{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bb1af397-500c-4a88-8168-abc57a1aa513",
   "metadata": {},
   "source": [
    "# End-to-end Learning with Autoencoders"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d79b1ed0-1dcc-4a62-a9dd-09c13eec0b80",
   "metadata": {},
   "source": [
    "In this notebook, you will learn how to implement an end-to-end communication system as an autoencoder [1].\n",
    "The implemented system is shown in the figure below.\n",
    "An additive white Gaussian noise (AWGN) channel is considered.\n",
    "On the transmitter side, joint training of the constellation geometry and bit-labeling is performed, as in [2].\n",
    "On the receiver side, a neural network-based demapper that computes log-likelihood ratios (LLRs) on the transmitted bits from the received samples is optimized.\n",
    "The considered autoencoder is benchmarked against a quadrature amplitude modulation (QAM) with Gray labeling and the optimal AWGN demapper."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4694269-5aee-4623-9ab0-6a9f84d9956c",
   "metadata": {},
   "source": [
    "![System Model](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABdwAAAODCAIAAAApX1QrAAAAAXNSR0IArs4c6QAAAHhlWElmTU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUAAAABAAAARgEoAAMAAAABAAIAAIdpAAQAAAABAAAATgAAAAAAAACYAAAAAQAAAJgAAAABAAOgAQADAAAAAQABAACgAgAEAAAAAQAABdygAwAEAAAAAQAAA4MAAAAAVoSI/AAAAAlwSFlzAAAXYAAAF2ABPHxl7AAAQABJREFUeAHsnQe0FFXWRiUIiAgIKgqYBRUlqCAYMWAYFdHR34A5y6hjRDAiRoygYhwDoIIBcwQTCCoIKIoREwYEE4qABAn/1jtTU/MSL3So7t693upVXXXDuftW9+v79TnnVlu2bNkKPiQgAQlIQAISkIAEJCABCUhAAhKQgAQyS6B6ZruzNwlIQAISkIAEJCABCUhAAhKQgAQkIIE/CSjKeB9IQAISkIAEJCABCUhAAhKQgAQkIIEsEFCUyQJ0u5SABCQgAQlIQAISkIAEJCABCUhAAooy3gMSkIAEJCABCUhAAhKQgAQkIAEJSCALBBRlsgDdLiUgAQlIQAISkIAEJCABCUhAAhKQgKKM94AEJCABCUhAAhKQgAQkIAEJSEACEsgCAUWZLEC3SwlIQAISkIAEJCABCUhAAhKQgAQkoCjjPSABCUhAAhKQgAQkIAEJSEACEpCABLJAQFEmC9DtUgISkIAEJCABCUhAAhKQgAQkIAEJKMp4D0hAAhKQgAQkIAEJSEACEpCABCQggSwQUJTJAnS7lIAEJCABCUhAAhKQgAQkIAEJSEACijLeAxKQgAQkIAEJSEACEpCABCQgAQlIIAsEFGWyAN0uJSABCUhAAhKQgAQkIAEJSEACEpCAooz3gAQkIAEJSEACEpCABCQgAQlIQAISyAIBRZksQLdLCUhAAhKQgAQkIAEJSEACEpCABCSgKOM9IAEJSEACEpCABCQgAQlIQAISkIAEskBAUSYL0O1SAhKQgAQkIAEJSEACEpCABCQgAQkoyngPSEACEpCABCQgAQlIQAISkIAEJCCBLBBQlMkCdLuUgAQkIAEJSEACEpCABCQgAQlIQAKKMt4DEpCABCQgAQlIQAISkIAEJCABCUggCwQUZbIA3S4lIAEJSEACEpCABCQgAQlIQAISkICijPeABCQgAQlIQAISkIAEJCABCUhAAhLIAgFFmSxAt0sJSEACEpCABCQgAQlIQAISkIAEJKAo4z0gAQlIQAISkIAEJCABCUhAAhKQgASyQEBRJgvQ7VICEpCABCQgAQlIQAISkIAEJCABCSjKeA9IQAISkIAEJCABCUhAAhKQgAQkIIEsEFCUyQJ0u5SABCQgAQlIQAISkIAEJCABCUhAAooy3gMSkIAEJCABCUhAAhKQgAQkIAEJSCALBBRlsgDdLiUgAQlIQAISkIAEJCABCUhAAhKQgKKM94AEJCABCUhAAhKQgAQkIAEJSEACEsgCAUWZLEC3SwlIQAISkIAEJCABCUhAAhKQgAQkoCjjPSABCUhAAhKQgAQkIAEJSEACEpCABLJAQFEmC9DtUgISkIAEJCABCUhAAhKQgAQkIAEJKMp4D0hAAhKQgAQkIAEJSEACEpCABCQggSwQqJmFPu1SAkklsGDBgnHjxvE8evRoDiIzw/nopQcSkIAEJCABCUhAAhJIK4Htt9++Zs1/L9Y4rlGjBt1xsNNOO0Xn02qAjUtAApkhUG3ZsmWZ6cleJJBAAr/++uuoUaPGjx8ftJi4EJNAazVJAhKQgAQkIAEJSEACEAjqTOfOndVovB8kkOsEFGVyfQa1v8IEghCDLwxyzOTJk+P169Sp06lTp9q1a/PvjYPoUjgfvfRAAhKQgAQkIAEJSEACaSUwduzYxYsXhy6iYw74EhudD1cVaNI6ETYugXQTUJRJN2HbTwqBErWYhg0btmvXrkGDBmeccYbKS1KmSjskIAEJSEACEpCABMok0K9fP+Lri2s0vXv37tOnD19ry6ztRQlIIEEEFGUSNBmakiYCyDFPPvnkgAEDgl9MEGLWXXddhBgUmTR1arMSkIAEJCABCUhAAhLIAIEg0PC8cOHC0F3wnbngggtUZzLA3y4kUEUCijJVBGj15BIoosVgKHJMt27d1GKSO2daJgEJSEACEpCABCRQWQLF1RkdZyrL0noSyBwBRZnMsbanjBEoIseoxWSMvB1JQAISkIAEJCABCWSdQBF1Rmkm6zOiARIog4CiTBlwvJR7BJRjcm/OtFgCEpCABCQgAQlIID0EUGcuueSSENakNJMexrYqgaoSUJSpKkHrJ4fAtGnT+vbtO2jQIEzSOyY586IlEpCABCQgAQlIQAJZJFBEmunVqxdflbNoj11LQAJxAooycRoe5yqBuIOMckyuzqJ2S0ACEpCABCQgAQmkjUBcmiHHIps0Kc2kDbYNS6ACBGrgz1aB4haVQPIIvPvuu/vvv//tt9/OvoAHH3wwByeffPKaa66ZPEu1SAISkIAEJCABCUhAAtkhwJZMNWvWpO8ZM2awlzYxTZ06dXJ7puxMhr1KIEZAT5kYDA9zjQAOMoMHD0bpx3C2uEZhPProo3NtENorAQlIQAISkIAEJCCBzBEYMGAAX5tnz55Nl7rMZI67PUmgFAKKMqWA8XTiCUQZZIxXSvxcaaAEJCABCUhAAhKQQLIIRNKMukyyJkZrCo+A4UuFN+d5MWJClg499NAXXngBB5kQH2u8Ul5MrIOQgAQkIAEJSEACEsgEgRC7NG7cuNGjRxvKlAni9iGBUgjoKVMKGE8nlUA8ZKlt27bstdSuXbukGpt7dv3xxx8PPPDAvHnzTjjhhFq1auXeALRYAhKQgAQkIAEJSKDcBCJ/GWo8+OCD3bp1M8tMueFZUAKpIaAokxqOtpIZAvGQpaOOOor/IqntFzHioYce+uCDDzbbbLNjjz02tY3nRGtz5szZcsstf/jhh6lTpzZp0iQnbNZICUhAAhKQgAQkIIGqEIikGUOZqoLRuhKoHIE/82/7kEBOEIgUmTTl9F22bNl3333HvyKEiebNm+++++48l5/M/Pnzn3vuuc8///yII45Ya621yl/RkhKQgAQkIAEJSEACEsgiAb4A0zvZf8NPnu6WncW5sOsCJFC9AMfskHORQLoVGZigqowcORJFhuO5c+c+/vjjFQKFlw1bQV1//fX4mFSoooUlIAEJSEACEpCABCSQXQLoMogyDRo0QJfp27cvGQOya4+9S6BwCCjKFM5c5/BIM6DIQAch5oknnqhfv36PHj3CcRwZfjSkQCPlSvzkokWLOBk/U9rxkiVLcMP58ssvOSixDC1/8cUXxA0Vv7pgwYLFixdzfunSpZ999lnYv7B4MYz59NNPp0+fXvxScALCi6e03n///Xe0pNBL8eqcoQWqz5gxo8hVTKIuJ+n9/fffL9H+IlV8KQEJSEACEpCABCSQQALqMgmcFE0qBAKKMoUwy7k9xswoMogLyBljx47dZJNNzj77bH4l+Oijj957773AjquTJk1Cr+natWtEE5GldevWLVu2RKp45JFHVl999aeffhpVYqeddqpevfrmm28eSv7yyy89e/YkP0uzZs023HDDNdZY45RTTon/+PDTTz9xpnHjxlxt2rTpBhtsQF6beC/0e/DBB999991ERbVo0YKO9tlnnx9//DEqw/ExxxxDMYxZZ511tthii9deey1cxf3nwgsvpFN632ijjVZddVVy5WBSVHfmzJkMim3FN954Y3awYisrBhtd5QCl5rzzzqMA1TEPG+6///6oAJdWXnnlIUOG0DU0KBBcjaICHkhAAhKQgAQkIAEJ5AoBdZlcmSntzCsC/ADuQwKJJYBrydFHH81bjjwy9957b/rs/O233y699FJ0Ddw1f/755yOPPHKVVVbBhzP0iDAxfvz4FVdccZdddols+PbbbxFQyDuDOvPOO+/sv//+CB+1a9dGlDnssMOQQiiJXHLAAQeg0VCM/YxoFlGDdkhYQ5uhABU50759e6rsvPPOFK5bty76Tujom2++Yfic5IHwcfjhh6+22mqUR4UJBXDqobVq1aqh6Zx00km0Rs78PfbYg6tEVLFxOJeQhI4//vjjjjsOC2lnm222Cb1Td8cdd6QAUg4tI7uETzc4INaE9jnPyU033fSyyy7ba6+9qM7Lxx57LFw966yzQhVM2nPPPQGCKBMu+SwBCUhAAhKQgAQkkIsE+vfvzy+UfMdDo+HHvFwcgjZLIIcI/BmV4EMCySTw1VdfoTKwMTOSwW233UagUPrsxNtl6623xlVkwoQJeJewI2CNGjU6duxIVBGdli3K4B1DAaxFs8CN5YUXXkANoREMJvUvMg1iCqE9hPkgWIwZMwZdBteVp556ipAfCqy00kp09P3331MFKeTaa6+l66Cq0HUQZZA8unfvzj9FWqB9/kdiKp3yeP7557mKGoKARReoS/j73HPPPVyiL+jhvYLLD5eQYBCP0GXq1as3fPhwCrz66qvURTD68MMPsRl16ZBDDqHxIMoQ6zR58uTAn0tEUdHCwIEDKbDddtuFuQiiTM2aNYn84iq9p2+ObFkCEpCABCQgAQlIIAME+NZ31VVXocvwPbZXr164eGegU7uQQMESMHyJBaaPJBIgcwreInjHoD4QI0PQDepAmgxFniCjyrvvvouzCbE/eJqgktAvOsubb75Znk6RUXBv4RmvE6pzzDMiS1A9DjzwQDxNEF9QQ9hsG98ZLr311ls8v/HGG8giuLc0atQIaYN/fsgxqCFTpkzhUyl0TZs4Ct1+++3EENEC4hEyCiILcU9YjoV4rxDfRBm6wMEHRxhcdbhE3mJaOOigg+iUS8QZEaOE7wxiELVCXRrfb7/9CNrCZtQifhhZe+21Q78IUi+//DKFqYLYxOj4x7z33ntzFbEmMo+XhF9xnvbpPdT1WQISkIAEJCABCUggRwnwle/MM8/s3bs3X2jJ+/viiy+WM4tijo5XsyWQXQKKMtnlb++lEsC5g1QsGVBksAAnkWeeeQZNZMstt8QzhSw2+KSgzuCW8uSTT5Zq4vIuoHrgvcJ/NTSREPVDDV62atWKS0Q/heS+eJcQJ0VQEg/Gu/322weT6D3qgf+Ikd6BiENqGDQRNB3yv5AemMZpE3kllOclAhaXSM2LktK2bduod8ZI5hcuff311zyjOlGA9DdRXYSVSPyiwCeffEKb/DMO5vFMa8XNo01ajqz1QAISkIAEJCABCUggpwnEdZmjjjoKn+6cHo7GSyDJBFxHJXl2Ctc2JAOyyeKdkW4fmYAYUYYkKTzfeeedgwYN4iSSBz8IIJ2MGDGCLDNoIuFk3D0k1C3jGVEj/KqAjBIvFhoJOgh+KPiwkI0liB2hGJdwWiGGKF4rfoySwkva4YHZHEeyS1SMS7ie8rKM3pF1KFBET4kEGi5hHs+77ror7jkcRA/Umbh5xXuPSnogAQlIQAISkIAEJJCLBIIuM3HiRH68JJvhsGHDCMDPxYFoswQSTkBRJuETVIjmochceeWViCPEEKU1ainAxSGFPDJ0Sppbwogi4kGpIcnLSy+9xP5ESCeILPHtqAkyCppFVCUc4P8SDhA7SODCS2KjojK8xIGFSyTK4ZlYIUSTDh06RBlzQ8m4MhLVLX6AGkIoE4bhL1PkKpdonEtsIxVd4iXGcGm99dYL0g9n2GY7KhAfFMXWX399LuHpg/9qVIaDcpoXr+KxBCQgAQlIQAISkEBuEUCXYZNNwvDZ2fOGG27g59KQADi3RqG1Ekg4AcOXEj5BBWdeUGRCKhn2/YlCadIHgjS95NxFc6E7NmCKHhdffDEaDdIMKWyRJ3AM4d8SGg2pZzCGfC4XXHABu2hHhqFTEPuD5hLpI5QnHiqIO6Ekksenn37KjtckcGEjJwq0a9eOAgRJkSWXl+GBTBN8WKLGSzvAZYYwK7QhfrvAJIohsvCDxo033sgl3Fu4xHbdIVswvjOEU9133320T+YaJCF6pwC9h7rkqWFQoTBN0QK6GAekPSbG6j/W1S6/eaWZ7XkJSEACEpCABCQggZwgwLdWvpmT/dDkMjkxXxqZiwT0lMnFWctnm8nnEiX3DZthp3W06BTsK0RGYfSUfffdN4QphR6RJLp168auT/wygHBDAXaexnuT/Ym23XZbhA88UOIOI0gV+No8/PDDl19+OUl8eUld4pLatGlD1l7qst01CgjJd0lYQ15eGkEWCZFBtMYBuXJJHEOuGXZNYnujW2+9dbljpxe8eFq0aEHyF7biZn9rNuF+5ZVX2GD79NNPZzeorbbaih2XaBwDGCxbNaErITZRkt532203tm3CdwaNBnsoidNQNKhgHmaPGjUKe/7+9783btwYXAwBuYdpWq55FpCABCQgAQlIQAISyHUCfG/Ep5v9mEguwxdg3L1zfUTaL4FkEWCd5kMCCSFA3tkTTzwxAxtgR+MldumOO+5A2sBvhQwy0flwgD5CElzyp+BLwtW3336bjY3wmsFnBB3kmmuuQekgRAg3E8rjBcPG0oggvMPxuwmbRlOLzbARQTiD1w+iD3sk4YxDGFToglr8b2O7a36FCK4olCFvLt40oQAuNshDKDvhJc/YTLgT8g24eEkLOO907twZDYUueODewq5JXAqhSey+ROOcp31S1Zx//vnRvobUnTRpEjbTBVcRaNiQG/EFtyDEndAjIs4JJ5wQEgBjG+0wdjL+hKvnnnsu4x08eHB46bMEJCABCUhAAhKQQP4RIFMh+4fydZHvzCT9zb8BOiIJZJFANfpOlkqkNYVKIApcQlMgjQuL/8yQIGYH1xW2mi4eIouogXpCJBG6DPbwklAjJBhkEXKy8G+Juji/kPMseJfwbiLMB18S8vsiLeHnyRA4SQtU5DzSDPsrUZFHNLpQgDJoQLTWpEkTNldC/kAqCtWRRegdL5VQhfIYTBc0FfVL+3QNQ0zlPB2hs4TyGBkap9OmTZvSFI3Heydy6ueff2ZQCEZcojBVosYpyRm6I6yJXjiPJZF5lJw1axY2B2ujZj2QgAQkIAEJSEACEsgnAqNHj8bXmy+NRM3jqR3/NptPw3QsEsg8AUWZzDO3x5IJECVENA1rftKa4C9TcqFknEWdwV+mNFsQTXgUL0AtNJQgo5RYN1Qsu0yJFaOTZXRRxqVQvexBUabq5kV2eiABCUhAAhKQgAQkkFsEcAC//vrrCWIiKSE+3fwml1v2a60EEkug1IVlYi3WsLwkgIvHAw88gH/HPvvsk4FUMlVkWFxwiTeIqlJiAU6WocjQQqhYdpl4R8WPy+iijEuhnRJtjndRdfPirXksAQlIQAISkIAEJJBDBIiUP+OMM7p06UIyxP79+8f3JM2hUWiqBBJIQFEmgZNSiCaR35c9sBs2bMh2RRkLXCpE0I5ZAhKQgAQkIAEJSEAClSJAyNJpp51G1L87MVWKn5UkUDIBRZmSuXg2kwSCmwyBS7179z7iiCMy2bV9SUACEpCABCQgAQlIQALlJMBeFjvssAOFBw4cyN4R5axlMQlIoAwCijJlwPFShggENxmyzB577LG6yWQIut1IQAISkIAEJCABCUigggQIYjr11FNxb3/99dfHjBnDXhAVbMDiEpBAUQKKMkWJ+DrDBCI3mcMPP1xFJsPw7U4CEpCABCQgAQlIQAIVIhCcZdjoU2eZCnGzsARKI6AoUxoZz2eIQOQmk/z8vhkiYjcSkIAEJCABCUhAAhJIKgGdZZI6M9qVqwQUZXJ15vLDbt1k8mMeHYUEJCABCUhAAhKQQOEQ0FmmcObakWaAgKJMBiDbRakEdJMpFY0XJCABCUhAAhKQgAQkkEgCcWeZBQsWJNJGjZJAzhBQlMmZqco/Q3WTyb85dUQSkIAEJCABCUhAAoVAIHKWmTBhgul+C2HGHWP6CCjKpI+tLS+HwC+//DJp0qRq1aqZ33c5pLwsAQlIQAISkIAEJCCBJBGInGVM95ukadGWnCSgKJOT05YfRs+ePXvy5MnrrLNO9+7d82NEjkICEpCABCQgAQlIQAIFQgBnmTp16rg3doFMt8NMHwFFmfSxteWyCITYpWbNmrkTdlmYvCYBCUhAAhKQgAQkIIFEEsBZpkOHDu6NncjJ0ahcIqAok0uzlU+2hhS/xi7l05w6FglIQAISkIAEJCCBgiJw6qmnNmzY0JwyBTXpDjblBBRlUo7UBpdPADeZ9957b9GiRcYuLR+WJSQgAQlIQAISkIAEJJBIAiHd75QpU8aMGaM0k8gp0qgcIKAokwOTlH8m4iYTsskYu5R/k+uIJCABCUhAAhKQgAQKhAARTIQvLVmyZNy4ce6NXSCT7jBTTkBRJuVIbXD5BIIoY+zS8klZQgISkIAEJCABCUhAAgkm0KNHjwYNGijKJHiKNC3pBBRlkj5DeWnfV1995b5LeTmzDkoCEpCABCQgAQlIoKAIhD2Yxo8fr6dMQc27g00hAUWZFMK0qXIRIKEMnjL16tVr27ZtrVq1ylXHQhKQgAQkIAEJSEACEpBA8ghEezBNmDDBtDLJmx8tygECijI5MEl5ZiKKDA/ytLdr1y7PhuZwJCABCUhAAhKQgAQkUGgESCtDXgIjmApt3h1vqggoyqSKpO2Ul0AQZSjNx3d561hOAhKQgAQkIAEJSEACEkgkAdPKJHJaNCpnCCjK5MxU5Y2hJJRBl3Ez7LyZUAciAQlIQAISkIAEJFDIBGrXrs3wTStTyPeAY68KAUWZqtCzbmUILP3rUb16dRPKVAafdSQgAQlIQAISkIAEJJAkAu3bt69Tp87ixYuTZJS2SCBnCCjK5MxU5YehZPnlgZtM69at82NEjkICEpCABCQgAQlIQAKFTCDk+sVfxly/hXwbOPZKE1CUqTQ6K1aGQEgos95665nltzL4rCMBCUhAAhKQgAQkIIHkEQi5fpcsWbJs2bLkWadFEkg0AUWZRE9P/hkXYpdIz26W3/ybXEckAQlIQAISkIAEJFCYBDp06KCnTGFOvaOuOgFFmaoztAUJSEACEpCABCQgAQlIQAKFS6Bjx46klXFX7MK9Axx5FQgoylQBnlUrTsCcMhVnZg0JSEACEpCABCQgAQkkmgBpZbCP8KVEW6lxEkgkAUWZRE5L/hpFlCkRTGy9ZPhS/k6yI5OABCQgAQlIQAISkIAEJCCBchFQlCkXJgtJQAISkIAEJCABCUhAAhKQgAQkIIHUElCUSS1PW1sOAcOXlgPIyxKQgAQkIAEJSEACEpCABCRQMAQUZQpmqpMx0LD7kuFLyZgNrZCABCQgAQlIQAISkEAKCLRv355EvxMnTlywYEEKmrMJCRQSAUWZQpptxyoBCUhAAhKQgAQkIAEJSCDVBEKi38WLF6e6YduTQP4TUJTJ/zl2hBKQgAQkIAEJSEACEpCABCQgAQkkkMCfW5f5kIAEyiCwcOHCl1566bbbbqMMDpm4ZZZR2EuJJdCpUyd+wwnPZ555Zu3atRNrqoZJQAISkIAEJCABCUhAAgVCQFGmQCbaYVaYQNBi+vfvjwrzxx9/LFq0KDShW2aFUSajwssvv4wh4blfv36oM9tvv33Pnj1VZ5IxP1ohAQlIQAISkIAEJCCBQiSgKFOIs+6YyyYQyTFvvvkmWkyNGjW6dOnSo0cPapHAjDRmZVf3ajIJjBs3DkEtPKO1oc6MHj167NixSjPJnC+tkoAEJCABCUhAAhKQQCEQUJQphFl2jBUgMHv2bNwobrrppmrVqu28887EuaDCrLjiirVq1QqthDRmFWjRoskgsOuuu2II+hrPvXv3HjBgwFVXXRWkGcSa8847T5eZZEyUVkhAAhKQgAQkIAEJSKCACJjot4Am26Eul8APP/xwwAEH4EaBCnPhhRc+/PDDnTt3btCgQd26ddFiwmO5jVggmQTC9KG88GBO0WW+//57ZnnZsmVXX311t27d5s2bl0zLtUoCEpCABCQgAQlIQAISyFcCijL5OrOOq8IEvv766xYtWhDegjPFY489ho9M0GIq3JAVcoFAJM08+eSTRKjhMtOsWTP8pHLBdm2UgAQkIAEJSEACEpCABPKEgKJMnkykw6giAXxkjjnmGLLJ7LnnnjjI7Ljjjizaq9im1ZNPgFkmrGn69OkocXPnzj344IP1l0n+rGmhBCQgAQlIQAISkIAE8oaAokzeTKUDqTwB/CNuuOGG1157rUmTJkOGDNFBpvIoc7AmYU1EMw0fPny33XYbNWrUtddeizaXg+PQZAlIQAISkIAEJCABCUgg9wgoyuTenGlxagkQtURmX9K+NmrU6N5770WRSW37tpYTBFZeeeVzzjkHgYb8Mq+++uqSJUtywmyNlIAEJCABCUhAAhKQgARymoCiTE5Pn8angMCvv/7KvsjsdX3WWWcRtZSCFm0iNwmQ1Llnz57k/b3uuusWLFiQm4PQaglIQAISkIAEJCABCUgglwgoyuTSbGlrygngJnPrrbeS3Ldhw4ZnnHEGjhIp78IGc4UAs8+WTOyDjkg3ZswYnWVyZeK0UwISkIAEJCABCUhAArlLQFEmd+dOy1NAADeZiRMnErrSo0cPM/umAGiON8E9EIKYdJbJ8ZnUfAlIQAISkIAEJCABCeQGAUWZ3JgnrUwTAUSZyZMnBzeZNHVhs7lFgCAmXGZGjx6ts0xuTZzWSkACEpCABCQgAQlIIBcJKMrk4qxpc2oIELv0wAMPNGvWTDeZ1ADNi1ZQZLbZZhsyyxC+xHNejMlBSEACEpCABCQgAQlIQAIJJaAok9CJ0awMEGDJvWjRourVqxu4lAHaOdQFEUwkfu7fv797Y+fQrGmqBCQgAQlIQAISkIAEcpGAokwuzpo2p4bA7Nmzp0yZ0qBBgw4dOqSmRVvJCwJBpFORyYvJdBASkIAEJCABCUhAAhJINAFFmURPj8allUAIUUGU6dixY1o7snEJSEACEpCABCQgAQlIQAISkEBxAooyxZl4plAIBE+ZatWquRN2oUx5+cbZqVMnbgk2Sl+8eHH5alhKAhKQgAQkIAEJSEACEpBAZQgoylSGmnXyg0DwlMmPsTiKFBIIIp2KTAqR2pQEJCABCUhAAhKQgAQkUCIBRZkSsXhSAhKQgAQkIAEJSEACEpCABCQgAQmkl4CiTHr52roEJCABCUhAAhKQgAQkIAEJSEACEiiRgKJMiVg8KQEJSEACEpCABCQgAQlIQAISkIAE0ktAUSa9fG1dAhKQgAQkIAEJSEACEpCABCQgAQmUSEBRpkQsnpSABCQgAQlIQAISkIAEJCABCUhAAukloCiTXr62LgEJSEACEpCABCQgAQlIQAISkIAESiSgKFMiFk9KQAISkIAEJCABCUhAAhKQgAQkIIH0ElCUSS9fW5eABCQgAQlIQAISkIAEJCABCUhAAiUSUJQpEYsnJSABCUhAAhKQgAQkIAEJSEACEpBAegkoyqSXr61LQAISkIAEJCABCUhAAhKQgAQkIIESCSjKlIjFkxKQgAQkIAEJSEACEpCABCQgAQlIIL0Eaqa3eVuXgASWR2DhwoVvvfXW4sWLyyi44oorbr311rVq1SqjTNmXaP/NN99s3Lhxq1atyi5Z/qtTp04999xzV1pppWHDhpVWa86cOUcdddTSpUvvvvtuei+tmOclIAEJSEACEpCABCQgAQkUIAFFmQKcdIecLAI//vjjQQcdhHhRhln169cfN27cOuusU0aZMi6hyDz99NNHHnlko0aNxo8fv+aaa5ZRuPyXsPzZZ59deeWVy6iC5ETXFFiwYEEZxbwkAQlIQAISkIAEJCABCUigAAkoyhTgpDvkZBGoWbPmBhts8Ntvv2HWsmXL5s6d+9VXX1WvXn399dfHCSXYWq9ePZxlKm13tWrV8LKhZQSUqrjbFDEAa5F7eBQ5H38ZysTPeCwBCUhAAhKQgAQkIAEJSEACgYCijHeCBLJMoEmTJiNGjEC8wI4//vhjypQpO+20E+rJk08+GXeNQZeptKE1atTYbbfdJk2a1KBBg1VXXbXS7VhRAhKQgAQkIAEJSEACEpCABFJIQFEmhTBtSgKVIYAbSyS4IMrUrVs3tIIus8oqq8RbJBRo8uTJeNa0a9eOcCcCmggg2mSTTTp06ICm8+WXX77//vvff//9WmuttcUWWzRr1izUJZ/LBx988Pvvv1Nm/vz5dMf5X3/99dNPP8UZh4Cmzz//nGY52bZt25YtW8Z75Bi3HXSi7777bo011mjfvn3z5s2LFOAlPjikxaEk1du0aVPE7OLlwxlMfeedd2bMmIEZm2+++WqrrVZaSc9LQAISkIAEJCABCUhAAhLISwKKMnk5rQ4qDwkgqUyfPr1Lly6oNtddd13Pnj2RQohpOuWUU1ZfffUePXq8/vrrS5YsoVhQeY4//vjLL7+cY+SYk046CWGFkKiNNtoIfxmKTZgw4ZBDDunWrRu+M6Tg5QzIaPmss84677zzOKYdRJbTTjtt9OjRUbN0d9xxx9F7UHYC5Xnz5iESzZw5E/UHlxw8cW644YYDDzywjDlAe7r66qsptmjRImphWOj67LPPrkqUVhk9ekkCEpCABCQgAQlIQAISkEACCbgldgInRZMkUDIBhBKEmB9++OEf//gH2sdhhx3GVkrdu3cn9Qw+MkgnnTp12n333dE1KHPrrbc+8sgjNIRnDedxRcG5BoEmNE3hWbNm3X///f/6179wtNl///3JNfPTTz9df/31b7/9NmWQXWj23XffxT2nY8eOe+21FwVmz549cODAoUOHFrGPZskifMABB9SpU+ebb75hu6Uy9mOiLuILghFpdPbYYw8Eo2222QaXn759+6LUFGnZlxKQgAQkIAEJSEACEpCABPKYgJ4yeTy5Di0/CeBXst56640cORJdhiS7+JigsNx8883rrrtuCD5C4Dj00EMnTpz4wAMPsK8TQgl6xxNPPMG+S0WIoLPccssteLUgwRBMtPPOO+PwwoZKW265JSWJbBowYAB5bTbddFMK4BGDdoM/zr333oseFDVFI7Qcgo+ojgMOoUy42+y9995sGhUViw5ee+21O+64Ax+Zhx9+mDIMB/uvuOKKq666ql+/fieccAJJdqLCHkhAAhKQgAQkIAEJSEACEshjAnrK5PHkOrT8JIAjDD4pJI5BbSEZDaIGZ/bZZx8ywiCdEHNEshiOUTpIFhMQ4PNSu3bt4jiIXcLRhuQ1KDvsk73LLrsQTEQLoSTN7rvvvgg0BCV9/fXXJKYJYs3UqVPjTVFs7bXXpgseqEV46GAG5ZFv4sWi4yFDhqDI4B2DxEO/jAIDCMKiHXQfwqyikh5IQAISkIAEJCABCUhAAhLIbwJ6yuT3/Dq6PCSAeNG1a9f4wAhrws+FVC+EIxFthLCCBw0ncZmJFyvxmNbCeZQUUvlSK2zOzUmOyQd87bXX4hqzYMGC0CzniY0qsSlO0gg5hkkGTBDThx9++Le//a14yZBUmKQ2SDnRVfoi0QwvqRid9EACEpCABCQgAQlIQAISkEB+E1CUye/5dXR5SADXmHiAD3IGQgYuLR999BG6yVZbbbXttttOmzbt8ccf51L5x4+eQiAS5UOt0CzOLGSrQS7BR4aNuumImKOym8U8Eg9TEkGnxN6xjfNRsFWRMq1bty5yxpcSkIAEJCABCUhAAhKQgATylYCiTL7OrOPKZwIIH9HwcI257bbbkE5wPHnsscdatGiBvMKWSYgyUZlKHIRm8Wpp3Ljxc889x8bbJAwmWQyizHJbCx46hDKVWBI/mp9//vnggw8+//zzixcgmqn4Sc9IQAISkIAEJCABCUhAAhLISwL/Xdrl5fAclATyngCRSkgwKCZHHHEEqWTIMoOuUWIGmQqhCM3ienP00UfjJlP+ZolaYutu+gpZhyP9iK2dggHsA8UBiYpDNhkSysQf5K+pkJ0WloAEJCABCUhAAhKQgAQkkLsEFGVyd+60XAJ/EsAvhny9Ia1vUEDI+XLDDTdwiXy6UYKYisIKzVKLtL6hWZq68sorizeLT82kSZNC+0QtnX766Ug57du3J5UvJ9kiCtcYDu68807s4YD9lWjwnXfeYYcm6oaKZBEONoeXPktAAhKQgAQkIAEJSEACEigEAooyhTDLjjGfCZCpl62XcGx59NFHySbD/tbbbbfdiy++yJgRQYYPH165wYdmqcsO2Z06dSLaCH+Z559/PrQ2dOjQqFm6Zi9tMs7ssMMOZLR59dVXcash6zDOO5RBfwlpie+6664OHTpwZvvtt0e4QUW68cYbN9tsM4xHvuHkBRdc8MYbb0TNeiABCUhAAhKQgAQkIAEJSCDvCSjK5P0UO8BcIoB/SnBLwejoIBpAdCY64BLZeY888sgzzzwTmWP8+PFPP/00+0wPGzbs4osvRi4ZNGhQqB6qRBXpiPPRy1AmfjI027t3by6xUxLZajh46KGHcJahFm4voQrP6DX4wowdOxZJZfbs2QhDr732GiJLVKBXr17seE1EFftkc5IAJRqhBTZ7+uKLL0aMGDFx4kQcavCgIXNNVMsDCUhAAhKQgAQkIAEJSEACeU+gWtkbqeT9+B1ghgn07dv3kksu6dOnD88Z7rp4d6Riwb+jc+fOo0aNKn41W2eI/ZkzZw69r7LKKkVEE96tIRypfv36QUCJjGTLapK2zJgxY6211iKaaaWVVmLLJPbJnjdvHt4oFEOg4Zha1OUlCs7cuXM5oHDUCMFEtIOHC0lewknOkJSXoKSmTZs2bNiQZqlIL1jINkk0iF5DpBKGzZw5E9tI7kumGIrFLecq7ZD997vvvuvYsWNoGfPmz5/PyVmzZpGimB7xzUG4KTKuyLYMHzBYBCY2kIrzybANdicBCUhAAhKQgARyiADfA7/66iv22WSTzRwyW1MlkHUC7r6U9SnQAAn8DwHkjNKEAASL0i4hheCugnQSqSFoK/xHRBAJrfMyXhd3lfjLUAZNhEfcGl7S5pprrllisygphCyF1LyELNFXVCzeCGZjHspLs2bNovNIMDwQntZff/0Sa0UlPZCABCQgAQlIQAISkIAEJJCvBBRl8nVmHVchEiiibqCG8Kg6iDKajTZLKk9fRdrBsPLUqrr9tiABCUhAAhKQgAQkIAEJSCCZBMwpk8x50SoJSEACEpCABCQgAQlIQAISkIAE8pyAokyeT7DDk4AEJCABCUhAAhKQgAQkIAEJSCCZBBRlkjkvWiUBCUhAAhKQgAQkIAEJSEACEpBAnhNQlMnzCXZ4EpCABCQgAQlIQAISkIAEJCABCSSTgKJMMudFqyQgAQlIQAISkIAEJCABCUhAAhLIcwKKMnk+wQ5PAhKQgAQkIAEJSEACEpCABCQggWQSUJRJ5rxolQQkIAEJSEACEpCABCQgAQlIQAJ5TkBRJs8n2OFJoEIEvv/++/3337979+6zZs2KKt5999377bffrbfeGp2p4sGPP/74zDPP/P7771Vsx+oSkIAEJCABCUhAAhKQgARymoCiTE5Pn8ZLoFQCf/zxx9dff13q5ZIuLFu2bN68ecglI0aMWLBgQVTk3XffffbZZ3mOzpTzYPHixV999VXxwgcddNDBBx980UUXFb/kGQlIQAISkIAEJCABCUhAAoVDQFGmcObakRYKAeSYoUOHtm3b9uqrr67omNFlUFJ4xCsuWbKEM0uXLo2fXO7xY4891qpVq759+xYvWa1aNdxkatSoUfySZyQgAQlIQAISkIAEJCABCRQOAUWZwplrR1ooBNBQJk6c+Pnnny9cuDCLY54wYcJnn30W97iJjBk+fPgrr7xSol4TlfFAAhKQgAQkIAEJSEACEpBA3hOomfcjdIASyBYBvE7IzPLxxx9/+eWXjRo1atOmTfPmzSNjuPrLL7+8//7706ZNa9as2aabbtq0adPo6q+//vrpp5+uv/76VERemTx5MpdwfmnZsmVUhoNFixahfRAi1KFDhxYtWnAGf5YpU6Z89913HPA8fvx43FKaNGmy7rrrRhURSj788MMPPvhglVVWodYmm2xSOacV+g19rbHGGu3bt4+PjvPffvstY5w5cyY20DVlGA7j+uSTT3hZu3bt+fPnr7TSSpFVc+fOpRa41lxzzc0333zttdeOLn300UcITK1bt8by9957DzJrrbUWQ27YsGFUxgMJSEACEpCABCQgAQlIQAI5R0BRJuemTINzgwDqw0033XTLLbcQp4PrCspI3bp1d9999/79+yNPoEc8+uij559/ProMV6tXr46+cMIJJ/Tq1atOnTqcQWo55JBDunXr1qBBA/LscoZh08JZZ5113nnncYxIccMNN9x+++3oPlytVasWsgtnOnbseNppp7399tsEHL344otjxoxZccUVyeFCSWqh1CCRnH766cgc1MIqKm6zzTbXXnvtZpttVn6yP/zww3HHHTd69GgaQXmhHXrhzHXXXccx7Zx88skMgQMM6NKlCwfkDx4yZMgbb7xBNpnQ0c0333z00UeH4+eee+6UU0756aefaBAaiETHHnvslVdeGVSbc8899/XXXx84cOA111yDwkV8FmWQeAYNGrTFFltwHBrxWQISkIAEJCABCUhAAhKQQG4RUJTJrfnS2twg8PPPP59xxhmPPPII5u6www6kVvnmm2/QI9jbCOcU3D2I30G2QIBAQ9l6662/+OKLp556ChGHPLvoDtTiEmrL/fffj2hCGRxDSLWLZnH99dfvscce7dq1e+eddy699FKUF9xnOnXqRBZeXEjwuEEc2XvvvRF98MFBtkAGqlmzJl3QJooMJ/fdd18Eo4022mjnnXfGEtL6ot2g2owaNWq11VYrJ19GQY8IQ9tttx2+PC+//DJtIppsueWWhx12GI107doVGzASh5e99tqLMzi28IzPDvYTu4QahbYSuiOvMAagXlEGq3DwGTZsGJs9/fbbbwhSlKGpOXPmIFohUdEyCYyBibNPnz59Hn74YbSq0I7PEpCABCQgAQlIQAISkIAEcouAokxuzZfW5gABpIqXXnrpoYceqlev3uWXX37UUUfh9xFEFkQQXD8QaPr164eDCT4jCDEE8qDFoG6cffbZSAxHHHEEIk4YJ4oMvjYHHngg7icIOggWRAOhzhDIgyRBa/Xr1yefLq43dDpp0iSCm3Ab+ec//0kxfGFoE9cVmgrRSage55xzDmoI7Tz44INoGcg0uN507twZVQhLUHnKyZdRDBgwYJ111iHqCtuwH0cYnFnuvffeIMqceeaZeNMgyqCzxG1ATsJfBqEKM0Jf6Eq9e/emBbSbBx54AEchyOBcAzc0Kbx+qBJKEmZ13333rbfeephNSbpAzZk9e7aiTDlnzWISkIAEJCABCUhAAhKQQNII6PaftBnRnpwngE8HPjJIFTvuuOPxxx+/8sorIzTwjM8I/im4h+CuQuYU4pUQRHD94Grjxo3//ve/IzcgMSDoRAi42r17d+qiO5BpZZdddkGPIJMLyktIuUJOGZxK0G4oibYSAnlQefCOoRG0GBrngfsMFVFecFHhJdIMPSKs0PIGG2xA11gVoo2irpd7gMcNfjF0gd8K6Wk4psrUqVNDxRJt4BLlGUs84Ii6+Phw6ZJLLoEJ5mHYoYceCitG98ILL4QGed5nn32wluqoXQR2MSgEnZA9JyrjgQQkIAEJSEACEpCABCQggRwioKdMDk2WpuYGAQQOctmiLCDKIJfEjUap4SriCM4gCBA4vERX0Uc23nhjnE1I6xud5ADpIbykLh4xVCSoB2mDkCVkEYKecG/hGbcRHGriFYscI8qEPDL4yxx++OFBtaEMDeK0wtXp06cXqVL2S+KVyESDawwOO1RHH6E83jFl1yp+FVZUZ/hbbbVVdJVRA4fNm8jpGz8JhPASFEDgGBoMISrjgQQkIAEJSEACEpCABCQggRwioCiTQ5OlqblBAHmCjYdQPUgEU9xi4piQM3AVwd0j7jDCMbFIIcqpeC3OoEcEiSdoEGR1Id8KmX1vu+22cePG4ZKDUwmBQmg9JVZH+MClhUtoGSG9S7wYvbPhUfxM2cckzSE1DC4/aEz4yOy0007EZBF7VQl9hKboi7HHaXAmDIQ0OiVaQuEgylSixxIb9KQEJCABCUhAAhKQgAQkIIHME1CUyTxze8xzAogFyDEzZswo0W2Eq6uvvjoSCS4ecRDIMcgTpUk58ZLRMXl5L774YuQYdinCY4UsNmzAdNFFF0UF4oIFmg5pgLmE/EHEU+QpExUOGkf0suwDNk5il25ioNg1iVQvtPbWW28hyhSvFbeh+FXOEJbFM0JVkauBXrC5yCVfSkACEpCABCQgAQlIQAISyA8C5pTJj3l0FAkiQOgNqU+IEiKBC+JLEcu4Sn5cpApEmSlTpoSrvPzxxx/J1Iu6Qd0iVcp4SQYWyhNGREQSPZIDOBRGgqHNuNKB5kJSXq6SEpitiwgXKvKgKa4GdxXqBgeW0Fo4GXdaYbslhsaG1rjJ4PJDXZLIhMLRcwg1ihL6RueLHGy44YacYX8lhh9dIrcORvKSzDLRSQ8kIAEJSEACEpCABCQgAQnkGQFFmTybUIeTfQIEFpGdl7ie11577YorriDlCjaRs3bs2LF33HEHokybNm222GKLkJNl7ty5XEXvuPrqq5Fp2DF6v/32K88YUDF69eoVZB00kZDShZPURUPBVQfXG7xXyArMGY55RuAgVXCoyIbWoRcMwCr2cuIlMsqqq66KcwrCzeDBg7E5lMG1hwN2zib/SzgTYotI6xv0GizHW4dLVIk8gIKTy/jx47/88ksuYUNxiYrzhE2RH4eDCy+8kBzJoeT555+PmgNJtsrmjA8JSEACEpCABCQgAQlIQAJ5ScDwpbycVgeVTQJIJORYOeCAAx599NH+/fs/8cQTbKuE4wkSRqNGjf72t7+hcZxxxhknnngil0hki6sIGW3Jz0JgESfRU3BUKXsACByIHWSTIRAJ/xf2k0ZkwcsGzYWKHLDpNa4r7EzElkxssE3vN95449Zbb33ZZZexhxEZf0kSjBpCNmIMINIKq1BhqEutXXfdlV2r77rrLvZFev755zlJxmLEF3qhfXbOZmg08swzz4wcOXK33XZjHyhCmdhPKtg8fPjwY489lmMugQKfF6q3bdsWeej2229nl+5QLHqmZYKw2AHq1VdfxWysIhEye2lTgOzFTZs2jUp6IAEJSEACEpCABCQgAQlIIM8I6CmTZxPqcBJBgDwp119/PVoDGgeCBVFFb775JpaRHBedAikEd5j77ruPOCbUB7QbyiDcDBw4kAQxwfckxP6E42hI8ZO0vNlmm5FRGC0DgQYd55hjjunduzeFqYW0gesNgUVIIYQaocLgrsL59u3bI6agrXz//fd4vmAYLaCYEP0UeqHZs88++8gjj8SrhVrh5LbbbovLD4oSKk/YpIny6Ep4A40ePfrBBx9EecHdhnQ2dHHnnXeGWi1atEDBwduFLuiL1qKNpcK4wnAojAz09NNPIx7h+ENiGljhiYPkhCWhZPw5NM5znEZ00gMJSEACEpCABCQgAQlIQAI5RODPxBM5ZK6m5jqBvn37XnLJJX369OE562NBUMClBV+SUaNGpdwY3llEBhGDQ2gSzyTlJS0uCgVCSUipS1gT53mQ0TZcRYlArwmWcBXdBDECP5TINiKekD/q1q2Lrw2BQrP+elCMBvGvoRGCj6LC9D5z5kw2RUJn4SpV6JqrBDpRj3ZQWGiKgCl0ogYNGlAs1MUNh6uILxQDTjhJawyEKs2bN0dL4mRogWIoUPTL6DAJ6YfwqBCORBnS3GDDtGnTaJxi9BVswDeHwlQJLymJvoM7D6FPGIypXKJNzAu9M0aAwAc7wxme2UwKyDQbWR5dqvoBfTFAgMd7rHqztiABCUhAAhKQgATylQA/MfJzIF/82HoiX8fouCSQDgKGL6WDqm1K4E8/DiQYHqgYyBzoJsHdI0KDFIJWgqaAR0mJV4v/P0Mp4BFaYHvsIHO0bNmSM4QsRS2HA/QOksjQCP0GGSicp+Qafz3WX399jIxfCgU4g1MMmkjIUxO1RoMoRME/hZOoFcRb0QUNhpOMqHXr1vHEMagq5CGmVhEbGHhoNnom1U4YDlYVp4GaE5WMDoI2FL30QAISkIAEJCABCUhAAhKQQM4RKLqQy7kBaLAEEk4APYJHaUYiZ6BHlHZ1ueepXlyOidcqo/GyK5ZoWBEFp3iZEgdbhg1xUzku3mCRAr6UgAQkIAEJSEACEpCABCSQTwRKXSvm0yAdiwQkIAEJSEACEpCABCQgAQlIQAISSBoBRZmkzYj2SEACEpCABCQgAQlIQAISkIAEJFAQBBRlCmKaHaQEJCABCUhAAhKQgAQkIAEJSEACSSOgKJO0GdEeCUhAAhKQgAQkIAEJSEACEpCABAqCgKJMQUyzg5SABCQgAQlIQAISkIAEJCABCUggaQQUZZI2I9ojAQlIQAISkIAEJCABCUhAAhKQQEEQUJQpiGl2kBKQgAQkIAEJSEACEpCABCQgAQkkjYCiTNJmRHskIAEJSEACEpCABCQgAQlIQAISKAgCijIFMc0OUgISkIAEJCABCUhAAhKQgAQkIIGkEVCUSdqMaI8EJCABCUhAAhKQgAQkIAEJSEACBUFAUaYgptlBSkACEpCABCQgAQlIQAISkIAEJJA0AooySZsR7ckcgfr167dq1eq333575513MterPUlAAhKQgAQkIAEJSEACEpCABP4iUFMOEihYAtWrV69Vq9bSpUsXLlxYsBAceHECEyZMWLJkSYcOHWrUqFH8qmckIAEJVJpA+HiZOHHi4sWLwzFNhZeVbtOKEpBAfhBo3759zZo1o68fvNxmm238KpIfk+soJFA2AUWZsvl4VQISKDgCY8eOZb20/fbb892o4AbvgCUggTQQQH8ZOHAgPwCEjxcOli1bFv0esGDBgjT0aZMSkECOEfj++++x+IUXXgh2165de6WVVuK4R48e2223nQJNjk2n5kqgIgRcclSElmXzi0Dz5s0PPPDA/v3733PPPZ06dcqvwTmayhMIKyW+DFWrVq3yrVhTAhIoeAJxLWbWrFkIMegv/AzesWPHIr+HKwEX/M0iAAn822ku7kP35ptv4rp71VVXBYEGdYaD8CwvCUggnwgoyuTTbDqWihHAI7RevXq//PLL6NGjK1bT0nlN4LbbbkOX8UtPXk+yg5NAegkEOebll18OWkzr1q3XXnvt8HM3/3dYVtE9z0H5rVOnTnqtsXUJSCAXCDRp0gQz99xzTwRcDvgqgoxLeCPSDK52P/zwA+oM5zmJM6+OM7kwpdoogfISUJQpLynL5R8BEv3iILPJJpvwpZlcv1tssUX+jdERVZQAigzfewjkbtSoUUXrWl4CEpBAXI6ZP38+QgzLp86dO+MLwz+dEIwgJQlIQALFCQR9tohKi1LTpUuX0047LVJn+vXrh6R71llnnXHGGUHhLd6UZyQggdwi4O5LuTVfWptKAiT63Wijjbp37z579myW4qls2rZylgDfdfhtyoQyOTuBGi6BbBLgXwlRsY888sj06dOPPvroTz755Morr9x///2bNWvGykpFJptzY98SyE0CaDQNGjRYd911995774suuohPlZNPPpkvrnjNtGzZ8uqrrw4x17k5OK2WgAT+TUBRxluhoAmsuuqq/P6w6aabjhw50o2xC/pW+GvwwU0Gt2F+fSryU5VwJCABCZRBAAeZo4466uKLL/76668333zzwYMHX3rppS1atGjYsKFaTBncvCQBCZSTQKTOnHfeeUGaITEw0gw/LhKGT+qZcrZjMQlIIIEEFGUSOCmalDkCwVmG3xy++eYbvk+ry2QOfSJ7Cm4yhBusscYaiTRQoyQggSQSQJE55JBDcJBZf/31kWMef/zx//u//1tttdVMFp7E2dImCeQ4geA4E6QZoq1HjBhx0EEHXXfddbrM5PjEan5BE1CUKejpd/AQwFlm9913b9u27WeffWYQUyHfEscee+yMGTNQ6Pr06aObTCHfCY5dAhUiEEKWvvzyS+KVhg8fjhxDsJLeMRViaGEJSKCiBII0M3To0FNPPTVEM7GdqLpMRTFaXgIJIaAok5CJ0GE0Vv4AAEAASURBVIysEcBZhk0x+G2TICa+T5NKLWum2HH2CKDIMPvt2rW74IILwvYH2bPFniUggZwhgCLTt29fQpaCnst/E+WYnJk8DZVA7hPAsReXGWKu2ZIJb191mdyfUkdQoAQUZQp04h12nEAUxPTrr78+8MAD6jJxOHl/HDJBoMjMmTOH2W/atGneD9kBSkACKSEQFBnSOhDziIcdqyPjlVIC1kYkIIHyE8BlRl2m/LgsKYFkElCUSea8aFWmCRCucthhh7311ltkBLjnnnvatGnDc6aNsL+ME4gyQbAzOt5S6623nmuqjE+CHUogJwlEigz/LMjpqyKTk7Oo0RLICwJBl+GHpT/++EN/mbyYUgdRcAQUZQpuyh1wiQRYitetW3fLLbccNmwY+Ro/+OADwlhI/as0UyKuPDgZHGTYqpZMEGyV8uCDD5IJYsUVV8yDoTkECUgg3QQiRQYfGT5GzOmbbuC2LwEJlE0AXYY9s++7776gy4wZM8b9mMom5lUJJIqAokyipkNjskyAOCZ2MGVPU/I14pHOVhrnnHMOXjMDBgzgK7h7M2V5eqrcPULMuHHj0No22mijrl27Mr8EKw0aNIitUvCQMhNElQHbgAQKggCfJFdccUUUtcRaqCCGvcIKH3744R133MGn6Pz588szZLbpxQORhDvLli0rT3nLSEACVSGA0/dee+31z3/+k/wyeH+PGjVKXaYqPK0rgUwSqJnJzuxLAskngMvMOuusw86CZE1Di8FThpz25HHEcv7b1a5dO/lD0MLSCPA1hUu//fYbK4oOHTp07NixZ8+eq6++unJMacQ8LwEJFCfAv4ZIkUlt1BL/bkIAwq677kpMZc2a//2Sxj8jfgBnoVWvXr3iJmXmzJtvvnnTTTfhVLjBBhuU52MTD8SnnnrqzjvvXGuttfRDzMwc2UuBE+Cbau/evZFB+Zgi6W+nTp1WXnnlAmfi8CWQEwT++/8+J8zVSAlkgAC6DPtkN2zYEGkGrxnW8MFNho0G889ZptZKKxx+4/9AXTR/hftP/58zefMCIaZGjRqEGyCubb/99nxTYZbzZnQORAISyAAB/h0899xzW2yxBSGuqVVkMH7s2LH44Pz+++9IM2Qf5xeCaEQssTi/zz77ZFGU4Z8g/xBRt5cuXRoZVsbB3Llzf/nll0WLFukpUwYlL0kgtQRCfpmJEyfiKXPzzTefeeaZ/qCYWsK2JoF0EFCUSQdV28wHAkGaQZ3h2+SVV17Jl1EOeM6HscXGsHDJb3dNbRM7sULtGvWnTXsvfiZvjvkFibHUr1+fyQ3HeTM0ByIBCWSGAEk0wz59BD/ySZLaTufNm0fjiC/8ADB06NBTTz01kmBQQ7haTjUktVbZmgQkkFsE0GXQYg4++GA+r9q3b7/zzjvzi1RuDUFrJVBoBBRlCm3GHW+FCfC1O4/9KRYsnl3ts/9hUr1GtXXXXfd/TvlCAhKQgARWWAE3mR9++AFvu7XXXjvlikwEmBRX33zzzS233NKtWzeCmMruCKUGk5o0aUKu+qgFDvgJgSxp8aAhop/QdKLfzCnAOo0IqR9//JGwKUYUXUIVmjlzJnVptlatWvFml3uMXwz28HtGEXuiivy8QfAXLxs3bhw3D9uwkDOYjYvNzz//TAH8GcPwMRKDiTaNjIwapMdZs2bhv8N/ataiES46Wrx4MS8ZI5Tw2cEkDCuxAEOmEWKyGjVqFBUo0gW4uBpf3NIFvdM+mTuwkJcEasULRC14IIEMEyAEcptttnn11VdxsuPAIKYM87c7CVSUgKJMRYlZXgISkIAEJCCBgiOAIkN+MRb/pGwoLg2kEMcuu+zCDoCTJ08mG8sll1xSWiLhqVOnshX3W2+9heKAmkCSLApvuOGGiBooGuQy5yTuNggrwbZjjz2WHL3kNW/VqhUCR5cuXdDfN95444cffhj3HDKg9+rVa/r06WSNIUHvnDlzqMVC7phjjjnyyCNxC1ruANE1Bg4cSAJ1BB26ZlMqUvzGa6FckGXm3nvv/fzzz9Ff0FwwiSw57F1FMS4RaoEZX3zxxciRIxkCAgq/9u+www7k03nyySfxFaIkzR5//PHBHjp69NFHH3rooWnTpqHpoB9RGPciIlVp8PXXXyenBiFgDJNR0ykOkgSuoqnBKhRgvdq8eXNUMNphJz6mdbvttvvHP/4RClAGqYVQMh4ISYAlSfxxxx2HWIa+wxDoAgupghxD9hwUmdNPP50RUdGHBLJLgLud3IjkgXr55Zd51lkmu9Nh7xJYLgFFmeUisoAEJCABCUhAAoVOAHcSxAuW9ClPJVOELNLAWWedhbgwZMgQAhBCMqwiZVAQ0EqmTJmCtoIW8OKLLz7xxBPffffdsGHD1lxzTeQPfG1oJ773ClcRL0K+c85/++23b7/9NkIG2gct4FSCsnD77bcjf6DabL755gz2mWeeueqqqwj5RGgoTRuKDCML29133400065du59++gklBWUHR5KoAKoW7j/kWWd9iObCQhEViU4RYrCB7jCJTD2UD0mOP/nkE9K64ZyCIILYhNDz6aef0ghqDrOARIImgsSDwISMxRIUfQptBSGJCUKIQauaMWMG+g6XUF6wChkLBYqRIqm0bt0aU2EyYsQIQOEoRIGPP/6YVD4UQHejAL4zSFQwoTp+B9CbNGkSuhX0AILljOW9996jWWxDMMLU+HijgXsggawQ4KY97bTTkCZ1lskKfzuVQIUIKMpUCJeFJSABCUhAAhIoOAJoASxsWP+n200mkMXjY88990RBwPcEXYDVfhHiqBvvv//+SSedxPa3hO2cf/75Bx10EI4wRCvgS4JeUKQ8L6OT0QECTdu2bemiWbNmKBfILtRFBsJzBNmCYhywokPsYJ/dskUZ5JIXXngBYeXaa6+lMBFAt95667/+9S88TYIlqEjBJQd3mM6dO1Pg+eefR5S5//770WgQZUIxTLrooovY4Am1COXlpZdeos0LL7zwwAMP5Aw+LGPGjHn33XcRUxBK2rRpc80112A8YU1oNIydl+zbTV+IMqFB/FkOOOAAHFgo/8Ybb2Dea6+9Ro9oLvECYMSnBpUnFEDloQAlkaXQp/Ah2nrrrRFcEGiw/5VXXsE7hn5pAQ8dQq7oAjkJaYZHaNZnCWSdAG9qPq94J+osk/W50AAJLJdA9eWWsIAEJCABCUhAAhIoZAIZc5MJkPHdIPQAj4/HHnts/PjxRfwvkEtQK3DowF+DQB6SAfO80047kdyEuKcihcuYNZSXyy+/HF0GTxlkEYSSbbfdlj10aQ2hhzMoEViCo0rwrymjKXx20F+oi1qB/IFgce6559Ia1UMtvHLwW0FsQuygZdpHGcG75KOPPiIuKWr5kEMOQX9hy21cV+gdJx3EJiSPcAanIRonOwzTQRU0Ixps2bIlohUNEopFrTBTUYPYgCcO8g3CDYiIS8IMfF4iShTA5wgIGBMKEBX11VdfUQBXHcQmqjAoLKEAu26tt956OMvQSOgCMYi6LH3RdOgiSswcGeCBBLJIAC0VKZNPDDTl5b6Ls2inXUtAAnrKeA9IQAISkIAEJCCBsgjgKZMxN5lgB+IFDiN33HEHq6mtttoqbhzBQUTWYA/JTRBiwiU8SgjYQRmJhyzFaxU/ZsGGohHPtos3Cj+qkx4FMYJeaBMFhAYj55rijYQzuK4Qy0NriCPhDI2jUKBZhJfklyFcCO8Y3FWCzTSLtWgfpPWNbEb4oFZItYvUgj3xM7RJdBJqFP4vNEtKXUKHnn76aVyEUFIwFa0EXQbvldApz4gpmBSS73IcUhfTI8aEMqussgoiUbwAQhI6EemKIUAxvGMICgsmMUZEH6aDFkJ16IVoqahHDySQKAIEMeHexfuaN06iDNMYCUggTkBRJk7DYwlIQAISkIAEJPA/BCZMmIAIgpsGMkFYnP/P5fS8QLkgrcyzzz5LEA0LKn7ljpQR5BiOiU1AlEFTiPdP6BCyReQGElWJl4mOUUwi0SScvPHGG4lmIhcMgyXHCsIEXiFR+TIOMC8E8sT5xI+xmQIki8HxJO5OglCy2WabRTsWxatgGy/jZyjGy2hQJCQmkQ3RVXiv4CaDr0oQa4rYGbXwZ1vFdjGPn/nr+p/bnHOATsQilh7xjony/oaWMZiYpqiXvyjqeB7x8CBZBEhuHbZh4nMM97TovZYsK7VGAgVPQFGm4G8BAUhAAhKQgAQkUDqBsWPHsj5nbRO5pZReNpVXUBmOPvpoUu2SVgbfDaKNQuu4ZpC7BJPYGon9gOLCCufx3UCvQVbApyPyGeEApSaSM0q0EjcQsvPibMJmQ8gQNEKOFdLxlli4yEmcTfBqQcGhkXCJHpFIIgNIP4wHClFIJ554IhpKVB3jK5eHhcaZFxSr7t27kxeZ7L/EHPXr14+Yo6hxDqDEI5zBk4gEw8hDKEGRmBUvgF8M3jEITBQgpAsXGzCiT/Xs2TPuT8QxY4majXfnsQSSRoA3MrcrdzI+d+6NnbTZ0R4JRASU9iMUHkhAAhKQgAQkIIGiBEI6BhKLZFiUQbBg+2digtgeOxI7MA75A5cTrhJnxDMKQniw7go/g6M4oOAQzkOqF7QYlBGUHTLgRlFCRUf412sECxyCaJDG0VCIFXrnnXeQMEosXOQkjir4v6CShO2u6RF9Bx+WqDoFMBKVhww1qDDB4ChSqUhr5XmJwoKAwuhQT0gugxMTfj3sOVWkLj2yoRLBTZzHU4AEPZQnxCnyF6AAezaFHDEUwGbGTgEgk1uHNDFhf6V/I65fn/UtiEBdpCNfSiCxBMiZzX0bMjEl1kgNk0CBE9BTpsBvAIcvAQlIQAISkECpBELsEpcz7ylDp2gNp5xyCp4gRZJ0slsQ8gHRRjiGEF+DgsBmTKRrYVNqnFwQj0gkgULBrkO77777F198QeF4Pt0SR4sDDilX0C9wDCGBLoIO20UTylRi4SInSbNCEhxEorPPPnvHHXdE3wn6S1SMApwfOnQoMVmYhMsM9pALhk7ZXKlFixZRyXIeIDyhnjDSRx55hAwvyEBsioTwhKIUb4GFKHl5GD5uRyQVRmHp2rUr/gJRGfLm3HnnnYg1+O9Qna2d9t1331CAXaJ4sNf4mWeeyQFw0G5oimPSMMeDsKLWPJBAAgnwacAtjdcbDyOYEjhBmiQBCCjKeBtIQAISkIAEJCCBkgmE2CV+asZzpOQSqTsbPHHikTJ4ZJDul5wyuHuQ8jby70A0QUpg7yRS5yKdYAIlET4QDvDj4CXiCAoClwYNGkSWmZNPPvnjjz+mMMV4BF+PIh4fKDtXXHEFGwmhbrDzNKLDCSecQJpbOqUKbYaDIrXC6HF+YX9rjtkYmx7xK2HLJASd55577q8OqzEo9u0myGjIkCGDBw+mEWQUJCc2bAqRRMUbL36GWtGDq6TUQW/CzwUj8QxCNcOxheTBlAlW8YxiBTcEJgQgbECRQWHBbSdegFoUQJeBQLzAhhtuyB5ScCCXMPsKB3sQd9hvm+7CuII9UWseSCCBBEIEE25rPMqOYUyg8ZokgQIh8N98aQUyYIeZXQJ9+/ble1ufPn3Ct7fsGmPvEFiweHafV/69WUYAUqdmg767/CocCUhAAhKAAGlK+IfFSp7/XKxt0sqEnCYkRsF5hBQtcWUBxxMcOkJADdJAsIH1FQFKnCeTC5eIykHdiIQbJA9qoVAQ48MG1egONI7bCP4gBDKwMEPOoB30hXhMFm1S69tvv+WAEJ4QE8So0XpQNKhOiBAni5gX7CE2KhQgBoohoL+QdQXzMCw4ldApMg0OMjjj8Iy1WEVrXGWwxRsv8QwNYjDtYw/t0w7aExzI/4LjDNFMPGgWHQpBiq8cAAkZZ6iI2RiDYERdbB45ciQzi50IWBSjEQqEuK1QgDJ0gc1cYuBEkFGYB30FUYYQKuBjP4FOAYLPEkgmAd4LxDAiuZ533nl8AqTPSD5h+BBj13k+W9LXiy1LIP8I6CmTf3PqiCQgAQlIQAISyD0CLPWJvgluKXHrgxYQP8MxKysUBOQSHDqoEtdWuIrMgTaB1oBWEi4hRlA4NM4zAUTFO6JN9AWKIaCEWkgnUTF8hWgkelnEHtSioNegB9F7KIYBUXkOqM4DZYT2eRkJTDRVvPHlnsFCekQiCa3RaTiIG8ZJCrBfElYFV5f4VY45SUetWrWKGokXoAuUGh6sMEMLNBgVQPoh8CoaYHTeAwkkjUAQYvSUSdq8aI8EIgKKMhEKDyQgAQlIQAISkMD/ECCnDLpG+/btg0jxP9fS8KKiK3zKR24dxc3hatzseOPx4yIV41pJkWJFXhapyEsKlFE9lI8XiLdQvPHynImrJMXLh/YpEy8W7zQqUPxk/Aw2l2h2aT3G63osgawTCJ9g4dMs68ZogAQkUJyAokxxJp6RgAQkIAEJSEACfxLgt2We0x24JOuUE8BRiIUoTi44wpTYeCiAHw2PEgt4UgJ5QyB8goVPs7wZlAORQD4RUJTJp9l0LBKQgAQkIAEJpJLAxIkTSSySMU+ZVJpe2G1tvvnm5NHAQSZktCkOIypAFFLxq56RgAQkIAEJZIyAokzGUNuRBCQgAQlIQAI5RiDsRa2nTI5N2workPyYRxlmE/aFH00ZBbwkgbwhEGTlIDHnzaAciATyicB/05Xl06gciwQkIAEJSEACEpCABCQgAQkEWTlIzNKQgAQSSEBRJoGTokkSkIAEJCABCUhAAhKQgAQkIAEJ5D8BRZn8n2NHKAEJSEACEpCABCQgAQlIQAISkEACCSjKJHBSNEkCEpCABCQgAQlIQAISkIAEJCCB/CegKJP/c+wIJSABCUhAAhKQgAQkIAEJSEACEkggAUWZBE6KJklAAhKQgAQkIAEJSEACEpCABCSQ/wQUZfJ/jh2hBCQgAQlIQAISkIAEJCABCUhAAgkkoCiTwEnRJAlIQAISkIAEJCABCUhAAhKQgATyn4CiTP7PsSOUgAQkIAEJSEACEpCABCQgAQlIIIEEFGUSOCmaJAEJSEACEpCABCQgAQlIQAISkED+E6iZ/0N0hBKQwH8ILF66cOy0/nMW/fifEytwJjoOB4uXLnj647PjJ1eptcZOG/SKn/FYAhKQgAQkIAEJSEACEpCABKpOQFGm6gxtQQI5Q6DaCissWWHZhOl3lGExMk2RAl02vKyM8l6SgAQkIAEJSEACEpCABCQggcoRMHypctysJYGcJFCjeq12ax5So0bdhUvmRX/FRxJd4qBmjZW3bHpE8TKekYAEJCABCUhAAhKQgAQkIIEqElCUqSJAq0sgtwhUa1BnrY5NTyi/0ds2P7VercblL29JCUhAAhKQgAQkIAEJSEACEignAUWZcoKymATyhEDN6rU7ND++bq3VlzueaitUW7nWGp3W6bHCCoQ9+ZCABCQgAQlIQAISkIAEJCCBFBNQlEkxUJuTQOIJlNdZpkb12rrJJH42NVACEpCABCQgAQlIQAISyGECijI5PHmaLoHKESiPswxuMvXrNNNNpnKErSUBCUhAAhKQgAQkIAEJSKA8BBRlykPJMhLIMwLBWeb4MkaFmwypZ8wmUwYiL0lAAhKQgAQkIAEJSEACEqgiAUWZKgK0ugRyksBfzjInlJZZJrjJtG+OamM2mZycX42WgAQkIAEJSEACEpCABHKCgKJMTkyTRkog5QTKcpb5j5tMo5T3aoMSkIAEJCABCUhAAhKQgAQkEBGoGR15IAEJFBSB4Cwz/ru7fl/0Y3zguslENL799ts333xz7ty5CxcuXHnlldddd91tttlmxRVXjAqk6uDss8+uU6dOz549GzZsmKo2i7Tz4YcfDho0qHXr1vvss8+qq65a5KovJRAILFu2bMaMGaNGjVqwYEG47Zs3b96xY0fu/3QgWrp06ZdffvnBBx/svffeNWrUSEkXvI/mzJlz++23p6Q1G5GABCQgAQlIQALpJqAok27Cti+BxBL4t7PMq9Ouipuom0xE4+233z7rrLPmzZvHmWrVqiHHtGrVCmmDZWr16ql0MxwxYkTdunVPO+20qOuUH/zwww8vvvjikiVLdt11V0WZlOPNmwa5QyZPnvzPf/4TuQSBhvuc23699da78sord9lll5QPE+nn3HPPfe21155++ulOnTqlpH3uc+52RZmUwLQRCUhAAhKQgAQyQCCV64oMmGsXEpBACgkUzyyjm0wcL54Cs2bN2mOPPR555JEBAwZsttlmOM70799/9uzZ8WJVP6YjHqyBq95UaS2wxl60aNEff/yR1l5K693zOUSA++TXX3/t0KHDjTfeeMEFF3DbT5o0CXUy5bc9TFB8unXrtttuu2200UapQsRbCa0nVa3ZjgQkIAEJSEACEkg3AT1l0k3Y9iWQZAJFnWV0kyk+W2uvvTbhG8RWNGvWbN99950wYQKrvlCMtR/BFyxiN91001q1asXrUuazzz7Dp6Z+/fp42USXfv/99y+++GKllVbiUu3ataPzHFCFICNcZuixSCjHzJkziaXCgCZNmkROOpTnQXkEF66itjRt2pSW421i21dffYUNeEDEz3ssgTIIcPttvPHGBx100OLFi/fcc098ZLhpP/roo+DMwv3GTUWIEAF9DRo0iLfDTfjzzz9zN6751yN+6bfffqMRvLS488PtTWFiAzt37rz11ls3atQI9xZapl5UiyoU4BLBfZzEZ23q1KmE+K2zzjpF3iBcpVPeXC1btoyqeyABCUhAAhKQgARygoCiTE5Mk0ZKIF0E4plldJMpkTIiSM2aNVkWfvfddywa8SAIYspVV101dOhQXGlYW7JK/L//+78TTzxxlVVWoRFiPXCuYZFJrbZt2xJJscYaayCgcHDXXXdRBZlmtdVWw/uAWkFGYb3K6pclKNVZtfbt23fnnXdG6Hn33XcvueQSIkoQgGhthx12ILSkXbt2mPTggw/edNNNOBqMGTPm448/xgxSxlx++eVbbLFFEG6eeOIJnB3QhiiMLvP111+XOEBPSqA4AXxYghSCRMi9igqDrscDZ7Hzzz+f25X3Ardu165dL7roosaNG/Ny4sSJHKPd4JDFe2TzzTe/7rrrNtlkE7SVa6655v7770ci5FakQRxw9tprL+727bffHnGH3t9///3DDz+cuqSz2XDDDYM9xxxzDBroq6++yjvi6quvvvfee3kTcW8jTfbp0weFNBTDth49erz33nu8BdZff33UGYwvPiLPSEACEpCABCQggWQSMHwpmfOiVRLIGIH/bsOkm0yJ0MeNG0fI0lFHHcVaFE+BE044ISgvP/300y+//ELq37/97W94slxxxRUvvPAC0gkyCqvH+fPnH3fccagnHLBWpGX0EdaouB4ccsghxGsg2ZDf96WXXmKlylUqbrXVVmeccQZaDEvT448/now2rIHr1avHahPvgAMPPJDUHqTe6NWrF7IOVVjTsjamWVwJjjjiCNwW3njjjYceeuj777/n6pNPPnnppZd+8sknLJtRi3CoYanMeR8SKD8Bblc0EXS9tdZaC7GP2ylIJ7i3oJiQ/ZcUS9yQKDI4eaEwjh8/nvuNdwrPhDuh5nBjozDecsst3KVU2XbbbSl5yimnEBKF5njwwQcj33zzzTe8R3hToKcMGzYsmMe9/eyzz26wwQbc2LRw/fXX817gPcUb5PPPP+cN8tZbb1ESxWe//fbjfbTddtv17t2b90hIAlX+MVpSAhKQgAQkIAEJZJkA34R8SCBjBPjNnzueHzkz1qMdlYPA0p/mfdH31TX6jdlwzsKfylG+UIo8/PDDaBn4mOAIwzO3Lm4pLClDZhZWksQu/fjjj0gkAwcORDc59dRTkWlQZFh/snbFM4Wr6DVoK59++inxIKwY8SagDFUGDx5MsUMPPRRxh4QaBEDRMl4DvOQNQndHH300kSD0xfnp06dTBbUFlQf/Bc6wDA6dnnzyybxkAcx6uE2bNl26dOElOUFYqWL8HXfcgQE0i+cOvZBLmHVvocyf46w4Ae63xx9/HE8uwoh23HFH7qjVV1+dmKNbb72V4KAjjzySmwqHL+5M7jGCifBqoQDbJ3HDI69cfPHF3N7IIjyzixOtcVvy1sBfjHuYKtyNyCvIMTh8YR1l0GJ4Z3GJ25vbvkWLFihBXMLni/OIjNzPnKcjbl3uc7rGGC4hj1IMhzV8Zw477DDeOMiUPPMuw+CKD73UGiFECwtLLeEFCUhAAoknkJmPMmR0Pp+nTZuWeB4aKIFkETB8iY8OHxJIPQHc7EePHn3PPffQNMfvvPNO6vtIXYvVay5tvu3PSxfPuu3wLdloKHUNp6Wl9u3bEwRBGBF5JQhbCMFEaenpr0ZZ75155pkEROCHcs4557ClNJslsUTEd+Dll19+7LHHEFxYCrIQZdHIehLfGdSWp556iuAOPALIk8qikfgjFpOsFQlfCvFKLC/xkSHsKHjK0D5rV3wBeODYcvfdd+MIwJ0TIo/uu+8+4jiQePiiw0m8bFi7hiETDEJsFKtWokV4xhIKsFpGMGJ1uuWWWxLrQUlMymRMByiGDx+OkxFdc8wjWOtz1Qlww+O0Ep6Z8WOPPbbqbRZvgTcXkgp3Ebc0Ny1yJAfEFvGMsIjLDFUowEvEGm5jdlBCasGfi9uYS2g3oQAeMTizcIybGM98A+LuRaZEx+ElTmfc4RzwQNzB1wbXG8LxCGviADcZ3m7oibRABBNqTiiJ4sMBHmQ84zuGQIm4SQgVL3HeSfcHQrAhi8/8N7ntttsCVY55v2fRmBR2zd523M/hmRuJGU9h4ylsik+z6MONWUA0TGHjWWwqMx8sVR8gnx5jx47lLUBT4eOo6m0mpIUMf7dJyKg1QwISCAQUZbwTJJBKAnw/Rovh6wKxJ/jth+/NrEOS/7155k9/cpg/OwfSjoTwHGKFMJjAItQZdBC8VNK0GGOdgPCBfwq+A8QE4UeAOkPO3X/961+EDvHjPGlKEUQIqUBeYa7Ra7hENBOLyZEjRxKRRKgFaS9YwWIwqkpYhVKSijjIhMwdf9L/z4OOWFsir/CNkxUsPgiEROGtQ3wTy1F6DB2F4ig+IZEww2chzRdWWsaLB5GIlS3xI/9p9c9dvaPjNB2E5QorFoaJneG2ZxQ80tRjATbLPIb0QNyNzD7JVoiqI54oheoMt2iIQkIc5E5GeUT1QBPE2wXlBW8XHoE8iXVZRaM2cokfSLmf4zPC3YiswxkO0C7DJe5PQpB428ZLcsy4cPtCyB4yZAg3DBWJWkLcIXgqlCzSAl5jnEcPokx8u+4M3OdFLM/My6DF8P+FfytIumCkX95i4SAzNqS1F9ymuJ+ff/55ZhAJDy8tbmzu6oSoM/EPNz5d+f8ODQTBoKqnlUxmGs/AB0tVBhK0mJtvvhlRmKDg8OWKBsNEVKXl5NTN8Heb5AxcSyQgAQgoyngbSCA1BIIcg189iTD5xkyj0TIp/ACVmm5sZYUV+E7Gmg3PEb6loX+ReIKMEqS8/cc//oECkiZpBvCoJ2gxfHNFccNRha4xA42GsCBii3AiCJPDgpbEGeQAxqGGQKTLLruMn79QdlBM9t57b3xnOI6mkSVu/GU4zzdOvmji/0JT1157Lb4GSE4sTvB2IU0MuTai6vGD+FoUUyOBJl4mfcf8XMxymh8wWSSzYkFXIoFI2KyHNVVCllXpG34mW+ajJvhH8MzKhHA5NBocRti1nfs/JdIM9xJKB1rkSSedRLroV155hVA+ElHj/c6theNM/GbjmAc+L/iCkQwb9TCiwRobBxZctLp37046mOg8ByW+T9FZUBLpEQEIA8JY8KChPE40vMHjLfDe4SXyJYFFdBS/lGfHQY5B4UWi5ZMBSnwOhM+N4DOVH+PlM4T7OTwj7OKH+Prrr995550k4eLGzuJnSJBjSKyOgxhaMx9uxJaGDzdmAVEyP/hn4IOlcqAiOQYfOu4Qft7gUwInWVrjXyT/XivXbAJrZeu7TQJRaJIECpCAokwBTrpDTj0BvijgsoEiwzdmvqvxdYGwEVYv4XszK5YSVyCpt6MwWgzxOCwR+YmYL+uIIyxHEcLILsEZQo3SRJvJffHFF/lGyE+4eILgyfL3v/8d1wDcBFhD0nX41ZrfTvnizlr0gAMOmDJlCncFaTVwimENybKKrC4sMrkl+KKJ+MKSkmPmjVrR7CEz0QLfOxkIQUyUJB8qoRysPAkVoWToKCpf/IDCtMyP6izX8V8IS9b0/aLOCortdVhNMTS0mJCTmDisMBF8b+ZR3EjPVI4A84jwwTPbD/HMW4D7H0UMbYK1Om2mRJcJtjGheMpwn5PhhY3hkQO4mbmv2C07FOB9h1KDDEfWGC4988wzQTrBMD4VuQf4JES7fO65504//XR0FmpxP3Mbh2zZoZHomfuE2D2yVvNGI2yQ1S+Xgk8NbkHnnntu8RbYcQy/MLZ2ikQfuo4azIMDZhZnKMbIRw27VqH1828F5uFzg7dYOMiDkRIQyocbedN5Jgk66YS4sRFESIzFLUToaFZ0GWIwcdtBISIuDy0mfLjx6cr/d5jz0RrEwTzgzxsnYx8s5cfFPztugxtuuIEPGfQX/oHyTAhw+HJFO2Eiyt9gkktm67tNkplomwQKiACfwj4kkDEC+Zfol4U6DhGsjviKwAKG33hxQOVkxpDaEWtRJBK2gGF9wlqFBSTf3lKCJST6JTbqwgsvZEnAAb/5E3lBLBIeAYQpkbsXb2rWDGguXOIe4DdtXKXwlKE83yPD79jklyHVLt4umMcOwchG7BzM5tb8+B/UHHxt+GbPBk8oOGEHGUqy6w0CECVZvhLWwbKT3KiEI7EGCxmFQ6JfpCg8FBgva10WbKxRceTBo55eKMxLWsBI0qmykEh5ol8yg7B8Yh9iHHNI8soChunA7JTwt5HyEGDecdQiwQpaDPcGQga3R3kqlliGucP5i7uR/dpDAWQXWubNhZMXLmksh1g5cOuyUuUZNwE8uVgzE3VCLXrv2bMn4Xu8H1Fw8Cwg4A51kkuoM2gK5513Hm8WAvpC44iPLML5ysVtE51h7c1A+NE4nGFVhq8Eeg03My1wP7Mqw/UmXMUkCuNExg1PsBVjp2R+JPrljUwyY5AyQN7CfMoFTTYMPO+f+cxEiuI2435A3eMTjE+bTI6a3vmthaBRlv381oL0zM1cOB9uqf1gqcTE8amC/yn/0PlvyOcPHyl8uQq7GVaitVyskvLvNpCMf9imiYmJftME1mbznsCfWfd8SCBjBPJPlMETIaRZ3X333fktVzkmY/dSkY74+sKXZlQJpAd+7mOpxle6ImUq+pKkMPwyz3oSWQTPFyQYvABYGPC9nC+sCCisEjnftGlT3EP4oZ6VAydxCmCdyVKKX1ZZsiKyBOWFNBysSDlPazyIhGIZyXIaqxBl+NKP2awt+dqE08Gjjz4apCXyaLAi5R6jIzx0WKJQmN/MSeHBNsN00a9fvyDK8PPyTjvthEkIQ7TJL8ysq7ENqQh1JqQBxluB8xXlUEb5/fffH+A0jmG0XDgrljKYZOUSNyQpOYj1QPvgpkW2YElfCUuYQZRl7nm0tlCd+4qQNG5XRGfeVvgvcPuxTOXe454kmUu439AL2M0azZH7DU2EB5s3YQPVww7WvDu47bmKmon3DedpH1Fmr732Yp2A9BNZy47aSJ/xe4m7nbdeaIFGiKtCmgktINngS4KTBfchnaIE8Q7iho9aq/pBZlYyxe0MHgFMKNPKB0UYb/Fi+X0GaYafPfBPYU75NEN3QyvJzJAJHeUmJ106zmhEyxaUHBARTtUHS9Rg+Q/4TOAfH+9r/q3zzz3SbcvfQn6UTOF3m8x8lCnK5MeN5ygyT6AaXRaQX5BDzTYBEjeiy5BoI6gz2Tanqv2z2GYpTkzKHnvswY/D/MPj95yqNmr9yhJAESNigp/Q+XmN4CZWj3yfq2xjf9ZDFgl6B8f8WM0v8Cx3WVWGaCCuslLCZYbVAmIKn6VBoWNlyEKCL5QYg09BWIhSnUY4wxcsVpgsL7lbuMSXfi6ROZh1JosuWuM8LdBguJf4TkwV9A465TzlCZuiX3QQxksvlKe1YBK6D2awVGZRzQHdYT+PsJql/WAMXi1/Dq/KD4Jl8NZhrU5CH0QlfCiCGVVu2AYqQ4AZ55YgLS4uV8go5IdmMV+Jhlh8krskaCuhOncOig+3ImskblfuKG5C7n8WyUw69yT3GyXRR7jEzYlDAeJjaCHcitzDPLi9uYfDHcszVbCZT1F6RJEM7xFO0jstoIGG3sNzaIE3QmiBOzm0wFWq0y+/onP78Y7DTk7yhopXr8oxA+SNhgFRj1VprZx1cWpDbALC4MGD0WFRwSI+5Wwhb4rh98dHGYo2rosI03hsoVOne3SoAHxdYdLxeeQNxUcon//p7jSZ7afqg6VCo0PkJYyRH7r4pYEEbXyY5FOYUoVQUDhV320y81GGjok3MZ59QZ2p6GAtL4HCJcCnrQ8JZIxAPnnK8DUddYnvCsQusYrIGEM7KpsAa7Ndd90VOSaFcUxl9Bj3x2E1GP2azQEvS6yIC0BULBSInAKKXwoF4i1zJt5piV3ET1aocLxi2ce4EeExgTaE90TYDars8l7NDAFuFUKZcK9AvKhKHFPZ1nIDl3ZfcSm6n4s0UtrtXaRYhd47UV0GziN6mcKDoMWwPk9hm2U3hcMdqhaTyFSmaVBlG5DAq+huRL2hgOMMxYdPWi0kBJW92Flbks0EhT2tfeVK49yHGfhgCTTQIAjL5Z84/8r5h54riNJtZ9W/22Tmo0xPmXTfCbafrwTyecOCwlXaHHn6CfDDHRlDCDjn1xt+uMNZIP192kO5CPBjPmFBFCWMCA8UvkqWq1plC8W9TvihPvo1m4PS3Eb4xTUqFrqNfoMtfikUiLfMmXinyzW8QoWX21oowM/IZH6dOnUqP1mzRiKfTjkrWizdBLhVcKwgCREiAlsyEVuUjh65gUu7r7gU3c9Fui7t9i5SrELvnaguA+cRvczdAzKX46PBAoxJZCrzY1BVnw6cB/FYQat6//33SbuLz1TV2yyxBRJjEROK1oz3Hz6w+MiUWKzQTnIfZuCDBao43PHNiingmH/l/EMvNNSljTfD321KM8PzEpBAmgjkwzeYNKGxWQmUQYAFD98bKECmAwKeyyjppcwT4Oe1IUOG8CWS3zn5zS3zBuR9j4MGDWJpREIZ3JHwyMj78ebWAFGKycFBTBkefKzwc8t4rWU5iiJDmqquXbsWcshG8TuBFNH8291vv/0IUB07dmzxAik5Q+NksUFrZv8vZIiUtJkfjWTmg4V/2bwFeCaCj3/l+YEuVaPwu02qSNqOBBJIQFEmgZOiSUkngJsM6QZ5xheUX3LMI5O0CeO7I9lD2QKJr9c4oqfbWSZpw0+3PbjJkLIEqmwQ26JFC3/JTzfwiraPpwm5A1jSE19Gxqs0OctU1CrLl4cAIhpSGiVJLhsy+JSnVoGUIT8R2aNZl7JiZ1/2dDjL4CZDy7TPby1dunQpzeerQIAXGWYGPlhwk+EtQGw4X6vIka8oWWQK/G5TBIgvJfD/7N0HnCVF2fbhZZecM0iOEiSDhBdEUVSSgookQRFzTqAIiAl4UQQRkaB+iIiImBEQEBQQfEEkqSggSTJIjruw7H7XTLHNYWbO7OzO9MwJd/9gtk93dXX1v0M9dddTT3USgYgynXQ3cy2jREBcSXabaDKGuPs7SmfNaWaEANtFq8YRcZaZEWxDSvuXv/xFMFe+GKbaSaNlSMhGPZHmkya98S9xlhl19sM6oVEzgvV86EMfMg905M7+KOkyIr8aWFSTs4w42Rb5O0sUgf786/6wePi9AhQZPsijGVe7/5W27JYusW2uuuoqw29FlO9zIwSGN3rRXqGg++zS/WB6B3NunHDCCaeeeurf//53Gl+fNPkZAq1MIKJMK9+dlK1FCfjQ609jMZRmf4uWsuuLpUOVZKD+jrPMyD4L1157rZ5MbjKZW2FkwY5sbmx3vf36/OMsM7Jga83tsssu0y7lI5AWaTPOhhSZVZ10UoezTJk1xotDlGlWgC7fXuuHxcPvFShuMl3OeZDL7wbbhjc6Ye7GG2/sw+Ff//rXwQcfbAC1yN99dp1++um+nJ/73OfM1MY45y7tr/Qix/dJmZ8h0JoEIsq05n1JqVqXQBm7xGiIm0zr3qTekrEdX/nKV5rwhYgmVHuLl7Zdimfs0q233iqUTNxkWvyW6dPmxyc0Bl0mHYYtfrNK8crADW4yvJziJtPslpHazYsk4q8HWxu+WbKZ2K6vRaiajTbaKG4yg9Cr78PiM+UVUFnHTWYQ/nZ1g23D0ia7sN/6oDDV4yOPPGJOtP7D0p966ik+vEJBff3rX//4xz8u2p3J1Fks/eWbPnnmZwi0CIGIMi1yI1KMtiFAdFdbFLukbQrdrQXVT8J8Yee1fqP0M5/5zIEHHiiA9AzdK068n/3sZ3nqzuiBM3SWxsRPPvmkhpAZSZpNvtOYOOtjS4C3hRa+79VJJ500tiWZobPvu+++prSboUM6IzEHNC3S+MhM925u0bvQUCzTTTz0BFQeix4XtcbQj+rClDV9WDz8XgE88wpM96FqI9tmutcysgl8G3bddVfRHj/96U+bso0/XTUG6r777tOl5B0f2TMmtxAYKQIRZUaKZPLpFgKa90austvWX3/9brnmtr3OYtsVO6/FL8J8H7///e9n1Fy45557HHXdddeJczQ6F/jPf/5TI3/NNddMNKXRAT6csxTtWFPHLRtOPqN8rHfhzDPPHOWTtsLprrzySt2/PDUSqmnw20E0UQX7Wo6s2i5ysEU0H8vgBejyvTV9WDz8XgEPv1egywlP9/LbyLaZ7rWMbAJfhrnmmmu++eZbccUV55lnHv1VnGvEl3nPe94jTLhFxKhjjz027jMjiz25jQiBiDIjgjGZdBEBVqDeOUahL3sXXXYutWYCniuL9vMMnYfflqO4+M7ogTN0lsbEJaCMQTHm92ncnvUQGCkCHukZVSdH6tRjm08JKKOnN6LMmNyIElDG2CjLmBSgy09aAsp4+L0CXY4ilz9MAoYynXvuuffff/8mm2yyyCKLHHfccb/85S/ZLbvvvjvrfbbZZss3dpiEc3gdBGatI9PkGQIdTEDrV5tBTxE9voMvszMurfQ5//Wvfx3Z0AN6U/W9LLfccup1D4MRPXPPPbfOmQqaMc+eE7IFD4XSqXvbbbeRTkTG9VMyOejAYfrryfEsVQfKjSuK3JZddtk+44P0It5xxx08YpzX6KHqkP4rfHRNTLDUUkuVc/VPMJwtLpwSZA6UxLwYDsbRObY49HmohjMrNuuWy7fQqh7Lqthlo59rrbVW9QA7ETFFF67n37PqGeY9Lo3x/3JYbLHFPJMlB8m8NTozdVd6XHVpGv9fZT7gijPedNNN3inPf59X4/bbb/dMrrTSSgMe2EYbixRVx2vbRhCGUtTizNLj1nLXXUNJP8Q0qgmLr7pliId0Z7IR+bAMiM4rIPO8AgPCadxYk23TeIo2XRfl94gjjlAH8SN+2cte9uY3v1mIrmI7ve1tbxMj2VBxFhTTq00vMMXuYAKpeGq/uXfeeafBBUKIa8OQaTfYYIMZHS4rIsb1118vbNXLX/7y2os7vROYyOOss856wxvesMMOO0wvbfaHwBgTKLZdaeqMSFE4SX3lK18Rz5/CItLkJz7xCa1EcwGI8y/mfxnRwxR4y1veov1pakYBcddYYw3Nzr/97W/kDC3P97///SaE8lMLVjP1yCOP5E9L41A8DddtttlGY9W6VseXv/zlrbbaquwy/6ufJhwhzVB/3v3ud3/sYx/r/yXho2sK8D/96U9EmUUXXVQsav1CuolG5NqTSdsRKNpx0ZFnovBeHJNZnHPOOQQRVuzmm29uRgzPLav3N7/5jb0qNbXSnnvuuc8++3i8+YSbFMNPM5LaS8TxPHvITznlFGKin7vtttuhhx5aUn7nO9+hxfz73//WDNZvyVYWnVHi/uV0xq997Ws/+MEPrDjjEkss8cUvfpGpLSXXki984QvEGutLL730QQcd9KY3val/DtnSYQSKbtIroYxkoN8Oo1Tf5Qzzw1Jfwbon5xG3bToGnY+DbgBebxQZ5pCg3YwovlcC8H31q18l44oEPHi3VsegyIW0HYEMX6rxlmk+/ehHP9IqY7D6FmhTFZP07rvvnqGz+pT89re/bZEBkNqNv/vd7/pPUzdDV5TEIdCmBMTi/cMf/vDa1752jz320ETUzuQcS6AxF2MVapdwST0hv+rxu/fee0Xh/e9//0tG2WyzzfjLaE/6Auy1114OvOWWW374wx8WFQYQ7dgNN9xQAD9aDN2HpHL11Vf7jFBwSDCUWUdpJGu4MjW0UTWV+2CMj24fIPk50wQ8eOaS95ixX4XdNQ+x2EwaY3xSHnjgAc/e1ltvTUn5xz/+QZQ0Zw3pRwJ+XnoR3v72t5NmuIl5vE888cQdd9yROOg5F29Yj6WUdlnxXniwPfAe6Z/+9KdCMw5YWlUn7VIxBAXwanhrvBp/+ctfJBbKkQS58847s7NFc0ycowEBZmMIhEAIdAkBjjC/+MUvVE/GLmmqMKtcuID3us1UaocddhjjTS8X+61LgOQy24hAPGVqvFnsSN2GLEWfgFe96lXM2SuuuEJzbkZjMehX9PlgCtdY1iFnrW/KN87fIR+RhCHQOQR07HsTKSwi3WpD8lWhy2y//fZanjrtecnqv+JK5gXhO2OXK+cdQ0MxgbR27Bvf+EZ6jS8DpzkvtTatML3UnDLKY9VVV9XVb9CHXUaLfPOb35QtTwRSCzGUe4K2qBEfjnJGjga77LJLH7Lx0e0DJD9nmgD7VdBNX3s6COnkfe97HzFFbhy1eLtYKCDkSN4uhx9+OBvX9PP26pMklLzrXe9SbemxPOqoo8glnFk80l4NLwJNf++995bSzwMOOKA4tlBwXv3qV7OkbfFm2Vst1EmPOtVGgAAPvzfrf/7nf8yY+/3vf3/jjTe+4YYbVKwc1rxBnEl5h1UHZiUEQiAEQqDbCGhhGSeuzmKtsZ2M8xL/0UZV2LbbbmuLSbLVRDwrWVzdBifX2+IE4ilT1w3SiKLIGMjAeNWa0oiy6GbnSleaak7M6uVKp2tRq6zYu42l0Yuo/TbgpCo2EoB9caqucjlQhdnBJQe56ZaspBMnkpulaMbSWNFpL4eql14aLn86MNnZhCSmsDEXNpYMbZe+v7eOU7gEQ7Qc1Vj4rIdARxLwFrsujUANSF335V3mF6ClKoyct4nTLPcWXjDGH5XIF0QZOosWLGdabUsphZKh5lBw1l13XW8W/4IiuRrEwXSQrQFHRjk5hDuA1qyYOP7q+XG4gdD0nVe84hXcbbyPfb4bfHTlwC+Ph4JyykqTtSNvRC6qbgIkFX0Jnkn+nnoX1TWeWCf1RBm4R68hgnjyf/azn3l6qYGlPJ55PjUeb88hk9doo/K0e6TJLioUNWNJKR9KikfUwj6mzqiV1Dt9rssr4B3hdEbK/NKXvnTIIYcUHxnuY1LutNNOqh66D8lGqSTrc3h+hkAIhEAIdBUBlYvKyxBXkWU0xDRSNJHUDrq4+MuISqaD/OGHH+4qJrnYtiAQT5m6bhNnbK0msVcEjDAGvpym0WT0UTBOXvuNkqLPkAM2ZVfjTUpqiL5HViaVREPOB0WTrOQg8Wmnnab/nA5ii/Ye9ZdJymg+++yzma2vf/3r9VUKLaGbnVu4Vp9kzqUHnkmtD3OdddYxGIqHeRmCJP93vvOdOip5pBOPKTvsaQa35qXeSDkogzMKJaBUTsduZiKXwujk//a3v8041jKkTLPRhdFKI7DAyd+OJEDv8IL8+Mc/NozoV7/6lfeUPuItMz/6H//4R4OV/vznP6v+tRX16hcC2qUViiLTVO9I0XQGdILTwqS/kEc1hlkPviGC1JTD/dWOdRRRppJNyyn46PqYfPe73y0fBONKjJlq/OxUJclKCAxOwHPrqabIeOb5qggio1NBXaMyooyog1Q0nFbUIMT9qoaSZ3lKrZB1/K2e/xJYseoq6HP2Uvf1H9urZ6KkdJayorrhvMPXzM9vfOMblB1jrAywUucec8wxxNA+OednCIRACIRAOxLwhWduVSXX7aRby0+OyVUUPz/VMkYtVcmsMHs+8IEPaPvwi9EfIAqE/gDOlawprSc9Bwn024gr6y1CIKJMXTeC/wsBxbdA46r/OQgi/LRJJwYg6E403kHPttYUy1Jzi2fNr3/9azEpmJ58bYgyVdNLMmqLvkH9iqxb7UBhR31cWKLcZ/iQO4qVLDQjxYTbHt9y/e2SCUgh4KhOS9qNlqQcDKqyi/RDymE9M6+50vAtd7iGnKamLcxfZxTJnOErPWtbAkVyRf4y04lKrlGnqO8dvaZqbfa/5GwJgQ4gYHyE15OIKSgpcdNf7UBvh8i+YgB77wSUYQ14c4vpMN1LbvbKeJ29UIRUbzd9hzRTpfTy+oD4y/tAR1DjKbyJ8dFtBJL14RBQJRmFpx5RH4mFpM7iB0645wFOIjzzzDM9mSK8UCene5bq6W2WkuhvV3HGaUzDO8xPLjk6CRq3k4T8tFfBjIfS6+Dto13qb2hMlvUQCIEQCIG2I1CqjAsuuKCS9V2CNgg3ZCs897WMqmpFGlZZ+Vlt5BEjoKcKQgR6TSFezPrM7NUU4uysNdR2TFLgjicQUaauW+yTodXEK7sarNR4JtIvBxMfkY9+9KPsSAMdDcLXJ0lq0R8ocqHx+dQWygjlhQ/LJZdc4nDfFE4rmmf6LU2EYYuYo0XBkQ9zVjJuLyxp29m4Go3CT/haicKo/bb22mvrz/SN8y0TDkOnul0kIQ1Lqo3mnAxZukoiNw1LC0dxgpGG6L777lsG/3MT0AqVUiaEGAoOHx9NUArUgFcqZZYQ6BgCXFe8SsJecB/wll188cVlgKFQpt4jjVXKZpmCsbIMhn7tjS4zxFPeavqFvIZOx19AV4933E+hVX0TeBbwXCPZeIv1/JhuyYk46dhSfHS9vPHRHTr8pOxPQGeAh9CTRpE3hk6Xo6rEp97DRoU0BbUEorrQ7hs9ZfrnM90tRvDJnMHNN0diPRMqUFWYLo0SqkYHwGc/+9lSxTipAhRPNK8bXUbvAjdPnZ/6GKZ7riQIgRAIgRBocQLikQn+oHJpLKeqgQkkNp/mVeN26yoLLab99ttPjWDUrS0qFM774ngyxmzRmOLLrBLR2LFoKPXJIT9DYMwJRJSp6xZoKQ3SKuMjYxAQYYWHi0+MoJ5rrbWW4fTaWhQZhqaInlpWxe70ESlZlTgvcma/ahAqurFFFuKIvnpjKPRn3n777dpstvhCMaNZzKxbao4BlkQZZq72GyPbx4u8IgdHOZGNpaPSun5RKkzhokPe4ljidEmvSMUpncwsMRudRsMi95X0uayLZvINgdYgoIOFKso7zGvlFeZTVqp/doCYLyZaotFor1JGZqK8ZvalsXrXROgwnRMbwndAg/ODH/wg2ZQwSrH1phusQRsyJQ3Dgmect9XXgCRkdIlJuL3+8dGdCfg5pA8B2gcFn084d05uXyomj7pKxLo6iPxB0OeoxTNFSj2Qnsk+OQz+kyMYH0wVlhVPu0pq1113VRU6aocddrjmmmt0Dxjhaz4ysxaq8jjpkDudmremqqf4tPMLUzkaJlxCzJThuoOfN3tDIARCIARanACbqlkJm7U1NKYYSI1HaT1Zqi26tSg11c+shECrEYgoU9cd4SNDvKB0sDhLs606k55GHdoaVDzAfURs95koHw7bdYnrMKeMSFAdUkQZ4+01+azzUqli7pJX9Fj6EtGDtc2oJMxTDi+acMxl9qtORYk5s0hptBERx3kVr2ROLbZiORFmAABAAElEQVT0ajtP2OI72PgpZGfrw2QB8/2rClNWNAX5kzvRGWecIfQMZx8xZUo/Z5+U+RkCnUHAi0ki4YHiLfN2ayXSUMp76q30igl6yo+GJuKdKpdsu6W6/P4/7aoSUFiILxzctHKJnsYVyqqciP+tQB4GHuo48tLRX/jlkn4sQgJ/61vfuvDCC80J5Wd8dCvaWRkOAU+ayov5S3b35KtlOFcSZWiFAiqZ3Jq7pddB6GvOLKJcG+RfnuTqeR78pzx1TqiSnMjynve8h3Oop12ZyTG2Gw9ImhE7xouggjvllFOKk6Y3TnVTjtJVQC3isKYmVRKRgIdzyTk2BEIgBEKgUwlEkenUO9sx1xVRpq5baYYUWowAE/rPi8NLdSZtKpYum7Lawszl26IHnrkpsSaZvZVpWyWzV55GNJA/KlXFXhsdpQPTYCKRxjmcG2O/zTbbXHfddeeddx5FRvcjYYX92qsaz80adoqSLdnI0tu46+nb1w9pqc6oqIohcf/CSCZbjUO9mlqM4gsYhyXcbx+husoqKyHQ7gQ88/xpPfOcYL2MWqSe9kpyJcR4TbQMjSqqrpROyg6oImVoWHIrq15e4Xjl5rWVs+BNXNi8m6I1eVVlIvMi2vrrLeNYx4fO4WRT0o9vSHkrOebw3PEW81ww0Mm7Hx/din9WZpoAfYQHCr8t7jA6A0j5qglPu4Ucs9dee3nkbPSsqr9s8fRKT1spsr5kb33rW0vEMWXwhNNu+IJ5a0qRVFjUFoHndQnwGLW9enH0AXAHs10aiZ3Fe8cvXW+ENM6ovvPwW7i4KxtPT7WPjal9Zvp258AQCIEQCIEQCIExJBBRpi74ZqnQyuL+rQvRGASNKGcSFULob14wRr/z9yaaGLXEGGWqaokxKyWjnuj3E6ZXn6QhSKV8RcEh9LA79dLLkKMKM5dcopGmzabhJzFPbwGGrWsZaviJcyH0r0EWZoCylwnrr0WXpnEQZay+MI2WxnM1EtGYtCgn75s+XjCKVJyAjODgUCMTA50Mu4hZ3Agw6x1GwONtIaM0drkIfaq7XlSL/mOX+swF4x1vBOJ9t5QtxSnGujdX5t7WxpQUHyd1uPeu8dTSaLg6xHvtg+AoTVbvbJ80jVllPQSGSMCjZSlPnaerOko1ZBFGsXrMvBSePQuxskpWaf1lC4mfam9dneWvxOQbNWD/R9q57LKUik9i2Vq8TX1ejfL69Hkfy+nyNwRCIARCIARCIATahUBEmbrulL4+87EJ/Pm///u/4u/qD9TfaEWnoj5Ag+R1oRuMQIthpxJuKBoiR1g3zQT3FhOO6hWkd0hAoyF/KCi9hs5iF+9uU1/TZYykYPjqRRROnCFrAIWh/sxcoo+OSlqM7fJ5zWteI7EcGLUcbTQg+YEb5sDAFUCxxBOVsv8kGgpgDiYDlKRxRhqQuL90JVnpnDQfNgcfjUmSjdyoPFqDdQFNviHQMgSqtmgpkfAWXijvhfEX3gKv3kyUtAzccGC10j+TxoZxn72NRWpc75MsP0NgRgn0ii0vkQhLDo2P2SBP5uCnG/xAp248vNmr0ViSxvRZD4EQCIEQCIEQCIG2IBBRpq7bxEwUcsUAH+EeiCAWnX66Fj/96U+TPww9oMiIYnjCCSdQOrTiBIYgyuiTpJ4QWSwm+LToAxSDhjHKPJXMnNm6Lr/73e+WMIfyMa6hCmAhIi/ZZaeddirjIwxW4jKjf1I4AH9dqny4lItbLiDoN77xDUXSwc7znF+PGMP9jW8ikZmYlFBJhA3WHSqTMkxDJnITVZQzjmKQbChNfRwB6oKbfEOglQhwIvM6i9lEiiWStlLRuqIsFGEDu3ys+FZUF8xrz+JjJTpPkaTLLgI3pZujR2NiG2VCVrORol1lQkPnBiVcOvWZfGCXAWLVgb6fDhHqy8Y+eoFIXrwRfST7nL3KuZtXVI7G4mGrYupmDrn2EAiBEAiBEAiBECgEIsrU+CSw+ykmhBJRXQzwKUEi/KWqkD/E5RXX03ZDHmzkhm0cE7vfYh5rA5E0BqghdjH3Wf/FCUXrgtZjglJhI2g09hJE+HWXy5CJ8C5l8H/ZQoLhPkPZqa5TYtNmK5hTy9YhpBYtSVkJDGxLlZtDWM/ms6AriXxRZiFVeGkU1SHCPfIGMjxKy0c+SpIey4pzVrqHAG8yLwXd08vbPVfdOlfKtVB0ZBPxmJ2nKhVXRI6KRsoQu0WorbbTSoSDJQrQx6uN5tIyzZC/u++++8EHH2y7b+/1119vTiuiM22FguAWGyhKR5Dhnnvu6RsuDV/FL3zhC0XXrnKzQuI54IADSEUmct55550bd2VdjcZ5U5zsKJh5GEIgBEIgBEIgBEIAgYgy9T4G1AqLnlXxOwkW7PjqfOx7fiV2sfX7aBnVLon7N/NoHxY6i9z67LVltdVWa8yNwiLGRGMyaUq4X9pNY/6alFUPcFVIK6Qfqg3JRjkda6n2ytni0vpsrxJkJQS6gYAXthsuszWvkTsMZYQEY+CYQLO+t6Wcvm8cl4g1gnBVogx5RfQrIg495WMf+5iPm8S+YLxdODP6IPt+2kKANg7UpOMUHHIMD0daDPVcxC4TBgkHRpSRzCeRG+O1117L4dGg0cb5mB1lvCpRxlGlPPnbSMArk7emEUjWQyAEQiAEQiAEuplA+nVH4+7TRPQNNsoZ1VntatRQqu1W7LI0bmlcd9SAe/vk5qQDJptu/o3nsl7yaXYJA27vk0N+hkAIhMCIEzBrsgDqZBQrInNV+QuLLn4WTcTIo9tvv71sN9roiiuuMM8dKYcjTNn42GOPEVaEzeLux4PGRuOVTEBOgjEa9Dvf+Y5YYKeeeqr5zkk8JkHn+dL4xaPvOPURRxwhn5Jh/oZACIRACIRACIRACITA0Ak0bfMPPYukDIEQCIEQCIHRJ0Bz4bpy8803O/W/epeqDKJlid7FVZAQI/RV2W5dbCzrhpSScsrGRx99lChjYKZI6pxljOv83e9+R5ehyJiG2YzjnG6MHpWhAadmHC/z1lUnskIS4j7zox/9iO9M4/ash0AIhEAIhEAIhEAIhMB0CUSUmS6iJAiBEAiBEGhFAlxjiCwGUQptLowLbaVyVzEe00YyCv3l8ssvV3ouLUL8VqKMYU1FQykJDNI0kZwBTaar+/nPf84XRqggc+E1Bgk2Nsq4URG1+rO47777BF8XgKb/rmwJgRAIgRAIgRAIgRAIgUEIRJQZBE52hUAIhEAItC4B3i6GDq233nrbbbedwUdEGe4yVXFtMYKJTCMNnxorV199tdC/hBWxYG666SY6ju0Cx9x5553ElxKwmTcNNxmuMW984xsbo55X2fZfEXRml1124Y8jtLCBUf0TZEsIhEAIhEAIhEAIhEAINCMQUaYZmWwPgRAIgRBoXQLcXogyRhuJsLvTTjuJ0fu3v/3tuuuuq8YQCU9uqBHnFykFiOERwzuG+PKmN73JyCbT24kvY+xS8Z0xd7WZrc2NzefFX3PnlaC/5frlaSNZpyyknEYuFByizGabbcZTRtBfuk/j3qyHQAiEQAiEQAiEQAiEwCAEIsoMAie7QiAEQiAEWpQA/YXnCy+Vtddee6ONNqKzmO3IxnvvvbeUmByz7LLL2ku4ob9QW/w1x9yuu+766le/2kYaTRm7xCNmww03FFaGnvLAAw843NxAjbPRmZ7JRNr8cSzGNB177LGNUMRxN/uSqbLNi/fLX/7yjDPOaNyb9RAIgRAIgRAIgRAIgRAYhEBEmUHgZFcIhEAIhEArEuCrwk3GDErUELIL/xeiTBnBVIX1VW7CikFJRiRdcMEF9BrzLpmVifvMq171qjKbtRFPQgU7vMy7RF4hrDhQABrhe6srl5i7jdNdeeWVItSY0anaVVYIOvJ83/veJyTNSSedVLxv+qTJzxAIgRAIgRAIgcEJPPPcY1ff86N7Hr/2+SnPDp4ye0OgkwhElOmku5lrCYEQCIGuIFBC/BpVRJFZaaWVxo8fT5Qx4MgwJTrLc889VyiQaYwq8pOSwoGFR4yfhiatuuqqBivJ5Pvf/z7xRZQZnjIOKaF85Ub0Ia9UKE3AdOCBB0r8ute9rtrYZ8W5TNW0ww47UGRMpM1tp0+C/AyBEAiBEAiBEBicwPNTn/3bfaf/7J/vOe/mL0SaGZxV9nYSgVk76WJyLSEQAiEQAt1AgJtMidr7s5/9zLpLNhxJ7N7HH3+cRwxPlhIRxiikFVdckQRDfxE+xkYDlyQ29fVWW2111FFH/eUvfzGbkkmaypxKPGVEoiGviDjDueb1r3/9hAkTpCfWOFZuv/3tb5vhJeWsvPLK733veznUiG7jJ62nWeJsD4EQCIEQCIEQGIjA1Keee/jex6959Jnb//3wBasuvPV6S+6+xLxrThg/+0CJsy0EOoRAPGU65EbmMmaCwJPP3nfV3Sff+N9zZ+LYHBICHUDgnw/89qYHz3/muUfb61pKiF+xY+aZZx4uLSZLsogFQ1IxEzZRhiZSXRH9hXeM4UjcaqgzFrvILkYbEVkcbuySBObALodQUsy7JKDvH/7whxmd4trZjY36yEc+UsLTVGXIShcSeOSZO559/iUBobsQwhhe8h2PXv6P+3/1+KQXIkyNYUm689ScHf58x7F3P3Ztd15+rnr4BKaOm/p0rzRz5d3fj9fM8Hkmh9YnEE+Z1r9HKWFdBJ6bMumOx6649ZE//vnOVTZaau+1l9y5rjONRL5Tpkwxg69+e5FNG/OzXYvU/C/alo3bsx4C0yVw9+NX3fjQ7xaac/m1l9xFZ9Rcsy043UNaIQHZhXeM4C9iuAi7WxWpDEcSzdcIpm222cYwJbvoLybGFueFC8ymm27K58VG6gkPmvXXX////u//xJ2xvcrESCV5/v73vze7tuC+ZsvefvvtvVzkG9k2yj3VIY0rPG6cWpwa02M3bs96txH494Pn3/jIuWsuutPai+80+6w9gYpGbTHyTmhqT6yBe5TKpZdeWsRrQuGoFaAVTvTAUzdcftfx886+xJqL7bj6YtvNP8coua0Rf0m6vh4CUeGvylY741988VqBzOiUQTSQ6+7/2V/v+cFKC71m/SX3WnqB9UbnvM4iXrtvtfnyJk+ezEDy9V5++eUXXnjhUStATjSCBIo088xzj8RrZgSpJqvWJBBRpjXvS0o1SgQmT5n4wFM3Pvj0LQ8+fcNf7zm5laUZdsZhhx2mw1/zspGOURsf+MAH9tprr/e///2N27MeAtMl8NyUZx56+uZ7n/j7PU9c8/f5zmgLaUZTU9AWsWMEhTEEiWdKdZmrrLIKseZPf/oT1YZAY0omu+aee+6Xv/zljHKNojJ2qaQvI5ioJ6LSaDVVmXCfodF84hOf+Na3vmVwE1Hm//2//+dYTSxt3dtvv13jtsg91SGNKzxulllmmT333FPOjm3clfWuIvDU5Af//d/z73n06r89cMZ6i+/2isV3nH3WeUaHwG9+85sf/ehHTzzxhBapYXSeWAqmp9oEYTR9W0anGGN7lslTJj068Y57n7juvieu++d/fzNq0ow6+pprrvn4xz9OncEfhPLFWGeddd71rneV2FVjS2bUzj7xuUfve/JvDz9z662PXDRq0gzsvCbp9eBb/CTEuwVcID/84Q8PEhRs1LDkRDNBINLMTEDLIW1HIKJM292yFHjkCUyZOpku8/Az/2llaUaf2znnnMPC0AWq57NQ0BHEL0DTUZszoszIPxndkePkKc88+PTNj068uy2kmX//+9/aPOZRIqaYeqnxFi2wwALC/XJ1IYhYiihDJaG/GKykjUrHqdJznDEx05lnnqmxWgLHVLv0rL773e/WuS2yrx7X4qEmH6OlNt5441122YUvTElcDXpqbOjyTdAA+9CHPnTMMcdUeWal6whMHWcEx8MTb3ts0l0PPPGP6x44fb3F91hz8TeNgjRj9neqpSpDDGztUgKlaNbeCDWFeNXeBZ5iXXI7SDOPTLzjiWfvHzVpRh0tspU54Mi7amqKACVXQCtfEmIxXcbS54PT2ffC8Ni7n7t6NKUZArqn3ceZBKlSMNCVTDPbbLNxX9p5552/+MUvdgz/HfYf97Ob9lDjDPERevb5p6Q87W97zDXvUA8ZYs6NyZ589gE/f379+xZ5eCa9856fMum/T93YmGdZjzTTn0m2dBKBGl/LTsKUa+kGAq0szbDz2NnFsLjwwgvf+c53ljtClDn//PMZ36y9brhHucb6CLSLNLPCCit8+ctf5smiwdMnki5lZKeddlpvvfW8EZJVrCTTca1dqr+02shG52Vz+OGHr7vuutXGsiKfJZdcUlbGNz300ENsem0qo5w41JBynNcsTlIyhQ2MEneGH42lMRPyzRve8AZiEGWncXvWu4eA9kO52OenPvfwM7c9NvHu+5+4/toHfrL+Eu9Yc/HtZ5tQu9cMyfJrX/uaRinnsj//+c/G01122WVG5J166qkaq5We2A13ZPSlGVQNkDz00EN9iCjIhj1ycTVY0qdJOPBGl71u4O8aR1+a8bXH/xWveIXnn+1EIhcm7Pjjj6eYk2Y6A/uKG4y79dHfD/1aWLkS3/zQ7+eYOPSDZjjl5Ck9sx/e+vBFD802w8eWA3w8p059vtnBkWaakcn2dicQUabd72DKP8IEWlOa4RGtk4cE42r17VeijDFN5557rupL6FMLH4GCo2y5+eabH3nkETE1Vl99dU3KitSTTz6p704/nnEWZorR5tRqZSlqcPZJo+HKstTpxD1HM9XwkAEHZj/88MM6Y80BrL0qn9JkLVkplb1cDxx+xx13yIqpxIVbU6E6V1Zah0DrSzPkDz4yzYjRTSx99hrBRHnxUvTZ7tXgzd6sm9GIDwa9o/S7WrwvffwLNGu9Ds3aV3ZxSeiqpm8fvF3/8yXPW4/XzDO39nrNXH/t/T9Zf8k91lhsh9kmvKgSjjgug+zoMuV14CPGa0YI6iuvvPKUU0755Cc/WQ3BIziqKYj+Ukrjua3cvrRmuYn5evukq0ok425GZ1QplNhMpcyqJ8lMeaYC8v33pjReC7eR2267TUXgmy9/uTV74xqPqmN9AGlm0W3nn/OFSnPEz+izA7vK1zfE90flSE0u6gy1t5JxVevgo4e2GpnTqy9PKYyoKLjBZXvh7y/+KuJGyHJQQd9yyy1uE1fBRulZPjJx41S+siXGOXwM/URGU5rxGKNBoweBFibEu+FLF1988Ve/+tXXvva1lSWDHnQQ4YaPZ7uRj0fXx9/Ue+4OIwdGlowKqA9kRo5BtZ5wu9z3xmepT/7uL7OqMcFw1sfPyh2vRwGZocUhz7/k4zRDR08/ccl7Su1n6QkDnFgz078fSdE+BCLKtM+9qqekf7375KvuObmevAfI9a93/8dWZzzxyosG2D26mwT6fXTi7QOes9WkGVYvOUNRVfB628zXqxOeXzofddaw7dQZ8gqbmP3HwhN9hqe6DjrbWW8an7vuuqvxFJqIJSvWOcuDqcHLungQsJX33nvv3XffnQlYpZGeWc8NRxpWOJtDGt7XlVWtPD/84Q9PPvlklj3bRRq2jgRiapQ0GgBnnHHGjjvuaK/gqSIgSmNlQOw1bZxRF9+aitGC2d7/1D8nTX6if8FaX5rpX+bBtwxoB7PaG9s2A+bgFZBmusn6H+vAAU/aP2XdW+ZeYNzmH/rvKde+dZZxZpjq+c//PYsiVj97tk3b1bi9Z+P4aen9KGl6/y3rvZn5XXa9kG/Prt7sXzhNz1G9/5WN5WR9NvYe8eKBL6afdtKeBOW/3nNW6XuKO217WetJVorYs/2Fy5Gm50fDTztKyhez6k304rG9u2ehp9h+1T0/mu9Jza0X9vacaZzgLL1Z+tuz9GTYu9Zz3D1P/k1Qkd7tL/4R/fShZ24hzdz/xN+vve/09V+2++qLkmZ6gk/Xuvgsb7fddiTIX/3qV6Z1F3GDKOPr/Ytf/MLXW8tfTUFz1KpXU7zjHe8owroWKZc0goKahUeYnw7R5nzTm94kkFnxU/MxP/3009U+9BqviY2+/8b3ER3UI7/73e9UEFq8pAE1ghrEsWoHGb6AahiXvcZrxk1c7ScnX3P1gHk88sx/NNj676qkmXufnBZrpk5pRgFcKRpbbrnlHnvs8dnPflZVLja/gZAqa+TxP+uss0hXKnQedgQXAPnZqaAFpeLZBOMOO+zw4x//mPJCXHBrCMFkNeqAXVSeo446yqBOObiDDIPXvOY1n//8553Xzbr66qvFxtKjQ1PwuXPs5ptvTptYYYUV+pOZ0S1zLzhu8w8/cPI1Ow54ICPq0Wf+M+Cu0ZRmSgF8iglk+++/P1HGs0qaNMWeXTfddBNjiRWkpwof/DfYYIP99tvPXSgHfv3rX5f4yCOP/OY3v8nWKoYQw8n2wpBJI1v3lE+ls3jR3Fl7y+H98/c2fepTn6IWOV1Jk7/DIVB5zfgQv2zedReZe6VMmz0cnjl2bAlElBlb/mN/9kcm3n7rIxePWjke6fWZZC3d+sjAtfWolWQoJ2odaYbtxW4oZSZ/GMHEvGOfWfHXdgkE2nj961/f0yqYZRbRZ1h7Om3IMbbTbvzUUySBxMwIGyUj7oi74ViyC6tCHFNmnGgaVRor/GjK3DSGxEujp0gaFrldFubgd77zHcYiM5GNyPhm9HDDkea9732vBMxK55KJs+geZJRrDDS65PRmU++fGXXxrbc0rZQ7D+Ep4/q2G6sCDijNVHuz0i4EZp1j3MvWevqG/57dU+AeDaH3n56/1dKzrXd7w789O30kev6ZtjT+aNwzbXsRKKal7v132q4eLaNx6fk1bdNL90wrSO/Whl29qaf9nvbvC1n2/Jy2qfHfaWdoSFZWX8BQbX/xoGlr1S6N2yk99dYltx0x57zT9vb8O+3/6swNh9j3zORHnu8dLPDC5oZ/6AJFmrnvib9de+/p6y/1jtUW2bZuaYaYYrQdlYQioImuOEIm+XprN6oFtBJ9/0n5lBcCii88xwqyvnrHB5zaQrWnpDhW+19KDiBvfvObVUZGRdFl7CXf0wWMk3rb295WGpzk+G9/+9sqF4nt5UopK14JstVDUDSdBiozvLrQ0uOmLnjzTQ/eOuCRU/gQ9BPFqpRuwaPP3PHkpPsbpZlqbx0rBBEuoqiqf/nFaLrD+I1vfOOnP/2p6tJQSs4X8PKEVV0SaEz6RgLgnUFSKfU1Pw4Hukf6XdCjl6l5TzzxxF//+teydRMZA2hzR1L+YjPofZGnqpnDCOyXX365DIkLjiq623CudNbZxy299jM3Pfi7ZpkM7sTRX5qZd9yKzbIake2sFFftoaVkEWXQY+0wbJhJm2yyCWKsl/KE//znPy+uNJ52wePJWI5y+6BzrFeGW9mxxx6rVN4gehnyxemSduldKKUdMH/8HX7aaafxnOrzeR2Ra+zCTBaac8W1lnzbGotuu/i8a8424SVuSl1II5fc1gQiyrT17UvhR4NAH2lmsee2Go2zvvQczAWCiJ5GAyJMLqOrs4gyLGwbmdqXXnop26IcxOxgrhmvZPCF3jZmhJ4cpgbToYgyJZm+uxNOOMHENPrrWA+Ca7Ct2dBM7cpck/l3v/tdJrVDTPN0yCGHnH322XqNmN0sD71wRx99NAvSsfo/ucAw+3Qo/eAHP9D79Na3vrWYNY5lRCqSuT+cS56j7EEwcy6+hVL+9pFm/vv0AOH3QqmlCcwybsJsPUFnewpZp9d6S0MYRuHKuLdHJ90xx4haTL3SzM2PTbpTDNpr5z9t/Ze9Q10zjGJO/1DNdd9eooAPMgHF91+Df9999zUHvG8+pYZMY64xvhVcXcg0JUdDZvjOGDNrEI1hGuKkXnLJJRqffC5ULlqtPvuGhGjlkhJUQ+JnE4C0PPmAaNByruQaQ0TgLMmn5nvf+56zqMjUUOqC6Re6eYpZxo+bOv7555vHnmh+6At7+kgzdz1//7yLTPegmUxAqFLnaszjrK+Ca4YQJyplHRUcW1TiKmu9GqrgP/7xj9BBVJ1JFfzpT3/asQ5UvZIMxJIjbNG5JFbzIqwXBH93xE8HumtHHHEEs2GzzTZTHaNNOOD08bGPfcwh9LIRmBmAtDlhZobPVNdlpVGaWWzWjZZYedzEBxv3j+Q62cvoLfKKh5Mp8pWvfIVKxUwiLNKtnMnDzFmYiSUAkPei8dy2rLjiimQyegqw/hLUoOZrxkeMtQOyW0w1cxcc2Cx/t4mzs/vLH2cmHDAbi5T1F+WYedaYZ/bFxs8yrO9JeIbAmBPIEzzmtyAFaA8CRZp5fNK9Dz87wyN4h3+FDGiVvSpcxxczQrxG1hh32dLJo1dTD491doC+F8nMkM1csE4xYW1zUeGyq4ensSTsZhpN0UdYG9yq2WqMQvnwfy4puUMztUsawQIOPvhgwo2uTqa27Sxs+fPH4Qxf6S/EF7OxcpbhnsPQLPkoEltQDo0FyHobESjSzNPPPea/Nip2ihoCLU6ALvDgMzc/Oukukzebor7W0vqeqxRKNUHZ5xTDfZLaos1ffFs01I1v0vIn1lQjOBwlMCovAxWBYw3DUZVo/NNiOGgUYcUhFAeqjapE1SM3ioNMbDEeii5QkjkXNYE0wF+Aqwg9otbrHWLmRZoxkPPpOcfNs3DlwDXEo2cgGSwYatg7RuhZ8eA4JRlWZkI39Gzki6RKhUgli3DJmh+HNEW1EZGEf6uaV+1PIKPjEBpkwh4Ak7LgrvG70YvDYDAqyg3SF8LXtThl8FFVucuf7jACoswMXPp0kpJm7pl87WPjHl50xXF3PzydxMPZXXQQjzHfYT5KBBQWC3mx8FlhhRWg/slPfgJRoyijz6mKPubxJsrQvCib0iOsPEULc3c8/8RHW5rlv+222xrT7e0wiKkUZjiX89vDx/3k9NOMuhpiJsdP2GPSuKd2W/u0+eYf6iFDzLkx2amzv+/xcQ+8dc3vLbVs3/hujckGWZ84+dE/3nrYID1A0+SY7RafZ/XIMYOQzK72IhBRpr3uV0o7ZgQmzDL7sgttvP6Se054bLlx484fzXIwsHTgMLxYbHrP9EPqaSGO6GpT/Zct7GYmAocXRrCyMct0/vBq0YcjWADrjfHBmGssNiukqC02spgJNzozWYo6kSpRpjGNdcOt+dwy5fndEGUUw7HMcZ1LVc664xSY0UOXqUQZvsHMlypNVtqOwGwT5l114deus+Ru58xz+O3jXqLutd21pMAh0GoEpkx9fpG5V51l3G3jmg8nHH6Zfd59nDUFaejGuagaNB0J/aqPkrmvt+a9r3cZylo2khI05ktloRZQTWiXcrdRy3C0JNZokXIcUEnxuNF8LU1E3QD6EpzuoIMOqoIKO0T9ojJSW6nRWkSUMeRh2fk3XmeJXZ+65IpH7j65X0Dw4YN/IYdSF+sO4bcCDkS28DnCraRAnkcqPrxpIOIaYzvyelboL9ZJMPi7I0i6BapjehkfDXU9mYZ/02677UbEwVb/ilO4s7xZeXmU/N1fSpljDUMuW1rhL4ebeedYct0l9lh2rq2+fvUOE3ocTepaaF6yZiaxYQC0zheY31B1PjfFOkmr2mKFX3Dl1cXdDH/3CHBWjd4mIqOjWEG8jN/znve4uQ5plr/t+LOO5NB4iplbv/2qcasu9Pqhv0fFl2S1RWfgkJko2Gzje+KXr7LIa5ZfbPmZONwhJtW+/M7jBjx2mhxjsNIa88wW75gBIWVjuxKIKNOudy7lHjUClRyz4oKbLzjncvc9P0DswFoLw641sJ9NxhpgnJmpl/f4L3/5SwYcc00MXRaz/hniCF9l1gZjjqFAMWGC6wIVWI7lTa9hkA1STlkx5hzbR7tpPIRdwjohyhTLRm+evXqKStdflVK/qHW9RtUWtkuxKastWWkXApUcs8wC6y84x3JzzXpiu5Q85XyBwCwh0boExs8y+6qLvn6DJd+x9Pzrf2rCxs+MGyDw9kiVniCiFhALlm5CSdE4pM5owFupTkGdt66uqVqh1a6yQtPxMdekVAHRF0ScVXHwjtT5r54SU+ajH/2ooU8qHcqC5qIzNlYQ4q06kIcOZadPzqP/s5JjVlh484XmXO68cfc92+PFUsuCgxqZhwVc+lfAgQhJPBv5QKoCxd9tAqpPUYhidBZ/7XIL3D4jy2QimjLnDkIbn1kDnUTwcX8dK6X72Ji/nElpAtD0yXlMflZyzFpL7Ljw3CtPnTjXkw+Pq899Cn+LK+W0W6wX63g28mG3WCo3sf5YpC+iDMPMXr1ThgEadMaFmZTjLTjwwAMplc3yZ6FZ3IURsYimPC8M+2z+61/OQbbMxCGD5NZs13DO0ntFfeutF+WYDFZqBj3b25xARJk2v4HDLv5GS+298sJbDTuboWYw6Q8nXzbu5A2X3vsDr9x7qMfUlu6JSfeZB+rGB89tdoY+cswcs/Y4GBus3Sx9TduLKMMOMPiZdGJeBmacEL/MMhrN1ltvrXa3i1Zi5BEnW8PUxZqxy7hlFrBeSgKNoUmDF09uxlqz4QaPwsumlE8ZrOQU1Bldo8Lg9c+80TWGHdM/wahtmVEX31Er2Jif6Kp7T7n5wfMmPf/kgCXpI8ckhN6AlFp/49OPjLvs+MVOOOFEE1X0xDp4Ia6MN37a+gsr01qA0372pO5J3Nuj27M27fBp673XXn6UrEojshz3wnpPZtVJrb6QSU9uJcOeTBrSvCR9766SrOfQ6pDefF7YXq2XE/acwKaS+Qvrvfn3rvdeWsmqbCznnpa+N4vewpTtpTATxp8ybtyz3CTnns93rMpZeab0nKu3AD3be1O/kGDq1Puf+scDT/1L27mn4AMtZglZdZE3mht76fnXW3Cu5XUvz9IznVNdi0+3esGYI4NZfJCNl1GnqC98vStPlurc2o3NPtrqiCqZFf0BBsiohoyp4XHgL5ne0Ax/yQH8OuVPg2g8xLqBNhL02TijP/910bitNtqdb8iAB9704LnX3X/6gBMwST/b+LmXXaDHO6bIMSp3AoH/BsxqRDbq8DjvvPPoX2hrk7sdCKjKzbWko6XPKShWvGCIaH22l5/VLVD1UxAMHzYAR4Rg/kplIicaWRkPxWtDPNo+7X+H97/jA55o8I1PPzru0uMW/+53vzdgMmPxzr1pf1NJDLi3jxwz/xxLevgfm1jv2FjjhkhjHntjrgtbZOgp/fVH0tiAxS4bK/5+IinEnjjKTCxRZvzVrWWAOevI3kHybwVRcpBrbKldkWNa6nakMPURiChTH9v2yHmhuVbw36iVdaG5LnKuheZcfqWFXj1qJ212okcm/ofdNuDeJnLMgGlr30iU4QKjdtf9xRoog5hKiF+msP40g5gYGb/5zW/M0cDmKJbfW97yFsHneDs7vHSaDVJQfW68annTOMsgkV+k4Y8jH/2c/uoO5QvNuLHiwEHyH9tdM+riO7alHc2z3/7In24bLwZs33NOk2N2XWaBDXjHRI7pC6itfk+eNO6ev8+9xuLb95R6miDRcAW9m6b9LhpDn18vHDRt6wu59P58ya6eH9P+79nbu/MlK9WmxuN6pZCeZJbqkBfWX/zdkKpvIV9y5LQjev59Yb33n2nbexI37Cs/X9z5wlrvPy+szzre6IZnX7PSZ+dfoOjyfTJoKNm0M8r1ktu/+eDT/34hvnLPWV5cJoyf4+WLbCOy79LzrUOOmXV87T4jhsmUsL5qECqAliRfAGI9KV+rvr9o8mJZh7BmlA3vD/nQcbhsqJu4b/ipZWsYFOdKNdcQspnhJI/cPW7WR1ZZY7FtBzzy8Ul3/eO/c/Tf1SvHbLLOkm9fYaEteMcUOaZ/spHdYqyx5ro6Wn8GNwrhZt0RiETnoc4MpwIlMSBMlNELwgwwfEbfDJeZ4uuh9ueSU+ZPHNkrkpsPy71/n6sZf1r/H289tP9JB5Rj+icb8S0C4R1//PGy5dtiTutisbB8DGJyR4ZzOn5n66yzjrvJBUkfFWvNxAvuqTxHJP/hlK3dj40c0+53MOWfIQIRZWYIVxJ3PoGWkmMKbpqLoLlsCG4vtuhbM2SJ4VvGLjGy7WKNMb+YYowAaXTZ8silyFhn8/GdsUKv0U1a9c/4yc/FAHWJjT8/9NBDnYgl0WjA6dbjYl1MdnLMF77wBaYkg7IEi2F5m4zziiuu0BdnJojSs8qDl61vlzO2yDJzLr4tUvhaizGL+Ute2jncXnKM+b9IkNo57OxGz6xaoc1c5t4yr5u5yfRgCy1Z+rFnLquZOGrK5HG+bD0H1ugKMBPlao9DigOFsasLzLnA0Es816wL9ve8oL/0yjF7LDXfugvOvdyss9Qox2iN09nJAZQXYX2NeBViTJxRYchUIrwzfL014MXUMNyVquLShBox47I0Qw8dKlqqqZp5CvDsoM5wOuBxqSrRYSB/NRf3BG+oJquqSm2ibSzEWImWMnSYA6bsdUKa0GzshjnH+jzsL8oxC26x0NzLzzFhvv43aMATzdxGEFwv1DpFjCrixmLsjLhsoh3bqDcFInWl8HBujduhQjeUTM+HsD6N8yQOcnZRY/7617+q9MkKMiTEWFdrq/TFUTYSDX9Rab/+9a+XvhYCBG3IdrdjkGyHvqvUrQOmn9AzpuYld2CU5RifXPz5CvnwMo2IkiK5sH8OOOAAppGOJaaOMUdmE7OrTDHJKBLo2tTXg3RN9blY4/XcX84yRuqx0PB3Xl5RAvMNmD9XJn7N7hQ1rU9W+dlIgByz/EJb9Ex0ncFKjVyy3tEEIsp09O3Nxc0IgRaUYxRfHa/KZ0tpxanLbWH1sthYAPohi+lW6TWMOdbAa1/7WoObzjnnnH322UcfmnH+LA8HshENOxeLrlBht5lXmxlBqWEISqb7tFjnTlrSOIRzOMWnGIssG4ajWQlkKwGb0l62JrPPUGp+7MrJd4bp6RB9RyWT/G0LAu0lxxSkYov+7Gc/8+BpZrS+KCOEp/mGSZzmPRllUaYtnsDOLiQ5ZrVFtlvvZbst1TNYadla5ZhCUoOc34TawRdeu5RMr+no623oEH2Eb4VJf4SAPfHEEwnrWqESUOe1Y4280Mj3qZ/uHSHBm6HZIdR8ojyBQFB5zVHVE+3e6cSbV0HwB+FEQCwgD/Gd0QzWrzB03We6xZhugj5yzJwT5uujF0w3h5lIAA75lQpAHfPuU8SQMbCrtP8NEyZ+CQ9nMa2hWa4JVUa+iEVC3jK+aSi6APJG37hr8BoMpR7X1KeRGTVGZTOjopkZVfqsAhW9G4q/zhU2gNjAM3FFM33IKMsxpZywf+5zn/OY6UkiFLpwQsz+++9PrpLAfdERxZuYLma2SqH3GFRl1khj+rwUQ7xYmqaprE499VQ6FyOKIuP9YoM1y989oorqxyp9ZkM8S7clm3PCgq9f9YtzTpg/Myt1263v8uuNKNPlD0Auv4dAa8ox5d7QR9Ti1rm0FCmEPa1PjN1QVuwqK0wB/WbsM1IL40AHphH+LAPGGUPEdo7llkqU4VmjTcvXwIoOIg69AgSyleVWTu0vC4MZR4uRhmXDUmTTM7VLGg3LQw45RJP4e9/7Hs8d1g+zmznOj6b0u5ay+ZtOoQppC660oxxTMHom6YBaOx68FgTbp0ilB9tr4rXqsys/O4/A1GnzKM06fq7VF92eHPOynsFKvGMGGFMzspdfvs+qA2oILxWtdHKAYGTaipr6JYIGCX6//fYjEX7/+9/nPaHD32OpYc+NRf3S+MWWW8mwFLKslwTqBZ96x6pfHK624h1AiXBGTVziozpL7aBXgF7jcA9/8akZiuIzIkzGRI4piIhcdDEKF31K7F5agMpXHVqunVhmVNeXv/xlQi2XIhFhuM+4ZIqM/owi2pZ8GuH3uQVupbY9/yaDiGWrHtcdoo4uMzQRfYhi2v+8k0qHivQSuMUjwnYomYyJHFOIeRrx95wLkMS8ob+YIEnPVhUsiaFCTBEIhi6jMwkcB3pBGkczDXgLysbyCni/9G9RvjzwvNKYQ8wtZwRnwPwlsL28g0MB2J1pZp0w+2Jzr9ad156r7mYCEWW6+e7n2sfNPmHely+2zSsW26nMrDQtlG8LkWG6bbXVVoxm5qz1UjJSi4qfzVGMAxvZcNxuObbohWMHf/WrX9X5w2OF766FncFAN9q50dSW7Be/+IUeVG3aovhIxoJpvHgGX0mj6SsN+0OPXGMaFsbHP/7xHXbYQRefU7DmiTLOaKXkY24O8306tjHbrLcIgdknzLPmYm9eZZHXJ3ZMi9yRFKNjCAh8Q45ZY9E3r7vUbkvNu84CPd4xtcsxhR4fSRIMxcRPTX2fcS3GElu3qjLsIha8973v5W7J4cLX2+edguDrrUlZ6hq1iZE18tGSr+4LB0mDL7Rs6QsqFG4vMlHX0Bzl4Fj1iNNJ77Ovh0AT1CgqNYiKg3uIekeaURBlZp8w90oLbrXiwq9aoXew0uh4x7hq7W2XbKLrQgwo/FXfrp2A1chfPf7KV76SCrP33nsXgOC7BWpPJB2OJEcMUgvUJTd/+XRccMEFMnT73KYvfelLAv2Ww1W+2Jb7IqWfRkWReNTOEiiGnItIVOVW58osC8zxslUWfn2ZWamE8q3zdC/kjTCkFX+IsIIaDQ9kI3+3xmsCFz5eAW+Kd4Qp1eh0eeSRR9Jc3IjGkhtxRoIk9NhIgtRNxReJHeUWUznZSMXQGjB/bwGfmlF4/hsLnPUQCIG2IBBRpi1uUwpZC4H5Zl9yixU+OWXKc/PPsVQLyjHlmtkQDAVLIwJ2hv7GPlsau7/YGQwLY47YGcW8Zi6wVBodCpgFDmHwWZgsjXpNlbON+vcGT8Pp2tIsDfPdUmWYlZYisMFS75pl3JR5Z1+ixUP5ero0KviWG4jBsPZsNyqDkHqwdRfz/PfE8lH3vjQ+z8RKgybK4XZ5IL0O1Y3wmrDINaW0Z9jf+vz91eCRrPRdUz9tMS7DsbbIx7mslBNV+VhhqSuhHGidxbh3SHkBG5NlvRsIrLDQ5issuPni86y+4FzLTBgtOaaA9YJYhgKZRGLRbvcGaUP2eVa9ZVw8+uSjYW+pNmqCelO8oV6T/jl40SzcEyRQ44ymg8Cqi7xhuQU3m3eOxUdNjilMVNkklUZvi4pV/xVMNNE5FlUAGz9c1AQxaPocRVzYcsstq434+xD5xOEPb5/WvhwIavhLIGc3qDqw7pXZJsy13cu/MdfsC4+aHFNdkY/zEPkDYmCXwXdqAQ9/fz4DjsJu5O/2WeD1BoHfh/9086/KnJUQCIEQiCiTZ6B7Ccw6fo5F5npJB0gbsehT9/cvOQusdFdWu1hsltJ9Wm3stSKmEztgpNJUJ81KixBYeK6RCfdY3+WQOUxmwcncfBa6Ij29DG7+WXrgt9tuu3JeCsjRRx8tagPdxBYmslCavLc0SBjKDhd2wV+jCbR8dBdrr+65554777xz6TUVa0Bvs5wdIiS2Xn3J9Ccz6/l5UVUcqGvaKA+xABjf5557ri3eL92nYnZU4wGFMFBIPdtEGSa+d01z1zDDd7/73fTQ+hAl59YksOKCr+KsMX6WNrCyhvKFHwSyd8FbOUiC3ppntDnMN8cS8417UTwapHhjvsuHqL8cMPRSOXxw/m6QfpehZzgiKU1xveR8tcy6NSLFa8wEQPVC45YZXXf7BrmDw89/RsuT9CEQAu1IYLSryXZklDKHQAiEQAiMCQEyxzHHHCOMtFaHLl86i0BIQiRQTKpefSII73G9zZQUey1izRRBRGvEuAze5hxYdCnrTL7++usFaaLvSF+cyygsN954o5gCZpRnPdsuQqc5aOTJK4d3Or1GCG3zp1B2FIBMw4KX2Hnt0pVK5SHWCPcoSIew3DxudE1TiExtI2e+8SJKitYxJgBz0rEiMOes0+bPHqsS5LwhEAIhEAIhEAJtQiCiTJvcqBQzBEaIgGYqb2etxBJKcMBcqzSD978NeGw2hsBIEeACI1ISzxQe5uZcF0NUlzuxw8JZpuqZNMiC54uJwCgmlBG+KgKLXnnllRxbpDGUTwAmviocW4gy1BYTxIqELTR144g/ZRZBQ6xrY/H4zphTxnwowjc6b7mc4mVDoxEtlf+OE/GvIeUIEkmUEQ9bQE1/Regw5YfxCxxnHC6Qp0m7xZgQWWmksCSfEAiBEAiBEAiBEAiBTiIQUaaT7mauJQSmT6D4Apgps3I06H9MlYY6039vtoTA6BAwdwx5hVuKiTMsRUYkiBiU5BG1lGJwgdlmm2022mgjjyuBhuzCjYWgw72FFsPb5R3veAfvfaoK5xd+LgI0cr3hUNN4FWJwGg9FPfFeGADFveX0009/6KGHCEDFs13mlCDxs8XIcKBkfGSIO1xjuMmY5kM0bqcTY1XwTmVTTn9JPyY4oxBFlGmknfUQCIEQCIEQCIEQCIGKQESZCkVWQqBbCIggUE1Z3eyah5Km2bHZHgIjQkB4FwF6ebiYcKRy7KJ0eDgb8xc7yeCgIiCSYMqkJBxVTFBaklFYBIu55JJLbLGdWGN8E7WlMRN5Gv1UlEoqjHxkSP0xa0wRZSTmOFYUGesljQFKEhgAxUdG5gIGH3bYYcZblZyNmTJVrTNSiBrPlfUQCIEQCIEQCIEQCIEQqAhElKlQZCUEQiAEQqCFCJjzyCKUzHQ1xKrQtBUL4cZIJQFfLBxVzNorDA1xxDCotdZaS8gYUYEJLtVRfVYcbtyTv7xd5NNnb/lJvqHRlARyJsfYTsex0GiqQwwVJN84abUlKyEQAiEQAiEQAiEQAiHQSCCiTCONrIdACIRACLQKAWOOLLxaTGY0c2Uqo5DEfyHHiCxj7l5Sizg1NJrpZijl4GmqBCLdlDi+BkaJLFPcbapj/TSLbfUzKyEQAiEQAiEQAiEQAiHQSCCiTCONrIdACIRACLQKAeFy+cgYGcS3RbSXSgQZevkEhbnssssEmhGSZvvtt+fbYt6lxRZbbOg5DCWl8VPC0EgpbA3fmQH9YnjclPJbMSGU0DZDyTlpQiAEQiAEQiAEQiAEOp5Aonh2/C3OBYZACIRAWxIQSdcirMwvfvEL2koZcCQizIUXXnjdddcN/ZIMYhKLlyJDEKHvmM6JdGKLYUdDz2SQlAYo8cSxyPy444674447JHYKoWQE+hUZx0/DmvjLmKhboJmzzz6bfDNIhtkVAiEQAiEQAiEQAiHQPQTiKdM99zpXGgIhEALtRMBoox122MEMRxdddBFpY4011uCTQqOheuy6664mop7uxYjLay4kYWUE+pUDXUZo3iLoCCtjaqetttpquplMNwHBxcAlM5oddNBB9CPBff0kytxyyy2cYszV/ZnPfEYmYhVvvvnmZ511lkjA5Jtvf/vb0805CUIgBEIgBEIgBEIgBDqeQESZjr/FucAQCIEQaEsCNBSiDD+Uk0466c9//vO//vUv0Vt4u4ieu9RSSxkNVAYElb/VFTb+XGKJJcyHTRzhXCMWL02H0LPffvuRaUzGRB8hygyYidzKdoJLybkx28YtJQEvmF122UWM4e9973u0HjFrFFVAnE033dTUUSU9UeYjH/mI8puQ+9JLLy0b8zcEQiAEQiAEQiAEQqDLCUSU6fIHIJcfAiEQAq1LQFiZ7bbbzgRMJBUDlxTUlsUXX5woM88887zhDW/gk0L7aPSa2WuvvV71qlcJ2rLMMstQSTbYYIOjjjrq5ptvLtFkbDfdNTeZf//732V+a1Nuf/e73zWeaKWVVqpAUFIuuOACpzDBNm1ISBplcOoqAbln3333fde73iU3G0k2tuyxxx6bbbaZ6MJi2Qj9q5wOWXrppctRZnQSGWe55ZYjEoleXGWVlRAIgRAIgRAIgRAIgW4mEFGmm+9+rj0EQiAEWp0AZWT11Vd/+ctfXuKw8ECppjeig1j6XADVw1JtJKkY98S5RkgaGk05VvxgWowRRpLJn3BTpS8rPF+23HLLaiMNyFL9tCJbRbJUG+kyjlpvvfWEsFFUJ3K6am9Z4fVDRXLqxJTpQyY/QyAEQiAEQiAEQqBrCUSU6dpbnwsPgRAIgbYhYJQQHWSmi0vKsVSHE1D6KybV3mGukGMsg2TSpzCDpMyuEAiBEAiBEAiBEAiBjieQ2Zc6/hbnAkMgBEIgBEIgBEIgBEIgBEIgBEIgBFqRQESZVrwrKVMIhEAIhEAIhEAIhEAIhEAIhEAIhEDHE4go0/G3OBcYAiEQAiEQAiEQAiEQAiEQAiEQAiHQigQiyrTiXUmZQiAEQiAEQiAEQiAEQiAEQiAEQiAEOp5ARJmOv8W5wBAIgRAIgRAIgRAIgRAIgRAIgRAIgVYkEFGmFe9KyhQCIRACIRACIRACIRACIRACIRACIdDxBCLKdPwtzgWGQAiEQAiEQAiEQAiEQAiEQAiEQAi0IoGIMq14V1KmEAiBEAiBEAiBEAiBEAiBEAiBEAiBjicQUabjb3EuMARCIARCIARCIARCIARCIARCIARCoBUJRJRpxbuSMoVACIRACIRACIRACIRACIRACIRACHQ8gYgyHX+Lc4EhEAIhEAIhEAIhEAIhEAIhEAIhEAKtSCCiTCvelZSplQnMPvvsa6655rPPPvvPf/6zlcuZsiHw97///fnnn1977bUnTJgQICNCYJVVVpl33nlvvvnmJ598ckQyTCb1ESifqfLJqu8syXmkCJQvVflqjVSeHZnPfb3Lkr3LCF5gybBkPoLZdl5WNX1YVNNeAVW2V6DzoI3sFcW2GVmeyS0EWoFARJlWuAspQzsRGD9+/Pzzzz9lypTHH3+8ncrdlWW99NJLJ0+evMACC8wyyyxdCWDkL5oiM+uss1JkgB353JPjiBKYOHHi5ZdfXj5ZI5pxMquFQPlSPfbYY1OnTq3lBJ2S6e29y5y9ywheU8nQW2MZwWw7Lyt86viwqKa9Ah5+r0DnQRvZK4ptM7I8k1sItAKBiDKtcBdShnYioFG68847a5T+/Oc/b6dyd2VZS/MmnjIjePO32WYb/cnnnnuu/uQRzDZZ1UGgaMfxlKmDbR157rHHHm7WaaedxhOhjvw7Js9e2WRicWwZwYtar3e5tncZwWw7L6uaPiwefq+Ah98r0HnQRvaKYtuMLM/kFgKtQCCiTCvchZShnQjwsF1mmWV42N51113tVO6uLGtp3myxxRYZvjRS919DaI455qDITJo0aaTyTD41ESjasfu16aab1nSKZDuCBJZbbjnOAnfccUc8ZQanWnSTFVZYYfnllx885Qzt5aax4IIL3njjjTfccMMMHdhtiWv6sHj4vQJ8MM8+++xuQzqj1xvbZkaJJX0ItD6BiDKtf49SwtYioDNHd5rIGuzChJVprXvz0tKwWoQ+0bwhyhhx89Kd+TWTBDz5nn+ijOefaT6TueSw+gm4Oz/96U9pxwZlRJSpn/cInIFPn5dLteLlirNMM6DgXHTRRTwFiDKWZslmYjvFGX+6jNFR8QRsBrC+D4u+E/zXWmuthx9+OGFlmvG3PbbNIHCyKwTal0BEmfa9dyn52BAQoGHxxRffeOONtXZOOumksSlEzjoEAjqceVlzh15sscUSU2YIwIaUxPC9jTbaiMh1+umnx1lsSMjGKJEOZzfI/dpll12EwRqjUuS0M0CAp8arXvWqp5566rjjjkvMsmbgHu1dtN5JjQTHZslmYrvcVl99dUKPgCmWmcihGw6p78Oiml5kkUV0onDDPP7447sB5sxdY2ybmeOWo0KgxQlElGnxG5TitSKB+eabb5999nnmmWfOOuusOMu04h3q7Uo6+eST9Tbvv//+hm+0ZiHbtFRiKi299NICDV5xxRVxlmnNm+i+eP7FvdLO/+QnP9mahUyp+hP40Ic+xBnT8I2rrroqzjL9+Yhm5cHmLPOa17zm1a9+df8Ew9yyySab0HqMYOKME2eZ/jDr/rB4+L0CdJ8zzzwz5sqMwgAAQABJREFUzjL9+dvCTSa2zYBksjEE2p1ARJl2v4Mp/xgQ4CawzjrrHHTQQXGWGQP6Qzvl4YcfftNNN+2+++6G23BuGtpBSTUkApz89913X3/jLDMkXmORiDPBkUce6e9uu+0mBtZYFCHnnBkCYmp87WtfI8d8//vfj7NMf4Kao7/+9a9XXHFFooxxRv0TDHOLPL0yMifKxFmmP8y6Pywq69VWW+2www574IEH4izTn78tsW0GxJKNIdABBNJW6YCbmEsYAwLzzDOPcTFMQ+3Sr3zlK2NQgpyyOYEy4trQ9AMPPDBuMs05zfweczBtvvnm+qu//OUvC9wz8xnlyBoIlKAPWjWbbbZZ3GRqAFxvlmqWDTbYgCjw+c9/XnCNek/WVrlzk/HNmWuuuegmdbjJFBjrrruu75uYNUcffbQzthWhegs7Oh+W2Wabba+99lpjjTU4+nkF6r2kdss9tk273bGUNwRmgEBEmRmAlaQhUBHQn7PSSit9/etfv/fee8swgWpXVsaWAKuFTGbOVHEZBAiIm0wdt0OkEp5iXDCM4PvhD38YP/86IM9cnhpOnCy0JxdddFF/4yYzcxjH8CgjzvgIGMdxxhlneLnc0DEsTOucmj7CR0AI3p122mnvvfeuw02mXKzIMkQfuozhmWWoVOtAGMOSjNqHRWSZl73sZd/5zncefPBB/FXoY3jVLXXq2DYtdTtSmBAYcQIRZUYcaTLsFgL6c0Sk0zQVdO2QQw7Rq9MtV97C16lj7Ytf/KKBS/yfBWOOIlPfvTIu7HOf+5xBTCSAE044IbpMfaiHnjPHik9/+tMGLj399NNf/epX11xzzaEfm5StQ8A0TG6fcbJHHXWU9yu6zLHHHquqNZ7ofe97H0VmiSWWqPVmyd9ZDGKiBFE2uefUerrWz3yUPywqbtW3Svz+++/XxRJdxhMS26b1X5OUMASGSSDTxA4TYA7vagIGMb3//e+H4NBDDz344IP/8pe/sCSEQe1qKGN08YIC/uhHP9KxpnuNMfee97yHajZGZemW0+pMFpHxC1/4woknnmgQkwCZZEqzonTL9bfSdYpC4vvDsYJ7hca8uCT5ELXS/Znhsrh9Jo+je5LYhJM35ZlhTTzUZjijNj/Ah0WHhw/7Lbfc8sEPfvCjH/3o8ssvPwrXZBCTIPF8c4SwoTgTaHzuuvDjNlYfFtW3StyNPuCAA3S0iHu94YYbegVG4da32ili27TaHUl5QqAmAhFlagKbbLuCgP4cfrbMRLYaUUaX5vnnn8+I8TN91KP2BBST5Q9/+MOtt9667LLLnnLKKaSxhRZaKNNg130LNBF32GEHLjMkgAsvvPDiiy/+61//yvPf88+Dpu6zJ/9CoLSaTj311Msuu0zrUeudk4WASpkGu62fELfPXObqEaLnr371q/POO4/30//8z/94uYxsautLG2LhixzDV+WGG27wPeeORxbhw0JzHGIOw0lmEJOZmLjJ0INEjuMsoxhd9XEb2w+L6tsATJ1eHnjS5EknnWRKsueff95PfmTDubNtdGxsmza6WSlqCAyfwCxTp04dfi7JIQSGSEBY0C996Uv6Pfwd4iFtkey5557jWX3MMcewnk2fYbj7UkstpVeHARF1po47yFhhn/FqZjiam5kcY1YIcy194hOf0CjNqKU6mA+Sp+YTHw2ipFA+Wk2ef14zpWM56swg3GZ6l8ee94RRLZwIMCfHGETJsWKfffb5wAc+kG/OTIMd8EDPs7CvvjCivQyYoNaNbjRPNI1SxaBN+EusoTurXDpPnfEl8VQTYsiLRQd56KGHtt56az0ffFW4ptaKesDMjaDhLEMSuu2223zNOvvj1oIfFp81Wv+3vvWtn/zkJ5yk8F9sscVEAu5UdaZW2wa9UfiUrbDCCv/5z3+Efxodp7YBX9tsDIF2JBBRph3vWhuXuVNFGbfEOA71kM40HWtaShMmTCjqTPqr63heGRYEZQ1Rf62TY4QA4LLBCIgiUwfw6eapEcUIq0JjMv6KOqPDebrHJsGMEtBQMV8yXfKuu+6ybvH877rrrqzhRPadUZjTTT86LZlBiuEue7l++tOfqlxoFm5xUWc671vn6tSkPiaTJk2yTo7xYGt++7CP4ZeEHqdyv+iii/AnFdHmijozhkUa5GkZzi5fkhb8sCgV/gQ7/PXEcKIp6syYiKTDwTuUY2u1bUbnUxZRZig3OmlCoD+BiDL9mWRLjQT0+PGR2XHHHffdd19N6BrPNEZZF2mG4wbTrSz6OceoLJ18Wg7MZC9XyB3JUHNzk0eOaYX7rfWi9XjjjTdOe/yv1b5qhYJ1WBm4SBR3GCPIvAJeh8gx9d3i0WnJTLf8RZrRkV69XPwapntUeyVgFZS4OfzsBKgqOnuLaB9FmtHvUvHvvI9bK39YijTDX6ni711or8d7KKWt1bYZnU9ZRJmh3OikCYH+BEZjaG7/s2ZL1xJgXVn42+sE60gIhruvvPLKNAJKQXET1e/UkVc6theli6yEjCnuSAkfM7a3ozo7m0+3tpnIX/3qV2vDeAW87NXerIwUAS4SxQWveOTFHW+kwLZyPhxkLOuss872229fXi7N1FYu8EyUjSJTQsZwRVlkkUVGJ3zMEMvp42ZZbbXVfNxK5d55H7dW/rAoG8tKg3/99dcv/P0d4r1ro2TtbtvohiQW6zPovMGVbfQUpahtSiCiTJveuBS7pQmwHhbuXVq6lClcCNRDgPCqTWWpJ/vkGgLdS4AAZyFGdy+CMb3yfNzGFP84HTDG7lnGthg5ezMCuiGJxb5RzOBmabI9BEJgQAJ5ZwbEko0hEAIhEAIhEAIhEAIhEAIhEAIhEAIhUC+BiDL18k3uIRACIRACIRAC7UugRHkos6K071Wk5CEQAt1MoHzBqpg1NaHI8KWawCbbbiAQUaYb7nILXWMZ1CA8nkD6LVSsFCUEQiAEQiAEBiJQojwIYGGut4H2Z1sIhEAItDqB8gWrYtbUVNxq+FIi/dVEONl2MIGIMh18c1vx0kyRI06b+VnEz2/F8qVMIRACIRACIRACIRACIdBBBEbHU6aDgOVSQmC0CUSUGW3iXX6+nrmX5pxz0qRJnTr7Upff31x+CIRACHQYgQxf6rAbmssJgS4kUHnK1HrtGb5UK95k3tkEIsp09v1tuavL8KWWuyUpUAiEQAiEQHMCW2yxhanHL7300smTJzdPlT0hEAIh0O0ELr/8ct2um266qf7XbmeR6w+BGSQQUWYGgSX58Aj0OsrMOXHixHjKDA9kjg6BEAiBEBgNAlVMmdE4Wc4RAiEQAjUQOO2005599tm6A/1WMWUyJXYN9zBZdjiBiDIdfoNb8PKKswxRJrF+W/DupEghEAIhEAKNBKrhS0KhPf/88427sh4CIRACrU9AQJnbbrtNqHJ+f7POOmtNBc7YpZrAJtsuIRBRpktudAtdZnGWefTRRxPrt4XuSooSAiEQAiEwEAGeMuuss46OBNVWJmAaiFC2hUAItDSBMvpyjz32WGyxxeqbF8nYJY7wGbvU0o9CCtfCBCLKtPDN6dCirde7RJTp0NubywqBEAiBTiPwoQ99aI455ij+/512bbmeEAiBTidQRJnllluuPkUGwiLKzD///LWepdPvVa6vewlElOneez9WV77aaqutvvrq4sDHU2asbkHOGwIhEAIhMHQC22+/PZ//s88++5prrskIpqFzS8oQCIFWIECU8eHiKTP77LPXV54S5XfnnXeed9556ztLcg6BTiUQUaZT72zrXpfhSyv0Lvfdd1/CyrTufUrJQiAEQiAEeglkBFMehBAIgTYlwMXvv//97+67777KKqvUF3/35z//+T333KPPdaWVVqovbE2b3oIUOwSGQiCizFAoJc0IE6DJGMN01113+YiPcNbJLgRCIARCIARGmkBGMI000eQXAiEwGgQOP/xwoV7qHrvEpJ88eXICyozGHc05OpRARJkOvbGtfVnLL788UWbSpEmcZVq7pCldCIRACIRACIwzgmn99dc/99xzTznlFDPLhkgIhEAItD4BbjJ80kUor3vskk5W0dCJMsJvtT6WlDAEWpBARJkWvCmdX6TiKePzLaxMRjB1/v3OFYZACIRAmxMwgunDH/6wDuf//d//9bfNrybFD4EQ6AoCxU2GIlPr2CWTYd96663C1sRTpiueqlxkPQQiytTDNbkOSkBYmSWXXJI0c8MNN2QE06CosjMEQiAEQqAlCBRnmbvvvvvHP/5xnGVa4pakECEQAs0JFDcZ0WQOPvjgWh1YyrxLQvwutdRS9YWtaX6h2RMCnUAgokwn3MV2vIZlllnG5/uhhx4SE74dy58yh0AIhEAIdBWB4iyjbaPz+corr8w0TF1193OxIdB2BKpoMoIG1KqVnHTSSY8//vgb3vCGueaaq+0opcAh0CIEIsq0yI3oumIsssgi22yzDWeZzMHUdfc+FxwCIRAC7UmgcpY57rjjhEVrz4tIqUMgBDqfQOUms/fee9c6EzaHd27vq6222pZbbskRvvPJ5gpDoB4CEWXq4Zpcp0fAhHlmzuMskzmYpocq+0MgBEIgBFqCQOUsc/bZZyfib0vckhQiBEKgHwGKzFe+8hXRr/bff/9VV121VjeZat6lxRdfvNYT9bvKbAiBjiIQUaajbmd7XQwncJFljGAyn0XC/bbXvUtpQyAEQqA7CXCWOeKII8SUEfGX036Cy3TnY5CrDoGWJVAUmZtuuumwww7jwFK3UFLmXdpnn33mm2++lmWSgoVA6xOIKNP696hjSzjvvPOaGDvhfjv2BufCQiAEQqDjCHCWecc73vGud73L2Fu6TILLdNwdzgWFQBsT+Pvf/37ooYdSZMT3NXBpttlmq/Viytilt7zlLZzfucDXeq5kHgKdTWDCl770pc6+wlxdyxIg3gsJJjbYhRdeqLNxiy22WHjhhVu2tClYCIRACIRACCAgbsI666zz5JNPClRv2pF3vvOdtc5sEuYhEAIhMEQCwgJcc801Zcal5ZZbrm43mfe///1c3U888cSVV1657nMNkUCShUCbEoinTJveuA4pdhXu15DUa6+9tkOuKpcRAiEQAiHQ0QS0dg444ICNNtron//853bbbfd///d/mYypo294Li4EWp0AH5ltt93Wt2i33XYzB3bdoWTgiJtMqz8TKV9bEYinTFvdro4rbOUsc/7550+ZMoXA33GXmAsKgRAIgRDoQALGMW288ca0GEF/NYS47nOfmTBhQgdeai4pBEKgtQmII3PggQf+6U9/esUrXnHMMceMQigZPOIm09oPRUrXZgQiyrTZDeu84vID5/h98cUXP/DAA6v0Lp13jbmiEAiBEAiBziOwxBJLlHFMF1xwgSEDAqVFl+m8u5wrCoEWJ1Ai+/KUMWpJcF9foVEYScRN5uSTT+abI3KNT1+LI0rxQqD1CUSUaf171OElrJxlzMH04IMP6nhMZJkOv+W5vBAIgRDoFAL8ZYouI76MQbjRZTrlxuY6QqA9CHz+858/7rjjBHYpcWTWXnvtUVBkDNvcb7/9brvttk984hMbbLBB3eGE2+NOpJQhMDwCEWWGxy9HjwQBzjKCy/i4X3311fPPP7+IvyORa/IIgRAIgRAIgdoJ9NFlzjzzzOeee07TKEOZakefE4RAFxPgIPOZz3zmrLPOuueeezjIfOQjH+FuPgqKDOS//e1vTz/9dOG0PvCBDyy66KKzzDJLF9+HXHoIjAyBiDIjwzG5DIeAKoR3jJmYfvzjH2capuGQzLEhEAIhEAKjT6DoMm9+85ufeeYZIWYMZYo0M/p3IWcMgS4hYKTSkUceefzxx1911VWEGNLMPvvss8wyy4yOImPg0hFHHHHnnXeadGnNNdeM+twlT10us24CEWXqJpz8h0RAiMSFFlpo4sSJ55xzzqOPPsq0HdJhSRQCIRACIRACLUCALrP88suvu+66pso2N5MBuZFmWuC2pAgh0FEEihxz9NFHX3jhhVxUyDGf+tSnXve61+naHDV3FfF9Oba/5S1vSTSZjnq2cjFjTWCWqVOnjnUZcv4Q6CEwefLkyy677L3vfS/RXX2zzTbbhEsIhEAIhEAItBeBO+64w2SChx9++A9+8AO+nwQaYs2ee+45++yz77HHHv621+WktCEQAmNLgBBjljeDlfjFPPzww//5z3+WXXZZn5TXvva1K6200oILLjhqcgwO3GQ++MEPbrXVVgcddJCZnnSpji2cnD0EOoZARJmOuZWdcCH3338/Oeab3/zmpptuuv/++0eX6YSbmmsIgRAIge4jQJq5/fbbjcnVjhIAeOmll9ZwItBwpVlvvfX0PZSgMwk9032PRq44BAYjUCQYKcqKv+Yn1W3pk8KRfK211tprr73GRI5RJIrMIYcc8o9//EORNtlkkygyg93I7AuBGSQQUWYGgSV5nQTUOtdddx31/Y9//OPWW29NoDFWts4TJu8QCIEQCIEQqIuAdtQjjzzy2GOPlYhp+rpFT9Oz7XyGO5Fpyt+6Tp98QyAE2o2Az0UZxFBW/LUULWbDDTc0TInn3Sh7xxSEZlwyXqkMXDrhhBNM0NFuaFPeEGhpAhFlWvr2dGHhhJW54oorOH4bjf/JT36Sv0wXQsglh0AIhEAIdBKBMqbJ36LOuLSqG9zAhE660lxLCITAcAhU3nOVMx3fujHUYqpr2XfffWkx2267bQYuVUyyEgIjSCCizAjCTFYjQ4AuI4CZr/8888zDTzKDmEYGa3IJgRAIgRAYawJFnVEKvd/lb0L7jfU9yflDoIUIVN5zVhTL3zHxi+lDxMClz372s8zyk046af3118/ApT588jMEhk8g8ZmGzzA5jDCBOeecc6ONNtppp50El+EyI/foMiOMONmFQAiEQAiMBQFhZcbitDlnCIRACMwkgRJKhqD8ta99zRzYUWRmkmMOC4FBCWRK7EHxZOcYEaDLGK162223XXrppWIlLrnkkgkuM0a3IqcNgRAIgRAIgRAIgRDoRgJVcF9zYH/84x83D/doTvbUjcRzzd1KIKJMt9751r7u8ePHG0C74oor3n333dFlWvtepXQhEAIhEAIhEAIhEAKdRqBRkRFKRv+omeM67SJzPSHQGgQiyrTGfUgp+hHgHslBptJlHnzwwY033phS0y9hNoRACIRACIRACIRACIRACIwYAdMt7bfffiZF5SOT4L4jhjUZhUATAhFlmoDJ5hYg0KjLXHLJJXfddVcJQd8CRUsRQiAEQiAEQiAEQiAEQqADCVBkDFYyHaoIj1FkOvAG55Jaj0BEmda7JylRA4FKl7nzzjsvuOCCG264gbNM4ss0EMpqCIRACIRACIRACIRACIwMgaLI6A3dcccdo8iMDNPkEgLTI5DZl6ZHKPvHmoCgv5tsssn++++vIHSZJ5980krmYxrr25Lzh0AIhEAIhEAIhEAIdBQBcWSOOeaYyy+/PIpMR93XXEzLE4inTMvfohRw3LjKXyZxf/M4hEAIhEAIhEAIhEAIhMCIEyiRfa+66qqMWhpxtskwBAYnEFFmcD7Z2yoE6DILLrjgbLPNdssttxjENHny5BVWWEEk4FYpX8oRAiEQAiEQAiEQAiEQAu1JgHfMcccd5++GG2744Q9/mJf67LPP3p6XklKHQPsRiCjTfvesa0tsHBMhZqGFFrrxxhvVGffdd19Rauadd96uZZILD4EQCIEQCIEQCIEQCIGZJmAmjXPOOefYY489//zz119/fRED3vjGN7K6ZzrDHBgCITCjBGaZOnXqjB6T9CEwhgQeffTR88477/TTT7/22msXWGABwWV22203szKNYZFy6hAIgRAIgRAIgRAIgRBoOwK6OU899VRBGydNmrTpppvuueeer3vd66LItN19TIHbnUA8Zdr9DnZd+dUTZl9ae+21jWa6/vrr1SJkmgxl6rrnIBccAiEQAiEQAiEQAiEwDAIUmcMPP/yMM85YccUVP/jBD+6zzz6vfOUrM2ppGERzaAjMJIGIMjMJLoeNIYES93ettdaqhjLdfvvtRjPRaxJlZgzvS04dAiEQAiEQAiEQAiHQ+gT6D1l6+9vfzooeP3586xc+JQyBziOQ4Uudd0+76IrKUKZf//rXQv/edtttYsV/8pOfzFCmLnoCcqkhEAIhEAIhEAIhEAJDJnDHHXdccskll1566UUXXZQhS0PGloQhUC+BiDL18k3udROYOHEiN5krrrji6KOPpsu8ZtoSaaZu8sk/BEIgBEIgBEIgBEKgjQjQYk455ZQ//OEP//nPf/Rlbr311gznlVdemRN6G11FihoCnUcgokzn3dNuvCIuMxdffDHJ3xJpphufgFxzCIRACIRACIRACIRAEwLFNaaYyksvvfQuu+yy9957r7baapFjmgDL5hAYVQIRZUYVd05WKwHSzG9+85uTTz7ZSqSZWlEn8xAIgRAIgRAIgRAIgdYn0EeO2XLLLXnHmLp0nnnmaf3Cp4Qh0CUEIsp0yY3ulsskx5gqm09mH2nGJH+ZoalbHoJcZwiEQAiEQAiEQAh0N4ESO+bWW28VQYaDDO+YIsdQZMy1lIC+3f105OpbjkBEmZa7JSnQ8An0l2aKKLP66qurihJuZviEk0MIhEAIhEAIhEAIhECrEaDFCLZoKVoMz/Flllkmckyr3aaUJwT6EIgo0wdIfnYOgSLNPPbYY2UALQ+aBRZYgChTBBqOM/Gd6ZybnSsJgRAIgRAIgRAIgW4lQIuZMmVKGanEO4YoM3XqVDC22GILpq8l3jHd+mjkutuDQESZ9rhPKeVwCFBnShhguox182cvv/zyRZThO8NxRpAzP/UkDOcsOTYEQiAEQiAEQiAEQiAERodAJcQUFaaIMo2uMexbokzkmNG5HTlLCAyHQESZ4dDLse1EoNFxpqgzxXemEmWKLqMCo840/m2ni0xZQyAEQiAEQiAEQiAEOohAEV/KBZXRSfSXaoV3TBFihIkhway00koMWuOVosV00COQS+l8AhFlOv8e5wr7EKjUmUqamThx4uWXX16EmAkTJkSU6UMsP0MgBEIgBEIgBEIgBMaEQH9RxtAkG9mrRYgpHjHlb7SYMblHOWkIDJNARJlhAszh7U2gCDSTJk0iykyePPmuu+7q/7e9rzClD4EQCIEQCIEQCIEQaGcCyy23XJkvyQpHGOvVCu8YHYpVgna+ypQ9BLqXQESZ7r33ufI+BBrlmOeff74SaPoky88QCIEQCIEQCIEQCIEQGDUCleZiRWDEIsoUmWbUypAThUAI1Ecgokx9bJNzCIRACIRACIRACIRACIRACIRACIRACDQlML7pnuwIgRAIgRAIgRAIgRAIgRAIgRAIgRAIgRCojUBEmdrQJuMQCIEQCIEQCIEQCIEQCIEQCIEQCIEQaE4gokxzNtkTAiEQAiEQAiEQAiEQAiEQAiEQAiEQArURiChTG9pkHAIhEAIhEAIhEAIhEAIhEAIhEAIhEALNCUSUac4me0IgBEIgBEIgBEIgBEIgBEIgBEIgBEKgNgIRZWpDm4xDIARCIARCIARCIARCIARCIARCIARCoDmBiDLN2WRPCIRACIRACIRACIRACIRACIRACIRACNRGIKJMbWiTcQiEQAiEQAiEQAiEQAiEQAiEQAiEQAg0JxBRpjmb7AmBEAiBEAiBEAiBEAiBEAiBEAiBEAiB2ghElKkNbTIOgRAIgRAIgRAIgRAIgRAIgRAIgRAIgeYEIso0Z5M9IRACIRACIRACIRACIRACIRACIRACIVAbgYgytaFNxiEQAiEQAiEQAiEQAiEQAiEQAiEQAiHQnEBEmeZssicEQiAEQiAEQiAEQiAEQiAEQiAEQiAEaiMQUaY2tMk4BEIgBEIgBEIgBEIgBEIgBEIgBEIgBJoTiCjTnE32hEAIhEAIhEAIhEAIhEAIhEAIhEAIhEBtBCLK1IY2GYdACIRACIRACIRACIRACIRACIRACIRAcwIRZZqzyZ4QCIEQCIEQCIEQCIEQCIEQCIEQCIEQqI1ARJna0CbjEAiBEAiBEAiBEAiBEAiBEAiBEAiBEGhOIKJMczbZEwIhEAIhEAIhEAIhEAIhEAIhEAIhEAK1EYgoUxvaZBwCIRACIRACIRACIRACIRACIRACIRACzQlElGnOJntCIARCIARCIARCIARCIARCIARCIARCoDYCEWVqQ5uMQyAEQiAEQiAEQiAEQiAEQiAEQiAEQqA5gYgyzdlkTwiEQAiEQAiEQAiEQAiEQAiEQAiEQAjURiCiTG1ok3EIhEAIhEAIhEAIhEAIhEAIhEAIhEAINCcQUaY5m+wJgRAIgRAIgRAIgRAIgRAIgRAIgRAIgdoIRJSpDW0yDoEQCIEQCIEQCIEQCIEQCIEQCIEQCIHmBCLKNGeTPSEQAiEQAiEQAiEQAiEQAiEQAiEQAiFQG4GIMrWhTcYhEAIhEAIhEAIhEAIhEAIhEAIhEAIh0JxARJnmbLInBEIgBEIgBEIgBEIgBEIgBEIgBEIgBGojEFGmNrTJOARCIARCIARCIARCIARCIARCIARCIASaE4go05xN9oRACIRACIRACIRACIRACIRACIRACIRAbQQiytSGNhmHQAiEQAiEQAiEQAiEQAiEQAiEQAiEQHMCEWWas8meEAiBEAiBEAiBEAiBEAiBEAiBEAiBEKiNQESZ2tAm4xAIgRAIgRAIgRAIgRAIgRAIgRAIgRBoTiCiTHM22RMCIRACIRACIRACIRACIRACIRACIRACtRGIKFMb2mQcAiEQAiEQAiEQAiEQAiEQAiEQAiEQAs0JRJRpziZ7QiAEQiAEQiAEQiAEQiAEQiAEQiAEQqA2AhFlakObjEMgBEIgBEIgBEIgBEIgBEIgBEIgBEKgOYGIMs3ZZE8IhEAIhEAIhEAIhEAIhEAIhEAIhEAI1EYgokxtaJNxCIRACIRACIRACIRACIRACIRACIRACDQnEFGmOZvsCYEQCIEQCIEQCIEQCIEQCIEQCIEQCIHaCESUqQ1tMg6BEAiBEAiBEAiBEAiBEAiBEAiBEAiB5gQiyjRnkz0hEAIhEAIhEAIhEAIhEAIhEAIhEAIhUBuBiDK1oU3GIRACIRACIRACIRACIRACIRACIRACIdCcQESZ5myyJwRCIARCIARCIARCIARCIARCIARCIARqIxBRpja0yTgEQiAEQiAEQiAEQiAEQiAEQiAEQiAEmhOIKNOcTfaEQAiEQAiEQAiEQAiEQAiEQAiEQAiEQG0EIsrUhjYZh0AIhEAIhEAIhEAIhEAIhEAIhEAIhEBzAhFlmrPJnhAIgRAIgRAIgRAIgRAIgRAIgRAIgRCojUBEmdrQJuMQCIEQCIEQCIEQCIEQCIEQCIEQCIEQaE4gokxzNtkTAiEQAiEQAiEQAiEQAiEQAiEQAiEQArURiChTG9pkHAIhEAIhEAIhEAIhEAIhEAIhEAIhEALNCUSUac4me0IgBEIgBEIgBEIgBEIgBEIgBEIgBEKgNgIRZWpDm4xDIARCIARCIARCIARCIARCIARCIARCoDmBiDLN2WRPCIRACIRACIRACIRACIRACIRACIRACNRGIKJMbWiTcQiEQAiEQAiEQAiEQAiEQAiEQAiEQAg0JxBRpjmb7AmBEAiBEAiBEAiBEAiBEAiBEAiBEAiB2ghElKkNbTIOgRAIgRAIgRAIgRD4/+ydd6AU1dmHvYACSrMCNooNC8WGHVCxN0zQIBaMXWzYIrFCEnsDxRI1EWuIGsWCioUiqGBFQQWMVEWsgAqCtO8J58tk3b1l7727e3d2n/0D5s6c+syc3XN+877vkYAEJCABCUhAAmUTUJQpm41XJCABCUhAAhKQgAQkIAEJSEACEpBA1ggoymQNrQVLQAISkIAEJCABCUhAAhKQgAQkIIGyCSjKlM3GKxKQgAQkIAEJSEACEpCABCQgAQlIIGsEFGWyhtaCJSABCUhAAhKQgAQkIAEJSEACEpBA2QQUZcpm4xUJSEACEpCABCQgAQlIQAISkIAEJJA1AooyWUNrwRKQgAQkIAEJSEACEpCABCQgAQlIoGwCdcq+5BUJSEACMSYwa9asFStWRP+uXLkyxp2x6RKQgAQkIIE8IFC7du2NN96YhtSpU6dly5bhOA/aZRMkIAEJxJiAokyMb55Nl4AEEgkE/WXs2LHLly/neMaMGYmiDMeJiT2WgAQkIAEJSKCyBNBikkSZunXr7rrrrmo0lSVpeglIQAIRgRLfHkcsPJCABGJHIBJipk+fHlQYRJlly5ZxnlljrVq1Nt1005KSEv7lOHa9s8ESkIAEJCCB/CHAz+vnn39Oe3j5wUH4s169epEos9lmm4XjPffcM3+abUskIAEJ5DkBRZk8v0E2TwISKJ0Asstrr702evRoJoUIMdOmTQsqDBNBjKtRYTCrDqJM9G/pBXlWAhKQgAQkIIE0CESiTDjgX16H8EGgWbJkybhx4yKBpnPnzvwcYz6jOpMGV5NIQALFTkBRptifAPsvgXgRCFpMsItBlAlaTKdOnRBiovkfs0BNY+J1W22tBCQgAQnEkUDQZRBlFi9ejCgT/sWOhuPwo6w6E8fbapslIIEcE1CUyTFwq5OABKpIIDKNGTVqVKTFtGrVCosYRBmFmCpiNZsEJCABCUggQwSCKINSwy91CPEW1BnizvTt21fDmQxhthgJSKDQCCjKFNodtT8SKDwCmEYzt8NTKcgxu+++O1oMQkyXLl1at25tsJjCu+P2SAISkIAEYk0AXSaEeOOHm59vpZlY300bLwEJZJuAoky2CVu+BCRQdQLIMUGLCVFjkGMwhA5aTAgZU/WizSkBCUhAAhKQQJYJBKuZJGnm0ksv5UccR+MsV27xEpCABOJBQFEmHvfJVkqgCAkwh3vggQf4l77jmh5Zx2D/XIQ07LIEJCABCUggpgSSpBlsXdmn6YQTTjAMcExvqM2WgAQyS0BRJrM8LU0CEsgAAQxkghyDIoNpzIknnsi8TU+lDJC1CAlIQAISkEANEUiSZrB7DdavSjM1dEOsVgISyBcCijL5cidshwQkEAgEOWbw4MHYNgdFhnmbcCQgAQlIQAISKAACSDMjRox4+OGH2UiRWDP77rvv5Zdfri5TAHfWLkhAAlUmoChTZXRmlIAEMkwgRJDp168fB7169UKL4YM0k+FqLE4CEpCABCQggZojsGLFCn7op06devXVV6PL8Fu/9957H3fccUaZqbl7Ys0SkEBNElCUqUn61i0BCUQEEiPIYCCDNKMcE8HxQAISkIAEJFBgBII308iRI7GamTt3Li9jjDJTYLfY7khAAmkSqM3KJ82kJpOABCSQJQIoMv379x86dGj79u379OlDEBkVmSyhtlgJSEACEpBAPhCoVasW0eL22muvRo0afffdd88999znn3/Or7/2Mvlwd2yDBCSQSwKKMrmkbV0SkEApBAgiM3DgQHQZ3pKhyHTr1q1JkyalpPOUBCQgAQlIQAKFRaB27dq8j9l4440JMTNmzBh1mcK6vfZGAhJIi4CiTFqYTCQBCWSJAIoM9noTJkxAkeGgQ4cOWarIYiUgAQlIQAISyEMCmMwEAxkcmrCXmT17NkpN27Zt87CpNkkCEpBANgjUyUahlikBCUggHQJBkSFlUGR0WUoHmmkkIAEJSEACBUagTp06Xbt2DY5LQ4YMmTVrFh3s2bNngXXT7khAAhIolYCWMqVi8aQEJJB1ApEic9VVVxlEJuu4rUACEpCABCSQxwRKSkrWWWeddu3aNWzY8P3333/jjTf4U3uZPL5jNk0CEsgYAUWZjKG0IAlIIH0CSYpMoQaRwTeemeUaa6yx1lprMd1Mn48pJSABCUhAAsVGgB/Kddddl9C/DRo0INKcukyxPQD2VwJFS0BRpmhvvR2XQI0RYKZ1/vnnz5gx49Zbb8VGJgftmDx58qRJk6ZNm0YcwZkzZ86bN48JX926dbNdNb7xd911F1PMzTfffOnSpU8++eR7773HMTJNtqu2fAlIQAISkEAcCRBQBnsZdZk43jvbLAEJVI2AMWWqxs1cEpBAFQmgyLD7NYoMcWRyo8jQ0L/+9a8ffPDB8uXLOSagYOPGjbfeeuujjz6aHR/4s4o9SSMbMtDrr7++zz77ELzw22+/vfnmm9n1s0uXLhjOpJHbJBKQgAQkIIFiJMCrCyYJTBVuuOGGv/zlL8Sa2XPPPYsRhH2WgASKg0AWVyPFAdBeSkAClSAQFBn+DZF9K5GzeklHr/o0a9Zs2223JZww9jIDBgy47rrrfvjhh+oVXIncGObgG7/HHntoJlMJaiaVgAQkIIGiJBB0mR49enz66afoMmPHji1KDHZaAhIoCgJayhTFbbaTEsgHArzywkYGRaZz587sfp37vZbOPPPMNm3aLF68+N133+3du/fQoUP79u2baCzzyy+/INOwB0SjRo1SLWh+/vnnn376CSOX+vXrpwaIWbBgAVkwt069FOA3bdq0T58+zDLXW2+9cAbzGbLw4c/58+ej2tSrV6/U7BUWng/31zZIQAISkIAEMkWAH8ctt9zyiiuuoMDHHnuMP+++++6wPVOmqrAcCUhAAnlCQFEmT26EzZBA4ROoWUUGvsR22WCDDZjYMatjy6dvvvnm66+/XrlyJZe+/PLLESNGTJ069fvvv8ebnZRYSnfq1IljrhKD5tlnnyUwDeIIosw222xz1FFHBRekFStWvPnmm+PHjyemL3oKyguS0w477LD66qsn3VGcp15++WWqO/XUUxF9CDFz2223URHmM+PGjfvss8/IssUWWxxxxBGoNkGaSb/wpLr8UwISkIAEJBB3AkGXueyyy9ghe+TIkY8++iivUuLeKdsvAQlIIJWAokwqE89IQAKZJ4CBzODBg7GOwUaGoCqZr6AyJX7yySdYxKy55ppoKEH+4C3cPffcw/wPlWTRokUff/zxq6++OmjQIPQXTrJX1B133EH6DTfc8Kuvvnr++ef333//IMoMHz6cZB999NEmm2xCvBjEHXp65ZVX7rjjjkm6DGY4qDDoLBhjI8rw5+23345VDlXQHqxvZs+ezfmFCxeecsoplS28Mr03rQQkIAEJSCAeBIIuc+mll2Lfym8x71R69uwZj6bbSglIQAJpE1CUSRuVCSUggaoSQKfATIbc2KfUoCLD5kfYpKC5sMvm3Llzjz/++NatWzPho2FoJYcccsj222/fvHlzZBEkGKxa+GA7TQL0GrQYDKc322wzDpBsEGjIhaENG0ixodJZZ52FZQ1WNs8888xTTz2FdrPVVlutvfbaicCwkeFDRfzL+VV/rSQSMMY4J5xwAoGH2TwbfYeYxN27d0eUqVThiRV5LAEJSEACEigYAry92HvvvXmfYdDfgrmndkQCEkgioCiTBMQ/JSCBzBOIHJdytt1SqX14+OGHMUhhFyQUGTQR1BAiuTRs2BBjGXQQDpo0aRIy4tmEkIT9Cz5HXCUZ59dZZx1UG4LCHHzwwcwROfPOO++wqRPOSti2IMSQElMg3KBefPFFDIKSRJlSm0R7zjvvPEQZgtHg90SYmylTpuAtRWnVL7zUGj0pAQlIQAISiBeBEPQXJybMWgnS/9xzz8Wr/bZWAhKQQPkEFGXK5+NVCUigugRQN/iE4L7VLat6+X/729/ir0R4XZyMhg0bxtyOEDMEESTCLs5H+BC99NJLuCBhKcMxyX788Ue0G7yQMKJ58MEHmQi+/fbbu6z64GeEBDNx4kTCBpMMCxdi9NI68mKJEz7BIqb8JlPOoYceiiJDsvXXX5/9odhmAtcqDGoqLJwGlF+4VyUgAQlIQAIFQCByYmIPphBcRiemAritdkECEogIKMpEKDyQgAQyTwA5Jjgu5UMomWOPPZYtsZnbEa8Xv6QLLrjg/vvvx1AFNQRPJULeoMWgkqDC4IiELBLhOPfcc9FNxowZc8sttxACpmvXrmeffTbheFFwSIZZzVtvvRVsZ8jSsWNH7F/4pCOaYK0T/KdCXSEXehCCTvULj9rvgQQkIAEJSCDWBPitxIMYLQYnpmuuuQaXYXdiivUNtfESkEAiAUWZRBoeS0ACGSZAWL5gJlODoWRSu9S4ceNu3bpdcsklc+bMYaNrYu7eeOONaC5nnHHGbrvthi6DUxKBY6KMbJD0xz/+cZ999mGjJXygCNCLH9NBBx2ExxPTxJ122gm3rGDtErKEEMJR9vQPEnWcjBeefjNMKQEJSEACEsg3AjgxEQwOYxnce92JKd/uju2RgASqQ0BRpjr0zCsBCZRH4D9uS6NGkQIzmfLS5fwadihE5yVeDOoMkzxCxuCXhNEKFjEEhaE506dPjxqFZIODEtY0hx12GE5YM2fOJPILNjXYyxCdF7MaItQQ1peMQVKhcLJQbFRC1Q6yWnjVmmQuCUhAAhKQQE0R4C0IVq5hJyZekGA1o7FMTd0L65WABDJLQFEmszwtTQIS+B8BzGRmzJjRokWLPDGTmTp1aojzQuSXIUOGIJ2wcTUGKXgz4XzEn0GL4U+0JHyIQk8IDDx69Gi8lgjcy0kC0DAvJBcSDFoMYg3BX7CyIbgMzkdLly7F9YmMISTw/1hU/iirhVe+OeaQgAQkIAEJ1DABfqx5O7L77rvzdgSTGSPL1PD9sHoJSCBDBBRlMgTSYiQggV8TQI4hSgvn8sFMJkThJboNTkYYyGDbgi6z3Xbb4ZTEJcQUvJaIHdinTx+2UsIKBl2GYDHB8oXtHi6++GJexxGPhhC8r7zyCscEjkGdwa3pmGOO4X3dRRddxBnCBmN0g0az11573XTTTezWFEpI/RcsiScjcsg90aUKC49yeSABCUhAAhIoEgL8+Pbu3ZtdDu+8807C8GPxWiQdt5sSkEABE6idD+ulAuZr1yRQtATOP//8CRMmYCYTpJma5TBt2jT8jJjJ4aOEYxGuRvvvv3/fvn2JFMgZ9BEUEISYn376iai9zZs3v/DCCzF4IVgM8gr7ZHMJHWf27Nn4MZESCYbXdMFBib2YiEGzZMkSMn711VdEqMHt6Oijj27Tpg0v9MjC/ta81uMkggtOUhtvvDHhbAg6g+iT+GfgM2nSJNpDAvQd0pdfeM0itXYJSEACEpBA7gnw40igfV6fYNPKTy0/yrlvgzVKQAISyCyBkmDMn9lCLU0CEihyApjJtGrVCghsb0QQ3FjQQCXBUwkHJUQZZBrckdBxkEhC45Fj2A4JMYW5YLBnSewUX6TkxfqGV3aIOMEKhgSUiV6DMU44wzEn0YZC3qQ/OYn3E4Y8JIhK4GRZhYdC/FcCEpCABCRQVAT4oRw3btzhhx/OC49hw4ZpLFNUd9/OSqAgCSjKFORttVMSqGECmODhK4SZDOpMDTfF6iUgAQlIQAISKCwCixYtOvXUU4ksc++99xpZprDurb2RQDES+E/8Aj8SkIAEMkuALYooUO/IzFK1NAlIQAISkIAEIIBJ6ZlnnonBKZFlcDGWiQQkIIFYE9BSJta3z8ZLIB8JBN8lzWTy8d7YJglIQAISkEBBECD0PnH3cTom6C8R4gqiT3ZCAhIoUgJayhTpjbfbEsgegYEDB1J4XELJZI+DJUtAAhKQgAQkkCUCRNNn9yXCrrFJYpaqsFgJSEACuSGgKJMbztYigWIhwJ7QbIhAb9nhqFj6bD8lIAEJSEACEsgtAT2Ycsvb2iQggSwSUJTJIlyLlkAREkCRCTthaylThHffLktAAhKQgARyQ4DtEdu1a9egQYP3339/7NixuanUWiQgAQlkg4CiTDaoWqYEipfA6NGj6byKTPE+AfZcAhKQgAQkkBMCwYOJHbIVZXLC20okIIFsEVCUyRZZy5VAcRLQd6k477u9loAEJCABCeSYQPBgItavokyOyVudBCSQWQLuvpRZnpYmgaImEPZdatKkybx584oahJ2XgAQkIAEJSCD7BMIeTCHcb+PGjbNfoTVIQAISyDwBLWUyz9QSJVC0BAYPHkzfO3fuXLQE7LgEJCABCUhAAjkjQGQZ9sPGWGbYsGE5q9SKJCABCWSWgKJMZnlamgSKmsCCBQvof5cuXYqagp2XgAQkIAEJSCAnBAgrgyjzyy+/PProozmp0EokIAEJZJ6AokzmmVqiBIqWQAgooyhTtA+AHZeABCQgAQnkkkAQZXBfCq+Fclm1dUlAAhLIFAFjymSKpOVIQAKrlZSUQIG5kSwkIAEJSEACEpBADgggxxDMrlGjRrNmzTKsTA6AW4UEJJBxAlrKZBypBUqgSAnMnz+fnjMxKtL+220JSEACEpCABHJOgBdCaDFsjD1x4sScV26FEpCABDJAQFEmAxAtQgISgEDwXTLKrw+DBCQgAQlIQAI5I0Cs37Zt2+asOiuSgAQkkHECijIZR2qBEihSAhMmTKDnHTp0KNL+220JSEACEpCABGqCAJYyYVfsmqjcOiUgAQlUl4CiTHUJml8CEggEQow93Zd8HiQgAQlIQAISyBkBN2DKGWorkoAEskRAUSZLYC1WAkVHQEuZorvldlgCEpCABCRQ0wR0X6rpO2D9EpBAdQkoylSXoPklIAEJSEACEpCABCQgAQlIQAISkEAVCCjKVAGaWSQgAQlIQAISkIAEJCABCUhAAhKQQHUJKMpUl6D5JSCBQMAtsX0SJCABCUhAAhLIMYGwJTaBfkNsuxzXbnUSkIAEqk+ghK+w6pdiCRKQgASYFQHBrxSfBAlIQAISkIAEckkAOYZ9BtiDKbwfymXV1iUBCUig+gS0lKk+Q0uQgAQkIAEJSEACEpCABCQgAQlIQAKVJqAoU2lkZpCABCQgAQlIQAISkIAEJCABCUhAAtUnoChTfYaWIAEJSEACEpCABCQgAQlIQAISkIAEKk1AUabSyMwgAQlIQAISkIAEJCABCUhAAhKQgASqT0BRpvoMLUECEpCABCQgAQlIQAISkIAEJCABCVSagKJMpZGZQQISkIAEJCABCUhAAhKQgAQkIAEJVJ+Aokz1GVqCBCQgAQlIQAISkIAEJCABCUhAAhKoNAFFmUojM4MEJCABCUhAAhKQgAQkIAEJSEACEqg+gTrVL8ISJCABCWSVwPz58xcsWMC/Wa3FwrNKoF69enXr1qWKJqs+Wa3LwiUgAQlIQAISkIAEJBAXAooycblTtlMCRUcAFWbmzJn8O2HChIkTJ/Lv8uXLi45CoXS42aoPvenQoUOXLl0aN26sOlMo99Z+SEACEpCABCQgAQlUnUDJypUrq57bnBKQgAT+S6CkpITDjHylBDlm1KhRgwcPRoth9c4ank+o4r8V+n+cCCz+7wejpzZt2gRppnPnzkozcbqLtlUCEpBAXhLglyVMFTSqzcv7Y6MkIIEKCCjKVADIyxKQQJoEMiXKzJgx4+mnnw5yTPv27akdw4q2bduyjK9du3aajTFZvhGY+98PKtvkyZO5y61atQrSzBFHHMFkOt8abHskIAEJSCAuBBRl4nKnbKcEJFAqAd2XSsXiSQlIoGYIfPDBBwMGDECRadGiBWYUHNMOlu410xprzQ4BdBluMZZQTz31FMfY0HTr1q1p06bZqc1SJSABCUhAAhKQgAQkkL8EtJTJ33tjyyQQLwLVt5RBkTnxxBNZpWMg06dPH47jRcDWVooAN/ruu+8eOnQoT84Zqz7qMpUCaGIJSEACEggEtJTxSZCABGJNQFEm1rfPxksgjwhUU5QJigxeLSgyGMhoHZNHtzZrTcGlCZMZPkuWLEGDQ5lRl8kabAuWgAQkULAEFGUK9tbaMQkUB4FaxdFNeykBCeQ1AbSYYCPTq1cvvFpUZPL6bmWucezI1LdvXzQ4vNWCOmOMxszRtSQJSEACEpCABCQggRgQUJSJwU2yiRIobAIoMv379w9eSyGITGH3194lETjwwAPxVmN3rSFDhgwfPpwQM0kJ/FMCEpCABCQgAQlIQAKFSkBRplDvrP2SQGwIoMhgJRFsJWLTaBuaUQK77ror0sz06dPRZaZMmZLRsi1MAhKQgAQkIAEJSEAC+UtAUSZ/740tk0AxEMBMBkWGnvbr10+vpWK446X2ET+mHj16oMtgMIX/msYypVLypAQkIAEJSEACEpBA4RFQlCm8e2qPJBAnAgMHDqS5mMm411KcblsW2ookhy6DExOizOTJk7NQg0VKQAISkIAEJCABCUgg7wgoyuTdLbFBEigqAqzA6S9mMkXVaztbKgGcmPiMGzdOY5lS+XhSAhKQgAQkIAEJSKDwCCjKFN49tUcSiA0BfJdwV9FMJjY3LMsNxYmpZcuWbMDEU8Fu2VmuzeIlIAEJSEACEpCABCRQ8wQUZWr+HtgCCRQtgRBNRselon0AUjvepk2brbbaasGCBe6NnQrHMxKQgAQkIAEJSEAChUdAUabw7qk9kkBsCLD2pq1NmjSJTYttaJYJEFmGD5YyfLJclcVLQAISkIAEJCABCUig5gkoytT8PbAFEihaAiGgTJcuXYqWgB2XgAQkIAEJSEACEpCABIqZgKJMMd99+y6BGiYQrCGwjKjhdlh93hCot+rDltjuip0398SGSEACEpCABCQgAQlkkYCiTBbhWrQEJCABCVSKALF++RDl10C/leJmYglIQAISkIAEJCCBmBJQlInpjbPZEpCABCQgAQlIQAISkIAEJCABCcSbgKJMvO+frZeABCQgAQlIQAISkIAEJCABCUggpgQUZWJ642y2BCQgAQlIQAISkIAEJCABCUhAAvEmoCgT7/tn6yUgAQlIQAISkIAEJCABCUhAAhKIKQFFmZjeOJstAQlIQAISkIAEJCABCUhAAhKQQLwJKMrE+/7ZeglIQAISkIAEJCABCUhAAhKQgARiSkBRJqY3zmZLQAISkIAEJCABCUhAAhKQgAQkEG8CijLxvn+2XgISkIAEJCABCUhAAhKQgAQkIIGYElCUiemNs9kSkIAEJCABCUhAAhKQgAQkIAEJxJuAoky875+tl4AEJCABCUhAAhKQgAQkIAEJSCCmBBRlYnrjbLYEJCABCUhAAhKQgAQkIAEJSEAC8SZQJ97Nt/USkIAE8pLA8uXLFy5cuGDBgpKSkrVWfdZYY41yWkrKWrVqNWjQgPRJyX7++eeVK1fWr18/6dKPP/5ImXySzidl908JSEACEpCABCQgAQlIIG8JKMrk7a2xYRKQQFwJfPvttxMnTpw0adLkyZPpQ8uWLVu3bt2+fftWrVrVrl271F4NHTq0YcOGBx10EOJLUoLXXnsN/eXggw9ec801o0uIPk8//XSLFi06dOhAxui8BxKQgAQkIAEJSEACEpBAjAgoysToZtlUCUggBgS+/PLLRx555IYbbsD4BRMZ7F8wmVm6dOnRRx991llndezYcfXVV0/qxvz58wcNGlS3bt1ddtllo402Srp6xx13fPLJJ9tvvz3KTmQUM3fu3CuvvHKvvfa6/PLLFWWSiPmnBCQgAQlIQAISkIAE4kJAUSYud8p2SkACMSCwZMmSu+66a+DAgVjE7LPPPu3atePg3//+91tvvfXYY499+umnDz744FZbbYVSk9iZV155ZcaMGT/88MOUKVOaNm1ap87/vpkpkOyfffbZBx98sPHGGyPchIyjRo0iPfY4GNEkFuWxBCQgAQlIQAISkIAEJBAjAr9aGMSo3TZVAhKQQB4SwGVp8ODBRIHp0aMHB9dff/0111zz8MMPX3LJJc2bN3/33XeHDBny008/Jbb8l19+eeKJJzjJwTPPPJN0FbEGixtiyjz//POLFi2KMo4cOZI/UXmwslmxYkV03gMJSEACEpCABCQgAQlIIEYEFGVidLNsqgQkkO8E7rvvvu+++26LLbZAhcHmJTSXWLxnnnkm8WLq1av3wAMPEHEGkSXqCYYwb7zxBiYw2LQV5y8AAEAASURBVNSgvJA98SqOS4g1JMY0Zt68eeESNjLjxo1bvHgxCs706dMTxZqoWA8kIAEJSEACEpCABCQggfwnoCiT//fIFkpAAvEgQOCY0aNHYyZz2GGHNWnSJKnRRx11FMFfZs6cOXv2bML0Rldxa8LaZb/99iOaDG5K77zzTlBhQoKPP/44/Mmljz76iCo4P378+G+++SYINHg8JRnXRCV7IAEJSEACEpCABCQgAQnkOQFFmTy/QTZPAhKIDQFMYIjwglbCRktR8Jeo9dtuuy0mM/w5a9asZcuWhfPIMcOGDUN2+d3vfhe2XvrXv/6VKLIEUWb99dcnDM0LL7xAzGAyvvrqq1jHrLfeepxElDGsTATZAwlIQAISkIAEJCABCcSLgKJMvO6XrZWABPKXwBdffBHUlnXWWSd16+t11103nGR7pkiUIcQv/kfYyOy0005HHHEEm16PGDGCckKYGJKhuYSdmzC9ITEiDqF/x4wZw0lMbxo0aKAok78PhC2TgAQkIAEJSEACEpBARQQUZSoi5HUJSEAC6RGIpJakzZWi3OE8qkrwPMJAhhC/GL8cfPDBaC677bYbYWgIHIMhDD5Q5ML0hq2vOTjmmGOIU4N8M3ny5A8//JCDZs2aHXvssY0bN8buhjA0xvqNIHsgAQlIQAISkIAEJCCBGBFQlInRzbKpEpBAXhNgy+rIFiYxakxoNFFgQnSYyI4mhPhFT0GUwUYGXWb33XcnGPDTTz/NjkvkCr5LmNhsuummIQ0eTOzQhO8SMWjYWhsPJpQgY/3m9WNh4yQgAQlIQAISkIAEJFA2AUWZstl4RQISkEBlCGywwQb4E5WUlLAxNuYwSVmDwsJVnJXq1KnD1RDil+i/2MUQ35c9mIJew85K06ZNQ20JWbbeemuC0SDKNGrUCCMadmhC3DnwwAPJiC6z1lpr6cGUhNo/JSABCUhAAhKQgAQkEBcC/1kY+JGABCQggeoTQDrp0KHDjBkziN178skno6EgwYRi0WiCpxKyCyLL6quvTnSY5557DpsXXJl69+4dPJtQZ9jomngxmMO0bds27IeN8kLJBA/ebLPNXn/9dcQaDGd22GEHYglT1NixY4Mo07x58+p3wRIkIAEJSEACEpCABCQggVwS0FIml7StSwISKHACxx9/PN5GWLiwiRLxenFiQnP54YcfCN/74osvornsv//++Bwh1hC1F/kGtQWbFz4HrPp069Ztl1124SSyDpFiEi1l0HHYngkvJwjiuxS23N5mm22wlJk6daobMBX4g2X3JCABCUhAAhKQgAQKlICWMgV6Y+2WBCRQEwS6du2655574mF0zTXXYAXTqVMnosxg8HLLLbeg0bRo0eKMM87AgiaE+GXrawxeBg0aRHzfqLHINz179iQLTkwEncEuJljKkABB58477yQwMCJOUGcQZXCYwlsKBQcBKHXLp6hYDyQgAQlIQAISkIAEJCCBPCSgKJOHN8UmSUACcSWAS1G/fv1o/ahRo66//vqbb74ZS5kQXwb15IILLthxxx2xeUFzIYIM5w8//HBMXRJ7i6UMQWeICvzII498//33xP1t3bp1iEHTrl27zTffnOwUQkXkatmy5dprr427U4j1S5SZxKI8loAEJCABCUhAAhKQgATynICiTJ7fIJsnAQnEjABxXrCLueeee4jdi7aCAQv/zpkzB68lLGXY5RozGWxbMHJBbcHmpX79+ok9RKPp3r07iRFfdt11V5yh+ITYNBjCcInSEGJCFuxlMMYhHg2JU/d7SizWYwlIQAISkIAEJCABCUggDwmU8BY3D5tlkyQggdgRCMJBpb5SqpAlRljwPJo9ezYSDB5JAwYM+OyzzwjQi3/Tlltu2axZMwSajh07BoOX9DtFeBrEl8aNG4fAwOlnjFFKTI369+9/1VVXBZujGLXcpkpAAhKQQI0QWLBgAaHW+HEkiH6NNMBKJSABCVSHgJYy1aFnXglIQAJlEsDnqFWrVlzG5wj56dFHHyWyL/8S7nfFihXHHHMMxjKVFWWIR1NmfV6QgAQkIAEJSEACEpCABOJGQFEmbnfM9kpAAnEjgNsR8X332WefkSNHjh8/noi/6DK77bZbZRWZuPXb9kpAAhKQgAQkIAEJSEACFRBQlKkAkJclIAEJZIQAXkt8Tj/9dEpjYyZsZwjim5GSLUQCEpCABCQgAQlIQAISiCkBRZmY3jibLQEJxJhA2NA6xh2w6RKQgAQkIAEJSEACEpBAJgjUykQhliEBCUhAAhKQgAQkIAEJSEACEpCABCRQOQKKMpXjZWoJSEACEpCABCQgAQlIQAISkIAEJJARAooyGcFoIRKQgAQkIAEJSEACEpCABCQgAQlIoHIEFGUqx8vUEpCABCQgAQlIQAISkIAEJCABCUggIwQM9JsRjBYiAQlIIAMEli9fPnXq1KVLl26++eYhGHDqmepX89NPP61cuXKttdaqVUtdvvo4LUECEpCABCQgAQlIQAJVJ+CMvOrszCkBCUigLAJLlixB+CjralnnFy5ceMkll5x00knTpk1bsWIFyVLPlJU39fwvv/wSCkm8tGDBgmeffXbIkCFffPFFFVqYWJTHEpCABCQgAQlIQAISkEA1CWgpU02AZpeABCTwKwIoHZMnT/7kk0923333pk2blpSU/OpyuX9gIzNhwoTZs2ejxQTFJPVMuQX8/0XyIut8/PHH22233aabblq7du0o15QpU6677jouPf744zRvjTXWiC55IAEJSEACEpCABCQgAQnkmICWMjkGbnUSkECBE0BG+cMf/tCzZ090GZyPaqS3y5Ytu/7664899tjXX38de5nENqyzzjrt27dHMFp//fUTxZrENB5LQAISkIAEJCABCUhAArkhoKVMbjhbiwQkUAME0EQwOfn5558JntKgQYP69esnNoKrRFfh6uqrr06AlXr16kVXFy1axNWGDRvi/vPjjz+Spm7duvxZp07yd+YPP/ywePFi4r9QAkYxqCH4B2HtgvsSVXNM4VSdGL0FlYRLJMBKhVyUHNWb/gENoxDaSRZKoIqobbScVk2aNImWk4BjdCJaSAI6gnXMlVdeiRyz4YYbJooytAoapKQ0WkWzo8bQQfoFPTpIgXzISI1Va3lUrAcSkIAEJCABCUhAAhKQQPICQyISkIAECoPA559/jq3KZ599NmPGDASXLbbYok2bNttss02QZr7//vsPPviABNOnT1977bVbt26N/QhpgrQxcuTIOXPmHHLIIZ9++umHH344a9Ys7Ep23HHHnXfeuVGjRoEPMVlmzpyJ9kH5ZN9yyy232mor9IsXX3wRuYQ0w4cPx4cI/aJbt24oICgaeBXRKgrEv2nu3LkbbLABGdu1a9eqVatIUkkH/jfffIMX0kcffUQDKHa99dbDTalt27bB+OXrr79+5ZVX+Jeixo4di9SCerLffvttttlm48aNo0coL8guBx10EAINaRBxQkdo1fz582kVHaFVzZo1C6rNO++8Q65dd90VfWfixIkQQ+IhATRInKg3pdN400hAAhKQgAQkIAEJSEACEQFFmQiFBxKQQOEQQG3561//+re//Q1ZoXHjxpiBoMIgWxBIpWXLlkgPf//732+88UY0lCZNmnAVCePAAw+84oorEBrIcs8997zwwguoD48++ihXMTAhCyLFrbfe2r17dwQUrEVuu+22hx56CGsUyudPxJE77rgDfeeuu+5CvADlP//5T2xhOI9+gfxBrn//+9/kevDBB7FDQRuiWOSbQw89lOC+HTp0SF+XGTFixKWXXoqsg7aCwkIb0GXOOOOM008/vXnz5oSkeeCBB/iXNgwbNuzVV19FN6EBm2yyyXPPPQcB9CbaifISRBl0pQEDBjz99NM0APWK0iDQq1ev3r17oxaR96mnnrr//vuRlpCxvvzyS2x8sACCW9++fX//+9+vu+66hfPc2BMJSEACEpCABCQgAQnkloCiTG55W5sEJJB9AkgGaBYIEMS4xR4EKxLOYCSCRIKUQP3sPYQLD7LI0UcfvcsuuyC4cAYLF4xKHnvssSBVYNWCyLLXqg9KxMsvv4whyU033XTwwQdjLIOhCtoKsshRRx215557YjUzZswY9rFGHEHLQK2YN29ejx49ML1B4MAchn9Raq6++mrKR+k47LDDWrRogdXM0FUf6kKswZomTTY4UmEUs/3222+99dZoKO++++77779/5513YsxCAxBfTjzxROx3sBLC2GeHHXag4whD/Lv33nsjqTzxxBNYx4S60KqIPoMiQ6uQXSBGDGC2Zxo0aBBFnXbaaUFzoYW0fKeddqL7lEBnEWgQtrp06aIok+ZdM5kEJCABCUhAAhKQgARSCSjKpDLxjAQkEG8CyCsvvfQSLjb9+vVDGQnhUTBmQXNBlEEcQb/ARgbl4oYbbiA2Cr1FzvjNb36D1vDGG2+gmIT+Ew333nvvxfaEPzE5QcFB/kDFIMtXX30VdA1MRfbdd1/0DmQaxBfqOv744xFfEGWOPPJIJJ3I/gWXKAohwTXXXINVTgjIgvHOOeecgzJz7rnn4gqUJveuXbti0tKxY0fqRSJBRsFMhkA2fDhJgzHnwdgHUWb//ff/7W9/Gzy2KByrHDSU1157DZkp1IWj01tvvUVjkLEQZYCGZRAGOGT/xz/+QTsjzQVZB/kGXQaBCa8oasR/inIINxP1Mc32m0wCEpCABCQgAQlIQAISCAQUZXwSJCCBQiOAxoGygCCCzhIUGXqI3MCHAyQMrEjwKkJWCGc4ibULVifsVYQog3FNILLHHnuwV1E4JkEoCuuYjTbaiPgsaB/fffcdqg2SBHkxiik/ugr+UAhDmKJgdYIOEorF5gVhBX1n6tSpeE6FkxX+SxbMZHCGwkeJ9qDL4MRE7ThhITZVmD0xwfjx4+nFbrvtRsScQIPCsa955plncAH79ttviSgc0uNgFcUG5hgHKM6TF9SKMolIPZaABCQgAQlIQAISkED6BBRl0mdlSglIIB4EsBBBSkBoKFUsIGovV1FDcCBKlFHQWd58881wNbWfuCxhIcJ5hBVUFSSYk046Cf8dpBZ0DaxRjjvuOMxeIpuU1BLQUJBOCKl75plnJtaLqIE1Cv9SbGquUs8Q6Bczlvvuu4/4u1i1IKbggUV2TIGwWyk1S1kn0XTQceg75URpNt54YzpCUVSE4hOdjw5IjHbDn+SNPKGiqx5IQAISkIAEJCABCUhAAmkSUJRJE5TJJCCB2BAgggwKBU5GmMOkNhqVgatoCklXgyFMWZYmJE5UUij27LPPJmbwI488gk8T/lDErLn22msxz0mtMZzBbQoxaNtttyVN4vbb4SoSEk0i9G9Z2aPzFIIYhA8UykinTp0IGUMznnzySVyKojTpH0CDAul7Yu+Qn8KfSEVcTS0NGuGTeskzEpCABCQgAQlIQAISkED6BBRl0mdlSglIIB4EECkwliHIS6lGHJiBoDgg3KCSRPtbkxKfJgSIJPOZcjqMcnHEEUcccMABBAkmAPB77713991349cTuURhF5No/ELJbIaNKHP55ZdHXlFJ5aN0hDNR3tQzeAwR8IXGX3zxxQhDIXQx/WXj6qTS+BNrl1JVlSglUY1RiAiRk2gRQwRiDILoIGFuUvWjKK8HEpCABCQgAQlIQAISkEA1CdSqZn6zS0ACEsg3AgSjxXGJraCJihK587BLEa5JqCS46iBkYAMyfPjwyC5m4sSJRMmlI+3btw9uShV2atq0aSg7hMglYDDqDFoMsggFovgEoxs8ldBWKCdIM7SKxKNHj2YL6nCeS2gfKCCReESzgwiCXxItJEHqGYoNigkBiYOoRDOIK0xP+YS6aAMZEXTYFipY3yTKQ4ldY2NsPLnoO5oU2blEemIS47iEesUeT2nSSCzTYwlIQAISkIAEJCABCUggTQJayqQJymQSkEBsCPTs2RN3Hixl2Pf6rLPOatasGXIDm0bjZ4SHERYrhx9+OB5AAwcOxE6EDaTROG688Ua2VWrTpg3bFSFnVNhVJB62WGJDIqLzIlsg0FBF48aNUUNQXjDVQQB66qmnCAnMxtKcJCVRh2kVBjV4HtEqLmHDQiPxe7rgggvIgoaCPkK0GmSXhx9+GCsVdllKPUOE3bD9E6ISwg2tpQQ2VKJJc+bMIawM+gueUPSLuDDs5I0IxXbXNDLoL0ldw9KH/bBxfcIPC22I1hJymN2vEXoIhEwjk9L7pwQkIAEJSEACEpCABCSQQQIVrz0yWJlFSUACEsgBAVyEzjvvPJyJRowYgVrBhkcLFy5k82Zkjt69e2Mmw/7T+Pu8/fbbvXr1QrPgEj5BqCGXXHIJCg7iCB/aGf4ttcFE7UXIeOCBB4K2QrhcgvUeddRRYYNtDqZPn45oQuRgjFmol82MKJ9W3XbbbezxxHlahU0KBinILux2RDkhzA0baWO0QuGY+bBJE+YqSWcQlfbbbz8i+7LhN+F+UXYwk6F2rFoweCEXPcLchj2kMP+hLtQo2sBVAgMTgCZ0J+oaSg0aFnwQYsBFSnZ0Ig3bgXMe0SoxfZSrVCaelIAEJCABCUhAAhKQgAQqS0BRprLETC8BCcSAAFYeiCCDBw/G6gSXImxYttlmGwxPguaCRQxhWQYMGECQF0xLkDmIs3vKKafsu+++wVtnu+224zy6CUYuUW/ZIRv7EQxVOElQmGOOOYb9s4ntgmUKdSGUnHbaaSgspGcnJkSTl156iUKwZGEn6WB9g8yBAQutwjuJGC4UwkbUVE0kmuDxRN7u3btzzK7elBBOJp2hqEsvvRQrGEQlLGWQgdBfevTogcHLmDFjyEV/EWXwqOL42WefZVtrskTRYUCBdQ8nOQhdw/0KSejxxx9HaaJAGoMPFzTQawKNzTffnL7TR4yAIhqkQQ+Kuhad90ACEpCABCQgAQlIQAISSJ9ASVmBBtIvwpQSkIAEIBDMKCr1lVKFLJVFjWiCIQkGIBjIpBp6oEFg5MIlElS2ZNIjrGDqQrHIK6mbYaOJUDuFo9QkVc159B3MW6KowEm141GF8U7z5s2DmsPV1DNUjcMR5UexeNGAqIiWRFoSFfFBuKENQWFJqijxTxpMLahOqX1JTJbtYyyA+vfvf9VVV3GQ7bosXwISkIAECoAAP6n8GvKygR+yAuiOXZCABIqNgJYyxXbH7a8EiosAaks5ggseQ5jDVJkIliO4F5WVnQkin1Kvlt8qsiDWJOk1qWfwSEoqPDhPJZ6ssKLExOU0ODGZxxKQgAQkIAEJSEACEpBApgj8zzI/UyVajgQkIAEJSEACEpCABCQgAQlIQAISkECFBBRlKkRkAglIQAISkIAEJCABCUhAAhKQgAQkkHkCijKZZ2qJEpCABCQgAQlIQAISkIAEJCABCUigQgKKMhUiMoEEJCABCUhAAhKQgAQkIAEJSEACEsg8AUWZzDO1RAlIQAISkIAEJCABCUhAAhKQgAQkUCEBRZkKEZlAAhKQgAQkIAEJSEACEpCABCQgAQlknoCiTOaZWqIEJCABCUhAAhKQgAQkIAEJSEACEqiQgKJMhYhMIAEJSEACEpCABCQgAQlIQAISkIAEMk9AUSbzTC1RAhKQgAQkIAEJSEACEpCABCQgAQlUSEBRpkJEJpCABCQgAQlIQAISkIAEJCABCUhAApknoCiTeaaWKAEJSEACEpCABCQgAQlIQAISkIAEKiSgKFMhIhNIQALZIlCvXj2KXrx4cbYqsNy4EVi26lNn1Sdubbe9EpCABCQgAQlIQAISqDQBRZlKIzODBCSQKQK77rorRY0bNy5TBVpO3AnMWPXZeNUn7n2x/RKQgAQkIAEJSEACEqiQgKJMhYhMIAEJZItA3bp1KXrBggXZqsBy40bg81Wf2rVrYysTt7bbXglIQAISkIAEJCABCVSagKJMpZGZQQISyBSBLl26UNSAAQMyVaDlxJ0AvmxLlixpueoT977YfglIQAISkIAEJCABCVRIQFGmQkQmkIAEskUAUYawMqzD+WSrDsuNDwEeg1GjRuHOpigTn5tmSyUgAQlIQAISkIAEqkVAUaZa+MwsAQlUhwAxZTp37swifPjw4dUpx7yFQYAngQ863eabb05UmcLolL2QgAQkIAEJSEACEpBAOQQUZcqB4yUJSCDrBA488EDquO666zSWyTrr/K6AB2D+/Pn4LmE/xSe/G2vrJCABCUhAAhKQgAQkkBkCijKZ4WgpEpBA1Qj06dOncePG2EdoLFM1gAWTa+zYsTfffPOECRMwngrbchVM1+yIBCQgAQlIQAISkIAEyiKgKFMWGc9LQAI5ItCvXz88VjCW+eqrr3JUpdXkGQHMZCZPnszOS127dtVMJs9ujs2RgAQkIAEJSEACEsgiAUWZLMK1aAlIIB0CGMsccMABGMt069YNB5Z0spimkAigyLz44ouPPPJIgwYNevXqpZlMId1c+yIBCUhAAhKQgAQkUD4BRZny+XhVAhLIBYG+ffs2bdoUXaZ///7qMrkgnjd1BEXm+uuvx3Hp2GOP7d69e940zYZIQAISkIAEJCABCUgg6wQUZbKO2AokIIEKCWAcMXToUILLDBgwAF0GPybW6hXmMkGsCSxbtgwBbtiwYSgykyZNOuSQQ3RcivUNtfESkIAEJCABCUhAAlUgULJy5coqZDOLBCQggSQCJSUlnKnOVwqKDJFlFixY0KFDh+DT1KRJk6Ra/LMACCDH/PTTT1OmTBkyZAhiHNIMXksXXXSR22AXwM21CxKQgARyT4CZAxMGXu1obJt7+NYoAQlUn4CiTPUZWoIEJPAfAtUXZSgEDyakGSKMMMFCl+FDDOC6qz7169cXdKwJoMXwwQYqyDFPPPHEt99+u+222x533HF4LanIxPrm2ngJSEACNUhAUaYG4Vu1BCRQfQKKMtVnaAkSkMB/CGRElAko0WX48L6LaRaeTXx23nnnXXbZpU6dOmg04o4dgSDHsLkSH3Q35Ji5c+cS1nfffffFQIb7G7se2WAJSEACEsgfAooy+XMvbIkEJFAFAooyVYBmFglIoBQCGRRlQumRNMOfS5YsCVFmUGdq165dSvWxPbXW2quV/Dq618J5q61cEdv+lNbwIMegqQVZba211mrZsiXWMRrIlEbLcxKQgAQkUDkCijKV42VqCUggzwgoyuTZDbE5EogtgYyLMoEE0gwHb7/99vjx44PzS2wJld7wfS7+Zs21f6XBjLhx/UXzfq3TlJ41NmeDHIODEh/sYtRiYnPnbKgEJCCBOBBQlInDXbKNEpBAmQQUZcpE4wUJSKBSBLIkyiS1AXVm+fLlSSdj/efwH49ctHJuYhcOaPjUmiXNEs/E/TjIMXHvhe2XgAQkIIH8JKAok5/3xVZJQAJpEqiTZjqTSUACEsgHArgv5UMzMtiGUa/VXfTr7b+33377teu1yGAVFiUBCUhAAhKQgAQkIAEJ5CeBgrKQz0/EtkoCEpCABCQgAQlIQAISkIAEJCABCaQSUJRJZeIZCUhAAhKQgAQkIAEJSEACEpCABCSQdQKKMllHbAUSkIAEJCABCUhAAhKQgAQkIAEJSCCVgKJMKhPPSEACEpCABCQgAQlIQAISkIAEJCCBrBNQlMk6YiuQgAQkIAEJSEACEpCABCQgAQlIQAKpBBRlUpl4RgISkIAEJCABCUhAAhKQgAQkIAEJZJ2AokzWEVuBBCQgAQlIQAISkIAEJCABCUhAAhJIJaAok8rEMxKQgAQkIAEJSEACEpCABCQgAQlIIOsEFGWyjtgKJCABCUhAAhKQgAQkIAEJSEACEpBAKgFFmVQmnpGABCQgAQlIQAISkIAEJCABCUhAAlknoCiTdcRWIAEJSEACEpCABCQgAQlIQAISkIAEUgkoyqQy8YwEJCABCUhAAhKQgAQkIAEJSEACEsg6AUWZrCO2AglIQAISkIAEJCABCUhAAhKQgAQkkEpAUSaViWckIAEJSEACEpCABCQgAQlIQAISkEDWCSjKZB2xFUhAAhKQgAQkIAEJSEACEpCABCQggVQCijKpTDwjAQlIQAISkIAEJCABCUhAAhKQgASyTkBRJuuIrUACEpCABCQgAQlIQAISkIAEJCABCaQSUJRJZeIZCUhAAhKQgAQkIAEJSEACEpCABCSQdQKKMllHbAUSkIAEJCABCUhAAhKQgAQkIAEJSCCVgKJMKhPPSEACEpCABCQgAQlIQAISkIAEJCCBrBNQlMk6YiuQgAQkIAEJSEACEpCABCQgAQlIQAKpBBRlUpl4RgISkIAEJCABCUhAAhKQgAQkIAEJZJ2AokzWEVuBBCQgAQlIQAISkIAEJCABCUhAAhJIJaAok8rEMxKQgAQkIAEJSEACEpCABCQgAQlIIOsEFGWyjtgKJCABCUhAAhKQgAQkIAEJSEACEpBAKoE6qac8IwEJSEACWSKwZNmPq622YuVqK6PyV662IjoOB0uW/bB42fyEkyX16jRabbWShDMeSkACEpCABCQgAQlIQAKFQEBRphDuon2QgATiQuD9uUO+WfjJ8pXLogb/vDRRf/nP6dEzb6lbu2GUoFHdjfbY5My6/9Fl/EhAAhKQgAQkIAEJSEACBUVAUaagbqedkYAE8pxArdVqvz/noYVLvy2nne99MTi6WrJarb1bX75aiWYyERIPJCABCUhAAhKQgAQkUDgEjClTOPfSnkhAAvlPoH3To9Zdc4taJbXTbGrDus23b9ZjjdoN0kxvMglIQAISkIAEJCABCUggRgQUZWJ0s2yqBCQQewJ1V2+480an1K+zdjo9wUxmp41Oblx/oxIDyqTDyzQSkIAEJCABCUhAAhKIGwFFmbjdMdsrAQnEnED6xjL/NZP5X3yZmHfd5ktAAhKQgAQkIAEJSEACvyKgKPMrHP4hAQlIINsE/mss06T8ijSTKZ+PVyUgAQlIQAISkIAEJFAABBRlCuAm2gUJSCBmBFYZy2xZfmQZzWRidlNtrgQkIAEJSEACEpCABCpPQFGm8szMIQEJSKB6BCo0ltFMpnqAzS0BCUhAAhKQgAQkIIF4EFCUicd9spUSkECBESjfWEYzmQK73XZHAhKQgAQkIAEJSEACpRKoU+pZT0pAAhKQQFYJBGOZ7xZNXbj0u6SKNJNJAuKfEihyAosXL166dOnKVZ/atWvXrVt39dVXzx6TRYsWUX4Gq1iwYAHtb9y4cQbLzF73LVkCEpCABCSQYwKKMjkGbnUSkIAE/p8AxjJvf3Hfzz+8tWLl8kQomskEGitWrPjll1/CWrSkpKRWrVqsRevUyfzPFlXMmzevXr16jRo1SrwRGTymL1RBgeuuu24Gi7WoIiHw0ksvffTRRz/99BMPUsOGDTt27Ni2bdv11lsPgSbjBFBkhgwZ0qpVq913350Rl5Hyb7/99rfffvsvf/kLzc5IgRYiAQlIQAISKCQCmZ/dFhId+yIBCUggewRKNZbRTCYC/v33348dO3bSpEmYCbD43GCDDdq3b8+iDukEjSZKVv0DlruXXnrpPvvsc9FFF1W/tFJLoC/nnHMOus/9999fagJPSqAcAg888MCwYcPWWmstREk0REbEIYcc8uc//3nLLbdErCwnYxUuTZ48+ZprrmnduvXDDz/MoKtCCalZpk6dOn78+B9++CH1kmckIAEJSEACElCU8RmQgAQkUGMEUo1lNJOJbsaXX37JsvDZZ59FhcHrgRf4vLc/88wzzz77bGwEomTVP2CtOG7cuE022aT6RZVVAiY/7777bv369ctK4HkJVEggaCWzZs266667hg4d2q5duz59+mA4Q0YcmxBrEGhKNSXDvoarXEqyrCk1F0LPySefvPXWWzPuli//jxFfYi6KIhcVBWGU42XLlnFMmlSplOykL7VJFXbWBBKQgAQkIIHiIaAoUzz32p5KQAJ5RyDJWEYzmdQ71KFDh/PPP58l4vDhwx988MGnn356++23P+KII0JKFoSs+lgQ8knKy3nWhCwIE9eKrCE5yb+kTzUxIAsFriosubRQEaUl5qIcPpwpKyNXw7I2qW3+KYEqENhpp514+HneMLk699xzx4wZc/rppzdo0ABVce7cuf/+979R/TbddFPMW1BqwmOPGojf3FdffTVz5sx11lln44035irJeDJLzUViRJ/99ttv/fXXRwmdMmXKjz/+iEzTpEmTUOAXX3xBaSiYOOL9/PPPHE+bNo0mUXLTpk2JGhMGCI891mEURTQZLpGSGqvQZbNIQAISkIAEioGAokwx3GX7KAEJ5C+BRGMZzWRS7xMLSBZ7OC6tvfbaLBFHjBgxf/58krHM++abbz7//PPvvvuuWbNmLVu25MV+CIHBpW+//ZbzX3/9NaExWD2yHGVNSEiOsDpFYWnRogW5yBLWkKgqXJ04cSJrTlatrGxZha6xxhrkWrhwIblYXrI6xadjo402YsWLOoPpAee5itkOzcCuh1o233xz2snylYz4mHCSSySmYaozqTfXM1UgwKPFoOC55YDsjILrr78e/yYeMB5jlJTzzjsPgzIeS55zhJurrrrqk08+IT1/8mzffffde+65J+JLqbmQPi+77DKe9t///vc33XQTeTFVu+2224455hgee6ogLsxTTz1FjBgKwRfv3nvvRXahJTzkZDnrrLMYibTq448/vvLKK6mdS4wIBiNtq0JnzSIBCUhAAhIoBgKKMsVwl+2jBCSQvwQiY5lFS+fttNHJjetvVLJaJgOm5G/P02sZK0nkEhZ1RH5B4Nh22215b09WQp+yPuQkq1AMB3beeWfcmvbdd18Wn3h2sJ787LPPsHlZc801u3fvfvPNN7N2feihhwYOHMgaEtGEZS3nL7zwQlQbSsM36plnnhk1alR4pY8G1K9fv7322gvN5fHHH7/iiivIRUUE9SCWBytPEiC40IDRo0d36dKFZSomCVR35JFHstZFBiL9k08+ib8J9ZKLZpB+iy22SK/TppJAKQQQGWfPns1wGDx4MM8bjyK6IQ8qiswOO+yA+RjGKTx1xJrBkuV3v/sdIuYpp5yCmHjaaacxQHgCecK5hFDCk1lqLso544wzrrvuulB9z549ecJffPFFIi4hymCMgxceYxDLNRQZvKiQYBCAGKSEvLnzzjuxrzn11FNp2x/+8Ic33njjsMMOO+CAA2gz2g1tK6VLnpKABCQgAQlIYLXVFGV8CiQggcIkgJ3CkiVL+Df/u9espPMay5stWbZyo1r7zvtmYUnJz3neZpaCwZcB1wnekGe1tdOnT//73/+OnvLOO+8gbeDKRCgNakSIwS0C0xXWh1i4sNqkMdi/cJ41JMtXXDC6du363nvvYWiD5sILfxarW221FYtG0jz33HNsMUM5yDT8S3coDWcQ3uq//PLLCDSE/mXZSYEoLNgXHHzwwdT16quvPvbYY7zzZ5FJLha3M2bMoAoSU8U///lPDHleeOGFo446igPWpeTlX+xuWKDSC7Lk5kPDwvPPEMhNjUVVS7QnEd46YSDkpvtoKww3nm1EkL333vvwww/n8UM05DG79dZbN9xwQ5qxzTbb9O7dm6cUyWbAgAEYc11yySUXXHABkgpX0RP594MPPign14EHHohpTOgRx6iWb775Jt5PHDCyEBlPOOEEnnakTxqDKQ06DomRKS+++GIe/kMPPXTkyJEMyYMOOqh///6bbbYZVzGcYViFMvPnXzCGYcJB/rSqsFsSxg6jhrFT2D21dxKQgAQqRSC7k+lKNcXEEpCABKpPgEk2sRKwd2ANz1qCLT9iMeFev/3CWmusefr1F674JQZfyyz8cPzhZmFs0qZNGyxEsqfOYHLCAg8ZiPvIv6wJcU1CneHVPVv28k6ez4QJE7jpLFBZOrJAxawGGYXoGxgLYCmA7QwGNWgizZs3xzVjjz32oOWsJLnE44GnBn9izNKpUyfCpnKMuQ0LBlatiCy4YLDI5BJCDBVhI3DSSSexPQ0WNKTkg1KD1sPalWUqq2LWpVzFNOAf//gHxjgUiB0ByWhtDhalQYuBGDQIXczn/fffX9VM/8kkAWK7BC2Sm8uTRtG5UWeQIxkFPP8oHXfccQcqCWIl33g8vRhtMTpoCU8pjwFpSMnAwcmO55lHMbH/iDJl5UpysiMjgwiNhn3QcM1DlGTgM4IYNTxjjCysYxj7oV4GBa6FNACZkoewc+fOGM6EekmZS/UqsbOpx5DB2ojW8o0RhgkmeKnJPJNxAowaxg7FIs0wdsKoUZ3JOGcLlIAE4kggBrP/OGK1zRKQQO4JBDmGKAbEO2AJgbMJMz8++bMYKIfJ53NWK6m12tIls1fGIfACbhFBpGC19pvf/AYbkxNPPBHfHJaFYbFaTk8re4lJPG/jcRFCcLnhhhuuvvpqVoOXX345txXdDYsA/Clw0ECsIUYMa63ddttt1113pWGsWjETOP7445GQWCKilfBIYB2D2kIbyMLalQPsDpKahHaDlQEeIjhrhJSYvSCyYLNDLvQ+lp2RLwZlhkUF1jeEm2ENTF0sTVmXslxnXRoKp7WsS5MqyuCfQY6hL3h14VSCSsWTT5P4N4O1WFQgwLMEcKyQMEVhbQnkvn37olYkaR8Zx/WnP/2Jh/noo4/mLuMuhD7IHUdGQZThOeRDjfyLBQ2DkZFI4BhaxWOZNCrLyYXcmdRshFeiayMpBvclCqdAHjC6T70M+aheDNPwZiKWTRhTmLAF6TapwBr8M8gxfH3xPfDEE08QHTkMkyQ+NdjCwq6aUYNQSB8Tx84f//jHcBcKu+/2TgISkED5BBRlyufjVQlIIB4EWAm/8sorQY7hzS3mEgQEYX2+yy67OOHO+C3EGQFlhH95z4yrAhoEr5r3339/zE+wJUELyHiNiBqs93jnj+g2derUTz/9lD1fiItBvR07duRe0xKagVSERIIXEmF6WUmyozYOF7hvYMNCg/k3ahtBebFewbGIk6gtiQ0Ohj/8G16nUw6RU1lq8jjxXD3//PMkZiVMgsRcaC48aUHoYcmBqQILY7ylEtNk6ZheswzGPyvIMRCgX+hBiFOYC2Wp0mIuFjWQ5f3bb7+NpRVqHQ8ed5zAtwwBnpCsqsAsX7H2wvILgRKLFZ5enjruNcIlnnfhpvAQhjZgqIJJCxISsmmQTnhUeFAJK1NOrqQ7y7OEJHrffff97W9/Q/imjxSLAIoCxdCjXgx2kupl9FER38lolKFeEoShkVR4Lv+k8ZMmTXrkkUeQYxieKEo77rgjd41hAp9ctqRo62LUMHb48sSIleGDfyg3hZN8taLoaTJTtA+GHZeABCCgKONjIAEJxJ4AK9JbbrmFt9bBWoGQCixXmOclLZtj38+86QDLmKgtrHAgjzRDsFuCiWIyQGjPSPuIkmXkgBvKYhLvpA8//PC1115DkcFkhrikqCdEr0B/CbUgoBA749hjj0WUwbIGDwvciFhJ4mmF+UxkCxAWqGRJEmUwhGHNxtqVpRrSD0oHB4MGDSKKB+/Y0YOwyim/OyxEsRGgVVjWsEAlcfZWpPQCix6iC7PCoa7tttsO2w3lmPJvUDWvJj7/PB48/0gzHEAekQILqazqMtRO/F0EaOLIECAJLYbgvkSYRg/loeWp4wFGwWSk8B2IfMPARBxkULAYnjNnDhoK28wjSpaVKxUO8XqR/HCFw+4Mfz2GIQY7dBMdnFhLDCgeeNz3UKYQiRBreAi5RL00g6hM1BviZ6eWnLMzYGFjKeJ/Y+PD4p+gyFgAQVI5Jme3IFSEChYOgrEMX87ofXy78hWNyYy6TI5vh9VJQAL5Q0BRJn/uhS2RgASqQgBFhiUKcV55R008S+Z2vEyuSkHmqRIB1jZ8kGZ4dY9pOvu28OaTGCu8iK5SecmZeNmOtIG7EKs+3q+ypxK3mxUm5glE1mDJR2wIYrjgqcRL17Ay5Jg2sFhFm0MnQkZBZGEhiokNbgusMFmvskhjVYAVQIjEgbRBOdTCshY/KZagZMfwhzIpjVpYZ2Jrw/41PG8UjhUAy9rktv73bxQZ0rMPMd5VwKFMhCQ6knH3FprNqrtXr15ECcEwB5sgXFr+2wr/zwUBhBg+KDI8+ew2TZXokpiiZFWX4VsOl7p//etf+Nkdd9xxLGtRGbA1YNAhVrI9GbFmEE3OPfdchgy2IWgxxKPBWwfhEvEOG7GTTz6ZlXCpuVKpMRAYPhjdEF8JGxkSoOxQL2oUsYcJ7YTiieEDNVImTz4OVqi0hGQiaAvuhwwZWsVQSi05N2cY7Jj5oMjwpQGxiy66KFFWy00brCWJAD/TfF/xJPMUhXcqJOAXnKcoKaV/SkACEigGAooyxXCX7aMECpYAa2OW6LiuoMg8+uijmNlrHVMjNxvpgZfnBNMlFAtrHmxYCKObEV0GMxBWvKgbGK1wzJKPl/OsKnlvj/MINbIaJIDLW2+9xdIL9wRECtbGHGA4gOzCpZCFl/aYz9A2nhNEFqzocXMjZdu2bSHGm3yWkeeccw4BZcjC63R0JawPeMDw12Api88IqwWcp1CF6BcRT9liqSzUrMmx02G9QZxgwhuRkVbBBERlZanC+WAjQzQf/FPoLDsc66xUBYwZyYIug/rAveD559FC+0O2yKAuwyPEQxsERBqMKMmTTIRpxJd77rmH/cUYCAwBakQ0wcsvmKph1YIEQwxgmsQjjRZJI8MlHk6sEkrNRS08wHyjIiYGOBwQSobHHmUnMoKjs4wg9CDcGHkUaSHxsIMLFfVee+21iCCMTYYt0icfpMwob0aYp1kIYhA3hZU/3w9IUXDTOiZNdDlIhjTDHeGxRE1G0eNB5fuZZykHVVuFBCQggbwiUJI9m+q86qeNkYAEsk0grEBy+ZXCgpmpP7NtXn7yThi7aBWZbN/lCsvHZKZfv37cGuwFWAJVZxmGrxA3l012qZT1JKtE3tj36NGDt9xM5ZFdePtN/FH0FCIHoUqEd/UEHuYSpi6IICwvuYRsh7CCIQzhUdkAG2clnlXe7aPs4PrE4hlxhzA0bC6DdEIynIDYzZcnioCmYSuZm2++mcJx08D0hjawlKUNGNsTTpi1Lg9hUEMQBzEcQMrBl4p38hgy4C2FkQ4ZKQpjGf4lPmuFDNNJwDIY+yAiv7L+RwBCFFCRSYdbttOwtsRpDrsSnHfw5cmgLlN+y3keMIRBJSEcb2qlPNVYgTGCeBQTyyk/V2JKBgIDDV2GkZh4nhJ4whlo1Jv69cv3AFoM6k9SrsQSsnqMIoOiinNfUGTQjlVksgq8yoVj5IhzHAG8eLPCN2cV/Jh42PjqJiPfvVVuhhklIAEJ1BQBRZmaIm+9Eig0AjkWZSJFhkkY/in5ts1Hod3dyvSHN+ToMligEPCF+DLZW4+hAPIY8Bo/LESZ1rN0ZNnJEpGlIOsx/g0hXaLmE96CLPyLrUG0diUligk2MljWcBW/JEqIsnAQXKgQmCicSqmILCxxE9OUekxGnJ5CiJlSE1TtZKTIYAKASIRgVLVyzJUNAngPYdKF3kcg6qTHLxvVWWZZBCJFBpWWWD9YZJSV0vP5QICvSnww+QY+//zzuV+VFfTJqCiTD/fRNkhAAlUjkMUdOqvWIHNJQAISSIcANhR8mLcRMSEjbjLpVGqadAhgzIJrA+5FmAzg7JNOlqqlQVVhFo4eF+QVFBP+DHoKShBWA6lLYl7mo7lgJhMpMlTNU4QNC+IOMV/w/khSZEiAqkKuYGVARp63dBSZkJGUGVcMWb2EyL5Y9+C9UjV65soSAb6RsJHB546NupAFs1SLxVZIAM9BbNywZSPoEl9KFaY3Qc0S4HuVscO3MUGs8Q+t2cZYuwQkIIEcE1CUyTFwq5OABDJAgHdiRPdgwYOPDNshp5rNZ6AOi6gqARwECAJK2FHceQjA7Lq0qiBLz4eZDJFTYYvkRKRMvZZKx1RzZ4ktTQQi7g6qsZ4UNXUfMJMh1hKRhnGH4etIr6WauhHp18vvOL/m/KaThbHDHUw/ryklIAEJxJ2Aokzc76Dtl0AxEmDGxltQTCEIpeFeS3n4BBByhYUQtwZRJqvGMnnY92w3CTMZnn/e/7POdK+lbNOuWvl8LxGOGmMxwhgpSlaNYTVz8QPB9k/Eu+ncuTNfR9Uszey5IcBPBtHW0dHYNUxjmdwwtxYJSCBPCCjK5MmNsBkSkEAlCDDhDmYylXU7r0QdJq0eAeIu8+FO8aleSeb+FQFeIAczGTbQ+dUF/8gnAtwdvp184V9T9wQbmWAmwwq/ptpgvVUgwKhh7PAtx9ipQnazSEACEogpAUWZmN44my2B4iWA7xKbv2omk+dPAHYc7ELNjjAsjTQWyNTNwnfpm2++0UwmUzyzVw5OTMTIIAYzOx9lrxZLLpUAS3piLWsmUyqcPD+JExNjhzBejB09mPL8Ztk8CUgggwQUZTII06IkIIFcECCCJrvk6LiUC9bVq4MwtJttthmrIz2Yqgfyf7nxXWJDbiIH67j0Pyj5erTvvvsScBovjAIQJZHCUQPZTSxfYf+qXZiS8cFrScelX3GJyR+MGsYOoybHHkzsyoeEWupDzn7wpaqrZGG3db6WGSA//vgjydieLyaYbaYEJJBfBBRl8ut+2BoJSKBCAigypMH5PHEDnQpzmSD3BMKiKCyQcl97+jVifsKsml2xS52Ol1MO6Xkbn+Ngrjz/PPmGUirnvuTJpSAcBxE5T5pU5WYMHDjwtNNOmzJlSpVLyGVG1vOsllnb88llvdaVEQJ8uTF2+KJj7GSkwDQL4eXBvffe+84776Smf/zxx7mUev6LL74YNGgQm3xdc801t956KxsOMkaQZlJTekYCEpBA+QT8uSqfj1clIIG8I0CMEubcxCtxwp139yaeDcIE4P777//kk0/YzGibbbZJvxMYAV111VUYBDEjTz9XdVKGgDKEXdAEoDoYzVtZAlOnTkVdRbusbMYaSb8qnsyMAw88EA/KGmmAlcaRAHLM+eeff+WVV+62225J7f/LX/7y6aefnnfeeUmvgpBg/vjHP/KF3Lhx42Bog6J04403YslYq5avvZMo+qcEJFAeAb8yyqPjNQlIIA8J4GrO7Ae3c3fCzsO7k9ikVS+q66Cg8Uk8n2/HvJLlHSl75WCCXqm2Ya/OPP6jjz6qVK7qJKaptJN5vzthVwdjbvJym1jCccsy7tFQ6pjiW5G6MPtK7B1VhzMcJF4Nf5IlShylLLWcKFniAVkwFqMxHCSe55hCSj2flCxLf36+6kNYKz5ZqsJis0cgWAKGRzR7tWSw5K5duz722GN/+9vf2HOQHwWUHYT+UD69+OWXX1IHSAZrtygJSKAwCGgpUxj30V5IQAISyDsCYVEUFkgZbByLTD68hwyvIjlmEs8nqoIZcJgEc5IDkpGGJSIiUZSFPxH1UnW9sJIkZWKBoWTWmXxKzRVVzQF1kazUEhKTeVzwBIJwHETkTHUWYeX777+fNm0aakiLFi3WX399wgmz6ps3b96sWbNmz5690UYbcX6dddZBEqJSnPK+/vrrpk2bfvXVV9OnTyf9Jptswot9Fo28+Sc4EXZeXGVckJKhyhkOiM/dvHnzDTfccIMNNuBJTm08w4pQGpRJS8jLSKcQjAU4Do2ZOXPmokWLaAwlNGzYMIy71HI8I4FUAnzHMnb4FmXspF7NwzOMqU6dOtGwAw44YPjw4YxE7C4ZEQwlPhxvueWWa6+9drNmzfKw8TZJAhLIEwKl/NbmSctshgQkIAEJSCCRAIoJ7yFZOjLTZY7LUnCNNdZgzcn0nQUkG3KRGE3kyy+/ZOFav359Fp+sDFkrskTkDFNnJsokpgTWkyw7W7duzdVQBYtJ1rSsdXERIj4xy1cWk0GaYSVM7BiuUggVsdSk9tTFKs0jGQtamtSqVat1112XtXFi+z2WQDUJvPzyy/369UNe4TFGjsF7jpfzkydPPv300z/++GOeSeSSHXbY4YILLmB9iC7zz3/+E8+LQw455JVXXsH5CCVljz324PF+6aWXGErELuUlP2/4GQukvPzyy9dbbz2SUTgDp0OHDtdddx2uHGyFk9RsBgLxPoiygXEZNVLv73//+7POOgt3oddffx1vPjYIYyitueaa55577sknn8xoSirBPyVQeAT49eFnCFGJJ59fDSLjoNEwfPhpYFQStzhV6y88CPZIAhKoGgFFmapxM5cEJCABCeSaAA78t9122zPPPINuwkz38MMPP/TQQ5944gliuxAI4OCDD6ZB6DWsCZn+8q6VmXE4YCmL5oJGw4qUafFrr73GjJk1LbFLWdaGbrDQZQHJ7JkPE+tTTjnl7LPPZo1K1EYKYWXLC08qZcLNMpVcLFkT+8/SdNKkSRdddBEOTWFS3r1791tuucVZeCIlj6tJAJXkgw8+IPgRMsqwYcMwikEQQfLgyURe2WuvvXgIn3rqKeJc7LTTTgiIVMdgeeGFF1BMUAnRX4jJ9eGHH55zzjnYsDzwwAOINRTIcx4ahhB58cUXo1Q+u+rzhz/8gRCnSU5ADBAUGT5IMCw7+ZOW3HnnnTTj1FNP/etf/0oVaDSEZxo9ejRpGHfV7LXZJZDPBNA6ifiLIsMPDT8TPPmMvpEjRzJ2CP7Vp08fBgIqp78F+XwTbZsEapyAokyN3wIbkKcE+H1l2caH9vE2nmllpQywWaExFeZVJNbg+TAlZWFJY5hwYz6Qp8RtlgQqIsACEtf9jh07or8QeZQ1J68f8cJg+kukFRalDFUWpQR5ad++/X777TdmzBiMYjAa/9Of/oT5DEtQVo9t27a94oorcN9gpwwWq7169WJBS80MDfZhRehB4nn00UdZWzKHvuyyyyZOnIgFAVssXXjhhdtuuy3T60ceeQRrhQcffDCxvZgMjBgxAnv7/fffn5a89957WOU4C09E5HH1CfCThOSH+LLLLrv07t07PGCYgD3//PP8ZqE8IpGguaCzoE5G7hIYzmBKg9SI7QyP8bHHHstCsUmTJogvCJFojlFkU4TIY445hrGApHjEEUewyGRkkTKx5QyQoUOHIgYhTTIAucRwQ8phNCGS0gZaxSDlGMmG1ibm9VgChUcAGfStt97iNwULMnRSfh2YLvIKgYHATwY/K7/97W8dCIV33+2RBDJLQFEmszwtrUAIYNTNvJNfWRZ4zHG32247JsH8m373mDozQ73vvvuY8rIHRPoZs5GSLrBqZddGJuKHHXZYNqqwTAnkgAAOEaw88c//zW9+g+SBBEOlO+64I3ExcJdApuEVJYoMNi/IIqxaEWV4UY+kwhjkTSZ+GU8//TRLWfwp5syZg1LJZPqNN97o3Lkz5Wy66aasSFmdMl5YUp5wwgksdFmX8tqfMMC/+93vMMYhGRF2mXy/++67SDD4MUW95kUo/k1Y35CAxBjaVErGjcrxQALlEOjRoweedzzAJ554Io8oK0B0FryNeESxgmEUBP8+3grwlHI+FIUbXVgTYh3DASJLeDjbtWtHdhzuopSRjEiyffbZh4efqDEMpcQmYQvAUKIErGMQeriEEMOPJsIlB7hKMRIvvfRS1BwUz6233prXElGxieV4LIHCIIDZGoaTb775JqIMdpoImvQLUTJsks24wE4N2xneihVGf+2FBCSQDQLuvpQNqpYZbwJMankNyKt43ijyWv7JJ5/kfSBLLKab6XeMpSOrvvHjx/N+Pv1cWUrJBJ15PDMG1qVZqsJiJZADAugvrA8ZlWxvgSbCZJeBxsKSCTH2LKxIWV4ipOKmwclgFIZwE6LGcMB5Xu9jvEZT+XerrbZCPCUKTFLLSUOsGZQavgowOmC5SwQZqg7JwjFLUKI5JmakcBaxLGXvuOMOvjFYkSL6JCbwWALVJ4Cwznt4bMRuv/121n6IJvww8U6+Z8+eBIVh1XfkkUciTfIMRzoN7LjRAABAAElEQVRLOZXyuJYjl1AaygsmYMiUiYWw8mTgML4YU1iZ8UGLRAalatygUItoIVoMlmiY5OBsyGBJzO6xBAqMAA//kCFDrr32WiRO3n4hWdJBVH5mkkgzeMuiVHI+aRwVGAS7IwEJVJOAljLVBGj2QiPABBf3BBzjebWIKEOsRN7ysSrDFym8lg8dZinIS3smvkxqkxCggIT3+Unnw5/8KpOXjNFb9DB1TiyHBNFVcpGAeXN0hsKpgnlw4mSaLPzJJ1yNmhoaw7+pjQnzA1qSeskzEshPAl26dGHWSzCL4Ch00kkn4ZSBjwaKCVYtiDKM1uC7hI9S+V1gsCQOuqTEjCCkHIYeug/SDLFpCNwbpWFAMXyIIxCd4YCGYaZO2wYOHMhy9MUXX+zbty+2DOXUkpjdYwmkSYDHHm+I66+/ftCgQTfccAOPOsdYad14441EcuFZRQ1ETCz1az/NKkjGbwoWZ/ygYA7GsOIHKDz2XGLEochgTYb4GI0LrjKmuMqoQS3ip/Puu+8eMGAAjcSDo1J2puk30pQSyB8C+P394x//eO6553jmifrENz/SJCHPCKrNlLJ///7dunVjtPqLkD+3zJZIIK8IuB7Lq9thY2qeAH74rKmYg+Ihf95554UGJUb0ZDHGq3UCVWACg80qzvzIN0gkpGRWyr6knJ8xYwZW4uFtSdQlvBvYk4VpLge4VDCv5ZU781fWkGTkxT4/1RwEJ4g2bdqEjBSIKTgV8daFRSAlYExOG/DXIKoi68ZQKTWSJrwyZWaMsxVdIC8GMkTcIEwja8uoJUzZ6QJKE/NstuGgGczjo6seSCCfCRA3kTAWTHxZjjIDxtoFhw48hhhBhHFhfDEAcW7CrYNBWuWOMAwxumHlickMFjEsTbEOCKVhOMCYZXzhP8UZhhtjkIHM+GUcYWFHLGFEmauvvhqTGUYiQ7XKzTCjBJII8H2OAUuIUU1UI3Zc4mufR50vc1QSnkO8lviT3wi+//kzKXuFf/J486FMnn+MXFDt0VPwUeJnIvy+sKpkqckPHAGeeNp55vn5Y7xgO4P9DkOGnc7IRSNx4vvXv/6FI3CSfFlhG0wggRokgGEXE62oAXzDR3uHcT4oj+Fq8N1LTIkwij8sIil+r+iVbIvGZA/rTjRKtirjtQFWbIoyETEPJCCBRAKKMok0PC52AkxhUTcIv8K8k7eRqTiY6RJoBm95IkqwKmMxxvSUxSELRRLzrpKtMQgmGuy6E396EWh4YcKrEibKweaF6Sw23vhQUBHTaMxzWP4hlxDzgt91XI0on0kwIUXZ/wLDV/Y9JWTAn//85zAnoOozzjiDS8yJcbDCYxlD8VGjRjGfZro8btw4asS3mXkA5fAOP8hGNJLlJTsC8IYzyDRYm990001MIFI76xkJ5BsBRgp2K2iRe+65J/FiGGssHWkk0gy6DFsdMYLYdAmBJoyySrWf4YbyElakOEPhe0gUAARZlE1Wv3wtMLFmHPEVwf5KSLFMrzGoYfFJG1h5MvmmbczakUfRZWgG62fOK8pU6i6YuHwC/Gqgy+++++5ohTztuEXwE4DfEHFe2CCM5R/SJI8uP1VIKnhVlF9a6lWURJ5wfrwIic2Tj1cUbwj4BcFIjSHAapORhSEMv0dYwbB5GS1hyUp8a353MBBAwWF/NH6/GI/82CEPEaQJz77UijwjgfwkgJKIoVnUNh54zB7Dn8cff3x0ngM89SK9JpxnsLABH9HHeKtH8DIsJZlc8WNBhEFSMha0TU4E6LEEJJBIQFEmkYbHxU4AwYL5LhNQXjmy1krFQUwWglmw0CIgKJNRBBqmsOwtyhYVqCQoMn//+987derEhJXpMnNW3h9SCIs9FpBE/OXtPT/SvGAPu41S3T333EPkXWax/GYjrLAgvP/++zGNYTsMZtu8eyQQKW9jDjroIBLgTsVqE7NYftfxkmAJyo89xYYq2C+GLMwAUGFYOiIAMUVm7YrogyE6ygsLWlLih8XWMywdeZMfZhsY+6T21DMSyEMC+CihM+JPwfBk5YksssUWW9BOxBHUE2RNIrkwRqrmK4GoiujJkMGODH2TcYrQiZUZCgvuUQxANBeqYCxjT8clpFjkGOLIoA2xAzGGdbwI5QuBYDSMXNbGNIMW5iFGmxRTAnyT85zz7Y12j7EM3hD8KKBRYpOCRsPPCpsuYfzITwN7h6Gt8BOD7o+VJT8iQc1HweELPwr0y8DhCUeaj0RMfsj40aEihhh7J7FtU9h6ae+99+aHD6GH2vkRYTlKmUSyx1SHxCgy/PBhTcMxLyr4CcOOhsKRclia8sMXU+A2u6gI8EjzrDIEeICjjoeBwzBhIpd4ngTMqRhcZEmUHYn6hKDJuGAAshMfUZ/4OeA9H85NvDyIivVAAhKQQDIBvn38SEACgQCLLt4EMhnlRUcqE3QW7FaYaCKRhKu8VyekBcoIWgzGKcyS2ReGOStXMdjGDSpc4n0jwSb42cbbgl9xPryH5/05xt74QWDsSo2IKcx0eRWJ4IKhDf4XzKf5XWcOzSUaxjsWysdrg8KZ+PISngkEK1JysUxlSs1bfc6HqxRLYt7Ys2jkDJMJ0lAsUk4wpaFhxEOlWK5m6hO+XDJVWjnlsAihLix9yknjpTwhgE0WN4vX+9VvD483zvk4KxGiguFDAEUGFM9zKJkxRaANnnmWo+EMEioKJuYt6COcQUlBJ0UxYcbMn9ioE5oRywIWlrzPx/wNOYahzZIVmxd2ayJGI6ZzpMTogMKRU1nuchWBBpMErAC4xMhCmUWgwfANAzS+HxBGEW54X8q+M8OHDydNZj9M9OEZVg6ZLdnSskEgS19WfLHzzZ/UYJ5whkP4FWAdyO8Oj25SmnL+RMRHf+G3jIyMHX6AUhNTIAMnKpYBQmLGQnQmysLPHO8kQmOikzk7yODXTs7abEWJBIIlL8Mn8WT5x1XIUn6BGbnKKAhzwoyUZiESkEABE9BShvmtHwn8PwFeiSCCMOCZ76ZCYQGGiTh2KCzewlVkDt6iY6KCCMJVJqBIJ8GclaKid4+UxgtM/uX1JqoKeZkxkxj9hV9r3iuGcBi4H7N9BicJmUEcUxQZXs7TmKOOOoq5bwhjgXkO2SmckDS8hCcLE2LOYBrDejK80mF+zCqUKmhbCHtBgsiXClGJ5SIaDW9Zsc05+uijeclJpaTxI4F8JsDjvd+qD2IiwwcpMwwxxghPOzNyhlj79u0ZTaEXiJ4Yr0U9QojEmIVPOMNIx76AD38yvrA5x+CcMoPKk/jmk7GDzcvgwYORZfEKpBwGUSiEcYeb0kMPPcQKlkL4QkAzYhbOGAz2BVHtHkgggwSiL/bEMnkyoz95RPlij/6s1AGjgCe51CyMBQZOdIkBWFYtWN9EyTyQQNESYCiVNZqKlokdl4AESiXgltilYvFkkRJgiYUxKusrguCyrEqiwDqQBRtpMIeJLrEg5DzKCBtgB7+n6FJ0wFoRmxquBsciSsDIBWNvAgFQHSYtwRaG1+yoMJwnggwLPF74Y0bO0o6X9qw5eT/JIhP78KhYDqidJnHA5DjyQuIkb/7RWVCIEhOHY9qA2QLeFkyvMQhClEFpSk3mGQnkLQEWjbxBjURP3slj/EIkJryHcCmqgscQY4GRxVjjgJVtoiITQUASYjmKGBopMomXOBlm3rSK0V1sigxfcajSEZBwwEk+fJUlnecLNvXblTMkTi2ExHz18S2HEkeCdIoiDd+B/6k7JX1Sdv9MJMAvFMOKJzmI+4mXPM4sAZ724NocFcsTy6POE5s6BJhglHoyJOZqVAgHDDfOM1gYMojISaOPWphLJKaPjjnPJyl9dNUDCUhAAhLINgFfj2ebsOXHiQBCBh4KLL3wRcK2hVCFYeHHTAV3JK7iHMEciEVg6BWzH2xSOI8gwlqOZMFOlUkts5+o52Etx3z39ttvZ8EWzlNOKJwDwjESAoZoFFxl60Rey2PkQqAKpm6YybBE5P08LyTDsgRVhRKYcuF/xDow9ZUptfOWkmIxLKcZYYad2B4m3xgFELKO4DLUgjUBelDUWg8kEC8C+BZh+YXVGFYqOBkRRCNe7Y97a1lMYgNIoJ/IhJAecZL4I3wlYriEu1nUR84QDIh7FBk0hUuETsclk285ojCEMywpcdJBH+fO8i/faXw5B+u/SBdDhkOyYYsr1LSoCg4QwRG4kb+DJVTiJY/LIkAkNZz48M6DW1lpPF99AvysYwbLzIHYc0weQoHMJfhBZwgg++JiGT3PjBeiBfH8E50nSsyPO/5lbA7ArAMZOlhIIdzw2BMYi/FCOQxAxguWg7xDYu4RauHtEdGyePGTZMdE7a+//jrub1jpGpi8+rfYEiQgAQlUgYCWMlWAZpaCJYBKwtKCCBHMXS6//PIpU6YwTUFnYf5ErBl0GaY4zIcIxU8oUCQSrGNee+01lBQiU7Dw4BKLE0IFo5jgTMEnkEJGwe2IBQbu+pTGeTJSOC+mSEClIZgof4ZIGYgmp5xyCvMkJmGEySANB5i9kJeqEWhoFZGASY9shKFN0v1gPkdUObKwKxOTudCY4OVEShqJaQx6DYIO4YqZ87GmTSrBPyUQIwJETySEBB5/yIvsQhqjlhdGU1kKYnlHtHK+W6Ie8e2E7RKeksTxiU5ywLcoHmTXXntt4kkkY6L/IBPffffd4Txl4svJ9yr+asSyRXQjBjORMvmTb+MoLz5l2BsmbpUSLrEzOqXxTR6l9KBCAshk7KZEOKRIEagwiwmqQIAfX2YCvKFJjBqLBElEOWQahtKXX34ZFcsP98MPP9y3b19+yqOTzAHYIYgnnHc5TAYYPvzKs9sA0ZdR1gjtT1x/QtqxPyNmthyHVzIMTyJSMSSJAx3mHokFslcjAfKQMqOTHkhAAhKQQC4JaCmTS9rWFQMCvMJlKsO7XGLmE7yTVQHTF97ionGgyBCvF6sW3jUhmiCI8HKJ2T8BRwkmyuSJA3aHYTclMmKczIvi0GF8GVhXUOZll1322GOPMfdlA9Gw6iA6L2mCBxMn2X80GMJQEZNjFjAckACZhl2fqA4ZBVsAZlesRtCPCISRyhSVhyoIFUykUgLN4BvFSzM8oUJKGkYhdIrX2rya4x0a65zUQjwjgbgQQNPkE5fWFlg7+W5hNcjXIF9WLCb5UgodxNqCr0G+OZGtSROML/jiQrNmhyzWpSwjoxf4fBlinEhGvqz4l+8ovjD5isMihqjJ6N2888cEgJjKyNmRvhwqQuNmkyAWk0muneGq/0ogrwigj+BczMsVXuoQtjzytWSSgI0SMwHGAlOF6AuN1z/87vMvE5Jo7x6EGMYLUwVGBy9yGALYuv7pT3/iPLZmmMYweeB1C9a+1JUo/YCCcUpocyY2TFSCrW5e8bExEpCABIqWgJYyRXvr7XjpBJimEHkX6aR79+5IIcyEeDHLXid4+iBz4EnEi18uYX7MzIYVwsknn8wZcmFCfMMNN2AtjxUx2RFuEHHISPwLBB3mWw8++CCWxiwtiH9BGqQQLoVGYLTCxrqYtwS7GE7i8YQ2xEvgkIZXl+RllUIMGt6bsekMUzE2y0AMwjsJU51QUdQlCiQwDW/SmALSGKaAbKTKnjJITjSVxpCSF9EsjXirxvonyuiBBCQggfQJ8IafbzbSs8jk7X2UERWGbye+bfiSRIUJ51FnkK35UkJYQayJEiPrIBMjwZCFqwRODooMsg4bkKNu33rrrU888QSLWL6sePkfZQwHLGKxo0l6+Z+Uxj8lkA8EGCYoldjJ4gSNcR/DJ7SKkYJzNFHDEVZ4nsNJREyETv4MKgw6SzhPXgRKZiNY4HISJz6s0lA5kXjuvPNOhgyWZRjjMKyYk/A6J7gwR92nNOYziaY30SUPJCABCUigpghoKVNT5K03fwkwPUK/uP/++5nlMyVC8kh0wOYVFjb2XOLlFecRbkJPmPegmDAZ4jUv1sVMmBI98znGJxxfcWZOzMPwG0+cJ3HM5tZsqRvtZMH6BLkncZmBssNqJJjkMA8LezxRNecJpcEnCShFMT/DB4o3byRObAx6DSsf3Kk4T/akjP4pAQlIIB0CLBpZ2iFA89IewQXdBKOV8FXDtyixsfAmQ4bG1xKvCgrkCy34RwR1hnf1oRaUGg54e88XI9+rvPPnTxacCMdR+BjO8P4/bHUcciX+i/MUkWUwtKHexPMeSyCvCKBR/l97Zx4vRXXm78sioCgaE4e4gBqTONGo4KCCooIYNcHBFR0xajSZmEVRIgojqKCgEjdMoiZxEuOWuA2I6BgdjCBRiOAPjeLPBcVoNG5oUEBAlnngZI5t31t9+97b1be6+6k/+lNddeo9p56q013nW+/7Ht7KcJey4DWGtsJTQWghjxOkTEKsRGThKYIXMzxIIGiGOGiijPnLxhON/3QOxDeNtz70EQziJkMvw3UXaYa3O/F8eVFEsFL8GlfotrjPEKDN44oZuCIWVyQgAQm0LgEfX1qXv7VnmgCCC+pMriITm8suHo+iIhO3sxLS+uaKIHEv4gteyrwQy1Vkwl58YaIiE7bg7VJ/DhcO5EkrKjLRctIKgyXmbKrfGOzQDBWZJG5ul4AEGiWAx1/IEU52Xn6vGEDOnz8/HoWegqsLaU2nTZuGCsyCxs0olJ+jsDGIzgg0FOCoELs0depUxpn8HjJJXK4iE83WX2Fgic3TTz+dwWr9vW6RQEYIcNsjYiK78OdL+DAiC85f9IvQPOLv0C75U2Y7kUdsxCMGX13+rLm9ESsfe+wxNgZXGorxEgjvWjL+ovLwqMBbn1xFpsAp07mo64EHHkCUoQsXKOkuCUhAAhIoGwFFmbKhtiIJSEACEpBA9RDA74/oSJRfEsTywp8B3m9/+9t4eowkyWnFa3ne/JOkHPGFIShfyXiFjBIEGpz+5s6dyzoDRQpzLGnI+cQhMXoQBIMcGJdYRVghnpTYUowMHTqUlBl5e/0qgYwQIF6YkCJudYKXjznmGLpPmPMoNI/+QhgyOguyC9olYg29htwxvIahyyDKUJiN7A0biXXCFEYoxhudvNxwlIz9hZVcAhgk3I8tRF7PmjUrqkK5ZVyXgAQkIIEyE1CUKTNwq5OABCQgAQlUPIEgspBJFAGFt/TEIrGF/C9EXoRzY8TIuLFr164EXJA+A8mGV/o4vzBHDJ41FOYrnyF2ieykIelpyEuaNy8vo0rCN3AfCAthHbn4iAQh6QyHEBvFpDbYzN3rugSyQADtg87CLYqAcvjhhxN8RNdgC10jNi9EMJFxiaz/qC1k4if6GPeZH/zgB0z9jriJNIMfGX2BkvjaEAqNpwy9Az+1mGMba2idbA+dhcLEOsUqWMHDl1mxiTTEOJM9UTJ3r+sSkIAEJNAqBBRlWgW7lUpAAhKQgAQqmAADQjLyIrIQdkTMJtPS4TJDbovcQSbbg/6CgwAjQGIxmF8G+YYU5owbCaBgqBmyzAwaNCiwYNjJCi/zc9GwkfTkzFW3+/oFl4Hcvawz0CWtDPm/yEcTHAryCvhVAq1LgBS/OIUtWLCAgGL0FGKQ+cR3htg9+kJoG7c9Gf1RFZEmkVToLySnoweh4DDREmFNdBnyBBP3R/YlugIl0W44Fi+bOG8AX9nIVI+hv6Dp1M/lj6sakzCimTKd/Lhx46KQ2rqIrF0CEpBALRNQlKnlq++5S0ACEpCABJpMgJfzMcUvoUOkvWCUSNpRRom33XZbDIgIEUyk0kCpQXzBZYaBIjEa/fv3J6vFiy++iFjD+39cXWLSX/bSmry390g/QY4p0FCSoPPyn7qYtw5vggIl3SWB8hPgngzJX/r06YPUgqsLqalRQ5jeMeZCQtYk5T8TwBOjdO+995JEBu2GYmiOAwYMQJqcMmUKsUvMJU934JPyuNtwLsg0JKCJJ0XGGXJsE/TUYEa8UIwDx48fjzrDDAP0WTpOPNwVCUhAAhIoPwFFmfIzt0YJSEACEpBABRMIKX4ZyDFcZMFBhpQuCC6IMrz5J8lFODcGhwwpGfhR4Oqrr0ajCR4xjCfJIIOvDdl8cRNgfBimZ+IoBqV8otfkZochjylZNuJUwUngmJuJl//MN5w0SVPSgW6XQKoE6BchxS93PpF6dAfmVOK2x3EMZxn8X2LtIS4JpxjmMqMYUw2QZSZIM7iVIW7SCyiDAMohQZShW9FZ8MGJRuhx9KyHH354yJAhcWPeCn2zR48eI0eOpBteeumlf/nLX/IK+FUCEpCABMpJQFGmnLStSwISyCfw0ar31679h/N2/j6/S6DKCaxdserD1WsrbwIU4iOYrzqkyWAqa6QQZvMlnws+LyR8mTx5crxueMSQwALPGjL4Es10yCGHsIthJBFMrCCg8MkkTWwJhxCpQTIaXvvfcsstHBU2FvnJEPeaa65hyEroR5GHWCz7BFauXrpqzfK6un/MUpT9BtdvITIlTmGoKriADR8+nP6CColLF4IItzrqSZiJjANxoiGCia+vvPIK64gvuIkhvjAfPHolMYD4yyCD0ukojIca/QvhBkkUJ5oQylS/9qQt9Jfjjz/+0EMPJbRKUebjNR99vPof+bCSiLldAhKQQHoEFGXSY6tlCUigcQIPLhjzztIXV635KPvP3A06eDMujRkBGj9bS0ggh8DyVR/Oef2Gvy5+YuXqZWvrKkaapCNMnz6dHKUoKbzGJ1CChZwXX/3qVwliwonm7rvvjnoKL+QJLOKk0WtI5UtJ1oMHTUwcgygTqbAenGV4e09YR26nI/opFkta6dmz59lnn42sk1TA7RVHYP7b97zw7rQPV7y1pq5SUzgjmjD1GBl2u3XrhqfM+h6zPampWXCiwcklyojkRdp5551D2FEQZcL1wlkmzEeGsokFEtOE7eT3Rd9ExMGD5oYbbqCPxK7HHxPGC19uNJ3zzjsvN0lw4fJVvHfBooeefPP2j1YtXrO2EWhVDMFTk4AEWpGAokwrwrdqCUig7vG/Xn/17J5T/v+wt5c8n2VphneJOJkzL0beNSNTAO85eeGZt92vEmiUAO//578z+fq5A+6a/53X/j6nUqQZBn433ngjwgev6xkKMqtuWMhLOnbsWIaCfI3BFAg3TNaLCsMLf97JRya8pQ9fmTiGfBlxOwNXjPDJq3syoZLwAucCvG/oaGwPxXAQiOXrr5DWlJw19be7pUIJvPHBvDvnn3Tjk0c8+cbtH654c9XaFdlX8HNR899B4hg8xZBgfvOb3/xfd5lFdhj6EYIIwUpINvEQ9BccZOhf7CK/ddjORjzO2IhSg1daLEwM1FFHHYWO+eqrr44ePfpHP/rRs88+i8sMvjnEOgWzBfoLUinHklyG7hlt1ubK+x/95a75p1wzu/cTb9yiNFOb94BnLYHWJaAo07r8rV0CEqhjaPr467/IuDTDU/Wxxx6LC0Deu0eeqo855hhe6cfkpl5RCTSJAG7zT735u1/OPaAipBnucwIriMVAbTn66KNzz5QtzBSDyMJ7e4Kbwi4GhLzVJ14DnSVXlKEwg0k+EVBItJFrZ/Dgwcwdw2wyRGqQFCM4F/B52WWXUZ7tpJjJLZ+3zsD1P//zPxms5m33a0UTeG3x7NufOQFp5pGFV767dEEFSTO4yZDiF12SOCM6Qu5VCP4v3OczZsyISZSQXZAj8SMjJTC9KZSn++AgE/LLsD0aoX/hoXbxxRezl/+mn//85+T37d69OwIQf0wom+SXYdL6WL7+CrFRdEwmqkegqb+31ra8s+w5pZlau+ierwQyQsCf4IxcCJshgVonkHFp5umnn8YF4J577skVZXAUx02Gh+n58+cTtVHrl9DzbwGBSpFmuP+ZRwk/FyIsDj/88LwzZiN+MezNnRgbJYVpswnKYInlGaMy8mToyK64MawwziSkghwZYd4ZAjpIxoEWwyv973znO4888ghJN0JJKkLQ4TPPAlM4XXnllbmTBOcV8GuFEkCaeWDBuTfMG1hB0gxZY5gMm5ClAw44AG0llzz9pV+/ftyoeNMw13XYheZIzmySyOAak1uYVDIE9yG+sCt3OwFNdKI77rjj5JNPZuYmrPHHRCdCixk4cCC5mcixHcrT6egvyDQIl7kWqJGMv6g5dNXc7TW7rjRTs5feE5dAKxJo49vdVqRv1RKoJgI8BXI6Tf1JGTVto/VRS58i0b5tp923PGnfbc/cfKNtWa+r+1S0Ak+QuGcze0XeC/ZPmSj1l1NPPZWIfd5b4oW+5ZZbhpNduHAh40bCK3ixf8455/CwG6tFwWGhGK8f856AeWKGEtv5pAyHNK9Mbl0hj0B9O8F+aMC6Bq1eTRkezeOxaa8wCQhRJ3w6IU591EtWvn3rn499+b3pebs2aLvhTv90eN/uQz+/ya4btOvYpu6TERSvvnnfjuiAu0reUVn+StwfIUhhuuvYTm5aTgdnAWSXuDFvhZwyJANGCSJnTW7/yivW4NdnnnkGJ4IGd5VtY6v8WJXt7MpT0X3PnzP3jV8t+/i9vOo+t9GX/mWrk3ftevRmG3Vv34bom0/+Jir9Z4d4WGZl4o8mV8Shv/Cvx64CKWAow4H8JZHdCQ81fu3zoCV95UAcdigf0tkkFSvbdv7i6Tv8xXPKRVbajENyLT/26jVTnjstd0tY32Kjf95/+3O+2vXIju02attG0ao+IbdIQAKlIVC+R/PStFcrEpBAiwmsXL2kxTYSDZTEePCa+X9/u7GANJPYgnR2MMBD0WBgiQvAkUceyctJ6nnooYeWLVs3XwN+NNFThhWeDinPYJJnXMIuSD6KR3p8CckuAv7xMCc3x7x58/jEX4DRI/7qTSpDvTQJJ4IXXngBmwxcGa6TS5VRbkgQwICWqCsawytQCpNLkmlZaQ8eCjzvsqU8S7sN6ta2XVmSG6M8DS5bLTBZ09DUY8Fr5tm3706SZsrWwlJVRD5Rljxr9A7cB/I25n1FQMz1r8nbW/hrqysyoXkbbFjHhV65+hNlrXCz3ZtHYE3dyga1/neXvYjXzBNv3JAkzeTZqaCvqP/8mOc1mP4Sc2Pn7YpfKYOUwxK3FLnCgXjiFFm4bMVC3ymyuvgXE1eKPDAU46mjwfLBa2bGwh8rzTTIx40SkECpCCjKlIqkdiRQGQRWrP7w2j/tm15br/lT3yYZT3oSwkiuNNOn2/e32PiLG7RFCvnkdWiTKmpJYXSTF198kXeJGJk6dSrT+kZRJqT4JXwpzmlKiBOOM2SZwU2GQ9BNeMk/YcIE4vaJ3scC65MmTeIJeNGiRegmlEFPwe38iiuuYJ6a4stwIF7xl1xyCdoQtVMd1qiL2pmPg3eMxFUNGzbsscceYz4akoA899xzFCAPCMJNOUWZXQ6qW/7VX1/zp/tacgmq8tg1a1f9ffmrSaeWI80c1rf7GcFrJqmw2zNLYPC4upvmf71tu2J9FjJ7Iq3VsA9X/G3F6g+Sak+QZpKKu71iCHTYsG7Q2A+Lf6JYvmTdHzRPOMUfksti2cpFuV/z1pVm8oD4VQISKDkBRZmSI9WgBDJNYO3aNW8ueSq9JpbceJRmdut6XN9tz0CaSa/xSZbxeUF8IQiIF7ZhDiY8X3CrnjNnDnoK7xiJY3rvvfd4P8k6+gjeK4T99+jRA4fwxx9/nL3Dhw/nK8pLiHuiImbcIGXAnnvuScaB3//+95T55je/iXRCJsgY7lS4DK0iDeprr72GEMNsqegspNv485//PHToUNIK5CZVRe4hH8eJJ56IQWqJ06kmnW9pt3fevG5t5zffXPJmac3WiLX10sxtz749JXjNrFz9qZy4NQKhok9zi+3r3v7o6Yo+hew3Pk+aWe2sxtm/Zo21sE3bus9t14THlRVL11lM9QlHaaaxi+Z+CUig+QQUZZrPziMlIIGyEUCaeeJvNzz11u96dB3CMLVs9YaKQpZfoiHQXFA9Zs6cSQ5FFJAQGUS8UgggQvjAzwVXFBIBMA1NSLjIPMFhFgwmD+ao4AiDWcSRyy+/PMR04IaDbwvZaq699tqJEydGH/UCZXCEOf/888kdQGgSM24wcypyD9FVJ5xwAhLPzTffzHbimEL7qZcypL+Jck+ZAVpdCwlEr5lNV/wjx20LDXq4BKqPQJRm/qwCXH1XNzNnFKWZ/l84t8eWx7Vr40gqM9fGhkigkgnoT1vJV8+2S6CWCLRZH7i04P0/EPRR5vOOogyCC2lfmH2GVDIEDeE+w3QYzJRBDpeYVgZFhllpkG+IYJo+fTqhT8Q6oZjg0kL0UGw5iRijRIKag5hCulNCjUI8VChWoMyrr746a9YsvHJIcIP4ghyDQoQjD8oLmRoRiZjOI9Y1YMAAknfE6uJ2VyqIAGF7a+vWvvFhim5uFUTDpkqgQQJt27T7YMWbH6x4vcG9bpRASQi0bdN+8Yq/Pv3WnYuWvlQSgxqRgAQkoL7rPSABCWSdAHJMu7YdN+7w+d7dvrfXNt+Z0G6H5XWLy9noKMqgtlx22WXk+iVT74wZM/CRIb/Mk08+SR4ZyoS0MmzEV+XSSy9lilNUEtQZtrDCBDTkl0lqNm44qDl4vuQKN3mFc8vgXINZCowfP566YknaQK4ZNCDiquLGffbZJyNzasQmuVI8AeSYdm07cf//y1YnbtntkIl1exd/rCUlUCMEkGPat92o26Z79t7m1FXbzJped1WNnLinWU4CyDHMCLnD5v37bz9y2838KS4ne+uSQJUTUJSp8gvs6UmgPoGO7RMnoK1fuClb1k3q1FTjK1YVmgoqyDGbdNxqr22+ixyz0QafbUp7SlMWPYU8vigdaCJMk0SgEOFLP/vZz5jNly2kiUFqyfWUue2220ivS1oZUvniosIhd9xxB6FJhVuDmwxKCpIK2XmpscHCuWXeeOONIPH07t27/qwZzJnatWvXaIRYpzivU9xYtpXVH9e1WQMh5qx1ySOwduXq5UyMnrc1fs2VYxhqdum0FXdd3OtKRRBY+VFdl01T+smtCAAtbeSqNSvXrFnZ8G/ietu5csyOnzu4Y/suk9rPb2mtHp8BAvSdTTYpuu+sG9A05yEknOjqNR+vWrOiwEkrxxSA4y4JSKDlBBRlWs5QCxKoJAKIJqf2mplGiy+q64nZphq/5k+9V69t4EkoyjH7dD9tz63/valaTwlPkEAhnFww+JWvfIUIIMKFmGea5C9sIXapc+fO5PRFcaAYKXvJI3PRRReR9Pe000674IILgn8KXi0LFiwo3KS//vWviCwk6+3SpQvqTIOFc8swszWzBbOMGDHiwAMPxB+n/iGkIq6/sfxbnn6w7mu7n3LqqaeWv+qM17js4/fuf2HE6x/Ord/O+nJM/TJuqQgCd42ue2Tm/U0YW1bEWZWxkY/+5er570xavqqBCZjqyzFlbJdVpUsARWbqmI1nzCj2ceWDxUt+XLdvs59w/vzWHdMXXtLgKSnHNIjFjRKQQGkJKMqUlqfWJJB1Am3q2m3dpUd6rWyq8TZMsfDpd6DZkWMCpRC7RHoXUrcgl/zrv/7rj3/8Y2QaBJowNzYyCm4pRB4988wzJHbB1YVdP/zhD5mhCQuEEaG24PyS5/+C6028CjjIMKkTzjW77bZbrktLgTI77rjjhhtuSKwT+YZ32WUXJn4K1ohponaWaLzVV5a+V9du2eebemO0erPL0IAlK9/uuEH+e+DKkmNImRQ8xXLv2zKga14VdDEODDmemmehGUe9s7Buy4132bSL02Y1A966Qzp32IJRcd7B2ZFj+JXmV5dP4k8z9cObR6zivq5dU/fuwiY8rmy8dl1Qc7OfcP7y90frI1KOqc/ELRKQQEoE8v/nUqpGsxKQgAQaJZA1OSY0OIgyO++8c3jgxkVlr732Ym5pHGeIXcJXhWKso8iwhC0MU5966imUGkaqzHJN9hlkF/xocvPF4DvDhNl4uFD417/+9dSpU5Fvjj/+eHxtIqgCZfDQwU/npptuYlolprgm2Q3aEGODKVOmINCQRCbXTjToSpYJVJYcA0kUmdGjR9NBcIPCgyzLbGkbybCZ44zB85133mmKpYxfrALNy44cExpJQneyetEXvve97/Xt27dAy91VQQSUYyroYtlUCVQHAUWZ6riOnoUEKptANuWYwDSIMqSPiW9BDz300GnTpg0cOBBlJJRBlCGCidmyhw4d2rNnT9SWb3/72z169EAlee655yiD+MJsTcQfhTmw2TJp0iTmWkLiwcXmb3/7G+IOEycNHjw42my0DOFR5BimeVR6xRVX4MiDKaSf3Xff/Re/+AXuM6FtfmafQMXJMQEpeuLcuXOZ650ekX3ICKOk6KadrGS/tbawPoG2bTfo0Lbz1l3+pffW3/3yFod0at+lfpnyb2Geu9mzZ/NTz9x85a/dGktOQDmm5Eg1KAEJFENAUaYYSpaRgATSIkBAEHMZkMq31XPHJJ0hKXVxQkHjiKLMwQcfTDRTiF0KRyHZ8O6dkpzOVVddddZZZ/3xj39EoyFQAg8Cvp577rnMYD1nzhxmvw6HMEc182qHBMC4uhx11FGUQVjJbUbhMnji4Bdz3XXX/e53v0PWQQkiEzCvanFb2G677bCDEoS/DFl+TbKbSzVT66QC4v4PMyuFVL6Zap6NkUAWCCDHdGq/2T9/7ht7dzut6yY7Z0SOyQIZ21BCAsoxJYSpKQlIoKkEFGWaSszyEpBAKQl8puP2e3b7duum8i18PtOnT88rwGxHqCHbbrttiF1iL4lmWGKxu+++G6cV/AiIJAq5NohOIvsv6ySCCcWOPfbYH/3oRySgwQgST1R8ohFWGi2DiHP++eePGjUKUYbyNCxXf0FLYnLuXIOuZ4oACZU26dBt/+32y74cQ9RPWLhRG7xXAUt0HmUaLMB2ugNpldaJsO3b85l7IdiFgMjGUIxdGGFLLMN2jg31UjiUj70vFmOFYuylPIdTPq+i3JKuVwqBju032eWfju75+eO33nT39m07ZqHZ4TYL9zM3W1KTQo/gVgxLbrFwD3N/xl5DmXC7hl7Aev2jsBBtUiD0iGg2dBOOCs1jO+vNKBMNrutpq1eHijAVt7PCdj6D8XgKeXXlls/+ers2HXb83Ned6Dr7V8oWSqBaCSjKVOuV9bwkUBkETu/zeId2G1VGW3NayUTXOd8aWGVS6rytm2++ed4WNBqUnbyNeV+LKcOj8DbbbJN3oF+zT6DzBp89cudfdGj3D50umw1mpEc2lpdeegmvLvJbM9fYrrvuShRervxHOqTXXnuNUCYC9PAFY5p2/LPCCJPxIcoj2ZHIuPT222/j3sWk8nhyBXWSveTGfvzxx0lcjaRIrN8TTzzBeI/QP5Jed+rUKTBhvjOMkCkJdzDWmc6se/fuffr0wVruWJG8HkyCNm/ePDRKvMzwX6NfkH41m2BtVZEE9t/u7PbtOrSp+5QoUOSxaRQj/I37HEfIt956i45AB2HJq4gy3O2U4YZkTj3iW/nL4J7ntxoJg/uTue3pKdz/3M+UIY6V25X7mVzUzz///LPPPkthusCXv/zl0NEo+dFHH3EgNoPzJofvtNNOhLsGKYQoKnoo3YSuwQsAIltRLSlAh8VfMpQhxorthcuEE6FHY4S6XnjhBcR9uid2cuvC8ZPu2atXL9Kr0X8pTNwuXTKPQwV97bnV8Xt1+/cKarBNlYAEqo0AP/QuEpCABFpOIPw4ttxOoxYY71EX8z03WjKbBYYMGcLjLMFKDEeTWlhMmaRjM7WdxDdcrDFjxmSqVZXbmFdeeQWeaHnlOQVGcXiKkaUIhYXBIQJHcOz61a9+RQMY3e2///5s6devH5ojBRj7sey9996IOBRgsEoaVMQU2kwxFEbsYIQJ4xFQKMAAdebMdVPeEuVHnlTKYCSoOWeccQZ7w2kOGDCAjTfeeCM+ZaGWAAEZKBTgk940fPhwaqeW0E4+R44ciaIUyjBI5igWRtTxqLRXKv3HKm0+KdlP72cHWZC0vmjuqIGhR3DLcXMikUyePDmcDgrF9ddfTydlF2W4IZFFyBeGxkGP4J/rpz/9KXPz4VxJHjH2cttjAdXmP/7jP7jVY09BUpw4cWLoBcgx5513HvoONinAJ6oNuZzQdEKlqC10NHYhynDns2CZhY2IJtRLMQSURstQjLhaUp7RnnCO4TQPOOAAwm+xg2aKcko/4hSYiJAQXepCnLr44otDS0ryCSWqoPsUb60ZhxRv3JISkIAE0iagp8y6RzQXCUhAAmUjwCMsrxx5XA6DzwbrLaZMgwe6UQKlIsDo69FHH/3617/OsJAxJ0mUSFrEeIzhX+7cXuylGAUYts2fPz/kTiKq7pZbbuEO5xCGgrgAoNSwzkxkRPah6SD0nHzyybGpDALpFAwyGVLec889+A5cffXVJFpi1BrK8DB0yimnMFAkpo+390hF+BqMGDGClNsUoA2nn346k5HxVp+jMIJjzn333cf4mV0MHQv0tdgGVyRQmABuLNdee+3ll19O1+B+xucL1ZIM7rhuxQMpQ3ArsiPKOz2C+xw18MEHHySmFdWAWzT4iC1atOi///u/uVGPO+44XGCQJvEm43YlNxnT6tG/uMNRD5mYj4r22GMP7n+KocXwlaPoAtzh3Pz0qRtuuCH6lHG3o04i9+CbQxnqJZ38iSeeSCPJXBYaWbgMPjKPPPIIbwXoMtSFiIP2Slei537rW98iHjba4RTo5jjLsOAoxyERgisSkIAEJNBkAmmrPtqXgARqhED49SnDyVb6y2febfJoy3M8D8dJuIopk3Rspran98o6U6dZtsaU01OGMSSzvzMOxB0GYSWcI8NRgozCevCUoQB6DYXZiP8L4zR+Cnilz162MMZjQMiAk3WOJeJjv/324xCyZbOFLhA8ZXgnf/vtt7OFBSOES2CEDNlhC+4DfEWgwQchbPnNb37DFhaiNthy//33s47ZBx54IBTg85JLLmEjvgPBNUZPmUim6ldS+tlBm0AfQTE555xzQo/gBkYZIXdY9JTBe4us8HiODBs2jCAmUNMF+MHHrwQ548orr6Sn4CnDnUm8EvIHBbiHmS8PEQRHsAkTJrCFnsJMYSiMhDUhA4UtKJXYoUbcVbiZkRoxQixh8BcLnjIcMnbsWMqzBMGIltBgpq5nS/CUKVyGzsI5ovIcf/zxwZ2NxjDHH9GF2CF/GV+Dpww9q3///qEDrq+wlB9Q4uz0lCklU21JQALZJpCVGF1+fF0kIAEJ1AIBYu8ZkfLOnzFk0vkWUybpWLdLoOUEGPghvjDdNcMzhnm4ugSbqCdBMYlV4PPFu/0glbI+aNAgdjFyw5OFlTByY9RK/AVhFIxCSZOB8bA3GsF94JhjjglfcRM76KCDWOf1eyzACpYZYYYtcRLuUAavHLbvueeeqEh4CoSFgBHawyCWsJFcO65LoBkE0FaY+hq5HFWR2a9Dj+A3nMA9lmAQZxZ6DRIG8U2HH344X7kV8Z1B0WBePPQLvFdCSfoR0UnBEQxvF7QVfGRYcE6hAHs5hH8BZJF33nknbKE8IghqBfczzaBq+iYF6FnBJp9sRNkJX7n5sfBv//ZvNAN/mWLK0Fl4W0CYEsIN/jUYp/00mxbSJdGYcM+Jdth42GGH8Rm3uCIBCUhAAs0mYPhSs9F5oAQkIAEJSKA6CaCq4N7CuZEzgvFkkSfJYBK1EVWF11GMRTkKOwzzeKt/6623so5GgyLDXmIfCtjErYC9jAaTyjAMpi5MhTJhrIjog2dB7iFURI245+RudF0CzSBANhn8U7iHSXnL0qAF7vngy0bO3agbUpIbFUWDfoHG0eCBbOR+zt1Fv0OFIeMvNbKdO5n8vjfffDM+NbjMIL7gWYNOxFLAJjXij4MFYgaxkGs/rueWoZEhFAslCOEpluFYBBq6Eq5qcSMt/NrXvha/uiIBCUhAAi0hoCjTEnoeKwEJSEACEqhCAgzDGMgx8EMfYQTYvDPECMrOwIEDFy5ciJMLuSdwBGBLcGwpYJOX/AX2souG0SrGuqEYMRes4MKDx0H9A8nrUX+jWyTQJAJoH3ipcOPhG5LkHoKoERxb6DUklMmzjzsYKVryNhb4Sl1xL+4wl1122U9+8hNiiJibDBcbbv677rorFmhwJbQ2SCpBJK1fLLcMWbGD3w2CC902rzCiDFXHjahI0UUobnRFAhKQgASaR0BRpnncPEoCEpCABCRQtQQYcZFPlOEcg0zCH0J20qaeLWPUa665BkUGueTee+/dYYcdeGlPBtNGRZmmVsQc27jeHHHEEePGjWv02CjlNFrSAhKIBNApWOgRKBf4ZxHgE3fFFeKJiB5C5mAu6tz8u7EAKwUcW3KL5a7TawhZIrkMjmDMwUSGbBRJgqSILgwaUG7h3HUORK+kLyOJIiQ1qMvkliEVFO45WCA4ixzDDfZ6u08uYdclIAEJlIrAp7wlS2VUOxKQgAQkIAEJVC4BBnIkf+HdOIoM0yExcovngtRS5MCMFBWMG5lSl8QWKDLBAnEQ0VSpVsidgSnaSXbhaJNG0tT4NUR88JUwkLjRFQkUSQCvEHK+0C/IGkN0UuwCrKDUBCPMIPaFL3yBMkTMkcE69hoKkHY3HlJkjbEYdzXJaIhUwvg3v/lN7mQM8jXYj7WH8rm1EG1E6mv8zujL0RorSWUoiX5Kr0d44sDYfg4pvtfnVuS6BCQgAQkUSUBRpkhQFpOABCQgAQnUCgEGlgzPyO6JhsLULUyIi3cACwl6L7744iJT52IExwGGdrzV51jGli+99FKYNYYxXuG0Mk0C/d3vfpe3+tTChE34DuARQHWkZaWpcWCJs8D222+PWTwOSlh1k9pp4colgKfJbrvthixCX2BOd3QZ7jFupOeffz66q+ApQw4XcjC9/PLLzG89b948yiBwkPOIQ1Bzmnf6qCTIPfQm4qfIMkM/Iq0Mc2xjFq0nTEkWLPOVpDA441AvObAnTpxIam38X5hKKVZdoAwVId/Q66mIadRQVLHDgrMbveapp56KRlyRgAQkIIHSEjB8qbQ8tSYBCUhAAhKoBgLIHMwqzcAPISbMF8YUSwwsGdQxCmWQ1uhJ8uKdOWgefPDByZMnM4hlSmDGdbjeMHbFCKl/f/jDHzZqpJgCTOI7YsQI5J7rr79+0qRJ5GFltEzyGsaxjDBDIg+CSshdevXVV990002Mq0NC1mKMW0YCgcCBBx7ItEe//OUvUVi4zYj0QfJDZ0S4RPgIZUh0ffbZZ6MSooaQViY4iCFq4C92yimnoNQkweT+TNpF2BTTbDPr9vz588kfzAoph+mVaCjvv/8+FZ1wwgnhWORI5tumu22xxRZUSnJiGsaM9blZhwuXwSHooosuQt+kp9N+uhItR+jhTI866ihispIa6XYJSEACEmgJAT1lWkLPYyUggU8I8IDIFwIWPtnkmgQkULEEUDQYCpIChlwtDNXI+4uwQjcfMGBAmPc6pAhl1IfIEs+SjWxBvqEkosyQIUNGjhzJFt7bM6BlyMqIcfz48YwzedXPURSjcJzrOtjBIBvJaRq+ssLXvAwXoRaqCGUuuOACRozk8kDuQfp58803d955Z4bHeC6EAnziR8PCWNqfqcjEleIJkGkF7Y+sLriTcAuheuAF06dPH+5q7k+mMcIUEgY64J133okUwhYkG7xamCH7uOOOO+mkkyhAB6FMSPISq6YXcJOzsBI2EqNEHwm3PYfQEydMmMAtjZsMfmrc9niyUAueO/QsxJpwFNvJr0QZuiotpDuMGjUKpZK+HOsqXIZ6yeZL7BJyKnlzcPlBkSFu68gjjzzjjDOCkdBn2UvhaLbVV0KnjgBbvT02QAISkECTCLTJC0Zt0sEWloAEJBAJ9O/fn/Hbww8/3K9fv7gxjRUGY2EiDB5Y07CvzRISGDNmzNixY/lkzFxCszVritfjDLq23XbbMjt6kMCCsR9Duy233DIMPpt0CYiAYFYXQplCelQePHjDz9CRXMJNslNMYbwDCOggJWqeiBOPZSBNWpmkWY1jsZKs+GNVEoxNNZL2zw7CH7cZtzFiZdJtxt5ly5bh0oLUgqjR1FNosDxBf2SroQPiCBN0FjomnYs2kHQGvzOCm3DGwXGG7kbEEypSrh2OxdulcJnc8ngA0VPq28ktk9I6f/FBdSWQqpgqUGSCkIRPHznFiznEMhKQgAQyRcDwpUxdDhsjAQlIQAISyBwB3q7zTr7ZzVrvBPAPtxeM8Oaf0SxLsw0WOJABcOExMMJQ0IYKGHGXBAoQwNWFWLwCBdjFTY6cEdIYFS5Z/F7kmDwdk45Zvx+h0TTaW4spg8Nao3aKb7wlJSABCUigAIFPHBoLFHKXBCQgAQlIQAISkIAEJCCBDBII4UuZiqjKICWbJAEJZJaAokxmL40Nk0CFEQgPQ+HBqMKabnMlIAEJSEACFUuAaCYcW/DNKZBUBeedRstUKAAePJhtjYcQUvxU6CnYbAlIoMYJGL5U4zeApy+BkhEglcxDDz3EtCZMUVEyoxqSgAQkIAEJSKAgAVJNXXjhhWSx+cpXvpJUkCwtjZZJOjb725kfKvuNtIUSkIAEkggoyiSRcbsEJNA0An379uWAP/7xj007zNISkIAEJCABCbSAAGmb9t5778IGSIXTaJnCFjK7F0Vm1qxZuAIZvpTZa2TDJCCBwgQMXyrMx70SkECxBILXtOFLxfKynAQkIAEJSEACLSbAXFfoMjyEhJdDLbanAQlIQALlJqAoU27i1ieBaiXAwxCPRHjKqMtU6yX2vCQgAQlIQAJZI8Bk4XjKmFMma9fF9khAAsUTUJQpnpUlJSCBRggYwdQIIHdLQAISkIAEJFA6ArwHCnHTTBleOqtakoAEJFBWAooyZcVtZRKobgLhkYh3VtV9mp6dBCQgAQlIQAJZIBCmXuLxY9iwYVloj22QgAQk0AwCijLNgOYhEpBAwwRGjhzJjnHjxjW8260SkIAEJCABCUigpAR4FUSWXz1lSgpVYxKQQFkJKMqUFbeVSaC6CZBTxrQy1X2JPTsJSEACEpBAdggsXbr0qquu0lMmO1fElkhAAs0goCjTDGgeIgEJNEyAnDKmlWkYjVslIAEJSEACEigpARPKlBSnxiQggVYjoCjTauitWAJVSaAMaWVwxgGdczxl//5Zs35pu37JfmsrpYXe/5VypcJvVLheldLm6mhn+M0Jvz/VcUa1dhbF9x1KPvroo7rJ1Nod4vlKoPoIKMpU3zX1jCTQmgRGjx5N9aSVSU806d27N+Oc2bNnp1dFaxKsorpfXb90X79U0Wm15ql06tSpV69ey5cvnzt3bmu2w7qLIBB+o8LvVRHFLVIyAuE3J/z+lMyohspFgH92+g7/8vSdRuvkx5Cpl0wo0ygoC0hAAhknoCiT8Qtk8yRQYQQIX+JZioekhx56KNWmO8dTqnhLYjx6yvDEXBKDGokEvP8jisyuBNW4Xbt2mW1hdTcM/qtXr67uc6zWsyuy71BsxowZs2bN6tKli1MvVevN4HlJoEYIKMrUyIX2NCVQPgIDBgygsksvvTSlKkOEFIn9HJemRLhUZl9++eWFCxdut34plU3tcP8vXrz4uuuuE0XGCTBcZNAYdOqMN7XKmseLAbQw3g2wVNmp1cLpBKmFixhS1BU45VAS/8Hhw4cXKOYuCUhAAtknoCiT/WtkCyVQYQSIYOJxiqclljSazuPXxhtvzCvQjz/+OA372iwJAdxkHnnkEQZFijIl4RmMbLbZZqeddhoDTu7/lPpXCVtby6a4OkE4VpQp/22w33779evXj3q5CvwQlb8B1tgSAmE2pWJEGeRpelkxJVvSHo+VgAQkUAYCijJlgGwVEqgtAgxCcJZhNJ5SBFOfPn145p4zZ84f/vAHx6WZvbdeWb/wuLz99tuT4iGz7ayshuEms++++/bo0WPmzJmkt6ysxtdUa/kB5NeJqtjAnQAAF2VJREFU68XvlRFMZb70/ODwH0HU5PTp03HWK3PtVtcSAvSa4N+00UYb0XcKmCKbzMSJEynQaMkCRtwlAQlIICMEFGUyciFshgSqikDa6X55C8po//LLL1+yZElVgauWk+HB+oUXXmA4xJViqZbTysR54KuP7vn3v//9Zz/7maJkJi5JvUYwXAyxSyNHjuR61dvvhtQJfGH9grMeuozOMqnjLl0F/KfTd+g19J3CVullhDAXU7KwHfdKQAISyAIBRZksXAXbIIFqI8CgkVfE6TnLkNKPxH6k99NZJpu3DmOhSy65hBk09t9/f26GbDayQlu16aabfv/736fxOstk9gpeccUV3P80j18qfgkz284qbhg/O8jBr7/+us4yFXSV0VkuvvhiIpIaTdxLyQkTJhRTsoJO36ZKQAK1TEBRppavvucugRQJjBkzBuvpzY3NazQyy+Asg8tAiqeh6aYTwH1jwYIFzEd7wAEH6CbTdH6NH0FmmTPPPJNXyj/96U9Nd904r/KWYLgY4i90kykv+Pza+PHhJ4hroXafjyar3/lN43rxz96omwz/MrrJZPUy2i4JSKA5BNqFgVNzDvUYCUhAAskEeFHJBEwEsOyzzz5f/OIXkws2cw/R5k888cTDDz+8cuXKvfbayxiBZnIs9WE8K0+bNo3ImmXLlg0dOvTQQw8tdQ3aq+Nu32WXXR5bvxDHt8cee/AplywQCC/wb775Zn4Akcw23HDDLLSqNttAZhnSyuC1xzRw3bp1I+N427a+iczuvcD7Fd7iTJ48+Rvf+AYuMAUayr/MAw88gDC9+eab/9d//VeBku6SgAQkUCkEFGUq5UrZTglUHgEGiuT6Jd/riSeemMbTMM/c99xzDxKAukxGbo6gyIwfP57ApSOOOOKEE04g1iYjbauyZmywwQaf+cxnGJAQxKcuk5GLGxSZELj0k5/8ZMcdd0zjdy8jJ1sRzcCnjAime++9V10m49cLRebCCy9Ei+FnDU0fEa1Agz/44IObbrrpySefvOCCCwonAy5gxF0SkIAEMkVAUSZTl8PGSKCqCKTtLMNzG244d999N97p6DK9evViFoaqIlg5J8MMzYxIuRBkBHj88ccPPPBAZm7Gm6NyzqDCWooQs8MOO+y000533HEHEhhfd999d5SaCjuNamkucuRHH3102WWXochwLc455xwUSS9Hq19eROFdd92VMfzUqVNx29x6663xl3EyrFa/LrkNoO+8//77+MigyHz2s58dNWrU4MGDcwvkrfNHc/7551NYN5k8Mn6VgAQqmoCiTEVfPhsvgawTYHzCTAq8pRwyZAjrJW8uugwjn7lz5yIHkPf0kEMO6dChQxoVlbzlVWMwyDFcAh6Uf/7znzPp0re+9S1SMKrIpH2Juc8ZZMIfZxni+ObPn3/wwQcTr+H9nzb5XPtBjuFXjmC9W2+9Ffh33XXXySefrCKTS6kV19FlcL544403+IPgMiHcI2XiwaQTUytelFA1fefDDz+8//77Tz/9dKKWtthiiylTphRWZDhw0aJFRx99NHlnzjvvPN1kWv0i2gAJSKBUBNqsXbu2VLa0IwEJSKA+AYQSwr/J23fRRRelNFxkUMqT3LvvvkvmP6Y7GTFiRIiaMdFM/ctRqi1oAfx94KA0b968a6+99r777iNH45e+9KWTTjoJAY7IslJVpJ3CBLjnic445ZRTYtpL5mZixh/UGef9KYyuJXsZT3I4zMlLSrpxRDGADxgwYPjw4f3799cXoyVs0ziWvOPkOCPmBT8LRvL8HzExHP9HYUmjRm0mEQh9Z+nSpYQeM08Zf9/8Uw8cOPCss85qVGTh8p177rlkayLvDApOUhVul4AEJFBxBBRlKu6S2WAJVBgBBi2MUngO+/3vf8+b/PRajz8zDhqEpgdphhEpiQCDLsOTd3r11prlIMc8/fTTixcv/u1vfxvkGGj37NnzBz/4AYpMrQHJwvlyLRhzoo4FaQZdBl0yzJxN87z/S3iNwpCSkDFWJk6cGOQY7n+iNUnx07lz5xLWpanSEuDPiG7CJNlBmuGShYVa9J0pLeoGrdFlWOg78A9yDD9NdBkeDG6//fYGD8nbiMcT7oH8uJFNhhcweXv9KgEJSKByCSjKVO61s+USqBgCTMGDs0zv3r3/53/+J23vlSjNQIcBamDE+zffXZfqdglyDNZAyliUp2qyNjD+51VncFAqVUXaaSqBXGmGY8P9zzVieqammrJ8EgFe7KNLspc7P9z//LLpIJOEK4PbozSDQMCl5JNG4tynf1+qFwvU9J1QReg7PAwwbXkxDjLhKJxhBw0ahG8mmctUZFK9WBqXgATKT0BRpvzMrVECtUigDEFMuViRZhiR8h6bN3JsD4/duQVcbzYBtBgiNTicrDFqMc3GmN6BSDPXXXdd+Ay1eP+XkDbjyWBNLaaEVMtvCmkmLtS+Zv1S/mbUVI2h7/DZJC0mIIrTM2211VbMqFVT3DxZCUigFggoytTCVfYcJdD6BHj8ZUYehBLyX+CrHAc25WlZfLldnuqquxa0GD1iKugS0+nmzJlTQQ3OeFN1u8v4BWpe80g6w9K8Yz2qGAKo+Y2mjEmyE1LJ8K6F6ZmYSKvZdpLsu10CEpBAqxNQlGn1S2ADJFArBAjmZ7oEXtqnnVymVoB6nhKQgAQkIIFqJ2AqmWq/wp6fBCRQpyjjTSABCZSPQEwugy5jRszycbcmCUhAAhKQQAUSCKlkyGV24YUXmkqmAi+gTZaABIoi0LaoUhaSgAQkUAoCxC4xayyhTKSYCdleSmFVGxKQgAQkIAEJVBsBUsmQ1pcAzIMOOkhFptqurucjAQnkEFCUyYHhqgQkkD6B0aNH4yODLjN27Fh1mfR5W4MEJCABCUig8gjE5L6kkmGCs8o7AVssAQlIoGgCijJFo7KgBCRQCgJ9+/YNsUukmFGXKQVRbUhAAhKQgASqikBUZLp27Tp58mST+1bV1fVkJCCBegTMKVMPiRskIIH0CcTJmEaOHHnBBRd06tQp/TqtQQISkIAEJCCBrBOIiozTLWX9Utk+CUigRATajRkzpkSmNCMBCUigWALdu3dnVmykmRkzZqxevRr3mTJPkl1sQy0nAQlIQAISkEC5CCxatGjcuHFhAuxRo0YNHjy4XDVbjwQkIIFWI6Ao02rorVgCNU4gCDGzZ89++OGH0WVwTu7QoUONM/H0JSABCUhAAjVL4K233jrssMPuvPNOopamTJmiIlOzd4InLoFaI2D4Uq1dcc9XAtkigLMMMzEtXboUjYa5mTp27GgoU7aukK2RgAQkIAEJpExg1apVKDKoMMy1RNSSeWRS5q15CUggWwRM9Jut62FrJFBrBELe30033ZTnsM0228zUv7V2A3i+EpCABCRQ4wSWLFkyadKkbbbZZt68eXjKqMjU+P3g6UugBgkoytTgRfeUJZAtAugyZPUjvxVuMk7JlK1rY2skIAEJSEACaRIgicz5559/7LHHdu7cedCgQXfddZdzLaXJW9sSkEAWCZhTJotXxTZJoAYJhBQzMfXvHnvsAQSz/9bgneApS0ACEpBALRBYvnw5IUtHHHEESWQIWWIqxiuuuKIWTtxzlIAEJJBHwJwyeUD8KgEJtCYBPGVYeFBbsWIFs2WPHj26Xbt2ZplpzUti3RKQgAQkIIGSEiCDzOLFi8ePH88sS7x9Ia0vuowOMiVlrDEJSKCSCCjKVNLVsq0SqBEC6DJEM6HL4D7DwtszdZkaufSepgQkIAEJVDEB5BhS+0+bNg2nGHLJEbJ08MEH33777VV8yp6aBCQggUYJtG+0hAUkIAEJlJkAPjLUiDTDExsBTayfeeaZJANWminzhbA6CUhAAhKQQEkI5Moxs2bNQo4hp+9ZZ52lg0xJ8GpEAhKoaAJ6ylT05bPxEqhyAutimf4vmmnYsGEjRoxQmqnyS+7pSUACEpBAdRHIk2OIV+KvfNSoUfytV9eJejYSkIAEmklAUaaZ4DxMAhIoGwF0mYkTJzJDEwFNQZrZeOONzTVTNv5WJAEJSEACEmgGgfpyDA4yBx54oA4yzYDpIRKQQBUTUJSp4ovrqUmgqgiQDnDChAlBmsHbeZ999hk+fDhv2zhJw5qq6kp7MhKQgAQkUMkE0GJ4iULO/unTp5M7hmAlvGOUYyr5ktp2CUggXQKKMuny1boEJFBaAlGawWxwnOnYsSMZZ4LvDI99LKWtUWsSkIAEJCABCRQm8PHHH69evyxZsoRkcFGL4T+aFyf9+vXTO6YwQPdKQAK1TEBRppavvucugUolgDSDIhNimngI5KVc8J0JszXx/EdwUzg3ZZpKvca2WwISkIAEskqAv921a9cGFYY2IsQ8+uijM2fO5JMM/fzzosXwaaRSVi+g7ZKABLJFQFEmW9fD1khAAk0igDozY8YMXsrxRMiBuQJNcJnp3bs3Sk1Yp0BcaVItFpaABCQgAQnUOAGEmECAuKS5c+fyagQVJqwEIaZNmzYdOnTAcZW/XTxYeTvizEo1fs94+hKQQJEEFGWKBGUxCUgg0wSC78zs2bMRaHhejM+OQaaJTecBMTrRxI2uSEACEpCABCRQgAASDMpLLBDecPAZ/lIRYnr16kWWN7SYPfbYIxZzRQISkIAEiiGgKFMMJctIQAKVRICcgnhQB10myDRRo4krlXQ+tlUCEpCABCTQ2gSiqykxwkgwBCjtu+++YUUhprUvjvVLQAKVTUBRprKvn62XgASaRAC9Bt+ZJh1iYQlIQAISkECNE0CCUXmp8XvA05eABNIjoCiTHlstS0ACEpCABCQgAQlIQAISkIAEJCCBRAJtE/e4QwISkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1AooyqaHVsAQkIAEJSEACEpCABCQgAQlIQAISSCagKJPMxj0SkIAEJCABCUhAAhKQgAQkIAEJSCA1Av8Ljs2nhJ1sTksAAAAASUVORK5CYII=)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2920adb0-9dd0-484d-9496-cfbf87af5eaa",
   "metadata": {},
   "source": [
    "Two algorithms for training the autoencoder are implemented in this notebook:\n",
    "\n",
    "* Conventional stochastic gradient descent (SGD) with backpropagation, which assumes a differentiable channel model and therefore optimizes the end-to-end system by backpropagating the gradients through the channel (see, e.g., [1]).\n",
    "* The training algorithm from [3], which does not assume a differentiable channel model, and which trains the end-to-end system by alternating between conventional training of the receiver and reinforcement learning (RL)-based training of the transmitter. Compared to [3], an additional step of fine-tuning of the receiver is performed after alternating training.\n",
    "\n",
    "**Note:** For an introduction to the implementation of differentiable communication systems and their optimization through SGD and backpropagation with Sionna, please refer to [the Part 2 of the Sionna tutorial for Beginners](https://nvlabs.github.io/sionna/phy/tutorials/notebooks/Sionna_tutorial_part2.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3287acf2-0dcd-419f-8cfc-27ea29ca3af6",
   "metadata": {},
   "source": [
    "* [Configuration and Imports](#Configuration-and-Imports)\n",
    "* [Simulation Parameters](#Simulation-Parameters)\n",
    "* [Neural Demapper](#Neural-Demapper)\n",
    "* [Trainable End-to-end System: Conventional Training](#Trainable-End-to-end-System:-Conventional-Training)\n",
    "* [Trainable End-to-end System: RL-based Training](#Trainable-End-to-end-System:-RL-based-Training)\n",
    "* [Evaluation](#Evaluation)\n",
    "* [Visualizing the Learned Constellations](#Visualizing-the-Learned-Constellations)\n",
    "* [References](#References)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51d35352-0937-4710-9f2c-7e50f819ea2c",
   "metadata": {},
   "source": [
    "## Configuration and Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2e201e1d-e0d8-4133-b251-14823c25f68b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T15:56:39.570679Z",
     "iopub.status.busy": "2026-08-06T15:56:39.570564Z",
     "iopub.status.idle": "2026-08-06T15:56:42.639134Z",
     "shell.execute_reply": "2026-08-06T15:56:42.638079Z"
    }
   },
   "outputs": [],
   "source": [
    "# Import Sionna\n",
    "try:\n",
    "    import sionna.phy\n",
    "except ImportError as e:\n",
    "    import os\n",
    "    import sys\n",
    "    if 'google.colab' in sys.modules:\n",
    "       # Install Sionna in Google Colab\n",
    "       print(\"Installing Sionna and restarting the runtime. Please run the cell again.\")\n",
    "       os.system(\"pip install sionna\")\n",
    "       os.kill(os.getpid(), 5)\n",
    "    else:\n",
    "       raise e\n",
    "\n",
    "import pickle\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch._dynamo\n",
    "\n",
    "from sionna.phy import Block\n",
    "from sionna.phy.channel import AWGN\n",
    "from sionna.phy.utils import ebnodb2no, expand_to_rank, sim_ber\n",
    "from sionna.phy.fec.ldpc import LDPC5GEncoder, LDPC5GDecoder\n",
    "from sionna.phy.mapping import Mapper, Demapper, Constellation, BinarySource\n",
    "\n",
    "sionna.phy.config.seed = 42  # Set seed for reproducible random number generation\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fca65b28-74a4-4cd9-8c66-748259d05e57",
   "metadata": {},
   "source": [
    "## Simulation Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ddfd420b-0f45-468a-b292-8aaea2ce280f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T15:56:42.641927Z",
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   "source": [
    "###############################################\n",
    "# SNR range for evaluation and training [dB]\n",
    "###############################################\n",
    "ebno_db_min = 4.0\n",
    "ebno_db_max = 8.0\n",
    "\n",
    "###############################################\n",
    "# Modulation and coding configuration\n",
    "###############################################\n",
    "num_bits_per_symbol = 6  # Baseline is 64-QAM\n",
    "modulation_order = 2**num_bits_per_symbol\n",
    "coderate = 0.5  # Coderate for the outer code\n",
    "n = 1500  # Codeword length [bit]. Must be a multiple of num_bits_per_symbol\n",
    "num_symbols_per_codeword = n // num_bits_per_symbol  # Number of modulated baseband symbols per codeword\n",
    "k = int(n * coderate)  # Number of information bits per codeword\n",
    "\n",
    "###############################################\n",
    "# Training configuration\n",
    "###############################################\n",
    "num_training_iterations_conventional = 10000  # Number of training iterations for conventional training\n",
    "# Number of training iterations with RL-based training for the alternating training phase and fine-tuning of the receiver phase\n",
    "num_training_iterations_rl_alt = 7000\n",
    "num_training_iterations_rl_finetuning = 3000\n",
    "training_batch_size = 128  # Training batch size\n",
    "rl_perturbation_var = 0.01  # Variance of the perturbation used for RL-based training of the transmitter\n",
    "model_weights_path_conventional_training = \"awgn_autoencoder_weights_conventional_training\"  # Filename to save the autoencoder weights once conventional training is done\n",
    "model_weights_path_rl_training = \"awgn_autoencoder_weights_rl_training\"  # Filename to save the autoencoder weights once RL-based training is done\n",
    "\n",
    "###############################################\n",
    "# Evaluation configuration\n",
    "###############################################\n",
    "results_filename = \"awgn_autoencoder_results\"  # Location to save the results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d711f03c-93b9-4524-a7a6-63a35b825623",
   "metadata": {},
   "source": [
    "## Neural Demapper"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caff5b7e-0ae4-4890-a45b-378afc5bf9a5",
   "metadata": {},
   "source": [
    "The neural network-based demapper shown in the figure above is made of three dense layers with ReLU activation.\n",
    "\n",
    "The input of the demapper consists of a received sample $y \\in \\mathbb{C}$ and the noise power spectral density $N_0$ in log-10 scale to handle different orders of magnitude for the SNR.\n",
    "\n",
    "As the neural network can only process real-valued inputs, these values are fed as a 3-dimensional vector\n",
    "\n",
    "$$\\left[ \\mathcal{R}(y), \\mathcal{I}(y), \\log_{10}(N_0) \\right]$$\n",
    "\n",
    "where $\\mathcal{R}(y)$ and $\\mathcal{I}(y)$ refer to the real and imaginary component of $y$, respectively.\n",
    "\n",
    "The output of the neural network-based demapper consists of LLRs on the `num_bits_per_symbol` bits mapped to a constellation point. Therefore, the last layer consists of ``num_bits_per_symbol`` units.\n",
    "\n",
    "**Note**: The neural network-based demapper processes the received samples $y$ forming a block individually. The [neural receiver notebook](https://nvlabs.github.io/sionna/phy/tutorials/notebooks/Neural_Receiver.html) provides an example of a more advanced neural network-based receiver that jointly processes a resource grid of received symbols."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3e636efd-9a46-4fd1-8790-b4cb1b63f31e",
   "metadata": {
    "execution": {
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   "source": [
    "class NeuralDemapper(nn.Module):\n",
    "    \"\"\"Neural network-based demapper with three dense layers and ReLU activation.\n",
    "    \n",
    "    The input of the demapper consists of a received sample y ∈ ℂ and the noise\n",
    "    variance. The output is a vector of LLRs for each bit carried by a symbol.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        \n",
    "        self._dense_1 = nn.Linear(3, 128)\n",
    "        self._dense_2 = nn.Linear(128, 128)\n",
    "        self._dense_3 = nn.Linear(128, num_bits_per_symbol)  # Output LLRs for every bit\n",
    "\n",
    "    def forward(self, y, no):\n",
    "        # Using log10 scale helps with the performance\n",
    "        no_db = torch.log10(no)\n",
    "        \n",
    "        # Stacking the real and imaginary components of the complex received samples\n",
    "        # and the noise variance\n",
    "        no_db = no_db.expand(-1, num_symbols_per_codeword)  # [batch size, num_symbols_per_codeword]\n",
    "        z = torch.stack([y.real, y.imag, no_db], dim=2)  # [batch size, num_symbols_per_codeword, 3]\n",
    "        \n",
    "        llr = F.relu(self._dense_1(z))\n",
    "        llr = F.relu(self._dense_2(llr))\n",
    "        llr = self._dense_3(llr)  # [batch size, num_symbols_per_codeword, num_bits_per_symbol]\n",
    "        \n",
    "        return llr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d232019-1c8b-4d51-b125-a1f0a4189dac",
   "metadata": {},
   "source": [
    "## Trainable End-to-end System: Conventional Training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4cdeeb8-879c-4ae1-a734-6a04ffa48dd3",
   "metadata": {},
   "source": [
    "The following cell defines an end-to-end communication system that transmits bits modulated using a trainable constellation over an AWGN channel.\n",
    "\n",
    "The receiver uses the previously defined neural network-based demapper to compute LLRs on the transmitted (coded) bits.\n",
    "\n",
    "As in [1], the constellation and neural network-based demapper are jointly trained through SGD and backpropagation using the binary cross entropy (BCE) as loss function.\n",
    "\n",
    "Training on the BCE is known to be equivalent to maximizing an achievable information rate [2].\n",
    "\n",
    "The following model can be instantiated either for training (`training = True`) or evaluation (`training = False`).\n",
    "\n",
    "In the former case, the BCE is returned and no outer code is used to reduce computational complexity and as it does not impact the training of the constellation or demapper.\n",
    "\n",
    "When setting `training` to `False`, an LDPC outer code from 5G NR is applied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2709aae3-7d40-4f35-a56f-79bd09a81e56",
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    "execution": {
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   "source": [
    "class E2ESystemConventionalTraining(nn.Module):\n",
    "    \"\"\"End-to-end communication system for conventional training.\n",
    "    \n",
    "    This system transmits bits modulated using a trainable constellation over\n",
    "    an AWGN channel. The receiver uses a neural network-based demapper.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, training):\n",
    "        super().__init__()\n",
    "        \n",
    "        self._training = training\n",
    "        \n",
    "        ################\n",
    "        ## Transmitter\n",
    "        ################\n",
    "        self._binary_source = BinarySource()\n",
    "        # To reduce the computational complexity of training, the outer code is not used when training\n",
    "        if not self._training:\n",
    "            # num_bits_per_symbol is required for the interleaver\n",
    "            self._encoder = LDPC5GEncoder(k, n, num_bits_per_symbol)\n",
    "        \n",
    "        # Trainable constellation\n",
    "        # We initialize a custom constellation with QAM points\n",
    "        qam_points = Constellation(\"qam\", num_bits_per_symbol).points\n",
    "        self.points_r = nn.Parameter(qam_points.real.clone())\n",
    "        self.points_i = nn.Parameter(qam_points.imag.clone())\n",
    "\n",
    "        self.constellation = Constellation(\"custom\",\n",
    "                                           num_bits_per_symbol,\n",
    "                                           points=torch.complex(self.points_r, self.points_i),\n",
    "                                           normalize=True,\n",
    "                                           center=True)\n",
    "       \n",
    "        \n",
    "        self._mapper = Mapper(constellation=self.constellation)\n",
    "        \n",
    "        ################\n",
    "        ## Channel\n",
    "        ################\n",
    "        self._channel = AWGN()\n",
    "        \n",
    "        ################\n",
    "        ## Receiver\n",
    "        ################\n",
    "        # We use the previously defined neural network for demapping\n",
    "        self._demapper = NeuralDemapper()\n",
    "        # To reduce the computational complexity of training, the outer code is not used when training\n",
    "        if not self._training:\n",
    "            self._decoder = LDPC5GDecoder(self._encoder, hard_out=True)\n",
    "\n",
    "    def forward(self, batch_size, ebno_db):\n",
    "        \n",
    "        # Update constellation points from trainable parameters (creates fresh graph)\n",
    "        self.constellation.points = torch.complex(self.points_r, self.points_i)\n",
    "\n",
    "        # If `ebno_db` is a scalar, a tensor with shape [batch size] is created\n",
    "        if ebno_db.dim() == 0:\n",
    "            ebno_db = ebno_db.expand(batch_size)\n",
    "        no = ebnodb2no(ebno_db, num_bits_per_symbol, coderate)\n",
    "        no = expand_to_rank(no, 2)\n",
    "        \n",
    "        ################\n",
    "        ## Transmitter\n",
    "        ################\n",
    "        # Outer coding is only performed if not training\n",
    "        if self._training:\n",
    "            c = self._binary_source([batch_size, n])\n",
    "        else:\n",
    "            b = self._binary_source([batch_size, k])\n",
    "            c = self._encoder(b)\n",
    "        # Modulation\n",
    "        x = self._mapper(c)  # x [batch size, num_symbols_per_codeword]\n",
    "        \n",
    "        ################\n",
    "        ## Channel\n",
    "        ################\n",
    "        y = self._channel(x, no)  # [batch size, num_symbols_per_codeword]\n",
    "        \n",
    "        ################\n",
    "        ## Receiver\n",
    "        ################\n",
    "        llr = self._demapper(y, no)\n",
    "        llr = llr.reshape(batch_size, n)\n",
    "        # If training, outer decoding is not performed and the BCE is returned\n",
    "        if self._training:\n",
    "            loss = F.binary_cross_entropy_with_logits(llr, c)\n",
    "            return loss\n",
    "        else:\n",
    "            # Outer decoding\n",
    "            b_hat = self._decoder(llr)\n",
    "            return b, b_hat  # Ground truth and reconstructed information bits returned for BER/BLER computation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f347823-c28d-4a00-8a02-f9b5e6d450f3",
   "metadata": {},
   "source": [
    "A simple training loop is defined in the next cell, which performs `num_training_iterations_conventional` training iterations of SGD. Training is done over a range of SNR, by randomly sampling a batch of SNR values at each iteration."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d95542ed-a6be-4c72-bae5-5154ded0340b",
   "metadata": {},
   "source": [
    "**Note:** For an introduction to the implementation of differentiable communication systems and their optimization through SGD and backpropagation with Sionna, please refer to [the Part 2 of the Sionna tutorial for Beginners](https://nvlabs.github.io/sionna/phy/tutorials/notebooks/Sionna_tutorial_part2.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ab429a86-f606-420c-8ad1-e0efc6874584",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-08-06T15:56:42.662810Z"
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   },
   "outputs": [],
   "source": [
    "def conventional_training(model):\n",
    "    \"\"\"Train the model using conventional SGD with backpropagation.\"\"\"\n",
    "    # Optimizer used to apply gradients\n",
    "    optimizer = torch.optim.Adam(model.parameters())\n",
    "    device = sionna.phy.config.device\n",
    "    \n",
    "    for i in range(num_training_iterations_conventional):\n",
    "        optimizer.zero_grad()\n",
    "        # Sampling a batch of SNRs\n",
    "        ebno_db = torch.empty(training_batch_size, device=device).uniform_(ebno_db_min, ebno_db_max)\n",
    "        # Forward pass\n",
    "        loss = model(training_batch_size, ebno_db)\n",
    "        # Computing and applying gradients\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        # Printing periodically the progress\n",
    "        if i % 100 == 0:\n",
    "            print(f'Iteration {i}/{num_training_iterations_conventional}  BCE: {loss.item():.4f}', end='\\r')\n",
    "    print()\n",
    "    model.eval()\n",
    "    optimizer.zero_grad(set_to_none=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5959e24-1107-4271-be5c-944eacdc3033",
   "metadata": {},
   "source": [
    "The next cell defines a utility function for saving the model weights to a PyTorch checkpoint using [`torch.save`](https://docs.pytorch.org/docs/stable/generated/torch.save.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f32c0d68-55f1-41e1-88da-28b96c3cac0c",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "def save_weights(model, model_weights_path):\n",
    "    \"\"\"Save model weights using torch.save.\"\"\"\n",
    "    torch.save(model._orig_mod.state_dict(), model_weights_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d6ad9e4-7df4-4988-bbb4-dedd0ba50f47",
   "metadata": {},
   "source": [
    "In the next cell, an instance of the model defined previously is instantiated and trained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5971c6da-52bf-46ab-91f0-57baf560964d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T15:56:42.669786Z",
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     "shell.execute_reply": "2026-08-06T15:57:01.413933Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 9900/10000  BCE: 0.2910\r\n"
     ]
    }
   ],
   "source": [
    "# Instantiate and train the end-to-end system\n",
    "device = sionna.phy.config.device\n",
    "model = torch.compile(E2ESystemConventionalTraining(training=True).to(device), mode=\"reduce-overhead\")\n",
    "conventional_training(model)\n",
    "save_weights(model, model_weights_path_conventional_training)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e884151-1047-4cb7-b1ee-5610924128a3",
   "metadata": {},
   "source": [
    "## Trainable End-to-end System: RL-based Training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d960a61-c6f2-400e-8a6b-ea1c7c2d2db7",
   "metadata": {},
   "source": [
    "The following cell defines the same end-to-end system as before, but stop the gradients after the channel to simulate a non-differentiable channel.\n",
    "\n",
    "To jointly train the transmitter and receiver over a non-differentiable channel, we follow [3], which key idea is to alternate between:\n",
    "\n",
    "- Training of the receiver on the BCE using conventional backpropagation and SGD.\n",
    "- Training of the transmitter by applying (known) perturbations to the transmitter output to enable estimation of the gradient of the transmitter weights with respect to an approximation of the loss function.\n",
    "\n",
    "When `training` is set to `True`, both losses for training the receiver and the transmitter are returned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "60f00146-1051-4406-a3cd-4a6e3305b515",
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    "execution": {
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   },
   "outputs": [],
   "source": [
    "class E2ESystemRLTraining(nn.Module):\n",
    "    \"\"\"End-to-end communication system for RL-based training.\n",
    "    \n",
    "    This system is similar to E2ESystemConventionalTraining but stops gradients\n",
    "    after the channel to simulate a non-differentiable channel. Training uses\n",
    "    reinforcement learning with perturbation-based exploration.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, training):\n",
    "        super().__init__()\n",
    "        \n",
    "        self._training = training\n",
    "        \n",
    "        ################\n",
    "        ## Transmitter\n",
    "        ################\n",
    "        self._binary_source = BinarySource()\n",
    "        # To reduce the computational complexity of training, the outer code is not used when training\n",
    "        if not self._training:\n",
    "            self._encoder = LDPC5GEncoder(k, n, num_bits_per_symbol)\n",
    "        \n",
    "        # Trainable constellation\n",
    "        # We initialize a custom constellation with QAM points\n",
    "        qam_points = Constellation(\"qam\", num_bits_per_symbol).points\n",
    "        self.points_r = nn.Parameter(qam_points.real.clone())\n",
    "        self.points_i = nn.Parameter(qam_points.imag.clone())\n",
    "\n",
    "        self.constellation = Constellation(\"custom\",\n",
    "                                           num_bits_per_symbol,\n",
    "                                           points=torch.complex(self.points_r, self.points_i),\n",
    "                                           normalize=True,\n",
    "                                           center=True)\n",
    "        \n",
    "        self._mapper = Mapper(constellation=self.constellation)\n",
    "        \n",
    "        ################\n",
    "        ## Channel\n",
    "        ################\n",
    "        self._channel = AWGN()\n",
    "        \n",
    "        ################\n",
    "        ## Receiver\n",
    "        ################\n",
    "        # We use the previously defined neural network for demapping\n",
    "        self._demapper = NeuralDemapper()\n",
    "        # To reduce the computational complexity of training, the outer code is not used when training\n",
    "        if not self._training:\n",
    "            self._decoder = LDPC5GDecoder(self._encoder, hard_out=True)\n",
    "\n",
    "    def forward(self, batch_size, ebno_db, perturbation_variance=0.0):\n",
    "        # If `ebno_db` is a scalar, a tensor with shape [batch size] is created\n",
    "        if ebno_db.dim() == 0:\n",
    "            ebno_db = ebno_db.expand(batch_size)\n",
    "        no = ebnodb2no(ebno_db, num_bits_per_symbol, coderate)\n",
    "        no = expand_to_rank(no, 2)\n",
    "        \n",
    "        ################\n",
    "        ## Transmitter\n",
    "        ################\n",
    "        # Outer coding is only performed if not training\n",
    "        if self._training:\n",
    "            c = self._binary_source([batch_size, n])\n",
    "        else:\n",
    "            b = self._binary_source([batch_size, k])\n",
    "            c = self._encoder(b)\n",
    "        # Modulation\n",
    "        # Update constellation points from trainable parameters\n",
    "        self.constellation.points = torch.complex(self.points_r, self.points_i)\n",
    "        x = self._mapper(c)  # x [batch size, num_symbols_per_codeword]\n",
    "        \n",
    "        # Adding perturbation\n",
    "        # If ``perturbation_variance`` is 0, then the added perturbation is null\n",
    "        std = (0.5 * perturbation_variance) ** 0.5\n",
    "        epsilon_r = torch.randn(x.shape, device=x.device, dtype=x.real.dtype) * std\n",
    "        epsilon_i = torch.randn(x.shape, device=x.device, dtype=x.real.dtype) * std\n",
    "        epsilon = torch.complex(epsilon_r, epsilon_i)  # [batch size, num_symbols_per_codeword]\n",
    "        x_p = x + epsilon  # [batch size, num_symbols_per_codeword]\n",
    "        \n",
    "        ################\n",
    "        ## Channel\n",
    "        ################\n",
    "        y = self._channel(x_p, no)  # [batch size, num_symbols_per_codeword]\n",
    "        y = y.detach()  # Stop gradient here\n",
    "        \n",
    "        ################\n",
    "        ## Receiver\n",
    "        ################\n",
    "        llr = self._demapper(y, no)\n",
    "        \n",
    "        # If training, outer decoding is not performed\n",
    "        if self._training:\n",
    "            # Average BCE for each baseband symbol and each batch example\n",
    "            c_reshaped = c.reshape(-1, num_symbols_per_codeword, num_bits_per_symbol)\n",
    "            bce = F.binary_cross_entropy_with_logits(llr, c_reshaped, reduction='none').mean(dim=2)  # Average over the bits mapped to a same baseband symbol\n",
    "            # The RX loss is the usual average BCE\n",
    "            rx_loss = bce.mean()\n",
    "            # From the TX side, the BCE is seen as a feedback from the RX through which backpropagation is not possible\n",
    "            bce = bce.detach()  # [batch size, num_symbols_per_codeword]\n",
    "            x_p = x_p.detach()\n",
    "            p = x_p - x  # [batch size, num_symbols_per_codeword] Gradient is backpropagated through `x`\n",
    "            tx_loss = p.real.square() + p.imag.square()  # [batch size, num_symbols_per_codeword]\n",
    "            tx_loss = -bce * tx_loss / rl_perturbation_var  # [batch size, num_symbols_per_codeword]\n",
    "            tx_loss = tx_loss.mean()\n",
    "            return tx_loss, rx_loss\n",
    "        else:\n",
    "            llr = llr.reshape(-1, n)  # Reshape as expected by the outer decoder\n",
    "            b_hat = self._decoder(llr)\n",
    "            return b, b_hat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d65a5114-3cf3-4911-9e15-2863f350ba7c",
   "metadata": {},
   "source": [
    "The next cell implements the training algorithm from [3], which alternates between conventional training of the neural network-based receiver, and RL-based training of the transmitter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8368cd0f-6ecf-4ded-a0a8-fc595a518086",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T15:57:01.427270Z",
     "iopub.status.busy": "2026-08-06T15:57:01.427146Z",
     "iopub.status.idle": "2026-08-06T15:57:01.431676Z",
     "shell.execute_reply": "2026-08-06T15:57:01.431032Z"
    }
   },
   "outputs": [],
   "source": [
    "def rl_based_training(model):\n",
    "    \"\"\"Train the model using RL-based training algorithm.\"\"\"\n",
    "    # Optimizers used to apply gradients\n",
    "    optimizer_tx = torch.optim.Adam(model.parameters())  # For training the transmitter\n",
    "    optimizer_rx = torch.optim.Adam(model.parameters())  # For training the receiver\n",
    "    device = sionna.phy.config.device\n",
    "    \n",
    "    # Training loop\n",
    "    for i in range(num_training_iterations_rl_alt):\n",
    "        # 10 steps of receiver training are performed to keep it ahead of the transmitter\n",
    "        # as it is used for computing the losses when training the transmitter\n",
    "        for _ in range(10):\n",
    "            optimizer_rx.zero_grad()\n",
    "            # Sampling a batch of SNRs\n",
    "            ebno_db = torch.empty(training_batch_size, device=device).uniform_(ebno_db_min, ebno_db_max)\n",
    "            # Forward pass - keep only the RX loss, no perturbation\n",
    "            _, rx_loss = model(training_batch_size, ebno_db)\n",
    "            rx_loss.backward()\n",
    "            optimizer_rx.step()\n",
    "        # One step of transmitter training\n",
    "        optimizer_tx.zero_grad()\n",
    "        # Sampling a batch of SNRs\n",
    "        ebno_db = torch.empty(training_batch_size, device=device).uniform_(ebno_db_min, ebno_db_max)\n",
    "        # Forward pass - keep only the TX loss, perturbations added for RL exploration\n",
    "        tx_loss, _ = model(training_batch_size, ebno_db, rl_perturbation_var)\n",
    "        tx_loss.backward()\n",
    "        optimizer_tx.step()\n",
    "        # Printing periodically the progress\n",
    "        if i % 100 == 0:\n",
    "            print(f'Iteration {i}/{num_training_iterations_rl_alt}  BCE {rx_loss.item():.4f}', end='\\r')\n",
    "    print()  # Line break\n",
    "    \n",
    "    # Once alternating training is done, the receiver is fine-tuned\n",
    "    print('Receiver fine-tuning... ')\n",
    "    for i in range(num_training_iterations_rl_finetuning):\n",
    "        optimizer_rx.zero_grad()\n",
    "        ebno_db = torch.empty(training_batch_size, device=device).uniform_(ebno_db_min, ebno_db_max)\n",
    "        _, rx_loss = model(training_batch_size, ebno_db)\n",
    "        rx_loss.backward()\n",
    "        optimizer_rx.step()\n",
    "        if i % 100 == 0:\n",
    "            print(f'Iteration {i}/{num_training_iterations_rl_finetuning}  BCE {rx_loss.item():.4f}', end='\\r')\n",
    "    print()\n",
    "    model.eval()\n",
    "    optimizer_tx.zero_grad(set_to_none=True)\n",
    "    optimizer_rx.zero_grad(set_to_none=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdd091ad-2ec7-4375-8715-a9516b5a4a4d",
   "metadata": {},
   "source": [
    "In the next cell, an instance of the model defined previously is instantiated and trained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f05bcfed-6836-4ee7-9619-9b8ea01245e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T15:57:01.433318Z",
     "iopub.status.busy": "2026-08-06T15:57:01.433200Z",
     "iopub.status.idle": "2026-08-06T15:58:56.928494Z",
     "shell.execute_reply": "2026-08-06T15:58:56.927348Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 6900/7000  BCE 0.3026\r\n",
      "Receiver fine-tuning... \n",
      "Iteration 2900/3000  BCE 0.2842\r\n"
     ]
    }
   ],
   "source": [
    "# Instantiate and train the end-to-end system\n",
    "model = torch.compile(E2ESystemRLTraining(training=True).to(device), mode=\"reduce-overhead\")\n",
    "rl_based_training(model)\n",
    "save_weights(model, model_weights_path_rl_training)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd0345da-9e49-4917-8197-0787254b5cf7",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4344594-739d-485f-8ee0-fbb9c8cb9055",
   "metadata": {},
   "source": [
    "The following cell implements a baseline which uses QAM with Gray labeling and conventional demapping for AWGN channel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f81f69a9-cc45-42ad-92a3-8ea469b1363e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T15:58:56.930720Z",
     "iopub.status.busy": "2026-08-06T15:58:56.930575Z",
     "iopub.status.idle": "2026-08-06T15:58:56.935569Z",
     "shell.execute_reply": "2026-08-06T15:58:56.934621Z"
    }
   },
   "outputs": [],
   "source": [
    "class Baseline(nn.Module):\n",
    "    \"\"\"Baseline system using QAM with Gray labeling and conventional demapping.\"\"\"\n",
    "\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        \n",
    "        ################\n",
    "        ## Transmitter\n",
    "        ################\n",
    "        self._binary_source = BinarySource()\n",
    "        self._encoder = LDPC5GEncoder(k, n, num_bits_per_symbol)\n",
    "        constellation = Constellation(\"qam\", num_bits_per_symbol)\n",
    "        self.constellation = constellation\n",
    "        self._mapper = Mapper(constellation=constellation)\n",
    "        \n",
    "        ################\n",
    "        ## Channel\n",
    "        ################\n",
    "        self._channel = AWGN()\n",
    "        \n",
    "        ################\n",
    "        ## Receiver\n",
    "        ################\n",
    "        self._demapper = Demapper(\"app\", constellation=constellation)\n",
    "        self._decoder = LDPC5GDecoder(self._encoder, hard_out=True)\n",
    "\n",
    "    def forward(self, batch_size, ebno_db):\n",
    "        # If `ebno_db` is a scalar, a tensor with shape [batch size] is created\n",
    "        if ebno_db.dim() == 0:\n",
    "            ebno_db = ebno_db.expand(batch_size)\n",
    "        no = ebnodb2no(ebno_db, num_bits_per_symbol, coderate)\n",
    "        no = expand_to_rank(no, 2)\n",
    "        \n",
    "        ################\n",
    "        ## Transmitter\n",
    "        ################\n",
    "        b = self._binary_source([batch_size, k])\n",
    "        c = self._encoder(b)\n",
    "        # Modulation\n",
    "        x = self._mapper(c)  # x [batch size, num_symbols_per_codeword]\n",
    "        \n",
    "        ################\n",
    "        ## Channel\n",
    "        ################\n",
    "        y = self._channel(x, no)  # [batch size, num_symbols_per_codeword]\n",
    "        \n",
    "        ################\n",
    "        ## Receiver\n",
    "        ################\n",
    "        llr = self._demapper(y, no)\n",
    "        # Outer decoding\n",
    "        b_hat = self._decoder(llr)\n",
    "        return b, b_hat  # Ground truth and reconstructed information bits returned for BER/BLER computation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9bf294cb-a410-46cf-9844-0e80ed8e1b79",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T15:58:56.937289Z",
     "iopub.status.busy": "2026-08-06T15:58:56.937162Z",
     "iopub.status.idle": "2026-08-06T15:58:56.939828Z",
     "shell.execute_reply": "2026-08-06T15:58:56.939040Z"
    }
   },
   "outputs": [],
   "source": [
    "# Range of SNRs over which the systems are evaluated\n",
    "ebno_dbs = np.arange(ebno_db_min,  # Min SNR for evaluation\n",
    "                     ebno_db_max,  # Max SNR for evaluation\n",
    "                     0.5)  # Step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6e921887-3d86-4ef1-b550-69bf086a4972",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T15:58:56.941588Z",
     "iopub.status.busy": "2026-08-06T15:58:56.941464Z",
     "iopub.status.idle": "2026-08-06T15:58:56.944456Z",
     "shell.execute_reply": "2026-08-06T15:58:56.943673Z"
    }
   },
   "outputs": [],
   "source": [
    "# Utility function to load and set weights of a model\n",
    "def load_weights(model, model_weights_path):\n",
    "    \"\"\"Load and set weights of a model.\"\"\"\n",
    "    device = sionna.phy.config.device\n",
    "    state = torch.load(model_weights_path, map_location=device)\n",
    "    model.load_state_dict(state, strict=False)\n",
    "    # Update the constellation points from the loaded parameters\n",
    "    model.constellation.points = torch.complex(model.points_r, model.points_i)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "125b4bd1-911f-4f19-b989-575c765e80d1",
   "metadata": {},
   "source": [
    "The next cell evaluate the baseline and the two autoencoder-based communication systems, trained with different method.\n",
    "The results are stored in the dictionary ``BLER``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ff0fe56c-6e7b-41f7-9b6d-a7919699ec7e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T15:58:56.946313Z",
     "iopub.status.busy": "2026-08-06T15:58:56.946183Z",
     "iopub.status.idle": "2026-08-06T16:00:11.269393Z",
     "shell.execute_reply": "2026-08-06T16:00:11.268450Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      4.0 | 1.2373e-01 | 1.0000e+00 |       95022 |      768000 |         1024 |        1024 |        16.9 |reached target block errors\n",
      "      4.5 | 9.6982e-02 | 9.9805e-01 |       74482 |      768000 |         1022 |        1024 |         0.0 |reached target block errors\n",
      "      5.0 | 5.7046e-02 | 9.0972e-01 |       49288 |      864000 |         1048 |        1152 |         0.0 |reached target block errors\n",
      "      5.5 | 1.9187e-02 | 5.0732e-01 |       29471 |     1536000 |         1039 |        2048 |         0.0 |reached target block errors\n",
      "      6.0 | 2.3527e-03 | 1.1070e-01 |       16036 |     6816000 |         1006 |        9088 |         0.2 |reached target block errors\n",
      "      6.5 | 1.1462e-04 | 9.1237e-03 |        9441 |    82368000 |         1002 |      109824 |         1.9 |reached target block errors\n",
      "      7.0 | 6.1875e-06 | 6.1719e-04 |         594 |    96000000 |           79 |      128000 |         2.2 |reached max iterations\n",
      "      7.5 | 2.1875e-07 | 3.9063e-05 |          21 |    96000000 |            5 |      128000 |         2.2 |reached max iterations\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      4.0 | 1.0718e-01 | 9.9707e-01 |       82314 |      768000 |         1021 |        1024 |        17.0 |reached target block errors\n",
      "      4.5 | 6.6189e-02 | 9.1667e-01 |       57187 |      864000 |         1056 |        1152 |         0.0 |reached target block errors\n",
      "      5.0 | 2.5553e-02 | 5.4896e-01 |       36797 |     1440000 |         1054 |        1920 |         0.0 |reached target block errors\n",
      "      5.5 | 3.9193e-03 | 1.5487e-01 |       19189 |     4896000 |         1011 |        6528 |         0.1 |reached target block errors\n",
      "      6.0 | 2.6280e-04 | 1.6242e-02 |       12135 |    46176000 |         1000 |       61568 |         1.1 |reached target block errors\n",
      "      6.5 | 1.0969e-05 | 1.1719e-03 |        1053 |    96000000 |          150 |      128000 |         2.4 |reached max iterations\n",
      "      7.0 | 1.2292e-06 | 1.2500e-04 |         118 |    96000000 |           16 |      128000 |         2.4 |reached max iterations\n",
      "      7.5 | 1.5625e-07 | 2.3438e-05 |          15 |    96000000 |            3 |      128000 |         2.4 |reached max iterations\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      4.0 | 1.0687e-01 | 9.9707e-01 |       82075 |      768000 |         1021 |        1024 |        16.7 |reached target block errors\n",
      "      4.5 | 6.7356e-02 | 9.1580e-01 |       58196 |      864000 |         1055 |        1152 |         0.0 |reached target block errors\n",
      "      5.0 | 2.3694e-02 | 5.4740e-01 |       34120 |     1440000 |         1051 |        1920 |         0.0 |reached target block errors\n",
      "      5.5 | 3.6458e-03 | 1.4007e-01 |       19600 |     5376000 |         1004 |        7168 |         0.1 |reached target block errors\n",
      "      6.0 | 2.5888e-04 | 1.5171e-02 |       12824 |    49536000 |         1002 |       66048 |         1.2 |reached target block errors\n",
      "      6.5 | 1.0073e-05 | 9.6875e-04 |         967 |    96000000 |          124 |      128000 |         2.4 |reached max iterations\n",
      "      7.0 | 1.2292e-06 | 8.5937e-05 |         118 |    96000000 |           11 |      128000 |         2.4 |reached max iterations\n",
      "      7.5 | 3.1250e-08 | 1.5625e-05 |           3 |    96000000 |            2 |      128000 |         2.4 |reached max iterations\n"
     ]
    }
   ],
   "source": [
    "torch._dynamo.reset()\n",
    "torch.cuda.empty_cache() \n",
    "\n",
    "# Dictionary storing the results\n",
    "BLER = {}\n",
    "\n",
    "with torch.no_grad():  \n",
    "\n",
    "    model_baseline = Baseline().to(device)\n",
    "    _, bler = sim_ber(model_baseline, ebno_dbs, batch_size=128, num_target_block_errors=1000, max_mc_iter=1000, compile_mode=\"reduce-overhead\")\n",
    "    BLER['baseline'] = bler.cpu().numpy()\n",
    "\n",
    "    model_conventional = E2ESystemConventionalTraining(training=False).to(device)\n",
    "    load_weights(model_conventional, model_weights_path_conventional_training)\n",
    "    _, bler = sim_ber(model_conventional, ebno_dbs, batch_size=128, num_target_block_errors=1000, max_mc_iter=1000, compile_mode=\"reduce-overhead\")\n",
    "    BLER['autoencoder-conv'] = bler.cpu().numpy()\n",
    "\n",
    "    model_rl = E2ESystemRLTraining(training=False).to(device)\n",
    "    load_weights(model_rl, model_weights_path_rl_training)\n",
    "    _, bler = sim_ber(model_rl, ebno_dbs, batch_size=128, num_target_block_errors=1000, max_mc_iter=1000, compile_mode=\"reduce-overhead\")\n",
    "    BLER['autoencoder-rl'] = bler.cpu().numpy()\n",
    "\n",
    "with open(results_filename, 'wb') as f:\n",
    "    pickle.dump((ebno_dbs, BLER), f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "905f3b1a-f906-4eab-b081-2705c9e1f6ee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T16:00:11.271857Z",
     "iopub.status.busy": "2026-08-06T16:00:11.271718Z",
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     "shell.execute_reply": "2026-08-06T16:00:11.608641Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "# Baseline - Perfect CSI\n",
    "plt.semilogy(ebno_dbs, BLER['baseline'], 'o-', c='C0', label='Baseline')\n",
    "# Autoencoder - conventional training\n",
    "plt.semilogy(ebno_dbs, BLER['autoencoder-conv'], 'x-.', c='C1', label='Autoencoder - conventional training')\n",
    "# Autoencoder - RL-based training\n",
    "plt.semilogy(ebno_dbs, BLER['autoencoder-rl'], 'o-.', c='C2', label='Autoencoder - RL-based training')\n",
    "\n",
    "plt.xlabel(r\"$E_b/N_0$ (dB)\")\n",
    "plt.ylabel(\"BLER\")\n",
    "plt.grid(which=\"both\")\n",
    "plt.ylim((1e-4, 1.0))\n",
    "plt.legend()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b35d47f5-226f-435d-bf65-d46cd0fad5ca",
   "metadata": {},
   "source": [
    "## Visualizing the Learned Constellations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "99fab7bb-d5fd-4173-9acc-688fb395c3a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T16:00:11.611714Z",
     "iopub.status.busy": "2026-08-06T16:00:11.611578Z",
     "iopub.status.idle": "2026-08-06T16:00:11.991028Z",
     "shell.execute_reply": "2026-08-06T16:00:11.990113Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_conventional = E2ESystemConventionalTraining(training=True).to(device)\n",
    "load_weights(model_conventional, model_weights_path_conventional_training)\n",
    "model_conventional.constellation.points = torch.complex(model_conventional.points_r, model_conventional.points_i)\n",
    "fig = model_conventional.constellation.show()\n",
    "fig.suptitle('Conventional training');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "772edc3d-3eb5-4219-a8d2-2853f463f84e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T16:00:11.993253Z",
     "iopub.status.busy": "2026-08-06T16:00:11.993114Z",
     "iopub.status.idle": "2026-08-06T16:00:12.375606Z",
     "shell.execute_reply": "2026-08-06T16:00:12.374719Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_rl = E2ESystemRLTraining(training=False).to(device)\n",
    "load_weights(model_rl, model_weights_path_rl_training)\n",
    "model_rl.constellation.points = torch.complex(model_rl.points_r, model_rl.points_i)\n",
    "fig = model_rl.constellation.show()\n",
    "fig.suptitle('RL-based training');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2774433f-05e1-4421-9e9f-a24dd2f3fd64",
   "metadata": {},
   "source": [
    "## References"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e67ab4d0-2aa8-453d-a810-3c5f105567dd",
   "metadata": {},
   "source": [
    "[1] T. O’Shea and J. Hoydis, \"An Introduction to Deep Learning for the Physical Layer,\" in IEEE Transactions on Cognitive Communications and Networking, vol. 3, no. 4, pp. 563-575, Dec. 2017, doi: 10.1109/TCCN.2017.2758370.\n",
    "\n",
    "[2] S. Cammerer, F. Ait Aoudia, S. Dörner, M. Stark, J. Hoydis and S. ten Brink, \"Trainable Communication Systems: Concepts and Prototype,\" in IEEE Transactions on Communications, vol. 68, no. 9, pp. 5489-5503, Sept. 2020, doi: 10.1109/TCOMM.2020.3002915.\n",
    "\n",
    "[3] F. Ait Aoudia and J. Hoydis, \"Model-Free Training of End-to-End Communication Systems,\" in IEEE Journal on Selected Areas in Communications, vol. 37, no. 11, pp. 2503-2516, Nov. 2019, doi: 10.1109/JSAC.2019.2933891."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.12"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}