diff --git a/docs/quickstart.ipynb b/docs/quickstart.ipynb
index fbfa0d66..efdc4eae 100644
--- a/docs/quickstart.ipynb
+++ b/docs/quickstart.ipynb
@@ -34,14 +34,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
+   "execution_count": 81,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T18:27:34.451257Z",
+     "start_time": "2024-06-20T18:27:34.363655Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/html": [
        "\n",
-       "            <div id=\"yFkvbG\"></div>\n",
+       "            <div id=\"Whzm5k\"></div>\n",
        "            <script type=\"text/javascript\" data-lets-plot-script=\"library\">\n",
        "                if(!window.letsPlotCallQueue) {\n",
        "                    window.letsPlotCallQueue = [];\n",
@@ -52,7 +57,7 @@
        "                (function() {\n",
        "                    var script = document.createElement(\"script\");\n",
        "                    script.type = \"text/javascript\";\n",
-       "                    script.src = \"https://cdn.jsdelivr.net/gh/JetBrains/lets-plot@v4.3.2/js-package/distr/lets-plot.min.js\";\n",
+       "                    script.src = \"https://cdn.jsdelivr.net/gh/JetBrains/lets-plot@v4.3.3/js-package/distr/lets-plot.min.js\";\n",
        "                    script.onload = function() {\n",
        "                        window.letsPlotCall = function(f) {f();};\n",
        "                        window.letsPlotCallQueue.forEach(function(f) {f();});\n",
@@ -65,9 +70,9 @@
        "                        var div = document.createElement(\"div\");\n",
        "                        div.style.color = 'darkred';\n",
        "                        div.textContent = 'Error loading Lets-Plot JS';\n",
-       "                        document.getElementById(\"yFkvbG\").appendChild(div);\n",
+       "                        document.getElementById(\"Whzm5k\").appendChild(div);\n",
        "                    };\n",
-       "                    var e = document.getElementById(\"yFkvbG\");\n",
+       "                    var e = document.getElementById(\"Whzm5k\");\n",
        "                    e.appendChild(script);\n",
        "                })()\n",
        "            </script>\n",
@@ -85,15 +90,19 @@
       "  %reload_ext autoreload\n",
       "The watermark extension is already loaded. To reload it, use:\n",
       "  %reload_ext watermark\n",
-      "pyfixest  : 0.20.2\n",
+      "scipy     : 1.13.1\n",
+      "matplotlib: 3.9.0\n",
+      "pyfixest  : 0.22.0\n",
+      "numpy     : 2.0.0\n",
+      "sys       : 3.12.3 (main, Apr  9 2024, 08:09:14) [Clang 15.0.0 (clang-1500.3.9.4)]\n",
       "pandas    : 2.2.2\n",
-      "matplotlib: 3.8.4\n",
       "\n"
      ]
     }
    ],
    "source": [
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import pandas as pd\n",
     "from lets_plot import LetsPlot\n",
     "\n",
@@ -114,8 +123,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T12:50:44.678367Z",
+     "start_time": "2024-06-20T12:50:44.577935Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -242,7 +256,7 @@
        "4 -3.115669  0.790815  "
       ]
      },
-     "execution_count": 41,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -255,8 +269,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T12:51:29.018667Z",
+     "start_time": "2024-06-20T12:51:28.926233Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -310,12 +329,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T12:52:13.464432Z",
+     "start_time": "2024-06-20T12:52:13.110601Z"
+    }
+   },
    "outputs": [
     {
      "data": {
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bukPW2hFdszndUaA0AAAAAAAAKEcUk8i7pJ/WyGrJIewzCQAAAAAAsP9gKTfyrsaNyuzDdY6hJwcAAAAAoFz4dl/e3QO5owlC3o0JtirkhEZ0zezY9AKlAQAAAAAAQDlixiTyzhijM5qO021b71PGZmQHJfeZkLKLI7IpR07Mk3NkUv4hGZmgFHOiOrPl5FLHBgAAAAAAQBFRTKIgTmw8Uo92P6u2O3qUeL5WyaQj67nbf+5uDsvcY1V7dr/mnTBTUyMTSpgWAAAAAAC8Fadyo9AoJlEQARNQ5PY6xZ/15KV2fZh5qYCUkgbuqFdrXbM0vgQhAQAAAAAAUDJU3yiIR556Qkuf79ltKbmjbCKgO371qra0txcpGQAAAAAAAMoBxSQK4rZbnlI2kduE3EzS0S1/uLvAiQAAAAAAAFBOWMqNvGvb2q6+Dpv7Bb6jlx7rkj5RuEwAAAAAAGBkfMt8NhQWjzDk3Zq21SO+xvckz/MLkAYAAAAAAADliGISededjo/4GmMkiWISAAAAAABgf8FSbuRd06hxsiNYyS1JbjQrGXpyAAAAAADKhSdT6giocjRByLupzRNVP3NAUm7tpBPyFD0qJdfh4QgAAAAAALC/oAlC3o2J1Sq7oEl+JLfxXsho8vwjCxsKAAAAAAAAZYWl3CiI0TNm6Y1TX1PzIkduas9Tv72oVce50uFTZhQxHQAAAAAAGA6ncqPQeIQh73xr9dLgesVnOeo4W0q1WnkRK/vm0m7fGfq/E1OstvyDlBpldHfH4hKnBgAAAAAAQDExYxJ5ty7erazvSZJSk6QtF0qBTimyTnJSVn6NFJ8u+TV/u+b1/rYSpQUAAAAAAEApUEwi7wa81C7H3mRbpHiLr5CTVcoPyO5msq5nfbmczA0AAAAAALBfoJhE3tW4IW3bVdKRr0OaNuj0cUvUEEzIWiPHWG2IN+kvm+dq5cAo6c3RlJIAAAAAAJQPT3s+MwLIB4pJ5N2UWLMCjqvaQFKXznxIjaG4Im52pzEz69s1MdatVQOj9PNVx2pW7egSpQUAAAAAAEApUEwi7xxjdN64WRpd+3U1h/vl7uEDllggo5n1W/TPM57SvJrzixsSAAAAAAAAJUUxiYI4ecxqbczE91hKbhNyfM2o7dCBNT2SJhUjGgAAAAAAyIFv2XINhUUxibyz1qrbv11Bx9v+ve61Ua1/rlGp/oBizWlNO65LseaMJCnsprQ5c6PqAteWKjIAAAAAAACKjGISeZe062RtSpK06aV6PfKtGRpoCyvV78paR07A1+Pf9zRmTr9OunylGsanNOC9UuLUAAAUVjqV0crXNsrL+qptiCpSG5ExbCgPAACA/RfFJPLOs/2yklY80KqHrj5Aqf7gTj/3s45SfY7WPdWsW/95gc79/qsaPUOy1pMxbmlCAwBQIFvbenXXL5/Qsw8vleMYWWtljFEkFtJZHzxGx585X4EAz38AAKD8eCzlRoHxCEPeuYqpa3VED31z11LyrRLdId3xmYOVSRpKSQBA1Vnx6nr950U/08N3vaiBvoT6euLq7x36Z/umHv36+4v0jUt/qVQyU+qoAAAAQNFRTCLvIs4UPfWzMUr15TYhNz0Q0Bv3zSpwKgAAiqttY7e+/aXfq783IWt3PyaVzGjtijZ9+wu3FjccAAAAUAYoJpF3yXhG659ukpTbvlnZpKvnftVS2FAAABTZbT95RAO9iWHHZTOe1q7cotXLNhchFQAAAFA+KCaRd23rOuU64RFdk+jzZfc0nQQAgAqTGEzp1WdX5zx+sC+pP970eAETAQAAjJwvUzFfqEwUk8g7L+sp19mSO6KYBABUizde3yTr+yO6ZsWrGwqUBgAAAChPFJPIu6bRDXvcS2tPXNeR4/BwBABUh3QyI98b2ZOhzwd0AAAA2M/kdjoJMALNYxpUP6Zeg73xnMZbIx1x5iEFTgUAQPHU1EflBnb+wM06UqbOkXWN3ISvQGLnIjIQcIsZEQAAYFieZQIRCotiEgWRPW6cvDVtctPDz/6wIUfBYyYUIRUAAMVxwJwJ24vJbNSoe35UvbMjsmZosxMrKRD31fJcXHWr03Ido6NOOaikmQEAAIBio/pG3sXTab3emFF8aq384N73mvRCjjqPbtWdG1cVKR0AAIXnBhwd/875So8Oas35Teo6JCov5siPOvKiQ/9MtwS05eRarX9XvcK1Yb39/CNLHRsAAAAoKmZMIu829PXJOEYdp4+V/1iHapf1yWR8OTucAeAHjGzAqPPYURqY0yCTSstaK2M4SQsAUB2OPG++vhdeIS+y5+c2G3KUHBtU3/sb1TqmoYjpAAAAhudb3qOjsCgmkXdZzx9ao2aMOk8Yrb4jmzRv3Rq5y1Py05Ibkwbm1uv1cRNk39xPy775xV95AIBq8ctVi5XdSym5jQ0YbQ15WtHdqQObWoqQDAAAACgPFJPIu9G1Ndq2s+T7Zr2mzxzxjMKOp7pwevuYnmRY/emQvvDwKXqhbbwCjpHDbEkAQJWIZzL668a1yvWc7b50Sje+9pK+/rZTC5oLAAAAKCcUk8i71poajaur1QcPelz/NP9l1e9QSG7TGEmpMZLSD8/4sz7/0Oka3/zOEiQFAKAwlnS2y7e51pJDnmvbWKA0AAAA+8bjaBIUGMUkCuLfjonqkKZXdltK7qghktI1Jy9Sm/2XIiUDAKDw4tmMvBEWkyMbDQAAAFQ+qm8UxFFj71R9OJXT2NpQVhNjfy5wIgAAiqc+FFbAGdnLLJctTQAAALCfoZhE3hnbrYCW5jw+4GQVsb8vYCIAAIrr4JbRI9o72Ug6aeK0wgUCAAAAyhDFJPLO0WZlsiNcvuYnJOsXKBEAAMUVdF29Y+oBOc+CrAuF9aGD5hc4FQAAwMj41lTMFyoTxSQKIpXJjmh8OusVKAkAAKXxsbkL1RqNDTsuFgjo9CkzNKG2vgipAAAAgPJBMYm8a+9tkB3hFv4DSUcyPBwBANWjIRzRDaedo/E1dYoGdn/eYF0wpNOnHKDPHfa2IqcDAAAASo8mCHnX0R/Uii2jcx6fyjq648X58nyWcgMAqsvYmlrdctb79fnD3qYpdQ1qCIfVGImoPhzWSROn6nunnKUvHXnCiPajBAAAKBZfTsV8FdPXv/51zZo1S7fffvs+Xf+73/1Os2bN0qxZs/KcrPLs/uN74O8QDLi68aGjNXPsFtVH07JWWrxxjB5bPkX9ibBa6wZ1xsErNam5T5KUSId098vzdd7JvCkDAFSfsBvQWdNn6azps1TfGFXW+go7rrq746WOBgAAgBFatGiRbr755n2+fu3atbrqqqvymKiyUUwi7ya0NGjxxvG66fFDNXPsVv3ggaPUnwqrLxGRJBn5uvmpQzSxqU//+va/6oaHj5DrNsowWwQAUOUCjqOwG5DnsUoAAACg0jz44IO67LLL5O/jik/P83T55ZcrHucD6m0oJpF3kVBAR8+arF88nlLA8ZT13Z1+buWoLxHRa4mIPnHjexRwgvp/Zy8oTVgAAAAAALBbHqddS5J839f3v/99XXfddftcSkrSddddp5deeil/waoAxSQK4tiDpuj+l1bsUkq+lec78q2nww+cWKRkAAAU34AX1yM9T2vpppVK+xk1But1XM0RmltzoBwOfwMAAChbjz32mK655hotX75ckjR37lwtWbJkxP+eV155Rdddd50ikYhOOeUU/elPf8p31IpEMYmC+MuLy3MeG3BdPfTKGzr3mIMLmAgAgOLzra/ftN+lZ/peVsqmlbXe9p+91veGwk5IF409X7NrZpQwJQAAAPbkoosukiQFg0FdfPHFOvvss3X66aeP6N+RSCT0+c9/XtlsVpdffrn6+voKEbUi8RE98m4gkdLitW05j89kPf3hycUFTAQAQPFZa/XDTTfrid4XNOgndiolJSnhJ9WT7dMPN92sxQO5f6AHAACA4jHG6IwzztCdd96pSy+9VI4z8irtm9/8ptasWaOjjz5aF154YQFSVi5mTCLvNnf3a6Tn2MRTGVlrOQAHAFA1Hu97Xq8PrlTKpvc6Lu4n9JPNv9XVMy5XxAkXKR0AAMDwfPaY1L333qtp06bt8/WPPPKIfvOb36iurk5XXXUVvcdbMGMSeedbK2tHdo2VRnwNAADl7E+dDw9bSm6Tthk90ftCgRMBAABgpP6eUrKrq0tXXHGFJOmKK67Q+PHj8xWralBMIu9GN9SM+Jqg68hx+NQAAFAdNqa2aNBL5Dw+bTNa1P14ARMBAACg2K688kp1dHTotNNO07nnnlvqOGWJpdzIu6bamCa1Nui19e05jQ84Rm9fOLPAqQAAKJ6tmW55b9lTcjhxP1mgNAAAlFbWenqqe7l+u+UJdWb7ZSUFjasTm+bqnNFHqDVUV+qI2APfMp9tX9122226//771dLSoq997WuljlO2eIShID588kLFwsGcxkZCQZ1z1NwCJwIAoHgcOdIIFwKwbgAAUI3aU736xOIb9J21f9KqRLt6Mwn1ZRLqTA/ojrZndMlrP9Ef254tdUwgr9avX69vfOMbkqSvfvWram5uLnGi8sWMSRTErEmj5QWsbGrvb7SspIaWmJpqo8WKBgBAwY0Pj5Yrd0TXNAcaCpQGAIDS6MkM6nPLblJnZmC3P/dlNeAl9ctNj8l1XJ01amGREwL55/u+Lr/8cg0ODuq8887TaaedVupIZY1iEgXxhxcWKxnw5IUk9819/3csKO2b38iGpY5UXC9v2KIFk8aVICkAAPnXEmxSg1+nQcVzGm+SRmeOO7nAqQAAKK6fbnhQXXsoJXcU91P65cZHdHLzXMXccBGSIVceazpGbNOmTXrhhaFDDW+//Xbdfvvtex0/a9YsSdK5556rq6++uuD5yg1LuZF3vrW648XXlfGt/LCUqZUytVbJUb4SYzwlW31lYlaZGskGpYFUWr968qVSxwYAIK/cP9XI5tZLysaNwitjhQ0EAEARxb2Unu1dOTQpJQcpP6u/bH25oJkAlB9mTCLv1nX2yPOHnn68iFVqTFZ+SEM1uNHQdElfcuNG4TZXTtZoeVtHCRMDAJBffd2D2vJAv7xkSO470jJ76BytL6nfKPO/Ud094UkdvHB6UXMCAFAoL/atVmYEB8GlbVb3bX1Z7xlzZAFTAYU3YcKE7TMm9+T666/X9ddfL0nbxwaDuZ3TUW0oJpF38XRG1lplajylJvjaZYstI8mRvDqrRDSr6Lqhh6Hn+3IdJvECACrfmhVt8j0r/96w7FZHgfNSUsTK1A793GYlJSW7zlX2xojU5Wh9ig/pAADVoy+bUNrPjuiaAS9ZoDRA8RhjVFNTs9cxO5aQw42tdhSTBRCPx3XuuedqzZo1uvTSS/XpT3+61JGKqiYcVDboKzVuN6XkjszQUu7E5KzqN4YoJQEAVSObzsq+uXrAPhtU5tmAzIGezGxPJmJlu4z854JS7w7PfTbXxW4AAJS/sBOUa1z5NvdyMmSoKMqNb9ljEoXFb30BXH311VqzZk2pY5TMpOZG9Tekct7B1DpS7Vg2OAYAVI/GlloFgq6U2PYdI7siILtizy+9QpH9c/kOAKA6HVw7SREnoIyXWzFpZHRYA1uaAPsbpqjl2cMPP6zf/va3pY5RUikvq1TMV86Hd7nSlshgQTMBAFBMU2eNUzCc++e/gaCrE886pICJAAAortHhBk2KtOY8vtYN67wxRxUwEYByRDGZR11dXbriiitKHaPklmxtk8357LUhcS8tyxI2AECVcByjM847XKEcy8lwJKhT33NYgVMBAFBcH590qmrdyLDjwiagQ+qmamy4sfChMCK+dSrmq1gmTpyoZcuWadmyZTrvvPP26d/x6U9/evu/Y39HMZlHX/7yl7V169Z9fmBWi7aBkc9+tJKy1s9/GAAASuSM9x+h8VNaFQju/eVWtCas9//LSapr3MPR3QAAVKiZNeP1/6a8c6/lZMQJ6qDaifrctLOLmAxAuaCYzJNbb71VDzzwgCZMmLDfz5psCkU1wgmTkpUChocjAKB6BEMBXfG9D2vmvEmqqd/1DVkwFFBNfUQf+OQpOuWchSVICABA4b2tabaumfVhva1xturciOoDUdUFomoIxjQh3KyLJ52hrx34AQWdvZ2cCqBacfhNHqxbt07//d//LWOMrrrqKtXW1pY6UknNiNTLZCSb66PLSqEuyRhO+wIAVJdwNKQv/N+HtO6Ndt39qye0/NX18j1fsZqITjjrEJ307gWK1Q6/xA0AgEo2JTpKX5pxrgaySfWGBuXJV3OwVrWpaKmjYRh+zodHAPuGYvLv5HmeLr/8csXjcf3jP/6jjjqKzXoHN/eodllKffPCUi4fenlSyyNbZf/JUk4CAKrS5Bmj9an/fI+am2vkuo48z1dXFwe/AQD2L7WBiCY3tvztuTDFcyGwv6OY/Dtdf/31evHFFzVjxgz927/9W1H/201NsbIs8jpqw5pw32bFp0xVtkF73zDAk8KbpdYXtqqpKSbHYTl3OXMcs/2fzc01JU6DfcE9rHzcwwrl90jJ38r0PCrfJmWcUWqu+aAUOk5iK5OKwu9g5eMeVjbuX+XjHgLYEcXk32Hx4sX64Q9/qEAgoGuuuUbhcLio//1AoDz34Bg3bbSiWxNqvXmdtn5gsrJNkox2Lig9SXaolBx7w1JFHVfBIA/HSmGMkeuWXymO3HEPKx/3sDJY68n2fU1K3iPZhKT09p+ZzAuSiUgN18oJH126kNgn/A5WPu5hZeP+VT7uIQCJYnKfJZNJff7zn1cmk9GnP/1pHXzwwUXPkM16ZTljsr61XpNnT1D/E8sU6M+o54ypSk1whwpKRzKeFOyQImtSarpnpaI9KZ3xubPleZzKXe4cx8gYI2utfH+kJxyhHHAPKx/3sIJYK/VdKqWfkFFiNz/vk2yfbM+l8ur+VwofV/yMGDF+Bysf97Cycf8qX6XeQ9fdP1c4eLb8OgdUF4rJfXTNNddo1apVmjdvni6++OKSZOjujpfkv5uLsy97l1YvXiet7FJkZZcyE+qUOKBZNuzKHcgounSrAl1Db9LCjTU64YMnsNdWBRjaG83I9y33q0JxDysf97ByhPw7VeM/KcfsppTcgbF98vs+p27nLsnEipQO+4rfwcrHPaxs3L/KV6n3cNSoulJHAKoSxeQ+eOyxx3TzzTcrHA7rm9/8pgIB/t/4VvNOnKsTLjhOj9zymBL9SYU29iu0sX+XcbH6mD52zUfUOLqhBCkBACicqP8zOSa3DxH97KDCwbuUMhcUOBUAAABQPmjU9sE999wjSUqlUjrzzDP3Ovb73/++vv/970uSHnjgAU2cOLHg+crFR772ATWMqtc91/1ZmVRWqXhq+89i9VEFIyF9/Fv/qIVnLChdSAAACsC1K5RNdSkQyW18IJBR2Pu1Ug7FJAAAKB++3T+XsKN4KCZRMMYYnfOZs/SOj5+mx29/Si/c+6KSg0nVt9br+A8cp0NOPljOfrpPBwCguhm7Wb6XHn7gDrxUtxQsUCAAAACgDFFM7oOvfvWr+o//+I+9jlm4cKEk6ROf+IQ+8YlPSJJisf1z36hwLKxTPnyi3vf/zpTrOvI8v6L2EgEAYKTa1nZpQvPIrkmnslJtYfIAAFBqg71xPfz7Z7T8+VXKpLMaPalFJ7z3SE0/eFKpowEoIYrJfRAKhRQKhXIaGwwGVVNTU+BEAACgnLRtbtLY+pGdYtmxJaLGlgIFAgCgRLysp1987Q96btGrSiUyyqazQz94aqWe/ssrqmuq0f/7vws18YCxpQ2K3fI5lRsFxjpaFMXqtVv1m9ue0fU/f0S3/fF5be0cKHUkAAAKJmvHamNb7h9M9qeCuv1XBxYwEQAAxed7vv7nEz/Vk/e8qMHexN9KyTcN9ia0Zc1W/fdHr9e6ZZtLlBJAKTFjEgX1yuIN+umvHlNvf0IDA0OH3ziOUSwa0qSJzfrkx07S+LGNpQ0JAECeTZk1Xv/94EJ9a/pf1RAcfq/J3mxITyVm6WNFyAYAQLHc8/OH9cYr65ROZvY6bqBnUP97yc/0v/d9kXMIgP0Mv/EomEceX6Zv/eA+bdzcs72UlCTftxoYTOn1ZZt1xdf/oFVrO0qYEgCA/NvoxvWwM1XXr5qvvsyet3/xrdSRiurC587U0uP2z72oAQDVyfd93X/zE0olcjsMLjmY0it/XVbgVADKDTMmC2TZsv37L9S16zv1s5sf12B8709C/f1J/fe37tH3rvmQohGOIgUAVIcnO9bLDxr9aNUCrY/X6d9nPauaQFpNoaHnxYxvNJANaUlfi77w6gnalKyVidgSpwYAIH+WPrdKmdTeZ0ruKN6f1N0/fVgLTjyogKkwUr7YYxKFRTGJgrj1juc0OJgafqCkeCKtR/66TO847eACpwIAoDhSvieZoRfy92yZoXu2TNcRTW06pmWTagJpbU7U6p4t09WR+tssSUsvCQCoIu3rOpXI8T3hNls3dhUoDYByRTGJvBuMp7T49Y1/+4Zv5Q6kFBjMSL6VdY28+rC8aFAyRum0p7v+/DLFJACgakRSARlfsts3zTF6tnusnu3e84mjxitKNAAAisI4zrbP6EZwDbPzgP0NxSTyrq29T8YYyVoFepIK9qUk3+40AdxNZiVjlGqNya8JaTCekrV26DoAACpcczws+SO4wJfC7Wz9DQCoHhNmjFakJqLB3njO14yfPrqAibAvfMt7dBQWr4CRd74/tBYt2JlQsDcp85ZSUpKMlYxvFe6Iy33zYByWsAEAqsVgKq1Im5NzOWl8KbKFl2UAgOoxY/5kRWvCO33PSrKOkXUdvfXtX019VO/++ClFywegPPAKGHnX0lwjvz+pwEBKZpiy0VirUGdCjm/lMG0fAFAlJrY0KrLFkZvU8OVkVopudBSybjGiAQBQFMYYnf2JUxWpCcsayauNykZCkoZW1yngyq+LyoaGFnLWN9dq1mHTShsaQNGxlBt519RYo2B3QukdSslYTUazDupSJJJVf39IS5e0KJt9sxf3rSZFI6UJCwBAAcyaOEpGUv1rAfXPysqLSvatr7r8oZmS0Y2OIm2u6mOhUkQFAKBgTjzvCD390Gta/PhyOfGh1XTb+Z40kJCMkdNcq8/d8M9s7VWGfMt8NhQWxSTyrrdrQE5maHrI2PEDuvBjS3ToEW1yjOS4vrysI983WnTfFP3uV7M1MBDS1lUdJU4NAEAeJT3J9aSsK7fTkd8s2agv7TAp0iSNTMoo0OdInq+sct+DCwCASrD2jTa99sRyKZPdZXsvaWiLL1krvyeuX37/fv3rf19Q7IgASozqG3nX2dYnOdKsgzr1vz94SMefvEH19RnV1mUUi3mqq8+ooTGts89bqe/++AG1tCaUzXjb96YEAKDSPbd+raKrBxUfZ5VpNEOzQZKunMG/fRnPkQJGA5OsshGrAbev1LEBAMir//vcr+Wnd19K7sT39dI9L2prW08RUgEoJxSTyDvjGNU09Ok///sJNTSm5ezhURYMWrWOiuuqbz+irDJi1j4AoFps3LpVXkTK1tidZknulivFJ1rVPp8pSjYAAIqhs6NPXavbhy8l32R9Xzdee29BM2HkfGsq5guViWISeRdsCuv957+u2rr0sGNdV2psTOnwo9eznwgAoGo4cavuQ6Kywdye26xrlBjNHpMAgOrx51ueGjrkJkfGSosffa2AiQCUI/aYRN6tHmjXySdukPvmDJGk7+gv3ZP1QM9Exb2AmgIpvad1tY6q2yLXSDW1WX3gQ0vlWyuHchIAUAVqG2rk9ef++a8NGvXMiRUwEQAAxdXX2S/r25xnTEqSn84WLA+A8kQxibwb7Fkq22RkrXRT2yz9umOmUr6jpA1uH/PC4CiFjacvTnpexzVs0ejWuBK+J8flIQkAqHzRlhrZTSO7xkZYyAIAqB6RWHhou64RHCVg9rQPGICqxW898q4x5EhG+s7G+bqpfZZ6vfBOpaQkDXghdWaj+q+1R+qB7gkaesbySxMYAIA8GzW2Uc5we0u+RSjMh3MAgOpx3DsP0R4PHNgNK2ncgeMKFwj7xJepmC9UJopJ5N3k1ul6Md6qu7umatDf+35ZA35I39ywUF2ZkAIue2sBAKrDtPomuaERTBGR1FrH8yAAoHocOG+SInXRnMcb19FHv/DuAiYCUI4oJpF348bM1Y1bZ2tgmFJym7gX0PVr5xc4FQAAxVMTSasmOvwhcNsE3KxmTt5SwEQAABTfR754juTmUDsYqXlis2YtmFL4UADKCsUk8u6N+CatStbnPD4rV09kRxUwEQAAxdWR2aS5U9sUcHPbxD/gempqppgEAFSX489aoLM+fsrey0kj1bTU6erffaZ4wZAz35qK+UJlophE3i3telHp7Mg21kp7AWV8r0CJAAAoLiurMc0Dmj5h+HIyFMzo6Hkr5DgjW/oNAEAluODSM/TZ739UTRNbhgpKxwydMeA6CkSCOuH8Y/S9+7+oSE241FEBlAC7rCPv4gNJOcaOaPNZK0mepxGfFAAAQBlqCDTLNa5mTdmsaCSlZWsmyvMcZbyhl15GvgIBX3WxhBbMXK2aaFphw+oBAEB1OvT42Tr0z7PVsbFL65dtVjqV0bjJLZp00AQ5nMQN7NcoJpF3U0Lj5QSXylfuJWPQeHLd4PADAQCoAKMCExQxMSU0qBljtur8KUtVl3D0fPs4xTNBjYsN6JBxm/WS36A1mRoFTEhH1p1a6tgAABTUqAnNmjVvklzXkef56uoaLHUkDIMl0ig0iknk3dRRU9TQk1SnanIab7PSAbF2OQ5/4QEAqoMxRsfWvUPP9f9c/9LymmpMVuEmqzPHr91p3BF+h5YkG3RH/xzNjx1borQAAABAaVBMIu8G7RLFX2qUnZOVCeWwX5ZnlHqjRv7BPtP4AQBVY1Y4rCNDS1S/lz0mY46n+ZEeNbmvKGTYWwsAAAD7F1og5N36jX3qX1cv//Ua2fTeZ0HalJH3dIPeWDxF1uZ2cikAAJXA976sWmf457aw42tyqE8D2buKkAoAAAAoH8yYRN5taW+U1C27Jio/aeTMG5QCdvvsSWslpY2UcOW9WCf1BeQZKx6OAIBqkfHbNDbQqVx3KQlIcrzvSMGzC5oLAABgJNhjEoVGE4S8M2qV1WoZSXZLRN6WsNSclRmTkgla2ZQjuzEsDfzt4Wet5Fs7guNyAAAoX32ZW9XsSMrxtbxjpNFurzgCAAAAAPsTiknkXdA10k5bSxqpKyjbFdTedpx0OfwGAFAlMnarzAif1ozJYV9mAAAq2IDXp47+VfKNpzqnQVE7SmakT5gAqgrFJPIuGgzIGinX91dWkjV2aIk3AABVIJNtUNZxFHT9nK9hqRQAoFptTq/Vg71/0Ob0WhljZWXlGFcBhXRU7Wk6vPYkuYZ6ohzx+gSFxuE3yLtoXUhOypPd6/zIHVlFOjIyzJgEAFSJzWuPkp/rOm5JvpVe62ssXCAAAErk1cGndHPHt7U69ZqSdlAJP66kn1DcG1Cf16WHe+/QTR3XKmszpY4KoAQoJpF3tU5I0c6MfFfDVpNWku9aeVE7grdvAACUt7b+Ab0y2CQ/x8/oEr6jP/dMKGwoAACKbH1qpe7ruUVJG9/jmIzS2pJep9s6ry9iMgDlgmISedfghjU4yVXN+pT8wNC8ybe+L9u2fNsP+GpYkZTqAizlBgBUjb5kvx7sHZPTWGulQT+gLZlYgVMBAFBc9/f8bq+l5DaestqQekNdmbYipMJI+DIV84XKRDGJvGtpqVUm5sqPWdWvTErWkxew8tw3vwJW2aCVSXpqXJpU74GuHN+Rw1JuAECVaK5r1JG1nTltamKMVOt6anLSBc8FAECxdGXb1e115Dw+YeN6ov++AiYCUI7YXRZ5lwpYBTxHHQtDanw9IyftKTnK0Y575jpZq2inp665ASVbAwr0B0sXGACAPJtWO03z6rbKffO5r70tqjt+f6BefG60MmlXDY0pnXXOGzr+pI0KhnyFjacTTFdpQwMAkEcbUm8o7adGcIXVmtTSguUBUJ4oJpF3XYMJOTWurGvVPz0sWcm8ZVq1FzTqmR2SHMmLWrkpI8/35TpM4gUAVL65B/TISRtlMkb/e9URevG50RocDMr3h57nNm+q1ZrV9frxD+fr0//2oo49fpOOHtetkbx9AwCgnGVtRr68EV8DYP9CMYm8CwVcxWt9BeJGxhrJWoXW9ym6tEtO0pNXF1J8XquyrVFZ38pNGMWjnhzDUm4AQHVwnD65vqMrv3icXn+tRZm0u8uYZCKoZCKob3/zMHme0bEnDFBMAgCqRsytU9CElLa5P7tFndoCJsK+8C3v01FYFJPIu7pYWJ7vy7FGkdc71fznNTKprNzE0Kdl1kh1T2+W1xBW57kHKD02pnTMylBMAgCqhFVM9/9p+h5LyR3FB0P63rcWasHhT0oNRQoIAECBzQjPkWsCUo7FZEAhHVZ7QoFTASg3rJtF3nUkBuVmHNU9s0Utd6xUoCe1vZSUJGMlN5FVaMugRv9iicLrB2RSks+x3ACAKpG1B+l3v5kxbCm5zWA8pD/dfUSBUwEAUDxBJ6xZkQUyb9YOvjXqTYfVlqzVlkSdtqZqlPbdHcYHNT92bKniAigRiknk3Zb+AQU3DajhgXVyk3vfU8RNZDXq16/LxLNFSgcAQOGtWTmont5w7hf40u2/ZbokAKC6nNr4PtU6jepMxbQh3qieTEwJL6ykH9JANqwtiXptjDfI2JhObzhfIWcEz50oCt+aivlCZaKYRN7VBUNqeGj9sKXkNibtq/aFNllmTAIAqsSK1zfKH9l+/+rv50M6AEB1CZuoMtnpGsxG5cuR3amCMPLlKGMD2pxsUEtgeslyAigdiknkXYMfUHhN79++YYw0qkmaPU2ac4A0c6rUWLf9x07WV91TWziRGwBQNVYs36iRftzGx3MAgGpzf/fjWp3cKH+YcUk/re9s+IV8O9xIANWGw2+Qd/2burX9mWf8KGncaMkxkrvDPlv1NZJvpVXrpZ5+OfGsrOUAHABAdQjFQkMfzI2gbvQjfEAHAKgevvV1f/dflbLpnMYn/aRei6/QwTWzCpwMI8ESaRQar4CRd5s6e+VkfWnKeGn8GCkY2LmUlKRAQAoFpQMmS80NMllPWW+Ea94AAChTmclGXiy3g28kyXeNBifGCpgIAIDiWpFYo7SfyXl83E/qT52PFDARgHJEMYm8iyczUl2tNKpZCgzzpiwQkKZNlNyAHMPDEQBQHaKzohqcFJPvDj/LwEqyAaP+2SxkAQBUj/Z0p5J+akTXdGS6CpQGQLmiCULeGTcrTRwzfCm5jetIY1slDr8BAFSJUCCgjJtQYmxkr+WklWSDjvqn1CrTwOE3AIDqYYzRSBcBs2gY2P9QTCLvwtGgFIvkfoHjSKObZXk0AgCqxIKmiYo9vEqJ8VENTK2VF3Hlu0bWWslaWTO0fDvdGFLvQQ3ykn1q+OuWUscGACBvxoZGKeKER3wNyotvTcV8oTJRBSHvGpoaRn60qOPI4eEIAKgSXQ9sVHzeKPVNNkqODclOyypsOuXGe+TEe+QOdivc2qP0pIAGJrrqX9ik2FMsXwMAVI8ZkckKj6CYjDlRndlyUuECAShLNEHIu4ANS8ERfloRHNnJpQAAlLONKzZpcOFYhf2sJjy7XOEXu5Rpd+SnzNBX0iiz1FfT8+s1ec1q+UFHAwtGlzo2AAB5Y4zRWc0nKWJyKydr3ZhmRacXOBWAcsMu68i7uqYaOWHJjmCf40DMp5YEAFSNrVHJTXia9uc1Snc40u6WF1mj7IAr9/mMDnDXaPWhE4sfFACAAjqh8UgtS6zWKwNLldzLG8R6t1afnfgxGcNy3HJjWSKNAmPGJPJuzGhHE2a1536B8XXYO5bJdXg4AgCqQ2ReRNM3rlem0+y+lNyBl3DkPJ9W1CaLlA4AgOIwxujj4y7QGc3Hq9aNKrxD8Wgk1TghHRidqi9PuUSjQs2lCwqgZJgxibxrqO9T04Q+bXhlrHI6V80aTZjbJmstn5ABAKpCw/ge6UVP1nNzGp+NO5q8nsNvAADVKKvTGh7UMTWLdNeGGVqaGKWsHDW7cZ09fpWm1jiqcU+R1FjqoABKgGISedeW6NWyx6Ypp1LyTU/fPlfvO9SO6BoAAMqVt/kNWX8EF/hG/qvZguUBAKAUrPU0mLlId76e1W9f+oBSXlCD6YgkKeBk9dfAwTp47Hr9v7d9XBPqfiTXmV3ixHgrn/foKDDWziLvXl6SVHogOIIrjDYtGa2RvH8DAKCcOUkrmx3ZC3nrFSgMAAAlkvJ+qh88Wa8bnztWXYm67aWkJGX9gAbSUT2z/gD9vz+erXW9l8nyZAjsdygmkXdbVnkyZoQ1o2+VTTNTBABQJSLNMu7IjnVjNxMAQDWx1te9yx/TAytnK57Z88ncvnXUOVirL//lZGW8R4uYEEA5oJhE3tUaR8HwyD7pcgO+3EBu+3ABAFDuGg44ZMRFY8N8Dr8BAFSPjP+Ufv3igr2WkttYOeqKx/TSlt8VIRlGwremYr5QmSgmkXfRhi4ZZ2SzROpa4nL4ewQAUCUcM1pNRyYk5fZ8GIh5SpweK2woAACKaGnHWg2kQjmP709F9ZuXxhQwEYByRDGJvMuEt2rq/E1SjrtGBiMZLXzn68pkM4UNBgBAkfhtWbVPrs19fMZoqT++gIkAACiu1V2+4tnci0lJWtvdWJgwAMoWxSTyznMjWviOpYrU5lY0RmrTOmDhRjkOS7kBANWhxYspdV9QyvEky6zjaPSf2WsZAFA9rEbLjmwhnXw7kkNUAVQDiknk3diGuWocO6gzL31CkZrUngcaXzWNcb3vigdlXVFMAgCqhpdKK9SR+37LTkqKvNxfwEQAABTX+LqDFA2O7EO3MXVNBUqDfWWtqZgvVCaKSeTdQZMOUFtbo6bMa9P7v/ygJh+8WZGalAKhrBzXUzCSUaQmpTnHrdGH/vs+1TbG9cwLs+U6PBwBANUhaK2cEU4TCeS4HyUAAJXgsAnjFXIjOY+PBdP64CHHFDARkD9f//rXNWvWLN1+++3Dju3q6tJ3v/tdnXfeeTrssMN08MEH6/jjj9ell16qRx55pAhpy1ug1AFQfcY31+uRO96m88ffo9bJvXrvFx/RYE9EG5aOUjYVUKQmrUkHtykUGfr0LDEYVH/vqSVODQBAHlmrUNBXKpv7p/ehEJ/0AwCqh+s4OnfOobr5peeU8oafhBIN1ultU6cUIRnw91m0aJFuvvnmnMY+99xzuvTSS9Xd3b3T99vb23X//ffr/vvv17nnnqtvfOMbct39cxUpxSTyzhijk+ecqaefX6WjD3tN4ZinmsakZh29fpexyXhQ9zx0tC469ZQSJAUAoDBGTaiRG/Al5f4Cs75hL9ufAABQgS5cuEDPbNig5Vvbld7LDif14aC++Y53KMAqurLjs0R6Jw8++KAuu+wy+f7wh/1u2bJFF198sfr7+9XY2KjPfOYzOuGEExQOh7VixQr94Ac/0HPPPac//OEPam1t1ec+97ki/C8oP/zWoyDmH9Kplnm9euTpQ9TfH1EqsfMbs8RgUF2dtXrwmQWae+I6jRmTLlFSAADyb+oBbUpkc//810qae1JPwfIAAFAKAdfRd88+S8dPna76cEiBt3RcdaGgxtbW6n/fdZZmjx5VmpBADnzf13e/+11dcsklymRyO+j3Rz/6kfr7+xUOh3XTTTfpgx/8oCZOnKhRo0bp2GOP1a9+9SudfvrpkqRf/OIXam9vL+T/hLLFjEkUxLL0HxSOpjXt2E16fctEdaxo0qTWrQoHMxpIRLSlr1kT57XpwGPWSzJamrhTR9Z+stSxAQDIi97+PnnBqJxEPLcLjNFjyybrI4WNBQBA0YVcV185/RR1xuP6/atL9Gp7mzKer7H1dXrP7NlaMG6sjGFWHsrXY489pmuuuUbLly+XJM2dO1dLliwZ9rr77rtPknTWWWdp1qxZu/zcGKPLLrtM999/vzKZjB5//HGde+65+Q1fASgmkXeDXocSfo8kyU8bDbzhKOOktaGxTnKsbMwo05bSwKqAooekZIzV+vSTOlIUkwCA6vDnR/tlIyEpkZG8jLS31T6OZGIxDa7dP/cVAgDsH1piMV24YIEGUil5vlVzfUwhh+e+csdp19JFF10kSQoGg7r44ot19tlnb5/puCfb9pQ0xmj+/Pl7HDd58uTtf2bGJJAnCX/oFzDR6Wr1mlYFD/QVrdvxHZmVe5RVbyKmjgdqNPv4DtmIlW89OYYnJgBA5Vu9OSRjpfpzszKJlPofDsqmJJv+2y46Tq0vJyKNujShjl/XyOsrYWAAAApoY3uPfvuXF/Xca+vkOEZWkpHUUBvV+acfquMPnSHHoQBDeTLG6PTTT9dll12mGTNmaMOGDcNe09TUpCeffFLpdFrW2j2OW7du3fY/19fX5yVvpaGYRN65Jqhs3GjNplaFDvAVDHlqCCQUc9NyZOXJqN+NqD8QkQ51tOzxUTrklLgMW54CAKpEKBpRdGZKo87vlVvrq/WjCfX+JaT4M0H5KaNAs6/Gd6cVnZ+VrBSe1qY3vjCx1LEBAMi7Z5es1bdvflgD8ZTeWs/0DiT1g989poefX6Er/vntCri8J0T5uffeezVt2rR9ujYUCu3157fccsv2Px922GH79N+odBSTyLs6d7zeeCGswEGeRtf0q8ZJy8hq2wFrAUmBQFxNwbi63Rp1z4uqZ0WjzNF8QgYAqA7jp7Roy7guubVDKwbcGqn53LSaz93NYW9Gcms9RY7KbSN1AAAqxRsbtup/3ywlJSk2Kq6asYNyAr7SAyH1rGpQIpXRqys26zu/flj/9pFTSpwY2NW+lpLDefHFF/XrX/9aknTEEUdo5syZBfnvlDuKSeSdY4PKjDcaX9+riMnIdXadtrzte41uXKr3teWNumLHBACgYDb1b1FoRu5Fo1tj1fru7gImAgCg+H78hyc0EE+p8YBuTT5+owKxjNyQJ+NIXtqR9Yy6ljVr3aMT9dzr69Xe1a/Rzbw3LCc+e0wWxKpVq3TJJZfI8zyFw2F9+ctfLnWkkmGeNPJu+ZrlqmtKKqysAu6e91KQpIDrqyGQlHNAb5HSAQBQeP2mQzY4smvCzbuZTQkAQIXa2jOgdZu7Nf6oTZrxjjWKtiQVjHpyXMkYKRD2FYx5GjV/qw7+yGtK+XHd8fArpY4NFNzKlSv1j//4j+rs7JQkfeUrX9Hs2bNLnKp0KCaRd72JLtWHkwoG9nYE6d+4xld9KKlsNlvgZAAAFIcbNRrx/AImJAAAqsjilZsVmtChcUe0KRDx9jjOca3CDSkdeN5yPbtk3R7HAdXgueee04c+9KHtJ3B/6Utf0rnnnlviVKXFUm7kXSCaVcDJrZSUJMeRaiMpuS4ncgMAqsP4xjF6w3Mk5f586KX5vBgAUD2S6awmvG3dTqVkz0BMPf0x+b6jSCijMc09cl0rx5UiTUmZelbSlZu9HCiNEfrjH/+oK664Qul0Wo7j6Ctf+YrOP//8UscqOYpJ5F1dc63s4AinfVjJypcR5SQAoPJt3JKRN8lRMJrbeC8jtb0wRppT2FwAABSLqetVMDK0Km7T1kYtXT1RGd/IcX05juRnHb2ycrLGtXZr7rQNCkY9tR62ocSpgcL44Q9/qO985zuSpGg0qm9961s69dRTS5yqPFBMIu/64rnPDtnGyshawzI2AEBV6O8aVEfXKE0+fqNMLs9tRmpf3FLwXAAAFEvzpLScHk/L1o7Tho5mNY/pU11DQrLa/r4vm3HVvqVej754kI5bsFQNExIlzQwUwle+8pXtp2+3trbquuuu0/z580ucqnxQTCLvtvZFpREepJZMhuTk9M4NAIDyl2hPKDwzlfsFvqNQDYffAACqhxswau+uV1tfvaYc2C7X9Xf5sC4Q8DVhcpfiAxE9veQAvfPQLaUJiz3ymT30d7nmmmu2l5JTp07VT37yE02aNKnEqcoLmxkh7zq9lNZtHCU/x4mT6bSrF5dMk5frBQAAlLmWequWA7pzmy0pyQn4Gnckb8YAANWjzm3Qqi2jNX5ylwKBXUvJbVzXKlabUH1rXMlErLghgQK6//779dOf/lTSUCl58803U0ruBjMmkXcJE9dzK6Zr8oQORSKZYcenM0EtHxwlywcxAIAqMf6ArNaO4ONf40hNU/sKFwgAgCLLxLvVOHpAgcDwE1BcV6qpS2rVpqh0QBHCAQWWTqf1ta99TZIUCoV0zTXXKBqNanBwcI/XBINBhUKhYkUsGxSTyLuWqKOOcEwP/XW+Tjn+FYXDuy8nrZUSibDu/suRSoxxOPYGAFA1Dpw7Tut6R3aMpXFYOQAAqB4vd61S6M3Db3LhOL4GgjwXojrce++9amtrkzRUUuZy+vall16qT3/604WOVnYoJpF3zbEa+RGrld2tSvzlSB17xGtqbBxQIODJdX1ls448z9WmzS164rmD1NkSkjW+jGFnAQBAdYhkR++0J5O1UudgrTr6a5X1XEVDaU1o7FE09LcP7yzPgwCAKtLhD8oxuX9I5zhSqHYE+zOjKCxLG/fJyy+/XOoIFYNiEnlX4/pyZJVtstowUKs77z9GtZGEJozpUiCQVSoV1LrNo5RSQMnxnmzYKmQ8+bJy2VgXAFAF1qwPSE1Dz2nruxr1+uYJyvqOMt7QSy8jX8vbxqohmtChk9cqEsiofUsjy9cAAFUj7NRI/ghXDxQoC5BPEydO1LJly/Y65sorr9SVV15ZpESVjWISedcYSiriZhT3wvJqreIHZJXZLKUfCclJuvJqA+pf6Muv97ZfE3Q9ORrZkxYAAOWqvSehzfEmJaKO3ugYray/80suK0cZz9HWgVo9unyWjpryhlavHCsdV6LAAADkWX3bKNmmFZIzglmTAwUMBKAsUUwi7ybXNMg4kjwptD6u1t+uU2RdXCbtS56VDTpqud1ocH6Dtr5/sryGoCbW9ohD4gEA1cJkfC3fOEHpUd4upeRbRiqVDerJ1QfI7Q8XLR8AAIXWvKVBqrM5tw6+J4UedKVjCpsLI+OzlBsFRhOEvLMar2mt7apZ0q2J/7NUNa/3yx305GSsHF9yU77cQU91T3Vp8leWKNo5qKmjO2UMf+EBAKrD7OYWJWrMMKXk32SzrhRk5QAAoHrUhmIyzw/ts5yTpBR7NlrQTADKD8Uk8q7b69PUVJfG3LBa7qC3x3HGSoHejMZds1x1AU8252csAADKW7QxLCeU+8mi1jHyGziJFABQPWYdPl2h3welnuHf5/kJyf7O1/zDZhUhGYByQjGJvPPkqeNn/l5LyR0FBrPquV+y7DEJAKgSnW5CITe358FtnNpsgdIAAFB8reObNKF5jDJX+LIdVjax6xjrSXbAyr/VV+SxqM786InFD4q9srZyvlCZKCaRd+FUTL2PZpVrz2gTUtvP03IMD0cAQHXoHHxVzgg2+5ekcIhiEgBQXT747+9WLB5R5lJf3g2+/PVWtv/Nr14r/8++Mp/35dzraM5RMzR6UkupIwMoMg6/Qd4NrE3JWqOcm0lJmXbJWss+kwCAqjCldoOCjiONYNJkYzBVuEAAAJTAtDkTNfm4TVr2YLP8R638R998j7jD20UbkOqn9+mUDx1cspwASodiEnnXvrlHNj2yYtJLG1nfyrgUkwCAyje3Oaj6QFw9mUhO46NORh+ZvKTAqQAAKK6bf/UNrRs9RQOH18pZH1dqVFSZlojkGDnJrCJr+2QjIaVmTNDTW76hg7zfyXFZSQfsTygmkXdOOCRPZkT7BFj6SABAFcn2HaCPTfi1/mf1ERr0QsOOj7hZvb1hbRGSAQBQHL7va422qK19jhItIdlRtUMzJd9cJefHghpoikhWchNW9z95nCa4v9Xb3/EPpQ2OnVjerKPA+CgCeec2R+U15955WyMlFjTIOPyFBwCoDs880agzm9foba0bFXMzex3bEEjp2oMf1kOLphcpHQAAhffww3do8bIDFQ+FZF1JjtleSm5njOQYeVGjuBfUK5seKElWAKVDMYm82xpNqvec0QrEclvK7dZIvee3ciY3AKBqZDJWf3zgQF0z7xFNNAMyvpX8nccY38rxrC6Z9JIOb27XXX+aVZqwAAAUwAsrlmjQhIdah+HOEnCMkm5Iy7dMK0o2AOWDpdzIu3SgXQvOGtToZQNa8mRMqYS7x7GxOk9nf6JTN06fJt/6csyexwIAUCkmTGnRT1Lj9PD9U9SxrkEtaySvNqvEBEd+QArEpdpVvlI1Ad2w9XAtT9epv46XZQCA6tHV5ytjAkOlpLFqntytyYduUqQuLRkr33PUtqxVm5aMUSYRkozUGa8vdWy8BUu5UWi8AkbeRaIJXTLjVc3/+VZde9lkPfdQnQZ63Z3+QgsEfUVrfH3g0+06+xNbldm4XJx7AwCoFtMPHa+tT9drzfIGtT6VkclYOVbSCztPm/TdtFLrHd2Tnaup799YmrAAABTAgN8kGSlUk9L8dy1VIJJVMOztMMLTxEO2aNycdq15dqK2vD5GSQ2/LzOA6sJSbuTd+Kin6bE+BYLSoWcaBaZOVKC1TgoEJNeVgkEFRjep7uDxmnOcr5Bj9d4xb8gXzSQAoDrcsvJPGlhRr9an0nLTb5aSu+F4UrjbV/PzGfVkaosbEgCAAmptHqdgJKMF57yuSH3qLaXkEMe1CkY8TTtyg8bMaqehAPZDzJhE3jW7Ucla3fKrWbrtlpnqqQ2r+6JmJQ5wZI2RyVrVvZxV8qmsvnLFsfr3/3haMxd0KWmt6CYBANVg9WC7apc4ctLDj3V8Kdzlq+v1WumMwmcDAKAYps4JqH36egVj6WG3mAyEPU07ar0GNvEhXbnxWcqNAqOYRN41Beu17OUW/f73M7XqnFolp7jyIxo6hU2SZNRzXFB9RwTV+3RA3/z6kfrfnz6oyGg+HgMAVIfBuFWkzcv58zYnI8WW+cMPBACgQoyfKLVkeuTk+DbPca0mLGgrbCgAZYcmCHn3/Nr1uu2XB2rleXVKHODKj5kdSsk3uUZ+zKj3mKDWHx3Tn+6eMfxJbQAAVIimrlZpD8u3d8dIinRQTAIAqkdHuFvGzf3J0A36qp/eW8BEAMoRMybz4JFHHtFtt92ml156SV1dXQqFQpoyZYpOPPFEXXjhhWpubi51xKLasK5Dz4dGKzXBkQ3uvWy0EaOew0P6w28O0FkXJhQORouUEgCAwhmlUVpnt47sohEUmQAAlLu+rCvHHdmHbiMdj8KzvD5BgVFM/h2y2ay+8IUv6K677trp+5lMRq+99ppee+01/e53v9MPfvADHXrooSVKWXyDnZ7aj47IhnObAemHpfZDI3IVLHAyAACKw3VSYksmAMD+rDedkQ0YjeSTN48nT2C/w1Luv8O3vvWt7aXkqaeeqt/85jd66qmndNddd+lzn/ucYrGYOjs7dfHFF6utbf/ZK8NraJUXG8ETimMUn+WKz8YAANUiMi41ovPcrCSviTdjAIDqUe+Olefn/tzmW6l7oKaAiQCUI4rJfdTW1qabbrpJkvTud79bP/zhD7Vw4UI1NTVp5syZ+vjHP66bbrpJgUBAPT09uv7660ucuHgyo2Ia8Xo0Y+W4BYkDAEDRRYOesi0m52dD60ru9GxBMwEAUEyt7U2KJ0M5j896jvoWNRYuEICyRDG5jxYtWqRsdugNxGc/+9ndjpk3b55OO+00SdLDDz9crGglN7p+jMwIS0YTlFjzBgCoFmOdMaqdnZQfHL6c9B3JazaadcyaYkQDAKAoarYEtfrhscqkh39zmM0adXXUK/IY23uVG2tNxXyhMlFM7qP29nZFIhG1trZqwoQJexw3ZcqU7eP3F2P76mRz/2BMkuT6vlyHhyMAoDqsDQ7oknc8rewcR37QaE8r2XxXSje4Cs30ddpBq4obEgCAAnL9rDpTzVr8ylSl91JOZrOOentqtfiFaUq18J4Q2N9w+M0++uxnP6vPfvazGhgY2Ou4tWvXSpIaGhqKEassNIfCiobSGsyEpRx22HLkKxZMy/pWxuVTDgBA5Rs7ZqlOGL1Oq45fot8F5yrd5irWnpHJ/m3+ZKbGVWJMUPWtKf3gI3ersXHvrykAAKgkq0MbZCNS25YWDQ5GdeCsDWpq7pc09C7RWqNs1tWqN8Zqw7rRkoy2zh7hDBcAFY9i8u9UW1u7x5+1tbXpoYcekiQddthhxYpUcv01SY22A1rXFcrhVDUr1/XV0jAo41BKAgCqw7jooFxZfeqUZzS+sU/XP3Sk0jN9ZWqtjPHlZ10Fu43mNHboq+c+oEnNfYr7jhKlDg4AQJ5sDfXL813JSAP9Mb343EwFgxnV1iXlOL5SyaAGBqLacTJLpoGDB8oNS6RRaBSTBWKt1ZVXXqlUKiVJ+uAHP5j3/0ZTU0zGlN9fEoGMr9BKTxMae7Sxp1Ge3dN0fKuA42tKc7dcY9XYHJNrmLpfzpw3y2PHMWpu5sS8SsQ9rHzcw8owx8yQTQ/9ecbsNs1uXa4t6ZhSvitrJNdKETerBbUdamj4Wx3JPS1//A5WPu5hZeP+VY4+Py7ZyE6L6DKZoLq79ryPpO9zX4H9DcVkgVx11VXbD7x517vepaOPPjrv/41AoDw/TYoFh6bf14QzmtLSpfb+WsXTIckOndVtzNBzU10kqVF1Awo4Vr6VXNehmKwQxhi5LLuvaNzDysc9LG9jgqfKT31Hi3rH6RedszTov/km7M2nOc9IgzaoR/vHanGiSVdPfEapZEC1Ls+DlYLfwcrHPaxs3L/yN9gdlHnzPWCuTMLI5bkQ2K9QTOaZtVZXX321brzxRknSzJkz9dWvfrUg/61s1ivLGZMTQ9r+qVg44GlSU6883yieDspaI8exioXS2nHltiMr+ZInvySZkRvHMTLGyFor3x/JSwyUC+5h5eMeVoYlz6c12DpavxjYoZTcDV+OOrMR/cfGwzXhMV9f+izPg+WO38HKxz2sbNy/ylHv1kh+UspxPo3JWkV7fXleeT4X7q+FKb9lKDSKyTxKp9O64oor9Mc//lGSNGPGDP3sZz9TTU1hpqJ3d8cL8u/9e/UmVitoPHk7PAO5jlVdJL3b8dZKdW5SnZ19MqY8Z4FiSHNzjVzXyPeturoGSx0H+4B7WPm4h5XhoVdX68Wp85RoGf4DRCuj3mxIKzaO08Xc07LH72Dl4x5WNu5f5RhTM1HRrmWKt7rScLNbrZXxpEDKlO19HTWqrtQRgKq0f1b+BdDT06OPfexj20vJuXPn6le/+pVGjRpV4mTFt7azUyfULR9qHHPgGk/H1a0UH3gCAKrFVtuvwWjuH7bFbVDhI/sKmAgAgOJ696HzFRm0Cg5I8vbyZs9amaxUu8HX5Cn73/tnYH9HMZkH69at0wUXXKBnn31WknT88cfrl7/8pZqbm0ucrDQ6e7N6W91qTQz3DFtOOvJ1ZO1aHVm7TjbHIhMAgHIXmWQkZ2TPa25ztkBpAAAovmmjm+WFHNVstop0Wpms3bmg9Ie+F4hL9WutArI67cg5pQsMoCQoJv9OK1as0AUXXKA1a9ZIks4//3z96Ec/Ktjy7UqQ6OuUkfTJsY9qWqRTYZPZzSiriMno6NrVOq/lJRntdFgbAAAVbUxNRGW4DTQAAEXzypY2+WPDkmsV7ZIaVlrFtliFuq1CPUNlZf0aq7r1Vo71lYo4emD9mlLHxltYayrmC5WJPSb/DuvXr9c//dM/qaurS5L0mc98Rp/61KdKnKr0/GBKkhR2PH1q7KNam2rWop5ZWp9ultXQQTdzYpt1csMKjQoOSBraUDeT9eS6PCQBAJWvKVsjayQTzv0akypcHgAAim19T6/iIU/OWKPYZivHNwr3S+H+nVcUWMcqG3Y0ON5oQ09vidICKBVaoH2UyWR02WWXqaOjQ5L0xS9+UR/96EdLG6pMRMKtMmboycYYaWqkS58Y+5SixpUjyZOUsNmdzt8eyIQ1IcoEXgBAdRjtt8qkjVST23JuPyMFnohJxxQ4GAAARZapc9Qfsop2DC3bltGbW34ZWVdKthilG4xYagDsnygm99Fvf/tbLV68WJL0zne+U+9///s1OLj308P2l+Xdx0yZr6c6J+m4MSsVMY5anZBCxpGRGXoOkmQVVtxm1emnNZB19Yd1C3TFIcFSRwcAIC8mTBmlgSdjqqkblBMYvpy0MqptaylCMgAAimNSY4NiwaD6U2n5YaPBiUbGs3JSkrFGfkDywzuXkRMb6kuUFnvEURAoMIrJfXTjjTdu//O9996re++9d9hrli1bVshIZWN03Tzds2Sejh+1QeNDrlxj5HnS5k21SiQCqqtPa/SYuGpNQFHX1ZK0FE82ljo2AAB50zi2Tm2DjZrQm1W4IbXXctJLO+pY3KKG0/bPQ/MAANVp/tgxigaGisltnLqs3LEZyZFM2pHfG5b8oXKyLhTSRw49pFRxAZQIxeQ+6Orq0rp160odo2xt6k6opS6lsW5E8X6jO247QH++a7r8N59wrJVqajN67wXLdPo71mmcG1LC56EIAKger/ZukukKadOT4zVm4RZFW5Jygr7MDruW+Bkj6xt1vDJKA1tq9Jp6SpYXAIB8M8boHw6ZpxuefV6Z2n5FJg7KuFYmYGWMZD0p4g8o0xNScl2tasNhHTZxQqljAygy2qB90NzcvN/MftwXa/u36APjVqprU0RX/OsJ6u0Nycu6O43p7wvrJ9fN1713zdCV1/5Vh47fJM/35TrsMwkAqHxLN3dIvpWyRn33jlEiklHN/F6FJiVkXCs/4Wpwcb2Sb9QoFXNk6owy7aVODQBAfr1v3lz9qec5tYUHZN6yesC4knGtQi0pheuz+sqsc+Swz2TZ4bRrFBrFJPLOBvo1M9ujL/zrSerqjO5xXCoZ1NrV9frGvx+rf/3fZ9jrGABQNbJpK+tJsQ4rJyv56aD6/9q6yzgjKdwvSb6yTTwRAgCqy5M9yzVY1yXj73lLE+NITtjXDe336v9aPlq8cADKAtPTkHfTmuK65/cz1NsTGnas5znatLFW615qkCPekAEAqkOjN6Bgv+RkNeyzm7FD5aRJs7s8AKC63LjxEQ36qWHH+bLakurRisHNRUgFoJxQTCLvalIBPXTfZHne0PLtbMyo49iwVn20Tis/Xq/VH65T9/yQtm0rOTgQ0u2/ninJL11oAADyqH/FOoUGhi8lt7NSuCNTyEgAABTV6ni7erODOY/v9xL63ZYnC5gIQDliKTfy7oGX22WtkZW05bSo+g4KyQ8aKTD09izTJLW1umo/IarRjyTU9GpaG9bWyTJnEgBQJTq3ZmV8X7l+BmwkBRI8CwIAqsfK+BYl/PTO3/Qkp9dInmRrrWx012tQXiwLOlBgFJPIuw39SWV9o03viqn/gJBsaNc3WjZkZENG7SdHZQNSeHlWyXRa0XC4BIkBAMivULRRxmuT3OHHbmN44Q8AqCKe9eW/2WqZPqPwEwEFF+9QQVjJb/GVOiGj7AxfMkPXANi/UEwi71pGh9UxJaqB6cHdlpI78iOOOo6Lqn5jWuEQD0cAQHVoijRJdmSzPjj0EgBQTVpDdXIyRmaTUezWkEzCyLzlyc7Z4Mq9zVFmpqfEe9JqcmMlSgugVNhjEnl33MGz1X5ETH4kt4eXHzJqe1tUjhnBtBIAAMrYG5vWj/hVllfHLBEAQPU4KDRB2mQV+11ITtzZpZTcxqSMgktdRe4KauaG0UVOieFYayrmC5WJKWrIu78uTyjdOIKS0TXqmxaWtVbG8JcJAKDy9TpJZZo8BXuHng+tkZItUrpRso7kpKVohxTcdiaA5yk+h8NvAADVY+lzaxW9KyQnMfwndSZjFFrqavXajdLbixAOQNmgmETePfjKa7KNI7zIkRKZjGKhUCEiAQBQVM3jW+ROWyrn6Ub1tQSUGDtUSO6452SqWXKyUv0bVi2xuHoa2GQSAFA9tmzskdOf+8QTkzbqyiYKmAhAOaKYRN75vSmpceTXeZmMRDEJAKgC7zj+CGV67tYD2QVKpF1ZdzcHwQUlLyj1zJYik5Oa1B0vQVIAAApj89aekW2g7BulQ2xrUnZYIo0CY49J5N28WIs00r+7PKk2xkbHAIDqMH/CaL3UPU1xP7TbUnJHNmDU1tasI6b3FikdAACFl2xKSt7IrrFMnQL2OxSTyLuTjlugQGdGynVFmpVqXuwpZCQAAIrq1a4HtaW9+c3128PzfUePdY8rcCoAAIpnIJaWAiPcpiTCjElgf0MxibwL18fV+td2ye7wJORJgX6jYI+RO2B2Li19q/pNyZx7TAAAyt1TbZvkZUfwMssatW9tLFgeAACKbrwzss3jXF/m4FTB4gAoT0yURt45wY165zkrdPujIfUfNVrRzY6C3W95c+ZIqVZfiTG+Jt2yUvM+lJaRr51OBQAAoEJ1xgOydmSnbHsenxcDAKrHwOKUnCkp+Usjymmvr5BkooMFz4WRscwgQoFRTCLv+vu6NPeYNj3ywhFyXnUV3Nin2Asb5Ha+uam/kdITGxVYOEGRzTHNOF067vDX5flWAXpJAEAVaA3NkLR4p++FnIwm13cp7GbUk4pp40CTRr4pMwAAlSGy0Sh4SL9SG0LSwDBv9MK+AkcNyr/Hly4uTj4A5YFiEnnnWVePPTlPvfFaNdy7WG5PQk56512Pwyu3KrS+W+mpzXrluNk6smel7JgSBQYAIM+iNiqTtrJBaXS0T/8w+ym9bfwKSUNVpJU0mAnr1uVH6L61B8uzrgLpEZ4QAABAGRtfU6cXXgyr7mM9yvy8UUoaKfPW1QFWilq5R8aVPsRT8GY+sAP2NxSTyLuxjRP05FNzVH/3ErldCZndzP02kkzKU+iNTknSH5qP0/vmsIQNAFAdlr28VoHurA6YvUX/dcydqgsm5Lzlaa4hnNS/zH9Yb5+6WP++6L2qfXRQ+nBp8gIAkG+zZo/Xjf56JXqTGvPZTtmXQso+ViuberN8tJI7KyXnhLgytY42Pz1GEyd1lTY0dsVSbhQYxSTyLpkeLXdpp9ze5G5LyR05WV+hNV3qfWOcjKGYBABUh4FlXZrdt1Zf/uj9aggn9zguGshqekO7vnL8nbrhB0cUMSEAAIW1qrdLNmDU316ndDykUQd1KnpUp2xWUlayQclaR53rG9SzskEKGXXOrS11bABFRjGJvHvu9S5FX9kkk/VzGm9SniIvbVTW8xVwKScBAJUvGAzogre/oLrQnkvJbUKurwNq2jXlwO4iJAMAoDg2J+PyQ0Pv71IDIZm2gOa1rlZjy6Akq6znaskbk7WpIybZoVmUqYZQCRMDKAWKSeTd2tVrZUawT5aRFNzYK2t9SRSTAIDKN3d+RAcd2b7L8u09qa1P6+0fWVbYUAAAFFGkJiwlpFgkqXNOfErRSEqRUHaHEVkddtBKHXLgGj3xymwtXTNZxmGPyXJjLfcEhUULhLxzEsPPDtmVle/nNsMSAIByd+jRm0f8Kmv6AvbVAgBUjyMPm6moSei9pzyuhrrBt5SSQwKuVSSc0dsOeV0zp6xXS4D3hMD+hhmTyLvaUGRoGuQIua6b/zAAAJTAzFZH3fGR7RYfiLC7PACgerQMPK9jD1ummmhSw02EDIeyOm7+6wo5yyR9qij5AJQHiknkXWN9s4w7sjdXzhhXxjBFHABQHULuJCkwsue1SJBZIgCA6jEw+JoOPGBzztuaBAKeph+8qbChMHJ8booCYyk38s6tlaa9O65ct6KwYUdHf7hLlqXcAIAqYXWswoFdl6ztiecbOYoUMBEAAMU1WNevYCD3swcCAV/twfoCJgJQjigmkXctY3t1wSeWy20cvpm0jhSd6Ok9F7wuY3g4AgCqQ8ZbKpuV/BxnGTjGKpXMvcgEAKDcBWpHy7Mje4+XtsECpQFQrmiCkHdTJsbUMiqlD/9gtdTg7Hm/yeDQEu7P//JluUEjY5gxCQCoDptWrlMq5SiRDQ5bTmZ8R+39NcqkeR4EAFSPUeMOk3nLOmDPGsW9oAa9kFJ+QPYtz5Gu6oqYEEA5YI9J5N3Epgna0BbR7+98p9KHBRXs2iDzRo9krORrqA53HfkzR8k2jtZPbhyvz3zmD3IcHo4AgOqwdmVaPfFpSk8I6G0NqxR2sgq+5QM430pZOdqQbdSrifE6IruuRGkBAMi/MaE5Mv0BWddXyg+oK1OjlD/0ns9qaP6KI6v6QFwNgaQkaULsmNIFxm7ZXPdoA/YRTRDyLpsZp4ceOUT9/VEp5Co+4wANHCvZZEJOxpMfCch1IqrZYhVISe3tjXrqydma/K5SJwcAID9Wr/OlOTWKhDJ6NDFDo91+HRDaqpiTkWRlrVGbV6fV6RYN2rCckNVDz87SnOmlTg4AQH7UumOVcaKKZ7PqzNQq2RlW14PNiq+skZ81CjZl1Xxyp7IHGQ36EY0J9mpy6ORSxwZQZBSTyLsnn31Rf31srrJy1X2Qq0yNkQ0aacdp+b5VcpQU7fBl10Z0zx+O1PvPspzMDQCoCoHJG+QFt61PM2r36tWe2Lah/7Z5In/jBq3CB7PHJACgevRlu7UpGVUmLa372WTF34jJTzhDBw1ISm+REmsickLjNfGidbIzpOcHX9D48JwSJwdQTOwxibxbs7xTqWRAXXNcpeu3lZJv4Qx9PzHaUd9UR4OpqDLpdPHDAgBQANlgRk5gT5tL7v5DODfCHpMAgOqxPLlYqYyjVd+ersHXa+XHA9tLyW38REDZ3qDWXTdFPW/UaUn8+RKlxR7ZCvpCRaKYRN7VxGoVH+0qGzOSs/cZkDZglBzlKBMzclwm8AIAqoPvNcmOsGd86wEAAABUsqWDb6jjz61KbY7IZvdePfjxgNbfMFmpbLxI6QCUC4pJ5N3hh83X4ERX1s1tWbYNSIMTXAUCboGTAQBQHJn4QSPeLL6/raZAaQAAKL6BdK96Hm+WzeRWO9iMo/5XagucCkC5YYoa8q5hYp2cmC/vzWn6ASerQ6eu0iGTVysSzGggGdGTK2dr+eYJsjKSjLKj2FsSAFA9vOYm9Wyo05hZ3TmNT8cDWnb/VOmowuYCAKBYBl6vlc325DzeT7rqWtQqnV24TNgXvFdHYVFMIu96Ekn5jpE8q5MPelWnzH1FAddTJPi3Tf0PHLtZGS+g3zx5vJZtnigTsvJ8X67DJF4AQOXrjQ9o7V+mq+nTLysU2/uhNr4nJfvC6n4+WqR0AAAUXmZTWH5qZO/vMt3BAqUBUK5ogZB3SX+DfBm9+9BndPq8l1QbSe1USkpSLJxWQyyuC497SPMmrVbWGjmcyA0AqBLhTFap1xy9fNtMpeN7/hzY96REb0R//e6hCieTRUwIAEBh1fv1GulbvKBhw2Vgf0MxibxLa41mjNmkow9crmgos9exsXBaFxz9uOpqB2WtV6SEAAAUVoPpVCCb0Zb7m/TEDQvUubZBqcGgsmlHvmeUSbhKDQS15ukJevCbR0krMgq6nMoNAKge82Z2yIRHVjROmdpTmDBAnn3961/XrFmzdPvttw87NpPJ6KabbtL73vc+HXrooVqwYIHOOussffvb31ZPT0/hw5Y5lnIj77Zu7Nc7DnlBsVA6p/HhQFrHH7xYvrU05QCAqjBpXEDPuFbB/rQGngnq0ZWHKzo6oZbpfXJDvlJ9QbUtbZbptwp1xmU8q5pmZkwCAKpHyzRfmYiRm+NB2zbqyx6b23tIFBGTWHexaNEi3XzzzTmNTaVSuuiii/TMM8/s9P2VK1dq5cqVuv322/XTn/5UM2fOLETUikAxibxLbfU0/sCenMcHXKsjJ6ySfEfiYG4AQBUYNa5JtaMTSvWHFEhkFdjYJ7/D1ZaVjbKO5HhW4figjD/0aj8Uy2j+uatLnBoAgPx5cNWg+o711XC/kZPa+5pua6zStUaL6xqLEw7YRw8++KAuu+wy+X5uK12++MUv6plnnlEwGNSll16qd73rXQqFQnrkkUf0P//zP2pvb9fFF1+su+++W7FYbMR5Tj311BFfkwtjjBYtWlSQf/dbUUwi79K1KYWdkS3LjjhZGccTzSQAoBocMOooHf6Bn+qh/5uv9ODQRv5O2pOT3v3zYyiWVeuBI38xCgBAuVrb1qiBmV1ytki1r1i56d2Xk9ZYZeuk9vdKsSSH36A8+b6v73//+7ruuutyLiVfffVV3XPPPZKkK664Qv/wD/+w/Wfvf//7NXfuXJ1//vnauHGjbrrpJl188cUjzrVx48acxxpjZO3up8C+9WemiGeAUEwi72KjIgo5vvZ+BunOIm5WWWuoJQEAVaGhpkWRyQ2adcZ6vXLXdJm9PCm6UU+nffl5zWi8ongBAQAosKjXoJC2qucER+kWqekJK5OR3ORQ4eG7VjYoJaZK3adIfkTKdvGOsOywlFuPPfaYrrnmGi1fvlySNHfuXC1ZsmTY637+859LkiZOnKjzzz9/l5/PmTNH73nPe3Trrbfq1ltv3adi8ogjjtjrz19++WVlMhlZaxWLxfT/2bvvOLvqOv/jr+8559bpLZNeCAkJkNB7F7CgIigL9oZl1XV1dfXnuiu6rAV17b2hAorI0mwoIkV6DyGV9JAymcn0mVvPOd/fH0NCQtq9eO+dmZv38/G4j4wz3zPzlntnzj2f8/1+vgsXLmTatGnU1NSQz+fp7Oxk0aJFdHZ2YoyhpaWFV7ziFUXn+EeoMCkl1xyvw1qgiAK7ZyDm6uUoIiLV455Np7EtGCB3iEvN+hQmtDj+yLt7C1jP4Cdd0gvreGDNQiZOnDa6gUVERErosoXH8VDHGnAgdQSkDofYZohutTg++A2Qmg02NjI+QsD0zfHRDS2yF+95z3sAiEQi/PM//zMXXngh559//n6PsdZy3333AXDOOefgunsvup977rnceOONbNq0iRUrVjBv3ryisl177bX7/NpXv/pVHnvsMWpqavj4xz/OG97wBmKx2F6z/vWvf+XKK6+ku7ubTCbDF77whaJy/CNUCZLSG3Coa4G+Aoc7WCZZQ2B9XKOp+yIiMv51pYZZscLgDNbAZIf0pATRnhzR3hzGt4Rxh3R7gjAx8ib1maWz6Y4/zqmTp49ychERkdI4cl4biWcg3WbBMWAgO3XksTdBzuWDp6lEIWOPMYbzzz+fj370o8yePZtNmzYd8JhNmzYxMDAAjMyw3JfDDz9858dLliwpujC5L/feey8/+9nPiEQiXH311Rx11FH7HGuM4eUvfzmzZ8/mkksu4eabb+bkk0/mta99bUmyHIg2QZaS6+1PMtWCV+CcbxeYEEIYVK6HgYiISDmt7N6O6QnBPv9WyxhyLTGGDq1jcF49wzNrdxYlAQhh67q+UckqIiJSDlu3/Z2apRFMxjngcmCTg/iSCA89s60y4aRw1oyfR5ncfvvtfOc732H27NkFH7Nr78epU/dRjQfa2tqIREYmaBVS8CzUNddcA8DrX//6/RYldzV79mwuvfRSrLVcf/31JctyICpMSsklko3YgRhzvOCAxckIlmMiPk88MQXPUz8RERGpDhu29e6zufjeGAy2r7BG6iIiIuPBsxu6yQ1FqP97DGfYQG4vg4KRomRiWYT4hghL1tdVPKfIgcyaNavoY3p7e3d+XF9fv89xjuNQU1MDsHOGZSksXboUY8wBe1C+2I4i5qpVq0qW5UBUmJSSm3zoMDf/fi5teYcjIz5xLJHdCpQj/7vOhJwQ84mkPe5fOoHQ6oJMRESqg/VDTLHN4tVcXkREqkjedwlCB3fY0HpThIa/e3jbDU4aTAacYah5xqX15ijJ5SOTVPKBShRSHbLZ7M6P4/H9907d0fdx12P+UalU6iV9zx3F0Xw+X7IsB6IGDlJyyajD4mfqeXZ9E4cd1sNZ8Ty9oWFb4OADMWOZ7IbUOjCU9/jjX2YRrRkqZq8cERGRMc04DtZQfHFSRESkSjTUzybuPYzf64I1JDe6JDfufZWcxeIT0jhFJQqpDvva7KZSpkyZwvr167nrrrt4wxveUPBxv/vd7wA45JBDyhVtD7odISXXGsbp6q7jnZvO5dl0HRnr0ORY5kcDFkQD5kZCEgbSocMfe6byee94Vt47GcfRUm4REakOE1prd35sgdCFfAJyNS88/Nju7ZBssvI5RUREyuXo+cdgh0b6Sx5oEooBIpmQl5+0oBLRpAjWjp/HWJJIJHZ+fKBZizu+fqCZlcU4++yzsdZy1113ceONNxZ0zDe+8Q2efPJJjDG88pWvLFmWA9HtCCm5/k0BXSdFOX3OKu7MNrIkjHNcfIBpXgYDBBiezSV5OltHj2c4bFYHW5sasaGPcfSSFBGR8a8pmSCIARmLnxjZifTFt4PDyMjDyYHxLflarR0QEZHq4fshJmcKXhlnLeSG1WNSqsOufSUHBwf3OS4MQ4aHhwFoamoq2c9/97vfzU033cTg4CBXXHEFDz30EJdddhlHHXXUbgXQoaEhHn30UX75y1/y6KOPAiOb9bz1rW8tWZYDURVISm7L9u00njLIzPoeom5ITxjlr6nWvY6NuQEnTVnPzWcfg7WOlnOLiEhV2NQ9gN/qY3u8kaLk3k5wz38ujFqceEjgaCGLiIhUj6ef2UjohxS6UNNguPOuJbzuVceUN5hIBcycOXPnx1u2bOG4447b67iurq6d/RwnTZpUsp/f1tbG97//fd7znveQyWS4/fbbuf322zHG0NDQQDweJ51OMzAwsHPDRmstLS0tXH311SSTlVvKo3fAUnKLgzTHT36OmBsUND7q+Bxx6BYC65c5mYiISGVsGxoiEgvApYD1a4bQMbjxyjUZFxERKbctm7dgbXFTT3p6SrcrsZSIHUePMWTChAk0NjYCsGzZsn2OW7p06c6PDz/88JJmOP744/nNb37DiSeeiLUWay1hGNLb28vWrVvp6+sjDMOdX7vgggu49dZbmTZtWklzHIhmTErJ9ZKiNZYpeLzjwJzWTgJjiJQxl4iISKU4niXsiezeRHJ/QoNrwvKGEhERqaCuratH1mebwouTfk7nQqkeZ511Frfddhv33HMPn/zkJzF7+V246667gJEZjvPmzSt5hnnz5nHNNdewfPly7r33XhYtWkR3dzcDAwM0NDTQ1tbGcccdx/nnn1/xguQOKkxKyUV8p+ibFY6xuCEjM0tERETGucBmCy9KAliD7dXtORERqR6t9bniZrFZSyS//01CRMaTiy++mNtuu421a9fy61//mre85S27fX3ZsmXceuutALzjHe/Ya+GyVObPn8/8+fPL9v3/ESpMSsk19yfxi61MBmDUW0tERKpE90AKip30EarTsoiIVA+n0cH4IdYt8DovtDh+rryhpHhFLseXF5xyyim87GUv46677uILX/gCnZ2dXHLJJcTjce69916++tWvks/nmTp1Km9605tGO+6oUWFSSi4xWM+wbyFa+DF+n4uZUL5MIiIileTb4MC9JUVERKrYtgnNeP1ryLfUwIGKk2GIk/EZPCRWmXAiFXLVVVdx+eWX88wzz/DDH/6QH/7wh7t9vbW1lauvvpra2tpRSjj6VJiUkpvS2MZDj9Uw6cyBgtqJ5PMO637diLlSV3AiIlIdZrc2c4/ZVNQxTqSwTeNERETGg7wbIdPmEu9MkW9NYl0H60HoGjBgQnByFhOGONkAry9F76ENox1bpKQaGhq4/vrruf766/n973/PmjVryOVyTJkyhXPOOYf3vve9tLS0vOTvv2N5tjFmt012/tFl2y/+fuWkwqSU3LzjZ3Pd+xtpPS5FtPbAO20HQy5b/pTA+byWcouISHWYO6MVL5LD9+MFjY9E8ngNavgvIiLVoyleR+9xSSb/qR/SWXJTaka+4Dw/IcVaSBqc4ZBkR4rehTVEEromlLFv6tSprFy5suDxkUiEt7/97bz97W8veRZr995Hb1+fH4tUmJSSa5nczNBwE4t/kWThu1YTrdl3cTI7EOGRry6gfWGigglFRETK65DkBM5cuIiHFh1NOrv/4qTrBExs7iUxqULhREREKuCs6Ufx09qVdJ3cSHTI4AQvWiH3/PK6oNaj/+gmhmcaFkzWUu6xxoyf+tZB6YQTTijq82ORCpNScmk/S9haT8eTLtnBOEe+ZRWJ5ixe0sdxIPANQcalb10dS66bS6orQW7WgWdWioiIjBeZdIbTj1zN1OQAv/rruQSuw177mwQhtSbN5Rf8mcWbp1U+qIiISJmsSfcQy+bxhqJ7FiV3YTA4eUvNlpDeY7R6QKQY1157bVGfH4tUmJSSW7p+HdYdOfH0rmrkvs+dQM3EYVrn9+IlA3IDUbY93UJu4IXdcbIdDtZaTCFNKUVERMa4m5/4DfMPMzz51TlMGOyhf04dmQlRdkw6MBZMaKldm6K5Y4DFyUOYf+mGUc0sIiJSSn35FM5z+y9K7mAwOFkY6NKu3CJj0bJly7j22msxxvDFL36xpN9bhUkpuc7BfmD3+d7DHTUMd9Ts97ggCPA8vSRFRGT8S2f7WHzzLAa74ni+peWpAULXkG/wsM5Is//IgI8BfCIs/sNMZp27FVpHO7mIiEhpdG4ZxMkWPt7xDbklIbyufJnkJdBSbgG2bNnCLbfcosKkjA9uzMMxll0n4SdaMrQc1ocbD8gPe3QubsZPR3Z+3WBxXLfyYUVERMrA85OsvGMaof/Cuc0JLLGe/F7H59IeS34/g9ccVqmEIiIi5fXoqudwAqCIRXFun1bQiRxsVJiUkpsx1cV1QkKgYcYgR7x5FbWTUrixAMe1BHmHMO+wfXkjy35zKJneOA3TB4s5X4mIiIxpTamFhMGDBY+3gcOaeyfBv5cxlIiISAV1bx/GGIst4krPzWt6nsjBRoVJKblktI+athSRmQMc874Ve+zK7cVCiIVMOm47zXP6eejLR9M+vxsIAc2aFBGR8a8mupbQd4o7KNQtOhERqR5ONkcs5jOcLXyn7YZYpoyJ5CWxen8i5VXkO2aRAwvzeWxoOOa9exYld2UciDfmOfkTT7NtcQtWN8dERKRKNDeuw3GL21nUcbQTqYiIVI8zD+sg4hV+bkvGsrzprKfLmEhExiIVJqXknl2f5ZBXbiKS3HdRclde0qdhxhDG6OUoIiLV4fCjHJwiT2uT5veWJ4yIiMgoWDgVLjxpGVGvsOvCRNTnvOPXlDmViIw1qgRJyXVv7aXtiF4KrTNG4iGzXr6JvL/3DQFERETGmwn1RzPt6E4K3coyVpvjjDeuKG8oERGRCnLjSd523iJmtPVi7P5nTiYiOb74zr9CVMuGxxw7jh4yLqkwKSWXqM0RFtkqMlrv4+jlKCIiVSJmDudl71hKvP7AN90cL6B15gAnHa4ZkyIiUj02dk2hbyiG81QKrz+H8UMIX1Q98kNMLqB2TT8DG11WdLeMTlgRGTWqBEnJTWmbii2yMGk9i+Pp7piIiFQH1+nhuBkDvOzTTxKpy4PZ+218JxpQNyPNxZ97iEluTYVTioiIlE9HZhLX/egIurclqF07RP3yPmJdadyUj5P28fpz1K4bpGFJL/52w7e/diw/e/So0Y4tIhWmXbml5NaGCUyRNcYQg1GdXEREqkaEKI04h8EpX1nOmhsn0floExiLDUc2gHPjIbNe18G0l3fSG2nENWppIiIi1aOpboAH7p1CEIxc5wW1luGj82RmhlgXvAGL82CANwRYyGVdtt1VC28a3dwiUlkqTErJ9WUiZPMx4gU2OQ5DWNU3gUxrnoQXLXM6ERGR8vOZx8OZJD4uyYlZFnx4PX5mI0MbEwQ5h1hDnpqpmZ038obCCJuDidTonZmIiFSJ9X/pIp93sBHouCxKdppLGAeckZNfvg0ykx2cvKX9hhxs9Iivy41uaNmTejdKmWmKmpRcYyzJExumkR8u7OXlpx0WdU5RUVJERKpGxnpsDWK7fc6LhzTOHablyEFqp2V2W12QxeWxTHOFU4qIiJRP77Ys1oHNl8dIH+ISJs3OouQONmEI6h063hIjPd3BBKqCiRxsVJiUkju8sZ3ttyTofjKBn97/mu78kMPqXzWTfypCaHUSEhGR6tDjryWwxd1w6w66y5RGRESk8pKxgJ5zPPJtDkT2f10YJg3b3hgreq8CERn/VJiUkvNwaLgvxaIrJrH1rjpyAw7hi9pm+RlDrt9h1U9b2PirJpr/MoS2vhERkWoRkCOXC4o7JiysBYqIiMh4MP/YOIPHRbDRwq70wggMH62eJmOOHUcPGZf0Wy+l15nCBEBoWPqVdlZf3cKMS3qZcNowbsziDzls+mM9m26vJ0iN3BKLdWQxxe6YIyIiMka5QQ25AOJFHJNOFVfIFBERGctmndOEXZYueLyNG/IvK2MgERmTVJiUkuvZ1IfJv3C7Irvd49kftvHsD9v2eYwJLIEf4Hqauy8iIuPfs8uHCSYUvjAlCAwrlk+A6WUMJSIiUkHPDjZio1uKOiaV0KJOkYONfuul5FzPxfhhUceYINSMSRERqRorBjpZsWQ6vl/gRnB5jye3zipzKhERkcq5b8PqoisOoeapjD3WjJ+HjEuaMSkl19xaC9ayY7vRaJPP9Nf30X76MG48JD/osukP9Wy5o54gM3KmCvM+jqs6uYiIVIfeXMDSx2dzyKwOGpqGcdx9Nz7KZT0WP3oowZZkBROKiIiUlxOkcYwhLGJHm5infssi5ZbJZBgaGqKxsRHPK6wsePrpp/O3v/2tLHlUmJSSc7DYTBYSUQ7/t24mvWwQLxniRHaM8KmZvp057+nm2R+38Nzv6nFyWcIwxHFUnBQRkfGvoaaFwO/k9htP5fyLHqGuMUU8sftOcEFg8PMeix+dzbKnDsHsp3gpIiIy3jRag+tYwgJbKDsmpClaeE9KESnM0NAQN910E/feey+LFi0inR75PTPG0NjYyAknnMB5553Hq171qn0WKuPxOFOmTClLPhUmpeS8iEvE+Bz52V7aTknhJfe80PISFhKWw/55O8b4bP2/hJZyi4hI1Zhc04QB8rkIf/rtabRN7GPhiatoae8HIAgcVi+bysrFM8mkYsDOhQYiIiJVYd6MWjYHW9k2WE9oDzQBxRJ1A5xQN+lESunGG2/k61//On19fQBY+8LvmLWWnp4e7rjjDu644w5+8IMf8LnPfY4TTzyxohlVmJSSmzBzAhNflqP1xL0XJXcVqbPMeW8f+SWNKkyKiEjVcBIOOBZCAxi6OhpZ8vhsJk7fTiQSMDwYZ/2qyTuLkgAmoosxERGpHrapjeTARibUDNKfSpAOo3sd5xCQ8HwmNvUzmFZbk7HG6O3JuPWLX/yCL3/5y8BIETIajTJ37lwmT55MPB4nlUqxadMmVq9eje/7rF27lssvv5wf//jHnHLKKRXLqcKklFyyLsFhH8oSqSvsL5hXYznpv+rKnEpERKRyahscksks6YEEM+du5rjTVhCJBMSeX84dBIaFJ66mp6ueB+9cyNBwgtkLNo9yahERkdIJrEMu73DVYbfxTN8krtt0Iik/xmAQAwwxJ0/UCTixcT1vm/Yot/Ys4P7MvNGOLVIVFi9ezJe//GWstSQSCf7lX/6Fyy67jNra2j3G9vf386tf/Yof/vCH5HI5PvzhD/OnP/2JCRMmVCSrGvpJyaWCbtymbMHjHQ9y01aVMZGIiEhlxaJbmTVpM0cev4pTzllCbX1mZ1ESwHUt8USeSdO6efVlD9BQM8TCI1ePYmIREZHSmhpt4/WtT9PkpjindTX/Ouse2mMDxE2emMkRd/JcNHER75v5AG2xYS5re4pkNhzt2CJV4Re/+AXWWjzP4yc/+QmXX375XouSAA0NDXzwgx/kBz/4AY7jMDw8zM9//vOKZVVhUkquL7WVwH+hw7G1sH57K3cuPYI/PX009yyfT+dA/W7H5LNZQltgV2QREZExbmpNnEiHZcGxa4nG973DqDGQqMnxioseYXBJY+UCioiIlNmU9XWc0rieQT/OhxZfypdWvYLVqQlkbJSsjdLvJ/nN5uN5z1Nv4e6uQ4mYgFMGNo52bHkxO44estNjjz2GMYZLLrmE448/vqBjTjvtNC688EKstdx1111lTvgCLeWWkuvZ1A/P78C9fPNk7l91GEHokvV3bsvNyq2TiUdzvOLIxUxs7Ce0FqwBtZkUEZEqMNjpMPOsLUQT+y5K7ioS9+lfr75aIiJSPbYM3klT3uP/rbyIrmwNIe4eY/LWIx94/GD9mTjGctacJaOQVKT67Njs5oQTTijquFNPPZVbb72VLVu2lCHV3pV0xuTb3vY2nnvuuVJ+SxmHzHAjGHh83SzuWXk4qVx8t6IkQMaP0peq5dYnj2djdzO5Pkd3OEREpGoMbjck2zIFj48kA6ae1lHGRCIiIpWVy23jN1uP22dRclfDQYwfrD+DWDxXoXQi1a2lpQUY6R9ZDN8fuane0NBQ8kz7UtLC5GOPPcaFF17IL3/5y1J+WxlncpE4yx9s5fF1h5B7UUHyxbJ+lNufPobl19VhHE2XFBGR6jBstmKc4u64xRsL788sIiIy1oUmzgM9sw9YlNwhF3rc1zu7zKlEDg6nnHIK1lpuueUWrC38Pekdd9yBMYaTTjqpjOl2V/Iek5lMhquuuoo3velNrFu3rtTfXsaBRHMdj3XPJetHCxqfC1yWe/NGlnOLiIhUgYF8pvj2JLo/JyIiVaQzfhi+LbzkkA0j/KnriDImEjl4fOADH6Curo4lS5bw2c9+ljA88MZS119/Pffccw/RaJQPfOADFUg5oqSFyUsvvRQAay2LFi3ioosu4ic/+UlB/wGkepiIYbitruDxoXXpX9iI62gvJhERqQ5DwzUEtrhKY25Y50EREake7dNcMmFx21oMB4kypRE5uEyfPp0f//jHtLS0cOONN/L617+e2267ja6urt3GpdNpHnroIf71X/+VK6+8Esdx+Ld/+zeSySRbtmzZ66PUSrr5zZVXXsnFF1/MFVdcwapVq8hms3z961/njjvu4Etf+hKHHnpoKX+cjFE5LwD3+c1sCmQ9CK3FMZouIiIi41/fcos7M0qckT49/WuSrP2/SfSuqAVrcGMhU8/rYtorOonWB+R9h2WPtYEmioiISJVors1iQihi0iR1iXz5AokcRM4++2wAcrkc1lpWrlzJpz71KQASiQQ1NTXkcjkGBwd3LvXe8e+Xv/xlvvzlL+/1+xpjWLZsWUmzlvzW/DHHHMOtt97Kxz72MRKJBNZalixZwsUXX8z3v/99giAo9Y+UMWZrZqDoY7SIW0REqkku0cdTKw8hNRDhkU/P47HPzWXbw83k+qLk+iOkO2Os/u1k7vvQQjb8qQ0/cFmSmTPasUVEREpmQX0tNa7/os9aIk5AzM1jXnwVaC0nN/VWLJ8Uxtjx85AXdHR00NHRwcDASH3GWrvzkUql6Orqor+/nzAMd37+xeP29Si1ks6Y3MF1Xd73vvdxwQUX8N///d/cd9995PN5vvOd7/DXv/6VL37xi8yfP78cP1rGADuULWq25PNHYbGowZaIiFSDfKvPqnWTsL9wyXd42GDPe8HWd8gPOTx73VSeWTUTf1phvZlFRETGg0mJY5mWfIplwy1EHZ/Dm7aysHUTnhlp9WaATcONPNE1ne2ZOhq8HG+ekhnd0CJV4uKLLx7tCAUrS2Fyh6lTp/KTn/yE22+/nS996Ut0dnayYsUK/umf/on3vOc9fOhDHyIS2f+uzTL+BH15jBtgg0JfXhYnmseEUOCGbSIiImNa/dQETX/tJrM1inOAVttB2sM8aIhepOVrIiJSPfrCbVzYtJZt+RivnbWYmOvjObvPtjq0YTtTa/pY1tPO8m1T8SKbRimtSHX50pe+NNoRClbWwuQOr3rVqzjzzDP59re/za9+9St83+dHP/oRd955J+94xzsKKk5edNFF5Q8qJRF3I0TIkjNuYTMnDdRsGCY0tvS9BUREREbBzNYcyRW5AxYldzB5y8zntpc3lIiISAWZ8HFuWz2HCxcuJhnZ9823uOdzREsH4WCUgaE07dr/ZmwpejWkSHEqUpgEqKmp4T/+4z+47LLL+MIXvsADDzzAmjVruOKKKw54rDFmzBYmV65cyU9/+lMeeeQRenp6aGxs5Mgjj+TNb34zZ5555mjHGxXtM1tp+uUAXed5hJkoOBanNo+Jh8DIpjhhysWmImDBTWSofzSNa1SWFBGR6tD79EY8ayn0lptjwVup5kgiIlI91g5sYua0HpLRA68IiLkBR0zbxK9WHMHn2ioQTkTGjIoVJndYvnw5a9euxTy/+3I5GmdWyt/+9jc+8pGPkM+/8Ie2q6uLu+++m7vvvpu3ve1t/Nd//dcoJhwds5vb8HLQ2DJIKhfDNy7WgRc23LY4tT6m1idifVrNIOH8lp2vCRERkfFuoMOHsLj+JEFe50EREakej25OMmnCmoLHR1yfwUiBSw1EZL+2bNnyD3+PyZMnlyDJgVWsMLlo0SKuuuoqnn76aWCkINna2sob3vCGcdlnctmyZXzsYx8jn8+zYMECPvnJTzJnzhw2bdrED3/4Q+68806uvfZaZs2axVve8pbRjltRNrQ0XxLQ7RgGSYC755Y2OyZHBr5LbW2O4OhKpxQRESmf3GAzAYNFtSjJFdybWUREZOzryYZMcoKCx3uu5ZBJ28qYSF6S8TuX7KD2spe97B+a/GWMYdmyZSVMtG9lfwfc29vLV77yFW699VbghRmSb3jDG/h//+//UV9fX+4IZfGtb32LTCbDjBkz+OUvf0lNTQ0ATU1NfPe73+WjH/0of/7zn/n2t7/N6173Ompra0c5ceVs7Osl0pqiq2cKNrL/X4TQM6wdaOOoac9hrdWsSRERqQrZ9llYs7ioY4abtCu3iIhUj5yN4BbabPl5EbfwQqaI7N94WaFc1sLkTTfdxFe/+lX6+/t3/geZOXMmV155JSeeeGI5f3RZrVmzhnvuuQeA97///TuLkjsYY/jUpz7FHXfcQV9fH3/961/H1Vbt/6juYBtruidgo4UVGQPXYftAgtBaXBUmRUSkCoStNWTaPWo2+gWN92OG7oU1Bx4oIiIyTuS3+AQzHZwiipNhqoyBRA4ihdSgstksg4ODrF69mq1bt2KMYcGCBbzzne8sf8BdlKUwuWbNGj772c/yxBNPACNVWs/zeM973sMHP/hBotHxPSPgvvvuA0YKkOecc85ex0yaNIn58+ezdOlS7rzzzoOqMLkp3UHaFrE834EtuUZUkxQRkWrRPrGBvpMSxLcN4Wb3f7faGvDrHLJTi+tJKSIiMpZtXlHDrBMcIl5hhckgNHTcVQtzyxxM5CDwpS99qajxixYt4tOf/jTPPPMMixcv5lOf+lSZku2ppNsg53I5vvGNb3DRRRfxxBNPYK3FWsvRRx/NLbfcwkc/+tFxX5SEkQ18YKQRaHNz8z7HHX744QAsXbq0IrnGip6+nqKPCa1DPtC0fRERqQ5nTZuKmQRbz64liO37zlvoQL7e4bnXNnBkW/HnTxERkbEq1RdlYGms4PF+2mH975vKmEheEjuOHvKSHX300Vx99dXU1tbyy1/+koceeqhiP7ukhclXv/rV/PjHPyafz2Otpba2ls985jNcf/31zJkzp5Q/alRt3rwZgKlTp+533I4djDo6OvD9wpZyVYNkmHwJR1kc/SUREZEqMbNmBY3RDMOHRHnutQ0MTY0QxAxBZGSz7iBq8BOG3oUJ1v9TE2Hc8NY5xfWkFBERGcti7XUs+0Eb2aEDL9TM51y2/KWOvuT43INCpBpMnDiRN7zhDVhrue666yr2c0u6lPu5557buXnJueeeyxVXXEF7e3spf8SY0NvbC0BDQ8N+x9XV1QEjS9kHBgb2O7uymhzVNAvbv7qoYxwfIt74n00rIiICsKbzUXqyccCQbfPY/NoG3FRIfFseJwA/YUhPioCzYzal5f5tUzhj9mimFhERKZ1kNMEWr5W/3NHO+a96img0j+vuORklm/VYv76dh1fOIX+E2pqIjKYjjjgCgKeffrpiP7PkPSbb2tr4zGc+w/nnn1/qbz1mZLNZAGKx/U9Lj8fjOz/O5XIlz9HUlByTu1intzcQcQNyuEAh+SyJaJ7mZjX9H+uc5y+gHcfo+Rqn9ByOf3oOx4cnlvjk7O4rCIKkw/Csfb13MNy1dSZf1XM65ul3cPzTczi+6fkbP2bXt7DiuD4G+lxuuvUMFhyxjrlzNu38ujGWnt46nlo0hy1bWzBNIV4m1PM6xhgtbDyoDAwMADA4OFixn1nSwuQb3/hG/v3f/53a2tpSftsxx3XHxl0czxsbOV5s1fBm4tE8+ZxbwOJsizEWpzYgsCFRr6wbxUuJGGNw3bFXFJfC6Tkc//Qcjm0bsxMIbLaoY9Khh+uWtMuOlJF+B8c/PYfjm56/sS+SiMDz57V0Os5jDx/Gspum0ZgfxnVChoMY/e315OtHrgHDqIPJGp0LRUZJEATcfPPNAEyYMKFiP7ekVaDPfe5zpfx2Y1YikQAOPAsyk8ns/PhAsytfCt8PxuSMyZTtAceQjOdIZaLYfc6aHClK1iZyWGuw1hIEhe3YJqPDcQzGjDxXYahbZ+ORnsPxT8/h+FDXOBVj1hR1jAWdB8cB/Q6Of3oOxzc9f+PHpJlNsBbA0rB0iJpNGUxgMaEhxCWOT3RNL0Hcofv4BvxaD+rcMXsuVMFUqlEYhgwNDbFs2TJ++MMfsnTpUowxnHbaaRXLoOlpL8GO3pEHmtq6Ywqs67oH7Ef5UvT2pkr+PUthaiICQMQLqU1kSeciBMHuW9sYIOIFxKN5HAdsAD09Q3iOXpJjWXNzDa5rCENLT8/waMeRl0DP4fin53B8OLKpFSimMGmpq0/pOR0H9Ds4/uk5HN/0/I0fiejIHgKtj/YT687hBLt/3QBu3uLmAyY80EvnqU0k2mrH7PPa1lY32hFGh+r/49L8+fNf8rHRaJR3v/vdJUyzfyr5vwSzZs0CYMuWLfsdt3XrVgDa29txnIPnP/Wcmtm4jNzlct2RGZF1yQzJeI5kLE9NPEd9TYZkfKQoCVDnplWUFBGRqtEXbqGxaYhC3807bsjESb3lDSUiIlJBLTUJGjak91qUfDE3Z2l7pI/WuuT+B4pIQay1L+mRSCT46le/yvTp0yuWVZWgl2Du3LnAyC7kQ0ND++ypuWzZMuAfq1SPR3NqZjGlY5hN7bUwMnkSx4Gos48p+RmYniquD5eIiMhY5pCjvmmI7u76A9YmLRbrQFtNf2XCiYiIVMCx06eQXJ06YFFyB5O3HOmVvgWayMHohBNOKGic4zhEo1FaWlpYsGABr3rVq2hubi5zut2pMPkSnHXWWfzP//wPQRBwzz338JrXvGaPMVu3bmX58uUAnHHGGZWOOKo6tveT/lsz5jU+tinc/7zcPDgbI6SfqYfq3chdREQOMo4zSOe2RsJkiEk5YMHspeeyxYJnsfGQoFdvy0REpHo8+uQzmCL6gLq+Zcmdy+Gtry5jKimalnKPS9dee+1oRyjYwbO+uISmTZvGcccdB8B3vvOdPXpNWmu56qqrCMOQpqYmXve6141GzFGzZsMSjG+I/V8tptuBzD4GZsBZFyH6lyT9PUmCcGw2ORYRESlWPB9jKB8DB2xNiI1arLEjsyN3/OtabCLExi0hDos2zxzt2CIiIiXz96VLMfniqlrpznyZ0ojIWKXC5Ev0H//xHziOw/r163nzm9/M/fffT09PD0uXLuXDH/4wf/7znwH48Ic/TDJ5cPXJCINBcqGHyTrEflNH9I4aTIcLGQNpM1KQXOMRu62W6J+TGGsIMdjQH+3oIiIiJbFk0y535QwQsyMFytoQm3zhX7znvw4M2+hoRBURESmLTOCzl8UC+6XJeSLlNzg4yLZt23Zu2DzatGboJVqwYAFf+MIX+MxnPsOzzz7L5ZdfvseYd73rXbzlLW8ZhXSja+qkNnLhWgxgrMFdH8FdHxlZruaACfc8O+VCD8/TBZmIiFSH0I+DSe3+SfOif0VERKpYvDFKGDE42cLLjflGzZ0SKbXh4WF++9vfcuedd/LMM8+Qz78wMzkejzN37lzOOeccLrvsMpqamiqeT4XJf8DrX/96jjjiCH72s5/xyCOP0N3dTTKZ5Mgjj+TNb34z55133mhHHBVO8xRCD9zn97OJJXIcddqzzD1qI9GYT3o4xtMPHMqKp2YSBi4AmYm6NyYiItWjPeFj7C4zPyxE+iHabTABhHHItFvCXXr8F7o5gIiIyHgwMGCwEQMFFiaDmGHo0HiZU0mxjC7Vx7UHHniAT37yk/T09AAjrQd3lU6nWbx4MYsXL+a6667jC1/4AmeddVZFM6ow+Q867LDD+MpXvjLaMcaU57KdpI/MU/NYhLNf+xRHnbYKL+ITib7QQ7J9ejfnXvI4f73xRJY9M5P00T5+GOA57igmFxERKY35jR4mb8CDWAfUrXZwfHD8kemS1lhq1xry9Zb+wy2hBzMzfaMbWkREpIR6tqYYOjzChMVD5DORA473kgE2r1V0IqVy33338YEPfIAgCLDW4nkehx56KNOmTSMej5NKpdiwYQNr164lDEO2b9/OBz/4QX76059yyimnVCynCpNScplMhuyMLJfMepR5xz9HLLFn78hY3Ie4zyve+DCem+Lulpk4RmvbRESkOvT0R6nbaMl5DrXPmZ0FyR2MNRgfoj3Q8qih96iQxm2aMikiItUjcEMuee0TbI/XsPzRqeT2WZy01DVluPzLd/HZa95c0Ywi1WpwcJB///d/x/d9HMfhXe96F5dffjktLS17jO3s7OTqq6/mmmuuIQgCPv7xj3PHHXdQW1tbkaxq4CAllxhMcPSW55h3zMa9FiV3FU/mOffip5m0qQ+sCpMiIlIdViyqoW5rSO2GPYuSuzIY3Jyh+QmHzo7GygUUEREps+gEj7MXLOPI120h1ZYkdA12lwqEBULXENS6JE61tE0Y5MSjVo1aXtkHa8bPQ3a67rrr6O/vxxjDF7/4RT75yU/utSgJMGHCBD71qU/xxS9+EYDe3l5uu+22imVVYVJKbqjb42VHP0ustrCZH5GEz5nBs2gPNhERqRY1gUfej+DsZcO3vXFCQy6nhSwiIlI9Zk5fy6YtLfz85pcz2NZA/4JmhqfVkm2JkW2KkmlPMDivkYG5zazbPolf3Ho+F5z0xGjHFqkK99xzD8YYTjvtNC666KKCjrnooos444wzsNbypz/9qbwBd6HCpJTclo0raJicLni8G4HDz+rAqi4pIiJVYsjxi7/fVmARU0REZDyocXu56a+nM5xOjHzCMeRb4qRm1JGaVU9mSg1hbGSPgVw+wtJV0wlzKlGIlML69esBOP/884s6bscmzhs3bix1pH3Sb72UXDzaW/TFmOtZ8rlceQKJiIhUWH9r8Zu56f6ciIhUk/zWCFs6mwsen8rE+NsDR5Uxkbwkdhw9ZKdUKgVAfX19UcfV1dUB0N/fX/JM+6LCpJTcxLaWov8mWCAWj5UjjoiISMUNpHMUuxTAupoxKSIi1WPjs+1k84W3KbHW4ZlnZ5YvkMhBpKmpCYB169YVddyOmZY7jq8EFSal5OYcfQimyFdWNh3BaFduERGpEvVEICyuMGmKHC8iIjKWDXXHCILiVhCkhqJlSiNycFm4cCHWWm6++WZyBa5OzWaz3HTTTRhjWLBgQZkTvkCFSSm5pSu6WNXRXvD4rO9y15L5BOH+d/AWEREZL+xzPZjC9oAbEVji21JlyyMiIlJpoedjwrC4YwrfqkBE9uO1r30tAJs3b+bf//3fyWaz+x2fzWb5xCc+webNmwG44IILyp5xBxUmpeQG+0N+v/0YhvOF3e3KBBHuz80lLOYCTkREZAyLBt3EOlPgF3ZBZkJLvCtT5lQiIiKV0z4/jc0XsSouCIh2DZQvkLwkxo6fh7zg5S9/OccccwzWWv7617/ymte8hl/+8pesWLGC4eFhrLUMDw+zYsUKfvGLX/Da176Wv/71rxhjWLhwYUULk4U3fBAp0KCXYHN/M79ddQKXzXmMZGTf04YHczG+98x5ZGJRPK/4jQJERETGosYmj2h3ltTcOCZnMMG+L8xCzxLrHcLRDToREakizccm8e5OkW+vBefABUoTQqJFUyZFSsEYwze/+U3e+c53sm7dOjZt2sRVV12132OstUybNo3vfve7FUo5QjMmpeRapjYDlsc6Z/PjpWexZbiBoXx0Z6utIDQM5aOs7G3na0+9ik1DzZjQkteUSRERqRLnnn8YqcNd+s/K4TfksDYkV2NJtRpSbYZ0i8GPWKwNSS3MsP1Sl0RbYf1/RERExoPpwy7RjiFMzj/whnD5gPiaHrzausqEEzkItLe3c8MNN3DJJZfgui7W2n0+XNfl9a9/PTfffDNtbW0VzakZk1JyE2rrMFgshmf7JvH5x17H5JpeDm/aTCKSoz+b4OntM+jPJV84yEDU1ctRRESqw8ypp9N/3mPUPuPjbndJtTtYz+w2YyR0DSaApjvydL/OMPVd8VFMLCIiUlqJvEcwqYG6p7YydNQkwogDkRetkgtDTGCJr+3BzVlctIpuzNES6XGtvr6ez3/+8/zrv/4rDzzwAIsWLaKnp4fh4WESiQStra0sWLCAs846q+IFyR1UCZKSO6F9CsYzu/392jLcxJbhfW83n4zpBCQiItVjY7qf2CDYziSpSRHw9lykYiMONgJ98+tp+uMAy/95dN4MioiIlENdIoFXmyY/rZXk4k6ChijZqfXYmAcGCC3RjkEi3VlwDPnpLcSiQ6MdW6Qq/PGPf6Suro4zzjgDYwwTJkzg4osv5uKLLx7taHtQYVJKLhVmqK/NMDAcI7QvXIg5WYuTtwQxg428MGMk6vg0Nuk2jIiIVI/N6X4iT3gMTdx7UXJXNuowOKcOuzQFJ1cooIiISJlNOLSeeHSQTH8WL5UhMpQmvrl/j3HWgI1FCW3A/AXbRiGpSPX5yU9+wsqVKzn11FP52c9+Ntpx9kuFSSm5bdl+Tpi0gQfXzySVilG/LEvLo1nctAUzsltWttlh+ykJUoc4NCRSTK0fJLQWxxSxa5uIiMgYNdSdIt0Sx0YKa+cdug4sVq9lERGpHvGmRpLp5fjbUphw3xNRjAWyOSLPdjLxc72VCygF0W7X49Nzzz0HwFlnnTXKSQ5MhUkpudAGHNq4hbbJfTz6X/NwhyxOfvcxyS0BU/4wRDAZzvv8ItJObHTCioiIlMG2Vb2EXhE321xDqlk9JkVEpHrETRNDS7P7LUruYCyQztG1vB1mlz+byMGioaFhtCMckHbllpILU134aYcVV84g0hvuUZTcwc1CfFPA0q/OosZNa7akiIhUjQ2di7FFnteKKmSKiIiMcZsfSRJmilgNEFoW/7C2fIFE/gEPPfQQH/rQhzj99NM58sgjOemkk3jHO97BrbfeShiGox1vD6eddhrWWv7whz+MdpQD0oxJKbnsUJrNf2kl2xNhpKvxvoV5l/5VNQwsq8Vv8/G0M7eIiFSBPN1A+2jHEBERGTVP37ca7MhsSQuENTHyExsIk7GRFl9+iNc5gNczhAktBhjaMjyqmUX25stf/jJXX331bp/r6+vj4Ycf5uGHH+b3v/893/ve94jHx87ql89+9rM8++yz3H///XzgAx/gve99LwsXLsTzxl7NZewlknEvlfbYfHsbYb6wnbb9YY/1N7XjnqOduUVEpDqELQa2FnmQqyZOIiJSPbZ1D4AF6zpk5kwc2Y3be+Gaz0YgP7WZ/OQmohu24/WnsGNw5tlB7yB/e3LjjTfuLEoeffTRfPSjH2XOnDl0dHTw85//nD/84Q/cf//9XHnllXzxi18c5bQvuPHGGzn33HO57rrruOeee7jnnnuIRCJMmzaNuro6otHofo83xvDLX/6yIllVmJSSG+pLEvrFdQnoX1VLaC2ulnOLiEgVsInJ4OYotGuOyYfkp/nlDSUiIlJB0cY4eIbMYZOxUQ+cvVzruQ64kJvZCuu7iOT20QdMZJT8+Mc/BmDu3Llcc801xGIj+2O0trbyta99DYA//OEP3HzzzXzkIx+hvX1srJj55je/iXlRfSWXy7F27doDHmut3ePYclKPSSm5of4MQa64l1bgu1BAU2QREZHxoKHZJTMzAL+wmR82AvkWnQdFRKR6nH2hS35yMzbq7r0ouSvPJTejjaPOG6hMOJEC9PX1sXHjRgAuvPDCnUXJXb3pTW8CRop5ixcvrmi+A7HW7vbY2+f29qg0zZiUkqtPxAgCp6iqdxiaA5+sRERExonowBbCujqy7SGxTkZmhOyNtWAsw7N96uPZimYUEREppzlT0/gtNeAUeGXoGCYcW95M8hIcxPdNnV1eu76/95UtkUhkr+NH24oVK0Y7QsHGzn81qRq1ngtFTvsNEi7uGPolFhER+Uds22ypS6bJTwhJTw8JXQu79s0KLViLX2MZPjTAiYe0NA6OXmAREZESe2hJQCReRFXLdfjLQ4eWL5BIkerr65k5cyYAf/zjH8nlcnuMuemmm4CRAuWCBQsqGa9qqBIkJdcYi5BtimILrE2GrsGvNYSBGh2LiEh1SG+vp61pEM8NCeotqTk+qVkh2daAXHNAZlLI8FyfzIwAoiHJeJZsRpvAiYhI9bj/2RxB/kUlB2tHbs4FdueO3bsKMypRyNjy8Y9/HMdxWLVqFe9617t4+OGH6e7uZsWKFXzmM5/hhhtuAOADH/gAEyZMGOW045OWckvJNbTU4WbThJ6Hkw/ZX30yNBDEHBJbunH2tcxNRERknJk97RDWDPcxc3InG7a0EYQOYdIhTO5+EeY5PolYnqkTu9m4oWmU0oqIiJRePNKP40ZHNkYNLU42wM3tPhnFOoYg7mI9A8ZQG9tzRpqMLnMQL+UGePnLX853v/tdvvKVr/D444/zjne8Y7evT5o0iY9+9KNcdNFFoxOwCqgwKSU38ZB2mjoH2HDCVFoWD2Axe93RyYYhOJa+WUnOTU0ZhaQiIiLl8cpjj+K3y5dzWFsHbz3+QR7aMocHO+Y836bJYC00xYY5b9pSpjd2c/W604ltGuXQIiIiJXTI9DRroi5BxsEb9sGyx6QVE1jMsE8YdQgSDvNnbxuVrCL7MzQ0RDKZ3OvXuru7efLJJznzzDNpbm6ucLJ9mz9//ks6znEcotEo9fX1TJo0icMPP5xXv/rVHHfccSVO+AIVJqXkjDHMvvQE+n/7JPgG21C3+47bFnAMoXFgYID65TnO+MIbRy2viIhIqR06awrTH9rOBc1LqI1led3sRbxm1tP0ZGvIBx610Qz10QwAfmC4tP1xcm3njXJqERGR0jlhfjt/IoU37O931p0BnFyIdSynnbqhYvlECvH5z3+ea6+9FhjZgfttb3sb06ZNo7e3lzvuuINvfvOb3HDDDTz++ONcc801tLa2jnLiES91d+0gCEin06TTaTo7O3n66ae5/vrr+ad/+ieuuOIKPK/0ZUQVJqUslqzcAngEU5rAcUf6h1ie3310xw7cFupiuNt6uHPxGk467YhRTi0iIlIaNlzLJcc+QzL6wk7brmNpSwztMdZzLZNq+jnyrNuAf61gShERkfKpi5yCn76LSAEFEgO4OcufVs/nBO3MLWPEgw8+uLMo+fGPf5z3ve99O7/W3t7O2972Nk444QTe9KY3sWbNGr7+9a/zxS9+cbTi7uZjH/sYQRBw9dVXMzQ0hLWWxsZGjj32WKZPn048HieTybBlyxaeeuopurq6MMbgui4nnHAC+Xye7u5u1q9fj7WWG2+8kUgkwmc+85mSZ1VTPym5bX2D5J/Zjpn4fFESXihGus7zRUkAA66HaW9i6S1PjVpeERGRUsv4d9DUOvjCKe8AamI5nhqMlDeUiIhIBf3f3zbgDBU+a8taePqhljImEinOb3/7W2Ckj+Tll1++1zHz5s3jjW8cWQF62223kU6nK5Zvf973vvexcuVKBgcHSSaTXHnlldx33318//vf51Of+hQf/ehH+dSnPsW3v/1t/v73v/P1r3+dpqYmgiBg6tSpXHfdddx+++384Q9/4Oijj8Zay69//WueeeaZkmdVYVJKbuXy53Ca41hT2MvLTYBJW4JQu3KLiEh1WNq1ec9GWgfgJbIHHiQiIjJObNn4LCYofLxjwes4yHdakTFl/fr1ABx11FG4rrvPcSeeeCIAvu+zcePGSkQ7oD/+8Y/8+c9/xvM8fvKTn3DppZcSiez9JrgxhgsuuICf//znRKNR/u///o+//e1vAMyePZuf/vSnO5eo33TTTSXPqsKklFx2MItJxij0iszPRfAmJHbvQykiIjKOre+K4RS7jeXBvu2liIhUlWwqwBQ596SYQqZIueXzeQByucJ3iy9mbDndcMMNGGO4+OKLOfbYwvojHHbYYVxyySVYa7nuuut2fr62tpbXve51WGt58sknS55VhUkpuawdwosWdwYynoPj6uUoIiLVIczNJgiLmzIZhPu+Ey8iIjLe1MV8bLGXeEWuNpAKsOPoUWKzZs0C4Mknn9xvwfHxxx8HwPM8ZsyYUfogL8HKlSuBF2ZzFuqYY44BYNmyZbt9/pBDDgFg27ZtJUi3O1WCpOQGYquL/ptgnJFpzyIiItVgWvIEwr1cjdkQ/LzDi/cB8EPD4lWHVCidiIhI+dW157BF3HMLDZiJuiaUseOCCy4AoK+vj2984xt7HbN69Wp+/etfA3DmmWdSX19fsXz7s6PX5Y5Zn4XKZkdaC724EJtIJAAYHh4uQbrdaVduKblYTfEnE+OGYANAjf9FRGT8O/HwQ/jf+w/jzKOegRBWPjWDh/6ygOGBxMiSbWuYMW8rp7z8Gdqn9ZLNRwi3vny0Y4uIiJSM15omO6WB5PrCxtsIuOcOljWTSDFe9apXccMNN/Dwww9z9dVXs2nTJt7+9rcze/ZsUqkUd999N9/5zndIpVLU1dXxyU9+crQj7zR58mQ2bNjA3Xffzetf//qCj7v33nuBkV3Hd9XR0QFAY2NjyTLuoMKklNzc9kNJNt5HdjhW0HhjQmYdv5lINF7mZCIiIpXhOg7PpY5j09Yt/Pknp5MajJPP7n7zbeWTM1m/YhJzjt6Id9Qwl515xiilFRERKb0OP0rvOQGx37i4B9ioOIzA4DEWt0kTVcaag7kFtjGG73znO3zkIx/hwQcf5I477uCOO+7YY1xrayvf/va3dy79HgvOOOMM1q9fz5133smtt97KRRdddMBj/vSnP3HHHXdgjOHMM8/c7Ws7Pj937tySZ9VSbim5ltYsR756FZF4YVOGo8k8E88ufZ8CERGR0RLYkC01PdzwlZfTv712j6LkDtlUjGWPHsITt8zj9vRjFU4pIiJSPn29dYRNsO0Si5+0hPuoPgQxGFxg6T8VsjnNnZKxpb6+np/97Gd861vf4pxzzqGtrY1IJEJdXR0LFizgIx/5CH/605847rjjRjvqbi6//HJqamoA+PSnP81nP/tZ1qxZs9exa9eu5fOf/zyf+MQngJFl2+985zsB6Ozs5H/+539YtGgRAOeee27Js+q3Xkpua3Yd/TNdphzWxablE/D3c3KJJnIc+6qVPJA+hHcGGSKuZk2KiMj4t2RwA+a2CPgOmP138g99F3eL4Z6ly/jXGa+tUEIREZHyak4aoiYg0+aw5Z1Q94yl7ikwOzp/WchMh4ETIff8qlF/UCUKGXscx+GVr3wlr3zlK0c7SsEmTpzI97//fd7//veTyWT47W9/y29/+1uampqYPHky8XicVCrFli1b6O/vB8BaSyQS4Stf+QqTJ08G4Ec/+tHOHpqTJk3ikksuKXlW/dZLya0bijO9vpdDPvYsf/nRSWx4ehLZVAS7yyYArhfgxXxOeO0yjn31SrZv9DB6OYqISJVY1rsOsy0GToHbixqDc0cULihvLhERkUoJ6wzZ0AUDNg4DJ8DA8WDyYAIIY+y2hjOCT344Omp5ZR8O4qXc491JJ53Eddddx+c///mdMx57enro7e3dOcbusiPjvHnzuPLKK1m4cOHOzz333HNYa5k6dSrf+973iMUKa9lXDFWCpORqvGm0xvtwvZA3/MuDdG6t47E/H8bap6YQ5B2iiTyHn7mOY162hub6DEPWcPqkVQecUSIiIjJePHXnyuIOMAa6dTEmIiLVY4sXJZ8JiSR87I4KpAEb3XutKxd6+LXqNidSSkceeSS/+c1veOSRR7j77rt59NFH6ezspK+vj3g8zsSJEznqqKN45Stfyemnn455UV3mNa95DW9+85s59dRTiUbL815VhUkpuRk1Q2zzLceYgJ986Xi6fZcjL1rLKa9/BmPAhrDp6RZu/5/jOP2EbZz0xmd52glwVZgUEZEqEWxPA6W/oywiIjJepHOG0LiErsV4dv87XATgpzysq8KkSDmcdNJJnHTSSUUfd+GFF5Yhze5UmJSSqw3yRFyfb/3nqSxf2kIu6/Hsg5Of/6oFXihA3rq2gbxvmHzpeqwNMEYnIhERGf9iYaT4pU9aKiUiIlUkOmhxPA+zNQKTs5AIwGXXy0EIgNDA5hhe3iVoyY1SWhEZLaoCScmtX5eg54EJPLu8mVz2hdq3tRYb2t16GKRSEW6/5VDqOuKMnKVERETGv3lHNex+4XUg1mIC/8DjRERExonp+QjugIsJHdgUh21R8Bm5EWeBEBh0YV0Ck/YwATT2qzA55thx9JBxSYVJKTnHj3Lbr+eRTkdGipC2HwbXYbeswXasxW5eg8luhDCFtZbhYY8Hbz6EIAxHO7qIiEhJzJ+Vwvg7rr4KYQla1dJERESqh1eTxgnAeiEckoKJOYgwcuPOMFKNqA9gdgrbkMdYQ208P7qhRaTiVJiUkqv1fPp6Y9jQxwyuI9jchb8lIByCcBDCYcivzxFu3YIbPEcYGh57YBIRVxdkIiJSHZLNQwSHB5hEATfd3BB3ehYzNSh/MBERkQpxGmPghjAjDVG79wVyDiOfb89i6/MMZxIVTikio009JqXkoqYGG4aYwc34neHIFP29CIcgn88Rmb6JMGwnDMDRK1JERKpANtdI7ekbcIxh+IYJ2LQDdi834KIh7sQcNRd2kbs/WfmgIiIiZRKvyWAneODZne1NnEEHZ9CBEGzMEjQ/33fy+eJkuFlzp8YaoyXSUmYqA0nJPbBliDDWR9C776LkDjYLfkeWsDkNjmZMiohIdVi5th2vfg1OLdS9ZyuZR+rJP1Oz28puUxMQO3WA6OEpjAPeUdnRCywiIlJi0xoTkMmAA26XS3R1FJM3GN+MnA9dsI7Fb/fJzc6BA16TekyKHGxUmJSS2x4fIr1qGFvg9VU4CPnhXtL5HDWxeHnDiYiIVMCmngFMw8jHTm1I8tw+7Nl9hL0e1jc4NSFO3e5Lt906LeUWEZHq0dDUBB1biayLEN0YHSlI7ioAExgimyN4PR6p41OYJm0EJ3KwUWFSSi6aShEW2bM4t8En5unlKCIi1WG4v2+PzxkX3Nb9XHBp5YCIiFSRXDgRt2fb3ouSuzDWQBoSixKEp6gwOeZoKbeUmRo4SMnNqW1+aX+89AdPRESqRCQNBMUVGm1ahUkREakeeX+Y2JrYfouSOxhrMCmDGdRFocjBRoVJKbkpTZN2NjcuhuvubZs2ERGR8afZqcNmCj8Z2hz4f9fmNyIiUj2Gtq/H5Ao/FxrfkNigEoXIwUa/9VJy9W4zpshXVnQGGKOZIiIiUh0mTJ9J/m81hRcn84b84+qzLCIi1aOvexBTxMpsg4H+SPkCiciYpMKklFzXti5qX+ViC+yVFcYdmt/mEAYH2MJbRERknPBSMYJHYgRrogfcDM6mDNlf12MG1VdLRESqhwlDil1K54cqUYw1xo6fh4xP+q2Xkhty+hk4egph4sCb2VgDQWOc9LQmrFVhUkREqkOucxAnnSf3i3r8JxPYYYN90cZwNmUI+x2y1zYSrojh5lSYFBGR6hFPmqKrRYEmTIocdLQNspRcw6QmOp9px17WQssNSzBpHyfc84QUeg5BU5zufzqSzNoM5hzVyUVEpDr0Dw1i/ACTDcndVEf+L3V4Jw/jzsthIhY76JB/MEm4MgaBxR3OvpT2zCIiImNWrCUyshFcoSc4a3EaD7DMQESqjgqTUnJDbh0AQXOCrnceTc0TW0ku3gbWjuy8bcBGXYZOnEL6iAngOaSH4ziOCpMiIlIdYpNDDJCtCxk+JE7jeh97f4LsXXU7x7hxn3yNoXeex8S70zgRrUESEZHqsebpBCawWBcoZD8BC+HmaNlzSZH09kTKTIVJKbnh0GDtyInHJiIMnT6doVOn4fZlMPkAG/cI6mO7nZxCa8gFPlFXL0kRERn/JhxST77FoevCJP9x5h+ZU7uNFY9OY+miGeSyHo3NQ5x4zrO0z+zlh0+dzUPxOcy6p3e0Y4uIiJRMbmsSN53FTxpw7P6Lk9bipi0m61YuoIiMCaoCScn1Dg4TvLhpsWMImhP7PCYMHSKOTkIiIlId5hwxje6Tn+Gq827kkMYuYm7Aaeeu4LRzV+wx9kPH3o1rAzpyE0chqYiISHkk+iMYskSGLX7SYB07sqx71wLl86vq3LTFDYCspueJHGy0dlZKLuv7hAXuyD3C4idD8kFQtkwiIiKV1BafwblnLmdmw3Zi7v7Pb7XRLO877j7yR9Xtd5yIiMh4Utsaw9gAA0RSFi9lcfJAYCG0GH9klmRk6PmiJOAOZUYzsuyNHUcPGZc0Y1JKzuYsuYaA2HYXU0CnY+tAvi7EVY9JERGpEs9s38bFhz1Jwitsp+24m+eY9qfLnEpERKSCmgJMKsAmR8oOTghOZj/VI98nOlzYeVNEqocqQVJyh9U0ETqWyKAPe9mNe1fGD4l1Z5/fE0f7kYqISHVYve4vxNzCL66ibsCrDllcxkQiIiKVlR2wRDqGIV/YyjiT8XHCWJlTichYo8KklNzMukaaF2doe7CPaF8ekwv3HGQtJheS3JSh5bFBmpbnCtqoTUREZDwI+rfgOcW1KIl7+TKlERERqTzXeERMHK97GPL7uVlnLSadI7ktV9ju3SJSVVSYlJJrm9xAy6IMXs7SsHSYyGCIyY30EcECocXJQbzbp351Gi9rmfREFqOTkIiIVIlIJsTa3c9rDpAwLjXGJb63t2B5NUcSEZHqcfjsmeAY4imP6NYhyOZ3L1CGIWR9nN4UyS1ZjBOBenWbG2uMHT8PGZ/0Wy8l198zTMQahtqjDB5aBxGHSBbI7hgxcqEW1EbpOaaJpsV91GiSiIiIVJFk6+FY/1aIQRSHZidK3LjAyFnQYrHAQJinz+axQMfaGma0jmJoERGREppQV0cYNXgZiAUxopvz5E2eIG7AgJMLifgujvHAcQgdiDXERzu2iFSYZkxKyaWGsuQbPAYPrcNG9v8Ss1GHvgUNhFFDGOxlybeIiMg4NGveYTy3somEcZnsJqhxPFxjcI3BMQbXOHjGodGJMtVNkE5FufWe40Y7toiISMmEKZ/0RI/w+UtCYwxRoiQyERLpCLEgNlKUBKwBv8bBy+qaUORgoxmTUnJe3GVgSvyARckdQs9hoMXDcVUnFxGR6rBhSYZN983nVcc/hrufViWOMXjWYUbUZeXiaRVMKCIiUl5DYY7eIw2Z+gjNK/I4mRDT3QcDQyNtvmJRmNBMUBsj2+IwNNUjH1dhcszREmkpMxUmpeSGGvLk6qOFH+Aahqcksdaqz6SIiFSFdc908upzN+M5Bz6vOcZA3uP4tm0VSCYiIlIZXfU5ck2GfMIQebaL+kXbwLeYcKT4aAG6ugla43Sdcyj5Voe85qqIHHT0ay8lt7V/8PnOWYULPUNgdXdMRESqQxh0c8QR2wseX1fn8+Y3LS9jIhERkcqqba+BPEy4bg11D3Xg5IKdRUkY6bls8gGRrcNM+eYyvK4MTlxzp0QONipMSslF8y7WK+6lZQFHL0cREakSc4/YjuMUd5Nu9uy+8oQREREZBf2ZDPX3d5Jc1ou7n96RBvAG80z+4QryuaByAaUwdhw9ZFxSJUhKLtuV2bHxdsGsY9AqbhERqRbts2twinyXZTRJREREqkhfNkXT37bstyi5K3fYJ7K8v8ypRGSs0VtgKbmMnyN0wfULG28BPwG+HxCJ6CUpIiLjX0vjTHxb3B23nKe1AyIiUj02P7UBZ8cMSAdqTzG0Xu4QnQYYsFnovTWk90ZL0AduOqDhjs3wmdFMLSKVpve/UnJhIkK+Fgq+HjPg14DnuWXNJSIiUimTG88jHyn8bVbeN9zbO7eMiURERCqrd0UXbibAa4PZN7pM/rxD8iiD12zwmgyRiYbWd7vMvtml8aKRi8doV3qUU4tIpakwKSXX1NJAEBuZBXmg4qQ1kG0A60Jo1RRCRESqw7K+bm5ZN4dsUNhbrQwuP33m6PKGEhERqaB4GOI2wMyfu0SnG7z6PS8OnSh4DYb2j7o0vMbgueoxOdYYO34eMj6pMCklF7EjL6tcA+STI8XHF/+NsAasM1KUDJL6IyIiItWla2iYq393IluzNeTD/d+lGw48rnnmSDqXJyuUTkREpPzcaTkmfNTBm2AwB6g8uHXQ/jGX+KGVySYiY4cKk1Jy9cbDBICBfAOk2iFXB0EEAg/86EhBMtU+UpTEgjtscbT7jYiIVImurn4ymSjv/MEbWD3cwKAf2WNMPjQM+h6/WHwkP//dKTA0CkFFRETKJJwXUHemi1Ngxy6TMEz4qEoUIgcb7TQiJTe4qZtob0C2zRspfTvg14089spCvCNbyYgiIiJlNbh1OcbC0FCCt339Mk46Zi0fOvcRpseHMQZ8a/j71sl895Yz6O6pB8D4Wj4gIiLVw7QOY2KFj3ejlth0FSbHHL09kTJTYVJKLp3Ok1zWg39yG0HM7H9ebgiRvoDk45vxg4CIp5ekiIiMf3Z4G9gGwPLWS+8jMWmAu/P1hPnGnWMitSFveOu9PHrnkSxaNgsnF45aXhERkVLLD3s4rcVVtWyBvZlFpHqoCiQl1zatheQzW8hNbyIzKUIQY6Q4uetK7RCwEBm0xDvzRDf1qSgpIiJVY3qLj5MLeM/778Kvy5Pby126PA4YOP78JUQ8n2W3NY9CUhERkfIwwy7GWHa/ENw/x+gmncjBRpUgKbl586fhuh51f12JeflhBDUR8jUQxBk5J4XgpSAyFOIM56n7wzJqF0wf7dgiIiIlM2fuEVw4fCv5Wh/7fA/l7ECEvnV1BHmXWF2OpkMHcFxLHoejz17B1j8eNsqpRURESqcmH+IYS1hwYdKScHJlzSTF00a1Um4qTErJRTyXhZedytM//Cv1ty0hffx06gKPeE8eE1pC15BujxEOD5N8fCMmhDf+22tHO7aIiEjJ9DlHMuHUq8k5hoHnalj+f4fQt7YeG4ANHZxIiHEt08/cwqGv3kjgGU5+77bRji0iIlIyjhPgmYCcLWx5toOlwUuXOZWIjDUqTEpZvPdfXs2H71xMpM8ntiEPxsc8P2PExeKtS4GFIFlL4sTpnHPGEaOcWEREpHQGBx8lFfXY/lQjT/9sHvnU7rtyB7mRLUrX3D6NrY+3cdqnn6Jh9sBoRBURESmLsNbS7AzTGdQT7nfjATCExJ08jnZaETnoqLOslEVzQ5LDjpyLU1+HcZydRckdjDEYx8Fra+WVZx2D6+qlKCIi1cOGa+nfnOTpq/csSu42LnBJdSZ4+GtHkQrdCiYUEREpr8FcLS6W1sggDvvuHWkIiTo+EyIDbPdrKphQRMYCVYOkLO7/82LWLN6EKaCfyB+ueZCuLb0VSCUiIlIZqVyE1bfOJD+876LkDjZ0SG+P0/1sY/mDiYiIVIizzSNtI9R6OSbF+kk4WRxCHELM8/96BDR7w0yO9pMLPZZtnzTaseXF7Dh6yLikwqSUxQ0/vLvgsTa0/Oo7d5YxjYiISGX52dn0rq4veHx+OMLqP2gjOBERqR7TBodY1D2VXOASc3wmxQaYFu+hPTpAe3SQSbE+psV7aIhksBYG/Ri9nZoxKXKwUWFSSq5rax8Dfamijln08JoypREREak8JzWTMCx0F9IR/RvrypRGRESk8vq2J9jU08TSzonkgpF2Ja6xJNw8STdHzAkwBoLQMJSPc8+SuQSrk6OcWkQqTZvfSMmtXrqJ0AMnX/gxASFhaHGc4i7iRERExqK8yRPkvQIamrwgLHDXUhERkfGgKzGR+C3b2bxhMgP/UcNRUzcS9/JETIgxFj90CTGs729h5fVTST7ikDomPtqx5cW0RFrKTIVJKbnBTBYiQBGFSeuBDUNw1PhfRETGvyABoYFizmpW9+ZERKSKBG6M+DMB6Rn1DFzXzKbpUzjlvFUc2rqdiAnozSX420PzGFzchDMINbaf6PbcaMcWkQpTYVJKrqc5W/RdFVsDjnbmFhGRalEbKaphTmggNUFvy0REpIr0DJNriePXeLzmpJW85xVPEI/6NNRkAcjmXf75Nat4bO4U/vfmMxicWU9y4+AohxaRStM7YCm5fBLycyC6mIKWsNkopM+CfBgQdfWSFBGR8a93OMXwRI+653xMITfrXBiedOAdvEVERMaLhM0z2BrnXy56hNeetIK6xO6zIWORgFgk4OyF6zhiRifv+frryES1gm6s0YIOKTdNUZOSa3VqSZ0LJhkWNN7GLZnjwNPLUUREqsSAn2FoaoRkcwbXDfY7Np7IER4aEEb11l9ERKpHNnB42XFrufCk5XsUJXfluZYJDUN84wO3k22NVTChiIwFqgRJyR3XPoPY5Bxnf3gxsdr99QixJBqyvPLKx3EGI1rKLSIiVcNLuCycvpHPf/U6ZszuJFmb2WOM6wYkazK85tJH+Pp/XIONqLu8iIhUj4b6et736sepTRx48wHXhfbGYebN7alAMhEZS7RuVkpuQnsDL3tuI4efsYmW1iH+/qMjGdiWJDfsYUMHxwuIxAMmzu/l7A8+Q6QlzwWduhgTEZHq0V5Xx+vPv4NEbY6P/88tbFgzgdtvOo71q9ux1hCN5jnlnBWc+Yol1NVnMNZw5uHLRzu2iIhIyZx5ItQlswWPb6jJ8vbzF5UvkLw0ulSXMlNhUkoutD7Hzd6E41gmH9HLG799H32ba3huUSu5lEeiIcesk7aRaHhhNuWpJy8bxcQiIiKllYh101o7gAWMgZmHdvKB/3f7PsdbY3nPCX+vXEAREZEymzezi2TswLMldzV32vYypRGRsUqFSSm5TLieSMRh145ajVOGaZwyvM9jQm9L+YOJiIhUyGH1T7DrrjeBhYEwxrCNYi14JqTByZIwPub51pKN8X2fJ0VERMabrnwnTkE7wL3Aje2/L7OIVB8VJqXkQgqfrr8rawOM0S5sIiIy/jVGNwFgLWwPEwyEMSxgd7T3tpAKIjhYJrrDJBwfU+TFm4iIyFi2ZThBOvCocwufNdkdxGgrYyYRGXtUmPwHrVq1imuvvZZHHnmEjo4OANrb2znppJN4+9vfzpw5c0Y5YeV5pp7i9xU1KkqKiEjVqHE8sNAR1DBsIy8UJHcR4hACW4JaJjFEvaNZIiIiUj02987Dc39f8PgghJs3zeb9h5cxlBRN902l3LQN8j/g2muv5aKLLuKGG25g/fr1ZDIZMpkMGzZs4Le//S0XXXQR119//WjHrLiYmYpj4kUdU+8eV6Y0IiIilbd2eCIDYXSfRcldhTh0BLWYIFGhdCIiIuV3dP18rG+xBRa2DJB+rq6smURk7FFh8iW66667+PznP4/v+8ydO5fvfe97PPDAA9x555187WtfY8qUKfi+z3//939z7733jnbcijLGMM2bjlPg9l0eMC1yTHlDiYiIVNCygUa6wtoDFiV3CDHcOTS1zKlEREQqZ1JyBUGanb2UD8Rx4J+ma1NUkYONCpMv0de+9jUAZs2axW9+8xvOO+88WltbmTZtGq95zWu45ZZbmDp1KtZavvrVr45y2gqzPrPcZdQYizlAcdLFMsEJaDN/qVA4ERGR8luf7SAdFt6ixGJIRrT5jYiIVI/ezU+TqAl3+9yBZk9OrO8vYyJ5Sew4esi4pMLkS7B69WpWr14NwPvf/35qamr2GNPQ0MC73/1uYKQP5ebNmyuacTS5rMcxlhNjPnXG4u31L8TI59udkAXRAJc1Fc8pIiJSLq4/WPQxMafwzQFERETGut7OfqwdmS65NZfkB1sO58bth7Ay1cDadB0PD0zgfzcdxf39E3cWLK3aLYscdLT5zUuwadMmamtrGRoaYuHChfscN2PGjJ0fd3Z2MmXKlErEG3WGNGCJGDgl5tMdGtbkXYbsyPxJA7Q4IYdEQuqdkTOQhZGzkDbAERGRKpBLRV7Cjfvit44TEREZq7Z2NpIeclgcaebT609mINh73+Xbe2Zwcv02rpzxCBtXxzlk+iiEFZFRo8LkS3D22WfzxBNPMDw8TDy+701eNmzYsPPj+vr6SkQbE0Lq2HFxZQy0upZW1wdGpu7vs8eIipIiIlIlBp+biZ3/UFHHDPmxMqURERGpvGc2trKsv5Erhk5mINj3OW44jPJA/yS+sOY4en6R5zMvq2BIOTAtkZYyU2HyH7C3Jdw7hGHIb3/7WwBaWlqYNWtWpWKNupAZWF448QQWlmUaeDrbRCZ0qHN8Tkp2MyMyvLNI6XP06IQVEREpg0np2WQDj/jzN+YOzHLnyiO44NSyxhIREamYxJQGvrjyOAYmHvjGW8Z6/H1oCpmIbtKJHGxUmCyTn/3sZ6xYsQKAN77xjThO6dt5NjUlMYVucVZpqXcSDn2DR9N1/HFwCr41ZOwLL7fFmSYSjs+bG9YzI+YSafwQzZF9F3plbHAcs/Pf5mY9X+ORnsPxT8/h+HDcETOp8XM40ZCwgJbeDhZvG3pOxwH9Do5/eg7HNz1/40dPLkdXXV3B44f8CJwR1fMqcpA5aAuT3//+9/nWt75V1DEXX3wxV1111QHH/eUvf+Eb3/gGADNnzuTyyy9/SRkPxPPG7tLnzbyWv/fewKJcw24FyR1S1iMVePyk91COM1kubltAxNVeTOOFMQbXHaNFcSmInsPxT8/h2FY3dZCJsUHyjqEvjO+3OOkSMsUb4A0nPYmrc+G4od/B8U/P4fim52/s8yeFENn1E+CujuBs8sA32IaA4Ig8tv75nbs9A9NzOheKHGQO2sJkudx+++184hOfIAgCamtr+c53vrPfJd//CN8PxuyMyW8v+TODkTYibrjfcRnrcU8+hrv2aS465JgKpZOXynEMxhistYShmo2MR3oOxz89h+NDXWITES+g3vXxCOkOk1jYpUBpcbBECWj3homakIm1AwTB/s+bMvr0Ozj+6Tkc3/T8jR+uB7iABe/RGN7TMQgMJj9yDWuNxXsmRtgUkn/lMLbOYkw4Zs+FB2vB1OjXTMrsoC1MvulNb+IVr3hFUcfUHWAa+nXXXccXvvAFwjCkpqaGH/3oR8ydO/cfiblfvb2psn3vf0Q28NniP0BzvLATiuOE3LjhFs5sLN9/KymN5uYaXNcQhpaenuHRjiMvgZ7D8U/P4fgQ5nO4kZHzYIObo97JjawWCCOEGDwTUu/kiJgXzpURN9BzOg7od3D803M4vun5Gz9iwxnIQeTeJO46D5PfvbBnrIGMwdlqiN5QR+6fhjCZ/Jh9XtvaCl+WLiKFO2gLk01NTTQ1NZXke4VhyBe/+EWuvfZaABobG/nJT37CwoULS/L9x5tnBzdTFy/8ZOI6lob657DWjtkZoCIiIsWIxSMEvgvRkcKjMVBjfGqcfW+GE1idA0VEpHrU9wVsWxLZa1FyVwaDSRmit9RgGjrhggqGFJFRd9AWJkslnU7zsY99jLvuuguAadOm8ZOf/OSg2oX7xfr8bizFXVy5JiCwAZ7RS1JERMa/7X6KeAGb3uwQhLBo1QymqauJiIhUiZpkFu+Juv0WJXdlMgZv/djdR+GgpaXcUmaqAv0DhoaGePe7383TTz8NwFFHHcUPfvADWlpaRjnZ6KqPxl7Sca45OHt2iIhI9dn8XAfJKTESNof7fHOmYT/KulQL2dCjMZLmkOR2diwUsMbQm02MYmIREZHSqp8WYh5npM9kAUzOIWxtLGckERmDVJh8iXK5HO973/t2FiXPOeccvvnNbxKPx0c52eibUzMdp3f3zwVDDpnVMcKMwa0NiR+WwdllhzbPRDEqTIqISLXIDNCVT2Jdh9A33LD5OJ7qnwaAxeCakIgTcGH7Yl7evpwl2YlMn9k1yqFFRERKx/HT4BTXPs3T5bTIQUeFyZfoK1/5Ck888QQAr3jFK/j617+O5+k/J0DSraPRmcwQG8lt9dh+QxOppXEIwQYG41lwof7UYVre0AdJy7HJM0c7toiISMlEbZKNQSt/6JjCks4ppPJRops9Ih0uJoB83DI8y+e6/Inc1rmQk6as5/jIhtGOLSIiUjJ+EMEaimry5Thjc0fug5l25R4xNDTENddcw5133snGjRvJZrNMnjyZs846i8svv5z29vbRjjhuqZL2EqxcuZLrrrsOgBkzZnDFFVeQzWbJZrP7PCYej+O6B0+/jIvbLuUHf/8+Hd9sIRza/f+3zY382/fXOoYeTzLtim7OXVjcDukiIiJjmWensTq9nqc6puOujNO4KgIBOMHI5ZnFEtvkEcZjDJzg8RCHMGHqwCinFhERKZ2e3CRMmAen8NJkPjh4rpll/FixYgXvfe976ezs3O3z69evZ/369dx2220H9QbI/ygVJl+Ca665BmtHbhts2LCB0047raBjTjrppHJHGzNq+tvp/vYkwqFg34NCg9/t0nfVLKK/Ul8tERGpHsNJw4qeiUSeShB9zsPxd78oMxhMHpy8ofa+BOkzQlY16U67iIhUD3dbA4TbKXgvuCCE7fmyZhIpVldXF+94xzvo6+ujrq6Of/u3f+Pss8/G933uvvtuvvnNb9LX18eHPvQhbr/9dmpra0c78rijpn4vwY6+krJvv7/2QXKDhUzDN6T7fR7/+8qyZxIREamUjDdM/6qGvRYlX8zJGxIPJFm5XYVJERGpHunOYbyBDISFrQU2ocXtz5U5lUhxrrrqKvr6+kgmk/z85z/nLW95C1OmTGHGjBm8853v5Jvf/CYAnZ2d3HbbbaMbdpzSjMmX4A9/+MNoRxjTclmfx+5ZsXNW6YFkUjl+d80DnPyyw8ucTEREpDK2DGWJLo8dsCi5g/ENqbV1cOBFGCIiIuNCOp/GHcgSxj3CeGT/S7qDkNi2IWwxDSmlMg7iHpPbt2/n9ttvB+ADH/gACxYs2GPM2WefzcyZM9m8eTNLly6tdMSqoMKklFzHc93FdTgGersGsdZijM5EIiIy/vVtrMNkUgWPN4Eh+my0jIlEREQqq6beMBS1ONuGyTUnCGqjI9eJzvMLN62F0GKCkNi2YRw/xHe0lFvGjr/85S8EQUAikeCtb33rPsf97ne/IxaLVTBZdVFhUkoun/Nf0l0VG1qMq8KkiIiMf3agBhOmizrGZHUOFBGR6hEF3GRAkPWI9aSxvWn8uihh/Pnduv2QyEAWJz/SAsxJBsRi3aMbWmQXixcvBmDBggUkk8ndvpbP54lEIgAqSv6DVJiUkqtvqin6GOMYHFctT0VEpDocM3EazyzpopglBKaQ1swiIiLjRFN7SGpKPz23NGEzDsZCZCAHA3vvI+nWhbS+JVPhlHIg5iBeyr1q1SoAZs6cCcDf/vY3rrvuOhYtWkQqlaKtrY3zzjuPD3zgA7S3q1f4S6VKkJRc26RG6hpe2GU79Ax9hyXYfF4jm17VxJaXNTA0LfZC/xADx5w2Z3TCioiIlMHpxx5K1A2KOqatVsvXRESkesSmGhovGKTm6BROfH/nRIvb4DPlU9uIzdTqARk7Ojs7AWhoaOCKK67ggx/8IA8++CCp1Ei7nq6uLq6//nouvPBCnnrqqdGMOq6pMCll8Zq3nko0EaFnYQ0bX9dC71E1ZCdEyTVFyEyM0XVSHRsubmVoWoxkTYxXv+nk0Y4sIiJSMtPbNjO5cajg8V7E532vXFzGRCIiIpXVdGgLxsCkD2+n8VUDOHUBJr7L8gBjcWoD4nOyTL9yK9F2H2pUopCxY3h4GIBbb72VG264geOPP55f/epXLF68mIceeogrrriCZDJJX18fH/zgB3cWMqU4WsotZXHcOfP4wkP3M9zmYSN7nlxs1MECXSfVEe11mDS9pfIhRUREysTQw/te+TRfuOFUUpkDb2pTH89y3jHrGa5ANhERkUpI18cIrEPECWn9p36aL+pn8OEa0svihDlDdIJPwzlDRCb4LxxjtBHcmHMQL+XOZEZaC3R1dXHSSSfxs5/9bLe+km95y1uYO3cub3/72+np6eHHP/4x//Vf/zWakccl3Y6QsvjOw48wNCmy16LkrmzUYVM7PLBhY4WSiYiIVEKMsxZu5pXHPktNbO+9tHaoT2b46uV/JuodxO/8RUSk6nSkPMJdei372y3pRwYYvmcbqXs7GL63m8yqHDYcOf/5ocNzQ42jlFZkT/F4fOfHn/rUp3YWJXd1wgkncNZZZwFwxx13VCxbNVFhUkrOWsufnn0WTOH9Qb75wENlTCQiIlJZPvOx1vDx1z/AO89/ksaaNMndCpSW+mSGWe09fO+Dv2f+tO34zB+1vCIiIqW2att2Ng81kEvBxo8FrH1zQM8NluyzkFsHw49YNv9nyKoLAlKLLX7o0JuNH/gbi1RITc3Ixr51dXUcfvjh+xx34oknArBt2zaGhgpv5SMjtJRbSm5xRwdBWNysj45B/fKKiEj1sKaBNZnpzE0s5S3nPM2lZzzDPYtn8cDy6WRyESY0DnHhSSs4dHIPAAN+lO28kQZ3lIOLiIiUSP9QgJNI4H8iJFhnsXtZQBAOjjw2fiQg99/NBDM0d0rGjqlTp9LV1UUsFtvvuNra2p0fZzKZ3f63HJgKk1JyK3u6GGlEUcyOapZ8EBBxdUUmIiLjXz4M+Oqm2fzvrFU0eDkiXsj5x67h/GPX7GWs4blsDbf0DfKB6aMQVkREpAzqMDT833P0rAsx++9qQjgA7pXdHP/r/sqEk8IdxJ1m5s+fz1NPPUVPTw9DQ0P7LDhu374dgEgkQnNzcyUjVgXdjpCSy5J+Scc5RSz9FhERGctWDm/m2VQNn91wIv3+vhv5Z0OHjdk6PrL2DO7vfbaCCUVERMrr7Knbyf4xe8Ci5A6RnM/C5evKG0qkCGeffTYAYRhy55137nPcAw88AMDChQtxHJXZiqX/YlJyh7fniz/IqDApIiLVYzDIkA3zPDw4kfevOpt7+iYz4HukApd04DDoe3TlY/xi2zze/ey5DAVR0kGBV24iIiLjwLxtMWy+8Gs8f9jh6V82li+QSJFOO+00pkyZAsA3vvGNnTMjd/XnP/+Zxx9/HICLL764ovmqhZZyS8m1xl1MLMBmC315WSItaSAEtJRbRETGv75cZmdTE8eGrHkwwo8eO5atkxuxUQevL8/J+c1MeFmI8SxYyIXBaMcWEREpmVSHJTtc3FyogY59rzKQ0WEO4qXcnudx5ZVX8t73vpeOjg4uvfRSPvKRj3DyySfj+z6///3v+e53vwvA0Ucfzetf//pRTjw+qTApJTeYSRKbmCKzoY5C+0w6yTwWp6iulCIiImNV3Iw0ST8x2Mz633n89LRjsC93IDFygZYF7hqow9kWcv665dx/2lyGgrpRTCwiIlJaA9vrKHZRXBjoilDGltNPP53//d//5dOf/jSbN2/mk5/85B5jjjjiCL71rW/has+Ml0SFSSk5x0wik4oSnTxEbkst+y9OWhKH9pHu0a5VIiJSPeJOlBn+ICsfS9B/ZgMk93yjaus9gnr4S+3hnPvQCu489uRRSCoiIlIem4cjRBMh6cHCZ03mA3Wbk7Hn1a9+Nccccww///nP+fvf/05HRwexWIxZs2Zx4YUX8oY3vIF4PD7aMcctFSal5CLWIch5xKYN4SZC0utrwXfYebvMWjBgYgGJ2QMYzxJ0OOoxKSIiVWNmTTPNT3Wz8ahpey1K7srWudw1/zBOzPdVJpyIiEgFhC1RfL/wa7x4rU8qVHFnzDmIl3LvavLkyfznf/4n//mf/znaUaqOCpNScn4+JNmYwRhL7dIs7X/IkqvxSE2KYKMObiqkZnMWl5DBd0JulkNNJCQIQ1ztYCUiIlVgohewZPJEqClsSU8YdQiWb4EzyxxMRESkQuqmNJDLuHiRkCC//+s841iyKRdmqTApcrBRYVJKzjGGWFOWmt9b4veMLOSO53zivf4eY+t/AIPvCHFmpjRjUkREqsadj91G0FDE26yEw+L21vIFEhERqbAFhx/KXRiCvIPjhoT7WKZtjMWGAIbktOaKZhSR0afpaVJ6yTzRZ4OdRcn9MSHU/RIcm8WoMCkiIlViTX8P1inuvBbU6X6xiIhUD3/l4EgbL9hZlDTOLuuCjQUs1hp2XDlGNuYqnFIOxFg7bh4yPqkwKSVnHUvNzWHhO2yHUHM7BCO3yURERMY9J5ooeidSERGRarLhmY17zFSx4S6f2KUguUNqa2/5g4nImKLCpJRcvr8fp6fwqzEDRB63uEYvRxERqQ7HzDsTguLu3EczQZnSiIiIVF7n1t499k050JkxyO/Z/ktEqpvWDEnJdS9/ruhjTABBEOC6hW0SICIiMpYtbJ9H9CmfbIHLs00qoDlbX+ZUIiIilZOLuYxMQ7HYqEcwtRWCEKd3GMIQElHClnqcbb04vUMjB0W03EDkYKPCpJRcPmMLX8a9izCwqC4pIiLVYMn2TuoeDcidG2AL2JnbyYUMduhiTEREqofXmsZGHGwyjn/MHIi44Di8uIFXOKUVp6MXd+l6nCbtyj3mqHWjlJnWzkrJTZsxq/iDDESiqpOLiEh12LZ9O+5jURKP+Jjh/S/RdgZ9an7sUP9YX2XCiYiIVMCUI1xsXRL/2DkQi4Czj/JDxCOc2Ix/+HScY9oqG1JERp0qQVJyc4+YixuBIF/4Ma1TIuULJCIiUmF9m9dgQ0j8PgrbfTKvAOvxwuzJ0GKGQ9zekJrrXLwuQ1CvZQMiIlI9Zk6tJThqJkQLuNaLuNhJLdTMyJQ9l4iMLSpMSsk5juHkV8zkgT+sZ49t2PbK8rZ/uaDMqURERCqn0fPg+Z1HEw95xB+B7PyQ/GF5bBTcPog94eJ17fJWTEulRESkikxsmQCx3sLPbxGXtogKk2ON0fsTKTMVJqUsms84EufOdYQZ2H9x0mLaDMlD2iuUTEREpPwmTGjCiViC56+vTAiRtQ5h2sNGDO6wxd2+e5ctL653/iIiUj1uvDWFwVL4DgSGdatry5pJRMYeFSal5Ky13Lx4OZ1ntDHl/i5Iw16Lk8YSthg2n9DG9Y8s5nOvO7fSUUVERMri6MNPwI3eSR7ItRl6zouQnumCAWvABBYTQP2jPo0P+LiELHiVLsZERKR6bN/Uiy3ynlsmrbYmIgcbFSal5Db3DZDK5wnjLs+d005DxyDt6/uxGYNxLGHg4DSGPDejlXRrHIzhyY1bRju2iIhIyXhOjENfO8RjDzfTcXGcMLn7Dbods0f6zoowtMBj5vVDnHPh0aOQVEREpDxirgOBhWJqjcH+N4yTUaAFHVJmKkxKyQ2ks8//8bJcduqDvPqoJ4m4Pk5gyWY84sk8OeuRDzx+cNf5PLp2LmAJwhB3Xzu1iYiIjCPZcIDa83rZNnUyYWzfS9hsxJBvha4PenTE7ucItHpARESqw1knzeGZjsdxnu9cEqvLMvv0jbQd2oPjhaR646y5bwbb1zQBhtBYkvnsqGYWkcpTYVJKLh7xMAbed/adnHXYMpKx3MgXIhCNB89/mANyfPi8P/OTe/M8teEYFSVFRKRq9PhreXj7FIJYAec215CORFg2sIlzG8qfTUREpBIWnn8E2b8/QU1fyHFvWsKEw7bjxQJcb2QKXtO0Qdpm95LPejzy86Pp7qxn0sVzRjm1iFSaKkFSctObGzly6lrOPGz5C0XJfaiNZ3nPWXcxb1KhDZFFRETGvv78MJuG6goenw0jPNypjeBERKR6PNHVQf8RcPqHHmfSkZ3EanzCrEPv8lq6F9cxtDlOtManpjnDaf/8BLHjBlhRlx7t2CJSYZoxKSXnuQ7vPO1RamKFTcNPRHK876zVZU4lIiJSOZtS4OcciLzwOZMKiG7MYHIhQb1HfsZIn+UdOoZrRiGpiIhIeazs6uSYI9fRMHWA9OYYq38zmZ4l9QBYC8axRGoDZl28lakv287LLnyKa+7X0oGxxqjHpJSZCpNScqHtoqWus+DxnhvSVn8n8N/lCyUiIlJBTr6FMDAQAW9rloYbt5FYNDjyxdCCY7Axh4ELWhl8ZQtEHMKsdiIVEZHqsa5nGwsXbKT7sQaWfH8m/tCe5Yf8YIQVV09n633NLPiPdUxv2zYKSUVkNKkwKSUX2q04Jlfk5l1prA0xRt0FRERk/EttGMQEEH1mkLZvbMQZCtijaclQQOMNHdTc18u2/56NzamtiYiIVI9JjV0Mr42z5Hsz8Yf3XXoIMi59K2tZ+d1pHPe+dRVMKCJjgapAUnoWDPvvLbmnLOx5ySYiIjIutacTRDdnaPvGRty9FSWf5+QskecyTPjCOhqf1E6kIiJSPdrqAtb8cvJ+i5I7hDmX7sUNRPrDCiSTothx9JBxSTMmpeQipvi/Ci4WY1SYFBGR6lBTG6f5Z1txhwIAbMIhd34d+RNrIOpgun1if+rHfSaNE0Bkc5a6u4fgc6ObW0REpFTmRCdz3+augsfnhx22/mECHFPGUCIy5qgwKSXnGoe4iZGymYLGG6De1BDaAIz6a4mIyPg3aUYb7uY01kDmXS3kXt4AUQPxFxarBMcmIR2S/HIH3vIMdkA7kYqISPUYXlVLmC1i8ol16F/cVL5AIjImaSm3lFxIHY1OPUFY2MvLhg51TlJFSRERqRpLnniW0DGkP9FO7oIGqHd3K0oC2DoXOyHC8Ocmkz8ywXDf0CilFRERKYNUPaFfXMnBz0TKFEZeKmPHz0PGJxUmpeRCZtAxMJGfPXo2Q9nYfsf2ZxJ8/m8XkQ6Oq1A6ERGR8rtr+Qr8M2rIn1ADyQPceKtzSX96ImFULU1ERKR6bMsMY2LFVYtynnpMihxstJRbSs8Yvr/oVfzxWZ/NA02876S/URfLUB8fWdodhIbhXIzNA818+/5X0D3cwO/XzeMV80Y5t4iISImkY5C7tAlqClsNYGMO+bNry5xKRESkcszcIYxrC959wERD6s7S6gGRg40Kk1Jy6Xyev2/wsIQ8sfkQ3n/zIRza0sHxU9dQG8vSk6rlgfVz2TbUuPOYaxZtV2FSRESqRv3Rzdj8hsIPSDhk36C+WiIiUkWSKWqOTjP4YA3YA68KMFFL41mDFQgmImOJCpNScpv6BzDsfuJZ3T2R1d0T93lMbzqDtdqZW0REqsOc5mbu73Mo6qxWq17LIiJSPWzK0vLGHtLL4/g9LuznrOgkA1ov68WYoHIBpTDq3Shlph6TUnK5ICj6b5cFQqu/eCIiUh36egbAKe5mm67FRESkmmy7bwAnYpn22a1EJvg4ib2c6JwQpyag5dI+Gs8dIvOsJqqIHGw0Y1JKrrUmWfQxrjG4jurkIiJSHZycGdkdsojNRd0+3aATEZHqse7pYVpOMcSmBcz82maGnkjSc0sD+R4PLBjPUn/GEE2vHMRrCggGYPvNCbh0tJOLSCWpMCkl115bS2tNkoFsFoDJtYO8c8HTnDV9A3HXZzAX5ZZn53HjisMZyMUwBs4+ZObohhYRESmhqRPbiKwPyScLu+nmhgHxR114bZmDiYiIVEiqxif8RZxJ/5bCrYW6E1PUnZja61jrQ77DJb2uiDt6UhFG902lzDRFTcri7cccTdSxXHX2ndx08Y286fAlTK8fYEJNitlNffzLcY/xl8t+xTuOXETUcXjTUQtHO7KIiEjJHHnIdObHOogYv6DxUTdg8lFayy0iItUjOivB4BNx+n4fI9jPZtthHvJdhk3/VYPfoLlTIgcb/dZLWURcw7fOu50TJ28mGdnzoizuBcS9gA8d+wQJz+I6/zQKKUVERMpjQjTBvMM7CIctKwfaydt9v+WKOXnOnbiCtqkzKxdQRESkzCYc2cTa/9tE9y8TZDc4tL4ji1sT4tSCcSDMgM0Zhh726PpRgrzvMviK2tGOLSIVpsKklMWqjuv50DFb9lqU3FVdLMc7FzzF1cvv5u3HX1ihdCIiIuXV7a8iGvU4KrqZGi/L4r6p+KGzs0BpCIk4AfWRDKe2rqUuksWYraOcWkREpHSO9Wt5+oRaGh4YYOieGEP3RInPD4jP9TEx8Dsdhh6OYDMjG96EtQ4LT9oyyqllD9qkVspMhUkpua6hYd4w915qo/mCxtdEs8xI3gKoMCkiItXBJ4sL+MCcui5Oa1xDPufxbGoCmSBCc2SYw+q20UeSwTD+/FF64y8iItVj9oTNHPumzSxd20p8S4bmeRkOeWsP9XOzGAP5IYcNTQ1s+XMDWTx639LMpw+9e7Rji0iFqTApJdefXs9hieGCx3sOnDN9Ffkw1M7cIiJSFaKmFsd4xMhzcmI9URMQSYSc3LBh5xhrIY/D5nwjy3PtOHpbJiIi1WRylvfU38e/f+BSTp62mtqGLNH6cOeXY80Bh72/m0Pf3csDT8/hzQvvoa42O4qBRWQ06B2wlFzS68UExc36cG1IQIj2YxIRkWrQ6h2GZ0NOTK4nYfIYs+cYYyBKyNRIL9YarKstuUVEpHqkYlEmmJDLTn2EVBhhb3NQvITFSwS87LTlzE50kM+pRCFysNFvvZRcnVsDxW4sGlqMccuSR0REpNIc4zI5yBDfR1FyVxFjmRrpI5M/ojLhREREKiDtT2VV2EoWd69Fyd04hiXZycy2/RXJJoUz6jQjZabpaVJyPdtbDngR9mL9/Ql11hIRkaqR97O0x9bgFHg+9Ah4ruea8oYSERGpoGk1PWwNGrAFlh0CHIZMpMypRGSsUWFSSs4Pojz6xDTC8MBjAVJpj5t+d5Q2+xIRkaqxpvceHAo8EQKOA5MaHi5jIhERkcrKOV1kg8ILjSEO6/NNZUwkImORCpNSci2tdfz6hsMZHIoVNH5wKMa9903HKXRaiYiIyBj3XMdyXKfwwiSAa4obLyIiMpatGvJxijwX5q1KFGOOHUcPGZf0Wy8llx9MM5gxfOVbpzEwGN3nuDCE7p4En/rvc0k42yqYUEREpLy2PddHGBb3NksrB0REpJqkg3ZCW9zkk0AlCpGDjn7rpeT6uro5/S2LWbxiAv/vs+ezeHU9G7MZ1uaHWOcPsTY/zKZMjvufmMS/fOLVdPcnOPPdiwkDzRQREZHq0LO8mZDiLsaWrZlUpjQiIiKV15mJkA8L3+A0tNCRritjIhEZi7Qrt5ScU9fF9CnbWHDWGs49cwm0DpB3feD52dXGknZyNB66kde8xbDNTzLxyAGMlnKLiEiVaKufyKJl0zj9mFUFbYAzkInztx/N4YLjy59NRESkEnI2zlAuTsIbKmh8NvB4snMGHFLmYFIUdZqRctOMSSm55qk1ODHLW975d6bN2k5NbXaPMY4DNXVZjj9tNWeftQwTNVDEJgEiIiJj2cJjT+COr81hMJM44Nis77J2Uxu9K9TwX0REqkf/QMADWw8hExx41mQQGrZnaukeqq1AMhEZS1SYlJLLejGaIhmiJsB1998wy3NDap088UgeYwqf5i8iIjKWzTlyKsPb6vnup06jL5XAD/c+bTKVi7B2ywR+8e5jOO68IyucUkREpHzq/To2DzTxcMcsMv6+r/X80NCTTfKnDUfipFWiEDnYaCm3lNywn6TVTRW0dA3AwVLrBIQ2xDE6EYmIyPgXjUU448ITuPs3D/Lv117MBecv5WVTV+78ugH6cwluWHUcy7/eTGM8wuvf9bLRCywiIlJiCxKz+d32lSztnUJPtoZTJ66lMZrGMSNdmAPrEFiHJT2TeHL7dOyQpblHMybHHG3OJ2WmwqSUXNTpwhax+9r/b+++46Sq7v+Pv++9U7Y3WJZeRBakKaLYNXZMTEF/lmDE8o1dYoxRMaJRkwiWmK+J5vs1sQZ71K+xE0WwBdEgSqQJKGVhlzbsLtum3fv7Y2UFt83A3Jmd2dfz8ZiHy865lzccL2f2c8851zCkQqtRRpwPCQAAoCv74QVH6285X6q+r0f/s/pYPfjlUeqXUy2fFVF1KEebmwokScZZtgasyFWfgT1SnBgAgMQZ0aeXCv+wSTVn9FKlivT8lwcq39uk0uwdsgxH9WGfKhsK5ciQbEee7SHtu4MqGNDdUJhEwvX0SNtD8c58NNW8xyTLuQEAmeGBf72j+v7elqEt7Hi0pr5nq3aO39T8/RpUXV+votzcJKcEAMAd1Rv/o4K5AUX6elV/RLGcXEs7wlnaEc7avWHUkVUdVq/ffKnoAeukK/8rNYEBpATrZpFwPrNAVpz7RfoMD3tMAgAyhuM4eim0Pub7bY5l6PbXXnc3FAAASfTRwoUyQlGVPLhRRc9UydwellEf+aZB2Ja5I6LsT3eoz7RV8m6PaMeKhtQFBpASzJhEwpnGPvIY8T1h2zBKXEoDAEDyvbdkhaKeOLYoMaV3jM3uBQIAIMnqGhvl2M37Khe8sU35s7ep6YB8NY3Ile035K0KKfeDalk7oi3HRBqZrNLVGKyuh8soTCLhbEW1w/arwAzG9ACcqCMFbJ8oTQIAMsVrK5bH/SnLjqeQCQBAF9cULpTts2QFmwuPhiNlL9qh7EU72j0mZGW1+x6AzMRSbiRc0F6nGrtIdgwPs3EcKSJLO+yIHIdbMQCAzNAYbUx1BAAAUqqusL+cOG66Rf2mag7p42IiAF0RhUkknK2worK0IZKviGOovXqj7UhhmaqI5Kt5gn98y78BAOiq9imRFOf9NjPsShQAAFJiQ1ONdhzUU7YVW3HS8ZqqObSHy6kQN8dJnxfSEoVJJJz36/0iQ/JoXaRQ1bZfEcdQxDEU/fq/YcfQNjtb6yKFsmXKkMHDbwAAGeOs8aUyQ3EcEJWKd9ljCwCAdBdSo7afMkDh0qxOi5PRbEubf7KvHB8lCqC74apHwvnNPi3FyahMbbVztSZSpI3RfFVG87Qhkq81kSJV29lyZEgyVOQ5JrWhAQBIoH5F/VTWEFRWTNVJR9lWSD8ft9z1XAAAJEuJPyhZpjb8fJRCQ3KlPKPVk1SMHMnOt7Tp/GFqHFEkq5GbdEB3Q2ESrujjmyJTOS2/dmQo6HjU6HgVkkfaZf9JS3nq4/tJClICAOCOqEbo5pPe1ncHf6ZsT7DddoZsFfoadfUBszVx9PAkJgQAwF37NpkyIraKejRp4rObdcTfatX/ByHlDIwqq09URWMj2v/WOp0wt1pjT9kiRRyVfLQt1bHxLYaTPi+kJ57KDVf08JykbZHXVRf9TLba/4HMVI7KvGcoyxyQxHQAALjLkSNPQb1OKNiispxavbD6INWF/aqP+CUZ8hgR+ayohhVV6exhC1Toa9IGW+qX6uAAACRIsdFXZeuW6nsTP1euPySNkIpnRNpsO27QGtlBqXoZJQqgu+GqhysMw9SwrLv1ZdMtqo3+W1Ht0K5PATDklals9fZNVh/vlNQFBQDABfX2Utlff8wa03ODxvTcoPU7SrSyppeCEa+K/A3av+d65Xi/Weq9JfKW+vmnpioyAAAJFWry68ixK5Xj63xbE783ovGDv9K7OfsmIRmAroTCJFxjGl7tm/07NdnrVRV6UrXOfDlOWJaRo2LzFPXyTZLXKE51TAAAEi7sbJej3WeFDMgPaEB+oN1jHKf9FQYAAKQbu4ehnuPqZcT2UG55PLb6nbbD3VAAuhwKk3Bd3ZYCffDsgVq8oFChpohy8v064qSROubUbHnzU50OAIDEM+WTEfdW3mz9DQDIHI35q5XrjX3jP8vvqN/x1e4Fwp5h70a4jMIkXBON2Hrozle16F8r1VgflB1t/hdt22bp+Yfe1ctP/Es/Ou9InXzGhBQnBQAgsXKtEYq30JhjlbsTBgCAFMjOCcv0xVfV8mbxVG6gu+HWPFzhOI7+eNPz+njectXXNrUUJXcKhyKqr23SCw+/p1efnJ+ilAAAuMNjFCrPGhtze0v56uu7wMVEAAAkV8jnkW3HV3KIRilRAN0NVz1c8dHc5Vq2aK2CTeEO2zXWB/Xy4/O1eeP2JCUDACA5+vsulaWCGFpa8pv9lGfGXsgEAKCrK+0/RtGIFXN7O2po3bZeLibCnjCc9HkhPVGYhCv+8bf31dTQ+dPXpObi5OtPL3A5EQAAyZVjDdU+/ps7LE4a8irLGKDh2ffKiPXpAAAApIHC/DJV1+bE3N6JGvrK3sfFRAC6IgqTSLhtm2pVvbUu5vaO4+ijectdTAQAQGoUeY/QiOz7VWgdKUsFsow8WUaOvGahPEaJenvP1cicR+QxYplZCQBA+ijP66X8vCaZsjtta8hWvq9B+xb2SUIyAF0JD79BwlVvi70ouZMdtWVHbZkWtXIAQGbJsfZVefZdCjvVMnO+koyQPEax7LqhMozYl7gBAJBO9snNUWWkSis/6CVrlC1PTlRmG8NeuN5U7Rf5GjVqnU4fwBZfXY7DGmm4i8IkEs7jtaQ9WI1mmCxhAwBkLq9RpJLsI2VZpqJRW4H6+lRHAgDANTXRj7RpUQ+9f+8YefMj2mdSpXofsb2lzmUYjho2ZWn1s321ZWGh1o8s1bl3z5N0cSpjA0gyCpNIuN79i+O+qVJQnMveWgAAAACQISJOteY+sJ+iTZaiTZaW/mWwlj8yUNm9gjK9joLbvQrVeFvaV60sUdVXFRozOoWhASQd62aRcP5sn8Ydvm/Msyb92V6des5h7oYCAAAAACTNlrVS3dbdH35jh03Vb8jWjjU5uxUlJckJGXpv1pBkRgTQBTBjEq74wblHaNG/Vqq+tqnTtrn5WTrkuJFJSAUAAAAASIblC/PkhL+Zr+L3R3TM8eu1/4Gb5fPZqtyYqzdeGaKNFfmSmtut/7wwZXnRNoMtJuEyCpNwRe8BJbr0xh/of3/7kup3tFOcNKTC4lxN+8M58vn5XxEAAAAAMkXV1qjkNO8led5PP9ex312r1VaWNhk+RWSo4MBt+tXJGxTZ5NPMWw9RVWWeImEWdQLdDdUguGb/w/bV9X+YrCfve0vrV29WKBRRJByV3++V5TU15uB99OMrjldxz/xURwUAAAAAJJAvzy/Hkm749Ydq2L9JLzg91dTgUfXmPNkRU1mFQa3pUa/sPram3/eebr/qSG2oLUl1bABJRmESrhpc3lu/+uNPFNhcq/UrNykUDCs3P0tDRvZTdq4/1fEAAAAAAC4YeeAg5dfOUtXYiFZX9dTnb+6rqmU9ZRiSvl4e7C8IasQJX6phnKlr7pyvX907JaWZ0QaWcsNlFCaRFCW9CrTviD6yLFPRqK1AoD7VkQAAAAAALjl4v4Eq7L1eb34+UB89NVbhBq++/YTUUINPi/4+UusW9pVx4UJddkFuasICcWpoaNCkSZO0Zs0aXXnllZo6dWqqI6UtNnAAAAAAAAAJlW0u1oKNZfroybEKN/j07aLkTpGgV1tXFeuDp8do8JDXkxsS2EMzZ87UmjVrUh0jI1CYBAAAAAAACbUlvED/fnGEwo2+TttGwx5tXFaq9yobk5AM8TCc9Hkly7x58/TMM88k7zfMcBQmAQAAAABAQq3fVqfqqtgfdBpu9Oijt4e4mAjYe4FAQDfeeGOqY2QU9ph0wTXXXKNXXnlFEyZM0KxZs1IdBwAApNiaLzfrH8/9WyuXb5RtO8rO9umY40fquIljlJeXlep4AAAk3KqVRYqGgrEf4JjasLTUvUBAAkyfPl1bt27VaaedphdeeCHVcTIChckEe/XVV/XKK6+kOgYAAOgCmhpDuus3L2n9uq2q2/HND2fV2xv03NML9Mr/faIzfnKYTjxlbApTAgCQeEZ4qJzosriOsSNel9IAe+/vf/+75syZo379+unGG2+kMJkgFCYTqKqqSrfeemuqYwAAgC4gHI7otl89p4p12xSJ2K3ej4SjqgtH9fTfPpDk6MRT9k9+SAAAXFKS55fptRUNxb6DnM+fxI0CERubPpGkdevW6fbbb5dhGJoxY4by8vJSHSljsMdkgjiOo2nTpqmmpibVUQAAQBfw+j8WaUNFoM2i5K4aG0J69vH5qq1pSFIyAADc5x+wSbbR9pO422J6oooO5uE36Hqi0aiuu+46NTQ0aMqUKTrkkENSHSmjUJhMkMcee0zz58/X0KFDtf/+zHgAAKA7s21Hs1/9TOFQNKb2TU1hvfn6YpdTAQCQPGsbQjIGBSXFNuPO8Uhb+7GoE13PAw88oEWLFmno0KG65pprUh0n41CYTICVK1fqnnvukcfj0Z133im/35/qSAAAIIW+XLVJ4XBsRUlJikZszXtziYuJAABIrpqolH1ctYy8zsdDwx+V/8ga5fZkxmSX46TRywWff/65/vznP1PvcRGFyb0UCoV07bXXKhgM6rLLLtPo0aNTHQkAAKRYzfYGReIoTEpSKBRxKQ0AAMkXaLJkFUaUd+5mGQURydfG1iaGI2VF5T+8VlkT6pSVG0p+UKAdTU1NuvbaaxUOh6n3uIh50nvp3nvv1bJlyzRmzBhdeumlqY4DAAC6AK/PkmHGvq+WJBlx7MMFAEBXl2N6ZRiSWRxRwaUbFVqWo+D8Atl1liRDMh359quX/5Adsoqab+YZTJ1CF3LnnXfqyy+/pN7jMgqTe+Hjjz/Www8/rKysLN1xxx3yePjrBAAA0uB9SmXHuaao38AeLqUBACD58q082VFDlukoYpiq39dRaEhd85uOJEMKGY4cr6kcJyrDkBTmZ+quxuimD+V+77339MQTT8jv91PvcVm3/Zv985//rHvvvTeuYyZNmqSZM2dKkurq6nT99dfLtm394he/0NChQ92I2aHi4py0ml1hfj1zxDQNlZTkpjgN9gR9mP7ow/RHH6aHkpJcBctM6avY2kf90j7fHUifpgGuwfRHH6Y3+i99nLzPwXp/1Tw1hg3VBLPk7LqT3Nc/xkYdqTaUpaaIV0WeRpU27EO/okt49dVXJUnBYFDf/e53O2x733336b777pMkzZkzR/3793c9XybptoXJvfWb3/xGGzZs0KGHHqopU6akJIPHY6Xk991bhmHIstKnoIrW6MP0Rx+mP/qwa/uyJqDAQZYKN0pWsOO2jiFFcqVXo1/q59ZxyQmIvcY1mP7ow/RG/3V9Bwzsr6YFBarNj8hRR31lKGSbqgln6baDfyDLYj030J1028Lkj3/8Y5188slxHZOfny9JeuONN/Tiiy8qPz9fM2bMSNmsxUgkmnYzJg3DkOM4su1uOh88zdGH6Y8+TH/0YXr4snqbavMiajrKUtm7UVnt7OVvm1IkT6r4nld5dXWKRtt4MAC6FK7B9Ecfpjf6L73U+5xOipI7mQo5hhqjjV12LOy2BVOne15nt912m2666aYO2xx44IGSpEsuuUSXXHKJJCknJ8f1bJmm2xYmi4uLVVxcHPdxmzdv1q9//WtJ0o033qi+ffsmOlrMtm9vSNnvvSdKSnJlWYZs21EgUJ/qONgD9GH6ow/TH32YHrZsqlU0Yqt+kKX6AYZy10Vlhr/1wd6Qon5TWw6xZGcZCjaE6NM0JDeXkwAASC1JREFUwDWY/ujD9Eb/pY9Pq9Yp6o9KMRUmm7ednPHpC3ok73JXc+2p0tL8VEdAEvl8Pvl8vpjaer1e5eayBcGe6raFyT31/vvvq7q6WpI0bdo0TZs2rd22H330kYYPHy5JmjFjhk477bRkROxybMfRJ7Vf6pOqL1UfDarEm6cj8kZo35zeqY4GAIA7KhpkRB31elfyVZuK5pqK2o6MiCNDjhzDkOMxJMNQj08lx5KUFUl1agAAEuaVqo/jfAycoa3+anfCAOiyKEzCVW9v+1wPV7ytkBNRffSbTbZetxapwJOja4Z8X8NzUzfrFAAANxSHvSpYIfkDkhn9+pumIcdntPohzQxLPRY68pj10gXJTgoAgDt2OHXxH2R2z2XDQHdGYTJOP/jBDzrdm/Kiiy7SwoULNX78eP31r3+VJPn9/mTE61Ker/pQz1T+S/V2UNphSpv8UtiQ/LZ29Atqh79JN698Rr/aZ5L2Lxic6rgAACRMflGOClbH3t6ISnlb+VgGAMgc+R6/1M4ey+3pprs4dmkGtWK4jE/AcfJ4PPJ4Ov5rsyyr5b/ddZ+B5XUb9EzVfNVvjsj4uECqtaSwIcMx5JiO5HGksrDqDm7QzC9f1F9GX6J8T3aqYwMAkBBWUa6MqNG8RDsGpm0o2rPA3VAAACTRdwccoA9XfxnHcm5HpVb3m9CD9LVixYpUR8gI3JCAK56ofF/1X0VlzCmQsdUrI2TKcJo3PTZsQ0bIlNb7ZLxWqIbtEc3e8mlqAwMAkEDbaxqkeJ/e6YmxigkAQBoYmz8s7ic6j4uOcCkNgK6KwiQSrjbSoOUbK2XMz2suQLbDkCGjyVT0zVy9VLkwiQkBAHCX4zjx/iymcDjaeSMAANLEvKXL5UTjKDk40r8q1riWB3vISaMX0hKFSSTcpmCNIp/5pJAR2wEhU3WrHdnx/gQHAEAXFQ1GFeMo2MKJUJgEAGSOJTXr4zvAMLSjsMGdMAC6LPaYdMGsWbNSHSGlgsGIIhWWjBh/JDMihiL/8Ur/z+VgAAAkicc0ZNiOHCvG8qTtKJv7xQCADOKYThtTob49GeVb46TFZBWgu6EwiYSL1hpfP7orjrkiwVjLmAAAdH3Z2T55ZCjkOJIRwwhnSJ5Y2gEAkCYGl5RKAUl+yTJs9cqqVZ/cWpmGI309PFaHsrShvkgNkeaH3mRFfakNjVYMVjbCZdyaR8JlGz5Zcf6v5TUslnIDADLG0GFlCpuKrSj5tfxe+e4FAgAgyU4Ztr8Mx1COJ6QDelRoQF61/FZUXtOW17LlMW31zGrQqOIqDczbJoUcndBjdKpjA0gyZkwi4UoKc+Q3vWpQOOZjsiyvLJM6OQAgM6ypCMjjMRWO2rEdYBiqaQy5GwoAgCRqcpqUHw1pWFGlfFb746HHtFWWvUN2vqmCnoyFQHdDJQgJ17MoT2XFsc/6MAzp6AP3dTERAADJVbujUaYnvo9ZrBwAAGSSZfWrNKj/5g6Lkjt5TEd9Cmr07+pP3Q8GoEuhMAlXnHXygcrO8sbUNjfLpx99Z6zLiQAASB6/zyOPFd/HLIM9JgEAGWR11QZle2JfRWcajmRsdTER9oidRi+kJQqTcMXhY4doXHl/+X0d7xaQk+XV2SePV1kP9tUCAGSOfYf0irvQOGxoL5fSAACQfNXBjTKM2FcDmIaU42tyMRGArojCJFxhGIbOnzxCPfbbKisrIpm7374wPFF5siIacvQmnXjU4NSEBADAJbm5fo0Z2S/m9nm5fp3+g/EuJgIAILnyormKdy2AabN6AOhuePgNXFEXrdHTgXs14MRalR3m1aZPShVYVSw7YsqTFVHp6K0qHb1NHp80a8s9uqjsJpkGdXIAQOY4+/QJ+s/SDaqrD3bYzmMZ6t+3WMP2YcYkACBz9M7vobW2ZFixH2OGYtsODMljsAc2XEZhEq6Yv+OfarDrJEm+vLAGHL1RA47e2KqdI6k2GtDqps81LJt9JgEAmaNfn2L96ooGzbw/otp6n9TGvBG/N6z+fep18y++wx6TAICMcvjgw7So4nlFYpw3acrWEGO0y6kAdDUUJpFwUSei/zTMlxPj7rNBp1Hv1r5CYRIAkFG89gc6ZMTf9efpph57aZw+XDxAkuQ4hkzTVpYvov934hL98LilsrzvqNp5WTKYKQIAyAyekKVBvjqttmN7noBftvarrnY3FIAuh8IkEm5bpErxzvbeHtnsThgAAFIkx/mzTNWpX5n0q4veUUOjV6srShQKWSoqaNI+/QPaOUnSVoN8zlyFjJNSGxoAgASpWvWSvtt/gx5q3FcNTselB78R1fjsbdq355uSrk1OQMSGldxwGZv6IeFCdkh78q+X7cQ2wxIAgK7OdCpkqnK37+VkhzVm2CaNH7VRQwd8U5SUJFP1ynYeSXJKAADcs63hKw3KbdB/Fa9SrhGW2c6KuiwjorFZ2zWpoEL5+fVJTgkg1ZgxiYTLsfKat9GKozZpyODhNwCAjGFpjaRoXMeY2uJKFgAAUqEq1KSobWgff72uLV2md+p76ePGHi2r6xwZ6uNt1El5lRrm2yHDkMJRU77UxgaQZBQmkXDFVqn8RrYaFfvdriFZ+7mYCACAZLMV/+oB1koBADLH+uzBamzyyOcLqdAK6wcFG/S9/A2qjnoVtD0q9gSVbX4zi9K2pf9sKNX4vikMDSDpKEwi4QzD0KH5J+mt6ucUUajT9llGjg7PPyUJyQAASA5bA2Tblsw4FgPYKnItDwAAyebrOULbarJUWBBSY9TSixuG6i9f7a/qsL/lOd0nlq3RJUP+o33yalRX59Vftx2g8SlNjVbifYAEECcKk3DFAblHaFH9e9oS3ii7g6VsXsOv4dnj1Ns3IInpAABwV9QYoprtXvXoEVv7pkZLm+t/oNwyd3MBAJAsp/YZpTvmHqwr/Z/q4s9PUm3Yp0bbu1ub5yrK9c+qwTpv4BIda65XjwFjUpQWQKqwqR9c4TG8mlL6S/X2DlSWkdNmmywjR6OyD9L3is9NcjoAANwVbAzp8UeHq77+m3vAUcdQRVOuVjcUKBD279a+qcmjJx/KTXZMAABcU5ZVpC8KynX2J6dqUzC3VVFSkhyZqolk6aE1Y/Tj1d/TFUO/k/ygAFKKGZNwjd/M1gW9pmlt8Au9v+NVbY5UqHmLY1P7+Efq8PxTVOplAxEAQOZZvXSj3p83WGPHrlf5hG16rnqYnq0aJttpXrzmSOrrr9dlA/6jCd4q3X7LIVq/fkNqQwMAkGC9PaVa63Q+vtXbPnlMQ3lm25NakDoGK7nhMgqTcJVhGBqcNVyDs4arpCRXlmUqGrUVCMT+YBwAANJNY0NIdtTR7x6aoO1ej4IeSxFZu7WpjmTp+hVHKGdDRAVLHOXn2e2cDQCA9FMXDmpZ9TapZUfJTjiG/m/dUp237zhXcwHoWljKDQAAkGC5+VmKFpmqPD9L9R5fq6LkTg2OV9t6ZqnqTJ8si49lAIDM8eGWCjVGwjG3jzi2Xl6/3MVEALoiZkwCAAAk2NCRfbX5KEPRGFakOT5DTYMtDT5xkPvBAABIkppQk8K2HfOESUmqDja5Fwh7hqdyw2XcmgcAAEiwiOWofj+PZMb205idLW09NI6f3AAA6OK21jU0b6och6ZwxJ0wALosCpMAAAAJtrS2Sl5f28u322QY+k9wk3uBAABIsgLHH19h0pG8QUoUQHfDVQ8AAJBgDdGQ7DgfY2k7PPwGAJA5sk2vjNi3mJRsyVsXx009ABmBPSYBAAASLM/jV7x1RsvgfjEAIHPUNwTlrfYoVBpRO8+A+4YtmY2GFEpKNMTB4L4pXMYnYAAAgAQbVdBbwUg09gMcaZCnp3uBAABIsnyvX0bIkCdgSlG1v6zblowmQ56ApawoMyaB7obCJAAAQIJV1dbJU+eTYp1lEJW2rGOaCAAgc/gsS2ZIspos+TZ7ZNYbzQXKXV5GSPIETHkDlgwZ8jg8CA7obljKDQAAkGAVNbXybPMrlBOUzE72moxK2uFRfR1PIgUAZI4sy5IVkhxLkgx5qz1yqh05HkmGZEQlw/66EGlLngbJ42fuVJfjxPlodSBOXPUAAAAuMG1T+ipbCn49Q6QtEUnVHqnSH9eDSwEA6OqCTVEZtmQ1SWZIzUu2HUNmxJAZNmREjeaVBdHmoqQRlcwgoyHQ3TBjEgAAIMEGFBbKlCFvjSlncY6cHmFF+4Yl6+sfuAzJ2GHJ2uiTUW/J9ko9emSnNjQAAAlkO7Y8ISmcJVmh5uKk7VHzjElJcpq/bzjNXxu2ZFks5Qa6GwqTAAAACTagqFDesKFGRzJlSFt9srb65BhO83qVqGTomx++rIj03WHDUhcYAIAE612UryzblB20FfVLhtE83unbO5c4al7K3Sj16pmXgqToEJNY4TIKkwAAAAkWjkYVDdn69rwPw2lnWbcjbdxSm4xoAAAkxZjBvRVpCsvyWZIt2X7JMaSWwfHrgpcZbp45aYWjmtCjZ6riAkgR9phEUjTWB/Xp/FX611tL9Pm/v1Ik0t5mWwAApL+lGzfLMuJbjvbBqrUupQEAIPlWLVqrgnU7pIgtKyp5G5pfVlPzy9MkeeslT+jrWmXE1pcv/yfVsQEkGTMm4arNG7fr+Qff0eKPvpQcybYdWZYpwzR01MQx+v65Ryg3PyvVMQEASKgdTSFF7PjWPkVt26U0AAAk36a1W+VfulXeQp/CxVmSx5ThfL2c+1uMYERF8zdqi5+fDbsag6dyw2UUJuGa1Us36PfXP6u6msY233/j7x/ro3nLddOfp6i4Z36S0wEA4J5snyfuGZNGnO0BAOjqTEnF71eo9sAyBfvkybEMyfpm4aYRjMoMR1W4YKO8NSGpNw+CA7oblnLDFYHNtbpn2t/bLUpKkh21tXVTjW6f+jhLuwEAGWVkn14y4yw0jhvQ16U0AAAkX+8hpcrK9ctwpMKFm9Tzn2uUuyIg75YGebY1yr++VkXzN6jHP9c0FyUl9RnMHpNAd0NhEq545Yn5HRYlWzhS7fZ6LfpgpfuhAABIkmyfV4fuM6DVw2/ak5/l1+RD93c1EwAAyVQ+brD82b6WX5uhqPKWB1TyXoV6vLNeRR9XyRdoahkrcwuydepPj01NWAApQ2ESCRcORfTh20vlxLgXRWNDSP947AOXUwEAkFwXHDlehdmd75XltUyN7NtL+/bqkYRUAAAkh2EYOvW/jlVWjr/le019/Np+YIECEwpVu1+ebM83t/ByC7M18pB9UxEVHXGc9HkhLbHHJBKucn0g7mO2baqR4zjsrwUAyBi9C/N11Q+Ga8aLCxUOmXKc1veDLU9UxT2CuvH7R6YgIQAA7jr+7MO0/OPVenfTem08vEDRLEt2limZhoyQLSPiKH95nYYuatK1D1zEz4NAN0RhEgkXDoalPbhZ4diODIuBCACQGbaFq7TQ86QmnBLS2mW9VbXmm32zHEfyZ4U1aORG9R1Uo+er79N5pdfxAxkAIKMYhiHf+SNU9Z8mhc3df0h0fKYcn1R7QIEqD++rrLLcFKUEkEoUJpFwBUXxDyiGYci02FkAAJA55ta8qEa7Xv5sqfzA9dr3gAo11vllR0x5s8LKyglLar6XtzVcqY2hr9TPv09qQwMAkEDz1n+lZ75YoqDZ/swVxzS0Kdign897XY+cPCmJ6RATO9UBkOmoBCHhSvsWKbeg8z21WhjS/oexlwgAIHM02vVaG1qx2/dM01FuQZPySxpaipI7NTkNem/Ha8mMCACA6/5n8ceqC4c6bWc7jirqarU8sDUJqQB0JRQm4YpTzzlc/mxvTG1zcrN06jmHupwIAIDkqQytVcSOxnXMxtBXLqUBACD5Vm7fpu1NjTG3rw0F9ejSRS4mAtAVUZiEK448eYwG7lsmj9fqsF1Wtk9Hf3es+g0uTVIyAADcF3ZCCtmRuI6Jt5AJAEBXtmL7VjVEwp033MXywBaX0mBPGY6TNi+kJwqTcIXlMXX9PT/WiAMGtrms2zAN5eZn6dgfjtOPrzg+BQkBAHCR7VfYju8DcmOUTZwAAJkjYtuy4ywWRWzGQqC74eE3cI3P79V1v/+x1qyo0stP/EsrF6+X4ziyPJYOOma4Jp55iHr2Lkx1TAAAEm57fY4cJ/YnbNu2tGF7iTTIxVAAACRRr5xcZXu8u+wx6Sg7J6TsnCaZpqNw2KPamhw59jfzpXpm82RuoLuhMAnXDR7eW1NvO00lJbmyLFPRqK1AoD7VsQAAcE3QtrUh0EuDSitldfAk0p0itqUNgT5JSAYAQHJM6N1fXrO56FhQVKfefbfLsmxZHluGIUWjhvo6hmqrc1RZ0UNZhk+TR4xJcWoAyUZhEgAAIMGKfNmqCPRTaeE25fqCMjvYPCccNbV6S6kcOzt5AQEAcJnHNDUht7c+LfpEPcpqZXl2v1FnWY4kR0UldcrNa9K6Jf10bP8hqQmL9rF3I1zGHpMAAAAJNrq4lwx59d7Kcu0IZikUaf0wONuWQhFLqzb30peb++iUfsNSkBQAAPdUL1upHr1aFyV3ZZqS1xfRPv0rVbl+exLTAegKmDEJAACQYJZhatLAkfrbqkWat3y4eldsVsmrlYquikiOZHglHZur9UcOUn1urgq8fp09ZGyqYwMAkDAV67ap4eAqWd7OZ9yZpuTkhfX0W3N13U/PSEI6AF0FhUkAAAAXTBk6Tm9+sUzOb/8t7+YmhRuiu71vP1evsldWqO7Ufpo09VSVZrHhPwAgc/x7zTI5eVHF+ig4I9fR6l5fuJoJe4Cl3HAZS7kBAABcYIVsDbpjhbLWN8j6VlFSkkxbshqiKnm1Sr1mb05BQgAA3LPF2ib54ytqRcuaXEoDoKuiMAkAAOCCV/5ntrat2ypFO/6hLFof0sv3va7qzTVJSgYAgPuyc72Kebrk10zWdALdDoVJAACABLOjtuY8OlfhYCSm9k31TZr94FsupwIAIHlGDRksIxRfZbLYV+hSGuwxO41eSEsUJgEAABJs9aKvFAnHVpSUpGg4qvee/ZeLiQAASK4xBcPltbyxHxA09MN+x7kXCECXRGESAAAgwWq31sZVmJSkcDDsUhoAAJLPY1g6pucEKRzbrMlsb5YmFI91ORWArobCJAAAQIL5sn0yzfg+ZhlmnBtxAQDQxZ1edpLy/DlSZ8/AcaQL+p0uj8Emk0B3w1UPAACQYEPGDpIRZ2FyyNjB7oQBACBFPq1bpqginT4ExzCkVwJv68CCUckJhpgZTnxPVgfixYxJAACABMsrztN+h5bH3D63KFeTfvF9FxMBAJB8L259U412sNN2jqRt4e36qnG9+6EAdCkUJgEAAFxw5q9OU15xbqftLI+p3kN6adhBQ5OQCgCA5FjXtFG10bqY29fbjXotMM+9QAC6JAqTAAAALug/vJ+uuP/iDouTXr9HfYb21rRnrpFhsMckACBzrA9WqtFuiuuYrxorXEqDPeY46fNCWmKPSQAAAJfsf/wY3fyPaXp2xv9p2fwVkhw5UUeW15LltXTi+cfqe5dNlC/bl+qoAAAkVE14hxynef/IWNXbje4FAtAlUZgEAABwUf/h/fSLR69U3fY6bf1qk8LBsAp65Kt03z5xP7kbAIB0sb5pa9zHhO2IC0kAdGUUJgEAAJIgrzhPA4eWybJMRaO2AoH6VEcCAMA1oUj8N99sVuN2PSyRhsu4TQ8AAAAAABLKa2bFXdOybcudMAC6LAqTAAAAAAAgocbklsuRoUijJTvacdto2FAkZMhr90pOOABdBoVJAAAAAACQUIeW7KOaiiJVvNNPjZtzFA2acuzd20RDzYXLygW9VfVhX53V6/jUhAWQMuwxCQAAAAAAEsoyTEVXlykaCqvq4z7yZIdVuE+1sns0yTAdRZo8ql5dpMYt2ZIMGR5b/VWa6tj4NvaYhMsoTAIAAAAAgIT6aktAwcZvpkhGGr3atqT9wqMTMfX0h4t1yw+ZNQl0JxQmAQAAAABAQi2r2qJgpJPNJb/ls4pKl9IAe++dd97R888/r08//VSBQEA+n0+DBg3SMcccoylTpqikpCTVEdMShUkAAAAAAJBQa7Zsj/uYuqaQC0mwV+zOm2S6SCSiadOm6eWXX97t++FwWEuXLtXSpUv17LPP6v7779e4ceNSlDJ98fAbAAAAAACQUNFvP+kmJuxniK7n97//fUtR8vjjj9dTTz2lDz/8UC+//LJ++ctfKicnR9u2bdOll16qTZs2pTht+qEwCQAAAAAAEmpwz+K4j8n2eV1IAuy5TZs26W9/+5sk6fvf/77+/Oc/68ADD1RxcbHKy8t10UUX6W9/+5s8Ho+qq6v1wAMPpDhx+qEwCQAAAAAAEmpoaQ95rfhKDsN781TursZwnLR5ueGtt95SJBKRJF199dVtthkzZoxOOOEESdK8efNcyZHJKEwCAAAAAICE2q9PqUpysmNun+X16NzD2J8PXcvmzZuVlZWlnj17ql+/fu22GzRoUEt7xIfCJAAAQBLUhUP6eFOF3t3wlZYGNstx6c4+AABdgWEYmnL4gcr2fvPMXSPiyLvDlrfWltXoSLuMhb3yczWmX1kqogLtuvrqq/XZZ59p9uzZHbZbu3atJKmwsDAZsTIKT+UGAABw0bq6aj3wxcf6cMt6GYZkO448piWvYerHQ8bojMFj5Lf4SAYAyDynjCnXko2b9M6i1TIrgvI0Ot8838aQHFNq7GEqb2Ce7jrjFBmGkdK8aAM3UiVJeXl57b63adMmzZ07V5I0fvz4ZEXKGHwKBgAAcMnHWyt0w8J/qiYcbPP9/13xsV6rWKm/HvEj5Xp8SU4HAIC7DMPQhPwyffLVcoWDbRS4olL+FkdDsv0qyspKfkBgLzmOo5tvvlnBYPNnvcmTJ6c4UfphKTcAAIAL1tRt17QOipKSFLSj+qpuu65a8CpLuwEAGWf5F5V6+PH3FQ5G223jRB2trwjo9/f/M4nJgMSYMWNGywNvTj31VB166KGpDZSGKEwCAAC44H+Wf6TaDoqSO0UcW1/Vbdfn1ZuSkAoAgOR59Kl/qb4h1Gm7cDiqFSurVFlV7X4oIAEcx9GMGTP02GOPSZLKy8t12223pThVeqIwCQAAkGC1oaD+vW1D7O3DQT288hMXEwEAkFxVm2pUtakm5vb1DUG9+Nqn7gXCnrGd9HklSSgU0nXXXadHH31UkjR06FA9/PDDys3NTVqGTMIekwAAAAm2rGaz7DiXZi9hxiQAIIOsWFWlYCgSc3vHkf6ztMLFRMDeq66u1pVXXqmPP/5YkjRq1Cg9+OCDKikpSXGy9MWMSQAAgARrikbiLkyywyQAIJOEQlFFI3Zcx4TD7e9FCaTaunXrdNZZZ7UUJY866ijNmjWLouReYsYkAABAghX5suUxTSmOn6+8puVeIAAAkqwgP0s+v0dNTeGYj8nP48ncXQ4P55MkrVy5UlOmTFEgEJAknXnmmfr1r38tj4ey2t7ibxAAACDBRhf3ai5MxshjGDql3zAXEwEAkFwHjBkgjxX7WOjzWTrp2FEuJgL2zPr163XBBRe0FCWvuuoqXX755SlOlTlYyg0AAJBglmFq0sCR8hqxfdTK9vh09pCxLqcCACB5/H6vDjloH5mmEVN7n9ej7xw13OVUQHzC4bB+/vOfa8uWLZKkG264gaJkgjFjEgAAwAVTho7TmxtXqaK+VnYHO0jmeryaPGSsSrN4kiMAILP85MxDtfjz9doaqOtwRXBOjk8XnHOEsvze5IVDbLr5Uu5nnnlGn3/+uSTplFNO0RlnnKH6+voOj+Hp3PGhMAkAAOCCHI9XDx1xmi754EVV1Ncq9O0NJx0p2/Ro8pD99V/DxqcmJAAALsrPy9LtN5+mW2a+pOqaBtU3hHZ73+MxleX36rwfH65jjmC2JLqexx57rOXr119/Xa+//nqnx6xYscLNSBmHwiQAAIBLtmytk/NRWNkeQ3YfKZr99RuO5NtmqCDg0RebNim8T1Q+Lx/LAACZp7goV3+4/WwtXrJez/1joao218pxHPn9Hh131H46+fhRPPQGXVIgENC6detSHSPj8QkYAADABZuqd+jGv72hmoagLBkqqGm9PC2sqJYHt+i2p9/Sb8+dmIKUAAC4zzQNHTBmoA4YM1AlJbmyLFPRqK1AoOMlsUAqlZSUMPsxCShMAgAAuOBvby9UbUNTp+3CkaiWV2zR6sptGtqnRxKSAQCQGmE7qjW12xWVoxJfducHIPW6+R6TcB+FSQAAgARrDIa1YMX6Dh55s7sdjUE9895n+tWZx7maCwCAVNjUWKdZqz/V7I0rZRrNT+m2HUcDcgv102EH6bDSATKM2J7eDSCzUJgEAABIsOUVm+XEOcPgs682upQGAIDUWbh1g6Z98k/VhoKyv3XLrjrUpBs/eVNH9Bqo28ad0FK0BNB9mKkOAAAAkGkaQxFF7fgKkzZLpQAAGWb1joCuWzhb1aGmVkXJneoiIb27aY3u+M+7SU6HmNhO+ryQlpgxuZds29aLL76ol156SStWrNCOHTtUXFysCRMm6IILLtDo0aNTHREAACRZfrZflhXfrA+Pyf1iAEBmuWfJB6oNBztt1xiN6K3K1fpp+UEqzcpNQjIAXQWfgPdCTU2NzjnnHN1www2aP3++AoGAwuGwNm/erFdeeUVnnnmmnn766VTHBAAASTaif6ksI/aPWaZh6IiRQ1xMBABAcm1tatDymi0xt6+PhPT0V4tdTASgK6IwuYccx9Fll12mTz75RIZh6Pzzz9drr72md999V/fee6/69eunaDSqW2+9VYsX848rAADdiddj6YQD9pVpxjZrMjfLq/93xBiXUwEAkDz/3lahYDQSc/uo4+jtyi9dTIQ94tjp80JaojC5h5599lktXLhQknTLLbfohhtu0NChQ1VWVqaJEyfqqaeeUl5enmzb1oMPPpjitAAAINnOPvoA9czvfDlals+j4/cfpt7F+UlIBQBAcjREwgrb8RWL4ilkAsgMFCb30KxZsyRJRx55pM4+++xW75eVlel73/ueTNPUihUrkh0PAACkWEFOlu756ffVpzhf2T5vm23ysnw6Yf99dcnEQ5OcDgAAd+V7/fJZVlzH5Hh8LqUB0FXx8Js9sHLlSq1cuVKSdNFFF7Xbbvr06fr1r38tK85/jAEAQGYoLczVX6f+P7235Cs9/d6nCtQ1yjQMOY6jcfv001lH76+hvXukOiYAAAl3SM8B8pmWmmKcBekzTZ3Sv9zlVAC6GgqTe2DnnpFer1cHHnjgbu+Fw2F5vc2zInw+7vYAANDdeT2Wjtt/Xx23/74qKsqRLUce01QgUJ/qaAAAuKbA59dBPfrp7arY9o3Msrw6fdAol1Mhbo6T6gTIcCzl3gM7Z0v269dPPp9Py5cv1y9/+UsdeuihGj16tMaPH68rrriCh94AAIDdmKYhv5f7wgCA7uHnIw9XntH5hB1P1NCZg0aryJeVhFQAuhI+Ge+BLVu2SJKKior0wgsv6Oabb1Y4HG55v66uTm+99Zbmzp2r6dOna/Lkya7kKC7OkWHE9rTPrmDnk0lN01BJSecPA0DXQx+mP/ow/dGH6Y3+S3/0YfqjD9Mb/ZdeNm2pVdm7jhoPkmyv5Hy7AuFIRljKXyM1VdWq5HD6FOhuum1h8s9//rPuvffeuI6ZNGmSZs6cqfr65qVX69at00033aRevXrp2muv1VFHHSXLsjR//nzdcccdWrt2rW677TYNHDhQRx55ZML/DB5Peu5daRiGLCt9CqpojT5Mf/Rh+qMP0xv9l/7ow/RHH6Y3+i89/M9DcxWpCqvvG1LdIKl2+O7FyawqqXC55Kt19Fn+em3aXKu+fYpSlhdtsFnKDXd128Lk3mhsbJQkBQIB9ezZU08//bTKyspa3j/++ON1wAEH6Ec/+pE2b96sO++805XCZCQSTbsZk8bXG/7b/OOWlujD9Ecfpj/6ML3Rf+mPPkx/9GF6o//SR9WmGq2tCEiSzIhUsLr55ZjNLyMi7frT7I66oJ78+wJdfcWJqQncCctiJzzADd22MPnjH/9YJ598clzH5OfnS5Kys7NbvnfRRRftVpTcqUePHrrwwgs1c+ZMrVixQmvXrtWgQYP2LvS3bN/ekNDzua2kJFeWZci2HTb8T1P0YfqjD9MffZje6L/0Rx+mP/owvdF/6WP+R6vV1BRu9X3Dbn59m+M4WrDwyy7br6Wl+amOAGSkbluYLC4uVnFx8R4dm5v7zb4XhxxySLvtDj744JavV61alfDCJAAAAAAAXVEwFFE00kYFsgPhcNSlNNhjPJUbLmMu8h7o379/y9c+X/tPGMvLy2v5OhgMupoJAAAAAICuoiA/Wz5/fM9FyMv1u5QGQFdFYXIPjBgxouXrioqKdttt3bq15etevXq5mgkAAAAAgK5i/9ED5LFiL0z6vJZO+M5IFxMB6IooTO6Bww8/XF6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bukPW2hFdszndUaA0AAAAAAAAKEcUk8i7pJ/WyGrJIewzCQAAAAAAsP9gKTfyrsaNyuzDdY6hJwcAAAAAoFz4dl/e3QO5owlC3o0JtirkhEZ0zezY9AKlAQAAAAAAQDlixiTyzhijM5qO021b71PGZmQHJfeZkLKLI7IpR07Mk3NkUv4hGZmgFHOiOrPl5FLHBgAAAAAAQBFRTKIgTmw8Uo92P6u2O3qUeL5WyaQj67nbf+5uDsvcY1V7dr/mnTBTUyMTSpgWAAAAAAC8Fadyo9AoJlEQARNQ5PY6xZ/15KV2fZh5qYCUkgbuqFdrXbM0vgQhAQAAAAAAUDJU3yiIR556Qkuf79ltKbmjbCKgO371qra0txcpGQAAAAAAAMoBxSQK4rZbnlI2kduE3EzS0S1/uLvAiQAAAAAAAFBOWMqNvGvb2q6+Dpv7Bb6jlx7rkj5RuEwAAAAAAGBkfMt8NhQWjzDk3Zq21SO+xvckz/MLkAYAAAAAAADliGISededjo/4GmMkiWISAAAAAABgf8FSbuRd06hxsiNYyS1JbjQrGXpyAAAAAADKhSdT6giocjRByLupzRNVP3NAUm7tpBPyFD0qJdfh4QgAAAAAALC/oAlC3o2J1Sq7oEl+JLfxXsho8vwjCxsKAAAAAAAAZYWl3CiI0TNm6Y1TX1PzIkduas9Tv72oVce50uFTZhQxHQAAAAAAGA6ncqPQeIQh73xr9dLgesVnOeo4W0q1WnkRK/vm0m7fGfq/E1OstvyDlBpldHfH4hKnBgAAAAAAQDExYxJ5ty7erazvSZJSk6QtF0qBTimyTnJSVn6NFJ8u+TV/u+b1/rYSpQUAAAAAAEApUEwi7wa81C7H3mRbpHiLr5CTVcoPyO5msq5nfbmczA0AAAAAALBfoJhE3tW4IW3bVdKRr0OaNuj0cUvUEEzIWiPHWG2IN+kvm+dq5cAo6c3RlJIAAAAAAJQPT3s+MwLIB4pJ5N2UWLMCjqvaQFKXznxIjaG4Im52pzEz69s1MdatVQOj9PNVx2pW7egSpQUAAAAAAEApUEwi7xxjdN64WRpd+3U1h/vl7uEDllggo5n1W/TPM57SvJrzixsSAAAAAAAAJUUxiYI4ecxqbczE91hKbhNyfM2o7dCBNT2SJhUjGgAAAAAAyIFv2XINhUUxibyz1qrbv11Bx9v+ve61Ua1/rlGp/oBizWlNO65LseaMJCnsprQ5c6PqAteWKjIAAAAAAACKjGISeZe062RtSpK06aV6PfKtGRpoCyvV78paR07A1+Pf9zRmTr9OunylGsanNOC9UuLUAAAUVjqV0crXNsrL+qptiCpSG5ExbCgPAACA/RfFJPLOs/2yklY80KqHrj5Aqf7gTj/3s45SfY7WPdWsW/95gc79/qsaPUOy1pMxbmlCAwBQIFvbenXXL5/Qsw8vleMYWWtljFEkFtJZHzxGx585X4EAz38AAKD8eCzlRoHxCEPeuYqpa3VED31z11LyrRLdId3xmYOVSRpKSQBA1Vnx6nr950U/08N3vaiBvoT6euLq7x36Z/umHv36+4v0jUt/qVQyU+qoAAAAQNFRTCLvIs4UPfWzMUr15TYhNz0Q0Bv3zSpwKgAAiqttY7e+/aXfq783IWt3PyaVzGjtijZ9+wu3FjccAAAAUAYoJpF3yXhG659ukpTbvlnZpKvnftVS2FAAABTZbT95RAO9iWHHZTOe1q7cotXLNhchFQAAAFA+KCaRd23rOuU64RFdk+jzZfc0nQQAgAqTGEzp1WdX5zx+sC+pP970eAETAQAAjJwvUzFfqEwUk8g7L+sp19mSO6KYBABUizde3yTr+yO6ZsWrGwqUBgAAAChPFJPIu6bRDXvcS2tPXNeR4/BwBABUh3QyI98b2ZOhzwd0AAAA2M/kdjoJMALNYxpUP6Zeg73xnMZbIx1x5iEFTgUAQPHU1EflBnb+wM06UqbOkXWN3ISvQGLnIjIQcIsZEQAAYFieZQIRCotiEgWRPW6cvDVtctPDz/6wIUfBYyYUIRUAAMVxwJwJ24vJbNSoe35UvbMjsmZosxMrKRD31fJcXHWr03Ido6NOOaikmQEAAIBio/pG3sXTab3emFF8aq384N73mvRCjjqPbtWdG1cVKR0AAIXnBhwd/875So8Oas35Teo6JCov5siPOvKiQ/9MtwS05eRarX9XvcK1Yb39/CNLHRsAAAAoKmZMIu829PXJOEYdp4+V/1iHapf1yWR8OTucAeAHjGzAqPPYURqY0yCTSstaK2M4SQsAUB2OPG++vhdeIS+y5+c2G3KUHBtU3/sb1TqmoYjpAAAAhudb3qOjsCgmkXdZzx9ao2aMOk8Yrb4jmzRv3Rq5y1Py05Ibkwbm1uv1cRNk39xPy775xV95AIBq8ctVi5XdSym5jQ0YbQ15WtHdqQObWoqQDAAAACgPFJPIu9G1Ndq2s+T7Zr2mzxzxjMKOp7pwevuYnmRY/emQvvDwKXqhbbwCjpHDbEkAQJWIZzL668a1yvWc7b50Sje+9pK+/rZTC5oLAAAAKCcUk8i71poajaur1QcPelz/NP9l1e9QSG7TGEmpMZLSD8/4sz7/0Oka3/zOEiQFAKAwlnS2y7e51pJDnmvbWKA0AAAA+8bjaBIUGMUkCuLfjonqkKZXdltK7qghktI1Jy9Sm/2XIiUDAKDw4tmMvBEWkyMbDQAAAFQ+qm8UxFFj71R9OJXT2NpQVhNjfy5wIgAAiqc+FFbAGdnLLJctTQAAALCfoZhE3hnbrYCW5jw+4GQVsb8vYCIAAIrr4JbRI9o72Ug6aeK0wgUCAAAAyhDFJPLO0WZlsiNcvuYnJOsXKBEAAMUVdF29Y+oBOc+CrAuF9aGD5hc4FQAAwMj41lTMFyoTxSQKIpXJjmh8OusVKAkAAKXxsbkL1RqNDTsuFgjo9CkzNKG2vgipAAAAgPJBMYm8a+9tkB3hFv4DSUcyPBwBANWjIRzRDaedo/E1dYoGdn/eYF0wpNOnHKDPHfa2IqcDAAAASo8mCHnX0R/Uii2jcx6fyjq648X58nyWcgMAqsvYmlrdctb79fnD3qYpdQ1qCIfVGImoPhzWSROn6nunnKUvHXnCiPajBAAAKBZfTsV8FdPXv/51zZo1S7fffvs+Xf+73/1Os2bN0qxZs/KcrPLs/uN74O8QDLi68aGjNXPsFtVH07JWWrxxjB5bPkX9ibBa6wZ1xsErNam5T5KUSId098vzdd7JvCkDAFSfsBvQWdNn6azps1TfGFXW+go7rrq746WOBgAAgBFatGiRbr755n2+fu3atbrqqqvymKiyUUwi7ya0NGjxxvG66fFDNXPsVv3ggaPUnwqrLxGRJBn5uvmpQzSxqU//+va/6oaHj5DrNsowWwQAUOUCjqOwG5DnsUoAAACg0jz44IO67LLL5O/jik/P83T55ZcrHucD6m0oJpF3kVBAR8+arF88nlLA8ZT13Z1+buWoLxHRa4mIPnHjexRwgvp/Zy8oTVgAAAAAALBbHqddS5J839f3v/99XXfddftcSkrSddddp5deeil/waoAxSQK4tiDpuj+l1bsUkq+lec78q2nww+cWKRkAAAU34AX1yM9T2vpppVK+xk1But1XM0RmltzoBwOfwMAAChbjz32mK655hotX75ckjR37lwtWbJkxP+eV155Rdddd50ikYhOOeUU/elPf8p31IpEMYmC+MuLy3MeG3BdPfTKGzr3mIMLmAgAgOLzra/ftN+lZ/peVsqmlbXe9p+91veGwk5IF409X7NrZpQwJQAAAPbkoosukiQFg0FdfPHFOvvss3X66aeP6N+RSCT0+c9/XtlsVpdffrn6+voKEbUi8RE98m4gkdLitW05j89kPf3hycUFTAQAQPFZa/XDTTfrid4XNOgndiolJSnhJ9WT7dMPN92sxQO5f6AHAACA4jHG6IwzztCdd96pSy+9VI4z8irtm9/8ptasWaOjjz5aF154YQFSVi5mTCLvNnf3a6Tn2MRTGVlrOQAHAFA1Hu97Xq8PrlTKpvc6Lu4n9JPNv9XVMy5XxAkXKR0AAMDwfPaY1L333qtp06bt8/WPPPKIfvOb36iurk5XXXUVvcdbMGMSeedbK2tHdo2VRnwNAADl7E+dDw9bSm6Tthk90ftCgRMBAABgpP6eUrKrq0tXXHGFJOmKK67Q+PHj8xWralBMIu9GN9SM+Jqg68hx+NQAAFAdNqa2aNBL5Dw+bTNa1P14ARMBAACg2K688kp1dHTotNNO07nnnlvqOGWJpdzIu6bamCa1Nui19e05jQ84Rm9fOLPAqQAAKJ6tmW55b9lTcjhxP1mgNAAAlFbWenqqe7l+u+UJdWb7ZSUFjasTm+bqnNFHqDVUV+qI2APfMp9tX9122226//771dLSoq997WuljlO2eIShID588kLFwsGcxkZCQZ1z1NwCJwIAoHgcOdIIFwKwbgAAUI3aU736xOIb9J21f9KqRLt6Mwn1ZRLqTA/ojrZndMlrP9Ef254tdUwgr9avX69vfOMbkqSvfvWram5uLnGi8sWMSRTErEmj5QWsbGrvb7SspIaWmJpqo8WKBgBAwY0Pj5Yrd0TXNAcaCpQGAIDS6MkM6nPLblJnZmC3P/dlNeAl9ctNj8l1XJ01amGREwL55/u+Lr/8cg0ODuq8887TaaedVupIZY1iEgXxhxcWKxnw5IUk9819/3csKO2b38iGpY5UXC9v2KIFk8aVICkAAPnXEmxSg1+nQcVzGm+SRmeOO7nAqQAAKK6fbnhQXXsoJXcU91P65cZHdHLzXMXccBGSIVceazpGbNOmTXrhhaFDDW+//Xbdfvvtex0/a9YsSdK5556rq6++uuD5yg1LuZF3vrW648XXlfGt/LCUqZUytVbJUb4SYzwlW31lYlaZGskGpYFUWr968qVSxwYAIK/cP9XI5tZLysaNwitjhQ0EAEARxb2Unu1dOTQpJQcpP6u/bH25oJkAlB9mTCLv1nX2yPOHnn68iFVqTFZ+SEM1uNHQdElfcuNG4TZXTtZoeVtHCRMDAJBffd2D2vJAv7xkSO470jJ76BytL6nfKPO/Ud094UkdvHB6UXMCAFAoL/atVmYEB8GlbVb3bX1Z7xlzZAFTAYU3YcKE7TMm9+T666/X9ddfL0nbxwaDuZ3TUW0oJpF38XRG1lplajylJvjaZYstI8mRvDqrRDSr6Lqhh6Hn+3IdJvECACrfmhVt8j0r/96w7FZHgfNSUsTK1A793GYlJSW7zlX2xojU5Wh9ig/pAADVoy+bUNrPjuiaAS9ZoDRA8RhjVFNTs9cxO5aQw42tdhSTBRCPx3XuuedqzZo1uvTSS/XpT3+61JGKqiYcVDboKzVuN6XkjszQUu7E5KzqN4YoJQEAVSObzsq+uXrAPhtU5tmAzIGezGxPJmJlu4z854JS7w7PfTbXxW4AAJS/sBOUa1z5NvdyMmSoKMqNb9ljEoXFb30BXH311VqzZk2pY5TMpOZG9Tekct7B1DpS7Vg2OAYAVI/GlloFgq6U2PYdI7siILtizy+9QpH9c/kOAKA6HVw7SREnoIyXWzFpZHRYA1uaAPsbpqjl2cMPP6zf/va3pY5RUikvq1TMV86Hd7nSlshgQTMBAFBMU2eNUzCc++e/gaCrE886pICJAAAortHhBk2KtOY8vtYN67wxRxUwEYByRDGZR11dXbriiitKHaPklmxtk8357LUhcS8tyxI2AECVcByjM847XKEcy8lwJKhT33NYgVMBAFBcH590qmrdyLDjwiagQ+qmamy4sfChMCK+dSrmq1gmTpyoZcuWadmyZTrvvPP26d/x6U9/evu/Y39HMZlHX/7yl7V169Z9fmBWi7aBkc9+tJKy1s9/GAAASuSM9x+h8VNaFQju/eVWtCas9//LSapr3MPR3QAAVKiZNeP1/6a8c6/lZMQJ6qDaifrctLOLmAxAuaCYzJNbb71VDzzwgCZMmLDfz5psCkU1wgmTkpUChocjAKB6BEMBXfG9D2vmvEmqqd/1DVkwFFBNfUQf+OQpOuWchSVICABA4b2tabaumfVhva1xturciOoDUdUFomoIxjQh3KyLJ52hrx34AQWdvZ2cCqBacfhNHqxbt07//d//LWOMrrrqKtXW1pY6UknNiNTLZCSb66PLSqEuyRhO+wIAVJdwNKQv/N+HtO6Ndt39qye0/NX18j1fsZqITjjrEJ307gWK1Q6/xA0AgEo2JTpKX5pxrgaySfWGBuXJV3OwVrWpaKmjYRh+zodHAPuGYvLv5HmeLr/8csXjcf3jP/6jjjqKzXoHN/eodllKffPCUi4fenlSyyNbZf/JUk4CAKrS5Bmj9an/fI+am2vkuo48z1dXFwe/AQD2L7WBiCY3tvztuTDFcyGwv6OY/Dtdf/31evHFFzVjxgz927/9W1H/201NsbIs8jpqw5pw32bFp0xVtkF73zDAk8KbpdYXtqqpKSbHYTl3OXMcs/2fzc01JU6DfcE9rHzcwwrl90jJ38r0PCrfJmWcUWqu+aAUOk5iK5OKwu9g5eMeVjbuX+XjHgLYEcXk32Hx4sX64Q9/qEAgoGuuuUbhcLio//1AoDz34Bg3bbSiWxNqvXmdtn5gsrJNkox2Lig9SXaolBx7w1JFHVfBIA/HSmGMkeuWXymO3HEPKx/3sDJY68n2fU1K3iPZhKT09p+ZzAuSiUgN18oJH126kNgn/A5WPu5hZeP+VT7uIQCJYnKfJZNJff7zn1cmk9GnP/1pHXzwwUXPkM16ZTljsr61XpNnT1D/E8sU6M+o54ypSk1whwpKRzKeFOyQImtSarpnpaI9KZ3xubPleZzKXe4cx8gYI2utfH+kJxyhHHAPKx/3sIJYK/VdKqWfkFFiNz/vk2yfbM+l8ur+VwofV/yMGDF+Bysf97Cycf8qX6XeQ9fdP1c4eLb8OgdUF4rJfXTNNddo1apVmjdvni6++OKSZOjujpfkv5uLsy97l1YvXiet7FJkZZcyE+qUOKBZNuzKHcgounSrAl1Db9LCjTU64YMnsNdWBRjaG83I9y33q0JxDysf97ByhPw7VeM/KcfsppTcgbF98vs+p27nLsnEipQO+4rfwcrHPaxs3L/KV6n3cNSoulJHAKoSxeQ+eOyxx3TzzTcrHA7rm9/8pgIB/t/4VvNOnKsTLjhOj9zymBL9SYU29iu0sX+XcbH6mD52zUfUOLqhBCkBACicqP8zOSa3DxH97KDCwbuUMhcUOBUAAABQPmjU9sE999wjSUqlUjrzzDP3Ovb73/++vv/970uSHnjgAU2cOLHg+crFR772ATWMqtc91/1ZmVRWqXhq+89i9VEFIyF9/Fv/qIVnLChdSAAACsC1K5RNdSkQyW18IJBR2Pu1Ug7FJAAAKB++3T+XsKN4KCZRMMYYnfOZs/SOj5+mx29/Si/c+6KSg0nVt9br+A8cp0NOPljOfrpPBwCguhm7Wb6XHn7gDrxUtxQsUCAAAACgDFFM7oOvfvWr+o//+I+9jlm4cKEk6ROf+IQ+8YlPSJJisf1z36hwLKxTPnyi3vf/zpTrOvI8v6L2EgEAYKTa1nZpQvPIrkmnslJtYfIAAFBqg71xPfz7Z7T8+VXKpLMaPalFJ7z3SE0/eFKpowEoIYrJfRAKhRQKhXIaGwwGVVNTU+BEAACgnLRtbtLY+pGdYtmxJaLGlgIFAgCgRLysp1987Q96btGrSiUyyqazQz94aqWe/ssrqmuq0f/7vws18YCxpQ2K3fI5lRsFxjpaFMXqtVv1m9ue0fU/f0S3/fF5be0cKHUkAAAKJmvHamNb7h9M9qeCuv1XBxYwEQAAxed7vv7nEz/Vk/e8qMHexN9KyTcN9ia0Zc1W/fdHr9e6ZZtLlBJAKTFjEgX1yuIN+umvHlNvf0IDA0OH3ziOUSwa0qSJzfrkx07S+LGNpQ0JAECeTZk1Xv/94EJ9a/pf1RAcfq/J3mxITyVm6WNFyAYAQLHc8/OH9cYr65ROZvY6bqBnUP97yc/0v/d9kXMIgP0Mv/EomEceX6Zv/eA+bdzcs72UlCTftxoYTOn1ZZt1xdf/oFVrO0qYEgCA/NvoxvWwM1XXr5qvvsyet3/xrdSRiurC587U0uP2z72oAQDVyfd93X/zE0olcjsMLjmY0it/XVbgVADKDTMmC2TZsv37L9S16zv1s5sf12B8709C/f1J/fe37tH3rvmQohGOIgUAVIcnO9bLDxr9aNUCrY/X6d9nPauaQFpNoaHnxYxvNJANaUlfi77w6gnalKyVidgSpwYAIH+WPrdKmdTeZ0ruKN6f1N0/fVgLTjyogKkwUr7YYxKFRTGJgrj1juc0OJgafqCkeCKtR/66TO847eACpwIAoDhSvieZoRfy92yZoXu2TNcRTW06pmWTagJpbU7U6p4t09WR+tssSUsvCQCoIu3rOpXI8T3hNls3dhUoDYByRTGJvBuMp7T49Y1/+4Zv5Q6kFBjMSL6VdY28+rC8aFAyRum0p7v+/DLFJACgakRSARlfsts3zTF6tnusnu3e84mjxitKNAAAisI4zrbP6EZwDbPzgP0NxSTyrq29T8YYyVoFepIK9qUk3+40AdxNZiVjlGqNya8JaTCekrV26DoAACpcczws+SO4wJfC7Wz9DQCoHhNmjFakJqLB3njO14yfPrqAibAvfMt7dBQWr4CRd74/tBYt2JlQsDcp85ZSUpKMlYxvFe6Iy33zYByWsAEAqsVgKq1Im5NzOWl8KbKFl2UAgOoxY/5kRWvCO33PSrKOkXUdvfXtX019VO/++ClFywegPPAKGHnX0lwjvz+pwEBKZpiy0VirUGdCjm/lMG0fAFAlJrY0KrLFkZvU8OVkVopudBSybjGiAQBQFMYYnf2JUxWpCcsayauNykZCkoZW1yngyq+LyoaGFnLWN9dq1mHTShsaQNGxlBt519RYo2B3QukdSslYTUazDupSJJJVf39IS5e0KJt9sxf3rSZFI6UJCwBAAcyaOEpGUv1rAfXPysqLSvatr7r8oZmS0Y2OIm2u6mOhUkQFAKBgTjzvCD390Gta/PhyOfGh1XTb+Z40kJCMkdNcq8/d8M9s7VWGfMt8NhQWxSTyrrdrQE5maHrI2PEDuvBjS3ToEW1yjOS4vrysI983WnTfFP3uV7M1MBDS1lUdJU4NAEAeJT3J9aSsK7fTkd8s2agv7TAp0iSNTMoo0OdInq+sct+DCwCASrD2jTa99sRyKZPdZXsvaWiLL1krvyeuX37/fv3rf19Q7IgASozqG3nX2dYnOdKsgzr1vz94SMefvEH19RnV1mUUi3mqq8+ooTGts89bqe/++AG1tCaUzXjb96YEAKDSPbd+raKrBxUfZ5VpNEOzQZKunMG/fRnPkQJGA5OsshGrAbev1LEBAMir//vcr+Wnd19K7sT39dI9L2prW08RUgEoJxSTyDvjGNU09Ok///sJNTSm5ezhURYMWrWOiuuqbz+irDJi1j4AoFps3LpVXkTK1tidZknulivFJ1rVPp8pSjYAAIqhs6NPXavbhy8l32R9Xzdee29BM2HkfGsq5guViWISeRdsCuv957+u2rr0sGNdV2psTOnwo9eznwgAoGo4cavuQ6Kywdye26xrlBjNHpMAgOrx51ueGjrkJkfGSosffa2AiQCUI/aYRN6tHmjXySdukPvmDJGk7+gv3ZP1QM9Exb2AmgIpvad1tY6q2yLXSDW1WX3gQ0vlWyuHchIAUAVqG2rk9ef++a8NGvXMiRUwEQAAxdXX2S/r25xnTEqSn84WLA+A8kQxibwb7Fkq22RkrXRT2yz9umOmUr6jpA1uH/PC4CiFjacvTnpexzVs0ejWuBK+J8flIQkAqHzRlhrZTSO7xkZYyAIAqB6RWHhou64RHCVg9rQPGICqxW898q4x5EhG+s7G+bqpfZZ6vfBOpaQkDXghdWaj+q+1R+qB7gkaesbySxMYAIA8GzW2Uc5we0u+RSjMh3MAgOpx3DsP0R4PHNgNK2ncgeMKFwj7xJepmC9UJopJ5N3k1ul6Md6qu7umatDf+35ZA35I39ywUF2ZkAIue2sBAKrDtPomuaERTBGR1FrH8yAAoHocOG+SInXRnMcb19FHv/DuAiYCUI4oJpF348bM1Y1bZ2tgmFJym7gX0PVr5xc4FQAAxVMTSasmOvwhcNsE3KxmTt5SwEQAABTfR754juTmUDsYqXlis2YtmFL4UADKCsUk8u6N+CatStbnPD4rV09kRxUwEQAAxdWR2aS5U9sUcHPbxD/gempqppgEAFSX489aoLM+fsrey0kj1bTU6erffaZ4wZAz35qK+UJlophE3i3telHp7Mg21kp7AWV8r0CJAAAoLiurMc0Dmj5h+HIyFMzo6Hkr5DgjW/oNAEAluODSM/TZ739UTRNbhgpKxwydMeA6CkSCOuH8Y/S9+7+oSE241FEBlAC7rCPv4gNJOcaOaPNZK0mepxGfFAAAQBlqCDTLNa5mTdmsaCSlZWsmyvMcZbyhl15GvgIBX3WxhBbMXK2aaFphw+oBAEB1OvT42Tr0z7PVsbFL65dtVjqV0bjJLZp00AQ5nMQN7NcoJpF3U0Lj5QSXylfuJWPQeHLd4PADAQCoAKMCExQxMSU0qBljtur8KUtVl3D0fPs4xTNBjYsN6JBxm/WS36A1mRoFTEhH1p1a6tgAABTUqAnNmjVvklzXkef56uoaLHUkDIMl0ig0iknk3dRRU9TQk1SnanIab7PSAbF2OQ5/4QEAqoMxRsfWvUPP9f9c/9LymmpMVuEmqzPHr91p3BF+h5YkG3RH/xzNjx1borQAAABAaVBMIu8G7RLFX2qUnZOVCeWwX5ZnlHqjRv7BPtP4AQBVY1Y4rCNDS1S/lz0mY46n+ZEeNbmvKGTYWwsAAAD7F1og5N36jX3qX1cv//Ua2fTeZ0HalJH3dIPeWDxF1uZ2cikAAJXA976sWmf457aw42tyqE8D2buKkAoAAAAoH8yYRN5taW+U1C27Jio/aeTMG5QCdvvsSWslpY2UcOW9WCf1BeQZKx6OAIBqkfHbNDbQqVx3KQlIcrzvSMGzC5oLAABgJNhjEoVGE4S8M2qV1WoZSXZLRN6WsNSclRmTkgla2ZQjuzEsDfzt4Wet5Fs7guNyAAAoX32ZW9XsSMrxtbxjpNFurzgCAAAAAPsTiknkXdA10k5bSxqpKyjbFdTedpx0OfwGAFAlMnarzAif1ozJYV9mAAAq2IDXp47+VfKNpzqnQVE7SmakT5gAqgrFJPIuGgzIGinX91dWkjV2aIk3AABVIJNtUNZxFHT9nK9hqRQAoFptTq/Vg71/0Ob0WhljZWXlGFcBhXRU7Wk6vPYkuYZ6ohzx+gSFxuE3yLtoXUhOypPd6/zIHVlFOjIyzJgEAFSJzWuPkp/rOm5JvpVe62ssXCAAAErk1cGndHPHt7U69ZqSdlAJP66kn1DcG1Cf16WHe+/QTR3XKmszpY4KoAQoJpF3tU5I0c6MfFfDVpNWku9aeVE7grdvAACUt7b+Ab0y2CQ/x8/oEr6jP/dMKGwoAACKbH1qpe7ruUVJG9/jmIzS2pJep9s6ry9iMgDlgmISedfghjU4yVXN+pT8wNC8ybe+L9u2fNsP+GpYkZTqAizlBgBUjb5kvx7sHZPTWGulQT+gLZlYgVMBAFBc9/f8bq+l5DaestqQekNdmbYipMJI+DIV84XKRDGJvGtpqVUm5sqPWdWvTErWkxew8tw3vwJW2aCVSXpqXJpU74GuHN+Rw1JuAECVaK5r1JG1nTltamKMVOt6anLSBc8FAECxdGXb1e115Dw+YeN6ov++AiYCUI7YXRZ5lwpYBTxHHQtDanw9IyftKTnK0Y575jpZq2inp665ASVbAwr0B0sXGACAPJtWO03z6rbKffO5r70tqjt+f6BefG60MmlXDY0pnXXOGzr+pI0KhnyFjacTTFdpQwMAkEcbUm8o7adGcIXVmtTSguUBUJ4oJpF3XYMJOTWurGvVPz0sWcm8ZVq1FzTqmR2SHMmLWrkpI8/35TpM4gUAVL65B/TISRtlMkb/e9URevG50RocDMr3h57nNm+q1ZrV9frxD+fr0//2oo49fpOOHtetkbx9AwCgnGVtRr68EV8DYP9CMYm8CwVcxWt9BeJGxhrJWoXW9ym6tEtO0pNXF1J8XquyrVFZ38pNGMWjnhzDUm4AQHVwnD65vqMrv3icXn+tRZm0u8uYZCKoZCKob3/zMHme0bEnDFBMAgCqRsytU9CElLa5P7tFndoCJsK+8C3v01FYFJPIu7pYWJ7vy7FGkdc71fznNTKprNzE0Kdl1kh1T2+W1xBW57kHKD02pnTMylBMAgCqhFVM9/9p+h5LyR3FB0P63rcWasHhT0oNRQoIAECBzQjPkWsCUo7FZEAhHVZ7QoFTASg3rJtF3nUkBuVmHNU9s0Utd6xUoCe1vZSUJGMlN5FVaMugRv9iicLrB2RSks+x3ACAKpG1B+l3v5kxbCm5zWA8pD/dfUSBUwEAUDxBJ6xZkQUyb9YOvjXqTYfVlqzVlkSdtqZqlPbdHcYHNT92bKniAigRiknk3Zb+AQU3DajhgXVyk3vfU8RNZDXq16/LxLNFSgcAQOGtWTmont5w7hf40u2/ZbokAKC6nNr4PtU6jepMxbQh3qieTEwJL6ykH9JANqwtiXptjDfI2JhObzhfIWcEz50oCt+aivlCZaKYRN7VBUNqeGj9sKXkNibtq/aFNllmTAIAqsSK1zfKH9l+/+rv50M6AEB1CZuoMtnpGsxG5cuR3amCMPLlKGMD2pxsUEtgeslyAigdiknkXYMfUHhN79++YYw0qkmaPU2ac4A0c6rUWLf9x07WV91TWziRGwBQNVYs36iRftzGx3MAgGpzf/fjWp3cKH+YcUk/re9s+IV8O9xIANWGw2+Qd/2burX9mWf8KGncaMkxkrvDPlv1NZJvpVXrpZ5+OfGsrOUAHABAdQjFQkMfzI2gbvQjfEAHAKgevvV1f/dflbLpnMYn/aRei6/QwTWzCpwMI8ESaRQar4CRd5s6e+VkfWnKeGn8GCkY2LmUlKRAQAoFpQMmS80NMllPWW+Ea94AAChTmclGXiy3g28kyXeNBifGCpgIAIDiWpFYo7SfyXl83E/qT52PFDARgHJEMYm8iyczUl2tNKpZCgzzpiwQkKZNlNyAHMPDEQBQHaKzohqcFJPvDj/LwEqyAaP+2SxkAQBUj/Z0p5J+akTXdGS6CpQGQLmiCULeGTcrTRwzfCm5jetIY1slDr8BAFSJUCCgjJtQYmxkr+WklWSDjvqn1CrTwOE3AIDqYYzRSBcBs2gY2P9QTCLvwtGgFIvkfoHjSKObZXk0AgCqxIKmiYo9vEqJ8VENTK2VF3Hlu0bWWslaWTO0fDvdGFLvQQ3ykn1q+OuWUscGACBvxoZGKeKER3wNyotvTcV8oTJRBSHvGpoaRn60qOPI4eEIAKgSXQ9sVHzeKPVNNkqODclOyypsOuXGe+TEe+QOdivc2qP0pIAGJrrqX9ik2FMsXwMAVI8ZkckKj6CYjDlRndlyUuECAShLNEHIu4ANS8ERfloRHNnJpQAAlLONKzZpcOFYhf2sJjy7XOEXu5Rpd+SnzNBX0iiz1FfT8+s1ec1q+UFHAwtGlzo2AAB5Y4zRWc0nKWJyKydr3ZhmRacXOBWAcsMu68i7uqYaOWHJjmCf40DMp5YEAFSNrVHJTXia9uc1Snc40u6WF1mj7IAr9/mMDnDXaPWhE4sfFACAAjqh8UgtS6zWKwNLldzLG8R6t1afnfgxGcNy3HJjWSKNAmPGJPJuzGhHE2a1536B8XXYO5bJdXg4AgCqQ2ReRNM3rlem0+y+lNyBl3DkPJ9W1CaLlA4AgOIwxujj4y7QGc3Hq9aNKrxD8Wgk1TghHRidqi9PuUSjQs2lCwqgZJgxibxrqO9T04Q+bXhlrHI6V80aTZjbJmstn5ABAKpCw/ge6UVP1nNzGp+NO5q8nsNvAADVKKvTGh7UMTWLdNeGGVqaGKWsHDW7cZ09fpWm1jiqcU+R1FjqoABKgGISedeW6NWyx6Ypp1LyTU/fPlfvO9SO6BoAAMqVt/kNWX8EF/hG/qvZguUBAKAUrPU0mLlId76e1W9f+oBSXlCD6YgkKeBk9dfAwTp47Hr9v7d9XBPqfiTXmV3ixHgrn/foKDDWziLvXl6SVHogOIIrjDYtGa2RvH8DAKCcOUkrmx3ZC3nrFSgMAAAlkvJ+qh88Wa8bnztWXYm67aWkJGX9gAbSUT2z/gD9vz+erXW9l8nyZAjsdygmkXdbVnkyZoQ1o2+VTTNTBABQJSLNMu7IjnVjNxMAQDWx1te9yx/TAytnK57Z88ncvnXUOVirL//lZGW8R4uYEEA5oJhE3tUaR8HwyD7pcgO+3EBu+3ABAFDuGg44ZMRFY8N8Dr8BAFSPjP+Ufv3igr2WkttYOeqKx/TSlt8VIRlGwremYr5QmSgmkXfRhi4ZZ2SzROpa4nL4ewQAUCUcM1pNRyYk5fZ8GIh5SpweK2woAACKaGnHWg2kQjmP709F9ZuXxhQwEYByRDGJvMuEt2rq/E1SjrtGBiMZLXzn68pkM4UNBgBAkfhtWbVPrs19fMZoqT++gIkAACiu1V2+4tnci0lJWtvdWJgwAMoWxSTyznMjWviOpYrU5lY0RmrTOmDhRjkOS7kBANWhxYspdV9QyvEky6zjaPSf2WsZAFA9rEbLjmwhnXw7kkNUAVQDiknk3diGuWocO6gzL31CkZrUngcaXzWNcb3vigdlXVFMAgCqhpdKK9SR+37LTkqKvNxfwEQAABTX+LqDFA2O7EO3MXVNBUqDfWWtqZgvVCaKSeTdQZMOUFtbo6bMa9P7v/ygJh+8WZGalAKhrBzXUzCSUaQmpTnHrdGH/vs+1TbG9cwLs+U6PBwBANUhaK2cEU4TCeS4HyUAAJXgsAnjFXIjOY+PBdP64CHHFDARkD9f//rXNWvWLN1+++3Dju3q6tJ3v/tdnXfeeTrssMN08MEH6/jjj9ell16qRx55pAhpy1ug1AFQfcY31+uRO96m88ffo9bJvXrvFx/RYE9EG5aOUjYVUKQmrUkHtykUGfr0LDEYVH/vqSVODQBAHlmrUNBXKpv7p/ehEJ/0AwCqh+s4OnfOobr5peeU8oafhBIN1ultU6cUIRnw91m0aJFuvvnmnMY+99xzuvTSS9Xd3b3T99vb23X//ffr/vvv17nnnqtvfOMbct39cxUpxSTyzhijk+ecqaefX6WjD3tN4ZinmsakZh29fpexyXhQ9zx0tC469ZQSJAUAoDBGTaiRG/Al5f4Cs75hL9ufAABQgS5cuEDPbNig5Vvbld7LDif14aC++Y53KMAqurLjs0R6Jw8++KAuu+wy+f7wh/1u2bJFF198sfr7+9XY2KjPfOYzOuGEExQOh7VixQr94Ac/0HPPPac//OEPam1t1ec+97ki/C8oP/zWoyDmH9Kplnm9euTpQ9TfH1EqsfMbs8RgUF2dtXrwmQWae+I6jRmTLlFSAADyb+oBbUpkc//810qae1JPwfIAAFAKAdfRd88+S8dPna76cEiBt3RcdaGgxtbW6n/fdZZmjx5VmpBADnzf13e/+11dcsklymRyO+j3Rz/6kfr7+xUOh3XTTTfpgx/8oCZOnKhRo0bp2GOP1a9+9SudfvrpkqRf/OIXam9vL+T/hLLFjEkUxLL0HxSOpjXt2E16fctEdaxo0qTWrQoHMxpIRLSlr1kT57XpwGPWSzJamrhTR9Z+stSxAQDIi97+PnnBqJxEPLcLjNFjyybrI4WNBQBA0YVcV185/RR1xuP6/atL9Gp7mzKer7H1dXrP7NlaMG6sjGFWHsrXY489pmuuuUbLly+XJM2dO1dLliwZ9rr77rtPknTWWWdp1qxZu/zcGKPLLrtM999/vzKZjB5//HGde+65+Q1fASgmkXeDXocSfo8kyU8bDbzhKOOktaGxTnKsbMwo05bSwKqAooekZIzV+vSTOlIUkwCA6vDnR/tlIyEpkZG8jLS31T6OZGIxDa7dP/cVAgDsH1piMV24YIEGUil5vlVzfUwhh+e+csdp19JFF10kSQoGg7r44ot19tlnb5/puCfb9pQ0xmj+/Pl7HDd58uTtf2bGJJAnCX/oFzDR6Wr1mlYFD/QVrdvxHZmVe5RVbyKmjgdqNPv4DtmIlW89OYYnJgBA5Vu9OSRjpfpzszKJlPofDsqmJJv+2y46Tq0vJyKNujShjl/XyOsrYWAAAApoY3uPfvuXF/Xca+vkOEZWkpHUUBvV+acfquMPnSHHoQBDeTLG6PTTT9dll12mGTNmaMOGDcNe09TUpCeffFLpdFrW2j2OW7du3fY/19fX5yVvpaGYRN65Jqhs3GjNplaFDvAVDHlqCCQUc9NyZOXJqN+NqD8QkQ51tOzxUTrklLgMW54CAKpEKBpRdGZKo87vlVvrq/WjCfX+JaT4M0H5KaNAs6/Gd6cVnZ+VrBSe1qY3vjCx1LEBAMi7Z5es1bdvflgD8ZTeWs/0DiT1g989poefX6Er/vntCri8J0T5uffeezVt2rR9ujYUCu3157fccsv2Px922GH79N+odBSTyLs6d7zeeCGswEGeRtf0q8ZJy8hq2wFrAUmBQFxNwbi63Rp1z4uqZ0WjzNF8QgYAqA7jp7Roy7guubVDKwbcGqn53LSaz93NYW9Gcms9RY7KbSN1AAAqxRsbtup/3ywlJSk2Kq6asYNyAr7SAyH1rGpQIpXRqys26zu/flj/9pFTSpwY2NW+lpLDefHFF/XrX/9aknTEEUdo5syZBfnvlDuKSeSdY4PKjDcaX9+riMnIdXadtrzte41uXKr3teWNumLHBACgYDb1b1FoRu5Fo1tj1fru7gImAgCg+H78hyc0EE+p8YBuTT5+owKxjNyQJ+NIXtqR9Yy6ljVr3aMT9dzr69Xe1a/Rzbw3LCc+e0wWxKpVq3TJJZfI8zyFw2F9+ctfLnWkkmGeNPJu+ZrlqmtKKqysAu6e91KQpIDrqyGQlHNAb5HSAQBQeP2mQzY4smvCzbuZTQkAQIXa2jOgdZu7Nf6oTZrxjjWKtiQVjHpyXMkYKRD2FYx5GjV/qw7+yGtK+XHd8fArpY4NFNzKlSv1j//4j+rs7JQkfeUrX9Hs2bNLnKp0KCaRd72JLtWHkwoG9nYE6d+4xld9KKlsNlvgZAAAFIcbNRrx/AImJAAAqsjilZsVmtChcUe0KRDx9jjOca3CDSkdeN5yPbtk3R7HAdXgueee04c+9KHtJ3B/6Utf0rnnnlviVKXFUm7kXSCaVcDJrZSUJMeRaiMpuS4ncgMAqsP4xjF6w3Mk5f586KX5vBgAUD2S6awmvG3dTqVkz0BMPf0x+b6jSCijMc09cl0rx5UiTUmZelbSlZu9HCiNEfrjH/+oK664Qul0Wo7j6Ctf+YrOP//8UscqOYpJ5F1dc63s4AinfVjJypcR5SQAoPJt3JKRN8lRMJrbeC8jtb0wRppT2FwAABSLqetVMDK0Km7T1kYtXT1RGd/IcX05juRnHb2ycrLGtXZr7rQNCkY9tR62ocSpgcL44Q9/qO985zuSpGg0qm9961s69dRTS5yqPFBMIu/64rnPDtnGyshawzI2AEBV6O8aVEfXKE0+fqNMLs9tRmpf3FLwXAAAFEvzpLScHk/L1o7Tho5mNY/pU11DQrLa/r4vm3HVvqVej754kI5bsFQNExIlzQwUwle+8pXtp2+3trbquuuu0/z580ucqnxQTCLvtvZFpREepJZMhuTk9M4NAIDyl2hPKDwzlfsFvqNQDYffAACqhxswau+uV1tfvaYc2C7X9Xf5sC4Q8DVhcpfiAxE9veQAvfPQLaUJiz3ymT30d7nmmmu2l5JTp07VT37yE02aNKnEqcoLmxkh7zq9lNZtHCU/x4mT6bSrF5dMk5frBQAAlLmWequWA7pzmy0pyQn4Gnckb8YAANWjzm3Qqi2jNX5ylwKBXUvJbVzXKlabUH1rXMlErLghgQK6//779dOf/lTSUCl58803U0ruBjMmkXcJE9dzK6Zr8oQORSKZYcenM0EtHxwlywcxAIAqMf6ArNaO4ONf40hNU/sKFwgAgCLLxLvVOHpAgcDwE1BcV6qpS2rVpqh0QBHCAQWWTqf1ta99TZIUCoV0zTXXKBqNanBwcI/XBINBhUKhYkUsGxSTyLuWqKOOcEwP/XW+Tjn+FYXDuy8nrZUSibDu/suRSoxxOPYGAFA1Dpw7Tut6R3aMpXFYOQAAqB4vd61S6M3Db3LhOL4GgjwXojrce++9amtrkzRUUuZy+vall16qT3/604WOVnYoJpF3zbEa+RGrld2tSvzlSB17xGtqbBxQIODJdX1ls448z9WmzS164rmD1NkSkjW+jGFnAQBAdYhkR++0J5O1UudgrTr6a5X1XEVDaU1o7FE09LcP7yzPgwCAKtLhD8oxuX9I5zhSqHYE+zOjKCxLG/fJyy+/XOoIFYNiEnlX4/pyZJVtstowUKs77z9GtZGEJozpUiCQVSoV1LrNo5RSQMnxnmzYKmQ8+bJy2VgXAFAF1qwPSE1Dz2nruxr1+uYJyvqOMt7QSy8jX8vbxqohmtChk9cqEsiofUsjy9cAAFUj7NRI/ghXDxQoC5BPEydO1LJly/Y65sorr9SVV15ZpESVjWISedcYSiriZhT3wvJqreIHZJXZLKUfCclJuvJqA+pf6Muv97ZfE3Q9ORrZkxYAAOWqvSehzfEmJaKO3ugYray/80suK0cZz9HWgVo9unyWjpryhlavHCsdV6LAAADkWX3bKNmmFZIzglmTAwUMBKAsUUwi7ybXNMg4kjwptD6u1t+uU2RdXCbtS56VDTpqud1ocH6Dtr5/sryGoCbW9ohD4gEA1cJkfC3fOEHpUd4upeRbRiqVDerJ1QfI7Q8XLR8AAIXWvKVBqrM5tw6+J4UedKVjCpsLI+OzlBsFRhOEvLMar2mt7apZ0q2J/7NUNa/3yx305GSsHF9yU77cQU91T3Vp8leWKNo5qKmjO2UMf+EBAKrD7OYWJWrMMKXk32SzrhRk5QAAoHrUhmIyzw/ts5yTpBR7NlrQTADKD8Uk8q7b69PUVJfG3LBa7qC3x3HGSoHejMZds1x1AU8252csAADKW7QxLCeU+8mi1jHyGziJFABQPWYdPl2h3welnuHf5/kJyf7O1/zDZhUhGYByQjGJvPPkqeNn/l5LyR0FBrPquV+y7DEJAKgSnW5CITe358FtnNpsgdIAAFB8reObNKF5jDJX+LIdVjax6xjrSXbAyr/VV+SxqM786InFD4q9srZyvlCZKCaRd+FUTL2PZpVrz2gTUtvP03IMD0cAQHXoHHxVzgg2+5ekcIhiEgBQXT747+9WLB5R5lJf3g2+/PVWtv/Nr14r/8++Mp/35dzraM5RMzR6UkupIwMoMg6/Qd4NrE3JWqOcm0lJmXbJWss+kwCAqjCldoOCjiONYNJkYzBVuEAAAJTAtDkTNfm4TVr2YLP8R638R998j7jD20UbkOqn9+mUDx1cspwASodiEnnXvrlHNj2yYtJLG1nfyrgUkwCAyje3Oaj6QFw9mUhO46NORh+ZvKTAqQAAKK6bf/UNrRs9RQOH18pZH1dqVFSZlojkGDnJrCJr+2QjIaVmTNDTW76hg7zfyXFZSQfsTygmkXdOOCRPZkT7BFj6SABAFcn2HaCPTfi1/mf1ERr0QsOOj7hZvb1hbRGSAQBQHL7va422qK19jhItIdlRtUMzJd9cJefHghpoikhWchNW9z95nCa4v9Xb3/EPpQ2OnVjerKPA+CgCeec2R+U15955WyMlFjTIOPyFBwCoDs880agzm9foba0bFXMzex3bEEjp2oMf1kOLphcpHQAAhffww3do8bIDFQ+FZF1JjtleSm5njOQYeVGjuBfUK5seKElWAKVDMYm82xpNqvec0QrEclvK7dZIvee3ciY3AKBqZDJWf3zgQF0z7xFNNAMyvpX8nccY38rxrC6Z9JIOb27XXX+aVZqwAAAUwAsrlmjQhIdah+HOEnCMkm5Iy7dMK0o2AOWDpdzIu3SgXQvOGtToZQNa8mRMqYS7x7GxOk9nf6JTN06fJt/6csyexwIAUCkmTGnRT1Lj9PD9U9SxrkEtaySvNqvEBEd+QArEpdpVvlI1Ad2w9XAtT9epv46XZQCA6tHV5ytjAkOlpLFqntytyYduUqQuLRkr33PUtqxVm5aMUSYRkozUGa8vdWy8BUu5UWi8AkbeRaIJXTLjVc3/+VZde9lkPfdQnQZ63Z3+QgsEfUVrfH3g0+06+xNbldm4XJx7AwCoFtMPHa+tT9drzfIGtT6VkclYOVbSCztPm/TdtFLrHd2Tnaup799YmrAAABTAgN8kGSlUk9L8dy1VIJJVMOztMMLTxEO2aNycdq15dqK2vD5GSQ2/LzOA6sJSbuTd+Kin6bE+BYLSoWcaBaZOVKC1TgoEJNeVgkEFRjep7uDxmnOcr5Bj9d4xb8gXzSQAoDrcsvJPGlhRr9an0nLTb5aSu+F4UrjbV/PzGfVkaosbEgCAAmptHqdgJKMF57yuSH3qLaXkEMe1CkY8TTtyg8bMaqehAPZDzJhE3jW7Ucla3fKrWbrtlpnqqQ2r+6JmJQ5wZI2RyVrVvZxV8qmsvnLFsfr3/3haMxd0KWmt6CYBANVg9WC7apc4ctLDj3V8Kdzlq+v1WumMwmcDAKAYps4JqH36egVj6WG3mAyEPU07ar0GNvEhXbnxWcqNAqOYRN41Beu17OUW/f73M7XqnFolp7jyIxo6hU2SZNRzXFB9RwTV+3RA3/z6kfrfnz6oyGg+HgMAVIfBuFWkzcv58zYnI8WW+cMPBACgQoyfKLVkeuTk+DbPca0mLGgrbCgAZYcmCHn3/Nr1uu2XB2rleXVKHODKj5kdSsk3uUZ+zKj3mKDWHx3Tn+6eMfxJbQAAVIimrlZpD8u3d8dIinRQTAIAqkdHuFvGzf3J0A36qp/eW8BEAMoRMybz4JFHHtFtt92ml156SV1dXQqFQpoyZYpOPPFEXXjhhWpubi51xKLasK5Dz4dGKzXBkQ3uvWy0EaOew0P6w28O0FkXJhQORouUEgCAwhmlUVpnt47sohEUmQAAlLu+rCvHHdmHbiMdj8KzvD5BgVFM/h2y2ay+8IUv6K677trp+5lMRq+99ppee+01/e53v9MPfvADHXrooSVKWXyDnZ7aj47IhnObAemHpfZDI3IVLHAyAACKw3VSYksmAMD+rDedkQ0YjeSTN48nT2C/w1Luv8O3vvWt7aXkqaeeqt/85jd66qmndNddd+lzn/ucYrGYOjs7dfHFF6utbf/ZK8NraJUXG8ETimMUn+WKz8YAANUiMi41ovPcrCSviTdjAIDqUe+Olefn/tzmW6l7oKaAiQCUI4rJfdTW1qabbrpJkvTud79bP/zhD7Vw4UI1NTVp5syZ+vjHP66bbrpJgUBAPT09uv7660ucuHgyo2Ia8Xo0Y+W4BYkDAEDRRYOesi0m52dD60ru9GxBMwEAUEyt7U2KJ0M5j896jvoWNRYuEICyRDG5jxYtWqRsdugNxGc/+9ndjpk3b55OO+00SdLDDz9crGglN7p+jMwIS0YTlFjzBgCoFmOdMaqdnZQfHL6c9B3JazaadcyaYkQDAKAoarYEtfrhscqkh39zmM0adXXUK/IY23uVG2tNxXyhMlFM7qP29nZFIhG1trZqwoQJexw3ZcqU7eP3F2P76mRz/2BMkuT6vlyHhyMAoDqsDQ7oknc8rewcR37QaE8r2XxXSje4Cs30ddpBq4obEgCAAnL9rDpTzVr8ylSl91JOZrOOentqtfiFaUq18J4Q2N9w+M0++uxnP6vPfvazGhgY2Ou4tWvXSpIaGhqKEassNIfCiobSGsyEpRx22HLkKxZMy/pWxuVTDgBA5Rs7ZqlOGL1Oq45fot8F5yrd5irWnpHJ/m3+ZKbGVWJMUPWtKf3gI3ersXHvrykAAKgkq0MbZCNS25YWDQ5GdeCsDWpq7pc09C7RWqNs1tWqN8Zqw7rRkoy2zh7hDBcAFY9i8u9UW1u7x5+1tbXpoYcekiQddthhxYpUcv01SY22A1rXFcrhVDUr1/XV0jAo41BKAgCqw7jooFxZfeqUZzS+sU/XP3Sk0jN9ZWqtjPHlZ10Fu43mNHboq+c+oEnNfYr7jhKlDg4AQJ5sDfXL813JSAP9Mb343EwFgxnV1iXlOL5SyaAGBqLacTJLpoGDB8oNS6RRaBSTBWKt1ZVXXqlUKiVJ+uAHP5j3/0ZTU0zGlN9fEoGMr9BKTxMae7Sxp1Ge3dN0fKuA42tKc7dcY9XYHJNrmLpfzpw3y2PHMWpu5sS8SsQ9rHzcw8owx8yQTQ/9ecbsNs1uXa4t6ZhSvitrJNdKETerBbUdamj4Wx3JPS1//A5WPu5hZeP+VY4+Py7ZyE6L6DKZoLq79ryPpO9zX4H9DcVkgVx11VXbD7x517vepaOPPjrv/41AoDw/TYoFh6bf14QzmtLSpfb+WsXTIckOndVtzNBzU10kqVF1Awo4Vr6VXNehmKwQxhi5LLuvaNzDysc9LG9jgqfKT31Hi3rH6RedszTov/km7M2nOc9IgzaoR/vHanGiSVdPfEapZEC1Ls+DlYLfwcrHPaxs3L/yN9gdlHnzPWCuTMLI5bkQ2K9QTOaZtVZXX321brzxRknSzJkz9dWvfrUg/61s1ivLGZMTQ9r+qVg44GlSU6883yieDspaI8exioXS2nHltiMr+ZInvySZkRvHMTLGyFor3x/JSwyUC+5h5eMeVoYlz6c12DpavxjYoZTcDV+OOrMR/cfGwzXhMV9f+izPg+WO38HKxz2sbNy/ylHv1kh+UspxPo3JWkV7fXleeT4X7q+FKb9lKDSKyTxKp9O64oor9Mc//lGSNGPGDP3sZz9TTU1hpqJ3d8cL8u/9e/UmVitoPHk7PAO5jlVdJL3b8dZKdW5SnZ19MqY8Z4FiSHNzjVzXyPeturoGSx0H+4B7WPm4h5XhoVdX68Wp85RoGf4DRCuj3mxIKzaO08Xc07LH72Dl4x5WNu5f5RhTM1HRrmWKt7rScLNbrZXxpEDKlO19HTWqrtQRgKq0f1b+BdDT06OPfexj20vJuXPn6le/+pVGjRpV4mTFt7azUyfULR9qHHPgGk/H1a0UH3gCAKrFVtuvwWjuH7bFbVDhI/sKmAgAgOJ696HzFRm0Cg5I8vbyZs9amaxUu8HX5Cn73/tnYH9HMZkH69at0wUXXKBnn31WknT88cfrl7/8pZqbm0ucrDQ6e7N6W91qTQz3DFtOOvJ1ZO1aHVm7TjbHIhMAgHIXmWQkZ2TPa25ztkBpAAAovmmjm+WFHNVstop0Wpms3bmg9Ie+F4hL9WutArI67cg5pQsMoCQoJv9OK1as0AUXXKA1a9ZIks4//3z96Ec/Ktjy7UqQ6OuUkfTJsY9qWqRTYZPZzSiriMno6NrVOq/lJRntdFgbAAAVbUxNRGW4DTQAAEXzypY2+WPDkmsV7ZIaVlrFtliFuq1CPUNlZf0aq7r1Vo71lYo4emD9mlLHxltYayrmC5WJPSb/DuvXr9c//dM/qaurS5L0mc98Rp/61KdKnKr0/GBKkhR2PH1q7KNam2rWop5ZWp9ultXQQTdzYpt1csMKjQoOSBraUDeT9eS6PCQBAJWvKVsjayQTzv0akypcHgAAim19T6/iIU/OWKPYZivHNwr3S+H+nVcUWMcqG3Y0ON5oQ09vidICKBVaoH2UyWR02WWXqaOjQ5L0xS9+UR/96EdLG6pMRMKtMmboycYYaWqkS58Y+5SixpUjyZOUsNmdzt8eyIQ1IcoEXgBAdRjtt8qkjVST23JuPyMFnohJxxQ4GAAARZapc9Qfsop2DC3bltGbW34ZWVdKthilG4xYagDsnygm99Fvf/tbLV68WJL0zne+U+9///s1OLj308P2l+Xdx0yZr6c6J+m4MSsVMY5anZBCxpGRGXoOkmQVVtxm1emnNZB19Yd1C3TFIcFSRwcAIC8mTBmlgSdjqqkblBMYvpy0MqptaylCMgAAimNSY4NiwaD6U2n5YaPBiUbGs3JSkrFGfkDywzuXkRMb6kuUFnvEURAoMIrJfXTjjTdu//O9996re++9d9hrli1bVshIZWN03Tzds2Sejh+1QeNDrlxj5HnS5k21SiQCqqtPa/SYuGpNQFHX1ZK0FE82ljo2AAB50zi2Tm2DjZrQm1W4IbXXctJLO+pY3KKG0/bPQ/MAANVp/tgxigaGisltnLqs3LEZyZFM2pHfG5b8oXKyLhTSRw49pFRxAZQIxeQ+6Orq0rp160odo2xt6k6opS6lsW5E8X6jO247QH++a7r8N59wrJVqajN67wXLdPo71mmcG1LC56EIAKger/ZukukKadOT4zVm4RZFW5Jygr7MDruW+Bkj6xt1vDJKA1tq9Jp6SpYXAIB8M8boHw6ZpxuefV6Z2n5FJg7KuFYmYGWMZD0p4g8o0xNScl2tasNhHTZxQqljAygy2qB90NzcvN/MftwXa/u36APjVqprU0RX/OsJ6u0Nycu6O43p7wvrJ9fN1713zdCV1/5Vh47fJM/35TrsMwkAqHxLN3dIvpWyRn33jlEiklHN/F6FJiVkXCs/4Wpwcb2Sb9QoFXNk6owy7aVODQBAfr1v3lz9qec5tYUHZN6yesC4knGtQi0pheuz+sqsc+Swz2TZ4bRrFBrFJPLOBvo1M9ujL/zrSerqjO5xXCoZ1NrV9frGvx+rf/3fZ9jrGABQNbJpK+tJsQ4rJyv56aD6/9q6yzgjKdwvSb6yTTwRAgCqy5M9yzVY1yXj73lLE+NITtjXDe336v9aPlq8cADKAtPTkHfTmuK65/cz1NsTGnas5znatLFW615qkCPekAEAqkOjN6Bgv+RkNeyzm7FD5aRJs7s8AKC63LjxEQ36qWHH+bLakurRisHNRUgFoJxQTCLvalIBPXTfZHne0PLtbMyo49iwVn20Tis/Xq/VH65T9/yQtm0rOTgQ0u2/ninJL11oAADyqH/FOoUGhi8lt7NSuCNTyEgAABTV6ni7erODOY/v9xL63ZYnC5gIQDliKTfy7oGX22WtkZW05bSo+g4KyQ8aKTD09izTJLW1umo/IarRjyTU9GpaG9bWyTJnEgBQJTq3ZmV8X7l+BmwkBRI8CwIAqsfK+BYl/PTO3/Qkp9dInmRrrWx012tQXiwLOlBgFJPIuw39SWV9o03viqn/gJBsaNc3WjZkZENG7SdHZQNSeHlWyXRa0XC4BIkBAMivULRRxmuT3OHHbmN44Q8AqCKe9eW/2WqZPqPwEwEFF+9QQVjJb/GVOiGj7AxfMkPXANi/UEwi71pGh9UxJaqB6cHdlpI78iOOOo6Lqn5jWuEQD0cAQHVoijRJdmSzPjj0EgBQTVpDdXIyRmaTUezWkEzCyLzlyc7Z4Mq9zVFmpqfEe9JqcmMlSgugVNhjEnl33MGz1X5ETH4kt4eXHzJqe1tUjhnBtBIAAMrYG5vWj/hVllfHLBEAQPU4KDRB2mQV+11ITtzZpZTcxqSMgktdRe4KauaG0UVOieFYayrmC5WJKWrIu78uTyjdOIKS0TXqmxaWtVbG8JcJAKDy9TpJZZo8BXuHng+tkZItUrpRso7kpKVohxTcdiaA5yk+h8NvAADVY+lzaxW9KyQnMfwndSZjFFrqavXajdLbixAOQNmgmETePfjKa7KNI7zIkRKZjGKhUCEiAQBQVM3jW+ROWyrn6Ub1tQSUGDtUSO6452SqWXKyUv0bVi2xuHoa2GQSAFA9tmzskdOf+8QTkzbqyiYKmAhAOaKYRN75vSmpceTXeZmMRDEJAKgC7zj+CGV67tYD2QVKpF1ZdzcHwQUlLyj1zJYik5Oa1B0vQVIAAApj89aekW2g7BulQ2xrUnZYIo0CY49J5N28WIs00r+7PKk2xkbHAIDqMH/CaL3UPU1xP7TbUnJHNmDU1tasI6b3FikdAACFl2xKSt7IrrFMnQL2OxSTyLuTjlugQGdGynVFmpVqXuwpZCQAAIrq1a4HtaW9+c3128PzfUePdY8rcCoAAIpnIJaWAiPcpiTCjElgf0MxibwL18fV+td2ye7wJORJgX6jYI+RO2B2Li19q/pNyZx7TAAAyt1TbZvkZUfwMssatW9tLFgeAACKbrwzss3jXF/m4FTB4gAoT0yURt45wY165zkrdPujIfUfNVrRzY6C3W95c+ZIqVZfiTG+Jt2yUvM+lJaRr51OBQAAoEJ1xgOydmSnbHsenxcDAKrHwOKUnCkp+Usjymmvr5BkooMFz4WRscwgQoFRTCLv+vu6NPeYNj3ywhFyXnUV3Nin2Asb5Ha+uam/kdITGxVYOEGRzTHNOF067vDX5flWAXpJAEAVaA3NkLR4p++FnIwm13cp7GbUk4pp40CTRr4pMwAAlSGy0Sh4SL9SG0LSwDBv9MK+AkcNyr/Hly4uTj4A5YFiEnnnWVePPTlPvfFaNdy7WG5PQk56512Pwyu3KrS+W+mpzXrluNk6smel7JgSBQYAIM+iNiqTtrJBaXS0T/8w+ym9bfwKSUNVpJU0mAnr1uVH6L61B8uzrgLpEZ4QAABAGRtfU6cXXgyr7mM9yvy8UUoaKfPW1QFWilq5R8aVPsRT8GY+sAP2NxSTyLuxjRP05FNzVH/3ErldCZndzP02kkzKU+iNTknSH5qP0/vmsIQNAFAdlr28VoHurA6YvUX/dcydqgsm5Lzlaa4hnNS/zH9Yb5+6WP++6L2qfXRQ+nBp8gIAkG+zZo/Xjf56JXqTGvPZTtmXQso+ViuberN8tJI7KyXnhLgytY42Pz1GEyd1lTY0dsVSbhQYxSTyLpkeLXdpp9ze5G5LyR05WV+hNV3qfWOcjKGYBABUh4FlXZrdt1Zf/uj9aggn9zguGshqekO7vnL8nbrhB0cUMSEAAIW1qrdLNmDU316ndDykUQd1KnpUp2xWUlayQclaR53rG9SzskEKGXXOrS11bABFRjGJvHvu9S5FX9kkk/VzGm9SniIvbVTW8xVwKScBAJUvGAzogre/oLrQnkvJbUKurwNq2jXlwO4iJAMAoDg2J+PyQ0Pv71IDIZm2gOa1rlZjy6Akq6znaskbk7WpIybZoVmUqYZQCRMDKAWKSeTd2tVrZUawT5aRFNzYK2t9SRSTAIDKN3d+RAcd2b7L8u09qa1P6+0fWVbYUAAAFFGkJiwlpFgkqXNOfErRSEqRUHaHEVkddtBKHXLgGj3xymwtXTNZxmGPyXJjLfcEhUULhLxzEsPPDtmVle/nNsMSAIByd+jRm0f8Kmv6AvbVAgBUjyMPm6moSei9pzyuhrrBt5SSQwKuVSSc0dsOeV0zp6xXS4D3hMD+hhmTyLvaUGRoGuQIua6b/zAAAJTAzFZH3fGR7RYfiLC7PACgerQMPK9jD1ummmhSw02EDIeyOm7+6wo5yyR9qij5AJQHiknkXWN9s4w7sjdXzhhXxjBFHABQHULuJCkwsue1SJBZIgCA6jEw+JoOPGBzztuaBAKeph+8qbChMHJ8booCYyk38s6tlaa9O65ct6KwYUdHf7hLlqXcAIAqYXWswoFdl6ztiecbOYoUMBEAAMU1WNevYCD3swcCAV/twfoCJgJQjigmkXctY3t1wSeWy20cvpm0jhSd6Ok9F7wuY3g4AgCqQ8ZbKpuV/BxnGTjGKpXMvcgEAKDcBWpHy7Mje4+XtsECpQFQrmiCkHdTJsbUMiqlD/9gtdTg7Hm/yeDQEu7P//JluUEjY5gxCQCoDptWrlMq5SiRDQ5bTmZ8R+39NcqkeR4EAFSPUeMOk3nLOmDPGsW9oAa9kFJ+QPYtz5Gu6oqYEEA5YI9J5N3Epgna0BbR7+98p9KHBRXs2iDzRo9krORrqA53HfkzR8k2jtZPbhyvz3zmD3IcHo4AgOqwdmVaPfFpSk8I6G0NqxR2sgq+5QM430pZOdqQbdSrifE6IruuRGkBAMi/MaE5Mv0BWddXyg+oK1OjlD/0ns9qaP6KI6v6QFwNgaQkaULsmNIFxm7ZXPdoA/YRTRDyLpsZp4ceOUT9/VEp5Co+4wANHCvZZEJOxpMfCch1IqrZYhVISe3tjXrqydma/K5SJwcAID9Wr/OlOTWKhDJ6NDFDo91+HRDaqpiTkWRlrVGbV6fV6RYN2rCckNVDz87SnOmlTg4AQH7UumOVcaKKZ7PqzNQq2RlW14PNiq+skZ81CjZl1Xxyp7IHGQ36EY0J9mpy6ORSxwZQZBSTyLsnn31Rf31srrJy1X2Qq0yNkQ0aacdp+b5VcpQU7fBl10Z0zx+O1PvPspzMDQCoCoHJG+QFt61PM2r36tWe2Lah/7Z5In/jBq3CB7PHJACgevRlu7UpGVUmLa372WTF34jJTzhDBw1ISm+REmsickLjNfGidbIzpOcHX9D48JwSJwdQTOwxibxbs7xTqWRAXXNcpeu3lZJv4Qx9PzHaUd9UR4OpqDLpdPHDAgBQANlgRk5gT5tL7v5DODfCHpMAgOqxPLlYqYyjVd+ersHXa+XHA9tLyW38REDZ3qDWXTdFPW/UaUn8+RKlxR7ZCvpCRaKYRN7VxGoVH+0qGzOSs/cZkDZglBzlKBMzclwm8AIAqoPvNcmOsGd86wEAAABUsqWDb6jjz61KbY7IZvdePfjxgNbfMFmpbLxI6QCUC4pJ5N3hh83X4ERX1s1tWbYNSIMTXAUCboGTAQBQHJn4QSPeLL6/raZAaQAAKL6BdK96Hm+WzeRWO9iMo/5XagucCkC5YYoa8q5hYp2cmC/vzWn6ASerQ6eu0iGTVysSzGggGdGTK2dr+eYJsjKSjLKj2FsSAFA9vOYm9Wyo05hZ3TmNT8cDWnb/VOmowuYCAKBYBl6vlc325DzeT7rqWtQqnV24TNgXvFdHYVFMIu96Ekn5jpE8q5MPelWnzH1FAddTJPi3Tf0PHLtZGS+g3zx5vJZtnigTsvJ8X67DJF4AQOXrjQ9o7V+mq+nTLysU2/uhNr4nJfvC6n4+WqR0AAAUXmZTWH5qZO/vMt3BAqUBUK5ogZB3SX+DfBm9+9BndPq8l1QbSe1USkpSLJxWQyyuC497SPMmrVbWGjmcyA0AqBLhTFap1xy9fNtMpeN7/hzY96REb0R//e6hCieTRUwIAEBh1fv1GulbvKBhw2Vgf0MxibxLa41mjNmkow9crmgos9exsXBaFxz9uOpqB2WtV6SEAAAUVoPpVCCb0Zb7m/TEDQvUubZBqcGgsmlHvmeUSbhKDQS15ukJevCbR0krMgq6nMoNAKge82Z2yIRHVjROmdpTmDBAnn3961/XrFmzdPvttw87NpPJ6KabbtL73vc+HXrooVqwYIHOOussffvb31ZPT0/hw5Y5lnIj77Zu7Nc7DnlBsVA6p/HhQFrHH7xYvrU05QCAqjBpXEDPuFbB/rQGngnq0ZWHKzo6oZbpfXJDvlJ9QbUtbZbptwp1xmU8q5pmZkwCAKpHyzRfmYiRm+NB2zbqyx6b23tIFBGTWHexaNEi3XzzzTmNTaVSuuiii/TMM8/s9P2VK1dq5cqVuv322/XTn/5UM2fOLETUikAxibxLbfU0/sCenMcHXKsjJ6ySfEfiYG4AQBUYNa5JtaMTSvWHFEhkFdjYJ7/D1ZaVjbKO5HhW4figjD/0aj8Uy2j+uatLnBoAgPx5cNWg+o711XC/kZPa+5pua6zStUaL6xqLEw7YRw8++KAuu+wy+X5uK12++MUv6plnnlEwGNSll16qd73rXQqFQnrkkUf0P//zP2pvb9fFF1+su+++W7FYbMR5Tj311BFfkwtjjBYtWlSQf/dbUUwi79K1KYWdkS3LjjhZGccTzSQAoBocMOooHf6Bn+qh/5uv9ODQRv5O2pOT3v3zYyiWVeuBI38xCgBAuVrb1qiBmV1ytki1r1i56d2Xk9ZYZeuk9vdKsSSH36A8+b6v73//+7ruuutyLiVfffVV3XPPPZKkK664Qv/wD/+w/Wfvf//7NXfuXJ1//vnauHGjbrrpJl188cUjzrVx48acxxpjZO3up8C+9WemiGeAUEwi72KjIgo5vvZ+BunOIm5WWWuoJQEAVaGhpkWRyQ2adcZ6vXLXdJm9PCm6UU+nffl5zWi8ongBAQAosKjXoJC2qucER+kWqekJK5OR3ORQ4eG7VjYoJaZK3adIfkTKdvGOsOywlFuPPfaYrrnmGi1fvlySNHfuXC1ZsmTY637+859LkiZOnKjzzz9/l5/PmTNH73nPe3Trrbfq1ltv3adi8ogjjtjrz19++WVlMhlZaxWLxfT/2bvvOLvqOv/jr+8559bpLZNeCAkJkNB7F7CgIigL9oZl1XV1dfXnuiu6rAV17b2hAorI0mwoIkV6DyGV9JAymcn0mVvPOd/fH0NCQtq9eO+dmZv38/G4j4wz3zPzlntnzj2f8/1+vgsXLmTatGnU1NSQz+fp7Oxk0aJFdHZ2YoyhpaWFV7ziFUXn+EeoMCkl1xyvw1qgiAK7ZyDm6uUoIiLV455Np7EtGCB3iEvN+hQmtDj+yLt7C1jP4Cdd0gvreGDNQiZOnDa6gUVERErosoXH8VDHGnAgdQSkDofYZohutTg++A2Qmg02NjI+QsD0zfHRDS2yF+95z3sAiEQi/PM//zMXXngh559//n6PsdZy3333AXDOOefgunsvup977rnceOONbNq0iRUrVjBv3ryisl177bX7/NpXv/pVHnvsMWpqavj4xz/OG97wBmKx2F6z/vWvf+XKK6+ku7ubTCbDF77whaJy/CNUCZLSG3Coa4G+Aoc7WCZZQ2B9XKOp+yIiMv51pYZZscLgDNbAZIf0pATRnhzR3hzGt4Rxh3R7gjAx8ib1maWz6Y4/zqmTp49ychERkdI4cl4biWcg3WbBMWAgO3XksTdBzuWDp6lEIWOPMYbzzz+fj370o8yePZtNmzYd8JhNmzYxMDAAjMyw3JfDDz9858dLliwpujC5L/feey8/+9nPiEQiXH311Rx11FH7HGuM4eUvfzmzZ8/mkksu4eabb+bkk0/mta99bUmyHIg2QZaS6+1PMtWCV+CcbxeYEEIYVK6HgYiISDmt7N6O6QnBPv9WyxhyLTGGDq1jcF49wzNrdxYlAQhh67q+UckqIiJSDlu3/Z2apRFMxjngcmCTg/iSCA89s60y4aRw1oyfR5ncfvvtfOc732H27NkFH7Nr78epU/dRjQfa2tqIREYmaBVS8CzUNddcA8DrX//6/RYldzV79mwuvfRSrLVcf/31JctyICpMSsklko3YgRhzvOCAxckIlmMiPk88MQXPUz8RERGpDhu29e6zufjeGAy2r7BG6iIiIuPBsxu6yQ1FqP97DGfYQG4vg4KRomRiWYT4hghL1tdVPKfIgcyaNavoY3p7e3d+XF9fv89xjuNQU1MDsHOGZSksXboUY8wBe1C+2I4i5qpVq0qW5UBUmJSSm3zoMDf/fi5teYcjIz5xLJHdCpQj/7vOhJwQ84mkPe5fOoHQ6oJMRESqg/VDTLHN4tVcXkREqkjedwlCB3fY0HpThIa/e3jbDU4aTAacYah5xqX15ijJ5SOTVPKBShRSHbLZ7M6P4/H9907d0fdx12P+UalU6iV9zx3F0Xw+X7IsB6IGDlJyyajD4mfqeXZ9E4cd1sNZ8Ty9oWFb4OADMWOZ7IbUOjCU9/jjX2YRrRkqZq8cERGRMc04DtZQfHFSRESkSjTUzybuPYzf64I1JDe6JDfufZWcxeIT0jhFJQqpDvva7KZSpkyZwvr167nrrrt4wxveUPBxv/vd7wA45JBDyhVtD7odISXXGsbp6q7jnZvO5dl0HRnr0ORY5kcDFkQD5kZCEgbSocMfe6byee94Vt47GcfRUm4REakOE1prd35sgdCFfAJyNS88/Nju7ZBssvI5RUREyuXo+cdgh0b6Sx5oEooBIpmQl5+0oBLRpAjWjp/HWJJIJHZ+fKBZizu+fqCZlcU4++yzsdZy1113ceONNxZ0zDe+8Q2efPJJjDG88pWvLFmWA9HtCCm5/k0BXSdFOX3OKu7MNrIkjHNcfIBpXgYDBBiezSV5OltHj2c4bFYHW5sasaGPcfSSFBGR8a8pmSCIARmLnxjZifTFt4PDyMjDyYHxLflarR0QEZHq4fshJmcKXhlnLeSG1WNSqsOufSUHBwf3OS4MQ4aHhwFoamoq2c9/97vfzU033cTg4CBXXHEFDz30EJdddhlHHXXUbgXQoaEhHn30UX75y1/y6KOPAiOb9bz1rW8tWZYDURVISm7L9u00njLIzPoeom5ITxjlr6nWvY6NuQEnTVnPzWcfg7WOlnOLiEhV2NQ9gN/qY3u8kaLk3k5wz38ujFqceEjgaCGLiIhUj6ef2UjohxS6UNNguPOuJbzuVceUN5hIBcycOXPnx1u2bOG4447b67iurq6d/RwnTZpUsp/f1tbG97//fd7znveQyWS4/fbbuf322zHG0NDQQDweJ51OMzAwsHPDRmstLS0tXH311SSTlVvKo3fAUnKLgzTHT36OmBsUND7q+Bxx6BYC65c5mYiISGVsGxoiEgvApYD1a4bQMbjxyjUZFxERKbctm7dgbXFTT3p6SrcrsZSIHUePMWTChAk0NjYCsGzZsn2OW7p06c6PDz/88JJmOP744/nNb37DiSeeiLUWay1hGNLb28vWrVvp6+sjDMOdX7vgggu49dZbmTZtWklzHIhmTErJ9ZKiNZYpeLzjwJzWTgJjiJQxl4iISKU4niXsiezeRHJ/QoNrwvKGEhERqaCuratH1mebwouTfk7nQqkeZ511Frfddhv33HMPn/zkJzF7+V246667gJEZjvPmzSt5hnnz5nHNNdewfPly7r33XhYtWkR3dzcDAwM0NDTQ1tbGcccdx/nnn1/xguQOKkxKyUV8p+ibFY6xuCEjM0tERETGucBmCy9KAliD7dXtORERqR6t9bniZrFZSyS//01CRMaTiy++mNtuu421a9fy61//mre85S27fX3ZsmXceuutALzjHe/Ya+GyVObPn8/8+fPL9v3/ESpMSsk19yfxi61MBmDUW0tERKpE90AKip30EarTsoiIVA+n0cH4IdYt8DovtDh+rryhpHhFLseXF5xyyim87GUv46677uILX/gCnZ2dXHLJJcTjce69916++tWvks/nmTp1Km9605tGO+6oUWFSSi4xWM+wbyFa+DF+n4uZUL5MIiIileTb4MC9JUVERKrYtgnNeP1ryLfUwIGKk2GIk/EZPCRWmXAiFXLVVVdx+eWX88wzz/DDH/6QH/7wh7t9vbW1lauvvpra2tpRSjj6VJiUkpvS2MZDj9Uw6cyBgtqJ5PMO637diLlSV3AiIlIdZrc2c4/ZVNQxTqSwTeNERETGg7wbIdPmEu9MkW9NYl0H60HoGjBgQnByFhOGONkAry9F76ENox1bpKQaGhq4/vrruf766/n973/PmjVryOVyTJkyhXPOOYf3vve9tLS0vOTvv2N5tjFmt012/tFl2y/+fuWkwqSU3LzjZ3Pd+xtpPS5FtPbAO20HQy5b/pTA+byWcouISHWYO6MVL5LD9+MFjY9E8ngNavgvIiLVoyleR+9xSSb/qR/SWXJTaka+4Dw/IcVaSBqc4ZBkR4rehTVEEromlLFv6tSprFy5suDxkUiEt7/97bz97W8veRZr995Hb1+fH4tUmJSSa5nczNBwE4t/kWThu1YTrdl3cTI7EOGRry6gfWGigglFRETK65DkBM5cuIiHFh1NOrv/4qTrBExs7iUxqULhREREKuCs6Ufx09qVdJ3cSHTI4AQvWiH3/PK6oNaj/+gmhmcaFkzWUu6xxoyf+tZB6YQTTijq82ORCpNScmk/S9haT8eTLtnBOEe+ZRWJ5ixe0sdxIPANQcalb10dS66bS6orQW7WgWdWioiIjBeZdIbTj1zN1OQAv/rruQSuw177mwQhtSbN5Rf8mcWbp1U+qIiISJmsSfcQy+bxhqJ7FiV3YTA4eUvNlpDeY7R6QKQY1157bVGfH4tUmJSSW7p+HdYdOfH0rmrkvs+dQM3EYVrn9+IlA3IDUbY93UJu4IXdcbIdDtZaTCFNKUVERMa4m5/4DfMPMzz51TlMGOyhf04dmQlRdkw6MBZMaKldm6K5Y4DFyUOYf+mGUc0sIiJSSn35FM5z+y9K7mAwOFkY6NKu3CJj0bJly7j22msxxvDFL36xpN9bhUkpuc7BfmD3+d7DHTUMd9Ts97ggCPA8vSRFRGT8S2f7WHzzLAa74ni+peWpAULXkG/wsM5Is//IgI8BfCIs/sNMZp27FVpHO7mIiEhpdG4ZxMkWPt7xDbklIbyufJnkJdBSbgG2bNnCLbfcosKkjA9uzMMxll0n4SdaMrQc1ocbD8gPe3QubsZPR3Z+3WBxXLfyYUVERMrA85OsvGMaof/Cuc0JLLGe/F7H59IeS34/g9ccVqmEIiIi5fXoqudwAqCIRXFun1bQiRxsVJiUkpsx1cV1QkKgYcYgR7x5FbWTUrixAMe1BHmHMO+wfXkjy35zKJneOA3TB4s5X4mIiIxpTamFhMGDBY+3gcOaeyfBv5cxlIiISAV1bx/GGIst4krPzWt6nsjBRoVJKblktI+athSRmQMc874Ve+zK7cVCiIVMOm47zXP6eejLR9M+vxsIAc2aFBGR8a8mupbQd4o7KNQtOhERqR5ONkcs5jOcLXyn7YZYpoyJ5CWxen8i5VXkO2aRAwvzeWxoOOa9exYld2UciDfmOfkTT7NtcQtWN8dERKRKNDeuw3GL21nUcbQTqYiIVI8zD+sg4hV+bkvGsrzprKfLmEhExiIVJqXknl2f5ZBXbiKS3HdRclde0qdhxhDG6OUoIiLV4fCjHJwiT2uT5veWJ4yIiMgoWDgVLjxpGVGvsOvCRNTnvOPXlDmViIw1qgRJyXVv7aXtiF4KrTNG4iGzXr6JvL/3DQFERETGmwn1RzPt6E4K3coyVpvjjDeuKG8oERGRCnLjSd523iJmtPVi7P5nTiYiOb74zr9CVMuGxxw7jh4yLqkwKSWXqM0RFtkqMlrv4+jlKCIiVSJmDudl71hKvP7AN90cL6B15gAnHa4ZkyIiUj02dk2hbyiG81QKrz+H8UMIX1Q98kNMLqB2TT8DG11WdLeMTlgRGTWqBEnJTWmbii2yMGk9i+Pp7piIiFQH1+nhuBkDvOzTTxKpy4PZ+218JxpQNyPNxZ97iEluTYVTioiIlE9HZhLX/egIurclqF07RP3yPmJdadyUj5P28fpz1K4bpGFJL/52w7e/diw/e/So0Y4tIhWmXbml5NaGCUyRNcYQg1GdXEREqkaEKI04h8EpX1nOmhsn0floExiLDUc2gHPjIbNe18G0l3fSG2nENWppIiIi1aOpboAH7p1CEIxc5wW1luGj82RmhlgXvAGL82CANwRYyGVdtt1VC28a3dwiUlkqTErJ9WUiZPMx4gU2OQ5DWNU3gUxrnoQXLXM6ERGR8vOZx8OZJD4uyYlZFnx4PX5mI0MbEwQ5h1hDnpqpmZ038obCCJuDidTonZmIiFSJ9X/pIp93sBHouCxKdppLGAeckZNfvg0ykx2cvKX9hhxs9Iivy41uaNmTejdKmWmKmpRcYyzJExumkR8u7OXlpx0WdU5RUVJERKpGxnpsDWK7fc6LhzTOHablyEFqp2V2W12QxeWxTHOFU4qIiJRP77Ys1oHNl8dIH+ISJs3OouQONmEI6h063hIjPd3BBKqCiRxsVJiUkju8sZ3ttyTofjKBn97/mu78kMPqXzWTfypCaHUSEhGR6tDjryWwxd1w6w66y5RGRESk8pKxgJ5zPPJtDkT2f10YJg3b3hgreq8CERn/VJiUkvNwaLgvxaIrJrH1rjpyAw7hi9pm+RlDrt9h1U9b2PirJpr/MoS2vhERkWoRkCOXC4o7JiysBYqIiMh4MP/YOIPHRbDRwq70wggMH62eJmOOHUcPGZf0Wy+l15nCBEBoWPqVdlZf3cKMS3qZcNowbsziDzls+mM9m26vJ0iN3BKLdWQxxe6YIyIiMka5QQ25AOJFHJNOFVfIFBERGctmndOEXZYueLyNG/IvK2MgERmTVJiUkuvZ1IfJv3C7Irvd49kftvHsD9v2eYwJLIEf4Hqauy8iIuPfs8uHCSYUvjAlCAwrlk+A6WUMJSIiUkHPDjZio1uKOiaV0KJOkYONfuul5FzPxfhhUceYINSMSRERqRorBjpZsWQ6vl/gRnB5jye3zipzKhERkcq5b8PqoisOoeapjD3WjJ+HjEuaMSkl19xaC9ayY7vRaJPP9Nf30X76MG48JD/osukP9Wy5o54gM3KmCvM+jqs6uYiIVIfeXMDSx2dzyKwOGpqGcdx9Nz7KZT0WP3oowZZkBROKiIiUlxOkcYwhLGJHm5infssi5ZbJZBgaGqKxsRHPK6wsePrpp/O3v/2tLHlUmJSSc7DYTBYSUQ7/t24mvWwQLxniRHaM8KmZvp057+nm2R+38Nzv6nFyWcIwxHFUnBQRkfGvoaaFwO/k9htP5fyLHqGuMUU8sftOcEFg8PMeix+dzbKnDsHsp3gpIiIy3jRag+tYwgJbKDsmpClaeE9KESnM0NAQN910E/feey+LFi0inR75PTPG0NjYyAknnMB5553Hq171qn0WKuPxOFOmTClLPhUmpeS8iEvE+Bz52V7aTknhJfe80PISFhKWw/55O8b4bP2/hJZyi4hI1Zhc04QB8rkIf/rtabRN7GPhiatoae8HIAgcVi+bysrFM8mkYsDOhQYiIiJVYd6MWjYHW9k2WE9oDzQBxRJ1A5xQN+lESunGG2/k61//On19fQBY+8LvmLWWnp4e7rjjDu644w5+8IMf8LnPfY4TTzyxohlVmJSSmzBzAhNflqP1xL0XJXcVqbPMeW8f+SWNKkyKiEjVcBIOOBZCAxi6OhpZ8vhsJk7fTiQSMDwYZ/2qyTuLkgAmoosxERGpHrapjeTARibUDNKfSpAOo3sd5xCQ8HwmNvUzmFZbk7HG6O3JuPWLX/yCL3/5y8BIETIajTJ37lwmT55MPB4nlUqxadMmVq9eje/7rF27lssvv5wf//jHnHLKKRXLqcKklFyyLsFhH8oSqSvsL5hXYznpv+rKnEpERKRyahscksks6YEEM+du5rjTVhCJBMSeX84dBIaFJ66mp6ueB+9cyNBwgtkLNo9yahERkdIJrEMu73DVYbfxTN8krtt0Iik/xmAQAwwxJ0/UCTixcT1vm/Yot/Ys4P7MvNGOLVIVFi9ezJe//GWstSQSCf7lX/6Fyy67jNra2j3G9vf386tf/Yof/vCH5HI5PvzhD/OnP/2JCRMmVCSrGvpJyaWCbtymbMHjHQ9y01aVMZGIiEhlxaJbmTVpM0cev4pTzllCbX1mZ1ESwHUt8USeSdO6efVlD9BQM8TCI1ePYmIREZHSmhpt4/WtT9PkpjindTX/Ouse2mMDxE2emMkRd/JcNHER75v5AG2xYS5re4pkNhzt2CJV4Re/+AXWWjzP4yc/+QmXX375XouSAA0NDXzwgx/kBz/4AY7jMDw8zM9//vOKZVVhUkquL7WVwH+hw7G1sH57K3cuPYI/PX009yyfT+dA/W7H5LNZQltgV2QREZExbmpNnEiHZcGxa4nG973DqDGQqMnxioseYXBJY+UCioiIlNmU9XWc0rieQT/OhxZfypdWvYLVqQlkbJSsjdLvJ/nN5uN5z1Nv4e6uQ4mYgFMGNo52bHkxO44estNjjz2GMYZLLrmE448/vqBjTjvtNC688EKstdx1111lTvgCLeWWkuvZ1A/P78C9fPNk7l91GEHokvV3bsvNyq2TiUdzvOLIxUxs7Ce0FqwBtZkUEZEqMNjpMPOsLUQT+y5K7ioS9+lfr75aIiJSPbYM3klT3uP/rbyIrmwNIe4eY/LWIx94/GD9mTjGctacJaOQVKT67Njs5oQTTijquFNPPZVbb72VLVu2lCHV3pV0xuTb3vY2nnvuuVJ+SxmHzHAjGHh83SzuWXk4qVx8t6IkQMaP0peq5dYnj2djdzO5Pkd3OEREpGoMbjck2zIFj48kA6ae1lHGRCIiIpWVy23jN1uP22dRclfDQYwfrD+DWDxXoXQi1a2lpQUY6R9ZDN8fuane0NBQ8kz7UtLC5GOPPcaFF17IL3/5y1J+WxlncpE4yx9s5fF1h5B7UUHyxbJ+lNufPobl19VhHE2XFBGR6jBstmKc4u64xRsL788sIiIy1oUmzgM9sw9YlNwhF3rc1zu7zKlEDg6nnHIK1lpuueUWrC38Pekdd9yBMYaTTjqpjOl2V/Iek5lMhquuuoo3velNrFu3rtTfXsaBRHMdj3XPJetHCxqfC1yWe/NGlnOLiIhUgYF8pvj2JLo/JyIiVaQzfhi+LbzkkA0j/KnriDImEjl4fOADH6Curo4lS5bw2c9+ljA88MZS119/Pffccw/RaJQPfOADFUg5oqSFyUsvvRQAay2LFi3ioosu4ic/+UlB/wGkepiIYbitruDxoXXpX9iI62gvJhERqQ5DwzUEtrhKY25Y50EREake7dNcMmFx21oMB4kypRE5uEyfPp0f//jHtLS0cOONN/L617+e2267ja6urt3GpdNpHnroIf71X/+VK6+8Esdx+Ld/+zeSySRbtmzZ66PUSrr5zZVXXsnFF1/MFVdcwapVq8hms3z961/njjvu4Etf+hKHHnpoKX+cjFE5LwD3+c1sCmQ9CK3FMZouIiIi41/fcos7M0qckT49/WuSrP2/SfSuqAVrcGMhU8/rYtorOonWB+R9h2WPtYEmioiISJVors1iQihi0iR1iXz5AokcRM4++2wAcrkc1lpWrlzJpz71KQASiQQ1NTXkcjkGBwd3LvXe8e+Xv/xlvvzlL+/1+xpjWLZsWUmzlvzW/DHHHMOtt97Kxz72MRKJBNZalixZwsUXX8z3v/99giAo9Y+UMWZrZqDoY7SIW0REqkku0cdTKw8hNRDhkU/P47HPzWXbw83k+qLk+iOkO2Os/u1k7vvQQjb8qQ0/cFmSmTPasUVEREpmQX0tNa7/os9aIk5AzM1jXnwVaC0nN/VWLJ8Uxtjx85AXdHR00NHRwcDASH3GWrvzkUql6Orqor+/nzAMd37+xeP29Si1ks6Y3MF1Xd73vvdxwQUX8N///d/cd9995PN5vvOd7/DXv/6VL37xi8yfP78cP1rGADuULWq25PNHYbGowZaIiFSDfKvPqnWTsL9wyXd42GDPe8HWd8gPOTx73VSeWTUTf1phvZlFRETGg0mJY5mWfIplwy1EHZ/Dm7aysHUTnhlp9WaATcONPNE1ne2ZOhq8HG+ekhnd0CJV4uKLLx7tCAUrS2Fyh6lTp/KTn/yE22+/nS996Ut0dnayYsUK/umf/on3vOc9fOhDHyIS2f+uzTL+BH15jBtgg0JfXhYnmseEUOCGbSIiImNa/dQETX/tJrM1inOAVttB2sM8aIhepOVrIiJSPfrCbVzYtJZt+RivnbWYmOvjObvPtjq0YTtTa/pY1tPO8m1T8SKbRimtSHX50pe+NNoRClbWwuQOr3rVqzjzzDP59re/za9+9St83+dHP/oRd955J+94xzsKKk5edNFF5Q8qJRF3I0TIkjNuYTMnDdRsGCY0tvS9BUREREbBzNYcyRW5AxYldzB5y8zntpc3lIiISAWZ8HFuWz2HCxcuJhnZ9823uOdzREsH4WCUgaE07dr/ZmwpejWkSHEqUpgEqKmp4T/+4z+47LLL+MIXvsADDzzAmjVruOKKKw54rDFmzBYmV65cyU9/+lMeeeQRenp6aGxs5Mgjj+TNb34zZ5555mjHGxXtM1tp+uUAXed5hJkoOBanNo+Jh8DIpjhhysWmImDBTWSofzSNa1SWFBGR6tD79EY8ayn0lptjwVup5kgiIlI91g5sYua0HpLRA68IiLkBR0zbxK9WHMHn2ioQTkTGjIoVJndYvnw5a9euxTy/+3I5GmdWyt/+9jc+8pGPkM+/8Ie2q6uLu+++m7vvvpu3ve1t/Nd//dcoJhwds5vb8HLQ2DJIKhfDNy7WgRc23LY4tT6m1idifVrNIOH8lp2vCRERkfFuoMOHsLj+JEFe50EREakej25OMmnCmoLHR1yfwUiBSw1EZL+2bNnyD3+PyZMnlyDJgVWsMLlo0SKuuuoqnn76aWCkINna2sob3vCGcdlnctmyZXzsYx8jn8+zYMECPvnJTzJnzhw2bdrED3/4Q+68806uvfZaZs2axVve8pbRjltRNrQ0XxLQ7RgGSYC755Y2OyZHBr5LbW2O4OhKpxQRESmf3GAzAYNFtSjJFdybWUREZOzryYZMcoKCx3uu5ZBJ28qYSF6S8TuX7KD2spe97B+a/GWMYdmyZSVMtG9lfwfc29vLV77yFW699VbghRmSb3jDG/h//+//UV9fX+4IZfGtb32LTCbDjBkz+OUvf0lNTQ0ATU1NfPe73+WjH/0of/7zn/n2t7/N6173Ompra0c5ceVs7Osl0pqiq2cKNrL/X4TQM6wdaOOoac9hrdWsSRERqQrZ9llYs7ioY4abtCu3iIhUj5yN4BbabPl5EbfwQqaI7N94WaFc1sLkTTfdxFe/+lX6+/t3/geZOXMmV155JSeeeGI5f3RZrVmzhnvuuQeA97///TuLkjsYY/jUpz7FHXfcQV9fH3/961/H1Vbt/6juYBtruidgo4UVGQPXYftAgtBaXBUmRUSkCoStNWTaPWo2+gWN92OG7oU1Bx4oIiIyTuS3+AQzHZwiipNhqoyBRA4ihdSgstksg4ODrF69mq1bt2KMYcGCBbzzne8sf8BdlKUwuWbNGj772c/yxBNPACNVWs/zeM973sMHP/hBotHxPSPgvvvuA0YKkOecc85ex0yaNIn58+ezdOlS7rzzzoOqMLkp3UHaFrE834EtuUZUkxQRkWrRPrGBvpMSxLcN4Wb3f7faGvDrHLJTi+tJKSIiMpZtXlHDrBMcIl5hhckgNHTcVQtzyxxM5CDwpS99qajxixYt4tOf/jTPPPMMixcv5lOf+lSZku2ppNsg53I5vvGNb3DRRRfxxBNPYK3FWsvRRx/NLbfcwkc/+tFxX5SEkQ18YKQRaHNz8z7HHX744QAsXbq0IrnGip6+nqKPCa1DPtC0fRERqQ5nTZuKmQRbz64liO37zlvoQL7e4bnXNnBkW/HnTxERkbEq1RdlYGms4PF+2mH975vKmEheEjuOHvKSHX300Vx99dXU1tbyy1/+koceeqhiP7ukhclXv/rV/PjHPyafz2Otpba2ls985jNcf/31zJkzp5Q/alRt3rwZgKlTp+533I4djDo6OvD9wpZyVYNkmHwJR1kc/SUREZEqMbNmBY3RDMOHRHnutQ0MTY0QxAxBZGSz7iBq8BOG3oUJ1v9TE2Hc8NY5xfWkFBERGcti7XUs+0Eb2aEDL9TM51y2/KWOvuT43INCpBpMnDiRN7zhDVhrue666yr2c0u6lPu5557buXnJueeeyxVXXEF7e3spf8SY0NvbC0BDQ8N+x9XV1QEjS9kHBgb2O7uymhzVNAvbv7qoYxwfIt74n00rIiICsKbzUXqyccCQbfPY/NoG3FRIfFseJwA/YUhPioCzYzal5f5tUzhj9mimFhERKZ1kNMEWr5W/3NHO+a96img0j+vuORklm/VYv76dh1fOIX+E2pqIjKYjjjgCgKeffrpiP7PkPSbb2tr4zGc+w/nnn1/qbz1mZLNZAGKx/U9Lj8fjOz/O5XIlz9HUlByTu1intzcQcQNyuEAh+SyJaJ7mZjX9H+uc5y+gHcfo+Rqn9ByOf3oOx4cnlvjk7O4rCIKkw/Csfb13MNy1dSZf1XM65ul3cPzTczi+6fkbP2bXt7DiuD4G+lxuuvUMFhyxjrlzNu38ujGWnt46nlo0hy1bWzBNIV4m1PM6xhgtbDyoDAwMADA4OFixn1nSwuQb3/hG/v3f/53a2tpSftsxx3XHxl0czxsbOV5s1fBm4tE8+ZxbwOJsizEWpzYgsCFRr6wbxUuJGGNw3bFXFJfC6Tkc//Qcjm0bsxMIbLaoY9Khh+uWtMuOlJF+B8c/PYfjm56/sS+SiMDz57V0Os5jDx/Gspum0ZgfxnVChoMY/e315OtHrgHDqIPJGp0LRUZJEATcfPPNAEyYMKFiP7ekVaDPfe5zpfx2Y1YikQAOPAsyk8ns/PhAsytfCt8PxuSMyZTtAceQjOdIZaLYfc6aHClK1iZyWGuw1hIEhe3YJqPDcQzGjDxXYahbZ+ORnsPxT8/h+FDXOBVj1hR1jAWdB8cB/Q6Of3oOxzc9f+PHpJlNsBbA0rB0iJpNGUxgMaEhxCWOT3RNL0Hcofv4BvxaD+rcMXsuVMFUqlEYhgwNDbFs2TJ++MMfsnTpUowxnHbaaRXLoOlpL8GO3pEHmtq6Ywqs67oH7Ef5UvT2pkr+PUthaiICQMQLqU1kSeciBMHuW9sYIOIFxKN5HAdsAD09Q3iOXpJjWXNzDa5rCENLT8/waMeRl0DP4fin53B8OLKpFSimMGmpq0/pOR0H9Ds4/uk5HN/0/I0fiejIHgKtj/YT687hBLt/3QBu3uLmAyY80EvnqU0k2mrH7PPa1lY32hFGh+r/49L8+fNf8rHRaJR3v/vdJUyzfyr5vwSzZs0CYMuWLfsdt3XrVgDa29txnIPnP/Wcmtm4jNzlct2RGZF1yQzJeI5kLE9NPEd9TYZkfKQoCVDnplWUFBGRqtEXbqGxaYhC3807bsjESb3lDSUiIlJBLTUJGjak91qUfDE3Z2l7pI/WuuT+B4pIQay1L+mRSCT46le/yvTp0yuWVZWgl2Du3LnAyC7kQ0ND++ypuWzZMuAfq1SPR3NqZjGlY5hN7bUwMnkSx4Gos48p+RmYniquD5eIiMhY5pCjvmmI7u76A9YmLRbrQFtNf2XCiYiIVMCx06eQXJ06YFFyB5O3HOmVvgWayMHohBNOKGic4zhEo1FaWlpYsGABr3rVq2hubi5zut2pMPkSnHXWWfzP//wPQRBwzz338JrXvGaPMVu3bmX58uUAnHHGGZWOOKo6tveT/lsz5jU+tinc/7zcPDgbI6SfqYfq3chdREQOMo4zSOe2RsJkiEk5YMHspeeyxYJnsfGQoFdvy0REpHo8+uQzmCL6gLq+Zcmdy+Gtry5jKimalnKPS9dee+1oRyjYwbO+uISmTZvGcccdB8B3vvOdPXpNWmu56qqrCMOQpqYmXve6141GzFGzZsMSjG+I/V8tptuBzD4GZsBZFyH6lyT9PUmCcGw2ORYRESlWPB9jKB8DB2xNiI1arLEjsyN3/OtabCLExi0hDos2zxzt2CIiIiXz96VLMfniqlrpznyZ0ojIWKXC5Ev0H//xHziOw/r163nzm9/M/fffT09PD0uXLuXDH/4wf/7znwH48Ic/TDJ5cPXJCINBcqGHyTrEflNH9I4aTIcLGQNpM1KQXOMRu62W6J+TGGsIMdjQH+3oIiIiJbFk0y535QwQsyMFytoQm3zhX7znvw4M2+hoRBURESmLTOCzl8UC+6XJeSLlNzg4yLZt23Zu2DzatGboJVqwYAFf+MIX+MxnPsOzzz7L5ZdfvseYd73rXbzlLW8ZhXSja+qkNnLhWgxgrMFdH8FdHxlZruaACfc8O+VCD8/TBZmIiFSH0I+DSe3+SfOif0VERKpYvDFKGDE42cLLjflGzZ0SKbXh4WF++9vfcuedd/LMM8+Qz78wMzkejzN37lzOOeccLrvsMpqamiqeT4XJf8DrX/96jjjiCH72s5/xyCOP0N3dTTKZ5Mgjj+TNb34z55133mhHHBVO8xRCD9zn97OJJXIcddqzzD1qI9GYT3o4xtMPHMqKp2YSBi4AmYm6NyYiItWjPeFj7C4zPyxE+iHabTABhHHItFvCXXr8F7o5gIiIyHgwMGCwEQMFFiaDmGHo0HiZU0mxjC7Vx7UHHniAT37yk/T09AAjrQd3lU6nWbx4MYsXL+a6667jC1/4AmeddVZFM6ow+Q867LDD+MpXvjLaMcaU57KdpI/MU/NYhLNf+xRHnbYKL+ITib7QQ7J9ejfnXvI4f73xRJY9M5P00T5+GOA57igmFxERKY35jR4mb8CDWAfUrXZwfHD8kemS1lhq1xry9Zb+wy2hBzMzfaMbWkREpIR6tqYYOjzChMVD5DORA473kgE2r1V0IqVy33338YEPfIAgCLDW4nkehx56KNOmTSMej5NKpdiwYQNr164lDEO2b9/OBz/4QX76059yyimnVCynCpNScplMhuyMLJfMepR5xz9HLLFn78hY3Ie4zyve+DCem+Lulpk4RmvbRESkOvT0R6nbaMl5DrXPmZ0FyR2MNRgfoj3Q8qih96iQxm2aMikiItUjcEMuee0TbI/XsPzRqeT2WZy01DVluPzLd/HZa95c0Ywi1WpwcJB///d/x/d9HMfhXe96F5dffjktLS17jO3s7OTqq6/mmmuuIQgCPv7xj3PHHXdQW1tbkaxq4CAllxhMcPSW55h3zMa9FiV3FU/mOffip5m0qQ+sCpMiIlIdViyqoW5rSO2GPYuSuzIY3Jyh+QmHzo7GygUUEREps+gEj7MXLOPI120h1ZYkdA12lwqEBULXENS6JE61tE0Y5MSjVo1aXtkHa8bPQ3a67rrr6O/vxxjDF7/4RT75yU/utSgJMGHCBD71qU/xxS9+EYDe3l5uu+22imVVYVJKbqjb42VHP0ustrCZH5GEz5nBs2gPNhERqRY1gUfej+DsZcO3vXFCQy6nhSwiIlI9Zk5fy6YtLfz85pcz2NZA/4JmhqfVkm2JkW2KkmlPMDivkYG5zazbPolf3Ho+F5z0xGjHFqkK99xzD8YYTjvtNC666KKCjrnooos444wzsNbypz/9qbwBd6HCpJTclo0raJicLni8G4HDz+rAqi4pIiJVYsjxi7/fVmARU0REZDyocXu56a+nM5xOjHzCMeRb4qRm1JGaVU9mSg1hbGSPgVw+wtJV0wlzKlGIlML69esBOP/884s6bscmzhs3bix1pH3Sb72UXDzaW/TFmOtZ8rlceQKJiIhUWH9r8Zu56f6ciIhUk/zWCFs6mwsen8rE+NsDR5Uxkbwkdhw9ZKdUKgVAfX19UcfV1dUB0N/fX/JM+6LCpJTcxLaWov8mWCAWj5UjjoiISMUNpHMUuxTAupoxKSIi1WPjs+1k84W3KbHW4ZlnZ5YvkMhBpKmpCYB169YVddyOmZY7jq8EFSal5OYcfQimyFdWNh3BaFduERGpEvVEICyuMGmKHC8iIjKWDXXHCILiVhCkhqJlSiNycFm4cCHWWm6++WZyBa5OzWaz3HTTTRhjWLBgQZkTvkCFSSm5pSu6WNXRXvD4rO9y15L5BOH+d/AWEREZL+xzPZjC9oAbEVji21JlyyMiIlJpoedjwrC4YwrfqkBE9uO1r30tAJs3b+bf//3fyWaz+x2fzWb5xCc+webNmwG44IILyp5xBxUmpeQG+0N+v/0YhvOF3e3KBBHuz80lLOYCTkREZAyLBt3EOlPgF3ZBZkJLvCtT5lQiIiKV0z4/jc0XsSouCIh2DZQvkLwkxo6fh7zg5S9/OccccwzWWv7617/ymte8hl/+8pesWLGC4eFhrLUMDw+zYsUKfvGLX/Da176Wv/71rxhjWLhwYUULk4U3fBAp0KCXYHN/M79ddQKXzXmMZGTf04YHczG+98x5ZGJRPK/4jQJERETGosYmj2h3ltTcOCZnMMG+L8xCzxLrHcLRDToREakizccm8e5OkW+vBefABUoTQqJFUyZFSsEYwze/+U3e+c53sm7dOjZt2sRVV12132OstUybNo3vfve7FUo5QjMmpeRapjYDlsc6Z/PjpWexZbiBoXx0Z6utIDQM5aOs7G3na0+9ik1DzZjQkteUSRERqRLnnn8YqcNd+s/K4TfksDYkV2NJtRpSbYZ0i8GPWKwNSS3MsP1Sl0RbYf1/RERExoPpwy7RjiFMzj/whnD5gPiaHrzausqEEzkItLe3c8MNN3DJJZfgui7W2n0+XNfl9a9/PTfffDNtbW0VzakZk1JyE2rrMFgshmf7JvH5x17H5JpeDm/aTCKSoz+b4OntM+jPJV84yEDU1ctRRESqw8ypp9N/3mPUPuPjbndJtTtYz+w2YyR0DSaApjvydL/OMPVd8VFMLCIiUlqJvEcwqYG6p7YydNQkwogDkRetkgtDTGCJr+3BzVlctIpuzNES6XGtvr6ez3/+8/zrv/4rDzzwAIsWLaKnp4fh4WESiQStra0sWLCAs846q+IFyR1UCZKSO6F9CsYzu/392jLcxJbhfW83n4zpBCQiItVjY7qf2CDYziSpSRHw9lykYiMONgJ98+tp+uMAy/95dN4MioiIlENdIoFXmyY/rZXk4k6ChijZqfXYmAcGCC3RjkEi3VlwDPnpLcSiQ6MdW6Qq/PGPf6Suro4zzjgDYwwTJkzg4osv5uKLLx7taHtQYVJKLhVmqK/NMDAcI7QvXIg5WYuTtwQxg428MGMk6vg0Nuk2jIiIVI/N6X4iT3gMTdx7UXJXNuowOKcOuzQFJ1cooIiISJlNOLSeeHSQTH8WL5UhMpQmvrl/j3HWgI1FCW3A/AXbRiGpSPX5yU9+wsqVKzn11FP52c9+Ntpx9kuFSSm5bdl+Tpi0gQfXzySVilG/LEvLo1nctAUzsltWttlh+ykJUoc4NCRSTK0fJLQWxxSxa5uIiMgYNdSdIt0Sx0YKa+cdug4sVq9lERGpHvGmRpLp5fjbUphw3xNRjAWyOSLPdjLxc72VCygF0W7X49Nzzz0HwFlnnTXKSQ5MhUkpudAGHNq4hbbJfTz6X/NwhyxOfvcxyS0BU/4wRDAZzvv8ItJObHTCioiIlMG2Vb2EXhE321xDqlk9JkVEpHrETRNDS7P7LUruYCyQztG1vB1mlz+byMGioaFhtCMckHbllpILU134aYcVV84g0hvuUZTcwc1CfFPA0q/OosZNa7akiIhUjQ2di7FFnteKKmSKiIiMcZsfSRJmilgNEFoW/7C2fIFE/gEPPfQQH/rQhzj99NM58sgjOemkk3jHO97BrbfeShiGox1vD6eddhrWWv7whz+MdpQD0oxJKbnsUJrNf2kl2xNhpKvxvoV5l/5VNQwsq8Vv8/G0M7eIiFSBPN1A+2jHEBERGTVP37ca7MhsSQuENTHyExsIk7GRFl9+iNc5gNczhAktBhjaMjyqmUX25stf/jJXX331bp/r6+vj4Ycf5uGHH+b3v/893/ve94jHx87ql89+9rM8++yz3H///XzgAx/gve99LwsXLsTzxl7NZewlknEvlfbYfHsbYb6wnbb9YY/1N7XjnqOduUVEpDqELQa2FnmQqyZOIiJSPbZ1D4AF6zpk5kwc2Y3be+Gaz0YgP7WZ/OQmohu24/WnsGNw5tlB7yB/e3LjjTfuLEoeffTRfPSjH2XOnDl0dHTw85//nD/84Q/cf//9XHnllXzxi18c5bQvuPHGGzn33HO57rrruOeee7jnnnuIRCJMmzaNuro6otHofo83xvDLX/6yIllVmJSSG+pLEvrFdQnoX1VLaC2ulnOLiEgVsInJ4OYotGuOyYfkp/nlDSUiIlJB0cY4eIbMYZOxUQ+cvVzruQ64kJvZCuu7iOT20QdMZJT8+Mc/BmDu3Llcc801xGIj+2O0trbyta99DYA//OEP3HzzzXzkIx+hvX1srJj55je/iXlRfSWXy7F27doDHmut3ePYclKPSSm5of4MQa64l1bgu1BAU2QREZHxoKHZJTMzAL+wmR82AvkWnQdFRKR6nH2hS35yMzbq7r0ouSvPJTejjaPOG6hMOJEC9PX1sXHjRgAuvPDCnUXJXb3pTW8CRop5ixcvrmi+A7HW7vbY2+f29qg0zZiUkqtPxAgCp6iqdxiaA5+sRERExonowBbCujqy7SGxTkZmhOyNtWAsw7N96uPZimYUEREppzlT0/gtNeAUeGXoGCYcW95M8hIcxPdNnV1eu76/95UtkUhkr+NH24oVK0Y7QsHGzn81qRq1ngtFTvsNEi7uGPolFhER+Uds22ypS6bJTwhJTw8JXQu79s0KLViLX2MZPjTAiYe0NA6OXmAREZESe2hJQCReRFXLdfjLQ4eWL5BIkerr65k5cyYAf/zjH8nlcnuMuemmm4CRAuWCBQsqGa9qqBIkJdcYi5BtimILrE2GrsGvNYSBGh2LiEh1SG+vp61pEM8NCeotqTk+qVkh2daAXHNAZlLI8FyfzIwAoiHJeJZsRpvAiYhI9bj/2RxB/kUlB2tHbs4FdueO3bsKMypRyNjy8Y9/HMdxWLVqFe9617t4+OGH6e7uZsWKFXzmM5/hhhtuAOADH/gAEyZMGOW045OWckvJNbTU4WbThJ6Hkw/ZX30yNBDEHBJbunH2tcxNRERknJk97RDWDPcxc3InG7a0EYQOYdIhTO5+EeY5PolYnqkTu9m4oWmU0oqIiJRePNKP40ZHNkYNLU42wM3tPhnFOoYg7mI9A8ZQG9tzRpqMLnMQL+UGePnLX853v/tdvvKVr/D444/zjne8Y7evT5o0iY9+9KNcdNFFoxOwCqgwKSU38ZB2mjoH2HDCVFoWD2Axe93RyYYhOJa+WUnOTU0ZhaQiIiLl8cpjj+K3y5dzWFsHbz3+QR7aMocHO+Y836bJYC00xYY5b9pSpjd2c/W604ltGuXQIiIiJXTI9DRroi5BxsEb9sGyx6QVE1jMsE8YdQgSDvNnbxuVrCL7MzQ0RDKZ3OvXuru7efLJJznzzDNpbm6ucLJ9mz9//ks6znEcotEo9fX1TJo0icMPP5xXv/rVHHfccSVO+AIVJqXkjDHMvvQE+n/7JPgG21C3+47bFnAMoXFgYID65TnO+MIbRy2viIhIqR06awrTH9rOBc1LqI1led3sRbxm1tP0ZGvIBx610Qz10QwAfmC4tP1xcm3njXJqERGR0jlhfjt/IoU37O931p0BnFyIdSynnbqhYvlECvH5z3+ea6+9FhjZgfttb3sb06ZNo7e3lzvuuINvfvOb3HDDDTz++ONcc801tLa2jnLiES91d+0gCEin06TTaTo7O3n66ae5/vrr+ad/+ieuuOIKPK/0ZUQVJqUslqzcAngEU5rAcUf6h1ie3310xw7cFupiuNt6uHPxGk467YhRTi0iIlIaNlzLJcc+QzL6wk7brmNpSwztMdZzLZNq+jnyrNuAf61gShERkfKpi5yCn76LSAEFEgO4OcufVs/nBO3MLWPEgw8+uLMo+fGPf5z3ve99O7/W3t7O2972Nk444QTe9KY3sWbNGr7+9a/zxS9+cbTi7uZjH/sYQRBw9dVXMzQ0hLWWxsZGjj32WKZPn048HieTybBlyxaeeuopurq6MMbgui4nnHAC+Xye7u5u1q9fj7WWG2+8kUgkwmc+85mSZ1VTPym5bX2D5J/Zjpn4fFESXihGus7zRUkAA66HaW9i6S1PjVpeERGRUsv4d9DUOvjCKe8AamI5nhqMlDeUiIhIBf3f3zbgDBU+a8taePqhljImEinOb3/7W2Ckj+Tll1++1zHz5s3jjW8cWQF62223kU6nK5Zvf973vvexcuVKBgcHSSaTXHnlldx33318//vf51Of+hQf/ehH+dSnPsW3v/1t/v73v/P1r3+dpqYmgiBg6tSpXHfdddx+++384Q9/4Oijj8Zay69//WueeeaZkmdVYVJKbuXy53Ca41hT2MvLTYBJW4JQu3KLiEh1WNq1ec9GWgfgJbIHHiQiIjJObNn4LCYofLxjwes4yHdakTFl/fr1ABx11FG4rrvPcSeeeCIAvu+zcePGSkQ7oD/+8Y/8+c9/xvM8fvKTn3DppZcSiez9JrgxhgsuuICf//znRKNR/u///o+//e1vAMyePZuf/vSnO5eo33TTTSXPqsKklFx2MItJxij0iszPRfAmJHbvQykiIjKOre+K4RS7jeXBvu2liIhUlWwqwBQ596SYQqZIueXzeQByucJ3iy9mbDndcMMNGGO4+OKLOfbYwvojHHbYYVxyySVYa7nuuut2fr62tpbXve51WGt58sknS55VhUkpuawdwosWdwYynoPj6uUoIiLVIczNJgiLmzIZhPu+Ey8iIjLe1MV8bLGXeEWuNpAKsOPoUWKzZs0C4Mknn9xvwfHxxx8HwPM8ZsyYUfogL8HKlSuBF2ZzFuqYY44BYNmyZbt9/pBDDgFg27ZtJUi3O1WCpOQGYquL/ptgnJFpzyIiItVgWvIEwr1cjdkQ/LzDi/cB8EPD4lWHVCidiIhI+dW157BF3HMLDZiJuiaUseOCCy4AoK+vj2984xt7HbN69Wp+/etfA3DmmWdSX19fsXz7s6PX5Y5Zn4XKZkdaC724EJtIJAAYHh4uQbrdaVduKblYTfEnE+OGYANAjf9FRGT8O/HwQ/jf+w/jzKOegRBWPjWDh/6ygOGBxMiSbWuYMW8rp7z8Gdqn9ZLNRwi3vny0Y4uIiJSM15omO6WB5PrCxtsIuOcOljWTSDFe9apXccMNN/Dwww9z9dVXs2nTJt7+9rcze/ZsUqkUd999N9/5zndIpVLU1dXxyU9+crQj7zR58mQ2bNjA3Xffzetf//qCj7v33nuBkV3Hd9XR0QFAY2NjyTLuoMKklNzc9kNJNt5HdjhW0HhjQmYdv5lINF7mZCIiIpXhOg7PpY5j09Yt/Pknp5MajJPP7n7zbeWTM1m/YhJzjt6Id9Qwl515xiilFRERKb0OP0rvOQGx37i4B9ioOIzA4DEWt0kTVcaag7kFtjGG73znO3zkIx/hwQcf5I477uCOO+7YY1xrayvf/va3dy79HgvOOOMM1q9fz5133smtt97KRRdddMBj/vSnP3HHHXdgjOHMM8/c7Ws7Pj937tySZ9VSbim5ltYsR756FZF4YVOGo8k8E88ufZ8CERGR0RLYkC01PdzwlZfTv712j6LkDtlUjGWPHsITt8zj9vRjFU4pIiJSPn29dYRNsO0Si5+0hPuoPgQxGFxg6T8VsjnNnZKxpb6+np/97Gd861vf4pxzzqGtrY1IJEJdXR0LFizgIx/5CH/605847rjjRjvqbi6//HJqamoA+PSnP81nP/tZ1qxZs9exa9eu5fOf/zyf+MQngJFl2+985zsB6Ozs5H/+539YtGgRAOeee27Js+q3Xkpua3Yd/TNdphzWxablE/D3c3KJJnIc+6qVPJA+hHcGGSKuZk2KiMj4t2RwA+a2CPgOmP138g99F3eL4Z6ly/jXGa+tUEIREZHyak4aoiYg0+aw5Z1Q94yl7ikwOzp/WchMh4ETIff8qlF/UCUKGXscx+GVr3wlr3zlK0c7SsEmTpzI97//fd7//veTyWT47W9/y29/+1uampqYPHky8XicVCrFli1b6O/vB8BaSyQS4Stf+QqTJ08G4Ec/+tHOHpqTJk3ikksuKXlW/dZLya0bijO9vpdDPvYsf/nRSWx4ehLZVAS7yyYArhfgxXxOeO0yjn31SrZv9DB6OYqISJVY1rsOsy0GToHbixqDc0cULihvLhERkUoJ6wzZ0AUDNg4DJ8DA8WDyYAIIY+y2hjOCT344Omp5ZR8O4qXc491JJ53Eddddx+c///mdMx57enro7e3dOcbusiPjvHnzuPLKK1m4cOHOzz333HNYa5k6dSrf+973iMUKa9lXDFWCpORqvGm0xvtwvZA3/MuDdG6t47E/H8bap6YQ5B2iiTyHn7mOY162hub6DEPWcPqkVQecUSIiIjJePHXnyuIOMAa6dTEmIiLVY4sXJZ8JiSR87I4KpAEb3XutKxd6+LXqNidSSkceeSS/+c1veOSRR7j77rt59NFH6ezspK+vj3g8zsSJEznqqKN45Stfyemnn455UV3mNa95DW9+85s59dRTiUbL815VhUkpuRk1Q2zzLceYgJ986Xi6fZcjL1rLKa9/BmPAhrDp6RZu/5/jOP2EbZz0xmd52glwVZgUEZEqEWxPA6W/oywiIjJepHOG0LiErsV4dv87XATgpzysq8KkSDmcdNJJnHTSSUUfd+GFF5Yhze5UmJSSqw3yRFyfb/3nqSxf2kIu6/Hsg5Of/6oFXihA3rq2gbxvmHzpeqwNMEYnIhERGf9iYaT4pU9aKiUiIlUkOmhxPA+zNQKTs5AIwGXXy0EIgNDA5hhe3iVoyY1SWhEZLaoCScmtX5eg54EJPLu8mVz2hdq3tRYb2t16GKRSEW6/5VDqOuKMnKVERETGv3lHNex+4XUg1mIC/8DjRERExonp+QjugIsJHdgUh21R8Bm5EWeBEBh0YV0Ck/YwATT2qzA55thx9JBxSYVJKTnHj3Lbr+eRTkdGipC2HwbXYbeswXasxW5eg8luhDCFtZbhYY8Hbz6EIAxHO7qIiEhJzJ+Vwvg7rr4KYQla1dJERESqh1eTxgnAeiEckoKJOYgwcuPOMFKNqA9gdgrbkMdYQ208P7qhRaTiVJiUkqv1fPp6Y9jQxwyuI9jchb8lIByCcBDCYcivzxFu3YIbPEcYGh57YBIRVxdkIiJSHZLNQwSHB5hEATfd3BB3ehYzNSh/MBERkQpxGmPghjAjDVG79wVyDiOfb89i6/MMZxIVTikio009JqXkoqYGG4aYwc34neHIFP29CIcgn88Rmb6JMGwnDMDRK1JERKpANtdI7ekbcIxh+IYJ2LQDdi834KIh7sQcNRd2kbs/WfmgIiIiZRKvyWAneODZne1NnEEHZ9CBEGzMEjQ/33fy+eJkuFlzp8YaoyXSUmYqA0nJPbBliDDWR9C776LkDjYLfkeWsDkNjmZMiohIdVi5th2vfg1OLdS9ZyuZR+rJP1Oz28puUxMQO3WA6OEpjAPeUdnRCywiIlJi0xoTkMmAA26XS3R1FJM3GN+MnA9dsI7Fb/fJzc6BA16TekyKHGxUmJSS2x4fIr1qGFvg9VU4CPnhXtL5HDWxeHnDiYiIVMCmngFMw8jHTm1I8tw+7Nl9hL0e1jc4NSFO3e5Lt906LeUWEZHq0dDUBB1biayLEN0YHSlI7ioAExgimyN4PR6p41OYJm0EJ3KwUWFSSi6aShEW2bM4t8En5unlKCIi1WG4v2+PzxkX3Nb9XHBp5YCIiFSRXDgRt2fb3ouSuzDWQBoSixKEp6gwOeZoKbeUmRo4SMnNqW1+aX+89AdPRESqRCQNBMUVGm1ahUkREakeeX+Y2JrYfouSOxhrMCmDGdRFocjBRoVJKbkpTZN2NjcuhuvubZs2ERGR8afZqcNmCj8Z2hz4f9fmNyIiUj2Gtq/H5Ao/FxrfkNigEoXIwUa/9VJy9W4zpshXVnQGGKOZIiIiUh0mTJ9J/m81hRcn84b84+qzLCIi1aOvexBTxMpsg4H+SPkCiciYpMKklFzXti5qX+ViC+yVFcYdmt/mEAYH2MJbRERknPBSMYJHYgRrogfcDM6mDNlf12MG1VdLRESqhwlDil1K54cqUYw1xo6fh4xP+q2Xkhty+hk4egph4sCb2VgDQWOc9LQmrFVhUkREqkOucxAnnSf3i3r8JxPYYYN90cZwNmUI+x2y1zYSrojh5lSYFBGR6hFPmqKrRYEmTIocdLQNspRcw6QmOp9px17WQssNSzBpHyfc84QUeg5BU5zufzqSzNoM5hzVyUVEpDr0Dw1i/ACTDcndVEf+L3V4Jw/jzsthIhY76JB/MEm4MgaBxR3OvpT2zCIiImNWrCUyshFcoSc4a3EaD7DMQESqjgqTUnJDbh0AQXOCrnceTc0TW0ku3gbWjuy8bcBGXYZOnEL6iAngOaSH4ziOCpMiIlIdYpNDDJCtCxk+JE7jeh97f4LsXXU7x7hxn3yNoXeex8S70zgRrUESEZHqsebpBCawWBcoZD8BC+HmaNlzSZH09kTKTIVJKbnh0GDtyInHJiIMnT6doVOn4fZlMPkAG/cI6mO7nZxCa8gFPlFXL0kRERn/JhxST77FoevCJP9x5h+ZU7uNFY9OY+miGeSyHo3NQ5x4zrO0z+zlh0+dzUPxOcy6p3e0Y4uIiJRMbmsSN53FTxpw7P6Lk9bipi0m61YuoIiMCaoCScn1Dg4TvLhpsWMImhP7PCYMHSKOTkIiIlId5hwxje6Tn+Gq827kkMYuYm7Aaeeu4LRzV+wx9kPH3o1rAzpyE0chqYiISHkk+iMYskSGLX7SYB07sqx71wLl86vq3LTFDYCspueJHGy0dlZKLuv7hAXuyD3C4idD8kFQtkwiIiKV1BafwblnLmdmw3Zi7v7Pb7XRLO877j7yR9Xtd5yIiMh4Utsaw9gAA0RSFi9lcfJAYCG0GH9klmRk6PmiJOAOZUYzsuyNHUcPGZc0Y1JKzuYsuYaA2HYXU0CnY+tAvi7EVY9JERGpEs9s38bFhz1Jwitsp+24m+eY9qfLnEpERKSCmgJMKsAmR8oOTghOZj/VI98nOlzYeVNEqocqQVJyh9U0ETqWyKAPe9mNe1fGD4l1Z5/fE0f7kYqISHVYve4vxNzCL66ibsCrDllcxkQiIiKVlR2wRDqGIV/YyjiT8XHCWJlTichYo8KklNzMukaaF2doe7CPaF8ekwv3HGQtJheS3JSh5bFBmpbnCtqoTUREZDwI+rfgOcW1KIl7+TKlERERqTzXeERMHK97GPL7uVlnLSadI7ktV9ju3SJSVVSYlJJrm9xAy6IMXs7SsHSYyGCIyY30EcECocXJQbzbp351Gi9rmfREFqOTkIiIVIlIJsTa3c9rDpAwLjXGJb63t2B5NUcSEZHqcfjsmeAY4imP6NYhyOZ3L1CGIWR9nN4UyS1ZjBOBenWbG2uMHT8PGZ/0Wy8l198zTMQahtqjDB5aBxGHSBbI7hgxcqEW1EbpOaaJpsV91GiSiIiIVJFk6+FY/1aIQRSHZidK3LjAyFnQYrHAQJinz+axQMfaGma0jmJoERGREppQV0cYNXgZiAUxopvz5E2eIG7AgJMLifgujvHAcQgdiDXERzu2iFSYZkxKyaWGsuQbPAYPrcNG9v8Ss1GHvgUNhFFDGOxlybeIiMg4NGveYTy3somEcZnsJqhxPFxjcI3BMQbXOHjGodGJMtVNkE5FufWe40Y7toiISMmEKZ/0RI/w+UtCYwxRoiQyERLpCLEgNlKUBKwBv8bBy+qaUORgoxmTUnJe3GVgSvyARckdQs9hoMXDcVUnFxGR6rBhSYZN983nVcc/hrufViWOMXjWYUbUZeXiaRVMKCIiUl5DYY7eIw2Z+gjNK/I4mRDT3QcDQyNtvmJRmNBMUBsj2+IwNNUjH1dhcszREmkpMxUmpeSGGvLk6qOFH+Aahqcksdaqz6SIiFSFdc908upzN+M5Bz6vOcZA3uP4tm0VSCYiIlIZXfU5ck2GfMIQebaL+kXbwLeYcKT4aAG6ugla43Sdcyj5Voe85qqIHHT0ay8lt7V/8PnOWYULPUNgdXdMRESqQxh0c8QR2wseX1fn8+Y3LS9jIhERkcqqba+BPEy4bg11D3Xg5IKdRUkY6bls8gGRrcNM+eYyvK4MTlxzp0QONipMSslF8y7WK+6lZQFHL0cREakSc4/YjuMUd5Nu9uy+8oQREREZBf2ZDPX3d5Jc1ou7n96RBvAG80z+4QryuaByAaUwdhw9ZFxSJUhKLtuV2bHxdsGsY9AqbhERqRbts2twinyXZTRJREREqkhfNkXT37bstyi5K3fYJ7K8v8ypRGSs0VtgKbmMnyN0wfULG28BPwG+HxCJ6CUpIiLjX0vjTHxb3B23nKe1AyIiUj02P7UBZ8cMSAdqTzG0Xu4QnQYYsFnovTWk90ZL0AduOqDhjs3wmdFMLSKVpve/UnJhIkK+Fgq+HjPg14DnuWXNJSIiUimTG88jHyn8bVbeN9zbO7eMiURERCqrd0UXbibAa4PZN7pM/rxD8iiD12zwmgyRiYbWd7vMvtml8aKRi8doV3qUU4tIpakwKSXX1NJAEBuZBXmg4qQ1kG0A60Jo1RRCRESqw7K+bm5ZN4dsUNhbrQwuP33m6PKGEhERqaB4GOI2wMyfu0SnG7z6PS8OnSh4DYb2j7o0vMbgueoxOdYYO34eMj6pMCklF7EjL6tcA+STI8XHF/+NsAasM1KUDJL6IyIiItWla2iYq393IluzNeTD/d+lGw48rnnmSDqXJyuUTkREpPzcaTkmfNTBm2AwB6g8uHXQ/jGX+KGVySYiY4cKk1Jy9cbDBICBfAOk2iFXB0EEAg/86EhBMtU+UpTEgjtscbT7jYiIVImurn4ymSjv/MEbWD3cwKAf2WNMPjQM+h6/WHwkP//dKTA0CkFFRETKJJwXUHemi1Ngxy6TMEz4qEoUIgcb7TQiJTe4qZtob0C2zRspfTvg14089spCvCNbyYgiIiJlNbh1OcbC0FCCt339Mk46Zi0fOvcRpseHMQZ8a/j71sl895Yz6O6pB8D4Wj4gIiLVw7QOY2KFj3ejlth0FSbHHL09kTJTYVJKLp3Ok1zWg39yG0HM7H9ebgiRvoDk45vxg4CIp5ekiIiMf3Z4G9gGwPLWS+8jMWmAu/P1hPnGnWMitSFveOu9PHrnkSxaNgsnF45aXhERkVLLD3s4rcVVtWyBvZlFpHqoCiQl1zatheQzW8hNbyIzKUIQY6Q4uetK7RCwEBm0xDvzRDf1qSgpIiJVY3qLj5MLeM/778Kvy5Pby126PA4YOP78JUQ8n2W3NY9CUhERkfIwwy7GWHa/ENw/x+gmncjBRpUgKbl586fhuh51f12JeflhBDUR8jUQxBk5J4XgpSAyFOIM56n7wzJqF0wf7dgiIiIlM2fuEVw4fCv5Wh/7fA/l7ECEvnV1BHmXWF2OpkMHcFxLHoejz17B1j8eNsqpRURESqcmH+IYS1hwYdKScHJlzSTF00a1Um4qTErJRTyXhZedytM//Cv1ty0hffx06gKPeE8eE1pC15BujxEOD5N8fCMmhDf+22tHO7aIiEjJ9DlHMuHUq8k5hoHnalj+f4fQt7YeG4ANHZxIiHEt08/cwqGv3kjgGU5+77bRji0iIlIyjhPgmYCcLWx5toOlwUuXOZWIjDUqTEpZvPdfXs2H71xMpM8ntiEPxsc8P2PExeKtS4GFIFlL4sTpnHPGEaOcWEREpHQGBx8lFfXY/lQjT/9sHvnU7rtyB7mRLUrX3D6NrY+3cdqnn6Jh9sBoRBURESmLsNbS7AzTGdQT7nfjATCExJ08jnZaETnoqLOslEVzQ5LDjpyLU1+HcZydRckdjDEYx8Fra+WVZx2D6+qlKCIi1cOGa+nfnOTpq/csSu42LnBJdSZ4+GtHkQrdCiYUEREpr8FcLS6W1sggDvvuHWkIiTo+EyIDbPdrKphQRMYCVYOkLO7/82LWLN6EKaCfyB+ueZCuLb0VSCUiIlIZqVyE1bfOJD+876LkDjZ0SG+P0/1sY/mDiYiIVIizzSNtI9R6OSbF+kk4WRxCHELM8/96BDR7w0yO9pMLPZZtnzTaseXF7Dh6yLikwqSUxQ0/vLvgsTa0/Oo7d5YxjYiISGX52dn0rq4veHx+OMLqP2gjOBERqR7TBodY1D2VXOASc3wmxQaYFu+hPTpAe3SQSbE+psV7aIhksBYG/Ri9nZoxKXKwUWFSSq5rax8Dfamijln08JoypREREak8JzWTMCx0F9IR/RvrypRGRESk8vq2J9jU08TSzonkgpF2Ja6xJNw8STdHzAkwBoLQMJSPc8+SuQSrk6OcWkQqTZvfSMmtXrqJ0AMnX/gxASFhaHGc4i7iRERExqK8yRPkvQIamrwgLHDXUhERkfGgKzGR+C3b2bxhMgP/UcNRUzcS9/JETIgxFj90CTGs729h5fVTST7ikDomPtqx5cW0RFrKTIVJKbnBTBYiQBGFSeuBDUNw1PhfRETGvyABoYFizmpW9+ZERKSKBG6M+DMB6Rn1DFzXzKbpUzjlvFUc2rqdiAnozSX420PzGFzchDMINbaf6PbcaMcWkQpTYVJKrqc5W/RdFVsDjnbmFhGRalEbKaphTmggNUFvy0REpIr0DJNriePXeLzmpJW85xVPEI/6NNRkAcjmXf75Nat4bO4U/vfmMxicWU9y4+AohxaRStM7YCm5fBLycyC6mIKWsNkopM+CfBgQdfWSFBGR8a93OMXwRI+653xMITfrXBiedOAdvEVERMaLhM0z2BrnXy56hNeetIK6xO6zIWORgFgk4OyF6zhiRifv+frryES1gm6s0YIOKTdNUZOSa3VqSZ0LJhkWNN7GLZnjwNPLUUREqsSAn2FoaoRkcwbXDfY7Np7IER4aEEb11l9ERKpHNnB42XFrufCk5XsUJXfluZYJDUN84wO3k22NVTChiIwFqgRJyR3XPoPY5Bxnf3gxsdr99QixJBqyvPLKx3EGI1rKLSIiVcNLuCycvpHPf/U6ZszuJFmb2WOM6wYkazK85tJH+Pp/XIONqLu8iIhUj4b6et736sepTRx48wHXhfbGYebN7alAMhEZS7RuVkpuQnsDL3tuI4efsYmW1iH+/qMjGdiWJDfsYUMHxwuIxAMmzu/l7A8+Q6QlzwWduhgTEZHq0V5Xx+vPv4NEbY6P/88tbFgzgdtvOo71q9ux1hCN5jnlnBWc+Yol1NVnMNZw5uHLRzu2iIhIyZx5ItQlswWPb6jJ8vbzF5UvkLw0ulSXMlNhUkoutD7Hzd6E41gmH9HLG799H32ba3huUSu5lEeiIcesk7aRaHhhNuWpJy8bxcQiIiKllYh101o7gAWMgZmHdvKB/3f7PsdbY3nPCX+vXEAREZEymzezi2TswLMldzV32vYypRGRsUqFSSm5TLieSMRh145ajVOGaZwyvM9jQm9L+YOJiIhUyGH1T7DrrjeBhYEwxrCNYi14JqTByZIwPub51pKN8X2fJ0VERMabrnwnTkE7wL3Aje2/L7OIVB8VJqXkQgqfrr8rawOM0S5sIiIy/jVGNwFgLWwPEwyEMSxgd7T3tpAKIjhYJrrDJBwfU+TFm4iIyFi2ZThBOvCocwufNdkdxGgrYyYRGXtUmPwHrVq1imuvvZZHHnmEjo4OANrb2znppJN4+9vfzpw5c0Y5YeV5pp7i9xU1KkqKiEjVqHE8sNAR1DBsIy8UJHcR4hACW4JaJjFEvaNZIiIiUj02987Dc39f8PgghJs3zeb9h5cxlBRN902l3LQN8j/g2muv5aKLLuKGG25g/fr1ZDIZMpkMGzZs4Le//S0XXXQR119//WjHrLiYmYpj4kUdU+8eV6Y0IiIilbd2eCIDYXSfRcldhTh0BLWYIFGhdCIiIuV3dP18rG+xBRa2DJB+rq6smURk7FFh8iW66667+PznP4/v+8ydO5fvfe97PPDAA9x555187WtfY8qUKfi+z3//939z7733jnbcijLGMM2bjlPg9l0eMC1yTHlDiYiIVNCygUa6wtoDFiV3CDHcOTS1zKlEREQqZ1JyBUGanb2UD8Rx4J+ma1NUkYONCpMv0de+9jUAZs2axW9+8xvOO+88WltbmTZtGq95zWu45ZZbmDp1KtZavvrVr45y2gqzPrPcZdQYizlAcdLFMsEJaDN/qVA4ERGR8luf7SAdFt6ixGJIRrT5jYiIVI/ezU+TqAl3+9yBZk9OrO8vYyJ5Sew4esi4pMLkS7B69WpWr14NwPvf/35qamr2GNPQ0MC73/1uYKQP5ebNmyuacTS5rMcxlhNjPnXG4u31L8TI59udkAXRAJc1Fc8pIiJSLq4/WPQxMafwzQFERETGut7OfqwdmS65NZfkB1sO58bth7Ay1cDadB0PD0zgfzcdxf39E3cWLK3aLYscdLT5zUuwadMmamtrGRoaYuHChfscN2PGjJ0fd3Z2MmXKlErEG3WGNGCJGDgl5tMdGtbkXYbsyPxJA7Q4IYdEQuqdkTOQhZGzkDbAERGRKpBLRV7Cjfvit44TEREZq7Z2NpIeclgcaebT609mINh73+Xbe2Zwcv02rpzxCBtXxzlk+iiEFZFRo8LkS3D22WfzxBNPMDw8TDy+701eNmzYsPPj+vr6SkQbE0Lq2HFxZQy0upZW1wdGpu7vs8eIipIiIlIlBp+biZ3/UFHHDPmxMqURERGpvGc2trKsv5Erhk5mINj3OW44jPJA/yS+sOY4en6R5zMvq2BIOTAtkZYyU2HyH7C3Jdw7hGHIb3/7WwBaWlqYNWtWpWKNupAZWF448QQWlmUaeDrbRCZ0qHN8Tkp2MyMyvLNI6XP06IQVEREpg0np2WQDj/jzN+YOzHLnyiO44NSyxhIREamYxJQGvrjyOAYmHvjGW8Z6/H1oCpmIbtKJHGxUmCyTn/3sZ6xYsQKAN77xjThO6dt5NjUlMYVucVZpqXcSDn2DR9N1/HFwCr41ZOwLL7fFmSYSjs+bG9YzI+YSafwQzZF9F3plbHAcs/Pf5mY9X+ORnsPxT8/h+HDcETOp8XM40ZCwgJbeDhZvG3pOxwH9Do5/eg7HNz1/40dPLkdXXV3B44f8CJwR1fMqcpA5aAuT3//+9/nWt75V1DEXX3wxV1111QHH/eUvf+Eb3/gGADNnzuTyyy9/SRkPxPPG7tLnzbyWv/fewKJcw24FyR1S1iMVePyk91COM1kubltAxNVeTOOFMQbXHaNFcSmInsPxT8/h2FY3dZCJsUHyjqEvjO+3OOkSMsUb4A0nPYmrc+G4od/B8U/P4fim52/s8yeFENn1E+CujuBs8sA32IaA4Ig8tv75nbs9A9NzOheKHGQO2sJkudx+++184hOfIAgCamtr+c53vrPfJd//CN8PxuyMyW8v+TODkTYibrjfcRnrcU8+hrv2aS465JgKpZOXynEMxhistYShmo2MR3oOxz89h+NDXWITES+g3vXxCOkOk1jYpUBpcbBECWj3homakIm1AwTB/s+bMvr0Ozj+6Tkc3/T8jR+uB7iABe/RGN7TMQgMJj9yDWuNxXsmRtgUkn/lMLbOYkw4Zs+FB2vB1OjXTMrsoC1MvulNb+IVr3hFUcfUHWAa+nXXXccXvvAFwjCkpqaGH/3oR8ydO/cfiblfvb2psn3vf0Q28NniP0BzvLATiuOE3LjhFs5sLN9/KymN5uYaXNcQhpaenuHRjiMvgZ7D8U/P4fgQ5nO4kZHzYIObo97JjawWCCOEGDwTUu/kiJgXzpURN9BzOg7od3D803M4vun5Gz9iwxnIQeTeJO46D5PfvbBnrIGMwdlqiN5QR+6fhjCZ/Jh9XtvaCl+WLiKFO2gLk01NTTQ1NZXke4VhyBe/+EWuvfZaABobG/nJT37CwoULS/L9x5tnBzdTFy/8ZOI6lob657DWjtkZoCIiIsWIxSMEvgvRkcKjMVBjfGqcfW+GE1idA0VEpHrU9wVsWxLZa1FyVwaDSRmit9RgGjrhggqGFJFRd9AWJkslnU7zsY99jLvuuguAadOm8ZOf/OSg2oX7xfr8bizFXVy5JiCwAZ7RS1JERMa/7X6KeAGb3uwQhLBo1QymqauJiIhUiZpkFu+Juv0WJXdlMgZv/djdR+GgpaXcUmaqAv0DhoaGePe7383TTz8NwFFHHcUPfvADWlpaRjnZ6KqPxl7Sca45OHt2iIhI9dn8XAfJKTESNof7fHOmYT/KulQL2dCjMZLmkOR2diwUsMbQm02MYmIREZHSqp8WYh5npM9kAUzOIWxtLGckERmDVJh8iXK5HO973/t2FiXPOeccvvnNbxKPx0c52eibUzMdp3f3zwVDDpnVMcKMwa0NiR+WwdllhzbPRDEqTIqISLXIDNCVT2Jdh9A33LD5OJ7qnwaAxeCakIgTcGH7Yl7evpwl2YlMn9k1yqFFRERKx/HT4BTXPs3T5bTIQUeFyZfoK1/5Ck888QQAr3jFK/j617+O5+k/J0DSraPRmcwQG8lt9dh+QxOppXEIwQYG41lwof7UYVre0AdJy7HJM0c7toiISMlEbZKNQSt/6JjCks4ppPJRops9Ih0uJoB83DI8y+e6/Inc1rmQk6as5/jIhtGOLSIiUjJ+EMEaimry5Thjc0fug5l25R4xNDTENddcw5133snGjRvJZrNMnjyZs846i8svv5z29vbRjjhuqZL2EqxcuZLrrrsOgBkzZnDFFVeQzWbJZrP7PCYej+O6B0+/jIvbLuUHf/8+Hd9sIRza/f+3zY382/fXOoYeTzLtim7OXVjcDukiIiJjmWensTq9nqc6puOujNO4KgIBOMHI5ZnFEtvkEcZjDJzg8RCHMGHqwCinFhERKZ2e3CRMmAen8NJkPjh4rpll/FixYgXvfe976ezs3O3z69evZ/369dx2220H9QbI/ygVJl+Ca665BmtHbhts2LCB0047raBjTjrppHJHGzNq+tvp/vYkwqFg34NCg9/t0nfVLKK/Ul8tERGpHsNJw4qeiUSeShB9zsPxd78oMxhMHpy8ofa+BOkzQlY16U67iIhUD3dbA4TbKXgvuCCE7fmyZhIpVldXF+94xzvo6+ujrq6Of/u3f+Pss8/G933uvvtuvvnNb9LX18eHPvQhbr/9dmpra0c78rijpn4vwY6+krJvv7/2QXKDhUzDN6T7fR7/+8qyZxIREamUjDdM/6qGvRYlX8zJGxIPJFm5XYVJERGpHunOYbyBDISFrQU2ocXtz5U5lUhxrrrqKvr6+kgmk/z85z/nLW95C1OmTGHGjBm8853v5Jvf/CYAnZ2d3HbbbaMbdpzSjMmX4A9/+MNoRxjTclmfx+5ZsXNW6YFkUjl+d80DnPyyw8ucTEREpDK2DGWJLo8dsCi5g/ENqbV1cOBFGCIiIuNCOp/GHcgSxj3CeGT/S7qDkNi2IWwxDSmlMg7iHpPbt2/n9ttvB+ADH/gACxYs2GPM2WefzcyZM9m8eTNLly6tdMSqoMKklFzHc93FdTgGersGsdZijM5EIiIy/vVtrMNkUgWPN4Eh+my0jIlEREQqq6beMBS1ONuGyTUnCGqjI9eJzvMLN62F0GKCkNi2YRw/xHe0lFvGjr/85S8EQUAikeCtb33rPsf97ne/IxaLVTBZdVFhUkoun/Nf0l0VG1qMq8KkiIiMf3agBhOmizrGZHUOFBGR6hEF3GRAkPWI9aSxvWn8uihh/Pnduv2QyEAWJz/SAsxJBsRi3aMbWmQXixcvBmDBggUkk8ndvpbP54lEIgAqSv6DVJiUkqtvqin6GOMYHFctT0VEpDocM3EazyzpopglBKaQ1swiIiLjRFN7SGpKPz23NGEzDsZCZCAHA3vvI+nWhbS+JVPhlHIg5iBeyr1q1SoAZs6cCcDf/vY3rrvuOhYtWkQqlaKtrY3zzjuPD3zgA7S3q1f4S6VKkJRc26RG6hpe2GU79Ax9hyXYfF4jm17VxJaXNTA0LfZC/xADx5w2Z3TCioiIlMHpxx5K1A2KOqatVsvXRESkesSmGhovGKTm6BROfH/nRIvb4DPlU9uIzdTqARk7Ojs7AWhoaOCKK67ggx/8IA8++CCp1Ei7nq6uLq6//nouvPBCnnrqqdGMOq6pMCll8Zq3nko0EaFnYQ0bX9dC71E1ZCdEyTVFyEyM0XVSHRsubmVoWoxkTYxXv+nk0Y4sIiJSMtPbNjO5cajg8V7E532vXFzGRCIiIpXVdGgLxsCkD2+n8VUDOHUBJr7L8gBjcWoD4nOyTL9yK9F2H2pUopCxY3h4GIBbb72VG264geOPP55f/epXLF68mIceeogrrriCZDJJX18fH/zgB3cWMqU4WsotZXHcOfP4wkP3M9zmYSN7nlxs1MECXSfVEe11mDS9pfIhRUREysTQw/te+TRfuOFUUpkDb2pTH89y3jHrGa5ANhERkUpI18cIrEPECWn9p36aL+pn8OEa0svihDlDdIJPwzlDRCb4LxxjtBHcmHMQL+XOZEZaC3R1dXHSSSfxs5/9bLe+km95y1uYO3cub3/72+np6eHHP/4x//Vf/zWakccl3Y6QsvjOw48wNCmy16LkrmzUYVM7PLBhY4WSiYiIVEKMsxZu5pXHPktNbO+9tHaoT2b46uV/JuodxO/8RUSk6nSkPMJdei372y3pRwYYvmcbqXs7GL63m8yqHDYcOf/5ocNzQ42jlFZkT/F4fOfHn/rUp3YWJXd1wgkncNZZZwFwxx13VCxbNVFhUkrOWsufnn0WTOH9Qb75wENlTCQiIlJZPvOx1vDx1z/AO89/ksaaNMndCpSW+mSGWe09fO+Dv2f+tO34zB+1vCIiIqW2att2Ng81kEvBxo8FrH1zQM8NluyzkFsHw49YNv9nyKoLAlKLLX7o0JuNH/gbi1RITc3Ixr51dXUcfvjh+xx34oknArBt2zaGhgpv5SMjtJRbSm5xRwdBWNysj45B/fKKiEj1sKaBNZnpzE0s5S3nPM2lZzzDPYtn8cDy6WRyESY0DnHhSSs4dHIPAAN+lO28kQZ3lIOLiIiUSP9QgJNI4H8iJFhnsXtZQBAOjjw2fiQg99/NBDM0d0rGjqlTp9LV1UUsFtvvuNra2p0fZzKZ3f63HJgKk1JyK3u6GGlEUcyOapZ8EBBxdUUmIiLjXz4M+Oqm2fzvrFU0eDkiXsj5x67h/GPX7GWs4blsDbf0DfKB6aMQVkREpAzqMDT833P0rAsx++9qQjgA7pXdHP/r/sqEk8IdxJ1m5s+fz1NPPUVPTw9DQ0P7LDhu374dgEgkQnNzcyUjVgXdjpCSy5J+Scc5RSz9FhERGctWDm/m2VQNn91wIv3+vhv5Z0OHjdk6PrL2DO7vfbaCCUVERMrr7Knbyf4xe8Ci5A6RnM/C5evKG0qkCGeffTYAYRhy55137nPcAw88AMDChQtxHJXZiqX/YlJyh7fniz/IqDApIiLVYzDIkA3zPDw4kfevOpt7+iYz4HukApd04DDoe3TlY/xi2zze/ey5DAVR0kGBV24iIiLjwLxtMWy+8Gs8f9jh6V82li+QSJFOO+00pkyZAsA3vvGNnTMjd/XnP/+Zxx9/HICLL764ovmqhZZyS8m1xl1MLMBmC315WSItaSAEtJRbRETGv75cZmdTE8eGrHkwwo8eO5atkxuxUQevL8/J+c1MeFmI8SxYyIXBaMcWEREpmVSHJTtc3FyogY59rzKQ0WEO4qXcnudx5ZVX8t73vpeOjg4uvfRSPvKRj3DyySfj+z6///3v+e53vwvA0Ucfzetf//pRTjw+qTApJTeYSRKbmCKzoY5C+0w6yTwWp6iulCIiImNV3Iw0ST8x2Mz633n89LRjsC93IDFygZYF7hqow9kWcv665dx/2lyGgrpRTCwiIlJaA9vrKHZRXBjoilDGltNPP53//d//5dOf/jSbN2/mk5/85B5jjjjiCL71rW/has+Ml0SFSSk5x0wik4oSnTxEbkst+y9OWhKH9pHu0a5VIiJSPeJOlBn+ICsfS9B/ZgMk93yjaus9gnr4S+3hnPvQCu489uRRSCoiIlIem4cjRBMh6cHCZ03mA3Wbk7Hn1a9+Nccccww///nP+fvf/05HRwexWIxZs2Zx4YUX8oY3vIF4PD7aMcctFSal5CLWIch5xKYN4SZC0utrwXfYebvMWjBgYgGJ2QMYzxJ0OOoxKSIiVWNmTTPNT3Wz8ahpey1K7srWudw1/zBOzPdVJpyIiEgFhC1RfL/wa7x4rU8qVHFnzDmIl3LvavLkyfznf/4n//mf/znaUaqOCpNScn4+JNmYwRhL7dIs7X/IkqvxSE2KYKMObiqkZnMWl5DBd0JulkNNJCQIQ1ztYCUiIlVgohewZPJEqClsSU8YdQiWb4EzyxxMRESkQuqmNJDLuHiRkCC//+s841iyKRdmqTApcrBRYVJKzjGGWFOWmt9b4veMLOSO53zivf4eY+t/AIPvCHFmpjRjUkREqsadj91G0FDE26yEw+L21vIFEhERqbAFhx/KXRiCvIPjhoT7WKZtjMWGAIbktOaKZhSR0afpaVJ6yTzRZ4OdRcn9MSHU/RIcm8WoMCkiIlViTX8P1inuvBbU6X6xiIhUD3/l4EgbL9hZlDTOLuuCjQUs1hp2XDlGNuYqnFIOxFg7bh4yPqkwKSVnHUvNzWHhO2yHUHM7BCO3yURERMY9J5ooeidSERGRarLhmY17zFSx4S6f2KUguUNqa2/5g4nImKLCpJRcvr8fp6fwqzEDRB63uEYvRxERqQ7HzDsTguLu3EczQZnSiIiIVF7n1t499k050JkxyO/Z/ktEqpvWDEnJdS9/ruhjTABBEOC6hW0SICIiMpYtbJ9H9CmfbIHLs00qoDlbX+ZUIiIilZOLuYxMQ7HYqEcwtRWCEKd3GMIQElHClnqcbb04vUMjB0W03EDkYKPCpJRcPmMLX8a9izCwqC4pIiLVYMn2TuoeDcidG2AL2JnbyYUMduhiTEREqofXmsZGHGwyjn/MHIi44Di8uIFXOKUVp6MXd+l6nCbtyj3mqHWjlJnWzkrJTZsxq/iDDESiqpOLiEh12LZ9O+5jURKP+Jjh/S/RdgZ9an7sUP9YX2XCiYiIVMCUI1xsXRL/2DkQi4Czj/JDxCOc2Ix/+HScY9oqG1JERp0qQVJyc4+YixuBIF/4Ma1TIuULJCIiUmF9m9dgQ0j8PgrbfTKvAOvxwuzJ0GKGQ9zekJrrXLwuQ1CvZQMiIlI9Zk6tJThqJkQLuNaLuNhJLdTMyJQ9l4iMLSpMSsk5juHkV8zkgT+sZ49t2PbK8rZ/uaDMqURERCqn0fPg+Z1HEw95xB+B7PyQ/GF5bBTcPog94eJ17fJWTEulRESkikxsmQCx3sLPbxGXtogKk2ON0fsTKTMVJqUsms84EufOdYQZ2H9x0mLaDMlD2iuUTEREpPwmTGjCiViC56+vTAiRtQ5h2sNGDO6wxd2+e5ctL653/iIiUj1uvDWFwVL4DgSGdatry5pJRMYeFSal5Ky13Lx4OZ1ntDHl/i5Iw16Lk8YSthg2n9DG9Y8s5nOvO7fSUUVERMri6MNPwI3eSR7ItRl6zouQnumCAWvABBYTQP2jPo0P+LiELHiVLsZERKR6bN/Uiy3ynlsmrbYmIgcbFSal5Db3DZDK5wnjLs+d005DxyDt6/uxGYNxLGHg4DSGPDejlXRrHIzhyY1bRju2iIhIyXhOjENfO8RjDzfTcXGcMLn7Dbods0f6zoowtMBj5vVDnHPh0aOQVEREpDxirgOBhWJqjcH+N4yTUaAFHVJmKkxKyQ2ks8//8bJcduqDvPqoJ4m4Pk5gyWY84sk8OeuRDzx+cNf5PLp2LmAJwhB3Xzu1iYiIjCPZcIDa83rZNnUyYWzfS9hsxJBvha4PenTE7ucItHpARESqw1knzeGZjsdxnu9cEqvLMvv0jbQd2oPjhaR646y5bwbb1zQBhtBYkvnsqGYWkcpTYVJKLh7xMAbed/adnHXYMpKx3MgXIhCNB89/mANyfPi8P/OTe/M8teEYFSVFRKRq9PhreXj7FIJYAec215CORFg2sIlzG8qfTUREpBIWnn8E2b8/QU1fyHFvWsKEw7bjxQJcb2QKXtO0Qdpm95LPejzy86Pp7qxn0sVzRjm1iFSaKkFSctObGzly6lrOPGz5C0XJfaiNZ3nPWXcxb1KhDZFFRETGvv78MJuG6goenw0jPNypjeBERKR6PNHVQf8RcPqHHmfSkZ3EanzCrEPv8lq6F9cxtDlOtManpjnDaf/8BLHjBlhRlx7t2CJSYZoxKSXnuQ7vPO1RamKFTcNPRHK876zVZU4lIiJSOZtS4OcciLzwOZMKiG7MYHIhQb1HfsZIn+UdOoZrRiGpiIhIeazs6uSYI9fRMHWA9OYYq38zmZ4l9QBYC8axRGoDZl28lakv287LLnyKa+7X0oGxxqjHpJSZCpNScqHtoqWus+DxnhvSVn8n8N/lCyUiIlJBTr6FMDAQAW9rloYbt5FYNDjyxdCCY7Axh4ELWhl8ZQtEHMKsdiIVEZHqsa5nGwsXbKT7sQaWfH8m/tCe5Yf8YIQVV09n633NLPiPdUxv2zYKSUVkNKkwKSUX2q04Jlfk5l1prA0xRt0FRERk/EttGMQEEH1mkLZvbMQZCtijaclQQOMNHdTc18u2/56NzamtiYiIVI9JjV0Mr42z5Hsz8Yf3XXoIMi59K2tZ+d1pHPe+dRVMKCJjgapAUnoWDPvvLbmnLOx5ySYiIjIutacTRDdnaPvGRty9FSWf5+QskecyTPjCOhqf1E6kIiJSPdrqAtb8cvJ+i5I7hDmX7sUNRPrDCiSTothx9JBxSTMmpeQipvi/Ci4WY1SYFBGR6lBTG6f5Z1txhwIAbMIhd34d+RNrIOpgun1if+rHfSaNE0Bkc5a6u4fgc6ObW0REpFTmRCdz3+augsfnhx22/mECHFPGUCIy5qgwKSXnGoe4iZGymYLGG6De1BDaAIz6a4mIyPg3aUYb7uY01kDmXS3kXt4AUQPxFxarBMcmIR2S/HIH3vIMdkA7kYqISPUYXlVLmC1i8ol16F/cVL5AIjImaSm3lFxIHY1OPUFY2MvLhg51TlJFSRERqRpLnniW0DGkP9FO7oIGqHd3K0oC2DoXOyHC8Ocmkz8ywXDf0CilFRERKYNUPaFfXMnBz0TKFEZeKmPHz0PGJxUmpeRCZtAxMJGfPXo2Q9nYfsf2ZxJ8/m8XkQ6Oq1A6ERGR8rtr+Qr8M2rIn1ADyQPceKtzSX96ImFULU1ERKR6bMsMY2LFVYtynnpMihxstJRbSs8Yvr/oVfzxWZ/NA02876S/URfLUB8fWdodhIbhXIzNA818+/5X0D3cwO/XzeMV80Y5t4iISImkY5C7tAlqClsNYGMO+bNry5xKRESkcszcIYxrC959wERD6s7S6gGRg40Kk1Jy6Xyev2/wsIQ8sfkQ3n/zIRza0sHxU9dQG8vSk6rlgfVz2TbUuPOYaxZtV2FSRESqRv3Rzdj8hsIPSDhk36C+WiIiUkWSKWqOTjP4YA3YA68KMFFL41mDFQgmImOJCpNScpv6BzDsfuJZ3T2R1d0T93lMbzqDtdqZW0REqsOc5mbu73Mo6qxWq17LIiJSPWzK0vLGHtLL4/g9LuznrOgkA1ov68WYoHIBpTDq3Shlph6TUnK5ICj6b5cFQqu/eCIiUh36egbAKe5mm67FRESkmmy7bwAnYpn22a1EJvg4ib2c6JwQpyag5dI+Gs8dIvOsJqqIHGw0Y1JKrrUmWfQxrjG4jurkIiJSHZycGdkdsojNRd0+3aATEZHqse7pYVpOMcSmBcz82maGnkjSc0sD+R4PLBjPUn/GEE2vHMRrCggGYPvNCbh0tJOLSCWpMCkl115bS2tNkoFsFoDJtYO8c8HTnDV9A3HXZzAX5ZZn53HjisMZyMUwBs4+ZObohhYRESmhqRPbiKwPyScLu+nmhgHxR114bZmDiYiIVEiqxif8RZxJ/5bCrYW6E1PUnZja61jrQ77DJb2uiDt6UhFG902lzDRFTcri7cccTdSxXHX2ndx08Y286fAlTK8fYEJNitlNffzLcY/xl8t+xTuOXETUcXjTUQtHO7KIiEjJHHnIdObHOogYv6DxUTdg8lFayy0iItUjOivB4BNx+n4fI9jPZtthHvJdhk3/VYPfoLlTIgcb/dZLWURcw7fOu50TJ28mGdnzoizuBcS9gA8d+wQJz+I6/zQKKUVERMpjQjTBvMM7CIctKwfaydt9v+WKOXnOnbiCtqkzKxdQRESkzCYc2cTa/9tE9y8TZDc4tL4ji1sT4tSCcSDMgM0Zhh726PpRgrzvMviK2tGOLSIVpsKklMWqjuv50DFb9lqU3FVdLMc7FzzF1cvv5u3HX1ihdCIiIuXV7a8iGvU4KrqZGi/L4r6p+KGzs0BpCIk4AfWRDKe2rqUuksWYraOcWkREpHSO9Wt5+oRaGh4YYOieGEP3RInPD4jP9TEx8Dsdhh6OYDMjG96EtQ4LT9oyyqllD9qkVspMhUkpua6hYd4w915qo/mCxtdEs8xI3gKoMCkiItXBJ4sL+MCcui5Oa1xDPufxbGoCmSBCc2SYw+q20UeSwTD+/FF64y8iItVj9oTNHPumzSxd20p8S4bmeRkOeWsP9XOzGAP5IYcNTQ1s+XMDWTx639LMpw+9e7Rji0iFqTApJdefXs9hieGCx3sOnDN9Ffkw1M7cIiJSFaKmFsd4xMhzcmI9URMQSYSc3LBh5xhrIY/D5nwjy3PtOHpbJiIi1WRylvfU38e/f+BSTp62mtqGLNH6cOeXY80Bh72/m0Pf3csDT8/hzQvvoa42O4qBRWQ06B2wlFzS68UExc36cG1IQIj2YxIRkWrQ6h2GZ0NOTK4nYfIYs+cYYyBKyNRIL9YarKstuUVEpHqkYlEmmJDLTn2EVBhhb3NQvITFSwS87LTlzE50kM+pRCFysNFvvZRcnVsDxW4sGlqMccuSR0REpNIc4zI5yBDfR1FyVxFjmRrpI5M/ojLhREREKiDtT2VV2EoWd69Fyd04hiXZycy2/RXJJoUz6jQjZabpaVJyPdtbDngR9mL9/Ql11hIRkaqR97O0x9bgFHg+9Ah4ruea8oYSERGpoGk1PWwNGrAFlh0CHIZMpMypRGSsUWFSSs4Pojz6xDTC8MBjAVJpj5t+d5Q2+xIRkaqxpvceHAo8EQKOA5MaHi5jIhERkcrKOV1kg8ILjSEO6/NNZUwkImORCpNSci2tdfz6hsMZHIoVNH5wKMa9903HKXRaiYiIyBj3XMdyXKfwwiSAa4obLyIiMpatGvJxijwX5q1KFGOOHUcPGZf0Wy8llx9MM5gxfOVbpzEwGN3nuDCE7p4En/rvc0k42yqYUEREpLy2PddHGBb3NksrB0REpJqkg3ZCW9zkk0AlCpGDjn7rpeT6uro5/S2LWbxiAv/vs+ezeHU9G7MZ1uaHWOcPsTY/zKZMjvufmMS/fOLVdPcnOPPdiwkDzRQREZHq0LO8mZDiLsaWrZlUpjQiIiKV15mJkA8L3+A0tNCRritjIhEZi7Qrt5ScU9fF9CnbWHDWGs49cwm0DpB3feD52dXGknZyNB66kde8xbDNTzLxyAGMlnKLiEiVaKufyKJl0zj9mFUFbYAzkInztx/N4YLjy59NRESkEnI2zlAuTsIbKmh8NvB4snMGHFLmYFIUdZqRctOMSSm55qk1ODHLW975d6bN2k5NbXaPMY4DNXVZjj9tNWeftQwTNVDEJgEiIiJj2cJjT+COr81hMJM44Nis77J2Uxu9K9TwX0REqkf/QMADWw8hExx41mQQGrZnaukeqq1AMhEZS1SYlJLLejGaIhmiJsB1998wy3NDap088UgeYwqf5i8iIjKWzTlyKsPb6vnup06jL5XAD/c+bTKVi7B2ywR+8e5jOO68IyucUkREpHzq/To2DzTxcMcsMv6+r/X80NCTTfKnDUfipFWiEDnYaCm3lNywn6TVTRW0dA3AwVLrBIQ2xDE6EYmIyPgXjUU448ITuPs3D/Lv117MBecv5WVTV+78ugH6cwluWHUcy7/eTGM8wuvf9bLRCywiIlJiCxKz+d32lSztnUJPtoZTJ66lMZrGMSNdmAPrEFiHJT2TeHL7dOyQpblHMybHHG3OJ2WmwqSUXNTpwhax+9r/b+++46Sq7v+Pv++9U7Y3WJZeRBakKaLYNXZMTEF/lmDE8o1dYoxRMaJRkwiWmK+J5vs1sQZ71K+xE0WwBdEgSqQJKGVhlzbsLtum3fv7Y2UFt83A3Jmd2dfz8ZiHy865lzccL2f2c8851zCkQqtRRpwPCQAAoCv74QVH6285X6q+r0f/s/pYPfjlUeqXUy2fFVF1KEebmwokScZZtgasyFWfgT1SnBgAgMQZ0aeXCv+wSTVn9FKlivT8lwcq39uk0uwdsgxH9WGfKhsK5ciQbEee7SHtu4MqGNDdUJhEwvX0SNtD8c58NNW8xyTLuQEAmeGBf72j+v7elqEt7Hi0pr5nq3aO39T8/RpUXV+votzcJKcEAMAd1Rv/o4K5AUX6elV/RLGcXEs7wlnaEc7avWHUkVUdVq/ffKnoAeukK/8rNYEBpATrZpFwPrNAVpz7RfoMD3tMAgAyhuM4eim0Pub7bY5l6PbXXnc3FAAASfTRwoUyQlGVPLhRRc9UydwellEf+aZB2Ja5I6LsT3eoz7RV8m6PaMeKhtQFBpASzJhEwpnGPvIY8T1h2zBKXEoDAEDyvbdkhaKeOLYoMaV3jM3uBQIAIMnqGhvl2M37Khe8sU35s7ep6YB8NY3Ile035K0KKfeDalk7oi3HRBqZrNLVGKyuh8soTCLhbEW1w/arwAzG9ACcqCMFbJ8oTQIAMsVrK5bH/SnLjqeQCQBAF9cULpTts2QFmwuPhiNlL9qh7EU72j0mZGW1+x6AzMRSbiRc0F6nGrtIdgwPs3EcKSJLO+yIHIdbMQCAzNAYbUx1BAAAUqqusL+cOG66Rf2mag7p42IiAF0RhUkknK2worK0IZKviGOovXqj7UhhmaqI5Kt5gn98y78BAOiq9imRFOf9NjPsShQAAFJiQ1ONdhzUU7YVW3HS8ZqqObSHy6kQN8dJnxfSEoVJJJz36/0iQ/JoXaRQ1bZfEcdQxDEU/fq/YcfQNjtb6yKFsmXKkMHDbwAAGeOs8aUyQ3EcEJWKd9ljCwCAdBdSo7afMkDh0qxOi5PRbEubf7KvHB8lCqC74apHwvnNPi3FyahMbbVztSZSpI3RfFVG87Qhkq81kSJV29lyZEgyVOQ5JrWhAQBIoH5F/VTWEFRWTNVJR9lWSD8ft9z1XAAAJEuJPyhZpjb8fJRCQ3KlPKPVk1SMHMnOt7Tp/GFqHFEkq5GbdEB3Q2ESrujjmyJTOS2/dmQo6HjU6HgVkkfaZf9JS3nq4/tJClICAOCOqEbo5pPe1ncHf6ZsT7DddoZsFfoadfUBszVx9PAkJgQAwF37NpkyIraKejRp4rObdcTfatX/ByHlDIwqq09URWMj2v/WOp0wt1pjT9kiRRyVfLQt1bHxLYaTPi+kJ57KDVf08JykbZHXVRf9TLba/4HMVI7KvGcoyxyQxHQAALjLkSNPQb1OKNiispxavbD6INWF/aqP+CUZ8hgR+ayohhVV6exhC1Toa9IGW+qX6uAAACRIsdFXZeuW6nsTP1euPySNkIpnRNpsO27QGtlBqXoZJQqgu+GqhysMw9SwrLv1ZdMtqo3+W1Ht0K5PATDklals9fZNVh/vlNQFBQDABfX2Utlff8wa03ODxvTcoPU7SrSyppeCEa+K/A3av+d65Xi/Weq9JfKW+vmnpioyAAAJFWry68ixK5Xj63xbE783ovGDv9K7OfsmIRmAroTCJFxjGl7tm/07NdnrVRV6UrXOfDlOWJaRo2LzFPXyTZLXKE51TAAAEi7sbJej3WeFDMgPaEB+oN1jHKf9FQYAAKQbu4ehnuPqZcT2UG55PLb6nbbD3VAAuhwKk3Bd3ZYCffDsgVq8oFChpohy8v064qSROubUbHnzU50OAIDEM+WTEfdW3mz9DQDIHI35q5XrjX3jP8vvqN/x1e4Fwp5h70a4jMIkXBON2Hrozle16F8r1VgflB1t/hdt22bp+Yfe1ctP/Es/Ou9InXzGhBQnBQAgsXKtEYq30JhjlbsTBgCAFMjOCcv0xVfV8mbxVG6gu+HWPFzhOI7+eNPz+njectXXNrUUJXcKhyKqr23SCw+/p1efnJ+ilAAAuMNjFCrPGhtze0v56uu7wMVEAAAkV8jnkW3HV3KIRilRAN0NVz1c8dHc5Vq2aK2CTeEO2zXWB/Xy4/O1eeP2JCUDACA5+vsulaWCGFpa8pv9lGfGXsgEAKCrK+0/RtGIFXN7O2po3bZeLibCnjCc9HkhPVGYhCv+8bf31dTQ+dPXpObi5OtPL3A5EQAAyZVjDdU+/ps7LE4a8irLGKDh2ffKiPXpAAAApIHC/DJV1+bE3N6JGvrK3sfFRAC6IgqTSLhtm2pVvbUu5vaO4+ijectdTAQAQGoUeY/QiOz7VWgdKUsFsow8WUaOvGahPEaJenvP1cicR+QxYplZCQBA+ijP66X8vCaZsjtta8hWvq9B+xb2SUIyAF0JD79BwlVvi70ouZMdtWVHbZkWtXIAQGbJsfZVefZdCjvVMnO+koyQPEax7LqhMozYl7gBAJBO9snNUWWkSis/6CVrlC1PTlRmG8NeuN5U7Rf5GjVqnU4fwBZfXY7DGmm4i8IkEs7jtaQ9WI1mmCxhAwBkLq9RpJLsI2VZpqJRW4H6+lRHAgDANTXRj7RpUQ+9f+8YefMj2mdSpXofsb2lzmUYjho2ZWn1s321ZWGh1o8s1bl3z5N0cSpjA0gyCpNIuN79i+O+qVJQnMveWgAAAACQISJOteY+sJ+iTZaiTZaW/mWwlj8yUNm9gjK9joLbvQrVeFvaV60sUdVXFRozOoWhASQd62aRcP5sn8Ydvm/Msyb92V6des5h7oYCAAAAACTNlrVS3dbdH35jh03Vb8jWjjU5uxUlJckJGXpv1pBkRgTQBTBjEq74wblHaNG/Vqq+tqnTtrn5WTrkuJFJSAUAAAAASIblC/PkhL+Zr+L3R3TM8eu1/4Gb5fPZqtyYqzdeGaKNFfmSmtut/7wwZXnRNoMtJuEyCpNwRe8BJbr0xh/of3/7kup3tFOcNKTC4lxN+8M58vn5XxEAAAAAMkXV1qjkNO8led5PP9ex312r1VaWNhk+RWSo4MBt+tXJGxTZ5NPMWw9RVWWeImEWdQLdDdUguGb/w/bV9X+YrCfve0vrV29WKBRRJByV3++V5TU15uB99OMrjldxz/xURwUAAAAAJJAvzy/Hkm749Ydq2L9JLzg91dTgUfXmPNkRU1mFQa3pUa/sPram3/eebr/qSG2oLUl1bABJRmESrhpc3lu/+uNPFNhcq/UrNykUDCs3P0tDRvZTdq4/1fEAAAAAAC4YeeAg5dfOUtXYiFZX9dTnb+6rqmU9ZRiSvl4e7C8IasQJX6phnKlr7pyvX907JaWZ0QaWcsNlFCaRFCW9CrTviD6yLFPRqK1AoD7VkQAAAAAALjl4v4Eq7L1eb34+UB89NVbhBq++/YTUUINPi/4+UusW9pVx4UJddkFuasICcWpoaNCkSZO0Zs0aXXnllZo6dWqqI6UtNnAAAAAAAAAJlW0u1oKNZfroybEKN/j07aLkTpGgV1tXFeuDp8do8JDXkxsS2EMzZ87UmjVrUh0jI1CYBAAAAAAACbUlvED/fnGEwo2+TttGwx5tXFaq9yobk5AM8TCc9Hkly7x58/TMM88k7zfMcBQmAQAAAABAQq3fVqfqqtgfdBpu9Oijt4e4mAjYe4FAQDfeeGOqY2QU9ph0wTXXXKNXXnlFEyZM0KxZs1IdBwAApNiaLzfrH8/9WyuXb5RtO8rO9umY40fquIljlJeXlep4AAAk3KqVRYqGgrEf4JjasLTUvUBAAkyfPl1bt27VaaedphdeeCHVcTIChckEe/XVV/XKK6+kOgYAAOgCmhpDuus3L2n9uq2q2/HND2fV2xv03NML9Mr/faIzfnKYTjxlbApTAgCQeEZ4qJzosriOsSNel9IAe+/vf/+75syZo379+unGG2+kMJkgFCYTqKqqSrfeemuqYwAAgC4gHI7otl89p4p12xSJ2K3ej4SjqgtH9fTfPpDk6MRT9k9+SAAAXFKS55fptRUNxb6DnM+fxI0CERubPpGkdevW6fbbb5dhGJoxY4by8vJSHSljsMdkgjiOo2nTpqmmpibVUQAAQBfw+j8WaUNFoM2i5K4aG0J69vH5qq1pSFIyAADc5x+wSbbR9pO422J6oooO5uE36Hqi0aiuu+46NTQ0aMqUKTrkkENSHSmjUJhMkMcee0zz58/X0KFDtf/+zHgAAKA7s21Hs1/9TOFQNKb2TU1hvfn6YpdTAQCQPGsbQjIGBSXFNuPO8Uhb+7GoE13PAw88oEWLFmno0KG65pprUh0n41CYTICVK1fqnnvukcfj0Z133im/35/qSAAAIIW+XLVJ4XBsRUlJikZszXtziYuJAABIrpqolH1ctYy8zsdDwx+V/8ga5fZkxmSX46TRywWff/65/vznP1PvcRGFyb0UCoV07bXXKhgM6rLLLtPo0aNTHQkAAKRYzfYGReIoTEpSKBRxKQ0AAMkXaLJkFUaUd+5mGQURydfG1iaGI2VF5T+8VlkT6pSVG0p+UKAdTU1NuvbaaxUOh6n3uIh50nvp3nvv1bJlyzRmzBhdeumlqY4DAAC6AK/PkmHGvq+WJBlx7MMFAEBXl2N6ZRiSWRxRwaUbFVqWo+D8Atl1liRDMh359quX/5Adsoqab+YZTJ1CF3LnnXfqyy+/pN7jMgqTe+Hjjz/Www8/rKysLN1xxx3yePjrBAAA0uB9SmXHuaao38AeLqUBACD58q082VFDlukoYpiq39dRaEhd85uOJEMKGY4cr6kcJyrDkBTmZ+quxuimD+V+77339MQTT8jv91PvcVm3/Zv985//rHvvvTeuYyZNmqSZM2dKkurq6nT99dfLtm394he/0NChQ92I2aHi4py0ml1hfj1zxDQNlZTkpjgN9gR9mP7ow/RHH6aHkpJcBctM6avY2kf90j7fHUifpgGuwfRHH6Y3+i99nLzPwXp/1Tw1hg3VBLPk7LqT3Nc/xkYdqTaUpaaIV0WeRpU27EO/okt49dVXJUnBYFDf/e53O2x733336b777pMkzZkzR/3793c9XybptoXJvfWb3/xGGzZs0KGHHqopU6akJIPHY6Xk991bhmHIstKnoIrW6MP0Rx+mP/qwa/uyJqDAQZYKN0pWsOO2jiFFcqVXo1/q59ZxyQmIvcY1mP7ow/RG/3V9Bwzsr6YFBarNj8hRR31lKGSbqgln6baDfyDLYj030J1028Lkj3/8Y5188slxHZOfny9JeuONN/Tiiy8qPz9fM2bMSNmsxUgkmnYzJg3DkOM4su1uOh88zdGH6Y8+TH/0YXr4snqbavMiajrKUtm7UVnt7OVvm1IkT6r4nld5dXWKRtt4MAC6FK7B9Ecfpjf6L73U+5xOipI7mQo5hhqjjV12LOy2BVOne15nt912m2666aYO2xx44IGSpEsuuUSXXHKJJCknJ8f1bJmm2xYmi4uLVVxcHPdxmzdv1q9//WtJ0o033qi+ffsmOlrMtm9vSNnvvSdKSnJlWYZs21EgUJ/qONgD9GH6ow/TH32YHrZsqlU0Yqt+kKX6AYZy10Vlhr/1wd6Qon5TWw6xZGcZCjaE6NM0JDeXkwAASC1JREFUwDWY/ujD9Eb/pY9Pq9Yp6o9KMRUmm7ednPHpC3ok73JXc+2p0tL8VEdAEvl8Pvl8vpjaer1e5eayBcGe6raFyT31/vvvq7q6WpI0bdo0TZs2rd22H330kYYPHy5JmjFjhk477bRkROxybMfRJ7Vf6pOqL1UfDarEm6cj8kZo35zeqY4GAIA7KhpkRB31elfyVZuK5pqK2o6MiCNDjhzDkOMxJMNQj08lx5KUFUl1agAAEuaVqo/jfAycoa3+anfCAOiyKEzCVW9v+1wPV7ytkBNRffSbTbZetxapwJOja4Z8X8NzUzfrFAAANxSHvSpYIfkDkhn9+pumIcdntPohzQxLPRY68pj10gXJTgoAgDt2OHXxH2R2z2XDQHdGYTJOP/jBDzrdm/Kiiy7SwoULNX78eP31r3+VJPn9/mTE61Ker/pQz1T+S/V2UNphSpv8UtiQ/LZ29Atqh79JN698Rr/aZ5L2Lxic6rgAACRMflGOClbH3t6ISnlb+VgGAMgc+R6/1M4ey+3pprs4dmkGtWK4jE/AcfJ4PPJ4Ov5rsyyr5b/ddZ+B5XUb9EzVfNVvjsj4uECqtaSwIcMx5JiO5HGksrDqDm7QzC9f1F9GX6J8T3aqYwMAkBBWUa6MqNG8RDsGpm0o2rPA3VAAACTRdwccoA9XfxnHcm5HpVb3m9CD9LVixYpUR8gI3JCAK56ofF/1X0VlzCmQsdUrI2TKcJo3PTZsQ0bIlNb7ZLxWqIbtEc3e8mlqAwMAkEDbaxqkeJ/e6YmxigkAQBoYmz8s7ic6j4uOcCkNgK6KwiQSrjbSoOUbK2XMz2suQLbDkCGjyVT0zVy9VLkwiQkBAHCX4zjx/iymcDjaeSMAANLEvKXL5UTjKDk40r8q1riWB3vISaMX0hKFSSTcpmCNIp/5pJAR2wEhU3WrHdnx/gQHAEAXFQ1GFeMo2MKJUJgEAGSOJTXr4zvAMLSjsMGdMAC6LPaYdMGsWbNSHSGlgsGIIhWWjBh/JDMihiL/8Ur/z+VgAAAkicc0ZNiOHCvG8qTtKJv7xQCADOKYThtTob49GeVb46TFZBWgu6EwiYSL1hpfP7orjrkiwVjLmAAAdH3Z2T55ZCjkOJIRwwhnSJ5Y2gEAkCYGl5RKAUl+yTJs9cqqVZ/cWpmGI309PFaHsrShvkgNkeaH3mRFfakNjVYMVjbCZdyaR8JlGz5Zcf6v5TUslnIDADLG0GFlCpuKrSj5tfxe+e4FAgAgyU4Ztr8Mx1COJ6QDelRoQF61/FZUXtOW17LlMW31zGrQqOIqDczbJoUcndBjdKpjA0gyZkwi4UoKc+Q3vWpQOOZjsiyvLJM6OQAgM6ypCMjjMRWO2rEdYBiqaQy5GwoAgCRqcpqUHw1pWFGlfFb746HHtFWWvUN2vqmCnoyFQHdDJQgJ17MoT2XFsc/6MAzp6AP3dTERAADJVbujUaYnvo9ZrBwAAGSSZfWrNKj/5g6Lkjt5TEd9Cmr07+pP3Q8GoEuhMAlXnHXygcrO8sbUNjfLpx99Z6zLiQAASB6/zyOPFd/HLIM9JgEAGWR11QZle2JfRWcajmRsdTER9oidRi+kJQqTcMXhY4doXHl/+X0d7xaQk+XV2SePV1kP9tUCAGSOfYf0irvQOGxoL5fSAACQfNXBjTKM2FcDmIaU42tyMRGArojCJFxhGIbOnzxCPfbbKisrIpm7374wPFF5siIacvQmnXjU4NSEBADAJbm5fo0Z2S/m9nm5fp3+g/EuJgIAILnyormKdy2AabN6AOhuePgNXFEXrdHTgXs14MRalR3m1aZPShVYVSw7YsqTFVHp6K0qHb1NHp80a8s9uqjsJpkGdXIAQOY4+/QJ+s/SDaqrD3bYzmMZ6t+3WMP2YcYkACBz9M7vobW2ZFixH2OGYtsODMljsAc2XEZhEq6Yv+OfarDrJEm+vLAGHL1RA47e2KqdI6k2GtDqps81LJt9JgEAmaNfn2L96ooGzbw/otp6n9TGvBG/N6z+fep18y++wx6TAICMcvjgw7So4nlFYpw3acrWEGO0y6kAdDUUJpFwUSei/zTMlxPj7rNBp1Hv1r5CYRIAkFG89gc6ZMTf9efpph57aZw+XDxAkuQ4hkzTVpYvov934hL98LilsrzvqNp5WTKYKQIAyAyekKVBvjqttmN7noBftvarrnY3FIAuh8IkEm5bpErxzvbeHtnsThgAAFIkx/mzTNWpX5n0q4veUUOjV6srShQKWSoqaNI+/QPaOUnSVoN8zlyFjJNSGxoAgASpWvWSvtt/gx5q3FcNTselB78R1fjsbdq355uSrk1OQMSGldxwGZv6IeFCdkh78q+X7cQ2wxIAgK7OdCpkqnK37+VkhzVm2CaNH7VRQwd8U5SUJFP1ynYeSXJKAADcs63hKw3KbdB/Fa9SrhGW2c6KuiwjorFZ2zWpoEL5+fVJTgkg1ZgxiYTLsfKat9GKozZpyODhNwCAjGFpjaRoXMeY2uJKFgAAUqEq1KSobWgff72uLV2md+p76ePGHi2r6xwZ6uNt1El5lRrm2yHDkMJRU77UxgaQZBQmkXDFVqn8RrYaFfvdriFZ+7mYCACAZLMV/+oB1koBADLH+uzBamzyyOcLqdAK6wcFG/S9/A2qjnoVtD0q9gSVbX4zi9K2pf9sKNX4vikMDSDpKEwi4QzD0KH5J+mt6ucUUajT9llGjg7PPyUJyQAASA5bA2Tblsw4FgPYKnItDwAAyebrOULbarJUWBBSY9TSixuG6i9f7a/qsL/lOd0nlq3RJUP+o33yalRX59Vftx2g8SlNjVbifYAEECcKk3DFAblHaFH9e9oS3ii7g6VsXsOv4dnj1Ns3IInpAABwV9QYoprtXvXoEVv7pkZLm+t/oNwyd3MBAJAsp/YZpTvmHqwr/Z/q4s9PUm3Yp0bbu1ub5yrK9c+qwTpv4BIda65XjwFjUpQWQKqwqR9c4TG8mlL6S/X2DlSWkdNmmywjR6OyD9L3is9NcjoAANwVbAzp8UeHq77+m3vAUcdQRVOuVjcUKBD279a+qcmjJx/KTXZMAABcU5ZVpC8KynX2J6dqUzC3VVFSkhyZqolk6aE1Y/Tj1d/TFUO/k/ygAFKKGZNwjd/M1gW9pmlt8Au9v+NVbY5UqHmLY1P7+Efq8PxTVOplAxEAQOZZvXSj3p83WGPHrlf5hG16rnqYnq0aJttpXrzmSOrrr9dlA/6jCd4q3X7LIVq/fkNqQwMAkGC9PaVa63Q+vtXbPnlMQ3lm25NakDoGK7nhMgqTcJVhGBqcNVyDs4arpCRXlmUqGrUVCMT+YBwAANJNY0NIdtTR7x6aoO1ej4IeSxFZu7WpjmTp+hVHKGdDRAVLHOXn2e2cDQCA9FMXDmpZ9TapZUfJTjiG/m/dUp237zhXcwHoWljKDQAAkGC5+VmKFpmqPD9L9R5fq6LkTg2OV9t6ZqnqTJ8si49lAIDM8eGWCjVGwjG3jzi2Xl6/3MVEALoiZkwCAAAk2NCRfbX5KEPRGFakOT5DTYMtDT5xkPvBAABIkppQk8K2HfOESUmqDja5Fwh7hqdyw2XcmgcAAEiwiOWofj+PZMb205idLW09NI6f3AAA6OK21jU0b6och6ZwxJ0wALosCpMAAAAJtrS2Sl5f28u322QY+k9wk3uBAABIsgLHH19h0pG8QUoUQHfDVQ8AAJBgDdGQ7DgfY2k7PPwGAJA5sk2vjNi3mJRsyVsXx009ABmBPSYBAAASLM/jV7x1RsvgfjEAIHPUNwTlrfYoVBpRO8+A+4YtmY2GFEpKNMTB4L4pXMYnYAAAgAQbVdBbwUg09gMcaZCnp3uBAABIsnyvX0bIkCdgSlG1v6zblowmQ56ApawoMyaB7obCJAAAQIJV1dbJU+eTYp1lEJW2rGOaCAAgc/gsS2ZIspos+TZ7ZNYbzQXKXV5GSPIETHkDlgwZ8jg8CA7obljKDQAAkGAVNbXybPMrlBOUzE72moxK2uFRfR1PIgUAZI4sy5IVkhxLkgx5qz1yqh05HkmGZEQlw/66EGlLngbJ42fuVJfjxPlodSBOXPUAAAAuMG1T+ipbCn49Q6QtEUnVHqnSH9eDSwEA6OqCTVEZtmQ1SWZIzUu2HUNmxJAZNmREjeaVBdHmoqQRlcwgoyHQ3TBjEgAAIMEGFBbKlCFvjSlncY6cHmFF+4Yl6+sfuAzJ2GHJ2uiTUW/J9ko9emSnNjQAAAlkO7Y8ISmcJVmh5uKk7VHzjElJcpq/bzjNXxu2ZFks5Qa6GwqTAAAACTagqFDesKFGRzJlSFt9srb65BhO83qVqGTomx++rIj03WHDUhcYAIAE612UryzblB20FfVLhtE83unbO5c4al7K3Sj16pmXgqToEJNY4TIKkwAAAAkWjkYVDdn69rwPw2lnWbcjbdxSm4xoAAAkxZjBvRVpCsvyWZIt2X7JMaSWwfHrgpcZbp45aYWjmtCjZ6riAkgR9phEUjTWB/Xp/FX611tL9Pm/v1Ik0t5mWwAApL+lGzfLMuJbjvbBqrUupQEAIPlWLVqrgnU7pIgtKyp5G5pfVlPzy9MkeeslT+jrWmXE1pcv/yfVsQEkGTMm4arNG7fr+Qff0eKPvpQcybYdWZYpwzR01MQx+v65Ryg3PyvVMQEASKgdTSFF7PjWPkVt26U0AAAk36a1W+VfulXeQp/CxVmSx5ThfL2c+1uMYERF8zdqi5+fDbsag6dyw2UUJuGa1Us36PfXP6u6msY233/j7x/ro3nLddOfp6i4Z36S0wEA4J5snyfuGZNGnO0BAOjqTEnF71eo9sAyBfvkybEMyfpm4aYRjMoMR1W4YKO8NSGpNw+CA7oblnLDFYHNtbpn2t/bLUpKkh21tXVTjW6f+jhLuwEAGWVkn14y4yw0jhvQ16U0AAAkX+8hpcrK9ctwpMKFm9Tzn2uUuyIg75YGebY1yr++VkXzN6jHP9c0FyUl9RnMHpNAd0NhEq545Yn5HRYlWzhS7fZ6LfpgpfuhAABIkmyfV4fuM6DVw2/ak5/l1+RD93c1EwAAyVQ+brD82b6WX5uhqPKWB1TyXoV6vLNeRR9XyRdoahkrcwuydepPj01NWAApQ2ESCRcORfTh20vlxLgXRWNDSP947AOXUwEAkFwXHDlehdmd75XltUyN7NtL+/bqkYRUAAAkh2EYOvW/jlVWjr/le019/Np+YIECEwpVu1+ebM83t/ByC7M18pB9UxEVHXGc9HkhLbHHJBKucn0g7mO2baqR4zjsrwUAyBi9C/N11Q+Ga8aLCxUOmXKc1veDLU9UxT2CuvH7R6YgIQAA7jr+7MO0/OPVenfTem08vEDRLEt2limZhoyQLSPiKH95nYYuatK1D1zEz4NAN0RhEgkXDoalPbhZ4diODIuBCACQGbaFq7TQ86QmnBLS2mW9VbXmm32zHEfyZ4U1aORG9R1Uo+er79N5pdfxAxkAIKMYhiHf+SNU9Z8mhc3df0h0fKYcn1R7QIEqD++rrLLcFKUEkEoUJpFwBUXxDyiGYci02FkAAJA55ta8qEa7Xv5sqfzA9dr3gAo11vllR0x5s8LKyglLar6XtzVcqY2hr9TPv09qQwMAkEDz1n+lZ75YoqDZ/swVxzS0Kdign897XY+cPCmJ6RATO9UBkOmoBCHhSvsWKbeg8z21WhjS/oexlwgAIHM02vVaG1qx2/dM01FuQZPySxpaipI7NTkNem/Ha8mMCACA6/5n8ceqC4c6bWc7jirqarU8sDUJqQB0JRQm4YpTzzlc/mxvTG1zcrN06jmHupwIAIDkqQytVcSOxnXMxtBXLqUBACD5Vm7fpu1NjTG3rw0F9ejSRS4mAtAVUZiEK448eYwG7lsmj9fqsF1Wtk9Hf3es+g0uTVIyAADcF3ZCCtmRuI6Jt5AJAEBXtmL7VjVEwp033MXywBaX0mBPGY6TNi+kJwqTcIXlMXX9PT/WiAMGtrms2zAN5eZn6dgfjtOPrzg+BQkBAHCR7VfYju8DcmOUTZwAAJkjYtuy4ywWRWzGQqC74eE3cI3P79V1v/+x1qyo0stP/EsrF6+X4ziyPJYOOma4Jp55iHr2Lkx1TAAAEm57fY4cJ/YnbNu2tGF7iTTIxVAAACRRr5xcZXu8u+wx6Sg7J6TsnCaZpqNw2KPamhw59jfzpXpm82RuoLuhMAnXDR7eW1NvO00lJbmyLFPRqK1AoD7VsQAAcE3QtrUh0EuDSitldfAk0p0itqUNgT5JSAYAQHJM6N1fXrO56FhQVKfefbfLsmxZHluGIUWjhvo6hmqrc1RZ0UNZhk+TR4xJcWoAyUZhEgAAIMGKfNmqCPRTaeE25fqCMjvYPCccNbV6S6kcOzt5AQEAcJnHNDUht7c+LfpEPcpqZXl2v1FnWY4kR0UldcrNa9K6Jf10bP8hqQmL9rF3I1zGHpMAAAAJNrq4lwx59d7Kcu0IZikUaf0wONuWQhFLqzb30peb++iUfsNSkBQAAPdUL1upHr1aFyV3ZZqS1xfRPv0rVbl+exLTAegKmDEJAACQYJZhatLAkfrbqkWat3y4eldsVsmrlYquikiOZHglHZur9UcOUn1urgq8fp09ZGyqYwMAkDAV67ap4eAqWd7OZ9yZpuTkhfX0W3N13U/PSEI6AF0FhUkAAAAXTBk6Tm9+sUzOb/8t7+YmhRuiu71vP1evsldWqO7Ufpo09VSVZrHhPwAgc/x7zTI5eVHF+ig4I9fR6l5fuJoJe4Cl3HAZS7kBAABcYIVsDbpjhbLWN8j6VlFSkkxbshqiKnm1Sr1mb05BQgAA3LPF2ib54ytqRcuaXEoDoKuiMAkAAOCCV/5ntrat2ypFO/6hLFof0sv3va7qzTVJSgYAgPuyc72Kebrk10zWdALdDoVJAACABLOjtuY8OlfhYCSm9k31TZr94FsupwIAIHlGDRksIxRfZbLYV+hSGuwxO41eSEsUJgEAABJs9aKvFAnHVpSUpGg4qvee/ZeLiQAASK4xBcPltbyxHxA09MN+x7kXCECXRGESAAAgwWq31sZVmJSkcDDsUhoAAJLPY1g6pucEKRzbrMlsb5YmFI91ORWArobCJAAAQIL5sn0yzfg+ZhlmnBtxAQDQxZ1edpLy/DlSZ8/AcaQL+p0uj8Emk0B3w1UPAACQYEPGDpIRZ2FyyNjB7oQBACBFPq1bpqginT4ExzCkVwJv68CCUckJhpgZTnxPVgfixYxJAACABMsrztN+h5bH3D63KFeTfvF9FxMBAJB8L259U412sNN2jqRt4e36qnG9+6EAdCkUJgEAAFxw5q9OU15xbqftLI+p3kN6adhBQ5OQCgCA5FjXtFG10bqY29fbjXotMM+9QAC6JAqTAAAALug/vJ+uuP/iDouTXr9HfYb21rRnrpFhsMckACBzrA9WqtFuiuuYrxorXEqDPeY46fNCWmKPSQAAAJfsf/wY3fyPaXp2xv9p2fwVkhw5UUeW15LltXTi+cfqe5dNlC/bl+qoAAAkVE14hxynef/IWNXbje4FAtAlUZgEAABwUf/h/fSLR69U3fY6bf1qk8LBsAp65Kt03z5xP7kbAIB0sb5pa9zHhO2IC0kAdGUUJgEAAJIgrzhPA4eWybJMRaO2AoH6VEcCAMA1oUj8N99sVuN2PSyRhsu4TQ8AAAAAABLKa2bFXdOybcudMAC6LAqTAAAAAAAgocbklsuRoUijJTvacdto2FAkZMhr90pOOABdBoVJAAAAAACQUIeW7KOaiiJVvNNPjZtzFA2acuzd20RDzYXLygW9VfVhX53V6/jUhAWQMuwxCQAAAAAAEsoyTEVXlykaCqvq4z7yZIdVuE+1sns0yTAdRZo8ql5dpMYt2ZIMGR5b/VWa6tj4NvaYhMsoTAIAAAAAgIT6aktAwcZvpkhGGr3atqT9wqMTMfX0h4t1yw+ZNQl0JxQmAQAAAABAQi2r2qJgpJPNJb/ls4pKl9IAe++dd97R888/r08//VSBQEA+n0+DBg3SMcccoylTpqikpCTVEdMShUkAAAAAAJBQa7Zsj/uYuqaQC0mwV+zOm2S6SCSiadOm6eWXX97t++FwWEuXLtXSpUv17LPP6v7779e4ceNSlDJ98fAbAAAAAACQUNFvP+kmJuxniK7n97//fUtR8vjjj9dTTz2lDz/8UC+//LJ++ctfKicnR9u2bdOll16qTZs2pTht+qEwCQAAAAAAEmpwz+K4j8n2eV1IAuy5TZs26W9/+5sk6fvf/77+/Oc/68ADD1RxcbHKy8t10UUX6W9/+5s8Ho+qq6v1wAMPpDhx+qEwCQAAAAAAEmpoaQ95rfhKDsN781TursZwnLR5ueGtt95SJBKRJF199dVtthkzZoxOOOEESdK8efNcyZHJKEwCAAAAAICE2q9PqUpysmNun+X16NzD2J8PXcvmzZuVlZWlnj17ql+/fu22GzRoUEt7xIfCJAAAQBLUhUP6eFOF3t3wlZYGNstx6c4+AABdgWEYmnL4gcr2fvPMXSPiyLvDlrfWltXoSLuMhb3yczWmX1kqogLtuvrqq/XZZ59p9uzZHbZbu3atJKmwsDAZsTIKT+UGAABw0bq6aj3wxcf6cMt6GYZkO448piWvYerHQ8bojMFj5Lf4SAYAyDynjCnXko2b9M6i1TIrgvI0Ot8838aQHFNq7GEqb2Ce7jrjFBmGkdK8aAM3UiVJeXl57b63adMmzZ07V5I0fvz4ZEXKGHwKBgAAcMnHWyt0w8J/qiYcbPP9/13xsV6rWKm/HvEj5Xp8SU4HAIC7DMPQhPwyffLVcoWDbRS4olL+FkdDsv0qyspKfkBgLzmOo5tvvlnBYPNnvcmTJ6c4UfphKTcAAIAL1tRt17QOipKSFLSj+qpuu65a8CpLuwEAGWf5F5V6+PH3FQ5G223jRB2trwjo9/f/M4nJgMSYMWNGywNvTj31VB166KGpDZSGKEwCAAC44H+Wf6TaDoqSO0UcW1/Vbdfn1ZuSkAoAgOR59Kl/qb4h1Gm7cDiqFSurVFlV7X4oIAEcx9GMGTP02GOPSZLKy8t12223pThVeqIwCQAAkGC1oaD+vW1D7O3DQT288hMXEwEAkFxVm2pUtakm5vb1DUG9+Nqn7gXCnrGd9HklSSgU0nXXXadHH31UkjR06FA9/PDDys3NTVqGTMIekwAAAAm2rGaz7DiXZi9hxiQAIIOsWFWlYCgSc3vHkf6ztMLFRMDeq66u1pVXXqmPP/5YkjRq1Cg9+OCDKikpSXGy9MWMSQAAgARrikbiLkyywyQAIJOEQlFFI3Zcx4TD7e9FCaTaunXrdNZZZ7UUJY866ijNmjWLouReYsYkAABAghX5suUxTSmOn6+8puVeIAAAkqwgP0s+v0dNTeGYj8nP48ncXQ4P55MkrVy5UlOmTFEgEJAknXnmmfr1r38tj4ey2t7ibxAAACDBRhf3ai5MxshjGDql3zAXEwEAkFwHjBkgjxX7WOjzWTrp2FEuJgL2zPr163XBBRe0FCWvuuoqXX755SlOlTlYyg0AAJBglmFq0sCR8hqxfdTK9vh09pCxLqcCACB5/H6vDjloH5mmEVN7n9ej7xw13OVUQHzC4bB+/vOfa8uWLZKkG264gaJkgjFjEgAAwAVTho7TmxtXqaK+VnYHO0jmeryaPGSsSrN4kiMAILP85MxDtfjz9doaqOtwRXBOjk8XnHOEsvze5IVDbLr5Uu5nnnlGn3/+uSTplFNO0RlnnKH6+voOj+Hp3PGhMAkAAOCCHI9XDx1xmi754EVV1Ncq9O0NJx0p2/Ro8pD99V/DxqcmJAAALsrPy9LtN5+mW2a+pOqaBtU3hHZ73+MxleX36rwfH65jjmC2JLqexx57rOXr119/Xa+//nqnx6xYscLNSBmHwiQAAIBLtmytk/NRWNkeQ3YfKZr99RuO5NtmqCDg0RebNim8T1Q+Lx/LAACZp7goV3+4/WwtXrJez/1joao218pxHPn9Hh131H46+fhRPPQGXVIgENC6detSHSPj8QkYAADABZuqd+jGv72hmoagLBkqqGm9PC2sqJYHt+i2p9/Sb8+dmIKUAAC4zzQNHTBmoA4YM1AlJbmyLFPRqK1AoOMlsUAqlZSUMPsxCShMAgAAuOBvby9UbUNTp+3CkaiWV2zR6sptGtqnRxKSAQCQGmE7qjW12xWVoxJfducHIPW6+R6TcB+FSQAAgARrDIa1YMX6Dh55s7sdjUE9895n+tWZx7maCwCAVNjUWKdZqz/V7I0rZRrNT+m2HUcDcgv102EH6bDSATKM2J7eDSCzUJgEAABIsOUVm+XEOcPgs682upQGAIDUWbh1g6Z98k/VhoKyv3XLrjrUpBs/eVNH9Bqo28ad0FK0BNB9mKkOAAAAkGkaQxFF7fgKkzZLpQAAGWb1joCuWzhb1aGmVkXJneoiIb27aY3u+M+7SU6HmNhO+ryQlpgxuZds29aLL76ol156SStWrNCOHTtUXFysCRMm6IILLtDo0aNTHREAACRZfrZflhXfrA+Pyf1iAEBmuWfJB6oNBztt1xiN6K3K1fpp+UEqzcpNQjIAXQWfgPdCTU2NzjnnHN1www2aP3++AoGAwuGwNm/erFdeeUVnnnmmnn766VTHBAAASTaif6ksI/aPWaZh6IiRQ1xMBABAcm1tatDymi0xt6+PhPT0V4tdTASgK6IwuYccx9Fll12mTz75RIZh6Pzzz9drr72md999V/fee6/69eunaDSqW2+9VYsX848rAADdiddj6YQD9pVpxjZrMjfLq/93xBiXUwEAkDz/3lahYDQSc/uo4+jtyi9dTIQ94tjp80JaojC5h5599lktXLhQknTLLbfohhtu0NChQ1VWVqaJEyfqqaeeUl5enmzb1oMPPpjitAAAINnOPvoA9czvfDlals+j4/cfpt7F+UlIBQBAcjREwgrb8RWL4ilkAsgMFCb30KxZsyRJRx55pM4+++xW75eVlel73/ueTNPUihUrkh0PAACkWEFOlu756ffVpzhf2T5vm23ysnw6Yf99dcnEQ5OcDgAAd+V7/fJZVlzH5Hh8LqUB0FXx8Js9sHLlSq1cuVKSdNFFF7Xbbvr06fr1r38tK85/jAEAQGYoLczVX6f+P7235Cs9/d6nCtQ1yjQMOY6jcfv001lH76+hvXukOiYAAAl3SM8B8pmWmmKcBekzTZ3Sv9zlVAC6GgqTe2DnnpFer1cHHnjgbu+Fw2F5vc2zInw+7vYAANDdeT2Wjtt/Xx23/74qKsqRLUce01QgUJ/qaAAAuKbA59dBPfrp7arY9o3Msrw6fdAol1Mhbo6T6gTIcCzl3gM7Z0v269dPPp9Py5cv1y9/+UsdeuihGj16tMaPH68rrriCh94AAIDdmKYhv5f7wgCA7uHnIw9XntH5hB1P1NCZg0aryJeVhFQAuhI+Ge+BLVu2SJKKior0wgsv6Oabb1Y4HG55v66uTm+99Zbmzp2r6dOna/Lkya7kKC7OkWHE9rTPrmDnk0lN01BJSecPA0DXQx+mP/ow/dGH6Y3+S3/0YfqjD9Mb/ZdeNm2pVdm7jhoPkmyv5Hy7AuFIRljKXyM1VdWq5HD6FOhuum1h8s9//rPuvffeuI6ZNGmSZs6cqfr65qVX69at00033aRevXrp2muv1VFHHSXLsjR//nzdcccdWrt2rW677TYNHDhQRx55ZML/DB5Peu5daRiGLCt9CqpojT5Mf/Rh+qMP0xv9l/7ow/RHH6Y3+i89/M9DcxWpCqvvG1LdIKl2+O7FyawqqXC55Kt19Fn+em3aXKu+fYpSlhdtsFnKDXd128Lk3mhsbJQkBQIB9ezZU08//bTKyspa3j/++ON1wAEH6Ec/+pE2b96sO++805XCZCQSTbsZk8bXG/7b/OOWlujD9Ecfpj/6ML3Rf+mPPkx/9GF6o//SR9WmGq2tCEiSzIhUsLr55ZjNLyMi7frT7I66oJ78+wJdfcWJqQncCctiJzzADd22MPnjH/9YJ598clzH5OfnS5Kys7NbvnfRRRftVpTcqUePHrrwwgs1c+ZMrVixQmvXrtWgQYP2LvS3bN/ekNDzua2kJFeWZci2HTb8T1P0YfqjD9MffZje6L/0Rx+mP/owvdF/6WP+R6vV1BRu9X3Dbn59m+M4WrDwyy7br6Wl+amOAGSkbluYLC4uVnFx8R4dm5v7zb4XhxxySLvtDj744JavV61alfDCJAAAAAAAXVEwFFE00kYFsgPhcNSlNNhjPJUbLmMu8h7o379/y9c+X/tPGMvLy2v5OhgMupoJAAAAAICuoiA/Wz5/fM9FyMv1u5QGQFdFYXIPjBgxouXrioqKdttt3bq15etevXq5mgkAAAAAgK5i/9ED5LFiL0z6vJZO+M5IFxMB6IooTO6Bww8/XF6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",
       "text/plain": [
        "<Figure size 800x550 with 2 Axes>"
       ]
@@ -342,8 +366,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T12:52:45.405874Z",
+     "start_time": "2024-06-20T12:52:43.077373Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -351,7 +380,7 @@
        "pyfixest.estimation.feols_.Feols"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -379,8 +408,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T12:54:29.304086Z",
+     "start_time": "2024-06-20T12:54:29.247938Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -415,8 +449,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-17T10:07:06.333906Z",
+     "start_time": "2024-06-17T10:07:06.320559Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -452,8 +491,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T13:16:14.216051Z",
+     "start_time": "2024-06-20T13:16:14.174099Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -463,7 +507,7 @@
        "Name: Estimate, dtype: float64"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -474,8 +518,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
+   "execution_count": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T13:16:17.654474Z",
+     "start_time": "2024-06-20T13:16:17.640205Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -485,7 +534,7 @@
        "Name: Std. Error, dtype: float64"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -505,8 +554,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-17T10:07:08.284417Z",
+     "start_time": "2024-06-17T10:07:08.261571Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -538,14 +592,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We see that the `X1` coefficient is `-1.0`, which is less than the value from the OLS model above (which was `0.949`). Where is the difference coming from?\n",
+    "We see that the `X1` coefficient is `-1.0`, which is less than the value from the OLS model above (which was `-1.019`). Where is the difference coming from?\n",
     "Well, in the fixed effect model we are interested in controlling for the feature `group_id`. One possibility to do this is by adding a simple dummy variable for each level of `group_id`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T13:18:53.334139Z",
+     "start_time": "2024-06-20T13:18:53.251232Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -600,8 +659,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
+   "execution_count": 12,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T13:19:50.267084Z",
+     "start_time": "2024-06-20T13:19:50.206870Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -667,7 +731,7 @@
     "- `iid`: assumes that the error variance is spherical, i.e. errors are homoskedastic and not correlated (independent and identically distributed errors have a spherical error variance).\n",
     "- `HC1-3`: heteroskedasticity-robust standard errors according to White (1980) and MacKinnon and White (1985). See [Econometric Computing with HC and HAC\n",
     "Covariance Matrix Estimators](https://cran.r-project.org/web/packages/sandwich/vignettes/sandwich.pdf) from the [`sandwich`](https://cran.r-project.org/web/packages/sandwich/) package for more details.\n",
-    "- `CRV1` and `CRV3`: cluster robust standard errors according to Cameron, Gelbach, and Miller (2011). See [A Practitioner’s Guide to Cluster-Robust Inference](https://cameron.econ.ucdavis.edu/research/Cameron_Miller_JHR_2015_February.pdf).  For   `CRV1` and `CRV3` one should pass a dictionaty of the form `{\"CRV1\": \"clustervar\"}`. "
+    "- `CRV1` and `CRV3`: cluster robust standard errors according to Cameron, Gelbach, and Miller (2011). See [A Practitioner’s Guide to Cluster-Robust Inference](https://cameron.econ.ucdavis.edu/research/Cameron_Miller_JHR_2015_February.pdf).  For   `CRV1` and `CRV3` one should pass a dictionary of the form `{\"CRV1\": \"clustervar\"}`. "
    ]
   },
   {
@@ -679,8 +743,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T13:36:26.067184Z",
+     "start_time": "2024-06-20T13:36:24.567752Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -735,22 +804,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
+   "execution_count": 17,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T13:51:16.035319Z",
+     "start_time": "2024-06-20T13:51:15.714005Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "param                            X1\n",
-       "t value           7.568059291000728\n",
-       "Pr(>|t|)                        0.0\n",
-       "bootstrap_type                   11\n",
-       "inference             CRV(group_id)\n",
-       "impose_null                    True\n",
+       "param                             X1\n",
+       "t value           -8.567586579080423\n",
+       "Pr(>|t|)                         0.0\n",
+       "bootstrap_type                    11\n",
+       "inference              CRV(group_id)\n",
+       "impose_null                     True\n",
        "dtype: object"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -776,8 +850,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
+   "execution_count": 18,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T13:51:45.202727Z",
+     "start_time": "2024-06-20T13:51:42.160812Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -861,7 +940,7 @@
        "4      9.21034  13.0    8.0   44.0      1.0"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -881,12 +960,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
+   "execution_count": 19,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T14:14:24.822497Z",
+     "start_time": "2024-06-20T14:14:23.773427Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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330pji8VifvGLX2T58uXZddddc91112XnnXdOly5dsvvuu2f48OHZZZddUiwWc/nll68S1G6scwMAAAAATU+zDJhnz56d+++/P0ly6qmnpm/fvqv0+eIXv5htttkmrVq1yqRJk1ZqGz58eJJkjz32yL777rvasZ///OeTJLfddttKbU888UReffXVJMmZZ56Zli1X3ga7devWOffcc5Mk/9//9//lmWeeaRJzAwAAAABNT7MMmB988MEsW7Ys7dq1y7e+9a16+91zzz2ZOHFifvazn9Wdmzt3bp5//vkkyQEHHFDv2Nq2Z555Ju+//37d+UcffTRJsummm2aPPfZY7dhdd901Xbp0SZKMGTOmScwNAAAAADQ9zTJgfuGFF5Ikffv2Tfv27Vdqq6mpqTtu06bNKmNffvnluofj7bjjjvXOscMOOyRZsW/x5MmTVxqfJNtvv31atGix2rGFQqFu/EdXT2/McwMAAAAATU/LtXdpel577bUkyTbbbJMkGTt2bG6++eY899xzWbhwYbp165b+/fvn1FNPzZZbbrnS2OnTp9cd9+zZs945evToUXf81ltvrTJ+TWOTZKuttqp37MY4NwAAAADQ9DTLFcwzZ85MknTq1CkXXnhhTjvttDz11FNZuHBhkmTWrFkZPXp0DjvssLqH2tWaM2dO3fGmm25a7xybbLJJ3fEHH3ywyvhOnTqtscaOHTvWO3ZjnBsAAAAAaHqa5QrmBQsWJEnuuuuuzJo1K7vvvnvOPvvs9O3bNwsWLMj999+fX/ziF5k7d25OO+203H333dliiy2SJIsXL667Ttu2beud46NtHx1Te7y67Tc+qrZ9dWM3xrkbUpcu7VMoFBrl2pRPVVWh7r9du3aocDVQfu4BmjOvf5o79wCwNv5saLq8B9AUNcuAedGiRUlWrFTea6+9csMNN6RVq1ZJVoSrxx13XHr37p2BAwemuro6119/fc4///wkqXfv4nXVokWLLF++vOSxG+vcDallyw2nFj6+QqGQFi18YEDz5R6gOfP6p7lzDwD1adGiWX7hvFnxHkBT0iwD5rZt29ZthzFkyJC6cPmj9thjj/Tr1y+PPPJIHnroobqAuV27dnV9Fi9enJYtV/9bWBti185Xq127dqmpqcmSJUvWWOPqVhtvzHM3pKVLl1nB3ARUVRVSKBRSLBazfHmx0uVA2bkHaM68/mnu3APA2ixbVtriMDZ83gPYUDTkB1nNMmDu0KFDFi5cmI4dO6ZPnz719ttzzz3zyCOPZMaMGZk/f3422WSTlfYfnj9/fjp0WP3XGebNm1d33KVLl7rjjh075oMPPlipfXVq9y/+6NiNee6GNGfOwka5LuXVtWuHtGhRyPLlxVRXL6h0OVB27gGaM69/mjv3ALA2/mxourwHsKHo1q1jg12rWX7nomfPnknWvhfxRx9YV7syd5tttqk7N3369HrHvv3223XH3bt3rzvedtttV2lfnXfeeSdJstVWW9Wd25jnBgAAAACanmYZMO+www5Jkurq6syfP7/efrNnz06StGrVKl27dk2SfOpTn6rbnuGll16qd+zkyZOTrNhTZ/vtt68737t37yTJyy+/nGJx9V+FKBaLddeurXVjnxsAAAAAaHqaZcD8xS9+MUmyfPnyjBkzpt5+Tz75ZJJk5513TlXVit+qTTbZJLvttluSZNy4cfWOrW3beeed07lz57rz++67b5Lkvffey3PPPbfasc8++2zmzJmTJNlnn33qzm/McwMAAAAATU+zDJi/8IUvpEePHkmSK6+8sm6l8kc98MADeeaZZ5IkRx555EptRxxxRJLkiSeeyPjx41cZO378+Dz11FNJkhNPPHGltr322qtu7iuuuGKVB+4tWbIkv/jFL5KsWDX80ZB3Y54bAAAAAGh6mmXA3LJly1x00UWpqqrKu+++m2OOOSZ33313ZsyYkenTp+e6667LOeeckyT57Gc/m6OOOmql8UcddVTdwwHPPPPMDBs2LO+++27efffdDBs2LGeeeWaS5DOf+UwOOuiglcZWVVVl6NChSVasFv72t7+dv//975kzZ07+/ve/59vf/naeffbZFAqFfP/736/blmJjnxsAAAAAaHoKxfo2420G7rvvvvz4xz+ue4Dfv9pxxx1z7bXX5t/+7d9WaZs+fXpOOOGETJs2bbVjt91229xyyy11ezf/q2uuuSZXXXXVatsKhUJ+/OMfZ+DAgatt35jnbgizZs1rtGtTPiuenFuVZcuWe3IuzZJ7gObM65/mzj1AY/v2ZfVva8jGYdiQ/StdAo3EewAbim7dOjbYtZp1wJwkb7/9doYPH57HHnss7777btq0aZNtt902hx12WL72ta+lbdu29Y5dsGBBRowYkQcffDDTpk3LsmXL0qtXrxx44IEZNGhQOnTosMa5n3nmmYwcOTJ///vfM3fu3Gy66abZZZddcuKJJ2bPPfdc49iNee6PS8DcNHhTpblzD9Ccef3T3LkHaGwC5o2fgLnp8h7AhkLATLMmYG4avKnS3LkHaM68/mnu3AM0NgHzxk/A3HR5D2BD0ZABc7PcgxkAAAAAgI9PwAwAAAAAQEkEzAAAAAAAlETADAAAAABASQTMAAAAAACURMAMAAAAAEBJBMwAAAAAAJREwAwAAAAAQEkEzAAAAAAAlETADAAAAABASQTMAAAAAACURMAMAAAAAEBJBMwAAAAAAJREwAwAAAAAQEkEzAAAAAAAlETADAAAAABASQTMAAAAAACURMAMAAAAAEBJBMwAAAAAAJREwAwAAAAAQEkEzAAAAAAAlETADAAAAABASQTMAAAAAACURMAMAAAAAEBJBMwAAAAAAJREwAwAAAAAQEkEzAAAAAAAlETADAAAAABASQTMAAAAAACURMAMAAAAAEBJyh4wv/baa+WeEgAAAACARlD2gPmwww7LUUcdlREjRmTWrFnlnh4AAAAAgAZS9oC5WCzmpZdeyuWXX54vfvGLOemkk3LPPffkww8/LHcpAAAAAAB8DGUPmE877bR84hOfSLFYzLJly/LUU0/lRz/6UT7/+c/nhz/8YR5//PEUi8VylwUAAAAAwHoqe8D8ve99Lw8++GBuu+22fOtb30rXrl1TLBbz4Ycf5t57781//Md/ZJ999slll12WyZMnl7s8AAAAAADWUdkD5lo777xzzj///Dz++OP5/e9/n8MPPzzt2rVLsVjM7Nmzc+ONN+ZrX/tavvrVr+b666/PO++8U6lSAQAAAABYjYoFzHUFVFXl3//933P55Zfnqaeeyn//939n//33T5s2bVIsFvP666/nyiuvzAEHHJDjjz8+f/jDHzJ//vxKlw0AAAAA0Oy1rHQBH9W2bdsccsghOeSQQ7J48eL85S9/ycMPP5y77747S5cuzTPPPJNnnnkmP/3pT3PggQfmm9/8ZnbZZZdKlw0AAAAA0CxVfAXz6nzwwQd56KGHcs899+Shhx7KsmXLUigUUiwWUywWs2jRotxzzz0ZMGBATjrppLz11luVLhkAAAAAoNnZYFYwf/jhh3n44Ydz33335cknn8yyZcuSJMViMUnSp0+fHH744dl5553z0EMP5U9/+lNmzZqVJ598Mt/85jczatSo9OrVq5K/BAAAAACAZqWiAXNNTU0effTR3HfffRk/fnwWLVqU5P9C5S222CKHHnpojjjiiHzqU5+qG7fLLrvkBz/4Qa644oqMHDky7733Xn7xi1/kN7/5TUV+HQAAAAAAzVHZA+ZisZinn346f/rTn/Lwww9n3rx5deeTpF27dunfv3+OOOKIfP7zn0+hUFjtdVq2bJmhQ4fm/vvvz6xZszJhwoSy/RoAAAAAAKhAwLzPPvvkvffeS/J/oXKhUMhee+2VI444IgceeGDat2+/TtcqFArp2bNnZs2aVbelBgAAAAAA5VH2gHn27Nl1x5/85Cdz+OGH57DDDkv37t1Lul51dXW23nrr7LPPPg1VIgAAAAAA66DsAXPnzp3zla98pe6BfR/Xgw8+2ABVAQAAAACwvsoeMD/xxBNp2XLdpl24cOE6b5cBAAAAAEB5VZV7wtpwefny5bnrrrty0kkn5YUXXlil3/vvv5899tgjxx57bO69995ylwkAAAAAwFqUfQVzksycOTOnnnpqJk+enCQ5+OCDV9kuY+rUqVm2bFmee+65PPfcc7nrrrty1VVXpUOHDpUoGQAAAACAf1H2Fcw1NTX5zne+k8mTJ6dYLKZQKGTRokWr9Gvfvn0OOuigtG/fPsViMU899VS+973vlbtcAAAAAADqUfaA+fbbb8+rr76aJPn85z+fRx55JN/61rdW6bfddtvlV7/6VcaPH5+DDjqoLmT+85//XO6SAQAAAABYjbIHzPfff3+S5JOf/GR+97vfZcstt1xj/44dO+aKK67INttskyS58847G7tEAAAAAADWQdkD5ldffTWFQiFHH310WrRosU5jWrdunaOOOirFYjGTJk1q5AoBAAAAAFgXZX/I34cffpgk2WKLLdZrXM+ePZMk8+fPb/CaAAAAADYU375sXKVL4GMYNmT/SpcAZVX2Fcy1W2JMnTp1vca9++67SZJOnTo1eE0AAAAAAKy/sgfMn/70p1MsFnPnnXempqZmncYsW7Ysf/zjH1MoFNKnT59GrhAAAAAAgHVR9oD561//epJk2rRp+cEPfpCFCxeusf+SJUty/vnn57XXXkuSfPWrX230GgEAAAAAWLuy78Hcr1+/7LnnnpkwYUIefvjhPPfccznyyCOz6667pkePHmnTpk0WL16cd955J88991zuuuuuvP322ykUCunbt28OO+ywcpcMAAAAAMBqlD1gLhQKueqqq3LcccdlypQpmTVrVq6//vo1jikWi/nkJz+Z//mf/ylTlQAAAAAArE3Zt8hIks6dO+ePf/xjTj/99Gy66aYpFov1/nTo0CGDBg3KH/7wh2y22WaVKBcAAAAAgNUo+wrmWq1bt87gwYNz+umn59lnn83EiRMze/bsvP/++2nbtm0222yz7Ljjjtl9993Trl27SpUJAAAAAEA9KhYw16qqqsruu++e3XffvdKlAAAAAACwHiqyRQYAAAAAABs/ATMAAAAAACWp2BYZY8aMyR133JGJEyfmgw8+yNKlS1MsFtc6rlAoZPLkyWWoEAAAAACANalIwHzRRRdl9OjRSbJOoTIAAAAAABuesgfMjz32WG655ZYUCoUUi8W0bt06vXr1SseOHdOiRYtylwMAAAAAQInKHjD/7//+b93xGWeckf/4j/9I69aty10GAAAAAAAfU9kD5ueffz6FQiFf+tKXcsYZZ5R7egAAAAAAGkhVuSecN29ekmT//fcv99QAAAAAADSgsgfMm2+++YqJq8o+NQAAAAAADajsKe+ee+6ZJHnmmWfKPTUAAAAAAA2o7AHzwIED06JFi9x999155ZVXyj09AAAAAAANpOwBc58+fXLeeeelpqYmAwcOzM0335wZM2aUuwwAAAAAAD6mluWe8IILLkiSbL311vnnP/+ZSy65JJdccknat2+fjh07pkWLFmscXygUMmbMmHKUCgAAAADAGpQ9YL799ttTKBSSrAiLi8VikmTBggVZsGDBWsfXjgUAAAAAoLLKHjBvtdVW5Z4SAAAAAIBGUPaAedy4ceWeEgAAAACARlD2h/wBAAAAANA0CJgBAAAAAChJ2bfI+FevvfZaHnnkkUyaNCnV1dVZsGBB7rzzziTJo48+mn/+85856qijsskmm1S4UgAAAAAAPqpiAfOMGTNywQUX5PHHH687VywWUygU6v7/6aefzogRI3LNNdfkJz/5SQ455JBGr2vhwoU58sgj8+abb+aMM87I4MGDV9uvpqYmo0ePzj333JMpU6akWCymR48e6d+/fwYNGpTOnTuvcZ5XXnklv//97/PXv/411dXV6dy5c3baaacMGDAg++677xrHbsxzAwAAAABNR0UC5jfeeCPf+ta3MmfOnBSLxXr7TZ8+PcViMe+//35+8IMfZMGCBTn66KMbtbbLLrssb7755hr7LF68ON/5zncyYcKElc6//vrref3113PnnXfmhhtuSO/evVc7fuzYsTnzzDNTU1NTd27WrFl55JFH8sgjj+T444/P+eef3+TmBgAAAACalrLvwbxkyZKceuqpqa6uTqFQyJFHHpmbb745V1xxxSp9zzzzzBx22GFJVqxuvuSSSzJt2rRGq238+PG59dZb19pv6NChmTBhQlq1apWzzz47Y8eOzeOPP56LL744nTp1ysyZM3PKKadk4cKFq4ydPHlyvv/976empiZ9+/bNTTfdlKeffjp33HFH+vfvnyS56aabMmrUqCY1NwAAAADQ9JQ9YL799tvzz3/+M4VCIZdffnkuvfTS7L777mnfvv0qfbfbbrtcccUVufjii5OsWEF7yy23NEpd1dXVOe+889ba78UXX8x9992XJDnvvPNyyimnpGfPntliiy1y9NFHZ8SIEWnVqlWmT5+ekSNHrjL+17/+dRYtWpRevXrlxhtvzJ577pkuXbqkb9++ufrqq3PQQQclSa666qrMnz+/ycwNAAAAADQ9ZQ+YH3jggRQKhey777459NBD12nM17/+9fTr1y/FYjFPPvlko9R1/vnnZ/bs2TnqqKPW2G/48OFJkp49e+aYY45Zpb1Pnz454ogjkqwI0z9qypQpGT9+fJLku9/9bjp06LBSe6FQyJAhQ1JVVZW5c+fm4YcfbhJzAwAAAABNU9kD5tdffz1Jsv/++6/XuNqHz7311lsNXtPtt9+esWPHpkePHmtcxVwsFuseSrjffvulRYsWq+13wAEH1NX68ssv152vHVsoFLLffvutdmz37t2zww47JEnGjBnTJOYGAAAAAJqmsgfMtVsvdOrUab3GdenSJUmydOnSBq1n6tSp+dnPfpZCoZBLL700m2yySb1933rrrXzwwQdJkh133LHefn369Kk7njhxYt3xSy+9lCTZaqut0rVr17WOnzRpUpOYGwAAAABomsoeMNcGxeu7EnnKlClJssZwdH0tW7YsP/zhD7Nw4cIMHDgwe+211xr7T58+ve64Z8+e9fbr1q1bWrVqlWTlX2ft+DWNTVaEwEny7rvv1gXqG/PcAAAAAEDTVPaAeeedd06xWMzdd9+dYrG4TmMWLlyYO+64I4VCIX379m2wWn7729/mH//4R7bbbrv84Ac/WGv/OXPm1B1vuumm9farqqqq2+O4duXvR8evbfV2x44dk6zYmqJ2/MY8NwAAAADQNLUs94SHHnpoxowZk9dffz2XXHJJzj///DX2nz9/fs4666zMmDEjhUIhBx98cIPUMXHixFx77bVp2bJlrrjiirRp02atYxYvXlx33LZt2zX2rb3eR8fUHq9tro9ee8mSJRv93A2tS5f2KRQKjXZ9yqOqqlD3365dO6ylNzQ97gGaM69/mjv3AEDTtqY/270H0BSVPWA+8MADs8cee+Rvf/tbRo0aleeffz5HHnlkZs+eXdfnzTffzMyZMzNhwoTcdtttmTVrVt3q5UMOOeRj17Bo0aKce+65qampyeDBg7PTTjut07j6Hm63rj7O+I157obWsuWGVQ8fT6FQSIsWPjCg+XIP0Jx5/dPcuQcAmqYWLda+YYD3AJqSsgfMSXL11Vfn+OOPz6uvvpqJEyfWPRCudlXqv65SLhaL2XrrrXPNNdc0yPxXXHFF3njjjfTt2zennHLKOo9r165d3fHaVujWtn90xW/t+NqVwfVZtGhR3XHtiuCNee6GtnTpMiuYm4CqqkIKhUKKxWKWL1+37XKgKXEP0Jx5/dPcuQcAmrZly5bX2+Y9gA3FunwQsq4qEjB36tQpt99+e/77v/87t912Wz788MN6+7Zs2TKHHnpofvzjH9ftD/xxPP744xk1alTatGmTyy+/PC1brvtvwUf3H543b169/ZYvX54FCxYk+b+HGib/t7/xmsYm/7d/cYsWLer2TN6Y525oc+YsbLRrUz5du3ZIixaFLF9eTHX1gkqXA2XnHqA58/qnuXMPADRta/qz3XsAG4pu3T5+zlqrIgFzsmJ17NChQ3P66afn0UcfzfPPP5+ZM2dm/vz5adu2bTbbbLPsvPPO2XfffbPllls22Lz33XdfkhUrbde23cbVV1+dq6++OkkyduzYbLPNNnVtb7/9dnbbbbfVjps1a1ZqamqSJN27d687v+2222bChAl5++231zjvO++8kyTZcsstU1W14tOEjXluAAAAAKBpqljAXGvTTTfNoYcemkMPPbTSpazVFltskc6dO2fu3LmZPHlyvTVPmjSp7rhPnz51x717906STJs2LfPnz88mm2yy2vGTJ09Okuywww5NYm4AAAAAoGlquM02NhIXXXRRnn322TX+1Prud79bd65Hjx5Jkn79+iVJxo8fn2Jx9XvljBs3LknSrVu3bL/99nXna8cuW7Ys48ePX+3Yd955Jy+99FKSZJ999lmpbWOdGwAAAABomppdwNy6det06NBhjT+1WrVqVXeu9qFyRx55ZJLkjTfeyC233LLK9SdPnpy77rorSXLCCSes9DC6rbfeum57id/85jer7GdcLBZz2WWXZfny5enSpUsOP/zwldo31rkBAAAAgKap7FtkHHDAAR9rfKFQyJgxYxqomvX3uc99Lvvvv3/GjRuXSy65JDNnzszXv/71tG3bNo8++mh+/vOfp6amJj179syxxx67yvihQ4fmmGOOyZtvvpkBAwbkRz/6Ufr06ZN33nkn//M//5OHH344STJ48OC0b9++ycwNAAAAADQ9hWJ9+x00ku233z6FQqHebRZq1a6A/dd+hUKhbhuHxvLpT386SXLGGWdk8ODBq7S///77Oemkk/Liiy+udvzmm2+eW265Jb169Vpt+5133pkLLrggS5cuXW37oEGDMmTIkNW2bcxzN5RZs+atvRMbvBVPzq3KsmXLPTmXZsk9QHPm9U9z5x6gsX37snGVLgGatWFD9q+3zXsAG4pu3To22LXKvoJ5q622WmufJUuWZN68eVm8eHGSFaHyDjvskL59+zZ2eeukU6dOGT16dEaPHp177703U6ZMyZIlS9KjR4/st99+Ofnkk7PZZpvVO/6oo47KjjvumBtuuCF//etf895776V9+/bZaaedMmDAgPTv379Jzg0AAAAANC1lX8G8PqZNm5b7778/1113XWpqanL55ZfnkEMOqXRZVJgVzE2DT21p7twDNGde/zR37gEamxXMUFlWMLMxaMgVzBv0Q/623nrr/Md//Eeuu+66FIvFnHfeeXnzzTcrXRYAAAAAANnAA+Zae+65Zw488MB8+OGHGTZsWKXLAQAAAAAgG0nAnCSf+9znkiRPPvlkhSsBAAAAACDZiALmqqoVpc6ePbvClQAAAAAAkGxEAfP48eOTJB07NtwG1AAAAAAAlK5lpQtYmw8//DC/+93v8tBDD6VQKGTXXXetdEkAAAAAAKQCAfPAgQPXqd+yZcsyf/78TJ06NYsWLao7P2DAgMYqDQAAAACA9VD2gHnChAkpFArr3L9YLNYdn3jiidl7770boywAAAAAANZTRbbI+GhoXJ9CoZDWrVtns802y0477ZSvfe1r6devXxmqAwAAAABgXZQ9YH755ZfLPSUAAAAAAI2gqtIFAAAAAACwcRIwAwAAAABQEgEzAAAAAAAlKfsezAMHDmyU6xYKhdx4442Ncm0AAAAAAFZV9oB5woQJKRQKKRaLKRQKq7QXi8UkWW1bfeq7FgAAAAAAjafsAfPee++dQqGQt956K9OmTas73759+2yzzTbp0KFDampqMnPmzLz99ttJUhdIAwAAAACw4Sh7wDxixIiMGTMm5557bpLkS1/6Uk466aR89rOfXaXvjBkzMmrUqAwbNizFYjHnn39+jj322DJXDAAAAADA6pT9IX+vvvpqzjnnnCxatCjnnntufvOb36w2XE6SLbfcMt///vdzzTXXpFgs5pJLLskLL7xQ3oIBAAAAAFitsgfM119/fRYtWpTPf/7zOemkk9ZpTL9+/XLYYYdl6dKl+f3vf9/IFQIAAAAAsC7KHjDXPuTv4IMPXq9xX/jCF5Ikf/vb3xqjLAAAAAAA1lPZA+YPPvggSdKmTZv1GrdkyZIkyaJFixq8JgAAAAAA1l/ZA+YePXokSZ566qn1Gvfwww8nSbbeeusGrwkAAAAAgPVX9oB57733TrFYzD333JNHH310ncbcfvvtGT9+fAqFQg488MBGrhAAAAAAgHVR9oD5xBNPTMuWLbN8+fKcfvrp+fnPf55p06attu+rr76aH//4x7nwwguTJFtssUVOPPHEMlYLAAAAAEB9WpZ7wq233jqXXHJJhgwZkmXLlmXYsGEZNmxYunbtmu7du6dt27b58MMPM3369Lz//vtJkmKxmI4dO+a6665Lhw4dyl0yAAAAAACrUfaAOUkOP/zwbLLJJvnZz36W6dOnJ0nee++9VFdX1/UpFot1x5/97Gdz+eWXp1evXmWvFQAAAACA1atIwJwkBxxwQPbZZ5+MHTs2jzzySCZNmpTZs2dnwYIF6dChQ7p3757PfvazOeigg7L33ntXqkwAAAAAAOpRsYA5SVq3bp2DDz44Bx98cCXLAAAAAACgBGV/yB8AAAAAAE1DRVcwJ8myZcsyceLETJw4MdXV1VmwYEGGDBmSJHnttdeybNmybL/99hWuEgAAAACAf1WxgHn58uUZNmxYbrzxxsyePXulttqA+b777stvf/vbfO5zn8tPfvITD/kDAAAAANiAVGSLjAULFmTgwIH55S9/mVmzZqVYLNb9fNRbb72VYrGYv/zlL/na176WF154oRLlAgAAAACwGhUJmM8+++w888wzKRaL6dWrV773ve/l5JNPXqXf/vvvn0984hMpFouZP39+vve972X+/PkVqBgAAAAAgH9V9oB57Nixeeyxx1IoFDJgwIDcd999Oe200/KZz3xmlb6HHHJI/vznP+fII49MksyYMSO33XZbuUsGAAAAAGA1yh4w33XXXUmS3r1754ILLkjLlmveBrply5b52c9+lk996lNJkjFjxjR2iQAAAAAArIOyB8zPP/98CoVCDj/88BQKhXUaUygUcsQRR6RYLGbKlCmNXCEAAAAAAOui7AHznDlzkiRbbbXVeo3r3r17khUPCAQAAAAAoPLKHjBvsskmSf4vaF5XM2fOTJJ06tSpwWsCAAAAAGD9lT1g/n//7/8lScaNG7de4+69994UCoVst912jVEWAAAAAADrqewBc//+/VMsFvPEE0/kT3/60zqN+fWvf51JkyYlSfbff//GLA8AAAAAgHVU9oD5m9/8Zt1+yj/60Y9yxRVXZMqUKVmyZMlK/WpqajJhwoSccsopue6661IoFLLZZpvlG9/4RrlLBgAAAABgNVqWe8I2bdrkmmuuyfHHH58FCxZk+PDhGT58+Ep9/v3f/z3vv/9+li5dmiQpFotp06ZNrrrqqrRr167cJQMAAAAAsBplX8GcJH369Mltt92WPn36pFgs1v0UCoUkyezZs1NTU1N3vlevXrnpppuy6667VqJcAAAAAABWo+wrmGttt912ufPOO/Pkk0/mgQceyPPPP58ZM2ZkwYIFadu2bTbbbLP07ds3BxxwQL785S+nRYsWlSoVAAAAAIDVKHvAPGPGjGyxxRZ1q5W/8IUv5Atf+EK5ywAAAAAA4GMq+xYZ5513Xvbdd99ce+215Z4aAAAAAIAGVPYVzC+99FKqq6szb968ck8NAAAAAEADKvsK5tpgeccddyz31AAAAAAANKCyB8yf/OQnkySvvvpquacGAAAAAKABlT1gPvvss1MoFDJy5Mg8+OCD5Z4eAAAAAIAGUvY9mLfffvv853/+Zy655JKcddZZ+cQnPpHddtstn/zkJ7PpppumdevWa73GEUcc0fiFAgAAAACwRmUPmL/4xS/WHReLxUydOjVTp05d5/GFQkHADAAAAACwASh7wFwsFtf4/wAAAAAAbBzKHjBfeuml5Z4SAAAAAIBGUPaA+cgjjyz3lAAAAAAANIJGC5j/9re/JUk+9alPpXPnzo01DQAAAAAAFVLVWBc+/vjjM3DgwPz9739vrCkAAAAAAKigsm+R8a+GDh2aJBk4cGB22GGHClcDAAAAAMC6arQVzOvqj3/8Y+666668/fbblS4FAAAAAID1UPGAGQAAAACAjZOAGQAAAACAkgiYAQAAAAAoiYAZAAAAAICSCJgBAAAAACiJgBkAAAAAgJIImAEAAAAAKImAGQAAAACAkrRs7AmefvrpzJs3r8H6JckRRxzxMasCAAAAAODjavSA+eabb15je6FQWKd+H+0vYAYAAAAAqLxGDZiLxWJjXh4AAAAAgApqtID5jDPOaKxLAwAAAACwARAwAwAAAABQkqpKFwAAAAAAwMZJwAwAAAAAQEkEzAAAAAAAlETADAAAAABASQTMAAAAAACURMAMAAAAAEBJBMwAAAAAAJREwAwAAAAAQElaVrqADcGjjz6aP/zhD3nuuedSXV2d1q1bp1evXunXr18GDhyYrl27rnZcTU1NRo8enXvuuSdTpkxJsVhMjx490r9//wwaNCidO3de47yvvPJKfv/73+evf/1rqqur07lz5+y0004ZMGBA9t133zWO3ZjnBgAAAACahkKxWCxWuohKWbp0aYYMGZJ777233j6bbbZZrrnmmuyyyy4rnV+8eHG+853vZMKECasdt8UWW+SGG25I7969V9s+duzYnHnmmampqVlt+/HHH5/zzz9/tW0b89wNYdaseY12bcqna9cOadGiKsuWLU919YJKlwNl5x6gOfP6p7lzD9DYvn3ZuEqXAM3asCH719vmPYANRbduHRvsWs16i4xf/vKXdeHyAQcckNGjR+fpp5/Ovffem3POOSft27fPe++9l1NOOSUzZsxYaezQoUMzYcKEtGrVKmeffXbGjh2bxx9/PBdffHE6deqUmTNn5pRTTsnChQtXmXfy5Mn5/ve/n5qamvTt2zc33XRTnn766dxxxx3p379/kuSmm27KqFGjVlv3xjo3AAAAANC0NNuAecaMGRk5cmSS5NBDD821116bXXfdNV26dEnv3r1z8sknZ+TIkWnZsmXmzp2b3/72t3VjX3zxxdx3331JkvPOOy+nnHJKevbsmS222CJHH310RowYkVatWmX69Ol1c3zUr3/96yxatCi9evXKjTfemD333DNdunRJ3759c/XVV+eggw5Kklx11VWZP3/+SmM35rkBAAAAgKal2QbMY8aMydKlS5MkZ5999mr79O3bt25V7/jx4+vODx8+PEnSs2fPHHPMMauM69OnT4444ogkye23375S25QpU+qu9d3vfjcdOnRYqb1QKGTIkCGpqqrK3Llz8/DDD6/UvrHODQAAAAA0Pc02YJ45c2batm2bzTffPD169Ki3X69ever6J0mxWMzjjz+eJNlvv/3SokWL1Y474IADkiRvvfVWXn755brztWMLhUL222+/1Y7t3r17dthhhyQrgvBaG/PcAAAAAEDT02wD5rPPPjvPP/98HnzwwTX2++c//5kk6dSpU5IVwekHH3yQJNlxxx3rHdenT5+644kTJ9Ydv/TSS0mSrbbaKl27dl3r+EmTJtWd25jnBgAAAACanmYbMNfaZJNN6m2bMWNGHnnkkSTJbrvtliSZPn16XXvPnj3rHdutW7e0atUqyYpwtlbt+DWNTVaEwEny7rvv1m3lsTHPDQAAAAA0Pc0+YK5PsVjMhRdemMWLFydJBgwYkCSZM2dOXZ9NN9203vFVVVV1exzXrvz96PjaFdH16dixY10dteM35rkBAAAAgKanZaUL2FBdeumldQ/E++pXv5q99947SeoC5yRp27btGq/Rpk2bVcbUHte21eej116yZMlGP3dD6tKlfQqFQqNcm/KpqirU/bdr1w5r6Q1Nj3uA5szrn+bOPQDQtK3pz3bvATRFAuZ/USwWc9lll+XGG29MkvTu3TsXXXRRXXt9D7dbVx9n/MY8d0Nq2XLDqYWPr1AopEULHxjQfLkHaM68/mnu3AMATVOLFmvfMMB7AE2JgPkjlixZkvPOOy/33HNPkmS77bbLsGHD6rZ8SJJ27drVHa9thW5t+0dX/NaOr10ZXJ9FixbVHdeuCN6Y525IS5cus4K5CaiqKqRQKKRYLGb58mKly4Gycw/QnHn909y5BwCatmXLltfb5j2ADcW6fBCyrgTM/39z587NGWeckb/97W9Jkh133DG///3v07Vr15X6fXT/4Xnz5tV7veXLl2fBggVJki5dutSdr93feE1jk//bv7hFixZ1eyZvzHM3pDlzFjbKdSmvrl07pEWLQpYvL6a6ekGly4Gycw/QnHn909y5BwCatjX92e49gA1Ft24dG+xaHvKXZOrUqfnGN75RFy7vs88+uemmm1YJl5Nkm222qTt+++23673mrFmzUlNTkyTp3r173fltt912rWOT5J133kmSbLnllqmqqtro5wYAAAAAmp5mHzC/9tpr+cY3vpE333wzSXLMMcfkuuuuW2lbjI/aYost0rlz5yTJ5MmT673upEmT6o779OlTd9y7d+8kybRp0zJ//vx6x9dee4cddmgScwMAAAAATU+zDpinTZuWQYMGpbq6Okly5pln5qc//WlatlzzziH9+vVLkowfPz7F4ur3yxk3blySpFu3btl+++1XGbts2bKMHz9+tWPfeeedvPTSS0lWrKZuCnMDAAAAAE1Psw2Ya2pqctZZZ2XWrFlJkqFDh+a0005bp7FHHnlkkuSNN97ILbfcskr75MmTc9dddyVJTjjhhJUeSLf11ltnt912S5L85je/WWU/42KxmMsuuyzLly9Ply5dcvjhhzeJuQEAAACApqfZBsy33nprJk6cmCQ5+OCDc/TRR2fBggVr/Kn1uc99Lvvvv3+S5JJLLsmVV16ZadOmZdasWbnjjjsyaNCg1NTUpGfPnjn22GNXmXvo0KGpqqrKm2++mQEDBuSJJ55IdXV1Jk2alMGDB+eBBx5IkgwePDjt27dfaezGPDcAAAAA0LQUivXtddDEfelLX8rUqVPXa8wrr7xSd/z+++/npJNOyosvvrjavptvvnluueWW9OrVa7Xtd955Zy644IIsXbp0te2DBg3KkCFDVtu2Mc/dEGbNmrf2TmzwVjw5tyrLli335FyaJfcAzZnXP82de4DG9u3LxlW6BGjWhg3Zv9427wFsKLp169hg11rzZsNNVHV19XqHy/+qU6dOGT16dEaPHp177703U6ZMyZIlS9KjR4/st99+Ofnkk7PZZpvVO/6oo47KjjvumBtuuCF//etf895776V9+/bZaaedMmDAgPTv379Jzg0AAAAANB3NdgUzGy8rmJsGn9rS3LkHaM68/mnu3AM0NiuYobKsYGZj0JArmJvtHswAAAAAAHw8AmYAAAAAAEoiYAYAAAAAoCQCZgAAAAAASiJgBgAAAACgJAJmAAAAAABKImAGAAAAAKAkAmYAAAAAAEoiYAYAAAAAoCQtK10AAAAAG45vXzau0iUAABsRK5gBAAAAACiJgBkAAAAAgJIImAEAAAAAKImAGQAAAACAkgiYAQAAAAAoiYAZAAAAAICSCJgBAAAAACiJgBkAAAAAgJIImAEAAAAAKImAGQAAAACAkgiYAQAAAAAoiYAZAAAAAICSCJgBAAAAACiJgBkAAAAAgJIImAEAAAAAKImAGQAAAACAkgiYAQAAAAAoiYAZAAAAAICSCJgBAAAAACiJgBkAAAAAgJIImAEAAAAAKImAGQAAAACAkgiYAQAAAAAoiYAZAAAAAICSCJgBAAAAACiJgBkAAAAAgJIImAEAAAAAKImAGQAAAACAkgiYAQAAAAAoiYAZAAAAAICSCJgBAAAAACiJgBkAAAAAgJIImAEAAAAAKImAGQAAAACAkgiYAQAAAAAoiYAZAAAAAICSCJgBAAAAACiJgBkAAAAAgJIImAEAAAAAKImAGQAAAACAkgiYAQAAAAAoiYAZAAAAAICSCJgBAAAAACiJgBkAAAAAgJIImAEAAAAAKImAGQAAAACAkgiYAQAAAAAoiYAZAAAAAICSCJgBAAAAACiJgBkAAAAAgJIImAEAAAAAKImAGQAAAACAkgiYAQAAAAAoiYAZAAAAAICSCJgBAAAAACiJgBkAAAAAgJIImAEAAAAAKEnLShcAAAA0Ld++bFylSwAAoEysYAYAAAAAoCQCZgAAAAAASiJgBgAAAACgJPZgBgAAAIAG4lkEG79hQ/avdAkbFSuYAQAAAAAoiYAZAAAAAICSCJgBAAAAACiJgBkAAAAAgJIImAEAAAAAKImAGQAAAACAkgiYAQAAAAAoiYAZAAAAAICStKx0AWycXnnllfz+97/PX//611RXV6dz587ZaaedMmDAgOy7776VLg8AAAAAKAMBM+tt7NixOfPMM1NTU1N3btasWXnkkUfyyCOP5Pjjj8/5559fwQoBAAAAgHKwRQbrZfLkyfn+97+fmpqa9O3bNzfddFOefvrp3HHHHenfv3+S5KabbsqoUaMqXCkAAAAA0NisYGa9/PrXv86iRYvSq1ev3HjjjenQoUOSpEuXLrn66qtz1lln5YEHHshVV12Vww8/PJtsskmFKwYANjbfvmxcpUsAAADWkRXMrLMpU6Zk/PjxSZLvfve7deFyrUKhkCFDhqSqqipz587Nww8/XIEqAQAAAIByETCzzh5//PEkK4Lk/fbbb7V9unfvnh122CFJMmbMmLLVBgAAAACUn4CZdfbSSy8lSbbaaqt07dq13n59+vRJkkyaNKksdQEAAAAAlSFgZp1Nnz49SdKzZ8819ttqq62SJO+++26WLl3a6HUBAAAAAJXhIX+sszlz5iRJOnXqtMZ+HTt2TJIUi8V88MEHa1ztDAANzQPiAAAAykfAzDpbvHhxkqRNmzZr7Ne2bdu64yVLljR4HV26tE+hUGjw61JeVVWFuv927dphLb2hfI744b2VLgEAAIAKklOsHwEz66xFixaVLiFJ0rLlhlEHDaNQKKRFCx8YsOG495eHV7oEAAAA2GjYg5l11q5duyRrX5W8aNGiuuO1rXYGAAAAADZeAmbWWe3eyvPmzVtjvw8++CDJihXPa9uvGQAAAADYeAmYWWfbbrttkuTtt99eY7933nknSbLlllumqspLDAAAAACaKukf66x3795JkmnTpmX+/Pn19ps8eXKSZIcddihLXQAAAABAZQiYWWf9+vVLkixbtizjx49fbZ933nknL730UpJkn332KVdpAAAAAEAFCJhZZ1tvvXV22223JMlvfvObVfZiLhaLueyyy7J8+fJ06dIlhx9+eCXKBAAAAADKRMDMehk6dGiqqqry5ptvZsCAAXniiSdSXV2dSZMmZfDgwXnggQeSJIMHD0779u0rXC0AAAAA0JgKxWKxWOki2LjceeedueCCC7J06dLVtg8aNChDhgwpc1UAAAAAQLkJmCnJK6+8khtuuCF//etf895776V9+/bZaaedMmDAgPTv37/S5QEAAAAAZSBgBgAAAACgJPZgBgAAAACgJAJmAAAAAABKImAGAAAAAKAkAmYAAAAAAEoiYAYAAAAAoCQCZgAAAAAASiJgBgAAAACgJAJmAAAAAABK0rLSBQDNy6OPPpo//OEPee6551JdXZ3WrVunV69e6devXwYOHJiuXbtWukQom4ULF+bII4/Mm2++mTPOOCODBw+udEnQqObPn5+RI0dmzJgxmTp1ahYvXpytttoq/fr1y0knnZQtt9yy0iVCo/nLX/6Sm2++Oc8//3zmzp2bDh06ZPvtt8+RRx6Zww47LFVV1v7QtFx88cW56aabcumll+aoo45aY9+ampqMHj0699xzT6ZMmZJisZgePXqkf//+GTRoUDp37lyeoqEBrc89UF1dnZtvvjnjx4/PP//5zyxevDhdunTJZz7zmRx99NHp169fmaqG0giYgbJYunRphgwZknvvvXel8zU1NZk8eXImT56c2267Lddcc0122WWXClUJ5XXZZZflzTffrHQZUBYvv/xyTj755MycOXOl82+++WbefPPN3H333fnd736XnXfeuUIVQuO5/PLLM2zYsJXOzZ07N08//XSefvrp3HvvvbnmmmvStm3bClUIDWvMmDEZNWrUOvVdvHhxvvOd72TChAkrnX/99dfz+uuv584778wNN9yQ3r17N0ap0CjW5x545plncsYZZ2TOnDkrnZ85c2YefvjhPPzwwznyyCNzySWXpEWLFo1RLnxsPiYHyuKXv/xlXbh8wAEHZPTo0XX/oDrnnHPSvn37vPfeeznllFMyY8aMClcLjW/8+PG59dZbK10GlMWsWbNywgknZObMmenYsWMuvPDCjBs3Lg899FCGDh2adu3aZe7cuTn99NMzf/78SpcLDer222+vC5c/+9nPZsSIEXnyySfzhz/8IV/96leTJE888UQuuuiiSpYJDWbcuHE566yzsnz58nXqP3To0EyYMCGtWrXK2WefnbFjx+bxxx/PxRdfnE6dOmXmzJk55ZRTsnDhwkauHBrG+twD7777bk455ZTMmTMnnTt3zk9+8pOMHTs2TzzxRIYPH57dd989SfLHP/4xV155ZWOXDiUTMAONbsaMGRk5cmSS5NBDD821116bXXfdNV26dEnv3r1z8sknZ+TIkWnZsmXmzp2b3/72txWuGBpXdXV1zjvvvEqXAWVz2WWXZe7cuWnfvn2GDx+e4447Lj169EivXr1y4okn5le/+lWSFSt17r777soWCw3s+uuvT5L07t07I0eOzOc+97lsvvnm2WmnnfLLX/6yLmS+8847fcjORm358uW56qqrcvrpp6empmadxrz44ou57777kiTnnXdeTjnllPTs2TNbbLFFjj766IwYMSKtWrXK9OnT6/49ARuqUu6B6667LvPmzUubNm0ycuTIDBgwID179ky3bt3y+c9/PjfffHO+9KUvJUlGjBixyjfBYEMhYAYa3ZgxY7J06dIkydlnn73aPn379k3//v2TrFjZCU3Z+eefn9mzZ691LzZoCmbPnp37778/SXLqqaemb9++q/T54he/mG222SatWrXKpEmTyl0iNJq5c+dm6tSpSZLDDjssbdq0WaXPsccemyQpFot54YUXylofNJTHH388hx9+eK655posX748O+644zqNGz58eJKkZ8+eOeaYY1Zp79OnT4444ogkK74NABuqUu+BBx98MEnyla98JZ/+9KdXaS8UCjnrrLOSrNhe8sknn2ywmqEhCZiBRjdz5sy0bds2m2++eXr06FFvv169etX1h6bq9ttvz9ixY9OjRw+rmGkWHnzwwSxbtizt2rXLt771rXr73XPPPZk4cWJ+9rOflbE6aFwffXBf7Yft/6pVq1ar7Q8bk+985zt59dVX06pVqwwePLjumylrUiwW8/jjjydJ9ttvv3r3lj3ggAOSJG+99VZefvnlBqsZGlIp90DtnsuFQmGNz6D4xCc+UXfs38psqPwNBmh0Z599dp5//vm6T2fr889//jNJ0qlTp3KUBWU3derU/OxnP0uhUMill16aTTbZpNIlQaOrXZHZt2/ftG/ffqW2j359dHUrO2Fjt+mmm2abbbZJktx3331ZsmTJKn3+8Ic/JFkRNK9uhT9sDAqFQr785S/n7rvvzhlnnLFOH5a89dZb+eCDD5Jkjas9+/TpU3c8ceLEj18sNIJS7oEuXbrkL3/5S1544YU1frOx9pswyYr3FdgQtax0AUDzsaYwbcaMGXnkkUeSJLvttlu5SoKyWbZsWX74wx9m4cKFOeGEE7LXXntVuiQoi9deey1J6kK2sWPH5uabb85zzz2XhQsXplu3bunfv39OPfXUbLnllhWsFBrHD37wg5x55pl57bXXMmjQoAwePDif+tSnMmvWrIw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330pji8VifvGLX2T58uXZddddc91112XnnXdOly5dsvvuu2f48OHZZZddUiwWc/nll68S1G6scwMAAAAATU+zDJhnz56d+++/P0ly6qmnpm/fvqv0+eIXv5htttkmrVq1yqRJk1ZqGz58eJJkjz32yL777rvasZ///OeTJLfddttKbU888UReffXVJMmZZ56Zli1X3ga7devWOffcc5Mk/9//9//lmWeeaRJzAwAAAABNT7MMmB988MEsW7Ys7dq1y7e+9a16+91zzz2ZOHFifvazn9Wdmzt3bp5//vkkyQEHHFDv2Nq2Z555Ju+//37d+UcffTRJsummm2aPPfZY7dhdd901Xbp0SZKMGTOmScwNAAAAADQ9zTJgfuGFF5Ikffv2Tfv27Vdqq6mpqTtu06bNKmNffvnluofj7bjjjvXOscMOOyRZsW/x5MmTVxqfJNtvv31atGix2rGFQqFu/EdXT2/McwMAAAAATU/LtXdpel577bUkyTbbbJMkGTt2bG6++eY899xzWbhwYbp165b+/fvn1FNPzZZbbrnS2OnTp9cd9+zZs945evToUXf81ltvrTJ+TWOTZKuttqp37MY4NwAAAADQ9DTLFcwzZ85MknTq1CkXXnhhTjvttDz11FNZuHBhkmTWrFkZPXp0DjvssLqH2tWaM2dO3fGmm25a7xybbLJJ3fEHH3ywyvhOnTqtscaOHTvWO3ZjnBsAAAAAaHqa5QrmBQsWJEnuuuuuzJo1K7vvvnvOPvvs9O3bNwsWLMj999+fX/ziF5k7d25OO+203H333dliiy2SJIsXL667Ttu2beud46NtHx1Te7y67Tc+qrZ9dWM3xrkbUpcu7VMoFBrl2pRPVVWh7r9du3aocDVQfu4BmjOvf5o79wCwNv5saLq8B9AUNcuAedGiRUlWrFTea6+9csMNN6RVq1ZJVoSrxx13XHr37p2BAwemuro6119/fc4///wkqXfv4nXVokWLLF++vOSxG+vcDallyw2nFj6+QqGQFi18YEDz5R6gOfP6p7lzDwD1adGiWX7hvFnxHkBT0iwD5rZt29ZthzFkyJC6cPmj9thjj/Tr1y+PPPJIHnroobqAuV27dnV9Fi9enJYtV/9bWBti185Xq127dqmpqcmSJUvWWOPqVhtvzHM3pKVLl1nB3ARUVRVSKBRSLBazfHmx0uVA2bkHaM68/mnu3APA2ixbVtriMDZ83gPYUDTkB1nNMmDu0KFDFi5cmI4dO6ZPnz719ttzzz3zyCOPZMaMGZk/f3422WSTlfYfnj9/fjp0WP3XGebNm1d33KVLl7rjjh075oMPPlipfXVq9y/+6NiNee6GNGfOwka5LuXVtWuHtGhRyPLlxVRXL6h0OVB27gGaM69/mjv3ALA2/mxourwHsKHo1q1jg12rWX7nomfPnknWvhfxRx9YV7syd5tttqk7N3369HrHvv3223XH3bt3rzvedtttV2lfnXfeeSdJstVWW9Wd25jnBgAAAACanmYZMO+www5Jkurq6syfP7/efrNnz06StGrVKl27dk2SfOpTn6rbnuGll16qd+zkyZOTrNhTZ/vtt68737t37yTJyy+/nGJx9V+FKBaLddeurXVjnxsAAAAAaHqaZcD8xS9+MUmyfPnyjBkzpt5+Tz75ZJJk5513TlXVit+qTTbZJLvttluSZNy4cfWOrW3beeed07lz57rz++67b5Lkvffey3PPPbfasc8++2zmzJmTJNlnn33qzm/McwMAAAAATU+zDJi/8IUvpEePHkmSK6+8sm6l8kc98MADeeaZZ5IkRx555EptRxxxRJLkiSeeyPjx41cZO378+Dz11FNJkhNPPHGltr322qtu7iuuuGKVB+4tWbIkv/jFL5KsWDX80ZB3Y54bAAAAAGh6mmXA3LJly1x00UWpqqrKu+++m2OOOSZ33313ZsyYkenTp+e6667LOeeckyT57Gc/m6OOOmql8UcddVTdwwHPPPPMDBs2LO+++27efffdDBs2LGeeeWaS5DOf+UwOOuiglcZWVVVl6NChSVasFv72t7+dv//975kzZ07+/ve/59vf/naeffbZFAqFfP/736/blmJjnxsAAAAAaHoKxfo2420G7rvvvvz4xz+ue4Dfv9pxxx1z7bXX5t/+7d9WaZs+fXpOOOGETJs2bbVjt91229xyyy11ezf/q2uuuSZXXXXVatsKhUJ+/OMfZ+DAgatt35jnbgizZs1rtGtTPiuenFuVZcuWe3IuzZJ7gObM65/mzj1AY/v2ZfVva8jGYdiQ/StdAo3EewAbim7dOjbYtZp1wJwkb7/9doYPH57HHnss7777btq0aZNtt902hx12WL72ta+lbdu29Y5dsGBBRowYkQcffDDTpk3LsmXL0qtXrxx44IEZNGhQOnTosMa5n3nmmYwcOTJ///vfM3fu3Gy66abZZZddcuKJJ2bPPfdc49iNee6PS8DcNHhTpblzD9Ccef3T3LkHaGwC5o2fgLnp8h7AhkLATLMmYG4avKnS3LkHaM68/mnu3AM0NgHzxk/A3HR5D2BD0ZABc7PcgxkAAAAAgI9PwAwAAAAAQEkEzAAAAAAAlETADAAAAABASQTMAAAAAACURMAMAAAAAEBJBMwAAAAAAJREwAwAAAAAQEkEzAAAAAAAlETADAAAAABASQTMAAAAAACURMAMAAAAAEBJBMwAAAAAAJREwAwAAAAAQEkEzAAAAAAAlETADAAAAABASQTMAAAAAACURMAMAAAAAEBJBMwAAAAAAJREwAwAAAAAQEkEzAAAAAAAlETADAAAAABASQTMAAAAAACURMAMAAAAAEBJBMwAAAAAAJREwAwAAAAAQEkEzAAAAAAAlETADAAAAABASQTMAAAAAACURMAMAAAAAEBJyh4wv/baa+WeEgAAAACARlD2gPmwww7LUUcdlREjRmTWrFnlnh4AAAAAgAZS9oC5WCzmpZdeyuWXX54vfvGLOemkk3LPPffkww8/LHcpAAAAAAB8DGUPmE877bR84hOfSLFYzLJly/LUU0/lRz/6UT7/+c/nhz/8YR5//PEUi8VylwUAAAAAwHoqe8D8ve99Lw8++GBuu+22fOtb30rXrl1TLBbz4Ycf5t57781//Md/ZJ999slll12WyZMnl7s8AAAAAADWUdkD5lo777xzzj///Dz++OP5/e9/n8MPPzzt2rVLsVjM7Nmzc+ONN+ZrX/tavvrVr+b666/PO++8U6lSAQAAAABYjYoFzHUFVFXl3//933P55Zfnqaeeyn//939n//33T5s2bVIsFvP666/nyiuvzAEHHJDjjz8+f/jDHzJ//vxKlw0AAAAA0Oy1rHQBH9W2bdsccsghOeSQQ7J48eL85S9/ycMPP5y77747S5cuzTPPPJNnnnkmP/3pT3PggQfmm9/8ZnbZZZdKlw0AAAAA0CxVfAXz6nzwwQd56KGHcs899+Shhx7KsmXLUigUUiwWUywWs2jRotxzzz0ZMGBATjrppLz11luVLhkAAAAAoNnZYFYwf/jhh3n44Ydz33335cknn8yyZcuSJMViMUnSp0+fHH744dl5553z0EMP5U9/+lNmzZqVJ598Mt/85jczatSo9OrVq5K/BAAAAACAZqWiAXNNTU0effTR3HfffRk/fnwWLVqU5P9C5S222CKHHnpojjjiiHzqU5+qG7fLLrvkBz/4Qa644oqMHDky7733Xn7xi1/kN7/5TUV+HQAAAAAAzVHZA+ZisZinn346f/rTn/Lwww9n3rx5deeTpF27dunfv3+OOOKIfP7zn0+hUFjtdVq2bJmhQ4fm/vvvz6xZszJhwoSy/RoAAAAAAKhAwLzPPvvkvffeS/J/oXKhUMhee+2VI444IgceeGDat2+/TtcqFArp2bNnZs2aVbelBgAAAAAA5VH2gHn27Nl1x5/85Cdz+OGH57DDDkv37t1Lul51dXW23nrr7LPPPg1VIgAAAAAA66DsAXPnzp3zla98pe6BfR/Xgw8+2ABVAQAAAACwvsoeMD/xxBNp2XLdpl24cOE6b5cBAAAAAEB5VZV7wtpwefny5bnrrrty0kkn5YUXXlil3/vvv5899tgjxx57bO69995ylwkAAAAAwFqUfQVzksycOTOnnnpqJk+enCQ5+OCDV9kuY+rUqVm2bFmee+65PPfcc7nrrrty1VVXpUOHDpUoGQAAAACAf1H2Fcw1NTX5zne+k8mTJ6dYLKZQKGTRokWr9Gvfvn0OOuigtG/fPsViMU899VS+973vlbtcAAAAAADqUfaA+fbbb8+rr76aJPn85z+fRx55JN/61rdW6bfddtvlV7/6VcaPH5+DDjqoLmT+85//XO6SAQAAAABYjbIHzPfff3+S5JOf/GR+97vfZcstt1xj/44dO+aKK67INttskyS58847G7tEAAAAAADWQdkD5ldffTWFQiFHH310WrRosU5jWrdunaOOOirFYjGTJk1q5AoBAAAAAFgXZX/I34cffpgk2WKLLdZrXM+ePZMk8+fPb/CaAAAAADYU375sXKVL4GMYNmT/SpcAZVX2Fcy1W2JMnTp1vca9++67SZJOnTo1eE0AAAAAAKy/sgfMn/70p1MsFnPnnXempqZmncYsW7Ysf/zjH1MoFNKnT59GrhAAAAAAgHVR9oD561//epJk2rRp+cEPfpCFCxeusf+SJUty/vnn57XXXkuSfPWrX230GgEAAAAAWLuy78Hcr1+/7LnnnpkwYUIefvjhPPfccznyyCOz6667pkePHmnTpk0WL16cd955J88991zuuuuuvP322ykUCunbt28OO+ywcpcMAAAAAMBqlD1gLhQKueqqq3LcccdlypQpmTVrVq6//vo1jikWi/nkJz+Z//mf/ylTlQAAAAAArE3Zt8hIks6dO+ePf/xjTj/99Gy66aYpFov1/nTo0CGDBg3KH/7wh2y22WaVKBcAAAAAgNUo+wrmWq1bt87gwYNz+umn59lnn83EiRMze/bsvP/++2nbtm0222yz7Ljjjtl9993Trl27SpUJAAAAAEA9KhYw16qqqsruu++e3XffvdKlAAAAAACwHiqyRQYAAAAAABs/ATMAAAAAACWp2BYZY8aMyR133JGJEyfmgw8+yNKlS1MsFtc6rlAoZPLkyWWoEAAAAACANalIwHzRRRdl9OjRSbJOoTIAAAAAABuesgfMjz32WG655ZYUCoUUi8W0bt06vXr1SseOHdOiRYtylwMAAAAAQInKHjD/7//+b93xGWeckf/4j/9I69aty10GAAAAAAAfU9kD5ueffz6FQiFf+tKXcsYZZ5R7egAAAAAAGkhVuSecN29ekmT//fcv99QAAAAAADSgsgfMm2+++YqJq8o+NQAAAAAADajsKe+ee+6ZJHnmmWfKPTUAAAAAAA2o7AHzwIED06JFi9x999155ZVXyj09AAAAAAANpOwBc58+fXLeeeelpqYmAwcOzM0335wZM2aUuwwAAAAAAD6mluWe8IILLkiSbL311vnnP/+ZSy65JJdccknat2+fjh07pkWLFmscXygUMmbMmHKUCgAAAADAGpQ9YL799ttTKBSSrAiLi8VikmTBggVZsGDBWsfXjgUAAAAAoLLKHjBvtdVW5Z4SAAAAAIBGUPaAedy4ceWeEgAAAACARlD2h/wBAAAAANA0CJgBAAAAAChJ2bfI+FevvfZaHnnkkUyaNCnV1dVZsGBB7rzzziTJo48+mn/+85856qijsskmm1S4UgAAAAAAPqpiAfOMGTNywQUX5PHHH687VywWUygU6v7/6aefzogRI3LNNdfkJz/5SQ455JBGr2vhwoU58sgj8+abb+aMM87I4MGDV9uvpqYmo0ePzj333JMpU6akWCymR48e6d+/fwYNGpTOnTuvcZ5XXnklv//97/PXv/411dXV6dy5c3baaacMGDAg++677xrHbsxzAwAAAABNR0UC5jfeeCPf+ta3MmfOnBSLxXr7TZ8+PcViMe+//35+8IMfZMGCBTn66KMbtbbLLrssb7755hr7LF68ON/5zncyYcKElc6//vrref3113PnnXfmhhtuSO/evVc7fuzYsTnzzDNTU1NTd27WrFl55JFH8sgjj+T444/P+eef3+TmBgAAAACalrLvwbxkyZKceuqpqa6uTqFQyJFHHpmbb745V1xxxSp9zzzzzBx22GFJVqxuvuSSSzJt2rRGq238+PG59dZb19pv6NChmTBhQlq1apWzzz47Y8eOzeOPP56LL744nTp1ysyZM3PKKadk4cKFq4ydPHlyvv/976empiZ9+/bNTTfdlKeffjp33HFH+vfvnyS56aabMmrUqCY1NwAAAADQ9JQ9YL799tvzz3/+M4VCIZdffnkuvfTS7L777mnfvv0qfbfbbrtcccUVufjii5OsWEF7yy23NEpd1dXVOe+889ba78UXX8x9992XJDnvvPNyyimnpGfPntliiy1y9NFHZ8SIEWnVqlWmT5+ekSNHrjL+17/+dRYtWpRevXrlxhtvzJ577pkuXbqkb9++ufrqq3PQQQclSa666qrMnz+/ycwNAAAAADQ9ZQ+YH3jggRQKhey777459NBD12nM17/+9fTr1y/FYjFPPvlko9R1/vnnZ/bs2TnqqKPW2G/48OFJkp49e+aYY45Zpb1Pnz454ogjkqwI0z9qypQpGT9+fJLku9/9bjp06LBSe6FQyJAhQ1JVVZW5c+fm4YcfbhJzAwAAAABNU9kD5tdffz1Jsv/++6/XuNqHz7311lsNXtPtt9+esWPHpkePHmtcxVwsFuseSrjffvulRYsWq+13wAEH1NX68ssv152vHVsoFLLffvutdmz37t2zww47JEnGjBnTJOYGAAAAAJqmsgfMtVsvdOrUab3GdenSJUmydOnSBq1n6tSp+dnPfpZCoZBLL700m2yySb1933rrrXzwwQdJkh133LHefn369Kk7njhxYt3xSy+9lCTZaqut0rVr17WOnzRpUpOYGwAAAABomsoeMNcGxeu7EnnKlClJssZwdH0tW7YsP/zhD7Nw4cIMHDgwe+211xr7T58+ve64Z8+e9fbr1q1bWrVqlWTlX2ft+DWNTVaEwEny7rvv1gXqG/PcAAAAAEDTVPaAeeedd06xWMzdd9+dYrG4TmMWLlyYO+64I4VCIX379m2wWn7729/mH//4R7bbbrv84Ac/WGv/OXPm1B1vuumm9farqqqq2+O4duXvR8evbfV2x44dk6zYmqJ2/MY8NwAAAADQNLUs94SHHnpoxowZk9dffz2XXHJJzj///DX2nz9/fs4666zMmDEjhUIhBx98cIPUMXHixFx77bVp2bJlrrjiirRp02atYxYvXlx33LZt2zX2rb3eR8fUHq9tro9ee8mSJRv93A2tS5f2KRQKjXZ9yqOqqlD3365dO6ylNzQ97gGaM69/mjv3AEDTtqY/270H0BSVPWA+8MADs8cee+Rvf/tbRo0aleeffz5HHnlkZs+eXdfnzTffzMyZMzNhwoTcdtttmTVrVt3q5UMOOeRj17Bo0aKce+65qampyeDBg7PTTjut07j6Hm63rj7O+I157obWsuWGVQ8fT6FQSIsWPjCg+XIP0Jx5/dPcuQcAmqYWLda+YYD3AJqSsgfMSXL11Vfn+OOPz6uvvpqJEyfWPRCudlXqv65SLhaL2XrrrXPNNdc0yPxXXHFF3njjjfTt2zennHLKOo9r165d3fHaVujWtn90xW/t+NqVwfVZtGhR3XHtiuCNee6GtnTpMiuYm4CqqkIKhUKKxWKWL1+37XKgKXEP0Jx5/dPcuQcAmrZly5bX2+Y9gA3FunwQsq4qEjB36tQpt99+e/77v/87t912Wz788MN6+7Zs2TKHHnpofvzjH9ftD/xxPP744xk1alTatGmTyy+/PC1brvtvwUf3H543b169/ZYvX54FCxYk+b+HGib/t7/xmsYm/7d/cYsWLer2TN6Y525oc+YsbLRrUz5du3ZIixaFLF9eTHX1gkqXA2XnHqA58/qnuXMPADRta/qz3XsAG4pu3T5+zlqrIgFzsmJ17NChQ3P66afn0UcfzfPPP5+ZM2dm/vz5adu2bTbbbLPsvPPO2XfffbPllls22Lz33XdfkhUrbde23cbVV1+dq6++OkkyduzYbLPNNnVtb7/9dnbbbbfVjps1a1ZqamqSJN27d687v+2222bChAl5++231zjvO++8kyTZcsstU1W14tOEjXluAAAAAKBpqljAXGvTTTfNoYcemkMPPbTSpazVFltskc6dO2fu3LmZPHlyvTVPmjSp7rhPnz51x717906STJs2LfPnz88mm2yy2vGTJ09Okuywww5NYm4AAAAAoGlquM02NhIXXXRRnn322TX+1Prud79bd65Hjx5Jkn79+iVJxo8fn2Jx9XvljBs3LknSrVu3bL/99nXna8cuW7Ys48ePX+3Yd955Jy+99FKSZJ999lmpbWOdGwAAAABomppdwNy6det06NBhjT+1WrVqVXeu9qFyRx55ZJLkjTfeyC233LLK9SdPnpy77rorSXLCCSes9DC6rbfeum57id/85jer7GdcLBZz2WWXZfny5enSpUsOP/zwldo31rkBAAAAgKap7FtkHHDAAR9rfKFQyJgxYxqomvX3uc99Lvvvv3/GjRuXSy65JDNnzszXv/71tG3bNo8++mh+/vOfp6amJj179syxxx67yvihQ4fmmGOOyZtvvpkBAwbkRz/6Ufr06ZN33nkn//M//5OHH344STJ48OC0b9++ycwNAAAAADQ9hWJ9+x00ku233z6FQqHebRZq1a6A/dd+hUKhbhuHxvLpT386SXLGGWdk8ODBq7S///77Oemkk/Liiy+udvzmm2+eW265Jb169Vpt+5133pkLLrggS5cuXW37oEGDMmTIkNW2bcxzN5RZs+atvRMbvBVPzq3KsmXLPTmXZsk9QHPm9U9z5x6gsX37snGVLgGatWFD9q+3zXsAG4pu3To22LXKvoJ5q622WmufJUuWZN68eVm8eHGSFaHyDjvskL59+zZ2eeukU6dOGT16dEaPHp177703U6ZMyZIlS9KjR4/st99+Ofnkk7PZZpvVO/6oo47KjjvumBtuuCF//etf895776V9+/bZaaedMmDAgPTv379Jzg0AAAAANC1lX8G8PqZNm5b7778/1113XWpqanL55ZfnkEMOqXRZVJgVzE2DT21p7twDNGde/zR37gEamxXMUFlWMLMxaMgVzBv0Q/623nrr/Md//Eeuu+66FIvFnHfeeXnzzTcrXRYAAAAAANnAA+Zae+65Zw488MB8+OGHGTZsWKXLAQAAAAAgG0nAnCSf+9znkiRPPvlkhSsBAAAAACDZiALmqqoVpc6ePbvClQAAAAAAkGxEAfP48eOTJB07NtwG1AAAAAAAlK5lpQtYmw8//DC/+93v8tBDD6VQKGTXXXetdEkAAAAAAKQCAfPAgQPXqd+yZcsyf/78TJ06NYsWLao7P2DAgMYqDQAAAACA9VD2gHnChAkpFArr3L9YLNYdn3jiidl7770boywAAAAAANZTRbbI+GhoXJ9CoZDWrVtns802y0477ZSvfe1r6devXxmqAwAAAABgXZQ9YH755ZfLPSUAAAAAAI2gqtIFAAAAAACwcRIwAwAAAABQEgEzAAAAAAAlKfsezAMHDmyU6xYKhdx4442Ncm0AAAAAAFZV9oB5woQJKRQKKRaLKRQKq7QXi8UkWW1bfeq7FgAAAAAAjafsAfPee++dQqGQt956K9OmTas73759+2yzzTbp0KFDampqMnPmzLz99ttJUhdIAwAAAACw4Sh7wDxixIiMGTMm5557bpLkS1/6Uk466aR89rOfXaXvjBkzMmrUqAwbNizFYjHnn39+jj322DJXDAAAAADA6pT9IX+vvvpqzjnnnCxatCjnnntufvOb36w2XE6SLbfcMt///vdzzTXXpFgs5pJLLskLL7xQ3oIBAAAAAFitsgfM119/fRYtWpTPf/7zOemkk9ZpTL9+/XLYYYdl6dKl+f3vf9/IFQIAAAAAsC7KHjDXPuTv4IMPXq9xX/jCF5Ikf/vb3xqjLAAAAAAA1lPZA+YPPvggSdKmTZv1GrdkyZIkyaJFixq8JgAAAAAA1l/ZA+YePXokSZ566qn1Gvfwww8nSbbeeusGrwkAAAAAgPVX9oB57733TrFYzD333JNHH310ncbcfvvtGT9+fAqFQg488MBGrhAAAAAAgHVR9oD5xBNPTMuWLbN8+fKcfvrp+fnPf55p06attu+rr76aH//4x7nwwguTJFtssUVOPPHEMlYLAAAAAEB9WpZ7wq233jqXXHJJhgwZkmXLlmXYsGEZNmxYunbtmu7du6dt27b58MMPM3369Lz//vtJkmKxmI4dO+a6665Lhw4dyl0yAAAAAACrUfaAOUkOP/zwbLLJJvnZz36W6dOnJ0nee++9VFdX1/UpFot1x5/97Gdz+eWXp1evXmWvFQAAAACA1atIwJwkBxxwQPbZZ5+MHTs2jzzySCZNmpTZs2dnwYIF6dChQ7p3757PfvazOeigg7L33ntXqkwAAAAAAOpRsYA5SVq3bp2DDz44Bx98cCXLAAAAAACgBGV/yB8AAAAAAE1DRVcwJ8myZcsyceLETJw4MdXV1VmwYEGGDBmSJHnttdeybNmybL/99hWuEgAAAACAf1WxgHn58uUZNmxYbrzxxsyePXulttqA+b777stvf/vbfO5zn8tPfvITD/kDAAAAANiAVGSLjAULFmTgwIH55S9/mVmzZqVYLNb9fNRbb72VYrGYv/zlL/na176WF154oRLlAgAAAACwGhUJmM8+++w888wzKRaL6dWrV773ve/l5JNPXqXf/vvvn0984hMpFouZP39+vve972X+/PkVqBgAAAAAgH9V9oB57Nixeeyxx1IoFDJgwIDcd999Oe200/KZz3xmlb6HHHJI/vznP+fII49MksyYMSO33XZbuUsGAAAAAGA1yh4w33XXXUmS3r1754ILLkjLlmveBrply5b52c9+lk996lNJkjFjxjR2iQAAAAAArIOyB8zPP/98CoVCDj/88BQKhXUaUygUcsQRR6RYLGbKlCmNXCEAAAAAAOui7AHznDlzkiRbbbXVeo3r3r17khUPCAQAAAAAoPLKHjBvsskmSf4vaF5XM2fOTJJ06tSpwWsCAAAAAGD9lT1g/n//7/8lScaNG7de4+69994UCoVst912jVEWAAAAAADrqewBc//+/VMsFvPEE0/kT3/60zqN+fWvf51JkyYlSfbff//GLA8AAAAAgHVU9oD5m9/8Zt1+yj/60Y9yxRVXZMqUKVmyZMlK/WpqajJhwoSccsopue6661IoFLLZZpvlG9/4RrlLBgAAAABgNVqWe8I2bdrkmmuuyfHHH58FCxZk+PDhGT58+Ep9/v3f/z3vv/9+li5dmiQpFotp06ZNrrrqqrRr167cJQMAAAAAsBplX8GcJH369Mltt92WPn36pFgs1v0UCoUkyezZs1NTU1N3vlevXrnpppuy6667VqJcAAAAAABWo+wrmGttt912ufPOO/Pkk0/mgQceyPPPP58ZM2ZkwYIFadu2bTbbbLP07ds3BxxwQL785S+nRYsWlSoVAAAAAIDVKHvAPGPGjGyxxRZ1q5W/8IUv5Atf+EK5ywAAAAAA4GMq+xYZ5513Xvbdd99ce+215Z4aAAAAAIAGVPYVzC+99FKqq6szb968ck8NAAAAAEADKvsK5tpgeccddyz31AAAAAAANKCyB8yf/OQnkySvvvpquacGAAAAAKABlT1gPvvss1MoFDJy5Mg8+OCD5Z4eAAAAAIAGUvY9mLfffvv853/+Zy655JKcddZZ+cQnPpHddtstn/zkJ7PpppumdevWa73GEUcc0fiFAgAAAACwRmUPmL/4xS/WHReLxUydOjVTp05d5/GFQkHADAAAAACwASh7wFwsFtf4/wAAAAAAbBzKHjBfeuml5Z4SAAAAAIBGUPaA+cgjjyz3lAAAAAAANIJGC5j/9re/JUk+9alPpXPnzo01DQAAAAAAFVLVWBc+/vjjM3DgwPz9739vrCkAAAAAAKigsm+R8a+GDh2aJBk4cGB22GGHClcDAAAAAMC6arQVzOvqj3/8Y+666668/fbblS4FAAAAAID1UPGAGQAAAACAjZOAGQAAAACAkgiYAQAAAAAoiYAZAAAAAICSCJgBAAAAACiJgBkAAAAAgJIImAEAAAAAKImAGQAAAACAkrRs7AmefvrpzJs3r8H6JckRRxzxMasCAAAAAODjavSA+eabb15je6FQWKd+H+0vYAYAAAAAqLxGDZiLxWJjXh4AAAAAgApqtID5jDPOaKxLAwAAAACwARAwAwAAAABQkqpKFwAAAAAAwMZJwAwAAAAAQEkEzAAAAAAAlETADAAAAABASQTMAAAAAACURMAMAAAAAEBJBMwAAAAAAJREwAwAAAAAQElaVrqADcGjjz6aP/zhD3nuuedSXV2d1q1bp1evXunXr18GDhyYrl27rnZcTU1NRo8enXvuuSdTpkxJsVhMjx490r9//wwaNCidO3de47yvvPJKfv/73+evf/1rqqur07lz5+y0004ZMGBA9t133zWO3ZjnBgAAAACahkKxWCxWuohKWbp0aYYMGZJ777233j6bbbZZrrnmmuyyyy4rnV+8eHG+853vZMKECasdt8UWW+SGG25I7969V9s+duzYnHnmmampqVlt+/HHH5/zzz9/tW0b89wNYdaseY12bcqna9cOadGiKsuWLU919YJKlwNl5x6gOfP6p7lzD9DYvn3ZuEqXAM3asCH719vmPYANRbduHRvsWs16i4xf/vKXdeHyAQcckNGjR+fpp5/Ovffem3POOSft27fPe++9l1NOOSUzZsxYaezQoUMzYcKEtGrVKmeffXbGjh2bxx9/PBdffHE6deqUmTNn5pRTTsnChQtXmXfy5Mn5/ve/n5qamvTt2zc33XRTnn766dxxxx3p379/kuSmm27KqFGjVlv3xjo3AAAAANC0NNuAecaMGRk5cmSS5NBDD821116bXXfdNV26dEnv3r1z8sknZ+TIkWnZsmXmzp2b3/72t3VjX3zxxdx3331JkvPOOy+nnHJKevbsmS222CJHH310RowYkVatWmX69Ol1c3zUr3/96yxatCi9evXKjTfemD333DNdunRJ3759c/XVV+eggw5Kklx11VWZP3/+SmM35rkBAAAAgKal2QbMY8aMydKlS5MkZ5999mr79O3bt25V7/jx4+vODx8+PEnSs2fPHHPMMauM69OnT4444ogkye23375S25QpU+qu9d3vfjcdOnRYqb1QKGTIkCGpqqrK3Llz8/DDD6/UvrHODQAAAAA0Pc02YJ45c2batm2bzTffPD169Ki3X69ever6J0mxWMzjjz+eJNlvv/3SokWL1Y474IADkiRvvfVWXn755brztWMLhUL222+/1Y7t3r17dthhhyQrgvBaG/PcAAAAAEDT02wD5rPPPjvPP/98HnzwwTX2++c//5kk6dSpU5IVwekHH3yQJNlxxx3rHdenT5+644kTJ9Ydv/TSS0mSrbbaKl27dl3r+EmTJtWd25jnBgAAAACanmYbMNfaZJNN6m2bMWNGHnnkkSTJbrvtliSZPn16XXvPnj3rHdutW7e0atUqyYpwtlbt+DWNTVaEwEny7rvv1m3lsTHPDQAAAAA0Pc0+YK5PsVjMhRdemMWLFydJBgwYkCSZM2dOXZ9NN9203vFVVVV1exzXrvz96PjaFdH16dixY10dteM35rkBAAAAgKanZaUL2FBdeumldQ/E++pXv5q99947SeoC5yRp27btGq/Rpk2bVcbUHte21eej116yZMlGP3dD6tKlfQqFQqNcm/KpqirU/bdr1w5r6Q1Nj3uA5szrn+bOPQDQtK3pz3bvATRFAuZ/USwWc9lll+XGG29MkvTu3TsXXXRRXXt9D7dbVx9n/MY8d0Nq2XLDqYWPr1AopEULHxjQfLkHaM68/mnu3AMATVOLFmvfMMB7AE2JgPkjlixZkvPOOy/33HNPkmS77bbLsGHD6rZ8SJJ27drVHa9thW5t+0dX/NaOr10ZXJ9FixbVHdeuCN6Y525IS5cus4K5CaiqKqRQKKRYLGb58mKly4Gycw/QnHn909y5BwCatmXLltfb5j2ADcW6fBCyrgTM/39z587NGWeckb/97W9Jkh133DG///3v07Vr15X6fXT/4Xnz5tV7veXLl2fBggVJki5dutSdr93feE1jk//bv7hFixZ1eyZvzHM3pDlzFjbKdSmvrl07pEWLQpYvL6a6ekGly4Gycw/QnHn909y5BwCatjX92e49gA1Ft24dG+xaHvKXZOrUqfnGN75RFy7vs88+uemmm1YJl5Nkm222qTt+++23673mrFmzUlNTkyTp3r173fltt912rWOT5J133kmSbLnllqmqqtro5wYAAAAAmp5mHzC/9tpr+cY3vpE333wzSXLMMcfkuuuuW2lbjI/aYost0rlz5yTJ5MmT673upEmT6o779OlTd9y7d+8kybRp0zJ//vx6x9dee4cddmgScwMAAAAATU+zDpinTZuWQYMGpbq6Okly5pln5qc//WlatlzzziH9+vVLkowfPz7F4ur3yxk3blySpFu3btl+++1XGbts2bKMHz9+tWPfeeedvPTSS0lWrKZuCnMDAAAAAE1Psw2Ya2pqctZZZ2XWrFlJkqFDh+a0005bp7FHHnlkkuSNN97ILbfcskr75MmTc9dddyVJTjjhhJUeSLf11ltnt912S5L85je/WWU/42KxmMsuuyzLly9Ply5dcvjhhzeJuQEAAACApqfZBsy33nprJk6cmCQ5+OCDc/TRR2fBggVr/Kn1uc99Lvvvv3+S5JJLLsmVV16ZadOmZdasWbnjjjsyaNCg1NTUpGfPnjn22GNXmXvo0KGpqqrKm2++mQEDBuSJJ55IdXV1Jk2alMGDB+eBBx5IkgwePDjt27dfaezGPDcAAAAA0LQUivXtddDEfelLX8rUqVPXa8wrr7xSd/z+++/npJNOyosvvrjavptvvnluueWW9OrVa7Xtd955Zy644IIsXbp0te2DBg3KkCFDVtu2Mc/dEGbNmrf2TmzwVjw5tyrLli335FyaJfcAzZnXP82de4DG9u3LxlW6BGjWhg3Zv9427wFsKLp169hg11rzZsNNVHV19XqHy/+qU6dOGT16dEaPHp177703U6ZMyZIlS9KjR4/st99+Ofnkk7PZZpvVO/6oo47KjjvumBtuuCF//etf895776V9+/bZaaedMmDAgPTv379Jzg0AAAAANB3NdgUzGy8rmJsGn9rS3LkHaM68/mnu3AM0NiuYobKsYGZj0JArmJvtHswAAAAAAHw8AmYAAAAAAEoiYAYAAAAAoCQCZgAAAAAASiJgBgAAAACgJAJmAAAAAABKImAGAAAAAKAkAmYAAAAAAEoiYAYAAAAAoCQtK10AAAAAG45vXzau0iUAABsRK5gBAAAAACiJgBkAAAAAgJIImAEAAAAAKImAGQAAAACAkgiYAQAAAAAoiYAZAAAAAICSCJgBAAAAACiJgBkAAAAAgJIImAEAAAAAKImAGQAAAACAkgiYAQAAAAAoiYAZAAAAAICSCJgBAAAAACiJgBkAAAAAgJIImAEAAAAAKImAGQAAAACAkgiYAQAAAAAoiYAZAAAAAICSCJgBAAAAACiJgBkAAAAAgJIImAEAAAAAKImAGQAAAACAkgiYAQAAAAAoiYAZAAAAAICSCJgBAAAAACiJgBkAAAAAgJIImAEAAAAAKImAGQAAAACAkgiYAQAAAAAoiYAZAAAAAICSCJgBAAAAACiJgBkAAAAAgJIImAEAAAAAKImAGQAAAACAkgiYAQAAAAAoiYAZAAAAAICSCJgBAAAAACiJgBkAAAAAgJIImAEAAAAAKImAGQAAAACAkgiYAQAAAAAoiYAZAAAAAICSCJgBAAAAACiJgBkAAAAAgJIImAEAAAAAKImAGQAAAACAkgiYAQAAAAAoiYAZAAAAAICSCJgBAAAAACiJgBkAAAAAgJIImAEAAAAAKImAGQAAAACAkgiYAQAAAAAoiYAZAAAAAICSCJgBAAAAACiJgBkAAAAAgJIImAEAAAAAKEnLShcAAAA0Ld++bFylSwAAoEysYAYAAAAAoCQCZgAAAAAASiJgBgAAAACgJPZgBgAAAIAG4lkEG79hQ/avdAkbFSuYAQAAAAAoiYAZAAAAAICSCJgBAAAAACiJgBkAAAAAgJIImAEAAAAAKImAGQAAAACAkgiYAQAAAAAoiYAZAAAAAICStKx0AWycXnnllfz+97/PX//611RXV6dz587ZaaedMmDAgOy7776VLg8AAAAAKAMBM+tt7NixOfPMM1NTU1N3btasWXnkkUfyyCOP5Pjjj8/5559fwQoBAAAAgHKwRQbrZfLkyfn+97+fmpqa9O3bNzfddFOefvrp3HHHHenfv3+S5KabbsqoUaMqXCkAAAAA0NisYGa9/PrXv86iRYvSq1ev3HjjjenQoUOSpEuXLrn66qtz1lln5YEHHshVV12Vww8/PJtsskmFKwYANjbfvmxcpUsAAADWkRXMrLMpU6Zk/PjxSZLvfve7deFyrUKhkCFDhqSqqipz587Nww8/XIEqAQAAAIByETCzzh5//PEkK4Lk/fbbb7V9unfvnh122CFJMmbMmLLVBgAAAACUn4CZdfbSSy8lSbbaaqt07dq13n59+vRJkkyaNKksdQEAAAAAlSFgZp1Nnz49SdKzZ8819ttqq62SJO+++26WLl3a6HUBAAAAAJXhIX+sszlz5iRJOnXqtMZ+HTt2TJIUi8V88MEHa1ztDAANzQPiAAAAykfAzDpbvHhxkqRNmzZr7Ne2bdu64yVLljR4HV26tE+hUGjw61JeVVWFuv927dphLb2hfI744b2VLgEAAIAKklOsHwEz66xFixaVLiFJ0rLlhlEHDaNQKKRFCx8YsOG495eHV7oEAAAA2GjYg5l11q5duyRrX5W8aNGiuuO1rXYGAAAAADZeAmbWWe3eyvPmzVtjvw8++CDJihXPa9uvGQAAAADYeAmYWWfbbrttkuTtt99eY7933nknSbLlllumqspLDAAAAACaKukf66x3795JkmnTpmX+/Pn19ps8eXKSZIcddihLXQAAAABAZQiYWWf9+vVLkixbtizjx49fbZ933nknL730UpJkn332KVdpAAAAAEAFCJhZZ1tvvXV22223JMlvfvObVfZiLhaLueyyy7J8+fJ06dIlhx9+eCXKBAAAAADKRMDMehk6dGiqqqry5ptvZsCAAXniiSdSXV2dSZMmZfDgwXnggQeSJIMHD0779u0rXC0AAAAA0JgKxWKxWOki2LjceeedueCCC7J06dLVtg8aNChDhgwpc1UAAAAAQLkJmCnJK6+8khtuuCF//etf895776V9+/bZaaedMmDAgPTv37/S5QEAAAAAZSBgBgAAAACgJPZgBgAAAACgJAJmAAAAAABKImAGAAAAAKAkAmYAAAAAAEoiYAYAAAAAoCQCZgAAAAAASiJgBgAAAACgJAJmAAAAAABK0rLSBQDNy6OPPpo//OEPee6551JdXZ3WrVunV69e6devXwYOHJiuXbtWukQom4ULF+bII4/Mm2++mTPOOCODBw+udEnQqObPn5+RI0dmzJgxmTp1ahYvXpytttoq/fr1y0knnZQtt9yy0iVCo/nLX/6Sm2++Oc8//3zmzp2bDh06ZPvtt8+RRx6Zww47LFVV1v7QtFx88cW56aabcumll+aoo45aY9+ampqMHj0699xzT6ZMmZJisZgePXqkf//+GTRoUDp37lyeoqEBrc89UF1dnZtvvjnjx4/PP//5zyxevDhdunTJZz7zmRx99NHp169fmaqG0giYgbJYunRphgwZknvvvXel8zU1NZk8eXImT56c2267Lddcc0122WWXClUJ5XXZZZflzTffrHQZUBYvv/xyTj755MycOXOl82+++WbefPPN3H333fnd736XnXfeuUIVQuO5/PLLM2zYsJXOzZ07N08//XSefvrp3HvvvbnmmmvStm3bClUIDWvMmDEZNWrUOvVdvHhxvvOd72TChAkrnX/99dfz+uuv584778wNN9yQ3r17N0ap0CjW5x545plncsYZZ2TOnDkrnZ85c2YefvjhPPzwwznyyCNzySWXpEWLFo1RLnxsPiYHyuKXv/xlXbh8wAEHZPTo0XX/oDrnnHPSvn37vPfeeznllFMyY8aMClcLjW/8+PG59dZbK10GlMWsWbNywgknZObMmenYsWMuvPDCjBs3Lg899FCGDh2adu3aZe7cuTn99NMzf/78SpcLDer222+vC5c/+9nPZsSIEXnyySfzhz/8IV/96leTJE888UQuuuiiSpYJDWbcuHE566yzsnz58nXqP3To0EyYMCGtWrXK2WefnbFjx+bxxx/PxRdfnE6dOmXmzJk55ZRTsnDhwkauHBrG+twD7777bk455ZTMmTMnnTt3zk9+8pOMHTs2TzzxRIYPH57dd989SfLHP/4xV155ZWOXDiUTMAONbsaMGRk5cmSS5NBDD821116bXXfdNV26dEnv3r1z8sknZ+TIkWnZsmXmzp2b3/72txWuGBpXdXV1zjvvvEqXAWVz2WWXZe7cuWnfvn2GDx+e4447Lj169EivXr1y4okn5le/+lWSFSt17r777soWCw3s+uuvT5L07t07I0eOzOc+97lsvvnm2WmnnfLLX/6yLmS+8847fcjORm358uW56qqrcvrpp6empmadxrz44ou57777kiTnnXdeTjnllPTs2TNbbLFFjj766IwYMSKtWrXK9OnT6/49ARuqUu6B6667LvPmzUubNm0ycuTIDBgwID179ky3bt3y+c9/PjfffHO+9KUvJUlGjBixyjfBYEMhYAYa3ZgxY7J06dIkydlnn73aPn379k3//v2TrFjZCU3Z+eefn9mzZ691LzZoCmbPnp37778/SXLqqaemb9++q/T54he/mG222SatWrXKpEmTyl0iNJq5c+dm6tSpSZLDDjssbdq0WaXPsccemyQpFot54YUXylofNJTHH388hx9+eK655posX748O+644zqNGz58eJKkZ8+eOeaYY1Zp79OnT4444ogkK74NABuqUu+BBx98MEnyla98JZ/+9KdXaS8UCjnrrLOSrNhe8sknn2ywmqEhCZiBRjdz5sy0bds2m2++eXr06FFvv169etX1h6bq9ttvz9ixY9OjRw+rmGkWHnzwwSxbtizt2rXLt771rXr73XPPPZk4cWJ+9rOflbE6aFwffXBf7Yft/6pVq1ar7Q8bk+985zt59dVX06pVqwwePLjumylrUiwW8/jjjydJ9ttvv3r3lj3ggAOSJG+99VZefvnlBqsZGlIp90DtnsuFQmGNz6D4xCc+UXfs38psqPwNBmh0Z599dp5//vm6T2fr889//jNJ0qlTp3KUBWU3derU/OxnP0uhUMill16aTTbZpNIlQaOrXZHZt2/ftG/ffqW2j359dHUrO2Fjt+mmm2abbbZJktx3331ZsmTJKn3+8Ic/JFkRNK9uhT9sDAqFQr785S/n7rvvzhlnnLFOH5a89dZb+eCDD5Jkjas9+/TpU3c8ceLEj18sNIJS7oEuXbrkL3/5S1544YU1frOx9pswyYr3FdgQtax0AUDzsaYwbcaMGXnkkUeSJLvttlu5SoKyWbZsWX74wx9m4cKFOeGEE7LXXntVuiQoi9deey1J6kK2sWPH5uabb85zzz2XhQsXplu3bunfv39OPfXUbLnllhWsFBrHD37wg5x55pl57bXXMmjQoAwePDif+tSnMmvWrIwaNSq33XZbkhVbyGyxxRYVrhZKc//992fbbbddrzHTp0+vO+7Zs2e9/bp165ZWrVqlpqYmb731Vsk1QmMq5R6o1bp16zW2/+///m/dsX8rs6ESMAMVVywWc+GFF2bx4sVJkgEDBlS4Imh4v/3tb/OPf/wj2223XX7wgx9Uuhwom9qvcnbq1CkXXnhhbr311pXaZ82aldGjR+f+++/Pddddl1122aUSZUKj+fKXv5yrr746V1xxRZ555pmccMIJK7V37949Z511Vt0+s7AxKiVYq90eIFnzqsyqqqp06NAhc+fOrVvxDBuaUsPltfnHP/6RW265JUmyxx57pHfv3o0yD3xctsgAKu7SSy+te7DfV7/61ey9996VLQga2MSJE3PttdemZcuWueKKK2wFQLOyYMGCJMldd92VW2+9NbvvvntGjRqVF154IX/5y19y4YUXpn379pk7d25OO+00ewvSJM2fP3+VLWJqvffee3n22WdTXV1d5qqgsmoXlyRJ27Zt19i39u9OHx0DTd0bb7yR008/PcuWLUubNm1y/vnnV7okqJeAGaiYYrGYSy+9NDfeeGOSpHfv3rnooosqXBU0rEWLFuXcc89NTU1NTj311Oy0006VLgnKatGiRUlWrFTea6+9MmLEiOy+++5p06ZNunbtmuOOOy7XX399qqqqUl1dneuvv77CFUPDuvjii/PDH/4wkydPzrHHHps///nPefHFF/PYY4/l/PPPT+vWrXPrrbfmW9/6VmbPnl3pcqFs6nuoH5C8/vrrOeGEE/Lee+8lSf7rv/4r22+/fYWrgvoJmIGKWLJkSX74wx9mxIgRSZLtttsuw4YNS4cOHSpbGDSwK664Im+88Ub69u2bU045pdLlQNl9dFXakCFD0qpVq1X67LHHHunXr1+S5KGHHipbbdDYnnrqqdx0001JVuzF/J//+Z/Zbrvt0rp162y55ZY5/vjjM2rUqLRv3z5TpkzJf//3f1e4Yiifdu3a1R2vbWVybfvaVjpDU/DMM8/kuOOOq/tW149//OMceeSRFa4K1kzADJTd3Llz8+1vfzv33HNPkhVPjb755pvTrVu3ClcGDevxxx/PqFGj0qZNm1x++eVp2dKjD2h+aj847NixY/r06VNvvz333DPJioe+zp8/vyy1QWOrfYBf9+7dc9JJJ622z/bbb59vfvObSZK77747H374Ydnqg0r66L7L8+bNq7ff8uXL67Zb6tKlS6PXBZV0zz33ZNCgQZk7d26qqqry05/+dJW9+2FDJGAGymrq1Kn5xje+kb/97W9Jkn322Sc33XRTunbtWuHKoOHdd999SVasujnkkEPy6U9/epWfWldffXXdOU9Ipynp2bNnkqx17/FNNtmk7rh2Ww3Y2L355ptJks985jNr3A6g9gOWpUuXZurUqeUoDSpum222qTt+++236+03a9as1NTUJFnxYQ00Vddee23OPffcLFmyJO3atcvVV1+dY445ptJlwToRMANl89prr+Ub3/hG3T+2jjnmmFx33XW2xQBownbYYYckSXV19RpXJtfuPduqVSsfOtJk1IZiS5YsWecx69MXNmZbbLFFOnfunCSZPHlyvf0mTZpUd7ymb8LAxuy//uu/8utf/zpJsvnmm2fkyJE54IADKlwVrDsBM1AW06ZNy6BBg+qekH7mmWfmpz/9qS0DaNIuuuiiPPvss2v8qfXd73637lyPHj0qWDU0rC9+8YtJVnzFecyYMfX2e/LJJ5MkO++8c6qq/BWVpmHbbbdNkjz77LNrDI6feeaZJEnLli3Tq1evstQGG4La/ffHjx+fYrG42j7jxo1LknTr1s1DzmiSrrjiitxyyy1JVqzs/9///d/svPPOFa4K1o+/vQONrqamJmeddVZmzZqVJBk6dGhOO+20ClcFja9169bp0KHDGn9qtWrVqu5coVCoYNXQsL7whS/UfWhy5ZVX1q1U/qgHHnigLmDzEBuakkMOOSTJiudPXHnllavt8/rrr9cFC/vuu+9K+9JCU1f7Z/4bb7xRdx981OTJk3PXXXclSU444QR/R6LJefjhh3PDDTckWREujxo1KltvvXWFq4L1J2AGGt2tt96aiRMnJkkOPvjgHH300VmwYMEafwBoGlq2bJmLLrooVVVVeffdd3PMMcfk7rvvzowZMzJ9+vRcd911Oeecc5Ikn/3sZ3PUUUdVuGJoOAcffHD23nvvJMmwYcMyePDg/O1vf0t1dXXeeuut3HTTTRkwYEAWLlyYjh075oc//GGFK4by+tznPpf9998/SXLJJZfkyiuvzLRp0zJr1qzccccdGTRoUGpqatKzZ88ce+yxFa4WGtaSJUvy05/+NMmKhSlXXHFF2rVrt8Z/J9tGiQ1VoVjf91AAGsiXvvSl9X5gzSuvvNJI1cCGpfZBf2eccUYGDx5c4Wqg8dx333358Y9/XO8D/Hbcccdce+21+bd/+7cyVwaN64MPPsiZZ56Zp556qt4+m2++ea666qrstttuZawMGs9bb71Vt3/spZdeusYPD99///2cdNJJefHFF1fbvvnmm+eWW26xfQwblXW5B+6+++71/mDRvxnYUNn8FGhU1dXVnoYOQL7yla9kl112yfDhw/PYY4/l3XffTZs2bbLtttvmsMMOy9e+9rW0bdu20mVCg9t0001zww035KGHHspdd92ViRMnZu7cuWnbtm222Wab7L///jnuuOPSqVOnSpcKFdGpU6eMHj06o0ePzr333pspU6ZkyZIl6dGjR/bbb7+cfPLJ2WyzzSpdJjS4559/vtIlQIOxghkAAAAAgJLYgxkAAAAAgJIImAEAAAAAKImAGQAAAACAkgiYAQAAAAAoiYAZAAAAAICSCJgBAAAAACiJgBkAAAAAgJIImAEAAAAAKImAGQAAAACAkgiYAQAAAAAoiYAZAAAAAICSCJgBAAAAACiJgBkAAAAAgJIImAEAAAAAKImAGQAAAACAkgiYAQAAAAAoiYAZAAAAAICSCJgBAAAAACiJgBkAAAAAgJIImAEAAAAAKImAGQAAAACAkgiYAQAAAAAoiYAZAAAAAICSCJgBAAAAACiJgBkAAAAAgJL8/wBHdAAytFNd7QAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 800x550 with 2 Axes>"
       ]
@@ -913,8 +997,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
+   "execution_count": 20,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T14:14:45.601334Z",
+     "start_time": "2024-06-20T14:14:42.883559Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -948,9 +1037,9 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CCV</th>\n",
-       "      <td>0.4656425903701483</td>\n",
+       "      <td>0.4656425903700911</td>\n",
        "      <td>0.00348</td>\n",
-       "      <td>133.820078</td>\n",
+       "      <td>133.820079</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.458657</td>\n",
        "      <td>0.472628</td>\n",
@@ -970,11 +1059,11 @@
       ],
       "text/plain": [
        "                Estimate Std. Error     t value Pr(>|t|)      2.5%     97.5%\n",
-       "CCV   0.4656425903701483    0.00348  133.820078      0.0  0.458657  0.472628\n",
+       "CCV   0.4656425903700911    0.00348  133.820079      0.0  0.458657  0.472628\n",
        "CRV1            0.465643   0.027142   17.155606      0.0  0.411152  0.520133"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1001,24 +1090,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
+   "execution_count": 21,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T14:25:55.694928Z",
+     "start_time": "2024-06-20T14:25:52.178379Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "H0                                     X1=0\n",
-       "ri-type                     randomization-c\n",
-       "Estimate                 -1.000085840074156\n",
-       "Pr(>|t|)                                0.0\n",
-       "Std. Error (Pr(>|t|))                   0.0\n",
-       "2.5% (Pr(>|t|))                         0.0\n",
-       "97.5% (Pr(>|t|))                        0.0\n",
-       "Cluster                            group_id\n",
+       "H0                                      X1=0\n",
+       "ri-type                      randomization-c\n",
+       "Estimate                 -1.0000858400741557\n",
+       "Pr(>|t|)                                 0.0\n",
+       "Std. Error (Pr(>|t|))                    0.0\n",
+       "2.5% (Pr(>|t|))                          0.0\n",
+       "97.5% (Pr(>|t|))                         0.0\n",
+       "Cluster                             group_id\n",
        "dtype: object"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1044,8 +1138,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
+   "execution_count": 22,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T14:27:40.631506Z",
+     "start_time": "2024-06-20T14:27:40.531769Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1123,7 +1222,7 @@
        "Bonferroni Pr(>|t|)  0.000  0.000"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1134,8 +1233,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
+   "execution_count": 23,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T14:28:22.456880Z",
+     "start_time": "2024-06-20T14:28:22.109139Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1195,8 +1299,8 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>RW Pr(&gt;|t|)</th>\n",
-       "      <td>0.308</td>\n",
-       "      <td>0.308</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1210,10 +1314,10 @@
        "Pr(>|t|)     0.000  0.000\n",
        "2.5%        -1.281 -1.245\n",
        "97.5%       -0.757 -0.755\n",
-       "RW Pr(>|t|)  0.308  0.308"
+       "RW Pr(>|t|)  0.000  0.000"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1231,8 +1335,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
+   "execution_count": 24,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T14:29:18.562673Z",
+     "start_time": "2024-06-20T14:29:18.466981Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1262,28 +1371,28 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Intercept</th>\n",
-       "      <td>-0.422921</td>\n",
-       "      <td>1.400832</td>\n",
+       "      <td>-0.424977</td>\n",
+       "      <td>1.402888</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>X1</th>\n",
-       "      <td>-1.160034</td>\n",
-       "      <td>-0.738848</td>\n",
+       "      <td>-1.160509</td>\n",
+       "      <td>-0.738374</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C(f1)[T.1.0]</th>\n",
-       "      <td>1.388184</td>\n",
-       "      <td>3.777114</td>\n",
+       "      <td>1.385491</td>\n",
+       "      <td>3.779808</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C(f1)[T.2.0]</th>\n",
-       "      <td>-2.834723</td>\n",
-       "      <td>-0.329145</td>\n",
+       "      <td>-2.837547</td>\n",
+       "      <td>-0.326321</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C(f1)[T.3.0]</th>\n",
-       "      <td>-1.604060</td>\n",
-       "      <td>0.979393</td>\n",
+       "      <td>-1.606973</td>\n",
+       "      <td>0.982306</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1291,14 +1400,14 @@
       ],
       "text/plain": [
        "                0.025%    0.975%\n",
-       "Intercept    -0.422921  1.400832\n",
-       "X1           -1.160034 -0.738848\n",
-       "C(f1)[T.1.0]  1.388184  3.777114\n",
-       "C(f1)[T.2.0] -2.834723 -0.329145\n",
-       "C(f1)[T.3.0] -1.604060  0.979393"
+       "Intercept    -0.424977  1.402888\n",
+       "X1           -1.160509 -0.738374\n",
+       "C(f1)[T.1.0]  1.385491  3.779808\n",
+       "C(f1)[T.2.0] -2.837547 -0.326321\n",
+       "C(f1)[T.3.0] -1.606973  0.982306"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1319,8 +1428,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
+   "execution_count": 25,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T14:31:29.429076Z",
+     "start_time": "2024-06-20T14:31:29.008470Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1453,7 +1567,7 @@
        "4       25           5  "
       ]
      },
-     "execution_count": 61,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1474,8 +1588,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
+   "execution_count": 26,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T14:32:37.568486Z",
+     "start_time": "2024-06-20T14:32:36.001311Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1527,8 +1646,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
+   "execution_count": 25,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-17T10:07:44.263843Z",
+     "start_time": "2024-06-17T10:07:44.240900Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1563,8 +1687,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
+   "execution_count": 27,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T14:35:44.788779Z",
+     "start_time": "2024-06-20T14:35:44.691030Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1608,12 +1737,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
+   "execution_count": 28,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T14:35:58.535732Z",
+     "start_time": "2024-06-20T14:35:58.268743Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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AAAAAAIApFBUBAAAAAAAAmEJREQAAAAAAAIApFBUBAAAAAAAAmEJREQAAAAAAAIApFBUBAAAAAAAAmEJREQAAAAAAAIApFBUBAAAAAAAAmEJREQAAAAAAAIApFBUBAAAAAAAAmPL/AXVULzvRZdojAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 800x550 with 2 Axes>"
       ]
@@ -1642,8 +1776,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
+   "execution_count": 29,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T14:36:18.961246Z",
+     "start_time": "2024-06-20T14:36:17.506421Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1680,7 +1819,7 @@
     "> - `sw`: this function is replaced sequentially by each of its arguments. For example, `y ~ x1 + sw(x2, x3)` leads to two estimations: `y ~ x1 + x2` and `y ~ x1 + x3`.\n",
     "> - `sw0`: identical to sw but first adds the empty element. E.g. `y ~ x1 + sw0(x2, x3)` leads to three estimations: `y ~ x1`, `y ~ x1 + x2` and `y ~ x1 + x3`.\n",
     "> - `csw`: it stands for cumulative stepwise. It adds to the formula each of its arguments sequentially. E.g. `y ~ x1 + csw(x2, x3)` will become `y ~ x1 + x2` and `y ~ x1 + x2 + x3`.\n",
-    "> - `csw0`: identical to csw but first adds the empty element. E.g. `y ~ x1 + csw0(x2, x3)` leads to three estimations: `y ~ x1`, `y ~ x1 + x`2 and `y ~ x1 + x2 + x3`.\n",
+    "> - `csw0`: identical to csw but first adds the empty element. E.g. `y ~ x1 + csw0(x2, x3)` leads to three estimations: `y ~ x1`, `y ~ x1 + x2` and `y ~ x1 + x2 + x3`.\n",
     "\n",
     "If multiple regression syntax is used, \n",
     "`feols()` and `fepois` returns an instance of a `FixestMulti` object, which essentially consists of a dicionary of `Fepois` or [Feols](/reference/Feols.qmd) instances."
@@ -1688,16 +1827,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
+   "execution_count": 30,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T14:37:13.147542Z",
+     "start_time": "2024-06-20T14:37:13.081920Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<pyfixest.estimation.FixestMulti_.FixestMulti at 0x1aa00d97790>"
+       "<pyfixest.estimation.FixestMulti_.FixestMulti at 0x30b07a120>"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1709,8 +1853,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
+   "execution_count": 31,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T14:37:13.940332Z",
+     "start_time": "2024-06-20T14:37:13.905472Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1769,8 +1918,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
+   "execution_count": 32,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T14:37:21.408530Z",
+     "start_time": "2024-06-20T14:37:21.309655Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1783,8 +1937,8 @@
       "Intercept      0.919*** (0.112)\n",
       "X1            -1.000*** (0.082)  -0.949*** (0.066)  -0.919*** (0.058)\n",
       "---------------------------------------------------------------------\n",
-      "f1                            -                  x                  x\n",
       "f2                            -                  -                  x\n",
+      "f1                            -                  x                  x\n",
       "---------------------------------------------------------------------\n",
       "R2                        0.123              0.437              0.609\n",
       "S.E. type                hetero             hetero             hetero\n",
@@ -1809,8 +1963,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
+   "execution_count": 33,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T14:51:43.693568Z",
+     "start_time": "2024-06-20T14:51:43.620992Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1880,7 +2039,7 @@
        "X1          -1.000086    0.082420 -12.134086  0.000000e+00 -1.161822 -0.838350"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1898,8 +2057,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
+   "execution_count": 34,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T14:51:45.963900Z",
+     "start_time": "2024-06-20T14:51:45.945606Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1976,7 +2140,7 @@
        "X1          -1.000086    0.082420 -12.134086  0.000000e+00 -1.161822 -0.838350"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2003,8 +2167,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
+   "execution_count": 35,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T14:52:08.867135Z",
+     "start_time": "2024-06-20T14:52:08.813846Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -2063,8 +2232,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
+   "execution_count": 36,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T14:52:16.143244Z",
+     "start_time": "2024-06-20T14:52:16.077847Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -2104,22 +2278,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
+   "execution_count": 37,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T14:52:20.031271Z",
+     "start_time": "2024-06-20T14:52:19.942556Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "   <div id=\"lDqApL\"></div>\n",
+       "   <div id=\"8GUhzm\"></div>\n",
        "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
        "       (function() {\n",
        "           var plotSpec={\n",
        "\"data\":{\n",
        "\"Coefficient\":[\"Intercept\",\"X1\",\"X1\",\"X1\",\"X1\",\"X1\",\"X1\",\"X1\",\"X1\",\"X1\"],\n",
        "\"Model\":[\"Y~X1\",\"Y~X1\",\"Y~X1|f1\",\"Y~X1|f1+f2\",\"Y~X1\",\"Y~X1|f1\",\"Y~X1|f1+f2\",\"Y~X1\",\"Y~X1|f1\",\"Y~X1|f1+f2\"],\n",
-       "\"Estimate\":[0.9185177588356426,-1.000085840074156,-0.9494410591286258,-0.9192548853827908,-1.000085840074156,-0.9494410591286258,-0.9192548853827908,-1.000085840074156,-0.9494410591286258,-0.9192548853827908],\n",
-       "\"2.5%\":[0.6990856170653885,-1.1663660739416457,-1.0839997603783968,-1.0334504235222999,-1.1663660739416457,-1.0839997603783968,-1.0334504235222999,-1.1663660739416457,-1.0839997603783968,-1.0334504235222999],\n",
-       "\"97.5%\":[1.1379499006058966,-0.8338056062066661,-0.8148823578788547,-0.8050593472432815,-0.8338056062066661,-0.8148823578788547,-0.8050593472432815,-0.8338056062066661,-0.8148823578788547,-0.8050593472432815]\n",
+       "\"Estimate\":[0.9185177588356424,-1.0000858400741557,-0.9494410591286259,-0.9192548853827907,-1.0000858400741557,-0.9494410591286259,-0.9192548853827907,-1.0000858400741557,-0.9494410591286259,-0.9192548853827907],\n",
+       "\"2.5%\":[0.6990856170653883,-1.1663660739416455,-1.0839997603783968,-1.0334504235222999,-1.1663660739416455,-1.0839997603783968,-1.0334504235222999,-1.1663660739416455,-1.0839997603783968,-1.0334504235222999],\n",
+       "\"97.5%\":[1.1379499006058964,-0.8338056062066659,-0.8148823578788549,-0.8050593472432814,-0.8338056062066659,-0.8148823578788549,-0.8050593472432814,-0.8338056062066659,-0.8148823578788549,-0.8050593472432814]\n",
        "},\n",
        "\"mapping\":{\n",
        "\"x\":\"Coefficient\",\n",
@@ -2188,7 +2367,7 @@
        "}],\n",
        "\"metainfo_list\":[]\n",
        "};\n",
-       "           var plotContainer = document.getElementById(\"lDqApL\");\n",
+       "           var plotContainer = document.getElementById(\"8GUhzm\");\n",
        "           window.letsPlotCall(function() {{\n",
        "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
        "           }});\n",
@@ -2213,17 +2392,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
+   "execution_count": 38,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T14:52:31.039586Z",
+     "start_time": "2024-06-20T14:52:30.711211Z"
+    }
+   },
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
-       "<seaborn._core.plot.Plot at 0x1aa001a9060>"
+       "<seaborn._core.plot.Plot at 0x30a7e1c10>"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 38,
      "metadata": {
       "image/png": {
        "height": 444.125,
@@ -2248,8 +2432,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
+   "execution_count": 39,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T14:52:52.979503Z",
+     "start_time": "2024-06-20T14:52:52.083775Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -2394,7 +2583,7 @@
        "4               -6 -0.017603   0         0.0  6.918492  "
       ]
      },
-     "execution_count": 76,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2408,22 +2597,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {},
+   "execution_count": 40,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T14:52:57.080804Z",
+     "start_time": "2024-06-20T14:52:55.340670Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/html": [
-       "   <div id=\"G8gIP2\"></div>\n",
+       "   <div id=\"soq2ad\"></div>\n",
        "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
        "       (function() {\n",
        "           var plotSpec={\n",
        "\"data\":{\n",
        "\"Model\":[\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var_hat~i(rel_year,ref=-1.0)\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\",\"dep_var~i(rel_year,ref=-1.0)|state+year\"],\n",
        "\"Coefficient\":[\"C(rel_year, contr.treatment(base=-1.0))[T.-20.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-19.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-18.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-17.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-16.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-15.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-14.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-13.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-12.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-11.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-10.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-9.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-8.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-7.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-6.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-5.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-4.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-3.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-2.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.0.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.1.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.2.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.3.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.4.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.5.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.6.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.7.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.8.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.9.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.10.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.11.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.12.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.13.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.14.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.15.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.16.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.17.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.18.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.19.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.20.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.inf]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-20.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-19.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-18.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-17.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-16.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-15.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-14.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-13.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-12.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-11.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-10.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-9.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-8.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-7.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-6.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-5.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-4.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-3.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.-2.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.0.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.1.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.2.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.3.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.4.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.5.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.6.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.7.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.8.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.9.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.10.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.11.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.12.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.13.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.14.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.15.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.16.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.17.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.18.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.19.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.20.0]\",\"C(rel_year, contr.treatment(base=-1.0))[T.inf]\"],\n",
-       "\"Estimate\":[-0.07930892379170669,-0.027115309873244755,-0.027235435350764316,-0.03361637397576204,-0.05578068831093835,0.01674513656446518,-0.02086993744456097,-0.022209824688845536,0.005447073274778881,-0.04792474874869773,-0.03247855934296003,-0.03751722264066751,-0.030856942644321275,-0.007104690258118683,-0.039598001783394315,-0.07918308610819687,-0.03422655342074936,-0.015214092720010685,-0.038687531424549226,1.3044622847590652,1.5334860476936707,1.5971037484422894,1.718847309180147,1.8462967066123768,1.9905872778860922,2.0788829001715436,2.1653995661371632,2.258950448012607,2.2777630764062358,2.4147668193865734,2.4563352194604438,2.5072957344947397,2.4814015130523845,2.626326070707077,2.8126319507465785,2.762682379858839,2.811300393411503,2.7770354043917562,2.7861644754695067,2.8148582432189846,0.030505181948300618,-0.09944466441181407,6.23519975400307E-4,0.0041247661867057995,0.021899085459484872,-0.03693340882836111,0.06957783836542085,0.03773442317032304,0.06177914337449611,0.08991270466792296,9.822487465557272E-4,-0.11303282051981747,-0.06922462711945501,-0.061289513093629025,-0.0020224309370890357,-0.05580979472634313,-0.06500868193865345,-0.009849959807181893,0.04633807990097424,0.015947312893775302,1.406404490822593,1.6605518884804105,1.7287900538935237,1.8548389960027065,1.9586760781129549,2.082161283635855,2.1910619949213292,2.279072842371443,2.3645934393351222,2.372162693677256,2.6492711880908235,2.753590871536464,2.81393547422749,2.756069650642772,2.8634270216427646,2.9866523455332556,2.9630322165965404,2.972596421114107,2.9350513290126417,2.9187065136810078,2.979701202491609,0.12805306243573136],\n",
-       "\"2.5%\":[-0.1695600913438541,-0.13130815610041208,-0.12259738225267744,-0.1175645163481524,-0.15943222422456108,-0.08271384506652281,-0.11742994692058029,-0.10020049059609207,-0.08409592792949198,-0.16435158427962726,-0.09941164460147212,-0.09715638905461493,-0.09367244147088058,-0.07832139368493432,-0.09281052913292714,-0.1388263127648801,-0.08904078728121567,-0.07493744182060509,-0.10202240184199105,1.194594346575011,1.4072836249567104,1.4860049540989104,1.6001299023272977,1.7528192639692397,1.8801608387434234,1.9527955114720021,2.050837966769707,2.1453420283383227,2.173300068852982,2.2970296208225083,2.2958801505463473,2.3509943789870746,2.315656867630969,2.489639041038913,2.650494736809072,2.601104141911765,2.6557354064354373,2.61676210244634,2.6094181968232464,2.6520847796434914,-0.00971897358507584,-0.2646972304933436,-0.16769018688345885,-0.17734940744468852,-0.15141773681724854,-0.23297450453669644,-0.09484387010242608,-0.13746745521816428,-0.10683560211191703,-0.08181351401233818,-0.15902123698075604,-0.2443506562456939,-0.18461172525475777,-0.18338272511269268,-0.1326928866666348,-0.1866256631156605,-0.1960991761768802,-0.11725094987359078,-0.0809912241439136,-0.11642114159510045,1.2938600189553549,1.5290095619722583,1.613915005015477,1.7368859662793261,1.814774185790572,1.9530023735556978,2.052486677327441,2.1273424786361748,2.2460668440010703,2.257759442916459,2.5356414801659573,2.6008255523798414,2.6534963681556536,2.5975551692265255,2.664416143565802,2.7979163360468178,2.7902393404784664,2.7857206716995413,2.7446638314603145,2.7484109878126053,2.8021561430453152,-0.030754737319463377],\n",
-       "\"97.5%\":[0.010942243760440729,0.07707753635392256,0.06812651155114882,0.05033176839662833,0.04787084760268437,0.11620411819545316,0.07569007203145836,0.05578084121840099,0.09499007447904974,0.0685020867822318,0.034454525915552044,0.022121943773279917,0.03195855618223803,0.06411201316869697,0.013614525566138513,-0.019539859451513618,0.02058768043971696,0.04450925638058373,0.024647338992892592,1.4143302229431194,1.659688470430631,1.7082025427856684,1.8375647160329964,1.9397741492555138,2.1010137170287613,2.204970288871085,2.2799611655046195,2.3725588676868914,2.3822260839594893,2.5325040179506386,2.61679028837454,2.663597090002405,2.6471461584738,2.7630131003752405,2.974769164684085,2.924260617805913,2.966865380387569,2.9373087063371726,2.962910754115767,2.9776317067944778,0.07072933748167708,0.06580790166971548,0.16893722683425946,0.1855989398181001,0.1952159077362183,0.15910768687997423,0.2339995468332678,0.21293630155881038,0.23039388886090925,0.2616389233481841,0.1609857344738675,0.018285015206058963,0.04616247101584775,0.060803698925434635,0.12864802479245674,0.07500607366297427,0.0660818122995733,0.09755103025922698,0.1736673839458621,0.14831576738265104,1.5189489626898312,1.7920942149885626,1.8436651027715705,1.972792025726087,2.1025779704353376,2.2113201937160123,2.3296373125152177,2.4308032061067113,2.483120034669174,2.486565944438053,2.7629008960156898,2.9063561906930864,2.9743745802993264,2.914584132059019,3.0624378997197272,3.1753883550196935,3.1358250927146143,3.1594721705286726,3.125438826564969,3.08900203954941,3.157246261937903,0.2868608621909261]\n",
+       "\"Estimate\":[-0.07930892379170355,-0.027115309873245487,-0.02723543535076407,-0.03361637397576078,-0.05578068831094041,0.01674513656446489,-0.02086993744456222,-0.022209824688846695,0.00544707327477867,-0.047924748748698044,-0.03247855934295879,-0.03751722264066802,-0.030856942644321136,-0.0071046902581183005,-0.03959800178339545,-0.07918308610819674,-0.034226553420749685,-0.015214092720010486,-0.03868753142454904,1.3044622847590654,1.5334860476936705,1.5971037484422894,1.7188473091801466,1.8462967066123774,1.990587277886093,2.0788829001715436,2.1653995661371637,2.258950448012608,2.2777630764062367,2.4147668193865743,2.456335219460444,2.5072957344947375,2.4814015130523837,2.6263260707070772,2.8126319507465793,2.76268237985884,2.8113003934115017,2.7770354043917576,2.786164475469507,2.8148582432189846,0.030505181948300656,-0.09944466441190238,6.23519975312305E-4,0.004124766186617726,0.021899085459397075,-0.036933408828448595,0.06957783836533336,0.03773442317023356,0.06177914337440952,0.08991270466783484,9.822487465090475E-4,-0.11303282051990572,-0.069224627119543,-0.061289513093716996,-0.002022430937176966,-0.055809794726430745,-0.06500868193874072,-0.009849959807273077,0.046338079900885,0.015947312893686037,1.4064044908225004,1.6605518884803165,1.7287900538934298,1.8548389960026122,1.958676078112863,2.082161283635763,2.1910619949212364,2.2790728423713498,2.364593439335033,2.372162693677187,2.649271188090729,2.7535908715363644,2.8139354742273897,2.7560696506426745,2.8634270216426674,2.986652345533159,2.9630322165964436,2.9725964211140092,2.9350513290125457,2.9187065136809247,2.97970120249151,0.12805306243564316],\n",
+       "\"2.5%\":[-0.16956009134385108,-0.1313081561004128,-0.12259738225267722,-0.11756451634815114,-0.15943222422456316,-0.08271384506652311,-0.11742994692058153,-0.10020049059609323,-0.08409592792949218,-0.16435158427962757,-0.0994116446014709,-0.09715638905461543,-0.09367244147088043,-0.07832139368493393,-0.09281052913292827,-0.13882631276487997,-0.08904078728121598,-0.07493744182060487,-0.10202240184199085,1.1945943465750113,1.4072836249567102,1.4860049540989104,1.6001299023272972,1.7528192639692404,1.880160838743424,1.9527955114720021,2.0508379667697074,2.1453420283383235,2.173300068852983,2.297029620822509,2.2958801505463478,2.350994378987072,2.315656867630968,2.4896390410389135,2.6504947368090725,2.6011041419117658,2.655735406435436,2.616762102446341,2.609418196823247,2.6520847796434914,-0.009718973585075782,-0.26469723049343724,-0.16769018688355777,-0.17734940744478747,-0.15141773681734672,-0.23297450453679372,-0.094843870102527,-0.1374674552182675,-0.10683560211202428,-0.08181351401244431,-0.1590212369808106,-0.24435065624578933,-0.18461172525484998,-0.18338272511278497,-0.13269288666672624,-0.1866256631157554,-0.19609917617697215,-0.11725094987368748,-0.0809912241440153,-0.11642114159519981,1.293860018955261,1.5290095619721664,1.61391500501539,1.7368859662792417,1.8147741857904802,1.9530023735556108,2.0524866773273467,2.127342478636081,2.2460668440009885,2.257759442916387,2.5356414801658698,2.600825552379751,2.6534963681555577,2.597555169226431,2.6644161435657083,2.797916336046727,2.7902393404783776,2.7857206716994485,2.744663831460226,2.7484109878125245,2.8021561430452193,-0.030754737319553332],\n",
+       "\"97.5%\":[0.010942243760443976,0.07707753635392181,0.06812651155114907,0.050331768396629575,0.04787084760268234,0.11620411819545289,0.07569007203145708,0.05578084121839985,0.09499007447904952,0.06850208678223146,0.03445452591555331,0.02212194377327939,0.031958556182238154,0.06411201316869733,0.013614525566137362,-0.019539859451513507,0.020587680439716607,0.0445092563805839,0.024647338992892766,1.4143302229431196,1.6596884704306307,1.7082025427856684,1.837564716032996,1.9397741492555145,2.1010137170287617,2.204970288871085,2.27996116550462,2.3725588676868923,2.38222608395949,2.5325040179506395,2.6167902883745406,2.663597090002403,2.647146158473799,2.763013100375241,2.974769164684086,2.924260617805914,2.9668653803875675,2.937308706337174,2.9629107541157675,2.9776317067944778,0.07072933748167709,0.0658079016696325,0.16893722683418236,0.18559893981802292,0.19521590773614084,0.15910768687989654,0.23399954683319374,0.21293630155873464,0.2303938888608433,0.261638923348114,0.1609857344738287,0.018285015205977903,0.04616247101576397,0.060803698925350994,0.1286480247923723,0.07500607366289391,0.06608181229949069,0.09755103025914133,0.17366738394578532,0.14831576738257188,1.51894896268974,1.7920942149884667,1.8436651027714694,1.9727920257259826,2.1025779704352456,2.211320193715915,2.329637312515126,2.4308032061066185,2.4831200346690774,2.486565944437987,2.762900896015588,2.9063561906929776,2.9743745802992216,2.914584132058918,3.0624378997196264,3.1753883550195905,3.1358250927145095,3.15947217052857,3.1254388265648654,3.089002039549325,3.157246261937801,0.2868608621908396]\n",
        "},\n",
        "\"mapping\":{\n",
        "\"x\":\"Coefficient\",\n",
@@ -2480,7 +2674,7 @@
        "}],\n",
        "\"metainfo_list\":[]\n",
        "};\n",
-       "           var plotContainer = document.getElementById(\"G8gIP2\");\n",
+       "           var plotContainer = document.getElementById(\"soq2ad\");\n",
        "           window.letsPlotCall(function() {{\n",
        "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
        "           }});\n",
@@ -2488,10 +2682,10 @@
        "   </script>"
       ],
       "text/plain": [
-       "<lets_plot.plot.core.PlotSpec at 0x1aa484d0df0>"
+       "<lets_plot.plot.core.PlotSpec at 0x305fdc260>"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2526,8 +2720,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
+   "execution_count": 41,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T14:52:58.547405Z",
+     "start_time": "2024-06-20T14:52:58.138322Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -2539,8 +2738,8 @@
       "------------------------------------------------\n",
       "ATT           2.135*** (0.044)  2.152*** (0.048)\n",
       "------------------------------------------------\n",
-      "state                        x                 -\n",
       "year                         x                 -\n",
+      "state                        x                 -\n",
       "------------------------------------------------\n",
       "R2                       0.758             0.338\n",
       "S.E. type            by: state              CRV1\n",
@@ -2580,11 +2779,185 @@
    "source": [
     "For more details see the vignette on [Difference-in-Differences Estimation](https://py-econometrics.github.io/pyfixest/difference-in-differences.html)."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Weighted Lease Squares (WLS)\n",
+    "\n",
+    "`PyFixest` supports estimation using Weighted least squares.  \n",
+    "This requires that the weights that are passed in are proportional to the inverse of the error variance.  \n",
+    "Essentially the weighted model,  \n",
+    "`Y*sqrt(w) = X*sqrt(w) + e*sqrt(w)` is assumed to have `Var(e*sqrt(w)) = constant`  \n",
+    "=> Var(e) * w = constant  \n",
+    "=> w = constant/Var(e)\n",
+    "\n",
+    "To demonstrate the application of WLS we borrow the approach demonstrated in Statsmodels (https://www.statsmodels.org/stable/examples/notebooks/generated/wls.html#Feasible-Weighted-Least-Squares-(2-stage-FWLS)) and generate sample data and assume the following:   \n",
+    "* Misspecification: true model is quadratic, but we only estimate a linear model \n",
+    "* Independent noise/error term \n",
+    "* Two groups for error variance, low and high variance groups \n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T18:06:59.477026Z",
+     "start_time": "2024-06-20T18:06:59.239296Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='x', ylabel='y'>"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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b7k9/+tNqHytGaPnuu+80dOhQz1xCQ4YM0YIFC/yaLkCSXnrpJaWkpKhr165VPrJQ/ngq/4WD0DV+/Hj17NlTffr0qXK78p+tNyu4UmcahpycHM+0Ae6FG/xFnWl4mjdvrtjYWEn/qgUVKb9Cc4cOHbxq22q16ic/+Um1bR8+fNhz0eNt2wgN06dP19y5cyVJcXFxWrp0qakFarKysnTvvffq9ttv12uvvVbltu46RA0Kffv379cvfvELde7cWWvWrKl0u9LSUs95s7efK3WmYdi+fbukske477zzTlNtUWfqBl+vpTmfCR6CVgRc7969JZV98btXt7veDz/8oEOHDkkqu8vijR49enjujG3ZsqXCbfLz87Vz506f2kVoOHXqlEaMGOF5pHbs2LGaOXOmwsPD/W6zadOmysnJuea4qIh7/iK73a477rjD77+HmtO4cWPl5uYqMzOz0kDUMAx99NFHkqSEhAT9+Mc/rrZd6kzDsG/fPs+IjM6dO5tqizrTMLnPdbZu3Vrp6B53DWnWrJlPixu52965c6fy8/OrbDsiIkI9evTwum3UrvT0dK1YsUJSWQjxzjvvmK5BzZo106VLl1RQUKDPP/+80u0OHjyoo0ePSpLuvvtuU38TwZeQkKCsrCwVFRV5Hv+uyJYtWzyP9PryuVJn6r//+7//k1QWXlU3j3x1qDOhz99rac5ngoOgFQF32223eS4i582bd8OcHIZhaM6cOXK5XGratKnuu+8+r9qNiYnxjDxatGhRhUPQ582bp8uXL8tms+nhhx82+U5QU5xOp8aNG6fs7GxJ0uTJk/X73//edLv9+vXznFikp6dXuEDb+vXrPQX+oYceqnBVV4Se//iP//D8PGvWrApPDN58803PDZ2RI0d6tdIqdaZhcN+Zt1gsnkUb/EWdaZjcCxYdO3bME5yVd/DgQX3wwQeSpOHDh/u00vPAgQMVFhamS5cuVThy6MyZM57FLR588MFr5lhD6Nq8ebMWLlwoqSxkfeutt3TbbbeZbjcsLMzzWPCBAwc8x115eXl5mjJliqSy77mHHnrI9N9FcMXFxelnP/uZJGnTpk3avXv3DdtkZ2dr9uzZkqT4+HifHg+nztRvxcXF+u677ySZv6EsUWdCnZlrac5ngoOgFUExefJkWa1WHT9+XKmpqfriiy88j2qOGTNG//u//ytJGjNmzDUr0klS37591bdvX02aNOmGdp9++mnZ7Xbl5uZq2LBh2rRpk3JycnT06FFNmzZNixYtkiQ98sgjio+PD/4bRUCsXLnSsyJ8v379NHjwYOXl5VX5X3mVHTMJCQl67LHHJJU9xjJkyBBt27ZN58+f19GjR/Xyyy/rmWeekVS2EuOYMWNq4N0iELp27aoBAwZIKpuDavjw4dq9e7dycnKUkZGhqVOn6pVXXpFUNkr1N7/5zTX7U2caNvdoi1tuucXr0JM6g/JSUlJ07733Siq72fPqq6/q1KlTys7O1vvvv68RI0bI6XTq1ltvvaH+SNLy5cs9x9SBAweuea1t27ZKTU2VJC1cuFDTpk3T0aNHlZOTo02bNmnYsGHKzc1VbGys0tLSgv9mYVpxcbFmzpwpqWzUTnp6uqKjo6s8z7n+pk1Vx8yoUaM8j39OmTJFc+fO1XfffaecnBxt2bJFDz30kOcG03PPPcfc4nXExIkTFRkZKcMw9Lvf/U6LFy/W8ePHlZ2drXXr1mno0KE6ffq0wsPDNWvWrBsWO6LONFzHjx9XSUmJJPk0Dz11pm4ycy3N+Uxw+P9MLlCFTp06adasWZo6daoOHz6skSNH3rDNiBEjNGzYsBt+//3330tShcW5ZcuW+stf/qIxY8bozJkz+sMf/nDDNn379tXEiRMD8C5QU/72t795ft64caM2btxY7T7ffvut5+eqjplx48YpNzdXK1eu1KFDhyos4klJSfrrX/96Q+iP0DZr1izl5eXps88+065du7Rr164btrnzzjs1b968Gxbco840bKdPn5Z07Yqo1aHO4Hpz5szRyJEj9c0332jBggVasGDBNa/HxcVp0aJFFYb5Fy9e9BxTBQUFN7w+ceJEnTp1Slu3btXKlSu1cuXKa1632+1asGCBZ3VnhLaNGzfq7NmzkspCV28Wvho9evQ1N2aqOmZatGihN998U7///e+VnZ2t119/Xa+//vo129hsNk2cOFH/+Z//afbtoIYkJiZq3rx5evrpp3X16lXNmTNHc+bMuWYbu92u//qv/9Jdd911w/7UmYar/BNZvowSpM7UTWavpTmfCTyCVgTNoEGDlJycrIULF2rXrl26cOGC7Ha7OnbsqNTU1GoXsanMz3/+c3300Uf67//+b33xxRc6e/asIiIilJiYqAcffFCDBg3yaUg7aldOTo5OnjwZtPatVqtmzJihfv366e2339bXX3+tixcvKiYmRu3atVP//v01ePBgU3PBonZERUXpjTfe0Mcff6xVq1bpm2++0ZUrV9SkSRMlJibqgQceUP/+/f2qB9SZ+s09pU2gHlGizjRMTZo00dtvv623335b69at09GjR1VcXKyEhATdc889euKJJ3TLLbf41XZkZKQWLFigDz74QKtXr1ZGRoYKCgrUvHlz3XXXXXriiScC8tg5asb+/fuD/jc6d+6s9evXa/ny5dqyZYu+//57lZaWqkWLFkpJSdEjjzyidu3aBb0fCKzevXtrw4YNWrJkibZv367MzExJZTeF7777bg0fPtzvgII6U39dvnzZ87MvN5WrQ50JPYG4luZ8JvAsRmUz3gIAAAAAAAAAvMIcrQAAAAAAAABgEkErAAAAAAAAAJhE0AoAAAAAAAAAJhG0AgAAAAAAAIBJBK0AAAAAAAAAYBJBKwAAAAAAAACYRNAKAAAAAAAAACYRtAIAAAAAAACASQStAAAAAAAAAGASQSsAAAAAAAAAmETQCgAAAAAAAAAmEbQCAAAAAAAAgEkErQAAAAAAAABgEkErAAAAAAAAAJhE0AoAAAAAAAAAJhG0AgAAAAAAAIBJBK0AAAAAAAAAYBJBKwAAAAAAAACYRNAKAAAAAAAAACYRtAIAAAAAAACASQStAAAAAAAAAGASQSsAAAAAAAAAmETQCgAAAAAAAAAmEbQCAAAAfjp//rx69uyp9u3bq3379vr0008r3C4vL0/33nuv2rdvr8TERH355Zc13FMAAAAEG0ErAAAA4Ke4uDhNnjzZ879nzZqlgoKCG7ZLT0/X6dOnJUm/+c1v9LOf/azG+ggAAICaQdAKAAAAmHD//ffrF7/4hSTp9OnTmj9//jWv79y5UytXrpQktW7dWhMnTqzpLgIAAKAGWAzDMGq7EwAAAEBdlpWVpf79++vq1auy2Wxas2aN2rVrp7y8PA0cOFCZmZmyWq1666231LVr19ruLgAAAIKAEa0AAACASfHx8Zo0aZIkyel0aubMmZKkP/3pT8rMzJQkjRw5kpAVAACgHmNEKwAAABAgw4cP186dOyVJTzzxhBYuXCiXyyWHw6FVq1YpIiKilnsIAACAYCFoBQAAAALk1KlTGjhwoPLz8z2/s9lseu+995SUlFSLPQMAAECwMXUAAAAAECC33XabnnrqqWt+N2rUKEJWAACABoCgFQAAAAig60PVsLCwWuoJAAAAahJTBwAAAAABUlRUpIEDB+r48eOe30VGRuqDDz7Qj3/849rrGAAAAIKOEa0AAABAgMybN88Tst5zzz2SysLX559/XoxvAAAAqN8IWgEAAIAA+Mc//qFFixZJktq0aaO//OUvGjBggCRp3759WrZsWW12DwAAAEHG1AEAAACASU6nUw8++KC+/fZbSdKSJUuUkpKi7Oxs9evXT1euXJHdbte6det066231nJvAQAAEAyMaAUAAABM+utf/+oJWQcOHKiUlBRJUrNmzTRu3DhJUn5+vqZOnVpbXQQAAECQEbQCAAAAJhw+fFgLFiyQJDVp0kTPPvvsNa+npqYqOTlZkvTVV1/pvffeq/E+AgAAIPgIWgEAAAA/lZaW6rnnnpPT6ZQkTZgwQbfccss121itVk2fPl1Wa9mp94svvqizZ8/WeF8BAAAQXAStAAAAgJ+WLFmib775RpLUpUsXDR48uMLtOnXqpNTUVEnSlStX9MILL9RYHwEAAFAzWAwLAAAAAAAAAExiRCsAAAAAAAAAmETQCgAAAAAAAAAmEbQCAAAAAAAAgEkErQAAAAAAAABgEkErAAAAAAAAAJhE0AoAAAAAAAAAJhG0AgAAAAAAAIBJBK0AAAAAAAAAYBJBKwAAAAAAAACYRNAKAAAAAAAAACYRtAIAAAAAAACASQStAAAAAAAAAGASQSsAAAAAAAAAmETQCgAAAAAAAAAmEbQCAAAAAAAAgEkErQAAAAAAAABgEkErAAAAAAAAAJhE0AoAAAAAAAAAJhG0AgAAAAAAAIBJBK0AAAAAAAAAYBJBKwAAAAAAAACYRNAKAAAAAAAAACYRtAIAAAAAAACASQStAAAAAAAAAGDS/wOYNutJiV2qgQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 800x550 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 486,
+       "width": 685
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.random.seed(1024)\n",
+    "nsample = 50\n",
+    "x = np.linspace(0, 20, nsample)\n",
+    "X = np.c_[np.ones_like(x), np.column_stack((x, (x - 5) ** 2))]\n",
+    "beta = [5.0, 0.5, -0.01]\n",
+    "sig = 0.5\n",
+    "w = np.ones(nsample)\n",
+    "w[nsample * 6 // 10 :] = 3\n",
+    "y_true = np.dot(X, beta)\n",
+    "e = np.random.normal(size=nsample)\n",
+    "y = y_true + sig * w * e\n",
+    "\n",
+    "data  = pd.DataFrame(np.c_[y, x], columns=['y', 'x'])\n",
+    "data['w'] = 1.0 / (w ** 2)\n",
+    "data.plot(x='x', y='y', kind='scatter', grid=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Starting off by fitting both OLS and WLS models and comparing them. It can be immediately observed that standard errors with WLS have reduced magnitude alongside slightly larger coefficient of 'x' which is closer to the True value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T18:36:49.980797Z",
+     "start_time": "2024-06-20T18:36:49.887531Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "###\n",
+      "\n",
+      "Estimation:  OLS\n",
+      "Dep. var.: y, Fixed effects: \n",
+      "Inference:  iid\n",
+      "Observations:  50\n",
+      "\n",
+      "| Coefficient   |   Estimate |   Std. Error |   t value |   Pr(>|t|) |   2.5% |   97.5% |\n",
+      "|:--------------|-----------:|-------------:|----------:|-----------:|-------:|--------:|\n",
+      "| Intercept     |      5.243 |        0.271 |    19.370 |      0.000 |  4.698 |   5.787 |\n",
+      "| x             |      0.435 |        0.023 |    18.647 |      0.000 |  0.388 |   0.482 |\n",
+      "---\n",
+      "RMSE: 0.952 R2: 0.879 \n",
+      "###\n",
+      "\n",
+      "Estimation:  OLS\n",
+      "Dep. var.: y, Fixed effects: \n",
+      "Inference:  iid\n",
+      "Observations:  50\n",
+      "\n",
+      "| Coefficient   |   Estimate |   Std. Error |   t value |   Pr(>|t|) |   2.5% |   97.5% |\n",
+      "|:--------------|-----------:|-------------:|----------:|-----------:|-------:|--------:|\n",
+      "| Intercept     |      5.247 |        0.143 |    36.790 |      0.000 |  4.960 |   5.534 |\n",
+      "| x             |      0.447 |        0.018 |    24.764 |      0.000 |  0.410 |   0.483 |\n",
+      "---\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "fit1 = pf.feols(fml=\"y ~ x\", data=data)\n",
+    "fit1.summary()\n",
+    "\n",
+    "# weights parameter accepts the name of the column in `data` that contains the weights\n",
+    "fit2 = pf.feols(fml=\"y ~ x\", data=data, weights='w')\n",
+    "fit2.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also compare the WLS standard errors to heteroscedasticity corrected OLS standard errors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-20T18:36:55.963625Z",
+     "start_time": "2024-06-20T18:36:55.910289Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "OLS       0.023321\n",
+       "hetero    0.028687\n",
+       "HC1       0.028687\n",
+       "HC2       0.029649\n",
+       "HC3       0.030645\n",
+       "WLS       0.018034\n",
+       "Name: se, dtype: float64"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hc_standard_errors = [fit1.vcov(vcov).se()['x'] for vcov in [\"iid\", \"hetero\", \"HC1\", \"HC2\", \"HC3\"]]\n",
+    "hc_standard_errors.append(fit2.se()['x'])\n",
+    "\n",
+    "pd.Series(hc_standard_errors, index=[\"OLS\", \"hetero\", \"HC1\", \"HC2\", \"HC3\", \"WLS\"], name='se')"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -2598,10 +2971,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
-  },
-  "orig_nbformat": 4
+   "version": "3.12.3"
+  }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }