{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Contoh Dasar Pandas\n",
    "\n",
    "Notebook ini akan memandu Anda melalui beberapa konsep dasar Pandas. Kita akan mulai dengan mengimpor pustaka-pustaka ilmu data yang umum digunakan:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seri\n",
    "\n",
    "Seri mirip dengan daftar atau array 1D, tetapi memiliki indeks. Semua operasi diselaraskan berdasarkan indeks.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1\n",
      "1    2\n",
      "2    3\n",
      "3    4\n",
      "4    5\n",
      "5    6\n",
      "6    7\n",
      "7    8\n",
      "8    9\n",
      "dtype: int64 0         I\n",
      "1      like\n",
      "2        to\n",
      "3       use\n",
      "4    Python\n",
      "5       and\n",
      "6    Pandas\n",
      "7      very\n",
      "8      much\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "a = pd.Series(range(1,10))\n",
    "b = pd.Series([\"I\",\"like\",\"to\",\"use\",\"Python\",\"and\",\"Pandas\",\"very\",\"much\"],index=range(0,9))\n",
    "print(a,b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Salah satu penggunaan umum dari series adalah **time series**. Dalam time series, indeks memiliki struktur khusus - biasanya berupa rentang tanggal atau waktu. Kita dapat membuat indeks seperti itu dengan `pd.date_range`.\n",
    "\n",
    "Misalkan kita memiliki sebuah series yang menunjukkan jumlah produk yang dibeli setiap hari, dan kita tahu bahwa setiap hari Minggu kita juga perlu mengambil satu item untuk diri kita sendiri. Berikut adalah cara memodelkannya menggunakan series:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of index is 366\n"
     ]
    },
    {
     "data": {
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GquYENBGb5h8QPOapUkXDBSy0VoNmrCZd2KexzAisJqSiOZqP0d9n0TSMm+pjUyPAOLQyMOb5KM1rKfvkuVZEfDwjNgAmstXa1tu0IhdbafgMJrF2ITCr36Rzh07tVju0ZxwVjcyXew6tNrI/4rl3kwi6SXGfa1PRyD45aWKPJpTS0/S5yBx7ZFN2jN3PUeAzlgYkBtaHqeHnzTLowHG3iM1pYNic8+lP2ib+vW4md/MU9rGhm6cXsSn/VHeSU6dzJjU25TE5NLtVpjmi9RGbU1GI2kQfG10YaZ6zWWOzhVS0GpsHFsXvXuiq1+oICCxvjpXT8LhEbGhg0yI2p9So4tRWIKDT2FghNvq1WSA2oVSapg33sTMlsOE1SE2gNrimdxOC2NQMLJpQRaOnNiuq17SmGnTOaJ4uscAmNMALQWx4EDzLZDuOuw1sTgPDG//0J8uBzTQNOrd64aRZ2RAqWt3AyxAPmMEDgtdraX14iuSTi/9DHQ+aWTkVNTZN1DzhIWwqGkFCtmAe4/jrbG7orCPaClAvG740RSPeM8HopWgRm1NrZ1IjWUVFizQVrVOiN00+JyNVY7PV4gHF/5OoKJ4K445rM4FNKR6QxIqGX5+KNr0qmikecHrej61GbEK/J4QixwObWc55JWYwbsUDTrnhjX/KWYsGpxitbiZ3/RTW2BiIjWfcPGP16VsOwEve81W467FC1//eQyvwkvd8FT5+o6mPTp8f2pyrKVPObZbD8ubWozacivaVuw7Bj737arjBoURzqhEbo8Zm0o3F0XyMZgmrFtoT60N4xR9eC//jK/d63+czHH+eh2fG0Fm/hAY2NZ65Y0ZgE/yxM8ay0xSxGacZ/LM/+Rb81qduO9VD2TKbVpZ8K02JBwg1Nk3S6ManqsYmn31m+R7HHjqJ4TVHWebrH5peQAD3/l4nhk4yGRpHxRf4/vdX1z8Cl/2nL8AP/IfPwfPe8WVnw3B6713z4Et3HoJ/9O6vws2PLNUan88eObYOP/Ffr4Err3u48r2zRhZ58BEa4GlVtDqIzez8lFY84DQylHveu60Huxd61t/rzuWNU0lFI5unC1bO81wpYWEg8fnvHYSHj63DN+87CgAA37zvKDx8bB3+9rsHjM8aVLQZIDb0ep2KOhsuV/jluw7DvuMb8OW7DonvHzegCjONmeIFEyI2DjibLpZVjtit+0/CvYdXp2qsSscfitogjfTsHXNKwanOomo04j1NaRDT2OkqHrB/aQNufHgJ/vaWA9VvfpzYgRO6l9DpToHCZ6ETU8Sm+RqbUYBj1rTleb4l4gHXPXBM3EMnMUwyXXrOdgAAePTE9AqHJhWtROOmoKLxxNrnv3cQVgdjGIwzOLo6gK/de1Q8Bl2jXIHDl+88BI8cX4ev3XOk1vh89p0Hj8GDR9fg8987WPnezJH8a8qswCa43UR1iwQrsJnhnG/FA04jw+LhPQs92LtoBzZ1JnOa5cbDfioRG5ejJtHJcFFS/5fj5s7QLPvY5LnZ5PJUBDYZQ7Lw/+Or8lhoZuVUiAc0ocrmWrTpuVWhIE00MKOZpNDj4DkvdBPodYrlrk42nKrRnK6KPNMYr7E5XRrgjWecAT0d7UyiouH9iaPZqqKhw76V84B+1Vb09GgioYDzZb6bGMeexpQqWhJBtwkqGuvjhvvKjrmiR45r7QlBbJDy3eT6hWMPeRZn3aDTRmwCa2zS8BobFP+YZbK9FQ84TYxK4e1Z7MHuxa71njqTmWftT8caG1MAAAOajP1fvP7YiU1jMcmEzzZlhaKV/p3zTrfCdIPOMrBJUczAFdhMH1hMY0ZgM6Xc8zgzFcVo1qhqoW1C55+OPxQ9wWByvpcoZZ86zxwVqThdnP4mjZ7TsMycng6WKSpQPRW7M9nMfkun9znTBp1onaT5PjZ07dkqS9kzMbPvKb+G76GTGCJb/TJ508RaJYoH1FVFo+IBjDGA17lfBmOuY9O13vUeJQve4FqhA5vqY7qUQ5sy7l+EoqKaIud+P/pRZ++YA4AZz/ma4kuTWhvYVNjaMFU3eu+2Huxd7FvvqfMscZ7pKa2xcTyE9Hzw3DfHDLEZacf+CHGGZrkp8Ez7KaGiMe416vQfX3cENpSKdirknhugBtJj0ClDF6cqFGTaDHya5cYGE3qcDRLY9MrApo7TSBGbxyN6wB2B/acJHc1ogPz4u+yWrQ/HsLSug+jTHbHBNcEIbEq6UpPd10cKsdm660HXu1nWOilJabaHTmKIbM0hYtOAg4/XvteJtdxzXSqaR+4Zn3Fcl13JKvqdroBNI3u1huc1HDtXnJMMhzWL5BfvMwMQvocpVTRvjU0x987dWQQ2W0FFm3Xipg1sKgxVkea6MSz0OrBHoqLVWESswGYLs/i8WaFr3BKdDJ1i9TvJnu93oEBNPyD8Wp0SKhqq5XAqmmMsdEOeVBVmGmtE7plME4N+RhGbisV/Wg4yH3uon4MI6XxXIzZ1nEZDPOBxiBzwjfh0UUY7E5SQmjR+3U/3Ght0UBKxxqZBKlqAY9a0bR1io79n/5TPHa7FiNg0QkVD8YApEBu61rqoaN0KpK8OYtNkHWQdxMZVh9qErZPkOtL2Qu9viKgBshLO3TkPADPuY5OaftOsrA1sKkzR0ErRgN0ksEkmWMg5FW0rEZuDy5smX9XxwIpUNAdiA+Cu22k8sGHHO6U1NqnOtgG4a2xGpxqxaSDQdHGcXUGO7xiTZrSsbF/gBobXfKE3aY0NEQ94HEIH/DqeLgICxjw7zWlZTRhHyk53VTSF2My8xma6hMgkRp+JraDlAEz/3OFaPNdojQ0GHrEKPkLQC+MYnj42CrGpCMZCamxUI9cG1woce0hixaUc2oThHtTvxLB9rluOKRSxwXOog9jMzk/BcQzTbKYU4zawqTC86Xu2FQENFQ/An+tkCU5lYMOzgi7nUHKGlYiA+l2fh6tup2k06nQIbDgVDfXYVwZjcUFoonh/GmtSPADAXCCNPjahiM2Eixm/98FUtPKc57qJ2pzrOCtLj3PEht+20waxCcjSPp4Mr/t5pXNxulPRcHixUGNT1/n1WYhj1rTxurOt+J5pn7sxR2yaoKKV597tTI7Y0HWbq3Li9tGtoKKFqKKNZ4HYpGHBElWRnUVgg8n1vYu92gGmrlFzvx/9KAxsZjnnx9nWrOtP2MDm5PoIvvvIEnz3kSWrqys1hOn2lLU1SEXrd2LYVqHmIRmHY7cysEG4+6wySAvJkLhV0UhgQ2RKQxCbPM/h0RMbtSN2/sBNG9jkeV4rU0YXMHQ8qE7/iXW7r44JxZ+hfWxovYORSaeIjX8ea0h8oiHYVDQ2dzZHqXqeHyX3tFEq2uPAwcZnDw2v4/Z+sZZNmzk+vLLZyMbommfT2uYo3VKBhEPLm0Hjx7UZG8me9uIBgtzzLBGbraQjGlS0GQaYptT6uued1YbjRMQmz2HqjLgWD4gmbr5K14JhmhnPAj7juC47/ZEAkRpdizUDKlrF3DOp2s0/t+if7l7sQQdrnWpS0fw1NsXxz9kCKlodn2Eae0IGNoNxCj/xB9fAT//xt+Cn//hb8JL3fNUKONAUYrNQQICI0uxd7CkYvk52hDtoW9nHBh2aC/csAICP06p/xgXTCmwoFc1AbPRnXQ/IX12/D170rqvhozfUa0zGr9W0gc2HvvUQvPhdV8Onbtof9H6pcJ4u3McEOprRoPOUIzaTigfonw3ExoHe+MYxaUaNj50v7D//Z9ep5/nFv3813HtoBQAoFa2jKA9P5D42f/b1B+FF77oaPnProwCg78sF5Zrw6BSBzYETG/CiK66GN15509TjDKGfTGL/1weuhxe/62pvMqspu/fQCrzgiq/Av//ErZXvxeuuApvTvMYmLZ+hWBAPaPJ+jQKz5k3aVlHRZonYAEx/H/Da90mDzrrOKA8M6R4YKh5A74frPSqwmQFig6wMl5lCJ7Ojou1Z7KlEQngfGz9FbpRmsLxZ+L6KijaD/oNodNxV13Uae0IGNifWR2qyxBHA8ubY2dBqY1jc5PlekdH8kYt3w4894yz4xRdfojT86yST8HhoW4nYYPH6rnk/T1OSbN7klDRKRXMorbnO7b7DqwAAcH/5f6g1TUW787HlYhxHwsYhFZUOKxqGPh7knjOHk0mdr2Aq2qQ1NmzsHCW9r7yHcVRkK+8t55ZCbHoxkXsOG8PmKDVQtscDYqOevfJ64TnhmjANqvjQ0TUYZ7n6jmksdQTQ09oDR1ZhMM62pJbo/iNrxVw8VH09MLF2Vpk4G8wwm9mEoX8y8xqbKdUUJ7FTg9g0U2OD0snFa9NdM9zjqNzzNIgNgBnYKPGAToV4QEC9nZonp6DGJkTcYBrDa7bQS8gzFjYvq54fen92lcn7rehjAwAwSGf3PU/IwIZmIs7bVcBvy5s2jQhATyDkNs51E/jLX74cfuUfPVVlq+pE6RYVbYbRMTderBfSEGswymCUZuo1LiIAUGSbEPYO6WOTTsibxmuFTcimDWyWN4p7EZrlMWVA8/J/ff8kyWcKvZ4KKlpmUNEmFA9wLNz1xAPK8TQlHsCOgxveU/YWGe+1QXFv8ZrPdancc9h1sHsHnPmBDd4/vA/auZg+247PexM0KoNX36CzMi1yWMfw+QhxFPC+zPWKtW004+LaaQ3nj9zHpsEam4AGg03blqmikUPTPXQSw2dujgQ2085xQzwgrkeBUsfggY2QKNJUNPkY9DvdyEPpwJ8CVTR6nWchS47n3IljTUWridi47hu9XvPl2pPlzdJ/pfEAzJZu+4QMbPREiWBHqTKxsilT0Uap+fBRw5emoaJtpfqNDmz8DbE46kLHLFHR1oYpnNwYiZ+VbDShczEsI/xzdxWQ6cYonUppbGVQjjnwAaPjHSnEhgQ2AnefXuNToYrWhJhD5nAy64gHKNWYScUDrMJT8zj4/TtK5GFtMIY0y9X9Weh1lPMe6qw8HgMbnlTgdJCpApsROgLTr2mzknsOkT9tylQdXsB8Q0dlgdRInM7zDZ8/Se65WcSm+dqJKqPTbaZUNLKG0T10EhuTZC1aU1S0bhJDktSjQKF5qWhK7tlPRTP2H2evm+J7mlSuVIhNYP0o/7kpw+cpiaPaz1gVYkOv1zwJimfFJDJ8hhk+W0/IwIZOlO2lAMCyY1HBSY3ZKGqailYHsSkebERNtrKPTSin1UAmxpkxyaU+NgC6+DWEn4zOcd1FAB2nPQs9dQ7H1iYvBK6L2JjZBiGwOQ2paI30sXE4meM6gc2UxZ12HxuG2JS/78TAZpgan5nvJtCryRPnTdFO83ruIFMbHUNYe5369YLclIpQA5u7Gdg0d+HxUFuB2CCHPMQ5xudqoaQ8A5zeAgJ4f0wqWrM1NlmWq/u1pYjNFjXo5Ndpml42vI+NdPz6xyzOvZdE0K1JgULzITYZ80dCEq0uRISva02YqrGpuI50SLMIbPCcOySwqdvHxnXf6HHo2jOrwKYVD5ihKcQmiVWG14XY4AODUCw1FdjUmMuYsdit+Ixbh9jg5K6jGz8Yp17EBh805AiH9E0ZTZiFw+PNdROlTre0NnmWa6WkH4YGpnRtEMUDhMCGnuMpoaIF3I8qSx0LtyvIEY+R25+vY5bcM9nA8jxXx92pakXGxvXuk+7ZoQsqLzB/PPSx4f2EzgjEZgZUtK0oRh8pKlo4YoN0EIDTu5cNXsdYQGyaCsgoH38WFB+XbR0VzbxO09TZKHo9ybo3Fth0YhW0TkpFw/jXoKKxBp0h1HjXcztqMKmCpqlop7bGZqz81UhLqgfX2CDq5Li25dijqEj0472YVZ0NvT6zXN+mCmyuuOIKiKII3vSmN6nX3vCGN0AURca/F7zgBdOOs1HDm9yhiI2jxmboQWymadC5a74sEt3CwAbnEWZ1slyWhKSnU1DR9BjxZ5z4F+0t1JRQ1cX4rAMhmJQOgt/Z78SqUepUiM1mTcRGkLKmD+eSUGNDF8VToopGTm2WfWyqFinlSE+siuausaFjUojNQAfk890E4jjS4gGBjhcGqnW7PZ/OpqkJYPxfJbkaYvh8NhE0mHOuQSralJTIOjZyoNuSqRob4pjOujv3NKYQG+JB6P2wmXHTebSVqmicsbAV3wMwnTLaWCUoIhVETDvHcY/rJvHEPYpwX0A5eUM8IHDtCVEdG82QilZFCzVU22aB2KT4rMVKeTC4xqaiRg3vASKv/bJMYVa139QfmuWzNXFgc8MNN8Cf/umfwrOf/Wzrb6985SvhscceU//+/u//fqpBNm1yjY2fiibV2GCyqlaDzjJjsbNEbIZbKPeMG04VXM170fgQm6c9aRsA6GxTSI3NpEo3+CD0OrGS3Z5UQCDPc3XPQ8dh1NikGWRZbjj3lXLPpxix2ZxwITGLI81rIP0sGS7+k/ZXsKlo+me6yKOyy9pgTBTRisW6rtwzSr0/aXvRw+rxIPeskwq4aZcOUSPiAU0iNvrnRqloCrFq7JBOw3EPxtVCAJpFENUWuTgVRgua0ZqusdmqZn7ctkwVrUHEhvopyQRqrZLR+uJJ7y3u2ejvSCqT3YqmoiHNescT0tt9Ngjc37IZz9Mx8Vc7NSmBKpHjChpzE3nFfWBW8/60Fg9YXV2F173udfD+978fdu/ebf293+/DOeeco/7t2bNn6oE2aTgpkiRS2Vist7Deqx5ud41N1cO0PhzDX9+8H5bWhsq5RXnVraWimU4MfY0ah+JpxhE3aXQ0VWCjEJvqbBcuwnUzSnit+p1YUdF8gc237j8KNz50XPzb5ijTSirBVDTzoeQPf1WNDW9QNgvDuXaiRI/GBp0jn8hZchVH0nOppKI5jkFt3/F1+LtbHxWzXjwoo3OHUlZ0jY2momFRpJZ7Dg1simuIgc2ZgNh8676jcPMjS86/q8CGIRfNIDaaDjKtolcdmmMd4+d9dHUAf/Pd/TOpf8P1Ic+rHR58LjuEDnJaBzboEAlyz009J3R9CTnmzY8swTfvOzr1924VFQ33SmyYPQ1ig3tZJ4mVk+pyfg8vb8Jf37y/8twQcex24tq1HQBF0gSfAWSoSOIBqubXiSpU7x2zbNAJ4H9+6Z+a+P6jqwP465v1mqT81TiqLaleVWOj1A0VYoO137NCbOTEaNM2UWDza7/2a/DqV78aXv7yl4t/v+aaa+DJT34yXHrppfArv/IrcPjwYeexBoMBLC8vG/9mbToCpjU2MmIzIu/llgTKPX/8hn3wGx+/Fd731fs0Fe0U1NjwYj0Aeex2jY05xrVhqq7hU8uGco8tb4qflaxKgtBlOrBJVI2SRP8CKDL8b/jgDfDzf34drA7soJXe79DnK2WIDX8wpbHwQGZS1CTUcK79/699AADsc5vEgXNlzGhRZSgVjR+P2n/42+/B/++vvgs3CMGoTUUjTg9ZLHcQKhomERRiU9NhPLqKgc2cd9yni60OxvD6D14Pv/QXNzjfoxEb8/cmERuA6TOXs6Ci5XkOeFicj//tS/fAv/vYrXDVbY818h3UaAO6qnVeU7uiiRrJbrVpuWf9WreiCLyuuaTlXeN5/Qeuhzd88HrnXh5qnLEwK8NTOn93Qed+7OQ0VLQyCCFZfdcle/cX7obf+Pit8NnbHvUek4oHdBSKGH5viwRH8TMmnDYNKpqZNHYiNgGBzUzEA4j/4ktIGtL0Dcz9P/rKvfAbH78V/q5sokwRm25NuecqdgxddwBIYDOzGhtCRTudApuPfvSjcNNNN8EVV1wh/v1Vr3oVXHnllXD11VfDH/zBH8ANN9wAL33pS2EwkGshrrjiCti5c6f6d+GFF9YdUm0bp/pm6hobF2KDEK+7xqYqsMHM732HVzVis1DW2Gxh3UUoYmNSrnKr6J3KUmpKXWZ91ikekCIdpGZgU16rfjdWRZKuB3x9mMKwrA+6+WE7g01rqkIpRnS4w3FmZbyW1kdOtS49LnmeNWXH14vzwsJ3fm6T9LKhh3AjNmFUNAD3hnu0lMuWAkQ+bvp1iNhEkeZyrw3GRo0NAEFsAhdU7AZ/we75ctyneWCzOYZRmsOJ9ZETMcGNhfexUeIBUzgGA8MRmO5aGVS0hhAbyUHCuXZUkGqf1ugmXrXOK5GapH4j2VNhkniAQmyaEg+ogdic2BjBSjn/XUJAoWag7DOkiuOzhmvWNEEURWww++56lpeIP+IzXCcL8YD69VN0f9wpNAAORYtDKImzoKKZ6IIHsQkIvOrYkZViLUK/UdXYJBMgNhU1NlQ8AIDU2MxMFY1c09Olxmbfvn3w67/+63DllVfC3Nyc+J6f/dmfhVe/+tVw2WWXwWte8xr43Oc+B/fccw9cddVV4vvf+ta3wsmTJ9W/ffv21T+LmibV2LjknumCwS1SVDT/9+GkOnBiQyE2+KCfmj42BLGpoKIB2NfmBHE8F0uJQK6yBOB+OCZGbFQGKa58wOk4rn/QRgBoIBs6Dnqthmmm7h3u7WmWW70IuFO2OZzt/caNB519fm4TITaOzCk9tyz3L+ohiA3OF+mR8IoHIF00jmERA5thqqloJWKDXO5RoMOInPeL9hQZ1a3k+U9inHYoGX/2FM+dOBeT0sgofWE0JcrStLMAwDKrjHs+i42cru1Vx6d0k7oB+KkwmkVGa7zGhooHVByT0oCnpY9xyvGsDL9nrjv9/VZUxiRSwaaTtlW+XiUvjdexCLbr97Gh92HHfCkeIMg91+pjI5wTpbw1WQdJx+9DT5uWpse9jq/VnTiq3QQXP5vnsq/HG+32avZ6q2tbhdh0qt+i7aabboLDhw/Dc5/7XPVamqbwta99Dd73vvfBYDCAJEmMz5x77rlw8cUXw7333ises9/vQ7/fn2DokxvdRLZXNujU3GduCOJUPUwqsFnagG2l40WpaHmeqyBpliYFNjJiY/7OFePQee9RtRRBwtmVpZxUwQQdp343rqxvovdEDGxIABI6Dl44jw//XDeBJI5gZXMMx9aGSrENwL6+s1ZG4xQjfm6TOHAuKgBfmEZpBklsPv/qcwFwPWb8pcCHQ+MGVUklHyJY7Bffvz4k4gFdpKKhA1F9D9YGYzhRol8Y2JzuiI3RTyHPxcUd52OmHPvidSPZkeu1rY4ZVLQpHcJZFI7T66MTMcXvs6Be0AC6ylGgwfkZQUXDBp1CjU1T1EGj43zFfDICmymv25aJB5TXsIp9EGJ0/lTVwyC6XiVWYDTonEDuGa9dEkfK7zGoaOWhqttP0J/9idgm6/HovfcdtyrwqmubTDafCnXUlVQ3fJYsgz7bn1P2HM+cirZFNTa1ApuXvexlcPvttxuv/eIv/iL84A/+ILzlLW+xghoAgGPHjsG+ffvg3HPPnW6kDZrZx8Yv96y4qwJio6hoFZMZb+bGKFX0Fiymy/NikmKDvFkaRariqFhYxCieOZYchcCgoN/VDxoexhAPcEzcJmps6jSeumXfCdgcpYaUKkVsgptdMYoeVWnbNd+Flc2xRaPiFK1ZU9FcneXRJkFsaAZ/7NlERmlmXGNzXPpn1/Oi6IzC33kxI1+wAYp5vUioaBvltVaBTQ3EBjf97XMdha6e7jU29Flw+ZZcaj1TdBC9/owzd4Dqs9AMZ4gZdV0NbYBSB3N8XmaRoTSoaJWIjc6cKvGAM0Du2ehjk/gd6rpGr18VAnicyP5Pey95EibNcrXXN2l4nfoNBLLKT+lUIza4bleJFaDz3KOIzQRUtF4SqzVYVEVTfWzk41QFDnRPmhli4znvxgObsQ+xqVcLWYXi8+e438XApvm1J89zs0Z3hlTbWoHN9u3b4bLLLjNeW1xchL1798Jll10Gq6ur8Pa3vx1+5md+Bs4991x46KGH4Ld/+7fhrLPOgp/+6Z9udODTGJ0o1YiNCZdSiyq4rPr79CQ5XPInsfgdoIiOacZ0VqY3z4LKlaW5GFzwB4AHNpjJniv7gxTHtmlELjSKZ41DjfaxGSrakivLYwZYt+47AZc/da96jRaYhgZYOVvAMLPSSwqVtoeOrVuSz3x8s0ZsVECDnGN2jSfJxBgbvUEP4YiN+zoaxf6VVDT775ts3BIC1EliRY1cG6TqWi8gFa2GeABu+ufvmifoYOXHTqkZyJqrxoZx0blzBTC5TCydW9MGNrOQUJXmDE7ZWWzk9BpUPXfUyZu15GoTJiM29Qqbq4wep8qRo82Rp72XfP4Px5nROLUpw2uIyaBpnhlFmY+J3HNFnd2hlU0YjjOn72EiNvWpaAOS+Jvv2X1seI2NK2iqoiTWqcWqY8NABLppaXqN2JjJlyQhcs8BcyXPc4N9I7JzysNsRR8b/v2nlXiAz5Ikgdtvvx1+6qd+Ci699FJ4/etfD5deeil8+9vfhu3btzf5VVMZVYJAuefVwVjOBvgadEYmWuEyaUKhehPA1jViw4WuQ2QDpXPmGXMrsNnAwMbWzDc7wsvOrkRbCzGKkCQVnF++qHM6GpX3Dg2w+HOIams9j/x0EzUudQzvnRuxmYSKpn92NegsfvfxkOnPjsCmHJvklPNxmzx4G7HZGKWwOiiu9VyvvnjAfiIcECoScqothO7nRmxIb6tJa2wapKK56rqmMaluB1+bxUY+rKGKNiK0ne4EClRbbUqAh+yLk0gC+4yuJ1UO41KDNTZ8/s9qf8brNNfBwGby60ZFjqrqT2ndxcGTm85j0v12kntLPz9fIgEboioaCpfIx6lqgGkEwA09MrRuB6Cijw0dXwN7xEDV2Jjsi6KPTTglkN8ric6pqGhYY1Pei8EMgg6J4TErq4XYSHbNNdeon+fn5+ELX/jCtIecuVFHCBEbgEJVaCdBUgCoWo1HFa1ikkkTaqGXQC+JYZhmWyb5jBMrjiNvVocv7LzHj0JsOonFq7ZrOmw0CscxDRWtSreev349kxA25Z5DAxvzfVJgY1HRmFPGFeaaNh3QyNdnWiqaz+EMXfyramzEPjYh4gFJrNAZAIBjpdIVV0Wrg9hcsHtBSdqe7n1sDDEH13PB+rhIdXeTqlqZcs/N1TnMUhUNxzkLTrlRKBso99yJqSra6YvYcG4+ANRuHlj5HeR+YfFz7KCEHWuwxsbaw9IUALrym6cwHCbSfxpBbJLYm7Sk7wUA2H9iHS7auyC+b0iCJXSoRzXWQC0XHSvEa0NQRcO1J6SPjYjYGHSrZuaeVD/qMpdS6KSGex3eJ63iS2uaq+8Df49PARe7mSgq2gwSsHxdmOX6Nnv+0wzsf371PvjgNx+c+PO8ZwCqkkh1NnjxpT42uKZXOTwSP3Oh1yGFWvrvn73tUfjdz9wxk0JlWmPjy+rwl7gq2smNYhPBovni2OZ3oEmTV1HRJg5sdDFjiPY9AMBNDy8Zi9Nkcs/m+9YwsEli2LNYCGBwKhp3yjY8gc2jJzbgzZ+8Fe581O7l9N4v3wN/+Z2HK8fIeblNyD27KE42YuO+jlW9CMZppuadNCd5YCP1OekkUTk3ijmJ9wKDnTqKL1hjc/6ueTXXpnkm7zu8Av/PJ26FR46tT3yMew6twJs/eSvsOy4fI0SdhyMVXBUNYHLEZmhQ0aZEbIS6rk/fcgD+y2fvnPg+pEJmFZfmWdAi6lDRqABGTwjA8zyHd3z2Tvjb7x5odIwfue4ReM8Xvl9bCY+rKQE0X2PD55DPmWtSFS1kD2vCFBWt0wAVjfSxqUKYqfPtq7OhVDSuxnXTw8fhtz51m4GUccNnqk+paOX+R3tK9SrqRgwRCcGXovOkqceY33N/g87qpF0d22SUbNFvC1hfLcTGw86xxQOauZA3P7IEb/nkbXB8bRiM2Hz8hn3w7s/XX5OonXGBzcn1EbznC3fDO666a+JJxKUqEbWRAhv13in62EjjnO8mpFBLb3rv/vzd8MFvPgR3CM7ttEZhRx/axF/j10XX2FCIukRsrJoOe/JOLB5A+thUSYviue5Z7MF8N4H1YWos4rSmKjQjzM8NEZt+J1Y1UycsxCYcMfm7Wx+Fj9+43wpgDq9swnu/fC/8l8/cWfmw88WwCcTGpDi54XlvVqsCrqfzRPo7D8gkSlwnjiCKIlgsAxnsTTLHVNFCnO4DS0XwcP7u+cq+ECH2gW8+BJ+4aT986ub9Ex/jyu88DB+/cb/Tua26xgA6COSBb3HtzPfUtdlR0Yqf3/OFu+HPv/Eg3PnYZGujoRrHqLMzp6JVHF8F50Q8gAZbdx9agT/7xoPwrs99v7HxrQ3G8Duf/h78z6/eX6mQxQ1vLw1sJlHO8hmfh779vtHAZqupaF29Lk3izBUS7cXP3SQ22g9IRu+P774r8YBOrBGb8rU//doD8NEb9sGX7jrk/LxJRSsRGyZljGMGCJV7Fs6HvNhUQpjfcz8bQf/chMAM+jlj5ScRmmqNfkI2YuP2xZR4QMN9bP702gfgYzfugy/ecdBK8A8de8Q7P3cX/PE198O+45M3rD3jApsBUYyYNLDRiE1x+lhnwylXAPqh6UmqaBVFevoY9t/nerGWnyWTCJ3lWahnKfGAKPJuQqHiAf1OYimw8M9KD4iSe665COgMEEGKHA8HReUwgKQPNkWhQhcjfm54j3od2j/FvG94fdFp9FHRMJu1wY6BvW+GaVbplKugkRWJI2oxyYJlUAFS+WeAisW/ArGh4/KJB0g0C0rjAQB1L446qGgh2XmK2CBYO002DlGWaWqsVsq1gQspoFWhYgD6edFyzzg/I6terq5R571JyV1cg/Ha4RpZ+5hb3McmVBUty3SRbyeR5Z5x3XCpd05iNz+ypNantUG9eZkSZwutUyObHGL8OL6A25R7no5GYyE2M6oFwGeQCndMEhTSedIhtDF3YBOI2BBVM77u4nz0rWdiYDO0Jf27FUifua75EZumaJD8nvvFA2aD2OC6ZyI24ZRAPhbpHDiltOk+NtqfTYP9BZwj3JeqY2dcYNOEtB+vm9HKaPam4WvQGTMalvv7zDckcUE3QP16uulhtD6LxZSqa/jqBuwaG7mPjYnY5OJnJQoGd75DDR0nWszo2gio2of0XorYhGZ5+NuwOL0IbIp7yR0EXJy29W1VGG54Xfi9Nxv9hXUw5wINCz27j0CoGRkpAynhWa2wxV9GbGwZUOPv5b3HAI0egzanA6CBjUlFC1VFG44zpV54fkPiARgoTfNc4zWo6r4NUJ2t5apgFMWdHLHR93BarrnkLOBm66Nz+ow+55yKN5M+NuQa+BwFej8N8QAqN1v+vD5MG8tKU0GVuuuCyvTOsI8Nf059+wUNbKaVkeXPzqxkaTVio+sCJwkK6fzpJnGw3DOAH7HBAvJuhzboLF7De+Ob1+jXdGmNzQjrKPX7sHFyCK1cWvsMmfuGbhW/5745bdKipxvAKM2sJLFmDRFKYMA84WP2qqIpxKbZPjaY/B2S80Jzydnj3Jom2XTmBTY1JCBdlhIFGgCtULYsSD5TigA3fKkuYjPfTSCKIj2JSKYTo/VZwN+4T3SIeECIKtoacyROqj42iZbCVZx1lu0SERszaxxqVO45roBkJdodvQ808xmK2FhUtE1dY6Nkhh2IzY4yePY5ZQjV8mtGf6+qkeGZaPwdAy9Xtj/kmADm4sgXylAqmvS2YQUVDe89XmdDqY0lH5CKhtki3FS7gdmox05uQJ4XgfvexZ56VibdtPI8V/2rpuHR4zUIUQJ0PVuWKppCNqkYymTjMxraTbnBS84CrhuTSqZLwRIPmpo0Guj7HAWTlhORRrKU9qnfs95QYe91JLCp60SkzCECaF4Vzco4O46b57kR2Eyr6MSfnWkRIJfh+SGjoPiu+mOnSYQiq18e37EGjAICmzzPSY2NXZOLz4svmYXnQvvYKCoaGRvOd6fgCX1uhXNqwifkxu95aNIORS4mNZpgGLE1qlBFK5NzAYt0SI0NXs+Yyz03tB5ujLRPw/c+aa6nBL2eZk0+8wKbbPrNk9fYaCqaUGPj6WMT3KCT/R0zNDw6ptH6bAKbMqCLIiXTKS0UrgURszYKsekkVsEon6tyjY0/8+wyKh5QWWOjuKMgQvMrkzTo5AHfwKairU+B2OBc49eMLghVmVXM5HCBBo3YTEdF8ynANEdFsz+P414oAzRTKafcgMs5geeKpp63QFU0pGect2seokg3vMtzmIgDf2xtqMY/zXONxwgrsvW/hzv2cdQAYkPm1tQNOoV5huOaFLERxQNy+ZlrwkzxAPfxqZPiknumjtb6hFQ8apujFG7Zd8L4vY6JVLTETW+exDjdxjWn14epcX2bFg+YlWopzr0eaWw+yXNDHcSEKp4GUNEeO7Epvo/W7fSS2NpDcW76rjWloi0wVTSpxsaJ2Dj6qKHNoo+Nbw/mxgPhaeps6P6s/CSlilavQWcIlZOLgGhVtGbmPK4rw3EWlAitI7jiszMwsCGb05SBDfIVfU06cdGQxAM4WuH+PvMGulSa6OYyCyoa7W7tRWwcp7NzvpA0xkw47WPjpKIJD8ikqmhDFdgkYq0FNV0UHVvZJgBWYxNKRWPvW1U1NokKbDj/H4+9fc5UhZEMHThf4WIoFY3D2IhiTCLj6ILaa6mikT+JiAyZJ7J4QDFuDBAlFIlT0dAWGGJTVae0n9TXAJiStpNsnJTHPo1amJYBdaCUmf8aFu8xn1PVaDFATanKZikeUGSQi9cmRSyk4HqWNTahgQ111jpxLPLcqRDBpDVG1G7bf5IhwTUDm3I4ktxzU84lT5y4Am7eO+xMUUWjdaDT1Cfp5GshnlLVxyY1AuYMjpS1iNToOkVV0XBO8/8lM2psCBUtz3PjWexW0Ksq5Z5ngdhwVTTPfeHTcpoxGIiNSlJq1lCdeRKE2KgEsNnHpin/k1LRLMRGoHgaa+YUwdWZF9gYzZgmm0AU2gMA2DFfIjaSKhoqg0g1NhWZEX4M3APmFWJjwn40Wp/FRpsRB7BOg060XazHz1xXiwdkZTZb6mPDTRW216ai6YWyqlGVzkTbG+44zQx6XXBgw96GiE030UpcXPQBx4eBjc8pUzC/B7KtQlzQKbUQm/7kNTZmc007K8/51/Ix/Fk1X43NONXZHgxSzIy+DmIBNO0OzRIPqHi2dA+bIrChvTMmWXMo3WOaDWNzbGc7qZn3ST4G7zelnCtSizbpEAeG3HOTiE1uOC+bDSA2PAEwk74NBhWtGrGJIo7YyAhYE72wrnvgmPH7Zs39RpJ7rnKo6xp33lzzvunAxqKizSqwweDQcc9DjbNKqpgkI4a27RcEBOg6JTXoHAYFNroGFVHzNCueZVM8ILzGRkq60H2nCVUygHqqaPw7pwlspH3QSEjXQNX5uEShKCUeUPzedB8bTOQOx0KNjYjYhK2ZVXbmBTY0KzkpYsO6Ju/wiAfQHhncEuLU+wxv6Dk75gBAc/45Fc1AbGai0oPOvj+wcT2Yu+Z5YBMbtUdpltuIjU/uua54AKmxqURshMUA38sznqEZan5uWGPTp6poFhUNA5vi2vmcMnyvvagSp65iwbGLw4v/t2GNzSRUtIoaG6R+BTcxq6SimX+nThfW2ISIB6DNdeuJBxzgiA2Z45OwtAzEpgEqmgv1qQoeAfT4eaPOOI5UomYSKlqe58Y9rNPITzIehNAxNVFjk7HnYxaJpFDRD55o63ZKuWd6Pcmx1hpAbHjD4onFA2ZYYxPSYBBACGymVkUzf5+ZKprao0CU+A61EcnoA1QzSXCfwcSNVGdD516HUKBw7VHiAb7ABlVMWePkDSKAEUdU6VI+TtXeMRKe62nNUkXzHJf7D9ME9psCnVetD0lEkojNIDYWFa3hGhs8n8HYVnSV9mEapE7zHJ+BgY1/kodYyhYCl9wzpT9IDTr5AvLuz38fXvner1kbDz54F5cdfhVio/rY4M2fbWCjN1DS4NITxXOzEBsi94yfCxIPcPS88Rl1nPqCGhs3XxDHKYe+otRf+PPr4Nc+cnMxXk5FG9jiAcM0M84ZHTJFRfM4EI2IB2RmJs1SRZuAt2oUpRtUtKw8djGfXbr0xTjk46ENfYENuWZzEmKTmc/oYs9EbBT1MxBmx0Dk/N0CFS1wzr73y/fAy//wWji6OvAiNmuDMbz6j74Ov3fVnZXHxOvgEswIq7EpPosbI93YOirTWzkU8bvppZlWFc2UGM8MlaJJEQujjw0T2ZiNeEAYrYIjjlKDTjo+lwzqx2/YBz/+nq/C/UdWveMapxnc9PASAAA89azFcnzmNf3ynYfgx959NdzIAiA0HowBaOdoWrROjTOwj82xpqloW9zHJo40YjMdFc1EbKTrlee5Wicu2lP4I5LkM97DXhJDZKC55vMSSkXrJnrP3hilck+9gBobKXDxITargzG88r1fg9//fL3+TzYVzX2efExNUdF4AjghfluI7xsir2yLBzSnijZOM7XfDceZdQ2l52oYuGZW2ZkX2DTAp+SOkKqxGZiIDVer4aZU0cr3ffa2x+D7B1esBnLoiPzDp58F3SSCH7l4FwCA1cdms8E+EJLRgnqfcoor67GDITZ9AbGxG3SyjvHEAaoTmFLHqd9JtPiBi5ZDFk7M5ONGyfvyuM732NoQvn7vUbjqtsdgnGbWua2RPjYLhP5E6WiaitYt/1aN2PgaX1aKB3DEpvxf19jUn1eujBmOFwN17+Kf+59bA4Jn1xnPuS9QIuj3dh2IzTyraatCTU6U82PPYh8ATMQmdM353O0H4b7Dq/DFOw4ZVA9+b2/ZdwLueHQZPnPrY5XHVIhNBf0SwP1c41tUHxuysWGSYhLEhmf4pq2x4QmsYY1nwGWSg4T/z6SPTSCtgovZ6H5LdhIBwN1z5rO3PwYPHVuH6x6QgxG0ExsjtQ4964KdAGAnTD5z26Ow7/gGXHvPEfEY3CEqxh9e2BxiIRleAIDja2aNyNRUtIDkXBNG69umoaJxxNoX2NDnChHpo2KNjbmm8j00SDyANPgEMJ1mGtTFnnpf/npljQ2bM3c9tgzfP7gCH79hn3Oc4th5cjFQFU36vY4ZdYpsD++S5r0h31GnxobLPTcx5zdYzTi/d5KP2xQVrVP9ltPLxgEFslVGoT0AUmOz4c7kS31s+ALioljh7z984S647T/9Y0JFM2E/g6O+VYiNp2kTt50WFS0xNrY0yy042VIXmZBKSI8ToopGM9E4Qjx/XkvllsU0M0G2KprmEHfLxnrDcVG/s6tIhhEqWnWNy9iB2IQWIQPYvFwV2PSbQWzMTaYYC87nUCqadN/pedl1WiVS17HFKopxmIvzIlNF4zU2VQX8NFtJj+sau2R4ba5/8JiB2PD+CJgtDQnyMate1ZSW/6xeE+4j7WMwjXgAz/iHyJH6jM85g4o2aY2NEPjRurYsyw0EeloL7WOTMrqz1KCTOlYuKhqqpVV1JafODKKZfF3CeelKsHGHCEBTu5ursQlDbI6vmev5tEnBEAesCaP1bYp+OE1gE1cjNvS1PYuFGJDcv68MbMq5yPfbkUJs3PdaITblOkr76+CubKw7rr28IinG92lquP8eWxvCxjBVe1WV2Q06fUk78/emEBuuBkmvVUgAHNTHJjef417HZBFNY0ZgM069CVs1xieqKlrV5h1i3BFyNeikk1vsY8NqbBTNwzGhOnFsPFi8UGvWiI2pila8JmZ2HZd1V6mKhjbXSaoRm5F7c6qzAVLHyeyE7N94Y0ElBqloKvvvWJxpgJplQh8bRUUr7qkSEBhQxCaciqZqbDyQbV3EBi+xy4EJMemeUZomBg6+rJaxOVWoormoaFSswqjBYFSMBS4e0GPiAWnmlW3GxbXXQc66/lsoFQ3P4boHj8OBpXX1Or+3qMAWgpLgZuNs0EmDR0nG3fi7+b4kkvs9hVrTiA0PoA0q2qSqaEIvJfpa02sufW59m/SIUE0AoFI8gPcV469XBe50H3Bx6jEYdzWn1A6Rfq3rcagnsfAamwJxwDVWUluqY1uF2ND6ti5SjKZo0InZfCX3LKwBdB5hYMMTugD6GmJQ0mGJUEUxCqSiFcfQ84N2vE8Ca4Lws9bfPbWF9HdfM1Ju1nrmmdOzk3s29/JOEtVCRa1rISWxy6+bRR8bmoAapXZiWFqnhoHJoCo74wKbRqhoKsNhigfwBp30u8Q+NmwBwXtiLcgOyeg+i45nLR7gK6iX3seN19j0u7FF0+HH4wvfyAgWagQ2JPsTE7lqJ2JDqWhsMUCp510LxcLuWojMhoOZXVTKFm5J8hm/U6mi+ahoDlqMIR5QkcXQqKEZZE/Vx4ZcHo4IAWjFNb8qmv+5HXiybjjmuW4i9mjgxbPbCBUtjvTmTJUNfRsVr6uLokgFN6FrDp7DYyc3jXWFZ6kwM17VSToldCxXEFTVK8ikd2XG++IYKh0Mn9VREQox+vFxalLRmkBs9JqtX2uajjYOPLaimiBig4XkVDyA1tg4EJs1hdhUzCU1vyOVXDNlZjM4tLypfpZszIKx4me9p0zS78n1HWrczsCmqLFBcZ5pA1Srxmbm4gHNUNF4jY20xtFrqhCbgY3YDB3HHGVFUkiponmpaCk7hlY/w3sZRcXaA+C+v9WIjTuhQ+9lncCmlipaQAARapLcM0186Aad1d8Rkhhw9rFpoMZmg/mzIdTSJ7Aqmj/zW+cYGrEpHKGVzZGxIKOjRpU7qHFnB9EDPrE5hxptq+We6TgmUkUT5J6502f3sTEfkIkRG0JHAgCrMSg3fGYSD2Kzuzwf15pFHzwJsUFTgU3PDl5UjU2/WhVNKc1Y4gH6M1WBCaegoQ+82J8csTELuXPjfwCAhW5NKpqI2OhxWVQ0hdjEpLZK/10VXyNiQ6hoC70ORJGpNgVQxQ03N3UAP71DMtfmxr/3wIkCzal6Fnxy2GhVPb5SI6lQ/i8U8U6SMPIF45MYRWK5KtqkNTZS4GcGH81KPgc36Ew11QRARmyoc+0SD8Davqq5RKktc+UeRBMmB09uqmSG65mmSB8aFdhpArWxmA+OOaUCm51lYDPlfbSSczOrsSn+j6ekovH1z0ftohRR3M8lxEbRcYX9tghcoXK8PPGHy+k4zcV1p6r3FoCc1KHzgs99uo5IIgku4/Pe28fGUkWbfL5sCus8FerQ+1/1dwTV2DjEAxqpsRmagQ2/LtJ3jAPXzCo78wIbmtmdcPHkii5YFD9Kc1NuLzMXDG4xeyDHarOU6VdcWY3zGWfdoFNSIhERmxo1Nng8PL6rPgLNgI1rBKb2IumH7jXMTxfl4hhYY7O7RGxc52sumLZ4AJpGbIrrQREbXmOzXjYo830fp0rVkXu2amzK4yCaNMliYdZmlJk6ch91jU3Y4i9NbUPumSM2Y01Fk1AFTsWgfWxwjgKYiI0vCONUNACoLHC1juF4n4XYBFLRJBlQblV0P4myQXn+0wU2XOZ82qw5OVaWM1W0yeSO5RojEtg01G0bwKRqAvidbb4fSeIBRmDjQGxw3am69vT78Pmg84uKXVTV2NAtLSGMhCbqbPh6UoXYnKsCm+nu45ZR0UiCtTMVFQ0TMSV1NiBp2U0ib5sLRGKUeECs6xPpnAhRRVPJSMKcoOfuaxZenJ9/76DrWp4DS07rn/cTSnCVWYiNZ33mfsGktd8AbJ1XpQ36WtWhLPLrKe0xmrJf/M5ZRNMYDWwGaWb1hJTWFlMV7QlUYxMiaRp6jITIw+KNpQ+5UltyFJRyKlrGnEo0RW1xUtHKGpstknsuAhuP3LPwWhTZqmhz5fhV/4vUXLAA/Nz7elQ0rYwFUN0zQTUjjTVdDr8bEZudZcbK5QjQh0wSD0DrJyYVzVRFK46BdVxpljsDADx+nptzqM7DTh3WnIx5sTd5g0562ip4J+dQVzxApKIZcs/m3xQVrZOI2Ui8f5J4wHzPRF0wwexLHEgS73UL6/n7ziudLt4l+7ETBeWnyhGUZEC5VdF0UyHw0Q6qLelax2yRkOkcW35/DSrahAGINAfpNWkSJef304/YmAk0Sb3PCOwEVbQ000m5asRG739zAhXNkCd3jBuvG31GKCOhicCGr8su5/LYrKlos1ZFiyJR4jvU9Hpl1thI2wyVFsf9nFPwAWzUmt5bmgBw1WDRY+hkpE5K0RpY3uSbWyYk1qj5AmD62amoaJ7z5Ldsuj42EmJTfAFl2oR8hxXYCBOCiwcoFlEDSR5ORcMx+/yFkSOZU9fOwMCmecQmiiLleFLFLC2j6EdseJbc5gbriUmNR8d0MjW9mFIn1xAP8DhAuCDhWJG2gIbZvg5x+vCzSE/iG7qLinZoedObadQ9bGyUSDLJYcOHGGtskIrmmkbUsSioaPL78Dphgf4qcTw4YgPgFhCgG7erOV9Vh3A6F+l4cWyTOG8ShQfHRGtYpqGiDY1rbf5dyT13Y7H5nEZszAATAGChq3+OoihIGU1t6mT+V2UVufGN5yllvxC6WB9e2SRiDP5jS9xrblXXWHLs8aWEOBgTBTZsI5wWsRlzKppRY2M7YvcdXoHvPrKk/t2674Q1HyVEi/pJTa65/Lt9joILsXGLB9jnvyYkU0K+T0ZsdGbb9UzrvUS/ZtRbTklFBBBoRcIxR2mmElVnzwqxmbEqWhxrmixe7+XNkaXe6TLup+gEheQ44nsjtSctb4ysgMIq/CdJ2fVAZglXRaOIsEFFY8qq3CSfYTjO4PCKXAfm6nWIVLSVzZHV1NUau3VMD2ITEECE2mBk+w6qhCCJa8k927Q897WNVWCjk+3T1slxVTRcw9EXkYJFg4o2RXB15sk9ewrFgo+hJop+oLbPdeDkxghObtBCX9Nh4hZHOtMAIPO26Zh5nQ466XgDZ4nY0CF1KGIjXEN8aaGXqHHMdROV3UPDQjPqEOFDPtdLYGUwtigqlrOR5XD/kVX4yfd+Df7pD58H//1fPEccP16jUMRGU2zsYkrcCJV4QAAEPs4yJ8LExQNQFY02Q5vvFQpy4yyHjWFq0foAzLk9HGdQtlEx1ZWqEJvcPAaaknueUhWNBzadUua6eM2T1RICEWp0nvC/G+IB5RSUGoXinDCoaEzes5cUkty+54v3xQGwaadVpkQj+h1YGYzhKWctwrfuP2bMf875HmcZJLEsRyqp5Vjf6aBgSK9ZVDQhAVDHeJfoaTP2Zg2VWXjKEwOfvGk//OYnbrWO8f951jnwx697rnhMja6TZ6vBGhv+LHhV0Sy5Z7veoqqPDUVxqq49vedzQrHwAaPvUkXiyKixoYjN9PtXiFztUumgxhHAk7YVC+aZ0qBTErhB9ahX/revQZYDfPO3XirW91LjFB+9HwvvJUkgpKKNS7SPKrZy34ciczSx4EtmDVhwRJkTyBCOo8igMKZ5bjmldBrgNfs3//smuPaeI3Dtm3/CSqK4xAYOnNiALMvhNf/jG7C0PoLrfvtlBlWZmi0e4H6mmqSimX1sip+NGpsacs88sJWeH0pFBtCITZYX75f6N4YarTUepplC8edVQ2934A3wBKuxMZ2sSY9hIyiopLQm1Ee4bi53slRvBEcGgQdInGs4S7lnuknEcaQL+TyTfZ489P1OrAIxNERwTBnH4m8uhMDmfebwwNE1yPOimZbL0HHqW4uknyoh1RPhJo6UpRBVtCxzv08hBeXxUHaVnmsnQPnGVSxsNuj0zwseHKFNhdgIGwXdTPH58CM25HgVVDS+MWBD1R1zXRFV8IkHzLNgXOoRYo5TI11dgWYT+lji+N70ikvhhU/dCz/zI+cDgHlP9vPAxrN5bnoCP/6dAAFyz2zNimPdKG+SoIRn16bOmrNAmKKZXBXt3sMrAFAEkRfsnoezSgf3/sNrxvt4gJ7nJqrZJBWNzy/fep6qxJcp90yvIf25ErGpIfesxAMcVDRnPRc5Blo0ZZ2WNc6AGpsNIgU/52AJ1DV+yjOvsYnMvWFtOIZHT27CweXNoHoyq0FngNwz9jDC+8XRId7LiyaBN4ZhzBKX3HNmsUf0saV42EBvyzlx7+FVGGc5PHR0zUtFoz8fWt6EOx9bhoeOrcPJjREcWbEbk6LhHMLp7VX8tMQDJp/7EhXNqLFJwuWefUIKaK4+NgDTz3uu8ovXEP1KSVGP1hU+ofrYUO72pFkh3jcAQK6P4Jk0bpQWk2VaKcSW2dOLCTXex2Ywmh1iw51szMCI6kkssgYoN46OOV184gHzXZmryRchushJ6ixoeJyeUIgoGc0ocqEBvD94LNcxRix7Ui0eYAbHdB4kcUS6N1cjRCYVTb8eKvcMADAgWXS8lymj9YQYdzKL//WzobONYXC9WGPjQSSwT8XebT1R5purDi6SebvAmnV2BSldavQcKBWtLk0Lx/TKy86Bv/q/XwDnlV2+DcTmBEdsPIGN0LjN+k4DkXGPif6sHNSpxQPCqRshxp0T+ixyxAYpDT//wovhG295Kbzv554jjoFSK1IW1ADMNrDx0SpU4itUFU0QD1gTJOZdRjPAWu5Znpeu54Qri6LVqQGoMr5XSHOK1pfgOjx1jU1msgMGDScZ9ffopAKidOM0866FktE+eXi84ljuvb0bRyUFXyvCUtM1NmawBMB8JG+tohkc0cSJpIoGICcP6WnoxBomg1NrXtBfzd5eAJ++5YD63ddTDs8fk5XePjZWADFNYGPv+7Q9iUZsqr9DSiLb7yn+x3tDA5tp10NLFa0cM15T6Tl9wqqipSyLPtExmCMEQHuQ2BzHbixfJqqUZNJAZMTGFg/gcs+zU0WjkzyJ/Xx6XHQWaGDTSSzYFmkMFGLG42mEwC33DFBmY8tzldRZ0LTcc3FcvCVhfWxYLRSiDRWBDW946KSiKcSmbNA5tPtJdBOtQe9EmWhg43BqKlXRaDE0WSRprUndBUOmounNFK+jL1MsFa5TM+SM2d+xs/juhZ5YxM/7RHWSWDkl82zO1kHN6Prgy4JKlrE1pkdqe9DBthGbaloHQBgVTXQQMvvvVD2wWVW06RxbjsjRjXyU5sZ9srLLjvOg1yTLbDGQaVR4uPHz9z1zPPElUTurqGhrBhWtAtUV5J7x/mWZFrQAcO9DPNOLNo0AhWucaNIx8T29jl6HpqailR9XlJkZyz0nkU4ODdPcS8uVjFNn1T0QERv0RYrvw8DmJEsqcrnnAtEt/rYRUO9XnAtLRhJ1Up14NOmMoj8irFuY2d8cZdazRucN37M/fcuj6mdfTzm85+gX+vwxPuSpAhsm95wRBoHpy1TPyRDEkzfaLVAhFH6abj1ct+SezYS5LB7wBA1sZtHHBsB2SgFsh4kbfp4X/vJ+EjhMLvdsN+gMg3gnMSOwifwKSBIVba4bC4FNoo4HwMQDMCqvyOSmxMFYG6ZO585WRQtEbAQlEfxbn2QnRM1/Mpax4Aih2Q06i7HShYUq0YVIAbua81VR0VzBEUXf6s4tOly8f5SmqVCQQMRGutY+8QCF2Cz2RNllnfHW9xOppXzO0gBDMvq61McmxNGgtVWqNwmZa/gdHLHxbYjU6XapQ6WeDR3A3vDzXK9N0yI2dTjpIWbeX7sHgtTM2OLyezKpaW6fZ5PJJH4sn5OgEm0JD4Jl1FakogkU6qrvS+JI0YtxXTmyOgiS8+XcfLQmEZuQBoNU5WsaZTFq6OzNB/TnmsbofaAJF99aKNmQBSu+Pjbcp3FJPuMY6BqIx98YhiVg3eIBcrNw15h9NZ6bo9SmonnqOQ8T+pmv0S+OHRO0oc2npd/rGG+US8+lE8fqHkwm9+wOGuk9aEoZjSfquXhAJqzBbYNOqCcXTC0VKGaaRmQ/tDwgQaOoh0uGmjohPEDSGabZyz1zWpSkLoWGb6VOYb+bGNE8AKl3IRQrJR7g4DtLzgZ9bdXRo0Fp4nOUKJO7XNONl2c5RoxqACBfhyHLkrvWkr4KbMrgWFHRzOw/XjvXouSqj6mD2IwdwVE30deh7oIh1dgMyQZJC19dVo3YuBEJVLDZsygjNpxjDgCwUN6LhZ6M2FRR0fiGW9Udmxp9Czp+Ug+dA6yvgp+KRq6Pa/441iDpNS5fLiGbdaxxKhpzTvj9ok6J7ryOGWs56ZFxxIbNwyb72FhUtHHmVBni8uLSHKXHk+SeTVU0//2jDAIu98xRRJfMLVdTQqtCpeuYxXxwqKIBFOfSVHNBntibWY0NqW/Te0PGCsirn0XelsK3t3ParlJG23QgNjSwKT+zLjx7klkJh0jvw7QxJJ1CVUizbhBdBjbjVBAkkj/LbWPkpr6rwKaPgY37OHaDzmkCG+Zz0DWa7OFB84IHNsK94g06AaAxSidH9vA+0YS5nRAjiM0Tqo9NWu+hl49hR6mI2IjiAR1HYFN+nDsJLgejltxzw1kiGpkXRZ72+NR7JSpaORmRutDvxKqju0JsMorYOAIbAR6lr7nqbAYs+0OvpTQNJA6vhdiQB0ymOZj30eWYaLlnRGzMGht9zXEc8r2lrzvFAyo2WXoatAAyiiKrb1KoSXNb0x9i9XyEZrVk8QA9Jr5RYJ+K3SSw8YkHAGge7zxXRasQD+AKa2h1qGh8MwIwM5/D0sm1amx84gEGYiO/T6JsGMcnf89z8/e4ItlRZVYfm2mz5uz+8qB5Q6Dt4jV20VRT5vBYVLQm+9ik5jrIr7c5LnPOSQjogCU6uENgUNEqrr3eC2Ii91x8HuckjqUKsbGekwpUuo652iZQo+pdjVHRcjM5NzMqGkm+0aasIbRTalycSFO+fIFN8V4XYiMpwqrAJrAW2IWkppmJVkUkuBERG5qQKH/G8W2ObDRXasQrGRVBsMau0IViH/H15bISKA0hNpSmD1DWRteQew4RNZARG6z9nm7ec6of/m6wR1L3vvEE62MTNml9lrKHG4AgNkI/gKoGnTkPbBgfXL3fCmxYjc0WIDY45pAGnbTwGoUDMBigaA4NHKpqbGQqmn7Npd3Pa2zMLtdCJiLTY3PV2BiIjXAd6EMmOUJoGGxtUwIUJRWNbf5ViiYu8YAhyZpWZTEM+dryvTjP+xOqBkmF/5T+1a1wglzHoObbzFHSda8R2Oi/8+sMoJ9nTkWros3h80ozlQD1xAOkZEZCOOqjNIPja0MiY43OoPv6BTXorAge+Wv0fiVRvU2Tm55r4cWtPuNS6/zabIwkxMZPUzWCJTGwaa7GRhUf96tr23gD56o+NgBgqWWtT4LYxKSPzRhRxCKwuWjPgnEe3KRMLx4ToJkaG+5MylQ0HRQ2lWlWAjgeWdomjNa30Xs+CHjWqXHEWqLrovF6HNW/jyUUFRWto+8vJo42QxEbR40NFeJJyPoI4GCQsOc2z3N17MFYoKIJsvaSZLZPPADPazGAisYv81SIDVsjqB9giAcEIOIpG7Po6wnP8aQJUG78+mJATBEbPn8MKtoUgdWZF9g02cdGcIQoYsM3HG4mFY0iSbKTxkUIuCqaxBtvyvgDHiT3LCE25ZhpTxvK6VX8ZFJ4+fCxNfjMrY9CluXW96W5mY11BjakSSMAQ2yES0W7OvMgbjwBFY3WD/E1kjfoxOCYbroA+jq5nD4XFa1OQZ1UY4PTblKqBr1lnOPcScxso3NcBhXN/rtB+yN/3xylSj57D1FFM5XabJQF74WLilaF2HCUlqKSVWZIqzsgfsyMP3l7XyUQvFQ0x3ygJokDmOPigQ1BbKLI6xBVGSpHbSupLTj3b35kCb5539Hax+NKfHzOmnSYMhi1MsP2WkOPb9XYNKmKVh5rGw1sHI4UT7T1FapIE2QMoWHZ0DVB9MZldC+YI2tCluVw4ERBj1QNZR3XBKe4JR5QofxYZfcdXoHPf+9gOU6NONNxU8Pgp5vERv0cfU6Prg7gz7/xIPzJNffDX3zzQTixbjdnvPaeI3D7/pPG9+AeFzovvnr3YfjegZNB7+X1bZSKRgOpMCqaia742BhcPGDHvKyKpqloev3E9ZVTjFxMBs6yMMSWylPE15RimqTkxhAYel6FeIAbscHrd96uOfXajnKNkhr98rEv9HE9c98H7oeGFPY7v5etEXS/T+JI+ZB5Xr0XhdSopcJzzBPuk9omW6OQPtztuOvhmhIPOPMadAq0mPrHKC4Yzfprp9RWl6lq0JnmbolBfOiiyOYj4waGTsss+9hw6oBP7pkXT9Kx6v/136iMo43YZPCmj90C333kBJyzc85aILg60cqmg4qWmsEIfRCL+2Q6sPg9sQDfcrlnALluwbyPeiOa6yaGY1Ul95yoa+53HF0PNd1YK1XRBNRHN9+akIomyT2TDbIZKlom/h3ra7pJBNv7HW8fG/qc7lksmq/yRqhVdBUnFc2TUeRG1wJ6nG4Sw+aoaDb52MlCeeq8XfPwaBnkhFLRqgQzXO/xOfJxPF22HbNri70OnFgfqczqGz5wPWyMUrj5P75CZYdDjJ8LTwZsDu0kUKUqGjsmd0gapaLhGlM63ENWOyG9lyM26MQlcWTVunDJ54lqbAhiA1CcPyqiXby3QGycQXSlKtpk1/I3Pn4r3Lb/JHzx3/0jdc9xvfUhNt0kMpIRwzSDubLZ7e9/7vvwiZv2q78dXB7Ab73qB9XvR1YG8IsfvB6evH0OvvPbL1PnVqfG5vDKJvzSX9wA5++ah2+85aWV7+f1bV0SlNFMdUjydsQSOz7aLE8CKcTGJR5AERuhxgbH3OuY84Aeg/exocEJ3RsHrjGn7rVgMEotZM/Ya8rjPfWsbbDv+Ab0khhe+LS98IU7DvnlnjEx4ZEmlr6v+N351krjawSu+4rOTnzWUZZB39HQWR6X29czAhuhae8kxucJrlndsqZsmNo1fKYPNPn3n3GBTZXyT4hpJRUbsVmXEBsXFa1cRwsVJJJlETIukmT09n6xqGAn9FkiNjgOdAx9zfh48SQARWxM5AaAcHrzXDl1+NmTGyM4tlookeD/fFwUVl3ecCE2fJH008j0xmufq0hFk6QxDRRBL8b9TuwNbNaZKhrnPrtgZIOK5qqx8SzGVBoSgNTYlPe8NyF3VqKRKWQjjoKoaK4aND1WWa4WA5vdC71iYSd1bWiSeMC//Ymnw4W7F+DVzz7X+J5qxMYOkgDqSSHT8dMNQzXkHWeKOrR9rkMKQn1UtOosblVgYyM2JhWtbq8eajjXUEBjVGaesSh5bZBOHNiMU7v3kq/GxlXLllUgNrOgonU7BUVKqotBw3OTFfQySOLERmx4YGP0salCdfX30TVwc5SqZrhn75grv9+eC5R67exjMyEV8dhq8bwfXh6o64LrbRUCQemjwzRTe9W37j8GAACXnLUIDx5dg31MtOPQ8iZkOcDxEsmpEsCR7MT6CPJcr1dVRtevyKixYeIBAdcRnUOtiuauc9IJqeI+7VB9bALEA5Qqmv3eHkO4KV1MI6k6YKeMCgA/1ZeL19D9c3NkK6marQCKn8/fPQ9/8M9/GHbMd+Grdx8uz8N9Xy3xAM8zZYsHTO678f0d1321NlT4PeY42Lg8fY0oswB9N0lWvo5ZVLQhBmllMnSYqsbr0hifUKpoo9S/eYeYtCirrvEDCrMGIjaZWzxAEipAQ9oGQAEFu7L0TZiF2CTurA4Ov9eJ1bg1BU2oscHrkGq61jyRe8bjDYUi4DTLDbSEq7OgWX1syOX0FsUJ0tb4fzeJvUWLrhobl4QwOnVrw7ER7Gr6X7mwVzgLAG4qmk/umQdneAw8/0khZqlBJ2bJgqloQhaNmpGlJMOjimgAJMAQ1gGagHjG2dvhN//xD6jiWLRexVh5/wY0SY3NZXqzACWwAWAGVXgf+x0i4elZz4zAzxGUVauimZ+jwTPtAD6ZeEAxvkVC3TCbzdWbc+Z6mgk1Jh5VNEetEA/8rMCmSVU0IpfLRWK42bV4es7oWgIe2MjZUHo8l9E9qUP6aw3Gmcrc7y2fNylTTQ/P5Z47U4oH4D3ZGGmEBh0tyTGjCIQR2JTXa//SOhw4sQGdOIJf+bGnAoCu2UPDc8bnCr93oUaNzYh9tsroo2g0b04zs6dXjUSKaqYZ43e4kxtcPIAnFIdCgseF2Ei+SoHYFj/3E2zkrf+m1sjyNd/6aq0FrP2B1cjV2Bv0/PiZ514Ar3jm2bBQzqd1nyqaqrEJoKIFICOhZgU2Y7N2kfqRVXWMfBxyLbJOAKNtE5rVT2JcThtRZcNnYIiN0b/PoyRZZWdcYFPFI69zDFM8wKyPAJApLtRcgQ1/EAHkOp0kjtQkWt4cW7rfk95UyXgw5+O0UqUM3JStGhtCRaM0HU5FozYcZ6KzQTcDV5NO3seGqox5i+JiW41MkvQVERsHdcUZ2JSLYJYXCy4NoAC0CIW0wPAN0ZR71n/bHKfOeeGiGiUqsJmQimZch3IDr6mKRocm3S+6oNF7wQObWLhfI+F5dhmOdeRwMl1UNP2sV36FGhsfD9I6hqlGZ/vdhEjkup/3TYOeUi0OUEXpANDnGpUBmAoIJsi2o+ON6xkvgq7r6KYs68oDUVE8oE4fGyGwaZL+S5H6queOq/rRrCzOUzxHXH95LxuDQh3o8OC8o8poWES+d1sZ2AjOBb1unF49bR8bXBs3SH8SHJ+0btLkI5VNxrXvugeOAwDAZefvhAt2zwOAjaogWoHPFadihyQZuQxxldH5nUSRUR9kCKmEUNGYn+JDP7h4ANbYuOSepV5ePBNf1WiRsyyyPLcoUIlnfTWfW9OhF+WehdYCNCDA+l9eA0KNIzZeNoLn+ahrPHE5YIgN3ZdqIzY+KhpJUCwIzeonMT5PVI1NEgXV2ABMviafcYGNqzC/3jHsyS6LB7iDEvp5TmuQUCUXnU3pyG+MrKxckxutxGkFcGRIiFKGFdig3HOFeIArsJH72FAqWkUfm479vZMiNp049gZ4BhWNnBsdQyfWFB5K3VsdjNU84MovPoqA+m4HFS3PPUpFPAON4gFYY6O4s3URG3ucNOivS0WTntvBSH6uj3HERijir2qkSw039JlS0VR9l/m6WszHGrGZ6ySVMuAAdlGpOIeYA8DNGfiyAt5JEkaqUzfKozLEpm5fE66ExD9vyF+XWb8+U0XjBbYWFW0L+th0O1GlGiGXe6YOOs5HPN6u+eI54NlUE7Hxnwff/1Qvm3GqEkt7F/vW+/V49e98X6Pd5Scx/K6N4Vgdo68CG2GNZtQq7jBd/2AR2Fx+yR61hhzjiA1BK0aZTkjN9WoENuVYXUkHbmZwaKo1mv3TQoIqc/1LIvdaxZNA251yzzZyLTXoxDFzo+dgNc5NCWITQEUzxQNM9LagornnJ1eDBdC+TEiNzWKAsAv/06R+aZ7nhjIugI3YFLL8UI7JPzf43JESVho509dnG+vHN6nx66sQmzh2tl3g93JSOtpUgc0VV1wBURTBm970JvVanufw9re/Hc477zyYn5+HH//xH4c77rhjmq8xjBeSTWI6ky5Q0YZ2ltHZoFMV6XGngjqlZjaOm9aRH1sw5LSSqdQ4LcrLaSXwJE5AG7mR5Z4l4QG04djmw4YjNrjQ2iotvgc2iXXBnXLK8b4mEfggcJuKVvxMz50u/HEcKXnI9eFYB1CsKFgMojhNyENLdNHR+OI7IIWHAHrTr7tYSEEJLVhVKEigKhq/1nmeO/vYUKlnABmxqUJWqdE6F8lURtOhihay5kiIMB1fkZUtzneuq6lovmPzDa9KHEAuHJaD55glO6arsUFHIDPGXDeDzwVYfFS0IbtnprCIHOylWW45oE3W2Bj9VaqawgrzhdeC4fF2LRT7Bc+mSvuWy3BuaBU2zanH4yBiQ8eAZqANDdfY4Oc2htphxcBLRiDMZ5+Lg1z/UBnYPHWPOqeltaGBQtH6ErrO4x4WQqOklJqQuU7nXiG1rlFvk3YakEgh6CCAX+hE1XOpGhu/3HOP+Eg+8QBu+Pk4EiSdM7tGyyd4YAuJmFQ07uAbzzwmFwWhKH4exvjL78D3uhB+ADuQnRStLFg65muYbKH+o6J7ViKz5u/BiI3gC09iG0PT/8Da44KKpgN5avz3SZNNEwc2N9xwA/zpn/4pPPvZzzZef/e73w1/+Id/CO973/vghhtugHPOOQde8YpXwMrKyqRfZdi4YvMOO4bp5AMQKprRoNOEbblJD2rxOdsRdCE2GgoeWYFNk3U2LuhXFA8wEBszkOmzWht6zAK5AuvvaMNUQGxy1qCzso+NhNjY14lS0ew+Ntop92Xi3VQ0PQZei7HQ17VaLrnnEMTGJR4A4HbAbEfNnOdN1Njoa6gdiqqCfD42m/ubO6lqGrEpMshSgMFVgXwWLPfMqWjlbQ5BM1yKUVruOSU9bOpT0QBkAYoqVIyvl+iMhRTwVhkG0dtIp26j2VxNR5cjNhYVTVBF4+IBAO5gL83t57BJVTRNaYwrVYZ4AgRAnwuOCc9x90KJ2LBs6rrQWNplvO4T17MjK1rchSI2XLnIQBtYjU03rg7SvWNTVLRMzfF+x+3I8fqSHqlnOry8CQ8eXYMoAnjuxXvUtRtnuUG9onvOKLWTc2MhCHaNAyAw+cGCQ4OKRtHrICoaQ2x86IcKgkwqmguxMWpsyuOvB1DRdCLS3q/T3BYP8I2Z7x0GFW1k97GRqGh0b5gn1EuXDVmiplaDzgn9UrrGzyuU10RsAPwNWM1xmfuihP5JiI3UrL6u5XmuEJtdpTIpRWy6jmQPv5dbSkVbXV2F173udfD+978fdu/erV7P8xze+973wtve9jZ47WtfC5dddhl86EMfgvX1dfjIRz4y0QC5NdGgU6ui6dPHKHV9mKoHCTdTZx+b8mVORRsLTpckHgCgoeBja0ML0mwysOGbmRIP8Cx+IhVNUdLogqU3Hlqfw53+4VjQnM/Muhun3DPrYwPgp9PhOIrgxSxo1YhN7EdsGNeZq+UA2I0ct5FGr5ryUXLnPTQNHpxRJ8sKbAIRG6vGhvVNCjVRPIBsplVdygEYnaBCZpfOh+NrhbO1Z7Grvo+PSXIMXaaDC3ntkIpmAUy6ZZVJ4iT0mMOxdvoLxMYd8KLxTbhKntyX3UajtWYAzSI2oywzxQNqUpO4xLhPFc0SD6CBjTBPip/ter9ZBDa9jl5Dnc+toL5JA3CqMLW7fA58qmhVGWNXjc2RUrVyvpvAXDcGjFm4c2GgDU3X2CjEZkwQm6Qct339Rmwvp72iEK35B+fsgJ3zXZjrJsppo3U2dM8Zp3pe+Dqku8YNEDbX8RpifRvW342YKlrIs6jXYjOwl1kIufFe9D/WhiabwicewGtTJD9FKaIJn6eomFJp9SSOeIsP3hbBQhSFdZAiEvMhiA3W2AQ06ORjnhStxH05ivQYeY0N/bkKScTrphIDoq9nHhNAblZf10aEbogtFzZH2meg7AVjzJavMxlqNFFg82u/9mvw6le/Gl7+8pcbrz/44INw8OBB+Mmf/En1Wr/fh5e85CXwrW99SzzWYDCA5eVl45/P6Ik3WWNDG6nhpjkWshbUYrKA8E7ZfIyuY6DcIs2W4QIQEtjkeQ7/9Qt3w0evf8T7Pr6Z+eSe8TlN4kg5w1YfG1pjU17GNM/F+hxahGnztc0H1IXY4ELZN0QLPA8sGYdaUHMW2MSRF7myqGiI2HRkKhoA6Yc0GAtqNbggVTudfiqaA7HhGXl0XFkfm7pZEJGKRqk2nowqgNmMDsAODvj5mVS0Yj4gYiM1kZQSFS7TmfAUfu+qO+ETN+4z/u6iotVpXulSQqT8f0VF61DExn1f+D2XNyn9+SpKB44DQCdopkFseIbThdh8676j8Na/vh1WK7KBnDbGO4FvCKpoPQmxcVCX00xCsJoMbPScrEJKpQazPVILRu/1zrLGxmrQaYgHhDk8CQ9sloseNtvnOpYEsfR5ALtZcWg2WbKc7KNUFQ3XW18fG15jMxxnqr7m+ZfsUe/fU9LRMGECYNbYUDo17/FDbWltCL/1qdvgpoeL7zD2igDHFk8F12Zcu1AmnY4HoGj++ba/uV1c+/HZ6rF9RkS4WBJgO1Fmpc+kUvUz6knLGhvOLPHU2FDqeEzGpVBtRGw86yuvjauqseFoL4D2FQC0P+KqsaGJhEXWoPMPv3g3XHndw+z95ucn9UtxjtE9gdfYAJh9rnymasVUYsB9bSXxgGkQG7o+b2e95OqIB2xZjc1HP/pRuOmmm+CKK66w/nbwYNEx+OyzzzZeP/vss9XfuF1xxRWwc+dO9e/CCy/0fn8TiI1ED5vraulf3mDRFZRQJQ9JOx3Azopyw4zJkZViU4ki/TBxjW/J9i9twPu+eh/83lV3ed9HHX2ACvEAAk+eu7NQkjm/VJQ5f1fx/3nl/wAmjEwRm3N2zEESR/APn3EWABTF7FIhahhiY9MCfRQemolQDluaG7LKtaloaqFwU9GobLiFknloGr7AxmpOGFxjY869ifvYkMNyOl+XIDahogb8bZyiY1LRELEx5Z5pUrRK5IMabv7fvO8ovP/rD8IVn/u+8XcXFa1Wg06B/gBAFNlSIvfcjYMkcvk9l+mX9O/+zCeOA8BGbCbJtuM9NFTRjH4cxc//85r74K+ufwS+ce8R7/F4dh7Pf0e5SfLu5wCkSJls0mNHsMcbA9NzaMIolafP6j64UQQZjRbX0s0ea2yo05HnOetjU+XwMCS3/C5EbPAaU7ELapTWHEXyc1JX3rs4rv55nSAIvhob/bwiYqOTaN9/rKC/P+eiXer9exYwsNHBDN1zRgSxobRnfu++dOch+OgN++D9X3sQAGxUocooVRqA1d+RuY17zv/4yr1w5XWPwHVlsEaN12j56lVGmbkndZNYOfq0zkbuY1NS0XiNjYTYCNRxmmDE88J4wyseYCE2+vfBOLMQMqNcQUDz54XkiHE+5PiqxibL4NETG/BHV98H7/is6WtZ+9vEVDSC4mNgw+pk6c9VNdgcsZHe7xUPmKLGZoOMG4+H5hMP4EyKLQls9u3bB7/+678OV155JczNzTnfxxe7PM+t19De+ta3wsmTJ9W/ffv2ie9DM7JuDdbYRFFkCQi4pF/RlHhAZtaJSMFXVY0NIjZzncTgCVcZZll8Ch90TPiAxx4nhmZT3vXaZ8Ff/coL4DkX7gIAgF944cXwl7/8fPilF1+i3m/UGqlND+ADb/hR+OSvvhCe/uRtABAm9+xq0In3jC60fi6xvsfUYaPf1Ylj0VFGs1XRip99VDQqG675zMV7Ool707fEA0hQywsZeSG5GqPFTzUXxUlqbFyFkXQzrapb4XPM6vjOnHaTisbkngXlrqoEBDV8zz2HVtXxqygYALIam8tUM1y25tEsvN7EEkJFc98X7nTLwTxx4isQHQB9riEFvFU2YNSNEUdsMBNfrq1V6xV/ppHXj8XOvhobqhzkqjui0vT8HJqwkcpYR2Q9d8k92/sRrbGhNS67FzR1CI3XLlb2sXEgNoeXiz0Is/guBUGJ3oM2DZ2Rfs/GiIoH6Dllf8bc1/AZG44z1WyU1gvhOmIgNoQlQBNtnZgIP7BrgDQdnMd07CENGjN2DfFaj3lCoHwfBvaSM85RK3+QgD6NXt9onS+abq5pO9R2g077e3hzTvp5SgO1Eq1szHlu1l/muRlkiqpowt4gNaB0rUH0XtM+NjifNkap1w+dNOFu1F3i8z+y71foM4b+QN9D5dS+nn4NyzKqUHWf4bVd6CaWj9SJbVl2NI42T4qi1wpsbrrpJjh8+DA897nPhU6nA51OB6699lr4oz/6I+h0Ogqp4ejM4cOHLRQHrd/vw44dO4x/PqsL+Urm4uQvMAEB3l+AG+WFumpseEDBTSM2ZWDTrVbRoYZRdVWBo1IHQSfbUzNAVdH2buvDC5+2VwWm/U4CP/aMJ4niAXRTiKMILtyzAM+5aLdxPtIiRCfzyuZY7NMi0Qd9fG7F4TX63ZiBVZJEXolbHqDie2gWqs+paATG5dkxXyDmQmzoNUWHw0VFczXojBkVrU5m2l60s3K8ejOtoqJVdWa2amzK96dZDic2kIpmIjZGHVsFKkqNU8wAAJbWCRXFQUWrg9go54E987RgUgU2ncnEA6oEKMQ5lvnnRzJF4TfeQ5yjYxK8Fd+dGWPgBenc+BCQ149OGG6clDZi1gPYKFhahdg0KfdMnOO6DToBWAafJDmQr07FAnizzioqml1jwxCbck/qOairOstrH3uaGht6PzZHqUr2+GpsNN3XrrHBgnicMwCa0nrMUWNDawOoKijfi/FectU6gLAaC16HR5scSzU2+vkRrgFLxuhGu/b36vfquYY+iBHYjN3PVIh4gFZVo5R1PTe4mJFrH5bWIrquFIFN8V0Yu0hCNYZ4QAViQ+81+oTjzERF6R7K/ZVJa2w2xyTZxahodG9TCdJAWXdfjY1GzihiU9abT9HHBuXo53qJxWrpEMEhvraECiVVWa3A5mUvexncfvvtcMstt6h/z3ve8+B1r3sd3HLLLfDUpz4VzjnnHPjSl76kPjMcDuHaa6+FF73oRRMNkFsziI3edKjxXjZq0XQ4TDTDybXW1XdV8P9xEzmsApukkrpAzejn4JnoPDLXiI07inehbNyoQ5SxBRvApFXw70szO9soZVKkINOXucgIckQRG/rgmFQ0AUWhQbRLPIA9tNuICAV3cLseyhG/LkO2cQLoDchFRePn4FJFq5MFsYOS3BhXl3QudwXXVVQ0q8am/PXE+lDxlzFTLXXV5siYz3j2CABgaV07OVVUtDDxAPMzaNR5oVQ0X60Ymk3Xs++hpAZkfsZ8jQeEeGmaqLEZZTlsCnRK/L+qzouPYcOB2NBrVoXmZmzvsBIBEyrwSGZS0SpqbIS1jTaSpVS7xb6dTeVc+HDEpvgOrGHB5JpGbGQKS+rYP+lrk8wh6hAW6ydmnD3rJnPUaSCCyme4bgJoGevjq/qZpw79OMsM2rYzsBmZ6zMNJmtR0colgqLeQzGwyY3zpcZVIX29hKT2E1jnyyl5dFz0+JYksYeKZiA2RLCIo36upJ+0jm0YgU2mkgjoN0koLXXcKxGbsb6e/UTv9ScJm8TVd8015hBTTZs7WlBGpuCH1tiY98BH86OIlqoTnkI8AM9lvpsYdVYApXiAo1G2RUWbMNnUqX6Ltu3bt8Nll11mvLa4uAh79+5Vr7/pTW+Cd77znfCMZzwDnvGMZ8A73/lOWFhYgJ/7uZ+baIDc6IMd4mRwyzJdyMzpYYvEKQXQcqhSlheAiAdkOUi9awAo9OtCbEwqWr8TOzNlknFKRp9NIj0OczPSQYH7vRLVQDIlHpCZ4gFoNFCTxAP4Yr2yOVZwKJokve2VeyYPrEuWuxNH3uvANxhejAcgyT1r1E8XEJeLN+unY56f7GSZgU1xTZyUFkcG2lJFq0VFM3/n4gG0jw1Ascn2Y3MO8mNU9Q/B70Aa2s75rtqIveIBIQ06hWf52OoQoASUXQ066xTWK6orp6LRGhshO1enxkYUoHA48dLfcRwAYU3yqgzvoS62zYxagbEKbGxHUDJnYIOIhUAB6nbo5m9fUy5IYCM2DdbYqOLrCPqZ/7mTssqUtjgi2W++R/GfAaoda153gFQV3IN4jQ136rlTTi1E4c9ldB1fH2oZXwy85Bob01HHMW+MUhX87SAF8ij5fHzdpYrGlD0d1wBpvipgN+ZZ9frKE4AmFU3fTzxn/F/yCXgQ4hM6kZK1CrHZMGWv6TEB3OuriNhIVDSC2PAaI5eSm3QpDcRmnEI/LQP0bgKbI5OVMQ1i0yMBBgDACYLsbxr3yPz8pM1pKRUN1woRsQlA+AECERuhNYHUrL6ubQyL8S/0bCpaN45VM+VKVbQJqWi1ApsQe/Ob3wwbGxvwxje+EZaWluDyyy+HL37xi7B9+/ZGjm8ojk0QGVNUI+FUtHLCrzLEpqrGhvdioQ8W5wBzw01EKcB0E2eWSDIKC/uKyTiVSwsf2N9BVdFCTCE2pI+NhNgMU1numT9wyxsjOHuHWcMlNbHz1tiQB5bSUuh3GcICFY4grbGhinBOuecBbdBZvKfrCcRciA2dA9WIjRwcTdOg05KyZNQI2oAQoFhs+2xVsegFVXLP5d+Pseac9FzoBjhiyJjPesJ7qPQr7RhPzaeex43TTNBUVnacMT61dmpchpt5FBXPp4hSVqDZ1vxgiN6k9RFFg9XiWDj/s9x0uBWVRqDuSMbHj04IOqlITaOUNjoP9XOtr6lLPKCbRAXlq0l5fYIipknxszshYc9fumZSOesFoccE58LXV0UzHR8bseGIqjy/6WuTOHf02aLnh4kkH9KNayteN/pMG4iNqrEp/p7nuaWKRp1u2nuKmg+xCWmszXtdmc17bfRHNZf2JMV4HyexbpRR0gG0D0J7+/iK/+1juhEbSezHYHZgUoUIMVGT1jGKBOdknSkC4JEo8U7n6kIXmwgXDA6exMJ7zQMbitjQPdiiok0Q1BfH1IgNrlVSjY0voUsNj9H3JAakJLaWe26AitaVqGhuxUVa1170L5tsDFMHNtdcc43xexRF8Pa3vx3e/va3T3to0eqqj3DjGXtqils4NGtsnKpoZAExVdFsmNJFRaNyiwBF9gwX6SAqmiB7KhnfjHTNgP1eCb71GaWwSBkAo8ZGQmzYA7osKKNJiI1PWpSeLw2AaM+IKIr8CjLsPuJ7uqXoQJrlgtyzXhTsBp3uzIndmMpGRuZLB8RZY+NyXLHGZoI+NpLYA4BJAaHPkTQH+TFciE2/E8NgnKm/LzHhAHouNJOJty6EiiY9y8cFKhp/Xn29Ibi5avhUYXNK5Z7jyoBiTIrDt/U6sDIYi9fZhU7wcaHxwNdXb+azUarvwQLp/bEqNI0MoaJR5UK0TYbYIIKD9SdR5MhqCtlbADCaBM53Exil40YDG1r3k4PppHDz9bEZjjPjWFKPCdyvFntJse5UUlTM7+PNlJHuh8G9VWMjrPFo0yjr0c9QFEWiGKHxHi5IezlWUs3murGxRu9hgU3Rud7cu1VyzqCiyckYHdjI88xlnAJEHT06TzKyzgHIzixXhdStHKS12A6itysqGkVsTBSo+Iy8vnprbIT2DDRJyBEba78RHBRXH52+oJ4n1ebO9fR5bIxSa08YEISU7iknjMCGIDboF5QJkkmYRPSYc91EPXObLPlUfE81dRlAz0meuKCWsvsAoCX1h2WPoBBRHm4bhIrG65ALlodGpKnh/rDY78DJjdHEVLT6Iz7FNi0VjWfsqS0o/jJTRXNkgqlT7Aq4qgqbd8yZGt9zdalo5AHzBUIWYuOpGeAa81VGFyyJs0lV3njGaSw0ypN62cjiAWHcUVMVjW0Cno3YRUWLY63qwR/6RUJF42PuegIxOygx52Cvo7n6rsDGoqKVx8C1eRJVNJ6NKgIJLbmJTU5dzfwA7GDAJXKATjFHbHYLiI3uSeRGYCWj9+tZ5+8EAJNvz6WD0VwZRclcVE7JeaEKOK6NimYo0bGtmkNBVLQxPiPF75M6pRSJ2N7X65lB8WHFzz4ahfT1vMZmXSlX6uQTrQmUHCUzk6vvJSYjmm2ITKholX1s7OQXzeBTaoymiehrjujGTob+O8fGVEHnGH25Su7Z1aepeC023lPHqBNL9wCkDvkSQryPzdFVrBcy99fdLLDhew11TA0qmqOoeSwE6iFS17hsiVQ0oV5H1za6n2veoDZUPGCHoqIRxEZKJDp8GKnZsdSgkyZAqUADgFuRUURsHPsfOtBm82bb/+qRxtwSHY0+bzFJjJ4kCTAjsCnPpRcYcLhM9bER5J6pD+rrU0RN1ahhHyhpb2b3AQCMMoBJBQRw3AtV4gFsbcFnB0GGLetjc6rN5E9O8HkyGXhWVul38z42jkww7qOcry1lb7ouKhpDbGpT0WoiNqpBpwfOlCa7zwzERghA6PlI0r/8AZUknyX0zOeI0V4itGiRizn41OG4opJuZmWr8KBRyXB9702KgCj37JA5pNnauYoaGVfjSzzXOqIUaLIwg0nTpM388Ppe98Ax+Efv/ipcc/dhJ+qDpqWCTaf9uI+KxpAjgEDxgPIabOt3VH8lKv3Ku9jr78VzD8/GWg06VcGklkLuk00MN543f/JW+Gd/8i01FrqJUpWeR09swEv/6zXwoW89ZHwvgIuKJm8iIckOn0kqQgCs4R+j0vjWKnoeuMbqGhtTGZDWn1CTULCcOTx4PTCgHqbZxNlWbirgoqpooxTe8MHr4Zf/4gZjLCIVjdDAVLCdxIacPB4DgxwMSKqLis29gFJrAfSe5BIPkJr6oU2D2FCaOA2KVcZZQimZA43PGPa/4vsrp6KtsMAmVDyAr8+1ERtWi+rqY4NzlKtRGmNme5rvOZYo3YjYLFciNub9xuVN2k8kKhtlLPA5FBMfghr+HkV6TXa1O1CURXovyh9pUBZFkRYQ8AU2iekjuKhoismBgVUDiA3eHy4ARMdTRffkNTah4gG9jqaXrwYICOR5Dr/4wevhX33oRrUmKXqgUGPTiasbdOrAZgtU0U4HGzk406FGF1yegFD63eXNrERsFD2Fw592xsWJ2LCurIbcc13ExvN+pefO+PTSs1FbFY3QgyS0h56PXWNjB1dSk05p8/f3sdHnS+sjXP18fDQHgCIgxvkWkweTw6yLgnhASA1DlXhAEdjURWwy4xyr+mlIJstgZ9bG12VB21fvPgKPHF+Hq79vBzauPjaYmcWNATPR20jRjrpfiNjQREUAYvMDZ2+HuW4Mr37WufCkbbb0axUVrQ7NhB+D1iwYcs+s4PrTtzwKNz68BPccKhoM4nvphjNOc7jhoePwwNE1uOq2x6yxTSIeEKLOJhmlblAHYpX1BwGQRTG40fmB54svKVW0UVoihzq7Si1h15SOAX/GIcwT+lxTymg0441ju3X/Cbjm7iPwle8fNuuPBASE9pCR1oA819+BtDQa2EiS+fz78FmaY9fOlnuWxT0kqvI0NTZS0XcUAfQSD2LDg7RyzEdXimeaIzZ7SlW09WEKm6MUTm7YinI0OYfH42uuRUUT9vyQc1WIDWFpiKponoSARUXz7jP2PopNt+87vEqOaTrFAIKKbE834+UmqaLRJCKfQ04qGkG/cZ3CwnRuiDxWITYAOgiSmlBy4QNctw0qGtlD8daHUsRcphAbsicoxIYGNmptCERsPFQ0qXQAQPsx6wECAiuDMXz17iPw5bsOKcl42seGs1q6CW3QaY4J5/m2uelQ9DMusHEFEHU/303srsmLTL9bylpQo7x7FxVNLyTyMfods/i6LmJDm2X5ekNwdTYfDxeHX5uKltMFS/+dno+9cGkUZ+e8racPACb1iRbReQq6x2RBpBkOLgiB67u4adINJjdpdnhOdoNOKh5gZv87jgxo8f2OjKByGqPKwMZZQ1GeY1U/Dckk/yjN9P1QgQ1bqLRcdW7TCxyUOcyiaZoZBqH2PcfLRRMdLqoEtaectQjf/Y8/Ce/6mWcpvr0h91w+Q24qWvWmNRaeAXrMQhWNUNEIfYf2ZTmwtAEA+n5RGdBxphv54TWoqj/kfHWeDZSktEOMjq9A74rjmYiNmdn2bcr0GvPEAXXeh2kmUmYAZElU6n9luZ5ntC6oqV42tB8SUkHuP7JG/m7fK7q2UTqyOsdOZKCSeAxEbHaSJJnPuaqqsbHEA8by8ys9b10hoAw1yUE2Jfl9jroDsWGJw+39jhrjsbWhjdikuVFDhHODSwPjnJfmcwgNjzuUSlgmlRt0+qho3E/xJtCEufa8p+wGAIDvHTipRG8424COFQ3RWU5VBJCpaJT2zROgLlo5FXLgvV246Robcr4O2qTrvgLYQRmuuabcs11jg+daV3wFTSM2OkGEc8Fs0Bn2PehT+MQDXOhrnSad9P7jnoV1UPMCFS2J3eIB+PviE42KZkK+E3zeEcED6KKpNUs8QHaY8BA+KprUfI1aFEWGgAANdIICm0DEhmeIfDxcn+qNZJXiAYFyz+hocsSGfkTKXPh6elDxgDGhounrYPNy0UwJT1PxzUVFWyByrByx8ffMMecJ72PT68QqsxqqiqblnhFdql9j49oceeYPryOOV8uhyvfcGOfYRGx0ltJ2WrkiDBeDCLH5XgJRFKn5dozW2GT2d+LxAcJQYk2D5Jmq4hi0R8dcNzbmJxVDOHCi3CQIRYHWMAyZc1VFRXMhNnp+6gRFHVPiD110rIr/zRobdNCqERs6zr6jsB0AYHOos9s8+STx0M1Mrs7M9zuJWssnpT5wk6hoxt+FDL9LPGBEzpFm2vHcUDzACGyCFDKL77DEA8rjqO7gFspuZtupqTk0SY2NsNZ0YrNPFje+R+P/SDXj4jxRFCnJ56W1oSVUM04zY4/ANclGbMqWECpgp/ezen3VSTIox437kL6f9H0+8QBO0/Y2nRYQmwt2L8D5u+ZhnOVw8yNLTELdTUVTPauEZ3ngQWwkyrpqGWHJPetnA99bVWMjKSFy/2vekyQcsaAM1zMjsCF7KI7R1y8mxKR1HgMoWj8aKqmua2wQsbHvk+tZ1kJa1eshDbZxz8LPzQuqaF2iisaDYk1Fq++rUDvzApvMnlC1Pi9k/tEWmH63xEelFqvssRnYSKocrmMAmFml+oiNfk+IsxDiZPs2Lskk8QAnFc3ToBObMPIaG6OpZmARHT0Hqp7GaUIubi//XkrviCO7IRyaKPccI6RdPd4FokhS/K83LXTyXM6Xq3YFfT50PCepsaGZtzTVCKW6DoQ6Q79jLNQtuBqJLvDARgiweRG/L1FRZVwhqRi//LzW6mPjyBLiYk4RSS73bGwSmP1SQgMxkQzPreC3iorGx25T0eT3VZnm0xf3ryMgNuM0M5DXkHpAABsRne/pvj8bI508CKmx4YEfnV+9CdBMn1HZcF7DQv8OIDubRsNG4mhRBw3n/upACGwCmjVrxMYcHwYDqGjF75VPXGaqGhthXewkukZS2qvw2uDzirQ1/HouzgOgn3sJsRllZv2Hi7LEkwomSyMg+cHWNhpAUGEILpIS0qDTV2PD1220yy/ZAwAA1z943AhkfeIBuNeJ4gFSg86Y7sPFa1YfG2uv0PeiOrCxkQmFnrO5OtdzU9F4UIZ728l1WRVN1dh4xIFCTDdtTtSxNGJj34dQWXcVcEl+R/mShdgQSn2V0fUB9yyliuYUD5CTJoqKhojNhL3FzrzAJjU3p9qf96ARSjwAu1oH19gwxIb22qlAbADMrJIR2ATV2Nja85JxxEYvMvZ7fcWhkpmITfEzDYp00bStipaS7L8LsaGbhiQeIEKsiK5EkZHl5rU6Phlfg4qWmc1HFWKTmNlO2rXX3nDcXFdcHBbVZmE6rWaNjXyf+XGHrLmX7mMTvljwRRu/h9eiOKlo5Lqh8fWVBzZZXtAPJcEIfr+qJNl9RqlouTqephBRSzxZUG5V4gFcypY6g/SZx+zXgNTjUMQK36uyxg4nHs1GbMyscR26HTVKRQPQ84yLB9Djeqlo5BpztKOb6Cz6+nAMLrqw1OuBHjfLeC3FdBlCbnRcUtNkSu+SarIoB52itlFkItAAmjoditikLPB2oWJdlqxA44pe1OrUolnjEj7TTai4hhux6TgSTVw8AABg7zZMaAwMJbDieJouHceEsjSUqWgj9gwCBNbYMPEA6h/QY6WlShseUqQxszVQ7ceeFgY8cfP8MrC57sHjxr7XFShQaDwJR80X2IjiAY71ldYG4/4p7X8GZZEcQj1bvGdhtw4Vrfj/hEM8AL+v13Hv7yFG+9jwIM4UDwj7Ho1Iu9/vYudoEaSagc0JFth0E9WME61LEkn0s1mmk03bSnXNSWsez7zApmLzrjJfoMF5hVVOE30YpSwBAKF7eAqbaVZpri4VLVAVjWfpghCbwCQ4Ljh0EhqIDW3QmdkLF34fUgR4jQ3NTND7xjd44xwUFc0MgHjPCFVrJGbCzLmWGo6QTEXDwGRzpCV98d77Mjp4DrhZDBRiox3tubp9bFhGvkpVTTIaqNLrqGtszI15pAIyHShY4gEWsoSLoHZCslxOQnBUQRKVCDUMbEZpDisDUzCkyya/el4CsrGuPh+4jmCWGGtS9EaVGc/wfkRsxpqigMdIM5uKZjSgFJwal2oep2XWFg8YcUegRGw2TUoNdcpCEJtCCYkHNrFWNBqlYodzek4uFCvNzUSFrj9rlorWiWNrbAAOKpqB2JQZzXGmgiBeQ4HXEMVuaILMdw859W3O6DWi60pc+5APJdVBen2HRMpAV9bYZCZtyAps5m3EBveZ42sjQRXNpFO7KEs458clkj8pFY036ORG61GkY+e5LYjjk9yWeiYB6MDmln0nlP/TTSIjQck/4xMP4HSuYlza53CJB/A1it4LLoFMrZNE4jH0dbaRXwC7Jw6AXR8k9cszEJvMfD4nSbgDAKu7NM/V8HsCGjoD6Ofc1+DWxc7RIkg1qWiI2HhqbDoJ9XHJnkDmtqKiPXH62NhoSK3PexblbTXFA2L1IDH4U5J7DkRs+gSxCXFAacbBG9g46z3cQUEwFa10nGmmx0BsEn0+otxz+ZoLsaEPjpS5kJw4Wuhv1Niw7KiuNTKPQRWX8LP4Fl+NzSKRu8UADbNePrlnnNe8p4beIHRWedMxL1xUNK0YVH+xkK+jVrfTNDszA0OLa/k+76oFokXcRSBpw/CU/ll8n53tDrW5bqLq6rCXzdCRzOBqbD6jwhXUeiqw0V2Zi7HrZ5E6kbrGBje8mMyhnASReK39a6OF6DmoaPXFA3SmsTgfO0AaZRlz5t1zkDreUnA4T7Louv7EEYg6EmF5ThD5WNPFmkZsep1IrrGRqGjGmqlpYFwggUuro3LRIimM9+2NXCmKUuW2z3VUrZou8HU7m9y00zXJ3mx/pkPWWn8fG3k95jU2AFTyeSAk0Uw69byDikYD4FGai3u+z/g1TOJITCRyNgjfOww2A+4znqbTkiALAMAlZy3CWdv6MBxncMNDS8XxOL3TIZRTVxUtzW06o4vqSwNAHxUNm2bTczQ+z9ZifV9tRIKPXWojIMo9Y2AzwdwvjknEAxITnUpi+zrWrrER7pOLnaMQm7pUNAGx8YkHmOugPp8nnngAy7rVNT9iY4oHjCqywe4Gnfpm6AJy96U2EJu6VDSy4PINaJxmcGh5sxxTeGBDHfgQ6wh8Sad4AMuw0kJ0bJ7Ga2zoPZOa8EmbqJTpofU8FhVNWFDp9KIbDO3bwh9ayoPH8wiSe2aIDac49AIQGzubV/yPm0a/xrxCk0QYDMSGIVe8YZ1MRZMdbBrYZHluSbniOOgxQqiePttN+PbF+P1UtBCnP3WgtF0W2KhAgDiqdKE/vjaE9eGY9TfQc9lXYyPtrdV9bCZEbFiNjSS2MmbOH+/kbo5TB9P8WN0kEhEbywmTEBtr3um5jWMPQck3RykcWx1430MDbjGwIecvIZPYmXs4tgUSOM0Os6qLvY43ecK/D9dAitjQvUhyPgA0fZnXLQD417gqE8UDktg6X2ocseW0F7nGppB5P742tKloDE2YLx08lyoaQCnJbQQfAWtE+XG6n0kJVEr7lY5tFvpjkIDf4Ua4uE8TRZGqs/n89x4Tx2MhNow2TU1u0KnXLt4rT1N9zeNINTYbEhUticSAzlV6oJIjwrFcqmjUqDKbXaQv3//DK5ve9cWQey7HiwG0WWPj/h7J5/OpolHaJTWl7hokHkACGwmxsVD3SMubs+cIDeWeB+NCaOfRMmAKtTMusDE27ymyQhI1bBsTD1BUNJd4AOGyGg+Tkb2RM4rULFW0OuIBFLFh73/rX98Ol7/zK3Dno8vBnNZizLnxnirD99GJ6aSiMX18yqvc46CiufoJ+TZRmumh2SCeHa3i9tLj0SwTnhN3WqIoUosCbpqarlU6CuKCVLyGn83KjDKVVZ5TiIu82LgUu/Be0AWuCsbmx6SITZrpWqUuC9o0hU43UKykorE+NvgdCmUUN0dEbCanogHo7O1SGdi4qGh1EBt8/LkjgHMGqR4cseFODADAoyc2dFEp63mjef65NbY6fWwsueeaCSMXJ9347tTsYRXSx4YG02gWYlNBRfNRl5V4QhwZqHKV/fyfXQcv/v2r4QSRCecm1dh04gjOKnsnGeiVgBJSwRV+jlxSGRNxC30tHe5HbMz1fY4hNmoMpDZS+rxMRZssOAZwyD0nJDEl0YWRpofiAQGIDfayObIiiAekup6lQGyK4/HAhjtkJlpaPYe0w65f4w4gvs9kg/DkhP6bZiG454DPp0E62pfvOlyMx/FMoS1OWmOTuvvYOMUDyFog7X+dJBZRH6WqxvaH+ZAam8S9nsniAW4Gyb7j6/DCK66Gf/uRm62/8WNSQRk8FTp+n9/z5k/eBi+44ivw/YPLOuDy9LFxCYHUEw/Qx10ZjOHkxkitSRJi04ljJchAg2L8OYpMSv7vXXUXvOhdV8N1DxyrHAvaGRXY5AwZmQaxkR7sBRalaphbdppwY8hzBqllWkHLJ1aAZqmiOTJlkvnknu87UjTcuv/IqiXx6W3QGTBmalqiWN8Penn7iW4qp2krmveJY8NNiBcHjh33LBacF+kcjBobBsW7kCtJ4jRXDhfA//d5F8Lzn7IHfqzsXk8NF/yTCrHhXF37oqOjQ1EL6tQEiQc4An0lHkAWmFCIl9ZbibVKDLmyVNGyzO5jwxt0lnPCoKLldvBEzyVjz9ck4gEAtjKai4qmm9BWH1NRfdhmwZMb6FDSYIUjGfuXNtT1KfobaHSHC0y4aFeu1/AeWQ06ayaMOFVKQs/GWW4E9F7VLuPZ5XQYE7Hh9SfqfVIfG3ZaqsYo0pt/iArPvYdXYXOUKdqFZJSK9oyzt8FLf/DJ8MafeDrsmC/pO0KvErreLigqCAnemPws3idXnyOXcToulXumCAdXOkTzBjZTKEOJ4gEOihEaZ1WE1Ng848nbAADgtv0nlNzzolJkzIzzw/vgEg8AMGmh+HuVSXQ+yc+gexaAnRSjgQ4+fyHiAdK9e/Wzz4UfuWgXXLB7Hi7cMw9veNFTjL/z9WthQrnnLLf72Oi+evJeEcd6zDIVLbLqL+nxeJJWi0LYjjutaQTQz4HxHioeoPYg9zx94OgapFluNEHlRq+Zte4J80Rap+86uAJ5XjRb1X1s3IGuSzxgm2pbUR3Y8GD7waNrcM+h4jwvOWtRRP40nVZO8CAVdzDO4KZHlgAAVMPqELPTGaexVWV+Q8z3YNMMRNF/w4+2UMfFUo7Jiw2TO4CSmapomrowrXiAogWRrDmnYMkNOuUo3mUYYFQhNgCaq2wgNkhF6mv40TyPKsTGPgcJwqZCBVWUPClLSalor372OfDqZ59rfS+ARl0wsLEQG09RpxHYEBpKL4lJ8X+YeACaK7ApGRleowpIWa4zP9yh4KpS2unOK59b1ceGOFgUyTPEA5hyl1b5mQyxCaaiIZoRsOa4khk8I2shNmlmPcMHCGIz1010PU1myz3XVUWjVCwAPzffZ64gl7+HPlP+ZsIksOFUtDg2lKqUpKlTFc2NYum9IIY6zWtxjnh7xRAqWjeJ4QNv+FEAAPjiHQfL77aDPLrPYAC0sjmygu0OCzjGJPAJ4d/zNZAGNnQvcjXR88k9+/aVKpOQ7AKxCUAglEplNRXt/7hwF/SSGA6vDNR+tHuxB2vDjWK9IjWmc05VNHPfpfczJKiTWBFScqYKsaFrTcTZGJ7khuTTnLWtD3/9xhc7x8zp9IuKNm1/j9igkwQvVh8bB1qs+9jouS2hLF0SDEgJHr4/zHkQG2yhgciwVL85MBCb4v9eRwfH3HCt9q0vQyOwMccr19jYx8JAZGOYKoSTJpHzPDfoj656at36pDrRw9eHv7/9MRiOM3jS9j5cctYiLBGZ7DgqvkvTXEnQTuYMXY+R3sZrr312RiE2rmi+jvmyTZidASgEBHw9bwDMyeDivkrN17iZqmh1+9gQxMbR7GiYZtZC6pN71g585dcXx4rM76PfA+AIbLBLcE4l/rRznOd0wptok/peH2JjdCzWCx53hF2BjUxFw/P1X5gFFtiE1DXhhtXvJOq6Y4ANUGxEVYiN63mg36/18cPUnygVzVBFYxQQriplIDYVzy2+d548f+NMlntWgh25KQntSxz4bC+RfAZohormoj/wYAmphbS2hW8SB5Y2jKJSTeegNTaFo0CH5qOYoo0sxGaybDt3zKW1bpSZPay8zYSJ42yKhRS0UuqUjByBqJT0cKGySQy1amxCevG4an/Uhk7HJaxv28s9YXnTlrTmxfS0gJ6jOZJxZ49S0SjCoXnwsrPpU0WbDLERqGixLW9NjTfxDaGizXUT+OELdwKApoUicktpUp04EilLWWYr/NG5HULDkxAbucbGFNxx+RlSHaKUhOFiC3WsToNOpCIbiE2ix4WnwdFil3hAHOn34v63SJKAnTgSaeUuf09LxguBTdlCAyWhJSTNaNAZ0MdGBzbufVddsyS2SiVMxMZNLUPq2MZIJ30oXZ5/hLbFoKaa1QdQ0fha87ffPQAABbUxikzxlI4jOUN/7iRazGVlYwRHy3rGlYCxoJ3Zgc0Ei6ckrYnWI1LLa8Ox3pyEDCSAKYfMN0QcW1UvHIDJ+9jkeQ7rhiqa7JyPSGATIvfsU72RDGsg6DWI2UKLx8KMgkRFw8Ayz+Xss6sbfBXEaqiisYXdlaW27mduZ5lcttgzN0Mcp27QaV9zOi8p318UD6iJ2NAgs44DB0CcTH4d0ZktC1axF4ZuLKqz2vZza34HpVrhUDPyHRJiUxwnd2bkQg0LiY+tYmBTzjWHo1wHseH0B+649DkVLc0spPDAiQ11v/udRAWSFLEBsDOBIYjNiDj2xf+TOaVcmU6usTGdwRAqGs3sFcctxrdAnBJdWM8znLYzzIM9DM6pfHsVYpPnOvj0rc9aJIWtWbihC1Q0Ooex/8rK5siqV+CiKaogvCIIQFPOniAeICE2LiUuSTUz8aDSVSY36Iy96zxvos2fMYmKBqDrSdBQAtoQD4gisY8Nv+8jPrcD+KpyYGNfzzQza4v4vRgJyR9vICgoTYaas0FncI2NXrtciI1PFQ2fHVwPtzOhCwlVd6HneF8lWhstfMdjc6OfwzHivi3O0zQAsSF7Pafd8wQPgDzPEGHZGKZWjQ0dBx+7VT+lyjLqqaIBABxeKQIRFKOgcwAThlK5BZ3L6Kc8cnxd/X2FiUr57MwKbBw3pY5J2TFqi6RoSmWDHIsAnQyuDqohjpdZY2PyC31WIBv6d9ekHY4zy0n0OTH4UnBgw8QDpM/hor0mUNFwDIskY29wmB1KLj4Vp1RAGgBop2rzOricPjTa36AKycJFAY3X88gbjn6oaWBLawiU3HNgHxs0SaEutMYmU5u8WSPEnVlefE1pUpYqGnNqFbc4iY1GmFJAS52plEhyTyoesGexePaOrw3UeIvvNNcHn9gGN9czX0lFcyA2Ayr3TPjV9L18ToT0sbHFA8LPkRqX5ZYdNFMVLYSKxuWe8Z7Q3iJSvwwAudbD6uOT6uZ3PYY4Vo0NwI+KuGTIJQqGpFSlEJuNETlHExHj3ei7SUyyxp6gi81PKp5g1NgITfQAKD2oacRGCGyIQ+uTyZcQmzgys/rUnn/JXuN3RG65sqdEWeJy+Vw8IASxwbdXU9EYGiQonxafFRAbSe7ZUUMYYjxhgY6/VxWNBjaEIkeRGPo3Vz0mFa/Bt2wjQXgniUTxAKllAAChoomIjVljI81zmlzUVDS3PxJCRcM1kTboRBNrbIQgEMe+PkzVtaMNgq3AkTWKRavTx8aV4MHkQc9ITpkJCBcVTbqWqy1iU30MV6CBqMHKYEwUKVyIjT4Gz7TiYoQZJZ/cM0dsaHM2n3FnxkVFG4wztZByPr2PhxuqisYVsSSqFm/21u/oiY3fR1WxKIfVpeRCoW1utDaEwroDhqK4YHur305m1+e4jG+mWu65OqPTIdljk4oWq8zL5sik6qG5ngc611VmOrCXDc2e00wx31B18TVms/PyvHKbisZ+x8/0u4mxOUkURHrts4wEhBP0sQEg0q8lD9hVs1MHzUgdzzzP3quN05B7Nten/ZSK1pHlngFsFE9GbExnA7+LU9HqigdwkRWJultI4tq0A8no+kMdNlxDaN2Ds++Q0HjXidhEkVUj5rLQJqMutB/PQaIu0bmtamwGY/V8WFQ0RomjTQp9iAnvYwOg1wVDFc2xD7mcIXrMphp0dhO5dgKNU6so7WX7XNeoJ6D23It3G4wLrLWj5+rqY2PXgHLEpvr5kdB/l9yzq5UEgEwt8+3tvuRjlXWMscrd49HUPu9QtOS1HVV9bKicMxqdq7QGR+xjw6lojv5E9LUFL2JDqGiZuQZJ/siAiOpIezeAGQxa+48RuMp0U1rovzmiNTZ6/JJfUxyTBTa9cMQGx0Fvz875Llz65O3qfNAUXbmKiiawpB6/NTbsRtYtcgXw19gAaHj1JIG9nH1sfIhNhoiNTJWgRrNkdeSe+UPpFw8Iz86GUq7QuHiA5GP2Ogn73abbUSogdTBcdD5vjQ2eA0NsVNPKhC+o5uftbtt5sKgCR2x8D7M6PtmgaBBoUtFI4CfMDVemMBYcmPAam+J/eh3TjPSYYQ6FrrFJy/MS5J7ZMGmDR6rWxwUvcBxozSA2ulkfgHYUQjrZu0yvMebrFhVNNbSkwVxxPhftWQAAgEMrm4pbXPSxkaloPPPoS1igszFkTk5T4gFiHxuO2HgcX1PuWaCidUvFnpFb7pkqMKnjOhCrOI6swNxlZnDmvk5SJp2eA967Qm2x+BsNznFPyHOAExsFTRKDJL6OjEjiJ0TumauiAWgqqVFj4xIPcMxver7TJB2pcQosdwp5IqJnBDZubaRt/Q5cdv5OACic3DmhQWscy5QlvvZS9UoAP1qm3iMEh9Jzk/HnxqKb26wSXodofK+AgoeaGdjofUpCX8UGnWRuZGR/pv/b6L5+H/dHthsKfrG4frn8PR8VDV/D4Ee6VgYVjck9+xAbAHfyhAoF8WQ6XRtcAiHUHzRrbKoRG/4sL7LWJz7DuX/ujjn12o8+ZY+ah5Lkd0+4ViYV7XEe2KRZDp++5QDsO75uZStcjtyJ9SF86qb94k2pUjpD/e6TRMnB2cfGo4qGYxun8oNFzZJ7DqyxsbT1rTFk6nWOVPkcNb3oer9emZJ7RkdJcPz5RFUZUuJIULTCCGwcGXBvHxtBJpIeFzd1nd2S0S60LNP9DSRuOTUe2HCJbV/9Q5fQYoZpZtQQUC685IC5FnF6P+rW2FDxAKpqw4vsnapoNCBk53/jQ8fhT665XymS9TsmFU1CT+gUoPSmSTKQACSwWWXiAS4qWoDD5pqv/JhK7plkufG+nLtrDvqdGPIc4JZHTqj3G9LQBhWNzVdP13GeOEk4YuM4xwMnNuBvvrtfQDfNuSBlOEfM+fNT0Yr/O3FkOVMAAPO9Erkcpgopt+S5BZqqJTNO1itcd77zwDH4s68/AIfLJnfWeTgaypnj12sF3zu480P3NJqV7ZMkD9Z/KVU0dp9ovZsveULHB2A+MzgXwxp02oGROocp+thIz1Y3MecAfwuf05T2IimiUXv+UwqqzI75jjoXuq4mcUQaOaYqSOCO6ShlBf7lz98/uAxfvvOQ+N1ScOhCbAz6YwBiQ6+XJRiS2e8PNZo8KmiPbsRGqv2h+wdHbFzMCcoY4Mmr7f1qKpqrxsbXx0ZR0Xomol6cQ/G/1McG56BPPADA7df5VdH07662EZSqtU5qbLqdSKEpfP5ooSiG2JTXdj2Aiob3/ylnLarXLic1bKbkNyZnMCgWEJs4UokWaiuDx1GNzXceOAa//tFb4D/93R125texeP6vrz0A//4Tt8JHb9hn/U07QvKpI2KzRJqvuYIgOve4coySBA2gymzrd9TN3z7XCZZ75llaV3A1HGcWChPS3DK4QSciNmNzsaLGM6p9QbqYct2HRmAjB6NeyWqy+VLHnnfydSFXkgJfFnhdaK0QgHb41MMsyj2XTlZiXgPNOy0oimphFRAXlZH3NFbrC5lJn0kbyyjVtV24cfE5q8UDNA2SZnPHaQZv+OAN8Puf/z6cKJMI2+e6xuYkOWD0XlJ64LR9bNaGKQzGqRhM0TGEoBnOGhuHKpoUrPSSGC7eW6A22C9lx3yXFI7mtaloqsiVUUiqnAu0//yZO+DffexW+PuyMzma1SPLIVsr0Q4kw+c5ZoENOq3UKdHXiyEjIt/e/B4V2CWRcoK//cAxeMdVd8F7vnC3OLYQKprZDV6mommpZn08ukdEUaQQBwz8VQNUEjzkOaUv+gvt0aRkwM4yuXZW2bySfh9fr+iawG2aGhtRPCA2FaLoWp/ntnhIKGIDAPCCpxZ1NnsW+4ROrZ+jONI1Nil5NiUqmtmgsxjTv/3Id+FfffhG2EcKoNFCVdHSLDfOmScEJDYDr0OU3u+qG/YZ9Zl6ndibgPX1sTHWdpSodjAn8DpR1TO0bUZgE4sUPJcQ0pxHFU1R0QTEBqnLEhWt50ErDcRGSEpSUZIe6Uelz48ENg7KMA1CCsRGI/Ku55IjZ2hKFW04dlLn0PC53butD7sXinUEny0AM9mA11KVWwh7Qq8jIzarNRCb076PDUq9HV0d2Aus44I/WjoCSC8xPuNwOtB2leooqOwA4M4GR1HhaGa5m4oW0qAziSP4g3/+w7C8OYJdCz0lHlAZ2LBsg73o6cCGj4PD1TRir6uKZiE2UmDjoOFQB5tCkGafAPmeeeuECMRKx4MLEu/nwxdUsY+NA7blhoV3apwBwSQNgA3xAMXVL/oUdJMYBmO73wk9Rq8TGwu2IR6Q2NfXZ5lxHYvPLpNu3ZjRpMXX4zRTmdUx6QvRTWLYHBUNOwfjTGWYXvuc8+EHztkOT3vSohFASM03E7ZpK2GJCREbujkWzRBN5199bw3ExuX42YiN2SdhTBr99Tox/O4/vaxASHKAvdt68PJ/cDbcXzZ4G2eZidiwDVoa5pidm+LbByI22MT0m/cdhX/y7PPIcc3Eg+Q0Fc5fdVAAYEqQGht6+fO8ah5H5J45YiM0sXOKJ0QR/NzlF8HJjRF878BJuPHhJXWu3IxGzA4qmhHYVFDR6LXm6+aO+S4cWxuqPVCJBxAVPTofu0nkTZ6gSXvgf/wnz4QbHjwOP3LRbvWaq1G0puTax560TqsYlz0nOgyxoedLz1HqoeRSREP7iR98Mvy7l18KP3rJbrh130kAMPejJI6MvmKbwwz6nUREbKSA91h535bWh3BhSS3l50GddVeDTuOcHYgNDYrN5A+QnzWSOAnC3WX7iK8WWJJ7pog99zGc4gHlobkIEIApHtCNdS1tCGLjpaIxVTSKTO5d7MHR1YGxf6YhiE3qrtEq/q6vYSVi40BFDcSG1oiXTW5HbA2mY7euT7kvZnnhM807RDgAKMshgvf+i+fAoyc24FkX7NTnI4gHVFHRpF5odahop31ggzdic5RaE8a1eC6X9THS36sCDVRHObxcLErdJHIWIAIUD1yW56IjXIzBzqhI9pof1o5CMBVt6Kei0S7wfDPjsrl0fHUXP1ycR4zaQs1CbIRi3TjSXG+zxqYce4DzgoaFc3FU3L8kjiDNcrWooJPuysTzZnGmkov/uiz0uCoac0iEDZzWihg1NoR3i/8XgY19zui4NYnYSH1sljeKBYZSB2mgamRhSB8bKodJnaX3/PMf1gE3CSAkuWeaTKD880kRm0L5qAi4VjfHTgRI97GpPqZLoMRWRbOpaJRa9cKn7YUXPs1Ub6I9DHyIjTTHUnYflHhAIGKD33fdg8eN1y3xAGGt431svI630IMKwFZF2xilCil31dgYfWwc9RlJHMHZO+bg7f/0h+BTN+2HGx9eEptF0s8AuNdnem5VVDS6p/H5sl1JPo+Nz1LxAOoYdJLYcB5dJjkzL3jqXiPLSr/H1crAh9hM1KCzvG4LvUQlZrqJqRDlKqSXVNGqEJskjuDXX/4MAAC489FlAGCBTRQp1HCcFYpTO6FrZdwLqreN2NA+U9wkNJqvD/g+45x5Hxsmu8+PWYxLt1ZAm4SKxqWpXYEvgNygkyZO+ByKHfOG7gGcZbOdqaLxOh3a24vTJucDVNHmhT42iPAPDMSm+H+aGhv6d6nGRlZFM49DxQNokJPEEXTjGDYhs8aGh7AadBIq2NpwHBTY9JIYXnLpk6y/02cIz6NDfIE0y8vAi1DRpCbPNVDg056Khjdvc2RnqV20ENwI5AXFn+HdrRCbzfJ9/kvEC+fR7Aad4ZdaZVRrIjYu1Mho0IlODHlYXQV7oVQ0XLCQsx5ERVPUJdzAIqOZE104XFS0kBobLpagEBvmSFsbhiAegJepkorGEBvFK/VkxGnwRh0KzlVGaouvF06fCTVIfWzCqWj6GHgNMXGw0EtU0K8C0pGp1kVV75RqDIHd44hRzWL8XhK0ODT9mxAPANCoDRZpS8fDcYX0sXGJb/D5i4gN3fAVYuRwPHRGnKmi8Rob4fZyVTR9zOL/qq71OGceOLKm1kd63Kb62FA5YVHuuayx2fAhNsK5WHLPrEFpMXZ9fSWrQ0VL4khA7cwk0Jg8B/y9vEaE19iMMnNP7MRaRS6kT1AVyinRRYpjuz9fhfr5DMdFHVZ0ytR3OyTDuew8QHWNDTVFRSv31IjcD93MsfArKlXRyvPA16S5JAnRBNXYOASCqH/BlSP1sdxIYojR59qssXE78n2BipbludUYUiPi5nFMIRHzb1Q8gAb1+JxTv4YnW5UqGqmdQltniA2dU3tKqqYp92zub5U1NgGBjVUna4gH6MQGtTUSpFF0o2OgWSxJ4RBEiglaWSUgwBNbkikaLaOiFZ/PjP+piBS+ty7CeNoHNnjRJMTGlZVCqoy0uFchNjhxD5XFo1UOE04Il3JMKGJDTdF6aiI21Bmn/OMQxAaNbv51qWic2kLNoqIx5ECpZSi0ypZ75hkbbx8bFpxxSWotTetAbATxgCpFPTReY6PrecqFz4MkdmKzxob3A+C0P2qpw3EV5Z5rIjaUp4uKgZTG1ScoI12kKdKFWcWUOPCWQ2ogNvL1psFo6D3xGSJsJ4hgCJ+vrs7YkrnGncSRIYnZF+SeXR3r0Vw9b2qpojlodhqVciA25PtueHBJ/aydq3LTEu6FJR6Q2gpX6ngkCUM3QPx5vlRF2yCqaK6GqkaNDW/CK1BcuZSydB5qnBXvkRx/XnCtG/Pa95sjDiqwIUi1gQ4lMbikYKmNU3PNdVmXURbRfKqZUv+gUEP0wWy8WASHUvHzSEJsjMAmnJCCTaallgW80Fxqq2DOi8yomZD3p+J/GsxSHwG/k1JyAWwmgdjHhilH6nHagWAdM+rdPDU2lIosUdGKv+McKo/taN2Ah04YegtgigfQYEBCQxPmf2HQkufmXphlufpdQmyQ0UMTdrx2UaaiVSA2ZM3g6x6+zn/m84oGIFSmme7drpYp0nTQymh+2roruUSN1wfS9/JEQDeJjST3ebvmDV8jxE77wAYf3E1SDIXmWjv9iI0/W4UT9+DJIrCporjgYezFzpz0dWoAKBXJV7hl1djQxZVcnIFRY2NSsOgYAcyFMHTIuDjjR8UamwpVNMzKeRGbWH7YRclqJ2Jjige49POtDSQjSi415Z47QqaVG+0ITYMEvmj4smR4+7njGrMNCcDsE+Qz2qBTITZl4mBBCGwG49RauAdjTSspxplbFDs+VleDTgCTPjhtHxsAfb9OEIl3Z8AVJB4gO7ZRFBnnOyfIPSsVvI48x4wgyCceIPU5YlxwNLzmWmrbEdiQ77v+wWPqZ+6c02s3T4qvLVTU4XzrwmIzmaERG9LHRs0jOcMp9bRQ34+IjeA0uOSo61DRJNRNPb8q8eXeHzji0GfOQSGhbSKfXcd6Rs2nakbNJefrWwdd2eQQw4SPURTuUZSk1y4iwTm+t6rGhhpet4EwJ+ZZPYYk90zPl/eekeaJhNjQ+YLZci5MYDFDhHtpiAc40J5JahJ9qmjUT+H1ImhiHxu8b471lRa323LPdJ5Exr6A30P/Tm2eUK1oUoiuo3KNTV+/t5wPShXNS0Uj/piw93J5bDuJG1k/+wIbitgYkumOmkMpGU0FBHzmapJMraf2BkRs9HtxHXZJt5+/a1719Qq10z6wUYjNOLMWSxfcjlQZX4G1i2OKVLRlxWv2LwC4iDgRmwkyynQx8HHR7T429qJfvG5nSJyBDfm5StYYjS8ach8bmYqmal6w0aNAlXJBnWqhDOAw24hNuYA4FlRORctynWGu8qGd4gGebCatFZFU0TiHXKI3oENtZ+T1z3URG7oB8Rob2oiUBqT8uRuwIIbW2Lj6xWTkPS75anqcaahoeB4nSyXEKBK+swYVDadjlYhGn2UEaeYXBUS4SUEQgERFE54JJR7A5idzLlw0Ivp9tM6GZ43p2ooFvuMst4IF1/otoYTF8cvARlBFs6loxf/0WeOxnjS/fBK2xevmuiq/R0aRAMi9VuIBbvSEb+Y2YpNZ8r14HF+fILUnVTwzrgaMPsQmpManaly8doIeVxKg4M8+VRgNNbx+SI2WEBvcb/naWdQ6mUheFbIn1SkZiE25JqW5v8bGxQgRA0GSpArd281j6vncSzRdKM/NNYPTqtAohZjPIdf6StFbO7AxqWhckp+OiQfhtF8cTRBTnwpVKw1VNKIaqAMbKN8Xl7/XR2z4fsh9KjoGl9wzRVaogljRg85GkwyqnjAfQnvZ1KKikcQ6fiVXiMR1E33B83fN16KVApwJgQ32YhEKpkUeeZoprqFYh+ChCQAU6kPUqrJaaiMR4Njif+2whhp1TH0CAvhg4QJu1DaQizMkQaFCbBxUNPpMVjWiROOLRhAVjTnYeD8kVTS9eMt0E+6I5TnpOaMUn0ykosODDSubbDrjBu1p0gadsUZbnI3mKsQDfFQ0V42NIR5Qu8ZGb0B4HKSiLRqIjT4uHxt+F97bNCfIhKM2wqCwOVCdLJ9ePABAI09IRZOOVeX0U5M6u6NRZ3eOLfY0UHMjNnq9MfvYVCM2SjXPcc1Nbr5/c7770AqcKANBnTXGbJw+DmbfR6mdmHL1sqHJIDNLXPy8QBAbd4AsbOTsnKS+W1VUtHGFwwrgp6JZcs+OOQ5gOm7Fe8rrKyA2Xb6eBSE2/nXMJR7gS9ZR6fK6hsEBdWLUOi3cTxdai3OhjjOE15ZTowFMhLB4D6ttHaeGb8KTDnISykw0FmPQvyCd2VJFc9Ty+pBvNY6KpG6VGQmATmSsUQaSqWrXzO+i95D3sXEhNrQGhM83QxWN1GHgUHyIDYAWb6GBDd7juW6skWxyDtv7HXWtN0mTXQB/0nFI5oy09/Lkn62KZl9H7g9T8QAexIo1h5SdIwU2RH3SZ7WoaIm91uL+wecy+gvn756vlaQAOAMCG7p5cEhMWrypGoSkbFNZY7NoBjZViA1uipbUssoauJ0cl9EJ4pN8xocQexAMHZvuMM3UAkEpWLif14niJeOReh3xAN4wE2tvzD428kasFzI3RZFnhPj3uRZUnDs4niy3lVxcxmtsOGrExwhAz9HkLg+Z4+6jouFChWPW309QgsAeSWiUXmhR0XoyFY0fG51uldHKwOnAUwTN1XyTbmCjCZ4vbttKhA2paBKkLqnn5XkOv3fVnfC33z1gvNdX90PXkzmG2ISIB+Ac4jRUTm+oU2PDVdFwLNzwvs53E8hzgBsfWireyxIPNBmEgQ3PYgMU83vf8XV48ydvhXsPrajXaTZXQmzmDFU0eVOVkh5WjY3gxFY1uJT6LnBz1Y/R1zgVTURseI0Ncw6ozDC/9i6kPycIQBWl1iUe4Co4Lr5fXpNDTBIP4EiUiV44EJsEEZs64gHmfkRvR1WNzSb7nScd8F7fuu8EvOWTt8GRlUGleAAGU5zWxp9LJ5sB5ZOF6zVJD5tifOZYTTqR/p6B45nEZaEQRDDH6UL61FqQ2HLPJhUttvwBPBYVgqDGA1YAvVdRqhr93sV+R+2vCrFhlOlpxAOkAICPwZW8WBWQFR+Vkz6iIhWt3Bf5cTdHKbztb26Hr959GAACAxshYFNrIfa+YwkhvM6PS8SGRr8IiSlKi5CVpNxCX6bE2cdmvmsU+FZlN5DbayvH8Ixc+GJCnUifA4qRNN50s8bGfJBE3Xwhq2NQ0WaK2GCNDcovR8b7BmJgI2eaXQVxdCx849IUh+J9Vo+L8n20QZty8qvknl2qaIISCJokHjAYa/lffM2ripbKjiu99D0BEfOZ3hw0nI2BzTZynjQg5XOWb3Rp7hEPUFQ0s+6IGqUcpAEweJVx8QDpWFIt1t2HVuD9X38Q3v357xvv9a0x9Hx1H5tyHqeZ0ylAw3vAs2g80JEQF96lHY07FwAOOkU5tudctAsAivMHsM+3IyA2NGjT48ngr28+AB+/cT9ced0j6nW6VnElJgAwpEdxvXehUNTBdvaxMTZbP+IwYgkjyfDZlHoxcCqaQiWFucIdc+4cUEl0jQr7Awt6WlWIjbOPjZeK5ke8fIbXdjvrT0LHSp+/IQuo0c7dNQ8AABfumQ/+bqXsyfYjABMhBLAz7vxZTNlcx/nwwW8+CB+7cR987nuPiUky6iOgU1mI1ujvS7NcDFasVgjC9eJBcF3j0tQdkhx1NVqkhmtXntuotpQ4ouNH6W1qVDyg26Fyz/ZnJds1XySxHzupFR5Vc06StKPnsdhL1LrtqrGR/FKTilZdY+PydYq/6f2CmoSs8OtrNJKtSGLjNeDCNN9+4Bhced0j8Edfubc8ZrWPq+oDjfXcTCJxKtq5O+cAAOAfnLujVpICAE7/PjYUdcHApt+JDQljaidJAbC/j43DcUhi2Dnf1U5OxeKP98mSB1ZSj/7vc1kviWEjs7Pf1NCZQS62i9c7HGdWgTAAOohmUS9dNEMTO75mUmh8kesxxAYnuUSVkpRf6PdYWR76wDKONt+4nIhNioGNAJ9XUdFcfWzIHOBjptlH2qDVEg9wUB/pMXkNxVRyz6QZn5Z7Lp7DBUOVhlDReGCDiA1RjXGKByACmmllHRcFsSnxAHS+T5Zyz1JQITXoRB4zDyp8GXH6HODcMuWeKwKbci7xTQxrbLpJ0YhNFA9wUNGktcHud6Cv9a4FM5HCHSZ6fF1jk1kb8Wicw+qgWGcpj5uiy1KmkmZTcb2vg9gkcWTMQVM8wO+Yh1DReOaRGg8WFOIoOAW8+L3HnINRmqtMOU+eVAkzuL5T+r4sLz7HE0n+Pjb1AxuN2Ji1EwAy5VvLwZvj+F8//1w4cGIDLt67GPzdPiraHENseB8bTgMdZWZfOxwnfn5jmIqolym6oRMCUu+aflyMCe9z3xHYSH1/Jm1mTMfX7cSqYTTdpwD0/Pf1U+NS67w+Bo2i33zcc71E9TTrxrbccxVb5jkX7YK7D63AjQ8dh1c882wA0PdojrAe6L690O+ov+GaG9Sgs6qPDaNh8XM118FyjWLfI9XC8L4xLjEV317F93Pc99bLmp4qJU96LI76Aeg5zKlo7/3Z58CDR9fgsvN3Pg7FA8hFXcHApuueQBSxkSVxqzO8lI4mZd2oVco9T7iYSLLH3HBBRSqaSzxgmGYGpKvGLmRJJqGiueR4qdHrGEf6+iueO4MfKbXGBbe7OvDKiI25cXWYM8ePgb9jASFV6aryoZM4Ygujncm2i0A1giHLPUfq7wBaqtY4b6Si8UwZ+V59fUOpaPbGsrIpyD0T+W4uU675wxF5ze5Mjd8DYC6mbipaM+IBmJFdWq9HRcONjTuRvjXGUEXrYnGqnseubCcfx8bQpgfQ44sCFSguwaiKeHnpc+uqRwHQmTxdJ8JQA3Le2ykVTTgmXkO62bucmR5xcvH6IHoYkvTgKntSjQ1fl7g1TUXTiI393mq558xq0FglS15VdyB9X3FONvLl62Mzkdxz+R20dk/3vbDPS8079qycs3MOnnvx7lrfrcQDUtPhBpDEA8w9ed2iyMuOPnXgJNSLXm9ck9LMTuDyvZ1/lh6Xrldjxz4aahyxof9LVCsXigqgrwVHFPgjRZOJfB/oJbFB5+WoOgJdrnn+/Ev2AIAphKKacxJU2ESgE+UT4DzgDTorA5sAVTQXAkfPx+5jYwc2iUqq2s9lVWuPHnsm0LQ6YPF/CBVN9+CyEXjVSJ4lhJ60va/uUV3E5rQPbKjDyaloEt0CNzoAB2Lj4TSj7SWBTdXi76Ki8Y2r7mLCEQ3JcEFFKppXPECAZcWCMsJLjSqQCX4c1+8ApqPWKWFsAF3DgZNc1WoItDoXPGst/DQTEZvv5XLPPMuDhtcSF85xmlsS0j4zJEvRISPXkytCjUgA3FNZV7MTPUAFFS2TAxsTsZEXK5fh26KIymkWry2Iqmg2yijRqzTCIKMH9BjcacV7SutwphEPUHLPZTG8SEUTMoq4sLuSGlKAb1DROmaDThPJkudYFRXNlzV0ITY8+Jc+T89RCwLkxv9Sg85FQzyAodpZpq4hdRbp9UsSeyME0M4mziNX7yZpbeOZQrmPjT9o4T8b56Xqx+w5yaloLuU/AKlBZ+nkG+IBZmBURaWj607VOkavtxF4+uSe1feHrS/U9PyMiLS1uU4bPV08Utl1rcv2I4mKtumoseHP4jjNRcaBcuDGmVpXq6hoaWYnBCRlOF6rKK1XkzQLp9Y1nNLI+F8SD/D1U9PIWPG7RsTltbQTRxalrEsCmy7xKXhS2VUTi07z9w6cVL4l0q4WuqYwAdpCr2MlB/F50Kpotj9B54O09/JgkD+bdE/Sz4J5HKnfjO/5qWrt4fJB8XcccwgVTVJ7482EORWNWp2eVABnQGBDHxiEwFAmVVq8jRobYXHFB8dXQIeSzwDVfNSELYj8eyZdfKVMCLeN8sGSxANMBywDqbeG5NTjj6GKaPQ4aKJ4gKGOEtl1OeVndJ8VGtjI19CFtkj67IqKxuSenX1symtJG6XhW0JqjyhHlwo24Cm4IPeOhdiYi6aXiqZqbAJU0QL72FDKBHf4Fw3xAA8VjfWxAaCCAmweRPgZD2IT0MSzjim5ZwetCUBvLPS2oVNtO+xuB8IUD8B7qt+3MbKvlTGO8jxdVDRKH+LKe1y9By0m85O/F43eU8xmam606ZzTtRUDG3qv0EbjXI2bHt8l99wxHAxzjlsZa8yepvZG3mfBn+lcYmDjD1oAAuSehTlJ0Tk6Bmn+csSGOz00WLRlkeWxmYiNf2+TuoMXxwDnmPGYknNXZZTSuMB6iPj62EyT1EDzJefmKmpseA0CRV7xdwCamc4rxQMWqCqalTS1gwgXFdO8XiayWtco24OL2QwDAhuTilZcs5jtz/yxy8gzSr8/jkpmBHGYOa28qp76gt0LcP6ueRhnOdz8yBIAEFU0itiQ52Sx31EJKavGhpyv3QyYIjZCYMOQetvXsYPKECoab1Au1RzGjiS2i4rG+zkFUdEY2lz8jMc3nw/pODMVD/iTP/kTePaznw07duyAHTt2wAtf+EL43Oc+p/7+hje8AaIoMv694AUvqDUgbnSDWS0jUioby22Z1NhIGbWR2kjcp04lnytV0RwLot2gs97ii+fo62OzyVTRRo5NdzhOxU1cFA/I7fdVmS0eYL/HQGxi20nGSS7X2OTGe9T3oLPJgwSBToffZyE2Dm6vQj+62hHxZeK5LRgLo/0wu7JwVDyANujUHFX3vFCOG1dFo03gavaxoVQ0PtcpZURJXWa5xTmX0JlNIdjB7ynGp4/BaTo0GJ2WNw5QT+6ZzhPax0CC+KuQS5zrdMPGgKW6xsbcxCRqH51iWaYDc5/DQeuXqNGMIq8T4RQvQx6V9LHhG2RBRbOdxSoqGoBZZyOek1A7xykj6r3k1zoNOt3KaW6H25J79rzXVWND1Zd4g0bJoaVm9vYQ36KsqKGwEyk+OXP6WkgzW2r0uPMqE88zzjYrYRoaKpqP+qOoaIyC40oycKEMjmyOUirmoz8nUdG4KhqAjNi4xTPscUyaBDKexXIu9gQ/ZZA6AhuyD7moaK4+Nkks76OaihZb56xr6ty+1+UlanN9SUdTVDSyhxpIGhUPGKeQ51pUiN4DX2LIK/fsUEWj98wl0CGJB4QgNq754JJ7x7Hi//Xknm0EXtdquhNCM62xueCCC+Bd73oX3HjjjXDjjTfCS1/6Uvipn/opuOOOO9R7XvnKV8Jjjz2m/v393/99rQFxoxsMFpqi0y9lhCoRmwCe6R6Diua/RNzH5dnASeSeAdzRMrX1UUlFw8BGCAYAwBBasMUDzPfSTr+hFiIeQOlRtJkWP4avjw0PMqsQG0qn453IeVGqSwpWU9HCKRwAcvdsOmZX3xzex4ZvXN2OuRhQc8n5moiNfX19RiUz+ZygjUjpd0rykADmRoeZMRdtDq+/JNVJpUybyNpS5a7iWG6HzQhsyDWUMrTSPKHjxACUPj94XSQ4vnivzohTw8+5Nlc6vy1Ej9zX2PFM0SyscnZVps18pqQ+NgD2nBunmZLKdQY2QpYYwOTA878ByA4+p4ygJQLv2y0MYDuK3EKoaIrG4Zkr26x+WKZzMEpza22kf5OM7gMhVGMViBE5Xz9iQwKbmogNff4wY26t09RRZ9L905iNHOvfFRWNITaY2LEQm9SsseFUtFGaiQlEOgac31mWW9fRDK5dKGzxv6R4OulaKSUZJOU8pzBMbKuoKfEAR0BOkS0jQYeJ0HJ/7iWRSlAo30sFT+5z4nU2G4IqGqXJd5LYEA+g46V0QN8986qiCbUoAAzpcFwr3HfpPFI1NoJEdFWi1lXnzWts6lDRusYcMpMVqjZRWDdnqor2mte8xvj9937v9+BP/uRP4Dvf+Q780A/9EAAA9Pt9OOecc2oNwmd0g8GIVPHIJcSmqsYmgLqyZ7Gvfg7tY4PW78SwPkynatAJECYe4O1jk5mLn3TeUnZWyiRVGb+WPlgTv9dVLyP1WeFZSf4Zi5crwPz8NvJMhmshwsCGOgoh14YqhiXComR3YdfzpCfQuuqpolUHNqF9bKiTyTNfi0IfGwAzuYDnwd/De9vwsaq6HMFpocFok+IBaKGqaJsj+oxlhjQ4gIwi4cYVRWDVEQCQWpkKxIabhIBJzzWAm4oGYAaN1ChVQhfAo3NuZtqMRoPkOeAO4CjNrU2SjpV2zAYwz50jNi7xAEkFyOfE0p5CeZ5ba1nTVDQfPSiJI9je7yjRHF2Aqx0Cni2tknuuS93sdmKAYWrsLRKNio4ZjT4TIUYVRPGZ5Ii71NPF9azUMb630GeC97FBKtG2fgdObowsVcQ0y0WRCYrYSK0DpAadEmIjCRNY66jwHE+LcElCB1JW30VFw3GNiRCPQmxcDToJwyYR6FgYZBSIjZlUDmHLYGBzy74TsDlKiSoaFQ8oPo9rWZ/IPdNbQ+ehKzHEf+av1eljwxP3iOLvXezDweXN8nOYGLATHvhx11rg8hVwzR6lhfR4CGKDxzIajTqoaNK+uWV9bNI0hY9+9KOwtrYGL3zhC9Xr11xzDTz5yU+GSy+9FH7lV34FDh8+7D3OYDCA5eVl4x81o8ZGyT0XEyvP7Q14hQQ2sipaNXVlz6K+iFUBCZ8UODbcGCetAeA1Njc+dBx+7N1Xw9XfP6TewwMb2tGed0KWilSl7OwkVDRXDYR0PgAlH5ZdVlyoeh65Z1cTsnGWw+HlTXjpH1wD7//aAw7anZwBkdRjiu8sfp8TEJKQa0N7vEgQeprl8L+/8zD8+Hu+Co8cWzceatprhqMIXiqaQmyY3DMNbLr15J6pE8OvP+3XQ6kAHLHh1A0At3gAV0WTnhtKC3PVX9UxnhmXjiVlQDcF5T4cF4AfsemXcqn8fQp56cjn4zpPvJ40eJQKhwFsYQL6qwsFpRlFPgc5VZSOkV5b7gCOskzVetHNk1IkpMJ+ABuxsQqVVYayOC7dJ3xUNBpIi1TmKaloVu+Giv1h+5xdxExRJa4YqRp0kvM9eLJYG//s6w8opDg4sBEcV2l9RZsGsdEKcRIVzUbSmkhqoPmC3XnWgR39CpzbnBY6SjMxMYfjHYwzta5K9V3Fd+okiU8cx1VjIwX2mu48mdtX9DIz5yCiFDc9vAQvftfV8He3PkqcdDuodVH3cfgWrZxS0YT6DKx3oeICeG2rqFYAAJectQhnbevDcJzBbftPEiqaHjsmKDDY1jU2meE3UKTBJUYEIO+9nHJus2Co/2QmR4pjpmotoKUUvO7R6InkSVAAuOu8aUJPaiLuO5Yp92yuhT5FUF5vWGW1Z/jtt98O27Ztg36/D7/6q78Kf/M3fwPPfOYzAQDgVa96FVx55ZVw9dVXwx/8wR/ADTfcAC996UthMBg4j3fFFVfAzp071b8LL7zQ+DvdXJR4gKdIC3tsADSD2FSJB3AYj0pRGzDllKpo19x9BPYd34Av3kECG9bHBsCu7UHDRVlEbALrA1wWoopGJ32S2Aon+OD22XkDeMQDCLx648NL8MCRNfjs7Y/pTISgAKc+W/4u0fEAbMSGBskhND1JPID+PEoz+PvbH4OHjq3Dt+4/amSXnry9mH/3HlpVn+OZHMmpUnK+Hm6z1ADVZzg1qCoaGg8I8Ng0uQCgM5xJoqkI1YiNHQzx99A+NtPQURZY3yEx0yg8K6YkuUyl4oabH80IUoehSjzA9VxuMDQbgNFQyPz2IXouSgjlgGsqmuyc07HTDWmDFc2OxrLcM12D6PrrrbFxOXap6eTgOVCLBcSmOC/JAbEdRW44L3itG4AdKFTRg5Bm3E0iKxAepRmR2jbXB3q/b3joODxwZA2uuv2xIGePmkQ18iGS9Lh1e9mMCG37uRfvgV4nhn9w7g4AkB0zV+3lJOYTwLEQm3Jtwh5NHImkicRinJyKlot0PkkcI839iI3Olst7sNTMc1LxAHpcVWNTXvuPXPcIHDixAZ//3mOkJ4s/8AWwpeZdiE0hvGPvY8+9eDf0khieed4OnXxiiI1vrkdRBJedX8yxB4+uEiqaXl+efvY22NbvwPNKCXFNRUtNH4/sQVZiyKCiuREbqZElALAEj7n+AuieMgAAZ23rW5/zIdiuRK1UPwVgJvQGI1vARLLnXLQbkjiCZ1+wi5wHBmiMihZQb1hltRt0/sAP/ADccsstcOLECfjUpz4Fr3/96+Haa6+FZz7zmfCzP/uz6n2XXXYZPO95z4OLL74YrrrqKnjta18rHu+tb30r/MZv/Ib6fXl52Qhu6Oai5J5ZVpLucSsDSkVz03V8GV4q9+xTTwOwe5rQon86ieoiNtzBx8V0jSyiKrAhMN0ozaDXiS3HVxfN6wFLiA3+2LgqGrln3Ti2HgK8zlIfG6d4AMniYeA2Gmv+sktRiX7WWSytApviffTPdWtspGAyzTQNZ2VzbPQYeM5FuyCOQMHJADb0LwU2+Kj4ZDZ1v5l6NTa8eBPADgj63Rg2RiksO6hoyJMe5zmRJ5YzpbjQi9LL6hq60bw6RmuFACqoaBSxGdsOBoC/rq7HMo3q+HEE4yzXNTYOZ831uqQ8Zzg1ZB31UdFc/G2dGY6sDcmu86DZ7gSiqHh+NiUqGso9CyqIcexWRatbY2MmmWR0h/+tCrFxSaaj7Opi395eORXNJ/cMoANDGrgZ4gFMdVNC3PDeDR3qmD6TxQPcDhEG6VRsJdRokPdbr/pB+PWXPUPdZxGBCFA4DTUfijffK35RqmjlXMX7u86RyDRnwYeJbI7GmSVeA2DeY6SipaSOCk0UD3AE63S9GgU4+lXWTWIYjDMLPcS9amVz7M288znDVdFcQkC8WS9+/2/+4x+AX/uJp8N8L1ECAHaNjf9895aJ7GNrQ3WP6fry5O1zcON/eLnyyeYI64H6DXG5R/K5n2WmmIRYY8NqaX11yxJrA3vY9DuxCrjp56Qam6r2FS6hoU0DfUqdAhbU/s/nnA//+IfOMa6rOo8AKlpdxKZ2YNPr9eDpT386AAA873nPgxtuuAH++3//7/C//tf/st577rnnwsUXXwz33nuv83j9fh/6/b7z7/QhVg06DeUfN2LDlacA7OJxyXZT8YDaNTa6sdbYs5lWGVcHw//Xy2uQZVoqlUazXG1Hv64dVDQJsamK4iWz0Bfho3SRS2K33DOet1Qv5BIPSDPtKBuKM+QcLMSmPJYrQ62oaAJHPCTm03KlZpFuohwFff9WNkcG/3n7XBd+6LydcPuBk+pzOH+U+piIRsqIjUFFE+S0fUaz57Yqmnlt8Ni8xgYDuCQum6iRuSsVmALQPgf2c0Pve5N9bPjxpXEZMuoOKhq+RayxKQO5OZbNR4dhUsRG6uciZucioVZNEA9w9bHpdRKNOqCjxlAzevy5TgLdOIZhmllUtHGWqXlIN3sqYCJt6ABSjY0j6VEey6CMsHWErkX0b1JiLISKhtSkxZ69dvBAoSrRhkkrSnOhdDOe9JHUw/DeDWg/s0CEkyo0olVRWDBIr43YMEdUapJI5+WQJIOmNRfNGQBgvuxpohEbpKKZjTvR0iw3xBbwXuhGnRn0suK6GkkFgYomBYjGvS2/J0QVLWXo3iRmITZsr1neHDvFAwDcDrtad5g/51oLpGulxANy85mvCuKRunV8dSjW2PDfcZ/bHKVAQd0k0vsbPQ+eAAmqsXH4KwAE9U0zVQdIkyl0bdSIje03VIoHJPazj+eNNhhnlniMy+xkVGQc3xcQd4loQ4hNjeHmee6kmh07dgz27dsH55577sTHp5uHhNjwxXNl04/Y8J4LkhkNOquoaOw4ePFHaW7QAWojNl2TsoD/Y/0CVWTa1u9otRFFD5E3XbqhKRqWFMXXQWw80oRodoNO27EDkB1v3gCQf0+R6R6X781AgqBdPXB0Jt4cL6eiGd8bcG3QWbaRqeIc0kxnq5fWR7pRafl3lKHE81CZl9hcDKjhefMxm+IBduDoM5wacRRZ15AHBHhsi4pWzsmYKNs45Z4ZYiMWVRMu9ahmBlqyRYY8SaosUjEu5RqPhUBcSg7oee6+R8XfXYiNfJ4bCpHVdD8TsdHPkA9hdSE2I7Lx8iaTHLGhY+x39ft5YDMcZ2oeOGtsyLF6gjOD5kt68PNx0R8BTMql5JiHUNFwjeaIJv1u1aCzgkqpqWh0/SzHZ1DRwhCbcRrm7FnjlZQzHVujS/mxynxokuSYabR2eiqaVzyA9bEZqsCmY/xO542RmFOIjQ5wdHCov9OUFS4RG4GKNiRBk6twW3qOm2hoypEa/r0rGyNLupgaD6gVYuOghFP01pXk4MfSirR+xx0NVXCPrw9VkMoFZahpxCa1WktIc5/vtT65Zy0Cwu+n/l3yfxGxWewnxti53yCrosnnqZV5zXV7YCA2VMCk3rzizZB9VDQAgO0CAu6yWivCb//2b8PXv/51eOihh+D222+Ht73tbXDNNdfA6173OlhdXYXf/M3fhG9/+9vw0EMPwTXXXAOvec1r4KyzzoKf/umfrvM1hkk0KeoUcOiS0mB8NTa+h3uuqydHleIKf2ho528aXEzaoFN35i4Rm/LBo7ze+W5iRdeubJnUoFOShKwzXO7oSwtJv0I8gGeCJJUkV3CS5TlsDCl/2V7Q7BqbMsPJiozRhr7AJuDiLCrExhWM6Wz18bLjPYB2Wp5PApuukK0RVdGERmEA5nWoi9jQLuN8U1royojNqkVF04iNqqFBZMJR7zFgzoJxPuVHaOf1abK2c93YmO/SMy89KzS5YGS0PQ4E3j8bsYnE99njkF+Xer9Iz7WEvBmIjYPrjufXT3QfG0VF89TYzHUT9bpfFc2mohXnYjv0ACZi00tiS72MByf08fbVoAGYdXDcwhCbsgbDQ0XDgAabL/LkEJpEResQJ58Xj3eYswCgHe8B6Wc2jXhAFeqjaoAcyTWX+WpmfA06m1FFk+cAgKfGpm/y/vF948wUDxgyx204zsRaVnoeKMwiiwfY95avoxLy2kRNEn/G+bplIDZiYCMfzyXiY8g9G9dKCn7N5FNKWBA+21M2ZD++NlTrEUeEqbnkniOSuJPuEVpIg05fspjOE1yDMOm/2DMRG359xSR2BRXNpYoGEE5Fk4wnTXxUNACA7TXqbGpR0Q4dOgS/8Au/AI899hjs3LkTnv3sZ8PnP/95eMUrXgEbGxtw++23w4c//GE4ceIEnHvuufATP/ET8LGPfQy2b99e52sMkzYPl/JPnudGg05fVrtqYd+z2IP14UZlQOKioo2yzNigQ3oGGMdRtSYmVQMn8DrpAxLHRe8TCgu6solidpbCk02oolUiNrbcs93HxnYgXE2rxoTmMkwz8sDS47PvQ7UdleUxx6upaPbDGnIvUe7ZpsBppwTv6dIaCWzKcf7oU3RgQxcMLxWNONRJHIkBIe1jI8nZcssEhxmguC58g8Sx2apoOkjBQ7goV5qK5qZk0Q2sCfGAKIpgsadldb2CBWSeOFXRPBsGXqO+B1UDcGe/qtajTow9onIxO0fVg6Tv5mpiaGYfG0yimGsNL2AHKFQF8XXOLR+lWjygCFIz6CQxEw+Qs7SGalGIk0McJleygX4PXUv5mNFcqKdCbPq2c+RSRXPViSAVja+fxWcza/5LAYBERQtNBEjiAdL6So0qP9Yxn7CP5Jg1gdai8etBk0ELDLHhVDS0uW4Cq4MxpCkXDygDGkK5EZU7aWBDVNG4DyQ36JSTFTRQaKKZMc4zTUUzr8Hy5ojUi9jz3/XsSdQ5+nuS8Ho7974wVoFN8XowYrM2JImnasRmc5QaDaxxnADmdbcbXHr62LioaAZapX8ejXOAnlnXNycENgrJlWrlKvvY8MCMJPRqUNG4qbUw088FgMyYAJATRS6rFdj8+Z//ufNv8/Pz8IUvfKHO4YJM2lw6SawKUnlfCUnnnlqoI7RnsQf7lzYCqGjm7+gIp47eMaFm1diUDgBCjuhU4QLY7cQAA1s6j5vRoFPI7PIHNcTqigcUjjcbV4KBTUmVEpqN2kohegNFKlpRIKuzPK4xcrUQjvwpKloFZchl+BD6KDLo1B1fsxGb3Ys9+IGzt8Pdh1asawdQHbTTwEaiomV58SxUwccZQWzofeX0reLYxTh5jQ1Or5hk3XSNDd+Qi/+D5Z4bknxd6Ce6X4hHFc2Ue5apaCFyz3zjdFEzuVWdJ6Vs0NjERenA19BcgT7deC0qGsuMcsQG5ywmY+a6MWyOMisAHpaBDXVIQuSeffcLx0b3CV9Xb/p3mcpsZ8C5YY2NF7GxFOUc9Is5ex3pqsAmrEHnkOwhtRGbjhmIAVQ7RDrhVC+wcfUZAqAUu+p9YRLz0RPnCGKT57nVoFO/D9E41qCTiWyM0lykfHeNhICe33wvl1BDl9KhKPc8xfXCZ6OnnnXzXg3HmULsfeqSaJZ4AJsySj2O7T/eZAbKPQcGcnvKGptjq0NF/fRS0To6sOH1Zj4qKJpPFS1EPKBIlBf76iBNAaCrEt4LPZOKphIeiT2uSsRGQGuL826Wiob1aFUIbB0BgelXhBmbtLl0E5luscy4/Wmm+7rQ1wDCEBv8Lp9Zcs+kj43adCYKbMxJhY4sRuZcb10VYrENk5tUd2KKB5Tvq1NjY4kHVAU2sbWRK7lnpdplbwz8OlJKkiEeIKBOLiqai3rjqrEJvS68wZz6XpKxxeDUCGzI+5GORjddbx8b2nOGBrCUikYQqBseOg7ffWQJvvvIEuxfWhfPQ1PRzLFJik8497nDSs8N7/OmUkVzITbuTYkGGbSx6TRGz0daWGkwhWsKRWxMsQs3FQ0dgjl23tzZdtXYVK1bFJGRqGgFDdT9vNLM6dpgDCdKmuSA0A0oFS3PqTKXvSnPdbXYAF+zLGSv3DBp0S8N+KTO7MXr1dSljMzjqkSM7xmTGi9ywzXaW2PDan9c+4xYY0Oa7dl9bOzMN455SGpyQtcxybmpSti56rQAip46LiTHpxBHke4DJzaMpMY08sV8zGh0DHSubY40zWzbHA9sNMoyNFCVAj3F06bJN6mPTb9jqoZyR9hwmh11CRIC0oSKHKei4fw4f9e8qu07ulrUXIcENko8gNXHHDy5CeNUq/hxxkBIE2Xly1T5eiUVbWmdUNE8gU1foKLhuUvtI/g64RMP0I0s5YRo8V2RtUbRZMq8WGNjo6hV18dJRRtTKloG01PRTB+3SkglxGqrom21SQ56QWmJgNMtsGiZZqtHaW5IyoY29NOBjf9muYp+x4yKVtd4rQlFbPJcyxvPIWITWGMj8enraJtLFpMMAoCDgsOKX/nGqhp0CrQVF9RpIDYjjVRJ/GVXgCGp7dDv5FS0UMaTzrTKlDtKC1kqnUeuoPb8S/bAX37nYVMqW1HR3IhNJ+EKMvpneh9+7v3XGZ//zL/9h/CsC3Yar7lU0aSMltS3g1pMkDoXFY1eHz52NFrIX8XLDbVFR98h9Z3ktSwvkCVT7tl+hqQsPN5LvnFKdCjJuuyY893EKMqnyJrk1HAHoXjNHkea5fBP3/cNOLY2hO+89WUmFY1I2ku9umiPC0onw/VhodeBpfWRJTKh167i/zgOo6L5aqI0LYXM44pETFd91n7GwuSe3apovA9Vldyzl4pGAhWemTXGSXrm+JBQyfi+AqDXBNcz5+qMfsu+E/B//s9vwusuvwh+76efZX3O19MHv+ub9x2F//zZO+HnX3CRqnGZhoaK5kISAMy5tjFKSY2N6T7h+0ak9gmguE8cZcHlgs499B36HVPgg9dkmPfW3w+MUqJ4EDyJ4bPGVdFe+LS98IU7DsLK5hiOrQ6N85HGpX5niE2a5XD7/pPwmvd9A/7Fj16orhNF+wEc4gFs3fPJ7lNDxGZ9mKq92F9jUyI249TyeaT2ETwwFREbhrxR2XTpHPpJXNDAxugX6mSKrIrm8fUcl8eN2JCWI8NUq4DOmor2eEJspKxYN9EPPl07T5ZSz7sXdGTHF1eVAah4uP/Zcy+A5168G17xzLO977MRG+1sTyNFa8s9F5Mpz4tMAcK9qBShmikxpSJu9LxxgaMPIc1s1jEpYKJmyT2zS4L3kyI2eW46Ja6smqmKRiiAtPGeIwPCM0VoLsSmiq+Ldtn5O+Ellz4JfuEFF4tjXiPZateG89IffDL82DPOgtddfpF6DZ0usUFnqh1qF2ITxxH80osvgQt2z6t/OGfvO7JiHVPRgphDLCE2lnQzu1RJpO/JpqNfiy0e4NnA8pzMjWkRmwpqE7mG+J1mryWBiibMlZf9g7PheRfvhtf+yAXG6zxgcS3ufN3ikttYXwVgPtcGYuMR+8Cfh2kK9x9ZgxPrIziyMjCpaOXxh2OT+ovfe/6ueXj1s86FX/qHlxivo2FQxymL6MBR1NgVoNPAWsoM8wwlpU9ZiJWV9HAjNmFUNHcfG3xGkEZdJff84qfvhcsv2QP/8vl6DaCdx7WSkOlYGYgNbeRXji3Uue2yfQXAFBSRTMoOAwDce2il/H/V+gyAP8jD16695wgAANx9cKVRxIY2yeVjSOJIzbG1wVhdc07HpUkwXn/HA2Ip+fbUJ22DVzzzbPjFF19iBjZWbZodrFi1igKltAkq2usuvwhe9LS98CMX7QYAgH/y7PPgORftgl94wcUqCEfExkcpVOMsh0Jrgu49XMyT7x9c0UF0wgMb97HrNOgEKHwoPN6J9SLZ4kNsdI2NTXtPVFBvI6ZoUo2NRCmkKCyvhdXJJWTyaFU0GbHBa0MSFKHiASkPbPTv1I+p+xyqdTaQinb29rnwY9caySkwyYGjWTcauGAGcM9iD46WWQMsrkJTwUaFI/Sip50FL/o3Z1WOz4qkSdYmNGMgmd2gU5/n6mCsGpFux4we24xdm67k8NbRNndZYmQX7L9zKppb7rm4fnmu0TaXeAA9F0prkRS1XBC4q2gRz6VKQcll/U4CH/ql51uv43lKdC1+TRb7HfjLX75c/PxQuL+mgowZSFL7ndc8E37nNc9Uv/+rD90IX77rkLFg8WNyCo9IRWNBIC3Ix3GgU4lohx0MaacZQKZNmIjN9FlIHCuanA3UP2cCFc3k1Jf3QRjT0560DT75b15kvR4qHsDXrcV+R611+J1SsE7H5HoWAPR1XCWdrDdGqaG+RRt0Sr26oiiC//m6H3GOWVHRHOp51Jmhn6VzZS5QPID3sRERGweaK+09IVS0VeJkcKNO5SitLrzdtdCDj/3rF4rjG2eU3440QB30qDGPbUckuI+NEORJTjk1KTsMoNflTcGxAyCIjad3FR5zeWPcGA1VfUei9zC+/y30EhiOM+X4AripaACmAiC9z/i7q9fa+/+v5wGA+exaVDSpxsaBfFNHtgnxgF944VPgF174FPX78y/ZA3/zxhcDgGYpKCqah9KLpqho5Vtp7en6cGz4I1XiAZxWXpU0QIuiCPYs9uDQsm5b4kNsqAAPp71zOhyAWS+aZrmoiqYkshOKtsQAkDlqNc0EoLOPDYokeRGbisDGg9isGoHNdFQ0l5+H9qs//jR4S+CxT3vERnLQO3Ekqk+g1PPuBR3JcMnJaehhkvHDUMTGB61XmVZFs+VQ14dj1Yh0x7xJeVKTxCG1SSexKAs7gXgAf38lFc2H2JBAgtPqLEUV8gDQBww3FameCA2vl7QQAeiHuei5YyIe0xiOSQxsApxzHxWNzm3q7FUFY1q+0nY4aLBExyfRbHgQyIMfWrjuqrHBvw/H9j1U7yH0QVfz1rpGxyoGU+Q1hdgYkq6241fHgbDkniukdNF41rgIas1x0p+L+jbzmHRO4/pAg471YQrDVKswUn43nYdV1CQ0RFtWHOp5hsR4kCqa4AgziinNrFYhNnjtpb2HB7C8hhOANOgUxQP0dw1JDUGtuULGZzXoFGhgNBij/Y6CvgtrN6XA3XW/0Yli108FNsI6A6DrjqSEAP+ulc1RpSNU17pGMsj8G863Exs6ieCiogGYPZvGGUNsxplVdM6Nni4PbGjjcZd4gKZl6deaUJD0GSI2y0o8wE3FRLPFA7QE/NogNeZa4khyoHEWTx2hDOozAgT2sRlp5A2/QkqUoi+Bgd9ASpgIEtm8bo4a9/co/dVEbDDhISSxQ8UDyHipeAbAdIFNj61VVX1s6thpH9hIWTGjQJb8GRGbnfNd5+JK6xCaMBcVjWZpJqqxYZNqyBGb8lxVZ2q2AYUgNtJDOIkqGn9/FRVNkntGZ5I6xxjUuSgHBmJDHDFcGM0+NrIDTQvRqVGlD9Pxs06tluF5rwUgNpL5qGiuGpsq2mW/o6F11zFjtrHI4gHm+PnmkBCd/02X3HNkZqK8RaI5lbieLRWNzqVUQGzGguNXB/XkNAtXAG03SbVrdaRgPXU4CAAyZXN1oDPTG8NUrWWF3LOegzQT7XR0E3le2IiNrgUpjmcGmSYVzY+wqQxleV/wUnBKpTRupQQmJIf4usrpakVfh9waozo2ufZjofg/xDSiRAJ7Rjmh4zQRG3fCQLIeo7wAVDuMOjtsXr+hCmzkpJsvw85fW94cT0X1lozeA4s+iYHNuq7j5TRl6lBusLXBrLHJK5uc0kaxPGMu9Sjie6MkINIkdU8yTLKi1REPoGsWInprwzFjIejPhSjn1Uli791mBjZ+uWedCMR1xVekLzV05QkRMbBhPgo1jqSukWSKXGNjI7lVvh4XsAKwg2wMbDijI8RwX8CGs8rnaiDwPu0DG1E8IInFAllEMbbPdZ10gmnoYZLZ4gF2Y61JoN8+yQoAmLzM9WGqsiKqgRubhCGqaBJlJVT73XfcEMTGpoNoGosWEDAjeR+FhmYO0OF08fPp7zrLI1PRegyxmXbeaMfRzlqGbDg+KpoLtp8OsdHHMGpshIwW38h4H4841g47HtfakGMzsPHx7VNCxZn2voQ6ygB6rlAHDcdB51Gd5546/z5HLY4jI7jmASZFs12qaD4qFt4fGnRsjEjTvSQ2soX0uK6eSPw6qBqbgSkegGscncd0bHQNme+R7K1YY2POM4OKxtcRNu5OIGIj/b5OnmvpGaGoJb1+odQwACr3bFPZJCoaR/uL94XNTalBpyRVTM2liob3V1pn8ty/X/LrszoYq+M10ceGfwff/3DOnij75FF1QDQq0WxS0XIjAB5S5U7P2qzXQvN6jdmxADziAQa602xSl9t2plolBjYO4Q5cd8aEirY2GDsTMiIVjTzzeZ4TdcXqZ2vPYl/9HEVuVUoAJvfMklj0PNDwHtHrwwOEoRB0qmBJOFeecDAadJJ1B8cl9Ser8vXwO7JcB8WcRoffO0lywUVF63amn5+nf2AjbC5dR1ZSoRjzHQ3Xs8XV1xV8ErMQG9SyT6frsUF5nADmhJIRG3OSSFSlODKbS0qKYHX7HKBViQd0Et3dvZPEFopArxGvL3JR+uiCtSIhNp6ABD+raDscsSFUtKpzq2N4niJiMyUVzXBeDWfVf0yq8sJNQe2xWXTuk3tG4xlritigcXREbeYK0bGvCS2MnYbuSc2gonmU2IrvLShI9HqpZpXkWapDW6ySMjXf60bOKGJDnRqaveTzQaKnrhjUzkxzwBkVLYQOZAU23WLMlngAo6J14sgZ8M1VqKJpxUdzDYkjWzzBVd8kPWM8ocD3J8ycFpK98n2k0vxVDTolQwcvTaU+NvaaTqkktREbtq/QYztRxUTee6V6UTT6XskRlZ7JpRI9aaKPDf8Oe84W8+1kqZrV78bWnO92dNIBA0iA4trxupiqOiUAveYMWDbfrPPKjb+pzwoOtk7qzoqKxhCbigQRgJ5DtPUErv2jVLdxSBjSKh6bPNcZQfND1uE9RHBqoZt4G1fr/TKDjAWoUgsNTkUDEAIbJvdcHAt9FHssXK1wjQiWLHRthU+pv06Vr0fnFH4P9xEwATbJ/kupaLRtwOOeipbnuUgHoFk3qY8NRWz45tR4jY2F2CAUSeWe619mn3jA+iAl6FSJ2PDARkBs+IYhITYTq6IFoBq0q66dJSWBTZejT7LzRL+GXh8sTKdvt75PITbaSaBGMyg0CJs6sFGITfNUNFPSl/DFAxEbqaiRZhar+9iY4+cZayoegOYUD/AqJBX/Z6SPzfTiAf6aDZQ0ByiuyTDNgMbCuM4YjSBngNgU7yX3wXONzYSFPDfwNf4zDbypeAClotGu6D76AD8fRFssKhr2sSFrtFkwrH82ETYpkWJeA0pv89UYAejncCSsoXw/4YpBtIjXZV3i+OPxquii5vjKNYAiNsyBkSSBAQr0jb6vyqSePlVMBJcgC95fCbHxNVClx6SG/b+aolbR7+VzQiE2ZTBFa83U52MdzK4TxGac5axOQe9XPqebqg8C6OAKHXbKCuHrqITYpBME0XWMIzYS6uGi+9HCfzo/MPnBE2tiwoscry5jhiI2PkU0ANKEPcstZoEUUOL9W+wlag/h9EKNiNuF/3KNjTk3MJBe6CcwR9DsJDHHJfl6Lv+Azm9NI2WBjUJs6s8pSkUzRGge71S0IjNqv07lnk3Epiyon+voi8Y2nqbkYdHo/YwitmlNUdiM2W/kY5pZt7EK4ngDN8woutTkqEmUgWlU0Vzfg6a66lYoM2mpa525KcZr3jPKQ6YmiQe4zl3iIgPohanLEJtpA2IMpOTApvrYGrGxHwzMAXC6TdVcn2PX2zhmThxCh9wuGu9jsyCgCfwc7UwjlGPJnGPndDWA6RdDE7GRj6WRELtOAJ83Oo/qzBUzG+n/HD0uR8VcDTrHhM7JT4/+LolbbAzHus9CEhsoW4h8MP8bCh7w5AsXC4ljDxWtQjyAOxh0E7cDGTnpMRKQhSoqmua6u50jicpXZ/6qPYYgZl2VNNJOlx6j/nldWBtDvovuP1IDZGpSdrg4hhbC4TUG9DpKY8NjnrtzDp60vXBCdWDTEGLjSQZp8QAMbBJrX6dS6Fwx0a7FwuSb+z7E7FjoUOtia7L+cfEAYQ2oUuCb1kJqbIy6V2FfzTJzXVe9CZl4jXQO9DlOs7yWENIeUmPjq6/hf0dfQ8lWK5+K1KUQgQepTx+A3ceGno8PsdFUtDKh4uhj40NsXEtPhyTzXPVxqw1R0cy5PH3gfVoHNr46ESmwWd7Q9CzVZM0q9pycHiYZp3HQDsnUmahrPYLYcNhybTgmNTZlYMP6DUjdnV0wMF38qrTNXVYlHgCgVVIKZSbzPfTB6DG0ylf0KI0T4VKJYqN/L76DykyicUW7OrSuKsNz4NlqgLANR3I00Kicp5l99B+T6vJzy0igW43YmBvCNk5FExAbixte3jOcktJzqgQGiPMwNWJDxQMcx9L86cz4bgDtNFDkr84zZDjwHn53MT79d67MRIMBCnb7+thEgrOxsskRG61iRx1AXc/mHjMPTl0ZUbymCrGJig7bvU4MUWR+jvYMkcQe8DvzvDieIYLBzp9fDypnzY07qBYVjXDdXUazrUoJrBa6p52UMQuM6N/QmhAPoGiuSqA41nlVQ8QbE5Jj8D3NbPRq309Mklx+yR5FecLApilauU88ANe7Q8ubAOBCbPSzZyA2TD0QQE6+WeNhCZw50koCwNwDXMI6huT7jMUDQmpsOo69lDJwaFCIPh1P1lXVQdIeZyEtGvYu6sDGp4gGYCJRmMjg6m70do8U2p1YTBw0WTyg+Fk6V1eNzUIvMQIbhSQlpn8IUO3rSfXOHLGZrsZG76fYy2bSY3E7rQMbV5+AbqK5rCJiM9/RmW2rQWfDNTYMFaALCt906hitseEPwfowJTU2JhUNFzupuVwIYtNEYOOal3hOSRxZVDcTsTE3Ux99ULqP+DljTI6FXzmBRmbL3DCM40xJRVNUn6Ed2IRsOBLVBACLJfV31BE88IkHuDo/S45bpXhAFAE/RVs8wE2T4q+dLDe9OPL3HQixKvEAgDDEhq43deYKvQ5VC7uB2AgNOqVsrSmbyq45rSHD+clqbCQqGoBWf/LNXVe9AjdeY4Njfc8/eza867XPMhynKIrUcfoVTs44y/3iAVawjc+YvYby525aKlrqoNn6jF5PfGY5bUVSzgKg4gFhexL2aqEqeVUCPOgYrrP1xKALOxIDADIN+md+5AJ4448/DX7jFT+g5sGGQ1lxUjPEA9ggnnnuDgAAuPGhJQAwa83QOkRYw5R7zuwmh0LyzR6PDoABKBWt3OM9iLVER50kiK5jO3hgIz6XMipGafF0bV1RfZfMhISEahvKlRlJLAc8W1TuuWoviSLdsFUFqKzGRpJb7yWxJQql3uORe5bulyo9KAMCGvx2iLAFjmee1AWhhbBzeF1Xk1S0LjkHKkPfxPN8WjfodEkWJ6TBo9nHhtTYxPLm1HSNTcKynZT/PM13qbqHcWbBlquDsaEABwDQQ4lShnIYY3Vs6I2rolWobHSSSNHINDJiBzZIXfA1YhMRG0kVjfwcRaShlgDR8qaDUmf2SQ3PQRIPCJkneA1dMuZ4HKPGpuK4faLyws2sd9DHlKg2do2NUNjOxmJzw81jSkkBvAdLZSHv9rnu1P2FtoVQ0UhWkT9fnIoWR7Zz5DO64VeLB+jjcsQmIWioxK+X0FLpOaFUtPWRpqL1O8Xno6hAQyahorkyorbcc/G5n/o/zhffP99LYGOUyn1saPY2y9W6lkjiAVb9HT5jE1DRSObUZSYFoz41miK7yiFIdNIIQFZlAtBFxqF7EibOKIKnrqXjGBjUrVt9ivT6whMDdB+QCreftL0Pb37lDxZjmjcd6Mb62Hh6f13+1D0AoK93nwX4AKWoUXlNfKpoAPIexY3fS3SK8XdaX2X3Zir+lxCbWVHRtjPxAHm/1j+7xH1oUIjuXVEbR9apAOVKKkJSZXtrUNEAAOY6MQzHmVr/uD8hyT33OjHxbWjNVW4EP2icRkYNry3S3PDZwuPPdWMYppnaV1RgQ+ZliFBUvxPDCuh1bpMl2RuhomWZuufTJijRTm/ExtFkkj7Ico1N1+AhU2u6xsZCbEjR6iTZODRaY8MLzdapKpqjQScufgs9G5bkvxuITaYdszpGNwKXogjPIpgcW30/VI3NKKuUAZVekzYNw3EUskZ5rnXdjUxYEoOvqLSuSTUM0rhc5qKijVlgUwdl6ivERqCiEUfdQGxC+thYPVbctB80a4P20A+xkJdvqJMYfU5czytFifm1ogW9dIyhRgt6JWoVNVcRffG9crbWh9jEAmJj9IUaavGAbhJDFEVqrqp+RLXEA+T7xeWeq64hboISJ9tEbMxO79Y6KDioxefsxBrfT/jviIjwgNM4PqnhmYRBQN+LDjSOWdr36P6xUVPuWTdd1IhNFaqPCY21oZkoGXoRGz8KRI0/703t5T6U+5nn7jCEOvqdQjnLFLeIxbnDVdEA9FrrO10+hjnmFLuacwIQdFmQfJ+VeAAPOGUqmpxwo3vUusBmiFmNjSjwQk6LoqEhycg9NahoADr4wbHi+H2BTZ8ENoORGfii0WumaGRSYENKD2jTTNzLcV9QiE3PRhKrxAMACBPISUUzEyt1jLZkwXVsLuDah9hpHdjg4sx7iRTF58XPUo3N9rmOvmiZGRk3ropGDtNJYuVA0yzNJLQ3X43N8fWRem37nF88wHDYHBQUA7FphIrmCGxYgasLUaHnTh96OUtjv6YkIh01Nq6xppl57dCZ56jcNIbzUgoiQgJgFxWNbmCFOk/1/UDzyT1Th9DsY1NdY8Pfw+lsAIJ4AA98PMEsBjacAjGJUUdUolAAmLRFfq14Y9xpnp8q8QDqHGyzgsdY0f3GQsKiqo+NKPdMVdES5H0X7wtCbNi1cGXlhg7ExmVYcxOC2JiN/uR1UH1WUAJTY8Ssd3kMi4pWXg+pOSeazlROpupHz3fTgdjQNZ3uH3VrbDBxZiI2fgoLJj04Kk3H4aqxCQlS+PPenCoaoaIJc+K5T9mjfkcHkt4LrqCJRkUeuIWooqHNMSqaq4cNgN4XzQAL59ps3D4u9yypormCGXrb14Qeb504rqSiUTEhQzEzYK7vIkFZlSoagL4XOFYcmkTvp8IAPSXUY1PVAJjcM0sEU6NNksdEZAv3YDwHvB4osU+DRsXO8Vwf3h9RqvcGqN6zxGMbVLRqpLuOndaBDS30n2MFUUqmt5xA4zRTm8qO+a6W7DSKpfSxm2vqRR7OKFKOWJpN1nwNTctG50bTNwCAQyeLAsYoAtjeNxt0KkqMQmz0YmP3byi/Q8jqzEIVTfVaEDIRch+b1OCqhiI2G5gNc4zJhcDgdRhl5gZAPztlXGONd9FACqrniYuKZvZP0WOOIjeChjbn4P0CgNMhDKGi8UVKQgtcjeX070JmrnyPpqI1gNjUoaJluTPjXKcpHDV676vEA3wiDp1YUwYzR42Ni7YCoJ0N6pSuD1OVMMGxYcZQF0G7x8yddtpckxpumiGZRAAdIFX1y6BiIBJiw4dOnQZu+Bo6Di4qmk8VTQVOY53JrxMIIxUQwK5xkhJ6Q6O589h4X5Vh4gyThgDVgSeuaTzz7quxqRPgcQe6qRobM/ll//3yS3RgQxU+6Tgk5HKUZWJDZQD/HOfPqVJFQ/GAsS+wKf43e1mdevEAw19y7M9S/Snd0wDceyVN1ioadUjCMIlhV9nLJoiKVt4LhdiUY5PqG2lSSBIPGDKGiBqTB7HpE3+PHktT0Vhg07MFgtIQxIYFNvy5xdOcmoo2NOvIprXTPLDREbehgpPorCTeHErt2T7XIYoL9oMNEDbZQ4wrbylerNA8rY7R7DelAQAAPLa8AQCF6hQuDrxDNF47H8VGQr0mFQ8w1E4qa2z8iA3tY2M0bgtVRatAbFw/K8SGZadDgrZQ4w7vk3fMqZ9DKAJUttGooaDIVqwRzpAAHikOYoPO8rCWeMD/296bx9lRlenjT92993Sn0+ksnQVCIjEkIWQHgQBhVzZBh0UQUREMKm6AIgGRiOM2DjriCIGvsqhsgwtxGDTMj2HPGGAwgksiIAmEbJ2kO919763fH3XPqfecOrXeukunz/P55NPpvlV1T1Wd7X3f533eAFQ02fOlym9wq2PD4FUsk0dsGmKI2BAHgDsVjRo2aioalyoO2U3CFOgUxAPkqFjCLsDpXsdG2tirZFfJPmyfpIpmtVekNniLB4hebQf9sHSqLBbiR/tk/Uu1gTIMUWDGjkQ7F3JnTS22gXRGFdhzYfOqUxXNXzyAFqbzyh/0AnuHnIqWFN8LU4MDRI9wWLlnFh3ZvS/Pqbp+9DnmJNgjOeTEHBvZsBEjYZ5tqlCODd0sqwz1hcSwySqMalUZA6BUt0aRv2h9T/CIDdv0Mccbp6J51HRRyX5XSjxAdjD5ORxoM+gcpIrYWPnL4h5QBTr3cWdOQG8ko6OFo6KJ40lVIJcWWM1KhgL9v+z4S0ljmoIydGjfYs+80RGxsX4XqGgBIuOcPcNyeVjxc+mUKGNQoKJxSfMRYNiwyc7qEHLEprQAl14OC5U3pJOW50TKOQHERSiOIkCAc1NAqy6XQ3uji/VuybB5a9cAAHGCz0heRvbs6ALr9FQ6B2Ex4KZChuh9UR9jyz0rIjaqHJt80fedqZ7tgCLHJqGYMACnkgpgP0O2WYvXsBHPH9NsFwYLcm2af0G9stRDlDCcHiQveEVsQlHRiPPBqukgbWBVERspP8IrsZ1eB7A3bHFEbKgR5kZF41WlTWfEZlCKlIale/jxxym85J6pcyV4xMa7f1sRG5HPz+YbtrH22pAK9ScSTjUp5ullG99iwA0Jz7FxfV/2XEyv6XX/9HpygU66lrD+7xqx8dgcsXYNEudXeEdSyRkxJBoE9Fmzdz6ooIAF3eyxsZUv2spFfnVsGD1SFg8QcmzyasdAkEhnkCT1KEj7rGGzJ7bxjSn7KVDRSIFOGf0uho23KpraUcRV0aQaRhScEqVgY8SVkyQjl04K+xY/uWc3x6MqxyaVNITIqpsjhdZxCpu/xiSfg0QNWO03Fl1irAhOAVTkuGVSRBWNGPkyzde+F+eYZqCO7AFyPhuT7B7YNRoVUdRCgL2eM8dGTIGQ2xMGlIrGC4yOBCoaneyEiE3CVqpik/eufjGRmNaT2b1vCFc/8CIef3Urv0ZcXguBxpGQ5J4L0bxx8rV6pZonqg2dnFSeV0RsnJ5Jp2HDxmPYXBKVdKMMOXwvbKiUVDSbqpEw1ANQLR7gpKK5RWlEJRXrp8xFFqlo5fUbOVLIis0BwfoJnfzo5EnpIYZhKNvuBruOjU+BTm4siXVEGKjzIZt0qgbRTTeDn3iA6v3KfTOOHBtaPM3dG0gjNi51bCI6M8QCnWEiNk66H40sMdBF3quOi6rdyhybFKNi+CePCvSKpOHoF20lBw3PsWF9zscL6GfYUOqgWMfGPiZhOMc0j/Z7KKC5UtF4gU6PHBuS+Bu1/ACbQ3nOj4KPny9a86dCAyEwPboxk+TPkTkP/QxPFkWUBVKCUNGCjBt5vFekjo3i3rKpJA6dNKr0fycVLZV0RkMZ+gbDR2zkbs3mV9bnBvN2DrLjXMUcUOk6NoD4bpQFOl2cjfQ5qJ5VImjEpnQZoY5NwL7OJJ+DGDbMicfSBNgjZT9/+szfcelP1uHVt3YLuVByXRjAVn+Vn1fQAp1UnIBBpqLRWnVs/NqUX/f7lJlAbNy2yUIRZVDR8sUiv25cVLS6lnv2yrFhEwp7OXYNm1IyPafsFPH//fkd3PPs6/jdn97m16hEHRtaGJEldbH2RkE2lUB+sMD5zUxilYFOInZHZ5QYJ2fRLWk2FlW0AFGN8aMs2tXYEv2KLh40GmMXhSs48l28vpfBFg9QH+e2gLENFZX0dNxbmd1GjjpRwyaMeAAgRSOlvqZSnnMDu08vwyZhGOhoyiCdNDCurUFp4MneOvmdycURDcM5Dt0SuYVjpD/JnPuomNjRgL9v6xOkP8XvJYaNw+MsJb6HzlET6VpekGmbyYQhfK8qgTxPFnmvqJhyPBFVNL6hKx1H6w8EaS+NpjOwRdKWewa/Fy9MaG8AYFWj9/peoY6NRKlU3a89h0t5bOR3TguKUKCTUtHYPYd1fsnHq6LL+aIJA+rcjqDUEcMw0JJLYWffEHr7hzC2NUeogupzWBRR3qDSiLA81xRC5H9UKmIjRPJd+vNR07vw9N+2o7u0holUNOfYYnA3bNzbI2/I5QKdQwX3vqNiYzCHn18OXzlozaXwzp4B5dwOuDsVDcNw7G8oZJqWnzODFuUN2j0OGNMM4C0+r3iBGZl9EjtkbGku+tvWvfjb1r3obMkINF5mEFH6GKWqUci1qShoTrXq/Imle2B7LeoAG8gX0ZBJhqtjwwybPMtjl5gCZVDRqGx2XKpodW7Y2BEPathYBTrFgWvXsGERG+bRMrl06Vu9FoWL1jEpFw4qmhAKLc9Dkk0nsXewwCM2oxrS2NFn09Jo57I7YKn2S+nZUc+hM8fGpmow+NEM3BDEsPn8CTNwwru7sfiA0Z7n8IhNochzR9w2Tl45NqLRmVD+P5GwJ1RmDO4oVbRmnNukj0c7DOTzBcMmgGcpSdpLufOyF5V9T5BcMlsVzYOKZhgY1ZjBg5cd7hohyUqGjaP4puFcnGQDSV6EvKhoDHHk2ADAnR9eiB19g+gk9EChLWTOkTnzssx62H5Cn5WveACNbiaTSCcNQoGzjUeV1KsqauZr2NCIjUTBkVW5lO0V3rkzx4bNY7bcczB61meWT8eyGV1YXKoxIoP1fUvIxfqblYPkY9gk7E0DBZUktjeZLqpoXuIBhIomO+SCQp4P2TOljpN8wYTpUjIhTP9szaUtw2bfkJDX5zZfsU2UnARO6Tcy7TVM/of8rCoiHuCy2bv4iCmYOb6VCwmIVDRnNJJB5TQCvDeVTlU028MNEPGAgHLP20tiK7QYZdxoKb2bjGJuB2QnoXN9yLtYNnKOp9tGmu5p8iEjNlccOw2HTxvN9yde4OIBAyIV7VPHHoRDJrRh7Stbcd+6N7B19wDv29mkuo6NOxWNOVad7aepB3INGwD43AkzcNzBY/ncSPfPfYN5y7AJsNeT28uMY6cyYRlUtIJNc20cCREbXlAqIYoHUOuddV4W1WAPnBZZM6XBEle0BnBuelm7aPG1ciI2gJ1j096UEQwbynPMSBEbFunwoqKplFOie5zt490m65ZcGodP61SeIxbotHM+hnySSlUTnEo8wGvzxiZUtgfYLhk2iQD3FhTywtfZnOGGShAD2DCsjeGglH8kS1uGidiwsVUoWrKkdJJiz4Q9s1kT2lyvQ6loqsrccn6HX2VqwFs8gCGOHBsA6OloRE9Ho+vn3AtK6gakk4Yg7U4NjDAI4o1kSEnRzXQigX1glE1a48s+h1bh9qSiKfpLvyrHhlPRSgXavCI2gtKP0+BtzckRm2DzZnM2hSMO6nT9nEZsaOFUP/l2m4omrhtU6plG5in6QlLRZIdcUDgNG+t32VHjzFZQn+8F1rbefXkhZ8NtbnGTexZzfeSITfQcm9jEAySHlwrZVBJHTR+j/O60Z8RG/SY8qWjS85WjhF7iAUmpD5umyR12bhHpOMCi527OGVHiWZqHEgaUvEk4hRncJfmtn4IqWsA9d2MmhfccNMb/QCjEA0r31ZhJ4dTZ42HAwH3r3sD2vYN8XaS54tSwV1HJAOdaTsELdJIcmywxCuS5MZkwkCkVFWVGRJBcRjdVtNipaKXnGERqOwgqF5OMAZSGlCObJqppLosHOKhoBdPhHYpTFUT2/qW5l9AMNVmrkOGGjXVvHZKnhVJwWBI29xwX/MUDVHLPphnNGIuSYO8bsSGbd/fQs/PvLPIgJi2r/w+IG1YA2CYZNkEU34LCmRCa4tSNoM+N9W26qeJebk5Jsb4nSOSNenMc9SUIFc0PQsQmmXA+Z0PMb1AaLY6Ijcr4UW+MKw17zrEneOZckGXWwzoGxHoYwSM2mVRC8NjSHBohcZhU4Y4iHjAkjUObiuYca17tVUVs5BwbtreJKzqaL5iCApAbz99ub8nwkKIdtK6aTP1l4KponnVsrO8cLBR5HkrYPixHyIT8A5LALBd3ZggbsQEs5yGlNrkta2zNkcsUiDk26mhYMLlnyVscUzI8nY8Cz8WO/DGxLWwacKOieYsHyBEbdR0brwKdbNz39ue586uSERv2blwND488Jq8501JF83f+0EhV2IhNGDBnYL9CqAgA2pus57Bt76AoHqCSe3Z5j16sCzr/uBlGMhq55DOLjFt/DyIeMCRFbGTDpjxVNJNHukeE3HOeeCSo4hK13vnAlTxfVCNbDgPHqQoiK3vwiE3R9I02+IF1VBaNam8SJ6QWRY4N6+RsEaYdRb5v6t1gCNLZVQgSxpfhpopmF+gs+C54qmfLnoHgiZaSPFXXYLQ3Vh+FGZKxqqJJ351LJfhiEDScyz2+gmFTap8henmC9D06IcpjpUg83b7XkVTR/CI26urRak+01zGyQkulQA1g9pyYUSqrEZbjGAhax4ZFiGXeOp8bVYavol/4US1pErhMReOV7L3EAygNNECODXuG5ToRaD0fSqn0qjAPEE+iZLRQFSp5wWcIUseGPY8dfYM8pyB0xEaewxSb8kLR3bCJErHZvS8vrBVu62gToaIxR1m+IEr3O3NsgosoVCxi4yMeoEJGckjI/YnL7EYQD3DUf2KGDVfvdHf60VwTwKahNWWSsUnqqtDiE7ERnIQeDhYZsqKmqyQ/6fvUmRM3uCpaabzL9zK6yaIz79g7yKWSxTo2dn8YcjFsPAt0kqjvgCTF74YGKcpEJfDdkJEMMfZdsmFTTh0bwGYljYyIDVGnEiM27nLPTiqas0p4XBMhIGmxJ2yPTaFo8o1y1Jo5LGzJjbZsSujkNMdGlre25Z7t5yavQ3KRUyBY0SYVEh4hZjcIRqFCFY3WsXFbRL1VZdwiNtKGW/JubdtTMmyanYZNublZqmrSPC8spJdwSKCiiRtXW8HM/5qGYbgKCATRumegi7wlHiAvXOI79yvg5va98nXlRMZKgVI3bdlLZtiUlGYk6l5QyHko3seW6GAKFTfBsCH7UeQglgAAZDtJREFUcpmqKEYtIJzvBfk7g4gHyOPQ+f5kuedgbfEDuy9KRZOTkL2qeueliE0QKloQ8QAWXd9emmcyqUTozaYcpZAjB1b7TYHLTxHGi83eT+++IVFW3idiUzRtD6/cDnlNzhN2hh+aMilh3a0mFc1xjsQKkNcWeTMpw3MzL83dtL4b/enlIGJzwPa9Vn5xRwVpaIDdV4JQ0eRmez1yOT/TT5Kf7r/iyqemkKlo8lcwtsfO/iFOs3KN2PjJPSsGWpbs9xgjyS9iIxvZ0ahopRybGAybjGDY2OVa4kBdGza0aBflD6aStl63nGNjR2xKnxeKjrB3nBa8rIpGvWXcwxcxQmTn2FgvPZtOCtSyFoW0Ipd75jk2RDzAsaEHbytDVFU00WMY7By3DYat9V7knmbXZEEPb7Nb7oArFY15t0qLwGiFeEC5XUdFL5CNcT+oqWguOTYBG0zlICnCiElQVSCLiibej5wA6iVTyqAaO7WK2FDxgLgjNqLcs/fkzhwlcr4L+17Z6QPAscgHzT+TYRs21nF2HRuPiI1EnZPfOzdshkQ6X7nzNBVyEerY+EjT2+IBalU0sU6aOHcylSTPHJvSuSyXLwqVUn5PKqptnkjByogasaF9ym1DRDcnLNont8OtwG2QcZNIGEL9prioaGIUM9g5okGZkMax7dxxi9iEEw+QIzbqDbHVFjZXWcdwZ12TWhglLrSU3ot7DgyZqxw5v97OEUFF1eX6dC2XnTlxglHRmECGfC+jGq0xbZrA27v3ARDr2KhqS8n35KWKxpwjg0QVjea4qsCiIUHrUQH2WsT2lQNcFU02bMI/Y7qn6x1ZERv7hTvEAyRVNDnHhirPyF7oOHNs5KR5OsnyYmgRv0/OscmmEkLht1aVeECehf6ZKpqH3LO0oQeiq6L5bRZUEEPL9nOjXg0etXNpDx0csuyv24bNGUkobQRNFra3BhlbBLyUXMJCnrxy6QSPOASO2CipaOJ7C1PHhrUDUEVsrJ9B32mWbLblxS0QFS1AxMaZY1OtiA3zgtpyz3bEprxNeZJSWlLe57Jrs/lBjkYmpAgk4IzYuIl9ePXvdNJ+fzYVrcA/c22vIB7gnmPDxQNC5HV5gebY0HnNz5Bzi8ZQyoiqAHT/UIFTyzypaKVz3+GGTfj+Kz/vtGDcltgKHlS0MPM7zbGhdDK3uSWRMPg6xZLm5dw9Zx0bW4wjUJvIxkqlChYFtJ8mgzqZpPwxOf+N7UPcCnQGZRwAhIomF+j0oOuy+YjRq0c31ThiQ/4chorG1g42Jfg5OgsmkXivgGHDjAg23uV7SScTfF5jgk/ppE/Exq2OjUeOzWBeXcdGhXgiNiXDJgbJdbpu9faPoIiNTUUzHHLPtAo4YFt8rR4Rm6mdTdb1YsyxkUPitBNS5aQo4Dk2pXvLphJSxMadisZ+NqT9xQPyiohNOapoUcQDBE8XqWPjLx5ADBvJi+BmzDhzjewNEEDC9qUEwDipaPKzyaaSPOIQlFKh8haXG7Hhai15dY5N0P7ADZukk4pmiQeQjYBiA++sY+N/TNUiNiQSIosHsL4TpJqzClRVzE9hxouKlki45c6JibRu49WLNivnEwB2HQdP8QDJgSEf6yjQWWb9L/l7w0Zs3FTRqJNFVcTTrkLuvUCzc9k80xJBrlyO7lKHGm1/pSI2CcO7WHEjV0az+ocs4OMesQm2NlOnXlweeUE8IOB8J4t+OKXNrd9dVdEiiAewfugpHsD7vvU7E8SppHAA4J9jEzRioyo8DBCnjs9+oFgk0vtlOkhUkAtUq7qgbERmU+ocG7f36JljQ6loQXNs5IhNgHnWjYrWkpPpoOH31JbCq3WRXf0jKGKT5wuJmGNDPb9s4Mo5NraUnJ1jc+KsbrTkUpg9sS22Nop1bMSJ7Z091sLlRUvwAs+x6bcNm0ZyLcFrRZR2ALvTekdsrJ9U6z6qlyNKVMPN8GD3PVggcs8BxAPkhDb53ai+l7aXR2yksL3o3fa8JV/IRm4uncCCKR1IJgwc4iGlTMHu2atAJ/8Z8F3YERuJiuZTjE8GpUepEpx9qWgOio13VCeXTlS04JzwvcQbaBs21niUx13oiI1PJItCjtiIqmgJwQBj8IrYeMkfM0oF/T6rjdZxwaho4nkydYg5o+w6Nv5JrUHAHRbForCI+yUhq6Ix9HdKRRskxk8fUUTz2vRzKtqe6BEbuX+pxAPyxSJPXJYRShVNkWPjd36TVMtGdpi4yT17yYZTqJx65SIlrBHBzpH7tlho1+4nrlQ0j+9xGjZsT1NyXuYDiAdIa1olpZ4BYPbEUcikEji0p135uad4ABkzcjvZ3HtoTzvGt+WE+m/CNYQxH1xpLyyyabXhRdEhGTaZVII4bf0jNnMmtiGZMDB74ijHtWl5j4GQERtZPMCbimYIbWTrXi6VFAtyR3zGrO9yKtqIqGNTZC9crGOTJnLPrPO6Fei0kquslzGpoxHPfem4SJrbbhA3COLA/fu2PgDAhFH+lWxVYMmCbH+STScFKhqd3GklWvpTzLHxj9hEVUXz0qd3PcfFa5wlg9/Pk0fPkw0b+pq9krMpJW/fUIFLD/ICnRGiUW5QVZM+d9EknHHohMDeCvau8xK/n7ZPlSTuBTvHxk0VLVzkh9VXoVAV6JQRjIpm/79aUs+0LYWivZg4c2yiOQaiyD3ziI3UP1VUNFkVzU3sQ+7rrOo8IBs2IqUiuHiAlR+ZShj8WclUtGLRe8wHhRCxIZQR+nhVhn9KMS8Coiqayvhh+SSyt1kGO5fNM1H6sIOPrxhX+aKJodIzZbVtVMf7gbWPqqL5zQdyLRsnFU0yGkPmpjFjyzDioxrRDXDQ+U5mAojUtAT3ZLPIpvwevNouvyM2t9oRG0YDVRg2Btvgi6po8mY7bkzrasYLXzneEdFgEOYauZ4W+ay9MYM3dvTzZ8WexT0fW4x8seiaT0INurgivyrIYh+q/iKr2FqqaGLOCkDq2Ejv8cRZ4/B/K7uU+wK63wubY8PWePZsA1HRmNwzKwaaTiKbSvJxHNW5YJ1XsMUDRkLEZohGbEodyTBKixNPjrOOddSxIapo7MXn0tZ14lTJkKMCdBD9Y2c/AGBCezTDRjbAZCqaKseGyz0rCnQ6N43WT+rZDSPvSxFFKlNM6LfvlYZr2YbMzZNHz5M3CIELdLINkGlyLnIqYXBPqigeUF7fke+DRSLDDGjbW6yK2JSoRryeTbBr5jgVTdxwhK0p4klFk7zlqkiLW+FB4TrkHcRVnDMIBMPGhYoWR8QzqNyzLL3MrhMsYuP8Xvn3hnQSjWmn8wRwUg885Z6lPATreNsAZ3OanGNTLoXE9t4Svr3hr+CoopkBRF7XhYrGPKHNPhF6uU9H6cMyjddQzHX5gq2K1pxxzz/0A5sHaR0b/4iNREWT5hXXAp0BPb9cJChGWrmfDLgKdI12RKRTtqHD+oaslue1nsjCRHLuF+uPXlQ0Lve8VyxhUEk0ZJKuEUuvZ0x/z6WTQhV6LnqSMDw38HZuUfSaYkGQk5656j3KVDRBFS1AxAZw3xfQHBu7QKf3WGB7QRY9DEKblveVzJDJpRNChCgKFQ2w50LWlhGRY8MWjVTSlqNlExlNEjNN06GKxuk6pI6Nn0UbBfLkY5A6CexlTRzlXs3cC3JHlcUD1Dk2knhAoIiNMwm9HLnnSDk2lIpGVNFonpXfNZw5NpSiQ79LiiSQ98XUY9qbMnxyjjdiI3nhfCYjFVRUNPccm2DXz7qIBwT10MrXUdaxkfIbgtSx8RMPkN95JUETcmW5Z65GGDJXgEG1+XcD679qKpo6YuOM6InzKINg2GSSwsIqyHnLyeueOTbOSA/7mSUiE4WiKdQ7KXfPSlXR6CLuR5tNSXMpgx8VjUUnGj2EA2i7GKL0Yfq83epy5Yt2YnFzGbVfWPtoxMbXsMl6U9Ec4gHEiRmoTSHzEoMgJTkIgkCOtKaFMWJT09l7cMsdUbZHWndo4VV6TRUDxan0WZ2IjR+88lXpWMxJCrBh9xSFYjFy9DwI5IhNUCoaWx+D5Nh4gaYe8BwbH+OCS1RLqmjeERsxwsSpaOmkWLeuTCoawwiJ2NhyyeylyDkErKYE68SydO5QwRSszLihigrQTt6cTUWusyEbYhkSsZFrH6SlkCEzVmQ1OVXbabmGqKpoXtxZN7iKBxAvga2Wo353bnV9rOv7fxdgR6cKRVOpHiPm2JQ3SdKF2DD8ebEqqKhosveFR24CNtdV7jnk4iBQ0aR3JtOAVJth+Xu8aBZA9YQDAJHmwELyDlW0iEXhqCHkLx5gCMfJVDQaqWBw1rGB8JOez9CQTgoeNC+6nNeGVKDrSIaNvEAOForcCCtX5IU6LOgi7ueoUMmp099TRP1KJR7Q6FHDBnCqeLVEyMFUGYv8M1rHRlLvYwhjeLNze/cNBY5INoakotHSDkFgiwTFt6bT+SgaFU2i2qYMhxPNYdh4igdIRlPKNlgB0dB2tEsybOTabLWCTN2nEHInJXZK2LzdQtF25lQixyYIFU2ZY5N0MiO8DFQ3COIBrI6Nz/7WVRXN4zRZPMBmPyWF/Wl5VDQblCFQDkK15t/+7d8we/ZstLa2orW1FUuWLMEjjzzCPzdNEytXrsT48ePR0NCAo48+Gi+//HLkxomqaFZT+cJMOKQsvyaZMPjEIaqi2QlPcUO1eaYva8KoBs9EUi/Im95syvZiyLSrNAlTFwmnPJW0E9bcvLNKz27YiI2PF1QFmXvPYHs1/OWevXJsEi7Xd1NFKxZNpWcrVsNGotxF6RteVLQU74NOI9sLbjk2ZkhqIpucMymLlklvL5kQVdEyivEoz4++EZsqUtEozUGuY+NQRQv5XlNCxMZ7Wmb370ZFS5H+zCAbqOw9OCJk5PeGtBix8aIeeKuikQ2a1D9z6aSwoA8MFYlzxfWSgUANGxqx8qp9Bdj3JufYsHecSSZI8i4xbEqbeD8qmuzdjBKx8eovPFJFqGiyAyBKjk3fYMEuYeDTvxn1jVGwnHVs3Ap0BjRsOOU8vk1rlMi8oBQoCaakk07Je9no9XICynsLWl/JNG2jVaUuKUdtmcOuGlQ0L3hSwqWIDTUCA0dsyH3nI87FQSA7yVVTtsOwSSYcRVYB21gIE7Fhc7EoHuC9v22UcmwCUdGIYVMkjhIaaQeiU9Hk8Z7LxOOoCHWViRMn4utf/zqef/55PP/88zjmmGNw2mmncePlG9/4Br797W/jlltuwXPPPYfu7m4sX74cu3fvjtQ46q1n9CTOzU7a3tPdRDiAbRQpNYsmPMUNujFVKQ5Fza8BnB3doqIxw0acILNJW1edVnVOJQ2elOZWv0WQe44lYhPsHLcoiphjU4Z4gKFukxslr2CKVDTVd8Sl1AQ4vT5B4UVFk6OGYevPyBXBw9YUYRM363OUA5805Do27guy1zH0GVY3YmP9tFTRRPGAfNHabEQtCqeSO3c9VqKiyYpYsmIka5/1eYIfB4Skonmo4HjdrxixEefnbDohFDbcly8ESmoNAjq/UVEULzqM1TZ1xGZQQUUbEqho1tjxEw+QHStRcmxUBTnl3ykVTf6OMJs9SmPbVRKS8FsfGB1Pjti4zTNhKZw25TzGiA25VtDnI0QxE7LcszPPsDkEvUp0xhnCXJgnBcC91CWZIA4zMGsdsRH2CR7iAbl0oiwqWpHQT+PsIwyyEREoYpNU17Hxiry5gc8/IerYcCraYAgqGnGi0jbLkfaoDga579Ykx+a9730vTj75ZEyfPh3Tp0/H1772NTQ3N+Ppp5+GaZr47ne/iy996Us488wzMWvWLNx5553o6+vD3XffHalxdg0Tg0db2MRH+e67SsV96ORNJ/dqUdHs4nXEsImoiAY4B082neDcZbn2AfXa9BFpyXTCjtjIg0/t2bV+huWlJjwmLDfYXmPx/CxJZM/zQe8fsXGIB7hFbBwGnvUzTyI2laKi0ckragQxraKiSZMUz7UJOOG4yz1bP0OLByg23YmEOF6CyD2rNjq0r0SleUYB60OqOjaAtcnlkYGQE72fWpzYDjFiI3jOSESCyrjLOVjc8HU8bylik1YbNk76k3ubxXwQiYpGqIuAOHeVy4236ThFYXyI0WVVe53jC1BT0ajxw2qV+IoHSBuQKKpo9Hk7ctmIQTeQLz9ik04muLHGig36nW+LB5QMm9J44Qp4DiqavdYHQWVybNyNxUDnJA1hnbFybKSNG41C+KwlcnSDvvN8wbTFLJTzqPWzaJq8hk06aUSiPcYJbxEf+/85SQE2aHcVnRmVzLERn7nK0B/dZEtSp0oOJ1W9OC/xADewOWSgjDo2ct6lClkSsaFR1lxKFA+IqjTsyLGptXhAoVDAvffei71792LJkiXYuHEjtmzZguOPP54fk81mcdRRR+HJJ590vc7AwAB6e3uFfww2FS3BXwrzFHLvadGO2NAFgi5ONOEpbqiiDnFFbGQLPJNM8kXTq/Irq6cAWJOtqkI5YA9GujhHVkWLEMZnmz958meeANO0NzpuGycxx8arjo37omXnGplKWcwoUtZuoO2IIhwAQEmDkTX7eXJ4QG8VV0VzpaIFNZCs67AJXOgXjoiNwmhxMb4p6PuoptxzQrlhtMfhEEl8DxttEJKOFdQS1bFcUIWcmzBoxMbemLPFU47keaqiZSTDxoN6EFQ8wC4qah3PNgjsXvpjNGwEVTQXKprKm2vnqEibb0JFU8s9s4hNOCpalIiNl3gAXfu4eIC0oQ1reLM2bisVFQ0s9zwoqqIxw8YpHhBN7jnO0g3ligekCA0UEFXRGGhdOb8pQl6z6LWHikWfAp12H9hB6NVRafFxwVM8QKJpsz6UMLyLwaquX6y4YeOfK9Xe5KwBxiOWVBWtECziQkGjyuxaoaloTAI/iNxzvsijrKmSkU2/Lw4qmqWkWgMqGgC89NJLaG5uRjabxaWXXooHH3wQM2fOxJYtWwAAY8eOFY4fO3Ys/0yFVatWoa2tjf/r6enhn9niAQZmjmvF0TPG4IIlUwAQLmXRRO8+RcRGqGNjJzzFDTmHABAXy7IiNrIqWjqBI6ePwYIp7fjggknCZ3TiYzlH7O88x0ZaxBslCx6IniMQRRKZRxfkyT+T4s+VRVDc5J7ZPaWThqfijGh4qb0thaJpFzKjhk0EKWs30IUuasSGK+0Iho31k93zEQd1YuGUDpx92MRA12RjQ07ytQvyBWvbGYdOwPzJ7Tjm4C4A4qKblBZnr8JyDCqPbK1ybFhT6KZMNmwKIZOg+bVDRGyOO7gL8ye344xDJ1jfJW1yKc8csIzTDZsth9Hk0Y3C93mp0DVkRJ67qkCn6jwZKk84FQ8A7EU51ogNkRKllEpxc6U4L2Ev5hSUikYL5DHYEZvKq6LJNCUKatCxNufS4iY7bP9kDoRn/rYdgN2P3NAkUdEGZcNGnmeK7tEHFeZNascR0zpx/uLJgY4PgrTHptsNrB8wRVS6VqSTKvGA4PQqIbqRNIRnY0Vs3ClMNGrLIjbtNc6vAZyOLgo6pVgRm/B0Q5pbVFFVNAcVzXkMjdiwuZNR3PcM5Pl8ESViw/qdaQL9Q9Z1whboDBKxofm8+6R9tNd6EBQCgyVGRlXoXcGMGTOwfv167Ny5E/fffz8uvPBCPP744/xz2bI2TdPT2r766qtx5ZVX8t97e3u5ccOVUkoJ8Hd8eCE/juZFqCI2bDAM5G3Phqw9HgdUoVW6kE8sJ8dGmrCyqQTGj2rALy5d6jjWMKx6ELv6h7j0dbI02bLryPMfLbzGELSytAy3In9eUEW42O/tjRls3zuIt3r3eV4zxT2/SaUKF7+mh5wuVZBhhlS7W8SmzDlSzLGJGL5VqqKJSb1jW3P4+aVLAl8zF5Pc89EzunD0jC7+e4pTR60+Su/fq/6Cfb7ze8U6NtWP2LDNGmB5pg3DWmCGCmZkKifl9/tt7g4a24L7PmHPAYJ4gGErz7GF669b92Db3kFkUwlexVquZ8NAn3dDOik4g6jYg/zuvOqJiDTQhPCTGzal/seUxYDyaZ/Ua00VgPyk6bnwjEuBTjcqGi/QGZKKFinHxkM8gArn0E1TNpXEUMFqY9j+ydr4+KtbAQCLpo72PJ5T0aSIDTPiWDKyzRoIt+40ZJL46SWLwtyCL5Iem243yDm/8jh2RGzCUNGkyGIyYSBhWLXF6LtV5ioSRsv2UpRtdI3zawCphpTUbDn/lEdsQiyTlMkj02/jhOx0VhnCLOLdP1Tge7C2hjRasinsHsjjzZ39mNbVEkkVjc6/LFLsq4pWGpOh6tgoqGhylD1s24Xrk/P8It1hELo1mUwG06ZNw/z587Fq1SrMmTMH//Iv/4Lu7m4AcERn3n77bUcUhyKbzXKVNfaPwauGCV28e3mODaWiWef0kYWyIlQ0H7nnsqhoUnv9Qo1s0tzVL/Kg3SI2LaTwGqMcBbHiVfDyxLiBDSjVxNPeaL3Lt3qtSdktRMna2ZhJOgaXuDC4b2aol0dFRYsiZe0GUf0tYo6NgkJYrnfKXRXN+hl1k8nGripCoNK+d1LRvI2faubYsP7EPF7JhOVFZZsZGrEJ+x78DD4vyFExmYr2zEbLyz5vUrudb8fmKjmB10MVTczlEdvoqYqmcCpkuEMiIVx7H4nYlLshoQ4L1zo2Siqa03FAfxfFA0iOzQArwug9ruWNaLSIjbthQyM2NMFcKLAaMqGatZEZbwundngeL0dsWB4AFXih0eGokc44IVA6Az4euS6TMI7J3MBAjV6/tUTFOOBlLHzEA1LE8bt9r7Uf6CARhFpBzLl0n3ssKloprzrE2qOqXVWJiI2laGr/7tZGto+gY4/tCd/YYRVwj1bHxj6WOfb9jIuGtExFCy4eMJB31oOMm4oWVw0bIIY6NqZpYmBgAFOnTkV3dzceffRR/tng4CAef/xxLF3qjDAEQd5j4FK+O4/YkE0Oe/F7SDQiSs0QP6hU0dhklkkl0FnGZOKUe/ZuP5s0mWHDnoFbjg1brPKk4GAxZE4FgyAeEPAx21Q05wksjPv2biti4xbqZPfUkE46chNkuUx+jstGgEZsaBg5ipS1G+hAjmpoq+Sey9Xsz/JJr7w6NjJYW1U5HV71FxjUcs/2/2tRx4Zt1lgEmPKdoxqYtH+H9X6JClkJBxXt2ZJhQzejXLjD43k3hqCieYoHKKTWZfEA5m2Mk4rGx7XpUcdG8RX8fUo5NkNkA6KiorFoU5NfxIY8K8OwpZHDgD5vR4HOpL25o97grGQAh4HsNDx00ijP4xtl8QAWsSHRKepEsdUE41+jg0JQFww4z2ckwyYtRdKcNOvgEsYqZxx3auWLvDisl3gAjdh0NFZvrnQDdSR4KTLSiE2YvkqdOpWkohmGIYwnNyNVadiU0hP+sdMybKLIPdO+wfa4fo5Sdyqa+zl2xKZg5/KwiE0MqmgiFS0+wybUjHrNNdfgpJNOQk9PD3bv3o17770Xa9euxZo1a2AYBj796U/jpptuwkEHHYSDDjoIN910ExobG3HuuedGatyQRyiRJnz3qqhopQe9uzSxsoSnuKFKhmN/mzCqoSwPv0M8wKfjN0mGTYp7R0UvLT8+k+Sh7d59Q2jIJG1Z1LB1OCJQ0WzxAOfxbEJ4e3cpYuMj99yQSSk8l2oPpdvmeahgF+ikiX9+dS/CgH53VGqkiopWrma/r9xzxPv2UuGSKTn0OH6Mj8BALcQD2MLAJuJ0KgEMFgRVtErm2MiQPc3UUDdNk+dFLCKGjTxXqdqRk6lonrVTgkVsbOqOuDjyHJvSZtcwgicMu34veQ6CeIDPXMUMMdO0zmXHMEeCtZbYxiwD28T7Fugkz645m4o0tmg+iBwVoHLwIhXNfQ70AzVI5kwc5bsJYWuRXMemIZNCKmFYzjQy1+Q92BnVAl0jAtOppTVMKLRbquVF0UD6ht9cLdRhE8ZNAfliEUMeG2KqGmvXZqt9xMZrLZXnHjMCLZ51n6JpcidtpaKAuXSSb/bd3iU3bMiYd0RsIlDRWIrBYKHIo6i+OTZSTjVb273mWbFejlgPUqjhFFkVTXSkxYVQhs1bb72FCy64AJs3b0ZbWxtmz56NNWvWYPny5QCAL3zhC+jv78dll12GHTt2YNGiRfjP//xPtLS0RGpcnnOa3TdAVo6NUzyAR2yYd7UCNDSrHfb/5eKI5QgHAAq5Zz/DxoeKJg9wwzDQkktjV/8Qdu8bwtjWHKksHa6tAm+9TPEAwM5xYe/WVe7ZYBEbZ6V7+iu9dbd6Ptv3DnDqFU20pPk5ZW+2YvBQqKho5fKJ3ahoUQu2MrD7ZefT6yiNlkARG/tvUfITooK1nXnms3xc2bSkfDGaISjUwwi5uZMpRtSweWNHP7b07it52dvJcc73AYjP1qGK5iX37GXYSPQc66f1N5vWUIrYlObrciOjAInqFyTxAB+hEzo/DBWKSCaS/DqABxVtUCza6gZByTGiYe4VsaHvf6BADZvwBQ8ZaMTGj4YGuFPRsqkEcukk9gzkBcnnqPWf4gQdd0HHr0xFk3Of5MuIERvva6sEb2j9pEEv8YCE7fhltdlqXcMGUDuC+e9kLObSCRgoM2JTej7l0sfdIEZA1ccwEaKsKmIjGzYRKMiDhSKfdwIbNlKOjScVjUVsCs6yKWLEJqphY58Xl9QzENKwue222zw/NwwDK1euxMqVK8tpE8cQqWMjg07ebPKkXGU2KbCNaiVq2AByImpCaFu5ho2jQGdAL9lObtiwTYTYLoqWnCU4wGoBRa6c7jFhuUGlIscwWipu5Ta5MaOjIaMQDyD3wBLXC0XTVe75ndIC0NaQdiRk2+3wvic/CBGbqOIBik2VXyFTP7DokUxFiyr/zcA2sHaEgHymitgEkXumG8MI+QlRwb6X5VJwdRjivY9qYAoRm5ALnBwt5Zsa0+T5NbMnjhJraCgiaHI7wlDRvBY21SacjXlZYYd5E+Ogjwh1bEgkWijWq1hb6L0MFYq8jZSKpirQaYsHeM/T9DlGNczFKJj47FkEJ0+paKmEIxcrDCjNO5Bhw8UDWB0bm8aSSyewZ0CMDucj5qbFiSjiAWkeSREjNwCQThmOdZTm2ISpYyM7TakqmkoenuZZMRZCRz2oonnQ/YSITSpJhGeC94kUue9KigcAomPS7V22K6hoE9stRUFGRYuSYwM452DfOjal9g4WrPqAxQARMbZ3LBRNnq8uK1mq2hIUdUFFqza4Co1HAjGVe25VRGwY/BLvo0IVqWDtLUc4AAifY+OI2DAqGhcPcHY+y2PYz/OUgnR2FaIsCrZh4zxertrrV8emIZ1yDGxVqNsybCQDqHTc1hLtzcuoKjvHhlwrap9Mp5ybKptPHK1dqoiNaZpgwlCRqWiSUe0rHiC130vuOWH4J2rHiYQcsaFUNDBVtGgGpiqqERSCEZ4wBBrKsxu3AXBuRhOKCJr8e04u0JmkC5kUsQkoHsALdKaYYcOoaCXDZjA+w4Zu7iifXPAaqyI25HNK96RUNKo8xhA0YkOfXVTDXOwv0jskm1+3HJuocs8JAzhscrvv8czJtm/I2kSxPIJsKsnnvX1DRfz3q1vRkkuRAp2VcUAGgTyOwpwjJ/cD1jOXryIUnfT5Di/F1SFCRfOM2BC5Z3lNrQW8FAkT0txjIrxhQiNVhYj5wkFBJZ/dmBzsmdN3xPaFLGLjJQLhhbB7XOqk2pcvIowqGmCzZ2zDZj+holUbXrxbatgo5Z6lcyoVsRGT0q3/szC8n9a/H2ibDcN/gPMcmz5RPIA9F9UmkCujlTptdMMmAj+Ze/H9DRu3Ojbsntsa0p6qaOz7BqFIti1dm0lLt3sYNuVS0eKQe6YceoayIzbMm0OUiqjabWQqmmTQ+OWSON+Z8xjmDR7dnK1qwTnWbxhnnU3E9H1ENTAbMkkYhkhzCgqBQmPQuRF44fVdAIAFU8TNKH8v0rhKSQtNg2vERqaiubdZkMAtfZ9daNiam+Q6NnFQ0QRVNJIr5pczR/9GBQRUVLRBVcTGZ4EWqWgRIzYu+YOAvUEaLBTEHJt0+DmagTl7Zk1oCyTYQQtR9g3Z7bCoaFY73tzZjxX3/AFNmSQWHzA6UrvihBBNCNgO1o+bS8/Er05XmDo2oihIKWLDaK9EPMAr8j1UMPF2SVm0sw6oaAKzwxGxsf+fTSe4UyWMWhZVOOURmwrlbeUCjKfu1hwAMTLLmDxv7d6HwXyRl+cIG7FwGjbe6wZTcrMKn+fBprYgVDQAPPLXEKthUwdUtGqDhadVliwtQKWWexbPqVSOjcoDceXyGZg1oQ0nvLu7rGtTC9zqlAENG1LHBgA+dtQBGNOSxXvnjHecwzyGbHBFLtBJHndQ7z6Xe1ZMPEEjNqceMh6bd+7D6YdOcIRD3Wg2Dk9R6ffXd/QBAMa15cTvVnjOooJS4qL2STbZiHVsys2xYVQ0O2LDjFwgutdL9mj6GjYeG22Gno5GXHvqTEwp03EQFuwZbN5lGcAzx1nS9LTSe1QDsyWXxqozDkFjNhW6j9FnbJAckiKRL+9uFaPHKmog4CP37KmKFi5i87EjD8DopgwvMsoWSRYNi4MXnySULJor5qdySBNz6RjjBRFTzhwbmqjf5CMeQJ9j1Bwbodimi3z0zr4hoaq5IM8asn8ue1cXrjhmGo492L10A0UmmeAiAXsH8jzHJlPKsQGAP23u5YyLLb3e6pfVQBTxgPlTOnDl8ulKwyyTSghzKCAafP5UNGd72LumUt5euYpsL9CUSWJqZ1Oge6okxCiU+Bl9/rlUEu8e34ovnDgDh0xoC3x9lcR7pYxlmhrg9h0nHdKNv2/bixNnjeN/62zOIJtKYCBfxNN/24YdfUPIJBOY1tUc6vudqQreY9owDDSkk+gbLGDfYDFQ8W1rTbGMode2WfujrlZLhCLrsR4EhWDYjJSIjV3Hxn3guso9J6pl2Nj/ZxPVIRPbcMjE4IPRDbTjBqEtNbqIBxw4phmfO2GG8hzmSWBhxiLhoodBFPEAHr5XLLKOiI3LwGlrTPN7s4rB2nlVbvkact9gx72501pcZQphlOKjXkiVadio5J7LncQ5FS3vvCYQrkgahVzHRl74ZTgiCC7385EjpkZrUBmQp6FFB1j0LrrJDVIbwA0fXDgpUru4pLYUhSkUTe6wkOv9qMQcAHEsNshUNEpliigewMbx1M4mYU5i12aGdRy8eFXEJhkgYgNY/XawAMmwKUVsFFQ0lncFhJN7jp5j4+4tZdGV7XsHxRwbSrUKuRHJpZO48nj1GqKCYRhozCTRuy+PvQMFQkWzDZu/bt3Lj9/0jvX/qBHnOOAVTXBDMmHgimMP4r/Lcs/FomjYNIaiotn/l0UKBMU7xf5IHj+HTemoiCpsWHiKB0hsBsMwcNnR00Jdn+4Liy77gLhA12+3r2jMpBzjxjAMTBjVgL+9sxcP/uEfAIA5PW2h9wPOAu7+5zPDpn+oEMiJbRhWrbbBfBF/K41RFnEScmwiKrzWbR2bSoJFbJRUNB5qLfLqxi01pqLF7RnICoaNf/ubXerYeIF5DJlkdhArXgWx8Fawc9iAUm1i5CrJQZ4tG4Ru58jiDvJxbKBPlEQfaPvimCPZ9aLWVVJR0cou0Ml57+qITdTr2pvu0nWEHBtvKlqyFIGoF8gL5MIpzLCx/j5YKKJQA9lap+Ss9fd9Q/aGUqYPuYkH0LHbIFHRslL+AIXXXMOilF7HyVS0eCI2bJNTFBbxII4KPsYIFY16yGXxABZpSicN3yRe2jei5tioEssZOlSGTTIpeHSroT7WzCWf80KODVuL/7p1Dz+WUaFrqYoWhYomQ5B7ThqOeUCgokWK2NjRYc+IjXTtRQEEH6oBr0Le9FFFdfrxfSFx0FWqNhIt1xDWeGLO0zX/ZxW0XzR1dOjvT0uiEUH2E+y59g3mA6cdsHl/Y8mwmVhqu6CKFvEZ03UkTipaXRs2Q3nmIVN4JEqjYGcpnwQQvV/yhFIx8QBFjk1cEKhoAQwzNmkyIyVIe1p5xKY8KlpZERuV3HNjMCqaDLr5cvP+u8mjMjDVEn6dCPfmBXYvcVLRytXszxIqGqsfQJ2N0aloYmRAqGPjQ0WrJd9eBdr2qZ1N6CrxpykVrZJF4dwgqx6yd8UcHIYBtEhRBB5B84rYeMk9S4uq31xjR2fVx8kFOuPNsRFzB4MoOKqUB5VUtGIRpmkrBvlFawBxMY8asRGpaOI4ooYNVXIrp0BnFDAFsD0DeTvHJm1T4pgHmKKWdWzclDBDXUOa3+Q5jo4nP+NdnWNjO7VswQWF41e6dhAlu2rAa36nzyPIfsfr+pTNEDY6GRS5AFQ0N7CoB1OBjPJ+5L4VRHyARQyFiI1P29m8z9aTCaOs/VHsVLQRY9h4RGzY4s0Mm4a0KPfrpKJVKGLjUxOhHNCNRJBOy/i7zNEeZJNr59iUJx4QhZ/Mc2wUhmsunRTEDtzEA2TQkKhbIU75++RFTKaixb3ZZu2Kk4rGjJyonm4WsSmathdaoKJFFg8QaVJiHRvvBTnoO68WaNtZtAYQN8HFMqhoUZGWDBv2kyV7NmecRSDd5Z7t/8tUNDq/ymPIb66RqTQy2PzGDIRYVNGSzLARFYCCqBzyXAaqPKigorEinntKVDS//BpAUkWLWsdGigxQUMNmgCtnGVKOTeX7Jy/SOVCw69gkbfEAKlRSzXa5QTR4o11DziGkc1wmKUtue19LFVlk/ZI5AAA1pVdQzUslMDsGanwc8FIklBUZy7k+rZFUqbmYbuzDMgtoKZBkwsC8AEqDMug8kkkmAq39LAK/jxg2fs9H7l9sf8T+bhjR5+uRSUULUMeGLd4yh1zm/OWqIPcc96RMvYtBIk7yohokBMsWVlnuOewGOYp4gDxZy6AFxYJGbESFKBfDxkdkQK4/5CcPGxbs++NVRSsKn4UF9ZCx+hKUH17uxMVzOujC71PHpp4jNtTDRqlotYjYyFQ09nPHXpZf49w8u8k9C+IBmSRSSTs3w8vR4jfX+I11R8QmDsOmdC9WTQv7b16SswyqiM2ggopmHWPywqJBJEstiqX1/yAKY+r2uUdsWI7Njr5BIWmfvr9KFS2kYI6pvZSKlk54rsWVog0FAX2O0ec7MkZSCeF+0kkjVFRIGbGRHAD0bxT0/R7aM6pijJWw8HISynVsyrk+HbeVmovFOjbhzqXO01njW30l4lWgc3BQWrtNRSsEdmLTeaMhnUR7o6hkmU74C1u5XnskRmzyATikfYr8GsC5wfMrbhkVdB6uxABiHTZIaFamQQQJ68tyz3QDEAYCHzi0eICLYUPoaEE37F45Nm7fR7vXqMa04zmKhk2gZniCFycss46NoIoWMdLGwKQgAdvbVRBU0SJdlkdOVRECVRRSpHbW1/REn61o2Njvo+ARZa4U3KhobCOuojuxR+scI86Fhhng5VDR2EbcL8emEnVsCkWTLOLBIrD8nRLjntdVk3InhopFLvUchIpGry875IJCkHuWnj2Tqy+atjR5NlVeHZsoYM9i70DBLtCZSnquxZWiDQWBV/5HlGukkwmHIqDgePN5B2I/FSOee0nExk82v17yawBvZgcXPzGiU5vY3DdQoDk2lTJsohvC1HkalSYoOJoCGjacijZYsGvU+UVsSP+a2N7AjRg2n5SjZEj7bpx1bOpr5yBhiEvYeodaAWc9AKfcc4WoaBXOCWCLQBCLvEmqeB1IPKBBitjEIPccmIrGxQPU7aTKaEE3ioLyj3RZOQGTX5t8vxytsT6P9x2ze4nKI2b3qKxjE3FBNgyD9zEmIMA2g4YRvX4Pu1dVhMBtMrYpg/UVsWFtnzCqAT0ddh6WoIoWcfyUA2boqqJigJrulJQoggyCeIBUR4KOLXnM+s01qYT3Isj63jt7rJob8TgQ7IhNkRTro9f2FQ9Q0D0zyYRAdR7KF7mDTZ6D3cCeZdSIDTUAZNp1OpngxizbvGSSScFrX42IIo/YDOQF2WmvtThqEnIcSIUwOoJcI5MSIzTpZIJLiQMBxAMUbBD2k0Y2Ve+Stn/RAeET0ysF+nrdqGi5dDLyesNzbAjNsVLRyVyIfCkZNGKzMIJwACDOpUEjNmxO3zdUIEWLg0dsaLt5xCaiCBIgjpfciDFsCjY/WIb8MlSqP3RsVLOOTZxgHTYTSO5ZpqKFiNiUcmzsQnahmilEbIJu6saWkq/HlnTRZXQ02X8PSlEQ6BZSO7rb1N9Hj1MZNvTzOCbJsS1WO8YrvisI2HulOTZsoYtqLAH2GBngVDTr7+VwlOX8D/oa3TbDSUNcxOsFTL//yOmdwt/Z/EQNm2q2nfUnJmYg91HPiI3UzFGNVo2Fsa1Z7gBgY4LWd3JS0bzvl429rpac8vPxo6y/s/wuNjeUg2SpjYWCWNPCIMaNu9wzM1bVdWwShCY8VDDtiE2AHBvAmoOSCYPfd1ikPSI2gE1HY5ALdFaD8sXr6fQPYmDIWccGcDoka0k/TScS6GjKoDGTjEQNAsTnKlMWMzxq6Yxeq5AUoj3W/5mTYWeJgu+We9uQtu6hJZvCoZNGhbyLykGM2IifsXcfVS0UsB3YzFFbyXlYMGxCrpHdrTmMacmiJZtyFE8OCtq3gjKSWP/pGywEVsAVDBuyZxnbmkXCsIuQRsHILNAZoI4Ng4pHnk4k+OavOjk28S8WrFOFkXvm7QmkiqaO2ISmokUw8E6c1Y27LlnkmthIJZ+Dhju9qGjfPmcuNr6zF+/qbhX+TruXLBwASJ68GDzxt5x7KF7b3ocDx4QryMWgoqL9Y0c/AGBcWzRjCWBjZAj7JCpaOcacvIj7iQdYxwIo1B8V7dTZ49HRlMHcnlHC36n0by1ybCaNbsT9n1jKFx157KrmRrfoTnM2hQcuWyps0H9w3mH4x84+TCEF/hxUNJ+574fnH4Y3d/ULkS6Ko6d34Y4PL8A7ewaRMIAjDupUHhcGqogNFVgoFkzXeU6uUwPYjgSqrsgKJYZRRQOAOz68ENv3Droaen6gc5LKQdDRlMGmUkE9wFnHphrdk81Fb+7cJ8o9k7X4gDHNeGNHH97ZY23Ua1mgM5Ew8ItLl2CoUIzsCJXfC6UysvksnUoAg4VIOTbMQfD30rt1e16ZVAL3f2IpEobT4VlLeOWrJkjEJiq6S33u9e39ju+LG4LKYMh9QSqZwH2XLsFQwcQoSQE2KKLk2DDjIWgdG/l76P6oqzWHBy47HGNa1I7pIKD9N04qWv30eAWGCu4J0XJHUnklWZE1oFpUtPivnw1h2DRKNIgghhZ7bnsHC8jTAoMhJwQxxBzsnGTCwOHT3DcwVPI5DvGAMS1Z5SBM+lDR4pZ77mrNce96FKioaP/YaU3kExWGWVBQyWeA0hIjX5K/N5XUtpu3sV4jNsmEgfccNMbx9xR5H5Wudu2Gw4iqjjOarYrYlKhoiv787vGio6G7Lcc3VAwOVTSfDanqGhSJhIGjZ3R5XiMs7BwbJ0XQ+mm6Gu00GsNgi9nY+Q77hooYKhRtVbSAVLSejkZXIy8IxEKQznug0W5AjNikEtWpD8U2Qa9t7+Mb/KwUOZrQ3gDTNLlhU2vBkKjOJgZZjYw6n+Q8M797FZ2F1jmsFMGmbXv5d7hhRndLmKZXBV7UfVtUJ/oGl61/b+6qvGFTDhUNACaPbvI/yANRcmyoYROJiibtj2QnX1iMSLlnNhmqXloQHjl9aJWjotE2xf84GY8xkipaIPEA+7ntGchHVkXjsr5G9HwMGZROEYd4gBvo/lplGNBNXDXUhPyQItQn9vOt3n0A1BGnoLCLdFrXjUO6mEk2J4UNpQW3yTihMILqGRkFFa2WGzTZWFHn2KiPDQp5M12P74rXsTEJrZJEbAD3vm2LBzjr2LB+myGRur6QVLRyQdcalQOro0l855mkXT+mWn2TbYI2kno1lioaSUYe1SDMWbVURYsD9NlmJPEA1qdYvwkjHsCuw4pHv76dRWyG1/Pyyldlc1E5VDT2fJjuTdUMmxpMf+kIERsqHlAww0dsynGcqlCpPXrdjgrTND356kG8klGSq8KiknLPQDhVtGTCECJTQRIxMySZs7c/zzcAUcUD4kya7ohg2Ih1AgIaNkKOjdOLGiUaVUnIVc+37NqHomnde2dT9LBwTorYBC3g5YUUX8St3+U6DyrYm8+6nZ4E0PdR8BA8qRaCzI0JaYMfFoYhFrqsZdK3G4SIjbSI+8lPU6U7BhUVDbAMHqZSVS3aj6i+5R+xserYiNHTSoNtgpgyG2BtkugGZkJ7g+AFrkcDOQxksYC0yrDhYh/e16LPgvVXZgQyhttwM2w869iUbqWcDW5nc1YSOakSFa0GGwPRsAn2zHLUsJGcPW4QIzbRo8wqVIqKVrejglIAVDQk+U8qHjndXFQqYiMM1Ap0btk76AeaZxNUOpN5dHv3DUVW13JTWSoH7U3hqWiZCBEb2mZVxKPeaqvIVLQ3Svk1E0Y1lPX8s1w8gEVsrL+XY6zyOjZMPMBwLvSOcxKMslH7Zx0Eaipa7doTZG7kEbSy8qesLzKM+ohkymB9Li+JB9Cfbn1bjoqy6wDOYqNDhSL2crnn6tQLEYqlKjobjXZnUiU1rlT8c7QXxkibzGTCQEo2bEZJhk0d9qMwSEoGp2joiPOa31qicprKVKCgFKR6gddaaquiRb+nhCTIUb2ITQ0MG5LnGDbHpm8ofB2bdNJAVxn5NCoIVLSRYdjYC4paFU1suqyuAkhSclXIsal1xAYQPYZBK7fbtWyGoquiuRT8Kwd0cY4kHhCyng4tPiV8HrN4QLmQN10sv0aVHxQGOSIFCSDwxOcFTrtQFeh0Ew9wSWyvVzAqWl4o0FnLiI08N6qcPmy8Rv8evlGrw2gNQKhoUh0bgIonuJ0rRkWt/3tQ0UKKB5QLoT6KYpzQaHc2KeZpVst4SCQMjCObTPb9OSnHZkK77QWuN8GQsEhLqmiyShr96UfZVtV8acqmMIqsUcMtYpNI2Gq1juLACWbYlLfBpc7Jyho2tY3YUKdB2Do2+wbDiweMayvPcaqCQEWLUeCrbkcFpQCoaB0O5R+fHJtKFeikzajEpjdMjg0ghvOCLhLMo2tR0aJtZmVPaBzoIKpoQa+bjhAeZoN1Aik+JXxeZxEbSn0yTZMropVt2LA6NnmJilZOxEbOaSDRGLeFvV7r2LiBvY/Bgr2BrmXbgwir8GJ4MURs6pU+xAy8fNF0igf4RGy4KlqJm2uatuKdTEXLF8IX6CwXdAOt2tRQw4bNiXaOTfWWfTon2U66/ThiI9SxUVPRuHiAXx0bF6cpfV6ZOh17XmD3Ld8+j9iUucEV+1Pl+nqtc2yiqKLlyhAPKHd/oQIbH7l0IlajqX4Nm6J3xEbur6oKznQyqJTcs1BEqwKTDIvUBO24lIoWtD0tRPK5EDFh3KZ2hDrNu13ZlG/FchlhKjsz0MKLKgjiAXWwjtBNTb5o4h87rUTScoQDABqxKck9x6iKJosHeFEr/Qq31hsoFY05ZGpJzXLOjR5yzzHUKKrXzSiN2DioaD7Ke3IdGxq5SafEDepggRTojJFO4QWhxolSPIBQ0VjEJl399yUaNtazYWtxay6FllxaFA8Yhht1CkEVLZkQnItsbeIFOkOJB9A8B/t5DbeIDeDuBLUjNuXdE80DqeQSQveUtaGihc+x4VQ0WsfGL2LDDJuYhQMAu//GqYgG1LNhU7C9YyrPrl+BTkBWXKg8Fa0S3vx5k9qRShiutV5kNGYpFS1gxIZT0eyITdiN2cT2BnQ2ZzB3Unuo87xgGAYWHzAanc3ZwN4CgdMdcLKZNaENqYSB97jUzvCqllwLUG7tUKEYGxWN8Wf/tnUPgHiUZWZNaEM6aXBZyAmjGtDVksWhHv3EL7G73kBrntSiQKeMIOIBh0xsQzJhYE4Zcp2sH9YrfYjn2FAqmkRzdOvbch0bJq/bkE6isbQIs+e6q3+oBhEbb4dah5RjAwAHdjajrSFdtkRrGNDNEGvHQWOb0ZJN4T3TLen0toY05vSMwoRRDRhdhvhJPSCbSuDgca04YEwTWhvSksiDKB4QShXNJQ90OBo2chSf4ZDSOuy1NgRBtVT2ak1FExlJwe6T1czZvneQiAN5nzO3Z5RVW8yjNEdUTB7dhPbGtFCuIA7UbR0bXsPGZXMjbxzUVDQSsakYFY0YNhXY9F64dArOmd8TOLGqmSSvBh1sNGITNWG8KZvCE188JrDIQVDc+eGFGAxRMI3SMoLOaYdP68RLK09wfcYqrnMtQSe0oQKhopXpUVkwtQM/fmIjnt24HQACy0F6YW7PKLx4nf1sGzJJ/PcXlnn2k+FKRRsqmJHrQMWJIDTdo2d04f88+nwQMMdJvb4nO2JjG5wGp6Kh9NPFsCE0NgB4pjQm5k0exQ258SVHwhs7+tHH6thUSxVNEQmgUBk2bY1pPHPNsRVTCFVBRUXrbM7iuS8fJ7TjgU8sRb5YHHbJ8DIMw8DDnzwcpmnNAfR+bCoaixp6X8tNGlmgog3D5+VGAz1y+hjPdTgo6PPZn8UDKA0x6L6L7RE27+rnjku/tp82dwKOn9kda3I/Q1tDGk9dfWzs+8a6NWzypafuFnWQX4a6QCeN2FReFa1SgyhMhxLEA4KqopVofL39+ciqaEBlnnEiYSCXCH7dKOIBgPczpmOuHiI2dMEbzBfx5s5SDZsyIzYLp3QAAP789h5s2zMQ2KPjB/nZ+vUTdnvDTe55sF7q2ASI2ADlq9DIOQP1BhqxkaOPflQ8WaDjmb9tAwAsmjqaH8NqZvxjZz/2lsQD5CLJlULKh4rWmEkim0pgIF8UNg2VWgfdMJEIA1CvstyOZMJAMsQ8X88QFOsUYins86gRm4n7acQGiEcZiz6fSjibGWh/rgX1mBq1QSM2Y1uySCYMgVobZK2qhFHDUJF9Y+xXjAl5n4iNbFCoNLCFHJtKUdEqnGMTFmKOTVAqmjPHZpjsKR2IUqDTD3SDXQ+ytoZh8EVy865+DBaKSBjwrOweBO1NGcwYa1Wrfm7TDpgxFOiMAiowMBxAaUssN7BeIjZWnarKLEo2Fa0+3xNrV1HIsbE+86vjQ6WcTdPkUcyFUzv4MWzT/o8d/VzuubkG4gGq528YBleVrKVXn24yg+YB7E8QI2slRwCvYxPcsKFrEM0hyaTqc+x5gZeGqNC60t2WI86xCho2pD9Xe40EotWxSSUT6G4V9wn14KyNG3W7fbWpaP4Rm5ZcSpmHE+XFhwX92lp0bhmCKlrAQd1K5J6jqqLVCzIRxAP8kKww3TAKWN9mVb27W3OxeO/Yxu3ZjdtjKdAZBcNN7plS0XiF+xr2E/rcVDL4cSFV51Q0L1U0L68x/TxfMPH3bX14e/cAMsmEkJ/CaB1/3bqHU3jjLDLnBbnCvQpMVbKWhg3dZFaTAlcvoM4ZtpeJJB6wH+XYcMn1Cs0b6WQCY0ub92rJPddiChT3t8H7gUxZHy7rbBjU7ajgxdBcVWvo4u3kkMvHDFfxgLCgyauBDZsGFrHJO5JshxuiUtG8QBWI6uAVA7Df7d+3xaOIxsAMm2c2bouskFcuUhJlo95BqWj1ELFJJPznxjjANmj1ql7H+q1Yx0bk97saNsRYZdGaOT1tjuKSgF0gFxCpwJWEasMso72UKFxLg4JuModjPki5oGMjI6mihaGi0f+3N6a5ilTcuQnVAHsmlWw6G5uVnIczyQR3bNeCyUHXxzBja6JEWR+uez0v1O2oYBuEtMsLS0oRGxUqVfzHrR31sMA3RahjQwt0Ms+jX/GwegUd4LFR0cizqAcqGmDf56ZSxCYujXlm2Pxxcy96+4cAVD9ULStX1TuUqmg1pGdRh4bb3BgH6p2KRnNs5IiNbODI4EVXi0UuHEBpaACE4pOApZhWrT5rGIavyAanotV488vmppEesbGpaN75XQz0vdIxZhgGd2S57Y/qGX5jLw6w51PJaLJhGHxfWZMCnYSGGIaRJDtB62DbGjvq9pZe22Z5wdw6ZhCvJNWNr9SGlF63HjqIIPccVDyg9Py27h7gfxsum0oZlcixoe+1Xp4Lu88NW3YDiC9iM7Y1hymjG2GathJU1alow1kVrQzxjbhANwyqGjZxgVPR6tRrzDaDqoiNn1EgRGw2WcIBC4lwAGBtJphEOgA0VUk4gIG13S2y2VGSTq51pITNTSMxx8YwDFLQVRTb8FtL6LwrO02ZsVhrozUKKlHMWwZ7PpVeuxgTqDaqaPZ4CkVFkyM2w2SdDYO6HRW/enEzAPdJO0jEhk0GQRUjosJvgakmBPGAoHVsSpuft3r3A8OGRmximmzEAp318VzYZmXD5l4AYkJpuWCe6af+am3oqt2tefHEOo0EyKCJ5oP52lPR6HdXNGLDNmp1Olew5zCYd6rV+ebYlPre69v78Pr2fiQMKGstUIdCtWrYMGT4RtklYtNceyoaMLIjNoDdlzgVLRXMsHGTewbsfldrozUK/MZeHOARrQqvIYwSWJs6NiRiE2KP64jY1MmeJk7U7ahY99oOAB6qaDTHxsUryc6ttMTlxUdMxfvmjBe8d7WCIB4QcFAfOKYZx88ci4ntDZjY3oCzD5uItgp6eiuJSosH1Mse7oLFk9HTYb2vuT2jcNzBXbFdm3mm/1SKBlV74rMjNnU7PQlgC8xr2/uwd7CAdNLgNU5qAdpfK5pjk6r8BqUcjG9rQCphYM9AHjslWuUHF07CoqkdDnoZA1Mde26TFbWcNaFNqXhGvZ/Vyq9huOjwKTjx3d2YMrpJ+fmJs7oxf3I7Tj90QlXbJeO0uRNwWB20o1ZgfYk5Ak45ZBwOm9yOkw8Z53mem3gAALz/sImYN2kUTprVHXNrK4/zF0/GEdM6K1oodvnMsVgwpR3nzO+p2HcAVp3Bo6aPwcHjWir6PSpQJ26YyN1IiNjUbR2bwXwRiaT75iZQjk3p3EoJBzBcc/LBFb1+GESJ2CQTBn70ofmValJVETTMHwbUiK6XSeCS9xyAS95zQEWuvUja7NUqx2a4UNEYxaRv0CrSOGfiqKrXC6Ggw76SEZuUtGGrNzRkkpg9sQ3/+9pORx2bc+b3eG56mFNooBSBk8cEA/V+NleZivbZ42d4fn7gmGbc94mlVWqNO2Z0t+D+OmhHrcD6EpsnZk8cFeh5CGqckpNy3qR2PHDZ4TG2snq4cOkUXLh0SkW/o6slh19cWvk+9/GjDsTHjzqw4t+jAjVmsiHWG9nppsUDagA3S1SUNHXJsSl5FCslHFCPoF7D4ULliROZgDUCwkCM2Oz/z3RiewPGk5o41TbmOAd7mPRfeY5yiwJUC0HmxjjADJp6nmfkvJig84JsrMnXYZhYw4iNxvBAyocy6AaviI2GRlS551w6ic5mm11UL4JIcaLuDZsgBTr9cmxq6T2tNpojiAfsT7CrOsd3TdrXRoJhYxiGsDmv9rzHJtr0cKGiSUXyam3YyDW+KgVGRatnyqAj+hiwqfLcuWCKM78GkCM22rDRcCKTjBbZpMp39cIU0Kgf0PyqsPlrbN7aX/tV/a5IJbgp7iQDKP+wxWkkJS02ZmmBzpFz3wy8+FmcEZs6q1VUDVAPdfWpaKWfw+RZi+IS6iTzakKI2FRBFa2eHSiHTWkXDPOgfYq+03d1t2BUqSaMDCraUa3inBrDC+XU5WLrWL3SPTVqBzrvhhWRYJHm/ZGGBoQ0bFatWoUFCxagpaUFXV1dOP300/HKK68Ix1x00UUwDEP4t3jx4sgNdFPcEeWeXSI2yZEXsWnKhC/QuT/BjtjEd+/ixii2y9Y1aNShVlS0et4wU1Aq2rvHt6GlgvSvIBDzDytPRatnA7Q1l8bM8a3896ALOWUKeEXgaqmKpjE8kCpjPtMRGw03CDk2IdMt2Ly1v/q+Q93W448/jssvvxxPP/00Hn30UeTzeRx//PHYu3evcNyJJ56IzZs383+/+c1vQjeMWaBe/G022F1zbBJMFW0/fXsK5NIJvhGv1/oSlQRbPOI06iglYCRQ0QDgwDFNvMBfzXJshsmsG3QTXC0kEgaviO3m9IkD6TI80dXEwikk+hiwL9N78nqnzdkURjVa60+169hoDA+kI1LRAHsdG4lOSg1vRM2xAWxltP01YhNq1VuzZo3w++rVq9HV1YV169bhyCOP5H/PZrPo7i5PhnDuxFF4fvM+z815MmGgUDQ95J5ZHZuRs+AYhoGmTAq7B/J1ndRbKQStERAWScNAAeaIMWxYns0j/7cFRrWpaMNsMQ+6Ca4mkoaBvOk+N8YBLh5Q5+9p4dQO3P4/GwGEiNiQe1o4xfudThjVgJ19Q1o8QEOJcqhoCR2x0XCBkGMT0nlfrQKmtUJZrrZdu3YBADo6xIl/7dq16OrqwvTp0/HRj34Ub7/9tus1BgYG0NvbK/wDgAWlxaTZY7FoKnGaO5rU/GeWzFlJZaB6RHvpeYzEZFaWLN0QM9+deWNHEo9+8QGWp7uxyo4BtkEcLtSehkwSCcOiLC7w2QRXC6yfjnaZG+NAc254vKeFUzv4+wm6AWD3dkBnE7pac57Hsjoy7S55OBojG2wdbo4QPWXnNmmjWUNCJpVAOmkgYYRXZJzSaeUG7q/7mcijxTRNXHnllTjiiCMwa9Ys/veTTjoJZ599NiZPnoyNGzfi2muvxTHHHIN169Yhm3UWsFy1ahWuv/56x9/PXTQJyDbg/YdNdG3DqjNn463efa7F8E4/dAK27x3EWR7X2B9x4+mz8MfNvTioq7nWTak6DhzTjGtOfhemj423YNbXz7L6mt8mZ3/COfN78FbvPpwy27uQXNy49KgDMa4th1N8CtjVC5qzKdx0xiHIpZOuTpZq4+azZuOdvYMV7a/vP2widu8bwgfmT6rYd8SBjqYMvnXOHAwMFQNvABZM6cBnjpuOpdPUMs8Unz7uIEztbKr6ONEYHvjiie/C2le2YskB/n1Jxo2nz8Jft+7BlE51EVaNkYt0MoF/fv8cDOaLoZ3Y07pacO2pMzG5o9H/4GEIwzRZ6bJwuPzyy/HrX/8aTzzxBCZOdDccNm/ejMmTJ+Pee+/FmWee6fh8YGAAAwMD/Pfe3l709PRg165daG1tdRyvoaGhoaGhoaGhoTEy0Nvbi7a2tkC2QaSIzYoVK/Dwww/jv//7vz2NGgAYN24cJk+ejD//+c/Kz7PZrDKSo6GhoaGhoaGhoaGhERShDBvTNLFixQo8+OCDWLt2LaZOnep7zrZt2/D6669j3DgdptfQ0NDQ0NDQ0NDQqAxCiQdcfvnl+OlPf4q7774bLS0t2LJlC7Zs2YL+/n4AwJ49e/C5z30OTz31FDZt2oS1a9five99Lzo7O3HGGWdU5AY0NDQ0NDQ0NDQ0NDRC5di4yb6uXr0aF110Efr7+3H66afjD3/4A3bu3Ilx48Zh2bJl+OpXv4qenp5A3xGGR6ehoaGhoaGhoaGhsf+iYjk2fjZQQ0MDfvvb34a5pIaGhoaGhoaGhoaGRtmo75LRGhoaGhoaGhoaGhoaAaANGw0NDQ0NDQ0NDQ2NYY+6K2fL6G69vb01bomGhoaGhoaGhoaGRi3BbIIgsgB1Z9hs27YNAAKLDWhoaGhoaGhoaGho7N/Ytm0b2traPI+pO8Omo6MDAPDaa6/5Nh4AFixYgOeeey7Sd9Xzub29vejp6cHrr78uKEBE/d56vlcKet/HHnvssGhzHOfK57m9/zi/s17P9bv3eng/1Tg3TB+I83vr5dyw9z+c3m2Yc1XPod7bHOe57Lwo42G43avbuUHvfX+fG0fq/oCd+9hjj0VaE4bDu/U7d9euXZg0aRK3EbxQd4ZNImGl/bS1tQV6cclkMrIs9HA4t7W1VTg26vcOh3ulaG1tHXZtLudct/Pk9x/nd9b7uW73Xk/vp9LnAsH6QNzfW0/nBr3/4fZuw55Ln8NwaXMc58rnhRkPw+1e/c71u/d6eD/VOHek7Q/kc8OuCcPp3fqdy2wELwx78YDLL79cn1un36nPre/v1OfW93eWi+H2jGt17nBr70g7d7i1d6SdO9zaq8+t7+8s91wgZIHOakAX6LQwUp/DSL1vGSP5OYzke6cY6c9hpN8/g34OFkbycxjJ904x0p/DSL7/MPdedxGbbDaL6667DtlsttZNqSlG6nMYqfctYyQ/h5F87xQj/TmM9Ptn0M/Bwkh+DiP53ilG+nMYyfcf5t7rLmKjoaGhoaGhoaGhoaERFnUXsdHQ0NDQ0NDQ0NDQ0AgLbdhoaGhoaGhoaGhoaAx7aMNGQ0NDQ0NDQ0NDQ2PYQxs2wwiGYeChhx6qdTM0NDQ0agY9D2poaGhouKHqhs1FF12E008/vdpfWze46KKLYBiG499f/vKXWjetYmD3fOmllzo+u+yyy2AYBi666KLqN6yGePLJJ5FMJnHiiSfWuikVh37/aoz0uZBhpD6HkTQHuOHtt9/Gxz/+cUyaNAnZbBbd3d044YQT8NRTT9W6aVXF66+/jo985CMYP348MpkMJk+ejE996lPYtm1boPPXrl0LwzCwc+fOyja0AmDrw9e//nXh7w899BAMw6hRq6oHuidMp9MYO3Ysli9fjttvvx3FYrHWzRuW0BGbGuDEE0/E5s2bhX9Tp06tdbMqip6eHtx7773o7+/nf9u3bx/uueceTJo0qaxrDw0Nldu8quP222/HihUr8MQTT+C1114r61qFQqHuJ8BKvn8NjeGIOOeA4YqzzjoLL7zwAu688068+uqrePjhh3H00Udj+/bttW5a1fC3v/0N8+fPx6uvvop77rkHf/nLX/DDH/4Qjz32GJYsWTIinkUul8PNN9+MHTt21LopNQHbE27atAmPPPIIli1bhk996lM49dRTkc/na928YYeaGjZr1qzBEUccgVGjRmH06NE49dRT8de//pV/vmnTJhiGgQceeADLli1DY2Mj5syZM+y9OcwzRf8lk0n88pe/xGGHHYZcLocDDjgA119/vaNTb968GSeddBIaGhowdepU/OIXv6jRXYTDvHnzMGnSJDzwwAP8bw888AB6enpw6KGH8r8F7RM///nPcfTRRyOXy+GnP/1pVe+lXOzduxc///nP8YlPfAKnnnoq7rjjDv4Z87z9+te/xpw5c5DL5bBo0SK89NJL/Jg77rgDo0aNwq9+9SvMnDkT2WwWf//732twJ8ER1/s/5phj8MlPflK49rZt25DNZvG73/2u8jdSIUyZMgXf/e53hb/NnTsXK1eu5L8bhoEf//jHOOOMM9DY2IiDDjoIDz/8cHUbWmEEeQ77A7zmADa+KVTe6xtvvBFdXV1oaWnBJZdcgquuugpz586tfONjws6dO/HEE0/g5ptvxrJlyzB58mQsXLgQV199NU455RQAwK5du/Cxj30MXV1daG1txTHHHIMXXniBX2PlypWYO3cubr31VvT09KCxsRFnn332sIpcXH755chkMvjP//xPHHXUUZg0aRJOOukk/Nd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      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start_date = \"Jan 1, 2020\"\n",
    "end_date = \"Dec 31, 2020\"\n",
    "idx = pd.date_range(start_date,end_date)\n",
    "print(f\"Length of index is {len(idx)}\")\n",
    "items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx)\n",
    "items_sold.plot(figsize=(10,3))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Additional items (10 item each week):\n",
      "2020-01-05    10\n",
      "2020-01-12    10\n",
      "2020-01-19    10\n",
      "2020-01-26    10\n",
      "2020-02-02    10\n",
      "2020-02-09    10\n",
      "2020-02-16    10\n",
      "2020-02-23    10\n",
      "2020-03-01    10\n",
      "2020-03-08    10\n",
      "2020-03-15    10\n",
      "2020-03-22    10\n",
      "2020-03-29    10\n",
      "2020-04-05    10\n",
      "2020-04-12    10\n",
      "2020-04-19    10\n",
      "2020-04-26    10\n",
      "2020-05-03    10\n",
      "2020-05-10    10\n",
      "2020-05-17    10\n",
      "2020-05-24    10\n",
      "2020-05-31    10\n",
      "2020-06-07    10\n",
      "2020-06-14    10\n",
      "2020-06-21    10\n",
      "2020-06-28    10\n",
      "2020-07-05    10\n",
      "2020-07-12    10\n",
      "2020-07-19    10\n",
      "2020-07-26    10\n",
      "2020-08-02    10\n",
      "2020-08-09    10\n",
      "2020-08-16    10\n",
      "2020-08-23    10\n",
      "2020-08-30    10\n",
      "2020-09-06    10\n",
      "2020-09-13    10\n",
      "2020-09-20    10\n",
      "2020-09-27    10\n",
      "2020-10-04    10\n",
      "2020-10-11    10\n",
      "2020-10-18    10\n",
      "2020-10-25    10\n",
      "2020-11-01    10\n",
      "2020-11-08    10\n",
      "2020-11-15    10\n",
      "2020-11-22    10\n",
      "2020-11-29    10\n",
      "2020-12-06    10\n",
      "2020-12-13    10\n",
      "2020-12-20    10\n",
      "2020-12-27    10\n",
      "Freq: W-SUN, dtype: int64\n",
      "Total items (sum of two series):\n",
      "2020-01-01     NaN\n",
      "2020-01-02     NaN\n",
      "2020-01-03     NaN\n",
      "2020-01-04     NaN\n",
      "2020-01-05    54.0\n",
      "              ... \n",
      "2020-12-27    43.0\n",
      "2020-12-28     NaN\n",
      "2020-12-29     NaN\n",
      "2020-12-30     NaN\n",
      "2020-12-31     NaN\n",
      "Length: 366, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "additional_items = pd.Series(10,index=pd.date_range(start_date,end_date,freq=\"W\"))\n",
    "print(f\"Additional items (10 item each week):\\n{additional_items}\")\n",
    "total_items = items_sold+additional_items\n",
    "print(f\"Total items (sum of two series):\\n{total_items}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seperti yang Anda lihat, kami mengalami masalah di sini, karena dalam seri mingguan hari-hari yang tidak disebutkan dianggap hilang (`NaN`), dan menambahkan `NaN` ke sebuah angka menghasilkan `NaN`. Untuk mendapatkan hasil yang benar, kita perlu menentukan `fill_value` saat menambahkan seri:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-01-01    26.0\n",
      "2020-01-02    25.0\n",
      "2020-01-03    37.0\n",
      "2020-01-04    30.0\n",
      "2020-01-05    54.0\n",
      "              ... \n",
      "2020-12-27    43.0\n",
      "2020-12-28    44.0\n",
      "2020-12-29    36.0\n",
      "2020-12-30    38.0\n",
      "2020-12-31    34.0\n",
      "Length: 366, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_items = items_sold.add(additional_items,fill_value=0)\n",
    "print(total_items)\n",
    "total_items.plot(figsize=(10,3))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "monthly = total_items.resample(\"1M\").mean()\n",
    "ax = monthly.plot(kind='bar',figsize=(10,3))\n",
    "ax.set_xticklabels([x.strftime(\"%b-%Y\") for x in monthly.index], rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataFrame\n",
    "\n",
    "Dataframe pada dasarnya adalah kumpulan series dengan indeks yang sama. Kita dapat menggabungkan beberapa series menjadi sebuah dataframe. Dengan series `a` dan `b` yang telah didefinisikan di atas:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>7</td>\n",
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       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I</td>\n",
       "      <td>like</td>\n",
       "      <td>to</td>\n",
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       "      <td>Python</td>\n",
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      ],
      "text/plain": [
       "   0     1   2    3       4    5       6     7     8\n",
       "0  1     2   3    4       5    6       7     8     9\n",
       "1  I  like  to  use  Python  and  Pandas  very  much"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([a,b])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kita juga dapat menggunakan Series sebagai kolom, dan menentukan nama kolom menggunakan kamus:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
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       "   A       B\n",
       "0  1       I\n",
       "1  2    like\n",
       "2  3      to\n",
       "3  4     use\n",
       "4  5  Python\n",
       "5  6     and\n",
       "6  7  Pandas\n",
       "7  8    very\n",
       "8  9    much"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({ 'A' : a, 'B' : b })\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hasil yang sama dapat dicapai dengan mentransposisi (dan kemudian mengganti nama kolom, untuk mencocokkan contoh sebelumnya):\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>like</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>to</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>use</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>and</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Pandas</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>very</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>much</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A       B\n",
       "0  1       I\n",
       "1  2    like\n",
       "2  3      to\n",
       "3  4     use\n",
       "4  5  Python\n",
       "5  6     and\n",
       "6  7  Pandas\n",
       "7  8    very\n",
       "8  9    much"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([a,b]).T.rename(columns={ 0 : 'A', 1 : 'B' })"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Memilih kolom** dari DataFrame dapat dilakukan seperti ini:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column A (series):\n",
      "0    1\n",
      "1    2\n",
      "2    3\n",
      "3    4\n",
      "4    5\n",
      "5    6\n",
      "6    7\n",
      "7    8\n",
      "8    9\n",
      "Name: A, dtype: int64\n",
      "Columns B and A (DataFrame):\n",
      "        B  A\n",
      "0       I  1\n",
      "1    like  2\n",
      "2      to  3\n",
      "3     use  4\n",
      "4  Python  5\n",
      "5     and  6\n",
      "6  Pandas  7\n",
      "7    very  8\n",
      "8    much  9\n"
     ]
    }
   ],
   "source": [
    "print(f\"Column A (series):\\n{df['A']}\")\n",
    "print(f\"Columns B and A (DataFrame):\\n{df[['B','A']]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Memilih baris** berdasarkan ekspresi filter:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>like</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>to</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>use</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A     B\n",
       "0  1     I\n",
       "1  2  like\n",
       "2  3    to\n",
       "3  4   use"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['A']<5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cara kerjanya adalah bahwa ekspresi `df['A']<5` mengembalikan seri boolean, yang menunjukkan apakah ekspresi tersebut `True` atau `False` untuk setiap elemen dalam seri. Ketika seri digunakan sebagai indeks, ini mengembalikan subset baris dalam DataFrame. Oleh karena itu, tidak memungkinkan untuk menggunakan ekspresi boolean Python sembarang, misalnya, menulis `df[df['A']>5 and df['A']<7]` akan salah. Sebagai gantinya, Anda harus menggunakan operasi khusus `&` pada seri boolean:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>and</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A    B\n",
       "5  6  and"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['A']>5) & (df['A']<7)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Membuat kolom komputasi baru**. Kita dapat dengan mudah membuat kolom komputasi baru untuk DataFrame kita dengan menggunakan ekspresi yang intuitif. Kode di bawah ini menghitung divergensi A dari nilai rata-ratanya.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>DivA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>I</td>\n",
       "      <td>-4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>like</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>to</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>use</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Python</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>and</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Pandas</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>very</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>much</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A       B  DivA\n",
       "0  1       I  -4.0\n",
       "1  2    like  -3.0\n",
       "2  3      to  -2.0\n",
       "3  4     use  -1.0\n",
       "4  5  Python   0.0\n",
       "5  6     and   1.0\n",
       "6  7  Pandas   2.0\n",
       "7  8    very   3.0\n",
       "8  9    much   4.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['DivA'] = df['A']-df['A'].mean()\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yang sebenarnya terjadi adalah kita menghitung sebuah deret, lalu menetapkan deret ini ke sisi kiri, menciptakan kolom lain.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# WRONG: df['ADescr'] = \"Low\" if df['A'] < 5 else \"Hi\"\n",
    "df['LenB'] = len(df['B']) # Wrong result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "      <th>B</th>\n",
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       "      <th>LenB</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>like</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>to</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>and</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Pandas</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>very</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>much</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A       B  DivA  LenB\n",
       "0  1       I  -4.0     1\n",
       "1  2    like  -3.0     4\n",
       "2  3      to  -2.0     2\n",
       "3  4     use  -1.0     3\n",
       "4  5  Python   0.0     6\n",
       "5  6     and   1.0     3\n",
       "6  7  Pandas   2.0     6\n",
       "7  8    very   3.0     4\n",
       "8  9    much   4.0     4"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['LenB'] = df['B'].apply(lambda x: len(x))\n",
    "# or\n",
    "df['LenB'] = df['B'].apply(len)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Memilih baris berdasarkan angka** dapat dilakukan menggunakan konstruk `iloc`. Sebagai contoh, untuk memilih 5 baris pertama dari DataFrame:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>DivA</th>\n",
       "      <th>LenB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>I</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>like</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>to</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>use</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Python</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A       B  DivA  LenB\n",
       "0  1       I  -4.0     1\n",
       "1  2    like  -3.0     4\n",
       "2  3      to  -2.0     2\n",
       "3  4     use  -1.0     3\n",
       "4  5  Python   0.0     6"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Pengelompokan** sering digunakan untuk mendapatkan hasil yang mirip dengan *pivot tables* di Excel. Misalkan kita ingin menghitung nilai rata-rata dari kolom `A` untuk setiap angka yang diberikan pada `LenB`. Maka kita dapat mengelompokkan DataFrame kita berdasarkan `LenB`, dan memanggil `mean`:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>DivA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LenB</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.333333</td>\n",
       "      <td>1.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             A      DivA\n",
       "LenB                    \n",
       "1     1.000000 -4.000000\n",
       "2     3.000000 -2.000000\n",
       "3     5.000000  0.000000\n",
       "4     6.333333  1.333333\n",
       "6     6.000000  1.000000"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(by='LenB').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jika kita perlu menghitung rata-rata dan jumlah elemen dalam grup, maka kita dapat menggunakan fungsi `aggregate` yang lebih kompleks:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "      <th>Mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LenB</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>6.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Count      Mean\n",
       "LenB                 \n",
       "1         1  1.000000\n",
       "2         1  3.000000\n",
       "3         2  5.000000\n",
       "4         3  6.333333\n",
       "6         2  6.000000"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(by='LenB') \\\n",
    " .aggregate({ 'DivA' : len, 'A' : lambda x: x.mean() }) \\\n",
    " .rename(columns={ 'DivA' : 'Count', 'A' : 'Mean'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mencetak dan Memplot\n",
    "\n",
    "Seorang Data Scientist sering kali harus mengeksplorasi data, sehingga penting untuk dapat memvisualisasikannya. Ketika DataFrame berukuran besar, sering kali kita hanya ingin memastikan bahwa semuanya berjalan dengan benar dengan mencetak beberapa baris pertama. Hal ini dapat dilakukan dengan memanggil `df.head()`. Jika Anda menjalankannya dari Jupyter Notebook, itu akan mencetak DataFrame dalam bentuk tabel yang rapi.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>DivA</th>\n",
       "      <th>LenB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>I</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>like</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>to</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>use</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Python</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A       B  DivA  LenB\n",
       "0  1       I  -4.0     1\n",
       "1  2    like  -3.0     4\n",
       "2  3      to  -2.0     2\n",
       "3  4     use  -1.0     3\n",
       "4  5  Python   0.0     6"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kami juga telah melihat penggunaan fungsi `plot` untuk memvisualisasikan beberapa kolom. Meskipun `plot` sangat berguna untuk banyak tugas dan mendukung berbagai jenis grafik melalui parameter `kind=`, Anda selalu dapat menggunakan pustaka `matplotlib` secara langsung untuk membuat grafik yang lebih kompleks. Kami akan membahas visualisasi data secara mendetail dalam pelajaran kursus terpisah.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['A'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
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    "df['A'].plot(kind='bar')\n",
    "plt.show()"
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    "Ikhtisar ini mencakup konsep-konsep paling penting dari Pandas, namun perpustakaan ini sangat kaya, dan tidak ada batasan untuk apa yang dapat Anda lakukan dengannya! Sekarang, mari kita terapkan pengetahuan ini untuk menyelesaikan masalah tertentu.\n"
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    "\n---\n\n**Penafian**:  \nDokumen ini telah diterjemahkan menggunakan layanan penerjemahan AI [Co-op Translator](https://github.com/Azure/co-op-translator). Meskipun kami berusaha untuk memberikan hasil yang akurat, harap diingat bahwa terjemahan otomatis mungkin mengandung kesalahan atau ketidakakuratan. Dokumen asli dalam bahasa aslinya harus dianggap sebagai sumber yang otoritatif. Untuk informasi yang bersifat kritis, disarankan menggunakan jasa penerjemahan profesional oleh manusia. Kami tidak bertanggung jawab atas kesalahpahaman atau penafsiran yang keliru yang timbul dari penggunaan terjemahan ini.\n"
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