/j42Pw/RQhgp4SZO+iojjupCjnOuR7A2x0/hBA3whhDzM0xCJ4bjMPXHEbjJmF1uCZPgz1T9yDxv4mIfzAeTEon/KR/5oeEYHlyMu44erRrrNVoxLn792Pn2LEI9vISMTrS5YorgEcfBerquseWLwfefVe8mDwQYwxDgoYgp7L7XLugrgCnJ53ey73EZ6vCzGjs/RhnxTm3qjCjNczcwKpVwu3QUGDePHFiIW5tt1qN/1VUCMauiojApIAAkSIihBDxeNIcpFQqRXR0tPP3UncTBQUFCgAYOnRom9SslYVer0dBQYESAFJTUwVJqdraWmlpaak3AAwfPvykq8sAICwsrOvfuqGhoV95iM5kXVxcnDYgIKDXxNa+ffvMK+F6Ta7V19d3Pb95XIRYctgpOWPs9o4FKwkhxOUphyox6p9RGPrKUDBvYVKM6zkKHy1EzpwctJW0iRQhcUW3x8bihqgowdixtjZcdugQDJZ924g4fHyEFWaAqX2ZeQKNOIRlW0bLaidn1J8KM1dJmNW01qBVJzx/TgqihJlL49w6YXbRRQBdsEHs4NFjwvdslVSK54dQlSohhBAyWMrKymRtbW0SAIiOjhasYZebm6tob29nAJCRkSH4Ur927Vq/zg6gkydPttkSsaysTHbvvfdGjxgxIl2lUo1SKpWjk5KShl9++eXxubm5XfP/KSkpXVVt9fX1fVaYFRUVeXUmttLT0/tM1h06dMgHMK1PlpSUpOvt2IaGBilguvgyJSWF1vQjPXLkKfnrAI4xxu5jjPn0eTQhhDg5JmWIuzcO4/aMg2q8ymp/w/oG7Bq5C9XfVYsQHXFVb6SkYKJK+Hr6va4OTxQW9nAP4nC33grIzC6O02iATz8VLx4PNSRQOLF6rMH5E2a21zBzzZaMpepSwbaESRCtonWHXFp2NpCfLxy77DJxYiFubUNDA/6orxeMPRIfjyhvur6WEEIIGSyVlZVdJ61SqVRwBW52dnZXZdbIkSMFlVmrVq0K6by9YMECYVslAD/++KNq+PDhw1999dWo+vp62bRp05pmzZrVaDAY2KpVq8IOHjyo6Dw2NTW13dfX1wigq6KtN7t27eo6JjMzs9eKMY1Gw44fP67oeJ5ej21tbWWdVXOJiYltSqWSrkgmPXJkwkwNIArASwCKGGNLGGP9WuSHMTaaMbaQMRZn1wgJIeQk+Kb7YvTW0Uh4MsHqXVVfr8eB8w8g76Y8GFpPuu0z8SDeEgm+GT4c4RZX9D9XXIwfqin56hRiYoDzzxeOvfeeKftBHMZyvazCeudPKtuqMLNsySjtV2d/8ZU0lQi2o/yiIJNQt3eX9vnnwu2kJGDSJHFiIW6Lc45HLKrLIuVy3BUbK1JEhBBCiHtSqVRdk1CdrRk75ebmdhWzjBkzpivZtGnTJp81a9YEAaZ2jFOmTBEkov766y/fiy++OFmr1UrefffdwtLS0n1r1qw59ttvvx0rLi7e//333+fPmDGjpfN4qVSKzMzMFgCorKz0On78eK+tC/bu3dvvNcmys7OVho61UIcPH95rwmzLli0+Op2Odfx5W3o7lhBHJsziADwCoApACID/wpQ4e5oxFtzHfWcD+AmA88+EEEI8kkQmQdJTSRi1YRS8462vji1fUY69M/eCG2lCnfQtVqHA18OHQ2Yxu35NXh6K2qjNp1O46abu23FxwCWXALpeO0CQQRYfEC/YPtF0QqRI+q8/FWau0pLRMmEW4x8jUiRkUHAOfPutcOzSS12n5JG4jF9qa7GtqUkw9nhCAnxc5WoBQgghxEUkJSXpAgICDABw5MgR5dtvv901/37gwAElYEpodSbMtm3bprzggguSjR1X9D399NOClhIajYZdffXVSW1tbZL333//2E033WS1LsHixYvVUVFRgvXB5syZ01WltnHjRt/eYt63b19XIq+vNcnMq+SysrJ6Ta5t2bLFr/P2vHnzrKrmCDHnsFNyznkT5/xFAAkAbgRwFEAAgEdhSpy9zBiLsLwfYywGQGcvEJppJoQ4tcBpgRiXMw5hF4ZZ7Yu9NxZMQhNPpH+mBwZi2VDhGk0Nej0uPngQOsuSFOJ4M2eakma//AIUFgKPPw7I5WJH5VHiAoSNByqbK9FucO5W9Ja5B6PRddcws0yYxflTIwiXtncvUFwsHLvwQlFCIe6Lc47/FhUJxoYoFLjeYv1WQgghhJw6qVSK8847r7Zz+7bbbku64IILEr/88suAw4cPKwEgLCysffXq1YEXX3xxwmmnnZZeWVnpBQA33XRT5bnnniu4wuWNN94ILSkp8T7jjDPqL7vssn4nnS666KKGztt//fWX9XomZg4dOqQETNVxw4YN6/XkrqcqOVs2b97sBwDe3t78/PPPb+rtWEIcekrOGAuEqcrsagAKdCfAfAHcC6CQMXaIMbafMbaXMbYfQAGAkR3HHnBkvIQQcjK8Ar2Q8WUGUj9KhcTX9DYbfVs0Ii62uiaAkF7dHhODi8KEydftTU14jNYzEx9jwLvvAgsWuE4PPTdjmaDh4ChtKu3haOdgq8LMkqsU9FgmzGL9qZ2aS/v+e+H2kCFAZqY4sRC39Vd9PXap1YKxpYmJkLvKlQKEEEKIi3n55ZfL0tPTu6qvvvnmm5BLLrkkuby8XA4AFRUV8muvvXbI6tWrQw0GA/Py8uKPPPJI6bvvvlti+Vg//vhjIADcdNNNNQOJYcSIEdrRo0e3AMDPP/8crNVqbZ7xaLVaVlhY2K81yQBBlRwfO3Zsj8c3NjZKNm/eHAAAc+bMaQgJCaH1UkivHPbNlDEWCWAPgCcATIapRSPr+EHHbwWAVADpMCXJMgDIO/a1AbjTUfESQsipYIwh6poojNs9DpHXRSL5lWSxQyIuiDGGFampGKoQtBvHSydOYE1tbQ/3IsQzBCoC4esl7Ojh7G0ZbVWYWXKV/Kvl3zUlzFycZcLs3HNdJ3tLXMbzFlWMQxUKXBweLlI0hBBCiPsLCQkxbN++Pe/OO+8sDwwM1Pd0nL+/v+HSSy+t3rt37/7nnnuuwtYxhw4d8gGAmTNnNg80jhtvvLEKAOrr62XffPONv61jcnJyFJ3rjGVkZPTaYhEA8vLylACQmJioVSqVPXal+9///hfUmaS74447qgYaO/E8jryU6ymY2jEyAEYAawC8DmApgKcBPAPgeZjWKWMwVZRxAI0wVZ+lc843OjBeQgg5ZT7DfJD2QRok3nTlLDk5/jIZVg8fDi+LicsrDx9GmVYrUlSEiI8xZtWW8USj6yfMXKXQwmoNMxWtYeayjh4F9u8Xjp17rjixELe1o6kJfzc0CMYejI+HzFXe9AghhBAX5e/vb1y+fHlZeXl57g8//JA/c+bMrnaKV1xxRfWaNWvyqqqqclatWlWckZHRYxvExsZGmUQigUqlGvAaEdddd11ddHR0OwB88sknobaOmTBhgoZzns05z/7kk096PbE7fvy4V0NDgwwAzCvobPnwww/DAWDkyJEtZ5xxxoCTfcTzOPLb6fyO3/UARnHOF3DO7+acL+WcP8k5f4JzvgRAGoB7ANTClDjzB/AfAC0OjJUQQhyqragNRj2tS0VsG6tS4f8s1jOr0elwzeHD4LZ6uhHiISzbMjp7hZnlvLCt5QhdYe6Yc269hlkArWHmsiyryyIigMmTxYmFuK2XLKrLouRyXBUZKVI0hBBCiOdRKBR80aJF6qioKF3n2KOPPlpx5plnNnt7e/c5seDr62swGo3Izc1V9HWsJS8vLzzwwANlAPDXX38F7ty5c8CPYW7Hjh3KztsjR47ssR3jzz//rNq/f78PACxdutS5+/cTp+HIU/JImCrGPuec97gWGedcxzlfDlNrxk9gSppNBvALY0zZ0/0IIcRVtVe1Y8/0Pdg3fx90tbq+70A80h0xMVgUEiIYW1tfj7fLykSKiBDxWSXMqMLMIRraGtCqE17ISS0ZXdgvvwi3Fy1yjRcicRklbW34oUa43Mm9sbHwptcZIYQQ4nAHDx5UAoCfn59h2LBhPVaUWZo6dWoTANx3332xjY2Ngg/xo0ePen3//fc2Wy12uv3222tTUlI0RqMRS5YsOaX2FDk5OT6dt7OysnqsMHv22WejOmNfvHixuqfjCDHnyG+oDR2/+9UrlHNezzm/BsCjMCXNJgB40T6hEUKIOIw6Iw5ccADaYi3q/6xH9vhsNO+jCnFijTGGj9LSECOXC8YfKChAXmuf7b0JcUtWLRldrsLMOmHmCstGWVaXAUC0KlqESMgpU6uBrVuFY2efLU4sxG29V14Og9m2r0SCG6LpPYMQQghxNKPRiCNHjigBICUlpU0ygItXXnzxxdKAgADD+vXrA5KSkjJnzJiRfMYZZwxNS0vLSE1NHbl582bf3u4vk8nw+uuvFwPAunXrAjdv3uzT2/G9yc3N7SqqmTBhgs0Ks9WrVwf8+++/Ki8vL/72228X2zqGEFscmTDL7fg9cSB34py/AGATTEkzOnsjhLiVggcK0Lixq3002grbsHvyblR/Wy1iVMRZBXt54X9paYIxjdGIKw8dgt5WbzfiWG1twDffAOecA6xZI3Y0HsHVWjK6S4VZqVrYzSTcNxxyqbyHo4lT+/tvQG+2/ruXFzBzpmjhEPejNRqxwqIa/orISATIZCJFRAghhHiuw4cPy1tbWyVA32t/WRo5cqR2x44dBy+66KIapVJp3Lp1q//WrVtVRqMRl19+edW1115b19djnHnmmc2d65RNmzbtpK/8PXz4sA8ABAYG6hMTE222arrooosaOefZ7e3tu0eOHEkLwJN+c+S31G8AzAZwJmPsDM75HwO4718ATgMQZZfICCFEJOEXhaN6dTXaK7qr4I0tRhz4zwEkPJaAxKWJYBIXKDcgDjM3OBi3x8TgzdLuCesdajWeKy7GE4mJ4gXm6ZYtA55+GmhoMG0rFMD8+b3ehZw6qwozJ2/JaJkMs7UEoStUmFU0Vwi2o/zoK7rL+v134fa0aYCfnzixELf0bXU1qnTCeazbY06pCxMhhBBCTlJ2dnZXVVdva3/1ZNiwYe1ffvll0eBGNTAajYYVFhZ6A0BaWtqA/wyE9MWR17B+BOB4x3N+zxi7fgD37fxGre/1KEIIcTEBkwMwdtdYqCaorPYVPVOEA+cfgKHFYOOexJO9OGQIhimFy3r+9/hx7FFTS27RKJXdyTIA+PFH4TaxC/MKM39vf4T4hEBncN61IC2TYZxbfxV3hYRZZXOlYDvCL0KkSMgp4Rz4w+IaxjPOECcW4rY+LC8XbM8ICMBw3147NhFCCCHETsxbGY4ePdol13bYvXu3wmAwMADIyMighBkZdA5LmHHOdQCuAKAD4A3gPcbYNsbYIsZYj5VujLFRAC4FwAGU9XQcIYS4Ku8Yb4zaMAqR10Ra7av5oQZ7Z+6FtoKqx0k3H6kUn6anQ2o2ZgBwfV4etWYUy0UXmVqZdWpvNyXNiF0lByfj2J3H0PJoCxofbkTe7Xnwknr1fUeRWK9hZn2MSyTMWiwSZr6UMHNJR48ChYXCsTPPFCcW4paK29rwj8XFIzfT2mWEEEKIaF5++eXyzpaI8+bNaxE7npMxdepUTeef4cMPP3TuFiPEJTl0lQTO+RaYkmbtMK1JNgHAdwAqGWM/MMaeZIxdxhg7u+P3WwC2AOi8BG2dI+MlhBBHkSqkSP0wFcmvJ0OQBQGg3qXG7om70by/WZzgiFOa4O+PRxMSBGO7m5vxakmJSBF5uOBg6xaMX38tTiwexEvqhaSgJPh4nfR60Q5lXWFmnR2jhBlxmHUWp1aRkcDIkeLEQtzSqspKmHeeDZBKsSg0VLR4CCGEEEII6YvDlxXnnH8F03pkO2BKmjEAQQDOBvAEgJUAfuj4fTMARcddmwG87OBwCSHEYRhjiL0jFllrsyALFBbeaou12DNlD+r+6HMNVeJBliQkIMNHmCh44vhxHG11yc4Kru+CC4Tba9dSW0Yi4C5rmFFLRjexcaNwe84c13gBEpfAOcfKCDyKBgABAABJREFUSuF7xYXh4VBKpT3cgxBCCCGEEPE5PGEGAJzzXZzzSQDOALAaQBO6k2e2fo4DOJNzflyMeAkhxJGCTg/C6G2joUhSCMYNagNyF+Si7D3qTktMvCUSfJCaCvPpzTajETfl54Pbmokn9nX22YBc3r2t0wE//yxePMTpWOYijEaqMCMi4RzYtEk4NmOGOLEQt7S3tRWHLS7guTKC3isIIYQQQohzEyVh1olzvo5zfglMFWZjAFwH4DkA7wJ4D8DjAM4EkMw53yZaoIQQ4mC+ab4Y8+8Y+E/2F+4wAPk35+PYY8coIUIAAJMDAnBbTIxg7O+GBvyvokKkiDxYQABwxhnCMWrLSMxQhRlxGseOAWUWF+BMny5OLMQt/Vgn7IqQpFBgakCASNEQQgghhBDSP6ImzDpxk72c8/9xzh/jnN/KOb+Fc/4s53wt59zGkuiEEOLe5GFyZP2dhbCLwqz2FT9bjPyb8mHU09sjAZ5LSkKct7dg7IGCAtTqdCJF5MEs2zL+8QfQ2ChOLMTpuMMaZgajATWtNYIxqjBzQZbtGMPDgZQUcWIhbunn+nrB9n/CwsCc/Q2OEEIIIYR4PKdImBFCCLFNqpAi4/MMxC+Jt9pX/n45Dl5wEIY2gwiREWeiksnwzrBhgrE6vR6PFRaKFJEHO+ccYVvG9nZqy0i6uENLxurWanAIS+OowswFWSbMZsxw/hcfcRmlEgkOaTSCsXNDQ0WKhhBCCCGEkP6TiR0AIYSQ3jEJw5BnhkCRqED+TfmAWVFZzQ81yD0zF5k/ZkIWQG/pnmxBSAguCAvD19XVAExXxHgzBs45XdHtSAEBwLx5wC+/dI/9+CNw+eXixUSchnVLRtdLmFm2Y2RgCPWhiXCXY7l+GbVjJIPoX5nwO2mkXI6J/v49HE0IIY5jNBpRX1+P2tpamz81NTWC7TLL9sWEEELcHs2uEkKIi4i+PhpeoV44ePFBcG331f2NGxqxZ8YejFwzEt5R3r08AnF3/zd0KH6trcVYlQpvpKQgy89P7JA803nnCRNmf/xhqjQzrzwjgyavJg+vbn8VFc0VqGiugFQixZZrt4gdlk3WLRmtj7FMqjmbqpYqwXaoTyhkEjqlcCm1tUBBgXBs2jRxYiFuaZeXl2B7UUgIJM5+NQAhxOW0t7fbTHL1lPyqra1FfX09jEZa1sCeuK0rwgghbsFT/n/T2S0hhLiQsMVhyPojC/vO2QdDU3crxpacFuw5bQ9G/T0KiniFiBESMcUrFNg5dizSfXyoqkxMZ50l3FarTe3P5swRJx4319DWgPey3+valkvlTltZaV1hZn2ME4YtUKupFWxTdZkL2rVLuK1UAiNGiBMLcTsaAHlSqWDsbGrHSAjpBecczc3NA05+NTc3ix06EWoGYDAajVJn/S5OCDl5nHMYjUYpAANM/9/dFiXMCCHExQTOCMSoDaOQe2YudJW6rvG2gjbsmW5KmimHKEWMkIgpw9dX7BBIRAQwYQKwY0f32C+/UMLMTiL9IgXb7YZ21LfVI1gZLFJEPevPGmbOrk5TJ9h2xr9n0oedO4Xbo0cDMjotJIPjgEwGg9mbnYwxzAgIEDEiQogjdbY87Exw9TcBptPp+n5w4uyOG43GDM45a25u9lGpVK1iB0QIGTzNzc0+nHNmNBo1AI6LHY890ZkRIYS4INUoFcZsHYOceTloK2jrGtcWabuSZj7DfESMkBAPd/bZwoTZzz8Dr77q/OVDLijCL8JqrLql2ikTOe5QYWaZMAvxCREpEnLSLBNm48eLEwdxS7kWyddJ/v7wo4QsIS6Jc46WlhbU1NSguroaNTU1gh9bY7W1tS7T8tDHxwchISGCn9DQUMF2WVkZHnroIbFDdRV/Go3G0znnutra2hA/P79WqjIjxD1wzlFbWxvCOdcZjcY2AH+KHZM90TdXQghxUcohSozePBo5s3PQerD74q320nZT0uyvUfAdTtVGhIhi4ULg8ce7t48dAw4fBtLTxYvJTSlkCvh6+aJF19I1Ztk20Flccw0wY4YpKWYw6NDa2mB1jLPPK1CFmRughBmxI8uE2ezAQHECIYRY0Wq1XdVdtpJdtsa1Wq3YYfdLYGCgVbKrpyRY549S2XdXlk2bNjkgerexGYBWr9dXtrS0xBQVFcWFhITUUuKMENfV0TLXp7a2NqSlpcVXr9eXAtDC9P/dbVHCjBBCXJh3pDdGrR+FnLk5aMnpnizWVeqwd+ZeZP2ZBb8sPxEjJMRDZWUBMTFAaWn32C+/UMLMTkJ8QtDS2P0eWNNaI2I0Pbvoou7bGo0eq1Y1WR3j7PMJVhVmSqowcymlpUB5uXCMEmZkkDTq9SiyKKWdHRQkUjSEuDeDwdDV+rC3ii/zMbVaLXbYfZLJZP1KdpmPBwUFQUaVrKIbO3Zsa3Z29iMGg+ElAGhubo5oaWlJYIxxiURiYIzZ6K1ACHFWHe0XpZxzxjnX6fX6UoPBUA/gkbFjx7p1y1X6RCGEEBcnD5Nj1N+jkHtGLtS7uk+CdDU67D19L0b9Mwp+IylpRoBSrRZtRiOG9uNqSnKKGDNVmb33XvfYb78BDzwgXkxuLNQnFMWNxV3bta3OWWFmyRVbMlpW7wUpaDLclUj27hUOBAQAycmixELcz56WFnCzNzEvxjDB31/EiAhxDZxzqNXqfrU87Byvq6sDt/VFwon4+PjYTHj1NqZSqUDVSK5r7NixG7Ozsx80GAzPGwwGtUQiUUgkEiUAqdixEUJOisFoNGo62jBqYUqWbRQ7KHsTJWHGGPvbbJNzzmf38/hqAM9xznPsFhwhhLggr2AvZP2ZhdyzctG0tbtiQV+nR86cHIzaMAq+6dSe0VM16fV4qbgYy0pKMC0gAGuzssQOyTOcdZYwYbZ1K9DaCvjQ+oKDzbLKyVkrzCy5YsKM1jBzbZKDB4UDWVnWi+sRcpJ2NjcLtkf5+cGbXl/EA3HO0djYiOrqalRXV6Oqqqrrtq3t6upqtLe3ix12r7y8vBAaGorQ0FCEhYV13Tb/MR/vb8tD4n46kmZzAUwzGo1zjEZjIgC6gpcQ19QM4DhMa5ZtdvfKsk5iVZjNBMABsI7f/T0eAM5njK0G8Djn/JhdoiOEEBckC5Bh5B8jsf/s/WhY39A1rqvWIWe2KWnmk0IT9Z4mW63G/NxcVOt0AIB19fVYV1eHucG07pDdzZgBSKWAwWDabm8HNm8G5s0TNy43FOoTKth21jXMrFlnx1wtYUZrmLkWZpkwGzFCnECIW9rV0iLYnqBSiRQJIYOLc46GhoYBJcB0Hd+9nRFjDMHBwf1OfoWGhlLlFxmQjkn1tR0/hBDiUsRsyTjQT1pm9vtiAP9hjH3AOb9tcMMihBDXJfOTIfOXTOTOz0Xjpsau8fbyduScnoNRG0dBmURX+nmSdB8feFmc3D5x/DjmBAXRSa+9BQQAEyYA27Z1j/35JyXM7MCywoxaMtoPJcxcm1WF2fDh4gRC3NIuiwqzidSOkTgpo9E44ASYXq8XO+weqVSqAVV/BQUFQSqlDnmEEEKILWIlzK45yeN9AEwHMBtAKICbAVDCjBBCzEh9pcj8NRO583LRtL27PaO2RGtKmm0YBUW8QsQIiSP5SKV4KjERN+bnd41tb2rCH3V1ODOEWqnZ3Zw51gkzMugsK8xqNNSS0R4451YJM1rDzHUwvR4sL084mJkpTjDE7dTpdCi3qKgZSxVmxEGMRiPq6+sHlAAzdHYAcDJyubwrudVT8st8X0hICLy9vcUOmxBCCHEboiTMOOefnMLx7wAAY2wUgLmDGBYhhLgNmUqGzDWZyJmTg+bs7qt92463IWduDkZvHg15mFzECIkjXR0ZieeLi1HY1tY19sTx4zgjOJiqzOxtzhzg6ae7t/fsAWpqgNDQnu9DBsxyHS1XqTBztZaM6nY19EbhFfa0hpnr8C0vB7NcI4cqzMggOdQqXNLCizGk0PpF5BS0traisrISVVVVqKqqEtwuKyvD4cOH0djYiLa2NtTW1jptAszHxwfh4eEICwvr+rG13ZkE8/X1pe/nhBBCiIjEbMl4SjjnewHsFTkMQghxWl6BXsham4W9s/aiJbd7TQlNvgb7ztqHrL+zIFO57McAGQAviQSPJyTgWrPKgp1qNX6trcVCStzY16RJgI8PYD6R+M8/wAUXiBeTG7KqMGt1zgqzwkKgvh4wGgGNhqGuzvqKcGeeI7OsLgOs22ES5+VfXCwciI4GaD1LMkgOWqxflqxQwEsiESka4oyMRiPq6up6TIJZ3m6xeE05C19f334lwDpv+/jQGtKEEEKIK6GZUkIIcWNewV7I+jMLe2fuRevB7gl79S41Dpx3AJm/ZkIip8kMT3BFRASeLSpCgVmV2ZPHj2NBSAhdxWpPcjkwYwawZk332J9/UsJskFmtYaZxzgqzhx4Cvv66c0uB+fNjrI5x5v+OjW2Ngm0Jk8BP7idSNGSg/MrKhAMZGeIEQtzSQYsKs3SqLvMIbW1tVgmvnpJgztoG0c/Pb0AJMCW9tgkhhBC3RgkzQghxc/IwObLWZmH31N3QFmm7xuv/rMfhqw4jfVU6mMSJZ2jJoJBJJHgyMRFXHj7cNba7uRm/1NbibKoys6/Zs4UJs02bxIvFTdlqycg5d7pksGWxha01zJy5IKNJ2yTYVslVTvd3THrmW14uHEhJEScQ4pYOWyTMUimp4JI456ivr++1Asx8rKmpqe8HdTCVStVn0st8W6GgtZ0JIYQQ0s2hCTPGmA+AmwHMB5AI08INlQCOAdgHIAdALue8vKfHIIQQMnDeMd7I+iMLe6btga6me0H2+j/r0VbUBmUSTWp4gkvCw/FMURHyNZqusReLiylhZm/Tpwu3Dx2idcwGmWVLRgM3oFHbiEBFoDgB9cAyt2Q0utYaZpYJM39vf5EiISfDKmE2dKg4gRC3VGxWwQ4AQ7ytW84ScRiNRtTU1KCyshIVFRWC37YSYnq9vu8HdSC5XI6IiAiEh4d3/Q4ODkZ9fT38/f1x+umnIzY2tmsdMEqAEUIIIeRUOCxhxhjzB7AJwAiLXUkAJlkcWwsgF6YEWk7H7QOccx0IIYScFJ9UH2T+lom9s/bC2GKEd4I3stZmUbLMg8gkEixJSMBVZlVmW5qasKWxEVMDAkSMzM2NGmW9jtnWrcA554gWkrsJVlqvw1SvqXf6hBnnlDAjjuNbUSEcSE4WJxDidjjnOKHVCsZi5XKRovEMnZVg5gkwy2RY5++qqiqna4UYGBholQTr6ba/v79VNbNGo8HatWsBAHPnzqU2iYQQQggZNI6sMHsMQCYADlNlGQA0AVAAsPw2HQpgVsdPJz1jLB/CJFoO59zizI8QQkhP/Mf7Y8R3I3DskWPI/DkT3tF09a+nuSQ8HI8VFgomtl4sLsZPmZkiRuXmvLyASZOAv//uHtu8mRJmg8jXyxdSJoWBd08INmobe7mHOKxbMrpWwkzdrhZsU8LMdUjb2qCorxcOUoUZGSSNej3UFgmZOKowGzDOOZqamvpMgHX+1umc53pimUzWr+RXREQEwsLCIKeEKiGEEEKclCMTZovMbr8I4P8457UAwBiLhSmZNhJAVsfvVABSs/t4Acjo+Lmkc5AxVoPu5Nn99vwDEEKIOwieF4ygOUG0bpmH8pJIcG9sLO4pKOga+7m2FgdaWjDc11fEyNzctGnChBmtYzaoGGPw9/ZHfVt3QqCxzfkSZv1JhjlzwsxqDTNvlUiRkIGyqi4DKGFGBs0hi/XLACCGEiJdWlpa+kyAdd5us2htKSZ/f/8+k1+dY0FBQbSmJSGEEELcgiMTZnEwVZf9yTl/xHwH57wEQAmANZ1jjDE5gOHoTqBlwZRUs1zwIwzAbACnA6CEGSGE9AMlyzzb9VFR+G9REerN1qh4qbgYn6SnixiVm5s2TbidnW1q0ejjI048bihAESBMmDlhhZklzsWOYGAsE2YB3tTK1VUoq6qEA9HRALUwI6eAc471DQ14q7QU39fUWO7EtQUFuCMuDjMDA90ykdLe3o6KigqUl5f3mgCrqKhAS0uL2OF2CQ0NRUREBCIjIxEREdFjW8SwsDBqc0gIIYQQj+TIhFkzAG8AG/tzMOe8HcCejp8ujLEoCJNotqrRCCGEENIDP5kMt8fE4Omioq6xz6uq8NyQIYihFkr2MWmSqR+f0Wja1umAnTuBGTPEjcuNWCZvnLHCzNVZVZjJqcLMVSjr6oQDcXHiBELcwm61GlceOoQDNirLAACM4fu6OnxfV4fhPj5YmZ6OMSrnf7/gnKO5uRnl5eWCn87EmPlPneX/KREFBgZ2JcAiIyMFt81/h4eHw8vLS+xwCSGEEEKcmiMTZvkAJsPUWvGkcc7LAZQD+L1zrKMaLeOUout+rBkA1vfz8D8452faeAwGU9vIawGMBuALU8wbAbzJOd85GLESQoi9tFe1wyvEC0zqflcEE5M7YmLwfydOQNORwNFzjhVlZVialCRyZG5KpQJGjQJ27+4e27aNEmaDKEBhkTBzgQozV2OZMKM1zFyHorZWOBATI04gxOWtq6vDufv3o6XzApA+HGhtxfQ9e/D9iBGYGxxs5+hsMxqNqKmpsUp82UqEtfaUBHQwlUrVY+LLPCkWEREBb7rYiRCXRXOQhBDifByZMFsNYAqAMYP9wB3VaHsH6eFGnsqdGWNKAF8DWGCxK7Hj5zLG2BLO+Yun8jyEEGIvjVsbceD8A4i8LhJDnhkidjjETsLkclwREYEV5eVdY++VlWFJQgLkEomIkbmxSZOECbNdu8SLxQ1RhZn9UcLMdSkpYUYGwW61ekDJsk4tRiPO3b8fG0ePHtRKs862iLYSX+ZjlZWV0Ju1oRaLUqm0qgDrKSnmQy2bCfEUNAdJCCFOxpEJsw8B3A3gDMZYEue80IHPPRCdH1ZqAFf2cWyljbEP0P1BdQjAio7jxgC4GYAfgBcYY6Wc889OPVxCCBk8ZSvKcOT2I+A6juJni6Eao0LYeWFih0Xs5PaYGEHCrFKnw9fV1bgsIkLEqNzYuHHCbUqYDSqqMLM/dbtasK3ydv4Wa8TEqsIsNlacQIjL4pzjykOHBpws69RiNOKqw4eRO25cn2uaqdXqXqvAOsdqLV/XIpDL5YiIiEBUVFSf1WB+fn5uuZ4bIeSU0BwkIYQ4GYclzDjnrYyxywGsA/AFY2w259x5Vr/t1vlhtZ9z/sNA7sgYmwvg0o7NvwEs4Jy3dWx/wRj7CMAmACEAXmOM/cg5V9t4KEIIcbjW/FYcue0IuJ53jR2+6jB80nzgm+ErYmTEXjL9/DAjIAAbGrsTC2+UlFDCzF4sE2ZFRUB1NRBGSenBQBVm9tfc3izYpjXMXAdVmJFTtb6hoec1y/ppf0sLPj94EPF1dSgrK0NpaSnKysqsflpaxJ8mUKlUiIqKEvxERkZajQUFBVESjBByKmgOkhBCnIzDEmaMsc8AHIDpaoe7AWxhjF3DOd/jqBj6whiTABjesbn/JB7i/o7fegDXm31QAQA454cYY3cA+BymD6wbACw7yXAJIWRQ+QzzwdBXh+LoHUe7xgzNBuxfvB9jdoyBVyAtEu6O7oiNFSTM/lWrsbOpCeP9qdXaoEtPB3x8APMJx127gPnzxYvJjVglzKjCbNC16oST5b5yupjCVSjq6oQDlDAjA/R2WdmgPM7lX38NLF06KI91MsLCwvpMhEVGRsLXl97fCCH2RXOQhBDinBzZkvFSAJ1lCxymqyh2McbWA/gZwDYA+zjnYq6yOxSmxTEBYN9A7sgYCwYwp2Pz915aTq4G8BqAcAAXgD6sCCFOJOa2GKh3qVH5SXe3B80RDQ5fcRgjfhwBJqEraN3NopAQxHp7o0Sr7Rp7q7QUH1PCbPDJZMDo0cCWLd1jlDAbNNSS0f4sE2ZKmVKkSMiAaDTwsqwMiooSJxbiUgwGA6qqqpBfWorv1GpgMCqpTjvN+uKRU+Tl5WWz+styLDw8HF5edAEYIcRp0BwkIYQ4IUcmzADA/Bs279ie2fEDAJwxVgAgx/yHc37CQfGZL7Y5oA8rAKcBkHTc/qungzjnRsbYBpg+qCYyxoI45/UDfC5CCLELxhiGvTMMLftb0Jzd3Xqr9pdanHjlBOIfiBcxOmIPMokEt0RHY0lh9znW19XVeD0lBf4yR39N8ADjxlknzMigoJaM9meVMPOihJlLsLXOU2io4+MgToNzjtraWkEbRFvtESsqKmA0GoGEBODjjwfnyaVS0+uvuLjPQ1UqVb8SYcHBwdQWkRDiimgOkhBCnJAjZ8ISYfowMP9JASA1O4YBSO74Ob9rkLEGALkwJc/utmOMmWa393c895COePQAjnLOe/pmP8Lyvr042PGbdTznxoGHSggh9iFVSjHi+xHIHpsNXbWua/zYI8cQMC0AAZMDerk3cUXXRUXhicJCGDq2W41GfFlVhRujo0WNyy2NHy/c3rlTnDjcEFWY2Z9GpxFs+3j5iBQJGQhm2Y6RMSAwUJRYiP1pNBqUlpaitLQUJSUlXb/NE2Hl5eVob2/v/4MqBzc5rggJQbxCgejoaKufqKgoREdHIzIyEn5+foP6vIQQ4mRoDpIQQpyQwxJmHW/yxQB+6RxjjHnD1K/XPImWCSDM4u5BAGYAmA7T+mf20nl1RyWAsxhjDwNINz+AMbYbwBLO+e8W9000u328j+cxr5hLGHiYhBBiX4o4BTK+yEDO3JzuZroG4ODFBzFuzzh4BVM7G3cSIZdjYUgIfjSrQviovJwSZvYwblz37dhYIDMT0GgGfTLSE6nkKsF2c3tzD0eSk6XRCxNm1JLRNbB6iwvJg4JMVT7EpXDO0dDQIEiCmf/uvF1nmSAdDBpN38cMwO5Nm5BOa4QRQgjNQRJCiBMStdcS51wLYHfHTxfGWCSsq9HSANh7hrbzwyoCwCc9HDMGwBrG2JOc8/+ajZsn+Wr6eB7zs5iQgYVICCGOETQ7CAmPJ6Dov0VdY9piLQ5fcxgjfhhBrW/czHVRUYKE2b9qNQ60tGA4TWgNruRkYMMGU6IsKEjsaNyKr1z4Wm1pbxEpEvfEObdqyUgVZi7CMoESHCxOHKRHBoMBlZWVNhNh5r81g5y46i9JXR2MBsOgJFpljCHG23sQoiKEEJdHc5CEEOKEnHJxEs55BYAKAGs7xxhjMpiSZnbBGPMFMMRs6CiApQD+BNAAU/vIGwHcBlMZ81LGWDHn/OOO481nDNr6eDrz/QOeaWCMxfZxSHjnDa1WK9qJFXF/bW1tNm8T9xHxQATq/qmDepO6a6z2p1oU/l8hom6Pclgc9Fqzv5k+Pojw8kKlrrsN54oTJ/BCgmddhOiQ11pnW0b6fB5UEqNEsN2qa3W670AGgxf6+vrtbDF3atNb/39gBua08ZJuxspKmKcnjEFB0NK/m8O0tbVZrRNm+buiogIGg6HvBxtkjDGEhYVZrRHW+dPZIjE0NBRXHjuG7weheu2coCB46XTQmH3fIGSg6NyAOIpWqzXfDO9rPo5zXtKfx3WlOUhCCPE0Tpkw+3/27jw+yup6/PjnTvZ9TyAJCQEStiAQFkHZRMGlqKC1rgVt3a2ttdpWrd9u1mrtz7pUq9W64F5R0LogqGwqChJ2wh4IJGQn+zoz9/fHZJmZ7MlkluS8X695ZZ47z/PkJAwzk3uec257tNZGuu7L2xcjsbyJBACbgfO11pVWj+8G7lRKfQ+80jT2d6XUCq11Fba/S5t31HZYP96bf4MTXe9isX37dqqr5Qpr0f82bpQ22AOVukERvCsYQ3nrZPTx+4+z12cv5hSz0+OR51r/OdvPj/f9/Vu2X8nLY86BA57zYcHB5LnmWXJqbZd4qDXWsvqz1RiUoYMjnC8vLwMY1uk+a9as6fRxV6kytm1xueXrLRzxPeKCaERPpGZmMs5qu9Bs5js3fZ55murqakpKSigpKaG4uJjS0tKWr83jlZWVXZ+oH/j7+xMdHU1UVBSRkZFERkba3I+MjCQ8PBxv747f5QsLCyksLAQgw8uLlQ5YU2xyTg5rsrP7fB4hmsnnNdGfDh48aL35cTcO6W4bFk+agxRCiEFFXiibaK13NV3hMRSotnujst7vVaXUZcAlWEqZLwOWA9aXafrS+RuW9UWePVhtWQghnE9HamrvqiXoj63tzpRREfhEIFV/r+r/ZrnCac5tbLRJmFUYDOzw9maq0ejCqIToHj9D2xZf9eZ6ArxknS1HqDe3/Wjb3u9cuB9vu2oyY6BcXN4ddXV1FBcXU1xc3JIQs7+5qsIyLCyMqKgom1tzQqz5Fujgf+d0k4kkk4mcPrRlTDKZSHdBJZ0QQrgbmYMUQgj3JQkzK1prDeR1Y9fXsLxZAczG8mZlfdmtP52/Wflb3e/NX1mdX5ps6YP8McDkyZM5++yze/EthOhaXV1dyxV9c+bMwd/fv4sjhMdaCDlVOZz6f6dahryOezHmmzEk/SWp37+9PNec59W9e9lS1fqWdiQpiftHjXJhRM4lzzXPVVhdCFm2YzPnzCQ2KLb9A1zgnXe6vsJg4cKFToik546cPgL7bMcuWngR/t7yf8TdqU8+sdkeMnKk2z7PnKWurq5lXTDrW25ubsv46dOnnR6Xt7c38fHxxMfHk5CQ0OZ+QkICQ4YMwc9Fa4C9VV3Nwn37qDb3vMNAkMHAW+npTJa1UYUDyOc14SxBtq9ZPwB2OercHjQHKYQQg0q/JMyUUhO01rv749x232eS1npHf3+fduy3uh/f9LXMaiwSKO/keOuVtot6+s276omslBrSfN/Pz4+AALmyWvQ/f39/ea4NcKkPp1LxeQXVO1vbvJ564hRxl8YRPjfcaXHIc61/XTtkCFsOH27Z/l9ZGdrXl8A+XFHuqeS55lmivaLbjJm9zG71b9id/0buFK81c4XtBLlCER4cjlLd7TwkXMVYUWGz7e3vj7ebPs8coaGhgby8PE6cONHu7eTJkxQV9fhPsD4LDg4mMTGRhIQEEhISWu5bf42JicFgcJ82svbOCghgZXo6S/bs6VHSLMhgYGV6OmdFRna9sxA9JJ/XRH+yu0ChsLtrlDmYS+cghRBisOmvCrNMpdRy4GGttcMXNlBKjQP+DFyKa6rk2luh2LqxcRLQWWN26wqx4w6JSAgh+pnB18DY18eybco2dIO2DGrIWpbFtF3T8A6VouWB4EcxMfzy8GGap8GqTCY+Linhilj3qdIRoj0BPm0ny6obZR1XR6lttL0gOcAnQJJl7kxrWL8ennkGr/fft33s+eehuBhuvx3mzQMP+nc0Go2cOnXKJvllnxArKCjActG+80RHR5OYmNhuEqz5a2hoqFNj6i8LIiPZOHkyS7Oy2FtT0+X+6UFBvDpmDBkhIU6ITgghBiSZgxRCCCfqr9lNL+B6YKlS6j3gOa31+r6cUCnlBVzYdN5Lm76Hw/4SUkpdAEwCYoG/aK1LOtk9wep+c38y6yY144ENnRw/vumrBvb2LFIhhHCd4PRgRjw8giP3tF4LUX+8nsN3H2bMi2NcGJlwlCF+fpwTHs4XZWUtY28VFkrCrD+ZTFBTAzKZ2CcGZSDAO4BaY2tip6ax68lc0T32v8sAb7ma321lZsLSpbDX8mdGm3SY1rBiheU2fjwsXw4ZGU4Psz1lZWXk5OSQk5PD8ePHbe6fOHGCvLw8zL1oB9gXERERDBs2rMNbQkLCoGsHlxESwu5p09hQVsYzeXmsLCrCfmWyiyMiuDspibnhUokqhBD2ZA5SCCHcV38lzOYCzwDpwBXAFUqpE8D7WF7Ev9VaF3R1kqbWgmcC84ErgZjmh4DdwB0OjPly4Mam+5uAlZ3se57V/W+bvm7GctWHD3AO8Gx7BzYl/uY2be7UWpf1Ml4hhHCJxF8mUvy/Yso3tHZ9yP9PPnFXxxFxboQLIxOOclVsrE3C7JOSEsqNRsK8pYrQYVautExSHzoEhw/D1VfDyy+7OiqPF+QbZJMwq26QCjNHaTDZrhHv5+2aNZREF9auhSVLoLqbz/29e2HOHMtr0oIF/Rpac3VYe8mw5vsVdu0j+1toaCiJiYkdJsMSExPt168RTZRSzIuIYF5EBIX19cRt3mzz+IOJiUyLkM+FQgjRAZmDFEIIN9UvM19a601KqUlYqsH+D0t5cBLwi6YbTQm0/UApUAJUAqFYeutGAmOBRKvTNl+Wdhz4E/Cq1tqRlxd+Seub1S108GallIoBbm7arAHeA9BalymlvgAuAC5RSiVprXPaOcVVtCb+/uug2IUQwmmUQTH21bFsnbAVU2Xr9cQHbj7AtN3T8AocfGtdDTSXx8Rw+6FDNDa1tKrXmv8VF3PdkCFdHCm6LTsbVq1q3T54sMNdRfcF+gTabEuFmeM0mm27AfkYfFwUiehQZmbPkmXNqqstx23c2KdKs6qqqk6TYSdPnsRksq9D6j+BgYE2ia/2EmIDpU2iq8X6+THE15f8htbEek59PdNcGJMQQrg5mYMUQgg31W+Xijcls15qWsvsKuB2YIbVLsOw7aPbHuveDd9gqVr7r9a6P/7SWgWcxJKkO18p9Sut9f+zCUapcCxVclFNQ/9Pa33aapd/YHmz8gXeUkpdoLWutDp+HPBk02Yl8EI//BxCCNHv/JP9GfHoCA7dfqhlrO5oHcd+f4yRj410YWTCESJ8fDg/MpKPSlo7g3xQUiIJM0dKS7PdloSZQwT52FaCyBpmjtNoskuYeUnCzK1obWnD2NNkWbPqali2DHbtandNM7PZTEFBQYfJsOPHj3P69Ol2Ttw//Pz8bJJg7SXEIiIipBWgEyX7+bVJmAkhhOjQKmQOUggh3FK/91bSWhuB14HXlVJpwGXA+cBUoLP+FpXAVmA1sFJrfaSTfR0RZ61S6hbgQyzro/1dKXU+sAIox9Lz9yagebZwA/CQ3TnWNK3ZdjlwFrBTKfUv4AQwGbgNaF6g5F6tdXF//kxCCNGf4m+Jp/DNQsq/am3NeOLxE8ReFUvIFFmLydMtjo62SZh9WlJCncmEv5dUEDpEaqrtdnExnD4N0r6qT2KCYiitLSXIN4hAn8A2FWei96TCzM2tX9+yZlmv7dnDtscfZ0d4uE1CrHn9sMbGxq7P4SBDhw4lKSmJpKQkkpOTSUpKskmGxcTESDLMzST7+/NdZcs8LTkNDZ3sLYQQg5vMQQohhPty6mIkWuuDwCPAI0opA5ACDMfSgtEPqMfSnjEbOKZ1Uy8o58X3iVLqauAlIBhY0HSz9yFwrda6vb8ClmJ5Q1qI5ef7m/23Af6otX7eYYELIYQLKIMi7YU0vp/4Pbqh6eXaDPt/up8pW6dg8DG4NkDRJ4uiolBY3rQAqs1mviwr46KoqM4OE92VkgJeXmDdnuzQIZg+3XUxDQAbru9svXPRF1Jh5uaebXfpkh47cs89Lf2h+ou/v3+bZJj1/cTERPz8ZI08TzMyIMBmO6u2toM9hRBCgMxBCiGEu3JqwsxaU8vGI003t6G1flcp9RXwM+BCYBSW8uYCLItrvqq1/qST42uUUhcA1wDLgElAOFAMfAU8qbX+uj9/BiGEcJagMUEkP5jMsQePtYxV76wm95lcht3VVddd4c7ifH05KzSUrysqWsY+KC6WhJmj+PpakmaHD7eOHTwoCTPhtqTCzI1VVKBXrsQR9VaXYZl1q+xqx07ExMR0mAxLSkqS6rABKj3ItnnMvhpZQ1IIIboic5BCCOF+nJYwU0r9HtgF7Orv9op9pbU+BTzQdOvN8Rp4o+kmhBADWtKvkyj6bxHVu1vXTTn2+2PEXhWL3xC5QtyTXRodbZMw+7CkhH9pjUEmOh0jNdU2YXboUMf7CuFi9hVmvl6+LopkcKqrq+P48eMcO3as5WvzzefQITaYHLPEszeQAOzv4HEfHx+GDRvWYTJs2LBhBAZKK9TBaIJdwuxEQwPlRiNh3i67RlcIITyCzEEKIYR7cean19/T1NlJKVUN7KUpgQbsxpJIK3NiPEIIIRzA4Gsg7fk0tp+1vWXMVGHi6G+OMvbVsS6MTPTVpdHR/ProUQBifHy4KDKSSpNJJr8cJS0NPv20dVsSZsKNtakwk5aMDlVbW8vx48fbJMOab/n5+R0eO83BsWSMGkXauHHtJsSGDBmCwSAtl0VbowMD8VYKo9WqCnuqqzk7LMyFUQkhhBBCCNEzzp7xar4kPRiY3nRrfVCpXGA7lrLhz7TWu5wbnhBCiN4ImxlG3LI4Cl4taBkrWF5A/M3xhJ0tEyWeKi0wkL+mpDArLIyZYWF4SWWZY6Wk2G7n5LgmDiG6oc0aZtKSsUeMRiMnT54kOzubo0eP2nzNzs6moKCg65N0oMqBcQK88eGHMFYueBE942swkObvzz6rtct2VVVJwkwIIYQQQngUZybMzgPOACY0fR0HBNjtk4ilC8gi4BGl1Fbgfq31l06MUwghRC+MfHQkxSuLMVW0toU69LNDTPl+CspLEi2e6rfJya4OYeBKSrLdPn7cNXEI0Q1SYdY5rTUlJSXtJsSOHj1KTk4ORqOxX773ScCIg/6w8/aGhARHnEkMQmcEBtokzL6tqOA2eT4JIYQQQggP4rSEWVPSqyXxpSwrPafSmkBrTqal0FqJNg1Yq5T6m9b6PmfFKoQQoud843xJ+VMKh+9qXZOpakcVeS/kkXCrTJYI0YZ9MjIvDxobwUcSEcL9SIWZpW3isWPH2k2IZWdnU1lZ2e8xREZGMnz48Da36qefJmzt2r5/gyVLIDS07+cRg9K04GDeLilp2d5stQ6qEEIIIYQQnsBli5A0LUp5sOn2XvO4UioUmA1cB1wBGIBfK6VKtdaPuSJWIYQQ3RN/RzynXjxF9Z7qlrFjvz9G3LVxeIfIuldC2LBPmJnNkJsLw4e7JBwhOjMYKsxMJhO5ublt2iU23z916lS/xxAVFdVuQmz48OEkJycTEhLS/oHBweCIhNntt/f9HGLQmmH3/DxUW0tRQwMxvr4uikgIIYQQQoiecbvZS611BfAx8LFS6u/ASiytGv+klHpXa33MlfEJIYTomMHbwKinRrFz/s6WscbCRk78/QQpf0zp5EghBqHISAgMhJqa1rGcHEmY9cFz3z/H8p3LaTA1UG+q56JRF/HogkddHdaAYDKbbLarG6qpqK8g1M+zqpEqKio4cuQIR44c4ejRozZVYsePH6exsbHrk/RBcHAwI0aMICUlhZSUFJv7ycnJBAcH9+7E8+bB+PGwd2/vg0tPh7lze3+8GPQmBAbipzX1Vmuebq6o4JLoaBdGJYQQQgghRPc5LWGmlPozsKvpdrCpwqxTWuttSqmLga2AL3AP8LN+DVQIIUSfRJwTQeQPIin9uLRl7MTfTxB/azx+Q/1cGJkQbkYpS5VZVlbrmKxj1icnK06y+eTmlu3xMeNdGM3AoLVm/bH1vLvvXZvxz458RuSjkSwZu4Tbp97OvOHzUMr161VqrSkoKGhJih05coTDhw+33C8uLu7X7+/l5UVycrJNMsz6a1RUVP/8npSC5cthzhyoru56f3tBQfDqq5bzCNFL3kqRajKxx7t1mmFdWZkkzIQQQgghhMdwZoXZA0BzkqxOKZVFawJtN7BLa11kf5DWeqdS6lXgp8BVSqlfaK1N9vsJIYRwHyMfHUnpp6Vgtmyba8wc+8MxRj8/2rWBCeFukpJsE2Y5Oa6LZQDw9bJt+9VganBRJAND5qlMlq5cyt6i9quWTNrEin0rWLFvBeNjxrN8yXIyhmb0e1xGo5GcnJx2k2JHjx6lujcJox6IjY3tMCGWmJiIt7eLmnhkZMDKlZZ1yHryOwgKshyX0f//dmLgm2A02iTMPist7WRvIYQQQggh3Iuz/5prvmQxAJjcdGt9UKlCmpJnVrd9wGosCbMIYAbwtZPiFUII0QtB44MY+pOhnHqxdb2XUy+eIvEXiQSNC3JhZKKvGs1mtldVEezlxbgg+bfsM/t1zKTCrE8kYeY4a4+sZck7S6hu7F7iZW/RXua8PIeVV65kwcgFff7+NTU1HD161CYp1pwYO378OEajsc/foyOBgYEdJsSGDx/e+7aJzrBgAWzcCEuXdq89Y3q6pbJMkmXCQSYbjbxltZ1VU8OJujqG+fu7LCYhhBBCCCG6y5kJs6nAGcAEq6+xdvvENY2dazVmAvKstq9USp3UWsuMkhBCuLHhfxxOwZsFmGuay8zg2O+PMf5daZHmif5bWMi/8/LYXFFBjdnMzUOH8vxoqRjss8RE2+1Tp9rfT3SLuyfMfvQjyzJTAI2NjRQWlvDFF8mdH+QCmacye5Qsa1bdWM2Sd5aw8YaN3ao0Ky0ttWmXaH3Ly8vr8vjeUkoxbNgwRowYwciRI9usJRYbG+sW7SV7LSMDdu+GDRvgmWfQ772Hsu6GbzDA5ZfD7bdb1izz5J9VuJ2RJhPBZjNVBkPL2NrTp/nJ0KEujEoIIYQQQojucVrCTGudCWRajymlYrEkz6wTaeMA60VuvIEkq+07gDuUUhXAHqyq0bTWmxFCCOEW/OL9GParYRz/c+v1DUUriqjaXUXwBDe+Ol+060R9PV+UlbVsbyovd10wA0lcnO12QYFr4hgg/Lxs10l0t4TZxRdbbgC1tUZeeKHMpfG0R2vN0pVLe5wsa1bdWM2yVcvYdesulFKUlJRw6NChNrcjR45QZvWa4mh+fn6kpKQwcuRIRo4cyahRo1ruDx8+HD+/Ab6mplIwbx7Mm4fxttvwee651seuvBLefNNloYmBzQs4w2jkG9/WCxg+KSmRhJkQQgghhPAILmqwb6G1LgQ+b7oBoJQyAGnYJtIm0po0a74EMgw4q+kGlvXRXPrzCCGEsJV4dyK5T+diLGttnZXzcA7j3hrnwqhEb8wOC7PZzqqpoaihgRhf3w6OEN0yZIjtdn6+a+IYINy9wqw7KiogNNR133/9sfUdrlnWXXsK9zD2wrEUbink9OnTDoqsrbCwsJYkmH1iLCEhAYNVhcugFhlpu93gef8vhGeZbJcw+7S0lBqTiUAvLxdGJYQQQgghRNfcLsGktTYD+5tu/wVQSl0EfNS0y8dALpZkWjogZQpCCOGmfMJ9SPxlIsd+fwyDv4H4W+MZ9pthrg5L9MLk4GACDQZqzOaWsa/Ky1kSE+PCqAYA+4RZQQFoLS3SeskTEmYVFXDiBKxd68Vzz01q83hkJCxZYumWN2+e858Kz37/rEPOcyD0ADggVzZ06NA2SbHmxFhkZKRnt050lpAQ2+2qKtfEIQaN6UYj/wKaPzHUmM2sLi3lMvnMIIQQQggh3JzTEmZKqadpbZ+4W2td04PDL7e6/7jWep3VeVNorUQTQgjhZhJ/kYipykTiLxPxGzrAW2ANYD4GAzNDQ9u0ZZSEWR/Zt2RsaICyMoiIcEk4ns5dE2Zaw/r18MwzsHIlWPLOvkBkm31NJlixwnIbPx6WL7csSeUMeSV5rMxa6ZiTjcXSZL2+8928vLxITk62aZnYfBsxYgRBQUGOiWcQ0/a/w8pK1wQiBo0wrZkdGsqGioqWsfeKiiRhJoQQQggh3J4zK8zuwNI2EUArpbKxWn8MyxpkR+wPUkpdC9zQtFkCbLJ+XGudDWQDH/RT3EIIIfrAO8ybkX8b6eowhAPMDg+3SZh9ZzURJnrJPmEGliozSZj1io+Xj822OyTMMjNh6VLY24suh3v3wpw5liTbggWOiae2trbdNcUOHTpEvinf8ondEQxACFAP3t7ejBgxgtTU1JZbc4IsKSkJHx+frs4m+iI83Ha7tNQlYYjBZXFkpE3C7H8lJdSZTPhLW0YhhBBCCOHGnN2SUVl9HQmMABa3PKhUDZAF5AAmYAyWtotgSbb9S2vduhCOEEIIIZxmul1br+1VVRjNZrxlnaDe8/e3TGZbJSLJz4cxY1wVkUdTuFd7vrVrLe0Vq6t7f47qass5Nm7sfqWZ2WzmxIkTHDx4kAMHDrTcDh48SE5ODlrr9g9M6H2c7XniX0+waPIikpOT8fZ2u07wg4a2r+opLHRNIGJQuSQigruPHWu5YrbSZOJ/JSVcERvr0riEEEIIIYTojDP/cp2EpXVi820CMNRunyBgStOtWfPMx3bgkf4NUQghhBAdmWqXMKs1m8mqqWFCsCwn2idxcW0TZsLjZWb2PVnWrLoali2DXbts1zQrKyuzSYo13z906BC1tbU9/0YOLshbOHchI2OkwtjVdHS07UBpKTQ2glT2iX401NeXueHhrLd6f3slP18SZkIIIYQQwq05LWGmtW5uvdhCKRVF2yTaOCDQardS4DXg91rrXvzlL4QQQghHiPH1JdnPj+P1rYsSfV9ZKQmzvoqJgQMHWrelXZrH09rShtERybJme/bAbbe9jcn0RUuCrNDRlUIVgBlLO8U+8jZ4kxDq4JI10SttKswASkpgyBDnByMGleuHDLFJmK0uLSWvvp54P1nTVgghhBBCuCeX9kbRWpcA65puLZRS0UAUUA3k6g77xgghhBDCmaaGhLRJmN0w1L5gXPSI/Xplp0+7Jo4BqN5YT0V9BaF+oU79vuvX927Nsq48/7wBeNEh50pISCAtLa3NumIP7n6QlQdW9vn8S8YscfrvXXQgKgqtFMr6T6qCAkmYiX73w5gYfnboEFUmE2DJx79eUMCvk5JcG5gQQgghhBAdcMvFBLTWxUCxq+MQQgjRv+pO1FF7qJaI+RFd7yzcwtSQEN4rbn2L3lpZ6cJoBojwcNtt6/aMolu01qw/tp6/f/N3m/Fj5ceIfDSSJWOXcPvU25k3fB5K9f86Z88+219nvgwIAbr3/y4kJITRo0eTlpbG6NGjW26pqakEBQW1e8ydgXc6JGF2+7Tb+3wO4SBeXjSEhOBXUdE6VlTkunjEoBHk5cUVMTG8bNVq+MVTp7hn2DAMTngtFkIIIYQQoqfcMmEmhBBiYKvaVcWJv5+g8K1CfKJ9mHF8BgZfB/QAE/1uWqhtxcjOqioazWZ8DPLv12tSYdYnmacyWbpyKXuL2i/pMmkTK/atYMW+FYyPGc/yJcvJGJrRb/FUVMDKlZrWZXgdyRtIAPa3jHh5eZGSkmKTEGtOkA0ZMqTHCcJ5w+cxPmZ8h7/P7kiPTWdu8txeHy8cryE0VBJmwiVuGDLEJmF2qLaWz0pLuTAqyoVRCSGEEEII0T5JmAkhhHCqmkM1fD/x+5bthvwGilcWE3ulLALvCSbbrVfWoDWHamsZ10G1iugGSZj12toja1nyzhKqG7u3WNjeor3MeXkOK69cyYKRC/r0vc1mMzk5OWRlZbF///6W2+7dJkymr/p07s5ccsl1nH22T0tybMSIEfj6+jrs/Eopli9ZzpyX53T792otyCeIVxe/6pRKPtF9deHhhJw82TqQm+u6YMSgMissjAlBQey2WtTxyZMnJWEmhBBCCCHckiTMhBBCOFVgaiBhs8Mo31TeMpb7r1xJmHmISB8f4n19yWtoaBnbU10tCbO+kJaMvZJ5KrNHybJm1Y3VLHlnCRtv2NitSjOj0cjRo0fJyspi37597Nu3j6ysLLKysqipqWnniGk9iqenHnnkAcaO7ddvQcbQDFZeubLHv98gnyBWXrmyXyv4RO/UxsTYDuTkuCYQMegopfh5QgI3HTzYMvbZ6dPsr65mjHx2EEIIIYQQbkYSZkIIIZwu/vZ4m4RZ+YZyqvdWEzReJk48QXpQkE3CbG91z6tQhBWpMOsxrTVLVy7tVQUUWJJmy1YtY9etu1oqoerr6zl06FCbxNiBAwdosHq+d62qVzF1h7c3JCT02+ltLBi5gI03bOy03aW19Nh0Xl38qiTL3FSbhNnx464JRAxK18TF8ZujRyk1GlvGns7N5Zm0NBdGJYQQQgghRFuSMBNCCOF0MZfFcDj2MI2FjS1juf/KJe2fMnHiCcYHBbHGKqmzRxJmfSMVZj22/tj6Pq2xBbCncA8/fvDHVO+tJisri8OHD2MymRwQ3UnASH98zF6yBOyWEexXGUMz2H3bbjYc38AdH9/BvuJ9No97G7xZMmYJt0+7nbnJc6UNoxurkQoz4UKBXl7cHB/PI1bPu5fy83kwOZkhfn4ujEwIIYQQQghbBlcHIIQQYvAx+BoYetNQm7GC5QUYK40dHCHcSbpdCyWpMOsjqTDrsWe/f9Yh53nj4BusWrWKAwcO9DpZFhYWxowZM7jhhht49NFH+eCD1zn//FqHxGfv9tv75bSdUkoxb/g8rplwjc34/OHzKfl1Cf+94r/MGz5PkmVuTirMhKvdHh+Pt9XrRJ3ZzOPW6+oJIYQQQgjhBqTCTAghhEvE3xxPzl9zwGzZNlWaKHy7kPib4l0bmOiSfcLsUG0tdSYT/l5eLorIw9lXmJWXg9YgCYh2VdRXsDJrpWNONhbwA+q73jU2NpaxY8cybtw4xo0b13J/yJAhbZJFISHw2WeOCbFZejrMnevYc/aEr5evzbaftx+hfk4sdxN90qbC7PRpqKy0PFmFcIJh/v4sjYvjpfz8lrFnc3P59bBhRPv6dnKkEEIIIYQQziMJMyGEEC7hn+RP1KIoSj4saRnLfzVfEmYeYFxgoM22GUvSbEJwsGsC8nR2v0+0hoYGkDZVNmpqasjKyuKzzM8waUe0TsTSayEEm4RZYmJim8TY2LFjiY6O7vZp582D8eNhb9+6RrYICoJXX3VtDtU+YdZg6sm6bsLVatt7/h4/bsnECuEk9yUl8Up+fvO1UlSbzTxx8iQPjRjh0riEEEIIIYRoJgkzIYQQLjP0J0NtEmYVX1dQc7iGwFGBnRwlXC3Y25t4X1/yGlonzI9Iwqz3/P3bjtXVDdqEWUNDAwcOHGDPnj3s3buXPXv2sGfPHo4ePYrWGhKAmxz3/a776XWcO+bclsRYqAMWCVMKli+HOXOgrx1Lg4Jg5UrIyOhzWH1inzBrNDd2sKdwR2Y/P+rCwvAvL28dzM6WhJlwqlGBgVwTF8frBQUtY0/l5nJXYqJUmQkhhBBCCLcgCTMhhBAuE3lhJD7RPjQWt068FrxaQMqfU1wYleiOkQEBtgmzujoXRuPhOkqYhYU5PxYnMhqNHDlyxCYptnfvXg4ePIjR2Ml6hg4ubLr/V/czNmasY0+KJcG1ciUsWdL7pFl6uqWyzNXJMgAfLx+bbakw8zzV8fG2CbMDB+Dii10XkBiUHkhK4o2CAnTTdqXJxEPHj/NEaqpL4xJCCCGEEAIkYSaEEMKFDL4GYq+NJffJ3Jax/OX5DP/jcJRB1m9yZyP8/dlkNfF6pLbWhdF4uICAtmMD6PdpNpvJycmxSYrt2bOHrKws6uu7sXiYvQosfUANfY/N2+BNQmhC30/UgQULYONGWLq0++0Zvbzgssvg9tsta5a5y1J20pLR81UlJBCVldU6sH+/64IRg9aYoCCuiY3ljcLClrFn8/L4eWIiI9p7PxRCCCGEEMKJJGEmhBDCpYYsG2KTMKvPqadsfRkR8yNcGJXoyki7SS1JmPVBe60XPbRi7/Tp0+zevZtdu3a1fN2zZw9VVVUOOb9SipHDRlJTXkNeRF6fz7dkzBJC/fregrEzGRmwezds2ADLlkFOTuf7l5aCA7pCOpwkzDxfVYJdcvjAAdcEIga9h1JSeLeoiAZtqTNr1JrfZWfz5rhxLo5MCCGEI2zbts0ATAUuAUYCoYCbXAYmhPBgGssltEeAD4Hvp0yZYu78kJ6ThJkQQgiXCp4UTNCEIKp3t/YsK3y7UBJmbk4SZg7k7W25WbchdPOEWWNjIwcPHmTXrl02ybETJ0447HskJSUxfvx40tPTSU9PZ/z48YwdO5bAwEDWZa9j/vL5ff4et0+73QGRdk0pmDfPsqbZ66+3js+Ykcu339omMdwxWQaSMBsIKiVhJtzE8IAAfpaQwOMnT7aMvVVYyN2JiUx11xdBIYQQ3bJt27YxwN+AeKWUr5eXVxDg5eKwhBADh8lkMo3XWl8A5G3btu3eKVOmOPQPG0mYCSGEcCmlFHE/juPor4+2jBWvLCb12VQM3g7ouSb6hX3C7Hh9PUazGW+D/Jv1ip+fbcKsuNh1sVjRWlNQUNCSGGtOju3bt4+GBsckTOLi4mySYunp6YwbN46wTtZwmzd8HuNjxrO3qJt9DtuRHpvO3OS5vT7eEYKCOlmrzc34GGzXMGs0NXawp3BXbSrMioqgpASiolwTkBjU7k9O5j+nTlFuMrWM3Xn4MF9PnozBXXrRCiGE6JFt27bFAv8wGAzDvL29hxgMhgCllDYYDCallO7yBEII0QmttTKbzV7e3t7KbDbXGo1Gf7PZ/MS2bduWTZkypbDrM3SPJMyEEEK4XMwPY2wSZo3FjZStLyPyvEgXRiU6M9Lf32bbqDUn6utJkfVHuk9rWL8ennkGampsH7vgAliyxLKQ1bx5TlnIqra2ln379rWpGisqKnLI+SMiItokxsaPH090dHSPz6WUYvmS5cx5eQ7VjdVdH2AnyCeIVxe/ipJJ2W6TCjPPVxMXh/bxQTVaJTv374ezz3ZdUGLQivLx4f7kZH5ztPXz37cVFbycn89Phw51YWRCCCF6Y9u2bQp4XCkV7+PjM8zf398YHR19MjQ0tMpgMEiyTAjhEGazWVVUVAQXFxdH19XVDWtoaDBqrR/ftm3bj6dMmeKQ1xpJmAkhhHC5gJQAQqaGUPl9ZctY0btFkjBzY1E+PgQaDNSYW9tF50rCrPsyM2HpUtjbQYWUyQQrVlhu48fD8uWWxbAcQGtNTk4OO3futEmOHTx4ELO57+2/fX19GTt2LGeccQZnnHEGEyZMYMKECQwdOtShCaqMoRmsvHIlS95Z0qOkWZBPECuvXEnGUMf8PgcLSZh5Pu3tjR41CpWV1Tq4a5ckzITL/CIxkRdPneKQVVvn3x49ypLoaCJ9fDo5UgghhBsaBYzx8fEZ4uvrq5OTk3N8fHxMXR4lhBA9YDAYdHh4eGVQUFBNdnZ2itZ6aENDgxHLa9AhR3wPSZgJIYRwCzFXxNgkzIrfLyb1GWnL6K6UUsT7+XHYapLrlINa9A14a9daqsequ5nk2bvXsvjVypWwYEGPvlVDQwNZWVns2LHD5lZWVtbzuNuRmJjYkhhrvqWlpeHjpInOBSMXsPGGjSxdubRb7RnTY9N5dfGrkizrBUmYDQzmM87AYJ0w27HDZbEI4Wcw8HRqKhfs2tUyVtzYyAPZ2fwrLc2FkQkhhOiFswFlMBiCIiIiCiRZJoToTz4+PqaIiIiSwsLCOEABZyEJMyGEEANJzBUxHP2NbVvG8o3lRMyPcGFUojPxvr42CbM8SZh1LTOzZ8myZtXVluM2buyw0qysrIydO3faJMb27t1LY2Pf15oKCgoiPT3dJjE2YcIEIiJc//8zY2gGu2/bzYbjG7jvi/v49uS3No97G7xZMmYJt0+7nbnJc6UNYy/ZJ8wazbKGmSfSZ5wB77zTOiAJM+Fi50dGcnl0NO9Zrd35fF4e18bGMis83HWBCSGE6KkzDQZDEKBCQkKqXB2MEGLgCwkJqS4sLFQGgyHIbDafCbzqiPM6NWGmlNoKLAfe1lo7ZkEMIYQQA0JASgDBU4Kp2tb62brkoxJJmLmxeD8/m+28+noXReIhtLa0YexpsqxZdTUsW4beuZOcEyfaVI0dO3aszyEqpRg5cmSbqrGUlBQMBvet9lRKMW/4PO6ZeQ8/fPeHLePDw4az87adhPqFujC6gcHHy7ZqUCrMPJN54kTbgV27wGgEb7mOUrjO46NG8WlpaUubZw389MABdkydSoCXl2uDE0II0V0xBoPB12AwmP39/eXKKiFEv/P3928wGAxmg8HgazabYxx1Xmf/ZTQFyAD+n1JqLfA6sFJrXefkOIQQQrih6IujbRNmH5cw6vFRLoxIdCbe17biRCrMurB+fcdrlnXXnj1cHBrKx71NulkJDw9n4sSJNomx8ePHExQU1Odzuws/bz9JljmIfYWZ0WzErM0YlPsmUkVb5jPOsB2oq4ODB2HcONcEJASQ5O/PH4cP596jrZ0GDtbW8n/HjvHYyJEujEwIIUQPBAIGg8HQ90WRhRCim5RSZsAAOGwiw9kJsxwgqen7XtB0q1JKvQ+8rrX+wsnxCCGEcCORP4jk2B+OtWzXHqyl5nANKkFaqLkjqTDroWefdchpllZX83EPj0lJSWHSpEk2t2HDhkl7QtFt9gkzsFSZ+Xv7uyAa0WvR0ZCQALm5rWM7dkjCTLjcL4cNY0VREd9Vtq5n+/iJE1weHc2MsDAXRiaEEKKHtKsDEEIMHkoph7/mODVhprUerpQ6G7gWuAKIAkKApcBSpdQp4A3gDa31ro7PJIQQYiAKyQjBJ86HxoLWDg6ln5QSdVOUC6MSHbGvMMuVhFnHKipg5UqHnOoyLB+eKtt5zMfHh/T0dJvE2BlnnEH4IFkHRsvf5/0mwDugzVhtY60kzDzRpEm2CbPvv4drrnFZOEIAeCnFy2PGMPn776nXltdyM7Bs/34yp04lSFozCiGEEEIIJ3B6s3qt9dfA10qpn2OpMLsGuARL6W48cA9wj1JqD5b1zt7SWuc5O04hhBDOpwyKqAujyH8lv2Ws5OMSSZi5qTi7hFlxo7Sqb09JSQkHV61ipsnkkPN5AwlAfni4TWJs8uTJjBkzBl/ftpVAg4XJbPs79jLIBKujBPm27XBR3VhNRICsM+lxpk2Dj63qVDdvdl0sQlgZGxTEH4YP577s7Jaxg7W13HX4MC+MHu3CyIQQQgghxGDhstWdtdZG4CPgI6VUELAES+XZeYAXMAH4G/CIUmo98Brwvta6qv0zCiGEGAgifxBpkzArW1+GqdoxiQbhWBHeth8jyoxGtNaDus1fYWEh27ZtIzMzk8zMTLZt28bx48eZBmxx4Pf5ctUqhlxyyaD+Xben0WybtG2vjaDonSCfdhJmDX1fS0+4wFln2W5nZlrWMvOXakHhevcMG8b7xcVstWrN+OKpU5wfEcEPY2NdGJkQQgghhBgMXJYws6a1rgZeB15XSkUBi5puC7F0HZrfdHtWKbUSeFlr/aWz41RKhQN7sVTCvaq1vr6TfRVwNfATYDKWhedOARuBf2qtt/Z3vEII4YkiF0RaLptoypHpBk3Vd3KthDuyT5iZgEqTiVBvt/h40a+01pw6dapNcizXus2ZFUc/g4empYEky9poMDXYbPsYfFwUycDj4+WDj8HHJilZ3SgJM4905pmW14+mtnc0NFiSZvaJNCFcwNtg4I2xY5n8/fdUm80t4zcdPMi00FCSJbErhBgEZP5RCCFcx+1mtLTWJUqpN4ECoAxYhmXqVGFp23gNcI1S6hDwB631204M7wksb1adUkoFAO8CP7B7aHjT7Vql1ANa60cdHJ8QQng87zBvQqeFUvFtRctYxcYKkHk8txPh0zYZcdpoHJAJs/z8fL7//nu2bt3K999/z7Zt2ygoKOj28ScBIw764OXtDQkJjjjTgNNokgqz/hTkG0RZXVnLtlSYeajQUEhPh927W8e++UYSZsJtpAYG8kxaGtfv398yVmY0cu2+faybNAkfg8GF0QkhhFM8gcw/CiGES7jNjJZSyg+4GLgKS2VZc9+X5sund2LpZnQxMARIA95QSl0GXK217td+XUqpH2BJ3nXHi7S+WWUB/8aSAMwAbgWCsbSazNVav+7oWIUQwtOFzwuXhJkHCPHywgCYrcZONzZ6/NXfVVVVHD58mJ07d7Jjxw62bt3KyZMn+3TOBj8/NgQGcu7p030PcMkSy4S3aCPEL4Qx0WNoNDXSYGpgSPAQV4c0oAT52CXMpMLMc82caZswk3XMhJtZGhfHZ6WlvFVY2DL2dUUFvz56lH+MGuXCyIQQon/J/KMQA88bb7wR9vrrr0ft2rUrqLi42CcoKMiUnJxcv2jRotO//OUviyIjI81dn6V7jEYj27dv99+8eXPQtm3bAnfu3Bl04MCBgLq6OgPAZZddVvLee+8d6+75SktLDQ888MDQ//3vf5GFhYU+kZGRxvPPP//0ww8/fCohIcHoqLjdiUsTZkopL2ABltLhxVheyKE1SXYKeBNYrrXe3XTMbcBFWNY3GwNcDvwO+GM/xhmO5U2nO/suwFIFB/Al8AOtdV3T9ltKqZeATUAU8IRS6gOtdWU7pxJCiEErfF44OY/ktGxXf18NdYBn52EGHINShHt7U2ps/Yx02uhZn5eqq6vJzMxsqRzbsmULR44c6dM5AwMDmTx5MhkZGWRkZDBlyhTGjBmDz1dfwfz5fQ/69tv7fo4B6poJ13DNhGu63lH0SpCv7TpmUmHmwc46C/5t9efNN99YWjRKq1fhJpRS/CstjW8rKsiuq2sZf+LkSaaHhHB1XJwLoxNCiP4h849CDCzl5eWGyy67LOXLL78Mtx5vaGjwPn36tPeOHTuCXnzxxdjXX3/96LnnnuuQP65+8IMfjFyzZk14lzt2Q35+vtesWbPGHDlypGU2rqCgwGf58uWxa9asCd+4ceOB1NTUhs7O4YlckjBTSs3C8qL+Qywv3NCaJKsFVgHLgbVaa5sMa9P2R0qpbcARLNOny+jHhBmtpdBlQHgX+97T9NUI3Gj1ZgWA1jpLKXUnlkRgFHAT8LgDYxVCCI8XenYoyluhjZb1VbRR473fG+Mkz0rGDAYRHpQwq6+vZ9euXWzdurUlQbZv3z7M5t5fzBUaGsrkyZOZMmVKS4IsLS0NLy+vtjvPmwfjx8Pevb3/IdLTYe7c3h8vRB8E+dglzKTCzHPZt1/Mz4eDB2H0aNfEI0Q7wry9+e+4cczavp365jX3gBsPHCA9KIgJwcGdHC2EEB7pCWT+UYgBwWg0cvHFF4/ctGlTKEBUVJTx2muvLRo3blxdaWmp14oVKyIzMzOD8/PzfS+77LLUdevW7c/IyKjr6rxdMZlsm/CFhYWZwsPDjcePH/fr6bnuuOOOYUeOHPEPCwszXX/99YVjx46ty87O9n3ppZdi8/PzfZctWzb8m2++OdjXmN2NUxNmSqm/AVcCic1DTV81sAFLkuxdrXVVV+fSWp9SSu0CpmNp0dgv7Eqh78FS7tzRvpHAeU2bq7XW2R3s+g6WN8FY4ArkDUsIIWx4B3sTMi2Eis2tbRm99nhJwswNRfj4gNWV32VukjAzGo1kZWW1JMe2bt3Krl27aGxs7PrgDgQHBzNlyhSmTZvGlClTmDJlCiNHjsTQ3bVUlILly2HOHKjuRaIhKAhefVUqQITLSIXZADJqFMTHQ15e69gXX0jCTLidqaGhPJuWxk8PHGgZqzGbuWzvXrZkZLS7nqoQQngimX8UYmD5xz/+Ed2cLBs5cmTdunXrDgwbNqxlwuS+++4ruvnmmxNfeOGFuIqKCq+bb745+fvvvz/Q8Rm7Z+rUqdWjR4+umzp1as3MmTOrx4wZ0/DUU09F/eIXvxjek/PU1dWpjz/+ONLX11evW7du/+TJk1smfm655ZaSjIyMcZs3bw7Jzs72SUlJ6f1EixtydoXZPViSY80zPQeB14DXtNY5HR7VsebM6CEHxNaGXSn0v4EvujhkNtA8a9bhvlprs1JqA5Y3qzOVUhFaawcsaiKEEANH+Nxwm4SZ9wFv6ql3YUSiPU+PGkVxYyPlRiNGIDUggAqjkVBv537EOHnyJN9++y3fffcd3333Hdu2baOmpqbX5/Px8WHSpEmceeaZTJs2jWnTpnVcOdYTGRmwcqVlHbKeJM2CgizHZWT07fsL0QdSYTaAKAXnnguvvdY69sUX0vJVuKWfDB3KdxUV/PvUqZaxw7W1/HDvXlafcQY+3b1wRQgh3JTMPwpnqaurU08//XTUypUrI/fv3x9QWVnpFR0d3XjOOedU/PWvf81LTk5uBCgpKfEaPnz4hKqqKq8///nPJ373u98VdnVu0cpoNPLYY4/FN2+/8sor2dbJsmbPPvvsyU2bNoXu378/YNu2bcHvv/9+6GWXXVZhv19PPPLII/l9Ob5Zfn6+d319vRozZkytdbIMICUlpXHKlClVX3zxRfiRI0d8JWHWd6exXOGwXGv9XR/P9RcgHzjc56ja9wSWUugTwL1AZBf7p1vd39PFvvuavipgArCxF/EJIcSAFXJmiM2212EvcNgyqKKvtNasLyvjmdxcVhUXY1307wUsiYnh9vh45oWHoxxcEVVVVcW2bdtsEmR51lUSPeTl5cWECROYOnUqEydOpLGxkaSkJC666CICAgIcGHmTBQtg40ZYurR77RnT0y2VZZIsEy4mFWYDzPz5tgmzdevAbAZJPgg39FRqKturqtha2br8zpdlZdx28CAvjB7t8M8aQgjhZE8g84+in61ZsyboJz/5yYjc3Fxf6/H8/Hzft956K3r9+vWh27dv3xcXF2d6/PHHY6qqqryioqKMd911V5GrYvZUn376aUhRUZEPwLRp06pmzZrV7tW83t7e3HrrrQV33XXXcIA333wzsq8JM0eJjY01ent76+zsbP/du3f7TZgwoeUK9ry8PO+dO3cGASQlJQ2oZBk4P2F2GfCx1tohv0it9QpHnKc9dqXQt2itK5pKnjsz3Or+sS72PWF1P7ln0QkhxMAXemaozbaqVRhyZRLPHWRWVrI0K4u9HVRwmYAVRUWsKCpifGAgy8eOJSMkpN19u2I2m8nKyuK7775rSZDt2bOn1+uOKaUYPXo0U6dObakcmzRpUktirLa2ljVr1vTq3D2SkQG7d8OGDfDMM7DC7iONlxdcdpml2mPuXGnDKNyCVJgNMOeea7t9+jTs2CHJeeGW/AwG3hs/numZmeQ3tK4t/5/8fEYHBnJvUpILoxNCiN6T+UfhDC+++GLEbbfdlmI0GpVSitmzZ5dPnTq1urCw0OeDDz6IrKys9Dp16pTvI488Evfwww+f+ve//x0LcOutt+YHBwfrrs4vbH388cdhzfcXLlxY1tm+S5YsKb/rrrsAWL9+fVhn+zpTYGCgPu+888pWr14dMWfOnDHXX3990ZgxY+pycnJ8X3755Zji4mKfjIyMqrS0tIauz+ZZnJow01qvcub36y27UujlWutPu3lojNX94i72LbW6H9XN8wOglErsYpfY5jv19fXU1tb25PRCdFud1bpF1veFcIhw8E3wpSG39b3X66CXPNdc7Ivycq46eJDqbias9tbUMGf7dt5OS+PcsK4/+xUUFNisO7Zt2zYqra4m76nk5OSW9cYyMjKYPHkyoaGhbfZrfq90+uvamWfCmWfi/7//oepbW47WffQReu7c5kD6Pw7hFCaTD119/Hbnz21+Btt1ostry906XmHR4etadDR+o0ZhONzarKNx9WqMY8c6MzwxgPT3e2g08G5qKguzsqi1+hzym6NHSfTyYnFkV/PLYqCQv0OFs9TX2ywJENvVfJzW+mRPzu/u849iYNi4cWPgHXfckWI0GpWvr69evnz5kSuvvLK8+fGtW7cWzpw5c7zJZGL9+vWhzz77bENxcbFPWFiY6d5775Xqsl7Yt29fS6uaM888s9O1IpKSkoxDhgxpyM/P9y0pKfHOy8vzjo+Pd4vF4Z999tkTs2bNCsrLy/N94oknhlo/FhMT07h8+fJjLgqtX7miJWOLpismLgBmAHFAKJYX+jxgA/C51toVWconsJRC5wN39eC4QKv7XX1qs348sMO92nei610stm/fTnVP1kgRopc2bpSqfuF4gcMC8cltXczd65CXPNdc6IjBwAPBwdT1sNqp2mzmiqws/lJVxUirCa6GhgaOHj3KwYMHW26Fhb1vjR4UFERaWhppaWmkpqaSmppKmFWSrr6+nm+//bbb53Pac01rLmmw/biz+cABKuplzb6BJi8vAxjW6T5OqXDspaJc27+XDx075NbxirbsX9fOGDWKFKuEWdlbb/HN+PHODksMQP35HvoLb28eDQxEN30e0cCygwf5Q3U1402mzg8WA478bSD608GDB603P+7GIT1tC/EE7j3/2K/y8lw7L90dfn7oqCg6fXPpyc8RH49TEyFms5k777wzqaGhQQE89NBDOdbJMoBp06bVnX322eUbN24MO3XqlO/TTz89BODGG28sCAsL63Zrl88++yy4sLDQIf+mM2bMqElNTfXYyqWjR4/6N99PS0vr8g/7xMTEhvz8fF+A3bt3+8fHx1f1Z3zdlZKS0rh169as++67b+iaNWvCi4uLfSIiIowLFiwo++tf/5qXlJTkFok9R3PJC5NSKg7L+mPLaF2k0t49QLVS6m/A35yVOLMrhb6th4thWv8+u/rPYP24279BCCGEKxjTjPh8a5swE66hgScDA3ucLGtWpxSP+/lxzRdfcGD/fg4cOMCRI0cwGnv3+crLy4vhw4e3JMjS0tIYOnQoBg9ce0eZTCht2+XC7OPTwd6iM//J/Q/bK7bja/DF1+DLuZHnsiBqgavDGjD8Df4227VmqS7zdIWTJ5OyenXLdlRWFt5VVRiDg10YlRCdm2E0srSujlet1hltVIq/BAXxUFUVI3rZtlkIIZxJ5h8hIYGJro6hKxdcwOlPP+VoZ/v05OfQmm19j6r7Vq1aFbpr164ggPT09Jpf/epX7VYkjhgxon7jxo0UFBT4AAQHB5t++9vf9uhq1gcffDBh69atDvkQ+eSTTx5LTU0tccS5XKGysrJl8mrIkCFdTnpERES07FNaWupWE1/x8fHGV1999QQ9KODxdE5/oVRKjcVSPRZF11deBAN/BH6klJrbwzeP3sQWTmsp9Du9aCFpPWvgS+dvWtY9bXqaDOz80mQ4g6YrXyZPnszZZ5/dw9ML0T11dXUtV/TNmTMHf3//Lo4QomfKfcrZv3x/y7bXCS9mnz2bgKCATo4S/WFDeTk5+/d3vWMncn19eeyzz2Dnzh4fO2zYMKZNm8b06dPbrDvmSC55XWun5eTZ8+ejk2WJgZ56aeVLnCxq7YTzw0k/ZOHZC10Yka133uk6EbpwofvEa+/g9wd5p+Cdlm3/MH+3jldYdPq6dtZZ6Mcfb2kJazCZWKA1Jvl3Fb3gzPfQBVpjyM7m5aLWytcapXgkIoIvxo9npPxdMqDJ36HCWYKCbNZv/QGwyxHn9aD5R+HhXnrppejm+/fcc8+pji4wjYiIsKmiW7ZsWVF0dLSUbfdSTU1Nyy86MDCwyyt5/P39W/apqKhwq4TZYOTUhJlSKgJYi6X9OFjeaJ4FvgJygBosSbIUYDZwGzAWGA982JQ068/LxZ7AUgpdDNzZi+OtyyX96fwNy/oTXY8uz+2qJ7JSakjzfT8/v36ZUBTCnr+/vzzXhMN5TbX9nKAaFOqUImCCPNec7T9HO72orvsWL+4yYRYcHMy0adM488wzW25Dhw7t9Jj+4LTXtdNtrwfyj4kBeU3tsQa7hgShAaFu9d7k1Y0/fdwpXnsxITE225WNlW4dr2irzetaQACccw5YVZn5fvYZXHedC6ITA4kz3kP/PXYsp7VmVXHrxfKFRiMXHzjA15MnE+/n18nRYqCQv0NFf/KzfR0p7OkaZZ14Ag+YfxSezWQysWHDhlCAkJAQk30rRmtmq+psf39/8wMPPFDQ0++3ZcuWA70KtB9Nnz59tKOq3p5//vmjN998c78W8wj34OwKs7uxvCFo4Gng7nYSYBXATmCnUuo54F/AT4GzgOuBl/ojMLtS6Du11r1Z1LDM6n4k0OELUdPjzWQBRSGEaIdvrC8+sT40Fja2jNXsqSFiQoQLoxp8KoxGVhY56K1q9mwIDIQay7q3SinS09NtkmPjxo3DqzuZhYGioqLtWGio8+MYAGobbecA/L3linNHCvMLs9kuqytzTSDCsRYtskmY8cknYDJ1L8MrhAt5Gwy8NXYsF+7ezfqyspbxY3V1nLtzJ+snTSLO19d1AQohRDtk/lE4y7fffhtQVVXlBTB16tQqf39/3dG+2mqJgGuvvbZo6NChA3JtKmcJDAw0N1eK1dTUGLpaC66urq6lIi00NFQq+1zM2QmzJViSZZu11nd1tbPW2qiUugWYDGRgeUPpl4QZcIXV/beUUm91sf8ypVTzG9wftdZ/AKxXA00Csjs53rqt4vFuRymEEINMUHoQZV+WAaB9NcYS+dzmbAfLyjpf5bgnvLw4e/FiFo4ezVlnncX06dMJHezJIfuEWUAAyBpmvVJntF3zPMDHva44/9GPYPx4y/3GxkYKC0v44gvPab0Z7h9usy0JswFi0SL42c9at0tL4dtvQdq6Cw/g7+XFB+npnLNjB5lVrQUX+2tqmL9jB19K0kwI4X5k/rFJbi4979XvZH5+dJhkauauP8fWrVsDm+9PnDixprN9TSaTAvD19dUPPvhgj6vL3NXixYtL09PTO/3Zu2v8+PF1Xe9lERISYmpOmOXn53uHhYV12g719OnTLTmayMhISZi5mLMTZiObvi7v7gFaa7NS6mUsCbNJ/RGUA+2zuj8ey1ptHWmaLkEDe/stIiGE8HBJv04i5qYYMssyMceaib0w1tUhDXjFxcV8/fXXbNq0iU2bNrGtuhr++U+Hnf/xf/2L6YM9SWbNPmEmv5teqzXaVpgFeLtXwuziiy03gNpaIy+8UObSeHoqzL9thZnWGqW6WpZYuLXkZEhPhz17WsdWrpSEmfAYod7efHrGGczevp2Dta3vA/tqaji3KWkWK0kzIcTAMiDmH+PjGRBXw7rrz3HkyJGWdhujRo3qMNlTV1enVq1aFQkwdOjQhuTk5MaO9u3MZ599FlxYWOiQXMOMGTNqUlNT+7zm3v333++SqsoRI0bU5ebm+gIcPHjQb/To0Z3+LCdPnmz5oDJhwoRuJ+ZE/3B2wqwGy2KUbVe371zzk7s//xp/CljVxT6xwPNN99c1HQOwv+nrZqAR8AHOwbI+WxtKKS9gbtPmTq11Wa8iFkKIQSDy/Ehqa2sxr+nPJSwHL601x48fZ9OmTXz11Vds2rSJrKws252SHVsBEyJtvmyV23VQkYRZr9m3ZHS3CjNPZ19hZtImqhurCfZ1yLIAwpUuvdQ2Yfbf/8Jjj4EkQ4WHiPX15YuJE5m3YwdH6lrnmfbW1HDuzp18OXEiMZI0E0K4B5l/FE5RWlra8od3Zy0BH3vssZjm5I6Xl1eXFXUdefDBBxMctV7Yk08+eSw1NbXEEedyhXHjxtVu2rQpFOC7774LvPjiizvMhZw4ccI7Pz/fFyAyMtIYHx/vlgnYwcTZCbPdwGxgPtBVybG1KU1f93S6Vx9orTOBzM72UUoNt9rM0VqvsjtHmVLqC+AC4BKlVJLWOqedU10FNK+a/t9eBy2EEEL0kNlsZu/evS3VY1999RUnT3axdnVRkcPWs/FWigTbxbOFVJg5jLtXmHk6+4QZQHlduSTMBoIf/Qj+8pfW7RMn4LvvYMYM18UkRA8l+vuzbtIk5u3YwVGrpNme6mrm7djB2okTiZfPIEIIF5P5R+Es1uuSlZeXG9rbp6CgwOvxxx8f2rzdl4SZaHXRRReVP//883EAa9euDXvooYc6bHP5/vvvt7TxOOecczpbj1A4ibMTZk8Bc4DrlVJva62/6OoApdQo4FYspcPtXjHhZv6B5Q3LF0sv4gu01i1ZZKXUOODJps1K4AXnhyiEEGKwqK+v5/vvv2+pHvv6668pKyvr2UlqauCrr2Du3K737cKS6GhCvZ398cPN2SfMwsLa3090yb7CzN/bv4M9RW+E+rVN5pbVlZEQmuCCaIRDTZgAY8bA/v2tY+++Kwkz4XGGWSXNsq2SZvtqapi1fTufT5zIiAC5mEIIMSDI/KPoVGJiYktrxW+++Sb4jjvuKLXf54477hhWVlbW8gd681pmvbFly5YDvT12oLnooosqo6OjG4uLi322bNkS8tVXXwXOmjWrzVpqRqOR5557Lq55++qrr27zbyScr93scn/RWr8P/A3wAj5USt2tlOrw06pS6gdYSo+DgXe01q87J9Le01qvAd5r2jwL2KmUulcpdZVS6lHgWyCq6fF7tdbFrohTCCHEwFRZWcnq1at54IEHmDNnDmFhYcyaNYvf/va3fPzxx91OlgUGBjJ//nx+//vf8/nnn/PxTTc5JL7b4+Mdcp4BRSrMHKZNhZm0ZHQob4M3Ib4hNmPl9XIR5ICgFFxxhe3Yu++CWdohC8+T1JQ0G+5ve9FEdl0ds7ZvZ291tYsiE0IIx5H5R9GVM888s+UN77333ov++uuvbf44uv/++4e89957UQDBwcEmgJycHL/Dhw/7ODfSgcfb25t77rnnVPP2DTfckJKbm9vmyuE77rgjcf/+/QEAGRkZVZdffnmF/T7Npk+fPlopNUUpNeWpp56K6mg/0XdOvcRbKfUnoAE4AIwGHgP+oJT6BjiI5YoHPyARmNn0VQFmIEEp9WUHp9Za63P7OfyeWAqEAAuBFCxJQmsa+KPW+nn7A4UQQoieqKio4KuvvmLDhg2sX7+ebdu2YTKZenye6OhoZs2axezZs5k1axaTJ0/Gx6f1c7LWmvEnT7K3ps1FUd2WHhTE3PDwXh8/YMkaZg5hMpuoabR9ftond0TfhfuHU9nQ2oL/dO1pF0YjHOpHP4I//7l1+8QJ+PZbOOss18UkRC8l+/uzYdIkztu5k0O1rRdTnGpoYM727aw+4wymyfutEMLzyfyj6NCll15aER8f35CXl+dbX1+vFixYMObSSy8tjY+Pb9iwYUPotm3bggEmTpxYvXjx4tN//OMfE41Go1q0aFHqFVdcUbJw4cLK2bNn934CYJC7++67iz788MPwb775JvTw4cP+kyZNGnfttdcWjxs3rra0tNT73XffjczMzAwGCAkJMT3//PPHHfF99+/f7/vss89GW4/t2bMn0Pr+z3/+c5srmc8777zKSy65pMN11gYbZ/dE+h2WF2usvgYDC5pu7dFYkmazOnhcWZ3LLWita5RSFwDXAMuASUA4UAx8BTyptf7aZQEKIYTwWOXl5Xz11VesX7+e9evXk5mZibkXFQApKSktCbLZs2czevRolOq4+4JSiuVjxzJn+3aqe/H9ggwGXh0zptPvMWgVFdluR8nFYr1R3di2YkDW1nK8MP8wTlScaNmWCrMBZPx4GDsWsrJax157TRJmwmMl+fuzafJkzt+5k51WVWWlRiPzd+7kvfHjWRgZ6cIIhRCib2T+UXTG29ubV1555ejixYvTampqDLW1tYa3337bJpGSnp5e88knnxyuqqoyPProo/F1dXWGAwcOBDz00EOJY8aMOeyq2AcCHx8fPvrooyOXX375iHXr1oUVFxf7PPnkk0Pt94uLi2t87bXXjkydOrWuvfP01OHDh/2efvrpNt+n2cGDBwMOHjxoU23o7e2NJMxauWIRkfZmyjxi9kxrfYxuxqotKyu+0XQTQggheqWsrIxNmzaxfv16NmzYwPbt23ucIFNKMWHChJbk2KxZs0hI6PmaQxkhIaxMT2fJnj09SpoFGQysTE8nI0SqfdpVWGi7HRvrmjg8XFVDVZuxED/3es5lZ0NpU1f6ujpFWZmfawPqhXD/cJvt0lppsz9gKAVXXw3/93+tY2+/Df/4B/jLeoDCM8X5+rJu0iR+sHs3m61aIFeZTFy0axf/Hj2anwztcE5JCCFcQuYfhaMsWLCgeuvWrXv/8pe/DNm0aVNoUVGRj9FoVDExMY3nnntu+TPPPHMyNDTUDPDuu+8evvvuu4cdOXIkwGw2M23atNquzi86FxERYf7yyy8Pv/766+GvvfZa1K5duwJLS0t9AgMDTUlJSfUXX3zx6V/+8pfFUVFRPW8TJPqNUxNmWmunrpkmhBBiYKjZXYPfW36oMsXB/xwkYFgAac+kuTqsflFaWmqTINuxYweWv4G6z8fHh+nTp7ckyM466yzCHdQKcUFkJBsnT2ZpVla32jOmBwXx6pgxkizrjH2FWUyMa+LwcJX1bS+Ic7cKs//7P3i9ZUVef847L7HNPlpb8hbuKibQ9vlZXCPLYQwoS5faJszKyuDDDy3tGoXwUBE+PqydOJHL9uxhzenWNrIm4KcHDnC0tpY/p6RIFbwQQogBady4cQ1vvPFGTlf7LVq0qHLRokX7nBHTYHPdddeVXXfddWV9OceWLVsOdGe/RYsWVWqtt/Xlew12rqgwE0IIIXqkdn8t/u9Yrm4/zWnqRjukUt0tnD59mg0bNrBu3To2bNjArl27epwg8/X15cwzz2TevHnMnTuXmTNnEhgY2PWBvZQREsLuadPYUFbGD/fupcRotHncWymWREdze3w8c8PDZQKqK5Iwcwj7CjNfL198vXxdFE3vuXvCLDrQposLRdVFHewpPFJyMsyfD19aLR39yiuSMBMeL8jLiw8nTGBZVhbv2L3v/iUnh2N1dfxnzBj8DHKNrxBCCCHEYCYJMyGEEG7PJ87HZruxoNFFkfRddXU1X3/9NV988QVffvllr9Yg8/PzY8aMGS0JshkzZhAQEND1gQ6klGJeRAQJfn42CbNHUlK4LSGBUG/5iNEtWrdtySgJs16xT5i5W3UZWP65rSnVNjnew3y507WpMKuVCrMB5/rrbRNmn30GeXkQH9/hIUJ4Aj+DgTfHjSP56FH+duKEzWNvFBZysr6ed8ePJ8bX8y62EEIIIYQQjuHS2SylVAawGJgOxAFBQA2QC+wCPpLFKYUQQtgnzIxlRkx1Jrz8vVwUUfc1NDSwZcuWlgTZ5s2baWzsWcLP39+fmTNnMnfuXObNm8eZZ56Jv5usJ1Npsm21PSYoSJJlPVFdDXV2FZOyhlmvVDbYtmT01ISZ2QxebvzSFhNkmzCTCrMB6LLL4PbboaopCW02w2uvwW9+49q4hHAAg1I8OnIkKf7+3HHoENaXLG0oL2fatm18MGECE4Pd7z1ECCGEEEL0P5fMaCmlhgAvAwvtHwI0MAG4APi1UupL4Hqtda5zoxRCCOEu7BNmYKky80p2v1llk8nEzp07WxJkmzZtorq6ukfnCAgI4KyzzmpJkE2fPh0/P79+irhv7BNmIe480++O7NsxglSY9ZJ9hVmIr/utm9ed6jF3rzCzb8koa5gNQEFBcMUV8PLLrWMvvAD33gvSrk4MELcmJJDk78+P9u6l2qrS/3h9PWdlZvLKmDFcIRewCCGEEEIMOk5PmCmlUoCvsVSUWa/QcBpLdVkwEGY1Ph/IVEpN11ofd1qgQggh3IZXmBfaR6MaW982GvIb8E92fZWV1poDBw60JMjWrVvHaasF5bvD39+fs846i/nz5zN37lymTZvmtgkye5V265eFSsKsZ+zbMfr6Qoj7JXo8gSe2ZGwv99DDDq1OZ9+SsahGKswGpJ/+1DZhduSIpTXjhRe6LiYhHOyiqCg2Tp7MJbt3k9vQ0DJeYzbzo337eKCqij+lpGBw54UlhRBCCCGEQzk1YaaUMgAfAEOahrYAfwO+1FqXWe0XDZwL3A1MA2KA95RSZ2qtbS9lF0IIMeAppdDhGlVklTAraOjkiP6Vm5vL2rVrW5JkeXl5PTrey8uL6dOnc+655zJ//nxmzpzpNi0We6LBbKbeLgMQIu0Ye6a99ctkYq5XKuttWzKG+Llf4nEgrGHWXoWZ1holz9uB5ayz4IwzYNeu1rFnn5WEmRhwMkJC+H7KFC7fu5dvKipsHvtLTg47q6tZPmYMET5tux0IIYQQQoiBx9mzWkuBdCxtF58Hfqa1bnMdrda6GHhHKfUu8AxwCzAZuBR433nhCiGEcBc6SINVIYOp0nnXT1RXV7Nx40bWrFnD2rVr2bt3b4/PMXHixJYE2Zw5cwgZAFVExe2sxRYlCbOeOXnSdjsx0TVxDACeWGHWXo7J7SvM7NYwM5qNlNeXE+4f7pqARP9QCu64A265pXXs44/h2DEYPtxVUQnRL4b4+fHlpEn87NAhXjx1yuaxj0pKyNi2jf+OG8e00FAXRSiEEEIIIZzF2bNaP2r6upcOkmXWtNZmpdQdwNnAeOB6JGEmhBCDkg6wnWmuPliNscKId6jj38rMZjM7duxgzZo1rFmzhq+//pqGhp5VtKWmprYkyM455xyio6O7PsjDFNr9TgxApFyB3TOSMHOYygbbCjPPSJh5foUZWKrMJGE2AF17rWXdsuaqG63huefgkUdcG5cQ/cDPYODfaWlMDg7mF4cPY7R6MT5WV8fZ27fz+MiR3JGQIBW1QgghhBADmLMTZpOwVJct7ypZ1qwpafYq8BiWpJkQQohBRGtN+YZyDKdsF/vJ+VMOOX/JIWZJDPG3xxM+L7xPExgnT55k7dq1rFmzhs8//5zi4uIeHZ+QkNCSIJs/fz7Dhg3rdSyeotCuwizGx0fW+eipEydstyVh1msV9battEJ83a+KcyAkzAJ9Agn0CaSmsaZlrKi6iFGRo1wYlegXQUFw/fXw1FOtYy++CP/3fxAY6LKwhOgvSiluT0hgXGAgV+zbZ1NJ36g1dx4+zMbycl4cPZpQqagXQgghhBiQnP0pL6rp67EeHpfT9DXecaEIIYRwd5WZlWQtzaJmbw0GDG13MEHRiiKKVhQROD6QscvHEpLRvUny6upqNmzY0FJFlpWV1aPYwsLCOPfccznvvPM499xzSU1NHXRXHNtXmMX6+rooEg8mFWYOU1ZXZrMd4R/hmkA6MRBaMoKlyiynPKdlu6imqJO9hUe7/XbbhFlJCbzyimVciAFqXkQEO6ZO5ap9+/iqvNzmsXeLitheVcXb48YxZQC01xZCCCGEELacnTCrACKBoT08bkjT18pO9xJCCDFglK4tZc+SPZiruzd7XLO3hu1ztpO+Mp3IBZFtHtdas3PnTj799NOWNouN7azB1REvLy9mzpzJggULWLhwIVOnTsV7kF9dbF9hFivtGHvOPmE2CCoT+8vputM22xEBkjDrLzGBMTYJs8LqQhdGI/rV6NFwwQWwenXr2OOPW9Y28/JyXVxC9LMEPz++nDiR32Vn8ze7avDDtbXMyMzkz8OHc29SEl6D7IIpIYQQQoiBzNkzfbuBecCVwNM9OO6qpq/7HB2QEEII91OZWdmjZFkzc7WZPUv2MHnjZEIyQjh9+jRr165l9erVrF69mlN2C7l3JTU1lYULF7JgwQLOOeccQmWxdxtSYdZHWktLRgeyrzBzxzW1BkJLRoAhwUNstk9V9uy1VXiYe++1TZgdOQIrV8IPf+i6mIRwAh+DgUdHjmR2WBhL9+/ntNHY8phRa+7LzubT0lJeGzuWJH9/F0YqhBBCCCEcxdkJsw+wJMxmKqXu1Vo/1tUBSql7gZlY1j77oH/DE0II4Wpaa7KWZvU4WdbMXG1m40Ub+evIv/Ltd99iMpm6fWx4eDjnnXdeS5Js+PDhvYphsJAKsz4qLYW6OtsxSZj12ula2wozz0iYdb2POxoabNssIq8yz0WRCKc45xyYMgW2bWsd+9vf4PLL238SCzHALIqOZvvUqVy5dy/fVdo2vdlYXs4ZW7fyr7Q0ro6Lc1GEQgghhBDCUdpZEKZfvQDkNt1/RCn1hlJqUns7KqUmKaXeBB7BkizLB553SpRCCCFcpmx9GTV7a/p0jqCCIKq+qeoyWebt7c3s2bP585//zLfffktxcTHvvvsuN910kyTLuuFkfb3NdpxUmPXM8eO220rB0J52rRbNPHENM4OhbXbME1oyxofYLit8qkoqzAY0pSxVZta2boUNG1wTjxAukOzvz6bJk/ldcnKbSZRyk4lrsrK4dt8+SnvQ7lsIIYQQQrgfp1aYaa1rlFJXA58CgVhaLV6llCoDsoFqIAhIAcKbDlNAPXCF1rpvM6hCCCHcXt6zjqlUWMxidrKzzfiIESO44IILOP/885k3b560WeyDHLvqqGRpR9QzR47Ybg8bBpJ07BWttbRkdKKhIbaJXUmYDQKXXw4pKZCd3Tr2l7/AvHkuC0kIZ/MxGPhzSgrnR0Tw4/37OWb3OejNwkK+OH2a59LSWBwT46IohRBCCCFEXzi7JSNa66+UUucArwNpTcMRtCbIwJIka3YYuEZr/b1zIhRCCOEqxgojRSuLHHKu2cwmkEBMfibmzZvHhRdeyIUXXkhqaipKWkj1mdaaHLsKsyQ/PxdF46HOPBNeftmSODtyBCLcryLKU9Q01tBotr2q3zMSZm338cQKM2nJOAh4e8Pdd8Odd7aOff45fPUVzJrluriEcIFZ4eHsmDqVOw8d4rWCApvHChobWbJ3L1fFxvL0qFFEy4UwQgghhBAexekJMwCt9fdKqXHAZcAPgBlALBCCpcosH9iKZc2ylVprD5g6EEII0Vf1J+uh+0uOdcoLL1b8awVzl84lMDDQMScVLYobG6m1m9mXBe97KCkJrr/e1VEMCPbVZQARAe6XgGzbkrHrfdyRfcIsvyofszZjUM7u9i6c6qc/hYcfhlNWFYW//z188YXrYhLCRcK8vVk+diwXRUZy68GDlNu1AX+7qdrs2dRUfhgb66IohRBCCCFETzn1r1ql1FVKqQuUUkprbdZar9Ba36C1Hqu1jtJa+2qtI5q2l2qt35NkmRBCDA6NjY1s3bjVoec8O+NsSZb1E/vqMi8gXq6iFi5yuu50m7EwvzAXRNK5thVmbT/mekLCbGiwbUtGo9lIcU2xi6IRThMQAPfdZzv25ZewcaNr4hHCDVwVF8fe6dNZFBXV5rGixkau2LePy/fsIdfuc5MQQgghhHBPzr4M9DfAx8BqJ39fIYQQbqi0tJQ333yTq6++mtjYWK6/7XqHnt8rxMuh5xOt7NcvS/Dzw7u9chkhnMC+wizULxQvg/v9/x8oLRnjguNQ2AYvbRkHiZtugnjbCkP+8AeXhCKEu0jw8+PD9HReHzuWCO+2TXzeLy5m7JYtPHXyJCZPuCpCCCGEEGIQc/bM1oimrx87+fsKIYRwA1prsrKyeOyxx5g7dy6xsbFce+21vP3225SVlVFEESYH9WRU3gq/BFlTq78cqa212ZZ2jMKVTtfaVpi5Y3UZDJyEmbfBm9gg2xZjpypPdbC3GFD8/dtWma1bJ20ZxaCnlOLauDj2TpvG4ujoNo9Xmkz84vBhpm/bxvcVFS6IUAghhBBCdIerLgUvddH3FUII4WRGo5H169dz1113kZqayrhx4/j1r3/Nxo0bMdmt91BDDZvY5JDvG70kGu9QlyzVOSgctEuYpQUEuCgSIWjTDjA6sO1kpTtou4ZZ20oDTyk+sF/HTCrMBpEbb4SEBNuxX//aM7K9QvSzoX5+vD9+PG+OHUtUO9VmmVVVTM/M5M5Dhyg3Gl0QoRBCCCGE6IyzE2ZrAAVc4+TvK4QQwolqampYtWoV119/PXFxcZxzzjk8+eSTHDlypMtj943a55AY4m+P73on0WsHa2psttNkrTjhQvYJs5igGBdF0rnuJMMkYSbcnr8//N//2Y5lZsJbb7kmHiHcjFKKq+Pi2D99Oj8ZMqTN4xr4Z24uY7ds4bX8fMye8sIvhBBCiG47ffq04ZVXXglfunRp0uTJk8dERERM9Pb2zggODp6ckpIyfsmSJcNXrFgRau7Hi87eeOONsAsvvHBEQkLCBD8/v4zIyMiJkydPHvPggw/GlZaWdisvVFpaarjtttsSEhMTJ/j6+mYMGTLkjGXLlg3Lzc0dsFeoO/sHuwOYAJyvlPoAeBTYorWWS6uEEMLDFRcX89FHH7Fq1SrWrFlDrV0FUkf8/f0577zzWLRoET/4wQ9ISEhg64St1Oyt6frgDgSlBxE+N7zXx4uuSYWZcCdFNUU221Jh1v8SQxNttnPKc1wUiXCJn/wEnngCsrJax+6/Hy6/3JJQE0IQ7evLf8aMYdmQIdx68CBZdhcbnWpoYOn+/Tybl8dTo0YxLTTURZEKIYQQwpH+8Ic/xD3yyCMJ9fX1bZrwV1dXq+rqav9jx475r1q1KmrKlClVb731VnZqamqDo75/eXm54bLLLkv58ssvw63HGxoavE+fPu29Y8eOoBdffDH29ddfP3ruuedWd3Se/Px8r1mzZo05cuRIywf8goICn+XLl8euWbMmfOPGjQccGbe7cHbC7EbgA+BOYFHTrUEpdRQoA+q7OF5rrc/t1wiFEEJ0W3Z2NqtWrWLVqlV89dVXdPfKmISEBBYtWsSiRYuYP38+gXbVSWOXj2X7nO2Yq3t+pY0hyMCYV8eg2lscSDhEhdHIqQbbz0SjpcKsZ955B2pqYNw4GDsWZJKsT+wTZjGBnlFhplTb7JindLVLDku22T5eftxFkQiX8PaGRx+FSy5pHcvJgaefhnvvdV1cQrihOeHh7Jg6lf934gR/On6cOrsX+m8rKpiemcn1Q4bwcEoKQ/1kDV4hhBDCkx08eNCvOVkWGxvbOGvWrIqMjIya2NjYxrq6OsO3334btGrVqqiamhrDtm3bgs8555zR3333XVZCQkKfi4qMRiMXX3zxyE2bNoUCREVFGa+99tqicePG1ZWWlnqtWLEiMjMzMzg/P9/3sssuS123bt3+jIyMuvbOdccddww7cuSIf1hYmOn6668vHDt2bF12drbvSy+9FJufn++7bNmy4d98883BvsbsbpydMHsISwcCa37AmG4cq9o5VgghhBNprdmxY0dLkmzXrl3dPnby5MlceumlXHLJJUyaNKnThFZIRgjpK9PZs2RPj5JmhiAD6SvTCckI6fYxoucO2VWXKWCkVJj1zP/7f7B1a+v288/DzTe7Lh4P16Ylo8ckzLrex10lh0vCbNBbtAjmzIGNG1vHHn7YUn0WFeW6uIRwQ74GA/clJ3NlbCw/O3SIT0vbLuv+Sn4+K4qKeDA5mV8kJuJncNWS80IIIYToC6UUZ599dsWvfvWrgksuuaTCy8vL5vE777yz5ODBg/nnn39+6rFjx/xzc3N977rrrsR33333WF+/9z/+8Y/o5mTZyJEj69atW3dg2LBhLYm4++67r+jmm29OfOGFF+IqKiq8br755uTvv//+gP156urq1Mcffxzp6+ur161bt3/y5MktSbVbbrmlJCMjY9zmzZtDsrOzfVJSUhr7Grc7ccUnMGV3a2+svRtWX4UQQjiJyWRi48aN/OIXv2D48OFkZGTwpz/9qctkmZeXF/Pnz+epp57i2LFjZGZm8vvf/57Jkyd3q/orckEkkzdOJnB89yqXgtKDmLxxMpELIru1v+i9A3YthYb7+8ukTk9oDfvs1upLSXFNLANEUbVntmRs76XQYyvMyo6jPSXbJxxDKfj7323HysrggQdcEo4QnmBEQAAfT5jA/9LTGdXOxUZVJhO/OXqUcVu28E5hoaxvJoQQwqHq6urUY489Fn3WWWelRUZGTvTx8ckYOnTohGuuuSb5+PHjPs37lZSUeIWEhExSSk156KGHYl0Zsyd6/PHHc7/66qtDS5YsaZMsa5aWltbw5ptvHm3e/uijjyIqKyv7NLFiNBp57LHHWhabfuWVV7Ktk2XNnn322ZNjxoypBdi2bVvw+++/36blTX5+vnd9fb0aMWJEnXWyDCAlJaVxypQpVQBHjhzx7UvM7sips1taa0Nfb86MVwghBiuj0cgXX3zBbbfdRnx8PHPnzuWpp54iJ6fzNWoCAwO57LLLWL58OYWFhXzxxRfceeedJCcnd3pcR0IyQpi2expjV4/FHNZ2Fll5K2KuiGHiuolM3TVVKsucZHe1bYvrMdKOsWdOnAC73yHjxrkmlgGiTUvGIM+oMPPkNczsK8zqTfUUVhe6KBrhMtOmwVVX2Y79+9/w/feuiUcID6CUYlF0NHumTeNvI0YQ0s5E2tG6Oq7at48zMzNZd/q0C6IUQggx0KxZsyZo1KhR6b/+9a+TN2/eHHL69Glvo9Go8vPzfd96663omTNnjikoKPACePzxx2Oqqqq8oqKijHfddVdRV+cWtuLi4kzd2W/mzJm1w4cPrwOoq6sz7Nu3r099mT/99NOQoqIiH4Bp06ZVzZo1q6a9/by9vbn11lsLmrfffPPNNleex8bGGr29vXV2drb/7t27beLKy8vz3rlzZxBAUlLSgKouA+e3ZOw1pVQAMExrPeD6YgohhDtoaGjgyy+/ZMWKFaxatYqSkpJuHRcdHc0ll1zC4sWLOe+88whwcGs+pRShc0IxR5sxlLdeNzH8oeEk3pmId6jHvJUNGDurqmy2JwYHuygSD2VfXRYaCvHx7e8rumVu8lySw5IpqimiuKaYuKA4V4fUrrbVY567htnQ4KF4G7wxmlsvWDxefpy4YPf83Yt+9Le/wYcfWtZlBEvW9/bb4dtvQaqPheiQn8HAvUlJ/Dgujvuzs3k5P7/NPt9XVjJ/504ujIzk0REjmCCfuYQQQvTCiy++GHHbbbelGI1GpZRi9uzZ5VOnTq0uLCz0+eCDDyIrKyu9Tp065fvII4/EPfzww6f+/e9/xwLceuut+cHBwR5ySZ9nCg4ObvkLsLq6uk8fnj/++OOw5vsLFy4s62zfJUuWlN91110ArF+/Psz+8cDAQH3eeeeVrV69OmLOnDljrr/++qIxY8bU5eTk+L788ssxxcXFPhkZGVVpaWkN9sd6OqfOMiqlsgEzcIvW+vMeHHcF8BaQByT1U3hCCDHo1NXVsXbtWlasWMGHH35IWVlZt44bMWIEixcvZvHixZx11ll0VGLuSKrKtndZ0PggSZa5SJuEWVCQiyLxUPYJs3Hj2u/NJ7rtpUtfcnUI3dK2wqzrfdyVl8GLYaHDyC7Lbhk7Xnac6QnTXRiVcIlhw+DBB+G++1rHtm6Fl16CG290XVxCeIghfn68NGYMt8XHc9fhw3xTUdFmn09LS1ldWsrSuDj+lJJCkr+/CyIVQgjhiTZu3Bh4xx13pBiNRuXr66uXL19+5Morryxvfnzr1q2FM2fOHG8ymVi/fn3os88+21BcXOwTFhZmuvfee6W6rB/V1dWpY8eOtVRvjRo1qr4v59u3b1/LFexnnnlmu9VlzZKSkoxDhgxpyM/P9y0pKfHOy8vzjo+Pt2nf+Oyzz56YNWtWUF5enu8TTzwx1PqxmJiYxuXLlx/rS7zuytkzjclYLqXtTe8mAyCXrAohRB/V1NSwevVqVqxYwf/+9z+q7JIfHTnjjDO4/PLLWbJkCenp6d1ah8yRVI3t9/MOl2SZKxQ3NJDXYHsBkVSY9dCOHbbb0o5x0OhOS0ZPqTADSApLsk2YlR93YTTCpe6+G15+GQ5aNQP57W9hyRKIinJdXEJ4kGmhoXw1eTKriou57+hRDtTW2jyugVcLCnirsJCbhg7l/uRk4v361LlJCCFcKq8yz+3/qPfz8tNRgVGdttfryc8RHxLfZj2p/mQ2m7nzzjuTGhoaFMBDDz2UY50sA5g2bVrd2WefXb5x48awU6dO+T799NNDAG688caCsLB21sbowGeffRZcWFjokH/TGTNm1KSmpg64yiV7//73vyOrqqq8AMaNG1eTlJTUp+fH0aNHW66oSUtL6zL5lpiY2JCfn+8LsHv3bv/4+HibCcKUlJTGrVu3Zt13331D16xZE15cXOwTERFhXLBgQdlf//rXvL7G66765YVJKRULdHbJU6xSqjuVYgYgAvhZ03b3ZnWFEELYqK6u5uOPP+bdd9/lk08+oaam0wtNWkydOpXLL7+cyy+/nNTU1H6OsmPaqFHVtgkznwifDvYW/em00cjssDB2VlVRYTLhbzCQ6uA2nAPe9u2225MnuyYO4XT2CbP2rjvwlAozgOHhw9lwfEPL9vEySZgNWr6+8PTTcP75rWMlJZZE2quvui4uITyMUoolMTFcHBXFf/Lz+cOxY+TbXajUoDXP5OXxn/x8bouP5zdJScT5+rooYiGE6L2ExxMmujqGrlww6oLTn1776dHO9unJz6F/r7f1ParuW7VqVeiuXbuCANLT02t+9atfFbe334gRI+o3btxIQUGBD0BwcLDpt7/9bY8WKH7wwQcTtm7d6pCraZ988sljqamp3VsnxEPl5eV5//GPf0xs3v7Nb35zqq/nrKysbGn/NGTIkC6TWRERES37lJaWtts6Kj4+3vjqq6+eAE70NT5P0V+Z/MuAZ9oZb54CeL4X59TAd72OSAghBpn6+npWr17N22+/zYcfftjtJNnMmTNbkmTDhw/v3yC7qbGwEWW2nVn2HSoTA66QGhjIxsmT0VpzvK6O7Lo6vGWNmu6rrYWsLNsxSZgNGu0lzAwGjdnq9c2TKsySw5JttqXCbJBbuBAuvxzee691bPlyuPpquOAC18UlhAfyNhi4JT6ea2Nj+cfJk/ztxAmqTLYFDnVmM/84eZLn8/L4WUIC9w4bRrQkzoQQQlh56aWXopvv33PPPacMHfztHhERYfMms2zZsqLo6OhOK+tE79XV1alLLrlkZGlpqTfAeeedV7Z06dKyvp63pqam5R84MDCwy78s/f39W/apqKjo/7VWPES/JMy01s8ppa4BZjnwtKXAbxx4PiGEGHAaGxv5/PPPeeedd1i5ciUV7ayBYK9pwVd++MMfsmTJEhITE7s8xtka8myvrFU+Cp9oqTBzJaUUwwMCGC7VZT2zZw9YT3gpBRPd/sJK4SBtE2YayzVhqsN93FlyuCTMhJ0nn4S1a8H688ctt1he+0JCXBeXEB4q2NubB4cP55b4eP58/Dj/zsujwe6NosZs5m8nTvBsXh4/T0jgl4mJkjgTQgiByWRiw4YNoQAhISEm+1aM1sxWV+35+/ubH3jggYKefr8tW7Yc6FWg/Wj69OmjHVX19vzzzx+9+eabT/f1PCaTiSuvvHL4tm3bggGGDRtW/+abbx7rc4DCYfqzV+wNwHV2Y7/HMivwX2B/N85hBmqAk8BarXWpQyMUQogBoOlDEO+88w7vvfceJSVdV617eXkxb948fvjDH7J48WKGDBnihEh7r+GUbcLMd6gvyuDcNdSEcAj7doxpaSBrwA0a9tVjBoPGYLAd96SE2fDw4TbbR0qPoLV2+hqXwo0kJMDf/w4339w6lpMD990H//yn6+ISwsPF+vrydGoq9w4bxsPHj/Of/HyMdm8YVSYTD+fk8MTJk9wSH8+vhg0jQdY4E0KIQevbb78NaF4fa+rUqVX+/v4d/qWhrd5Trr322qKhQ4cOyLWpXM1sNnPdddclf/jhh5EAQ4cObVi7du3BmJgYh1TzBQYGmpsrxWpqagxdrUFXV1fXUpEWGhoqFYVN+i1hprU+AvzRekwp9fumu29prT/sr+8thBADndlsZvPmzbz99tusWLGC/Pz8Lo/x8vJi/vz5/OhHP2Lx4sVER0d3eYy7aDzVaLPtFy9//AsPJeuXDWr2CTNLXkl3uo87GxU5yma7urGa/Kp8hoYMdVFEwi3ceCO89RasW9c69swzcOWVMHu26+ISYgBI8vfnudGj+W1SEn8+fpxX8/Oxn92qaWrV+M/cXK4fMoTfJCUxUjoCCCHcUO7duTtdHUNX/Lz8uryczV1/jq1btwY23584cWKna3SYTCYF4Ovrqx988MEeV5e5q8WLF5emp6d3b32SLowfP76uL8ebzWZ+/OMfJ7399tvRAHFxcY1r1649OHr06Iauju2ukJAQU3PCLD8/3zssLKzTc58+fbolNxQZGSkJsyb9WWHWnuYEWneqy4QQQljRWrN9+3befPNN/vvf/3LiRNfrbSqlmDNnDldeeSWXX345sbGxTojU8dpUmMVLmxnhoSRh5nDldeUE+wbjZXD/luttE2aWCrPO9nFniaGJ+Hv7U2ds/dvxUOkhSZgNdkrBCy/AhAmWdRubXX897NghrRmFcIDhAQH8Z8wY7mtKnL1eUID920ej1rxw6hT/OXWKq2JjuS8piXSpahdCuJH4kPgBUcXkrj/HkSNH/Jvvjxo1qsNkT11dnVq1alVLxVNycnJjR/t25rPPPgsuLCx0SK5hxowZNampqX1OJN1///1Fjoinr8xmM0uXLk168803YwBiY2MbP//88wPjx4+vd+T3GTFiRF1ubq4vwMGDB/26SsadPHmyZXJtwoQJfUoIDiROTZhprf/Y9V5CCCGsHTt2jDfeeIM33niDrKysbh0zY8YMrrzySq644goSEhL6OcL+15Br+x4vFWbCIzU2wq5dtmOSMOuzS9++lE05m4gLimNoyFD+b87/cemYS10dVrvatmQE+wozT2rJaFAGRkaMZG/R3paxw6WHmZM8x4VRCbcwciQ89BD86letY0ePwl13wX/+47KwhBhoRgUG8urYsdyfnMxfjh/nzYKCNhVnZuDNwkLeLCzkkqgofjVsGLPDwqR9rhBCDHClpaUtVxR21prvsccei2lOsnh5efX6r5EHH3wwwVHrhT355JPHUlNTu15vxAM0J8veeOONGICYmJjGzz///EB6erpDk2UA48aNq920aVMowHfffRd48cUXV3a074kTJ7zz8/N9ASIjI43x8e6Z+HUFQ9e79B+lVIZS6k9KqdVKqe1KqYNKqR1KqY+VUn9VSp3tyviEEMJVSktLef7555k9ezYpKSn87ne/6zJZNnnyZB599FGys7PZvHkzd91114BIlgHUH7X9HOGf4t/BnkK4sV27bKstADIyXBPLAJJXmYdZmzlVdYrMU5k21U7upr0KM/v5Sk9KmAGkRqXabB8qOeSiSITb+cUv4Gy7P+deegnef9818QgxgI0ODGT52LEcOvNMbouPx6+DZNiHJSXM3bGD6ZmZvFVQQKMnlTULIYToEet1ycrLy9vNARQUFHg9/vjjLe0h+pIwE211lCybMGGCw5NlABdddFF58/21a9eGdbbv+++/3/L4OeecU97ZvoONs1syAqCUGgK8DCy0fwjLZbYTgAuAXyulvgSu11rnOjdKIYRwrrq6Oj766CNef/11PvnkExobu66CHzt2LFdffTVXXnklaWlpTojSNeqO2E6AB4ySdRiczaw1O6qqmBAUhI99DznRPd9+a7udlgZRUa6JZQA5VXXKZjs+JN5FkXStOwkzT5u7HBVhu47ZoVJJmIkmXl7w2mswcSJUWl3cetNNMGMGxLvv/1UhPFVKQADPpqXxYHIyj588yb9yc6lu543l+8pKrsnK4jdHj/LzhARuio8nzNsl00NCCCH6SWJiYsuk0jfffBN8xx13lNrvc8cddwwrKytreQNoXsusN7Zs2XKgt8cOVMuWLWtJlkVHRzeuXbv2wBlnnNEvyTKAiy66qDI6OrqxuLjYZ8uWLSFfffVV4KxZs9qs4WY0GnnuuefimrevvvrqNs+NwczpM15KqRQgE0uyTFndyoBcoMJufD6QqZRKdnasQgjR38xmM+vWrePGG29kyJAhXHHFFXzwwQedJsuGDx/O/fffz65du9i7dy8PPvjggE6WmapNNBbY/j4CRkrCzNn2VVczZds2wr76ijnbt3PvkSNyVXJPbd5suz1jhmviGECqGqqoaqiyGXPn9bNMdn2yDAZL0syax1eYScJMWEtJgaefth0rLYUbbvC87LAQHmSonx+PjRxJzsyZ/GH4cCI6SIadqK/n3qNHSdy8mV8ePswx+0p4IYQQHuvMM8+sbr7/3nvvRX/99dc2Eyn333//kPfeey8KIDg42ASQk5Pjd/jwYR/nRjowLVu2bNjrr7/ekiz7/PPPD0ycOLHXybLp06ePVkpNUUpNeeqpp9q98tbb25t77rmn5YrSG264ISU3N7fNh4A77rgjcf/+/QEAGRkZVZdffnlFb+MaiJx6CZFSygB8AAxpGtoC/A34UmtdZrVfNHAucDcwDYgB3lNKnam1tm/JLYQQHmfPnj289tprvPnmm5w8ebLL/SMiIrjyyiu57rrrOOusswbVmgPGciMhc0Io31uOocRynYf/CGnJ6GzrysoAqDWb2VRezvG6Oh4bOdK1QXka+4TZzJmuiWMAOVV5qs3Y0GD3TZjdfTcUFVnyBPX1jURFVXh8hVlqpG3C7HDpYbTWg+p9SnRh6VL46CNYsaJ1bM0aeOQRuP9+18UlxCAQ6ePD74cP5+7ERF7Kz+fJkyfJrmvburjKZOKJkyd56uRJLomO5mcJCcwPD5fXciGE8GCXXnppRXx8fENeXp5vfX29WrBgwZhLL720ND4+vmHDhg2h27ZtCwaYOHFi9eLFi0//8Y9/TDQajWrRokWpV1xxRcnChQsrZ8+e3aY6SXTt5z//efzy5ctjAZRS3HTTTYW7du0K2LVrV6dXf8+YMaMmNTW1oS/f++677y768MMPw7/55pvQw4cP+0+aNGnctddeWzxu3Lja0tJS73fffTcyMzMzGCAkJMT0/PPPH+/L9xuInF1zvxRIx9J28XngZ1rrNtMCWuti4B2l1LvAM8AtwGTgUkCa3gshPFJJSQlvvfUWr7zyCtu2betyfz8/Py655BKuu+46LrjgAnx9fZ0Qpfvxi/dj3OpxrFmzBuph9ujZeAV4dX2gcKj1TQmzZueEh7skDo9VWAhHj9qOScKsz+zbMYb4hhDkG+SiaLp2ww2t92trjaxZUzngKsxqGmvIq8wjIXRgrKEpHEApeO45+OYbyMtrHX/wQTjrLJg3z2WhCTFYhHh784vERH6WkMCq4mL+34kTbK5oezG5GVhVXMyq4mLGBAZyR3w8S4cMIVTaNQohhMfx9vbmlVdeObp48eK0mpoaQ21treHtt9+Ott4nPT295pNPPjlcVVVlePTRR+Pr6uoMBw4cCHjooYcSx4wZc9hVsXu6b7/9NqT5vtaav/71r9364+jJJ588lpqaWtKX7+3j48NHH3105PLLLx+xbt26sOLiYp8nn3yyzVWlcXFxja+99tqRqVOnuu8i4C7i7JaMP2r6upcOkmXWmh6/A9jTNHR9/4VmSykVp5R6SCm1XSlVqZSqUkplKaWeUEqN68bxSil1jVLqc6VUiVKqTimVrZR6VSk1zRk/gxDC9YxGIx999BE//OEPGTp0KHfeeWenyTKlFPPnz+c///kPBQUF/Pe//+WSSy4ZtMmyNvwgIE3aMTqbWeu2CbOICNcE46ns1y8LCoLx410TywCSV5lns+3O65d1xH5JQE9LmMWHxBPgbfu6fLhU/rYWdqKi4PXXbZ/wZjNcdRXk57suLiEGGS+luDwmhm8yMtg8eTI/jInpcFJof00Ndx4+TMLmzdxx8CD7qqs72FMI0Rcy/yj604IFC6q3bt2695prrikaNmxYvb+/v9nb21sPHTq04brrriv6+uuvD8THxxvT0tIa3n333cOpqam1hqbPa9OmTZM+vR4qIiLC/OWXXx5+7bXXjixcuLBsyJAhDb6+vjo8PNx4xhlnVD/wwAMn9+7du3fBggXy5t4OZ18mNAlLddnyrpJlzbTWZqXUq8BjgFNmlpRSC4C3gUi7h8Y03W5XSv1Ba/1wB8cHAO8CP7B7aHjT7Vql1ANa60cdGbcQwn3s2bOHV155hddff52CgoIu9584cSLXXnstV199NYmJiU6IUIju21NdTanRaDM2TyrMesa+HeP06SBXa/eZfcLMndcv65hthszTWjIalIGRkSPZU7inZexAyQHmDp/rwqiEWzrnHPjTn+B3v2sdKyiAq6+Gzz8HL6keF8KZZoSF8W5YGNm1tTx58iT/yc+nyn6xTSztGp/Ny+PZvDzmhYdzR3w8l0RH42t/xYcQosdk/lE4w7hx4xreeOONnK72W7RoUeWiRYv2OSOmgW7Lli0H3OGc1113Xdl1111X5uhYBjpnz9Q0L0h3rIfHNf+n7vfLhpVSE4EPgeYFcj4GPgMqsCT8bgECgL8opSq01v9s5zQv0vpmlQX8GygAMoBbgWDgEaVUrtb69X76UYQQTtbTlovx8fFcd911XHfddUyYMMEJEQrRO+vsqstS/P1J9pd15Hrk669tt2fMcE0cA0xOue3ffUlhSS6KpPfs5xs9LWEGMDZ6rE3CbG/hXhdGI9zafffBV1/B6tWtY+vXW9ozPtzuXKAQop+lBATwRGoqf0pJYXl+Ps/k5bG/pv0la9aXlbG+rIxYHx+WDRnCjUOHkhYY6OSIhRgYZP5RCCHck7MvCWpukt3Ty3+HNH2tdGAsHXma1jer27TWi7TWT2utX9Va/xKYCJQ1Pf4XpVSo9cFNV4dc07T5JZChtX5Ca/2W1vpeYDrQ3Iv0CaVUCEIIj9XTlot+fn5cddVVrF69mpycHB599FFJlgm39+Xp0zbbsn5ZD9XUwHff2Y6dfbZrYhlgjpfbrk+cFOp5CTP7CjNPa8kIkB6bbrO9t0gSZqIDBgO89hrYV9P/9a/wzjuuiUkIAUCotzc/S0xk37RpfD5xIkuiozucMCpsbOSxEycYvWULc7dv5/X8fGrbqU4TQnRK5h+FEMINOTththtQwJU9PO6qpq/9Whba1Bt4dtPmGq31c/b7aK0PAX9r2gwFLrTb5Z6mr0bgRq21zcJ5Wuss4M6mzSjgJgeELoRwsqNHj/LAAw+QlJTExRdfzHvvvUdjY2OH+8+YMYPnnnuO/Px83nrrLc4//3y8pPWQ8AD1ZjNfSMKsbzZvhoaG1m0vL5g9u+P9RbcNhAozpWy3PTFhNj7Gtmu6JMxEp6Kj4b//bduW9oYboBsV+kKI/qWU4tyICN5PT+fYjBk8kJREjI9Ph/tvLC/nx/v3E795M3ceOsSuqionRiuEZ5L5RyGEcF/OTph90PR1plLq3u4c0LTfTCyX337Qxe59FYnlqowC4L+d7LfT6n5y8x2lVCRwXtPmaq11dgfHvwMUNt2/onehCiGcrb6+nnfeeYfzzjuPkSNH8vDDD3Pq1KkO94+Pj+e3v/0tWVlZbN68mVtuuYVwSTQID7OprIxqqx5xCjg/0r7FvujUunW221OmQGho+/uKHpGEmXsYF2O7Hn1+VT6ltaUuikZ4hJkz4cknbcdqa+HSSyE/3zUxCSHaGObvz0MjRnBi5kxeHzuW2WFhHe5bZjTyz9xcJn7/PdO3beOZ3FxKOrmgUIhBTuYfhRDCTTl7DbMXsFwBEY+lh+4k4DGt9Q77HZse+zWWajQN5APP92dwWuuvgHO7sat1DxHrv+hm05qE/KKT72NWSm3A8mZ1plIqQmt9uqP9hRCutW/fPl588UWWL19OSUlJp/v6+fmxZMkSrr/+es477zypIuujmgM1+MT5gJ+rIxm8Pi21nfSeFhJCjK+vi6LxUPYJs3nzXBLGQFPTWENxTbHNmLsnzLZvB6PR0pWuoUFRW+uNUrYZMk9cw2xU5Ch8DD40mlsnRvcW7mV2slRSik7cdhvs2gXPW/2Jl5sLl11med30kzd/IdyFn8HAtXFxXBsXx/7qal48dYpXCwoo7iAhtrWykq2Vlfzy8GEWRUWxNC6Oi6Ki8LVfuFOIQUrmH4UQwn05NWGmta5RSl0NfAoEYmm1eJVSqgzIBqqBICAFCG86TAH1wBVa6/ZXnnWipqs4ftW0WQN8YvWw9QIOe+hcc3tJBUwANjokQCGEQ1RXV/Puu+/ywgsv8M0333S5//Tp0/nJT37ClVdeKVVkDrTvmn1UZVbhn+ZPQEIA9RfXuzqkQecTu4TZhVJd1jPV1bBli+3YOee4JpYB5kT5iTZjw8KGuSCS7vvBD6C1MNmfP/4xYkBUmPl4+TA6ejR7Cls//u4tkoSZ6IJS8NRTkJUFG63+FNq8GW68EZYvb1uCKYRwuTFBQfx91Cj+MmIEHxYX88KpU6w93f78e6PWrCwuZmVxMVHe3lwdF8fSuDimhoSg5P+3EJ2S+UchhHANZ1eYobX+Sil1DvA6kNY0HEFrggwsL+LNDgPXaK2/d06EbSml/LGUPl8K/AJLhRzA3Vpr60ubh1vdP9bFaa1neZI73EsI4VSZmZm88MILvPnmm1RUVHS6b0REBD/+8Y/56U9/yhlnnOGkCAcPU62J6l3VANQdrMP3oC8N5zZ0cZRwpOzaWvbX2F6rclFUlIui8VBff20pKWrm5QWzZrkungHEvh1jZEAkwb7BLoqme+yrx5TSA6LCDCzrmNkkzAplHTPRDb6+sGIFTJsGx4+3jr/+OgwbBg8/7LrYhBCd8jMYuCI2litiY8mureWl/HxeOnWKvIb2P6+XNLVs/GduLmMDA1naVLE2zN/fyZEL4b5k/lEIIVzP6QkzAK31900LXF4G/ACYAcQCIViqzPKBrVjWLFuptXbZ1IFSagpgn6zLB36htbbvMxxjdb+Yzllfst+j2UelVGIXu8Q236mvr6e2trYnpxei2+rq6tq972lqampaqskyMzO73H/OnDlcf/31XHrppQQEBADI/7N+UPldJdrYOpGsDRrTSJNHP9c8zSq7dWRivL0Z7+09oJ/vjn5d8167Fh+rbfOUKdR7eVnW6hF9crj4sM12Ykii2z83zWZ/rK8LsyTMbPepq6unttbzsmZpEWk227sLdrv9v8dg4faf14KDUf/9L37z56Oqq1vH//pXGoYMwXTTTa6LTfSI2z/XRL8ZAtw/ZAi/jovj8/Jy3igq4qPTp6nvoGw6q6aG+7KzuS87m7NCQrgiKoolkZHE+vi0u789ea4JZ6mvt+lwEtvVfJzW+mRvv5c7zj8KIcRg5JKEGVj66AIrmm7urL3ePlHAlUqp7VrrQ1bjgVb3u/rUZv14YId7ta9tD6IObN++nWrrPzyF6CcbN3peVX9ubi6rV6/myy+/7PL/SXh4OPPnz+e8884jPt5ykdemTZucEeag5fs/XwIIaNk2J5ohwDOfa57q1cBAsJq4GFdTw+dr17owIudyxHNt7nvv2ZTQHx42jKw1a/p8XgEbTm2w2fav82eNm/9u6+ouwHpRRoOBNhVm27Ztx8urwMmR9V1DmW1FwY68HW7/7zEYufN7aOwvf8mZDz+MwarM0ueXv2R7QQH506e7MDLRG+78XBP9ywD8GFgCfOPjwzpfX7K8O556+qaykm8qK/lVdjZnGI3MbmzkzMZGulszLs810Z8OHjxovflxNw7pS69Rd5x/FEKIQcdlCTMPUgTcCRRiefP6MTARS3XcuUqp+Vrr5pIU699nVwvtWD8u/w5COInRaGTLli2sXr2aXbt2dbqvUorJkyezYMECpk2bhncnf+gJx/PeZ/v7NqWaXBTJ4FSpFLvtnvPTO1jYXbTPr7SU8Oxsm7HCjAwXRTPwFDYU2mxH+0a7KJLu09p2DqW9CrO+zbO4TpJ/ks12ubGc0sZSIn1k3UPRPYVTp7Lr1luZ9OyzLWPKbGbK3//ON3/+M6dHj3ZhdEKIngoGFjY2srCxkVMGA+t9fFjv60uBwdDu/mal2OHjww4fH/6lNRlNybNpjY1I00YxSMj8oxBCuAGXvVAqpZKBaK31NrvxC4AHgDOAKmAD8LDWuqtFLPuF1vpr4Gur+P4B/Au4GQgD3lBKpWutTYB13xlfOn/T8rO639NFebpa0f4Mmq58mTx5MmeffXYPTy9E99TV1bVc0Tdnzhz83bj//MmTJ3n55Zd5+eWXybdrM2cvISGBZcuWsWzZMoYN6+q/m+gP2qzJvDETI61rPxnHWe67+3NtoHitqAjz0aMt2/5Kce/cuQR7ebkwqv7nyNc1r9des9nWoaFM+dnPbKr2RO899uZjcLp1e1b6LBZOX+i6gLrBy8v2395gaLuG2RlnTGLhQs9ryWjWZn5z5DdUNVa1jIWPCWfhSPf+NxkMPOnzGgsX0hgais8jj7QMeTc0MPuRR6hfvRo9YYILgxNd8ajnmnC6ZYDWmm8qK3m9uJhVpaWUm9q/IM6oFFt8fNji40OgwcCF4eEsiYxkYXg4wV5e8lwTThMUFGS9+QOg86tu+8BN5x+FEGLQcXrCTCkVDbwMXASsxvKG0/zYlcAbWC6tVVjWNLsSuFQpdYXW+hNnx2tPa21WSt2OZd21M4AxwELgUywJvmb+dP6GZf2JrkcLPHTVE1kpNaT5vp+fX8saS0L0J39/f7d7rpnNZr744gv+9a9/8eGHH2Lq4A+yZueffz633347F110kVSTuVjVniqMxUabMWO6Zdsdn2sD0f/Ky222L4iKIia4u81xBoY+P9e+/NJmU513HgGhoX2MSjTLLret3hsbN9btXxvMdnkwpWhTYebr64eb/xgdmjR0El/lfNWyvbd0L0vSl7gwImHPI95DH34Y8vPhlVdahtTp0/hffDFs3AhSaeYRPOK5JlzivMBAzouLo95s5rPSUt4uLOSD4mJq7N8km9SYzbxXWsp7paX4GwxcEBnJotBQ/LFUsclzTfQnPz/rXBOFfVmjrKfcYf5RCCEGo/Zr4fuJUsoAfI4lWaaAUVaP+QNPN8WkgDzgQNP9AOA1pVSM/Tldoelqjv9YDTWXcJVZjXXVf8b68SIHhCWEaFJZWcnTTz/N2LFjWbhwIStXruwwWRYVFcW9997L4cOHWb16NZdccokky9xA2foym22/ZD90XPuLhgvHqzQaWVNaajN2ebT7t7tzK0Yj2K/fdMEFrollAKpprCGvMs9mbGTESBdF031tE2ZtWzJ2MF/oESYPmWyzvT1/u4siER5NKfj3v9u+ZhYWwnnnwbFjLglLCOFYfgYDl0RH8+a4cRSefTZvjxvH4uhofNv2Km5RZzazqriYG48e5frQUP4UGMjLhYUUNUjRjBiYZP5RCCGcz6kJM+A6LFdFAGRiab3Y7FIgGtDAJ8BwrfVYLL16zUA4cIvTIu3aAav7sU1frVcDtV3IoS3rPm/HHRKREIPc4cOHueuuu0hISODnP/+5/QK9NmbOnMny5cs5efIkf/vb3xg50v0nWgeTsnVlNtshs0NcE8gg9XFJCQ26NUHpoxSLoqJcGJEH2rIFTp+2HbvwQtfEMgAdPX20zdiIiBEuiKRn7JNhBgNtWjJqD742IGOo7Rp9macyO9hTiC74+MB778GcObbjJ09akmanTrkmLiFEvwjy8uLK2FhWpqdTcNZZvDR6NOdHRNBZI3CjUmT6+HBHdjZDvvmGc3bs4J8nT3Kyrs5pcQvhJDL/KIQQTuTshNllTV+PAGdrrVe08xjAn7XWRgCt9SrgbSyVZhf3Z3BKqWVKqXeVUplKqfAudrcuaa5s+rrPamx8F8c3P66Bvd2PUghhTWvN2rVrWbRoEWlpaTz55JNUVla2u29QUBA333wz27dv55tvvuHHP/6x9Lt3Q9qsKdtQZjMWOkfa2DnTW4WFNtvnRkQQLutu9cynn9pup6dDYqJrYhmAjpQesdkeEjyEIN+gDvZ2H92pMPPkhJl9hdmxsmOcrj3dwd5CdCEwEP73P5g2zXb8yBGYP1+SZkIMUOE+PtwwdCirJ07k1Fln8VxaGgu6SJ6ZgfVlZdx5+DDDvv2WjO+/5w/Z2WRWVqI9+Y1VDFgy/yiEEO7L2QmzDCwv0C9qrVv66yqlvLD04QXI11p/Z3dc82II/X3p8ATgh8Bk4Nwu9j3T6v7+pq+bgcam++d0dGDTzzu3aXOn1rqsx5EKMchVV1fzr3/9i/Hjx7Nw4UI+/vjjDv8YGjduHE8//TS5ubk8//zzTJo0ybnBih6p3FaJscR2/TJJmDlPcUMDn9i1Y/xRjFt0RPYsq1bZbks7Roc6cto2YTYqclQHe7qX9hNmutN9PMm4mHH4evnajO3I3+GaYMTAEBpquQBhwgTb8f37Ye5cS8WZEGLAivH15Zb4eNZMnEjh2Wfz8ujRXBwV1WnbRoDtVVX88fhxpmzbxrDNm7n1wAE+KSmhros1rYVwIpl/FEIIN+XshFnzjFu23fhMIAxLMu3zdo5rvjS1v2dMV1vdv72jnZRSkcCNTZuNwMcATW88XzSNX6KU6qgs+ipafxf/7W2wQgxG2dnZ3HPPPSQmJnL77beTlZXV7n5KKS699FK+/PJL9uzZw89+9jPCwsKcHK3ojZKPS2y2A8cE4pfk18HewtHeLSrCaJV89jcYuFwSZj1z+DDs2WM7tnixS0IZqOwrzDxh/TLoqCVj5/t4Eh8vHybE2iY2pC2j+P/s3Xd4FFUXB+DfbEvvjSQQEiAFEnqXXhVBQUCULl1AAQX8QERFkSJKl6ZSojSRIiAliECkhw4BAumQ3nvb3fn+mJSdzYbUzWQ3532eebJzZ3ZzEsKWe+acW202NsC5c4C7O3/8+XMuaRYZKUxchJBaZS2V4gNHRxxv2RKR7dphfnY2XisogLHo1dNaUfn52B4Tg8EPH8LmyhUMe/gQv8bEII7WPSPCovlHQgipo2o7YVZ09YN6DzTVRT38NNyvceHX9BqPiO9fAPcLb/dlGGaB+gkMw5gDOIKSF5wdLMuqrjq/rvCrDMB+hmHM1O7fAsCGwt0MAD/XUOyE6C2WZXHlyhUMHz4czZo1w48//ojU1FSN51pYWGD+/PkICQnBsWPH0KdPHzDlXIFI6pbkU/zqJus3y1vDmNSk3+PiePtDbWxgLpEIFI2OOnqUv+/gAHTpIkwseio4JZi3r6sJM00VZrreOUq9LeOdWEqYkRrg4AD8+2/ppFloKJc0Cw8XJCxCiDDMJRL0KCjAZ9nZeNG+PY56e2OcgwMsy3nPmq1U4q+kJEwNCoLj1avocvs2loWH42Z6OpS6/gJMdA3NPxJCSB1V2zNgwQBaA+gE4DeV8aL1yxQATqvfCcB74KrPgjQcqzEsyyoZhpkM4D8AxgDWMAwzAMBhAGng+v5OBeBYeJe7AD5Teww/hmEOAxgB4DUA9xmG2QrgBbhS65kAil7EFrIsm6jNn4kQXaZQKHDs2DH88MMPuH79+ivP9fLywpw5czB+/HiYmprWUoSkpuXH5SMjgL8Gnc1gG4GiqX9CcnJwNZ1/bco4BweBotFh6gmzt98GxK9aeYNUVqkKM+u6nzBj2dLJME0VZro+X9fOsR33DrnQzaibwgVD9EvDhsDFi0C/flxLxiLh4VzS7Nw5wMNDqOgIIQIxEokwzM4Ow+zsUKBU4kpaGo4nJeF4YiJCcnPLvB8L4EZGBm5kZODr8HDYSqUYaGWFQdbWGGhtDXuZrMz7ElJdNP9ICCF1V20nzPwAtAEwhWGYSwBOgXvC9wT3fuU8y7LFpQUMw0gArAf3RM8Wnq9VLMveYRjmTXClyvbg1lYbqOHUMwBGsyybreHYBHAvSgMBuAH4Xv3bAFjGsuz2GgucED2SlZWFXbt2Yd26dQgNDS3zPIZhMHjwYMyZMwf9+/enSjI9kHSa345RbCaGRXcL5CnyyrgHqUl71arLbCQSvG5NFX6VkpsLqK0Bh3feESYWPSVXyhGRFsEb04U1zDS1WtTHCrMuDfnVlMHJwUjISoCdCbV2JTXAyYlLmvXtCzx+XDIeGQl0786td9a+vWDhEUKEJRWJ0NvKCr2trPBj06Z4mp2NE4XJs2vp6XhV1+PEggLsi4/Hvvh4AEB7U1O8YW2NQTY26GxmBkk5rR8JqSyafySEkLqpthNmm8D15jUBcFDD8eIndoZh5gH4AoBV4VAigG1ajg8AwLLsJYZhvADMBvA2uISeIYB4AFcB7GFZtszkHcuy2QzDvAFgDICJ4JKEluB+hssANrAse0WbPwMhuigmJgabN2/G1q1bkZKSUuZ5ZmZmmDx5Mj766CM0a1b3J0lJxSX/zU80WA2wgkgmAnIECqgeUbIsdsfG8sbet7eHlCYHKsfQEHjyhJvIPXqUq3jo21foqPRKRGoE5Eo5b0wXWjKWnTAr/zxd0tKhJYwkRsiRlzxx34i6gSEeQwSMiugVB4eSSrOHD0vGExKA3r2BY8e4Y4SQeo1hGDQ3MUFzExN85uKCxPx8nEpOxomkJJxJTkamQvHK+9/OzMTtzEx8FxkJS4kEA6ys8Ia1NfpbWcHFUH2VEUKqhuYfCSGk7qnVhBnLslEMwwwHlyyzUj0EYCnLshdUxswAFF3WngRgqGr1mbaxLJsCYHnhVpX7swD2Fm6EkFcIDAzEjz/+iL179yL/FYsvu7i4YN68eZgyZQrMzc1rMUJSGxTZCiSd4leYUTvG2nM+JQVham1rqB1jFTEM4O3NbV98IXQ0eudJ4hPevrWRNayN6n4lpKZEmKaWjLqeMJOIJOjo3BH+Ef7FY9dfXqeEGalZdnbAhQvA668Dt2+XjGdmAoMGAXv3Au++K1x8hJA6x1Ymw4QGDTChQQPkKZXwT03F6eRknE5OxtNsTYU7JVLlchxKSMChhAQAgLuREfpbWaG/lRX6WFrCSiqtjR+B6CmafySkfrhy5YqRr6+vjb+/v3lsbKw0KytLbGlpKbe3ty9o165dVp8+fTLGjx+fItHiGvIjRoxwPXLkSPFE2yeffBKzdu3a6FfdJzk5WbRkyRLHEydOWMfHx0utra3lr7/+esqKFStinJ2d5a+6r66q7QozsCz7D8MwngDGgrtyIgnAnyzL3lc79QmAUACHAPxIvXYJ0S8sy8Lf3x+rV6/G6dOali4s0b59eyxYsAAjR46ENl84iLCSTydDma0yUywGbN6ihFlt2RETw9tvaWKCzpSYJnXQkwR+wqy5bXOdaMlbVoWZSMTvwVjOBe86oWvDrryE2bWX1wSMhugtGxsuafbOO8D58yXjBQXAe+9xFWezZgkXHyGkzjIQiTDA2hoDrK2xFkB4Tg7OpqTgTHIy/klJKbf67HlODp7n5GBrdDQYAO3NzIoTaN3MzWFIa9cSQggplJycLJo+fbrLkSNHbFi1/vsJCQnShIQEaWBgoPFvv/1m99Zbb92ztbXVyifCP/74w1w1WVYRsbGx4u7du3uFhIQUl1bHxcVJfX197f38/Cz9/f2D3N3dy6580FGCzDwXJr82lHPOnwD+rJ2ICCG1RalU4u+//8bKlStx7dqrJ9CGDBmCBQsWoGfPnjoxGUqqJ/5QPG/fsrclZHa02HZtiMvPx7FE/nUp0x0d6f8dqZPUK8ya2zYXKJLKqWjCTK4H1+ipr2N2M+omFEoFxCKaQCQ1zMwM+PtvYPx44NChknGWBWbPBoKDgTVrAJq8JoS8gquREWYYGWGGkxPylUpcSUvDmcLqs4dZWa+8LwvgVkYGbmVkYFVkJAwYBt0tLIoTaG3NzCCm99SEEFIvxcXFifv16+cRGBhoDAD29vYFgwYNSmndunWOpaWlIj09XfT8+XPDixcvmj9+/NhYW3EkJyeL5s6d2xgAjIyMlDk5ORVae2P27NmNQkJCDC0sLBQffPBBfPPmzXPDwsJkO3futI+NjZVNnDjR9erVq8+0FbdQqFSDEFIr5HI5Dh48iFWrVuHRo0dlnmdgYIAJEybg008/hZeXVy1GSISkyFEg6SS/HaP9u/YCRVP/7ImNhVzlSidDkYjaMZI6q1TCzE53E2YiESAW8w/oQ4WZesIsMz8TjxMeo6VDS4EiInrNwADYv59r07hlC//YunVASAjXotHUVJj4CCE6RSYSoY+VFfpYWWF106aIysvDmeRknE1OxvmUFCSXc2VLHsvifGoqzqemYnFYGCzEYvSwtEQvCwv0srREW1NTSGiNYEIIqRdGjhzZpChZNm3atLj169dHGRsbsxpOjQoPD5daWFho5dPgRx991Cg2NlbWoEGD/CFDhqT88ssv5U745ObmMn///be1TCZjL1y48LRt27bFa3jMmDEjqV27di2uXbtmFhYWJnVzcyvQRtxCoVdpQohW5ebmYuvWrfDw8MC4cePKTJbZ2Njgyy+/REREBHbs2EHJsnom+XQylFkqk8YiwPYdW+ECqkeULIuf1doxvmdnB0tai4HUQSzLamzJqAvqU0vGBqYN4GrpyhujtoxEq8RiYPNm4OuvSx87fhzo0QN4+bLWwyKE6D5nAwNMcXTEH97eSOjWDbfbt8eqJk0wwMoKhhVIfKUpFDiZlISFoaHodOcOrK9cwaAHD7A6MhLX09JQoOuLlxJCdEpubi6zZs0a29dee83D2tq6tVQqbefo6NhyzJgxjSMiIoonAZKSksRmZmZtGIZpv3z5crqauQo2btxoc/nyZXMAGDt2bMKOHTtelpEsAwC4uroWSLUwD3P8+HGzAwcO2ALA2rVrI83MzCr0whMbGyvJy8tjmjRpkquaLAMANze3gvbt22cCQEhIiN61htJKhRnDMP8W3mRZlu2nYbyqeI9HCKm70tPTsXXrVqxbtw5xcXFlnte4cWMsXLgQkyZNgrGx1qqPSR0Xf0CtHWMvS8js9e41t076JyUFwTk5vLHpTk4CRUPIq8VmxiItL403pusVZupzbfqQMAO4dczCU8OL9/+L/A/T208XLiCi/xgG+OorwNUVmDaNW8usyL17QOfOwIkTQLt2QkVICNFxIoZBOzMztDMzw/9cXJCrUOBaejrOpaTgfEoKbmVkoLxZyAyFAmeSk3EmORkAYCISoVth9VkvS0t0NDODjCrQCCFa4OfnZzJ58uQmUVFRvMmW2NhY2f79+20vXrxofvfu3ccODg6KtWvX2mVmZoptbGzk8+bNSxAqZl22fv36BgBgbGys3LhxoyBXbmVkZIhmz57dmGVZDB48OGX06NFpAQEBJhW5r729vVwikbBhYWGGDx8+NGjZsmVe0bHo6GjJ/fv3TQDAxcVFr6rLAO21ZOwNrpVzRccrgqnGfQkhtSQxMRHr16/H5s2bkZaWVuZ5LVq0wKJFi/D+++9DG1dQEN1RkFKAxL/462fZjbITKJr6Z73aFffexsboam4uUDQ67PffgYEDAXu6+E6b1NsxGkuN4WLhIlA0lVNWhZk+tmQEgO4u3bH/0f7i/Uvhl8CyLK2NSLRv4kQuaTZ8OFA4IQ0AiI4GuncHfv4ZGDtWsPAIIfrDUCwubt8IAKkFBbiYmop/UlJwPjUVT7Ozy32MLKUSfikp8EtJAQAYiUR4zdwc3Sws0M3CAl3MzWEuodVUCCHV88svv1jNnDnTTS6XMwzDoEePHmkdOnTIio+Pl/7111/WGRkZ4piYGNmqVascVqxYEbNjxw57APjwww9jTU1NaT6+kvz8/EzCwsIMAaB///6p1tbWgpQTz5kzx/nly5cGFhYWiq1bt0ZW5r7GxsZs//79U8+cOWPVs2dPrw8++CDBy8srNzIyUrZr1y67xMREabt27TI9PDzytRW/ULT5qltWgos+JROihxISErBlyxZs3rwZWa9YGLlz585YvHgx3nrrLYjoyjkCrrqMzS95uWBkDOxHUdKhNjzNysJp1clEAHMaNqQJ7cp68AAYP55rCda/PzBmDDB6NEAXA9Q49XaMnjaeEDG68VpSn1oyAkBv1968/RfpLxCeGg43KzdhAiL1S69ewPXrwODBwPPnJeM5OcC4cUBAALBmDT1PE0JqlKVUimF2dhhmx138F52Xh0upqbiYmopLqakIUuvqoEmOUlm8BhrAraPSytQU3VSSaC6Ghlr8KQgh+sbf39949uzZbnK5nJHJZKyvr2/Ie++9V3yFe0BAQHzXrl29FQoFLl68aL5ly5b8xMREqYWFhWLhwoVUXVYF//77r1nR7U6dOmUBwJ49eyx37txpGxgYaJKeni62tLSUt2jRIvudd95JmTVrVlJNFxOcO3fOxNfX1x4Ali1b9qJRo0avXoRTgy1btrzo3r27SXR0tGz9+vWOqsfs7OwKfH19w2so3DpFKwkzlmU1zlyUNU4I0V2pqak4duwYxowZg+xXXEE3YMAALF68GL1796bJeMITuyeWt2871BZSa5rAqg0bo6J4+9YSCcY5lLv2K1G3cyf3VaEAzp4FHj2i6gUteZzwmLffwq6FQJFUXtktGfUzYdbctjnsjO2QkF3yGfti+EVKmJHa4+4OXLsGjBgBXLrEP7ZhA3D3LvDHHwC97hFCtMTJwACjHRwwuvB5JjYvD/5pacUJtMcVqEBTAriXmYl7mZn4KToaANDQwICXQGtlYgIJXYxKCNFAqVTi448/dsnPz2cAYPny5ZGqyTIA6NixY263bt3S/P39LWJiYmSbNm1qAABTp06Ns7CwqHBl1NmzZ03j4+NrJNfQpUuXbHd3d52tXLpz505x20MHB4eC119/vamfn5+l6jkJCQnSS5cuWVy6dMnip59+cjh+/Hiwl5dXjfzM2dnZzPTp012VSiW6du2aMXfu3KSqPI6bm1tBQEDAk8WLFzv6+flZJiYmSq2srOQDBgxIXblyZbSLi0ulk3C6gOq6CSFVEhsbi5UrV2Lbtm3Iz9f8fM4wDN555x0sXrwYHTp0qOUIiS7IepqFjBsZvLEGExsIFE39klxQgD2x/GTlDCcnGIvFAkWko/LyuHaMqiZO5KrNSI1Tb8nY3FY31i8DXlVhxh/Tl4QZwzDo5doLfz7+s3jsYsRFTGo7ScCoSL1jYwP4+QGffAJs2cI/5u8PtG8PHDkCdOokTHyEkHqlgYEBRtnbY1RhC++E/HxeAu3hKzq1qHqZl4eDCQk4mMBdlGIqFqOzmVlxC8dO5uawoQpaoguio+v+vLSBAQsbm1e/Q6/Mz+HkVKsJhmPHjpk/ePDABAB8fHyy58+fn6jpvCZNmuT5+/sjLi5OCgCmpqaKRYsWxWs6tyxLly51DggIMK1+1MCGDRvC3d3dq5TkqQvi4+OLn4S/++47p/DwcEOpVMoOHz48qVu3bplSqZS9f/++0f79++3S0tLEz58/N+rXr5/nnTt3Hjs4OFT7E+Gnn37qHB4ebmhoaKj8+eefI6rzWE5OTvI9e/a8APCiunHpirr/xEQIqVNiYmLw/fffY9u2bcjNzdV4jkgkwtixY/H555/Dy8urliMkuiRuTxxvX+oghdXrVgJFU7/8HBODbJUZfAnDYJaTk4AR6ajjx4EktffxkyghoA0sy+J+3H3emLe9t0DRVF59a8kIAL0b9+YlzC6FX3rF2YRoiUwG/PQT0KEDMHMmd6FDkagobl2z778H5s4FqAsCIaQW2clkGGFnhxGFLRyTCgrwX2oqLqel4Up6Om5nZKCALX/poEyFgtfGEQCaGRmhi7k5OpuZobO5OVqbmkJGVWikrnF2bi10COV6440UnD4d+spzKvNzsOzt6oZUGTt37rQtur1gwYKYspZGsbKy4n0KmThxYoKtra0efTKpXWlpacVX0IaHhxuam5srTp06FdStWzdeb97FixfH9e3b1zMkJMQwOjpa9sknnzjv27evUmuNqbt06ZLxL7/84gAACxYsiPb29s4r7z6EjxJmhJAKiYmJwapVq7Bjx44yE2VisRjjxo3DkiVL4O7uXssREl2jzFciZmcMb8xhnANEEvogp215SiU2vnzJG3vXzg4NaT2Eyitqx1ikVy+gWTNhYtFzURlRSM7hr7nX2qHuf8YuUlZLRrGYf0CuR00tern24u1HpEUgPDUcrpauwgRE6rdJkwAfH2D4cED1NbCggKtAu3AB2LULsLYWLkZCSL1mo7YGWo5CgVsZGbhSmEC7mpaG5Aq+UQjOyUFwTg5+j+MuUDRgGLQ1MytOoHUxN4eroSEtl0CIHlMoFLh06ZI5AJiZmSnUWzGqUqp8WDE0NFQuWbIkrqxzy3Lz5s2gKgWqRZ06dfKsqaq37du3h06fPj2lIueyLMt7cl22bNkL9WQZALi4uMh/++230Ndee60FABw+fNg2OTn5pbW1dYVbYarKzc1lpk2b5qpQKNCiRYvsr776qtL/jkSghBnDMG0ATAbQEYAlACm4tUzLw7Is21R7kRFC1CUmJmL16tXYvHlzuRVlX331FZo2pf+ipGISjiSgIL6AN+Y4ybGMs0lNepiZiSy1MpZ5DRsKFI0Oe/GCW7NM1ZQpwsRSDzibOSN0Tijux93H/dj7eJr0VKcSL/WxwqyFXQvYGtsiMbuk88uFsAvUlpEIp2NH4PZtYNSo0uuaHT8OtGkD7N8PdOsmSHiEEKLKSCxGD0tL9LC0BAAoWRZB2dlcBVphEi04p9T8q0Z5LIvr6em4np7OVdcCsJNK0VmlCq29mRmsqZUjIXrj+vXrRpmZmWIA6NChQ6ahoWGZJausSjXr2LFjExwdHfXoMr7aZ2JiUvypzsjISPnhhx8ml3Vu165dc1q3bp11//59k/z8fOaff/4xHTVqVHpVvu+iRYscnz9/biQWi7F9+/YIiYRqpaqi1n9rDMP8D8B3AFQzrRW9pKX8WnRCSI1IS0vD2rVrsW7dOmRkZGg8RyKRoHfv3hg5ciQmTJgAIyOjWo6S6LLoLdG8fYteFjDxNinjbFKTOpibI6JrV2yNisLaly/hY2KCTubmQoele3bvBlTb5JibAyNGCBaOvmMYBm5WbnCzcsMwr2FCh1NpZVWY6esaZgAgYkTo1bgXDj85XDx2LvQcJcyIsOztgXPngKVLgdWr+cdevOAqhb/9FvjsM1qPkhBSp4gYBs1NTNDcxATTClupx+Xn42phAu1aejruZGYiV9ObDg0SCgpwMikJJ1Xai7sZGqK9mRk6mJmhvakp2puZwYqSaITopICAAOOi261bt85+1bkKhYIBAJlMxi5dulRvqpKGDRuW7OPj88qfvaK8vb01VxFoYGFhUfypzsPDI+dVyUoAKEqYAUBwcLBBVeK7du2a0U8//dQAAKZOnRrXvXv3Gvm566NaTZgxDNMFwArwE2QZANIBUOaakDogKysLmzdvxurVq5GSornSWCKRYNKkSfj0008RFFTnKq6JDsh8mIm0//jdAJxnOQsUTf1kIZFgUePGmNOwIRIKCsq/A+GTy4Ht2/ljo0cDxsaazyf1nqZEmL5XmAHAgCYDSiXMlKwSIoba7xIBSaXAqlVA797AhAlAQkLJMYUC+Pxz4MwZYM8ewNVVqCgJIaRcDjIZ3rGzwzuFbRwLlEo8yMrCjfT04i2oglVoABCWm4uw3Fz8qfK82EQ1iWZmhnamppREIzUjKup++ScJzMCg/OKNOvpzhISEFK+50KxZszKTPbm5ucyxY8esAcDR0TG/cePGVZogOHv2rGl8fHyN5Bq6dOmS7e7unl/dx/n8888Tyj+r5jVr1iz32rVrZgDXDrO881UTbOnp6VW6YuuXX36xlcvljEgkglQqZT/77DONLZyuXr1qqnq76DwvL6/cyZMnV6jlpL6r7QqzOeCSZSyAPQC+YVk2vJZjIIRokJeXhx07duC7775DXJzmi0mKEmVLlixB48aNkZOTQwkzUiXRW/nVZbIGMtgOsy3jbKJNxmIxGtMV9JX311/F7WyKTZsmTCxEJ2hKhInF+p8we73Z67z9xOxE3Im5gw5OHQSKiBAVb7wB3LsHjBvHrWGmyt8faNUK2LgRmDgRoHV+CCE6QCoSoX1hYmuWM3dBYkpBAQIyMkqSaBkZSKzEBXOhubkIzc3FIZUkWlMNSTRLSqKRynJy0o/iiTr6cyQnJxd/0LewsCiz9HTNmjV2UVFRMgAQi8VV7u62dOlS55paL2zDhg3h7u7uSeWfWTe1bt06+7fffgMAZGRklDvhkpaWpvpvVaVPhEVtNZVKJTZv3tygIve5ceOG2Y0bN8wAoF+/fqmUMOPUdsKsJ7hk2QWWZSfX8vcmhGggl8uxZ88efPPNN4iMjNR4DsMwGDduHK1RRmpEQWoB4n7jJ2UdpzlCJKNqA6JDNm/m73ftCrRvL0wsRCfINXyMFon0P2HmaukKTxtPBCWVXGBzJvgMJcxI3eHkxLVoXL4c+OYbfv/UjAxg0iTuIokdO4DCCg5CCNElVlIpBlpbY6C1NQBuUjU0Nxc30tNxszCBdjcjA3lsxefJQ3JzEZKbiz9UkmiuhoZoY2rK21wMDMDQBQeECEJ1XbK0tDSNEy5xcXHitWvXFlciVSdhRkoMGzYsbeHChWBZFs+ePTPKzc1lXtWWsagdIwA0b968wq0fiXbUdsKsqHxgfy1/X0KIGpZlcezYMSxevPiVVWIjRozAN998gxYtWtRidESfxWyPgSJTZUZYDDhO11gpTkjd9OgRcPEif+yjjwQJhegO9USYSMSCYaD3CTMAeKPZG7yE2eng0/ii5xcCRkSIGrEY+OoroE8frkVjRAT/+LFjwNWrXCveYcOEiJAQQmoMwzBoamSEpkZGGOPgAIBr5fg4Oxu3MjJwOyMDtzIy8CAzs1JJtPDcXITn5uJYYmLxmKVEgtYmJrwkWgsTE8jUF3ElhNS4hg0bFpeSXr161XT27NnJ6ufMnj27UWpqanF+oGgts6q4efMmtaAq1LRp04KOHTtm3Lx50ywnJ0e0bds263nz5mmsmLt27ZpRUcLMxMRE2b9//8yqfM+dO3e+2Llz54vyzvv000+d1q1b5wgAn3zySczatWujy7tPfVPbr1BFl55U6R+eEFIzrl69iu7du2P48OFlJssGDRqEW7du4c8//6RkGakxyjwlXm54yRuzG24Hw4aGZdyDkDrop5/4+w4OwMiRwsRCdIZ6hVlRJ1T1hJmmSjRd90azN3j7N17eQGpuqjDBEPIqPXsCDx5wVWXq4uOBd94B3nsPKKN9OSGE6CqpSITWpqaY4uiILR4euNm+PTJ69MDd9u3xs4cHZjg6ooOZGWSVrBZLlctxKS0NG6KiMCkoCG1v34bpf/+hTUAAJj55gnUvXuBCSgqSaU1lQmpc586ds4puHz582PbKlStGqsc///zzBocPH7YBAFNTUwUAREZGGgQHB1N/1Rrw3XffFa/h8NVXXzVS//0DwIsXLyQTJkxoUrQ/efLkOFNTU41XKnTq1MmTYZj2DMO037hxo412oiZA7VeYXQcwHEBHAAdr+XsTUu8FBQVh8eLFOHr0aJnn9OrVC8uXL0f37t1rMTJSX8TtjUN+DH/d1kYLGwkUTf3Bsiy1QqkpaWlAYS/yYtOnAzKZMPEQnaFeOVaUMFPveqKPFWY9G/eEgdgAeYo8AICCVeB86HmMaDFC4MgI0cDcHNi5E3j7bW5tSpVKCQDAH38A//wDrFsHjB9Pa5sRQvSWVCRCGzMztDEzw9TCsXylEoFZWcWVaLczM/EgMxP5lahEK2BZ3M/Kwv2sLPiqXIDQ0MAAPiYmaGliUvy1ubExDGm9ZUKqZOjQoelOTk750dHRsry8PGbAgAFeQ4cOTXZycsq/dOmS+e3bt00BoHXr1lnDhg1LWbZsWUO5XM4MGTLE/d13300aOHBgRo8ePbKF/jl0Vf/+/bNmzpwZu3Xr1gbp6eniPn36NB8xYkRSt27dMqVSKXvv3j2j/fv32xWtX+bt7Z29atWqGKHjJrWfMPsJXMJsKsMwG1iWLbdMkBBSfbGxsVi2bBl+/vlnKMqYievQoQNWrFiB/v3708Q60QpWyeLFD/ynfcveljDvaC5QRPXH7thY/BYXh+/c3NDVwkLocHTbzz8DWVkl+2IxMGOGcPHUE6MOjYKFgQU6OXdCR+eO8LH3gURU229jq0e9ckxSGH59qDAzlhqjl2sv+IX4FY+den6KEmakbhs2jFufcto04MQJ/rHkZGDiRGDfPq5NY+PGgoRICCG1TSYSoa2ZGdqamWFa4Vi+Uokn2dm4n5mJeypbSiXf1LzMy8PLvDycSS7pGicC4G5khJamprxkWlMjI4hp3oKQV5JIJNi9e3fosGHDPLKzs0U5OTmiAwcO2Kqe4+Pjk33q1KngzMxM0erVq51yc3NFQUFBRsuXL2/o5eUVLFTs+mLLli1RYrEYW7dubVBQUMAcOHDAVv3fAAC6d++efuTIkVBjY2NaQ64OqNWZBpZlLzIMswrAYgCXGIaZA+AUy7LKcu5KCKmCjIwM/Pjjj/jhhx+QpTrBq6JJkyZYuXIl3n33XUqUEa1KOpWE7Cf8i5Ooukz78pRKLAsPR0ReHl67exeDra2xumlTeJuYlH9nwpefz1UUqHrnHcDZWZh46oms/CwcfnIYSlaJX+7+AgC4POkyurl0Eziyyimrwqw+rGEGAG80fYOXMDvx7AQUSgXEIrpqnNRhDg7AX39xlcWffMIlylSdPQt4ewPLl3NrWUp0K5FPCCE1QVbYzrG1qSkmFI6xLIuXeXnFybO7mZm4n5mJ0NzcSj22EkBQTg6CcnLwZ0JC8biRSIQWxsZcEk0lmeYok9G8BiEqBgwYkBUQEBD43XffNfjvv//MExISpHK5nLGzsyvo169f2k8//fTS3NxcCQCHDh0K/vTTTxuFhIQYKZVKdOzYMUfo+PXBpk2bosaMGZO8fft2W39/f/P4+HiZXC5nrK2tC9q3b585fvz4pFGjRqULHScpUavv6BmG+RxAFoAgAJ4A/gKQxzDMcwBpAMq7/IRlWbafdqMkRPcpFArs3LkTS5cuRVwZayzY2Njgyy+/xIcffggZtRIjtSDtchpv39jbGNaDrAWKpv7YER2NiLy84v2/k5PxccOGlDCrin37gGi19XAXLBAmlnrkbuxdKFWurRIzYrR1bCtgRFVT0QozfU2YDfUaik/9Pi3eT8hOwNUXV9GjcQ8BoyKkAhgGmDABeP11YO5c4KDaygJZWVwybedOYMsWgNqaE0IIGIZBI0NDNDI0xFu2JcUUaXI5HhQm0Yoq0h5lZSGvEi0dASBHqcTtzEzczszkrStpJZGgubExWhS2c2xhbIzmJiZoZGAAESXSSD3VokWL/L1790aWd96QIUMyhgwZ8rg2YqpvunbtmtO1a9dqddq7efNmUE3Esnbt2ui1a9dGl39m/VXbl8AtB1D0Klj01RCATwXuy6jchxBShosXL2LevHm4f/++xuNGRkb45JNP8Nlnn8GCWrORWtR0VVPYvWOH8K/DkXwmGY0WNKKr/7QstaAAy8LDeWM9LCww0MpKmIB0mVIJrFnDH+vZE+jcWZh46pGbUTd5+z72PjCWGgsUTdXV9wqzJlZN0MqhFR7EPSgeO/b0GCXMiO5wcAAOHABGjwZmzSp9AcXDh0CPHlyrxtWrufMJIYTwWEgk6GFpiR6WlsVjBUolgrKz8SgrCw+zsoq/hlWyGg0AUuRyXE1Px9V0frGGsUiE5oXJsxbGxsVJtSaGhpCIRNX9sQghhOgRIXpGaJodreiMKc2sElKG0NBQLFy4EEeOHNF4XCQSYdKkSVi2bBmcqX0YEYh5Z3O0Ot0K6TfSYdrOVOhw9N7yiAgkqZW1rHBzo0RlVZw+DTxWu9jus8+EiaWeUU+YdXLuJFAk1aNeYVbfEmYA8I7XO7yE2dGnR/HDwB/oOYnolqFDgd69gf/9j1u/TN2ePcCxY1ybxpkzS/6zE0II0UgqEsHH1BQ+pqZ4X2U8Uy5HYHY2HmZmFifSHmVlIb6goNLfI1u1Ik2FjGHgUZhAK0qitTA2hoexMQwokUYIIfVSba9hRq82hNSw9PR0rFixAuvWrUN+fr7Gc958802sXr0aPj4VKeYkRPvMO5sLHYLeC87OxsaoKN7YcFtbdFe5mpNUwvff8/e9vYFBg4SJpZ4JiA7g7Xd06ihQJNXj6Qn8+COXOFMoAKmUm+ypTwmzYV7DsOzSsuL9sNQwPIh7gNYNWgsYFSFVYGEBbNsGjB/PVZs9eMA/npYGfPwx8MsvwNq1QN++wsRJCCE6zFQiQWdzc3Q25392jM/PL65Ce1jY0vFRVhaylMoyHqls+SxbfH9VIgBNjIxgq752JSGEEL2nU6sSMwzThmXZe0LHQUhdoFAosHv3bixZsqTMdcq8vb2xbt06DBgwoJajI4QI7X+hoShQWQtAyjD4vmlTASPSYVeuAP7+/LEFCwC66lTrErMTEZoSyhvT1QozV1fg05IlvJCTo4CfX/1KmLV2aA1XS1eEp4YXj/0V9BclzIju6tYNuH2bW7ts6VJArQUY7t8H+vUD3nqLa+vr6SlMnIQQokfsZTL0lcnQV6XNvJJlEZGbi8fZ2XiSlYXH2dl4nJWFJ9nZSK/CmyslgOCcHASXcVEyIYQQ/VXnZ3oYhjFnGGYmwzC3ANwSOh5C6oLLly+jY8eOmDp1qsZkmY2NDbZs2YJ79+5RsoyQeuhSaiqOJCbyxuY4O6OpkZFAEem4r7/m7zs5AWPGCBJKfXP95XXevpHECN723gJFox1iMT9hpt66UZ8wDINhnsN4Y4efHBYmGEJqikQCzJkDPH0KjB2r+ZwTJwAfH2DuXCApqXbjI4SQekDEMHAzMsJgGxsscHHBTi8vXG/fHqnduyOqa1eca9UKG5s1w0wnJ/SysICdVCp0yIQQQuqoOlthxjBMTwBTAYwAYAhu/TL2lXciRM/FxcXhs88+g6+vr8bjEokEH3/8Mb788ktYUts1QuoluVKJecHBvDEbiQRfNG4sUEQ67vJl4J9/+GP/+x8gkwkTTz1zOfIyb7+TcydIRHX27WuV1KcKM4Bry7j+xvri/QdxD/A44TFa2LUQLihCaoKjI/D778C0acBHHwGPHvGPy+XAxo2Ary/w5ZdcK0cDA2FiJYSQeoJhGDgZGMDJwAD9ra15xxLz8/EkO7t4K6pIe5GXJ1C0hBBC6oI6VWHGMIwDwzD/YxgmCMAFAGMBGIFLlgGAnk8hEKKZXC7Hpk2b4OHhUWaybMiQIQgMDMTatWspWUbqBKW88j3kSfVtiY7GPbXFrJe5ucGSrqKsGvXqMkdHbjKU1Ir/Iv/j7fdw6SFQJNpT3xJm3V26w9HUkTe2/+F+gaIhRAt69QLu3uXWOLOzK308NZXrz+rpCezerf//6QkhpI6ylcnQw9IS052csK5ZM5xt3RqRXbsivXt33GzXDr5eXhhvYiJ0mIQQQmqZ4AkzhmFEDMO8xTDMMQAvAKwA0AxckqwoUfYAwEIAdHk8qXcuX76M9u3bY86cOUhXXxcB3DplZ8+exYkTJ+Dh4SFAhISUpshWIMA7AGFfhUGeocf9xeqYmLw8fBEWxhvzNjbGdEfHMu5BXkV05Qpw/jx/cNEigFpb1oqcghwERAXwxrq7dBcoGu2pbwkzsUiM933e543tf7QfLEuNJIgekUiAGTOA4GDudUNTJVlEBDBpEtCyJXD4MED/BwghpE4wk0jQ0dwc4xs0wDQzM6HDIYQQUssES5gxDNOUYZgV4JJkxwC8Ba5FZFGiLBrADwBasyzbhmXZH1mWjREqXkJqW1xcHD744AP06NEDDx48KHXc3NwcGzZswL179zBw4EABIiSkbC83vETOsxxEfBOBG81uIGpLFFglTQRp2/yQEGSozbZv9fCAVCT49TE6SbJiBX/A0RGYPl2YYOqhW9G3UKAsKN4XMSJ0bdRVwIi0Q30NM31PmAHAaJ/RvP2QlBAERAeUcTYhOszcHFi5klvf7P33NZ/z5AkwciTQsSPg50eJM0IIIYQQQgRUqzNoDMMYMAwzlmGYCwCeAfgfgAbgV5OxAAYCaMSy7Gcsyz6szRgJEZpCocDmzZvh6emJPXv2aDxn/PjxCAoKwpw5cyCR6NdaLkT35SfkI3JVZPF+QXwBEo8mljzLE604n5KC/fHxvLGJDg7oQS1aq8T2wQOIL1zgDy5eDBgaChNQPaTejrG1Q2uYG5gLFI321LcKMwDo4NQBzayb8cb2PdwnUDSE1AJXV2D/fuDqVaBHGa1lb98GXn8d6NsXuHSpVsMjhBBCCCGEcGolYcYwTFuGYTYDiAHgC6AnSpJkSgBnAJwsOp9l2X9Y6stC6qF79+6hS5cu+Pjjj5GWllbqeMuWLeHv7w9fX180aNBAgAgJKV/YkjAo0vkzvk2+bwKGoYyZtuQplZj97BlvzEoiwfdNmwoUkY5TKuGtfsECrV1W6y5HXubt63o7xthY4Pp1ICCAW94oNJR7TqyPCTOGYUpVmR0MPAiFsh788KR+69qVS4adOgW0aaP5nIsXgd69ubXQ/vmHKs4IIYQQQgipRVpLmDEMY84wzCyGYW4DuAVgJgBLlCTKbgGYB8CJZdk3AVzRViyE1HVZWVn47LPP0KFDB9y6davUcXNzc6xfvx537txBj7KuSiWkDki/lY6YX/jdc+3H2sOsLfV+16blEREIysnhja1s0gT2MplAEek25ytXYBkSwh/86iuqLqtFCqUCV19c5Y3pesLs2DFurrxTJ6BdO2D6dCmA+pkwA4AxLcfw9mMzY3E25KxA0RBSixgGGDSIqyg7eBAoaw1if39gwACgWzfg9GlKnBFCCCGEEFILtJIwYxjmN3DVZJsAtEVJkiwEwDcAPFmW7cSy7EaWZRO0EQMhuuLMmTPw8fHBmjVroNAwS1bUfnHu3LnUfpHUaaySxfOPnnONdQuJTERosqqJcEHVA3czMrAyIoI31snMDFMdHQWKSMfl56P577/zxzw9gSlThImnnrobexdpefxKa11PmMnl/P2il/T6mjDzsvVCB6cOvLFf7/4qUDSECEAkAkaNAgIDgZ9/Bho21HzetWvAm29y2fbjxwGlsnbjJIQQQgghpB7RVoXZWABG4JJkzwF8D6ATy7LuLMt+zbLscy19X0J0RlxcHMaMGYNBgwYhPDy81HEvLy9cunSJ2i8SnRG7JxYZNzJ4Y65LXWHYkKpytKVAqcTkoCCozq9LGAbbPTwgphaYVSL+9VeYxMXxB1euLMlukFpxPvQ8b9/TxhNOZk4CRVMz1BNhYnHRV37CTD2xps+mtOUnoo8HHUd8VnwZZxOipyQSYOpU4Plz4KefgEaNNJ936xYwdCjQsiWwaxeQl1e7cRJCCCGEEFIPaHMNMxZALLj1yW6DS5wRUu+xLItff/0VzZs3x/79+0sdl8lkWLZsGe7du4eePXsKECEhlVeQWoDQRaG8MSN3IzScV8bV0qRGrI6MxL3MTN7Y5y4uaGNGLTCrJD0d0pUr+WOvvQYMGyZIOPXZP2H/8Pb7ufUTKJKao54IK0qY1dcKMwAY7TMaRhKj4n25Uo7f7v8mYESECMjQEJg1CwgOBnbsAFxdNZ/3+DEweTLQpAnw/feAhnWPCSGEEEIIIVWjrYTZU3DVZQ0AfATgAIB4hmH+YRhmGsMwFlr6voTUaWFhYejfvz+mTp2KlJSUUsd79uyJ+/fv48svv4SBgYEAERJSNWFLwlAQX8Aba7axGUQG2rwuo357lJmJb9RaMfqYmGBJ48YCRaQHli0Dk5jIH/v+e269GVJrcuW5uBx5mTfWv0l/gaKpOWVVmNXnhJmFoQVGthjJG/v17q9gaa0mUp/JZMC0acCzZ8DOnUCzZprPi44G/vc/riJtwQLg5cvajZMQQgghhBA9pJWZTJZlWwDoCGAzgERwyTMpgD4AtgGIZRjmCMMwIxiGkWkjBkLqEqVSiZ9++gktW7bEv//+W+q4lZUVfvnlF1y4cAFeXl4CREhI1aVdSUP01mjemM1QG9i8YSNQRPovX6nEB0+fokBlUlkEYJenJ2QiSlJWyZMnwMaNvCHFkCFAt24CBVR/XX1xFbny3OJ9ESNCb9fewgVUQ2gNM83U2zI+SXyCqy+uChQNIXWIVApMmsS9Pv3+O9CihebzMjKAH38E3NyAceOAmzdrN05CCCGEEEL0iNZm1ViWvc2y7BwATgDeAnAIQB645JkBgKEA/gAQxzDMLwBaaisWQoQUGhqKvn374qOPPkJWVlap46NHj8aTJ08wZcoUiGiim+gYZZ4SQdOCuCa8hURGIjRbV8bV0KRGfB0ejttqrRgXNmqEDubmAkWk41gWmDePl9FQSCQoUG/PSGrFP6H8doztHdvDyshKoGhqTlktGdXXMCvgF+vqvZ6Ne6KZNf81Y8utLQJFQ0gdJJEAY8cCDx8CJ08CvXppPk8uB/buBTp35ra9e4H8/NqNlRBCCCGEEB2n9dl5lmUVLMv+zbLse+BaNE4H8F/hYQaABYBJAEYX3YdhmB7ajosQbVMqldi0aRNatmyJS5culTreqFEjnDp1Cvv27YODg4MAERJSfRErI5D9JJs35rbcDUZuRmXcg1SXf2oqVkVG8sa8jI3xdVlrnZDy/fUX4OfHGwoZOhRs06YCBVS/nQ87z9vXh3aMQNktGSUSJW+8viXMGIbBtHbTeGOHAg8hNjNWoIgIqaNEImDwYODiReDGDWDkSG5Mk5s3uWozFxfgq6+AmJhaDZUQQgghhBBdVavlLCzLprMs+wvLsr0ANAHwFYBn4BJnDEpqFC4yDPOSYZh1DMN0rs0YCakJwcHB6NOnD+bMmYPs7OxSx2fMmIFHjx5h0KBBAkRHSM3ICsxC5Ap+4sasgxmc5zgLFJH+Sy0owPgnT1QL+iBlGOxt3hyGRbPvpHJycoBPPuEP2djg2ciRZdyBaFNyTjJuRd/ijelLwkw9ESaVcv+TJZL6XWEGcG0ZDSWGxfsFygJsv7VdwIgIqeM6dQIOHeLWOZs1CzAq40KluDjgm2+4xNno0cDly1xVNSGEEEIIIUQjwfq/sSwbwbLstyzLegHoCmArgGSUJM8cAcwBcJVhmFCGYVYxDNNWqHgJqQiWZbF582a0atUK/v7+pY43btwY586dw7Zt22BOrdOIDlPKlQiaGgS2QGXSRQx4/uIJkYRai2rL7OfPEZmXxxtb7uaGdmZmAkWkB86dA8LDeUOBEydCUdbkI9GqM8FnoGRLKq6MJEZ4rdFrAkZUc0onzLivYjG/wqw+dlCzMbbB2JZjeWNbb21FvqIe/jIIqYymTYGffgIiI4GVK4FGjTSfJ5cDBw4APXoAPj7A+vVAUlKthkoIIYQQQoguqBOzmizL3mBZdja4JNk7AI4AKEBJ8qwxgIUAAmorJoZhrBiGWcwwzBWGYZIYhslnGCaeYZh/GIaZwTCMrJz7MwzDjCk8P4lhmFyGYcIYhtnDMEzH2vo5SO2Jjo7GG2+8gY8//hg5OTmljs+cORMPHz5E//76caU8qd9efP8C6dfTeWMun7nAtLWpQBHpv99jY7EvPp431tvSEvPLmhwjFfP221x7Kw8PAIDitdcQ1YM6Qwvl5LOTvP1+TfrxKo90mXoiTCIp+koVZgDwcaePeftxWXE4FHhIoGgI0TG2tsCiRUBoKPDnn2WvcwYAjx9zldXOzlzbxkuXqOqMEEIERnOQhBBSd9SJhFkRlmXlLMv+xbLsSHDrnc0CcBUliTOmNuJgGKYvgCAAKwC8BsAagBSAHYB+ALYBuMMwjFsZ9zcCcALA3sLzrQEYAHAFMAHANYZh/qfdn4LUpj///BMtW7aEn9oaOADg6uqK8+fPY8uWLTCjKhCiBzLuZiD8q3DemJG7ERovbSxMQPXA06wsfPjsGW/MUiKBr5cXxEytvDTqt169gPv3gaVLUfDjjwD9TgUhV8pxJvgMb2yI+xCBoql56okwWeG0h/oaZvWxwgwAWjdojZ6Ne/LGNtzYAJYm8gmpOIkEGDGCuxDk/n1g2rSy2zXm5QF79wK9ewPNmwM//ggkJtZmtIQQQkBzkIToK7lcjoCAAMONGzfaTJw4sVGbNm28jIyM2jIM055hmPYjRoxwFfLxKqJTp06eRY9fkS0oKOiVyf3k5GTRzJkznRs2bNhSJpO1a9CgQauJEyc2ioqKktR07NVRp4JRxbJsKrgXhW0MwzQB9yQ/9pV3qgEMw7QG90JjXDjkB+AYgCRwlW4TAXgXbn4Mw3QsjFXVLwAGF95+AmAHgDgA7QB8CMAUwCqGYaJYlv1daz8M0bq0tDTMmTMHvr6+Go/PmjULq1evhqkpVd0Q/aDIVeDJuCdg5SoTmCLAa48XxEa0hpY2ZCkUGBkYiCwlf1J9u4cHGhnqR+VNnWBoCHzzDdicHEDDxQ9E+669uIaU3BTe2JvubwoUTc2raEtGuZwr9qiPeds5nebAP6KkpXVAdAAuhl9EH7c+AkZFiI5q1QrYsQNYtQrYuRPYvh0IDtZ8blAQsGABsHgx8NZbwIQJwKBBJZl9QgghWkFzkITor8GDBzf18/OzrKuPV9tiY2PF3bt39woJCSmeyIqLi5P6+vra+/n5Wfr7+we5u7vXictH62zCTBXLsqEAvi7ctG0jSl6oZrIsu031IMMw6wDsBpe8awZgKYD5KscHABhTuPsvgMEsy+YW7u9nGGYngP8A2ABYzzDMXyzLZmjpZyFa5O/vjwkTJiAiIqLUMScnJ+zatQsDBw4UIDJCtCdsSRiyH2fzxlwWucCiq4VAEek3lmUx69kzBGbzf+dTGjTAKHt7gaIiRDv+fv43b7+1Q2s0stCflqOlE2bchQfqFWYAlzQrSqjVJ0O9hsLV0hXhqeHFYysvr6SEGSHVYW3NJcM+/ZSrPPv5Z+DwYc39XwsKgCNHuM3WFhg9Gpg4EWjXrn5m8QkhRPtoDpIQPaVQKHj7FhYWCktLS3lERIRBXXi8yvL19Q0p7xwnJyd5Wcdmz57dKCQkxNDCwkLxwQcfxDdv3jw3LCxMtnPnTvvY2FjZxIkTXa9evfqsrPvXJp1ImNUWhmG8ABT1gjmm/kIFcG0jGYaZCqAPACcAkxiG+Yxl2aK/2gWFX+UApqq8UBXd/wnDMB8D2AfuBWsagLU1/9MQbcnPz8eXX36J77//XmOboHfffRfbtm2DtbW1ANERoj0pF1Pwct1L3phpW1O4fuUqTED1wM7YWPjGxfHGWpmYYJO7u0AREaI96uuXDXYfXMaZuqmsCjP1NcwAri1jfUyYSUQSLHxtIWafml08di70HG5F30IHpw4CRkaIHhCJgL59uS0hAfD15SrQnpUxL5GYCGzaxG3e3lzibNw4wNGxduMmhBA9RXOQhOi3Dh06ZHl6euZ26NAhu2vXrlleXl75GzdutJk7d65rXXi8yho/fnxqVe+bm5vL/P3339YymYy9cOHC07Zt2xY/V82YMSOpXbt2La5du2YWFhYmdXNzE3xV7zq1hlkd0F/l9m9lnVT4AlQ0q2MFwB0AGIaxVnmMMyzLhpXxEAcBxBfefrfK0ZJaFxoaih49emD16tWlkmXm5ub47bffcPDgQUqWEb2Tn5iPJ+OeACp/9owBg+a/NYdIRi8l2nA/MxMfPX/OGzMTi3HI2xtGYmp/SfRLaEooAhMCeWNDPPRn/TKg9NpkJQmz0hVmmgo/6otJbSbBwcSBN7bq8iqBoiFET9nZAfPnA0+fApcuAWPGAAavuDg5MBD47DOgYUPg9deB3buBtLRaC5cQQvQUzUGSWpebm8usWbPG9rXXXvOwtrZuLZVK2zk6OrYcM2ZM44iIiOJL9pKSksRmZmZtGIZpv3z5cmpvUwWrVq2K/emnn6ImTZqU4uXlVe1WgzX9eLUpNjZWkpeXxzRp0iRXNVkGAG5ubgXt27fPBICQkJA60Q+cZjn5FAACAaQDKK8EMFnltlXh1x4o+Z2eL+uOLMsqAVwq3O3MMIxVWeeSuuPQoUNo27Ytbt68WepYr1698ODBA4wbNw4MtSsheoZVsnj6wVPkR/Ffj5usaAITbxOBotJvifn5GPboEXLV1i371dMTHsbGZdyLlCsmBnjyROgoiAZHnhzh7dsa26KTcyeBotGOiq5hBpROrtUnRlIjzOsyjzd25MkRPE18KkxAhOgzhgF69gT27uVeI7dvB157rezzlUpunc9JkwB7e+Cdd4CDB4GsrNqLmRBC9AfNQZJa5efnZ9KsWTOfzz77rPG1a9fMUlJSJHK5nImNjZXt37/ftmvXrl5xcXFiAFi7dq1dZmam2MbGRj5v3rwEoWMnus3e3l4ukUjYsLAww4cPH/Ku0oqOjpbcv3/fBABcXFzqxKWjlDBTwbLsVpZlfViWtWBZ9lE5p7dQuZ1U+NVHZay8+z8u/MoAaFmJMEkty87OxowZMzBq1Cikp6fzjkmlUnz//fc4f/48GjduLFCEhGjXy3Uvkfx3Mm/Msp8lGs5rKFBE+q1AqcSox48Rnsu76AYfOzvjXVq3rOpYFpgyBWjbFli7lpv0I3XGn4//5O0P8xwGsUi/KinLbslIFWbqZnaYCXMD8+J9FiyW+y8XMCJC6gErK2D6dODKFa5N49KlwKs+3+TnA8eOAe+/zyXPRo+G6MQJiOr7ExghhFQQzUGS2vTLL79YDR482DMqKkrGMAx69uyZ9umnn0aPGzcuwczMTAEAMTExslWrVjnk5OQwO3bssAeADz/8MNbU1LR0D3lCKsHY2Jjt379/al5eHtOzZ0+vTz75xGn79u3WS5YsadChQ4fmiYmJ0nbt2mV6eHjUiUtHaQ2zKmAYxgnAG4W7iQCCC2+7qpwWXs7DvFC5TZmWOiowMBDvvfceAgMDSx1zd3fHwYMH0bZtWwEiI6R2pF1PQ+iiUN6Y1EGK5r83ByOiakptmB8SggupqbyxzmZmWNO0qTAB6YtffwVOn+Zuz58P/PUXd0V9Q0r8Ci0yLRI3om7wxka2GClQNNpTmTXM6vt8s4WhBWZ3nI2Vl1cWj+17uA+Lui+Cj73PK+5JCKkR7u7AN98AX38N+PsDe/YAf/4JZGZqPj87GzhwAAYHDuANY2PEdOkCkVwODBny6laPhBBCykVzkKS6/P39jWfPnu0ml8sZmUzG+vr6hrz33nvFvZUDAgLiu3bt6q1QKHDx4kXzLVu25CcmJkotLCwUCxcupOoyAgDo06dPs8DAQOOUlBSJkZGR0t7evqBDhw6ZY8eOTX7rrbcyyrv/li1bXnTv3t0kOjpatn79et6iuHZ2dgW+vr7hWgu+kihhVjU/AijqqXmgsLwZAOxUzkks5zFUyzVsKvPNGYYpb3avuAQhLy8POTk5lXl4AoBlWezevRsLFizQ+PsbM2YM1q1bBzMzs3r9+81VqYDJVauGIbpPniJH4HuBYOWqC5cBTX9tCqWFslb/9uvL39ru+HhsiorijTlKpdjXrBmUeXmov8821cOEh8Pgk0+gmuJlg4KQCwBqf8f15W+tLjn44CBv38rQCl0bdNW719fcXBkA1ao5LiumqcIsPT0XOTn1+0LOWW1nYfPNzcjI5z57sWCx5J8lOPDOAYEj0z30vEaqpXNnbluzBuJTpyA+dAgiPz8wZfSOlWZnw+Xff4F//wVrZgbFG29AMXQolAMHAqamtRw80Vf0vEZqS15enuqufXnzcSzLvtRCGILOQWpTdF5enZ+XNhCJWBupVPGqcyrzczgZGMirH1XFKZVKfPzxxy75+fkMACxfvjxSNVkGAB07dszt1q1bmr+/v0VMTIxs06ZNDQBg6tSpcRYWFhVuzXL27FnT+Pj4Gvk37dKlS7a7u3udqDYinIsXL1oU3c7IyBBnZGSIQ0JCDA8ePGjbpUuXjAMHDoQ1bty4zEs/3dzcCgICAp4sXrzY0c/PzzIxMVFqZWUlHzBgQOrKlSujXVxcavX/xqvU+SemuoZhmBkA3i/czQSwUuWw6sIy5b1rUz1e2QVpXpR/Cufu3bvIop7ylZKXl4ctW7bg0qVLpY4ZGhpixowZ6NOnD65duyZAdHWXv7+/0CGQmqQEjFcaQxop5Q3njszFDfkNwE+guKDff2vnDQwAQ8PifSnL4pOUFDy8eBEPBYxLlzFyObovWQJDtavib0ybhrjbt195X33+W6tLdj3fxdtvZ9wOF85fECga7UlI6A7V+YmIiGfw8gLE4tKJsYsXryIsrNyL9PTem1Zv4mBcSUL1+PPj2HJsC5oZNxMwKt1Gz2ukWiwsgKlTIXn/fTjduAHn//6D7YMHEJXR5pjJyIDk0CFIDh2CQipFfNu2iOnSBbEdO6LAzKyWgyf6ip7XiDY9e8ZbWuzvCtylRtuw1JE5SK1xvnattdAxlOcNa+uU061ahb7qnMr8HGzv3q/+EFrDjh07Zv7gwQMTAPDx8cmeP3++xuRqkyZN8vz9/REXFycFAFNTU8WiRYviK/O9li5d6hwQEFAjV8ds2LAh3N3dPan8M4m2mZubK7p3757erl27LGdn5wKxWMxGRUXJLl68aObv72/BsiyuX79u9tprr3ldu3btyasSX05OTvI9e/a8QCVyG0KghFklMAwzDMBPKkMzWZaNVtlX/X3yLkPRQPU4/TvUEdHR0Vi9ejUiIiJKHXN1dcXChQvh7OwsQGSE1C6DgwaQBvCTZfIWcuS9X95TG6mO8Xl5aKhUYouREQoYBrNycuCheOXFbKQcXvv3wzooiDcW0b8/4jp0ECgioiopPwlPsp7wxrpadhUoGu1SKPhLBxclyhgGEIuVvOPq59ZXQ+2H4lTiKWQoSpKHe2P24qumXwkYFSFEbmqKyH79ENmvH2SpqXC6ehXOly/D9vHjMu8jLiiA482bcLx5E0qRCIktWyK2c2fEduiAHFqjlRBCSqE5SFITdu7caVt0e8GCBTEikebPGVZWVryJh4kTJybY2trSZEQ9t2LFipfdu3fPNjQ01NT+JM7f39/4/fffbxoTEyOLjo6WjR8/3vXSpUvBGs7VKfQkWUEMwwwHcAAlvXQ2sCz7u9ppqr2DZHj1C5ZqM/fKlpg2Kud4KxRe+dK2bVt069atkg9fPx0/fhyLFi1Cenp6qWMffvghVqxYAUOVyg/Ctb8ouqKvZ8+e9PvREyknU/DsIO9KOkhsJWhzrA0MGgqzDkV9+lsbCGB4ZiYupKXhM0rQV4vo/HnIjhzhjSnd3GD/++8YaG6u8T716W+tLth8a3PJEuQALAwsMH/YfBhI9G/Nmy+/5P9M3t4eACIBAAYGDLKzS461b98FHTvW75aMRUKtQ/HFpS+K9+9m3IXMU4bejXsLF5SOoec1onWjRgEAUkNC8GLdOjhduwbrp0/BsJqfx0RKJezv34f9/ftotWMHlD4+ULzxBpRvvgllx46AWKzxfoQUoec1UltMTExUdwcDeFAb37eOzUESHaVQKHDp0iVzADAzM1Oot2JUpVSpFjc0NFQuWbIkrrLf7+bNm0Hln1W7OnXq5FlTVW/bt28PnT59ekpNPJau6N+//yvb1vXs2TP75MmTz7t27doiPz+f8ff3t7h06ZJxr169sl91v7qOEmYVwDDMFADbUPL72gPgEw2nqvZ7MsSrX6xU39FVapGO8noiMwzToOi2gYEBjIyMKvPw9Y5cLseSJUvw/ffflzpmZmaG3bt3Y/jw4QJEplsMDQ3pb00PZD3NQsiUEP6gGPD+wxuW7paCxKSuPvyt9TQyQk87u/JPJGWLiwOmTgVUJ+ukUoj++ANGDg4Veoj68LcmtANP+OtRve35NizNLIUJRsvUi0WNjUuqeKX8gl6IRIagPz3OJ90+webbmxGbGVs8tujiItyZfgdiEU2qVxY9rxGtatoUoW+/jdC338bAVq1gdO4ccPgw8O+/gLzsZSlEjx5B9OgR8MMPgK0t8OabwFtvAQMHAmVc4EJIEXpeI9pkYMC74CleS2uU8dS1OUiiu65fv26UmZkpBoAOHTpkllElBABgVT43jx07NsHR0bHOrCdF6rZ27drlvvPOO0kHDx60BYBjx45ZUsJMzzEM8xWAr1WGdgKYxrIaL5dLVbltDaDMzH3h8SIJVY2PVE9cXBxGjx6NCxdKr5Xi4+ODw4cPw8PDQ4DICKl98nQ5At8JhCKDP6vb9IemsOpjJVBUhFSBUglMmMAlzVStWgVQK8Y6IzA+EHdi7vDGxrcaL1A02pevdi2vVMqq3H71ufWZsdQYX/f6Gh/+/WHx2IO4B/j17q+Y3n66gJERQl6pQQNg+nRuS0kBTp4EjhwBzpwBcl+x1E5iIuDry21SKdCzJzBkCPDGG4CnJ9fHlhBC9FR9m4OM6tr1vtAxlMdAJCq37UNd/TkCAgKK16tr3br1KxMYCoWCAQCZTMYuXbq00tVlddWwYcOSfXx8aiR54+3tXd5agfVW7969M4oSZkFBQTpf9k0JszIwDCMCsBWA6ifxDQA+KeOFCgBUe5i5AAh7xbdQbatYesEsonUBAQEYNmwYoqOjSx0bO3Ystm/frl5+T4jeYhUsnox7guyn/PcR9mPt0XBuQ4GiIqSKvvsO8PPjj735JjBvniDhEM1+e/Abb9/JzAl93foKFI32FRTw91WTZOoJM/Vz67sp7abgp4Cf8DD+YfHYF/9+gfe834OFoYWAkRFCKsTKChg/ntsyM4GzZ4Fjx4BTp4Dk5LLvV1AAnD/PbZ98Ari4cFVnr78O9OvHPS4hhOiB+joH6WRgoBdVTHX15wgJCSlOXDRr1qzMZE9ubi5z7NgxawBwdHTMb9y4cZU+jZw9e9Y0Pj6+RnINXbp0yXZ3d6/2ZYSff/55nUkQ6zMHB4fi/wNpaWk63waEEmYaFL5Q+QIYqzK8lGXZ5eXcVXWVY28Al15xrnfhVxZAYKWDJNXy+++/Y+rUqcjL41esS6VSrF+/HjNnzgRDVy+SeiTksxAknUjijZm2MYXnDk/6v6AFh+LjMdDaGhYSehmucadOAV99xR9zcgJ27wbKWOCYCGN+1/loaN4Qvvd9ERAdgDE+Y/S6xd6rEmYyGQuAKfPc+k4ikmDd6+vQ/7f+xWMJ2QlY8d8KrB6wWsDICCGVZmoKjBjBbQoFcO0aV3128iQQWM7H4shI4JdfuE0kAjp14pJnr78OdOwI0PsqQogOojlIoi3JycnFH64sLCyUZZ23Zs0au6ioKBkAiMXiKi+kvHTpUueaWi9sw4YN4e7u7knln0nqAtVEqYWFheJV5+oCmjnSbAtKXqiUAGZW4IUKAK4BKJri6FPWSQzDiAH0Kty9z7JsahXjJJWkUCjw2WefYfz48aWSZQ0bNoS/vz9mzZpFCQJSr0Rti8LLtfxW7BJrCbyPekNsrL+T10L5LTYWox4/Ru979xBPfddqVnAwMHYsf90ykQjYuxegNeHqHDsTO3zU6SPcnHYTT2Y/wdwuc4UOSauowqx6+jXph7c93+aNrbu+Dk8SnggUESGk2sRioHt3rmXyo0dAaCiwcSOXAJPJXn1fpRK4fh1Ytgx47TXudX7kSODnn7nHKbMggxBC6hyagyRaoVqcmJaWpjEHEBcXJ167dq1j0X51Emak/rp48aJZ0W13d3edb11Jl2CpYRjmAwAzCneVAD5gWfa3su9RgmXZVIZhzgN4A8DbDMO4sCwbqeHU9wEUzdz9Uc2QSQWlpqZizJgxOH36dKljffr0wcGDB2FHE6qknkn2S8bzj57zxhgpA58jPjBypcWza9pfiYmY9PQpAOBeZiZ63L2Lc61bw8VQ51s8Cy8rCxg+HEhN5Y+vXg307i1ERKQSvGy9hA5B60qvYVaSGKM1zCrmhwE/4PTz0yhQcr+4AmUBZpycgYsfXISIoesACdF5bm7Axx9zW2Ym8M8/wIkT3LpnGtro86SmAocPcxsANG4M9OkD9O3LfW1ILcYJIXUPzUESbWrYsGHxZXhXr141nT17dqk+yLNnz26UmppanB8oWsusKm7evBlU1fsS3fXgwQODo0eP2hTtDx069FXrKeoE+mSpgmEYB3A9gossqOgLlYp1hV9lAPYzDGOmepBhmBYq3yMDwM9ViZVUzrNnz9ClSxeNybKPP/4YZ8+epWQZqXcUWQo8GfcEUCuW9vzZE5a9LAWJSZ9dSEnBe4GBvF/3s5wcHEtMFCwmvcGywJQpwMOH/PFRo4D584WJiRA1VGFWfe427vi066e8sf8i/8Pue7uFCYgQoj2mpsCwYcCvvwIvX3Kv8T/+yK1hZmBQ/v0jIrh2zBMmAI0aAR4ewIwZwMGDQFyctqMnhJBy0Rwk0bbOnTtnFd0+fPiw7ZUrV3hXRX/++ecNDh8+bAMApqamCgCIjIw0CA4OVvt0QvRNp06dPBmGac8wTPuNGzfaaDpn+fLl9ufOnTN51eNcuXLFaNCgQR55eXkMAHTr1i29b9++Wa+6jy6gCjO+eQDMC29HAghjGGZYBe53p+gqDpZl/RiGOQxgBIDXANxnGGYrgBcA2gKYCaDoBWwhy7I0U6plfn5+GDVqFNLS+AluqVSKn376CdOmTRMoMkKEJTYRw/uwNx698wjyJG59TpclLmgwsYHAkemfW+npePvRI+SptQea37AhPnZ2FigqPbJyJTcBpsrHh5tkoxa7pI4onTBjVW7zj1GFWdm+7PUlDgYeRHhqePHYAr8FGOIxBPYm9sIFRgjRHobhXtd9fIBPPwVycgB/f8DPDzh7tvy1zwDg+XNu27GD2/f25qrPevfm2kLa0/MHIaTWzQPNQRItGjp0aLqTk1N+dHS0LC8vjxkwYIDX0KFDk52cnPIvXbpkfvv2bVMAaN26ddawYcNSli1b1lAulzNDhgxxf/fdd5MGDhyY0aNHj2yhfw5d9fTpU9mWLVtsVccePXpkrHp7zpw5TqrH+/fvn/H2229n1MbjlefixYtmS5cubdSoUaO87t27p/v4+OTY2NgoxGIxGx0dLb148aL5pUuXLJRKbnk8Jyen/N9//z28Kt+rrqGEGd9EldsuAI5W8H6TAOxW2Z8A7gVpIAA3AN+rnc8CWMay7PaqhUkqaseOHZg1axYUCn4JjZ2dHY4cOYLu3bsLFBkhdYNlD0u0u94ODwc/hGkbU7h94yZ0SHrnbkYGBj54gEy156EpDRpgTdOmtGZidR06BCxZwh+zsACOHOGuTiekjnh1hRn7ynNJCWOpMba8uQVv7nuzeCwlNwWfnv0Uvw//XcDICCG1xsiIW+fs9de5qrOXL4Fz57jk2b//AgkJ5T9GYCC3bdrE7Xt6comzHj24zc2NLrohhGgbzUESrZJIJNi9e3fosGHDPLKzs0U5OTmiAwcO8BIuPj4+2adOnQrOzMwUrV692ik3N1cUFBRktHz58oZeXl7BQsWuD4KDgw02bdrkWNbxZ8+eGT179oxX9SeRSFBWgqumH6+iXrx4YbB///5XtmXr3r17+m+//Rbu6uqqF59kqSVjIYZhbAGU+UdXGSzLZoPrITwOwDkACeAW4owBcAhAD5Zll9XE9yKaKZVKLFq0CDNmzCiVLGvTpg1u3bpFyTJCChk3M0a76+3gtdsLjIgmBmrS/cxM9L9/HylyOW98hK0ttnt6UrKsum7e5FotqWIY4PffAXd3YWIiRAO5nOscqkom03wboAqz8gxyH4T3vN/jje19uBfHnh4TJiBCiLAaNgQmTQIOHODaLT58CGzcyLV0tLSs2GMEBXGV6R98ADRtyj3me+8BmzcD9+4Bap8pCSGkOmgOktSWAQMGZAUEBASOGTMmoVGjRnmGhoZKiUTCOjo65o8bNy7hypUrQU5OTnIPD4/8Q4cOBbu7u+eIRFy6oGPHjjkCh08EtGHDhpdr166NeP/99xNbtmyZ5eTklG9sbKyUSCSspaWl3NvbO3vixInx58+ff/rff/8915dkGUAVZsUKy5JrbOaSZVkWwN7CjdSi3NxcTJw4EX/8UXot03fffRe7du2CickrW7ASUu9IrahFdU17kJmJfvfuIVktWdbfygp7W7SAmJJl1RMRAbz9NpCbyx9fswYYMkSYmEi5niU9g7u1e71LFqv/mQKAoWHJbVrDrPLWvb4OZ4LPIC2vpOX29BPT8Vqj16g1IyH1mWr7xo8/5hJd9+8DFy5w1Wf+/kBmZvmPEx0N/PEHtwFc9fprr3FVaF27Ah07UiU7IaTKaA6S1KYWLVrk7927N7K884YMGZIxZMiQx7URU30wZMiQDJZlb9fFx7t582ZQeed4e3vneXt75wGod61cqcKM6JXExET069dPY7Js8eLFOHDgACXLCCFa9zAzE/3u30eSWrKsj6Ul/vLxgYGIXn6rJT0deOst7ipyVdOmcWubkDopISsBrba2gvcWb2y4vgEpOSlCh1Rr8vJKj8lktIZZdTiaOWL9G+t5YwnZCfjw5Idg1cv5CCH1l1gMtGsHzJ8P/OY/QYkAAKoHSURBVP03kJwMXL0KfPcdMGBAxZNeaWnA6dNcG+i+fbkEWuvWwIcfArt3A0+fAoVreBBCCCGEEN1FM3ZEbzx//hxdu3bF1atXeeNisRg///wzVqxYARFNUpN6Ki9Gw2wt0YpHhcmyRLUSkV4WFjjRsiWMxWKBItMTeXnAO+9w7ZZU9esH/PQTrTdSh229tRV5ijw8SXyCeWfnwW2DG7IL6sca0uVVmFFLxqqZ2HoihnoO5Y0dfXoUvvd9BYqIEFLnSaVchdjnnwN+fkBKCnDrFrBuHTB8OGD3yiU6SiiVwIMHwPbtXDvI5s0BW1tg0CBg2TJuPbWU+nNhCCGEEEKIvqCWjEQv3Lp1C4MGDUJiIr9K1MzMDH/++ScGDhwoUGSECC/iuwi8+OEFWp1tBfNO5kKHo9dupafj9QcPSrVh7Glhgb9btYIJJcuqR6EAxo3jWiqp8vICDh0qXaZD6ow8eR62BGzhjQ31GgpjqbFAEdUuTRVmBgYltw0N+RVRmhJspDSGYbDjrR248uIKErNL3gPOOTMH3V26o6l1UwGjI4ToBIkEaN+e2+bN4xacfPYMuHwZ+O8/bgsNrdhjpaQAZ85wW5HmzYHOnbkWjh06AK1a8a+YIIQQQgghdQolzIjO++eff/DOO+8gU60XfaNGjfD333+jZcuWAkVGiPAif4hE2BdhAID7/e+j1ZlWsHjNQuCo9JN/aiqGPHyIDLVF4XtYWODvli0pWVZdLAvMmQP8+Sd/3M4OOHkSsLISJi5SIfsf7UdcFr+F5iddPhEomtqnKQHGT5iVfz7RzN7EHtuHbMeIP0YUj6XnpWPUn6NwdfJVGEgMXnFvQghRwzCApye3TZnCjUVHc4mzy5eB69eBe/cAtYujyvTkCbft3s3tSyRAy5Zc8qxo8/EpXWpMCCGEEEIEQf3piE77448/8Oabb5ZKlrVt2xbXr1+nZBmp115ufInQhSVXxCoyFHjwxgPkx1Ovr5p2JikJrz94UCpZ1t3CAqdatoSphK5PqbZvvwW28CuUYGrKrSfSlKpI6jKWZbH22lreWB/XPmjToI0wAQlAvcJMIuGW1SminjDLydF+TPpkePPhmNh6Im/sTswdLPBbIFBEhBC94uQEvPcesGkTEBDAraX633/AmjXAiBHc8YqSy4G7d4GffwZmzOAq28zMuAq0mTOBnTu5Vo8VTcgRQgghhJAaRTN4RGdt3boVs2fPLrWwe//+/XHkyBGYmZkJFBkhwoveHo3gucGlxpusagKZPV3BWpMOJyRg9OPHKFB7LhpgZYWjPj5UWVYTtmwBvvqKPyaTAceOcRNNpE77N+xfPIznrzn3addPBYpGGOoVY+oJMmrJWH2b39yMG1E38DTxaclYwGb0cu2FkS1GChgZIUTvGBkB3btzW5EXL7jqs+vXgWvXgDt3NPfj1SQ/n1tH7dYtYNu2ku/RqhXQpk3J1rIlYGJSwz8MIYQQQghRRQkzonNYlsW3336Lr9QnTwGMGjUKvr6+MDCg9juk/orZGYNnHz4rNd50bVM4z3IWICL9tSsmBlODgqBUGx9ma4sDLVrAQESF3NX266/A7Nn8MYYBfv8d6NdPmJhIpay8vJK372HjgTfd3xQoGmGoz5mWTpjx9ylhVnmmMlMcevcQOv3cCTnykhK9KcenoJVDK3jYeAgYHSFE7zVqxG3vvsvt5+UB9+9zCbSAAOD2beDpU67FdEXk5AA3bnBbEYYBPDz4SbQ2bYAGDWr2ZyGEEEIIqccoYUZ0Csuy+PTTT7F+/fpSx2bNmoWNGzdCTNUcpB6L2haF5zOflxp3W+mGRp80EiAi/cSyLJZHRODL8PBSx8Y5OGCXpycklCyrPl9fYNq00uObN5dMSJE67dqLazgfdp43Nq/zPIiY+vX/o1kz7s82L4/bpFL+cWrJWDN87H2wadAmTD0xtXgsPS8db+9/G9enXoeloaVwwRFC6hcDA6BTJ24rkpHBtWMsSqDdugU8L/2+vUwsCwQFcdvBgyXjDg78BFrr1oC7O9f/lxBCCCGEVAq9gyI6Q6lUYvbs2dhW1KZCxddff40vv/wSDMMIEBkhdcOL9S8Q8klIqXHXr13ReFFjASLST3KlErOeP8fPMTGljn3o5ISf3N0houei6tu/H5g0qfSV2MuWAbNmCRMTqbTl/y3n7TuaOmJS20kCRSOchg1LF0qqJsWowqzmTG47GRcjLuL3B78XjwUlBWHM4TE4MfoExCK6sIoQIhAzM6BnT24rkppakjy7dYu7HRZWuceNiwPOnuW2IgYGQPPmgI8P18rRx4fbGjXiKtUIIYQQQohGlDAjOkGhUGDatGnYtWsXb5xhGGzevBmzaPKU1HMRKyMQ9nnpD9cun7ug8ZeULKspWQoF3n/8GCeTkkod+1+jRljZpAkl7mvCn38C48cDSrVml0uWAEuXChMTqbQ7MXdw6vkp3tjC1xbCUGJYxj3qL1rDrOYwDINtg7fhUfwj3Iu9Vzx+Ovg0Fp9fjO8HfC9ccIQQos7SkmsxrdpmOjGRWwPt/n3g3j1ue/q09PuiV8nLK7mvKnPzkuSZ6mZnV+0fhRBCCCFEH1DCjNR5crkcEydOxL59+3jjYrEYv/32G0aPHi1QZIQIj2VZhH8djohvIkodc13misZLG1MCp4bE5+fjrYcPcTMjgzfOANjQrBk+bthQmMD0zcGDwLhxgELBH1+4EPj2W7oqWocs9+dXl9ka22J6++kCRVO3UYVZzTKRmeCv9/9Chx0dkJCdUDy+5uoaNLdtXi+rHAkhOsTWFhg4kNuK5OQAjx6VJMHu3gUePACysir32OnpwNWr3KbKwaEkeebtzVWnNW8O2NhU96chhBBCCNEplDAjdVp+fj7GjBmDw4cP88alUikOHDiA4cOHCxQZIcJjWRah/wvFizUvSh1rsroJXD5zESAq/fQkKwtvPXyIELVZbAOGwd4WLTCCrsqtGbt3A1OmlL6Ceu5cYPVqSpbpkLsxd3H06VHe2Pyu82EiMxEoorqN1jCreS4WLjjy3hH03dMXBcqC4vFpJ6bB0cwRbzR7Q8DoCCGkkoyMgI4dua2IUgmEhJQk0O7f575qaBterrg4bjvPX3cUdnaAlxeXPCv62rw519qR1uslhBBCiB6ihBmps/Lz8zFy5EicOHGCNy6TyXD48GEMGTJEoMgIEZ5SrsSz6c8Quyu21LFmG5uh4cdU7VRTziQl4b3Hj5GuVvFkJZHgRMuW6GZhIVBkembLltKLPAHAzJnAunWULNMxi88v5u1bGVphVkdqn1wWasmoHd1dumPr4K2YemJq8ZiCVWDkHyNxYeIFdHTu+Ip7E0JIHScSAe7u3PbuuyXjiYlcNVrR9vAh9zU9vfLfIyGB2/77jz9ubMwl0FSTaF5eXCwyWfV+LkIIIYQQAVHCjNRJBQUFeP/990sly4yMjHDs2DEMVG1PQUg9o8hW4PF7j5F0Um0dLQbw2O4Bp2lOwgSmZ1iWxcaoKHwaHAz1FSMaGxjgTKtW8DKhapkasXWr5mTZxx8D69dTskzHXAi7gLMhZ3ljC19bCHMDc4EiqvuoJaP2TGk3BUFJQVhzdU3xWFZBFgbvG4yrU66imXUzAaMjhBAtsLUFevfmtiIsC7x8yU+iBQYCjx9X7UUnO5tbZ+3OHf64WAw0bQp4epYk8zw8uM3JiarSCCGEEFLnUcKM1DlyuRzjx4/H0aP8Vk4mJiY4efIkequ+8SeknilILsDDtx8i/YraFaJiwGunFxpMaCBMYHqmQKnER8+fY4eGljYdzMxw3McHjgYGAkSmp3r14iZ3EhNLxv73P2DlSkqW6RiWZbHo/CLemKOpI+Z2mStQRLqBWjJq16r+qxCdEY29D/cWjyVkJ2DgbwPhP8kfDc2pKpsQoucYhmuj2KgRMGhQybhCwbV1LKpCe/gQePoUePYMKCgo+/HKolBw9332rPQxI6OSJFpRIq3otr09vecjhBBCSJ1ACTNSpygUCkyePBkHDx7kjZuYmODs2bPo1q2bQJERIrzcl7l48MYDZAdm88ZFhiK0+KMFbN+yFSgy/ZJUUICRgYG4mJpa6th7dnbY6eUFY7G49gPTZy1aAH5+QN++QGoq8O23wJIlNHGig448OYKbUTd5Y1/2+hLGUmOBIqobnj4FoqK4DlYmJty8oGo3V0qYaZeIEWHn0J2Iy4rDP6H/FI+HpYahn28/XJx4EY5mjgJGSAghAhGLSyrARowoGZfLgdBQ7gXsyRP+lpFRte+VkwM8eMBt6szNNSfSmjYFbGzoPSEhhBBCag0lzEidoVQqMWPGDPz222+8cSMjI5w6dYqSZaRey3qchQdvPEDeizzeuMRSgpYnW8KiG62jVRNuZ2RgxKNHiMjLK3VsmasrljZuDIY+sGtH27bA6dPAjRvAXKpG0kX5ivxSa5e5W7tjStspAkVUd2zfznUXLTJ5MrB5c8m++hpmGp6CSDXJxDIcHnUYvXf3xt3Yu8Xjz5Keof9v/XFh4gXYm9gLGCEhhNQhEklJIu3tt0vGWRaIidGcSNPQmaHC0tOB27e5TZ25OdCkCZc8a9IEaNIEImdnGMfEIMfOrurfkxBCCCFEA0qYkTqBZVl8/PHH+PXXX3njBgYGOH78OHr27ClQZIQIL/mfZASODIQiTcEblznL0Ppsa5h40zpaNeHXmBjMfvYMeSx/4tpIJMIeLy+8a08TqVrXpQu3EZ204foGPE9+zhtb3nc5pGKpQBHVHdn8wmAYqxXcqVeYFRRwXa2omLVmmRuY4/TY0+i1uxeCkoKKxx8nPEZ/3/74d+K/sDWmam1CCCkTw3BrkTk5cZ0BVKWlcYm0p0+B58+5toxFX9VfCCsjPR24d4/bChkAGACAFYnANmrEJdNUEmrFt62sqv59CSGEEFIvUcKM1AlffvkltmzZwhuTyWQ4evQo+vfvL1BUhAgv+udoPJv5DODnymDkaYTWfq1h6GKo+Y6kwnIVCswJDsbPGq6KdZLJcLxlS7Q3MxMgMkJ0R3RGNL7x/4Y31tm5M0a2GClQRHVLVhZ/30TtOgcjo9L3yc0tfR6pPgdTB/w78V/02t0LwcnBxeMP4x+i9+7eODf+HLVnJISQqrCwADp35jZVRVVpRQm0ou3ZM279tGqUVTNKJZiICCAiAvj339InWFlxiTM3N25r3BhwdeW+Nm4M0Ht8QgghhKihhBkR3MaNG7F8+XLemEQiwaFDhzBIdUFiQuoRVskidFEoXqx5UeqYWWcztDzZEjJbmQCR6ZfI3FyMCAzELQ1rMXS3sMAfLVrA0cBAgMj00OXLQGwsMJISKPpo0T+LkJmfyRvbNGgTRIxIoIjqlvIqzAwM+JWtALfUCyXMtMPJzAn/TvgXPXf3RHhqePF4YEIguu/qjn/G/wM3KzfhAiSEEH2iWpXWuzf/mEIBvHhRuiLt+XMgLIw7Xh0pKWW3egQAa2t+Aq3odtFXS0taP40QQgipZyhhRgS1d+9ezFVbq0YkEmHfvn14W7VXOiH1iCJbgSfjnyDxSGKpY3Yj7eDl6wWxEfXpqgk/RUVpTJbNdXbGmqZNIRXRZH+N2LMHmD6dm3Bo1Kj0lcdEp117cQ2/PeCvPzq5zWR0dO4oUER1T3kJM00VZjk52ouHAI0sGuHCxAvotbsXItMii8dDU0LRY1cP/DPhH3jZegkYISGE1ANiMZeccnUFBgzgH5PLuWRaaChXiRYSwiXRQkLAhoSASUur/vdPTua2O3c0Hzc3L51MK9oaNQIcHAD6vEAIIYToFUqYEcGcPn0aH3zwQanxbdu24d133639gAipA/Ki8vBo2CNk3CqdxHFZ5AK379zAiOgqx5ryrZsb/ktLw7X0dACAsUiEXzw9MdrBQeDI9IRCASxeDKxZUzI2bBhw8yY3yUB0XoGiAB/+/SFvzNzAHCv6rRAoorqpvJaMmirJ1O9Dap6rpSv8P/BH/9/689ozRmVEoceuHjg15hQlfgkhRCgSSUkrxX79eIdyc3Jw4fBhmMTGomuDBpC9fFmSWAsNBSIjAaWy+jGkpwMPH3KbJlIp4OzMva8t2ho25O/b2lKVGiGE1ENyuRx37941vHbtmsnt27eN79+/bxIUFGSUm5srAoDhw4cnHT58OLyyj3v37l3DjRs32l29etUsJiZGlpeXJ7KxsSlo3rx59qhRo1KmTp2aLK6hxbAvXbpkfPXqVZNbt26ZBAUFGaWkpEhSUlIkcrmcMTMzUzRt2jS3R48e6TNmzEjy8PDIL+/xkpOTRUuWLHE8ceKEdXx8vNTa2lr++uuvp6xYsSLG2dlZXiNB1wBKmBFBXLt2DSNGjIBczv+/8N1332HatGkCRUWIsNKupOHRiEcoiCvgjTMSBh7bPeA4mdZUqWkykQiHvL3R7tYtmEskOOLtjZampkKHpR8yMoCxY4ETJ/jjsbHA+vXAjz8KEhapWT9c/QEP4h7wxr7u9TUcTCnprKq8CjOJBDA05NYtK6Kh+JVoQWPLxvhv0n8Y+NtAPIwvmRBNzE5Er929sHf4XrzT/B0BIySEEKJJgZkZUs3MoBg4sHSpdkEBt65ZUQItPJzbL/oaG1tDQRRwjxkeXvY5Bgb8JJp6Qq1hQ641JCXVCCFErwwePLipn5+fZU09nlwux5w5c5y3bdvWgGX5Lf1jYmJkMTExsn///ddyx44ddocPHw51dXUtKOOhKmzQoEGeOTk5Gkupk5OTJcnJyaYBAQGmmzdvdvz000+jV65cWeYLbGxsrLh79+5eISEhhkVjcXFxUl9fX3s/Pz9Lf3//IHd393KTbrWBEmak1j1//hxDhgxBjlqvoblz52Lx4sUCRUWIsKJ3ROP5R8/BFvBf9CSWEngf9oZVXyuBItN/zgYGONuqFdyMjGAhoZfFGvHsGTB8OBAYWPrYJ58A339f+zGRGheUGIRll5bxxlrat8RHnT4SKKK6q7yEGcBVmVHCTBgNTBvg4gcX8ebeN3Ej6kbxeI48ByP+GIEfBv6AT7p8AoYmMwkhRDdIpUCzZtymSW4uV4VWlEArSqYV7UdFAWzp9UWrJC+vpKVkWYyMuOSZszO3OTmVfC3aHB255BshhBCdoFBbh9PCwkJhaWkpj4iIqNKT+cSJE1327dtnBwBisRhvvvlmcp8+fTLMzMwUYWFhBocOHbJ+/vy50Z07d0wHDBjgce3atae2trbVXAwUsLa2lrdu3TrLx8cn283NLd/S0lKRn5/PhIeHy/z8/Czu3Lljmp+fz6xatcq5oKCA+eGHH2I0Pc7s2bMbhYSEGFpYWCg++OCD+ObNm+eGhYXJdu7caR8bGyubOHGi69WrV59VN96aQDODpFYlJSXhzTffRHJyMm987NixWLt2LU1EkHpHma/E8znPEbO99OuJYRNDtPy7JUy8NPTqIjWqjZmZ0CHoj6NHgYkTS8/2SyTAtm3AlCnCxEVqlJJVYvrJ6chT5BWPiRgRfnn7F0jFUgEjq5vUE2aaWjCamgJJSSX71JKxdlkbWeOfCf9g+MHhOBd6rnicBYv5fvMRkhyCDYM2QCKij0+EEKLzDA0BDw9u0yQ/H3j5kp9MU02qvXzJrbFWU3JyuAvOnpUzT2hjUzqRprrv7AzY23NrwxFCCBFUhw4dsjw9PXM7dOiQ3bVr1ywvL6/8jRs32sydO9e1so919OhR86JkmZGRkfLo0aPPX3/99UzVc5YtWxY7fvz4xgcOHLANDg42nDdvnvPvv/8eqfkRK+bSpUtP2rdvnysqY73OlStXxm7evNlmzpw5rizLYv369Y4fffRRonp1W25uLvP3339by2Qy9sKFC0/btm1bfKnojBkzktq1a9fi2rVrZmFhYVI3N7dqV8ZVF33iI7UmLy8Pw4YNQ3BwMG/8jTfewK5du1DWfz5C9FVebB4CRwYi/Up6qWNWA63QYn8LSK1p4rk6AtLTsTwiAvtbtIAxfXDULrkc+OILYPXq0sesrYEjR4BevWo/LqIVO27vgH+EP29sbue56OTcSaCI6jb15JemCjP1brCZmaXPIdplKjPF32P+xsy/Z+LXu7/yjm25tQVPEp/g4MiDsDOxEyhCQgghtUImA5o04TZNFAogLg548YLbXr4suV20Hx1dM+uoqUpK4rYHD8o+RyQCGjQonVBzdOTGizZ7e64SjxBSr+Tm5jKbNm2yOXr0qPXTp0+NMjIyxLa2tgV9+vRJX7lyZXTjxo0LACApKUns6uraMjMzU/ztt9+++OKLL+KFjl3XrFq1qob6/wLr16+3L7q9aNGiKPVkGQBIJBL4+vpGXL9+3TQ8PNzwwIEDdkuXLo319PSscpvDjh075pZ3zkcffZR05MgRqwsXLlgoFArm2LFj5vPmzUtSPSc2NlaSl5fHeHl55agmywDAzc2toH379pnnz5+3DAkJkVHCjNQbLMti8uTJuHz5Mm+8bdu2OHToEKT0Ro3UM2lX0hD4XiDyo0q/bjX6rBGarGgCRkwVl1VVoFTiu4gILI+IgALAotBQbHR3Fzos/RUfD7z/PnDhQuljrVpxybKmTWs/LqIVz5OeY77ffN6Yq6Urvu3zrUAR1X0VqTBTL3SlhJkwpGIpfn7rZzS1aorP//2cd+xC+AW039Eeh0cdRkfnjgJFSAghRHBicUlCqnNnzefI5UBMTNkJtRcvuLXUaqr1YxGlkkvWRUeXf66tLeDgwE+kFW2OjiXHrK25RBwhRKf5+fmZTJ48uUlUVJRMdTw2Nla2f/9+24sXL5rfvXv3sYODg2Lt2rV2mZmZYhsbG/m8efMShIqZcK0db968aQYADMNgxowZSWWdK5VK8e677yavWbPGSaFQYM+ePdYrVqyoscRdWZo3b55z4cIFCwCIjY0tNclvb28vl0gkbFhYmOHDhw8NWrZsWdyqJjo6WnL//n0TAHBxcRE8WQZQwozUkq+//hr79u3jjTk7O+PEiRMwVb+kmhA9xrIsXvz4AqGLQgG1TsIiIxE8d3rC4X0HYYLTE0+zsjD+6VPcUmkJuCkqCm/Z2GCAtbWAkekpf39gzBhurQd148YB27drLqchOqlAUYBxR8chu4CfAdo+ZDtMZNQ+VhOlkuu0pKoiFWa0hplwGIbB4h6L4Wblhg+OfcBrPfoi/QV67OqBLYO3YHLbyQJGSQghpE6TSLh1yRo1Kvuc/HwusVWUQIuO5t5TFyW8ijb1NxI1JTGR2zStO6xKItGcWFMfc3DgrgCipTYIqXN++eUXq5kzZ7rJ5XKGYRj06NEjrUOHDlnx8fHSv/76yzojI0McExMjW7VqlcOKFStiduzYYQ8AH374YaypqWkNZ/ZJZcTFxUlyc3NFAGBlZSV3cHB45bpkXl5exRVcZ86csayNhFloaGjxumwNGjQolfQyNjZm+/fvn3rmzBmrnj17en3wwQcJXl5euZGRkbJdu3bZJSYmStu1a5fp4eFR5Wq4mkQJM6J1+/btwzfffMMbMzExwcmTJ+Hs7CxQVITUvoKUAjz94CmSjpe+GMSgsQF8jvnArA2tpVVVSpbFT1FR+Cw0FLka2p9sjY6mhFlNksuB5cuBb78t3W5GKgXWrwdmzqQPzHrmu/++w82om7yxD9t/iIFNBwoUUd2naY6rIgkzWsNMeO/7vA83SzeM+GMEojJKLgrIU+RhyvEpuBJ5BRsHbaRkMSGEkKqRyQBXV24rC8sCqalctVpRMk09qRYVxVWr1eS6aqrkcu57aLpATp2BAWBnx7V8tLcv/zZdWEeI1vn7+xvPnj3bTS6XMzKZjPX19Q1577330oqOBwQExHft2tVboVDg4sWL5lu2bMlPTEyUWlhYKBYuXEjVZQJjq1GJ/OzZM6MaDEWjffv2WZw7d84KAAwMDNgRI0akaTpvy5YtL7p3724SHR0tW79+vaPqMTs7uwJfX99wbcdaUZQwI1p1584dTJkyhTcmEolw8OBBtGnTRpigCBFIxs0Mjckyy36WaLG/BWR2Mg33IhURmpODaUFB+Dc1tdQxMYAvXV3xuYtLrcelt168AMaOBf77r/QxZ2fgzz+BLl1qPy6iVddfXsdy/+W8MXdrd/ww8AeBItIN6u0YAc0tGWkNs7qpc8POuD39Nkb9OarUun077+3ElRdXsH/EfrR1bCtQhIQQQvQawwBWVtzWokXZ5ymVQEJC6YRa0e3YWG6Li+PWYNOWvDyuYu7ly4qdb2LCT6Spb6rHbG25JCPRG3nReXV+XlpkIGKlNtJX/qepzM9h4GSgpcy2ZkqlEh9//LFLfn4+AwDLly+PVE2WAdw6Vd26dUvz9/e3iImJkW3atKkBAEydOjXOwsKiwosxnj171jQ+Pr5G/k27dOmS7e7uXieqjYRW1M5QLpczKSkpkoSEBLGdnV2Zf5NBQUHF1V5ZWVmisLAwaU2sC3b69GnTxMRECQDk5eUxL168kJ0/f978ypUr5gAgFovZNWvWRDRq1Ejj37ibm1tBQEDAk8WLFzv6+flZJiYmSq2srOQDBgxIXblyZbSLi0ut/t94lTr/xER0V0JCAt555x3k5vLXB9y4cSMGDx4sUFSECMf6dWu4LHJB5KpIboABGn/ZGK5LXWm9sipSsCw2vXyJJWFhyNZQVeZlbIzfvLzQwdxcgOj01NGjwJQpQEpK6WP9+gH79nEfaIleSc5Jxvt/vg8FW/K+XMyIsXf4XqquKYemSrGKVJhRwqzucDB1wD/j/8HCcwux4cYG3rGgpCB0+bULvu//PeZ0ngOGqmoJIYQIQSTiWiI6OABtX3ERh1IJJCVxibOiJJqmLSYGSE7WftxZWdwWHl6x8y0tS5JnNjbc16JNdb/otpUVt+YcqZOuOV9rLXQM5bF+wzql1elWoa86pzI/R2+29+3qR1Vxx44dM3/w4IEJAPj4+GTPnz8/UdN5TZo0yfP390dcXJwUAExNTRWLFi2Kr8z3Wrp0qXNAQECNrLuzYcOGcHd39zLX6qpPpFIp2rRpk3Xr1i1TlmWxfft2my+++ELjv41cLsehQ4dsVMeSkpLENZEwW7RoUcOivyVVDMOgY8eOGV9//XX0oEGDXvkp1snJSb5nz54XAF5UNx5tooQZ0YqCggKMGjUKkZGRvPGZM2di9uzZAkVFiPBcv3VF2tU0ZD/ORvO9zWE9kFoEVtXjrCxMCQrC9fR0jcfnODtjVZMmMKIPSDUjIwNYsADYsaP0MbGYa8/42We0ILgeUrJKTDg6ARFpEbzxr3p9hY7OHQWKSneoP0WJRNzF1GrXE9EaZnWcVCzF+jfWo7NzZ0w/OR2Z+SWfBfMV+Zh3dh5OB5/GL2//gobmDQWMlBBCCHkFkYir2rKzA3x8Xn1ufj4QH8+vTlNNqKnu19aVPqmp3PbsWcXOL6rQKyuhpmmfkmxEj+zcudO26PaCBQtiRGV8XreysuJVLE2cODHB1tZWi+WopDImT56ccOvWLVMAWLlypXOXLl2y+vfvz7s0U6FQ4IMPPnAJCwszVB1PTU3V6hOavb19QZ8+fdJbtGiRV/7ZuoESZkQrFixYgIsXL/LGunfvjvXr1wsSDyF1hUgiQosDLcAqWBg2NCz/DqSUfKUSP4SHY3lEBPI19HJuZGCAnZ6e6E/rldUcf3/ggw+AsLDSx1xdgf37qQWjHlt9eTX+fv43b6y7S3cs7rFYoIh0S5paB3dzc81L+9EaZrphdMvR6OjcEaMPj8at6Fu8Y2dDzsJ7izfWvb4Ok9pMomozQgghuk0mAxo25LbyZGVxLSHj47ktLo7bVx1TvV1Q7WKHimFZrlKuMtVyKkm2BtQCkugwhUKBS5cumQOAmZmZQr0VoyqlSsceQ0ND5ZIlS+Iq+/1u3rwZVKVAtahTp06eNVX1tn379tDp06draLWjfdOnT0/et2+fzeXLl82zs7NFb7zxhufgwYNT+vTpk25ubq4MDQ2VHTp0yObZs2dGVlZW8vz8fFFWVpYI4JZGqgn3799/WnQ7PT1dFBgYaHDkyBHLbdu2Oaxevdp5+/btDrt27QodNmyYzl/6SQkzUuN8fX2xceNG3pizszP+/PNPyOjNBiEwcDQo/ySi0TOxGEsDA/FQ06JAAGY5OWFVkyYwk9DLW43IyQGWLAHWr+c+bKobNQrYvp1rjUL00oWwC/jiwhe8MXsTexwceRASEf0/qwj1CrOyOsRSS0bd0cy6Ga5MvoIl55fgh2v8NfzS89Ix5fgU/Pn4T+x4awdVmxFCCKkfTEy4zdW1/HNZlnuDVF5SrWg/IYFrJVlbVJJsRrX3XQmpcdevXzfKzMwUA0CHDh0yDQ0NNXyo57Aqn/fHjh2b4OjoWGfWkyKAWCzG8ePHQ9577z23c+fOWSoUCub48ePWx48f510pbmdnV3Do0KHgt956y6NozNbWtsb/Lc3NzZVdu3bN6dq1a86UKVOSevbs6ZWQkCAdNWqU++XLl5906tQpp6a/Z22imQ5SowIDA/Hhhx/yxgwMDHD06FE4ODgIFBUhtSP7WTaCpgfBc4cnjD00LFBDqixZLsdWQ0P4yWRgNSTL3I2M8IunJ3pS4qbm3LoFTJgAPHlS+piJCbBhAzB5suZSGaIXItMi8d6f70HJlkxQiBgRDow4ACczJwEj0y2UMNNPMrEMawauwYCmA/DBsQ8QkxnDO346+DR8tvhgZb+VmN5+OsQiau1ECCGEAOA+P1hYcJuHR/nnK5VcAks1wZaUVPI1KQlITOTvU29rndA1qut9oWMoj8hAVGaSqUhd/TkCAgKKJ6Zat26t+arjQgqFggEAmUzGLl26tNLVZXXVsGHDkn18fF75s1eUt7d3bvlnaY+VlZXSz88v5NixY2a7du2yvX37tmlSUpJEJBLB2dk5b/DgwamLFi2Ks7S0VGZnZ4sBbn0xZ2dnrSY/vby88r/66quXH330kVtBQQHzzTffOJ48efKV6/7VdZQwIzUmKysL7777LnJy+EnkHTt2oGNHWuOE6C+WZRG7MxbP5z6HMkuJx2Meo93VdhDJaC2n6mJZFr5xcVgYHIwEg9KVeWIACxo1wleurrRWWU3JyQG++QZYswZQaGhZ3r07sHs30LRprYdGapeNkQ16ufbCn4//LB77pvc36OPWR8CodI+mloyaUMJMNw1sOhCBswIx7+w8+N735R1Ly0vDrFOzsPv+bmwdvBXtHNsJFCUhhBCiw0SikvXFKiovrySRVvS1aCtrn9581ToDJwO9qGKqqz9HSEhI8TogzZo1KzPZk5ubyxw7dswaABwdHfMbN25cpZ6pZ8+eNY2Pj6+RXEOXLl2y3d3d86v7OJ9//nlCTcRTlwwbNizjVW0Pb968aaQonMtp3Lhxro2NjdbXohs+fHj6Rx99BAC4fv26mba/n7ZRwozUmNmzZ+OJWiXCzJkzMWHCBIEiIkT78qLzEDQtCMmnSnqiZ97ORNjSMDRdTQmF6niUmYlZz5/jP/XZ5kKtTEyw08sL7c10/rW47vj3X2DGDCA4uPQxAwPgu++AefNoEex6wkRmgoMjD+Lri1/jW/9v8ab7m7RuWRWoV5hZWGg+T/2pTP1+pO6yMrLCnmF7MLL5SMw4OaNUtdnNqJvo+HNHzO44G9/2+RYWhmX8ERBCCCGkZhgYAE5O3FZReXmlEmqJ168D69ZpL05CtCg5Obn4g7uFhUWZfU3XrFljFxUVJQMAsVhcbkVdWZYuXepcU+uFbdiwIdzd3T2pJh6rvjl37lzxJ8suXbrUypUAlpaWxUm59PR0nZ8wovIHUiN2796NPXv28Mbatm2LtWvXChQRIdrFsizi9sYhwCeAlywrknQyCYocrV/EoZcy5HJ8FhKCtrdva0yWGYtE+L5JE9xq356SZTUlOZlrr9ivn+ZkWfv2wJ07wPz5lCyrZ0SMCN/0+QZH3zuKfcP3QcTQW8fKqmhLRvWOsimCLCdNquMtz7fwaNYjTGhd+mIxJavEppub4LnZE7/c+QUKJb1HIIQQQuoUAwPA2Rlo1Qro2xcYNQoZ77wjdFSEVJnqumRpaWkaP8jFxcWJ165d61i0X52EGRGeUqnE3r17bYr2p0+fXisVdo8ePSpuCWVlZVUnKy4rgyrMSLUFBgZi1qxZvDEzMzP88ccfMDQ0LONehOiu/Ph8PPvwGRKPJmo87jjDEc3WNoPYiBILlaFkWfjGxmJxWBhi8zVX3ncpKMCejh3hQWuV1QyWBQ4eBObO5RbVVieVAkuWAJ9/zt0m9dYwr2FCh6CzKtqS0cqq9P0UCspR6xprI2vsGbYHE1tPxKy/ZyEoKYh3PC4rDtNOTMPGGxvx48AfMaDpAIEiJYQQQggh+qxhw4bFrRWvXr1qOnv27FJXe8+ePbtRampqcX6gaC2zqrh582ZQ+WcRbfrhhx/snjx5YgwAHTt2zOzTp0+NrN9Wnk2bNtkX3e7QoYPO97ely4RJteTm5uL9998vtW7ZL7/8gmbNmgkUFSHaE/9nPAK8AzQmy2QNZPA57gPPbZ4QG9MMZ2VcTUtD5zt3MCkoSGOyzNXAAF9kZWFRdjYaaVjLjFRBUBAwaBAwerTmZFnXrsDdu8BXX1GyjJBqqGhLRk3XAVBbRt3V160v7n94H9/1/Q6GktIXkD2Mf4iBvw/EkH1D8CThiYZHIIQQQgghpOo6d+6cVXT78OHDtleuXDFSPf755583OHz4sA0AmJqaKgAgMjLSIDg4mCYA6qDz58+b5OTkaExoKpVK/PDDD7aff/55IwAwMjJS7tq1K7ysx+rUqZMnwzDtGYZpv3HjRhtN53z//fd2J06cMFMqy+zmCblcjs8//7zB77//blc0NmvWLA0TTLqFKsxItXzxxRd49OgRb2zWrFkYNWqUQBERoh35cfl4Puc5Ev7QXM1sP8Ye7hvdIbWh9xWV8SI3F/8LDcV+TQkbAFKGwf9cXDDPzg6Xz5+v5ej0VGYmsHw5sHYtUKBhLV8zM2DVKuDDD7kFtgkh1VLRlozqFWYA15ZR0zjRDQYSA3ze43OM9hmNOWfm4OSzk6XO+fv53zgdfBoTWk/Alz2/hJuVmwCREkIIIYQQfTN06NB0Jyen/OjoaFleXh4zYMAAr6FDhyY7OTnlX7p0yfz27dumANC6deusYcOGpSxbtqyhXC5nhgwZ4v7uu+8mDRw4MKNHjx61UqGkj54+fSrbsmWLrerYo0ePjFVvz5kzh7fQYv/+/TPefvvtDE2Pt3z5csfbt2+b9urVK61Dhw5Zzs7OBfn5+UxISIjByZMnrZ49e2YEAIaGhkpfX98Qb2/vvOrEf+PGDZP//e9/Lg0aNMjv0aNHuo+PT469vb3cwMCATUlJET969Mjo7NmzltHR0bKi+8yePTt28ODBOl9hRgkzUmUXLlwotUZZq1at8OOPPwoUESE1j2VZxO6KRciCEMhTSrfhldpK4bHNA3Yj7DTcm5QlW6HAmhcvsDoyEjllXK0yyNoa65o1g6excakqVlIFLAv88Qe3DllUlOZzhg4FNm8GGjas3diIoAKiAvBH4B9YPWA1rVGmBRVtyWhiAkgkgFzlpYbWMdMPblZuODH6BM6FnMN8v/l4GP+Qd1zJKrH73m7sfbAXU9tNxZIeS+Bs7ixQtIQQQgghRB9IJBLs3r07dNiwYR7Z2dminJwc0YEDB3gJHB8fn+xTp04FZ2ZmilavXu2Um5srCgoKMlq+fHlDLy8vDQuck4oKDg422LRpk2NZx589e2ZUlOQqIpFIUFbCDAAyMjLEJ0+etD558qS1puNNmzbN3bp1a/iAAQOyNB2vitjYWNmhQ4dsDx06VOY5pqamii+++CLqf//7X62smaZtlDAjVZKamoqJEyfyFpA0MDDA3r17ad0yojeyg7PxbMYzpP6bqvG47XBbeGz1gMxepvE4KU2uVGJ3bCy+Cg9HdBnrlHkaGWFts2Z400ZjVTipiidPgFmzgIsXNR93cgI2bABGjACYKrcsJzroTswdDPx9IFJzUxGZHgnfYb4wkFDb05pU0ZaMDMO1ZUxU6fibmqqtqIgQBjQdgLsz7mLn3Z1YemEp4rLieMcLlAXYemsrdt7diZkdZuJ/3f+HBqYNBIqWEEIIIYTougEDBmQFBAQEfvfddw3+++8/84SEBKlcLmfs7OwK+vXrl/bTTz+9NDc3VwLAoUOHgj/99NNGISEhRkqlEh07dqQrl+uQ5cuXRx09ejTrypUrZi9evDBISkqSMAwDGxubgpYtW2YPHTo0dfLkycnSGlpS4+eff44cOnRo6qVLl8wePnxoHBkZaZCamiqRy+WMkZGRwsbGRt68efPsAQMGpE+cODHFxsZGUSPfuA6ghFkFMQzzE4BZAJaxLPt1Bc5/E8BMAJ0AWAKIB3ATwFaWZf/RXqS1Y/bs2Xjx4gVvbOXKlfDx8REoIkJqjrJAiRc/vkDEsggoc0tXP0msJHDf7A770fZgKLlQISzL4nhSEhaHhuJJtuaKfguxGF+7umK2szOk1AqwZkVEaE6WSaXAJ58AS5cCpqa1HhYR1t2Yu+jv2x+puakAgD8C/0BSdhKOvX8MpjL6e6gp6hVmZmZln2tlxU+YUYWZ/hGLxJjWfhre93kfq6+sxrrr65BdwH9dzFPkYf2N9dh6aysmtZmEBa8tQFPrpgJFTAghhBAh0DwkqSktWrTI37t3b2R55w0ZMiRjyJAhj2sjpvpgyJAhGSzL3q6px+vWrVtOt27dcgDEVPexbt68GVTeOdbW1soJEyakTpgwIbW630/X0IxkBTAM0xfAhxU8V8QwzM8A/gYwBIA9ABmAhgCGAzjHMMwmRodn2Q8fPox9+/bxxvr27Yu5c+cKFBEhNSf9Vjpud7yNsMVhGpNl9qPt0elpJziMcaBkWQVdSUtDj7t3MezRI43JMhGAD52c8LxzZ8xr1IiSZdrw+uvAgAH8sQEDgAcPgNWrKVlWD92LvYf+v/VHSi4/IyMWiSEV0VqMNSk5mb9vrbF5Bkd9vTJKmOkvMwMzLO+7HGFzw/BJl09gIC5d2ZmnyMO229vgsdkDYw6Pwf3Y+wJESgghhJDaRvOQhBAiHJqVLAfDMO0BHEXFf1ffAphaePsFgMUARgNYBqCoj+dHAJbUYJi1Jjk5GbNnz+aNWVpaYvfu3RDRJDfRYQUpBXg2+xnudLqDrPulW/0auBig5d8t0WJfC2rBWEFPsrIw7OFDdL97F1fUe5IV6m9lhbsdOmCrhwfsZPR71RqGAdas4b66uABHjgBnzwJeXkJHRgRwO/o2+vn2Q3IOP5PTv0l/HHvvGLVkrEEKRemk16u6zaonzKglo/6zN7HH2tfXImROCGZ1mKUxYa1kldj/aD/abG+DwfsG41zIOV5bdEIIIYToD5qHJIQQYVFLxldgGGYQgH0AylievdT5XgD+V7gbCKAHy7IpKse3A/AH0AzAUoZhfmNZNqJmo9au+fPnIy6Ov97C5s2b0ahRI4EiIqR6WCWLWN9YhH4WioKEgtInMIDzHGe4LXeDxJSeMitqVUQEloSFoXSNHqetqSlWN2mCAa8qtSAVx7KAUgmIxWWf07o1lygbOBAwNq692EidcjH8It7e/zYy8vnrCPd164u/3v8LRlKjMu5JqiIlhfvvqcrWVvO5AFWY1WfO5s74afBPWNhtIVb8twJ77u9BvqL0Wp+nnp/Cqeen0MKuBT7u9DHGtxoPE5mJABETQgghpKbRPCQhhAiPSoI0YBjGgGGYZQBOguv7W1GfACiarZyp+iIFACzLxgCYULgrAzC/mqHWqnPnzmH37t28sSFDhmDMmDHCBERINWXcy8DdHncRNClIY7LMpKUJ2l1rB/f17pQsq6S2ZmYak2VuhobY17w5brVvT8mymnL5MtC7N7B2bfnnDhtGybJ67NjTY3jj9zdKJct6u/bG8fePw1hKfxs1LSmp9FhlKszU2zkS/edq6Yodb+1A2NwwLOi6oMz1BB8nPMbMv2ei4bqGWOi3EOGp4bUbKCGEEEJqDM1DEkJI3UEJMzUMw/QH8ATAl+B+P1kAyp2FZBhGBGBk4e4jlmX/03Qey7LXABQt+DdCV3oIZ2ZmYvr06bwxMzMzbN26ldZxIjqnILUAz+c8x+32t5F+tXSrQJGRCG4r3ND+dnuYd67QhV1EzUArK7xmXvK7s5VKsaFZMzzp1AmjHRwgoueN6rt9Gxg0COjRA/D359YiK6P1JSG77+3GiD9GIE+Rxxvv69YXJ0efpAoVLUlM5O+bmAAGr+h4qV59Fh9f8zER3eBk5oQ1A9cgYl4Evun9DWyNNZcmpuam4odrP6DJhiZ4c++bOPrkKAoUGirmCSGEEFIn0TwkIYTULZQwK20cALfC27cAdAJwogL3awmgqFzifDnn/lv41QlAq8oGKIQvv/wS4eHhvLE1a9agYcOGwgRESBWwShYxu2Jw0/MmojZFQVMJlO07tuj0pBMaL24MkZSeIquKYRh86+YGE5EIS1xcENK5M+Y0bAgDWuuw+gIDgREjgA4dgDNnSsaTkoD16wULi9RNLMti2cVlmPTXJChZ/pPe8ObDcWrMKUqWaZF6hdmr2jECgL09f58SZsTayBpLey1F+NxwbB28Fc1tm2s8jwWL08GnMfyP4XBZ74LF/yxGcHJwLUdLCCGEkCqgeUhCCKlDaOZSswQAswF0YVn2cQXv46Ny+1E556o+ZuvKBCaEhw8fYuPGjbyxXr16Ydq0aQJFREjlpfqn4nbH2wiaHISC+NJXXhs2NUTLUy3hc8QHho0NBYhQd0Tk5mJBcDCyFYpXntfH0hIvunbF8iZNYC6hlpbV9ugRMGYM0LIltxaZJocOcWuZEQIgV56LcUfH4etLX5c6NqXtFPwx8g8YSF5R7kSqTT1h9qp2jADg4MDfp4QZKWIiM8GH/2/vvuPkqur/j7/ObO8tW5JsekJ6SAggvRfpIIiADRERFb8W1K+KXwF/36+i2IEvIooNRUSkI9/Qew0J6QkhbbNJtve+M+f3x53dndlM292Z2TLv5+NxH3PuveeeezYc7s6ezz3nHH4tG7+4kf/7xP9xzrxzguY90HqAW169hXm3zeOUP53C3zb+jQ53RxxrKyIiIkOkfkgRkTFCPZgHux1n3t+h/lU50ye9K0zeCp/0jCHeJ66stVx33XW4fTrG09LSuPvuu3FppIiMAx07O9jxrR3U/LMm4HlXuovp353OtG9OIyk9KWAecWxpa+OWPXv4a3U1vdYyPT2d/wgxytQYQ0FKShxrOEGtXg3/8z/w0EPB85SWwg03wDXXgJ7NAtS01XDh/RfyWsVrB5371jHf4pbTbtGUynEweErGoQbMqqqiWx8Z/4wxnDHnDM6Ycwbv173PbW/dxp/e+xPNXYGn5H1+1/M8v+t50lxpHJV3FOyAcxaeQ7JLfwaKiIiMEeqHFBEZQ/SX0iDW2neGeWmxT7o2aC6H7xLuYbpODmaMCTcPYv+EPl1dXXR0DP+N0vvvv5+XXnrJ79jXv/51ysvLR1SuTAydnZ0B02NBb3Mv+27dx4HbDmC7bcA8+efkM+PWGaTPTKfbdoOadEBr2tr46b59PFxfj++/5E/27OHTBQWkxiFAM5bbWqy43niD5FtuIWnVqqB5bEEBvV//Or3XXussjuTxgJ7NIzIR2traqrVc/vDl7G7a7XfcZVzceuqtfOGwL4zbn228qapKBgZeHCgo6KWjwxnlHKit5eYaYGCUc3MzNDR0kK6BzxJAeWY5Pz7px3z/mO/zr63/4g/r/sAblW8EzNvl6eLFhhd58Z8vUpJZwkcXfpTLFl3GYWWHKXguUTMRfofK+KC2JvHS1eW3/m9JuP44a+3eod5jPPRDiogkEgXMoifTJx3uG5vv+cyguYKrCJ/FsWbNGtra2oZxC2hvb+frX/+637GSkhKWL1/OqhAduJKYBgdWR40bUp5NIf2v6biaAgdy3OVuOj/TSdPKJnZv2w3b4lzHcWJTUhL/TEvj3SCjxCq7u/neiy9yWs/BU1zG0phpa7FgLZPWreOQf/6T4vXrg2brTU9n+wUX8MH559OblQWvvhrHSiaO8djWnq17lrv23uW8BOAjw5XB9TOvZ07tHP0Oj6P33luO70u8ra17WLXq4P+3+9paa2sKcLbfuQcffJniYgXCJbRSSvl28bfZk72Hp+uf5oX6F2hxtwTMW91ezR2r7+CO1XdQmlrKMfnHcEz+MczNmKvgmUTNePwdKuOT2prE0rZtfp0FT0RwSTx/kcazH1JEJGEoYBY9vv+WXUFzHXx+zP43uP/++2loaPA79tnPfpa0NK13ImOQheS3k0m/N52kPYGnVvTkeOi6vIvuM7rH8P95o8sNvJ2czCNpaWwOse5YqrWc0d3Nob298avcBGZ6e5ny6qvMfeQR8nfsCJqvOyuLHeeey45zz6UnJyeONZSxrsfTw92Vd7Oq7uBgWHFKMTfMvoGZGTPjX7EE19Dg/50pPz/0V8SsrB6Skz309g688NHUlKaAmURsesZ0Pjv1s3xy8id5s+lNnq9/nrUta/EQeH3Lqu4qHqp+iIeqH6I4pZij84/mmPxjOCTzEFxGU/yKiIiMYROuH1JEZCzQQzJ6fHsyUsPk9e096Q6aK7hpYc4vw/vmy4oVKzj22GOHfIOdO3fy5JNP+h0744wz+N73vqc3T6VfZ2dn/xt9J5xwAumjNGdUx7YOdn5xJy2vBX6T2iQbSq8tZep3ppJcoMdeIG1uN3+pqeH2AwfY0RX8u3ZuUhLXlpbypbIyiuO4PtlYaWtR19xM8h/+QNIdd+DaG3z2DltcTO+Xv4z7mmuYkZurSedjaDy2tR0NO/j0Y59mdd3qg84dNfUo/nbB3yjLLhuFmsmNN/oHzI47bg5nnDETCN7WSkoM+/YNXDNr1lGccUbgYIdIKOdyLgAV9RXc+u9bebHhRd5vfz9o/pqeGh6teZRHax5lcvZkzp93PmfPPZvjpx1PevLYfxbK6BuPv0NlfFJbk3jJysry3T0HWDdKVQkknv2QEbPWqtNQROImFs8c9RxHT6tPOty3Nd/zQ35lONycyMaY/l6xtLQ0MjIyhnoLfvCDH9DjM81aamoqt99+O5mZGrktgaWnpw+rrUWDq9RF29rAU48WnVfEnFvnkDlfbTeQ/V1d3FZZyW/27aMhxGix4pQUvlZezhenTiUvxMizeBjNthY1FRXw61/Db3/rLFIUzJQp8M1vYq65hpTMTOIXohQY+23NWstf1v2FLz35JVq7Ww86/x9H/ge3nnErqUnh/n6WWKmq8t+fMSOVQE3Kt62VluIXMGtsTAt4jUikphVO49ziczm3+FxmrpzJv97/F/euv5cdDcFHNO9v3c9da+7irjV3kZWSxelzTufceedy9ryzmZwzOY61l/FqrP8OlYlDbU1iadAMS9XDWaMshuLWDxmhVsDt8XiSrLV62V5EYs5ai8fjScKZMOvgTpFhUsAsehp90oVh8vqer4l+VUbmrbfe4v777/c7dt111zFv3rxRqpFIaGllaUy7fhq7/9/u/mNZy7KY89M5FJ4e7n/HxLSutZWfV1Twt+pqeqwNmq88LY1vTpvG1ZMnk5kUeKpLiZC18MYbcNtt8MADEGo6yzlz4Prr4TOfAb0xKwE0dTbxhSe+wH0b7jvoXGZKJnefdzdXLL1iFGomvh55xAl+7d/vbIsXh7+mtNR//8CB2NRNEtMhRYdwc/nN3HTSTaw5sIYHNj7Ag5sf5P364CPP2nraeHjLwzy85WEADp9yeH/w7LDJh5Hk0vcDERGRUdDokx4L/ZC7PB7PImutaW1tzczJyWmP0X1ERABobW3NtNYaj8fTAeyKVrkKmEWP70qg08Pk9Z1ScXfQXKPAWss3v/lNv2P5+fnccMMNo1QjkchM+8Y09t25j6TsJGb+v5mUXlGKcemNJl+9Hg+P1dVxe2UlzzU2hsy7Ijub66dN49LiYlJcWsNkRDo64L774PbbYc2a0HmPOcYJlF1wAShAKUG8tPslPvXQp9jddPBXiHmF83jw0gdZWrp0FGomgx155NCvKS/336+oiE5dRHwZYzhs8mEcNvkwfnjqD9lQvYF/bvon/9z8TzbVbAp57Tv73uGdfe9w04s3UZhRyCmzTuH02adz+uzTmVUwK04/gYiISMIba/2Qz3g8nlOstT11dXVF2dnZ7RplJiKxYq2lrq6uyFrb4/F4OoFnolW2AmbR4/uXZbj3h33Pr49BXYbtscce658LvM8NN9xAYaFG6cjo6anrwd3uJn1a8JE2ybnJLHt6GVkLs3ClKcDjq6a7m9/t38+d+/ZREWJ9MoBzCgu5fto0TsrP1xQKI7VzJ9x5J/z+91BfHzyfMXDRRU6g7Jhj4lc/GXdau1v59jPf5o637wh4/qrlV/Grs35Fdmp2nGsm0TR9UHfHnj2jUw9JHMYYlpYuZWnpUm4++WY212zmoS0P8fi2x3lj7xtYgo9Er++odwJtm/4JwJyCOU7wbM7pnDLrFPLT8+P0U4iIiCScsdYP+QrQ1dvbW9XW1jZ19+7d04qKiuoUOBORaLLW0tramllXV1fU1taW1dvbWwl04TyDokIBs+jZClQBpcDJYfKe4v2sBzbEslJD4fF4+N73vud3bMaMGVx33XWjVCNJdD0NPez9xV72/nIvBWcUsOSfS0Lmz1meE6eajQ9vNTdze2Ul91dX0x1i2sV0l4tPlZbytfJyFvgvaixD5fHA00/DHXfA44870zAGk5npTLn41a/C3Llxq6KMT8/seIarH7064Kiy/PR8fnvub/no4o+OQs0k2qZN89/fPabmIpBEsLB4IQuLF/Ld479LTVsN/97+bx7f9jj/98H/0dwVYt1N4IOGD/hg9Qf8ZvVvcBkXy8uWc+KMEzlhxgkcP/14ijKL4vRTiIiITHhjqh9y5cqV7atXr/6O2+3+CUBra2tpW1vbDGOMdblcbmNMiD+ORUTC806/mGStNdbant7e3kq3290AfGflypVRmwZWAbMosdZ6jDEPAdcChxtjjrLWvjE4nzHmWOAw7+4/rbWeeNYzlIceeoj16/1fNPnv//5v0rV+jsRZb1Mve3+1l4qfV+BucgNQ+2AtLWtbFBQLo9Pt5h81NdxeWcnbLS0h8xanpPClqVP54pQpFKemxqmGE9TevXDPPc4Wrnd75kz44hfhqqugSB2HElp9Rz3/+fR/8rs1vwt4/sQZJ/KXi/7CtLxpAc/L+DN4hJmmZJTRVJxVzKcO/RSfOvRTdLu7eXn3yzzx/hM8+f6TbK3bGvJaj/Xw7v53eXf/u/zijV8AsKRkSX8A7YQZJ1CWXRaPH0NERGTCGYv9kCtXrnxp9erV33K73T9yu90tLpcr3eVyZQBab0BEosXt8Xg6vNMwduEEy14Kd9FQKGAWXbcBV+P8u/7ZGHOCtbZ/qXZjzBTgT95dN/DLuNcwCI/Hw8033+x3bNGiRVxxxRWjVCNJRL3NvVTeXknFTyvobeg96Pyum3ax9GGtyxPIprY27t6/nz8fOEB978H/dr4+lJPDdVOn8tGSEtK0Ptnw9fTAE0/A3XfDU085o8tCOfNMuO46OOssrU8mYXmshz+s+QPffvbb1LbXHnQ+IzmDH576Q7585JdJcqk9TSSDA2bNzdDUBHl5o1MfkT6pSamcOvtUTp19Kj8/8+dUNFXw9I6neXrH0zyz45mAz6rBNlRvYEP1hv6pZQ8pOoRjpx3LUeVHcXT50SwqXqRnmoiISOTGXD+kN2h2OnCcx+M5zePxzAQ0Z7yIREsrsAtnzbJXojmyrI8CZlFkrd1kjPkVcD0wD3jPGHMHzkKc84EvAiXe7LdaazePTk0PFmh02Y033ohLnekSBz11Pez99V4qf11Jb2OQYI+BpOwkPD0eXClqlwDtbjcP1NRw9759vNoceoqkVGO4rKSE66ZO5Yjc3DjVcILauRN++1v44x/hwIHQeXNznWkXv/hFOOSQuFRPxr9397/LF5/4Im9Wvhnw/IkzTuT35/+eOYVz4lwziYfy8oOPVVQoYCZjz7S8aVy14iquWnEVHuthXdU6nv7ACaC9tPslutyh100F2Fa3jW112/jD2j8AkJOaw5FTj+So8qP6t0mZk2L9o4iIiIxLY7Uf0tuBvcq7iYiMKwqYRd9/ApOAT+P8Uro5QJ7fATfEs1KhBBpdtnjxYi655JJRqpEkiq79XVT8rIJ9v9mHpy346JziS4uZeeNMshZpfS2A91pbuXvfPu6tqqLJ7Q6Zd1paGl+YMoWrJ0/WtIvR8swzcMstofMceih8/vPwyU9Ctl6mk8jUttdy4/M3cuc7d2I5eIr/7NRsfnLaT/j84Z/HZfTiwESVng4lJVBdPXBs925YEnoZT5FR1bde2fKy5Xzz2G/S0dPBW5Vv8eLuF3lp90u8VvEaHb0dYctp6W7h2Z3P8uzOZ/uPzS2cy1HlR3H45MNZOWUly8uWk52q360iIiJe464fUkRkLFPALMqstW7gSmPMP4DPA0cCRUAT8CZwp7X2iVGs4kEeffRRjS6TuOrY1UHFTyrYf89+bFfwdV8nfWQSM2+aSfZSdYo09/byj+pq7t6/n7fCrE0GcEp+PtdNncp5RUUk6//l6LrsMvja16Ctzf94Tg5ccQVcfTWsXAnGjE79ZNzp6u3il2/8kh++8kOauwKPFv3Iwo/wizN/wfS86QHPy9jzve9BcbGzbOHMmbBgAaSlRXbtzJn+AbMPPohBBUViKCMlgxNnnsiJM08EoNvdzep9q/sDaK/seYWW7vDfZwC2129ne/127l13LwAGw/xJ81k5eaWzTVnJirIV5KRpnVsREUk847EfUkRkLFPALALW2heAIfV8WmufBJ6MSYWi7Kc//anf/uLFi7n44otHqTYykbVtaWPPj/ZQ9dcqZ/bsICZdOIkZ359Bzgp1fNR0d/O17dv5V20tHWHWyCpJSeEzZWVcPXkyczMz41TDCcZaqKlxhncEk5PjBM1+/3tn/+ij4XOfg49+VKPJZFg81sMdb98RMFg2r3Aet511G2fOPXMUaibD1dEB//M//sfWrYOlES7DOX8+vPXWwP7WrdGrm8hoSE1K5ehpR3P0tKP59nHfptfTy3sH3uOVPa/wZuWbvL73dXY17oqoLItlS+0WttRu4a/r/wo4QbRDig7hsMmHcdjkw1hWuoxlpcsozSrF6AUWEREZZyZ6P6SIyFimgFmCe/3113n11Vf9jn3nO9/R6DKJqqbXm6j4aQW1D9USYJYxhwtKLi9h+renk71EQYc+ecnJ/F9DQ9BgmQHOKCjgc1OmcF5REan6f3d4PvgA7r8f/vpXp6f7gw9CjxC79loncHb11bB4cfzqKRNSRkoG/3PK//Cphz81cCw5g++d8D2uP/p60pIjHJYkY8aePQcfmzkz8usHL3m4bduIqiMy5iS7klk5xRkd1udA6wHe3OsEz97Y+wZv73ub9p7I1vC2WLbWbWVr3Vbu23Bf//HizGKWli5lWYkTQFtaupTFxYvJSMmI+s8kIiIiIiLjnwJmCe5nP/uZ3355eTmXXnrpKNVGJhLrttQ+WkvFTytofi3wFGMAJsVQdmUZ0/9zOhlz1HkxWKrLxRUlJfy6stLv+JTUVK6aPJnPlpUxM0P/bsOyezf84x9OoGz1av9zr74Kxx0X/NrDD3c2kSj5+LKP84s3fsGaA2v4+NKP8z+n/A8z8meMdrVkmN5/33+/qMiJsUdKATNJRGXZZVyw4AIuWHABAL2eXtZXref1va/zzr53WL1/NRurN+K2oddv9VXTXsNzO5/juZ3P9R9zGRfzCuc5AbSSpSwqXsTC4oXMLZxLapLWexURERERSWQKmCWw7du3869//cvv2Fe/+lVSUlJGqUYyEbjb3Rz40wH2/nwvHduDL+7uynAx5fNTKL++nPTy9DjWcOzo8nj4v/p6zigoID0pKWi+T5eV8evKSpKAs4uKuHryZM4uLNTaZMOxdy888IATJHvzzeD5/vKX0AEzkSGw1lLXUcekzElB87iMi7vOvQuXcfmNuJDxacsW//0FC4Z2/fz5/vt79kB7O2i2XUkkya5kVkxewYrJK/qPdfR0sK5qHe/uf5fV+1ezev9qNlRvoNfTG3G5HuvpH432wKYH+o8nmSTmFs5lYfFCFk7ybsULWTBpAdmpmv1ARERERCQRKGCWwH75y19i7cD8eLm5uXzuc58bxRrJeNZT3cOBPxxg3x376KntCZovKTeJqddNpfyr5aQWJ95bvG5reaGxkfuqqniwtpbG3l4eWryYC4uLg16zIjube+bP55yiIkpSE+/fbMQqK+Ghh5wg2SuvRHbN228765lp3RMZAY/18NjWx/jxqz+mpbuF9659D5cJHug+YuoRcaydxNLmzf77Qw2YzZ178LHt22HZsuHXSWQiyEjJ4EPlH+JD5R/qP9bZ28n6qvX9QbT3qt5jQ/WGiKdz7OO27v5A2sM87HduWu60/kDagkkLmFs4l3mF8yjPLSfJFfylJxERERERGV8UMEtQra2t/PnPf/Y7ds0115CbmztKNZLxylXhIvWxVNa8tAbbGWyBMkiblkb5V8uZfPVkknMT69FjreWtlhbuq6ri/poaDnR3+53/W3V1yICZMYbPTJ4c62pOLFu3OkGyhx6Ct96K7JriYrjkErj8cjj2WAXLZNi6eru4d9293PrarWyt29p//J+b/smlizXtcSIYPMJs4cKhXZ+VBeXlzqDYPps2KWAmEkh6cjpHTD3C76UDj/Wwo2EH66vW817Ve6yvXs/6qvVsr9+ODbqgbnAVzRVUNFew6oNVfsfTktKYXTCbeUXzmFswl3lF85hXOI+5hXOZljct5EsSIiIiIiIy9iRWr7X0u++++2hpaenfd7lcXHfddaNYIxlPrNtS93gde361h5znnUVZgnU+ZC/PZto3plF8aTGulMTqNNjY1sZ9VVXcV13Njs7OoPkeq6ujpbeXnGQ9kofNWnjnHSdA9vDDBw/vCKawED7yEfjYx+Ckk0D/DWQEGjsbueudu/jlm7/kQOuBg87f/OLNXLzwYo1GmOCsHfkIM4AlS/wDZu+9B5ddNrK6iSQKl3Ext3AucwvnctHCi/qPt3W3sbFmI+ur1rOuah0bajawuWYz+1v3D+s+Xe4uNtduZnPtwd870pLSmFM4p3802uyC2czMn8ms/FnMzJ9JRorWoBURERERGWvUM5iArLXceeedfsfOPvtsZsyYMUo1kvGip6GHA/ccoPL2Sjp3BQ8AARR+uJBp35hG/in5mAQaqbOprY1/1tTwz5oa1re1RXTNETk5HOjuVsBsqHp64OWXB4Jkvj3LoeTlwYUXOkGy004DrdsoI7S+aj13vH0H9667l7ae4P/ft3W3sadpD7MKZsWxdhJvVVXQ0OB/bDgBsxUr4KmnBvbXrBlZvUQEslKzOHLqkRw59Ui/442djWyp3cLmms39AbDNNZvZ0bBjWCPSwAmmbarZxKaaTQHPl2aVMqtgVn8AbVb+LGYVOOnpedNJTdI03CIiIiIi8abe2QT09ttvs2ZQr8u11147SrWR8aB1QyuVt1VSdW8VnnZP0HwmxVD68VLKry8ne0liLI5urWWdN0j2YE0Nm9sjWy9jRXY2l5eU8LGSEqanp8e4lhNHamMjSffeC08/DatWQVNTZBfm5cG558Kll8KZZ0JaWmwrKhNej7uHh7c8zB1v38GLu18MmXdZ6TK+dcy3uHTxpaQkKUA70Q0ObGVlwcyZQy9nxYqDy9XSiiKxkZ+ez1HlR3FU+VF+xzt7O9lWt80vkPZ+3fu8X/8+rd2tI7pnVVsVVW1VvLH3jYPOuYyLqTlTnUBawSym5U5jWu40ynPLmZbnpPPTE+ulNBERERGReFDALAH95je/8dufMWMGH/7wh0epNjJWeXo91D1eR+VtlTQ+1xg6b56H8s+XM+MrM0ibMvEDEdZaVre0OEGy2lq2d3REdN28jAwuLynh8pISFmRlxbiWE4THA2vWkPzww5xw//3kb9+OsRG+6T15MlxwAVx0kTPdYqre1JaRq2iq4A9r/8Bdq+9iX8u+kHlPmXUK3zrmW5wx5wx1aiaQd9/131++HJKGMQvn8uX++9XVsH8/TJky3JqJyFClJ6ezrHQZy0r9FxC01lLdVs379e+zvX57fxBte/32qATTPNbTv27ay3teDpgnOzXbCaR5A2h+ae9nVqq+b4qIiIiIDIUCZgmmra2NBx54wO/YNddcQ9JwenJkQuo60MW+3+xj/+/2013ZHTJv5vJMak+opee4HqadO420jIkbLPNYy5vNzTzonW5xd1dXRNdNTU3lspISLi8t5bDsbHWaR6KlxRlB9sQT8OSTcOAAKUBBJNfOm+cEyC66CI48ElyJtW6exEZXbxePbH2Ee9bcw6oPVoWcnivJJHHxoov55jHf5PAph8exljJWDA6YHXbY8MqZMweys6HVp9999WoFzETGAmMMpdmllGaXctz04/zOWWupaqvqD6Rtr9/O9obt7GzYya7GXdS010SlDq3drUHXT+tTkF7AtLxpTMmZwuTsyUzJmeK3Tc6eTFl2mUY/i4iIiIh4KWCWYB555BFafXpeXC4Xn/nMZ0axRjLWdGzvYPfNu4OeN8mGSRdPovw/yklZnsLTTz8dx9rFV4fbzTMNDTxaV8djtbVU9fREdN3U1FQuKS7m4uJijs3Lw6UgWWhut9MLvGqVEyh77TXo7Y38+pUrB4JkCxdqvjKJmnXV6/jbpr9x7/p7qe+oD5m3NKuUa1ZewzUrr6E8tzxONZSxaHDAbOXK4ZXjcjnXvugz4+crr8B55w2/biISe8YYyrLLKMsuOyiYBk6ga1fjLnY17mJnw052Njpb335TV4TTTUegobOBhs4G1lWtC15fDMVZxX5BtMFBtSk5UyjJKlFgTUREREQmPAXMEsy9997rt3/aaacxefLkUaqNjEV5x+aRuSiT9k3+a3GlFKcw5fNTmHLtFNKmOiPJOiKcinA8qe7u5vG6Oh6trWVVQwMdnuBrtvmamZ7OxZMmcUlxMUfm5ipIFs6uXQMBsmefhYaGiC+12dmYM86Ac86Bs85ypl4UiZI9TXv4V9W/eKnhJXat3RU2/zHTjuG6I67j4kUXk5qkaT8TXU2N83jzNdwRZgDHH+8fMHs58MxsIjKOZKdms6RkCUtKlgQ839jZOBBIa9jJnqY9/dMzVjRVUNVWFdX6WJzpJavbqll7YG3IvIUZhZRmOSPrSrOcrSSrpH8/NzmXqq4q8lPyo1pHEREREZF4UcAsgVRVVbFq1Sq/Y5/85CdHqTYyVhljmHLtFLb/x3YAco7MYeqXplLysRJcaRNvejtrLVvb23nEGyR7vbk5xGRr/uZmZHBJcTGXFBdrusWhsBaOPRb2hV7/yZdn7lx2LFpE1cqVHPaVr5CRlxfDCkqiqW6r5oGND3Dfhvt4teLVsPlz03K5bPFlfP7wz3PY5BFEQ2TCGRzQyspyBr4O1/HH+++/8w60t0Nm5vDLFJGxLT89nxWTV7Bi8oqA57t6u6hsqaSiaSCINjio1tAZ+YtIQ1HfUU99R33IaSD7ZG/O7p+2siSrpD/AVppdSnFmMZMyJ/VvRZlFeulERERERMYEBcwSyN///nfcbnf/fmZmJhdeeOHoVUjiqnNPJwf+dIDqv1ez4tUVpOQHn1Kl9JOltG9uZ/LnJpOzIieOtYyPbo+HV5qaeLKujkfr6nh/CCPlFmVmcrE3SLY0K0tBsuEwBk4/Hf70p+B5UlLgxBOdUWTnnENXeTkb+wL+qepQkZGra6/jsW2P8fcNf+eZHc/gtu6w15w08ySuWn4VFy+6mMwURSzkYC+95L9/7LGQPIJv20cf7UzN2DfYuacH3nwTTj55+GWKyPiWlpzG7ILZzC6YHTRPW3dbf/CssqWSfS372Neyj/2t+53PFuezxxPZdOPD0drTSmtDKx80fBBR/ty0XL8g2qTMSUzKGEgXZ/kH2QrSC0hyaR1uEREREYkuBcwSyD/+8Q+//Y985CNkZ2ePUm0kHtydbmofruXAPQdoeKaBvqFT1X+vZuq1U4Nel5KfwiH/e0icahkfFZ2d/Lu+nifr6ni2sZFWd/jOcQAXcFxeHhdMmsR5RUXM02v9gbW1wauvwgsvOPOHPfEE5OcHzx8oYLZokXP8jDPghBPA9/k0Aaf/lPiraKrg4S0P89CWh3hp90sRBcnKc8u58tAruXL5lcwpnBOHWsp4NjhgdsIJIysvJwdWrHCWeeyzapUCZiISWlZqFgsmLWDBpAVB81hrqe+o7w+mDQ6q7WvZR2VLJQdaD9DrGcLassPU3NVMc1czOxp2RJTfYCjMKPQbpVaQXkBhRmH/Z2FGIQUZ/sfy0/MVaBMRERGRoBQwSxD79+/n9ddf9zt2+eWXj1JtJJastbS+28r+e/ZT/bdqehsP/gP3wB8OhAyYTTS/3ruXr2zfHnH+LJeLDxcWcsGkSZxdVERRihY4P0h7O7z2mhMge/55eOst6PVpa6+8AueeG/z6006DkhI49VQnQHbaaVBeHvNqS+LZUruFhzY/xENbHuLtfW9HdE1WUhbH5B3D107/GmcccoY61iQidXWwdq3/sZEGzMB5j8A3YPbEE/CjH428XBFJbMYYijKLKMosYmnp0qD5PNZDbXst1W3VVLVWUdVWdXC6rap/v9vdHZf6Wyx1HXXUddSxtW7rkK7NS8ujIKNgIMCWUUBh+sHBNd+AW15aHjlpObjMxJuiXkREREQGKGCWIB555BGsHViZKScnh1NPPXUUayTR1l3TTdVfqzjwhwO0rWsLmbflrRbatrSRtSArTrUbXUfmhJ9WckpqKudPmsT5RUWcnJ9PepI6yP3U1DgBsldecUaSvfOOMzdYMM8/HzpgVloKBw440zOKRFFnbycv7nqRJ99/kn9v/zfv178f0XWZKZmcP/98PjLvI5gPDCmuFE6acZKCZRKxp55ylmjsk5EBRxwx8nLPPRduuWVgf/162LMHpk8fedkiIuG4jIuSrBJKskpYUrIkZN729nYefuphGnsaOWTFITT2NlLV6hNU8wbWattrqW2vjdlaa+E0dTXR1NXELnYN6TqDITctl7z0PPLT88lLyyMvPc/5TPMeC3TO51hmSqamdBcREREZwxQwSxAPPfSQ3/4555xDWlraKNVGosXd6ab+iXqq7q2i7ok6bI8NmT8pL4nSK0op+0wZmfPH/9SCrb29vNjURJbLxUkFBUHzHZGbS1FyMnW9/qPtDs/J4azCQs4vKuKwnBxc+uPVYS1s3z4QHHvlFdg6tDd3eeGF8Hn07y1R1tbdRtnPymjtbo0of3pyOmfOOZNLF1/K+fPPJzs1m46ODlbtXBXjmspE9Pjj/vunnQbp6SMv96ijoLAQ6usHjj32GHzpSyMvW0QkmowxZCVlkZWUxbHTjiUjIyNk/l5PL3XtziixviBaTVuN/357DXXtA/st3S1x+mkOZrH9wbY9TXuGVUaSSeoPrPkG1foCarlpueSk5ZCTmtP/2XcsNy23/3hWitZSFhEREYkFBcwSQGNjI88995zfsYsuumiUaiMjZT2WpleaqPpLFdUPVONuCr8GT/6p+Uy+ajKTLppEUsb4HS3R6/HwTksLTzc08HRDA683N9NrLWcXFoYMmCUZw5mFhTxVX88ZhYWcXVjImYWFlKSmxrH2Y1h3N6xZMxAge/VVqK4eejlJSc5wipNPhlNOiX49RcLISs1iSckS3tj7RtA8+en5nHvIuVy04CLOnHMmWamJMdJWYqu31xlh5ivUINuhSEqCs8+Ge+8dOPa3vylgJiLjX7IrmdLsUkqzSyO+pqu366AAW217LfUd9TR0NvR/NnQ46b6ty90Vw58kcm7r7p9KciQM5qDAml9QLUDA7aA83nRWShYpSZqCXkRERAQUMEsIq1atotdnZE1aWhpnnXXWKNZIhqNtcxtVf6mi6q9VdO0J/wdf2ow0Jn9mMqWfLiVjZui3O8cqay3vd3TwjDdA9nxDA03ugwOELzY20u3xkOoKvqbAbfPmkZecTFKiv4lprTOX15tvDmyrV0Nn59DLSkqClSudANlJJ8Gxx0IE01+KDFVnbyevVbzG6xWv893jvxvyjeqz5p51UMBscvZkLlxwIRctuIiTZp6kTiGJumeegcZG/2PnnBO98i+91D9g9tpr8MEHMGdO9O4hIjIepCWnMSVnClNypgzpuo6ejv6AWn1HfX9ArT/I1tEQMODW0NmAx3pi9NMMn8XS3NVMc1czRGHQXYorhazULLJTs8lK8X569/2OpWT55Qt2Td9+enK6RsKJiIjIuKKAWQJYtcp/aqmTTz6ZHHVqjwtdB7qovq+aqnuraH03/BRjrnQXky6exOSrJpN/Uj7GNf7+OKns6uLFxkae8wbJ9nSFDw62eTy83tzMifn5QfMUpiRoB3lzM7z9tn+ArKpqeGVlZjpzgx17LBx3nJPOzY1ufUVw3h5fvX81z+98nud2Pcere17tfzP8kkWXMH/S/KDXnjX3LG5+8WY+NPVDnD3vbM6edzbLy5bjMsED6iIj5RvMAmew7dSp0Sv/zDOhqAjqfAYk3Hsv3Hhj9O4hIjKRZaRkkJGSMeRAm8d6aO5qpqnTmYrR97Oxs/HgdIBzbT2h15ceC3o8PTR2NtLY2RjVcl3G1R9YCxRUy0rNIjM5k8yUg7eMlIzAx5MzMG5Dm7uNNJeWmRAREZHoUsAsAQwOmJ155pmjVBOJRE99D7UP1VJ9fzUNzzZAuBcaDeSflE/pJ0opvriY5Lzx9b/1vq4uXmhs7N/e7+gY0vXJxnBMbi4eG3r9toTQ2wsbNw4Ext54AzZvdkaVDUdZmRMY6wuQHXooJGrgUWKqsbOR1ype45U9r/DKnld4q/KtoFMnPbvz2ZABs5VTVlL9jWqKMotiVV0RP62tMGipWD7xiejeIzUVLrsM7rhj4Njdd8N3v6vHsohILLmMi/z0fPLT84ddRo+7xwm6+QTcGjsb/dJ9+y3dLc7W5Xw2dzX3p7vd3dH7weLEYz39P1OsJK9P9gumBQ2+RRiYS09O798ykjOczxTnM8WVohFzIiIiE9z46lmXIdu9ezcVFRV+x84444xRqo0E09vUS+0j3iDZ0w3YnvABjszFmZR9soySK0pIn5Yeh1pGx/5BAbJtQwyQASzOzOS0ggJOLyzkxLw8spMT8FHW0wObNjnTKfZt7703vKkV+yxaNBAcO/ZYmD0b9AehxEBFU0V/cOzlPS+zoXoDlsgCu8/tfI4vHvHFoOddxqVgmcTVX/8K7e0D+0lJTnAr2q680j9gVlkJDzwAV1wR/XuJiEj0pCSlUJRZNOLvJ93ublq6vEG0IEE1v2NB8jR3NdPe0x7+huNEr6d3YHrKGHMZ10HBNN+A2kHHkkKcG8Kx9OR0zZYgIiISJwnYy5xY3n77bb/9qVOnsnDhwlGqjfjqbe2l7rE6qu+vpv6pemxX+M7i1MmplFxRQtkny8haljUu3m7b751isS9AtnUYAbKy1FQnQFZQwGkFBUxJS/CpN375S/j2tyGC6SqDKiyEI4+ED33I2Y480pnvSyTKmjqbeGffO7xV+RZv7XuLtyrfYl/LviGX4zIuVk5eycrJK2NQS5HhsRZ+/Wv/Yx/+MJSURP9ehx/uvMvw6qsDx37+c7j8cr3bICKSCFKTUqMSeANwe9y0dLfQ2t1KW3eb89nTFnS/P93TFjL/RArEBeKxHtp72kfl50xNSu0PnqUlpTmfyWmkJaUF/4w0X3Jaf7nhjqUlpZHsSh4XfREiIiLDoYDZBLd69Wq//TPOOENfbEaRu8NN3RN11NxfQ90TdXg6wi8gnZSdxKSLJ1H6iVIKTi7AJI3d/37WWrZ1dPByYyOvNDXxSlMTHwxjxFNeUhIn5Odzcn4+pxcUsDhrfAQHo6Kjw+mBzcwMnqe0dGjBspQUWL58IDj2oQ/B3LnqYZWo6+rtYl3VOr/g2JbaLcMqy2BYXLKYk2eezKmzTuXEmSeOaDokkVh45hlnsK+vL385dvf7+tf9A2arV8Njj8H558funiIiMvEkuZJGPNVkIH0BpcGBtL50oKBbe087HT0dtPe29wejOno6+tNtPW109HTQ0dsx4QNyoXS7u+l2d8dlJF04BhNRYK3vMz05ndSk1CFvaUlpw7pOAT0RERkJBcwmuI0bN/rtn3LKKaNUk8TV29pL/VP11P6rlrrH6nC3usNeY9IMRecUUfKxEorOKSIpKykONR26bo+HbUlJbEpK4vfbtvF6ayu1PT1DLqcvQHaSdzs0O5ukif4F11rYt8+ZRvG992DdOudz61ZnYZqrrgp+7cowI2xmz/YPji1fDunjZ9pOGR9au1tZX7WeNQfWsPbAWtYcWMO6qnXDXl8jLSmNI6ceyXHTj+O46cdxdPnRFGQURLnWItFjLdx0k/+xBQsgljNfX3ABzJkDH3wwcOzb34azz4ZEnJ1YRETGFpdxkZ2aTXZqdkzKt9bS0dtBfUs9Tz37FN2eblZ8aAU2yQ4E2Lrb+oNrfcG3vqBbe097f2AuUFCuvaedzt5OOno78NjwL7cmKouls7eTzt5OGMGkJ7EUraBc9c7q0f5RREQkzvSn9QTX2Njot3/ssceOTkUSTG9rL7UP1VL7r1rqn6rH0xn+y7ZJMRSeWUjxx4qZdP4kknPH3v+ezb29vN7c3D967M3mZjqyvX8MNTREXE7uoADZ8okeIOvqcoYg+AbG3nsP6uoC53/vvdDlzZ0LOTnQ0gKzZjkBtL5txQqYNCn6P4MktOq2atbsHwiMrT2wlm112yJedyyQgvSC/uDYcdOPY+XklaQlJ/h0qzKuPPkkvPaa/7GvfjW2g3eTkuAHP4CPf3zg2ObNcNdd8KUvxe6+IiIiY4ExhsyUTEyGoTi1GIDlpcvJyMiI+r163D39QaGO3o7+dGdvJx09HQcdH3ysb7/vWEdvB129XcHPe/e73GM0AjXO9I3IG7GmkRchIiLjy9jrkZeYKSsrY+bMmaNdjYTQU93Dlk9FMA1ZEhScVkDJx0qYdOEkUgpSYl+5YfivnTt5vK6Oda2tDOc9u5ykJE7Iy+sPkK3IyZmYATJrYc8e2LjR2foCY1u2QG9v5OWEC5i5XPDcc85IssLCkdVZxEd7TzubazazoXoDG2s2sqF6A2sPrGV/6/4RlZualMrysuUcOeVIjpzqbPOK5mnxchm3urvhW9/yPzZzJnzmM7G/92WXwa23wtq1A8e+/W047zyYPj329xcREUkEKUkppCSlkJOWE9f7eqynP7AWKMDW5e5yPnu76HJ3Bfzsy9d/LIJrAl3rtuFnxxEREZloFDBLIMccc4zmcY6TjNkZZC/PpnVt68EnXZB/Ur4TJPvIJFInpca/gkO0vrWVta0BfpYgpqSmcnxeHsd5t6UTbQSZtbB370BgrG/btAmG8O8U1IYNzj1C/ZsdfvjI7yMyyJee/BJ/XPvHEZezYNICJzDmDZAtK12m0WMyodx668Frl918M6TG4Ve6ywU//zn4zrLd2gpXXw3//rczCk1ERETGJ5dxkZGSQUZK9EfNDZXb4z44+DaEQJ1vkK7H3dM/6qvL3UWPp4eu3q6B/cHnw+xrykwREYkVBcwSiKZjHDlrLa3vtdLyZgtTPj8lZN5JH5k0EDBLgoJTCpj0kUlMunASaWWj23HstpaNbW280dzMm83N3DFvHukhetiOys3lkWDTBwLT3W5OnzyZkwoLOS4vjxnp6RMjONu3zligwFhzlBZbzsmBZcvg0EOdbdkyWLo0tnN6ScJo7Gxka+1WttZtZWvtVr505JeYkhP82bW4ePGQyncZFwsnLWR52XJWlK1gedlyVk5ZGfUF5EXGkk2b4P/9P/9jK1b4T5MYayefDJ/9LPz+9wPHnn7aqdfgddVEREREhiPJlURWahZZZI12VQ7i9rjp8ThBtK7erv50f1Ctd+RBuR53D3t27OEpnhrtH1dEROJIAbMEctRRR412FcYlT5eHhucbqHusjrrH6uiqcOYULzq3iLSpwQNfxRcX0/JOC8UfKabovCJSCkdnukVrLfu6u3mnpYU3m5t5o7mZt1taaHUPTK9w9eTJHJ2XF7SMo3Jz+9OpxnBETg7H5eVxREYGHe+8Q461nDFrVkzmjo+L3l7YudOZOnHrVudz82anV3TQOoAjMnu2f2Ds0EOdObxcmpZOhq/H3cOOhh39QbGtdVvZVreNrXVbqW7zX6T6uOnHhQyYLSlZEvRcZkomy0qXsbx0OSsmr2BF2QqWlCwZE2+/isRLWxt89KPO0pR9XC64++74j+z66U+dddT2+8yY+oMfOO9cXHxxfOsiIiIiEk9JriSSXEmkJ6dDDN9HfvnllxUwExFJMAqYJQhjDIceeuhoV2Pc6K7ppv7JemofraVhVQPu1oPn7q57vC7kKLOsRVksfWRpLKsZULU3OPZOSwtvez8PdIde7PaN5uaQAbPDc3L48ezZHJ2byxE5Of2j0To6OlhlbVTrH3Pr18O77/oHx7Zvh56e6N0jLw8WL3Z6LX1HjfkEHkWGoqu3i91Nu/mg/gN2NOzgg4YP2F6/na11W9nRsINeT2Rr5G2t28pZ884Ken5x8WIMhtkFs1lcspglxUtYUrKEFZNXMK9wHkkuzfUmicvjcUZ1DZ6K8StfgZUr41+f/Hz4+9+dqRn73oGxFq64Ap54Ak47Lf51EhERERERERnPFDBLEHPnziUra+wNox8rrLW0b26n7rE6ah+tpfn1ZggTB6p9tDbstIyxVt/Tw+pBwbEK39feI/RGmOkFs5OT+db06cOt5tjywx86PYzRkJPjBMYGb1OmaEpFGbL6jnq/gJjvZ0VTBTbcQykCW2u3hjxfnltO63dbyUzJHPG9RCaab34T7r/f/9jy5c6vldFywgnw4x/DN74xcKy7G84/H/75Tzj77NGrm4iIiIiIiMh4o4BZgli2bNloV2HMcXe4aXyhkfp/11P3ZB2dH3RGfG36rHSylsY3ANnU28sab1CsL0C2ozPyOgcyKz2do3Jz+XBhYZRqGSe9vbBnjzMybPt2eP/9gfTzz0NZWfBrFywY+v2ysmDRIicYtmTJQGCsvFyBMYmItZbGzkZ2N+1md+NudjftZk/THnY37WZHww52NOygsbMxJvdOMknMLpjN/EnzWV62PGReY4yCZSKDWAs33AA//7n/8exs+Mc/ID19dOrV5+tfd34N3nXXwLGODidodtddzqg4EREREREREQlPAbMEoekYHe3vt1P/73rq/11P4wuNeDo9kV1oIPeoXIrOK2LS+ZPIXJSJiVGgpG/NsbWtraxpaXE+W1tHHBzLS0piZU4OH8rN5ajcXD6Um0tpamqUah0DPT2we/fBAbHt2531xoJNobh9e+iA2fz5wc9NmuScX7DA+ewLkk2frnXGJCSP9bC/ZX/AgFjffmt3a0zrUJJVwiFFhzC/aL6zTXI+ZxXMIjVpDP+/LjKGWQtf+hLceaf/8eRkePBBmDdvdOrlyxi44w5oaHACeH3cbrj6anjtNbj9dhivy4yKiIiIiIiIxIsCZgkiUUeY9Y8ie7Keun8PbRSZK9NF4ZmFFJ1XRNE5RaSWRL/D2W0t2zs6WNPSwprW1v7gWM0I19PKcrk4LCeHw3NyOML7OScjA9dYGw3V1uYEv3bscD59R4zt2jWwKMtQbN8Oxx0X/PzChXDIIQOBsb7g2Pz5TsBMZJAedw9tPW3kp+cHzdPe0075L8pjXpdJmZOYXTCbOQVzmF0wuz9AdkjRIRRkFMT8/iKJxhhnJNlgv/89nHFG/OsTTFIS3HsvpKY6n77uuccJmt19d+hfjyIiIiIiIiKJTgGzBLFgONPQjVPDHkUGpE5NZdJ5kyg6v4j8k/NJSk+KWr063W42tLWxxhsUW9vayrrWVto8kdcvkHSXi+XZ2X7BsfmZmSSNheBYT48zdeLOnQNbX3Bs506oqYn+Pd9/P/T5Qw+FraHXcZLEYK2luauZypZKKpsrqWypZG/z3v503/HqtmrOnnc2j1/xeNCyslOzKcwopL6jfkR1SjJJzMif4RcUm1MwhzmFc5iVP4u89LwRlS8iQ/fDH8Lq1fDcc85g49/+Fj71qdGu1cFSUuBPf3IGWf/0p/7ntmyB4493RpzdfLOz1KaIiIiIiIiI+FPALAEYY5g5c+ZoVyNmehp7aHyukYanG6h/un5Io8hwOVMtFp5VSNHZRWSvyI76VIs37NjBI7W1bGlvZxjjpfykGMOyrCwO7xs9lpvLosxMUkZrukBrSauvJ7O6mqS6Oqis9A+OVVTACAOCIeXlOfNhzZ3rbPPmwZFHxu5+Mi50u7upbqvmQOuBsFtbT1tEZVa2VIbNMyNvRtiAmcEwJWcKM/JnMCNvBtPzpjMzf2Z/cGx63nRSklIiqpOIxEdyMvz973DMMU7w7KMfHe0aBedywa23wmGHwec+5wzk9vW738Ff/wr/8R9w/fVQXDw69RQREREREREZixQwSwDl5eWkpaWNdjWixtPtofn1ZuqfrqfhmQZa3m6BIcRkUkpTKPxwIUVnFVFwegEphcPvnG7q7SUvOfT/RhVdXWxsbx9y2TlJSSzPzmZ5djYrvNuirCxSRys4VlMDd93ljBjbvRv27CF91y4+PMK11cIqLPQPiPWl586FoiJnviyZ8HrcPTT0NNDU28Szu56lobthIPDV5h8EG+kor0D2Nu8Nm2dG/gw21Wxiet50v4DYjLwZ/ftTc6dqPTGRcai4GDZudKY8HA8uvxxWrIBPfMIZHeerowN+/GP41a+c81/5CixZMjr1FBERERERERlLFDBLAHPmzBntKoyItZa2jW00PNNAw9MNNL7YiKdtCBEyF+QenUvRWUUUnlVI9vJsjGtoQZZuj4et7e2sb2tjXWsr69vaWN/WRkVXF9XHHENxiB60pVlZYcsvS03tD4r1Bchmx2PNsaam/uAXpaVwxBHB83Z1wX/9l9+hqNWurAxmzYI5cw4OihUWRusuMka9XvE6Oxp2UNNeQ01bjfPpm26roaGzYeCCUZhRs7a9lq7eLtKSg798cO9F95KRkoHLjFJQW0SGbM8eJ3CUmgo/+lHovOMlWNZnwQJ44w24/Xa44QYY/O5OZ6cz4ux3v3MGZ3/yk3DZZVrOU0RERERERBKXAmYJYDwGzNxtbmr+VUPD0w00PNNA9/7uIV2fWpZK4YcLKTyr0BlFVhDZKLIej4ftHR1sam9nU1sbm9rb2djWxpb2dnqsDXjN+rY2ThlCwGxeRoZfYGx5djZlsRgB2NXlTJFYWQl79/qNDOtPNzcP5P/MZ0IHzCZPhqQkcA9jYsncXJg92wmK+W6zZ8PMmZCRMfQyZczo6u2iobOBuvY66jvqqe+op66jjrr2Ojp6O/j+id8Pef33nv8ez+18Lk61Dawwo5CpOVMpzy1nas5UpuZOPegz3MiwrNTwwXERGX3d3fDEE/DHPzqfbjdkZsI3vuEMXJ5IkpPhq191ppH8wQ/g978P/Gv8rbec7WtfgxNOgPPOg3PPdd5bEREREREREUkUCpglgBkzZox2FYbM0+Nhy5VbIp5q0ZXuIu/4PApOL6DgtAJnFFmI0VldHg/vt7f7BcY2tbWxraMjaGAsmPVtbZxSUBD0/BG5udw5bx5Ls7NZlpVFTpgpHCPS0uIEwfqCYYHSNTVDK3PPntDnk5KgvNwJtA3iTk7GzJqFyzco5psuKNDUieNAt7t7IOA1KPgVMO3NE2odsGRXMv91wn+F/P+xODM2i+gku5IpzSqlLLss4FaaVcqUnClMyZlCRoqCtiITWUcHPPccPPooPPgg1NX5n29vhzvvhO99b3TqF2tTpzqzKl9/vbMO29/+Bj09B+fr7XX+nZ57zgmeHXIInHSSE0Q78UTna4CIiIiIiIjIRKWAWQIoKysb7SocxN3pJik9Kej5lPwUco7IoeXNlqB5sldkOwGy0wvIOzaPpIyDy+t0u9na0cHGtja/wNj2jg6GMU4qoPWtrSHPF6WkcO3UqZEVZq3Tizc4CDY4GOY7MixaAgTCDnLppc40jjNmwPTpdJWV8cLu3XQWFnLGhz9MhkaKjSqP9dDa3UpjZ2NEW1NXU3/wq76jntbu0G15OHo9vTR2NlKQETyoPNSA2aSMSZTl+AS/sgIEw7JLKcwo1PSIIgnK44FNm+DFF+GZZ2DVqoOnJBzsf/8Xvv1tZ1TWRHXIIc7IultucX7e3/4WqqqC59+2zdl++1tnf+ZMZ/rGww6DlSudddIm2qg8ERERERERSVwTuEtgdBlnOMXlwFXACiAL2A+8BNxurX07XnUpLS2N162C6mnsoemlJhqfb6Th+QZ6G3s5audRIUedFJxW4BcwS5ueRsHpBRSeXkj+KfmkFg9Mj1bb3c2Wxha2dnSwtb2drd7RYzs6OiIdpBaRaWlpLM3KYmlWFsuys1malcX8zMzwF/YFwvbvH9j27Ts4vW+fM5VivE2eDFOmhM/3k5/47Xo6OuhsCz66SIamx91DS3cLzV3NNHU2sbhkMcmu4I/pn7z6E+7bcB9NnU39ATCPjWaLj46a9pqQAbPZBbNZXLyY4qxiijO9W5b/Z05SDhvf2khOcg5nnXmWgrMi4qepCdasgdWr4dVX4aWXDh5FFkxmJlx1lTOiaiIHy3yVlTlTNH7/+/D00/DnP8PDDzvrmoWya5ez/eMfA8emT4dFi2DhQmfdtL7P4tgMHhYRERGZUMZS/6GIiChgFhPGmAzgAeCcQadmerePG2NusNb+OB71KSkpicdt/PQ299L0ykCArHVN60HTK3bu6CRjTvBO76Jzimjb0Ebh6c46ZK45aezo7OTV9na2tu9n65aB4Fh9b29U61+WmsqizEwWZ2WxMDOTJVlZLMnKoiBl0Fpobjc0NkJhYegCly+HdeuiWseIZWU5cyhNm9Y/Mqz/c/p051ws1lBLEB7roa27jeauZpq7mvsDXv37XYP2B5/32e/s9e+p3Pf1fUzOmRz03gdaD7D2wNoY/4RDk5uWS2FGIUUZRRRkFFCcWUyKK/Qagl87+mt87eivhczT0dFBZUplNKsqIuNQTw988AFs2eJsa9c6QbLt24de1vHHw5VXwiWXOEttJqLkZDjrLGdrbXVG4z32GDz+OFRXR1ZG39KoTz3lf7ygwJmVeebMgW3GjIHP3FzN1iwiIiKJbaz1H4qIiAJmsfI7Bn7ZbQZ+C1QBhwHXAtnALcaYSmvtvbGuTDxGmHVXddP4ciNNLzfR9HITre8dHCAbrOH5hpABs9Qjs/nDTzPY2l7H1voKdu7rjOpoMYDytDQWZWayKCur/3NhRgaFnZ3OHEV9W3W18+k7Kmz/fudYaakzTWIosXrNurDQCXiVlzsLlPSlfffVI3WQ9p72/ikI27rbaO1uPWhr6xk43hfUGhz8au5qprW7FcvQ1r2LVGNnY8iAWX56fkzuC/6Br8KMwv7Nd78o0/9cQXoBKUmhg2MiIuG0tDizBPtuW7c6AbIPPnDW2RoOlwuOPRbOPx8uugjmzIluvce77Gy48EJn83jg3XedkXovvggvvwwNDUMrr6HB2d59N/D5jAxngHtZmfPZt/XtFxc70z0WFUFOjr7KiIiIyIQ0pvoPRUREAbOoM8acDlzh3X0OOMda2zds5D5jzD3Ay0AR8EtjzCPW2uALdUVBtEeYWWvp3NHpFyDreL9jyOVUPl/PlKuDTwOY5nLxm3376PCMPEw2Mz2dRSkpLHK7WdTezqK6Ohbu309uX9DLNyhWVRV+TiJfVVXOSLOk4GuyMTl40CMgY5weo2BBsPJyZwrFSKaDHKfcHjftPe2097QHDGL5Bbd8g149rdxy6i1My5sWtOyfvPoTbn7x5jj+NMPT2NkY8nyogJnBkJeeR356/sFbmv/+4OCXAl8iEgvd3QO/Zg8c8N8qKweCY/X10bvnjBlw4olw6qlw9tkwaVL0yp7IXC44/HBn+/rXnQDahg3w1ltOAGz1anjvvZHNIt3RATt2OFs4KSnOO0J9ATTfraDAeTcoNxfy8gKnU1PD30NEREQknsZi/6GIiChgFgvf8H72Alf7/LIDwFq72RjzZeBvOL/0Pgf8PJYVyhxhUMW6LW0b2vwCZN37u4dcTm8q7F6axNuHWl5f5iHnQ128EiK/yxgOycjgvQjXyEq1lrmdncxvamJ+dTWL9uxh0bZtLNiwgayKiuG/kh6O2w21tc5Is2B8A2bFxf6vUvtuU6Y4wbDJk53eoTGqx91DW08bdS117O/aT5eni7f2vYXb5QS52rrb+oNdgba2nvDnu91Db2N9vnbU10IGzLJTs4dddrwYDC3dob8Lnz77dP54wR8DBsVy0nJwGVecaisiicRaaGtzAlt925FHOiOUgvnOd+CWW2Jbr5QUWLoUDjsMjjvOCZTNnBnbeyYKlwuWLXO2Pj09sHkzbNzofPZNk7ltW/SXY+3pGQi2Dkdamn8ALSvL2TIz/T8DHQt0Lj3dKTMtzUknJ2sEnIiIiAzZmOs/FBERBcyiyhhTCJzm3X3KWrszSNb7gV8CJcBHieEvvPT0dMwQ/4J3d7hpeaeFpled4FjTq024m9xDvndPMmxaBO8dCmtWwMbF0JM6UM4kd/hRXAuamngv2b+ZljU1Mb+igvk7dzK/ooIFe/Ywv6KCGQcOkByF0WjDsm9f6IDZ9dfDl7/s5Inya87WWno8PXT0dNDZ20lHb0fQdGdvJx09HX5p33zfPPabzC2cG/Ret715G19f9XV6PQGCj1uj+mONSGt3a8jzWSlZMbt3enI6uWm5fltOak7o/bScg67JTMkMG/BaWLyQhcULY/aziMj4Z60TvOjogPZ257Mv3drqTH8YaGtuDn1u8Dsoq1c7gapg8vKi+3OVlcH8+bBwoXPflSth8WItyRlPKSkHB9HAeY9o505n27VrYNu50xlBuG9f/Ova1eVMJBDpumxD5XINBM/6gmmBPoOdS0tz/j37ttTUwOlw+4HSqanO/6/WKqgnIiIyVozF/kMREXEoYBZdxwN9PdzPBstkrfUYY17E+WX3IWNMgbV2iCtDRCY1THDGWktXRRdNrzXR/Hozza8307qmFds79DWZ2jOcoNi6ZbB+KWxeCN0hOq5qe3porKkhP8T6Xh95913mbt3aHxQ7ZO9e8iIccRYVWVlOkKu0FEpK/EeC+Y4MCzPt5f5MD1s7PqBj94bIAllDCHR19nbisdEJFF625LKQAbNkV3LgYNkYEy5g5jvCLDUplezUbLJTs8lKyepP+25ZKVnkpecdFOgaHOzKSc3RVIYiAjid0729Tkd9d/fAp2+6q8sJ+hQWBi9n/Xp4/PGBazo7Dw58DU77HuvocOoSa+GmUSwrG3qZJSXOCLEZM2D2bCc4tmCB82+Wnz+cWko8JCXB3LnOFkhXlxM027dvYEpO3+Vh+/br6pyRZeOBxzPw/9vYlAFcgMvlITnZkJzs/HdKTsYvHY9jvvvGOJ8ul7P1pYdzLNT+SO4Rqjxjgn/6pkVERAIYc/2HIiLiUMAsupb4pDeEybvJ+2mApcBLsajQ4ICZp8tDy7st/cGxptea6N43vKnv6gucwFjftn0ueEIs4zWYy+1mz/79IQNml/b0cOk99wyrfsF0ZWfQXphDa0EWLfmZNOen05SXTn1uCg25KdTmJFOb46IqC5pT3HT1dtHl7qKzt4Ku3u1OuqWTroYuutZ30dXbxWOXP8bpc04Pes/Htz3ONY9fE9WfIxY6ekP39GSmjN6aaQYTMJgVKLg1K39WyLIuWXQJ5x5yLlmpWaQmaWETkZHyeJzN7R7YfPejcW5wvsMPd95dCGbLFnjySafDvbfX2XzTg/f70l1dKVRWHonbbbjjjlSsDV7G4PIGB8Qi8eijcN55wc+vWQPf/e7Q/nuMhoYwf7b7BsySk539sjLnfZS+9IwZA9v06ZCREds6y+hIS4NZs5wtFGudEY11daG3piZn1GNzs3/aPfTJESY8j8fV/5yS+BkcQAsUVBvKuaHk6Qv2xfIevpvHk0JV1eEYY/nrX1P6A6OBNt9/m3Cbb95Q1w333FDuMZJ7xqrugfIG+nlCffq210jzR5p3uJ+hznV3u9i8uRBjLLm5LtLTh/dzjfbPMZIyx+LP0ZcOVc+h/hzhrhlq/jFkzPUfioiIQwGz6Jrpk94VJm+FT3pG1GvilZKUQuUDVVS+XE/L682Y9zpwDfNt3copA8GxdctgbznOr+sQknt7mbV/P3MrK5m7bx9zKyuZt3cvcysrmVFVxZ77f3PwXD4+1rgrWRFB3WoyoSoLqrOgKttJB/qsyYSulA4gRGCoy7vVRnBjr87e0NNLpiWPjzmiOnqGFzAzGDJTMslMySQrNas/fdCWHOZ8SiZZKf7nM1IyyEnNIT156NOLBpORkkFGytB7Yq0dGKnRlw60hTo/lGvz8kJ3GLe1OdNLjeSesfpZfM8tWRJ6xtL9+53p3EKV2dXlYu1aZy3A1lYXKSlDq1dfQKcvHepYsHMXXQSLFgX/Odatgz//eeT3iXX+H/4QTg8e3+fxx+FLXxpacGs0PPAAXHJJ8PNr1jiz4Q5dMjA5bK5oCddxPdanGHS5nBFy4ZYJPfZY2LDBCYwVFDjXiYRizMCaY+GCa4NZ64z2GhxMa2pygnBtbc5IzECfgdK+eeIxYlMmFt/vIhNfMjB1tCshCSENZ4COyMiFCrpZOzNWt/UteFeYvHHpPxQREYcCZtHlO1QqXLjFd/KioqHcxBhTHiZL/zv3XVVdPHbpQ0MpHgA3UDkVPpgL2+c4n00FPhna6F+zKqmnh6l1dZRXVzO9upry2lqmVVdTXlPD5NragI2s0buteuA5Tig+JGg9nqjo4PHZ0JgOjWnQkOFNpw+km9LAhut4s0CLd4uBB+5bS/6p+UHPP/PcLtgbm3sH5UkCTyrGnYbxeDd3Gqb/WCq40zG9aWCdY8/TRMkpLwctctMr2eT/5c/e69KhN53eTgOeNFJTUnFbQzPQ5BO0CMUGzefG2oH/YIPz+eYPdOw73+nm5JOD3/zFFw0/+EFqwLIGB1wco/M62o03dnPqqcF7V55/3nDjjWO8N51o/hx9/x3WRaVeQ+V2d3PyyaF/jp/9bOz/93jttW7S04P/HO++a9izZ+z/HBs3dlNaGvzneP99g9OZMratW9dNSUn8f460NEtGBmRkWDIzITPT+ezbz8py0r6fvnny8iAnx8nX17nwcvBfH/3CTd2YSLq6uti2bRsAWVlZpI316Og4l5bmTPMZZhbtkPrWBOzpMXR3G3p6DF1dpn+/bwu176Txpl39x9xup9zeXmdzu/tGsg7sO/mM95j/vscz9l6dFxERGQ+C90sAHPDdmRKuP85aG2nPT1z6D0VEZOiM1WuSUWOMWQX0vbefYa0NOuzIGHMW8KR39wZr7Q+HcB/9RxMRERERERERERkjrLURvcESr/5DEREZOk2IE12+g6m6wuT1Pa+RfiIiIiIiIiIiIhOf+g9FRMYoPWijy3cBqFRC/9LznXdnqEtvTwtzfi7wvDd9IVA5xPJFIlUCPOFNnwNUj2JdZGJTW5N4UVuTeFFbk3hRW5N4UVuTeFFbk3iZCjzsTZ8MbI9SufHqPxQRkSFSwCy6Wn3S6YT+hZfuk+4ImiuAcHMiG/+VSlcPYQ5lkSEZNH/3OrU1iRW1NYkXtTWJF7U1iRe1NYkXtTWJF7U1iRdjjO8iZtuj2Nbi0n8oIiJDpykZo6vRJ10YJq/v+ZroV0VERERERERERETGmEaftPoPRUTGEAXMomubT3p6mLy+0yrujkFdREREREREREREZGxR/6GIyBilgFl0bfJJLw6Tt++8BTbGpjoiIiIiIiIiIiIyhqj/UERkjFLALLpeB3q86ZODZTLGJAEnenffs9Y2xrheIiIiIiIiIiIiMvrUfygiMkYpYBZF3l9cz3p3zzfGBBtWfRlQ7E3/I9b1EhERERERERERkdGn/kMRkbFLAbPo+4X3MxW4zxiT43vSGLMI+JV3twW4O451ExERERERERERkdGl/kMRkTEoebQrMNFYa1cZYx4ELgaOAd4zxtwJVAArgC8Afb8Ev2mtrR2dmoqIiIiIiIiIiEi8qf9QRGRsMtba0a7DhGOMyQQeAs4IksUCN1trb45frURERERERERERGQsUP+hiMjYo4BZjBhjDHAF8GlgOZAP1AKvAL+y1r46apUTERERERERERGRUaX+QxGRsUUBMxEREREREREREREREUlortGugIiIiIiIiIiIiIiIiMhoUsBMREREREREREREREREEpoCZiIiIiIiIiIiIiIiIpLQFDATERERERERERERERGRhKaAmYiIiIiIiIiIiIiIiCQ0BcxEREREREREREREREQkoSlgJiIiIiIiIiIiIiIiIglNAbNRYBxXGGOeMcbUGWM6jTE7jTF/MsYcEaV7LDHG3GOM2WWM6TLGVBtjXjTGXGWMSYpXGTK6Yt3WvOVfaIx50BhT4S2/2Riz3hjzM2PM9AjK2GmMsRFuZSOts8RGHNpaVNqJnmvjXyzamjFm5hDal98WpDw91yYoY8wd3v9uN0WpvBnGmF8bY7Z523K9MeZNY8xXjTEZ8SpDxp4YtLWTjTF/NcbsMMa0G2PajDFbjTG/McYsjuD654fwXDsqGnWW+IhmW4tWO9FzbWKKRlsb7vc1Y8zMAGXpuTZBGGMKjDHfMca8apy/D7qN83feM8aYzxtjUqNwD/WxiYgkCGNtwL4eiRHvF/wHgHOCZHEDN1hrfzyCe3wWuBNICZLlZeBCa219LMuQ0RXrtmaMKQDuB04Pka0LuMZa++cgZeQCjYCJ8LaTrbUHhlJPib04tLWotBM918a/WLU1byfKzmFUyVpr/V4+0nNt4jLGnAI8jfPC2c3W2ptGWN7ZwN+BnCBZNgDnWWt3xbIMGXui2daMMWnAPcAVIbJ5cJ6dt4QopxYoivC2R1tr34i8ljJaYvBcG3E70XNtYopWWzNBXlaKwAxr7Z5BZem5NgF429bfgeIQ2TbiPDeG831ffWwiIgkmebQrkIB+x0BH32bgt0AVcBhwLZAN3GKMqbTW3jvUwo0xZwJ343TUtQO/Ad4BSoCrgSXA8cDfjTEfttZ6YlGGjAkxa2vGGBfwMHCC99Ae4PfAVm+5ZwIXA2nAH40xDdbaxwIUtZSBTuX/wWlnoTQMpZ4SNzF9rhGFdqLn2oQRq7ZWDVwUYd7/8t4P4MYA5/Vcm4CMMSuBh4jS7AzGmKXAg0A6TqD398BLOB3EnwSOwXkuPWKMOdpa2x6LMmTsiXZbw2kXfcGyWpzn6DqcDrsTgU8AqcCPjDEt1to7AtRpKgOdyncDT4a559Yo1FtiLAbPtRG3Ez3XJqYot7VIv699HviwN31PgGCZnmsTgDHmUOAxINN7aBVOP0UdMAP4NLDYu60yxhxhrW0c4j3UxyYikmistdritOGMxLHe7VkgfdD5hTh/yFrvZ84Qy08FdnivbwEOC3D+Xz51uDwWZWgb/S0Obe0zg8rPCpDnPKDHm2fv4Dp483zBp5xDRvvfTdvYa2vRaCd6rk2MLR5tLYI6XOFThyfwjtQflEfPtQm2AWfhBDatz3bTCMt8xVtOL3DWoHMGuM3nXt+JVRnaxtYW7bYGnOxTzjqgJECeI4Fmb542oCxIvfrKOWO0/520jXyL0XNtxO1Ez7WJt8WirUVwz+O9bcgCa4DUIPXSc22cb8CLPv8drw1wPhm41yfPz4ZYvvrYtGnTpi0BN61hFl/f8H72Aldbazt9T1prNwNf9u4WAZ8bYvkXA7O86Z9Ya98dVH438Cmct20AvhOjMmT0xbqtfd772Ql83FrbNjiDdUaU3ebdnYrzR8lgy3zK2T7EOsjYEOu2BiNvJ3quTQzxaGtBGWOmAH0jL2qBq6y1NkBWPdcmCGNMmjHmZuBxID+K5R4NHOvd/ZO19t++573t6is4oygBrjfG+E3fE40yZOyIVVtj4PsawKestdWDM1hr3wK+593NBC4PUM4yn/SG6FVP4i2GbQ1G2E70XJtYYtzWQt03C/gjkISzPMDHvd/1B9NzbZwzxixgYMabh621vxmcx1rbizOCa5/30GeGuFaY+thERBKQAmZxYowpBE7z7j5lg8+dfD/O1FAAHx3ibT7m/bQ4Q7wPYq1txfkCCbDUGHNIDMqQURTrtmaMycF5GxngaRt67Z0HfNIfCnC+7w+VTVbTDow7cXquwcjbiZ5r41wc21oov2Kgw+cb1tqqIPn0XJsAjDGn4XTKfh/n+3Ib8PMoFf8xn/T/BsrgbTt954qAU2JQhowBMW5rfc/N9dbatSHyRfp9rd5auy/AeRkHYtzWYOTtRM+1CSIObS2Um4DZ3vSPrLWbguTTc238O80n/Zdgmbwv2T3u3S0A5g3hHupjExFJQAqYxc/xDPx7Pxssk/ePgBe9ux8yxhQM4R4nej/XWWtrQuR7zic9eNRPNMqQ0RXrtjYJeBs4AGwJk9d3wVq/8o0xBmeuboD1Ed5bxpaYP9ei1E70XBv/4vE7NCjvYuKXeHdfsdb+KUg+Pdcmjk8w8DbwOzgvigRai3M4+p5JDThTRQXj+0z68KBz0ShDxoaYtDVjTCawDWda7M1hsgf9vubV17Gs59r4FsvnGoy8nei5NnHEuq0FZIyZjzMKEeAD4JYQ2fVcG//cwEacaYW3hckb7vdcMOpjExFJQMmjXYEEssQnHW7If99bUAZYirPQcUjeRWvzh1g+wKHRLEPGhJi2Ne/IjkBvHweyyCddN+jcDCDXm94AYIzJxWlP2cB+nDei3RHeS+Ivpm3Na0TtRM+1CSMebS2UH/ukrw+RT8+1iaUG5031u6y1bmNMyUgL9E4DtNC7G24U4laczqAk/L+vjbgMGXOi3taste3AMRFmD/p9zTvl3Xzvbt9zLROnPeXjTFH7XpApz2TsiXpbg5G3Ez3XJqSYtLUwfgj0TdP5HWttV6BMeq5NDNbaO4E7I8weql8iIPWxiYgkLgXM4memT3pXmLwVPukZMSi/EvDgvK3vW340ypDRN9MnvStM3uG0taG4yif9xqBzvvPGe4wx/wLOx/njt0+dMeZ24MfW2o4Y1E9GZqZPeleYvMNtayNtJ0Opo55rY9dMn/SuMHmj+lwzxlwAHO7dfdi73k8weq5NHLcDX4jBf6MyIM2b3hUqo7eDcT9Qjn9bjkYZMnbEqq0NRajvawsZ6IBuMsb8AbgMSPfJ0+I9fpO1tiF21ZQRimVbG2k70XNtYon7c80Ysxz4iHd3jbX2gRDZ9VxLIN51iPtGo9YS+RrDM33Su8LkVR+biMgEoikZ46fYJ10bJq/vcPGiaJdvre0BWgOUH40yZPTFuq1FxBhzJnCud7caeHpQFt+O5Z8BF+HfqdxXpxuBF4wxk6JZP4mKeLS1kbYTPdcmhtF8rn3dJ/2TMHn1XJsgrLXvxKijbyhtGQba87C+r4UoQ8aIGLa1iBhjlgCf9+52AQ8OyuL7XPsucCX+ncoAOcB/AG8ZY2YjY1KM29pI24meaxPIKD3XvuaTvjVMXj3XEsvPgFRv+u9DWGNYfWwiIglKAbP4yfRJd4bJ63s+M2iu4Zfvm8f3umiUIaMv1m0tLGPMNODPPof+O8AfTb5/qHTjzDG/EOePlSnA53CmLwNn3vv7jTF6Zo0t8WhrI20neq5NDKPyXDPGHAqc4N19zVr7ephL9FyTcPR9TcYMY0we8AADIy3usNZWDsrm+1yzwF3ACpz2VIwzKqNv7Zi5wGPeqc0ksYy0nei5JsPmne7xY97d3TjPtVD0XEsQxpjP4/z3BCcY9aMhXK7vbCIiCUqdNPHjO/1lwLm0g5yPdNrMoZTvm8f3umiUIaMv1m0tJGNMMbAK6Jun/mmcaTkGm+r97AROtdZ+x1q7xVrbZa3db639HXAEzmL1AKcw8IeQjA3xaGsjbSd6rk0Mo/Vc+w+fdLi3lUHPNQlP39dkTPB2/j4GLPAe2gDcECBruffTAh+z1l5rrV1rre2w1tZaa+/HeQFgrTffIuArsau5jFEjbSd6rslIfJ6BKT1/aa3tDZNfz7UEYIy5ELjD59AXrLX7hlCEvrOJiCQoBczix3d0TWrQXI40n3SkC80OpXzfe/iWH40yZPTFuq0FZYwpA55noPNlO/Bxa60dnNdaeyyQCxxirX0lUHnet5x9O6w/N9I6SlTFvK1FoZ3ouTYxxP25ZozJBq7w7u4BHg13jZ5rEgF9X5NR532+PQkc7z1UC3zEWnvQ2+/W2iuALGBOsDWBrLVN+K+DpudagolCO9FzTYbFGGMYaEsdwO/DXaPn2sRnjPkI8A8Gpkb/lbX23iEWo+9sIiIJSgGz+Gn1SQ+eH3sw3/ORzv09lPJ98/iWH40yZPTFuq0FZIyZA7wMLPYeqgBOs9bWBLvGWttira0IU/SjQLM3fYymLxtT4tLWRthO9FybGEbjufZhn7IejHS9Az3XJAx9X5NR5Z0J4FngRO+hRuBMa+37wa6x1rZba3eGKtdauwbY6N2dZYyZGiq/TDwjbCd6rslwrQSmedNPWWtbIrlIz7WJyxjzWeB+BqYb/hP+a9xFSt/ZREQSlDpo4qfRJ10YJq/v+aDBhuGWb4xJAbIDlB+NMmT0NfqkY9HWDmKMOQJ4DWeOd4BdwInW2t3DLbOPtdYN9HXipKEFcMeSRp90XNpaMCHaSWOQOhxEz7UxrdEnHa+2dr5P+p8jKOcgeq4ltEafdLi27JtnWN/XQpQhCcj7ctNrOFONAdTjvNz0bpRuscUnPSVKZcrEE6idNPoc03NNhiJm39e89FwbR4wxNwK/Y2BKw3uAqwLNeBOBRp+0+thERBKIAmbxs80nPT1M3mk+6UgDDkMpfyoD/+19y49GGTL6Yt3W/BhjzsSZhrFvzbJNwHHh3tgbop4oliXRE9e2FoFA7UTPtYkh3s+1JOBs724l8PpwyglDz7UEZK3dD/S9/R6yLXvb4WTvbn9bjkYZkniMMYfh/3LTPpyXm1ZH8TZ6rkkkDmoneq7JCPQFzLpw1mWMNj3XxgFjjMsYcxdwk8/hXwFXRzpLRADqYxMRSVAKmMXPJp/04qC5/M9bBqYACMlaWwdUD7F8gPXRLEPGhJi2NV/GmPNwphbL8h56EzjBu0ZPqOtmGWO+Yoz5oTHmnAhu1Tf9RTfO29AyNsS0rUWjnei5NmHE7bnmdTgDo74ejeStVD3XZAg2ez8Xhcm3gIG1NwY/k6JRhiQIY8yRwHMMvNz0Ps7LTRvCXDfJGHOdMeb/GWM+GcGtfKcr2z+82sp4E8V2oueaDIl3/exDvbvPRTIdo55rE493avM/A9f4HP4va+1XhzmyDFAfm4hIIlPALH5eZ+DtpJODZfK+Mde3psB71trGIdzjRe/nCmNMXoh8pwS4JpplyOiKR1vDGHMi8AADi9c+DZzq/VIYzhTgl8B38P9iG+g+hzAwYuRt71RmMjbEuq1Fq53ouTb+xeW55uMYn/SbEV6j55pEqu/5UmKMCdV5Esn3tZGUIQnAGLMQeAro+/23lshnAkgDbgO+B3wjzH3ycF42AKi01u4dVoVlPIpWO9FzTYbqaJ90pN/X9FybeP4X+Lg37QG+YK397yiVrT42EZEEpIBZnHg77Z717p5vjAk2HPsyoNib/scQb/Og9zMZ+EKgDMaYHOBK7+5Wa+17MShDRlE82poxpgQnWJbmPfQ4cJ61ti3CIlYDzd70h0PUEZw/Zvr8ZSj1lNiKQ1uLVjvRc22ci9PvUF+H+6Qjna5MzzWJ1IM+6S8HyuAN/n7Ru9sErIpBGTLBGWPScdpKgffQ28DJ1trq4FcN8M4Y0Ded1DJjzFEhsl8PZHjTeq4lkCi2Ez3XZKiG/H1Nz7WJxRhzJfB5764HuNJa+5so3kJ9bCIiCUgBs/j6hfczFbjP+0uxnzFmEc48y+DM4X73EMt/GOh7W/T7xpjjBpWfivNFr2+x0Z/HqAwZfbFua7cx0Cn9FvBRa21XpBdbazt97pkK/M0Ykzs4nzHmG0DfVBnvA38aYj0l9mLW1qLYTh5Gz7WJINbPNV99b7Z3MzBFVEh6rkmkrLVv4qwlBfA5Y8xHfc8bYwzwa5xpxwD+11rbEe0yJCHcDCz0pncCZw1j5O1tPuk/GWOmDM5gjLkMZ3QtQB0Dz2tJHCNuJ3quyTD4jkRcO4Tr9FybAIwxpQx89wf4hrU22oHNh1Efm4hIwjEjmNJXhsEY80/gYu/uTuBOoAJYgfO2SV8H4LXW2rsGXXsS8Lx390Vr7UkByj8PeAQwOB19vwNexVmL5XPAUm/WV3EW+j5oGqholCGjL1ZtzRizAGc9IeM99E1gewRVqrXWvuJTTh7O1BnzvYf2AnfhvPFXDHwMON57rgU4xVr7TgT3kTiL5XMtWu1Ez7WJIda/Q33ytgDZwH5r7UGdKCGu03NtAhvUhm621t4UJN9MBjpGdltrZwbIcxjOVKOpOOvt/RX4PyAT+BRwrDfr+8Bh1trWWJQhY1M02poxphDYw8A6s7cyEIwIpd1a2z9qxxiTjLP+Wd+zqw74LU7ndB5wPnCu91wvcJG19vEI7iNjQLSea9FqJ3quTVzR/B3qk3c9sMS7m2at7Y6wLnquTQDGmB8B3/bu7gG+EuGl71pr93jLOAn1sYmIyGDWWm1x3HC+7P8fzh8AgTYPcGOQa0/yyfdCiHtci/NLONg93gKKwtRzxGVom5htDfhRiDJDbQe1WZw1fN4Oc90e4NjR/vfUFv+2Fu12oufa+N/i9Ds01yff5mHUUc+1CboNakM3hcg30yffrhD5LgBaQ7ST94HZYeo04jK0jb0tGm0NZ4qq4XxfO6jNAvk466CFuq4OuHC0/+20xb+tRbud6Lk2Mbdo/w715q335usYRn30XBvnG7BvmL/nrvQpw7ddvhDiXupj06ZNm7YE2jQlY5xZa9uBDwOfAJ4GaoAeYD/OmlDHW2tvHuE9foPztv3vgF1AF86b7K8D1+F00tXFugwZXTFsa0vDZ4m4jhU4izV/Cvg3UO2tYy3OW9DfABZZa1+N1j0l+mL9XItWO9FzbfyLx+9QBkapATQOo456rklErLWPAH1TiW4DOoB2YA1wA7DCWrsj1mXIhBXN72uNwFnAhcBDOJ2UPUADzrpBNwILrbUPR+ueMv5Eq53ouSZD0PedrXGoF+q5Nr4ZYyYBk+N1P/WxiYgkFk3JKCIiIiIiIiIiIiIiIglNI8xEREREREREREREREQkoSlgJiIiIiIiIiIiIiIiIglNATMRERERERERERERERFJaAqYiYiIiIiIiIiIiIiISEJTwExEREREREREREREREQSmgJmIiIiIiIiIiIiIiIiktAUMBMREREREREREREREZGEpoCZiIiIiIiIiIiIiIiIJDQFzERERERERERERERERCShKWAmIiIiIiIiIiIiIiIiCU0BMxEREREREREREREREUloCpiJiIiIiIiIiIiIiIhIQlPATERERERERERERERERBKaAmYiIiIiIiIiIiIiIiKS0BQwExERERERERERERERkYSmgJmIiIiIiIiIiIiIiIgkNAXMREREREREREREREREJKEpYCYiIiIiIiIiIiIiIiIJTQEzERERkQnIGPNHY4wdwbZ8tH8GCc04vmCMKRvtuowVxphdQdpz/hDKuDJIGX+MXc1FRERERERktClgJiIiIiIyzhhj5gIvAv8LpI9ydURERERERETGveTRroCIiIiIxNx/ARuGeM3OWFREouYTwPGjXYkxrAa4xme/bQjXPgdc5E2XAHdFq1IiIiIiIiIydilgJiIiIjLxvWKtfWG0KyESR+3W2oeHc6G1dg+wB8AYMzOKdRIREREREZExTFMyioiIiIiIiIiIiIiISEJTwExERERERIIyxswwxljv9jfvsSxjzJeNMa8aY2qMMR3GmG3GmFuNMXmjXWcRERERERGRoVLATERERESCMsaUGmPqfAIm5wfJl22M2eXN4zHGnO5zru/a//buf8QY86Ixpt4Y02mM2WiM+W9jTGEE9ckyxnzFGPO8MabWGNNtjNlvjPm3MeYzxpikENf21eN73v2TjDEPGGP2GmO6jDGVxpj7jDFHR1CPJGPMVcaYp4wxB7z1OOCtx6eMMUG/Z4+kHsaYm4wxFrjR5/BOb3m7wtV7mJb5pDcaY44B1gG/Bo4BJgHpwDzgG8ArxpjsGNVFREREREREJCYUMBMRERGRoKy1VcBXfQ792hiTGSDrrcAMb/pOa+3TgcozxvwYeBA4ASgA0oBFwA3AemPMomB1McYcB2wBfgmcBBQBKUAZ8GHgHmCtMWZ2uJ/LGPM/wHPAJcBUIBWYAlwGvGqM+XaIa2cCa4DfA2cCpd56lHrr8SfgTWPM5FjWI458A2aTgKeB6cATwP8Dvg8845NnCXBt3GonIiIiIiIiEgXGWjvadRARERGRKDPG/BH4tHf3ZGvtCyMs73HgHO/uj6213/Y5dzLwLGCA7cBya22bz/m+L5xbgfmAByeo9CyQA3wK6BtNVQcstdbuH3T/w4EXgCzvodeB+4H9wExvGYu956qAw6y1+waVMbgeLThBr7eAbOBy4GRvHgscbq19d1AZJcC7OMEtgDe89diHE7j7GM6oK4D3gZXW2pZo1cMYswBYgBNQ+5g3z+eBaqDdWruKKDPG3A9c6nPoHeBya+32QfluAf7Tu/uctfbUaNclHO8ouxnAbmvtzCiUNxPY6d39k7X2ypGWKSIiIiIiImOTAmYiIiIiE1AMAmblwEYgF+gBVlhrN3qn3lsHzMIJhB1vrX1t0LW+Xzg7gQt8AzvGGAP8Cviy99AfrLVX+ZxP8t57vvfQd6y1twy6RzJwO07wCGCVtfbMEPXYDZxkrd01KM8fgCu9u3dYa68bdP5fwEXe3e9aa3/EIN5RYX3Hf2Ot/UIM6nETA9Myzhp8fTQZYzbjBOkANgDHDA4CevNNAmq8uzustXNiVadgFDATERERERGR4dKUjCIiIiIT3/M+62ZFsl04uABr7V7gm97dFOA2b/pHOMEygFsHB8sC+P7gUVDWeYPraziBN4BPe4MvfT7CQLDs/sHBMm8ZvcCXcEZpAZzhHZUWzFeCBJl+4JM+wveEMWY+cKF391+BgmXeutyCMxoO4EpjTHE06xFPxpi+tcn6fCZQsAzAWlsLNPddGuu6iYiIiIiIiESTAmYiIiIiEhFr7W9x1tsCONk7Bd8XvfvrcdayCqUN+N8gZbt9zrlw1gLrc45P+ich6ucedP7CIFm7cNbfClTGTpwpEgHyB52+hIFA0D3B6jHofDpwWpTrEU+LgSRv+gVr7TvBMnpHCqZ7d6tiXTERERERERGRaEoe7QqIiIiISMz9F85UepF6M8S5q3GCY1kMrFfVA3zKWtsdptzXfNc2C+AVn/RRwL0+aYDGwWuKBfCcT/rIIHne945IC6YZZ221lEHHP+STnh1oJJ4P31FlK4H7oliPeFrmk34sTN5yINWb3hGb6oiIiIiIiIjEhgJmIiIiIhPfKyNdw6yPtXanMea7OGuO9bnZWrs2gsu3hTm/xydd6pMu8X7uJAxrbYMxpgEoGFSGr6YwxfQFsQbPxjDVJ/3rcHXxURLk+HDrEU++AbPXw+Rd6pNeH4O6jFiYICc4/6/UxqMuIiIiIiIiMrYoYCYiIiIiQ7V20H6oUVK+Aq595aPdJ53rk87xfoYanTa4nAKcUXCBRFrfwXLDZwkoJ8jx4dYjnvoCZpbwQTDf4Np7sanOiD0U5vzJDKw/JyIiIiIiIglEATMRERERiZgxJh24e9Dhm4wxD1trt4a5PC3M+WyftO8onzYgj+ABsGDltIfMNXS+5WVYazujXP5Y1Ddq7ANrbWuYvIf6pNfFqD4iIiIiIiIiMTGa07uIiIiIyPhzE3CIN/249zMd+L0xxoS5tjzM+Vk+6X0+6QMBzgdkjCnGCa4BVIbLP0TVPunJUS57zDHGTGZgLbZIRoz1jTCrs9ZG+98+Kqy1Jsz2wmjXUUREREREREaHAmYiIiIiEhFjzErgG97dbcAlwH3e/WOBL4cp4ugwQbXjfdIv+qTf9n7mG2MOC3OPU3zS0R7l9JZP+qRQGY0xJxtj7jfG3GKMOS7K9YiXiKdYNMakAfO9uxpdJiIiIiIiIuOOAmYiIiIiEpYxJgW4B0jyHvqCtbYL+DrQ5D32Q2PMzBDFlAPnhij/C97dNuAZn9OP+KS/FaKOScD1PoceD5Z3mHzr8RVvnYO5CbgU+E9gUpTrAeDxScfqO/1Q1iRbzEDbGKvrl4mIiIiIiIgEpYCZiIiIiETiOwwEUO611j4HYK09AHzPezyLg9c3G+w3xph5vge8ga7/BRZ6D906aH2wh4Dt3vTHjDH/ObhQbxm3A0d4Dz1vrX057E81BNbat4AXvLuHAncHCpoZY24GTvDuvg88Fs16ePmup1YQg/JhaAEz3/XLFDATERERERGRcSd5tCsgIiIiIjF3nDEmf4jX7LfWvglgjFkC3OA93oD/KC5wgl1XAiuB04wxV1trfxegTAtMAVYbY34DrMZZI+szQN9Ui5uAH/tdZK3bGHMF8DKQBtxijLkA+DvO+mbTgU8DS7yX1Hn3Y+Ez3noXeu9xtDHmj8AOnHXNLsGZnhKgF/istdYdg3rs9Un/whhzO4C19h9RvEdfwKzRWrs7wrygKRlFRERERERkHFLATERERGTi+3/DuOYR4ELvyK17gFTv8f+01lb7ZrTWeowx1wJv4sxg8FNjzL+ttZWDynwVqAQ+BnwzwD1fB84bNLqs7x5vG2NOAx4AyoCjvdtga4GLrbUVkf2YQ2Ot3WWMOR54GJgHHAL8MEDWJuBT0R7l5uNpoB4ncHe8d7PGmCestW0jLdw7cm6BdzeSAFjfCLNeYONI7y8iIiIiIiISb5qSUURERERC+RoD0xy+BgQaOYa19h2ckWYAecBvAmRzW2svwxmZ9SbOWmWtwCvANcAJ1tq6YBWx1r6CE6T6Bs5oszqgG9gFPIGzZtiR1todkf94Q2et3YQzmu3zwP/hjHLrAVpwRp/9N7DAWvtoDOtQB5zivX8jTqBqPzAtSrdYwECQNJKA2VLv5zbv2nYiIiIiIiIi44qx1o52HURERERkAjPG9H3hfNFae9Jo1kWizxgzBWfkIMB91torRrEuu4AZwG5r7cwolDcT2Ond/ZO19sqRlikiIiIiIiJjk0aYiYiIiIjISBzqk35v1GohIiIiIiIiMgIKmImIiIiIyEgs80lHMn2jiIiIiIiIyJiTPNoVEBERERGRcW0sjjDLNMZc6LP/hLW2J5ILjTHTgcO8uyXRrpiIiIiIiIiMTQqYiYiIiIjISPSNMKu11u4b1ZoMKAYe8tkvABojvPYU4A/RrpCIiIiIiIiMbZqSUUREREREhsUYkwbM9+5qOkYREREREREZtzTCTERERERiylprRrsOEjOLGPibYtQDZtbamVEo44/AH0dajoiIiIiIiIwvxlo72nUQERERERERERERERERGTWaklFEREREREREREREREQSmgJmIiIiIiIiIiIiIiIiktAUMBMREREREREREREREZGEpoCZiIiIiIiIiIiIiIiIJDQFzERERERERERERERERCShKWAmIiIiIiIiIiIiIiIiCU0BMxEREREREREREREREUloCpiJiIiIiIiIiIiIiIhIQlPATERERERERERERERERBKaAmYiIiIiIiIiIiIiIiKS0BQwExERERERERERERERkYSmgJmIiIiIiIiIiIiIiIgkNAXMREREREREREREREREJKEpYCYiIiIiIiIiIiIiIiIJTQEzERERERERERERERERSWgKmImIiIiIiIiIiIiIiEhCU8BMREREREREREREREREEpoCZiIiIiIiIiIiIiIiIpLQFDATERERERERERERERGRhKaAmYiIiIiIiIiIiIiIiCQ0BcxEREREREREREREREQkof1/AnMYsdbP5DEAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 800x400 with 2 Axes>"
       ]
      },
      "metadata": {
-      "image/png": {
-       "height": 374,
-       "width": 870
-      },
       "needs_background": "light"
      },
      "output_type": "display_data"
diff --git a/Packages/gme/core/equations.pyi b/Packages/gme/core/equations.pyi
index a09df92..6ae7118 100644
--- a/Packages/gme/core/equations.pyi
+++ b/Packages/gme/core/equations.pyi
@@ -17,9 +17,29 @@ class EquationsBase:
     beta_type: Eq
     varphi_type: Eq
     do_raw: Eq
-    def __init__(self, parameters: Optional[Dict] = ..., eta_: Rational = ..., mu_: Rational = ..., beta_type: str = ..., varphi_type: str = ..., do_raw: bool = ...) -> None: ...
+    def __init__(
+        self,
+        parameters: Optional[Dict] = ...,
+        eta_: Rational = ...,
+        mu_: Rational = ...,
+        beta_type: str = ...,
+        varphi_type: str = ...,
+        do_raw: bool = ...,
+    ) -> None: ...
 
-class EquationsMixedIn(EquationsBase, RpMixin, XiMixin, VarphiMixin, FundamentalMixin, HamiltonsMixin, NdimMixin, ProfileMixin, AnglesMixin, MetricTensorMixin, PxpolyMixin):
+class EquationsMixedIn(
+    EquationsBase,
+    RpMixin,
+    XiMixin,
+    VarphiMixin,
+    FundamentalMixin,
+    HamiltonsMixin,
+    NdimMixin,
+    ProfileMixin,
+    AnglesMixin,
+    MetricTensorMixin,
+    PxpolyMixin,
+):
     def __init__(self, **kwargs) -> None: ...
 
 class Equations(EquationsMixedIn):
diff --git a/Packages/gme/core/equations_extended.pyi b/Packages/gme/core/equations_extended.pyi
index a43af45..72acb59 100644
--- a/Packages/gme/core/equations_extended.pyi
+++ b/Packages/gme/core/equations_extended.pyi
@@ -13,11 +13,15 @@ class EquationsIdtx(Equations, IdtxMixin):
 
 class EquationsIbc(Equations, IbcMixin):
     ibc_type: str
-    def __init__(self, parameters: Optional[Dict] = ..., ibc_type: str = ..., **kwargs) -> None: ...
+    def __init__(
+        self, parameters: Optional[Dict] = ..., ibc_type: str = ..., **kwargs
+    ) -> None: ...
 
 class EquationsIdtxIbc(EquationsIdtx, IbcMixin):
     ibc_type: str
-    def __init__(self, parameters: Optional[Dict] = ..., ibc_type: str = ..., **kwargs) -> None: ...
+    def __init__(
+        self, parameters: Optional[Dict] = ..., ibc_type: str = ..., **kwargs
+    ) -> None: ...
 
 class EquationsSetupOnly(EquationsMixedIn, GeodesicMixin, IdtxMixin, IbcMixin):
     ibc_type: str
diff --git a/Packages/gme/core/equations_subset.pyi b/Packages/gme/core/equations_subset.pyi
index 2c2aca4..5d0201c 100644
--- a/Packages/gme/core/equations_subset.pyi
+++ b/Packages/gme/core/equations_subset.pyi
@@ -7,4 +7,10 @@ class EquationSubset:
     poly_px_xiv0_eqn: Eq
     xiv0_xih0_Ci_eqn: Eq
     hamiltons_eqns: Eq
-    def __init__(self, gmeq: Type[Equations], parameters: Dict, do_ndim: bool = ..., do_revert: bool = ...) -> None: ...
+    def __init__(
+        self,
+        gmeq: Type[Equations],
+        parameters: Dict,
+        do_ndim: bool = ...,
+        do_revert: bool = ...,
+    ) -> None: ...
diff --git a/Packages/gme/core/pxpoly.pyi b/Packages/gme/core/pxpoly.pyi
index 60d0b7e..e75c9fc 100644
--- a/Packages/gme/core/pxpoly.pyi
+++ b/Packages/gme/core/pxpoly.pyi
@@ -12,4 +12,6 @@ class PxpolyMixin:
     poly_px_xiv_varphi_eqn: Eq
     poly_px_xiv_eqn: Eq
     poly_px_xiv0_eqn: Eq
-    def define_px_poly_eqn(self, eta_choice: Rational = ..., do_ndim: bool = ...) -> None: ...
+    def define_px_poly_eqn(
+        self, eta_choice: Rational = ..., do_ndim: bool = ...
+    ) -> None: ...
diff --git a/Packages/gme/core/utils.pyi b/Packages/gme/core/utils.pyi
index 6bc15af..53ddc79 100644
--- a/Packages/gme/core/utils.pyi
+++ b/Packages/gme/core/utils.pyi
@@ -1,9 +1,36 @@
 from sympy import Eq, Poly, Rational, Symbol
 from typing import Any, Dict, Optional, Tuple
 
-def pxpz0_from_xiv0(parameters: Dict[str, Any], pz0_xiv0_eqn: Eq, poly_px_xiv0_eqn: Eq) -> Tuple[Any, Any]: ...
-def gradient_value(x_: float, pz_: float, px_poly_eqn: Eq, do_use_newton: bool = ...) -> float: ...
-def px_value_search(x_: float, pz_: float, px_poly_eqn: Eq, method: str = ..., px_guess: float = ..., px_var_: Symbol = ..., pz_var_: Symbol = ..., bracket: Tuple[float, float] = ...) -> float: ...
-def px_value(x_: float, pz_: float, px_poly_eqn: Eq, px_var_: Symbol = ..., pz_var_: Symbol = ...) -> float: ...
-def find_dzdx_poly_root(dzdx_poly_: Poly, xhat_: float, xivhat0_: float, guess: float = ..., eta_: Optional[Rational] = ..., method: str = ..., bracket: Tuple[float, float] = ...) -> Any: ...
+def pxpz0_from_xiv0(
+    parameters: Dict[str, Any], pz0_xiv0_eqn: Eq, poly_px_xiv0_eqn: Eq
+) -> Tuple[Any, Any]: ...
+def gradient_value(
+    x_: float, pz_: float, px_poly_eqn: Eq, do_use_newton: bool = ...
+) -> float: ...
+def px_value_search(
+    x_: float,
+    pz_: float,
+    px_poly_eqn: Eq,
+    method: str = ...,
+    px_guess: float = ...,
+    px_var_: Symbol = ...,
+    pz_var_: Symbol = ...,
+    bracket: Tuple[float, float] = ...,
+) -> float: ...
+def px_value(
+    x_: float,
+    pz_: float,
+    px_poly_eqn: Eq,
+    px_var_: Symbol = ...,
+    pz_var_: Symbol = ...,
+) -> float: ...
+def find_dzdx_poly_root(
+    dzdx_poly_: Poly,
+    xhat_: float,
+    xivhat0_: float,
+    guess: float = ...,
+    eta_: Optional[Rational] = ...,
+    method: str = ...,
+    bracket: Tuple[float, float] = ...,
+) -> Any: ...
 def make_dzdx_poly(dzdx_Ci_polylike_eqn_: Eq, sub_: Dict) -> Poly: ...
diff --git a/Packages/gme/knickpoints/base.pyi b/Packages/gme/knickpoints/base.pyi
index 0646edd..0e70dae 100644
--- a/Packages/gme/knickpoints/base.pyi
+++ b/Packages/gme/knickpoints/base.pyi
@@ -11,7 +11,9 @@ class InitialProfileSolution(BaseSolution):
     rz_initial_surface_eqn: Eq
     ic: Tuple[float, float, float, float]
     def __init__(self, gmeq: Equations, parameters: Dict, **kwargs) -> None: ...
-    def initial_conditions(self, x_: float) -> Tuple[float, float, float, float]: ...
+    def initial_conditions(
+        self, x_: float
+    ) -> Tuple[float, float, float, float]: ...
     def solve(self, report_pc_step: int = ...) -> None: ...
 
 class InitialCornerSolution(BaseSolution):
@@ -25,7 +27,9 @@ class InitialCornerSolution(BaseSolution):
     rdot: Matrix
     ic: Tuple[float, float, float, float]
     def __init__(self, gmeq: Equations, parameters: Dict, **kwargs) -> None: ...
-    def initial_conditions(self, beta0_) -> Tuple[float, float, float, float]: ...
+    def initial_conditions(
+        self, beta0_
+    ) -> Tuple[float, float, float, float]: ...
     def solve(self, report_pc_step: int = ..., verbose: bool = ...) -> None: ...
 
 class CompositeSolution(BaseSolution, metaclass=abc.ABCMeta):
@@ -40,6 +44,13 @@ class CompositeSolution(BaseSolution, metaclass=abc.ABCMeta):
     vbs: Any
     n_rays: int
     def __init__(self, gmeq: Equations, parameters: Dict, **kwargs) -> None: ...
-    def create_solutions(self, t_end: float = ..., t_slip_end: float = ..., do_solns: Dict = ..., n_rays: Dict = ..., n_t: Dict = ...) -> None: ...
+    def create_solutions(
+        self,
+        t_end: float = ...,
+        t_slip_end: float = ...,
+        do_solns: Dict = ...,
+        n_rays: Dict = ...,
+        n_t: Dict = ...,
+    ) -> None: ...
     def solve(self) -> None: ...
     def merge_rays(self) -> None: ...
diff --git a/Packages/gme/ode/base.pyi b/Packages/gme/ode/base.pyi
index 3e4f15c..35e87b0 100644
--- a/Packages/gme/ode/base.pyi
+++ b/Packages/gme/ode/base.pyi
@@ -42,13 +42,43 @@ class BaseSolution(ABC, metaclass=abc.ABCMeta):
     cx_v_lambda: Any
     vx_interp_fast: Any
     vx_interp_slow: Any
-    def __init__(self, gmeq: Equations, parameters: Dict, choice: str = ..., method: str = ..., do_dense: bool = ..., x_stop: float = ..., t_end: float = ..., t_slip_end: float = ..., t_distribn: float = ..., n_rays: int = ..., n_t: int = ..., tp_xiv0_list: Optional[List[Tuple[float, float]]] = ..., customize_t_fn: Optional[Callable] = ...): ...
+    def __init__(
+        self,
+        gmeq: Equations,
+        parameters: Dict,
+        choice: str = ...,
+        method: str = ...,
+        do_dense: bool = ...,
+        x_stop: float = ...,
+        t_end: float = ...,
+        t_slip_end: float = ...,
+        t_distribn: float = ...,
+        n_rays: int = ...,
+        n_t: int = ...,
+        tp_xiv0_list: Optional[List[Tuple[float, float]]] = ...,
+        customize_t_fn: Optional[Callable] = ...,
+    ): ...
     @abstractmethod
-    def initial_conditions(self, t_lag: float = ..., xiv_0_: float = ...) -> Tuple[float, float, float, float]: ...
+    def initial_conditions(
+        self, t_lag: float = ..., xiv_0_: float = ...
+    ) -> Tuple[float, float, float, float]: ...
     @abstractmethod
     def solve(self) -> None: ...
-    def make_model(self) -> Callable[[float, Tuple[Any, Any, Any, Any]], np.ndarray]: ...
-    def postprocessing(self, spline_order: int = ..., extrapolation_mode: int = ...) -> None: ...
-    def resolve_isochrones(self, x_subset: int = ..., t_isochrone_max: float = ..., n_isochrones: int = ..., n_resample_pts: int = ..., tolerance: float = ..., do_eliminate_caustics: bool = ..., dont_crop_cusps: bool = ...) -> None: ...
+    def make_model(
+        self,
+    ) -> Callable[[float, Tuple[Any, Any, Any, Any]], np.ndarray]: ...
+    def postprocessing(
+        self, spline_order: int = ..., extrapolation_mode: int = ...
+    ) -> None: ...
+    def resolve_isochrones(
+        self,
+        x_subset: int = ...,
+        t_isochrone_max: float = ...,
+        n_isochrones: int = ...,
+        n_resample_pts: int = ...,
+        tolerance: float = ...,
+        do_eliminate_caustics: bool = ...,
+        dont_crop_cusps: bool = ...,
+    ) -> None: ...
     def measure_cusp_propagation(self) -> None: ...
     def save(self, rpt_arrays: Dict, idx: int) -> None: ...
diff --git a/Packages/gme/ode/extended.pyi b/Packages/gme/ode/extended.pyi
index 2244f44..4b048b2 100644
--- a/Packages/gme/ode/extended.pyi
+++ b/Packages/gme/ode/extended.pyi
@@ -1,7 +1,11 @@
 import abc
 import numpy as np
 from gme.core.equations import Equations
-from gme.core.equations_extended import EquationsGeodesic, EquationsIbc, EquationsIdtx
+from gme.core.equations_extended import (
+    EquationsGeodesic,
+    EquationsIbc,
+    EquationsIdtx,
+)
 from gme.ode.base import BaseSolution
 from typing import Any, Callable, Dict, List, Union
 
@@ -68,4 +72,9 @@ class ExtendedSolution(BaseSolution, metaclass=abc.ABCMeta):
     xiv_p_interp: Callable
     alpha_interp: Callable
     h_interp: Callable
-    def __init__(self, gmeq: Union[Equations, EquationsGeodesic, EquationsIdtx, EquationsIbc], parameters: Dict, **kwargs) -> None: ...
+    def __init__(
+        self,
+        gmeq: Union[Equations, EquationsGeodesic, EquationsIdtx, EquationsIbc],
+        parameters: Dict,
+        **kwargs
+    ) -> None: ...
diff --git a/Packages/gme/ode/single_ray.pyi b/Packages/gme/ode/single_ray.pyi
index ae7d687..9d0e5cf 100644
--- a/Packages/gme/ode/single_ray.pyi
+++ b/Packages/gme/ode/single_ray.pyi
@@ -46,6 +46,10 @@ class SingleRaySolution(ExtendedSolution):
     xiv_v_interp: InterpolatedUnivariateSpline
     uhorizontal_p_interp: InterpolatedUnivariateSpline
     uhorizontal_v_interp: InterpolatedUnivariateSpline
-    def initial_conditions(self, t_lag: float = ..., xiv_0_: float = ...) -> Tuple[float, float, float, float]: ...
+    def initial_conditions(
+        self, t_lag: float = ..., xiv_0_: float = ...
+    ) -> Tuple[float, float, float, float]: ...
     def solve(self) -> None: ...
-    def postprocessing(self, spline_order: int = ..., extrapolation_mode: int = ...) -> None: ...
+    def postprocessing(
+        self, spline_order: int = ..., extrapolation_mode: int = ...
+    ) -> None: ...
diff --git a/Packages/gme/ode/solve.pyi b/Packages/gme/ode/solve.pyi
index 13260a4..88e3381 100644
--- a/Packages/gme/ode/solve.pyi
+++ b/Packages/gme/ode/solve.pyi
@@ -1,5 +1,21 @@
 import numpy as np
 from typing import Any, Callable, Dict, Tuple
 
-def solve_ODE_system(model: Callable, method: str, do_dense: bool, ic: Tuple[float, float, float, float], t_array: np.ndarray, x_stop: float = ...) -> Any: ...
-def solve_Hamiltons_equations(model: Callable, method: str, do_dense: bool, ic: Tuple[float, float, float, float], parameters: Dict, t_array: np.ndarray, x_stop: float = ..., t_lag: float = ...) -> Tuple[Any, Dict[str, np.ndarray]]: ...
+def solve_ODE_system(
+    model: Callable,
+    method: str,
+    do_dense: bool,
+    ic: Tuple[float, float, float, float],
+    t_array: np.ndarray,
+    x_stop: float = ...,
+) -> Any: ...
+def solve_Hamiltons_equations(
+    model: Callable,
+    method: str,
+    do_dense: bool,
+    ic: Tuple[float, float, float, float],
+    parameters: Dict,
+    t_array: np.ndarray,
+    x_stop: float = ...,
+    t_lag: float = ...,
+) -> Tuple[Any, Dict[str, np.ndarray]]: ...
diff --git a/Packages/gme/ode/time_invariant.pyi b/Packages/gme/ode/time_invariant.pyi
index 8843bd0..d67d7d9 100644
--- a/Packages/gme/ode/time_invariant.pyi
+++ b/Packages/gme/ode/time_invariant.pyi
@@ -15,5 +15,13 @@ class TimeInvariantSolution(SingleRaySolution):
     beta_ts_error_interp: InterpolatedUnivariateSpline
     h_x_array: np.ndarray
     h_z_array: np.ndarray
-    def postprocessing(self, spline_order: int = ..., extrapolation_mode: int = ...) -> None: ...
-    def integrate_h_profile(self, n_pts: int = ..., x_max: float = ..., do_truncate: bool = ..., do_use_newton: bool = ...) -> None: ...
+    def postprocessing(
+        self, spline_order: int = ..., extrapolation_mode: int = ...
+    ) -> None: ...
+    def integrate_h_profile(
+        self,
+        n_pts: int = ...,
+        x_max: float = ...,
+        do_truncate: bool = ...,
+        do_use_newton: bool = ...,
+    ) -> None: ...
diff --git a/Packages/gme/ode/utils.pyi b/Packages/gme/ode/utils.pyi
index 7b8dd80..ccdc1db 100644
--- a/Packages/gme/ode/utils.pyi
+++ b/Packages/gme/ode/utils.pyi
@@ -1 +1,7 @@
-def report_progress(i: int, n: int, progress_was: float = ..., pc_step: float = ..., is_initial_step: bool = ...) -> float: ...
+def report_progress(
+    i: int,
+    n: int,
+    progress_was: float = ...,
+    pc_step: float = ...,
+    is_initial_step: bool = ...,
+) -> float: ...
diff --git a/Packages/gme/ode/velocity_boundary.pyi b/Packages/gme/ode/velocity_boundary.pyi
index b0c5226..c180fe0 100644
--- a/Packages/gme/ode/velocity_boundary.pyi
+++ b/Packages/gme/ode/velocity_boundary.pyi
@@ -6,6 +6,8 @@ class VelocityBoundarySolution(ExtendedSolution):
     n_rays: int
     ic_list: List
     ivp_solns_list: List
-    def initial_conditions(self, t_lag: float = ..., xiv_0_: float = ...) -> Tuple[float, float, float, float]: ...
+    def initial_conditions(
+        self, t_lag: float = ..., xiv_0_: float = ...
+    ) -> Tuple[float, float, float, float]: ...
     t_ensemble_max: float
     def solve(self, report_pc_step: int = ...) -> None: ...
diff --git a/Packages/gme/plot/alphabeta.pyi b/Packages/gme/plot/alphabeta.pyi
index 95a903c..1570c4d 100644
--- a/Packages/gme/plot/alphabeta.pyi
+++ b/Packages/gme/plot/alphabeta.pyi
@@ -4,7 +4,47 @@ from gme.plot.base import Graphing
 from typing import Optional, Tuple
 
 class AlphaBeta(Graphing):
-    def alpha_beta(self, gmeq: Equations, name: str, alpha_array: np.ndarray, beta_array: np.ndarray, tanalpha_ext_: float, tanbeta_crit_: float, fig_size: Optional[Tuple[float, float]] = ..., dpi: Optional[int] = ...) -> None: ...
-    def beta_anisotropy(self, gmeq: Equations, name: str, alpha_array: np.ndarray, beta_array: np.ndarray, tanalpha_ext_: float, tanbeta_crit_: float, fig_size: Optional[Tuple[float, float]] = ..., dpi: Optional[int] = ...) -> None: ...
-    def alpha_anisotropy(self, gmeq: Equations, name: str, alpha_array: np.ndarray, beta_array: np.ndarray, tanalpha_ext_: float, tanbeta_crit_: float, fig_size: Optional[Tuple[float, float]] = ..., dpi: Optional[int] = ...) -> None: ...
-    def alpha_image(self, gmeq: Equations, name: str, alpha_array: np.ndarray, beta_array: np.ndarray, tanalpha_ext_: float, tanbeta_crit_: float, fig_size: Optional[Tuple[float, float]] = ..., dpi: Optional[int] = ...) -> None: ...
+    def alpha_beta(
+        self,
+        gmeq: Equations,
+        name: str,
+        alpha_array: np.ndarray,
+        beta_array: np.ndarray,
+        tanalpha_ext_: float,
+        tanbeta_crit_: float,
+        fig_size: Optional[Tuple[float, float]] = ...,
+        dpi: Optional[int] = ...,
+    ) -> None: ...
+    def beta_anisotropy(
+        self,
+        gmeq: Equations,
+        name: str,
+        alpha_array: np.ndarray,
+        beta_array: np.ndarray,
+        tanalpha_ext_: float,
+        tanbeta_crit_: float,
+        fig_size: Optional[Tuple[float, float]] = ...,
+        dpi: Optional[int] = ...,
+    ) -> None: ...
+    def alpha_anisotropy(
+        self,
+        gmeq: Equations,
+        name: str,
+        alpha_array: np.ndarray,
+        beta_array: np.ndarray,
+        tanalpha_ext_: float,
+        tanbeta_crit_: float,
+        fig_size: Optional[Tuple[float, float]] = ...,
+        dpi: Optional[int] = ...,
+    ) -> None: ...
+    def alpha_image(
+        self,
+        gmeq: Equations,
+        name: str,
+        alpha_array: np.ndarray,
+        beta_array: np.ndarray,
+        tanalpha_ext_: float,
+        tanbeta_crit_: float,
+        fig_size: Optional[Tuple[float, float]] = ...,
+        dpi: Optional[int] = ...,
+    ) -> None: ...
diff --git a/Packages/gme/plot/base.pyi b/Packages/gme/plot/base.pyi
index 07767ca..717d095 100644
--- a/Packages/gme/plot/base.pyi
+++ b/Packages/gme/plot/base.pyi
@@ -4,8 +4,50 @@ from matplotlib.pyplot import Axes
 from typing import Dict, List, Optional, Tuple
 
 class Graphing(GraphingBase):
-    def mycolors(self, i: int, r: int, n: int, do_smooth: bool = ..., cmap_choice: str = ...) -> List[str]: ...
-    def gray_color(self, i_isochrone: int = ..., n_isochrones: int = ...) -> str: ...
+    def mycolors(
+        self,
+        i: int,
+        r: int,
+        n: int,
+        do_smooth: bool = ...,
+        cmap_choice: str = ...,
+    ) -> List[str]: ...
+    def gray_color(
+        self, i_isochrone: int = ..., n_isochrones: int = ...
+    ) -> str: ...
     def correct_quadrant(self, angle: float) -> float: ...
-    def draw_rays_with_arrows_simple(self, axes: Axes, sub: Dict, xi_vh_ratio: float, t_array: np.ndarray, rx_array: np.ndarray, rz_array: np.ndarray, v_array: Optional[np.ndarray] = ..., n_t: Optional[int] = ..., n_rays: int = ..., ls: str = ..., sf: float = ..., color: Optional[str] = ..., do_labels: bool = ..., do_one_ray: bool = ...) -> None: ...
-    def arrow_annotate_ray_custom(self, rx_array: np.ndarray, rz_array: np.ndarray, axes: Axes, i_ray: int, i_ray_step: int, n_rays: int, n_arrows: int, arrow_sf: float = ..., arrow_offset: int = ..., x_limits: Optional[Tuple[Optional[float], Optional[float]]] = ..., y_limits: Optional[Tuple[Optional[float], Optional[float]]] = ..., line_style: str = ..., line_width: float = ..., ray_label: str = ..., do_smooth_colors: bool = ...) -> None: ...
+    def draw_rays_with_arrows_simple(
+        self,
+        axes: Axes,
+        sub: Dict,
+        xi_vh_ratio: float,
+        t_array: np.ndarray,
+        rx_array: np.ndarray,
+        rz_array: np.ndarray,
+        v_array: Optional[np.ndarray] = ...,
+        n_t: Optional[int] = ...,
+        n_rays: int = ...,
+        ls: str = ...,
+        sf: float = ...,
+        color: Optional[str] = ...,
+        do_labels: bool = ...,
+        do_one_ray: bool = ...,
+    ) -> None: ...
+    def arrow_annotate_ray_custom(
+        self,
+        rx_array: np.ndarray,
+        rz_array: np.ndarray,
+        axes: Axes,
+        i_ray: int,
+        i_ray_step: int,
+        n_rays: int,
+        n_arrows: int,
+        arrow_sf: float = ...,
+        arrow_offset: int = ...,
+        x_limits: Optional[Tuple[Optional[float], Optional[float]]] = ...,
+        y_limits: Optional[Tuple[Optional[float], Optional[float]]] = ...,
+        line_style: str = ...,
+        line_width: float = ...,
+        ray_label: str = ...,
+        do_smooth_colors: bool = ...,
+    ) -> None: ...
diff --git a/Packages/gme/plot/cusp_velocity.pyi b/Packages/gme/plot/cusp_velocity.pyi
index be903a4..d202666 100644
--- a/Packages/gme/plot/cusp_velocity.pyi
+++ b/Packages/gme/plot/cusp_velocity.pyi
@@ -4,4 +4,18 @@ from gme.plot.base import Graphing
 from typing import Dict, Optional, Tuple
 
 class CuspVelocity(Graphing):
-    def profile_cusp_horizontal_speed(self, gmes: VelocityBoundarySolution, gmeq: Equations, sub: Dict, name: str, fig_size: Optional[Tuple[float, float]] = ..., dpi: Optional[int] = ..., x_limits: Tuple[float, float] = ..., y_limits: Tuple[Optional[float], Optional[float]] = ..., t_limits: Tuple[Optional[float], Optional[float]] = ..., legend_loc: str = ..., do_x: bool = ..., do_infer_initiation: bool = ...) -> None: ...
+    def profile_cusp_horizontal_speed(
+        self,
+        gmes: VelocityBoundarySolution,
+        gmeq: Equations,
+        sub: Dict,
+        name: str,
+        fig_size: Optional[Tuple[float, float]] = ...,
+        dpi: Optional[int] = ...,
+        x_limits: Tuple[float, float] = ...,
+        y_limits: Tuple[Optional[float], Optional[float]] = ...,
+        t_limits: Tuple[Optional[float], Optional[float]] = ...,
+        legend_loc: str = ...,
+        do_x: bool = ...,
+        do_infer_initiation: bool = ...,
+    ) -> None: ...
diff --git a/Packages/gme/plot/flow_model.pyi b/Packages/gme/plot/flow_model.pyi
index df8f95a..a613d57 100644
--- a/Packages/gme/plot/flow_model.pyi
+++ b/Packages/gme/plot/flow_model.pyi
@@ -3,4 +3,15 @@ from gme.plot.base import Graphing
 from typing import Dict, Optional, Tuple
 
 class FlowModel(Graphing):
-    def profile_flow_model(self, gmeq: Equations, sub: Dict, name: str, fig_size: Optional[Tuple[float, float]] = ..., dpi: Optional[int] = ..., n_points: int = ..., subtitle: str = ..., do_subtitling: bool = ..., do_extra_annotations: bool = ...) -> None: ...
+    def profile_flow_model(
+        self,
+        gmeq: Equations,
+        sub: Dict,
+        name: str,
+        fig_size: Optional[Tuple[float, float]] = ...,
+        dpi: Optional[int] = ...,
+        n_points: int = ...,
+        subtitle: str = ...,
+        do_subtitling: bool = ...,
+        do_extra_annotations: bool = ...,
+    ) -> None: ...
diff --git a/Packages/gme/plot/indicatrix_new.pyi b/Packages/gme/plot/indicatrix_new.pyi
index b62b1e3..70c49e5 100644
--- a/Packages/gme/plot/indicatrix_new.pyi
+++ b/Packages/gme/plot/indicatrix_new.pyi
@@ -15,7 +15,30 @@ class IndicatrixNew(Graphing):
     v_from_gstar_lambda: Any
     v_infc_array: np.ndarray
     v_supc_array: np.ndarray
-    def __init__(self, gmeq: Union[Equations, EquationsIdtx], pr: Parameters, sub_: Dict, varphi_: float = ...): ...
-    def convex_concave_annotations(self, do_zoom: bool, eta_: float) -> None: ...
-    def Fstar_F_rectlinear(self, gmeq: Union[Equations, EquationsIdtx], job_name: str, pr: Parameters, do_zoom: bool = ..., fig_size: Optional[Tuple[float, float]] = ..., dpi: Optional[int] = ...) -> None: ...
-    def Fstar_F_polar(self, gmeq: Union[Equations, EquationsIdtx], job_name: str, pr: Parameters, fig_size: Optional[Tuple[float, float]] = ..., dpi: Optional[int] = ...) -> None: ...
+    def __init__(
+        self,
+        gmeq: Union[Equations, EquationsIdtx],
+        pr: Parameters,
+        sub_: Dict,
+        varphi_: float = ...,
+    ): ...
+    def convex_concave_annotations(
+        self, do_zoom: bool, eta_: float
+    ) -> None: ...
+    def Fstar_F_rectlinear(
+        self,
+        gmeq: Union[Equations, EquationsIdtx],
+        job_name: str,
+        pr: Parameters,
+        do_zoom: bool = ...,
+        fig_size: Optional[Tuple[float, float]] = ...,
+        dpi: Optional[int] = ...,
+    ) -> None: ...
+    def Fstar_F_polar(
+        self,
+        gmeq: Union[Equations, EquationsIdtx],
+        job_name: str,
+        pr: Parameters,
+        fig_size: Optional[Tuple[float, float]] = ...,
+        dpi: Optional[int] = ...,
+    ) -> None: ...
diff --git a/Packages/gme/plot/indicatrix_old.pyi b/Packages/gme/plot/indicatrix_old.pyi
index e9f85d1..0b83772 100644
--- a/Packages/gme/plot/indicatrix_old.pyi
+++ b/Packages/gme/plot/indicatrix_old.pyi
@@ -5,15 +5,104 @@ from sympy import Eq
 from typing import Callable, Dict, Tuple
 
 class IndicatrixOld(Graphing):
-    def comparison_logpolar(self, gmeq: Eq, name: str, fig_size: Tuple[float, float] = ..., dpi: float = ..., varphi_: float = ..., n_points: int = ..., idtx_pz_min: float = ..., idtx_pz_max: float = ..., fgtx_pz_min: float = ..., fgtx_pz_max: float = ..., y_limits: Tuple[float, float] = ...) -> None: ...
-    def text_labels(self, gmeq: Eq, varphi_: float, px_: float, pz_: float, rdotx_: float, rdotz_: float, zoom_factor: float, do_text_labels: bool) -> None: ...
-    def arrows(self, px_: float, pz_: float, rdotx_: float, rdotz_: float) -> None: ...
-    def lines_and_points(self, pd: Dict, axes: Axes, zoomx: np.ndarray, do_pz: bool, do_shapes: bool) -> None: ...
-    def annotations(self, axes: Axes, beta_: float, tanalpha_: float) -> None: ...
-    def legend(self, gmeq: Eq, axes: Axes, do_legend: bool, do_half: bool, do_ray_slowness: bool = ...) -> None: ...
-    def figuratrix(self, gmeq: Eq, varphi_: float, n_points: int, pz_min_: float = ..., pz_max_: float = ...) -> Tuple[np.ndarray, np.ndarray, Eq]: ...
-    def indicatrix(self, gmeq: Eq, varphi_: float, n_points: int, pz_min_: float = ..., pz_max_: float = ...) -> Tuple[np.ndarray, np.ndarray, Eq, Eq]: ...
-    def plot_figuratrix(self, fgtx_px_array: np.ndarray, fgtx_pz_array: np.ndarray, maybe_recip_fn: Callable, do_ray_slowness: bool = ...) -> None: ...
-    def plot_indicatrix(self, idtx_rdotx_array: np.ndarray, idtx_rdotz_array: np.ndarray, maybe_recip_fn: Callable, do_ray_slowness: bool = ...) -> None: ...
-    def plot_unit_circle(self, varphi_: float, do_varphi_circle: bool) -> None: ...
-    def relative_geometry(self, gmeq: Eq, name: str, fig_size: Tuple[float, float] = ..., dpi: float = ..., varphi_: float = ..., zoom_factor: float = ..., do_half: bool = ..., do_legend: bool = ..., do_text_labels: bool = ..., do_arrows: bool = ..., do_lines_points: bool = ..., do_shapes: bool = ..., do_varphi_circle: bool = ..., do_pz: bool = ..., do_ray_slowness: bool = ..., x_max: float = ..., n_points: int = ..., pz_min_: float = ...) -> None: ...
+    def comparison_logpolar(
+        self,
+        gmeq: Eq,
+        name: str,
+        fig_size: Tuple[float, float] = ...,
+        dpi: float = ...,
+        varphi_: float = ...,
+        n_points: int = ...,
+        idtx_pz_min: float = ...,
+        idtx_pz_max: float = ...,
+        fgtx_pz_min: float = ...,
+        fgtx_pz_max: float = ...,
+        y_limits: Tuple[float, float] = ...,
+    ) -> None: ...
+    def text_labels(
+        self,
+        gmeq: Eq,
+        varphi_: float,
+        px_: float,
+        pz_: float,
+        rdotx_: float,
+        rdotz_: float,
+        zoom_factor: float,
+        do_text_labels: bool,
+    ) -> None: ...
+    def arrows(
+        self, px_: float, pz_: float, rdotx_: float, rdotz_: float
+    ) -> None: ...
+    def lines_and_points(
+        self,
+        pd: Dict,
+        axes: Axes,
+        zoomx: np.ndarray,
+        do_pz: bool,
+        do_shapes: bool,
+    ) -> None: ...
+    def annotations(
+        self, axes: Axes, beta_: float, tanalpha_: float
+    ) -> None: ...
+    def legend(
+        self,
+        gmeq: Eq,
+        axes: Axes,
+        do_legend: bool,
+        do_half: bool,
+        do_ray_slowness: bool = ...,
+    ) -> None: ...
+    def figuratrix(
+        self,
+        gmeq: Eq,
+        varphi_: float,
+        n_points: int,
+        pz_min_: float = ...,
+        pz_max_: float = ...,
+    ) -> Tuple[np.ndarray, np.ndarray, Eq]: ...
+    def indicatrix(
+        self,
+        gmeq: Eq,
+        varphi_: float,
+        n_points: int,
+        pz_min_: float = ...,
+        pz_max_: float = ...,
+    ) -> Tuple[np.ndarray, np.ndarray, Eq, Eq]: ...
+    def plot_figuratrix(
+        self,
+        fgtx_px_array: np.ndarray,
+        fgtx_pz_array: np.ndarray,
+        maybe_recip_fn: Callable,
+        do_ray_slowness: bool = ...,
+    ) -> None: ...
+    def plot_indicatrix(
+        self,
+        idtx_rdotx_array: np.ndarray,
+        idtx_rdotz_array: np.ndarray,
+        maybe_recip_fn: Callable,
+        do_ray_slowness: bool = ...,
+    ) -> None: ...
+    def plot_unit_circle(
+        self, varphi_: float, do_varphi_circle: bool
+    ) -> None: ...
+    def relative_geometry(
+        self,
+        gmeq: Eq,
+        name: str,
+        fig_size: Tuple[float, float] = ...,
+        dpi: float = ...,
+        varphi_: float = ...,
+        zoom_factor: float = ...,
+        do_half: bool = ...,
+        do_legend: bool = ...,
+        do_text_labels: bool = ...,
+        do_arrows: bool = ...,
+        do_lines_points: bool = ...,
+        do_shapes: bool = ...,
+        do_varphi_circle: bool = ...,
+        do_pz: bool = ...,
+        do_ray_slowness: bool = ...,
+        x_max: float = ...,
+        n_points: int = ...,
+        pz_min_: float = ...,
+    ) -> None: ...
diff --git a/Packages/gme/plot/manuscript.pyi b/Packages/gme/plot/manuscript.pyi
index 214ba3c..5357d03 100644
--- a/Packages/gme/plot/manuscript.pyi
+++ b/Packages/gme/plot/manuscript.pyi
@@ -2,6 +2,29 @@ from gme.plot.base import Graphing
 from typing import Any, Optional, Tuple
 
 class Manuscript(Graphing):
-    def point_pairing(self, name: str, fig_size: Optional[Tuple[float, float]] = ..., dpi: Optional[int] = ...) -> None: ...
-    def covector_isochrones(self, name: str, fig_size: Optional[Tuple[float, float]] = ..., dpi: Optional[int] = ...) -> None: ...
-    def huygens_wavelets(self, gmes, gmeq, sub, name, fig_size: Any | None = ..., dpi: Any | None = ..., do_ray_conjugacy: bool = ..., do_fast: bool = ..., do_legend: bool = ..., legend_fontsize: int = ..., annotation_fontsize: int = ...) -> None: ...
+    def point_pairing(
+        self,
+        name: str,
+        fig_size: Optional[Tuple[float, float]] = ...,
+        dpi: Optional[int] = ...,
+    ) -> None: ...
+    def covector_isochrones(
+        self,
+        name: str,
+        fig_size: Optional[Tuple[float, float]] = ...,
+        dpi: Optional[int] = ...,
+    ) -> None: ...
+    def huygens_wavelets(
+        self,
+        gmes,
+        gmeq,
+        sub,
+        name,
+        fig_size: Any | None = ...,
+        dpi: Any | None = ...,
+        do_ray_conjugacy: bool = ...,
+        do_fast: bool = ...,
+        do_legend: bool = ...,
+        legend_fontsize: int = ...,
+        annotation_fontsize: int = ...,
+    ) -> None: ...
diff --git a/Packages/gme/plot/ray_angles.pyi b/Packages/gme/plot/ray_angles.pyi
index e7efc05..c42a0d2 100644
--- a/Packages/gme/plot/ray_angles.pyi
+++ b/Packages/gme/plot/ray_angles.pyi
@@ -1,5 +1,9 @@
 from gme.core.equations import Equations
-from gme.core.equations_extended import EquationsGeodesic, EquationsIbc, EquationsIdtx
+from gme.core.equations_extended import (
+    EquationsGeodesic,
+    EquationsIbc,
+    EquationsIdtx,
+)
 from gme.ode.single_ray import SingleRaySolution
 from gme.ode.time_invariant import TimeInvariantSolution
 from gme.ode.velocity_boundary import VelocityBoundarySolution
@@ -7,8 +11,82 @@ from gme.plot.base import Graphing
 from typing import Dict, Optional, Tuple, Union
 
 class RayAngles(Graphing):
-    def alpha_beta(self, gmes: Union[SingleRaySolution, TimeInvariantSolution, VelocityBoundarySolution], gmeq: Union[Equations, EquationsGeodesic, EquationsIdtx, EquationsIbc], sub: Dict, name: str, fig_size: Optional[Tuple[float, float]] = ..., dpi: Optional[int] = ..., aspect: float = ..., n_points: int = ..., x_limits: Optional[Tuple[Optional[float], Optional[float]]] = ..., y_limits: Optional[Tuple[Optional[float], Optional[float]]] = ..., do_legend: bool = ..., do_pub_label: bool = ..., pub_label_xy: Tuple[float, float] = ..., pub_label: str = ..., do_etaxi_label: bool = ..., eta_label_xy: Tuple[float, float] = ...) -> None: ...
-    def angular_disparity(self, gmes: Union[SingleRaySolution, TimeInvariantSolution, VelocityBoundarySolution], gmeq: Union[Equations, EquationsGeodesic, EquationsIdtx, EquationsIbc], name: str, fig_size: Optional[Tuple[float, float]] = ..., dpi: Optional[int] = ..., n_points: int = ..., x_limits: Optional[Tuple[Optional[float], Optional[float]]] = ..., y_limits: Optional[Tuple[Optional[float], Optional[float]]] = ..., do_legend: bool = ..., aspect: float = ..., do_pub_label: bool = ..., pub_label: str = ..., pub_label_xy: Tuple[float, float] = ..., eta_label_xy: Tuple[float, float] = ...) -> None: ...
-    def profile_angular_disparity(self, gmes: Union[SingleRaySolution, TimeInvariantSolution, VelocityBoundarySolution], gmeq: Union[Equations, EquationsGeodesic, EquationsIdtx, EquationsIbc], sub: Dict, name: str, fig_size: Optional[Tuple[float, float]] = ..., dpi: Optional[int] = ..., n_points: int = ..., do_pub_label: bool = ..., pub_label: str = ..., pub_label_xy: Tuple[float, float] = ..., eta_label_xy: Tuple[float, float] = ..., var_label_xy: Tuple[float, float] = ...) -> None: ...
-    def profile_alpha(self, gmes: Union[SingleRaySolution, TimeInvariantSolution, VelocityBoundarySolution], gmeq: Union[Equations, EquationsGeodesic, EquationsIdtx, EquationsIbc], sub: Dict, name: str, fig_size: Optional[Tuple[float, float]] = ..., dpi: Optional[int] = ..., n_points: int = ..., do_legend: bool = ..., eta_label_xy: Tuple[float, float] = ...) -> None: ...
-    def psi_eta_alpha(self, gmeq: Union[Equations, EquationsGeodesic, EquationsIdtx, EquationsIbc], name: str, fig_size: Optional[Tuple[float, float]] = ..., dpi: Optional[int] = ..., n_points: int = ...) -> None: ...
+    def alpha_beta(
+        self,
+        gmes: Union[
+            SingleRaySolution, TimeInvariantSolution, VelocityBoundarySolution
+        ],
+        gmeq: Union[Equations, EquationsGeodesic, EquationsIdtx, EquationsIbc],
+        sub: Dict,
+        name: str,
+        fig_size: Optional[Tuple[float, float]] = ...,
+        dpi: Optional[int] = ...,
+        aspect: float = ...,
+        n_points: int = ...,
+        x_limits: Optional[Tuple[Optional[float], Optional[float]]] = ...,
+        y_limits: Optional[Tuple[Optional[float], Optional[float]]] = ...,
+        do_legend: bool = ...,
+        do_pub_label: bool = ...,
+        pub_label_xy: Tuple[float, float] = ...,
+        pub_label: str = ...,
+        do_etaxi_label: bool = ...,
+        eta_label_xy: Tuple[float, float] = ...,
+    ) -> None: ...
+    def angular_disparity(
+        self,
+        gmes: Union[
+            SingleRaySolution, TimeInvariantSolution, VelocityBoundarySolution
+        ],
+        gmeq: Union[Equations, EquationsGeodesic, EquationsIdtx, EquationsIbc],
+        name: str,
+        fig_size: Optional[Tuple[float, float]] = ...,
+        dpi: Optional[int] = ...,
+        n_points: int = ...,
+        x_limits: Optional[Tuple[Optional[float], Optional[float]]] = ...,
+        y_limits: Optional[Tuple[Optional[float], Optional[float]]] = ...,
+        do_legend: bool = ...,
+        aspect: float = ...,
+        do_pub_label: bool = ...,
+        pub_label: str = ...,
+        pub_label_xy: Tuple[float, float] = ...,
+        eta_label_xy: Tuple[float, float] = ...,
+    ) -> None: ...
+    def profile_angular_disparity(
+        self,
+        gmes: Union[
+            SingleRaySolution, TimeInvariantSolution, VelocityBoundarySolution
+        ],
+        gmeq: Union[Equations, EquationsGeodesic, EquationsIdtx, EquationsIbc],
+        sub: Dict,
+        name: str,
+        fig_size: Optional[Tuple[float, float]] = ...,
+        dpi: Optional[int] = ...,
+        n_points: int = ...,
+        do_pub_label: bool = ...,
+        pub_label: str = ...,
+        pub_label_xy: Tuple[float, float] = ...,
+        eta_label_xy: Tuple[float, float] = ...,
+        var_label_xy: Tuple[float, float] = ...,
+    ) -> None: ...
+    def profile_alpha(
+        self,
+        gmes: Union[
+            SingleRaySolution, TimeInvariantSolution, VelocityBoundarySolution
+        ],
+        gmeq: Union[Equations, EquationsGeodesic, EquationsIdtx, EquationsIbc],
+        sub: Dict,
+        name: str,
+        fig_size: Optional[Tuple[float, float]] = ...,
+        dpi: Optional[int] = ...,
+        n_points: int = ...,
+        do_legend: bool = ...,
+        eta_label_xy: Tuple[float, float] = ...,
+    ) -> None: ...
+    def psi_eta_alpha(
+        self,
+        gmeq: Union[Equations, EquationsGeodesic, EquationsIdtx, EquationsIbc],
+        name: str,
+        fig_size: Optional[Tuple[float, float]] = ...,
+        dpi: Optional[int] = ...,
+        n_points: int = ...,
+    ) -> None: ...
diff --git a/Packages/gme/plot/ray_geodesics.pyi b/Packages/gme/plot/ray_geodesics.pyi
index 3e3fa2f..fb41ebb 100644
--- a/Packages/gme/plot/ray_geodesics.pyi
+++ b/Packages/gme/plot/ray_geodesics.pyi
@@ -14,5 +14,27 @@ class RayGeodesics(Graphing):
     gstar_matrices_array: List
     g_matrices_list: List
     g_matrices_array: List
-    def __init__(self, gmes: TimeInvariantSolution, gmeq: EquationsGeodesic, n_points: int, do_recompute: bool = ...) -> None: ...
-    def profile_g_properties(self, gmes: TimeInvariantSolution, gmeq: EquationsGeodesic, sub: Dict, name: str, fig_size: Optional[Tuple[float, float]] = ..., dpi: Optional[int] = ..., y_limits: Optional[Tuple[Optional[float], Optional[float]]] = ..., do_gstar: bool = ..., do_det: bool = ..., do_eigenvectors: bool = ..., eta_label_xy: Optional[Tuple[float, float]] = ..., do_etaxi_label: bool = ..., legend_loc: str = ..., do_pv: bool = ...) -> None: ...
+    def __init__(
+        self,
+        gmes: TimeInvariantSolution,
+        gmeq: EquationsGeodesic,
+        n_points: int,
+        do_recompute: bool = ...,
+    ) -> None: ...
+    def profile_g_properties(
+        self,
+        gmes: TimeInvariantSolution,
+        gmeq: EquationsGeodesic,
+        sub: Dict,
+        name: str,
+        fig_size: Optional[Tuple[float, float]] = ...,
+        dpi: Optional[int] = ...,
+        y_limits: Optional[Tuple[Optional[float], Optional[float]]] = ...,
+        do_gstar: bool = ...,
+        do_det: bool = ...,
+        do_eigenvectors: bool = ...,
+        eta_label_xy: Optional[Tuple[float, float]] = ...,
+        do_etaxi_label: bool = ...,
+        legend_loc: str = ...,
+        do_pv: bool = ...,
+    ) -> None: ...
diff --git a/Packages/gme/plot/ray_profiles.pyi b/Packages/gme/plot/ray_profiles.pyi
index db6d7a1..ce91742 100644
--- a/Packages/gme/plot/ray_profiles.pyi
+++ b/Packages/gme/plot/ray_profiles.pyi
@@ -5,6 +5,67 @@ from gme.plot.base import Graphing
 from typing import Any, Dict, Optional, Tuple
 
 class RayProfiles(Graphing):
-    def profile_ray(self, gmes: SingleRaySolution, gmeq: Equations, sub: Dict, name: str, fig_size: Optional[Tuple[float, float]] = ..., dpi: Optional[int] = ..., y_limits: Optional[Tuple[Optional[float], Optional[float]]] = ..., n_points: int = ..., aspect: Optional[float] = ..., do_schematic: bool = ..., do_ndim: bool = ..., do_simple: bool = ..., do_t_sampling: bool = ..., do_pub_label: bool = ..., pub_label: str = ..., pub_label_xy: Tuple[float, float] = ..., do_etaxi_label: bool = ..., eta_label_xy: Any | None = ...) -> None: ...
-    def profile_h_rays(self, gmes: TimeInvariantSolution, gmeq: Equations, sub: Dict, name: str, fig_size: Optional[Tuple[float, float]] = ..., dpi: Optional[int] = ..., x_limits: Optional[Tuple[Optional[float], Optional[float]]] = ..., y_limits: Optional[Tuple[Optional[float], Optional[float]]] = ..., n_points: int = ..., do_direct: bool = ..., n_rays: int = ..., do_schematic: bool = ..., do_legend: bool = ..., do_fault_bdry: bool = ..., do_compute_xivh_ratio: bool = ..., do_one_ray: bool = ..., do_t_sampling: bool = ..., do_pub_label: bool = ..., pub_label: str = ..., pub_label_xy: Tuple[float, float] = ..., do_etaxi_label: bool = ..., eta_label_xy: Tuple[float, float] = ...) -> None: ...
-    def profile_h(self, gmes: TimeInvariantSolution, gmeq: Equations, sub: Dict, name: str, fig_size: Optional[Tuple[float, float]] = ..., dpi: Optional[int] = ..., y_limits: Optional[Tuple[Optional[float], Optional[float]]] = ..., do_legend: bool = ..., do_profile_points: bool = ..., profile_subsetting: int = ..., do_pub_label: bool = ..., pub_label: str = ..., pub_label_xy: Tuple[float, float] = ..., do_etaxi_label: bool = ..., eta_label_xy: Tuple[float, float] = ...) -> None: ...
+    def profile_ray(
+        self,
+        gmes: SingleRaySolution,
+        gmeq: Equations,
+        sub: Dict,
+        name: str,
+        fig_size: Optional[Tuple[float, float]] = ...,
+        dpi: Optional[int] = ...,
+        y_limits: Optional[Tuple[Optional[float], Optional[float]]] = ...,
+        n_points: int = ...,
+        aspect: Optional[float] = ...,
+        do_schematic: bool = ...,
+        do_ndim: bool = ...,
+        do_simple: bool = ...,
+        do_t_sampling: bool = ...,
+        do_pub_label: bool = ...,
+        pub_label: str = ...,
+        pub_label_xy: Tuple[float, float] = ...,
+        do_etaxi_label: bool = ...,
+        eta_label_xy: Any | None = ...,
+    ) -> None: ...
+    def profile_h_rays(
+        self,
+        gmes: TimeInvariantSolution,
+        gmeq: Equations,
+        sub: Dict,
+        name: str,
+        fig_size: Optional[Tuple[float, float]] = ...,
+        dpi: Optional[int] = ...,
+        x_limits: Optional[Tuple[Optional[float], Optional[float]]] = ...,
+        y_limits: Optional[Tuple[Optional[float], Optional[float]]] = ...,
+        n_points: int = ...,
+        do_direct: bool = ...,
+        n_rays: int = ...,
+        do_schematic: bool = ...,
+        do_legend: bool = ...,
+        do_fault_bdry: bool = ...,
+        do_compute_xivh_ratio: bool = ...,
+        do_one_ray: bool = ...,
+        do_t_sampling: bool = ...,
+        do_pub_label: bool = ...,
+        pub_label: str = ...,
+        pub_label_xy: Tuple[float, float] = ...,
+        do_etaxi_label: bool = ...,
+        eta_label_xy: Tuple[float, float] = ...,
+    ) -> None: ...
+    def profile_h(
+        self,
+        gmes: TimeInvariantSolution,
+        gmeq: Equations,
+        sub: Dict,
+        name: str,
+        fig_size: Optional[Tuple[float, float]] = ...,
+        dpi: Optional[int] = ...,
+        y_limits: Optional[Tuple[Optional[float], Optional[float]]] = ...,
+        do_legend: bool = ...,
+        do_profile_points: bool = ...,
+        profile_subsetting: int = ...,
+        do_pub_label: bool = ...,
+        pub_label: str = ...,
+        pub_label_xy: Tuple[float, float] = ...,
+        do_etaxi_label: bool = ...,
+        eta_label_xy: Tuple[float, float] = ...,
+    ) -> None: ...
diff --git a/Packages/gme/plot/ray_velocities.pyi b/Packages/gme/plot/ray_velocities.pyi
index f2d80ac..b1be8d9 100644
--- a/Packages/gme/plot/ray_velocities.pyi
+++ b/Packages/gme/plot/ray_velocities.pyi
@@ -4,5 +4,40 @@ from gme.plot.base import Graphing
 from typing import Dict, Optional, Tuple
 
 class RayVelocities(Graphing):
-    def profile_v(self, gmes: SingleRaySolution, gmeq: Equations, sub: Dict, name: str, fig_size: Optional[Tuple[float, float]] = ..., dpi: Optional[int] = ..., n_points: int = ..., do_pub_label: bool = ..., pub_label: str = ..., pub_label_xy: Tuple[float, float] = ..., do_etaxi_label: bool = ..., eta_label_xy: Tuple[float, float] = ..., var_label_xy: Tuple[float, float] = ..., xi_norm: Optional[float] = ..., legend_loc: str = ..., do_mod_v: bool = ...) -> None: ...
-    def profile_vdot(self, gmes: SingleRaySolution, gmeq: Equations, sub: Dict, name: str, fig_size: Optional[Tuple[float, float]] = ..., dpi: Optional[int] = ..., n_points: int = ..., do_pub_label: bool = ..., pub_label: str = ..., do_etaxi_label: bool = ..., xi_norm: Optional[float] = ..., legend_loc: str = ..., do_legend: bool = ..., do_mod_vdot: bool = ..., do_geodesic: bool = ...) -> None: ...
+    def profile_v(
+        self,
+        gmes: SingleRaySolution,
+        gmeq: Equations,
+        sub: Dict,
+        name: str,
+        fig_size: Optional[Tuple[float, float]] = ...,
+        dpi: Optional[int] = ...,
+        n_points: int = ...,
+        do_pub_label: bool = ...,
+        pub_label: str = ...,
+        pub_label_xy: Tuple[float, float] = ...,
+        do_etaxi_label: bool = ...,
+        eta_label_xy: Tuple[float, float] = ...,
+        var_label_xy: Tuple[float, float] = ...,
+        xi_norm: Optional[float] = ...,
+        legend_loc: str = ...,
+        do_mod_v: bool = ...,
+    ) -> None: ...
+    def profile_vdot(
+        self,
+        gmes: SingleRaySolution,
+        gmeq: Equations,
+        sub: Dict,
+        name: str,
+        fig_size: Optional[Tuple[float, float]] = ...,
+        dpi: Optional[int] = ...,
+        n_points: int = ...,
+        do_pub_label: bool = ...,
+        pub_label: str = ...,
+        do_etaxi_label: bool = ...,
+        xi_norm: Optional[float] = ...,
+        legend_loc: str = ...,
+        do_legend: bool = ...,
+        do_mod_vdot: bool = ...,
+        do_geodesic: bool = ...,
+    ) -> None: ...
diff --git a/Packages/gme/plot/slicing.pyi b/Packages/gme/plot/slicing.pyi
index 97ac81a..dae93b3 100644
--- a/Packages/gme/plot/slicing.pyi
+++ b/Packages/gme/plot/slicing.pyi
@@ -10,26 +10,44 @@ class SlicingMath:
     gstarhat_eqn: Eq
     pxhat_lambda: Any
     pzhat_lambda: Any
-    def __init__(self, gmeq: Equations, sub_: Dict, var_list: List[Symbol], do_modv: bool = ...): ...
+    def __init__(
+        self,
+        gmeq: Equations,
+        sub_: Dict,
+        var_list: List[Symbol],
+        do_modv: bool = ...,
+    ): ...
     H_lambda: Any
     def define_H_lambda(self, sub_: Dict, var_list: List[Symbol]) -> None: ...
     d2Hdpzhat2_lambda: Any
-    def define_d2Hdpzhat2_lambda(self, sub_: Dict, var_list: List[Symbol]) -> None: ...
+    def define_d2Hdpzhat2_lambda(
+        self, sub_: Dict, var_list: List[Symbol]
+    ) -> None: ...
     detHessianSqrd_lambda: Any
-    def define_detHessianSqrd_lambda(self, sub_: Dict, var_list: List[Symbol]) -> None: ...
+    def define_detHessianSqrd_lambda(
+        self, sub_: Dict, var_list: List[Symbol]
+    ) -> None: ...
     Ci_lambda: Any
     def define_Ci_lambda(self, sub_: Dict, var_list: List[Symbol]) -> None: ...
     gstar_signature_lambda: Any
-    def define_Hessian_eigenvals(self, sub_: Dict, var_list: List[Symbol]) -> None: ...
+    def define_Hessian_eigenvals(
+        self, sub_: Dict, var_list: List[Symbol]
+    ) -> None: ...
     gstarhat_lambda: Any
-    def define_gstarhat_lambda(self, sub_: Dict, var_list: List[Symbol]) -> None: ...
+    def define_gstarhat_lambda(
+        self, sub_: Dict, var_list: List[Symbol]
+    ) -> None: ...
     v_pxpzhat_lambda: Any
     def define_v_pxpzhat_lambda(self, sub_: Dict) -> None: ...
     modv_pxpzhat_lambda: Any
     def define_modv_pxpzhat_lambda(self, sub_: Dict) -> None: ...
     def pxhatsqrd_Ci_polylike_eqn(self, sub_: Dict, pzhat_: float) -> Eq: ...
-    def pxhat_Ci_soln(self, eqn_: Eq, sub_: Dict, rxhat_: float, tolerance: float = ...) -> float: ...
-    def pxpzhat0_values(self, contour_values_: Union[List[float], Tuple[float]], sub_: Dict) -> List[Tuple[float, float]]: ...
+    def pxhat_Ci_soln(
+        self, eqn_: Eq, sub_: Dict, rxhat_: float, tolerance: float = ...
+    ) -> float: ...
+    def pxpzhat0_values(
+        self, contour_values_: Union[List[float], Tuple[float]], sub_: Dict
+    ) -> List[Tuple[float, float]]: ...
     def get_rxhat_pzhat(self, sub_: Dict[Any, Any]) -> List[float]: ...
 
 class SlicingPlots(Graphing):
@@ -39,9 +57,50 @@ class SlicingPlots(Graphing):
     grid_array: np.ndarray
     pxpzhat_grids: List[np.ndarray]
     rxpxhat_grids: List[np.ndarray]
-    def __init__(self, gmeq: Equations, grid_res: int = ..., dpi: int = ..., font_size: int = ...) -> None: ...
-    def plot_dHdp_slice(self, sm: SlicingMath, sub_: Dict, psub_: Dict, pxhat_: float, do_detHessian: bool = ..., do_at_rxcrit: bool = ...) -> str: ...
-    def plot_modv_slice(self, sm: SlicingMath, sub_: Dict, psub_: Dict, do_at_rxcrit: bool = ...) -> str: ...
-    def H_rxpx_contours(self, sm: SlicingMath, sub_: Dict, psf: float, do_Ci: bool, **kwargs) -> str: ...
-    def H_pxpz_contours(self, sm: SlicingMath, sub_: Dict, psf: float, do_Ci: bool, **kwargs) -> str: ...
-    def plot_Hetc_contours(self, sm: SlicingMath, grids_: Tuple[Any, Any], sub_: Dict, do_Ci: bool, do_modv: bool = ..., do_fmt_labels: bool = ..., do_aspect: bool = ..., do_rxpx: bool = ..., pxpz_points: Any | None = ..., do_log2H: bool = ..., do_siggrid: bool = ..., do_black_contours: bool = ..., do_grid: bool = ..., do_at_rxcrit: bool = ..., contour_nlevels: Optional[Union[int, List, Tuple]] = ..., contour_range: Tuple[float, float] = ..., v_contour_range: Tuple[float, float] = ..., contour_values: Optional[List[float]] = ..., contour_label_locs: Optional[List] = ...) -> str: ...
+    def __init__(
+        self,
+        gmeq: Equations,
+        grid_res: int = ...,
+        dpi: int = ...,
+        font_size: int = ...,
+    ) -> None: ...
+    def plot_dHdp_slice(
+        self,
+        sm: SlicingMath,
+        sub_: Dict,
+        psub_: Dict,
+        pxhat_: float,
+        do_detHessian: bool = ...,
+        do_at_rxcrit: bool = ...,
+    ) -> str: ...
+    def plot_modv_slice(
+        self, sm: SlicingMath, sub_: Dict, psub_: Dict, do_at_rxcrit: bool = ...
+    ) -> str: ...
+    def H_rxpx_contours(
+        self, sm: SlicingMath, sub_: Dict, psf: float, do_Ci: bool, **kwargs
+    ) -> str: ...
+    def H_pxpz_contours(
+        self, sm: SlicingMath, sub_: Dict, psf: float, do_Ci: bool, **kwargs
+    ) -> str: ...
+    def plot_Hetc_contours(
+        self,
+        sm: SlicingMath,
+        grids_: Tuple[Any, Any],
+        sub_: Dict,
+        do_Ci: bool,
+        do_modv: bool = ...,
+        do_fmt_labels: bool = ...,
+        do_aspect: bool = ...,
+        do_rxpx: bool = ...,
+        pxpz_points: Any | None = ...,
+        do_log2H: bool = ...,
+        do_siggrid: bool = ...,
+        do_black_contours: bool = ...,
+        do_grid: bool = ...,
+        do_at_rxcrit: bool = ...,
+        contour_nlevels: Optional[Union[int, List, Tuple]] = ...,
+        contour_range: Tuple[float, float] = ...,
+        v_contour_range: Tuple[float, float] = ...,
+        contour_values: Optional[List[float]] = ...,
+        contour_label_locs: Optional[List] = ...,
+    ) -> str: ...
diff --git a/Packages/gme/plot/time_dependent.pyi b/Packages/gme/plot/time_dependent.pyi
index 38d77da..7c66dbc 100644
--- a/Packages/gme/plot/time_dependent.pyi
+++ b/Packages/gme/plot/time_dependent.pyi
@@ -4,4 +4,41 @@ from gme.plot.base import Graphing
 from typing import Dict, Optional, Tuple
 
 class TimeDependent(Graphing):
-    def profile_isochrones(self, gmes: VelocityBoundarySolution, gmeq: Equations, sub: Dict, name: str, fig_size: Optional[Tuple[float, float]] = ..., dpi: Optional[int] = ..., do_zero_isochrone: bool = ..., do_rays: bool = ..., ray_subsetting: int = ..., ray_lw: float = ..., ray_ls: str = ..., ray_label: str = ..., do_isochrones: bool = ..., isochrone_subsetting: int = ..., isochrone_lw: float = ..., isochrone_ls: str = ..., do_annotate_rays: bool = ..., n_arrows: int = ..., arrow_sf: float = ..., arrow_offset: int = ..., do_annotate_cusps: bool = ..., cusp_lw: float = ..., do_smooth_colors: bool = ..., x_limits: Tuple[float, float] = ..., y_limits: Tuple[float, float] = ..., aspect: float = ..., do_legend: bool = ..., do_alt_legend: bool = ..., do_grid: bool = ..., do_infer_initiation: bool = ..., do_pub_label: bool = ..., do_etaxi_label: bool = ..., pub_label: Optional[str] = ..., eta_label_xy: Tuple[float, float] = ..., pub_label_xy: Tuple[float, float] = ...) -> None: ...
+    def profile_isochrones(
+        self,
+        gmes: VelocityBoundarySolution,
+        gmeq: Equations,
+        sub: Dict,
+        name: str,
+        fig_size: Optional[Tuple[float, float]] = ...,
+        dpi: Optional[int] = ...,
+        do_zero_isochrone: bool = ...,
+        do_rays: bool = ...,
+        ray_subsetting: int = ...,
+        ray_lw: float = ...,
+        ray_ls: str = ...,
+        ray_label: str = ...,
+        do_isochrones: bool = ...,
+        isochrone_subsetting: int = ...,
+        isochrone_lw: float = ...,
+        isochrone_ls: str = ...,
+        do_annotate_rays: bool = ...,
+        n_arrows: int = ...,
+        arrow_sf: float = ...,
+        arrow_offset: int = ...,
+        do_annotate_cusps: bool = ...,
+        cusp_lw: float = ...,
+        do_smooth_colors: bool = ...,
+        x_limits: Tuple[float, float] = ...,
+        y_limits: Tuple[float, float] = ...,
+        aspect: float = ...,
+        do_legend: bool = ...,
+        do_alt_legend: bool = ...,
+        do_grid: bool = ...,
+        do_infer_initiation: bool = ...,
+        do_pub_label: bool = ...,
+        do_etaxi_label: bool = ...,
+        pub_label: Optional[str] = ...,
+        eta_label_xy: Tuple[float, float] = ...,
+        pub_label_xy: Tuple[float, float] = ...,
+    ) -> None: ...
diff --git a/Packages/gme/plot/time_invariant.pyi b/Packages/gme/plot/time_invariant.pyi
index 6aec8f6..c3421fa 100644
--- a/Packages/gme/plot/time_invariant.pyi
+++ b/Packages/gme/plot/time_invariant.pyi
@@ -5,10 +5,116 @@ from typing import Dict, Optional, Tuple
 
 class TimeInvariant(Graphing):
     fc: str
-    def profile_aniso(self, gmes: TimeInvariantSolution, gmeq: Equations, sub: Dict, name: str, fig_size: Optional[Tuple[float, float]] = ..., dpi: Optional[int] = ..., n_points: int = ..., xf_stop: float = ..., sf: Optional[Tuple[float, float]] = ..., n_arrows: int = ..., y_limits: Optional[Tuple[float, float]] = ..., v_scale: float = ..., v_exponent: float = ..., do_pub_label: bool = ..., pub_label: str = ..., eta_label_xy: Optional[Tuple[float, float]] = ...) -> None: ...
-    def profile_beta(self, gmes: TimeInvariantSolution, gmeq: Equations, sub: Dict, name: str, fig_size: Optional[Tuple[float, float]] = ..., dpi: Optional[int] = ..., n_points: int = ..., xf_stop: float = ..., legend_loc: str = ..., eta_label_xy: Tuple[float, float] = ..., pub_label_xy: Tuple[float, float] = ..., do_etaxi_label: bool = ..., do_pub_label: bool = ..., pub_label: str = ...) -> None: ...
-    def profile_beta_error(self, gmes: TimeInvariantSolution, gmeq: Equations, sub: Dict, name: str, fig_size: Optional[Tuple[float, float]] = ..., dpi: Optional[int] = ..., n_points: int = ..., eta_label_xy: Tuple[float, float] = ..., xf_stop: float = ...) -> None: ...
-    def profile_xi(self, gmes: TimeInvariantSolution, gmeq: Equations, sub: Dict, name: str, fig_size: Optional[Tuple[float, float]] = ..., dpi: Optional[int] = ..., xf_stop: float = ..., n_points: int = ..., pub_label_xy: Tuple[float, float] = ..., eta_label_xy: Tuple[float, float] = ..., var_label_xy: Tuple[float, float] = ..., do_etaxi_label: bool = ..., do_pub_label: bool = ..., pub_label: str = ..., xi_norm: Optional[float] = ...) -> None: ...
-    def profile_xihorizontal(self, gmes: TimeInvariantSolution, gmeq: Equations, sub: Dict, name: str, fig_size: Optional[Tuple[float, float]] = ..., dpi: Optional[int] = ..., xf_stop: float = ..., n_points: int = ..., pub_label_xy: Tuple[float, float] = ..., eta_label_xy: Tuple[float, float] = ..., var_label_xy: Tuple[float, float] = ..., do_etaxi_label: bool = ..., do_pub_label: bool = ..., pub_label: str = ..., xi_norm: Optional[float] = ...) -> None: ...
-    def profile_xivertical(self, gmes: TimeInvariantSolution, gmeq: Equations, sub: Dict, name: str, fig_size: Optional[Tuple[float, float]] = ..., dpi: Optional[int] = ..., xf_stop: float = ..., n_points: int = ..., y_limits: Optional[Tuple[float, float]] = ..., pub_label_xy: Tuple[float, float] = ..., eta_label_xy: Tuple[float, float] = ..., var_label_xy: Tuple[float, float] = ..., do_etaxi_label: bool = ..., do_pub_label: bool = ..., pub_label: str = ..., xi_norm: Optional[float] = ...) -> None: ...
-    def profile_ensemble(self, gmes: TimeInvariantSolution, pr_choices: Dict, name: str, fig_size: Optional[Tuple[float, float]] = ..., dpi: Optional[int] = ..., aspect: Optional[float] = ..., do_direct: bool = ...) -> None: ...
+    def profile_aniso(
+        self,
+        gmes: TimeInvariantSolution,
+        gmeq: Equations,
+        sub: Dict,
+        name: str,
+        fig_size: Optional[Tuple[float, float]] = ...,
+        dpi: Optional[int] = ...,
+        n_points: int = ...,
+        xf_stop: float = ...,
+        sf: Optional[Tuple[float, float]] = ...,
+        n_arrows: int = ...,
+        y_limits: Optional[Tuple[float, float]] = ...,
+        v_scale: float = ...,
+        v_exponent: float = ...,
+        do_pub_label: bool = ...,
+        pub_label: str = ...,
+        eta_label_xy: Optional[Tuple[float, float]] = ...,
+    ) -> None: ...
+    def profile_beta(
+        self,
+        gmes: TimeInvariantSolution,
+        gmeq: Equations,
+        sub: Dict,
+        name: str,
+        fig_size: Optional[Tuple[float, float]] = ...,
+        dpi: Optional[int] = ...,
+        n_points: int = ...,
+        xf_stop: float = ...,
+        legend_loc: str = ...,
+        eta_label_xy: Tuple[float, float] = ...,
+        pub_label_xy: Tuple[float, float] = ...,
+        do_etaxi_label: bool = ...,
+        do_pub_label: bool = ...,
+        pub_label: str = ...,
+    ) -> None: ...
+    def profile_beta_error(
+        self,
+        gmes: TimeInvariantSolution,
+        gmeq: Equations,
+        sub: Dict,
+        name: str,
+        fig_size: Optional[Tuple[float, float]] = ...,
+        dpi: Optional[int] = ...,
+        n_points: int = ...,
+        eta_label_xy: Tuple[float, float] = ...,
+        xf_stop: float = ...,
+    ) -> None: ...
+    def profile_xi(
+        self,
+        gmes: TimeInvariantSolution,
+        gmeq: Equations,
+        sub: Dict,
+        name: str,
+        fig_size: Optional[Tuple[float, float]] = ...,
+        dpi: Optional[int] = ...,
+        xf_stop: float = ...,
+        n_points: int = ...,
+        pub_label_xy: Tuple[float, float] = ...,
+        eta_label_xy: Tuple[float, float] = ...,
+        var_label_xy: Tuple[float, float] = ...,
+        do_etaxi_label: bool = ...,
+        do_pub_label: bool = ...,
+        pub_label: str = ...,
+        xi_norm: Optional[float] = ...,
+    ) -> None: ...
+    def profile_xihorizontal(
+        self,
+        gmes: TimeInvariantSolution,
+        gmeq: Equations,
+        sub: Dict,
+        name: str,
+        fig_size: Optional[Tuple[float, float]] = ...,
+        dpi: Optional[int] = ...,
+        xf_stop: float = ...,
+        n_points: int = ...,
+        pub_label_xy: Tuple[float, float] = ...,
+        eta_label_xy: Tuple[float, float] = ...,
+        var_label_xy: Tuple[float, float] = ...,
+        do_etaxi_label: bool = ...,
+        do_pub_label: bool = ...,
+        pub_label: str = ...,
+        xi_norm: Optional[float] = ...,
+    ) -> None: ...
+    def profile_xivertical(
+        self,
+        gmes: TimeInvariantSolution,
+        gmeq: Equations,
+        sub: Dict,
+        name: str,
+        fig_size: Optional[Tuple[float, float]] = ...,
+        dpi: Optional[int] = ...,
+        xf_stop: float = ...,
+        n_points: int = ...,
+        y_limits: Optional[Tuple[float, float]] = ...,
+        pub_label_xy: Tuple[float, float] = ...,
+        eta_label_xy: Tuple[float, float] = ...,
+        var_label_xy: Tuple[float, float] = ...,
+        do_etaxi_label: bool = ...,
+        do_pub_label: bool = ...,
+        pub_label: str = ...,
+        xi_norm: Optional[float] = ...,
+    ) -> None: ...
+    def profile_ensemble(
+        self,
+        gmes: TimeInvariantSolution,
+        pr_choices: Dict,
+        name: str,
+        fig_size: Optional[Tuple[float, float]] = ...,
+        dpi: Optional[int] = ...,
+        aspect: Optional[float] = ...,
+        do_direct: bool = ...,
+    ) -> None: ...
diff --git a/Plots/Theory/psi_eta_alpha.pdf b/Plots/Theory/psi_eta_alpha.pdf
index d0bbd62..9fa1f14 100644
Binary files a/Plots/Theory/psi_eta_alpha.pdf and b/Plots/Theory/psi_eta_alpha.pdf differ
diff --git a/Sphinx/source/conf.py b/Sphinx/source/conf.py
index 764f7c9..fe4e920 100644
--- a/Sphinx/source/conf.py
+++ b/Sphinx/source/conf.py
@@ -1,3 +1,5 @@
+"""Sphinx config."""
+
 # Configuration file for the Sphinx documentation builder.
 #
 # This file only contains a selection of the most common options. For a full
@@ -12,17 +14,22 @@
 #
 import os
 import sys
-for dir_ in (   ('..'),('../..'),
-                ('../../Packages'),
-                ('../../../GMPLib/Packages/gmplib'),
-                ('../../Packages/gme/core'),
-                ('../../Packages/gme/ode'),
-                ('../../Packages/gme/plot'),
-                ('../../Packages/gme/knickpoints') ):
+
+for dir_ in (
+    (".."),
+    ("../.."),
+    ("../../Packages"),
+    ("../../../GMPLib/Packages/gmplib"),
+    ("../../Packages/gme/core"),
+    ("../../Packages/gme/ode"),
+    ("../../Packages/gme/plot"),
+    ("../../Packages/gme/knickpoints"),
+):
     path_ = os.path.abspath(dir_)
     # print(path_)
     sys.path.append(path_)
 from pprint import PrettyPrinter
+
 pp = PrettyPrinter(indent=4).pprint
 pp(sys.path)
 
@@ -32,16 +39,15 @@
 
 # -- Project information -----------------------------------------------------
 
-project = 'GME'
-copyright = '2021, CPS'
-author = 'CPS'
+project = "GME"
+copyright = "2021, CPS"
+author = "CPS"
 
 # The full version, including alpha/beta/rc tags
-release = '1.0'
+release = "1.0"
 
 # The master toctree document.
-master_doc = 'index'
-
+master_doc = "index"
 
 
 # -- General configuration ---------------------------------------------------
@@ -50,29 +56,29 @@
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
 extensions = [
-    'sphinx.ext.autodoc',
-    'sphinx.ext.githubpages',
-    'sphinx.ext.intersphinx',
-    'sphinx.ext.todo',
-    'sphinx.ext.napoleon',
+    "sphinx.ext.autodoc",
+    "sphinx.ext.githubpages",
+    "sphinx.ext.intersphinx",
+    "sphinx.ext.todo",
+    "sphinx.ext.napoleon",
     # 'sphinx.ext.imgmath',
-    'sphinx.ext.mathjax',
-    'recommonmark',
-    'sphinx.ext.inheritance_diagram'
+    "sphinx.ext.mathjax",
+    "recommonmark",
+    "sphinx.ext.inheritance_diagram",
 ]
 
 # Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
+templates_path = ["_templates"]
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
 # This pattern also affects html_static_path and html_extra_path.
-#exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+# exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
 
 source_parsers = {
-   '.md': 'recommonmark.parser.CommonMarkParser',
+    ".md": "recommonmark.parser.CommonMarkParser",
 }
-source_suffix = ['.rst', '.md']
+source_suffix = [".rst", ".md"]
 
 # The name of the Pygments (syntax highlighting) style to use.
 # pygments_style = 'sphinx'
@@ -81,7 +87,6 @@
 todo_include_todos = True
 
 
-
 # -- Options for HTML output -------------------------------------------------
 
 # The theme to use for HTML and HTML Help pages.  See the documentation for
@@ -95,20 +100,21 @@
 #     import sphinx_guillotina_theme
 #     sphinx_guillotina_theme.setup(app)
 
-html_theme = 'sphinx_py3doc_enhanced_theme'
+html_theme = "sphinx_py3doc_enhanced_theme"
 import sphinx_py3doc_enhanced_theme
+
 html_theme_path = [sphinx_py3doc_enhanced_theme.get_html_theme_path()]
 
 html_theme_options = {
-    'githuburl': 'https://github.com/geomorphysics/GME/',
-    'bodyfont': '"Lucida Grande",Arial,sans-serif',
-    'headfont': '"Lucida Grande",Arial,sans-serif',
-    'codefont': 'monospace,sans-serif',
-    'linkcolor': '#0072AA',
-    'visitedlinkcolor': '#6363bb',
-    'extrastyling': False,
+    "githuburl": "https://github.com/geomorphysics/GME/",
+    "bodyfont": '"Lucida Grande",Arial,sans-serif',
+    "headfont": '"Lucida Grande",Arial,sans-serif',
+    "codefont": "monospace,sans-serif",
+    "linkcolor": "#0072AA",
+    "visitedlinkcolor": "#6363bb",
+    "extrastyling": False,
 }
-pygments_style = 'friendly'
+pygments_style = "friendly"
 
 
 # html_theme_options = {
@@ -128,16 +134,16 @@
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
+html_static_path = ["_static"]
 
 
 html_sidebars = {
-    '**': [
+    "**": [
         # 'about.html',
-        'searchbox.html',
+        "searchbox.html",
         # 'relations.html',
-        'globaltoc.html',
-        'localtoc.html',
+        "globaltoc.html",
+        "localtoc.html",
         # 'sourcelink.html',
     ]
 }
@@ -146,18 +152,20 @@
 # python -m sphinx.ext.intersphinx 'http://python-eve.org/objects.inv'
 intersphinx_mapping = {
     # https://geomorphysics.github.io/GMPLib
-    'gmplib': ('/Users/colinstark/Projects/GMPLib',
-               '/Users/colinstark/Projects/GMPLib/objects.inv'),
-    'sphinx': ('https://www.sphinx-doc.org/en/master', None),
-    'python': ('https://docs.python.org/3', None),
-    'matplotlib': ('https://matplotlib.org/stable', None),
+    "gmplib": (
+        "/Users/colinstark/Projects/GMPLib",
+        "/Users/colinstark/Projects/GMPLib/objects.inv",
+    ),
+    "sphinx": ("https://www.sphinx-doc.org/en/master", None),
+    "python": ("https://docs.python.org/3", None),
+    "matplotlib": ("https://matplotlib.org/stable", None),
     # 'mpl_toolkits': ('https://matplotlib.org', None),
-    'np': ('https://numpy.org/doc/stable', None),
-    'numpy': ('https://numpy.org/doc/stable', None),
-    'scipy': ('https://docs.scipy.org/doc/scipy/reference', None),
-    'sympy': ('https://docs.sympy.org/latest/', None),
-    'PIL': ('https://pillow.readthedocs.io/en/latest/', None),
-    'IPython': ('http://ipython.org/ipython-doc/stable', None)
+    "np": ("https://numpy.org/doc/stable", None),
+    "numpy": ("https://numpy.org/doc/stable", None),
+    "scipy": ("https://docs.scipy.org/doc/scipy", None),
+    "sympy": ("https://docs.sympy.org/latest/", None),
+    "PIL": ("https://pillow.readthedocs.io/en/latest/", None),
+    "IPython": ("http://ipython.org/ipython-doc/stable", None),
 }
 
 
@@ -176,15 +184,19 @@
 napoleon_use_rtype = True
 
 
-
 # app setup hook
 def setup(app):
-    app.add_config_value('recommonmark_config', {
-        #'url_resolver': lambda url: github_doc_root + url,
-        'auto_toc_tree_section': 'Contents',
-        'enable_math': False,
-        'enable_inline_math': False,
-        'enable_eval_rst': True,
-        # 'enable_auto_doc_ref': True,
-    }, True)
+    """App setup hook."""
+    app.add_config_value(
+        "recommonmark_config",
+        {
+            #'url_resolver': lambda url: github_doc_root + url,
+            "auto_toc_tree_section": "Contents",
+            "enable_math": False,
+            "enable_inline_math": False,
+            "enable_eval_rst": True,
+            # 'enable_auto_doc_ref': True,
+        },
+        True,
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,16,17,18,20,21,22,23,25,26,27,34,36],derive_by_arrai:15,describ:[2,9,12],design:0,desir:2,destroi:9,det:[20,24,48,51],det_gstar:[20,24],det_gstar_varphi_pxpz_eqn:20,detail:[2,29,32],detect:7,determin:9,dethessian:51,dethessiansqrd_lambda:51,detj:24,develop:[0,1,7,9],dfrac:[10,14,16,17,18,20,22,23,26,27,53],dg_ij_rk_lambda:15,dg_rk_ij_mat:15,dh:51,dhdpxhat_:51,dhdpzhat_:51,dhdx_arrai:[32,36],dhdx_interp:36,diagon:15,dict:[10,11,12,13,14,15,17,18,20,21,22,23,25,26,27,29,31,32,34,35,36,37,38,40,41,42,44,45,46,47,48,49,50,51,52,53],dictionari:[2,11,15,29,31,32,39,40,41,42,46,47,49,50,51,52,53],did:35,did_clip_at_x1:31,diff:[15,16,17,20,21,25,51],differ:[9,29,38,52,53],differenti:[0,9,53],dimens:22,dimension:[19,21,22],dimensionless:[21,41,42,53],dimx_limit:28,dimx_limits_zoom:28,dimz_limit:28,dimz_limits_zoom:28,dip:47,direct:[6,9,27,35,40,44,45,49,53],directli:[49,53],directori:[3,8],disabl:[10,12,13,14,15,16,17,18,20,21,22,23,26,27,42,44,51,53],discuss:6,dispar:[9,47],displai:[15,45],disposit:38,dissolut:6,distanc:[9,11,38,41,42,46,47,48,49,50,52,53],divid:[9,35,49,51,53],divisor:40,do_alt_legend:52,do_annot:45,do_annotate_cusp:52,do_annotate_rai:52,do_arrow:45,do_aspect:51,do_at_rxcrit:51,do_black_contour:51,do_ci:51,do_compute_xivh_ratio:49,do_dash:46,do_dens:[28,29,31,34,35,38],do_det:48,do_deth:51,do_dethessian:51,do_direct:[49,53],do_eigenvector:48,do_eliminate_caust:[28,31],do_eta_xi:53,do_etaxi_label:[28,47,48,49,50,52,53],do_extra_annot:[28,42],do_fast:46,do_fault_bdri:49,do_fmt_label:51,do_geodes:[11,28,50],do_grid:[51,52],do_gstar:48,do_half:45,do_huygens_wavelet:28,do_ic:28,do_idtx:11,do_infer_initi:[41,52],do_ip:28,do_isochron:52,do_isochrone_p:52,do_label:[40,46,49],do_legend:[45,46,47,49,50,52],do_lines_point:45,do_log2h:51,do_mani:46,do_mod_v:[48,50],do_mod_vdot:50,do_modv:51,do_ndim:[11,13,22,49],do_new:26,do_new_varphi_model:11,do_noth:11,do_one_rai:[40,49],do_primari:46,do_profile_extra:28,do_profile_point:49,do_profile_schemat:28,do_pts_onli:46,do_pub_label:[28,47,48,49,50,52,53],do_pv:48,do_pz:45,do_rai:52,do_raw:[11,12,27],do_ray_conjugaci:[28,46],do_ray_slow:45,do_recomput:48,do_revert:13,do_rxpx:51,do_schemat:49,do_shap:45,do_siggrid:51,do_simpl:49,do_smooth:[40,52,53],do_smooth_color:[40,52],do_soln:29,do_subtitl:42,do_t_sampl:49,do_text_label:45,do_trunc:36,do_use_newton:[25,36],do_varphi_circl:45,do_vb:28,do_verbos:29,do_x:41,do_zero_isochron:52,do_zoom:44,doc:[10,11,12,13,14,15,16,17,18,20,21,22,23,25,26,27,29,31,32,34,35,36,38,39,40,41,42,44,45,46,47,48,49,50,51,52,53],document:11,doe:9,doesn:[10,12,13,14,15,16,17,18,20,21,22,23,26,27,31],doi:6,domain:[2,22,31,35,38],don:[11,12,26,31,36],done:35,dont_crop_cusp:31,dop853:[28,31],dot:[15,16,18,24,34,41,45,48,50,52],doubl:27,downarrow:[10,17,22,24,26,27,34,46,53],downarrow_0:24,downarrow_:24,downstream:11,downward:9,dp:[31,51],dp_fudg:46,dpi:[28,39,40,41,42,44,45,46,47,48,49,50,51,52,53],dpoly_lambda:25,dpx0_poly_lambda:25,dpx_:46,dpx_poly_lambda:25,dpxdpz:24,dpz_:46,dr:31,drainag:[9,49],draw:[41,46,52],draw_rays_with_arrows_simpl:[40,49],driven:9,drop:[40,41],drpdt_eqn_matrix:31,drpdt_raw_lambda:31,drvdt_eqn_matrix:31,drvdt_raw_lambda:31,drx_:46,drxdrz:24,drz:46,drzdrx:24,dt:[31,35],dt_:46,dtype:51,dual:[9,20],dummi:[16,20,31],dummysolut:29,durat:38,dv:31,dx:[46,53],dx_:[46,49,52],dx_fudg:46,dxy:46,dxy_b:46,dxy_c:46,dy:46,dy_:[49,52],dynam:[6,9],dz:53,dz_:46,dzdx:[24,25],dzdx_ci_polylike_eqn:37,dzdx_ci_polylike_eqn_:25,dzdx_eqn_:25,dzdx_poly_:[25,37],dzdx_poly_root:25,dzdxhat_arrai:37,e2d:[10,13,15,16,17,21,22,23,26,27,34,36,44,51],e:[1,2,11,17,31,47,48,53],each:[1,2,8,15,31,36,38,47,48,49,50,53],earth:6,easier:15,ec:[45,46,53],edg:38,edgecolor:[46,49],effect:49,effici:8,eig:48,eigenvalu:[20,48,51],eigenvect:20,eigenvector:[20,48],eigh:[48,51],either:[3,9],elem_:48,element:[2,31,53],element_:31,elev:[46,49,52,53],elif:[26,29,40,48,53],elimin:[29,31],eliminate_caust:31,els:[12,13,18,22,25,26,27,29,31,35,36,37,38,39,40,41,44,45,46,47,48,49,50,51,52,53],elsewher:8,embed:46,empti:[29,31],empty_lik:36,enact:3,encapsul:9,encount:9,end:[16,20,29,36,37,49],endpoint:[29,35,37,46,51],enough:46,ensembl:[9,11,19,31,53],ensur:3,enter:3,entri:45,enumer:[29,31,35,40,46,51,52,53],environ:[3,8],epsilon:24,eq:[10,11,13,14,15,16,17,18,20,21,22,23,25,26,27,29,37,44,45,51],eq_:31,eqn:[16,17,20,22,26,27,31],eqn_:51,equal:[10,11,14,15,16,17,18,20,22,23,25,26,27,45,51],equat:[2,10,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,29,31,32,34,35,36,38,39,40,41,42,44,45,46,47,48,49,50,51,52,53],equations_extend:[32,34,36,38,44,47,48],equationsbas:11,equationsgeodes:[12,32,34,36,38,47,48],equationsibc:[12,32,34,36,38,47],equationsidtx:[12,32,34,36,38,44,47],equationsidtxibc:12,equationsmixedin:[11,12],equationssetup:12,equationssetuponli:12,equationsubset:13,equival:[3,9,11],erod:9,eros:[2,6,7,11,19,22,26,27,34,42,45,46,49,50,52,53],erosion:[0,9,46],error:[10,12,13,14,15,16,17,18,20,21,22,23,26,27,31,53],essenti:33,estim:[37,53],esurf:6,eta:[2,10,11,13,14,15,16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