{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fc6520b1",
   "metadata": {},
   "source": [
    "# Instruments\n",
    "\n",
    "While `phoptic` was primarily designed for use with the OPTICAM-MX instrument, it includes an interface for compatibility with other instruments. In this notebook, we will explore this interface.\n",
    "\n",
    "## Generating observations from a new instrument\n",
    "\n",
    "First, let's generate some observations that are not compatible with the instruments currently supported by `phoptic`. For the data, we'll generate a 1 s $U$-band exposure from an instrument that has a gain of 100 electrons/ADU. We'll set the time of the observation to 00:00 on January 1st, 2026, with a pointing of 0 RA, 0 DEC:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fa88898c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T12:23:22.918608Z",
     "iopub.status.busy": "2026-06-16T12:23:22.918523Z",
     "iopub.status.idle": "2026-06-16T12:23:23.504859Z",
     "shell.execute_reply": "2026-06-16T12:23:23.504376Z"
    }
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "from photutils.datasets import make_100gaussians_image\n",
    "from astropy.io import fits\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "out_dir = Path('out')\n",
    "\n",
    "image = make_100gaussians_image()\n",
    "\n",
    "hdu = fits.PrimaryHDU(image)  # image data\n",
    "\n",
    "hdu.header['FILTER'] = 'U'  # the observation filter\n",
    "hdu.header['BINNING'] = '1 1'  # the image binning (i.e., 1x1)\n",
    "hdu.header['GAIN'] = 100.  # the detector gain in adu/photon\n",
    "hdu.header['EXPTIME'] = 1.  # the exposure time in seconds\n",
    "hdu.header['RA'] = '0'  # the RA of the image in degrees\n",
    "hdu.header['DEC'] = '0'  # the DEC of the image in degrees\n",
    "hdu.header[\"DATE-OBS\"] = f\"2026-01-01T00:00:00.000\"  # observation timestamp\n",
    "hdu.header['INSTRUME'] = 'SYNTHETICAM'  # name of the instrument\n",
    "hdu.header['RDNOISE'] = 0.  # the instrument's read noise in electrons/pixel\n",
    "hdu.header['AIRMASS'] = 1.  # the observation's airmass\n",
    "\n",
    "save_path = out_dir / 'data' / 'simple'  # directory to which image will be saved\n",
    "\n",
    "# create save directory if it does not already exist\n",
    "if not save_path.is_dir():\n",
    "    save_path.mkdir(parents=True)\n",
    "\n",
    "# save image\n",
    "file_path = save_path / 'image.fits.gz'\n",
    "hdu.writeto(\n",
    "    file_path,\n",
    "    overwrite=True,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3695f8aa",
   "metadata": {},
   "source": [
    "Let's take a look at one of these images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ed49c0f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T12:23:23.505857Z",
     "iopub.status.busy": "2026-06-16T12:23:23.505739Z",
     "iopub.status.idle": "2026-06-16T12:23:23.750641Z",
     "shell.execute_reply": "2026-06-16T12:23:23.750154Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2zNXVVSbhyoTMz88na6GqAwj4iKrPOrFrwej29jZbAPYi4om9qjawuLiYlfdgMIhutxtffvllrK6uJtO4sbGRa6aVVSs0BUVExO3tbbTb7aSze71e9Pv96HQ6M0xZt9uN6+vrvBd2jd2pVLyKfzQaxdXVVfqxStieaGtYC77WarXi+vo6Hh8fk10Eeh8fH5OxAD7sBQZScrc3FxcXyU5gCo+PjzPx26fz8/OIiLQtYMU//PXu7i5ev34dBwcHsbi4mN9Vfd2ee1aJ8vLyMtkfMRBbe3FxkXFjY2MjLi4u4vr6Ou7u7mJjYyNubm6S8drd3U3WowJ938muausnIuLh4SGazWbc3d3l92qbdjqdiHhK1BhFMRrImJubS1CGIRNnXWzfelX2LiLi7Ows+v3+DJOIEXh8fEwWeGlpKc7OzrLo81xLS0txe3sbW1tbcXp6mrFse3s7W9gLCwvx7t27ZJCqfUREdLvdBAVYQKz8/f19gghxZXd3N9vxCt+lpaW0mdXV1bQBsUPM5AOnp6exsbExk4PkC2x4RCRzzsYrsy/2W4f7+/tYWlrKPMfe5Apdi28LUD54BsUGS+IRU3Re2xZaITZJQLq+vo6zs7NE14CAqhlld319nQG30WjE4eFhHB4eZjBULQuM+pgMFH3W7XbTsVQYnmF3dzeN9vHxMfvWq6ur2bscjUZxcXGRiRWlrj2FUhwMBnFychJ3d3exs7MTr169SiODgNvtdlb+KNHJZBKffPJJJuqrq6usihj6/f19bG1tRUSkIzE6ld/p6ekMpaulcXJykuDm/v4+71VSBRB7vV7uneoVI4ZtqUYfEUnxotfn5+djfX09lpeX8/eWl5ej2+0mharak5zu7u6yL66aiIhsFQKcEZHgiG1Y+8vLy2Qibm9vc41Vmz7P+gkaKnbAiN2wTYBldXU1tra2MsljwkajUdLjGBTgQLUPDF1fX8f8/HxWYSpJ/eOq+8CsaXkKbEDu0dFRnJycZBu10+nM+IHeNeYFaDo6Oprp61ew79ntC6AuaVxeXsbNzU0mntqGs9e1naaFBLShm8UGbT0AjjYG+7C8vJxsjqp2MpkkYBWMtQ3Y7Orq6ozfnJ+f52diHE5OTnJtu91ufkdlAhVLz5lbCcNzA4bAwmg0iuPj4wTDCgDP4l47nU7c3d0l8zIcDuPFixepexkMBln01NbtZDJJFow/fvrpp8ngAlfYI/FZq9s91hgOZN3e3maBYu9WVlai2+0mq2Bfax4AAKy/4lGB0+/3s6itzANgqd0hF/AdPnl9fZ3sZy0G3r9/n2uraGq1WgnmxcLT09O4vr5ONkuLdG5uLnZ2dnJPd3Z28rvEJjZBe4KRfffuXfR6vYzbmAwsDb+7urrKAtF68aH5+flot9vJbLJhYB4ben19Hevr6xmPMcrW/DkbrfjGam1ubuZz/LTrg2dQbm5uYmNjI0U8KpradxMYaUsqDTYej2N1dTWTgOrFglcRGINDIdfgK0GpIFBgxFKcr9/vJ20qCANEqidGa6Ml9H6/H+12Oy4vL+P29ja2t7ezSmSE2A7J+ebmJitiNDFK9Pz8PCvatbW1NLq/83f+Tgbv29vb+PTTT+Pg4CArFfQ18WS3202GZWVlJYGG56wBZHl5Oaneq6urpMwjIhH4zc1Ngp5a8aB9JRm9W4nf3mjhadHc399n1bC2tpaCZzSpBKmtQYQM5etj00JwZhoPVWQFAvYc6Oh0Opl8K7sGRFhPdoiWfXh4mKGfVShHR0fJ2LAt9oshwLb4XdX827dvMwkA2Fp36+vrcXV1lfeGOq8BhU6KnWFVGo1GHBwcZLvEfmxubsbFxUUyINYE7RsRWRm+fPkyTk5OskUUESl8rAwp3dNkMkmdC/vnP9jFWq3Vqhs4dD+0GdgJ2o1er5drQdTIv+mjIiKr0WqT/E7xANioOKumaDAYJMvA7mrFWgXgWD1i2ePj4wT3QL2KmH3d3d1l4hDPxEsJDxC0xhgaNkZIDxjQhVgrYlrJfjgcRqfTidPT0xQL06ER2WKput1unJ6eztgG0BoRMy05gP38/HyGEaELk8wXFhayJQ0sVabl/Pw8W3FALsE0APccNNM0si3fjc2Ym5tLkbp7lR8qs8RWxZ/Hx8cYDAapCzo8PMwiEcvLh4ihtZoUm7UbQPeEQcQmYx3lFjEQmN3Z2UlBf9U8Xl5eJjt3dnaWf6eQvL+/z7WvOdP6KKT6/X62en/a9cEDFKie4XBuQUU1IQhJmNB9xBM6J/6LiGQSCD8r41IrdsYIZUsIkGJ1LobK0FBoNzc32dLodDq5gcSmgtf8/Hz0er2cApifn0+hHGdWUYzH47i/v09aljGsr68nCNDuiHjqfe7t7cX/+//+vwlSGCUHECz0HNfW1qLZbCYtLdjp21rjo6OjpCatj2pydXU1hVqEoIIVIIVFur6+Tk0OACIRAh2ekTOp2rBnggpqHvUIDN3f389UByo+4IFtbGxspE6l1+tlkJW0tHkuLi5SWH15eZn2Vr9PdQLIaluokjm8dsr8/PwMXX51dZV77l4l7Lu7u/j000/TnlHHzWYzBcQS5+3tbWxubsb5+XlOIvR6vWyRaAOheatOQTus0WjMaEokdRWmoOg5VldX89kingLY5eVlMkO1eJA8+AN7ub29Tf8jdNW+YBNAtZ8XqFXOKuqlpaUU3F5dXcXBwUEGd3ZHF/AcnCwvL2efXhVtzSOmuieTF1WIXouhyuJZ99evX2eLRkwz5XV9fZ3tMHvFFiRhrBbxOPZMa1IcFUfYvlbOw8NDFjHaRli6x8fH2N/fz9bEYDBIBpKdabHXdqx1NJWDgVhfX49er5egl05Gi6PT6cQ333yT+6IIAfp9LrajFjhYKQlaXFPUKDYBM2tXRd7YBiCs6jEkY3ZfW6uEyL6LbdIosmuav7u7u2wdyTkYLjkD63l8fJyMJsClzew55D5x5e7uLjqdTlxeXuZ36kBcXFwkUwyYfec734n/5//5f/JeAHqgyu9eXl4my6dlfHV1FS9fvoyzs7OYm5tLse23uT74Fo9el2DfaDRyAwXjdrsdq6urWT1V0RWHFzQrIIiIrGTX1taykpbY/TdkKwmh+vXSfa/qzFhyRGTr5u7uLg4ODnIMUMKnL7i7u8uEiTHiYNXpJV7gSADwbJeXlzOaHQ53cnKSLY8XL15kUGg2m3F8fBwRU/pUhS65Pzw8xNXVVQIOxnp6eprVCmZHktzY2PixiRMVHMbHGCCth3UGRiNiJnhqR/g7AQXjA8zaUxWrCqm2ngg/redgMEjwcXt7m4mJvYxGoxmGTmDxuxXkAq8cvd/vx/b2dlbmAFClWre2tpJ5UoHRZpydnWUr8ubmJn2h1WrFN998M0NXAz39fj82NjYyUHc6nTg4OEg9h/FSAddaszctKest+F9eXiYN7++0o4BFiVfgB9iwIHWt2FPVQbEV/ksTEhE5puyeiHqxFxV4V8Dm+Xwu1sN3ixHul+BQUVR1FdaU/kpLCNiqrV/t2729vaTnTRS+efMmVldX4/T0NKtzCQgrorViXTFefGlnZycmk0m8e/curq+vZ6phoEKrLSIyKWsb0aLUdt3S0lK2m7e3t5NxI7BVBHjGh4eHWF9fT+aX9o3oezAYZEFhFBWDt7W1lb7y+PgYp6en2bbl3xUws/Gql/BZVfAaETPtGK05omFAS8EpRgHqtYVFn2TtAUgMglbv5eVlXF1dpWj+9vY29R30d5h+zJTnruAJGMdM6hbc3Nyk6B5AwExNJpN8/udtHaBJywhAiojY2dmJ8Xgcn3/+eezt7eX+s3u5TussIhK8a9HRT+kQ+I5vc33wAKUq2uuYp8pEsLeYkrkqnKGpmpy/UZFnRCRwcPaJ5B0x7Y9CkxFPwOn8/DwTbh0v5rgRkYGw0t4RkVUgyr3S7GdnZ5lMjceqICKeUKygLcBr9Tw8PMSrV69+jMZVhQpyi4uLWZ1zCmul162SqmJjNKqKz/kfJjrQnLQ/1llQsY5oUQEPEFRdr6ysxOrqao5gAn+Xl5dxcXGRgQ+wEcgEMGsFkFQwgSr19xJ5FVGzN3oYlKrvFfRub28zqWhLoGAB5fF4HL/1W7+VYEAFqrfs3q+urlL3YA1QuLRAwJjn1O6ozIHvvri4SJHtixcvsuWzubmZFHNEZKLDJgK3w+Ewvve97+XeAYP67EDr4eFhMoIU/pKOdWUb/pEMNzc3E2Cwf8/RaDSy1cA22LUCA2jgp0AHPYIkwqYB4zrZVRN/RGS7qhYofE680MIA6PlLZZDW1tYS+AKFxor5kKRVNUECvgsTVPeBEFd7EbtEp+ReaMasLzABSJyenqYOxRk0fq8Wd+INsE/sr9gCbgEJQFvcMYhQBxMiIoXg/MFIv6Qtrpoqcv6R/eFL2iZ8mvhVHDUBBDyxSzoLfr2wsJAgQH5gK/IMNqzmFOtzd3eX+jr/PJcdKG6rFg0oqTEpYgootXa0LmvMEcvOz8/j4OAgix/2BAArzDY3N/N+Ly8vE5B6VvctNij4xDbroc16fX0dp6enafc+89tcH3yLRzW/vLycyE4QYkDABYpUlRYRiZAZV1V7+ywLzjkgctVLxLTfJhmiJiud6GIMFeRA2hxU4pN4K13s+wVhRiYgLCwsZG9T8PWZ19fX8f79+2Q7Op1ObG9vx/HxcSYu/cWVlZU4OzvLYCs5CRS7u7szAtp+v5+iwEbjaawQc0Hs1m63c+4e8FABAWHWBUsj+RiNs3cqrIuLixmNDWf1+ZwkInJ/UKSV0uWAqiT6GNVFRCTIiZhOFAgs9CsVaHoeyYMdVZur7T9C1ToOih0RgFSAkiGGjj6lapzsHX2QaSuJw719/fXXKW4WmJ8nYK0r9P/6+nr8H//H/5F7VKeA3J/WHCGu6mx7eztGo1FOIqisnBuEcmd3Z2dn6WeAiuRWJ1xqgqv7wCbca6X/2QW7E1N8RhXDYtwqI+VZ+bnftw8RkZNLdeqH/RCgsjXPghkUByR/fu7n6a0ISxUVtF/agVXMDRBqQ1rfdrud9tTv93P9HNzF5upoL/Ah9hBj06r83M/9XPzwhz/MM5Mk27u7uzwIEntbJx6fM37WuYq4sbNaxe5X/MAA1eJRruAzVc/El8VZn0HgyjcBYW1N7eEKvtwLUKhA9Fw1odOzsC2fU3UwdaIRW+pcoXa7nWe7+O7nZ2/5fBN6V1dXOdwhHiwsPB0mqE28uroaEZFtQX56dHSUMdgeVq2ebobJSGyPdngF2D/p+uABikQoKKHX9Zxr75uOgEhSQkZ7RsRM20IFboMFiToKCGBAqRVNAhkR0x40DYjEoPrmCMAHSpdhoeIAAT3q+fn5ODo6islkksm6jl9OJpM8oEtFWjUFvV4vtre38zn05Ik4zeRjc/QeBYPahwVIDg8P8/sqCyQg+A7Ar7JW1t86El+5P1Q+piZiOkaIRncS42QyybFIYI6NWG+AVMtEdQ/MVqFXZVmq+DoiUvtgjznp2tpajhCaXhDA2VwN2g5hEswnk0kmbvbk3it7QoPAXn12p9OJw8PD2NraSmAkiUmCqjFnCqFrtSToCJrN6eFzGBTB3JRCHZm2huvr6zPVbqvVirOzsxnw7+8lObQ3QGAtiAYxMACoSlKQFOj5CzaET/Bl60cv4fsjpuLcSoFbfywLuzQFw67H43FqsTBV7PXh4SGDtAICkLFugCYgVFucFfxWwMDHtIRNLx0dHc1UrNovWlr1+Wjx+Gin08nKvxYO9fur3/o8wH04HMbBwUEWLtWuW61WntlT46w4VFtv2HGgWftbEVqHI5xnJUZXgatrOBymfslRFfYOq1vZK6wTNow/NBpPQmFaKq0LhQ/2RAzl6/PzT+cy0dxpEzpRWQy6vLxMP+l2u8ma062JAQcHB9HtdnNtFhcX85gIfwb4y31OqmYH/Et+cAr5d7/73WRBxUdxE3uP+RMDV1dXY39/P87OzjJnAobi77e5PvgWD2cgmuOgxFK1shJEJVUJTRDweZgRo6o1oVlkziBZ1XYQ4/KPpIgSVW37XnSaZOSz9Jg5aFWiu+96FPzXX3+dKnAB3L3X0et3797lva2srMT5+Xl+hsO+OJ5EGBFJ63EqNK7esZ4jpC7AUNrbn/v7+5lxOG2a+m8Hx6l4K5ip51zc3Nxkj7z2Va1TbX2pgIGD2sqLmOp0BEfBxD4AUu4FPTs3N5fndVTmjKDt8PAw23o1gKysrOTonsCPicAUmO56fHxMIVzVHLhHolKJpAKBOtJcz1mJmCaCy8vL9BO27TuwLqpEa9nr9ZJKX15eTvuQONiNz1UkrKyszEz6AAT+23QXn67tDclbW9e6oeZVxlXDFTFlWrGU1qMyqs6h4M8CsvU0mkzThj2ovXagit0QKNc2jX98Nz/Rp2drTk9lx5gt7IU4QcMmdtRx4ffv38/8LJDAXsS9iCeQU0foq0jSvSpOJDYaCfHLyH59TqJLz0LMSrNShZbiSBWtSqxayZi8zc3NBJwKDMMRNGl17wFMoFPbSwtJi5auwp/5fPsoXng+9st/2WDVysk5TnHe2dlJXWOdVsL8i59AOAAt/ipYrb8pHnbjM0ajUdrL5eVl7O3t5XcB54CPdhAmV5HwxRdfRESkrkosc3+9Xi91bIAXxk48tcZ0VNXuftL1wQOUirghWX+ORRCkiDkrgnWML41FFe9wit9J6CgJ1XMO9PzribQElYxXdaXSrEd864NXEeXCwkK+b0VF4FyP2k6qfX2gqU5ItNvtrEwleveMNo2IbAtxGkwSVK3FIVGjPLUSIqZ9/yructhX7dMLtpC/MU/0uNFFScrvApYo/pWVlWi32wlM7EudRrJWKhXvmREwgVcz/YJLFTMbnZSUI56SHMAqmbsPlYWqT8Vi/U5PT1NILCFXwKolA4icn58nAKGWr+yeBIqV87l6yf7f+juP4ObmJvb29jI4qcpU0aZZ6H4EPtRzbZEBB/rnDw8PedItf1C9VV0JcKgthenD4AmKQGgVbVpjcQCoU9Hym+cTdYSRkkldO/ZQ27lE8OjpmjwkWQURcALw7O7uzrQvJHUxgN/Ym4jI+wfY0etYKT6KxlfFikERUyG/vTLmf3FxkYVc1UCMx+OctmA3k8kkXrx4kUeo00XYd5+pWBGf+HWj0cix+AqAFBiVrdZKEuckXi0swEWsBzTEdDENoAI2AEIFAn2iHCHmsA124DnZPx+yVlotPo/t131U1DpzRYypoFXblI3WlqSfefv2bdpTZUSAQG2z09PTzDdzc3PJ/qytraUvYvY8i3ju+6pmkvyAHSmsJpPJzKSfwyeHw2FqVTDG8hE7rWdC/aTrgwcoUFmn08m2jktFK5GpKgQdQk0UsMQnGTJQrQ6gIGL63gbfpxWjnywQ1UoXG1JFd7XifXh4yMN+BL7KHnAawssqjBRk/DmWBv0ZERnU65HpKMRer5eVBnZFkiXI9OcCAYoWU6OaqBVe7a06j0QPFQ2LIVheXs7EKjmqeiUMQaK2Z/SGLy8vE5TVszP0ZFUJBHIc0fpLfM9BH8AGQFrbOnqKdauaIv+2VsacBckKIIjqagC9urrKZ6/9dUp6VbN7qADBM3mvRkTktARBrykMrESt6mtrgu14/5Iq0D4sLCykmLXabsT0ZWuDwSDFxnxP+9Gz1zYjAMbHaYa63W4KCIGBqoew1oClfcXAVBq7ig4jpqea1h6653FhMnwm37SXEqk9wvT0er30G4BZzJFwq9A3IhIQ1ynFOmEi0dm7lZWVHOdfWnp6YeTu7m7eIz0LYGV/3KvpwMrWbm5uJgDWVpC0ibS73W62h6pIlPbF3kjKwIBCgN1I0tbx9vY2zs7OEiRVbaBpNX6APbC29lnMw+5ggPhZLcBqgq6AxfeK3WL98vJyAm1xQjyxpvXdQN1uNzqdTk53Kp6xTuyV3gxbDJTWfMQ2AD5CaPtjksdrE7C51sn9inXsnx0RWbMf3weEXF1dJSB9XhhXbV09Y2wyeTqRmN18m+uDByiC+vr6eo5TVQqOU0bEjPpbAru6usp+mHn09fX1pNMiIpkHSRSYgAhVkdB/BTemVwQfQEX1okqCPAmeIFcjZ7XlEhEzBl6nSWp/3fQQerUKbqH8+q4UjmBtaHtqcuaMxJCtVivfbCrRqSz0s9Hq2gb1DAjJGdD0bHNz07NMKstTtRwAmAqpVvOVxmcP4/E4qxhJBXOlJaFdpMqo7UFVycrKSmxtbSUb5nsFokph0xsBOSpQwV7VI7iYkpKkBDgnEJse4PR61vW8CuBHsK9jy7XHLLmwvwq+9ebthQo1IlIUPRw+jbv2er34+uuvs51BN/K85y250ybYG3/XaDRid3c310pyECABmgreVZzssrZtgEwJhZ3xNckfm1LBJGDdaDRmAPnW1lYWIGJMRMy0CbCixKN18kayV7jw7Zp8JENBHABVWGHwJImqOdOOwwo57AtwkWwlaOtvEoYQnV9jPg8ODpItUFRhYQ8PD3Pd7bHPNU1iukscECv4UmU5gabKVtoLNmFPsON1XyMiAaMYK4kq+DArtQVcNYnYS3/G/yV0xR2GHFsmzldNDZDqHgDOi4uLLM4APsAfoBqPn8aOz8/PM4+4F/ogYE4eFEvFqnp42srKSmoFAWw50/7USTKFawXqhPry1eLiYmrtxN2XL1/mz/u3dRaDvs31jwRAGQwG8e7du1Q1Q8HVmVSURt5oBFCsNoEBMmqVuUAouFQ9Su1DVmpcpVPbIpVNqGCBgRFvjcfjfI+CoG5jGRXBkf43rQqQpFp7Tgs/1+NgDvr9fo4c1jWbTCYpfCWeFNAZe8S04kM/ckr33263Z5JerUQd2lUFdL6bU0rGkgPDxy7o09Zec0Qk/aoPW0+BdL/2qyYj4NYeacMQswlYAr4KUvDDeAj+v1NbgnNXgFBPZjWdAAxol7GDlZWV6PV6cXFxkWwCUbCkeH5+ngEzImbEosfHxzkSzw5UaDQJ7lVrsLaJ/Cya3/tZ2Lv9I3ZWIZpkMVKpkvUyzqq/qeyKpCD4CuCmCCIiQbmkxQb9U3Uf9ty+CP78DTsmzpyfn8f19XXGgwpiK/tQW5NaoIChZCD5mgwUS2jIKr0vDgCKz/UI2keqYj4YEVmEOItDdS+R1gKNP41GT5MnQIhCwus4PDvdi9Y5vwZ6gSaTQ+Iroen9/f3Mu3CAEa09+8mXsZrWQSH3vIKvBYNCzB4Ac3yuMpAYWu0PoEYcUchhW2oLW6z3+daiAlLHPZydneW5ST6rstb2iK967YI4K6/pHvizmu8wnlXvATTXQwnv7u7SHyMi46KcOBgM8nsqkKy6TYAS6Ps//8//M0HiwsJCnm1j//6x0aBAoBywnhZaaWtJEEKXLCqtFxE5Nos6rXoMyczi1hbS/f39zGmhteKMiJnq2P3VpKRipeVwPxgdG+/zVV8cSCAErARdxnZ1dZUJ1H1YO5/NWImjrKHxP8b//ECtypxQxFtfgrdms5kOiQGhe0EPCyBaQdpR7q+idiCEsFaCEyRqf7XRaGTrq7aerK/Pqwfqcf7nmpv5+acTfU9OTjIJPQdVqnfsB5asthYEgzo6DLBJyl7kKKgZE1X9Ly8vzzAK7HNxcTH72dglNl1bD1phwEOdSqJ98o6QKkSdm3saH67fPx6PUwyN1UIZa8FJpMS2Ajf7qvYEgFUKurYXBWRAHVAAhiJmzydC2ddJEGyCmOD32A1Whi/6HcG5sjR8ugIckzLn5+epvZHsa+uw+sRkMkmwKVH7+xqvatUuOZvCkqRrwrD3g8H0ILJ6voVnFN+qWN8+iA+ArhOcm81m+g5g7eyd2tbx52xfjKz7W9srVeMC3FTBNzD28PCQGqvKtEREglhrJzZgPiRMvgok1QJnMpnMgHX7CBhJ2FqR2nRzc3Op82InZAUE4IDp/Px8jlq7B2B2bm4up8TEkU6nk4e0ab/VF3SyR/fL79kCbZjCrr4So57FVYcPTk5Ocu/cpyIU08nn5TXdBi1wwNza/rTrgwcokGClkwUchlkrarS3fziyoOUSuIzPbm1tZUVWDbQCnueaAsCI4wl4VRBY6XO9Q8Ggth4YD5bCSZaek2AV64O+BgyMg5licQ/WDENgPa6urvL14BFPh1NZz8fHxzy/QgvDdXZ2NvOSMGwCQ3fqosCElUDbcuSIaXXq77UqgAJqdUm5nk6rwscIWBeI3r457RO7ppKRoMbj8cx4roqSTUVMR54Hg0EcHBzkmtRKDqsmWQNaEZFTLSpeTo7RwJg5XrtOgBEE27+5ubmkXTEZhHnz8/Oxvb2dkzIqOqDt+Pg47Wk0GsXh4WGyU9ojdcwZQ+AfdDLQDqgIUk4vHY2mb43VypDMm81mTgTZa4WGZ6xMCQArmREXAyoqWEETOFQtY4UkquoPmAXrWJkM7cJK39fjC+xprUKfFwRLS0u5tmwKKxsRGa9qQcOP+DytgLXxLPUt5IeHh5kQ60mijtyXsHy2AkRrYGlpKf2kxlLtRGLjXq+XRYj1t4fiCttmB8BdFcwDt5WdGo/H0e1289lrtQ/4YE8jppNk9tKARN3PWkxWgG8PfRYfxab6e0UAlh14Eq9Md8kDl5eXuUYRkVICJy1jwIndDWBgQ+pkKvbb7wHebDEi0hcdUXB2dpbAng0rAmu+xMZbX2vJN927teQzFbzVFirg2Ov1Usxf32D+k64PHqBY6Odq7noaHlGmkzglOMlIIIKCJSUiVklVD1bQ1x5BVQq8kkedAHGP4/E4qTtJjVHc3NykaBDI8RkO+nH/7lGVCEFDu9odEnQVZarUJXQVKScFKt6/f5/ADFBgbBKsQAhsRUS2klQHCwsLMwyGtY2YVqXWwvOsra3F1tbWTDKsyen+/j4+//zzTOCS9GTydGaBZFHBFAaq0sT23pqrhiSoGgiBDRUaeyHyi5i+kbXaAHuTGP0s5s3eS0iSl1aG78cGeRZ7zfaJilVZxge1uCKegKbKCFCsOiN0PJ+JiBSlDgaDPNDLeRwAOLD55s2bnOJQGVb9y2g0yjcs1+ROY1TXsbZYga8qMK96H88QEcna8WlMoN+VsIyNszkJGmuDTcQ+VTuhkar0PW0bBpA98i3+4fsjpnorR8ZXwWbV04kh/E6iUiQ8PExPUVVUVYGwahqD4aC28/PzvP/KJIubw+EwEyswQFNS99/LBhWL9Q3h9l4rQJLn/xVIik+12KsFifgj8dbYpQiQsP2bYLPaEODsWWvRUP3Xdx0dHc28yqIy3JXtAvg8o7hGS9jv9zO2aadX7UuNL2K/fEGHhq1ko2xE8VRbopUJrDqSant8GShzX+JbnQJyKJ34ag2AvjrhJf5aV/m5aux+2vXBAxQXisqDeyW8UwgFfXqMiKkYkHPW6kkFSh9wdXWV2hWbUcfjGG5N8DWAVsQKrAgyqmrJG5X+vK2jh0elzyndn4D0vNLa2tpKIZXk6x4q1V2rS9ThxcVFfjY6U4UeEZnkUPVVDFpR88bGRh5kVR28ikVV6QAHyrOq+mvVDGRUARvwMRgM4urqKs7Pz+Pm5iaPl8YSAYvWCvCLmDIi2ArrjbGKiExItbK2JxzSevjsjY2NrDwEVZ/l3iUhzIGAJQheX1/PjPahgtHsEmoVXwPYEne1fyDY/auojKGq0vSQq7izBhmtvbdv36Zgjmg3IhJgEulVyh0wtaY1wNFdsE3f7/cBfoyVtao2ASSzO2spGVkn96ZI8bvWGdjA/tSEWqfGAAyJvDJ2gBTAXyv8+fn59JHaKnh+EKV4Zg/dlzeSK5pQ/+xIm0eCAx49Uy0CFEOenY9iYK37+vr6zBSe9p244jnqpCGgAJyoygEtfidm8/0vvvgiQTO/B1DEUXZS23z2uQKSylCIh9pJ4jDQUJm1yqjWdjFmwnO4NwwhxkiS73a7eXxEBXrYl9riqcBHC0fxUeNH1UE6HNHzY1/kHXo2a0eTqMih4fIySp/z27/92zP+4D1MNeaw32azmUUwMC2+/WMDUAQnTuOfOnoHtdegLvEzAp8hqFSKHdNSqdT6bhNJFlJdXV3NCgYShyYlNUGqVi8OmhIUaTJQvzb97Owsjo6O0oEYp3vkUFWEV/v31QEYLONiYBHTI/UF2yogreAJO1JbWtgWz6eSbbVasbe3l0nA70kcEt/Dw0MeEGQfUMkSUKMxPTvl/Pw831GkahHwtBYEf3sgQXOc+k6Lql2olWxNhKp0wbECPUG5BjVtGm2CiMhE7NmBPXoTP2/tJLyIJ8GoxDscDhN8s+8Kuk9OTmbsiRbJVBDAaVKs/lyj0cgAW/+8JiAAvvaoJaKq+9BCqAnWZ0RMp2IqQ1N749VPK7so8GE6KwOBZWBn4oLkZC/oZaqOq4I5MUQS9Dz2j++yVz9zf38fm5ubeYYMHxG42RWq3bNV0B4RKWQkhHa0Air99PQ0AVMtFDC+vluhBkhLPoCX+yOoZdPWA61fK+NGYyootl7GumuR5VmtFXBjLzAttYWIERBL2CdWiO2JHfwaIDFeq1XmxGM/A2gAku7DvhonprN5zhBU0AyQA33Vbufn5+Ply5dZqFTGXvu0ggRgqcYtgLLqDSOeCsUqzpcP/X0V4pqG9Oxra2sZk+UBnwWk8/WVlZWIiGTKfK74Iz/2+/0cg9/d3f2xrsW31aB88EfdcxbsQUW0j49Ps/iMvgZPidwFKTNCrRYJvI4+cpjaMrH4jLJWCYy39jUrExMxfcW15CnACc4CVk0keozoP5UYAMHIauKtrR7BzXetrKzkqByju729jZ2dnTg7O8u2g2SgQtOuiYhsV3hnjt52o/E0RXR+fp7na9RJGBUOgW7VRwhE9sl+2zMgyn1VUFBbHv6po5w18Po9Aa5qBR4fp2dV2CPAC7Vf78PnREzbPp7JpEBlzoDQiMigr/KtOhQ2Y0pDQByPxwlYJNmTk5MMjMPhMPb29lLvIRgD4AI22xCoTeX4DIkaI8YGUMUmCTqdzky7oK4BtqDdbidNbY/4Cj2O51YpC8r2hB/ad0mFP7Jl4MIz1AQrEdaTLyXG2p77nSpANhMRM0mtCi7pD55rSWq71rr4b4JM9+p7a3vS+lSGTcL0yoT6Wg2JgYia3WPdxFJ2HhE5saKYsd/uU/xRHGHX2P1oNJp5cSFdDIG/WOjYf60GcVHsEGf5l1gAsEp8z1scnl3irLGgaif4LjurzIvWRUTM2JF7d3/uq/qDQgi4xMSKH4pcRS+Grba5jBjX4gfI93P8fzKZxPb2doqGq94LM20d3dvd3V38zM/8TLx//z4HNWj1DEnIKablCHQjpi/GtCZVyC0+NpvNbBfJEd/m+uAZFJtDg6DPK5EKNtVJMSQCs43CUnBQkzdVN0CX4rtsnO+lI4CAJSuJUTD0e/W/z8/Ps/qrAUiiUJ3UP+NsTo6UTBgWNoBz1hYTilkgrg4mSUZECuWAgypM1LOsL11071WXozpw79plVYha+/8owbr2kletrIBRgrN6X35GZaeykDhr260CFXsigGl1SDCmoNbW1tIxqw0ANjRL/r625+qotMqjMnQRkS0En+G/JT1rRlRnv6x/DboSunZOZR2BzCoUNR1SaWH2K2lhHDwfzVN9D9Pi4mLSx1UIKPi6AGtFA2A0mUySJaWxqsnGmtdR1CpGl9SxKMCkJCbx8Gm2IXlWAFmZuwpExKCaQFSb9XP5meBfE6BWaGU+3MfJycnM73tOuqXaorm5uUlxaz3ynv3UdrdnMIklPlpjazAej7MdgcHxd+ycLbA7yUrhYf0qcI+IbCVXhlY8YMs0TRVIV90IhtHvVgBXGSOtRaAHsNJOqvZkTU08OfVZG7YyFPQitSCpgniDHNvb2wl42as1ZSdeFio+iUU+33PXvRIb+QXNGF/kfxjZelyDQun9+/cJZCIiD4msrUbHWdhLf29/IyJF+IAPNkzMc1RFLWp/0vXBMyjX19cpiCU6rDSSAK0asZjO3AAOUIS13VAFcIywUo0cobaWagvApTfJANwLIZ0kV6ckVldXo9frpereZ3B8qPz+/j42NjZiNBrl2ycZPpo8Yvo+FIHP+Cijrmc4PD+PhFML7Kp4TlIPkBsMBnmUtn1QsdfES7xVq6Gqw8EACWAqar/rvmljIiKnXKyTPnWtoKqwjHNVIMJugCXVbkRkUq4UvzViW/Y2IhIoYqImk0m+RVSFaI3oD2qgriK5CrIr6+ReK/MzGj0dJV/PprDOKkQntWLQrAeK2Vq02+24uLhIbUCr1coRa+trDa2JPbOvNBQSo2moiCkwEVDrOmOD2Htlw9DigiV/9Fk1CEq2ldGs1W0VRks+FcxjG91PZaVqBV81ERg4QBcgYN9iB11OBWLLy8vpP+yZKLWOdTqlsx5c2O8/veAPsyYRSr61QMFiAp+KOsyLdZCMR6OnycKtra345ptvci3FFAAKYOUbVSdWtSG1QNRGVBzVtjJdjSRZY20d1fY57Mi6SuraWrWNaZzafrAZMc9za7+xsSps5Y+1DYgdqexF1bc42dXaAJLdbjdBdY3/zhhiC16IWz8DWDk5OcliDGtd9x+jZ/jBoMPvVKTx22azmbo0fs5/KrDyrh56x9pqZgO+69tcHzyDgnqvZ0lUiq9SsVo26DELJRDYHMmYYzNg2hInxkZMq9WIaYUv4QE9AlUVQBHO1pMsPQ9HX1xczIqZgzCIxcXFH3u3i8Tos7VMJGmOy9nrYVHQMUNGzaqMJDVrSVNhTavRnZ6e5poATwSTzylZgVSwtebWsAr9BBXsCueprJOgBbVXsTEHfp7sJLx+vz8DXlTdxLmq2NqH93m1V/9c6yHxCLqSg9ZjDbpsmEDb92Oi6sRC/TxJ3T8qZf5g/a2bKh3QjZi2z9jb4+NjvnpBuxTw8LnuoQI3Z7tEPCWidrudEy+eUQBnj858qRWi5GjNa1Cs7RvfYz+fx4BabdYqXpvHvWPi0Nz1iP1arT9v9/D9WsXPz8/nM1sPhVLE9BUdQGK1rXqsgM80tswnnDrKFnwGNtXzVv2KSyEWETMvZFS8KXI8a6vVSvAwHA7j9PR0JvFXIXfE9L1VklkdGdfCtRbu03fb13qSq/iuYleh28vKptTv9R2VsfPfbMR4u3sSL/3jeUzBidfVB9matk1t19SJGDFNTHCqtTwQEfnWYGtSi03fYboPULJXnU4n2bO5ubl8u3273c5n0x5yjzc3N3l+Cl+QXzy/NiB/kPMUkeL63d1dvH//PgGbnzFJyUafs2g/6frgGZTKcEDAAmXVIURMp3aqMHU4fHp1OuewuLW/V0Vateft4B0jlY1GI9sadAaV7q9I3fepQBqNp9MLJ5On/v7p6elMNVh7ooKgcysiIo+y5pARMdNOcl+C7crKSrIoPq8mjNovHQ6HmXD8PeBgD4AeTImkKYkJ6gKLIElXU9+SKXEyYmvs4CdVlmevTuLSHwWEOLzEDKDWg6Nq8KlAsyYBa1SZFmC2ipIbjcbMwU5AaW3BqTRru6JSz91uN8XY2jUqJdqCus61z1sZrslkktNoApznxcLVBCORaMuxJyCRv9RW0Wg0yoO7/H3E9A29PldxILg708E+CuYCr3X2rHxIn1wArOCGjbGtqmmoNgIojUajfG+MZwJG6LzYI/tQDWot80/riOkDLpxq7R7Ycq3AKwMBhGjN9Xq96Ha72W6dn59PJgrLcH//9Cbb+lk1+bJdrUlAFNgg9q/Pu7DwdGZOp9NJVkp13O/3E0xcXV3l9A1AY80Bm/X19Tx7xYACv66TIhWcVAZHImZLnrHaCHBfD84E1ICOyo5W4IQxqXo3MV+RImmzg7OzsxmQwMcqc/Tc3yMi7Q0rJX4DF3zRHlxfX+dxBoAQG61CdExM1ZsAIO5TvgGeb25uMs7XVuBgMJh5jYKCqrIrdIJi4Pn5ebaAgVtrU1nF2mH4SdcHz6BERCJrQVxPPiJmjNE/qpTr6+tMVIKLpOwflblEw1gJ/XwPNkJSrcBCYPXzPs/mj0ajHM2rWhYJX9ATFCrjogKravYKNgT8Ov0ymUzy+yIiDbIyGP1+PzY2NvIeneSIohVAKnNSn1FF//DwEOvr63kiJrX+cyBoLLxWugJWxPRcDEG9Co8jYmaEWOBcXV3NN8F2u90cIWUTAirqHs3uWSp4FHBqBa6XLOgBPioQVQWGp7IMVWjrc13z8/NpTxFTZqPqRAQJ73/x5/X+3U9EZHDTfsF81HX2Pb7z/Pw8A6rAaGywPiv7ryxT1exIGg7SIrqUxCWis7OzbDFKFhJBFV8CX3V8GKgTtK1/BZnWbW1tLba3t2cmACVCI9meS6CXfGo/HVPFDisYrkCo0utVmBoRqROw/7UtNRgMUux4fX2drTWFCLur7bkaf0yv8A+ieUUae7fH7r1WwLVt12q1Yn9/f8Z/6pAA8W1lYjudTsaDmgB9X22ziXFAM/uw5tozEh2fqnGnMtC16FTAYCD5SNUSAchVoyj5W0frDRRZI78nTnomQB8LSZfENtlt1QQ5LsDhioY6xDU/w6brSxOBPew95r7VauVRAPZYHHXAojYOW7bWGHmvPsDgAP5skr9qS9FB1iKvTiJ9m+sfCYASMUW09RLAGE2lhKuRClwRU1W2YEQEWIFJ7c05YTEiMlkymiqo8vccW4WprRARGUBq26jqBKrDuX+JogoaVSHuR+UuAQl+EZEBhnMwyCrSrWciCEoCKyOutGnE9ORF36FX6j5/JyErx5XQ/dx4PE7Vv567fTBGXBMHZ1FBW1N9YHbgs2uAiphWToSvtXrD4Ejq7EQgt4ZsqrI3GDVtA/vr82vrLSKydyyJs9da3dbfGQyezq3RU8ZsTSaT1DSoqLUIHFXunqr+RsITZJyL4M8rgJIcamLjPyZGCAaxJtW2+TCg6LI2/r6CitFoNNN2rKDXXvBfnwMgVeZVwJe8BFdJoU5b1fZv3WM2UNtLgnEVStd2Sn2poOQp9gAQ5+fnyTR4blM34knEVMxLBAy8sBHJ075sbGwkc+n+tRLEzhpX6Q+8OdqzszHPgj1VGGgVukdaF3tZWWJFTz0fRrIFet1T1ZYBa4AqkFQL0hrz2QN/tp5igH0G4N17ZSStLVtS+LCjKl4XoxSRgBjmz95V9pa/s+3KKDYajQQj8gK7jHjSoLAhbCkJRH3ha22hOYah2+1mDjBleXt7G71ebyY+X15e/lgxUKeX7GnVeymqK/v9064PvsUzHA5je3s7D6+KmL4AjHHVY/BrQGFckiS0XMdQBfia0Bg65FqDX9UvVMqcwWI73CPjRMdJIA6Yo9TnzP5hcOh54OW5IwnUjnn2MwzHPaDZI6ZJQfDk0MCJ9eJM7iniKeFWyhATUM8QENB8fwUrVfApUdU2RtWv1NaB765oXSUDENY2hsRf+6z21X9XFgs9jB2xjyhuScz91cpBsK8amnpwkkSqyhIEqqaBvboHz+p3JdRaQVZgrK3Alup/A+W3t7fZ1gFEgIO9vb1oNBozbzdFTaOWtS0qW6CiYwubm5sZyLRI+J5kA/zVMVu+Cvj2+/18waaADyBILPZO0tYakSSsC1urlD4w4Oe03qrY0+9V9tX0DH/1HdVH2J/kVZ9xMBjE9vZ2Av/KrviOOjEitkmq/BT4MgDw+Pj0sjeaNsms2jSfqfolQEdr0HpU5kS8EDMXFxdzDF388n21nVeZ79o+E7ewCvWIh3a7nexKjVX2nP0BMUCNvazTXlU3VvURbFprzP7WabbKeAIJERHHx8cJksWkun+AA/+3h63W0+huRKStXl5eZi7QquGzzWYz20KVida24V/+oQWs8VPcd16U1qX2sTWpgxYVOHU6nRnWkr9hQSs4VIhVZuvbXB88g8LQq0LYAljcq6urdGLBQPKAmAW5SnP7M1V5dXzVMMeycRa+Vv+VLqz9aYdFCRicQXKqTluTYA3GEVNRLOOt/c6bm5t0WMHB80tK/rsG6kpjYiEEOwj87OwsGRwOAjCphKr4uO5TFShWylmQkKwkeMFT4BfcJJ0KdOyDZ8CkuO/KYqkUnOkhaLhXQbjegwCH2bGelV2p56LoRQ+HwzyATVA1xhgxdeLaewc07F99KZv7qW3K+mp1P2M/2FGlpiMiGSwJst/v58slMVf1ZZH26Lk2RotONY2N8fy1lVRBvQrbiZl+vu6f+6yAzj56xurH7KQGyKo3ipgevia4+hwsXRVSV7tSNWLqrIVEXxnSqnere4uVeC7ori1en2FCwj77PP+uGi/Jr36GdX/37l1MJpPUZrgHIOl30kpJPvU9N34Ww4HV4dMAeMR0igjgq+0AZ/KwZ8lVoSZZRkxbjNbAxeeBiNrGabVaecDac7arAn+x5/b2NuOA9r57F/sVUuyzxmifI3ZgnquQ2r641+d2w789s3is6FHcAa9ArsIaiK+gfHNzM/0n4gkA1Tem+5zx+EnXWOOf4y2IkIH0yuoC7/yF/WPhiWtr+7H64U+6PngGhQDruREKugzff6OqbTpUWVXMEdNFrCIk5064qoHajFrRcQIB+OTkZKYHzOE4brPZjJ2dnRmR0urq6o/RzHqLPh9alswZKwBSmZT6DJVK5HTuaW5uLsdUBU8Jw/fPz89ntah6U31V2pUAVrJVpaFXa6JUvavE6qRDq/U0smqKQAvJFNFzAZd/as8bjSthCoj2q7ZP7J3ghC6tvXI2hyaWUN1HbR/WFhsGSYIFAGsCB0B8n8pmfn4+j6BGnWIV5ufnc+JAa6VOakhoAC06t2ptgCeMwHg8TkoXMK+tpcXFpzN8ACKTayrRCsw9P+re5wJ3ETEzxVD3r7YHMEqeS5IVLO1TZfq8R6syPGhn/iZRSMDOjKispMk1n80GiUkVCrXlWRNZbX95Nslvbm5upo1WW8RVvMmW+IW/AzKsXwX+EdNzRbBlChlC89pusB7Aa8R0lL+2BxSJbLjVetIveTcQLYJ1EAdqQVZbDuISf3VcQgXrxMM1DmAQsbZVGF7P+GDjtQVvn4BF4Le2gCtYAfLZmauuibZU3Tdri3m/vr6Odrud8c5eVyDiZa/8n0/W6UCgDiO5sbExcxBlfet1o/H0nq6vv/46+v1+CsEr0BVT+UHtLlhrtsc2gZ7KUvGzKiGw9t/m+uAZFA9aKXqotfYSGV2tgB1cAxHq4wlAgmntb9sQf1aNvqJsCYaDRExP2BMYaitKcpqbe3oJGUfnFNTSGAPBuNKkxFWeu9/v55HKkomgJRC4pyqyZJgMVl/XKbOSaF3T5eXlrLJqlS2Q1wDAoU0/zc3Nxd3dXWpaBFhOwalV9JzsOSCtYNC6NptPOiHOHDGd5Kh96krHW6+VlZXY29vLA54qu1b72P7NftyXXjqhrImP2q6rFTYnFkwrAMUe1ICuisOgqfxru6+21eiJ3L/ADJC5L8mgtqiIdNmqREvw2mg0Zo7nd/FJzI2qjo0I/s1mM89miYgZX2MjlTXgL9as3W6n/4kH7s9z1YJCcldp1tYsX/V5YkBlQARnUytLS0/vpaHBqGCPbbA9//j+iKdJtSpQ9DO1DSjZKbCqLgnT6XsqEOUL1tYzRkTqJLyt3M9pCwAvaH6Azj5gHzc2NvKejM+Kk+59YWFhZvqM7btPrdDKPNYJSc/J9viB5xKPa4LUcnMflQ2r4JE/KjafAxe/xwbr8AJwrSVa2xe+twJKLBMb0Gbnp3UCrAJ38YzdVR1SBcsYFbYhxymG5ubm4uDgIGOIl1TyNaCGn7Hv2qqujC178H21JcS/2Bwb+7YMygcPUATpKmpSpVekXhFfFfnZWPRYZVae61ZQYQy3AhJGD5X7fIZYHUC1KnAQLRrXi5gVZXHITqcz85ZYP1OTGhCCOiWSc7/od/9fNQgcY3V1NdE7UZw+Jcd/7rj+vNVq5WvoMR4ABuBkHwT12mOurSr7WB3WCaMCq2RX1eYVNPq96twCkGQqSdTW2e80aaB14DlQ0BI02+p0OllVuqc6DSMgA2tYtNrW0FqMmLJ5gEFlwdi1IGyvagUm0Xh+NLa1Flwri9ZutxMUV7CCras9fiOlEiq2re7LcDicGYHt9/sztlyFzfbAoYsS7/P9ZVOLi4uZOAW+CsDm5ubyvUO1V17bu0Dl8z3gixWc1M/wZ/WUZDoXANE/ETGTLE0K2sfK+l1cXKQN+mzP6Rwg7TT/Xe231Wplmw5b47kASOATO6LyB64xrmINhqeeWVSLJcmU7sTPV50Xm6+MHh/AKImHzWYz16tOjTnTh90AnNolfNWfA5TAQG37VjBRY4PE7Z5qxa+ArSwKUPkcLNdhgwqa+YFhBpoj9z4ajXLyCuiqrZ/aHWg2m1kcyoP8Hqtr3diHwtZaVDsFgDBimKj6lvrFxcUUvLtHTJ6Y+fDwkPYnbogJ7Xb7W+X3Dx6gVKexsQyqOkCdWqkUon/8ThX8REwDHkd8zqRUpMsAbURt32AUGLU2h7MLODRBqSSDTfj5n//5mRP/gCDAxPcKQBJw1YQARp5B4PMcnNPfc8IqFpMcm82nNyi7jzpmGhEZBCUt+pXhcJgHYQlSgkmt/vWnOefzoGbc1f6osDyfc184RF2rKiYk2BRMAKiIp4B9cnKSGqZaRdffEQBqwK7JdjJ5OgytUvWCRLWTKki0J3VSRrAUqGovFztze3ubFH5tJ/j9ut6SpGeph2EJ3sPhMHq93oy/+e6IyKDPD1VcfrYG3MpAAR/E1GzD82EwI6bjr4K9NbSPz7U8lfWsyURCq8WHi18CSvUdNip9e4/BE0+eAxmHA/J3TKnYozUosNcEzM+AgPr8nqHVauX6AWHaA2xU8SVhiAESnXjFvgAu9thsNuP4+DgTmzYA4FWZGb9Hu6Fqrrbr3/zBFFdtcwEj7gG4ZWsKMUDMMxmnfs4w+zxgR4y1/4BQ1Zrw3Vo0sbVqo/UFoT7TyeDuUWypxRgbqsMX4gCwKL4CXb7bffLPOo3GtsQ19/R8CMI+KXa9oFb8ZF8K1Y2NjZkCZ2NjIwELRq8W0VVsjdVXDMsvk8nkH5938UTEzLQN4xa8bJ7eZaMxPUyttjdqwEN5Sz4qeIFTBeT7VF4QagUi6DKAQ9C0qZJ7rUqBJNWIBMfpbm5uElDoj9ex5uvr64iI6PV6mdxRgozQJQBzDmALfacHX1sTtb8qQXQ6naS4jVX6TvqT8Xicla6qgTBNwBF0BAkVqf1TiRixM/qIDaqAo1ZVHFk/XMKrPVTBBqCtlZ+EVMWztVUgOI7HT4cVobAls8rEdTqdvMc60YUdqbZgn7XZBDJB0Oc/b2Pe3t7G+vp6BnBrYQ1qG83fYcxqtUmkaI0Gg0HaXG2bVEGpCsqa+D0v42NnbJH9Cp58xudjDFyqNb5D/8M2IqbJsOoS6kvs6h6LE7W65qsAvATBfoEtQb624YwFWyctswoWxSA2V1mh6v8YL3vku/2ds5yALfdQWyteobG1tZUAdDwe5yGA3tmjgKm6rMos1URc/bUe8iU51bjJj6yB5F/fn2UNgCBjr/UexPaqgXmuN6o6tPpz4odCVIFQW29VK0LLpThjE2L38zYbRoa9A6uA02g0ysQO9FU2TgwGdisAoVsUc2vRVguyqsOKiHxJY9UbVdutoN+e1+9uNpuxubmZ7H7E9FytWgTwq0oI+B7xxfp7nhpXftL1wQOUWpVFTFXzKmxBuNKzfk6QfE511lYGZyIgk9BqYuKEDNsG1UAs4QqMqFNsQdUPqH7d19zcXPzmb/5mHsTmu2sQw8g4sfL4+DguLi7i6Ogoj56vSaMaJJU+5xEMVL2ofg7r+zFXKn3jrval6kCGw2HqYSRyeh9Jz3MIXto87l1CW11dzTXTMhMUrbvkVH9XcuRgz1mfGqw8Y612HGhUvwvLU7/X2qg8tGAqMBVAfa+gao/YkP2oyc06VfbKqwr05qsgrt4v+7L/gvZgMMhjuSMigYqEUqvf5+Cktj8AW2uoUq7PKPHUlh8/tJ/s3ppIKlWfUyvrWrXXFo3gX23b+tVn4qMAe/Xb2s6sTJt2RLWfx8fHuLi4iEajMfPm2IhpG4fv12m9mkT5k6RfX+pZNTPW138/Pk5P6tTajYhspzkHpSYgz1nbwBJqo/H0nhZvncY4KByqbgJjU/+toMGG8BHP5f6BDglWVV/PorF+En5twc3NzcX6+npExAw4E2sl89rCA0hqm98+VmansgNVL1aLDkUY26D5qO3DOoRRfbu2hmsMVVTKG2xCkXp/fx9bW1sZR+sZJHd3d7G9vZ2xQNFaW1Xb29tZENRWlb1l6/Ka/Fj9WFGEvZN/qti3ss6GDNjBt7k+eIAyNzd94VPtkz6vpgVoxsqJbEDENADVNoC2Sb/fz/G82rO2WRGRRl6rckYrENUWTaUSVRz6fLVf6e+ccmm6otlsJluiXQFRHx8fx+npaRwcHOR/c17B5nn/UaDgRNZKv1f7ifFJ9lX/4b5Vif6/PldEZJ+WoK4mjnqGCgak6jUwN0bI7Yn1F8ztqeeMmB5YVBOqpC/ockKXJMG+KltjvQAt+yvo1crUvZ2cnMywcs/1O77b+Qf11OBm80ljYnycjQpmKhb9YYHVc9b2Zm17Vf2Kv0PZA/jAGbuuDKJj2QeDQSbU2kKzLhGRn1UDNyDh791bnZapNsI2JQ0+Zx1r0PU7tQ8O5Nh7yYxfVj0HH7YvtXK+vr6eGR2v9mAtAZrfSazNPypgqvqz2pqtwBUja/+rnsI90DBhME1+8SmfSW/kmR8fH2eqfbFBvKoVtLazvwOSrTGft84RkfGDf2OqOp1O7lnd21qBAxwViLjvyoiKcxGRNhwxy75Zf3aA8dQap0HBbtgfQNHv1nbu82lQIE6LtbI21cYAD/vpcvaMe4x4AhfOL7FHmKh63lQFQ+wb6ACCFCdsqQIUsX9ubi7a7XbuvTYeVs4+AyMRTwXOmzdvZvy8AkS2+tOuDx6gCAz1lDu9eMZQ+7AW6PlCCUQRU7qwUt81AAqeKPCKNm1+7SnXvmJVetvMyWSSCbvReBJZcYJ6KmJNjJKBZ649WPdzeXkZJycncXBwEO/fv4/z8/NkRQQXwkH3VIO5BEyfUNXZql+ngtZqlbAKsgemOAIhsPe8COZzc3OZfO2r56lr52eJeYG8ugeCJgeVUJ4DKftQqefn1TmgKHipmhqNRr53ovawfabj92ugt3d+XqWIiq42xs4E/FqlqLokdaJh0xNVaAhwvXnzJilirBi9g/aSe63AV5VrTSpLobqSEIbDYQZVFZcE72AniZf9tFqtDIBAkktCwl5V1oF+QLVZ16O2BOpn8Q1JQWKqAKnaQ20x1Om+2ppiv76v2+3mOkVEPo99VkSocCOmUzHVN62HdeTrtapnB5KugkYlW59Bq08x59/0AKpvcUXVrqUpcbNp3y2h+czJZDLzDi/PikWtQAN4ur+/T3E1FhgIA1L8U9ugbEmMpjNy1UKyssMKDYUO/zSV4v+rCJj9i2XVzuyRdq02xnPQWVsq8lZd81okYDrFEbYJPJ6dnc1MMNViDlvBfjqdzgzz+eWXX6YI157xCZ+xuLgY3W43bX99fX3mOA+xVttQHnG8ATGzgrGypLUA/EnXBw9QON/c3Oy7IRieoGlzTOJExAwirnS24BARMwEPE1Kp4VrZW3SBsBq3z+BwAgrDajQaWd1IFgIB4BTxlIhsMKMVLFS4i4uL8eLFi0w819fXcXl5GZeXl2n8ULb7rhqGWhFYWz1zR6M3m804Pz+febY6OkeP47kARWuDkagUI/q70qM1QTSbzRx39v8RkU6hZeTzamKpmhJBuu4R1q2yXJWJqS8wA/Iq/Q/UqbaAqbrXo9HTdIzvc1++S+C1b0avn4/m6q1HRAZ8lDTBKFDpeWv1PRgMYnNzM+8P6BPcMQDuSzJGXQu4wFbVPQC09v78/Dx90r9rS6raMFvxvVpBEbNtV4FUALSOdZ8qe1VbULW3D5RU7RV/BgbYrft2cBUfrOP1VVQJ6PtOkz2VdfLzg8FgZhy6tkF8t6QkjlX/508+QyFWkw0QBaBjtxRzleECEuwxrZs1UAzUk7H5OFEmAHp3d/djgmH7qWXnzI6rq6sZNuR5K1R8rSxvZQhqIQBU0x6JabW91Wg0cpKsPr+YD0Sxg8pYVaaDvdh3jAHNV0TkfbA3rXC/v7a2lr5s/zyTzwXOTk9PZ9hS907IzL5ooWqh4Kr2rZgAuNkN5ruCw06nk+Blfn56xpLPVCCNx+N4+/ZtAhR7yf5rMfaTrg8eoDAq/2632zPHtjPiChpUhbXyY2QASG0lcAxBs+oTgAkBVKLhgJIk40KzRUwnBqq4S3UrCTJC1RTgVWl9SUHgXV5ejrW1tXjz5k2e48Fpbm9vM8HrtQogHFOVyFi1eCIin02Sipi+qG9+/uk8hbu7u7i6usoqok52AEEcyFkt7XZ7hjaVrGhTrL9KTPD2tlyViDWLmIq2anKuNDCQ4XNqC0dQFMzZUaWGrY+1q+LjCkzYIdAgsPk79qRiZX+1tSS4VuZChebzJX7PWicgIp5e5a7FhUkxkQCg8QO2hp0bDKaH/wEtg8Egut1u7ktNcGhgIkw9aILS+mK7iNmWBX+wroAfbc14PM5DwNiDXnstOPx5TVhAEZsE6hQ1vrdW3vauvjy06pXcH8Bjz2oVLs5U4WgFwoqUqkeo9gVw8SGgEUCz//VYAwlRjBL/VLeYTkmVjiPiqY0A+HmlB5thVxiXWkgo7CSg2tpxv36eP1or7Uwnn7Inz+Tnm83pFJ+9tv9AEPChUHBvlQn3meyejVr3qrWzFjVGVdusrFNtG4txDty0b1hkjDg7YrcOXXtewDUajbi8vEzfYDfVZkejUbbs+HEVOddWIHAspijgASK5Ql4gkH5eKNWBBu0sbA87qHGntrF+0vXBAxRB0INX+jYi8s8rqqxIt/aiJbJaYVf1cU0sAlmtoCvt6d+1lcM5BGzfCXzUCREVOdqdITJAhsBJ6iSDCmNzczN2d3fj1atXCdxqkHbfGxsbMyNsnpVhaykI4KPR09uX/T2AULUCo9Fo5myNSvXao8osqNaqwJWzaoVETM8lUQEKArXV87yKr1Qwhsn3ctzHx8d88dz8/HyOQgOqAo6kjZatdCjANjc3fYur5C54ov0FY7bp2bzJVdCpL06r68V2KuCtOifvXHEBpwILUOKFhAsLC8mqCMD0RgAxwFiDadXCqASBwgqgUPJoYvtsTYEJYNibqCUwCaYyXIJjbc9U5k7CBqr5N18CGCsQALSxIuyFn/I3exgRaUO1XcOP/b3v9CyeWxsPM0gkyZZcl5eXM7FCa60mRW1FnyshWV/3XlsbFaC5Z9+xvLycjIuzbsQzjCWdhlHW8XicLLSKGsCOmJ5erS2A/ZFwq0aI/Vadlvv0fNba81c2w5/XogZIxCLXY/kr0wZ4VK0VpoXN2Uf3WRkYDFMVmD8XVJtiBFzYoeJWmwarSucl1mBGtVW1EgEOQAdAJdjvdDqZy7A21pzd87eqDwNQagungkE+WIsC66XVyGZrbPpJ1wd/1D0HlRgrre/P6mJGTN8dgF5kqFVAW3ucEpgAxYg4ParS79QWjAABOQsIlaLEbvj5TqeTCXAweDru3phj1U+okFQt/n99fT3p/fX19VhfX59563JEpEZEgq/KawnGukVMj9mubxWem5vLcxFUVrUFFREzwURSkVSN4kqWgkNlliRjI9fNZjMrvbrPEVNleD0MizNUWlwC5nzATAVlHPR5C8T/szXnwrAZQEaiUIn47tpykJSti3Xy5y5BEsCz99o/qkqsQk2Ude0dmicgttvtODo6SpH5xcVFMgCAbk3Og8EgdnZ2MtANh8O4vLxMAOeFZAsLC1lZ2zt0suqv2pm9Yg/201q0Wq18pUJdf0WCP7M+tVL259X2MRiuWhVjJ6oQsoJp311boGxdwpQsJYr6nT7PCDj7k3DYyM7OTpyens6IRz2H9piDuLCg8/PzcXl5OdNuxD5INisrK/lmcRoQGjJFR6v19OI6b7bFClfgo/3Y7/dn2CxsT20ViF1ikhhatSKbm5vJLDw+Ps7EGElNXBgMBglkaSLY1NzcXN5Tq9WKy8vLBFpaWS7HE6j2sZ7+W2tEkq8tlef2JV7aez5HrK4o1n7lTxiMKvo1/VWPSBgMBj824l41gTX3mYgCWPm99ZKPagvbml9dXWWBs7CwkD8PhNtPa1A1hECt9mDtPmiHiruVbf5J1wfPoFAvQ/qSAYNGa6LLKwsRET9mUM7lsMBATu0NYweqOrw68t3dXQZQVRx6UFBiIIxQ0rDpvotae25uLvb29jII1jYG469iL5+xvr4enU4nNjY28qwS9CiBrHXz/4yKY0jE7lf19PDw9C4VLZ77+/scZxuNRnF5eZmfHREJBB1Mx7ABE0m80oFAmT9Hx6qIaD4kOKODvg9SByiBKcHOVQOMfeP0lf1xn5JrrdrqWtYevsBXR0IrcLLmKlXBwj57zueVJbv7/ve/n0muVt61DSlRCoJG1iOmmqnLy8usruwnMCIoqvp8Vq1Gb25uZo5MX1hYyAkt6yuxsKHhcPpmYMmIjd3e3s4klRpQsWHWwne4d4xmxFSzYG9qEWP9K9ig2WKXbE2QrcJcQMVnKUisawWu9rmCKr4VMdUiCPrOFZLgxSJsjKk64BMo8Bz15/zu1dXVzFlA4prE7JBIe8SmKwPijJKzs7NkTx4eHmbai1XvVROqxLa1tZUxqrJXmAZ2ilUWExQ8FdRau8pUiR18ExipvsMPKztamVnxQpJ2nkhtI2tNKlDdn7xSWxkSPL+37hFPZ1ZVZrsys+IuO/RZAJ5/s8lq1/v7+zOs1dLSUhY1Bi34gnwGrGOhqgCXkBlbo0Cu6+WFpb7XHteinpD8p12/Jwal1+vFX/7Lfzn+l//lf4nBYBD/5D/5T8a//W//27G3t5c/81f+yl+J//K//C9nfm9jYyP+1t/6WzN/9rf/9t+OX/mVX4mvvvoqfu7nfi7+/X//34/vf//7v5fbiYjZkb/ak3vOqOiJ2wyOXc8cqQAkYiq8kiSAjIjIoO2/a5UlgQo0tfdcWw3OJ2Ak1Tn18SMiwUtFphGRFfT19fVMAFQJPddbrK2t5QsAHx4ekomotHhlFQCVGmStn+BdGYyq5anBQ+JwH/WU3Np+kaSsewUN9kRlwJH9vXvg0L5XxVpBBXBSf7YCAPdb7QB48rlYtQpyKvMjEFQ7UWVURi0iZgKFda+9Zzahted3tS8uLi7ytNvaGlQlC4R3d3fJcJ2fn8fm5mZWPNghDJl1W1x8ehGgZz4+Ps42lPURrAT7Sv0uLS1Fr9fLSpfdLy4uZmLG5rHBup+SRhVLVz91X5Xip9mqonnBvYL7apdYSECk/r59pgtA1UvyEpHEaXqGnY9GoywyJCe/aw3ZosoaSABC6prXtZB0nLxcQbAEh42trGEFVRK6z7q9vY2NjY18kSO78zyKOODy9vY2Njc3k+F6Hhs3NjZm3rBtzTEHWD12DxR6lspGSdyAE1D0vJV/eXmZ+1HjabPZzAKyAvjn8cx31ZaLdWXb2I1qf1W36PPYa32PkWRufTCp/MOe+jzMoqKHvWJL/S6fF4+149hpFd7XQxmbzWYy1Pa70Zi+bBZwZud+115ETI/VAMB9lraUwqbb7abv/7Tr98Sg/LP/7D8bw+Ew/t1/99+N//A//A/j//6//+/4p/6pfyouLi7yZ46Pj6PVasVf+2t/Lf/5q3/1r858zt/9u383/uSf/JPx3e9+N/6L/+K/iLW1tfjFX/zF+PLLL38vtxMRU0Hncy2HhWd4xhAhT/37GvAZmCN+I6YMC7oXWmWoDFfgMXZa+4J+prIo7ru2njARKtOI6fsyNjY2ZsZWBWLP6lKFqqifU94SJBqvCgyBPeuhv/u8BQFoMUTOWvvq7jFimmDRlAKpXrX9EHwjpuJmSBy4rNMJ9t3vQuvb29vpIBKdBFUr8EpvchiBz37ZB+xYBVGCu8BgPSR6SaACneFwmO22mtRUKsSIwMnztgxdBpp7YWEhfvu3f/vHEiRmsVZz9oOdAlP1zblnZ2e5D2tra3FzczNjTyolQRrDpUJdWFiI169fp70By54RO9VsPgkdAcWISGGoNiWAXIO0dbOXFdAAK5UJ82cCOLuogt/aovFzQF0Vp1sDRY1kvLa2lgWPCS7rNDc3l+0cFa5nwGBgrUajUQq9MR/0KFg5EyBVfA6AAPb1v+29++Wj8/Pzsbe3F+PxODqdTtpBHeHd3NzM2MnHiKZvb2+TMbu8vExGzNQGX6J/sJZaWOxNAq9JXeJnb81mM4GhZMr2rKe4WQsRvqq9Zc3te41R4oT/B5gUD+4fA1CTb80lEZHAv+oWxY/Hx8d8pYR7cFSDfOPvMEJAUp2krAMIWB96GsA1IvIkcv/PxhTn7G9vby8HDupBi81mM8EJewL0AG3gW26wVlXDU4tV7bZvc/2eGJT/7X/73zKYRET8kT/yR2Jrayv+xt/4G/Ev/ov/Yv758vLyT2RD/pP/5D+JP/7H/3j8Z//ZfxYREb/4i78Y/+v/+r/Gf/6f/+fxl/7SX/q93FJOEhj/qwFL0q3JHnhQwdTkxpkZWk16NXjReHi7r43wnYJwBS/upTIGUOj6+nqK0KBeAr5Koak4UIymfVR0QEREZAU0Pz+fFQznUiE5JhuV6jM4nMRbNRtoUUHW31XWSnJ6nnzruSr17yHxra2tOD8/zyBS6VwVeMTsmKG9FZRpFQS9Gqxq1QpYVMYoIvKZ/D5dhD444BEROWbLtlQ/VRsB+HQ6nbxXzspe7YMgaz3si3uoGhSXhCyJ7e7uZiB7fHzMNWWbetJsT/Crycw6rK6uZvWFERKcvHGbnVUQpq2K5SGYBBwwE9ZXW9CauXfrAEDzS/4jCNr7mngq1W6PiMirndJP+LnqnwBCZcv4SGV7iBbtK1urTF63280kJi4QimIXsCjAgs8w4ivZNxqN6Ha7+bzWWxFm/xUbkkmn04l3797NnCfEfiRJMVJrhV3t7+8neAXeq+aOvmg4HOZ5N3xLzGL/lXGqEyPuEVNTtTXWH7ih3agsmj0Wc62Ny7MA2HzWvT6v/IE5sY24nO3xl5o7xuNxxmWFW5188ZzumbZO3GYHFZz6fFNw2sBs0D/um39hM+thi57Vi2nJI3weu7AW+/v7OR0o/2B/xBY56OXLl/lz4r7npJuSf+vz/qTr98SgVHASMR0dqjR3RMQPf/jD+ON//I/Hn/yTfzL+nX/n35l52VhExN/8m38z/tSf+lP5/41GI/7Un/pT8Tf/5t/8vdxOREQmXL1aaFsQYZBVU1Bp3xoYOY6JGCrsWilWmtj3CKhGXesmSgLPv09wcgED6DQiRkH94uIiA6A2B+eNmH2Ne006kLUqmeNVLc14PM6zCAQfQaAelEYDsrq6mhWNkWbvazAR5BA2jBQQc3FxkQbKaSF37QUO5s3LlcJ2/3WKqgY+I84CL2SvYt/Z2cngq2qqLAw7kTwrxS251WRoLwVCVbu9o2avrwColHPVeFR6H5OAqel2uzlVU9tl/FL17veNj/pstlz9mO/SVQDR1suzVD0Wn5PI2bFkNhgM4t27d8l81aqqsh4AuGd2n7XFBgzUShVow1LMz8/PHH6oYltYWIhut5ush4Co4gPWI6bnqkhuETHjA+PxOAGyPbm5ucm2Wh2DxcbxPS3YStdLeu6NvoB9s1n7I8no7dcYJq48Pj4miHSxASDCuCtmoNfrxfLycr70E+CkUVHx27N6SKQiqRZl9cV7ETFTLPBPv1OZFi83rX7MPtmMAo3f1WEF9+YCFgHVelYOpqeCF3ZaBexV1yEu8EW2XuUDdaKqtvMrQMQWA2J8ok5muc+auzApDw8POZ3H3913ZcmxUtvb2zMgzjNUQIN1IfYHoiIi9vb2ZljguqZ7e3tZvGxtbeU0FxDCPl2YRnmuitR/0vX/aYrnP/6P/+NYXV2Nf+af+WfyzxYXF+OXf/mX45/75/65uLi4iD//5/98/Pf//X8fv/Zrv5ZtivPz83jx4sXMZ7148SK++eab3/W7qNVd5sgFs9rPlGBUX4yDWLXSeoKuwGijBat6yFjEE5C4vLxMA60COA5tgwGCGvR8BmNk/CpEgV1AXVtbi/Pz8wwI+qcCp9/z/55HENRCqtQuBxyNRnF6eppJvlLa1SAjnvQu19fXWWUxwDpZUZkiFaAAKbBjMfyZz6CFMHJZqyzBS7tKj3Z9fT2urq5mqkuBvLZntCsE85qM3UPElOL3HFX4VnUWQKSAS99U9TIVhAwGgzg/P58RJw6Hw2TiahtCjxkVTktifzEplPrNZjMnA1ZXV+Pi4iJ9wYinhG3Cw/M81/8IWlocDw8P0e124+zsLDVLnk0bCdiLmG3LsQlCT4EJY0isubm5GYeHhzOshkRrvwXo2krFJmk/ANf+qUVJxPQ9SxJ4ZSs8N4Gf5/DdtaVVAZvELLgDYpWdq0CkgqDRaDRzjLnnx9xETBP83NxcrpeEqR3j++wljYHPspZimhZOp9OJdrud9jAej/Mo/IgnBhab2Ww24+TkJFscg8Eg21ImviJiZkpkNHo6lFAS7Ha7M+0ObC/mQmVdX0VRNXTaGTVW8ntXZV35P4AjBteY7zPrQW32yHdFTE+8ru16sRHrJi6wUXvvfrFStRCq2pXaaqy6FYDH5z3XKPnOu7u7aLfbKbGowGQymaQtAXXyp+daXV1N0I5ZrG2hjY2N6PV6Wai5VwwkggBT45mBNOdc1Rj4ba5/4Cme/+6/++/iL/7Fvxj/7X/738bOzk7++Z/7c38u/vyf//PxT//T/3T88//8Px9/42/8jbi6uor/6r/6ryJiGijqiFlEzIy5/k7XX/gLfyHa7Xb+8+bNm4iI1DUIXAKGpCs4qyYlxpWVlUSxKHyLXnUbDLwaecT0VNgq1pPY6j9VlV1/l+FETCcrIPFaud7d3cXGxsYMI8Gg6xQR0ECI6XvRkp1OJ6tw97q0tJStJcHcd6+uruYBb7WXGvE0OdXtdmNzc3OGWcAscdZOp5NJFngyTqiSq+JQTing2i/rooVCQCeYRUydmopdwrW3qgFJXUCy71X4Vtkdew7cYEXqq+H9HDtgO/ZXMJVo2Ze+srMHKiXa6XSy8ouIBL+ef39/P9kDQM4bntmmYKDKJ9JzorDkKQDVSRQ2CjA5RRZArqPv/mFXtDKSsO8HgAVDeygosnH3js5nC/bkuX1oX1rvWl16nqoHATxpirAAih2gCxtaW4r17BCVNPBaJ66ASJ+tlQf8CPIrKyvpZ9aGTfOF0WiUugVtQz5bWcdapdaWAl3c1dVVtoDqG8QlQs9dx419Lh8wFfjq1atYXV2Nzc3NfDcMMIE1EJ8BJ3sDMCl2nJ2DJbMnteWmSKytoxozMCNsYDAYJOtqIqwWkFgWDKj9s6bsTdwDbBQhvut5fGezWH2AG+umxa3AU3yIDa1WK4G/vWY/NT6PRqNkUwChi4uLBKeNxvQdaoCBtq33nz0+PiZjVpk7e/b+/fs8fwdo3traioingZmqjeHzj4+POYjBR6vwuJ4L9W2ufyCA8tf+2l+LP/Nn/kz8N//NfxP/wr/wL8z83XNk1Ol04p/4J/6J+Ht/7+9FRCTNdXZ2NvNzZ2dnsb29/bt+57/37/17eVz75eVlsi0VtXpwo502eW5uLvUijIFxATQqC4FHwgJOJN6qpJesajDjUL63qpqr4zK4Sn8LhipgTiYACgCCmBflOcdkbW0tpzQkQBSiA4EYHFoaUGSUhG739/fZS2XwThmsoI0DeMmdJMgWGD3H9N4Pzoi2dQy4f1TBPp/oTKtKhW3/ntOr+vfaJZxVYpC0JKYa+AQmvVkajIWFhWyVcLw6WYWhq/SxYFJbQ/UgJs/qhFVJRUvPnvMb63tycpIMTAUlCwtPo+WCMfAn0GoD8R33ihWpGhBUfKWoX7x4kX8vuNcAVkWhS0tLMyyUdyydnp5mggIYUNBV/+He2RAaHTDETGgBaNmwUcULn9rc3MzPB4CASaADq2EiEMCr7KRx+ouLi7i9vY1er5e+WA8M9Hv2xbWwsJCJTqtD8rEHkqG96nQ6WaFi5ar2ApAQj5znUts64p69ZCPeMu4QPaCLT9SzVAiYfYbDw/gn5pEuB6MD7PIxrXNFZV3n53oSLalqCz6HH9d2oPjtyId6zIM1EKPlDjGQ/dQkWsfera1zVmpbnU1GRBYOQCY7iJgWpJUR9Fw1Ltd2fQXDnqEWJU7+tddzc08HcD5nzuvrI7rdbq7x8vLyzCmy1sHED6bXuTJVJiEf2Tv35jUQ2vhsjn18m+v3DFD+h//hf4g//af/dPzX//V/Hf/Kv/KvfKvfeffuXU7FNJvN+MN/+A/H//6//+8zP/O3/tbfij/2x/7Y7/oZ+qP1n4jIwMGY6jkZFeFVw2UsxEaqO6iSAfjv6ggMXwKUdAR0wZCRmaYQlOq/K31Zq/fnPdvXr18nG6P9gXFZWVmJzz77LBM/4OAtlADK/f19toqgXAlMlYlulpwlq52dnQxKDOvu7m7mPRZaHJeXlzO9c0lHsJScK1Ohcmw2m7GzszNTCQNuVVPid33W4uJiJh/skfUydXF0dDRzvo0kEDFNGJIVsCCg1BbG8zNR3GNlO6yl6k512Ww2M6BvbW0l0KGhwGJVsAHcEJLW1w+ouiSDejWbzRTHomv5wuLiYuzt7WVStj/sWFUvaQwGT+/ueXh4iPPz85nvqeLRWgDUtqZntIZaPvf399kulXwkUv7LryoTosdde/0AICDMhgFZCbLuERaCDdh7z1GTOr8W8Pv9fpyfn8fR0VGcnZ3F6elpXFxcZNKvExX182viN4FSQbFkgGnzzKa5rJM1AbIAq5qwgVHahc3NzQR6ddTWn9lPScl6iYsKPZ+9uLgYnU4nT13e3NyMjY2NLCw8u4KBHwDzPlMyqy0fQK/GZLZaD1jjL9ZcnLeGwA9bjojcT8BY/mCrQLBLQeZ3xTp+Zo0xOZ7FAZmVfa+MOyDBR/kgtg+wYB/u1T/1nrTnxDYsWcQT66F9ORgMUuNorBij5XlM2aytrSU70ul0YnNzM1nAdrsdEZEsqJhUwUqVIdze3ubot+f+NtfvCaD89b/+1+Nf/pf/5fgrf+WvxJ/+03/6d/yZ/+A/+A/i9PQ0Ip6CwH/0H/1H8cUXX8S/9C/9S/kzv/RLvxR//a//9fi//q//KyIi/qf/6X+K//l//p/jX//X//Xfy+1ERKRT17EqbQvOIMHUA8aqI0CXmAcgR5CojhMxZQYkDEFaYKg95RrcHN9dx0z9ozKu78PwPQKfsTDVRMSTQQEKGA9tDUxM7VVzfKh9MpmksBVSF9QjnkASFfp4PJ5hDKrT+Pm1tbVYXV2NdrudbRD3WgWNEdNzQax/q9WKs7OzDOY1uBuJ1jqq7RWBWWCRhKyTvVMZSxCSr3sTHNDB1hF4ek5ZWn/VZcR0TNGx5bV94HcwJCsrK1lJOnK+nvirIqkndaqA3Dtnp4mxrlVfASD7b0Hbszebzbi4uEg7qmOA7JEdPaeyaQ7YFdbFiDIBbqXDqx2osARuya/T6WQSYqtYPFW6ZKyytzdVL+IaDAbR6/VmxjAlLW0GQu+IyCSCMQO47TXw2ev1otfrxdnZWbKUDsHD1LRaTyez0hV53tpi0fasAErLazAYxOHhYQLIu7u76Ha7yU4R/UrAko3Y54WN/t/ZKKurq7G1tZX6BN9tTcQW4HNtbS1Bj6SF8VldXU0NWQXvRqI3NjaygKttvArMMSueq2ozxL1aUFWGrcZbF1ZDnKgaMxebvbu7y9gWETOMLVZhd3d3ZkIQQGDXWlU+o7IzNHNsSqygH6vyAy/cA5axK1UYjJmybwClWF0LnPqOslarlYCVnwwGg3xHGxsRI93T/f19XF1dJcNWtXa1bVPbor6bH5hk1Gb/NtfvSST7r/6r/2q0Wq34lV/5lfiVX/mV/PM/+2f/bPzZP/tnIyLik08+iT/yR/5IVnC7u7vxP/6P/2P8iT/xJ/Ln/7V/7V+L3/zN34w/8Sf+RI4q/YW/8BdmJnu+7cUIBF8Vd72gbwtHu1EV515tbQMZuuReq3aOUXUJdWNrdQ4kcQzJh+HWP3t8fIzT09OsNqH0OtLp+4CRiIhvvvkm+8i1Z6+VU1+eKBiNx08CxqOjo5z6YOQR01YdRAwICA7adI43h7wl1apFwTZJ+BcXF8mCEd9VgZW1B6hULQJgrRLdV52kwVp5O6p/JMXar3avLusL3NmHyuoIMPYb7ev3q+4ACKuBVdCWcLFf7ChiyhiaSpJYVP0Ah/aEAC85qUrv7++j1+vleCKb1/KUrBYXF7Ma0vsWmIHslZWV6PV6ORY/mUzFdyhtQJHNS+qABDuX4AFPgLbb7WbbAwMi2NXKU9WJ7vc83kSugmYn7klQV9Sw7XqInmpfldrtduPq6ipPyRU7gCLtyaWlpXw9PU0dCr6yG9W2AUGskvUCLvnqw8NDbG5uxuXl5QyTWtva2EKgz75ubGzkcxppXVxcjN3d3Tg4OMgYU0dA+UplWVXk9tJnAY+Pj4+Z5DY3N/OeAC1MiriIsQBcvB4CM6Zw29nZyRFn6yfJiRd1zWqsqgwbv66tB/GX0L2ypbV1CajU3IBpG41GWTT5f/9WcGAiah4QL53OCgiwb98jztF9iR3YUz8nx8kbvpf/eacayYA1kDcjpqx9p9OJXq+XrKJc4v7YgP1bX1+Ps7OzmXF19qM4ruLYn6Q3rVdjUiHnT7l+4zd+43cUt+zu7sbu7u7Mn719+3bmzZS/03V9fR2Hh4fx6tWrGe3Ct7murq6i3W7HL//yL8+chSD4oZ1sNsFaPbdCchGIIc2IKQVsgSUc9GTE7Ls3OGCdVqhtn4hpUPL/DM131ZZA1TDURFB1MCpM1bPE1e12k/K7vLzM46t9P0OpGgXPDOjU/qgWDlFtFc/5bEHKPpj6sX6vX7+Ok5OTHAsnDOXMepkShzXodDp5EKCkKXBjFOr0CtobqAGMnld11kASsU9spgopK7VveqsmS0FQQqy2zC6cO8ImKq1aK17AVqAZjUYpKr24uIjNzc0MRNV2BAEtL+LJKszzO7QOEdOD4G5ubmJraysDsmPvBcFKzdub6+vrvP8qyAVEsTR0N69fv87qjV0Rbz4H7lUbU8EPwKQo4X8muwAD4OXw8DCBWGXsBEz7IXlgp2qSMNGhOqZtOjs7i5OTkxxr397eju3t7VhaWop2u537VCt4fsK3rG0VXQLl9/f3M2wp2xRvJDRvvZX0ms1mfP/734/f+I3fyArYmkVMBxVM6pig0/bAaPg8e69tIU76u08++SR6vV4cHx9nlb65uZkABmilU8MA2O+aBL180udjXtgze1Kg2f+q96lAhl/YW+xXjcd8iB3RRxHDAxWYCQUBIFBjF5sBhvkckMrOAA1vAwccWq3WzIshIyKf1X3U+ChOAVDad1ihes7P/PzTEfOnp6cZoxRZNHraS9vb28nu6BJU0FZHo/meWPnwMD2l9ubmJr+L3QL4v/IrvxKXl5cp1/idrt8Tg/ILv/AL3/pnX79+/VN/xovs/r9eELdEFzHtDUJyta9XKUJOKYhGzM7fM7jaJtAaqe2ECoL8HuDDuFGUvovBQuaYl0olR0QewMQwdnd3s/XCIBiXNRU0iWa3trbi4uIi77ueKSBAHB0dJZqmW0D1YR4c8FYZIGsIuEn0zWYztra24v7+Piluz9Xv93P0++HhIStbLErE9PXeKnvUZwVMwJv9BSJrNYSZAKpQkvbiubjS97OViOl7O+xBbUtg3gQNAbRW2Wj4KvglGpasazUIZFc9hO+nPRG8m81mHsL18uXLDCiV4ZFUnrNGV1dXmThoArA1QJdxY/eO8Wm323Fzc5OjyBKA9alUM32S4CTgtVqtePXqVXzxxRfpK/WlaGxVYJMYsB8rKys/dpQ3/2IjfMQ615ZbTd4CsM/HuujVq8CBpwria4KsWh4/y1ZqcsSYiGGmzYgy6z0tLCzE8fFxFjjAM4ofuCFEVAzwMUyV57eP4luj0Yjvfve7cXh4mCBM8RYRGZ8uLy9jdXU1280LCwtxfn4eDw8POQk2Pz+fB1kqquhatDUxjdaKPYiH5+fn0Wg0crTVcyo06tprRWkpsDmxuAK8iMi47WesnzhOK0PEbn/9PTvkp+IDG8AoKGzYamVg5ufn86gNAnITOOzfPVWwA6Dxledtvdr+qbmP7yuA3IdnYNt1aKHZbGa7shaU7k9+klPobYyUe7UGIXbV0H1bDcoH/zbjCh4YfdVIAAu1hyxIqszqOQaVlpecASDOFPHjFFWlG9FxAiKjUyk1m82k7wVPgQ3diI3g2AyAGM49qXgklLm5uTg5Ocn79J4VokLB6Pr6Oj8PvU4UVZ1OYOj3+wmMVDJo0UoLY3UAEJV7s9mM/f39ZGAYftUTQPESn3ZEs9nMSkN1hz3xzJKsRBIRCersVRW+1urcGkdMqeca9CUqVaq9tuaVvcNOrK2tZWAyNgjE1MD2vL21vb2dfVt7UEWTtQUHxAg6KysrcX5+nglIJVZbGcA4MAIQOwSuMhe+a3FxMcXPgsvKyko+I5ra91hvrT2+xaf0qbFlnqG2qKovaVVgHNzf8fFxtj+tH4ZXLx5YlmQEZT48mUxS8FenRqyRJMEWa2GhSqw/W9us7Iz91CIDIKTdANrqqx0q04N1BHLFFJ8NzNTj0CVNiVa1Xdk+L20bj8fxzTffRL/fn/ne9fX1OD4+zjesYxbETsmS3deWuPgs2QMu1ge7qtDQgvS77AvItzcYg+ftO+CYlqS22+3pxsZG7rvWirVxr5joOhWEGbQHYjk2Q6E3HA5Ti+KE6OpLbAYzoQissYSmp9frxWAwmJnMEcsBTWCvtsYajScRK5bG27AJuzFv2qQKkgqkFDfilXjDtuRGDI085HkqkHr16lUWMPZXvP1p1wf/NmNGCvUz5NrDqwJXxqaqjJi+/0DlJdnWs1GGw+k7aypVWgOaAM7Q6ymKFSCoZlCI6E1AQGVYqVbBMWLa9vE7tVKnL6jBVPKG3Gv1EvEEYoyAYn1UbZ6rAqVms5kH71S6s45SVvW8KvXs7CzRNqr1F37hFzLpREzFpbWSeHycjjFjNZ7rNQAhz80ePGt1SgALpXx5eZk2UwXQEm0VcFZxHEDC7qpNSrqSjV6w56wnaVZGTQK6urrK951oJdzc3MT5+Xl+b50cIkyvwl+JQgCXsATG2paUQPw8kPfq1atsIQraWkZbW1tJSbNb32ndJUA+Cky12+2c+oh4ApO7u7sJEH0eH/TM2jKj0SirMq2kympJDIK0dcEIKRQUDkbrrQ0/U8gAqxFTIEOPtLW1FZubm3lyKx9zRIC9rxM+7qe2FgEMtq2dJs7wEzEBM6IaxchgR3yH/eTPKloaAoWS4kTh4H58xu7ubszPz8f29nb66Hg8jl6vN1PVA/j1uao2KWIqYBf71tbWchJPoWZPa6uecBXwAxCxlpioqjX0u2xLDLm7u5t5+3YFMnwDOwq01aMEaC3YHAaR4NTz23/TMxWgiQ/VHpaXl/No+Npy1KqUszwTcFBZWD6pIOC/CnJvtBejFL58LCJmdFrtdjtZLWy3ts7q6mrGUIUCPdFkMoler5dAT+yVe3/a9cEzKKokTi9AogM5BAOUfDl6ragjpi+2ez6XXyk+zoYhiZjtdaLvJXQiVYYUETM0oUpEUgZiVMiQJ8QKkVchHgO0HrXf5x4wNRFPJ/d6F4P7hoY5iECLKncxaGcsCLzaFZWRqsKvGqgkly+//DJBjMkPQfLy8jIinqrpo6Oj3Ls6taAqk/gEQXR/1UwJJrWPbKQYa0EsWulUe6TSqpqdKvKz9hFT0aP2jkrFPQp49knV8c033yQwqQmm3utoNMpWm70GrLTa6iRWxOx7j+p6eLbaduEXi4uLcXFxke0Geoerq6vY39+Pr7/+Oj9LW6RqPVTU9tZou33odrv5ojl28/j4mGLQOn3m89bX13MqZX5++v4ZwZhuIiKyKuRfv/ALvxDb29vxd//u380k7L4xOFVkyMd9JuZCArXu2qcSld+vUxLV16uoub40DQjBlhjNxHY5mh670Wg0ssLFYtWYBuwQuttHdrS3txfv3r37sdY1e9ve3o6jo6NsrUqgfAXYajafjgcw1m6cVXEnFlRtz/z8fOzv78e7d+8SINYhBs8gNlcdDHuu2hA+iM2RB8RAPl0ZIgCBD4p1VXztnuy/Z3FImjURH8UbrSxgVkJ3gmsdKHh+2KI9EoO0dwwksDMA0/c+Pj69H2c4fBKdf/311zm+XHNWBcnj8Ti63W6cnJzE/f19tNvtma4DAHhzcxPr6+uZdxQM9sLvia+dTieur68zNrtn7TNygJ92ffAABRpF5aG6BF1JHFr0O1VcV2nj2luWbBgYI42YApmI6RHfnLzVamX/VbUpOQABqM7FxcVkDZyyGvHEJKgurq6uYmdnJ6uDh4eHaLfbM5WuACSJCzbYnPq+EC0W64IWvbi4iP39/VxL1F0dA/Z3VSz7+vXr+PLLL/M7t7a2YmlpKc7Pz1MnA0Ry1OdrFzEdqwZ8KjVuGkJSklQajUYq5ev5NFVox2lUQ9YAxV5Fqs9p/oiYYYoECjS5BDAej+N73/tenJ+fJyvjd1DGEVNQUFsdbE9F56VrdWLAmLqqrdls/tjLJjFsW1tbcXBwkNWKtWWDaOp+v59UMKqYLWgHGF+vU0peV0HDALAKbATItfVUafIKGtmsNbDeGxsbcXFxMXMirJNB2a37qxR1FRcDEBLJ27dv4/DwMKlw3zWZTNJPq+hdAK++BJxiEAiRIyIrydoaVjnX6RV2UKdAaPEUI5IyPYbk1u/381CuVqsVGxsb8f79++z1i3fin6oVK6zStk7auva7Hsx3dXWVQAnLNhwO8yWk1qW2lgBd4NQkWS3OTFlpzV5fX0e73Y63b9/mNE8tGCNiZroFwPD99oRWY3l5OU5PT7PlU1u7ikV6HaCR7Uj04pT2mPuuxYnYKQ7To/FNryfAfmhHarGLM4oM8ZRWAyMjhmN1t7a28gWdAG0VyEY8nfTqua1TxBNbDthgncXomju1kzqdThweHs4UnBXsaBHPz8/Hz/7sz8b5+fnM2Dj/wJb57sq2/6TrH4kWj4AfETOsRm0BAB0MUsKV4CUySRj6rvQeZ6yVQRVm+iyXACXRQJ2qR8GcQwscggQGZn5+PitZqL6eaCsIChR6opgKBueNqhVQCcDaNv1+P+nF2q+nGeAwktHZ2Vm8ffs216rS5YKJxK8/i3ZX7an4T05OMlEIko+Pj9nWQLX7h3rcmqJf694T3bGJOkY4Ho/z2Hf9/4j4se+wdxgo1X1EJKPz8PAQ79+/zzNrgEuUdG27AAO1DWivBeb5+fnY2tqKnZ2d6Ha7sbu7G3t7e9HpdJKeJ8bDlkhGEkMV7voZlSSbZht+x8mb1gGg8bM0Tf1+P0+ipEkBno0dEyECx/VFdd5ay/6Gw2HqRthP1SDRErGxjY2NeP36dbZ36rSMpKYAkMQIN9Hgld0TA6oOp56vUuMMAMHfJWDVNNuQIIEJMUpyr0BNdS4eOIvn5uYm2u12rK2t5QgzgKbNqtWmpdTtdvNlley8aqrYtuPK7Vtl3QCMRqMR29vbMy0xxcHa2lqsr69nMnJkfx2dtibsRFKvbAEAZ/xXcahlIW4Ql1t/+4xBYcemh4AyrRu/a90VTvajAuWHh4dss/KRytrVFtXGxkbGOGtuouXu7i5bV+KZ+CYmEMliQOuZWOKFIqjb7WbbUM5wGjNwOB6Pc/LR3joWQKFib6uezjQYgP3w8BBnZ2exu7ub8aVO9Ii5WNu3b9/mIAbRby0I65lJ1fZ/0vXBMyicuuorGJykiXaOeDJKQintjIhZypBBP+/hM76I6YSBIBgRaZSC23Oxqc98eHiI4+Pj/GzgQiKbn3863r2KV1H+krjAIqnQdzw8POT7Eup5D6PRKF6+fJmGV8GagOI5d3Z24ujoKCuuzc3N6PV6qdmQHDc3N3PKRlBBT7vfWil6k7DAoo8u0Arg9YyQ2jJTjanWVThsQJUmqAGXy8vL6TieW+UvYRGPVlZAAhVcapsPYLK/EU9VSxWWshOVzdzc3Mw4L2AqQHBelVoNrMZW2Qpwh5kiUvWs7lmbRDKWJOy3ypgGALvXbrfzdRRsPSKyUgMKAESB0z1gVSKm7a6VlZXcr7q/gj9fXV5ezpYQUKS9U0+rFIQrS+oztNaA11rdVo1X1QkJmoK2YgWot0cVtFatFBBDryWoK3hqchI3BHctDC3LiJh52WMFZpICoIn1M01lbJvAcnFxMTVKm5ubMZlM8mRi4ND6VUbCWUKKLMVFu93On6sifHbLVwBxdkpkWw9+1HZiM7XFLE5UgFh1gBgZPuedbCbcHh8fk+mt7QoMpD3TApyfn8/hBfZYT9HFuGgV1fZjzR/PdT8YMM+ktV7bj/Pz8zkdqYiwvz7bvnkHGh8A+AAexQ07F0O05rTutK7YvPVYW1tLhqa234woY1a0NS8uLnKdDD9YMyxdv9/Pt1Z77m9z/SMBUGq/v2pKKsCQ1NFmergCJ0ZCUtVTI8gTOFVclbpHjXEEmyMwAiiQONprc3Mzq4zBYJCVuaDNUTA1KHqAAeWLGsSeOAjt7Owsq8r5+fno9XozbyRW4To8DYpXWQlAnCAiUgBVR5k5v8Cq7SFBYD4qnap1RMgmCEqmk8kk+5cYoYjpRAnhZhXQRcTMSYcqKj8jQUlWAlYdYVSpALT2pgI6SQaDJcDUig5bgGnyTKjzeoAe7QGdkYPg6smcj4+Psb+/H+fn55koUMkCpjUELvyu+6UXWltbi06nk68+aLVasbW1FcfHx3mwF3sWONlK9SdgF8hE+9YqyX4Doyoqtv283UdUCARUYaqKWCVXE6A1Zx+VZvYeG8UA7QeqGUtSkx5/q9N3FZzYy4hIvVYVlftuRYIEyw5UsVgvYM89WXuMCrDs2PKFhYXUp6ytrcXFxUW2eYhePfdwOIyNjY3UdLEdvmr9X7x4Ee/fv8+iZ2Hh6b1OPhvbQaOnBSRpeT0CgPhcC4I1azQasbu7GycnJ3kvtb2IqeEj4/HTkQpauXyusmCeh68BwmIHkFS1a9aaj7pXPgO48f3aLouYCtIVIFrpdVprOBxmSwajpijyPQSknkUbc2NjI8EWDchgMIif+ZmfiV/7tV/LFrXnttf8TcHqfVTstsZJe0pAvr29HfPz81kUKDTqoXsRka+8AFSAxvX19VheXp7RGNLYAJ9s9lvl92/1U7/Pr8pUVCV5FQTWCjjiaWElPzR/RKShSlKMv/bQBVuOQuvCESvFLFA5JrgKZx10BRRVKhjFWtkb38/JK9IfjUbJtGgJqdIg2FrFC7CCNYNGtdMeAFKCM00AOpwRWw+OwmCtCUCn/1xFasZoBVGgT5I37oiedjy3xP5c11HFnxzIHrAXbBcHdEQ/Aaa9Y0NsR0KT2IEUDJlgZ9zXzwpGKmLJtFYwlY3zpu2ISNaFkxO4ra2tZdUlYWOTBOwqzlMBATJAt6mDu7u7uLu7i/39/WzV+IzxeBztdjuZtCp+k1isg3vc3t5O3xKcVNJsXVIAvuoEF9sEwCWJ2srigxHTMyBqq7ayYdipuj6AA/9GQ3s2zKR2wGAwmGFKTSmh9e2/P+O/fM0ZFUB5q/V0VpG9xbhVllN8u76+znNIKmCnVzs/P8/Ep9peXV3NEc+NjY1ktSQKreCIpxe20s/Nzc0lANne3s64Yu8ippqbCqRUx75PTPF3iiP/bc348tLSUpydnc3o6STrql2QXMV1oM5eaTMAropF8TsiZlqZWjuTyWQG6Pk8PlzZAT4/NzcXOzs7cXFxka0PDF4FgEAPoAZI0LkoSOSMCiKBoL29vfi1X/u1jBlaSHIIjYuTo8fjcZyfn2fhRbMiHomTld04OTlJzUv1IwVkRMT+/n4Ka9mtvGm/R6NR7O3tpS4LYN/f348f/vCHPy2tP63Zt/qp38cXA2I0NtdCViGRn6fxYOQMAAvhgCwGLdk+V4bXCp6RV0PXd5MMfJ+AD3BETEdCn/f29A8FK9R0xDQACtLOi4h4qji3trbyeTmU6gtLA82abdfi6fV6sbCwkO0ITEcV8B0eHs60LzirSlXgWVlZidXV1Xj//n0Gt0q7qzSczeLUXL18gVD17jMIR0ejUQoVK4NmHeoEgWq7go+IyCpV1S5xAUj2tlZRglndSz1h+2btBFoVE2CmSpWgVFnaG/6/1+vleQaOx8cOAY2SKhB1e3ub1bDjznd3dzOw0QvU9k99U+3Z2VnSzJJK1Ws4jOrm5iYrIizV7e1tnJycxM7OTqyurmZrUqKzBu7z/Pw8n7fb7cbBwUFS1NaePysK3Eu73Z7RBbmvfr8f9/dP74HZ2tqKL7/8Mg/+kpTZlMpbspVoBdoKwCOmr4IAUiXa2m5yz3xWIUMfdH19nSezVg0BMI49qowHUWudqnBqM8DDP03CYRHsdUTkKDJmRkLCKABKkg5GF4tVTx5dXFzMtg/dk4qcXUtQg8Egn//8/Dx1b/QZtfquZ61gKzCNYiKmiQ0DvoBiBY3YBnvhH/6GvRDDgajnp2WbpFRkViFr1ThGRL6eRLyohyjWWGavK5sI4IjfWuj0XwAT5pr9YVGsV7vdjkajkWegiGPWlt9gwsXeeiJ2nUDC0gM8GKpOp5OHfWIy/QzAzW6+rQblgxfJAhiO1ddOiIhMMEa16lSPtovFu7y8nHnVumAvQBFvcjbnpCwuLuYoF0pRAGXQwBB6ss6Xc+hKG25vb2dQaTab8eLFixSQaWkRL3pDabvdziAGEN3d3WWF41k4kxaK1pIAMDf39HJCjgIZ14AfEZkwBVRIm4M4NZRegSZleXk5Xrx4kZ+peqSzwARYP0BnY2MjFhcX49WrVzNBwqFMKoKq9XHVfcC4VPDE8QnE6AWqLqH2gSs16q3EKvPR6OkgI4BKWxCgAF7dS/1cPW6Hon366afZSwcA7APAWKlp/gAo2h+9dNWO3+n3+1kVWZfr6+s4OjqKr776KsGEqhWAqVTu4uJiapEAmJOTk2Qaer1eVmQ18UvIKHKAeTgcxunpae5R1Q1hE+oZDPf399kvB575Mk2Al/kBNVorQKg4UUWWl5eXcXV1lcLa6+vrnCKqEx/sCDCsItrKpHj5IXH88vJydDqdGR0Yf6z0+uLi0xuDxRfs3tzcXLZs/tAf+kMxNzeXMcBL2fr9fuzt7WXRICaMRqP03yqU1JahW2C74onkCmDSuhCnS3TWBsu1srIS+/v7qfEAjCTh2koBUqrOy+dWwauY457FqqoNMrJMRCq+un+6N/vomSub+fj4mC1v+iz269wVIMn3eh4+I1Y/tw3fC0iJ1+wOeAYi+OfDw9Nbxb3cEiCvrUWxAkuv+OIvYgoGxxSZNicQUYs/+qIqbfjud7+bsf/09DQ2NjZSwyLfii31njCPP+364AGKBMdB9Do3Nzdjc3MzwUPEdBRXUKngQ5CsUwER05f9mWypL04S2DgQI0VxqgBsLNQscFZ9AdTeaDTi8PBwBnWfnZ3l70go+/v7ERHZ6nB/qiDVNgbAfdT+cWVwoOlmszlzsJsRNEljMBjE1dVVfPrpp3maKNTMOSMihVX39/eZvFTzx8fHCdIcgW/NKxgyzSHRDYfDZHY4EeZA8K49Y1WfnwVcaF+0fepR6aZHVHmVHSP6Uj3Pz8/n2R4Cx/r6evbjK+W8vb2dbyd1D9YcQMCICDanp6dxcHCQ+h4AyLqgVgloJdCbm5u0c/Z6eXmZFbGKy1TI9vZ27pe2GmrfdId9tIaV9WGrEdPTWTc2NlLbxL4Az8qOKQQknWazmUBtbW0tNjY2ot1u55h9BYxLS09viO10OjOTFJgZgZV+QtJgp0C4/68HFrI/e+L3xQUgw88I5sBwPeRxPB4nwAaSJHp2aj8AD75ZQRG2RdGh8v/VX/3VTF4R0wLl5cuXebjW7u5utuUww6aFTMcYYZXotNd89sLCQrTb7bi9vY3d3d0ZJraycHWMdWtra2Y6SvFDP8JmFEIKKSBUwQjcAop17/h2o9H4sUJNjLfmtUXqv+uBh9gy7cgqvrYntY2trX5wcJD6Lfq67e3tZF8w81XAX4s+8R040irlOyaG2Mv8/HwcHR3lGlTgdnt7m9NH2CgMktbryspK2lm73c5TbwH7zz77LCKeQKFpPbFQEbG8vBzv3r1LYCfmsdnRaJRv+fYcABl28addHzxAsXmMQqLh1FoPgma73c7qGzpldFUtLgBUg6itAbSjSoEBAzER0zckR0yrCuN/ApV2DQcjCoPwCZ8IqYbDYSZs7BAKXAtnMnkaIby/v08tA2Ovqna9d8biHo+OjnKCqIpuVXMQ8cXFRSZXwi+gZG1tLY6Pj7Mf2m63M5kuLS3lmR72QrWlUkO5CwL2YTAYxNraWrYLBD6fiZmgx9ja2kqmQQXH+QXRjY2NGZZAEgUsBWn/xmZhWdhBxJNeRIWxsbERq6urqeD3c1U0iapGq9sTf4ex2traSoGn9VBhHhwc5PkPbMs6AgzOKqDh8f2mFiRUjJVJrJo8tEqNN0ZE+oWDqVDfAL91ZKOSnfUE3oyTY/aAY4yCN6tKnDQ+FxcXcXJykvooe6bS5WMOEeRn2gIKmKpFqSO39kZgXV9fz/HaKp4ERuqoe6vVim63m3S3exmNRgkcxCHaMIkPM6gg8NlsfX5+PnZ3d+P169czrQx6s8fHxzg5OcmEAgzbC7Fla2sr2zrLy8vR6/WS2q8vGa3VNjsHamprfX5++l6xk5OTvF/Pq4CKmLbr6ksExULxiX9ohYqPQI99fHx8jMvLywRS2h3u1f1V8CWW1/ad9amFS8RUpF0T7GAwSN2UwxiBg6WlpTg5OYmDg4OMWxgUAueIyMJOcSaOPJ90Gg6HcXx8nL/Dzl2AtCk368XmABctUxNhXupoL7F6b9++zVxT4xOblisvLy/j5uYmhfUR0xOY7Rumy+DJ5ubmt36v3wevQUEhendBHSmGKv3bSCzDtYic1YJGRFKvwIlKT1KLmCrgVST+rIroJDBGoVcuSEoOo9EokWpFyi76DEkeaOj3+xmUer1egpuISGMDgiIiNTFVeMiBut1uCr2ur6/z8yMi2zNVIFnP4FhZWYnvf//78YMf/CC1Ie4FkAFEODvk7v69hVl7qwIpwbEmA8lUO0hAj4gMOJeXl0lLoqUFGC0LzBNaufaRteDYC/YDI6Y9R/goGLJBwXtzczNpVqOizebTyxSBO9qROiElgEhS7XY7A7/WVBWprq6u5lkmxq5ri0FbByDkGxiuw8PDDOL9fj/+wB/4A/H+/ftsK7BxtuZZAHXAiY046Kvb7ebUEB/DCNpTYmXAS89a+6TT6WT7I2JaOWKVKkjyHbQPlQ0T2FXatV1Txae+w73o5/tMp2VqS9ZReWvFv+t5GvYBkED305BpaR4fHyd4o0Vw7MBw+DTeSXwKlAOxz8dPHQaJ0ajMhNYNYMuPer1evuxTUlQ0+HcVL4stbOKTTz6Jr7/+euYwPVoTPiTWbm5u5tuQAXRABeAAYBQVo9EokzZ/Fxv4fNW+sGuFkXgCbNDi2Lv19fVknMV4+kF+4I3f8ksd6/Vd2qraJ1W7VjUhgDUpQGXcTdqIg4ASYO1ZK2sH5Dw+PmYucOIvWx+NRrG7u5tC1svLy+h2u3F4eBhbW1sZ38R9bVjj8YYbFKMIArlBTqtalcPDw9y3n3Z98AyKh0ZdRUQqmSE+aBPrURO2intubi6rUP39iGlPDnVcgxqDqeNrElJF2ipHwfnNmzcZQPWkBRJaDO0G7BB2YHt7O5NcFc85J+Jnf/ZnE8FyBs+GOlRpawXo4QtqABN1uJNACR/pTOrhVBERx8fHuQ4nJyfZ5lpbW4uXL1/OGLvgWM8uIbxyKF1EJOWN5SHAs54SD0EnmhF9XccJTbgIBioEQR74RAlHRO7Z6upqAmH7JXCyK5Mvkmkdl/YMXkxHVIjx0Mq6u7tL5gBIUp2g9AVjNri7uxuffPJJJuqISOBTmbO1tbV49epV6j4mk+k7OwCvbrebrJmXfG1sbOTaCZ7Wx4itfwDKq6ur1DqocKs26+7uLl/RQAtk716+fJn6rqoh0Opkb1UHFhF5SJ7PxIBKQmyZYJg91/eCSNwYI0yQdlNttb579y5bghJFtZ2aBLQM2QsWUKzA7o7H01Or3b9ki/2VdJxKXYGpNwtLduvr67G3txcvX77MRPY8/gGn/JNWbXNzMx4fH+O73/1uxgiVOnZNlW70u9/v58+en5/H4uJiauXsFVsWp/v9fp7TMhqNUhdWC0xFIjB8fn6eTG2d/LLfWCaAmQ7s+vo6W84AnKtqWsR/fm+qrJ6to1Vpz+xHZeAAeYmdLYoziiw+DWBW9j8ics2JoxuNRuzv72eeq8WCe9jb28uYWPe7SgwUydqA/AygqAdZttvtZLvsuzgqD/KHiJiRHgBpdIfftsXzwTMoUK6KlEGqljgVB/R3ETEjggVk9D71C1VogIYeq360RBUReRKfxI0uJKAcDKYvSao9V71Wl6pfcKwsDG2CY/EZrspbMETjWwcJmJ5BH5QjSVCTySRZjMPDw5ySQf9xSM+krSQ5Ooip2Xw6ywAjc3V1lZqNbrcba2trcXBwkIHa/dA91HfRREQGpspQSXyA3cLC06FIm5ub+TsStM9BW3MWAEfSA0isIzATMVXJ+zl2U4WGwBFtDSq11Xo6I+Hs7Cz79fUNzICPCrcq31W8tCAqOAeWnZ6eZgDwrOyhMm2np6d5Kunp6WkMBoNMiuyZLUVEHB0dpS2rlAEpPoL+xZqsr6/nvkuqg8EgLi4u8m3LDgsDMrEDmIGvvvpqpshgr/au2kHVK2DRFhcXZ3QLWnieg4bBXtdqtbYrVMtaQdox/FKFf3d3l8JUMYU9KwRU0/bHHqqwJYWXL1/G27dv87NR49q2rVYrz8HBABBQmwyy7vbZHn7yySfx7t27nABx5shnn30WBwcHOY4vYbJ7DJoxdbZfxZARTy3tN2/e5NuPj46OsoA08aLFpuUlZjsUUlxh284/cQAYEMLvCES1AsX8Wmhq5UdMAWgtSOwD9kiBAHhhVLVcfLdXkDjt1/4AlKurq7G/vx+9Xi8n9qo/Azz1HK6IyNipnQxYeR4t0C+//DLbe+LD+vp6HB4exuvXr2N+fj52dnbSviIiTk5OZuz85OQklpaWstU7HE7fcK09J49gwp2788knn8Th4WHmDflSW/HFixfZfgVSImImZ/6064MHKJxKUkJV1p5kv9+PnZ2dPCKckKeKq/wZQ1IJVbpRMGZgkgodhL5m1YwIgsCNxBoRWUmMRk+n+xGQeY8D0Rr6vgpqI2ImWKoiUHECtyCHOQIoaGU4XhXOYV0YlXV6eHhIoBERqWtA66rGBVQtNQkB41NH/+q7WJzFgYFwUuHzCao3b97keR61HeO+nTWhVVKrBjolzgLxc1j/rqJhwJBTAbicsY5uY3fsPT0BulSVS1uDNrefzmGRdLVqMCf+DB1Pz0K/JNl7r4kpC1SrnjnGjT0T9amWrFWn04l3795lNUp87fwFzIyWVhWfqwoxEZLC5eVlfOc730maF3Vvr+uBenUSQVVZJ9GsP/BAT0Lsh6XADlYhIp9kD7XNhkG4vb3N5Cgxm6ZbWVlJ+8IG1fFM7J2TkrEw5+fnWVnyO7Z6fHycpwIrBDybKTdgezwez5yPs7Ozk343HD6dZ+SYdbalBV6nVbQC2u123NzcxPLycmrQhsNhvHz5Mr744oss9Iy/0k1IjgsLC3FxcZFj3xhCVfrDw0Pq8/iZ/Zbca2GCITORBHjzGcVU9X2xXMyr7HVEJJOGzTFRxJ8VqjRm3kBcR6zFIYCdrkqewISYDlWs2mtAxM8Mh9OX62nVAFXPY0vEdJrJgIR2kdOBCXeBLe0wHQCFLYbb55nm4W9VJAxY7e3txcXFRczNzeW7shYWFnJKbmVlJc7OzmJ5eTk1ZdZUIYrR+jbXB9/i0Qu+uLiYOdHRSXnHx8dxeHgY79+/T2rPphMyVq1DRKQamxjSJmI6+v2nE/e2t7cTjQqEVVQLpFCfc2jJkYCV6HU0GsX+/n5WgXNzc3F4eJjPagqj9v0ZOSMW3AEootjaZ0Wdq1wkEg6gfVSRseBBH8LIBXkgam9vLyci9PZrUOx2u1n5qXR97+vXr7MC0SKIiNjZ2Ul63d+1Wq0Ug5mOIU717HqmtU2HwULDSmKcFWiTNNiSlwqur69nu0My1gKTVNHUAg1mreqU9K7dE4ZgMpnktIfgDXBhe4BCgXY8HuchW6adiOQAcnskgFWdiaklwkDBrB5tXfUqzmWQSG5ubuLly5exvb2dVbK1cP/39/dJl29tbSU7B1wIuNZekmBH3uxr8gCAq8Hcn3nmdrudU0nAnOdju5JAtQOtFS3Hzc3NFIlL6Pf393F6ehpXV1cpjKZRqGseMdW43N7exuHhYepzHFEgeUdMEyjW5nlLWpypWjoJ0/gpYXZEZGsWu8lexZhG4+l8DFMrjcbTpCNfj5i+uNT0n/vi89hClTcQpRXs4EdtM+tu7Pn+/j4LTPbt72qBsra2NqOzwZ4qAq25qZlaLC4vL+eRDArKqsUDOunS+Mfx8XEcHx/H3d1d9Hq9ODw8zO+ukgGCeX7qdGXPJFEDKVrA/uFf7J7tVCaNxsqhktpe4sPOzs6MZu/x8TE+++yzGU0kNss91SlEukHv+3rx4kWyi9fX1zlJ12g08hR07SR2+fDwkEBawSTubW5uZsuv6it/0tWY1CbcB3RdXV1Fu92OX/qlX5rRmkjsxGt/7+/9vZxy+Jmf+Zn47ne/m2dLnJ+fp2EDJ6g9yVmC4dROWfSdkD4GxQmhklc1PH1aCDtieiJixHRkGlpXidGiREzPwWDwqNrhcJhtH8GIAVbhYB29k2RrshSIOEDV1WxtbeWYL+eX+ARMz720tBSXl5c/NrLc6XSS5RCIJAvTHRKNUU/gyZpxyPv7+6TRARQCTveigqK5ELgkWwkFsDCpohIWyOgSsDECBif1jOynAgrVXA3O+ty1Ur68vMxWQa046HNevHiRCaSO6er9AmbeEOu7JLTT09M8NIzdCvyO4nbq6Pr6epyfn8fLly/zwCUaBjbz2WefxZdffhnz8/NxcnISP//zPx/v3r1Lv6gicUyCal8CdVpp7ctrf0l0GDjr7Kh+iUKAtI8YRXR8ZQQjpiCAnoNPSsjusSZZ6yzZra6uJuNJrOi/K1Blaxi4zz77LD7//POMY3ReGNPBYBDb29t5sJ2zSoxrayVgtMQy/qMCBgZ6vV6CauwPVk4V6/kUD1qxEkltiXoerTrtTOAdUFGYqaYVRCYAvUxO/PLn4hzRMzBRQbR9tIdiv7aLv1NImdR63s7W7vZnlaGwXoob92Ct+HXVXShCSADYirVqtVqpzavPtrm5mSDRdKZ2EhG/HOGzFZXsATjgNz5Xiw+7WI/P4Icmt168eBEHBwe57xgazyYODgaDPJVYMSH3KUoipgy7QnFhYSHz0XA4jL/0l/5S5ojf7frgWzyAAwODYhmYxbq+vo6Tk5OsxP3bhulVMn50OiNmpLV/HBEZMCWDiKn2ASjRQsF+0KIwMiBJwq2nK9ZeuvvTr5WoMSKqV9R0reoYGg0Io1Td0RgIIowLG0TfgcXwjNpGnU4naT49WICLat85DkatOYiqT7BRVVkbbSf97m63m0BobW0t6XXnekQ8ObJ+ueAY8aRZ2tjYiKOjoxltDpAjoKFDARHgiAYDIyUJATuqBUGm2tZkMkm2DwMkka2vr+fES7vdzlMf7bFpHp8HeNV+dsRUva/qJlKWqNy7c06++OKLDFY+g1bEfvlcmgUnDf/oRz/KNdvb28vze9w3Jsi0FmbG9FH9PftnHL2+0VhQlKQVIMCtYsFBZ+12OwGL6i8iMhbYG8UD26BhkIgB2tpe9bPaufaWz0j+9kYS1R784Q9/ODMJ403dXraIVcHoVeH6cDjMNqopFJNRElqdtKiMxcrKShwfH+d313uLiGwHNBqNPIr9D//hPxx/+2//7fw+TIGiQ5uKbkTMMELbbDbjk08+ycLOPp6fn8+0bLUDgRqsnraJGAQoaz9GRII7AFcx4v+1AO0bYMEexCWH8VVtIHBd95/PWj/3w7Yx20CPeF4ZRC189lP1eUAFP6y6KvGKzxmpXlhYyLNxasFrsor/8nuMlmf0KhNslOLfWhmbtl9VtyeOO7tIQUfvpRBSbNKKybM/7frgWzyDwSAXuFbK6Lvt7e3Y3Nycmc4YDofp2JxGD399fT1HCQUwmwDFowVV1fXgIMGKcrrqAqqzQf2EiRcXFxm4e71eUvgSVhV9VUGtyrlOyBg9reOMmI4/+Af/YI4Qa/FgKxwzv729nf9NFyIZmyKqTIegSkjLqUej0UyLSZvLvjmNUQtHDx+TAUA6wIuTotY9H0Fxr9dLjYV+eT20KOIpEH/99dfZ69XGYT+CmgTmXmqic19YEPRp1QBxbKOhEqkXOJrIEVCurq4yUDlUzaFZFQh1Op0MwDs7O7G1tZUV/M7OToIVgMoYKVZgdXU1GZP379/nPr958yafp74HhU03m08TZLX9Z/z59PQ0A7NnrnotDFsdR2afS0tLsb29nTYI2EhC2jP86+HhIY6OjnKahG1VPQTQxuc2NjZySoaImR3yaaOn6+vrCZScYwOEVNDDD00lsC/foeVHrIoK1yKrbcyHh4dspTWbzRSlOrtF8rQPVSgK7LOjekDi8fFxVruSGSAT8TRBaOJmb28vGYTLy8tot9vx5ZdfJjPCDl6+fBn39/fx8uXLXCvgEhsnGUqAJycnaccKP/8Gvpz+W9lrNog19P+14Li/v8/zUYCWOvSABVfsAHEXFxdxdHQU5+fn+U6ZqvWoBWun04mtra1kXXwmRg+LWtngqgdSoJkWBBgNCACvQCChOf2N4QR+pXCuf062AJBU5s8/CsrBYJAgUtsXe/rpp58mK0m4rkj3IkE2DdyLLewb4PYuJOz3xsZGFsVs7addHzxAqZW9I+Elirm5udjc3IyXL1/G3t5e9mAt5PLycuzv78fLly+z/6YCr47iVfSQvU2rYlUB7Pr6OvvletWqMgEY6PFyKE4nyDFarRsBWfUrCdtojIneM8Bl2sLJmK1WK370ox+lWh1jobWiojo7O8tWhRHnwWCQ6Lv2XOvYqPVbWlqKly9fZsJVSbx48SLvD5NEmHdychIR04P3MAB1wqHRaKSuwpHKWBgVNadRDRBHR0zHPiWWyWR63H9tM6luBDyvnMcMRUSCS9SnakhwU8HSghCkqrJoKbTbfCZgend3F2/fvk1RM92HUxo3NjbySHksFoGt5EB/cXp6mp/b6/Vic3Mztra2MpCPx08HM1nfx8fHBHfWy2QVRikisl3HZlVsk8kkaVsVsspPwvLcDhSsFSvKX4UqwFn/y8vLODs7y8qb5gMTVtsXAAAGVSsFs6Mivr+/T5Fhnf5iu5XJUQBhDrAaWFTJUJsRSwe0sTP2KB5o3WIMre/S0lIWYXWUttls5qsEJMLKENQJKW1XSfLx8TFtYWFhYebVFTc3N3F6ehp3d3czp8/+gT/wB7JFcXBwEOfn59lyAQYxWfwchV9bRGxOS4vdAMjiFt1X1Zlo9Wl/aPVitsWg0WiUayIu1qlLAIBAVJLFwojJ2kqV0cbUY/Ls+3g8znNriKfFKPvIJ+/v7+Pi4iLtDuvALvibiT+2LbZYL0yNFzoqlBTi4j/ARVuJVVcE9Xq9aDQacXx8nPvNvoF3+Yj4l4/yc3Fnbm4uGXpMVr//dGp4PaDw21wfPEBBqdYRRPQp5+90OvHpp5/GmzdvUkU+Go1y0ocDV92ADXx8fIyzs7M86p0Tquq63W50u93cOCKmdrudh2NFRIrQOA+nwwxUqlgAkUD9nt/RU+XATvKjkvdzngWFKQHV0yY5Yl0vyQYlrt0CtUdMe5gCvlNn6XoeHh6yirCWDqkj5sOgMOKI6duCaTFUPVpcQB5mRztAX5g4izB3OBymg/d6vWSX6AgwPYCrkTtsAHZGADRmBwirSDinZzChMxw+HahVGTTrbZrGfahmfI42XdUi+G9UvoO7JBLUM0Ho7u5udLvdTOASk6AYEbG7u5sJo9frpV01m82stIB1CQTbhZ04OTmJn/u5n4uIKYto74EWIF4wByw8CybOWgr81Tbs8/X1dZ75AQzUlkZtIdQprY2NjWwFPjw8ZNuo0+kk+weo0BLNzT2dxVPZHdqJKrqtLCpqW+EB5A2HT2+hliD5N0qdf7JriZGN1VYy2t2eEKsCzmKL/1adO09lb28vzs/PU+xaD72UfFX1k8kkfvCDH8zouiSvmgSJn7GY2l5sVSFRp7ok0r29vbQbgNWeWd9Op5PFhDUhKNaywcwAIvyhJubV1dX45JNPcsKFL0vChLbaQ47YVwSwf34BaJ2dnSXIJjam0fCOJwy2osUzaJXz+c3NzdQNbWxsJEBRpLC9jY2NOD09je3t7Wg2m/Hq1auZE6wVU9rwEZEMiNaxV3Q480grR9x1vhKwoZAhIsdS7+3tJROI/fzud7+be8RfCYh/2vXBa1BUSwI7dO7kvY2NjfiDf/APxhdffJEJLWI6904pzoiIzgRFlV+ldCs99e7duwxEVV+iCtZyqsp1n/G8zyk5b29vR6/XmznSfWVlJQ4PD1OT4RlVulXAyaG1kzi/IHJ0dJRGuL29HUdHR0mRWxstm+qEqvh+vx/7+/vJvNzc3MwwS/XI7qpHkEwjImnDiKlIT6X9ySefZB9W4qraDglPtQloSYACKvaGbkdwsl4RkaABQKziXevpLA+HLlUKv/bxsTHz8/MZAFC/Eh66uY7eXV5eJjiWbAWtOr5LwOu/BRagW0Vk/K/RaGS7yDHTS0tLcXp6GouLi3ky6/39fWxtbWW16/0kWmDj8ThevXoVR0dHGYgJWy8vL2N9fT36/X588cUXCWrpFiQZbUbJQYUrWUlKR0dHaQuqXUlRtVs1SVtbW7G+vp4MkLWu5xlFTJkQtseeavKWeJ2bon21t7cX4/E427BLS0sJdFWsgBZAwU+xZpgLxYnvJZJ1XALAy/fPzs4SzC8tPb3bCMPkTJ2bm5ts19bR2OXl5SxaWq1WMkm+my5CfKlTV+Px9MC42soQe968eZPne8zPz8fLly+j1+slIKO3A8weHp5OFcW4aA0pCrAi2FKsJHErZhIQp3VyiqmzaDCJ/Knffzo9l99jpzGlQK+WtXa+5zdCj70mqpUD6p9JzDUPic9zc3N5H/YFOz8/P59tvKpppK2pp0JHRBY3n3zySRwdHcXP/uzPpu1pcTo7ZmdnJ7766qvUHvEFbajj4+N84anWl5hFa2goxP2Ivwq/d+/e5dppW/Kvm5ub+PVf//VkA0166VT8tOuDByg2T58VBQc5Xl5eJqp//gI9wAEoqVM3VeiEWWDk2IZW6+kocpUBClIiYggYBRunUjEm6DsEVmJZ519wYNUAfQxAw7icc/HJJ5/Er//6r+fJuIIIgKCHu7e3l2JRoKZOXaBHAR0BwYFAqkr0s+AlgFblPbaJwZsOAEC0qi4uLuLdu3cZLFGNBIQRkaIrVGFljKylNYuYJiGjjhIlGpYdAG1AhmkW7I/PF9iJ3wBaoBgAApyAU8kE+PFeIGJI1SSGzPkpbEASodp3DokTVLFhNfH4tyR6e3sbr1+/zoPUHHPtbJb7+/v4Y3/sj8Xf+Tt/J18V//j4mAe76UtjXxYWFuL4+DhZxM3Nzfjqq6/Sbvr9p4PSXr16le0BjIJRbAGcv1QGBavA/9rtdjIRGFBAhm9LEjQNWEWJhNbs4uJiZswzYvrCT7bMb2qbp7YgI6atQ4AFs1C1Z6p9la+CQpUP2KDtPbs11uvHQGhf0CXQ1Fi72ur2fQA67QpgA/i494jIVo9zLiqIASTZlxYB38PO8gnAwBHrS0tLKdhVCBLn0rOYJtPCBE75PJ1f9THsaafTybjQaDTi7OwsW+n2yvlMfAw4xJby4aqbUnSJkYCKIlZSxyiRDZjEOTw8TOCwsLCQ2i+jy+LPxsZG9Hq9BPW6A+7Bd79//z7m5+fjV3/1V9N2xW4Fytu3b7PFrSDwrGxtaWl6SrPYDmysrKykALfT6ST7HjFl8ADBCs7FUMWMuKp9rKj7adcH3+LZ2dmJiMhDYWhLjGQyxk6nkw5WFfqSnoSnypAwVLmqeD9fqwlUqgBbhbQcZ25uLo/iV40wdvcyGExfjOawNai2Bg5vE1WlMAxV8uHhYezv72fvuoIpFXWz2cxTBAExPW7oV/CG2K+vr+P9+/fRarXi6Ogox0Frm2lpaSnPEdBz5XgqIetnLYko6+m97oMzdbvdHC2VqBj/Z599lmDMPrp/wELFSBuktaRFIbBICg8PD3F2dhZv376N8/PzpOWBT2fgEC86JVYSe/6iPcFvd3c3A+/8/HwKe60/+6kaEgBREtAmAkjQ9kDj3t5ePrOKqNVqZUIGaCQbyUzA+tVf/dXY3t7OqYmHh4fsrUtY1sne+Luzs7P0r6pfMalDc1LfN1Sfu9L+QMvFxUVWlmtra7G1tRURkRUhdk9C5l+qZQJkfnx7e5uVOp9hj3w3YjotxRcFWXS2ZAfwignsx7SWlos2Qa/XS6F3r9fLNpPj2L236eHhId68eZP+hHV1hIIjA1TMVUjvPB/nf6yursbDw0N89tlnsbq6Gufn5zktp3iQtGiNxCd6KQfWeT4stMk9z+rsqNHo6VA+IEac0S6IiJkRaMcbKDRHo+l4MaDpvtyDo9jF5vv7+xzD5yuKAL4psW5vb8d3vvOdvBegQQxiB9pOtCFkBNXHMYFsSOuIjxD7V73K6upqgnaxqE5c0u7QpYjFq6ur8erVq5m2K+3NePx0BIDpMDGUjYsJ7HQ8fno57HA4jNevX2dLB/vvPoFAzA/wurm5maJZ04ZVrA4Q0TVicnq93rfK7x88QFEVqYIFq/fv388EHSN5tf9NOMth0P2q7YhIQFPFZZKZCtDYF1qcs+zv72cl9fj4GAcHBxlk6nkkAkR9gRShlQv9XKnXhYWFDIKVKalsD5QtOXNiY1+mgAQXupWIJ4fV/rFmEdPAbexRC6e2P1SwBKZ1xLRW391uN2lzvwdQCvpEvaouwl4anffv3ycYXF1dzX+AU2yN57R39D5saG5uLnVDgKnPdRAgoRyBqlaO57u+vo7vf//7sb+/n20+4rT7+/sUIy4vP72FWDCcTCaxvb2dSntiaNWSSrm+7BBNbc8qDY2WFzhpbiRZUy1XV1f5XhNVpfv+o3/0j+YL3H7rt34rVlZWotvt5vcAusDD2dlZao/0zmkriBAFrNPT05hMpse3V4bCWCVgp9KW+Gi8CFqBXpUtMMFntQWBC8mGJoDdPDw8nZR8dnaWB8SpDEej0Uzf3Du0JFY2zE9pxbAKNTkBZbV9glV0vgQm7OTkJHUMEqPCS+FRGUafr23h2dfW1mJpaSl+9KMfJWhX5ADYtDl1tBpY8L1VgwMgAIS9Xu93nMZqNpvJnrZardRHGFsHcoAjIIAept1uJ0jwXiagk+DbNB6WVAwDqNmBQuXu7i5OT0/j4OAgW7MRkYWolgWALmZGRP53BfVibgXnpjIBJWttz6rWA4MCPNdXSriXiEjh+snJSYJkY8FY9dPT09ScKAxfvHiRE2ZAju/AuIhNNGGKj0ajMdOSA9RardZM267mQMDN8/K/5eXlf7xEstA+tbQEX1/GJREzHFSYpINZqeIziZCRSmzor1pNQbYqcHTyF198kdXHwsJCjlNqa0hqTpMEboAaFTItACo8IpKZIcQSKNCDngeq932U4T5TBW6cjhANPb23t5ftid3d3dSlCBqYhLu7u2QnFhcX45NPPkk0rQLCNnU6nRxrRT/TsTitEJhcXFzMynN5eTkBkdFdfW7VXq/Xi6Ojo3j//n0yB1UvgA4fDqenoAqSWh2oYWJFYs+FhYWZfjxwIbEIvH//7//9ODw8THt5/fp1gue1tbU4PDxMatlnsWWH7UVE9spr6woTBxjpywNjEZGBc3l5OV69ehXD4dOr2gGW0WgUR0dHeVKqUVWtEhM4v/Vbv5UnqErG1qWOWE8mT0fJA3eHh4cJUIwcCkxLS0vR7Xbz+4jnMAvaHqZLAEztSwkKeNSKJIIFNitABfyAmYWFhazq2YG1WVxczDFjfgi8A3LAksQq5rgv2gVMh1ONgQZMS01mkk09uh1IFdC1Sv28+yWcnkwm8eLFi4iImbaUvb68vMzJRQWK+EbgqQCqCRLgrfoJgMq0SbPZTC2U9gu/0ZbsdDp51o/vATJ99tbWVrTb7dja2spxaRX/8fFxtpUwAkapW62ncWji+ogpy+1ZFEobGxuxv7+fBaZkyZb4dsRU8Ixp8tl1SkbbsjI7jUYjp8TsQ8QTuHn16lV+BjZHO5rvbW9vxy/+4i+m37sfQGdubi5PftaGUZywZXHOOLkTjx2Fr3h58eJFtrzH46c3Hju117t7gNzNzc3Y3d3N/3737l0yI/Pz8xkPvvOd76SWyLP5GWv6ba4PHqBUCl2lZTO3trYy4Ev+All9GyMhnYAlQNRAgopnVKhgIlE0o6C2s7OTQYPzMvwKUgQAiB+d7OwWVRgD1Hpy763W04iZBKiFMxqNcjQa8t/b28tZdtWJPmw9fRUVqErEngAmxGzeHzQ3N5cvpRKsaYAipiyU74t4egGZd5QQUzrBVcKRgF+/fh3dbjcp+YjI6h0DIyEDp0Ym+/1+ao84qEoULTkajbJPHjEdMcfoVNBZe9tYizoBJHBZt/v7+/j8889TpzQYDPIMCVVYnZrxnSrEWrlKglT8GDfJCpB0oufCwkJ8/vnneeYLkC1xWy8BVRCXHIyun5+fx/b2doxGozg4OIjhcBi7u7sJelTf2o17e3vR6XSykqN94gOmGH7u534uWSxglsjTJMRgMD14jX11Op14/fp1VoQYCC00iUf7hmAXuLHfEU/jkRGRflInROg9fL8kuLAwPbkX4NLS4SeSJj2Cwqhqs7Tu7OHOzk6ytFXM2u/382wk4AToW1lZydaAWAQ8VC0AVmY8Hsfp6Wmsra1l2452SjuhsodYXp+vqjYVaGxV/NS+Y5t8ykSJwxYlMSP9iozB4OlAPi/AxGxiViOeWASxqbaPj4+P4/T0NNda/FGkVbbIJJu2Fd+SK7SYMY9+X/sCK0BSgN1zL2QC7JKPYci010z7dDqdGZ+/uLiIX//1X0/Qg8mi2RIT6itABoNB7OzsZFsdMMK+vXz5MtkkjGC73Y7Dw8MsUvjg6elp+v+nn34aBwcHGXfY6Oeffx6NRiMPdMSktFqt+Oqrr2IwGOQ6AlkAo1cb/LTrgxfJojEFeeOuj4+P8fbt2+h0OtkPdJywVgXxl4qrTq5IOAIsWt24GDSOHVEhCoboV5MSxgtVoYReEiDazCmy+tyChQqDah0zZP4dZTccDjOgYjMEhTq9EjF9saLgIzB7EZ+DgFSqNAzOYpCwtTYc/43+AxD0KJ0YKxAdHh7G9vZ2nJ2dpVP5mSoClZiwQ+4Z4+H0RX1xz1iPI68HbrVarfxMArTJZDJzqBxWBeuip353dzfz7h/CZgyOvi/A8/Lly2RqsBGoffdDNK3N4jOAYUeY6+nWZ6A70SZst9vx/v377OUTYd7e3sbLly/jRz/6UdK1KuiVlae3D//8z/98HBwcxFdffZVCU2JqQbDz/3+pmXF6/5ydncXj42Ps7OzkIU9VY8VHl5eXU49C2xMRCY6wLphNZ54sLCxkda29p1VqDdm4oG7draUWZgV9ko62I22Y/fcdQOvy8nIWGsB2rYIVR/W0YHovNuPIf/dBgLi8/PSuKqPHBLr8CUhVlWIPu91uXFxcJCggtPS74ockoSD64osvZl4pAOgdHx/PTJrwCXqURqORIBawpWkQbwAOcbbVasXW1lYWId1uN46Pj3MftHfEcYmPT1oXMQrTVqvxCsS1WzAfBgyAl+f75vfrqzCAGTGOHQHFWvtVs2K8X0wHOp2Xo0BR3Gm10ajVKSBgZX5+Prrd7syEku/WUru4uIjt7e04ODiInZ2dzHXYr4WFhXj79m2CFuzI/f3T25SxlyaIKgPzwx/+MDVNwE1EJDDRRrRm9kks8DPW36nG3+b64N/F8+f+3J/LNoJRNMmGk2BYbIyfYejOL1lcXMxxP0G1AgSBIiLy5zEzKNgaeFTEKhCOV4OFXjoGKCLy/4lanSugPy+oGz/kQJVqxNJERIpofaaWhyqyjio/PDzE3t5e0ra7u7v5Zly9ff19orLV1dWZCtf4q/aMEVXraC2cCbC1tZXjgEdHR9niUD25fK/j8gFCgRkdKvE6v8Wf2y/BxVkPgk5EJHtDy6Dq8VyYjoiYEbsKgALqy5cv4+7ubkbfJDgCfD5ra2srDg4OsuqhJYqI6PV6+f3ebG1iR7sGuPGMEZFi1Z//+Z9PYarnqor7iEi61ns7/J338ZgyUvmOx+N48eJFjgSriIwpX15eJqNWAxqf81LBlZWVODo6mnkTOeAmsWpfSKzAnckTPe/6OgKVmn2phQkf9jPaucA2Blbv38+x7cpKWIfT09NkhVTiQFa/38/nBZ4UD76H//Atcch30M2Y8JLM+v1+vHr1Kt69e5eMoaTIf7SMMK1eJil+bG1txcrKSrx9+zZ1E1iUylqIXZI0FmE4HMb+/n6KkMWFm5ub+N73vhdffPFFREQWFAC4RAnssjVtbf5resW+ACMAewWPwDtm2ropGulq6pH5WC5FofYdnQmQ/bw912q1kumjjxkMBvH111+n7+3v78fOzk5sb29nwaIoAQC0Jb0OAdutCBT/7+7u8jUdpmqAClNu2vMHBwextbWVRxiwLWyVuI0Fxr64N0yce1BweV6gHpMsrk8mT+9rI/zVavKMgIzc8xf/4l/8qe/i+eAByr/1b/1b2R5B/6MGjYDWU/cqEpSEtHtqbxX9qkqOiJnkpBKuJ7hWEMOhJFW/XzfXv92zFojKo7ZcKlKXXO/v72Nvby9H8mrSE7i1ODAknPz+/j5fDtVut/NzgB1rgUY8OTlJJ8BIcRD3g4K2djURXF5exuvXrzNRXl9fJ4hy355bJaSKFNhUT3WKBOhQAWM6Njc3k5asCn4OWIODtpAernVWVRLKAkRAjKCtWkLn0lQAShGR1YXRQc+AVmZ/7k+imkwmKQ6mDbC23/3ud+OLL77IhACA6y/XsxrYvSrp4OAg//vq6ir29/eTbgaiu91urKysxFdffTWjTVhcXMx2zdbWVrx79y4TJX3Aw8NDtg4kNNov4/+Eo9if4+PjGb0NUR2bjHjqY3/66afx27/923F9fZ3g2rkumEXfoxqurbzaKkDLszkBWesAMOHvdVLH/gjaEZFJiEi86s0iIllFINzPsidtMnvFJmuLGEgE9GvbeH19Pd6/f58syqtXr+K3fuu3soi6vb3NdkK73Z554aFDFx0lAIArEggit7e3U6CPEWbTQCe7Ns5svehJMNYvX76M4+Pj9F/rZO0VeoCwQoxN1XH0KkCtfjA3N5d75n6fayEwEhGRBUAVnLsHMY7eprbCrq+v4+3bt/Hll19mq/N73/te7O7uxu7ubhYK7MG6sXOTM9iktbW1fH9WBWr3909vSq4nBNfR98rEum9gpRatr169ymf1Z4oRwB1oHQyezu7BagH89V1Q7oHPKaqxn+/evUuioNFofKuXBX7wGpTa6z06Oorb29uk3fUGgQeTORwdlRUR+Rl+hk4BPVmRdFXRC8D6jozc+3UuLi7ydEwBXxIQmDY3N3O+vAZofc7nCdmmN5vN+Oqrr9JgGbAERmNBT+BIbsFH9XJ9fZ3VMNFgPZfhhz/8YaJ8lezV1dXM+CNdT7fbzUDI+bWmzs/PIyJmXr9uTVXMFRAasZZIOVtE5AFmqiyXpNBsPh2CVQWZpo6MrWK8JDVsh6pCojKV4DmtL9Din+FwmElYb1pFCTh6jbn2AiBhdNwIsfuz7k7KJCJ+fHyML774IttDbEV/WyC3F4K7/WXTgsi7d+/yIKaaBLyNlvLfmQrAvVbo+fl57O3t5bt+JBAVsfXb39+P73//+7Gy8vR6eGDx4uIi++CqYWJczAFR5Q9+8IMfa4exIUJVlHNEpPgUawEk2AMsG7sBhgR/SQNQIZ4WP7y7a3FxMV68eJGTZ5VRmZ+fj/39/RzLVKn7vd3d3Zifnx50ZT+toSkc4sXaFrHv8/Pz8f79+3yt/f7+fvzoRz/KmCeuRTwBIyPoYpq1Byh8bx2z9fbciJix693d3WS4iJwjIo6OjnIMfTgcxtbWVrx58yaZjB/96EdxcXGR72nxs+xGYVnPGlFgsuMaG/mOil3Lzn4uLCykFsnz2nMsC32QYpPta1ECwloyiq6I6UgxnQsm5vj4eIbhZDtihhObDQAsLS3FH/2jfzRub28zhwAq7XZ75tBRrUdMELBgvRQnjmrgH41GI/fa+g4Gg9T7KWTv7u7yTdeK4cfHxxTDi5/AbdUUyT1v377N9+L5nm9zffAMyi//8i9HRGRfTt+R02rTqLQjpspsx/rW1ozqu9ls5vkEtCOqd+wEoMIg0FwCW0RkIJSQbRoxJEPiTO6L852ens6IvdDWt7e3M++fQfOZ4HGEsV6nPqN3sNAtvHz5MvvX9B3ugUZAwNQKc5CS6mprayuOj4+TqTk8PMxqrjoX59RrrifMqsA4/Pb2diwvL+foorVSqVTRKMqz1ZoerARcqPTohDAnwE5t7ajC/L3qDzth76oqnealjsZ6LhMFQOHu7m5ERIKEOkWkYgKAms1mBqrBYJB6nDo2a1+MHhPHRjxVgTQK7s2BVUZq9/b2UrhYp4RarVYK8bRPVVPsUoCUIPyzvLw8M44twfBRf6/qN4Vnf+wtVgXbYt216CKmbxyv0zk0BRI6+4t4StL0TShr+hnPzhYrEBPErTdWzN/TcahA3acevWqenUREtpsIbiMi18kaEwEDmfRbt7e3sbKyEltbWzEcDuPrr7+O/f39OD09zXYg/QSWVvHCdsRG4ACI5YP0DZI73zLFJa4+Pj7G3t5evH//PgGxgqq2z+1TBfj2YWnp6Yj3Xq8XL168yOkngBAgqi0J96zdUhlmwtxaPFgPwlqaqNreIR4HmoEggLM+H0CnuIh4emXEb/7mb8Zv/MZvxOXlZaytrcUf+kN/KPb29hL4APZePugcJcwU0OlcmohIEMv/DU9UNtX/16lEcoCVlZVsIY9Go/j000+zlS7+Pj4+JnPmDCQ5R5xSRN/d3eVpy5XdNi339ddfJ4NWtSkKHvqlfyxaPP/mv/lvphFyRnoUC2NSRuCRIKDto6OjTDTADSqNsAeVp+8qgRGI1ckTlaLRW7T/1dVVUrqCvP6kdpLkKmhxwG63G4eHh8latNvtODo6SupQIJibm0s9iVbM9fV17OzsRKvVivfv36d4iejXs3NWI5KSHHATMdVdULCrdoAsYtmIp5bbV199FXNzc6kj8L2CAPAoEVSdivupAe354UhaToTIEZGiViJBSU3fW4IRtNyHfqo9rp+nvy8Ro9Al/9qjlmgWFxdTGyIBAklaeZwVayEo7uzsxPX1dVbbpht8JxZMMmCTEmJEzJz9IWkJdA4NdI/2HaCqLSy2pS2njXN9fR27u7szbBaA6b4cbU14PRqN8pA2/kfv0mw28ztMzHlHjnNntFomk9kjwdH7EujW1lbaHoZJ0JaQrJP7IsykEbG31oDepxYrEdMEAoT4PML20WiUYvB6dLgWLt2IqQ96jpubmzytuIIjVTKWU1uIPe3u7sbf//t/P2OYicB3794lAKkCUxocBy9qm2BHFRPz8/MJ2k2m9Pv9+N73vpcnEYtFJhkVGFo3QBpGVMsDAHU/fkebJSJSewSYaHuxIcxH9VvABUPm+9k4Zqi26Oha7F1EZEtI+1vyreyOl8MeHR3lVOTe3l7qfBQGdFVeXULEihmqdgF0AXb9/tNrRk5OThJcYNQAyJWVlfjOd74TP/jBD1K0PDc3l4CX7g7A2dnZiZOTkwQa5+fnP5YrayeBkL2eRCxG18LF0IJ9rxq8o6Oj+Mt/+S//ow9QfumXfimTDsQJTEREVqcqSzQdJIiiVP3Xt4zW8ShJCjXLmNCgRKgAhSQ3Go0ywGxvb2d7aWFhIV/ypoJVlajkJMzl5eU4OTmJ169fR6vVyiOjVdiSI6fXI1RdWwfjdtAx1oboDoV7f3+fwcXUgWCkTSBxRkSONkpYEhr6mYAQ+8TZl5eX4/j4OBG7JLu5uZnTVIII9iLi6cVuNAwCmv3AhNBjSDCSjvsErIxJYgwkH9WhCsreqCiJ46ytJGhNan+YPQoorpOTkzww7eTkJCe4JN/hcHp2hyCGPtX+GI2eJslQt16gJ5izKzY8mUxyP1WBbFTiss6qHu9dYrtOwOx2u3nfVaRcR2exC4AzgTA/9byKCyO77o+/EcCurKzEu3fvMlGglAFUSYWPV/CmejaZs7CwkCJ1INAbuBURtT8fMT1OvNFoxO7ubtLkbBX7AKyORqN8NcDm5mbqsaqeh5BxfX09jo+PY2dnJ87OznJc9/z8PN68eZPvp+ILq6ur+dbYq6ur2NvbyxaRpN/pdGJzczPbqtoOGD0n6j4+Pka73U4BqYPivvOd7+RrDrrdbk6JvH37Nl6/fh0RU3EzdqfdbudrK87OzpLdwcS5tEIB88po8sWq56pAvrbbCV4l56ohtP9s3br7nRoHMbUYEuPx9WckbC2hqo/CmGkjLyws5Iiyz5CnKuOv2ARcCHu924ggmC3STQISABmb3tnZiW+++SbHyrFn/h5IWV1dTfZZnAX0gDYghI6N6PzVq1fJvGCBEAO17WqPxTlTmre3t/Gf/qf/6T/6AOXf+Df+jTw0zMuzBFj0lAOzUIfGPc/PzxOxerMkUe3/j7x7i5W0TeuCf1WtWvv9fnX36n77nXlnA4waBpyEQALGmGjEYAZjgiRkBGeIEWM0akKUGDzBA0NI2AWFIHiERk8QPeAAjBExURgYmRnemXfT+7VftfbbqvoOlr+rrlozzNvjh19ovko63b1W1VPPc9/X5n/9r81dxzqjOyFEDhg6FH1DpBwkB4IKdHaEa4qKOQYtlOoZgAaG+OzsLM9mES1jW7rdmz53/fJaoykyha01IxyAiEHxXHXYDx48yFZs1D/Dz6BxfPLGjUYjlasWCHO6HO3e3l62D4qqKC+Do8tHhAfBVyfDsNVIF8WvNgirc3Z2NlAQi41qtVrpaOxbTekALpxPq9XKGSwRkUqMARPlWif3qwuj270Zl2/Q1fDwzfwSh0/qJBAlttvtTPOcn5/H4uJinJ+fx2uvvRZf/OIXY3l5OQGVVMPR0VEWC4+OjmbabHNzM++ZA5ifn89UhtRV3XftgzrKRJhkZ2xsLJaWljJnbY0ARkwW41oLoStAAFR0DmDqRJGVyr+8vEwWhgPxUtBtve0d+aOfjLLnxJLVFJUhiDq11Ht59lrTgl1ynxF9B764uJipUbrnfrBhZNz6cGqrq6uxvb0dnU4n1tfXsxDWus3Pzycgcl/Wi3yL8CNioF7n6OgoIm4AoxSR3wt4OCYzfB4/fpwOUAABIApMsC/SU+yVOjG6YY6OOg1ttXt7ewMsliJu31eBYC3cNuNH5E+e/Js+Y4tq6gdrzhYB8BH9c56ApJqSU3tTJ7TWImpAgW1lizVHYCj5n06nkx1wgPL19c2sHKk8588BmWSWH5qYmIjd3d14//vfH2+99VbaiL29vQEmTJBTD9h98eJF2taxsbH0C+fn5zk6o9u9Gf2wsrIS77zzToyOjqZe0HeDTdVDCSQmJibix3/8x98ToLzyc1D0zWtnFY2guyJiYNqhvKhCQsyJwklFrBy2Km4OBiWG4oI4pWG0zt6OBFBn0kgEHKvR7d4M5wEelpeXUxG8ajcNBRWlMnSuw3E6Qh57waCqh6jGENNDMUZHR7PFGF0ndYbir84YS4HNUavS7XYHaH0DreRNK+iolL+UhDZR+20QljY4xrMyFZ1OJwfoiWwjIhYWFrJw2LNwxBGR38OQc5i18h/YY8wYSY4cnRpxA6Q5ADnnO3fuJHDy3ehjBXxYEGsN3EXEwMjqd955Jy4uLuLFixcD8suoX1xcxPr6eo5M/8IXvhB37tyJD3zgA/Hbv/3beZ16voxnu7i4SKOPiRkbG4v19fV4++23Y3t7O0/jxTjoOFJYKJWi2Pf6+jqZy9tMpY4FBh07IlCo3TCc0vT0dBZ422fzXzgfjtjcCUyYdacHigd10dTJmzUSNwsJOAJuOBIAzZ5aCzJE1u0vZhbT0W63486dOwnu1UeQC7VEovmaXqzyqBNrc3Mz90B9nYCr3W5nmzPa3zgC+48FlXbSRus65t4402phYSEDFNfHvJkoKwAA2tvtdty9ezfOz89jf38/g0cOlH2qz6jrrdafYbjYEswW+fV59S9AoM+wId4jcBKgYK5qhwp9q6BSvQcbx/HTZYC21sQpgq32D3iW8j45OUlGqtu9mSUESLP1i4uLsbe3l8G5msCDg4OcEq5AO6I/RI+fOjs7y7Z1fk3Qdn5+cxyE+s1a21IB6Orqaqb7DAIVoLvey7y+agal1+vFW2+9FVdXV/G+970v6avbr42NjXjy5Em8733vy8O9/k/e8we9MCif+tSnsjVTGkEkGBFJK+7t7aXzmJubS+deHb2IVY7f9EnRAeNzfn6eETm6XRdIzXtGRNJdCsWkIhi0q6urzE93Op342q/92njzzTfT6DCUGIGZmZkscKKMBH94eDiNB4cr/dLr9fLYeA5TT70pn8+fP89Oo+fPnyfb8sYbb+SQH4oF2XsWAEX0RHEVQtaWRYDHfAifj4hsT0U1t1qtLN5SYMZguAeKJfrjpCiB3vtarwDAWUNr7Vo+z3BhoiIi6wdQwSKgSjvX1JjI/sGDB1lEHBFprBlfYETN0vX1dXbWAOG+9/r6Otvjh4eHk4kjz2ovfFctLlUcaBBas9mMD33oQ/HpT386C5O1ES4uLuaAKZHw2dlZAtydnZ14+PBhvP3229FsNnMgngiLIQQKtDAC0yMjI3mSKubImVHX19c5h0dRNJbAXnBKIug6qwYbwokLCrAL9K+Ce+CBLtNxjCaQggUC4DgWzl0HWsQN+JHmqalYz4JJIW+CEpG0FLU5Jvfv389hanX+yPr6ejx9+jTlfW9vL8FBRD9N4dlr0aefLyws5JlKd+/eTdujhZzuYgAUIkvz0aG7d+9GRGSAYmgku4BtwFgCcGovsNgcYy1Y5dSB4tqxoh6MTgousAS1cBZIYosrIwKYVcYGKJmcnMyUDIAG2GKrzC8CPjQGDA8PZ72OgANzgREyRwpQEMRVlqoCbP6oAiV6S4fVH/FH2KXFxcX4/Oc/H+PjN4dKPn/+PN544414/PjxwPwnz4lVUXfYbDZTzuqYDqk+RIHgThB3cXERP/VTP/WH22b80z/90/Hw4cP483/+z8df/st/Oe7evRu/8Au/MPCebrcb3//93x8PHz6MT3ziE3Hv3r34R//oH33V73nZl7av9fX1FPbp6emB9lzGmBK8ePEihSQikrK2cZwOlgD4AIru3buXwhkR2T4LUUspzc/Pf8mhYlgS0fP5+Xk8e/Ys62AMUhKhOaVWQefOzk5S45TURMDnz59n4ZtC2LW1taxP2Nvbi93d3TS+IgxRy9jYzdjux48fJxNxdnYWb7/9dtZYAD8Mc0Qk28TY1gIsAErHRaXPd3d3sxaIk5aGm5ubS2Vj2NXIVCMfEWmgGGtOfWVlJSMwKF7l+tjY2EDqxX3VtI7I3TNSas5SKiSiT+NTxtoaXIfWAZcq7z37/Px8nrGj3dYcB6/19fV8FjMptHZycuov1ImgrXXrXF1dZSsjdufw8DA+97nPZWrtjTfeSLbs6dOnSbMrIPVsL168iPHx8QQQ2nE5ijo4bXd3N7vEOFat+M7dUXck9x0RCaDoaj1EMqLfCjs3N5froF7m7OwsC4SxK6JAETLghpZXo1HBHpnQ2izdcHFxEYeHh3nwIWd6dXU1UFeCJWFftF5idTEkWEf6jtVaXl7OVOj19fXA+V7aiR8+fDhQJGnCcsTNie8YzImJiTzuwkRhaenJycnY2dnJ1C4m9uLiIpaXl7PI28RiOqdTw7ONjNycx4SJPT8/T9mpaW+6aD+BfGBBcKILy35L6bInggK1VBcXF2lXJiYm8sgDBZ3abWdnZ5P1EYxhjq+urnJsfgXTfseX8BsaJdrtdmxsbOTU66Ojo6zh2draikePHmVnG9AMiPZ6N6MFBMlsnfWp9VDT09Oxu7s7MLeI/dJuTrY2NjbS5gB61sdkWqBdbYsuM4Hf0NBQ2vw6pl9AJTVPT+s5ZmpELy8v014Bg+/1+qpSPNvb2/Ebv/EbedjRz/7sz8b3fd/3xUc/+tH4E3/iT0TEDYj5pV/6pfj0pz8dH/7wh+O//bf/Ft/6rd8a3/AN3xAf//jHX/o9L/tCYznkTZR0dHSUFOT29nZG+JiWxcXFaDab2cYr6kW1NZvNuHv3biJE0ZWIWJUyA0cZUYSMUh0gZnMYJMacM6inc25ubqYDqIVGUlOYBHltiqU9zWF14+PjWYQrT0m4RFCo3qmpqdjc3Mzi3ampqSyGkjeGijkgTIOanRqlSimtrq7myb0KQLUcUhDKB3go5NWqPDIyEvfu3cu5J7qyasU7KlfR1/X1dRblyZ3WdJ2uoVoQKzpjrCicFk/ghIJJJwIGCv6s69zcXGxtbWWBnxy0NlfGT5snYPbkyZNYWVnJlkRdAcZYc7IKr8kjMCSHDPCqKaAj2hrdU+3iURytXisi0rBJ0XQ6/XZ8EWTEYJRuvRT0Kgasc2s4udv1Oehg0TSDKYKM6A83jIg8Xr7Z7A+4Yxgra4kmx1Jw6gILzmdxcTF2dnYyeuQIpSntfUQMsDfn5+c5BVa+vdVqxYMHD7KYkA6tra3F5uZmpklXVlbi8ePHEREJEsi76F1QZBaGvdSuq2NRzd3IyEg8evQo1tbW4v3vf38ymNPT0/HkyZOUV7VowMru7m5ERKZstQLv7OzEyspKbG5uZpuziFnK3PUU7EZEfjYicvKsYEe6t3YE1gnGXmQAGKodWpWlqw0N5j+pGVPUyVZrO2dvBC11sjZwQP50S0qFAgb8APtQU8I6YuzJyclJ1tbt7e0lQGCTMZbOEcPysIemxWIxzLhaWFiI8/PzeP/735+syfT0dOzt7SX4wZaT/wokajE4WWfrTBheXV1NG4Bhury8zHRvbSFXV6kOqLbBv8zr/1WRbK93M8r2J37iJ+JTn/pURER8wzd8Q3z0ox+Nf/kv/2W+7y/8hb8QQ0ND8R/+w3946fe81wub8QM/8AMDaAxSVP1PwG2A4iiCqmBWBMVh1HZYIKPZ7E/HwxRosar1CBA9ZEwhpFe2trZysM3k5GQyLTXXJ+K5e/dupjsYVwbVyHfdFBMTE7G9vZ2GamTk5ujt1dXVLGJ644034t13380ovP2/T6EFljg7Ba41lQKBizZWVlZyPLU1FLFH9Ke8yj0/fPgwK/s5BCkeEYmCUQVZGJiab8Vu2QvfxfEASdJKwCIDaJ/soxyxqLy29wGSZAsDQMlqOy7Zq6eHqtaP6E845lgiIgGVeif7gR2L6B9FAETfu3cv3n777QScDLGZMArgFFbu7u7mrB8AXcvo3NxcjI2NZa0R1sP5P+6R/AOWCnw5CmvPYQBtR0dHOewrIrL2xtqKrBh+EZsuEKDGe32Hs20i+qek6hKy1hxJLcjVVm5Og32ttUO15dWzc3LqZBSOSnkqCmV4pU6ll6VAapBgpAHQ/L73vS+ePn2aU5efP3+eHW5mwoyNjcW7774b4+Pj8fDhw9jc3EwwyB5IcdovTmt+fj6ePXuW+q1ddXd3N1lOqQj24/nz5zE3Nxftdju7+w4PD2NqaipTv9LegKjn4pSkHCL6QzKxkeoW7JffS90aIYA9AjzoK2bM2mAQapACXGIYpWYBVsydupzbxeGYI0CXfcD4jI6O5rlltX6vptPYsvn5+ZTBiJvpvFNTU/H8+fO8HhBrLebm5pLxr00B6joAo9XV1Xj27FmsrKxEs9mM3d3dtBn0x4wtPs5z0TVHWPBttW5Hd6X/Sz85cFCdnBSeIvfZ2dm09ZjRRqMRP/ZjP/Z/t0j2d37nd7JGIeLGsH/mM5+JT37ykwPv+9jHPhY/+7M/+9Lv+XIvtJ1XbZ+VN4VA5ZE5KY5Fodjm5mYKNPBRO2+AixodcorViTGQjJScdp0twXHpMqq0eq/XG4jSa8TpZzpcKJpc4+HhYQwN3cw1IYARkTUkT548SaMsFzk9PZ1FtqK4qampZCLUhdR2XHUkhqednd2cVooRcrYPgAbIoD7R2K1WK958882Yn59PYKOwVaulQrpar4JGbzQaeeJtZSjkZTFpKuQZB/tcHeTV1VU6Dw43ou+IOS5rCpTU4kMdDiInz4wVqkWeIjvPZHaN75XmW1tbS/Cg3klkitGYm5uLZ8+eJW1dQYfctdoAxsm9SWVeXFzkEet7e3t5Fs/+/n7MzMzEs2fPotfrJX0sbaqYVU2BwX+np6d5fhPWjOPVUSaNJkXlPQIBTlZawih+nSuVCQM6OSG6Zd/UI3C4ZIUemLoZEZn2Y/jb//uwS/Lrs55HAa3rczyeydrrjmo2b85a0mmzv7+fhbv2VYGjmqSzs7Os+cBwYV+xpufn5/Hmm29mQMWhYI/Jnf3r9Xrx6NGjLJKU7pNevL6+Tn0ELtUiHR4e5nlZbF1ExNOnT5PZjYjc71qPBYhgquyHAWIKZDkwgF83DgaNk8bosO21JsJ+RMSXHJ4KnNTJuABFo9EYCDqAdGuLUdTpCXywARcXF7G5uZmgim4CPfwUPaydZwAh1joi4vXXX4/t7e2YmpqKdrudXXmcP6CB0cdc0GVpOn5obGws1tbW4smTJwmWderQOSw+xhVr5lA/gIMPNFlWB5sgTbpfIbege3NzM2uNRkdHszbuvV7/xwDl9PQ0vvd7vze+5Vu+Jf7Mn/kzERFJ5dwueEVRv+x7vtzrR37kR+KHf/iHv+TnWAnCIj1iDglHxQDW+gXKxugwRoSXksitQ7CMioJKOWzRI0fEmXFQ7pdBqULMuDI+FEtahBKizYAGDI42t729vUzpYHkYucqGzM/Px6NHjyIi0pA8fPgwtre3sy5lYmIiHjx4kBXgOzs7aWywJaOjowkggDpFw1iQy8ubU24JuPx3RdNyqCLr6enpnNA4NDSU7Z7a8KpRYvhVl/tMjR6s+eLiYjQajez8ICfWoUZYigLVxXBK1rTSsRGRTsFzqDtwLysrK3FwcJAOEMPGENZomMGIiAStzizh2Mix1Fyz2cyIW0RszgsGiawrXpTiEnm+8847yR612+10uFtbW2n0GU6OLSKyfbZGfvZWZC3A4DRFxsCPgmq5bIWCgCAAjDpXr1JTNhGRciWwQMefn5/HwcFByq/vrKALYwdYcdaYG/p6+7wfqZVaq1JfivClWo0T9150udQABkm0rZAd82tdDe9bWlqKR48epbPCHEvzHh4eZv0OHdrY2MjUG3nDaHGaDnM0MbTZvBlrv729nfJxm51Q0O9nAAHgrjYLSFpdXR3YZzItuFM4au/VPNR0TmXXPP/ExEQ6fuy4VK7gBlMkTeE+ajt0PQ+HTaFfUu7qztTBsCvGxNfjJ7a3t2NtbS0Z4NPT09je3k5789Zbb2UwLU0p3aJL6unTp9nBqh4K84rB7XQ68eDBgzg8PIwnT56k/HY6nSxa5uusc/VRz58/Hxihz7aOjo7mOAT67h4iIhk2TSgCv4j+sRMOtHyv1/8RQLm4uIiPf/zjcXJyEv/pP/2ngcglIgaKCyNu8ly1a+K93vPlXj/4gz8Yf+/v/b38/+HhYdy/fz+N0MTERDx+/Dg3QNSBjRgdHc1JjWgr1B7lZ5gtNgbFBjJUVXFqPYhrU1iOTv5eYaZNs6Eikpoe8TmsCUEcGhrKug3jrrX4KarU5if6YDhcjzOUfwWmHNYmtz86OhpvvvlmpgowBa6ztraW7A6QVFNZzsG5vr7Omhhpp5GRkYxMAUzXNiRqfHw8f885AjPACafQarWyHZRzBO5QmfXANQZN1EM2dcjU9sA67wbwY7iqvEREytTq6mpERA7VU79Qa2NQv4w5pQboOE15XkCm0WgkoJdeibgBZaKw3d3dmJubS6PO0GjxFYk5+qA+c511Iso2/wYIXltbi62trWQs1dSos2i1WsnK2J9er5cHTALFHLe0C7CuRV1qo84tqvuH6andXLWmTF1UTdvVOTsiTXJPLkX61k4gUod5kTF7yz7oXLJnEZF659/ar2dmZmJrayunwe7u7g4wEdKB+/v78cYbb8T29nYWSEbctM1jmWpUjF2QSsbIqYuig2zx7OzsQCAEBOp4HB0dzc4s6VWg8c6dOwOFlcaZ6xKzdtPT07k3BoSNjIzEixcvYmxsLNM1NSqvAYC0ZEQ/hcepqonwM6Cb7QOu7JfnVg9G1oGEGpCqm3Nf9ltXDtvE6dOH8fGbM8gcwkivpN6kYZrN5kCNipqrD33oQ/H7v//7GYhi3XXNCLBqAGrQH7uJ1bQHAkzlDkNDQwkoMMvarz27Fuler5dNGmSYPdQ1JOhnIwWV8/PzaTMmJyezk/S9Xl81QLm8vIyPf/zj8fbbb8ev//qvpyGOiCwue/bs2cBnnj17Fg8ePHjp93y5l86A2y9RlDkmFNC0RkqJxpP3pISMFApePQSaHftSc3GMIIqe8LqXiBgwRhQay8Bgc6C+ryqGWSvuqyJwjI3o8YMf/GCu5+XlZfam+1yr1Uoqe319fWBqochXseGDBw8ychTJADpV8RTfKeJCn9eZF5yjCu/9/f1YX19PqtjzOg+l1iGgX1HBEZHUIqMnwsEm1XNxXrx4kXsu8jR9NqLf+SOFxblh3Rh1ILS2/toDBWsM18jISLbfKqz2fiCngiEFmAcHBzExMZE1QVdXVwPzBZypgwJvNBoZIV9dXcX6+np2mQFsDJCIWOpOy3YtfKuFuqJlxpmTsH/O27C+um+q3tE9BgvYATassyDFd4ly2Yler5eGDLhE6bt/BpLe0oFaH4MF4uwAfoyeTgTsJ2aH/q+srES3283C8ojIupGIfhv7N37jN8ZnP/vZTDfX4kN1WIBDLQLt9W7OxFF4TPYxA0CV0ejNZjMePnwY+/v72c5KTsfHx2NnZyfW1tZS9qqjMZVamkEn2e7ubtaNsC8mUitqx8I9efIkZcaZXVdXV9l6jQW2rxGRsq47Ebsn6LodTAkyFQ7rvHnzzTdTn2q9mtQglk03piJ6TlpwiDkfGxvLIJYck89aU6iN3x9yHzF4+jqmCCNVW+j93rRbfkCQ4J4F2W+99VamlgCwj3zkI/F7v/d7sbi4GPv7+8k0Ly4uJtADHABCQUU926vT6eQBj43GzTC/J0+eJMCqtUKtViuff2hoaKB2c2xsLNlbZQQRMdCmf3FxEQcHB7G/v//S3TteX1WRLHDy5ptvxq/92q9lN099fdd3fVc8fvw4/ut//a8RcWOQP/jBD8Z3fMd3xI/+6I++9Hve61XnoNgki1tb1aDBiEigIWUjx8w5YUMIcW11rcxBbUeziQqlVK/XYVHAAmCjSpyBnp2dzd8xdgrpIvp1Njs7O7GxsZGtyyjAy8ubwWt+ByC4PmfRbN50Jj19+jRTYoxDt9uNg4ODZKNEg5yiVMrjx4/TSMujLy4uxuPHj5PmRG3KoYsIOX0pEErMaar/wU55v5w6h8sxcui1g6KmYThrhVrYiW63m2yM58UQiDTsL8fGQANEjJf9cW1pJD9H3aPNPWNtNaTcEZEMyMrKSoJOAJaBAYSBwVrA3Ov1Mj0gimS0Im6A09bWVoJdtO3CwkKCISkooNizSpNJQ7gH62KPTTvmRD7ykY/E06dPo9Pp5Gmox8fH2QGmyFTraq34r4O2AAIFhtJXCvRardbANGkdVxcXFwN1L+oCgBesVER8ScrPdSrgUQ+2vLycHSDSmQIg6V0tmlqeFUOTI3qvMw1IABTImpHnVRfGxsZybgx5cujn3NxcRuMRkbpUmQX6Uuf31K4Q6WNr4T7X19fjC1/4QnQ6nXj/+98fGxsbmfaRouMgq63e3d3NWiWdY7qGjo+P027TE8zdxMREtu26d/djhIJxEI3GzaF1UpnVjqi/Ik+YdSyemVnqB6enp1OvBEH2RIcW3wMcAjpS8UC+n0lR1WGRtU5QHY0SAt83Pz+fhc9q/548eRKLi4sDtZMAmvRata1qVjCIbJojQ4BDKV0F47WuTKChoF9dDp9hnwBTR1dsbGxkkbD1/pmf+Zk/3FH3H//4x+NXf/VX4+d+7udyEE/ETdSN/fjsZz8bH/vYx+K7v/u749u//dvjX//rfx2//uu/Hr/927+dgOZl3vNer3oWDyUSsTM8nCHBFR1zLLV6n7ACIHWuBUeFMgV6tHTawFoQBWHXXLnPn5+f56YYoy13W1vaaouayETXi/SG9wIxCijl2NHDnLjniIikYFHNuo2urvoHxi0vL8fz58/TYa+ursbGxka8//3vj+fPn2fRrDNZOI+IGLjH09PTmPvfp/uur6/HxsZGGkf1AdJgAJ/IUYEgh9Nut2NxcTHa7XYWefkOn2dQRELW7HZqTkTK8cpF+z8DR7aAC2yN9dKSC/AartTr9XKWBZYAWI2INJAip7W1tZyLgwGsL/cAXOzu7mZUjNr1M7LZ6XRSnt3vwcFBgkhGU9toNTS1E46zxlLV+Qn0a2pqKkeNqzkA2nTebG1t5fsZdMBcqkl69O7duzlcT2dbTbnWFlWOjqMA3mqbMH0ZHh7OlKDzdyL6Y8uvr68TQFhv6SVsi7UESrxq6zrgJCUCcAEdgAJHhqHEdngu96dzCKjFMgkC/Lymo8/Pb4aufeYzn8naEmks4JUzdLo5Gzg6Opq2SdqY7gkeRkZGEpQAFKenp/H666/H5z73uawDMpvn6upqYJ4N+ZdKazQaqcO13kgwA0CoVdJxQ9dq6o4uAr3Sc9LIgICATSrIfgoyu91udlzu7u7meHkpE/YKQAbUrZ0hmFK6Ozs7WXjMLgi2qo9SquD5KvDGNuuU29raGmCI2+12fOADH8gaHPa+svnOfKrpSDbMdc/PzzOFUzt7Li4uYmlpKbue+FHgE2to9IU9I28/8RM/8YcLUP7sn/2zA500Xt/7vd8b3/u935v//8xnPhM/+qM/Go8fP44PfOAD8Q//4T+M973vfQOfeZn3fKUXgPJ3/s7fiYjIlMXW1lamGkRqEYNH3NfJn+hE0VJEZJU7YZXuYEQoFifDIDE+UiFasBQ1MmrAjfoErWcR/TkgGxsbea8V6TPMBjHJ3StWZRhrbczoaH+aoII0f+bm5jJVUkdRj4zcnDhZCyFFPKIDoMGJpRyUCFxFOcSudsS6MoSKzLTHAgDdbjfu3LkTL168SFoxop/u036L0eHIKF9E5O/ttb2rLcYMnPSL/RSJm+DKaEjZ1WJdeWIGUC0KWXNYmzk4gJFnoNiKUE9OTuL111+P4+PjNAAA1MHBQSwuLmb3CufrWrdTQq1WK+7fvx+PHj1KgIxdHBsby/QP42POhTWL6HchiGzrsywsLCSgYbyAdd0s0qBk9OLiIulla6eNkXMR8QH3ETfpoLt378a7776b69FsNjMnv7W1lbMfFJOSCQYUxY42j4hk06wbWwGc6twQCGCY3K96KlG/YyAi+sdxRESepSSgefjwYc4Iwg5E3Dgmrd6Y0BqNA/pbW1tZR1aLPSuLdnp6mnoErEuH+d6JiZvp0Q8ePEgZw1QCBCsrK3F0dBSrq6vx/PnztE8cdLVBQAZZ6Xa7Of5cW/PKykq02+0M8GoxM2CG1cB+ACEzMzMxNTWVM64EE2rtasoFuOn1emlHawoO02kA3cnJSc4ack/kB6POPwBW7Ao7rxsIW66TS6pQMKO2xH3XmqV6b9YFM249TNxdXV1NcEv2zs/PY319PYNga8fP1bbsmurGikuhGTSnu0nQdufOnRwIV9m5280BrhkRA0D/D51B+aP0AlD+xt/4GxlFye3WaEitCRpM5MJI12pudLCCNtG3XKNIISLSEFDEWoQFjUrv+B5KIupCzaMiUb81HQLJz83NZcSnbSsivuSQN3Ryp9NJmlfEosUMQ3FycpKnFlNy0RuEHxGpEKi9o6OjHOPufozh9vnawUSRRW1ra2tJl+/t7eW6K1imSAo2OW8OqzJifs5Z6IywhtaYM5PWU0A9Ojqa9RQo2YgYUDSsCUoWFQ1MAonuWxSrGl6KRyRBPkQpomK/V9QrcgKkRE0iN4WJCtaWl5fj2bNn8dprr8WLFy8y+uz1enl+z/n5eZ6RwQF0OjcnbTuheGxsLPPbhuQxktKf0gy36eHKMqJ/1UtFRKYYAVR6AgR4NuCB8XddBo/TUugLONEXVL3vpaeKg7GiwKp9qo5eDRTQIJ0iUADYrAO5wJAI5rAxWDgGXvG85zGDQrGo73ENzpUD2d3djQ984APx/PnztEXAsaGPnU4n9V43jwAKO+aZFX/ShePj41haWorR0dF46623sniZba01e5yleRpDQ0MJHoBedmZoaCjrT7AHAoA6OCyiX4SM2SOvCj3poDSD0Qi11knqCuOCYcRQCmgAI7a6NkRwvuTf9wMWWEeFoeSOHSd/KysrWbjsvTW1Yj/MchFICyIElXWQ5e7ubh5zoFCdHd3Z2cnWcozf8vJyrK+vx2/8xm8k6ATaNTdghWstDnsI0JIXe1KD/pq+U68HzJo59pM/+ZN//AHK93zP92TBDjpTYdfV1VV2iPi79uf7jIUkSBiPy8vL3NzaWiyqgMKBDNeqxX+1QNd1RfIATEQMODW1BWdnZwkSRPqUGOVodoVro2m1pDI+WAnAQc5QXnBzczNpTsDAvxlMSL+2hHt+50BIZ3CenKwoVwFXnRVSnUZF8BypVjsA7+7du3l9azg2NhbPnj1LY+J5sSHm5szMzCTFCniKsL1fekstiw4GUZ19ZTgjIo2VFAJFrUWVmASAGtjDRlTnzOg5tO3i4iLu3r0bz58/j+Xl5Yzg0LXmKYhEgXGdYzs7Ozke355yvKhfEZXPicoA3tPT04wEOSTpEMaHbknNYNU4IwYfe6Og1X3ToVogSraxMDX9JnKN6AP0ms7B2l1dXeX0VcZU8CFdp0tFQIPRwJLUdLB/1xoSILV2DLEd9LWCaDUnbAK6PCKSBeVYvNS01NkcQDMGSJQaEQl81f74flG8/1sLdL5i4XrSN12ZK6fZuldpNoCTQ6sAC5tMh+xv7ZSMiIH3VZB3586d7K4CmgGaytACO+qO/J5uYefMvGHjjXdgU+yf/ceOspM6t7AHNRVTmZfKmAKkOmjYfk6eXrNpEZGdWxERq6urcXFxkYw7MMbnYDawshisTqeTdZqzs7PRbrezaL6yK2ybwlw/M2PJwMJOp5PpYRDC5+g2ey2VbJ6O9OAfeornj9ILQPlbf+tvJeKHhuVLZ2ZmcsF0A1SkB6xERIIV9HJEZPGfQilGD5qP6CNkCJcQcHgEnrN2DdEpqhUipqQUR3RenYp6h4cPH2ZVu0I+Lbl37tyJiMjzbhRXnp2dxYMHD+LFixfx4MGDpCS3t7dzgBqHbbYLulTKAONE+IAcpzVjNRTvyY9znFiriEglrBEyxqXWftjzmlu3/oCEqAYo0GGk6E7vvhSNCEwkyzAwSIwbYCoFofOJgQEsamQ9PT0dOzs7afxFRIwQxo5RBybJMNaIw1bnw2CKFmuxsHuqhnBkpH9+SEQf6No/tDADLZpvNpsZeaPUyQOKGKC8f/9+fOELXxiY/QFY+puZUfNgjzhWhXWG8elY4TTHx8ezwLQyODphat2S55Fu8T70PYdI9zkjoCgi0qmQM8/rfmt6SA2PyFpxsYJxNoJzqw4WHU9GyD1GABAClCIiawfUDLFtS0tLMTk5Ge+8804OfMOAPHnyJJ18LVReX1/PAW1SH2p0RMuc6dnZWaajR0ZGsn5BcTLG1j5iAck04MKm6ooiszWd5xpSjRoCAJDaWl/TKfRYakVtU6PRSDAN/ElrA0GCEvtCRjltaWD6Y10ANL+rIFpBPrBTUx8TExPZ2UWngL/T09O4e/du2p2Dg4M8U4e9FEB3u920Ndav1+vlxHSp4MvLm2MzNjY2cgS9e5qdnc3uI/WE5LbWOHnGmi41KNXIC2vAXgCRQA32/Z/9s3/2xx+g/M2/+TezkNQ5MiMjN7M2FOtBjLVSmyD3/neLH2GquTJApRay1VRQRJ9BiOgPoKqFlbVFzLWABVEg5C6qgGgJXM3NmiA7NDSUp6ZKGaDSNjY2kjZVWEsh0eKUfWpqKutdUJCibOeRiEQVxDLG2mM9K4X23dp65YtPTk5yXwgupkievtVq5Xh4bWki4na7nbn+ypABLdXAAJGVro+I/D+gKLUgAjGHRZRXqXyqIrqp+ft2u52GscqolIRIFJsjFciJ1cif8WV0b9+/nDHHq9aoAkSdNLOzs1ncGBEpR9aBowCaq1wDU8baA+cLCwuxsbGR6TfR+Ec/+tH4zd/8zQRV7pNzeu211+Ltt9/OdTo+Ps46KsDAM0ZEglk6owsCawJA1NZ/bIjU4+np6UAxqvUBfjgsqZ7a7VGLdgU2dA2rVe0F+lzrOVtQo0zrrnB5eXk5Op1OMhKuIyrGPAJpnLricPNEqk1zgJ8WVzImesZoffGLX4ylpaW0CdYA5c+BkROOml40mzfDLtfW1nIAoah5YWFhoNbCDI9Wq5VMiBoH4A0orulYIJeeVtCLyas1ZPSFzcQSAXe1DVlXDGDN/pNfrIu2b7YFoJXaU+8TEQOt6/yCGVuYwFp6IPhVdsBHsJHj4+M5eNQhkrUM4fj4OD784Q/HF7/4xZTH1157LZ49e5YB0OrqagLiJ0+epJxhqe/cuZNpwfX19djb28tAGxMjuKZv1kdphaL4tbW1ZPVbrVY8e/YslpeXM6Vuz9rtdvyrf/Wv/vgDlL/7d/9utrbVjg0CzaAQchEKMFALtKoR8X/I+3ZO1PVV+qMPVYbbTAJYlU6qRaRQWxgjIt8j383YYWl8320KlRE3gZZxqnUNDO7+/n4enmdEsijS+kRE1kWI0irAU7VveuXBwUHMzc2lwss11sI2ax4RGalhKAxJQovqLmLc1HFY08pwiXg5YI7WDIca9UTcgMmab2dwav2MllQUOuMFiEZEdkUBmWh8xcLoTmunPdGkTOkT8y/I2NnZzRkqwKxKfJ913YWFhTw6gdwr1q2snboIkbn5BTMzM3luTERkisdsCKCAgXZ/73//+3PNHz9+HHfu3IlOpxNPnz5NkKA40P1LYaqFef311+PFixeZLrW3hr5JKXoORcCeE5gFlkWqWCl5demJ6enp/HetNdL1xniqeaF3qGxAVTABfMqv21upMp14QE8tkO92uwMzTGrKBxAkk2RJB0QFfLpi2CSHQNIxLGet06F77Ijvqx2J7rmmvGoBrA4uaUuyWYtUazeSNAVQKip3nYjIfaj1TeyfFC2mnM2ubHdE5LgAa7e8vJwp8EajEfv7+3kcyN7eXoJUhfz2rHZKSXcDIYKr2v2jZm1paSnfR84xDwIf96qu7OLiIl577bXY2trKe9FdGdFndrBTZF865/Hjx2nf6IeUzPDwcE4ZHh8fj9XV1Xjy5EmysBhS8qYzkhyb9st++JyUvNTT+fl5tkJj3QC5Crh1zx0eHr5UDcr/q7N4/ii8pEB0C4joq2AQaO1ZznUwrlpLJhAj182gMHYoQpRsRGTEYMYBtKhYF2UK9Ij+pCQUdzICCpsWFxfTuXKCNl9L8PBwfyS2FraVlZWkdp8+fRpzc3OZGmCsgS6OkBEmLCh391nrLUQXaElRpkPqHBzFuC0vL6diyb3Pz88PFOaKooCGsbGxpCc50cPDwzyV09RJ+wWwyMdSDJGSAmZGvnYeSVMAjFgNlLaR2ZyOSEgUfnZ2Fnt7e/ketKYiMyDS71Ck0gCuOTw8PHA+k46UtbW1BHoK8SL6Y9w5lIj+MDPR4+1oUqW+dArQiu7FTonMxsfH44Mf/GA8ffo0Li4u4gMf+EA8evQoCy+vr6+zfdLZOePj47G8vJzgTMGeqB3rp0YgItKBiPxv1zxgr+TMa8qvRtfOIQLMNzc3E0wCPuj4iMh1EwgISKTW1INZV/pgzzgmzrbua0QkIBwdvZnuDChzttKa/jZNdm1tLR4/fpzBj6ALY6kQuA7Bk/atTEJtcybfGC1sANu3tbWVgZRrc0JAztLSUkREtNvtOD4+jqurmzH15BaAE8TQZWlnQFx3I9tsrZw/xIFVlkW91cXFxcC5U4o0gbyTk5N4++23s5vzjTfeiNdee20gha+TiKwAYwKbycnJuHv3bg43A778TooSeFNTpzAb6On1eplmVn/Bbul4A7Ic4ihowTaQU/fe7d50NgLkte7HhPFGoxHLy8v5fkdoXF9f5ynnALhi6cqs6pIzRA+7wofVOp1auL6/vx/X19eZpnL/bOb6+nq8ePEiWaSXeb3yAIUB4/iroUGji/w4Q5GWFAogw/iIZGuxanXOwA4DR2AplQ4UDEqljUXmaL06/AdCNSBoZ2cnxsbG4s6dOzlci+Mx3E0NyP7+fgKZra2tbEU9OTmJ+fn52NjYyJwrJxlxg87RzPLKAI0IkhJhn7QmAy2rq6u59grTPJuZJQyg5+31ellM60yRCoZEd9ba7Ibl5eU4OzsboLoZRUxBRCTrwQDZ3/Pz83R+NUqsAAfgYdxrpb3PKKKcnJxMehbQtO9YkZWVldja2hpIUYlQhodvWtoZEQddXlxcxIMHD+Ly8jIdh3kFDNje3l6u19zcXLJDBwcHSenKSZvPgCkR9csV6+Yif77n8ePH0enctGCSQTVNWk/Pz88zPdTpdLLgWipMBL2+vp5rIFLlxOXSq9OiB9aebgJTdIqzFXUzvvS6HnQHnAMDfiYIMUHVunKM9BlbUWvJjo+Pk8ECsKzj3t5eXF5exvPnzwdm/ADdAqC7d+9m55O2doBInZROlGazmQFWBQSVpZDuVdQqJdhqteK1116Lz33uc7G0tJQdLdKJPmsNOU3HFmANDDiUDpIyMesoIhKsWUOAXMrN0RwAdK1J0dEFaEX0D/ME8DxTLdxst9vx9OnTnLMkrWWonvQze6Hg2PAzwP3tt99Om2w/OX+yJmisDRfqLQBnTOi9e/fysD5B8MOHD2NraytTiK5TmeB6KK7UjImvS0tLsbOzE3fu3ImdnZ0cXYFBEbRjvDRVtFqtZEiNCmCzzs9vDqGkz/TzNqsEOHuxd/yDkgDtyldXV7G9vZ3+FvPyXq9XPsXzfd/3fVnoJOIAWBxaVc9A4QQjBltJOU2Rsc6UGg0x7moIFIOKglTzi+r9nJNHM9po45lVplcmR3cD5IzWQ8N+zdd8TTx69CiOjo5ySJ6TjRUiQf/y76+99loaZQZWZG3NdOGIBDi9brd/eBuaErVb86kiym63G6+//nqCPTnx4+PjRNgMFXADQFEYRYk1VVYPEay1GwxUHZ/NwC8tLUW3228vrLUpjCbAab+ltBiLiH4kgIa1Rt4DWEkzkC3THGvEw7mNjIxkSy/jqWBWTp6Tt07y76JUzmJ3dzeL24DdmgoUQWupdN1qZDmnCoKAppmZmXSKFQBJD1Td5Hg5F0WLU1NTA0e++5lIm1y6Pieh0wtA1H3QbrfzQLgaZfpsbXvnOMmM90VEdsBIb1SAc3V1M++DncGEMdBYuIjIoGF4eDiLadH4ggLrqIsJWxtxEy1r05VOApYxKGNjY3H37t347Gc/m4CYwZ+cnMw6A+lZuqleBXAX5GCJzs7O4p133omJiYlkJ8z96PVuzt7SOizdyp5iuNTDTExM5BlDQKmTrz2XmiFFmufn5znXRbAh8KMf2DHpOO+1vxsbG/HpT386i6w/+MEPxkc+8pFYXFwcqCljOzAaNQUsFS+QE4jRJ3tlMKUAc39/P5lttiAiEnRKA2IsBErsKXnodG7a/tUpSakIoACNWkeJ5VKD9Nprr8X09HS88847qduuNTo6mgCOLEqJ+26jAEzo9jxsJfa56unIyEgG6Bh9+iYgA/QODw9fqovnlWdQKNjo6Gi2LynaQdlNT09nru3g4CABS0Qknc/oisoIaq3wVsg2NDSURUCEk4DUoj0olJIr0MOEKD5SNyAnzXmIwBgBrECj0YjPf/7zWa/x7rvvphCj6Hu9/vTOycnJ2Nraiq2trZxlEhE5iTQikmK+vr7OsxUYavMsRLDy4nWCIODAIFcFjIjstBgfH4+9vb3MQ6+vr2fU7I+Be1ISKN8KGjnE2ykaXRraL4eGhtJAikorsyBKpOT+X8/ZiYj8TO0QcA3At9YdWRdpLZ+XtqtFnp1OJyvn3QsDPD09nYWXADTgi7ZlQJwY3W63M+UhypRiazab2VYeERkZYbkAvEajkYPC3NPz58/j3r17WSA7NTUVKysr8e6772Zd0m2Qg6WUmpT6UlznOQUAGBEzOEZGRnJKrsFrnU5nYJ6MlNHtrq9OpzNwKB695KB9Hhi07nRYeoEsqyehB4CmP55LnZVaJACes3OyMLDP3kxMTMT6+no8efIko3aMRLPZTCewuLgYb7/9dsoZp6Sj5xu/8RvjM5/5zEC3IT0mdxiky8ubQYS7u7vJpO3v70e73U5w6LWzsxMRkYW70pGA3+joaKZcMEjeL7VVWWJpQCly+2bNfa4O7BNU0sXaRWgPHzx4kOmplZWVASeOiausde1QAWJqt6G147ClALEV7l8qrwYnnklQZh2w1mySmTWeXweSAILsqZ0h5+re6DzWYm9vL9cYk4PFwUoLZMi7dJ1ACWuIAeOH2E1F8tJ2GEAsmZknExM3h9rW0Rx08j39+6vOoPz1v/7Xv2TkthRKRH8CJmEgYCJQwoA+Q00z/JQQq+IzlA2S9B1Qrg0CKGqagZHjPBwwp9AI82GITi1IJTCAjTQEpZqcnMxhT9C9yEcR1e2Jp6IIhhoFKsI01nloaCid1tnZzZkcp6enaXBrjr/T6WRdDspcLllLHINgbWunyNXVVUaA7hVFXenFWrOgyA1gVYTmvbcZFw4eUFCjJLcKtGLAKLxoWMTDeFjDmoqznr6LMVSgy2mLXJw+quV4fX095Z1jBWgjIvPIanYqSJ6ens692N3dzc91u92MZtS/LCws5HRWnUGVVbJ//pbvVv8k2o6IZLAYPwATAJADB2aBF+uoQLwWlVtTLa7YMYGGf3NYdA5z1GjcDBlrNBqZbuMARMq+v9VqDaR+RNgA2d7eXmxsbKROVydq7cmXAEjxKF2rqQOOtRaxc9zVid0eQIj1kEIUcIyNjeXQvpGRkXjjjTfi2bNnWeyOUdzf38+aNTIhpXhxcZGD49hR7LLn5NQVdQLltzttgDd2lS5LPwkYgYPz85ujQKyZmriIGGADJicnY3V1NZ49e5apBLOT1CDqOryd8q/F9XXuEZ2qxczk2YC0yoAeHx9nm3aj0UggQs/ZdYCUPLumFJl2f88vuJVqrLVsCpMFKsoGpMtdA3iRbonoB3QCkTqLhxxLoQtGBd/sKgLAcM5erxcf/OAHs319c3Mzz9V68eJFsqbuf3t7O5rNZvzzf/7P//h38Xzf931fGl0GprZxcQzaWCkLQaUstWgHZV8LYOtsjkptoRtF0QTDJEbvla+UckK1coCcNKOn8GppaSkZD6hcZKxOYHl5eSC6cW6Ce1SodXx8nPU35+fnsbq6mnU0ChTlsWuxsLylNTBIrNm8OXzw6uoqB5nV81LqEeUc8dra2kDqCehAD3oG66OCHv0vfRYRafhrxTvnzrkxipxBLfytg9lqkaz3cC7ADxaC4cDkcPQ+GxH5/aLvRuPmJGkOxHfqCJmYmMgWb8/Z7XZjY2MjjZ41kcqwdhH9Sn/GzUF8iq+ddkp+IyLBCdntdm/GkQPFWgftD4dTz3xqt9sD94UxqMW7HC36mlMXUNBPDBDwJafu2oCTCJD+YYFcV6oVxU73axeGqLKutfuxJrUAUQrWmgGqteumgjnXsi+uUdvF62A3E0KxM9hCgMV92w8nDfsujkm0reNP1M2ZkT961Gw2c/qoUQ3YKpF5rVej965R02f2peoNtkUtl3UhG2QZoFAXJRDRWUJnazODfWMba/pHDY6GCdes9TB8AMb08vIypxtL5aqRcb/13sxmISdqM6QtBQnAOVmvtYh1eOPh4WE8fPgwnj59mgyK+5f+15FF99U7OpkeUL68vEybiy0jZ9h9E29NS9bZR99ruzz2iX5cXFzEvXv3Uh7Y/tptVOstBfu6Enu9XvzUT/3UewKU5svBgT+6L0aPAET0j7S3KSIDRg51LGqKiGy/A2go+tzcXKyvr6cxYMlV55UAAQAASURBVDjMCmHMGRpCb0gZukvetbb/NZv90dyiSYaR0AMDFXw5z0OkiAGSU5b78/fu7m4OHqpzN3Z2dlLxvH9qaioF0Dqikms9zr1792JiYiJ2dnZic3Mz6WJUuNknTpyV31XIWA348vJyXFxcxMLCQjpU0ZRD7URYEZEsme4WtQYQOgMKtAInEZEAj3EV7VRDJKK1V1g1UWxtncNamLFTO4hq8WXtvhDldTo381FEeM+ePYuDg4M4OTmJx48fx4sXLzKiqcBpaGgo1tbWslIf0+EsIl0CTuNeWFiIFy9eZAQkVVK7RKQ22u12Mnuzs7Px2muvDTANh4eH8fnPfz4PfuMIUNaVZSITBwcHGYmKrDl6aTAMWm1RlwenL0CXQEARNnYJiELvVwAJTETElxTTo/HVlWAqOWSOXLcOW4KdAgrJHj1TY9Nut7OoVUpJJNxutwcK9NkfDsgRF9fX13kUATajdhO12+1kdKWLPR9QJGXoO3ynInCMH5bDyAJ2aW1tLXq9Xjx8+DDlcWlpKRYWFtI5jYzctCNvbW0lOJNGqg6qgiPrISgk5+zJ5ORkTE9Ppw3G5KmnkHJXVEonpKIxPQpz2TTPzw5bM2tYC+7pvzUH/mvRqO7NyjyY+Eun6Juif+lzReOPHj1KtqLRuDksE/ON4djd3U3mQwqFvtqL9fX1mJ2djV6vF9vb23FxcTFQVI/pBnBOT0+T2bh///5AGYBDcat9vB088BXkB7jc39/PoNl369J8mdcrz6B8z/d8TxraiMhcJmHATKDAvbArtQKbUcB02HhAA6q2yJw4sCPNIqUgwrPEkDeKlJL4/koDixp8F1ZImqrmzBlhfegoYN8R0Z8Bweg9efIkkXPthrl37152JYjeOJzV1dUsSmS43UNNn3S73Xj48GGenNlqtXIgkHSPezs5OYmVlZXY2dlJY2u9a55b7QWwI/9b6VvAiAHm9KwdRzw8PJzORAGmCEF+vtVqxYsXL9LIY7Vq/YpnF7nV3DVDYq9qigj4qgV46hhQ+rUd1HfVmqFms5m07L1793K4kjTB+fl5PHjwIGswRETSbmRTrQXatzpukaAoUQqSka61NSI8U3YnJydTjzgFkRP985yej9MRYaPJJycnY3FxMd56660vSTVIsUbcAGdyi4XTIdJsNrM7TUSPvfI89sX1OEsAUAqSsQbCOF0gmT4qBqxdKuwJsFhTwZ7BHBNDFn2GIwecFaBiYkymNjJAdDw+Ph6PHz/OOodaNyGSlt6MiAQsnL9UmXTcyclJ3L9/P168eJFsV0RkGkHkX1OvQ0NDMT8/n8/pHgHrWs8jLUxOpG1rAbt6HE0DHCS2EOCvLDkAKMDQ2QI81OJ2zhiQjujX8WBBMDhscWW/AdiTk5PswjTA7PT0NP/G1Kqd3NnZiaWlpQx8amBW7QKG/vr6Olv3IyIPaG21WvG+970vPve5z+WsHUW00qVqt24/M8ZR2hC4FMgJbhS2185FXYkAJztFb+hQu92On//5n//jz6DUzhgRpEW2QDpLvER12A5KJwVS52bI04l+UJuiA8YKYmas6wAvTlP06MCt119/PecBnJ2dxeHhYUaJqNFKlXESNV/rGaQeGo1GrK6uJkKPuIk+a+RAyClbRKSx7PV6sbu7m21ovq8OwvN+k17dnzSJ1ADjoUK9nqOB/Tg5OYnd3d2MdrE+nLdKc0rFoDGck5OT8Sf/5J/M9x4cHKQjPjw8TJmoRgXlaG1Etfah3W5nwXVlVKQ90P72gOFmVIylZgAZLgbJ9/Z6/dbzOsU1InJ4HJan2+1my6p8PjD47NmzHBEPNE9OTsbTp0+TkfIc8uitVis7AQBn+1LvD6CKuBnapyCTYeJYrLN9YVwV5lbHDcgBGFWXOMp79+6l4To6Oop33nlnIMXnOa+urnIuh2h1bGwsJ6bWwAOjhCFkB+wLWVaTYR0ECY6lYFN0ymCYgB6MpuBDu+zCwkJMTk4m44eRsl+tViud6+joaA5h44jIKlbQvnW7N/OG1P1wqjp/AGgpR9+H3RXEKeAcGRmJ5eXl1EOpbesKEGlEINtkSIpPOhADwl5fXl7GxsZGdie5B/ZapH96epptzzVF0mq1YmFhIR48eJC65dXr9WJ/fz/BSUTkvQFmbJA6QHZNPZzOItNcMTZS5RgCAUREpON3jzWdub29Hefn5zlNWrBobpR0qHTP1dVVNgMIqNjDhYWF7NQbGRmJBw8eZFu6Giyp1kePHmUtHhaWz3BIK4Cv0Hh5eTkBt8YSQYP7B4J9jy4/etFoNPLE71rcf3l5M2p/dnY22cz3er3yXTycKETMSFBqYMPiRUTmHeU9I/oj6zl/DEytLBfRcRzyfYw6B1ijY3lfh74xABER7777bs4qmZubi1arPwadQZKTFbVXEMCwYH2gccyFZ5ycnMzTapvNZiJcTphA6roAAii/WhAHWXEuETHQ8UGgARQUq/ZGDkzFOWDDcDCkalcgcv3zETcR0ubmZhr0w8PD+O///b8nZVmLM6UH1OHU9tmrq6scxa9jR9QYEQNgAuVtBoe6IrUnDIvia9dXgCedpOAPGBNZ2GupSC+sIOB1//792N7eTsAmt8wpMt5y9eRa18DFxUWONudAGez5+fl0Kgxdq9XK9xtmJtqWM8cKtVo38xnkvf2crEmZVkNuj92nduGISAfDOEsdSG0a2GbdFNBas4hIOa3t2aJFoEm0WIEaBlJ0rJ6i1+sNTN5E6w8PD8f8/Hw+K9uxt7cXS0tL2Zq8uLiYulGjZ/eK5WGj2BnsGcbGSbV0XtEywMhJYXOxaH4uTVxZRd81NjaWJ1ofHh7mvCYjBdQXbG1txdzcXDIwWAq2lr1lJ+hPXXvrCvgJNNXqDA3ddFfOzs7ms5s7tL+/H1tbW2kPFc7yCbXLEtDWWOC+1EfwHTWQAxY9s9rFiMjJ2QbRASIRkam96pf4AeCX7BpWqQZF4az3kwvvUYhsna6vr3M2kTUFPnVDtlqtTPFIdwFH0ngyBbISnc7NQD3zVjwjdq/O5BJYzc3NpQ/Bokgn0ruLi4s8OuVlAcorn+L57u/+7oiIgehMlMD4mWSJLmUgK1pdWlqK4eHhRHyoS9F3jSQqcBHNcDg2HCKXP5Yr9POI/hTaOtmSYA4N9cd3+xyabXV1NRkKiNhkRXNYfAfaHs0v4uFo5FLHx8ez4LE6d8xKRMSTJ08GalgYmLW1tWSoFB9KNVgfLETt9qkFik5cjYh0HBsbG3kdhriOz5ayqikhTl+0iPGpLaO1kBpzgr5kWHUhYVfqLADs0+XlZQIPMnf7Xmu0a+iSolKAjaxiBnQwjIyMZF6c0ZBu037abrfj9ddfj3fffTd/T2YYSjKuxufy8mZ0+N27d2N7ezsNVQXAs7Oz6aAuLi6S9RNlkiEDsABf7ePWnGFWv4Et4rikEawpOcFYqZsg5/RW26N2b3J8u44IcyZQiYicI9LtduOtt97KVGKdYVEZFevvZ9KDCsMBJAW+tWg4IpLBqZ9nxHVj1PbziMhAiw1zLbJp+Jq0Wa11q92KtZDXdev8jWazGWtra8n6+A5nSwG7ZrC0Wq3s6ltZWclzv6QRAD2pKPfDhgIxdIRtqmlpaXpMhFQuWQYmMCER/QNBMUhScz5XgSumWZ1KLdYHPski2x0RKfe+GzshUKzzWdgboODw8HCgwQIQN6W40WjktTDJbBp2guxImSuApWPz8/Px5ptv5vlHtZvMyAaAogL1VqsVKysree9sjsNq7ZV0JRBlXAGmEnsEEEZEpjnJh5lPp6en8dM//dP//0jx1DYphVa1qLO2TfmZdIbUAOqckKPvqsHgaG0EKhDLgEaLiIwKUHUXFxcDKZGFhYUEPBGRAhVxQ2nX8fAUTREch2j4UH1Oiuh+j46OkuLb3d2N3d3dePDgQSwsLMTMzEwK8/n5eRZkuQ95ye3t7XRGHJTBSnXcuhNvrcvy8nJGAwAd5UCbSlEdHx/nzBiTZd0bByJyqkxYZaoiIqNhhV8AplHzFLMaMHKETpeKmp+fT+cIMHBYHAOHiblRi4H2rpH7zMxMAlksGDq+5tAjbgC4z2OijJcHUsiTKbTm0HgmHTUiH0Vz1kkNCTYQfV9nA6n5AARFbmanuL5n297ezjoQLdKuL7WhLof8V5rempIDJ/SqrSLnag0ibhguKR3FlORJtFY7dTqdTjx//jz29/dzKCEQAxyRGcEDeRgZGUn2DqMiRcwZ2sexsbFkTejl0dFRHB8fDwD6oaGhBHDqg4Ac6SbF/u6p3W4PMArdbjfXQ8Ek1seMiq/7uq9LAKqFf3R0NJ4+fZpTq9XoLC8vx9raWkT06/qwj1I8Q0M300OdcIzaV1smGjeHiiwBGaJw4ES9CacpXc4OK1JVPMspApUCG+kOwQdbRiYEBVL6HHCv1xtIodTaCrWDbBvbRJ4q01I7fRQEq7m6f/9+prt0LgI7gE49r8tgS6njx48fZ4MGNsTvvvCFLyS4IcN37tzJQMh6XF5eZj0SG31ycpKst0F9WCYABwuOvdfppY6KTQDgBT7GEPg+evkyr1eeQfnkJz+ZQghYRPSHA1XqskYSDEmtPrawiljRpBQAIrXRhI+yiqrURlAehZiAje+jAAzS2dnZAGWuwEiEUSN+QEekxqBJC7lXtCqafXV1NRqNRjJFvkf7mgIuTr5SjLVwa3V1NZ4+fRqvvfZabGxs5P0bNiY/bXAaZ7m3t5etiJ5pYWEhDg8PMwIXPUtX2TMgVEvu+fl5FmVyLBGR62qNFDRG9B1C3TPUL3ABUFp3IANtWyMZDEONCkXrp6enuW7of1FaRGTkrJDYkCdR29raWs4XMYGzDpfSzfPixYtk7sg/epnTVQOB7kcDt1r98zTUaUREAk31ABGRbcVk0B6RU50UtYVavQf2zv4AC8BLrS2gL8B4Ze0UAouq1dPUaFCu/NmzZwOspWL2iEiQynDXwIbO+iwgyHirG4iIBL4A2c7OThYgXl9fx8rKSnQ6nTwXqNO5mXtk3g05Be5dk71xpETEYD2IKF+qNqI/sZSuYy6Aa4W3p6ensbq6GhcXF9lWK+VUC5fHxsYGzoNSV6GzR92RdSE7zrnhuASBWDrFmjWorKlVrdQVvAKu9It8GmZJ/0xnNjeldgXyD1IaikRdHxNuDzCPXoIH71HLJgABZth3LJY1qjafnXZ9tgv7BbxpmGAnKsNSOwkBacC6Tp6enp6Ox48fJzAyl0i9Ftliqwx5o7vVr/r+urf1pOq1tbX4/Oc/n1PS+dhKGExNTcXu7m78zM/8zB//OSif/OQnM/plvCsQgMQVCtYOCs5Z7lKU732E2+9t8N7eXqJS3yG6U/hIadGiNQIVGUVEOiHfA1Ax1LdpXkCg1+tlbYhiQwZEZFWBjmc5OjrKaaB1rWq0AZApeFOTcn19nZ0F8/Pz0el0ckqr4kuU7vLycg4HU+uDiahtrJgiUbHIgVPBILivyr54RpEVx0FJrdnk5GTs7OykcWfY5F1d6/ZJqGNjY5lb9R0iMPRuRAwUMFZGBkMXERm1OYTs6Ogo2wk5rf39/Uxp1BH3QItrcSQAuEjK2mC2AEO5b6yJAm7gRAfPs2fP4s6dO0mrA5n2AMihD+SV81AnIiUIXKq7qcxVlTv7L8VWZbnWKBmuhiVTdAcsSJFZIzaAg5ZWk1MX9auZ8AwcE53RXVfbVckJcGW/MU+1u4UOWwNAbG9vLyNaNRPW++rqKlZWVrJAX5QNdHBk7sdBgQ5kFNHq1ANg2Qo6THYANPZLupiuaH+310NDN6Pr2+12nJ2dJavkfrCMGId6gjMGptYLYkqlGcgcO1TtQZ0tJaCQosPS1JQJ+avtzXSp2thqb9QaseV0TsMApgeYrl1IlfWQwidvygfUjQD8CwsLeTxALQq2BhH94uw6xdvvDfgE7Hu9XtrgWi9n7VxL6ldTgfc6l0gaeXFxMetH6Py9e/fi+fPnadMxtA4kpAP0FSOjPusXf/EX//ineK6v+0OebAzDouOm1iKg9Sni5ORk3L9/P6M518QWOKGxFklGRAqtLg3gptlsxszMTFYqN5vNpKf9Tl95PX2Wc8H0KISC1rERGxsbSQdThsqE+A6RF6TLybrm+fl5UoGcAwVmyBXTAX+KSR88eJBGjjF0fxzPkydP0rFyHIBZp9PJEc4irZpCAQb8YRDtHfS/u7ubdR0KFuu/AU21LcazR9yM7WaYsSBAbKvVGpg9AVh2Op2sVapU9NjYWAIBE3sjYoAhYkjeeuutrB/ilMyMIZcM3vn5edYFMf5kGwjpdDpZoIwhMLXRuouUpFlE6GZQ3LlzJ4uFXVOUBYzcvXs3C2PVCdT6C/degc/U1FS2JZML3WgodM6As5HeEXVG9KfoTkxMxPz8/EDaqxZPi0zJvZojeyGKQ6Gjp72nAieGHLuiC03dB5BMFz0P0EX2a7cNp9NoNBIwo/U5fyyb3P35+c0U0rm5uYzq62nrCizJZ6vVip2dnbR3npmTV/uCCVKXh7VbWFhIJoQ+bG9vx97eXqbRtMHu7OzE+vp6FvBiSIC90dGbA+2ABcXyrdbN7KPFxcWBLi9AjOzaZz8HLnXH1dq9sbGb6bkAGfvKlus+sTdSZ7XLrdfrZb0i+2EvMMDqqdTYmPHDDrj/Op1cjYbvmJ6ezgDv4cOHOVDOzBBpMgFKbeRg224XAGuM8Py9Xi+Hd56cnOQ8JfdlPo/PkhGga3t7O46OjjI1owjbdcbGxuLp06fJSPNZWMO1tbW0cWwN+z45OTnAsHyl1yvfxVNrDrRwQXgoJVEHoRdtm/dB8LEiojyKExED7W7Dw8MD0yg5/VqQhDG4vLxMAUX111Y2ztjmQ7o6bKQJut1ufpZRXlpaygiG0ZFLB1goifH4U1NT8e677ybtqgUaYyD3T0lPT0+zQHJ7ezvGx8fji1/8YiwvL8fw8HDmrlGJtbhQHYoowRreuXMn20ZnZmbSYVMQUbDCwYh+tC4lYF8xJOZBWL/KljFuqOOI/mA+zhsdyphiq7R9czT1ZV04UOCAIVZUx4lhLtSRAC3krbZ2c4rkttls5umlnvnFixfZHba9vZ3O1FqhpFHqUjJnZ2fxoQ99KH7v934v1xiLBJzK16svUMtk+vDW1lbO61DnofDaQC8ASbGg+oeIyM6xhYWFlHUyqQgTSxARWWRMnrEYlQVrNpsZ+QLd2A/vqcWyc3NzMTQ0FM+fP891dx/SUPL7zWYz00HYGTpT61ZE2rXw2n5X9qHWjtWusFobArS4LgZP0OL+XE/6Qz3S4uJiPH36NEZGRrI4XcpseHg4i9Dd08HBQTx58mQAmEktKLZn76QApL9GRm7mB0m36PzZ29vL/SJn0mFf/OIX03kZJFnBkiCNXSGbl5eXefYNH1CDGU7bfQFz7Juf315be4XF9f3eg7EDQK6urrLOhnNuNBrZdQQoYXUFCDoksYKArftRV1kZJM9QgQ69qDVTc3NzOYCz1+vlZGiHigoiHeZHzwVkgHxlf4EzxfVA3v379/O7lEaY5K3+ptPpxJ07d9I+Ybrq2I+v9HrlGRSRjKgrIjLirzRWrUmoeeUaeRPyWqBVwQQAU1MuFcUCM3LDgMH19XXm6cxMqEpIWGuBpmebmpqKiYmJuHv3bhapomJ3d3fT4ED2qMUq2GjUsbGxrGWgqCcnJzmRNCJSQQ4PD2N/fz8iIs/OGRsby/oLkbWCO4al1m+cn59n4ZXoOSLSMGJfRDrQOoVVq2Itbu+R9Jn7rNFipay9D0h0DTnwiMj1sveMAYdaqUl71m63M99O1vw7IrJlu06W5Oimp6djYWEh6eknT55kNOv+6/ej/y8uLrL4VzQ6Ojoa9+7di7Gxsfjar/3a3EcOicOq3VXmtDCeahMiIu/53r17WeQogsI0onM5N58xLh2wxJh5bobWntojMuL6WDk6xJBjm6Qhzdmous5IAp7ACxsBLJBxTAu9JmvWnw2g0zVF7J7Hx8djfn4+wTMGw5wV73ePEZE1Y3Nzc5lOQJljT7B3nIoTdCMi7ty5EwsLC8minJ2dZZqQ42c/sE8meqpzIFNHR0d5ndrqjsHhcNy3lAC7gollX9WoAKp1PEKv14t33303J3qzR9gFrLKCV2zayMhIBoZ1PgnQWTt2fIY++xmbW2fLRERO4bZX5FGNiXW2F7WYPCKSWazNEroK2RJ6Sw+Bdi34atGwHJjiWn9TBz3WmirXEaizcU+fPk1/ND4+nscYmBdlr4AV66lL0BoDpl69Xi82Nzej2+3GixcvBgbyuR+y7jDR+fn5zGi87OuVByhQcUQ/yuIoGTc1BaLlSuuhvKvBqVXYlNg0w4jISJ/xq8NuRkZGYmpqKg1BpSYj+hsX0W8r5tRu08DGoz9//jy++MUvZrqpUvitVivzjoo2dYfUmRsid5Ea2lJkTTl0QrhnKYidnZ10pouLi9lZU/PvnI3iRlTi2tpaKqbnBRLVqHAEIizpBKwFozE6Ohpzc3NZGS7qrXS7mQfyw2o9RDBozApG5YQ9B8NdGTTGzZ7eLoaWqpCHF41VWjUispWP0TYng7EEshgURltaz/OqiAcEz8/P49GjR0lN37lzZ6AOYHx8PA9W41xmZmZicnIynjx5kkWngPDOzk6mVMj8xsZGgjzPp8V2ZmYmtra2YmpqKrs26JfIf25uLtM89gEj6Tmmp6eziFMkaR329vaSzr6+vs5jFm6nmchbramYmJiIsbGxgRkQ9WgG8uFZAUuRX0QM1ASwL4CQycFqrOjy2NhY3LlzJyYmJlLO6dbV1VVOUa7t4aOjo+kc2CLyrnOIPVA0LF0qiHn06FHOL5FucubO+Pj4QIdMbY1WwIqdVQQ8NTUVCwsLWfMgVa2Whk5ERA4BVGj8sY99bKB7Re2L6wOYgiHOnm5yfIBKtZ+OmsA8VDauBgyVIceIsFNSd5gC/8ayKc4GvPgZf9h/9Vf2hK3HSDUajewM5EPm5+dTPtgjAROwSR4FCZUlrsHwgwcPEhRKsz179iw7x+gkJpRtjog8ruDy8jLm5uay2F2ggHE3/0btCBs9PDwcKysrqYt86tjYzeRv7K99e5nXKw9QUOCEsRabcVQE08wFhpFRQHvVGhB0lboUqK8WDjJanBjnxlijTwlTRP+sAjRybYdlDOsAK1EyAQeirq9v5qEwqlA6BG4NIiKdIwFHO4qw1TwwYIeHh9l9IGpglAzbefToURoBAGNtbS2L5yhJs9mMra2trAeK6Bcuo1FrezfHTMHq+Rg1X4/6dz3rqehSfQKlr0bv6OgodnZ2Ynt7O3Z3d9Phcixy/mRG7loUwmAo1BStSHeQJfKp0FDtBTlQ39NoNDKlRp7dy4sXL2JzczNBp3vSZooBi4iUZy3gqFstmgokpZKGhoby+Tnz2dnZPKBwYWEhHj16lAxJt9vNwWNYN/cqtcZhmzY7OjqaBZcoc84Bo0RvxsbGsoOsnjoO8KkZkmaVAhKAMOque3BwkOkzQMWeGczXbDZzbww+4/BqkEKeMXecjfQWvVTrJdon0zs7O3kvp6enmfYEKOTmXYNcR0TWTbERhstxmFpOKwvlnBqpE2AOxa74GttZh76RleHh4eyG0fVivL1CYsyo9IKpsUAeBvW3fuu3UsbIuj3G5FUwomaBPajn7rgOMCGtRk6rvVWEXFOCEZHAxGci+jUede9q+y0AptsPMKhdSOrOIiIHpEnd13Oq2HS6cnJykmes1fpArfsR/cNiAaLavQVkfu5zn4vZ2dk8eHJiYiJ9IlB+cXERL168GEh7V5vV6XRia2srVlZWMvXLLllfNSyCRYGmIZzsl2tWUEZ/Xub1ytegRPRz7Ci1brc/IK226VXDQeBQTox77eS5DSxsssgcxcdA19H0m5ubSccBFBwVI82Y1LkCqvf39vby+Gr3LdpEiR4cHAwMi2Ls0b0GT9UCJtF5RAwIu/dSaHUhJsgqhANkUJJLS0uxu7ubRa9y+s7TEFlUJTXAi3MBglST15ZHQg3gAJOKYz0bZsrfcuT2UufV+fl5bG5uxuPHj+P6un8AGibEd2DFRBOcZo2+pPesZ7fbzVSEPeYw1f8AeeouagEbdss+SaWYe6CuguKTVxQxB28AGKNtBlCr1Yr5+fl02I3GzdwKBYaMlHNCMAucLiM0NzeXDladhOmm5sxgKaUUm81mMgSARW2bJJ/qt1DtHDfHAmhgAIEisiQa59joLXBi/wA9xrSmF3T9SXW4z4hIJgJg48zdawWiR0dHsbS0lBEqIO89gI8Ulb0H+qXxyIo08+PHjweK3cleDdTYF+wWmzI5OZlr5feYjIiblJTnFLjs7u5Gs9mM119/PR4/fpzy5YykZ8+eZVBlfc1z6fVuukk2Nzfz/tg8IHZ2djb1kxwquLWugDxnShZu1wRhtLE+tYOSj9Ddhjkmq94PcACzfEaz2cxT3/kE8k+/gHApKs58eXl5YEDezs5OMlVsIXsMqNFH6RN7ND4+nuvj+uSz0+nkfrk/dpdM1tlOVWcEdQJI9Xe1c05nnWcGhutxAvRBMI/JcY/A6Mu8XnmAgqbnMAgphF/TD5X646hFHShGaQTGlpOzmeo7GG6I2oZJIxF2SHJkZCTzeAsLCwl2UIsKyFqtVhoA91CRvVyva1PQWhB7eHgYq6urOSK+5jEjIg0viledgPykdYiIPJ5dAaRIZGlpKTY2NnJ8vvoJZ4JERObVtfRiGUSMGBGCv7u7G9fX13lUPMfCKdWcvz00TwVYENXYJ0CJsdvd3Y3nz5/nQYCt1s3cDJG/63JoWCWOK6JP7dfOjfv370e73Y7t7e1MYUj7UPSLi4t49uxZ1jExBIySiJhRVDvCMamQB4RarZsZCRwesCL6PTg4yNQk6vn58+extbWVbOHe3l4CB4aDzNSCQG2s9APFrRaEgzQrRb5aayHw67l9PwcBBGGg7DV2hIww6ENDNyP8a4dDZTsZRtGzOpOao19fX4+hoZtTvX2eMVXwyNByBFKeQ0ND+Zwciv20Jg5aq/VJAgD2xFpjFQRWaoXU42COfJcUKxkF0tkRfyo7UOts/M2xdrv9NnDrQCaBqbfeeitBhr3b3NyM2dnZPNnWMDc1PqL1paWl1EMB2wc+8IF48803c2x+Zco8E+BmTcmA1Bm5Ayjppr3HCgn0gL7bwLrem2CP3k9MTGSNjjlPagbtBV+j3bcW+AMG5Id8eQ5yW9uhHQSJWdS9dX19nV1EOjRrx1GtYamFu7WWrrYi1yL2bvem43FxcXEgzWOGj0YLnXeA1traWp6nZk/4pWofyBWw9DKvVx6g2FQOsEYVlW0wxIjRqamFWnUO9akoh/xOT0+zZRPg4Nwj+oN2KPvx8XE6O5smL8eZE04CT6A8k0hSsShHImqrG03pRIxofgAMSKg0OeGVX44YTGGJIlqtVo4nXl9fj3fffTer6An/1dXNfBEdD9IJVTAZNzUXxisDBPLr2BzKo16ltusBbSK+iH4BbQWiGDWfkzcHTq1XZUwi+oP+rF9EDABZoCLiRumfPHmSQElL9ezsbOzu7iYtj+FyjxUIK5rW3WJvAS/3/OTJkxgeHs5zQoBGBlrqwfh7cn1xcZFFcKJBxpZT2d/fj9XV1TzrSPcLR8pBWHPOoObJtUpiKYERrJSCbcwXZzI5OZkHmEX0Z5pocVQ3AUzbF6e3jo3dnCGj+L2yidIWCn6B3HfeeSdZLixrxE13Fr2ynwKObrebhawiXKkG3zs0dDMXyL2SKekQa+X+fAfZAjw4PmsJdGi71Tp+eHg4cJDdwcFBOkp7sbOzM1CUzqFIZ2Obq1PH/FWWc2lpKddfsFSLwNlNcuC51IeZpRER0W63486dO7m/Ozs7Cd7ol3Xl2DCOdLPutZfvotfAqrSfZxMASE8BB9It2C6MH9uGJSfP7FREZDpWUTp5w2Z6L1tJ/gCjiMjOP/fte4B89qmeraR7zXgAzJVASumBI0XIp33XQKFOqNFoxIsXLxKMA4Hs4vPnzzPIID/21T3zlz5PNwytfJnXK1+DAmTMzc1lyywlwRhU1M0xASYzMzOxuLiYLXG6MjhBuWkR4dLSUqysrAxQyJShpiIqPUgZUImoXAbcv2vNCoeO2lXkZxAWZ+xZGo1GLC4upkH4/d///QQJKsivr69znDmF4bBGR0djZ2cnFYpgE6RutxuvvfZavHjxIkFNZW30u6ME9fLffolo0Z8EH/rnGETNIhTGBd2JWZHKqSAoItLwci4M//DwcDx8+DD/rK+vD7BVEX22TKQIzFlz7+H0IiJlDihrNpuxs7OTnRjYAd9zfHwc7XY75ufno9W6OZisFipaV9EfGfe7/f39jAAVhLp+s9nMYUnAIKPAkA0PD+dMnVrAvLm5OaATtS7BM5MrDBxDRea1+kf0z0ihB9pRTQ/GKkptYmnUbmDOgNiql91uNwEUEFuL0tUy1Nkp0pBYqbrHPntwcBB7e3vZEWNeSnXCcvOcS0QMpLhevHiRKaRK2XsucoItEFxwYM5BcXJsZSV1ZQA6c3NzA1054+PjCT7ZPGwkcKhuZHd3N1PUWAXvw/phMKQbgGEBH6C0uLg40DVDPwVGtY4DC6ilHLMVEXk9gLjW2dWUIHnynTUgci2t6dbKOgoI2RKAgi130nDETdpraGgomwcEU9gbIIEu0IOrq5shZwIQNV4zMzMDLDbQCsCwo37mverw3F8NrqRbms1mrK6uxoMHD+L4+DgLZAU7Jm9vb28nW4gldl2sLf2uvglAIftjY2PJyFrLq6urLOgGwpEGNch/mdcrz6BwJg63iuhH0haHAat5whrN1vMYUIM2yabLO9oA1+VgCXvtDhHRAiZmokDbcq/oMNEBJZDegZTlnZ2iOTU1FRsbG2l8Op1OolnrIOeoIM0pkwTNPULzz549y/Y+lK17MMnTOqmc1znkPrUeElBOtubGGVOGe2ZmZsAIXVxcZCRLua1lpTCluew9hyDPjEER+aC5FbVRUjIjv8qhKdSj5CIqzo3T0x0W0a9f4vgiIgvf3AMAZM329vaybiRi8AiGiMhaBEZBe/DJyUlOu2WwaiTMYFsfOsAARvQPpgTCVd1XZoTRZNCkTQDZ/f39BFvSBVJd1osDkNbEHDohFTgHMESGInQyBsTRu8pA2nO6UyNfQQoWUfqg1plh7ny+1uvUWjG1PjXwuQ2u1FPVGSL0zoh+lLkUEv0XXEmxzM7Oxs7OTrRarYySBSjSAfQFC1kPz1RTgLa3R845UiTrXqQbK3hXcIk9rXNDRkdHY2trKx2Zk64FVibXcoQOudvf34+ZmZlMuWKLNzc3E1Cvr6/HkydPBtLbFbDVmiC1H9hh6XfpH+ynf2Oq2W92kw1lH+gG28QmqE/BlrFTRjEsLi4mS17ZWOlG3wGs1rk65ODg4CBmZ2fT1nW7NwMhsaq1TkiQsbq6OgAW/c73AT3VDgjkpRxrOtf6VWbR/8nk6OhobG5uDjA9/Bbd4nfrEQJf6fXKAxSRnVx+s9nMdEStbq4Ly/EwiISh5lYJK2aE0/J9tfWO0DE+AAxlBoDkIhmHy8vLpF2NJRaB1nu4vr45Vlt3iujV+GoO24RJkS7B9LzVeO7u7sadO3dyYI7UTh2ZzIjUqEN3EEMgUgDYFKFxIiIZ9Pbk5GRsbW1ltOD9Ozs7sbq6mszDyspKRnDun+Iq4sTAMEYcTi1stceKoDFGgEgtvrTe9l104jpkQK1LRAzcH4AFsCkMZvDch2MMyIBoXGTveyL6NVaKLQ8ODpLh4pDJC6DKKABJ9rzW0DQa/bkWw8PD+f1DQ0PJznS7N107cuLy8caVy6lH9Idl1aLRWjfkHjgHTh3A5ezk6OkX403PyEFNa9X0LkPK6GImFIuTRYbbfUf0z1ohI+7dfgI3HDAdiIiUR4wehgd17h4FK9WAd7vdPEIAuFaD4nml80ZGRpLCV+ugpsCZWGpRLi/7w7Rqmknkbx1rpxPG0j5YO85le3s7U2nGHeh4seac+cTERNZR1CBIGy8dUuOg26t2q4yPj2dru6L92yBbUaxOMfV9QHll3mqnivexl3RNGlLgpGtHCzlAhAkEgOkDmTZEUeG3ejzXNhOLXQI4KzO7uroaZ2dnuYZYo4hI+7i6upry+c4778TExEQyUwB9RL/bZ2VlJU9XrvJLLt3T8vJy1rNh2Py+FpNHRMok+eIHdS4ZCwHg1ZTcV3q98gCFcYPS5ubmsnWK8po/wSAy3IypaIATE0lXGgpSrI6Z45FrBADQciKLWqBFkSk+pY3oV2crQGS8RBZSR9C2WgTOjGHQFeK9p6enOWGw0sgMuXHkaF2K7/kZaaOwCTDjiWGpU3wj+uCuph6ePn2aBhJbwEHv7++nsWu322ls6j4waktLSzm1klHx+7Ozs3Si0mMYKpEMIFcjZMhfwWZNM4mkgDT772dSON1uNwdv7e/vJxCWix0ZGcnDuER45NGgOspt8iODc319nblyYMecC2so8iSDirmxWZ4LyO31elmjIGryvHSE/FuXOrfGvpK7WhRe1xw1rTuMg8Ck1eJYwMa1dZBVIy5Fo44IONWJUIskgdRaz+D5gBT7JwjxO+CUocZoSL/WLsFaB+YznB7dv7y8jKWlpWQ16Dej7Xk5tcvL/gGd1blhNuradDqdZOR0f21sbKQ9IBPkHMszMzOTh1KyWWyOmgQ2bH5+PnXefVs7MkuvjcRnT9hZaTZ2ggzWFCkgYW2lMiP6Lc/eIzVFxty/1JX95EzrWpNtekdPam2dfQM4ye75+XlO3tX4oFh2ZGQk5boOdMNkqMthMwRbwH21u/yAQt1aJ9dqtTK9p0bP+5eXl+Ptt99OXbEXhgfWtnbrFtEfQMrmKPYGZnRnVnDv+4EYgAxDWWdAYddf5vXKAxR99wAEx9/r9bKYjQJVxasFbbUOhMFg5Dg+iFyhl89D4RS8FpZyMJQBWKnfSUnNXojoMxUAlffYdELRarVyvkJtB6tAZnj4ZngOh4+qj4gcEPXixYuszKYQjUYjT9EUJXNgomcGHfgR0XJqjLwhRyLDi4ub0dkAGjDkULKay2UgoHtAFJNVI3LXYZCazeaXgD2RWERk6gDVWwFVrUv4cnl8YBUoRYnXVEuNpDl9hX4iDfJRa29qDYVpxhy5yafWStqn1WrlwY1kpsqvPWSI0MY1PWHvrB3mQORnrSsDcHh4mM7SuojCazt5p9PJXLvUD9Zgd3c3wS090TpJt8gfcFufEzhTS3BbNipbQr7R15wywGftvV96yl4C235vz60rRgcIIxfqN8gPwBsRCWQV43s+Ou/P/v5+AlpzQmpXB92kI0tLSxkAYUA4Vq3ZWFksbB3SKKXiO2v9RU1zRgx2BS0vL8fe3l7WYC0sLCSrQ0/pdA2Y6loaW0CfKpipe9ts3pyQ7lo1lQiEjoyMZM1bbQzgK+wbPfZd0m5kjA5hCui2QnO/q4xp7TSq9re+j56ro2GrpUMFOAACvXWGjj/AQLPZTOapyhn9wmrU+j3+ogI04N/PrDcmErDDwAJZ5AwYI/e1m7POsfpKr1e+SPbi4iIj6YuLi0SlIp+ISHSO1rq6umlB1i2CHrOoNqcKlOFglFNhqZoGSkdoq7OuzkJ0GBFpADEQoj/fWdvQKKbCPzUgBAiTA0RJV11eXsb29nbMzs7G8vJyClKtHWE8a+1JRL/AUWGe+6iTH2sE5WA9CLyenVKpVwrC4KqRQJHK8XOEOpdcS4rF9wIflE3EoANBhCJfLKI3TRPtXo2G9r0ande9tN/WzPtFB2ZN+E5AACjF1IhMGS1t0rUYzX2ZRFqNsOMRRIeAD2fOiNSaFvNUADbsAgqdkxNhi6bsa6XGGVZUtNwyhk/NDSekuK7ZvOn40X1ivzxPr3czPt7QP3S1dBE9s06c2NjYWLJsagEY9uqopUswSe6hMjPkjXMS+NhLslVryMhZTduJ+DkqdovcAtb0V5GhVCv2IyJyDayPaFZRLWd6fn6ehZAcDLnrdrsZZGFEDdyyXgDX6upqLC4uJhtZ055sVK0JIl8YBJNxMQPYIICPLla9ury8TFDqVevVKmvpGva1sg4+p1ZLqh2wYlvZJ7aXHkX0T8KOGGSEpX5q3ZzPsLFYPCxYLXhlxxWsA44ClYg+W0xu7LsCZYXE7t21Ly4uYnZ2Nju2amD7kY98JM90Ozw8jKdPn2aw6D7ojmMl2I1qRzBgtTD59u8EBNYGSNWZ+jKvV55B4WQJR42MWq1WVvlToFoo5I/2ToVkNVdJgGquvNfrZWQC3DBi0Lc24zq6emhoKKNnBX82dmhoKCOBSq2bynh6epoHnjGg8uSVaq9oe39/P40utoQQyqkyJvPz89k+SqAoLHChkt15NL1eL42ZqnKOSKElh6IGxedq4Sbh59BPTk4yhyr/yxlUR1Wnr1a603fY84hIA4014+QYrFo1X1McjLq1IAsAsHWamZlJ4FDbmMmj5xD1V6bm8vIyo3bpGg7L2jH8GJWhoaE8LBKAI9eiY3sM1KysrKRjYvwYxeosORbrExHpoBRIWgMFm2qZ6r0A80C8KJNx3d3djS9+8YvJTNXhXKurqzE6OpoyHBFZmFoBp725uLgYALKVflbAiIqvgIyN4KSA77W1tbi8vJk4K11QmVD1OAyvFCGHXeeLsEuNxk2n3e7ubt5zRCSQs/7y+xi2iMjuPUyHwWenp6exsrISZ2dn2Xp+uyZufHw8dTaif0YU+a11OvScvHnVw1Glj+y/1DHGCEiuA74iIj8vRQIM1MFqUkpY1/o+ui+wpMdsteehH91uN/WOnEtxsVWdTid1gt7Veg/3pHi01m9Zz16vf3JwZeX5Fy3LZLM+DxZZoFNZ3XpUCbaWbLFDtTbPGl9fX8eLFy+SGSN/IyMj8bnPfS5t7erqajx9+jTTsgCbe5yfn88hfIJ8IF9aUEBWSxGkHP272hU+6P83DAqhYmCxH1pMoXWUvIhRNTgFJYyEgOADARGRFCHlrqgfpYtuw3io3Tg6OsrogTEmfLOzs3mPCtYoWB16BRA1m808RZNCjoyMZM6X8gE7Iu86T4Hx8l2eu9PpxOLi4kDO3lpGRLZA+ndEZD4RihexVRpXBBjRr7xXsyCyYuQqA6JYDqg5Pz/POQjuj1KdnZ1lu7PK/JrfFdWZaQP0AIzYAIpv3YBAe83woC8ZzC8HiCIijQ1GjQH1+evr6zT4wNjExEQOYANyRbgMjLZ6jE1NJXEus7OzCZyfP38+0I3mBGj0K9aLIbKn1n9hYWGA7laDMD09nd1pGAkHSJINTkqxrTRFlemFhYWM2iYnJ+Pu3btJk3Ni9MoJq54f66UwT00AgGdd2u12ysTh4WHs7e0luKqpBHKBLlfgXdvvpTtqSlhtETmvxdxqcHTHuF5tG2bLIgbrLs7PzwfG0XuuVquVoE0gExHpyLV7qie5urrKOgB1T77Xd9IN4F3nDTuxtbWV0buifvd5cnIyMNRRYwBbOjMzMwBaa53e9fV11kcACJ7h8PAwtra2BgKnmtYg+3RFKoess7+CTzpU7bi1Jdt8BiarMqyK1auOADkLCwsD59pYC6yJeVvkbGZmJrsjFXm7xzrzJqLfteo+XMNn2GcpWcCILLne0NBQTigm+/W4i+Pj43j69Gk0Go3UWcAfw0Xf3Ad2PSJyPk5NFd1e75d5/bEAKBW91by/qMYG18KoGl0wHpUKFlVXQUBXVzoMKLmtODagOryRkZGBw7dQdQa3eQYsTqX6jSSXUsHccKxAxMjISHawVIHh9DkENKhn5YhbrVb2tVuTVqs1ULyqW6DX68X8/HymsxgiTqNG03VNsRIYHCwPVkh+vdW6GYSnzQ3IMX+hsjteAEBEZM1DZRc4WwaajNR6ErJSTwMmN7oMsHQ+i+52XaBMlI8JEHGTB888MjKSp4tyoFJeInxzcOR0tRaK8mqqAVgCWGvNC4NjXwAibKS6DPc1Pj6eBwGqDyHfHI01r1X/0hzm7lRAX1lNxh2oFWH97u/+btYgWH81UeR5eno6ASsDure3F8+fP89ImL6jleup4dZaMT3Z3NvbS9AIyNAf+1hrMDg3smxdao6/AmZ7AUC4HtsBgNN5J/+6LnkAyM/Pb45wEOQsLCzkCcb2GCtR2YOIm2JWdgrooFOYByknOnV8fBzT09OpkxjoOp+FTkhda39vNpsDwK2mhrXxA4KAmKgdW8vpk5sKmisbVGtR6IGRAV8uJVHrbMhrrTerbG8dI+B919fX8fz58wGGoKaUaxCiw6bT6cT29naeiEwOIm7SRdhpzl4AYB6WZ5b+uby8zMMaMct0RiDSaNzUBmpfrqmmChirvAHfbPfQ0FCCRqkeNkcdVZWlCibZi/d6vfIABTXqgU0RjOgXHCq+QjHW+Q6MiVHEnCNnWpG477OBCnQJB2WtAihaqtECh8CoYTp8njDV6G9/fz8iYsCAoh21iFGWiP4hg8CIdVEg2Ww2s4UUO0BZIiKZI0WHe3t7+Qy1+NVpshyAf9fn4EDcv2iTExSJA1YiL0JewZ7/A1/2SS7Z+zg7rBWwVz/P0FSHwwABnF4AYAWf0gGMJaNsnoE1rYWaNcUV0c916yoCKMlItzs4Ptr31SLrmi6q4Iyx8vwKAEWSHF41iFJSjAuDAhRjPvb29pJyr+ti7xg9NLUCOYyK72TkMVF0DjUNoACL6GRMmxcZ4vQ8i/VVk+X3GBlgVwRbn5esSj3SNUCoMnUiUAwA20MW6Zi9rk5FOsC/6SaHY85NvS/3g0on2+pRIiLBphoDqZnK7s3MzORMDWkA++/5XMsp0H5WU2mATK0X4qgAUekeKVDP4Xpa4mv9IJtLxtlC4B0T4XPsV2U9gbkawUszCVCr/aupXowwIFDlp6aD3Ru7Yy0qy+h0aAcw1vqk+fn5mJubSyDvvtiKOoel1keSa2vr94AFm6Y4G8ikF2o2sYXHx8exvb09EHDUeVe1ZVsak17fLrituggseSb1Mu/1euUBSqVia1Eaoff32dlZolpTFEXnFpKSUexa+2CzDw4O0omg3aBam+r9nHy3203UXiNr1+ds0O8Em2MSqRN2Bg2wqjUVETfG2LyUGrExMCIbI8IV4SmiRWtXkEDQbztMzpchpeyKO2udDSDG+GAbCHrt+JCyUXgqlYPBUEjZ7fbb6+o9Wvca9da2U/dUHVeN7imWmh7Gy3pE9IucGT2OlgGtzAygUGWFHNRCPevAMI6Ojsb8/HxcXl7mKPjKsszOzmY9h8izPq+6CDllg7nUHUkhqVXRoaWwT7GilBsWxx5hy9TSRMTABNo6fKzX6+VUTqDSPniP9XDaNqBfUyl0jnG3p9jTymBwQqenp7G/v58dELU+AcgQPWJp6n5gkzg9DoMT1qGCAVBcKHK+HehE9Ge0ABtSsFJ2dLk6+BrBAukYWfp2cXGRxaKHh4exvb2d4AYzaa1qoaeuQ8GHWjhBhhoJ92EQHaZMAS3bJpUu5QiUSWsCA+y0vXXv5oywP9Xp+4z9AajcSy2CPz8/HzjTqq4rnSYTQFO1U76zsiW1hKA+i3tR/xXRT5/47ogYODqFrVY86nnJApBu3wUB5Fo9XgWT9sNaSn+zczUtI/XHb9Z0taCqDleUlmIDK6tZawsraygQrz7lZV6vfJEsepvTpWi13U6qgIHDClh8jqNGJYQZ/coZM0I6ewAkeVpCT+AAnxqNyfUTAhEF4ENoXZtD4qxr69/c3Fx2AFGwJ0+eRKPRiDt37mTESjjkh2ttAZr96OgolpeX4+TkJKOGiD59LTqodTgQNePmOeyF5/V3ZQwq80SQRQC+g/ADYAwsB6nmAVtS9y6iX7SmPoKjqHUt9owi1egJ2HH4F3aNopMXgKgOcCMztRvLMyuWpqwcbY1uGCaFgww+RtB9YPKcQwOU1LOLGLtq2Bjy1157Lf7n//yfMTQ0lN0gJjNjNpziyhBxOmRYbjsicuAgg65bCwWPhYuI/JmokROt7ITrVHq4ppEA3Fo34TP2VW0FHQMYBRU1laeYj2y6P+DXPdBngwnJTg0kOBOdHVNTUwOHV9ZUG3ulPmBtbS1rsxxc532ml3KkJsRyWGQfNQ+8uw9rRVetibk6onK2Sy2comj1LrXg1NpcXl7m7BWAxHr5TgW0lV3rdm9mCGlAoMNVr+tMFWyMwwo52NrNQgZqChGTcn19nToGfLJr3i8Ycz9SaEC2gJjtr+xBTVORD+vs+62LtLpTluk/u3J4eJglCD7r+dkC6f/KNPMLgoW6BnSADeMLauqIjlamWVACkAre2DlBHpujFEHgzhe9zOurBijtdjt+8Rd/MX7rt34rvv/7vz++6Zu+aeD3v/zLvxz/7t/9u4GfTUxMxE/91E8N/GxzczN+7ud+Lh49ehQf+MAH4pOf/GSeffDVvColj16cmprKszAIKRpXxC+HVmnYRqM/1rimIQg0ReQIXLsa2Zpzq4VgldKvuXcC3mg0slhPZCi6YLSxDe6FAIgmI2JgmA/Fdf+Ut6bFGA+H921tbaUTmp2djYODg/w+Aom1UXzl+43T57BrFPv8+fOBVA2QV1saRQeiWmvB8FC0VquVhrPW/NT6AICTYtSct332XkCigiDrS8Y4zJpftZ+cq/0ZHh5OAFnbSSMiQW81YMAtA2i97Xu73U62TdqkVtuLzshVzQMfHBxkMS1Q5H2KZN988810+lr2XRdIAUjI5e3K/TqZshomnWGeX6QnYCD79ss6+7y6Hs/vWff29lJ2sCwVBNFB6U80v8i3tplWHSajAhf6yfH6HeNtpog9AQLa7Xb+TkoAE0eWq/54VbBbZ3Bgvnw/OVMcrT3dZOmzs7MMXhRMYlqc6eQ6bFTVIWyM67BndPH8/DyHxGHg6GrdzypvZGdubi71FuAmsxsbG1kDpVUfw1Mj8gou1QfRWfdfGTc6y37Qa4P32HoAqO4REIjNVlTvMwKf2yBGoFBTQOyaYBg70Wg0Yn5+Pl68eDEAcufn57MjTwAhVURWFUqT8+Pj4/SlZKkGwOTOq9u9mRgtbcZWWqtut5vHFMzMzGQNHB9Qa3mqblTAzlabnF7rBr/S66tK8fybf/Nv4mu/9mvjzTffjF/4hV+It95660ve8zu/8zvxX/7Lf4lv+7Zvyz/f8i3fMvCeZ8+exdd//dfHf/7P/zk+/OEPx7//9/8+Pvaxjw2cTPuyL9FALahyMFqN9CMiI52aV4YkCRfnWX/OORNcwo7O5OA4h5qTJcCMJ/oLlcZobWxsJFNRv6PmVufm5gZOxRXhUJ6aD221WvHkyZOspq7Kq7AP8vc3B6huo7bSYp0wKXK6NdKpHQxqWxja1dXVjBjqgDAgrR70KEXku3TviA6AGEbU8QFSKQylqLhGlBV41Foje+/6lMxnag2KCIKBo2w+12g0svVWhFjz6+Sq7jMjXRkWTkjNCZDtJfrysl4GptlHNK9nMcPHvdKFiEjQRKfIFoby8PAwqX0pIGkExaTSoDV3zjgzllUvrYk6AOmXKs+iQyBhYWFhoE5LJM9IYhUUVEb0240Bf8AnIgYK612nOkX7S9/V95Ab6Udsp24Nsqbuq0bXdEy9zdDQ0MBcEc9bB91JSXrG4eHhZFPMqhGQ+O6xsbHsegL66AinDKBhN7SQktvbNrLOTOLYFhYW0l64PjtJFiP6bNH+/n6mMrFbbLNnlKZR5MounJ2dDdgknVb21r3SSTbKXpFn6dVqyyuLSz6lzbSd1+CUXbNO7XY7DwP1WeviPgRGtbi01kNJvdR08+joaKa8aqoMKGKXalsynbZnleFlR+0pWSND9Ks2nuhCw/aQU2wN1qnRaKRtqyy5/fm/MgflT//pPx1f/OIXY2JiIn7yJ3/yD3zf8vJyfOITn/gDf/9P/+k/jcXFxfiVX/mVaLVa8alPfSreeOON+LEf+7H4J//kn3w1t5SKWlkOCy4KovBSPZwYRYroG59afQ/5QZ4WHEVVWRdOvqL3mt4Q9TcajaTF6gAmOVc0sImqIipzDqQAjo6OsvXx7OwsqTz0p5oXBsk6EDb0YJ2tcHh4GMvLy7GxsRE7OzvpxNDSAJVIivI520YLLwUSoQMoivFE6Q5Ro/Q1aq2DnTBe9hbD4nkpg/vC3tR0DENlDWrRrPVnKGo+mgKKTjFqNdKtKTz/j4hMURg3LzrTcoxFcC9AAQDouqJVEyZrlBYRefZU7T5i5BmE2dnZgbQHh6PmgPMg377b/dRUhLw44GjPAAn1W9goKUiAHdBk3N1T3Ssgy7EKdU9dG3CoTtR1sC3uKSIy+uWYatdfRORzRMTAtUTMQKIXdqDdbg+wLYBDRH+mEAfMAXIk9KkWH2JE1IGocbm8vIzl5eUBPdaVI03IGdaxBzrIOAsyWlMr1rrOgcJsSg9UmSOf9GVxcTHa7XZO9L09HI7N5Pzdp7WquigFplvl8PAwDg4O0iZbU/Y/4iaQYMcqC1lrVKRRK6squKz2XFqsBiXWg62p7DcmCIgRGNMJ63271o0PYssEHxiTWp/EXtR9wCAeHBwMyDAdOTo6ip2dnRx6ubm5mfZAaoeObm9vJ+s7MzOTrJRnsSfuG0ixfgAR3Wc/2V3/Pjg4yHT5y7y+Kgbl9ddfH4jg/qDX8+fP42//7b8d/+Af/IP4t//236Zh9PqVX/mV+M7v/M40ipOTk/GX/tJfil/+5V/+am4nX5w6VIe+pcQEoRpZ94T1QJFq+yWANSqjuKhPTgBiFa1zohDl1dVV9rq7D0YU+jfRVKqn0mXucW9vLyN3BkhRXkSkg9We6DCvmjethV2MomtFRKbGgAJ5YRGYs1Q8E4FH8VeKz2C2oaGh+P3f//10FtA4uh5bhQVCu3Ik+/v7cXR0lApqjkwFfrULh1GvRqruDcWrNSCiJ89TjScWo/6uAhjXJGdDQ0MDRdbPnj1LpyOqBlw56ohIBot8GoZkXStQZpi1dzNUmBnfDXDXNfJdDC4Q22g0cl0ZzJqa8rzuvX5PjdirAXZ9zFztCrCeCwsLeb1ms5l0dQ08Kn0eEQmGyZk1q+k1KRJ6pUOorr1n9afWprEPcv+VoaM/jLVC2m73ZnbR3t5eggrg1vO02+1koYDmqsMOkSOnNaW4vb2dqVTv8XzABLDKptym+9Ue1dk5ovlms5mt27fTJO7RDB0pQBNLIyILQ4GS2miA4WDPsMnui02oXWrOVQMK6iA6AEJtFlunpq+md6p8ssGcOR0mzxVE0E86IsAYHR1NpgQrI8ggO5gW+4jBs8+6nZyJo7vHc9qP0dHRZMncr2uw+dIwaq329/dTBnZ3d7NuiA9S+CzFZ70UarPB5MVa+n7MKaDrnmpdD3tRg30697Kv/ytdPF/zNV8T73vf+2JiYiJ+4Ad+IP7cn/tzuTHn5+fx7NmzePjw4cBnHj58+GVTRl7o5fonIpJFqIYe64EhwVoQXEwJEFGRYo3SOFNdP6JHUbr/7+/vD6Dtml+rFDXhHRkZSUClJbMWWVXUeXl5mSkr1yYsaHxpq4jIceGETuEbtiSiP9hHx0ez2RyoAheRMhJbW1sDoKp2L9X0FuMOGNbBUrWyf2ZmJpUSAyO1dnZ2FltbW1ms1+n0B2rt7u4OPK/vrZEeQyZitbcM4uzsbN5/7XgQYXqeiH5LX0Sk0WSka8ROprSqcs4iOoarghgy6PecY2UosCuApYhF9T/HLTIzzMveaS8H2LEeFxc3o7Dtg3SI9Ec1SAAsEO8ZOUidFmNjYzE3N5fXrDQzRxrRH+ZXQYIi2hqtSg3U/H9EJEh1rbOzs2SWfI/UGzmw7jVK7nZvBpAp/gSAajqHDHCgghi6RSdr9yCmB0jn2DCCipSHhoYGaoLoP50X0FRWGEi0Np6fbkozYRjIQS1Y9tzVoY6N3Ux93tvbG2DZ/FtBP0a1An2dXQ6WEwxhierYATbt+vo6ayD8n9Os3UrsNz2QhiSn3lMDjCor5LamWYBMgNh+0zEsn2DQvai/ku4mu5UlqEEWuyPNVYuwOW97r+ajpqXsL9+BWWRTqj7Y+4ODgwFGk6wALeRSQwOQBmh6TjKnRtCBpbUTR6q4pqmlIMl9BWrVf2FtXub1h97F86lPfSr+8T/+x/n/7/me74mPfOQj8XM/93PxqU99KqNAiNuLc/qDXj/yIz8SP/zDP/wlP7+6uspcHEW20fVQN4oD3VmoGuURAoamLnA1OP5Uo+H7RSJaKGvEV2tiOEZgijHnJKVogAhKJXqYnZ0d6DKRHqC85+fnGTkp3qTI9XRUh/wxsJTW81UK1f3s7e0N/O13FAqAsDaer6a9boMHOWBAEPPCWe/v7w+geODL99faoogvZRki+nMhajcEI8JwWN9q7DqdTtL4EZF7fXFxM9hM+kLhqAF1FZRyQgBbRP/wx2rYOQJ76721ziKif76U6x4eHiZw0IJqbRn4mlrx/FVm7A/D6fiDTqeTk2HJqOvW9Is8N7mu0RrmlfHUfu7eaipU+oZDt1b13quTEkRUHWKcK1Om48G+AS3VMQLFt3WcQ0Jf1/oU6+peRb1arBlke63otHYL2Wt6vri4GNvb2zE8PJyD+kTUIyMj8eLFi9xvzyjdwLmxWxW0As+cBvaGA+31bs5dcmo2+zc+Pp5HV6D62Qey32j0D8Mkn2pMMJ2Cs6qrWAB6S/arrRCp13o6NkP6g+74LCDluQRPvs8eVdtqjdQO1uckD9VmYNIjBufuVEBe17HT6cTKysoAg1afqdFoDJyMjMGt7fNAP7tnlg27z+Zj0oAG7N3c3Fwy+643MTGR8tZo9GsAATKtyNvb21l7Aqy4b4GDZwVUAeq6B2zye73+0BmUlZWVgf+///3vj49+9KPxm7/5mxERmSpot9sD79vb2/uKXTw/+IM/GAcHB/nnyZMnERFpfNB2lJVhkQ6QGqgO18KKVGq7XY3SCCXQweBKhTCG3iviYKgojOIs92yz3BMky4iLmAEdxkBu2r1SbgLsmtvb29Hr9WJra2tgqI6Ii4OK6I/Sds2FhYWIiFRSClNp1Ooka/0EpyUfXfOViis5QQyS/ZqZmUmwVFv0UIeKNNGlamMYbs9mX2rNSUSkY6ysyvDwcEbHHDvwGtGfgFgNqu/wnXXOgfOT7FkFIpV9Iy/uv+5dLTBjQDhGI+85c/U5VXYYYWBgeHg4ixi73W4Wtc7MzGQNC32q7fmcN5A/PDycacXKqMzOzsbc3FyutaLFmZmZARmqhYi3u7XoJLBofoX/12Jaz4kZtDZYSQEFYIGxwqDVAl73UnWIjI+OjiZDUjtV6A0wWbuO1BZhYD2LomdpHyxuRJ9plWb+3d/93XSujsIgS76r1Wrl8QOuw2FwPOS5MqYARkQM6Ii1IsfSwVgyAwgdn8A+0BOy6BoAJKdfa9fco3tiA60D4EXu6MJtwF/t5+1gQ0DjupVFqmUHFZxgidhZbAFZJ8uYOSzO7WLqCmRqJ1+9B91fWqUxPoJracyalsSe1hQlP2WNrDc9w6odHx8PTIyOiFxfsirtc/vojMqI0C8pWyTB7XW3dkCrNWHPX+b1/8mgNkgx4sYAfOhDH4rf+73fG3jP//pf/ys+8pGP/IHXGB29mYhY/0RE0qeMHUBCGOQnnR1QaTY5Ok6qdlFE9Mc3R/R7z7vd/nRByjY2dnMCaG17JJi+T/eKQj2bTAC8pxawVqTqeSjls2fPBgxVVVrgh4B6jna7PeAgGWhUcwUw0gLuT6QlQnX+hwitzhyoNG9tQ2M86jh14NL4cYa/plpqzlW0FhG5T7UzxnPXyEkXQGVeACRGBLARcXA2FWCJkMgVJQewXMccEI6udq3Iw1e6HCiw/hGRhgIzpK3PdzomQKpAWq6eY4KtARoBP/vI6VewaB3HxsZie3t7IG9fp3HW3DcWb35+fqDzSDqPE6zGFXiyxwxkrR2KiDTClXnyPmCHcb6+vs7OKWvM6HtWjoGMSXEBkOSt0ugMq3uttTaVcXTOTKfTGZjd4fvJPoPu5XnpM3snxaqWDIskakbJk2esAhCnoNJakjF/A/vkuaYVsIHs3YsXL9K5zc3NJeCpqWD7C7R4BvddHZyAqAZagq3KAlWGqnYs+gxbT79roOS5alqcrF5cXAx0wQgK6Cu56PV6A2xHlXfyIr17m8WujEG1dc7x8Xxsx8jISB5/wN6yHdLNbAgZGx4ezoYKemzNqr5WpgQIjogBVkq6R+rO1GHp4ZrqBYasn31rNpsDgSw/i01zny+b4vlDByj//t//+wF0+su//Mvx6U9/Ov7iX/yL+bPv+q7vil/6pV+Kzc3NiIj4whe+EP/xP/7H+Gt/7a991d9HoUQmDFetQ6A46N3annZxcZGni8rDVaTPONWcP0FhNGsUhFkgUCqsRVKKjG5HeV7SBxUNS9FwkPUQu+pMa2oAaEG7AxWuS8lR74SPYDlZmYBWpkTU3+32p7hqf+YYUNqiEQpZUfbY2Fg6nZrOIdgUUw0NIwPgeVbGtO55RB/Q1OI6z8Np1vwoJaqpEHtmj4COmuao62Iv7IHv4axEHoCSz5PZun8cooI1Bk57JtDi8LE6m0FkY4+tLRlyPwCDtbYWEf3ZINXgkmUszvDwcLIklWavXTbWSsqnglgGjA5hLGrdhHSw9b09wwZgwEpVpsua2k9Mqu+xzhgW+09Oa/1IrT+yHgIUukN/JicnsyCaTVGXUe3j9fV10vU1IDAhdnh4OJ4/f55dFZwSJ6s+wbM2m83swpDS8kc6Qz1F7e5TzyL9axQAeWK/Li4u8mRkXYccMBqfIyLHETEAWjyfwKc6OzpaDzGsa09fMMC1bsN3sX2VpawpwSqXZNj+0ZEqa/U+sBrku6ZryQy7Ueu47HWtVTQXBstR6368AAM1WrUone/wmYuLi5Rp67S9vZ3XUUBtqB1b1WzeND84t6k+t+DGela2iV+onUW3O0d7vV48ffp0YP3p5su8viqA8tnPfjY+8YlPZAvxv/gX/yI+8YlPxM///M/ne37t134tPvzhD8d3fud3xrd+67fGX/2rfzV+6Id+KP7KX/kr+Z6///f/fnzd131dfP3Xf318x3d8R3zTN31TfPu3f/tXbE3+g14MbaVfI/p98AwMw0O4Dg4OshCq5pk5dQ6kGkqRNATJ2Bqj79A/FLJrKhit+bpab4Imj4h0EtfX/cFKwBHakQLNz8/nPV9dXSUq7na7mVceHR1NARsbG8szPqDloaGhPPSr1l1wTFrCKFdEf0IjijKiP+1R6kGxrvcxcpyX03oZAodY6RCox5c7QFBELN/P+IjyMGQVvET0zz6xn4x5RJ+poWCMXi1MA24qG4Qdss9kToSD9WAQGWBO5fj4OFOBNcLkfO0lOfNvTqum1QAxBiGin5qrxtRzAgQAdnXqDIh/11ODG41GOtOIiJ2dnYHaEhFaZbHIoEgLA8Awo/jr6bjYlbrm9pp+kk8Ojb67VwGGZ5faI1PWr9qLGkBwzBWAXl5e5pj/yqaSrePj47QP9rwWvdd6IPfgOuhybAC5rYPY7PfExEQeLKmuwx6KTjkKzQR02N8VGHlGn5Geq/Ux7ADZVEdSWaR79+4NBHX0sb7oVI2w63pWpm96ejouLi4y2CFPlemw9+S1AqoaHEXEwL0JaitoqXaPLKnfqd0+NZ1GpmptVvUjdEVAaUge2aGH5ALIiog8m4q9qHV9ggy6LFARtFZ9ZIPf9773pX8CcAWtwLMaMvcNWKir83tgR9AFqPKT1ocOScf6LF/yXq+vqkh2fn4+vu3bvi0iIv+OiPjgBz+Y//7xH//xePr0afyP//E/YmJiIv7Un/pTsbq6OnCd8fHx+NVf/dX4jd/4jXj8+HH80A/9UHzjN37jV3Mr+WL0RD0VwaNt1RNYTAJZWQECXelOm2BxaxEU406xGAfCyfBVJM5woG3rYCf373kocES/sLBG6IzCbUdM+Oq6qG2pdHdEJGBT3wBN1zw3wwolUyRKrFC2RmPWsBrkmk9uNpuZDxVxiCxFOzX3GRE57r3WaVTAx0hjIqrRslc1P43pub6+znooTBB6vIJJ9Gk1yq7jnut7rKd1xs6411oIWKNqxoVTRasDZIz16elpzMzMJEBTEMdQkitrr4ZC6tBQqE7npgDYxMpar2MdagrJfu7u7sbw8HAeb2AiMcBBR4BRemf2Ri0wJo/YMylHEbnn4xjcj+mt1XBXJosN8PtaOOr7RN415VZBRXVg6lsYfbJeWaiaGqpFl/ZYOrkCo4uLi5z3gZFygNzW1laCBs8ojVODAvcyNDSUe6OuqOokeRRE0T+6oS5EVw45rWPbIyILVwV+bJjIG9CUgqh2zP6zD+w1Ha6pWfJ1eHiY7AuQLb3F0WOlq62srBR5r0AAYPHdFcRaK0Ew+764uBgHBwcJHitzUkc23AbkETdsoCF4rVYrgUcFitYACw8Y3H4Wz+PZPYN16fV6eWgoeydwwn5J2Y2NjQ2k7dlgxbLkSKBLHipAAazZLraY7oyO3kycdj7Sy7y+KoBy586dl2I51tfXY319/Su+p9FoxDd/8zfHN3/zN381t/AlL8I4NzeXh6kxLJxCZRAsFkNSqSbCBu3qfHEtgmfDKoXvZzUXHNFnFqBxgqYrwhkn0D6FqRXWNpSC/kG5TD836p6CKFJqtVrx7NmzGB8fz2mflITD5eDcD4NY87k1z+tzEZHKRFlFaZxLo9HItlfdJgwlpwHEWVs0KQeJYWIE1R7ppJHzZmiurq4GUh/X19cDtTRoW98FwFEge1dpYkaWseY43bMzUhgY6weYVAqWzJBJsiAy1oIYEVnEBgxG9NkpzgMoqXpQjejY2Fimwebn5wdqAyroMiL95OQkjREA6oh2BnNkZCRbysmrNCBHyolWA0vGrHUF954hYpAir8YfoOAcBRX+Pzk5GRMTEzkfhtHlHLToAh3uRRq15vV9Zx1+h3UjS/bFz8kUJ6duoAI2uXu6z7Bbv3q9y8vLgRlBnHVNS5Ht+rx+J21CF6yZ4IocexaTR6WH6r6aOMoB9nq9PE/mtvNkD9jDypCpc6g2hD4okD45OckgkQOmeyJ68nOb4aAvNXikf3WPasBD5gQKPlvTcMCcgIFtwBCTm1qrIr0LyAKDNT0CpLOz0kDWQfcmW62Ox2cwheyttTs9PY0333wz/SU7yrZWHQTG6NzU1FRsb2+n3EbcsKc1MCSD7GdN51lrLJR9fZnXK3+aMZRPmQmFHDw0TmEorYW2sDXylWu1kKh/Dt8fYAfwcQ33VR1DRea9Xi82NjZSUCpdyhFSMvdYjXNVsMoY1DH6rdbNAXcjIyM5CCuiX1DKeNehRirVGVr3AKj5v84B96QNstfrZW6fMgM41tzz+T7fRahFVvaOQxdF+p7qfCu4u52Xtv7VSJABYLCifD/jSBmh+t3o2RohU2yGuLJIFRxU4FBz1TpJUOj1PZ4/IvJsJQW/ctpk3mySiEijGxHJIDoagGOyd2oP6IXvJqMYhpq2EVEBX4w5OTWltjpwa91s3gxk297ezkicLtEzhbNSBZxPjTjtt64iuiCdVmuFGPvbqTAAVipD/YXn8n2VIar7Q0cFO9atzkdhGzgVa+FvwNKeXF7eHH8xPT2dDkygVYvJ6XNEJKNGl+iPdappS10XgIK9E+EDaJyQfVWrh0WiL2a7cI6cHCal1qLUGi7pmsqWsaECNE5bfUXV86p3VRcVYdLv+j72+DbjQu593nOwAeQAw2eN7SMmii2qTDb7DhjUdId02O06DfU5VZcAG+k2+zAyMpKDMzGVNatg3yuIVdDcbrczkL6tZ/SRfrPN3qtuqTKZAjQMkzQyppHdfpnXKw9QIiILdTAHFZFXupmg1FQOARaBXl9fZ4cE41o/W4WQwLsWw1ujbvdSaWwKQnlvAyQby0DW3Pr19XUKK4GtggEw1CI0xgxin56eHkiVELwq7AwNxbJWFIvzrYZVCyJw6OC4CghqRBHRP426MjfV2VHWiL7hr+mb2tlAuWpKxX7WyKU6MffNGEunkBHRKgc6MzMzcCAi8MPYMLwVEN9OWVUDJsogo/v7++lw7S2wyWC4fh2dXlNAahPIpO9WpyX69t3AX2VEFFgyvs1mM7+PnNsD30M2qsEG3sl4xE3Ui4FhACvDJcgAim8HGnTM2pB/xaBSoMCAdJFaL7UkwC8WqtL/Vd+qjbDOQIB7tXYCG8Xy1enW9ERlegykk0q43fVANqxFs3kzTE/7sWifLQRE/NzaVXmmj2wDJgabqHbPGUJslgDGvQoEFDbXlJa9orfYzJqCcY/eBwBFRDJfbKh7t/8RkfdSGdhqQ8kzW0Mfa0AoAAUUvepak296UuuifLdr0W9r41UBGfnd3NyMdrud60s+jbkA7qRrIvpMUU0tsftDQ0MxPz8/cFglu4ZZVp8IMJNPMs2mYVRNswZm1ZnpdlL/Uv2lNWGb6xrUM5Pe6/XKA5Q6RbY6ec44YnBoTkWJEf0BUDbchkLcBK0iUYKtsM0GQK/uQ+Rm832/qJqhkIdkIFGpnM/Y2FgeHGXCZEQkACGwlYrudDo5GVJqpFKTov+I/nkhalJckzOuIOf6+jp2dnYGqsUZLQ4hol9c5TPWQGHtxcXNZGCdDnUmQI3oPKf9rQWl9sFeen5GwXvsPTDCYFl/rIPWzWbzZrJubTl2T5UZAQp9V039cMicQo3orUtlk6w5Y4K1IqP2yrpLTUVEpk/I4v7+fhY+AwiMTk09RfTTU7UwjkxYH9cWedWDAIGICtQYYvvGaDuTCDhRuyTSFkn6P0bEPqKHRdXn5+epC+pyarqw6lqNYiu4rE6Fk1C7UeUK+NBdA7TWVLH9YqS1n1v/238ajUYWNtY9qn9LiWCI6FB1lpjS+vw1HWsfKvjyTApz3ROWqa5jZWcFVoYBSsWJ6MmyydSAPUap2+2m82UbBS3WraYw6apREUdHRwOyVuvQapBR0/HWrQal1ZFWxqUysrX13HqyuXVf2fnKEFbmjDwDLpgH4GB4eHigrsl1KzCfnZ1Nn6F+i25hJ2pXn9odf7BcygzIe32u+qzWQBahBvGACrkBxgVGdJXeDQ8P53dW+/wyrz/0SbL/X78clhcROcEwIgZydxUgUFLGxGKi8SL67WcM4e0cJfah5no5P5FmbZf9chF0BUbASC3eorQ1PTE0NBSzs7N5omRlbQAPg31q2ujw8HCAzXFo1MTERK6TQmL3J8qlWByZKElawL1ymNfX17G4uBi9Xi/BSo3g1IrUwT3qLWoEUZ2bf7uWeghMSAVzNUVR88sRkakz0SmFr3UNlc0QfXHSqPEKViqj5mfVAY2NjaWSc/IcIwfBkUbEAAtTDTGj6EA2a6KSXrTuGXu9foeOdayj4EVpHIjr93q9nICKYZM+Qv+LYmuqxL6o5SBLtdAXOLLfnh+rUOcluVYFTtbWzxhkYIcRBQoxJoDg7bRrdTYV6NbImB2oIKkWXNd6gtHR0WSp2CKBAAYEuKt1LjqzPCenUwFPBdUcHeYCOPHemhJUqColUl/0CEirrIJAodojtWuY0eqw6kn0x8fHsbq6mjpTGVyF2rWGy5raGywzmyMVPDExEcfHxwOpKmtIHmohOpnEfkjN+C4v+2i96EtlHu1zZUDphvWwTtWe1OsD4exJt9vNrkoMhz2s+6F7kX3FmrkH69ZoNPJcLj7Od87Pz0e32x1gLKVqybOAhA7Q5dt2UaeVgOh23RN5FZTpagRsImKARf9Kr1eeQalFZf5ofa1otuY9bTgBrnUFNdqN6Ct4RL+VjCEBPupMBd/J+dqompvnWKFWyjg1NTWgtByToUbdbjcPfvJ53wVMuT6j6Vq6QyB+ilzz0ZVGFEEQ+JqyqukwtGHN287MzMTi4mKuXwULKPDa7cGgM5Q17WCda31RNeaeq0Ys1iQi0vlVp0aJqsF2X5RMigRA89yATXWaUi6AnHVyz4CLZyGn9omBcU+3wa+9r6BWVKJIsO6RLizryrHPzc3lntWoChCpIFoBdh2ABQiq0QDIHZpG3urUYkay1WolBc2g1zQApyHiYtAFAxUIYG0U+VWa2p9aW1LBCT23ZpVVqbKGqaiMqveJbm9/pqYnrHm9F3LhGo1GI5kHnRf2l9xeXV3Fzs7OgFzQNfJGV0dGRmJubi7Xxr5JIblHtWe1roO8OnvFGtANQLgCfg7MnknfNBqNPB/JWrRaN8PByAa7hJmqk3g5vMqY1XNfakA5Pj6eYESxvP+TmarnlWmvKQk/q6C86ngNROmMycm3u6QEAJi5ypCZwcX/YN/tOf3gqzqdTtqviJv6PzKCHVUqQL4x50NDQ8nUkDmzTqyfGUb0R6BX/WtN7whab69/RKSesvPWtab4PSvZeK/XKw9Q6jHXNtLJjYyAhawFodVx1D8MsY2odHFEfyxyzd3KH2NOOEObWlM7jCtKjrGxkRSDsIgSXas6IwpQka/NPzg4GOgquH//fjr/GlWNjIxkLpuCURzouBYmEnbRiO8HQkRA6G0RCYfqc9ZblOlMC+shFSdCZDQYkUrbi+7k9v2/MgmAlzVUlHcbzHh+hopj42hEdZXVqfcCVNZ5J/Uzoo76PEND/bNpGDfOyr3bc2CW3DkzBeARaQFsACgn6AyapaWl6Ha7OeSNAdVy7p5qB4QovXYf1CiSowbuPcvtXDxdqBGga0dEno6qq0FEXx0uYGzNOC3vkz6qLcu128p+0Hd7yGHRc2tHLqrt8BldTvX0ZPpE/gE1es4p0JM6gbd2ZdS6Ce+l5xGRJ8oC3pwEh0Y2rAtQCzRVgF9Tf4C3+8IEeg6O3/eR08oENBo3w9wcyFrrxCqIqF03ldnwvpq2Yn/qGt8O+L5cLUlNobo3901uawdKDUoq8w5MW6OIyJqumnLye865pvbcG1aHvkT0T4MGTs/Pz1NWKgvuOuSenSH7tc5jYmIi53CRk1qHVgNcaysVh/Gtdoc9lp6sQEvgWYGc99Z9sobv9XrlAUqlCv2fIteovp6dA4kzdDVCYuhvU1IR/ToDhsmG2SD5c++r0ZlojyPnYBgKhjQikjXxPcCQZxIdUSj1KUCAzqDq1KB/Cg4k+bxrU0xK67tQwYyz56udRJ5/Z2cnZzowDJwJheMk6gGRKPWq0NIZ1j+iX0/CiVUmzHe53m2D5/PWgIGqYNU+AUlAnXvmUDiz2uVUa5QYcutYjZO/61pGRNa8kN1ajwNkuS/1Eq4LTADXETfOGFBTMHp6ehpnZ2cxMzOTBdWYx8oOVINcHYm1BaQAG7JFvmuNRC0MBqgr+LQet4vHrZH31ZkONX0A1HJcUgHWE9gmezUlUEEoJ0gGIiJ1oEbv9ZA+z4tZrPpSWUOO4OTkJGtoAIE6P6Q6u4mJiRzuBSCdnJxEu91OICIA0NINVNBX152cnExgWderFmRjQawLsOj31aGdnZ3F5OTkAIjDctW0ksBrdHQ0pqenB1Kz7CH2xJ66/4mJibzn2gBBXzAEaqbqcDgAhI2uALT+qSCQjdLeby/tJ8DR6/UG0r235ZitqoydWjvfWVkVgW1lHH3f9fXNEQ4jIyPJKFXZ97fmBOtt/ehZr9fLEgA2jxz6P6ACwNSga3p6OuXeq9lspv24Dfhr3ePp6WksLS3lOV63041/0OuVByiVcqyLJPWCLWBwIwYjIY6YYFKC6rDkfatjrYa2Rtpe1dExaq7j94pV6/t9N0PjuSL64IuAM5QE2bU5CQbw+vo6Njc3U0Er7YlG7Xa7sbCwkMpUBbrmr3u9m4ItAMkE2NrRw6AwZpyNqZYRkXlMTpwS1ZwxoxTRn7Bbp6AyUvbPs/o9R+N3Nc1V9ziinxOtv3d976GIgM1t+avOUtRpDe2f74gYbCOsRtu+mPLIyamrEAEBxdaRsaiTbxl7BtT9Ma5YHobQ/aCma+rCvdUW2aurq1hcXEzQU+tsPI85ONbU6H3sCxBhsimKutVqJcNWD/tkYE0jrh0xnrtGm+4dYKGXZMoe3p6wW1NE1cFVcFr/b185EFFvLQwGVj0zoICBta81+la74n5HR0fj/v37ue8cX+3wioh8n+cBMICQWncnJWfPrUOz2cyJpoph2TTAp9frD1Uj9+6BnLVarRwOxgHaY/KPhVlcXExwSK7ZMzbC99Zgip7WonIv+12LYSsrUdPX1YYCQzVdVcFFbR6ojC4ZqClGf9e6KPrMsXuP605MTMTOzk7KglqzWpdI3/2t5rAChso+awBoNpt5KKFAFWvtOdRMkU/ssO9Ro2WPOp2bCcBLS0sDwb/5V+pRqq/8Sq9XHqBQEpGAvyP60anNqcpXWRCOqhoQ7ADDJoJgRCL6Fd+EqkbiHE6N9ERy1XnUinXfJ2rgBGp65fZn3ZtrcIaEiYJwJqIbynl11Z80e3BwkEaKggErlUKmrJWd4UwiIlNHOjGg7wrKarU3ha9AAzBR3CnKZGREw5xazQWryxBNYIoi+nVJtQg1oj+hlmGq9SAMWP1TWQx/i2AqhVvrGyL6xc/Wt0Yy1RFHxIAhHx0dzfko9vW2LN0GLNrltQdPTEykQZTKkWYbHx9P5zM/P59GjFOK6LcM1nNDbrNCdE1RrOvcrhcB8Csoq6xNZTbsDVDSarUGmCPr2Wq1Bqjk8/PzZPK0sovoagqEHPs+ayrtJuqsbC2d5JClS2pNhvWxBo1GI+8fOBChckiemxFXlKxAtLI96i1qCqOmc6wBxomj7Xb7h2LWtAldEYjV/yvEVdBb2Wfdb56zRtgVZGLcgL/a/SVVxyaxoVgRv7cPNVXKntX6QvJ5W2/JIr2UEiG3Vd4qO25PGo3+uWPk2XOwiQK2Cjp8Xw32rDGWmA3sdPonltsXReSYPyDCWmF6fKamms3dce8VxCsAXlhYGKghAVCliwQCgke+QEfW8PBwdlitrq6mPZydnU19Vz9JP1/Kv7/Uu/4IvwjEbWRosR2sRxgpLkRNYUdHR/MY8YpoawuxSKAOryFANaUjDUMpIO0aXdTrKRK73UXU6/WSYtVaxjGdn59nxCoSqQaPcgEC8vCMv/TJyMhIrlGNgBg2Al/ZHMPC5ubmIqKfkqrAB92MJqWgDEotBGVgfSdwRbBvRzmicBFXNRaNRiNnXNRzYzxHzTG7jnVnEIGYSmtH9Iv6KFen05+14mfACOPhmUWtWJQa3dQaHkbB+gI9q6urA51lk5OTyc5YLxM/Dw4O4uDgICN34+p7vV6OMAdoZ2ZmsiXZ72t6Sh0C46h9tDqpjY2NpNbpHIdbGUopq/39/TTINS1Cxuggg0hGyR8HyGnWWqsalZMtDo48OY1Xuou+A7gcSWVFakrLS1BCFlxHaqTS5gAWWfG9/s+ZkDGgbWFhIdv9gZNut5tTs2tXmoClFpNub2/n4D7rac1rYIZdAlIrY2TNpTJ9lnwrolWLY08EI7Ozs7kPdAFodJ3ajFBPo6/gtdpqts6rsk0+Vxk6esbWuHfPWBlD9hxoqfoqMCRn9pftqazBbbtX0x8TExPJyNaRAJ4NK1ftDhl1f3NzcwNtyRVUSasIFO1/ROQBttanynin08kCefpf7XlNy1UfRodNPXeiu323XsB+3buv9Hrl24yhx+qIa1TEMFTKHiJtNpspCJxXRL+VlhOtrENEn8onjDYWKPKHUtQ6EkCJ0XYPVZhtaM0Xc9S387vDw/3zgigISt0aiKBHR0ej3W4nJezeRSkzMzMDDIJ1w8oQSJGgPH+N1ghsVa6IyAK6mmd3H6raK30s4uagKv1qnWsEjnkSUXpeIMh9u58a1dQI63b9BcUSufh/pZbrd9T6GN/RavUnG9f14qBEWRwLGdPqS46mpqYSfFXqm4FV62C/azEoeQZ6e72bmhQ56tpuK/ohC7Ozs7G3t5ftnhgCTiFiML2KLVS4VyPiiP6ZLJVp9Kempegsh1pTR35GBwBPqRDgmk4A8gAeBtWasxV01DOQkSov7tt3ep8/ZlBE9NOCFXDRZd9l/cgCloIDrfUonktRbgWGAKD7l26t4Fmhq+/3PdhO6QTpUCmqWkNHHr3c4/7+fjK63t/r9WJnZyeZGOCVLbAvnDCb6Gf02TrVdA49qwFCLdCujAxwLeVorxRTG2pWARh5YVtrPQiZ4TNqyqLeE9l0TxGRwN7+Wnt2BOAz82pvby/tBtto2q819pw1WK46as/N3DJGwjPs7u7m85NxgNm6VrBKRmdmZmJ3d3dAlvb395PxqT7NuVtV197r9coDFKiZc7QZEf36DxGwiLnmqPX793q9rFoWFdRUBmGtRXoMOsGqUQVU6Ttq/r5GQ1WYDJ7qdDp5Tg0mYHx8PDY2NtJw1Dwo2rRSzuo30IeElyLX1AIHbESxSFYEfzuqksfe3t5OsKLoFsOj1TsiMuKn/NA2RfBMjIXnnpycjP39/Uzd+b37WFxczBZMBiQiErDWE4HVRki7MIwRMWDsqoJzuuSg1gyJkChinWcCgEijdDqdzAsDHJwKx4VO5xw6nU7K49nZWezt7eXPnePk3jhMJ0j7t3sRzcpfAw0MCoBY2xVFh0bg04mjo6McuGaNOEZrHRE5mIlMcaQ1GKggvhYrezGE9Lw6BFG/nD5Z4lDpIRDBWQDjNeUrAsZkAHOVBbJGdK6+H5ivuljPKorogzLr2O124969e7G3tzdwai22ilMUlWKIK9AW6dbhh+5hZmYm9cIcHg4ZeKknB4+Ojmb3YwUwwGutf7LXHD5nJ9izXljkdrs90Lrqu7x3fn5+II1jbTltf8iEPfB8te3VoZVedT/JqmnXvsv+cNj8RU1xs5+YZzYV4GRLgMka0AJKgrSalmd/rTl9rMFKBcI+S875DnZM+odemMdTGRn+o6ajAOlaH4M5mZ6eTnbRs9bSgZoKFixhyOirjiW2uzKRX+n1ygOUmu+0sDabgkT0hwExzpV6jeizIhWJc1CABmdQaS/OQcRX29lcq0ZvhJIwKpRk6Bh0zrwKbAVVu7u72R0hKq61AgxXzT06g4WxjxiMSCid7yN8h4eHA6OsPYPrK7KsDpkCcsSMnPvnXAAH921PFVPV0285rxqBTk1NpfJVqhbooNyMJiWtDERNxfkOa1gZAterP8cCAV6ie9/v2UU+jIb78SycEbkhM05xbjabA4dsVVAsH89J1OepETQZlxaqOXunUjPK2CigZ2xsLE99HhkZid3d3Zibm8uIrMor3cJyAAGAG9AljYnpYADR/ehgsl/3qwIVhrqyTzW1WutN7Iv/q2moqaHaLVLZCWm1WhhaJy/Xrj7X98y+q9Zzccrke39/P9NxnMXc3FyCYcxoBSnz8/MJaCp758Rn8nl0dJQMTl0z9wm4TE9PR7vdTrvIPgABlcmsqUAOmG21TuxtBb/2TerOPlkr+uW+MWI1/VtrpoCEysxWIOMZFCDXdBunaW8qc2C9pOPIB8BxO8CprF9lLdgT60KeayEvfbGmhmhaa7rpPmZnZ/PIk5mZmYEjCcbGxpK9JLsnJycxPT2dHVcYOc9sPwQk9Jyu8g0V3Nyuy6rdQAZKuva9e/fi3XffHQDoL/N65QGKFzRPYdHcFFe0UNvQIvoto161iIfAVCQLTBDS6liAo4g+wKlGoP7f3xS3bq5rEgwDjnxPvS/PaQAQ4YuIVHDCdH5+nu3HETdG4u7du7G7u5v0PUXv9XoDFdyVsu31ellchaFiLAAVRauE+urqptvD9FIKUtMVBJ4zrx08FRC4d0ppfW8Dh+oA1ZfUPbOHrlcjBBGzNjz74lkYCmCughH3rO2aDDYajYHiZRH2bRDEQFZgxgEMDw/H4eFhXss9V6fFWI6O9gcIKlTE+JBRjlb0Sk+ALAYbA4OdME67OvxKKdf1sgY1BWbs+dzcXEbTFYRbMzl0/6YTXpgOlDOQYI8PDw+T8Wu327n2dND31YCi/tv61vx5TT+yC+hrAL8WvlemBsgE8iozUAtJ3R9b1Ol0cmZNBUg1cscmc7giWqDP3t5uIiDP9XoGs6lz8x2GBtYJu2rXOK7x8fGc50F36V5da9E/WauMcGVhrCNwSy+rjbZXnHTdN0Em2azpcbrF9gKGQGW919pRBlD4mbW4XbTMgQtiau0MmZeWqvbLmntugMXeCfDqWk1MTCS7CRx4XvtFFgzj892VQaq6yA4JhisgJUMyC3ShsjHk3fRz616Jha/0euUBiiilUq0RMVBNXh0E6pVD9Ad9pqiwRlfVoKkBgS5rDleOHGXq+/1tBgDDU+tcbgODCiJEkQ5DrNfiRFCtwMnMzEyuiSiGU67GwLHpHJTvpJiU10RZqaiIyEim1tJUapJjmZ6eHhh7L6XEkWIAGPrKBgAqHJ11cq2K4CuAqM6WYmCPKiNGud1zBSmccY2k/a5G7BVUVYbNmlSWrKYlACSREaUGhHu9Xhp/6Z3p6emU4Sr/lVLlRKXFpHrQ+ZU1Q8Fjv+gNo0PW63NV57W8vJyAs7asYtmwMah4hr7dbn8JPT01NZWMBDDuWqOjowmKRc81pWq96oGGInSG3x75HLnQiVDlnk5yoICidAt5qDUG2DDyQl5vO1SpQfIlTeqz0p1XV1eZ3rXXanHIFaeP2m80borEZ2dnc/w8NsX36NpTT+R5bxdTAi/Hx8cxNzcXR0dHOfukBms1QKt/yECVCXvtWY6Pj7MeTnHx5ORktNvtDJgq8HcK+9XVVYLbymBxsmTOPmPwHI3C7gmkgJNqB4BHoLrqLZmqQYr9qyyXQ/Rq+t1REnwOPbM+9ojdnpiYyIMT6Tu5t+5KE6rssZ98Dv0G5iNigJm2B5X9FsworLXv5Ob8/DxmZmZie3s795GeWb9erxebm5sDaf9KEnyl1ysPUGqVcqUV5WM57BrNEZiIPkKtcxxEStWZMVbV2IlWOFCOwuLX9AKjKefIsFAKglXrUAiXNr5aRzIycjPW+tmzZykwhJlRgHxrvYDv5NitR6V/GQDRNpZJDpVzd0/WvFL72il9FuuiOJlRFSFNTEzE4eFhRnH2yz2IlDhyRgII8wwYpNpKWNetMlr2/3Zax1rVuhVRE+ddn5cx6/V6CQw9m+/k0Or3fzm2gYME1iL650pdX1/H3t5erh95HBsbi+Xl5aTmGY1Op39WC4N+dHSUqYl62i55HR4ejvn5+ey06XRups5qdxXBM/jSC6KuWq9EphRfSldh5jgH7wVgMAw6k7At5EiEVqNqRdt+RufVY9UUjvdgPOku/aMTwF0Fsvaf8Y2I7FyohbMRkRGzqBaowuaJhoEWz+EoCI5rYWEh5x8pbK4MgfZza0QnMV3Su3ShnuzN/rBNlTnyXnvDrmBbb6eualpmZmZm4IwhkXxlfytTSedrDVit6+EYIyJTGxE3dXK1C469YyOkTNgEslNlv9YEkgt6WVmf26w4loLdqQAPiKv3bs3UiN29ezeePXuWNiMiUlZrvVMNUis4qeAQCAMo1MIA8+xbZa/Jo9RPBTsAjnWp+43Jrak8tl4q0T1MTk4OzK+pDPHLvF55gFKpRcJtUYGTWh8S0Ve+KoQiLk69GiGOzt8cJ3RMmEQ3NoOBcG/1+0TXFLs62IhI48SQS/UwhmjUoaGhjIYi+qkkucbKgszMzGTKRwHm/Px8bG5upsDUSnG0Ym1dZFgiIqlLSo7yr/MQPHd1hLcLNBmilZWVTHVVIMjxceQiHoaJMkoDAAO+sxod+1drQBR5YoAq0LU/ikgrQLLeZKpGL+QPpS7dU/ceuKgODauAaTg4OBiQMUZStGjKKGpd5MoA1xkWImosBSAq4gQEAVe5bbT2zMxMOnd7QAY8/9BQ/xAxsknv6Ee3280WUzlzAANIu7y8TGZMYSGDLHUrXw60eA7gD0MA7AAoapZqajFi8DRy4Nt31bQvGYiIZPQYXqkw8koOpJ4UMdfZIL1eL5aXl2N7eztBqT20PrUGpAYEETEwyC0iskurttfW9HAdNzA/Px+Xl5fZeQRcOPWW/ll/8uPeOWs6RWdr6qXORaJPnpO8+BkHWvek1ta4LtBEB8msNat1SWz5bcaKPKizA3zYayCw1pJUP+LZardg9RsABWBXywMajUa2/AP72FLsGf0CWIGJg4ODmJmZib29vYEgUCq+HklhQnktS3AWFpaGPghAKoiy39VO87PstbVXxK1+rAZlzWYza3mwQC/zeuUByunp6cAo31rUwwEyHJSnGq7bNQk2qlJ8DFZNj9QCNojQ94mGOSNK5+cEISJicXExdnd38xpYnrm5uTS6AAilro4aQIOKjTPnrG5H8JSlgoyZmZkUMMCq0tqeVzTLYDHa2qEZFdSziNA+YZKAqVrAe3x8HLOzs8mG+C7ptog+W1YBx9XVVT6TCI2xZJQZJIY1ot8SF9E3ghgPzxXRd2bkQ3RdKdaaYoqIZIUA1arEtdDO96N3ya9nU89j2qN9iIgcVMepADHAou8gs56v0+nE7u5uzM7ODhwSCWgCYfYE07WwsDBQ3N3pdGJmZmYgKvSsjLqaixphA6ezs7Mpq5ytOgkGGqsAoIyNjcXMzEymJumuVAKHqpODzGMUKksoWrRfZIwMCA4Am9upMfuLNifvHM7U1FRGrj4rSAD2TFbudDrRbrdjeno6bUNlGhQ4+n9tizUeoLK89owNEdX63fT0dLaCkjWAUJpYrRpdZy+lwD1/XZeISEfGCQFlupGANUxrBXj0x7rVou0afNZnkdKrUXxN1dGv2oVSAfDw8M2xCLXuRxBYU0z0znUrg8kuAVlAvMCZDfc5smwd6TP9cm1+CwhSU4Utqd1Py8vLsbu7m3YPYBGo8E1YHzo1OjoaBwcHA80ifJpAw7XYHMCrgrlaECtYwagY1oYJVZPyMq9XflAbo8exVepVdAxxGzIlj1zpO3nfmZmZWFpairW1tVhdXY2VlZVYWFjICYQzMzMxOzubUQ26ttfrZd6TMhFOjoXjrMPaCEeNQChkpW2d1yAq83wUGPioufkKSkTQfoYFarfbX6KEDKmUC9DHASqAM1BMlOakTU6bQ64pGWvhma3r/fv3B2bZiHCBmVo/Ues27D1Day3QnZgL6Qv75f4qcETLYjJcx2fs8/T0dHYZYWKsq/deXV2lAyab1Qj7v+mu9l+0dn19He12eyCFUhkce4CF8bnx8fFYWloaiDpnZ2cznTA9PZ2TT61rs9nMQ+fQ7eRGzYv32jeMwMrKysBAqEo5M5AREbOzsxmFn5+fx/b2du4PWfZ5z1SjNakCaz0yMhIrKyupA0BhbXWvwIlj51Bqmtf3kw+AEwMLsEf0gSGZA6arMxKpW0P5+7pXta7J3gAKbFGdUIqxxZTUEem1XRhYEmBcXvZb/mvNAnnjAEXoiqorGxnRrxvBSlxeXma6ZGhoKPWspp7YLm297It9co/sg2CkslYALhsFlNQhkPRVqob8uWdpC2tVgfbV1VUCBc/OtlU2AatDllqtVkxPT8fKysoAa+mFXQfu6Eyv18vBdRVUAdy+H3DHcroPelUzBBGRrJpAsQIdKRn7TY+qDySbgmkADUsvgND5Mzw8nOlGgNBxCxH9ydxACeapMmsv83rlAcrCwkIibQZ8YmIi2Yaaw4vo5/gYLA7KpleHZmMsNMWp1xSZVZqv0pLVqRDSiEhKlRARnFpMBu1zwpRJ4Vot7mTIfH/NRR8fH8f09HQWlEG5FxcXMTs7GxGRKY4KCtDlhNnzoucYxQqKrq+vY319PQ23aIEzFjUp6kL1SyX4bmCIcgELNZ9aDbhXZUNqxXz9XmxBTQ12u92sM6m1IGpQ7LHUFeAh4qjsl32o0VxNH0QMdopU0KU2BHPA+VkTEZLnIkOdTieBtHu9nUPHxHCc1qFeKyLy56Je0bvv5QTa7Xam5BYWFvJ7yBJn4mdjY2N5crZUB4bDcwIW1jCiXwjsurU7CuCvAUPVJ9epaVdAAqCr7cMMaHW+7iMi0rkBKO4J6HGN/f39gTRLTfFGRM4+ce8R/ZkdnU5noP6FLOi8qCkmcsSJ184dLAo7RC9qWht7K0L3LNfX13FwcJBgSerYntaUh/SJPaev7r0CCywWO0em1LtUcBVxAxpq5yHnyFbSczIGPAGiwFkNItwbsKZOiZ09Pz/PdGO1x3Se/BwfH2dXE1soHVI7hcgPmfc3sOf6tU6l7iGmStErna8A7+joKI6Pj+POnTsJJCobQlbUmjSbzbh7925+v9pFNrsekcF2Ly4upt0VNPiMtDGCAOC3F0Ao1o/Nea/XKw9QagHl1NTUl6BmP4fmGcLaOkqQCRLHQykJpQrziBuEzCFQEIrGiGI+RN+qxwkmChHVS3Aqsq95vIpkI/pFnbXVrebLK2gDTiL6c1kM8VGXIirv9Xr5fgavFtqKRHWT+Deh3dnZScPm+QkkY6uepq5BjZ7sAwoTsPSe6nz9X3RRmTSKwjDWVzU8FZhV41oBhe8EpHQx1WPHOWIMBTkS3TDY1UlW48mguYb3VnZL4ZnrA4/uVdQJuHPMtcq+RouMIINfo9Gzs7PM63v+2qGmfVA+3H66r+Xl5QQPjCXja4/pRp0/4bl1q2FBpWAMr7MPwBf59B1ac4eGhmJ+fj5mZ2eTsVpbW8tiYvdjHzjAmu6L6DtIjt9n6Yn7lPa8urqKhYWFiOi3RDcajYHU5W02j9xp0STnh4eHKTe1jqt2ibkPEbygo9VqZWec9ZHCq/uCpalss3uvBa/+WLOaPgPcauGkU5l9bmpqagBM9Hq9/M65ubkBJlFKg3zXFIJWeyMLrGsFPjUNxeFWG+96nkNNFPDn78nJyey+ouvYF/bWqxaC1jEM6unqC1u1uLiYOlbblTEzS0tLuba1Jo/OTExMxJMnT1JO2UWpVmdsnZ6e5pgAwYzvqkyWQN/f6k4E47otARZpLrIMRPkzOzsbQ0NDsba2lgTCe71eeYBSI1jOklBWwxvRNwQMDuTIcNwuqhJloMIIZAUbWpMBJd8n0qXY1UHWwrcaFbln0ZJ7rErks0AKg0NhFSMRCsa9AqOaThJBDQ/ftKkS6oj+nAj/rikI7xVBo3lNmI3o13kQfi/MxdXVVc6dAbR0CjFkqEvdDGhPxtJ6+wMk1aFZlEm9AOMlkiM3WCWGptYFUfQKIK2FPwBmHd7lTy0qrSwcdmllZSVmZmYG7sd1yYDnYJxqvQrg5x68nwxgJhhh0axoptLYHL7UEHYLO0DuXR+4q0BdCsLcl1qAW3VVSqLRaCSbxhmpBWDoyAPZwqTU6B1DRue63W46ZvePMaiyUYsqq85VurymLhlge8ixAMRShfTHfdUptlIOnU5noDjSM9Znnp6eTvbJz9xv3QOskP2okTzbwwFhaersmNv3jXVpNpupe2NjY+mUOEx6yBkLOgQQZK+2+gKigCRAx2ELMN0rBhHzUm0mIOPeMULAQK21qqkNOl9BFv13/VpUXBlRgQQ58X6Ar9qFq6urmJmZSfBBTsbHx2NxcTG63W5O8vVsmMuIfqeYwlfpo2bz5syuq6urtMmCOrIPzLp//kawTi8rEHd/s7OzMTk5GWtra3m9aptMCK9BUrPZzEYT4HJ4eDgHEd6eZfSVXq88QOEwatTPwVswhY4RkZE9o4yeq05NxDgyMhJLS0sxPT09oBAoNhtO4M36qBXXt+n2mvrwvUCJ+xVJoKgrQBH9YUbch0hE5MwQi8r8LfVTHbEiO50qXpw+CjSif9YFYELpGfipqamsX6npIA7h9PQ053VQZnR4fXZDxjBWvh/y9lmODGhRI8HI2TPKWGtJyE0tCq4Og4HnAKohRudjEgARoIUhEP00Go2MysgeA6ReBdiO6Dsn4JnT5pQi+iffMuqek6ER/Z6fn8f8/Hym/hToiaA5Ag4Crc74R8QAQLEPlcqvaQb1JqL06enpdNCez89cn0H27xqJczZ1jk1EDKRf6I69r+ARAFEwSnba7Xbs7+/n/mJgqhNyncqucoZzc3MxOzubulhTE4AjJ3l5eZn2wXWBjNupSsxA7caR2jXUbmxsLEESwKNIGBASaVsfzh2bTFfu3LmTjLI0pzQTWwmcAMY1XcTZ0FX7QP609wKGpghzirVuCQAme2xnrRUxXbraPTpSna/1qfUY7LIUSN03n7WH7IsaHb+vAe9teYzos3A1qLAumKLKQgMm9N1esTWuQ4YBG8xRu90eYHYrw3Z0dBQrKysZlB0fH2c6lm0nF57ZvmNxtYzPz8/nGgD3ZJlPrOUD5CAiEpg7vuQ2i/QHvV55gBIR6ewZSFHqbURNgNBWEZGGFe1dETHQQXhFPRGDk0vRa4rnGLoKBiBRiNjm1nwjYIG2BJ4oak1TARwcqWeRH6YYNZ9fUxjukWGGsFXxM3zSGChHiLmexmlNRkZGchaHNljPB3lfX18PpBcYIAqztbWV78PKEHbsFSU3OdHwJtEHh+bfDKwoh6OvNHiNwqthr+wWQ2RfJicnU2mtAxmRiqlFkRgDszG8t7aJ2i+UuBqFtbW1NEgXFxcxPz+fqQPXqUZ+fn4+wWNlGRkxTnBoqN8W7LM1LXd11T+DpnbKRUQar9spEA6mMioi+EoBVyq/0+lkzQOg5HkAITUKHECtD3JvddBiZZvIM6dZC6VbrVYezFZZLpG/PeJQOa3j4+OBceRV1yIiAxRyA2RVJomDrhH47OzsQPFrt9vNjo1aw1HvJyJy37a3t9OmKPxWkHx4eJhAiaPQrm2/IiJ/HhEDelG7eyr9j1GuB8XRffJTdYx+VRaLU5UOAWLUg0gz2GfrSEYq8+1+AH3rWNNgmCBMpCCBrSRrlZkEsu151TmpaCCODWIPFEuTRTopYBUgS/Vsb2/H0tJS7gO58xLkkVWyW1PglRETxEjt66xUJ2hN6LruTnNa1NuwT76vgvZGo/El06inp6dzbaT2nbD9Xq9XHqDUiAcFChVWpoBidLvdODg4GKjGhvyd9Ovn0gCKoSL6c0JqHz2aWeRWHRVBqVE4IZT7GxsbS6EcHR3N47e13YrECQGQAAygcyF1CsU51BoWisUReF70XbfbjeXl5ej1bgar3bt3b6AAkDND4zoDCAUK5dfcNSOwuLiYisrJRkSupemRtVJ9fn4+nai9xnBxIBE3UdrCwkIsLi7GzMxMAipovwJDLJvJmO4Zo2Tf/H9kZCSvfXJykmkpCsmJKXoEhP0MO1GvzViIeGuasdKt7leNh9RRbdGthYjmV1xdXUW73c57Ozo6ynkKnBoHy+hxKrWdm77YO3nkmtIEniuYrFONRYT0ClPCMc7NzcXc3FyCdnUkGJ5q9K1hRP8cngpmsCbAByP95dIX9XdkBXsE8JI5jrSmrbByDDSQZ31rLRrHUJ+51oxxyGRBytPe7u/vp6MDltg8clLBin12ffLl+cl/u92OJ0+exMXFRcoYhlPRrGFvlV3lgO318vJyRNwMTpucnMwaJrogHcDpa0d3yKFrqw3CyrKbw8PDmQoRpFRgWQuca71KDa4UndN1DBamdWJiIqanp5M1st/AD3lgi9ghMsAmYaaAM/dk79lmdkFhKxmrzQwG7AloKuPhuQTbAOTs7Gzus7XADtsPoO7g4CAWFhYGzu7xXGy3YEUg62eYsVqDWO/LfvJxAMvExET6h/d6vfJzUBqNRiwtLSUFatEYNI4VCmRcGB4Cj7kQWVvQ2pteaXCCW4WAghBkfyIiN1exUG3LAw4oak2HcHqcQbPZzMFqhFAtSI3gKLV78zv3wwBZQ4BBgSsBbbfbA51Nd+7cia2trYFCX7Mdal6fwgIv2BWGQS6cgtcCV/cp8kSh+70oy2Cg6kQAK+sAMFa2SvTtu2pdAkdaiwEjIo+pZ2CwO9gC4E+rLiBTnZV0wO38q/f4rkpTi5YBkc3NzQQI1omD5LQYXozX7UmS5+fn0W63U6ZqxFfZQvtkcmila4EeEVcF0XRsZWUlQVCv18u5NuSVnNVTZAUWmEkpEnIyOjoaU1NT8eDBg/j0pz+dMuH6wCcHExF5X+5/aGgo8/hVV3w3Rky6CvgAWhlf1zLAjgGu9WsiV8EIUG1NhoaGMqqUYjXXBqAYHR3NmTXSZ1Vv19bWYmNjIxqNm4PjXJvTJ1f0yOfICzp/bGws9vb2BoANUKgjCAPK4dIF17ZWtYYFM8YmrKysxP7+fgZsnc5Nce7Kykq02+2Yn5+PR48epXMXcCjIlA60X56HPFfbgQFkt8kcQAOYzM7OZv2GomK6SC9qHVmv158aXVOvNb2LTazMGf+kBlHwYW3IFibNeWMGNpItn60pLMGBoHx5eTkODw8T2NC5mhKbnZ3NYydq56sifGtFFhTFyxiYEm3fBcTsAf9q/QU/dPO9Xq88QBkZGcmx3JxTLRglwOirWpA5MjKS0Ty0X1M+lXZlZCL6h6L1er2cA1HTB61WKwVK8ZLPMM63mYzbFBlHfrsoztwSrWSoSMaNIRbdcYauTWj8vxbsRURek5Ml7NPT03F+fp6DdwzII6x7e3u5ByI8z0XRGBfKGREDYI/jFcXfv38/3nnnnTSY9uHy8jLm5uYiInJOB4MopcOhVapelFLBmpRHBUCVyq1RVy1Cq+xFRH/AU7fbn1GCKlWvA+Q4np4M2R80K6fsOzzbwcFB/o7DA3pEgQx57S5rNBp5EjEmkAGSm+52u9mlBngasY5NJJNAiGnGoiLX2NjYyGK+OqysGmbyLhoGBDzT1dXVl4z1r+nL3d3dTI3Qb10uNdVqb2qkDADUSE8Kyn7X8frVAYvoaxF7je51DNY2VTKGGVleXs6uCjS9z2AUgBC66DnU0FRwpc5nd3c3pqenY21tLeu6fK/nYavYIM90fHycke3Q0FAO5tO6yqbS6/n5+UzH1gLlGhTNz8/nkDn2odlsxu7u7pew1NfX17G5uZlAr6ac1QvRI9F3Td9Z54jI6cU1APVvAQ554DjZJiCTTEdEzqCqxfUYyIhI8MFeADeeg91nO9hsQLp2vxj0pu5jeno6dnZ2Bpot2C7yoSi6MmX8wfHxcTJghqNhv723ptsdrmn97eX09HSm+2dnZxNg6jSdmZlJFqZetwaGGCWM4Mu8XnmAIhrBLjACjDWBgfq9r9J6Ef2iz4jBiD2iPyGUAVRDYfgZgWBwFQRWpkCRFWESNYvuMBT1vJRaNc4A9Xr9Q+8oOOGoaBfQQjcycvLY2tdqLYyUUaPRGDj/xfkWhB5iZ+A4BUaq2+2fUuuckIh+URlAxcnVeh5rdHJyEnt7e7n+9rZGCgCciLcalkp3u+eaGuAMDw8PU2Gtp/1HpdauIRFxjfitCxBZjWGNHsiUgXkMAzCgQNG9isDm5uYyspWLv7q6OR16c3MzI6Rerxdzc3M5UbIWV8pzi4AajUYyhZwqxw1EmW9gTURh9sl6qXU5ODiI7e3tBCv00Jp3Ov1zfQAhYAeQdw+cDSdc6wgAH7VaaOcqY57/dqoT4wXE+Pntzii6WxnS6+vrLC6Wj6/gbHR0NAEEBqrW0xipvr29/SUshO+id5XpxChVWwS0C4KASUxCBee1Po8zpT8AC924uLiI5eXlOD4+Tn2TnlJ7tre3F0NDQwM2qabQAFE/d/9sDp1w//aATbGPzWYz5ufnY3t7O20apk8ASK7cKxtXWSDPXlOtgGYtEbC2Jp9KRUln1GMpKrDwnNgoXVe7u7vppDFBm5ub6SNqEWwFs2wc8K3DEsNf55NICUZE7q8zzxRyY5ut+fj4zcnedRK0oARIq3WZ7steYpGrrcLU1NR19XP0B9Cjr+/1euVrUFDdqHlKqZYDWBgZGckalevr/pTOWsQEIfuZmgCRNwQL5TOCjBBhr61ds7OzA1EqxZBjRzW3WjdTOdF21VADYdfX17GyspKgpLaJue7x8XEcHx9nZbd7QmlSQrRjNQ6EF3Cr9G2r1UqmKqJ/EBuq33EDivGOjo6SjtfZAyDWFNjp6Wns7u7m/Vf6j3OsgKPT6cSdO3dSaSqlr4iutryRDXUuIgc0JqaCoSEHl5eXcXBwEAcHB6lUNe1gnoZoTisririmiuqa1X0Q/TFqombrx3DVdNPU1FSeAHt1dZXfj70QxZAr6RxUsvtQa/DgwYNcF99ddUunEWpaFOUlNWEPvDihqampWFpaSgdCVq3l6upqdgxZ59tslt8z6taifl+l9v3OkLlWq5U1F7X2jL2odoMe1Hut9Q1XVzcdV8DYwcFBHB0dDaRYPb/UGFtBr4aGhr6E7RE46PBQ62P9OQ4yU4OHOhdpeHh4YDYU5ywV0Gw20y6Ojo5mLQxnLLIHlKRm62iD5eXlBIjLy8sxNTWVEXbdi4h+DYIRAphC4Ehq3RrNzc3l+4aGhvK8GrqGkWQn2YWa3vQ82DD76OfstpqTyjgDUhGDYLf6GN/j+TCV5NJ9Capcc3d3N8bHx2N1dTXlGsNEj+h8t3tTf8dOeBbgx7XJw8zMTKYasRSC7so+Wg91O4IG72cnR0b6RxOY02VPyDt5o7P2RNq3svsRkbVJ7MjLvF55BqVS6xH9iYEU1UtNgPdXdE2Iat5S9Cei956K2jk5m3A76mHkagFURAykdCIilcP/IVUTJimbmg7gB5iAto+OjhIguFf0HmcOeBBWgCsiMqqTL9Y5oHhOUdrGxkaicCwDitirFktidyo7woF5huHh4ZiZmUlnyqBy+s5gARwoCANY0zToRA6j5v8ZxRopuA6nFXHj8BX6XV1d5VAjLAYmDGVrf4Fj9DSDFRHJQkT053jUkdgMpXZRBtta1qJiTIK0wPHxcZydncXCwkI0Go0sivWcWBuMydTUVN4nZ8YxOIzs3r178eTJk7wv6aF2ux2NRiP1yTqKziMi5ufnc+2kprAyouuLi/68HwazOhCpQjozNDSU6UL6IF3mOgx8rRcQ6QkUgP76b3/skdqdyqq6Pj0HHDgUOifAIf+AkVoMzwg4k6nz8/NYWlqK3d3dZMZqDZX78D1DQ0Oxs7MzkD7Usirtd3V1lXOaFhYWEjj5PaAR0XfIIvDLy8uBA1GBFn8ePHgQb7/9duoXEFaLQwEQqWAMwPX1zXljmNkKjGrqb3x8PNtigTq6RcftIfsKVNQ9ZmPYbntdGRy6Tx/U17FFNRjGEI+MjKQtNmsKC14nZ9u3Or0ZAKRLd+7cSZtCN91/ROTQwvHx8QxgrMfY2Fhsb2/H5eVlLCwspF+YnZ3NdBQbzHZgWLDutzv+pAc9O8b45OQkbZS1mpubyzrEw8PDZPXYZqwP2a1g/iu9XnkGhVBRQlG4YjNoDZpjyEWRDHRE3/jUHC9aqiLziEijgkqr7b6idN+ByqVIFNxmVTpa4Si6UPQqYjfsh2FBQVIo7YHyygxERL8ORURS0yo6mXQxAALoRU7l6OgovuZrvia/01pQcGuimO/i4iJrVUQSACKDBvTt7u4mXe/5a0W9upulpaWMgLAs3ldTTeTATByKLhIAVBgMzAonw3gxQIwrZgLAc881zaiLhZNmjCL68xAYS+DK+td7Ip8cDUNXW8tdn2xiz3SPSKUtLi7G7OxsDv3SHlor7BnyiMhakuHh/kGR6+vrAwyG0dnqQebn53OfFcRWBoMTAtztK71jLDnSynQdHBzEzs5OPj8mB/CQ768dBdbfNWvKzV6IvgECKTHOtjJZIyMjCfCkyOw/0KllXDRbj8motPnp6WkCclGr9Akb4v6tIyBp7aw1Z2YmEBmzJsvLy6kDp6ensbKyEmNjYzE3NxfLy8t5TYxwjZa1Dfs+nSVSidYRI2Tvm81mLC8vpz7XVJlUWqNxU9g7NDSUZ0ixJ+SypoXdl7WonVuVQROQ1UJc8m0dKiPq/motYafTydbgGvFL7ZMJKT8MKPbYRFr2z6iJ6+vrBAxYlImJiQRgEZHBHtmdmJiIk5OTWFxczDSt9R4fH4/9/f3saKLzbAMfSbbZbfuKSVf0Pzc3NzAl3DgAuu7eut1u1ggJOgUjEZG6AAjyfTrMXub1ygOUiMiiLkqMEha1Ud7JycmkvwgdxsHiMRRSQxE3TsDQMMLFoDJ6DEFEDKRSoO96pkxEpGABG5SDUEGrrVYri7QwKGoBKLoR/4ylewACrFGj0UiwIuLEBDDY29vb6WyGhoZib28vxsbGspL+8vIyHj9+HK1WK9ugOWVdOu5Lt5JoSB6Xw61Gm3MSpddBXqhygMBzoCSdp1JTAxH9s4ysgfWF/CMiwYeiWAxIZcMqPb66upqshnuoKRspgZrrBtpEW6IeDk0UXQ1oTW1U+lYhnvXyPWohjHwHvrRILy0txcrKSiwuLuaxDp1OJ+tPOHhAN+KGBXvjjTdSr0R6QLdImHOP6A+Fq/MaFP1hfkRqCjB9rqYjgWssw507d9KJYiEYRXsYEUnbAwh0qRYMRwzWctQ0Q0R/GNvtGjRAfnp6OhYXF+Pu3btx7969HKdvjbANar0AhaGhoYHJz2yBsQL0sBaNA723O53M/sFMsD9kanZ2Ngt3h4eHM/WLtVBbNT09nYfezczMpC2p4L2yARX4HBwcZFoV2ImIdMSAqX0FpqQAzNk4OjpKp7i4uBgTExMJbAA5jI/9AJhq3ZC0NQa7Fr1W9uzy8jIDDHIqZXZ9fZ1rjvERjPINZBlj5Rr8Rk3RN5s3HTiKuNl6wzzJsO8HqNz3xcVFLC4u5v83NjZSdqampgZKEaRn5ubmBthqNtOp1kCy51e0vLi4mPU95EMZhOtbU2vGlkVEpnLYdYFEo9GIhYWFBDaVSX6v1yuf4uG0Ra1eNeomcAxqxOD5EdIcNp6jETVaTMaGM/QHYKh5UYWsdWDR0NBQRvbQqQhVTUp1KgcHBwPRAcG4urrKe6gFuhSCoawpI5E/I8cIUQKgZGRkZKDKHsvEWKPdATSfVwMyMnJzJDijggrUplfnxTgYjwG7f/9+7OzsJCUpV1qdyf7+fhq9OoLa4Ys6P8gAirrdbqcccODy84x9nVmgwJo8ACgin1qMXetEKpiqBYb2IqI/WDDixjE7U0bhqO8jD5VRIW8nJycpY4rUGCCOhHHgwD1/TTthaZaWlpKGXV5eToewvb2dn+EoOSfAREcXGdrf38+ivprmW19fz+txFkdHR8kGVGaJTgHZ0iSXl5eZ/pmamsrZOVgy7FlNvZIfco8RAkLu3LmTgYegY3V1NZ17ROQ+A5oYUU4RIBIUqQGqsinilYbb39+P6+vrPIfK+lW2Uqq3dnhVZ8PG+EwtsH3+/Hnq//X1TTed1Bsbc3h4mO2yAgvyjMW8vLyMdrudIEI6TtRtnc3mmZ2dzcPrzNSIiJzLYzzA9vZ2rvHJyUnaB0EPJ25tqw2UGvb8wJzOmhowst3sKPnDHJCVulacLzshTe9+gBbgSMDAPmMw2Qi2pgaWmM3h4eE4PDxMxpadVnCtOHZxcTEODw9jYmIix8Zju6zZ5eVl7O/vp173er1M/VjT3d3drBsycHN7ezsDaOMN+BlyzSawc/TFtaX6qo5W4FUzFy/t31/6nX9EX5Wi4gRRyNiBiBhwNpz4/0Pen8VIni5n4X9k1t61Ze1LbzNzps/iIywOMsLCFjtClhBwBwJuEDqstgGL9YYrkEFCYt8MFkYIfsIgsYkLGyEQQtjGbLYxZ53p7unq2itr7dor83+RfCIj6/h4xsjAfw4pjWamuyozv+8bb8QTTzwRb1V/W2j/EHOhC2U32JdKA56cnES73R7IwLy/bOe+SpuOJKI/bK4KV9XZZ2ZmBtrYGGSlL2WKmIBWqxVzc3OZHaExOTAAaWZmJo6Pj3Oyn8B1d3eX9U7vX2vFgFv9fkDDmzdvciBVLUE5fLIQgKCyPAJ5dcScFmDRarUSjFaHIGPBANQBTehmZYFaxpH1sAP77SBV3RE74kQ9SxUYVgcr0HpG+4ONAnY7nU5mQ5whxyCLmZ2dHWBYaklQuYuYGluIhVpdXc02cU6G41T+xCLUUovuoevr6xRlC46dTu/uEM/r+QXiKlYFmA4ODjJIAArWU/BhQwKz560lUs6zjo5XwrKPkgFlRICQP5idnU0GU2Zrnw0H5FTvD/Mj/J2bm8vRBRUMYRKtE9DIZvb397MFeni4N6gLOMNC1BIAQG+/lZ4FCGfa/tO7+B3+ptPpZDZvDs43fdM3ZeuoM4mNqmXtu7u7WF1dTd8hCPFzfn5ycjIBdwXwlaGyjhhJf1YTCz8/Ojqalzzyw0AGX8GvWFNsVv05ftZaVp1QLW36bJpFz6Y0KJFgW/xC1ULOz8/nucZu8MHiEIBTbxgHMmdnZxOcT09Px8zMTAJF7JN7qzyXJAXwkLxhDycmJrLtW9v47e1t7O7uxvX1dczNzeVzLS4uZtMGDQkGrZaYrSVhsyTFOhiBAKBInK3jR3l97AGKMgDnyiE8evQoESDWhNFzNpXGrBSeGhlHCkyoJwsmkLiAC30PDQ1lRmBT0FvKSXQMFXTU1itGzCmjzTATwI5OBeWdubm5WFlZibm5uQzy1QFVYEXIROegRBDRy3YAqydPniQ9632BFBqdhYWFDFbADeZJsCBcvri4SIGZbOXi4iKz4Z8pm/N89lpmTgQMCOgWAEStAXatMkw1+1W7ryIyTr92J/h7GTzwQddRtQOmYdqrOrcBgPAZET1HI5ul7mdnAk7NGomeBSXTJhcWFvI8TE5OxsnJSZYRZN014GG47Eej0RNXt9vt6HQ6CW44psoGEcRx6ko8bJywcHp6eiC4AkKeu4qWAcu65q1WK+//AYhqqRWYcM4ELo7anSdKqvV80hc9ePAglpeXMwFwTtgwJ31ycpKdDW41B05k4lUPxT9ERIIKQ/yUVFxrYF4I5gsDaH2wW2zBVQs1ox0ZGUmhpMyXmBhoomvY399PfY/yG8CnlBbRK0lvb2+nj7qvB1HykNyxJWLt29vbgWs0gIKrq6t4+vRpAkv7CNSxdSJvzFttM5e0OPMAcb0/p4I1iZGzpxzB9gA0yWUt393e3qZIn/+WpLFD4ILYmk6D3bOR4+Pj1JZgos10khjxoZ715OQkNWb2QlLi87HPwGXVPjkLYomzWjs/lc/29/czDrbb7Uz6lHqd13a7nSDE9Ruzs7PZHu7v2FIFjR/2+tiXeMbHe/eUHB8fZwBQG5RZQOZqmDauouhK1SmboOGgRUOrBGt1fxS14B8RSXUKbmqNnJagBTxp4xwe7g1Bev36ddLnHIWgygjX19ej3W4nch4bG0sh0/X1daytraXRcnpAlWyfCLZqGGhmZLI7Ozt5sDlhpZnDw8Mc22+64MHBQaLxnZ2dzGIj+hns3NxcnJ+fx8TERLazcq6EYzIozgc7Utvhqj5EtkKXIENBBwvEKEqBGqhBUXNk/t6ec5gCDAALpHA83gM4qYHLOjisc3NzSfWbESFT0hXADiJioLavc0B2hUXj8DjJm5ub3Cci0tHR0az9y3L9Dnt58+ZN0vFVs3Rzc5NsSEQv6Gol5uB0IphUSUujLOOc1hKhP6sdJmze7IiqeQKQOX+ZqPOuHdKzAbH+QZ/zAVhKgmRCR0lHFS4qB2NKBCjMK20aW6jAThLlvArUp6eneZ0CkGQ2ETBAi4IBxQ52Op1YXFxMhuTw8DDPEYDDTpU1h4d7AyXta0QkS1Z/TimBb+l2u+mrtIZXH6Z8xZ74XuVBPhYLbE+rHdQuIM9+38c6f+5eqvod59x7e9XSve/N3/Ob/Fi9+8n3qXsAPAITkiqxQZmSTYgDzhRg2Wq1Ym9vb+B3MHW1DMoOJycnc/rr7e1tLC4upu/y7FhYTJtx9vaA8NWaSSAMiLPWWBhMq3ER4+PjcXR0FGNjYzE3NxfHx8d5zitjbQZYs9nMrh4/91FeH3uAon4mS2Hw6r61/oyeajQaeXcEvcTJyUnSZ4wO0q9Co6pErmp/2bIMoma5DrwMgWNZWVlJR+53OS0trt1uNxYWFmJvby9p7/tGyXkweOgaPcfQgKE3b97E4uJiIm3dHP6bgd3c9IaBcQ5aGTEhWJKHDx/G+++/n5k7NurNmzfZzeOAP3jwIA+ejEKZJSIyOyJS8z4+lyMTZB1GTsjeWGuAkFPExnDayi4yy1rbtrcRkeBIsPOdBUX0suefmZnJQ6+1tlLcsjwOmS1xAIL/9fV1fPrTn44PPvggM//Hjx9ni/f4eP+m6dPT02RKsC9KhQAx0DM6OhorKyu5j2rx1sXo8UajEe12O6+TULKrgE4icHd3l9oN61TLH7JjzCabZLd0WHWYV822ZbRjY2PZzYM1iujP3Ijoa9CwcPYT44ABiehfWGcNnEl/V0s0goL9YX+AqOSn6sCqHisiUnOBTeh0ekJUA/gE5SqQVXo5Pj6OxcXFge/QaDRy3D1GSBnH0DE6IcGY+L1+15ubm3jnnXfigw8+yGDn3qmrq95VGILLgwcPYm5uLl68eJGaEz4M8PR7/Kznq/o4ego6MH6Xv7Rm/EWdvcT+nfPKDjqvtTGhAq8KUIHH+rvOJvCIIaoAyzmtP191MgC1F2GqBEK8Ur5uNBqxsrKS58x3o0HRDeT8Es4bhglEAwfKyKQOWFmlwIiI/f39jF31lmUM8NTUVI7bJ9q/vb1NTZ+ZTcYX8PP1Hi2i/JmZmWyLN1b/w17/yyWeKiz6eq8P+/uP+jMf9qoqfJkIQ5JpoP0Jm2gl6qhuQa0qvWWXlVZkmNAlgOBwQptYk1pPhNTVaonkZClYAJ9j2l9Evy3Mn1F412Ary3M4xsfH4/HjxxEReRBl2ah0FC3qzut+Jkokent7GwsLCxkYXr58GePj43k76v2WOYI/TpJDwQwI9FUT5MCaFQJMoYrNRVGiIXzlVNgFUCbrrJlBFSfXur6gaM28FwdQ9UmEwYKMQKIU4HBzYuwvos+mYAOwP/YSUH7+/HnuU0TEixcv0lFgoLQ0EmXLiF3Ep+Qj4HLgDx48yDtWBAbreX19Hfv7+1nSFKjV17EDdF8cFjDv5fMODg4SWBhSKKtzQZqgWVknwRPbIqOUwUsq1PcB0ZmZmQS1mMKzs7MsF0TEgDOu5doqogU6qq/Cqgp49bv6DhExUBYDbCUASrHsqJZgsZ8RvfIYZg2z5Rn9OXF5RH+0PwYYcwuw0TAoAQGKk5OT8eLFi4iIDHSYgZGRkbzmAlAALre3t3MtfY69sHZ8s70VUI1Wx9BF9EfLX15exuHh4QD7yU49J6BWbzq/r/uo4vBaAgakMQnOpBgAaNUrACYmJhIMVV0SvY1kiq8GKuqsLgCP/yWQjYj0F0Dj4eFhLC0txfj4eJYW+dLj4+NkRJaXl3MdlMw3NzdTS4PJEcfECLFEkoERA4bb7XYCEOfV+4h/yl5PnjzJ9wfolFRPT0/jxYsXmXi4euPDXj8ngHJ8fBx/+S//5fjsZz8bExMT8Q/+wT/4GX/ub/2tvxVPnjyJkZGRePfdd+Mf/+N//L/0Mx/lxeHbcHVqiDaip6c4PDxMQ0XhyvCheVSq3+cEGSZU7/BcXl7m+6Ar/Tfjrr3vfkaPueCH2vMcte1Zluq9ddU4qJW1QT9C9TQ1DoADUg8N8IAO9b2VghwmnxkRmZEBTsoLnAxEr7w1PDycExtlDIKbEgZhX2Uc6E9OTk7yUEREAi8OtGpNql5EfZWYklYFWECzo2ap2pWZ1LYJwDw7oHZychL7+/vpoARoQY9+RECptX7f1eRXn1szNxmIKbW+LxHdzc1N7O3tpSbD4beeCwsLWf5ZXl5O+42IzILsG/ugW+FwsSx0BEAeRw+oAAJDQ0OZXdUSyu7ubn5+FQ8ODw8no4bNkYVhgQTPiMigQIRHUG42RxVMKlei3Ctt7mzqqLHeLsesZ105wlwlQQwYnJqaivX19RQiylwlJ8CnoCPosx06CcJYTIdnvru7i/X19QFWBdh1RuylTBugq3OaBEMdIA8ePMirOrCnAjy9DfBkf2v50Nwidrm4uJg25DzywZICDApgYVRBo9HIidIAvGzfZ+rCUt60t5VdtZdYO351eno6299pLIaHhwfE0r6HhINvUTIzQVg3kr2vZUN+C9uNtfe5khxgHDDAQNh37+cak+Hh4Zifnx84Z9aP+DUicvrszc1NzM3Nxc3NTbz11lvp7+6Xb2r5kB9VdrRmnkPJnb5H9WF+fj7m5uZia2troHQuSbKm9pJtfpTXzwmg/IN/8A/iS1/6UvzDf/gPv+7P/It/8S/i9//+3x9/7s/9uTg9PY3v+Z7vid/yW35L/NiP/djP6Wc+6sshrFfHU9lXsSsETxjJwGRgEf2L+8bHx5PtkA3YHIZBxyAT4dBtvH8LyLIsgj8Og+OUeRKBosu0Ez979ixH4QMSjFFAjYjMjoCuKgh89uxZonafW9Gw0sTTp0+z1ISZciiUJBxgHQ7WXo2Vo8MmaZmE7mtpwEGrQMbQIK14tRVS7dyzLy8vx+c+97mIiGxDPjo6SiEiJTtGpR5K7wG0VuU+lhDoq3oSzkjpAHOCIbq8vIyDg4O0DQeWcwKgMDB+7vj4OOcY0KWY6qo7qtbGMSeV1ZFdnZ2d5e3HHLCyDYrYd3UGFhcXIyJS9OsiuvvZGUcrWGFElEPpLLAGSj+jo6PpVGu3nH9XvQlAqtTnygRZWrvdHmiJtM5AtiQCM1J1V5gMiQvgUJ/JGaldUkp5gKtaf7vdTtA6NNQbOkaYjlmsE5SVcdgYoAGU7u7uRkQkWKQDuy/4ZAe0RG+//Xa+3/j4eKyvr6evHBnpDQtbXV3NRKgmd1iYoaGh2NzcTJY2IpJhePDgQXzyk5/MAXPWXFIFxAIgbE15A/iu4m++pTIVs7OzsbKykuAJe8RvK7FYMyCpJpT+m30AArWjkS3x6WIFwOAs2h8lC+CilvgEfM/GNmm4amfRgwcP8gJE/s/54LcI7rvdnohV+ztxN9va3NwcaKIAdNvtdoyNjcXGxkZ+97m5ubwI8vDwMI6PjzN22cv5+fkUbGuW8EzYRpUJfkriVctSmJTKEPuemP4PezW6HxXK3P/FRiP+3t/7e/Hbf/tvH/jzX/WrflXMz88PMCLf+q3fGp/4xCfi7//9v/+Rf+bDXicnJzE7Oxu/63f9rjzEBKQM3oGGqGt54/b2NssEsiSOkAPmkKrYq75HRL+8FNEf8BQRmXXLGBwkAjCAJKJ/MZZDw8H7fBmFwzY+Pp4T+9Bxh4eHWYe+vb2N1dXV2NjYyFkHPk9GRBzH4T18+DABgdJJLXFBzYwQlVlV+RB4RGTXhe4chxjAQH/SjMzOzsbOzk4yINaRlsZ+1Izs5uYms2jf0/erYJGDQdvLoH2+WjVhnczN0fBsnpXTEHxrpqjUV+vDEZHAS1aqBEkgTNS8v78/cPMoB19LSru7u0k921cj79lgLVtitOzf22+/HS9fvoxnz57Fq1evMkMFwAka2enq6mqcnp7G9XVv9Ln5FUays8GpqamYm5tL1ubi4iIvAEQnj472bh+3BoAToZ3A7VxKEipjQEcDwNnbKoh0jjyXmjv7ZIs1+JlJQYSso826+HnljVrmODs7y8DHFgRkJSOBXMdb1UZMTk7GwcFB6tMuLi5iZWUln3FnZydbkK+urqLdbuezoNPb7XaWfKvuxF5iknx3Nqj8UWf3eObDw8NcFz5WSVaw3tvbS83c9fV1+qPa1s5v8s/WubIWPtt5IgblJ/0eoPr8+fNs9/W8GJSIvnygMsIYP6wLFsb+1+9EkwH8+hz24/0wDLULkq7NZ3tuZ522TqKg5LGwsJA+w95I6NbW1pJptn8+g79+++234/Xr17m3t7e96c78kvL87u5uMuz0RXt7eyn8xWSJh363xgFApNls5kj9g4ODaDQa0Wq1YmJiIra3t7PsZz1brVa8ePEi/ubf/Jt5rcbXe/28thl3u934sR/7sfjlv/yXD/z5r/yVvzJ+5Ed+5CP/zM/1M4kb670JkB4H5cBhDajkoUcDxeqBldUBBf47ol9Xpxk4PT0dCAScLKTLcVVdRu0UgMBtZn0GhzCiFwDb7XYG4PPz89jf34/R0f6AtW63Gzs7O7G6uhq3t7fxzjvv5GcODw8PXETouVHk1O03NzfZLlzFxmYcCEAYFIcQ4+IytYuLi1hcXEwQoHuA3gX42N3dzYySw1AHF8Q4NQwGER8nwiF7NiCiMmRAlMyR3dC1OOgRMbC/jUYj5zHUTIgDOTw8zDY9GpRutydwRn0T9rFHmTxW7PXr16mD8dxDQ0MDzMnW1tZApgr4YII48VoyqUFTaeHi4iK2trbi5uYm5yxgC9klELq9vZ1A+/DwMLUwyhGAgEBizbysW7vdTnDi+0T0W5CHh4fj8ePHud9Kto8ePcq1xvL5b3qZ8fHx3Bt6nW63mxoMAaXOXRFQR0dHBy6pq0JVe22WDxDoz5V7+B31/G63mywssE2QTW+jDFrLRcDi1NRUdgHu7u7mfTTKQgA/EBkRyaI6QxMTEwlgK5NbO+AqIPyWb/mWbIuN6CVfn/zkJ3NfJYK1DbjdbsfS0lIyNnwdRhFTOT09ne/JDy0tLWVyom2ZJspQOGy37/TmzZvY29tLO6QrAayBHbf3joyMJCPg74Aha+87iRGew6A0/gSLzn7Zj1kmtQS9traW38esIgMLMcl8ON97dXUVGxsbyTo3Go3cy6mpqUw0fTY74LPoRJQ9AXkMpzJvp9O7iFCnqisGrJn9oakZHx8fiGn0ZnXExebmZoJmeqH9/f1s25dU82HKPx/2+nnt4iHe0SHjtby8HDs7Ox/5Z36mV6UbIyLFnFUoiaKUafl5VGNEZLblzyvtp7VWNlZ/R+DXJsXBXl9fp9LdYfR3UKigVGuk77zzThwdHWXnhYzUd5XxKKNE9EduDw0NxdLSUoqmWq3WQCb47rvvxk//9E/H9vZ2vPXWW/Hee++lk5mamkphVRV9GsiFBhRsXeNe1eQCmdY3LMTFxUUiac56ZGQkW6UjegCHIBC1vLW1lYdwbW0tdTeCHKfqMJsvwKl3u93U2Qj89ZBXtgPVHBFJ7cqoKpWLwbJGjUYjaUzZEoDIKcuIBHuOqQqt3Qj96NGjFP0pi1Qgar2UwCJioI47MzMTp6ensb6+nuOvtVui+i8uLmJnZycdP6CN0cLe6GATuIGNmZmZFDvL+ACFiBi4F8qaY+Q4z9PT02TxdHx9+ctfTpDI9qpgjwaF7goLZC86nU6WejBHVQCNWZLlARG1o8e6OveeQ1Cz1sAKgCwrRXdj1oBSLEGj0chSHABXW2+rgFN5lC0uLCzE8fFxHBwcZMno5OQkJiZ6F+fRj7DTZrN3583BwUG2AgM31mRpaSmOjo7i4cOHsbGxkXqORqORXYMffPBB+i2i4ufPnycbWMWU1oc/0SIswFWQUpk+7a6SqNp56b/5YLNhlCFmZ2eTsT44OEgfXxnOBw964/uVpCIiWQ3+k+3Zb9+z2n59JudXN8rQUH/ei+8J2AMfLlGsdinoe0+C46rZqGyQn1UeUTLhD6anp2N3dzf95OjoaLz33nvxbd/2bfHFL34xxxBggE5PTzNBVYqemZnJybO1zGj9AMQ6rRc7xP7ZF/E5H3RzcxOvX78e0LdImKqI/md7/W8Z1GYB6//f/0If5Wfq63u/93tjdnY2/9GZwrHs7OykU4qIBBjoQhmmDJeT8FJDdfX17OxsREQGSmAHihQsUbdKFTocDPiptXHGd3V1Fdvb2+lcsA2+u555m+xSMoIuYrca+CP6lxRiVNQf1VMnJiai3W7H9PR0HB0dxfn5ebz//vuxt7c3MCDt1atX6WjroVOq8qxaYwkth4b6d43oEKliNgDC+zQajQQsxMqCk7WM6I8o94wYGqCQcxsbG4ujo6NUmiuzVdtSqri9vU0Agy4VdOo+KPdgBVCVQGsVE9MSKVWNjIzE3t5eDjwCtGZnZ2N3dzcBBYdYp2kSFNc2Yu2qbqW9u7tLUEuj4fvt7Oyko9DC2W63E0SxSVcDcBxq8JT39lpZgHNWhwcyAMMK6p0nIuzR0dEEI/4/IuKtt96KkZGRDLB0H9hFwHd+fj7tWlBTBsDcARM0Aurlzrr1draVfjhmAETSgqUyEBJro5sNsyQLt2aC16NHj/L8VYGjMmlNFHwHM52UGz2PzhDnDAs5PT0dh4eHmTE3m804ODhIZhO4Hh0djY2NjQQlbPDJkyepmQKgMZOCkvcBwiQESrI3Nze5dwCPZLG2BPNnWBXMA7Dvu2GefJ/b29uc1i1IEr9KiJSD6TDYMyCD7fVdsQY+q/5Op9PJ+4mwhIAbJrheg4JZYYPOMfZgcnIyk6uIyLNfmX4g3/cE1mnqADFiWme31WplHBkZGYkvfvGLcXV1FYeHh8nwYsUjIpaWlqLZ7HWRSUCcP5+HTbbnOs7qPKTKmvJddV/5AkwrtioiPjJA+XllUKanp2NycjIFXl67u7uxurr6kX/mZ3r9iT/xJ+J7vud78v9PTk7i8ePHmcmi0NbW1uL58+eJAivih+IcHMhTGx6RH3DDWRH1oag4RxRhRH+yIsrR73Y6nWQ4qp5EgBSUZT0MoNZMbazPFbAcPI5NoKytrRwooKZuqV1OiWFsbCxnFURE1kRrCcotmlXkBl07kHd3d5lZ675pNpv53J4LYyOT2NnZycCKgcLMeDYHxoV1lcav9yOhaqempnLKbW3fFfQEIgJQNuJn7wsaIyKzmTqMzHsJZNZD+WplZSXfU3nGNMuLi/7t07L0RqMRi4uLeajrmhNYHxwc5LOjfIeGhhKk1SFpykvAliAjqz09PY3l5eXUm3B8wHTE195ddXR0FBMTE7G0tDQQNNg8Rsa+yRbpTLBFV1dXKQDtdDoD97fQN7AFdn9/4BM7rhM9BSZ3lwDlPocQk4Dbi0+oIKcmPp6JjsTa1NIh7djIyEhsbm4meJNpX15eJhtA98FvOLto8uHh4bwnqbZwY+iWl5czIfD5glUN2s4UoFpLmphr3V6uVpARWzesCd0CZojdeYalpaVsb9XiTqg/OzubQKsKnDG5zgutGMCC/eQL7Yd/KxkAkIAKJgdLDaAQa2MZDH/EXgDTVVztvBK02rsqfjbHqM4TYdfYZc/DR/CrPkOzAPA9NzeXtyVbAzZuL+wHf06D5hxUBtQYjp2dnUzuAF++Abjh47e2trL8CnCaf4MFfPjwYX7PR48epT7NWmGqgOmP8vp5ZVAajUZ827d9W/ybf/NvBv78X//rfx3f9m3f9pF/5md6oYDrPxGRGyNzcOkRagxyV4c7OjpKbUREZEDjFDgllJvbXjE0gjXkX2unNmBubi6vMQe6ZGrVOQsC3gPzgeJkHLX+Tl8gONaBUp6HsXMWsveLi4v4lm/5lgw2Ajjk7r3VKpUnfL8K+hzOOvofZW1t3GkCSBgO5aBhrU5OTgYuWJPlGgXNke/u7ubNqjWbJx5UK63r0el0klWha5EpqfvWEhoHXOvirVYrsywZlPWrmRIa3HcQjFzWVQXX1hITB/DWrigsERuI6AHHdrsdc3NzMTIyEk+ePIm33347AcDFxUW0Wq04OTlJ9gxDhCXjtFZXV1ODI2gL0MBQDSjEm9bI968zNSJ64NukXQOgZI/KhvRDNUhFRAbS+yXPyhTu7+/nGWZ3Eb0OrgcPHsSTJ0+SueQg6Rlki2hmmb73qHONdIT5PkCN82UPBcHKQLVarVhYWIiIviC3lhLpF4Af78knoPuJzP0+Nmp2djaDkT3RSSU40bzUDopmsxlzc3OxtLSUrIQM203HyjDsq9PpxMrKSvpIpUsludrifXx8HLu7u7G9vZ0MC30RP0hDNzQ0lNm/Ujlm2D4JrhI5ZZ9Wq5VtsPZYsoQtorNwtti983t+fh4vX77MycZAEPv3vvyB+MAW+A5aPokQ5tmLrgbDKsmQOLNPYldnXeJlwBqwKdmUHBlAOD4+HnNzcwM3h19eXiYgMareOAd+iz7J98BkkRfQ1mxubqYdSPD59ru7u0w0IiJ2dnaSBQJ8ZmZmsuwJ3H/Y6+cEULyxNxc4PEBExB/5I38k/uW//Jfxfd/3fbG1tRXf+73fGz/5kz85wH58lJ/5qK+pqal4++23k3aE3AVRJaCaPTtM0F5EZGaLZdAyy1iUYwgybYqfr4row8PDODk5iYODg2i323lg1GMdFJspC9YVoDWQIj+iz7ioNy8uLuamEzLV7NC0SP8/PDwcKysr8Z/+03/KeQAyuVpWaTabsb+/H48fP050fHBwEKOjo3mJFMP03ayNAwYAHR0dZTmkivI4N1fWy6Z8fg2kgpN23Onp6WwHls3QDHHUsk9sFwo9oq9dsm+AqOdC6dZSIeBhrkrVKnCkqHCAg6aAY+YAtZ4SMAtuhpiZeeDfumIEW5mT77e3t5fzPpTLOp1OPHv2LB326elpZkS3t7cZnLa2tvL5OA7TR1HJyilDQ0M5VMw5qcMPlZPYBqGhjO3u7i5evnyZgmwOfGFhIUE/B4uBBKxl5pUFAw4BCsDq+Pg4NjY2soWbU2cf2CY2Yl1rKZUzZ9eVgdjZ2UnxMWYNyLZOWDdZNWa2+hRnGdNahbYYOmCksgbuzvIPUF9nB2F6r66u8r+npqZiY2MjhfTYMfvl52oZqdvtxuvXrzMb7nQ68alPfSrW19dTA6QNvpbQsXv8G01WLV0fHx/H8fFxDmoDpPgVIB0zKcE5PDyM6+vrOD4+Th9Rmx0AOaUW5UwgpZb9hoeHU1hNC2atamLgrEmW9vf34/DwMN+fjbkZ2jNHRIL32hY9MjKSjFiz2cxLXZXJJXDKThhWbIyuGgAGMzcyMpJgppax+T0xil+1Ns5Qo9GbZAs48nl8AfDt3wDwwcFBnuNaJuUjafYwL9PT05lUftjr59Rm/G/+zb+J7/iO7/iaP/8dv+N3xF//6389//8f/sN/GH/qT/2p+OCDD+LZs2fxp//0n45f9+t+3cDvfJSf+dle2oz/5J/8k3lYOe7R0dFYWlqKzc3NpEUXFhbS+U5PT2c7LgcjkxHIOBuIGg2sPl83AODRUVF1K91uN/Us5+fnsbq6mh0fUHpVqfud8fHxZAscrKurq3j8+HFmJ0Rqe3t72Smyu7sbjx8/Hqito1DVtpeWlrK1zeRAl8r5/pz3w4cPs9XPTAzlGd+1Btp6CNRKKc2VDprNZrz99tvx6tWriIh0dLJAI/M9I/1K7R6h3L+9vc0sXBACAq1tRM9hGGZF5e/zZLCYM2sNvAClEZGMVy0BcWAOvIDm8HsO4KLur4C9sbGR331paSlZLDY6Ozubjsn7X11dxdraWu4VVmd/fz/BK6em7s7xDQ0NJcjd29uLpaWlDPK3t7cxPz8fBwcHGbDUprGISoKycmCQLV9cXCR4lhjc3fUGygGZzk7VlHBo1kkgJdLGdBBC0gdxor4/NpBtVqEj21V6EUyVIKpwmcATYKksn0zWGfN97D3NRavVyvuW2KTz0mq1YnNzM3Uqzu319XXOAQG2gcR2u51gQNaLHVT2BQqIz3d3dzMBevvtt2Nvby/t3DRia80X+XNnhi+iISOmn5iYiI2NjRge7o3ZX1tbixcvXuR5pc25P1vKOmG66VCAtMoyyPz5UxotoN15cN7rngK/yvEYFIlr1R/xW+zPeZNgTU1N5ZqaxfQzaUn4FImgWUz8Y7vdHmDTgOybm94Frq9fv46pqalYWFhINoIWhn3QIPKd5+fnWaLDWvBZgBQdku8qMQSOJDB7e3upxzw4OIiISPaoDhDkKzY3N3PicdXw0flJ9DH2FxcX8bf+1t/60Dbj/+U5KP+3XwDKH/7DfzgiIpXmMjabj763sAxZ4DAApw4XAzBkBbJymRL1Mh2BLoLKxjD6iP5GcU5Ebt1ufyQ5TYCAS1Phvx886N08SnUOQGGJZH9YhIuLi1Tsz8/PJ9twcHCQ300Aw4Q5lHrxlb7UCxcXF+P169f5c1UjI9hYt4ieVkAHAifBKah7ylDQx0Rl1qfZ7HWnEKTJcARpmYKDr0xGtxERmRHIVDlme8ah2KuIvugL/SwYec+ISBrUegIv9lD5zXuzz263G8vLyymU29jYiJ2dneyuWVtbi4WFhSwRcH7Hx8fZdSMAc5jKA2hUbF63241v+qZvivfffz+DHzCHlYgY7GywFwKqC/4AEGvqfCl5el/B277QIQjAHB2G5MGDB2mHgrqgQGRpHeswLl0JAClbuA86BA0BpgJqbZyCFyAlQ6Q70HlWZ23UwKWsUidlAtUAF7CrvKBTit0aCnZ2dhaPHz9OhuH8/Dw+9alPJQigPwFCrA220wRhzJVzryS0uroa+/v7WTbRycFOATXBpgqBMdCNRiPm5+dTx4Bxsd6NRm8Wxu7ubrLHlRFVOgbA6twMYMHv6WQyTwbwAZJr+Yp/xwgARAYwOq9+HuvnOwN9GAbPj0lwZuvPYUXEGAleBQg0PQCRclHVKymX1rvfgGL+FGB68OBBbG5uxvz8fExOTsb29nbGDM0Qn/vc5+Lly5d5f9Xm5mbMzMykrgsotqbioNK6UQPswRDOy8vLLPOxKayhJEsHIABfm1bY7F/4C3/h/+wclP8bL4airAJtciw289133x1QinNoxJSCFcet9KFuJxuhS4jogSK3QgoW6MPaGcIRUJxz5g7t7e1tdtWo7/tzoEa5xDNXJb+s8OjoKGZmZpLR2drayrowehlYefr0aXYSYY+ur6/zOveIHkKuQ9b29/djfn4+Wq1WHgY6GA5EOUamx2BR3Q8e9K60dzgEPM7dnkVE0okEbrIroruqYfGMlS6udP7u7m4yQQIiW/Ad1PwBpxpcag29XoS1vLycpS+0LYcta6g6FYEEcyNweX/PeX19PTA7QkbW6fS6C4i95+fnE1BjuTAtAuJ7772XwT4iktkhSH369Gnaf6fTE3WzAQD/5uYmgXhEZCBQSvXnAB7gzoFh8CIiZydo2xwbG4tPfOITmaEDd7e3t9kBNTMzk4C3Ct+vr6/zTivaJAFA4FI+XVpaynWLiAxawK3PVIrjtP17e3s7R57XcozgTHzuvCs9Kv85284+W2232wMjDjqdzoBuqdVqxcbGRvq029vb1A3VDJ/jv7m5yTIj38J+Hj16lAEK+6dl1HNhSQk7sSX0N4TOr1+/TtBQBZ6652Tn1sd3d+aquPf09DRevXqVoLaK3peWlrL8CHizY3vANginaS907REF8+W17ONsTk1N5QRlvhbAVsJRnpyeno6HDx9mCRuwr2ACMB4eHk7/jaXCeGDa+NCDg4PcU35wZmYmmxU89+joaCwsLKT2zVnjZ1dWVuL9999PNhA4evDgQaysrOSASwCddunNmzexurqaoNz50ZlYGeg656aWfnWNmX+FWWk0Gjnp+H6TzNd7fewZlO/8zu+M2dnZHA+sHorlcN8OR2AR0eeEXGjEiEgal3HJvCP6Q6gIXW2Q7NbPdLu98dOm1qpf+r1KS9YZAsR2hIY1+5B5YXnUZ9WmAZyISLEtB8xpV1U1pgSVK5OI6AUarXgONEPjPCBmQEx2CMCh8h1ogdYBq7RvzTxpUVCYGAyaHGvjdZ/dqACxjjKP6IMEWaF1sb/2gPOSkcsAZMscHRG2soE6uqycPostOPAoWKWXg4ODLDtOTEzEu+++m5oCgYkT2t/fTwZQ6UVAYNN1yJ7P8Jqbm0twxskrbdJLtVqtOD4+TgZMtlS1TvYTk1VHmY+P9y6PbLVauW6oXc+FOXNm7J3MmB15P512FYBgPesFmrp6IiLLWuzO7953qGxMglH1DIKm7+y/z87OMrnBALEhYkgsLceORbu7u4v5+fmI6E9LPjo6yszfmpnCOz09neAZuImIr2G8BFNAuQqXJyYmUqsCjLPleiHn6upqdDqd2NzczH3DnlSxNX8hwMq8BSPlG0BWUqBEUNnpVquVNsrXKeOyM0kJf8M30zXc3NykffBzAKY1sl5VA0gfYl91AWKWAVwdP2b6aAjgRzCDlVmsZSaAmu0qETWbzSyx831Koeyv1WqlLAALq9ylFAtgLi8vpy+9vLzMwYbYrLm5uZx9gslkD61WK5Om4+PjZOutlY4yYAYDLrYtLS2lNOHi4iJZf35XzGm32/EDP/AD3/glns9//vPZxql9S621dr84QOgsdKZM25/XLgJZMoFbRP++HpsqGEX0nIDuGmUGdVS1RfMlgBPZVRUaMwqHjoOkyyA4wlIILpyIz+PEdK4oxQBWyiTHx8cxOTmZw52gX2Ue5QOZG00CDQWxYHUmb7/9dl7/TudQM+fr6+vUtIyPj2fXBycqo7AunL91Bd4cLCJHwAeDoLxQD7Ug6XmBnTrd0KGrQLa2IUZEOlc/I7hwvBxtVcJzqvZ4dHR0QFTNflw+p3Sp9dMzYyXa7XYyHisrK/HBBx+k3QNZVU+iW0Q29+jRozg6OsrpjgIgRkZgHB4eTvEjmweOOV16JjZVgWsV8nU6nVhbW8tMHlUu+KHUZZmc9s3NTTIbm5ubCazsC2cOJN3XFQBxPoNgEnPiPADnvvfNzU3qH1Dfddhj1csMDQ3F06dP4+TkJJkLYIGGwJ+xh1oakLhIDjCNx8fHqflw9vg5nYObm5vpP3xG1Y0AX8ADbY2EaGhoaOBiPn4JSLm8vIyHDx8mszU7O5vdXe4Fo39wxioodBaUErW2C6RY6OqzgHhB3ZoBPsTSJycn6Q+BBgCY6NgcHSDcc/sOGIGIfpLJX2gLZ1OY+QrgamDvdDoJmp0ZiQ6gY14IH6Z0LZmqYlN6pQ8++CB9HeAOECqjt9vtePLkSbz//vvpr8Uqvnpubm7golMJrpKqUiig/OjRo3jx4sUAGFRRiIj0R5J79kyioNyj3f/y8jL+zt/5O9/4JZ5ms5niT62X0H1E5KYJJjLGiD51LsviGGqWo25NA+HATE9PJ1szOzubNJwWSgHu7u4uFcvtdjs1HmNjYxlQI/qDawT8iEjq9MmTJ3F3dxdPnz7NDF37H6ru4uIiRYwcodHKMmFU7e3tbV7eNjQ0FI8ePRoolUVEoufasbCwsDBwMy+EPz8/n6UvWdvW1lZ2ESjTVLbJ+6FNHbZat63G3u12s4VOQOBslensmwm1avoYKOBA2U7gBz5RnQAOEGqwFGCi1MEGMGSo7eHh4ezCkGWg8zlPzsCgLGUzs16urnoXg8mSDPpCe6vdj42N5djvjY2NgRq374q2X1tbS+0QUL6zs5OBYXh4OIWtsk+tzrJ7e6iUeHfXu/EYu1f3BBD2s2xyYmIiJ2BycgLF3NxczM3N5dmuHWM3N72ZEXQsaHTXwnc6nWw7rd/V3mpbXlhYyPXU4eSm4rOzs3Tcgg0gjsGjEWCD4+PjsbCwkBny8+fPs9TM7jBeleXU6QRQeF4AFUCpGT9AoJTqQkcXxtVnJ/gFwrBcOlHY+Pz8fLz99tsR0euKNMdEO+zi4mLa09HRUezs7OSFcxWEDw31ZpjotLm7u0uGqQqDnXHlGOfH+ZAkAQ4YECV5pRxtyUSckjKlEDbEX9Oi+DP7Aow5UxJBSZJbzvknWq96ZUCz2euutF815tROH8xqFY0eHR3FW2+9lYJa+qp69cbZ2Vm8evVqoExIxzY3N5fDDmn0tra2MglxHpWCnDUMKyClHCemuFjQXrEHJSRnssbTbrebDLtEGYPkDOjG/Civn9dBbf83XpCjTJ9By+YsoEV78OBBLC4uDoim/LxgVDPe4+PjPCCV3pWVywaUUzgbtPfQ0FAeWF0+dV6BVki0WKPRyHYsAdWo+u3t7UTrr169SsTuDiJgZ39/f2BMOaNRi5ycnIyNjY3UHIyMjKRjMTLb72B5iKUEakwGo+90OlkKcDAiIgERB7S3t5clBpltRF8h3u12Y319PZ4/f55U8NnZWczMzMTu7m5SyDIKXT9V9IdW393dzXJNLSlwnuh9zgvg08LHQVXxs3Zd+46pAW4Icmv2BCRERB5wA52Wlpayu8ntoOzaXUWcKWejXITJ0uEiq3dvB0Gjn62anuvr6wHBYxVyA3VYv8nJyQyAHObt7W2Wz/b29jIYqXlHRH532aoskQMHnAUudDXGp9FopCgPQ6ILT3CtAmmCbvs8MzOTZZHKeMo4PavvcHl5+TXdYxXQYfmwRT4bowCUYY3MzpidnY39/f0EpbX8C0DR6NB6VaDLNtvtdiZCAoqR6tZ0amoqNjc3s2zgjBI5CoiCrozWrbWXl5fJ1gAJAo01HB8fj1evXsWjR49SXLu2tha7u7txdnaWwBHTBnhpBBDYX716lefl+Pg4tWPK9crugnbtuJQYSEh2d3fT5gEQ6xbRZ0Qq6Kut4cogtevL2T07O4u9vb30YZilytKMjo6mnfGHvi/fwd/zAeaXHBwcxJe//OWByeOTk70bsiP6F1z67IODgxzNcHd3l7qzeuawHBj1VqsVOzs7uTYkBY1GI6c7Y9H4dMMo+STzYqoPkBjStBmlUa9cYINYquHh4dSjfWh8/7iXeH7n7/ydKchjrARijLHObYGMLVpFyn5HhhoReUjoHhzSiMhMVT1ZNkNEWYM4o+cY0V+yQUHce0O3VVzoz1DnAIiMQdAR0I6OjmJpaSl2d3fzYGBlqpiQoR0cHKR6fGZmJkWXx8fHsb6+nkAMIndArZX6pSyI45Q9OGjYmU6nE+vr6/HBBx+k03WwdRGgP4eGhtLBoSmpyjmTZrMZ8/PzA/NtOEkUPKEyMAloWlMZJsdWS1ee03OjzOfn5+Pu7i7ZCPtBs8P2/E5l+IaGevcqvXz5MktNdC/j4+OxsrISe3t78dZbb+VdU3QXSlICL00EtmN0dDSOjo7iyZMnsbm5OXDnjCmVSmCYL0zW7OxsbG9vJ7XtuTimycnJdHREmVgZ4AsD0e124/Hjx3l52MbGRor8zHkAiur+Vk2Hcql9BCxqAqI0A4SyG2U4z1bZN+wghiAi0h/wG/zB4uJi6tGcZyUte6J85ve1fdNjeZ6qo5EwDA/35t4YM09HUVu0rcHNzU08fPgwXr16lcJUehAAb2JiIiczK+XovsDeLS4uJgB58+ZNLCwspH3WUQsofN/t+vo6FhcXo91ux+rq6kBrOP+iu8TeSQaJd527CuSVbWjjBEBgFpDWBUUn6Jzb//X19ZiamooXL14kMAEmKpNegX9EDFyUaO/ZXC11EaAr03gOnTj+zGca329itaTJunieiMhuRyJlZSnvOTc3l2fP93Jm+EH+iz1LZmrFwRUJw8PDMT8/n9/DGRFTsB8AszuAJF5iztVV73qTg4OD9DXT09PJeAFgJBF/42/8jW98Dcp3f/d3p9HWuj9xVG1brbXplZWVHPhFEyATpdVAt1eBZERfn1JFZliTKnRlQLIIgOKdd95JUZyszRwT2aKgPj8/H+12e4D+pNh3ADqdTmaDdUqq59elYyia0sbKykqi5/Hx8Tg4OMjMwHMSh7mcS7bBCBlbHa5FVyFDj+gF3UePHsWrV6+i2WzmbaVVSV6FhQL72NjYAOiUnXB0wF2dr1EdnzIalkIpw9pilQSdmrnJ0NkDYDk1NZVlEJk2R6qrgeDNHl1fX+dB3N/fzym72DJBFLuGKbm4uIj5+flk7dD8RoPTSVSW7rOf/Wx84QtfGABCWkLfeeed+OCDDzLLodnSys2WBajJyckMdtoViW7Z1v12VIwDRsH8IVm1ToIXL15klstO/IyzpnzJ5jlBwL3uVa1914Dpd6vwUcAGnLWGViYSkJCN+5xajrm+vk6WsAZfDK6S3tBQb94LbRd7A6roWtTsZfPWoPoSc5WMrHfOp6enc6AakDUy0ruMcXZ2NnVgdFqCXfV5QGtEJJjWAk0EK5lxrrGaSpDKsI1GI/b29rIEy6cAB4KipID2aWiod5XA5uZmagNHRkYSTAIm9Yze3d1l27mf5Qf4SkCTfSs/Yk2q3k/2T7MkcWRXfC4/69mI+z/xiU/kkE7AA9Dge7DsV1dXeaFmt9tNVhXwjogstbN/d1xhSHVfYnGmp6fzu7BvJVj+RfnYc3gfAEZMYeeuJPE8n/nMZ+L169dZuVhcXIyXL1/G+vp67O/vp/6n3kPE75ydncXf/tt/+xsfoHznd35nBirUYW3TUjOkF3AYq2jL4fKzggpHWQ0YiobCOUZZrE1hxBGRhwUdrDWU6vr29jZRMcMRvLyX4VcOzfDwcCwsLGSpRiavZdDY6qOjoyxpKXn5PM4U0q1lJ06OI4roq+4hZizI8vJybG5upgGvr6/H4eFhTrhFIwNqHK6Acnp6mqUlB6LT6SSIATQ4ZA7J2rZareh2uxlwp6amYnt7O8s7ylx+B1Nl3wGWKrK0vxGRTJagKLtlKwRnhIEcVWVi7COwzIZ0aaBjZcpAM4dJCF0zdWAN+0f0qcQVEam/UYpy3N3TUwfRNZvNWFxcTIEmkPlt3/Zt8e/+3b9LZ2r2ivfn7Gv7p+/DyckKPb/OGYyWujbqGiBDHfs5+yaIABRuaG02m8kSWH9JC2YDELfP1pVtSzAkC0qALggEUumB6vPVriBnaHl5OVvcBbq7u7tkiwA5c4b4h5WVlfjpn/7pWF9fz2zaaABaAeXCepMsoM4W7VudbaIEoLysC1ESoqVX5wt/ortmb28vpqam8h6ZhYWFBOt0Jey32WzGw4cPY2trK0s8CwsLqcljt7pUAAh2X7sLJYASS8HXf2MBqjC3sqj8QbU7wbh2gtknvsG+VmadjyBEZc9KZI8ePYrnz5+nPWD2JGMmMxO9SqIvLy/jm77pm3LQKBsFBNbW1ga6qK6urmJpaSm2t7cHqgY1Cec3zBTqdnsdr37u+Pg477iqJc8HDx5k6f/y8jJWV1djc3MzS57E+VhQLCWmJqLXJi1B29vbi5ubXsv2//f//X/f+ADlj/2xPxYRkRSlLNnB4swcBJkQuqtexV0dnsMmmPqZiH7XAU0LBkcwEWDVdU1tXV5eTqBR7/kALoikbm9vEzBQRssAaoAXWOgoBCO9+ktLS3F2dpZ0KUfJqDkChurA3d3dJRDyfTE51lJnwdraWnZUcPKyIcFCyYD+ZH9/P4OlQF9nhyhD0DTQOsicqohNthoRCU5Qvo1GIzY2NtIeUOyyC3tWM9uDg4OkLpX8ZDpVhCcTjBisL3Mc2iHV9WsbMy2Fn9VhwXEBGYeHhzkW2mj/0dHRWF1djbGxsXj16lV2p6FPrb2gNTMzkwwcmhdQozPodvszPKj4Z2ZmkgK2Jz4DMwBEoL0978jISF4IVgWLyhBKRL4Ptsnfsefj4+O8mE2CQXuFlVCHd76Vb51ntgKI+36cve/FF2CNiLQrjQ7oAujsrmbtWqiVU2TiWEXiY5+9tLSUYJANWF9n0edah7OzsxwcZr+UdQnZif11/tzc3MTjx4/zXp9GoxGrq6uxsbER3W43Owzvd8DYp0ajketE72ACqfVwRnT3RESW+q6urmJ1dTXZNwng9PR0bG5uJjt3eHiY5YSI/uWMdErKn/d1cM68s1oHB9ahY3y/8Q/AtFKEcmen00nRu/XwO0pVwJ8BathrJU6sAbBcWbCqjbu87F05srCwEC9evMjzf3R0lL5mbm4udnd3B5hxoMy+sW1aEMPhgPHNzc2UKpihUgeFTkxMpJavsuV8sFEZ4iM94uXlZayvr6dubG1tLV6/fp0dPs4nsIc1+3+CQfmjf/SPZqCohzmifwV2FTESGinREDdF9GdkyHoFMWLO0dHRbJeq9G1tMwMgIvo3wAoAddZKNVYOF9g5PDzMICI7qdnn3NxcbGxsJHInUNQR8+zZszg/P8+ryrUQU+4rixDOMnLOdGdnJx4+fBh7e3tZB6YbGR4eziwb9a8+DCBVxb0suHY+MH6ZSp0fU52/Z6r0Z0TkOtDf1HY268yp1Vo++zAVlAaotnZW+n50dDQ7lNCebMn+1rkZMlsZIbp1eHg453rQWphDILArG3FaypG1/CGrUz8W0A3z8n1q58nQUK/t9cWLF7lvHBa9CpDW7XbjrbfeiufPn8f6+nqC6g8++CAdKUG2TpJWq5V3AGFcUOe7u7u5T5w6MFaFpqYgWyvTjiMiM0/6FIkF4A7EPHz4MF6/fp2ZnO4MARGIrF0VgowsHPtII6DFmK6Bj6mib2dbAAWMACdMJQG535+cnMyAQgztezlX6HEspu+r3AvoRkTe3aR923nDPi0vL8fz58+TqcU6sa2qpwGI64VyQKqkw3Rrepfz8/OcSGu/5ufnY2NjIwHExMREAgxCYCUjvsp+ADvYYSyY71Zb5iWF9hGQi4jY3t7Os6Nkby+d96ql0MVTy9NiSi2XVP2QRJh+CNPt99gf0KPNWCt9t9v9mqGBGLn19fX46le/mho9bB59D91jbZVfXV3NCcSm+fr/6+vebKa1tbVkNvg5jQtEtnVGlwRZ7JMU3d3d5c3ey8vL8ZWvfCWTW5OjJTL8VKPRiIODg/j+7//+b3yA8vnPfz7nXezv72c9nT5C3VEJqNbLUcIoQXR07dpQ/2bgBEfdbjcnklatB8eC3hIkZXWCTnXUDg3hp4OMdtN5AxnXOryf8/6e0Z9VMOD/HfCrq6tYX1+Pra2tePjwYVJ3Do9aPdrOVfDVGciiVlZWEkFbE4bt8sGdnZ2Ym5tLUCQ7wjycnp5mixt2SFuqFtHqUAAh2TZ9DqA3MTERL168SMoRm1YZsHpNgQBWO1zqYCffudLGAKcMqR7iOpNlbm4uGRIAAT3K8Xc6ndja2kqQC4CgdGVk2BEiU1kn3QJ6VebIOZqRUPUvSkccbR0mdx+4ExZb5/X19fjSl76Uo9XrKG/Mk6C/urqaNlkzPc775uYmnZcA0ul0UuviOYD+OliwdvBIQFDpEX1Rc0TkgC0ZKKcO3NGBVRbm7u4ug5MynKmjZ2dnyaSwG8C7Cg4xZVULJwhcX1/HkydPYn9/PyIiWSAX+11f98bTK+88evQoyzZra2vx/Pnz1GS47+vq6ira7Xae71arlR1SfFVlZolmHz16FKenp9mFwbddXV3Fpz71qfjSl76UPggQ2NvbS53U6elpLC8vZ9JCq8VfODPWEUiqei2ABphkE0pintPfKwWzr9pWfXZ2Fq1WKxMSjALGxPrz1ZhQyYKz7jtISulGrGXVtvAB9kRZiK1V3+jP2bXza32BDX6utu5iNNnMzMxMtrfz2+z2/fffH4h7CwsL2RFG1GxPhoaGknWVfPtzNkKXtLu7m6ApYnDgYtXpOH90Us1mM/7m3/yb3/gA5ff8nt+TGTInjEav2Qb6twYI9LPMRlCE2rX9ej8gBvVaqdwqyBNA6zXj6tGoZQOo0HCCH+NEedZSjIPFoGQtgqeprXVy69raWn4/4KSqxtWyDw4OYnl5Oak54rnaejYyMhLPnj2Ln/qpn0qGYG1tLSYmelN6t7a2Bij5ra2tGBnptTCjTiMis2tBEd2MYreP5tPIkgAGqN516lV06NBzAMo4ta4qE5dR2duIyOesGqMqmvQC+GqZrWZVnIgAAzCh+AFSwlp0rkBW/1FuxC5ZR2XAOq1Y8BNYUcJq/91uN4WVzWYzM1mCbRmecqY118kxOTmZXWH2qo7Gr7McdH44m36WgM4QOGdDl4ASo+xTecdQQhlYROSzOgcCbZ1tI+ADoF9Pr1OdamUBa2Jhj25ve5fuEV8rr0mKnHXrT/fm+8hWR0ZGsi23si+1603Q1P4/Pz+fd07VssfExERqM6w9Kr/dbmfydnp6mm3BrptQVp2cnExGQ3eaUhetjWwck2BIW83kHz58GNvb23Fzc5NzVSoowoIpm9LNCIz8ojPqjiF6NjcHaznm2zB9WDBnvyYBQFlNtCSM9qXOpnHGscn16gAl8ohIoBLRv/KB762i1YheeXV4eDjnAdWhexK4hYWFBLJf+tKXkmnEZK6trcV7772XifQ777yTmhdCeqXWnZ2dFE5PTk7G0tJSJvZXV1e5d8qiSskE/5hhon4MD+CnUWBqairX6/KyN62cnSkZXl31rl35S3/pL33jA5Q/+Af/YCJPiHpnZyf7/AXkOhNCdguwoAoj+q3IEZHBwSGSxUVEghQ/ozyB6agiySrA4/BkfMAP6txBk13LhGU0ETFw8DiOyvxwgtCyEsV9tXnNuE9OTmJtbS2zWodX5r+9vZ3thL4ngCMQHh4extraWtYlCb84Xv34U1NT8fr166xjCtTWRrsecFCd3ejoaFKktWNDuWF/fz8dNgBR9wi7wVlZM8FfuQMLoMSwvLwcl5eXA50CwKNAgkEDdq2TrFFmLxh1Or2hYWYsCMzKae5S0snk+YEBDm1nZyd+8S/+xfHVr341HR17qJ0f1d7YtzIBAaD5GjQRFRSpt9/e3ub605nYw9HR0cxaT09PU6vjor1KJcuusAEy6cXFxXj//fej0Wikpkj26VzRiTizzqOaOvYJwPIdAAy/ax3ts0zSM9OVVGaFfdhnCVIVLqK9Bai6X5jIiEg2wWfbM3fmuBtle3s7lpaWBrQdRLPAO4CCGXb2aOEAEDoJZUA6oG63m4kJEIVFqP6GpmZvby+TIawxoWlNotzj4/xgMOfn52Nvby9ZUWeR9qyK4WspVxLBfpxlfreOYuBjIiLF31XQze6bzeaAdkTJTsKDwal+XteVxJHNCN7Vj9h/58H+YEXo4La3twc0Kp4DWCYAx/jxOWII9vvg4CC2traydEQcOzHRu20aMOWHhoZ6Q/aur68TUFxdXWU3lTNuPZVt+Sa3M+sSNX+l0+lkVxfAiT39vu/7vm98gPLd3/3dA4GEY0IXUp9XBqTW2OpwMQ6FkcnqOO5K7TM8jIjs3cYSSnIGyhDoN8aFiak13Ao6KurmxGREumUo/CF/VLsaer0Hh/CSSt+BMaDOd5Gp+tmnT5+myEr2oCSl3FIzLhnSxcVFlhKwRChq3QOLi4sJbnQbacnm8H2e94zo35nEmXh+tVPtfO6HoL/QlePQKO1VYFODJragtt4Bntbvvv6p6gTGxsYyeLDLoaGhePjwYbx8+TKdBVbNmgpc9Y4SIBODtLW1lUOmnAOg8TOf+Uz8yI/8SAYA2ZhsHlhXMsQYmZ3DEcqOFxcXc+gbNoYj9j4GBAJSMzMzMTc3F3t7e8kMHhwc5EVrRK/AwNLSUgJaGoiTk5NsrRwZGYnXr1+nk4zodw1phbZWQ0NDOQnz+vo6Dg8PB0pxtVMjoq8ZUxau71//G3CSbTo3VRTNNjArAqyfZyv2WfJC5AqQzs/Px+vXr7OMAYxF9O5FwWQACgBGu91OJlCwGB7uDciampoa6HxzntkrJtDvehb7B+ApIcjYdf8NDw/n3T7Y0Z2dndyXyqRi1sbGxtInOXs65AAo6y0x4kPttS67+2cRgyIxrGJs31fZUSIly4+InCAMLPg7/qhO6vZsmCRdedq4JYHWjM0BHxLH6+vrePjwYc58wUo9f/48y3UYFeDDdzAvCTMCcGHZsLnAWrPZTADpu2OmOp1OMlNse3V1NU5PT3Nwm3gleahxQWeplzMREf9vlHh+3+/7fQNUJwOJiAGqvgIEkz4ZoEubxsd7I6tvbm5iY2MjwUClzImM6BIItBiAGuPYWO/ipLu7u5w/EBF5OyvBVO1EQVmenZ1lBw5gATBERNKNELqbTycnJ2N6ejq2t7fz3omdnZ1YW1tL4a2sBitUaWTPRDDo+QVZAWd2djZ2d3cHULF1HB8fT2cKLHHOIyMj2ZLMqKenp+Pk5CSdpumFMq+xsbE8cKOjozn0SkunwOyAKJOh13U70AoI5oBqFXHJWDhh5QW1akPLMEyy+hpYZJocYLfbTcHpyclJPHnyJA83h66ExWk9fPgwHSDGhrbIMDadCnQQnl8AcLkY1sMlbVX/EtHLVtG/GCvCTZSvshDtUkSkmNLdJxy1/d/a2kp9CwAM/GOZateFVy2BoMGBTesqQLDL2j3jpYQWEckiHh0dfU325/0iIs9ZLQEAK3WvBRo2c3V1lefrrbfeioieOBO7wtb8vFKAbNT+KqO5kE3nz8OHD1Pf5e+B3bom/I/W7JWVlXj9+nX6QnqOuvZ8CnbHBZ5Y1cXFxdjd3R3oHCEirQJeHWAYZ35XotdoNDJQEWdq43a2gd2IXvny1atXMTIykgPysB2YIaBV9g7wCWlsGiDHcNSSY2XM/L3yr5EU2m8lLHXEAtbm7bffjqOjo2TMJGi1LA18aNIQO9rtdoJe352omD+r4nF+yt7zHXy7Th3XlkiUAS2+Qhlyeno6VldX44MPPoh33nkngdLBwUF85jOfydlV9IkaN5Sl6XiIYjFRYpKSK//i+/7ZP/tnv/EByuc///kMwLI95Ri1U4gNjcihVo1Hzbxl5MAOpO2964Y4iDJz2gltz6Ojo0lHMw6fCdH6XIe8ahR0bAimnJgDhAUxYloQr/MvqrPmaAw+u7m5yTkLIyO9q9S/9Vu/NX7iJ34iIiLLZEojnkkLoxtrfWffDY1tVgMwJ7tGn6olX19fD4xuFww5zfX19bi5uclrutHtMjslvVq+ojcReGqJpLYKq1ezn1of111g7WQ66O7q5K0Ru5HJybBlKBGR4ERgsBaf/OQnY2trK50qIS8QTYvAJqz76upq7O/vZzcBan99fT3XTNnu0aNH8aUvfSnZlGazGY8ePcqzgdbF/mmP5Vxl2rIhMzCqEDSiP6KbOJng27qwefuxvr4eOzs7aWduPq3gQJJhje+zJ7VTTiZPGAmEC9TsTHAHzgArgUVQM69ibW0tjo+P00FXH+G7eT/rYv+bzWZeDskfyP6tt2AgCFmfiEjAWrNvWoWdnZ1YXl5On9TpdHLWjfIW0PpN3/RNeaGh71i1ENZVGdja8CmNRiPZANdgOANsVofPyMhIlg10HQGXdT6Mz+SnsYF1YJrkjM/BMmGS+WQgCSBW4gUGMQxAVAVezWZzYOLr3d1d+hb2AIwptURE6vr4A0kNnR0b0HFYx1NE9LuFKgt7enqaGiUNEe4wAwguLy9jbW0tmRKAGWiWMBvuVhMe9sPv8VdiFhvodDrx+PHjePnyZQIQgN9aYjhNKOZPfL6kkQ/4f6KL5w/8gT8QNzc38dZbb8XGxkZutgDoAMgEIyKHLEXEgLiy6iocwtr9oVVqZmYma+3U1N4buqbd4LAZfEQk8o3oz1S5vb3NDpfaOsdZAhVajmvgrRNBBTPGJmtVy8ca0WFgJGZnZzMYAz0OkLt9aglBsAc8iPJ2d3fj3XffzZsvofXb29tYXFzMUoZ5CYYAbW9vx8zMTMzMzMRXv/rVWF5ejogeK/Pw4cNklgRi3U4YK4FWBs4BciSjo707LDiNWjKq9H9EJEsE+aOTr6+vk9aM6DsU2QtHjmGrbIUOh8XFxYFR0EpF9o8+g+OUZVfwpBvH5V0RkbV9jsLdG1hFAU6ZR5cZ8IpFwMzRHFU2TmfJmzdvYnl5ObVSEf2WawGFHVdtmDOEpcMe+H4yauUV2S17AdIlEVUAK1uWVTuLAoos/n57cC0NKxV4HvvsjCqZ1vkUAoDPEIT8uWBP+0Ho6iJJe6zsK3hU5kjG+eDBgwHanR7jyZMncXR0lOJzwI5olw6AbWNJsKK7u7s5l0eXBsBD86U0XAXhEsG9vb1YWFiIg4OD9ANAD2CI1eMPJGYSkeHh4djb28tMPyLS10ZEipFbrVaMjvYuI1Ue4yeHh3u3XBsj4Hzx9co5AioAU7t1gFFnmG9mE3y70hgwLTHQZfPJT34yNjY20jdERA6iM6ANaOOr7FeVFmDvgIjNzc0stUb0Bi5iYNgb4FCH2SnFKMuyx9oIMjs7m4zg7W1vUCCgSJpAWyiploRKup2bqp2zvsrs1uz/iRLPH/pDf2ggY47o1xxl7qurqwkogAwGPDo6mlNKBVsUryyHhqBOkux0OqlMVn83FbRuEh2MVuabm5uBQTkMWmlBNqLuPzs7G5OTk1nmALRq7fPZs2fx6tWrDAacBU2AmqeOkQqKlJIiIhkMf/8Lf+EvjP/yX/5LHuYqktvd3c0gj1Ug+KwzLWoborHXY2Nj8fjx44iIeP78eToNgZIzhMg5YgKx6gTrwYyIfA+gM6J/t01EZBYr25dxyfwwVhyEjAcIWlpaSpZKUAIAiQABJICpUp6GXU1OTubYaMLJiPgaBge7R5NxdHQUa2trafM7Ozt5zw0nUjMz4MoV6qOjo3mLLOCOjQKclUF0F7ldWD1c2/T6+nredcKO6blkdkomrVYrNjc3c20EZbNudnd3Y319Pfc4oj/vReD0nm/evMlAyZ6cY3M+nC37zG5qSbAyY9ba88tknUXap8q4oeQryKlidR0xbMXfXV9f5+wTYtJGo5HdFex0eXk5S8ICFxvxvPv7+3Fzc5PiVMJlGjuMF+qd/gUDOzQ0lOCaTejAU7rUvmvdBfLFxcWBsvfDhw/j6OhoYCzC+vp6ssrOnqC7tLSUIJfQ3tBI3UL8l7WWLGpPVnaq82/sh4tYadWqoJV9KYsBSdg9gN96z87OxthYb5CcGFBZVLZJSErAOzw8nAkY4G1sgEm62FRJlzhATErfw2fzXysrKwl0MXZG6Vsjz8RWMLbeC7Mi9hnM5zJLe1CBt1ZhTBYQawaM5JU/8tkAJSD9l//yX/7GByjf9V3flSIePfGNRiNbGWspB40f0W/7tWjKMlXLMjo6mgdddi1jrJQfh1aFWQIR2k2G4l4SCHR6ejqpTYDGrAYI2iFwJ83NzU1qOXT/RERSiQ7GyclJztmgb3CYapZTqdDd3d1kYiIiD/DJyUneORIRidrRr5/4xCdid3c3HSnQVzUZd3d38elPfzp++qd/OilEjlJmJfjPzc3FxMREDhKrTMJ9AbF66tnZWa47pgPgAiCIhgHWSsfK7DhuWV4FnBylbieZNX2AgGZonAwONQ0I1BKH/XVZIFsYH+9dE7C/v5/Ol7PifOxtFXLLxgSo7v/sztB54/qDi4uLODk5iaWlpRgZ6U1/1e3R7XZjeXk5NSaXl5dZm6/3aRA2y560MHuWyq7VrjaTkw8ODga6lJQqPcPBwUFqXQTI6+vrZAmtNRE24GO9MCIADTvxmda7luUqS3A/yFV9Ue0o0aF2dHSUgFJ2Kaj5e7+HFQCOzVbBVvk8CQL2UsmLn9BZAnTd3t5mctNoNHKtnOVGozEwPXpycjLBI82FDL22zQqwkiQg+X43k2CGSa3Bus7LUYIDoLAYtDoLCwsp2LQWhlEKyPZCmbiWA7FvACY2ta4fnUS32412u53+UJnJGa3lHmuBjZUIA09LS0txcXERR0dHEREJiMwkYevOFU0Wf1ZLPNgO+pjz8/N4/PhxdDqdvACUzQIV9+ciASmAM30X/6tBYnp6OjqdToINOhM2KDHGFn3wwQfZ3YjF4v+qpIBWka8DSn/gB37gQwHK8Nf9m4/JC30ugFGv02DIqKH30dHRHOU9NzcX5+fneVU6yjKin8miyWo72+TkZP4/h0eFLwsX9CMiHZwAiHqMiOwjB3YEX4bCqGUZgm273U7nhMZtNBqZcarFQ/YRkR0WBMICv7ZN/yYSPTw8zNtOBWZr8fbbb8fu7m4eghcvXsT6+npmCvv7+xmca8lic3Pza6agYm6AQ8JZNCqa1CGoGp2qAVIPrf35MimMimCujbaWQIAHwNCNzjQY3W53QCgK+BK8KrtVUTC2zWwHTABmSACXDVaFvy4xc0ZkvrXNGHXabDZjZWUly46NRmOgg8UcCgFHWyk9Aweko0c2zdlrLa3i5OHh4Zyt4TOxafV+GFqDqampLEFiBIGEiYmJZJaurq5S+Dg+Pp5CTJosgbV2QAAk1ty/ZXemIg8PD8fTp09jZGQkNjc34/z8PGZnZwdYPyJxQMjZqKJe5QssmXKq4NVo9OaDAHhYVOdhdHQ0g6o9oSVbWlrKkqrhk7Xzh8+pmqputxuf+tSnsj0b44OlINgkbia6Ba74jcosY41pT25vb3NWknMZEcm08LWC7f7+fszOzsbp6WkGQAJO/31wcJBaM34c6JGR0zixs5mZmQS7rhLZ2NhIvy8RqGUTe9XpdNKG2b25KXX2iefiO4aGhpId9T3pvPgR9nh7ezvQDox5w/Q5h++8804y39iXehkqZoSvwMi5B0lZpg7wtIeSa8wzgIsB4X/rVSq1C88+iXX0I3yQkh2/zU/s7u6m7TWbzbyChb2KTXzoh70+9gzKH//jfzwz9RoIsROYAI5XVsuAORqOx3Q9Tp9TsLAVlQquaqOcvXo7gd/ExEQ8e/YsxY/q7IwYK0EMV8smlZ0RqJeXl+Pw8DCpeiyRGqfavd/XkaOWqaOAAVHXHx0dpXFD5fUW0tHR3kjk9957LwOuOQwyD+2qdd18X44H8HL/Byfld/2+w27tIyKzdJ00zFd2U2lac1wmJibi/fffTyo9oi9qFJR0EAiCpi1yjJzl2tpaZvr3uz4IjzlIn1czO1oHINQ/S0tLyWzUdlhOBPNjZgqgHdFrgxTM6p0mWJJut9c6/OrVqwQ4ApOR3FdXVzmimwM0CfXurjdzg+MFRmSwmIrDw8MBoLa+vh7Hx8c5jl7tX0DCfAKVvpvvZyiW+naj0Yh2u50JwNnZWczMzCSNXIOJAGj9MHE6fZRwKuspOai6BCxsp9PJDgkvayc7lWhIcszdMJUZmOSPiGqxMPUfbJBzz2fIQK3jmzdvYn19PcGY9xJUMAy+gyCNYaldIdgJ845cXWB9+EvMa2VGsBXm32CGLi8vY35+PiYmJnJkO72dSxuBg4gY6JIZHh7O0oBSiD2URNUSGZ+GxRUYJYqYZwDP7ziPnu/urj8qQQKBpdE+LWDXMjG23Eu5xHfkR9+8eZPJ8dDQUDx58iSvBKi6NrHMe/OB/AmmDUOGZecvAWdxUWlNXKv6M0Dk+vo679UBuIAZ4mdnlBYG6HafF7AUEclUj4yMxOzsbLI829vbH0mD8rFnUFCq6uMRkZmMcoi6XhW1ydwqyp6amsrsurbhGXzEuckSZHjVwUPh6psova985StZxyWwq+AD7caZYkMM4hkeHs7Axbmg3gTxz372s/GFL3whDdQUPxmgz+HIOF01eFSndjJ1YHSoz5W57ezsxCc+8YlsBxSoObDFxcV49epVtNvtnIWghdE6+zw0dhWMHh0dZcAQHHQR1IyUk63tc/XfboiViXB4QJX1qmxKo9GI169fZzAVqDEv9SqFiEh2jJASnbmzsxOdTie+5Vu+Jba3t2N7ezsWFxfTTjE6tS1b5xS2IaIHwIyLr5obdg4koJXVeTF5HEa9PVlHiVZOjI6MWWvwyspK7O3tpWMHfIAGAuKJiYkBgE+D4uwAXDL++t/OkQyu0Whk9xhgXoEqhlB3ivKE5yUaNVemtmYrB9fAi0nUiSW40ljUzH51dTUiItuzMVo6S9iYZ8ceXFxcZGAyERo48JzLy8uZnAhs4+Pj8fr165x1xB/xQUBPTbRarVYmasT3dUicGRoYAOddAmbv+AbnsJaxiC8jItkInYX8FiCyubmZpQwAHmMgiLqoEzvA3wAeEgKaKmceUxwRA2U5QVmSdXNzk1diACN8jrMscQWUJJiAvq4lv0dDpZRUWRLno9vtpk0YRIjJevHiRbLhx8fH0Wq1kvGrQuz5+fkEVQArxoLPqQwsn6Vd22RmrdO13Pfo0aMs17qQcHx8PHZ2drLcPzU1le3m9m9rays1eZJt54Ffx8QBQpubmznm46O8PvYMynd/93cPCJeMeq916Pn5+Zibm4u7u7vMupQKXKo3NTWVaNHdAqh4iB71HxGJ+CMi64MOM80J46zC3IhIdItyZbxul7Xh2vS8Lydda8ERkRmSdtJf8kt+SfzET/xEMg03Nzfx6U9/Or785S/nICffUb3aM1bAxlmh5nd2dvL76qCRJdxnerApR0dHCU6wBZiNGgCwJwAOlsuwIiOh/e7R0dGAlgDAkRUJ/Mo6MiPrj1GqrYH2ELNibQVsv+s5BOUqwMVMXF1dZTYZ0RdiKvFV1sZzq7f7uzpDQo1f5uw9ut1ujj1XMgAAATaDv2SBOkCIndk+2/Xi6C4vL9OBuSSSo6o3IhNiKtNdX19nyzv20byb58+fZyBgB9ZPGyU7N2uCEB64pBMyewXgwRoI1M6o4C2wctAVgFSdQnXiQ0P9e5l8dqfTycm6PgMoiOiXb9mHbFNZ2mf5/KurqwHtQC0lA9f0VWNjY9Fut3Of+CR6h06nM/DfWF06C++3tLQUm5ubyabpIpOYYYEI7E9PT7NFts4tquyT74998p6YLsyJZ8PsKgVVZo/t15IHGyXoxUxZf4Jy68aGnBnspnPq/Dk/WDClZOtby0S62IAYZ41Y+fb2NkFaLUne3t7mQMv7389N7/wWdt5VIe6/4b8lbdgUjQoSsIjIEgs/R7vGX/GF2oDNSqogSVLt1vHKnAGHKysrsbm5mTpFZwAjxP9ic25vb+Nv/I2/8aEMSvPrQ4CPx+vq6irefffddMCoMX3gb970bjzd3d3NOSJHR0exv78f5+fnidJlC1VQy7nL3Bn46OhozM7OxsTERLz77rvxmc98JktJELRuA4FT/z8xlWBZ67jaKRk6pD42NpYlJIechoZxXl1d5V0VX/ziF9OAIyLm5+fzXpx6sybxpawHfQf9OkzHx8epA1ADd9ja7XbMz8/nMCW6B3fEjI6O5gRaJSxiTw5C9kB30mg04vHjx3F3dxebm5tJ86ILNzc3EwDIpgSU2pHAeQE28/PzeamZYCwLrzombag6JeyVgKXd0+E9OTnJFklADYtQg09EL2hVsbHMQ1DgtIkwq56GnbAvVLo24Lm5uVhYWMgMWDBnP7qA6KWUQTliNkZHUf/75uYmJ/+2Wq1YWVmJiD44v7u7ywFzCwsLWXZ99uxZfn9gfWdnJ4O01kZ0thbamnmyeywMxkGpC4PjWWt2Dvh5XiUAQYhIUIABlASY4eHhZDYB2V/6S39pPH36NJ167WSROUf07wniB3ToCGhYGywQ8AZo8AXLy8sJsKpOBkh2zgkv6zNfXFzk3JX9/f1YWFjI8uDc3Fx2d1WhseycrkLS4AoGQbLdbifgc5eWv5OMDQ8PZ7dPnYuhlTgiYmVlJZnetbW1WFtbi1arNaDnwxzZe/qQ09PTePXqVXZYSq7MJrEvbAeIiohk0YD7WtJot9t5ZulSXFyqpAo8AWx+TxzBSElKlJ0IT++XfzDKdGx8pQScn2o0GgMl4qozqQwKECv5dGbEHTcai0fe9+qqd9ns2tpa3N3dpa3zD4bA3d7extbWVtzd3WU3n1lM/FO3203NpzXgnz7K62PPoPzu3/27UwkOeEDTaOrZ2dmkNNHFtb6PAZBlCITYEgcXAKrCUjS5IASocCDoMpqOiP4wI87p+vp64E4FxhcR+bn0Lg5mncOCxXC4OIiZmZmcPihDRC3qvFDicTjR0ZTyugoo2lGjnpXgDWUtqxseHs7sUua4srKSv0svI6jQ0kT0OymUOThr/xBxooyxTtZdVmDapgAYEek4ZMZedT+AXM4CGFFuAbZ8j0q9qqcLyJyg/v9a82cjxLjod0GBo9ve3v6aAM05ywxrPZkzog+xd0qYtdvD+xhjzaEr5UVEsku0AzJ53UYobfagnIpKr2WI+fn5ODk5ieXl5dje3k5W4+HDh6mhEcQODw8zW7NH7JyzNlLbXgD3wI1SgH0RMIBjgMV+s10ZtmetWhCMQkRkCcm+1BJnHSXgZuJOpxO/4Bf8gmi321kukIXaLwB5cnIydSBYPeee3Rs1UDtiHjx4kGdvZKR/LYbSrVJYROSZshYEpNbg+Pg4Rev0BRg6a6Tkd3p6Go8ePcrkZWtrKxmEp0+fpuBXSQxj6KUELikzJFC3kgsPI/pdd/RBznPVBAEz9lUC4WedF+cWSAV2vfysGTL2AAAEPms5jCbMeSa6x+7RudQuMSJVnXtKS+xCgo2Va7fb6cfZuKF9hhaKHZOTk1mC3N7ezmGDFfT67hH90nFEpBZMovDmzZuUGBhRocvP9GHngxxAwowVPD8/j7/yV/7KN36b8W/9rb81pqenUyOBMREUARJ97GNjY0kfylC8HKZ6gCNioAdf4JBNEQw50JWOF+CqOlzg4twFzsXFxWQC7mecsg1IVGbN4cm6Pe/Q0FAaggMBfNRAheb33gSl9AMMUHb/rd/6rfGjP/qj0Wg00tjR8YRrr169iohIMCQDdeW7jA0oW1lZieXl5Xjx4kVm4xExsH7WA+oWiDgcgCOiDwwXFxdjYmIiNjc3c01Q88PDwwPCXBkoKt66CeT2ws9UHcv9tkHB0ufRI1WdBUegY4VuxPuhWWmC7u5647B1AdXL1CL6rbwYg+Pj43j77bdjZ2cn31OA47xpbkZGerfp0gB0u9149913s8tDMASodGUooypbPX78OK6ve5dKoo7V5zudTg6V08VUtSuCgrZmZTpsljJX1dJ4Fu3c1RnTH1l/AEepg/O1ZrVzTyaOJawaCzNW7u7uYm5uLsWqgAsgrwxQa/1KgQKJlu92u5136Chb1jKzIIo1k3ErG2N4AXwdGo8ePcqSrzUEIpWIVldXY2NjI21kZ2cnzxWQUe2evzQIrc4V0bWlNFt1G8TW1pjgGyPDn2JYrR+NjHXAAFvDOstFaToikh2tXUpDQ70ZK4TWteQEkNR23Fq2rUmf7+95lOHZQEQMlFlrMtRqteLs7CybBpwnwT+iB9r29vZiaWkpmRF2Qgi+tbWVCTgGDaMMiDvXunFoLNfX12NraytFzezBzcU3N73295WVlUwUdH4eHx/H48eP018B4wsLC/H06dP48pe/nOcPqFT+VX0AhMbGxuLP/Jk/840PUL7ru74rZzfYUBkrVb3DSwDF2Cu7UVtiIyL/jhEwfiJPgicajlqHdOAFjhpoI/pIXvZwc3MTq6urcXV1lc8lM68ZtYAQ0a+/1nquzMTgnZ2dnXj69Gns7e3F7OxsBqDb2/5UV4buwFald0TPoTx58iS++MUvxvDwcDpOmTq63NCgw8PDzAJevXo1oHTneDAed3d38fjx46QCfX/MlPHu2Jn7AJFuReeM4HFz0xvD74AAnJwY+tgdNpyhZ7YvuiwEZxSoDMlhBBbQ0JVhEdArgOHk2ZwMPiIyewaEAFYzXtTn/T/HJtNcWFiInZ2dHJKFCauUKmbRiz06JwKZO6ROTk7i6dOnsbW1leChCspRx7WurtZcO7EAN8DAugl8zmb9e/X8+x0ygo61tO/Oms8HDCQf/txIeQyjIOI5vD+gomxQ7bTW9gGhqsnBTPE1gLz7bcbHx/O+FEmFEhl20zNhqpwf0zzb7XayM/ybkrU/GxkZycsfO53eyPLXr1/nfKdWq5VddkCJbB47aI/ZI5CAuVQOJyCmU5qenk7md3FxMV6+fJnD35wJM1x8rkydn8G6sREAB4sBNFTGD2AFIPnnquXRxBARCaKsCT/HPwLCt7e3sba2lt1sGG3NB5VJwOZG9IXYtdQLpAL7/H3t/sGEqxDwmxhX82rYIxa92+3G06dP4/j4ODsm671jzgRG0TmujCIAp/zGfwMmADi9U72Kxd9dXl5mCRUwVkb9KBqUj30XT6fTv6GXylw/uFbgiBio3VUxF8MSCGUa1eHZzIj+LZUCG9Qp4PhcIlf1XtmiYCjwOfg0DDoQ1DSPj49jeXk5p7Qyco4Wcof4q0AJNSdoyZqnpqYGLsDSnaKTZGlpKQVSExMT8fz581wDDkT2GhEDwGl+fj7b4sbGxmJlZSVevnyZAM0hfPjwYQJKYGF6ejov23LQzd+4vu7PauAcJyYmkkalYVBS4NwjIg8TW+BYapcCdgnjpjTisNMwELBF9K8LIGgVcNH0bLO2G0dEKuo9p/dnX2YYHBwcZNY8NzeXzyV4HB0d5XAlwVYJUobKkcvmACS0u/cTUC4vL+PZs2fx3nvvZeY4MjKSk4l9P5nV3NxcjIyM5HPSuszMzOQsHBoLzgxtXQWIACjWxPwRQt7FxcWB1vk6nwOgElzti7+L6OtBsIEGRwEFApYzX+cVWU9dY2xeoLCvd3d3OTUWWyeI64IaHR3N+2kAIhowYkT747uZe0TgaC6ONWerXp55aWkpM3RCyzdv3sTu7m5eGWE8QaPRSDErhk75CXNRW2uxjkoHL1++jKWlpTg4OMiApRtSeeO9996Lhw8fxvb2djKvVZhbS9FKijWod7vd3H/2w19YW4kNwOgM67wbGRlJJoPvsacCM2CE9by97c9rqaCSzz8+Pk4gxQ4wPGKLuUOY7dPT0/wzDGHrf94wbSwEgHhychLz8/OZaCmjimsYOowREAAYSlZdvlkvyuUTscDYDfNRdO74DKCqipzrOTM1PCIy7gFGnU4nbYq/+7DXxx6gyDxkYoDH2tpadh9gDTiBBw8eZIbhRU1dWxH1bZ+cnAxMVkTnOiSYlojIICrrr5kPWs2wL6yPAyeQ2XhBKSLyHpqvfOUryQbomhDoFhYWksFgCIIkYZmy0PPnz6PZbMbS0lK8efMmD61bbIE3WZhA7HMMoVIuWVpaSiq6am8YNPrWwK6XL1/mJVgyu4hIhyi7OTs7i4cPH8be3l5mREi/qvuoI5/R4lp9gZbKWGC3rL1pkGqysoFut5uTGWtmBgg2m81sIY2IrLGzDc4WK3B2dpaULoYB6KXaj4jUu/juHKl/gLfaEo8Cpr2pnQM1+9GB8NWvfjUiIs8DOnhrayu1MaOjozmoTvmg3kek5jw5OZn0ubOoK6LOOFHaEhhrB5jndVcMKrmWVuqwLu26gOHi4uLATCRidmtWA66EgzhUqyiAIwtEmUfEQFcbRrP+TmVaIiKZXOfJWjgb7FU3DP+AbXSPmHPuriw20mq1kvmkrSB6dIWFhGBpaSna7XZekcF/edHe+Q7YNOxVLS/Sy2HXrIGs+fq6Nxjw4cOHWSIWdDc2NuLhw4cR0QeNSn/uBNve3k4wQDxf9YMycEwOVg4jyl5q4FYmBTr4ZSCI362suRIsbRiWzbkicMf4KKVoisCCsEVamfPz83j06FH6OEAJYAd++QnsiTlMZjL5DDbFl2BhJBR0hXxARMTy8nLeZ+T5nc/z8/NYXl6Ot99+O77whS/kdxdDsXtsBWu4uLiYXVpjY2Px6NGjOD09TUBLt8hff5TXN0SJh4ND9dWDB92ph0ZElgdkwtCdiZJ1+ExtPcSARPQvBXQ4HAJGKLOpegl1a9+LgAhN7L2wLRExICzFAB0dHWWm6nlqOyCRJlYI46LOSmdjlLFDUb/f0FB/UJz1IYgdHR3N7zA+Pp51dIGydukQUelmIBRzMCIia9oydvM2BAcXCm5ubqZj4QzRyHX0e6VRgRqHX6toRCSLsbCwkKJa6yiwVxCEgeBYq3alOmhBRPblcN/e3sajR4/iwYMH8eLFi2QJ/Lwhejc3vXkNFxcX8eTJkwz42AxZT2VeIiKzTnorTJxnUgLCxGkdN2iKgp/jrZeiYRiAHbS0rgSMzpMnT+L169cR0R+OCLACIwIbO2W/7JB90A0Q5EoeFhYWcg9dlnZ3d5dU+NjYWOzs7KRtAZWe4eLiItdEokKEXktUtfSLea1txGyqguTK4PBJwPDNTe9SU50OV1dXsba2Fnt7e9FqtWJ/f3+g3d56Y6tknq1WK7a3txMosxlzY2juJDkYivPz81hcXMwzj03CinofPgnzcF/8bzaJQO8vgqk+AACOaElEQVSuHAHRudMFZQ0reFGaExBpkCr7ae1ogwhnBWXPBqQro7CdWuIhInZ+a2mR6FyiohyCRbMH7K/b7WZrPr3e/Px83k8mFkREvP3229kxWrUowJMLRI+OjgbEvnQsn/3sZ+PVq1cJAA4ODhLAsFcAXDOBJM24+ohI3ZfnlgQ3Go1YWVmJg4ODBDLY8YODgwTj/CF/wr+JuWKLhKYm21hUQ1KBvb/21/7aN74G5Xf/7t+dDqjew1KZjcPDw3QIVfGso0MLarPZzLYs8xUI2yIiKUPBvv6Zw8zga6dDFagqWVQmAN0pK6/OydySiL4mogZCjM3l5WUGasOyMDjouIj+BVnqsd5bVirLc1EZo2ZgVTxYFfUyU3T12tpafPGLX4yIyGxuamoqB405xO4oEnQnJydja2srpqamBoZsOYzEViMjI9nOaj3U2gUN2ZMaMsAlo5EFAR/X19ep2QCEKOw5Dt1g2qgjIlmBOlDKGikRAjt1VLwsjbiTTkLAuC8StlayRu3jnAFgbk+73W7OkWFfMlYMCDZG2UiLL1HdwsJC7O/v577a9/u36jabzQxsgrlzwE6qJkg2BsAqqdZAQydjr9huFX8LlFg+/3CotVXUvlfNSJ27IZOvgknsF9bSHBrJALAKMLFPzC42q3b1ETZizrACnq2u2/n5eV4Oubu7m9mqEtfQ0FDq0CqbMD09PaD90gYseCgz6MCyTiMjI1k6ZPOAAFbF/tBq+DwlRnYq4Dn71V/ac6CGn7WmEjQAB+s1OjqapUPMFj++sbGRSaE9tJ5s0H4DLbUcSAvDzq6urpIp8L35E1oYzKoyPyCKIa1MK39kYjemopZf+WK/K2H13XT7YPPEEokkzcj8/PzX3NfFxrA+t7e32cmjrAz8AS+1Lbu2CN/d3WUnUAXUyvQYHN/ROa3My1//63/9G38OiptiLZzWRt0MlPYRfQGiLhiMSLPZzJZNjggyRSnXGix2IqKvZ8BMnJ2dJRgyndHMkuXl5TT8VquVGghBFuK8rytotVqpGVlcXIy5ubmYnJyM5eXlBB8ElA67O0CqqBAFjHqU0fq9drudVBxqLqKva+E8Op3egCqof35+PnU119e9+0t+4id+ItG4aZVKcTIswl2BXAkEZerA1dKUbOrs7Czee++97DwQlLS/0YEIZOjL2i4qmPjOsqaIyFowAeLk5GTMzc1Ft9tNPUYVCVYaWLC8u7tLTYEBghXwCQIOL1Ga+S01cAHfmAIiuYhIQLO6uppBqn6uWRLmHggs4+PjsbKykhmiOz0wixER77//fmbQAOzIyEjs7OzE3t5eOiAA+PHjx1m2k5WfnZ3loCn75EwJAoC8M4lRqzcGC0RKHsvLy7G0tJRnX4nK50VEOvaRkd60XGUBNH5EZBnl+Pg4gXKr1Urbw8LUtmrABIiTDBmH3ul0MjBLXCRRIyMjA5dxSpLqzKWHDx9mwDo6OkpWKqIX2D/zmc+knzOfw3cQfLA9zWYzXr16lULl6+vrbBseGRlJcOa7Li0tDQy6rPqIxcXFLGUoMRCQAvlYZrYmycIQ8DnX172LCQXrRqMR6+vryWp6JnNRhoeHE1RVgb7WWWc5on+VBd/Bf0lGiMtl/848fZT3WFlZSRbJ+XPO+Xl2i4WwZ4CO76G8BMy7/bn65J2dndQfAQGYO9KF2hU6MjKSccU9PRIQdvbo0aN8ds/i+05NTcVbb701wCrPz89n4gSEsQP6TJUF53xxcTGf0+djxyTjEkQx86O8PvYMyh//4388g3stw9Qyid5/ARZLIRsQHCqS1A5JtAUxy5wYGcOrHTGGLUGLd3d3WWYS0AjyIiIz8So2ld2pwzLAlZWVbMX0u91u/5bJiEhKWubBiIzP9vkRkZm8wAFkyKowAa1WK6lJupyqTDeDQXbb7Xbz4A0NDcXy8nLs7OxkHfXw8DDpfZQs7U+t6dfuIw4Yohc0lPEEd1m59dNWWksvtbU0oueo/AwAJegBNko2Mgn6JpksJoEz4ERqJ5AJw7o62FUtJdROMFqfiMgy5MzMTHzhC1/IoWzn5+exurqaGoWISOcAiE1OTqZDPDs7i4WFhdjc3IyVlZU4PDxMlkYpgY0AIJgWmajr7gHA29veNFBrX+vudWS57LLT6Q0KVKP3nX0GkCfIei5X1wPlvoszTJ8gY5cNYn8EOMDH37Pjepb5FVl27bSKiOzQ4wPq92WvACN2QHIzNzcXEZGZPnDCb/Fj1gR7I2CMjo4mUBCsZdxsBwDxu3wXBm9+fj42NjbyzF1eXmYJlhAT+K2smmBtRIGyH7E7EAhgsmv6M+vrPFbQKlBWLY8M/fb2NsE78TihLEBShdKAKaDsvNtPvtL3tw6SAixcZbR1r9HWSbSUDbG3VXQtINOMAYmAq/V+/PhxtkBbd0wK9gg7W8GvmSlnZ2exsrKSkgHsh3Zuzyh2vnnzJt566614//3346233opXr15l7OTDsEuYZqVcPmt1dTWePn0aP/mTPzngy2sDx/X1dSYHOzs7qVv8/u///m/8Es/nP//5vCsE0qRRqCPmO51OdkXQqeg6gLI5mIhImrEOThKIBUQGXUVGDBSzIRDKHmoN9+rqKtbX1+PNmzdp3OjY8/PzbFMU5PTiV+ElBM9J1RJGq9WKvb29uLy8jJWVldjY2Ijx8fHMOgE4pYaLi4s4Pj6O09PTvNiQUa6urmZmgx4GiurFV6hMFPb4+Hhe5S4w39zcZKtsZXlQ6pyHUkqdJ1Dr5H6e0+FIOCdUJAddqeSqGYqIdH7WsZaUHLqaIQIpAIqMRdnCd8ceoYk9J40PGwJy1OcxQbIw7EbtCGOLQApwwxlXUXjVCkT09SHHx8cJvGrpTS2ZQ765ucl6tjUB+OlOsARcij0AgmqZAHMGfAIgbtPGDvierVYrwaPgBKDSPdzc3MT29nZ+PsAis5Y1e29/JrsHYgTL6mQ5eEAUNS95YPvYKT6K3Sof1YFlPkfCg2GMiOzcodfRvbOxsZGMpe67iYmJnHwKENARLS0tpS88Pz/PgWLsu979RVPFzgVAgygvLy/jU5/6VGxvb+clhYIfFoFParVaea+UPfDeu7u7GZz9uffBDtQEQTLlfZxhoE+wtI8ST7/HXpXXa6J6c9Of5MuP6C7UNsynAft8BDG981o7OiUVvoM1dW5qeaomNPXeKM/rZ4B/QlNNB2xWJcCaKttjqPgs64RhOT09jV/0i35R/NiP/ViWusQx7OLk5GSsrq7GixcvMvFWDVDmc17EHWtiD/ktHV5//s//+W98gPKH/tAfSodk4itULACjbqFVYh0AQimhihpRaoIYyrMKFGWDtQat7kY0aoPoLTgCRod2jehf772zs5MHXJkAO+GzTQBVYnEwARJAxcFQ7qgdR3V6Jad/dnYWu7u7mdERsC4vL6cqm5izghTMChpPYFFeoE/gQI3EdmhpG2S0gjV6XWumIGefIvp0LvAWESkUVbMVxJrNZq6roCCwynw5sNr9A/gCqjJSGaDnEDgFT6JfPyMD1UFQA6CMfmZmJksu19fXCWJlP9fX13kBHOGybMtas0UdHcR/2DoOHnBh3xGR68bxos8BNWyPTEk2RgdFa1RLqJ5Pdkff9ObNmxSwYzawJ0qgFewT/5mWurm5mcCQ5iEiMpPn9GvwcdZougDa+wGqahXsn7IOZqkykJUpUd6kI1ImYw/YBsAdyAVkCZx1J+3t7SVQ50OAvfuAA5OmY4u9z8zMpBar2+2NZTenxaWRdSw78Sp7BuyPj49jYWEhrq56gxrZt+98eHiYpUAt48p+gqT35ZeJevnL29vbZGqV0CVjWAHgLqIPQiRFAC9RP6CPYVO+UFKvLFrVp2gIOD8/j4WFhZxRwsZqcuL3MbTmCM3NzcXe3t6AtsYZ5Q8iIks7hMIYejqP5eXl1AGaiVLnUvkOVVe0urqao/U1AgDMkk9demLL+Ph4TjHmn/hlZ1A5nA3aB8mU71W1axICTRB/8S/+xW/8OSiMg9M0+yGiN5Njf39/oIbskDsElYLVomawVq0bK30I9BZe9nR3d5fDhIzaZuwOKmpOsL64uMiMBvVM4MupuPlWFjQ21pstQkh6e9u7/4YxEYYxJpScAE20RwispijbJnrkgMfGepctPn78OO/aOD8/z0sXI3oBbXV1NUsWDjcq1vdaXV2Nvb29PHQAoOCg9FY7Eypdq0tCkK4MiEwYyNOWDGgKOjMzMzE7Oxv7+/vpsAQZ5YbJyclYWlqKk5OTFJHJ6Hwmhy3AyYYxHwAekEP0CJT6/xoEr6+vc9quFvPakmeqpiDk5yMitSychTPB7nXsVNvH0NB7YPgAWfqIxcXFgW6eTqeTWRPQtLS0FFtbWxERSY0L1Ohvd9q4MRWYByCxjJOTkyk8Rs8DbIBas9nM0fi0NhICZ9bvCKCCv0y3DnKM6Hf8Oft1dpHnUo6R4FSgFxFJ39NUEK3LNvksWXLrf94OzK8QqUb0bwjG5LKlypCZ1EsPwqcJzIeHhzlD4/LyMlttATSt9LQoJycnOVWUngBI3t3dzaCHAXBDuecVZJWXAAugLCKSQQbO+MXp6ekBPR1fgAVUzrH2SkDAWdXMPXjwYKDbxRkGjPjj2pZb704DbHUIOWfYH2D//Lx3UR6wjV2Zn59Pwbt1q8y6ZEV7tX2tvtez1AFsS0tLeX2DRMLsL4nBmzdv4pf9sl8WP/RDP5TrgvXGhLbb7XjnnXfiS1/6UiwsLKQGhr+pfhXQdju4W7ybzWbqJREGNDxXV1d5FrGzWGVr+1FeH3uAUrsfLKRWvJ2dnawvKuMIyrJi3R+QvSE8DldEZGa9vLwcw8PDsbm5mZ/vvbRcOpA+T0CqB1RQVq/m/CP6tXvf22RJQknipsnJyTg+Po719fXY2NhImlA2Z00YiSxyeXk5has6Q4AFiBpy10XEcQlidTx0s9nMQVnqjWdnZ/Et3/It8d//+39POtIBkO3XtlH3Szx8+DAvn/J+te27OutK6daykDWcmJhI8Z8AQmcE/FGdA64yNSJcGUpEJPWO5eC8K4Oke4FD9fOAhn2lP1CS8+eVut7f308BuKvW6VbcUWMGCLsD0og2OT8B03+/9dZb8aUvfSlBqxLY8PBwio6JGzc3N3PuhRZLrZPVmX7wwQfxzjvvxOHhYRwcHKSglnO/vr7OiyXtmX0CepyZmu0phZyfn6cGCsOFMQJGvOfw8HAKiGtGyqkDxpOTkxnsgQ/lAmzY3V1/gnMt4wpE91tqASU2ipEQECsTxoaBbu9Zhe7q90qdn/vc5+K///f/Hs1mf05SZe4EMkmCKzQmJydjYWEhdnd3BwAdG2TPGEGZtttwDw4OUkPR7XbTBufm5jJx499qe3EdH8BfANDKfMCHrpZ2u52+qNls5rylylIKfL4/G5IUiAVAqcTN99PVBghL1Fx6Sn/ofFadG6G8ZOjo6Cg/V7n85cuXycrw6bWkWgF6LXN6FtN9MdHi0MHBQXziE59I8KqMokMOCP6pn/qpmJycTGG6eSVsDfADbtbX1/OaipubmzwvGHoApzYYSKT5IH7CwD6/PzU1leVlnZ5ViP+zvT72JZ7f9bt+V2ZUnFOl6WQmnKkszSLLhJV5OE9ZsWwQdch5RfRvcgVkvGdEpJaFU9NiJiOz0RGR4Ofs7CwWFxczgMrQ/TxUzbESadZDLwMHFmQVEb3sbnJyMjY3NzNzazb7Y9c3NjbyvgogC4NhRgza1/rKEAVEh5DuodbHUdUCqTkIyliy94jItkUHFiqnkqfJsaYCPNrd5Eh2AUBWYe19fVAFKYKffZbRCXjYGSDLehDZVUrboacnEKRRxRH9SyFrV0Rt6wUOAELZk+BS2RS6Hhn1w4cPU/x6fHyc17o7N2rKBNmNRiOePXsWu7u7CSr9viyxiqKr9qleN6/eXPVVwD6QXAGmf5Qd7JGWZp0Tyge1JOUsmoVCPyOrt5bOcu0CiegHVWUH5UelJWcCEAQE2BeWFPW+srKS3U51Pokg6XvxJRIk9ukcY4/4EJqu0dHRWFpaykmfQBpWSWt+1bQo+UX09QnK0UAgeyPWVFZwjrFqtHsbGxsZxL13ve6BQHxiYiKWl5czqO3u7ibAU8rBtjkzmCaMQWW9gZ5ashPg2afzDfjfT274YMHSfkomaiJLwzM01BvZD/T7e34MqK0szcHBQQK1yvjPzs5mmdGdPhH9mSX+zs9jpvzZ8fFxfOpTn4rnz5+nb1ZWj4jsrAH+AZOasEti7s+dqne5AbwSswqasXgaIpwN+yzBcz7rRbd/7s/9uf/zJZ7/+B//Y/yH//AfBv5sbGwsfu/v/b0Df3Z7exs//MM/HC9fvoxnz57Fr/7Vv3ogmH7UF8CApjRFENpD6VYRpYUHAjhqGQCU7uBV9O3zlCMgY9QYMMQYOWbGXN9naGgo1tbW8vr5u7u7ODg4GJgoymFwNjX7QK0yik6nN/Nlbm4up/kJrsarE1rJDrT2KuWgCcfHx2N+fj5ByO3tbSqwCaEwE7V05uADLsAilsTP1KmnKHVr3u32ppXKmOpV6NYNGBgeHk6mwX5ERN5i7bApa0H+WCDvVZX7nU6vE0smCaQJoMBFbSPExClL+Zw610YWXn8PuKyskQwdFQpsKuk5U0oM9kPLIpAe0QM+u7u7qWVoNBp58RiAYy1MVdUSHNHvcojo6zY4H4Aaq+b3qmZKiUw26bnfvHmTWTcnyJ6cmfHx8WTv/Bm7VRqoe6f+TUCo3u982It6zp1nINjvVAAkWOrk8F2xBHzH3t5eshgE+b63TLVOs/WqwNaZBJ6wXgAAdtjnCQwREZubm/Ho0aMU3bMP/xaMJD0rKyvxla98JTuwsGyybW3gEZGXfLKFTqeTz6uEhDETDL34WwPpTk9Pc00iYmB4JQB7v5QeEVkCAU4kEcS2gKSOLqCUnsp51GXpHFQxvH/4kvPz8/x+VZPCH1RGpwIeQK42MQDiYpMzAgSqBgAPwCNbA7L4ilarFa9evYqxsbHY399PoId9Yc80bxcXF7G4uJjnRflLMtNo9GfeOHe+v9JfRCQbjmlRauPfu91ufPM3f3P8t//235LV4iMajUZOH/4or593gPLDP/zD8Vf/6l+N3/ybf3P+mQfwOjs7i1/za35N7O3txbd/+7fHn/7Tfzp+wS/4BfHP//k//8jUj5eDr9bWbrfzv6vWgjMCWCIiHVB1Tv6MUVUqDq3ugDWbzVhZWYmbm5sc8NZsNtNAa3YfEQNdLJy9+iQnZ+Nl8T4noudQ0dyysSrgRRO/evUqtSICEJ3F6OhozmCowkU1W7VMGZ9xyz4bLbi9vT2gNsdG1SxEC5z1tAatViuzIdke53N11RsYZkAYWh7DMT8/n9mvw1A7djgsIMl+VIZFfZTTEHRkdGNjY3lXC7q9/gwhHOBgL9gIm9O5UVv+7FkVDqKzlVVMmF1YWIitra10sBT62juJQm9ubrKbA1U/Pj4eOzs7OZk0Igbq6YSxHBQK9+TkJO9qEoBk6MTZ6scupeNsFxYWEhhXVb+ACGgqm0REOulaq2ZHET2Ng+cCxusIAVkx29fdIumoWffc3Fw0m80EbPaQABCVzta8BAy+DMh2nmTrhKkyfPZdu4YA0ojeuPE6hFEJVYZMvNpqtWJiYiK/t9IHILO4uDjQ4RcRuZaCvQGEbNKk0MXFxbi87I1SX1tby7WT/UocBGBnAhMKQGHCDIjzszMzM7G9vZ0sCgArcGE87CVfQiBOVO95KoiR0CrTAA21nM5/1vZqnS/Oos8mrqX5oqcTzOsYCD7OHB/P4d+3t7c5UI4NKFVj4cSH7e3tZJfo/MQEya9Epa6PigGG675u0tmrz1+F+2KDcqipvEAEltT+6MAiPahCcCyKs2ZIJ8Hw7u5ussPA0Ed5/W/RoDx9+jT+wl/4C1/37//Mn/kz8fr16/jJn/zJmJubi1evXsVnP/vZ+L7v+774/b//9/+cPuvNmzd5NXXtCImIRHcC+uLiYjooTApjj+hPJJTRyuIE8qur3u2hshiHHZpnHAwvol/2qU6z1vPVaX2eqbLoeAFIoBwbG0sEXhX2HPnFxUWsrKykg3RB3+XlZXziE5+Ily9fxvT0dCr4OVGjlB1UgVim4/4HTk8QrEIvN6I+ffo0R40Lvuqsq6ur0Wq14otf/GJmbQJnRP/+EsJB4LJ2WAhQQKihY7JrLI7/V75zg+rw8HDWVQ3Asn93d3eJ9lGs/hzAqOPblfK8qrbp8vIyB1xxKMoWQBq2bGdnJx2mDJSDqmK/Wh5zvwrg4FZtjB1AaUR2HaglO52ens7LJJ0bNzBjHXVrqenXbpx6N84HH3yQn22vZLFKNRycLgoiTICW40ehW+sKZNDCOg0ELyDHnsmqlURprehCJCrKl2wcyBOYOfPandXtdhPoA3bAH9Bs/e2d2TOCh8ANoNMC+bd7hJx9Z8/PYgsiIrtpAIA69JDuxloDkGZyWGNTnq2nMiA9lysF+Cz7LAnhR3TQCexYCX7t7u5uYIaK88GvRkSWMvlFWX5EZNMCnx0RmVzS9gEalZWrU7Qj+oyg70iAjvlQFuODMUo6jDBadWoqQEQveHFxkTOD2Lt/ADrlI8AcKKWVqayMJOj09DTefffd2NjYyG5LMUpSVOUJ2FvnhG/0fPatdjgBHe12O1vcvZ+EybPyPxGRQF1SLF5hmwDNj/L63zJJ9ujoKL7/+78//v7f//vxpS996Wv+/gd/8AfjN//m35xiwcePH8ev//W/Pn7wB3/w5/xZ+rw5Q0an9MIZXl5exvb29kBXBmDSaDSyzu3vOQC0oLsUfMbh4WHs7+9nXY+BC3AMVsbDAQM7NBICkO9pbLUAjQIUVNR8ZX43Nzep+o+INHbP1+1283Pef//9pOdklwSu6uecJ2Bn/DkkLZg4mIyOAHJ2djY2Njbi4uIiNjY20okCGKenpykg29jYiNPT0wRDgg5leNUcVBU8xM8RC/yyhIjIgEK0WkdWK924jM0hm5ycjPHx8bw0kd4G41IFc7JhQV35AJPj74AG+iAARWYqgLs4MSJSY1G1MXX4Ei0Vp0Z/QOcUEfk960wLQQDbwlatmeyQY+cQI/psG7EyJ++7cLZ17dm7TL+yXIDe2Fjvzh8lLRmYvY3o33UE/Ps7769kx85oKrCR9kqpD4DAtgDJ1sJ3sJ5VhAmkcLKVsXLOJQREi0tLSzlvxIRQrOfd3V3aiIAQEXkvVqfTm9+EwgdmlBgBQeJywUgwrlo3FPvNzU3eDj083BuaBZRX8e709HR2q9CvdDq9jg16gjdv+jcwdzqdmJubS+aMDVbwieER4E9PTxMMAy/YrYjIcobkU9D185IUrK/vh1lS0mo2mwlE+Xznq/pjbOX6+npcXvambe/v78fR0VH6dMxXFVYDR8oZbABLBPgqCRoiWZ+16h+VozUVKDWJMa5RuL29za6qy8vLBFgYG/6ET7i8vIx33nknAUi9I8fFnxihbrebF0tKlmdnZ1MK4HweHh4mOInogUtnQUmzioDn5uaS4fqw1/8WgHJ+fh7/7t/9u/jBH/zB+OZv/ub4nu/5nvy76+vr+OpXvxqf/vSnB37n05/+dPyP//E/vu57Xl317hOp/0REBhqBiBFArYKXgI0GQ9uqIzrcDL22gzrwgIjFhkI5esgR9VZFutgSM1loUuhlZI7VCaL1tre3MzMUjGqmIOOJiGRprq/7lwGiKu/u7jKrY1zaExuNRjx8+DDu7nrt0vv7+3F9fZ1dHUadY27Gxsbi4OAge+y101G/K13ojJLxuHXYvlQxrwyp2+210xEzyr61O8/Ozsby8nJOT+RcZ2Zmck8ddAGpti2j1bVi2odau+cIqyhSiaiyYxH9riyZXt0fFOz19XU6FMwJVfvd3V3s7u5mV83MzEwOwZKB1XY/v6OLo2b4VPICvpImXYF1VmLqdruxs7MTNzc38c4778Ti4mJ2JxE5j4+P5zOoiztv1vH2tjdfSBAD0gR93we7VOl5tjExMZE2WDVbgBN9g6AgKLMrl5gJJHW/lXEAUtqlhYWFZBkkNLQ4MlfrK7sVkGqp6We6lM3vbm5uJrDvdDpZcjU0z89pMUax8xuSBYCUmPH6+jrngdBGVNaInzN8kf0AIr6nUehASbfbzQ44Za8q/FxZWUkfJ1lROlZKAnJcn6GExX/Yj9o8IPACw2yQHfDT/EYVljvTtEZHR0d5SR8giznDwrqzDGg7OjrK71jv+xkeHo5Hjx7F2tpaJo069uyJMqiEiR8z+ZaAGxCQvCiXSBSVyHTEsLfp6emYnp7OpFC5lEjVZ3kebBVbWFtby3EDW1tbMTQ0lDeGY47YhUSNLubysjeYULK4t7eX3Zp8nNJ+1YACo+Jojc20TR/2+nkHKL/hN/yG+OpXvxp/9+/+3fhn/+yfxQ/90A/FX/yLfzH+yT/5JxHRH82OyvMyf+Trvb73e783Zmdn85/Hjx9HRCQdSuPgUBOF1lq0w1eDlQyMY60BmLEzBAea84mINHZZqEDiICnlqI8yZnoGtKFgUQcd1VbVOlpeMBAIaQaGh4ezT17AlRXoHjk5OYm33norNSt18qA7TIA3zAGBXW1BZWzT09Np3JimZrOZinRUoPs4qkBRduGFQVAn9XcEeO12O7a3t5OdoWu4urrKu3QuLi6SYubclWIcXs6hdiUInrWODUDUrp/KlrEDehNDymR7Ef05PQAssRrBobKZIK/EuLm5GePj4wlYIvrdRgATjcjd3V12XXjvy8vLvGSOLQKuaHHBTAb3+vXrpMq1J3JkbNfnVQYNyMf8CFoyaYOgBCSlU6UXQ9foA+wJkAsAerELwYRNYbB0vFQRK0eOCeQodRjVsg2QyxYkNe7RsRf1e2nbp2XDtgoqtBPOpdkwyslHR0exsrIST548ydIq/4YpwQRdXl4mOKwATFmBcNbzWG/lacG/0eh1ybx8+TIZBQLxTqcT7XY7Dg8P4+23307fqjTkmQ4PD+P09DTvPcOwjYyMZCZeBzAqO1eg51xdXV3F3t5elnPqdQr2QFLBR2Gu5ufnc5Caki9QZO0Jl5VklHwBCgDfOvP51kLJ7PDwMIWyVXdk/z2nc8cXKdsCf+xEDMJ+VblARC8BWFxcHOhuctfP6GjvYlaAho35zCdPnsSbN29ie3s7y363t7cpMwCajMk3m0vXZet/3gOHEMCe1kYJHYCVSTMrC9MC3Enc2PeHvX7eAco3f/M3p+o6IuJX/IpfEZ/73OfiX/2rfxURMXB/Rn2hm77e60/8iT8Rx8fH+c+rV68ioj/OGoshG68ojc4CWhfYIUKLe3NzE8vLy/Hs2bOvAS1VPwChmtbXbrdzrL4A6FChrVHO4+O9K+9RYPPz8zE3N5eHqRoqLcXNzU3Wthmpg1fLULU9FVIH0tSzqbMJlhqN3oVY8/PzudYjIyMZMCYmJgamD97e3ubUUNRlpWgXFhYyEHM8nGQVKgMygjyhG2BW58PU+rLWPN9FJisrRo8Kxpw91sD+c6S1BVwmCMQKlDJ+syiATKyW8hN7AMr8HQYBsEPry+iVeogPZVH2MCKyxAOYazHf29vL2Rpsit0pV2IciOk4ZGthvWpmOjExEXt7e0lVy4JMz6RX6nQ6KZyuA98mJiZSAC2gnJ+fD5TNCNqVQ7yf0t7FxUWCbEEGYDQIT6Lh3HDsbMT+YkX4GiBBkIjo3+xsqmllS2XLAqa/B2rZ1Ph47wLG8fHxODg4yEBcGQF+CztIkLi3txe7u7sZYGs5iV87PDxMoA8kzc3NxcjISDx9+jSZTAJoE3UFzIjeZO2tra28+K+yyQC1tWw2m/H8+fNcE4ACExoRCcTYM9bBWbKX9oQNYgPp3MbGxlIf53s4K84NG6itwd5PgsX/YNwwR0Am4A2A8tnYVoJmAJINiAdshb1UkbezPjo6Go8fP07/ZrSAkinfxZ86H8TmwH7VCzkPfJLPMfiQX15dXU3A12630/4l3aenp5k0SFDHxsbiK1/5SjQajXj9+nV+T7FmbW0t/TStI7CDicFiYc4jIisPSsvWngbpw17/Rwa1qWFF9DKCJ0+exHvvvTfwM++99148e/bs676Hw3z/tbOzk4tv6BZaGzVKhKX+bdOVaNCEnU4nNjc382A6aFCtQwcAoA9rNuVz6uGoiurb29v8GQFOVtDtdtNpj4yM5D0aDtDQ0FDSuQsLC/mMGBMUZW37e+utt+LFixfZPup5IH8UNMTte1YHAcA9ePAgtre3k5XodDopjKNFkJFWPZCMj7jNCPtK26ILIyIv4qoaCUwAetTfn52d5dyVCngiIu2Bg5JN13ZimY8gBCw0m80U7wIw9zMbWp2I/q2os7OzaS+15Gfk9dbW1gCtjSkCAtSJlWqwI1p3t7e3E3jLjKs2B8AjnrWnfl4boXLa9PR0bGxsDOh+TJxV3uHYtbYDOjpYjo6OkqFTUvTcMvuZmZl4+fJlLC8vp51aWyUdzyEJEMSUmiQBzi8msopjTautMyx0xrBpuhznXHkhoq/hIqLnlAWQ2qKqO6kGLnfKePFHtClKAYA92+AHlGJcBAmgYgTqcC2AQdB+//33s0RlbzGVNXhERALjqlvBBCrLLC0t5fh6Z8iev3z5MveEoNx70x3Itq0h/1CzcEwrliUiUtgPYGKpTMWOiCyvycSrL6k6DXthf+hR/Hll3itwd96sj5Ib1k+iVbv5fJfz8/MUl7IpTCTfrzGiMot8m/udJLSSDMwVP+H37YPKxIsXL1Kb1mg0shGF2NcdS8CDvSVbsCZXV1cJpLe2ttKf0mzSFALB4qO4gNFR5r+7uxuwl4/y+nlnUL7whS8M/P9P//RPx3/+z/85vv3bvz3/7Df+xt8Y/+gf/aN0tEdHR/Ev/sW/iN/0m37Tz/nz1KjV2QmQIFN6BwDAZkT0Kb1GozGQ3UT079Th0GoWZcqe7E8QRGMKlIyPTka9rjIrmA3Zt6x2eXk5y0my4gqU/DdRF6c9Pj4ec3Nz2ab63nvvZbCLiAz61gCi9TOemZDOQUYdCiz1/hfiyOXl5cx2ZZSAhVku1hMDI1DaQ6WCbrebUyq9Z61jc7yckDKBtSFUE6Q481oeqDXtWkNWfqhqe3tZadtnz54lVcoONjc3M8gODQ3ltfGXl5fp5NgWIFvbGWW719fXCVzZLmAF/MnqOKL67ICOZ5+bm0sxGyc3PDyc1C/7sy66c2S/taOGc66gTZmCrsIUTF0Kr1+/jqGhodRJVBtSMiIarOVOU3Tv7noTddm/vVFqAKLevHmT3SbOmcBJjLy0tJQM3PV1fxYO26s6h9PT0yzf0AscHBzk+vEhft93Apjr3UIcc9VMra+vJ6DQ2u6/W//zgkSZMqCG+ZNMGCyHIWUDzhf9A6BqRtDu7m5eoKc0BzxG9KdkDw0N5RyUiIhXr17FxMRE0veA9u1tb1ZSp9O/VFRCsLCwEPPz80nzYycqw1fLYc70+vp6ggDt/c4/fYcymTNQ2QnjH0ZHRxNoAXpK0lqaJSzW4tGjRzE7OxuTk5PJNvPvmBt7IBZg2ay7JAqDw9/SJiqVVl0UH6TEfnl5mUJmM59OT08TqPos+zM2Nha/8Bf+wjg5OcmyFt8/NjYWa2tr2VUmEVEiwhSzO4yQlneJgySUrWGcnj9/PtBN6xmciSoi/yivn/dJsr/m1/yamJmZic997nOxv78fP/ADPxC//Jf/8vjH//gfp6PY3d2Nb/3Wb43V1dX4tb/218Y//af/NBqNRvz7f//vc5E/7GWS7Hd+53cmwq50PQRf65AYBAdHJ0EdwyxwVIqXM46IRMvapWpgrXX6WgfXOgiZAxfqfzZRMKlKdnVl7+P7VC2Nso9DODLSGxT1zjvvxPb2dgYSgSAi0nkDaMoxFU27JRrDBNV/+7d/e/zoj/5oBhQBmbJcjRdg4xQAMKhfpuX766uXHXFIUPx9FsYAItkEByS7rDoQWiQOlxOxHr6X7wuwypLr+lWxqvtiBFF25QXYVqW+ch+Bsr3EKik9CMJqzRiAKpiWTek+MrvGObCGgvH1de9elXa7nd1v9tf3MQ1YUHUO2FpluDyruStzc3PJcmg9Z/u1kwFL5KqF2u1grUZG+oPXFhYWUqNiLSpgZ+MANGA6PDyc4Muf19Ko/46IBKy1S+frtZJiWIhQb29vU4dVy0Cely4IIIqIPDe+B59iHWg0fLZ/s0GJibPg+wCyQLyzNTU1lYJ1LCgbury8jMXFxQQosv2I/qC1iBgY4ObPJiYmElBhrU9OTmJtbS329vbSx9IdYtnoe4aHh5NVdU6VIIB2/iCiL9hlI/adXVhjyZByMdDi72oZ3+fyqQAJRpg9iB3akIEj+8yvGuFgX/i8iEifwj7pwiSAfr6W8awze8aqY5vYsVvia0nq6dOnA3esdbvdWFxcTB+gEQTj632Xl5djf38/O8LcteNnq0yCLVp/56uWigAdYvMf+IEf+NBJsj/vDMq/+lf/Kj7/+c/H3d1dPHz4MP7ZP/tn8c//+T8f0KUsLy/Hf/2v/zV+22/7bXF5eRnf9V3fFT/yIz/ykcFJfQEGgpJMA3NSRXQMU1bsIAkStU5rYdF2nJUNEggj+lM9IXuUq6BHta884L8rQnYAtFwKCD6PQ6i6hNqGiQHwHHXuhzq7UlftMCGGRbldX/cvvHv+/Hk6XTqZoaGh+NEf/dFYXl6Oubm5vAiNk5Jlci6VrcE0Ed2iEh04HREOSaVPgUjO1LqzAUxaZS+AHyxFRAyITKtYy2GqI7DtjWeh+p+ZmUlbJTQmruQsKuOxvLw8UFKzHtiqmqFxruzQGnFgMhxrBCwBPRwix8Hub29v07Y4CzYuK6VZ0s1RBefawIEtZ0FQ2NvbS2Ek9sb31YFH1yPA14DE8QtGslzMxOVlb3w9fY6OBSJ5fiAi0gdU7YO1FfCBOvYDHApiRNeVcfTdiVJ1NbF/NitBAnxoB9inmSVYT6DJXkhAWq3WgLYjIrLsUAWds7OzqfWxnoTAnU4nhzvu7e0l8xHRL4HSoxGoVhDu3GB56x01pi1fXl7GW2+9lYyIAClpGhoaipWVlYjogxNrInuvug9+hP0A3gJg1cwIjrrlqn/jk3yOF5s0QRtbg8XDeHpWfo+9GllAA0Xo3el0cuzF+Ph43mNkurNysv2zd2bKVGZVPKsx6+bmJucQNRqNnOIsgWI3EhFJo9vpJcpTU1NZAuaH7R37MBUa8F9bW0uQY58AHn4OU4QhdWa0cFddUxW9/2yvn3cNSqPRiO/4ju+I7/iO7/hZf252dvbnPJTtZ3pRegMBNB4WjVNxKGQhhIF+hrNHpwsUMk806/DwcLIdnBnBbxXoVWq+1srvizAFLI7N7/ueqER6F+1bHABj4ig4COi2sgYOhUwcSGLYUL5syDwQQT4iMqDu7u7mmsliOFkZiYN7cHAw0CGjtllLYuhvLEu32026V7ZYKdhWqxV7e3t5oNGHWBUgyXtU4RYmhmPwvoI2tq3RaMSjR49SYMsR1AALpAqEVbALWFbdTtU7nJ2dxcLCQgKcuseNRiOWlpbyGgTBt4Li2v0gG67sgf3wPZQQdHoQ8vquCwsLub6eBRi8vr7On/f3VaeDxaKB0GJK6e8f2bJ1kFRUhsG4dWxgrVc774IM4KGb7PDwcGCOA9uQtQG6zjggIQBqm6+JiXNPJ8Nm/T174dxl/S4VHRvrzXrBTvge1sz0UEPniJQlHa4UoHuRmLDHzc3NAY2D8y3w1C4YAVWpwPsQPguk5l8oxQA6AAbgsLCwkJ08EZElFevFx7omAxNCsIy1GR0dHWgJlqGzF0PkrKcEQzkPuABUu91udhWJC2yCDdunTqfXjSSO8NmYbL6nAlzsI9t0r5mSZkTkKAHjApxJ4Hl0dDQ2NjZibm4uQb04ocQMbPD5uty63W6CmjqkjX6FxoXNWkegx23t2Gg2TgtjDc/Pz2NxcTFev36dJAPdJf8/PNybpYN9dqadC/tVWW4z0D7s9bG/zbh2ZgARxH2ytjovg2MRoAUugem+KNOictZ1YzjUSlcKXihtCmpZgTkH6pxEhDL/3d3dDKoQLUNkIDJAQAtLIxORIXFmaqzNZjOFZgwyIvLA0ImMj4/Hq1evciItsEbMJ2gQQrmrR5nB4XCgONKI+BpgSBxXxa0civ3BZPhZGYZaNaduTDywI6hiBWhLDN3jvDudTuo4ZLS+xyc/+cm4vr6OH//xH0/ghaUihJWBCTao2Yh+Scd3okMBGDkQdkQIOjU1lVoHToVug7OqpTdBxj5ybMof4+PjKVClBdEtAMxxTi6TlLHJYDnuqok5Pj4eoPJ1DngphSj9AV++E3tjJ84uRswzAWbAvFHrWLK7u7tkCGTZFXCgp50rQa0K14E+DIEMGtDhZ3wP7GTVRQlOwE9EZFmXk5a1+l0XrzlPxOC0CRhQZb/aisqPYDqAVHMw6mhxwLnqz+hOGo1GnhV+oyYHzWYzEz9nTteZmRk1e9ZGz0aBIUkaduX169cxOTmZ5QbBljatluYkFaenp+kzfLZXLdt5fkwgu2BfQ0NDcXBwkFk+plZsEHydTVofew0k86W1vMqPuPDUrdIus5ycnEw2xV7aD2DCnopH9g3zJtmTkKlavHnzJtfI8wLBJknv7OxkqZbv4LfEFjYoBmJYJQR0lQcHBwnonC/gx/iQkZGRAZGwePBhr489QLGR6lh1gA4BJefHoNDWEf0DI3thFLIbjkimJNOuYCairw6vpR713/39/QzAnBa2gMZA3TkiEgXLfBmnDJfzM2JeyUk26LMFdEAnYvA+n4hIJx0RA9+vjvJvNBp5zXqr1Yrh4eFYW1vL4WKesyJnQ+JoLYAcmbdnvK8BEIgFJocQqLNmDgfHq73S3nseLA6H0Gw2M2BUZwl8NpvNFBqen5/Hv/23/zZLQcBv7QjDBNRBWLJf+xwRyZLUWi0g6fZq9mXtLy4uYn5+PrtzImJgGFedP6Bc2O120yH4GSWdu7u7BCnX19extbWVpSN0/9nZWczPzw8wY0tLSwPzcYx113XwwQcfJDsnO65aGkFfl4ix+7Vd3j7oDrq4uEgHWYGcu1kEQ2VDLa/Eh5eXl3F6eppndWtrKz9HABJEtMlWWwA8gTEAhuCS3QI43jciUpNkvSrLRawMOALxGDZrQCCLBQAiFhYWUny8t7cXS0tLyUjxE0A3oOsc1LtVfM7Z2dnAfTq+r4DFj1RdTW2nx0hh4rA+EgW+yfPy0fZLAklwadxE1QLRKYyMjKSmjr1FRILnyp4AoQJh1Y1JgCpLzv/YXzFAcoV9qL4D4KPj0KkC2HouCSCxMcCFjaldM8Dw5GTvrqSFhYXslomIgRJou91OP0PvVf0tlgwzwsfx5fymZJt9KtNiZ46OjuL29nbgfjcsmRhiT/g88VXZanR0NJOz+6zoz/b62AMUztKmovorm2FstOwb1Q4kACWCR0R/PLD3qHXi2onBiWFmOKWqUK/dQrV0UelXLxvfaDRy4BJnvrOzE9PT03nLpM9Rr5d9caSVMkTRQtAyg4j+gLQ3b96kYQlADo2hXAcHB/ndrOPMzEweYIbdbrfj9PQ0lftbW1sJ+jifKm6rZRxr5mCZDQJg1MPCKUb0KNXp6en8XsoN2JZKo1YAyKlXyrfS/7J34Ifmp2akVazG+Vd2BuCoIElAZzs0QbKUiEhwVieh0sOYF4FKRyELfLQJ+/v7OSVXZru4uJjAgoPVrs025ufnY3d3NzqdzoBgEkuBnavlMbb+7Nmz+PEf//EBEKDEUcWwzgUAbZYDariCb6CCzdojLdKAiv0USOhhqk9gc56J1otNoKKd2brXgrx9q6wNgSHKXQ0fk8aHVB1SLf9W29/a2sr3Y8NuBBZIAQwCZqyMoIchqLcYW0M2bZwBcaw/B06cSYC1zjUSvJUJfSesD9/jbGNVJid79ygB0tg1LHM909Uu+A32I2GrZTe2VNtx6z7UMre9qD4YU1QBYxWZ35cDeP/p6emBe3BICvxZZez5DJ1Gs7OzMTc3F3t7e8n60HzVrjwsmnKm9VWirGVzTCt2hc0BLdbb3t7d3cXr16+j2ezPpXFdw9zcXM4e8rMAjniCAa1+zrnEdPlevsuHvf63jLr/P/mS9TnAp6ensbe3l4dBuymqy50dkGOz2czhUTIy9HBEH6Awfk61iuxqeybwgl6EkKtQ8+rqKuv5nEmlfAVxtWIHMiIyo6oXMNWShADru/jOo6OjsbS0lCrvmqkoMw0NDeXtyg7c5ORkZvRYAIyT73dwcJDZRv1sbMvJycmASE1wd5A5GMEK6MJ+YceAPkyMQGywFYfFmQCtGAsB1GfcF6EJnhGRqnWHvDpYztgeAE2ypYjIQzkyMpJZufKYgGafqzgXyOQUPYNAdX19nYO2lPiqtsfeDQ31BiO5HLLeB9Vs9i4nnJ+fz8ypMjc141FTp6+QJdnX6uyxAGdnZ/HjP/7jeeaUP60rZkfw0A7JhrA3WmOdH4HcWeTkBAOaMKDG7znrgh5g6M+AEuexMpGGutnXeu5rwJMQYG6urq4GWFp2i/lhC8PDvdZSTl32akZK1U+Nj49Hq9VKoTLbrJfRKVU6L3VOD7sURPx5u93O7JbvBESrVkgi6Bwq3dDh8aVKyXQoS0tLeU6VLiViyoq1hFfBW83OJZ91L7FHfLa9ESD9GT9p/enqgAW/b5/r5wLZ/ESdGQVMKP9JfJTAaaMqSPVcBrP53Nq94zOUJT3z2tpaAqWaULHX0dHR1AFJIuv+sQOxQeyUvEtkXO56fX2dyZeEj02OjY3FyspKgpeIGCiRSSAxoFhi4OujvD72DIrgA0ne3t5mHbtmdw47NM/hR8SA8pwzdUirFqUGuZqtREQ6Bd+lBqqaPWFwtN5F9K8Er1lK1TqgaoGwOttCex6j5XxHR0fzvgbrhB6VRWNqxsbGYn5+PodHUXxH9G6mfvXqVQZz9UvrrW5PI7K4uBg7OzsZyGoraM1EHWRzHQTsZrOZztufYxCgdHvh/Q8PD/PzrLO9NL3QZ1fBoO+PIUN12q9aerm/TxxC7awxg0aXBwAoYHLmDx48SGoe/eu7cbwyKYe8Dj2q3SaA1c3NTczPz2dgqyWao6OjnEQcMXi1vFudsWBKKOh7dXXBzzPJwt3thIkEyOoaAZ/WUnCS1bmewURizt8zNBqNvKzMmdBeq3SlS0C50poI+Bw+1qTb7aZfuL4enCYseERE7gf2xn7VzBXQoCkAbGmzXL1AB2ftla2qAJX/qVofVHtlAUwPdeutxArjKqDZP0PgJEkrKysJfrUGHx4epp9hXxWgC1T2HiBnD4IfW6mZeU0Eqx7h+vo61tbWMuGxP3RmbA6L5AzwC/Pz8wNdlQTa9rpOU7ZPnU4nmVDrAWAAegJ4BbX3S9JugK/xo5arsXLOrHISEMlGlRBN9eV7nFcxi/7GmTI0jk0AAs4MHYikFBMGiEtu6JswTpWBGRvrXTuwvLycPuno6Cjm5+dTwA8804dVJhtLZZ1pKnWiftjrY8+guGFRBm3IDTEhEAJMqBmj79TJq2iUABINV50JapeRjI31RmOrYTsIDJqTYUR+X527BmV/XwNsBUWyN6CAw7+6ukrBo+9ECOnQoMsNkJIdcu5GpnOeHLo7XoAkokOZjFrz6OhoLC4uxubm5kAmDdgAe7VryJqgkSvVzaFVpkJArp0ymB8OxedwphxBLXfVjqI6+Ol+hsRmZEJjY2NJ42LlKqC9u7vLu4K63W4OK+O4laMuL3uDl1x6CURXGhegs8fWAAsIEAA+NAmywW63mxqlmZmZODs7i7OzswRzgqmXtQboZmdnBzp+Wq1WgkAg27pbJ85xZGQkM0ufOT09nRoqNnF4eBgR/Vq/QIel41Cvr69jbm4u2QAlJ0mCwFIDk2djL3W0PKd5cnKSHW83N71JzMfHx7G/v59+QmstUAR4AZUAGSc9OTkZ+/v7sbe3FxGRoAk9DkDIzCUgdBY0KXwHoG6/rLX12drayvJrnU1j9gkhuvu9FhYW4uTkJFkTZwFjwvdULdfq6mqeddoK/oQN8FvONe2R8+IMAs37+/vZqvvixYs4+p83qbuZF1isZTraBskF0MhXR/RHszvf/Kx/JAy+r2QCE8/H+F16LczO/b3f3t7O0iWfwL7YL3u8/x7alv1MRA8I124be+jPJSzj4+PphzyLOFHZac8xPz+fa68EaR2BbmcHowhQY0vF0QcPHgyUa4FZ32NqaioFwYAPdkYHpzX6sNfHHqCYUMiY62wRDoYRc44oXIwKFMuwzbQQmCP6CBCCV6O0wRF9ytnneAEaMzMzmQn7Gd+tUv4oXZ83MTGRxlDbGP2ZoWcRfdGrejIgQnBJqS7w0UcQtEHWssQ3b97E48eP84BA6oRzFxe9OxnOz8+TOeHQgSX/AGAYDFmitXCwvPw9J0j0S7DpPe5f8ObQoHsxNdZfkJFxcqCAl0PO4bIhQZjzEDTQxg6ezB3NDGwS+LJFjlWZxd4I/vYbiydbqgG4OgglIuBG8JSZqafb706nk0Eae+N7qj0L3oYGymrb7XbOSnnw4MHATAzfz/mS6ekciog8D9rQ2QxHL+NT6llZWck5CzpigFkZmfq7ZMVemrnBL9CamAkB/Eb0ywpAPPBqroy9qrq0OggPyG82myki5mOcKZfN3S/rAgBXV1cJbmX2l5eXA6A4IrLkITEx90hJFeh0DqseQQnj9vY2O350K9YEouqfsCXVlrC0nqGumdJTBTHKldgf9urPASn+QHbvDBIY80dKo74bIMjvSuD4DeeGnVYtDV8BlPKtVS/h2bw/VhmDJ5EVH6ypgY6SjJmZmSyR8jd0XD6DT+AjiJlvb29je3s7vz/gat/YN7EsFgtgjIhMdPgX9lC7LAE457myTXd3/RZmfg2DVcGlZBmr6n1rfPzZXh97gDI1NRULCws5OloG4FBxEA4Kg2MojGFiYiJ7syHfiD7YqENmGAmDADi8F1QsUJnO6QZd2YWBPlCqTZP1VeGW7yijVBetk1WJlWSJtbZ6c3OTojt/NzY2lpRh7VQCgNB2l5f9cf4yNU5a8LWWR0dHGZx9jozDQQQQranvWFms0dH+bAy/hwGqZbWrq6ucnwAw3tzcpCOO6AEd7YvaEwUcoKzWYVHzVZVeRZYOGQB1P0PD4MhE2Yt6cXW8U1NTsbi4mJ/DZt3MDJigjjkGn2NmiMBRp0ze3d1ljdkeVUar0+nkNe6e1X6a02EfKvhvNBo5UdT52traioj+PJjj4+OsVwNB3l8wdJYwns6n55XZ3d3dxe7ubkRE3mYuKGAIfG/nrj6ncwn8W38JjKFT1pOjrqwlJ191JAKvYEZvAFg4d8AfxovvAEb5o4uLi4GBa8498MsGqj+o7IGA6HsK7MSaEh/P4vftm0y+spMyc6UwmiR+oSZUVffHV5ycnKT/HR4eTrsSsARlZ5IP5DudE/bKJ0VE+o8Khq1nLXNGRD4Txso5rixFFdNjsLyPZJXPwVwoGYkn/BCgYdSFkfrDw8PJPth3QLbqEl3TwkcNDQ3lPU8S6fuMsI7LiEjWDKumTA1ouIJjfn4+EwwAsiZ4zg1/KYGhR7TubFw3Yy19KzeyGQntR3l97AEKw3UvhD5z4qP7A6vceFk1EdC4VuP7QYQRKx9xNFX4V0VwApLfRdFV9kEdl2N1gM7Pz/OyLptPz3B+fh67u7uJjtvtdho7uhsAQQdqKet2uznKvGaJ2s/uT6f1/BG94UtTU1PJyBwcHKQzGBkZifX19XQKsoPKPhhhbw0dIk7BQeCgaveGlz0EfOqfccoOQtWaWGdZea2J1gmuMicZB2fz4MGDvI8D+1EzUp8pu+M8sGqADNAZ0b+2vtLhtRRUHbL3UnqqWpvh4f5dOtZlZWVlgF3iQGk41PE5WE7NecA6sYFOpxNHR0epa+Kg0O6VFtatgMlpNPoCZiyKDFc2CCBgr2pXlHMjG7c2AIWzTCsD9NSgUcsO7E4gZ4N8R+204DfQ/zVzZkMAJFB5enqaDFsVxdMOyFqBC6UwiYVOLywcnYHgWK828GeycqJIIEMwsT9KA4KkYG3Nh4aGBgZOAlqmlbL9xcXFvEPJM1tT66NNX1mm6pHYGrajJh3EtgJqBc2V3QOWlIudU/v24MGDBN3WQGJir/gWrFhE7zJCsaCK/mvyCFCzT59tfZxnZ4dPtgaSiTo6wX1GlS3C7GJZ+A3vAxjaLyV2Prj6Pn4T0FbuqSMGaBb9WyKP/Y6IBJgAs2f3s8CPhNv5q/Oz+MfqZ36218ceoMi0RkdHs/zisN//GYZUDTYiBjINqJ3ho+0MS4P+gBsOSlbis/y+1mfIXa0PJYliq0psz3M/GPrOPpNhcDYEjhC5jqXb29u8Jyei5zBrCxvUrMtDxiqTYNTWS6kF9a+l2Ht7Hoei0WhkUBG8HGjr5rtivqByn+m90eIcjAxHpoKCxzZVRwHE+Dl7KFutFCSnXifU1pKT7E1GaA1lyJyU78DhAUBDQ0MZnAQYDmVsbGxgKmnVJAkCtaV8dLQ3pK7Z7N9h4s/s4d3dXe4H6tWcBoPV2JYAzyERU8o87+7uckpsFf4ZF19Lhr4LGxYQTCS1L7UrjW7Gd1lcXEw7BPxq55V19aolMWfSXggOlclgVxH9bhcgzx5h2qpGy9oThFaaXsC9urrKEuLBwUHeRuz8CphskZATSBDksUzOj+GP1d9dX/cGE7JZ+wlQXF5extLS0tesRWUSAUwJSLPZm/L89OnTGBnp3XSs+QBDgXW17vwM5rLOgGk0GnmzPX1HZT9qedzeVf0Z1rjVaqWGg+9wJnShmDkiAWRPyjEVnHlm5xpjWstWgrorLzAexKgVrNZE13kSazCHbBQow1CaPFt9UrV9CREGs5Z4gNuIyA5Q53JoaCg7e7DOGMmVlZW0MX4Og2b/ajnonXfeSdbG2a8VizpCwD+Vkf6oGpSPfRcPAxfYOV/OT/Dws5UOtVEyhIj+3TvNZq8XXPbJYICCOp1R5lgNkmFXNF2dPKPBpnB+Mh2BWocBZwqN7+/v52eOjY1lhttutzPwVLHvxcVFfOITn4j9/f0sC2BaZmdnM0jKsJrNXhcCcSh2CaCpJZA6j0T2A6hZA0DJc9T1YugOopfyDjBiTdCb9koNVFCoLZyCZAUfPh9VPTw8nM+JjvXeu7u7A7YhmNhHzsJ3t95AjkPO2QKTviuhaM2iKzNXa/6EdRgG2SemopZ6rGcVz0XEwJoLcD7TuhhvfXZ2lkJbjIssFg0NyBHRajf1DCbFAnsYm6GhoQyWQF4NUvQFEZHgRfCxD5iJquVwId39coiAUzs5AFOjvWtZpWogqubl7OwsNV+1lKvWXge68TWyXWfDGas6FpocJTrPhD21Zkpg2BNMG3sC/pWPAJTaJqpUYI6N4Kkc4HzRt9zc3MTm5maWq4AwDHX1wVgF/+18OTPKZnwZv1MFnrUbqO6HtaqzoWZnZ/MM1kTR+1Wf6Rxi05RLJBxAQy1r1c91TjALfEBldio7VktPwGtEJGDHaEmWdFFildmcn/M7WtjrwMi6VsAXfRCQiyHGVDvz/ETEoJC/lgUlIveZMayX4XvYa2svOQcele2B1A97fewBStVqCJAOLWBSAzKDioiBjhJ1Nxulzbb+edUeMO77GbKsRfkCuMB+QO3QvcwPwpehCRwRkc6GgprDdOAdFAHX393c3CSr8ubNm/jyl7+cWZk1M6qeA2W0nOLZ2VnMzMxkW6OBSktLS/m8gqa6vcDCeXlOAaV2EzFoGZlOggcPHmSHS3VyldLGKlVHyMECMxH9AFWdYnWgnHtt+wY+a7lkbGwsWbaqoRFg6gyS6lxohFCimKSIGAguyiDWzn7WMiEHu7y8HLu7u+lIrZP1FIhlf3RPb968yYsfgWJAHnASeLBkAiHWog5t8/OyOIJkf1br8UDK9fX1QAeN78Ge2HNEfE1ZqWbUvpu9de6rFsHa+g5+x8/5WUGy6q5krSMjvZk4VV+BIayatqrF8J0AQ/6JJgkljp3qdrvZVs5/mJ4a0StRbm5uDlz0pzRW98+ME2eARkui8+rVqwQerVYru2kM49KWzF6wxVUkieK33tagzoOyhs6AsyVYV/G/IMu2BNB6Rp1FYBAzd3t7m5PCBdWFhYXUb8jwKwB1jqo4nT+pTJ+zpBwBcCpt1VKhPfBvGhJB3vtLYgDuCnw9h4SOXVkHCajYIjEFZs7PzxM8jo6Oxu7ubj4DGwKklZaqvqjakPhXu0rreYmIgXu4KgD0mXx/1WLxMc73h70+9iUeIkX0nUDLYVeqU928ij79HpGQAMfwqP8520ppcqoOoeBpM6HHqjSP6KPUiMiMDDqmiZAhRvQn12IhBDfBUaZ0c3MTS0tLaVzYI+CA02bsss1aNpqfn0/aFPtwenqaCNg/WtAiIoEJB+LQMUqgT+Dz/NYP3V3r1DIVmQeHxchlS3QHZt9wjvXPOdOK/gEaQeC+VsTBrNQqJ8gOIvp6hIj+rcB+lkiMaLkyXrI2YmkUr9+tIk1lIWWZZrMZm5ubAx1FdE+ADj2TAFLZH3qmGsQFAudjc3MzRkdHc9hYq9UayCprp1llpCIiGQbgorJebIIWRuZ5fHycgLuCcbZTu7M4ebfXKntYZ+/vu0oOKoDjxP1etZHKwtbs21mRKdp7gldOXoADZJxtrIeAIsvVRYPlaDabeavv7e1t3oXEz2E+nH82ACQoR+u0q2JH6zA83LvgTVY7MtIbxLi/v592dR+I17H5/JCSycLCQoLoWpatftGfE17bD9oZP48xrODdvjhf/JB15ifuJ30VpAKXfL/3iYhcu1r61SggltTyfLVnvt539zl8h/K3cmnVkVjHOr+HX5EQ1mRRHIuIgeSvJgFDQ70RE170SeIaG60ME2CCCXWOAGqAG5hW8qZfMdU2IvL7+n+Cf0lf1RZ+2OtjD1A4Pw4ZqscaHB4eDoxhh9orFR/RnxxKS6BnuzoBhuRAoDYjYqBr5Pb2NjMh2QZaT4Dg4B3M2saKFq9toQyqsgmEfcooFfk7LDs7O3F3d5caEo61imq1nFq34eHhePXqVdaSZdKCOVZGVus9KzIW3GtQrW2S1qQKSgETB9f6ADUVKHifSq1af3/nZwVgzpKNeDbvV7+z36/Brl565r05zQpUOZmIyGDnYHKqAp0/4zT8I/OQdSm/qbtXETE9h/PAKfusKrz0XekYancScA/oy3JNU33z5k0cHBxkeaBmgPYTQ8Spa4/k0ARTder7+p8q9AacIyK1ErqtnElBWC1/f38/wYKf935VKwLscMLsxM8KktiimswAvwArgTVHDCyzVyxELSlgVgHYKop3DjEfBKvOv0zbc9sv/+18VeZBGdBY/MrEakMXpLwHEGc/aZgqk8x2aumOlmF8vDf5VrZfuyyVD+1nROT6O6dVy+UM2gsMimcD7GgmABZnSFJQfWBNGu1PZcElD7VkXxnuOsHaGWfPU1NT+ZnOkvMZ0b+Rma/y3LQeQEL9TO/PvzjnwKSfUx4EepyvWj7rdruxtbWVflzL/cnJSSaknoPN2ye+yvsSv1c/eh+IAbWqBB91kuzHHqBUUZt5B1XNzlFzwIyr1tMvLi5ib28vjo+Ps1YXEQlkZAmMozI0slJB1UFBkxEvVVGZS/SwEnt7e/n7EZGGXRkNGRFn6LMZA0GUoAcAOQhoX98Rjci4Hew6h0NQlZEom8kKlVgcBoaM1oyIFFA6+BE9o5f5VqFsdUoCFeMXuGtG6FntQd2PyrZ4VXErdqMO94qIgfIBR4z5sH8RkTMgrIP1qd9T4Nve3k4Hy0nK/O4zOt6vlgkEgZGRkdjZ2UnGotK13oddYjAiIoPs7OxsjI6OZldOu91OR17H76t5W3fUMifOHtmVYFcHMNWzZZ0FlMp21Kyr0+nk2HNraC8i+gLDymBZMwAf8JAt1iRE8K8dU76ncq+1r9/Te9RMsIJdQd3+8x8RMZBJs89KrcvUlQEFRgxLtT0+ydms2iL+xEBF++Q73t31hgjK+HXmKanSTvB1NDaVAbJufIizS6PgfNVbjw3+u7m5yTkvunec1RqgaQLZYgVs99lY5wdIxFbztZJQzII5MfyKgO+76kipeiLMuWB7dnYWp6encX19Hevr6wN7xFbtNf8n8dXaawyD55G0mupr//gXoMW+ewa2g7liHz7b2AylMyClsjSSBMmi9zGBV7mVPwP+nFu2XZMz4KaWZp39utcf5fWx16Aw4urcZWtqjwIeg67ZNG0K1oSTqbXj2uoHgNQs3UaqV9ZaJ8MFmIASd29UGqzS8QKUu3EgWAdXjfn8/DyV8oxW8K3OWxmB/mVkZCQDlIOqC6pORGSUy8vLyUoJHByTrFFAnpqailarlYGR3iaiF9iNTa7OrQIEz+igyxwBCRdloRzR+JytPZcd3Q8+Ef27OgAZz3uf5agZqOdWIqylilre8Vyc3+XlZY4k58DYrmcCzqyDjJgzwcTc3Nxk+ycm4b6oUVAUwDyPkhpQxyFfXV3F/v7+wJmpQE55ynPWqbYcW+1Woerf2NjILNO6eybfudasfU49F0NDQykOruUG+1t1DvXFkVd9kgzZ7zpz7E79XrZrxHp10sqE1kUJwFmpwVSZWBCK6IEsF/ix6aOjowwOWCS2prX/fkbtO/jOBKMm0preOzIyktOeLy8vU4vCX7H7brebWjRgpwLtm5ubmJubi+Pj42TVKoiL6GsAgTS+BXhm80rIfM590OjMsj/nj1+ua12ZLz6xJitAdtWtSPScpcqkiB3+G/Dlk4GBTqcTP/VTP5XlHUxELbU5S/fLIzWp9TzK98CpOMIf1Sm/1q3b7Q019Pn+3owSdsynSHIkF3yDa1dev36dbF1lsAEcQCyizzjRKwEgYgSSoNlsxsHBQYIXw+MODg5+9sDuHH+kn/r/4xfnE9GfjsfYoGwLxdFURb2WMw7E5XhVcY2ZcBhqWYjBVsqYYwF8ONmI/iRaoMrvcvYYnBqQq2Gh7XR/yCQZLvbGAWTY29vbcXp6mu24sgu/ay0iItevlmg4NoCIIA0gQR0T3KnzovI5ARSfrMZ3tl91/kgNUgJiRGT9PaJ/148yH+EYexAc/bdyxNTU1IDGwVpcXl5m7b0O6PJd7J1/O+jYJt8HQKismGet4Nc6V/FlxGC3jfkMhIvmCtze3mbttwbLiMh999ycJntVcnQ+7HlEr6tF9la7FpQ4PV9d+6oN6nQ6A7oJnWXWmX4kIga+S7PZzCwSgKkC1aoFAWoEBvvg74Bu9iaoqdXbT/ZfWS0B0zAppSrPB0RcXl6mDsaLH6j6tFpGqlqjWnJy1uoQRCXfajPj4+MDycfNzU28/fbbGUS63V47s33gxyQ/Ef1ZHtgY99f4/FoeoXGosz/YEH+kxdTa+u58XE0kBG12XdnHOm9GEun/7ZMXcOXzAE9+1B7UVtm6d5V5tUdYhlo68zM+jz34XNohLDNWWkypyfHm5maCU3tuzdzXZNhfTW4lRJgfv0P/4RzTn7F/ySUgjz1rNpt5Mej19XWyVsPDw6ldUYJkt2IZ1lM88twYfH7BmvMdk5OTeY59v4/y+tgDlNrjbfGBEVNSoWdoUAC1oFCuf9fuCRvvdxk2yhfTQWvC0WBcdCvIljktP4NOvh+QBSWHJCLS4fgcB072AQQot8icqzjT4ZI9VHqwdvOg7TmHk5OTZJvc53J+fh5zc3O5VhH9KwHm5+cjItKg7UOz2YwvfvGLqbUxUE9mgz2xHhU8mjdQO3d8tp+vgmR/b99kpuyEnqbWiqtzMrE0IjILrJQ+lqfWjGVCasJ0TwINbQD2YnZ2doAijYh0bBH922eHh4dTc1TFxTJ87YnA0sjISAowaZ4qZcteBF5/3mg0slME4MEa1D2sWezJyUkmCpxaBeQCLYCBYfEd6j4T/tVSqosO7a39FwiAYgJme1O7Smr5zRkCggQfzyVpccYbjUbOnRAsPCN2QSJhbTn1iK9lBdkA31BZVEkFAOxz6Uasq9foaG9A1/j4eJbwPJfxARKW+0JF7d8uVLzPXAIdbAGovLu7y4CKcQIKDOLjJ/k958znA4AVKNUEr5am/L298r7YDjeWOxt8E3/Fdwr6ddx/9a+CMB/M/3lP39XPVn8wPT09oDXERClx+66e3zl3Xo05oF2q5ZjK2FfG0n03b968iYcPHyZ4rQwtGwHUsFQuFcUQTU5Oph9bWlpK5r1q3SqTXUXoyjpiq7UTZ61HZaA/6utjD1CazWYakI2v9C+AYfNr9ludWQUH/rxmtgI7FGmzbBDDFeg4b7S5zcHCqNc6iH4/okeVHhwcDBxQVGXtgqkUJMO3+YyZ0M6hN4gHS1G7ZQQmnUSEkT4vou9sBQe/bzgcB4sVajabmZmYYoupsY5+/vT0NGvIJycn6QRubm5SaFjp1sqWCWa+IwcksFrXiH7XUh18V+vHHAkWiiNRruDgqzNjW2j0qhlQG69CSt+LjfneAqTvxC7Zife3BpwX++KIiFjZmO8CoEX0x4lzaoC5IX6eD0jDLrBLgAa4qmeq3skBeLBBZzYikpan/yIOVTYZHh6Ozc3N3APnkS3Zs5GRkYEpxc6A711bn9kRnVOdEiyQAGLegx3SIBDgs1lnxV0y9tZZ8f91Xbx/9R+YWuvrbCj11NkogPB/+k//KfcBKzM9PR1bW1sJXADIWhazzsBwo9G7C8efGQlASOts8jf+ub6+jt3d3QSXQLsyQy2tS0iUBCVJ9s172ns+oAJN4Io9Vl2DM+b8SEQr4AGQsIc1wfKM/h9TVs+OUpEzZ09rOQurouQBuGNrnEH+QxOCP3fZrT0FCICxyk52Op0snWDrgeCI/iwimjHfga07kxi4qk+yh55FIqbLkC2bTcM+JO/OgvPhzGPzPuz1sdegyBw5FEGiagrOzs4SeVZqT5lDfb9mDicnJ1lHZqwOEefpwFXxZET/Rk0OSKDx/wJoROShI0L0vg6eAywDrA7W88n4q9Ld+1TldURkRuTQ+z01YahZuaaKH2UtjBYT4DNHR0fj5OQkFhYWYnNzM7NswaiKA29vb7MMIUO0/lUHVNuZ/bz9qPqhy8vLZFEEMwDNc2GklNK8anknol9LV0fnmDhubBt7qACqBu4a0NVp2RH2AGCpzq8yfXQrtX3bZ42Pj+fFcIKGDNGzzMzMZPC/D2Tq+nFabrsV/DAX9oTdCJ5uofX9gA+dc/eH5t0vtSgtOGccaERkScu6AxkAlfdx9irriTEUnJwZgQIrVSlna1bBK4cPGNzv6rF+ngEgxDpVMaeW93rdRkRfLyM418y/BubKDo6OjsbBwcFA+dWZNTjr+Pg49vb2sn2bD1Ie2N/fT7vAYNXkzTkihJ+ZmUlfY/9MhxUUK3j2qmxCs9nMmUCYJr6ulohl8N7L2lRRKrbWnvDhNcmoIK+2H7PVqqXjGyu7UjVKGF/XNDjT9rsGYz7EHrtm5D6DAmjyKZKdmgw7C8DO9fV1dlIquX/wwQcDJXPDQ4Gf4+PjPJu1bKaDTMziB2rSUtvUJZrAyu3tbfoY++kc1mpCfWYJ20d5fewZFA8vq7HRlVWAFqv4LmKwXY0hCQ7VOBhfRYAV3FSxVrPZn7sQ0a9fVwW8bM0mowYdUt/BJnJItc3YwVLf8/5VFMlp1OzWga8t0jplBCY/U0sZVeHPOWnt5BQcAJQvMGP91SU5QWCx6gJkJmNjY7lf1ul+Ka8+j/XDpnW73QSmDofPqboTGRfGifPy80pKdBMcBNvzTNazsiqjo6Op05GpAFHq71UjwR4FL4Hdi91xiFV5z4ZRrBUMY94E1G63mxlafY5aPkLHV4pdhic7FfgiImlcg8J07QjcWgudBQDL2rirBnBXG3fO7FsVyVbgjuEQXCqtbJ+VoqzV/XIdZozDlj1XhgTb41LMWtIAXr0Edt+Hzdb/9gyeTZC19jLyCuAxHd5D4Gg0GilIJ9J01oG109PT/O/6e7U7h73U0qlzyufRgdVSh78TcGuHCM0X/1Xbcvlbz6aRgA9lo3U/JTjOcT2zynhY3jop21rXhCNi8Mb5qqOosSEiBq4m4YsODw+Team/W9mJ+8xeLZ/wr84u38+f2X/22Wq1UmuysLCQz1HL/0CesyvRqZet1mSKXsf6Suo8f9X01A4s685H2+v7tikOW7f7Oryv9/rYA5SRkZGs/3nZpBoEa92e4/f/nBWDM08F6q7BpwIZKn8MAZDA+GqQqoe1lp7oABqNRuoRqriyMi/qz56HEXAyAhhDFggYoqw4op9tqj/K0qwZZzM6OppUr9KV9eK8BJI6b4NjrHVsg6HuBw5OgkGjNTnp2vrnzwRInSMyXUFXJlJFiPc/TzDhJGu5otqQPaggkfNiS9VxOvSEd76vtaiOQf1WNuh9jo+PBzI4TqPqKO7u7nJaMidcnx9I4wQxRxXAs2tZ0s7OzkCg8dyAHyfH8VQNk8yKo6s6FLaBEu92+yO5rcnu7m6eq1rHBzrevHkT29vbeb7qPtZ2eustQ8UA1SFVmFdrWtfZs1XmtIKfWlbjvIGzKtKsTCdHjlFSxvASOCQobAloYfcCh2Soagq8gHvMQgV2GAWJkjKr82GP2MjFxUXMzs6mQLbqKYaHh/PC0qoJ8d38v4xfgDLSv2r6PGcVx1pf5eiIfnlWuQuDbN8AQnvEhg4PDwfAoxd/PjTUH4pWtR+EqAC6i0/tH1DPb0g2qn1jiTEgtZTsz2sSWbV4fKjvwC6xNLUaAFT6TMnA6upqjIyMZPeWhExyJclxXnxPzBPWkcaIT9X9Z80xJvxxLY+pBIiFHxWgfOxLPBH9dk1Gis2oFBe9xPX1dTpI4OM+/Sco1bol8OLQcYACTFVIV2OrGb8gFBEDQAJSZWycYWVVfJagUEtUjLjVaiXlzqiOj4/TYXo/geP8/DxvtuRY7n933/Pw8HDAYAGHWp8VkLVKAxU1w/K7Dx48yAFjSkzeAwjwHP7fOthfWX9F55gfTreyQmyg0r81W6ifBdxVFgHQqaWdGtyq5qJmTdbHqP+IyMzbfgwN9S5Tq9Mma8kHdRzRC/i6pmgAZObHx8cJetlmBQvWeW9vLx0aG7SmAHMNxC40c6acM5msrFlLa9V7WfPNzc0BgffR0VGObLeeAgHdEhAP5NorzrrqxARJDh3lXbN2gdG+YhycjXpmsTgcK1/CuQpItWukMl80A9aWf6mMC7sZGRnJz6rAUUtvq9UaGP6ImavlWO3f/AEfVkGrGUTKFIAuG2HzvsPp6WnMzc1l0HdGnAlJS500bS3o3+xBPXsSIQADSPcdsAfWRylCkuLPx8fHU9tRyyQ+Q/kUyB4a6gvFa+xQjqtJVGWZrZ9zi53iE9ipEQ4VZFbtD5Dvu9T402w24/j4OJPPqtdRNvVv++xVheRKjSMjvenAtIUjIyPJKrtyAngdHe3fzaUM6dwrIUtEnf+IXjOE84ftMxnZmvk3P8EuPsrrY8+gQHwOFcOsIqaIPp1FV1HpNBRv1XAIXrVGzagjIh1YFUQ6wH5X9icbldk5pP7fhhuOQ/HO+XiuqitxsASNOppeIK+Ds2og8zuex8TYSs1x/J7boZcdMzyOu9FoRKvVyt/htGrQJuC7vb1NxbgW5Qq46v7YDwG2fragDLx5zoh+G2ItH1V6HeOkHVzA9bsVsACSMnFA0/fFeNkTAQ9bw74wURVE1YwDKCByxDbU7qsa6LBht7e9dmMBTLaK7WBr9Z4TpUjBX2bP3rxoRjyn0hSwz3kBnTV436dyOW3nQvDkMCNiIEBW1sXLvnomAN69MzJY7FfVBtV2Td+FzQiYMljg2ntNTU3F/Px8LC4uxtLSUiwtLcXy8nLMzc1Fq9VKlkHQ9RyCMkBinoszgdqvOp9ms5nrLJmqAF+w48usizNOL1Pr/sSvleFwGapAXgNqpfHNPKlnwh4pJfCB1kuA9f7Hx8fpC819qVc1VFbPefe+fJr1tIcGQtYyg73sdHrdYpoCqo6F/3V+79trLYko71amGFtcS8bW2vvUJMYeYGDd1M1veX7JCHBeEw2Ml4SUv/ZMRg+wE3s9PDwcGxsbCa4Be4mIl+9ibfkH+15LZACjZKKWxWvsrEyytTav6mdis36m18ceoAjWaDG1aS+HhDPm+GrwR++hGdF8DgdnCJRoU7MxnIJ/IvqdJDbKoVcHhphrLb1S/7WWWDUUlNaMwCG6ubnJW1kdOgJVwETd3FpxqGNjYznOPKIvOqvvJSOu7bOQOZo+oj/62EH1fWdmZmJycjJLEgSTKN9aelBbrWJBAMD3rgG7rhuneXR0FFtbW3noATzf3UEVwOse2bfKvFQBnu/KRqqmgROqAdBnYXkAoqGhoVhZWUkQpFPCnsm+PZvPrNQ4R0RALLuVgbP76enpmJqaGrjxuCrzZXZAQ22Vv7m5ifn5+fz5+vzVSQvOgl2ddaJMUcF2RA8cc4D2VScLoFcDtHPrrGF2/Ix14BMA0poM2GN7Yx0FUMJldq+UOzMzk//wF8o6uh4ETOsDILHVnZ2dLDdUpreCNODCc87NzQ3Mn/Fep6en2SIMCAOz9lNgYb9GCWxubqa/BFAruJdcSECs9/Dw8NeAH2BKRs7fsqta7qQFtGfWuQKUmjQKyoKrcyrIsp37Nn92dpZt217Eyb5jBUdsVCmrMqxsTwKDbaLzU76QADkzkiC6u1qS50uq7TWb/TkxnlPpVTKgGcAoCf6g3W4P6A/rXU7V59MsAstAlxvJ5+fns4OyAjx7AVz43oAve5aEVpbLcxIJV2D4Ya+PPUCpgKC2EnPCtX5bKX3OpGoCBJ9aS4uIpKoFGE7W70QMagQYHwd63yEySNm4n5dtGoXvcNVyQWVeWq1WBgldE5C4nxkdHc0yTqX6asblszm8+1kNpF7LW5wkunN8fDyFezVYeW4HnFM+PDyMq6urODs7i3a7PTCd08/Uro/6TN5P10BdSyzYzxSAgEtZ2MXFRc7K4WAqHe5ZlWcqXX6fnbpfNjJngDOzxzVTkq0bchQxePmlIGXd/Vn9LFmTeRocgSzb87HP+fn5gXkYVXfk/dm8z8QkoNk5ReUWtDZgXZmRqqmIiATBtaTAAds7tWtlD07ePlXnVrVavr8OhypUBAIqA8fe6p8DMMAx/1HPKJaG+Jc9Kv9w4vwFRlNGXLUDHD8f499HR0fRaDTi6OhoIPvF3vEVbI8tCtzWCjhh/8qvd3d3sbOzkwHec9byF+A8Nzc3UAKsz+hzK/NVg7egCRgIZPc1dpWdcJarvTuP9pzdSUDr2as6FPNWPJu9Zx/Wig/F9AikfE4t07OFClqsGSDJ51fdIH+yt7c3YMOSZme0AjC2wl8B3s63Zwf07ksGKltfS16VTbHWmFt+pf6ec+qZfWZlU4Gp6qOshX2pMeOjvD72GpQ6/MeCo6qADYGiZvkyRMJWDiYiEvnWWnv9vZqNocQ4DwapTe7+Z99HykRnnEZlDBhRbTXmjKqjsPmMbGRkJGuZ9B0cv8DNeHx+zfocjvq5HNDw8HA+o4yuBjl/bs0YMyZA8GLYNYuCrDudThwcHAwcDOvO2WERBNkqhqwMjoyxajkEj4gYqEdXx+YfGZRnrM9UxWLeFyNVASfn4z04kIiIra2tLM/5TGDT+rOPynr4fyJAYKAyfxH90fLKRcpKtUQ4NjYWp6enKQistXcBRksrR1mfH3gWrK2jAVbtdjsiepQ0gWul260xh15FdHd3d9n94739PnBetWIRfVBah0YJ2LUMAJywRWdcIPTfzqJzXrN1Ad6aAXURg23PWA3ArZZRfFcBVDCkEyGkHBvrTRqt3UF+p4ItItQK7P9/7Z17jNRX+f+fnZllr7Mze0HY0qVIi2jBaIOiRaDljxKsqaCxStWmXlIboiYNrcFqE62aIG2TorE2kfpP1aCxSZtU1NZEMIqFJnRToBdsRZct0rLL7uzs7LKX2Tm/PzavM+/P7LLM+vvK7rbnSciys+fzmfM557m8n8t5Psgz68rL2nQORDwqK8ebMXIfolgq+wBXjQQpCCAFMjAw4AGCypDqM9ayUBjvbcSJrurqav9iRPhMU6HoVZ4LA4kDimOVzWYjUVH2FZlhLQFO8AR6I5/PW1NTkz/ZhSzDU4VCwcss+0z00Tnn+6TAK+gArSdEhpF11kpfr6LgAIDCmmtUn1oPTlFSi8Jcz54963uWKO92d3fbO97xDs+76AdS8qU1U9ocT9dEwVUikfCgTffmbZPiUeOhTXlgMsZghFhYFtHMIqE9kB+boUpMPRX1iNXoERrVuSlw0NwcCpY58X0qhCiP0ugN76MpVQwoZULddXV11tPTEzkWyLw4YQSjEq2gERQMSloARQ5AqKwc7wOj7wXhOVGohDZhUAClerYYdFUAmrckxaL1DYSamQeeEWFI0lcQYxA8bX2PAtQICd4T4UuArNm48OZyuUh9C7wEWIzFYr6rLAZIDWt/f7/19fX5EwZcC3DC+AAyNZyrnhbPjbLW1Av7QEjdbPwkleaZ+cnY0rfq4kVRE8M8eW6u13wzRh2lRWEuRkLTLvByabdOvhdnA54lpM4/ZI1nzmaz3iDxNzOLAGjVD6yz8gdRGI0w6tqjtPUoKHtCylDvCx/pqQ411hg1+ERfkwEo0MJL5UktIIcPicyh2zCkpAhICWvRPEaUyC17qFEzZEQBGMWwOFPsOdfjNGAEMb5a71coFGuFkD+VWZwnjXiwFoBJZEB5gWZsZuYjOmbFwliuhTcAJcyD7q6antSUq0a8NLrLvsEvRA0BxSo37JPqIkAfxH6g/+hzhHwq+IVPFchjBwDHZuYLcNEbCxcuNOdcpJsxfE8EHr1NlEZr1tRRxVFTRwBgwrzeNn1QWFCaUSnCLBVcPJdYLBbpGKhePMAFQSAyArNpQSTAA8KDVY8DDxSmraio8AoNz8wsmgIxM4+ENSSNkKpnhPdOGoL3HWDcEHCehxAepwC0UA7Fh/EA/SqYKy20AhTBgMwbwQQtU5CKV4AHQaQK5Y3iQREgiBhJgBMRLBQhjYkU1BEOZe20FgFgyX6inAjjquJnj1BsWmAMoFMFp/dGODUvi2JijoSVzYpRJP4P72D48PBIQXAd+6TpGBQmcySSoHVMABheWTAwMOA9dzPzz6MhdA2To3CGh4e9FwkYyefz1t3d7WsUSD1hjEk16jXaMZXnx2hqhFND9MiRpgM016+nGhSADg8P+06wyAffhzFlnAIy6kV0nVHEKtcALgUsZsXTIBrRBMAAjhoaGiLhcnhJO0Ur/5Pqo0cLssdYdZIUKGN44CXqEUjfEYFNpVK+twqFu/l83hYsWODBBu/0Ib3JGPZInQ4MK3qZz4aHh625udmGh4e9A8Y+aiRB1xl5wVlCj/GvsrLSGhsbPb8xB00bah0Fp7HgC6KagGnKA9BdGlHF0cQu8JwK+uD/0vob9CmE3YLf2UNOyRCFo28SfMbpLMApTQIrKiq8TdGaIxwAdBHtLgAxRGQUpKN/OfnEfNAbOMj0CsI+EHVT52EqmvMAhc3W3DmLSfgORjQrduSDmc3M5+pR6GbFRlsoQRgAQw3jwbQcOwMwodT01AgeB5sJIif6ggDCHChMPQIJSCHMPjY2fmoDw0HenmgOXgPH0yhSQ7gymUwEcXO0N5vNeuWBAgHglAplZWVlpAdK6QkcNWgYKS3e1QJAvKbq6movVCgOBBRjh4HEiKPsabSFwFLgyH3V6MIv8IgCXISQdUe4MCbqnaKcACMAFooM4aXSOhLqpjBmGGUFini55PbHxsYiXpSZ+fC/pip4BhqN9fb2+r4xeOUoCpQnClgjMYlEwh9f5hqUHjKILJBaqK6u9uFleBDlDyBEYQ0ODvpmVwr2IOTYzCJyoKlNviORSET2WKNjvAMIZV9a14AMYaSRPXUSNL3DPqNbEomEL85k/tTSYKT0J0AFXcUc6NGCR8vpOgAEP5k7z8paAeyRaWp6AFFEaNUh0iPMGD5kATlg7XBqNAqmUV8iJOg3QIruK7oQvUjUE09fUyiqQ0qja6TFcTQhvg9HiGgIoI/x6Ez0G/oLGYTn1MagT1XvIi+sN2uL/uHZeSswcoTOpT4Ix7IUgCOzyIzWIOlPLQLmlJeeXMXmUXgNz5w8edIXxrIvZuZ7ndCbiFQxvA9/A8y4lj1qaGiwVCrleQwnQYHYVPSWASgKHPRUhTbrgqk1RKhH5zD8CHapQGFUQOTk8hEimBrlZzYx3AgYwCBrDh8kz/1gXj3WR8iN7ys1ilRVMxc8q0KhYAsXLvRGoKenx+LxuC+U0/wgAqNzIjKE0mI9WRuUqVnU82dt6diqKS6MJ9cCKlB+GDk+4x02GA2+C4WKJwdYQGGgBNW4UM3e1NQU8cjNikeataIfJaohTuarjZo0RKshbvhR61b43lhs/FipAlc8cEhfWaB8g9HBO4SXNB3IPemGSpGhpj14lwZeOl6ynrLh3gCQRCJhLS0t/lQA+w7wwlh2d3dP8Jx5bpQxni5yghIEAANA9ZQV8q6yh+PBsWNkUMGMWTHtyx6QziDsHYvFJpzK4zq8VOaKgUYWNdrEnpAKQTb1ODRgDV7p6+uz7u5ub0BYe4ALJ7wADqwpnjVzK404oBvr6+u9LKn8qKGmr0l1dbXvwksXUXTUvHnz7M0337RkMunlRgEKoAte4/7wKkZU0365XM66urr8mqKn1SGEt7TOAVkojVyPjo53veWt3+rQsv6MZ70A/xoVQN454aWRV0CKAib4BjlhjeFD1gMdhq7UaBYAtLScAB2k+6ARUo08s4bIcH9/vy1evDhic4gysyecjGMuPBcOr6bszczXOGJnSnWvtsqHn8stkp3zAEXROR7/2NiYPypLUSDGg5CXhvlLPQoNy2k1PgZQvU4z8/0VCP0iAFospOkZs2J3ShgWxgJAwHjkjREAvFsNY+MV8wyM0UZJCPbIyIivJ4jFYj5iAnqHoRAg+hGYRXtvcG+8DOZHCkbTYwiohtFZA4AaOX9da+0PoR6Jhn1Li28RCowlykXBaWkEDR7RWgq859K6Ib6XOfIcZsViWMbxDIxRY8ozoKyISugxaH02jCEpKMA3imx0dNS3ciecrMW6KHoMBspWa6ZaWlp89IxnZF3z+bz3xKhdophRAT6girQS66GAk0ge/+A9TpkRVkYJI2vwPJEufkI8H8YXI8b+Y6xY/1K9QbpS+ZnrAGClwBCvmiii1h8B9lHkKl/ahAyDo2ld9gvAqHuvHqqe7lD9B08pCCgUCpEj3BhElSkinBgo5P38+fO+YyztCtBXpH1Lix81PQE4YC1UP2Ko0VEaXUPu0dXoZvYBvitNPcPTGnlSHaJOLXpLi5h5NuRIwQ/Xqj1gjugSdYRUPvRUGHxNOo4UK/NGV/BcgEPAQV1dnX9ueIE1AQRimwAz3Ic0Hp/zDLyxW1NyVVVVkZdYaj0edg0bpLUx2AlNKcNzKrdT0ZwHKGpkNKeuqNSsGFlBMFB8nKZBiaLM1SiiHMyK7y3JZrORNr6gcAU5GEr+rykPPFSUKs/AnPVoKUVLFRXjOVpeLEjUAaOMskex8pPiYY4BqxFnnfjcbLwIC0+MZ9bUlNZ58LsajVwuFznRkc/nff0Jc8HgEWKlcZvW/GBIEUoYHAPMPgGUNNqDQuLZAJPcGxBL6gJPnYJpVXrsHd+NIVCjqYaLOVHLQii/NGLHd9XX10fAGH/XvC88gtKprq6OHAXEmKkS5v4acWKfUFxm48anpaXFGxKtu0BBU5dSXV1tLS0tfq85Lo6RQU6QDfYJAEjqAuAKgObZoFwu5/kOJQqPwHOaelGnAEVcV1fnC/NYF/UyS/kWnkEulW9Y39KUMmAEXmc+Cn7UQQE48x3qtADI6uvrfV+VUl3FXDTihCzi9PDcGHxkDb1IQ0gAI2O1t4YaIXRbPp/3r1QACNHplrSYFstqVIM14Xm0DkOjLXjt1NJxP5w4jCD7DCBkXQCARF75jPXV1Dr9Ukr7ltDbhv1AzjWajiyTElXbgM7WaImeltK10UgwNDY2Zs3NzX6PNPKEndA1hQ9Jb9L9lfVBD9XX11s6nbbTp0974KuZArr5Mkf4kzUdGxuLFIHDM+w/ehl9p5FpnnlkZMQymYx1dXVZOTTnAQrMiXIhp5tMJn2dA94yi45xIKTNxpsVma+mpsaH3WEirkNBlHoDfBdvLdUiRS3Y4rNSBaYhTNJHhOQx7Ap+tPCOOakQEjFRgTErNoJSJYTxYa1Q4NSVaIEexkbzpQgcb8FEqer+aCoMpQy65ydK3Mx8nxfyuAg/v5sVaye4HgGqrKz0x2rz+bzvmaARLBSRFtThLSu4YzwKSKMkGAXWA8PLM5emG/TFkKQReC6MqSors2JTOy30xNsH+HE9PAvg5Dq8KjOL9ILBMxoYGPB1WKwpPEXKBy8xn8/7dunkuDXszJpQswMYwkMDWLCPRGxIATI3eI/v1XSGplLgf/YIkKqpDYCBghhAPoqYaCEeMAARZQ8AVABJBEH5XPUR68lzMT99RxPAgXXjGZkjESEiGoDPUi9b6zD0VCK8hg5jPVk/jRhrXUw8HrfW1lYbHR31US2cwNKUBMYKftUUCLoKHkWOzIrpC+ZAxAYQBwBkPWpray2dTkciGXrSxsz8cwMozYovlWWvifggK6pDmBMAEH2pvEfjQ/ZTT8QQ6TIzH/2G/1lnmtaVOkIaoWXtmJ/enzQePAmYKQXW9fX13vZxOlPfOs290S2sJ3xExJV90WhfPB63dDodSZ3D6wBydAI2CCDD/S9Gcx6gAB4wtFpZrb0TyN2q4JhZJG2gdRZ4aii20rw3zF9bW2stLS1emZoVX59uZr6ug/crgKopJGUjE4mEz20iBGwm5/VRjMyX2geeB0CC4OHtmlkkfKjeYGlYGHBWeuST5nE8P3UqWvGvEQyeqbq62nvfmuYA8OARadGrom0VXA3NkyPWhmmATzUSCJi+RI60jwJCPGg1Hni36lmjMPlMgSX7wB5haAGOKCu8R76PvC5rz/2ZI+lDFChhWiJIRBjUS2Mf8JxqamosnU5HPCS83kKhYF1dXd4DRklSsBiLjb8jBK/07Nmzfk2JjjAHFHAsFvNdg3kFAvdEbinmhnfYC00bUYSHXMC3pZENVeasMXup7QfojKkRJF0zgAAGTkP+8ClrQuSEa+B7faUDBoC9hF9HR0d98eDIyHivEwq5idASIXXOeUPT2Njo9QvpEQxkacoH+dEILbLH/Hp7e/0JSKK6RLkAE+po9fb2ep7juZgzuk9rJQB4FKprfYhex/7yXdlsNhLN0/SGRh/0JbGa9qJ2ChnVdA5yp5+hi4eHh/0ek3JkHPwAeKiqGn/3laZQcGLVkUA/YBPgL9KY8LKmNLPZrJd7nCn4CZ2oEWX4lGtwHqlPISVLJJf6IuZJeo+9ZZ4AlsrKSl+bosCD6DjRcK7jmQcHBy2bzfomoJo+KofmfKM20hrawZJNw5sDbWPgFIWymOpVxOPRY1h41aBkFDhCCHBBkSFQeC0IkeYPmY8WjKHgEQCUbzKZ9G3sNS/L/M3Mo33qFUCpyvyEesm5wojaaRIjz0vcABCsJYpLAZtZ8d1EPCuAghA+e6BpGYwGIAnGR4HhySFEpOPoc4GyZk8VxJgVDQ/eJs8C6FMhQUkRPmU/tKmZKicUsYbrFYSggAFPXIcHDK9hKDDGKA0aK7Gv9fX1du7cOT8Wr47j1ShA9tnMIh1qafDFu5RQ9BhAeIu90nA/a8UpAIq34W2ARjqd9kdM1XCyN2bmeRDS9AtzqKqq8vUcKNrBwcEJx0KRBXUwNBULf7OP7Ad7pZEovGY8PfYGXuB+fI86LRq5IaKo0RuaBcLHaqiI4OHhYgA0jcpJsOHhYUun077YGe8VQMT+a8po0aJFnufQF6R0VBdhhAG0qVTK+vv77ezZs2Y2/rZkDBUAHj2ALmM/eXatcVCQoAWjGGgiSQqsFeQjuxRqci+zYppfI6ysA/pcU0M4TzhBqvc1LaF8RaRDjTn8T+SBsXqvWCzmT3UNDw9HDDlroQ6PWbTtP/pIwSbzJ0pTW1sbAR7oIABUMpm0uro632KCvjdE/DmdBZBhDzSSiDxgE5g383LO+RcRjowUX0FBTQrriT7SNZiK5nwEhYXCq2JRFR1jHFAcWggHMzGW9+SA8mAWDVsCUKj3UAStSpGUAYABhaM5UU0TIbQ63qyI4DFCWoRVGuaGATXMicLiHigQDQlrmBdFixBzv7GxMW+E8vm8DyMXCgX/GnLt+zA2NuY/x8g1NTVFQsPNzc3eUyNdoEWtKAeiSHqcFCPGs2uYXIGEejM8J9ewRwi9enUo38kKGpkv+8E8uSepHgAHwEAjMzyn1mJoMSw/WWOMH6AEo68GnuJCDC3RJuaBAWL/9dUQrAlKG2XE/hOF1LQBhbDsGwZQnzWTyXge5/mQLQUCPCftuFljLdRWEI8SJjKCt6mKUSMsQ0NDlslkfDoTnsAQxuNxS6VS3vCqwef7+A6N7LFXgCn2A8PPZ5piYH2RkaqqKsvlcl4HYDwBExh+AKJGRNF7rENjY2Ok9olol1mxs7CCep0vfEKEkj2DD+EF5FwLfnk+TaMh22YW0Z2aCuAf0TL2Qx0E/q+pN6JFygeM4Rrkg/oKDCR8zvdotIy9xZHRpo7wN/ICT5qZjw6gS4iUM2ctqgaQaUSBlKc6g0Q1AALclwaOyrukalgf6kASiYR1dXX5/aTPjurVfD4fqddSR1/TrNhNeEZlhZOavLqE01BazgAflUtzPoKSSqV8NERPDaDsNOWgitSs+HZVvBANoZmZN2REaIikaNW5hvxBsYo42WgNNfJThQImMCsCJ9oL473yDAAYmmuVRosGBgb8UTwMIPdGcROe534oXTy6rq4ur5CZk7Y6Z63Mxs/UDwwMWG1trRconh1vSfuvaHQI44PQJZNJ6+rqslQq5U8N4GFVVVX5F9DpaYdMJhOp4wFMcY0KnTZIg08UqGn0i73DqJhZJPypBos1QtHw3PAR68xpJY1EFAoFH/1SY4QyQiEACvF6+E7WG8MM/+lpMOZPjQ4RQfgfDx0Z4bQNBlybNqGQ4L3m5mZvbOPxuP89Ho9bNpuN8CKyw/NWVVVNOLqI99bc3GynT5/2ERSiIJonhwcBCaU5cjXIZlGwj5yxRyh6DIXKFnUO1BNR16NpB8YTnTCzyAkQ7q/Hx+EtQK3qJa0f0/Qz/E8NEmtPNJY5NDQ0WE9Pj42MjFhLS4tPXZSCf4waoDKbzVoymfRr09jY6L358+fP+9QU+qR0XdkPorU4kABYdC3ggP3i+traWuvp6Ym8MJXIA/VTFOuqvtSoODIBoIUvGKfF0mYWWTfAKzzKHnEUnOu4B7KssqKRB605A6BxH74Tg6+vPuGZtS4Jx0LTVABZ5Bddg2OiDeFw8HiRIDwAzxPFow6Rdayrq4sc3CAy1NjY6B0XnFnt7cP+sM44tfDNxWjGIijPPvusffKTn7RVq1bZ1q1b7fjx4//VfRAMUC5IUHPHCB9RltKXeeE9qEfJ4ppF36SLkqA4DmBCMasKNoKjQotB0AgPjI6hBGSh5EDCZtF36PD9NHLiGevr6yNV1KVvC9aUl4YX8/nia8TJGcJQ5ECVgc3MRwbwZABd9GxAoaNMMYzMBw+VEydm5lMMAMXGxkaP9vGGUQYARlITGEn2DMHBM4TUg9TICSmuyXK8AFT1BEZHR31uVfPNWpwN/wGO4TfAEcaPNcVb1NQL17N/yj8YFhR0ZWWxcyZ7CC/Buw0NDZEcOABZT5LhLaZSKV9zFIvFfC0BP1k3FC3ygCcFv5R679R0lEYroVwuZ/X19d6IkV7UiBjPDV+aFR0K9IL2N1HwqGkC9oKiZtabJozUzqizoWkYSKOfY2NjvqYB2TWLdmM2M78G8B3rjJOlHq7yA2vP/bg3TRwxLoAtDAWt9EdGxt9oixxUVVX5o+oAA01pMQ7Qxn6qTHMt/AIPaUE9NUGAKOS6tbU14sQhQ8gVOgoZQD61xpC91JSxRgKwC9QP6okY9AV8xDprGplID/eDt50rvpJD+VBlqTRarj1S4HEtFNeIInsNWAOQwjPoTN07Td2hOzh5pFkFdCoRcmSZDAE2Et3Fd7AWONuaJdAsA2utJRIK0qeiGQEo7e3ttmHDBrvqqqts9+7dVl9fb2vXrrV///vf076XImYYj6iDMoSZ+TwviFGBAZugiBH0TTjerJjnhGm1QY4W/HFPzVVrtbbeS712FKqGxXgOWhADUhB0XqcNgNAUBsLOEU0YS3OHePSEk/kOaiH0FBTvVWEuo6Oj/iQIHgBh0YqKCg/cKioqvBFGCSP82Ww2Urg4b948y+Vy3qNEQOPxuH/NPcpLjUGpkQOQEk0gZIthQFEQmmSeTU1Nlkql/J4qT2i6hzXWUywoeuZBSoP9Q+gBQOqdI9x40ygZeATjgicJSMA44dUrwCpNq+TzeVu0aJEvMGY+gCIzi4SZUWSsBSDNzLyyBBy0tLTY0NCQ9fT0eMNMweLQ0JDlcjl/rJN7qlyxJ+wjPMAL6yhA1HCxpgvYFwyrWdS75qfKha6ZygeyzJxo4W5mPl3AEV1Nd7BfWiNDBIk0BGuDPqDxIsaD/db5s94UtGoRPqepkFHWgXYBlZWVPqpKV2HqMYgW8J1EClgjjeCiWwDc3FN5l/VAzjQSyHUK6OBJ0gu8OBN9BoCHD9FjKtNa76cASU/WaH0L6wrf8azU8uiL9+AbjD6GFT1N5MGsCDQh1kr/NjIyYj09PT66y3zr6up8LQjzZ/3Rp+gPwDU/4/F4pKdROp02syJQYw+IkrKm8GtDQ0MEdCITjGUvNOWsJx21mJrxrBkOD7oqnU77wEE5NCMA5Qc/+IFde+21dv/999u6detsz549tmDBAnvwwQenfS/SBxreUuMBmjMzv7ggY5Q2YACFDDDRanaiA6BN7Y6neVQ+07wtXghMBzBBmSijwdRq9Nra2iJvvKUTJCFAwowoVvKdFC7CvM3NzV74WQ8EleOq3IO0kx5Jjcfj1tvb6z0pPGDqEvL5vA8dIvTqnWQyGc/oKjB4p3hBRHZYw2w2G2mDjmHjOTVMynqoYJjZBIONx4MAc18NqbNGsVjMewLwAyAP4Kn1IHh0ZsWeBZqHBmCSAwegUqeD8kW5AqoxOIAls+IRdu6Pd0JUh+JxwJWCNwAxChHFoulETY2wDgoy2X8MH+/eALz39vb6xl40aCuNtsELgG1NPwCuKNAsNYpEWKqqqjx4Ru7U21fAAa9o6lSjZYzRFDBrzHXZbNa/Qwle0NNYWgxYV1fnjRj1WNQIoCvgW+Ra09RE/rT7K+A1n89bQ0OD50WiI6TEOB1XWshJczCcnNHR8feZqZ4EsDrnLJ1Oe0dNT+SRkiB1id4sFAoTImzoBbxus/FUA9cAeDX9QoQBOdA0NnoCGVRnANlQXUCKkr+hb/ipBf2aSqFfioJPQLJGC7TmkL/xDJyIw9mj5gigAYBB32gkhr0fHBy0lpYWGx0d9RFtrY8CbJCanT9/fqRODzugKXD0Ic8GcGV/cQrMLPI5+8naEQFnvYm647BqShrnvxyaEYCyf/9+u/HGG/3vFRUVduONN9r+/funfS8MjVmxvwMGAUMIoQQ0NMn1CIyG0tRowayaFsKYariNkKq2D2Y+WtvCRuNhka7gM5gUw6RRHmo58Ax4dsYQGdAIBNEGPAKQu6ajeH7niqd8mpqa/CkC0kqsaX19vW9zjTdJDhvDwJqjwFDO6hXV1NRYMpmMpJF03dSTwdtCWHgG7qWGH+Wv4zAc2pRIIx7xeNxyuZxvPmRmfs1LUxR65FTzxPA0yo4xhcJ4G2n2QWtflJ/UqwOwwGNcx9FUvHAAG8Ynnx9/QylAnLRBbW2tB5FaR8RaNDc3R8LcfM9kdRZExFDeb7zxhge1Z8+etWw26yMmAA+N2DnnLJVKeTAByDAr1vqQP6+urvYAiLXScSrzmkLl+fCgNU2INwwogefoFQEwwdCgrFlTTU+VRl8bGhq8ceJZ0TcYfwwbgM7M/FvHeSbmrOCb9BweuEYjAcHsHXKCAeUa0r46bwDo2NhY5Kjy2NiY1y+6nhhcvov7A0zgGU2Rw7/oP40WIHvsBQ4aYB/dAKhAJ3M/UgmkTLiG7yI6SS0F689YjDa6RPUFupW5AEzRHeqMqkPLyUNKB/hu9DdGXddvZGTEO2U4xehiIvSxWMzXX+qhCQXTgGItC6ioqIi8Z43onr6vDL2ALRgYGPBgirYaOPnwJvPEeWhpabH6+nqrqqqydDrt93Oyt1ZPRZe8SJaXlrW2tkY+b21ttc7OzgteB8KGQPtspgobRo3wpeZ8ETQWifwujKwGwMy84JpZRFnhyWiODg/IzPyxUT3CqIzI96vhp/iRMDpKoKenx38v6RAz84oE451KpbyXCkNkMhnv9WYyGYvFYr4AVqMfhULBH4cDEQ8ODtq8efNsYGDAeza65ij7eDxu/f393vhQBAXIQfBoyIWnqSctODpcWvALgGHvC4WCV2LcQ48dsqcYGjorAkDYS7wv/U4Em+fAOBAZwgOjAI214DqMGOvPfAqFgu/rQEh0cHDQGhoavPFmzRSIwveAx4aGhkjLce7X0NDgn4k3wPI3FAseNMVueNgcPQZkIA8o/Z6eHmtoaPCeGTyUyWQiUUgMNp44pz1Q9mbFImIiIhhG5oI3x3UaLicixP7xd2psUP7IMs/Ac8An6AWt32DtMMZcMzo66k+h6RFVwB6NDZEhfYU8EUCcnIqKClu+fLmdPn3ap0oUWCr4w6PWk12M1agtKQ54EDBE5IiiR6JiKgODg4OWSqX8fmM40V3MkevhEYxvX1+fJRIJD+SRZXgD3QnvArCJlqD7mD9gN5vN+jUk5aU6Vh04XS8AAnZC689ISWj9kKYBNZXL/bEZGFfkSOtTampqvDOAXiNdg4wBrAuFQiT93tfXZ/X19d6OoXeZB3NHX4yOjtq5c+c8cNSom0be9eWvXV1dPl1H92CVV/YjnU5bLpfzzRoZB4B/73vfa88995xfUwVaADnkEwCPLGoaCJupp8pYjwvRJQcoKJ3SIhmQ3IVo586ddt999034/NFHH/2/nWCgQIECBQoU6H9O/f39kVNhpXTJAQr9Cs6dOxf5/Ny5c77r6WR0zz332Pbt2/3vmUzGrrjiCjt16tSUDxjof0/ZbNba2tqss7PTe/OBZo7CfsweCnsxeyjsxewh58Z7jl122WVTjrvkACUWi9n73/9+O3z4sG3bts1//ve//91WrVp1wevIbZVSKpUKzDZLiFRAoNlBYT9mD4W9mD0U9mJ2UDmBhRkpkr3jjjvs8ccftyNHjpiZ2TPPPGMHDhywr3zlKzMxnUCBAgUKFCjQLKMZ6ST7pS99yU6cOGEf+chHrLm52Xp7e23nzp2Rkz2BAgUKFChQoLcvzVir+127dtm9995rb7zxhi1atMgfiSqXqqqq7Dvf+c6kaZ9Al5bCXswuCvsxeyjsxeyhsBdzjyrcxc75BAoUKFCgQIECXWKa828zDhQoUKBAgQK99SgAlECBAgUKFCjQrKMAUAIFChQoUKBAs45mrEj2/4eGh4ft4MGDlsvlbPXq1bZw4cKZntJblo4ePWovvfSSXX/99ZOus3POnnvuOTtz5oxdffXV9q53veu/GhNoajp//ry1t7dbX1+frVixwhYvXjzpuJdfftleeeUVa2trs1WrVkXeHDudMYGmphMnTtiJEydswYIF9oEPfMC3dFd6/fXX7ciRI5ZOp23NmjWTviCtnDGByqODBw9aZ2enbdq0yb/RF8pkMnbw4EGLx+O2du1a/86n6Y4JdInJzTF69dVX3ZIlS9zy5cvd+vXrXW1trfv5z38+09N6y9GBAwfcmjVr3LJly5yZuT/84Q8TxmSzWbdu3TrX2trqbrjhBldXV+fuvPPOaY8JNDU9/PDDbvHixW7NmjVu06ZNrra21n3ta1+LjCkUCu722293yWTSbdy40c2fP9/dcMMNbnBwcFpjAk1NJ0+edOvXr3crVqxwH//4x93SpUvdkiVL3LFjxyLjdu/e7WpqatyGDRvc0qVL3bvf/W73+uuvT3tMoPLo+eefd8lk0pmZa29vj/zt6aefdqlUyn3oQx9y11xzjWtubnZ/+9vfpj0m0KWnOQdQrrvuOrdx40aXz+edc8498sgjbt68ea6jo2OGZ/bWoqeeesr99a9/dV1dXRcEKHfddZd75zvf6c6dO+ecc+7w4cMuFou53/3ud9MaE2hqeuyxx1x3d7f//fnnn3eJRML95je/8Z/t3bvXVVVVeUN55swZt3DhQvfd7353WmMCTU1Hjx51R44c8b+PjY25DRs2uI0bN/rPjh8/7mKxmPvtb3/rnHNuaGjIrV692m3ZsmVaYwKVR/39/W758uVu165dEwDKwMCAmz9/vtuxY4f/7Mtf/rJbsmSJGx0dLXtMoJmhOQVQOjs7nZm5ffv2+c9GRkZcOp12Dz744AzO7K1LUwGUBQsWuPvuuy/y2fr1693WrVunNSbQ9GnZsmXunnvu8b9/7GMfczfddFNkzPbt291VV101rTGBpk+33367u/baa/3v3/72t11bW1tkzGOPPebi8bjLZDJljwlUHt16663uzjvvdO3t7RMAyhNPPOEqKircmTNn/GcvvfSSMzN34MCBsscEmhmaU0Wyx44dMzOzlStX+s8qKytt+fLl/m+BLg11dXXZm2++GdkLs/FXc7MX5YwJNH06efKk/etf/4qs67FjxyZd59dee82/2rycMYHKo3379tkvf/lL+8Y3vmF//OMf7f777/d/u9A6j42N2csvv1z2mEAXp1/84hfW3t5uP/zhDyf9+7Fjx6ylpSVSP/ee97zHKisrvQ4qZ0ygmaE5VSTb19dnZmZNTU2Rz5ubmy2TyczAjN6+VM5ehP36v6ehoSH73Oc+Z+973/vs5ptv9p/39fVNus78raampqwxgcqjP/3pT9bR0WHt7e129dVXR97K2tfXZ5dffnlkPOussnGxMYGmpldffdW2b99uf/7zny/YHXYynjcza2xsjOzFxcYEmhmaUwAFJszlcpEK61wuZ62trTM1rbcl6V4o5XI5q66uLntMoPJpZGTEPvWpT1l3d7f95S9/iZz4qKqqmnSdzSyyHxcbE6g82r17t5mZ5fN5+/SnP22f+MQn7IUXXjCzsBeXir7+9a/b6tWr7cUXX7QXX3zROjo6zMzs6aeftqGhIfvwhz886TqbTdRTFxsTaGZoTqV4rrzySjMzO3XqVOTzU6dO2dKlS2diSm9buuyyy6y6unrCXnR0dPi9KGdMoPJoZGTEbr75ZnvllVds//79EY/dbFw2JlvnxsZGf+SynDGBpkeJRMJuueUWO3r0qGWzWTO78Dqbmef7csYEmppWrVplyWTSnnzySXvyySdt//79Zma2f/9+O3LkiJmNr3NXV5cNDQ3567q7u21wcDCyFxcbE2iGaKaLYKZDhULBtbW1ubvvvtt/dujQIWdm4UjY/4imKpLdvHmzu/766/3vvb29LplMut27d09rTKCpaWRkxG3evNldeeWVrrOzc9Ix3//+992CBQvcwMCAc278dMmqVavc5z//+WmNCTQ1/ec//5nw2Y4dO1xjY6M/WfjUU0+5iooK949//MOP+cIXvuBWrlzpfy9nTKDp0WRFsp2dnS6RSLhf//rX/rOf/OQnrqamxvX29pY9JtDM0JwCKM459/jjj7tEIuHuvvtu99BDD7m2tjb3mc98Zqan9Zajjo4Ot3fvXvezn/3MmZn75je/6fbu3eteeOEFP+b48eMumUy6rVu3uocffth98IMfdCtXroz01ShnTKCp6dZbb3WJRMI98MADbu/evf7foUOH/Ji+vj63bNkyt3btWvfTn/7UbdmyxTU1NbnXXnttWmMCTU333nuvu+mmm9xDDz3k9uzZ4774xS+6efPmRXoxFQoFt2nTJrds2TL34x//2H31q191iUTCPfPMM9MaE2h6NBlAcc65b33rWy6VSrmdO3e6733ve66mpsY98MAD0x4T6NLTnHyb8aFDh+xXv/qV5XI5W7dund12222TdnIM9N/Ts88+az/60Y8mfL5582a75ZZb/O///Oc/bc+ePXbmzBlbsWKFbdu2zZLJZOSacsYEujDddddddvr06QmfX3fddbZt2zb/e29vrz3yyCN24sQJu/zyy+2OO+6Y0HG2nDGBpqYDBw7Yvn37rLe315YsWWKf/exnJ6QCRkdH7dFHH7XDhw9bOp222267za655pppjwlUPnV0dNiOHTts165ddsUVV0T+9sQTT9jvf/97i8VitmXLFvvoRz864fpyxgS6tDQnAUqgQIECBQoU6K1Nc6pINlCgQIECBQr09qAAUAIFChQoUKBAs44CQAkUKFCgQIECzToKACVQoECBAgUKNOsoAJRAgQIFChQo0KyjAFACBQoUKFCgQLOOAkAJFChQoECBAs06CgAlUKBAgQIFCjTrKACUQIECBQoUKNCsowBQAgUKFChQoECzjgJACRQoUKBAgQLNOgoAJVCgQIECBQo06+j/Ad4S8zFKpPkaAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from astropy.visualization import simple_norm\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "\n",
    "# get image data from file\n",
    "with fits.open(file_path) as hdul:\n",
    "    data = np.asarray(hdul[0].data, dtype=np.float64)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "im = ax.imshow(\n",
    "    data,\n",
    "    norm=simple_norm(data, stretch='log'),\n",
    "    cmap='Greys',\n",
    "    origin='lower',\n",
    "    )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac17ddc2",
   "metadata": {},
   "source": [
    "The image looks good.\n",
    "\n",
    "Let's see what happens when we try to use the OPTICAM-MX instrument to reduce these data. Before performing reduction with a particular instrument, it's a good idea to call the instrument's `run_checks()` method. This method takes a path to an image and runs a series of checks to ensure that the data are consistent with those produced by the instrument:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "130e118a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T12:23:23.751557Z",
     "iopub.status.busy": "2026-06-16T12:23:23.751423Z",
     "iopub.status.idle": "2026-06-16T12:23:25.065397Z",
     "shell.execute_reply": "2026-06-16T12:23:25.064903Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OPTICAM] Checking instrument OPTICAM_MX.\n",
      "[OPTICAM] ERROR: OPTICAM_MX.get_exptime() failed due to the following exception: \"Keyword 'EXPOSURE' not found.\"\n",
      "[OPTICAM] ERROR: OPTICAM_MX.dateobs_kw (UT) is not a valid header keyword for file /tmp/tmp_wnl6b0w/out/data/simple/image.fits.gz extension 0.\n",
      "[OPTICAM] ERROR: OPTICAM_MX.pixel_scales does not contain a corresponding value for the camera None.\n",
      "[OPTICAM] ERROR: Failed to parse the MJD of the image due the following exception: \"Keyword 'UT' not found.\". This is likely due to an incorrect keyword being passed to dateobs_kw and/or your images do not give timestamps in FITS format. In the latter case, you will need to define a custom instrument with a custom get_mjd() method. See https://opticam.readthedocs.io/en/latest/_executed/instruments.html#Defining-an-instrument-from-the-opticam.Instrument-base-class for details.\n",
      "[OPTICAM] WARNING: OPTICAM_MX.dark_curr_kw (DARKCURR) is not a valid header keyword for file /tmp/tmp_wnl6b0w/out/data/simple/image.fits.gz extension 0. If no dark current is listed in the image headers, you will need to use a `opticam.DarkNoiseCorrector` instance to correct for dark noise. See https://opticam.readthedocs.io/en/latest/_executed/applying_corrections.html#Dark-noise for details.\n",
      "[OPTICAM] OPTICAM_MX failed 4 checks.\n",
      "[OPTICAM] OPTICAM_MX triggered a warning during 1 check. Warnings may be ignored provided their caveats are satisfied.\n"
     ]
    }
   ],
   "source": [
    "import phoptic\n",
    "\n",
    "\n",
    "instrument = phoptic.OPTICAM_MX()\n",
    "instrument.run_checks(file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3981e974",
   "metadata": {},
   "source": [
    "In this case, we can see that we have several errors, and one warning:\n",
    "- The first error is caused by `OPTICAM_MX` using a different header keyword for the image's exposure time. Typically, the keyword \"EXPTIME\" is used to identify the image's exposure time; this is the case for these data. However, `OPTICAM_MX` uses the \"EXPOSURE\" keyword instead.\n",
    "- The second error is again caused by a header keyword mismatch, this time for the timestamp keyword. Typically, the keyword \"DATE-OBS\" is used to identify the image's timestamp; this is the case for these data. However, `OPTICAM_MX` uses the \"UT\" keyword instead.\n",
    "- The third error is due to the data using the $U$-band filter, which is not supported by `OPTICAM_MX` and therefore has no corresponding pixel scale.\n",
    "- The final error is related to the second error: the Modified Julian Date (MJD) of the image cannot be inferred because of the timestamp keyword mismatch between these data and `OPTICAM_MX`.\n",
    "- The warning is due to these data not containing a \"DARKCURR\" keyword in the header. `phoptic` can use the dark current value (represented by the header keyword \"DARKCURR\") to infer and subtract the dark noise from the image. If this keyword does not exist, then dark noise can be removed using a `DarkNoiseCorrector` instance instead (see the [corrections tutorial](applying_corrections.ipynb) for more details). This warning can therefore be ignored in this case.\n",
    "\n",
    "There are also a number of other issues with using `OPTICAM_MX` that didn't result in any errors being raised. Let's look at `OPTICAM_MX` to see what the instrument represents:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8681a461",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T12:23:25.066461Z",
     "iopub.status.busy": "2026-06-16T12:23:25.066311Z",
     "iopub.status.idle": "2026-06-16T12:23:25.068078Z",
     "shell.execute_reply": "2026-06-16T12:23:25.067773Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OPTICAM_MX(diameter=<Quantity 2.1 m>, location=<EarthLocation (-2352558.38786513, -4940313.54119147, 3271528.71037222) m>, pixel_scales={'1': 0.1397, '2': 0.1406, '3': 0.1661}, airmass_kw='AIRMASS', binning_kw='BINNING', camera_kw='INSTRUME', dark_curr_kw='DARKCURR', dateobs_kw='UT', dec_kw='DEC', exptime_kw='EXPOSURE', filter_kw='FILTER', gain_kw='GAIN', ra_kw='RA', read_noise_kw='RDNOISE')\n"
     ]
    }
   ],
   "source": [
    "print(instrument)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9babaf8c",
   "metadata": {},
   "source": [
    "We can see that `OPTICAM_MX` has a predefined position, a dictionary of pixel scales for each camera, a read noise value, and a number of header keywords. Many of these parameters are likely not be representative of our new instrument. Notably, location and read noise values. The location of the instrument is used when applying Barycentric corrections, and the read noise is used to calculate the total photometric error. Our new instrument therefore needs to redefine many of these parameters for accurate results.\n",
    "\n",
    "There are two ways to define a new instrument. The simplest is to generate a configuration file template, edit the necessary values, and then use that configuration file to define a new instrument. This works well provided the instrument adheres to the conventional FITS header keywords (discussed in more detail below) and represents values in FITS format. If this is not the case, then you will need to define your instrument as a class that inherits from the `phoptic.Instrument` base class.\n",
    "\n",
    "## Defining an instrument from a configuration file\n",
    "\n",
    "In this case, our data use conventional FITS header keywords and all values are given in FITS format. Explicitly:\n",
    "- The corresponding filter is given by the \"FILTER\" keyword.\n",
    "- The image binning is given by the \"BINNING\" keyword.\n",
    "- The detector's gain value is given by the \"GAIN\" keyword in electrons/ADU.\n",
    "- The corresponding exposure time is given by the \"EXPTIME\" keyword in seconds.\n",
    "- The corresponding RA is given by the \"RA\" keyword.\n",
    "- The corresponding DEC is given by the \"DEC\" keyword.\n",
    "- The corresponding timestamp is given by the \"DATE-OBS\" keyword in FITS format (YYYY-MM-DDTHH:MM:SS.sss).\n",
    "\n",
    "If any of these values were given by different keywords or were in different units/formats, then it would be necessary to define the instrument using a custom class instead of from configuration file.\n",
    "\n",
    "To create a configuration file, it is suggested to generate and then edit a configuration template. Configuration templates can be generated using the `generate_instrument_json_template()` function. This function takes a directory path and writes an `instrument_template.json` file to that directory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eca47b77",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T12:23:25.068754Z",
     "iopub.status.busy": "2026-06-16T12:23:25.068687Z",
     "iopub.status.idle": "2026-06-16T12:23:25.070097Z",
     "shell.execute_reply": "2026-06-16T12:23:25.069822Z"
    }
   },
   "outputs": [],
   "source": [
    "phoptic.generate_instrument_json_template(out_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d61aa57a",
   "metadata": {},
   "source": [
    "Let's take a look at the contents of this template:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b4932189",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T12:23:25.070737Z",
     "iopub.status.busy": "2026-06-16T12:23:25.070671Z",
     "iopub.status.idle": "2026-06-16T12:23:25.072631Z",
     "shell.execute_reply": "2026-06-16T12:23:25.072331Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "longitude\n",
      "0.0\n",
      "\n",
      "_longitude_description\n",
      "The East longitude of the observatory in degrees.\n",
      "\n",
      "latitude\n",
      "0.0\n",
      "\n",
      "_latitude_description\n",
      "The latitude of the observatory in degrees.\n",
      "\n",
      "height\n",
      "0.0\n",
      "\n",
      "_height_description\n",
      "The height of the observatory in metres.\n",
      "\n",
      "diameter\n",
      "0.0\n",
      "\n",
      "_diameter_description\n",
      "The diameter of the telescope's aperture in metres.\n",
      "\n",
      "pixel_scales\n",
      "{'camera_1': 0.0, 'camera_2': 0.0}\n",
      "\n",
      "_pixel_scales_description\n",
      "The pixel-scale in arcsec/pixel for each filter.\n",
      "\n",
      "airmass_kw\n",
      "AIRMASS\n",
      "\n",
      "_airmass_kw_description\n",
      "The header keyword that corresponds to the observation's airmass.\n",
      "\n",
      "read_noise_kw\n",
      "RDNOISE\n",
      "\n",
      "_read_noise_kw_description\n",
      "The header keyword that corresponds to the detector's readout noise in electrons/pixel.\n",
      "\n",
      "binning_kw\n",
      "BINNING\n",
      "\n",
      "_binning_kw_description\n",
      "The header keyword that corresponds to the binning mode.\n",
      "\n",
      "dark_curr_kw\n",
      "DARKCURR\n",
      "\n",
      "_dark_curr_kw_description\n",
      "The header keyword that corresponds to the detector's dark current in electrons/pixel/s.\n",
      "\n",
      "exptime_kw\n",
      "EXPTIME\n",
      "\n",
      "_exptime_kw_description\n",
      "The header keyword that corresponds to the exposure time in seconds.\n",
      "\n",
      "filter_kw\n",
      "FILTER\n",
      "\n",
      "_filter_kw_description\n",
      "The header keyword that corresponds to the image filter.\n",
      "\n",
      "gain_kw\n",
      "GAIN\n",
      "\n",
      "_gain_kw_description\n",
      "The header keyword that corresponds to the detector's gain value in electrons/ADU.\n",
      "\n",
      "dateobs_kw\n",
      "DATE-OBS\n",
      "\n",
      "_dateobs_kw_description\n",
      "The header keyword that corresponds to the image's timestamp in ISO 8601/FITS format (i.e., YYYY-MM-DDTHH:MM:SS[.sss]). If your instrument does not give timestamps in this format, you will need to define the instrument with a custom get_mjd() method. See https://opticam.readthedocs.io/en/latest/_executed/instruments.html#Defining-an-instrument-from-the-opticam.Instrument-base-class for details.\n",
      "\n",
      "ra_kw\n",
      "RA\n",
      "\n",
      "_ra_kw_description\n",
      "The header keyword that corresponds to the image's RA in units of hour angle. If your instrument does not give the RA in units of hour angle, you will need to define the instrument with a custom get_sky_coord() method. See https://opticam.readthedocs.io/en/latest/autoapi/opticam/instruments/index.html#opticam.instruments.Instrument.get_sky_coord for details.\n",
      "\n",
      "dec_kw\n",
      "DEC\n",
      "\n",
      "_dec_kw_description\n",
      "The header keyword that corresponds to the image's DEC in units of degrees. If your instrument does not give the DEC in units of degrees, you will need to define the instrument with a custom get_sky_coord() method. See https://opticam.readthedocs.io/en/latest/autoapi/opticam/instruments/index.html#opticam.instruments.Instrument.get_sky_coord for details.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "\n",
    "with open(out_dir / 'instrument_template.json', 'r') as file:\n",
    "    config_dict = json.load(file)\n",
    "\n",
    "for k, v in config_dict.items():\n",
    "    print(k)\n",
    "    print(v)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bae2cb3",
   "metadata": {},
   "source": [
    "As we can see, the template lists a number of parameters that can be configured, including descriptions for each parameter. In practise, one would probably edit this template in their preferred text editor. For demonstrative purposes, however, I will edit the dictionary in this notebook.\n",
    "\n",
    "For this example, I'll ignore the location parameters (longitude, latitude, and height), as well as the read noise, since these values vary on an instrument-by-instrument basis. The only parameter that needs changing to prevent errors is `pixel_scales`, since the `exptime_kw` and `timestamp_kw` parameters of the template are already set to the conventional \"EXPTIME\" and \"DATE-OBS\" values, respectively:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "89a606c9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T12:23:25.073368Z",
     "iopub.status.busy": "2026-06-16T12:23:25.073302Z",
     "iopub.status.idle": "2026-06-16T12:23:25.074559Z",
     "shell.execute_reply": "2026-06-16T12:23:25.074351Z"
    }
   },
   "outputs": [],
   "source": [
    "config_dict['pixel_scales'] = {\n",
    "    'SYNTHETICAM': 1.0,  # 1.0 arcsec/pixel\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42384d84",
   "metadata": {},
   "source": [
    "Let's save this edited config file under a new name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f3cffb28",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T12:23:25.075277Z",
     "iopub.status.busy": "2026-06-16T12:23:25.075214Z",
     "iopub.status.idle": "2026-06-16T12:23:25.076659Z",
     "shell.execute_reply": "2026-06-16T12:23:25.076407Z"
    }
   },
   "outputs": [],
   "source": [
    "with open(out_dir / 'new_instrument_config.json', 'w') as file:\n",
    "    json.dump(\n",
    "        config_dict,\n",
    "        file,\n",
    "        indent=4,\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60a2848f",
   "metadata": {},
   "source": [
    "Now we can use this file to create our instrument using the `from_json()` method of `phoptic.Instrument`. This method takes either a path to a config JSON file, or a config can be passed directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eef74d7a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T12:23:25.077329Z",
     "iopub.status.busy": "2026-06-16T12:23:25.077258Z",
     "iopub.status.idle": "2026-06-16T12:23:25.079477Z",
     "shell.execute_reply": "2026-06-16T12:23:25.079219Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using the saved config file\n",
    "new_instrument = phoptic.Instrument.from_json(file_path=out_dir / 'new_instrument_config.json')\n",
    "\n",
    "# using the config dictionary\n",
    "new_instrument_alt = phoptic.Instrument.from_json(config=config_dict)\n",
    "\n",
    "new_instrument == new_instrument_alt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f1216a82",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T12:23:25.080077Z",
     "iopub.status.busy": "2026-06-16T12:23:25.080012Z",
     "iopub.status.idle": "2026-06-16T12:23:25.081233Z",
     "shell.execute_reply": "2026-06-16T12:23:25.081052Z"
    },
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "assert new_instrument == new_instrument_alt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a3f36ec",
   "metadata": {},
   "source": [
    "Let's take a look at our new instrument and check it's using the correct values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0aa625a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T12:23:25.081906Z",
     "iopub.status.busy": "2026-06-16T12:23:25.081844Z",
     "iopub.status.idle": "2026-06-16T12:23:25.083265Z",
     "shell.execute_reply": "2026-06-16T12:23:25.083067Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instrument(diameter=<Quantity 0. m>, location=<EarthLocation (6378137., 0., 0.) m>, pixel_scales={'SYNTHETICAM': 1.0}, airmass_kw='AIRMASS', binning_kw='BINNING', camera_kw='INSTRUME', dark_curr_kw='DARKCURR', dateobs_kw='DATE-OBS', dec_kw='DEC', exptime_kw='EXPTIME', filter_kw='FILTER', gain_kw='GAIN', ra_kw='RA', read_noise_kw='RDNOISE')\n"
     ]
    }
   ],
   "source": [
    "print(new_instrument)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a69d8972",
   "metadata": {},
   "source": [
    "Looks good!\n",
    "\n",
    "New let's run the instrument checks for this new instrument:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d09d402b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T12:23:25.083944Z",
     "iopub.status.busy": "2026-06-16T12:23:25.083881Z",
     "iopub.status.idle": "2026-06-16T12:23:25.089373Z",
     "shell.execute_reply": "2026-06-16T12:23:25.089103Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OPTICAM] Checking instrument Instrument.\n",
      "[OPTICAM] WARNING: Instrument.dark_curr_kw (DARKCURR) is not a valid header keyword for file /tmp/tmp_wnl6b0w/out/data/simple/image.fits.gz extension 0. If no dark current is listed in the image headers, you will need to use a `opticam.DarkNoiseCorrector` instance to correct for dark noise. See https://opticam.readthedocs.io/en/latest/_executed/applying_corrections.html#Dark-noise for details.\n",
      "[OPTICAM] Instrument sucessfully passed all checks.\n",
      "[OPTICAM] Instrument triggered a warning during 1 check. Warnings may be ignored provided their caveats are satisfied.\n"
     ]
    }
   ],
   "source": [
    "new_instrument.run_checks(file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5624df97",
   "metadata": {},
   "source": [
    "No more errors! We still have a warning about the dark current keyword, but we can safely ignore this for now.\n",
    "\n",
    "As noted above, creating instruments from a configuration template works well provided the instrument follows the convetional FITS header structure. Now let's take a look at what to do if this is not the case:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5cc9d969",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T12:23:25.090012Z",
     "iopub.status.busy": "2026-06-16T12:23:25.089945Z",
     "iopub.status.idle": "2026-06-16T12:23:25.138016Z",
     "shell.execute_reply": "2026-06-16T12:23:25.137659Z"
    }
   },
   "outputs": [],
   "source": [
    "from astropy.time import Time\n",
    "\n",
    "image = make_100gaussians_image()\n",
    "\n",
    "hdu = fits.PrimaryHDU(image)  # image data\n",
    "\n",
    "hdu.header['FILTER'] = 'I'  # the observation filter\n",
    "hdu.header['BINNING'] = '2x2'  # the image binning\n",
    "hdu.header['GAIN'] = 500.  # the detector gain in adu/photon\n",
    "hdu.header['EXPOSURE'] = 1.  # the exposure time in seconds\n",
    "hdu.header['RA'] = '12 30 49.4'  # the RA of the image in degrees\n",
    "hdu.header['DEC'] = '+12 23 28.0'  # the DEC of the image in degrees\n",
    "hdu.header['INSTRUME'] = 'SYNTHETICAM'  # the instrument (synthetic camera)\n",
    "hdu.header['RDNOISE'] = 0.  # the instrument's read noise in electrons/pixel\n",
    "hdu.header['AIRMASS'] = 1.  # the observation's airmass\n",
    "\n",
    "\n",
    "fits_time = f\"2026-01-01T00:00:00.000\"  # observation timestamp\n",
    "mjd_time = float(np.asarray(Time(fits_time, format='fits').mjd))  # convert time from FITS format to MJD\n",
    "hdu.header['DATE-OBS'] = mjd_time\n",
    "\n",
    "save_path = out_dir / 'data' / 'custom'  # directory to which image will be saved\n",
    "\n",
    "# create save directory if it does not already exist\n",
    "if not save_path.is_dir():\n",
    "    save_path.mkdir(parents=True)\n",
    "\n",
    "# save image\n",
    "new_file_path = save_path / 'image.fits.gz'\n",
    "hdu.writeto(\n",
    "    new_file_path,\n",
    "    overwrite=True,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c436f9cc",
   "metadata": {},
   "source": [
    "These new data are very similar to the previous example, but replace the \"EXPTIME\" keyword with \"EXPOSURE\", and store the image's timestamp in MJD format instead of the conventional FITS format.\n",
    "\n",
    "Let's look at the header of this image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7d25c66e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T12:23:25.139025Z",
     "iopub.status.busy": "2026-06-16T12:23:25.138958Z",
     "iopub.status.idle": "2026-06-16T12:23:25.144032Z",
     "shell.execute_reply": "2026-06-16T12:23:25.143774Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SIMPLE  =                    T / conforms to FITS standard                      \n",
      "BITPIX  =                  -64 / array data type                                \n",
      "NAXIS   =                    2 / number of array dimensions                     \n",
      "NAXIS1  =                  500                                                  \n",
      "NAXIS2  =                  300                                                  \n",
      "EXTEND  =                    T                                                  \n",
      "FILTER  = 'I       '                                                            \n",
      "BINNING = '2x2     '                                                            \n",
      "GAIN    =                500.0                                                  \n",
      "EXPOSURE=                  1.0                                                  \n",
      "RA      = '12 30 49.4'                                                          \n",
      "DEC     = '+12 23 28.0'                                                         \n",
      "INSTRUME= 'SYNTHETICAM'                                                         \n",
      "RDNOISE =                  0.0                                                  \n",
      "AIRMASS =                  1.0                                                  \n",
      "DATE-OBS=              61041.0                                                  \n"
     ]
    }
   ],
   "source": [
    "header = fits.getheader(new_file_path)\n",
    "print(repr(header))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beb2c96b",
   "metadata": {},
   "source": [
    "Predictably, these new data will fail with both `OPTICAM_MX` and our newly created instrument:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b3493c6c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T12:23:25.144825Z",
     "iopub.status.busy": "2026-06-16T12:23:25.144759Z",
     "iopub.status.idle": "2026-06-16T12:23:25.150164Z",
     "shell.execute_reply": "2026-06-16T12:23:25.149837Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OPTICAM] Checking instrument OPTICAM_MX.\n",
      "[OPTICAM] ERROR: OPTICAM_MX.dateobs_kw (UT) is not a valid header keyword for file /tmp/tmp_wnl6b0w/out/data/custom/image.fits.gz extension 0.\n",
      "[OPTICAM] ERROR: OPTICAM_MX.pixel_scales does not contain a corresponding value for the camera None.\n",
      "[OPTICAM] ERROR: Failed to parse the MJD of the image due the following exception: \"Keyword 'UT' not found.\". This is likely due to an incorrect keyword being passed to dateobs_kw and/or your images do not give timestamps in FITS format. In the latter case, you will need to define a custom instrument with a custom get_mjd() method. See https://opticam.readthedocs.io/en/latest/_executed/instruments.html#Defining-an-instrument-from-the-opticam.Instrument-base-class for details.\n",
      "[OPTICAM] WARNING: OPTICAM_MX.dark_curr_kw (DARKCURR) is not a valid header keyword for file /tmp/tmp_wnl6b0w/out/data/custom/image.fits.gz extension 0. If no dark current is listed in the image headers, you will need to use a `opticam.DarkNoiseCorrector` instance to correct for dark noise. See https://opticam.readthedocs.io/en/latest/_executed/applying_corrections.html#Dark-noise for details.\n",
      "[OPTICAM] OPTICAM_MX failed 3 checks.\n",
      "[OPTICAM] OPTICAM_MX triggered a warning during 1 check. Warnings may be ignored provided their caveats are satisfied.\n"
     ]
    }
   ],
   "source": [
    "instrument.run_checks(new_file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f7ccbc3e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T12:23:25.150908Z",
     "iopub.status.busy": "2026-06-16T12:23:25.150843Z",
     "iopub.status.idle": "2026-06-16T12:23:25.156080Z",
     "shell.execute_reply": "2026-06-16T12:23:25.155774Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OPTICAM] Checking instrument Instrument.\n",
      "[OPTICAM] ERROR: Instrument.get_exptime() failed due to the following exception: \"Keyword 'EXPTIME' not found.\"\n",
      "[OPTICAM] ERROR: Failed to parse the MJD of the image due the following exception: Input values did not match the format class fits:\n",
      "ValueError: Time 61041.0 does not match fits format. This is likely due to an incorrect keyword being passed to dateobs_kw and/or your images do not give timestamps in FITS format. In the latter case, you will need to define a custom instrument with a custom get_mjd() method. See https://opticam.readthedocs.io/en/latest/_executed/instruments.html#Defining-an-instrument-from-the-opticam.Instrument-base-class for details.\n",
      "[OPTICAM] WARNING: Instrument.dark_curr_kw (DARKCURR) is not a valid header keyword for file /tmp/tmp_wnl6b0w/out/data/custom/image.fits.gz extension 0. If no dark current is listed in the image headers, you will need to use a `opticam.DarkNoiseCorrector` instance to correct for dark noise. See https://opticam.readthedocs.io/en/latest/_executed/applying_corrections.html#Dark-noise for details.\n",
      "[OPTICAM] Instrument failed 2 checks.\n",
      "[OPTICAM] Instrument triggered a warning during 1 check. Warnings may be ignored provided their caveats are satisfied.\n"
     ]
    }
   ],
   "source": [
    "new_instrument.run_checks(new_file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebe22897",
   "metadata": {},
   "source": [
    "## Defining an instrument from the phoptic.Instrument base class\n",
    "\n",
    "To reduce these data, we cannot create a new instrument from a configuration file because the timestamps are not in FITS format. Instead, we need to define a custom class that inherits from `phoptic.Instrument` and implement a custom `get_mjd()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a97b07bc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T12:23:25.156915Z",
     "iopub.status.busy": "2026-06-16T12:23:25.156849Z",
     "iopub.status.idle": "2026-06-16T12:23:25.158575Z",
     "shell.execute_reply": "2026-06-16T12:23:25.158300Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing out/custom_instrument.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile out/custom_instrument.py\n",
    "\n",
    "import phoptic\n",
    "\n",
    "\n",
    "class CustomInstrument(phoptic.Instrument):\n",
    "    \n",
    "    def get_mjd(self, file=None, header=None):\n",
    "        \n",
    "        if header is None:\n",
    "            header = file.get_header()\n",
    "        \n",
    "        return float(header[self.dateobs_kw])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90533bee",
   "metadata": {},
   "source": [
    "Due to compatibility issues, routines defined inside of IPython notebooks cannot be used by `multiprocessing` (used by `phoptic`). To overcome this issue, we have used the `%%writefile` magic command to write our `CustomInstrument` class to a temporary module called `custom_instrument.py` in our `out` directory. This is required in Python $\\geq$ 3.14.\n",
    "\n",
    "The `get_mjd()` method of an `Instrument` instance takes two inputs: `file` and `header`. `file` will take a [`phoptic.MEFSlice`](https://phoptic.readthedocs.io/en/latest/autoapi/phoptic/mef_slice/index.html#phoptic.mef_slice.MEFSlice) instance, while `header` will take an `astropy.io.fits.Header` instance. `phoptic`'s `MEFSlice` class is used internally to represent a single extension of a multi-extension FITS file (or the only extension of a standard FITS file). This allows `phoptic` to easily get the header of a FITS HDU by calling the `get_header()` method of `MEFSlice`. When called, `phoptic` will only define one of these parameters, depending on whether the image is already open or not, and so both parameters should assume default values (i.e., `None`). The value returned by `get_mjd()` should be the MJD of the image as a `float`.\n",
    "\n",
    "In this example, the MJD is given by the header, so it can be returned directly. If the header gives the timestamp in an alternative format, it may be convenient to use the [`astropy.time`](https://docs.astropy.org/en/stable/time/index.html) module to convert it to MJD.\n",
    "\n",
    "To instance this `CustomInstrument`, we can again generate and edit a configuration template:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "428570b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T12:23:25.159253Z",
     "iopub.status.busy": "2026-06-16T12:23:25.159189Z",
     "iopub.status.idle": "2026-06-16T12:23:25.161522Z",
     "shell.execute_reply": "2026-06-16T12:23:25.161331Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CustomInstrument(diameter=<Quantity 0. m>, location=<EarthLocation (6378137., 0., 0.) m>, pixel_scales={'SYNTHETICAM': 1.0}, airmass_kw='AIRMASS', binning_kw='BINNING', camera_kw='INSTRUME', dark_curr_kw='DARKCURR', dateobs_kw='DATE-OBS', dec_kw='DEC', exptime_kw='EXPOSURE', filter_kw='FILTER', gain_kw='GAIN', ra_kw='RA', read_noise_kw='RDNOISE')\n"
     ]
    }
   ],
   "source": [
    "from out.custom_instrument import CustomInstrument\n",
    "\n",
    "with open(out_dir / 'instrument_template.json', 'r') as file:\n",
    "    custom_config = json.load(file)\n",
    "\n",
    "custom_config['exptime_kw'] = 'EXPOSURE'  # change EXPTIME keyword\n",
    "custom_config['pixel_scales'] = {\n",
    "    'SYNTHETICAM': 1.0,  # 1.0 arcsec/pixel\n",
    "    }\n",
    "\n",
    "custom_instrument = CustomInstrument.from_json(config=custom_config)\n",
    "\n",
    "print(custom_instrument)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "178533e8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T12:23:25.162314Z",
     "iopub.status.busy": "2026-06-16T12:23:25.162249Z",
     "iopub.status.idle": "2026-06-16T12:23:25.167539Z",
     "shell.execute_reply": "2026-06-16T12:23:25.167211Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OPTICAM] Checking instrument CustomInstrument.\n",
      "[OPTICAM] WARNING: CustomInstrument.dark_curr_kw (DARKCURR) is not a valid header keyword for file /tmp/tmp_wnl6b0w/out/data/custom/image.fits.gz extension 0. If no dark current is listed in the image headers, you will need to use a `opticam.DarkNoiseCorrector` instance to correct for dark noise. See https://opticam.readthedocs.io/en/latest/_executed/applying_corrections.html#Dark-noise for details.\n",
      "[OPTICAM] CustomInstrument sucessfully passed all checks.\n",
      "[OPTICAM] CustomInstrument triggered a warning during 1 check. Warnings may be ignored provided their caveats are satisfied.\n"
     ]
    }
   ],
   "source": [
    "custom_instrument.run_checks(new_file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec75f029",
   "metadata": {},
   "source": [
    "Alternatively, we could define the instrument by hardcoding the required parameters, which is a bit more convenient for reusing this instrument many times:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f6bdd5ea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T12:23:25.168129Z",
     "iopub.status.busy": "2026-06-16T12:23:25.168064Z",
     "iopub.status.idle": "2026-06-16T12:23:25.169788Z",
     "shell.execute_reply": "2026-06-16T12:23:25.169524Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing out/custom_instrument_class.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile out/custom_instrument_class.py\n",
    "from astropy.coordinates import EarthLocation\n",
    "from astropy.io import fits\n",
    "from astropy import units as u\n",
    "\n",
    "import phoptic\n",
    "\n",
    "\n",
    "__all__ = ['CustomInstrumentClass']\n",
    "\n",
    "\n",
    "class CustomInstrumentClass(phoptic.Instrument):\n",
    "    \n",
    "    def __init__(\n",
    "        self,\n",
    "        diameter=1.0 * u.m,  # telescope aperture\n",
    "        location = EarthLocation.from_geodetic(\n",
    "            lon=0.0,  # observatory longitude in degrees\n",
    "            lat=0.0,  # observatory East latitude in degrees\n",
    "            height=0.0,  # observatory height in meters\n",
    "            ),\n",
    "        pixel_scales = {\n",
    "            'SYNTHETICAM': 1.0,\n",
    "            },\n",
    "        exptime_kw = 'EXPOSURE',\n",
    "        ):\n",
    "        \n",
    "        super().__init__(\n",
    "            diameter=diameter,\n",
    "            location=location,\n",
    "            pixel_scales=pixel_scales,\n",
    "            exptime_kw=exptime_kw,\n",
    "            )\n",
    "    \n",
    "    def get_mjd(\n",
    "        self,\n",
    "        file=None,\n",
    "        header=None,\n",
    "        ):\n",
    "        \n",
    "        if header is None:\n",
    "            header = file.get_header()\n",
    "        \n",
    "        return float(header[self.dateobs_kw])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fd97b458",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T12:23:25.170385Z",
     "iopub.status.busy": "2026-06-16T12:23:25.170320Z",
     "iopub.status.idle": "2026-06-16T12:23:25.172444Z",
     "shell.execute_reply": "2026-06-16T12:23:25.172095Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CustomInstrumentClass(diameter=<Quantity 1. m>, location=<EarthLocation (6378137., 0., 0.) m>, pixel_scales={'SYNTHETICAM': 1.0}, airmass_kw='AIRMASS', binning_kw='BINNING', camera_kw='INSTRUME', dark_curr_kw='DARKCURR', dateobs_kw='DATE-OBS', dec_kw='DEC', exptime_kw='EXPOSURE', filter_kw='FILTER', gain_kw='GAIN', ra_kw='RA', read_noise_kw='RDNOISE')\n"
     ]
    }
   ],
   "source": [
    "from out.custom_instrument_class import CustomInstrumentClass\n",
    "\n",
    "custom_instrument_class = CustomInstrumentClass()\n",
    "\n",
    "print(custom_instrument_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ff0f5ce3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T12:23:25.173045Z",
     "iopub.status.busy": "2026-06-16T12:23:25.172979Z",
     "iopub.status.idle": "2026-06-16T12:23:25.178226Z",
     "shell.execute_reply": "2026-06-16T12:23:25.178009Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OPTICAM] Checking instrument CustomInstrumentClass.\n",
      "[OPTICAM] WARNING: CustomInstrumentClass.dark_curr_kw (DARKCURR) is not a valid header keyword for file /tmp/tmp_wnl6b0w/out/data/custom/image.fits.gz extension 0. If no dark current is listed in the image headers, you will need to use a `opticam.DarkNoiseCorrector` instance to correct for dark noise. See https://opticam.readthedocs.io/en/latest/_executed/applying_corrections.html#Dark-noise for details.\n",
      "[OPTICAM] CustomInstrumentClass sucessfully passed all checks.\n",
      "[OPTICAM] CustomInstrumentClass triggered a warning during 1 check. Warnings may be ignored provided their caveats are satisfied.\n"
     ]
    }
   ],
   "source": [
    "custom_instrument_class.run_checks(new_file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ccde946",
   "metadata": {},
   "source": [
    "While creating custom instrument classes is more advanced than editing a configuration template, it is far more configurable. Additionally, instrument classes only need to be created once, and can then be reused indefinitely (unless the instrument changes the format of its data products). If you write a class for an instrument, we encourage you to consider opening a [pull request](https://github.com/OPTICAM-instrument/phoptic/pulls) to merge your instrument into `phoptic`.\n",
    "\n",
    "## Gotchas\n",
    "\n",
    "Of course, not all instruments operate in the same way and may not have some of the keywords assumed by `phoptic`. In these cases, values can be faked by creating methods that return fixed values, or infer values from other key words.\n",
    "\n",
    "### Example: missing keyword\n",
    "\n",
    "As an example, let's imagine we have an instrument that does not include a \"BINNING\" keyword in its data products. Assuming all images will have the same binning value, we can fix this by returning a fixed binning value for all images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cbae0d74",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T12:23:25.178932Z",
     "iopub.status.busy": "2026-06-16T12:23:25.178867Z",
     "iopub.status.idle": "2026-06-16T12:23:25.180368Z",
     "shell.execute_reply": "2026-06-16T12:23:25.180160Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing out/instrument_without_binning.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile out/instrument_without_binning.py\n",
    "import phoptic\n",
    "\n",
    "\n",
    "class InstrumentWithoutBinning(phoptic.Instrument):\n",
    "    \n",
    "    def get_binning(self, file=None, header=None):\n",
    "        \n",
    "        return '1x1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cb4311d9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T12:23:25.181021Z",
     "iopub.status.busy": "2026-06-16T12:23:25.180931Z",
     "iopub.status.idle": "2026-06-16T12:23:25.182922Z",
     "shell.execute_reply": "2026-06-16T12:23:25.182666Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "InstrumentWithoutBinning(diameter=<Quantity 0. m>, location=<EarthLocation (6378137., 0., 0.) m>, pixel_scales={'SYNTHETICAM': 1.0}, airmass_kw='AIRMASS', binning_kw='BINNING', camera_kw='INSTRUME', dark_curr_kw='DARKCURR', dateobs_kw='DATE-OBS', dec_kw='DEC', exptime_kw='EXPTIME', filter_kw='FILTER', gain_kw='GAIN', ra_kw='RA', read_noise_kw='RDNOISE')\n"
     ]
    }
   ],
   "source": [
    "from out.instrument_without_binning import InstrumentWithoutBinning\n",
    "\n",
    "instrument_without_binning = InstrumentWithoutBinning.from_json(config=config_dict)\n",
    "\n",
    "print(instrument_without_binning)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7e62995",
   "metadata": {},
   "source": [
    "All methods of an `Instrument` take two parameters: `file` and `header`. When an `Instument`'s methods are called, only one of `file` or `header` are passed, so custom methods should set defaults for each parameter and define the method to return the same value regardless of which parameter is defined. In this example, however, neither parameter is needed.\n",
    "\n",
    "Now let's generate an image without a binning keyword:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2648b650",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T12:23:25.183518Z",
     "iopub.status.busy": "2026-06-16T12:23:25.183452Z",
     "iopub.status.idle": "2026-06-16T12:23:25.218211Z",
     "shell.execute_reply": "2026-06-16T12:23:25.217859Z"
    }
   },
   "outputs": [],
   "source": [
    "image = make_100gaussians_image()\n",
    "\n",
    "hdu = fits.PrimaryHDU(image)  # image data\n",
    "\n",
    "hdu.header['FILTER'] = 'U'\n",
    "hdu.header['GAIN'] = 1.\n",
    "hdu.header['EXPTIME'] = 1.\n",
    "hdu.header['RA'] = '12 30 49.4'\n",
    "hdu.header['DEC'] = '+12 23 28.0'\n",
    "hdu.header['DATE-OBS'] = f\"2026-01-01T00:00:00.000\"\n",
    "hdu.header['INSTRUME'] = 'SYNTHETICAM'\n",
    "hdu.header['RDNOISE'] = 0.\n",
    "hdu.header['AIRMASS'] = 0.\n",
    "\n",
    "new_file_path = out_dir / 'data' / 'no_binnning'\n",
    "hdu.writeto(\n",
    "    new_file_path,\n",
    "    overwrite=True,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5015fd9",
   "metadata": {},
   "source": [
    "When we check a previous instrument that would otherwise work:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6db9e360",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T12:23:25.219043Z",
     "iopub.status.busy": "2026-06-16T12:23:25.218978Z",
     "iopub.status.idle": "2026-06-16T12:23:25.221657Z",
     "shell.execute_reply": "2026-06-16T12:23:25.221378Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OPTICAM] Checking instrument Instrument.\n",
      "[OPTICAM] ERROR: failed to read image binning for file /tmp/tmp_wnl6b0w/out/data/no_binnning extension 0 due to the exception \"Keyword 'BINNING' not found.\"\n",
      "[OPTICAM] WARNING: Instrument.dark_curr_kw (DARKCURR) is not a valid header keyword for file /tmp/tmp_wnl6b0w/out/data/no_binnning extension 0. If no dark current is listed in the image headers, you will need to use a `opticam.DarkNoiseCorrector` instance to correct for dark noise. See https://opticam.readthedocs.io/en/latest/_executed/applying_corrections.html#Dark-noise for details.\n",
      "[OPTICAM] Instrument failed 1 check.\n",
      "[OPTICAM] Instrument triggered a warning during 1 check. Warnings may be ignored provided their caveats are satisfied.\n"
     ]
    }
   ],
   "source": [
    "new_instrument.run_checks(new_file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d803fad0",
   "metadata": {},
   "source": [
    "We see that an error is raised. With our new instrument, however:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0c8fcb5f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T12:23:25.222421Z",
     "iopub.status.busy": "2026-06-16T12:23:25.222354Z",
     "iopub.status.idle": "2026-06-16T12:23:25.224466Z",
     "shell.execute_reply": "2026-06-16T12:23:25.224236Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OPTICAM] Checking instrument InstrumentWithoutBinning.\n",
      "[OPTICAM] WARNING: InstrumentWithoutBinning.dark_curr_kw (DARKCURR) is not a valid header keyword for file /tmp/tmp_wnl6b0w/out/data/no_binnning extension 0. If no dark current is listed in the image headers, you will need to use a `opticam.DarkNoiseCorrector` instance to correct for dark noise. See https://opticam.readthedocs.io/en/latest/_executed/applying_corrections.html#Dark-noise for details.\n",
      "[OPTICAM] InstrumentWithoutBinning sucessfully passed all checks.\n",
      "[OPTICAM] InstrumentWithoutBinning triggered a warning during 1 check. Warnings may be ignored provided their caveats are satisfied.\n"
     ]
    }
   ],
   "source": [
    "instrument_without_binning.run_checks(new_file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d24c633",
   "metadata": {},
   "source": [
    "We don't get any errors.\n",
    "\n",
    "However, this will cause issues if the binning changes between images. In this case, the binning information should be inferred from other keywords (e.g., `NAXIS1` and `NAXIS2`).\n",
    "\n",
    "For example, if your instrument has a resolution of 2048x2048, then you can infer the binning mode by dividing the instrument's resolution by the image resolution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "13589094",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T12:23:25.225363Z",
     "iopub.status.busy": "2026-06-16T12:23:25.225288Z",
     "iopub.status.idle": "2026-06-16T12:23:25.227097Z",
     "shell.execute_reply": "2026-06-16T12:23:25.226753Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing out/instrument_with_inferred_binning.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile out/instrument_with_inferred_binning.py\n",
    "import phoptic\n",
    "\n",
    "\n",
    "class InstrumentWithInferredBinning(phoptic.Instrument):\n",
    "    \n",
    "    def get_binning(self, file=None, header=None):\n",
    "        \n",
    "        if header is None:\n",
    "            header = file.get_header()\n",
    "        \n",
    "        x_size = int(header['NAXIS1'])\n",
    "        y_size = int(header['NAXIS2'])\n",
    "        \n",
    "        # divide instrument resolution by image resolution\n",
    "        x_binning = 2048 // x_size\n",
    "        y_binning = 2048 // y_size\n",
    "        \n",
    "        return f'{x_binning}x{y_binning}'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "600e9224",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-16T12:23:25.227840Z",
     "iopub.status.busy": "2026-06-16T12:23:25.227763Z",
     "iopub.status.idle": "2026-06-16T12:23:25.230489Z",
     "shell.execute_reply": "2026-06-16T12:23:25.230179Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OPTICAM] Checking instrument InstrumentWithInferredBinning.\n",
      "[OPTICAM] WARNING: InstrumentWithInferredBinning.dark_curr_kw (DARKCURR) is not a valid header keyword for file /tmp/tmp_wnl6b0w/out/data/no_binnning extension 0. If no dark current is listed in the image headers, you will need to use a `opticam.DarkNoiseCorrector` instance to correct for dark noise. See https://opticam.readthedocs.io/en/latest/_executed/applying_corrections.html#Dark-noise for details.\n",
      "[OPTICAM] InstrumentWithInferredBinning sucessfully passed all checks.\n",
      "[OPTICAM] InstrumentWithInferredBinning triggered a warning during 1 check. Warnings may be ignored provided their caveats are satisfied.\n"
     ]
    }
   ],
   "source": [
    "from out.instrument_with_inferred_binning import InstrumentWithInferredBinning\n",
    "\n",
    "\n",
    "instrument_with_inferred_binning = InstrumentWithInferredBinning.from_json(config=config_dict)\n",
    "instrument_with_inferred_binning.run_checks(new_file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ca778a9",
   "metadata": {},
   "source": [
    "The above can be extended to more-or-less any case in which an instrument does not include required keywords in its data products."
   ]
  }
 ],
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