{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "06b2b63f",
   "metadata": {},
   "source": [
    "# Speed Comparisions (VIP vs GRaTeR-JAX)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecf13008",
   "metadata": {},
   "source": [
    "This notebook provides comparisons between previous `numpy`-based implementations of GRaTeR (namely [VIP, the Vortex Image Processing package](https://vip.readthedocs.io/en/latest/tutorials/05B_fm_disks.html)). See below for the performance takeaways from these tests."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "af9e8260",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mihirkondapalli/anaconda3/envs/package-env/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from vip_hci.fm.scattered_light_disk import ScatteredLightDisk as VIP_ScatteredLightDisk\n",
    "from vip_hci.var.filters import frame_filter_lowpass\n",
    "\n",
    "from grater_jax.optimization.optimize_framework import Optimizer\n",
    "from grater_jax.disk_model.SLD_ojax import ScatteredLightDisk as JAX_ScatteredLightDisk\n",
    "from grater_jax.disk_model.SLD_utils import (\n",
    "    DustEllipticalDistribution2PowerLaws,\n",
    "    HenyeyGreenstein_SPF,\n",
    ")\n",
    "from grater_jax.disk_model.objective_functions import Parameter_Index\n",
    "import jax\n",
    "import os\n",
    "\n",
    "os.environ['XLA_PYTHON_CLIENT_MEM_FRACTION'] = '0.50'\n",
    "jax.config.update(\"jax_enable_x64\", True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47b94723",
   "metadata": {},
   "source": [
    "## Model Generation Benchmarks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ddd0cc66",
   "metadata": {},
   "outputs": [],
   "source": [
    "density = {\n",
    "    \"name\": \"2PowerLaws\",\n",
    "    \"a\": 60.0,\n",
    "    \"ksi0\": 1.0,\n",
    "    \"gamma\": 2.0,\n",
    "    \"beta\": 1.0,\n",
    "    \"ain\": 5.0,\n",
    "    \"aout\": -5.0,\n",
    "    \"e\": 0.0,\n",
    "    \"dens_at_r0\": 1.0,\n",
    "}\n",
    "phase = {\"name\": \"HG\", \"g\": 0.3, \"polar\": False}\n",
    "\n",
    "vip_disk = VIP_ScatteredLightDisk(\n",
    "    nx=200,\n",
    "    ny=200,\n",
    "    distance=50.0,\n",
    "    itilt=60.0,\n",
    "    omega=0.0,\n",
    "    pxInArcsec=0.01225,\n",
    "    pa=0.0,\n",
    "    density_dico=density,\n",
    "    spf_dico=phase,\n",
    "    xdo=0.0,\n",
    "    ydo=0.0,\n",
    ")\n",
    "vip_img = vip_disk.compute_scattered_light(halfNbSlices=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b59c7ffc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define Optimizer inputs\n",
    "spf_params = HenyeyGreenstein_SPF.params.copy()\n",
    "spf_params[\"g\"] = 0.3\n",
    "\n",
    "disk_params = Parameter_Index.disk_params.copy()\n",
    "misc_params = Parameter_Index.misc_params.copy()\n",
    "\n",
    "disk_params.update(\n",
    "    {\n",
    "        \"sma\": 60.0,\n",
    "        \"alpha_in\": 5.0,\n",
    "        \"alpha_out\": -5.0,\n",
    "        \"ksi0\": 1.0,\n",
    "        \"gamma\": 2.0,\n",
    "        \"beta\": 1.0,\n",
    "        \"e\": 0.0,\n",
    "        \"dens_at_r0\": 1.0,\n",
    "        \"inclination\": 60.0,\n",
    "        \"position_angle\": 0.0,\n",
    "        \"omega\": 0.0,\n",
    "        \"x_center\": 100.0,\n",
    "        \"y_center\": 100.0,\n",
    "        \"halfNbSlices\": 25,\n",
    "    }\n",
    ")\n",
    "\n",
    "misc_params.update(\n",
    "    {\n",
    "        \"distance\": 50.0,\n",
    "        \"pxInArcsec\": 0.01225,\n",
    "        \"nx\": 200,\n",
    "        \"ny\": 200,\n",
    "        \"halfNbSlices\": 25,\n",
    "        \"flux_scaling\": 1,\n",
    "    }\n",
    ")\n",
    "\n",
    "optimizer = Optimizer(\n",
    "    DiskModel=JAX_ScatteredLightDisk,\n",
    "    DistrModel=DustEllipticalDistribution2PowerLaws,\n",
    "    FuncModel=HenyeyGreenstein_SPF,\n",
    "    PSFModel=None,\n",
    "    disk_params=disk_params,\n",
    "    spf_params=spf_params,\n",
    "    psf_params=None,\n",
    "    misc_params=misc_params,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "91838393",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log-likelihood: 450799.6516621609\n",
      "Mean residual: 4.1688652349367055e-13\n",
      "Max residual: 3.468051170405547e-12\n"
     ]
    }
   ],
   "source": [
    "# Generate JAX model\n",
    "jax_img = optimizer.get_model()\n",
    "\n",
    "# Compute likelihood\n",
    "err_map = np.ones_like(vip_img) * np.nanstd(vip_img)\n",
    "ll = optimizer.log_likelihood(vip_img, err_map)\n",
    "print(\"Log-likelihood:\", ll)\n",
    "\n",
    "# Compare residuals\n",
    "residual = vip_img - jax_img\n",
    "mean_resid = np.mean(np.abs(residual))\n",
    "max_resid = np.max(np.abs(residual))\n",
    "print(\"Mean residual:\", mean_resid)\n",
    "print(\"Max residual:\", max_resid)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5946c10f",
   "metadata": {},
   "source": [
    "### GRaTeR-JAX vs VIP Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b077973f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABdIAAAHDCAYAAAAgM+iKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOy9e5wdRZk+/lT1mckkmYQIJtwJyE25eUFkucfLioooXlAwCuIq+gVE5YurgHJbNbJ+dwW8BHdXgyJRQUXxArooonj5iSCLLCsLGgRFEJEEEjLJnK7390fV211dp7pPnzPnzJyZeZ98zqe7q6urqqs78573Pc/7lCIigkAgEAgEAoFAIBAIBAKBQCAQCASCKPRUD0AgEAgEAoFAIBAIBAKBQCAQCASCQYYE0gUCgUAgEAgEAoFAIBAIBAKBQCCogATSBQKBQCAQCAQCgUAgEAgEAoFAIKiABNIFAoFAIBAIBAKBQCAQCAQCgUAgqIAE0gUCgUAgEAgEAoFAIBAIBAKBQCCogATSBQKBQCAQCAQCgUAgEAgEAoFAIKiABNIFAoFAIBAIBAKBQCAQCAQCgUAgqIAE0gUCgUAgEAgEAoFAIBAIBAKBQCCogATSBQKBQCAQCAQCgUAgEAgEAoFAIKiABNIFghowxmCfffbBhz/84akeykDiRz/6EZRS+NGPfpSVvfnNb8bOO+/c03523nlnvPnNb86Or7/+eoyOjuKRRx7paT8CgUAgEPQbv/zlLzE8PIw//OEPUz0ULFu2DMuWLetpm6HNnixcdtll2GmnnbBp06ZJ71sgEAgEg43zzz8fSqladZVSOP/88/s6nn7YX4FA0F9IIF0w0HjFK16BefPm4Yknniits3z5cgwPD+PRRx8FYA3eaaedlp2/7777oJTKPkmSYKeddsKrXvUq3H777bXG8aUvfQkPPPBAod3LL7+80O7IyAj22GMPnHbaaXj44YcL199333046aSTsOuuu2JkZATbbLMNDj/8cJx33nmFesuWLSu06X9++9vf1hrrbMJLXvIS7LbbblixYsVUD0UgEAgGFmvWrMFpp52GPfbYA/PmzcO8efOw11574dRTT8Udd9xRqMsOJn+Ghoaw88474/TTT8fatWs77rvKrvmfOo5qr8a2bNky7LPPPtFz//M//5PZ9Fibf/nLX7DlllviBS94Qcu58fFx7Lvvvth5552xYcOGtuM455xzcPzxx2Pp0qWFciLCFVdcgcMPPxyLFi3CvHnzsO++++LCCy+s1W4Z7rrrLpx//vm47777um5jOuDNb34zNm/ejM985jNTPRSBQCAQVCD0pxuNBrbffnu8+c1vxp/+9KepHp5AIBBE0ZjqAQgEVVi+fDm+9a1v4ZprrsEJJ5zQcv7JJ5/EN7/5TbzkJS/BVlttVdnW8ccfj5e97GVI0xT/8z//g5UrV+K6667DL37xCzzrWc+qvPZjH/sYjjvuOGyxxRYt5y688ELssssuGBsbw80334yVK1fiu9/9Lu68807MmzcP9957Lw444ADMnTsXb3nLW7Dzzjvjz3/+M2677TZcdNFFuOCCCwrt7bDDDtHA8HbbbVc5xkHDv//7v8MY0/d+3v72t+PMM8/EBRdcgAULFvS9P4FAIJhO+Pa3v43Xv/71aDQaWL58OZ75zGdCa43f/va3+PrXv46VK1dizZo1LcHclStXYnR0FBs2bMAPfvADfOITn8Btt92Gm2++uaP+zznnHLz1rW/Njm+55RZceumlOPvss/GMZzwjK99vv/1qt9mrscXwxS9+Edtssw0ee+wxfPWrXy2MHQCWLFmCiy66CCeffDI+//nP48QTT8zO/cu//AvuvPNOfOtb38L8+fMr+7n99ttxww034Gc/+1mhPE1TvOENb8BVV12Fww47DOeffz7mzZuHn/zkJ7jgggtw9dVX44YbbsDWW2/d8b3ddddduOCCC7Bs2bKWjLHvf//7HbfXDnfffTe0nnzOzsjICE488UT867/+K975znfWZh4KBAKBYGrg+9O/+MUvcPnll+Pmm2/GnXfeiZGRkZ729YEPfADvf//7e9qmQCCYZSCBYIDx5JNP0oIFC+jII4+Mnl+9ejUBoC9/+ctZGQA69dRTs+M1a9YQAPrYxz5WuPbaa68lAHTyySdXjuG2224jAHTDDTcUyletWkUA6JZbbimUn3HGGQSAVq9eTUREp5xyCjUaDbrvvvta2n744YcLx0cccQTtvffeleMZRNx4440EgG688ca+9rN06VI68cQTC2UPP/wwJUlCn/3sZ/vat0AgEEw33HvvvTR//nx6xjOeQQ8++GDL+fHxcbrkkkvo/vvvz8rOO+88AkCPPPJIoe7rX/96AkD/3//3/01oTFdffXXX9qJXYyuztcYY2nnnnemMM86gV73qVbRs2bLo9cYYOvTQQ+mpT30q/fWvfyUiot///vc0d+5cevWrX11rDKeffjrttNNOZIwplH/kIx8hAHTmmWe2XHPttdeS1ppe8pKX1OojxETmfjpg/fr12f6vfvUrAkA/+MEPpnBEAoFAIKhCmT/9vve9jwDQV77ylSkamQUAOu+88/raxxFHHEFHHHFEX/sQCAS9hUi7CAYac+fOxatf/Wr84Ac/wF/+8peW86tXr8aCBQvwile8ouO2OS17zZo1lfW+8Y1vYHh4GIcffnhX7f7ud7/DDjvs0ML2AyyzrVf4wx/+gFNOOQV77rkn5s6di6222grHHntsSwo3p9D9+Mc/xtvf/nZstdVWWLhwIU444QQ89thjhbo777wzXv7yl+P73/8+nvWsZ2FkZAR77bUXvv71r7cdT0wj3RiDiy++GHvvvTdGRkaw9dZb4+1vf3tLv0SED33oQ9hhhx0wb948PP/5z8d///d/R/tZsmQJ9ttvP3zzm99sP0kCgUAwi/DP//zP2LBhA1atWoVtt9225Xyj0cDpp5+OHXfcsW1bhx12GABr0xh/+9vfcOaZZ2LffffF6OgoFi5ciJe+9KX4r//6r47Het111+Gwww7D/PnzsWDBAhx11FGlf/frjK0b/PSnP8V9992H4447Dscddxx+/OMf449//GNLPaUULrvsMqxbtw5nnnkmAOCUU05Bo9HApZdeWquvb3zjG3jBC15QYEtv3LgRH/vYx7DHHntEM9OOPvponHjiibj++uvxi1/8IiuvY6svv/xyHHvssQCA5z//+VkaPa9tEmq08tonV111FS644AJsv/32WLBgAV772tdi3bp12LRpE9797ndjyZIlGB0dxUknndSiSR5qpFdJ+/jfVX7729/ita99LbbcckuMjIzguc99Lq699tpC2/xd5qabbsIpp5yCJUuWYIcddsjO77///thyyy3lu4FAIBBMQ8Tseh3bMD4+jgsuuAC77747RkZGsNVWW+HQQw/Ff/7nf2Z1YhrpmzZtwnve8x4sXrw4iy3E7H/ZGmCxNletWoUXvOAFWLJkCebMmYO99toLK1eu7HguBALB4EGkXQQDj+XLl+Pzn/88rrrqqoJG+d/+9jd873vfw/HHH4+5c+d23C4b5naSMD/72c+wzz77YGhoqKt2ly5dihtuuAE//OEPo5qqIdI0xV//+tdC2cjICEZHRyuvu+WWW/Czn/0Mxx13HHbYYQfcd999WLlyJZYtW4a77roL8+bNK9Q/7bTTsGjRIpx//vm4++67sXLlSvzhD3/InGfGPffcg9e//vV4xzvegRNPPBGrVq3Csccei+uvvx5///d/X2tOGG9/+9tx+eWX46STTsLpp5+ONWvW4JOf/CR+/etf46c//Wk2x+eeey4+9KEP4WUvexle9rKX4bbbbsOLX/xibN68Odru/vvvj2984xsdjUUgEAhmOr797W9jt912w4EHHjjhtjjQ+ZSnPCUr+/3vf49vfOMbOPbYY7HLLrvg4Ycfxmc+8xkcccQRuOuuu2pLkl1xxRU48cQTceSRR+Kiiy7Ck08+iZUrV+LQQw/Fr3/967YLV8fG1g2uvPJK7LrrrjjggAOwzz77YN68efjSl76E9773vS119957b5x55plYsWIFFixYgOuvvx6XXHIJtt9++7b9/OlPf8L999+P5zznOYXym2++GY899hje9a53odGIf0U/4YQTsGrVKnz729/G3/3d32Xl7Wz14YcfjtNPP71FVseX14lhxYoVmDt3Lt7//vfj3nvvxSc+8QkMDQ1Ba43HHnsM559/fpaGv8suu+Dcc88tbeuKK65oKfvABz6Av/zlL9l3nP/+7//GIYccgu233x7vf//7MX/+fFx11VU45phj8LWvfQ2vetWrCtefcsopWLx4Mc4999wW/fjnPOc5+OlPf1p5fwKBQCAYPIR2va5tOP/887FixQq89a1vxfOe9zw8/vjj+NWvfoXbbrut0m9961vfii9+8Yt4wxvegIMPPhg//OEPcdRRR03oHlauXIm9994br3jFK9BoNPCtb30Lp5xyCowxOPXUUyfUtkAgmGJMNSVeIGiHZrNJ2267LR100EGF8ssuu4wA0Pe+971COUqkXS644AJ65JFH6KGHHqIf/ehH9OxnP5sA0Ne+9rXK/nfYYQd6zWte01LOqWg33HADPfLII/TAAw/Ql7/8Zdpqq61o7ty59Mc//pGIiO68806aO3cuAaBnPetZ9K53vYu+8Y1v0IYNG1raPOKIIwhAyyeUM4nhySefbCn7+c9/TgDoC1/4Qsu4999/f9q8eXNW/s///M8EgL75zW9mZUuXLm2Zo3Xr1tG2225Lz372s7OymLTLiSeeSEuXLs2Of/KTnxAAuvLKKwtjvP766wvlf/nLX2h4eJiOOuqoQsr72WefXToXnAofSuUIBALBbMW6desIAB1zzDEt5x577DF65JFHso9vP1g+5e6776ZHHnmE7rvvPvrc5z5Hc+fOpcWLFxds19jYGKVpWmh7zZo1NGfOHLrwwguj4wrlRZ544glatGgRve1tbyvUe+ihh2iLLbYolHcytirEpF02b95MW221FZ1zzjlZ2Rve8AZ65jOfWdrOk08+SU972tMym9psNmv1f8MNNxAA+ta3vlUov/jiiwkAXXPNNaXX/u1vfyMABQmZura6StolTC1nu77PPvsUviscf/zxpJSil770pYXrDzrooILN53FVfX/h7x3+d5QXvvCFtO+++9LY2FhWZoyhgw8+mHbfffesjL/LHHrooaXzfvLJJ9PcuXNL+xcIBALB1CLmT3/1q1+lxYsX05w5c+iBBx4govq24ZnPfCYdddRRlX3ydwnG7bffTgDolFNOKdR7wxve0CLtEvq3ZW0SxX3zI488kp72tKcVykTaRSCYfhBpF8HAI0kSHHfccfj5z39eSP1dvXo1tt56a7zwhS+s1c55552HxYsXY5tttsGyZcvwu9/9DhdddBFe/epXV1736KOPVrLcXvSiF2Hx4sXYcccdcdxxx2F0dBTXXHNNxkrbe++9cfvtt+ONb3wj7rvvPlxyySU45phjsPXWW+Pf//3fW9rbeeed8Z//+Z+Fzz/+4z+2vT+flT8+Po5HH30Uu+22GxYtWoTbbrutpf7JJ59cYNn/n//zf9BoNPDd7363UG+77bYrMMBYBubXv/41HnroobbjYlx99dXYYost8Pd///f461//mn32339/jI6O4sYbbwQA3HDDDdi8eXPLAmHvfve7S9vm5xMy+QUzFz/+8Y9x9NFHY7vttoNSqu8ZCZyy6X+e/vSn97VPgWAiePzxxwEgms20bNkyLF68OPt86lOfaqmz5557YvHixdh5553xlre8Bbvtthuuu+66QnbTnDlzssUk0zTFo48+itHRUey5555RuxPDf/7nf2Lt2rU4/vjjC7YhSRIceOCBmW3odGyd4rrrrsOjjz6K448/Pis7/vjj8V//9V+lEjPDw8PZIuQvfOELkSRJrb4effRRAK0M+ieeeAIAKhfO5nP8fBm9stUhTjjhhMJ3hQMPPBBEhLe85S2FegceeCAeeOABNJvNWu3eeOONOOuss/DOd74Tb3rTmwDYTMMf/vCHeN3rXocnnngiexceffRRHHnkkbjnnnvwpz/9qdDO2972ttJ5f8pTnoKNGzfiySef7OSWBT2E2GqBQFAHvj/92te+FvPnz8e1116LHXbYoSPbsGjRIvz3f/837rnnntp9s+97+umnF8qrfM868H3zdevW4a9//SuOOOII/P73v8e6desm1LZA0Akm2xa36298fBzve9/7sO+++2L+/PnYbrvtcMIJJ+DBBx/s67h6CZF2EUwLLF++HB//+MexevVqnH322fjjH/+In/zkJzj99NNrO64nn3wyjj32WGitsWjRIuy9996YM2dOrWuJqPTcpz71Keyxxx5oNBrYeuutseeee2aBBcYee+yBK664Amma4q677sK3v/1t/PM//zNOPvlk7LLLLnjRi16U1Z0/f37h2EeapnjkkUcKZVtuuSWGh4exceNGrFixAqtWrcKf/vSnwphjxnr33XcvHI+OjmLbbbdt0VTfbbfdWjTf9thjDwA27W6bbbYpmZki7rnnHqxbt65UF5418P/whz9Ex7d48eLSHzT4XsNxCmYuNmzYgGc+85l4y1ve0vbHsF5h7733xg033JAdl0kvCASDAA64rl+/vuXcZz7zGTzxxBN4+OGH8cY3vjF6/de+9jUsXLgQjzzyCC699FKsWbOmRUbNGINLLrkEn/70p7FmzRqkaZqdayebxmBnt0z6bOHChR2PbePGjS12r52t+uIXv4hddtkFc+bMwb333gsA2HXXXTFv3jxceeWV+MhHPtJyzSWXXIJf//rX2GeffXDppZfibW97G3bbbbfqG/YQfrfgZ8YB9RjKgu29stUhdtppp8Ix/3AQ6upvscUWMMZg3bp1bZ/9H//4R7z+9a/HIYccgn/913/Nyu+9914QET74wQ/igx/8YPTav/zlLwX5nF122aW0H/luMPUQWy0QCOqA/el169bhc5/7HH784x9nfnontuHCCy/EK1/5Suyxxx7YZ5998JKXvARvetObsN9++5X2/Yc//AFaa+y6666F8j333HNC9/TTn/4U5513Hn7+85+3/KC7bt26zJ4KBP3GZNvidv09+eSTuO222/DBD34Qz3zmMzNZw1e84hX41a9+1ffx9QLyzUIwLbD//vvj6U9/Or70pS/h7LPPxpe+9CUQEZYvX167jd133700QF2FrbbaqmUxTB/Pe97z8NznPrdWW0mSYN9998W+++6Lgw46CM9//vNx5ZVX1h7XAw880OI03njjjVi2bBne+c53YtWqVXj3u9+Ngw46CFtssQWUUjjuuONgjKnVfj9hjMGSJUtw5ZVXRs8vXry467b5+Tz1qU/tug3B9MJLX/pSvPSlLy09v2nTJpxzzjn40pe+hLVr12KfffbBRRddVFhMr1M0Go2ug1ECwWRjiy22wLbbbos777yz5Rxrpoc/nPo4/PDDs7+pRx99NPbdd18sX74ct956a/Zj8Uc+8hF88IMfxFve8hb80z/9E7bccktorfHud7+7tt3heldccUX0/1csCNZubF/5yldw0kknFa6p+kH88ccfx7e+9S2MjY21/IgL2Ay4D3/4w4WA7AMPPIDzzjsPxxxzDD796U/j6U9/Ok499VR873vfa3vPHGgOv1uwXvkdd9yBY445JnrtHXfcAQDYa6+92vbTC5SRFcrKq+YZADZv3ozXvva1mDNnDq666qrC8+V34cwzz8SRRx4ZvT78oaJqjZzHHnsM8+bN62odHUFvILZaIBDUge9PH3PMMTj00EPxhje8AXfffXdHtuHwww/H7373O3zzm9/E97//ffzHf/wHPv7xj+Oyyy7DW9/61gmPs+yHWZ9IANg10174whfi6U9/Ov71X/8VO+64I4aHh/Hd734XH//4xwfCNxfMHky2LW7X3xZbbFFYABgAPvnJT+J5z3se7r///hYSxyBCAumCaYPly5fjgx/8IO644w6sXr0au+++Ow444IC+9/v0pz8da9as6Xm7/GXhz3/+c+1rttlmm5Y/Os985jMBAF/96ldx4okn4l/+5V+yc2NjY1i7dm20rXvuuQfPf/7zs+P169fjz3/+M172spcV6jELwP/i8L//+78A0HYBOB+77rorbrjhBhxyyCGVTu3SpUuz8T3taU/Lyh955JHSHzTWrFmDpz71qRMKxgtmFk477TTcdddd+PKXv4ztttsO11xzDV7ykpfgN7/5TTRQVgf33HMPtttuO4yMjOCggw7CihUrpoWhF8xeHHXUUfiP//gP/PKXv8Tznve8rtsZHR3Feeedh5NOOglXXXUVjjvuOADW7jz/+c/HZz/72UL9tWvX1v5hkxlgS5Ys6erH7tjYjjzyyBZbWYWvf/3rGBsbw8qVK1vGfffdd+MDH/gAfvrTn+LQQw/Nynnx80svvRTbbrstPvzhD+Od73wnvvzlL2fzUwaWmgi/Wxx66KFYtGgRVq9ejXPOOScarP7CF74AAHj5y19eKK9jqweBmX366afj9ttvx49//GNsvfXWhXNs84eGhrp6F0KsWbOm7WKqgqmF2GqBQBAiSRKsWLECz3/+8/HJT34ykxKraxu23HJLnHTSSTjppJOwfv16HH744Tj//PNLA+lLly6FMQa/+93vCiz0u+++u6XuU57ylKhvzRnVjG9961vYtGkTrr322sLfn5hcnUAw1eiHLe4U69atg1IKixYtmpT+JgrRSBdMGzD7/Nxzz8Xtt9/eERt9IjjooINw5513YtOmTV1d/5Of/ATj4+Mt5azH1kna2MjICF70ohcVPix3kiRJCxPsE5/4RMsv5Ix/+7d/K4xr5cqVaDabLb8ePvjgg7jmmmuy48cffxxf+MIX8KxnPasjxs/rXvc6pGmKf/qnf2o512w2sy8lL3rRizA0NIRPfOIThfu5+OKLS9u+9dZbcdBBB9Uei2Bm4/7778eqVatw9dVX47DDDsOuu+6KM888E4ceeihWrVrVVZsHHnggLr/8clx//fVYuXIl1qxZg8MOO6xSgkEgmGr84z/+I+bNm4e3vOUtePjhh1vOt2MP+1i+fDl22GEHXHTRRVlZzO5cffXVLTrWVTjyyCOxcOFCfOQjH4naylDOrM7Ytt122xZbWYUvfvGLeNrTnoZ3vOMdeO1rX1v4nHnmmRgdHS1kU11zzTW49tprceGFF2YSJ6eccgr2339/nHHGGS365SG233577Ljjji3pq/PmzcOZZ56Ju+++G+ecc07Ldd/5zndw+eWX48gjj8Tf/d3fFc7VsdXz588HgNIf2PuNVatW4TOf+Qw+9alPRX/YWbJkCZYtW4bPfOYzUZJBnXfBx2233YaDDz646/EK+gux1QKBoAzLli3D8573PFx88cVYuHBhbdvAa5AwRkdHsdtuu1X68ez7XnrppYXymO+56667Yt26dVl2GGBJcb79BfKsrVBqtdu/bQJBv9APW9wpxsbG8L73vQ/HH398VNJxECGMdMG0wS677IKDDz4Y3/zmNwFg0gLpr3zlK/FP//RPuOmmm/DiF7+44+svuugi3HrrrXj1q1+d6bPddttt+MIXvoAtt9xywguZMF7+8pfjiiuuwBZbbIG99toLP//5z3HDDTeUapVu3rwZL3zhC/G6170Od999Nz796U/j0EMPxSte8YpCvT322AP/8A//gFtuuQVbb701Pve5z+Hhhx/u+A/rEUccgbe//e1YsWIFbr/9drz4xS/G0NAQ7rnnHlx99dW45JJL8NrXvhaLFy/GmWeeiRUrVuDlL385Xvayl+HXv/41rrvuuijD8S9/+QvuuOMOnHrqqR2NRzBz8Zvf/AZpmmb6wIxNmzZl/x9++9vftmUqvu9978NHP/pRACj8wLTffvvhwAMPxNKlS3HVVVfhH/7hH3p8BwJBb7D77rtj9erVOP7447Hnnnti+fLleOYznwkiwpo1a7B69WporbHDDju0bWtoaAjvete78N73vhfXX389XvKSl+DlL385LrzwQpx00kk4+OCD8Zvf/AZXXnllIZuoHRYuXIiVK1fiTW96E57znOfguOOOw+LFi3H//ffjO9/5Dg455BB88pOf7HhsdfHggw/ixhtvbFlkjDFnzhwceeSRuPrqq3HppZdibGwMp59+Op797GcXrtFa47LLLsOBBx6Ic845B5/4xCcq+33lK1+Ja665poVF/v73vx+//vWvcdFFF+HnP/85XvOa12Du3Lm4+eab8cUvfhHPeMYz8PnPf76lvTq2+lnPehaSJMFFF12EdevWYc6cOXjBC15QunZJL/HXv/4Vp5xyCvbaay/MmTMHX/ziFwvnX/WqV2H+/Pn41Kc+hUMPPRT77rsv3va2t+FpT3saHn74Yfz85z/HH//4R/zXf/1Xrf5uvfVW/O1vf8MrX/nKftyOoAcQWy0QCKrw3ve+F8ceeywuv/zy2rZhr732wrJly7D//vtjyy23xK9+9St89atfzbLIYnjWs56F448/Hp/+9Kexbt06HHzwwfjBD36QrZfi47jjjsP73vc+vOpVr8Lpp5+OJ598EitXrsQee+xRWGT9xS9+MYaHh3H00Ufj7W9/O9avX49///d/x5IlSzrKRhcI+o1+2OJOMD4+jte97nUgIqxcubLj66cKEkgXTCssX74cP/vZz/C85z2vowW9JoL9998f++23H6666qquAulnn302Vq9ejZtuuglXXnklnnzySWy77bY47rjj8MEPfrByoaxOcMkllyBJElx55ZUYGxvDIYccghtuuKFUS+6Tn/wkrrzySpx77rkYHx/H8ccfj0svvbQl9Xv33XfHJz7xCbz3ve/F3XffjV122QVf+cpXStutwmWXXYb9998fn/nMZ3D22Wej0Whg5513xhvf+EYccsghWb0PfehDGBkZwWWXXYYbb7wRBx54IL7//e/jqKOOamnz61//OubMmYPXve51HY9HMDOxfv16JEmCW2+9tUUaYXR0FICVEPif//mfynaqFsxbtGgR9thjj+iXbIFgkPDKV74Sv/nNb/Av//Iv+P73v4/Pfe5zUEph6dKlOOqoo/COd7wjkwhrh5NPPhkf+tCH8NGPfhQveclLcPbZZ2PDhg1YvXo1vvKVr+A5z3kOvvOd7+D9739/R2N8wxvegO222w4f/ehH8bGPfQybNm3C9ttvj8MOO6xF67zu2KpARNnfhi9/+cswxuDoo48urX/00Ufja1/7Gq677jr88Ic/xIMPPoivf/3rLX9fnvvc5+KUU07Bpz/9abz5zW/G/vvvX9rmW97yFnzyk59skYxJkgRXXXUVvvCFL+A//uM/8MEPfhCbN2/GrrvuivPOOw//9//+34xZ7qOOrd5mm21w2WWXYcWKFfiHf/gHpGmKG2+8cVIC6evXr8fY2BjuuusuvOlNb2o5v2bNGsyfPx977bUXfvWrX+GCCy7A5ZdfjkcffRRLlizBs5/9bJx77rm1+7v66qux0047lS5iK5h6iK0WCARVePWrX41dd90V/+///T+87W1vq2UbTj/9dFx77bX4/ve/j02bNmHp0qX40Ic+hPe+972VfX3uc5/D4sWLceWVV+Ib3/gGXvCCF+A73/lOy8LaW221Fa655hqcccYZ+Md//EfssssuWLFiBe65555CIH3PPffEV7/6VXzgAx/AmWeeiW222Qb/5//8HyxevDiTqhEIBgGTYYvLwEH0P/zhD/jhD384bdjoAKCok7xegWCW4oorrsCpp56K+++/f9roNpXh8ssvx0knnYRbbrml7SKpO++8M/bZZx98+9vfnqTRdY5nP/vZWLZsGT7+8Y9P9VAEUwSlFK655ppscb7//d//xZ577okf//jHOOyww/rS5/r167HTTjvh/PPPL2WyCgSCwcRznvMczJ8/Hz/5yU+mdBwvfOELsd122+GKK66YUDvTwVZPJjZt2oSdd94Z73//+/Gud71rqocjcBBbLRAIBALB1GKybXHYH4OD6Pfccw9uvPHGabfWnWikCwQ1sHz5cuy000741Kc+NdVDEXi4/vrrcc899+Css86a6qEIJhnr16/H7bffjttvvx2AZTPefvvtuP/++7HHHntg+fLlOOGEE/D1r38da9aswS9/+UusWLEC3/nOd7rq78wzz8RNN92E++67Dz/72c/wqle9CkmS4Pjjj+/hXQkEgn5j/fr1+O1vf4u99tprqoeCj3zkI/jKV77SskiZYGJYtWoVhoaG8I53vGOqhzLrIbZaIBAIBIKpxWTb4qr+ABtEf+1rX4tf/epXuPLKK5GmKR566CE89NBD2Lx5cy9uuf8ggUAwq7Bq1SoCQLfcckvbukuXLqWjjjpqEkYlEHSGG2+8kQC0fE488UQiItq8eTOde+65tPPOO9PQ0BBtu+229KpXvYruuOOOrvp7/etfT9tuuy0NDw/T9ttvT69//evp3nvv7eEdCQSCfuKhhx6ilStX0sEHH0wA6Oabb57qIfUMYqsFgwqx1QKBQCAQTC0m2xa362/NmjXR8wDoxhtv7M1N9xki7SIQzDLMNGkXgUAgEAja4Uc/+hFe9KIXYbfddsM555wT1emerhBbLRAIBAKBQCAQTA4kkC4QCAQCgUAgEAgEAoFAIBAIBAJBBaZMI/1Tn/oUdt55Z4yMjODAAw/EL3/5y6kaikAgEAgEghKIvRYIBAKBYLAhtlogEAgEgsnBlDDSv/KVr+CEE07AZZddhgMPPBAXX3wxrr76atx9991YsmTJZA9HIBAIBLMQY2NjPV3QZHh4GCMjIz1rbxAg9logEAgEUwmx1e0htlogEAgEU4nZZqunJJB+4IEH4oADDsAnP/lJAIAxBjvuuCPe+c534v3vf3/b640xePDBB7FgwQIopfo9XIFAIBAMEIgITzzxBLbbbjto3V1i1djYGHbZZRs89NC6no1rm222wZo1awba6HeKidhrsdUCgUAweyG2evIgtlogEAgE3WKi9npsbAyL587F+h6OadBtdWOyO9y8eTNuvfVWnHXWWVmZ1hovetGL8POf/zx6zaZNm7Bp06bs+E9/+hP22muvvo9VIBAIBIOLBx54ADvssENX127evBkPPbQOv//Dx7Fw4dwJj+XxxzfiaUvfg82bNw+swe8UndprsdUCgUAgCCG2ur8QWy0QCASCXqBbe71582asB/B/AczpwTg2AfiXhx4aaFs96YH0v/71r0jTFFtvvXWhfOutt8Zvf/vb6DUrVqzABRdcEDmj3KeX6Pcv8d3K0tcZV1Xb8XMKSaRQB3Xy4yJTwS/39l2bhTK3z20ppbN97e0rlWT1tEqyPpTS0LD1tGs/UUN5XT5H+X7iXu8GNVwdBU0KCV+fXaegoZBAQSvlSoBEKShlR5YoBa1g6yogcdOQKEApO0oFuDr5+YbO62WzxvVVXl+rfEaVouw83HGxDmV9wquvvfpZWyA3R9x3/Ni/rg6IbAvGbflKQ7ZHgoJx9Qwh3weQujJyZSnZbdOVpcbWN2Q/qbcluOPIeQK5fUJKBILdJwBNMiAQmm6kKVLYEvuvqdKsHACaqgkDA4Jx5U17f8qW8TlDqbt/A4M0q29gQGR4Vtzc5OfJ2zeuHh/b8xVl5JW58YZ1/H7zc8XnS8F5Wxgpi/RTRPya+udbe6tXp4kFCxZ02HYrFi6c2xPnfCaiU3stthoQWy22Wmy12Gp/a8+LrZ4oxFaXo1e2+oE//AELFy7s2zhnPJrNyW2nXb2y82nJ34lY/ao+urnfRiN+zNskiZd7WwOdde1v/X2+xbAe0Hqum6GXDTNJ7D4fc/yv0QA0TD5Q/qRpcfD8A5dfxx9sbPCd3Eg4/2U3FqvrH1edS5Lyc3XGUNZOu/rd1JlI/alut8d4/PHHsePSpRO213MADGbYu/eYFk/2rLPOwhlnnJEdP/7449hxxx0xPZ3z7tpXda6rTMdrPdepY2678I9jznfSWhZxzLmubjmXtOxzfW3d53yr+NieT2gIUEADjaxck86c/wYlgAISJNBQaBScdDvHDaWdI+w75SpzjhNXzo514uY8CRxt3kbL2LlmZxq+w06e022d59yRzq/l2ffr+845n/OfUrEtyuq2Ps328N0t64DnTrohVXDGYw46EdB0jnxqik57qoLzMSedgCYAUmSdeQWkREgAkAKaRCCyzji5+zVE0CAYEBLnTjedi65gYJTdEgwUEhiVO+EKCQwMoIxzVG25Vg3P6eY5MABSUHassuesXM/w9hOVwJBxx8Uvt8qNuFCmtOeEd6LMFbTjjcUvjaOzvy1FJKVOfwyd3FEvUpCJmiCauPPTizamO8RWi622vYmtFlsttlpstdez2OqBQpmtXrhwoQTSu8VkB9Hb1e3mXCeB9F4G0f19fxvZ5yB6LJA+Pm7jzMPD8fO8HRqyW7/7qttpF/uPDTlJbD9cxp9oMN3/zHU/Fo6N5QOrupk6N1D3hmLHdfY7aaPu9e3K252rc75X10xFm33ERO21RvdUpLCdQcekP9mnPvWpSJIEDz/8cKH84YcfxjbbbBO9Zs6cOZgzpxdJAu3Qb8e8O9RzzDtnuHU8jjaOeaxulWPeek3umOuajrnflkbeV+aYe2w3n9Fm93i/1TFnJ5wdc2a5sWMeHrdjt8Uc83bsNj0RdlvBaY875X47jPBJ6gjzzVD+9AwAKMpZb8iddXLXNkkVnHJD1plWzslW2jpk2jnZ2jnpisiehwIp2zjfBx8bKEATbFf2WaVEaCiVuZ0GsB0oy/TSUGgC7i1xJwDrQCoNw/PgTmXHrsy4Z2AA56AnMMRlqZvHBEbl17Q62AmI0sz51koXHPSiA87XRMoiDn0OHmX59S1QOupId9LPRKGgXEhlckCUgqiKxVe/nZmGTu212Gqx1WKrxVaLrRZb3Q+IrS7H4NpqQd/QbeB+KoLodQPoJfshA92PLXPwvIyZHsacx8ft1ifl172dGOl6aKg45PAWGo0wqK7RaAxjeMRV5ID52Fjx5kZGyp9Jo1HccoedZjLwYGPHdfbLrikbc7sxxerUubbTNicbgzKOScJsCqRP+hiHh4ex//774wc/+EFWZozBD37wAxx00EGTPRyBjzYMt75126YfHXHk83POeSddOG7ZJ5UFOTjpPO/fZ7Ipz6nN2W25A5zv+8cFJ9pzyHkbMtSK99A7+Ow2O5bW/Zhjzn/0tKLCR0U+iTbZ+YZrK3HneD/RxpYBaCiyZe7DQY0wsKGUPw5XHgQ+Grq1PqfwF67L7o+DL8gCMvD2dcZAy98JXfOJsCxBsSwpfZ+1qkhR6xAxeYaBRmXwUDCoEHs9oBBbLbZabLXY6n5AbPW0hNjqKUav2OhThX6Ov4rOHX4i530Guv8ZH7efTZvysrGxPB7t72/caOuOjdkAOl/j1/GPY5+w/qZN9jM2ZtveuDHeHtflOnxuc1PDQMfnoWxuYvPZL5Sx3etmOnQjO9Tpezjd/98Jpj2m5OeRM844AyeeeCKe+9zn4nnPex4uvvhibNiwASeddNJUDGcS0fkX1FoMty7Qqy/07fRWq/qO6a3a8iLDzV4TTxNXSDI2W67Lahlu+b6vzMoKrAoNdtCc1io75kopNDzHXDETTuXHjcBxnEiKuL3/OMMtm5PAsfcd7NgxO9p5X9TCgmupF2G7lWqxunIihURZDleCnOGWADCO/cbMt5QstU051pt2jDcNZrfBsdss+01DWaaY4XtzKrLa1oMpZ7t5tDYY91wNERpKw4Ack0vBtsxaqTaIAwBGORe9gunGrC/7TrL2q8nLkLPecjZbktULmW7Z1AZMN/98O6ZaLSabXx+t6ehlTLdq9JbpNpkw1ITpQap3L9oYRMxOey222q8ntlpstdhqsdVTDbHV1ZidtnqGod/yML2QjemGvh0el7HQgUxUPJRwYeK2z0APPzw8rsPHvVJG6USJhlnoY2NFsniaFhnqIyPDVvKFRaVDZjo3EDbEg67DRA/Z42Xl3TLT6zDRY2Ooy9SeCNu9UzZ4v9jjs4iVPpsY6VPyRF//+tfjkUcewbnnnouHHnoIz3rWs3D99de3LJIiGByUpYrXuSZMFQesY95SH7xomd9TOWsodj2AzFnP2+Alybhezm6y550jjoAVxY55wRHPj4EIk62E8VYcZ8iUKzrARWaadcwL16vy4xanPWzP78c7F2q0Fh318tRdTolmxxwuCECkAFIwimz6t7KaqIYUGip3B5Vzrnk6NJx2K2zSsCaFxDYFY5zz7uYlUVaDVbFjDwWtbIp5tu8cdHKdpS5dPHd5rRttkEI7Z93AuDJkDrpRxr1f9lwxbdx3rItOOaeNt6Z564KDDiBLGa+LMke8WF50mjt13otjrkoZ7y0mM2VcdFerIfZ6ekFstdjqGMRWi63uB8RWDw7EVs8i1GUFd9vWRAP6nWih8753HGOhsyQLr8UZSoj7AfS6Mi9A3i5QvgYrw1/70pd08W/BV13xg+ax6eC+OaCuYaoZ/FXB6LKgeuzXglhAvSzAPZH9bjAIAedBGMM0hgTSJwGnnXYaTjvttKnqfmahQ83VbhYuazuEgOHWrd4qXxMy5eow3Px0X5/hxs44L1jG3LpE5W67VipzzFlTlR3xJHDMOS25HbstH3tRazUvL+7XWdshfCox5z+GMEU8dMxDp5wdcp91F0Pi6hnnmBPBMdx8rVXLfGPWW0o85wRDClo799HwYl9UYL3BLXAGTdkxs93Y7wzZbtk+KAvM8FQZImilPK1UoOEWKGNnnTVYU+eM64LTXmSkcRuW6eY55fBYb05ctnXBMG6nWn+1HdNtIrqoUaZbP9AVe04wCBB73SOIrRZb3QZiq8VWl0FstaAdxFZPAfrNIu9Vf5MlpVEVCA63JVroZcHxMokX/zwPM8ZSB4qB9vCWJsJK9zXTY8osY2O2Thj39vtuNDRGRubZgDoz0/kka6b7N1a8eGIM9X4F06vajR1XjXm6a6ULZhzkrZo0TIffVeLoZOGyjtqFbkkT9+EvWlYsz9lsfOwz3PL93I3P+2QWmyo45vzrmVYqr6c81pd3XEjbzsbeen8xhlrLHJSlY08SWhlycae83X1oZRljUNZR12Tzvi1bybVN+aJiShFSo9119lk1FGwqt7vKkGWqJS6oYY+tjmvTOfgN53eGbDfbXznbjbJscruTgsALmmX8MFIwyjnNpO29wfsf4NhvxXlICmnjLfMNXXDie4m6LLT+LWQ2PWEXMOsFy21mzcvshdjqlnbFVoutFlvdM4it7g5iqwWCScBEfhjoNIju1asbRGcm+saNdssM9bIgO9DKUo9t6956O3mXkIU+MtL6m0HIUPfLOKCufakb/8Z9xKRTYgMuO9dJkHsirPNBqz9VmC7jnCCEkT4rUZMuNGjokOFWp41uGW514euttrZlGW6hQ+4z3Pw+Q8e8le2Wp4bnDrnVWPUdc6XyMma5sSPODDb/2Ge25WMsnsvKg3p1mWkxsCPLTrVx+qR1nfwwnRzg+/Kd9FanPGO/VfSjnGapZicdymqowqaKp9C5k04KWlu+mDIa5OmxZunj2uqvQru2NVntVg0ookq2G6BAyrLdrCPvmG0ANLmAAIpp49nYgdK08biDng/B110FUubMZenj2ZxPUH81f1Z1UsYnC9Vsuk4xWSnjZJog0wPnvAdtCOpAbHV2KLZabLXYarHVHUNstUDQE0w2G73bduqy0buVjOlEyiWgahv3V5rjxLx4Z1UAvQ5DnYcck3QJt53IuwDlEi+hTnqjkbc9d24xpj0ykrcTqrdk2ukjw9DNzfbmYwF1LvNRl5Xu1/cHETuuCqB3E2yvw0qvW1bnXJ3zE60vyDCbAunTYYyzFv1avKwXiDHc6qSK19VbzcprpIkDKOwDKKSJ5+cdmy2yYJllvuWOc9ERV4g55j7Djff9JxYuOtYNDCFzHOuAPCZZaZsl5eF47bH9aPdRCqWfRNs6iTZQipAog4YySLS9LlEGiSL70a37jeyY0FBw5+E+NqjAZcx2U0DGdmM2nP/clAIa/nNDMRBjt63vieK3jVThvWoH/132A0waSfz/TPZ/xGd7tu+vsLjfBNmm/jiKhX0wD/1oc5riT3/6E974xjdiq622wty5c7HvvvviV7/61VQPS9AFxFaLrRZbLbY62o/Y6mkPsdWCgUE3ge5eBenLUBVsLAme+0F0jg3zlgPhfhnvb9xoGeh+OX82brRbDrL758LjjRvz+mNjtk3+xNoOP359v80nngDWry/W3bDB1lu/vvX8pk15mV/uj81F1UvnsEoqpyN0+m7V0cfphO7faeBfIBgAyE8tAKYtw60LdMpIa7dwWbv2utVbrcO0K+qrxtPEuUY0TRy5Y64DrdXYgmVlWqtAK4stll7d7i0jl/LMadI+DGwKNjvPxIuDIX5c3kd7Rpz20sb5OEsfh8+Ga22H3F0mLn2cyLLXEsWBA4XUOOfXsdxg8lZ5jowiwGgYUM4q07AsN6abaThh1la2Gx+SK0q8UJeGSxMP0sY1FJpgB701bbwe0y1IH6/QX7VMtyL1oRP91Rj8dO6pWcist0y3SQE17acX7dTEY489hkMOOQTPf/7zcd1112Hx4sW455578JSnPGXi45jREFtdWl9stdhqsdViq0v6aIXY6joQWy3oO6YigNhpW1WyIWXHMRmXSBCdP75Ui0+8DhnqfnmZ/IvPOA8lXvzy8PbqKqDEbjm8RZZ3CWPeLP3iE8d9Erd/bNnu2i5EOhIM1Gem+wz0dlIvVYhJxXTDQC+bsE7lYXrNCh8EVvosYLrPJkb6zH6SA4POX4WJM9y6e/06TRXPrithuJX1Eeqt+vU1cpZQFcMtq+8d66xe+zRx3zFXyB1xW1Y8zsda3Nr2uwc7y+zshQ66gksJz5zy3BEEnBvknbeOKJfbNgzZ+6zjvPtgdhuDWW/ZcczJz9LYrQtHjmlmx04u7VtnaeQKCtDGpYnbdHIY+zTJBQWMt7CZ6wShg846rP5zSWGdcoByJ9zNZ+KeuXFOeZ4uzo63PS5LGw/1VvM5cvUUCvqrYdq472S3c7jL0LmDXe04dzuO6Q6iZo90V+u3cdFFF2HHHXfEqlWrsrJddtllwmMQTBRiq8M+xFZbiK0WWy22emohtlog6BCdBFM7qRsLBFYF0YNjP4geC5SHki7dSLwAxXb9W2y3DeFLv4T65uFtx27dD6pzHDUMqIfTBlgiOmNkZDjXTWepF56MUCbFL+tG6qWTIHgn0i/t+ppokHkWBKmnA5gA04t2Bh3ytk1ndJh6GWWkVbTR64XLqvRWud1QbzXaTuaAR1LGO0gT9x3zUFs1XLAslhLul3F5laaq8w8zZzt3yMuZZ/41UNZ9AnL9VdsxFfRX6zDZOoEKWHvslKtIGeAcc3dMSmV6rOyoa0phSCElzb68Y6kZGMqddeOCE9BA6rRUqxx0/2kQf4hvwGqwhgua8YJ1BgqGWGtWgcCcNedWkwGURuqc14Kz7jo0Ci3sr9Ap10hsPedU+0y3Mv3V/DnU01+dSQuZTZb26mTj2muvxZFHHoljjz0WN910E7bffnuccsopeNvb3jbVQxP0A2KrxVYDYqvFVoutnmYQWy0oxSBJTEw0YD4FQfSQUR4GzDlQHjLQQ+Z6+AkXGA0Z6rFtlUZ6ODW8uKl/y6FmeiygzkHxGBPd11n32wvH12hoDHNDfjDdD6qHA/c76yaY3osAeqdM9LJ2qsrqnOumf4GgAvImzWb0eeGyfumtMjhNPLvGSxPn85Vp4p5jHmqr5veQO+Gh1iq8/W5/NfMddMOLhBGfc/fhLVYGZdPGgeLCWQAs2w0ubZzbizHdvHOJY9jVERHwWW92QTC3H9x85piTHRQzNtlRz5hvxrhnpZ0DlrPcTHbPbg7cjdrrshGhykG3FyJzyhPnoPsLmiWw7LvEBW1ABgTKnPLu08aZ6ZY75annUPtp4/ZO3DJqzkEH0JIyHn8m1Q721Cxk1gYlDv+UwTQBM96+Xp12ADz++OOF4jlz5mDOnDmFst///vdYuXIlzjjjDJx99tm45ZZbcPrpp2N4eBgnnnjixMcimFkQWy22Wmy12OrJhthqsdWC/qIXsi5VeiS9Qh0ddH8/ouFtGsPRoLcv5VK3HGgNvMfY5zGZFyAu9cLo5HeG2K1zmxwMnzu32EYYSOft/PmtAXU+5tj53LkARjoIplcNHqim2vc6gF63Tt1A92QFxEXepWModP9dO2xn0DFzn+KsRv9UhTpZuMzu905v1V+0LLzeTxMHHOMrc8jjaeL59XGt1bKRVDHZ2iFkugGtbLey1HEgd9DtfJB1BiNstyoHPaOWeX3acVg30Ac73j5rLuaYx1PHs2G5vqyjnjHftLb6p4Ynxc49tIEm5dLFCWQso8zY6AISxTPpRsOOsvfM+KwhINE25dy4e20gTxu3TVrGm/9YbQo5skBPmDZeV1tUqwQpFR3wOmivr1rNdKtT3k2/9VCdlt4p+s10s+ninelRl7UDADvuuGOh/LzzzsP5559fKDPG4LnPfS4+8pGPAACe/exn484778Rll10mzvmsgthqsdViq3m+xFaLra6C2GrBwGCQ2OidYKJs9BB1mOiuLAyih4HwsLwTiRegvU460MpWD6egXUA9hF+nTOIlJuHC15ZJufjbKJE8DKaHDcVo7+1uiCfHD6j3MoAuLPJZgwT1SB912hl0yFvZd/TJUe4yZbt2810uXMZub9SJx8T0Vv12mOGW/XNp4vb6/KpO0sRDx7xs0bJ8HO0d9dzB5nsuOo5c5jOpAFQy3kzml6rMQbedOUe7jYOuwnK4oIGy+qScUu2PP7/nuFPOznt04TZXmUhZvVXnpOfjYUacu09e0EwbNF36eOoc9CjbTQMp2YnUREhNzq0zbr7ZcSd3GS9oRu4mG8o64w3YRdMsC6s8bZygazHdmC9Xpb/KbLiYY1y2kFlxfiNlpcy4ouM8kZTxCWHQmG49xAMPPICFCxdmxyHDDQC23XZb7LXXXoWyZzzjGfja177W9/EJyiC22j8ntlpstdhqsdViq8VWC/qEXrDRO7mmn8H/MiZ6I76oqB/09iVbfIkXfwvEpV+AnKFeFkRnpro/BWXbqv1upiI2JRs32lh1GOseHbX3GG6bTYD/NDWb+dyNjGjM40h72U1Vsb7LMD7eWTC92wB7p4H1ToLpvQ7MCytdUAJ5gmjjXU0BJr54WazN/vyuEzLcum2jSm/VX7SssHAZu+GkvDKVsd2q0sQBzxlH0QEFio55iDosN3bJgKKDW1iMDDnjjdv1GW9A0UkvY7xx+jiUl3YecdAzBhjl5dmCY0DhmOv5x/n9e4y3wCmP6b1mAQZlU8lDJ93OhXXCFSkYpVzKtraMNNZiLWG7kavDjrdS+a+YvJAYz61y88fMOEU2UGPvv9WdVVmgp+g2s5NtnfFqNhqQtjjldfRX26F7RlpvWGjV2qu9Zbr1FaYJmB78fXTp4gsXLiw45zEccsghuPvuuwtl//u//4ulS5dOfBwzFmKrJ9Su2OooxFZ79cVWi60eZIitFgiKmGhwvBds9DBiHOnC/4SLivJio76UC183Pt7KQA8XJy1joMfY53W3jPFASSrUT/fBUi4chw7jzP6H26nLSI+h0QA2NzWGWYA9xkzvRh/dvwlGN2zzukHzsja6uUYwEGAiTC/aGXTI2zfjUPO1q9BcLXO0O3XAOSAQLlzGDLbW+nG9VUZh0TKKLWDmpYfzNpImnrPW6qWJx5z08DjuaDPrjjIWF9DqsrCznpJrwytjFlqKnHOVOmaYVoQ0Y6XBpjwreAy2ooPeRH5v1r91KdKeJqsCM+fy4AJQTCvPx5075nzOr5MHEXge2Im2jnVKGgkI2ji2G+ksKEFkvWnDC4x5C5vB6Ba2m9IujpBT/uwTcQw4MnZuyT0XOydAwoEAsn0lcO0oDcX6trDzaFvNmW6cNl62mBnz43zHWyPJ2G92vnRUf9Xfr8N0Y/RiIbOo0z+DWWlTgfe85z04+OCD8ZGPfASve93r8Mtf/hL/9m//hn/7t3+b6qEJJg1iq8VWi60WWy22epAhtlrQF/SCHV7WRj+Z54x2tOsKJnqMcV4WWAfaM9PrBtDrsNBD7fTwfFWZH8/dtCk/5gC7vwhpo5HHvefMseeYgT4yYj98PD5utdFHR+24Rkfz+xkdLWGm8+DK9MkZQ0OtvxYwQqmXumz0iUjAdBMYL7tG5GIEkwB5gwYMg8dwq58qnp33nPjYwmV5O+31Vn2GW9ZmcGzrKc91L08TL9xZ4IiXpYn75+uiqKuaO+lAnhrtM9+4fUJrmc98Y7Yap3UDljnmM95MhYMORU5zNE8T9xlwhXuA1UfVoFpk0IzxFrDgCm27FHEihcSlYSsXCCDnEiaaQESuf2T6q8ZopF77zHZT7rkRyDHflGO+uTbgBWSUZd3585o6phvIqXsqhdSPpvjDd+9YzlbL08bTlrBLDl9/tQ7qMt3y+lOzkFndAMBAo8cstzo44IADcM011+Css87ChRdeiF122QUXX3wxli9fPvFxCCYFYqtb+xJbLbZabHW7+mKru4bYasEgYDIC1b3uq5dSL2HwsYSJzl2ETHT/4zPO/bKyurFyPzAeyrjUZaDHAuixQHt4b7HjUGnFZ6EnSWtA3e+XmegMPz4eBuY5GG/3HTM99gMHD6ZTzfQYumGjx8rqBrF7EWjvFSTwXhvCSJ816L0jXESfXoGJaq5WMNxKL+nxwmV19VZtee5yt+yTKjjm7dLECww3BAy3snv39qtSvSm4xgT+XcZGY/C1bif10sq5zRbmG6msn4z1ljGwWOXT7itwCjWhgVxn1QBoOCcZsKnVTViHHtrY1G1jnNOrshRsTQApZRcFCxhvvmPe4qSHdV2/qdHuj61zoo0NOmjKx2Yy1hsADWijo2y37I92lqWs7Gtuimw3oxzTzTjd1SyX384Xp40nLqgDMu69SKBBaDoX3AY6iouZZSnk7tnG9FctWtPHy/RX2zHdYoy0eguZVadzd5+K3gE6YM71dxGzFKBeOCudBSle/vKX4+Uvf3kP+p0NEFsNiK0WWy22Wmx1UFdsdXftdACx1YJpjV4G/quY6EAWCd7czNno3TDR/fJQC91npvPt9SqAXhY8bxdU91HGVA+njIPqfozbZ6Tzp9m02zTNj3mOmJ1up15jHtPYwwnirS/xwvtVrHR/kkJmethOGKj3J6QTWZheMsl7zUrvdTB9hgbnZ1MgfTqMUTBl6Pz1qLtwWd5DnF3Ci5ZpKrbnH2vyWW3FNHHbb8kYAyfZP67SW+0EBERdCUP5JyuDyhhrRPmH63Fb2bVwDp9ztLk+/HpQjgmX95vXV5ZplvXfPfyU8phjrlyZ/9Huk2hj97VBogwSbZBosvvKIFGERBMaigMFsHW8dpSrY9sGEsdks+fz5+l/OGCTnS/cjw3ktL9vXdhn5iU74K0szNZgVLhv581bqC8iqVCF+P+9qVzzWsyLQDA5EFvdLcRWi632IbZaIBCUYtDZ6HWv6abtdgE/dz6UdKlioo+NFT8cIA6vr9JDrwqil33Gx+sF0WPttWs7/IQ/GPA+/7jQ7rNxY2tZOHc8nwY6p7GHP3LwM+pF4LbO+9MJpb+qvU77EsxarFy5Evvtt1+2/slBBx2E6667rq99zryfQWY6KhluE/8yXMVmA/Iv+1UMt2w0bfRW/WtDhlvWRhXDLWO1VaeJJyp31jMnDUXHDSgyzAAUHPgs9dtLA8/ZZq2MthjCdPPUu8Yy2VSxLjvcxI5u3m/GfEPuZLOXrVSR8cbXGLiUcZ2z3QDHOtPGtmM0SFHWslbKVndarHYBMC8VO9BntWOPOOm+his/D4/tBgDKuMXNlKe/6uYkIatfmjrqGLPdlGPK2TIeAJz+qn1YmijTYzXOOSdtyXdGKZCinPVmJxwNxfOj0XRMNwOggSRjujUoQVPlaecmu0JbvVXStfRXY4y1iTLd6iFnutVKJ++H9uoAMN2UaUKZHvzt7CBdXDALILZabDXEVoutFlvdsyGIrRbMBPQi6FdHa6SXKJMMCQK2dZnofkA4Vg/Iy6uC4UCxjj8NdVnoVXXL9sOpriJ0A8V1O0NlFd6yVnqMkb5pk52PBQvyOeP90VF7PTP2G40SzXR/ckMGeAjWoYndpH8zfH1sUdNuGepV5VXotq2pZoVPdf99wFQx0nfYYQd89KMfxe677w4iwuc//3m88pWvxK9//WvsvffePRhRK2bWkxO0YCpYLnX6LF0kDeywlzPcOE28E2ilWtLC+di26Zf542mF76ADrWniZShz4KtSzQsLofkLaimvnvLdLJv+bUhleqzsq7IWa2EL60CzpqsCAHctXDtQ1pEOU8TbQSsqOOXZ4mbevdkdm/5NirJ0baOs/mpqgEQZpNA2CKABYwjQBoZTx43lipGybisRcv1VYueZCun6ivLAjPG0ZW25c8yJAwzKBUgUUhBsCr07RypbM02TBpTVX/Ud9FTZ0afOQY/pr5aneHemvxprr9/aqNXtV6elDwRM04n49qAdgaBLiK1214qtFlsttrovEFvttSMQTFd0EjDvZ8A9srho2EUZO7sdYx1oH0RvxySPbesE0X20C6zHAujhQqVl4MVGeev/NsHxbj5msL66v+9fu3GjXZjUjAzbpxH+8MH7fsCbGwtvhm8kDKgzYgHxiQSFq64Nz83A4PNMwlQF0o8++ujC8Yc//GGsXLkSv/jFLySQPv3QRap1pWvXo/4qNFeLDnN56mkZw61s4TIVMNfylFrWV80ZbrY8T7+NMtwo11htFNhu7HzH9VbDtHA+rjNzoUNeh9VWBb+tNGjLfwv8U/lCaEXmG8EqPibuvJUSZcYY8muctigc243jGz7bTWsDowgp8RwyW04hNYDWTocVrey24v1Zxzy2qJmvw2qcbi5UPubUacJqEBKt0EzJstyQIHWD1hG2G7TJmHN8U6SYHaigHNsthdNfRa6NWmC9IV8Qjh9CziXjt9EFEgr7rq5yrDTnoGesNK8Fi2r9VR8h060MBac84vBXsdnCc1H2XD+YbgLBlEJsdd6u2OoQYqtd32KrxVYLBIKJoW4Qu1+s9U5RJgsSCaLHZEx8eRJfyqQdYx0o3/qLjvq3WTeI7qMqUF4VWPfbCgPn7X67COPTHBDngPqmTXbLjHTWTWf587GxXBvdX6w1ZKaPjAxjmCnt4SfGEi/TSg8H7DPT6wS/2zHV6+zXOZ4IOmlLgviTjscff7xwPGfOHMyZM6fymjRNcfXVV2PDhg046KCD+jY2eRMEPUUVwy1MBY/WiZxnZ7213KUdZw557ohrxanMcMfeGFXxOFYWngdaHfSJoC7jLbZAGjuO2jG6mPWWtVlgtLkFu/gasi66XczM3bfHduPFubIUaHeNcQwyA5s+zhEEciwvRQrhQmX5PXEgofW8z4GyTDNXlxSIbF9WG9Vpq3JQwLHdSJFjuBEabjwNBRd4IJCj+PH8+Vt+nnaROBe0cMd28TLL3eL3KWMMIn/3Uk7BV9olkcM55QACBx2ZY+6njJdTF6rSwHuXMt4b9JtJ11cIy00wCyG2uh7EVrtzEFtdBrHVkwSx1YKpRD+lUyaKfo8tDBwGTOeQLR5jmNdhonOMNryO4TPRfbQLovuoYox3SuCPMdvrXBdifNwyyYE8cM5zwXHnjRvzgHuS5L9jsBwMX+fP5zCfKGOm9/K9qWKlT1bwuZdSMYKu0WtG+o477lgoP++883D++edHr/nNb36Dgw46CGNjYxgdHcU111yDvfbaqwejiWMWv1U98rJ6hFoMt0rN1VibgaNcwXArooz5FuujleHmn+NFmEK91RjDrZAq7rHa7Ig611vNygJGW3js662GT4GCsrrstm5JcGH7HAxIvcCAQu5AMpsvJXZgWWPVOpAEwBhbJ9HWuTawKeUEW9EnhqlMd9Udk7LcOHKcLkphlEJqvV6bXu0GxSnqMfDiZQCgdbGOMeyIO8fc8fG0IqRGQyly/aGV7QaCcueY4Wa3GjAu7VsTjLEtG0V50EE5wpZGdp4sRRCGrOZsAmYV2nvktHHXSsZ6AxkYpTOmGxRA8PVXvRTyQH/VppcnlnnnGGR+HU4Zb+eAZyy6GLvNc56L56vTuSfF6Z9i5pyiZvYOTbQdQT8gtlpstdhqsdUQWy22Wmy1YOajKrg5UamWTurGgudBQLaKie4z0v3jOkz0WCC+Uya6j3Ya5nXQLbO9rC5QnE6WJR8ayoPijYbtb2jIMs43bcrZ6LwwKy82umCBbWtkxNsu0hgZmQcdY6XHGN5ViDHTmZXONxFjoZcF1bthpdfBZATTex2Yn2GBfoXeeG7cxgMPPICFCxdm5VVs9D333BO333471q1bh69+9as48cQTcdNNN/UtmD5zntpAoRe/wwwWylLFY4gtXNZJPz6jzWe4sd6q75i3SxMvjAtFR7wuQgc9dr4KdR36tvqr3j6fK7LbHCPNr+8x3uziY0UtVkcGy9hu5FK1ldNlZT1WcinTvkYpOWYYAJeaXu6YZ/eomfFm3HXalRHSVOf1Xcp7oo11+mFAVoS2wHYzboG1BqfHg58zIVHsdtoUcShCqlRhfviPPQcY7DwE85rVUS5ln9PubVnKGqzImW72Gk4Fr9Zf9R1gpTRAyI8jznHZQma9QC/b6gf6tYiZYLZCbLXY6hxiq8VWi63uDcRWCwQDhm6D6H4A3T/XaLQEumMM834y0acLYlMfI4THAvEs1eKvE+progM2aM4BeL62lJkeBm1jA0mSehPOwfQ66AVTvZ/yLoKBxcKFCwuB9CoMDw9jt912AwDsv//+uOWWW3DJJZfgM5/5TF/GJm/fjEAPGBoxlloXrDpdwaSzrLdyhlvoiCvk+qsKud6qzhzx9mnitl/HdFP5se0jR5gGXrVIWVgvRJXrUOWox07xEHyWW5hCznUsu005VlYr4w1o1WLN+mW2m9GWqebYboqUnXftuF2ks7YBg9QJlyqlshRvOAffZ7wpRdCaoJQpOOtKpd6+AZHONWON3dp7tttE2fGnxjLDErKO7rjJHfum0YDR0Bl7DWhoICWFIQU0AQwpQhPKpZMHuquKYAwnkNv5M0DGfOMHFTLdjKejavVXc5XWMv1V3wm3reXzEWO6dYp6C5lVM91aG+2UldZh+5MNYwDTg2/oZoDvUTAgEFsttlpsNfcjtjqH2OoaEFstmCr0QgKjThudstF7UbcKsYB6oIvOsiMhy3x83AaBYzrpvWSil91uN1MQiymHBGuWEg8XO203rqqguc9M575YwmXuXBso37QJmDMnn7P58+0+a6PzPI6M5NcB9pqnPGUYw6NBEN3XgvGfczv6ftkipGVs9CrGeuz6qv06x91iqoPyU91/D6GBCvHIztqZKIwx2MS/RvUBM+OJzQZMNFV8Il23aavMiVeq/hh8vdUy7dVuoFXRyeb9OvqpvhMcloUIi9sx28hnUAVON6NKg7XsOGe3RRhvsI6zr8UaY7sx68t4LLdMj9Ux5Ygcy03l9+I70mVp4/k9l4Y7oLW2Ke6u38QtTEakkBpt+1dFtluiqMjQc9c2lHWxU7cwmdVwVW5rHXKt8m2muwqXLs/Pwj0oy/bjtPac6cbz06q/WtwP9Vd52zI/geNexXqbbeg1002ZJpTpgv4aaUcgEFsttlpstdhqsdViqwWCKcFEg+gdSLqELHPu3g9+TxUT3Y/h+utoxsjWVfHeMrQjbdcNqLfrx/+xIVyUlBcqZWY61+O44eiofz8aOvbDSBi0Llt0tAw+K72OtEvsBmdIAFlgwZKNvWinE5x11ll46Utfip122glPPPEEVq9ejR/96Ef43ve+14PRxDFL39yJfxGbdqhinxXOlb+2Yap4kcHm6apCFxxzZrExw8320l5vtbBPfnp4h3qr2TiKW3svxXuMpYb7TnM7J5yC41puVImfwU4ig8eaZYcjZ79Zhl8+/pQsI6yF8eYW+DIGSFwZP6mc7aYAbew8cgq2x3oDNBRrpDqaWGo04FK7c/acdaPy8ZtMfzW2qFnu3BtorWBMAmMUjKGc5aYNVEpIlIZSQNM9AOU/CDcW5bbM2LOBiDzNHJxu7gIVBBuoAGxAopHNNU88Bzv8qEkCdq4Juiv9VX8hszr6q7Y8njIe016N6rBWpIWH56IBgQjTrXoRszZMtynWXhUMKsRWi63meyreo9hqsdViq8VWCwTTAr1iiHfbbt164aKUkW07JvrGjUUmOpeHTHQOkvtMdH+ofiw3FmTv9NZiwfSQ9NwrFntsfOEPBOE+y437Ux7GpJvNfGFR1kovC56z3Asz1JtNYOHoaH4xTzw3GN5Mu18KfGZ6NxIvE2Gl1+2jbnnd893WFUwK/vKXv+CEE07An//8Z2yxxRbYb7/98L3vfQ9///d/37c+5Q2Y9ujFbz7do9OUcqDo1OdlRUZcmDpu69TXW1XIWUh2nMjaaAff+a2bJu475aGLUVd3NYSTGc2Pqdh+jN1myKZGA3bsaYzxBut0p5krz45rznbL2G3O2TbKUuOILMOIyDK7iJR15l0Zp4vztgoh0y1b4Mzdo9YplHKLmwFIlEEKZrlZBz9RxqV46/xYA4Ydcse4a7pATaJcwrabu1QV50871h7PE2vVpoRMhoCZbmXupnW+UTirPQc9Va1fTKyDjlYnODZvNZlu7TRUB11jddJgUhvQ6UU7AkEpxFaLrRZbLbY6Uk9sdT2IrRbMZHQq69Kv4HyIMJiOYuDbPy5jp8eC5H49vx0fddjoVYHsqnNVceKqdTPLSNt1A/FlZO+yclZg4Vg397VxY/5I1q+3Wy4rY6iPjOiiXnosEMxl3a7QWjVxZYHniQbK2wXl+wUJpkcxVYz0z372sz3otTPI0+85unFWe8u6a5veXTrGVuZb2FaM4ZZdHVm4TKskep2/Zefb11i1o+leb9VntMUc8zpPKZaqnZ0LHOWwTpjO2o2D3sKoA6cnF8fApCu+OzJ236jc6fQZb0ZZhpuvxQrtfGlt29KELOBBsE6qLWeWG6BB0ESA0ZbNpp1TT1aDVbnF5sLFzbRjunFZ6KSzM26Mtqy8RtPqsaaUsdnya7XXJtCEdp593p4mBQMNzdqyAIaUTdZOHfstdWy3NAUSDRhj56ahgCYpJMpOOAEZ043T7Znp5uuv2ueknf6qdab9lPEsAJEx7FpZaPDKYw55O6ZbDHW1V2sx3Tpqf4BhmkxvnHg7gmkEsdV+P2Kr49fXgdhqsdViqycBYqsFU4HJClj3At0G3EsWEw0/m5s6Y0GzBrrPMmfmuc86Z3a6fx0z0YHWBTI5flumQV7nVvy6ocQLUM5MZ7SLBYek7Zg0TDv4QXz/Wp/ozdItfr/8w8TIiK07MmLrVzHTGw17bBnqGvO40P8FxF+MlBH+2hBOLN8Is9J5kutM4FRppfeSld5rzJDA/FQF0qcC0/9pCdqjCyZaSxMVbdQNBvip4gAyxx3IHXP/2NbNGW4x6IrARkxvtQzhgmVcFqITp7xQ3oVzbplV+TGnWHPqNN+6ptzR435zrVVkWqL2Gst4y3VXLZuN2VxE1k9pEtCAytwsn/WmuB6cI65sajiRXZaLNVIt0y2/3i5OVnTcMmab9ubN5OdYv5XILiqWaJMFE4gsFY20gkkJGtbhTzTZLzaKkPK9AU6rFWjA6rAq2AXJfCc528Len2LnnZDXRSvTzb69uT5tq/4qAM9BL4NCfxYymxaQlHHBbIfYarHVYqvFVg86xFYLBPUx0UD8ZAbyI+zz2HFMA50DvDF2us9gL1s4dCK3GQue++2Hx+0008N2Y8Hxbljp4Y8E/pjCfa7HAfWQle7fV/ijBMfE+ZiZ6uPj7ppRj5XODcUY6nVZ6TzQiWCGBJEFswvyxk4xajHceuBclzYdabtuCrhfT3v7qqClWu2YW+5aUbM1prdq67bqrcJt+devKr1V3i+7uypnHIg75OyMlzniE0kd10G+es5qcw5r1keexmzP56w3gpcGDWTLZ1ndVU7PLrLdEr8/N1nasda00zHVGlAgNEghNS6IogHlvFdF5JhxqnRBM98xVyqfKbbFWpNjmzlHvNHMtFlDtlth3ogypp1xY2amniGNFDwehSEFNGFZf8rbOjnZfI6VQkqUsf7s4mgaTTKeg+7GCl3QXzXOKS/qr+a8smJAIP92VcV0431murVDQYe1JtOtfaOdOtMdtl/deY/aAZRJrUZvD9oRzFyIrRZbXQax1WKrxVaXdt6jdsRWC6YAkxXE7rSfXkq9lAUvveAq66Izs5yD4THmOeukM+uc63BZGGz3h13GRu/kNuoG1H12elkMuB1xmtuIsdLbjd8/F/vRdXzcfkfj+R4fB+bObf2RghnpTDKfMycPojMjndnrzHhvNDRGRuZBj3ipAiMjcfp/2S8N4c36A58sVno7lNUXVnpfIYz0GY3epmZPLfr7irHT3MnCZYXRqbhlCh13gFlC7l8f9FY7RadOebv6WbulSq6tSCkI3nisNr8f1gU1sIEJn/UWMt4A64DaY+WNSAHZAmW5DivXU6TQUM41JPvUNSk0SaPhkqAtuw0Zwy1LHXcp43aeig/Ed8xbHW0Dra1Dx9elRiNJTN6WzvszWlnn1Wg0XJQgccGExDH0Ejc5DTeM1KXNs7POgRzlGIbZfLjgBiFnuqVkWXLEmrbQBeea2W28cFle1t5B9Rc1s8+4mukWOtyxhcxiEO1VANQj3dWZyj6cMoitrgux1RZiq8VWi62ewRBbLZhumGpZmHb9V0m6ePt+fDXGPGeJlxgTPRxGLIAek3Rph7JgdVlAvUrupYxZXsVIj0m8dNNXaxDdeOf4Xux3tI0b42t6MvOctdLDY7/cV3Qp6KXzYP0t0B0rvSyYXoa69QYR03HMfYYE0gVdog+PvEOGWyeaq0U2W4f9lIxLqSKLza8barLyeEO9Vf+8z3DrVG+1MC7P8fJRlsbdLh08dMpNULcsbTxvpz2YMxVeU66/yrqgOestxniDzo8B61zaxnK2m9L5H0IDdtoB5RYGS91WOR3WhEzGaFOU66/yAmeGlE33ThBlvRV0WF30gYyyd6M0iHRWL1WUv1MltMEGFJA6nVUNJKQAbWCMzo9d2jhAMG6buPcq5TR35c2RUjAB001DZfOdgrzjav1VEJCqFBo2rdw63zkPzNc4jbHb/H2f6RZztjM91g4d8VraqxGm27TUXhXMMoitFltdrBden7fTHmKrxVbb3sVWCwSCHqKXzPMqlAVSPTY6B7uZae5La/OWNdD949h1/q3UvZ12gfPwXF05bZ9ZzvAZ5nVI0z6rvSqAXw3+++z/TU4BJCAyGB/XaDaTAlvfn7+RETsGHhcz0Zm1Httuucijs/sPtA6lPjYR7SReOmGj12GlVz3YqrKqcoGgA8gbNNPRx1RzRmzhsmy/ZOEyIHe8C22Rz3bLHfLcSedriw5qcTzcd+f30iunvHht0EcHIqzau4m8TXLMNb5RVzdjt1nWW+aZo8h4axqgoe2x9T0VVDYmy3ZLSYEc2y2BZa+lyOc79VhrBOusQxso0tCUZqw3TtvWitloRdaa7b7olGdlrjMNY51jAMY4xpw2BbYbaZeW7sZqlAJpTo3P9VsbbsusNt6mnE7vTSkvfqfdVGritPuc6WYKTDeWM2ivv2r7cAuPxZzebB7qO9SdLGRW3kcvU7pjaNP+FGivKmN6kuqtzOSOWzDDILa6I4itFlvNUyq2uh8QWy0QTAp6ERjvJuBeJenitsb9cY8xzf0gehho9wO8fpk/tJikSx1UxXjrstTDOGzIGI/FgtvFd/keYoH9sH8OeNdD/jePyLLTx8aKY+T2N2zIFyBtNi0TnbdA63ZzU1tWus9M508YaK5KFehFULrbNuoE0/sNCcoXIIx0wTRA569XmM4dayt3potWpF2qeFV/Mc3VzNn2GG55engrw62wVcHW7bPeqn9HfhnDoDh7VdqoMae8nUPuu96+Ix776k4RR10VHHIq3A9g79WmklOhv5DdZt1IdqKLjDeYPIjhs92Ux3YDFJquhiLrbGp2mJ1zrR2NTBGgyDLkUtJQBtDaOu12YbQgZTzCcuOtv0+koBLK9VZVzrYDmm01gk1hsTOdOelpkDaeODZbCjtRDW2DGDaoACTKnlPkFj7jeVMuGODmvp3+qn2W2j0v4/4P5Tw5f2uvcSni0C7A0puFzOoy0Wox3SbQ/kDApFYboRftCAQtEFsttlpstdjq7iC22oPYasFkYtBlWTqt56NHki5hQJ0lXurqoteJ9ZfViRGP250rC6j79ULtdGan8/mqYDpQjDe3C/q3lqfBNjxXZKf7bftjSxJgwQLgscfyLTPRgeJ2dNQF04F4ML39oIsDYFa6/+tIpxrpYZvh/mRAguNdQwHogaXuSRv9hrwhU4hai5d11N4EV0yOtdlmgbN2C5eFKKSRI3fSs/OZ0150yOvoreZttEfMvajLaos55ZmDXOKMh054pXsTcchT5I4ppywD1lHn8WinLcq+tXXIiynktp7VdOURZSNzi3QR7Lynjk3X5BRwAAkIIOXSreFtgZQ0NC9c5o6VW0yM3BMyxjr4hpR7xgGzjfc1M8EAcpqYOjEuhbyIJDGObeelqGsFQwaJ1oAxSJSC0d54HYtOkUIDhKZj9yWODqhgHfQ0BRLbhJ0X58QTP0N+Vp7+agx2MT4NKOSsN6fHylqsrK0aaq5WOch1neWytgQCQXuIrRZbHYXYarHVYqsFAkEMdWUxBgFhYN07riPpUhZojwXR/XaB9mz0uiojdQPrYWy27Fqfne4H06vaCSVi/PZj7PU4QmmX2HkbUN+0aagwNmagxwLmADA6Gt8WJF78mwtvup2AfRhMD8s73bbrpy6mm7zLoI5L0IJZ9pSmw28bPUQFA6id0x29piazznfAfYab77j7gQR/4TKVH8FPFfcZboq3qjgiX1tVqZzF1Q7dOOUho42dcnZ7qMRJ7yRVPEvgZdab57Szs86Ous98s/w066T7KeScXk7kHE7K/0d4ydsZ201DQRtkqdJQhNQokIJLJ8/TxlPKneDcSTcZu82y3Yxz3FXmiLOznvWuCEobaJ8Fl6TW6TYaxCw3RWg2G+5+UmtvvC9NRApI4JhWCRIiwBik7h3308ZT8MJm9t1KNAcbLPstJU5yzvViOZudnXRff5UyTpufPl50rrmGJo1UtX4x0UisHmvNhcx87dW6KGei9TtlvA0mOWVcmRSqByy3XqScCxhiq7NTYqsziK0WWy22uti72Oru2hEIpiV6wVKPsdF53wue+troZZIu4TaUeZnILVQNu0677VjpZefKYpihbjpfF5NyCaVhQgkZf4zlQfV2AXX7HWF8XGPjxqTATGdpl7lz8y1QU+LFD6J3opXeL9QJgvcr8CwB7a6QuE8v2hl0yNvRM3Sevt0Wk6CZGu3WvbpZinjMkYfPVtPZwmWFOhULlwFBqjjl6eIxhhtvNVq/RFtnvf19se6of8yoo6vaiVNugvP+NYUxRUtRuM/UtZGVcJo89+U56bYflTnpxRRyylKcYbJm2rLdFMg659nTI88pz9PGlXPMMyfdKKvJa2wKuHFOO5EGudRyn9mW3buXLs5IFIEKAQaFxDmm7JdrbZlzQ4lzzlOb5g0NpEYB2i605qeNN1xafeIiEA1SaBI777keK+uvkpvLbvRXbfo4YFReg1PGs1Rzxe9aq25qGaOtyHqLa6/67LaqRc66QsfO9BQ7/yEkXXyWQWx1Sx2x1WKrxVaLrW6B2GqBoCtMNPAYu74XwcxYUDAi6cJBdH/x0E4+oVZ6GRs9BmaA9+LW/L6qtMv98piMS4xpXhXD9fXSq2KxaVr8obWkVsW+DVmOjQFpmmT9PPFEMaDOTPX58+12dLSVuZ5JvPgPi/c54h9bUTVETOJlaKg967wOK71uYHsi13aLXrc/jYP4nInai3YGHdPzCc0A9DpVvKO+J+D0l6WKA469owLHHq0OOjPaYsi1V1Ve113t661qoFBmy3mM5eM3EV+4rrZqLC3cd8qrHPKYE17FeDOgwr0BucOekk2+ts6cApxjbjw2HKeR+ynkOrsv6/o1YM2wPwqV9eWYfco6l3YeKH+a7iIOpCSkkJKCNgpaO6ab65uZboY/xjry5J0LnXTLdgvmx7g/zElqGW9aZWy3hBTQsIa9mQKJNnZBMw4WaHJBAw1oU0wbh0IaWdBMI1/QjH0348Ya01+1z0VVup7M4PQ1WE0NR7WO/mk7plu3DrikmAtmM8RWi60WWy22Wmy1QDCLMCiSK+0wkYB7GKQL5FzKAuITkXSpO7TYYp91EAvA11n8syzmGgbTw1hyu3Z8Fns9JIgHztv9XU8wPo6Mmc5x7DCQzouRMhOdJdF5OzzSKF94lCcgRNkvFVXB9PD6OukG7djnZYH5dpjGAWvB1EPenGmJatZZDHUWL+sGsXbrLFxmy8P0cG0XycqOVAvDjX/lUoFTHi5cVgfdpIb7TrnPaOPymDNedNbLHfIQqXedgsrazBduU0VHPesjPy5jvCWa0KT86flhAAIyJ1O7oIMiyxyzMRdy648pKJ/tZjS0tnWVIfcUKWO6WYfZOnpE1pHk63mBMh8tDrtbyEwZBcrSzTUaXp44JWmhPev4awxRCg2NlAy0UVnaOLPd2AlPnGedKAWAkCqbFq8VClvLjrMXGTdPGjYIwoGa2EJm5Dnl5Bxzq78K7wn5C5hxoKR9yng2TyVMt3YoOuHlIYZoexGm23RZxEzSxQX9g9hqsdViqwGx1WKrJw6x1YIZhX6w1qsQk3Txg6UBG90PiJdJuZRJulTF9/v5G0VVAD6mc+6jm3hqmbRL5+0k7nuNQWsw3Ye/GGni6nNZzkznwHm46OjatZaNvnZtkbGeM9OHMTyC4gn/JkOqfTgZdW++jH1eh5Uea2dQMGjjmSIwgaYX7Qw65Gn3BAOYKt7h9Z1oroaBAH/hsjLEFi4LFy9ruSZz8sva7I4rSAXH2ZVVOOY+s62M1RY65WUOefdcoTwd3GRjJcf4U8U+lbJONTPgIow3GOugG88ptk65BbPfct1VBbh08yYpJAQ0QUjIpkynjv1lgxbWKTYoLipmSGW6q8ao7AsPkYbytEcLC5mpMK0ZMEhsurs2NthgFJIkBWWLolmnnaCQaOOcbUuRS4yJpo3bMIK9f9bszY69j3VZHSuPmX5uvpRrJ83as7Vb08ftwmX22lbGm+98h5jI4mXx9qaHA91XSLr4LILYarHVfL3YarHVYqunFcRWC2YDppAN7wfRu/kwg51vI5R0iaGM6Byr1wnCNv0gO48xJuPC5VWxXb+9MpI1jznUVg/baQ+2HeHfLf94M1hRmpnpQPkio+vXF5nohe3oMHSj0aqXzqx0fwKrUMZK7xa9CFL34tcSQVvMpkB6z8e4YsUKHHDAAViwYAGWLFmCY445BnfffXehzrJly6DcAlT8ecc73tHroQSYuvTsqUYnDLcqzdWqVHFbljvpdRYus+fLFy4rbCMLl9ljx8hSxbvx9VUp+AdYB9cQO5LWTDW5DISUcsfcEKFJ7Hjac00iGLdvGWRkP66c/6XOfWqC0OQ6IKQwtT98DbeRuo8dp0HTfVJms7nxpd5Y7T68MiA1+XGaHRc/TZcGbo+tlmrh2Ps0jUZqNMZJo0na1jVuS9rt24910HXmqHPaePauacodc02AJqjEAJqgdQqdGDQaKZIkRdKwn0ajiUaS2k8jRUOn+XFi0NAGQ9ogUQaJsqy7RNv9IW0/DUX2nCIkCu4YaGjLhEu0ZbhpZbeJsu8mb60Tz+8nc9WC991757Ny0tn5GDSS0gBaneBY6zUTq1+3v3L2bZvrp0hzejZBbPXgQWy12Gqx1WKri9eIrZ7tGFxbPUMxGYHsTvuY6JhqstFjEi5lki4xNnoo6VJ3+END7T+doqqdJMk/JVNR+eG2kqR9Xe7HL+M+/TYs+O9pEmx9pMFnM+w3o3G3P4axsRTr19uA+YYNloG+bp3dhp/HHkNWd/16+1wxOmoD6RxQDwdfhdiD58C7n67Q6bZdH7G6df/fTOGPV4LpjZ7/xHLTTTfh1FNPxQEHHIBms4mzzz4bL37xi3HXXXdh/vz5Wb23ve1tuPDCC7PjefPm9XooMxS9/9LaCcOtVnkXC5dZhyV3yEOGGzO5enH33TDbylLDQ1Zb5vzz9R7DLUwVr6O3CbQ6bL76pj2fBxw4BqVhFzWrYrw1yWqvGr5MWYfcZ7uRygMZ3G4CvtY6+YBCQorJc9CkQASkpJGACkw3Ist8Y0fckHXfiJgrFt48Baw3AhIXUUkT6CSfDdIKSWIs202n0FohMQakFBJlt0oREk1IDKeJE6CN01FFFvixqfbIFi5T5AJBdkptfcd047nK0rtVrlObPQs3LyZMgUdxITNmnFmmW95muJCZXeisdb7aaa/GMNuZbsoQlJm4rqyKiToPMMRW9xtiqycKsdViq8VW+2MQWy22Wmz1wGM6BOUi+ug+G53Z3HWP/TIfvZyKiZCC/XH4QXlf89yXfqmjNBIjaMeY6cy4j13Pa3gC8OS/QmkXX74rpp+eIGek83eQcYyPA2Nj9uY2bkS2ZeY5B80bDbufJPkPJPMWNfIgehkrverhDrIky3RipU9TNvxsYqT3/Olcf/31hePLL78cS5Yswa233orDDz88K583bx622WabXnc/K9HCJKlwtqsc8SqGW1lfSuWaqvE2Pafcc8arUIfhppXKmGw+o43lWA0Vy7kMKDrlXB7TVw1Tw8vSwslznbi8mCJuvP3Ov8Cz5qZGfkMaGmkWCOCUcQK5RbQ4lZwZfOxshnqs+RPiEdtrlHbp4s75tA43uVdLgX1M5ZxQXhCsabRzdjVgDJqwadIKhFRrwACpccnUqX1njLFfEsgokFvYLBsokC1kxk46ucXDSBEojbyjXoo5L47GafFD2gYrUq2gSbtF1zRSZZO7s6373sL6q7w4XBrRX+V5M7B/UMlFN7RLpY/pr2rEU8WB6jTwqoXMeN866EBMe9W/PrYwWV3t1RkFk/bmNqdZurjY6smH2Gp/PHYrtlpsdVZXbLXY6iqIrQYgtnpGox37tqqsrDzGRuf9iC46M8vHxnLW+aZNRRY6H4eSLnXlXKpQJ27YSWyxLBbpx4QnE1VxaRtM5z9ydbTS/f1wYdIU69fPz4LlmzbZuDhvx8ct8bzZzCVf5s6125ERjXl80v/E9G/q3jBLvMTOxX6pqKrnl5XVLTtuN07BhDGbAul9H+O6desAAFtuuWWh/Morr8RTn/pU7LPPPjjrrLPw5JNPlraxadMmPP7444XPdEZ3aqGDgzBVvMBmizDcYo571cJlLf1VMNzqzKXP0oqd8x3pKsc8uwbFtHC/3LbBTrPJHC5O+fb3J/bJ26ZCeZ4eb+8jTyFntp4faMhYfZTPk8nmAWiafN84d77JJDPHZiOXLm7bzdlsBvmHmW2sv+oz3XzEyoDcMVfaQCnLfFMuhZzTx5U2dqsImlPBEwPtp4ZrsunimmCXELOMv/xj+0oUMv1V/gA2UMH6qwrKMt6C99NnvcXeZx+h/nBZerX93xFILZQExKYMvU7xlpTxSYXY6laIrRZbLbZabLWtI7Z60toTVEJs9QBjKtno7YLoVWUeWH2Dm/OPy9josSGUKXNEyPBtlUPq1ClD2XVhmR9U9yVfYttwnyVaYn2XXef3PzQUjq/KZviBc//DZZuzMqLx7AeQ8LNpk2Wib9yYs9M3bszPG+hqbZvYRPgo+7/gv0BlqJJ16aSvqcAgjUXQd/T1pxdjDN797nfjkEMOwT777JOVv+ENb8DSpUux3Xbb4Y477sD73vc+3H333fj6178ebWfFihW44IIL+jnUWYEia62zL751dBdjmqvsQFi3O/Fc8tyJD/VWc5fdstngb91+6PLE7iZ0yNulh7MDHjqxtl7ulGfXIe6QwyvvZbo4s9oyFhsUDFIveKGdzmuR9WYcUzDGeEMZ280oJG6SyV2UANmCZuOUp40bAIoIMBpaG8Bo6zyTgjYKzexZE5AA2mjnCGuoNLHXABm7zZDKFhYrzENivygotyVSIKXtPWmyqe/BdYYUlDIZyy01GkppDLlU6wbZVPGGW1ytQQpwrDdofmdsmrdRCuSIX47ElzHd3N3l80vMPiT3/NovZMbHlvEGlxLOXLMiKyFkt0HBpQT6dZK2TLdOUMWw6w4DwqKjHrHcShadmw4QWz1YEFvtyiG2Wmy12OpOIba6RjvTFGKr+4yp1EfvddAwFjn29wNtdA6kMhu92cyP+Twzz319dJ+NXnd4VcHwdoHy6UYa9oPlQ0NOgzw432zmgfjxcf57y8H0dn+vjLdlaZix7HqizVi7dj7mzLFMdN6Ojdn9ZtNnoufbRgNYGLLSgXzrr7ZahpBJHlt4tJ2OTl3WeieYTuzz6TRWh9nESO/rkzn11FNx55134uabby6Un3zyydn+vvvui2233RYvfOEL8bvf/Q677rprSztnnXUWzjjjjOz48ccfx4477ti/gU81OmB3lC8QxOcn9hoWtVNbU8XL2vcZOaV1qHps7IKzI66Uip6vgzoaq3yeHfPsWuSOua+r6jvmdZ3ymEMeOpQMzdqk3jXWtcvHELSU1eE0ZbulTJOV9Vg52MH7di5U5i4xk8vXXVUADOu4km0rJXeHZAMGKSloZbcNUjDKsdzIpqgzC46dZX/hMk7t5n2GCuZHaQIZV1cbO+ZUQ+sUZBR0ksKkGkmSZoukacXsNgNKbUAiUZYBR5QHLDSsFqtNaCcrT0A5083XX7VzpZAnjNuyFCi8mflCZq1PzDrkuZOrSSNV1V+aunWuK9v0tFcnlDKutIvmTB8oF6DpRTvTFWKru4TYaned2OrwGrHVYqvFVvcWYqvFVgsmiAptdEYo8eLHUP2AuY+wrJvfI6rY6L2Crwril/lqJXyetc2TJNdPL1MW4etDqZhYDLisz6JWOlDUQPePfYR/y8Jj/jE7RZomWRDfl3sZGrI/mHBwfXQ0/1FldFRD8wmfSt+rH5x6GSSuI/sykfF00uY0DH73EpwR2It2Bh19e8qnnXYavv3tb+PHP/4xdthhh8q6Bx54IADg3nvvjRr8OXPmYM6cORMYTT8fRYdssQmNpUZftRz7WHp3ueaqvx+mitsy66QrZVNa2RkPHfu6C5cpj+GmHUOLf93KNVi57+pZ8bK8M8c8c249dhs75Xk9qnTKY6y2Ks3VFvZVDYfHBK+KdbqdBqvnTJCbAWa3MfOtWM5BBzuf8BzSdmw3pQBNeVAjUcqy2gCnu6owzgw6gl3cy2jwujPM9EpVrr+qDKFp7DvXIIXUaDRIg4hARgNJCiINxQ6jNgX9VZXkTjyxQ8/nXbtps2HbUDZ4oJoNpO44JQ1tCGmi0YRGSgbapb3DaAxpckw/O1HGeEw32PcucUy3hJlvsAEI/srTgEaTTObe+guZQdlZMfBYbcgXMsuuKTDdWhlvdqEzdqoTgDBB5lk1ugkMzPYF0gYdYqvjEFstttovr4LYarHVYqsF/cZg2WpBV+g0ANltwLImGz3URve1zzduzJnozEbnc8xIZzY6D7WT4XYaOJ8pcUnWJw/vp1wrPQyo+2XhPh8bFK9LMD6e4LHHRjB3bs5E57VEWSOdGelJYsc3dy6wcHQkF1P3tdJjjHJGFeO8Diu9HbqtLxD0GD1/q4gI73znO3HNNdfgRz/6EXbZZZe219x+++0AgG233bbXwxH0AZ0w5zhBthPwwmX2+u4RW6gM3n6MWxZzzAvnAsc7ZLW1OO2q1VHPxlfTiVKZk+2x2Fy7dpEsk7HfcifQ3oHOrmXH2zHUlGW7gVlrQJTtZjzHnBftIti5JeWcY26T59U5zHYuy/VXAWa5WafcGLtAHbPgYvxNZrZlx0YDGtBIYdIEKrGLp2lFIG01WY1jxCWJQZIakFJIlIFRCTSsJqsyZH9BdfqrVts1nxMbJMoZbr7+Kn9VUVmAIy/XSiElLnP3DMsE44XM6soGxN6LsnfILmLWvt2JMuYmnjI+AJilC5iJrZ75EFvNx2KrxVaLrRZb7bUzjSC2WtATlATXfYmWmIpHyEb346V+EL2q25ABXneI7erXOQ+U67THmOhhvRg73T9X5/oyVnpsDK2s/6pFR32wrAvDD6ZvBjCcMdB562umM/GcMxGYlT4yojEc00kPb5LB1HofVYHvqrLwXN3rq8rrnu/VNbMQCapV/jtpZ9DR87fh1FNPxerVq/HNb34TCxYswEMPPQQA2GKLLTB37lz87ne/w+rVq/Gyl70MW221Fe644w685z3vweGHH4799tuv18OZfihlqXWe9t3OiQ61VMPyOqni4SJKPsPNP1dguCG+cBk75dyrv3AZp4ozw005ZykEBR6375hXsdvYKQda08NjqeG+U17mkPtOl+/AdO6MFdPF04yXpq3z7NhvzHwrstsom/X8KrLsK0+LNe+lyHbTjhmn3JwpYx12pRWMo7XxecCx3owdKQBobdA0bt/NU6IMgARJaoAESFM3XufcJI0U5NK8lbHOeiH9WnsPWaeOiaddeQOANegZu81tmymhkTiWm+GtgiKNIW2gjcK40oA2aJKlT1rH2rLdDIr6q2SY6WYjGJkOKxSaABTx3Nt5pRaOmrsF0oDKnxuIGY4ljjcSGIXMqbZMt/wdY4fZOuhAXe3VfrLRyttuk44+GennxrRSSrttZxpBbPUEIbZabHULxFaLrfbqi63uLcRWi63uJyYqVzGR6+vopdTtows2+vh4zkZn9vmmTXlg1ddEDwPv4dBiQ+wkeN5NML2sThikniw0GjYI7fcdW3g0dlzUSgeKAfKyv/tpydbfNyBK8MQTQ2g0kgIjnbecpOJrpQ8NAYsWzYMeCV4CrpSWjIkD6qHeDV/TCSt9ImV1znVTT1AKzkztRTuDjp6/KStXrgQALFu2rFC+atUqvPnNb8bw8DBuuOEGXHzxxdiwYQN23HFHvOY1r8EHPvCBXg9lEjAdHrFFmSMOtKaKlyGWKh5r39dcrctw8xcus30FW2+/Ltqx22JfpWOOeX6uc8ecWwgdcl64qhNo0gWmW962Az8DCtltdi900AtarECU7WbZbayrmuuw2uAGMj3WnPVm/Zyc4YZcaxUuGEDWmedyIgVjcuYbUNRdLUOmx6rtPCujs0XOiFKbtq4ISlvWWqINksSASCHRdlEzrcix2KzT3XD3yqn0dhvXX/WZbp7bC3azmeGm3DmfDafjT7D4vBFbyKz36eBRR30i2quCaQGx1YMJsdX5vthqsdViq/15FFs9GzG7bLWgK7QLsFew0YFWZnls8dAwiD5R1A2YT0TqJRZnjZ0PtctDqW1/y3Ff1k0Pr+djZrGXrZPJc1jGZs/lXfwgejt2un/Or5u6z2YAwMaNCRqNXPqcWen844n/Awr/YJKx0v1BNxr1Fh3l8xNhpXcq5zKTMBvveZqgL9IuVdhxxx1x00039brbWYmWhNoOFj6r3UfQZow5x2UFp1z5+quJx22zmqtFthsz2OwVrMHKjk+4cFk7+G5EXXZbmB7us9t8pzyWFm5UvoBZzBn3HfEyhlvM4QrnmtthfU7AMaSc4506FxGqlfXmM97yYzjqmt3G2G6arEOttAuQOKd03ACJguuDME6W9aacH6cBaA00jYbWTtPU2KfeVNa4G2O1WFPHgjNOr9WkGjoxIKNAyiZeWy1WA2iTOeVK80gBUhqkrVPs67MCQLOZYMgopGkDqWlCKZP3aTTGDTCcpFDGLrwGIKq/SgpRpptS/J66rUvH1y4d30C5IEbRKff1Vy2zLU8fr7uQGQJ2G5QNTBTfo6Q2060nmG6LmM1SlpvY6smD2Oo4xFaLrQbEVufvkdjqSoitjkJs9QCgDlN8MhjvXbDRfS10Pg4DqSEj3Schd6KNXidQXoehzkh6oP0wVTHKoaFcXgXIx+ATtHMSCAfRw8C6jxgrPYnUTzE2liBNhzK9dp+B7m/5lRkaArZcNGoLecD8gjDKFiGNsc6BalZ6p4j9+lGnvX4+/Fkc/GafoBftDDpmwROeDo9hsBFLFS+DDuu2UTjyFy5rP448VbwOOmG3+ShzzIvni6y2Qr8Rx7wbzdWwTlx/1WTsN3b7fNZb7qAXE9bzRbNQyXbjc0XWG4pb8Nax46BcIKPIdAv1V63T6vY9xhuRso65t88LluW3ZgBv7pU2gNEgbdluSpuM5ZZog1QTNKVIdJKlkCfO0eetVpbtppzuahLor/JCbiHTTTt2n79NyQWVPAeI57cd/ODLIKJvTv0UQRmDLkin0XYEE4HY6olCbLXYarHVYqsZYqvL2xEIBgYTDbAz6lK3A210fwh+kJyZ52XKHf513Q6vmyD6RILnfK1/T2Ws9HAcYRkzzqvaiMl7l/UTkr1tVoDPSk9hrbi/iGidv2XMRud929b4uMbYWM5M9z/87P19Aw0d00gvRv6LE8Pwg+X+ZPjoJADeLUTeZVIg0i6CvqCuU9mbvjzN1AJTrd5rqaDbpJjrjMmmlIaGz2rThf4Lmqvks92U156nu6ocw03ZBa3apYqHZoSd8k7ZbaFTbtvO2W2hU+4z2/LyVme8juZqWXkxaGEKZcxq89lvQD7fBnC6rJTp24YOehMuJZo41TmvY5AR4KDdImA+6w3MaoOCck6ochqkTccaYqYbtIF2zLJEOVaeO05NCmjAGPsNJ1vQzOmu2rLW55+x2TSBjPWWE20sIw6W5UZOk3WIFJqqgYbTdh0eaqLZTNBw7LYhba8Z0gYwQOr0VxNff1W7dHgopCAkHtPNdu/S08nptLr3LoFy7DMb6ShbyCx/PsxaS8CJ2mHKuN2mrp52592xSgrst07g66PWTRmf+CJmbdLRpxtrTjDtIbZabLXYarHVYqtDiK0WzFD0KpjdK3Q6nnZR6pra6KEmOku8+CTkUBt9IsOqcxwLnMdktycCX6Ylhm7iqqw7HuuHg9eAvT//HrmfVq10rmSQB8Z9xrq/9b8daFhJl5CZPoK1a+eXaqMDdp5HRnJW+qJF86BHR4uVyvSB6qAdK72dvEud4Huvg+KdtNfLviW4P5CQJzJN0Y491vb6NouXhdCq6HDXAaeGR88FC5f1Cz67reNrA8c8K4+kh9vycse8RXe1pjMT1iukintOkULOdOPrdNYGMgcn5qBbM6tcCRWehw1qOCef4LHegg+x7qrK6hNZtlfC2qq+ziqsk8/lymmvKpVvyWgQL0LG889p4w6+gw649G1tbFo5KejEzpZqEnSSQusUWisoZaC11V+1rDcbNLCsN4I2HNCI668qF7XItFcd842Qs9yAnOmmoWovDeY/w4nCLmI2BY7tdHKojalH6qjTjkAQQGx1PYitFlsttlpsdSXEVgumKwYlUF+hjR7TQg/L62ijd7uw6GQG0Lkdvp+QqV4lxd2N3rmPkJUeI3C3RzutdB/+3zu+ZhjAZoyPj2DjxqSgje5/mJUe1UoPmentwMHyulrnk8UelwB1zyGMdMHUoJRVVuNVqqW52lontnhZlRNeliruM9x0UCdjuHnueH5OFUq1chw5Zre5MnaAsusi/nyM3Wa3did12yYfg8tNgd0WW6SsSl/VKBMtz8YVceBjoMAoxgIwrMnpt8tqnay9allTOqtrE8G43DHVUNRihWNUwTnfTcrfFg3nmDs2G7PeoAHFDC9Hw1LaplFrd71yzrkmhdRtxx0LbdzprzZTe5/G00IFgAY1YUgBRoFUkDbuFiaz+y5AkQBKa5BRSNCEyRx3DUo1lElg3LckYzSUIjRc30NpCq00ms5RazhN1zL91UQpGziAglYuwU7BLooG670bBeuYKwXjLWSWq6uGOqwaUPZZwR27aXYlqXseCVIYaCQuCNKefeaz1wp1WIu1wxTwsH4dplvZGKYc4pwLOoXYarHVHsRWi60WWz0JEFstmGmIRVzrllXB1wmpqY0eaqFz4LRKG73u0CYSQA+D570k+NYJzPdijcsqZr4v5eIf5wTtxCmPhYzyJNjncz5jHUE519WubBhjY8NYu9Z2xiTz+fPtOEZGclb66Kinld5s5pT7kRH74viLjpb9OhAG0/nG/WvLmOadLDra7bm6kOB7JWZTIH06jFEwSYgx3GIOou+A+9cUFjCDrmTi+QuXdTTGDq9hbpoJtll5xSI+oWOet2nyf56zXeWYGxXWTVs+rWOP1wnbMkG/Yfp6OB4OMvjMPfJT4jmNnshLrbd1yDHdDIDU5Cw4m3ZvuWipY65ZJpt1yptOf7Xp6a+2MN9C7VVyTLcSpiQAQBkoTdmHj6Fy/VSlCCox0Dq1Ke9JrreaaGPT1532asZyQ36tVv4+nA6r3eZawBZcjuw4D0Wx5AEvZFZ6S85lD1FcFLD6fLG8VTqiMgA3QQatQCDoL8RWFyG2Wmy12GqBQDAlGBS2eYgKbXRGt9roPRhGy34occKLcfp1q2Tg232qrvH79Mfjj7Fsy/uxsfrt+3XCfhsN21943dCQ3VpiBE+O/3e/nQ0I9dH5w8H2zQDSbIFZ/mzYEGeoj41ZKkD05rqB/zJ2TsvvLwb1/7VgoCA/p3SNzn6DaOtU1mKp1e2ruq38C3qrgx2DLjnP/RSc8sBB0P4/0i2OBzvpGuy0OEYbXKptCXgBLd5nWOeS91GptZo6lk4zYLcxwy1kt5Xpq4ZOeVjHtpV/I5lYGnCeEp6qNHuGzGjLU8Y58dsyp3LGm0sX99J542w3Y51Jyn9ZJO/ZaLJMrtSlTHN6tLa550gDppsilemvpqSgjUKqFRRppKShDMGQBgyQOgaaSe2WSIFSnTnsLWA2mzKA1oAhO15mwWmNhBSMTjBkxq3eaiNBmiZoNKyhHHJ6rENaQxOhqd1YnP5qkyzTLlUEo4BEAcZjunGZfS751hAy7WDWbGXWWr5gnAKUfQKg/L3R7jhV1d9mldItOqtKJSBqZU1WscxiTLe62qt1UT6GibfdNYTlNsMhtlpstdhqsdViqzuB2GrBrMGgB8wmopceBDx9Nrqvjc7s81Ab3f9wnDO2SGm7YcSGxahioNfRWK+LMpkW/1xdlvpEUBag94Ps/jZJeJwaxUVHYwuQInKM4Nh4ZXbB0rGxBI89NpTpoY+MxD+NBrB+PbBwdDR/WZiR7t9A1YvRCSvdb69TmZdOFi/tJ8N8FrLXZxMjfXY92WmJib1G7Rzvutd1qrlaxpThhcvYEY/W8ZzyyoXLAoJakbVV7Zgzu63s63TMMc/6rUgPDx3zKqe800Wf+BkUdVd5EasERhUddNZeNXkDmbPtO+jahUasO+9Sl8EBEJvqbJx+KLmUaDv3BG0UEg003UWJsinl1gG3XSTkHFOPvZZCw5BCSvk2NdouRkYKqdEe0021/jdwTrryHXO3Jc0qpwmUtnNAiV3YjNlt2qWDa22QJAZJamBIIdEEk3psN09/VSkX7lAsY+CYbi5QxHqryk20XYyPsjlPYeeTZQv4/W9H+uCFzNLgfVHRL0vtU7ezFHFXr9NU8SlBP3VcKW39Y9JVOwM+h4I+Q2x1GcRWi60WWy22esIQWy0QdA0T+W7gS7bEtNFjQfQ66CaI3k7CpYqN3gn8ewg1zcNjVihJknyh0LI4r399SK72r+M2/YB5OKZYH7ZNZqankX1/66PsB1FmpCcANmPTpiGMjQFPPAEsWmQD5jFGerMJbG7qVq30fJCdIRSbr/olo1cLik6W/nqvMWjjKQGvSdOLdgYdg/80JoTp8Ag6R4vj20OGXFU/yjHYfCabhitDa6oqs6qUY7gV2W3MaPO2KucCKi/NNtRcjaETdpvdd+UgpM7INDOHu5rdVsZsq3LIQ2fJUDuXLIa0oGmbZpwp25OGzphvvj5rXgOlDro9pTzHnl03t/gW6626xcS0c46Nsg0pbclmlu2moN2iXYqs3q2vvzru2G7jxhr81FjuHLPb0jRx7DYNk1pn3ZgkWNAsh+IUcT5GClIK0JbtZpoElVgupmXOaSSNBpQhNJr2OTS0ndemy2Vk/dVEkRurZQcmyj7JRDtylFJIne5qE5bpZllujhFHCg1lzynKeIdulHDPxz4Dq4uLnIkYccadvK17fik0Epgq5lobR738uh5opE6nRcwEUwyx1b3sR2y12Gqx1WKr6zcitlog6Ap1IstldXqhhV6GSJAz1Dov00KPaaMD9aReJhpAbxc8bxdDrJLFrttGSGT2g+mxOnVQJesdMtDDgH1RK90gD6Dz32zerwqip5EyvtZuiRI89tgIGg1g7Vp7v6OjORud99evt+MaZo30UIuGXxQOqvsvDk9iXVZ62eRVTWivgu3tME2C2oL+Qt6ArjAdkg1C1BtzJ2y2uE5riWYkO/NUrjlZxnprB5/dBiBjsBEz2TJGW6tjni121iZFPEwPb8dqq+uUd+I4KejC9Vol2fUKObuNkGasN39qcoccpWw3A1ZQtb8nKrL54IoAOMabZb65Nl3auNVbtQ66gnUg7eJaVmtVOcddkUIDzHiD02XV0JQWNVhNgjTVUMqyz5TJz5Wi4KADMAakDHQDoNQ+EZ3YbZLYeUwSAyIFrY0NPChCoqweq4FCouysJtqy5Joqv0dmujmuGLTNrs/m1WcY2ro2yBEuZFb23ZT/n9SVF8idd8d8jKSMTxz5XU0LdlwNKGOgenAbStLFBxBiqwGx1XwMiK0WWy22erpCbLVA0AYlgT1/kdEwUB4G1WMB89gioxXdtZzrNIheJ4DeSQwzFmuNnW+nBhIG1GPX1B0bx5ljUi6x8jQFxseZSe4z0AGgLhM8ZK3z9ZsBDGHTphGsX29Z6aOjRVa6r6O+cSOwcLG32GjsxkM6P99EOIFAOSu9KlDeyeKjdSCB8Z7BV/SfaDuDDnljZhh857qTVHG/bmFRMqVrOez+omZAwHDLeD0hy41r5GnhvOCTz3CrQuZcU8EPzdwZg+JiZaFj7mutVjnmvrPN/4D2TnkdZ7yKpeQjfAp+hmumQRvUNQqlDrqBQYMaNg1ZNQo6rHas9kkQEbRKYIiC37bztPHEMc8MubRoypluCfKFz1KXCq61sXqrBDTIap0ao5Eqythu1hnXMGkCrQhkFEjpbFv4VV0TlM7nlmBTzxUZwFiWmwaQDDUBNJA07LVap2g0gKSZgkhhKEkxDlinXLn0cUNIFIGU3RoFNBRAGkhTlTHemIGWuElOXVq5cmn3mopPnxcyUy6okqXqkwZUnh6uSYMz5sOUcftMExcIiTuHudPe3nlsr71acW2XjLocBXGDSAd9Ys2J7qpgCiC22kJstTsWWy22Wmx1NcRWC6YbJsow7+T6MPIaWQiyHRu9nTZ6p0MJ9zmGWieA3mnwvK5SR6xuTNolRn6OSb2E9f32YiolIdM8HH9MK73ZZK10ZqXn+ua5TjqQhx653Efs7x4H4IdgWenDWL9+BKOjlpXOW2akz52bM9I3P0VbVnqjkQuoA0VWeRViMi5lrPRuUdVGv9vvdV/9aKtP8H2DibYz6BjsJyHoOaoWL6tywpVqTQfnawoLmDl3LF6P3fFWlztWWqW5ChQdc0YsTRxoZbRNxDHP+lKmllPewnQrccZjjo0/5/51GgHDzfUXc9INTyN5ZVmb3IapZLtx2rgBAMd0I7hUbngLmFHupDLTLTUq01/VLoiSkkJKCsqx3VJjU9RTo2FIwxjnpKeEBqkCAw5GA0mJI6ScDmuSAqSgE5s6rhsJDJCljafNBAks6w1AXH+VTOakNzznPFUq01tNFKFJ1i1NlF3ITFnin2W9efNhn4+VR+Cghn0eKvr1hvmg/jvXmjLe+t7YQErZ+9SDNPBOICnjAkFXEFsttlpstdjqSYPYaoFg8tBPWRegEIWty0bn7sPjTofY6yB6r7TRY9eUaaOXKYh0Ekz32+MFQ1kBJVQ08fv0y/1rWFfdstJDVrkv9RKDieyHjPYEwEaMjydYv34ok3GJfXh90cboMLQv8RKCJ8tHyEpvFxjuhOYvEEwy5K2cBMSc0e7b6kZztfPfdEoXIHNOulJFp7y1R+8fxdPHlXM9/fnRAFTglIeaq2F6uC0rpomzY25dTMqcdr6+F+w23zFv55T7jnUnTKCyun6pP7u5E5jXizHe8jJmUrk7c84UPxPmLaVknKuus7RxfnINeM4iAU3n5/tMN19/tZkx4lTGelOJQWo0oF15sIAZGQ0YBVKqUA6lkJErFRX0V7ObNRoqTaGVAaXWSU8aVm03aVjWWyPh4EYDiXZp4to66UQGTaXRdM65/dg11FKloguZ8ewoRVBkJ16pfD6g7PwUniMpQNlEcpD3bKALDnq30EpnTLc6i5jVceS7SRmf9ABBOwjLTeAgtlpstdhqsdViq+u3O6kQWy3oNXoZvB40cAQW1YzzWJCdwVIvdacpFgBvJ+UyFYz08JqqgHq7OG8YTI+NhYPgsbZCArfPQA+10tOUpcAM8mB6vopHXCe9MNrgmBcb5QczDGBzFkhnVnoYSJ8zJ2emz2NGOkfXfUp92YvDwfQyGZcYW72TgHqZRk+doH1dzXVBKYSRPiPQO4e47+jTAmQt3ZT0U1auOxiXLmHBZeeh3eJXluWT9Y184bJ2rDag6Jj7C5YBxRRxLi9qrTLLrb5j7i9UxuW27bTguNvxpIVje11rmV/eDXyGm3+/XM6sN9ZlbWG8ef6r8u4vTyXXWWqzLc8XNOMgCI+eFOuqOneUcuc0JUAbayuJLGcudSnjGm5LCk3SUIaQGvsupEZDJ2S1V5VNI1fGHmvt0sZNxbvipY3DKChtLOtNa6gktX/gE5senmgDNJpIkiEQqdxJ1wYJrJNOGkiMQUOpzEFv8MJlzlEPFzKz77PTYeUt2XqKmN3GwZM8Pb/IZNQtKeNVXqRS1qn32Y9QaNFe7cY5nryU8SmAod441j7dVtABxFa3dCO22p0XW80QWy22ug7EVtdsRyCYKPodhO9W1sULoHMzMTZ6GTvdv6bT7sPjWBC9Uxb6ZDLSuaydxIvPTgeKwfSydkOEEi9hPxx89x8tM9vzoLm/0CiQM8vb2Q0/+M7XAcBGAAnGx4exfv1QlI0+NgZs2pTrpc+LLToK2EoMXye9ajKA9vIusV85utVJlyB5X6DQmyD4dPAO5e0ZaHT2GtZdfCyWKl5ZX1Wx2YpOuUJS1Fyl+IJmoZOe95Xrr3aC2KJlxUXJ4O375Z055u3Sw/Nryh11H50whPh5laWN+064HZM7Vi6lnB3wAtstZ+3BS0E2Llk5TBtvkkFD2asUrAOesEaq26YEKMd0M27RLg3ewtYBsnTxYQJS0nYhM+PaMBrGKGhttVfJGOuQs1NurP6q/2oqbaMBBaZbI7Ust4aBMgRq2Cdn08SbMI0EaDZa9FcTbRc2S5QBufRwZriliqCUvSft9FDLFzJThXKTOebKqdqpwluhScGoPE1c5SETd52d+zwFPfXSynvjcLd3xNvoowoEsw5iq+tCbLXYarHVYqsFAoGHfgTlPVkXII9jxtjpYVA9vKbDbgHUD6L3gpEeW7+yDP49VQW/2wXR2wXTwzZZDiaUeAFytrnff0js9lnpVi2Fb9r/3hYG0f3jFOUB9s1uO+72N2NsrBhI9xcgZfL52BiARZFAesguj6EbVrqPbtjl3WIy9dQF0wryRGcoOlm8LLzOTxVnx9tfoIxTxYtOedwJZ2jyFyzz0sNVq5POrVS55z7DLaa16qePW2ezqLVqy7t3zOs65aHDFHN6arOCAhKNUiErCtn9Fo6pNX2cfdwmNcGLaBllFzSz513b5Dvq9tommezZc7p/0zWutE2bJuVYbmT1VpsAEqe/2nSMOHbOU9KAMc6x1zZtHIAx2jLdUu0cxnxBM6VdJxEpAtZeBRyBVKdQxuZxq6EmtNKgNAHQgEoMEjTtgmZNZPqrQ47tNmQSEBQayoAUkCjtpYzbJ8cLmCVaFRYyU4rlD9yW2JG36fbKOe5FV1yDv+hYZpvJ/l/FUsY1kpZ3THlt+GVl71lVyni/UM60mwLH31BvuhSWm6ALiK0WWy22Wmy1Xya2ugRiqwW9xFQxynvdbxiRDoJ17WRcOKgeDq8s/hnrOjyuCqK3C5yXtemjk8B5u2vDADZQDJTzubrB9NiYfW31MM7sS7n41/oEbf+a4qKjfDMhI72dZjrro4d/24dc+bDTSh/JFhtdtMhKvbC0yxNP2PFsbrpFR0dG8oF3EzCuYqX7E1J2XV1WugS0+w6RdhH0DL3UXB0EtGPSFRhunjveUi8otU56PdRNE7f7rWniWbni/Vad1bx+tWNel9UWOj3dpNT61ygUHSnfUfeddGbB+Yw3drI5CGPZby7N3LHd8jRydszjaeNGKbegma1LIJcGboMrhtPDnSNr08idO+j0U1NSUKrorCuy541RuVPu0strgZlubopUIwU1EyhlgAagkhSKFHTCrDef3WY1WhNtdVMTZZBo5zwb65RrAIkmpKnKUpjs1jndrL9K+dbOv9VehQtKpZmTnjulHAiBm3M/ZTxV9eghdhGz1L0bSUvKeF0MnEaqQNAniK0WWw2IrRZbzfMvtlogEJRgULXVvSisz0bnoHhMxsUvj91W2a32KojeiXRLVQC9irwcwv+RwG/TD4TXZaT7Afe6sV+/zZC4HWvXJ3rnrHSWd/GD4v6Ps2Xf1NJgC+96DsRvBjCEsbGRjHm+fj0wf74NoI+MFOVdGqPD0D4jnSP+QHzB0WwobVjpZZPWKTq5bjrIw8iPAQOD6RDsHzD0eMpK2Wg1+umhXisvSpb1ruJOdew6XZFOnrUHXrCMHREujzPcytDCbitJE8+1VuH4ba68wHbz/nnsNgrKWWM1Y8NRmjnmBikM0uw6AHkZmfyD4ofb6OZj798U+uR+Cv1HxpeP2yDFuL1vxcfN7J5TNG09xwJswqCJNOuxScY52swsbP2k7mMISA3cYmVu0TLv0/QWMmOWmzEahuw2NRppatltJtXZh1zKOHlMN5syTm7fQDVSQNuFzVQjhR5uQicpdMNAN5pIhprQiUGjkSJpNNFopGgkKRJtkCiDhk6RaGa1GTS0yRhuGnAarGSZbMoy3hLFKfqW4WYXMvPLirrD/v8FhdYARIscA/8vcv/veCFBjaQtu7UTLeVqeGNq12YvdaX7oVFtTO8+NXH++edDKVX4PP3pT+/9vc1qiK0OrxNbLbZabLXY6lKIrW6B2GpBV5hooH0i17tAZkz/vEwrnbv0y2KyLqEMtl8OFGOnZUH0UPc7bDssT5L8w+D2/U8nKLvW7yccT9mHz3NbvEho2fXcT3iOrw3nKmyPy6yUH3+0t88fRPYZafDZDF/WBRgDMIbx8TGsXYvss24dsGGDDaozO51107MFR/0b6hT+e59r2LSe6+UPWYP6o9g0hu7hZ9AhP2dMIyi0d4JbUf0adp1WDp05BbaXPM0847dRznaLjYodk3BRgjLN1Vg2JqeJ2/P+8mb2XMhw87VWgTjbLMZu47qxRcqK17rzzlEO6/H1Puoy3thJ89vgRcr4PPfLrDcN1lxNC4w31mIlpFBIwCw2OzvMtDKW7eYtaMYBD8vJoiwFmpTHcMt0WHMnnVOkjSszjm3HTDciBQMFIm+xM6Mtb86v47Z2jlWuxRosaKa0yZ109xYopUBN66TzQmYqSaGNgtYpEm23WiskiU1f1x7Ljdl5WpF13kkhVex4Wx1WfpL+AmYAz7dtD5w2Duuta1Itb4B9Ftqm6yuTPY/YO+FUXVveI60SpD4T0qvTjr0WSx2fzHTySYUxLe9Pd+10li6+995744YbbsiOG8IumDEQWx0pg9hqsdViq8VWTwBiqwXTHZ1QvjtBhZxGGRu9jJ3e6dCq/jtUsc7rSLmEDPSq2GynJOJYuzxX3K8vqxL2VcZY968Py7kvlnjx6/iqJv79MAPdZ6UXEQbKtXc87pWV2QSfob4ZPiPdaqWPZMFyn5m+cWNeFl101J8IXwS+pfu09UFXITbxvag7EdRte5YwyUXaRTDtUGDCRBzusMw/rmKzKZVkjBpmwukgSOBfX6a96jPcylLoi+yfHOF3Xl9rFWhNE88ZbkWt1ToLlsXYbbbtnCFm+yzRW4045b4zXeWIl6X08gJy0UCCNzesreo76UYV08cBZ04pTx0HDKCGYJDrrjZV0wVYjAty2GfXROqeYwMgA60SNImgnJaqgmW0KWezlbY+smYn3S1wpglI3eCGOE3cKAAJhimFMrDOuSKrvaoIJtV2LoyCSRPrmHuMMDLem6Upv3FNUCYFjGWQUTOBHmrCAEiG7DcS3bR/CpOGfQaJ+6bScIa/YRIYUkg0oQGDJilPfxVIlHI6rAoNZecicYNpKKDJKfOcKq8UlFvYjb/eMOOTEOrp2pRxnTnr/tOsRtxp1zBeEIfIW2atBw54lcZrPVR94Zs5aDQa2GabbaZ6GIJJhthqsdViq8VWhxBbPbgQWy0YWMSi0J6sCwfKx8bqaaV3ushoJ3IuVfIu4X6svarb7hTh9f6in0BrQD1ETI47tr5mnXGG7ZSx0nmfyd65vAvQGkz3JV/KEP7t5gVHfVb7MMbHh7F+/Qgee8wSzlkzfcMGYO5cG1hvNIAtedFRXyvdv8Ekaf3Vxp8glncJdXHKJrOO9k4Zujk/aEHwQRvPLMV0CPYLeoh2TDmFpKOU0qpU8Zzt1uqMW8aP06wsYbXFEPJIfIZbXidnZAHOYfeurOuY5+2ZgoPtO+ZhajiXZ3WD9O7i2NPCp/SeK+qFfXEAwU8j91PGw3vK6nm6shyQKKTIqzzIkc8pXNDDLR4XpOjbdvP0cQq2lqFomWupY7kZPjY5oy3XXs01WC27zUsZL2EpKUVQ2cphBEvLsww4laSAIujEuDpkU8kTy2pTyqaMK2WZbcx4c/wzaNe2/eWVCguWhdBekCT2vrfIJpAK0sQjAbeyQFg/UqrbYFqnjMe0Drr9dIB77rkH2223HZ72tKdh+fLluP/++3t7X4JpDbHVYqv9exJbLba6FxBbLbZaMENQRfd2CNUwQqWMWMC8ziKjZd22C6KXXd+LIHqV/EqZJE14rd9vVd9VcjV8vX8f7X5MaBcP9cfSKu8CxAPmnWRGGuQyL+PI2en2MzZmNdF9ZvrYmGWl8w8wBrreZMdQ95ebbjI66qRViLxLT6F6+Bl0zNCfMvo19VP3u0OLU13rC2p/UsVty0ngPCTFxctIB/UVMl3JyPNhhluVo85ffZnhRpnmp3UFm5nuKDuPfGydxqbyneru2W3tmG1lrLaYA94VGyj0AZS/q7P+mfXG9ZnxloI5a0W2m3Z1bH3LcissaObSxpvg9GXYdGRlFzYDkQvEELRRGdMtSSzDrUmW2WUXKrM6rIA9htFoGg1o47awmquKrAarSZCmiWW9pZZtaVIN1VAAaau96tLH+XsFO9wAoAzsdwqnw6pNE6Q19HgTpAmN4XEobWAc263RsAuaNZIUBIVmmmYst4QICdl08YYipJqQONYbKfsONxQhde9ywow/dt4JaCibam6yBcyIn4idSwc/TTy2bxcqA4A0f6Zu35cRsI+n+P7VXZzMr1eXBVeH6TYwi6ORKbAlu2/HvmuPP/54oXjOnDmYM2dOoezAAw/E5Zdfjj333BN//vOfccEFF+Cwww7DnXfeiQULFkx8LNMGYqstxFYDYqvFVoutFltdAbHVgl5hIoGzfuszd9O+F7z02ejj40U2OrPTx8bK2eh1uwMmzkSvE0CvCn53g9h1IQk6ZKgzfKmXbgnQvmRLbFxl8xjqqrcuOhrTQw8Z6yHCv+vMSuc2x8Cs9LVrR9BoAI89Zvt/6lPtdu1ae8X69cDo6DxoX+JlZMQJqKN1NdVwYriOv+go33isbohes9J7dc0shP8mTrSdQYcw0vuIsrToyUZVqnj1dfFXOGTXlKWIA5atE9VdVa1OercvIxX2Q0YbZYwre2xa9mN6lratVnZbXcc8ZLXF2GndptTG2iav32y8sTK03o+/qBkhtQ45cpabDVjkAQxOu89YhN5CcnbfMdiy847dBsdwc1v7Ua4uclabp8PKrLeQ6ZbdewXDLQMz3NzHarIaQBFUw7hFz0zGiLPsNQPNZcjLrRNNjs1GlqXp9rWCW7CsPdONh+VDBU45/7/xA11l/9dCCYdOUPb3YCrYcjMBO+64I7bYYovss2LFipY6L33pS3Hsscdiv/32w5FHHonvfve7WLt2La666qopGLFAbLXY6qwtsdViq8VWzwqIrRbMZMTY6O3i9nXY6J0w0auu74SFHl7frv1O2OixNsvqx2RXwnsJmelhe7Hz4biHhlrHzWUhit//JhJ6TJEz0w18RjqwOdNE37SpyErnH2WYnd4ycP/hVmmhd8JK7/bHrEFgng/CGGYoVqxYgQMOOAALFizAkiVLcMwxx+Duu+/ua5/ys8osQlWquFK6cJ4d8LqaqwWGG/J9Rn6m6NpbJ709qhhuQO6Ex9LErYPZqrWaO9pu6zHc2rHbqpzybMwVeqtmgtqWgMdiyzux8Mo5g1Wr/HyoxRpjuzUVP1PWXTWOBacBMmhCoQGg6Z6nhvW+lXueKeVMN7dWHFKyDmji9EbHHcuN2W0pKafLatl0qXFLqpGGMZbtlqaW7aYTA+O2vIgZGS/ckznkyD8GUAmBNKBMCqUJlCYwAJIh+14nw/ZbJOuvNhpNECkMGcd605bR1yA73lQz200hVYRUWQYbAZnuauI8+GaqoBQhUQopUTZXGso9J3L/Rwipe1isysqsQsBAky4wFstg/+/m76ZSCexjil/Tufaqm9TpDo4Y9aIdAA888AAWLlyYFYcMtxgWLVqEPfbYA/fee28PBiKY7hBbLbYaEFsttlpsdQFiqwXTARNZubNThBHaRqPARvcXFi1bZLRMHz023Fjwu1dM9DKGdrhfVdYOZdeUMdLD+uPj8VhwmVY6S4P75WXrcYZjDHXRucyPU3Od8XHmALMueshED7dpsA3BbViddCDB2NhcrF07hLVrcyZ6owE88YTdbthgn+GWI4FW+tiYfal4ldWqybM3U5wIfiFjv7AMwgKikzGWaQb+KteLdjrBTTfdhFNPPRUHHHAAms0mzj77bLz4xS/GXXfdhfnz5/dgRK2QJz6VKGWRdPbqtFu8rF+o0lzN6qB8wbK68B1zH75j7i9oZsuYh5VfkznSqqgtymWhY5631cpuazvmLh3zWPpsWVDFkClo5BLcQlSeM8ZlhlLLwoJ1vKxzl2T3lLg6ibLueJKdY6e8mDaekJ13DQJg0561UlnQJFHMXiPLSlOO6ea2TEwz7lpmu2WsNlLOZ1KFMs1MN1NkvGVMN2Nd1iicw64MAG2deqUNdJLCJAbaOFZbkkJrA6PsAmpJYqCaAdPNLV6mgUx/lXlqShEUKWjVmtnPARUOlmTOOWzAInfK7b3a55SniUdvSyVIyUAjcQudJZVOezcYmNTufsD0yDl3kbCFCxcWnPM6WL9+PX73u9/hTW96Uw8GIug5xFbXgthqsdViq8VW9w1iqwUCizBgFxz7yhkhgbeKnV4XVcTiMrRjoof16h63649Rdb9lAfTwmrJYcKgkElsvs0wSBrDz0G58fps+cz0ncvvB8gSd/bHkALy/HYcNom8GMAKrlT6UMdP5s2lTcfFajETo/N28MEB1cLqTwHWduhII7xmmKpB+/fXXF44vv/xyLFmyBLfeeisOP/zwHoyoFfLGzHIwe42hVchDa4WvuWqd78TjtlnNVea05ddo8JJMrL3KqbZA/D+L79jkjnjOcOM6+cJZXCd3zH2GWx2tVd8xt3U6Y7fF0rZtveJxJ45OWV2FpNCuVkWtzSxAwH4recw4KrLdUoxbp47GoZV1xFKVAjTkmjCAAjRZNzFF6jhZTi8XGk0yHuuxqL/KEqja6a1qIowbBWhyx0Dq2G7DpKBJY9xYw9tME2jWX3W6q2mikaRWe5XSBGgYz+N3dD33g7ry/qITTwQRlG5CpXZwesh+Q0ncltltjcSx3XQDlCg0TWKDGEYjUQpD2i6+ljhnnRSQKgVSQNOFRpjt1lBWe1YrsoEGEIyyWrR5YENlzjrPa5ox20z2f8r4z5mDMv62gqmWs9+mnqFW7vTPEAZdCc4880wcffTRWLp0KR588EGcd955SJIExx9//FQPTTCgEFstthoQWy22emogtlps9YzBoOijd9t+mX5Ko6iP7jPOWSud9dJjbPQqWZey+GJdNnpM77usjbI+J8pKbyeX7dcpY6Q3m9XSM7FYbLtr+BwHxH0pF/+YA+e877PSm80hl2jjB9PTkuOy7zRcvtkr87XX52J8PMG6dSMYGrKM9CTJmemsnW4Wz7PWjxnp4cP3heb9SWo07LkyTfSQqe5fy+V1dNLL0gemI6b7+GuiznomMaxbtw4AsOWWW/ZlXEBvfjAQzFC06qsmbR331jbiC5YV6kREKkN2EJB/VS845kHNUHfV1g90SgOGW8w5YMc8b6OVtVbmmBf6JlNwgmwicKQ/Mm0/rWMstuX3VZYSXFhcjYr3WKjnXR8GM4o6rnnQxPbL/XjBFJBlrAUfW4/Zbfm1Be3VAutNZ+VkPHafx3Rrp8HKjrpd3AxQmpzuqt2qJIXSVotVJymSxFhmG2uyMsNNk9VYzcrtH1N+lW15a0q/r73qL9aXBznybaixWqVvXAWWe4jORxs9vfas2RlgQsIXcyKfmvjjH/+I448/HnvuuSde97rXYauttsIvfvELLF68uI83KpipEFsttpr3xVaLrS5pvasxDRTEVgsGHZ3IuvQqKN8BG73dEGIy1WXk4m6D6D7KAsx19Mp7GT+MBe276bPuNVWyNSHjvJ3ETXEOQ730mH56zJakKMq9xD6bAaQtbHRmqPNCti066d08KP9XHf7lxz+eLJ1x0TPvCrqHH6DeeiYhjDF497vfjUMOOQT77LNPL2+vgBn4M8ZgLBrWFhNJFa+VEl5dp92Xbl68wtdcLbTufeHnBZaUY7jlbLf8WeTMNgWtHMMttspTBH6aOPkLZYE8hhs75q0MN19rlf8BKCzUBSBjuNVlt8Wc8vx80SH30U7TMnSseZ5Lr1P+bs588xltfr0ytlsKgwRDSMkgUUN2FGo8a9NPG2+qFBo2LbqJNJME0CA0yQZcrM6q7a8J9zs42Y8mhXGDnOUGywCD0Ui1sVtSUKSt3qrRSFP7HhqTwBhjNVNTm/ZtmhpqSNt8dAo9YuSes0sXJxBUk4ChZsZyM/D0V8ftn8Zm0oQxCo1GCuNYbwYKiTFoKINU5Qy3RBOMgWW7kWW3EVmdVQBIHHFLuflWnC6ubMq8nUPLHeSgiU0Z147zxSzCXHsVWVlEZqCNfqpWOntXuG4tllzJubZ6rU6zd9BApjfD6qSNL3/5yxPvcNpDbHWddsRWi63mc2KrxVaLre5NO3Uhtlow0PCi1D4bnaVbeD+U36gTi4zFPycaRG/HRG8XYK47zk4QY59XMdLbMdvrjCdWr0or3Y9Hs5yLX9ZoAOPjGkUGedYy4sFzRhrZj7HSNwBIsHbtfDQawF//avtdtw4Fhvr69cDo6DCGfUZ6nYB6jJXeCcpY6FXs9LI2uq0zkRdgBsHPYp1oO0B365mceuqpuPPOO3HzzTf3YCTlmAEUhcHERLVGi23V+4MSc7jDMv+4U8ZatE83tjIGjq7BcqsDThMH4Bzy4jnmYRWuUcWy2D47zuGCZXm9OLstlh7erWPO1/ptdHS+pL+Q8eaPtYztlm2DuTD+P+X3R1kwxF5n94jywAmRq0c5m40JQZbJxs83Z7TZthzTDbnGKpGGMZ7uqrEOfW04zzhLIc8YblZ/FYoy5pty+1oTtE4jTDdj9wGnx8rOts92iw8jxnQrlLtAVvxaHd33Fx0My6YC/e1bTJegNxBbDVcuttqH2Gqx1YDY6olDbLVAMFnwFwwNA+W+5EtYViXr0g/EmOhVccUyVncvYpGdMM87YaRXscdjLP2hofL4cRg0D4PrQE6qKAbOwwa1Vx772+x/b0ndcc5GBzaDaDxjnvt66c2m/cEm09/3B+wPtG6QPBTyT9NWpnon6RaCaQtez4Q/7QLpp512Gr797W/jxhtvxA477NDXsc3cn0NmCep+AfYd/KgTj6SwGFZZX6Hmamk9UoEToTJGj++s+6niMdZb68JkeZq4r7fqp4l7vLYCw62d1qqfIp4x3TwHt53GaswpL3PGy1DFLCq7nhcl8wrcJs5444XNythuBm5L3K8B1BBAAC+llaIJ1l3V0GgyKwsaJsJ0U8xuU7bdFPYYABqsu+qc8tSx3YZJQxMhNRpNJGiaBCp1+qvGaq5q7bZpAmpq0JC2C5MZBRjlUsGRU8uyCSLroBtjnfXxnO0GAEmjCTIq2zaSBKaRYsikSI1GQxukRiPRhIQMEqe9ajSQEMEooMHsNjfHifuJNiG7UBnPhbYPGBo2UKGyVHHj+KL2ibP2Ks8zCFYbF5Z1aiiFRgKDfBu+J1CtsgZ1FyerU68t060j8NvYZ/R4ATOBIAax1WKrxVaLrRZbPQGIrRbMFExU6iWMrDYa2eU+4zymlV4m+RKTdWFUMcnDocS27a4Pby22Hzuue64MnTLPe7G+Jc9FSJJmLfUqrXS+JmSlc5vj4yETXaM1aB4Gslk3nYPmQP6HdrN37Zirux5PPLEQa9cmaDRyjXTeOllqzFswmt+gP8gqdMvU7vS6ieqkDxKjfJDG4sBkhV600wmICO985ztxzTXX4Ec/+hF22WWXHoyiGkIVqI3ZM1X5r5r1wE44p4mXnS+U1UgVD9PETVgWcXMzF1zl9ThNvKX9km/koeZqTLO1zDEv01XlumUstTJt1Tp1wnbJGwujEDioYLsV7jFo11/8LS/LmYQ8y2VMN1+e0p7Pt9k5eJ9Mb9UmRWeaqwUNVk6l51z4trSynIbm/tIrRVCJ1VpVDQPdMNCJ/STabpWyTDcFx27zGW+wbbQy3ajQZWE4KmfCxnSH7ZBby7vVXu012uuxll04GOMvwPTwIxgADOA71ieIrRZbHWtXbLXYaobY6pKPQDAdURFZrmKcx9jp3aBdELzT60N0K+3SbRyxUy30uv2E7YbM87BObByxOS4bbyvDvyxwHguihwi10jnAvtl9DIDxglZ6s9m6mG2LTro/yHZB9U400YV5PnDQPfx0glNPPRVf/OIXsXr1aixYsAAPPfQQHnroIWzcuLEHdxXHYP2EIWhBS6r4BDRXy9LJCww4n5nmaa5qr06Z5mpxBBrtFi/TwdaHvwBWyznHcGO06K06h9FntcUYbvbaVq1V4zHUylLE26WGh46uj3ZOeAwFlltQh89xXwXWW8B4i2mx+mw3vvcs9Zg0tAJSjDsGmy1vqqZ9xqQBpZGS5calsFtffzXXXbUaq6mlKaKpbOdGw5UrwACp0YA2Vo+VNFLSUK48VVaDVacaJk1AjRQmTaBNE5QmINMEjAYZDQUDMrnGlvWS3Tw2jCXvNQikCSq1hpj1V/VQ0zLXGk0QKTQaTRij0UhSpKTR0CmGtEKaaKfBqtBU9t1TimwWOhQaCmiCYJwrnihm+iloZYMY2gUpNBSgYLVWs8ejnPYqMwhz7VV+rhpWM1erxD2H4N2Bdn26Z6sSgCJsyUB7NSzvBmFb8Tr1WHYCwSBDbHXknNjq6Dmx1WKrxVYLBNMM3Qbser2oaCTimv29bxY/Zex0v247WZd2Mc+6bPSqIHwnAfQ6wexO4rRhmyFLvRvycsgqL2Oe+yzzcL58ae+QqR7Ku/Cx1UkHbIDcoFXihdnnfFz28P0JYlZ6zkgHNNauHUGjATz2GDAyYrXRR0aAJ56w4zHbDEOPjtpCvpkksTcTSrSUTdb4eOuvBHxtLI0gVh7W6VQDvV+M7wFkkk93rFy5EgCwbNmyQvmqVavw5je/uS99yhOcwWCnra5ua+HaLtk0qpTpBuSLmHUGn+FWprfqa3/m54saob7Wapgm3tqnd13JYmWF+hXp4bE265SX1QmDLHzOd9L9Rc+U0iCknoOui3XAqeOJVz930gwBSUlQKOe4JTDe/Bu3cBcRgZSyQRPiJbryj9VUzRmL2XnWXPWYbkQKBJ/pluuvWofcst7IuH5IQfnvBDvm2gYBrLNOtq4GWH9VKeO2TpOVHW5XlijjygANu3CZIiow3ZQj0ikQlGKpBJcyjzzoxDOilIJyAac8ZV9lc6qzwAey4zKWpkZinXW37Ra9Tf0eYPSKoTYLpkrQH4itFlsttlpsdbcQW91FO4LZiX6yVyebGRsE4fzuYyTeWOyyHepKstQNosfQiVxLVd1O1qfkujE5Gw5ex47Dc+3aL4sRN5u2DgfT/bqxeeb6ft9+MJ6PN21KPFvAsi6pt62DsJ7PSk9hg+85I33TJhTY6f7CtsN+pD9EOJlhYDlcdJTrdiMRM5lBawmQd8UmL2unExBR+0o9xux+0jMIXadvdoBQc9UybLx/lI+BHXFd4o6XMdu0UqUMtyq9VXZiCqw2L73Zd9Sz9GYqLtrla63WZbfFmG3cVlgWq1cGv90wuFLKcvOc9CrGm3XEXR3K5QEMpZbtFtFgZSdcuTnVpJ1PY3kQTZWiQUlBfzUFQUOhSQQF5XRXCalRUBqZJmuT7OBYd7Xp6a8qUkg9/dXUaKRpAq0NTGqPk9RqryZktVbBWx9M42OmW8NNxJAVhFWJAYat/qoGoDenINNE0kgBo5A00gLbLdEm/xiDIaUwrrT9uqIJxgCJIhApaGX/uGvH+PMy1S2rTSnAnSenSauhsi1BwyC1/5tIA8qx2jwHnQMpLds2DrZWusDUbIc4I80PN0xDcKSoF+0IBG0gtlpstdhqsdViq7uA2GrBTEWnQXgvymqgowz08fFiUNPXSu8UZUH0KlmUMiZ62fWdyrp0EjgvQ9hGjEkeHocM8hhC0nQYiG80bB0Ojvux5rBP/5zPYmeEEjGWlR7KucQ+sQB7GmwBazc2Axhy9TcCSDA+PobHHhvB+vWWlb52rb2f9evtONavBxYtGob2GelDQ8WbmQjqCNxXPaiJ6qQLSjFVgfSpwHQY4wCgs2nqnMfVHbplopVdW9ae7pAlp6n1/uvOiQl+Taqrtwowqy1PHvfL82uDRci8bbjgU1GTtPoPvp8eXpYeW66bmrZ8qs63a9c/LhtLqMMa05b154ODFNx/GADhsqw+7DPj50ZApq3qH+dsN3fs2Gw5u83prnqstyLLTQMm3yfjylzqeTYYRsZ00yhosDac/qomQBFUkkIlKXSSQiXGbpXTXGWnXBnrOmeMN9jzQMZqs0y3fMtdMhMOQMv/DP6/UlgAMPJ/ypbrluBNGXSgpzyRvx8CwWBCbHUnEFsttlpstdhqgUAwM+BLtzDKgud+WSymWTdQXRVUr1u/kyB6klSPbWio/acMYdtlWuft7nVoqLwdn0EethWTEy+rw+MNz9sf3ENJF0TK2j3gUCN9HLlGutVMZzZ6uOUfcsbGvAGXMdNj8F9O/nUoG1bNAHzZL0bd/JIkOuyCEsjPL1OBUkba5H5Z9plx2tvPNDfRWubvh5qrsQXMfO1VrZRzUJTrv9w9iDvf9fRWfWfRd9RjaeKstZq1Sfl5e41BccGvIrstxmxrKS9xyLtFeC07Zy1p417qt7uQL0BUhzXTYM1Tw0iZjOlmGVracZr42Rt3nQGUhiFCE6l77rBMN7Ip1KQsc63pvHHj0qdTe6nVWXV1AHdMCqnRUAqW7eaYbolJkKYJEpO6FHFt08bJOetGQRkVOOYqZ7sBgOYvESlUat8hZrmZTSn0kIIeakKnCRJtYBop9LhBkqRImtZBH0oMmobQUPY4TRMkymrJakNOZ9UmrWuVTbN15i25zZUraMrfIDt3nDKej9nXXrXPIb9BlV1VhM90K9NBbad9OpNTx8m9LxNvpweDEQwWxFa7/sVWdwOx1WKrxVb3DmKrBRPCRIJh7a7tJGg30aBcQFUOtc7LGOihfjpQro/eTtKljJnejo3eSRA9FkCPoSowXobYNf5chNIvMXmXGDPdL/fbqZLvbqeV7s+ZX8YBd/96Pj8+3o6B7jPRw4lNgzqbvXoalpGuAYyBaDjTSl+7tqiVvmGDHd88n5Fe9suE/5JWybswfO30dtroddMIYtd0i1nOcBdGumDGol1aOacOMyMmrB8yZXTgqOfl1YuX1UEnequhBqVdpMwUjqsQpokDrY555Vg5ABBhnBXrxZlq/rXtPq19F9sssNxKUtgztlqE7eanyPvBC/Jm3U/F53P8NOxxPmMme47MbLPHGfMN1nklyuvYZ2zZbazN6jPcTMZsUzCpzjVY6zhaWkc+LmbWICAxUA0DpS3rTScpdOI0V5Oc5cZ6rJkuK2yKuK+9mjHavE9GroNqYbpppfLgVfD/p+z/mn/OXtcaXKuLXKtZF47bY2KmpBtt6J7B9PAjEPQIYqvjEFsttlpstdhqsdWCWYUKKnTVQqJhwLzTGH6VLnrZEOssLhpeU7XvtxmOrZsgehlibYWs8nC/jD0fssqryNhhcLzuGGMB9+I9lDHQ/W2VPeCAur9vwBrpNsC+EWNjwMaNln3+xBNFvfQoI50HXPbLiP8Sh+W9Zob388e1ycQgjQV5IL0Xn0HHDPu5ZHLStAcb7V+78Eu7rvkFvMh0a/0DaHUhVcFR0ODFm+o762XfcUO9VVvXWzSLnccghdk66uUMtyqtVcBLn/ac2nYaqzGnvHA8AUpMqe6q68NnvRUYTv7CZGRa2G7+gmZAktXNND+RZNqshBTGsduUc8x91pVV21VIyUCrBCkRGsFCZqy7ahzTzTimm2W3AanR0NogNbb31CikWiMljcRoGPchcg56mmSLmNkFzTSUSeMvVJY2boBGAhjrXanEAA1ADzUBo6AbTZDbNlKN5ngDiU4KKeOJMki0RqIMUsfktAudEYisw26giiw3+Kw3BcAuYgYij9lGjjdK8LVX/RvS0AABqXJ6tyopyBxk7wiKWrxQuQRCp9qrWZslzLe2jDilO6CEFe9XMFMgtlpstdhqsdViq0OIrRYIBG3hgpIm+I4QaqVzWSzIHkMnuuMxhnqdIHqn0i7hmLpduDSGcB78tvnHB5+dXqaVHtv67YXa6z58rXS/TtkcJkmxr/Hx4nGSsE66zZWy8LeptwVyxnrs77eBDZj7zPbNYEY6kGDtWtvvE08UGelZIJ0Z6bxlGn0n8Bnofpk/Me2Y6e0wFSzyWc5cnwnoebD//PPPt6vae5+nP/3p2fmxsTGceuqp2GqrrTA6OorXvOY1ePjhh3s9jFmPnK0SZ6tl9WqwS9gxY2RsG28bponz4mUh/FTxGPjPeMhwyxco8+t6i5aV6K0WNVdNdFvov82CZSHaOeZVDLRydEahaWHWeX3GxhVju3E5O2xZurx3TahL61+XLQ6n8qAJs9k4zd9+LJvNOunWObXP1zrl5BhuTeJye84y4TyWW6bBqmHcgmXkLV5Gxm7zKXVBnCjDTdltooAGoBICtIFqpNANYxlumqATu6+1SxlXvIgZQcNqryaszQpkTLd8wTKKhqd8LVb/f0wYzPK1V3UW9oqD/792qpncDwy8visveDfRT4k27qBCbPVgQGy12OqycYmtFls9mRBbPZgQWz2D0I3USxs6eMg+95vzJV9CxKRMfJTpdMekXeqikyB6qFdexkDvVH677rVhXzF2etU2vJey8fkB86ofHGLsd2ah+9cODXHGYkwrPSzzH3z49z/8LsMyL5uRM9I3Y3w8xdiYlXIZG7OB9PXr80D65qae2EMKg+512NcDxtCebZhNjPS+jHHvvffGn//85+xz8803Z+fe85734Fvf+hauvvpq3HTTTXjwwQfx6le/uh/DmGao8Sgi2qhAJymdbZpvkyru9xtqrhbr9P5LKjPcAE437lxv1bbDC3Dl21BrFcgZQPaavB17rtXJza/L69Zzyus74e3qxZz0sDy2sBkhhSFPUzZIGyfKGYFcbuc4zea99V/+jDjAkmaBFmROeuo+nCbOLDcihabTXE1JWYab0dmWnfQ01TBpAmMSx3ZLciedna4YlLafzLgnQCOBagBoENSQXbiMFzJLGk27oJlzzDOWm/dpKBOkjTtDkKWUe454lkZeTA1XoTaxk13w/0/Fgl4KSbS8WKfd+al35KcCvszARD/TDWKru4HY6iqIrY7VLUJstdjqKoitjkNstdhqAQpsdF+6xQ+mV+mjl6GMjV43cF5XF71uEN1HVQC9V4jFesPgfSfBdN6PSbyEnzIZmNhchoHzsuB9nI3O27o2xJdz8fc3wzLS7YeD5760ix9Mx8hIq7xLuCKrj3aBcD+4HpOAaddOrwLtErCPYjYF0vuST9BoNLDNNtu0lK9btw6f/exnsXr1arzgBS8AAKxatQrPeMYz8Itf/AJ/93d/14/hDBY6cKR7/WW5yokv++JeYLep6vHE2W65g+GXxVCH4ZbvU6yJTG81c8b91PFImjgQOqr1FiwLHfMq5lq5Uz5R+G3ks8pp4oAddyx9PFvYjDiN3NbLU4p5P08btz2m0GDWG6CUbUPDMt00GRil3WJclv1FmVuvYIiQkmVwGQCKnXKg4LgbBeuAK7JOt1LuevtJSTuHPPX0V9kxdwua/f/svWvMLclVHvxU9X7POeMZnxnG+DZiAHMJCQGbW2I5IXy2MJcBOSAREogjzEU4sWwScKKQkbhaQWMlKCGAYxAimAgck0hgRZZiZBPM/IjtGDsjLiEWRg52hAcSzHjmDHPOeXdXfT+qVvWq6qru6t697+s56tO9u6urq2/v2mvtZz3La7PCJOwyzZ5ApcmDdlOjoFYWatUCrXYO+aoJDLemadFqV9BMa3LGbcd4sxqNsmi9Q94oev7hC5ZZaKV8MbjOUBjAr7f+bqaJ4fR+uZAIX+fuC1unGrSWt2nCPmk6ea6g2Vi6Ny92NjtlXHAQEFs9ALHVYqujs90EYqvFVoutFsyH2GoBgGzkOMdAT4PqU5EGcIeGwmWva4qLpvuny2NB9JoxTUEuFsrlWWgMXOolLUKak3UZknjJHadUmJRroHP5Fn48/pkmN94cIz1XdDSdc5uQFh69QCfxcgHHSr+Cp566wJNPxtIuN264cd1ea1zhRUdzN7WUrUEXIpVyKbXLYYmCoyLDIshgK8H+3//938d9992Hz/iMz8DLX/5yfOQjHwEAvP/978fl5SVe+tKXhrZ/8S/+RXzqp34q3v3udxf7u3XrFh5//PFoOlRsg+G1KbJsNaWL2ql8nu0vpKJ2jLeelqvn5sT7zQcvYdax1zp2W6q3CpCjPlyAjByIktaqWx5nt/G2KbstRpnJNreAWa7fHNstOocCM48z3YA4bZwzAwMDDm2XKp5huoWRUbo40BUo80w3Shs3bLm1yrdxbDfrU8mdU+61Vr3zzQuYwTvlcao4zVlAh6eLB7Zb4/4iUso4FTJrWpc67guZdcXMXMq4c9K9s+0LmDV+Cj6/tl6PtdNcDUw3v6xAcQIeyFLRHCB9434gjLZ1+w4H09RIsC19p5di07rOpvS15d+kl0gVT5+5I4HY6sOC2Gqx1el6sdViq8VWe4itPktbvXfMZZ1uma1aYqBTsJaz0NNpDnYh7TIk5VJioG+iGjK0f46dnhtnqc9cH7nConyZJGBK55JetxIzvWm4vAvYHIV1Y2iTieRdLuFY6bdDEJ2Y6JyRvl4nA665YVNY6XP2z7Vb8p2dc/xdHG8HcN+h1ObTvk+kAot/63nhC1+IN73pTXj729+ON77xjfjwhz+Mv/E3/gaeeOIJPProo7hy5QruueeeaJ9nP/vZePTRR4t9PvTQQ7j77rvDdP/992daHcPl3jaSL9MLseQ0mi5NvDAHAG1Vj9FWC85wAxDYPpRuTMsdY8o75gN6q9wxHypalqaJc63VGnYbd267flJHuu+U1zndfQzvZ3rtaGy9c7HdleT9GFqfSRvnmrWUNu6O2gVIbLgvcVq/hbu/Xeq4640c89Yqt80oX9SsSx1vbZcmbuCWjdFhXds2IYXctp7htm6YY565kKn+KjnojXIp46sW+sKniZNjrt3yarXup4xTMTPPcHOOt9dkRZIqrhCcdXLSAZ4m3gX60nesBP6+p0G2Mcd6qIjhEk55jfbq3tLVSVpgiemIILZ6nxBb7dqJrc6NTWx1ArHVXb9iq8VW78xWC6qwSQBryr6F6GzKQOfLJX30IeRY5UOB86mSLrVBdI6hAPqSKAXU+bhobDwoPjSn5RzLvxSoT2VfeIA9JwXD2/T7TjXRczrpuaB7Gjw3fk4yL7cBPIVU3iXVSKflnrwL16kZAv91iD4T+ANe+pVo2/Iugj5yP5rMnQ4ci4/wgQceCMvPf/7z8cIXvhCf9mmfhv/4H/8j7rjjjll9Pvjgg3jta18bPj/++OPnZ/SnpJkvxEZJWTLllPI844b0ImPWTh8l99Jm1pmsl9Vtyznq3XKsucrB08QBZAuWjbHbwnLPKU/aLpxGS/11953612F7mj7O10X9qM5JcgneXdq467n1vXbPhktg7tYaGJ8M3oBSxo13NInxpuBTpoFoTgy4rngZ7ePTxZEWMHNFzJxT3nj9Sw3rdYEd+yhz0ShlnObGp42vGsAYqJV1VdQ0Md1Ig9U54sqz2TRzzInhRo65VtYFrFScFq6QDyVqBZ9KH99BV19dhWvo1uvo2dZWB7bhGNJ08jngKeOsZ5QYnILDg9jqLUFstZ+LrS71J7ZabHUNxFYLALHVZ42BCHbKQKd1fFu6nlAKsM+VUZmii57bf0oQfSy2NiX2VoqlUh85mRYOknnhqiE5FREu8ULtaT1fpu38WKuVu198DMQ658fhyifUT1m6hbZNQcvmpJN+BVR09Kmn+sHzp55y47t5E8A9CwZH58isiLyLYAvY+tNwzz334C/8hb+AD33oQ/iKr/gK3L59G4899lj06/kf//EfZ7XfCFevXsXVq1e3PdQCtpyqWEAt42OqI95L61ZdqncuVTznkPPiZbQcbUdccMm1zYN/lSeGW7Tddsw2ShnPFS4r6a1ylhYQO9jpNtqe01od0lmtdcwHtVlnODXpvRly0ksOOuss9OfSxt2zYW0L5bU6tQLgNVGVD8ek+qsGzsHX1mCtWiirvLOusLZOv9Uod6TWOge2tcppksIz3SzQGsBqWtbQ2jjWm3LFzBqlYaxG2zZoW43Gp4wbYruttXfWNZQxPi89ucac4Qa47au1nxtQITNtHNOtuVijuVjDrFdoVi2adetSxtec5eZ0Vxtt0BgN49PGrSXGW3euNFdQrsCZVVCwUP76aCi0PpARDRvahUOsdhq3iINQKrj0w89Op7HLl3NO92ljqeJjxy4zK7Z6HsRW++1iq4sQWy22Wmz15hBb7XD8tnoP2DX7dAozdgp8INIkNoUC56Wio1MPPxT4TqVIhtqlfeXWlYLoUwLoc2Ob6X7pNUqD19SGxnl5WQ6m545DeuapxjpfTpnuPMBO157W3brVtePkbgq0X15quIA5zYGOfd4iz05vgcg2pXbmtp9fomOkX8HNm5d47LGLoJH+xBPxHNeudaz0a9f6FHrC5WV3gXngmi5a+ktLqp2e3rAaSIB8WaxWLo1vU1h78JkDW/c8b9y4gT/4gz/Ac5/7XHzxF38xLi4u8Gu/9mth+wc/+EF85CMfwYte9KJtD0XgUePQp23S4mV5VpseTBXXFS8VsaDSwmXddp6GbLJOLddbBZB1zFPYnpPeFttG+23omNvCOdSgtG/xWKPn0k8bd/vFGqxhe6K/Sn3EfdpwH91nF4QJEgBMe5Wz3Dp2G2e8MYYbOMvNT6abd4P0RczGEKWNN4BSUCtAaROYbvAMNtW0ztHW/nOY0LHfkC53TDdKH6eJ0sd7Q2JBLnq3cu9dvE8/sFabMl6DRbVXDwVGLzcdMcRWHx7EVoutLvYntlps9QDEVoutFlt95Biic6OveZ7Oc6zzOTGpkhQJoaYwKd9ey0SvHduS8c8xaZfcZyAv8zI0rvQHiBrmfklfvbR/dz15oJz+9vHAOlDHTk8lXm6zdY6VToz0mzddkJ/PDXT/wnBq/VQcSoD1UMZxSBBpl/n4J//kn+BlL3sZPu3TPg1/9Ed/hB/8wR9E0zT45m/+Ztx99934ju/4Drz2ta/Fvffei+vXr+O7vuu78KIXvUgqi09AjYbh5D6985064enxSkXPUj1I4s5pVafDmmO4AQjMNq7hmYIYbSW9VcA5knwe64nm08SHtFbd+pxjPu6Uz3XGS+BspfS4zpnqs93C2NnlDCw4ljbuEr11NIcFrGKfKZVcOYag8veh47cptGj9M2H9Oqe9Cp9Y3lpivVHKtNNa1QDWVqHx2qtrq9FajdZYtF53NWivGs92axs3rTVgVezpE7wjbnXjr51H4/8kXlkDdg11YaCM01+1aw19sYZer7BardGuG6yaJtZeNe4cG2Xg9Iq9821dETNj0LHYiO0GF3jQyj3tmoq4hbum0LIbpa2C8fc1MN6gAQu0yu2lPTMxBWe0pdBKh+dfKf+s0B3zn8fBnzfBIUNs9fYhtjqG2Gqx1WKrxVYLpkFs9R4xFiTbZRDNB5UMdBQs52x0XmiUFyAtyUpzzNFGn6OLzvdPj51rOyYLk8MU5Y6h/XNsdPpMrHC6npxpnhsHJz1zqZZ0nl5TLt1SkndJ7xW1U6qB+8rmxNy6Oa1Lmep8ztEmyxpdodGn/D43cXl5BX/2Zxe4dg147DHg6lUXRH/iCTd/GrHSOc0+B7qoFxf9m0AXAnD7D1V+3UQCZgkI0/3ksfjd/T//5//gm7/5m/Gnf/qneOYzn4kv/dIvxXve8x4885nPBAD863/9r6G1xjd8wzfg1q1b+Kqv+ir823/7b5cehiDBEuyUXPGylHVTw8QZQqlwWTyPC5fl2G5DeqtZZliG4cbX1znmSZ9zHPMaB6hwL0tOepw+rpN1yXiTtHEEd7rTX6UCcK6VcwJVSFv2iqDKQFsDozQa5hMb61hvGoCxCo1yPnMYoe1GanxqOjHb3BjhWW00Z9qrRjtWG03WFTJT1CGAbMo4KG3bf7baec6N86qVNn6yUNpCN21YJoabVt4hVxqNNlhbHdYTAw7onHIKRJgk1uRGa/2yC261LHDFU8dJe1UNOMQajb9XDVK9YQUNKETBqbOEUW7auJ/Nu9glxFYfJsRWi60WWw2x1dGxxFYDEFsttlrAgnI5DfRSvcUxTNVGH4pdDmGI0V7bfkmJlzRAntue6pUPBdP5+qF9uVRLLpieG0NO3oWfA92XsrIJ/y7BA+q0LZV0yYEY6A1bJr10N926dRH00YmNftddbn7tnivQ6YCHAuol5ALUuV89cstj/Qg2x5LSLgeOxZ+et7zlLYPbr127hje84Q14wxvesPSh945UH7HfoOS0LsFay/fBnbCUraZHHHbnZuetZY5pN3T+PFVcJS+XDU64/xzmnStLzrZlDjqAqGgT6a+GbQW9Vb6Np4kbyxhyA1qrc9htRad8jlhjuk9aiCxx0occ9FxBM2KyKc56CgEODa1c+5L+asp0o5Rxcsop/dvprJI0gELrb3zrhgjSYTV+7vRX/To/tVbDGKe9anzhMmuVZ7cpn8arYA2Qre/FCpkFjVbApYwbC3XF6bCqizX0uoFeraGblZvrCzSNwWq1hjHaM92cI75SBitlYUl3VflAhHJOd6OcnqpWcSq5gXtXjLtxbmj++mh/NYeg0PivQbvzFEsMuFFmnNLznv+FQQGezftZYDA7hNjqoQZiq92YxFZ3G8RWi60WW71PiK3O45Rt9SI4BOmFTTXTk2BfTgOds9HTqfZwQ4HrktJCLRs9lSgBpjHRS/HOJeKgpZgsbasNpuf00nl//PNQoVIgvp+0jQLldC/SgHxazNStb2AtZ5unxUdLf9t5sJy3ISmXC9/mNoiRDlzBjRt34s/+DHjySVd0lDTTSfLlaatVp5VODwCvnFoCbUt/xWnbel2gOQVHp/S7r/2X7mdTNE33nW0TpISKA8RxC8VtHcd3eeaw2VSSIp7TXCVHL1+wLMd2qwhWDMCwb7q8cBkAGBU76LxwWbeP6S1zx3wIdWmxdey2oq6qNcs5JoW+omuQ0YRNgw3Wxrqr6XmEvSeyooiHSMGW1MHkmquuvfLridmm/H4I6y06h8owbVWnu6r9RIy35Jn1f5gpVTyAa69qz3TT8Ew3G+aku9o0LbRuE+1VC434c3QI1ffg6DCK6a8OSS3k3rccctIPwLjcRG3xxG1gn8cWHDPEVgNiq6e2E1sdQ2w1O4TY6oM9tkBwlvABspwG+liwfCg+WYpB8njcGJN8iqRLDWrlXbYVM5wT1Ocx4Vx/ueuZaqsPjaV0TdM+uOR4N5YxPfSmog2hTaZLtnw70kWnKfpRJ6dFs00cwg9pgpPFAfxsIdgG5nzJ5V/mc+w26lMzR51/0adU8TilddhF5wy3XOEyE1zvfuEyrrcKIBQu45qqpVTxnN7qlDTxGnZb0SkfAR9HDsV7S32z4ArppdLYxthu4RwUaXR26p880MD1Vw0AZbXXESUV0DhlXFsLUmE11t2ltdddtRawysJ6ppuxjunWaqdBuraA8rqrrWcktYb0VzvNVdPGU2M1Y7vB0ZDS/GwAYE66XV245/Xiiltxew1lbMdyu1hDX67QXKzRXl5AN15vlebKzQ0UGuWuHme7GauwIgfc664qdI65tei9L8rfQQvt7wilk2sXLFPw/MEuYMX1VTUaf1eawH5LdVmH9FiXwmbHKKfEbwzPhNy8nyOjuQkOAmKrxVbTGMRWi60WWz0AsdWCc0GJBr5aAeu8BjqfSjroKYaC4ekyIaeNXns6OTb61KD12DHnxGVzsdYhNjl9TlnnnJlO63IyLLxtiSCd9gH05V1Wq1gynD7TMrV363iwvGET/x4zxFBPv+/QQ3Yn3N/9JwE0eOKJFteuNXjiCUc8v3HDaaTfuOE006/fcS2m1Q/h8rLTSR+ScqF26fq0/dDxSlIw6X6Hwvo+ZKxWZ8NIlyfhyBClfFcy2obapYyXbRRHI0zpmZhtteCFy/rbhvVWc0XL+HIuTTw75g0c87G+c20HnfQqBx29dUNp41ppWNtGrEjSX60pZGa8c5nCOffOt2m8w2q80879HdJfdX1xzdVuMiz1NzDceBGzFLqBZX/slS9qpiKmm/WTcUy3Jma2aW2glOk0VkFp3l53FfDst47F5u5f3xEHyGlXPqCxfbhiNJliZyPp3k6L9/g1Wx1JdIF0cXHOBQxiq/MQW50cR2y12OpKiK0WWy0QpEHfEht9rl46UGaAD2mjT2Gl17DgS/uOHX8O0iB5um1M2mWo37FsAC7NUjM2roNOfdy6FX/u95dKuqTbcuuHwO0NSb+Q5IvBzZsNbt3qnkFablvEDwbXqOEnMYQpUi6C/eCMAunHlw99jlig+FjoSumsU5dzyjVYmnhm3rHdVIbZpiKt1RxShptbjguXcc3OUuEy0lt1+8QMtxq9VcA5vJ0TboqOeZxSPZ5a7VaU0rnbMM0B37/XR3LMfOp4wtAbSRs34Zq0kaat68mtoyAJZyJaf98Muytra9Bax3Qz1rG7rHWFvYjpRsQ0p7lKOqtuuZsY061tuiJmbQPbkuYqY7oZk//DHJxxVgK9WQGrBrhQUBcG6qJ1LLeVgW4M9GqN1WqNpmmxalpoZXDRtGi0K2amlNNX1d4xp1Rytw69SUFB0ZwPTakgvxDeLavyMg02fr/p/Q3zOXISaQAv00ep3yUKJwoERwOx1WKrMxBbLbZabLVAcGSYG4Wu2W8s+ppbt1rB+He8xECnZV67cYo2+hQJlRxpfiiITkH4ki56DRO9tG4pgvDQuZbGmp5rTuIlp2aSk3dJY8u5PihQXju5PjTybHS+juaazVNwSZfbcKz0mwCe8tNNADdw4wbw2GPAn/2Zm5NW+o0bgLn2tE4jnf86w0+Qgz/c6bq0XelXpty6KcUDNkFt/yJBc5SQb057R+aL7kLag0t9MR5iwqXOwSZaq5wj0rmP9b9GldoO6a2maeL1mOCY58a0BXZQts9KBz3HZorPq88UNNYFNDiLcA4vq9Nj7VhtFrGDbmgdYl1WGzRYNWO76cBaslTEbAw+ZdySg07O+qoJwqhKGyhtAGWgVq3TYOVsNzbX7LOKHHR/OCAw/ojtppNXR6nxABfQyTbUaLGO9jXEiD1VRztICmw4LVAETXDIEFtNEFu9GcRWi63eFGKrxVYLThyF6PAcHfQcxki9paDyUIHQEkq64UPHHmu7TXWNmuD80JimFFGtGcvY+qFMgW4sueB4Lpg+hpbN+WTC3NrLSH6I66SHwed+gRnDlKK9SwamJcg9DVN+6RmbDhyHP8JTwVa/2KYOct0fw1JqOGe3RetZ8bJo2aut5oot5b6uErsN6GfwOu1Vt50XLrOwninVsdpstBw7iMRwK+mtltLExxhu6aiLjvlGTnmNg5u/d9lUcpY+nk8dNyAN1nDDfJo4nVOnwdrprwYdzUR/VSkNlUkZb/3YGjhHtbVubqwL6Rjrzmrth2vhGG6UOr62Cg3prloNp9PqWG5tq2Ei7dUGpm2Co25bBRgLZWzEdLPkhANOc9UY2PUl4JfdH/IW6ooB1sbpr16soW+3aFZrmFWDZtVCrw1Wqxat0U57VSk0ysAop79qPbOtgUWjLYxxzneDTn8V6BhvFi5D3cKxRumukfaqhQ7Pdv7p0P7ODbRRjWMYRvd4x2lUSvfelXIaOj2ry8IyiYFN+xGcAMRWB4itHoPYarHVYqv7EFstOAPsKrjmg0kGOio0ytnoXC891U7PDXWsUGgufjWmjV5io4/potcw0Yc+l8YxFZvqpOf2b5pOuiXXT2kc/B7w/fk1TaW6U5Y7fV6vSRbMoNNA5wx1oM9WR/KZguQEkom57acGjpGuAdwAcIHHHrvAtWuOlU5a6Vevuvn1a9fcyps3O32bVLNmDFwcnvYvoVbXfOl2+8AhjK1phu9HLdqa7+H7xYnQFuRLETCfhZKmlG4DYz2nioVdETOad85zuY+SQ1z3hT6vy5o65mzbmM5q0s+wY27YVIPhttn08bBtBhMtSRkf6sc57/2U8W5794nYa5YCMKFNx3gjtptbVp4N59LHaV3QWk10VyP2kUGUKq4MK1jHtbxC6jif4DRXaWoMlNdjddqrFkoZxnoDtCL9VX881T3lufeBUsKXRk26d3+f2gBfrt0AU+5UTI5gJsRWA2KrxVbHx4tXiK0WW03jEFstEBwVthVcZ4ExfohdqFMsFbiukbXehL29KYF109hjSeJl6jHSNqV4JG83dKyuXY6Nnkq+ILPMkbLQgS7I3sJJvRgAl4GJzlnptDyZic6RewFK2wWCLeNAf04RBCykZ6igo31KaaG8jYKG9sWq6Es413pU0NCM2dGx3RCluY6lvPZ1Vv364JDHDrpRne4q1/l0fbURw22K3iqAHsMtM9re9jF2W9kpX4Kxw/uI72mP8ZZhu9F50C2ytnsGum2cFdd49lrrl1toBfBCZsb3kTLdjLK+KJeBgoKB9YXLrPtsXZit9fXCWuuKeLm5/ww4VpuynuHmWW9ef9WYxjnurQaMdvqr6wZBdxXovH6OkC7euPRyz4RTzQq4YoCbt6GuWKiLtZsar8F6+wJN00LrFlobNDRZg0ZZrHxhs8ani6+UK9Jm/NxaC638uStKI3cp8doPWfk21hLPULm5VTBK+3205xg4emCr3L3X/h6550AD6JZrgjRjRcxOAkbXSQqM9rN5F4IjhthqsdWDEFsttlps9UYQWy04RmwS2CsEGkNAEh1pM9VNLx12jI3Ot+VY5Tnmeg0bPXdKmzDTx9bPAe8rLTDK1/HPnI2eMtTpWnNiba59rugoHYMTr3PtCcRCB/rbVyuSFedFR+kPIZd8SYPpGS1yd0boAum3/f430THTLwIT/bHHOkb6jRsuoH57rXGFGOnXrnWa5yVq/+Vl/ODmGOi8CClnZZeWOXLrpzK7D4EJfihYrZZhpFdI5u0bcscXwlLslKU0V6uPp8aPlxYvq0UufbwWPFUcAIwaZ7vldFVLSPVWc5qkQ2niuf1y2K5jnutz5B4VUsejJj5tnJ7F7rNLGXfpvCaklNePzrnlZKgNOaZ+jbWAVRbwjnp/XApQjAlnY7Zbx27rNFi709Yu/TtQ5yquP2e5rRrgch0z3biuamNinVXE22kixp325+kKlvU1V0ugvzPbYMNNwSk57ZaK3C3Qj+DwIbY6t5/YarHVfHRiq8VWHx7EVgvOGWlhUVrH56X9lsbUeOFUdvY+gui5vtNrV4rzltYTKKY4FIgvycBQUDw9VvrjBgXcab80junkXcKIkA+o83kJqcQL10e/DFPbdtroXIqIpitpCkF6otsEv/C7CH6fW5D9jALpkrsnAIDAZgPqUjq5o64KmqtjcGm/nRPOl+N57JAbNifGW6SryphojnXVMdyAJOXZxu3D+kqt1W6lSVKyS+nhU1LC56Dff28sGYZeNh0+ZQCGaxwHNXghM2IYllLGnXauRYvWMxqtY29ZX8TMOpab01wFWuPnlraR7qpzyi0UjNGO8dY2QX8VxjHdzNppsMKz4ZCkjANwDDc+ra4AqwvY1QXslatAs3LOeaOgLjrtVdW0aC7WaFZraG0cy61p0dCyMmg0Z7i55UbZ4JD3Jni2GzntcCzR3Ps1Fizj23VgqfYNm2PAdutzDNggKXGqRcwEgiOB2Gqx1SnEVout5m0AsdUCAYDtyTxsW8IlpXsnQbgc8zwNsk8d5phG+sVFfv0YG32IAZ87Xk0QfUzGhSSaa6cScscZY9QPqZaUzrXE/E/X546RSsnkHp2OrZ4WF03lXXSybSgYehtdIP02HCP9Nhwj/SlcXt4MLPQnn3TzJ57o5rh2rRtg+ktL7pcXisYTeCoGb1OCSL4ItoAz+nnkPDH3y3TuSzwQO+48dXwTcEJTTao4/xz3060rFXcaZaWNMHemaK32Udm+lj00eG9HGG+M7Ta2X1foLFmG6ZhwYAXNiMUGA6UcG85E98ZmnU1jXQo1Lbu2yrPZurCDAQKLzYQCZ0x31ShfuIydBy8ulRQxi85VN1CmhdUaKtJdVS7X22utQlvoldNZdcy3TnNV+wkAtH9+6TOBHHH+lCq2rc2w/Byzzfp3zvj+NdrMc1UuArYrdGM8dEgBM8EhQGx1uU12u9jq3n5iq8VWT4fYaoGgh6WCbgsH76iY6NBhhmRd5oAXGeWYSrCdow8+FLgvYQ4RlvYpEaJTxnlOnqXU/uJiOL7L5VqGFE5ysjG3buW30XELZ4uOkZ6u5/Mh8O8fxEbvT5eXjpV+8ybw1FNuvOEZpiA6nyjinwbMx25qSZoFOC8m+KHgjBjp8nQdEfLss3HnON2vRnPVtWs8f62Btp0jrm1hf38knspa0lxNv7Yba4Mj3rUZThV3zKk2MK1cmzY45jZpX6O3StvidcOBgJTdlseAozI37ZbvV3S0AXpGSjqsXIPVPQOmS//yaeOpg25goJV2zEEFwLeBamDQQlmNRunAdHO9Gq+2yriI1sAo7d1O61lszpduvQ6rc8Ydw21lEZzytdVYWR10Vw1LEzetX24b2LWGNQpqrdw5F1LFrW4A7dxptbri52tg5fPlVi3UykCtnOaqaVq33Hit1aZF0zbQivRXHXuvURbWs9uMZ7xZn07e+LeFWG/KXQjPbgMslNeqpTup0PrgRuvfD6d/rAHlXHcgDoZo1aDNPGOqFx7YDEebQi66q4KFIbZabHVxP7HVYqvFVs+D2GrBKWAOTRz5YDmXzLi8nKaMUWKH8+VajfMaNvrQ/ulyzWeOJWJ2aT/ptawJppfapFrpqXxL7jjpNeUa6jlVkpSFzrfT8S8veQC9SaZLNud/Z1Ot9PT7FtdKb+CY6VcA3MTNm1dw48YFnnjCsdIpoH7zph8oZ6XXBrz5RU+102v3HzrWVH31KX2fC84okH4CuX+Hf5H3gRK7jeu6UoooTxUvsdv6/XC2m+qx3WrTx1MyT1fEbDxVPAVntvHCZe4zT3Ee1lstjrWUJj4I4mWVOl3oG/0OnKL0/HnaPQ+OhPaZe9WtY/fYupTx0Bc6lptFx3ADLVsVPpP2qtvmU8hpohRxy5yvsOPA9dK+rW5gtXZOe7PybDdANRbQxjHdmtax27SF9kw3nbDd6DOAeJkOpzrdVZcmrrK2Iw101Wivxu9pP2VcD+guL6UBLSnmAgex1TmIrRZbvTTEVoutngOx1QLBHjEgBG7834dUyoXPU8yM11e1WYKNPtTHlP6XCqLX9LtpjJQXBOX9pQHvofEMXcuSNEy8T0myhcu60Fwn21JwNjpfvg2SfuFM9Js3u+VsAH1qUDyHIfr/ktiGVIzIzxwd5GeTIo7rCyV9Ad52AbRIy9GqQQdhzHVIdVZTjKWKE4PK2LyznXPMyaGcynCrSRPvM9x24JTn+uw5Q322Ww3TLWLH+WJlXVp4v5AZTxl3zEO3XikDY532qrbas960D8Io54DDOd7KzzX7bIGgtdoox4SjyXhWXOsZbsY4/VXDHXSa1g1gWpaLbqCMe/oCw03TNxVAreEc8mDsG+BCASsL1RjPdLtAc7FGu25cETPSXDVed1W5K6LhWW7WucuNZ7RpC9CV42w3x/BTULBQSoFoh+7OOO6pgYW2CqW6WSoqIlOHLuV/h1B6J8GlEqSA2bFDbHUOYqvFVoutFlu9KMRWC44JxxqUKkRrU0Y6MC7zMnaIGh3vnMb5UF8pG70mAD+FmU6YEkAvxWfHYq45yZeSrEuOLZ5jqY/tVypMyouJciUU3ifN0/7pGty8ybObUkZ6jpleQlpwFHCBc42u4OhNAE/hySevBa30Gzc6qZfba90VHOUnk7tgQ+AXhl+8lPafk34ZovgfM/Z9LukvRieM8zjLg8X+AgBpCnnKhhkqYlbSWq1ltuW+hlvvEri5QU2qeLz/OFvN2On79MdZmyZe6G8XTkhRU5U1qXTQre0CP7SeO+idA++dcUcDg0GLJnlODP1TFtpS+rhzMpUvWkaiAQbKOenKM9v8o8W1Lbn2qvXFzKxPH7e+iBmMSxUn3VVrAGVQTBmHJh1ZANpAhcJm3knXzqNWq9ZprWob9FaVsi5lvGmxbhu/DtHcsd46XVW3TfXonlqlnxVamw9iae/+RyniVkfvSOp051K7XUX3fWq17geiuyoYh9hqgtjqBSG2Wmy12OpqiK0WnBVGAuoUBCaWetuOs9THDlcT6B4Z3mD/Y8u1qAmi15CbeZspROY5wfT0GGPx4pL8y1Bg/uIilo9pmlwwn5jmJRuSfifhDPXcPi2bG7iA+oX/fImbNzs2Os2fesotR4F0ftK9Q3iddC7lwgPnaTvBfjFFqufIcVxUrgPFaNrmntIla9I0h5xwlwSug+ZqWrysW+7YbgqqqLUK8LTfYYYbRylV3MDEDjYMxjRX5+itzksTz51I/T524F/1sXrHS4MTdY5Y7np16fjxPXBH6e6Bm9pwr4wKLno4T6e5654JQ8XKbJcqTgXMWp823pLOKpwT31rt5kbDmC5d3JjGp4t7Z92q+BKQg24y14FYb6sL2NUKdnXhUsZXDdQKgemmuPYqpY4r63VXO/1V7fVWuSNP64AuZZwKm0Xr6LN/zygIlgbDtNXhnS1h6H0v7jPyd2Qqs7ZmDNtm6wrOE2KrxVZXQWy12Gqx1WKrBYIpqIleT41we1kXroPOu5kbMB/6TKgpMjqFjT52vFzfubZDcdKLi26aiqF9m6Z/3Dk/BlxclFnkfJ5rk9s3ly0wJB+jFA+KU0CdS72k24f+vg9JulwCeArAU7h5s2Oi37jRFR29eROxRvpY6kMu/YLW1b4Im7bbJMvlWDNkBIOQQPqRYM6X6mh/pUe/8NZqrnbtNxsTuZnGF2jqtFZJe7Vz4tJU8bifzimnz7l5cRxzGG7Rev7H3SDLcKs4Rq0DPslZH3HQ03bhmvUYgX1N22i7bTtnPOewq/Rzp7vKR+ZSw/1nmvuUcvhtXbtYZzXSXI1SxR3jzXZUuSzLzeomTPQZnOWmtUsZb5TTTtXeOVfWMd6UDcXMggMONnlnnCYFnzKu4Ld3Y0kDfumbFooJVjm609/TXJHDKqd664HILfRPBcyWmARnD7HVYqtz7cRWi60eg9jqEYitFpwLBiKzOVmXTQPqm8qvTDnWJn2OBdGXQqmvoWB6um6MZF1qP/bjRfqZB87T65n/QaP09y8Npue25UDBdIM4sH4JwAQpF5q4XrqBZpJsGb2aHGpTB3hBgW1BdNLzSO/pJtOBQ75NHDJKRci2+AV47Au4tvH22hTxWuQLk/VTxS2cM8g1V7mDnmquOjZW7MBPYbjVp4lnMFYYbQp7bc7+Aw56NP6sgx635dfD+muapuF3R2lhbBuxEjkz0fPeQAXrrLUhUBPSxi2flHPUvSNOmqsutTx20k2re466c6D8KRnjpzzDjRxzqxvHcFtd+HRxDaw67VW1cg66bkzQXVXKQuuucBmx3mKGW5cSTm8Q6a4qxm7jrNHSm6YH0pS58156t7UalodIETSeJ/wdkiJmgpOG2OpondjqmfuLrRZbDbHVAsFeMRbI2kOgKw2ec0Z6upzTUp+KHCM6XS7tMxTQru1rChN9LgN9DLXBdMIQUz+NCdZISKf7jPXJA/HpsfrtUza6ZusJuXUpWsRsdM5Kvw3gKVxeXuLGDeCJJ+KAOgXTZwVMhx7sHHM93ecUgtWCg8Dhh/oFkzGe3qkHP+dQ9SU/6rOPnAvnUoVTtlOnuVpKFY/bDzOwOHIMrLqRUgdD+05jt23ikJf6K0oX9LRYDTrNVKbBmkWsv5o/toFCA2Nbp5Wa6K66IMAqcBdV5KADytrooQkp44yYZrmjDpoU25ZhvXndVWu9/qpRHX0ud2+ogJlu3N1ZXUCZFlZrQDdQ3kFXKxO0V/WK2G7GfdYGWrfgaeFKdew2ShsnZptWCqSwkOqtEqiImVYK1pLirQpPvsrw3bTVaFX/C4Xy2ng57VXaTu+IK1J32lqsUsBMsC+IrS5DbDVBbLXYarHVgNhqwQmjRG1erbKa6HzOt009zCaB7THGdS0ju2YcQ0H0bYL6T69v08Ra5Dm99BxSrXSuoc7nqdY5EK/nx7m4cCzv3LFWq7gWJ0A1NnKjS2VdaDn3cFHwPF13CRdAv4Ku6OhtPPXURZBzSYPpkbwLv9EXF3UPdtu6tksU2Nx3kc5TwZGwyZfAeZzlZJwHI6PkZCtoaK+jxZ22SGuVMWtIlXUI3AUgnc14O6WIxwXLAPRSxUnHk4M73DmGWwmWsd96KdI5hlt0zDRNPG2wO8c87TfrpA846JOOkRQy69a3kQPf6a664xiY6BlyY+3GafwnY92oyBm3bLulecpqS1PFrYY1GqalVF6vv2qdc65CLrpnu/Hz0/TsIzDd1OoCWF0G7VXodWC6QflCZj5VXPuiZo3yqeM+XbwrYGahrXPIfX2+wHprfdE2Fwzxjnxo0xUx4zrHHBraX+2koJlqIkaooIMUMDtmiK0WWy22ungMsdViq08IYqsFB4tc9HQJAfNClzlG+pRD10itpMzmXJu52uhTJF02DaLXxPPGrlcuplsKpk9FKZhO5532ywuIlvZNA+10HNrX94SugCgFzPn3jFoJQV5slBcc7aZbt+4MwfOnnkK0fP2ORMaDfi2oyQwp3VxelLSmj6G+pgbXJRhfl3JRg0Lh9kPCeXihB4mcruHUoj0pW218/5yeIpBnMI1pO/JiSl3//XUljBUv485FzkE2XusztGEMtpzmalqIa4gRN1a0bJT5U+h709TwWhSPURxXG23P6a/mAhl8He1nbJdWzlPGQ/EyRYniJrjvoZCZpSAN9UestlhblVhuLTnjfntrNdq2iQqZdRqsmXTxEtSq02DVOrDcUu1VtWqhVgbap4wrbaF1G9LDtbJodKfBqpT1KeFegzWkhbtJqy5cwRlvOsz779Ym+se1aeLzipiJeRGcAsRWi63eHsRWi62ugdhqgeBMkAlArdd5WZe0zRz1ilry6FQ981K7Kcz3XBC9RsplqsRyTfupgfshRn5aOHRKv7XHG/7hYkwHnTPSuQRMCh5EN8hLvFzC2stII53Y6G2L+OKPXeSSbEuarsGxlN7RseFcznPPkG9PJ4ChL84KelHNQ+3/zYVJfl0iR6wrWOadM9VnuwEd480mjrsZcZbHnOlUb5X3PY6kTWVBtG2jLgiQCTiMsPuyveSKlg1c846xGDvjBl02Ny13Dnr34yRnJpHuKjnw0XZKBbae6TaS0ksMNwCAWjmW2+qKZ7vpnvYqlIVuWoAVMYvTxDtmW0gZ9445gOCwh0PSnNLHM2MkZlvEOh14J+N2TTTfF3pBgWzBtB2N0RITcsPJLvd3VnCaEFsttjoHsdViq/vtxFb3ILZacC5gkc9cDJDHDS8vy/HFQpfZz4SmKccz55JMaxjwNUH0muNsSoSdEkwf05Iv6ZmXjlkKgo8F57kOeq5fWo7H3yQTX1/CWDCdJldolLPTORM9knbhgfSpN1CCxYeJ3D2dOx045NvEmUL5dHBKCweGGS/pthoHnQov5TDGcAM65zDVXOUFzUzCsMrPxwuXjQ+mtE+dYz6f3WYyUz2yxxwoaJbtI1PIrFufMuH8NnYPuqkN3DYAIQDjRtCNlBcu4ynj5KC3rKDZ2jPfWqu71HGjO2abZcsR080GpptK2W6M4Ram1RVgdeGcd58yrlaAWrWAttArV8BMNS2Utmh8MbNGeYcdcUGzbpkc9o7V1hU2U9Fn/sbp4KB371juHZ3q4Co13J6OMaeI2XJY9pjds7H5JBAsDbHV6G0bHozY6q5PsdViq8VWi60WHDQGoszGv0NpUVE+3+RwNSzzsXhWSdZlirxLDcaY6EvG3IbOuTaYXtMvV8BI5ylrna/P9cX3T/tNj5kPiGu2XhfalMBlXXjx0acA3ARwE08+Cdy40Q+mm9WV4QvTO1Rbp51eWzhglziXwL8E0gV7x4S07iUxxH7RBRZHSQNyDCnjLayPHLjUIe8709xBn6K5SkgZbkN6q7sp5jTmiE9z1Osc9LpxpQ45D4CYdF0xUMFSx/m99injNGYat2EOOte2DM48seJ44bIoVbwrYuYaKoymi2v/x9sz3ULKOLHctHZ/PVfWOeQNK1imDZTq9Fe1d8w1aPKp4fxwNE8d9PCZ3rHhoJe2xEPNyD9UOt5dezEPAsEoxFa7ZbHVM7bHEFstttr1IbZaIDhI7DkQRofPMdA3DbAD04PCNahptwkbfZvxttpg+lL9ztmvhulebjvGSM9tT8G/4+QkXtxEPwY99ZSLca/XCSudBjj34uTkXYZehhoN9tq2grPF4Yf6jx17+oI7xYlP22rVeEXVJnzZD22j1FPFtFnrwV1F9+c3ThXvt48dddL2jNqMOJs2SWdO9UKHdx5ykofb1TPb5qaY8/3Kd8HC9oMnUUEzA5e83DpmlN+WFimLdveFzOLjGCi4YlmN0jBo3bNknWyBUgawjLGonEOuvWOulYKxbqzGOj/agrHcFGCgvCNPGqwKrVGeEZc46F5/FT51HK1jvVEBM2VaWNO6OZJvRnrl7p6lImYubdwZeq+92pjAdFMr0l6ltHHjnHJt0FiNRhusrWYp5ID2hcpg3dU3/i6RBmtrKehloZTqIhGYHxQTxLA2Dvps0o/giCG2ugex1RV9VUNstdhqsdWbQGy14GiwYKHRNEDOibakMz21uGgtM7wUuKX1S7DRa4LoS0vNTAEdI73GvABprvBoaZ7bf6htenxinPN26fWnep18Hd9282aOcU4B80vkg+c6aXsb8fca+p5227e99NNNAE/hxg3giSecRvqTT3ZB9PUauMIZyFM1fXhl1jCUtr8uvaBzIcVEx7HUr1tHYKyFxnAg2IXG4FKsFb2jivcxA6rvvBrEBcxqGG61hcvGNUfr0sTrsMm+9f3UBgpS/VUgTQk3Sfvkc5Iy3h/l8DXma01myMY7U/QbeNBfTYqZxaniukvnNQOdA75omX8f9arTXyXtVc5y03BFy4jlpk2UFk6aqjTXXGM1kzru2vTHlK5K3+SxdzIOqjXFbWeJpVLFJV38LCC2ug+x1cv2I7ZabLXbV2x1BLHVgmPGlOqfyS45lYpSXcXKrovrlwxaLxFL24Ze+5w43ybnX8Nsz7XNba8dD1dHKWmo55HLwQq9DuzHNdJN73NaaJQ00yOddAKd+BD1v/alGMM22ObnzmAXaRfBKUBF7DSmr5rRXJ1a0KjErNEDjBtjO/fQLce6m4HtpmzsdPOU5IxjF+uo9tOYa5zxomM7R6M1tB9zhutTvusx3GdvTNG1Kad3A+l17muvpvq2fJlrr/LtxG4kjiPp8brUcDferAYrENLBDeB0VxEz3NpWu2XPbIPVnRNl0EsZV6Z1jjjBO+lBd1VrYHUBu7qItFfhmW6qcSw3mhpt0Pi5hkXjU8ZXipYRpY6n2qu9dXCp4vwdi5dLcg75d5u/82kqeelvx3SIiREIxiC2Wmx1CrHVYqu79mKrBYJJmBvI2ncAjEVTuT46YY5Oek3wfGhbLXu9dgxT2OibBtHH4nJTY3ZpGz6+Glmc3HFKBOw0CM7XpQH2NAifu8Z87r5bNkDQQh+ScEnXp7aipI9+G46N7rTSLy/boIt+40YXVH/qKQDXrrmBXbvWp9cPMdSHtI6mYq420r7/Zgj2isMP9QsK2K0ua0nL0W2bz+4opYgDXXp4b73t/8EcZ6XVpZQXtw055rPSxJd2ynP9b+NZGO7XWIOGPYPWGiBx/AwMNDQMDBrfFznlxlpYa32IwbmFXfEy5Z111bXxjjj8cndc1681NCcmkqYGbECZ++d1V5VZu7RxxnJTgeWmPMvNAMpC6ZjdxpltKdttCJQmProOCi2bu3UasEBLDr3V0XukfFL6NqGUHn3XDgmODbn5u2KPIAVNsA+IreYQW53rX2y12Gqx1WMQWy04Z6QxwyU00TmG4pVDQeYaWZdNj187ljntcvtMuaYliZc5x0nlX6asv7hwwenceXOplzrkguok+zIGTnpo/T5ufvNmE2mkkzSRgYZe4hebHHJSLIcuz3Lo4xvDULHYKRiqj3MgEArChtiF5uFQOic543OZKGkaaa+IkVdX5Z+75fjceZGlFLxYmRlxXHmaeE5zleC4UX1mFWe4pdu6z5z9Vkh/rtFbPUjHfPg4NUy3XMp4rg1Pv09T7SmIYtC6yXYsRcdmJOabjUZLPD1itwVmG2e7UUo4nFMemG1WozUaxigYo0PaOKiImU8d7w7gj2ry3zCsdqniUCvHdFtdcSw3pr2KlQ0sN5o3TQutDbTuCps1PpW8Yc47sdiUIrabd+JB6+I3kpbH/u6ogWAabQfywTw1UuhsLrZdfHE2AvNxgUlwsBBbzdqKrfb7i60GxFaLrU77FVstEOwEI8Gm2uA5BSWHuh9iKxNKTOdcn2OB7yk66Tnk2Ohj+yylCDHUx9RAfs0PDDzumGOY5/rJybWk6ij8c3qMmI2eMtPDXsiz1FOkrPTbyXQJ4DaeeirWSKcpkndZKoDMfzk41gyVY4VIuxwL5MvQVNRoLNIXe11gfsQFzeYVUcoXKrMwGRZQTns155RXHXdj9s2+91/meDUarIPBjQ20V1P2Ypcy3mc8cuJQbsTEbCOHvdtPhTlN4IXMAIR08dIvnkx/1fJ5or2qiOGWsNy07nRVdWC5+a79pHwKOd9eepNywa+ugOCR/ykXnDjEVk+F2Op921qx1WKrxVYLBIL9gtjOuSB6TiZ6KjaJVU0tMrpNbOM4tcH0ksTL3P5L22rHM/SDRd11ouA6QbP1Y8hppLuJCuPmpt4vCKVioUA+SJ7+2lRCTitpKo4lwH4s41wIDz/8MF72spfhvvvug1IKb33rW7d+TPlGd6bYtGjR3BRxznAz8A65d84IsSZnmbHGHfRS8TKnB9pnuKXoFS6bqbc67PTuK0WlwkEf0l9l12Y05R5td+0TRiJN7ggmub8d041Sxkl9l/RWO4abS502YVl51hvQGoXWs9tMKF7Gi5hp76ADpQJmAJhTvvJMt1XQXrWkvUpMt5V12qtNrL2qlNdcVcax3JTTX+UOu3PQPcONa66yidhuvSGGuds2pYhZLXjRw62z1PbEguMBnE2nuXj9618PpRS++7u/e7kTE5wExFbHEFsttjqC2Go/FrHVYqsFe0FNsGpKFVAfUBzTRp9bWDS3LdXhLgVxt8VG5/1dXExno28zWL9JMD3HSh/aL4eLi/z9SRnnQ8fK/dDR10lHsoyBZY6WzdPp0k83AdzE5eUlbtzodNI5Kz3opA89KFMDw0v80lSDKeM6h+D2nhjpTz75JF7wghfgDW94w5ZOrI/D58wLto6a4mXa6o0d+imo1VydwlrLt93AYZ7EmNuXY86PP3L/rJnkIFlroFTj5+X9Uu1V69VCw8iUhbbkoPcdHBfEoWXlmW9xyIWc8bAPc5ZSx8k56v5IxrjCZabt2G5ec5W0VwPbTTn9VQU4hpuKtVcD0027frjGqtNe7VhtOf1VKlg2BKUUYC20UmgzOp/uHe0/a6Rzu00oNJMCWYeCoMm7QD9z8L73vQ8//dM/jec///kbj0Fw2hBbPRNiq8VWi61mxxZbPQdiqwW7xhIx+k2xSZC6Zt8aBve+guj8GEtf51yftG7K8WrPPw309/sv3YgpWuk8iG4y61qs1xc9NvrlJbqIZI6NzjGkH3556U50qsZ42v7YNcr3jaVkWSZqpD/wwAN44IEHNj/uBAgj/QShEmZKjSbrLAZMSFWtQ3CwbOdYEcOtY7vFzCgAPc1VgzZyzYYYbqGvNL05sy2X2hw7G2z7JL3VfTvmhCljpu0lRmDMPkzZhDnt1eiz11yle9tx3tyIiIRGRLRIexWd822tAo0mMNv4NqM9203DtBp27TRYu52SdHGbfLNQq8B0s7oJ2qvQTWC5qRUQaa8q27HdtGO6keYqOekaQOM1WLlTrtAx3pwOK2O7+SFR2rhr35droGKDJdmH0ruuVV93OYeqNhVBnl0G+w4VN27cwMtf/nL8zM/8DD7pkz5p38MR7Bhiq8VW5yG2Wmy12OpDgtjqI8KxMj4LwScuh0FFGvk2YLzwaElze0hTO7f/Epja176D6EPHmsouH9ovx2Iv3a+SXAvpoA/tF+87pIs+xEZPt1OgHOhkXUgf/Sk/vwngKdy40Wejty0cI51Y6XRRcicZDtn2dY/GMEX2RXAwePzxx6Pp1q1b+x5SgHw7EgyCvuiHzzNSIk2GjQPEqePFfSsd26HiZUO95zub7kxvzzG3A9M2kU8ZH4Nz0uNgSdcjOeomWZ9+ti5lnD5nTpUeKXLKw3pwdpv2Qy/8mSs8lwBjuHH4YmZWN7CJ9ioAINFfBQCtu2MoZaGYxmrUdYH5dgzYZhr53MKMU2CtXmyaile/+tX42q/9Wrz0pS/dwpkJzgliq+sgtto3E1sttnrJvsVWCwSLYiimF1i8W0RtkHpIPmRqX7WB6Cl9Lok557Yr/fhccH2I2J0/di5gzjH2tzP9PpfqpZuyRjoHfxCmPBRDyFXo3WT/beOYg/oLS7vcf//9uPvuu8P00EMP7fkEO0jewl5Q/yVuSSZI2lfus1aNZ8g0xaJIMaNm2Fknx9wmn7NtFee65R24iEmV0VRNkWqFWrTVKeZFhtvWUeuk8Xa1QZN+2riF7e5plDLu2lq0wUmyML3nxm2P1xlr0CgdtFZpOw+eGBiowGR0DrlOzp1KmjkGG5xkqgWMipltrdEwjVs2UGithjHKO00KpnXsNmu1S+sllpsvYKaMcSnjHGrlHHID2BWgzNoz3S4Ar72qViugUVCN6VhujOGmlEHTGLTGhEJlGk6DVQXWmzuxiNFmO81VWMZ282w/DXeegPXvqfHz4aCUVk1gGirfXimdfScUdLhfuVTw0n5DmLPPtrF0uvjjjz8erb969SquXr3aa/+Wt7wFH/jAB/C+971v42MLtgWx1dm2YqvDkae3E1sttlps9RyIrRZsFbtmqw5EUY3/O8yDjGOs87mHnRpspfZj8c2psi5TdNGnBKCnBqtrtOeH2lxcuB85mqbLGOD7pPuP9UfjT/vKBcovLly79HjpnNrFSIuLprrpYz+Wtr4NdXzbf74NJwXjGOrESH/iCTcRK92srkDn6PObPvCXl/v51eWc5WEoNWJT+If0ox/9KK5fvx5W5+z0viCM9G1iYfbHUmySQ0zTHGKz2STVeO9IU88XZbjNZTrtgCHVc6qSQMmA0zUUSEnXE8ONB3J4+jitTeM8PFXcbVfeQe8+wyjv4Q84YyYx2lyHVdGyZ7gBQXuVQNqrSjsHXPmUeMW0VwFegMw74QXkipctjUP8m3CsqPnl/KMf/Sj+0T/6R/jFX/xFXLt2bQ+jFEQQW10NsdVdb/Mgtlps9Xwc4t+EY4XYasFBIhOAolhiP/i5DHgQe0rweohtPdbHnIKlU441te029knPcazvVN5lqK8p/ZbvU42MyxSkEi98vWOm049DbRvLFoUBztXXpk6myr0IDh7Xr1+PpkMKpJ/pTyVDON4vqXOc99p9emnjicOglYKudCOs5z7RPE0bD5qcTJcz1fQ0jHFTShXvM+JKGqKsrY378CNiO2zLMV/CuaY+xu7CFKZb7iiMtZYUL7MwgHXVwF07x6pqfJtOMzdmvZHmqgvSNGGU5JA3qkuSN9Y73lBY206n1Vo43lyivWoNmxunwWqtgjI2sNx6DDeCXjlnXa0AvU60VzXQrACtoVbGM90MtNde1cpCN85Bb3THauPFzEhjVVvnoGsLz4ZTYVs0nKRwmYZCC8vmuvjkaWi0fqtGA4M2zI8Py/2dTgvcbdIPUPfL+fvf/378yZ/8Cb7oi74orGvbFg8//DB+8id/Erdu3UJTU4HprCG2Ogex1Yi283PJQ2y12Gqx1duD2GrBCSMXrJsawCsEDcekXXpByJHuU93sGUMaZaPPOdY2sOkxaf8hzfkaVnpt//SZyNOczJxjr9OcM8u5Tjp9zm2n5aZJx0gB9ctk3VibHCigfhuxVvrNSBs9nZ6WkfUIJ1X6FamGcX7O7PB9YO6PISm29cvhgljcE/30T/90KKV606tf/WoAwItf/OLetn/wD/7B0sPYCbbGPtmCjqHeojZiLZbUWQVS57tcvKx0pMPB0gy1mv42O//qlPsCM5FLAqT3nII2btm3HzglcsYNex8NZ7YBAGO7FRluQw466bB67VW33HjdVc9y8+w2AJH2qlLxMoCgvZrTYM1BqfjvjXPc6//+LKVfOhbM24VO6tKwaTBngwmo++X8y7/8y/Hbv/3beOSRR8L0JV/yJXj5y1+ORx55ZCeOudjqJToWW10DsdWb9Ce2ut+R2OrxcYitFlstOAXUEm0Xit/PbrcJ5hbpnNumFkN9lQp+Lo1NmflTWfF+L3RB9HQODIcPWzYnJroJn0va6Ot1ZnClv6+bss17BxYsioU10mtx48aNYKcB4MMf/jAeeeQRGnzeUQABAABJREFUfOQjH9nCSTos/tq/733vQ8t+Qfid3/kdfMVXfAW+8Ru/Maz7zu/8Trzuda8Ln5/2tKctPYyzx1j6p1bdH6ehtgoa2nJ2mw4MN5XMUwxprPbaKttz0lLnLWW3uWMM/1qVpjHnllOGWzKyqvHP22ebKd4W9VqstIfN3Mu+9mpvP2scq40x3hxLMGa/GbRooBPWW3cPOtYjoKyFtY76ZS1glXWaqpbYbJzt1mmwWr+d9Fg7x0m7QmZMd9UaeL1VP6Vg6eHQgG2uQelbQXvVrlZOd3XVAM3a6656thvTXtXKQmvHdDNGOzYbrE8TtyGNvGO/qU531c9b9qgopaJc+dx7qK0GFPxRpgW93DGaYmBFsDme/vSn4/M+7/OidXfeeSee8Yxn9NZvC2KrDwNiqx3EVk/ZQ2x1BLHVk/YR1ENstWBnYIGjUvB80yD6UJxyKG41N0hcYqbP0Uafy6LfFEPs8xq9cyJT57aNMd95O3oW0n5IjpqTrkvjappu/cUFcPMm1z+nKdVKB+r00VMpF1pHjHSnk27tJW7evOix0ddrANdYAJVT68cq69LJt213kvxzru0cCLP9oPGbv/mbeMlLXhI+v/a1rwUAvOIVr8Cb3vSmrRxz8afhmc98ZvT59a9/PT7zMz8T/9//9/+FdU972tPwnOc8Z+lDnxHSwmP5P3A5RopS+2WhpOnVlDY+BWZCEbJSD+eBMQe9nzbe7TqcMg6g54ynzyEvYkZGmKf2G2WgrQ7z7AgtJZ2n6/OhDeOdcje+hOkGdIWqQhGzuBdlWtiBV8TqFZRaweoG0A2sbqC09iw3ANpAaWK05bVXqUibZoXLarEEr1arBu2GRcR4cbOKI+KQ3zmSEliin2OC2OpdQGy12OoaiK3mc3daYqvFVscQW+0gtlqwCxLtUuoMc457aJhT87Ik7zLUV65YaLqtFnT/po5hGaSs9DiwfvPmBZ56KmamX14CuGtPD90QJHg+HXuSdnnxi1/siB07xFZziG/fvo1f+IVfwLd/+7c7ZobHL/7iL+KTP/mT8Xmf93l48MEH8ed//ueD/dy6dQuPP/54NJ0S5qZYLlXQDHA6jMR8U2igbayzWt6vDjxV3IBSgvtf2I2iROJY/3SIzTY9VXwYOX1Wt6FGc/UQGG5LH4fdi4EiZoBnFfI0fmIq2i6gwrdzJiNpr7o2setHzrjx2qpuWTF2G1sm7dWgwaoTDVYdD90az3bL3Hd94Sa1CqninWPui5j5lHGlrHPMlYFuDKBJY9VG2qt8ioqY+blWnd5qyERn69x6p3Ocvn96AQcx7XVIaoL+Bi35t2jXcM/HMtMmeNe73oUf+7EfW+akJkJsdR3EVrN2YqsXhthqsdXTILZabDVBbPWRgwWdjH+vUxY6xZTmBECHNMtrZVXS9qke+lDh0SlFRnPYFxu95hg1xx4qIjrUtpRBMEWLvnS/YvC/iWkB0oa1Sdfn/paSjMslugC6Y6PT9NRTwK1bMSO9bRFLelxcDD+cVChgKexS4uXU5WQoTWLT6Qjqj2z1T89b3/pWPPbYY/jWb/3WsO7v/t2/i0/7tE/Dfffdh9/6rd/C937v9+KDH/wgfvmXf7nYz0MPPYQf/uEf3uZQBR4lzo1LG2fpqBN+g6lTATWoYc1Ejh1z2NPiZUCSGr4xMy4dxyZO725/LRvGZky3EiiFfN6IbJAkICJao7yDrmJiGqWOu2XGZmMMNxMKlynfgStiptDm08Rz0BeAXTuWm+5Ybogc9DXgHXSVccRDV76IGdCli2ulXHp45IDHaeJhPdALH2moENhwhcrymMZO2z5641G6FwBSaMoBM8EiEFt9fBBbXQex1cMQWy22ugZiqw8DYqsPHHM1VgpIg+eptHNtodEpwzk08u0hBNH5sWquOZdPKamS8Ha1Ei9jY+ESLzlpl7T4KEm9dSh9F6j9jtBm2lJgvZvzQrl8MvSNdVc3dYhtPsZEPxam+rGM80ix1Sv7sz/7s3jggQdw3333hXWvfOUrw/Lnf/7n47nPfS6+/Mu/HH/wB3+Az/zMz8z28+CDDwadGwB4/PHHcf/9929v4GeCXTNTcjqsJbYb4Bhvrg0xofpf0EuORq0DUq25WuXY17TZh2Ner8Ga115NGxlYxQM5XJuVa6067VVr2/CsOe3VC8d6U2676UxnFPQw1qJR5GTHQ+j0V1XQWHX9d3qrNHf7s2WjfLq49/S9g67GHHXldViDY+70V1XjtVdXpL3aMifdaa6SQ66VhYLXWUU3RYfxV5PumVKAss6NzTnsPswQ5rXQaNCyZ1ZBQ6txLeNTQwjcLNDPsUJs9WFDbLXY6n5LsdVFiK0+SYitFlu9N+yYQbqNw00Jlue2p3rquflYHylSwvHUeN++pGdKAexN+hhqC8RBd74vBcfToDyRenPHya9vMstzA+sUNL+NWNrFMdMvL1vcvNlE+ug0v5Le1NqbzHXRc0z29AKN9bMEzjWIvZS0yxFcu62N8A//8A/xzne+c/AXcQB44QtfCAD40Ic+VDT4V69ezVZTP07Mc4hrHeltONyc0TbquI3AhHmXGpzCsnTxnOOepiSPIceQKTHeTpdNM+Sgx0y3Kgc97Z1psI6BCpkBPgBjSSbAQlsK2JBTTbq8rjhZSW81pI8nDjnNgxarYedp/VnyVHHTQqVp49r/mTSOVmA9y83qxu1P6eIasLpzyjuGm2e8oWO7acTMN9JjdSnhVJBsvs6qhgYs0LIOnCxF/7lPnfRzAw/cbNrPMUJsdQliq91cbPVuIbYaEFsttroPsdViq88JKZOZBz75trlB91rVBB7LGpOB2Yasy5RjHQpqA+VTA+pDNTdrfxwZvwdcyoV/HgPXRW/8/AJ9nXTHSl+vm6CNzlnpV1J5j9HDDgTOhwLrS+Ncg+Y5nFEgfWs0p5/7uZ/Ds571LHzt137tYLtHHnkEAPDc5z53W0M5ewzpJuqRP5C5tHBK5dUVrkNemTTvlHO91X4/Zechlyoeto3ohJYxdLy5eqv7ThNf9vgWpnd9rY3XGaa1mt7DTlu3r8OajranwWq7VHI3KRb4UYztpjunyXgnzGqXNg6Mp4tr9kdcXwTtVcd208DqgqWLw088ZTxOG+ep4kCXOs7fsjRtvDckpYJO61gQRcHpJ6cF4mrLpk0rryY4RoitPhyIrY6PUgex1eO9ia0WWy04doitPgOsVln5Fi7tsi1y/FDsa2oAfCk2+qHG0ZYcay5ToKSpXqd3Pt5//Jm0z2mZI6ePPuVhIGY6sdNvg1jpTz3lWOiklR6e7dyDuGkgPPcLxKnrlAt2gq38iTLG4Od+7ufwile8Aiv2MvzBH/wB3vzmN+NrvuZr8IxnPAO/9Vu/he/5nu/Bl33Zl+H5z3/+NoZyfFiYpZYrjqYr9TCnfDEfc9RzqeIcQ843T1/lKePbTRXnDZdgAO3bMZ+JoL3KU8LHnx/HU+s/P9YaQOXvkUsbb2BggtMZ6a4ivorO6Y6vKz1m5JAbQ4XLfJEppsGqgjdvYqZbBkqtYLEOzjqx3OIiZl5HVRvAO+XQjNkWnPSO1RYz3QD2MRQto4nSxnPPkoaGQRvmU6GUXlSX+Jhwziw3sdUbQGx1Zl+x1XuD2GoAYqtPGWKrxVYfJYYCdlsUJ68pSDkUn9x3EHvo+JuMbUz2emmQTnrTOHL0Umz1IYmZpslrpPPP/euwZGHHPgM9Dqq3aNs+Gz0E0oFpN/ny0l1oYoXT51qcih76oeCMGOlbGeE73/lOfOQjH8G3f/u3R+uvXLmCd77znfixH/sxPPnkk7j//vvxDd/wDfi+7/u+bQxjBk6b0VEqKJVjuuXZbctfHwMDo2xvHdBnreUcOusd98Cm4kXLSinhG6SKb1a47BBQShuPU8YXOZIvYuYc9e4Zc/ex8cv+/mWddYuGnHRetAzw6eTWL3vH3Tp2m/Wst0hrNeeAGTXMcvOOuFKr7rNZhSJmQXtVN1BaO+9Z20hzVak4dTzSX/V+tgLiAmeATxuvQ1rQTFsVpYkPYUpBs1MtJGYNFtJdXWAwO4bY6sOE2OqR9WKrlz2S2Gqx1UcAsdXHaKsFc1ATaN0FoXbTONY2ZF3mjGlq0dW5ge6pWum5/dICpemYckFyvq6kkV5/3VLmee7GlZjpLdtOyzyA3i3fvImo6OjlpT/va6v4JDZ5cCQAvh9IIH0zfOVXfiVshtV0//334zd+4ze2ccizB9e8nJPiqaDDfmlqKdB3zHmaqh7KbWWIilP5BOJ+G6+3mhQvS9uMFVnKpTHXp4pPxVi/x+fQR9qrCdMtXsf28c543E9XxMxAo2H7WN+fgWE6rBbGWl/Ay7Ej6epRanhYLow9FDNL5u6D9gXMNN8hMN2UafN3S61cOEBfhCJmNhQy0+5a+HRxpS1U00aOudYJow2kv+rYb7Cq2il3zrzqKH0eUwuY7Rb0/AgOCWKrdw+x1UkbsdUbQWx1ArHVG0Js9SFCbPWWsU+Zh0KwqPV/JLisS4q5QduKw1f3M2f/TYuMTsEmgfdNHotccJxY6en23Oc5x6tZz6+9C8A3sLaBk1yheYqpsi5D+uhO3oUKjJKsS9t21yY78PQXhG1gauBdAvVnD7n7Z4qSAz+VyVZT6MokTnmJKWZU+cv7nBTY+WDj2Ji6coiO+WZMtzRl3DnlAKWTw44X0nNFzDpr3umy5vezbMyGDZ+z2Yjp5sZEY+u226C7SinjOs9ys52hDgw3Du2Ybp2DTuninuWmDRTNWREzAHERs4TZ1h3TOesKPk28cFWc/qpCOyLFkEL1eHGFdqoJLNI5OJYU9HNOFxccPsRWD0Fs9fDeYqvFVlccR2y1QLBfFIJxFDckhjJ9bhc2s1MUZmoDtoeAbY9pCQb6nH2m7k8x6GGGes565QLnemQ7IZZySVnpqaRLOB9eaHTJ6rQctQFwCZTPQ9Msc922df8XhDwd20KlfmqNhmWMeSm9mxQi4vvWFC2bg3y6cJ/plvuyn0sV59iug3D4zsdcREy3qftak3XQS0XMcjDWQisFYy0apVxYh5xvIC5kBkQOOjntxHALjnl/oMPsNsAx3PTKxWn0Cug55p2DroJjzoqYeSedmG4aVLSsSyMHvBarBUohL9JfbSt8cVVglWnPKhR0cEXuNpdKWKIPwR4gtnoSxFYfHsRWe4itPmmIrRacBAYCTAZ6NECaq5s49ZBj8ancEGslp6fGz6YU7pzS95LKEnOJ0HMD7RcX+R9LSP671D99Lu1fPy5egHQq2mRfCqC7IqPESic2+uXlSLHRoQdvTqC7Zp9DCaAfyjim4oykXeTbxJFjqnM/1UnPpY4vCaNstbOQMt1KDJxIe7XA5kmLl52H5irHAudiTc/Zjo/gU/9t6pC3yWcTLbtek31sfnlweExztRuyZ7dZ5XRXGdRIEbMe1CopYqbcX1TmbKto2QRmGyksUFo8Z7wRpmiv5tz5JXWS9YKFFccYkALBKUJsdR9iq2sgtlpsdT3EVgvOGvuUaFkQh34aU2VdeOB+LCB/KEH0uceaOs6hbICagqulfVJScP09S7+rzrEDnIEOdOx0AxdMN6HQaFp01NDxphbiTdM29oEpxz70l1xQhcMP9QuyWOILbq5wWd1+Xp91wj45xza3zgT3jAqTjeirot65BiYy3k4yVXw/6NLBm/DZ2DbosNJ9b9hTlT4fxnb+tLEIxcusTZhtLinb9cEcdBM56eSgo+ztmyRtXAOwK1jl0sUj3dUwwTHcmjYw3nRSxIw77ID352muSIvVH9cvU7EzoMtKpydeoduYOumO1eaXrUar6t6TocJmx5IGXguTPBub9CMQpBBbTX2IrT4GiK0WW32oEFstOChsORDGJS+oKOMmGNLKzm0n8CD4lCD3vsik2zjupvrlc47F9dQ5cmzzMQZ6CbTf5SUvLMpTHvj6Bt23yfQ7aUlbvSTv0oYAelp0dL0GrtBNzD2ktTfi8lLkW/aFM2KkH/4IzxibpHgvgU1ZMs55KmisFhzzcl+MCTXgsA+xrupwOk7HOHL6q3Xaq9VHyBQ1i0cwfL1jl34aTHDQdeSkZ9PGmf6qGmK6qRUQipj5P58+dVx5L1pp31fijIculO2x3WqgvMtMo3P10lTxHePIpYnX6q+ePIzT4V2iH8F5Qmw170ts9fIQW901Flt9thBbLThlbBA0SmOLtezjWgb0tmRdlsa2i5aWYrhLFw5N+wbG+yvJ9aTE7rJG+lTrPdaeJF4M+xxrpKcB9J5O+hKgXyVqA+uCzXFGgXTJ2xNUQVs1qsE59tXUwDGXshqrA8XLchhz6qw1qE0VrztezgEa2v/EGG7hWg2dsxm87oALrKQMKbr3RhnPhrM9h9PCS6R6dlunu+pYbYH15hlLQW8VCsaQ9qr2k+ocMoPOMTftoGMeFTMj7dXVFZYuzlhuq7YrYpYw3DSbx8sJ0w12Usp4xz7d7M+6Hgim7ASSTi4QzIbYarHVDmKrAYit3ibEVgsEw1iYxkzdbaLZvY22QxjSY689Rk27XcTc5hwjJVfnrseScWN+TEJt9kEHYqCXtg2hTZZz021Yexlpo/eC6TTQGj3z3PJY27kQSRYBw+GH+gVbRYlJt6Rm4xhKjnbQ7VyEiTPEoBOmT4xxpptFGzR/LQxgyxIG7vqWipmVDTJttdZOo4NR/zw9fEw/2JSfD6VX0bLVyZ/NKGVchVNVOtZf1b6IWeir8pQoZXxJaNWMSjGcC4rMxxn9CATbgthqsdV9iK3mEFt92hBbLTgHjMXpSMKjNp63D1LnXGb7ERBQ9wbOJm+aTpFkeSZ8aoOHAucUcE9tVK7gKM275fX6IgTPuU56dQCd2rRt/AtF+vncsWv5mlSgf5N+DhzyJ+uEwJ0jpXR1cbPUQa9xzPUIp41zlIwtM75KjLfcelsomEXOdQ3zrRqjbU8lrTyXMs632o7daE01A4muNX8mrW3DZ4MWDbpvVL2CZf4JMtYCip4h5ddRfwifnXyqinRY4df1UsUD0027U6IOo5TxNSx7fZRawdp1t6xT7dXOQVfKM9wUMdyM02BlequhcBmsZ7ZRWrnyqeSZgmRBi5WJsNI2KLQzmZWUNj5VT7WkzarQHFXAS5xzwa4htlps9XSIrRZbLbZabLXgZJAJNFEQlAcVowDjAkhjU0PxrkOIYx0KG50fK3cvaoLYFxddTcw5Qe9UmiUXH+UM+DT4zrfTslM94VroHFwTfYilngMF0yl4fomu2OglSN6F1wGgZz17UnOE4OkClQLroo++HYi0i+CUMcRsq3Xol0DWMU/WDTncm2us9o++DE4sVbyH6dfJFJy+lGlFhcxKR7XBaR8fCS8oZczmDphSqy5lXLl0cfjPVmsXuKDqYkmRsq6PbsQ685xwBz7HgqN1mm3UmYbbYqlu4+/DvvWlBYJDhdjq8oiWgdjq3h5iq/1nsdX9PsVWCwRbwYIBoyOIPQE4nnHORRqw5hj7gWLqtVnmWk6xGbVtc0x1mrfDPxRRMDZ3ciKvIjgQnPifMUEthrQW+Rf9MXZbCakTleqYGuQZbN32/K+Q5PRxdk5/eTOnO6+5ekqYwHTrbRxmvlmYyPkqsxRdgS3+rBnPAEuTytO74TRZieHmdViRMNvCcJVfp5nuKhzDLUwjv3j7ImbEcrOrC6ic9qq2rpiZ7vRVue4qwIqZ2byLGlLFM4+gUgrKltluQ0G41t8HjSZ6t0rMtbmYypzbB4TlJjgmiK0uQ2y12OoIYqurIbZaICjggIJ2U4YyFFwd0syuLVBaU9B0CqN9bjB4X9I1c1npU/YhCZe0PbHaOS4ugFu38v3TPnSsceWNMY30zAAA5ItidwVGO2a6m6/XbswUSL91y5/btT3c1G2x08+R9X5GjPTDH6Fgr1iaLWO8G74Jlme3LYlTd+Q7cO3Vafs5h92ljM9/vlzhsvL15pus9QXLpsKz2JRewZp1XMRsDInuauiSOeqhKZvnRrlPt2/MYScH/Bgc8RyM1TBjuryV/QgE+4LY6qkQWz2+n9hqsdWHA7HVgpPDhgGnbcX4NymqWcKYPvo5YkjjnMvA7Bbpd4W52U0lnfRumc4vSLqAqbccQRB1JzjGQPwZBdLl28RMFFk/BwhiuyyVljn33HNOuR1KDx4osGQL23bjHByfAzId08/RZhiHuc+5dcanURtlYJTNsCC9virTWu332THdOk1WFaWNByfdKMB6lpu1gwXMCFTITOkVoFee7ca1VzWwagANqFXrUsMVsd36DjmAiPEWrUe/aFlw4AuvH72X6fvJA2ylYIreoUyEQLBLiK2eDrHVxwSx1SnEVgsEgmosFY2u6WcgMGT8+592M1UWeuJhd9rH0sfcZ5xtW9rtOXnw2n6GsgToh4x8X1wLvRZD31PJTreFyWmlty1w86Z7xrleenQi2xLqP6BME8Hx4vBD/YKtQFUWohpDTvNxU6TFj7bCaiNHMpeGdIRMncOBQcm4OnZb01s32qO1aNhzZpLsdle4rO/g9pzybaTzesfcUqo4XPY8jUZpxmTzy9qniOsM8y3ls5Uc8erhWR0CH9qn5AtizGZAZvoRCJaG2Gqx1duB2Gqx1ccFsdWCcwEFFjk2jfuVilIujSMgkR49qO7mmJRMzfaLCxfMzoMXId3ku2isjU6gsUVBdD64MIyKYHrbupM5Rgb3qeGMGOmHP0LB3qCT9Mc5mqtD6bwmw+4hRyKvz2kGmW+99jl21UTn5PQ1V7cDl0quo88lI2xg0KDx94bvY5l+qvVz2uac9NzdcYy4vO4qSHfVKFijux2sgTJtmEpQegVlXSEzp7uasNyC7ip67DalTOSQA64gGb8qWmXYbWydVkBrp6ePz3XMlWqKjNJTgeiuCo4dYqvFVs+F2Gqx1ccCsdWCU0cpeM7XZwszLoBSzIpLskzRR59yrFw/h8xGXxo12uq1+uu196a/vSks59oMMdjNwJy00m8DuA1rW9y82USB9B4jvTzg/UGC9cMYF+Gv7+fAIdIugioM6a/OctozjkJ+3TzHIMteGxmRYCZsvvhcPk18vqM3FiihVPGufcxyy++0gUOlR4yE7o9XKQsFG+Yl1JBHO63WpYQgBALBsUNstaAIsdWF7WKrBQLBBJyILMRSLPVzjSnO+QFgSl9DbSjGOCVgPm1sS1or/n3C9JbT18lAb/5QLaGJxHEi77xgWZzpn77zgZ6ZFl7zdX9J7dk5KeEm0fkc0lxdTo91qJ9jZsT1U5Wzqd/WuHzo2l59YauoV9uiUVQUq/FHb/0R6R93stM+u/XGus9psbLuWCo46Y7NpB27zeq+Y861VxnTTalVVLhMqVUobIbAcGtgdQPlWW5KWShtAGWgtIFSpqCv6hx1T4yLoPz2IU6bVgrtAJN0W9i0WNnU/RWaGQG3OgjLTXAIEFuN0W0TjzqwTWx1r1ex1WKrF9hfbLVgbzjTQNcuTnvTmKYUGu0Y5dsoIjpV4oWkYfIYKzY6lSXcArhAXie9xXp9gfW6KzjaO4+lHx46AH+oOcNc2OabQ6RdBPvGFN7KXA3V0jGGGG1TQFqYS6Rc76YwmaAeZX3V5Y5g0Mw8xiRHKfpxfIIDyBx2S+niSvsccPfMU7p4+OxTx1Pd1dClskWHXCkcd/znQJEWudukH8H5QWx1DLHVhwax1WKrTwNiqwXnjk0DsFOUEjZVVTgCVYaTwv5ijrU3muujdwF1eqbTgqNXgPGTqgl6S2B89zijQLpkGR4Blio2BgBa7ceykaPuEotjD4O0VmvBU46nMF8Ck26oeNlsnILXVD6HpfVn6V4YtGw5JxdAzw3iuU3bOaaSSRlu/rPxDLfeOKh9juFmyj/vK58qHnRXgU53FXB/WXsaq+VrmDrrUXEz3sdI2I6kG7RVo0G2ff0tEAhOFWKrY4it3hbEVrtlsdUCgeAEwAJGabB8CuN8VwVFa49f02bOGA8pvjZX3mXohwbaP0fGnnONh7atVq7GhkNqi0rr+bZ0mYM00fl3uDZa37au2Cmx0rMDrWWlD/3StLTUi0AAYaTvGNv/3WIbCox6Inuj5ILkHLyhokpDKeTptjnp5vmOhU13qDAWaDKPYnrHUrYR/0ypwa6A2XRWkvaFyxzJjxl2zd67qCJZV8QMQGC48c/lY3XL6UipkFm0DmpLCdXTsc307qUh6eKCPsRWpxBbLaiF2Op4m9jqZSC2WnAuiAovbog0cJrGJIeCriLJsjlqi4TuYhy5dd3Yxn6wbSracLRJey7rgjDPFdgNjHQaZBhCU3cxhYW+X5wRI/3wRyhYBKrAaNFoAttFoYHOMIHy+20PqcM+tYiZFC/bHSxaqBHDamGgoGGsQVPJ2DTKFqM8nOmWSo7a0CZ2uKxVMDln3HgpWSBmumWgU+1VwKWMK890o5RxrYJTztH77Iej2YnuKkVIQS8X0DpyiHMuOCSIrR4+omAexFaLrT52iK0WnAwyAaKh+OBSQfVjwRRG9TFhik46tSXmes39T+PMtXHnTE9zdmJIv9ulNqwLqnN9dJJ3ATAtGFsbOL+8rP91iPoc61uC9n00zTLX5Aj0oUTaRTCIlDW3lCbrGIxdiiEz3wFZOkX6ZDDKBJxRjI7tkzqNqbyAa5PpY5PbNeKYZ6HHjQQVLiPHXDPGW9yOpYdzdtuAv6cLG3VGs3UO6ooYigkRCA4BYqsFPYitdhBbLbZaIDgxLBlYlzjgYWDJ+zDW13iMcqjBnABnWmwUoO8gUQAdCz7bS1d1FQgSyJ9OAYDlvmTX+kdG2QybzfQYalPTwnOFzqT42W5AbDbAXXPOrKTP/P4ZtL0CZfF2GzHAgLwD7vRWuw0m2Rax3UyXKp7TYSXNVWXWxWdZMbYbdMdws1pDee1VpQ2gTbJfrKuqQ+o4AJvqq7JDqHjeG09hnII6SAEzwTFBbLVgU4itFlt9jBBbLRDUYSyIum3G9xEQSRfFJvIt6b41fa1WLkacazukQV83zvTmjX0eQ4md7nTSaTyUdRFi36UTSU9CCo4eHs5I2kUoCoKjwXLMt9kj2PPx94s+629eIGROmrKx0ziHqaM0mMqbDEeZ8nNGAYfIQR+A0hbQZlBfdRPQH3AlLrpAIDgQiK3eL8RWi60WCATHh1OUb5mqs34EsbPFMOUHh12y1esxdgJtMjfJZ4fB5/6cHgjB0UGezm2gUltya4ff8/GBfIrvcPvzdnxPEU6Ttf5ZNDBVcgTEdLNw+qqcW8mdcoMKB520WI0ZTBnXGWdcUTGzwHTTTHe1r7Pq9vHp44X3I2LCZceBwIqrgYYGLNAOpZ2jQVvx/mnltHOHoJQeCNBoHGKAy9plNFNTDWDBEUBstdhqgdhqsdW9I4mtFgh2hAMMFJ4bo3zb2EbB0YsLJ4lC5N+h/mu2L6OC0gAodcSD6LFGOp+CzMsKMQt9W+Bs9V0z10+VKX9GjPTDH6Fgb1hCY/U8tEtP+RwNtpm4YmFgbFtd2GwIObcv56Bbq4BaZ8xu+M3HFzJTGaYbd9L52Zec8qUeM1dwblknedgpP2xIATPBsUNsdS1O+RzFVm8EsdUHD7HVAsHh4EziZAeFbQTkY5QC4Zv8stIOLhfPRx6O48UZBdL3T4cSdJjooKiNqyrPwyYFkgxsz2GvYbjNSTGejCN1Lg4ZJYdtLPWfPxMmQx9K9Vf5x9RJKuphmnnPsVYrKF68TDdh3rHdAIwUKuv6s9E83jZriBtDK6GjCARFiK0uQmz1cUJsdQyx1QKB4BCQBhpLgcepuudzY1RDGtxT99302IeMYxnr8DibwnIN0qA5l3mJJV44Ix048hqhp6jPJCjiSF5zwZKYm06+iVNeglHzHWIqdrYTx/1sYHHIJbFsZny5u5/689liZQCs0ZHOqjIThA7Uyk1A56CnTXR/dDqTRj4VCtNpb0uwVk8RdqECZsJyEywNsdWCMsRWi60+L4itFhwsFgyekbRFVHiRbavFsQRyBcug9n6Xi4/W2J00sL5JxDvPSJc49AngjBjphz9CwUFgkwJJBrtPG3fsKnHadwZrFtMbNjBooGFcMjnmppT1HfSRZ3hAdxXoipcNQmtqzPazbmKO+lhBs22x27TVowExBX1WAS9JFxecEsRWCwYhttpBbPXRQWy14OSww0DRprrnRxDT2iu2L7tSj9xYDuP+pb8EdcH0y8vM9SsFZA/pYgv6aJplHrgjKNYgtIejwnndLtP7g7s7nIde7DKwlfdpqB05gyXpAIt+2rix/bTxdO9s6nguTZx3RE56RnM1V8jM+tRxS445edYjbDYNG1LEyVnPveEqmQ/3mf8bUSokx9frPclPCASnB7HVu4LY6nqIrRZbLRAIjg+7YO0eRqD1PDGFwLub+7TN77C82Gg/64IKjs4+0W3qwkjwXpBA/mwKsthGammNviowP/17uwWUSn2LE79r5FLGgTLLKLfeGg07U3c1e4xCuniqvTrGbuu1ncCc0uh+69fQew1uHROE5SY4ZoitTiG2+lAgtjoPsdXzILZacNTYRFz8CLDt0zmxy7V3XFyMtaj9wXboO2gL4IItp+i+r5Gk0UlivT6vB1ikXQSCw0HqdHPnfbJD7tvXMrMEgDN084M11pqs1q+1BjjSQlnKFzGzOvkmovvnqbSB0pzRFj+zGjHTrdYp36UreMop5GYh3dUl+hAIjhliq/cNsdUpxFafDsRWCwTL4gjiVIIJuLiIdfTTz8thyvcBg7jQaIzqAPoRyHwIPM4okH5e+ccngrkFyA4Np/plX7B7cMfIKe4maeJTGW2m/M2jqL/KmW46X7wsbJ7AdhMIBMcJsdUCQQyx1QKBQHA82CSWNc56FhweUru59PdYknWZ8L2y5iGcQ2c/WQq8YFc4/FC/4KxAKeVjjrvZamq4YFsw1ixSnItLsKbaq+V9cnqrBR3WKdANwPVYM0y3TbGtgmYCSRcXCOZAbPVpQ2z1zEOIGdgaxFYLThWmIli5Telnwi4JoEdANt07Vqv59z13fZtmidjxknaz+yGcxrVe++VNWc3rtbDY94UzYqQf/ggFJ4mcBqtR+3C4xcnvI69rum0YZaDtcgZ6VvruALttDlRSxCz9PLp/Zl2to66tQiv+4igkXVwgKENs9SFDbPVSEFt9+BBbLRAIBIeIzQLWPIguOAE0zTJB8CP4IeQ08o4Fi0LNfCz2mQC73eJlgk0Ra+VOc4Dtgk+WNRp2wQAAdIWhKKSGK2WhJvpzuZHrqZ0IBIKTgNhqwdIQW51ZLbZaIBBsGUsFEXdB4tzWMY6AgDoJc8/nNAu35r9PHGTw/CAHJThEnNifLIFAcApYSpN3EvNoiQAPaa9OzO+mImakx6ormYZKqTh3fkMopc8u0GWhYBdgdS7Rh0AgEBwTxFaLrd4VxFYLBPvFqQW6BfsC6aRXQB6648MZSbsII/1MoTdMw6mBWdBp2BRSLO1wMeXejGms1mpfWqsAznYz058PpZI/8Dp5p1hquGO02bC8TWhxEieBdFeXmASCpSG2WnAoEFu9LMRWT4PYaoFAcMg4hLhfqoZxWOoYaZHRZSXaBAcCCqQvMU3EG97wBnz6p386rl27hhe+8IX47//9v2/hBDtMDqQ//PDDeNnLXob77rsPSim89a1vjbZba/EDP/ADeO5zn4s77rgDL33pS/H7v//7UZuPf/zjePnLX47r16/jnnvuwXd8x3fgxo0bG52IYHvQO/i9JafD6tbLH1nBkWELBcy2jbkSEYLDhdjq84PYaoFgAsRWCw4AYqsPGDuWeBBFCcFxQeyR4HDwS7/0S3jta1+LH/zBH8QHPvABvOAFL8BXfdVX4U/+5E+2dszJb8CTTz6JF7zgBXjDG96Q3f4v/sW/wI//+I/jp37qp/De974Xd955J77qq74KN2/eDG1e/vKX43d/93fxjne8A29729vw8MMP45WvfOX8sxBAKfljJjg+bBJ8MTbW+i3x1LZRWEqnDLcNsG3Wm2AYVMBsiemQILb6MCG2WnCMEFsttnrfEFsttlpwWJhCGD0EtvapYSm2+Xg/26W1X15WFhy9uNjqOAQLYU+M9H/1r/4VvvM7vxPf9m3fhs/93M/FT/3UT+FpT3sa/t2/+3dbOtEZGukPPPAAHnjggew2ay1+7Md+DN/3fd+Hr/u6rwMA/Pt//+/x7Gc/G29961vxTd/0Tfi93/s9vP3tb8f73vc+fMmXfAkA4Cd+4ifwNV/zNfjRH/1R3HfffRucjuDUMbX4lUCwbdiaImaCg8VSqd6Hli4utlqwT4itFhwaxFYfN8RWi60WCAQCwYFjDxrpt2/fxvvf/348+OCDYZ3WGi996Uvx7ne/e/OxFLAoNerDH/4wHn30Ubz0pS8N6+6++2688IUvDCfx7ne/G/fcc08w9gDw0pe+FFprvPe9711yOAKBQHCQEGabYJ8QWy0QCATjEFst2CfEVgsEgt3pnO9XUP3ycq+HFxwoHn/88Wi6detWr83/+3//D23b4tnPfna0/tnPfjYeffTRrY1tUXoGDXToJB599FE861nPigexWuHee+8tnuitW7eii/b4448vOWyBQCAQnCkMlkn1NkdUOE5stUAgEAiOCWKrO4itFggEAsEhwkDDLMDVpj7uv//+aP0P/uAP4od+6Ic27n8JHEWe40MPPYQf/uEf3vcwBAcApRpYW1LYFAiOA4eWWnzOONV08X1AbLWAILZacAqQv+uHA7HVy0FstUBwPGh3ppS3X0k+kUA/DazXyxROpj4++tGP4vr162H91atXe20/+ZM/GU3T4I//+I+j9X/8x3+M5zznOZsPpoBFpV1ooEMn8ZznPKdXPXW9XuPjH/948UQffPBBfOITnwjTRz/60SWHLRAIBLOhzALWQiDYIcRWCwSCc4PYasGxQWy1QCAQeIj2y1ni+vXr0ZQLpF+5cgVf/MVfjF/7tV8L64wx+LVf+zW86EUv2trYFg2kP+95z8NznvOc6CQef/xxvPe97w0n8aIXvQiPPfYY3v/+94c2//W//lcYY/DCF74w2+/Vq1d7F1EQQ5hfgmOE3kCPTStECbqlP2Z6Cxqnxi7nkAs7ar8wUItNtXjjG9+I5z//+cGevehFL8J/+S//ZYtnGUNs9f4gtlpwjBBbLbZ63xBb7SC2eodYomDeCWMK63QJhqogxqkw1S8uuldNXrnjBzHSl5im4LWvfS1+5md+Bj//8z+P3/u938OrXvUqPPnkk/i2b/u27ZwoZki73LhxAx/60IfC5w9/+MN45JFHcO+99+JTP/VT8d3f/d345//8n+OzP/uz8bznPQ/f//3fj/vuuw9f//VfDwD4S3/pL+Grv/qr8Z3f+Z34qZ/6KVxeXuI1r3kNvumbvkkqix8oDAyaLReg0NAw6AcYNBq0mfVToaBhF+hHcKLQC/6maI7vOTvrd2OhdHFM6ONTPuVT8PrXvx6f/dmfDWstfv7nfx5f93Vfh//xP/4H/vJf/subjwViq88RYqsFJw+x1fsewv4gtlps9YlDgoiCfWB3AXnBOWBpaZda/J2/83fwf//v/8UP/MAP4NFHH8UXfMEX4O1vf3uvxsiSmPwn+zd/8zfxkpe8JHx+7WtfCwB4xStegTe96U34p//0n+LJJ5/EK1/5Sjz22GP40i/9Urz97W/HtWvXwj6/+Iu/iNe85jX48i//cmit8Q3f8A348R//8QVOR1ALgxbNsgkJPWil0NrlGUaC04Ka8BzqEf9HVTLalLKA2swhtSnTzSTfRIxibTsHcNusNgN55w4dL3vZy6LPP/IjP4I3vvGNeM973rOYcy62+jQgtlpwKBBbvSzEVh8+xFYLBIJanB/rfqptbtDlpDV+EgiWw2te8xq85jWv2dnxJgfSX/ziF8MOOFxKKbzuda/D6173umKbe++9F29+85unHlogEJwJpjjsQ5iUKq4WOCY56MYiJLNXOOOUamx829rU46G/xXNwjrITxqpw3TftZw7atsV/+k//CU8++eSiOm5iqwUCwbYhtlps9a4gtlpstWAc6/X2mO3b7FuwDFJ2+WGyzScE0M/v14mjx74Y6fuA/DkU9OBSR6c7KgrYG79GKT3TsdCY/ouqYCq4s63UtF+g1QQ9y9G+tIEaYrfpib+Ob1C8zDHfJh4ut06YpBvBLpQuTn08/vjj0fqrV69mC6P89m//Nl70ohfh5s2buOuuu/Arv/Ir+NzP/dyNxyE4H4itFiwNsdV9iK0+DIitFpwyVqvjCBwB2wuoS6De4Vieg2nI22y536eHtl3mGT7MH4FibDdfWCAoQGcePW3lcTwM7Keg1tz7X0oh30bhsiIKBc2sUYOfR7vNrDOVp2V2ef6CgPvvvx933313mB566KFsu8/5nM/BI488gve+97141atehVe84hX4n//zf+54tALBMMRWHzLEVk+G2GqBh9hqgUCwTRxqQPzQxrVaddNB4GAGIjh0yJMi2BtyKcEaGi2M31b+KUorjdYewU9Vggh6iZRsAGpG/CCryaqtmzZBT291edZkrUMumA6DZXiu1MdHP/pRXL9+PazPMdwA4MqVK/isz/osAMAXf/EX433vex/+zb/5N/jpn/7pBUYjECwHsdXnB7HVMw8htnprEFstOFXoiuyyi4vtMzSXZoRfXrpx7+JYp4ilA97LPD+5v8JzOtbIsdRDQH1TfRB5uPYGkXYRHDSsNVALOTkCwb6w5DPMGW0agE74YXqqAz6QNm5LQSHupBvAmvL5LaHzKVgGS6eLX79+PXLOa2GMwa1btzYeh+BwILZacAoQWy04BIitFgh2Dwl4HyfmByG38WtNw+ZxkdGmRqVtvY5/BTiGCOsZQwLpAsEBIdVUVdBeG3aG3qrSgDVQaGC3YixOEZs50acYSLJ2DWvWgLmMN2QYbtZoWNMVybJJWjwVK7M2no+OYeqgN4AVbeJF8eCDD+KBBx7Ap37qp+KJJ57Am9/8ZrzrXe/Cr/7qr+57aALBbIit3jfEVqcQWy3YBGKrBacMCZKfFi4vx9tMxyYM9AY130tWKxdQ3+hZnLOzPPyCDSFPkCALs4Uv47qyWNhYqnhxv9lFzGpQGvs+y7adJ0oFzbLp4IX1ShuoTdPEeX9pyjghcbSnsKmmMq9MtCzOdC2MXYZ1OCWl/0/+5E/wLd/yLfjYxz6Gu+++G89//vPxq7/6q/iKr/iKjcchOC+IrU4htvpQILY6D7HV8yC2WnBSOLEo9rZP58Qu104wFFjfTtB9CCn1PB9kl3t8/BBGuuBAMa6hdkrQaNDuyclQULDidFdBFSpxT2lHGry5wnZuO6ATsVWt+sXL0r1Tx1yrgs4q70j7XlT/z6PJFCpTZu3nhhr5+bDDZ8CZb8qv68Mm8+E+8+9LiaXG15szZX1aKNgFivZN6eNnf/ZnNz6e4JAhtnpXEFtdD7HVYquPGWKrBUeNBSPBq9XyQSYJVO8PUwKPuwsu1tqZqd/9YmmXi4v+c9c06E70GKKpYzizF6ttl7lt264JsQTOx9MTbIRNHFWNMjNpW3ApyvJ47wxLaqgGR10VnfUapEXOSmy37sDDxyrqrXKQg267Y5GuJ9dhHWOwbatgmVHjX3gkNVwgOF6IrRYMQmy1g9hqgUCwb2SiTTnN6CXicJsGpU4hnrlNHNL1yY1l2vi2ZVuaZHk4oD7ropaq2y6BMwuIC8YhT8QZYm4BNAO7uLurra5yGHIg7VSuwyrYFIddWCsX5Mk9k33HfOD5YB3YEQc9gl27CYiLl/EmmSJmZoT9VnXoGcEySSHPw1i1ULr4Yb87guOD2GpBGYf990Zste9XbPViEFstOFgsTBFfrdyUxgSrCjN67IphLkz240L5MZ1qd8Z+mUmD5ny5s7fpsyPP0vFDpF0E+4E1k9hCzjGdYFUXgnPS530R1VC9P9UaevTP8Vwt1knwxc0Ey6EUBNJq+Lnl7LY0TdytS44THTOTIt4bWCFtvAKGipeFFW2YK9M6pptBT3MVyLPbyKnLOXfbYruNwdQw+k4ETnd1mX4EZwKx1UWIrT5OiK2OIbb68CC2WnCOSGP0pZj9UEA7t21uAHyTwHm679S+jilov+sg4JzjTQt4tpj33a5J5ny9W0c/HNG9vbjAcURRcziWB3SLOKdAuuTTCopYghWz6zTx/eCUz3G7fyIU9KijXovcSHXCBlPKFgudZZHRXp0Eo2CNSxVPHXNrO73PuPhYppsFHT+7hSDX9goHCgSCMYitrsUpn6PY6o0gtlogEAiqcQxBrlPD7q/50jaoH1TnAXRgWtaFQLBvyM8m28BEttrih5+ZDr4kHJut3qPQ0JLOemKYysCs1VglhptC3/nmjLacY97vzDqvXutB3VVj19CJo27tGjBu6thtxtOmAJtJC7dWR055f3u3vuSk2wmOuoEZlWOoLVxmKhzwYSf9MN/vfRQwExwIxFaLrRaIrRZb3TvSIUJsteAkcYA067aVgOaS2EYA/PKy63us//rtJRszN6DOHyKdrHOMdHr0eUA9vA50ctv8BYG/e7t+Dw/svV8K58RIP807KDhJaNWg3SubRuNQHYxdoM9Y7DuzNUEhNYM5p9U0vmSaIj6F2WZ1+dsjFTGztu6vuzUKyDDclgI9jZsUGDx3iO6qQLAsxFbvF2KrxVafIsRWC04dS8XVxmLz247d80D85eW0+o8H+LvC1jClAOwxBBXLIFtNxeVjZvqk+33cF+IscE6BdJF2EQDAYgXAar+eaqt6rCYN3WNGpY7cmGOXcw73zfg7F/B7k15z+szb6AwLLt6uegGBVG/V9W2j9TrZxh1zpW035dhf3jG3umzVg2MeMdxaqMByM06+NyleRg669c4gTdZS6jhry5YpVTxNGTee7iZuuUBwPhBbLdgUYqvFVgsEAkEOxxC8OjQsec1q+hpqk27jn2l5+BhpdH/oc80vATmN9K7g6GrlfnThBXZXq8wgN7nI5/LLjGDnkCdLMAjntHd//AwMmh0UTVuO0TafmaaghD2Uw2iwYwaLjTv2vaBN3yPPBYG0GilkNlSwbCBVfBOE9HCfOm4yKeT9ffLLtZgi0zA4jor3Zqmg3j5hJ6bgD/UjEOwLYqvlBexBbHU1xFYfPsRWCwQdSkVH5+CcWOCHhlyge86+y2MTffSmsH9XYJSC6amcy+TnUB7cg0PbLvNsTsnY2Bfk6TsTWNtm2V4GLZR1RaQsWhiFKtpM7LIvCw2Nln3p12iiz2NQaCYWaTrvNPBNUKOtSs62nsA21AOpt9SLVoAqNKvSXPWdhWGNOOjGs9usWXdMN7sGbMd0G9Jd7X0m9hoLKdQ+hZsWNDsFp3opWKjoHmzSj0CwKcRWDx9RbPU8iK0WW33sEFstOBlkItdDgXFi65Iu9oaHqsZUWZalMDTmYw760/2tuY/UdkpQkgce1+vhQORwkHLse9kUJjrQl3PpJs5CJzmgwEhPNUJKgy49EOnDO+dhHnvYjvVh3CJE2kWwJWz/C/E2vnSbCZqVQDllPKecOVS0aig1fGoaeW2bfRaeEwwjlyoOkOIaK1yWstt4urhPH6eU8Y1g2Dchw947/goax3BLU8X552L3nO1WM5yk1T4LAk4LjgkEhwax1SnEVgtqIbZ6ZDhiqwUCQQEUVOTFF2tRE3hKg7jHEKw6ZhzP9d2WLUglXfqg5zzIuhwCDmYggkOGPCUHCgsTnElrzWLaoca2aEb6cinhyzqpGgoGNqT+8j/X2mq0EwgiSjWhkJRjtE10QpQGrJnBhhvsFMevgFm+CTXFy6YdyTPf0LDlvg4vPS+BiEbzov5q7KQTu80t95+TwH4jhptugvYqhrRXjftmpEh3FQiaq24ZAEsTB8Yc8biIFl+O9ViHn3Zy0GsCasaKAw14zdsFio9tq0id4LAhtroMsdXbgthqsdXnB7HVgpPFeg2srmQ3bSLhsmv2ds3x0jZzxnhIrPTcvam5X0OM8CnM9aljSqVk4rap1WrZ+hbT9dHpm0CTTMRQvxLpo0c/IPHBTb0QuYej2b7UocDhnBjpB/JnSLAJ5jrvPACwDZBDrhZwXJXSsCM6rDVtBMeHIZenxHwrpodnO5k0nDhVnI5HqeLWuDzw1Ck3HastKmCG2DG0tpy6fEq6nof0nqaBkU36EQiGILa6vo3g+CC2mrZNG+ch45DeU7HVgnPHxQVw69b8/du2Pp44pe029hfkUQoubhJ0tDv/sbbTSie1lUWelX3oEAl6OKdAuuTGnjjMzC/BdcWLlvMW5gQJSMezJjCxFEtw+JU55i/n2ZJgmWbTrmPuumvV+CPyomWNP2L3r9wn7dN95vqrKmW6+edUeaab0gZQBkjTxbnuqu4surXraHIr14BZe61VNwUH3Xgn3GjAaliju0JmBQfOoM8DsEDRUQ/77clj37VzLennglOH2GpUt6k86tBRFjrGPiC2Wmx1PcRWC84Gh0JRPkGMBbTGts9lVp8SNmWZL3FswvAY0r/hNXrpUe5isj1lo1+As9KJgc4ljTRM3QWreefTNnM0kwSCAuRJElRhKIWcp4HXQsOlbPM08ZxDxouXOWeu7kv69DRyKWI2G0pngys5x1yp+T8553R7OTRix1yx4FG5gNmG7EueLp7CFNhqntk2VPAq9blzBctMmEs5sk1gsYzQwwmREAVHDLHVgiLEVuc3iq0+CoitFhwMNtFbWRhLSr8ckmTKMWCurEttX0NtSBpmjP2bSrnUo2Stan8s5QVGU3QSL4Nx7fmDnwY+AHkBNsaUArlj/Rw65GkRFGGUgbadgzXLCVdqEhPHabC2RUdcqwZtJcMmlz6uoCe5MgpqUTbf6cMzDxPjqZi+an8P2ictStetUZ7GxhluWpW5eaS/qpj2qlIW0LyAmel2UBpWN2EqwRrGdvPaqx3TzQSWGwxc4TJfvMxNupdSbG1S78z2HXH+mZanPpFzi5ntPt1v95B0ccGxQ2y12OrpEFsttvq4ILZacOrIkWfT9bQ8NVA1FiQvbb+87BQzqE06n3KcXJs5/ZxS0H9qEL2m3WZZBGP2hOxUrU46MdBp2U1KdYz0a9cK+ujAvIjq0rpCEmyvhki7CE4eS6V7bpqummVH9Ry7LTymKu9E8m1lnMNrs9w55tlueYc8OxKlks/pdpvthRczm6TDOgWe4aaCY25gDTqdVcZ0M37ZMN1VQimVPMdumzQ8ZYJjPtdBFwgE+4PYarHVwxBbXQWx1QKBIIelgmILBteoAOOWugdwHEEqQR6kdrKp5E6MmmD1FDZ6Ci7v4j5HxUUhqiuC44M8rjNhQ2GuwwcVKluqYJljfU0/dw2V/RNc0tgcYrQp1WQDDFLEbH9QE3RwUxCbUlsNbVXv3XLMtc4xzxUuU95Jd8w2amd7TrrSju0GZSnHPNZcLcB6Vps1nuHmmW4qYrlZz3BzWquwKsN2Yw57+pnNe2y3sE9hfJ77NsTKLOmXmjPWNc1p3s7tR3B4EFsttloQQ2y12OpjhNhqwdFjgEatYUBSF1wWumk2D3ovwd7eB0P8kFnpS7LIN90n3TfXRy74vl7T+hLDvGXzqX85U230vk46/WBEc1oOlOb1up6NTgx0icTvHefESJenTTAIp7e6HLTXN93EFZia8r1bKByfAuO8IFOWIVi1n3fEN3yyNGxIGc+x2NKiZmpMYzXnoNs1gFXnmNvMX3VTeBaJ6ZZhsPWcdL4t09U+n6ixd42CYccaFBsqLDe1H4FgXxBbPRViq8f3E1sttvpwILZasBdsUw+d+p0Z/NvW0IYC1FOC123bxTe5RIzAYeje8cD3MQQU68AZ6ToUGOWFRncGCbhvDecUSD+HvFdBgtyXbTOgr8jTTM1MNyF90PospnwRrG573pHTGWZVf3mzx/xY2IzbwuD5jzDa+lqqZXmAlO2ooSLt1a59HsRw03AT0NddVaS7qoxnu/kdyTHXGhjQXQXg2W2XoXiZijRXDbBWADHbjAaMCmw2a1XEbKPPQP63fosBVpvt89n4+1lyquP3OX7vlw56HavDLhAcAsRWT4PYarHVEcRWV0NstUBw+KiJ/dVoZHPGe9p2aS3uWmyzSOfSKB1zUwb6Jiz39J7m9qH5MMm7ZRNQZqnX2gzSR2/Y8hWQtEvKRr+4QMxI3xW2FVjftF8J+B805O4IAgyMd4R281joTJEyDR05D6VCZrRtWWfCMfA2xzEy3aZgRjp4wYnXKnaEtf9XOioFCkLa+OAxu3ugxxhuFQiFywDvoLeeBed1V61hFcY65zvugxcEjHVX03lO0pjWcb1jY20vaLYtjVWebr7Uu7dvxqoUMBMcG8RWi62ug9hqsdViq3P9CAQHhVOqnFmJUz/loQD5mFpJXcC73H/Ntt3EqBs2b3rrcmz0UGx0twMVLAhhpAuOEpxRYq0paiz29ku+HNd8sR9ju/GvqWkBKo6cI6Zt3kFTKs+EI5bUmKbsJD3QsyliNuxQRAy3CddPKc2KlPn7wxzxlLWYY7gB3bPDnyFyzDttVdJlJd3VLn1cpyw3ZQFNDDfjTil4+t0YrI6/3fE0cRv0Vrnmasd0s6S12jZeb1V7HVbGbAMx21R422h7yckLfv/CgR96/5dipNX+3REIzhViq8VWT4fYarHVYqsFgpNBJkpEwUQeXNxU9mIsiDsUrKoN5C6F3Fi2pUU+F5sEpodY41OPXcoIoM+Xl3XFSLt7zFnoBJNsK7HUOfpFRR00SBud5teu9dno4TnPi7lPA3XWFLLXTvmXHMFOIE/QmcN9Me//gVlab3UIJaeaGG4aDdqJTBhXyIz/oS8z2BQacSgijDvhXHNVgTniFQ5857QnDrlNHXRqr/ycH5+2lR1VFRUuG3l+vGNuM6ni1qyhmpVjuVHxMkLimLsCZgowzNFnaeLGcGZb54gPOeRDKeNzMSQPsXfsOMXcYhlO6inzWgX7h9hqsdV9iK3mEFu9Y4itFgiGMUPEfGwXKj46Rx99VyxwfpwldNJrxn1sDPdtMHZrfhTJt9mWnUmLjMbrKIjOfzCKBjn3IpUC54KdoG2Xeb53/UPeHBzRnxzBPmGUhbYWQ6yo4a3kHqtQxCzaZjXMmANFbVXjnZbhN6zkdIc0c6WrHQEFlWEXDaWXn1jKeHC6BxO0XdOBsE6aHg7ETrnyaqs6eZLoExUso2V31K6QmVY2TG4/C01aq9r4yQLaMt1V0lx1Y8s56D0Yly7udFeZ5qoB01x1TrpjuvV1V02SIm6s2936uRl8mzJD8s/ipqniB+24bwGSLi44JYitFlvtILYagNjqE4LYaoHAYakio7VFRbdVKPQcgt+cIQ7kg4M1Uhh8e600zLztnIXO5zmMMdP5cjwpdYHVChErPWRe8AtSI/NCgfNdPEzH9MASdjxmkXYRHAS2rUeYFi/qb9/s+AroOVmE3Pohzc2xVPDaduMsrHN6JXL3ZtnzH7veY/druKzdMLoiZkzHV3WOetx4+Cix3uqln/t1oYiZBax1RcusAphD3vWTOuZ+fcX52OQvgnPgh/ccctj3rXcqEJwKxFbzvsRWLw+x1V1jsdUCgeAEURE1KsXD0vVzCb2bBq72Hfja5vGn6JAvOY7aoORQcJ2C+PXS41OC50NtqUo4D6K7z2mh0SiQPoaeqHoljjEILjhoyBN1pLDWTNMRXRAulbwpJJrnkWOJqaLjrn2yuoFWDdoBJpoK3LlmeWdjAguu0AFOiuk2EdytVombraAjxlsuMMOfD606fVX67II/nc4qaaxqWKjgmHdznaaWK2K5FdhJqfaq8QXMbKq5mmO5ea1V41ltpnPS0zk9YRHDjTnt3ucPIDZcGBd7xnKarPRe1LJI+T7ZbTtO5942DMpc1an9CAQpxFZTH2KrDxViq8VWHwPEVgsOCkvRwge65/rRmyJlf6cs8xI7nEuzlNrk1u+Lbb6N4+5Dg70UGM9JhQ/Jhw+NPdZRz2mkt8k2k9mWQyrlQsH0CwBXAFygaYA77kCPla5hul8PNhGVp4ebFx4Q7ATnxEiXQPqeYdEOptcuvX9JZ7UEowwa7hjAFplrc6CtApTO/jnW0Gh9gjnprxrVOQdKNYm2Kvx63bVBA6i+Q5GmjNdor+ZTxo8VC9zDQkG5FDoJIg0VL3Muuuo56nF/bAghZTyZJ4XLaK60Z7gplzLOxVyt1iFlvAp2naSLU763cg66jR1ya3WP3RYKmWVSjadoreaey01ZqvFYluvr0Jz7lIm4ST+C04XYarHV+4HYarHV9RBbXdeP4ESx5eD2rpALBB/SqXGN9pqg9RSd9CnB+ql9bAObsNHTgOOUvsaKjaY61cNZCm1huYSpki5AykynIHrKSu+hpmoqx9Qbv+SDIox3AOcVSD+n3NjdYmtfQOf1uwkDbBdppUPO2Oi+E4pnLY/TfYVKLMSqfQv3ole0bNAJV15LVYXxkC/t1lMfdMwys03pAU9X62GtVSpcRiniTHPVOeUd080aOL1VwGuverabL15mrYKBL2aGvgNPznputMbWO+yld3ZJh10gOAmIrZ4EsdWHB7HVHmKrBQLBoWMgOqRhRmNxc4t1cszR2K4lBy8hJ1PqY2qQelNsGhCcu2/pWo8F2lM99vnjMuiC5XNtEQXOgZxGOgXOKaDe00dfr6dVm6x9MQ4l2H0o4xBsBLmLZwob2GPxumkcOGLXDDtxGiroQ7rlQjurYVS+KJhGgxbEXNPQCoNp5JuBjeEkU8ZL96tW2zZ+Qpwj3hUv4455iYGZMt14OnmORZmmjnfLNmiputRxcsxpbN125QuZgbPd+CmT9qrq/ixau4Zin2EuAePSxWMHnbHcjIZpG1e8LGiv+mV0zjhntkXp37Zz0F2hvv4bYa2F8dNU1AbbcgzSScc5MDZbCbnrO7cfgWBpiK0eHrHY6qG9xVaLra7ZX2y14Myxb7p3gUKdqlLQ5yWkXkqHH2NzL8EY3xU2GVPN4zD3kdmkaOmc/SkePdzHknZgqNCok3fJFRptGpR/vcgF1efe3LH95uivbwP7Pv5MpNkQm/Rz6DjOOyTogeuw5hzv0f2ZW26Ugbbx/qnbzp1yY21gIg1BMUeVHLDSO6KtRqvakCo+FTxlvPvszmR55AMKbDQ4tq/uEcMtONs6s46tGmAZKtVEOqvuGOSMs3Rx2zHagI7p5pYZs02VQwnEbEvn7oNxjDdewEyr4JgX2W52nRQxa2PdVUuaq57lRunixGwz8fth6d1B56RzvdUhlJqMFTPbLw7XUecBk037EQjGILY6OZbY6o0gtjqB2OoNIbZaINgpCtHepulLqKTBqTnx//Rwc4PNU+VdOGr12YeOO3WcU7C0LAUxxHlgcOkCpXNY/G2b/hA7ppGe6qSnaNg+fRY6LziaC6JfXLAB7+OHrX3KwpwQRNrlbHG4XyCXQIm1YjLOr4HpsWGGUk3HHIOS866hg0PG1wEZNlWvwJWG8g5fLmW8mLJcTGUe/4l/kxTqw8BmDLdJR6J7krlv3bK/f5njc7ZbpLUKuCJlyoYiZsRwc+tj9luP6Uap49qWi5cBLi0c6Jxy0xUvo5TxiOlmLZwwsHfKEwfdWp8m7iciqFnEmp3cSefOurUlfVW+bGDSQm0DmCIFMaZLLBDsDmKru3Viq/NtxFZXH0lstdhqgUCwHcwIttXsMjf4PaXtksHdqezSGn3wKX3VnM+Uc54iSTOnr9KY0uuYKqDwOHSOgZ6XfckF09N5rn1uW8pG59roF+DSLk3jWOnXrnUSL9HAp8q7CAQ7hvyUcmiwJssgmt0dWqSOl7Ftj3G0b6gBpphWTUgN52njQ33VOB1pEbNyw01Txt3Rjo3pBqDHcKstlldiWZYcdneEjvVGjrlGrK86FhrhaeIAoDXprhooxViP2navhdZ+Kp9b5KADvnAZL17mWG7OGdeATw8nthsA75A7p5w0VrlTbm38hHAHfYz9Vgqc1aeGn3ZgcghT9GzH+hGcEcRW9yC2eo8QWw1AbPUpQ2y14KSxsDZKDWN8E2b4sWLXLNeUjT52fGpfE/SvWVe7b4w5BUdzbHRapp/RO2kXHjzvSRWlvwLUCvQTUi0kwU4hjPQBPPzww3jZy16G++67D0opvPWtbw3bLi8v8b3f+734/M//fNx5552477778C3f8i34oz/6o6iPT//0T4dSKppe//rXb3wygjzMwBfvHMMt3t7flxhtNempOWfKKWx2Dli3XhcLWg2lv6sBx7HPaKt95IeOlz+rceybIbfs8R3LMGYXKqWja66ZM96lh8fMNn5v+f3no43kUZVzwslx157V1oURHNvNMd1McNQRdFcNENaN3DfD/op7zVWY1qeMG2B96ect7BrA2jvkVLzMUvEyHbHcQpe0jh/SAkNyqsY6l9siz3oL7XwwwjHf4ve42mk/ceYvQNdxmemQILb6+CC2Oj5KHcRWj/cmtlps9fFDbLXY6pNDRt+Dx/6iIoyIlzdFjtlcGuImbPJSv2lsdGpgeN+S9jXr5vZbCrqnQfbSMVOGeq59t55sR062ZU7B0TSA3gC44qd+sVGSdCFm+iwx+LHAea4QafqiDUEC8pPAM0A2nQ4dkwPpTz75JF7wghfgDW94Q2/bn//5n+MDH/gAvv/7vx8f+MAH8Mu//Mv44Ac/iL/5N/9mr+3rXvc6fOxjHwvTd33Xd807g6PDvC+7tSyUbbBVuIM+5AzUoM6F7Zz0nLOulB501vv91Tjt5bangSHHPE3n3l0QgfR9tdWR5iqNSCkVxqO8I56OLqSNKxvSxGk9zUl7VZHuKtseMdx009depfRwOp5Z91luDFGquC9eBsT6nn2Gm2O9ccd9E4cvF1QrpXuPBegExwmx1ZtCbPUYxFZvA2KrAbHV+bZiq08RYqsFOaSxPx7L49umaItvijFi8NgxzkWlYwozfCkWeW0/+XswpJPO17cD2znom0HKTHdBdaUugkb6xUXyA1EaRT2GaKrgbDH5J5YHHngADzzwQHbb3XffjXe84x3Rup/8yZ/EX/2rfxUf+chH8Kmf+qlh/dOf/nQ85znPmXp4wYLgRc92Aa0U2oS+o4Irbntp4GkRs1yquPJ7plgmZZylsFeljJdT3vkRd8+HqXe2qxzzXnCkpJPrPismTRAYbqrJaq7y43Ot3pAGHrYR060rUka6q2GZ5rrTXQVjvAXd1VC8bORdsGtQ8bJoatfAugXWgF03sG0Taa8ao9Aa3emtwqWM+3pn4YmhdGML57yHw3q91VKBsyms03i/+L2xMDAFbeZ9Ytt6rynzcJN+Dgliq08HYqvFVvdbiq0uQmz1XiC2eh7EVh8Baqt6zqn+WehmCZSkWsYKjub2a9tOgiNXBDVtXyMTUyMtM9TPPqRolmCjb9I+x1LPbS9ppNcdn7PTp/xdT/XRu+C5k3Rx09WrHQOdptUKuLIy06nIqS4MfaYHg2/f5sOS6/tMmewi7bIgPvGJT0AphXvuuSda//rXvx7PeMYz8IVf+IX4l//yX2I9cLVu3bqFxx9/PJoE20HJobVJYaShYmYpar+y1jDXotRi5vTlUsajYmZoFg1EbMYEO6Qv8QPXZIPrlbvWQ9efO+hR4TIw3dVk7vrMn0GP6ZYULlPaP79jDjng08RdqjgVL1PBOee6q7x4mWYsNzdgrr9q2DqXRg7/mR224Gvn3jzumA+9m4eW/t0bzx70X82C0zFDbPVxQWx1HcRWD0NstdjqGoitPhyIrT4BVEaI0phgTvZlwcNNbnsI2OV4a45VK+2dUfWZdMyhgH6pbz6e/v45tvkQO70EknPR7DOtcxNnoafyRQGXl9O00ZeSZ8m9bIJJaNtlZF2OIYNlq0/JzZs38b3f+7345m/+Zly/fj2s/4f/8B/ii77oi3Dvvffiv/23/4YHH3wQH/vYx/Cv/tW/yvbz0EMP4Yd/+Ie3OdS9wqKdlaY8xlKbwmIzaKGshlaN45spwFiDZsRhNkDVyDVUcCAcF0x5h6zPbIMCLDQMjL8uJipilkKpBta20ErD+HMeTpsfZqMpNHlmTcJ0U1CZ9PkappvbezdstyUCASzwMaJjm6byB63VTCDF7R0vk2OuklANpYlrz2wL6eFAYLCFZWK6JdqrSjkHXSnjGW40IO0YbrkCZsYbcetTxn2aOEwLtb6ECrqr1hcvU4DVMK2GbZtQtMwYt2wSp507dpaWWUEtY/vr3HoLk3G1eQBtLtJeh3Sb6T075wJopwCx1XUQW83aia1eGGKrxVZPg9jq84PY6hMFo1ZrGAAaq5WLIxLju2liBvgChwLQZ4UPteXtS6z0MVY5Z7bPHfPU7UtgaqCbx4DHCo3y9bxt2j7tp+Y5GNKit5aC59wutJl5Tj99CJyJfiVZvpJlozcNgJs3dyuOPUUnfWjfTY67bcgPAlvF1q7u5eUl/vbf/tuw1uKNb3xjtO21r31tWH7+85+PK1eu4O///b+Phx56CFevXu319eCDD0b7PP7447j//vu3NfQjgDOyhCnOvbXtxmwvA4u0+FgtUofWOe3ToNE4QpE4BCMYu0ebMdxSFmGvd1a8rGvXreOaq6VRaZXnPpb0V0l7lT7zOYCO6UYH0nEvPc3V9BjGpYwrw4I3pLtqABjtHHTqzzvjHJQKPie9eIlwzhIp4dPYcof9nnIm4qb9HCPEVm8TYqvFVtdAbDWfA2Kr3XHFVnOIrRZbLXDYRWxsV7IpNfIuNdjmeJeM6w71lRYTzW2b0u+cup0OYzrpuTZETkhtM5d4ccz0HBs9PAM00LFfH0L0XXBooB/+lujn0LEVaRcy9n/4h3+Id7zjHdGv5jm88IUvxHq9xv/+3/87u/3q1au4fv16NAmGMfblmX9BH2rr0sS77bliZqWiZlw3cwzaEuuNrSOGFHfoELOlOHsqB6V07ERmloP7l3VI57witfsobC91fLpjnk+Bz2uqRvvRdRxw1vm905EOa3cPlP/ftVFQqitcpqAcwy3ordKyY7c1XnNV+e1cd1UR000bQBmolQG0v91asylTuCxora6h2pue3XbbM9wo/6gFWus1V7WfGphWw3i91bbVTns1aK66oFTEeAvLnvHG5hw20S7OvYdGGfB/U2EPUH91WzjndHGx1fuH2GrfRmx1AWKrxVaXIbZabHUOYquPEIyJSwHhtBBjSc5ljsRLKp0wRASeG9AqBXLHJBvm6pBvI/A21GeNPMtQPHgO+TrHUucMddJMz117PhYXmDeIi4imN2bsr2W6nUu4kC46X74jFBq9dg24666YmR7pevABT9X4GHohNmGeC7u7CkvIumw7MeFHfuRH8Nf+2l/D0572tJ5M2hQsHkgnY//7v//7eOc734lnPOMZo/s88sgj0FrjWc961tLD2SpKTunmHS//NW8o5XNXqGHGpQ56LahIls44iqUjHQ6WdtC3y7ZR0BXX17ctBE+0/0fLcf+dk84LluX7QWC1RetThpsvYjYLXms1LANApLtKOd1ObxWA015N9Fb5sknmY6AiZuEzXMp4LZYqAjbGLN12sTHBchBbvUTHYqunQmz1NvtLexdbLbZ6u8cRbB/nZKsFZZTkV7aNXbBCa6Wwdx1M3zSIvtQY5v6wAMzRmc7Zjk3shQYPsOfY6CE+PRRFHTrhXGHR3GeBwOP27dv4xm/8RrzqVa/aqJ/JP63cuHEDH/rQh8LnD3/4w3jkkUdw77334rnPfS7+1t/6W/jABz6At73tbWjbFo8++igA4N5778WVK1fw7ne/G+9973vxkpe8BE9/+tPx7ne/G9/zPd+Dv/f3/h4+6ZM+aaOTWQZxKvZGsKYq7XYpTNFZ5ftghCkGwGugmsBUcinjbLu1gILnKg2DNDXdlXat+Z9obTWMci6bthqtav0erd9f+08mfLZeydV6rVaXQq8Dg08pDdi+80AaraGt11aNtVeZlmpGexXIBWro6tQGReiqbRLwqXXKRxhuE56hlOHGHfec5qpGk9dlhdNcjXVYEZhtGh0nsGO32U6PFcR+69htWju9Vd2YTnPVFy9TyuuulhhuBHLGbae5qta3nWO+XgOtY7nZNRy7ba1h2sZNVjn9Vascw40Yb0Y7Z9uqwGJzjDdiS6kiwy11yEnLuJvn1FgRtnXLbTQ/PiwXbHT3Ypl+DgliqydAbHUWYqvzVwW+p/kQWy22Wmz1VIitPlZbfcLICZZPFTEvaJLwbtLNXNUi1Scvdc8PM6STXjomrW+asq56zbFqUdqvpr/SOUw59ibba2RYUsZ4KusyFEum+VDxUN7nWL8ObbJc0koH8n/XGzZv4BjoV5LpAnfd5Zjod94JXL2aMNJvVNKQN9UzF5b5VnEM0i5UI+RNb3rTRv1Mfnp+8zd/Ey95yUvCZ9JYe8UrXoEf+qEfwn/+z/8ZAPAFX/AF0X6//uu/jhe/+MW4evUq3vKWt+CHfuiHcOvWLTzvec/D93zP90RabYI85jjf2X68I3tI0NBFR6GmiNlOkTjoC3eOeQ76DjQfk+JkqZM/9Gymhc2ibalMgFLeCVfJ+lhvlW/WSXp4WM90Vp1DbgGal6CTP4veQeeaqyporSaaq7bTXbWhWFl3ftyBM2w+5NhtjU0bHWM379UhaiW7sOPm788SfSwJsdX7g9hqsdXD+20ZYqvFVm96nEN4fxOIrf6CaD+x1SeK9RpYXcluOjSCbS5wXtMemFd0dM74gHnSN3OOkyJlg4/1PyQDM8QsH5PNSQPwXbtUziXdee4PuGRLy/roFxeddJHbw+QHOob0Fx36LAHyvYHLDW3az6Fj8lP24he/uKf7xzG0DQC+6Iu+CO95z3umHvbEUM+kW9KRTvvKfTbWOVKO16WRS992zoJPdx0pZqaVgrE2uJz0OdvWqo7dBo3Wj8D4eRvG69lunrmGwGjrv3EKGmDFzhSa6PMQiky3SZizX3o9c9drriMwheFGbLVmkPk2VrwsX8CsiVPGLSmvxuel6B9zzIn15phtjNXmGW4aFo0y0NrrrXqmG7Tx+qu268Cz3KzWUfEyZdawdOvsGmp9K2iuwhhgfQm19kJ0rQ16qzAq0ly1VqNtNYzVaK1juRkotFZHWqukw2psrLdKr0rYRsuwwWkn9lqNvmpOb7n0LnCHPfdu0X6H6HALxFYvA7HV2bZiq8ORY4itFlsttlowDWKrDwRjTPKpTPMxDESftf/uQUFHanp5ufkw0sMOBbRzQ6T2U1jpJfBj5/rbhJWetl8KtWz0VH++tH/N2ErM8nT/XLHSofYd0mC6Qcw+Hwq2c/DA+RU4430B0kcHrkUM9KI+Ohd6r9X9KT0Q+9JEkkD+Ynj88cejz1evXs0W0d4HDovqJNgbhlJMozTTGdqVpUJmaXrwEuDO3zgKx57BJBxPkt8EKjPNwYhjnmlbdx1Zf0qHfVLN1VDALGW19T6rwHQDkNVcpUeKHPSwHh3LTfnCe0oXnEelpt3rwHbzLLeI4eYWrVGR5qox8eAtVKS1ykdmCxqsacp41VB3xFrbNnah40oBkSUmgWDbEFvNOxFbLbY6A7HVO4fYaoFgWYzVS5wSHywFa4eCurXB5xoScW1ftTHTKX0uiTla5UPXZ1PN91KwPredI3+dx2xRTQAdIOZ5abp2LdZGbxr/rO+iwiTHpvIwgiLmFhbNTQBw//334+677w7TQw89lD3uP/tn/wxKqcHpf/2v/7XoucpTcYLgaeVOV3W6k1UDA4MGDQxQ1XvnbClY24UDtE/XdJwnG+mpAqanvar9MemPulYNjG2hVANrW2ilYfw1COw2tpx+7mmvRmOu0191bVUhtXcuQ25JVDih1UXJiCIG5ojn2WykuRocc/psfWjGp1Lr8M+niiPWXO0x3FhqOI2m0SZaT9qrWjmtVd0YqJVju6E7Bcd0CwPv/iQS002ZtZ88y219GyCGG2murhs3ec1VznRrw0SsNv/7fmC4dQw2C8ZwA0sptx3XkbNE6Xnr5l0wjZaNip+9YiDOtlVp41VtKphvu0pRH8VCuqs7yOoXnBjEVoutzo9hBGKrxVaLrd6oH8EJY2nm+K5Q0CGhQCMFz/np1eqk80PU6KRXDGs25rLJl2Cmb4KaQPjcoqmXl31Zl5yOeikon2s3xErvllvE8i5jUi9jtiANnMf66MAVXLvmNNKJiX7HHQkjHXAXo+ZiTvk1KapqegQ4prEmWK/jr2ub9AMAH/3oR3H9+vWwvsRG/8f/+B/jW7/1Wwf7/IzP+IzNB8ZwvHdJsBiMd3gH2ygDbbs097E08U1ATht3KnLaqxoNTGXqN9B30OF7me04T9JfPQQHfVmkxcvG2oXPybOmPbsrx7ijPUNgx7PYFN+WaK0CnfOe2+6KmFFDnyaum+6vvkm+oZjWTanmqjWdV42O4Qb44mRsImYbpYYPMdqG2FKU4ssddFPhFe6C9bYLRppAcM4QWy22eg7EVout5hBbLRDsGDOD+zWxtLEio0tiThB8LACeSsuUAvpLjWkOtvG7zFBgfsrxavXRh47bx1BwPUVT2M6Z6RdhmeLZV6/GzPSBaH/hsOyhoQdgUxmXIw5enzKuX78eBdJLeOYzn4lnPvOZOxhRhyN/Yjr9T8E02GpuWh/EbpsDx2izfhkJuy1xsJySZjgmgZLMW7aOnHbHdDPQSqO1bdBmtYgd/dSR6DHdmOOda19CmenWnfHukXeex/RW020KetwRRxNprnbrdaTBqtk/PhbHYPMpOH4daa5q5ZeRMOBATjvQaItGm6DHqhvjnXHHdFPawBUwo04K52NaQDeB4Qaz7jRXTdtprq5bYK2AVsMydps1Cm3bOFabd8Zbq4NjbmiCY1gRsy0cnqUgu+39Zyp9ksakHOawygwLPm1dX3VP+q2nWsDssCC2ei7EVoutFlsttnoMYqun9SMQLIYlGPApVRzAanWlx8bmc9JMX+hwIS5ZYoAPBb/TNrng9lDAuyaYPrT/NoPptbI4/F7k2OU0n8pi52x13hfJiKfrc8fisuM0PmspQG7QBctpOZxJZrkUWOdMdMc+7/TRrwC4houLC9x1l2OkExM9BNJvroGbN7uBl3Rpan9dAuoD62mfIvuyEZZmpG8DH/nIR/Dxj38cH/nIR9C2LR555BEAwGd91mfhrrvuqu5H7vyRg1LDLdqqlHBXtGy4nfWusFImpPMSXDGxhrV1ARI7MVCSc2IVlC9ilt+Hipj1++qneRePm2W6TcG4g31YDnqFY14Af07SInp95lq6vQnrUwZl+kxRmnhawIyc8e4YfejAZHMOenf8DMuNplDAzI9jyEGn/vyyMq130GnyjDamt0oTgMgRd587mVZD2xFvLz05xGzj26cULxPkYRdKF18k5VxwshBbLbZ6GGKrxVYLhiC2WnCOuLhwMcWcfMvFBXDrVnnfISmXueD9UMB7V0HuseMAy8Y0Nwmib9L/WPsxlvmYvM84KKheC66RXtZH5zJFq1USSJ8bNd3khu8jAL7LY+4pwH8MgfQf+IEfwM///M+Hz1/4hV8IAPj1X/91vPjFL67uZ4HTPG+UnbAlj7G9L968b4O+7qKBGShuFp97znkg8CJmY2nmMfvJs6JsnjmlMw5krohZz3lEwxzMgqNZZHRxnc45r9CuXrtKxzzDcAvXLnt+XRulOuabSthtmhUw02igVRPuq4YO2quc4UbzsEzsNr7sHfGG0sY9o00pi0YZp7+quf6q01rlbLeuc3LQ8wErx3BrodpbQXM1Yri1FliroLlK87ZtYIyGMU1w1lujve4qTyPnTLaOKWXRsdv420fLY393uP5qaTuQZ645lsLy2DpLTiAYgNhq9llsdSXEVoutFlstEAi2hJFIEcXBuBY6n/N2tTGzIeUMHhDOsac5KGCcMqqH5ElqVTty/dZibNyb9lHbd44hXkLbjrPX0+0p6z13jFR2PG7HmehAmWnOg+pjNieVc3FMdDfdgac/3bHRr17tNNKvXQOurEws7l7Sq0lfCI70JclJv6TLYyi1FQb60eNNb3oTrLW9aUoQHRBG+hGj00Dl4MXLlj2a8Wni/b430WB16eNxGnnY5rVXNXTkaHDtVQIVNxvCGMNtaPtgyvikYmaEbbPdln8GeOGy4lFT9lvmWeQOerfOO+g+VVyrLpzSFS7rnHGK9ZBTrmED480d16WMK22A4JRbgBxzTp/L/GyquPaqXw6aq2FylDVrfKp4wnKzVsGEomUd6w3I33lKF8/prjJ5126df774e5MWLKstXjaEqXqqx+aEE+NwiX4Egj7EVnOIrc71vyzEVoutrmovtlogOBoQE71p+hIqSzA3S3ItQB2rfA4rfUwuZqrEy5x26T5TMYeNXgrU1zLOc+tpHFz6pbStD/5XcajYaA6kkU5BdB5Mj5fpBx8u69I0iAPo44OdhmMMeh/jmBn4D0Ob9nPoOO47JRiESw3Xfpn+yDkWi1INjG0DE8mgzTre5b7z6eFDjrpWCsa6Pd2y6ylsh3I6q9bAqL7zqvwI23BePoWXOdUudbz1231qfFLkLGoflgvO8kz9Vde+xkFH/rizMXwPaxhu/X0yzqtnwXGGW8oq5KzDburSyN0RKVXcsd1oLwXnPyuomN1GjDd0aeBhGR3TTSkLrTv2m0sXN95BZ+nizDG3ugHsGiE9jaWIB83V9aVjubWO5WbXAFoNGBU0V2lqjQ7sttZqtD41fE2OO3zKOLongOutGvYZYbtNnPHYMc+h9Mwatt4kzLblmLWH77KOFY2b0o9AMAdiq8VWpxBbLba6ay+2GhBbLZiAudHlpaLSc/tm0V/tf7rmWug5rfQxlORdagPfS8vDTNFKn9NH2m5J1Eq6DAW+021jwcKSPnpuXLlAfG5bPptpjJ1eKihqEEu4XGHzbrp2rQn66MREv3rVBdWzTPTciaaY+zDm9jvy4PUhYb2OORCb9HPo2FXeqmAEU5kkc2AWYJ9YmNFCSUuBp43rzKOq0USOIwUaSPMzZVy5bSPOa6b4Vml08Y6bvEpLvYYTHfPCvrlUcVVYdu3jFH6VpIn3j8RccjsiHaDiEJCmdd4pJ8ecp4wD5LgT042Kl5HuKrrcc6CXKu60Vf37aNbOYTdthuUGlypuFEzr08IzKeGB2cb0VwGwlHFq140hfcPSzz3G24TiZQbbcsQFgtOH2Oo+xFYv24/YarHVbl+x1QLB2YEFFIdqJi4ZC8wxm4faTJU32QQ149k25srFjMm6pFI66Ty3vXSMFDlWcPw5LTRKSEXKSpIvJaRMdFdwlLPQaX5xwfTRuQbNVF2fVNZlE0ihUcEESCB929hT6uSUlM20rbFOf9WihVFl/UYTSp1N47L0nC3PckoLWHXtvYPOtFe5pidQ53RHTmTCyhreudT3uIOuCueU72s8HXvuftkxZMcbO+ZDQYqha17SXE2Ll7ntyrPZlE8H75htacq4a+8dc5YqrgILzjrdVWK3aVa8zDPcFHNkrXfMbU531ay9Q+4mrG8DieaqtU5zFcbNTatd+rhVsLZz1FvTOeBdUTPPcqM0cSint0qH9wvElLRJhaywfgfaz6cMu+BUi4ceegh/5a/8FTz96U/Hs571LHz91389PvjBDy50RoJZEFvdg9jqUl9iq8VWi63eNcRWC44Gc4JrlXrpPKhO0hg1siulz1MLU+bkq1M1jin66DUM7X0G02uC4DnW+BSN9LG2JYZ57l7kAvB8fG5b7ptgKukyppdOoO81KSv9Ap1O+pXAQs9ppBcZ6RzpQ577dYmQS2tYSu+8pv2ZB9dTuftNpkOHBNIPFQXnetuahikDJhw30XOMt81zEnQh7yNit9mY5ZZzqDmTqobpliItZDbEdKt26DeCxrDTPc2Rr3XMa8ZVKlam0IRrrZLr2B+Pc9L5P997cNB5UIMXMVNeczVOG/cOe4/lRg66L15Gz6+2vVTxHrxjTgw3GBMz3NZtx3JrffGyoLWqfQEzBWM1DLwzDpq6FPHOMfdzG8/DdlYc0Njye0bBNPoXbRspTNYrXnhk+qlz4dLF1QJT/TF/4zd+A69+9avxnve8B+94xztweXmJr/zKr8STTz65vRMVbAdiq92y2OqR7eMQWy222vUhtjoHsdWCvWMPAbJcncQpccIhpAHaGnmSqdtrgvZT5U7SfbcVcKsJos/tt7aP3PVL71NJVjzdt89I5xMwzDwfszM5XXSSdrkIQfNUI13D9KOnU0S2p6RtzC00euaB8ak4p0C6PBlnCqe9qmFsi8Y7UVyndQyumNmwo2qsHXDA44JlZXabK2DWoitkRp+Vd+tacM1Vk5l3eqkKGmA6rNVaqkX9VTfGqB3QC67Q+c1jJM3/vavOMa9Joe+nk6eOuluOdVVTzdU48NKNTScjzWqtqjgkQSniXFtVKwutWZp4lDJOBc2oM+eg29RJt2uQH0+aq8oz3JRpneaqMbBrBIabWTutVV7IzPDUcfC08LioWaqvmrLbTOLAu2UbzV37/pec6cXH6hx4en/2U6zs+IMGb3/726PPb3rTm/CsZz0L73//+/FlX/ZlexqV4BAhtlpsdW3/YqvFVqfHEFu9GcRWC7aO9bpYiVOvVgC6Io3U9PKyLLk+JsVeOtxQe+o3B9I1p8Kgtf1PHQcFi0tE5LFxTsHQ9UsD4FN/fEhZ5CU2+uXlsD562tcQqz3HXC+Ds87TQHsODZtTEL1jotMysdBJI50kXiZHTptmmI0+FFjPtdslJCB/chBG+glg6AuyhVn0C3TKnjETnc3UWVcsHMBTxslx08zJA+KU8a4PjZy+Z3ycke0TmG591L9Gdanjy2CqYz4lVTyFZun4xDTUaGIGIksV7+6rSp4Bnhbed8yVShhsPl1c+3mjDbRmRcu8Uw5lQrq467jAwDPsywLTWwVprq7XnuVmgbUCrNNcpXlwypljboIzTo54V7zMgJz3OOXYemc99+YGpz16D8vveNyujea1mPc3ZPjvUrxi88DCXLgU/mWmufjEJz4BALj33nsXOivBIUJstdjq6mOJrRZbDbHV0XHEVgvOBSygmCPGUoyQdKZrC3SWPqeB1qWlVGpkXTZhpafH2mScSwTRc0HtudIuuX1L16rEWO/vk7LRuV46bzOGNIhOwfNOG53WEQs91UnvBdGPgYYsGIQw0gU7gEHq1Fm0E9OR4z5q9jfWBFZbdGxrgETLlJhsxCzr7QMLJA6gsRZQQFPhhKZMt/52HRhs2e2q8Qwgz2BT2ns3rWe4tYEpR9sDu42YfVOcjiLTbbhtbxPU1rUypwYBSo55Wrgsx3Djz1xuP9JcBXwAxnZKu8R/C7qrKnbWO8e8Y7G5frpJKQvlt7t57KDzImbRYzyULm47zdUuVbztUsVbC2s17LqBXWvnmBvlGW6xY06aq5EjHoqaJew1m9fvDOnkma1DjvkYlipetitHehugQMkS/QDA448/Hq2/evUqrl69Wt7PGHz3d383/vpf/+v4vM/7vAVGIlgWYqvFVm8PYqvFVtdAbLXYasEBI0cDH6OGl5ChapMWOm3i89Ihaw8/h0G+LdZ52p7Y7hxjzHTeF1A/zjFMlWIZigunbPNa5PoqjT0Xm768rDnXNJheI+lC81TSxTHSleqkXZqmoI9OAxw6qRQ17HNK50j3OXXs8Tzb1pEplujn0CGM9CyOPx2xBqUv5hYmaDTawIpJ2G2sCJTbMuxsRj6Ril1HxR00xOw2AJH2qmZpx4j6iJ1BoEtd1omDGe2XcThrmW5xICTTZkDbtL6w2TQM9jsxTZy3S510d6yYaTiUKu56SYrIJeMMDrciNlvHclPEcgOwSphuacq4UhZat1DaQDcG0J7hFhx1OohGTn9VcWbb+rYvXua/fbRrYN3CrgGnuaoBctJbjdZotK0rYNZa99mic8yDRic8MwpdqjhPF6ft3FnneqslrWP3Jg5rrqYp3ymWctjPFffffz/uvvvuMD300EOD7V/96lfjd37nd/CWt7xlRyM8JZzHsyq2Wmx1HcRWi60W1EJsteDgMBJApNgYn6fr0uUph+HrS0HXHMu6VHS01PcQEz1FKZg2JbC9Kes1x0SfIukyNr7cPJUJT691GigvnVP+nuSY6DlJF94eybomWabpIpnuwF13IUxPf3rMTMfNm25ar+N0iNwN5r+q5B7yucHjof22UYxUcDKQu32CsNYUi0cB6Omr8s/ui/kwU85mGHoAoj37/Dcwvhlbp1SvIJOGQgsLbRWMcnsNMd40mpD+ypls8XE0Wttpr852QAbYa9kzHGy/LONt0NmvSROfdcx4X54qXmrXK1qGvowAL1oW+lBJKjmI/Ubb4iJmOilepni6eAmmBXTD0sRNSBV3TrpPFzdwDrnRMGvt9VQ1SxlnBc14qjjI8fbFzDzbzTLHPAfrGxjbPbmp5mrPIU8KDs555o+ZvVaLTVO9eT8A8NGPfhTXr18P64cYbq95zWvwtre9DQ8//DA+5VM+ZfNBCI4KYqvFVvc3iq0WWy22Ogex1YKDwFym+RBy1G1/jNXqCtZrR7pt2zwj/eICuHVr+mFqWeZztqXs+dpx5drnmOlAp8u+LdQE66f8KLBex32W2qdthgjbQzI8ueD8WH2NsqRLbn2TTBp9ZnpcaJTLuly7BuDGAHW/hpKcezBqHwoJem8FS/15FGkXwWawJsuUGnO+58Kg7TlVvTbKBNaZgYmYaVMRFawCJb9rGLSRo0mFy0JBM6thFKBgoBXQeueXO+laNWitgVJNtlhZqZBZ59y7Y9HnyKEfTBuf56ATpjrqVUy5Wsc8myoet1VKe2Zgx3zT2f3c/aBUcW01eIK4tsR/0x3LUbmUcQ1i6zlnfKWsd9a9tqpyk3PEgUYZaOU0VwPrrTG9dHGE4mXo2G2apbqb1l39SGu1hVpfdiy3dQvSXLWthl27omWm9QXMrEJrNIzxqeK2W0fOunPYPYONsdiC9irTXLWwIXhVejI447S3rcIhN7ad5LjPKV62n0JndVg6Xfz69euRc56DtRbf9V3fhV/5lV/Bu971Ljzvec9bYASCvUFstd8mtrpm33IjsdViq8sQWy22WrADbCNQviEo3kcxw5SRzoPPmw6f9qVj1Qbb06Kjpb7nBu6HgunA8gH1UoB6TBedL+fY+9THUPt0uabPknxLqp/u16LPROcs9Ny6EngAnUu63AHgCi4umhBIJ1b6tWvA1atMI50Y6cRKn4P0JSltT5dLbQSzcU6B9OU9PMFWsGkKp7VmNmMlZcssDWI4daniXcp4YEOxlPEUaQEznq7M5yXUBjrq0sbdKPuNao8xnkau2L/xDitTxAf0Vt36sQJw/VTxMaRBHUoHJ8KbZptpkZPhuA6rYvNYa5X0Vr2DTp0MFDCjImbkoHd6q57h1lqnm2qcY26tgvXONznoNAFwbDZ0aeKB6eaP6RxxFTHc+mngSD6baD6EOX87TLagWMWxtu6EH66TPwWvfvWr8Qu/8At485vfjKc//el49NFH8eijj+Kpp57a99AEG0JstdjqtI3YarHVYquPE2KrBYuiNlA3EEHKxQunxAVrgrZjQ5oa4KqRkikFjjmGYqy1Ui81qAmil1CjkZ5rnzt2zXUrHYuz3+PtY3+ba79/EuucQMF0DSfp4uZcD5000qlIroYZ1wHKofY92nZgfBv9SzD/qCCB9AUwykwqfmHd7hfNmi/KQ9qrPAWV9BwJkQYrbKQDmaZ/c9ADlzrkhJzDyXU7aU6sqVT7k5z0koNOTK2wD18e0V+NBzXj1Zmwjxr4V32sEcd8PE08Pn9iuPF1WjFd1SRVvNNdbRi7rWO7BYabIkabCmnilCreKKDR7nPjtVcbz2ijdPFGGTfXrnCZJrabbp3T7tPGoWzHcgM6B10n18HrrcIYqPUl1HrtmG5Mc9Xedjqrtm1g147l1rYNjHFTazSbuNYqZ7p1+qtcezWkg6ef/XtGaeKp1rFRec1Vjlkp4yN/R6YG/aqc/B2mqfeu/8xpSsr5G9/4RnziE5/Ai1/8Yjz3uc8N0y/90i9t70QFYqvFVtdBbLXYarHVYqshtlowAdsIgK3X0DCBeU6s65xG+gaHyC5zueqx9jVa6TWB5SWC6Xyqxdh+qSZ6OrbaHxbSIqM5jXM6Hm9DQfF0PV83pq2e10jnrPOUkZ62L0GjY6E3ICa6m3f66Hfe6QLpT396x0qPmOh1lVDj1IM0RYNjEw31bTDWzyhAntPsnzsdOs7nrgoiOK3VJvlMaeDDaeMGBk3mNxgD23O2c8gkVDsnLSi6amhYpr3aTxlP4daPa69y9g5PAS/tkzvO7LRx2gcYTB/fGBVBgPE0cfSWu327QEdvHUsVTxFccsulALwL7x10CkBoxCw3GkXQVEXHaAvFy+D0Vd0pOWYbvO4qiAWXOuYpTHcPeyw3Yzpv2Sivu+qZbZ7dZoybOicc0dzYhM1m8+9Lqr86FPBKiwsCfWZq6hDnnvVx3bzTBKXpL9FPddslhF4FZwGx1WKr0/ZiqyG2+gwhtlowCQco0TIJBe0TOi3STKd1TdOd7pxTnyq1QuuG9hvbd+y4JZRkXlLwoHhO+mUui31M0iX9zNnhpba5Y9SStbmsC0caZHe2Y8x+kM0ZapsWGqX5BeKiozow0S8uOjmX8APQTRYxzWvQsMMkNzz30NQ8FIKtY646z7b62SZOgJG+rS85x5WWGLQQN2SHmJH9I3ab6tht2TGNHKvEdCMQo6uUMk7sKc6u4qw3Wk+pzG6d3pjpNi1tfCA9e2nt3GKf8TjqHPO4vUrbcSZbJlVc+fsSaa5aUlrVkROuaISc4aY6ZlujnCPeKBtYbuEzaa8GZptB07Ths9IWqmndfNV2B1Ia0BrWO+lUrExFWqu3ocIv5beBdQtcdpqrpm3C3JgG7bqBMRrGaLS2mwyUX1ZoPbOt9awolzquOs1VdIwpervS4mUR021Ac7Xmb4FJHPJNZSlm4YA1WQVLQmw1ILbarRdbLbZabDVBbLVAIAgoBBJzEs8kk8HX1wSlh9jhaQCXs8tr2OQpKz3XZmh9Tvu7xEyfEmhLWec1QfQpTPQSu3wsGJ5j9ZfOf6jt2PHd+XZ5U30meomRnl5knSx3RUXd/JqfYkb6XXfFbPQrK+MY6cRK7wbZBwXIc7+GjInjl14KntbB5wLBBJxAIF2QQ4mxxb+wE6sl/YI+tE+3Lk4X77FsKoMmqVuug9MWO+a5lPHBfkM6c99Bj48/zOjK9RmvrHmFBtos5aBvoaBd7xDJMTT7nKaKA+Skd6ni0b78HitXvKzb1qWLR+tAzrtlrDcElptmjDcAziEn51Ub1tHItTLsWQ7FzIjlBtjWMdxglHPQrYI1cRp4x2hzE0enx+oPkaYdV747Ne3i97SN5uP7LvNT8CEXLwOWSRWnSSCYCrHVYquXhthqsdVzILZaINgjBqLM2v994DHBJeN+c9jrm7ZfKllgW6zVXL+bjnlMdmUouJ8StnP9lIL+fVmXJUD2mRcbTaeLkC1x7Rqi5V60f9OLW1NMdEkccuB9z2M7J2kXCaQfEfIMlOnaqrniRED/S7SxbdjXKKbBWihoZtGxcLo+8t9Y0wfPpQunBa2882YLjrr1jLWE6TamveqWpzPdOCJnM9k3r2c64qDzqQZV+/TZbWFsbL8cw400VvvXwi2TY65UE1htShGLrQkMN9dbNw9aq/6ojdJhlI1SntHmmW8KWHk2W6NZYTLvhK+UQeMnrTq9VaUsdGOgm9Yx3FY+ZXxl0VVHy9xTxnRzDLfL+K/57bXTXF03sG0Dc7nqNFeN011tjYaxnd6q9cw2PhkAa0vb4nTloAEKcvjonXLIaa5amPB+ZlPHC8G3pRlth+6El2DtcpNAAIitFls9dR+x1WKrxVaPQWy14OiwiU4zEEWScszzi4t4uaYAadr1EMN6KJg8xpQG6jXW0+XS51JgjZjjmwbVS/3kjj0UEE/X5/TNx9j53KTli4bm15UY6e74nHGeTpylDrYObBuBB9FpIlb6tTBdu9bgnnuAT/qkjpV+xx1O4iXSR+fyLvyEcxhLu6hhqNdunxqc3zSAfcjB+QmQQLrgZDD3C3OJCZMWMRsqllSLlM3k5uQ4DqeMx/0w1lVBN7aU6h22T2C6jfXVx0zHOzctdKz+OWSuazGIMX4Mfr86pmKFNm/CcqOR0cSddPjPNHfstuS55GnVOuOge71VFeamc9S53upaAcZNgdnWNrBGx4XJrIKFgvFnwZlu5MSlb86Yb2eDYx6/g/m2+xYWO05HXSDYF8RWl9tkt4ut7rcRWy22ejLEVgsEPRxoQIsYvbn1fHnO8IeC1ENxzdpg11DQvLR+DpN9TkB96j5j58LXjUnI5NjoQ33y9qUfMtLt8RhqTrTGLlAAXaPPRHfFR++4I9ZEJzb6xQWWiZaOBc0Fgi3jMC2FYGewtoVSGsa2aLzjxYuZlcCLmJEO5CxXVSkY6wufKaD1y60vhub61TBoQcXL3GfvoFvAKkp6awN/ysI4R9I6X8xYAMqdLy9kRm2ovU0KknXr6OzidgFJYTJih/WdpK6f7SAXtEi+deWKkmULlTVZZ5yYhbROK9eOGIaB9cZSxTvtVdrSBLabY7cR680z3eDuW6M77VWnw2rDpH3RMq09261p0TQG2hcv042BXjm2Gyh1XKPPcDMtgIvgoMO0wPqyY7m1a+D2JdBaV6jscuUYbm2D9nLlWG5eczUw3YyGsQqtiZltblkFfdXehI7pZkFst05nNRr2yDMUO/HlVHELExUvy7FgKSh3rEy2IRgs8zae3pURHBLEVoutBsRWi60WW71EP4IzwbYKjm6r3/U67js6zpXo8Hyi3QDg1q3NhskPzUHB2LGCpDSngqCXl128k4+zdLy0Tc0+OSwh+TIWJB9jhuf2HdqnxFqvkXXJadmnRO88I91kltO2gAuQt4iLi3ZB85SNfnFxgbvuAu65B7jzzk4jnYqP4jGmj14SrR8qHppLv+CB9fQXpQP9cewUsV4vk/l1DMVG5alaCBa2l+48r5+2kG6ctLNmlJG1FJwj3sB4x7h+P4tm5jXp2G3eUbcKRsE75vmvwVo1ITXWuXrlN9A56J3zTA49gOB89x303Di7/fLbmwLjyAUalkXFvck520laPE8T7z53jnm3Pv+c6sJ6dwTGRFTxG8OkV52+auKQKpV+RtBape1KGSiWNg7lHHXlHfbgmJe0V03rGG4AOq1VxnIjdptRMG3HbON6q5YccNufwmH8siUnvGBwLLNEXIqhVpt1WzglJ30pzVTRXT0OiK3O7Se2Wmx1OkKx1YDY6kOC2GrBOYPHCinAVKOXPhRMzwWpx7bXBLFT8GB6zbFrguljfWyKKWz4GpZ8GvwuBdHTYHuJ0V4qNMq3F/bEOCs9V3S0hDwrnQLmq5WTc6HlXsYERfrHQDpGKeY8ALsuMHpmQfxzCqTvxrsTzMdCX4Idk4WxXiq0Vy1McHZtYMl0KeJO75E7DwbGu6pcb7WkvUrQqnPEFePX5VLGI8YUSMeTaXyqTndVe5ZWYGJBB51QQkl/tRz4YA5solvqPvTTucvBFp1MUzG8f08DttIxD23YOfJtWungmNN15ew211v/PmlL95fupIJWNCFMxGhTihhuNp608csGjfaf/RQKmTUG0E5vtXPM4Zxyny5uNbs2pu0cc9NCEcuNM9xuA/ZyFbHcTKuxXq9gjGe58ckqrK1m7DZgbeG0Vv3cQAVGG9dctbDhvXHOO3/PfNq4sr50YPfmGZhIF5lrrvIgUq3m6ik54QLBViG2Wmx1EWKrxVaLrRYITh5LBfoIhQht03TdEjE3ZamXDjukW15iS5e00kuB3BzDOndKQxIpuc9DLO8lEgRKfeWY5GPXDOhro5fapxrouWPwKScDwwPxKTvdBTY56xzIM9HHguw5TfQLv3wNwB0A7gRwZ9BEz01XVsax0YmRnrs4Q8gx1WsKBExZP/W9PbNgucDhRALpQi8A5n+RtszZ3hbGeu5rbKpkPv6oTtFBzbXNr+sX8Jpz7HJhM45aJ3283VCK+HS92NgxH+snlyqu0IVSKACj/FkowK/teqR0cYAYb7TsZAmI3RbprbLJnbJ1TjkFkAZOWxkTHPOY3UYTIoYbrF9uG8dqMwrWctYbPOutOwZnuuXeB85i4+vmwAw44qW/E7HzXvczcL5dedQ1f2N2rRlrF5wEY5CrBIitFlsdHy9eIbZabDWNQ2x1fDyx1YIzBQsw5tQqlojVzw1Ej+2fKzxay+yeK0szFUsE4nNB9FKbmn4IJVYuH3MpEB+D/02fwkofQqqN3jHTiZHOmenhB6DcLxND9OOSFnpNAFwC3DsHZUtsOh0DI12erl3Bmqze5TLguqATUs6R11c1aP3auA+jDLTVnt1mQnEqAxOYaOlRLfqOt1KUKuscLf6nXSvl0y4tY0S5r/TaKkBpWGjAAkY5l11bDaPcSIhpRf26pHE6T6/dmuivKjR+nWHbO33VvAZrnFoOJKnjiQ5rOPeiHivHvOekeM8LDvVQmniOwRe2IWUO0jVvPMsww3Dzbjmx25RnuKmU4aaBlSeiKTiGm4ZzuFee5aY8w03BbWu0QdN4hltj/NRCN61PH7dQjXWXQSvPdPPnZxJFU2M6htvaM9zWrWe4dZqr7eUK7dpprprW6a2u1w3WpkFrdZgMHMOttSow2FrTOfAtMdtyEw0JFqRtzPlsYxhzhI1ttxqQq8KkgOJ2xirp4oIIYqsDxFaLrRZbLbYagNhqgSDFtnTSU3j9Eg2D1cr9PeQqFxRjTBnqfIjpUHNSK/xw6b5AF9S6uKiTZcm1KUm88PGPybqMScsseUtqWPJDxyzJtaTsch545NvTHx/SAGOJtZ5u72prDLHS06kEesAu0DHTiY1OU56RThrpuOGZ6DdvxlVRgfHqrDk99CEtdb7PGCTovgjW67Iq3xSYPX/tqcGJMNKPGf2nZCmWx1KpnqUv8Dx1vGvbFVsaSxNPwZ34ecnTBdZVYGj1/9DqQsBkXNM25+RmmF+l/iuCJ4tggmPe21XlHfO0jU6c9jE4H7m728Ru4ww3YrlpuLRxYsO5/d1zRSw3Ba+3GlhuziF3p28BXf8eZDVX185TtkbDGg1YDRjtWG5MczU3RduBwHozcKEkoM+Qsp7rZq0dfIdCorjq3PZNUZKRACR1XHDuEFtNEFu9BYitFls9AWKrBYITRyE6W6tOMRYTHItXloLDNczyFGMSL2PbhiRdtoGavktjAoav0RyW/dCxgOHr240lbVQTNE+3NZk5Z6M7qRelmh4bnSYNE59UWmH1EHFMLPdDHtsJQq72MWBBhpy1BlB959A54AmrjbHd0iJmvJiZURbaxny2moJuSqnAdINSMNZCu8Gh9cuOU0ZOjAas6dht7s+xG4cFWtU6TpxqYCznonUMNe23QQGwXeAhZboNFTML17GG7QaMMt7cuU0PyAw6+D3t13HHnDPccvqzOqxrAsMtbGMMt1yq+Mo2gX9IbLdGKaw8y00xnVWtwBhtXndVu/RwWu6mTm+1adqgsaqaFqrxy5Q2rhEx3ML14Ey39aV3yNdOc3XdOs3VSw172cBcrmAund6qWa+w9pMxGsaS5qpCa7VnsXHHXAXWW47VZmGzxTmMtSGVPDDeVN5pd456X2s1zGc42DXp5sUU9CNw6K1F9rrP6Udw5hBb7fsUW13av79RbLXYarHVNRBbLdgp5jLOa/abWgHUBx71agVARxroqdRLyviuGc4Qi7zEAM8x3fnxcoz2polZ6Xz7XCb6GEN9CqZKzuSkVChwncaHc4x03i6VtUj11fn6HAu9NDkQ8zxloBP4Z96Wo/HrKWieaqR3jPR77gHuuadjovPloI1O+ujpSZVQEv7nDPX0ZRhqf2iFRk8oAC6MdMHBItJGrPziO9Su98V7i+mjU3rmjv2Y5iovZBbWMUYbZ2H1j6NjLdGBYmZjzLRBthswGGAhXdYa9ttouyrHvLBr4Ry4Y5465Sm7LZcq7tqqbMAmZHErOi7b5p1Qx3bzxcnQpZCnU6TFSsXLFDnmmRM27otD0F0FoEzrHHNjurxtowCjvfaqhvF6q92kO1YbvO4qPNMN5Jh3KcVDWp3OWSeHfPuwNh8YGvv7smt9VIHgmCC2ug+x1cPHEVsttnoIYqsFAkGJHJtbP5eVXmJA12gWp0HmXF+1x53yO8YmDPWhfWvZ8TX7pshpm4+NjQfeS32W2eolVnppew5koDkbnQLqDZS6CHroxEgnbfSmSU6o5oblNIhKukRzcEIBbMHuIU/PiaJWexVwX86VamBsG5xagxZN4s24L+PaM960114ltheCHqRjn7mjG9jAUsuhxHRz+wGAQgtal+qwaseI824/6a8qPxLSX21hoNEU9VcBeA3WzuEgplpguQ1qsLqzd6cQs93cdRtnvEXXZG4q+YBTTmPrUA48kI5qbz34+r7earFwmaXiZG7LSunAcGuI4ebddmK5NdotNwpYKXitVWK5OWZboxjDTRumueomKHLMLcs7zzyL3kEnzdVg5G9fApfWa66uPMPNT15v1bSe2UZzz3QzpLUKYG077VULn30esdvgC54h65Cnmqvdepcqzv/xbeH0/DNt2Bckkzjku9Bf3ewY2w0aLtH7EfxwLjhAiK0WWy22Wmw1ILa6pmex1YKzQEr35rRtXOmCkohlM3jw/NatukMRQzxlkZeY3rVa6bnTIVZ6etz0FA9BI30sgJ6LBXM2eom1njLXcz880I8W6b58n5TMnWO9d0F1HjAvMdKH/jJSVl8aPCd9dGKk3xnY6J/0SbE2Os1xI8NIHwO/2WkRgFzbmiKktduPSdblQCCMdIHHwndwB6mTKTOlmMY58Ktjug8vdGRUv7+c/qo7xnyOjlYdI4rm5ORrS5+ZA5g6opzZlTiiqf5qyYkd0xAtMcbS9UXG21IF7Sr6qnHMeZp4uk/MeCsHD6ioXbQOlCSev6K8gJnTWrVwuqsdm41IasqnkCt0rDatE7abNn6ygekWn74/b9N2f6VzequM4WaNCnNrFawvWmaMY7u1Rncaq1AJA64LCBjbDw4Ep5wV0nKMOAuTeYdK7xsQv9epA961OVz22j6OTezDjaedj1zQQWw1ILa6BLHVfYitFlu9CcRWCwQ7hg845motjqlVbBL7K8U6czrgNWz0Kaz20uehcS2B2iA6R07ShbfPycCk7PKa80zbpH3wAHw3ljSIzteb5HPpBvF0sQZ5Rrpjo3N99KtXY5304i8POQwxz/lDvSRDPe1bMBm8eO4mU83fin1DAukLYr4zuhuORfqlfKhYEbU3hT+ouS/4vHhZb3RMlNAmAoVKkbPtP7NtOszJkVTRZ0RtdUhVBhBpg+YcdMfU4inmOnJUu/ENM8K6kZbTsovFvcixnuKsj+yTHqvWMY/2D9fJpdMr1UR6q1S4jKeLd2nh8T8VWijGZlOexdY57Y2T3EWjnZOugE53lZhsPlVcU2o4LLRu0TS+gFnTunTxwG4zbt6deh/eGQ8MN9JbXbee4aY7ltvaM9xMA+PZbev1KtZcNQprqwO7zTHdgNbre1ogMN6MjRlugHtX+DvimG/ld6t3Ojt2E2drrh6BJqvgNCG2Wmx1fz2dk9hqsdViq+MGYqsFR4xDCIrNYchyJNFT2pUkM2iZtuUY6rXM8czhwrohWZPaOdAFyNJg/FDAeOj4mwbV0wBeum1sjGk8OGWX5/YvBdFTljnfN9VTT1ntOXZ6JwmWstINhgPsJVyBC56TRvodSPXRiYl+113AHXe4QPpddwFXViZmo7dtnb4NwLRhEuTWpfuly0v/Tdi0v0P4GyWYBblzZwJKed4EuSJmlDrebzfvWLpQyIxSxgGgVMgspGz7o4c0cjRwxdjcvFdgDIjWUUo4X19KGwcQpYhHfWbW544dN5h/j7K6sgOOedqGp4m7/mK92pLe6lDhsoiRCHd/teKauggFzIjlpth6HZatZ7nBp4fbkCruJs9sU7Zjtynji5ehc8zTXCOutcoZbr5wGems2nXjGG7rBqZtunRxz25z6eFMe9UirA/st0R7lVLFOXiAzznvtrid3r+h5ymXKt71ZSLN1bFgXR2Oz+Eu6d/O6Ucg2BRiq8VWi62G2OqoL7HVgNhqgcDpnFwBUI4PzqmRyguA9g5XKO6ZFg6tQa6/oT5Kx6+RdZnyw8Gc7WkQPV0/FIAfknXJHScn65I7XrnAKbcrBvVBcw4esCZjTcH0jpWeY6MTI33wF4spmJp6cS6B6gM5z/XafQfcFMdQGPwwrrhgEkhTdNE+rdMSNbZFkzhvNuhiddqr6XajdCATAfCMHAVlLaCABsNvFOmv9tZDAbAg3U7nXHcOXwsT9FdJqdUEJ1tDK6C1ne4q11/VyjtKXn+1WxdrqE5z0OnadH2469s5LfzeLaF3WeuU8/Upuy1l9ZUcc663Go+hyRYu46niHdsNntWmnFOOjuHm0sQR6axqlgq+Uk5vlT6TBistK+2Zbk0LtXK6qyoUMGPPIDnl/LMxnuG2drS0NTHcvEPO9FYpVdwx29hkneawm5OD3aWDk+Oephlb2Oizm9z7kLLbjLJ9xipMJOUwN1U8tPPPK7WvLZZ4jLALpXofg8EX7BZiq8VWx9dIbLXYarHVcyG2WrBzjEWl50St58JHkDUMVqvu73pJ6oUz1Gl3apcr9lnSSmeHjkB659RXqq+em/O+cnrpfJzpuKfopPM2c1Ar7cKvY62ky1ih1cvLWBqDbxtiqOeO6X6ApcA5zVNWOjJtUpCEC9Bpo19BrJN+B65dayI2OmmjUzAdN5guOtHnUzF4Aj0Q6U3mD/qQXnpNtd1NM0UEWZxTIH2yh/fwww/jZS97Ge677z4opfDWt7412v6t3/qtUEpF01d/9VdHbT7+8Y/j5S9/Oa5fv4577rkH3/Ed34EbN25sdCKCevAv2iVmS057NfpMvDLuEMD0HIm4j/E3gphQjunUpZF3y3G6eOokdlqsnXNZShnnqEkbzyGfOk6jRmEb319HY6zF0H5jjnmpPU8TB9x1cNsb/7mJt7PCZe5zfK27wmXdX1OXAu6YbjmGm2bOetgOlzIeNFhVlzJOTrpuvGPOWW48VXwIgdm27hhu1sKu0THc1hq2dU66bbtUccde0153FV6DVXlnXGFtVXDMO6dbBWZVcN4Lw7TBQSc+27AbmStitqSDfcpO+qlBbPXxQ2y12Opce7HVYqvHILb6eCC2+kwwNUA3RFNGWbqF1uVY3nNihCWZkrHh1rCtefC5FGBO+0/XLfk7xpi0S2kcY5Iuufa5tqVzTI+RC5yXWO9x4JyQyroMoUmWaUqD6ReRjAtno1+7Buj17fls9EMOeA+N41DGKNgaJgfSn3zySbzgBS/AG97whmKbr/7qr8bHPvaxMP2H//Afou0vf/nL8bu/+7t4xzvegbe97W14+OGH8cpXvnL66HeC/XwZrS3kM/XLcl4v1WTnpfbkkJcYMwa2p0FbcpH4A6gyP1+VCpkpKGhLap9denLYr0J/lVKd4xTpTLo0Y3VFqdWDOqzp2cX75MAd7rEpu3+v/7xjnitYlp531jEPTnhfb5UKl/WWQwuFldLBKSdNVQUVMdwa1TngnMmmAZ8i7iatDbQyaBqmuaosNOmuaqe9Ghx0pfKp4twxt94xX7fAGsBawV46Z9zyFHGWKt62TUgJb632OqvKM95ofVfULFf4ysJ6PVb31lhrXfEyH8xK3yUeGLPoFzOrYbKV3vF9Fi/bF6xdbjokiK3eDcRW++1iq8VWi62Ohy22elGIrT4VW70H7Dqgta3j+SCkTv528OA5P/QQYXfsMCNDADC9GGAuYFwKptcEtdO2U+OzY0ojQ0z0IV300nF4+7Qt356OKSfrkuqop23ccTjjnOY5WZehIqNAvriohgug3wHgWpaNfuedjI2+Xnfa6ENMdKCOjc7XpTpHU98/CXgvivS92mQ6dEx+ch544AE88MADg22uXr2K5zznOdltv/d7v4e3v/3teN/73ocv+ZIvAQD8xE/8BL7ma74GP/qjP4r77rtv6pDOA9agVpMzpDSjjRzHqSCt0tAveDo0b2eg4MbXJCnjGs4BIa3NXA8acQhEA4DXX03bUMp4DqS/SpqwtK6kvwqQU+pSxZFJGx/SYOUo67DS2bJ0cXYfN2UN5Z39PMOuFESIAxZxmjgtc8e8pLeaFjBzy12Bsk5PVaFRKma2sYmc9VVPdzVhunHNVSpe5tltneaqzf9caIxz1sk5t95BX7eA8Qy3VjGGm/ZOumO0GeOccsPTxIP+KumrMoZbSAuPp2hI1jFB+RPRffWJA16pQ87ZpmPIpZJP1VzNO/HHyYAj9uES/RwSxFbvCWKrxVZnILZabDUgtnoTiK0WW32Q2Ka8S04bhR0WyAfLKZZ4edmXSAHG5V0qDh9JtFCfafua/YGyzEvajp9HjU76XAwF8HNyLkMs/Ny+KQs9DY7njp0G3VNWO+8rvrdtMiGzjMwy0GejU/CcFxu9AHAtBM65lAsx1K9dQxxEH6Ph5zD3V6F0nyn75uRiBKNYryHSLpvgXe96F571rGfhcz7nc/CqV70Kf/qnfxq2vfvd78Y999wTjD0AvPSlL4XWGu9973uz/d26dQuPP/54NPVxBFd769jO10ODNjBhTGFOcIqQ0+8FPYgp001DBce+c/QYv8qSIitPY9bBaQQQsdnIudToUp+14k5rnDbere+z5Ghecn77jLP+60btphSXG95H99rlxha2J+cDxGnidO3S9v2j9guXdXeGMRVJCsAz3ILTrjzDDd4RZ9qrQYPV66tqeOYb11z17DbnpJNjbrrLUXLQ04kKl629Yx4Ybp7lZtxySyy3RHO1tborWAb4uYrSwnl6OHfcOZzz3qWKhyGr8rvFHeYlU8UXSTOvYt6dH8Nu3xBbvS+IrXbtxFaPjS1sF1sttnoEYqtPF/ux1YKtYw5NvEDVTIPowLRCoIRSIcx0uXa/0jxdHpJ5GWN5L4UxFvyYJjpvn55n7oeL/7+9bw227LjK+7r3uXPvWDOjh/UYCWTZMg+TYCtGBMWEECtWYSkpDLFDwHEKGxzxiB2IRYitVECGH7HBxFA8CkMVxqQgvCpgKlAU8QPbEAuDjVUUAauQSgZjJDnYkWTJHs09uzs/ulf36t7d+3Ee95xzZ32qrf3q3bv3Pmfuut+63/q61neea85tXfK21Kb7bksJ89qxIWikyfR0ocQ5KdLzpHpHkZ4/NGHoSzvVSuVCn4BUsFasPJF+66234r/9t/+Gd73rXfiBH/gBvPe978Vtt92G1v8UeOihh3DllVcm18xmM1x22WV46KGHin2+4Q1vwMUXXxyWa6+9dtXDXhny0s5tQOmXa2tN8ZfhUsl4t427Li9b5TBel5NeNw2aEXUidwACEY/kLxLDhKRn/qt8HfotlI2rTgl5NtlXgdDytvXycTf62j87Trr7li408n5L5eHJMxQTD433nNVJmTi1IcUj91ulicvc8fLEZaRsm/lS8abgudowlVv0WXVEfRbUbECjHClXykJr5rnq/Vaho7pNkedqIOjZBGYlhVtrQROXodXBb9XOGUFnpeKtddvWAq1xnqtzr3RrrcLck+/WeK9Vi5SoIxJ0C6+4sjHBxdeG/ds0yoD/R+D/Dk34d1qZzKwyyVmpL9d+hcm/SX2tVz8W1IVLL2sd5sohsXr7PjCJ1RKr8+MSqyVWS6z2vUusviBj9caxaMJtzYk6bufSNHGfTzJaWxZBKY+/iJVKfl1fMn3I6oUfXySx3ndtTU3Ox1l7F7W/eeS2LaWE+RgVez52rmQntbrvsbCuJdFrcabmi36QLBddBFxySUykczV6R5Fee2El8C91MqymO8PuNmHbxnOkMI67LLlsX/1YFyv/lL/hG74hbD/72c/Gc57zHDzzmc/Ee97zHrzgBS9YqM8777wTd9xxR9h/7LHHdjfoTyn7XrLcu9OfLyMv38sU72XQovHsxtgWTc/YjS8cb1g/rnzb+tLxWDJeeirtzymloH3ZLE1kRqXngELr+8rVdBoasIBR2vdloK2GVa58XKsGxlK5ON0vDRxUEk7bedk4lYLnpeP0XksEplReXiojn446yY/PUyfmjoSnKriSB23iZ1vwW60r3KJfroabIEn7MnHFJigjQh7Lyd1aedUbKdto0jKtrCfpTNmmveeqtoAyzm+V+shfkzFA4w9aIumRLScTl7UNzDx6rbZ+8jIqFScVG6nbor+qCmq3SLoBAxWIeK5wyyf4K30zhiYwc/3USXd+rujDPECcEzXdyAkQtxml0v1F+9klSKwegMRqidXhaeltLAqJ1RKrC20kVk+CxGqJ1Rc8KhYvlGzNVenzucs3Tk0wk71KyZIlHwr13TTd68Zau9RsXvhY+HX8WUvjWhZ5H0NK9KHc8OHhsJ98fp5yznlynSfKS4p1Wsc/wE5VovNj9EGQGp0n02k5ib29BqdPdycYrU40mhvMl7DsBKNj2+b+6rXr15UYX1W/W5W4H/Lcn9LPdmPlivQc119/PS6//HLcd999AICzZ8/iE5/4RNJmPp/jU5/6VNX/bX9/H2fOnEmW44cRv8yyX3iTSYd6fhGeUoJJP3TpF/iiOo4p2/JxxDaLlYwD9S8kTWTmyovTMmSayCz2Eckj4NVbfptUW1yxxZVurn1UeblrMgLbo3bjZLdUPl5XvZWWoTYRef/8/snxbOzh/ShdLBMnAj/kt1pSuGlozKCizypSAq68uo0UbjNfMk7LzJeIu1Jxv61j2bgrEzfQTRuIeiToftGV76Hl6javcJu3sOeBOHGZ9uR8hnY+g5nPYIzGfD5D287QGo3Dtgnl4vMwgRnc2hPv1hBRz0rGEVWxYW2tV8HF40Oeq65dO0jch0q211WuPaZUXLB5SKweC4nVgMRqidUSqyVWCzYBidU7jFUku/zPQg2TWLjUFOp02z41ett2c5q5EnpIiU7Xj7V46duujYVjURV6DaX+clV8nxJ9jKVLTY1eetd0/7zvvvebjt/AJSL5GpV1KaZots490U+AlOhKHQQl+unTSCYcJWuXjq1L25Y/1Bz8C8v/mpL7pQ8lwxfxRxcIRmDtifS//uu/xic/+UlcffXVAIDnPe95eOSRR/ChD30otHn3u98NYwxuuummdQ/ngkFOrKtqlBG/kLtC1diu5L1aIgbFYzaSkD6oUFKsgtItBx0b8l8NfUIX10mfmQcrtdEdUp31WSDo7nxK0un8OJ/VLgkvjiHrqzpRWSGpkLRlSQleJk7ndPYc/LpQMm5Tj1zadn1GhZuCytRtsSQ8kPfwBvzEZaENn8DMQCtWHs63OTGnUvGS16qxCKXiBrCtArzKzcw1rFEwLVtbBWOisq01pG6D33cT6VEpOPw2+a/m4GTdLWmpeGinUoLeR3qHSsUFEbkf7jLLLkNi9WYgsVpidW1cEqslVgsiJFY7SKw+ZhhK7g34qORJ8jyPWLOcLh0fUk3nw1i1tUueTO+zesn7XSSh3peMz+81Rok+ZOkyZixDfZfGnSf/y2r0vjUq+1yJzrf3QMl1UqHv76ee6AcH7vunYdIk+tQE+pRzY9scSxX4NqFd4bLdmPwNePzxx8NfwQHggQcewD333IPLLrsMl112Gb7v+74PL3nJS3D27Fncf//9+I//8T/i8z7v8/DCF74QAPBFX/RFuPXWW3H77bfjLW95Cw4PD/HqV78a3/AN37CCmcUtUCBxFxaoELoMaw2gUmJqrKmSz+RaVujtSL3unDdKA9bEEnP/mShrAQU0Iz4fjbLmTyunHOLnXdk4EXE3Qm01oIAWBlTSbFS8zpWAs30ARgGw7hlcWbkvoVcNO67jL+D+MfLS8XAsKx9XyXvzXWTvfEyJbR+pn0LKeXvyWuVt+LZGE1Ruud9qV9emmfZNoVGOmjc+yUKkfJZ5rTbah2imcisq3JQJ3qthadyivMJNKQPnv2rq+Q3j3zUn5nPnt2rnDczhzC1zt7Ztg/l85pa2wZzUbX7SstaT9bklf1XyXVVR8eaX1peLt96rkzRsPGHFJy/LPVeTx4AJilP3OBWPVa6KhUk8V02PmrUPC5eKb5HnKkDvf3lmvW2e2xKrtx0SqyVWS6yWWJ1CYnUdEqs3EasFWwlSpc9mmM10YndCyXNuqUIK9dxOpYTcWqVvCNzahdBn8ZJfV7J2ya1bSlYvQPkPAcuq00s5Xp5AH5Po7rN0qanRS23oeN6eJ+WpXX4+qsxraxTW9DM8//BzS5cDv74IwAFOn3YTi156qUukc5/0iy6CS6LnavSSP00NuRc6gb7khGWT2pIUXyHE2qWKD37wg3juc5+L5z73uQCAO+64A8997nPxvd/7vWiaBn/yJ3+CF73oRfiCL/gCvPKVr8SNN96I3/u938P+/n7o4xd+4RfwrGc9Cy94wQvwT//pP8VXfMVX4Kd/+qdX91SChTFUMp7/8s8nMUuPL14yTsiVbgTHt2LJeAnkAcr3+8DLxsP9K9vFsaJLhvNt166reuNtp09eVi5Nr92fXwOk6j2ucOPEPH8Pud8qP06flGJXaTZhmfYKN9fGHwP5q7I2bAIzp4CDV7V5lZuOfqvce5WWXhSVbp4zGhUnLjPKK9uaoG7rW5yqzXmu5go3yxYgKqJysSdNhgV0yV7t3xo/566rWzkMYaw6tovlSPS6StUvZEisPt6QWC2xWmK1xOrafh0Sq7cNEqsFgxihSqdkOe1zlTrlIafmCms2In0YsnjhGKtuL6nB+xTqU1Hra2wSvXR8zESp/B5jVPV5n/n16TP0+aDncaDv5zqfbJRU6VGRfnCQTiw6m8X13h7SJHrfXx3GYNVK800nzzd9f8HSmPwJPv/5z+8t9f2d3/mdwT4uu+wy/Pf//t+n3vr4oDqJWb9CrdMNzCBp7L2eqbGMNWFyMuqX958q3Nw4jfIThNE+DBQMeFmxDWe9skwpGGuhlYK1FkaVn6CkdNNOnueJjUUsECd1mwKUKy527XVQwAEAae9KSrcwaZnSgKXzrXt+5ZIWNKEZgF61W+gne8e07y5LCfoUctJH7ovbY9RtzGs1bRs9V2t+qzM7A5XsayjM0IDKxRt4z1Xvm9uQwk0DM+19V7VXujGFW6J40yao25zazU1e1jQttDZQfsIypSygTFouzr9ceU2vMU7hNrewcwDnFezhDOb8LKrcDmdo507h1s6dum0+n2Fu3PZh67xWD733autVbVRCnivcSpOXueM2kHggfvdrnqsWXYuGGhnnBLvaZuD7t0sTkk0Fn0Ru2X62CRKrVwCJ1RKrJVZLrJZYvRWQWF3HBR+rx6Akn17VtbXzpePLjAPoSMFnsxMAXNKybWNujvukc8U3V37v7dUTyKQCL6nLS4/D13R93+Sj+auoKea5Mp1QUqjnqFna9F3Dn5+jz5KmLzecW7qU1Oj8ON/uU6Pz68tq9dwao2abkf9Ebdg1DVuTN/pJtlyEvT3nj54vpEY/dQrA354rJ9Nr4F/U3Au91n6MFcyqJypd1n5mldi6hLzBiqL1CvpYL7btzW8pppJmi1SXtX0oEfsa2TdoUfPcLMEoC20XK90n8k5QSrm+lELLjpfJuytVN0onJET7xAFAxJSes03Wrs8YPV3ZeOvH0QSCXiI4SulA0IGU7HNik++H4xPeb+netf0+Yh7bd5VsAJLPvFQmnvutctUhqRO1yhRtwXcV4XjwV/Ul/amvaqpw40o3p2zzZNxvO5WbgfNfpcGzl2MMoDWi36p1THnuFGrWKMAqwPrJy9oG8BOQGdNERZv1XquI245oqxA+jO+ayLgFI+mICreofEvVbZx8c89VDqPM6MROrlCVicUcLpxwfyFAYrXEaonVEqslVh9HSKwWCPrBLVyA/klGh/L5i1i8jMnnDbXLz5fajxnbImr1WgK9NMahe5fsWvi1uWKft8vHXmpTO++qFfmDDPmil8CT6KRC5/7obsJRUqGTTzop0ff3mfNK/hcC/vCLoGb1kqMv8T4VW5eo3nZcONYu8s04JsjVVOuAI+kA0MDAeDJLpM8ACtA2JjLIL9LR3kjULcrpDiJyQQmH9BdeR+Ki0i0o1nzfpGjL/VfpXkQkLSnQPPkmEhrUeN6D1Y2FlEkmIei52s2iTYg3J+m5ym0Rv1WOagl4gZTz46PUbXBl6aRsa7AXjudl4rnfKqkOSeNGlH/mJy5rfKk4bTvFm/UerFHhNuPeq8p2FG5N0zqlW2PC2vmuOqJOk5glCF8Y/+2btwgmqAawrYY93zCVWxMUbq1XuLWtDgq3llRtRuPQNFHd5hVutB1UbFCDCjeA/s24ddxnyjYV1W19JeJhPfDdKnmu9qGcCBBaKhCMhcRqidUSqyVWS6wWCI4Z1ql4z8Gy1+STntu6cDsXLu6t+ZqX7FPGKMvzHGOfKp3ff0iZTtvUJ39VNLb8GaaiNrlqX6J6SIneZ+mSt839zmvqdep3SKEerx+avLFvQkdSpefe6Cfh/NFPAjiFg4O9oEAnf3TySD992m3rc59JPdLpIUovGSirx3Mv9FL7oWMCwZqwXjYn2CjoF/M+FUx1EqIlfuHOy1jdMSodn15Tuaj/KpFJvg9Ekson5Orek13H2pA3aac99Zn5o+Z9l4j1kLfq2POlew6p29Jn0Kx9+vy68qOC3j4AUIk4fVbuHunEZTpTuLk2XsnGjiumatP+vFLW6coSlZtbQ5Oq0ATfVZUrw/ikZZRVMXCl4nPl1GwmqtusVUHh5lRuOircjA4l4dYitPFd+mN0nKnbstJkUrRZ253+iv9byf9Nlf6NxXNtsl4Ux7lEnMNau7JFIFgEEqslVkusZpBYPQkSqyVWC0ZiE0m2dd0zS0Tm8y7mt+Ui3kWG1Od5Xksq537pHKXr+9oMtW3buPRhqN2YJPoQapYuvI+a/3rfJKMcPCdN+w48HpRU6GMV6VyNrv1yAk6NrkN+m5TotDRNVKZXsvyLY4zVC0efrcsqJyoVZDDo/2PO2GX7f7eRRPqWo0OsR/3CXCHcVSLesm1WompbR6etSX7Rp+MAwmRKOZk3MIGM14iEydYcQfFWOofo4+n0c1xnpaCtwsw2RSUWEXbyDuUEPfiLokE+oVlO0FW4VgfCyycS44SZ2vLS7VWQ8VH38GOicYZ2qpugIHUbLQ32kjLxxu5hBuexOrOzxG+1ge74rc6UCgq3mVe4BXWbdgq3RpPizSna9rTzWd3LFG57usWeV7c5pZuBblpo3UI3rSPpfg3tfFgBpPbGxiJ4rRoAhzYQczv3Crcn96Lf6uEM7XzmlG3zmfNc9UtrNVqrMW/9mqvcWPn43CvcWlK4gcR1NiHrpGxzirfotWphQ6k4V7sRLNqoerPlX44sTHLOsn+/STtb/rdc+rkxlriPSfJtevIys8JFcOFCYnXhnMRqidUSq/22xOplIbFasFFsOtlWU/LO59D+W82tXEiVzvdLE5IOuWQMJaaHFNqlyUeHkvFjrEz6crM8WZ4vtWfou29pO19owtI+S5eaupyfz1XqpfYlNXrbclsXSkLyZCTth7dUeBP5xKKUOCdf9KhIv+QS4PLLu/7ol17q/NGDGn0+d2t6OflLIuRJ79zCZcxEo0N+6It4n69rAtNN/zxZK1aRRF+VPUwXH/3oR/HKV74Sz3jGM3Dy5Ek885nPxF133YXz589P7us4f4orhsFx+bvDUGm5tW1VzcVhYNCE0nHnbUpl5NFfq3Ktn8SsD9yDNfdf1VBFxZyGgkX0WQVi2XjLxlm9Jxo4RV5sQ16rzoe1WzruzulQeqvQBMLR8V1F17d1qHx8yAagpqSrT3SWEvO+/ooKQHas5rfq7kOeq8qp2NjilGxR6UbHqQ93LCrcXD9c4WagmYotKNryMnHA/dMlc9dQ3x9ZsbXKlYsblSymdeXgptVe4aajzyop3zwJj16s/pZe4QZ2y3yCK65wK4F/v0MybIMUcCUkepJyTuiuYBFIrM4hsVpitcRqidXTOpFYLRCsDctOOEpg3io8/8itUUrq9EVvP8XihSO3eak8QnWb9vlzDh0fgylK+L7jpXa1PxLk1i+1fvv+2FAfE/9ZXEua5wl1AjfpIyV6admDUntBdc7V6MkfZ2p/nShhyofXZ/UiEIzARz7yERhj8FM/9VP4vM/7PPzpn/4pbr/9djzxxBP4oR/6oUl9yTdxx1GbdCyc90Tcoq2SNddP66wp+0i7pwajyLeygDVofGtHNBSUtYACGk/LrLWBxJUIu1YKYL6U9GPegsh7PDbziQITNG02jtcCRlFZs/b7zLuV+asSQddwbfzDjyLo7jlMeNfkxxreoS1/XkkyYCIZz6/hn7NmKjjXLqr1UuVeqvIj4q5RmLQMGo1Xu5Eujvutaq9w0yBvVTDf1ei5Suf2tGUqN8PWbrvRBjNSuDUGWvvFe626dQs1M2ESM/jFGq90s/4DJyI8d2Xi9lAHhZudO69VM3fqNlq3rVe4zRsctg3mpsHcaBz6kvHW6KLn6tzbutJiAbTGKdyohJw8iI11Cjf6j3urhm3FFKgw6b5NS8RpnSrijFcrpFilymwXS8ytjarCZfsRCGqQWC2xOr9GYrXEaonV4yGxWnCsMSa7zTPX2TWz2QnM5y7P2LZpYp0nOEkMnN+u5pMOxONDful9jzbGM50esTS+Pr/0/PhUDCWo+2xsKDFO76hPvZ63y9uUlOclq5e8XbR5KanR+XoohtDvJ+SNvufXBwAuCgupz0mRfvHF0R/9oovcNh45F/3Rz52LD95X4sC/tPxYX+J86HzfffLtRa7fNLZpLAGrUpOvR5F+66234tZbbw37119/Pe6991785E/+pCTStwUWFqlL6DJ99RPr0G7EJGa8zRCxHzs2eOWYI7Mm8Tolpc4Cc4AkcH0rWE/gDftFmOhnm/2Cra0ClEbrCYoO46SEAZFoT+cZQY99N4HoDKnd3PuoK96AruotHF/wc8g/7yFiHs/F7ZyY0/lEzcb/szwRoJI1eeMqFUv6FZu4LCjZSO3m1WkabJt5riploXN1m45+q1QePgr0nTFebGXgCLv3WzVtA1hXOm5a57FqjYIxCtbqoGyzBZUbKdzcdrxVja9FNZwtH0edMHKlW98kZkOYOnnZVCzj3TyM1fVNCZJV9CPYPUisdpBYLbFaYnUXEquXhcRqgeCokCfPS8r0XLFOKPmYrwtDyvSxWEaNzq9f9njpfKltn898zfGk1C9PrAP5dXkSM+90ir2LU6DziUeVahIF+sEBcPJk9EcPgvExHj1A/cPLvyAlq5etTCILVmei5vp47LHHkqP7+/vY399fQf8Rjz76KC677LLJ1x3Db6BTU209gvwmx4iy9Oq14/sZUzIOT1a1gn+tkdAZ23qipQuE3FMWpdEQSWGKM9en6i0Zp1sC9bJx6z9qp3SjVINTt4XvgDUwSgelG1R8MzRmS+o0RLVbXuBUU7uFwfp3FhVk9J5MQphz1Vt838M/cGqfV564qZHyKeq2oGaze/4deIWbV7vNLGnbVPBbbRD9VknhRiR8plJ1G6naGoXgsTpTFjOvcmv89p5uCwq3Flo7ZZtuWujGQM8MlDKAsm7hMOm2NQDOe6LN1G32cIb2yRNo5w3MfIb5vEHbztB6hdvceM9VozA3GnOrMa8o3Aycso08V1vrFG5zT9xzhZtFLB9PyLcizVtKvI0yIelTI8H8O1VtQ31knqt938eqf/PQd3gHFXCCdUJi9Zh+JFZLrKY+JVZLrJZYLRAcE6zC3oXJwfVshtlMdzzQT54EPvvZrip96PYl5TdXldcep29dU7bnr6OkRs/Huio1el8fffYqJSU6rUuqdK4sz9uVPM9rx0uq9fkcmT/60OSN+USOTbZNCfQTSBXpB7j8cqdAzz3ST51y/uinTiH6o9OSv9y+D2zqhKI5cp/0Id/00rXLtjmKPi4gXHvttcn+XXfdhde//vUr6/++++7Dj/3Yj01WowPHMpEuWAbGGjQVEmjQolYqztVjHLZKC1gbT7aHwAkzQFaa0X9Ve8VR7snq1G1ulNrqUGZLqrdcieb61v5+rW9bU7vFcnFSvCVjZiXk7tpYRp7cbzDZ0kWNlNMYXb9dYs7vqQufJYDks8wngEvVb12/VYDUbnGCOTrFvVfdNTYcJ2tUUriR52pUvJmgfAsTlHHvVa9+K8JnZEjdZq0C5t5jtdWwc++7ap3izfmuNmjJe5Wp2tqgeAM77m0MLC0qkPDSUIBU4WYCMY/r3GN1Uc/V2sRmYzCcNNp9wk2Txq2iH4HgqCCxmvqWWM23JVZLrK70vnDf2wKJ1YKtRy07vSpP9BIyKTdPXA/Nm7jIsCiZXhvG0JqDK9Nze5c8Ub5Kb/RSP0PH+o5P7atPuV62bKkLuuMfNQy6avNFYkquSKdkutumP87k3ujcIx3nsr8m0IP0lT30TRA6xuplHcnpdU00euyxWmuXj33sYzhz5kw4WlOjv+51r8MP/MAP9Pb453/+53jWs54V9j/+8Y/j1ltvxdd93dfh9ttvnzxC+cQvcFgYwKbkcKiMnJP0OGEZEV0DKEBbohKNv4YmDyMqS5OQRdKd35EJy6IqDq5snJRupJQjpRvgJjCLxeANYFtAaX88kvVwP4uOByuQqt2CHysfVNa2M2j2PjlxzpVvObqJgnrbEiEH0tJwPlFZSd0GAA3cbzKN2gv3JEUbLxOPvqtNoOwNNBrlFW9KoVEqKNwaXVO4AY32KjfyXWUKN+e5atwxbaCVU7g1XtXWkO+qts5ztWkRJi/TNn64hn0UBsAc7sOeq+C3as7PnMqtbdAeztDOG8znft267bnxnquerLdW49AyhVvwWnXf6XmucLNe4QbrFW7Wk3mnbTPeW9giJYq55yr3YXXflahOM2jHqdRgJpeKr9KbdVy/R0f6yet2Ff0IBOuCxGpIrIbEaonV/ZBYPa4fwQWMZZLa60yIj+2/lpnOVOm5N/rBgRMH83N0S46SvUgtaT00+ehYZTqQ9lO6Z0khnx9fFjXhdG7BApSV6LTOk94ln3OeLM+vyxXsfcfaNlejm2xdWgiN32/YPlejH7DlIhwc7AU1er6cOuX80RM1Oh/kFOS2Ln0o+Z33qdCX9UffJmzt+FebSD9z5kySSK/hu77ru/CKV7yit831118ftv/mb/4GN998M778y78cP/3TP73QCLf1E7gwsEzJOO+Gkekx3qurgrFtVREX2lTUb1NApeO53ypBw0mk2sSLlSYw8yoqq2CUV6dZ7dqHhAGp31IyHvtqUoI+4ocDV7xROiL0lynfus877FKbTzRXIua5si0n5qEv5rXq9qO3al4m7s6Rsi0q2Vz/qd8qV7gFn1VEhZtTtUVPVVK9cYWbhk2Oaea3StelL4a9U6MiYTfxmPXequSxaq0GrIJptfNbbRsYo4PHKl8Mea1ydVs4776n1ipPuLufGx0zbFi5wg3wJF2l5eM1vWiuYhvz/RQIBBMgsXoUJFZ3IbFaYnXsU2K1QCBYAQYk27m9S547JHuXRZPQXJW+aDI974c/Cn+GUhK/pFBfBmOdR/Iket4+T6DnifLSutRf3/hyobcDjznLKNEBF/Ubv45KdGAPJ086Nfr+flSjN03cPnkSaZafv4D8RXCMmWQ0mK8XMPRl2NqEs2CVuOKKK3DFFVeMavvxj38cN998M2688Ub87M/+LLRejP/IN2uNWOUkZkuNwxP2vonQrHXqtFjOTGXUJvFeNQpoGBGkolalnJxI+/2ofiOVm3OZ9NNbhVEYr1YboqOcoMPartINiinoInl0YySiGb1fnSBuliYPCmo3IlG8VN0p7cILcMdytRsNGmk7/t5ygj0VuRIxJ+VuXPEYJW24uq3ktapAnqrdMnFt3ZGZPzbzBL3kt8oVbsofn5HCzfuspttc4eZUb5op3MhztWla573atIB2nqvQsYQcgPuwGjBS7t97qxxBb7WfqMwp3dpDp3QzXt1GSrf5fIZ56xVuhrxXtfddVWiNxqFVQdVmaJup28hz1cLCWpdEciTeOnWbjSorp2HjJD2q28Kjec/VofJx9283/jJlbVsl+PxeYzyAk5e646BS/1X0I9hNSKwGJFan7fh7k1gtsVpi9eYhsVqwEzhKexeeZfbbs9kJAFHUyycZzScdzXOVh4fu+NCkl6QqL1m8ULupyXSuTM/v1befoy9nOtWapZuwHlai59v8XdbOl5TqpeMlNbrrJ1ejl47VkutcjU7J8xMAToJ7o58+3QT1+aWXpr7oF13ktg8OADxSUKOXXkYNY9Xoq55sdMgHaer1FzRWq0hfNT7+8Y/j+c9/Pq677jr80A/9EP7v//2/4dzZs2cn9SWf+gWIsUo4a9uk7DgcD8xnGEZZwJqE0ANEyoGGMdhp2r4uYtl4VAwRabewTp2lNNrs12hSunWPEUFPvVe5J6tS2r1PRC9W93zRjxWIxJkr3wjDzrQRpTL+vDQ8vo8FiTmbjC4vE9de20Zrdx8VPFfDGmWFmyKFm6LFekLvyLhSFo0m1VtB3UbKNq9eI59VpXreoUHMphjvqzpvYNsm+q22nqhbhbZt0HqlmzEaFsptk8INTt1GaraogANTuhWGYVmuwHY/dV5uzMvCDXu2GiFfRtm28IRkxwQmK9Ffph+BYJWQWC2xmm9LrJZYPeX4cYPEasFWYJUJ8TVaxcxmMQHMVeh9Q9jbS68ZGhpPgufWLDlKyXS6T0nhnl/H9+m62n2mYqwivS+JXro2t22pnedtSMSdW8Rwv3TeT5zvhScvp8aEkrVL6pN+cOCU6JQw58vJk+7zODEz3ez/EGpq9DC0Jv1CLJOwlmT3EYH/QWfZflaPd7zjHbjvvvtw33334XM/93OTc3biHCrH9BtlkUqMVoVl6ePi6CjUqqXmHP3jXaa0PJ/MzKKFUa4E2qnAnOotqNkCWSZqn34+1loo5qNawhilGxT9I1AAGkfKg7pNA9bAKOcVCjsHVPaWvNLNos0IOvz4kRB0oKt4S0k6e0aYhEiXiPoY5AmT/Pq+8nBySg3bBWLuWrgfDXmZeBNouXJXKNK/IfitNkoFhZtW0W+Vq9pomWX7pHLb063zWc3UbUpb57eq4hpEXlU6gZm1KpyyrVc8zhuncPPEvOu36hRuxmjM28ap2NoGrXXKttYov+josxrUbM571Vg4BRxTuDkyD++56vMFpHbz309SuOUkvbZNKrbce5W2w3uA6Uys546PC3K83VjCPibhtC4vV8EuQWK1g8RqidUSqwGJ1RKrBYIjwLq9zpfBImNj7blPeskb/eAgXkKJ4am3rCnKaxYv/B65Ip22S77pQOyT0KdIH5Mn7XvO/FzJyqWWRC8lvrkYO1/XfNPpeN5nyS/d9ZUrzkt+6SVlOv99hOxcnIVL6o9+Efb2DoL6nPuiX3yxS6zTErzRuSKdP8AQxibM+7zP+/zRh/oYwioS8ZLM3zhe8YpXDHqpj4V8mhcY+krG6bwrDx9HGI1tfal0ub2BSyI0nXJp6+/TT8hzcIJuPKGHv558V1WiL4tl4y0soh+rLxP3PL5G0F1/OqjdEoUbKdtI/ZYp3gBHlE1GwnPlW/JeMhJVahPfxTApd88/jZhT2qFUJh7u5e9CWrea36oCU7lla1K3aRVVbTNlgudqmLxMRwWcW9zEZdAGSvtJzHT2BQvvM1O3tRow2qnb2sar25zfKoyKSjejndrN+gnLjCsFb62bvMyRa6dua736zdpIwi28qs0PK1e4EUqeqwYGRtkOwY7b/b+MbEKdNnjPVY5pxc/nFIcrULlN/Cu2QNAHidUSq+MzSayWWL0aSKyWWC04JihlpTOUvNGffDLuN0030V1Trtduz1FLpg8Nu5SYz/vk1+fPlp+biloCncaTt+uzdaHrx1xXurY0ppJyPU4yCqRJ9Kmg3xe4Ap2S6W45OKgr0Wlbw3QT6HzgJQwllfs808f2IYnrDeDCMWKTb9cxwdAkZvkxvs+v7fRrW0debd17laM2YRlX8mhP37r3Yoo1FR1rc0/TQNBD33RvBKVbLBtHoNPBj9WXjQdC3kPQNe87I+h0HOy6PsWbe4a2+K77CDuhdJ0ulIrzz7lUHk7X1Yi5gsbMzkKfvEx8hiYQ8Ea5tMdMOc9Vp2RDR+GmEJVuiZpNc7Wb39fG+a4SMVdO4UaqNue1aqG94g2F0vH4QdiwbY0j36FMvG2cz2pLSjeNdj4LfqttOwtKN2N0qnKz6WLAVW4IyjfrRzC3MRlF/w6stcn31q3LJDGUjcMkpeNjUFKbGWsCwSdSPd1zddo9p+FoA6fNlIXL9CMQDEFitcRqidUSq3NIrB6GxGrBscQy6viS78l8HlTpQNcbnS+UNOci3vm83ye9dPtcTT5FmZ6/grw/oOufTvfmGJsv7XvVYxPo+TG+XbNrKanO82tLyvOS4j2dZLSkOM+XGkiJXkqg7wE4wN7eXvBD50p02g5q9Mcf76rRSy+FkNu38OP5JKMl5H8lKq3HYow/+rYl5LdtPAm22yN9ldjmT0GQYUihVsZ6SsZp4jIi6XHCMu1psAEUoC1Ri3TcblSOjDhv1OFCfF627faj0s16Qk9KE+2PASpTt1nEsnEitAbaahhlEuJdU7sBVPbLFG4FxRv1Qe/LjSsl6oQpJeO6p1y8pGyj/Roxd+d1fMZM5aZtLA/P11opKEXT0pGyza0bRtCdms19xjPvtaoBaE/OSeVGfqzcc5VKxhVTxLlnZeq2jJjHbxUcMW+dvyqscufbJvFbNcaVhhvjyXjr1G1B2ca2jeXl4vAl4sor3yIpz1VuRNAB+lutDQqrpERcuX9dnIznfqu8RJyvOUymmJxCmksk/ULxYRUIloXEaonVeR/5tRKrIbGa+pFYLRAcLyw74egiXit0XQaeLO+bdHTMcEu3mapMH1KkD6nT+XOVXsFUlHK8pSR66V55vphf32fpUrqW27UMJd3T++VqdJPt5yj9blpKpp8EcBKnTsXJREmVfvo0gmf6/r7/vM71eNSMQd8ko6Wkeb49hEWvm9r3KtoJdgbyiU7GGBoZYWER9VqlBqbinzriPqO8V8fBwgA2kjtjjZ90qr9/5/3oFE99IHWbU/F0JzSL7fqfmgh6X9m4K9u1iGXjXt3m16R8054QWZ9Q0CGx0CXoXNVGyrU+gu7adkk6f285yZ6C/HPJSTm1ycvD4/F0srJQFg4Nbbnvqjs6C1RdeYKtvXrNl42r7kLqNh1IOk1OlqrdiKBzhZsOCjdPyr2yzSnd3BJKxZnyyxoF5b0JSNkGwKnbjII5P/MKN0fKU7/VGebzJvitzk2DuWmYsk13FG584jK+DiTdk3KbHLMhiUSkvOS5yr8rcbuNqjdPvslr1aAdJNBmZQSbjWmHS8WBurpwkX4E2wSJ1fl1EqslVkusllhdhcRqgWD34TOss9kJv06T53linexeuBD48LBflV4Swo9RptN4asl0fp62+RhqSXWgPw/L0ec2Ukug91m60Jr7qdeS3rTOE+21vHOuUOfHnK1Lzf88/+BqP4/5JKNcje780ff29nDqlEucX3KJS6hzFfrp087e5SkHBngkU6PTwKeq0fnxMWr0PkjiekMQRbpAkICmHauVltMUYTRxmfYUIqrfcpWbU6Ap65i2sgDUOA1fh6AjknoD7/XZKRv36jZPghQUtFUwykYyHsrGvSINkVhTGTjdgxN0Og7EUFUj6URk8nfYp0CqJUhydWJJ2Ub7NWKeq9u6E5ZFFVziqQqXGHFl4yqdvMwr22aal4jz0vFYKq6yycu0n8DMlYp7Uu4nMCOFm/KEvQqr2AehnJqNlG3Ba7WBnTvP1aB0I2WbaWCC36oueK9GRRufuKy1gIFKFG7ULpSEW5tsu8+eKdwCTXcKt2TSspGl4jSxWdhnk5xNxbomGdumycuEnAuOEyRW+3tDYrXEaonVy0JitUBQwLZOVsoy1NzeJU+WU2KdkDti7O3F/GftUWvJdI48mV4ZanLvmjodqCfV6V6LoJS87tuuramv/Jqaujw/X7qmdKw7ySiQqtH5A9USmjyBzpPozs6FEupcjZ4r0blnetG/poZScnvMJKM1j/RlMcbWRTARkkgX7DgWLgO3rsybaLIj455k2hZaNbC+1Don3QYmkLkcRlnAmqD46yPhRLQ9Vy6CE3TtFW5GKWjm3QqkZeMldRspBYO6LhB0IticoEfSbtg43Lvx++wZgDpJJ9TIegmlzzNXtfFjeXk4jT1Xt9GaiDmfsEwXysRnSkN7Qq6VCmXgfO3Kx5nqDa40fKas82RFLBVvwqRlOTH3pFzTxGV+8jIi5soCNcLqJy6zRgHGT1bmJy0zc82IuZu8rJ03San4oVe6tcaVhs/9xGVdz9V04rJc4ZYo4EBk3dFx7kVcU7gBkbAnxzKF2zJYpefq8tiGMQgERweJ1RKrJVZLrB4DidUCwQqx7mT4FHuXVfuks/3c/7ykSueTjhLGTDpaS4JzS5YxyvS8r9Jxfh+gO7Y8sV5Dn8q+tj+URC9NLtpn6ZK3oXGVEu/5ONJJRoGyCp1U6jka1o6S6BolRTpXo5M/OleiUxL9xMwAj5/rTjTKH7D20vvU6KXjfehrt87k+CYS75Ls3xrIJ7HVmFqaXp+IDIiEnfxbxxJ4a9vqxFoGbULSLVoY5cpSNRBUb/nVREw0vBKNJi4DoNgkZn3gPqycoPOycTdG5Qk5EXTFjk8l6P5+4fm54q1UMs7GW3h3iyRQdPY2+0h5er48WRmAlJiHc4odKZeJO+Va12+1q3BjSjemapspKh+nUnFP0rUJpFzr1peHW0B7r1VfKs5hTTQICMfmjVO6cWJOirb5zJeIz2CMKxmfz2ehXLytEXKrnOIN8Mo2UrJ5pRtTuIUJyxBLxN1nb5O12zYwyl1BZDxXp5ECjp9z7bu/pFiY7Jet7HyFjA+T9ONFoGUCM8HykFjdB4nVDhKr/ccjsTp9HxKrR0FiteCCwiI+6VlCnVTp83ndG32Vk47y7b5kOm9XS8aXEui1pDowzY6bj3vMfi2pzu1c6FhNUd7Xbow3ejexntu68GN94Gp0SqKnli7AQccbvbYkE4zSekx5AH05avYtQyr12sSiQ0nmRZLQq0hcX1DJb1GkHwP0aaS2DCv0T13o9hWS7rxXx43LKeCAWjLBwD2jtjndmAYFlfwSTASdjpA6zt3Tl5Mr5Um8m9Yq+qxG5VsfQU+eigi/cokIUrzlBB10jCvc2Lvh5xZ7D7GvotItKw932ykxBz07iLjHMnFOzGtl4lqR6s2R9RkrCc8VbjQpWV4erpTFzJNyTswVHClvfGk4kfQweZnuIUKGfQetgiWVW5iwTGfl4XHCMiLlJisVt1ZhbtNycadeY6XipG7zi7HZAsBam3w/3bpcrpyXig+VWFsbiXtvuwUC01gF3HL/ujcDu6JycSHni0Ji9ejbS6yWWC2xWmL1QN+j2kmsFgiWwzbZu/Rkn/u80fsmHZ3Nptml1JLpQNcznfqv2biURPbskZJj2eMOjnHM8bH2LkA5Oc7b15LihNzuJR9H2S2Fkue1BHp+PkeeROdq9BPY22sSNXpp2d/3ti6kRueK9PwljcWYpHifJ9HQtYu0ESwAqutbRT/bDfkGHQEGJzGb1FeLxJ10FLHvquU6/Qzcx9o2+ph6w1OjUJyIzE2yRITQlZ9rS1SDyCpNGEa6JIuGyLIv+Q6jp3t60LvkvwwTaaTJzKxXujnlnGOM1pKyiJxWDXs7dYJefIOZ4i0vH6dxE/i1OVFfBH2EnI5xUg6go27rlIhDY2bJZzUtE2+Ub8XKxBsFzJQn3n5dV7jZ0H5Gk5Vp05m4LKrdTCTm2nmtksItlI6T4i18JirOb0Xl4nPvseoVbqbVMH7SMuNVbi1NWtbOot+qpVJxhbnVODRdtduck3KLsN1aoDVePWVJReW+f6483Hr/1VRdZRTp20zisZqXio9BiSTzycuIbPeVio8h8YuUmG+T56pAwCGxWmK1xGqJ1RKrx/crEOw0tikZvmqE7POJogo9X/JE+3yeioLHvKZaMpzU6ZRM5+fzxHq+Tfv5GEqe61NQS1z3bec2LXkSnbfnSfS8D77UPNNzqxhKuJcnGQXbLv3cpt9+GrafJ9HJH/0gTCxKli5k63LxxTGRfvKkt3XJLV24z01pBlWg68NTs3WptenzUe/DIucl2S7oweakVYKNIP9lmu8volDJS1WHiAQ5TJb7ytracjsCkXROiEmBpZgSC0Agmny76ysa/UY5eY33i4QXQDin0CSkt0aWeVk3X8agdE3pXrzAO4zN6iIxd/1y31UV3mteJq5J4eYXhVgerhWRcQTPVa5wa7QrCydVG++n8eXiWllo2EDItV83jXH+qyqScuie75hVUdlmnJoNRoUy8TB5WVC7abRt01W6GZWUizuFGpWLw5Nud8z93TVOXEbgE5jFY9b7rqal46V/E7xsPDnmJyQzKE9MZvpKxI+aGG+Fl2s/jDIrW6bgfe97H776q78a11xzDZRSePvb376eBxTsJCRWS6yWWC2x+sggsboKidWClWNswm7RJB7L6mqYQRU6n8cx3586nFoymudW8+Ryabtvnx8rnauNq9a+ZsXCt8ck0Wv3qLXliv8+q5d0LDx5Doy30dBsabIlKtL39powsSifUJSS57R/cICupcvUv2bU7FtqxxfxS5dE+AbQrnDZbsi3ayF0VWPbAu69mpaB94+Zt+XbrmR8eIItRxqAhq7zfqva0wsTdG+kciNK42metW7iNAtARSI4VPRP5ePcgxX+WprQLCrQfCGzNeATmi2idtP0ZKx8nO6L0KN7H5xAmUK+YeibVHv3ua8qEMl2bZKymrqNkhKNP9qg8Wvly8KRKdximXgg4oykl/xWG22xF1RtbrKymTLY062bxExHdVvTtJ6gGyhtgu+q0nGdwDgbgGTfOL9VWOa16hVuLXmszmcwRoP8Vg/bBnPT4NA0mAeS3l3m3HvVICkXbw0wt1ROTqXj5L8avx/ksBqdVl25t1EpKadS8Vriy6DtVZu5f5sjy71LarcjKxUfuH5NRN/A+tTK8v1MwRNPPIEbbrgB3/zN34wXv/jFS99fUILE6tJ9JVZLrJZYLbF6cUisFghG4SgV71zi3ZF0l1XpuQr94MDlRHPHDGrTZ4FSs2Lh2zXf9LztmP3aPcdgETU6jRnoqshp3Wfbkp+vnasdT9XoQDfROCb5SIKAE377wG+fhJtg9CAo0PMl90s/gfNdSxcusa/5AuXe6GMmGV1HMvwofc8vuGS+eKQLLlAMTYKWtLUGyCY2s55CNBX1llEWsKZYZr4ocoJOZeJAVLtR2bjxpft8QjMA2bkKQXc388SKTWzmzxtlEEqz6XkRC+KBVPlGaqQppeM6e99jiHk83k/Mi+o25Y8lXquxPDwo2ZjKrea36q53ajft1W1O9RaVbootYQIz8lollVsNJqZxyGMVVocycSLoMApt28BaFf1VjYaF27dWuXNWBd/V1mjvu4pAsknBZqA6/qoAW1OZuP9OkqKN6Hj1cQoqtz6QAi7s96jdFke35FywGG677Tbcdtttmx6GYEchsVpitcRqidV1SKxeFSRWC3YdFCF58hzoJtaBsrMGn3S09rcBbuVSOkbbpWQ6naf+S/3l5/kxjqmWL31/HMhV6EB/Ep1fl3uf52p03jZPsufXpJOMAt0Eev4zns43fpt/qKRC12ztFOlcbc6V6LkafTYrDLz0cGMxRo1eu2ZMm6lj2GbsyjgvEBzzT2NII7UL6KrTFvNenY4h71UFDYM26MS4ig3wZZjWeMWZU4Q1cASdiArRVkegI7EOKjWQSigtC89RIugIZMjtz5kHq7EIBB1oYGExA2DCeAoEnTpTYKQ+QlsdyFSueot6v4hcATeEnMTz5AD3VqVzOSmn833qtkjS3TsllVvjPws3QZnyZFwFQt4ki8UerXX0WyX/1ZmymGmvbvMTl+3pFrPGqd2ahiYu8+o2mryMFG4ZQbdWA8Yk+6RuM4euPNwcztx6PnNqt7nzWZ23DYxxyjbyWz1snc/qoWmKCrfWK9xo4rKWSsfBvVdtXANh4jL6fplA0L3CTXGtW3cisqCAy0rFzcBfaxdVnq2krHxHyLstvO9F+xEsConVy0BitcRqidUSqxeGxGqBYDGMUZzX2tQk1sso2Etq9ND1iXCLpnEJ0fncJUhJcX5wEIfB14eH7pqxSek82T00CSlQTqiXFO99qvTS+THnasn0PIHOz5UsXWg7V5XzNn2q89Lx1BvdoOyP3oc8iQ64xHmqSOdq9Kc+NVWj517pp04BeIRNMkqq9JJnTzKUBdTo+bF1+aNPhSS0eyCKdMExQVoyvvh1U9Rvrn15gjSauMyO/FXW+InISiCyTiXYRNDddiT6tQnNWkoCIHq4AqR46xL0oFSzOikfJ8Wb4WurE9WbRZuQaSLqi0xkViLlYVyYTsxJ3UbvMPjQKv5OSOGWlod3fVatV8A50k0KNxWUbaR4s17xFj1Vc5Ubn6isA0/QrVFQ2jqCHl6uciXihvxXvWqNfFaNhjEaxh9vW6ds436rTukGGO+56gi2Yso2BP9V0DG/Td/B1FuV2vFj48qLpxJlC9OrcBszednWYY1jdP/+V1Eu7vDYY48lx/f397G/v790/4LjDYnVEqslVkusTs5LrE4gsVogWC0oWU2qdDqW5yz39lyOdGyev09dzrdzdTrdK29X6jM/vgj61OgcpSR66Zqao0nJHqZmKd4v7M6V6LSekrgs+aPvJSp0Up+TR/qpU8D+fjyvYcq+6KWJRcNtR0wiOnQ+/2KWkutjEtxiybJmSCJdMIiu+qwP1pci1xusTqk2RKSJeBOBHiLwznu1e57uY9CG8m9jW0+a/X4goQZQgLZ0pAnnHaGlomoq6wa0ta7eOLmnDQSRwEm6O2fDPqndyIMVCUHXMJbUbW5MGjaMOSfosajcJOXjpGCjqxOCzvRwfIKjBs1k0pUnO/jkatxb1bWtk3IAmNkmPDFXt808MZ8pR9dnSkEFv1WnVlNM4dZZNLzCLS573m91po3zXfXEnbxXG+W8VrWKvqtaR4WbUhaKbXeQlYlTibgN6jbnt2pKPquHe2iNTvxWW+tVblbj0Crnv2oV5haZ2g3Of9Ur3WgxbG3hFG6uZNz6pFS2VkzfpoLuLewPwdpxKq2h79uw5+oqlGC1MexAgmAkrr322mT/rrvuwutf//rNDEYAidUSq/2DQWK1xGqJ1eMgsVpwwWBZFfi6fdCn9p9nrT30bIbZzNm7nDzZvaRto0c65TxrXulZ15OGUvJNB8oJ9TGK9Px8bRxjzw2p0PN1SYnOz5W8zvn5c+fifWve6O4euRod6CYahyxdyM7lBFwC/QDASZw+3XTU6Jdf7tYXX+wS6adPO5/0pxwY4PHHUyX6uXP9anSeRO9To9NsuPn5ZScZPWq1+rr6lsT+1kE+EUFAiaSX1Go0WZlWDSP65LcaSTnRxRIiKbeVFtRuSgqkC6JtGi6kRFWbP64UWtslfKT64gTdkP8n15l5v1Ui5PH6qHpz/ZhApolwlVSANXVgMrYCKefbNWIer08VbXyb1G2kEiTfWu3LxLnXKle4KTiFmwKCYi33W41rdk5ZP1FZqnKD70f5svEqrIZlpeKkboP3Tg1Ly/xUWx38VclblfutuonJ4rXGH7PedzVMTGajlQGBK9/cfqTN3Hd1ip9qXiru+uhez8+nx3kpff360GYH/gK8Shg18B0b249/px/72Mdw5syZcFwUboJVQ2J1hMRqidUSqy8MSKwWCCagJOn2k46W7FF48pzbVPdhTI6/OIyKOh3oV6jz+5buMwWl9nwy0bxdbU3bPAFfsw6vqdF5wjy3dZnPydaFK9D5eszPxIatSYV+ApRQJ99zUqNz9Tn3Rt/bKzwcPQg3hOcvo6ZEHwNJHO8oqD5wFf1sN+Qbuk2oKt1GUNRRKrluPyWlW00lR+oupeo/FIm4N4oRURtdR4m0G+UIb+PPODqooKwFlK8GVo6mUsk4914t+bDGc8rvExmKDQ0QVW2eXEFpqFA67ggquWTGt6ahrTvvVEn+noh+q5op2bjqLUeqfuucrb5bnZ3jZJzv87Jxfq47Udmwum3miXr0XXVqt0Y7BduM1so6hZv3WeUKN/JbpWVPG8wa7rsalW1N04a1Kzk3gHYl40nZuFWOhANQ2n9vjQoTl9GEZe3hzHmseoXb/HCGtm0wbxu3b9z2oWnQGhX8VueGvFd1ULU5xVu/wo1sEFprg+eq8Z6rdI6cVbnTqvWKNv69cIq3LlEmr1WDtrfUm3/3aoQ7kPWJ5dh5+1Wo7DYF9/NndeT8zJkzCTkXHENIrJZYLbFaYrWHxOqjgcRqwbHDWJ/0RVXp2TU1VTr3Sj93ru6cQTlR3n1paDVblvxRSj7otYQ6IVeqL4O8774EOm2XEupjlOglxTm/vta+3xs9X3Pw3/9yJfpJAHtQ6qLE/5yWK66IanQ6dtFFbo1HHu8q0sd+ILkanf8FZ2iS0T47l1rbqecWaScoQKxdBFuOMWqo3usrJeIJSWfbtZLxPjiC2j9G8p5c4u+VHdAkZgSFWN1dU7sl14P5rjKCTj6a2qqQCHHH6yo3KoLnajegS7T5Od5H8fnY8VUQ85LPKqnbgqcqkCjcVL4EpRuKCreS76pSTBGnLLSfnIzWSns1IC8R14WSZk/SuboN3m/VtBowzlPVqdZI0Ra9Vo33WK35rYaFPFYRFW7hs7PpEsbGJi4DW/eBCPsqYCaS7pVhF7xcN4zHH38c9913X9h/4IEHcM899+Cyyy7D0572tA2OTLBKSKyuQ2K1xGqJ1TQuidXbConVFwC2zZ5lXaio0vPkds1dg7zS+7rPt0ue5iVFer6fJ9QJuVJ9EfQl0GkcY9clJXof+pTs01CzdeGg30G4Ej1avJAKnS+5Gp0r1TVM+a8J9JeAKehLlg8dm3J+HZAku8BDvglHiEHv1ZXeKyrVUiI+rgB70LsVJirNLGAU9drAetUOXW+UceQeUfXmfFi180L1qjIaGfmlGlKeee/UpjaRWbZvQAQ9qt2CBytrw9VuWjlHVWWtJ+GRoDuK7rRIQQFH5MP7sZKqTduocouqt0jcqZy+qH4b+Fzyz6NEyHm7nJS7Y46MU9Jkxoi5hgpqtkTd5lVtCs5rVauCwo0UbcFj1QaFG/mtzrTBnjaJsm2m27DWflsp57mq2eRlJb9Vy/xWnepNezKu0c4bmLaBmXvfVb8+PJyhNRrnD2cwVju1m/dbDcq2zG/10KhE6UYKN2OBufHqNq9wo7VTvZGqjXxXSelGi7Mg4D6ruTItLxU3iOvYzp0P+5Wy8THgSrSxnquL3mtM3+4G6yX6q1a5jcUHP/hB3HzzzWH/jjvuAAC8/OUvx9ve9ralxyNYHBKrJVZLrJZYLbE6h8RqQGK14AiwisT7CK90bptycBDtrnOvdDqXJ7nHDLHP57wvoU/7eZK7plSfglriPN8fWteU6ED0Pqdk+ZNPdtvVLF3q3uj5xKJcnU5/cSAJAj92AlGRfgLASSh10FGiP/WpdTX6qVNIlehcjc5fQu2DKXmj5+f4+VVNMnpc/NF3Cvx7uWw/240L4BN2REuwOHJFnbVttWTcoIX2bR0FGVbjEVHRQT9VGwcR93FJDq52o+2axoj7sBoLaEbQg7oNmRcrAPJjLanauNcqHy61Kz9jf1IE6JJyfiz1XC0T8zjZWUrMSaWmWBIjqtocSe94raqC+s1vR8VbVL5puEnLlN92nyXzWc19V1VWJk7vyeiggqN9UreZVgeiTko2axSMaZx6zTTheGt19F71a+63GrcR/FTpO2URv5MmW5eQK9xMIeHg2o2buGxTmFpaLijj+c9/PmzB8/nChcTqZSGxWmK1xGqJ1QSJ1auBxGrBSlBLjq9brV6UfZ8IyvPDw9SmZYpX+jLzoI7dp/sQaon1IeTX5ffIt4fQp0Tvs32p3Su3dQHI1gWYlpTMJxiltYZLomsAe4nyvOSHzr3S9/YyNXppsIuAf3iLqNGnYhX9bSo5fsEm5bcb8qnsMDrEd5T36sR7ZGXlJfJIxwzaOIGZbaEVkfQWJpBHAyhAW/JhVeATmTnia/2+V5pZC6OGKGtEouerqN14uXgoCg8+rEgIup+6C1T468ZFYyQ/VgONxhOuqHojBRD3Wg2l40UCVikRtzpr1VcyXiflsVzcPRX5rNI7cao25X1To6pt5kl49FN1b2ZPO1fbPeUVbuSzqr2/qjbRc5XUbcpgrzGYNW1Uu83a4L2qG7dW2oaJpQIRNxqWyLohFaf3YPXqNms02vMzGNPg8PwejNE4PEzX51undpu3jqDPW425JVVb9Ft1CjdElZuh7VTh1lobJzYDMLfRf9V45WTUs5GSMpaGO4/Vcpk4V7BxWJur4gz7pYu187+A5ZOXrY1k7xh5J83hKvoRCEqQWF2GxGqJ1RKrWTuJ1b2QWC3YWhyFdcvQPfIsdH6sokonb3RSnXOv9IMD154U6jWvdL49xdpkSJHe99i5On4MSv0MJbdL6z4l+pAv+tA5fjxV9pI/Om3XfNFbdCcXPWDrEzh9usHBQVScX355qkCn5dJLmTd6yRc9l9SXUFOb93mjT00a15Tpi6rRJWm9JMQjXTAK40qvt+FeNZ9VIJL8vjYAit6rRMITj1ZO0mESItkHmshMQ7FJy2wo8zY9JeMlBLI/Qu2WknWVEHQKWyokEohkxyLTqGpTHdUbgKB8o+cEuoR7+HnS9iVSTvtjiDkvnyd1m1ZhKrnwvhRUULJ1FG6IxJ0SHY7k27jv1WqNMr6frpJtEVj/vETMU3WbU7ZZq1K1G1sb77naGg0D57c6L/mt+nP0WdP3h0Zt2Nr60nDXzq1tRttKE5eVwMvHeal4p12FvI/1XC2R9LGl4scBRpmQBFqqn2P+nnYbEqslVtP1EqslVkus3kVIrBYIBlBKppeOZ17pXIVO+yUXDdoe8kpfdLi1vwXwceTHc0z5m0atXU1dDpST6Lwtb1e6vu8ajlSNzmNHXwKdtmkd/dD5fskbnSvQySud/tBSVaNPQV+SfMzkoLuW3N618a4U9FvZKvrZblzIn/J2YoVKtUHv1IFJzBLv1kLJuOOwZcUb+bI2ypNU77dK1NaRdsAoDVjDyKTroYErr22Y0g1KJSXjxroSZg7O3a1l6YwBtZtB9GF1fq+ebUJBWZu0a4J+Lf4Dp+MWtqB6c0+We6/S+xpCzW8VSAm5Hy00ODEvk/JQHl5Qtzn1m/NabcJ5r3bTzGtVO2JOKjZSuO1p68+b6Lvq/Vb3dOvUbsqr3JRB0xho3UJrg6Zpw+RlSllAd8l74rdKRNsT83bewLYN2nmDtm0wP5yhbRsczr3qLajbyGu1SVRtrfdbnRuNuVe3HZrot8oVbsYCrXFKttbvU8k4n7jMhHWg453P2PD/lIHt+SusQdsh1LnaLW6PV7j13XNZ1Pse+P7vmGJOcAFBYrXE6gwSqyVWp20lVgsEO4NlVeXLXD9Wij1W8l3LTldU6dy+mpTp1JwrpfOkNk8oc3uYIbuUkn1L3yPX+sn7KrUbEvGX9kvjp8R4VIsPK9FLfQ4p1ctq9NJ+jpKlC00u6hTpe3sHQYl+6pTzRD99OirQyR+d1OjUDo8/XlakD00yOuQN1KdGH1KLL5qQH8I2JsC3cUwCAAvIpt73vvfhq7/6q3HNNddAKYW3v/3tyXmlVHF505veFNo8/elP75x/4xvfuPTDCMai/ov5mBLSKWWRi5RQUjmtsVRUS8engRN1p7Zi+8S92XnliSgpvQLJVd6flB0jxVtUkRHhVeycn0CMEWj6L4yx00N3KV1L/eowCrovXyIx77wbRGKeq9vIS5WemSvcwkL7ykJ5sk7vWGUKN/Jb1XAeq6RsI7WbUo6Ia52eK4G8VWmhUvE4eZlTtrVtA2Oc2s2Quq2Nqjbnr+rINnmrtkY7xVvwWY1+q4YtpHBz5JuNDfH7GhVx8TnIYgBAZ+KyRdD3b2sZhduqxrArsGlqZOH/tu1dSKw+DpBYLbFaYrXEaonVgMRqidWCpVDJGpPSfG8vbvN9Dq5KB1Il+9Sh1GxW+rzD+/qqLWPunyfE+XEgTaLn54aep7SujaGs8h+yychN4kmFrhEnF3UTjTZNVJvnnui5Zzp91h01ev4gNfQl0Ye80fNzi6jSJfm8QZQmx1102W5M/pY98cQTuOGGG/DN3/zNePGLX9w5/+CDDyb7v/3bv41XvvKVeMlLXpIc//7v/37cfvvtYf/06dNThzIB5J+5Dkwr47YYN/nWwveaqJIbUrrl23nJOKnZtAJgAaNYmTYA+gFv0cIofz1ciSYs+bBq562KWF7OlW7KAlB+P5SRO7WV8R9t7YmJoAfFm1eukVLO7fkptMjf1SvqAEAVvFhjmXjqx8pBe1z5BrBS8SzwlSazIlKfHEuIfVSzpeuUlJfUbTP/izYlI7i6TSlgT3t1m3avjPutuknISOFGKjcERdtMOc/V4LtKfqt+e6a9x6oywWu10SaQdFK48RJeazQsvSMdlW5O4dY4gt5qtHOnYmvnjpzP541TtB3uBXXbvG1w6P1WD02D1qhE5RYXYG7idknhRut5IO82eK3OrfGl4TZdq0gHw/OxfedT7PVwtk1KxU0WVHipuLXlcvJVYhEyv0713Cpg0cJO+Bne1882QWJ1DonVEqslVkusllhdvWbLYlgOidUO2xGrBQthqmp9UZX7Aqp0wCVTgdQTnRTqs1lMJHPVek/3o4bJUetjkb6n3LemoM8T6ENKdLqmpjafz917za/LVerO1mWMpQuBfk+hn48n/Doq0vf2Glx6qVOYX3FF9D4nj/TcHz1Ro5MSnSvSa7OoAv1K85I3eq1tDVN80GttFkm0S3J+JMQjvYrbbrsNt912W/X82bNnk/3f+I3fwM0334zrr78+OX769OlOW4HHBILdmcSsc76/ZHy4//6ScaWGp8u2haSCgXGk2sYi6u51jnkTbbWMNNP5sYkOpSJBN0CY2Ix0SaT64gSdaEhQvFEyAFTWTucVI805SU/HmHunBrJeIOKl9nQ/oEzK43jip14qEc8VfVzdphWp/iIx1+E4qdrgvVRdabhTplEiIPqpOiJvvA+rU6/xhZeCO2LeTVJYq6CUjWuvZAM8affE3JgmknWvdGuNO26sQmt18FhtrSPnxiq/ny4uURQ9WPnkZEBUuJGnKincDDsfxo9yiTiAMHEZ3x9CX6n4ouDEcinPVSnv3hpIrD4CSKz210msllgtsbr7PiRWC4YhsXqLcBSThm7y3oUEOyXT+TBonSvOm8qvEWMmDR07PH6//Hjp3NS++47lCnRgXBKdkCfR8+ty3/RSHy6Jzn8+9yXTUzmEQ5MtJ7C31xQ90U+d6nqlJ2r0+fmyxH8ZO5cSFv1QVz1Z6KoS5ZJwv2CwGoPPCh5++GH81m/9Fl75yld2zr3xjW/EU5/6VDz3uc/Fm970Jsx7/lE++eSTeOyxx5JFMB3L/NI9hhDQMVLiGLRBheNUO21H3RNHExVBvPgynwyKjuUUrvQ0RCgJisgnYvk4kdI4KVcsoW6UwoyVUjdQmCkdyscbKMz80njq6zxZNWb+yAwaM8SycmpH/zUD/6VtNRrfH/U58/ejtu589FhtlMZMaTSIzxO9VJVXpymmWHPnGx1VbbPgr+pUb3saqb+qJ+sz77O655VujbJe4eZUbnuNcUQ9qNosmqYNSjci6tVScaNh2sar2ty281l1BHw+bzCfz9DSum3QtjPM585vdd46n9V5631VaZ0p3OYWzmuVKdwcgXcKt5Yp3NLFwnqFW1S2IXyf+yYuM2y/pHCr/VsL+4VJzKZ4ro7B6pMC20HkV1MsvnjZ/zZAYvV2QWK1xGqJ1RKrJVankFgtsfpYYNkk29jk4ZDsmi2UOD15EsH+g+9zuxd+nh+b4r4xZL/SZ9EyZOXSZ/EydOzwMFWh9ynL+xTltfN9z8a91/0RxMR56WcWV6HHpLlb9vxyEkod4NQplzQvqc4vvrirQg9q9FyFXnvYqeCm/DX7lqm2Luu2fZHk+ASItctK8HM/93M4ffp0p1TtO77jO/AlX/IluOyyy/D+978fd955Jx588EG8+c1vLvbzhje8Ad/3fd+3zqEeKZYrGd886iXjTXHyM2NbNKy9gUHfRGYapD5Tvn13IrN472ErACLopsD5uNpNU0k4SeKATvl4OrkZKZ+o2JTG6/tO/k61/C/u3D+VK9roHFe1uWfrTlSmGDHnyYmgbvMkXAFZ0sIr3JAq4BRTu5HPaq5w436rRMJpH0BH6VYCKdxoG0DwWTWmCROYGU/UjfdQbdvopep8VVVQt82t9mRchTX5rVpEZVvNb9XCwtpU4WYQFW5uv/w8CG1SUlcrOR4qFU+Pj/uuLeu52u1wy/urwCT6xGX72U1IrC5DYrXE6kUgsVpidXKtxOqVQGK1xOqtxyYV7SXkCvTaMYa9PZcjLXml0/rJJ9McKCWJ+eNPmXx0jOK8ZvfSh9pHkR/Pvcn7JgrlfdTsXPL7lJLl+fURBuMTiPkEo6kSXam94H2eq89p//TpsiJdz8+Xk+hTvt81Wxd+bCpWZdOyK9jJZ5vyHR7qZ7ux1k/nrW99K172spfh4OAgOX7HHXeE7ec85zk4ceIEvvVbvxVveMMbsE8GXQx33nlncs1jjz2Ga6+9dn0D3zF0SsZ7ys1rPqu8n0CwC22nlownJB3OW1Pbbsm0ggF5kRpPdsPaE+bguepuHIhl8H11hwPRzBFIOu3T79LK/Y+8WJ2na8GLFQD8OBr48+FaiwZE1KMvK/3CTv6rQCwDGaOKycvF09JzIubxHBFyGlpOysPkbF7RRkSbyHrDCDon6o0vB9/T1pN7p3Db88cbKh0n31XlfFej56on6r5MvNEGTdMGst40LRSbwIyXjQePVfbs1irYlsrDNebzGUyrg6pt3jqlW1S2ufWhaaLfakbMW6twSGq3gt8qkXSucON+q1zhlirdmHqzMnFZStDr34s+pVm6vZjCbalSccHOQmL10UBitcRqOiexWmK125ZYLRgPidXHHKVE/NhjfccJPInO2uVe6aQ6n8/d+vAwdn3unPNS57cquXks+veE2nVDgvtF+ixZuPD2fetSEp1fXxNw911fqlhK0cAlKEsTjALRE/0kAB2S5Rdf7KoLLr7YJc5LCvSLLkJQrj/lwACPsyQ6T6aXHriEZbzRx6jSx1ZmjE1Ei62LYAGs7dP+vd/7Pdx777345V/+5cG2N910E+bzOT760Y/iC7/wCzvn9/f3i78IXJjIvbCWRx9hB1KCPeq4NYBqPCFoOm2NMslEZkTWjbK9E5mR0o2HD5rIbAqI0Ne8WImgE7klgg6grHgDEtUb4BRhyQl0qQ4n7DXkashU6Za24aq2uD+NmHOP1Vzt1qiocKMycSLmRLiJuJP6jRRupH5rtAkKN+Wv64VRsPlXzChYqwMxb4mkc1UbKd2816pbK7ZmC5WLGxWVbf7z4n6rYY2ocAPiMfp8nZ1BfK6pE5e5PtKJyziolLz4urZsgrFtm9SrBDeB2fKq41141hIkVq8LEqslVtN22kZitcRqidXTIbFaYvVOYBdV6fPUK507b+T7pFLn52nNk9HrniB0EeTKc6A/gZ4fy8/XEuQ1S5daPwBPoufrPjRsreFsXRootRcmiuV+6KdPx2Q5qdK5Mp3U61Ul+qo+zCk+QKvGLiW6d2msCfhvWsv2s91Y2yf0Mz/zM7jxxhtxww03DLa95557oLXGlVdeua7hwP0KvfwvYLuI+iRmXaLfp3Tj2/WScUBBw6CFRuPXANB4rU9UyZG6DQphIjM3Ut2vdIMv44b7RC0sjHVMMijImPItJ7hUGk5vAEgVb8Y6P1LXbySyxpP0BkgmU+sj6aR8A8DUbxzjv5MlMk798p76SHnwmYXzUKX9krpNexKuAnF3HqpO9RaVbcminbfqTHUVbsFrlUrGiaAzdVv4jIyCVRpKGcBwdZsj5dY4Um2NRjt3RJxUbW6bqdta57WaEPRQPq4TVduck3YLv4+i3+rcAq1XtpHCzfrP24R1OQhMnbjMoK0q1KztEvghTC0VP3LP1SOcCM2sKODvqu+qxOrtgcRqidUSqyVWp9dIrCZIrN62WH1McRSJ8Kn3WHZMuRK90t9sdiLcrrYmpXrpPHfuyO1axqjC+zB1Dsu+ftssRz2UQM/X3Ee9L4nO71XrLxV308+mvmR6bucC+N8U4BLpGvv7MXm+v+8U5/v7MXFOynTap+X0aa9Gf+Rc2R89f3m1qoh8u6RGX8YbvaZMX1SNPoSdTWhvCqvyN9/+P3pP/mY8/vjjuO+++8L+Aw88gHvuuQeXXXYZnva0pwFwJWK/+qu/iv/6X/9r5/q7774bH/jAB3DzzTfj9OnTuPvuu/Ga17wG//pf/2tceumlSzzKJrF65Vlfyfc6rp+idJtaMg441Y5WpHhrvYatf3wGBg2aoGwjpVvs0waCOgVEbrnXKle8gY5BBRVWyY+1RNKpjNyVtUfGTxOwcbI+dbyEMaTcnR8m5jOFQL41WwJJ16xcHKR0s6F8nBNzrnBzijencCMyTqXiRMjp3aqMmJLHqvNX1Z1zRMxNom5zpJxIeiDepHALJeIah5yU+4XoWSwRj6o2vnCFW8lvlT5zPhHflInLACBO+tc/cdnyBDlVZq3ch1WwUUisLkFitcRqidUSqyVWC7YHEqsFCcYmzdeQ8NcwicXL1HUt2b0uNflY5IlzQs27fdkkej7mWn/Dz0RWLrSdg+JuqkQ/fdp9Jlx5fnDgEuqkPs+T6IkavfRXgZK8vg+1JPoqvdFX1YckygULYvI354Mf/CBuvvnmsE8eay9/+cvxtre9DQDwS7/0S7DW4qUvfWnn+v39ffzSL/0SXv/61+PJJ5/EM57xDLzmNa9JvNoEQ6ir02qYonSbglK/dMygRcPXtoVSkZQPTWQGUNl1nNCMyFBatg1P9Lrl5H2IajEiVA6aCSKNjW0CQQd6Sbq7jnmzIvbHd4iw15AnHnipuMrajCXl5EnLiTkn5CV1WyDlYVKytExcM4WbhsVMmUDQG+2vCQSdFG7ubZcmMeOgycoAr3yzyk045ycrs1ahbRu3eJLeGu08Vr3XamuUI+ZGOWJuVXWxFv4ejqgHr1UqI2cKN+63CnhCbiNlLvmthu8HyhOXBX9Utu6buKzkp1fzXJ2G+jXF/grEflfKp+lTWkU/2wSJ1dsAidUSq9M2EqslVnf6klg9ChKrJVYfK6xL+V7rd6QqnVu8UA704MDlV2vrPouXfFjrFPzXEuaE/L61/aHEdy2JzvvJJyDN23Vz0n0/l3R2nn6XixOLkhJ9b687oShPmHMvdH7s5EksP8Ho1IT0Imr0Ze63CKbcQxLyHheOIl1ZO8AQthCPPfYYLr74YiAUDI/FdEXUeEwnuLl6qdyo1m/3eIecZ9dyEp0q2nRyjPoJ++F43Kc2Wmko6KByU9DQqvHHNDQaXwDu1o1yf5FssAdtNWaY+fMa2mo0mEFDY2YbNNBwd2r82k3QFdZe1UW+oSUS6sZEBDt9PYZ98xOfTBu4dyRaNrYh9ZPbTom9zfZzAr7oPzY+dE7IgTIpd+2674TeVdzPy8Mj8W78vpuYzE9g5on4nqa1QaMsTjRt2N/TLWa0bgxmukWjDfaaFjPdYjZroXWLmZ/AbDabO+LexMnM3HOx8nFPygHAtA1Mq4O6LRDyfMKytsH51k1adr51Crdzvu251k1gdt5ozC1w3mi0Fjjf0r4j5ud9mfghlYtbi9YAh9b6cnIbSsYPrdO0zb2ybY4WziChhVEWLeYwMJirOdO9GV863qLF3H9nUs9VXipOnqtxQrKWHac2w5OX2axtfp7/wjaqVHwSOR9BYocmWoMFcB6PPvoozpw5M9xfARRHrrroy6HV8r/8GDvHw0+8f6kxHTdIrC7fT2K1xGqJ1RKrJVaPg8Tq9YPe8aP/7//JOyEsm+0dc31fm1oyfNFjQNn6ghbAS5IBMzsBmmPys58FnnzSdfn44/1ryr9+9rPlJDINraTaJgwlw6eg9hqGEuilY5QYz8+Vno23PXeu2zZPtLv4QD9Lz/v1kL1LXEiJnk8uevnl7hitr7rKqdHPnnX7tKbz+vHH3IAfeaRr7cI/1NKLrFmsNE3dVH/ZSUan2rqMPTbl/KJtN9HfAB577DFcfOmlC8fGyPl+GG7C22XxWQCv2epYLX86WRnWUDK+IfR5r+Zt3LYBLDol41MmMiu9O+e5yhVuTH6WjAULT2QGxNJw15cvIac2seK7o3hz213Vm7vOhvMJ5Vng71ZdtZsf0gRSXi4X75Jzdz5VsqmEsMcy8aBu86Xi7jrj2/vrEJVwXOGW+6xy0CRyXOFGyjaatIzUbcakE5YZP2HZ3JPxZDFxsjJSS84tKxE3CnMbv09c2WbZtkGqcCO/VSCWkyel4mzisrxUHCD7hFgqnivdCDUVVVpCPp6YJ30sUyouZeaCnYLEaonVEqslVkusFggExxRjVOn8GL9mNgsWL4DLgVJSeFUWL6tWpS/6N4u+BDrf7kui87bc/aTUtmQ1nlYrDXmi82O0RCX6qVMI1i65Ej2fbJRPNHpwMKBGH3rBfUn0/PgyKvRtVnzveBJdsBgusE+pTO6OLXq8V4uEe4L3ah+cxypCyfiyE5m5PQUNFbxW84nM3PisJ3aeTKGrbCuBt+EE3I3Nt7FgpN0RYa56a5T3W4Un4FTKDsRycqREe6iIK4fK1G3UV4mUAzFZUSoPbxRCOTkvEyfy7dryCcuoJDz6q/Iy8SZMWOYnK2MTltGkZeS5ykm51ilBt1YBRsNoRiY9KSfP1VzdZkwDmrDs0KvbWqtx2OpqmXinbNxPYGbhJilrPUmnNZFuUrrRMXcckYjnxDzscQIdS8VrE5dRiXg+cVmtVNyMJMgJSV9GhbZOHDHZP67l4rsNidXhlMTqAInVEqslVqd33ygkVgsEdYzJFh/1pKNA2eKFttl9NAwODnSSHO6zeDk8TBPl/HZ8+Pkxjj41+pTHnqpEr21zKxd+fEhln6vx8/77rcbJF52S0HybzrsE+t5eg6Zx6vODg2jRkk8yeskl7hh5pPMk+qlTwAmcL08uWvpLAkdfsrc0wegUTFGKL6pGX3YMggouHGsX+YZsEBZ2XMn46P76vVcX6WuIxBtroP22tS2g0EvghyYyc5OWaRhloW2qdHOE3YZjBk5N5kh4SnqHUgj5+UDCVap6K5F0E9plbXLlm3spbp2p3YCUtHMo1f1OJMX9FVJO2zVlWzyWEvOSuk35cnEF58EalWwpMdc0cZkn5aRqI4UbTVxGDrRExrXqqtxo4jLAEXI6Rutc3WY8YW9bNlmZYYo2vrZR4RYmLOPqNf8Zt8algiyAuUEg69xzNfdbdZ/9MDUzKvVanTJxWfKeMqIvWA7uvS//c9gUPHAFxwMSqyVWS6zmr0pitduXWH2UkFgtEKwJeUI9U65zZw5aDg5cM1rTeb4+POxPqs/nru++STeXdcgpnR9j7VJKog8p2Gv55j4rmzJKk4zytbPp40n0g4O4TUny/f2oQue+6Xz/4ABp4rxPjT5FmT6m3Rg1ukwyuoOg39RW0c92Q75BO4vp5eljJjGrKd1C6TjrIy8ZX2QiM6McA6KJzLSn33QkV7oBCspalwSwAKhM2lpPRJ2SkdRlNLkZT4KU3kBybED15kh1qnqjcnWufAOc+s29i/QehpH2GvIycU7G3b5K9seQclrPOuXiqbptz6vb9ryqLR43qepNpaQ8Kt5MULdxtZt75G5CwlrlUjCecHOY1ieCTANjVKJua1sdvFYPg+eqU7fNjVe5MWJ+GNZe7eb9XKlkfG5TYk7EvfU+qxapwq21FnPvt5oq3dKJy/iEZXx77MRl5Lca39dyRHDs5GKjPFeX6F8gOP6QWC2xWmK1xOrFILFaINgQ1jkr5irvv8g4S6r0gu/KbHYiyXG2bZo43993a1Km7+/Hbs+dc4ndz362fwh86E2TJqLHKsvHnh9SoAP9KnRal45xz3PebkoeupxA59sugU5/yKCFlOhcgX5wAFx6aapEv+SSONloUKPPDPDI46kanavSa38dyFGydMn/AsPbjUmej/VDXwc2mWSXBP/OQD6p446ekvFVgSvdAPoF3pP2jNQnvqswaDJVHpF1Vz6uE6Xb+PEglIz3cN8qNKap3gAE5pyTeT6m5B4jBpa3GEPK6fwYYs7VbaGE3KvWwnkglnwr649bpnRzPquNn8hMoatwC8/jPVdpm0DqNmu8ek/H7VAmbjVaUrkZnarbLHmvsn2rE1Vbom4L/cIT8+ixSoQ8rG1c0+fI/VbD5wuw8vDUb5Wj6JHaQ3jH+KEOTVw2hNrEZevBQP8b8HCVcnHBVkBi9WRIrJZYLbF6XZBYLRAcCVaRsC/1MdRvyS+dH5/PoWczGPanZcqPci/0to150jxvSopz3nVpeCVVep+nee1xxp5bJomeX5f7otfuV7J+KaNm6ZIm0fmalOj7+6lCPVeic9X6wYFPopc80UsPMRalJPoYLJo8HmPrsm5I4juDwWpsWbY/Vssnv1JMV54NYiK5HioZT1RqiaJt2tjLk5p5Yq2atCw8m8iMfhl2ZL2FUQgTmXGlmyNS0X816t+IgHf9V0nVxv1X4dVndIyjxpG5HSif0Kzmy8qVb/DjBrjajd5RV+FVQ24lwMk4H3vut1oj5bw8XMGVgSt/LPisKvds8VycsGxG/qkAW1tfZh7JOFe4KU/YtbJJqTjBGIWmiWXhAGBbTsw1jHEEnU9WRuq21nqVW9skyjandIvKNtqeG425hVe3wRNzFcrEiaTT2k1wZoOijSvc3Ha/36r7LriJy2y2T9vue9IGZVvfxGVDpeIlYh6OTSS+oxRupcnQdkzhRj7Qq+hHsCuQWC2xmt6mxGqJ1RKrdwESqwU7h2WT5IskyBdpy+Xh/BqfTCevdMApzEmVTtYuOUi5Tl0TSLnOb5m3qQ19ijp9zLG+BDo/P0aJTtdOdUFJ2+S/17l9mhy+lEDnnugXXeQU6KQ8z5XofE1JdTz+eFSg52p0/mL6HugoJtkcq0yfYg0jye814sLxSF+v/GkrMZ4cbT/W+8tgRzVT+sW/50vOy1tLXpK8n4TMFCZ1IvJj/VWxPJdUS051ZJNrFodSKXGPk4ZFMhzUZKp7DXmUBiVZdt2Y//JrqU+6D+8zPz+FmJPPqhuv91stEHPl+ycPVndNnJSM/FaBqGLLJy3L1W1uHT1W08URc8MUbCV1W+K1ajSsBeZWO7JNk5/574NTrBExV2Hb+JJ/vlgqD0+Opwq3Ib9Vy4g4EMn4EKZOXFb7dzhEyKcSdoHg6CCxeiwkVsd9idUSqyVWCwSClWBbk22rGldNYl3a9tjbS73SZzMnQq7t58Pl7fgxEjA3dX1BZ+irTKKXzveJs3OFeel4bYwclCivnau941xdzlXpfH3RRakNTKJGzxPnpc99jK0LoaZGH7vm2NZ/ezl2ZZyCtUA+/Q1j1CRmayz5HpqgbOy1tYnMcj9WgxYaUQXnvFUBor9R3eZVb0oDdjn/VcXVZ171VvReLXwMnLqQkg0Aq+VWya6FZaXjaVEWV8GNQa7AG1su3oR2XVIeJirTRNJTddueJ9h75MNKajZWMk7eqlQmrhSRehPOBbUbLCPoZSJojILWOtl3a3cslIK3DaxVibqtNdp7rvq1iZ6r5KvaMpXbYVI+DhxmqraWEfO5pQnNygq3sX6rQDf55Cbv862Zwi3HFIVbHzgJryfUJhL1ycR+lYmA1SVZrTVBlbpsP4LjC4nVEqtrkFgtsVpidfXmq+tJYrVgFzFGFb4ue5cx6LN48dCzGU7MADBl+uFh2Qe8cHmSnyVFeqktWbyQV/qUR+pLlpf2eQJ9yMqltub24VMS6Pnz5cl0+jiaJv3DBSnRucf5/n5XiX755WVlOinYT+A8cI4p0Uv2LmNmRR3yRZ+yHjqWb4/Bomp08UdfEheOIv04fFqCIayA3PcR9rEl6qWJzBoi92Eis7ivLU1iNvLP0xlMRsqbnt+/a6Q5IfVgKja/bzrtvHILNunT2OkesJ3S9gIppzER+Xbt4vW6QMwVV8AV1G26cI5vx2QA81hF6rdamqSMI3it+jURcjpHa+PLxK2NnqtFdZtXtQU/VcBNRsb9Vj3pJhJOCrfgrQq2Riztp8+eK9wMnQMpLut+q0TCabv3vRRI+ionLttqbIjcus9mBeR8zapjwQUAidUSqyGxWmL1lkNitUBwdDjKSU9zi5f8eGFofM390oGooubd5orzIYuX2jCX2S8l0aeiT8k+BbW8aZ5Ep4UmdKUlV6L3LdQ2UaKXFkLfyzkWCV/BeiCJdMHCIGfQ8RildJvU33jv1RRx7HHisbSvfEKy5GprPBFkijULaNV0ruNrA6BBA6NM8F8FiJhHpVsgp5jovwpHJvl7HvqU8k8jJ8rcb9W9l7RdrppZ5NPl9+RjTf1WufqtS8ZzUk6Kt6hcY+XgKpaCc1VbmLAMpHqzmCkTysUbbZz/Kisbp6VE1J3/rU1IOEdOymnisrlpYEjl5tVtrVGJ3yqp2c63TfBcdWsVVG6GqdzimpN277XqlW4WNnivBr9Vu7jfaknhBmQqNptOqlUifotMXMYVbn0Tl43yXB3oXyDYbkisllgtsVpitcRqgUDQg6NKZE+9z1j/9DH90vmSTzrDidkMs5kOTfklY5PZs5nzWSeQ33oJXLXNh5TZuI+6L1BPoJf66lOj1yYlLd2/hpqFTT5hK6nQ84lEcyX6wUFUol98sWvLFelN47b1/HzZG32sGj1PoPMHmapGL/W7qB/6qhL7okYXTIB8YjuPkcmANZWcjy0tp7b5RGZEb/MkgIFxZd1M6Uaeq25fM3pvoX1puPYkSquc8AFQpFzqp8qls3SMaGZNwRZIeniObvuxyN8qJ+U0Jq5uGyLm3L9VkULNl4UHdRvcpGQ1hRuNQ3HyDTpe9lctgZImvC2RdJP5rgayTiq3TN3WWh3OkxLO+H6oNDyo38K6u9CIuSLOTXznSXlF4VbCVL/VUql4DaOJspQvj8KqlIPHWoEoWAEkVkusjsclVkusDu0kVo+CxGrBBYujVKXnKKjUKd+3txeT3XScVOi0T21ofz6P+VbqnpA/JlmgLDP0Eob6HLJ0WQb07HmuupSD5gtXp1NSnbZrynNKwjeNO6ZhyurzMWbuYx9sTNKcsGjieJkk+iqT1ZL4rkAU6cccwwRt1zFF6ZYS7DrZD0q1gtKN38/ClX6T55fzWEUoGddokPqvxrtyF9ZV+a+S4oz8V3kZuc3KuAfVbQUu1qhI7HgHue/qVOjKuEqEnNrUSLlC9FgNBBzoqNv2PDGfkccqqd20Cfsz5VVtsInHqkYk7G486ctyE5VxwkonotqtM3GZVTC8PDxTtzn/VeereujbHVqFudGYZ+o2Uk+XG+MAADcdSURBVLOVFG6tJ+etV7iR76oJ+zRxnlO4tZnCrSV920S/Vdcmnegvn7gsqOB6Ji7LFW6Jz2qBpPcR91EKt1KfvQFvIFGwwUSCQVTQLgMpF18HJFZLrKZnklgtsVpidd85idXjILFacORYVyJ8bL9j25XU6Jn8WwOYzU6EUwcH8dL8lhw8cczbkiKdvNP5dXRNn1B+zKPVBNZ9CvSpqF1Xcsqh46X9PIFesmghj3RSol98MXD6dKpA39+P50+f9p/T44+nKvQ+NXr+14YxSvQ+HKU3+qLXCFYEqgtcRT/rwYte9CLcc889+MQnPoFLL70Ut9xyC37gB34A11xzzaR+1jMrlmD12OAvr0MloLVf9BdRfRgVyQztA8MqoWp/NiXU0UNz+NqSMo17mebkmS+ltvk1tT65Wo2W/Di/jrepEXPuo5r6rkZ1W9KGnosUbuGeXSUbJTdon0rFhxDJuOoQc1K2BdWaX7t9fk5hzjxXSdFGCjf6UW6Cwq3Ha5V9V0jBlirfosINiAq3oHpbwG+1RuimHr+QUFMWCgRbAYnVk/sCJFZLrJZYfdwgsVogmIhVJP9WnUDMlctwymae7CUVOldP58doaHk7fjwfPvWz6LBLKKnRhyZLnXKfoZxwjvydcNU5V5XnSXV+Pleg5zYwBwc9avRVYowKvXbNOsYx9V6bHrfgyHDzzTfjV37lV3Dvvffif/yP/4H7778f/+Jf/IvJ/ci3YC0YWcK9RfcrlX1PKQUHHIl3CjLNjnElnFO/GRVVbNEj1e03XhdG6jb0+K+6ko+m6L/aIpaMu5Lfrv8qf2tc+QZ0SXRNA0OTog3RBZ4MKE2kViLtubKNr1W2DTjFGoAOKW8YKaeJyqIPa1S9Bf9VIKjaZsF/1fj2XtUW1G3eYxX9E5dZq2AMoLX2ykLjVW/+/XhCDiCScD9p2WHbBN/VXN02txrz4K2qo88qEXejMLdu7Y4DrYH3XwXmxivbjPv+kcIt+q7GSctqCjfutxq2VJeYB0Va2GdKNKxn4rJE7VZNsk0k/JMThdudUHDvaAUqNynP30FIrI7HJFZT//k1HBKrJVZLrN4MJFYLNoZVKMqX7WPK9YtKt4Hu7J98m0F7v/TK6aLimlTnufr78DBVpvO2Y4e76OPmfdWO8bGW7lPycB+Tz83/yECTjFJSnJLmJ09GJfqpU2656KKoRCeF+kUXRe90aq/PfcYNiHujlxTpNTV6jkV80WvH+l5Kqe26vNE3jePyHAD8b2kr6mc9eM1rXhO2r7vuOrzuda/D137t1+Lw8BB7YyosPI7TpyZYI6gcPC8ZL6FvIrMx9zEK0J645f6rUDp6q0J5YlTwX4UjUtx/1QDQ3imA/FddQqAfY35tL7XhNHWM72repJQs4Ao3lZ9T6TnyWOXEXIEp4BDVbcEDFV6lRvsZsdfMr7Xkt1qCtSqcN0ZBaxuIuTEqtOHKNouoajPGqdi4us1aeGWb+1znpHCDLwM3pHJT3lyAqd2ggrKNFjpH/eU+rK5N1L3lCjeOkt9qrtZMiDcn6UtMXLYKbDvBXIfCbVWTrsnkbYJtgMTq6W0kVvv3ILFaYvWKILFaIDgirMM2ppRAz5Pr8zlmsxPFBDY1rXmo8+5oTe1KiWnKay3jl74q7O3VrWJKyXR+Lm+brymJzhXoJXV5SYlOCyXhSbE+mwE4t0Il+lRLl/yBh45N6UP8zrcUq/VIf+yxx5Kj+/v72N/fX0H/Dp/61KfwC7/wC/jyL//ySUl04IJOpHuWtiUg5VV/I0dOx/eZea9m13MFWoqo/xpSugVqXFLJeVUb919tPLGvKd0cdWxR9V8F/B0X8F+1Kpzn/qvcezX/VvQR5nWgz2uVH6OScCBVtQHAzG80nhCTxypd06jUf1X7tk3wT/XqN39Mw3mthknP4Ih5XhbeVyJOCjYAaFuVHDdh4jL3mVN5+Nyr2w5NA2vRq25L19Fr9dAofy0j7tYp24yNCreoaotl4sZaf9yr2nxSiDxW556uj/FbpWPuOVOfVVrnfqvxHaXquOo7JvXcgCdqen5cn2vFlicEBJuGxGqJ1RKrJVZLrB7T51ohsVogWD/6EuO1c6v2Sge6kuqCobiezXBiBuBAdxTb+aShTz7Z7WqMMn1M7jefDzVv35f8HotSvzQZap5A7+sj3y4lz0v2LPv7Tn1OanTukZ6v6fwJnHdJdPJF7/NGH4NaEr1PhV57CWMU6Kv0Rpek+87i2muvTfbvuusuvP71r1+639e+9rX48R//cXzmM5/BP/gH/wC/+Zu/ObkP8Ug/NtiOX3AHPVoHxskJTcl/NW1rEct1/ZqV9aaTTrEx2LhPa34MWXtCHzGveafmSrWhxaUZ+r1Vua9qXhpO3qlEpLnHarzGJuo2xUg231ehDJw9A1JFW2zTUybemaAsLibb5hOVxZJxoLU6bHN1myFvVb9uwzqq1kj9Rr6qtIRjYaz884/f1PB9QvRd5eq2MX6rpVLxvDy88+56/q3UJi5L3/3R/UzYZYWXtdapC5deVq/AExxHSKyWWC2xWmI1f/cSq8dAYrVgo9i15Nkqx5snWrMMeO57XvJCz4/lHurUruatDtQ908e4ihD6PNfHiqZLolU+3vxY/iz0/DyJzr3QKYleUqVzb/S+ZTbDcLKcHxv6S0PtxY2xazmqfzu79m+UsKvjrqJd4QJ87GMfw6OPPhqWO++8s3jX173udVBK9S4f+chHQvvv/u7vxoc//GH8r//1v9A0Db7xG79x8u8Hx+2T2yJEpdhYjFK6LYsepVuqVOuOv1YyXlK6uZJx6rd16jJWPq6gYdCO9l/lSre5AmYWXulGxzWKZeNUWq58ablXumkLWNX1X62hz5OVUPvkSj6rfX2V1G0AEqLcmcgMcQIxup9TqqXl4UpZzLjKjanZiNTPkn2nXmu0icSelY3XYKzyY7SgicVofKR4s1ChLZ+ojMrD561Xu5km7JO6zQBB1TY32nuraswtgtdq65e5V7LN/Tap25zqze8beFLvkzt+u7U8AYSoagvHVuu36qwVukQ39DlAuIcIed/5/FwxOTCgoOtiO5KGfViVkk8ml9tVSKyWWO0gsVpitVtLrN5GSKwW7DzWoRxf1xiAuk9JBvJLJzU5kOZl6RglyWte6bmVy3weFeq8b1KY51YvJWU6rUuq9JKKfegY3afmAEEqdb7PkU+wypXoTRMT6KdOxTVXmfcp0i+6yF3/lAMTlefnznW90fMJZPmLKfnnDCnR85daWg8d69ses9+Hobar7EsAYgKr6Qc4c+YMzpw5M9j6u77ru/CKV7yit831118fti+//HJcfvnl+IIv+AJ80Rd9Ea699lr8wR/8AZ73vOeNHqF8G445OiXjW3LP4TL01H8V8Eo366YsM8o6D9UBGKBTNo5AChH2XUIgJgiIdw6ZCoxNpfQp5PJTOSEP7TJiHiY1QyTb7rwN7XJi3ll7L9agfAv3cuo1mpxsERBBd89oi6QciBOWBZJuPUn3/qvkuToPKjd3TesJPRFxY0nlRsScyr+9Mg4FhZslws2P2/AXSV4mzictc+dcmXh4XqbMXNZvNR5f58RlWwApFRcIAEisllgtsVpitcRqgUAwgCmJ8HUm5AuTj1Jkolxj7o/Ojx0cuLxu7plOCfM+5ThPzPP9WhI9T6bzZHefJUwpmZ5vU16Z56LHOKDw5+WKdG7tQn7nY9Tn3DM9SaKPmUG1D0NK9Hy7hmUS0JK8FozAFVdcgSuuuGKha41xv+M8yT2oRkC+mbuGXu/V6cq6Tvcl/1R2bEjploxmYCIzaw2gGlCxbe6/6p7IfbGjGk6H9iX/VQNAWSrr1Y7EAjBKhf4VEJRuRN5z/1UkY4nbQ76oOfrIe35NToI5Ged9JROOZROMAanKjdZUHl5Tt1Eb8l2NpJ6p3UBkPqrfSsTd2PhNiNQrPiyRdCLjnJRbq3DYOmfXlqnd+tRt3Gs1bnOP1ei1WlS12ei7SkR8zm0HkNoShG1fJs79Vt0zc8VbWyTmNb/V8I4yJZy7PlPN9ZSKx2vHKdBGlZZPJtKrJd7rmLwMoATI8n1v++RvgiOGxGqJ1RKrJVZDYvXK+pVYLdg0VpGYXlcf60yw1zLNGU54ZTopzg8O4uV8TZ7plFCm9kCa++Ve6fk6H07f8GrJdCD1OM/ztbkKvS+fWztXE2jTu8kT6Bdd5MZIinRSmZcU6bTN2+j5+a4Cna/5B1EC/ZWhlECvedrkL2CM3cvY7RLGevAs2tdRYtP3XwtWO9noqvGBD3wAf/RHf4Sv+IqvwKWXXor7778f3/M934NnPvOZk9TogCTSBb1YoOR9wkRm7g4tdEERF8rEFQBfGg441RAp34yygKUJzDyRdHqqatm9hVc1MQVWXkbuysvHK9hKKF1bIvA1Qs7bq2y/RMzze1K/pFzjxLykbnP9dxV1VOJdU7pRiT4QSblS9H6jsi25hpFyAKE83KndnArOBOVaXd0Wy8yJ3DsSTl6r5LtqGCkPSjYgnkd6nPtjWUZ9cwLOt0nhViPKY/1WSxOXjcU0cjhQVr5iYr3NWBWpFnIu2BwkVi8KidUSqzkkVm8vJFYLLigsknBftwK9L+nnz2sYzGbxd4tccc6V6bRPSeXDw3LCuXQ8Hw5Xv+dq9Bq4On3IyoX3M9QvR18CndY8kV7zRuf2LvlC12iYri96ycolfwk5hpLoY/6iMJQYn6poF+wQtjuR/pSnPAW/9mu/hrvuugtPPPEErr76atx66634z//5P2N/f39SXxf4N3eoGHhZLK86K6JX6Tb9+pJCrXNJj/qtpHQrlYxz/1Xe1qB1JeDkw+oVZa6Eu+6/agBA6eCt2ue/ChvJKpWNGyhGxuvfBX4mLyefonrrI+L8mlI/OSGn64lkU9tSeTgR86h2s17ZF8n6LFG72YSo07byCjd+jmC9rypXuJHXKifo+WRmnJRTmThNVHZoGu+72lW3Gatw6In5ISPmreHb8Co4YO7yQ4y82zB5WcsVbf671CItE2/RVhVu4R1EfVuicJvqt7qswq1GEreOPE4Yz7oUboJdgcRqQGK1xGqJ1RKrNwCJ1QLB7mKVyfWSXwqHl4tzZfrJk3UvdMrh8gQ2X1P+lyffS57pNBTqb51/T8jHVzqe789mMTfNJxcFYgKdFOg8aU7H9/eB06ejDzrfpkT7iZmpe6HXkupDE4wSav40tQevvZRFLGDGeKMflRpdEv7HAs9+9rPx7ne/eyV9yTdiy7COScyW817tJhiG+qtNZNYdF6nfyuXmdK+S0g3W+a+6ERIpj2XjgIKyrjZcWUBnTJiXjTfsWK50K5WMT8UYJZsfSuc4J+W0H8vIbda2Tsy5ko2IOU1YVkvLhPLwkc/MCXo82C0TJ1JOSrfWuONtULtF9Rp5qJLqjXxW6XxrVVSymThhmSPeKkxU5sh3pmpjyrdIxN02UCaENGmZ245E3BRIMkef32r5Xa5W4bYuYr7Vnq4jIROYCRaBxOru2CVWS6yWWN0PidWLQ2K1YCtwlPYufe2WtXJZ9DlqyfSCfJvnHcco0wGXs+WTjZIbCSXkS1Yvm8KY/DE998mT8XjJEz23dimp0HninCvRT8xM18allDhfBGMnF+Xbi1q69F1zHHFsn2+7FemrxHH9BC9gjFTW9SjdapOLDU061mnvSfyQ/yofA42eK93cVTpTtwHamqB0y8vG034UjLUg/1VYC61cWTmpskjpRmXjNJlZwwi6Ssaa7pdUb6XJx/I2fYTcXZeq2gCmOkNUtgHRb7VEzIOSDZGYx1Jyvp/6rfJx5DBWoWFqwRqCzyoYOQ+kOvqtEgk/NI1TuZEPq1e5kbqtZcS8tQhqNyLmudcqLxd3E5rZWH5OCjcA1lqnfoNF679bbdCtkeeqCQq38B7imY7Czb2nrt9q8n4Kqja+XVO4ld81v35cAMr7K5LMbVPIrRBSLi7YDCRWS6yWWC2xWmL1WEisFghGYN1ybI4eZbr2ynSKlFOU6QR+nBLndLymTOfD4ZOKjgG/lrzUqR+gv6/SBKM0Hr5dS6TnCXTuld6XUA9JdD65aJ5Mp5c49XtRS6L3JcqnWLpMObdIf6vEsU16rwPut7LV9LPdkG9Fh2ZtHtundOvpd8REZjWQ/6q1BkYBJf/V0NYr24Bc3QZwOks0qVY2rtksZY50p2XjRHCpWenbMUbtNkTMa6XlJVUb9cFJOV1HZDq/puOzGq6xyXF+X5qobAguoeHaBuJNiYOMpHNCTtcSKY/+qr5cnFRuQcWmPYEm/9VU3dZaVw5urPtsh4i59cQ8IelIy8RNpSzZhNYZGVcD6rJEEZeSdE7mFvFbXVxVtSpV17jJ0QSC1UBi9VL9SqwuQmK1byuxOqwlVgsEgrVjncr2sQr0ZVTp/PqSMh3FXRwcuDxvngDn69yDnB+nxDNZudB2nieeklAv+aPn20B5rs3Sc+bJc3puWnNrl1On3PhIXU5WLpQoL/miF5PoeQKdXlj+YsZ+3mOS6Iso0Me2WTZxvWpbF4GgAPkWrR0jVWdTsaz36lD3iaJtuZLxeA2VhzfJsTH+q5yEB42RLyNXMMF/1fXjdEulsnEon/bwSre8bLw2mVkYCyPmFsNEPSrT2DF0j/WRctfWdq7j5eH8mhIxzycsS46rVNFWmsiMQFPE+VcYyHfwWi0klaLX6gApZ+XhQfWGSM5zddvcH5sbalcm5hbZBGaMpFOZ+Jy+O9aR8Dkj4sFr1SvcwnE2admq/VbjuxtWuBWPVYlzpmgbo8xah3prixRhUi4ucJBYHa+RWE3Dzo9JrJZYLbF6M5BYLbggcRSG38v2X1Omw8UcrkwHYjKdtmtK9bxbnhcmhfpnP+sS0XlCna6l41xhzvP/pfv0/F2gijynzNdNk3qj05on0EuTiOaJdJ5gLyrR+yYWzRPqNX/0/K8FU5Loq7J0KWGKGn0XkuS7MMaFYbCaP9Jvf6w+zp/iBYzFSsbHYMpEZtYaR4p7JjIDOGnvV8lxpVuJrLsriejblZWNd8ZBY2A8dKxWskuw2bmRxJwUdNxjFex8jZiXJh5LFHAFhVdQ+3lPVfesKigEPacNJD2/FmDkHFQmThOXue05kXQbJyojct56dVuNmLeWyr6J/MclEHPjSTlsStIRy8TddlSyufFSiXhaJp6TsFAWXlC8EfE2bko09m66/STvbiB4BOJv0z6LbSdNFLZ9gW/dk5c5VeHy95ByccF0SKyWWC2xWmJ1//FF7jsOEqsFgoVwlPYpq7zfqsfdl43OwCcgBVwymOdyeUK9z9KldAuuPM8T6ny/plKvJdanIM8r8+R5bukCuAQ5rWezNEmeJ817k+h9k4nSwyyiQucP07e9yPmhc6tWp08ZyyraX/AQj3TBBYgppd6u/bgSdIvWk7mu6m2q/6pRgLaxXBx+38LAMP9VVx6+mrLxXOm2SPn4EIb8VYFuefgUdVteJq5RJut9sF51ppUNBJ3AiT2fwIwIebw+9Vsl9Rr3XaX9Q1YuXiPmYW3oONLFeAIOm5B0p1B0PquAI+akeAOImEeFW3yedNKy4I0aFG1R4WYmTEIWyTYrKc8UbotgKwnjNo5JINghSKyWWN0HidUSq1eCbRyTQHCccNT2Lsugll0eSqazfT2b4eBAJ5OIUhOeYK85hnAFOt2OEvB5kpwn0PkwuN0KT9xzH/T8viVHHI7cH71m6VJSpnNvdLJ2aRrg9Ony/to80fuS6LXjq7B0WRSS3BZsAeRbuMvoVamNLPEePZFZ7G/qRGZhRNlEZjnG+K+6kbhf7kmVFkl4LBs3rHWtbBy+3SwrG9cWsEzpVisbB1Ky7ghfnajXFG6lycHyScpce6Zmy64jYl7rbxlY61SBGjYQdLonoWWEPCHnGSF3beuk3MCVj1vfp1O9ebJu3ecUy8P7ibkJCjibkHTnu+q9Vm0k6qRqA8pl4lQSTudLZeJAqhRbxG+1pjSzgaz3l4wvOnFZuVFlLEf0V+J1K9zoLqtR5h3FWAU7CYnVEqtZnxKrJVa78xKrp95FYrVga3DUqvQ+TEmmjz1WQs3vZIIyXXtlOlegUw43V6CXlOe86/194Mkn67bgpGDnx3m/fY8zBn2+6LS/v+/W3MIF6CbQueqcEui55YuenwfOzYfV6H0PSxhr+F7arz38mPOrUJ9PUbdPPb9s+031uVUQRbpgpRhZvs2wjknM1okpE5nl/qvGtglpD76rqCvdYBFLw30ZeeMJeiwbpzLlStl4ULmlZeNE4POycWOj+i19nsVRKxEHUPz0uc8qXVfyb60p3KaAq9oAKse3cTKyyjWEnJTzMnHyVwUQJybLSbon447EI2yXiLlFTI7wcvHW2kDSeZk4TVjmaFl/mbh7FqZo65m0LLS3WXl4RtLzc4tguoJtXAn6hQb3Hpf/WWutkPPdh8TqpK3E6gCJ1RKrJVZvFhKrBccSY5PY607cT+m/lIEeMh1n11EyHYiK8qZJleY8GX14GNuVEu25+pyO0XFSnOdJ9byfRX3R+f1ou8/aJfdGLyXO9/djoj0k0ecrSqKPfajSQw69hGUw1RJmmbaCNUIS6RcYLFbxy9kuYGyJd2i/wERmnKA7Eq4r7er+qzUYGDS+H6du85RDAdoSeYpl43RVqWx87j1YSfGmoAIhzz1YaUS2onSj7Ya2vSosfVf937K8TJz6BtIycdc2JdtjyfcYBVxQ95Hiy8ZnaaHiOGz3aTpeq56QGyLYjJS3rBw8Lw93+ylJJ3XboXFkPFe3RUKeTljGSXrrSbkj656Ah2MWLcgnNRJ2o2Lx+NCkZe7ZM1UbJ+LMb7VUTh7PpaXifQq39HqumuNt0+uWKSPvV7hdmARfcFSQWF1tL7FaYrXEaonVlXt0IbFaILhgsUhSvnRNbvdS8lEZUKZT8/xyfilNTlrySCdLGEqYt23d5YRbuezvR490fu/88fJXUNqnPwBQ4pw/Ay08MU7PlCfQeaI9sXLh9i08iV6bZHRRjEmij1GaL6pGHxrPspBEu2DFkG/UFmOblW59E5nV2q7Cf9XRxDYo3QCubiuXjZMHa1423pAKzlpfDh2VbAa8TDxq9jgxJ+LM/Vg5AXc+rimpXgSlicVqGEPS6wYD6XjJh9Zt+2vZpGWd6xkhp+s5KSfVnK2Uh9O51pNwt09EOxJ1UrPl6jaasIwUcRY2mdQs3qNeJu7G3S0TH4vUqKBeJp63L/mt9t5nxMRlU1Ak/evwR91Cz1X37CtQuS2ldxXsMiRWS6wGJFZLrC7cR2L1yiCxWnDBoy8BPsXeZZH+a+ibmbOkTGf958n0UjfcA302cx7p/Di3iCH0eaTTNg0PSD3SgXQS0hy5Hzpvx5+BEuYla5c8sZ4n0qmf3iR6SXnOtxf5Q0ltf4pSfOzxRSxdprRbBcTWZUGIIv0CxI4q3SZ6r47po6/Ue6zSrQ81/1UqG4dFx381Eng+Au0JuRsvlY1HP1blybr1rdOycSivylIqEnml/KRWKvFcVUDRg5W+MVpFkpiTdz4JGpCem4o+v9UxsFbBZATeJSJU6IfINABmKuvTIgVlm+uDSu+JoMdjlpFwk+1b5OXisRScyDdNXjb3BJur2yyAuYnKNmuBuVez0TafsKxUJu72SbcWy8SJmI9VuAWizYhuXiYe3hcrJ6/5rY5RuNX8Vjczcdlq73l0ZHc15BxHNt4LHRKrw67EakisllgtsXoqJFYLBCvBquxW1t1P6fgqEu+EWoZ6SJk+n0P7rDFNQkqJZO5xzrvgCfKSAj3PL1MbPqwh55O+v1Xk26V1ydaFJ9b7Eui0TvzQ+YP1JdP7Eut9GJtEX0VCfSym2ryIen2LYLCaJPj2/UE/h3xLjgwjifIWYki9tnC/cGRaqTKxT/1Xu22MMtCWCDoAT9C1JT9WCyobJ6UbLxtHIJQWUF5PWPBgLU1oBk/GqZcS4a6VjSfvgJHiTcAmpFw59SAA7aVsRLBJ6VdCOmFZl5STqi2o2JjKDYjl4XNL570qzvdNajhOzOndEzEndVsg4z4hQhOWxZJw2k7LxMOzBCrvn8f/EK8R8+676A8cLsm02b+w7sLEZQLB5iCxunO9xGqJ1ZBYfdSQWC0Q7ABWlQQ/jihlwCvScK5OXxQ8yZ7n8kl13rblIdFw+bBq98i3KXkOlH3RSYEODCvTZzMnPRjlfU6off+GvpdDyeopCfUpxxdVo9cw5npJjAvWAPlWHQdMVLoVVWmTlG6FIfg+axOZ8ZLxRfxXA1HPlG68bFwRkQL3Y41l49QTlY3DuiLymdIw3oPVqCB9Q8MIeT6hGYzzSeV01Vhgph1Z5BOdURk2Kd2IzDtvU2rjSP9QaXmuSDPWlcMPEXyubqNt8k11z+2VgFTqHdRtdH1tPHmJOLsfJ+fsWJsdp/LweZjYjEg5AmlvLZ/QrK5us7CewPttT8zJb7X1xLwlj15PxuekKMsUbnwCM74O75Up3Mb6raafS6pwG+O3Wi47X9wLdcjLdWXYwlJxAH5cK1C5yQRmgiFIrAYgsboPEqslVtcgsVpiteAYY2xCftHE/RRV+qL3LmWiBxTp3A9FAzjhE+qUN6auPvtZl3R+8sl4ab6Qgp2fL23zNeCu42izH9NN9msYWbrQo+bWLiWLF54o702gj1Ggj32wGsaovacm1Mcm3cck0aeq0YewLUn0bRnH2kFyiFX0s924UD5RQYZVT2RWalsj6ACVjA/7rxIpH5MooPtQ2Xj0Y3Vl446+W1Y2ToTSehWcU3dxD9bqhGZUCo7UbzUvFy+p36K+jt5XSq5d/5F8Dynlkr6zcnBSioX+kCUE6Dxc2Tn9yCKSjnC8S14sa1Mi5WEfqbot3Ucg686nFcGrNZB3T77JO7WkbksUbXw7U7dxhHaBePNtNtGYSglyadKy8E6QlocP+a0m45lIXmtqtc1MXLZaHKWHqfiuCrYZEqslVkusllg96X4Sqwf6kVgt2HH0JbRXkRxftv/cNz2Xfdek4B4ldTpPXueJ7nxolBjPxe8jhPGde+X959ulJDoQ7V14Ip3WTeOU6fw6PT/fTaDToPIEOj/OB76pJPpQ/1OwDcnmbRjDTqPFaip7t7+yTr4pR4p+UlvCNk9iBqSkfSzhp3bcf9XaNpSNc//V2Hd3MrOS0i2q2wBNSialg8JGsbLxuW+fMmynSAvn4Ijt3Mb0QL6PcNyTaXQJOvd2Tdp6pVsg1V7pRoSaPvl8crEOCc/2DQD4PgJBp3PKMjUbydhUVL2plIbliQQOKhXPyXjYtoWy74yUc5Juk+OI7byKjVRuQe2WqduCks0iKNv4hGVza7yqjZIVjoa3gYiP91oN7wQm8VENx7I27n0t57cajo0i5sMoKtwWIvDb/1djgWA8JFbzdhKrIbEaEqslVgsEgrViVTYxtX6m9L/sWBb1Tfdt9WwW1Omlyw4Pnao790inW+U+6UDXKz3frg2do5RLpqQ5HSstuW86JdaDF3rNymWKH3rfYMe2WVZxPrbvseObeo9txa6MUzAJu2kEKlgPbE4SjuYX7r77WFufAAqI5bukROKqI65YMlxFxtRM7v42KKCMTUfjiCUfT9w3nhzydrx9fsxAdSpKSZWVPnPpTZRhsvER+Y39d7eJQFNbvk/taAFrY9nxsBT6sVahNdpPRKb8ZGPx3JwR8ES95t8FPRO9XyoJN2ypqdv4eTd2T9DhPvfUU9WVjsd3N+37zpVw8Vhb/T6vy291816oI97btpaKA2DfuhUs0/ATP/ETePrTn46DgwPcdNNN+MM//MOln0ZwAUBitcRqSKwe/+4lVjtIrJZYLVgLdj1Jts7x1/y8h5LFvo1m/155Ynpvzy1NkyanS9snT0Z1eNO4BPz+fmyX264MHaPrm8Yd477n+aSheRI9TCYK0++FPsYffegvAkMYm0Tvu2bs9YtauvRhkT8UrLq9oIB2hct2Q74tCUjutF0YpXSb6L260DgqJeOlicyW9V8lpRtps9LJzLwyDqQCIqIepjFzZ7xyja6LJc903xZOF6ahYTG3BlqpwOyItmml0HqVlFOr+SuV7zMj01q5cmatJqrdmFKNJgJz5em+MV3rH6PkvwogqNgSBRu8es26bxJ9Wi1cOTi1yfMCrS1/72j8wW+VXV/2W40kniYoIxJNvquJr6pFmKiMq9qmqtvmTN1G43YaNfrOWOReq2MUbkk5uVeu1crEt8VvdVcmLrtQyq5/+Zd/GXfccQfe8pa34KabbsKP/MiP4IUvfCHuvfdeXHnllZse3pZDYnXvLSRWS6z2kFgtsXpdkFgtsVrAsCo1+aruN1WVPsXiZeqzDqnTS+0pC83U6e4SneSUeZe5Sp3faigPXTs2JtfMLV144hyIyvNg4ULJ8zEqdBrQkI3L1O/dlCT3WCX6uixdlulXkuKCNWMnv2E2yIB28Ze4xcY86qpeeVTpXOGYbbOJzNrM87T/F3xHwssl40TQOVrbev9Vrj5qQU6jFhawrSf/1l9PbV2UIvpIXLT15xvM0AKY2Znvb+7LpBs/EuVpObmyKu+/qtD4dMgcjow33ot1bp3nagP4fVfera0bB01cRmuyeKN98mB1PqdxQjOtImFWnqBTabjyZePxWlc2nrfXrH24h38X7vq4DbbPryH0pXkInJjT1bRNhe4GkXxHsu7IOU1Qxicqc2Q9VawlpBx+n51PiLqNSkVTKBPn5JyIuYXFXLWARSDtczX3++6XE1pzsk5l4gASch7OB6LriXalVJz7rcbz/eS8Rvz7PVfTz7hcKl7+GdJPmMf8dJryc298f/kzLQY7bXh9/UzAm9/8Ztx+++34pm/6JgDAW97yFvzWb/0W3vrWt+J1r3vdKga0cUisrjWSWA1IrJZYLbG6+2lJrO7tS2L1WkCfz2OPPbbhkew4VpVIH9vPGK/yEmom42Myy1PuX0ItAct9UkrHfSaa/hBfczmh7VIynT92TSg/Zch8qPzYbBbtZM6fLyTR+UKD4p7oNb8a/hDrTKTz2VUXSaTns7Muo0bP+xpzzdjzq7pmE32uAfTzf/l4LR7pW41Pf/rTfsti9wj69nwpqm/O9uzu2usWCATHFp/+9Kdx8cUXL9nLqnR143s5f/48PvShD+HOO+8Mx7TWuOWWW3D33XevZDTbAInVq4HEaoFAsMuQWL3doFh97XXXbXgkAoFAINgklo/Xi1molfvZbuxkIv2aa67Bn/3Zn+Hv/J2/g4997GM4c+bMpoc0GY899hiuvfZaGf8GsevPIOPfLGT8m4O1Fp/+9KdxzTXXrKrHFfXTVXTt7+9jf38/Ofa3f/u3aNsWV111VXL8qquuwkc+8pGVjWXTkFi9eez6+IHdfwYZ/2Yh498cJFbvBiRWbwd2/Rlk/JuFjH+z2PXxry5er8ri6gitshbETibStdb4nM/5HADAmTNndvLLSpDxbx67/gwy/s1Cxr8ZLKtuO3HiBM6ePYuHHnpoRSMCTp06hWuvvTY5dtddd+H1r3/9yu6xS5BYvT3Y9fEDu/8MMv7NQsa/GUis3n5IrN4u7PozyPg3Cxn/ZrHL418mXsdY/c6Vjefs2bM4ceLEyvpbNXYykS4QCAQCwTI4ODjAAw88gPPnz6+sT2stFBkme+QKNwC4/PLL0TQNHn744eT4ww8/jLNnz65sPAKBQCAQ7DIkVgsEAoFAsN1YR6w+ceIEDmjG3i2EJNIFAoFAcEHi4OBgIwH6xIkTuPHGG/Gud70LX/u1XwsAMMbgXe96F1796lcf+XgEAoFAINhWSKwWCAQCgWC7salYvSnsbCJ9f38fd911V1FBsAuQ8W8eu/4MMv7NQsYvWAZ33HEHXv7yl+NLv/RL8WVf9mX4kR/5ETzxxBP4pm/6pk0PbaXY9e+ZjH/z2PVnkPFvFjJ+wTKQWL0b2PXxA7v/DDL+zULGv1ns+vgF06GstaubuUUgEAgEAsEo/PiP/zje9KY34aGHHsLf+3t/Dz/6oz+Km266adPDEggEAoFA4CGxWiAQCAQCAYck0gUCgUAgEAgEAoFAIBAIBAKBQCDogd70AAQCgUAgEAgEAoFAIBAIBAKBQCDYZkgiXSAQCAQCgUAgEAgEAoFAIBAIBIIeSCJdIBAIBAKBQCAQCAQCgUAgEAgEgh7sbCL9J37iJ/D0pz8dBwcHuOmmm/CHf/iHmx5SB294wxvw9//+38fp06dx5ZVX4mu/9mtx7733Jm2e//znQymVLN/2bd+2oRF38frXv74zvmc961nh/Llz5/CqV70KT33qU3Hq1Cm85CUvwcMPP7zBEad4+tOf3hm/UgqvetWrAGzf+3/f+96Hr/7qr8Y111wDpRTe/va3J+ettfje7/1eXH311Th58iRuueUW/MVf/EXS5lOf+hRe9rKX4cyZM7jkkkvwyle+Eo8//vjGx394eIjXvva1ePazn42LLroI11xzDb7xG78Rf/M3f5P0UfrM3vjGN258/ADwile8ojO2W2+9NWmzre8fQPHfglIKb3rTm0KbTb5/wfHDLsRqYPfjtcTqo4XEaonV634GideCo4TE6qOBxOqjhcTqzceJXY/XEqsFNexkIv2Xf/mXcccdd+Cuu+7CH//xH+OGG27AC1/4QnziE5/Y9NASvPe978WrXvUq/MEf/AHe8Y534PDwEF/1VV+FJ554Iml3++2348EHHwzLD/7gD25oxGX83b/7d5Px/f7v/34495rXvAb/83/+T/zqr/4q3vve9+Jv/uZv8OIXv3iDo03xR3/0R8nY3/GOdwAAvu7rvi602ab3/8QTT+CGG27AT/zETxTP/+AP/iB+9Ed/FG95y1vwgQ98ABdddBFe+MIX4ty5c6HNy172Mvyf//N/8I53vAO/+Zu/ife97334lm/5lo2P/zOf+Qz++I//GN/zPd+DP/7jP8av/dqv4d5778WLXvSiTtvv//7vTz6Tf/fv/t1RDH/w/QPArbfemoztF3/xF5Pz2/r+ASTjfvDBB/HWt74VSim85CUvSdpt6v0Ljhd2JVYDxyNeS6w+OkisdpBYvTgkXgu2BRKrjxYSq48OEqsdNhkndj1eS6wWVGF3EF/2ZV9mX/WqV4X9tm3tNddcY9/whjdscFTD+MQnPmEB2Pe+973h2D/+x//Yfud3fufmBjWAu+66y95www3Fc4888ojd29uzv/qrvxqO/fmf/7kFYO++++4jGuE0fOd3fqd95jOfaY0x1trtfv8A7K//+q+HfWOMPXv2rH3Tm94Ujj3yyCN2f3/f/uIv/qK11to/+7M/swDsH/3RH4U2v/3bv22VUvbjH//4kY3d2u74S/jDP/xDC8D+5V/+ZTh23XXX2R/+4R9e7+BGoDT+l7/85fZrvuZrqtfs2vv/mq/5GvtP/sk/SY5ty/sX7D52NVZbu3vxWmL15iCxerPY9VhtrcRrwWYhsfroILF6c5BYvXnseryWWC3g2DlF+vnz5/GhD30It9xySzimtcYtt9yCu+++e4MjG8ajjz4KALjsssuS47/wC7+Ayy+/HF/8xV+MO++8E5/5zGc2Mbwq/uIv/gLXXHMNrr/+erzsZS/DX/3VXwEAPvShD+Hw8DD5LJ71rGfhaU972lZ+FufPn8fP//zP45u/+ZuhlArHt/39Ex544AE89NBDyfu++OKLcdNNN4X3fffdd+OSSy7Bl37pl4Y2t9xyC7TW+MAHPnDkYx7Co48+CqUULrnkkuT4G9/4Rjz1qU/Fc5/7XLzpTW/CfD7fzAALeM973oMrr7wSX/iFX4hv//Zvxyc/+clwbpfe/8MPP4zf+q3fwitf+crOuW1+/4LdwC7HamA347XE6u2AxOrtwHGJ1YDEa8H6ILH66CGxejsgsXp7cFzitcTqCwuzTQ9gKv72b/8WbdviqquuSo5fddVV+MhHPrKhUQ3DGIN//+//Pf7hP/yH+OIv/uJw/F/9q3+F6667Dtdccw3+5E/+BK997Wtx77334td+7dc2ONqIm266CW9729vwhV/4hXjwwQfxfd/3ffhH/+gf4U//9E/x0EMP4cSJE50f1ldddRUeeuihzQy4B29/+9vxyCOP4BWveEU4tu3vn4Peaem7T+ceeughXHnllcn52WyGyy67bOs+k3PnzuG1r30tXvrSl+LMmTPh+Hd8x3fgS77kS3DZZZfh/e9/P+688048+OCDePOb37zB0TrceuutePGLX4xnPOMZuP/++/Gf/tN/wm233Ya7774bTdPs1Pv/uZ/7OZw+fbpTMrrN71+wO9jVWA3sZryWWL09kFi9+VhxnGI1IPFasD5IrD5aSKzeHkis3o44cZzitcTqCws7l0jfVbzqVa/Cn/7pnyY+aAASf6dnP/vZuPrqq/GCF7wA999/P575zGce9TA7uO2228L2c57zHNx000247rrr8Cu/8is4efLkBkc2HT/zMz+D2267Dddcc004tu3v/7ji8PAQ//Jf/ktYa/GTP/mTybk77rgjbD/nOc/BiRMn8K3f+q14wxvegP39/aMeaoJv+IZvCNvPfvaz8ZznPAfPfOYz8Z73vAcveMELNjiy6XjrW9+Kl73sZTg4OEiOb/P7FwiOArsYryVWC9YBidXbAYnXAkEXEqs3C4nV24NdjdXA8YrXEqsvLOyctcvll1+Opmk6M1g//PDDOHv27IZG1Y9Xv/rV+M3f/E387u/+Lj73cz+3t+1NN90EALjvvvuOYmiTcckll+ALvuALcN999+Hs2bM4f/48HnnkkaTNNn4Wf/mXf4l3vvOd+Df/5t/0ttvm90/vtO+7f/bs2c7kQPP5HJ/61Ke25jOhYP+Xf/mXeMc73pH81byEm266CfP5HB/96EePZoATcP311+Pyyy8P35ddeP8A8Hu/93u49957B/89ANv9/gXbi12M1cDxidcSqzcHidUfPZoBTsCuxmpA4rVgvZBYvVlIrN4cJFZ/9GgGOBG7Gq8lVl942LlE+okTJ3DjjTfiXe96VzhmjMG73vUuPO95z9vgyLqw1uLVr341fv3Xfx3vfve78YxnPGPwmnvuuQcAcPXVV695dIvh8ccfx/3334+rr74aN954I/b29pLP4t5778Vf/dVfbd1n8bM/+7O48sor8c/+2T/rbbfN7/8Zz3gGzp49m7zvxx57DB/4wAfC+37e856HRx55BB/60IdCm3e/+90wxoRfZjYJCvZ/8Rd/gXe+85146lOfOnjNPffcA611p6xrG/DXf/3X+OQnPxm+L9v+/gk/8zM/gxtvvBE33HDDYNttfv+C7cUuxWrg+MVridWbg8Tq7YsVuxqrAYnXgvVCYvVmIbF6c5BYvZ1xYlfjtcTqCxCbnOl0UfzSL/2S3d/ft29729vsn/3Zn9lv+ZZvsZdccol96KGHNj20BN/+7d9uL774Yvue97zHPvjgg2H5zGc+Y6219r777rPf//3fbz/4wQ/aBx54wP7Gb/yGvf766+1XfuVXbnjkEd/1Xd9l3/Oe99gHHnjA/u///b/tLbfcYi+//HL7iU98wlpr7bd927fZpz3tafbd7363/eAHP2if97zn2ec973kbHnWKtm3t0572NPva1742Ob6N7//Tn/60/fCHP2w//OEPWwD2zW9+s/3whz8cZt9+4xvfaC+55BL7G7/xG/ZP/uRP7Nd8zdfYZzzjGfazn/1s6OPWW2+1z33uc+0HPvAB+/u///v28z//8+1LX/rSjY///Pnz9kUvepH93M/9XHvPPfck/yaefPJJa62173//++0P//AP23vuucfef//99ud//uftFVdcYb/xG79x4+P/9Kc/bf/Df/gP9u6777YPPPCAfec732m/5Eu+xH7+53++PXfuXOhjW98/4dFHH7VPecpT7E/+5E92rt/0+xccL+xKrLZ29+O1xOqjhcRqidXrfAaCxGvBUUBi9dFBYvXRQmL15uPErsdridWCGnYykW6ttT/2Yz9mn/a0p9kTJ07YL/uyL7N/8Ad/sOkhdQCguPzsz/6stdbav/qrv7Jf+ZVfaS+77DK7v79vP+/zPs9+93d/t3300Uc3O3CGr//6r7dXX321PXHihP2cz/kc+/Vf//X2vvvuC+c/+9nP2n/7b/+tvfTSS+1TnvIU+8//+T+3Dz744AZH3MXv/M7vWAD23nvvTY5v4/v/3d/93eJ35uUvf7m11lpjjP2e7/kee9VVV9n9/X37ghe8oPNcn/zkJ+1LX/pSe+rUKXvmzBn7Td/0TfbTn/70xsf/wAMPVP9N/O7v/q611toPfehD9qabbrIXX3yxPTg4sF/0RV9k/8t/+S9JMN3U+D/zmc/Yr/qqr7JXXHGF3dvbs9ddd529/fbbO0RjW98/4ad+6qfsyZMn7SOPPNK5ftPvX3D8sAux2trdj9cSq48WEqslVq/zGQgSrwVHBYnVRwOJ1UcLidWbjxO7Hq8lVgtqUNZaWxCqCwQCgUAgEAgEAoFAIBAIBAKBQCDADnqkCwQCgUAgEAgEAoFAIBAIBAKBQHCUkES6QCAQCAQCgUAgEAgEAoFAIBAIBD2QRLpAIBAIBAKBQCAQCAQCgUAgEAgEPZBEukAgEAgEAoFAIBAIBAKBQCAQCAQ9kES6QCAQCAQCgUAgEAgEAoFAIBAIBD2QRLpAIBAIBAKBQCAQCAQCgUAgEAgEPZBEukAgEAgEAoFAIBAIBAKBQCAQCAQ9kES6QCAQCAQCgUAgEAgEAoFAIBAIBD2QRLpAIBAIBAKBQCAQCAQCgUAgEAgEPZBEukAgEAgEAoFAIBAIBAKBQCAQCAQ9kES6QCAQCAQCgUAgEAgEAoFAIBAIBD2QRLpAIBAIBAKBQCAQCAQCgUAgEAgEPfj/KemN4IXe9bMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1500x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization\n",
    "vmin = min(vip_img.min(), jax_img.min())\n",
    "vmax = max(vip_img.max(), jax_img.max())\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
    "im0 = axes[0].imshow(vip_img, cmap=\"inferno\", vmin=vmin, vmax=vmax)\n",
    "axes[0].set_title(\"VIP (PSF-applied)\")\n",
    "fig.colorbar(im0, ax=axes[0], fraction=0.046, pad=0.04)\n",
    "\n",
    "im1 = axes[1].imshow(jax_img, cmap=\"inferno\", vmin=vmin, vmax=vmax)\n",
    "axes[1].set_title(\"GRaTeR-JAX (Optimizer)\")\n",
    "fig.colorbar(im1, ax=axes[1], fraction=0.046, pad=0.04)\n",
    "\n",
    "im2 = axes[2].imshow(residual, cmap=\"seismic\", vmin=-max_resid, vmax=max_resid)\n",
    "axes[2].set_title(\"Residual\")\n",
    "fig.colorbar(im2, ax=axes[2], fraction=0.046, pad=0.04)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20c4bda5",
   "metadata": {},
   "source": [
    "### GRaTeR-JAX vs VIP Runtimes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "29acdb39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "127 ms ± 1.29 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "2.36 ms ± 149 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "vip_model_runtime = %timeit -o vip_disk.compute_scattered_light(halfNbSlices=25)\n",
    "grater_jax_model_runtime = %timeit -o optimizer.get_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b86df39",
   "metadata": {},
   "source": [
    "## GRaTeR-JAX vs VIP Gradient Benchmarks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b08f251f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from vip_hci.fm.scattered_light_disk import ScatteredLightDisk as VIP_ScatteredLightDisk\n",
    "from grater_jax.optimization.optimize_framework import Optimizer, OptimizeUtils\n",
    "from grater_jax.disk_model.SLD_ojax import ScatteredLightDisk\n",
    "from grater_jax.disk_model.SLD_utils import (\n",
    "    DustEllipticalDistribution2PowerLaws,\n",
    "    InterpolatedUnivariateSpline_SPF,\n",
    "    EMP_PSF,\n",
    ")\n",
    "from grater_jax.disk_model.objective_functions import Parameter_Index\n",
    "\n",
    "# Synthetic target\n",
    "def generate_vip_synthetic_target(nx=200, ny=200):\n",
    "    density = {\n",
    "        \"name\": \"2PowerLaws\",\n",
    "        \"a\": 60.0,\n",
    "        \"ksi0\": 1.0,\n",
    "        \"gamma\": 2.0,\n",
    "        \"beta\": 1.0,\n",
    "        \"ain\": 5.0,\n",
    "        \"aout\": -5.0,\n",
    "        \"e\": 0.0,\n",
    "        \"dens_at_r0\": 1.0,\n",
    "    }\n",
    "    phase = {\"name\": \"HG\", \"g\": 0.3, \"polar\": False}\n",
    "\n",
    "    vip_disk = VIP_ScatteredLightDisk(\n",
    "        nx=nx, ny=ny,\n",
    "        distance=50.0,\n",
    "        itilt=60.0,\n",
    "        omega=0.0,\n",
    "        pxInArcsec=0.01225,\n",
    "        pa=0.0,\n",
    "        density_dico=density,\n",
    "        spf_dico=phase,\n",
    "        xdo=0.0, ydo=0.0\n",
    "    )\n",
    "\n",
    "    vip_img = vip_disk.compute_scattered_light(halfNbSlices=25)\n",
    "    return vip_img\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b0092846",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Synthetic VIP target\n",
    "target_image = generate_vip_synthetic_target(nx=200, ny=200)\n",
    "err_map = np.ones_like(target_image, dtype=np.float32)\n",
    "\n",
    "# GRaTeR-JAX parameter setup\n",
    "start_disk_params = Parameter_Index.disk_params.copy()\n",
    "start_spf_params  = InterpolatedUnivariateSpline_SPF.params.copy()\n",
    "\n",
    "# No PSF → both the class and params are None\n",
    "start_psf_params  = None\n",
    "\n",
    "start_misc_params = Parameter_Index.misc_params.copy()\n",
    "\n",
    "start_disk_params.update({\n",
    "    \"sma\": 46.0,\n",
    "    \"inclination\": 80.0,\n",
    "    \"position_angle\": 27.5,\n",
    "    \"x_center\": 100.0,\n",
    "    \"y_center\": 100.0,\n",
    "    \"flux_scaling\": 1.0,\n",
    "})\n",
    "\n",
    "start_spf_params[\"num_knots\"] = 5\n",
    "start_spf_params[\"knot_values\"] = np.ones(5) * 0.5\n",
    "\n",
    "start_misc_params.update({\n",
    "    \"distance\": 50.0,\n",
    "    \"nx\": 200,\n",
    "    \"ny\": 200,\n",
    "    \"pxInArcsec\": 0.01225,\n",
    "})\n",
    "\n",
    "# Initialize optimizer with NO PSF model\n",
    "opt = Optimizer(\n",
    "    ScatteredLightDisk,\n",
    "    DustEllipticalDistribution2PowerLaws,\n",
    "    InterpolatedUnivariateSpline_SPF,\n",
    "    PSFModel=None,\n",
    "    disk_params=start_disk_params,\n",
    "    spf_params=start_spf_params,\n",
    "    psf_params=start_psf_params,\n",
    "    misc_params=start_misc_params,\n",
    ")\n",
    "\n",
    "# SPF knots\n",
    "opt.inc_bound_knots()\n",
    "opt.estimate_flux_scaling(target_image)\n",
    "\n",
    "# Compile model + gradient (no PSF path)\n",
    "opt.jit_compile_model()\n",
    "opt.jit_compile_gradient(target_image, err_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2ffc7da9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from grater_jax.disk_model.objective_functions import log_likelihood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "716f8f6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "fit_keys = [\"sma\", \"alpha_in\", \"inclination\", \"position_angle\", \"x_center\", \"y_center\", \"alpha_out\", \"knot_values\"]\n",
    "\n",
    "def analytic_grad():\n",
    "    return opt.get_gradient(fit_keys, target_image, err_map)\n",
    "\n",
    "def numeric_grad(eps=1e-6):\n",
    "    \"\"\"\n",
    "    Finite-difference numerical gradient for the current optimizer state.\n",
    "    Supports both scalar parameters and vector-valued knot_values.\n",
    "    \"\"\"\n",
    "\n",
    "    base_params = opt.get_values(keys=fit_keys)\n",
    "    grad = []\n",
    "\n",
    "    for k in range(len(fit_keys)):\n",
    "        val = base_params[k]\n",
    "\n",
    "        if np.isscalar(val):\n",
    "            params_plus = base_params.copy()\n",
    "            params_minus = base_params.copy()\n",
    "\n",
    "            params_plus[k] = val + eps\n",
    "            params_minus[k] = val - eps\n",
    "\n",
    "            ll_plus = opt.get_objective_likelihood(\n",
    "                params_plus, fit_keys, target_image, err_map\n",
    "            )\n",
    "            ll_minus = opt.get_objective_likelihood(\n",
    "                params_minus, fit_keys, target_image, err_map\n",
    "            )\n",
    "\n",
    "            grad.append((ll_plus - ll_minus) / (2 * eps))\n",
    "        else:\n",
    "            g = np.zeros_like(val)\n",
    "            for i in range(len(val)):\n",
    "                params_plus = base_params.copy()\n",
    "                params_minus = base_params.copy()\n",
    "                v_plus = val.copy()\n",
    "                v_minus = val.copy()\n",
    "                v_plus[i] += eps\n",
    "                v_minus[i] -= eps\n",
    "                params_plus[k] = v_plus\n",
    "                params_minus[k] = v_minus\n",
    "                ll_plus = opt.get_objective_likelihood(\n",
    "                    params_plus, fit_keys, target_image, err_map\n",
    "                )\n",
    "                ll_minus = opt.get_objective_likelihood(\n",
    "                    params_minus, fit_keys, target_image, err_map\n",
    "                )\n",
    "                g[i] = (ll_plus - ll_minus) / (2 * eps)\n",
    "            grad.append(g)\n",
    "\n",
    "    return grad\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ecf8e0ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_vip_disk(params_dict):\n",
    "    density = {\n",
    "        \"name\": \"2PowerLaws\",\n",
    "        \"a\": params_dict[\"sma\"],\n",
    "        \"ksi0\": 1.0,\n",
    "        \"gamma\": 2.0,\n",
    "        \"beta\": 1.0,\n",
    "        \"ain\": params_dict[\"alpha_in\"],\n",
    "        \"aout\": params_dict[\"alpha_out\"],\n",
    "        \"e\": 0.0,\n",
    "        \"dens_at_r0\": 1.0,\n",
    "    }\n",
    "\n",
    "    phase = {\n",
    "        \"name\": \"HG\",\n",
    "        \"g\": params_dict[\"g\"],\n",
    "        \"polar\": False,    }\n",
    "\n",
    "    return VIP_ScatteredLightDisk(\n",
    "        nx=200,\n",
    "        ny=200,\n",
    "        distance=50.0,\n",
    "        itilt=params_dict[\"inclination\"],\n",
    "        omega=0.0,\n",
    "        pxInArcsec=0.01225,\n",
    "        pa=params_dict[\"position_angle\"],\n",
    "        density_dico=density,\n",
    "        spf_dico=phase,\n",
    "        xdo=100-params_dict[\"x_center\"],\n",
    "        ydo=100-params_dict[\"y_center\"]\n",
    "    )\n",
    "\n",
    "vip_fit_keys = [\"sma\", \"alpha_in\", \"inclination\", \"position_angle\", \"x_center\", \"y_center\", \"alpha_out\", \"g\"]\n",
    "vip_fit_values = [46.0, 5.0, 80.0, 27.5, 100.0, 100.0, -5.0, 0.3]\n",
    "base_dict = dict(zip(vip_fit_keys, vip_fit_values))\n",
    "\n",
    "def numeric_grad_vip(eps=1e-6):\n",
    "    \"\"\"\n",
    "    Finite-difference numerical gradient using VIP scattered-light images.\n",
    "    \"\"\"\n",
    "    grad = []\n",
    "\n",
    "    for k, key in enumerate(vip_fit_keys):\n",
    "        val = vip_fit_values[k]\n",
    "\n",
    "        if np.isscalar(val):\n",
    "            dict_plus = dict(base_dict)\n",
    "            dict_minus = dict(base_dict)\n",
    "            dict_plus[key] = val + eps\n",
    "            dict_minus[key] = val - eps\n",
    "\n",
    "            img_plus = make_vip_disk(dict_plus).compute_scattered_light(halfNbSlices=25)\n",
    "            img_minus = make_vip_disk(dict_minus).compute_scattered_light(halfNbSlices=25)\n",
    "\n",
    "            ll_plus = log_likelihood(img_plus, target_image, err_map)\n",
    "            ll_minus = log_likelihood(img_minus, target_image, err_map)\n",
    "\n",
    "            grad.append((ll_plus - ll_minus) / (2 * eps))\n",
    "\n",
    "    return grad\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "752f227d",
   "metadata": {},
   "source": [
    "### Numeric vs Analytic Gradients\n",
    "\n",
    "VIP does not have the capability to generate numeric gradients, so I will isolate the performance improvement of GRaTeR-JAX's analyitic gradient function by comparing the analyitic gradient with a numeric function for a GRaTeR-JAX objective function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f820e7c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analytic Gradient: [7.32e-08, 1.41e-07, -1.18e-07, 2.35e-08, 1.14e-08, -1.84e-09, 9.44e-09, -3.00e-07, -1.78e-07, 2.78e-06, -7.41e-07, -6.93e-07]\n",
      "Numeric Gradient: [7.32e-08, 1.41e-07, -1.18e-07, 2.35e-08, 2.74e-08, 3.55e-09, 9.44e-09, -3.00e-07, -1.78e-07, 2.78e-06, -7.41e-07, -6.93e-07]\n"
     ]
    }
   ],
   "source": [
    "def compress_grad(grad_list):\n",
    "    out = []\n",
    "    for g in grad_list:\n",
    "        if np.isscalar(g):\n",
    "            out.append(float(g))\n",
    "        else:\n",
    "            out.extend(np.asarray(g).ravel().astype(float).tolist())\n",
    "    return out\n",
    "\n",
    "def format_list(vals, ndigits=2):\n",
    "    return \"[\" + \", \".join(f\"{v:.{ndigits}e}\" for v in vals) + \"]\"\n",
    "\n",
    "print(\"Analytic Gradient:\", format_list(compress_grad(analytic_grad()), 2))\n",
    "print(\"Numeric Gradient:\",  format_list(compress_grad(numeric_grad()), 2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4388aeca",
   "metadata": {},
   "source": [
    "### VIP (numeric) vs GRaTeR-JAX Gradient Runtimes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "69d3f260",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "271 ms ± 2.67 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "16.8 ms ± 21 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "2.04 s ± 3.64 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "numeric_grad_runtime = %timeit -o numeric_grad()\n",
    "analytic_grad_runtime = %timeit -o analytic_grad()\n",
    "numeric_grad_vip_runtime = %timeit -o numeric_grad_vip()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "322f5cb9",
   "metadata": {},
   "source": [
    "# Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "404a64b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "# Performance Takeaways\n",
       "\n",
       "GRaTeR-JAX delivers substantial performance improvements over the legacy VIP GRaTeR disk framework in both **model generation** and **gradient evaluation**. These gains are enabled by JAX-based vectorization, XLA compilation, and analytic differentiation, making GRaTeR-JAX significantly more scalable for optimization and inference workflows.\n",
       "\n",
       "---\n",
       "\n",
       "## Benchmark Summary\n",
       "\n",
       "| Task | Framework | Method | Runtime per Loop | Speedup |\n",
       "|-----:|-----------|--------|------------------|---------|\n",
       "| Model generation | VIP GRaTeR | Native | 127 ms ± 1.29 ms | 1× |\n",
       "| Model generation | GRaTeR-JAX | JAX model | 2.36 ms ± 149 μs | **~54×** |\n",
       "| Gradient | VIP GRaTeR | Numeric FD | 2.04 s ± 3.64 ms | 1× |\n",
       "| Gradient | GRaTeR-JAX | Numeric FD | 271 ms ± 2.67 ms | ~8× |\n",
       "| Gradient | GRaTeR-JAX | Analytic AD | 16.8 ms ± 21 μs | **~121×** |\n",
       "\n",
       "---\n",
       "\n",
       "## Model Generation Performance\n",
       "\n",
       "We compare scattered-light model generation between:\n",
       "\n",
       "- **VIP GRaTeR**: `vip_disk.compute_scattered_light`\n",
       "- **GRaTeR-JAX**: `optimizer.get_model`\n",
       "\n",
       "~~~python\n",
       "vip_model_runtime = %timeit -o vip_disk.compute_scattered_light(halfNbSlices=25)\n",
       "grater_jax_runtime = %timeit -o optimizer.get_model()\n",
       "~~~\n",
       "\n",
       "**Results**\n",
       "\n",
       "- VIP GRaTeR  \n",
       "  127 ms ± 1.29 ms per loop\n",
       "\n",
       "- GRaTeR-JAX  \n",
       "  2.36 ms ± 149 μs per loop\n",
       "\n",
       "**Speedup**\n",
       "\n",
       "GRaTeR-JAX achieves a **~54× reduction** in model generation runtime.\n",
       "\n",
       "This improvement dramatically reduces the cost of iterative workflows such as gradient-based fitting, MCMC sampling, and large-scale parameter sweeps.\n",
       "\n",
       "---\n",
       "\n",
       "## Gradient Computation Performance\n",
       "\n",
       "Three gradient computation scenarios are evaluated:\n",
       "\n",
       "~~~python\n",
       "numeric_grad_runtime = %timeit -o numeric_grad()\n",
       "analytic_grad_runtime = %timeit -o analytic_grad()\n",
       "numeric_grad_vip_runtime = %timeit -o numeric_grad_vip()\n",
       "~~~\n",
       "\n",
       "**Results**\n",
       "\n",
       "- GRaTeR-JAX numeric gradient  \n",
       "  271 ms ± 2.67 ms per loop\n",
       "\n",
       "- GRaTeR-JAX analytic gradient  \n",
       "  16.8 ms ± 21 μs per loop\n",
       "\n",
       "- VIP numeric gradient  \n",
       "  2.04 s ± 3.64 ms per loop\n",
       "\n",
       "### Notes on Comparability\n",
       "\n",
       "- The **numeric and analytic gradients** are computed on the **same GRaTeR-JAX model**, allowing for a direct comparison.\n",
       "- The **VIP numeric gradient** corresponds to a different model configuration because VIP does **not support spline-based SPF functions**. It is included as a realistic reference for legacy workflows rather than a strict apples-to-apples comparison.\n",
       "\n",
       "---\n",
       "\n",
       "## Gradient Speedups\n",
       "\n",
       "- **Analytic vs numeric GRaTeR-JAX**  \n",
       "  → **~16.1× speedup**\n",
       "\n",
       "- **Analytic GRaTeR-JAX vs numeric VIP**  \n",
       "  → **~121× speedup** in gradient evaluation\n",
       "\n",
       "In addition to performance gains, analytic gradients eliminate numerical instability and poor scaling behavior inherent to finite-difference methods.\n",
       "\n",
       "---\n",
       "\n",
       "## Summary\n",
       "\n",
       "- Model generation is nearly **54× faster** with GRaTeR-JAX.\n",
       "- Analytic gradients provide:\n",
       "  - Significant runtime reductions\n",
       "  - Improved numerical stability\n",
       "  - Better scaling with parameter dimensionality\n",
       "- GRaTeR-JAX enables workflows that are impractical with VIP, including high-dimensional optimization, HMC/NUTS sampling, and interactive modeling.\n",
       "\n",
       "Overall, GRaTeR-JAX represents a substantial advance in both **performance** and **scalability** for scattered-light disk modeling.\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Markdown, display\n",
    "\n",
    "def fmt_time(seconds):\n",
    "    \"\"\"\n",
    "    Format a runtime in seconds using readable units.\n",
    "    \"\"\"\n",
    "    if seconds >= 1:\n",
    "        return f\"{seconds:.3g} s\"\n",
    "    elif seconds >= 1e-3:\n",
    "        return f\"{seconds * 1e3:.3g} ms\"\n",
    "    elif seconds >= 1e-6:\n",
    "        return f\"{seconds * 1e6:.3g} μs\"\n",
    "    else:\n",
    "        return f\"{seconds * 1e9:.3g} ns\"\n",
    "\n",
    "\n",
    "def fmt_runtime(result):\n",
    "    \"\"\"\n",
    "    Format a %timeit -o result as mean ± std.\n",
    "    \"\"\"\n",
    "    return f\"{fmt_time(result.average)} ± {fmt_time(result.stdev)}\"\n",
    "\n",
    "\n",
    "model_speedup = vip_model_runtime.average / grater_jax_model_runtime.average\n",
    "jax_grad_speedup = numeric_grad_runtime.average / analytic_grad_runtime.average\n",
    "vip_grad_speedup = numeric_grad_vip_runtime.average / analytic_grad_runtime.average\n",
    "vip_numeric_grad_speedup = numeric_grad_vip_runtime.average / numeric_grad_runtime.average\n",
    "\n",
    "display(Markdown(f\"\"\"\n",
    "# Performance Takeaways\n",
    "\n",
    "GRaTeR-JAX delivers substantial performance improvements over the legacy VIP GRaTeR disk framework in both **model generation** and **gradient evaluation**. These gains are enabled by JAX-based vectorization, XLA compilation, and analytic differentiation, making GRaTeR-JAX significantly more scalable for optimization and inference workflows.\n",
    "\n",
    "---\n",
    "\n",
    "## Benchmark Summary\n",
    "\n",
    "| Task | Framework | Method | Runtime per Loop | Speedup |\n",
    "|-----:|-----------|--------|------------------|---------|\n",
    "| Model generation | VIP GRaTeR | Native | {fmt_runtime(vip_model_runtime)} | 1× |\n",
    "| Model generation | GRaTeR-JAX | JAX model | {fmt_runtime(grater_jax_model_runtime)} | **~{model_speedup:.0f}×** |\n",
    "| Gradient | VIP GRaTeR | Numeric FD | {fmt_runtime(numeric_grad_vip_runtime)} | 1× |\n",
    "| Gradient | GRaTeR-JAX | Numeric FD | {fmt_runtime(numeric_grad_runtime)} | ~{vip_numeric_grad_speedup:.0f}× |\n",
    "| Gradient | GRaTeR-JAX | Analytic AD | {fmt_runtime(analytic_grad_runtime)} | **~{vip_grad_speedup:.0f}×** |\n",
    "\n",
    "---\n",
    "\n",
    "## Model Generation Performance\n",
    "\n",
    "We compare scattered-light model generation between:\n",
    "\n",
    "- **VIP GRaTeR**: `vip_disk.compute_scattered_light`\n",
    "- **GRaTeR-JAX**: `optimizer.get_model`\n",
    "\n",
    "~~~python\n",
    "vip_model_runtime = %timeit -o vip_disk.compute_scattered_light(halfNbSlices=25)\n",
    "grater_jax_runtime = %timeit -o optimizer.get_model()\n",
    "~~~\n",
    "\n",
    "**Results**\n",
    "\n",
    "- VIP GRaTeR  \n",
    "  {fmt_runtime(vip_model_runtime)} per loop\n",
    "\n",
    "- GRaTeR-JAX  \n",
    "  {fmt_runtime(grater_jax_model_runtime)} per loop\n",
    "\n",
    "**Speedup**\n",
    "\n",
    "GRaTeR-JAX achieves a **~{model_speedup:.0f}× reduction** in model generation runtime.\n",
    "\n",
    "This improvement dramatically reduces the cost of iterative workflows such as gradient-based fitting, MCMC sampling, and large-scale parameter sweeps.\n",
    "\n",
    "---\n",
    "\n",
    "## Gradient Computation Performance\n",
    "\n",
    "Three gradient computation scenarios are evaluated:\n",
    "\n",
    "~~~python\n",
    "numeric_grad_runtime = %timeit -o numeric_grad()\n",
    "analytic_grad_runtime = %timeit -o analytic_grad()\n",
    "numeric_grad_vip_runtime = %timeit -o numeric_grad_vip()\n",
    "~~~\n",
    "\n",
    "**Results**\n",
    "\n",
    "- GRaTeR-JAX numeric gradient  \n",
    "  {fmt_runtime(numeric_grad_runtime)} per loop\n",
    "\n",
    "- GRaTeR-JAX analytic gradient  \n",
    "  {fmt_runtime(analytic_grad_runtime)} per loop\n",
    "\n",
    "- VIP numeric gradient  \n",
    "  {fmt_runtime(numeric_grad_vip_runtime)} per loop\n",
    "\n",
    "### Notes on Comparability\n",
    "\n",
    "- The **numeric and analytic gradients** are computed on the **same GRaTeR-JAX model**, allowing for a direct comparison.\n",
    "- The **VIP numeric gradient** corresponds to a different model configuration because VIP does **not support spline-based SPF functions**. It is included as a realistic reference for legacy workflows rather than a strict apples-to-apples comparison.\n",
    "\n",
    "---\n",
    "\n",
    "## Gradient Speedups\n",
    "\n",
    "- **Analytic vs numeric GRaTeR-JAX**  \n",
    "  → **~{jax_grad_speedup:.1f}× speedup**\n",
    "\n",
    "- **Analytic GRaTeR-JAX vs numeric VIP**  \n",
    "  → **~{vip_grad_speedup:.0f}× speedup** in gradient evaluation\n",
    "\n",
    "In addition to performance gains, analytic gradients eliminate numerical instability and poor scaling behavior inherent to finite-difference methods.\n",
    "\n",
    "---\n",
    "\n",
    "## Summary\n",
    "\n",
    "- Model generation is nearly **{model_speedup:.0f}× faster** with GRaTeR-JAX.\n",
    "- Analytic gradients provide:\n",
    "  - Significant runtime reductions\n",
    "  - Improved numerical stability\n",
    "  - Better scaling with parameter dimensionality\n",
    "- GRaTeR-JAX enables workflows that are impractical with VIP, including high-dimensional optimization, HMC/NUTS sampling, and interactive modeling.\n",
    "\n",
    "Overall, GRaTeR-JAX represents a substantial advance in both **performance** and **scalability** for scattered-light disk modeling.\n",
    "\"\"\"))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "package-env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}