{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "55255214",
   "metadata": {},
   "source": [
    "```{try_on_binder}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "05868d0b",
   "metadata": {
    "load": "myst_code_init.py",
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The pymor.discretizers.builtin.gui.jupyter extension is already loaded. To reload it, use:\n",
      "  %reload_ext pymor.discretizers.builtin.gui.jupyter\n"
     ]
    }
   ],
   "source": [
    "from IPython import get_ipython\n",
    "ip = get_ipython()\n",
    "if ip is not None:\n",
    "    ip.run_line_magic('load_ext', 'pymor.discretizers.builtin.gui.jupyter')\n",
    "    ip.run_line_magic('matplotlib', 'inline')\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning, module='torch')\n",
    "import pymor.tools.random\n",
    "pymor.tools.random._default_random_state = None\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ddf7124",
   "metadata": {},
   "source": [
    "# Tutorial: Model order reduction with artificial neural networks\n",
    "\n",
    "Recent success of artificial neural networks led to the development of several\n",
    "methods for model order reduction using neural networks. pyMOR provides the\n",
    "functionality for a simple approach developed by Hesthaven and Ubbiali in {cite}`HU18`.\n",
    "For training and evaluation of the neural networks, [PyTorch](<https://pytorch.org>) is used.\n",
    "\n",
    "In this tutorial we will learn about feedforward neural networks, the basic\n",
    "idea of the approach by Hesthaven et al., and how to use it in pyMOR.\n",
    "\n",
    "## Feedforward neural networks\n",
    "\n",
    "We aim at approximating a mapping {math}`h\\colon\\mathcal{P}\\rightarrow Y`\n",
    "between some input space {math}`\\mathcal{P}\\subset\\mathbb{R}^p` (in our case the\n",
    "parameter space) and an output space {math}`Y\\subset\\mathbb{R}^m` (in our case the\n",
    "reduced space), given a set {math}`S=\\{(\\mu_i,h(\\mu_i))\\in\\mathcal{P}\\times Y: i=1,\\dots,N\\}`\n",
    "of samples, by means of an artificial neural network. In this context, neural\n",
    "networks serve as a special class of functions that are able to \"learn\" the\n",
    "underlying structure of the sample set {math}`S` by adjusting their weights.\n",
    "More precisely, feedforward neural networks consist of several layers, each\n",
    "comprising a set of neurons that are connected to neurons in adjacent layers.\n",
    "A so-called \"weight\" is assigned to each of those connections. The weights in\n",
    "the neural network can be adjusted while fitting the neural network to the\n",
    "given sample set. For a given input {math}`\\mu\\in\\mathcal{P}`, the weights between the\n",
    "input layer and the first hidden layer (the one after the input layer) are\n",
    "multiplied with the respective values in {math}`\\mu` and summed up. Subsequently,\n",
    "a so-called \"bias\" (also adjustable during training) is added and the result is\n",
    "assigned to the corresponding neuron in the first hidden layer. Before passing\n",
    "those values to the following layer, a (non-linear) activation function\n",
    "{math}`\\rho\\colon\\mathbb{R}\\rightarrow\\mathbb{R}` is applied. If {math}`\\rho`\n",
    "is linear, the function implemented by the neural network is affine, since\n",
    "solely affine operations were performed. Hence, one usually chooses a\n",
    "non-linear activation function to introduce non-linearity in the neural network\n",
    "and thus increase its approximation capability. In some sense, the input\n",
    "{math}`\\mu` is passed through the neural network, affine-linearly combined with the\n",
    "other inputs and non-linearly transformed. These steps are repeated in several\n",
    "layers.\n",
    "\n",
    "The following figure shows a simple example of a neural network with two hidden\n",
    "layers, an input size of two and an output size of three. Each edge between\n",
    "neurons has a corresponding weight that is learnable in the training phase.\n",
    "\n",
    "```{image} neural_network.png\n",
    ":alt: Feedforward neural network\n",
    ":width: 100%\n",
    "```\n",
    "\n",
    "To train the neural network, one considers a so-called \"loss function\", that\n",
    "measures how the neural network performs on the training set {math}`S`, i.e.\n",
    "how accurately the neural network reproduces the output {math}`h(\\mu_i)` given\n",
    "the input {math}`\\mu_i`. The weights of the neural network are adjusted\n",
    "iteratively such that the loss function is successively minimized. To this end,\n",
    "one typically uses a Quasi-Newton method for small neural networks or a\n",
    "(stochastic) gradient descent method for deep neural networks (those with many\n",
    "hidden layers).\n",
    "\n",
    "A possibility to use feedforward neural networks in combination with reduced\n",
    "basis methods will be introduced in the following section.\n",
    "\n",
    "## A non-intrusive reduced order method using artificial neural networks\n",
    "\n",
    "We now assume that we are given a parametric pyMOR {{ Model }} for which we want\n",
    "to compute a reduced order surrogate {{ Model }} using a neural network. In this\n",
    "example, we consider the following two-dimensional diffusion problem with\n",
    "parametrized diffusion, right hand side and Dirichlet boundary condition:\n",
    "\n",
    "```{math}\n",
    "-\\nabla \\cdot \\big(\\sigma(x, \\mu) \\nabla u(x, \\mu) \\big) = f(x, \\mu),\\quad x=(x_1,x_2) \\in \\Omega,\n",
    "```\n",
    "\n",
    "on the domain {math}`\\Omega:= (0, 1)^2 \\subset \\mathbb{R}^2` with data\n",
    "functions {math}`f((x_1, x_2), \\mu) = 10 \\cdot \\mu + 0.1`,\n",
    "{math}`\\sigma((x_1, x_2), \\mu) = (1 - x_1) \\cdot \\mu + x_1`, where\n",
    "{math}`\\mu \\in (0.1, 1)` denotes the parameter. Further, we apply the\n",
    "Dirichlet boundary conditions\n",
    "\n",
    "```{math}\n",
    "u((x_1, x_2), \\mu) = 2x_1\\mu + 0.5,\\quad x=(x_1, x_2) \\in \\partial\\Omega.\n",
    "```\n",
    "\n",
    "We discretize the problem using pyMOR's builtin discretization toolkit as\n",
    "explained in {doc}`tutorial_builtin_discretizer`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1d85adce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "077f6b20e5834c4cb2e7c3987f7ae050",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pymor.basic import *\n",
    "\n",
    "problem = StationaryProblem(\n",
    "      domain=RectDomain(),\n",
    "\n",
    "      rhs=LincombFunction(\n",
    "          [ExpressionFunction('10', 2), ConstantFunction(1., 2)],\n",
    "          [ProjectionParameterFunctional('mu'), 0.1]),\n",
    "\n",
    "      diffusion=LincombFunction(\n",
    "          [ExpressionFunction('1 - x[0]', 2), ExpressionFunction('x[0]', 2)],\n",
    "          [ProjectionParameterFunctional('mu'), 1]),\n",
    "\n",
    "      dirichlet_data=LincombFunction(\n",
    "          [ExpressionFunction('2 * x[0]', 2), ConstantFunction(1., 2)],\n",
    "          [ProjectionParameterFunctional('mu'), 0.5]),\n",
    "\n",
    "      name='2DProblem'\n",
    "  )\n",
    "\n",
    "fom, _ = discretize_stationary_cg(problem, diameter=1/50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44ead40e",
   "metadata": {},
   "source": [
    "Since we employ a single {{ Parameter }}, and thus use the same range for each\n",
    "parameter, we can create the {{ ParameterSpace }} using the following line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "95d3d88c",
   "metadata": {},
   "outputs": [],
   "source": [
    "parameter_space = fom.parameters.space((0.1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10911d05",
   "metadata": {},
   "source": [
    "The main idea of the approach by Hesthaven et al. is to approximate the mapping\n",
    "from the {{ Parameters }} to the coefficients of the respective solution in a\n",
    "reduced basis by means of a neural network. Thus, in the online phase, one\n",
    "performs a forward pass of the {{ Parameters }} through the neural networks and\n",
    "obtains the approximated reduced coordinates. To derive the corresponding\n",
    "high-fidelity solution, one can further use the reduced basis and compute the\n",
    "linear combination defined by the reduced coefficients. The reduced basis is\n",
    "created via POD.\n",
    "\n",
    "The method described above is \"non-intrusive\", which means that no deep insight\n",
    "into the model or its implementation is required and it is completely\n",
    "sufficient to be able to generate full order snapshots for a randomly chosen\n",
    "set of parameters. This is one of the main advantages of the proposed approach,\n",
    "since one can simply train a neural network, check its performance and resort\n",
    "to a different method if the neural network does not provide proper\n",
    "approximation results.\n",
    "\n",
    "In pyMOR, there exists a training routine for feedforward neural networks. This\n",
    "procedure is part of a reductor and it is not necessary to write a custom\n",
    "training algorithm for each specific problem. However, it is sometimes\n",
    "necessary to try different architectures for the neural network to find the one\n",
    "that best fits the problem at hand. In the reductor, one can easily adjust the\n",
    "number of layers and the number of neurons in each hidden layer, for instance.\n",
    "Furthermore, it is also possible to change the deployed activation function.\n",
    "\n",
    "To train the neural network, we create a training and a validation set\n",
    "consisting of 100 and 20 randomly chosen {{ parameter_values }}, respectively:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b7ce6dac",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_set = parameter_space.sample_uniformly(100)\n",
    "validation_set = parameter_space.sample_randomly(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2af0a942",
   "metadata": {},
   "source": [
    "In this tutorial, we construct the reduced basis such that no more modes than\n",
    "required to bound the l2-approximation error by a given value are used.\n",
    "The l2-approximation error is  the error of the orthogonal projection (in the\n",
    "l2-sense) of the training snapshots onto the reduced basis. That is, we\n",
    "prescribe `l2_err` in the reductor. It is also possible to determine a relative\n",
    "or absolute tolerance (in the singular values) that should not be exceeded on\n",
    "the training set. Further, one can preset the size of the reduced basis.\n",
    "\n",
    "The training is aborted when a neural network that guarantees our prescribed\n",
    "tolerance is found. If we set `ann_mse` to `None`, this function will\n",
    "automatically train several neural networks with different initial weights and\n",
    "select the one leading to the best results on the validation set. We can also\n",
    "set `ann_mse` to `'like_basis'`. Then, the algorithm tries to train a neural\n",
    "network that leads to a mean squared error on the training set that is as small\n",
    "as the error of the reduced basis. If the maximal number of restarts is reached\n",
    "without finding a network that fulfills the tolerances, an exception is raised.\n",
    "In such a case, one could try to change the architecture of the neural network\n",
    "or switch to `ann_mse=None` which is guaranteed to produce a reduced order\n",
    "model (perhaps with insufficient approximation properties).\n",
    "\n",
    "We can now construct a reductor with prescribed error for the basis and mean\n",
    "squared error of the neural network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9d9dc46f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymor.reductors.neural_network import NeuralNetworkReductor\n",
    "\n",
    "reductor = NeuralNetworkReductor(fom,\n",
    "                                 training_set,\n",
    "                                 validation_set,\n",
    "                                 l2_err=1e-5,\n",
    "                                 ann_mse=1e-5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "403d64ef",
   "metadata": {},
   "source": [
    "To reduce the model, i.e. compute a reduced basis via POD and train the neural\n",
    "network, we use the respective function of the\n",
    "{class}`~pymor.reductors.neural_network.NeuralNetworkReductor`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "583009d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e51c4acef0d8431a8aaa1a737e2adff3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rom = reductor.reduce(restarts=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64a45166",
   "metadata": {},
   "source": [
    "We are now ready to test our reduced model by solving for a random parameter value\n",
    "the full problem and the reduced model and visualize the result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d87780ee",
   "metadata": {},
   "outputs": [
    {
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    {
     "data": {
      "image/png": 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ItLoeY8eOZelWr16Nyy67DF27dkWDBg1Qp04d7L777nj77bdZupkzZ+o1TbfddpvW97vvvitXHnfeeSc6deqE2rVrY99998WsWbMgpcRVV12FNm3aoKSkBAcddBAWLlzo1O2VV17B7rvvjjp16qBevXro378//vOf/7Brdeedd+q2Vx+FbDaL2267DV26dEGtWrXQvHlzDBs2DH/++WeF2pxCSum9XyurHQp5nqdPn47p06fnpXcannrqKQwYMADt2rXTYX369MHmm29ernX5U6ZMwU8//YSjjjqKhS9ZsgS1atVCw4YNWXjLli1RUlKif3fs2BHt27dnaYQQGDhwIFatWsWm/e2yyy7ODI/NNtsMXbp0wffff59Tz1WrVuGpp55Cr1690KZNG6YnADRv3tzRE4DW9aOPPsLatWvZTAUA+vdjjz2mw15//XV8++23uPzyy1FSUoLly5en9i233norunXrhoMPPhjZbDZ12cFzzz2HNWvW4LTTTtNhQgiceuqpmD17Nj766KOC616InkuWLEGzZs3Ys1C/fn3UrVuXXc80dOjQAcuXL8fq1at12G233YYWLVpg+PDhkFLir7/+ypnHwoULcckll+DKK6907qtCoGbElGWrNgTsvvvuAOD0C1OnTsWgQYPQuHFj1KpVCzvttBOef/55R/4///kP9tprL5SUlKBNmza4+uqrvSP6hfRTixYtwtlnn40OHTroqbFqFE9h1apVuPzyy7HpppuiuLgYbdu2xfnnn+/Yy1WrVuHss89G06ZNUa9ePRx44IGYPXt2vs2D0aNHo0uXLqhduzYaNWqEnXbaCY888ghL8+WXX6Jfv376ft57773x8ccfl5l3Gufq3bs34yo777wzgHgPFJs3+PagyJdbqXX5zz77LLbZZhsUFxejS5cuePXVV3PqrbiWlBJ33nmnY1d//vlnHHrooWjcuDFq166NHj16OEu5FAd77LHHcMkll6B169aoXbu27k9tUFs5atQotG/fHiUlJejVq5czZfqbb77B0KFD0alTJ9SqVQstWrTA8ccf7yy5U1zyu+++w5FHHolGjRpht912K1ceP/74I44++mg0aNAATZs2xaWXXgopJWbNmoWDDjoI9evXR4sWLbxLI/K5l4UQWLZsGR588EHd3vTe+fXXX3H88cejefPm+jo+8MADFWpzH9K4TEXbIW3fGh9XX758OaZOnerMcCsEb7/9NhYsWMDsIQCcfvrpWLZsmXO/5oNHHnkEQgg2g2DNmjVYsWKFww+aNWuGKIqY7cuXn9SsWRO77LKLU/7BBx8MAGVymRdeeAFLly71cq5GjRqhuLhYhxUVFen3OIX33nsPTZs2xV577aXDoijC4MGDMXfuXLzzzjs6PF8bPXXqVLzyyis477zz0KRJE6xcuRJr1qzxpn3yySex8847634RALbcckvsvffeZXLQtLrnq2e+fC8NzZo1Q+3atbFo0SIdtmjRIowbN04vE169erVjx2xMmDAB3377rbMsulBEUVQhHlMuB8XixYvxxx9/sM+6wu+//459990XM2fOxIUXXojRo0fjqKOOYgZbSokDDzwQo0aNwn777Ydbb70VW2yxBc477zycc845laLHbbfdhjZt2mDLLbfEhAkTMGHCBO+6aIXx48dj8ODByGQyuO6663DSSSfh6aefxm677cZuJgAoLS1F37590aRJE9x8883o1asXbrnlFtxzzz05dZowYQJ23313FBcXa5322GMPAMCJJ56Iyy67DDvuuKOernTdddc5LzQA8MMPP+CII47APvvsg9tvv73MjTb//PNP7L///ujatStuvPFGtGnTBqeeeiozYEuWLMF9992H3r1744YbbsAVV1yB+fPno2/fvt4pQOPGjcPo0aNx8skn45ZbbkHjxo0LzmPixIkYM2YM/vGPf+Dcc8/FO++8g8GDB+OSSy7Bq6++igsuuAAnn3wyXnjhBWca6YQJE9C/f3/UrVsXN9xwAy699FJ899132G233bSRGzZsmF4zr9qbTvkaNmwYzjvvPOy66664/fbbcdxxx2HixIno27ev0zkW2uYK2WxWv9CkoSLtUAj23ntvZ1pbIfj111/x+++/Y6eddnLiunXrhi+//LLgPNU6QNtg9O7dG0uWLMGwYcPw/fff47///S/Gjh2Lp59+GhdddFGZ+c6dOxcAsMkmm+RMJ6XEvHnzykz38ssvY9GiRY6ee+yxB6IowvDhw/Hxxx9j9uzZePnll3HNNddg4MCBei8DZXBsA6am7n3++ec67I033gAAFBcXY6eddkKdOnVQu3ZtHH744cxBtWTJEkyZMgU777wzLr74YjRo0AB169ZFp06dHEP95Zdfok6dOs4eKGp6aa5rl1b3fPUE4uv56quvYvTo0Zg5cyamTp2K008/HYsXL8bw4cOdMlesWIE//vgDM2fOxIMPPohx48ahZ8+erP3efPNN7LzzzvjXv/6lX0BbtmyJO+64w1uPSy+9FC1atMCwYcNS65oPFElfX1PrKxOqr2zUqJEO+89//oMePXrg+++/x4UXXohbbrkFderUwcCBA/HMM8/odHPnzsWee+6Jr776ChdeeCHOOussPPTQQ3r5VXnw119/Yffdd8fo0aOx77774vbbb8cpp5yCqVOnasdCNpvFgQceiJtvvhkHHHAARo8ejYEDB2LUqFE47LDDWH4nnngibrvtNuy77764/vrrUaNGDfTv3z8vXe69916ceeaZ2HrrrXHbbbdh5MiR2GGHHfDJJ5+wttp9993x9ddf4/zzz8ell16KGTNmoHfv3ixdebHVVlvhyiuvBBDvgWLzBhuFcqv3338fp512Gg4//HDceOONWLlyJQ455BDv/jkKdOr0Pvvsw+zqvHnzsMsuu+C1117DaaedhmuuuQYrV67EgQceyO4dhauuugovvfQSRowYgWuvvbbMZa8PPfQQ/vWvf+H000/HRRddhG+//RZ77bUX5s2bp9NMnjwZP//8M4477jiMHj0ahx9+OB577DHsv//+XjJ+6KGHYvny5bj22mtx0kknlSuPww47DNlsFtdffz26d++Oq6++Grfddhv22WcftG7dGjfccAM23XRTjBgxAu+++66Wy/denjBhAoqLi/V+ChMmTND92Lx589CjRw+88cYbOOOMM3D77bdj0003xQknnODduLTQNld6AiiTy5S3HQrBlClTsNVWW6X28/lA2Tuby3Tt2hVRFBXMZdasWYPHH38cu+yyC3MYlpSUoHv37hg/fjwmTpyIX375RTu/GjVqhJNPPjlnvvnyEyB/zjNx4kSUlJToZZUKvXv3xn/+8x9ceuml+OmnnzB9+nRcddVV+Oyzz3D++efrdKtWrfK+iPu4TL42WnGJ5s2bY++990ZJSQlKSkrQr18/5rTKZrP45ptvUjno9OnTsXTp0oLrnq+eiudceumleOutt/Drr7/inXfewfnnn4+dd97ZWfYDxA6I+fPn4//+7/9w4oknYsmSJYyHv//++1i5ciU23XRTDBo0CLVr10ZJSQl23XVX73vT0qVLccEFF+Diiy9GixYtUuuaD4QQFeMxhUy3UFMrfR8FpExttKdClXeJxzPPPFPmMpNnn31WApBXX301Cx80aJAUQsiffvopVa+06ee+aaVpSzzsuq1evVo2a9ZMbrPNNnLFihU63YsvvigByMsuu0yHDRkyRAKQV155Jcvzb3/7m+zatWtqnam8vcTjq6++kgDkiSeeyMJHjBghAci33npLh7Vv314CkK+++mqZZUkZT1UGIG+55RYdtmrVKrnDDjvIZs2aydWrV0sppVy7dq0zvezPP/+UzZs3l8cff7wOU1OG6tevL3///XeWvtA8mjZtKhctWqTDL7roIglAbr/99nLNmjU6/IgjjpA1a9bUU8+WLl0qGzZsKE866SRW1ty5c2WDBg1YeNoSDzUdb+LEiSz81VdfdcILbXOKvffeW7Zt29YbV9F2kDL/51mF5bNEK+0ZU1ONH3roISfuvPPOkwCYbmVh7dq1snnz5rJbt27euDPOOEPWqFFD92GZTEbeddddZea7YMEC2axZM7n77ruXmXbChAkSgLz//vtzpjvkkENkcXGx/PPPP524++67TzZs2JD1t0OGDGHX7vPPP5cA5FVXXcVk1f1Wt25dHXbggQdKALJJkybyqKOOkk8++aS89NJLZVFRkdxll130MokvvvhCp2vevLkcM2aMnDhxouzWrZsUQshXXnlF59m/f3/ZqVMnR/dly5ZJAPLCCy8suO756imllPPmzZN77703a6NNNtlEfvjhh94yr7vuOpZ27733lr/88ouOX7hwoS67bt268qabbpKTJk2S++23nwQgx44dy/L7+uuvZSaTka+99pqU0tzj5Vni8eWXX0oA8uGHH05ts+oGZR/feOMNOX/+fDlr1iz55JNPyqZNm8ri4mK2dGfvvfeW2267LXuWs9ms3GWXXdhSrLPOOksCkJ988okO+/3332WDBg0cW5xvP6WWRvmmyqr7acKECTKKIvnee++x+LFjx0oAevqzsqv2tPQjjzwyryUeBx10UOo9oDBw4EBZs2ZNOX36dB02Z84cWa9ePbnHHnvoMB+fSpt+3qtXL8Zbci3xsDlZIdwKgKxZsyYL+/rrryUAOXr06Jz1VvL2Eg91T9Brs3TpUtmxY0fZoUMHvZxAtUenTp3yWi6pbGVJSYmcPXu2Dv/kk08kAHn22WfrMF9+jz76qAQg3333XR2m+oAjjjjCSV9oHieffLIOW7t2rWzTpo0UQsjrr79eh//555+ypKSEXfN872Up05d4nHDCCbJly5bO1PjDDz9cNmjQQNel0DanmDVrlgQgL730Um98RdshbVmY77lRYfks0Up7xk4//XSZyWS8Mk2bNpWHH354mXlTvPDCCxKAHDNmjBM3bdo0ueOOOzJ71qlTJzl16tQy882Xn0gpZZ8+fWT9+vW9HEVhwYIFsmbNmnLw4MFO3F9//SUHDx4shRBaz9q1a8tnn32WpfvHP/4hoyhyltYefvjhEoA844wzpJSF2egzzzxTp91vv/3kpEmT5E033STr1q0rO3fuLJctWyallHL+/Pne9y8ppbzzzjslgNR2Tat7oVzixRdflC1btmTXs2/fvnLp0qXecrfYYgudrm7duvKSSy5hy6puvfVWXX63bt3kxIkT5ZgxY2Tz5s1lo0aN5Jw5c1h+I0aMkB07dtT2uSJLPIYPHy5r1arFuGohKNcMijvvvBOTJ09mn3UFNXX2xRdfTJ2i8/LLLyOTyeDMM89k4eeeey6klHjllVeqWk2Gzz77DL///jtOO+00tuFI//79seWWW3qne51yyins9+677+7sIpsvXn75ZQBwRjjOPfdcAHDK79ixI/r27Zt3/kVFRWzUsGbNmhg2bBh+//137e3MZDLak57NZrFw4UKsXbsWO+20k97okeKQQw5B06ZNWViheRx66KFo0KCB/t29e3cA8eaMRUVFLHz16tV6uc3kyZOxaNEiHHHEEWyWUCaTQffu3Z0lJT488cQTaNCgAfbZZx+WR9euXVG3bl0nj0LaPJvNYs6cOXjsscfw73//2+tVrYx2KBQzZ86s0NF/K1asAAA2BVBBPTcqTT548803MW/ePGdkHojvpc6dO6Nv37548MEHMWnSJBxwwAH4xz/+kXMn82w2i6OOOgqLFi0qc0duNYrfs2dP7w7fCkuWLMFLL72E/fff37s0oHXr1ujWrRtuu+02PPPMMzjnnHMwceJEdnLSjjvuiO7du+OGG27AuHHjMHPmTLzyyisYNmwYatSowdpNTS/ceeed8fDDD+OQQw7BlVdeiauuugoffvgh3nzzTZZuwYIFeO6553DqqafiyCOPxJtvvokmTZrg6quv1nmuWLGiXNctV93z1ROIR1e22GILDBkyBE888QQeeOABtGzZEn//+9/x008/OeUeccQRmDx5Mh555BE9ZdbXRgsWLMB9992HESNGYPDgwXjppZew9dZbs7oDwJlnnol+/fo5m3yWB1tvvTU22WQTjBo1Cl988YUzw646o0+fPmjatCnatm2LQYMGoU6dOnj++ef10p2FCxfirbfewuDBg7F06VLdLy5YsAB9+/bFtGnTdP/z8ssvo0ePHnoWDgA0bdrU+zzni6eeegrbb7+9nq5MoWauPPHEE9hqq62w5ZZbsr5bTTlWfbeyqzbPOOuss/LSpWHDhpg9ezY+/fRTb3xpaSlef/11DBw4EJ06ddLhLVu2xJFHHon333+/oOnzlYFCuVWfPn3QuXNn/Xu77bZD/fr1K8RlunXrppdKAEDdunVx8sknY+bMmfjuu+9Y+iFDhuS1xEth4MCBbAPCbt26oXv37vpaA3yW2sqVK/HHH3+gR48eAODlITaXK08eJ554ov6eyWSw0047QUrJTiBo2LAhtthiC9a2+d7LaZBS4qmnnsIBBxwAKSXLo2/fvli8eLGjbyFtvnz5ckybNg0XX3wxAJQ5A7O87VAIevfuDSllhTY+z7VJfq1atQriMUC8vKNGjRoYPHiwE1evXj106dIFp59+Op5++mmMGTMGa9euxcCBA3PObs+XnwDxBqpvvPEGrr/++pzLF5988kmsXr3a20cXFxdj8803x6BBg/Doo4/i4Ycfxk477YSjjz6azYA/8cQTkclkMHjwYHz44YeYPn06rrvuOj1DSrVdITZapW3RogVeeuklDB48GCNGjMC9996L6dOn62V1FeGgaXUvlEs0bdoUf/vb33DNNdfg2WefxRVXXIH33nsPxx13nLfccePG4dVXX8WYMWOw1VZbYcWKFWwprCpfCIE333wTRx55JE499VQ8++yz+PPPP/UydQD48ccfcfvtt+Omm27ytkGh6NWrF1auXInLLrsMM2fOLHNpiY1yneLRrVs37xSYdYFevXrhkEMOwciRIzFq1Cj07t0bAwcOxJFHHqkb9L///S9atWqldztVUNOP//vf/65TnVV5W2yxhRO35ZZb4v3332dhtWrVcl7OGzVq5OxdUEj5URRh0003ZeEtWrRAw4YNnfYo9AzqVq1aoU6dOixs8803BxC/tCrD++CDD+KWW27B1KlTmXPJV16aDoXkQfcxAKBf0tu2besNV+07bdo0AGBr4Cjq16/vDaeYNm0aFi9ejGbNmnnj7RMQCmnzX375Rafv2bNnmefDl7cd1jUUqfF1Yup4q0LI5sSJE5HJZJxp2QBw/fXX4/bbb8e0adP06SeDBw/GnnvuidNPPx0DBgxgzhuFf/zjH3j11Vfx0EMPsV2SbcydOxf9+/dHgwYN8OSTTyKTyaSmfeqpp7By5UqvUf/ggw8wYMAAfPzxx7rPHThwIOrXr4+RI0fi+OOPx9Zbb63zOeyww/R+PJlMBueccw7eeecd/PDDDzpP1YZHHHEEK+vII4/ERRddhA8//BB9+vTR6Tp27KidWkD8QnDAAQfg4Ycfxtq1a1FUVISSkpJyXbdcdc9XTyB2whUVFeGFF17Q6Q466CB9JKl9LGz79u313iJHHHEETj75ZPTp0wc//PCDnv4JxLtVDxo0SMtFUYTDDjsMl19+OX755Re0a9cOkyZNwocffph6vFehqFmzJp5++mkceOCB6Nq1K3r16uXsZ1Rdceedd2LzzTfH4sWL8cADD+Ddd99lROenn36ClBKXXnopLr30Um8ev//+O1q3bo3//ve/7L5T8NnRfDF9+nQccsghOdNMmzYN33//vWODqX6Asav0BbwQ/S644AK88cYb6NatGzbddFPsu+++OPLII/VO8/Pnz8fy5cu9+W211VbIZrOYNWsWunTpkld5lYFCuZVte4CKcxnfPUHLp6clFcplNttsMyfM3v9o4cKFGDlyJB577DHHji9evNiR9+lQaB4+G16rVi1nun2DBg3Y8pl87+U0zJ8/H4sWLcI999yTusS4IlzmxhtvxMiRIyGEwI033qhPOUpDedthXaOkpITtZ0SxcuXKgnjMX3/9heeee04v+6ZYu3Yt+vTpg969e7MBkz59+qBLly646aabcMMNNzh5FsJPJk2ahEsuuQQnnHACTj311Jy6Tpw4EY0bN0a/fv2cuDPOOAMff/wxvvjiC72UZ/DgwejSpQuGDx+ul6xtt912eOSRR3DKKafovrBFixa47bbbcOqpp2q+VoiNVmkHDx7MlhEdeuihOOaYY/Dhhx/ixBNPrBAHTat7IXr+/PPP2HPPPfHQQw9pO3XQQQfp/YReeeUVJ/+ePXvq74cffrjuC2+++WZW/gEHHMBO+uvRowc6duzIjqMdPnw4dtlllzJtZL44+OCDcc455+C6667Dddddh3HjxhW0F2W5HBTlQVkbPOYLIQSefPJJfPzxx3jhhRfw2muv4fjjj8ctt9yCjz/+uMJHLaZt0lNZ+ueDXJ1FRZBWNxuFdJ754uGHH8bQoUMxcOBAnHfeeWjWrJnej8O3saJPh0LzSGvHtHCZrP1Ua6YmTJjgXYPle3G1kc1m0axZM+9ZyAAcwlBImysv8Mcff4yrr74al112WU4nRXnbIReq4nlQGwH99ttvTtxvv/2Gxo0b5+3VXbFiBZ555hn06dPH2XAIiI8I3muvvZz+4sADD8Q555yDmTNnOg69kSNHYsyYMbj++utxzDHHpJa9ePFi9OvXD4sWLcJ7772HVq1a5dR14sSJaNCggXPWNBAfVda8eXPHIXzggQfiiiuuwIcffqgdFK1bt8b777+PadOmYe7cudhss83QokULtGrVSjsMAWh9fBtrAcZBlZZOpV2zZg2WLVuGBg0aoGXLlnj77bedTVvVtUxrg1x1z1fPn3/+Ga+++qpDoBs3bozddtsNH3zwgbdsikGDBuHee+/Fu+++i759++rNGxs2bOg8J7T8du3a4bzzzsOhhx6KmjVr6hlEatbDrFmzsHr16jLvAYo1a9ZgyJAhaNy4MUaNGrVBHeFNBy8GDhyI3XbbDUceeSR++OEH1K1bV/etI0aMSJ0xZj93FUF5+qlsNottt90Wt956qzfeduyWF1tttRV++OEHvPjii3j11Vfx1FNPYcyYMbjsssvY8cXlRS4uU1Ucw0ZFbExloCq4jBrZPe+887DDDjvo+3q//fbzrrf26VBoHr52zKdtK3ovK12OPvro1FH27bbbjv0upM2PPfZYbLPNNrjrrrvwz3/+E7169WIzpmyUtx3WNa9v2bIlSktL8fvvv7NBqtWrV2PBggUF2YNnn30Wy5cv9zrx3333XXz77bfO9d1ss82w1VZbeW1fIfxk8uTJOPbYY9G/f39n43sbv/zyC9577z2cfPLJqFGjBotbvXo17r//fpx//vnMQVCjRg3069cPd9xxB1avXq1nnQwaNAgHHnggvv76a5SWlmLHHXfUTnrFZQqx0WlcIpPJoEmTJppLKI6ZxkEBP5fJVfdC9Bw/fjxWrlzp8KEDDzwQQDxg5XP+KDRq1Ah77bUXJk6cqB0UZfE4Vfe33noLr776Kp5++mk2E3rt2rVYsWIFZs6cicaNG+c1QKswefJk3HbbbTjssMNw+OGHe53LuVDpDopGjRo5U1JXr17tveAVQY8ePdCjRw9cc801eOSRR3DUUUfhsccew4knnoj27dvjjTfewNKlS5mnf+rUqQDg7Mpv6w/EBJNOZfLNusj3hV+V98MPPzij8j/88ENOfSoD7du3RzabxbRp09gmdvPmzcOiRYsqXP6cOXOwbNkyNovixx9/BAC9oc+TTz6JTp064emnn2btVsiZ15WRRz5QI2LNmjUrc/lE2j3QuXNnvPHGG9h1110rnSTVqlUL+++/P/bff3+8++67eOqpp8qcRVFerKvnGYhfsJs2bYrPPvvMiZsyZUreG4cC8Znbvt2UFebNm+clJ2pWztq1a1n4nXfeiSuuuAJnnXVWzjPMV65ciQMOOAA//vgj3njjDe08SMNvv/2Gt99+G0OHDvU6XwrVE4jJiRoJ/O677/Dbb78xr3XXrl1x7733Okt55syZA8A4z1q1aoUWLVp4l/zMmTMHtWrV0v3rDjvsgPvuuw/ff/89q7MaFfFdu7Lqnq+eagO7tHbytZENNW1TjV5GUYQddtgBn376KSNOvvJnzZqFRx55xDl9AYiX3my//fYFnQX+7bffYsaMGRg/fnyZU2+rM5TzeM8998Qdd9yBCy+8UC9VqFGjRpl9a/v27fVsNgo6G0gh336qc+fOZc506dy5sz7HPpeNV3Z1+vTpbJaDT7801KlTB4cddhgOO+wwrF69Gn//+99xzTXX4KKLLkLTpk1Ru3Ztb35Tp05FFEU5XzB9bQLEXIYuGcmXxwCoELeqDLRv3z61PSqjfN/99uOPP2oe8+eff+LNN9/EyJEjcdlll+WUS0Nl5JEv8r2XAf99oDb0Ky0tLfN5LQ86deqETp06YaeddkLHjh3xwgsv5HRQlBeU11NU1WxqZe8+++wz7L///jr8s88+QzabLYjLTJw4EXXr1tUvqRSF2r5C+Mknn3yCgw8+GDvttBMef/zxMgfmHn30UUgpvZxrwYIFWLt2baqe2WzWiatZsyY7SUNtdKnuw0JsdNeuXQHA4RKrV6/GH3/8odNFUYRtt93Wy0E/+eQTdOrUyZk9VlbdC9Fz3rx5kFI6bZGL79lYsWIFm4WVVndVvtpo/ZdffgEAZ4NPJduxY0eMGjUq7yWMQLwVQ82aNfHggw+Wa8lIpR8z2rlzZ2f33HvuuafSPJV//vmn431XD7ualrP//vujtLTU2SF11KhREELk9ECpl1NaB3X8ko06derktT54p512QrNmzTB27Fg2deiVV17B999/n/eu3+WF6iDtl1jlda1o+WvXrmXHYq5evRp33303mjZtqh8O5Tmk1+6TTz7JefygjcrIIx/07dsX9evXx7XXXuvd52T+/Pn6u3LK2PfB4MGDUVpaiquuusqRX7t2baWtK2/Xrl2VrlEv5HmujGNGDznkELz44ouYNWuWDnvzzTfx448/4tBDD9Vha9aswdSpU1MdJY888ghq167tXWsOxF74yZMns2mgpaWlePzxx1GvXj02bXvSpEk488wzcdRRR6WORCn5ww47DB999BGeeOIJNvUuDY899pje1yJNz3nz5jlT/B999FEAwN/+9rfUvLPZLM4//3zUrl2brYM+6KCDUFxcjHHjxrHRuvvuuw8A9Mk0QLxr+qxZs9g+Q3/88Qeee+457LXXXno05KCDDkKNGjUwZswYnU5KibFjx6J169beY8vKqnu+em666aaIogiTJk1ifcPs2bPx3nvvsTaizy7F/fffDyEEdtxxR1b30tJS1vevXLkSEydOxNZbb61HJp555hnno5YVPfTQQxg1apS3zDSofQUqa6R+faJ37956/5SVK1eiWbNm6N27N+6++27vs0uvz/7774+PP/4YU6ZMYfG+WWn59lOHHHIIvv76a++JD+reGTx4MH799Vfce++9TpoVK1boo3YVj/jXv/7F0uTrLLanoNesWRNbb701pJRYs2YNMpkM9t13Xzz33HNsRGvevHl45JFHsNtuu+UczercuTM+/vhjNtXc7luBdBvmQ0W4VWVg//33x5QpU5jNX7ZsGe655x506NChTIdwWXj22WcZkZ8yZQo++eQTXS8fBwHyv+aVlUe+yPdeBvx8NpPJ4JBDDsFTTz3ldeyl9aeFQi3dqCou4+P1paWl3mUrlXHM6F577YXGjRvjrrvuYuF33XUXateuzTj3H3/8galTp2L58uVOPvPnz8cbb7yBgw8+WJ9iQaFmE9BjxIF4H5MffviB2b5C+Il6L+nQoQNefPHFvAbZHnnkEbRr147tD6PQrFkzNGzYEM888wzrj/766y+88MIL2HLLLXOWMW3aNIwdOxYDBgxgs0HztdG9e/fWM5rVUg0gPl2xtLSUcZ5Bgwbh008/ZU6KH374AW+99RbjoPnWvRA9N998c0gpnVPSfHzPtzxr5syZePPNN9mM2y222ALbb789nnvuOXZPv/7665g1a5au+1577eXlMk2bNsVOO+2EZ555BgcccIC3fmlYsmQJmjZtWu79LCp9BsWJJ56IU045BYcccgj22WcffP3113jttdfyOsYmHzz44IMYM2YMDj74YHTu3BlLly7Fvffei/r16+sX8QMOOAB77rkn/vnPf2LmzJnYfvvt8frrr+O5557DWWed5awZpdh3333Rrl07nHDCCTjvvPOQyWTwwAMPoGnTptrDpNC1a1fcdddduPrqq7HpppuiWbNm3n0LatSogRtuuAHHHXccevXqhSOOOALz5s3D7bffjg4dOuDss8+ulLZJw/bbb48hQ4bgnnvuwaJFi9CrVy9MmTIFDz74IAYOHIg999yzQvm3atUKN9xwA2bOnInNN98ckyZNwldffYV77rlHT3caMGAAnn76aRx88MHo378/ZsyYgbFjx2LrrbdOPRPYRmXkkQ/q16+Pu+66C8cccwx23HFHHH744fr6v/TSS9h11101QVMOmDPPPBN9+/ZFJpPB4Ycfjl69emHYsGG47rrr8NVXX2HfffdFjRo1MG3aNDzxxBO4/fbb2Xq08qKi5wyXhUKeZ7XBVUU2yrz44ovxxBNPYM8998Tw4cPx119/4aabbsK2227LNgn69ddfsdVWW2HIkCEYP348y2PhwoV45ZVXcMghh6Qu+brwwgtx9NFHo3v37jj55JNRUlKCRx99FJ9//jmuvvpqfd9OmTIFxx57LJo0aYK9997beTnaZZdd9Gjkueeei+effx4HHHAAFi5ciIcffpilPfroox09Jk6ciFatWqF3795ePc844wyMGzdOb+DZvn17vPPOO3j00Uexzz77sClzw4cPx8qVK7HDDjtgzZo1eOSRR/RzTtfutmjRAv/85z9x2WWXYb/99sPAgQPx9ddf495778URRxzBRi0uuugiPP744zjkkENwzjnnoEGDBhg7dizWrFmDa6+9Vqdr06YNzjrrLNx0001Ys2YNdt55Zzz77LN477339F4ghdY9Xz2bNm2K448/Hvfddx/23ntv/P3vf8fSpUsxZswYrFixgh0be8011+CDDz7Afvvth3bt2mHhwoV46qmn8Omnn+If//gHW14wbNgw3HfffTj99NPx448/ol27dpgwYQL++9//sr0uBg4c6OiuZkz069evYNunnueyjtzbUKCWwIwfPx6nnHIK7rzzTuy2227YdtttcdJJJ6FTp06YN28ePvroI8yePRtff/01AOD888/HhAkTsN9++2H48OGoU6cO7rnnHrRv3x7ffPMNKyPffuq8887Dk08+iUMPPRTHH388unbtioULF+L555/H2LFjsf322+OYY47B448/jlNOOQVvv/02dt11V5SWlmLq1Kl4/PHH8dprr2GnnXbCDjvsgCOOOAJjxozB4sWLscsuu+DNN9/0bsrqw7777osWLVpg1113RfPmzfH999/jjjvuQP/+/fUo3dVXX43Jkydjt912w2mnnYaioiLcfffdWLVqFW688cac+Z944ol48sknsd9++2Hw4MGYPn06Hn74YYf/dO7cGQ0bNsTYsWNRr1491KlTB927d/fuJVARblUZuPDCC/Hoo4+iX79+OPPMM9G4cWM8+OCDmDFjBp566qkKPzObbropdtttN5x66qlYtWoVbrvtNjRp0kQfg1i/fn3sscceuPHGG7FmzRq0bt0ar7/+OmbMmJF3GZWRR77I914GYi7zxhtv4NZbb0WrVq303kPXX3893n77bXTv3h0nnXQStt56ayxcuBBffPEF3njjDefI5/JAXbeq4jJdunRBjx49cNFFF2HhwoVo3LgxHnvsMe+I9JQpU7Dnnnvi8ssvL/dGmSUlJbjqqqtw+umn49BDD0Xfvn3x3nvv4eGHH8Y111yDxo0b67R33HEHRo4cibffftuxhZMmTcLatWtTnfhdu3bFPvvsgwcffBBLlizBvvvui99++w2jR49GSUkJG+3Ol58sXboUffv2xZ9//onzzjvP2US/c+fOjnPj22+/xTfffIMLL7zQOxMnk8lgxIgRuOSSS9CjRw8ce+yxKC0txf3334/Zs2c7umy99dY49NBD0a5dO8yYMQN33XUXGjdu7CwzyddGFxcX46abbsKQIUOwxx574JhjjsEvv/yC22+/HbvvvjubNXDaaafh3nvvRf/+/TFixAjUqFEDt956K5o3b64PFiik7oXoOXToUNx8880YNmwYvvzyS3Tp0gVffPEF7rvvPnTp0oUNuG277bbYe++9scMOO6BRo0aYNm0a7r//fqxZswbXX389K3/UqFHYZ599sNtuu2HYsGFYvHgxbr31Vmy++eZ6X5F27dp59ww666yz0Lx5cy/PKQtSyor1yYUc+aGO6sl1xGdpaam84IIL5CabbCJr164t+/btK3/66adKO2b0iy++kEcccYRs166dLC4uls2aNZMDBgyQn332GUu3dOlSefbZZ8tWrVrJGjVqyM0220zedNNN7Gg6Kf3HBH3++eeye/fusmbNmrJdu3by1ltv9R5TNHfuXNm/f39Zr149CUAf3eWrm5RSTpo0Sf7tb3+TxcXFsnHjxvKoo45iR1qpNrCPCZUy/WhGG2nya9askSNHjpQdO3aUNWrUkG3btpUXXXSRc2xjriNlfFDH5X322WeyZ8+eslatWrJ9+/byjjvuYOmy2ay89tprZfv27WVxcbH829/+Jl988UXnmuc6tqaieajr8sQTT7DwtPv67bffln379pUNGjSQtWrVkp07d5ZDhw5l99ratWvlP/7xD9m0aVN9fBLFPffcI7t27SpLSkpkvXr15LbbbivPP/98drRPoW1Ocfzxx8uioiLnvq6sdsj3eVb1qMgxowrffvut3HfffWXt2rVlw4YN5VFHHSXnzp3rrZvviC91hNrzzz+fU49XX31V9urVS26yySayZs2actttt3WOfMp1tDKso/nUkbtpHxtTp06VAOQ555yTU8+pU6fKQYMGybZt28oaNWrI9u3byxEjRuijsaiu22+/vaxTp46sV6+e3HvvvdkRwhTZbFaOHj1abr755ro/uOSSS/SxwBTTp0+XBx98sKxfv74sKSmRe+21l5wyZYqTrrS0VD+fNWvWlF26dEk9KjPfuuer55o1a+To0aPlDjvsIOvWrSvr1q0r99xzT6f+r7/+uhwwYIC2C/Xq1ZO77rqrHDdunPcZmjdvnhwyZIhs3LixLC4ult27d8/rOOCKHDP68ssvO0cpVnfk4galpaWyc+fOsnPnznLt2rVSyvieOvbYY2WLFi1kjRo1ZOvWreWAAQPkk08+yWS/+eYb2atXL1mrVi3ZunVredVVV8n777/fscWF9FMLFiyQZ5xxhmzdurWsWbOmbNOmjRwyZAg7RnH16tXyhhtukF26dJHFxcWyUaNGsmvXrnLkyJFy8eLFOt2KFSvkmWeeKZs0aSLr1KkjDzjgAH1kYlnHFN59991yjz32kE2aNJHFxcWyc+fO8rzzzmP5Sxlznr59+8q6devK2rVryz333NM5PjeNc9xyyy2ydevWsri4WO66667ys88+c44ZlVLK5557Tm699dayqKiI9Ws+TpYvt4LnmFAp049mtJEmP336dDlo0CDZsGFDWatWLdmtWzf54osvetvDtnFpoLbylltukW3btpXFxcVy9913l19//TVLO3v2bHnwwQfLhg0bygYNGshDDz1Uzpkzx7nmufqAiuaRxvN8/Uq+9/LUqVPlHnvsIUtKShzbOm/ePHn66adrG9SiRQu59957y3vuuUenKbTNbURRJE844QRvXGW0w/Tp02WfPn1kcXGxbN68ubz44ovl5MmTq+SYUYV77rlHbrHFFrJmzZqyc+fOctSoUc5zoupmP7tSStmjRw/ZrFkz3W/6sHz5cnnllVfKrbfeWpaUlMgGDRrIAQMGyC+//JKly5efqGch7eOr74UXXigByG+++SZVTymlPqa8YcOGsqSkRHbv3t3p86WMjxRt27atrFmzpmzVqpU85ZRT5Lx587x5FmKjH330Ubn99tvre+CMM86QS5YscdLNmjVLDho0SNavX1/WrVtXDhgwQE6bNs2bZ751z1fP2bNny+OPP1527NhR1qxZU7Zs2VKedNJJzr1/+eWXy5122kk2atRIFhUVyVatWsnDDz88VY/JkyfLHj16yFq1asnGjRvLY445Rv722285dZayYseMDh48WHbu3LnMMtIgpFxHuxUFbJTo3bs3/vjjj0rbwT6gMFx22WW46qqrcN9996Ffv35o2rSps0lPdcMVV1yBkSNHYv78+RBCODtTBwRsrFi6dClWrVqFgw46CIsXL2b95l9//YX58+fjsssuw8MPP4yff/654FMIAgICCsfMmTPRsWNH3HTTTRgxYsT6Vud/Eu3atUNRURGeeuoptG/fns0yqK7o0KEDevbsqWcs2KfZBQRsjJBSYsGCBZg1axZ23HFHp9+cP38+Zs6ciYEDB2LTTTfFO++8U65yNo45pAEB/6MYMmQIWrZsiRNPPBGtW7fO68SC6oKmTZtW+aZqAQHVCccccwyaNm3KjvZSOOOMM9CpUyc8/PDDGDx4cHBOBAQE/M/gvPPOwy+//IIdd9yR7QVU3fHYY4+hadOmOTfPDgjYmLB48WI0bdo09Tlt1qwZunXrhoULFxa0qaaNMIMioEIIMyjWP9auXYvvvvsOCxYs0OvRqjN+/vln/PzzzwDiI1vT9iAICNjY8M033+jNrerWrYsePXroOPUMt2/f3rsWNCAgoGoQZlBUD/z555+YOnUqpJTejZWrGz744AN9AlTbtm3ZaT4BARsr1q5dyzZu33zzzRlnefPNN1GvXj1svfXWqfvA5YPgoAioEIKDIiAgICAgIGBDRXBQBAQEBFQvBAdFQEBAQEBAQEBAQEBAQEDAekfYgyIgICAgICAgICAgICAgIGC9IzgoAgICAgICAgICAgICAgIC1juK1rcC1RXZbBZz5sxBvXr1IIRY3+oEBAQEBCSQUmLp0qVo1aoVoqjy/ewrV67E6tWrK5xPzZo1UatWrUrQKGBjR+AcAQEBAdUTgXOsewQHRQrmzJmDtm3brm81AgICAgJSMGvWLLRp06ZS81y5ciU6tq+Lub+XVjivFi1aYMaMGRsNYQioOgTOERAQEFC9ETjHukNwUKSgXr16AOKbsX79+utZm4CAgIAAhSVLlqBt27a6n65MrF69GnN/L8WMz9ujfr3yj5QsWZpFx67/xerVqzcKshBQtQicIyAgIKB6InCOdY/goEiBmmJZv379QBYCAgICqiGqcip8/XpRhchCQEAhCJwjICAgoHojcI51h+CgCAgICAgIsFAqsyitwCHcpTJbecoEBAQEBAQEbLQInIMjOCgCAgICAgIsZCGRRfnZQkVkAwICAgICAv53EDgHR3BQBAQEBAQEWMgii4qMR1RMOiAgICAgIOB/BYFzcITFLgEBAQEBAQEBAQEBAQEBAesdYQZFQEBAQECAhVIpUSrLP2WyIrIBAQEBAQEB/zsInIMjOCgCAgICAgIshPWgAQEBAQEBAesCgXNwhCUeAQEBAQEBAQEBAQEBAQEB6x1hBkVAQEBAQICFLCRKw2hGQEBAQEBAQBUjcA6O4KAICAgICAiwEKZbBgQEBAQEBKwLBM7BEZZ4BAQEBAQEBAQEBAQEBAQErHdUewfFu+++iwMOOACtWrWCEALPPvtsmTL//ve/seOOO6K4uBibbropxo8fX+V6BgQEBARsPFA7alfkE7BhIvCOgICAgIB1icA5OKq9g2LZsmXYfvvtceedd+aVfsaMGejfvz/23HNPfPXVVzjrrLNw4okn4rXXXqtiTQMCAgICNhZkK+ETsGEi8I6AgICAgHWJwDk4qv0eFP369UO/fv3yTj927Fh07NgRt9xyCwBgq622wvvvv49Ro0ahb9++qXKrVq3CqlWr9O8lS5aUX+kEa9aUFiwjBFCwE0wAeumRKLjI1KyqHOUszCeSK6u0OCEKl8mZX3WWyXVfSRknyFdOlHHppGT3of4qkX5/psQJCMgCb5LyyCi5dXf3u+WIsh5+b9vl0DlHe9M2okmklLEeHl2lFBDCU5YUgC88R5yAgBA1/DIBAesZ64J3VAXnWLu2tGL8IZ9wO074gytafNUJFo5CbWqqajn4WbXmD+WQAdLbqGCZxIzINHsmAUnsTF6cI638HPwhV3bl4yqVh7LfHSqLc6gIHxfIIUPiOOfw0c8yOEdO3hM4x8aCau+gKBQfffQR+vTpw8L69u2Ls846K6fcddddh5EjR1aaHhMeeh/jxr8XPysApBDmqcwIZMlDKel/mk6YOBGJ2DtmyUAAMhLxs2/HJeEgciBxWSqj8gLMvBr6Ai8AZMD1ZjJGByYTWd0IlY14f2b0RqreUslYOtC2Yr1fokNqXe20CkrG0kFCGr2tuoqIeDAtGSES4+XRjRk1SvZUnC0jZGpdRSQTGdtDICEiVXvegWciyfQWukwlo9rBvNBmIm6KjRGRiCIepmQz6rfKW/+XyERKNWmaTkhEyJpHQumWxBVFgESWpI/jBbLIkDKEajYhUSQkgGxirExeArF+QstIqMexSGQB0LSqbhI1RNzetAwBoEiUJmGSyQjEOghdLxMXIWt0YHoYmQg8L4FsUifJymf1gYzlSPsU0XIARFq3bKwbwGV1nCsTQSIS8X+A1AkSGYH4Gqr2gUSU/G/d5B7UrzMA1RWlqNiO2hWRDdiwUB7eUdmc47XX/g/X3/iSa+vjhzV+D/HwBwjB+YTNH3wyNn8g9tn7FiLg8h5VHpBqU207nMY5aH5pnEPpVyjnyMkfLD7i6F0g5/DpYHOO1Lp69JbWYIHhSoVxDqUDPNxCcw4IS4fycY7IukaKE0QR708p58jo9rE4R5Iv5xyAEFlkIpG8HXPOIZBFpHUShHMAmUhxAcFsNBz+YHMOyhs4F+A8RSacwwxu2pyjKJKAdDlHBqVab8oDKO/R8VCcoxQZK0x9p1wg0nkqfmXSMR0YR5CkvHTOEfMeP+eI+UOppbPheJRr5MM5mjW8HE3qD0N1ReAcHNV+iUehmDt3Lpo3b87CmjdvjiVLlmDFihWpchdddBEWL16sP7NmzaqQHsuXr9bfmXNCADIrtSGmRl8ZYSThNC5LjYb9X+Vr5acNjZU+7qpUvtxoSpIPffkHpPZ0SkuGG1OwMhUpkoKEWzpJK97Og+pN0zjkyZYh6VL11gabf5fKqDkfQdJJTRLMNVIfIyNIWv1ar+skPWUk+SWOCDuNV7coLkM7uRSSr4IaT5G8HIs4XIrE+CTGBUw8jgORUfUQgn7A8uTlxYYKyYusMci2YZb6BTx+4eV5KiOpDJsyuFESRj+qTP3ynOSnjXRiEFW4Sae+A4Z8ZBGJ2IiqdMqIQpeZ1S/psXGkYbScLIQOz5K4OD1rS9UWiUEWSXmUROj2suuS3F3m+nHjrz+kPQUM8dGy+k6wHFLsu0xS+GBIpMkvDivN/uGVqC4olRX/BPxvoDy8o7I5x5Ilphx7pFlmlf1xHflSeGSo3SFpbVvsyy/NDmcTLmBzDmrzs5Z8PKPL4iMUVActQ/KwbTzM93w5h7K3afxBWL9zcQ5h65OmA/sIIiMtvf2cA0Lq6+dwDiet+aRxDkRKd2HKsjmHfR+A2HjGOWKuRHkCv66GcyCxhxAm3Mc5mF2knCMJ55xDvcxmvZwjIvlGKo0aVEi4QUbZcOeTxjlMudRO63qA6J7cNTF/cDmH0PlbnEPwcqk+6ZyDtiV1OsS8RySyps0A9cJvyiAyUjp1UXFsMINwDtX+2olD7gO6aMHzeKZA3fEu58hmKz5LrSoROAfHRjeDorwoLi5GcXFxpeUXRfxRUjZTU3714g6h+l7j1RYmrYCH+EtACDUiQmJVftQwGhW0JZXWk5760s5kpCVjymHGNslf6c2dLCYL26Gg20fAT3jghjskIE3GqivVWxMHEFg60CloXAcRv9xDuA4YKUkb0nJogfzaGVjkQNLRKkNGhGpklaWw8yOKW+2tylaGJ/lhylZxTHfJwoVTD+54iJGFIQzcACl9lZE0qmd1XSNiwNW1E8l/OlpgmjD2oBtDb+qoy1d5IJs8a9xoUhmBrDb0ej6SJgvQhCGWUaMNxjGh0ip9lGHXl4OMOCjHAhuZ0LohyTtpZ60biN6244ITAQEy6iPAyjCzLIizAqYdbU4LwZ465/kBvS30DcR7MncpSfVCRdd0bmzrQQMqF5XNOZhfWgJSCPNc0keNGMFUziFdGWHJaHtdIOdg9pfG6d+mA5HEfpWHc1BUiHNoHQjnyCUj/DJpnMNxmOTiHJAx/6P1URfNWw4tMI1zQPMKFZmbcxg7yfOjnEMyPXTZxP4YGej8hCWj7E2hnAOQiCLJqunjHMKWcThHYjuFyzli+cQRQDgCn8FA7KiqC4xzgM08VDxBOViYjBp8MHUynEPN4CBlM6cG4R05OYfiSsqRARaei3PYPMrcmmpQKDfn0PeISsdfORjnsG9dm3OYe97mHNV7TD5wDo6NzkHRokULzJs3j4XNmzcP9evXR0lJyTrXJzEnyeNCHA7CihfEiCXxKo73sPE/PY2f2B7VmUoItkyDGQjh6uA1xjpcMuNPDYmjm9Y7SWvpoHVEUlfhkRFchuWbJuOtaxkywpOe5iesPGy9dZ0E11tBL90Q3nIAmX6NbB0i9dWSoXVHohwhnLq2wtKB3HvaErA60NEJ6cgwQmDpTUcfjLExL9jUcSCReOAjM7qgqwLX0EVCGhmtizHGUMaWkBlVljHsJA3ia5QRJs7kFRtW3/RHJN8j/Z+PALCpnkQmEhKR9DsDzEgIaWcookCdBlLXm9aJOhW4U4Lc6mo0xpLRsz1IubrtkhFM5lRJbpk43OOkkKZ9abu44QEBGweqG++A7mf9/EFzAco54OEPlgybDWrL2DbVPPTM7tr/dRyVgZ1P+TgH17MSOYe3rgXKJPBypTTOAcTOCbj5OMs98uYcJM7SWzsPaPq8OYcdRzgHzTcppyo5h8onF+fQzgkv5yBLELycg9o243BQXMDmHBGz34VzDpWn4RxZUmfjQFGcQ5clDIcwvMfYe8M51G/yycE5DA+y87Pr6uZHHRyATLiFTOUcpn3J/Ua4hb6f1O0QOMcGi43OQdGzZ0+8/PLLLGzy5Mno2bPnuldG9Y6WwXJHGZQB0r0sAGO4JM0L3DkhhTCkgRh9rkSSn10eLBn93ZAA5ZygYbaMVYzfeJJw6Ublzo/1UpZMlEMmn3JoXOKylaontPX21lW6etN29OrgkgEdk1JXH4HQoAbdltEGX3ni7ThbTeltGxWnp02y6hGvNx2ZEEaGvjAr0JkBvFp01IEbpdh4CpgpncZgqxdmaiRjKA++SWfqkxhZml8ikyEjKe40TPNb5cUJiSRpkDgHTPukjTqAtIeaUso/xPBb7YlEJiKm2y9jf7hzQmdKCYFIPjCfiJRhygdrH33fsbbaMJCFQGkFaE22ArIBGxaqFe9IQO0wpfTM0UANALHrNucAjB1O5Rw6KecPOu8cnMNeBsv+2zJOeWVzDh/KzTkE3IECoHycI7Hrvtmh3rYV6ZzD1sdXjr+uHieE0iKlrvlxDm4bKOcAXP6Qpjl7+bfuicI5RxaRMPbR/E/4g4dzaJuayjk4FzD8IZ1zmOUdNufgMtxRYdfJOAAY59A6cs6hbLF2DrByjA42Z+D8wb1UAllkkMI5iBNCD64orkTrou4JaeqvOAfnWKa9TXmcc9jtsyEhcA6Oau+g+Ouvv/DTTz/p3zNmzMBXX32Fxo0bo127drjooovw66+/4qGHHgIAnHLKKbjjjjtw/vnn4/jjj8dbb72Fxx9/HC+99NK6V17qP0DSwdnWzmvUtGGS+lHj8QKxybHiJFwj5CEKbjlwex0io50UZckoEmLH+cgF+Z8m4+hN6wO4ozxpdbWMvm90SO/xYTdpTt08MpRAAMQaKx3SyUXqJp8pusVhfLTBpOFkgKdRL+SujKDhVH2RtXQwIyF06iU1FtoBQJpAfY+SjSfpqAmT0aoRj7yamiiUoaIOBL4WUpejDKM29raRJrol4Xq0REgICLIsgzgXklFBmq/ZIEow3SKbHJAPX8NJyQ7ZRFN6ZJw6JetLRUwYIGjbZTkZkZYMjMF3SIy0CYxqV28PFMuncAIJAWFv2laNkZXJuvkKyAdsmNigeQfgcgEaZebpm0DKOTyOdZn09V7OUaA9y/3CbtIJO7xAzpHrvwSAjJuXo7fNOfKta2VwDl9+KZxDD24Afs6RypUs/kDyS+UcPg4DE54X5yB5cecClZcQEX+5h05jXmxtzuHYdYdzSIdz0NmAZXIOlUYo255cYkHlqd3kMlRfygnMPhWUc1AdXM4RadteNufg+1dwzgGofSwsfWH4g3Z22PzByzmkwzno4EhGD3aA65nKOaT1nyJwDiq/MaHaOyg+++wz7Lnnnvr3OeecAwAYMmQIxo8fj99++w2//PKLju/YsSNeeuklnH322bj99tvRpk0b3HfffTmPGK0K0A2rgPj5kMphQXpWNh0wgUzIBV9/STISZlxS5w2ej9pUSqhwbx7ud4lEJxVH8itTRoUTw2Ubdd+aTtt42s4MqSroawuiH9Vby3j0NvnEnb7eTErLCFeGGHst4+jtMdyShpHvUukpeXtbemsZtnROJjqq/KSW0dMcrfoKJaOMvq6qZDJsimVSV0CyHbQpodDOgEQHOtVSzUCQxCtuDCiRgUxGNSTUekVdfqKDmnZoyuHG2Oy6DRhjbl7M1ZpePeqhlk5Yddc7SifXznaAqJd5enKH2YXajLiY6ZhZTQDMJVFEgTgOoIiIQIRSTXbU/WOmdZLZDtKQrYic8KEMttqYzMRJRLrtJAuH1stMZVWExMSZ/+YKqXChZf0kwlxRdaxZVqZvWhwQsD6xIfKO5ctXG56hTtOgtsTQCccJkcY5OH9I4RzaNpaPc9DyqJ1zNupOk8mHc9hcwMM5vPyB5unRr/I4h4dv0Euk+IWHcwir/g7nAM8r5hxGh1TOwXSoXM4BbXsN59A2OsmvPJzD7N0Q/+ZcgMhYnIM5OBTnsGVycI54D4sob86hNn81nCNx1KRwDvV0Uc5Bl2JAcw4BIUotzkGdBnTJaMwRAKE5TCymeBTlD5RzwOIPSbt5OQdi5wSZOZoB5UqKcyTXiF5ri3OoW59yDhTIOdaWLk5NF1D9UO0dFL17946PSkrB+PHjvTJffvllFWpVNhYs+AsAfyF3XvyF+905QcJKp+PAw7xTEiNDIpiMJHp5PPtMhwSKfDhlUBKQEucNk/5yHBmqp5IBIHLpTeUl0duREVC7UPva1Ke3SGR8BMJbV22VPc6LtLKixIBF5jctJ1Y96ynHkAS7PmrKnIitiO70jXE1MlpWGxxl5CWJN5tfikQpZXiNhz7OSG1UpYw5m1URKY98YtiovuCGWsCQGah1lQJED/XfbOSkm0fEFTIbNtH0AGRMLNhGUyIx6lo3Q9GjhBREyhir0Y3kxV7NwrBng2gSBatOyvkg+CiNIgpqfwozk0Pd0nR0hNQLZt2nGomhR36x9a8qT6o/SNtpQiN1PL1l1U7ldj4C0JPG7O5BAFhb+ieqM0pRsemWFZENWL/YEHnH4sXLOb+g36kdtcKpHRawnA62jaY2S/22bRdSOIcvfVo5SrcUGe9JHZ56CSpP+YOnjXx2OG+uZJeTorfiD077pH2H0iGdczgzLhId9LCyV++YD2glInDHRiVwDsDwB5dzmBf0OJ3hD3rpQxKpHRJlcA5l4+L8PPxBxZWLc6gqZ/2cQwJClJqj1KHqlsI5BGLOAYlMROxwpGywyzmUI4KdNEacDZm8OIfRLTfnMM4Rm3M4/MHLOSTRxXAONjsFXM6NMw4bzlWE4V8sn+SjOYdgcQCwtvR3VGcEzsFR7R0UGyo6dmyKjz+Zrq0hozpSEAMgwJ4gYeyL+g3rO81LGULqRTckRXAZZpziH8zQw3z3GRtnyqgl49u9WseBQ9rl0PwiS4Ya4oir4DPs9kyStKmc8XBykg6SK5M2/VPpp35RmdT2kfH58IKI0OvtqasQ0A4K516wpz1qIUBkrJa2yonzpqMq8RniPogIiCIk3bwyXIkKxLDb54hn9M7Z2YRIEGNFyADVJ0N32xaGLGRsGSgDaxl2Uq+MHkVJjByUcyXZGIqMxqh8IyH1Od3QeRq9I58Oqixd1zjMHOFpdFDbxAnQcujLvHT0NgY2TTfjDOK6mbgMi6NOBgm9/AUCwlM3u74RkJKOXDsL+qWHpFV/axa1SpGqHghkIWBDQsuWDfV3Sb+oBzAi/wE/57DtVw7OQdM6Ay9WmBRSGwyvXfdwjvSBBVK+x3am6l0BzsFQAc4hCUdwOEdk/fbqQDy+RG+Vnz4Zycc5iM4x5xBOXSqTcwiY/PRMg8S+qZd8G0JwziFg0tGTINI5h3JelJ9zxHZT5aXa1NhoH+cwgxt6a3SdXy7OkYmUHWa3tJdzqDSKC3BbXD7O4dQVhnNkwJewUO6QEbS9OVcqL+ewNwgXAN8wlPz3cw7TxnHNjY7qmtSquZkjVZ0QOAdHcFBUOawbxmMdBUCmZVJHg0iOC7OyVE4PCSCS3DjqbslVgRodd++L5LGnvSTM6ECq84HmZ8nY4VR9RJYMwJwsTG+7XPLdmd6I3DJp7QHEIxv6yFBbxtMGSAwhIwyWDCDpmx0gyHGrZFREMFlp/uvsCCmwQaZM+ow+3dzJlEdkmLbKiMuYKFCDrI2sMtykCGXkBDXegHJSqJEUzjWVdx9ETuq62udv07LNsV6GCFCjKwQNj6cu8k2hVHsQ4uEz/MLMXtDXQphy6AZVyiCas8vpNaQzDMxH6c1HCEBk7NEFKsNHJUwbmLrCyVsiY5cj7NkPJF61lZQ8XkivjGkkkAdMEQZATS1O8YkFBARUAoQk9tGOlMlzSEfZVZR0T/ZQMlJFCGnZV8/RpAVwjjT+4F0OWgbnkFY4UZ/r4OMPVO98+INVTj4yVBeXc4gcaY2M9jhYetOXetYhRzbnSAYbtLziMJJmlxt5cY7kCvs4B1E/F+cw6Y3d10WQl34f54CPP6RyDpWnWqJh9FCDDz7OoexrRp8KQpdk5OYcfC8IZavz5xz0RV05E2zeUxbnoNTUyNAyOOew40y4mjVK6kHk6dhdvPrMzznM9Yw5h+0A4Zt95s85/A6NgOqO4KCoYhCTgbRuP35hV48TebASQ+QbYbePGdW2TbhHgTFjbxtvSiQ8IwWC/NdGnvz3GidVnifOW47KJzEavhEatxfjVfc5ZHwyGt5RJGF0sGR8I0SscbztK71tENfVP+qgZZz2EzycxUkyK0M6+ZlRlSQbSi7AdVGkIIpso8FlaDgnEibMEIisueTUUCcyfB8GZXzMulDbeWGm/Nkykiwn8cmQZtB6+E/eAEDIA3h9wEkCdQDYx3WC6BAJ6GNGOWlRG01RY6zSSCLDDbW91EKNgkSeOFpXSE5u4glVPgMuzZpTEqZvGY9MfAu6ji8dtwERhawUyKZtzZ+nfEDA+oKAZ8NLbc/S+UMa59AvxpErIyGYXReoRM6BFPtrZZmTc3h4gJdz2P89cl4dypCRgFmySeIY57D0LA/nEDStXVcfH6H5QcDLS+w6JdoVxDkQO0bMLSe5zincQv23w2ke3iWbZC8rL3/wcA7tuABxRBDeE0+4dWWcmZykPmmcg/MHpVs654C267YcQPkDnYVQIc4BOy6pq6WfsueG94DJ6D05iA4AvJxDIuF1KZwDqk7giG/BwDmU/MaE4KCoYngfDdVbQRkhkYxQ2CMO9iNKZeB9GulIAU3HCQkXZkRBUBme1BkhiCwS4zGYvC1S0ghCLiyDzHTw5etbX1mmDDGoJM6s85T6+jj5IU3GjpOeGRcg+bt6c+cET6NGGZRzg49+AJRsUSlBhhyMY4Q4E+CR0caeGkhwGQCUbJidsTmhUJsmaaNF9FYjIvZu2AJZ7SDhxMXIMD2TOLrXBftvpyPlZ4gnn3nm2UkeRu+4fOOEoE6NCKWgUyRBy1SzR1hdbaLAyQI9joy1j8zqfSLoJpeAIgpC62bvOm6ulU1MSBsofaRk9wLIdaCjJMzZRYmCGq3bQBGmWwZssFD8IRkc15Bgo/USlcQ5osrhHAB3NPD8rGxSZChSuYBYt5xDlMk5cuSH/GQK5hyJHU0tR9k9ZdNJOQCxCTZ/oI4qVTY4f9BxOj++n5SR546LfDgHCH/In3NIvSknf5F3+RC9izOOHS2bc/BBDG67KedQBeXiHDZ/YPVJ5Rzu/hVmFoRkulG7nrF0MHqXpnCOLAx/Kh/nYNxPcgdM4Bx++Y0JwUFRxTAzKHLdOHyZRhyiM/Bl6v0YB4DNSpBYgyTcJgB5OCccGZ8RBFINpOPIEB4ZXxukxZEwkascJiNdGSgilUMHe+SD5ueZDeLNT//3yGiiYI9EmBwj75x4mVxOqYkCM/pJvmr0RJDwiJIAWo5I1usR4w8A3lkQSb7xtESSNvmoUyqYjC7H6MYNkZGh0yKZYyKJi5iMyc8+gpPX1cTZhIAaZ3ftJTX6xoiaJR5KBzJ1UZWjy4C1VILrLUh5bLTEmm7JiBQhJvqkE3ZcmKmb8rsJVi+TN712+pqRkQ5FJsw9Iy2ZpB4S8M2uCAgIWDfwUnRKC4TLOTTWE+cAyuAPaXykOnMOGHtvT2ZJ5w/+vHLLlJdz+MtRNtq9qhbnYPXiL9U6XaID5wLUZih7X3HOIURW74FQGOcgcSAv6YIvK1GcQzknDB+hyzWyBXEOAbXpZWGcQyQ6SLqRpIdzII1zAIwPsBM+PO2geaSegZnGOUwbFMI5hLpmhD8wziHAnBE+zhGwcSEsBa4i/PLLgth40Q7bog3MsEplmEzHr8KTDBzDI+knsSRmFoZbHgTXghIFJafjCQHRYZLICFNuFsmHlO/Nj4yyZGHSwZLLJh8ZJR+apwAnF2WUY/KXbC+IOE6acLpITrWT8LVPLCMj6ZAsN79EQmQBkQWibDJ7I/kIGYeLrL7uOl7IZPZBNt6ESnmGk7WOerpklIxYCJg0ojQ5P1wZf5onn0YYG8W4nCjKQkTxC3BEyo9ENt6sSBth/uIbkfWXQpQmMqWJnokcedmON43K6g2i4pkHWQiU6o0i9a7TmiQkMuo7ShMdSpNRDHVWdzbJK6vTK89+JjlKK0IpMqIUGcSjAhnEYTFJKE1IRDbZ46FU11mnFSZOIIuMTqPCzchBJGh6Uo6qa3LEWCS53oqwRElcfEtlUZTklUE2kSlNdCvVumWEpYc+Rz2r21nIrIcosB5Fk0zdWySdAB31MT2JuafYCJjJwEoNrF4zz4mrTihNrlZFPoXguuuuw84774x69eqhWbNmGDhwIH744Ycy5Z544glsueWWqFWrFrbddlu8/PLLLF5KicsuuwwtW7ZESUkJ+vTpg2nTphWkW0D1x7x5i82z7DH/ALGdSbxxMOgUeXEOwMM5pLRSIC/O4XABEpfGR8rDOey4fDmH5nGkLcriHIon0A1/JICsyMUfCOcgv6VPRtchjXMQbuFwDsUlVNmUH0jDOSIZz/xQPMLHOSIJibgcxjkSXqFk6YkYMRfIGn5RBucwDnqXc8DiHFFBnIPyB0k4B9GLlG24RWky8zKreYniApEo9XIOgayXcwjIhD9kdRmUP3DOYbhSxPKVyMU5Mmmcg3GlUs61kEUmuU6ZhFNkkEVGJtxOcw5JOEcpua6q7U0ZQsZPer6cQ5Jewzi6oMNixwvnHEJ3MhyGc8xwI6sRAufgCA6KKkLt2jXNj+QpEvS3DaE8jYkls17CFWTkiSNGTcANK0uG/SbG0ZuPWtZB4kQU6+7oK9Lzs9NTvYVP38jz3VNXn4wy+rbeak6hr66qLWxniOUO9tbV1UH45ezyWJh0wnU2UWK4IzUaQJwEQiKKBB8lELAIAJU3GzypTZYiEROQSMQ7UGci9bIc55Uh+anNJaOEuBgZ4mRIystEiaGLYvKhdrHOJDJFkWom4qSIsqghSlEUKTKQGG0hUSRKURRJqI2tdHyURQ2RvMwnac3H/DYyqq5GL70hlohHCYpETEponAlPdrwGtN5FxAlijgiN27dIlKKIOGDUrtiKzGQgiZyqc6neiVsv7xDx7VEkssjoW5aMyMAQDX5MqWlnc/saOXcaqSEBavdtA4koCfXd3iD/Yf2ORAmqM2SyHrS8H1ngetB33nkHp59+Oj7++GNMnjwZa9aswb777otly5alynz44Yc44ogjcMIJJ+DLL7/EwIEDMXDgQHz77bc6zY033oh//etfGDt2LD755BPUqVMHffv2xcqVK8vdNgHVDyUlLudgD5/9nZglFUbtsIKPP9AXaS2jOxjhsZskjRUmqJ1Osak+/lAo57C/07rm4hzSls9RF2PPRQpXIo2ej96ms/Z+cnMlUaDexHGR6KT5hmpzD+eIIiSbW/KPsoVmZoQZnY8Y98jNOeIwP+eICOeIUjhHUcI5lD6Gc2QTzkHLiutVpF66GedQnEI5AST7FIlsbNctjqHkbM6REYntVnoLordIHBqMc0jNOYrIRpqqTTJ5cQ5zRKjiHEWEczDeoR0ZLufIaM5huEKEZLNONYBCOIfSI8qTc5hbVDqMA0mYEH7OocJ9EFGdlJjqgcA5OITMddj3/zCWLFmCBg0aYPHixahfv37B8vfe8zYenfRxYnCEWdupDZ5wvPOSGiOAbXYpLcNvPPoSMpl3Zo80UMPG1ocCWkbrBCtfK73RgTgotZ5cRlVVj5akyUQknSgsP4o4nHpejZCEzHHklzVCrGUAJEdnSUtIJobUziu+Duq35HrbMjDplOGXsB7DxOD6ZaTOG+DfM+zIL+XpAuheELGMEsqiyJJRcVEyiiBYTnEas2N0bEQU4RNEhhsiqUmCClNVos4RR0aYM8SVFqoO8eZUxvOu9n9QRp151ZM87V2w9Z4ReraFrZ/RQZcjlBHNxg4SWpdEH0dGx5tZHbZuGVEKd20oYEZ5qNFW9SG7jmvjb0Z8cu1vAV220ZtvdmXLkOtv5eW2W8J7PeWo/y0aXY+GdYegPKho/5xP3q//X3vUqVd+H/6ypVnsu+1/y63j/Pnz0axZM7zzzjvYY489vGkOO+wwLFu2DC+++KIO69GjB3bYYQeMHTsWUkq0atUK5557LkaMGAEAWLx4MZo3b47x48fj8MMPL1/lAiodFb2nn3xyCu686y1i8ymXgJdzAB4uoOxgIZwDVpllcA62n1UuzkHLpnkRGRYOrBPOEdcnX85BuEUuzpHE2Zwj1xKNinAOI2X0iVK4koiy5eAcavZDkgeVcfLzcw6jQsU4B2llzh8ETR/HaTss1MlcZXMONUuDLsdU+bonbwhtuynnYHoXwDlgyRj9VHzWOqbc6EA5h7HrMU8x/MHiAoxzxA4GzTms/TWqhnMAaqmMqY+59hlWDudNm9QfgU0anIvyIHCOGOuSc4QZFFUM0/2oqXW+VPHjI6wgZ1qTLSutMGV0JH38qcFLl1EGwV5GoSdZ2WRAmPR0JEMIUqb1HZ680wiJNz97GqX6RCaNITRZPfWRxqlP1gkXyfRLnh9ofnpqoySfZHlGRMMSvclv1t6J8YsJRNK5JzLaqAsTR8sSEXm5VumSeOrQcDaQcr6raYykuUlcxAw7HQkhsw+U0dH5JjMQoCaoSB1OCQSdqUFnZhgd1MhMlv8WiWEiSzeUEeN605EXZVCzlt5ERqjpjOSFW+tNp05KbcwjUB3MKIDQUy/dUQFhHQtKRw4yotRcV0uGPxKSt5MVDuvesGdA2HtD6F7Ck1+cgMoY4mMTfPMY+0cvhPPN2xFulFiyZAn7rFq1Ki+5xYsXAwAaN26cmuajjz5Cnz59WFjfvn3x0UcfAQBmzJiBuXPnsjQNGjRA9+7ddZqAjRPutGgem5AMHuT7TpHCH8wrRR6cw/5ZFucgH/3SavMRmD5pXXAOmz/k5hwxt8hGqp3y5xxmKYZ0PxXkHHrJhur7rTjd90dkOaDFOYSXcxD7w36bpRNeziFczqFsceGcQzrl0BkdGT1b0/CRWKaUcY4M4RxsuYfFOWz94vIqh3NQ54SPc8T8gS7Z5AMIphw6wzKLKFUma66LDjPXkU/AIc4azR+Ic6RKOIftwjOcw+2a8unUNj5sLJwjOCiqGPqhI7+cfRQgVS9KLC995UgxrBH07AzWNdiGnYIuW1D5wA2jajvG3VSHkQarqv5PooNDPIRHB7ssC3qdpzLIrJfytIP+SAiPjOpQnThAWzNpfxIZXzkiqas99dHk7ylH6cIMPP/t1lUtRzCG1aidZcs/FJFRVaIGXZA4Hm7uLh0OE6f3aCDrVdWMB+WFZ/nRF3H7t5axdrqW8RWnIxJ0ZELv2UBpslQ62HpDy3ByYPTW5SgdhMlXIGvOPaeESE3HBLyOiIytt5YpdWRo+zjXgtTZvBKY9hFMb1jXw84LJD9SV1ZOyj1MiIjddThEhcVuGCiVUYU/ANC2bVs0aNBAf6677royy85mszjrrLOw6667YptttklNN3fuXDRv3pyFNW/eHHPnztXxKiwtTcDGCUn+u/Y1T85hP8ApnMNxJlDYZQsr3FaYlOtwDpiXeaeulcE5bA7joLycQ9l9yWRycg4IyIjss6E/Vvk253D66opwDpnKOXgayjlk+TiHxR/y4RyRAPycg9tNde10fg7n4KP/yME5HP4AOz9oHXycQy2vSNUbnHPowQ2rvQ1/yKbyB3spitDfzdJPL+egOtj8wffxcA5QnpIH54AdXiDngK5DyqO7ASBwDo5wikcVQ9JHxffkSIDMY9LpzIuvcGTUVEUBYk+FZaR9+aW5o6ihtuVTwn0zKlR10ogK0yFXOQDPL1VGgJ18kYeMnvFBoeSEYIaWyqSRFcd46xjJzz0XJIYSBvA47Tn2tI/STbA6eEZMlIgiF052xNA67WGNPLC6qhkEKn/13czE0C/JHpmIGQ9lpJNyVN2S77FRE8ZQEUOoLjszrMmazFhG+mWgSIIiIWpURCT60TpkISCYzvGHb7wZ0bpKPiXSXH6ppyRSEhNzK07GTDlm6YTzkb44SrbscmJnTWTd20ZvMzOEP4IyV5eRQgJyy2xIUFulll8+bu9Zs2ax6ZbFxcVlyp5++un49ttv8f7775e7/ID/XVBe4H3wbCNocQ7GLWQegwdpnCNFzhmkyYMLAPDa9QpzDl9+qfwhB+dIqU95OAeoDlaN0jiH7fzwcg5PXPk4B7TdYirk4ByaC2ibb+IK5RwC2XipikoHiz+kcA62TIRxjqyXczAZm3NoJ4RassFf2MvLOWi4ktf7biScQ+ttcQ7GB7zLLWKZsjmHxS/KwzmIruSWAucc9JqbvHzIxTlSu7mUvKorAufgCA6KKoJk35LHx7j5TJSaVicFD9c9uIT9mMVTENXxpTSCJKUGkvxmIxbC+m3rn2b0hdIhpRwPCXCMvi0TAVZflioT6+B7wS+LFEmjC5GlR3Tpy6CJh7R0UAkkaz/BdIk7X7t9NBmItDiLY04LSyaKACmpUedxCszoC5UvjTNedKepkimWkiinZRIS4XiqBS9H3baCyID42fRIgjCkAiCbHSVrIs3RWMbgmRGJOLGqgxp5iNXOOjJ87aPSM0vaIKvJhyIk8SUyR2dFSTpztJitXza5DhKcGEm9JtKMNBC9I+gnmS0HESBExG0Hes/wUQybYKg6gV8HJa/jqFMnuX90m9ldCydJBtwpQ2H//l9C/fr1C1oPesYZZ+DFF1/Eu+++izZt2uRM26JFC8ybx09DmTdvHlq0aKHjVVjLli1Zmh122CFvnQI2RAjoozYK5BySGhoBCLoEgWZfFucQKTIe5OIcAHLzhwI4BwCzH4VFryrEOTzvFTbnULaLcQ5w/lBeziFoval2lHP4+IivrUXW4g1cprycQ5pqJ1UrB+eATDblJHZd27VsKufgtpNyjiyxZ5w/mNkaKINz8DJ0XVXZhXAOgDgQkv0jhHr68uMczNHAOIeMT2ZR6VI5h9XehHMw/qPyllRn5ZyAh3Oo/AB1MxjnknQevXw4h7mfAudQ2Fg4R1jiUUWYPn2eZSmSDoY+55ocJK8EMv6jjb6yatL8M+mEzibp3hlUEuacMAKQSDneixTrIwnMmPKaGYNrfbKA2ZwqKUdNWyQuZKOnR+9Y39gtk1VGX5D8IuuYMFAdJN+cSpcjGVHg9UmOz3J6THokKKDWy8WkIguRHPEZkwLldKDHjMYlCCKj9paAAJkGlxzXFGX12lA9xY3I8I0YszqdmS6pZM06ykwSbqYMqn0d4k49I5Sxs4/rNLs062mCgk5NlBD6uC0VB71btDq+U+0MrXQwR3St1WWrtZ/6uCxhyqDHbWXE2uSIK3VKSLLxkz4eK5lSCbMTdwRzikYGEhkp4RydBamP/4yP8EqOFtM6GRl1tJg6MSMj1bFe8Q7Z+uhQQY4WE+Z4VCOT1WQgo76r6yCTERtCgtSIjCZFlDgk5QOKfJl7AchqB5Aa+YiEyd91NFCZLCEU5t5TRMHIKN5sjd6QfmP1mtmozihN7uaKfAqBlBJnnHEGnnnmGbz11lvo2LFjmTI9e/bEm2++ycImT56Mnj17AgA6duyIFi1asDRLlizBJ598otMEbByYNftPK8Tz9p2Lc6g0hHNohiF5NmVyDpYWWsLHOVSYl3Oo/CymqrmKzTlQNueQBXAOxQUU57Dzy8k5PA4NxWG8nEP4OYfQnEOmcI6s5hxqWn1enEON8mvOkXCBqFSfDqI5h8jNOYD8OEcmcSxEkbKp6ZxDvWRTziGEOirU2McohXOYfRZc3sM5h9Txitfw/JKPCk+OGWWcA4pzlHLek8I5ohycI6M2qER8WofQ5aZzjijhD4r3GM5kc44kjToyNAfn4P/JDBG1d4WadQKYJTeac6h7QN3dhj8YzpE4eiDjGRX6tjdOkDTOAZ2GOyfK4hwrV09FdUbgHBxhBkUVoU2bxvj0sxkAoB8dZtApB6BPEnn7dzZvUmsvhR0n2JNo4mL3pWP81cs1CdcExWO8QeNgdKDymkjY5VC9iZxQRAFWOxD9pLTCIOKNoRAb8zhzUqY9IuLoLSGUjE9XW0aIWIbMWZSA2UzKJ0tnvQgSrNvb3AWaHESAM9IFaE93fJ64ME1gjXrQ7xkto/Q2JCSi+pBRfSYDAbqpUSYihoDIq5MyVAidLmlk4vyUXJEeLbGMijA66NGIRMbsZk0fj1ifoogbLpWnIhpuXWl+xKAp4iCyXIekvEjXVRljaANIN6cC1I7eSBwyqn4mvZJh7ZPcS3oDsOTa6bpJGN2EkdFTPnX+xjBDEw1VV5IfwDYZjeP4Y8TaR4UL08a6rklSem+w//qWNuUo1ChqgeoMuqazfPL2K1xunH766XjkkUfw3HPPoV69enq9ZoMGDVBSEh/Jeuyxx6J169Z6Tenw4cPRq1cv3HLLLejfvz8ee+wxfPbZZ7jnnnsAAEIInHXWWbj66qux2WaboWPHjrj00kvRqlUrDBw4sNx1C6h+aNmigRUinJ85OYfNBZKH3uYcQgCyXJzDX46IrDKt/85sTduuVynnoOV4OEcu/gCXcwghknxSZKNcnAOMP3AduG6McxCuwjmHVT5kKueg/EHlo5DRp25YnAPJvhJKTWKHDYfJIp1zJDZDFMI5+C1dOOeQOTiHTOUcEctP5sU5BOKjSY0OhgvQupoZGMrx4fIHNftBc4G8OIdM5xxQzgOlq2D5ZggX4DMg1HXMIo1zgOqt7wf1rFQt56hZoyOqMwLn4AgOiipCreIaAMyDEq815CMK2tBTUIOsrb/5WqYMfcKTLyw/SgzUdybDRJkK6ic7IoyUaRMJShy8cVaeAjwNIxlOOUlqu5eyR1qoDroEan18dacGPekalZFiFlCSpqajIjRDEm7LqPyA+Hx0opsQ5gOmt8nP7ZiJYRSANv7gay9Z1YlMDFuGGiAlQ3d5TrzjiRTdXFGQ/LRxE2oaofTIKN2Uocqht2cqppbRaaSVn00uaDzVGUQfmjcAYZXp1JXkJzw6OG2a1eUoguGMDjhtYMpxjhEVJi9zawuA1sdaPgO2P4Yqg8pQg8+vEQCmm6T3NO/toEiGbn9RAwEGd911FwCgd+/eLHzcuHEYOnQoAOCXX35BROZX77LLLnjkkUdwySWX4OKLL8Zmm22GZ599lm1ydf7552PZsmU4+eSTsWjRIuy222549dVXUatWrSqvU8C6Q1GRZfxsuwZla4UT6HAOGi24jLRlmA019otxDpYXSU/ibIPmcCWannEBS0a4MkD5OAdzymj7K0wmrO5cJk7C7XqaTC7OQQwBbP5ADJOVXxkySV2ZiDAfML0N77EuEeMPse0y/MG16y5/iJGLc9j55eYcsYz1Qi3M0gWjR8U4RwTJuEA657D5E//O7ajhAlRelcM2m7T4g55NIpS8pQMdsIFyipiZrMZxQNuHtkHWzdPiHNThEN8spG55cI6I3mswOpnrVjbngA6Dvgco54hE2Xsx/C+hunOO4KBYJxDQcyTpkyTjzlWNRjAHRtJpSGoVoOKlfkJtp4dOZMsknYjKz7t7tWM0ubw9iuEQgwJkBOy6Jr89TglKSPhIS9Kdl6E3D08hCjqdJNMoTZRUBlXEHS/VTXm9tea0gxVgG2sJUo42+gLESZDV6YQjk9X3hVYFRod4XaaqpsnPbBiV6EU883QvCKHrExMIs/GRX0aTg6QcM6jGZSKYY7OMjDKI0isTe+ntF23o/KKI66HyckcQXL319MPkv14eISxdkBwxZumtDL69A7Yux1MveGTMnc7rSeMM+fGUQ/VRn6RP4eWom0QRHXr7S90PRUyn5BYiJANMP/Xb7n1EsvaWONSYtNvNVFfEE2vLr22hsjKP0Y9///vfTtihhx6KQw89NFVGCIErr7wSV155ZUH6BGy4EEjuJ5u5I+EVKZxDE4F1wTmorkyHPDiHo3fZ/CGVc9j1UflVKucw7QfBZTTn0P/XNecwcWVyDlCeUDbnMDLcbumXYVKOciS4XMDHH5TOafwh6+EpPudBOuegl0id3JXGOSJSBnVCMP5QBucAVD5m6QTjSgVwDlW+3Q5wZCwuoK+Rqp+HczBng9Q8gR5nqovycg5ozsF5kH66nEcoH87hDqiobxL+w8+rHwLn4AgOinUJaqAsI2v/198TA5lrYylqdB0Z4mpMIwhUhvcinBDQp98JF1Z+6bvdeHWxiQgvz/8QeXXQessU3aQT5sTZSAy6sqbCkqL5UTKgw6wO1xAFdvUseVc7M7rBLivUC7l1N+hwbeQEVTV5mRf8EBmVLpOE0amOQDK1T+VDb2ca5pSTMvWQzEyw6xMJMr2PxMUv877LLl2Co3XIIiOEo7cyhJkk5whcb9UGvrpmku/8NlfrVe28/DKqPrRMkP/a4SLtDaIkbxN2z1j3muBf1YgMP0tcsnrYo0dGFkwmrRzP64s/WTVHvHq34jtqBwSsN6TYRwXbAukeSOTJOWDssJdzKB08eTicw4qzOpzCOAfrfE2dUsuxZGSVcA43zzI5B4TTFOmcg4QVwDl0nEc7m3OYYvLhHNzGU84h4OqdrKp1OIePC7C8SZ5KByPDOYfWAa6MsMoxOmS17eYfyjlMGVrGyzkMB6D/Hd2cuubiHPByDs3XkA/n4G3HZkgoGVUH54aUPH/Bv7qcw+VolcE5vLewnayaI3AOjuCgWCdI/M0pIxoS1vCDEon8cVJLeWTUjIyykDz9jCh4tE4jNb5wRiLgxjvrTMvKT8kk0xR9pMUrg0TGIgROOJVPjJ1DmJRRt6d1Jjl6jxAj6VieiYwiCqqzNwRAWv+5DJ+mp2TI+kmaFxKPu6W2sGSox1+1Az+2inv96SyJiObHyiEGW7hHbyoZ1T7xZBVrdoI2rNQrbvTW53rrcFpXo6faBMqdHcFHMMxojBoVoQaUyOgNotz2EchabQemn9vekulKr6s7o0NadeNxWm9px1ttp38TWU/3o8qCEyXNP+EVg3VIIZGUOjYgIKCKIdL5QyrnEOrlPE/OocLTOAfvcOLUinuUxR98eRUoo5dxFMI5VMaiijlH0pc7A0fl5hxmECJfzqGXKVQ151D2zLGb0JxD50FtqsVHGH9QTavlysM5EtsooG1++TmHn1v4OAeXobzDzzmMg6AAzkH4ZFmcw68zHUCyy/VxDus+IeP57D5IOEeaU0ylt8PK4hwSxlGierDAOTZsBAdFFWHlyjUA+OMmALYJk+oyzFYyJE51/lAPGQAhrBdoE25kkkeVu4BB911h47SWYTQPtvlNuQwtX5IwPe1TbXglrTjaUxHocsgGl/HURmGO6PLoyI02kYFk0zyNMSdkwy4nkWEvVbaMQzASMiAkOf6TjIYk+ZnrGhes1hCacJlcs6wx+OquUTKQ8dpYSUY6EpkoMjrHhieWiUCcBpqExDIZ5c1O9DYjIclmTuTeUjJqbaiWScqKUGrloWSkXorBjrCkMiBHcSW/M4mMWibA9c4mF1XCbLJk6spe4iEQ2TLawJtjwozBhQ5Xxtpc7iQ/UWrICzHKqr3jY8fcuur1r5aMvuY6PN6YTO9sTWWEKUfpZOqq2s5zjSCg9namjihKQJwj0UiPxJxXhKCo50gSGdrbmb5NPW7SyS0r16A6Y11vWBUQUBGsWVOqv8c2U+g+Nm/OEbmcg6YRKiYX51A6+DiHZbdTOQeRoy/xjEcQLsD5A+ccac6JMjkH6cQY50jhD17OAbDh69ycg/INgDk7qH2NbM6RKJWTc9BramQiVaayt5RzJDKFcg6lF7Xdfv4A2JxDbaqtHQCRLZMv51D8QXIdUjiH2RMjnXOop8bPOQx/iNReEoxzJFyALWvg+fGj34lMCn9gR51adc0kS4Upt6Gcw+idZW3n6C2Qk3PoTTYrgXOA5CuZzuY+l+RoUh/nIF2d/sX6E7kK1RmBc3AEB0UVYdbsheZp8X3AjSiF9/jNyCND8nJO9YAnDnCNtue/mqrJ+EZKuZooUBmrnmllqeO6hCJFEY2T9ly2ONxqD56fO7pgOjfp7CKuy/G6cyUvR+tuG3lSZ5q3MiTK+CM+Kkwk1jiCJGmkNhIqLO5nkr0gVDkR6bSFMey2d5xt8kQ9/sKMElBniDPyoQyljMvRnn1i1KNkbaqalonEmIn43Lrk5AtzOxrSYaZeQumTdKoZJqPqUApAJKdl0LaOJ8LFZIDvMK3rqskGH33ItYeG2rnaXEY6SuHKKB3M/h5KLonTBIpcByC+fkRGk0LQtaHGcRHfviYcMPUxo0Tm2po4cuSXuq+JbJTkaIgTzd/c0poHa2IA8NFCVya59ZMw+/kC1qyd54RVJ2T13VBe+Y2LLARUb8ydu8QTKszDiDw4RyLCbDyzy57vumM2v52TNZDOA3JxDgjLOUDzS/IUvMPOWZaXc2gdJKSysTQv9dtuH5TFOVL09nEOmsbDezSHSQygUI4FANSxoXV3OEeSLeUFZEaF7qxtziFMfXJxDjrTgW8knS2Qc8Qv1ow/RIDmHIDmIrk5B7GdMLYTIqGBMg7PWLMt4nxLIeByDjNrwrbPqnxiiwnniGB4lNYNSTkinXM4HAbJ9VF6q8tvcQtbhnMOAcoF0jmHyy0AU1dVjimL6K1vYYtzyOSaJyVRhwx3pLjLWBTn4DQ9N+cgTEVj1ZoZqM4InIMjOCiqCJtu2hyffZ48DGoYAMmDozzw6r8vA0kMo4KSgeXhj8yDyeToEy+JjGV8dRG2kafhJN5xVPhk7DytvKiOvnrqEQnLuCOSWibtyFDtf02MGHmL9tYnzs+MFEhIQ3o8OpidviXTWwjJd8bWcjI5pxxQu0/TOGcPiCSvDD3yi3S29lILkOpFEX2JJcdHOtMozXd1TJhTVSFRRKcjkiZTazz17aTyFiY/+4WZHo/KdZcoimJDKC0ZfmSoX2+zK7UysHT/CK4LJTHUYw/46koMsaODJDoQhwcoKXHLUXWi4Spf3T7S1cE9FtSU575X0BEOz3XVpIMSjxiqbaQVbuogwPW2H3FOCyISDnDCUFyjDaozSqVAadpC/DzlAwLWFdq2bWR+EMNeCOdgcYLIYD1xjqQsR6cknMnYeVp5KR2FzTlsHSqNc7g6sNkghHOocoW3w5acc7D8ysc5bNukwivGOXizKJsG2LZbLZfwVDXhAkbG2LqyOAd9KfZzDqJDUo5Ikcmkcg66VDJP/sCOLbX4g+BpKX8wG1W6nEOdhFY458iy+mhe6OEc1AFht0WG8ANb/1TOIfz8QT0qDufQMzlcGf6I5885atXcEtUZgXNwBAdFFUHY36QyctYNJInB9ORCOQHNTkCkzoxw8iOGwJ5iqfIXAJsKSvPVclxtJkMNvvFg+qHS2vqrcnKNfjBDn0xPUyMpJq3qpdXoBjXqZOTCGnmQIunobGsLJSJ1HrRzjDOQlgwhH0mcISPEaFr5s//kWtsybJqekDqeb0qV1flFHhmh/gtuGFR+aTJ6CqOgxsgeRbDK0UaPtB956VZlZCDBRmBA62Pn5xp3QO0mTfQG1ZvLxPF0p2tOjqJkR206iqBJg3BP5RBJfvw6uHXRZalbFXSEhcqA7Jqtbm++tpOlFb76WfeJtOKFcXoA/F4w9aXtqa6dzxg6PZ/+pZwrblxAQEClI80ZkYNzqKc6H86hZXJwDgDOrA2bc9gOAy/nIN9zcY40eDkHTNnevaeAgjiH4g/l4xzUUWDK83IOkT/noPkw/gDAyz0K4hyGP9g22vCRFM7BbB/nHBBk5iGZ2Sh0nNKRcw4gkUvhHCBlO/yByNgnh/GZCH776ucPuTkH5Q/qEqpy4lM+6HWXRE/Od3JzDpqv3e58BgSX4Xqzay4tR0w5OIfhD5XFOVxnROAcGz6Cg6KKISBZF+ODVAZMdxLqcXOPGVUJ0mW44dZx1KBbRlgThMiVSWwv8/wzGY9hZzI0Q3h0sMrz1VXnK3jdFFGw6wNi2IUTJ/hvUo4iCr6yNVFwdExkWHtTckBf/kzbUFXiPMldIqwmpZ55YRmexABAkB2oLZnIMmp88yTXECuj7ztm1AlPZCUSL72lh1cH3caqHDNt0hATGkbbIdmhW18O2m7cYUANnFqGoWYVqDSS6GAbZOWc4OGmHXzGX2hiAUuHJM4+DzxxXGa8MlRfUlcZ3y92uFBEQBM26lCJyyJdhoEU7F6g8RLWPaL+J51DjsfVEy68cdUVpajYjtqlkGUnCgioAqi+rSo5hy3LbaDlaLBlRG4ZZbttp4eWsWY4CpqJ9T2Vc6j87Ee8nJwj1lumcA7lpNCtlSSxOAerK3EalJdzkPbUqji/0zmHHqjw8Ae+obbhD8amEpvFnBOujHE08CUKqm62DtDlmJkV6U4DWieXc1D76dh1wnuE5CdlxM8K5xYuf6A2vGzOIQQ/rpMNGjg2vyzOAQiRTeEcMk/OQeoq/Zt4Cqmub/6cQ1Q653BdF4ZtpDk2qh8C5+AIDooqBiUI3DRRqC6ae/K111/JCZOOGl/qBKAjBb682Hdbxiof6reui/Xd7lmoDO1dfETBSuMc/8l08DgH7HowHci6UE9Pl6YfPTdc+HRzWiGREcb4mVEISlSkzpvGqatpVCBEgcxsAI3T8dQIqY8kBp8YXGWoSeeujZozg4Lk4YyOZM36SpA2grWGkRnXZEkFGTVRunGHAtXblRFMhusWe+hNHmwPDpWfrj+5LpITBX6UJyUXRhaStwkfjckS3mxfV7JLtqDtQ6dREhmZkAtyL/C6kkdC1Qv0v7rOKj/J7x19HWi51uuMJG2fKkPDzLVVYd7HcwNBVkbIVmDDquxGtmFVwIYHgQpyDpJO208JNnsgjXOA5J3GORw5wOUcwvru6UQcp4FPxuqw9EaVPrmCOQfK5hy2LoDFC4w8X/5hX8FK4hykzPi3yznsY0HZbAgB2JzDOAA8fITablsf6sCwOEdGlUN0VnbTvPjn5hzavnk4h4rzcaW4zKyXcxj+kNSN8SjKlfLnHBmlS96cI4U/pHAOypWg86K3a5a3qaB15VyAlsUeI3Vdqw3n8HQY1RSBc3AEB0UVg82giB3bnkQpR4ZG6rEjYbbxthGlhJdBFFI3ziKyTu/gCU9bAuUQBaIXIwNMxkMSqN7ekQ/LOcFkaDlWYSJZRWrJqI4eIHnqNMmxVU6djUFiHWySX5Sit/MCraIt40c79SghIsygJjpEjpwxuNTY20bNMdAgUxWFrYPkuzgTY+juTE3JgNJBGnKQxKn1n1Bptd6EVNDvyhg7IyLQG2vZ0xCVjK4PdXConcUhoTYvjWWypo4AOx6Vrxnl7SNEkr/gbWCcE+Tak2vOCZSRcfMjZdKTXnRPoGT4vWpkee+j9EaKjCIkPlCi4MYFBASsM4gULgDkzzngJmFiEkDGX05ezgmfjJUeJL2XcwAuFwAvx8sF7H0iEilnuYdHbxoe600dBVY5dl10vIdzJDrk4hzC2/mWh3MAzgs0yS+TnAqSi3OorCgX8PGHNM6h7XAK5/DznsrnHELVS6XVehP76OEc3HYbHUzb5sc5DO/Jn3MwGesaCSFTOYdxXFgyoPqAh+fiHKwnKZtz8EEZGo6cnMN+vEx84BwbK4KDooowf368ozY1wPE0KeExcPqVACL5L+kcK5jvvmUQahqiMu4qD57I1VFKeE/DUPKC5KnDJZwRF5bWZ9Rt/aEMutAGnI/IWI4Juw3svAHQ0QIh+G8nT52el6OpmU4nWVxcTwk9LVOlAZHX5fvzoy+xSg9tCPWtIYn32N3oycQbIwPw33o6oDa66hQPY3D0UZ5JORmiS2z8s4iE0EZfJDcAdaQIql/ivY2S8pxpl8LsqO2cBw5+9CerE8hJGUpGe/TtJSwxeYqS8pzRBZZfEqdHKNSeE+6xpRHRX8moax2RUQdGFkTS7uQ6UAOeIYSBy+Qw+loXEy4k0Y2N4lgODtreeoolNfweZ4UwzzfNk8ImCPkShbVrF3tCqw/CdMuADQlLlqww/IHwCv0AKyRxzM7DcA7NH3w2OMlQ78MQmSzz5hxELQiuA+McRN52alAZm8OkcQ7GBaLycQ6tC1D5nMMTxzhHordOh8I4hy6GzEawZzgou0IdJOXhHPF1zsE5kt90BgKSuCixm2bmBLFbxOZDQO+HoGc7OvyhbM5hlk7kxzkMV6LcwuUwZXMOyh+obuqoc9JW6hKS9lZLQQrhHO6ACNXR5hwmX8U5pHr6pAA0H8qHc8DoWGmcw1o67aRxsXbt757Q6oPAOTiCg6KKsHzFav2dH8cF75OjOiDqaFDh6UsS4hd9QOQ4/gvs+E6v0ab/I0NWhBWvHSCWcRcCqaMOWicrP3XmuJrtwOpJdLF1ZPtgwFOPKOnaUtrLzksZbHdo2Tb0KomAXhZhybARDJ03oF9CiVoqnq5xhDDrRqljQqssbIMnSTmx4cjo/IzBUuVHSgbmmLI4P3fphtIrw44JUzJmNELnR/TOCHs2gamPnnKoyoFETEb8Mko3eyQFgpAEYcJpfeyNOmMDbR0ZykiPIRDcOaGOVPXUVRt9MP3o2eEmLl4VrnbAptM74w+dXil0W0hSBt8gTOlvG31ertGb6CikVb7fyZD2+PA0RlekpPEhK5fniF3/yKJiu2Jny04SEFBpWL58Tdn8QUF6vyJ5S2Tpbf7Bjiq3vycfenynDkux6+qECmfmo80FSLiwymMVVjp5OYxxAtC20YMlaZzDCmP/C+Ec2vb6OIf6buL8nCPub3U7wASb+pafc8RxKl9jQ7UdplUVxp47x4dDLbeM7RnnHCQ+0V8tWdF7WWn7beyzknHscA7OwRwX2vZnHa4UN1Ea50BSryzhMHSmg+E3ul2UDvaRocSRYOy6cdCUj3PY/AG6PjbnECQNH0hK7ivafuA66Fuebf5pyjKPq8057HJpmwvY/KGsR678nOOvHLHrH4FzcAQHRRWhfbtN8OFHP5EQ7dtWP2OQEQX1X3UTLL0EGylQcUISBwTNCwBsouHJS49oePTwGmZB/LE2SUjilNHU5UUkPUgeiYF1Nt9SukOyTbOUDMubVEnbe+pcSH4LorfTmyUdvwmXhCj581L6URkAyVRKLqONt86PdNzCVD0+t5we7WUIAlU1IvoZJwQthxuoWIY2cVZroAy1PRVQkQg6GmDyM1MEVR5aVhhS4OpA17RSg8U3p/LJ2J52AeOgoOmVboZ0cJlI0LK4QU2tq7rmMrlGWk939gbVgc96MW0bea6PSHQ2/JgcnSr9m4DRR88uR9+Pnjh9D0G1K7kfGW/mHYrVgxHYIy/5GdiaNVrmlS4gIKBstGhRH0AKf6BQ9p92HipcWHIe/pA356B55eAcjAvYnAPgvIfoTJeQOJzDw2E0f7C4RCwniUzyslYFnEO9pPv4gwDI8g3p5KVldH7kBd4uH5RzWKpVgHOYAQ8TnyEynD+YDbM550hsdGTSK9nI4hw0vzTOYdthWDLl5Rw+/lDEOAcZhBDUQeHjHKZcXVcvF6COHVQ954CfcwCKd9h1jcGP8uScw89HKOdgXQS7blXPOTrllS6geiA4KKocsZWSutPP8SClGEIGOxvbeAOAEO5eEB4HBPuekAPbKSJFQkg8MgJw9tUgJlTnaeen9Gb6Ji9KUlk2EWegy5aS769ByYUeXaD1SRRT4aBkQpEQaelOjLxDOOIKqKmcNjGhU+7t/KJIMgMOkZwxrr3lIDrGBo5tDIWYQKkpkbqJyOgGtCE2xkfF6VF2TUrUVUpeijWJMI4JAdewKyeBMp586mhSVzKiYgy8sPawMDrRzaJUvSSUUSUzGoQpR5MVko9qQu8yEZ3O3CLmY64BN5YuiRHCTLkFQHbbpoQhfXQBnrRaP4t06OsquV4mzg1T9xTstCpW8N/qu9Bn3vPnQT8/EI5M+nd1b21IW1P5kdWLd8ovHxCwrpHKHyxbzDohHWwdM0oz8/CHCnMOu5g0zkFE7aWpXs6hZDV/QLxXhucNyR5oiXmI4g+VyDl0Z04dALk4B+EWNs8Q5jt3QqRwjuQL38dJ6ejjD4Zz+GZW5sM5eJzLOewjx7kzgXOSiCwXsTlHxuEclh22OId2kPg4B7X5lqOEcg4w+2s2t7T5gzO7k8TZJ4Kwuqpwi3PwDT4ryDngaTfKOZy4RE7m5hysHDVLBQa284QjcI7AOQyCg6LKIakl4BbLWAJIM6fOa3CdDSthwtgj7jP6Sk6FCzjdgqR5W0Zcxr2zO9UT8O+VYenJy5fecKEITpqMb8QHgBld8OlA5Kz8nI6MpvHICEg9uiHsOGa4ebYxUSC3QPJXTY2kZEMbL2XYideb74pNDJ8yDoIaL25c7CUAtBz7+E8V584yMC/skTDecEHuNj1lkskAZi8Ie10lcVI4dSWnaNBrQOpKzz+3HR6cGBi9uCElTgiZUlfSBjSe1p9P3XQJgWlvqY/8IrcPqyusupp7w6eDkqdPMy/XnphkT/FUpTGiYN3DNumww12RDZ8oAECpjFBagR21KyIbEFAuCEPXNfewOUcCyXgJkaWcw7at8PCHinCOFP6gZ0dYfCKNcwgi45YvvXttQXhkdLzw1j2OLyfn8JYDXo5VXlQuzsEvu7EzkvEFoWUJF7DtMLVpHs7BbF1O/gCdR2GcI1lWkcI59PIPh3NIzjlYPcrmHDZ/oJwDJFzxHlqX8nMOSwdfW9A87HpVJuegOtmcg9x7CoVyDn4dwQVY/f3hrkjgHEp+Y0JwUFQ5/I+TgQSi+AXBdAO5IZPRAOFJK6lhJcUyB4SrQYqRTpERhlwwHUQe5fienzSikKabiqNEIR+9VY5eGU84lUk9ecNDPBKZKHI7cSA2DoxAUIMg6As5qYqSAem8hSVDqkDzs2cOaDJgEww7juwyrcKjMmVcvdlZ6fTFXpCXbPbJWvqaMo2MZHnZTgNeHk8nrPzsdAAs5wSpq3PkF9FPcL0domDfBwCQ1JW3gd2+Vn6S3yPm9ksz3rDax9U9DWmPROqjkiM8ICCg6kHH572RwuUcJlp65fRMSW+WIrWoCnEO63ca5wDK4A/5cg6VFwBnjwiaY1l6p8nk4hyOnHRP66B8Qfj6bJkSXj7OEetg2VTCOai993OLsjkH7DjJ4wx/cDmHzXuU3i7ngC43jXOYY8VdO+zjCJpzpMQVwjmMLu5MDJtzgOlctZyDlZXKOcDqwOFyDp1Oep4jp375I3COjRPBQVHVINOVvKZDqO6EBJHfbAmF4AmcZRciKU+afG2+4RvhYGslabq0p14i3tjK0sGRIfo6G1CRj2/5iJZJIx62Y8D0pH6CAxIHS64MmbRybINv/sdGKL4MPF3EjglLecmHK2Py4wZXEBkIMC8+n3kARg7oUhFjzLOGgKj0yrA7R2+aumaEjI2olpE6P3YcmKpXEk5nfKjNpVS4TMphMxWcXaOtcpK2dI4bJcZdybmjIeQWEKVkdEI410jvgi5NXvqsdFhlq2tkhWk9BGDO/vbIEp1NfgA7YzwpF/q7datCejm6ypemN7BfXbiM6qO8j9NGgiwEoXLlkw8IWNfQ/MF3+8UdJACkHHsukv5IuHIenhDHJZwj8t/vVck5gDz4A9E/J+dQeQm/k4YNVDC+UHHOIWieqZxD8Qk+c8LYbMIBfJxDqnR+zgFbJoULVAbnMLehssN2OUCE0lTOwWeLVoxzCMh0/kBnGtB6Mc4h9QwL2+abSxiXmymDc7C6kmtkcw7NYZQMy5M6J0x5pq6ApKex2bJa67I4h0lj6mn0Tut+9KwNp5tJ5xy5+cjGg8A5OIKDoorw88/JcTZSaGMsJJJdpIVljOPRjHiLGhlPvSSGy7uRZQKVj9Q5JeHJng3MIsAvS8mHs+4zpTwa5xANavBVrLVRpkN0fDJELyYl4k6cruWEJcN1jA2gsejS+g9taKmMgNSky7zIGXLAiYLxhquNMp2RfmJIY7nEMCdGOSIdt+pqlKHRhADQXZi9cSLIzAN28gaTocssEv0Sw2PLwCsjoXafhlWOsGSM8ZXMaLPTPGRsnM0+FUb/2CDax4RB68DahxrRZEMue+MsfS3Y7IgkXLh1Ba2rlkmugwT0MVvWbAt97QXIikJ1r6j2MqsF6f2jBu5c5wS5jzwyqq8wMPJ0FANWPgq5CIcK57+dbqBg07hm7a8FSqxbhOmWARsS5sxZRAYu6KAI4Rwa8TJDh3MkcT6uYUNxgQpzDjuRp5Ox947Ii3OoMsriHI7Tw26ruP93+EMuziHzk2GcQ9lQtawjB+cw4TzOqGBsk74UymYQBwblHMLiD4CPP1QO51D8QS2P0HUvhHM4fIhzjlh/95hRyjnMUpBERtp6E2cMTB5VwjmgHBFZfV3YshbCOezlp9SuR9ZgCT3K3rQhkcnBOUDKyIdzQMtzB5Agzg2dRnczaZyD1MGKKy/nWLVmWoES6xaBc3AEB0UVYZNN6iXfJKSIEkNJHifLaNKuwTWYJC0x7A6oMSdkg+52zYwpJQqCZCFIHrQnsGWScCHSZdRxonaP4siwMkTSbnDkTBqbHCQJIpmSXregJ0+p9aG/VYesO2Kiu1naQQkCMcLKqAmjHl0OQmdJOISAhDEvd1IedwoIYtzVLth+mfjorPi3M7KhjTA1REiO75JaX9UM7Bgx3ZzSrzfVjTgqzOgGPc40rhMdneGzHaxyAJYfnDJIvQDoo7is+NhIc1Kk66raKTGn/LoSckFuNeWYoOGg7a3j6FFl6o9NwPiGWiBxek2oDgf5RtfmkvwSp6lNw5Fow9JaMWkzLuj3fJGJGpVDat2h4meSb1xkIaB6o1GjOvGX5OXB+6SKwjgHAM4fYPEIK43KzzmK1PPRnMPmApbtpuH5cg5A8qNOfeXQeqiZrB6uYtKkcA4l46TXreXJ0+YchiPo35bufDkp5yiRUDI25zD5VA7nUPmlcw5VVkTiDOcwdpPrkKQB5xwA15c7OkyZfs4hy8U5IqYbkXO4F68X29zS4hxaRxJvcw5665hNPNM5h60bPzLUtEH5OIfRg7a1jlP3gblFk29Sy3EZlz+YcoSTv4nn+2MJS7ZQFGWalUNq3SFwDo7goKgi1K9for+LbBYyo+fYQT2SapCCDlbo79I1vso4+RwNQsCdspn0IqoMBiWjfitSQ/L0HfHJZIQrQ/OPszUls52xjduW5aWV0WWRUQgImFEJ6ZWJk1H3rjSVYK1gymBxqq2jlHAB7mgg5dsG2hiUxNgJkxWbqqjD+Us4ffnXjhDmUACgT2BIDJcmBNRQqZd80+TCyo+Wa3RMP8ozk6I3PyaM5kl00O1FdZPEqGV1vnQzTEokBClHsvYBa4P4MvC68iPEBAm3DWFcpjqGNc7GTKJjMpISBemQEdMWYI4Lc/yahGDXG1zGKsdcI9foK9jOI3o9/Mbd3Fe+HNPKUaQnUS+VONhxUVQ7JWVAQEChKCmpASB5fhkZSF5KRNmcA57NsHV/IMhvmwsoUM7h6czK4hw0rWFL5eAc6nuFOYdKRzgHuEz8qSjnkLwcUh/XOWH4gx44sDkHKsY5uE3lnEMCpOls5wWxe4LLcYe7L9zHOYxdzRAZZu9hcw7jPCgP5zDt4H/Rp9eTtlvGY6O1kyQvzmH4QxrnUHG0HEHC/fY+P85Bnzd2n9icA8YBYaMinIPqQNuEplUpy8s5MlGDlJQB1RHBQVHlSB4x3aunPaY6uWt4femSHz6HAXvi7bzpf66mE677Rx9x8aVLfgjAu3eG2pYLEGCndtAMbL1FLCMg+I7fTEbycMfIc4NvCAQJJ7/j87ldGTNLIonSxoWQBE1WoMmD2h07qY42/vHIhSEVWibRwbSbRRR0NanBgXZAAGTUghhoM8PDGHB6RBdIuNk1m44YKEKkZhSYNtN1tWS43tTjn5SDLI9jbUfD1WXK6rJVu9hrSdm0SELQIo9svO+GRGQ5Mrx60+tt50P19pAruoaUtRkAOHtrgJRjiI26f5gsTP3U0wUBb36MPBLZWIbHsWfaSu9+F+x3GmFIIxHVFVkpkE0bVs5TPiBgnYI8wEImSzdSnjzpkysrXcIF3PLK4Bw5dLX5AwB9lKg6Qcwnus45B9O7vJyDfLe5hbYHVngZnAParifhAD9mNE/OISzOoWyIu1zT1C+j9MvFObQtj+OVTTXpVXgaf8im8oeY95SfcwBkFoeXcxCuxGy7j3PYXCDRfR1yDljx8a1K+EMOzkF5hzp9BHA5hyC3dj6cw83fzzlspPGMQjlHLgdGdUPgHBzBQVHlkNrgpT8mSRpBjC6F7wmWgMzEkZJ1Lea/k5cy+lac3xdKQI/NsnTynX3OjgizCAMgUmSM0VYdnjGWML2i0taS8YEacFYYIxaWTARDPqy8hK1D8p2++LOqgho/kheU48Kvhtn/wBoVEKQs2jaga0P56IGwws0gEp1pYf6b/HyjIhJq52z18u/KGDJCZcx3yxjrtk3aEsqAq3R2HZLynaOzEt2cdlP5cqLitBNs48qJB43PJUNJhDMIR+rK2ieRpdfCkWP14bJ0XbJzL/l+W4G++5bWyZePL31Z4RsashWcbrmxnUkesGEh1UmgnKSR4EeY2zI+uci2THlyjjRuk6a3sAKIPlXKOaiox96Xj3OQMA8f0dzCx0fy4BwufyB10MW7nIOmYQMQTCY35+B3g3IYuHaW2/90zqECjVyWcQ5bXi/fKINz6HazOIcZ5Cibc8BzXKfhAh4+QrkA+V9+zoGyy7GuUeGcgzsNXP5A7lfPNfT9Vu293jjHBuShCJyDIzgoqhxC//XvK0F9nzQ8+Zsy4wJJh+N1NNA4QeJ8WaUZVJ8M6fXonhVMh1RDDD3FUnv0SRqhnTge+M+FivOxLZ0ymt46ST01lYHI+Aw+rBkQzOBZxtvoRqYdCm4koQ0uz095z2k5zAgJpwmMDM0PxiiozZZMHHEiaKNPDQI3nuwjbIOryspaxpfqTY/vonJZQxSYQTazHHQbE13sXbBdvW2vvISQWU85Zs0ozc/W282TEgRLxtrl2rQHJxWMsEjX2MPJj74OIAnnz4rRgd/fgsm44fTpzdEVuCiH0fcdDhAQEFC5yMcRID0PIuUctl0HkM4RkjgvH0nhHGUeK+75lItzAECE/DmHkrc5h6pjwZwD7olj4DK+l/98OIetn4A0x4JWgHPQbNM4B918k9o8vWwihXOwpQ6aq3AnB33hZ5tYso/aSFJovlBezhGR/7qNwfWmeYHmZzl2yuQc+nSUdM5hy/g4h/7u5RyGD8GXXwGcg5WFAjmHzVtIOTSsUjhHSlzgHBs2goOiipDNEs83XbDpPCzxQyz1hg8EetqgZb2UwU5GJ5zeG+5IgjKs3t2wvQaXG2NKRLwkRPA0vB4wa0GtMtlRYJYMaBxJE8v745SMIlssihp6PTKQpItYLUmexKBY5aR5kpXRF6ocJmMRAi2T1fkZgxX/jyARRVL7q6jebDMpkF2fhc/RQHQT0C+6hotlyUgBlSPrJaUJj2DV1WPY2WZOQt2uWUSCp40cGUWETJsy54Sgs1PortU2obDaDpKP8KgXd30tpd6gSl9XYa4Rn5YKKLIVARCqHJD8LXJl3yfqEadOIiQ60nvH/M/GjShh5WvaWNUVpBxE6pFS94u5K9XuOGmEwn5REepPko39jNlCElwnHS7zeZVaf8jKCNkK7IpdEdmAgELhPk6JIaSPuwRk0hd4jxlNOhDdE6TxhVycw8Mf7DyELy9POloNXU8il5Nz+PJC+TiH5jBlcQ7r5Y8NTlicw9gckqcAoLmArSDnHPaoeBQZzsFlyuYcAO3/zd4Npkyjt3ZqSGXTXc6hbSK165pzcB2M06I8nIPvHeHjHJQ/uJzD6K3zITKap5CTzqD01uWYl3ovX7M5h2qjFM7BdSiEcxD+AM4LOeeQ7D5l/IHpbXRQD2y+nIM6oKCXmFQh5xBQp7Vr+J0TWTugWiFwDo7goKgi/PTzvOSbdcxoVgKZNF9k/FeyJ5A8YcohQWKyEoBeawqelohLGp486DIynYsUVjwx/j4nhS/ct3GWjFSWgufpdEGkqvQkDlZ94vShPSZgjLL1WxIjoHorIdS6uWThDTHEVEOROFV88e7MCWJYIxqWEAGY9IKUqXaKpvtHKMOr0vHju4wh40stpHEsiCwiCOPsEBLmGCzi2Vd1gNQyylkjkmPSYgJCdodO2jWT5KPWKprZHqqcUjOqoYhJ0j4ZVh/aPlkiE3vzo2QT0Iye7UDaVAoIUo4yvBHUEWJkREIA5hxvq5wk30hyHYTWIW4LdnSX1jvWQe1Sre67SBMn/nSbWRNk9oa6F8h1VWkVRKKD3vHamvobt63Z7TvWQd0bKg+tjJe8mGhTjgqzuhOtt7R+UyFF2GkcTb967S+oziiFQCmnQgXLBwSsK8yevZD8Mk+w2jBTD2gQyOQhdTiHNrGSdkj5cQ5ALyVlwcQ54uggwAZV2CkgRO98OYfSl/OYHJzDMyOBV4u8YOm0KZwDOTgHpI7XjvEkXC/tFJxzKEduLs4hqopz6Jdho4O2K6IszhF/N5xD6WNeoO0TPpRzhnOOOE2GHOstbJkUzuHwB6d96HHfsW2OUAqAHntuHBCx7XY5h5md4XIY1SbUeWM4h4/DxPtdgLaP0rFMziG85RjOIXR7a+eKurfU9U6euXhmbtVzDpoXiFyhnEPHSfUMUm1jrFr9A6ozAufgCA6KKkLnTs3w+eczAch4doRnk0nv1EQheBpY6a3/8SgFvynjl2/zcYiCirfDiBxb0+nJT3jCbZKhO6YIoLtpx9UUkJFyIpDOSclaR4TFoomMSCgViTNLN6RTvtKNdqTOqICANl4QySaVpJukTRxvQGXy0yoLbnBjucQ4kamXSU004WCnbhCDlFEy1BgLeqQWMSqJjlGkXqQN2TCzJuz8EoeBLoe+NFvOBBh5RXLo5lnGcQBkIjqqYQxvlBzrZS6JyTsTSUcGgu/QzfSXMfGJL5NVjm5nXoYmPpoMmiOshPSVA03I3GOzlDNBtRkx/rFKTl3Vdz7jhMjo+4wSEtPeqhxQUy4tcqFlaLciuQwEm87K48wvGkZHI3ja5D/vgkyceYRYegmgRqatKxQQEFAutGnTiPxKHAgee0ztteYPgkqatIpbOA4BH+cgeadxDgHwWRWWPsqWM33VIEdKPRyOQmRoGOMciSOAyXk4h8orrlOhnIO/vDHOIYiNYvGmw6ThjHNAaJvHTu3S+VmcIzESbFCC2Ix8OIdZnuHnHHEY5xx0VgfnHPS0L2rrXM5h6pObc0QRcSaoeEE3AZdWvOE9sGTSOUcaR6DOFh/n4A4ezjmynvwM71H50f+5OIddV3UrUv6QF+dI9FRcsizOoeToIwj2vXpwjpo1NneFAqotgoOiipCJ6FCFTEYJ6OOK2NMnlI/aPFfqv2PclTFORiKocRZ2el0IuNdRyQgiQ3sVX35EhsVZHYQtw+OEdeQXiZMiWafpyVD/JyQAsaeeNqmJSjpfLas63tios/yIjFSdsTCGnpYVQxl4QgZUZyssIqKdF/YIhksKTD4mjI44qHCdHxSXMvUxxlcZMZIXNUYeQ83yJPXyreOMR1OyXJ46XWCPuJh0kZd4cB1g6aaO23LihNRt45wJrmVsJ0WWXQ9jeMl1dYx7lukp9TWRySoj0+b+47uM0ysmEbyuEC5hgM5DX15Sf95LGKJgy9j5Gdi/NOm1yqJwJg4K71cv4lFA81uNcgjh5FqtEKZbBmxIUPZT22HzWPNw3UsnkBZHIAKac8AdtHA4hzEyqZxDywlLzs6P5MXi0niKp+Py6qB/xr25wzksncxyifJwDhMHxjmIrdMJ8+cc0KPolHPwl/myOAe1cQ7n0JUznCOyZSzOIeDhHCyNxw6rPJ3BBPNdzcSwN440NlXqT0U5h/pfFuewdWSOn5ycwzB9m3PQe4AOBnHOQfmDh3NA+nlhuTgHbRuLc+j65+IcHNWHc2TKkFy/CJyDIzgo1gUE2XTKGpFIdRPCY5RphJ3GKdN8VXkoYqANd1q+dv7CKkf9Vv4GSkBs4iGIDkl3q6am8fyEI2N0kSbccgQAsDa0UiMkMAacyCij5stLIu7V9KgILSMxGmwtISEMfL0meTkV5vhPY1SpEeIeeSTf2YwH6jSwZBT07s/Mm584C4Ta7ZqTDHuTJyTppJYB0ZEafXI5tCGP2yCShFCoNoHy+kuWp1R6S75fBqxy6Z4UIDK2Xnr0I4V42A6XOCLZDFOC5wWALRHReUh2D7u3veS3YyKnHnNHBwmYI7+sR1Ka+8LEKd2zpBSpv/llzLPmf+SFN46o4caTbisf6D4nTZFqiFJUbMpkaeWpEhBQMOJ+R5glGgr57Bzn6SiE9HCBMvLwcQ79OkbsIn89T3GWoBycw+YyLL9EGXpKGc3Pwx+EnY78zo9zmA/nFlrjnJzDpC2cc8DDHwrmHCQu0vpRpwjlHNQWUc4BSwefjKkPm9EAkHI4F7A5h7A4BywZm3Mo/mA7SpSMkHY85Ryca+i2s26V+HlwOYe59m4ZEeEPaZzDtAuMDpXAOfiy1iz5VTbn0EWBI3COdATOwREcFOsCko8++I7E0saXi2nDx32s5rGX4IbaJ6PzI4abxlGjbU+XdPafsHXzxNNNqBxHiID3qK7UjauI3gy6ZzQ5sGTC1phoTsiB0H/iODXxhXeExuj7enT1ws6y0gY7KZN26JpUOELQSxSsopRjQJdF9DcbQ7kyarqg693OGhIjqBwhCo6cIjEmb51GkwuLYDGCo66XMvpZQ0goWVJySoa2h6eu1PjStmPXiBh8fQ2YDB2VSNqHphc6K2O0BTXMErDaSgtI69hW4eqg627fdyRvCmbevTLwpOdt6cb7fihShYojr7eagICAyoAE9FKHVM7hk/FyDos/kHxofCrnkLwPYXlSvSwO49PNx28czsF0sJwDHhkb3v0qVO46H8s2FMI5SBy1mbbTQggB10HCOQdFVXIOOsNQ6QBLjnMOZbsUFOfgDhLGH5hNVI4LH+cgejPOodIR/kCulciTc/C2KItzCJh2MdfPxzlUXjbnUPEZUj7jHHqGTC7+YBwjtNxK5xwiTcaFzd/c/Hw/AucIiBEcFOsCQgIi8uyybSDtLyKWs7u9mFTEf+04JKMBvmJssuHqWIZuNrmwSAU12s6IBJK6pJEBn4wmLtIJU+WwI0NZj2s6c2nJOEaaEQJ7BMPIRGyPBGowJQzxSIgIizcG1Bh2LqNVVKMJybVnyy2IDJiMtfkTnfGgRzeoTp466PyyZudsphM5SlSYZhNWmniIjeap1pO6cpHImnpYepmZIHSWgjTtA6selJAQAoOkTnzJDml3YaaF8mvh0l+mg5SIj6gz8ZSQEFNu2ieR4XmqtvXrx/KwdHTT83sJTC63lRaJvzONQPikC+UOqu/bkPhCmG4ZsMGB8QfhnNQhdBKLPyi5KI1zeGRosZ5gxjnSOi0fRDrn8PKOfPiDo1v5OEdKB8vrZHERH+egXMMdFSbOb2qjEx0c/qDsJ7W1Fudw9roCl/FxDr6shL60JyebOY6GrMU5aJzNAzjnYGmJjDqW0+UchvfYnCNiXCA/zmEGUizOQXmhpTflHKrNQOJsMK4E0s66fQvjHIYvqdzBddezgYUlkx/noOGFcw5TRx8C5/AjcA6O4KCoIiz9ayX5JZJTEYQ+xSMXjHGmS0PS4wSSEYrE4FIZ9pIOE0enXOrvSltBRknA89PpSJ5qyrszIqGMOvnpbBTqIQqKBpmyrC5G8hEJk58AHW2I2ybpXNkZRMaRoGqnHBBGMv5vHBDK4GWhNrKynRPMwBLSYFQ00yh1ExIZ97gtGIOrdNAy6pQKop+Ws6dl0hf+bHJcp/uirw07lB7JztzOUWCGIAgiYx9PmhFkrSk50lTprdtYKsKQbKSpDWIyRVPEOqi8JGtja9RDylg3LROniR8/7phhdRXKgZDV+UndPtYGXELpZpMcdS8oImT25TDXSKbI0DcLkzcNBUmv2y7RkjtXOFGxTTt71k3mpL4cvrBCkOaYzWKlP6KaoFRGKK2Awa+IbEBAoVi5cg0AzhdibmD6AGqNaf9S6ZzD1gHpnEOSNDbnsDfd5vtIJHWjjxmxhTpfq/PSPMXhHKrXRyrn4OUQTS3+YLe2zTmkhMM52D4SwvxX3MPvnDBcgM+eIDYjF3/IwTm4zVN2Jkt0sJ0P1qwBkYVABLqUVOml+AN3JsTx5oQPv0wuzhHRZZkV4BxCCLbXls05GH+QEhCKF5ryzbIMc3IaO2FDGC6hZk5wzlE2fzAzO9I4h81fCuEc3Pmg247eWyQfQT42qgvnKM0urUCuVY/AOTiCg6KK8PvvS5JviVFPeot4s0g+IsAMvAq3IfhDTp9gnyFncTQPm3R45q5RomDnBcBxKAghIAWZ0UB1iaz/jGykjEh42geJTtoz7dtxW40AKEOtR0KSDpTIqE5ST/WjxIeUE0UkLZC8+CazDARRTyhjhPiFms3GMIaSnjgBMtrATuZIHAjKeJtdug0piYgRMiTCOCzcmRPmWFCh9TIGOr4VSH4C2rBnIEn5cX2UXpTAKAOv9YapTzytMT5PO/LKxDq6G3Ma3ZKnJ7lKyTrSHKM15uQNcv2SfJ09L1SdyPGgZv2lJL/BdUiesAiCxSndzaNCiElCXNxbWOqP6jdiCSPvdg2C1FWwNKbHsSDB7neqQxpcN0d+yDVrrHTtgnLkuO4gQc+1KZ98QMC6wsKFy+Iv9A3ABJhjRtNuS5uHIE/OYdtpWPyB5uWRUYMo3tM4KI+w4nNxDh9/UDI6P3jiPDIohHN4ZKj9QcIN/JwjEbFloOx0GueQermmyksPoIDaRRDOIXNwDmtJgc7LnaUohJkBYfMHw0nM6R60Ln6ZOD/NOUh9NLcpk3OYukcO71H5mqUe9lKMNM4RX9ZsCudQcrStqU03OoCUE5HyOOcw8arNoOK046U8nEOAWqY0zqHvS52XgVTXMJVzeLqYasI51q79rRw5rjsEzsERHBRVhM6dmmHKpz/DXqYhhQRkfM61Pf0SgJckCCQO/JTlG9rIqzKooE0QqMtSePLzydA4modHRvq6FVWhlGO89G7adp4qzpZJDBaxeKAzJ+IRcVMDwWSocackwf4utQgiahAlX+5BVFAv7doDLcyVd9aMagNK44hhRzzRxpRrRsntaYWUMMTkxnZOmLWkWkY3ZVYf6+XmJ1GUEJf4tzFu7pFfRiYjzIu7mTLpX9oCIhO3N9XN1ts4DwCyfAS8zrocSQ0vIWBC3QqcMBhSRNvDJQ5GB+j1pNI5vssatTKXXZM/WG1qPoKVoaZq+kyPPSLC9ZbwuCdymjB6z/KUtukjfRp55p2uQT3HkktJADWKWufQJCAgoBC0atWQPGDGPorEigoByCwAeyN7iz8kQfpZZryB8Ie8OYcVVxDnsJWhiSIVlcI5cvz3HjPq4RzxyLmKqyzOIXk6XSFjm8COJZf6uE7dENYLvp1fbKPLyzlU2YRXpHIO6eUP2qYKblO1bdT20eIpjD+AySrbrWw5nS1guADnHJlcnEO1qaT6Fc45KIdxOQcczsHqBKm5BSC4HjpvEB3otSsP5yDXXV8Pl3NQp4YNoxvfe6NyOAdNXfmco7hmOGZ0Q0JwUFQ1km2vjTPCelSzcHb2Y12JgLu+k1rwyI0SVhIm4+kp2F4OSEknrO8+h0lSPz1d0ifD2gJJFQWkTDo2S0Z3dmqKpZZTZ4zz2vJ6KKJg9VTKKuWQoYZeNWo8LdOEc3WMftywSE0yTBMpg5vVThLb2NHNpNRZ1GykQRtqQxSEZTxZnB0uTDmUKFAZelpHHG6ON+PGLSFRsJeJJHqrupJwyeoqiV6cLNgG3dGR1U2VR8kdJRm8HBAdMpI6YqisCdPXXTltCOGIZ8eYaZk2saBh5nYT+l6AU4/4PoF0SQD94pINSo6sh1iTJAPp5AUW6+1LoNpUOHEqvdN9CJJPjhGO6oQw3TJgg4ayW7ZxV5zDk16l9c2A8D7YJDhvzuHL2/otqA46gPQjif6yPJwDhHN4hnX1EaR0MAeoQs4BwzkESao4h6U33fsBFlcpi3NonuDhD2wDS2IHc3EONnND14/bdc451LKONA7j8grOOVxbn8o5kD/nYDoUwDlUuDnJhHIOszTVV04kaVmAOVmjbM4hmIzQ9aL3gZ9zqPvDtEUq57B5gX2f6yDjLErjHFQ8N+eI2852TajwwDnKlt+YsHHVplrCGWPgEACkZLHKQLmPIthT6NuYSvcHlrHnvV1Klmkytnzy37cBV/xf2NkZmTQdqGVylEveqqwZGFIRBdomREbQ/FT2NI6Vb8JZxyxMZ6487sY4KBljWGHJ2DtkU3LhHNdJlnSYOKOTOW7LGBa61IMtjxAAm8pJdKBLR4yx5caXj0jQ5RpZyylg5CJLRusosqwNaDkRaR/fkZ4mnJIYaeWn6pw1s050np46WDpkIK28YjsdWTI6L2mnT/S0yIOw9LZ37xYJ4QCk0xHHOgg9UmH+Uv15OSo25elznBOmnJRw8t2O81MLf3oWLjcYroCsFBX+FIp3330XBxxwAFq1agUhBJ599tmc6YcOHQohhPPp0qWLTnPFFVc48VtuuWXBugVsGDDPbQ7+YHEOE53SGdDvHv5QKOdAGn9Qtt2jG4QbXi7Ooey78wZm4rUuVcg5lLS93IPyB7YMQ8UBDuew96vwcg6YmRheLmBxDhPn5xxmFiWx54w/0HIMf0njHG5cfpzDzk9zDtputt7k8rr6u7abcg6wuKxTV/PJwXtYOYY/lJdz8PqUwTmS+y4X57BnQSie4D/tpWzO4XQhnm7G5OBfahI4x/8e5wgOiioHsZD2vaOc3EIwA6WP3tIWDmyNplorKewnH6YoSbK397ygcbRMactZMk6V1CiGlV6tDbXXqMbxUsvQ/MxaTmnJSJ0PN1rxb5pWveQ7RIGWQ4iC8MjQqYtmFNws64BQhEIaPYgMNQDOMVjqcibrNsFkiCEUPmOSJTMkJNdRGWmtG1hcXFeqN5HxGS5h5a8Ne9YjY/ITTAdFctQ6SpqnKSeC+ejrK7NuHUk5akOpVKJDvseXncvwDbY815VeX3K/6XvQ196CkxhjvJXeyX0Q0fuOkwt6P0Hdl0QG7D4yN7dNXJx7nuUngSjLnjfJ7sV04pD2SJly408aGUgbUA0wWLZsGbbffnvceeedeaW//fbb8dtvv+nPrFmz0LhxYxx66KEsXZcuXVi6999/vyrUD1jfIGRc8wcrPobIyR8051Dh1C77siwH50Dk4RtW+fq3rRuVyYdzqH6O5SmTN7z8OQd746og57DtgI8/UNviOifSOIeEwzmQTZwTfpsdOTbA8Af1m9pj5ZywbbeKQ9Lu+XIOOw5JmWVxDm6HOefwcaU0zgGZZeXa5fj4g3HE2PnJuL1zcA5zHfk1ZnY2D86BCnAOXYSHc/D7m8Txx4vcK9Y9XwbngIdzpHOLtPDAOSoD1ZlzhCUeVQ4JiPh1ImuNGJgnje9TIRDPwoSQySgDsdCq57JkqHDa6IMx2H41kRInWdkwm21JOLM4ZFJfu57GOSE8Mz8k/yrMD0HaiPdY6YTJ18b2KIZ3iiVMZyuIDpEA2fWZyhijQsvSzg6Y38rZEZETMWyywWdUmLwjSL23hC7GJijst8qPOF6IITBTLE1dGQlRhlJyfbRRU21IyjFHgxodnLpaJEaHCYuURJLlT9Mp4gHw+mc8dQXUJlnm/uI6QF8Xu+1oHqw8ci/YhI4ba6o3b39zD8VxaiTRxNllCy6j0/GbWKV1Y2h+LpJH2YFKmxZnh6sw0wZEa+sR3xBQigilFfDhl0e2X79+6NevX97pGzRogAYNGujfzz77LP78808cd9xxLF1RURFatGhRsD4BGx70s0n6FhYpAN9xonpghOxfwQSFhJTCHdZKY/+Uc/hg6waQ3pPHS08a89unbxkDIoo/OHrk5hxq2rhTmq+5yuQc3GYIS8Z98Swf5xCQyNC9qfLlHMymcs4RkbuHcqWMkMnSGW5HI+fEEJOf4Q60yVU5vIyyOQcdYMmPcyBpH2jdJGuTNM7hbkQep6kszsHy9nAOgOuWxjn0LaHSJP0Cm5lj6cl5hGTp6LNUXs7h4w80bb5xgXO48oWiOnOO4KCoIsyevTD5ZnpW70OZWBSBxCmh0pCnWuojrgSJEywPKkMJSlovIYRbjk7HjDIPpzLmRBBjyPlZzTbRoHGEINCWETyVMbbkr3KQqO/KslERZggQe671s6sIAe/Y9WkfJDwS3Pjo45iEu9O1Ls92AJBjnswmltaLq7B+a4NLDbtKa47oYgSHbJpk1kRSgwxi2DkJYF561V7sWC8QYmTyjpC18ssiStPbJ6P2fhDJEWaEwDAHANEtzkcdJap2/Db52XoDMvGNmevLCBakNYWSf+eEgl5Xky+/DjD6AKxOlATo8vVzIJU/k+Rj7lcqY/9WFCEtnueXNoXS/9vut4QVTv9zjRPqkEIU1qydh+qM8k6ZpPIAsGTJEhZeXFyM4uLiCumWhvvvvx99+vRB+/btWfi0adPQqlUr1KpVCz179sR1112Hdu3aVYkOAesHv/8eH6EX22jz0pCLc1CHgIBxaFDOQWW0ED0pw87bxzlEOudwTgMhMvbJHjk5h47jCklBQwh/IHmncQ4jJrVjRqcV9FhII6cdBQVzDmKffJwDxgYLIsNsnWfGQ6acnAM0ve9YUEmdJeaFXf2HLUP1svmIai8mQ21t0g6pnMMcoV4I51BHfOpLT+rkcg5TJjtFTcY6OJyDXXfAOZVDum3v8B4f51DtTe5Fyini+paPcxhIK28STpwbPs5BZSqbc9Dv5eEcq9fMRHVG4BwcYYlHFaGoRtLt2R78fF15MoVYpNy7dDofTc6+pPUIHnLgHPllZ0yNe5R0g3ba2NrxPIx1tfInyusFdTJF1pKhHyJDR0IUUWBT3YQhA2rdJ5tux6bfAVFk1mrqI7+UQUzi9RINGp+kz0T0twqL02eEmjapypHIiCyiCMTwQ6eln0gg+R6PVBRFJC/yn5XBws3RXnpNZtK0GSpHLmmcPkvyNJ9MMlpC95CIkKRPCIEK02tTUar3gjDrQpUusRyfmgmdl/DklxGqHIlM0nYZIqN0MHUGyZ/uXWHCM574iJRj6mqvbaWEkdYNrozg8Tw/z6eMR8R+hPMNt2GP5oGltZyDKWltCFGzjBQbB9q2batHHRo0aIDrrruuSsqZM2cOXnnlFZx44oksvHv37hg/fjxeffVV3HXXXZgxYwZ23313LF1avc+EDygMNWvSHWvKQPI+rIm8SKEmAp4Zj0kUMeEkefy7AM6hgijnELa8zTnoegpfXC7O4G4mUAbnkG56yjFSOEcsIz2cAw7n4PzB5RxCxxuO4uMclL/YnMPwED/nEIRzCA9PKCJ8IxJScxPFH7RtTnSIAJ1esLxiu5vJg3Mw/pCTc5Tq0z+YTBmco8jDH3ycQ9ltzkNofiD1STgHKOfIosjiHJTP0L0jaJzNOeJPDs4hqHPCxzn8Mj5OkotXpH9Eapzn8SkX54hRXs5RNS/p1Q0bC+cIMyiqCC2aN4AA2PRtqQwXfYzIT52GpHAfQyvcnq4gPGkI1CiGTHualaFN0lByQmWcWRKJMcl15JcTp/OzRy9oMunvyaxTPURSN/VDOQ0oQaDe7DiZNI6dpM7KoaQcED69lMFnoyXKEAhjaGgVtVNDl0tegHX5ksnTqZzUw24MKalTksY58kuFExl7WqEiAjFZ5SMGZkQC7L9v8yw2ssB0MKMmqTowGaMH3YgrDhOO3nZdjTPFjKRA5UkcLWpkiuvJ9XNluJ48LblNBM9Lx8nEIeaBufZWAs8xo3Y5boyRoSMLCmme6VwGPs23qkhJarwwL0I6nQSKMo1ylLb+kU3G3SoiDwCzZs1C/fr1dXhVjWQ8+OCDaNiwIQYOHMjC6fTN7bbbDt27d0f79u3x+OOP44QTTqgSXQLWPRo2rBN/kQDYMo0cnIPYeMo/TAgXywUBMwvUDrc5B+MPaZxD/Y9cmTg+ttH2Mha614SE5NX3ORuooqqkJE6CNKOlgeEcIuE/Ps5hfhsZqeurOIeg+dn198iY6f75c46oDM6hbJ1t12z+wDhCkq9pE6MDPYrcJyOIMaBxlHOAyhTEOUxb25wDJD8qUxHOwT8g+hfCOYgeVcY5LLaQxjmIXj7JCNITU305BxD/rlHUMkdp6x+Bc3AEB0WVQ5iRCm7tDNRzbHGI+MVf8CdYP5FJuO9etnsWu6zISgtOVFgxKboKaRELLSOgzhi3j/xSx3fBXsfKdJX+sBSZ2N9jaat0k4iPBRWu24RuBqSm28WVAms/bmhoHtwQ0emWRjYxoMnoBGBmFZgpg76XT2pUqeGJPfs03OgYe+19cfa0UE4UTNnMgQLuUWf7RBCSQEmLvQM1nZ5ov/zrC+U10PEP+5iw+JMl7WgbbptgJbrpernlCCHZiXDM+Dt58frHfJQbcmddMbmmsd6q3uSaC1JvUh91M4qEJNkJ4ltY6DaMw9XxZnZe6in1nVJeOTAlQGuv44RxTBClqjVKpUBpBaZbKtn69eszslAVkFLigQcewDHHHIOaNXPPTGnYsCE233xz/PTTT1WqU8B6hnoYU6dAwOEcMT8Q/odYGXtfdsItJucLBJHh/CG9Hppz6AysgiL4OQft4El+ufuiHJxDxVNnB+EcsZPB5hySzK6wbKDiHr44wLxESmPX1UtsYZwDqZwDBXAOVX+bP9Bmys05eBxoHOCPoxxCkHrm5BxZFq7bzSOjFBfl4RxWfpG6N1geZXOOOCCdcwDSPWZUtwfhZ1o/+rvyOIdXxroPqhXngOvErK4InINjg1niceedd6JDhw6oVasWunfvjilTpuRMf9ttt2GLLbZASUkJ2rZti7PPPhsrV65cR9pSKCOWan4BSHOCBu2dFFhvBJ0flTFrM6101KimPaQqH/Kx41n5Okwmu2dLZ0aGzEU59M7ZpiOn+UghoXbSNruB+zQkJIHkpfSI1O7FSl1BDbR5yUYSF/+XfLdrAfIhJIP0yDSN6vhVx21v+AgBxPsnwCUESjapC9czq+VUOECNPq8btP5ZMv3TJQq+HbKhSIRHjsnom8rk556GkSUG3C3LLgeJjHFO+GW8O3uTHb/p/UVHWRSx09Ni1e/I1ZvtzO1cD37dVX727GH6P3Jk4k8s45samRz5JYRHhrZXct8JIuP9VB1RUODdFr+n2MLQgErFO++8g59++imv0Ym//voL06dPR8uW1Xs0aX1jg+QczA5bL+YMhD8QOe/STsI5VBp7dqW/fKOCj8/kzTloGCRkRLlCUkzCGbycw+Y9VMb5lMU5AOaYsPKMl2LYnIPwhzTOoXmD4RxAvPTCvg50yYaPc/DVs5RzcCeE0sFnh6lNNUtKrJd8rx02tjCdc7hcgHIOELl4PwzLDjObn8Y5+EkeoGV6OYeE3qcijadQDpGDK6nrUSjniMrgHC5/oNeA3YbsfyRsOeVsycE5khyYHNPbx2GqH+cQUuZ+9QooN9YF59ggZlBMmjQJ55xzDsaOHYvu3bvjtttuQ9++ffHDDz+gWbNmTvpHHnkEF154IR544AHssssu+PHHH/XZrbfeeus61p50FZ4nNXYwCCeMy3oQlTHFyRptoCTEp4NSz5ufxxinymir7DHiQOqMDz2Dw+5hYYywV68UEmNGGty8dJw27kZGeMqH7vCIGDU0lFzobKU5c5wZGWWMaB4qX7WmkldPG32ryu6IxP+z96+x9yVVnT++1vn0BRjtRoRuGmwu6k90dAQGQ6e9JJA0IcR0Mo8kaJQQZaKhE8ce49CjwjgXeKKID3A66pDWmYzgqNGZQHCcnhDiiMMfsJ95GS4z3UB3AyI03Whfvmf9H+xaa73XqlX7XD638/my1zef7zmnqlbVqtp713rt2rWrBN4/1Pyzw6hBgSDP4bZeUL9QPthAqRwWhK+oizqhbWXQBtp2XTsQZSDh7nsqox3X/IDM7Van7HFktsX2MvsEzp9QppSnveo7DqSyGkTEvHLeWWd3metHRrLrAwkmImK2N7MuAjOc1IJVu8jDDz8cnjJ88pOfpHvuuYee9rSn0XOe8xy644476NOf/jT91m/9VtD7D//hP9BNN91E3/Ed39Hl+dM//dN066230nOf+1z6zGc+Q29+85vp6OiIXvOa1+xeqa8SucjM0fnjIn5n5hCi7t201gngGAQ+IDkx5mDIb3UKzFGUOWaOfsBAazTLD5uYA/NtcT6jcTvm0AcitlgmDg7g4ATYGfObZ46aH3Iz+Q1z1ukfVLgNuzEHBRtCGxEBc/SswrPMIX1em5iDqba58UMow9onh29gDuC62AbzzJFtQP1tmCPm59/zmOMhM4fNatqjvPOQhTmiXIgBire97W30+te/3rYxufPOO+k973kPvfOd76Q3vvGNXfo//dM/pe/5nu+hH/zBHyQiouc973n0mte8hv73//7fZ2r3JK0z4LbNaO6d2oJEQtxffZy6gtAjpC1IKd7gh4tRr2zuL1JRR03UPT2x/LqeQVwn1dWgJNSjxRQ9muj/o+uqsLn1zPAEIkUz0fTUuR9UINBBp6EOHKfYTXq988ZMc7g5z5UCSYYMKm++idq7l+3YonPWLbpI3DlnJ+1PCSbRaZlEIx0JdZt0p6cV0+GIgy1sMBBttO3IRNtOUllTWtL2Bxs0DQUdWEG86TDY5jpgYyvXbaCuzsFxt8+42nYEJ2sHTjrq8Lpj2NolxXk9rBlCefjpEusQYUI6Z1uBwpybGulmKyogqR6IZqny0vVN/P/9nfBZiciK1rL/JEPZQ/fDH/4wvfzlL7fft99+OxERvfa1r6W77rqL7r//frr33nuDzpe+9CX6vd/7PfqVX/mVMs9PfepT9JrXvIb+5m/+hp7xjGfQ937v99Kf/dmf0TOe8Yyd7ftqkYvNHE3USefTsGAOu2bVZwrHS3RF1DEHMgKlax6Zo7jUz5M5TCvHWZkyZA7s3UKfzGR/qEIkcYFuDC+YA33JLHMUfuYImAN9DD70qOLiYIf7VHylYcQccaBhHfxlGJiAJ/3xwUnPHJb3gDmYcK2tzBzrjjkyw/TMgYMTEpgj6iSmAO6xUwTqWPID4e/IWZk57Bj5oYdytmCOlWUTzlXNN0rPHDk/9P1nyRz6e1fm8AChfMUeqizMEeXgBygee+wx+shHPkJ33HGHha1WK7rlllvogx/8YKnz3d/93fSf/tN/og996EP00pe+lD7xiU/Qe9/7XvrhH/7hYTmPPvooPfroo/Y7b9Oyq3zsY7qFnl8UTESyFqIjxmvHeheH+AQH7bFj3oOciWgtCv0zF1/n5Vq2ChZV70HVRV/DgEAvyEz+vpcQyarrIadwe6LQEuOj1dwjo2L7xPkj1oTqaKGD98WrxJ27abkTC6DA4MBWSQcdntk4pdepfrgo05TEt6DCJxeqQ0RtcAKcLm63Feq1tnTVlqEZSMwGnrYgjfuHq23ReU+OzW0ITppTORTbnGnd4Em3/mplCczusDzUbtVJ+cnaoEbtJov3+gQooNZGzWk7QMUBkXiKiT2JCMDV6qTllrAkWh9u7UMtLVwTWo6dzw4DKn5OIoD4MdTLZOTcK2DIcVrCKO0IBqqyeryp6sPTtZ50pu9Cjz9xX2Ht4cglYrp0DKjZR/dlL3vZtMXjQO66664u7Nprr6WvfOUrQ513vetdO9vx1SwXlTk+9ekv+g+8U24LZprvzsyxqh6QFL1LSyJt2nSehRFkxBxog4aZTUXfwlswh9bjpJiD6x6OVRd8PrXkjOEQ3/HDgDl0xgMOdpjhmTmsDvpaJdkOWPPMob4TfGp3Y44ME2/U3Q+jD486hDZ0W5PCgEC6+e+Zw/mBWP3z9HcU2mLEHEQksJ4EDpCQEA2ZQ6ztVqHdwf402DJJwRzAZXsxRzHAMcccsdyeOYiqJVVmmCOk80slDx7k7/n3vsyh4Zkfqnxdx5kjlyMk9Ojjf0mHLAtzRDn4AYrPf/7zdOnSJbr++utD+PXXX09/+Zf1yfaDP/iD9PnPf56+93u/l0SEnnjiCfrxH/9x+pf/8l8Oy3nrW99Kv/ALv3Bidj/72V9H/7+PfJKmXqbdrsDj6uBUVaBHqU6X/ATBn17EkzI6eLyth3JSuXnxqZyHOW5z8l6+buuF0KB/3HrLvFAVM02LWhG509ey1UEnHWo6RB5nAxPg1LXTtvcB0UGzx68gzrb8ao6qf1cTRrrx1Q2AFB8YEDvU5thX4Ow4be/EFJyflROeVEBeBDAQQELoClvzQqy+6hz1Zh5v6ommpxGxjt4WV1h9cIRdIQMHVcic5xWWny9SRax2Vzo+qEOc22naPpWgXM3ziNd+vEN8fkUkx2sYh7gVHMsQLv7ko9NhDe0dPc6IxsttRUz4jM5PQW75xQEMP45R0iVbxue4bdzXtmmqAZJRQkyr36+44vpCYZFFzlcuKnNcf93X2vfMG0Q1c0yXJzj9rsNQ396SIAuAbM0c0Gl1sydSHuo4GAYpSuaIjul4zEFRJzKHlt36+pXrKK+Yr0P/A2lq5vBDtYKZnz7jMDIH5jvHHEcdc/jneEHMMXPE/F3nKDEHsfOBb9sJrKRx2gZgI9MGfhgwh86oyDpHVnYqS2BR8GCDpPaJrGS2gb/XK+gIj01ijiE/EB5rtrJW4sf1vJgj55HLquTQmePKo+duUdIihyIXZpHMXeT9738/veUtb6Ff/dVfpY9+9KP0+7//+/Se97yH/s2/+TdDnTvuuIO+9KUv2d999x3v6d6Tn9xWNdUpBcUdBmsvqr8bIBhXMHYHU7rpm/7P5oy7pJzKgb+8lakn9L/y4YiOxrMaO4ktVqVPaUB88aoEDPZEhzu7oUWiA7fv6P2JbNiGxVbyzk80GCrkU07hiQW5k9QbcsLvBHkROGm74caBBXd2Zrvm3z5xgMXzgoUheT6OWpw7neS0SZ0eDEqw18Udpz790Dqs/bOVkZ8k2IAFx3xsEIT7aaCrQsecMStorZMNhV2hvXGAqLUpO1yE11iYyBYKM91L0KYKkGAXnBP22xYNAyjQK9KuQz2PeofNou0B+5DD8dDBpJhff2lvAoU52WutysTt+n2+/KwUOyGm+ZWfz1vW4u+E7vd33jVY5KzkEJjjyiuPiEjdc33yMfyPgd7PbGYOC59jDvzijqnvL7ZgDrcPmIMGzCHbMwcuSO72ZuZQXzrT07EOd+t/4hl0DTLFR+Zwn+pcEAciAnMQ+sBjMAcyBUmq85hH8CjAoS1s1HL6Bwgavkr+3sLz7IXjMkcaeNHZI0c7MAeuk2HHCB64VMzh8ao/Yo62IKidEwfAHDPcsavMMccoLm8Tqt+3YQ6/9CJzrFZP3mzsOcrCHFEOfgbF05/+dDo6OqIHH3wwhD/44IP0zGc+s9T5+Z//efrhH/5h+rEf+zEiIvpH/+gf0SOPPEL/9J/+U/rZn/1ZWq36cZmrr7761PaKJRukoOC0pnELmV7dqK48HdiAcLH/wTFXTz4wm/RDZ2LMiT6xGAuTzmljKEMdeXiL0zrOaRR3uK6vUIAQzSUiSLRcdJEgK2NqZ2lJVyvQ0TQSAcC/TMfJ3hklgk5fzMmFAZEWd7Ryx1TNAoiDBm5DAINSp4cZhA4HhDgzol/vAd9/xDh0VOD80yAAPkVY2cCHg4I9IRIfFInlZLu9bivxtKtOh4JdJAgbXnex8AhuqIPp/ZC3tELBvnZ20ZEd71Z3ISJ7SsOQluz8jWBI7foQinjfCwKB5zd/GXZdRhE+SpPjZp9OcMwbhuw6qbqjqc+RmdHRw5P1Md8HPY7uIucnlzVzkPaXjJ1T3+mAlMyxQWymgzT94WubsT+ZQ4PAHNgfIXMk/TnmYM14xByCiaItOqhg+XDziCLGHGyRkTmiAUxiOnq43AcqCyBzBB+tD1JmmINmmcN/+yyHOeZA/5niEnPkhS817ogl5YN8Vev03NPzAzLKmDmcWeaYo+KHeeZI/NDOq2L5lw3M4YMUerbtzxwUfleCtxd+PZwtc+DAin1pdmzLHFk034U5LrYcfG2uuuoqeslLXkJ33323ha3Xa7r77rvp5ptvLnW+8pWvdEBwdNRuNc5jqzv3pO2TUq/RO1aLaemE04Vp8EFBV0Cv1yG76KutScO2Y6CCeeVOIqef+mYJnQsmMIeJf9YG4GxhtNwUsa6tNw0j/qnr6ldy1nKofDodHXN0Rjg909Mw6U0+CuquuD9O3Oqa9UIbdZLaI6TU91mhXma3dG2AQIBHENuwH2nnBhfT72o0Xt+DXYGOTpdcDXTKbcrAblI7rX5i7ed/1NUT2xzz8uMfj3V/DLxNh+dLPrbZhpBfQ2TOf+JPjKAMgrIqGZeT/roAsqnJXVlch1tcUebIhkqWXUYXuQhy2TOHtATFhWreYFUzR+gbUlEVc0grO3qamjkCUxThqjBkjhyuTUBEWzMHVq3aukF9VPK1mt82zBH9eK9D8H3FoEvuh6OfL3STD1GfGexFLuOk3xL0zJHsTu3AScfNUFaKZwA+ZDminjk280P8nL73Op5n9fBF7QZO2JI5rI6dv4wPs2zm58CPalvYK0DJF5vOTszRnwsj5kD7R/5bsxqVY38VP+zCHARxRZlz9mVZmONiy8HPoCCaVhV97WtfS9/1Xd9FL33pS+ntb387PfLII7bC9o/8yI/Qs5/9bHrrW99KRES33norve1tb6MXv/jFdNNNN9HHPvYx+vmf/3m69dZbDRrOVoSIud24++XF5M54rNpGACHNJp3ymhTy9y+31SGamW0h4FpSPlbXInYwJIYjqdEAdwJmB08aOjjR9ZCkIJUtB52iDFwLAosyB2afPqJvOur0tFOFkXUd0UdHuKrCMT/BcrSzFv8Eu/JK3BTKyTp5war4FxbeDE8mwIGn/LQcwvYBHVxvw+uKu3Xkpx/FIVW7bWEp9GXYjn37VFtxYToK+Xg7+9Zefd6YV04Ty/BTrHtSUORV5Z3PYk6fKIO3yeL1s2XcXDgNHkzM5sN6s7YLYpyfrEmXS9tff5GLKZcFc9D0dL5jDt7AHNQzx178UOnA5V/aINPNjPYXMUrKhTnN83T+XjMc29xlhzdIBLZn5uh0jsEc4AeCn8FB+sQC9qQfb3rVDwMLZOYI4ZlhYCZj9oUlc0B+ZnvHHP4XbIa6rng9yxwrOx7HZ45sV6xr/ptjjoIzsKwTZI6JfdLp1qU5WebIclbMMYrblzmYdSOBi+GLF+aIciEGKF796lfT5z73OXrTm95EDzzwAL3oRS+i973vfbaI1b333hueXvzcz/0cMTP93M/9HH3605+mZzzjGXTrrbfSv/t3/+7MbH7ssSf8B5MuukyyJmrzxkkHGpg4LF7VPyFov5hhkagG+zwVgI6UVQV6H535ky/+kB9FHcogwxo29UhZR0jsyUvW8d6yAAaepjgGKEFn38rEDju+c9darOVvo/Si5bpTwzJxFN8WJGo6eeEkKxccShwY0E+maTVpoggYrjNtJ5UBwx2uzo7BvG2ap6gOE9PanhaQxPZY2ZRAdZZYTtqClLWMSw4x6pTtacPaB1WEaHov0m3Qsgh0VlankY4OeKjtruNvN62JiNv7mS0dAAOhbYL2srcpe7sRxGEbxPZ2INBzq9Sh6drV80J39cJzweAi2OsXAIMOiv5Ol3EXH8KsX6jTDh1/ETeno30L2khFOkyPdfTXv54YaByGXBKmS8fYk/w4uoucr1xE5rh0aVo02G4XkQWadMzRks4xBxHwg0jz79z1F/k+IDAHsogyB9hjegN+6JjD7J58jqxi+sAc+hADZcQcRJa+DfFE5rCk2mK7MocmwkUQWzTyRMcOGg7rJJm/OlnmUD5giMvMYX4Y2sNsKPhhNeCHeebYxA9zzEEtv545nKEme+xBknHFGvhhXTAHEZvdxTHS42437p7frszhgxP7MYcz0e7MQSnNyJutVifDHHNxeRBkW+Ygmp6JrtsvkccHGochC3NEuRADFEREt912G912221l3Pvf//7w+4orrqA3v/nN9OY3v/kMLKvl3nv/Zvoi8WJTZx0GA9BBKxeoo053J8zgwIseRvM3Ry4U3v+UfJFnG4R6mzodNAhuOKrezD65i/dpXdyS5Hw4QIFBhzpIdlNwhgGaGEbM1QT2YrnLA6sXByEofZa7b3DbBqsrvz0pyGVrHORrTtIGS6D6UJ8jhBQmG5RYcbXY0trKCfY2MFCdsFq36Tj8EKHNDgrupNdthey1OXDPz+1GgMrbhBHBCtsAKkx9+6xYnT/ZuRLhJh5zajb2r0T7cfA8oN0DQMCnrAlP7fiko9Jhu8YqnWjTGCI4BQadGR+VupNhuaNwSflXgxqCIXBJS0rz2BP3jw1dZJFzlovGHJ+5/2/9R3Exdzfj6POJauYQsgcqmR80TJkEBw9wECHww9TJlnFz/BDevGctQ2IeJ8EcyhVE5A9IEnNAXmERyK2YQ3118o14wwzfbZC+pTsKMyZc99jMQa0cYI7ob2vm4BnmqPgBfSsuCrlCHeAHPIzdAMQsc/QsYOl1AER0UCWeC2PmUFbyAQkfOPA6WVzToWZjyRxCFNe9cGY5EeZgOjXmCHEjeLC6jOPm9Igic0B31aWxEOnT6vfHHv/4TImLHJpcmAGKiybf9E3XT9uMMvmCVO5Z/YqphhC5fxqhImvqnzJQndZ0YHAfyxgCBeqpnUFHny2oCtv0So0LH7mn0AKtlxTCuVvdll8khY4GA6SAU4+fMNpO+A6n3/xnGMEFGrFAg4SuqyQDC2sdBp1VdM72BIP9ZjwDw2oVR89V54hxfQ0J0HG0WsdyAJTi1lked8VIh3zrLK0rwg2CAsZ1dSWECzvMIV519GmD1Tkc3pivL7a19uNKlP5gkMLsIAjz4zwcnLDR+xg+HXPPp6GANRc61axTydCRi1BeRX4OKDbJ3EK1oycSROk6pv4pTH+Ji3UyVb5XXXHjtiafiywLVi1ykeTGb/h6/wGjEeGylRzQBMJytDFHVuH5/gLdeigG2AC6CDcb7SRIm802nIrM0VUCv+/KHFZOy6TdwGcbtmcOgkGR+Kk8EPvVKe4IecQSyJA5iOaZw31T5oexjq4/tS1z2A4bXdy0RTi+3hKYY+XHIPKDvwKRBynmmCPzgH7uzhy6wGccRKj4AU6NeebozpOTYo4YtzNzpPwopBvzwzirM2YOom5WBxHRVVe+YDuDz0kW5oiyDFCckrBfMUSs6zX4O5R23RVO3OJlmtK47iKImLm8sDsHnzqYkQ6CQ04fZm20GGk36cBBRKyueLqhCq+xcso19LoMPXIaGBBqvavrhEGF3IO1ejCz7/BRtIBIXJzKZ4P6PuRunw4cpLIhSXRaYB7U3cqCG2t3cOhwFXDUGU+ZMaUdQfInODubGgm/K51yOy/yBa00jILuOgEYEU7L5JGNTDEvxjbApxIADBlw9LckHUbI6HUIbQ12T23vUJJ0xMu0463HU9Dmdg4Tl+9+hlkwLWwW7lUHbu5VV1hdfZ8Dz+ZeXF9gpWy0iMLNhPUZIfc5HbY+7yLImqatu46jv8giZy/sFydce5uYw3SrnxVz4EAD/KaUphrI2DRO0jEHx3jkh8AcZVWULzB8N+bwPIE5IE6noEvmB6jlPHM45Q1ndBYPC7SEzBzZ3/QzCsjq3c0soLTbxAw/5HyRHyITIHOsk4773m2Yw+rfMUfy4Y0xwmAGZ1v3ZQ5p7dN0JNWn48PNzGE7dWzBHFO+c8zBJXN469WSHyYacxC1hyQ9JRwkc7T/7EwSzeuwffLCHFGWAYrTFrb/wgJPlcPunDC1ccfs4Jnqxa8wv8K5WxkFg2SnH+JKHbb3QnNmUxz10jrmygazorJbt/60eujQiDqUZBlMycTpjfiEo9v1zaBAAAzEdN3h1k7fR7XVQi0vQYkd4ypcBydw5Wmv34okTQdEG3FdBwAXHj9FWCUdzxMHTrx5PD/q8iMpbFNdnP6p6UnsndNVskvbUA8RvpupzrvTESJq0zV729Dhx4EVPZswL9MJZYMoWKarjoX7tPrbdFL4QKxO1LfnKtx45PzmHL7Y/1yEbyta6+oJhcWl3A1tpB5YXWSRRU5GmCTO2qw6GmkPSwbMUa3Kl5mjyxL4QfPLXIJZBubIcQN+GDMM1/mdEHN4mjnm4OjvE3N0TVcxh/4Zw0gwAXlkzBwU4iJzSGgKXNwbfSqxfz/akjmQH3S2ZM0cFNMb92zPHJv4ITOHsdWIH4jI14pIdu/MHDRkDiYfzDoOc1g9d2SOKm3WwTTGHM2Ii8Qc9m1hjgsrywDFGcrQ8RL5+585vLgRsQimekXu2JOPw5pIFd++21aks09dUn6cLDY9gW1TwWGrAzdrYlfDTQff9wy9KTpIc/qU8odyGJwiEYUBiQYG+ZUPddi2gCbqBDui3egctW66g4XpsqfxFaglOTidzulA4favi0GDBiSU3r8EnT69t0u34jcJ4R7dPnAyDU70T1IqHT8WunBnXY7AlER4+iFQ16J9Jufug1O5zirlDAhGaJjyLPlU2jmQzjEEEQKdDAqzcAC2YZhwrVnltc0lmqda4tMJBIrpU7r0moApXqk5ry7uglHCdKVu2ekN9BdZ5FwFTsH4kIPjmhMYVe3WAY7SmGOGL3B79M7JdPnGP9z+vLejv9UhLm6dToo5aBfmyPlDOUyd74zM0erGWseZNIV/RLsja4yZAwcGsk81DtiDObpBAY3j6lUQijZsYA5rz038APkTe7PGgYtkJ7RdtG/AHIL2U2g7tUHFZ1TrwBKfK3OI+Gsf9WscdQi3/6vLeE7Oizloi7hDkoU5oiwDFGch6iDVb3C+YPotO3XQYPjUgnsdMh0KPYaV1fV8EF/4fcHPLq5QGInAoEVw8vBbASLrAihEHTKnkG2OO3C4k6903Hn6QEOpY84RS0oO0PJUIMFBCq/HqnnL7Njrbbgmu6b3JbNONV0S/gJYYF3dSUeIgGmU7CP6rIDT6t3XNQJBBBzUGQGJ6xC0A8H/Zrf07aP5r9o5mds1bvkFAx5M9nSEKNkX6giOtF1MvVOPQILi5WB6jxs9FWC4xrI9XpPtBHXylVs57ymsBgVcITuED75rAJOupn0xnOhajjnd8jJbUXuRiyTTdYZ+fRNzaKLh7EwW6z17nRgcytoNFcJnjJvJiFPUCTCHdVqmQ8QcX8fQnPdiDr3pLsw3bgiB4FMLfsjMgfq1Ds0yh+7+kXX6m/zIHNkG5Ic4OLIFcwR+wLqO+eFIB3hCvFA/kxP4gZ0f4jHqmYPwOLf1oXLb7coc1eCEqWu7nBBzaHynY+3BRTjWpNcrbQCdk2COUfmWZhgw6OsOUBbmiLIMUJySfOELDxMRjA22dxNF7FZnitMNmwH684AAbidK4UNddutmEUSgR9D8QqeQegu7UcrpMBlrie275s/SfkuXr4Wh08fiRVobtJZit0cdh2qZ6xAhXjXAoDa9UvUInJ3Xvo0YNycjAA3oAJMOmWNyJ7fitiCjCK1gYSZq+jb9EvLz8HV4rWMqQFfBdsceBw0mhxtXoc464rYqxBgwaH16HU46ptdOINYdMmwqJ7QNbEWGOtrAYVsvijb0On4cvJypvdoR9vysPp6vtXezi0hsLRFvA4AROB0VPPT1g25tDXJR562zmvA4dgAAn1M58DslLt6SCt8iGMV0ksI3wUKVRuuCn1O6MCw4pU2DE3gkMCyUJNFO7UMurR8eWHsYsixYtchFki9/+e+ISP09mz/smKN1Qrwlc+BAA95EGHNI5AfMj5nizEvoOFVH30bpmAM7PkrMQRJ4BHWOxxx9f6zMYemPzRx+U5xnZ6jvqQbLJ+ZwPzbZOmAOgnCwYTJkM3NonO/MsTZ/uQ0/6AADNX44Qp0W53Yjc6hPTjMniGgTcxAJHcEW6tsyhw8SbGIOTC+A7pMO7cAc8eyKxzrHnQZzjCXTx5g5sKz5nLZhDv3/5JkDUzxx6W8G1h6GLMwRZRmgOCV56Mt/3180TapBAr1YERSqQYIqYtaxb+pBqndNFSrMAatt0ufHeiMjvihmdUfF8U/MWZMrccofhpRtlntLg1Mpw6sUq+icp/jpicdq5aCi74oSudOPUzXFnLg53FbOtHWXTtXzwQ0d1fenEuigpt03LF+IXzFFxw5gEVfCJhvpNx0sn9a0svcy+ycIK6YJFKANpiYtVtRubbGCwQm/aXcHnbfioubUtRzKNpQ6PjDhp6MPiKwgHp845HdQAwjgNmUJULEcD2ODsulXdepWzjuCQj7t623CyK6rKF426uRBiCxz7FEBBRVh/eBEH7bPKxpz74yu5aHdM1xkkUVKefjhR/2iTTK6dJE5drm8Q1r07ZS+Z2n+GhmIacwcgRdCHo05VjswB80wh8rKfwfmIPJ1qxiLi8zh9q0tD1+zAW5uNRyYYzVkjmmWAW4xuoL85pgjr1ulfn6OOZhwhywpmINCfVZt28ueOZwfqu0/87pUyg/c8cN2zOG7hKXBllJHjJXCsSQiXU+r5B6rq+vYWlnhtdnNzEGEu6Ex6eKiXucxc+S88fMkmCOXmcNOnjmkCBszRy4DZZY51l+csXyRQ5NlgOKU5HnPfTp98M8+No3kCbX3Ojk4X9Y49guTCG4S0GlnyVAAMgcjZWCGE/jRLbZZgYxChVC//gSRP10hiZXELb/gXQgDDn24oHm2diTyNvVEYgtvY2+LAwu2wrbVqdeJC2NqNtHx6juEtkuIAozFufPK4VZWyxy3/PL2EY8zFbH64FRAb8yW36q314DEpj7iiP6ajuypTFHXVBcELNy2VO1QKFL0xQUoUSc+kfD3cwnKM8jp6tkgahWaC9LHwRHMMw9muJ7GcR8HIEOQG9rYO3o/qaLzF1oNH2dU6eeBYBMojNKMIMJbIAJDSOSXYzin80AKrqyvT2utXGG68uhZM9afvyzTLRe5SHLDDU8NfFH1WERkD50Dc2S/OePnY2ZUMwFEj8Iyc6hNoy3UK3t2Yg7Iy5gD0u/EHM3v1/yQ/QXGxwUsXQfsaAWjn7J1C+zpvzNCXixTi+uYo8muzBH9nKb1ozhijskGoX4WwTpsrz7HHDGvET/4jFGyMiJzUKfjgzV+eJz9MnMo9yhz6E0wXhK9TmuflH8fF73x9swRY06SOcjsGmnM5db3LxiXOWEf5sD7pphshjmI6Korv2nG+vOXhTmiLAMUpy3qXPWKyZe3QNeUo8JjgyQal2BhOF1yk5kjyChMLnszUrBg72jAfGZXEJ1iCRmyGQ8ddQKT8A6e9WRMuBFrmKLfbGeIq8wf6aizCm1hw9PgcAWApumEG34G59ni2F6hkDDireMnvt0WNLMAAGneqgdOugeFPCgB4ZAfJR3fqrTFsxT5oa7QUSiDfGoobKmaZ2Lk/Kyd8rZehPWPbWrth/YWx72P83ojWKizR9h0p7m2X34uuWtl+z+f/7XDzldshpLiMi/SjsNHkvuJqI+4EydS24Cq9l1Qt+GNil3LXPcpByhrOt6CVZfbll+LXAyxa8xeZcDeraXRa7Djhz6txw94JN0sbCtZR/DLpktH+5NtmENa/1UxR+57t2QO3MJ8H+agTgdnJkD6NiiCdZo4od+aNDMH3nC7X0Q/vCVzEM7CiMxRsYA/xNieOXTQAwcaVqGd5vghl6HH5PSYwxaYbOlW4nH5uI94RGd7+Hkh5Fu0bsMckCcdHnM4EdXhvf4WzEEUBlN3Yo4LIAtzRFkGKM5UBt3DWkiuiL2JX6TQUyXPIZi4/amTLlfabmnzdEq9fqtwHWGXfI1zHGioOqruUmm9Cx9Bh41f8P3MFK9OR+0xFc6decuHpk5ttfJ2sU8Se93DI9wZrapBgBAuMCghpK+LoNMlio6dMC/Lj7q4mB86Ln+1JDowH0So7e63+ERQiE8e3AYvx12JD1zk/Kb4I2iPCA7VNqMUbeA+vzh4EIGkWu3aB2ncOeNpVMfB9FPIS92lH68cF50vQ7ilSoDALUX1ZCKHDa8h0stIKO/pzYPvmwYu8ClETqtnF57XdkZomyTnX7GA5S11/CKLLHIy4rfrFO7Yw/Wt24y6kvW3c8xBVRwwxU7MIUSy8ngvq2AO1QH/vzVzEIVZCuHLPsxBQrzCm86TYI7ap65W6p8Lvz5gjtFrj6vEFYEvTpQ58GFJimfnB6Jof+QHtH1bfkA78jajwA9bMAdlHaqZwwZV7Poh0B0xB5XMYYMTGp6YI3+rzvnTZI5KNjFH1R1UjJF/j5iDiHygcJBnl/fCHBdWlgGKM5E0rJ6F8yQv1Sp07K7IF87J0SMLprKKOCGiVd9ZlE9ZUKr1WBpYMOqZvg5ouHO2+PwuKOhUnT9xgw4Aj8lcBQopHQY1uAhFw834qqxrvuF24PH3SFM8FU8QFCh4nQYnvJxVYQM66WyXOshqte0AXbk+LF1zGxRxah9w/HkVbAo6BZB061tovggrvd25rgg7q5BWTfDBlGxb/5QF46ryqzrGtqqEpYYUdbfbOHn8Pbz0isEJjMtGMNUDGm53ulnZyr6Zx5wpagQhhy7LdMtFLqJIvvhKicwxvSqxmTlKfoCiBNIfizlGhRyVVZlnDnO+knQSI2zBHJkf3LgNzJF9KjBHvinWPHt+8MGJEXOoTyUIp8Yc3Wsneis4ZA7xtUQ56uiaFDVzVL17HHjwtvG6os3uf/udNyjUtWYOnzUC6XdgDkadGebIrOTft2cOK697rSPa4y2HNlB3WW7khyJ8G+boL4hxnDLH9KPX2485ZrhhYQ7Tv5xkGaA4bREi4mkwYQ0XkV0szQPk1zkmNWk6oGQ9iTRnPiAGLr6zg0FIu5o+47kt1lv10CB93i1g5raFfOhzNKwiMSg0SX/TnG2IHb93SerciSi8MxrsZNWBhY5YnyK4E4vVEVrRtNqz5RWm7+W6IUC486NWn1URx0S0onVy4DhggPn0TyvwqcaUV3wSguUQtacYAgBVgUJw/g2kBJ9IaDwMTrAfMxu8KcpR+NG6Uqgrghie1uvp3WDJIIP5ViBVSw9D8TsNwvW95/4pBpx0XVnj70P7Vit3/lvGrUY6xZaCRMVNB6q0upROP9+caBrxX+OWPyxZYGGRiyhMDf6Z+501qP1eRebQ2QqcuWITc2h8xw+QJHd9Qm1Nrtx/AHN0lRIadaNlj5K4p+rFmFL3t4E51G5KffxWzNHiKubgxByqszVzgI3Rt02DJsoV7qPBp4bZCsnfs+eVmSPMJu2YI/NHekUk8cMuzEFod8kcvvg5cbJ7ljliu2Gdow1+bqxWUjAHhWOTmYMI88A0mWk2MwfRdJ7kVx6qdLm80feTZI5ND1Hy5TzHHDQX167HdQxamOMykGWA4pTkk//3c9MXJtLHE+b8c2/SXNtaqLx4wzaj0ONNF+V0FUoees0OOse18H4LsLlxRh0RzaESbAoCcJN1GONJ3Ob812UXO3oFK23r+ESiYVDbokv1uNmEji3Wc/rLT/8dJgpnBvnFd0rXqRwhXexKn6DYThXs6cpBBnXKkrYMhfzDIMh0Arm9Lb+u7qw7fHj7Ma3NoeOq4ajjdkutk0Al19XAqLVHXADU7bYVuhUyQvtoO0cdBBc/f/rBBy+rP+0qZ27nXTHQVU2xnD57x1GVUf02fYgYOv+ZOAtHmGhh2t/gVRD6oGBXnz/e/Ph1ySVp8NRj0ONPfHpYh0UWWWQ3uf/+LxIRXnJ6E171PVPY2ki+MYdDR88cFiWe+ybmQCetg5dJB1/b6A2dYQ4wL9hQv7NX3KbA743M0RgDBmCROSgxhzSf68yhZfX8YLbYw43swwodvdFmYAbwhern/RVOaS0QdwtZkd/Im++0mQnkAwGUmAO5Z8AcVteOOdaBH2aZY5YflAPXxkU4mEAwgJHrmpnDj3Osm3NPZKX46oa23Txz4Gk2FeWDErsyR4jD3zjgVshuzEEhs32Yw6RgDotKZVbMUYxLBuZw3Xnm+PvH/8+8nYsclCwDFKckT/u6fxC7qNybULtm05WnF5jkK5TcsStTYP6mkwWuVxmEWxwAie4lju+UTvuQ98CgnRNCQ+qJ4TeMjqgjw9c+QMenWTaHA/HmnJiovEnV8JZH3uZKnyyYEzYnr/ptW1CKceqwdJswdFQhP4p2rfRJRgjz0fleBxwuOlX2WRB5a7Go4/kQ+1OPONCQyqb8SZ2OHl4Fi3prMdfB08G3Ei3KA0dNhS1xq1WxY7xKOkTTTnEOItkps83QIIibBhBrnezU82eeGtoyJKJ6+y4ufk8qPjiZdfRyySJV2g1xNrVSHJw2wYA5f+p1tK8a9TOxr/LvR0dPLaw+HFmeZixykeTaa59i33XxSIIPovoaDcyRhWN6H8ScObd3ZQ4umMNUBsyhnIJsEUyaYQ6N65jDmcGZI+bhTBGZw3xYy899O7KKP913/x05xrYMDewB60coY9Am5pCSOSLDzDMHDh7ozASv55g50DfHhyjACEMW2MQPFXNIoeMDLdX6HpuYo9oW1eslQYeI7PXOEXPkQQqycOrijsUcA37YizmK362UneNOizlyuaAYw5pcefSMIvRwZGGOKMsAxSnJtdc+Ra+TaaRWeLpIk6/VXaOmEfkWRi2sW3V6w+hie3qQF6pK17IFgjkxMWmXKiHc3YUAXFBMh6J1YLfAnb7UvVOrA7GXD9rNYTBMzRTf/qvqZXVtgnTdujPUKEcqDfdZHwkYMA7hReuX8lNgsaTstpszFIrlmW3ZeSncwG+tizad9s7g4M2BkyRnGl+piA48L/IJNiP0zOqIlzPSIUqDBg46PkAT6+rA4jp4DNh+RZxG26a6sOmPnLOec1m46VcugTmG42lRr3OiOlqTeE2FUxptq7PaToe5n5rZ1qoYQ0jqF1RHj5hgv2IjReU2oyv+BzPWn78ssLDIRZKnPOUqImrX7FpIjgAoyK/lWeYIpyxHveJ7uGGYY44Wv5E5dOZGYIuCObjLxUTMESfmIHIfWzJHvCGyPpCJkL60zxwxBxfMgX7LXlcwFogDCAoKI+bQhw6EOlYR/zQ/CMzhPn875phqvh4yh7JF9KnAAur/M3OcKD+cDHOo4EMhjBvpOI/or/2YI5ymZ8wcUzLp4iCLg2QOaQVtYo41EZEwHa2eOmP9+cvCHFGWAYozEB8nt7vqcLnhatauQ+M1Jog8cY4uhiJHQBC+J8CIXWzOAd+2bB2FOuTQy/blxc2SpIvPTh2/YFOMvnugFHFSfwfHS0JtX+8IFA4MGB4HAMwBhoGNNCVSnTRsUxZskDbqv8InDjE//D19+JMObBN3+v12ZAgqWge0H+PIbPAnJgFEZnQQPKpycHTcyyHyq6Z/8oH1Qx0c2NCyYvsiXGigTFu2ubtLOiE1gVoI404nhQMGjOOybTmd/y4u81kZ6fie4XoD0F2dRV6sqN63NFOYhhoMIPLp5peXH11kkQOTdiFy+049c1DBHCRE+eLseqwtrt1Z5kjhoa9hZYt8u1IwR9VDjZhD61vZE7qv2LFXnOEzO3PZc8wRWxGftk9YiH40Mkd4YyUMTAyYw27M92cO96HOMKEebfrvauUDAHlwIhRl9oxZYDM/nC5zTOnWfTktPu+6FsvDNsP2PV/mQFtzXO+i/fpemGORQ5BlgOIshWPX4U55GrjotuIi5wV7KoBA0WZLoI6/SuJ/saw2+AFmVYNuCAOYV7zIpQ8TCNcA6Dw6UIiKhDelFmODBnhzrjrZmZA5/dXKYSI4apgumTut1UrItx4jQujwVwvSqLxNC4V6an56yBhtnPJfBbtc8kCItksHK+Dg/JWWOLARthlV6AFQ4FAfh5NydWyDlWi3HrOVHY+RjtgxICsnHlcEnbhytp+RK/Yyi9PdbMv5YRy296qdB5V/G+kwU5u2yJ0NWcKpwWxPJtIps6PsigsbUuvTF3u/fB4Y+uctmwsarLF1sCJ0vH3FL1h1F7kcBTquzBxSMIdt7XmazFGyBJSBgwQ5jb4S2uWRuCAzR7X7x5kxB5G9joFs0fIzfkjMQeTMQQVzrLDdoSmsfPP3zhxoD8qYOZLvNOaYFrdERrCy8KEMo2+vWQCZY54f8HT0tNswBx67bZjDWxQHnSI5h9OevU0vV+Y4cZ3EHCoLcyzMobIMUJy2TEOFNLqCxDqdqGLC/UknVA8qIAAgIAQwGEmKs+cX8KqFRQDEVCLUOuuqF40rEplnL7cJUwdbOFMN759w6E3s4FLNOhSdWnTG058vDIXOA3QY8oA8devN3pG2UX87LfwG3yAGnCHOeNAW9iooKORqejl9nWbiJE+LjOWvKNdVw/NgQdbp4QenS7pIs8HzwTxthe6QF/VPhzBeemfvedYy0sl6+XuVFtPlGu/jiqbF3gb9yUzcSEZ2zTu7zSkw5ZpaXyK7aJ6vLNMtF7mIItWdD8om5ugz9EGJ+WxJIL0F1lkOmWNy0r1FQjR4uLGBOaqOH5hj6jHjQ5V64mrPHGg5+qZYnj/Nd9PgRpqpY4+8dXhYO4qj783M4bME4l/cMtTzOLIHLMgcMf88ABTaOtWrYyWadiKL/BD5YjM/HJc5XKcSFlznKg8GxTArbw/mIBqewuU25ZhvPp1H/HI85qjvVc6COU5SS1MuzHGxZRmgOCXxPYCn/5hb2Mq7e3OqVK0v3SLT+1kk5PuHV30Jg/4oHHpud9IxXNeIqPLR/cPL20tOnTZ873SgJx3rNFhhsfbSzzBrgb1rXZkXFrIXbsmdzPQjtvj0hMOdChMAgdeu16HojCIopCYFHc9PQKdPq041LAzVEjAJHVmLpqc5aqtoXaPzXWmzcrYBnoaEGRnNhkoHIGbV6bgjj09LulPAjlFY1IqxHaZPOOR+XNvhDiuZQx56cgTwGNpgl17Q6cvtdan4vVlnKqh3Lb1bnS7/VdFjjOO2cczbgsy0DvzmvMKWX6HzysNLhysLLCxyMUU7rtaJcWQO7dO6V8GJvLPM5y7qcNahISuE71syR92ZAnNkGzYwB+WytD72XVK4Okdnjpgvd7aGWRY7MkdoBhuEsMxq5qCkVzCHxRfMod+7dSXa75Vt8en+1f3wui0C2g+Y9IzgrODMAeVt5IfNzNFzSmQOMp14GCNzSFnXqS1OhjnmuSderufHHDVb8LkyBxG+2NHlLdCfoI51FjSnfVCyMEeUZYDilORjH//s9EVo2lag7TnOuHgVOjzBC4rNKQfPL2QLZ1pQow47L/H60+txlYJsdCRdslo+91lYYQzRoh+uw6kT4PQldJDt5tmScGQP7PDVEQiMdIeGaVmtiuFpFiJeCbRpuqm3qZcY35ydOip1Qt2WodRAxp1rtzWV6vDaXAKz2PZdvgWqGMRo/IoBFDSM1kTCNsWSzBGvSRcFXWHdSGjaiovttwIfkzrzNU37q6vjnfKbnixAndmddni6YW2n24Sl/Gg9OXrxJ012fMXrkLcJU50MG35uSGhTPLeCo7fzL94ez0FAnLIZ04Tza/B9qOP3DC1Nr8XwHS/pFaTIrraPi3XdZmAhmWlXMw5LbOPmpzTed6EOt/wef+L/bchlkUUW2Vbuu+8LMaB6dMh9ErtZYb3OOSQQeKhiOiPmKMqwNIg2oJe3GY3ZSmQYRSJ4ADRkjmySFMyB9bJikDn00/0pWjexRdHvK3OEyhOFrTd1ECN8pt0yiIgGzOE357qzV2SOiUNq5mAhOlopP0iIr5hD+cF2E1Hfq/Gdv1cdZyZve6n5AZgjvpoKzCGxfYwBShu2YQ4KrETQPtquROiF92eOanBiljkgvyynyRyZLUbhRKfLHJiDc8imTOaZ4+8f+4sNFi1ySLIMUJySPP95T6cPf+STsacqu5L0FXskbtAwuptiivFNwkVc6GAP1nUIHLuWLi+m6YmF9u5MhK+BVKOs0dtDuckW3RfazcAb0SmjOJCQqrdya81RMDoNdeDuxMpZDhR39wg6CUj6qZkRZrRc23asLViptq2YikEQHRypBjv0acS6K4OJbGvUPF3Rti+zOI1fw1Zl61Qv3WoVgAS+4zRUgnDXUbDyPPCJEcG5ckS5zagBhKRjBINRrG4VbYDjns4P/XQIZY8ziInptV4rCLFvMwAR80MckAAKKJxSYmaj+RUj0VbZBxqk+F7tLoL9Rp1nayDJoDDJFUc3brDkfGV5mrHIRZJnP/vr/IeQdUDBv6d+jImm1z2gg4zMwTFxjoficrryj5w5KJdRMUdL1zEHse3kMWaOUF0SRn5wy/VndxOb61QxB3tdSuYwf9YzB1H6TnGAxP4yc6Q/X7sh+k/fRhT8MCtzCAXfugVz6AOROBgCW52Cv9dBiBXkH2Y9VPxQMkfkD9xWlVL4NKCBNuzHHCtsF9qOOYg4MJeHbs8cnpfazV34JubwuOMxx8hzTTUf88hJMgfmWv8a5DTDHFdf+S0bLDlfWZgjyjJAcUpyxRWwKpMISXu0HxeUlemEgjB8ftF2Jw0X5OgCnbtwueU16nUCXLRPWyW79bACjt3hgKO+hkQPES3kFE/sAxOgMw1gTDas1KYwlU7TRifjNjoMeIRAPvA0orVPfDrSvgs3OBK33+LUTojX6ODg2Z+StATo+KzJhIjD7h0QjoMKTJ0D1TbAaaQ4xRL3Vnfpywl2E0dbWHXgOBQ6mG/exiy3AYX6xNvdPDiBTo9DfpzqnU5BjuGmk4AhnleoA1ABqUI7JMmLUuG1gxozl+VWsun9Tz8aGQHGNmjq8UBFfyOBJVT9wfTAE64SIWIevY17GLLAwiIXSVZhQMKcU2QOcebAGRD++gRXb3eU/LAvcwQ9xvDIHBavPILMEfRSHxYcDTJHvGXhlHYv5oDImjk0vfvNzAlTyjWEb2COwBvuEwVfpRwxQmAOJpZ1xxzN5MAc+PqrS88q+dWW6jVZHACo+CHYwl5PrAuZfb2/r16vGTOH2xWOIeio/ixzCLaf8l709tsyh6eNtBHalWoJW5CeEnNsErf0NJmjzy+UP2SOw77lXZgjymET4mUjerXA5dWuotBVdeeWdsQhJPi3uSKDjmfXJRuf0kzcRkjyTZB20L3Z7KMq0TtQ3yWBSpWkOQvZqI9OVyA/CWmqzjvYIF6mOT1OTo0wbv67drm4IwZBvisElzblU8eBosOFfNnPozCroT1B0KmZ+H5oveilw4OChpcjxAmCbBplgA2oN2fH7odGp3K6xEEU3IY1vJqS2ju8E2v5NPsk5hsOs8D38Mmm0y2qlXQ8v5lBCVJA6K6I1g4e29epymtzeHwZZKyDZ2Rl30hEuknfg1KSXkrB2gjbFLrIIoscT4aPTLdjjhQyitosA52BJydkjmzbHHPgFonbljTLHJ23SN851aqZHNb1wIcSg47ZmQP8QmKOuM2o999hzSqOu1v1u3BFvz6xwXrKT/p1LZwFkDnGLGCf5u/zQpVpUEcZpuOHVFYrJ2wlyt5GeQCJwYZ8tGvmkK2Yw/Vr5sh+WKG1P+SbmSPflEXmqeLavzTj4rSYY9s4t3w35iCqLv+ylcsS7fvCHBdeDns46XIQuNKkeRS94d5mZexhr0DkN+/wp5ejUB9HXOgQ6KA9lp+EfLFa9tnZkEb9QYsxh+DkFTziaLlV13pcvXOMTw9y88TXOvDJhcQ0TLEshvJSbd2OVB4nG0F6ZzPZc9TSqx1ukw8s4OKWlVPz8n2LLneg7tSqlbNJAYJT+eBw0WET6BikBD10RwhH0bGjMw7bh0Gd4tOewTmU7CbQwTaNdqdzSuuJlYD4eCkw2G0N382UqM5FLWdO5tL1Qx51WX2cD9OxfarW9rcadTlieRZR5f3RhdvyS9j3UN9Tf5FFzlOkdbDq881fz4jNpugituSHFL9Jh1L8JuZAvc3MEf2X84OnLZnDnhRsZg68eQwPHSBv1dnIHJbxHHOkLUbT8ayeOoaZEIT9swTm8B1AyD7LXV01POn0zIHtmvx9nm1RtI8PThTMwbGNOdnQl+/57cocPqPGyw91hrigk8sSssGoijko6ByPOao4ty6uH5FlpKclj3kkM4fszBzSNgaord5NFua42LIMUJy2YA8jeUBiuoxlRPQckllQGBzos5vdgnR0vdY3GtPQeg0K5ZufU4ew8lRuF3Zc/SMazjopbaUzOYzKcr+JrQSfUHhm0emHTp7xnUd3nPrqRISW6QjZO5eMjnJKt2rgE50p2NXZLRCHDhRnOkRHqDMiVgZu6FTX9h0Hbahra4xf2ysgoS01T4jrHKZIF4h2x4EjbTvPP9RJj3tun6rcUJdRfA1fI5meOE3Xq+Y8AgUvZ9PwxEivvi5H4aM4BQW0cCtk2AAKc4MTVd/ERLROdhyyrOFK2Vd/kUXOQwQvRLwJYmnnJTAHZ90oTM4UQ34YdRMriq96wPdTZQ7QHDJH88O5nH2YQ5QfuE9PtAVzJFWdOaHhePO8CgMCqBOZI9y082Abzc6ueKM5cURijsQ9rjWtr4XM4TrrkG9mjZ5Hmv0Fc5h+Gdfyq5iD5pij363jJJgjSzcLt7O9P7/3YQ4iCutmdXnOhJ8kc+DCmpE5BhywB3MMWUZq9jlkWZgjyjJAcUryyFceJSIABSKyRSWoXUytg2ViWutQX+sPRYdsKV54wSUIdPfqpFexw8idB3t/6+ZwYxmwR0js6Ys/AcH3yvxC8I5Iph1L1COqcws9crEfALwbaluGMZMuKKn1s2zVoVgvrTMOmPy9QKio6Yh+hfc42RyuOyRpDgmehoiCydrsxsUmzaHCnzlA8ESr7PD1ph5X6rZ3SQnyVMN9tD7PUnCY0D3HBfKb6rTSla21LGsit9/j3JnbbAs/UmCfVwXPCiaALFEQYGLyFbqnuseBnFUoYzq3cIqonjlYPndh6V3W9FlBQp5AKIWOH9CsG0VTVO5im+EKzDdjYBVexcV+oMKrehaExQEQeVwP9MEGxnbjLtGKdJCCSOhRWmSRRU5GHn30CSLy267ph4SnaurjidThk7lKIgrboJsOfK+Yg9Nlbmyxir+1r5EWqMwhM8wRBz7gh9DEIyJER3syB0+/I3N4ShFf2FKZw59kA3OofyyYgzrmyPzgftB9WfTDeASOSuYg4ubzp1CxNSM0Dy6ZA3bRQLPNDzcdWCOsYxvFrgFzMOvimdXsBi+LQhi0hdWh12UtP9gNfnCOOfR4Br46DnMMbIDPzcyhRBp12sHqwkbMUcedJXPo/7E+kTYq6/djjmhLzxyYy6X1IwPtRQ5RljUoTkkefOBLRJQ7oHRZwtWdw6tXpwR/ZDEHQubgTQfiu6menPKg6VKuZ2Ekx29qOgsk9Qq5N+2+NwcOear9Uxy7Y0SHy2RPK/CdTHRq4VOdDhPcDENcy0PDV+hwWZ29OtvJ4R4xluUj8EzTKtQry0cd3rpb/VqfeKwQFKxOa9MJa0hYnmsrj8w+oZUgKHgbTe+Eri3c3rskIUo6EZh8QMPR1wdGcGXuSdbRNsK2JtIBCK2L7W4i8b3V/P6q2wannkiXVs/HUE7xt0p61YyNeipo/NRLgpJ+lba63Kr8K9e9KbyKy99jGinTatwmnW3L0Z39KuB502QFPwABAABJREFU/NLnuvwOSXTBquP87Sof+MAH6NZbb6VnPetZxMz0B3/wB7Pp3//+908Anv4eeOCBkO4d73gHPe95z6MnPelJdNNNN9GHPvShnW1b5LDl85//sn1nuBjdO9KoM/CwdsGqKx/dDFh69N0tT5sxkf5sZ9DUabDwkDnmOk97IFLZNPw+YA6rLdyM23oJ7qvcP3urhtcS9FP9I+QzhbOHEwe9sMuW/mb1f2s6CrtiReZQP7piGCQY8EOXTm0kmRbNBBu8TpEfnKWQH1pbMlFmjsnfxgGY3fgh6lChEx/WZH7Adm7HEvihZ4WeOabTp2YOyuUUfyVz6DXR8jguc4x+57jqjwbpRuFVnBpYdzM+lyHryoxOlqpumKcyRyVPPPHpQcxhyMIcUZYBilOSb/zG6+AXeDkMkRiGSSsxnR3jCOI2MUD8It3NBeYV8uCcoI3m50e5c3mgQ2Lyxy6tRxa4wbcMdDEidht8xNWBw6aNhR5NnyyQbVmm5RmQaD7QwO64wcHbQEGc0oZODTtwYo8fdcirHGdVqEfwze5cfovLi0yqQz/itDglpDkqdagAjhiH+aPD7+FiOkZHUK7VZTpEcMjigMcq1dXjpNQhIlpJWnAL47o2a2mECF/TwM8Vx0Wocr6V062BqA6bwivo2uaPbZpnduTZYgtnsu3Nett7HaJ47UUdCXFZrjr6hjriQETfBz3O367yyCOP0Atf+EJ6xzvesZPeX/3VX9H9999vf9dd5/7n3e9+N91+++305je/mT760Y/SC1/4QnrlK19Jn/3sZ3e2b5HDFdxmVFew767XwVMOvEYZklhw6a/rcFOqHrcyhRssncyBBQXmmOGH/ZkD2kTAp+f8+PjMEV5vaQ6tYg7zQcYFPXPY71Yuw3esGRMPmUNnLMQzAHwup5sCOw/QB2HeAn44l9kvbG2MwGPmqPkh+82KR7LOZubwuFxHD4NmmGEO/NuBOXgw2LEnc+TFTq0cGvPFrn/zPDLPHOWi3syzzFEtjVkxxyTOHFXXdPVVh73N6MIcUZZXPM5EZHJqItOe4yZMvCaSo7Eas77ZF8NpcB7CrM0+TdYT8ONcxNeBDhmrWA6nROGNUdVpUyKl02kviqSpIyLthkmnGUIkk4T01uFlKEAtaQ5RYI9u0xHTDXZrWaRgYCZPaTVPsAErHQc0kh3UO8YMEBamK2dzUUZXtlZKnzqkeC1bcnoynSNyfbSfYcqmA1Qrx1b37uEDD0d2bB7m9SGAQ3dMU6Pr04+QJ2M+Gu4TDbklivlFx5glHvN40mI+GfnqnLwdcviY9EdvUNqVuzE/nGrZT3aOV6DZyaAjY51QDsP9RDJt6sda7ecGUr/K5VWvehW96lWv2lnvuuuuo6c+9all3Nve9jZ6/etfT6973euIiOjOO++k97znPfTOd76T3vjGNx7H3EUOUtiuWxaidWaO4gljvMIHl+iAO+LOFRt02u/xtufaw6Se0vhhB+Yg1YnMgZHCqSTow/pBkN2Zg81uIZpljsRKiTlCWSQbmGMdGAF5Bf1ZxRzxTNmdOYR05kMR3z2E0EbQ8ovdRwbM4XFgo5aXB5S6uvYcEpgjtcU2zBH5qmYOK+/cmUPjdhlddJ3+0t2fOSI/1MwRy8g6m5ijlbcwx1AOmTmWGRRnIbruhL6ykGVNRMS5LyLrDCodhYwUx92X8mcIVGec9xa3b9kLtWDpFiICy/PrHqqTegrs8ssbc9VhaAc1WH9Wr5Yk555X7+ZqL6eWTte2wK2spicF0lWXm051jETWsU5oQs5LI/WJTjJL7VmlctRBx5b0JuqfligcJadufw0UuIhr+eVpiqtmQzmyDja4fc2mcrqkg0J5iEQHGKKTn9WhuM1WzG/s5qvTvhVT5lWVgbbV5QiNveecV53T2T4/7GtKu2eXwR7kN1QR77cugJzUdMuHHnoo/D366MmvvfGiF72IbrjhBnrFK15B/+t//S8Lf+yxx+gjH/kI3XLLLRa2Wq3olltuoQ9+8IMnbsci5y8MF9mJMocXUP7MAwD7MgcTD1/3kJwYvUrJAT1zYAK9+Q5h4FROjjmIVqVz4o4fkDnyAP02zEGYnpwnfMeNWJeKOez7jsyBXJCrmlngOMzhAxPFrIpCx5no+MwR4wbnecEc2YZa6yyYo2U4DN+VR2odtrg+fC7uZJmDFua4wMyxDFCcpQx7JQeFeK1HZ9mBQeWgiMo5Y8Gxp15TinBBzZEN9hKdkG3HxVRub6p5xfc7m8Nsv827aH6g01WVIY7dyfmggjer/ZnT9wqa4wbdqlWjYxewc51G/eFPbYB6TvptXQeO7ca0Jl+JG/7UgVNsN7S702HQqfJj6vPDtEREOb9cV/Z2CPuSs4R6I4T4e71gd/vzpy55OzI/1+OWaHouTZ95mikCRMyvbdWln4V0p3A4ljMgEfR8emJ6hrLj376TM6NNakc/PVRKnc1STzW9ODgwLyc13fLGG2+ka6+91v7e+ta3npiNN9xwA9155530e7/3e/R7v/d7dOONN9LLXvYy+uhHP0pERJ///Ofp0qVLdP311we966+/vntndJHLT2avaIwE7rDB+0r5hJmDIU/3sDLuysDnuC+m+IpmKGvMHH7X53mZHzop5iD3qT1zEOni2B6J3OP+MNZ3njlWwWdr3r4Glq8sKbSJORhtQX+edMj8YuSDnkmKdpvlh5o5au6ZYw7klJ45CF7D2MQcFsdEua2GfxeOOfblkcq2nhNg2GxQo7qO+zLHrmRzXrIwR5TlFY/TFiG/nvU+ACKligAH3k2FhD6ivCSzTupXqqmV3ftarWMUFo9MOkIyDieantCsLNDSTV8lK01l2ipaEGY2SVQQ8q1JuzbSVpXc3MQr6PxTG6zIXzEJcEGts09x00JTTLrzidcLBgC6vKS9b+c60+e61Vfj0L5+azGLh3rkTttngqTDxDLYvmvKfyWV4/VVsLUu1D5XLb9eByEnlrPS5oynfVf38N3KwfQtv5Tedci26IrlbOPsp1+xXbkII0tZPa3TY1qVGB325vBddOJvCWdo1OFeR6isC+p1eZlOccRFder4QxTZc9Ep1Cciuu++++iaa66x8KuvvvrYtqm84AUvoBe84AX2+7u/+7vp4x//OP3yL/8y/cf/+B9PrJxFLpZIc3L9Fdy8IhPRWqZFhlDKdRNolh+67i05qU6n+WEYE9meObIwhKPOFszBq8QclL5uzRxM1HYcE5Lk17xnzQ9AuPEDAz+wMPmOXt7LKrNMaypUzEHGHFgP9b89c7Q2GTIHHELOzOH1iL4SbqQzc8BgRyxhfaLMQbSZOfJsFY0joW4gYI45MovVbRJlzpt8dTFHobOBOTRtOIO2ZI7qjDhEWZgjyjJAcUrymfu/SEQNFPQCgi2/7CJbqUOOyyzh9dRdstBZVjpMAAWV0xaPCtcCxE/hHMpgYnutg5ntqUXsVKCAIO5op6+exmYntB7YR6jdIVG7odZR+jjtsOUFAwhkDlJIn5CoUzMj0ki72+k67oyl9XhCBKtpe1e8hpFuH9mfIiEv9jIcnNrABalDdHTLdRWwNZQTOnffIsye3LR8DRzsXMmDCbFdSOBJBdhLWY+hHQM4ALwEHaL4pEPtRhDRdqcAPmC+6cQw/z3t1sv2m1Nau0TaeeFwwSEdUc5DUkpNFa1gCBeKNlTpsqsdpactwuNV6eWHbYGzjlAKreAndBX2TjseNcwVZ2xq2wkRPXHp84MaXF5yzTXXBFg4bXnpS19Kf/Inf0JERE9/+tPp6OiIHnzwwZDmwQcfpGc+85lnZtMipy9/8zcPExH69OYXMnMwdKjqoqyzbL/RHUKH0lDF8xvwQ9c5IXOscpTUzJHyy8zhD1a2ZQ6yCnDb/UPXoNjEHETUHhKAJOZw3+r8MPlO95vSzRpU45QPEnMAi6idzhzwVN9mSLjf1oqPmcNnUXb8wD7DwGzU9lCbkg7ygzNHvx5GNfMjt/s0c4I6G4bMEfLdnTlI2wHqOscczglwCAM/5Dofnzn06PWMcfLMMZJt8kK74iV8fOYwvT2Z4/EnPjWq2mUllwtzLK94nJrAuJ7eIKWOmsj65fLC7y9TiMA/6vM14TqfcLGHwrnugVAh9Uas77mOKhC8N/zuFDg8tDA1+1OHTQ4BLRvfRgrjvToryxCd4fTb3n20z2yqhxsEtPzNSTIbeGhZTBzs1/UadEVlXWHaHRSWj9t6qUPG7bqiM/Ztw9YhT2p1VIdeb/m1tjbKq2F7vm4f2n5EeS/1rBP1uEjPIbydB1qv1oa7vOSwgvdMyfKJaeJZR5ShI+RX5sEb7OjH7LMbzX+j8Dk9lWFfUdaXLXxkw9xnF1ZMWc1p59r/UEWowdO+f+dk9z333EM33HADERFdddVV9JKXvITuvvtui1+v13T33XfTzTfffE4WLnIaAuPaMD19u+uZTLW4CR/IKB/iLZnDfnOXSRgLwE5DQKcybpY5eoPL/pXjnw40TLNSnC+sPxswh+Wt4QT8oOUhW4TBjsgC1Ozw/N2fR+YA28HX18zhDIH8cJSYAx+EhLxAB1lE6zxmDv++Cn/KHHHmBNaBO/sxbHvmoBCe/f30Gsacf69nx27/8sP0Ow90SJd/XHtj00sb88xR6Y3zmtfZJCM7qvYjjvWqPrs892WO83LKW8rCHFGWGRSnJM+64euIqUHDWoiOeHpEkK6r/umGxzFTvVjUSPJQaBEdhH2kUaxsd5JhahTjaHn7zh6unYCAM57iKpOm3G3NCut4pWoi8hn6bE8MhNqAgMWpDThVUqZZA6tGbjoq3uJ8QUyGxpuudF7hHt19h05EbUYCPLXIddUBlebcwxMMIDDvqMnTExnIZMdLjPYAlRJuM1XfcBPkFx2imA21I8anOlgOkx5Bd9JVu011rk/P/NQjtiHaTcFJx3KsPM71he+cnzdYQZSfTdj3oc7cCK9dETHYztdaoxKRaQBspCPp0+Psqhrkz3btdv2PuF7OH+8BNAUz6sSS8KmrV4roiqOnD2p8GLLuzojd9XeVhx9+mD72sY/Z709+8pN0zz330NOe9jR6znOeQ3fccQd9+tOfpt/6rd8iIqK3v/3t9PznP5++/du/nf7+7/+efuM3foP+5//8n/Tf//t/tzxuv/12eu1rX0vf9V3fRS996Uvp7W9/Oz3yyCO2wvYil4c8/elfM/XfDVYpv67RZMQcLBTXj9pGIP+qb5hlDvYsXAdeQcl2A3NoiL0WYkxxsswhMi1uiU/RfQLKPHPg5HbjFCZ4ig92icYVnEE+sEE6C3IDc+jgQmSO2BqROSax10IHzKHtXjFH9rdj5kgMw7muXjcsyxfRxFdja+awzwFz9DMteh2r+5A5PM7tjm1AtD1zhFP7WMwx1clkD+aYi8PrfF/miCJwGZwec7AQXXnlYW9tvjBHlGWA4rRFe2AhklV9OcuapndBMcogY0PeubfaRofTb6JuMASHKQpSIH3qodPFfSXohhng0CHD3vtq56mvjmg0OlNOYTI5eRGa3gnN74qShqFTZiLWFcp9NkI0TkfwtZnwvVFxOFDzzR6/+cZwkTb6v4L8tGUFQSU6bFYbDTRg1kSAG9RRIIqO12AF0qoBE1Q5SGmZqBcOHNobyvD2jjNgPC5CC+q0t5ySQ4sAqOG6Rkh8ftY7eD95prbMob10cGHnb8wLvyGAUhfnvyXESkqpoUV4evVrm7jq8o/vgUoZ12EDa5zYjN1cTifssd1+3Ni3bOqjvorlwx/+ML385S+337fffjsREb32ta+lu+66i+6//3669957Lf6xxx6jf/7P/zl9+tOfpqc85Sn0nd/5nfQ//sf/CHm8+tWvps997nP0pje9iR544AF60YteRO973/u6RawWuYxEqA205yHfMXOI/TcjmR9Uh6Nq7kvmmEP7k22Yw3qpVQxX306ouSNzBJNPkDk0zmY6BOPcx7vvd9/K0sdNUcdlDkrMIVsxR/bjHI4cMsgMc2A8A390+fkx2JU5en6IzMEnwhzuPfPpvQ9z6JfjM0e85kvmSPxwPszhAxKa2WkxR2nkIkR02MyxDFCcqYy8IblXap/+fiUNLyy7Hito4Ph1yB0AF4J5Zpuzt+H0RCPZwEV6HfK0FZUpxzdgYHcyRPoUAxyfwQM4E9b0rbYNQnywITqrWE3XIfZysdUwr6jcBju4pQ9OTprd2YYJLlaa1hzw9IfbbRF5XAaIPJiwMttjmfo+Kb6z6YDgpx7WyV97QShwyKpmcoQVtcF2r7t0Za3CsaNCr46rnHs4FbkexKhkbAOH7yiOBFU4DcNsNW+QHnEgjn26aZ9xC2+Dg3M+2PsATREBe3xfwt5tQTmzMpr2pYy4TR4HILgq9r76u8rLXvYy6veAd7nrrrvC75/5mZ+hn/mZn9mY72233Ua33XbbzvYscrFFhOtHrqFzB+bY1GEO/H2VbCNzCNmMjXwLU/GGfR+8L3Zc5sBuWfRmHnynloG+0zOcZw6zL7eQMsegqWxrUqyn7ryRmIO0TPBjmTn8ldTED/DABn36tswRb4KROYQiA6CPxpquS+bQ8sbMgccp1zsdO4h3VtqeOWqe4GZD76cPnjkGXCEzszdOjjl66gjpT4o5tlI+HFmYI8oyQHEWwv6qRD4NhOtryy7LmblZCAH5s8/L9UIEwEENFakLwp87XgtCQrwqJmSZ44yOTrVWqzygAJ07DJ9Pna4+xZCi6frw4MzM6Scd/Ut1j1Mfc2crMF0yl5V1/LNbiAvyq0fPcdAA//zpS9/gDmvcheEMEjwmU342qILxUuk0uxX0CrtxWmaoq/THLrZh1QxC1XujNErfdFapoM2ntnTg4HYTnA+dWhmx6RKqr8kZSTuW7KS6RzmVfcOdOmwO+cWQtTDxMew9zmrciyyyrwh2Qlxdn3AZSo6bueXIwTP8sGlwQhW2Yg6UHVdNExHio4o5qGQOHeCvWCDeoGPEBuawARIrumMO3z0sDhoEBORo64g5mPtw84cFC+Sb+6h3kszhtoS2GTIHDZmDinC1X1+9jW0Q7c6yDXP0h3bih4o5hrIXc4zj9mGOfcsayl7M0Q88JJLv7RKxwZOtmWN2COXwZGGOKMsimWchwtZpRycJN7iFH+DqRSoiy6RbqRZ7yBEhYHhLbzMVuqSJQKBsU8hKugZFoTPVs6ccdR4Cv4ig32s6XOilJS/s9yobwRquwfHmeHry4TqcdNzBuSNGm4KTpgJuoH3ClE0i0mmYOP0TAcaeAkh2huJ1woEUcp3sPN2hc0xLugCW1i3nR4M6tUGV8jR1yMI2IvKFtjAvsw+uFdQpYaT9rZjb6yqDdg95TbJq05QyXPiboX2lxuFEXEdN8f3I1yyYzMHOMG4DKOS2INKrbt6Oqhx9IhOqKy2u0BK7K4rXzSKLLHLS0q5q6N8wjlbAHBxi2nVd9+dECUdSxzk7KIE68HnqzAH8EFRaRhVzTPF1DxVe8aQtmYPbjeRJMQfwlMYRCR1B5uHQpAdAPQtoOT4wwrSeUgyYw3v/yBw2y7TTiQMTOMOhZg6y/PoTS+iIhfKzHOcHKflhxBx0CsyR06vsxxy17MMclU2bwmfjTpw5Bg+AoJx9mWORiyfLDIpTko9//LP+AxZxmnxMMQ7OekHBBT8Rgz141HSWLU3rb1oeheD73qy/m8KmwbbwHlnzG0LU9gJPhbBrWQ9s1RbzDGzdk68HgZqYV9j2qX1yC8+dOb5jGEfP483+lK+yWz9FEJ+SoI7ntrb64GsGIjr7wade2vuVuq85w1OHdrxVR52kQ8U61EuPo3XR7SmB7U5CDjFhFw2e0krT8eOkB2dteXFnx9pmQPQ7b0x22YrfWmfQ8br4cSCZgM1nT4gd91h/sFOIKjDUT/tr1w0TtSciHNJS0iHTcSjGaY95ASuCcL+sJKbjVB7mx372VzZNuUVni2VkmS4t8UutcODpHkR7oVSXMRT5JRzLEUgXV49OdkIc3gwIET126bC3/NKVsY+jv8giZyWf/szf+o/W504XKPQL+fJsN876EEUX19RrXURCp4YMMTsggek1TPsC4IeJYcDnZ+ZoNm3FHFi2EOHWlbswB74maemKWYA7MwdN/ODM0dI25ihfQ6HIHNyqFfmB2uAE2rE2u5E57GDoGhbB5zqLYAOJ1rXp6OsJ2gYVc0wDDuuCObDtfDt0MjsycxC0iz6Q2Z459KRT5jCbicw232YU7DxH5nB9Cb/tlM52nQpzjOMq5iCKfn4f5vC4bMPJMcejj/11We6hyMIcUZYBilOSG254avsmpF0gw4UyffEOygcS2PsuppDeLswUbtkVdrQB2z4+OEAM5maCpJ64Zcb6dAbyYC5ygqpYOncQqGbvchZTJPU1Css75WUeu4lNFVTn2/J2U90x5625DOi0qmCD/rZ3BxEYSJ2MzoLwwQPLJ+eHYEITYHTvbOIaG4Rbc+UnIvnpR1/OCj/B2Ze2Qreen7zAoXW40HyIkg7age1Q6XAow+vNppNhwY5HCiOK4ICicBdBgMEGKvVymJ0u6X3N8H3wZKEO1bi52CK9DWYqgiN4KBzkMhFNtFTfZd1zCgVZORHFCTqZiAwSvvRyxdEzNtTufOU83gddZJF95elf/7UpxB9uTL/aOQlOeXrY3Hp96CiMOZhTLz8zMOHF4v1XDE/5401OxxyqhMwRMsOcXM+qB2yQmYOMDQYDD62D56BTM4f7sF2ZQ6As92e4RWfwqUI2UJN9c/Tj0XdrnToWsO9xQCXu3AVbhHLcgtzbNDMHDZmDUr5dHYLdRRtDPrswB4WyT5Y5CPLDMLN9S+bIunk/h4pzOp0TZo7ywUjBHFrGcZgjPra1wobMMfVnuzHHlVc8u444EFmYI8ryiscpyVOeclX7xmFYCy/mPEfNOlPuOyFMEzvFGD7SqSR3spN94qDQCTpzd9biWgEk/Du4DSYS8+JT2JoAWLRna3pWIuioE9EgB4GiTPF2MtvFj4ktBr5mrUzY2YLCSO2UmZmi0xJZ26Y5tER0k1OLThrbc0XieVmroEPGwxGfImBancKIAxbYirgPuQKAT3sUq7c7YVxokkNe8YmSdLZmUKAAONl14YBJOOpE7Huvh/M1Ta3MTrq6Fvw8iCDRnR+dXu3CR9eb2qCSnx7s+m/VdkAf/cvlVXZK+k3hvIobCMfjrTo+tK/t7yrtyV27Dvx/bYu+f2IhWtHVQ5sXWWSR3eTqq6fnTZMPatdh6hbyjEC8MEddyBxz4GcsZ5xXpRO2Jw+JE3Oom+ZNzOGWV8yRB128kpE5JOTE5j+QOdzKyBzBFGMO10XmIBzgldh/6rHR2ZCxbZpvLJgjP7xAS+NrFZ5lnCmp1kZ+wOZy34zlaLlcr1NB8HAGmMPauPvuzIGvvuSWn2OOVXGWZubwiH59rDnmmGyrmYPofJijz+tk/82Vp3Yehzn8uonMEcNjn7EVc6z+wdDmRQ5PlhkUpy06nC+TU8WpStb9p2mUqoZPNDDcvo/7h1pnjigGkjsajOnHQlt5wtQtDlFb1EWHPi9NmywltZGzSXxigsn0Zio6C7EbrTBrQNPBKwhYmBDZQk5WkpU5WgiyGKzQMsV30MhSOycEgko8PiJZq2vvRVodMiU5GMUzWLlO4crPGLOJHRiCnpEa6rSronDeFRzg9/F+4ykdfho8SJcOYTLnVNWnGVHYPV0fu86OUNlWF59s5KtFa1ifV0wODAzp7QBBDlZYzDyES3FnBFllnQOV5WnGIhdXmGweNA6W2qscwBzt8h54WL933uF0xss8DhpsZfn+zFFakb9vxxwisIvGMF/ywQO1reIeYA770wEHxkUiI3PoKxWd4XlRx43Mof5YOh0bJKhYaZCXzHCKj7j0f5zqo9vMatyIOarjZw9JOn6gjczBqRkm5uj9LDLXPsxRrnmxJ3NQUZ9mxLm509Nljq6w/l4Gv1cGYPgFcMcLc0RZBihOW/B8yYvWxOH5Ts1cHpNtxxWu+hYuoG/XKAxdW1coRHQ0o1PYMd5KFJxxFWdORWIvJeTvkxb54c02+GLYkjPlzUQ4WgqGAzTEaYHeDfadIE7RjBFi+eWZAiu0AaYN4pTMkFWzIW4z6oab84XpqfEVDunqjDpuX3vCUtlGun6FEBegoWnmIA/F3ofViuf6mI3RbxyZnUUbDMqyfDjVf2gdvGfa/uv9W//MYuMv5u4pwtjmM3QcCizFFMxKHPn9lw9JSEIp1BllOChJyq8HK8uK2otcWDGfy/2FD/yAwkQ+mzEzR9OT1Rb8AMwhRETb6ATzEnOgjUKDbUYFEqTeZQfmsMEaITpaqa/dkjkIfWYcDPC0vX2GherYrA+eFoNEZjG/zvg6K/hBYK3o45QfInNoGt39g+HGvmaOxGcdcwATUWwnYw4S6rln1D54C5tCRVlNgDlyW1EYyNnMHNUxGzPH+Ba80O3idmeOasvQg/AyJ8AcHlfTwTxz7Bh+oLIwR5TlFY8zEbErFa8XdXzjc2pwoc7qhAHlVpB/bnUdQ97dNC7BznWm/LIkIVr14Q4ruaNSG+Z6mSJObUQDu9Woo+BWma6q/8c9wt3WYlYFGFHZre+cVjo6mBDaf+Awgw5FJ6x4yFTv8EGkMyf6KYvZSXc2DlbhGerITF3Ntj52cFjDxJwqRX1spzzzJEycrljZ1ywp26jWifmOpkZGnXFtNs3fqOpRtzUZONT5VeepS6lZPDEplUO4bExySKILVh3nb5FFzkf8hj2igMxywDBGncngwp3rC2SQcKRTTR3n7ktRfskJUoaPmEPzn5m9TqXl1R0Z7D5WzkQQ3+EjPyRg8l05cDBAbRv5VF98M4Yrc/R6PXNgNeaYI3IMslLPG9Po1lRen99xmGPmWBRlZOYIHmmGOYa20eBYiMYdnzlmy0n5XmTm2Bi7F3PMFHOAsjBHlGUGxSnJE09c8h/qaNaTR9JzSIhtilvru00mh9smMsJohEB++WrVJxR5fEKEfBcNSXGqg/2zkPWIXZfAUs/mgPgVqRsST2d3ljrd3Qvz10OZoGcnxrjQ0STk0umO+rTB2ochf3Vm3HwlOnJpT1ikLZY9Qc1U7DpM39S2UkdsK0PbThiTjrGc4BMG3ZkD48jibI2IsKOFmGPVdQBQx5/8oM7a9BQMML+VtpFoO3s5+u6ortQc47z54xMVIl0tOi6wRfZAyNsgzuKI/iMNMCToUyjS2cmEaS255gHOk8mUNA4BLItr+ssV3MVXOmrH+JWMCho2AcMmyPC3sd2WeP1LerVM9fT/POEUy+nDNT/LQwiXden0dKa5kE7LVtvXRYmLLLLIPrJe+xUtcBeLPl+4MYder3A5Dpmj8UNmlCk/KvkBZ0sE5uCBDsbH3ouI2deowI7FOvHpJnvIHLwPc8SeKhRa8MOIOazXFe6Zo3GF3QTvwByRLXrmsAcoyicQt4k5Vo0feuZAny0Fp6yHzGGvkBhz6AOUyBx5gc6KOYh0gKS1NUXbWA/pgDlQupkUA+ZIJ0M6KyJzgGnAV+fDHGL13405NoVvYg4iOgZzCKRtegVz0FbMYVdYC3+8rNsihynLAMUpySc/+bnZeFEHqj2YhqlYr+vxBAMN0CtbVF0Q6Zy2+DfocIfhREQi8M4gAok7DnP0uYcldyY5VL+xFd6cqkFEyCJ1+toxq1PUfCD/oBPLZdIdNJpzhspxA59qtWkimMaY4lfEDg7YWMzhvc1Jx2FAjTXH2gDEFqFCgCFYFBPft5QJbo7QWYPN6gjDFMumg47ejVbQQWcOkBLa0o9DOEaMkFAseBny7o+P1tXCueUbL5/y3DJwKNKNPrGdsnRpYbDE22D8O90THFtG23eFLoPzldXbl22SkCb9BvCwOKnz7wNc97En7hvW6xBkeiKx/5G63J5mLHLY8unPfMG+281/vrA5dM5RWrBk5jiKaTRu9vTOvDHDHNN6GOOs7AFEKBN83gkzhyaIvjtXh6Hv9RtgMM/9VOqTNW/c/hN9r3LFit1nUmujFeTpMz575mCCQ42+j9XXrslnXqp9OhiBzKF2KXMI7PzRdBs/OI+Q2215c8qvZgmyuiKPxNM1IvCezMFoQzwvNjFH/5oHHFe0awvm6L+fLHPo2XeWzAFmwpXV2zfHHFkYGMbS7sAcKo89/rGZUs5fFuaIsrzicUryzd98vf+QoluCAXKbChiGDNOfqm9xAoYkIx3uv3IKl3y2K8CQdGWYqUZFMAoKTrAyhouY8ukK3pCGJwzq3GBoAirFufkZ8oFyudLJtoo7YinS+kwOKA8cerYvT5WzpzoZAqAR0KkG24jCayo5zsNjfmGBrgAOAB1DoCAY3NAnJWi3H3d3+hL1g32j8PwpJSho8wdQwHiu88xtFX7LWMdncmz7lKKu3xh05iVP5fTvTPi+al9mtA7L2tTFaL9g+jPXS1UBDbrqiuduKOl8RResOs7fIoucldz4DV9v36VwTuaaiUi3OKcQBp9bMMfw5mKLviD0R2hjQdg62yPYT37jtRVzDOqwkTlaXoE5mKYFz3M+6gsCcwALMPpRspksPXf1RuqDko5viOIrGsAP3Raq7B+ZOdTeFY9PhpF/QH6IxSGr5DhPn9nCHgolHiEb9CB70LMNc2A4HpaTZA4S1B353KhXpbHwhTliDYp7Ec+suE8ZVOKqK791Q0nnKwtzRFkGKE5JfHXb9l/7jDfD2u1hpwoi4SOG58ELiCpP0RnIGHUO4/fY2qip2Rfj14KuhNBfTCUWNnsuXmmm5qyrXjpnoIs1hffcFSjyDb4kh0JdfPdExJw+ePlmn7SXvxjaxJ0j5MMAUEZWzXEq/AgRy7pzJNw9UYj2RWeOOvkP4hIwmY228BX3+THqkrU3TuPc5OSy48W6TE8exJ5kdQMKKdwPBVibRqRcJ1qGtevPBQjpXuqdwqbZOptfwajq3pczjss5VdfrKBUne3sdPKO8VM+lqEe7Y+jsgOwy+OPfIosscprSegOZZuF1a+VLY47q5foRDFSDFxB0kswx+ebc5yI99fprUbgv7J0BB+7SUqwQY0pGVeMNXD8iM0dgG7EaWtYeD/45mCuFLaLgAczhN+A622Hq++eYA4qQET9M38t1SclfN3H/jAMKkS9KftB00I6hHOSRPZkjx50Gc0Sf7TrFAbWIy405QhtvyRyVbr7WjGGGzMEdc6gKc9ybYG5r1EUOT5YBitMWtv9ImPupis1pCXG4SsF31FAvphrSm9Ob0xlINdHDYCYJE9t7oV3nTEzlXCP10qH/GXdyRK3uEh2K4VbLj/PCm/akw52wDVwwpskSYSO2lnTtYo4uLUzlPimNuOOgCNpmenkaZKwSGxCkpwcAF5hf19Sxqim/lie0dbeHOcTFPweFft/zeOxQPN6d5VTX6VvunKrTxz8l/LZKmk51nlU6rtsdA7Ohrg+RHsNdnGBB/BBXX3ubgaFHJiqvyfpczzYUOvg+aK8ylrm4AxM5gb9FFjlrwSfZQv1NAsaNmGOWH/K9U6HDWWcgo35sI3MkfrCp/h1AkL9CMmKOvHhvwRxmaXNCqy2Zg0GHuPfR9cMQKJNjW6rutswR/GVeQDMzTLLN26D4k7idqfvGyAh9dTLDEDBM8QazUBgE2Z858q1xxRz9cTgOcygNYF7qNQ+BOerrcn/mqLObZ45tqMPGC/dkjov02sPCHFGWNSjOUpJzCA6d28kV+7fR3VPUSdl223rBH1fh2AMnj6NAkLcJcyNUF/OQ2mukpsCR6RyX66n5mmNuQBDWgcC/OcfFOqqKtgMMgL1oi633YItfuYMKUzoDkOSZHUKkTziwPCIiWgcd+97K0nc/g2Ey2bSCcuK0yNpxWx1SXRUU2OoSuz/P1+uJgyqU2gV1ApQIGRThk3WtVnhnlOG7/cVXY9JhI92fPDt7InSq6PTriw3zc51dYEC1VG/DRT0ov77Sd8tnWj+m6YTtwObdWoaTzY5wYJtcLEe67Em+yIUVvQRXMQhoP/KDXpvKCINsT4Q5XKt00huZw+7U0X+NmQN9yzbMgekq5sAHCJk5cjXdlyJz4F9iAJhFYM0T/Lr6YSZ8fWTq2zUuz+xQfgAbmy1CkHfBD0zir31AI3EqJ99c115O7U91hcU2cTt3qwudDXMwsQ+QbMMcsDaKvtowaoPIHPOt1DPHLp7eZRNzjPI8f+aQk2OOCyYLc0RZZlCciUhH+d6xTp/dxac96pAUBlFcjxjqgEcXJePuQq2q8xuM6Er6zOFU84a+bjDOsNapx2DFOsftjKub244Rg5NO8Tp9TDBUnwb0jd0cfm8DAkklunVXdLyTXbWOOuxB+xhz4BMcBab+qQ6T0Eo8vw7EhMLghuZnK4FnYem2DJ132wR1315nPr/sBtG80awPLLGOq+B1RfWwxrY2x9/jQZI8i2I0qyJL3Qp136C2FKc3xXeyNhawyCKLnILYrhfZ78JA+bb8wBg3Km/EHGVc61eq7kh5JN+d0AbmqDqkGebQnaB63zT2CWE0p4jkVBDePFc+UMcoehIQu3nvdIhqfkgzEKLRyi8Vw2zmh8wc9YwPbbeqrhLKKZlDat++slG102eOqh0q5rC44iFI1KvCx+fXaBffuWGKk2SOHHdazDHkh1YOFXETnw/6rDnmEB/cWBDk4skyQHFK8sUvPkJEPjAw/RBzvHaxcLv41skpg7frHHzLD30OloP+K1y24jqs9rDbQM0ZoOMgJhhpdkJhHWI2ndZZr8hGp4MzsJ4zuQ5YwTu6AXdK9hOIh6GHs/dp2VeTZrsJj5We6sndNprYfYm+2wmDAlpPr05yWvaaxZrcSWfHGqc34vug6lgtf2uW+BTAy3FHjydBhKK+8WxrMXaQiACCso7gEOArw4ra4L8RFLKTZTsYCTqo3qjG/7jUQen5zc+5Xid+89O0TxHLEXJ3HJ1yf5Z4C+d1PUb1zDZFO+cQo4cE1LEyua93ZX+IS1Ms8doUqSe8ElHY8gtlLY/M1uPcRU7gb5FFzkgeeeRRIkqecwNzhHMULl1kjsAWmTmI/GlzxRzkccYcYMN0A+E+Q9pdWvVWCuNj7V2Yg3rm8Fdtc88noV9D5sC6OXPgYIJYfcbMAb6fMfvMD9mA3tqaOWLVJ38a+cHrmfOLzBG2MwXmiAyj5aDdYLuADsw87fgBBs7cW5K3H1HS1TLUPtT1NsBvc8zR+WLBNozMkf1lqDKUuZE5ZJzfeTBH1R6e68kxR8cPhGfODHMwlKDMgZ0T5jdgjkuXvjhbj3OXhTmCLK94nJL87Re/ktyFf1u37/oEAa8xgXDU2XjeQccbXVoUdwNS90TEJCzRBgEdKKv1l5PBeWhaKP5mttcTwnTJlXfluAiSLqY0+RQJOskz0tSp4cBKTOO3ThiuLQUthFMZG0DYUxZrX9BBWtN8uyZVp7qetv7UOrbvuAZE9EqTTnjCYXUlohbnUx3bd1nD6y3uqqbfa3DkCC1rok5HjwnslR4+va4raF/Nf0U64s7Uu8xCxI8/tuFgV93ue/fJtQ5+hu/iOjlNrxPBoMxvWG7W6KVOUZ1b20kFF9KNhvY9jIZabDGrhInoEtjX5TK4aREiunTpSxttP1c57qrYl9l0y0UOW770pb/zfoydKwJzpMEF49rEAtswh7r+rDPiZGWO/JoIy5g5uk7UOsdWmoWn36rG8DnDHPa9Yw6yfBnvWs2vgQ8GvRFz6ILovl5EwQ8Wh42Rb+STDsW0yg8r0tdg3c6wpgO0L/KDVxMbVJmDtmIOn+EZBxYCX1Dmhyltv+5ExSCROZRzsD1WIU0SYA6MX7WImjl0kCCWo021E3NsiDt/5ujzP47MM4eEUGSObAMT0aW2aGZdkKfL8sSlz+1s95nKwhxBlhkUpyTPf94zyHxik7D9DhPcD3t4eEgAaYMU12WewZd8W3HRtm609QYBUHQLMingBfsVCBMi39GCPE8bfLEnOc1Fi5fvZkOc5AwmQ8JosExw4+aBy1I9mdqXDSrI8tBFo0J+NE0r1Ol7oXyaylsRjBY35ZCvpg0OK7rBeDMObsTCxfTi4ZaQH448K9Tkrt/yhrLNDhJaMcctzCgfYu5i8H3NXNbK7KtmZeQSmg5XpaANMbxb9yxI/5QhQkatO3TQXD+1IBqtbh6v6TAxUZhG/9COPJmxSpPLHN1QCMWFr4KepHCJU0P9iqHwni1RalM71xNQMA3fab/yimcNLD4MaQvlH+tvkUXOSp71rKeaL3K/lZjD3Ba73+J0LVeZjzuXWYnrSEAPgeEatwtzUGIOGDwIfmwDcwh8z8whVj9lAZ5hDk87xxy+9gFbf2vMAeVk5uh8T2COih965qCu706zWUp+8MERZw4wYsAcTglWNGhNDy/Gr7O6hQivbkOto1uYBx8Uks8zh6eKpUTmGM8liHT61c0clPKrmMPy28Acq5I5JLAyxtEMc1x91f83Y/H5y8IcUZYBitMWJvOCAu9QMTgzCTMDMN6S9DI4EavpnPM6TLTmaRVjSeGkFFHkkW7cUW8tKausI9FhBZeaeyLIwNcpxFHGOLVRk4vGgQ6RA8u0CjWULNopStfTBTbi9EOY2KZNtiibP8p2WDtnKeqotbNlO1X6J9Vsh6F2qNoOqKHNLdZhT7Z5fGw3Bh2yJyJYAhlhFRVqYeE1lVC+AAhpIQR55fqM6jmDKY3+8lORIbDN5Wd5jRCiOhaaVz56+uRs64yGOjxqNIJjvovYkwi9cVDImQExbRc4mfTU0HnW2Q4em73IIoucoNgAvUh4bzswh974oT9A5hg7ml70VZEq/iSZY01UO0LutzZH3YRX+CIAdmOZOaxPIy9X+aEraEfmmGYeIHNA46fqR/ua3T4i0spUfojMEauUZjHgQxjJ/JDpoKsu5UZA5gjuITNH4gfxyI3MkQ5731ah/M4LbcUc3IVvZg69ic7Z78Ico605u/qVoYfJHLaOXRg0mpjDrtd9mEMtWpjjspYLM0Dxjne8g573vOfRk570JLrpppvoQx/60Gz6L37xi/SGN7yBbrjhBrr66qvpW77lW+i9733vGVmbxXvqsu9ZE8mgUxKiWofqMQLuvhQ/i/7SXvnokkiX1mJEBh3dQHjSGfe/47JiOA4GFMnLMB/kYJ6mefZ5ppHY7IB7z0VKA9WYu5CEPZijz82DEEBhHaOIwc6ApUq7Pa10aRWkKtfpa3AkwXdoIT1bXO+s9GkTlmTfkFUKqWzrj1GEgnJrUhriBeWBHdMZLJLpaarYsU6V3uweePixzVNZtc5uwqR9T9UO+cil4mG4PgzizU0zvEAj/Lqi9nH+Frm4clGZA/tuoQFzSLzi8RnCHHO0Asrf+XWPDUZuzxyYYJY5Cj2ON8DTV0y3ZYeUdrLobn5nmEPLzswxtZf6s75SIlTyg+bdM0fzwAO/Hhf6xkGWSkfzm+n/qT9muzIH8sOquj2GBzmoQ02n4gcWDikzK+3LHDnc7R7ldRbMMa7PSTLH8PoalJ3zC7N8aE/mIArMEcpfmOOyZI4LsQbFu9/9brr99tvpzjvvpJtuuone/va30ytf+Ur6q7/6K7ruuuu69I899hi94hWvoOuuu45+93d/l5797GfT//t//4+e+tSnnr3xIHgDlx8c5Js7A34eX19VXDdo0XoESzeAldzLdg82it9hZB/+JDs7dN4aj9ts2dZSU8m+x7d7k+4ph22BSf5+qeZleeijE3Ed7RQF8/I8Ox2CNSJ0KinFT4a8KdXDwmDrLNQxW4mISd/lxHF7Nc/Xj3AYcP1epwcGfJIBXp7yWbmyBJhn/06sNaSQP+TA41La4Z/VAMmUvYTXocxptvAVTcqcRoyq05FQ3yTmN2UX89sOFCIa5DpOVlFrovaUC8/lbJGADulg3ghDu1Latwyu8zLZJ2BUGpQjPAsgrq2ILimdDs2NO5oL5EBtfvox9Be5kHKRmQP7BmSEbvA48cM2zFFdvsocc3xTKs0xx0g/T9Hbkjnsrgxn9IG1jIyQmMPZQ7r87IEHIcdE5nB+ALuMb3wNhbB2FrWdN4A57JkzrwfMsTb+6ZiDwEYbKJDGHBU/EMUzKa39YMwyZg4dkKiOST5T/LUXgeSRC5E5GJgDWTDopqL3Yo6g2zPHijimMZ2zYQ4csAse/ISZo7dwO+ao4vZljlH7LMyR9C8juRADFG9729vo9a9/Pb3uda8jIqI777yT3vOe99A73/lOeuMb39ilf+c730lf+MIX6E//9E/pyiuvJCKi5z3veWdpsos64uz0zUFNPWa3U473Z/X1Vemo3i46ImQLEJQOpIgr4AIjV3N2a5m1l4hJQscbnR1L3emqrWX1hewpRnSwk4Nb5Zsw1RuZyuo44s0xtU5ZJ8zlw+rvSmockw5ArDr7J8d8NHNcV6NwkqKtW9cvU5l9XfWuM8NG1E9KEx90w/J9WtEyIHmeBbNibrNzYunM7XWa4sA7KMRJlRk0cn4IDFW66nWN6kBEbEjfGPPJzljPGM/TbC/qGZ+OehnqpHcZnJhUpvpjXpp3/06qqYTyKYTXJ6Ovtn3BgGGRr0q5yMzBJLRudyX4wHG6+tzBjJiD1kR0VGZc8AMZCwwHGLZkjg2T0me6jYI5qjKT/twsEW0rnMHAQrSquKf5vjnmYMrMob+Zpu1U+vJzNaJPC8NQNPFDfjHDY7Ue0U9JYI5YoTYzoHS39VaeQkRHeSAGbDDmKPKLr6zkJGPmEO5nMJSzPxNzxPPOWaBjDspt5rLN4MQ+zJGpY8QcRMpK1A907Mkcq1XsM6a4/ZmjjIP650t2jjmkq4le1vUGxAtzXHw5+Fc8HnvsMfrIRz5Ct9xyi4WtViu65ZZb6IMf/GCp81//63+lm2++md7whjfQ9ddfT9/xHd9Bb3nLW+jSpUvDch599FF66KGHwt9x5P/+v8+TXR/WOfqWX9M1w/adqV2EqXfQQfQY6B/hZiGDA6bv4sQLDmZCOFMc5NS/fK0LOt7W4TUHAtWP3batTiWwuEsPVN4uLS7c1LIWb11uYy9wiGrH9ESCQ3sJ2cCAheIxaTqMzkdaBxvXsICmNPgg0tvlpkPwhMVwrD3FIH3yodlpo7SnG6J5apuqbjICw4niaLgdp+Sl23Hou/A1hUYHHXcvBDZoXfWbl6Q1xndgTcR1zFGWdrsjxnPfV+52t+m/+9M/wASCQjiGeYEohvz8vPK2iO0z5a1/bE8y8MXILn+udeAVXNCNxwDtqgTjsI/R8wPP5QoT9Tru4cX7qL5WUQfPTCaix5/4zNDeQ5BlwaqvTrmozHH//V8iIkQLIeK0zWhzcehbsXu3azffM3ec0D5GN+yoZuxQM4f6QdvyPHdE5f1FwRwEzJHrA8whIuH6HDEHlczhNpkfxv4ZtmarmGOqq+/OxVA59c24AKD28dxeja2Ywwb8WQi1InNgkyJzpAyNH9pnmHXq30OOxineTmaXvRYKzCFxQCIe2nW3ptccc9iQDCSX8FkzBy77pn9D5gAr1HdG5kAb92cO1a8YpGKOmJ+3y3GYgwi4A2p1UszB5MyBddiOOYBrQ1eyHXMIET36+MeH9h6CLMwR5eAHKD7/+c/TpUuX6Prrrw/h119/PT3wwAOlzic+8Qn63d/9Xbp06RK9973vpZ//+Z+nX/qlX6J/+2//7bCct771rXTttdfa34033ngsu6+55kn+A6YCYOeeByNCWgyaKYcHn5igPmdHuXJpw2Qa12p4FzVXBIJJawx7R7bpx1kTOR+HgYpC9AlC1QbY7tg5BmcBjWjVQe+n5XAMcmdZVJfBEUDds7vg6o+nB1nq7B1qKOUXdaITUcegr5KkqZpNJ28xmvMLYfDX68W6hhkNtihYXVesT6xT7QbrVbi9bSvZ1N7bSpVP/9sRQ21aEXf1jLn2OgwXRl/WXG01XYpl7ta8qZAjfxJjHKfwmB+nL7mdhIiOVl87Y/UBiJzA3yIXTi4qc3zt107M4X6r71PD1ubHFC2n8re1wqjX49C3EFGcjFeqFb1oVTfGD3UyuzCHq5X5ModBEfssdFj0hhA8dNfJJv9t5VC46+IWWPXVjLblOGiL0q/zxBzTrhhj5qh8d88cGIdluV39FuYUmUPqMrGs3jbwnwPmIIq2oTD5McqyKlkEW7TOz/LNf3OOe4t8/DfDOXFc5vC6pCEviKtrmwdUPGK60PB6iMxhV2j4zMwR8ktlWEhxnmr8avXUwuoDkoU5ghz8AMU+sl6v6brrrqNf+7Vfo5e85CX06le/mn72Z3+W7rzzzqHOHXfcQV/60pfs77777juWDU/7uq/xy8cci1+EduGId4ahwxS/2DoZnYSDcB5Ge+Z5jB07KE0qIVX+PiAiNA9GNdGCTdeUd+RsbSKj4gQ6QU4j58X7WaW5CboQJOKxg3pX9sA7oPG4SrMNnmJQHFqJdkmEAK7aXsuJ8xbQ+fvTl17HS8VPqFh3RtRTWmMu8emFvR8b8sG8+3NhbtyrOnabfg8dqzn26ma7H/GPufWxo0GVOZm5dKiHppbaztW+b8kYgWDBo6F2yedW6hvYvnm44DlZ9DKpUjrmtzr0AYpFFtlSDoE5vuZrroZf060FLppWMof9R/0IPvzcFXx3YQ5J/kfIxrKDDJkD803+IjxJBR89rg70o+Dzsd+LtYjhyAlTBLZzGgxId2tdlYfMEStXMQdD++g0+Mgc7fwYMgdBW46Yg6ysTczh9ojlh35rxBzxd/2QJP5txxwjX6vHPcdvYo55Xz9mAaaaOw6COXjanaRLPcMc0zGqmWP4eB84ep450rlfMEe+DmM5RFcefX1twyIHKQe/BsXTn/50Ojo6ogcffDCEP/jgg/TMZz6z1LnhhhvoyiuvpKMjf5Hy277t2+iBBx6gxx57jK666qpO5+qrr6arr766Cz8RaVs/yUrM67lrIZK1kBz1V9QsEwjVvYv0YV0+hRf09z97+2rM8De/uHP56XdXtZhf5ZQ7DUnvf/KgfQAkoLk7nejUo21dn0xEIhJXwd5KpznowobVYG0IobjPNZHCZD9dEzVVrxZ00ppnc+YS002RUsaJ6KwF6XUACirp3wvl5mCYymad8bTzdS3ySigUy9lx9sHG/OZw5PgyrRchbYFKP4f0DJkkXst6rWocSnckwfSoh0YoLCcddku61aThmsDfhyzHXRX7cltR+6tFLh/maA6o8OmyljY9bxMs4EAknShzaEomvJ3elznmypuS4i4aY+aAmx1Ju2hwkRwChGjaSrTw92uaYHtn5hjE5e+6SCgyh/OD27aJOTBvu/EcMkf9kMI3kMQ/YI6KH3RgpeCRyY6aOVIzBJljjhJPNzDHtr15vj3vy+n5IeofKHPQdE7uyhz5St2fOfQVcDiPt2COAP0HLgtzRDn4GRRXXXUVveQlL6G7777bwtbrNd1999108803lzrf8z3fQx/72MdovfYXKf/6r/+abrjhhhIUzk7yLRP8YPIFXAEC8gWMIngni+mTztgZq45Y2d3W5OZAahtCJ2B/EgPxqWweNLASatjQ7PLq2O602qsLnG7eDaaaTrujMlRj+AOdFeSLNulbLBHQOOj47iMaK8QrCe/5aThBfiFvgnqGlbh9+1EGezRP49CcJ62nQQXOOgANXVxfxvSHbd+den487A/juW9Hdau5DcCGLA4KIytiWv0WUubKDqTHCy/HIDDZfZqgYDJwQti79HZs0tnNf4uMF7QbOkkZHakDlszZu/wtciHlsmIO2N4ic4Awu79HBukSR70hc6Rk9Y9g3DxzjPItO5ENzLGqSqhuvDkyR2KEzByZH6ZEkTlMb8AcyDJdfTvG09dYdfAirpGlgx0Vc6CvwldcKuZgCBsxh4Zl5mCieeYY5KcMsw1z+PHwozbHHLH9at9NKW84RASa4W+um59jjvHlNWaOkd1nwhzUXaRmk34bMcdo4cu575VMzDG4T5hhjgsnC3OYHPwABRHR7bffTr/+679Ov/mbv0l/8Rd/QT/xEz9BjzzyiK2w/SM/8iN0xx13WPqf+ImfoC984Qv0kz/5k/TXf/3X9J73vIfe8pa30Bve8IazN17sv3iNg3PP15akb915x0S0qn2jJanihj0Ah0HJLq6IGI2ehwGNAiJ41W4XOaUfqRD1N/7VSkGhfKHVqrdv8k9SVEmdcVdy+785bMmxPtCQ73UVCEqdwnRpcd2TjGBJBqUpbNWfIaSQNnFB0ZPJwL1Kld8GHdIFsqoTpYLAGjZUynVEmoz3HC/gTj+HHfe4nDnZDi5OR6abh/i0ItpQi593/Zk/t7JSdSbMFTTrIwPoL7LI4cpFZo7gX1f1NbmJOUql3Pl2vq2I2tBPzDJHjpzpxy2zijk49/QpI0lxOjjRWzXLHMxpxoXqdYMIrmP5dmXpNqN9uDVPwRxT+cNeO0U1VgIb4g3j6K5nNFtSmUP2Zg7GvAY6ykojfpjzZ3P+cRgndVyls5k59vN9Y1Y6I+ag02cOSZ/bFqRMVOtchnftX0Vy8K94EBG9+tWvps997nP0pje9iR544AF60YteRO973/tsEat7772XVjD//8Ybb6Q/+qM/op/6qZ+i7/zO76RnP/vZ9JM/+ZP0L/7Fvzgzm+NU5smj6BaJ0uKFhXiVbz3Jf+vUfuHoQ2FwIwhviKstnRx4N0AKY5WJJnxvY/G7zT7bzia7Y09TDTWJtLy0yYRoctLMJAKDFMJEvDaIwI6NCSAhd07wNKDqtnrnmVeAjgMEun+4VYejTt8gk77a57NJ4mJUpbR5mtLqMB2BdVhXIsMFLiLpAwL6qkhtnw2qVE8uLA511AFNo9u+urXCFJtOtoVydhbXFWRx1IXq717HfjFTNXVnvKhVtK63b07n9KR37LXzxZaQFNo9gaimNEHu3bUi1OcBWWH5GC4h5vTb6iRkmW751SsXkTlc2uOD1u/pNsLSYmg1Pe2cY47uGq22EkXnkOM22KelSOpcRswxwQB834U5dJuh9IqH3csDRzBRm23R957oY3Nd9Wl/fnDDm5ij2Jpbd+zoyVCI2G/mMz+MenFlot5vbmAOiswR8ituV7dijo4fPFxwtob+FTrKCEyxvTcxx/jhxjxzjNd22Ic5RjZcPszhqU6GOey1DjvmfVzZn8nYskOVhTmiXIgBCiKi2267jW677bYy7v3vf38XdvPNN9Of/dmfnbJVY/n4xx/0S6E5PLvYcY4cXJFiHpvhKvVeNlxvNGWzFqr6VReBvpLw8hTPj8Cc7h2zmNdkTiqse7cx2yBxxsdU0JQdexJ0RqLvcgZQae8o6uwIUfjwPHGdChGeAEGI8rageZXwrr9s9jAmorbIFLS3EDcwiWtHRN21Qwfj4fKnDTYFEhf+wX48O2nxV1oQZjTcfvMUJiQ0PE/agkMGRFa3dQA4j/MpoNmR6voIE9s0t8/B9HD6V+3NXayQLxoFsQ2+4zFSDbF9yqc2cr1os75fyfY7xsWcOf8vnvc2gmXtEq87/nTX70DytY7hawAOtF261H35Ux4JE7Q/gp9WfkqGbyc//sS9MzU4AFFDj6O/yIWVi8Yc933qCylE+nPYfKw05rC7Q+OE3Pea0yJniabSS+KRMAlApNOx/mjEHJpnN3sjdTqdzg7MkezJ9zOBOcAmpbqSExpzoL/BrTWROfRwmP9DtiAhasyh8cocWP2KOcynlsxBBXNQ8M/YVs4WLR8m78k75tAs17EtweDIDwLIu4E50O7+YAyZo+ae2GbR784zh1dlxBxybOZAGyJz8Oj+vpSzZo6stQ1zzBgHuWBfgqFsh1gvscgyzhyPPv6Xm0o8X1mYI8iFeMXjIspznvv10EG2y0gXmJAYHrqo2GPGVxvwC3tyKtJgWmWOuL4FeCGUnGGI03wk9rqht6y8Eaw1ADe1sZONlusTCbxp5ebFPK/o0A2eciOY80txTDH/1K4RGNzhxLIThFDrDgWeBojaDbdo8NpKdrha1wqezGmCDSFNPH1aGIeFs+LfNBCE24WG8kqdyYa4fZW6AranKPmdUCafPZLbq58Cq22Xy4n16uxtpqijH5zJhKtm95/JPvF6U7ZlC1KIq3RzGV7BSsiDc9nVO5/zVlipzIRb0OV2nSR2DHGLYYmWtvw0j1DLwkSNv+Lo2TvYfx7Sn/m7/y2yyNnIs254agop+ojAHOqXo9PgfAFrR5o72/SVKaXjxBwZZljL23CtMDAHBpYq7lTZkm3LHH7TH0xmgrzAL5rtqc7ZvOjsOubouAM+Yz0qq2HdLGAOvYntmaOvK5pZ8aYzh65/EfXyrAkCf17zw/S3HXNw+NRXWKycFreJOdIhoMwcIX4L5sj5IXNoTIfW1Pv5OeZYpVT1OVzLWTHH/P0wnEEbmGOYjxWXztcBc3BI5Joad9UV3zxr8fnL6GrZ5e/ykWWA4pTkqithckq7a8bXKLSTxB1Iq06v6+QGI2z5tMwPTTDAbeAQYCOf7Sq3G3rMvEXbWGrO3PRjuLmh7jrycuKNOriuPCDSkkke/Ve3ZdtvuS1C3Ab7feYBig/wSmh/y9kcI0zGs+zbU44GAz4QImaDN4kUN852Nnjlwh9wJWxNmp9k53dGxXrrvr79lFB0QzplNLkRO3YSjp8eOwcijWGygRiO2aDT6LtUwJI02m7AGE9dQteHjnDk+KqFm7Irtz8gSA7B884AIUBlRVyGbyNlcRtGzN2FF+fAwP5aR5INOB17+rbK+YnrdO0rRMxHtMgii5yMHB35SkAs0w0rzljQ8G677+Sy+xsrioEcPkzmmMNxYcAcLbZkDgJfhz6olOMxBymjbc0cLWrtiRi/CYXZZ5hhZA6xNjNrbLFKTscHZzD2zKEDBNFuZBWCXxVztBDjljXEVStCwPELzJEJA8vSdLszhz182JE5QnbJkm2YI526iTn6fPvrYx/miO1wPsyR/XqsQ70I5u7MEc9P/74Lc/i1M2KOK8vyFzlMWQYozkL00T6so6Ad+eRTpOzRuPtCATi2fd3ILlocUwilcPqt33h6+t/1M2xOos+xSu9OpNIJzmLYFwrF6NbhljTF3QxQfTdx0mGIg05QJNx7h4cjVZ1Y4aOYzpc6cdfxY94fvr5NIo9lI8TqWj6J0Om7hThuYMgGKc+fTLTp7AptHVONS8zH2tshwl5vf3VJVGs/c2jrWquyoQPtHWV+kuRm0ZkMeu5u/3qJA2mouz3CqzTqfgGfXujvKW8FEA7hpj7I7mBFTuBvkUXOQww0JF13wBxEY+YAMc6Q7ZnD8usdDVlnIPhbvw2YQ2ieOaqyZ5gDsi2dk7/3HiOQH0qd4PeBOSqrG3NQYg68oap0vO8PxYf0obtVnXLxSLZTBH1pZo7cffu7/yiykTliffZnjp5h4deAOaoSN/EDMgdvqaNxJ8UcemzPjznI+42COcZ27cYcYnn1La3MwfAb84uv3YD6whwXWpYBirOU6qJkIrjjiHEtivQzXbezHTD3f+OdePJNpmcen1qk8GZ/91eV37S8Cm0mA24Bqq9LsI6mVwsgeRrs0rKM79vmnAqBTS1/8w4S42Aqy+Z+r6StYV/SNVvT6TiP+mbOoDEnK8rtHOvEXTzZ92qFbw7xAFIpD0w/J5aPz6edO7AFtPjf2IXmCyzquA3+Y19QOFFhdczVudKLw2HV0ezu1WSwsnksbbvgg5VzgIUPfOADdOutt9KznvUsYmb6gz/4g9n0v//7v0+veMUr6BnPeAZdc801dPPNN9Mf/dEfhTT/6l/9K59i2/6+9Vu/dXfjFrl4MuwvZ5ijRVeAvy1zhLdZy3LGF0fJHAzMQe2Z/hxzRLPbp/twnDlRMYf3q5FLSp8PP+rm3oY5KDEHGev0zDHX92KZEn7LaCCa++Y2naHdEtfcsnTznR7yQ05fM4fOEtmOOQh0qoc2nmJknzPHNtuRc69pfyfKHFs/iDgtkZ2ZQ2XEHLvyQ344hSmruJmsDlcW5giyDFCchdhTDHfLOjrNQtP7lZWU20xBtqPicjaFbozv35fTgJ07xubR8iJ67qKlKw2aZ2D5oJz4xX+q7YWONBu6MnCXEIhj8UEQ7JTtk7XKveOcBl1qu6v+1Bilk9Z6gzhdPEpS+vw1lJVOLcSEajBBqL0zOrJB+sLsmG97oobwOrKaYBrsoP64c/FaDU5trc4yr2fMcZvBiTydcm56ZZ1uvow+r3kgmlLk8xNsGl7jYzytDqtdWVz0J5pgkVl55JFH6IUvfCG94x3v2Cr9Bz7wAXrFK15B733ve+kjH/kIvfzlL6dbb72V/vzP/zyk+/Zv/3a6//777e9P/uRPTsP8RQ5FbFXs7B+93zMJ12V/5TKk2Zo59F572H0M+gjagjkq55SZg2LPXjFHFb6ZOTI/kDEHM4U62yseo9oG5jAyDGMUlTmc+t6KSUJ6v4srq6XxpffuUGLijdU4u1nmiDm51VX7qPfplh9RO4acUDOHkHPPaNCltHsL5uh0pB+Ci6/TVDpe4nGZY06vYo5NZezDHHrmlzaOGGHm6OTzXq1Q5lDtLsEis3LIzHFhdvG4aPJ3f/dY+xY8lt+g6tc2Gmm7cbQOVFYtkqIjCI4JbjKxb+su5ME1j+tIuI66U+n6eQvH79rra1cBy2Z3OycztVF87dSZdAXosGozWOirQye7GZsV07jTX0sbgTPPrcMTTELreEPPRCQ8bXVlh0xvbH0AAA8lbqkp0rZEbQdRb/IlQYiwvhMIJ4O1u7ZD/JuAQNfOwPdS1y0c9FN+CCtalwo448yIqX38jWbd2kxP3nZcebKhYywrn0Ict/YL50P7oo7GF/PsT1iG/6tTGs6qEMc8p0MDHcy1dsYjV6pTGitgqOvFpqe24PVTiSFvO7/illp1+mxjd+9in2y1yD1AflKhNkizN5/VLTqd0fr3GB206OPZ4+jvKK961avoVa961dbp3/72t4ffb3nLW+gP//AP6b/9t/9GL37xiy38iiuuoGc+85k727PIxZHHH79ERO3awvfokR+0o9NLG5iDiGwV412YQ5/0bnNzUDGH9pcVc1hBHJnD3o0XabukTb9jH4r5aY/pzMEFcwh5ePSmjTlIPB3EBsQze6VgDiiT2+sVLI0fLAfygyJlO05JYx2cOahjDn8pN9ZqtQNzaB4aHvODdIk5iCKHod/UmQ6NgqF2AnFkM/b2ZQ4rnj2lXgJ+SZw8c1QS6486mOvZMoeGbcUc5OdszRx6/OIKKsdljnC1tpN8Lc4c4cweMMd6/ZVh/Q5CFuYIssygOCX5zGe+2L75CRM6MabQkefTiovvUiVMSl3HJ6NzVlJCKGewbWhYGBMrY+8dSqqkppPkJLxGbHFuvN1wY2VSntiOBB3g1K4+TZOIplkQ4fUR7Pqhrm2gRdTLE+zrzV6KOk9id6AEcBEHRbz1mHz/8EiJ625f8eg4K1mXsSz+hAP10Q1R+M5NZ7JtRfrqhzreBiQpP7Y47srK0/f6Evt88NQZ/WV9Kr5nmbMjhEmO07OEY7ogvTPf5mnEScl0PrODLMX20NDx5R9BgGNkoRPT+/99+aN2xb/Hn/hsXc6BSFs26Fh/REQPPfRQ+Hv00UdPzeb1ek1f/vKX6WlPe1oI/z//5//Qs571LPrGb/xG+qEf+iG6994D3+J1kZ3ls599yL5XbCEp3AJVgAar67eUimESc3T9So4T991l/oUjsFuWajHLxA+cOqSeOdyzWzFMPpguOal5rWA3MocN4pMzRzgSZqNmrl7amSMwk3IHTmkomUMHHMj8sPrv2FBrYBHIsmhOLYVwYABMcH6odbnXMK5Q5vABkMwwnPKWsMPGNsxByTbXoc7uEVf4b+nioIG6sueYAwf7GFIdPHOYGSPmGLy+McMcw/lU0qf3S6znUi+n7joef+K+Yd0OQRbmiLIMUJySfNM3XQe/0FM2Ebz2ksdWX5XEO4aZuDycGIsMGoPVG8zOIZvgos6pjCncI7mNqoSZpVLkbSpsTyCyjllYrBQ6ORod49ewVkPGLjy+ATgVlN5iFNeJC0gxsbCVY22lQGHlt/w5OpvQ2qCzgjQQ3YVF9ekk6Q5B4THbsItZFrt8GRzvOGDisTlVH84cXxKpTpPOWfO4rlkHf3s+sZ2HsMFw7CDXcErZ/9Xymlh+/+84kvPYNV+RNINBvCvpatFlKfYxLC2dW37OMeUtX1Fn9DDpqiu+YVTSZSU33ngjXXvttfb31re+9dTK+sVf/EV6+OGH6Qd+4Acs7KabbqK77rqL3ve+99G///f/nj75yU/S933f99GXv/zlU7NjkbOXZz/766Yv6jur9+czcyCa9OMHs51yyRwSdWLf0/qy1BnhonklFxD1zJHT4A1Tq/uYOTjphVDHF3RcxVMeZA50wNzqxIwp3d8PmUP5QTwnba+VcYIyDaYBU5E5cp8Mhzz6GfgcdeKgU/uSalHImjkwz3gr244fE5TUa+7KHB1raMkFc6TTt2AO5MfIbfswh51zZvPlzhxY1uhiTjpchfN4lkrrTqrcr77qBbXOZSaXC3Msr3icicjUG4kvVKQj+HG16B4mmInWm3pRyGHctXnHkUPjrayHa/pu4herbfWUMMZEGuaZQRovNS4U5fo6BZJyevwCAZaFJgAPMDkEsTLsRpY9G33FAO0Wa4118DGh38Q80euYDQxppK1tMcVp/vpaRTwiCiRVOzPVnTx7mzZXUZ1C1eikVHXzbKk6i6ayWniAXAYnnAc8vJ1q9IunecYprHf/xCHlyDk+Wx+BzUP78G3lOOCQp1puvQo3E+nUx+ETServUewqTzOnpiPaX8cexhSecIQB2GhzAIYtq3PuMroh2kWfiO677z665pprLPjqq68+llkj+c//+T/TL/zCL9Af/uEf0nXX+QA5Tt/8zu/8Trrpppvouc99Lv3O7/wO/eiP/uip2LLIeUrrP9v1LND/6boIPiCZbjr1ss5ZFsyBUanojfZtZo7U721kDoQBMKVkDgnMYUmC7YMLH4oJnNDCOTGH9sm8chsyc7DaonllOzJzaHDQQf/rOjrRXpkj1tNbeZWOg/OD5oKx8TPnRVoeRbG6hrL0WHiicKrNnU+JOcz2xBydSpntXMhm5ui092AOjdmXHC4Gc+CdSuIHsyNoTEXgDcSQOSjUYmGOi88cywDFWUjrMaWNqhsfBEfm3gfdbO92PcLeJ4U0+ilpuqad96Cj+Uw2yfBVEMk6LZ++Q1Inxd2ggus4GGTLu5ttc7KUpmlG+7p1FoxMYjHBua8kKRFNA0gSbtz9MCXbxAc8pimc7qD9sKZReoAaDPdPJpF1GtkHkOT+eQNCWK8zhY+mSdVT8YR0XYMOQMI7r1kt6kTbBtMvE+TlmpV1JaHKESsOFKdps22MCiPHvo+7P6mplggMGR6I7CrrNdjf+xxBRrzitCPp3/7UkkYWjrZ8Ha6oDdldCF44ofdBr7nmmgALpyHvete76Md+7Mfov/yX/0K33HLLbNqnPvWp9C3f8i30sY997FRtWuS8xPuGwWRrd5Rw4ywpajhIkbI0vuA+zH6UOjVzTDckA36IUxohp4I5Aj/UzDENHtS9XmaO4INqR4PFRgefywHmCGt4haieH/TmG5kDMxRal2xBpMzR99nc2nQb5sj2dzqaZ6UTaxfDZphjsDL3QEeLkLINTp45KB2H4zFHYfFGOVnmINL6bh6kOAZzDPghD07kXDYxR6l5IWCjycIcQZZXPM5SkrMLEXhnB6NoM2vtlHFC1B3VEjTy703XRPIncz1B+VS/6eiNJIp26PkWDAOy09PuaMUKHwouXpDNIAif5OtKUI6vmkEt1rUYdCGotA5F2ArMVfua+s0gW1sBZBQ2TC2ztnclc2xYCyNoeTiWh++ZFkeiO0+t6+cZlzU8f2rAwcW0+imLm2CAUnqCc0sBjm3q8NyCVXPOvarrcfcUPz1pUy1leEsyY/tYZ1xc/YRsU+tsk2aR3eS3f/u36XWvex399m//Nn3/93//xvQPP/wwffzjH6cbbrjhDKxb5MxF4DobMUf2+VsyR9VRGHMM+aYI6G5wMWrMDyMbbFW8osw55iigIqXps4zMkfihYw7ITKAiWzAHsTNHBMO2FgSU7z56lF9kjjCwUOpM/FAxh7Syx1P04+AAh7JHda1ymX/dYRfmMDtGzFEUsx1ztDh9zfGYzFHX5iw95risyo65S3KkM0XU/DAn1fmzmScW2jgNOSvmWGZQnIX4fWlwWPa8nTFiEttHfDR4MIrboBM+p5J62yxGLEkY9GDyJxyqN90VTs6y8DnCZE8ksmNTpx87k9YuTa/bEUSIeMWUZwH4VMtmGicdnl5nPcpNADrBijajwb24BJ2V5Y5d9TrYEooSodXK03htxXTw3USL576uquk6kmNC3lAps60/EtTW+PAYb78WXpxbo+1HV5ptp8PNScXK6qlWOSJu30oHz0TVU4twbnSQOjjxy5CcR2yjKv/jSp5FQUTdb0w999SmardpQMO/9xojuBjobDFzom+1w5U83riP/q7y8MMPh6cMn/zkJ+mee+6hpz3tafSc5zyH7rjjDvr0pz9Nv/Vbv0VE0xTL1772tfQrv/IrdNNNN9EDDzxARERPfvKT6dprryUiop/+6Z+mW2+9lZ773OfSZz7zGXrzm99MR0dH9JrXvGb/yi1yuNL8FNNq2h0M/PFa+zv153COniRzhOs88wN0IP0gSuIKLENH1qu7olUx5gLMUXm6jjngLlqZI3SnQsRFOYZx+hXbQdJi35gX6mBVZd0OkbOFmrYSavwQWWmWH0QaK+EilzKvA7nWsy64YA6Aiq7zk0E5NXOgDd1AksadJHMUayjtyxyd/d3vfZljt7h9ZDfm2OTPB+22J3OUgxEyykmDx+19iLIwR5RlBsUpyYMPfomI9NLRKxK22clOVuBmganrQIMAcISLvQgP/levcNNJHb9e7G19hOyE82I6IVLgUzh2KLkYYatsuOkWuGNGGyTFkbQtPT3p1GTT1W08AHVlIhg997Zm0dkQzidYLx1swZs5Mh2kO7BB8+HYBgo802q7K8qDLpOOOgXPGO2N4dLZqOK2wNMVgXdDhz0ZPq+R+sFU4SnCcx5xu0P7FCdy9i9wOaSUSaeK695H1PgeEvCpiKYvMCwbDDpi+eyyqNSuUuWNv3WRKqLtQUn0oFobxXNOQCvnkscgpuMnYbGsfHpAcSH8iUt/O7T3ICRXaJ+/HeXDH/4wvfjFL7btum6//XZ68YtfTG9605uIiOj+++8Pq2H/2q/9Gj3xxBP0hje8gW644Qb7+8mf/ElL86lPfYpe85rX0Ate8AL6gR/4Afr6r/96+rM/+zN6xjOesbuBixys/O3fPkJEZAtk2gy67DuU2dF9ceo+B5yyLXNkvAnMESKaX6yYI0nkj9wReR/WMUfihxFzCDi2yUdjfzcxh/kn9ffAD0TOHOY9uvaRmjmAh4S5P0Sqm7AL/TyW54dC9L68MUeUmjncIwi0lsg8cxARbE3aYoUCc/QlEdQgcpazABtzBL8Ceh6S2mdL5qh81j7MoXGnwxyez3kzB8bM5ZMlbgzg7bo/c2yzzajn+fgT9w/tPQhZmCPIMoPilOSJJ54AIHAvxRSfnedLXarvXUfWC44TSo7YoBOTcv3+54yIsL9fGTyz1H2ujRbge5TpHVJ0gObJtQzx3+W7qtmW6MQ674SfnalqZ2c6fCFfpCqUozpixz7G+5OPSs+rkKdMVvGbp+k7FI2nXHr4ZFv1Xmguq8uDY96jcmJ5dXxZj0Kna5OUwfxTiN7OjW0JqHU6mDBfrv1m32kGbZm7giedHrn87dCBftextMEz5naDMWd3n5XQ47M65y7nsCf5y172stk1PO66667w+/3vf//GPN/1rnftbMciF0+euHSJiKhcP6LzTNUFCcKjdEWajj026XD8Pcscg7z0Lfm4Lyb0XL2TtLARc3Clg2Uy+asdNKhrYo5V+xb8ROVkeCLDvJ4B5mvJNbNuQLrnj+jT+rBer+IDCcUpQ+QqBGNNp2aOuOCknwdE68J3b+eXa+bwlp9jju73wIY57gnHiLZnDv8czDjYIr+zFGeOvObaBqlPa7uWR/r5iphjDkyLrWUDIfLYJivPVxbmCLLMoDglefazv96+46ihTXFrn1x4OSaqRg5cRudScd89r9aTDHY5PeTUBXP3zQcB8E2QkFcxKjoeTVaPrysU6Ig6E0t8rkLC0wwNzLjlMHWq0R2WCclnR3QzF2DEJAxB2BxXdMdYm5hfdYAk/VphLskzBiaDBBqeUUTbrS/R19WYQvyzdpf9OSBdeB8yGSEpxfj7/LDNWL8SgX/BnKFGQSeD/M4aGap6rNqJ0B1piXpBAm3B1ReSZZ1icTgi0lW8RzJacO/Ko+uq5Issssge8oynt0XR2uXM/k5lvDk8JebYLnrQGXQ+tc9A4g/PzwKkCwlZJfWtmEND1am2G4jgWSXdbgfmyM+jkU0yczTLpO6GfcZAKz3AxMkwB6MdDGp4883ho6OMaMN8iVMIzPIEQoP5KlNeXWbbMEefOh/3fZkjx8cb4u2Zozo38mzIUX5nJV092kWxP3NAYqmOBvwGHUcWZY6aO0Y4ctWVz60jFjlIWQYoTlEQCrruEyEBtpwMqTSo6pNGTmcwIDBrZXunRMIdLxP3PUooh3FIXVo9hOJDDc0NiaC8n0Fn3X5b+un9xVW6SS/rCG1YPCS27dSjArhEWLyHie3myiYTMnCK5SXRbvjWtQP7Ma1cuWqvOh1J0ycpDlQUtVqbQytairMByQtwtg90qviRjtofzi3Xya8z+bXSJTWpwGAEGTFdiQIpdzYgI57O8dGTi0N5okEEwFCApP4oYSkft3AsBvVjPSf1+vdPWyistI/8uj2cphuLnMDfIoucteiU/TQvPty+J+bQBKw7aZ8Rc+QMh8whNK1TkB3knswR6oj3wYk5so/bzBz5pkwCc+ANnW6dGMvhxEZxUHgyLx+7yBxEPT9oZOX3qsOqzKELgue8KuYg2pI5dG2xEEddWD9D5mSYI58o+zIHxo3c2XbMMaVTc09ygc2TlJof9mOOyA8YP88cfiIwhDtzSNIZdScHKwtzBFkGKM5Uqs5KA+CCg+A5mJ+dzZOdMf7Ieuo06lKGOrblVyqptBluTngFUwa5S2KdF8Yzxytw8lvS2413qkzJ2SseDJ4EE+ikNhFKebVK6rtupRvSd021Q8a6dtt3tVykrZytzjcRF5v9EnSZ2zuy+pQbHF1Z1Qro2p8IDIYUgOCDNRLqVeU1d4p2O8Yl/WRu+5TZfHf3Rf1xkFbgIU+3rJ6k8BQx9FFDm6V4crmpfBkACxWQbkpUDMwdsCywsMiFFOzTOPTdQfIdO5MvyXDMi3Qr5iCptzCvOglMtwtz0AxzcGKO7LRYgDvUrOx7PXqOOYhyOOiBz47wV3GK17ekSeCH7JszM1iMxDaoOCXugDHpZ+aYPkfMAeWk2rDZrbyBCdnKR+YYndPHYY4urX2OZ0ruN4uy1/GjcbjecbRI5j7MMccPI9mLOZp9F0YW5giyDFCciXR3scF55oGGcFvMg3OO6wu6SrvpnAVXtb2UmUrMp+iFbRTUHAzoJM9hN4ojIKgE4aKIsDHdnMdwkS6Y9og6GjA4DrZ+w6CdAKsgQ4WA1I6aXyrB8ujq6k5/eEw592b+e2UHA+NqUa2Rwx0NfHULtAbT+ttvZ8rRTfbQxA0yoNrBdYevwJyn1C/fuFXbWOcL1mmdcvxMPsVx3egfj7M89SKLLLKlwIDjeEwyBcCXEXMM8qv4YeOVrl3PXJrKxmowA4MLIzJzeNmDu9UGCNyFFd+xzAFzWM+Y9MLilqHn1pkLqSwuvwbpd0PbZLQv+N17whE/1Nwzzxzq7+eZg0McUbUz1MZza8afjUca+lwzc1Sn3n4DClEHr9U55jhEQY7bycriGG1kl32Y44DbbpHNsgxQnIkw8YrCtTJdWLAIoWBq7UwHHaN2CNW1l5xwJ/mK1t0oOv8JtzvVvexMOaK2YX1tcCIaLurptNJQJJFMe47nFmDykfTSC/kWXbgKt+6F1q9QPBnHVK9yXVZ1CFgOHb15Dh7STXEdzwxwWKmpcjW4jLvdyDDPsCsKlqU6Dn74V6UfhU9t3q/CPMW3RY7Kc5i7k3twmO371D7bOyM8A6XLjewk7s+HfYDkZGSudhLacsunOnpsZm4WujNOclkxEp9kcIwi26Z2UNbBiZzA3yKLnLWIXmOxj5+6fLi5TDe/xhyjm8y+W7bwnF20J0fK4CbyhJmDauYIDq1jjil8xalSTNTPZoj2jJhDV6GIzV0zB6fPnjmkXtOHpKAXL2vED1MzxCESL4w7Hd14dOQHlTnK02TQJ2bmICihYjzWOpUWAHNUetLbN50np8McBVlQZg7GBAfGHHNib5DN8EOlM8ccwxkpIsX5Szsxx4WQhTmCLAMUpySf+MTnQodkHhRXXcwL4WAHVXioEN6+hkUc6+s3pMfcuq5QnYQBTs5M7Z4pBDTteummKk6LSupKwF527MRDQa1t9NWODF9mRwOJ7Nhzc6NtEkNCL8rwgqjfdE9hK7AQO8kVUWqjNSH06XqaWFd1xgwHvtv2DO2AYxSOlkKMhkKwlMcV6qpBDDkPOrwhqzG2JmwjVXaeHC6LeIr4L5F68Eaz44GOl1IDVTCH8YqQeH6k8zs+5zk7j6Aw2W0zKt7mwfagR0HHz5M+RVUjHJTo2tK2/HJ9b7/YR+Hzwsef+PSmKp+v6Irax/lbZJEzks985ovTF7uc9Q6iJZD+JlvaRRrXuXXnlBdYxIGNYc83Yg5pPSwyjFDwc/XNSR/Rr+Cvt63HZY50w0h6U1vXVl9z3cQcfgigr0z+vdoulJuOsU2zJ94bu07oxQf8YAGUjjlJt92rPsfIzEGYqmAO7eX7ZpsGArptZYE5WB1HiMunQGRMb9+0dWXBHdkvjfihZg4pdZCvpviaOuaZw8+LQ2EOLbtmjsjOJ8McuYPS43QyzPHoY3+9RY3PURbmCLIMUJySXHfd1xJRu2xhEbnwIp9dlXknCsJf8XvkjSDlk2rIIHd5Ghg7lrbgTJd5G2Uuzv/+mkjK2eODIwvAAEmrZw7mgQXrirSTDfG21uZmL8CdusD3lo+mW4MJojNauD39Z7MqvnuJeUPAsBPudLSc0FrxXIiePYTHbh8cKue1Bqpy0tllTVhNX40hk3Pw8L6WY8daPxXSuHqNhLmu2I9HnaqzXjBtvVQbOur8edoyrAcsEAWndvsb2AaV8/sP6SNjQen6gtzzYjK5rIEcHX39OHKRRRbZSZ72tH8QfostRjT9ZiLyHa5aHwfwH8YiAFkwrrjf6wXYBu6hQ2YSgjcsRFwxRxmC4MQpZWQOkt4bK3NwjlETlBMwRee4auYwrlDTFAtbY+PCiOFVvZZImQMPKep4GZFxoOaxGSGd58lJJ7ZPPg5WTMccMVF/rNzAEXMIxEa9mfOkLEVGKuMZMTTHHGOl4zJHpXlRmEO/Dxe53Yc5psK6bDx8cMxnmufKK24YRy5ycLIMUJySfM3XPMkvWBw50D5Yb8zBkYeuag3J04AFC3wH+MA4lNH1ah1eME9ncHDnpXCMtB7F5fDhX+PbltrtUitnDTe1+QbasAOXumZM2w+P++hpjmNa+0AuQEAbiJBmo8SOc60QBzDnUwILB2gjxbHWhbVWytzA0yjM6zjefsoGegAi1oOUarX/9u/rDhcQCAU0/H3WzqKB36pyrc7ZcRtsah8srzrPsj1Jx863Oq+zkjiMmWmxOm5ZJ30yztfRL90zqKbTzmXLTqIOT9OirR9KpmG4Xn8rftJsfc9bbL22Y/wtsshZyZOedKV9ZxE//wI/ROZAl81tu4mNzAHhRPV5Pn5WkJijOT+JiUz2Zw71wDVzpA01Q37qTwQrxlVasLNgjoklkDlgiWdmY44pKjKHPTUODAZsotAQv3Ze3G2pjM5xEfiq4QGiaaeO0dHwGlAadBlkFqz1s4sbc0RPUjOHWluWtgNzVFKzUq8TrTw95jhr6ZkDflfMIX4EiSgcWdXZljn09y7MYVfRiDlWX7tFrc9PFuaIsgxQnInASHFr8dC96mN6puKqioJQse1sHsum6y2Iqiej/nXDk43iuxrW3aTUFmkxs05CVUIebBXIhvlX0MkwRtznp8cIm2Mqob15mXb1sBkJeeqK1kdSUwfzcl5tmEHyVqcBrYZStfdUz9hw4dTiWE60LDvZ/vhw8S2HxlOsP1aSTr/OeQ3tq+OyjM7TGMq1DkfImj3nz0jmns7gzJ9eB8+Q4oSgKmoYEUAD4V2frpRACtff3NOrgxE5gb9FFjlrEb05jyfh5KsK5tBP7rsD00PZ4trtmCPozzAH8bQ+UqU2QxQ1c6DlPXP0oak8qfqpsoOd8mrNHXw8ltMxh3PhKmWL80ezjhfmouuL5PWBeu6Idiu/IHNs0mMrvtbBOyXMRyyur9e2zBFjm8/pbOCNzJFd4KiEyYbxot213n7MoeaOmKP6fRbCxZGyuMwc4diOmSPMuhkyR8x3E3NUOgtzXGxZBijOUmBWQnQ6OSDrpd8zJ2EFBoyRzQRcI1HQqOz31cnO9eJjK/qY7FEseRiyCVHzOlKEg07nDCed6DZwGl1RPmYMWsYfGTxCjsEsQrebu27WOK70qt5nsr16taeyOcvQ7Qzzi3oZ/4wH8t9AunUnZgTP1U2XSyVjx95X1sHwUD3aCOBpwyEfRG5Y6KtWGUObSP8ELxd/mfnRRRY5HAnw7z/C/dqgEzUvU13AlR+m+lrumAP+puAZRqAZx5D8+rwVU/AuzGGcP6jrHHMQUZh5Xt68D/Sy9TZwUOxglmdEelnsNoR8lTmqvEIGSS/rRKfOKXb+/qi/Ud+OOWou1Jh9mWMbz14xT59mrsYnyxznyyNzjTqnV0fO8cMwpw3Msbttixy6LAMUZyC4lEz0hi18eGX28WXnXmkNqKHWq9+3m2Jq4+annnHnBWxpnMFWQSMrWOJilFuJpBFaLX9uYEcXRUoF9e+lRpul2djTRr9oUCwv5jGBVH/zXZwunSWVwzV4mLv57KI2nF3DE2tu14iBg5pLPVqQjHooimZV51Y9VDPvt5pOfkpF41dpzkq0NpUdDvblviRjy4tHCxv7mcEBl5ZfqQfX/qEO/SyyyIUXcyjFzf7GwcjCB2vMjAsdZrsHc5R3+LRh4ELzS8whtDtzUMUcmwZXZ5lDBrX1m+9YrcYcc31s5dCKIjYd+vqGHabhb2COnlVk9hyTzvjtmKOq7n7MMTgXtmCOPJBFAxtOiznOizzOijkgcqdgzW8Tcyxy8eSK8zbgcpVLl/ztNbuu1jItetT8ljATrVoHCA7GvrbVlFgYnjyQ3f8XS1t4HBVx3bXqnUrOL3TnqTeenmK3KaSh5/ZuCV+pnBwZrN4rMk3tb2mnd8nMiqYnFgd+3jpDfD0hd+ncht3WxLSyxSxVp5XabFAdnWK5lrwLxzpWz9jJK7gmopUACLG20PS+ZhwFbB26sLU+tsOU37rXsVLxYDTbuNkA+U2mrkhwB5EO+Ly98RjpseWUktVujoMibBoOdgYr6ZvrxHMurKWi79omJ7aC2vmRJLCtryPW1lsu2hTfG07Xj612HjP2Y3d2t9pzmOJPF7AuXW026uCvSlsktpcFMtNa+h1EXEcxHQ/SYcMD0/H4ZhmEWeQsxW5ueLrWtE+UNREdKXMQdL5kF3lmDnwqsjVzVHGS+5B55uhYZcQcFhdvhXTmA7cfK3XmOzDHik3Fy0kveOduwZhD2sahgVNq5kBWQ3fHiTl89y7kgCk/fFXF+aFgDtOLlqv+WsgYyFfh0NaKNGnMIUSr1CbIHNZ22QZprMHAHDzPHPjKCFF8soqndMUcMR2fHHNUhQQ5WeZwj342nmUfftiXOTSk5BEZ5ysitC5yGzPHpdKuQ5GFOaIsMyhOST7xic/6D+0ItZ9tvVsY2U8dua8/k045+NktZMsUOtCgY2VSujNMfzhSksVUkk3d6HMyrIjXxbq82Gj55MSKy62wITi1aqG+VNWcIac0qmPbmWq7cqflebZMxOqVbmDRBuHwtCW/tpAXElPHmlfLtvCZuuoq0HgEYjuow47nor1uknSi3T0AEOHhrlo7CurEc5bLdES9sx/p7CS83+3yWd1i17f+BFt+jTUrnXi+5zNiPqtqpe7tp1j6ufjY4/eOSjoMWbb8WuQCyX2f+tvpi95nAXeoBGbIaIHMUXXsWb/Fqf/r3kUv2aIP37jNKPUsgIt9xkybdPea7oe3Zg5VgX4PfW4eoFH/mMsZecFVZg6aYQ5x5qDgK6XxA2zzuTU/FD4lH6Km4zeOmltVV8bYkG3VFhUVEmVcjA2BPh/zm2OOkWzDHP0Ag8zq7CQdc2xHE2fFHKPSeubIbbDp/kH/36LtEu+bRtpmdL54L2fZZvRiyTJAcUryTd90vf9IjlD9gticSSb3t7GT5uxDJGYVZNRzVeEw9B6sG2zfE81P3WqXPBlddOLSHM9oOISZbGRUsBBowhDQvvvIaWV3Vdbkptc5DasLFz82QnZgAjyw5xUGTtDkzm60gFO57W9Gh0An1KtzetGp9/n5zI05iTruLOZOLcSa6cv4VRku7B6X71ZsctY92PpniBEMqxBrPr/Tlm4Fbf0HC0Sh42fSxeZQR6GTu/NxbsJ1yAC0/LSPNoRSiwbSoCuveM7mMhdZZJGt5MZveNr0hZubktYHwKUt4Gi6J3WbmKOSnZmDO+ZgYI5xdsWtXO7A0yB7OWa6gTmCP20OWP1jf0PtRY/8cBcO3rhjDuqZwxgR79MjsA2ZYxt+sGpybNO6rpPHHTPH4IQa2LCZH/bR6ZljmNdezDH3so6nGZaZlJA5RpqHzxwhNoWOWWAf5vBw7h5GzjGHylVXvmBzmYscjCwDFKckq7Ass3aaPsUvjGY3aoi32qbWX8a158Os5nxFKoWbE8yeG250ug5Sp+Ph7VzOtb/JqyZ3eT3F7SciXZXbR7lzRaEJUoaeR29VJaywBsfGi9HXa/QdV7CmGLjQDCyoGOXNTY35klDeeKPUUWrxG8yqYpBZSpBXsra2ZAQQDvE+YBSPo6btT0uOucB7ggZDlW3p03Pqw12nP0vqFbDzVVaFxajR/uZnvWhVRoIQ1zoUvwcQh9pRQ1t6KamwLI+JbCpYfuKkS6dzhC0Mjk9zz7b9dpZ8Qu/zt8giZyT+RFv7Qmn9uVg8692kwDvleJ6OztnY2U/5adBOzNH4YcAccY5C0qE8yR7je+bQWfvZlDnmCL9zXRJzICpVzFEPc7v9LHUTIXPYXWw0uajUbsyhOkRkA1J2PJULQcf1xn22tMz8NaKY1sOF9DRUveoUGrGGx23HHNE++Mu2p/J7gs86m5kDwxhCKuZgsO3CMkfQqZnDz4E55oDWmmEO2oI58FaM+cBveRfmCHLgR+tykX6sUMIX7ZU0dUxXcryAKnXdQD3Tp3CI2H2HDjzkJUU/07/PqIlwbYMqHhedxE5+qmvUM8gINzexhar2mXTiHsrZFMYYZhgMyZmx3fAFC1iPrHQ3W9NIs98kBgffSGOFebVvegyCbaBTHtZigAthZrQQGOp4M+Ujo7VxtfrUGvWMtYtjoo0raleAMN7FA51jb1Pt2Ku88JWfHpTn8zt9GdVvinOS7fGn10DYr17RiGiY86p1vO22nX55wLLAwiIXUThe5WGhgphwyByD7nIcl7JHtzu+DKTMj6nuPQJzJBYYMgern9mOORh0Aj8EGwWbzoqdljDomcNy55QNMENmGAHmqHxnxT324slO/KALiUY/EQ0v+FIGC39aWxdlmQ0j35l3V3O1VVHW+Lw6Heao7FUdhrSe14gfcn7Y3ofKHHrWnS5zuNT3D1nH2n6GOfbYpOz8ZGGOIMsAxVlKfoE/xI10BmHhhn37vPSCjmMO4PTTSV4+ZcHw+o5+bEOlpJC0we5SZ9SZM3ZcaFm+kZUWKpBnZSnTqm8xc+5Z9PlUzzc6mJBmE5S2uVuoHXsLn4GYfWXsJD3Xsp126DB3eYDu7bh9zbZx6HP5jLXPBxQ2idh/Y6kv11pptpaDrUQVyS/84MQii1xUKUcKcv9QOdUWM+eH5zqFXBYE22Q+SSnQQba/6oFIJ11fMtYpX2EsfH2wd9w81HmWzC8dMvV+HeOrG2a9KeT8pInbDXMe0KB9+GGDLx+wEg6O1Hr9r0r6Otc5IHPEeGjoHZljO344LamGiTaVed7MoedCCbuzMmKOcY0GGc68IjzWmTVtkQOXZYDiDERH/cJbAER+U5yHbUUHU6W7wPCmu7z22AZio05p2GTUeKohFLRL/8h9fjLzi0Qdxw43SqDDKTx8DW2rFenLUXiq7dY3Qz2WW35Vbnq8OdmifwoNwSkVpwLqUVVXSLTug6bUczefua0wrjhiup0qdzp1GZlF+wR9g1fAVJWVL5laB7foGp2B1RZmer1mnTyUdPYyW5/hrhxObf25urmsIqLMS/sTouKaLVjy0AVvqvb9W2SRMxfo3DNz+Ct/RcfbOvncJ1R9bZb8yuDogYIaxZJ0rGPIBmPZc3eeuzGHb9gw9o+j0rrZAQPmUEag8DgC8gF+6GP7nbfmmEMDRvwwYiWty7rT8X1TRsxRtbf6IfQ3wT+PmENySM0ctXZv+ybm2I4fYvg21wFp+fDZ61xM5qismN8JrGaOIT8U+W/SYerbrNJZmONiyjJAcUry0EN/R0TTBaej8SxinqC7nOEKcifXbhVHDrDUGTsCS2867tVcR4IjxEzCYkz6vXAs+vJi55xA2zTwvQrJLgBuhgXaIb1YGjo1HuhAPUU4rgAs0cB1iIj5OfgJqXufyhHIii0ub4GE0BG7dv+/2jZJD3A/xY6UPrpBiin9Km3MUrwKkvPrYqfXUfQd5r4PhGcsPJXgmu2v9BDw3izkVAEYZ50iDsNGzi9v72Xf0pMVtzuuWB2B4Wy9QVXeZB9RHpwQmquv6mq145CUp+ufcWUWkJaRbgVWwUC5cC0RrekrXX0OSuQE/hZZ5IzkK195bPrSfIUxB7jLcEpmfoD9tbdmDqJytmLw+yPmaM7Op9AnQ9P1E/kjSWYOoeTnInPUr4tqSq9EZg73NTLUWWNUxxxaRwmDKt7eyBxxp4J55iCLG227WPGD6pP0/MCk7bYfc6DfjONTEgzk+J/W3Jgj8GIaLvBTJabZlTkoJc07iYyYY5NcZObobY9l58GJyBwYgzr6LTNHNQw0/Z5jDtzafBvmuHTpS109D0oW5giyDFCcknz+bx4md2nRseI1Wp5PHC/TbTvD3dLWI/TTAlZSZjTqHG27rrAgI46W9LZZd4SgoO0Rtl91oME+DBcU6lyM7pltd7v6PKDVu62Iacci9I1KFNP3lR0MXbm4vn0WXhXh0TZ1obmTRpzzwQ1NiDMWRm9D+loK7vuxUivTL+2T2Lyu15c1V0MiCk885s5FtE7tHuU51JlJv8s1Q+SZ5TK2VDt1GV170zm5hxVi/2lOQ0Rwnb7FESnxkkRgRw2Mu3Tpb3c2e5FFFqnlS196xP1ZYovRdxOe/VlKxQ/jAqaIkjlwjafc8Q4McRc6YI6EH0yAFRVzYK47MEdem8pwQfAVTxnq2OsnsNhFzxyhxsYmyBzFEALFwY7MD9gYUH5gDr3lrDYkBYYaMgdTHjqI9o3PM6SmKH1JXITP5VkxR8cPHMOwDUc6O3vhxBxVbWfUzlXm148Y2DjLHIP8RszBPrtnxBw5jono0qXPztq9yGHJMkBxSvKNz39G5wp0pWXzSRIuOdKvHJQKKeKyTtXR9U6Mu7yk2bnJhAAB0R/iF/P19nZFsgX39p78NtO0KKU+KYhK6j69mORGBHVa/Xi68denCOjSwpJEenMtTGJtIF6n5onjruOtJKjg2mxFe1YUR4nR7hXZUxGsF6+IwlKaiA35GCGR4VMPhQx1B/3TCOEeQOLvHM9d2up7fwZ1KGj55ndDK81cToFupQj8QxnDwDytY37bAMVJSlmPcptRsobp4ZLsPMGc8aP4AadgD4PVjhwde6e4K4+e3UccksgJ/C2yyBnJDTd8nV+eQrarDt5TuxeJFyX2F2WfNjqXq/DCz3tEzxwWJ9QNVAeHUEINJ+6IzBFmgph+wRwEft325+TIHHZd4w228gr8JrJ2d+ZAjdxAQgwzJdYQPsccDlU6K4A75qj4YZJV04GsRAc9dEnKrDPgwoI5fD2MAXNg9anqNvMta2aOniWqX7swR5b61K7bIJ+e+zEHDxMeJnP4njt2XkvUi0oUYCCsIQJaWYdHzbKBOarjd9VV/18RekCyMEeQZYDilCV2zxLvI+2ClhBn51gLKnfkGIg60e3PU6buBdIWXm+T1WRN/QJOqimFa2MigddbfJElgTbwT7v5FK+TCdRRiGIktmH3fkcBZUTNqYOTCfxQbI3ZPoVg680AP0xrc2RxOqkoJQa7RVui/QlJsqE6DLYd2HSWpFiGbUF79emQ92cJNjUT2BNKkCJ9NWjBtqq5bRWFenqSJAeU4YVikqKmtU6WeopwDWEazHx4q2pXErb8onSuDpX0QvXzLx6LEQAw6ZZfor8t3I0Ixy8d64vgR5f3QRe5kGI3x6331stTL3BkDpXUgY52AavEb9y3NjAyB/hHtrhehdbQJyUfOnnB2GMNfYbZOmaOtSStxgdr6V8R2IY5OCYlPQ5CGOE+dE3+G3cSCa9wxLu1rfghPVYxhqGkk1OGvKy1x8xRSfD35iM4vCbiTCZWFw0h+FbP6pjCbSeVM2SOkavdhjmMty4cc7T/7PyX7pxUQaZU5hDejjlUMTOkGbElc1wEWZgjyjJAcSaiXXnwGvE7Vx3ZTOcnlZPCDiCF4Y+cIU8Z1uMNMtQZdUjDmyMm0q24DIRMh4d1CY6lyzu1EHgNYWxWCf93g6/DWSP+5ILzgWnGjfpAGa1U3OCHqyPdnorkF0bEdHJFp8jeUavt4/MnitdiesOnctX1DevkrONrHd7sI0dLs22XS/PDOv/6yO6+aLzl1yHBQJbqlQ98grGN5b6OzHQEd6std+e3HZf5lTcXWWSRUxWkjcFVrQ6t6NrnbriqLO2m8qSZoyi3f/0U+UG69ETuzziHF8wR4guHZotN1s5pyBwdP1DucbGg6U9kUA7NPDoCfkiFUTyyfsuoA1f1MR8xzK7MwWCDhxk/tDjB9OGTuhj93rWi1DYclzkq2W9GQ33kD505KtH2tu9z6VBkYt2yHQYyDQzGs25hjstbrjhvA76qpLiPzuHq5CyKx9dXFVcNDnT6ufcWIlrpjeZABjpVSTxKrzZLgwllDZ7cms1YFJq2GgvxMTNpbLGiNJIPfq23Q+1jpZZO2JO1TKYfq5yXebvaTRORvyYCztxX086tzUS0bjoS1gGNMzAmI2ygB0DD3wXVORVCR7OeONqg31bccrAm0oMnqDwdJ23TwTmn6330IvZaQJfCl1iP5pKfB73OoJiN0mMMV8FWTL1d21nLpi3jqut4bPtgZfiNNqRBtI157H2QzkeEhwNsW+svssiZCzxdHjFHFug85pijunytnLmy5pij6hYG/DBmDql1WPspBQt0fYk5UF2ZwwLF7rFXuefbijmKOrF+cLzBQsaoHB1Xe3a1b+3dfFyHUo/nUWAlzRwGicT78Mgc7bMNmLhJkTmUYeqtaEPDd5bbWhySYgbMwTGIOGoNnrzuzxyl5xIqX23cLL0OVr+/FA6DOSqJvFnFD2z3W4CU17wszLGF/mUkywyKMxJzxBAQxqCTf1KnPxwYHMXN3WkMHZ6Yc8JwX3E65QNOvS+g7jQkJBkYWL8w2pxi0mk2dKtIq18jb0OMm1woDfusrvOTQVWtnPqGj2hq1n6VayJipnXIkYNO13YSP7EYm4Ei+eg1xzo6F6SOsym2pbeJOqEGxUGfoHFkQzuBpTtEk9Mv6tOXijo0c7H0gvn15/2UoFoI6mBBoZ2sDTmrFJ3ten7r915jdJ16WZ0Rg2Og7zhvhyEHInICf4ssctZiO05wd9+Lu2h0LrUNMuzEHNXdYWdPEVAyx0yfg3EhyYA5MO+COex+dFDc5IJ65zTPArUdJYvYz36XDBkfohaefVDkh274otW1Z5F55rB1N9Bbov8vTgYd1Cmb1Vigt5+PwRxla5wocwzGz47FHAV1DPzqZckcFT8M81HF6tqLzJFvbRbmuNiyDFCcktx779/AOaOOAK4w7ajhisJpbjgw251zDB/os3Ovm5Q5hXWvaKgzMsBJovpZxzxqTGDXi8KAeXB0Bri+Afe2saZn64uMG9ogRtgWFJqUoe/CNlW/MpmuPZ/Q9ERgWj9irdPeoU6+oOcUrutUEzQBkZeBI/zRsRORrAAYoL1t+y72911bHUXtErR/SovvcgrpGtrSmjS/N8tQspdjttqfFDo82ZKchb8T6hFh/Y6i80S7EMoI2mHSxamA9ZrgUuh4fP1Op6RvAt+83aWYVshJ8+wk18W3JBOoRW9X1tGGV0jGdh24e+waMGiCDfE2y4car0E8ko8/8cBsXc9blvdBF7lI8uCDX5q+qN/UHhYZQeOb2LXJiTmqTtazJcu2Ikhkjioy8YNYxzIzXZ4pPK1m4AcsCfuenjmgPEAQ75+cOXTByTX42WC26fbMofE1c3h/rZmIsPn3iTnUbO2XpTEHDZhjYhdkjjXB7Aa9LxfeyBxEsHaVAIdIbC9kDqtrs5bBkDE/xLUn5pjDjsOQOShEdMwhhU46Ryt+qHFaOh0rJuS5iTlivTVv9Kco58kcXfl7MIclsOtoS+agfpC1GVEyh54LhjgUe5XHHv/kpqqeqyzMEWUZoDglecpTriKidhnqQkkMHRsR5ctSL1xOF+Q246ecPrsIFBlHMXE9bU1oOJ1N7F1WjOfyaw70MdY4WKOvQkz/wyCGlTMoK3STXKbAENYBkNSCYftT+CbhC9o7Okq6MGZlb73QE9IgbqQkoEPBPrdf86pnbqBTH6yt0dkwdkJ1jdmctqb3OmanW//oHN3ovBuEZdjYVgz8tkrrbvYspV8ZO55D9fmUVaynaZ+p/9F06VMX39NUpsMcfsf8PI8ctuInb7B0kUUW2Vae/OQr4w3chs4peD2JYRv7NS76BxoFUL6zTDYUzLGhI5OydM7jFb00frAbc6wHMEd1u1W9Md/PwqhZz29r2Zgj1EFfzSiZZcpMfCSllbGBe3AHF+jt55jDGcPztu8D5tCPfJOpnJKZwy2qbfDDWM0mGMgccwxQdMQPU3b1IqGzVwZv9r2j3LIPr9OeD3N0Mssc8+2DV+1WzDEIm2UOOC07Dln9g5mKLXJosgxQnJI8/elf6xcGPqagdOG0XrzrlKIvKuO2De9uvNDRJFjADaKy341bO80Rh3dZZdmJI8aOwB1t7vfWoUgdwOD25EMj4lBQfAqi7qtw2GisVI7Dn67Yb3EbYtslcJAWy92mYXATX4Vre3jL4fPvXiIM6O/NN7BxbxCHDccbdALZylh+SlEYmoOkypq6Vtwqzo9b3PIrfseBr5xr3Voy+HdaMngJxX7ZNVZOb6jzwFNDMB7yiENT7VvQIdcpTgqHvcIsITo6upYOWuQE/hZZ5Izkmmue4r296NC/+86SOZIbH/aHWzBHdVNRMkeXX8Ec+c4CUpUGwXz/02IOn7ThN+CdT839Y1hAMDJHqhzZwxqJ3Ye/FoEzLbZjDn1No2IOL2PUWU35MvyOVJBPkn4QBwcnsm1Yea9BHBgJLLIFc3Svt2zJHKO4fXTUn25ijuECsReIOTpbC34IckzmCOXswBwaeeXRdUXEAcnCHEGWAYqzEJ2Wn26Y/cpz54oJeI1AkfNsn1uAxPw5y6QzCIJNRITj771WTucllSOXyaDx3sZVadLpEJO/7lDoC3m/51wx2KKL8fqOx0Egv85Ogx7U6TEh6jU3nPv1OZ2q/EK3dpSVzA9UiG4/mU5I4KQqywCHXg6DDnc6eYfbETxWAw+l7Rt/b3CgoJevh01yGsBQAQ7+Dtcekw1SdK9vUf+ucwuOLB6OxaDunQ5D+GAGVrpJ2KFZz08mpt9/quVlBguLXBARvTEV/aHB1DHHKuodhzmkSFqLdgZ7MkfnwIlIes0TYY6Ubo0rSkK48UN6t17fLOlmNG7BHJa0YJVs3IgfuGUgyWSU+rCOHrKgb+wzLOva6RVxLLbwOWrsyhxszKERm5ljJPsyRw4bMcfgHno744r8TkrmmIMockFXz1NmDku8A3PEi+HAZWGOIMsAxZmKX5XdtYIvK7r/Ln2xSjUCW90AVDfWsWw9w8tSBganzIV8IGbmIpkWbOod3qz3THChxbJ1iNBYttIXwE4L464qMb5bByEZMhXRngqIfjbLdfXduAF553C9QvoNe1EudKZ6zA4pDA4d6njJW8yiiOzV/qp9zzfbMCppgjn9np8K1OCjabNOnXd7T7J4nzOm3WebsLORTfARnXk729PrG+P0UM5gwapZ28rFvDyuVhqz5iKLLHJCwsnHbWIOjdyCOarw6mrvyyp+b2KOURQuxLBJR3ZkDnTRRWelMxG8sbgNjjhz2ELTPpoA98o1c5SmazHKHHrzrZ5B/IBpr1/74eb5gTkCd8zwQ+XzuWU56sv7WRrbMUf0Zsdljv54j5hjNHSyDXNYnK6JgAulDc2+mMxRM8Wm+O2ZY1Ob6Boulc4cc+wMN4scjCwDFKctYv+hv7IrC++pg4p+48H1xfGC7i52GcflfOZfFagj6o5MPHnlvZrTr5cfKlS02XhgG1NZuW7/8JaXOxyKAwngcessJXy4zhTEKVLdWedc2VtIiuMjJB1IzXXbFjPUmb8dHDLoCBp5/HyrGkCawotXPTYZUBxTLr5lldFgBRU2YG5l3Gih2A1yUr5wfnZHtXWXQuv2FlgZcC73aQYyuFGZ72vitX7wIifwt8giZy3hNccimqh7uJGZo5QNF+2QR2YGNeosecgPo4GJIXPQZuaonvRUWVkZA+boA/PvzBz4tfeSDNtrCOjUzMFUMQfWdI2ZWHx1vLfonQc6syQpo7rOM8cox9EyU+P1I8am7cscw/ARt9Nm5tjVcZzULIo9HlMcmzliXPzspFgBMrvZ/rS8YE54YY4gywDFKUkEhMnh2vRJIhsNtJvvdaHftj7qOmJ1VNIFjW82rOAc6TfF2bX5O2IxMtwgDfxy3rVBnwZ0hpPDQFyluYFHaz9cVVmdXI0Vk1ObZjjUDYQjsZOtk3Hlftfm1JPT0xtY1h0/8ghTfv4AsMHxcKPTzohhb3AWXg2n+ffvCPqxq/osdfv9qcU03mY0HyPISygNukhQyK/bENH4QcOGbUb79yP9s3SyOpOiKGok3B431c0wl9PxcWH3wQmL7Kht1hZt/wEzdNcCaVOOGsZnq6QuppUzRLPDlAUWFrmI0vz51JdLYAJjjuL8RObo82xpxs5kfLrnsmAthJgfPNHOOoEfBkXsyRzumFscMEcua3i73KZbrPvENGYOvZlOOz4UzMEWPmKOljdX/OAuPXOCpor4GY9P9rbSfcO8qDx8k92TIdl/KXOUM1gBI6KOh3c0sBdz0I7MAcdy5oTchzn2Gb4/LnXsrV8wx3x67q7TLrsctgNz5GO9MMfFlmWA4pTk4x9/0H+s1Emk3jE/xVAvG65Sv5utOunQCVf9hHhn3o+dt7JsXyktXwGnz2vKpzI8W4ZbQMEgixoqEm9oBeMdLmzGHDGt1fG0KZb5ulyLT56bwvrtqTTctuWU3DAMZZKNhPufhHI0Y9uOLLdZ0xHRZp4GTyYo4GY3Udytg+3YmN1CYdzAAMdQIm6fZdMq29OjtZWLnCdBp29TgbaDlbgVGlKbel2r9c4ptGs8bhzaT0gCaFU3vWvC0zZP1hRakyT7yKdhDo6Uv2PZ7K5GhcjP//xqikPLOvzeVqrpozms21ccLiLmeugioWzh7GOK8hwGner6xymbQb+89qbvjz1xX1HSIossso/c96kvTF/sApwuPu5Hww0K7AY9uMHgaEIZG5kjda3xZhOYQ1lCtG8pmAM7jlQOS+6ljsEcfbLQV03lT/3b5HP8+zr1e+bPJbaTM0e7oUo3dWtIm5ljTRLLmWMO87E9c+DOXcpUPoShPFQ0q9piD3I0LDKHpWOMS8wBD2cyVxC53dFX+HEQ6XWIlDkKn741c5Axh9e1Yo7IGZYf047MofmPmaOalTNmjp4VtpFNzFHlVzFH4AuaYw6cr73FwEZgjjggN8sceD3BPcHfP/YXm8tc5GBkGaA4JfmGb3ia/9BOuhv1dlrw8QhuFKAxrqLvRIaLMvcMWThfrBCRzagKySrcd1rCDAkKY2yaHroktqmX3ol4+9iiN/DBNLVNnLUFWNXdT4Ky9B0nqxLwgla/X8SHvR7oRChlGmrv73oypycH1R084bHlsEBnTqxPXlDPzwesIbiDDHnQPtG9w2AJo0vBQZTKtpY2PcXJ52vldkeD8JNOjx7dccQwwUGEkXCoVa/juXYQuEFSzjvLTqAR2rPW6nIrzteZF3Gm+HBBRFCl0SJVWlYRxER05dEza50DkfPYk/wDH/gA3XrrrfSsZz2LmJn+4A/+YKPO+9//fvrH//gf09VXX03f/M3fTHfddVeX5h3veAc973nPoyc96Ul000030Yc+9KHdjVvkoOWGZz7VfzQ/0A8mInO0dZnatS1Rtf2I/ahQuqQHN7QaHvudxBw8ffIcc5BGJeboev+ssw9zUDdoYa5fsjdVnxrtRp862dn7Kk4zY5mp93/AHLgN65D9CPyWsgFHHxQXGQRmwDxmmCNvL4l2xOMR2zSL01P2OmzcU+XnZRXnwuC1jn2YQw2vmQPPAPjcmTm4Yw7krzH31EafFnOU+UF7+k4yVX5RZ445hm3GMcU+zIH92lVXfuOopIOQhTmiLAMUpyRPuvpK+DW5KXwCrl0uDh5UN1qikRonwXeVHWWWfOPuNnCh4wUEZ53ynZvepx252So0jeBL3YH7bAXIVYiGrxqgdw3Gt/xt9FsodFbSP/FQvfV6ylAkjeBj79tmTphzhfpNx0qPKkABHF/Ryhb16QckPK8pznt4XORqDb8l6KSBp9gM1j7YFkIcJ9OAziWittK2hPW/hJjWDTQlOAy3wUznesQ7HaLQftnubJt/h3OL6zrEvFLdO52+pOzAK+c991RDiGgNsyvykwvMeRvJOvWZpf/DNaFxMOOiLz11GtrKAAx+rU7fVvA0hSFc+6nAKELEjH3kIkREjzzyCL3whS+kd7zjHVul/+QnP0nf//3fTy9/+cvpnnvuoX/2z/4Z/diP/Rj90R/9kaV597vfTbfffju9+c1vpo9+9KP0whe+kF75ylfSZz/72dOqxiLnIFdcMeEcExG3R/J52QOGWQTaV7rPppo5yK9bXDhyyBzseZmIRg2Yo8WW7rHpzTKH1mHIHLFPHTHHeFnGEXM0VZ3lGvp0LpjDic2Zg+BpcLMZnFPwRAKsKNHnh/bAVhq0NfqZTcwRwtOf6+H5JkX+2kLoYSNzoKyJGlucPHOgXcdlDqKKH6q8NjEH0aikOTkJ5th2YKPjlHr8giJzQBwX5wT2P8dlDiunZo4VXz2u3FepHDJzXLFT6kX2FDaPyCvsyKeLy6b8jfqI6Hus3xrdgGZREOGmE2898i2K04eO8gpJGMrCKe5QixYJ60WsCPIad7Q6I0GEaBXqpE6u0G3t172i0cLVBk69lrvS2CGy2s0UOkR/grQOeall62Zzfh1AhCfHSumwarru1RLVIVp19Z1sq4/3ikTWxekztU2/dVe03/HDQ6Vw10zc6lq48lbX/nlIO39MZ8sTtm+1DXZHnay9Tjpm20xZUQfznlkDItgZ0/Wu2tNV37fJH39tFq7L0v6gvMRQJ17j1m+132qFPenL05gH3cwiUV71qlfRq171qq3T33nnnfT85z+ffumXfomIiL7t276N/uRP/oR++Zd/mV75ylcSEdHb3vY2ev3rX0+ve93rTOc973kPvfOd76Q3vvGNJ1+JRc5fdDqAUHSqPM8clbc15uDtehoiZxNb5zE8CmuR5gMZYmrm0LhRfyQk07hB4JRxH6nM0r9t0dZ0EkksQm6rUBykGFSHGz8EzsDsjDkE+EFaEbUfnrJq/lbL1dYwP9wln8Iq5iCGNog3n5k51IcLkbNSbqCtmIPJhwTmmaOyObRBwUpzzKGpAU0tZuTZt2GOLPswR3SNJ8Ec9dV6cMwRzuN55iAdyBswR+i/FubYSg6ZOZYZFGcpwzllgx4dpfmupFUms3QS/8ZP1GXY39gsBMzLS5o6YUl/GtHpuOW2FkOYJeHvLeL7klNpbbRe8N3G3umHUmrvPq9DalNzL4HO0l/pAutcsw1zg0voujflhOlVZ3uXk9s54ql08aqjZWbnD7M3Qk45D7S2lnDq2oukUt9Jb85h5hhxmV5tcxv8x/btm+05GdH3OfP7tfuWPrcN6z46s1t+XSSpTold/4jooYceCn+PPvroiZn4wQ9+kG655ZYQ9spXvpI++MEPEhHRY489Rh/5yEdCmtVqRbfccoulWeQylh2ZQ/KP4zCHfpTlzPX9BXNAIUxtp9E55ugkMgf6JqKeOfxVEG5xp8ccxhlpa1JXz8wx58+wXphqzBySvufi55ij6O422qUeObdKPzsjWrANc8Ry5mYV1oLMsc2WoYWm/Y3KwbUzTpc5Tk72YY6N+Z2gzsIcFA7K5cIcywDFWQi8C6DXC4PDleGLQ31PsM32oSMHM+cgu7xEY7hUHg1sjxLM3XRL8c0t02DpUpT1b+1VTkiRKhcffcVPrcL0yaWTklbHNeSJh6uCM1grqBMuwkPf0+nom7VQXtArYAcSVK8I6JObwvRmf3921bapDTKIE5t+OzSwy6uPkhBfV5ZbXROuhc/agoENUmtt8zRi33dEQxkzh3Vcbq1RnaMxdhBZBBu+D94Hvmhbfp3U+6A33ngjXXvttfb31re+9cRsfOCBB+j6668PYddffz099NBD9Hd/93f0+c9/ni5dulSmeeCBB07MjkUOTNT/ditBBu80SbiWpetcjsUcw+5jPKdSZ1EEu5oLnu1CUl2NOXbwMxY0cNI9P3j6sI6Vli/TZzk8P2AOK7pkjilqncPxk2MYybA6BMkLfogm9IyY25VpV+bAskaKY+boj+s8c5C3BYYNfoWQPZkj5iXdt768Q2QO2os5hsez5ZeFi285QdXPKHOUJV6wWRMLc0RZXvE4Jfn7v3/cf6jjar2Ndf4tzjiinVwex00JRpnjXVb/FfPrH9ynn1NAXKNKpkJw61HV03QittCTVywXokQxCLd2gXFxfUJh4ahDEIdl4vTAqeUUENbC0+sSoKOokN/Gw0Wo1mCT2Oe0i8gK2Yn9xxoBBehKRGjN7YlPq0p+RSbUldUGbqOH7Rg1nTUp3E210PqJvXiJOn4M+467wZMdZ2zvFa1pTUxER5CfGrgWoSPSc9GnUgbQsv+1HaY2sKm/7UTV87Q+VeM0Tf9/GtzI8BshB3TauZqnnuqUyMrJc0iDNWnA2YAB36nsdtgogWe76ZqViMRzdntgqLUkQY+ET6w52kBdmB5EXOE8R7crk/ClJ6HH6eBldyrr5L777qNrrrnGfl999fIe7CInL48/fomI2rWW30e0cDI/jher80PBHFCGvbIB4dYv7sAcxO4+vU+IPTh2usYcJH3BIaDq0bKxY+YgIaJVS1Eyh/tubCEblNDtzbn5X9ZelCmvAYC+Y03SPS1U5uABP6zFuSUyB9GaIT8mQru9QcSiIj9o6c4cRNxYYNp7zPGPU35iDNN5uXbA19z4AW3o/HCgswFzUDGA0jNHsEdPIfbLIOvvzxzkOtpAXXpt195/V8zhpTs3nxVz9DuZ9LmXx3lGa5456vZ05oBWae8l6foumTtwlkcMP7mZBKcmC3OYLAMUpyS25VclrWdMC0BPov57pV9rUNCYLgw6X72uA5S4Z/ZMMD8hv/nPPXhT41Xq3bGXKnWECOGi6NGEmJi9xpMdo07eHUcaZmjOIdWtKTCGhajeHaATc4kQ5fmxtanVQG/8WYdEHCFyE/jQQDzi/p6nOqZYJUK9FmdrYmga+O41RChzCKraoWPB1ApRZxK1IbdelZf+yAMVnHSingMIjBF1EmyeXbJ7XkbHDW04bfFtZXfzX1nLzoXBExmX8iJNcZ7DepRbF+zDYI8/8dXxBP+aa64JsHCS8sxnPpMefPDBEPbggw/SNddcQ09+8pPp6OiIjo6OyjTPfOZh76KyyG7ywANfsu8lW1TMgY4h3tEGPUs709eNmIPZbyxz9huZg7U+nTOOaeaYYyDOHHgDOq1lgYvsZRbAmynjhyFzQFyM6itJknxNLNHjlHOm0DURrQJzeLsK17em/kjH7Z502poYKdytiMxB5A9njqBtdOC/Yg58KFAyR2gg5KdKx20YMUe3HkRLN/9wxIXhfx0oOynmwHxG33PeZ8UcWO7oKqrPrai1DXMMy+iYw2W9x2sdjz7+yXHkZSSXC3Msr3icknzzN+P0FvOy02fzbFJdfK0nrReto+HFN+yvIEJShE2lxD/oynFWB4rg8CRR7C2DTnNm+rTenOLglRJxHea2T3VlovDwtUAhaLv2Oe29PdjnuwWFPcWx3FRVfVeVIK3+0HRrs6GRmb7DquFBx9vE07fRKcaVsrENmNrcCbOBdcVy04nl4z7SCg7e5Gztrp+CNlkYflY6eryxZSPQxXpAOek428reuV3hn+p0ZUgMQwn1TvnhE654vmU9tCE+Fdsk+86eYFi9Vf+fy6u3iNNnSwdPIFRxc18i8We48ZFOB0tGULzqihuH9h+E9Cfq7n+nLDfffDPdfffdIeyP//iP6eabbyYioquuuope8pKXhDTr9ZruvvtuS7PI5SE33uhbm5v/mfHN4X6OPZ7zuTs4j8t+QiBC8mVQM0fwTaOy1zS+nlK9zKcl/9qpF9doeRlz8/uD8pUfQp2NOfLrBhx0kAsCI6Bp4j49sI2k5tFjrutVMOgE+9CvNz5owCOJObTtNC36RNNL/ODwNGaOmh+UBcJwAuSBHLQbc1Q2VPjbHQvTU3/fr7GVv2fp0yNr5Lm8tQ1EF4c5PMWYOTD9vBEx98wcseQ+Lp9JT7rqW7cp9fzkuLyx/Wmxt5wlcywDFKck4dqx3gZGlJnAWfdnlg5GlCOLxUmoTqN6xVR/112L32xGw/2mNndKPgK+qeMTq8f0043ikIoo7BMGOqP3DN25eVrjojyIAOFr+55zBacMbSAcnajGqWNfUwaXvGiT2I4t5mi5P9qdA8vV5vxTrKx156TBcVPuv/KimFhAtTBV7vsqcOhPSE/bLxY11492zr7g62xHBwr5MA7KG9sQY2Row1jnNGQ6jbi778hSdwED+/BVLSsEv3KX3KaB8ygulYbMuR8rnYuc1Pugu8jDDz9M99xzD91zzz1ENG3pdc8999C9995LRER33HEH/ciP/Iil//Ef/3H6xCc+QT/zMz9Df/mXf0m/+qu/Sr/zO79DP/VTP2Vpbr/9dvr1X/91+s3f/E36i7/4C/qJn/gJeuSRR2yF7UUuN/EV7xmZQ/83FtnhBB11oJk5is6+v+R3Zw4HoZNnjnxjNV6zIjJHSCVM64pVYEHOOeZgaANh4BRC/+M+uueHijkgDm7GoQodc4QalDeAkTmib0wDDlbmcZmDQhpJIdHKOeaoX+fchzn6OOpSHIc5xjbM6JyC7MMc0oXmTOPrwF15OzAHQVzOcWGO7eSQmePCDFC84x3voOc973n0pCc9iW666Sb60Ic+tJXeu971LmJm+if/5J+croEjsZtTmqbbmRcsEg5urMqOQcY3YyREErbbasGzJ29xU0yTOxBbJhvD687eDarjpjc26i59dE8+tLv1hrF9Wluv6zwNqCpP3FVTXWE/AKDhffkep9t0cafTHzR1qxmWTE9ieHbBfRNNea2JUk+NPfbgGHVuYhudcdwoZMNhLf3KnE4uDeFrJFsDQ9KpbTsjYDCqHkFaL92UXsxvdr7oKMcxZMz2MzKf61e7fPjDH6YXv/jF9OIXv5iIJkf/4he/mN70pjcREdH9999v4EBE9PznP5/e85730B//8R/TC1/4QvqlX/ol+o3f+A3b7ouI6NWvfjX94i/+Ir3pTW+iF73oRXTPPffQ+973vm4Rq0WiXFTmYIODxBxFyhFzjMgw+MGk1G9JCeMKfck06jGZqF44nKl3hP2PTmmOOYjIXi21OK77MOtyB8xBlWk0DVKsJbfDiDmi3XCIWvItmIOK59ySB0LUBh8MyceqbOpZ0cELamug7M4c2TbVyMMa+bXYXndsYyX7MAfalmt3Gsyxq85Jyb7MMbrKR8zhIfswxxajJ4uUcsjMcSHWoHj3u99Nt99+O915551000030dvf/nZ65StfSX/1V39F11133VDv//7f/0s//dM/Td/3fd93htbOSBoVxQjcBlsgffkuacvA/HW+O1t1STeIuCNsPS6OiBKED6Ubrcy0MuWk23f14+Z1EUzalwEEsM/f8H3T9dWQfCcPHauVreMnaTCF1YbqNjeNuWidFGaYbYEsfBqiqznbIWp60CTh8Nk7ox3trUPazFucwlprT4t65mn85G1avRfoC35RJwLv9aIteIxcTa0AfTw87f1fW7kc0o4G5YbT+rqS8gAL2gs4tPXLnLHBs6+uTvvTk3DGpND5IZw8sLi375ZqCU20YR4EL4Qc19g9dF/2spcNIYyI6K677ip1/vzP/3w239tuu41uu+223Q36KpXLhTnwGg1nlQBzEPT1TLD+VZJNzFHwzaZLoGaOmRu1xCmDHLdmjl5bInPYnSv2bM4c7scg08AcjFrQbhVzpB6VqX/O09hHWkPERTm9nsGn8tSmmTl8nQl1+DWz7cYcXo0hc2i+kuNcj5JePp98vdLtmMP988kyx6bXJ06SOVo2KYT6mS+nIrszB64Zsg1zbHSXezHHmEUOUhbmCHIhZlC87W1vo9e//vX0ute9jv7hP/yHdOedd9JTnvIUeuc73znUuXTpEv3QD/0Q/cIv/AJ94zd+4xlaW0jtewi73eFFO4oYDVxwrzN/8ySdo+nsK67xuIp1jOkiuryli85upsy3UOpaDlhB9AuaxtMq0lLolBaMSAs6kl5nBrCESN9jjDIZylJsISbuVKuDWOmgmX4CYp7VO7UwCFQKp8/2fRpZ2dHxCOlaInk4yCC4Kn6mbcenXT+d2Q6f5HeEMc24RrWTpNmO/iTE9iMPpW4HCvk70bzrHtZ/cN5PNy4DIDiu4z0HOY/ploschlx05shr9BAlH1tMiTbdOeYY9MtD5oAiY4B0CTvm6LRGzJEL7PW3Z44MDPkn9wsCzzLHfB+qX0seqVTFBxyC1dmGZALDayOgZbq9ztTa+zAHl6uyMzBH4odWr7K+djO/G3PUto2Zg2g/5qiM9sN30szR50ea34yFx5XDYY56HRmeZY7qijhcWZgjysEPUDz22GP0kY98hG655RYLW61WdMstt9AHP/jBod6//tf/mq677jr60R/90a3KefTRR+mhhx4Kf8eRz33uy0SULmLomKyzwpHjnib6cIjDxawwr9HItqUXiOl0hPD9VA5JvRMYzTIoX3ZUnearsE0CdMDiDKgulG6om5OeOk0Ehv8/e38fu9121vWi32ve66Ut2NXCsq92U17cIBGoadNaojkaG4oxComaYqKtjdRE0z9Md4KSaFExKaAhjQmxHiwiUQ/EHV/+MKnGHmtC7Kb7QIhozuZABdoCa/UF2tLFZq313PM6f4zrfYwx7/v+Pc/veX6/1Tme/J77vucY1xjXGHPMcX3meG1PaNQ5Tq1UGZJRB9tYqjzVaTpm6J22dZd2RlXcvMpzFfe/WO03mQ7MsP0w4lRFCvfDNomEgoJMn7RG19QDZNaFHAAWSpSsfBhxUy6firkqPFhasLCr5jUkpnlyGbcD7hfTgaRTq4XLr+lqBoY40qEFmNINXqPv+Rrb/zkcg5nlyKq6aVXczgqdT/7UusrDmSlVn5HflkyeCVO32Ir1L9adrLHHM8eEfDei6zu1tMnQqjvaRqtuNKvuzvFzUx12t7sH5W4rc3z+878NQFlAX/zmzNFthhmuc33DG7DFWcxRL9TWhfuWbNYy9cxB+edIxt5/KQQdM0eMaMocoonZLN5mDuUHtWu2aWY92aPMcozMkTghbGKZmcPzyXC7rpE1W7oYI6TQgTm0fLRzIjEHB90Kc6yROcjtfdoIVJlD4uuYw+LJNUD54VzmsLTDNRT56YCOMMcab88Gc8RurrHt3maO+C/KjJmjPitB8hqYI3LMXTMHgK2ZI1djDr+vW8wRn/07dz451WF3N8/d+A6KT3/60zgej93alZe+9KV44onxMXU/+ZM/ife///344R/+4bPTec973oPHHnvM/l71qrvbYf63/+943m5+oRw1CGYyuPc/p1ds0s/cXevkuripE0ggM2zNJp0Sk5Yv7tqdujy60RRXp3VuhKY7Ag/HsRDVnxDP5Ux+BGjVZ2J5CQ9/1vkheQPB57BS0yPljQBaSrMNC29gEvRgiTODRe4hzh0I+rekeOJd1rrVY56ACdW1ni1PseMi6q1lEePrTxSJaeUwUa/oeqM76sCohq93syp3yiBjEIZd/YtcBp0+1goa2/nhoYzH1Kc0O1O9jze4urllCT8thlrn9f/N9aT+O/4BAPNvz1K6GW524y75292tc7eWOX77WatzceA5MccE9qM9Y5zXFFa+OLf57NuGQftRmWPEKaMHbPLMjS3SZH+OxBzhcfY3I9gLtrKG2cvMHGQ2NNhxwoA5HMV9kMGZI7Xh8pVpSUUU5YF68En/kr7FHPnkjkX2lYh5BRgLKL1AZn7Q8sm3ZOl0yLLxOoVymjNHfQlNneTs9nHGFsNrnd4SZrBc9lQzv8Uc1caf47Zny95L5vA4o/5XYg7kNqYSyOY7yuh3XJ+2EY7K9ZW/sJHSDXA7cyR34zsoLnW/9Vu/hb/wF/4CfviHfxiPP/742XLf/d3fjc997nP29/GPf/yu9PiK/6WlHQ2tPob2/ozmp+clq1FML+f1CQPmlbA0AKNgtWGwxtjSyXhiRpik0dngAm3q7Jo28mqwGdbwV8OpPddbeYqHq7uRyi/R2vttDS67n56bzOKvR3qa3mGoSY0xh/Aso1Ejw15HpbUH3vOlox5u0P36ksokGmggjqC4ofaOjwwbuXxC4ae4yxXqw6HEOTbKPTi4uRsZYI1zbFq22tlRXOriyE+vRZHl7bgiLHCKYxBfAFGHwWDsWf9CipznxqR0eSyj9S6XRF9OW8gQ/WINiUtS2P6rpaHPMXediKxxUC9j4Uawx8DDD718qu+NcH3Fvvxvd895d1OY42UveyxxhRv2AOvy0EbmMM7g/GKe3Kwun6jj6T1cUkyqhesU7UD0M35w+5zb8dBOlbammz0SrkfbPMoTFyG3eRnSmHUAYsQcLu3MofzAiDCQbCqLhLX9YbZmyITaVAB+ikiM9mzm6Pmh55ywCWYsE3JOycWY7XpfxCO7P8inpT2WCaXbXyfu4oufW011n4bmNacbU78Kc9T4rsocAO4pcyT9BrN1PP9jN2OOLt/M9Ur4xunx1K/nMMfIPfLwA17uf8qNGOLSv+eQu/GbZD7++OM4HA548skn0/Unn3wSL3vZy7rwH/3oR/HLv/zL+JN/8k/atXVtTedDDz2En//5n8dXf/VXd3KPPvooHn300XusPcITBWBhv8Dwxk4fttGZ5bOuRfUbgUQ00rXCdvG1wL6usfQqAOimWeibia5XWLLnCp6qrUn0DUifBhfvZcmhVwYO4+7VlgyvqbhBrZNioZYlhxmBDAKWKoNmCA4pftfPblG8rVJALJp0t5VC8bm2ZnQW5PGApgNjsVhzhpsefb/6CoLO6zgk5ZvhdpCN+hFIjPpyhf0URt0cocKkUEyaYg7N8CpV/WbprCJTw7ScxHznR3IUX33sYhw1PhQ5j4vTNV6RuTbEHV71T8qMACdigOo3unND3eWZsLYowcBkcyvRadX1nem2SqmXXbVTJ8VWu7a73T1gd9uZQ096ALNtENmuA9Y6rWK3FyqymxGPn9vKHCdlAnMo+3DWbzRt1NsuLnEOmGOgZ+pcTf653d2IAiu3IuvTmjEHcFRO4QYexhVijBYpA7UCrhUZP4za/fyCRtDFklEmWpbMHK5zs51rF7EyR19DZsxBxi9uw0OHAs24UIAMJPsJRK9hzs9wc+YY7Uei+jor9aluMQelz8uZo7obxxwlkrthjnpHL2GO1radxxxTeNzdrXE3fgbFI488gte+9rX44Ac/aNfWdcUHP/hBvPGNb+zCf93XfR1+7ud+zs51/dmf/Vn8qT/1p/BH/+gfxc/+7M/e9TTKu3EcnvxqH3ky7XrUOJjYVguX2+WTxru++IbmdRS4fQS4iH8dW4Tf1rbISAIYaRpf6ywIU/u8Dc0jGqxNIqv1A1j3aoDNdGg7bpPJGObUXgP7DDJW+K1Xn+LRWRKXjZZYfmI+EOJymZgHjjpr4xuNelDPtXedOOkdZTwObdg5FJf/R+WvQVg0Nxzlgyb5Hko6nK/78psy4wToeulHrqXdj1DYmsswFMZJJt7WUYXM8XGJb0u3cUrZ2Mf0ATi0urohIFs9Gsp0+ZmmgjRLq7jhVGoA6QihLq/9N83HzPe5cuTXvmHVF6e77cyR2+4JcwDg0jlxDnMMI7pakDIKusEc8efKWdG7Yg5P2XmDuqbQ96EwS2q2jc9kjsYPVT9ngcQJat8ZiTni0tgxcyw+I4SpbVaZp6m4vlIG1pnV8QOQ72JcZgpPB11xGVVoHrLhyjRjcbC+6sZ4KNynzBxpxm3HHEE+JH2KOVTvGXNURgCzz8rt/p5bzOE63BvmyFrXvPbfLDT3JQvszPFcZY4bP4MCaOeyvu1tb8PrXvc6vP71r8d73/tePPXUU3j7298OAHjrW9+KV77ylXjPe96D5z3vefj9v//3J/kXvehFANBdv3/Op/9b/cltf3hoi6P59ZHMrH4O091w03ru1mc6jSoE6Tz0CLOz1JgFlJEI0cKOFrXgDKSNq4KMQwTnuEW4R7bQKEd9OrDiTq7rUyb/MpbRrbdq6ecmPeUVkDPj+/QjKAQSCnr1NYihg2qD2jW4FxpCWbcLQv1NtCtdebsID/yaHIXvWYeRmqfrWu/LaGB4dj09kZ5P3u3LYTuu+ejDJemfCo/yohDTH6XJQLc5dn5Spg/uhdo9YDfmp8vkd3cr3W1mDu2UYOBsfkgWZnjKVAjYpVfjGL/0dELUXzrpJkNqlzDHfLx3w0XmMOMU4thgjs4069fEHH0rSyp7NnOsmJeidRn4b9LWetTQEUazMrPyNf45c9Rw6jI/DO7LpL5t8UNljvRrJjOpQMoc1dtK8UqcMGOOq1nIubW9Cj+MZ2tsyV2is9bSS5ljS4edOYr8c8jdig6Kt7zlLfjUpz6Fd7/73XjiiSfwmte8Bh/4wAdsE6uPfexjWJabPBmEbDVEBHmFAYhffI6sQ5CRp0cHV2UkqRIoXBukk5SiKjjMSh+kyC1Vt4GBzVGq4Q9htvQhMd5E1qOaOktIjUrobS3njsc/AsC2KzWHKX6MNk22RZ78VJB8ipvdVdZlHWQyrCpQu7dH+FTCNoqxBr1LfsFefwZl6HNJsuFf5IYzwjIThs3WYa57mwv/sY9lJNiQ6/VccBrKSAhdA0252AC2o8qWmidPDuW2mp5jyGDEEcMQFYCx4Z3Gp6MZk14473Ic+9ytSZzDxdwCXckU2zTwPv0eI+WZ4VEN1SIb47+PuCny3QK3w8IXrbvNzNFeLduMv8Qc+lJt7T+swXB/Asnaya76DvkB1mkwre4j5lhGfvWF39NFDXIXzJF2VjiDOVqxcJAeDz6cYg6ZPW+xsgBBY4RAhmcwhy87bRc58UNunxvfyL5YFJZC2tKSnh9amQZo7cpQyyGXVmQOq1vGsBTYqJTvlDnEotRBKADEZMtQUoxh35Xufk6Yw06FmzDH1OmIfuCEqzOH+NFc6lzmuAqFzDsn7h1zaL27hDmayN0zx61wO3Mkdys6KADgne98J975zncO/T70oQ9tyv7oj/7ovVfohPuf//OTxVC1R2jVh4bhraQ04Ks+cbGxYzcsHp9/2GaM4bpdiEa6M/JuPPWD7S3aZawhiQ9O5TI1OmI8bTM99ojjEV0610yNAUPWHpIbsNgY+zTE1fLJYf37ymLARLaykBnqYIRWblMmmRnc3srNPOq0vYX691MGyz4WPtWTBQl5oLf4yKwAv1mEtkfEgQwnXY4ZK1GbshX3ilAjH8o831aSNbKrlGuAKm7jIfG+rC6IhSJyBDMhOuQ0GhSspHtcRBkWHfLKROMc0zmkJtBQN4jVbDP3QMHkwJf5kn35QTlD3e9NhgmNTzVOudFOnJB+2swte3XlTiF8S2ejgwVRB+quV5aubg41nn/Nk8lQBtr83aXWAgjxMz5jnRZcwoaye/b4qwONd7e7m+FuG3P82q/9JgBpk6Q9c1uJ1GjoU1qZQ+2xvgHbS3/lBI1u1EfD4/BmBKIf4Es9InMkfhikM2KOmLa9fMMiMwtP28yhHKDgk/jBdJYOhs4Ou61dVEZtt7yEMwO0cEiXcOQVB4r3LtwjMIi52zeKgcQcFNMnNn6IuNdYqXVoEGXmYCKz3dZKi34r2l4ZXV43mAO8tmqkyKfMwdjgh1ZORKvlsektVWDED+LfWMm1IAScNSPuPBKZg1Hv74w5nBE0LEFtqtQnREbYYI4gn0rABkcQZLaZo1LHvWKO/Lt3M+ZQPU4xRwxfmUPbhnOZw8pwgzmefvYXBhrv7qa6mzkE8Bxwjz/+u8Jj4S9K1giQt0Pd8H9pCWojUB/Yej3KzZZhkKTT738p/watEcUWPKlMvd6cPlJzFF8bde3hOfkx6xQaIAJ5Jwr5CIY2TGrELT4xVOndlaU8yLzdWHFsEMkMbWz4AIQ9RChtX2HrNglY07Fh1IAg6hnKkyVOPQM8+rdZAkvIo/95mrC0VKbFF0jQZKjkJ+tYr5vcQEYLb43pRr3D7+TI85irESc/dP69n5bdKJ0af/QYLbzZlMHYQEfYiPfOzfbczX0zoFzifD2tRuUV3OuJp1PX7o5lgi7U31fqvvTusLz4onzcb7evB93dbXIvfvGX2Hdra7iMrNpmuNvMYZgSbScG7c+AVWrHufnRhDlogzkQFKDsM9S7VwnaeaG6zZljMl2/lIGCgjEH5zaeqwy5jIrb/hJWzoEl2D9tH4Zg1+1eRJnAHKDMHOlI08AWWd/KAvVej5hDWWTEHGIThAX8DizgsA34kC0Ixg89c4x4JOQ5xYmUp85RzaPKNOboTisRoVPMQUXmHH7ouecy5vD06J4yx4jJTju9t6eZQ78PmUPlMChLCvd+nPzw50OHl5yZhwfjdubIbu+guCb3whc+X76RWxz4g0ZqJLVScXmu4vI/tpjaX/xN4XuJY9SwpMaz0gf0ZUb1RgKKPG2Pi4x0DIRWPRk3hm3SUxugNW5eWTTlHIXJdPraJklhoygTdJjJxl8MK1HSTTeP0j2sUyMfNq3ipFyQ4WhEgdgAHxlIG4BGA5w26vJ0rfNBN/hC0B2IFwII9J0UOp7vwLAAJa5RZ8hqMl7eMxkDJcRjxyIO6re85VJn7Esas3AInyO3fWZ4r/eWMXaZ8b8Yn+NXjrHmO8Y9S19jpnJF4xvrmmptF5c+v52JZ6ttKU0Sv6lMHHUreVAO1z+tswu9YKj7jXF9Zbz8b3e7u0/u+c9/BICygDyH4WE1q7DFHINGtWOOcB3BT8UuYY78KkMg0SExh8pfO3M0uS3mSMUTNqE8hznAahFIdLBCmTKHJy3pq7HSuI058tlfkTlWYMgc1vlR2OIUc+gsCPcb653SMavi5TjkB3gZHYfMgU5mDel6/bt75gD6R+Kcpl2Zo9atm84c1S/a/xrfWNdUa7u4LmUO9TOZuCRZmEM8ioaZOcyPgcPy2FD3G+PuhjVOVcxb6PYOivvitCHnYp/V6HDcvDmRfK1vyQ6O3KSC0uYPGnrGXaNrmnHNVzLzBDBTr0anc0iHtp4tN8z9oE/V3T21QaJyfZafuBl2XiPpnQUj/Va5rx5tU5SA2C+V0jfjOlA9MEt33fQMBn+Fz8JQGa9bs4qioz4c4vJEo6GO6a/DMEEv0811mG/b1es2AoHqP4ILTMJrKmX+QJfeyM303pKpKygzUK1DLWZ6b+fPZwptrRmtMyccU0bYYVGnH2eVQeJRwmQoND16s5ldu9vd7u6BY2oGqA6O6KlKkTmATXNx2paP3UnmML6J/DCw0fbzLpijGNU5c4RXvcocfUbSdVaZmKS9pGdR/W2vVoE5tGNgZmtYQpWDSGAdHZ3HmDk2isd1Rs8cHOKLeqe8osY1Zo7YrV2ZA/Z7xBwkzEFWjnYdo/uaMzizrzN7d4pHaipXYY5TLDCWmTMHX8gc1e/umIPT9btljuaVOw/j9Z05npvu1uxB8Zxww410MISDzj868eBB91JqDkILk+LiGjB86d6qm/GMB0VwaVByi8LohlRiUHY4ULvMQNnnoBh5bnskONDIWkNak0xVIxqugmkji9ziTQXvebX+eM6NJJUCZsEoJt/7WWdraOeFNttxg+82i5N8XWrXKSJAwv12QCw3SGU8BfZ9LywuK3G56BWBgzRRva8SA1nppzKOG3sFjcN9dMjoKh97CNMseyXNtdwZNTehZEK+NL5YErF+5V0nosvxJZkLrV1etXqe2w65tf3WJK7w/HDyO1+nlvJ8ZkqL8PSbzmUpPhh3t1Mmn2vTLXd3Gx0lSO9favPP/sUzOMZwSOsqzCFWJgTMLZIxh/w0Oxx3MeQY/gRzLOcxh1mhAXO0gYdt5lgCc0SrYmVjlko9OH5AM974QOxtxxxrkQl5BRI/mC0vzMGS1xZ2xBzy/8XMAd/3IuZTZIyDPHIrcwrl7XLrhDlYb0kKPWeOIGzlUJkjaLfBHKG4jR/yEaE3gzkGD8pdudlSkNPMkalvS2bLbUqcwRy3we3Mkd0+g+K6XWntktHWT+orlhvFyTNGSM3FVr3s0hz58ZhJZo0iu8noYpvpQuBBL+bA8FXvAUiph095zDJp9gcQNgqr0zFrQj3B1U4I99JSqC/mnpfUKAd4iR4efmTWPUFdmhFHH1pWSl6DTFeeQX/u4kOcPdr/keNZ9qPBNc/0rD7U615POccR/QfVA+jL2jJpeR3J1fudZUcyXt5jd7nh3YKO2bM3e3/YAIWJr5fDtiadzGy6JvqyNhcauUtR64G44UNw4d/udnefXZxNhyVUw2Lapswxc1sP7TCuucyWpZsnMYKUE1rzqLneYg6eNLDyks5XYQ5NstpofUXuS8HeuzvmEH1L+gyEsSVKMtz5R7+ee2IrnZmjzrbMMtEWD92ZzBHj8326euaYNrdMgw70cT2x9IgxYoghKqZ8Vs9t5phbzXb9JjPHVdK+F8xhHUEbzDEv0ltmhHfmSG6fQXFNbtXjNQjQBVG2azSasYg2ZliviJKMXxeZkSE98cSP05nER7MGaNQ54W6pjbMZoPH54zlJLzeWuOIJGLMXIhqNGBg0IIyWiB/yujzt0W47Uoe4Qqqxd74JtdND5FQ26KgCAN/YiqsMoMe/xRzZzBKCHZ2V2hsdXbCXd0Is0SP8OFMgxC1TebvjS8MRXemO8IK1ba0VQ5sMIDtZ6z2Ws95XLFjkqLPkzLq4Fxev7MqDMehH0nrReZiI3y8nBbnL6ZnjoQ7e3TKp6bqzdFIzwtr8+eh5cwxOp/1qvDz8zaLvWJcqM9axgz2ZLVPtYdx8vF7n5FNrwu52t7u7df6YS8smO0JSYA452gFpLkAywDIDsL6ADxiBi196tLf0hLcElTnGnRDNZ75h5oQ5UMIFuf71PviJcpE5MpfI0aAnmAMbzEHxupk8yoxRfxs/eJsamUO1XHk1u05in+PsBGcOYcvCHN5+V+YIehFwZLaTRzw328xhVodiGsoi+eQOl5GrhTmApeV1wBwkZVptkdqzPDI7Zg67T4U5PE6uIieZI4YqKqdYO354QMyhZfAgmcPiK/vA1GTivZ4zx+bcz93dMLfPoLgm94v/80lvo/TBNYsMeWGTR4ilQQ1hvUWgvrVwH9sEyq5JXIh/cHmaXO++D14MtaG2OQMWl/clNzstu4cra6T0nHTScWUleV3L6aJulVuS8pLP4WWfvZHWayuHxkrTk3WasSFl7mVK1psfWPadcD0B3XQrp8fcjvXSTao46Nbp6EUJZjkFg3XKreQx7hjOWTdARjtsmqZ8yoZga0gv5qvt8q3xLPZpe1uIDrpRpv6Bw8aZkuaKRfLf66B6HlmvqwzSSsluZCSWZ6gfq82EyY9H+4sw6HGvWKFHwFUZIBvYeC2vqgz+oZ7kTas4/a6u1/c8v3FcPEwbQFtnzr3ua9HuVDrtGfYQcVaVARyPISZGrHVC3TN3PnYidw/Y8T34293u7pP7xCd+o1zhjXrI7ejKaBjNxR4D7nyGzBGS7Bq0wfOQgozalhBeX/opCkbmADBkjqi1Ndg5fcs+A9q/zkXOba7s7yDhV9Mjh4v84J9NdhWDlm2b88Oo/VfmsNmL3HTjxBxh4IvdRofiMv8V6BhGZZh9TysG2dGjHOIw/YQtZsxxBPcyQGCbPBt0lS4KZ46lZw7OzMEnmMO5J8/aWBNWU9Ivbt0Sq60yx4gfWnyROVqI08wxvsbgwkWhCptuN5M5rH6HeE8xx6CPKf8+gzm4+6JfPdTTz/z8idw9YFfby6v8PYfc3kFxTe6rv+olkE5x/Q9xCiCxXy9DGO2DB8BP6Ha51uhrTJbsqAu5dIl2MkBf0aX3QXvFs/rBOHBMo86XyMYlb3NAXWPCTF5O+pkKpeR6kF7M0+jZVQPs/UDeTd7AwQJCe/vtSCvOBany7D9gIOH9MqUBJS8/dmMJJqy0AHYkl0CQHtNFOt4Q4IhJDPPiSdiNItSufKse1OJE5ws5yoxSeM1jrVomN7lHmlefsjmaOppdgw1YetFntuwwVs5oQqu+/Xca+3cGLyeTr3H3WQ36TO1TfqdchJMECNoJN0hlNqLRKxafy5KPvmolv5l7+PCquecNcHQP/na3u/vlfs/veXG5kh/MUetPxadnDvITP85lDmDCD/mnXR60LR6ehrO1jCU6bohr+KmEo9JeOzeYiVVjEwKOZ9f7qV6JhQJzjNrSNkuS0ou7qSvyM+YwlogdDzHPgTnAMPuozJGsuB4ZGvedEH5w5lAPZw4rRxHSTgyf/zDS2+23FpMfud6XqzMH2zVjjmG5zplD4+uYg0OdKO7eMAc65qh1mMtn+s7+dD5I5jhlw0b8MMpPne2xJTNM5y6YI8b9yMP/64mUHqzbmSO7vYPimtxhCUWrI/+lYaFopWob62L9xlaMcUMpL6ryNYkMWwA1MgOZln584fFAlDZTKA0OhxkBaeibPEOI09F0JoTq6TIMiLEe0c48cyyRkRiUbIgDaYVI1tWNoMEA1PBSOj7TRkoQsmfhRSbSGrkfEO9dhKd4NrjXlfgXilj+lnwsKLkuOvrQr9WkcMb4QP/U0lHn5+yh+aGQHy7x9cY5AtUINDTvfpqrhxpNznNfHWMjgGqeqw7zkYYcp0U9vA9bMlsbSo1GO+r1OJtjDeMPVeZUes2TkDfxsmwhgmAfIo6EBF+ZY9yVB+Wy1h/1mFENQLSbn93t7l45fdFvzaYb9swc4YI+jNF887j9w+i6yHb8oCwzYQ4ayZQw9ZMKPHXMYW1VYI6UobJ0YoM5hrNKuiUAlTn8uqnKan85NHyReyh3EAR+OAbm8BdqmVlgfQe5gz+eshUZpqof+SFWBU3PjxQfyVD2jzoacyjDFH7o4iNjKzUOYx7hnjk6vU4zx+hkLpNLb3nbzOFuzhz9Xl1b9HDzmGNLZpbeqL5Uv6swR0PROXOkNE4yx6FLcXc31+2EeF+cWhQua+ApL7mYvGPUy+lUnWo3gbFtVchItFJkSpyENm1Sv6ufG1NkP1GOYgs9yEmXH1GgvkNp6HUdj66bJoMC6kCLtIDVoPRK2PIMzg1kW6pRGl0p1FWXbnS9S2IQy3Xd4EqNe/WrszJU32Swi8yKKuPaNwBI1hdqrFfEchXTwz2cWAjOmBeTmxvScVeAA0tvZvPGmpS+baXTf2tuRZyWWQ302I2Ma/w9zlP8Pv7Xy2xNyxynNAONLd20k2LczPTPl6OBAzXit8mmXfUo4PR95ndTHd+Dv93t7kG4sFTiXOZIj2h5Ps9ijkmjOHuhGMmQvvRF3UbMETMVZnn0jobXL2aOClcDfuj6gHU/Koztvb2EimcSZ0rLMPSadWyMmAMDGWTmGPJDWH4a45oxB5CXZ2hM+v+MOcyv6rfBHJgxB2aDV+3ajC3qdb+tY+N0LnOMfLaY4zQ/ZHfOsecPijlG+tr1e8AcJj/bKHRnjucsc+ybZF63i09n3c1nzP9dEGaYAY6GjjR+qZRc46oVdtmQscSyzta8uCUNkYR0hvnKimq0trVjBR6Ric0eQdslXwZB4FRePMhLf/xUk2PEBlP14xAm54ktC/WgyJxpH4TJuo1aZ9WA2bVS3sijL7nUWEDMRmVSXt3Qx4QXOVds1HbpfiEOfR6rH5Ka85qzMrjxPPCjcCVWIXL40JQjGjje1TSph0ERPmWDGEjHu/qU0VEe+04f1ag9lwOZgRHlJHXNLoyadkVE20eTVplY/vX6th0cI8ltM577kV+7u7VOH8HRENSEOaLp75gDuIgfhhMOtmRSJwpCw8zj5uQUc8RAdn3AHPLjPOYILaHa1DOYw1vYmNbAdkh+I3NE/JozB4b3NBZb45626Xqd1WG5YtdNW/0Zc7hgzxx0ijmsXAP3aH5TXF2WhnFl5pDYLB2/51vM4ZL+f3X3ijk8z+czh5HYDWQOvg/MgcHv3ndnjucac+wzKK7bqXUu9hEAbMpSbZPMiHI2PiG68NGlNx4V2FZxGFdMfBDJNN4JTLgN5c679nk0j9kLohrMkZCwCme/bACKDAuEUJ2aR6EMuJ+VIbc1TxahlN/Z+lU3jSpDSbYXqXq38Aox46mIMZ1KMPUl3/NKPL5HLqMGPox8DIZZ8npP18Pu3cCiKc5F3The7zSLwjOfjFYx2dHSB/ObptT7bMV3t1byXMioG2JWDbZiGedn/IS1Z3VSOjxJKbRZMx1vnON78Le73T0Ax7pbZMcCyhR9w0vyJjVkDg82Tq/8vpg56jMzbEZmY+mzBKPVnzBHl8QWc8xenUKA6Gdfx6eXITBHin9gE5MMRicraUpz5ug+uT8iPYYItSX4KHdUNQfxpz+JLzGUztClvuygZV2ZIxb1uKyuwhyN76jEk+/N0J1gjlidbxNznOvuF3MAkM61WUQ7c9yeTJ7v9hkU1+R+6wu/A0CaFR/iNgMYbBcI3HZxLrYgnkihz198FmOvbDUjnUwJvyVDkNH9pQQML8mE0DtNNqbcRxwT0OhYDHbo+/De/dgzHICEY8+4K6vNWRncwCo9/wvYtr/wvLWjNOOGSwtJGgQ5ajRlvMnQigPH4lCj13Zpbr9D+TDkuhalKWG7CsSjQVcAhAUsx3wuaFMiCSzwSGMZWrBgxRFaXlZIsvzDxwVkHAV60JzGB0lrYcYqS2QWrF4+4VauDCygcH/ySEV/+zW+di/8CNOIORGGvN4vgEwXzeaOibpjVVvHlOiUNoejUL5ZQ613CnYaTwwRdQrapWPMkoxu6GRrwiP4bFuQkVHXezYMzyzH1vadMFtx9vAZ9fPcpBBhHajmTaHPR9gGHVU8tp0rfmeYp93tbneXu9/5nWcBxBcztOd1wBx2ITyUfEXmKCbZPbV9jGkEfdWPSedUcm4+YlOktjvYGapGvzpyP5u1WZjD2uogs80cwZqWvLblEvI6nGxDZA6PX2cwNjXY7LvZVG77TSyhbb+UOWKbvMkc7LoZc4BAQiFRBgDWjjm8VFdkTmFJo8W5gliONCU58lPYZlVGkNjiUedT5gi3ILvLmUNQNjFHDL3FHEA7nt5T70kj6raKjB7TejZzINare8McW+4Ue9xP5tDZGD1zjHQbM8dx/a1h+N3dTLfPoLgm98lPfh5AbigI5aENpV/XbscBjmgHT7m003aUofI3iNDt8CQlZt9YKgpxbl4Hgin4sLkTCqJ8IemfU6Due7eyjmJj5rGqOefw2zaBFDlfy5nT0bWX9YbFY618Z29f22lpAAKMfkKH7wdBQbe8mSXMvxz3afFLXBw3yyLoDtxHlOPDTGZJMuqPFC5/z3cohtVjSmN5exmsdo9q2QNrMHbperivFYzr6EaNc+RG8cS0LnK8LTPTY+tJGaPARvhB58R5cfJGiK3ZEaMnr8nMn2sPG/+Odz4zVvgmuVqpLvnb3e7uo/uN3/iCfTfzRHPmqI4Aq7uXMEeSF6G46WA4CGrIHMTzFw0VOd2GDfwCC1H1k4/RsoKOOVibvgFzDDkB1lEQOcP5YcQcOVzW1m2yK5iZw/e5qPY22nCx9yPmCKeE1FKtzOFMk5nDdWxsY0eVpzwvLmflE/8iK+V8j/y2mMPLBn25ozFHnDGLFHbOHKeODK1uxhwA/MSWC9y9Zo4td27nRC83lNoIcTlzeE0sMZbnPv7dufPrG1rfELczh7m9g+Ka3Fd/1UvCL3l06pFfXPzD11Fd23p5mfklaKh+I6EQqOuoUNjgEj7Fk2XM+OkoTjnWCrKTtRuh0PywT++zfSYY2YhylFtCfOoXXuzDQ8yqC4d02NN1uVBwHNIr5anrNAHC0TazUp1DOrFgLI7F4jS97Tix0U3y8tJzvlU33enawCDB1eRxFziJhq9CQ4WdVeI7JgCKcn48ql5XHeIGnQYh5Glp8cS89hCSirGrkjWs1zm2/DQ/lv/9cxZXFx/1YdJvZjCzdb60azV/c739ep//KgNNr/weucETejLcKN4EB4UO7Gftywvu4YdeOU37JjhdD3o3f7vb3f1yr3jFi/2HtfmFOdbwq9A9x3D1+6AuT3kkdEbUNqvjh+KGpw2dkJkyR2CKzBxLsPFR4QlzqJ8lEJlDGca0TX9rAAVNf8wcbqc5ZkntesxDyKjaWuts0GuBrSpzRBaozBFtdnalo0D1Dcxh9jz4zZhD0/GX/cxqyhhu9yjlte986Zkj3odm83N8lXtynufMgXI9/r6YOciZYyR7r5ljxg9b16+LOcavH5xCTZkjNW0lTbm1I00efeSGHzO6M0dyewfFfXFsLUU2MARa1Z9HnYrxo7veXTMjN7Hnk5bGjRRKXwkFzxIHi3+Xhg0JwP4ftPDjximTSFwZM3LsxerZoXwt603ZIJe4ageGAkCakUBiENPoRUhXcnYU3Uanf3CS0d76yYv+YMfu3mjHDrBo6Psi6GTKtXRUGUbxUYoPIBwHdzPmJYGN/I5l1ecphokyfSfGyFBXPdLv8LY8Gj1pn9vrKkdulr75r9wZb558PxXf1I/8eWUg73Q/ldVnbgSl2+nY9VRdCLaOvYhEqHiO2dHd7e4GOTec/brtwhx6NeDHrO07ix/OkamfA+YYjnkw7FSxFGCLOUKve24NQ6MVy+EamMNGySfM4XGRxV+Zo4lTeDlH6lQC8vHuaXYh5xfzET+4PdYBmiXFPZIhKzBnhDTIQb2Mx9fbc8Q8Q9krd7JpmG2+0TDnMQdCOpcyx+hZuZQ52h+n61vx1etTVhkwR5U75/qm3z1nDuXhmkxhjhjVmcyxu9vn9g6K++G0x51oPmuheND0h8tw9It2uzP6E1dkRsdJddLxRXnUqRIVHMU1bP1I+meK4iIzgoW0xizlw43suPHL8SUDOpGxhjRBkRg7K4Je77Vkx9LSe1dv3qx8MAcmj7f+BWDYaKA56SAjFDyLq3aiRAPfG2z/Ppq+NxgRCjpVCY9zXBAKdSNw2Ci6brrkqXS24tuEmFnVPyO+i/xo/BRtx9eA4SKZuAdFJ7RZ4W6Pm9HoJX+72919dmRwcII50stvimAb6ifM0Z3csSU7kImBpm3i1BDqS0+9PG942yg/J/aqsydy2j1zNI9t5mgsUF+MnTla3P2gQD+44XGOmINNpi5cCfk50cif22Rdzhy5c8KvbTFHtOvzDoeeOcadF1vM0cJsM8cozhFznGr6Z0s07oY59Ps5zHFufGf7XZE5fL8VvdJ/S/HszPFFxxz7Jpn30U1MqLsyHZB1p6VZfNoABePDQNftlNLs36PtM3ZuuEy4EI0cY5yhqlMMD07HTEFAyjaUYrJmq3WYtvDx+K4WDYu/HKMJLzoqm3CwQZAr2+9BnWW8tQ3heF6OraPCsYBJr0phWO9u3M2bEI8Z1Q2/qrF1EAlyki/7FcqcQ/4YbeMmm/YZ1Scp57QBl+dHy9lT1V5qv/ltWQubcNRBddN6HKpAKm+WcuiqkRkjqSOaKkXA2Xia0qjY7CHS8k8qWTo5hWhMt9KfGFeNO+o1CDOKgQNEd35FdhsUen31+rRtCqMjgydm4ra02HrzuVluP/Jrd7fR1ZZq/FyrZ7Zv7V17CzrQsQDr9dIZn2RGv7eYo/Z2cJEtDWZaiplk5szhBiVsTSkPrcmoKoE5KDBHC3aaOXQj6q6ECC3wVmNR8y6MVJnD2Cm19hHWYHnVe2Y2uiSXf2snSlBDmYY9HZu7QBSrlcdCMP2UYc5hDtdyxBytsCtzpHvOfv0Uc6Q6oiVneoe0LT2Y3vHa/BnqmSPrcDlz1A6pGGqLOWq40fWZ390wh/pOKHxnjp05zO0zKK7b6fNB46YlGeXo7KV9Ei8hrGEcXJ/IbAwZp7i8O2Co+Bg6CH682cwfyAoS4PNLQ2mEIG1NY5axToxOGX+z5NQ2OcD0R0e5/MrRn8uUS4spFFs/pTAqn3vYue2kTYM1lKy/dVQlLLsQvzVN1Qzph7WjSQ/QYGRC8sM5TSvnZDz9RrayUPDw2tGVdyonmuggMGg6eFjL10BvBB1yGYydyfJ4qqPiRZ/OoM5VPSapchdf/DaOr8Y00udcv7GJHoWOeRn4hvyP0hldl8IeauFrkp9jFnR3u7thjsDW0TBlDsJwhz49CWISMXg0/HuK/6/CHKO04zqUU9c1wglzMABaojJFRWn/4majyhxNaoM5PJYzmEPP0oo2rm/fTzOHx1qZQ+93nYmADeZo+V9s5kfHEIU5XIUt5lgy5yTuQWCLzBzrxcyBjpXsfp1gjprXbeYY285LmQPx8wrM0TPMecwxql/XzRwxJ1dhjkFkU+bAzhy33u0zKK7JfeITv9G+pCEC9il9GnBh6zblEFQd8eD5I/9IDVy8ziGAXufyvDKsiyo9xhoOea6B930Hx9IYWXpBInb6yvlkZvDbuVHZpWO2qiOsK7fjP5d2DKgatBWMA1XdxFCtwLI0HeP60vjyvyz2ymz+4HYs1xLK8MiEA7H1/LcyVgPKOAgELKC0grMZe7I1cTpzYqV8FBc0PrQ02gFfsBGJdlDXigM4T9+zDg/vlbbjsMQv1QktG/l2AIversMRsgqVW1n7+AHhyIwDCEwcbqHG2SpPmxWjCRJWXpv+5ZiwFqsb7VgTtQy8bnvNYAYOUlapmsFlYj1qVVDCDo8Dq0ZM64IeieY1JD5aY9PYG0TTkcqzdo7bmtoYUqx6bE9ZpS6E1kG/Fu5FST/F3MkUPdhl4tN9586T48zcFDdnrPPld7e7++Q++cnPu+027kDPHCQGrDRiDUO4dRyvcCMSGtiw5Dwxh12ozAFvVyxMYQ7lhxFzxHjjMaNtg0H1F4m+wS/MATMQZHacckdEco05DouZxbAUQwYaGDaL02wgN6yrzKHHd65MgTmanDEBJL7AHPq72bV233rm0NRbWaxMdpshed1mDrd1ehJZY4Z28OeCo5cBgkxgDrfVkg/hGLfd7QB1MAvD+EwQlVFjQkR2NPs5zKHzN3wWSTulozKH2zPVJzOAHTNq0W8xh/utEguFq5E5dBYpSnrZbTPHtrvHzHGGu4Q5/PtlzDGOYyRT9GCv4/FJe+bZXx7k5Aa5nTmS2zsorsk99PAhAAHFj2zH2RuQeC2G0+/mBpWwvjjZtUnjVNNPPqN5QrW1CAnadPy6J0XKAJWM5ZxZtOYXpjFGiNBMRfJgYFXDxWIArGDVCInZEZ10GqZCh07jBNoUTu1o8CyoQSOBG7KNe5q3msb2fwMX0Q0EWprsInFpv3275ka1uYYJR2TwaKGW1nHSyTS5VbZ7WkEINbBpRtWkhDxNZFYx0rHDpYVNiz3gL73k9yJpnnVbSu1eJTaDMPm/lbfXkFjP68Cc+q3MqMs66vMx5FGEeljS6Uut/97HND6k6yo25JTMLD+uSQ5d44ttlS77GbUpMb3UvpUps9F/ntYjG1o/eLdPt9zdbXKPPHKQF/nmKj+M2sBod2s74S9pYzdjDpU5lzkIZMsi7RqX9Ivi9pI3ZA4J2DFH8CvRauQj5mgvUboppAIE2ouvdnQMmYOc57SNJOeHzByWerDrQScKWlthU2IBFp2Uj3hBYo5oU2f8cBRLHMtOX/KYmi08pDvudr1O2leZakE8bO1cceawToLASkd4B8qSah+ZX6gZJ5hD+aHqdTXmGFvZ8lxMnFdtZ4YRc2Rt5zHdK+Y45cZ5ijnp0+fBtS3mmOptg7r9/avp6O/2GD46zc9NcDtzZLd3UFyTe9lLH/MHxp446p7qbnTDPJCN0jmutAoj2e7BZ3vWQ9Pi3xkUdHEYiGBge0UE+ah/15BR1oJZe59bnOlIIY2DYEYYJKMQFHKiNCPHNlEkIZNpsykaEDS/JhZMA+U8N6iIeSCAl9b7HzpNGG1a4SLmxWc4WM5a6eg+G1IfiNR/bYaXQzoWrxhfUp7Rzaha4CVMl/H9HnyNKAA7CsxNMOxuxTkINpNCdNDOB7+uabVfq00c1bgXkRiZFzI9KzAoLq2InRQhA8H8ZMOfZ87oY1ZT1RCajwrWaaM0wA4s61PU3zG066J5iVJVuuqGgX9M4VxYGWulv2fIUK+wKcx+ZRDOv2u74OuD4yroBntxNnmrl4SHDi/udNjd7nZ3NfeiF32J/2AGaBnOft5ijnIgxmkX4h+9wOn1yhzqEV/wEnPUCJCZo0UY2ujQ2A5nYd4lc/iMi5BnYw6xaB1ztHj1hVuNkzLHym1EXplDl02Q8IPlIZxcsnKb1Rk7TqzkEi/2zNH02WIOGD+s4HZd2/WwXMj8BsyR4ot7OthtZCEQYJHuhkwKzg86C0Pz48Mkq3DCJcyBEJ+Tqu96Finm3jLH9HrHHF4+lzNHjv9umePqPOKSlTlGS1TuJXO4r+5hVyMhPPzQyzsddndz3b4Hxf1w0m5y170lzctqP4uMGNr+6cwtUk1rkPZUJjzIESBszbhCQdcA0/S7GnkFkKGOVQcxsHadPGj7klNxvWkQv+/tMM6rNXEuwr60Qosqr9nUm1j903+WUFs7SQ41eh3xKPq4WtBwKZRJlhuWpaXMnZ+fMV5dKJ/Bdc932CMDdd8MTTsefUYpP1Fmnk7NRzyqLGtf044yufyy4R+nMzfg6sfpV4zPYSg+WivYOo1qXOelc56O58rkcI5wo7g6GUID2Y0XlYH9b5V90I1PBDtWd/jM3kRX286r/O1udw/Cie2mC5mDVmTmOMUPIWzkB7A0A6NnIMhflTnqVwrTUK/MHJ1OE+boZDgxRzcjFWIpuLa/kTnElmhnBfya6mRsYuVa7CP7saVXZY5hOXTXQ8dD4rSSFqpcZAF/lW4ykTciP9Trcx7ZZg4Ev5pHZ45+icI2c3hZeEfBiDmy7NhlmUuZo+o9d1dlji2/m8AcnW7KHDo4tzPHrXP7DIr76jYeEu2aV+MXG35ty4uRHZ2sw/UHWcrB8AY/MWZcZDz8gCYGBjhGkHROesi0yQVhLaeYKOsADYnrDs6jNaIhbz6rIMiFfCXgsGmLoR9cpzNyXuOY4KQ6SZ9lhkU0UB7G82qm2JavwM90h+sEtBEJcNCdQrnaZlZRErbqJbdRbC+F/RFo7MtbLJuaKXh5pMLOy0TqgBZ06mqQsZGbVE98zSjCPdKSyUtG2DJEWkYU4wmylO5qkKn1n7PfYAFy186XC3HW0TxoSSdrcdLleM6zrrPYvb5wF240snEqwhhPX1ZjXaeb791Ud7cG/7bld3fPOcdM82GouNxBmCPNoBg1O6eaoa2mqjJHbJhHzNHxA5wPSsRp5mZKS0bIz2GOKDZlDrFSG8wR1dDUqu2vzGG2wZZWyL5TdnJWza+XU2UOECU7TCVdlwqzLCbM0aLMzGGLWYN5DlQTZnfUctAlM84PzhzCCR1zZL6KK3a0GD2dLeaIensnUGWOuM8JzD8zh7rID5ngsr9ndps5cuz1hxXBfWKOU2mcxyMt9L1iDpc8lzlunQ3emSO5fQbF/XBUmw+k3/XZGoXp48xNxKQty/6EoVBsUPv4tlqLYSp+deBdloAivTZzF7x/CS5p80iPCCnJkokBZDfGVUZfnFnDShppt2kNzmqSxiajm2aWCqbENbE+0RyuXSdDrD+Ur4V0Ri5OB+XyJ9p317Unul7Xc8/nMjkvNupAo7RruZSyKCOCfnsdOnp/7nTw+OY0Pa75bHrXdGa2pTwVxZ1v6M9z8/gu1W3bVvZlfUpGoexe5/i6nK4HvZu/q7gf+qEfwqtf/Wo873nPwxve8AZ85CMfmYb9I3/kj4CIur8/8Sf+hIX5i3/xL3b+3/qt33o15XZ3410aLZy1VcUOn8sc4/TG3sM4InNMGcaMcS876JxIQQcRnmKOalHuNXNYgW8wRxeFlZP7ReYYzRCwvKZUYkXI3NEF6bJwOXPMmrwRC4yYo/MbMIfNkKAaF5nMOkjDb1vPXpcwR/ab5fleMgeGz971MMdVrPNl/HB15pjHd6rcdubYdjeVOfYZFPfD6SgGl15VYmDR9VKcniJvyMJos8lJmGCwLd7cadyrEo282KxupkHSIrTAlC/X6yPjW2Xqmlj9SUE3Cp5xdkfuTWuBmcUsRUNB2tEwMuPaZ17vhZ+SkfaSZKCNSDQpCuWtYVaQrNeMqxejsW1XWUdHqO1XcQgwESXHa2hhgKN/cQLq2vbHTuWp5bNSzmtTdTEZL2+yctQdsL0YGGDdZTxcg+72DVsX6vUgyIDTvTT9Ql61JCj8H28EaRl2xcJt5/l4cVKaGqvFrCMbHTRYzOGahNWputT7qaZ9vet3gahyM3eZcR09xacQauA3me4Q63Mvs6EV6704BU9f3O4nfuIn8K53vQvve9/78IY3vAHvfe978eY3vxk///M/j5e85CVd+H/9r/81nnnmGfv9mc98Bt/0Td+EP/tn/2wK963f+q34p//0n9rvRx+92ZuF7e5uHMvMiWZwqbbJyhzhKsFPb4C2p/ERHTBH5zdSpb7Aqf28H8yhlzvmIHvFz8sh0DFH3leaQxuX7dmMOeykEDDiwSidzBnMkfJd9s9Sr7o8JHJh2yNL9SPRqH33vZ+yxY1tds8cvQwF5vCNLjVCP6XkFHMoC4yYw3aKMIbxGaqscYZ7BEQ95NQ3xOtd4YcSmDFHYFYg1a+7ZY41lbIyh97H62aOq/DIJcwxT2OLOeYyw8vmpxS3M8fc3WTm2Dsorsn94i/KEXoM2G5DZuACMAQL2dYWihkgeWDDXHI1NOoIvvlcsJs5QG17Y7gIHRj5lQli+qXMu2E9MtR0I4+06hDyLXZYfoqhCsro1EQd7lnF2C9anjINs3UQtBacZDcqbZpXBkh2bvLZhNp5QCLD4QhWOZZzlZNBdBoEtbK2l/EF0r/U/HV3awWKCF8rAJLlJYumCd8kK8Ej/PiuuCSl9Wu0jhCmDAFa0KrfKjKtKEh08PKKG1q2Y0Eh11n09Ibd/cIUSJajSUlBz4/YYjAOrNzh/voYEOK0Vc2vCjDiySlNw1GF9seKLT+xboVqiNjJMj6CtNU/QQry8JR0cRPHQWbRAgoaamgFuvQMZfW6a/V68xvvzH3Kzdd/ZixLuoXnjwcyGMpgKKON1ihvDMazdz5+fmYehIsP8VXlL3Q/+IM/iHe84x14+9vfDgB43/veh3//7/89fuRHfgR/42/8jS78l33Zl6XfP/7jP44XvOAFHSw8+uijeNnLXna5Qru7Ne4Tv/qb/qO9CaWH1fihMgcYegw2c27P6hu8vZSpvbSIYwAAIz/Vo7y52pT6yhyxgapNfDymXOy2JTKS0XwCvsyzMAezbqKt8cmsPNv50bt6GH7s+Yg5sDY7t1CML9j1xfPcOvcJB7GFzhyEVTnJypvMhq7cM4dmVZljDczR7nPjh7bZZi6oVdLxU7jkRAvhrjjYYf6yZERlWmeC53UN/KBpHbl11KwbzAHyrS/nzOFdKJEFIpNodVisB8h1aHVHN9vUimSVxe5zXHTi6WSnR4Oeyxztno6Zw8PMmCNHdlXm2Lqe9OyuzXmEU9n551WYo35L6VyROZ5+9v+aaH5D3M4cye1LPK7JvfKVL25fQtc4ebMpjSehb2m0uUFquFymPJCxJSyVk4BueqP5yXVTj/S6aDmUoZBwvk4gyJmUUVsMl0CI4iQ758XGxktn3ASq7XaZYFxk7WW+Dtt3q39ZI3nZl2mELNcAn1qohSz3amXCSgtWXgAstmQkbdqkIKY7bDPl+DQ9EI4cp1FG3aOMl4ddo0VuucfFRFgp/Nailt8rLWAZQ6lyPm1U/1qP00qLnflBwX8N8USdYen0101H080/Ve813ftYtoOlIjSemunpxGvNKfBl46nJUQqrfioT/Sze1MFUXT+dtn4f2aNRfDO7NYtD00/PweB6Jxem1M5k4pXoNSwjmj/NDz300qHWN8UR813/AcDnP//59Pf0008P03vmmWfw0z/903jTm95k15ZlwZve9CZ8+MMfPkvn97///fiO7/gOfMmXfEm6/qEPfQgveclL8LVf+7X4K3/lr+Azn/nMFUtldzfVvfQlL/Qf+jKb9l1AxxyBRuzBTc/qoE20QFdhDlzIHKOXIQ4spfmj6D1rcSJz9GVAlK/HACPmaBv6jZlD28MZc6y63IKVE3SjyKVtdGkN6gJwsN0mJ2E1TjeKxgyVOYwDQpht5lCvyA+weIwdEj94+avMWpgDZs+XcK8yc7gNOo85mJYU/ynmAI2Zw8rOrut90+LtmcPK5ALmsM/wfMW/bebYeg89nzlmYbau1TjG6W/zw/nMoXKDX1dkjocPXzHU+qa4nTmy2zsorsk9//mPyDe11tQ1LHWHbTXc2psM9A+zPXglXDL+QTZ1YgRZjp8jmfD0k3lwarJzA8BDMLGdvFl64pn6Zj+mL+mAuYcCcqPFLDtWgwENxyEO+eFfm+FR489Fi5W94Y+GdoWseSS5HkaTjiqjfmqU0V7qfQmq00ruKIkv4gIbtlY15DUaXrZbIZ0jbHG7MkvuHNH4tDMkdnig5DmwagQBN9Z5F3DWfBaDgeTHoZy0XB3O9LQTls+0ftTipKBT7IDJwKCzT1Qm7ozd4qMSNv71/1Zw0Du7GXTE+EZmfJSO/kOJ79zvIz/u/kdKYWTA2zMuMoP5k4OnN1XqrAOnexjvXdus7RF8MbhXvepVeOyxx+zvPe95zzDcpz/9aRyPR7z0pbnj5qUvfSmeeOKJk+l85CMfwX//7/8d3/md35muf+u3fit+7Md+DB/84Afx/d///fgv/+W/4I//8T+O4/F49Uzt7sa5hx9uE9cJAMnUyvq+PexOVbNG4xD1NVZ/bDIH/EtlGe4CDpjDApsPrAUxhYQ5ypEJY+YoWsQ2KzBHSBL6pnOKOdhkCnPwiDn8hhhzlBcts2nabgbmWD3qYBczc0R9Vc8hc0B0M/ub46zMgRA2Grr2EToILC6yARzrkIn507RCHeWin5boKeZIteQs5kDHHKOy9RNB5szh7BBlwv2aMAdMLtHGPWGOOnfy5jFHfPjDnR7MpriXzLEsz8cXg3uuMMe+xOO+OLIHipbQU2jTKiG9DeXx1+mVqQH3L11n//CNw71q45Kucy9PIJ/eSFWGhkJtJgW3aZxFr0ES4k82xZ6oirV06tpOIyVGX27a3HGYYtnnuFOQ19ZpFFbVtLi4rQs8hCO6tNnklWUTGJ3gqQ1tm65Jmr+YOktudLdua2lJNnvsS9VPC2nA5WW6YOXVpmsmwx+XgUhV0k6XttyDQg8lg7GAeAVTXNzgLp89HpzoraWS7pXpWg1m2JuiQjH7VM7sctoUvqkppODDpo+nH3M0eh6i36iujmQ07FxmWvPNnZvW1vVtP0p6zPIR9dGlRDqTK5a8xtKXqrZnXfIt/GDE88a6ESFeKg/g4x//OF74Qh/dvq79H97//vfjG77hG/D6178+Xf+O7/gO+/4N3/AN+MZv/EZ89Vd/NT70oQ/hj/2xP3YtuuzuATubaghEI8hqtyJzhOe1tN4iAwszmmAwTh+BbTx+0JxFmndgjlGkA/1IEhrp5talxCf7QhBvMEfxmzGH8lBbdRKYgzzEQOumnzFHDmPLPuzUMS0dyNKOpkNsalmWpx7A1qnhLLCOmSN8jxNkdS+JMXPAloj4IJvH2+w6oMs2VXAV5shcR2H5itbOkSv8UJgjvs4qG1d+cOYY1EG72VmGUlldjTnmuahunPerMAcA4c95nKPR6fvHHBMO0XeBC5ljHJeE35nj1jLHPoPifjipNCRvvrbjavC76OHh8j38xejSzq6x4tffWzLT5iDL1Dgbk5DHKb+bvxCLdoMHPeJUQFOXAe8OVys5xhzr3ZZ0YlzeqcIWbxw5yAZrGH2XT44NreoZw5aNkFIfckjfSo0lDxz+kir9FEMnwDzyUf1H819cB7LrvkSl6Gj1i3I45PLOenCWifdV0+tLORj9+BfyHXUqZWj6RP9QJoPq2j9H4VliyUUddWBmmcHC9r2Wmes4tjr9I2ljAOHKOrledRrHEtPZsoCjZ9yBsupdx2hORIR4j26Hu1c7ar/whS9MfzNYePzxx3E4HPDkk0+m608++eTJtZxPPfUUfvzHfxx/6S/9pZP5+qqv+io8/vjj+MVf/MXzCmJ3t8+pHb6QOaIpGnmObH2MrueHEPGg6bmEOZruzhQ1zsQc4Q/svqeZw+0iJL0t5lAbFrWszJFGqaNtCLGxR+a57Y1DShdFhiWBydy4Pv2wzASBOXA2cyDLWJzqn3umIisxInN4efe2s/BDKB+bpdGVOac0eZT2tIROM8e4DMuMFb53zBGt7SXMMcskwzs3ajojtuivb/HIvWGOsd5z5phen9/oG+l25shu76C4L47F3nB5IqXR2eqcyPPzmrjarsHDR5Pr6nnW80rx64RihsOkpxKq+c8yvYheo4lc8PeUiwXQr8XYDFRTfknNri6V6JpjL4I1RKHX23cy+WhAV/TTPn2q4YwaCb40I3c0jKccArpmtff3KW8pPs5hekNNtTrmvPalPpQZQ1z+vnKfnzWARo5HdenrULpXtUjD/zmdnLdhfIPr7bOvXbMjz0bTLM9JZ1aHT+k2c1P/Tet+heEIGpfP7pp75JFH8NrXvhYf/OAH7dq6rvjgBz+IN77xjZuy/+pf/Ss8/fTT+PN//s+fTOcTn/gEPvOZz+DlL3/5Xeu8u5vqpG3rGr4znr8Rc8iXkfQ9YY4kMhaadfJa8lPDPk9p2I6KvT2HOSzIBnP46+BANctq4Au1YzRijomtZ41yQX0xHzFHGqAoGfWyrMwRBx9KVjsdR3pqfF5ymR8maQ2ZY6sjZsQckcdyOaTlH0XnFZkxol6pvLq0eKhDus8pnZi37OqykJruFnP0HHWzmWObKi5nDtqZY9PddObYl3hck3vmmTv+g7zHn+WsKXuoiUG6l4A4f+DFsA1sXjV5XPzV+EU/8FjOrnHTJ07ZT4ZIjSDJxElVtOvm4vThBFPyE8jGxhnEj9RPdOIkk9HCRgBkumqbqadGlGVOn94ANdgyRTEWjBqpSB1WDm0pxYG8gL2jl+xEjhgXJB1iLyIGZDonsDDjEDpECG3n6iP7UVyrXNcplsxsR5XpBD6dSrtAjzFjkSObpn9IjXsrpCOAuJ3lEQDx0qZtsm8dxRKXYQPL8WKkt6fJ6A7Th6i3yokfkfu18iHobtY6HZcEDhfud6xueralKYuVXChbLWvyMjW54Ke+GqaVfXkeEL4EZlbJ+KjltBy1NK8sZdDu16gjxaeKzszpllGfQVL0PRcKGK2ezfScgUrVx/44l4/HENrIm+hmVHaJ/IXuXe96F972trfhda97HV7/+tfjve99L5566inbYfutb30rXvnKV3ZrSt///vfj27/92/HlX/7l6foXvvAF/J2/83fwp//0n8bLXvYyfPSjH8V3fdd34Wu+5mvw5je/+cpZ293Nc8dje80xdpB37DTTrTXGEGMCDbrJHOFLbGvq51WZg8uyg67tZWkftT1y4yLecUkhLE9XYg5mC8MlHWsNmRCP1CbL5Iw5hB+wjpkDsmxDdSvMsQTmACl3CH91zMF2GkdiDhCYnDlyMbYKEI//1FNAuDCHl+GCo21bGdNZmo3gdvxnXEIK2USSWZehjPnBmaPnB93vS09Fa0XtJ4hYfuD8kJiDKnNolrx8RsyhnRSHUuuVOTI/+IOgVQGk9VS58DzmSPxwFeaAlNEVmWPL3WvmmKdzuothxhwApG6IPvzsiZgesNuZI7m9g+Ka3Mc+JjuWFgMtdqx9p0F9UmO5KExQb/RjXOWCNsKpo0Jb/fjHIXyNsKyvtLRNJjcvbEZ94syQDtK1VjYYbignkMNUJaZuGlhba6Z7QWT9PI3YrNseEcjqKzAQQc7l9ru3pu0ig5N72ZbPRr1dB9ayk/JqhrI1nYv4OaSR6a2r8SJWHSX0UvPDkL0lNK9ylChWqwZLKPgVCxZem3EO5dPwwvO6mCnVXB9lPafqRpaWZj3vV0FgrNIp4umrrKK1HvlFWgaifYvdTfxK8d7lO1KNd60prnd//3gQPl6PfvmY1yw7iyucWNe5WLq5PtLweoxz5ncVi9WaHQp3KMfVtTuTa1aowT+WzzN3fv1i3e6ni1Mmryp/qXvLW96CT33qU3j3u9+NJ554Aq95zWvwgQ98wDax+tjHPoZlyT3CP//zP4+f/MmfxH/8j/+xi+9wOOC//bf/hn/2z/4ZPvvZz+IVr3gFvuVbvgXf+73fe23rUnf3YNyv/fpv+o+JPU7MUR5wH20MwjN+COnoppXd4Mg5zGHhJ8whX2bTvlv4QeszZISsn78uBuagqChnReLghNjh2SO+sm79kW1g5Acqha8DC5U5YjpqFxUsV7m6WBl6nvsjPsfMoXd9Bdkxmodww3xSP2RgQAul2XuVqfzgwyTV5mtZzPiBhR8yczCOOIJa50biHtg+FksqIWcOkpyojLJaZA69s3qPogwBtj9YrDmuW6wL/bzjIXOE3G0xR4rnQuZQnWYTnmfMgc3r2zxyVeZQyS3mGHNGiakwR/z+zLMfvVi3++l25shu76C4JvfVX/1S/J8//UvtebENhuQxVAM5aTQAyEZR+RoBwArw0j+oBHvv9QdZ4x7EVVTpPcLLnntp414m2lN8sc5+HFqVVAT6IOrmjrq+k8gMlAIDQ2aZEIshkrAsEepGX5rVlCGypaMk8VujF5YEqClWgrNlp6zXtHDba7K2g7H/ZAXJa33ohVe9lHdk9km7zrIhJaQDgw3GmElkCKA1nWW+RgOYKsICXljuQgqVjErGBN2QMuit90qBASyzZjzBY8lrbBg1r25MyfTW3uwIM25TND69ATEXTc/F0IQC/vTm0rEru9VkeWro4wwPrRcZGd2wl0E2T7HMQIha1j2b8nMcn7seNWbXx359/KdcDcvpk7pf23GH52ng+8hDr7pAsy8e9853vhPvfOc7h34f+tCHumtf+7VfO53+/vznPx//4T/8h3up3u5uqHvV7wkjWWobUZ5TmcHZXVcZGrDFVZhDG8e6WbY07WczB1DsL7ombsocK0B1R8TAHG3GQ2AO8y7MAcA6BYI8lXYtzqg0ftlkDtFb8reCsFBjDg4cZDMzBszhSzgwZA7tuKjMARBWXmW2JOz4VWWO1ZhDy6VlTjvDG2OxhFUZt6nKD3FWTLxH2/ywgJglPq8bKxbr9lBO0Y9eh54f0pyJcCJcZY5UH8mHjSJzMGI67TPvwBDyY7KFOcJzEAcZ7iVzxLQwksHtYo5pBBR+6HvKQOSRh7/2As2+eNxNZY69g+KaXJyV19YSyisTOTSAYUs+agtuxp+88XIPjNoESy4rEvXoA1t0aousDQ8vexRe9hjWcDbbmVMkMfoUjLIlZ/Fy397ZaEXwE2Ot4BMup/xoOdkkDZlJkfPszXCTp/DSDdn/wY28h0F4cadkSZkdAuIEkWb8dQkCwgs4rKc/Zt1l2rIT5jXJrLygdVJ4PhSajgzZ6ZsA8ldyRjXWGYw0r9ppoE5P1qiVZZVLPovB44syjMCk7J0KVQefIpunWgJUdAjmlv35QZKj8lslounN54uQpRPqtsWcAQAmMzeTXYkNZyD0bvBITn+5W1P5V79p4xBiHYXYBoV8j7Z0rFd81LZfu3yj3Zg3L5Pf3e7uu6MAEP7sWVsUlpl2rvYebPEDTrdvIzkzzeTezgYSxpiDUuDRmvLEHB0/yJc6tJiYA6nBHzEHSjzKHLEktFPD0wrMIfLxdIlN5mC2paoz5vByahFU5jC1E3OoFWRk5oh1ZcwcCOlA9F7jJsqqW7jpI+bIZTNnjhZf7nCIAy2jdHodvN7XZTnnMscqp51FVok0EeOrzBEfgHOYw902c3AKpT+ukzl6vnK3xSMe67UxR3x3iaEJXl/sedlmowfuduZIbu+guG5H9l86zzrVo2IgvSGQl0HK4YfHdyE3TGnjzQAZow05DRCCYY9Na2pMk46Dp4GCSRi09wkgLO9GErljRy4wc1ofaTMrZBMMnTERp2gyw0dPoistO4cfrfOAUzAA7Qxvams0zY+9kNg6oDLNrQwsSy1LAniVDpVayuSjJ0llxsok0zXVHHpqbTppGUUy45rXoqrfLK/ZWPs1TcdAqhikKBPLtHUqRL28HOqIQgyTUSCCE6f74H4aK6d49KoDRE1rvNXWEAC6eHs3M39bBvpyk6lH2I4kT8d2FVAYua2VoVy+W3sx2Mj0proHMd1yd7u7W9fWnWtDTZMHnhGXj1bmSDJq6gbMkaIc8AMwbydHLxVD5khq81CHuHQyJXoWc3C43oQqc3j6LWCdpWm8U45k13j96O5qH8UOS0dEemlmYJGeCF86CrOpbbR8wByl/Nt+YastxdGrahH1ONE849KZY2SlRst6lYu8gyAzh+W1u8POHIw461LLJ+rcfNZwpbPFhXNjedtsieIYOlgzsMjMiZXGzOF5Ms6bsMUW94Scd34dSk/Cx7D3jjm2ji09bdOvnTmoZw77tjPHrXV7B8V9dAQMOwgA+CxADg8XwUcZGK2hDAJmO4J99YSQL1L6MCfRdgbcljjEyE+1iPqTSkNGIaYJL3lU7muT8KhezxnLr55sgXTZiKarwXy6pmgZjU+8QRUcql8tg6BgTDMZ6Ti6EqaVjqCuGiMu1+u3dIqFgFY0m7ZaBYqhNNg1mkMZVVego8vzaDVwGWew2zNLJ6cBl0yJZSMc1+jKLw7+xEG3LsOwG1ZnAzkjJRnTOIw6Wb5nz7fW2uG0uKnQxrMy9tmCjymwhHo4NvLj2Pp77YKj/XUmMwJvrqsFchX53e3uQbruRc09LmKOYE/OYY5kKotK1W5U/bhGToPwwziugTk4yl+VOeSaFEpkjiZOiTWMOQow5tmqZDM9LO2YJ1bVKciR3U9TC+m2dU1WnOCbQ/sSiY4f4GUamSPPXaBQD5wFtvkBgR+QOlU6W2P/ky+1gbLcGGAzR9RYqYTOHSyjtl5H6ytzkJbPNTGHc9G9Yw6915e6B80c6qZyN83tzJHc3kFxPxxBbUMyeLHR7xpNk2FvtZL/ZOx38JBuvjzpg11akmBmm/CShbaeA3vJj/Z7CxLUJveXDYhqEXixjDXJUy3tqmmf/A2ofFQ/SbIbxJSiWHkO8h5ezFiSYclL27wpZckym/eKUI8FsNkVufuHw/85Piafwqn3TO/zKhUy10MKsmOjtlq5U1dGujdHDA8ssmQl1qhsheO9TR0JHSaw6NBGsNxIu0nmUDp2JdTHjJQz8ynx8kimlyKEhRWlruZnPMtx8FkGd9SnQmcd51rP3RYojO701vO9tTmcJjSqj62crqL97na3u8tceAkM5niLOdSErYU5nB9K+x0CTJnDVTnpOuYoBvIi9j6DOUaetvJfEwvc4zM0114QQF022tzk1UiTIGEO7ZSwToom281yVH6w/QYKc5Df84abzhwrWLcfKWUQlx54KWimZ8yhGz67efXp9FFvXWazCmjShAXGzBH5oWeOdhpbLW903GP3QTqQeubI+kTbrLNz8gtw7AbgcM2frDE/KJX01e+qzMGc9127CnOY3hPm2HJTtphdvyJzTMNsMIdL78xxG93eQXFN7jd+4wsA4oupHovkDSADsHOQuDxgESpsOh0VP29UOV53+xZ0CNfTxZCOWhzKzbUanbj+M15vm1eGPuVgk61ZiF+0R9d3r3TvUAi+AkIMMrfy4mRgykiCRuNUJUlyKpgjVhwo7HQdwGDVtH1ny4Yk4cU4ZrQdncU4xDOjJJ92nFRQcBUFGYwDxaUOHp8eXRVlFulwUOPkx2E1pY5hQy+CdsIsOPKKg9QW3UxKO0l0V26FGT3Wi1l27GZYGYB0x+x2bKnuam0yEt5HXFomjnJSSJPJ6Wj9tiNaQ9nqRlRxNEhnJatM3FWilV3cSNOvI+TJO8MCKJDvrBE1iTL5rrdvsTMrPmf1cTU/QtcBRgCOxYRmwOcEDCPgoBN+9bqWIZfr1cj3v3uZJJvyHOAz5av9Oq5fwE13z7Upk7t77rrf+q3/G4A+hyRN9oA5qPnXTR7t2Edk5ojM4M9vYA4O7VVlDv3O6BorZQ62TZwKW2hM4aXNmAP6gh5yMGMODjEG5qhyKbfyMs2y0eY5zKEK6hKZnjnkWHFGGP1X5hC24ByZnSZGaHtrSflvModgS2SVlT09ZY5mF5QVQjTBWacCxsyxkpz8IWVHkvcj+3Hjeu9ItrhcjB/GzJGPNG2UQsYPkTlk60luLzLGHACYWsfGQ2czh8/vqMwRuYeKjDIHyI9ITaexbTBHw9nKPVdjjtErwt0yhwqP+CEkdeOZI0rdOX4GN93tzOFu76C4Jvf53/qd8IDkZp8Hl7VxiEa/t4A1gl6+bsIY/YbypYWiYHytZ5xVt2LFFBw4TCOsZDKCBAOhCAqjhjpsdpl0VTPtTVI0Tr4eVV7KWZksbFoFOWM80k+Ie13bDtcL4oFS4QgqJtsLAdDOp9VuQNy8ClhxFKOzSMGyLDZbcTTDFTeQAlbr3DhY+s1PT9aIMjqroQFNrjyEQztsSzasdFxtuT/yESCWo8VyXrVOLOXGqnaL6MLpegulG4u2JRbt3HSFmD6vLf7F7mhzq93ppVs7uopV1nuUYIy8kyJW8XxGeHBlxk0M01U/K6Owq/nAr8poXLNjQXW21Oix75ZhhXT6J2esS3RrmfY5goQKGHXco0LSMO2Ntmrlz29I3gDHPM7AJfK72919cl/4wtNTbpjVxA7yB/wwc7WNLFgwZY7kF5jDPFWP8LsmlCbkn2IOfWdhfT0caiJRhRkUEQuMOfx33M+AdGmHvNDbRpJpU2ffYLK9+AbFhR9WGezxfSQyP9iyD+UHXhuHpCmoA+bQEqPFmCOyjYzIWIdInGmh+0U5c+TX5VW4Z0ml1fhhoXRwDJQ52uGfXGw3DflBR6aOshkjIe4jsVhsJqP3EAc5mHTCHIEf2O88VivOAffIBuaVOXRPr4uYA37UfeWMelKH34v7xxw13vj7/jHHJN4NBTaZY/3sWOimuJ05khvt1bK7e+Be/RWPtwZGnjJ9gYxGz/z0N/rGJbnRE1v9JZK02WSQI/vPdYjp+0v1ID2WyGNLGGEgHIel/va8pLhI8k6Fhkq68teSbXHb5AtbO9GUYCaAl6YDQ6YlBiWJ2maXJgPRV1+xg37scquwSutRb6NRutfDGvLE3DbTZDGHnm8WnfQ6eRIMiW/BypqW6K8yvODIbUnGqnkTE23XAJHXNBbTkTnHtzLJ6Itek84FXnBUGcRPEkChFJf+XvW73a7FytSua1pBt3WQDiRPjpFkeVUjq+03673jA1bWjgyE+JZhPrIfeRoU/XNVqNMLzT9U3V5m/D26Pj7qZKIOzNyvJ2W/Hv14cl39ht8lvqrfloslQ/KVgq9uzBbbmOZFePjwijNS2N3udneOe/nLXzR4qab0rT6j8Vp0/pyib5Cii29kigYnmCP+pHpl1PglYIl+G8zR6a37ZxTKikxTmSPYLyAzh8WlzAH4JujS6Olx4Z4OJGywMcEIsLJMtJsD5rAsBX6YMYfaabt8ijnQM8cq35053M6qPCS+yByrMUxlDl1iujiPzJgjyiHGd4o5AivNmIOUOfxeI7FAZA6W+BpzHC0+SDgyPdaSj8gczjc9c4TbWv4fM8c68h98j66G4U3mcI7IkZzHHFPOuIg5xg2PxnERcwB45OGvHsa3u5vp9hkU1+3ULjGAMrLdPJINzV6pVzz4a0s16GLVkdbhYz1Ifi5T+zKLH0NmQabWAbrxou6MPexkAUrXWOjJKK2KFhvDO130u3T+jp0JyOulLbFRWQLielGBEh2McBldz+hxRL3b+lF4gyiRsIUITafErTJtt+twDJYQC4HKUhKZ7UGqm8e5iu6L3JB0p+Q++Iu4dEIxdztjq0vHliKaSR0xIissnSVi01AlbLzzfrRYlgEjnXPu5ZWPIK01KOrgxradm67TGOOoh8aQS0D96rntGp/ekXo0aX6EBk/FyWu1uo7iG8WTZJinYXht63Xr0xtlkg6ybMg6Rwb7XXRpAIh7faS6oo9ybZfs2aVpO3TT3L6j9u5uo7NnTOeKU25jgfCMloaCB2Hdn8bP7V0wR/YfMUfseRCRurHgiDlGUY2Yg2KAoDLn7Fbm4I4FQqCzmYPkBA/Xl4OMzpbQdNjsJ1v5je2JzwiAcAaUYVD44QzmiNYTmDOHJFM2pfbr6b4xmU3QGY+VBTStOT9sMQdbOvEmnmaO3o2YAyBbxuJV2Suaz4zIzAH4st9s9y9njhkDXMIc8fe9Zo6Z31WZI3+/AnPcArczR3Z7B8V9dZS+WV1aGfwQpSD6+MU1VbllYX/wOMupgS1JZpPPfq2tsRzaamkA8l7J1vjYmrzgx9IoJ+ukkbcReyzFwLOm4Rlx4CBJp2aGhVlc4dhwgUu/tDViYWomBxnRU08g8Zx6ztXwcvSyKYh55F2FolFuSZAZZzVqHPzcwNdboeVC0A022ZMJ8WW9V9Ms6tfWZ6b8hNLVY0ttdCrowKXzIlRHK+Osg3aGULkKmxbp6aiB1vvqCz6SgbLyjqY9H1XmAtB/wWgAAL/fSURBVNIRQSHl4HdE3WQrl3eVMV2CzYv6JSNdZOIeLjHsFmTMJl9O7dDGCwaXzyozdn5nct2OI1g1voFaGo43dL9pbpi5C+V3t7v77OILTnxBS+0oc34xIG0GCnOkZzlcGDR4V2EOEDqo3mIOMK7GHHQGcwRd58whpZts9BWZQ1v4sOF5snMM0OI22u6rdazMmSNv+5hH6fPiWGwwx+KsUdb92D5diHdoizm8/GNeMz+wzRQ5nx8QyjTcy8Apud6fYo6JvR0wBwJzVLvGwJQ5uDCH63c5c1RtbxpzTP2uwhxwvtyZY0P+OeT2JR73xYVWY+Rm686nAi0yncoUkwjtYE39pHoxYJIZK9c1ZCma2RtSbINTEhMta6svn32TO5IJRllaeJuuWeUGMmYRGGGaobchMa54PTYyGQxcjz5rfq0vCQp/RV3AZnz4X51C2sv5Eoe+GGIc6Tr1ea3pwmBI80NDGdeFhnp3ZarlkMCMBvHXqZWQ2zjWG13Z6d+4PjK4G8Hj6DeUwbSazp7NDCznyZ2O7/zrMb4xeMx1GATd/L273e3u3rnUrk8ftmy9/b19mzn6NyIMH/q7Zo5R+MgPo2i2GqQBK42YgzJcdMzRLffolJ8wB/dlm+1m78FAeGHP/HAuczjzbPADRkUnXQw1n+LX29TwfcAc0TaPyyAzkPnTOfww44DtvMVwK3SZRi0bKtyzlVZgnwuZo6V1FebYqPZXYI7Z839V5jiXEarMJWntzPHcdfsMiut2jNYBQTKdrgxAtCmY3C3naEFls6VRbyOx9c4P0wTGT+nIyOv1WXfVWvw0jpKOjohTDKMiMf4ox5IXuR4HLdISjqKfXZaWOI+MtIZ2Baepmj7VUnuWYbpqL32U8cTUcMbmm6BHva5gHGwmRWnkZZaJpSV5XUE4cDPAdkRlyGo1XAsBR6a2C/gAaFjyxaXw28EsLcM+ctL0Z7vuudOYV65THhngRTa+8vKImq+cl0y0fJHMyPC8RZfTcR0IfXUBGMxtUyyK+QnWuB471vwl17FymWzz09EyDv/Xam8xcgYACprPjKWNbGz1pw18miZzaJhdP+fR3waFDa14LMtb39l/bb8E3RxHa/u7G/nd7e5+O4LMjiBKU5/ZAwBLZg7t5CZscIUyR5lyxqPGJQUI6dbrWzIjv0uZQzfZ0sacPKJLmQOMNqOBS/GcxRy6SSWKrmKHjSc0f5k5zA4YP+As5lDbxmj82U7C6AuWEflBmAOEI3QZQ59hBssG6X3h6zGf3SzHxAIjfvBOCo2/nWDmKUenzOHXhQMmzMFaPh1zRDLKzAFuM0mwxRyZrC5ijqjFZcxRdU0SV2KONj/33jDHpW5njp05qts7KK7J/dIvf6p9IagVtbWFqZWRLwTddLFZtNQA6ZS+0onRZJqfGp10mEYBhzglLqavM9E4to6lITSruHiDp9d55bZsg4C6u7a1Ed3bJjs0hMY8bUURyoeZfWtjs0hk+2BY467rH5mDrLBG6HlYmbEs5MsZLHF53ZS0KJShxrMKqOhRXmA5Qgw+vZEIcpxn028R2FFeasY/nkvOll/d6RsCN/H4MdvBOVoE0g2b2OZeLmp2BU4WoQ+meLJt27tBgca6UcgBQXXwSZALmBkHsOyp7eloUfmeHNmgHxDXX0YZ3ccjH2mlx69SlNF0laYo7zmh5dfuQQFphRmKXTFN2aP4+QkobOWtHSgdZ8tN8XrvbtbXx0xW5U+xeYSkemb5zMVcJV27OHN6/p0n170Odten8dEwQW0hnr3zqwNtbpAbZe5S+d3t7j65X//1zwLI7Ya/EGVHoa3UFiehge5TVNbwE8TeH1osLax598wR9SlvMWoXTG7EHAAgR30CoQ06lzlsREKEr8ochNDRUF4oE3OQ6ZmYg9pgk3Svg+S0DjOwcY+gwhy65wPOZQ64TaVQpgv5vgkt/swcTFLWyhxe/K040/3bZg6yQggMQn4bIj8AlJa89vxAJ/gBlldnBcgWLJlhiMISlAFz0IA5ADmQXW/qgDmOYCwhr2R1aD2LOSIrbTNH+y8v6U2PT+euwhw+1+U0d4yYgwfXa3r+/f4xx+88+wsDbW6Q25kjub2D4prcl734S9JDY4wQXpp06l9sA1geMA0fH3Lv3YYZYmtEgkznGP1zGwxz9IsNYY2v7UodrLkaEuux4K49M3go+UyWsQCDRW67VsZioyDjBt0aWBmp8IscXu7JgCFu8Lky2YZPbW2u9JiH+PVLOwoMWFay0RRTC/rCvsisBVjB8oK0Z0dLexEj1a47AJAdZ6Xwt8A7uNq52wIdyawsWGkNRs5dlMko2s4yVwPn/VMtllUMez3i8wgHku74Lmr1+JBkZL+Hkg5SOn7NZjHwanCRN79kwPKqZ4ML7MABa0mY4+e/x9RZvvmmW9nvaIBTK7GRWJIBfOPQGpewJbKUOka8O1R8UK7VOEaP/5a9GvsF2K/xU37ET0GIKcU9YADA4fCiDe12t7vdXeIee+wF9j2u2898kT/b9545zJ/GcnH3huGzX5mjNcaZOYwfPEzVYcYP9rInDVI86vzqzKGJDpiDfY5cfDnsmYMTW3XMETLqe2xpEi1WBiXmAK9thosxh+s9ZA5NovSS64zJI/sxppE5wNz4wZjD75PxA+vRqaeYox0ZqgMivul182ss4PxgHCsyq9l7T+sYwo75oXJKs/fOSlGGTe94dc4ccj9JOxEyc0S/uKEmhQ0zc+qSVtAt7oQxZw4EIM3x1Zk9lzBHLoNtrogzfiurjOx8Ta13PpulS/seMYe6hw+/eya1uxvo9g6Ka3KPPfYCf3ht9Nottj1kbPawmzbIlJuh1OCET2v2KxSYZACDEH/cEVkDjh5q1S+e2MGhNUydFnEqJZu5hfW0L6V5SS1Q+06hkHw5hviRAgB5Ax6UNsAJRr/Ndu0bP8hog+Wb29RY0kTJt6NcSlO8CnQcyBPTY8IAiSe8MDMDKy0AtyUSvnnRYmmvtIZ0dLtHtl2u2y/CUmVCT1Kc8MgySsMAQIvUAS4zN1QGaIaeJT6WtMSYQ0dAtJ9fTffqS1zML8t4uasMt6UiaW0R+18AQBs6A6eplK10XS4vkyELEWdh9Ca9fXcscOBLMxbCJqjVWGsM3SRC6q54quUh00cAqCMwsey2YWD0u6atri4pivK5c6LPseuaBUPTFvQTeQptnOrJhIW+ZEPjB+/2HbV3d5vcC17wCAB5/lYGH/JbyVWYI8mhZ46Z65hD7XJkjsFLU53lyZgwB0KDsgK8mMCcOZQzrsgcsZyii8yhs07mzCGJkPaTsL1BsmwqtkoJ+4tuexlW5lgoLEkMzLFSfKmHdXy0k7lwBnO4TW1Li5sODelGtluz5PygzKEMBPb8xHq0GI+cYg5NNTLHnB9WtLx6uc9kKMml+5uYw08KceYQvRHFIvdcxhx6zxcLh8Ac44Wj95o5NMismm8xB5XrlQOA62OOqveMOdosJMJhedFAi5vjdubIbu+guA/OzV80sMGf0Xq76xPcEUR0lD6iSJ/+WPSUqw1P9iNrDNIUSxI/HdnQxkJjY5TjpmAtkPLCTPlgNwQkmmftNfbju8he7MezMUL0rI1a01tlbNYC1SWsLQ0djIm3o6211LJhT4/R9pyQvKalMgBsjwdpXdlzhKMWb91tm5cAGdkgrnJSB1nawQ8+euEY1TpQ2lFc+R4Dvq4070whfvCVnLFjRteCxmvNhbxaLApWAjdd7RvLuP6UfiFdiWFQQrW4kUJq6Ufz59MufQrkVspIVzSHna0tfFRl7oXNGcHGzN9/+9yT6MLkJChv68dUV3tHGLdbN9LJ8qe7kt/d7h6IKy/h6JkDA+Ygscfj9uf8Z/cS5kjtxma4DeZgnVU3YQ4gLzPdZA7XqGeOHskicxAH2x2YQ1QquRXrEpjDbIYyR0wvMAfpG1jUgtlnHoQ2GqTLHi9jDl0qqXFtM0erUG3mp/CD7ZGhLCAzRdFmgTQOPMUckR9ivZrzg3XInMkcLAWu+1rVu5sHRrT89D7q9fg7k8S5zOEdG7Em+PvDvWSOde22khEd8uOQa+vY8cTvfjBH1TELSbw7c9xat3dQ3E8XLFs2ymr9ZnLlN8t/CzXRKp5bMX+oY8L1JX3UsqlbMH6pHzmDAcoXlASYbZ0kxPiZf0cL0iwz2/pPzYMadZXhIBLPHo/lEPPNtQCs84LKNSULXfKhsy7YgMGjjSaxdUQclnjdEzSAiUUDjTOUI3RZiMskWAh6APneEHw9Y3bclrWkzo5QQNZBkO+DGg8zwlyqmvTgc5Ip22Sxhwd86VOUIcB69yMKWH2o5aNaEMNmVcQKEQsZHPTW2SWl4Fxg5inLnTT9WLW8RKJhj8/YqGRH6lqcpQejlXWv1dRQFzcCAI23j3Ni1Xmc1pYOt8127qMZu7v1zjYE6JlDl3Zku9i/AJhHM5DOHIB3ep/DHLM3nRlzjIKMFtKLmc4jAZqOMAch7PkQ8lyZQ+y+zaK4J8yhdnDGHCFv6giALPPUkzwo7GfhseUEufSgBGvdMUe7z2PmAIUuoAuYQxlhtKmmdp6oxUyWk3P2x/yAwA+BBRD0Ruy6cuZwHXsZo1HrtYs50nTYysG10dtLwbaFG38hc3iHw8SKhulHd80ckR+4D+NeuVOEycu+lsOWu07m2Ez3ltngnTmy2zsortupIZo9dNKIxSaJs+f4oawbAJYfsQE6q85OVPSXxew/moqVIpuAxHT3wJEO2nCWUYw0vtMlFeCixK2GxKbTDVptbeLNmIcu22bYah+7Gu9kRt382iiCMx7UoMX41JqEsGbqREZHEzjowNbRkOtKW97Q9rVQvaPOMdsVM0b98Jo7tvoc61fUoS8f31Hb4cXYMaXu5WYciNirT0MZgGWt66g+kq2DzXkBbPf3VC66Blg26xrFSOPHxWbt1OvoHtesxyguTWdklIezSxzPTj3xvSqjkaO5mwEWxpdA0H3UyEDtOWZHd7e7G+MYA9vbuW3mSI2Sfp/O5kRmjvCyOepIzenN46t5YE1kJjBjjpqPzXRag0vxPZXQxTNmjtKOGnMENhgyh/6IL41kfv3sAZL9JyfMUbJW+SHZ6Djjw2TbMtdVbt6IOUK/V0cJzDRgDtXR2TG+TlO4luOd6D1gpZhOZY74pj7kB9FiZBtBfndiOsoVNZetuGnMHIAt/bH4z2SOLg1cH3NE7QDdp+x2MYfeh505bq/bOyiuyaV9GRA2ZVxGnQ6y8VHqAJD/GeEoJ/FY3G84T6uLHyitfmqhutGOGoe1hn5h3Fyp0aQsUz+rPnFYvcLAQMa2iBhllHLDrMZUTazP3eAcb5BJ/UnWaosZsevBQLIjhBrLRYCOwbGPwxKsx3+6ZguOsk+FG15XaA0jJKo2Y+mO29JNo1asgCzN8Py2+I6cD21Rg24yg0JpesMMveq9yvGf6WhSynovCcaaPjp9UsGlZa2Ns/TrOCEyEQxEB/YNMPPaRAbxgiPWtDaVAYBbykuMXFOM8dWHp9Q5g0imFEt5ZLygB24IDOPkEkH04HLaDPdpXQAKM53KtfS9FIKD4Q13PfleLr+73d13J0+47bVQmENs51oagsgcFjB9LY3Y4KUpuUnnhIputiGRBQylrsgcGl8a6Bg0ZJU5Rk0UDzQ35nAeuIg5WGyM8YTEJ8tWhswBn8FXmWNFe9FlVTdkTu1wnsrflmZE+7wCIFnKgY45hAW4vKBXfhgwh9Y5LS7ljlWWepzmh1CiYtdPyaxCQs4chR9IrfyIOZQFTjCHhxYWUN0MH2FzXhiJOYymL2COqJuni/ydQ70YS0+Zo4Yb9lqEHJxy94I5ZvHOruvz4f/fgm6KnTmS2zsorsn94kc/2b4wYMPYBFDcvMo+tEFFMgbm7K2AQEux3dY9OGiFJBAtHm0EgDgzwh7d0bVwnckVt80znSBaU1sAYZQlWxQZt4ru4KJcYG6zHklFdLdrb4R0xMI/AV5hI+y6iZU13Iw0dVKNEa0NfBbKuulmO0RhUyNZF3qwUQqdPtvkVimrtulTO9pTc6brMRtcqMFZxMiL3kHGUIfbbtoLteUaLVtsxXWwIazW4aHmUY8oU56zZQpyvTFOO8WkbX7Z4rNNsUjWeIJxtFECgQ80uQPr+tmGDmRc1cql2wVc8tqmD+vToHNAHGJMAbSODZVZTAMCsLZ1u1YfRQcsuMM6cyZuxeX198B+kofHx3a82WJXHFP00a57gHgc+TlihTqFtOoPv66OywtGeirK82oyg3jcjUF/S3bWMTGyp/7bG5yYRy3DZ+/8yoYWD97t0y13d5vcxz/+G/lCHTpsBsccYX7MqAkwQIsvUbT2koHR8aUWcfhqSfj7VPPj0n6MmCM2dknmQuZgzJlDnW2oGZkDbsCjDUrMoen5bAllDrJM+TIN3ajRFiPIvlQHSOeCxiMRbzGHHR0amINAxgJHjPlBj/UeMQcJc+gNW0VGx9f0xDE7ZlRUWlh4QJiD0TbjZvGzslqbvkeidkyqZlXy78whOlhxKz/EyhH5YcwchJAfu9XOD3prW/qrdVLogI2m1qJrympZtlkXrYQOipGBOY5W1r5Hl9f5trl4nImhIXRrz0uYg8JfvBYPrZkxh+fUr/U2W8LogNw1MweA4QzSu2GO33nm/7uhxYN3O3NktzXhfnd34b7y1Y+3L6GR8+ZDH/SBYLSqBHsZNyGexpa+pfc5Hl+v10zVnlSKjG8uRWgGi7yb2GY35PhDhNK6MaQB4tw8cWw5df0jE9I2jEwWB8Qc6DRGKLBAp0KShwmLZrXxW1MakjTFMGTp2ydaz3zU7cjhepRjwhELmBfJr8fBssGH6sZQuQVM7YCukQzTghWEVQBiFZlV0lnh+jYjvmClpemh5SNluNKCFQccg0zTtfkxlpRXQPKDBdr1oXqvIByp+Fm5uN4xLysOONIBR9Uz+S2i35LSYSxY6YAVi+sNJBmfG+H15KjlZzWylQ+DcJQRJD18zGRCXYv1jS2dFm/M12p5ziDAYDmGFean/vo7Xh89hjk+2HO0colvYNxjOqfiTo6CH4/D9HF6wzOCoIcOrxposLvd7e4q7pWvfLH/0LdB+Fis9QuHh5TMwy9k5qAxcwSZjh9iuHhdXxrZrxFCejO4FljKOpzDHH1Em8xh5VN5YIM5pK2PzAEoc8irbtyog6OtzKWnv50d5FW1MgcHm8qug8kBYF6MOdYhP0SeOZ85jqKPMscKZY6Dh1Xm4MYCatOlYMReNvt8THk6FOZAiG/ED5EFrsoch8QcQ1YKdaAxx8HCt3JQfljgXQteP0bMobpE5ohp2X3tZObMwYE5rF7L/6eYo/7ZM5HiOZ856qN8FeYY6XK3zPHow//rQIPd3VS3z6C4JvfQQ+EgR2bwImPNmQXS0o7USyl+HF4M1LMuB4myMWiSmSmqLzEaJjzVHI4MjX5xp+U449FGEEzPiYa1d4blFIhYAPbJKa9mLHRdJ4UyEyhbmbEs+brNWaAcVmNdGTgs2pyT665wFOjLRg1C2aVytpF4KSP4dz0yVEdCmGXkg+KRVhI9Z7YhlQkJ64hL0KoZKQZ0N22bLyABjtCjuGREwm5F3p27gYrfJ19XSqZflmEDMHVrHPUxGSS9Y71q9cA301LoVKOrMzbYvqshzyNEGuMK7X5wwx1y5P/bxaZTnEnhRTd6yiZ0bc80hTuDEL7XhGLZDkOlqIduNkVzFtc5fl2U+viVdmXWBvm61tDJwwDRDe8fb5R+d/K72919csuiFgJuI4h65qgtCLsdtQ0Tg58/71luyhz6uzYuwfwrW6QVE4U54ptLnG0Q2caYQ+PrHrlrZg7xGzOHyOleEeYpd4gBGHMAcWTamUN4JDCH2qa8f4TP3lRGMPvI2Q4rc7CdnBHyyu1leaHASoE5CJT4IZbRygtAvuSCyPlh1aIwq575wecWZOZYA8MQMOSHNiuROpnIHFrKlTlUt8oc6o6AHMkurGSCizFHKD1LU/c5y4Md+VtlDi3vyBw9P5zHHPGo01a69aHeZg6NLzyaU8ezM0RrNksOtvyujzlu+CvvzhzJ3XBCdPdDP/RDePWrX43nPe95eMMb3oCPfOQj07A//MM/jD/8h/8wXvziF+PFL34x3vSmN22Gv36nT0uuPNZATFsBNnjIsbXr0/Vig7iy0SzXVbC2d2pbGYOpQ8NmREY1Bq1Ip1SkJmlAh88WeeuiMtrwMMDaNRz/QFj1ekmz9frml+i2tIHAq/bWBy8gyXS91WE0w0c20EYCmKRHmfIfx9GYgR8UIvX34nmWuNOMgZUA9jg03pW19z7KuJ/Nigh/Ky84Aj4ro5OJMzfkT2ZstPB5BoTq38mgjeysoUxNnnW5SNQvylLxi/r25e0ytarEawvqfaj5zzI80K+vilF37vRW3bSu9XkaPRLjNMojMJEZuVN+Qxce11m6EULaKCn3njfY6XTLu/nb3e11t5o5JptJnsscXXQAsPI2cwxlCqfU96QN5ujj22COLX6YfN9kjlUV3GCOmA9hDrWz0flshKDDSeagKXPE+BJz4DRzxGtI8SJcb/YQFk/mh5hXpLggzIFOxlgJIQ8WbsYcOltj8bwlfjifORhj5rBZCTrLtdjnLX7I13uWu5w5eubKbDFmDveLj9QWK51mjvoIbvLG5DHameNytzNHdreig+InfuIn8K53vQvf8z3fg5/5mZ/BN33TN+HNb34zPvnJTw7Df+hDH8Kf+3N/Dv/5P/9nfPjDH8arXvUqfMu3fAt+9Vd/9T5rjvxERWPH2XvmtMM3LYFUvxJX8hvJkPe9jipznSY5uj7SoX/u2dIa+EiLFv9KbGpMIytpuFSeLheNq8YUG99OBLA+o9rYjX5rePvbkKmGAgO5kT6aTS25kYGYOZ7op2Va9bTppMUQcro+ys+WsaoyvC1HAhSdHiXOcO99CY9eJ8ROZ0svlkWpXwp4vgRiktcSR5zSCAUhjT/UpfkdKjrZJw3jOtk4TFMJUQwyxiuGaVXdOr8S/ykduutXyM/udvcg3K1mjpG7oE3Rvo2eH3RzwnE8NJJBZodz2WJ0XZO8J8xRtavMQSqDaaO3Ndg5ayfP4Yckz4EfNmSy34w55p1WlRHa5/yNzvWbpeMMU3Uzu959jvlhHI/enrHtnpfRjDnyi3/kijlzsJVT5YSeOejKzOFxjZnD05ncq2LXR8zRx3e+68p6kLHrZo6tvO/u9rpb0UHxgz/4g3jHO96Bt7/97fj6r/96vO9978MLXvAC/MiP/Mgw/L/4F/8Cf/Wv/lW85jWvwdd93dfhn/yTf4J1XfHBD37wPmsO70VY5g8YW7iBX3nCPBif0zad4z30NxDRzxKI0vqD4OKbol3TD5azyDdcyq4YuRFQJP+YhhhnjCDEZTrd1QiVS8kgWSeIQw0gyykKAKVRjqhzHHWIMyGsoR70yGvaXK7HDoU0IwNmgIajKdARjUFZIxjk9KcjEjk+WFyiN0U9FkunHxnQPTtg62Ht/klea6Wz0RuO10YyOT6Gg0GGgzb7g0Gom73EWQ8xzlY+sZ6IToj3KJeRj/pkt4bPGWRtAfAQcMLnLOzM7xKZmRs0FebROg1vETFMb8oFf7u7le5WMwcAfeBYvqrzjbK5mlGXPIc5Tjjjh9oY8DDJzr+T5QuZw8TmzGFlcxXmqHahS1ttvMtsMgeHF3rplFg7frhb5sCYOSJHBFua/YP9k7BrYg7v5DnNHNHOep77GQhj5jCbWvihpjNkDo2vMIfb2nH32jZz5PIeM4fLjZgj5ivdb+EGnRmTdFK5KXNgyByM8UyJ28ocI6ftz84ct9vd8AU5wDPPPIOf/umfxnd/93fbtWVZ8KY3vQkf/vCHz4rjt3/7t/Hss8/iy77sy6Zhnn76aTz99NP2+/Of//zVlQbw1G9LXMzQnYIBP07UjKP0+lvjw9mmEQQYqP2K9Y/00QuGz9deeRz2k0P9rR0OQLf+k6KeaYfrlpDK8MLwNaKej+rqac1xnZhlnPynXwinY0smPBk5osk2zGLbrZi56azlVNMjXc+q5asqcDstRRNi6E7PbLshM8ednNs9Oix+H23HZm7rQnWXao2jGS/Z5RpkO34T6/4KzW8F+Wbbcj0v0SWsJCZNimvltuM3CTQcscoO4X6vW97XmHXXmxjMTSa2ee10Dwaxrq/UnUjaqlNm6ZIIEEwkR3myriWVI8ySzCprQ8nMM0jKVMpGlznG6uGfunaURG/YfdP8Qsp2QVvD63WypUOs5727jOJXllG9Q33wmKDQskjdWUUzq/0EuedaQq6HrrWNZ8Fr9ZwdFRZT9rzm8hkKzPxOOJ58L1F3CWlYrTft8tO4yW7fUfuL091W5nj66TsAQnskbRSH5R6sDYNBCNLzqTxykjmCfGpnSgOUmIORGgjnh3CtMgfBz4WEMwcIcqLZmDn88K0Zc5S2yuLYYA5jBGWOki+W0mECLZ5IZg4pxcocCPsokB5jyW73NpiDWYtqzhx+qmpgDjQOIGaT0X0TSHjE7HMqKmqnhKCd6LWQn8ShzHFHmCOeTkHEwhxceESZo93meJzniDla/jNzHEhfxrn5UWUOUkJo/zgzB0QHCPeovT+XOVTnqzOHxjZnDqk5cv+8TvXMEdKXYJE5YpXVvTrqfhrh8R+6m8Qco4RiJ0tkjuP61BU0uH9uZ47sbvwMik9/+tM4Ho946Utfmq6/9KUvxRNPPHFWHH/9r/91vOIVr8Cb3vSmaZj3vOc9eOyxx+zvVa+6ux3mn3zic/Itm8hwTHP63Hj/MMf1SQ1vjxECuoYmvmViIGPKYeLhkaX41AYzpcbvXJfzE7Q2QhmUCqsgIRZgng6nlEO2Bpa5ny64MrCafxxhED+53mRKzz37tdbBoGsiYbqpwVpZ12yqri7TZhIAeQShjRwcEXrJobtUH8wv97SLzJpHQ1T2CF0bqmapfTIOOMo6zfxafsARkL8l5F935UbYndrXhbqM+LHrfQThDuL+ElG3uF5ycd1MJuenlVsMP4sv3ycvuyVdWycy7gf4Ttw6OiH3znHK6xa0A8cBX/2Ok6fFZdK7Q/uMoF8ehzpKEv1mMlcFhYvlBi8D+v3Z46euoMXudne97rYyx6c//Vtjj2jvK3tER733aOr1iB9SdKWRiLxgLz4xzRONCqNnDoR2dygz8eLIFpU5vPemV6A2wO1d1qauu17+gmQbYAfVGwuMmaP5c8ccyhA+kzIzR+uW0rDOHMfAHNE+rmI7k00tp4Jlm3lI9jkyBwJzMLtNhTECCls0u672ttct88PKyhvOHDazYsYcGDFH5aEllUHknjsdj3i8LrP0ZXpl5hiHb2W3hPgA48Upc3SPiTHHaE+rltaYOep3l7kdzBGdPtV37tyQJXe7O8vd+A6Ku3Xf933fhx//8R/Hv/k3/wbPe97zpuG++7u/G5/73Ofs7+Mf//hdpftVX/US+VY3lmLvRJjYQw2mzqBh2j057PPwz9SZ4PHr9y5sr4KF0O6WjnPCWsmaD9uMqrR81ltYhHInQ5ZRymItVw78YAZcXsRDJ4JuGc4WrwKKGHyGgYJej8dtaR7zZlMIccmUTG6bVjadAjBIOjZdMU3jXAqAwNPhuZ/BhPirMT/yQeBEO1vIdTMZl1MQOLJPJ2zxHbDywUZofC8IPzb1GPLYPvVI0Hw0qevvx5RFmWicueRpJGPgYnnqYSLqhXAfAO9IYv20aaJSd6y6qszSy5jcktIwPzk2LXU+laU4CSQ4X09+pOWD5F+hol6v10aymISt12uYU47B9iiNwj9y+D1nxPIAnfZS3s3f7r7o3INijnzMqNc90lMkgOlDP/PbGjiZsoZ+zuLjIFPCdCJ8HcxBY+Yw+1YgyJjDbWAanAi2FPB2HFhaB8aZzOHHc24wh8YTmAO84LhSSDvoV5gj2ZnkF6/3zOHLNzJz2IaUfAjHmcpmmdV2h3LSY9GPXBnmgCMfoEel+31W5jiUzUNPMAfPmIOCTXWZlocZc8jgyoA51rtijjhgo2FoEN85zKH7VPTM4YNmuWrPmEPzvrVxJjb87hVz0MR/5CJzjNyjj9zwY0Z35kjuxi/xePzxx3E4HPDkk0+m608++SRe9rKXbcr+g3/wD/B93/d9+E//6T/hG7/xGzfDPvroo3j00UfvWt/etWGJttwAabolANAK8GEsqWsVrMpxiw623CKG9Qe5eyGIMyOKmE3BpODPLsboZXTGQgsQaAM+xbBNL2QDBTuznABOMiYF7XioozEk8zI55gMyJU8LNw6GhJZNgUgbeGoRghPAqdEUfSl0KknaOjkw3T6GnvRlSzRUbF2BZREPzRupLpyWmxBasJWBQwgb7/tKwMLq50rocaI6fdFnxbapkTrTdAnHs61SbGri3NAs0CUSDMiUTClIhhzf1coBvMg0T8LKqx1v5vGJHzwdMKUpvNzJiH4iw6FmtIdnNRmtKfpr4VX0jb14sVK7Tv4YaX7QhVuh01NdBlMZ//TUHacZDFohU2DjuJ/nA/AXCQx1QvLrS7oPXU0VDa7V0hn58cB/pFmVTWlTAK3bYkPPpaIt+d3dOnf7mUOcmsfypKZlFyEoAB+9rw+3gkBpYBIjCONAfwcdOtUmjZW1Y12npncAWMJy/a6ZgzlvQaSWnDFhDmTljCfEBpidF7ZYGbTMmWMBNbvG0S/H11mxwhwE4LjKUemsVia+KKtefg2mp/DNhcyhS1J8NL8dEWpVIMRXmcPZdswcLGH0uPb2W5ljwcorDuR5HPGDvpSbE4YhRK4YyZzHHBS4x+36nDlaOqf4wcO6jvF6/+Bcwhwxfg2Zv51mjhldeblFyQfHHBrYOwhvgduZI7kbP4PikUcewWtf+9q02ZRuPvXGN75xKvcDP/AD+N7v/V584AMfwOte97r7oerEadMxoAJ1a/aLzUy6EAPokgXKQvpCPgKJWfr5Jf4MZ/HxEGTaPgrjCHnUw8dwozpomeqmXX7d3vJzXAlmqsxAxGTGzZ/2InuDhzQKwfCXL/8jm0nhPfQSjoM8YpbjFMvyx1ZMKbzKcPit+tqRX5T1cpn81+JsSyT6jacmoxCSP9VhTenUdIvuEZbC7RvLA31eXWZVvYd/zaz2IwWan+jnMmuRiXWhl8EgzfD0B/itdavPe7y/g7pg6fePy9YzPPPbsomz6xeBgl3jy9qZB+wIuLsjvx50BnZ3JXfbmaPjh5G7CnNUv8IcfI+Yo3up4Rhf4R5Ni1ubOIpwkznqbAn9OuWHwcvOCeYY+eXlnkAFqWbrir0PsyhGNhLCHL6pZAhnzNHHdy5z9PzQy9hyhk3miEtBtphDO3GiTfXPqd7lD0GHEeNZfCeYo/dfbMPLjpXQM4fWhTk/zDavHMnksr2EOTzXlzGHLj/NMdx05sDOHLfY3fgZFADwrne9C29729vwute9Dq9//evx3ve+F0899RTe/va3AwDe+ta34pWvfCXe8573AAC+//u/H+9+97vxL//lv8SrX/1qWzf6pV/6pfjSL/3S+6w9ifGmrkPXg5CPzOvDFEc+9OmKlrvIAGJIGHGXoajCvPZy89vqCa1h26du/tk8XO/WUxuuljiKIrLAlKRX3dNg079XPhZK/Mkm33pOw1MrCrYNmTzDBLYRGIUJHzlx/bWvmgbp6OaWbZAi9H0zpZkIGi4WAUuZENhGRVKjmm5M6MWnkKbdZBGhZlAOBjQCQmU0IBad5ZfgI2mWvuXADROzbLapL8s0GJ3jUB65v940CTIakslHDXJV7ubYiEwcZYj1El39sam40FEgSiN6FP7jIgNwqidVCyr3lcrn8MGaPJc6bjEGX9/bJs0bSZvcVMdB6nqc10iHJ/fb3e5uh7u1zBEaUq5GPTnKbZW1iej5wURafHFPq8QcXfq4EnNss0c46lRtLEkbaKPyJYYROkiDSVoI8W3GZGbMMc/PmDlgm1xOmSO0m3ozdMZDl+4J5tD5AaeYg9KXbIcTVnGwHOSFN+SHwD32q2OOYqMKc8zlIDNhPH8dK6l8yFwmjz6vBGGlwNwdw1ipRgzX/529TDpPyTnJHKZZwLjrZY7xg0nl3sT0jOkZSID1wJkjpzZijsFTtLtb4G5FB8Vb3vIWfOpTn8K73/1uPPHEE3jNa16DD3zgA7aJ1cc+9jEsi08G+Uf/6B/hmWeewZ/5M38mxfM93/M9+Nt/+2/fT9XhjZU82MNnubx0BSO2ilFOfgR/Qa5WfsGw4Qva9BcGNqN7oNVfDddUhs1oVPrgWUOmeaytiQWdychHjZZyGF1aEI2n7hrdwvtyETvbmlWlOImyf1WGAUkwYDbKo/F550BbndOmKPoUSc1Ei3dllh2mreAAAo7MOJDLRKjU0z3UrUxieNvu3NZBIf+tssZkSQbR4U936V4BLKxFeQBjtdVFLLoSFqzUdv5aUuEDOh1zCXpHt3Ke8qjmzE8iyTJHLFhkSm5k4iYDmXJZYJfbcg1/jDydI/I0MoIsk5GyiPyq6Ry5Tj3LnS1x9VVEpFjB4y+W+xDwpj5u5zvWzqvOI9Xk63AKxDxIZT66eIPdaLrVpfK7u5XuVjMH+eeUBUonvYlRa5OtAaH4p4agROhHNQzdPWWOSQrDlyC1Y5ErRp9D3beYo730B1Bz+61FdAFzrBxff8UvsYWjntkIYQR/CXXZZFNJliueyRxmPaWwR/zQlosoPwQZKycy1svM0ZZMLHUAJ8QL8lMomg0mMK/QR806gKbMIXoHhsovsDCZzBxyHwb8QALVkTmii7Nw1HaDF+iJZtFvxBxRbz0VJcZ1Xcxhp66IqyeljVwmYM9rt/xZQt8f5vDSSH7ch7vxbmeO5G5FBwUAvPOd78Q73/nOod+HPvSh9PuXf/mXr1+hE+7Xfv2zAOBr92jSiydGh1h2xtX6Gd7x/UgqB4rQxMNfgIPfrFVQO6Z2O4ax47yyTde4SHVbYGsgzbCoeoNp+5Z/pjAgEEKls1FD5jTPGpZdvyjDUcayJC9KTKCDgoEbMmbCop05QRcO8uvK9sIL6AswxFg7VCiwLAnU2g1cCQIzgG/31V7qmRkPLa0TioK/xvoQQSBSRltkNeZDxDZDtx3RpZDTZMAI8BNgRNMhn+mwgvAw1mLUskE07Qkgbpt4HWhte3aIkSNZH8pYcWAEiG1jBsyMhxCOTdP4JIQefQbzbwm3015Znh/dAYNBYmBX0VvjY4YcYcYCemRagCUdASTfZaKlvKDtBK77ebRik+Ne7dkir1tgHNifcdV75TZzJQKg1ROCdPO4I7SdubXv32ePSLw0PybM67qn5EWm5eZh9DHZgoZLgaIDQCtz12UU9s7x0xekcv/dfuTXF7e7bczxmc+0I/Rae+9t0pQ5gDRoEtuFi5iDy2d1J5iDN5hDw3TMwW6g6rr6mM+4O4Dv3TUInS4b2LRi7DZVEIthGeLQxjoXrKveBmn/OeybNdBB956oSEaAHPmdXw/XwBw6mKL34Cj2kXVmJreBkXOZQ22Q3zP2+5j4gfEwyqsoQeL3vCx6nCkWrGA8ZKEDcwAIp7NakRMOWFeeMseK1XTwV9U5c+hyicocms05cwBEa88cUkUWBo6kT5Zj7gG+FDoyBwDTgYIOfII5HoKejJKZY9GUyyyXGXNwuEdrZQ6E/UJUr6Bjzxx0K5jj2TufuCCV++925sju1nRQ3D7n2xQlk7NRgfRBXrgY6CBmRnoiG/3NDM4AYtAqJN6obz1VLhgsNejeypYIu8ipT8StnrS6lKebqpw0go2q2MNG4x9+8hquqQhBACIobTKEdW3N+YqldVKwy/pmmq4KU+vpb3e79Z7HvpVV7s2ytNkBemb1cdWZAp6Oxntn9XPBKZTPHQGdhRYr9yZCuLOuAjS5zI4sy0eIcOBcfndUmmSmgaS3SsfFAgobarawR24dI+2scVOgGU5RVadMqim7EwxxTIfshussEK8oR6xgGVHyEYTmfwclr+LnnRMRkkRv20ubUnyKDHk2RdP/aKtgl1ACzR3J8+Ml1I7uigAYkeFoMWfXYKIfdWA0eEO4ZkafFeJ6V1F49v0cl9G4992Xcuxudw/WzfZq6gP2l9QMVr6oTJFksNHGzBqM2rCVtitMBMgMNGQOIG1+Oct+VVQb+qh05IfKStGQR78zmENfEG1GJhMccCQ56RDQ+9dmPMCYA8ocrDyky0Ebpxg/BOYgtE0zCZDZB2RldWclLEPmmPPDytw2ySZqnQiJH2RnKhIdWG17uFc2S7Dpd5Rrp5gj1oEZcwAkbNGWacSKMmcOtjD5RVx0EH5YUiVhi0/jyjIed2YO/UUdw+TZFL6cZYs57oT8ROZonTd5NpHKHS1cdlo/uyoPWIdGZY7o+vhuOHPscHKr3I3fJPO2ule8/MXt8Ql2rX0pTwiHz0GHgIcuFxm+FlT/yuHEHOOMerCDQJKPMkFNCuHq9xQfDyCJkZqRaLjywteaPbIXMEvJyid8z6bQo2OJQ2d0pPlnFP7CI8AoabZjRm1DKA4bPTEl9T2cyASdOPytq69/bJtOBpmQjm1ghdEmmHKWOPcbOGm++uM/Xe90dBb0iKyDHNkZ/NJxW2Szz/z3+IhPPbf8mDaQkmO4WM82J/iRWP65hnxrbTG/VC9c7pjKDeEosAVxMy/Lq+pdNgr1exXrIYL+CP6U/vzRC3Gy1oXovN7lNHN9GbvBSCHFx5WDzlGX6mq4nF+U6/V3L7MNCqMVamDgocPjg9A3yNX29Sp/V3A/9EM/hFe/+tV43vOehze84Q34yEc+Mg37oz/6o21EM/zVoy2ZGe9+97vx8pe/HM9//vPxpje9Cb/wC79wNeV2d2Pd44+3vS7a8zV7Yjcc1zbgMuYgTbYkpzIdc+hPHsup7Jw5pNVj7rAqMUeMAyj8oPkOGxAG5ohLMnLsV2UOIE1TlbQhdnWTOUDt2NLAKWabzmCO1XSbMYdzQuUHZ4RwnGhknsIj0YZzYgTZSJMJ6wZzJPtszDE+4lOPF83MsWCbOfzvWDjA+a4/ZtR4SP203PjumSP/nc8cWe/F61+o8tvMUZ71dK3nB06Pz+1ijocfvuFHm+/MkdzeQXHtjrJxHhh9N5zhOgA/A5w60Rh9crMKeqIClz19+qhp8JX66AiU4CiZcV8T0ieUyiBIsWw43sFNTLxEyOWHyYdOhdKR4o26AEeRUShIty8YnKobczyWOBsFNbBu4NwQ65KMbCTJziXXsjCjrToMDN8xykRAECiws84tXjIDW42sG2uBuQgiQY96Nrj7ARGOjrzgqHEXmWMAoHTaSQAGB4cMVFamAay8jIoOQfcKKN5BEg11BYlc3uvET+Fw7eJwmWjAYfL5vmUAmEFGRAd11MUT/7QuVjcDh1TVh+G9vEyDwFb1Obqpjpjv+u9S9xM/8RN417vehe/5nu/Bz/zMz+Cbvumb8OY3vxmf/OQnpzIvfOEL8eu//uv29yu/8ivJ/wd+4AfwD//hP8T73vc+/NRP/RS+5Eu+BG9+85vxO7/zOxfrt7tb4OqDW6dEjJhDfzIyc1yQXlfbTwBz3ObCrmH8o9Mm+RE4tXbhkweRGD9EBa6JOeJARmn48ovnaeZwewtvWy9gDgCbzFH9xswBHPkgHQxqlzOPdJ0qUD9kOyyfR15afFxtI3lnDDh832aOxD0T5qgdHHdM78ocZWAkyGgnxYhhVnb+Op85/L5mv23mQOcH75AayCeOC1X3NHO4bY9pXo05YocaOvmdOXbmAPYOivvguHTfB6cPEHM/hZLbf8YRDBslsBGJ3DYMoyaVDSpEvyTDA78gU+NHCW/XmCzd2CoZMFCJL4KUds8Gw+v7T4XGjkPW2TuB2igEhRGZAmcBRDjJ6HcdSYm9x6Hhi73vUAhxmQgdOV4ot7hcMVCMGHeJy4qkvpwGw266eYO9Bl2i3qOjSaPxj/Fb2ZvM4GgtK3dKengHTj5arIVdoCAyO0osGh5NZxUdejDw67GDYR36Qcqn3tcATyijaox8f4oMuvsWylfr2ur1weEFKZ2YFoIMgl+Fglg/kgueCsxYl/CsufXOUBIemZLGOHpvBdILwijw7qbuB3/wB/GOd7wDb3/72/H1X//1eN/73ocXvOAF+JEf+ZGpDBHhZS97mf3pRo5Aa8/e+9734m/+zb+Jb/u2b8M3fuM34sd+7Mfwa7/2a/i3//bf3occ7e6+ulPGO/iN+CGOjsZj6+iMZ/dS5uARP0SZ8rYyje8c5gC1JRL1Dehi5qAgdwZzIP9O7b59H/ODdiqondENkDWiOXMQRsyRByqqHQPcRpd8d8wBxEGRart7m0qBK3rmQNSlMoLpMGaObIfdFtvM1BSnM0fVO7FS0a8yxxrzGsogXbfvc+bo+SEzh9vtu2EOBuus3cIcVr4D5ojMkPziYxLqR3JnMUfsiOnjLNFMoj/NHLNXr925u8nMsXdQXLsLj86oB48BOzorXGoXBntTa8tVoqsP90gkqTPzH12btR6jeNAa2bkiM9lJK8KD7wPuUjdtkFQlDjtpR2+7RlEEYOoa9gos2kA7PJDF1zXAdWQhxOWzJ3IjbL3hXa87klHsZRBkYlojUIgv+b2fjxQsyPrPZGazBoBh/qGbV1EADArXo+EfyYZpl/X6RIc8RTLrsZJ3UMW41N6OdF8H1+O9Qfotf0Em1Uf0z0SMY3xm+8ZzNHGan+F18FS3rZSmGlD6uPluvQd/AD7/+c+nv6effnqY3DPPPIOf/umfxpve9Ca7tiwL3vSmN+HDH/7wVM0vfOEL+Iqv+Aq86lWvwrd927fhf/yP/2F+v/RLv4QnnngixfnYY4/hDW94w2acu7u9Lj1/9WEzDhgwhwiMmQPjUY2aXry21RTxRLVTIDN4F2qfFzCH2eY5c3AJq5/EfTqnmMNnRozcoFAjP5SXwhlzdEtOw19ijsIwudMg84PPathijvyyvyYdYzr9TINTzOFpjZlj3KFQl8XGW5iXbETmqLNMMyfk6zDZyieBBUbcdQZz9Nd7Toj3a6Wc/lr8EcpslL9U7TB/JmbcczfMMXtceRDjVZkjNl23wu3MkdzeQXHtTpoJEiNVqUCsW2fA1FqPLJueA13WfybZKqZ2cBTd/C2k/x5/VznRuS3z2IivXE9Hk87S7UCDfWQhBrNw5CMaMqoAMUIRAKJMHtVAKf7eQKlbQ1re0UABGuIsgPbbRiDkT9dypiUbNguh6WZTBqE6ap6WBC5qkBRM6shJM6ALjqtOu1S9vXMgdpboS75Nn9RiNeO94KhLM8I1Tc+ng0aYgeU3j3DksuNSdjZqQQFGZK1pW+sZ0hnsbeHpaD2IU2Yd/MCTtamqc4lP64/6ZXCSvHaAwAB7faxpAUsCjjyTpK+b+n20XMOfmvynF8cAPTcPPPGrTZxdDwlehjMPzt2r6ZavetWr8Nhjj9nfe97znmF6n/70p3E8HtNoBAC89KUvxRNPPDGU+dqv/Vr8yI/8CP7dv/t3+Of//J9jXVd88zd/Mz7xibZbucpdEufuniNutIbCfocNCLl0yyfjmEV51rhsMcco7EjmfjEHtpnDtt+rDWWcXTnghxlzJL8ik0e2cWXmqHZzyhxQzvHfaVlnYg5depGZg4U5jp3ec+bQdI+rdy6cYo7jCeZYC3PEwYg6oJOZw3XLs056FrgKc8BYbunTCcwR7bnWkdHSD71HdWZtWpKj9e0kc8B0edDMMWwzAMy4Y2eOLz7m2E/xuCb30Y/q+h1/dAgAtzMa28OzuA9B1g4yO1gYQDSCYKZw9qMcmSQy2shpamz/+UWNCgAgx3vJRI0WNBwzmmQ0Lt18urQTrGdWLS0nqRGKmQ/XbPdtCmM2tQOlyIA5nLlOFp7Dd1r0OC/x0wgYvvO3wIkW9xJktBFeliarZ4BHulhZjgsLx46t7MeWUjwvnoEjGAsTILtgt6PECHcYeGjhdsRk8wbJUWAr9BgsPaKrldPKjMOyuumQ47vaCeerpbuEY8JIb7xVrbBz80rA0szLYluQt555MGQHd4lL7hXLkV9H1QF63NbS9GO0Hb/lzi7UdhTX8tAytRMrFHBMxq8vehMJ8B2tpRzQONB256Z2hNnCq+w03gRbWS1Y13Y86h3oZNZ8xJqno89Su1cLWn68drfjXgHGImWlx5FpDVs4Tzv071oXYvXW6ckSazjJQwNqiv5oiD8LRuiDLKH8XBSGa1Keq3JplTqW/TVHI5dea1I+7XoRjD+fOd7sI7/ulfv4xz+OF77whfb70UcfvWdxv/GNb8Qb3/hG+/3N3/zN+H2/7/fhH//jf4zv/d7vvWfp7O7mu1/9tc+GBzEwhDDFlDmo2T9rPiJzSFiNywZarsgcWLJqYLROj8AVVOOKiWhalTk4ChaZc5mDPT5VOnVUBMU5fK/MsQpTjJlDjzgfM8eq/BB0YOMd5Yem15GBgzEH0n3mCXMcGcDCJkNBB7U4jHaKSLNJi+i8yjW2UzmABXd4xUOBlUbMoeVtzHEkHJZgh+VkkFX1YOAY+IGI7JjRFt2YOZBkWlwPmT7OHK1TYZX72LNA1rtnDjWRmTl4wByud2Ilq3ZsJ6IsgTk0naPyXarGLqPMAThXnGaO8oBqHSvMoXw5Yg5lImUObQn0RJKLmAOtDsbHy7Ueubtjjqef+f8NY32uuecKc+wdFNfkXv7yF8k3BkgOK4rnAUYjjfgQkTWco2ec1Q/+gCaZEl5dfdjVmNaA8dSsLj4FltiREYxwahnU/rO9/oVMBMOt1jboQjatkZNM7BDoMhkAAdZBoEGzDh4N2XGjrXOB7chUFr1XyHFXcefvpK83xscVdpRWM5DNUutLPpiaMVMLB+C4LvaiXo8ZbUaNpLg5ySgMxOvgBSutIA63SKEJh2aEWDoiWE1900ujcBmCHrcFZhxEN+fXZh6J1tBfRQCv7ehNhSO7BWRAkjgZDZ5UN5VplaQd0cWyIFhBQg3gkVbo8Vqxz2zFIjLxeNR2T+4IcqxVB0nP0/E6dgRjMRhwDQDtnIgPtcNMu69IOjNIzhyPT2/7vmoU8OPIKpz0dp6mz71rm13sA6WB3Mht+W35G8sO3EOH330i1gfs9MXkbuTRNpSKsDBzjz/+OA6HA5588sl0/cknn8TLXvays5J8+OGH8Qf+wB/AL/7iLwKAyT355JN4+ctfnuJ8zWtec1acu7sd7vEvb6d4aDvtTtoZylWaiz97kxR85KWk8EqcsRDbH/s644cRc2zJaJrJngfmWLme+Tjlh8QcJRGSFzjzM5bQDG4wxwo5G17igstsMwentniVI8fNYohQxB67RWL/nTkauxjDJOYIdh3AOmUOPd4Sxj3OD81yLyjMgQVHVpnKD9wxh1nwlbAQYaUVB73v3GK5Yz0mYj8luSMv0qGBk8xBUujWCcKSV711vIyZA405VtL5BGPmoMQcykrt+5g5GqnUY0ZX0rBRB3nZJw+7xRzGD4E5IotsMYc/25k5GMoJc+ZQjqMidxXmoD6Ra2GOhx965YlYH7DbmSO5fYnHNbkXvOAR+05ytnSccxQfav2UdiL1Y5gLdpM4y+o7eF2qEaff6Z/YsjAtEW0Egz0us9FrlgnvcPl7zFWkH9YP8jCWjkqQrZsyfaIfgK63prZmFjHnD5uKD5mep+GpkBBhXWOZ+V3xaYghPm55ylMSY/mVaZFWBnHaIfJnmHKZ92AQHUYypoNO+Wtm0+IL4bKMT1Vkk/FdqWN8Np2yTLP0dJYw1TDrYKeVWN4WrHpUWbje1l66zJF1OZ3qu+Ty0TykY1Dj/Xa//tizIqNlo/e9TLPUqZy5HPVeh3II1309qW4Cmh7BLi6tP/F+rd111xVdfAgy9d5WnfOjVOOr39XVPFzi6pFfmsiCe9erfy0uzr2+6t8F7pFHHsFrX/tafPCDH7Rr67rigx/8YBqx2HLH4xE/93M/Z2DwlV/5lXjZy16W4vz85z+Pn/qpnzo7zt3dDvfoow85P8S6F75HS9oxR+WOM5gjhtOkjB9GzKH2uTKH6nSKORDiA8Lbe4gbThBT5igN3pQ5lAWoNJqcvgCr2+668bHFF5ijvUR1dwIdcygzSIFfjTkm/FCZw1hiLAPE/R2inx7n2e/xUJkjhRc7fQwymTnqpp1uvy9mDvgylhZmQc8cwW5exByiQ8ccQTcOrKR5DcyRmW8pn5czx4gP/NjSM5mDtpkDm8xROiluCnMsX3JhTPfZ7cyR3D6D4j44lp7t4VmeGmZ2cfQuPpPZ6HK0MQBG1y1VxgfGHuWBnz4HacZAFCD9QGvSgrEGyZKTAgn6nRg6RbFXiHs944hHyhw1ECFBl5gHap0UDdSSh4yqaA+QAoxAxoq+bCzv3qdsTTq1nu6lxgeGj3bk8mErcJdhlcGClVebIuhZbcr57AL3b4C2WnWscen9ieWt0yKXQIgRvnTGgvb1q1tVbbBN/WzZWbCQLFXR+iKGrd2bYOoIro/qjaw3eJVlJVJgFkb0jiUghhchXZt+K7/WMKXVy9udT2PkEHObcRPvjteK+K3eQ1864vETfMyELRUfH8l1x2MZPZixbvQjIqUWdt9HMQ6apk3XTX66RPgBuXhy0lXlL3Xvete78La3vQ2ve93r8PrXvx7vfe978dRTT+Htb387AOCtb30rXvnKV9qa0r/7d/8u/uAf/IP4mq/5Gnz2s5/F3//7fx+/8iu/gu/8zu9sOhDhr/21v4a/9/f+Hn7v7/29+Mqv/Er8rb/1t/CKV7wC3/7t3371zO3u5rsBcwz54cTDzPXHJc9ufd7JP8ykxTTOYA7uwhFI+YGzIIHmzMGwpSJj5sBAOfiDXfVUFkhmvzJH82S02RLWmZPKNDCHggb8q/FDvBmkcSNcD5Gz2KYpcyDNqp0xhwZZ17Z0xIuBAWp22G2QTviX+5D4gcxHZ1tU5gBkGYtCQvSR2aJLxxzNdg+ZAwsW+PJYpU2bI6FLFy5gDuMHY6Axc/itWcC0YsQcbfbLFnOM+eEUcyCF9npzijk0lvOZozYOOdVxXnbmiG5njuz2Dor76fS9ieGGQP/XRX9jseQ6o1YN90hG/1uyX3p2eSAjBiUOVOjlUYPhukUj6LaztecCB2ZA9SO+jmVFKBnD6Ap8sSQS7PrIcWkGT5ZxSqQJxCmxGptfEoOzUBEPjXUtUEmXdZlJ0S02tmY4BEyYotGtcrbQwkrYpnBy8BFju7LDh6cczLpUjFjfmkzWLxKPGTllEsi+HRLElyF7BDoCFGNpem/ltb+v3pufQcv1phJew7m5zExKMp02l0/VIeptD3+QsZyRl0+t4ApK7gZdEJyj3qr3QEaZYVAefB097Jca/LswvF8s7i1veQs+9alP4d3vfjeeeOIJvOY1r8EHPvAB23DqYx/7GJbFofI3f/M38Y53vANPPPEEXvziF+O1r30t/ut//a/4+q//egvzXd/1XXjqqafwl//yX8ZnP/tZ/KE/9IfwgQ98AM973vPue/52d/+dNcMj5ihuxvLW1ik/aFwh4MxUKj9019EDdWKOGlb06CZQWvOagcjyssUcYY+IqgiNm2TEnCbmwHaTGF/Tal7S6thmPO2FtTKHslie+O+60ARaesZrNr8uG4754E5GPd2OVO/2uy5mCDbV6uFp5jA7b8t2QkaEe9IMoVTPnDlYvoyYw+aHBOaIsShzeF6dfo17SvEwIqGk4aa2KmnAHH6rZ8yBxBw51jlz+Fjh+czhpBhZS64lBu1lerczx012N5k5iLsFebsD2pSUxx57DJ/73OfOWstT3Q//P/8z/l8/8X/4TMFlAS/UOl4JwEKQDX/bhoTSQcH6SWjGg4qMGKf2W+KmKON+tjKCvGHnBTksPEyUBUKYKCNuKBfDqA5g11OtvoX1qqeb9Nj1tK6UO52A1itN4TfKd50J0YdhXw8X/HT2BAV/TV+vxTAtm5xmFbSRCDYZ22wqpRlnBsRec7Q9HURmUV2CjI7q27pTtPwspKMFbLKAXNd0ED5VRsNA94dovxcxbJqu6YYop3q3NKIOC7LeTYfVOhdQdQhoYJMFVUbLDVlfzUvr1FhtfWoqU4bH3+kdw3r81KXFQW8tV6lOsXwS3nDKK4Byj4qM5THUeZWJZab1MYWNOBHrh4dRW++PZ4A6g8GqQ3AjWAh6n3KaWrQ2L3nR9+HFv+ut50VQ3N22z+fE/f9449/EQw9d/SX+zp3fwX/58N+7Fh1399xzd1un//f//SP4off9v4M9lhe4A2V+EDZgaSQSl0yYA4DJ3hVzAEluyA9RRsIDG8zRXS/MAQqsdC5zoO0rgetgDjZ/ErsE8zPtxszBSNczc2R+OJ85AgME3lB+WIrM+cxRbCrN7HC7dhCbukT7aMzhLNDpfQZzIOowYI7MD2un/1Lyr4sllhhW9eYBPwS9Yb9nzNGzkpbrOcwRuh66e4SY3oQ5Kl9W5tBOsna7XPfbwBxf/sL/DY8/9r+dF0FxO3Pcf7fPoLgmd+fO0b77js+t11eNqj43C/TYIQlmn9JirwIM3iFrDYX+5iKL6MchTNgx2wx77KKS37pKospoPCYWGpG0ARYj7UTIPGhtDDxCb3AMZssnKHR3M8Cc2y72dGJrZDuTi4wlqzMOBEBaiwuLF0ygQy5R3TSTWTfU9I2sdKRDOyrsNAQQjjKtcSFNRuIDgZixLFpcWuALVp2GSJ5O3MTrIPeOOYwZcUtjkR+6wSaLwVfddOdwLZIlgM4d0ZtBNh1zsXJk220ZkPygjUjYxEyROUCWdsh9JaDtck1tGuTKWjotPwvF0QEGoW3itfCKOEpAlqdmvNWItry2Ta8W9s2ranVchEF1L3CNj2STr5YnKQHSPEFOC2Houes6HTOn0/Q2YISUD8o9gk589YpLaHprVY+jYqvcfzX+cWKll0mWafVOOpwMjPKUUS0TKpIzN2KF2AZtzlbimc/W4WQP3tHa/u5Gfne7u19uXb1ViZ/6RsGAz2QU26ANSGIO7pkjrR5AlVEF4PY+hhnwQ9cYwa8nmaBfYg6NPzZMXOJTJVSQ0RpznMMc0N0I74I5QhaNOTScE2BjgcAjp5hD2CEzh0/ZHzIHNw5NzMEKAmIvpGOBRX21GcocqlNnz9QOKz8wteWbpkNbWqL8oDKNH9SiNrt+UDYuzMFgHIgCcwQWEE4x5hDGiczBiTmceyJzcGAOOyxO00E7GSwzB4HpIDLZrjsTCT8EFiDlQsyYg3GAsJLRIVl5I6WTmUNNzpg5co5mzAHIhpvBzzsTPFyd9aHMEUHhpjEH852T6T5ItzNHdnsHxTW5X/qlT8k333shLZNgsw0SShqXYGRJvsTlEWr/ksEOrtpqsKeb/DimkcNrPFz9Jw1D13FSdOIVbdphyb/CQJqSSNkvRxR1KJqr39LlKJRp9jNGoLw2k8GyH4U0zKQyCgx+0+weSTN+XB1C1ICszGLw/WboPWFGgAOdTNfWMOpu5LFnmtHWWK7BWKrROa6rbaO9hDt3BMB8NOOWT7wg0HpsZRBk2maWKw6SO4+vrRcFHyPiaeHjyOGoUzGq7YivpoNOl1RAVMhQKlpkHkfL6wELjiGH+idngQh4d3ltZ38INLhZbR0TR0vXTW80302HeL9XqN7ZNK/Q40z9wC8CyakfHGTY/l/h5RNX3Op5JICjgNYGSDm2Eb+8+rgudMndEA4P9VFag0R1IzA45WYyzHO/Z+58/MJUdre73c3cr/7ab/gP6fQF+SuFDHCjvrfHJoNE1kzmJcwRAsXwlkSIKybLuQnrmSTZ1BzfjDkg+0kl5pDrxhxVwXOZwwY0VAdVujIHD5jDw1DMLGSpBcuLarDtzHPmWNUMLn53hsyBoHJkDliXh8g4c7jm7YWaJKolMofabrVMovcR7cVdY3CbKraOjzgyFY474I4wB4w51AYziNd0y6bMYaUzY46230QD08gcJKV3tDu5hKutOI8D5jiI1Ycwh6fW84MzxxF+isuiFZW0jO6EpSVk8Z1iDr0TtlQW7WQWZ46I2I05vHZk5mjvJJw0J5Dv9WXh8tMX+xbrdY85uyEjnICQ8NqRr28xx7O/sB3p7m6U2zsorsl9zde8FP+fn/llxBdatv/rerLmEgSEJ8wMcLX6M9koI0mmzhER0E7+YU/k4Om3JlnBoOpihjxcE7hIMlBQ8eawVw5eQBF8GLZHQ+zd8XPIFR6oFKSXiM5ooVDYvBKwsK9XhYww6UsjUblfahpklJpJzjDXPLOPAqDtAN1MvS4PaCDSZGRTRrtRzfDqiAAYcq56i28Vw35kXYrhhkl7vlc1WDJq0iYjqgHSqZ8trZUOrY5glWmEAVrMAHHq+zniYFMQFxuhYBAOOBK3NBVmJO8qw7psw8pzacdgMWzjq5btdmY5iSaHAG0Amt4an9xPMpnmw+AEW0nveDuh3QOS13CvG4Co3vHpWqRTUcpfp9kCWHnBQnofYmYXO8JM0yKPXZ5JnTLrOqwKMFglMwrL/hVWI9ulIyAjUGWdLCu961G26ekYNjOjtebpvYJDG4U+wGgC1aMPfwVutGPuG+lL5Xe3u/vkXvV7vtx/MPvMTTOe6B9scVN+uESG0ZZmSJLnMkeS1wv60ZrZnjkK5FTm6GRQmKOLZ5s5oIMHcQNmjh0JNY4N5vAianygTMMAURucaHlS5qAw48Lvqb0aDpgDZzDHkXWJgoZbfCYvr8YcbWYBtTJgG0KRYgrMITMYnTmcHypzHLG0jhheCz8s8lLMvsG3FONRXv0rcwCHUI7cMYfy0IFWu1UAsNJSmKPlLTIHm60WGz1hjjifcsYc9Th0gNrybpVJ8Z1iDqkDhTl8kAtaO1qZnmAOtfuRExo/LM5rVvEpLZW6NuZIJTW4Jj9WhOc/Pr6DduuRh78ON9rtzJHc3kFxTc57hh0Mmh2slnlg+dVx+BgY5RquC6vfEertBrPUkYwat+Ujgkfwt+sSYQxD1Nqo0JEu6VNqmTiOTqRRjtggAgiG2DaTUl/9PXzL8kL1NbFNRqd05h7hxRr8ZhA8YmaftucGuOmwCjC4nhAZiZVgoyUt5kWWDTgVeScB5NzwNsKxkPdF2y7XgK2pJbTlDK3PRWIhnzmhELJoMQZDBtY1iq5DlvH4dC9q1s4VCMyI+dF76eMTKtMg7BBus97XhZfQQx9Nfxspsbyy31rCAUy6ZpPUhCtiNNiC9+yrHyhMoLROnjYawXrCCOKME4Ux1pxbHW4jPFq9LWW5txks3VEIQem6QrTWLV/isbTpvSZlOUDr0HGZmNuVIwTk2TJZI9eDB9/0V2210rPeSUAfy7PXj94Ip+8EdyO/u909KFdtNWAv3NMp0KMLG8xhXrV5ozOYg3vdYhyRZTrmqNeVOarfjDliVs5gjtauXTdzqGdjgWZ4cjo2A4N6HSJz6KzFnjmQZI684JBst9h7LOCVjTk4MIeWSuMHZw59jz7JHHYfMnOoDqYea6eHC4yYo3GKcJTqEKyjOs+r34dTzKEngizYZg7rhDnBHPqyX5ljBUv5Z4aZMQc07sIcdl9hq5lCfjTHejVWWMnB6stvfIbPEviBS34ic8T475Y58q9N5pAfp5ijPd43HEB25khu76C4ducWUzeWii6tx6J4PdS10TMVjV2ViX4RCgYy6YViFJ++Y6MYf7TrQx1G72DmF0Z2ysPExOO8rpPrUnodQJnxHtCVgUq5RvpCPXjCnQlS3ogcGCgmIwYojmpQiGSVgqRQWPqa2UY3uGjtr5wowElRJsCQTAjBSpK2NeBkDfcqsyW6PRMI1msf07IjQyu8YcFRevrtZT3o5tU7jjM08+1QAJj5Y3a9Szl4+eQSYlnjewjGTw3jGhZcwDoOWEYc3PhrGTe9FymDbE71WV0GMpxC+JOvo0gZQmPOagWWvErJxJlGWk9iOjGvrqtTuYUOz0StW3OXsSOntyXRu5vOBrvb3XPB+UuBjKgPjxkQcp8wx3DcZMQPG34nmWMQV2IOtVmnmCO+nIx0WJFmdiRVzmWOAEl3xxziF5ljqLu215czh6YzZo78Jpf4AZFWg+oD5sj8oOm0vTAaYjQ/6ygnZBawCH2QZ5FlKr6VCaEd5alyA+bAeczhnRXeCcAh/WWDOSyvCHkFwHJk6CHIZObwYRkPscjM1wE/sLNVzOmIOTx3MUS+R0eRyYswKnPkSs6k8Xk65zNHL9M6Ry5ljnGersocg6Zud7fE7R0U1+5IjPf2E2Ltrzxp9sCV3v8oQDlk+yaGb/QahIEKTKebCtNtYMEmfQ0mx1UuZMP1bZ+2LjQaCQGSlCOGUUm3F0WMs7es+cdgOme3GxjVANHlyDsjQACn0YyxZHVU7mx9PdUlQzle6r65WdBFGjB4ia/VyRirQdcNujTGUAl0w1MtlTbCTyaTFfHVnQwKRS6/pZKYNhT1knIIRZ9nBtRyU32o/G67WfguEx6Xr/HMjkMs0VSyLKvpzWeU0nIKulF/zf1c2+rtYzOj/JI9LipdRwg4C0Cfia59GLUNlkp6IjcN/aipuq2OmG2U66ryu9vdfXXRDBLyhkPRKSOMmKO8+CJcvzJzpDb8TOaIkUXmkP94EHjIHBRfT3N8U+bAvWSOoMz0/bDwiEZry0hiQi7Tv3h6i32KOfJtpnJftDzEAk2Yo1YVtVV9fDCdMkb6coA4kMMggML3Ml+/2W6SpTGVOZx9lDmcg3rm0BkYp5ij0E0onb58dZHGjDm2+SH7bzNHrLlq21kfSSivcQm/0LyeROLsXX1hkLIpj0oILffvEuYIbHMGc7gWc5Vvi9uZI7u9g+LaXXsybT3h8GGpU7zFka4pPD8pnRZYX1iDDe5bikn8ZkTki3b6pnxU+ZGftZ2M6bSPzmBrHGGq2ci4D9/cNB1tFdXSVNKJ1qzJ8FAGqImRGlSCj2hYtsMLed0JlWEydWobge1UkC4+QjtzOxhuWCdCS88GiczYN/WXhf0+wicjLvDNL2PRr1KGqh+BZWfsdvOpwCgBstSkQARDUlnbSR2hBC0k65RCgZukt4ZtYNjCtA2g8sahoreusRXNDYwVCcinQVrZMuFAbLOEYonqRqR2EgdrXluuKMqEvOoyIA4a6s7j/r4Qlgexz1hJjxLLFNeuTNnuc0ZlhTGv2v6oeD3x8H4HbZpzim/8WG49rlvu0vAP3O3rQXd3W502AhczR29SZ2ZWogkN4AZzlI6SaZxmHz2cRTNrPO4hc7SscOi4mMhUt8kcIQL1s8IJL4PUyxCxl8U5zBH3yojJnmCOxha5kBi6SeJp5gDcTq6Mtp+XZsV4pMkqP6zhHp/LHDG9I/ykDssTuw3vmUPiHzCHyp7DHNHZrFIrNb19PXP47aA20zMxR+OHlRsrGcOI8nPm0P01PKexU8n3wGo+8R6NmAMbzMFSH3NwsnK4fubol+308fZ+O3PcXrd3UFyT++xnn2pfzCAFy9vNddaJYM1xkIuNfHzafKYepSfb7Ei4aN/U8IfjwLJMDhcbXTDAtmkAzNZyvtQb8PA7TzXLIGKOkWaNxFHi1JrHiE2mNm1SNgcOlyN4iPFPUyIJfGTQQW8VeXmGTS/rKjpmxrLINM+Q8VVmUCxdZtsmVQeVITEsOtVQqklcvqFrGA+ShxUMIjGEsnHRgYBVpnnquj9e/Wx0NSQkOqtMy54a8LYPg84dWCE97qzTFiUduTGEBhJ3ZK3kQ5ZVBb0ljAL4VMy2odMqL+g5PhBhXTMUEEjCNZkDIS2DAC2yCziFKtT82uZNbVQj7iZNUgcIsCPOtJqsaFCn21W12Nre2UepNv5INP+j7a9BBshkGrauoQPUsNs4UCsDrcJq9uW/o95fk1J4kdGYACf2jeUAU4oyYQmP1ml7WsjuWe348Kcq6pAdlc+Rq37r+tRG6N3tbneXuKeeesZ/UGwLlD0QHsK+w7hjCwp+8XplDo1jlUZMB2MwZ46OHzRc8AOQ+xbuA3PwQCbHTx5Qd3BMIzYz5nCN7SuRjHgKAyzwzhEU5qjLQS5lDilkZ452kUQHW4AYmUNY4I4wx7LBHK1IInOwvVB7fIstgzlYcUlhROYg2ODAFnNAOmNWLszRrDn0CNIZc1CID2jHjGbmINOHA3OovdeBsyPncUH1mzNHiy8zR6vcR7mvyhzt7m0zBwfm0CUi3h3k+b+UOQ7wumX8wNoxVef1YpM5AD9u1ZJJzBE1jLWciv7RL3+OXPU7Hj+7EXp3N83tHRTX5H7zs7/dHr5gpGy3ZsTr7pLBRQ8Kdi3Jxaaqu5zjrWkGyNDfBLSNpeLTr3J6RBeQ9n7qXliiDM+vWTqjcLBiy3K+d1BWwtZeTvTWiyH/PqWwFBQ1Awv/GXQgG1UgneECBZ9MbXFQZV0FBgi2FIKoHQ2qHQYxSyx58ldbTV+O1SLGQuEQqgCCzYAsoTxaz7x2NNiJZJC1qWtbI3kQpS2rzLgDH8WwnZyZ29FVAIikZz/KrOElmCGzGliMctvsM+4CznzEnbXFsJDH1WBL9fYdJAgHEPuoRO7EOLRjvQRA8qGcwBFHQPMqaTTfhkd6ZnisEEfbmituvsXgdB1wc7pY+Swo61atUrXUXAs15QpYft8JsIPPFkST7R00IzO9DmVa5fJdvXOzEB+12lRofCMgGGuw7e4cP3ehxH12sb25qvzudnef3Oc+99sA1IaQ2+pk9LDJAB1fKBMMmGPkmg1A6giw75EXKj/MmGNLpqY78ijXpswR2GbQ8DWnw+7qVjHyM1bSi0PmqDL6MugdBRoXMwV+uIw5AMKySB2QG9OYQzsnrKqAWY5JR3th15tPLPxAsNkKqp51PqEto4wMdoR3NETmiPywiM1PzLGGmRPiD2bhnswciDLhFrVbsGwwx4rjKr8o2lDOrGQ5PdjmlwzfmLzFQFj5CLZZHtmC3sHRrvsBpO1o0sQPthz4BHOw85UyB4R7kNJX3xVHhmzCGbXz7oMRc0z5AboFuFKaV/E1pBsPjNf45syRH1pN6d4zx6cvlLjPbmeO5PYOimtyX/nq343/46c+muqLvfKaRYA8YRwqFlnHBMvuhXW2ocpZM1Qgwg11eXyXLKMGzX6Gxv1UPTcGolFjE3VtiuroStpDKupbZNI0xsRX0ijKrIM03Ns8ipIhgkRM0igGkuJYqBTiIzcDFq1FxWGtH9COCWuvm77RF0FHlUh69WHJiglqnuG0Bp/mB16tZ7xp3YyM7u7sR3aSmzWdsil+jMWWiTAQOgLcpB5FhoxyDnY6BUPgQIyYTr/Ue+QbOGWZxQqrSS5EvjM26yu+jgm0sG3GiHAkeV6ZViwGeMH8yVFlxDpXYZG6wQYaXs8WLNxe+A+xMsr0oBWQdEJeiWwGg97zdgMlr/IAehlIeTNkd26fFERMsrkY7N65FMVKa7Xdx8dUhzDbx+Q8h9ERfJqnySS44iQZUw3VJ/gC3chJAZtz3SMPv+KC0Pff7etBd3eb3Cte8aLABXEJR2AO+WlPtdguGwy5gDnMaViCvTxb3BS+h7iGpnuUqdBEE82ZAzEuZY64DOIUc6C0Z0E5K0edIWJ+agcuYQ4N0zOHIduAOWpUcU8jZQ4dEo/MAQCrzKBMPFKYQ0vKlxas0hmxwRwmI3YmMEfLQtNhxBztNDLdvHPOHLpRNYV/U+aAsoAWlgzVEApz2HCQlKse165VNrBSYg4CC4/c4XZsKZlf2+UKYvNHzLGilbfNbeCgN8mGn1Jum8xBbpfyZpvOHGmWJFulSLp5hQ11OTBHm9k64IcilxnBOaVtS35vmMM3Qa1ylzHHo498zQWh77/bmSO7vYPi2l0x2PE3w6c+trcxu2xfKijEAPXJFOOT4KISwOD3cAmJxTdIB2OZqluy20ThyC9OLU2ElbS0JEwVhbzoWXw65T0pikBWMfLoN4GJdD3HZ1NXg26ev9pkts8jQ0YuYC/LLeQCP9/bC68ZeNhx0RFNVplQ573vQbeUHY9vFbX8WFC/mdwiTemQlHfIatKBsYBWnzK5RL1D7rO5W0QSMu1Poagc3yWhCbKOVc9GB9lSlwZNzdAukoKXhK7d9DWKBkKhbtQX9CMrNGWTyLwAtFo6PhpJNlGHSnkzy8kpKTeSKjPakaaUyq7VkwaCDITrXoL99/bbdDH/7BxtVG9NlwcxRchRzfPd72ViHaHgdxkw3GjHyO3CVeR3t7v77CI/6NLB3hOJOcz7UuawdCivdIhy95I5ou76Nch0zFH5YYs5COHlvrX7eYyHkAykJ3RPmMP12GKOWGixcKUlVrseGSYwB5/JHHq0pDGM5e085hCDCe8oIGCNSy4oxVeXLbTSbHMm1TJV5mg2U0OqJpU5tF5G5vAudo1xzA/OHGrvfbCgnVgSZXxyDrVCDeWj9UiXu8RZDmzpnGYOhHKQiStJB7srgTkyK13AHOR3Y01lrGG1JLQ0M3PETopIMup7CXNs88jOHEn+OeT2Doprd9rsDh4jbQUZ4IXS5WiCqlN7OPTTxqjYsGArBjLiF3cgGtnA4Kf2tZsOGuxuHYUhwA1vjU/zMygi3UynlgfZkIB8ntLbYxyEpUwqVXOdsRF01mmZmteazrrq0VklOfbzvCPUqDG1mR3BgMHAqZSbMlOamqLeflRlBkHtiMhGSAMdU3zud7SppW1qYJRRHbQaqSa2iZRurhVHZdiOgTfT5pChKxfzLALflEqfKTVwi0BM/1TUZRAxxDrJ62q7pyt6uHYtj17e9j9rJ1q+D628FxtByvuX6BGoWcLTG7UEfqxazq12gABhYVCSGW/qX8PGkqigUEti5re73e3uQbhoObqnN/wc88M2c8zciC3umjkmDFOZA4oBAz2n/DBijpQO9TIaYxxFGek9zu0gbIgrMozI8Mq2aWOU1XX7I+ZoMyoGLDBiDsA6cYbMAX25DxkrzFHtzIw5ADmCFG1piWdnmzl0c+kRc4B1qWJegLBK2ZKUE4fyjptaVuZghA4CyT/gy1TUvqqWOuCypPsmeiO+hJc6xCgdRU0XPaJ12WAO5x632tyncJI5VsnrKeaIWlonV5dbX85RmYNLyl7DLmcOT210dWeO56LbOyiu3dXHhvuvS2h2ggHWjgaLwYwTrJGtUemocwci4VqxN0PVEhiUdoQHflxkOmd6F63N1ng/KncyVVGVCXmpYdXo97mcnvFOo3SGMYSwari4DxX3m6i6W89+UJkRjpPiFLz5yzIG85JRl+ERVNziOwz81NhxKW8FlT4+N4xq7lrPuBtH7VJQ00qAXMumD2As4bSUg5RDrB62c7jkXkcd3GgKnMgVElBaWDtANDG5rvfC9PC4/P95XmOFHh35FTfqJPTVNXYaONxEX88rBb0VDCrkt3pSH3Atv5i76jfKa07NnkkTEh1CQhUv83X3u/VOp03fjfzudnc/XeCH2vLaV3mmSexu8o5LEap04Ydop+vL8j1hjvA7cc+AOWY2vclcwBymd80kJoWS9ZwyR5WXi3l/q+If8x1VuAfM4fHPmUOXCpSpq+AycyDWA7V1tUKonYn2h+HM0dumyEUt9Ig5rBMBblfdMjkrEdA2lQ5lV5njYDNmHC7jy7czB4f9JriUnVf0GXO03+O85rDye8AcLc++7KFWrlPMgZDXyBxyJYb0+HQwq1YtWnPuNpkDga4uZY6t6ztzmPxzyO0dFNfu2oPjU82KN7Uw8TjRka0KUc1/Qx5fRjYQMVx7O+386qsYh+vDOPp3NwOFtEIiiFg6HPwUFCaEEbfrSPGZce80L6DggvbsruS7NrHL2OaVKhCVVS0kbm8k2yyKRRZfcoiv6qZGWqdPyh6XaQpffLkPxQqitoGVHhnabnOdWuhTHvV1+bj6nhfOZoyVFhCvVmotSPt/lc0a42aRbTlN26BTd5j2ERnYiEE8RrNtAbW0vS3s2DEd/V9kA6S2gMU6MqQc2hIZnRsRQMbMrpeRbjymYwK+/hJ2nflY1oUCOvXV9gWJeYXkNY4qyWiO7WTdVdm2S7qOWngN8SPRKDyX6hdncTBc76PkFp0EyVrg/JgTuC1Pkf1C9O74Xh1k5ZPzSrbSLDu2ut43J3VxR0Sv3u9Wuti7dlX53e3ufrpiL4d2G7AXzVS/z6nrhR+I0fbKMoN8It3wO27IaTaN/PtZOt1z5kAeCAhF5OxflD+HOQo/tEt8kjm4+LEumbiAOeIeEVTAYswP/vrX+IFNfW3dddnpiDl0dD5WROcHTlP+48twneHX7I6cPBKYQ6VGzGHck5hD/Z05KJRPKwfCEbIPBGXmWHEwulg8KdFZZm5SLDvp1mDfQyMPduiSkpJXkBzX7glsM0eLU5kjx7XNHC3u9sxG5ohLWLJEYI6kQc8cKjdmDs/rpcwRc52v78yR5J9Dbu+guCb3y7/iu8U24ykP7Brenuzp1EaoBU6NhBre3va1hkWe8tpu1d2dTaa1z2aR4vLJat9y464JIq2N1EuWVIAPGLzk+NonA7q+Ma71ZLTOgyCjaVl8QFnCWQglW8aWTm29jiSdFPCM67pTKMDAWsnWEEsYMyDeLB6PCgxiELiBDthfAkkU12O9oDIS1owmt6mLB82NsQ2BV8JCcfmDxMwMP6O67TlBQNuBG4yD1SqGHi2mx1dZqQUZ7eVeJO+6qzbLtMaHFpnZIMZYN7ZcAdcbYXrmkXFYVt+VWTaBakspVmuIbCYCtZ3LDxRbXLLNqwihfDQtakeOHVBlGISD+R3lvpHJylRNKwOS51DyaqWUO0hWbqfJtfW7CprNkD+ENRzfRVqDABa9Ca6DTLO15SkES4elQiyhPD3PHDYTzSZc49MlNP5MCLxJWcZHIi4bURmHGU84PF0pZpWIfuo/cs88+2sTn93tbneXul//9c8ByC/qZu/jEZ/yIuHMEd+Z9VUF3YNb+YEItv8eCHn5JlGWQZZTPe09P3ROdPtT2N5VgTmUQ67AHC2flTm84OrLa2IOK+Bt5og6mIv8EJemsnQyLBxeUKQlZZLlAFZiQ+bQJQ0snR5xRh+EBwkErNIZPmAOnZrC8I2lW0dNG07IzKHJzpljsRkLkTlaXg9LG4RQe6ubTZ5iDlveIsyhp6od7M4Fm7oyFtLdKk4xB0C0TPghcpTfbWUOYrZlRByvYzGGOUYZLVFhAS0DljwzY8IcjS0OoW5rnKo3yc0cMUfbrFv5SlF7whyYMwekqsbZmjCG4tBPN2cOf7TPZw6tmzFmv+vZzZjj6Wc/OvHZ3U10ewfFNbkXvvB59tDVh8dcMWBmFtVoI/ul+LRdiUazJFZtY0wvggPB06PagxfkbJ3nGgwwSuORW458fFfw15d4eZNKG13RGo3yWIekN8ONfcpgDZSvsxyHOSrgNHlR44h5E6ixXctJjwLz+By+mqBzEJkRWVeYMYsy+rIPwAyFLuk4sh6N5QWiUzuP8GOjlkB0R9lTgYjCkV9Nt+5ITJFR004gHPS6OD1KlNDWOToUsR2eZb36cs+0vBdqyzGiDnekxA9Gmc3ryISV9PjRUCdMbz1CTO8N4Q636YhtxIEtOpL7BEYa8QBaXCu1/MdjUwHgWZAY5LiEo8V/pFA+oYJofhxIvCyPtFo8cbOsFSRTRzmkIwCCKGPIBR29ca1ct7hlFUp8TNXPp6oqeHSurIGyR8/ymienxhIZAcNh+V2jVG6M23fU3t1tcr/rdz3flz8UM2g/irNLE+YI3vlB1vg5x0/snjXdyBxRuS3mMBNcmUP8rKMivsysbq5TOjpwMGEOxHbvbOYY6x1PYEjMwdTpZd9D58SlzGHBQzqROSBtPjFsfyWSdJw5mp0BQp8W4WLmiPzCdj3e4zYzo3W8L5vMwSDrBJkxBwA5YBM+41GZI/BD4x59QBbcEXtpR5BuMkcriBlzHGX+xTJiDrlflTkYKzgxh9/sMXO0T2WOuA9WKwOS8DxljjxrQY5/lzJbUjo+k1bvnzIHoAMffoS6alL3+6rM0eqjPgORObS2FnePmWNZXjS4enPczhzZ7R0U1+S+7MVfWh6Q0eOC7kmy5nAFsPia+q7eqVFiSoZUnvcUV0zHIIHgnRtwI8xVl/DEs7aUNb4Yh1vEPk0Vp/BberptFghrs0W5wYqwQiEPKa+U5F1Ge6a5iytd6HbY0oR8oyVfU0fAuvqUyUA2NgdmsGPXymxwkJrd2JPNmkZzR2YcyggPS5kuBJlxouFlwqV0APjGSovpudIK3b253a7mtwaZZryiMVltZENNlhrUFofKkC9xkONR1cghpMO0mkEkhRToyRp+X0m001GPMLlGdBC9EY9FW3z5iU1/bHprma1yX1WGuemwSg4bHIXZHEBbCqL3CA6BgB5nqsZa8iq6LqQTU1X3PFJhS4ZIZbyjSJ8dIpXhbmqkz43R+w1DFPfRmqMp5yfHHGkdGhm6MNJTrvtdHhvIETAcDje7g2JfD7q72+S+9EsfybZ4ghzbzBH44QzmqGZzVONTR0BhDuWVLs0EFlnv6sVlA4I0E0PF1Q9z5sjtY9ZhzhxAnCV5EXOYAY9+blOmzEE1vqswh+vpzOGFHTcWJcyZI/IDF36I8a3IzJFsNxV+MPuyhpf985jjKLM2euYAmI4Wg/uIH5w7eMgcqvWYOUwG7Z4fInNIWqeYYyGdERmZgwsLZObQo9JbWQd+2GAOX97EYNIHXjbBTM+OPiDrYDmG3iEdQKt7dlXmcKmuI8Ie0O4hwb1mjocPXz4Me2PczhzJ7R0U1+z8sarWNgXqYSIYxHitypVWY7zPBdCNdKg8jdKJMqM2J6YXjZsa7/obsdFTSAj2kUiWquj0MBIZ8rikke70rmoRbPQiRGXpaGAm5IYtzbII5JQMft7aB1ga6BAGU+w5/kw6+PnsMYxep6CDihOOq8wKCLNECLLLdTxaLEiJamkqpxpGq5lptLytgdRwRH4g18oLWodD1qHJOGKSzPIAZHcJhSOdyimSFHSIdYSk7Bby2SjGn3xAPq7TihW6jqcuWziCwHyETrvUUmfo9Edf1aq5aDqsBjqLjZQtOILTyiCDHFFEO2Q45PVou2nnbjeCrBNV024jW03vhXVkSmX8HvoZ49qZkE11XZHpqfpIjddCP+fc2ysPE++RlY/FFfXK7d2ouRo0M7vb3e6uw8ka87GffBa+mLFAkovMcYofahqFOSpSmBWOzFHYo3sFkh9bzJHyusEcTW9v3+4dc/jGiqnwrQMj29RRfptbfAbIXTOHvvBaxoOXLhvQ2XZz5ohSq9lGtk51AB1zRCWOUaWQ9ZWXwAI9c2h5R+YAwiBHYg5u/BBmImox+SxH3/NsxBzZiraODE24nTISurlOMoe/uG8zh+4lEfX1R9CPSnfL3pjjIEuB61CfzpLRQaS47IIa45nG4Tr8ZJJ+PkWkghFzZH6YM4f7zZljxDY7czxX3d5Bcc0uPYD17clceIGMBlDFLBjn0YGFUnST99vmF4w9xbBRJqSvMjR4wpNo9A8ets402GJNN83aShBSzYDIhcJghYaid3ivDjJI8aCUT4UM9fcplHpNmkeKGc1fY9OoYcd59Xg4aS4vmDpyMQGHvEM3FeMcdBCZ1gvfy9jGjByQRZedyBIJxDIi+EtxiE+x5QjgsPiIQawMFPRWgNPZDKXaIwKU3gdLjVjOHo/3z3vYVyk3NW5qaFeZEtr01h2nWfQmG9HwmcYcNJGRhXDL22wJhwvT137lGQiEsBEmZx+Fol7Gdy1HyaveK06FF+5xrPwGNtISSb2MEvGe5NGI8DQOZeK9rv7Z71ZCwj6asbvb7iLpJ8di17DNHPBOynvJHPoiehZzSNNdzOcwq6YTxulawAfEHCW7IRxlP+kNobrbZEjzrpmD4fwwZA59oZ4xR75RyhzW4hdTEpkjpkPQzSFbvBVnWlI9c6gd9jSL5QnMoctqlR9cxkvFWSnqrTKZlTTUEc5KkR+2mWOx2ZaXMEes91H39n3AD6Y3Or23mcPva1qJW/ihllNVzpkDFzBHiGJD5jLmqA3KDXY7cyS3d1Bcs+v79AauWNz86A0jdRl59mKDVddGJlsyNNJRlyxjUZUw40ZqmHzOVDHaJ91m2JJYzE+VSZQ1UHCk96h8qtgwKoGLYgiTalFHgSFtzKOMJkJi4Kko2Ebda5MNM6Z57WeUGWSPdaqhn6qxFBlHNu8IAGRDRlkTGvY0sxGkmC81ekdiO2Y0qUnOVKknn309Ktmf99wv7MBieU16+ku9/s7bhBaZcI/YfAmLrUSNazrbVFMyvWOMeaQgw0XuUMkyjh+hFGSzq0kFHz17mlK458lR2oeuc8PHj+psoqTFIH2r4bcFFXZY2N2tdSdr3j1gjmTMLmSO0cyGKXOM7HuUvRvmqGlgEnbGD0GnOT9MmGNDLbOZgxt5VeZInTvhPsyYA5BR8wk/XMIcqvScOfIx5UtMh5HsjUZtMwPYTyppe2D0zKE66GwQfZ9WxtE905U5bHYme/6VbTL3bPBD+I0kk5d+RBlw7nRRu09yXog6G7wAGRfeO+ZA+b992jGjQWKbOYC8kWZwtH1gxfBx3GSOPpWdOW6/2zsorsl5PdGHeGIlCePrKisvr51BVL/YmpGErcMMGy/503dwVXtkwJHV0d/JDm/IdcsyQzo1G2PlNJyvq0szNgiovf4pMb0ep1+imQGIMUwKEpCPS8oT9dsLd5+putYuQqAtUYh6h1LNDXQpCAUihQeWpQdFpu29LUeGBhkzX6xHWsXY8zGj7KEBkBz/masgAXZs6SI3Yg0yK1bbFFPTaMW82JpRiwdqjlUmLr2Q5Smsu5d7uTYZNkhbOr11+mTOU5NpS1c6GcQj0ayGYMUB8WhS3RiKBFfiBqBuPJd2tBhyGTRY8nWmWhe87GInClu52aaZVlGckuN+GH63tY5ldG3XvTNEjyTzBSB1UUqVQX3XCfnOVyIA3Rpg2N3ubqvbZI6xlzMH0HVIWINfhYKxHsVd0rkSc4To7ylzKC6FZIbKWbgZc/i6fhMYMgeMTRJzxOHyu2KOWBCZOczyOPZYYTHnIyy3maPZmbY8McucxRx2ksqYOZqNjXZY9jmw+JQRm01V2135gVg7PqAl3WxnYA6gLaEg6bw48JqYA2j3NeoNz80VmeOAduw5mb7R3ntHkzNHW07izOGdJdfBHPG1Qu12OxqVrMbESqH7lXGo9kIOegRpCH8pc5jvlDk8xztzPLfc3kFxTe6jH32yGOlggOITw/pAeoOoT58bTW3JHQDU6OoeCNqikDy9sYOEyB9eux5P16gGubVteQ8KucYM2xiyAw0KRid6aEDNLwU99Lfmi9F26z7mdKwIxbJQaKmajBtfmN7B+KdOG7lOHMozGHAmpN0YzcV1hkgZYS3MYLW9vFn0ln0dRBd92V/FWwtcZZZFAaXJmBljtFkTkj5LGbSzv5vC9tIvaz/ZjJusDRWyu4N4dJZOx2tGb2XZnBOMRWQIbefwh4h98y2SMsNiMiwyxAItYvxjb7+C17qu7dixgcwiMu2Fe5EeecLxuOIhqTe28ZXotGCV2y2mldB0WxkP0Yo7gAENyPfKOChoQs9496NO9YQPve3t2NIVD7GMvoBchv14VIM+1QFrgG030ix6w/KqglGGrLNCQXnh1oHh0z1kdIbbca9H3W08VGQG276qDketDi/sO3RHjE7LjAJCKEcpNIyaErLnK7cZzzz7Mdxot59Jvrtb5D7+id/MF9L6CxTmEPOo9qljDpXHgB8YWAGuzAHI8HeOT0VmzGH+E+bA6qY76RHtbImzY47glZijfo/5N70oFhHaCx7aaLykYceWnmAOk2HIi5mkpEP4I+Zgbse5tptxgjnYl4EOmIOFOTx6LXAYjxgjSP4rc9jsxsIcatMp8gM5CxhzrO36HekoGDFH5gc9SpQlPzJrVJiDmIEBP7SlKKvrbizgzKFsQ+B28ggtOPDqnU4SD4HaUem0CnOITaPWSRH5QWc3Es2Zw44ZlTqxROYQftDNP+22C3McJsyhx88bPyhzyD5blTnsaFIpH614DAg/MI4UZ1sofTAO0qFDQQet9pk52oPKWDvmYClDZumOoMocpg3ijNKeOXxmR3zsR8zx9LP/F26025kjub2D4prc//IVX47/86d/qf2wllsbGgy5AYC9bHZhJqxRZdLU/XDBZCg/sC2CYEc5J2CjDFAj5bpY+xHisY1/SzxcrtVnkOKX9oYE29ipwFUEGtMzyixZb+v9qDK67tOuh0xxLJBU2K6rwot+Vx1lh3C2hbOqA1thq36rwNcqL5q+oaV3VLTRDUrHdxFkxoJASCsk3aSK/Z6KIV8Rz/NYrVCbnwJGm3hIqZxDB8fiHSTPMgQGpIee3EgpbLABSOuxZ+j+EWs6Jqydc44gowAiholIRgKiQTy0Y8KYBWbY9p848oJV0jzQGvqhCM9aL7zM9mCW0l6lfNvIgk5n1A6RxnGrTz1tucEdedBMB6ixh4C9nFZiLLvYw9nKWu43AUcsNnKypAq1YLVqpn6ai9WqrR51qtMaj5JePC7MqqhW9VC5CXlNrMbmT0Edj2CTV71Tu4QAZSm25h5+6JW4yW4/8mt3t8m94uWP5Qu1k+Bu+KFyw0ymRZjagKsyBwBbe2YtjTc5iT0qI3TMEWVDFJ5O4Qe1gaLPkDmMK67AHJYHb4jZOmICc7CXdSrQgBNj5ggFwNz2DinMoRsVxk207XhSnWUgaVfm4AFzWL9H7PSAdjysPuDCZMeer+zM4YNWUmC2KbdEmZiDAnNQ4Ycxc5AxwjZzHEHGDwt8o0n7n5UfON1Pls6R5gfjhDFztA07lTkaSjo/+KzHzEqRH7aZgzNzSD2NzEGBOQjSAQSvC0ccRJ5tWUrTYZWjTgGnSnmMMGIOrfyZOeIjHut9mDMRfsdGQa4OmEMfmxlzPPLQV+Emu505sts7KK7JPfJwK9rWlrg17jsa4gsAspHtjFmWyRQAf6mkkE40miUNhxCMdVPjGqLCRv0nguyMHQWCfGgpNClLRwOFHsQ01Ssoa1FrWuHc83iEmCpsL/zgIqMvvK13PFjYcUZTSxgLvCsJ+BEaMToffcDCpbdUesENxmIJ+brMKMIyK8BHdrwg4x4SuQEnrLxgWTwVP8vaSyqbhdbBgZW7nvY2ws8h/lg+rrcWgsnw6IhW7QDW5SVmWsVErjbbg22kolUanYVS83rkRY7iyn4rFjH+vfFrJ3JontyqrrzY8ajq5WZbGbHFpetgdQZEnEmhJpXRwMuMv4CYdoLrjBdmn70BoMGT3S2fwHkE/Ox4xPu0QLuhNLc+iKYQUDvvI3b4tbiq1cPq1dQ6mbzWoahTe1YPuNGOOTROV5Tf3e7ukzvEOejA0PbnJzSHAwO8kncIRH7QC+cwhwaJPCHvrHXwZYs5on7BhGau0HSDXU/xXcocRMhHg17OHLENNebQspoyR5Ybg8XsuvitMXEpcG11dWbLPWCO4zEvsVQ5XZYxYg7GYi/irtVp5tCX4445mJ0fEG/mnDnAK9Axh59qofMDPEhL/RBKx5fByPKJAXOskNmmlH1GzOF3Uza1jHCeZDyL94I59C6nSTzQJb8hBXnoWv+d5seZQ3nNrbrmxplDfWJe9VqsWz60Et1lzBFTGDPHI7jRbmeO5PYOivviyCvOMn/80nVC6tgYyfSvD6EToYgpGLDFXWRQZCTpdMZ4TEeE4iQBk6lEVHTQ/HXxDZ4tPfKL2ZeqmDznfHh8BNYOiJAQCa3Z0aCBXmy5RCy7RDiaObiiFKCgayG1VMrNUABaKRWITj20KbTBCCg0tQ2r8qZLetKFlo821iz5UZlSqtKxQQO9xQSR7pLhZkAZzI1nq4t+coZODQ24o8wk/6X9GlYZIQlYossdgGbg8xH3B7RjsGyuhseFFbavR8nU0UZIalZlPwuJKc9MYCkD2MwhAtqSGZlKSVL5Y3VRQ7raRmLSzcIrbCkMeXhHK3amlHIC1/orQMetUyqCm7rcyRCRIHZSpAUacIir5eN1eDyKkSFTtayAmHPQV7nd7W5399b5q1nfxm/zwwAe1I9lA8JOBsOmoWMBVSdwwLnMAQSGmTHHkB9OM0dXDqeYIy5VMR1IrnPS25gDlzBH/BL4Q5UYQQ/gzFG58QRztBkV5zGH5oktGX/Z1OSdOdzCUWAO69gJ5aedApFh9B45P8T9nkiKeos5mg7bzNFsI9uMhMFLs8zYICk7Sx86F6PFkTexzKzkWVWGyXZYn0ibbRSZY8AP28wRZXzmjN0nSXGxAnNF18AcFGqE6qODKbF/yjcKz09S7qSYMceATY3rLmMOCrV0QE3Y3e1zewfF/XT2ggv75NBwRJfe8f0tJl+vMsUAqwU3e3fiMTWjvaVQaTOo+s3CDmQ6SMk2LflRF7lc//+3d/6xnhXl/X8/53O59+KXXVzc7i673UrAKv6AEiFsFktIdeNSjS1JG1EbWInFNkLSuIkKYlktVhZCCY3dSsSiNsGutVXSlM1aXbttVBoiLqkVxOJi0eCu0Ii7BWX3nnm+f8w8M8/MmfPZ+/sXz5tc9t5zzsw8M2fOPK8zZ36EMJ1OEUpwkDxyHlfntUz5dS6Ol5uFZDaog6ms5Z80PkFnFZ2/ht1ZdZVeiIvSPaPy7RucOIULGKXkZOSrBpCqpwBIdiuycxydV25qCkfIy9IfJxUmFXZfmHyhU68WlI2I0Gn7YzpM7sy03fFuUMxV1hkiAJHdWAVJTThXVnXO/kryHyZTmPyRSIUo8ODTSYt/cXEF6VhUUg5p4VNdSAypI/phSeVYQn+0Kvyv3iyoJ0LZ0O0Q64ZYEnKsGt9phjeZFlLd5rDwT0lZX7wKn3xAlx86XkuYI/Pplac+NF8ZC5yAOfQ6VXmieZx1fkDeTOm81vgBQ5hDjlYbxT7mSP/PuGMSzFFrRWaTOUofFM/GsqH8WoLqA8njmw5ziH+hkGh5j3gazCFbhuZhksfthpGPI90S96MbhzNHCpX+r1lJ/2gWKPviyjoQmYO6zIEsTPdBT1uGlo9G7T2gzhxFbiAfSDohK8zhkzoRc4SOC/W46DpRRt3HHABXm5lu+EUuY45M1kExLwqvHWEuYJR+I+h+3q2OovAvWqE57Q2jLi7DFO6o49OLhkb3qUQnT+h8BdFOO8anACQ6NUZ9CKeyW2dLwsi0xOgg5WtFjbZ0mCKdrNXXeebsF0TQIA75pTyAFKqUqnbWAmkgNYSOgt3yVYazeyAFxMWXfiI9Z7SoJqyPcW66U3M8qchxIB8XRhbEw/HelfUx0VKrwkhxiv2DrGHVYfKlOmIYLsOIbS6GkW8dkkU9aMWnKtMd0jIiqlAjXMhuIYBe1Tt9dWDW1dJX8DZ8TQiDNlWFb8J2ZcrdBhhKK2BrvPWVsRZGfpdhnzkKylzfVDxyf8TuUoTKffUxpcVYdVaQOm9AOboIsDXqHpUwVHX+5YpzuhyqcLRIZcMtTUtctVGYJ+SHwkcOZQ51Pv6hw4hfp8Td4rOqL/hlmBgAyW8ULr/sTK/xQ405dKBZYQ7k9sV0psIcIQ3vBkn1ppM07gDNJnMg+/giZeHCm3N3a/N0X3X7Jv58eswhazDowgvrLkyTOTq+jpN3HUCHAWSrUykvzRySVz0bRzMHAR1+yJlDqMCHZHAYhclhemcsdcUcmBRzCJOWzJGViLA3Is5CnnCm8mqfI6dYSd9hxxQWPe2GKu+rhNH8MCXmUPwwGeaQzqByU0NjjqUt66CYI/3iF8eUQ/WPR2zTlZfwQCCOCboVjNck70e5Tyt28IiS82U6wRkASPPVKm1U4XvSKAUdH9Lv3KjjKlq06Ryrc3E1bwUiOk5JmlO2QW2wQRaF1IUUAKQEJQmDRr/oKQMpNWyp3IKTajgVvTS84pFktW2hLrkvDYeySl5BnApkEetIWsFZNRQbeIDSXF0Znhh64GXxK0JaNdtJAx/mmjbEcVcQAoNZVqBOc0YTXIRmO4ThYDep+ZMNEM4RZLFND6lhdW5GXCxJ7B4expdpywEBArAMQlrRBllkk/wK2w0ElDgMvw2LV1GqI9ITL/M15SsOhVsk1Vzs9k9dWAeb/doWrKpWhA9mDMIwX6fzCr3USg4szIxBeNLiNmLEEWI8sqQwrgijl51iwO/UoYcaQ4abOr+MVazSEqdL8ZHEkupMGuBagBYjgoFapst3hkCe32QXqZ9UEv6sfnZ0t6huahwfg8lkmh0dP97Gdk5QXrt3jQXMC88c0dYac0g04i7VMfkjdiBo5lA8ciLm0O+2rH4ZxhyxZe5jDjGkhzniwo4lcwCIW41q5hB/HZkjFUxkjqxQkNp8xRxpuopiDjXVtcYckOOTYA6Z7qGZgwiKLcQPU/DriHwkzMFhwcfBMOZA2BY0ZNGR361qcswBgKjOHGjATH6NqQpziDcvmYOEOdS9zJnD8wMYYepCk4VJHXXJL0fmoNRBROwXCh/Is6J8t6/rmh+g/LWvOvrx9J9cOHCP1J7kv4VhGlBcnDsxjCvS6WeODj8UdneZI7VYDhSnlUyWOQCph3mrlzGHew6mpSProJgjPfnkM/4Xqpw8wTEuDotDZCiHW0h3fOgw1TQ5xSfn4oiIsPiTagVSGNntsPgaQZynn5NQaDSa/BiANNe0kxkVpjguQwClDYp5KO3O0q+VmDpXySuHtLJVkCSauIqhOpYVur4RoYGUoVtEkBWZIXND9VeucCOYKQFLhBV/XQQLkubcX+eE9pBMEMiQFcJ9VtM8w1bqAvshhHo3DsccR2Kk4pEheS7sbkbBufhwjh2aEEZ/HZD9vdvgQPR8zQmX9hhvkuHhxb2NTjpbvIoBdrKtFsV8MRo4btEEIIsLSMn/ufXpZL39A0ygRRPui6xjocO0BAwEgODnq8r2n8m2VEkcWjU3U5dDCpPcqw/ThjAafgBCGx+IPD7nNwILnR56XmgTbQBk8a+826OFGv6qbJf/0namqYrnbYxgQQ4IpZj7h15OTDxVP7FoJA3BDMKbTPOkn/70SObTM59WckNxntUvkTE4XcrIrwdmzhzan1aZAylM5eNstlDlbDBH9gaUHc+ZI6rGHBKMVWZVfMRUZw4JX2EOBvwil1DnwktdDEP5jWCQQAyyYfZEaWv6ePNmzhzCD/GDDcFP5XDJN0ucukktmQPMaIcxBwffTXJzCBPsvG9Glzn8aBgumIMw4RwaP5AhYwGHQeCHCnOAwM7zw0Axh+/caANjTZ45WrSRBZvAND5rPq8u+GzZ0YuoqTKHjP/Q/JCXQ8kcuQ2pCqeHSCgmjwtgsTumI3kaxhw6ti4rsYovP9NlDgkxGebQcYqOTfyoGmbxyJhDyzoo5khnnbUGD3zroDpC8f961ED2NOb+Inf+0r4rgNBQEI9phx4SYhUOjDhFQ4NIOcwve0ao+FXevJQReshmBgzBFtndo1g7KZVF4eSbWhgAMpQ+LTqlbNDx6c6D0A2s9y3311Pw2yqz8R4E96N3BMlIjdP+SBEUACYZgYBCAgjeBvmaRFmYYsHUaHdYIIlIlbEHHab0dUPmakq5+m3CZNSBDyUpNEiD31xIp4XavovF9YYGn4GmkUUjKWwf5u+DfAnwiQwg+4KLDWnkwyB8DZB7J9aQ4q2wRRb5uY5+H3K5FnFbLY5jEQDZiku+dBCa6vZdYMIEGj8agV0YHirwM4g30XMaR8RoqUHDFMoHsd4R0lBN2VqMYoxNOB5Go8Sq1yQg1CttqzDx65OqdjJ6Iy5AGR5qineS4Sjff9zPJ/WglgNDHG8S55xKSnkYQM+v5fLBlpji85kaMqkbaZE5Rhl69KT15UOyuGTDLU1LSBs2rEp/KKaYCnNkbJG9HMwic4jvj+1laF9K5lD80GEO5E3OVJgjNp8ZCyC66Cy+YOikmUNsEt+tDYy2UZc5og3NEOZAGvUihT+MOeLnb5cxR26QhivMiDmyrdI5fd+vMgf7uE/EHKT4QZjDMWPQpLpEGPipERAWSGE6/BDvSKPuX5c5XA8/uLDzlPBDGl0R9s+aBHPE7pjIHJ7TvF/3ZS151SM39CIsNeagwA+eYU7MHKzCVJkj3IuSOTBN5kgdFfki2g7pc0eqaXXmiN0+02SO8dFXYFHLmCOTdVDMuaKXC/+n/JRy9NUg5Tmpv6Uzgrzs6rCUkqmFkQa6hBikZz82Ix3nB1DTDaOfD73ekAYGKDtr6UhShHoYn1c99DQ552hDcOS1MAU6+NEE0QbdtIVmT+LKGkV/QG91xcrybCmjSDTdm8kkjTOpsitggxsVXxr9IPHJKty6IYZyEuX2kZIP6cBIyebbd6VcyCgQ+YATIgg3zG8ZmpxMGoFA6u+cPvUcUSJxd4DsktGEzgs9kljDIynH5oIbjStMa94KYRoALg7l9EdatX1XcqUUhrEqB8sBWOC3/IpuWt3OckcQgmxBFqCHXIaC3u5BGh4awvj+LkqdODF+lSd4B5++KKSLZEhu7s5RAIPUYP+3g9+VXe4rx3T0lnG6RlPMZZoLjepVyTQ93FzqhMlkmmt1kLWHObT/yHhB3puG8ENMqGSOMpxKe6GYA0DqVEGet8ymWWIO7yykgKSYKMur5ocTMgel+DLm8MMYUgblnMyFkdQLfvBpTIc5ui2+54ecs2rMoW0bxhydRaKDf2ydn1pCoRSSn/K/yd961ISujlNljiaWtnCO5weZdqI/LvUxh2cEvTVpmkjpQl1rsjS8Wm4wgAs7i+npFX5HEM0cwg+eYdLW4pLvyTCH5DVngWSb3FX5pcYcPkyNObyteSdFzgbp2pkxh7bFmGNpyjoo5li6EaotMAV54dNj2sSHiIMs3mzEuWYOnPNzVSjojjlPYSVM/h6Z4KSwGcgdeTyl7M46TORS9VUjczo96cQwBUClMFy1Qb/MlmEiWen8E0A6EPLzVUAD+WF9jUSgwwQjGhSF0GdcOMDcTUfdh+7XlwAtFfuY0yCP/LZScKL5EDxfFwnk/GgJbaAOA6hXY/JNPzmOYbRTcKHgpHpLZ4VM0yC15oTPg5+zKMXp9Es/yeJXgi6s4mpCbz7H20uquJkYCOlI3B4c9OKZCRE5LGoZuizUx7HGDylt9NJX4vzT30DeMdTGxaJS+aSWAdn/RSmv5bOhHXw6JmFA4vJd9mhLh0O6O3InZTVxwT0NEvkXEh22jE9SKsYBFenVzy1KOUHBmYQ3meZf8aW3ZA7x3RXm6OUH8b2Msonyp4cxx2TCFM1YHwsAyDoc4qmZMkeNH2aROQB0p7qp8u7wSDjE4uaKgpBpCx3mAKkVpMtCqPxeP5AdHs4c3B15GximtjWpZo6YZsEcMiYwhdHeSNmGtD2qfDEX75WYI40QlPNpXYkuc8gaWrIGBynmQGCONAok5wfiZCOF0RI15nAqjHh28f2kbikp3+rLNO84kec7beKZ/i9yvczRgOGUX9dhfF7ziTyhvDF55pA7XGcOIbf8eIxvFplDQhpzLE1ZB8UcKw1zQ3Ta8nQmJ0bpAkBBBCCL7sT4lMMnHQbqoScdARJdyLQCoNPMdL5WSPjSB4oNKixIxSeAkL/fFolV0qqYndlfsw1QXyI0HXDFsCKhsrwFcFCcUwlSzRCKAfOCgQKmvrxyXhgsxyGjKbgSRh9ALGfu5NWf0OCUBRHH1bHPlx1XKoRARzSDJURwuJyhR3RxrCpYlluZG1uaDQHr5FwJHOo+pQWUkH4YsoiW/nqDeF+1i2KOMcZRDh3YjC4vlZOPInWu5PnJ7ZahqlIzBYcE9JLdcs/zMFSkXb1HGZboB046nSjmFeCQr1o9QV7e8bGQdDoZBSBzatVIJDEtPsf6u8oSFDv/M5PwJtMCSZ71DnNkvrHCHNrTZWEQmp6ZM4dc0zmu3Gl2WMUrL3IZc0jaM2UOHU+pocyBimGd1FJEkTm4nzniFIKaLUOYo3s42I0pMod/yR/GHPlpfWG5ulHOHEVmInPUblW+RoWE4LjLF5QNfcwRPVEPczAQR0Ok+u8rIxMiczhkRJL4QdlN7DkgTV2oM4cuu8kwh49nNpjDL8IpHyX0FE/KSisf1auZoyzA3G4V1xSYI//ohTKjAKSTxphjaPhlJBv1Msei7lNWEVc8sg/dPdabkP9HnlhODVI6UTrAIiyrH32K1U8luD7HRRgq/+Y8fDxXptMXn4qTgPjZOk+H0tBKdU7yVobxx7kaJtoqL+wh/vynPB7YTMUXFe5ravh9SrEIuHvOO9oG7GQunziecC3XnL4P6xzFaW3px38xcJwXtwvnHVN5G+DAcNzAOQphvQN27O2Q+Bz0j/8O0Lo8PgcdVtkVz0mYlE857pg65eDgHVPrGhW3t2EChJbJx6fsbkNaLTfhWhUfhxEZjDwdTra0rG0Quwdow/lkm/zb+DBUhmlCmCZLS+yTDvFUfureF8edCiNpZP9W6qN8K+IsTJFOpa56CC5RCQBxvJdUnjNNSrt27cIZZ5yB8fFxbNq0CQ888EDvtXfddRcuvvhirFq1CqtWrcKWLVs617/zne/086vVz6WXXjrX2TAtahXOOByaEtYPYQ6KJ+rQEN+TC78eL5kCc2RJ9TFH6ddnkTkoXtfHHJz4oXZ8GHN02EKYoDw+CeaI7y6TZI6Kry2ZI09KXqKb6TGHOifF7P16zhytYo52lpgjDyP+ldBKmB7mcNwk24J9E8IVQ5ij7WGOtkxnGswh3NH2MgdhgnPekDy1HX7oskD5yHCcwlJnjpJN55c5jDwmo8XKHDaCYo50+PDPAYTHg1PPJUAdBiAA7MJwOUJ3aB+XF8d//P8G+THpz9W9yD5o6oWmAaq97BIJQ6Whz5VDHxlgNXyyhAxW4+uifW3ys1lckh81hLPoQ/Hh1Nh5X3ZA/FKj+hHiBw01/zPG1SbbZOREhBJC2FqssE9W0iYVucACONjtjZB7IAts+cmUqjQ59PSqMMpyPyS0ycuFiMAt+zmecY2CEA+Tn+LQ5A5B6pPslBa39YJMpWA1rzcVftuG1aVjPUwjeUjFJ19FZAEtEMetsGSoIIHAzuVbfJLY4G0chPtKcXihDxPXvJByJD/tgJgx0niwoVDABIZzDiPqS40fphkWhGRvW5pCIqWXD9309hGI9XBMVnM1CUx+W62R8IwRpCwavx0YUj8+xRw5b5PYHatC2EJM6lJIB+QnXzB8Qy1fbyQ+Zv/ox2GrobL6asoYAYU5sKl+h5LxO5ZkLyj+/g1AYb0NVnZLMyM4gpg3xxy3aCV5ukK8DgjbmHW/uzCAiYlnsKglFDuT8FPU5z//eWzfvh133nknNm3ahDvuuANbt27Fo48+ijVr1nSu379/P97+9rfjoosuwvj4OG655Ra88Y1vxHe/+11s2LAhXnfppZfi05/+dPx7bGxsenkyLVr97GfPdn22/FEyByNNY5B2uUv3eRio607AHCnZOnNkdk6HOUpuEBv6mCNjgdlljt50mHO2aAEO24V2mAM9zIFQ6JTST0b1MwcAf49i5N5eblUYSSiEYSqYA+H+tcgYQTMHiMFN/kKZFq8MZVEyBzhsJSoFFpiDS+YQLKLkh4Ovi2TBfqHGkcK+qTKHT6WBc4wBuRMyhy+6wBzh84Lcl8Qc3jcPsmmryQ87eN+ZMYe2O/hNzyIUWKBgDnig6GMO7jCHL2+9ZahmDgpf8JoeFmD4jV4aXdnJTxihEF/OHH6KLIMjC+jaNSPmUPWjxhyShvzOAI5N/ASLWsYcmayDYo40MTGR/ghPXHxUtKNT1+h1iJTXz0QO+ZpHFI4B+TxLOV6mK/HWtspEHia7nlM65TnSk+3rbUM3jLTn3LUbtfgKG3TZyhcMPlEYQmF3cMql3Sjymp3XEei8FpSnLvNtOXdAKE5w1KuGqXQ4bi0WrhfI0PuXN+ErDLHvaQ9z2GRRKw5xtcGxp50nUq90K+lw4+FA2R3ZiJDlL6234Oc5ppkEfgvLRmeVALBfu0Fe8uPOHyAwu7gdmXY8jv2ilC2l6/0q297RHne+z72BgAQAHmAi5NXvZ+4Nk/8z+221GqT5mR6OWr9YJOVbi8l+4QBhAI5rQviOCMZxF/IapvrEMpZbR3reZoOWGex8nWsiGEns/lgDmSXqZ706ZkwERz3I2gS/CnoqUw0G7M/F4ymvDBcWR5U9QCjaoYewkkoHcHE+b/ZwIQx/VQ8Pda5IVZuyGI9hUWsB5oPefvvtuPrqq3HVVVcBAO68807cd999uPvuu3Hdddd1rr/nnnuyvz/1qU/hH//xH7Fv3z5ceeWV8fjY2BjWrVs3ZXtMS0cTbWV4L+X/Zm5MP5Dxsrw9kPcQ1g81ephDjk+WObR/Lpmjlo0KP6AMo/hhGMP0sRKGhVHpTZY5uqykjB/GStmx8MswfihtAPLVGiHnSO0EUlzPijmK894Fcs4c4bSL7aSeUpo8jnS2R+Ygv705DWEOQH0ECSfbkNUac0y4OnO07EJdKJmDEU4FfuCY35YJjijbIUMK6biTsQrBFwbmcGGrzIa624zKV/+B2A3A757RQmghdqSQv2GtMIesqwXAd0QwjjvPNcIv3p5BfFb7mANgtSV7nTkAv5CmL8VWMUyqlC1c7GBLzAHInh8U40o2eH6QnE+OObjDHOmc7x8czhyywGp+jTFHqcXMHNZBMUfasOElAP47PjDZN0TtFILk/U+qFxfnO45MHHBwkvr9WI67coEs8WUSBuhsxRVN4+TPJO0YRtrLElqUwy9t5vDcUdMTn7Jbl0fwICmMKoLSwccwZTqQZrwEg/DVmeEXNIqOPDWWugMlqRZGyjCNbpCVkFJTjLSIVZYJsTKvGCzh2p4vG/4mp5sXrUayIVkMcQP+V18jHfvvAJKic6lzAyqMn4SQoIQgdc5XJmqE1rxz8yzqio4NPUNSF90gvqzHnTVY3LtPtQXnABKcPYW8DiJhMpiasNiVt0ScaOgWCFt++Rf1JvLiwHcKsLdPXupTGHhHH8DAD/sdCR0JHL7wcHhuwncK8gtuye8Ca7IYp2yPGumf/FBJB4ZfVVzicvHetsxxe9RIrCzzaxlNeAA4PBANh9Ckt3kTRMjX6k7QEMoxe9rSE+QUzOjzemZoioHjFXldZIyOdHvnF5Vm6WvGkSNHssNjY2PVrwnHjh3Dgw8+iOuvvz4ea5oGW7Zswf333z+pJJ977jkcP34cp512WnZ8//79WLNmDVatWoXXv/71+OhHP4qXvOQlU82RaRHrV1avSPwA9Uv5IhqUMUflGu4+4v3MEf51ZSLTZQ6dCUKXOZDS7bhRUvb3MYfYoNLVAwumwhwIzUSVOZirW1FQhR/AjLitJ6EoydBqFvwQ/fqUmEMKXi6sMEc5glPyA4Q3yhDWEahJhV+2lpKk94GJOfy9DyMaKszhzXapQwGpXgxjjriNZsEPmjk0PwAIPjUPI/xAFLZBFcsobP/J3hOmXSoGcb2OPuZoFXP46wbxQwQiP0yHOSSHTZU5oCxxzGgUK9WYg8ItThzl76DfQYT9yFIMY47wOYPyjhJhjrRWv+6o6DIHqTuXmCN/AIVchjGHbhrGRl6KRS1jjky2BsVcioE471u8ZXHe/xDKHgnS11TihY5OHHG8nrqrd3Pxqw7jusdR/N4Jo+1S11FfGDnuKnar/Ci+SOnoMHLOt6fR7/alkzlYJpDzw+Uooxp/Do666cTyKcpTxafBw/+fon3ZvYr3Wn7n/LgTG8q0/JcNjvVIpccAu2Q7x598qkc2d1HNi1RFhzTnEXAuxeH/bmIYbwaFuh3We3DSIZB+ZM0KPWdTxjzIuhAtUywKxz6MzMOM6zCEMH7OqbYh5bV1TQjTIFsTI5sjCvj5sTIfNaw/oea0+p8m/RttlvUimuIa+T3NORWbsnNFfE7FJ3/r6hDX+FDlJmEmgt0yF1fOt9zEMov3SJedqgOc3Q+KoNBtpTQMxKctHC3D6Lj7RMW/y18bN27EqaeeGn9uvvnm6nVPP/002rbF2rVrs+Nr167FoUOHJpXWBz7wAaxfvx5btmyJxy699FL87d/+Lfbt24dbbrkF//Zv/4bf/u3fRtu208+UaVGr9+kawhzxpXc6zEHTY47MZ2MIP2gbivinxRxAxgh9zJHzQ505wEPCIGeO7DW8YA75uJDi7mEOJpXIJJkjHldOBopFdIGGGIU5ynn/GXNIlMIc2SAe5W86zCG+Ihx3FebgqTMHT4I54hpbLGtgUJU5HKAYwZdDsk0xAIJfnwRz+PUnyrW70poVfYzQxxxtWGNM8lFjjpwfaAhzNIo5kDgJTVjPIrCSYoY6cwCMlOeSOaDuDZDfv7IedpkDWZh6+FJ6DMwLQ8uFOWwExRxLD5lMXfvwrRypPsI4hzBcwuGxypy+6l8MUJApXF58PI9Jp68QeU+3jy/NMYvH1YH4K0XTs3RqjQMRKunoyNLvlP8Z/4hhKvnpS1jvXFHGF8NUtrdInRbUY0ORaLRdGlB1PBRUFqIsYH2XVJiK5dEQ5k4kSM1zecOoexxhfmQkuW46LSMNu5Sj5EcXpOF5KR0Kjq4s0jgvkAE9sE+GSKZtqyjN6lFhOvOPwl96CGVKJ9hN+nnzIZyaeqFXePZfLppsqgWrMOnrhFQZDkDVxKGayPKQYFG+Jco1cctQlrymZ9tB5vVSltu4fZeuViGtNkxf0aN1vd1NXBfEhbeA+OVB/uGUz/ytRAGnLvTwAKT8hNEUKkz+SLBaMV7fORTrXixySTs9k/AAfvSjH2HlypXx8Fyt/7Bz507s3r0b+/fvx/j4eDz+tre9Lf5+zjnn4Nxzz8VZZ52F/fv34w1veMOc2GJaYEn7rvcmBJI/62EOOZCOzy1zVN1cxXfHpNW1M2YOxR4l95yQH2rpDgnj/SOl+5LltXgBmwvmyKagZo69MLbLHHG9rEw9zAGdToonxVBnDgbiyIJkGkcO7mOOjnqZI/n8OnOEKSTp6YjxCT/oudWJBWSqZum768wBBP9KwjLKd4erxW+KbcIcRPpNQsaGSD6mxhxtCJ8zR7lWhh9RgVhennvS9F4J08ccDWQeTckcJT/IvcyYjvW9CNfp+eSTZA5QToSLWsYcmayDYo416Uejy/qQQU6d64AqKPh3kG56qo2ppitDszrXhDaCiuvLRZCmrIot8VDV6aiLdJjStlr8ZZhu0jHv+ZZfJ7Ajllcs+P7rS/PE4ZWGlWno05TmfdbTIf1HvOkM6gyZjelHOMlxtLachixGmWU9xOXDUBxJWmYrY+SQXkynUhFKUJMvT2nxqQLo2Nvt8UERLtIz1FlzlnXWSZUeg4q/JYzvxOPw4UgvdpU6bpJl+fBDuRcCJf5UWlROth6rYWCW1xCTjG7Nr0/paBzg7GzZEenrQe2+AXkzI6Dg4SLFU95e6nsuiTvXL2rN0nDLlStXZrDQp9WrV2MwGODw4cPZ8cOHD59wLudtt92GnTt34qtf/SrOPffcodeeeeaZWL16NR577DHroFjWouwfAOqBpfzhVfxwQuYonEBsw0uGGcIcNTPlXM2ve1/TE99kVM1rhTnKRq2PH2oNZk+Y2uWTYo4yvukwh1ya3k1zOciKh5W8clgzoy+dKTCH1CtSEcRzdeaQDqxhzKHT0FluoOqRtqHzAcOfd4EfSIXxa14Ffqjwtp96IXZmHhVxjQpdDBXmEH6grDTrzKGn2Eq5cSigJsSp13PQ1SZjDoQlxQvmiOWmcqHT0p+LUm61TZo5CKnDZHLMAQlHeYjIM6qtqTFHTUuKNwBjjkI2xWPOpSpbp+Jx9ff0BSFU1opD1+fyoYOVMBKfvK1IGPkdHObUpePy00gLqt90OI8ri3tIOhA7i3NZOlCNNRdpqXPVMDzJMMqWlE7oACjXGdPxdYaKMjAkTD60Mv14uyk7Vp+a4S30TqipVx9GOKenF+i4yA/HlKF5cXhjGgop0TgdRv248CbsF+FUQ/ziUMUmTg3RWZXtqlqn7EP+r8vsVWE4D5NP2UD2I9t0tkxpGCLnYVqnhhvGa5oYX55fxPj00Ei91akeUskcpp5wgwk1FUYP85ShnXp4pB9iGYZVqrJLW6bVtwljNDH+fJoGwGEoZp5Oo8J0qmPKhzoW6168rwmhdBgU8fkLCOVaTRyO5xeatEZHR3H++edj37598ZhzDvv27cPmzZt7w91666246aabsHfvXlxwwQUnTOfHP/4x/vd//xenn376rNhtWnxieVx7n7f6iRkxRxk9hrAAEBaO5uxc6dd1fBlz6PhQ8ExxbYc5MEPmUPF2mMN1w3RYKaYzSebIjvPUmCOcy5kj8ID4ctd0tyBl/1rLUPFk6YRtz6fEHJRPg0Q/c2RTJRVziE9NvreHObjx6St+8DboKRvCHIoFXB4msoCbDnM0J2SOOKWWhzGHTAOBSiMxR7ZVemFDyQh9LODivyWn9DNHnNaBcgtUKsJ0quMQ5gBqzOFP5HUna8Eybk7p6Gfa1NViZw4bQTFHOnjwKfWXDOEG4tAD6d5V3cb+V79tU7b4kxr2ny04JddALTYToouD1BiAxCcdvUxpuy2JDx4Y4tab0F+RJR6opz5mLcFQSF/3XEt80nsepwKElXJ0I0KEbPtPSBGpeCE96lQJI/ZKOlIEck71zmvbfDrq63Wr8qoXwSQgLkZFKsIyTLxvyRgq2tq4+FPDugqEtMivfRkWTUpDEEIj3Eg1YsT9v+TmyjAHub8Mv2MDU7RBjZ7z0wvCNmGyvVccbuf8OT1yAiC0rUPTEFqp1yHOFgA5H4//uuBtI3g2GiFZeMqDGYWyke22mNkvQhXK2IWsyzBDv92WhwwCpzwSh6z7bUFlEKK3Wb7mNHAOGJDzo0EajgDp4KdLDOK9IfjdQho4MEbg1Arivswde1c8gAyX9DY1BLBrMGhcVmZSdxwYg3AiLpzJsrMKZ/XRu3q/k8kg1Ck/JNSXRSgWv7CVfHUgf/MnHGOE0hcVBqEhP87DwWEkGidn/X2XLUNl2kkE7dBIyEgPUm1aw+k+pIovIOILOR1NDc6x409iUctJV9FMwk9N27dvx7Zt23DBBRfgwgsvxB133IFnn302rrB95ZVXYsOGDXFO6S233IIbb7wRn/vc53DGGWfEeaOnnHIKTjnlFPzf//0fPvKRj+D3fu/3sG7dOvzgBz/A+9//frzsZS/D1q1bp58306LTk08+E39Pz5uS9jPSxgtzqBFRJXNonxH5gmeBOSLDSHtRMIf2qSVzAIkfxJ/J7ydiDkadHyrMAUq+MvobsaHGHEDOIyVzpOGA/cwR74HYzWougHLGfcwRbli2K5yUe8YcCtaYAl9xznpIfqbDHOKke5nD+9KcOfw0gLhVevSN4dYq5pC4QIS25cgVnjm8j25DcfYxxyDWWWEOAJyWvZS6oItKcMqXmawd0qAlYCT4gy5zJJ+XmIPitqWy0xVRYg5iv91qCz9Co84cUkb+vLASFHP48mswyHwVxeM5cwS/zo2viz3M4RxjhFwYuZEzB7PUTQkYRoc6YIQox2fFHINY3SSdgLfhWkeJOeJIkFCHEoeE+11lDs8yMrWlxhzPH/s+FrWMOTJZB8Ucac2aFeovSo5fOcASGKRRQ1rmNjUeyver57E7h1E5cmnckT/D8Vx0jjo+p8I0kwwjB5WT0vFlX3PE8co1PWFiGp38VM4RQG33+hPnVW4Gx/sSJVuLqSlvPjgl0Ohs9xoa6yKvMYJyoi4B1PqEI7iI5N6VNhCANnU2ZHmNwzE5hkkNdDge4dFXOOYAGWA4p+pgtCOARtjzy5vVoHV+xqQ4d3HMsn2X2B5fmpnSNqOst9vyV7ViaYAgqdEtc3RcjTh4lZbvp5f1InyELQ8gO2WkJaK8lce5CZ0efsiiVI4WLobJthNlxnFw7ExokD+L4lKZwpzQYBO70AFBHFeshpQPpZWvm/AwMBMmgLhat3ewHPMKSt8r9JZfDn7LL71NmFSdCX+HwjeTfOpHS9LBEp+McB/aACR61eyQX+oOR9XpCSLo+hY7K7OrvUZG8lWfF51mabjlVHT55Zfjqaeewo033ohDhw7hvPPOw969e+MiVk888QSaJtWAT3ziEzh27Bh+//d/P4tnx44d+PCHP4zBYID//M//xGc/+1k888wzWL9+Pd74xjfipptumrN5qaaF0Wmn/b/ozoqHLj2Ryl9lzKF8ubgs4iKCzH/n7UK8TBoDYZhJ80MPc/Rs/3lCfiB0/HAMU7OhhzmkjDosoJkDyoYT5jVnAdLXtIqVtJmh4e3s+qEYhpX/8+cLn5/ZTal8qszB/cxR5nWqzAGAqYlbbecFIEESczDgX9yJ0DruMgflzOGrQ2KOlll9ENHf5GvM4WNtwbGDPmMOBibkQ0SHOajCHF6eOQgUdslIu325wDC+nAhyTjOHrEuRblHJHBRYgjntbNbEYTv9zEEcFgUljnZF5iC/tbnsgdLEe+Q7GxBsy/hBMYeE0czhSHgtfwgd2hhfzhwU1pyIdBnP1ZmD4/FcmjkW+ahBY45M1kExRzrlFL9giHps/AlGWu26cGJyVbnGgwaO2CaSxKXekQu44J4wIel8YSeJD+lv1pCRzMyuqYUhta1WbDo0WEDlFQBLl6uOT8GS9rmk40OKbFhegdyGbp4ITrpyQ4TSk9uBjGgbpS8bKC4sOyJiBIRs+8/svhL0QlTJb1OeWdaJCXmlShDhIIRJ5aAad7E7y5yE4SLJ8Hrt/KgJQlqAKmU1bEGl4iMGHDOaRt0c9aqe1n2mACPpWSEiNKzXdKAQwkUHxhIbhXuHsEBmoFJ9/wbolFhYgFJvQpYca2JVn1YjbpjDF4XYU5ZupGMGkVMAEVx6GMrTqPvTZDZ4+/RCmRJfox4wQSuBgybkk8MXIxdyqDtD0hZiLj4APpb0euG/3kApdXPoBcXSfSvP+XKSc2Xf6iAr9VRqRIyG0qJKpqRrr70W1157bfXc/v37s79/+MMfDo3r5JNPxpe//OVZssy0mDU+flL0nZnjBLKOCe0OSuYQHz2UOYDs/VfizZgjxLFQzBHt7mOOggX6mCPmTZdhiCzLq9gwXeZgZVst4wiM0HLGSmItMYVFBwvDkXPFMOZIwYYwB1Ea2nhC5lBRdFiJVDo9zNEGfojrW/UxR/D5DD9KMfr1VA7e76aX3RkxByj521DJJGSXOShdzQ0cuXiVZo6WgUHDYWqkZg6GI9fLHIDDgACn7JbRIcOYo4EeJRksZGGBVGk1PzTq/nAgHb1laM4cDN+72GUOh8kxh74bk2UOxKtTOWjmGDQrYOpqsTKHdVDMi/TwSdKHc+crx7nwEcXLrjhRLo5rT8xFxJ0e/Uo6ZXwEZJ9MtTk6jPaVyudkdmfpFFmKYEIVGyQvVIQRG5oirzqMzqu2weXgAoRFg5wPXH4R4kBWTFzYEMIg/1JM6mW9k9fonEpDPTDE9DMADIbrz/dyLo6hLRIKYZiAbEFO8mFkzilFh+RPpuGNfrpFOueH/UlDrxNjBsJHmRgfh3JoZSRBQSwtN2gojKhQYCkjI0BlOgxm71ZJ5ZVUnRMHli9VSTGM6hcEh3TiKt8UrQ7zQl0YAYEII35oZF6U8uXFZ7sJdYQDAIht/kuABlCJ0bHLyk1W5CYMgHAuX7iqgWMHhJEZrCqeCzejUdM/mHSYSn8aEKbWiGX5ndJQkN/1rIIqJVBuuDyjIygbr0WoBfiaYTLNigh+ymb3c2KnIwHApJkDQPdtIISfLnOU/HBC5oC010V8uTvNbKgyh2KBavnoBlvbUGMOIK3jMJvMATVVA4EtVJj4AkcIHQ5dkbCA8MNMmQOEuMjQUObIb0ZiDs6P9zEHURzV2WUOCqMAU3zCHLEDbAhzyAu3Zg4m/fHBx8jsd6LQI1ZjteBUrzTXhWUtfdFR8qkM+OmcFeYA/AiRhvyIj8QcEsaptBVzxHEKJ2YO78s9cwCpM0KYw4Ey5ki+PGcOXbI5c/gbKdXLcQOQi82F7khw7KffDGOORCSAvveTYQ6C+teYY8lqySySuWvXLpxxxhkYHx/Hpk2b8MADDwy9/gtf+ALOPvtsjI+P45xzzsGePXvmydJ+yQuO/MR2WHoZY+VU/yI8Vgx1zJ+XMPLTCQNkcaV0wk84LY1YE23jOGwspecy22LvpL4Mxe+d/Pvr43TK2jVlfNreyjmUx1mFGZZO7SADYD8/TxaSgnYF7L9UdPOqjocfXba1dHx+qDc/fXlFkY4/LjRXxkchWBp0mBasCkFZ/SC4OgaoArYpDFXDpHPBjWZcQ/FHlBZcyos0pafSCV5PwqQFsNI5yassLCVxx3SYlH1QNqr4JE50w8gx+cqR5yflg3X63G9DVm25OMc+jE6HUYTpIFjtOpUfRhFXeX3q2EAs91Q+Ukb+95SyfCOJ8XDXFv/3EnOe5Wqs0/kxLVktaeYQP5j9qFZhmswBFMyBIowkPgnmAArmKFq33DmF8yG+Puao+dspMQenY7PFHFT8myn4dOLUxgpzeLsrzBFiK5kjP1/PZ1+YOWWOzC/080OVOdAXRv0+CeYQPx35oafIqsxRYwFOLCD+3mV5pcy2GnNMigVKhun41MkzB2bEHJoftLr8kKeR7kPJTDpM+biXzMG6AnXubZ05urYuARlzZFoSHRSf//znsX37duzYsQPf/va38Ru/8RvYunUrfvrTn1av/+Y3v4m3v/3teNe73oUDBw7gsssuw2WXXYb/+q//mmfLRfVKQ/pUX70qe8SGOaPoZDhzOlS7Nru+fp6K4xE0oh2cn9PhVPzR+RU7WsS4XH6+Fw5qYRj1vFZsKMOUP9X4wpo15Lrn/O9cj6+6XYLYRT1heq53wVnK+jkaZjph0vHoZMtydJTHwdoZePs6TlJW5Q7pZbteOHFuuZOJq027sg0NK00j/a13DHEIK1Qrp+uvDbtoZM7Yr1Q94cKq2RWH6yS8AglZFbuzWjb8ythigy+25OhbbuAwgENuc8uEFmFFbYEzzuMTG1jZUM9r9/ccgJoESNlPyA+jc66vY0KXR+18DkHxkQqP/zBnqM8loFp6xGB6IWrpM4c8avkDR+WDXA3I+bkePpBzU2YOHa5QlTlQj08fr/ru6TBHmd8h/DCZMMN440TM0bFbM0eNFfr4oY85AMUT3bjYTZE54sv9NJij8vIvzOEUy5TMUf70MUfGAoo58t03Kv5UMUfpG1tHaaeMGnOU8QXmaBXD1JgjSysyR2IK+RnGHG3BHNoGsbnKPtHuPFwvNyAxTO24Ux1VHaaZJHNk3DEF5qgeNi0pLYkOittvvx1XX301rrrqKrzqVa/CnXfeiRe96EW4++67q9f/5V/+JS699FK8733vwytf+UrcdNNNeO1rX4u/+qu/6k3j+eefx5EjR7Kfmaht004C8WHRD5ewenyO1LmCDzqdFNS9MItanJi6LDlZzp58RuHIxRTluDOHBMSXVHKIX0jA6Xg8p7PgUgdHlr5LacbzIleAhoqrk6++MLqFkzBOhSlawRpQdAAEUt4M//WjyCsDQANi6kAKxw4N+VqS7PaBtfOWNGUNg/x47J0WxyjJREhIHRUxryqdTt+K8/nhCAThxwEMjs5P9/jLosHM6aWZg/OJ6cQweuQDId8eNTi5AkQkLg0TbdEhEl/IOcAHdMeHty0CDRKs+M6NAbpbnaX8aKhpox35tqVx+UrWNiTAYUnLUbwF8YcHHoAkDyUERHuQ7EeyKYMW+M4Lp8o7frUKsJBvrybnmjw+XU1Q72sDNZCOo+zhis8AFcc52p6+pBXt2iITs5vxj2lpaikyh36emIoPCfFZR5UfOsxRUv1sMQcCC2j/LMkFv1P6ewmTMUeNH2osgFliDmVnxg+1MEVedRjNEhJnH3OUnTUZc5R2hxs7PeZQ52KaYTg/T5I5Yl77mIMjc2iTI3OUfk4xhx4VwJg6c2j/CMUR6QODikuF0VuaauZwAFqXmKOV41wwh2YsTszBgR/6mQMd5mgVc6ROgxpzUMEcTVa1PHc0GXPUOh86o14yftBl6mtK5DSk+4gQTyt/s24KusyRqgyVTUaeDrRUm6fqttAGo2AOtFjMMubItejXoDh27BgefPBBXH/99fFY0zTYsmUL7r///mqY+++/H9u3b8+Obd26Fffee29vOjfffDM+8pGPzIrNAPDiVS8CETAYUFiN2BOD32qQAGI0g7C8THiuJlr/st+MUFzBOIXxakaKMI59OuS3ImJKYdpwzaBpEmAQ0PoVh/z2PWFYHUuY8MAPCtta9pMBCQANijDs4x1plA3wzYPfepsxMtB7cgETwcsMSOVVwsCv4iw2iO0T4eEb1NIJYUaKMC37pYCakF9/vbd7IpDACNXC+OsbGXYY8jHBPrMjBBCawgYCmNOKt+Hccfh5eA0AUqvhsjSX7MtB36MJ9sseDUI6oDIMY6Tp2g0gy2usC4HWBvGe+7w7sAc+gt+pAwAR+zoXllAfNJQWbEwl6G2QsvM1HC37pc39tlkUdu1AmA/py3VAYZsv8i7MhfWiG7lPsYAcEFZ4bjAS55oS+VLwtaYJcxlTvzvzBECMARpfx8GIazqEpdcHcesqf45xHGC/LVbaKcSXA1Eb7KaYjp8/ehwOhAH8Yld+lXDxxhNgQrAhuH5igP0a4Q01aZvRQLDeBkZDI76rIYRjCQPpDAtxwSHU4pA/qQuytsaEvy/sl+IiEqd9DH4PFrVPH2R71gn4rcxkLrAMyDwe0htJi2KRxwWmiXAnGlWm8GWAtBiYevoAtBgZrMOilu7FmW5405LTUmWO005b4dvdQWj/S9+ECnM4/yJKgy4/DGUOFH54sswBqvCDP1f166E969jNFRtiXgt+CDZMhznEp1JTYaXwfPcxBwFoBqrVC8xBBAxAnXvUEgBmX3YqvuNhga4Oc5DihyLMBNJaApNljjak0whzhOMsXoaBk3qYgwhoZOVDxZKMxIXCDyxegRlNE5ZRDr69RQsK/FAyB8MBDIzEJTBPwBwQfw80NMiYg0PXCoX8erMHkTmYGAOMpHOUWMDvQpZYQEoIQA9zqHonYWgA71P9kRQm54cGwiOJOWRhy5I5eAhzeH9NvcxBNFCM4F/km/AyT0yKORh+j5Euc/gfzwIDYRVhGByHE8ormIM7zIEYFyI/iM3CHMdDPkYy5uDAHBT/U7Zxi0HzK1jUMubItOg7KJ5++mm0bRu3PBGtXbsW3/ve96phDh06VL1e9mut6frrr88A48iRI9i4ceO07X7b2zbjbW/bPO3wJpPJZFpAyWefGYU3LTUtVeZ4wxtehTe84VXTDm8ymUymBZQxR6ZF30ExXxobG7N94U0mk8lkMs25jDlMJpPJZKpr0XdQrF69GoPBAIcPH86OHz58GOvW1YcIr1u3bkrXm0wmk8mUycnquNPUMpsP+kKRMYfJZDKZ5l3GHJkW/SKZo6OjOP/887Fv3754zDmHffv2YfPm+hSKzZs3Z9cDwFe+8pXe600mk8lkylRbJn6qP6YlJ2MOk8lkMs27jDkyLfoRFACwfft2bNu2DRdccAEuvPBC3HHHHXj22Wdx1VVXAQCuvPJKbNiwATfffDMA4E/+5E9wySWX4C/+4i/w5je/Gbt378a3vvUtfPKTn1zIbJhMJpPJZFrkMuYwmUwmk2nhtCQ6KC6//HI89dRTuPHGG3Ho0CGcd9552Lt3b1yU6oknnkg7JwC46KKL8LnPfQ4f+tCH8MEPfhC//uu/jnvvvRevec1rFioLJpPJZFpCYufAMxhuudy2/HohyZjDZDKZTPMpY45cxIt9M/oF0pEjR3Dqqafi5z//OVauXLnQ5phMJpMpaC7bZ4n79SdfjhEanXY8E3wMX/vF582HmCYlYw6TyWRanDLmmH8t+jUoTCaTyWQymUwmk8lkMi1/LYkpHiaTyWQyzascA2R7kptMJpPJZJpjGXNksg4Kk8lkMplKMQOYyZZfywsWTCaTyWQyzZGMOTLZFA+TyWQymUwmk8lkMplMCy4bQWEymUwmUyF2DJ7BcEtbf9pkMplMJtNkZMyRyzooTCaTyWQqxQ4zG265vLb8MplMJpPJNEcy5shkHRQmk8lkMhWyrxkmk8lkMpnmQ8YcuWwNCpPJZDKZTCaTyWQymUwLLhtB0SPpiTpy5MgCW2IymUwmLWmX5/KLwQQ/P6MhkxM4PovWmJa7jDlMJpNpccqYY/5lHRQ9Onr0KABg48aNC2yJyWQymWo6evQoTj311FmNc3R0FOvWrcPXD+2ZcVzr1q3D6OjoLFhlWu4y5jCZTKbFLWOO+RPxcpu0MktyzuHJJ5/EihUrQERTDn/kyBFs3LgRP/rRj7By5co5sHBpyMrBykBk5WBlIJppOTAzjh49ivXr16NpZn+m4i9/+UscO3ZsxvGMjo5ifHx8FiwyLXcZc8yOrBysDERWDlYGImOOpScbQdGjpmnwq7/6qzOOZ+XKlS/oRkFk5WBlILJysDIQzaQcZvsrhtb4+PiycfKmpSFjjtmVlYOVgcjKwcpAZMyxdGSLZJpMJpPJZDKZTCaTyWRacFkHhclkMplMJpPJZDKZTKYFl3VQzJHGxsawY8cOjI2NLbQpCyorBysDkZWDlYHIysFkml3ZM+Vl5WBlILJysDIQWTksPdkimSaTyWQymUwmk8lkMpkWXDaCwmQymUwmk8lkMplMJtOCyzooTCaTyWQymUwmk8lkMi24rIPCZDKZTCaTyWQymUwm04LLOihMJpPJZDKZTCaTyWQyLbisg8JkMplMJpPJZDKZTCbTgss6KGagXbt24YwzzsD4+Dg2bdqEBx54YOj1X/jCF3D22WdjfHwc55xzDvbs2TNPls6dplIGd911Fy6++GKsWrUKq1atwpYtW05YZktFU60Lot27d4OIcNlll82tgfOkqZbDM888g2uuuQann346xsbG8PKXv3zJPxdTLYM77rgDr3jFK3DyySdj48aNeO9734tf/vKX82Tt7Ovf//3f8Za3vAXr168HEeHee+89YZj9+/fjta99LcbGxvCyl70Mn/nMZ+bcTpNpqcmYw8u4w5hDZMxhzAEYdyxLsWla2r17N4+OjvLdd9/N3/3ud/nqq6/mF7/4xXz48OHq9d/4xjd4MBjwrbfeyg8//DB/6EMf4pNOOom/853vzLPls6eplsE73vEO3rVrFx84cIAfeeQRfuc738mnnnoq//jHP55ny2dXUy0H0eOPP84bNmzgiy++mH/3d393foydQ021HJ5//nm+4IIL+E1vehN//etf58cff5z379/PDz300DxbPnuaahncc889PDY2xvfccw8//vjj/OUvf5lPP/10fu973zvPls+e9uzZwzfccAN/8YtfZAD8pS99aej1Bw8e5Be96EW8fft2fvjhh/njH/84DwYD3rt37/wYbDItARlzeBl3GHOIjDmMOUTGHctP1kExTV144YV8zTXXxL/btuX169fzzTffXL3+rW99K7/5zW/Ojm3atIn/6I/+aE7tnEtNtQxKTUxM8IoVK/izn/3sXJk4L5pOOUxMTPBFF13En/rUp3jbtm3LAhamWg6f+MQn+Mwzz+Rjx47Nl4lzrqmWwTXXXMOvf/3rs2Pbt2/n173udXNq53xpMqDw/ve/n1/96ldnxy6//HLeunXrHFpmMi0tGXN4GXcYc4iMOYw5ajLuWB6yKR7T0LFjx/Dggw9iy5Yt8VjTNNiyZQvuv//+apj7778/ux4Atm7d2nv9Ytd0yqDUc889h+PHj+O0006bKzPnXNMthz/7sz/DmjVr8K53vWs+zJxzTacc/umf/gmbN2/GNddcg7Vr1+I1r3kNPvaxj6Ft2/kye1Y1nTK46KKL8OCDD8YhmQcPHsSePXvwpje9aV5sXgxabm2jyTTbMubwMu4w5hAZcxhzzETLsX1cbhpZaAOWop5++mm0bYu1a9dmx9euXYvvfe971TCHDh2qXn/o0KE5s3MuNZ0yKPWBD3wA69ev7zQSS0nTKYevf/3r+Ju/+Rs89NBD82Dh/Gg65XDw4EF87Wtfwx/8wR9gz549eOyxx/Ce97wHx48fx44dO+bD7FnVdMrgHe94B55++mn85m/+JpgZExMT+OM//mN88IMfnA+TF4X62sYjR47gF7/4BU4++eQFssxkWhwy5vAy7jDmEBlzGHPMRMYdi182gsK0INq5cyd2796NL33pSxgfH19oc+ZNR48exRVXXIG77roLq1evXmhzFlTOOaxZswaf/OQncf755+Pyyy/HDTfcgDvvvHOhTZs37d+/Hx/72Mfw13/91/j2t7+NL37xi7jvvvtw0003LbRpJpPJtKz0QuQOY44kYw5jDtPSkY2gmIZWr16NwWCAw4cPZ8cPHz6MdevWVcOsW7duStcvdk2nDES33XYbdu7cia9+9as499xz59LMOddUy+EHP/gBfvjDH+Itb3lLPOacAwCMjIzg0UcfxVlnnTW3Rs+BplMfTj/9dJx00kkYDAbx2Ctf+UocOnQIx44dw+jo6JzaPNuaThn86Z/+Ka644gr84R/+IQDgnHPOwbPPPot3v/vduOGGG9A0y78Pua9tXLlypX3FMJlgzCEy7jDmEBlzGHPMRMYdi18vjJo4yxodHcX555+Pffv2xWPOOezbtw+bN2+uhtm8eXN2PQB85Stf6b1+sWs6ZQAAt956K2666Sbs3bsXF1xwwXyYOqeaajmcffbZ+M53voOHHnoo/vzO7/wOfuu3fgsPPfQQNm7cOJ/mz5qmUx9e97rX4bHHHouwBADf//73cfrppy85UACmVwbPPfdcBwgEnph57oxdRFpubaPJNNsy5vAy7jDmEBlzGHPMRMuxfVx2Wtg1Opeudu/ezWNjY/yZz3yGH374YX73u9/NL37xi/nQoUPMzHzFFVfwddddF6//xje+wSMjI3zbbbfxI488wjt27FjyW35NtQx27tzJo6Oj/A//8A/8k5/8JP4cPXp0obIwK5pqOZRaLitqT7UcnnjiCV6xYgVfe+21/Oijj/I///M/85o1a/ijH/3oQmVhxppqGezYsYNXrFjBf/d3f8cHDx7kf/mXf+GzzjqL3/rWty5UFmaso0eP8oEDB/jAgQMMgG+//XY+cOAA/8///A8zM1933XV8xRVXxOtlu6/3ve99/Mgjj/CuXbtsuy+TqZAxh5dxhzGHyJjDmENk3LH8ZB0UM9DHP/5x/rVf+zUeHR3lCy+8kP/jP/4jnrvkkkt427Zt2fV///d/zy9/+ct5dHSUX/3qV/N99903zxbPvqZSBi996UsZQOdnx44d82/4LGuqdUFrucAC89TL4Zvf/CZv2rSJx8bG+Mwzz+Q///M/54mJiXm2enY1lTI4fvw4f/jDH+azzjqLx8fHeePGjfye97yHf/azn82/4bOkf/3Xf60+55Lvbdu28SWXXNIJc9555/Ho6CifeeaZ/OlPf3re7TaZFruMObyMO4w5RMYcxhzMxh3LUcT8AhrTYzKZTCaTyWQymUwmk2lRytagMJlMJpPJZDKZTCaTybTgsg4Kk8lkMplMJpPJZDKZTAsu66AwmUwmk8lkMplMJpPJtOCyDgqTyWQymUwmk8lkMplMCy7roDCZTCaTyWQymUwmk8m04LIOCpPJZDKZTCaTyWQymUwLLuugMJlMJpPJZDKZTCaTybTgsg4Kk8lkMplMJpPJZDKZTAsu66AwmUwmk8lkMplMJpPJtOCyDgqTyWQymUwmk8lkMplMCy7roDCZTCaTyWQymUwmk8m04Pr/EcCr/JbUNagAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1280x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mu = parameter_space.sample_randomly()\n",
    "\n",
    "U = fom.solve(mu)\n",
    "U_red = rom.solve(mu)\n",
    "U_red_recon = reductor.reconstruct(U_red)\n",
    "\n",
    "fom.visualize((U, U_red_recon),\n",
    "              legend=(f'Full solution for parameter {mu}', f'Reduced solution for parameter {mu}'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05934cec",
   "metadata": {},
   "source": [
    "Finally, we measure the error of our neural network and the performance\n",
    "compared to the solution of the full order problem on a training set. To this\n",
    "end, we sample randomly some {{ parameter_values }} from our {{ ParameterSpace }}:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "14ec1506",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_set = parameter_space.sample_randomly(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "110e07ef",
   "metadata": {},
   "source": [
    "Next, we create empty solution arrays for the full and reduced solutions and an\n",
    "empty list for the speedups:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dd73cb0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "U = fom.solution_space.empty(reserve=len(test_set))\n",
    "U_red = fom.solution_space.empty(reserve=len(test_set))\n",
    "\n",
    "speedups = []"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59b59bcf",
   "metadata": {},
   "source": [
    "Now, we iterate over the test set, compute full and reduced solutions to the\n",
    "respective parameters and measure the speedup:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "39847335",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "de9cac31557542ec899b4d378fbb79b9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "for mu in test_set:\n",
    "    tic = time.perf_counter()\n",
    "    U.append(fom.solve(mu))\n",
    "    time_fom = time.perf_counter() - tic\n",
    "\n",
    "    tic = time.perf_counter()\n",
    "    U_red.append(reductor.reconstruct(rom.solve(mu)))\n",
    "    time_red = time.perf_counter() - tic\n",
    "\n",
    "    speedups.append(time_fom / time_red)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "032b13e0",
   "metadata": {},
   "source": [
    "We can now derive the absolute and relative errors on the training set as"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a18d91f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "absolute_errors = (U - U_red).norm()\n",
    "relative_errors = (U - U_red).norm() / U.norm()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53c6ecfb",
   "metadata": {},
   "source": [
    "The average absolute error amounts to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "142b7e30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.004755120599120671"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.average(absolute_errors)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0057bad",
   "metadata": {},
   "source": [
    "On the other hand, the average relative error is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "764dc0a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.2114466909876e-05"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.average(relative_errors)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb803e33",
   "metadata": {},
   "source": [
    "Using neural networks results in the following median speedup compared to\n",
    "solving the full order problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8c19769e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.618931481881127"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(speedups)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a50bc525",
   "metadata": {},
   "source": [
    "Since {class}`~pymor.reductors.neural_network.NeuralNetworkReductor` only calls\n",
    "the {meth}`~pymor.models.interface.Model.solve` method of the {{ Model }}, it can easily\n",
    "be applied to {{ Models }} originating from external solvers, without requiring any access to\n",
    "{{ Operators }} internal to the solver.\n",
    "\n",
    "## Direct approximation of output quantities\n",
    "\n",
    "Thus far, we were mainly interested in approximating the solution state\n",
    "{math}`u(\\mu)\\equiv u(\\cdot,\\mu)` for some parameter {math}`\\mu`. If we consider an output\n",
    "functional {math}`\\mathcal{J}(\\mu):= J(u(\\mu), \\mu)`, one can use the reduced solution\n",
    "{math}`u_N(\\mu)` for computing the output as {math}`\\mathcal{J}(\\mu)\\approx J(u_N(\\mu),\\mu)`.\n",
    "However, when dealing with neural networks, one could also think about directly learning the\n",
    "mapping from parameter to output. That is, one can use a neural network to approximate\n",
    "{math}`\\mathcal{J}\\colon\\mathcal{P}\\to\\mathbb{R}^q`, where {math}`q\\in\\mathbb{N}` denotes\n",
    "the output dimension.\n",
    "\n",
    "In the following, we will extend our problem from the last section by an output functional\n",
    "and use the {class}`~pymor.reductors.neural_network.NeuralNetworkStatefreeOutputReductor` to\n",
    "derive a reduced model that can solely be used to solve for the output quantity without\n",
    "computing a reduced state at all.\n",
    "\n",
    "For the definition of the output, we define the output of out problem as the l2-product of the\n",
    "solution with the right hand side respectively Dirichlet boundary data of our original problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b302e35f",
   "metadata": {},
   "outputs": [],
   "source": [
    "problem = problem.with_(outputs=[('l2', problem.rhs), ('l2_boundary', problem.dirichlet_data)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f9ab532",
   "metadata": {},
   "source": [
    "Consequently, the output dimension is {math}`q=2`. After adjusting the problem definition,\n",
    "we also have to update the full order model to be aware of the output quantities:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8ac0b0ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5d00888039c9402193929d54e1b83952",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fom, _ = discretize_stationary_cg(problem, diameter=1/50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aee880ca",
   "metadata": {},
   "source": [
    "We can now import the {class}`~pymor.reductors.neural_network.NeuralNetworkStatefreeOutputReductor`\n",
    "and initialize the reductor using the same data as before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "15537197",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymor.reductors.neural_network import NeuralNetworkStatefreeOutputReductor\n",
    "\n",
    "output_reductor = NeuralNetworkStatefreeOutputReductor(fom,\n",
    "                                                       training_set,\n",
    "                                                       validation_set,\n",
    "                                                       validation_loss=1e-5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b11b859",
   "metadata": {},
   "source": [
    "Similar to the `NeuralNetworkReductor`, we can call `reduce` to obtain a reduced order model.\n",
    "In this case, `reduce` trains a neural network to approximate the mapping from parameter to\n",
    "output directly. Therefore, we can only use the resulting reductor to solve for the outputs\n",
    "and not for state approximations. The `NeuralNetworkReductor` though can be used to do both by\n",
    "calling `solve` respectively `output` (if we had initialized the `NeuralNetworkReductor` with\n",
    "the problem including the output quantities).\n",
    "\n",
    "We now perform the reduction and run some tests with the resulting\n",
    "{class}`~pymor.models.neural_network.NeuralNetworkStatefreeOutputModel`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cd5f64a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9411507a98aa44d99b88fdfe06024107",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Accordion(children=(HTML(value='', layout=Layout(height='16em', overflow_y='auto', width='100%')),), selected_…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_rom = output_reductor.reduce(restarts=100)\n",
    "\n",
    "outputs = []\n",
    "outputs_red = []\n",
    "outputs_speedups = []\n",
    "\n",
    "for mu in test_set:\n",
    "    tic = time.perf_counter()\n",
    "    outputs.append(fom.output(mu=mu))\n",
    "    time_fom = time.perf_counter() - tic\n",
    "\n",
    "    tic = time.perf_counter()\n",
    "    outputs_red.append(output_rom.output(mu=mu))\n",
    "    time_red = time.perf_counter() - tic\n",
    "\n",
    "    outputs_speedups.append(time_fom / time_red)\n",
    "\n",
    "outputs = np.squeeze(np.array(outputs))\n",
    "outputs_red = np.squeeze(np.array(outputs_red))\n",
    "\n",
    "outputs_absolute_errors = np.abs(outputs - outputs_red)\n",
    "outputs_relative_errors = np.abs(outputs - outputs_red) / np.abs(outputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74e41997",
   "metadata": {},
   "source": [
    "The average absolute error (component-wise) on the training set is given by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8199ac62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.001393936984678279"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.average(outputs_absolute_errors)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51d18a42",
   "metadata": {},
   "source": [
    "The average relative error is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "84d07a24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00041834554676528087"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.average(outputs_relative_errors)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93779624",
   "metadata": {},
   "source": [
    "and the median of the speedups amounts to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fcffe5f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.421077704856241"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(outputs_speedups)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20ce7593",
   "metadata": {},
   "source": [
    "## Neural networks for instationary problems\n",
    "\n",
    "To solve instationary problems using neural networks, we have extended the\n",
    "{class}`~pymor.reductors.neural_network.NeuralNetworkReductor` to the\n",
    "{class}`~pymor.reductors.neural_network.NeuralNetworkInstationaryReductor`, which treats time\n",
    "as an additional parameter (see {cite}`WHR19`). The resulting\n",
    "{class}`~pymor.models.neural_network.NeuralNetworkInstationaryModel` passes the input, together\n",
    "with the current time instance, through the neural network in each time step to obtain reduced\n",
    "coefficients. In the same fashion, there exists a\n",
    "{class}`~pymor.reductors.neural_network.NeuralNetworkInstationaryStatefreeOutputReductor` and the\n",
    "corresponding {class}`~pymor.models.neural_network.NeuralNetworkInstationaryStatefreeOutputModel`.\n",
    "\n",
    "A slightly different approach that is also implemented in pyMOR and uses a different type of\n",
    "neural network is described in the following section.\n",
    "\n",
    "### Long short-term memory neural networks for instationary problems\n",
    "\n",
    "So-called *recurrent neural networks* are especially well-suited for capturing time-dependent\n",
    "dynamics. These types of neural networks can treat input sequences of variable length (in our case\n",
    "sequences with a variable number of time steps) and store internal states that are passed from one\n",
    "time step to the next. Therefore, these networks implement an internal memory that keeps\n",
    "information over time. Furthermore, for each element of the input sequence, the same neural\n",
    "network is applied.\n",
    "\n",
    "In the {class}`~pymor.models.neural_network.NeuralNetworkLSTMInstationaryModel` and the\n",
    "corresponding {class}`~pymor.reductors.neural_network.NeuralNetworkLSTMInstationaryReductor`,\n",
    "we make use of a specific type of recurrent neural network, namely a so-called\n",
    "*long short-term memory neural network (LSTM)*, first introduced in {cite}`HS97`, that tries to\n",
    "avoid problems like vanishing or exploding gradients that often occur during training of recurrent\n",
    "neural networks.\n",
    "\n",
    "#### The architecture of an LSTM neural network\n",
    "In an LSTM neural network, multiple so-called LSTM cells are chained with each other such that the\n",
    "cell state {math}`c_k` and the hidden state {math}`h_k` of the {math}`k`-th LSTM cell serve as the\n",
    "input hidden states for the {math}`k+1`-th LSTM cell. Therefore, information from former time\n",
    "steps can be available later. Each LSTM cell takes an input {math}`\\mu(t_k)` and produces an\n",
    "output {math}`o(t_k)`. The following figure shows the general structure of an LSTM neural network\n",
    "that is also implemented in the same way in pyMOR:\n",
    "\n",
    "```{image} lstm.svg\n",
    ":alt: Long short-term neural network\n",
    ":width: 100%\n",
    "```\n",
    "\n",
    "#### The LSTM cell\n",
    "The main building block of an LSTM network is the *LSTM cell*, which is denoted by {math}`\\Phi`,\n",
    "and sketched in the following figure:\n",
    "\n",
    "```{image} lstm_cell.svg\n",
    ":alt: LSTM cell\n",
    ":align: left\n",
    "```\n",
    "\n",
    "Here, {math}`\\mu(t_k)` denotes the input of the network at the current time instance {math}`t_k`,\n",
    "while {math}`o(t_k)` denotes the output. The two hidden states for time instance `t_k` are given\n",
    "as the cell state {math}`c_k` and the hidden state {math}`h_k` that also serves as the output.\n",
    "Squares represent layers similar to those used in feedforward neural networks, where inside the\n",
    "square the applied activation function is mentioned, and circles denote element-wise\n",
    "operations like element-wise multiplication ({math}`\\times`), element-wise addition ({math}`+`) or\n",
    "element-wise application of the hyperbolic tangent function ({math}`\\tanh`). The filled black\n",
    "circle represents the concatenation of the inputs. Furthermore, {math}`\\sigma` is the sigmoid\n",
    "activation function ({math}`\\sigma(x)=\\frac{1}{1+\\exp(-x)}`), and {math}`\\tanh` is the hyperbolic\n",
    "tangent activation function ({math}`\\tanh(x)=\\frac{\\exp(x)-\\exp(-x)}{\\exp(x)+\\exp(-x)}`) used for\n",
    "the respective layers in the LSTM network. Finally, the layer {math}`P` denotes a projection layer\n",
    "that projects vectors of the internal size to the hidden and output size. Hence, internally, the\n",
    "LSTM can deal with larger quantities and finally projects them onto a space with a desired size.\n",
    "Altogether, a single LSTM cell takes two hidden states and an input of the form\n",
    "{math}`(c_{k-1},h_{k-1},\\mu(t_k))` and transforms them into new hidden states and an output state\n",
    "of the form {math}`(c_k,h_k,o(t_k))`.\n",
    "\n",
    "We will take a closer look at the individual components of an LSTM cell in the subsequent\n",
    "paragraphs.\n",
    "\n",
    "##### The forget gate\n",
    "```{image} lstm_cell_forget_gate.svg\n",
    ":alt: Forget gate of an LSTM cell\n",
    ":align: right\n",
    "```\n",
    "As the name already suggests, the *forget gate* determines which part of the cell state\n",
    "{math}`c_{k-1}` the network forgets when moving to the next cell state {math}`c_k`. The main\n",
    "component of the forget gate is a neural network layer consisting of an affine-linear function\n",
    "with adjustable weights and biases followed by a sigmoid nonlinearity. By applying the sigmoid\n",
    "activation function, the output of the layer is scaled to lie between 0 and 1. The cell state\n",
    "{math}`c_{k-1}` from the previous cell is (point-wise) multiplied by the output of the layer in\n",
    "the forget gate. Hence, small values in the output of the layer correspond to parts of the cell\n",
    "state that are diminished, while values near 1 mean that the corresponding parts of the cell\n",
    "state remain intact. As input of the forget gate serves the pair {math}`(h_{k-1},\\mu(t_k))` and\n",
    "in the second step also the cell state {math}`c_{k-1}`.\n",
    "\n",
    "##### The input gate\n",
    "```{image} lstm_cell_input_gate.svg\n",
    ":alt: Input gate of an LSTM cell\n",
    ":align: right\n",
    "```\n",
    "To further change the cell state, an LSTM cell contains a so-called *input gate*. This gate mainly\n",
    "consists of two layers, a sigmoid layer and an hyperbolic tangent layer, acting on the pair\n",
    "{math}`(h_{k-1},\\mu(t_k))`. As in the forget gate, the sigmoid layer determines which parts of the\n",
    "cell state to adjust. On the other hand, the hyperbolic tangent layer determines how to adjust the\n",
    "cell state. Using the hyperbolic tangent as activation function scales the output to be between -1\n",
    "and 1, and allows for small updates of the cell state. To finally compute the update, the outputs\n",
    "of the sigmoid and the hyperbolic tangent layer are multiplied entry-wise. Afterwards, the update\n",
    "is added to the cell state (after the cell state passed the forget gate). The new cell state is\n",
    "now prepared to be passed to the subsequent LSTM cell.\n",
    "\n",
    "##### The output gate\n",
    "```{image} lstm_cell_output_gate.svg\n",
    ":alt: Output gate of an LSTM cell\n",
    ":align: right\n",
    "```\n",
    "For computing the output {math}`o(t_k)` (and the new hidden state {math}`h_k`), the updated cell\n",
    "state {math}`c_k` is first of all entry-wise transformed using a hyperbolic tangent function such\n",
    "that the result again takes values between -1 and 1. Simultaneously, a neural network layer with a\n",
    "sigmoid activation function is applied to the concatenated pair {math}`(h_{k-1},\\mu(t_k))` of\n",
    "hidden state and input. Both results are multiplied entry-wise. This results in a filtered version\n",
    "of the (normalized) cell state. Finally, a projection layer is applied such that the result of the\n",
    "output gate has the desired size and can take arbitrary real values (before, due to the sigmoid and\n",
    "hyperbolic tangent activation functions, the outcome was restricted to the interval from -1 to 1).\n",
    "The projection layer applies a linear function without an activation (similar to the last layer of\n",
    "a usual feedforward neural network but without bias). Altogether, the *output gate* produces an\n",
    "output {math}`o(t_k)` that is returned and a new hidden state {math}`h_k` that can be passed\n",
    "(together with the updated cell state {math}`c_k`) to the next LSTM cell.\n",
    "\n",
    "#### LSTMs for model order reduction\n",
    "The idea of the approach implemented in pyMOR is the following: Instead of passing the current\n",
    "time instance as an additional input of the neural network, we use an LSTM that takes at each time\n",
    "instance {math}`t_k` the (potentially) time-dependent input {math}`\\mu(t_k)` as an input and uses\n",
    "the hidden states of the former time step. The output {math}`o(t_k)` of the LSTM (and therefore\n",
    "also the hidden state {math}`h_k`) at time {math}`t_k` are either approximations of the reduced\n",
    "basis coefficients (similar to the\n",
    "{class}`~pymor.models.neural_network.NeuralNetworkInstationaryModel`) or approximations of the\n",
    "output quantities (similar to the\n",
    "{class}`~pymor.models.neural_network.NeuralNetworkInstationaryModel`). For state approximations\n",
    "using a reduced basis, one can apply the\n",
    "{class}`~pymor.reductors.neural_network.NeuralNetworkLSTMInstationaryReductor` and use the\n",
    "corresponding\n",
    "{class}`~pymor.models.neural_network.NeuralNetworkLSTMInstationaryModel`.\n",
    "For a direct approximation of outputs using LSTMs, we provide the\n",
    "{class}`~pymor.models.neural_network.NeuralNetworkLSTMInstationaryStatefreeOutputModel` and the\n",
    "corresponding\n",
    "{class}`~pymor.reductors.neural_network.NeuralNetworkLSTMInstationaryStatefreeOutputReductor`.\n",
    "\n",
    "Download the code:\n",
    "{download}`tutorial_mor_with_anns.md`\n",
    "{nb-download}`tutorial_mor_with_anns.ipynb`"
   ]
  }
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style=\"line-height:120%\">00:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.3545454545454545]} ...</p><p style=\"line-height:120%\">00:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.36363636363636365]} ...</p><p style=\"line-height:120%\">00:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.3727272727272727]} ...</p><p style=\"line-height:120%\">00:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.38181818181818183]} ...</p><p style=\"line-height:120%\">00:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.3909090909090909]} ...</p><p style=\"line-height:120%\">00:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.4]} ...</p><p style=\"line-height:120%\">00:25 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.40909090909090906]} ...</p><p style=\"line-height:120%\">00:25 |   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[0.5454545454545454]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5545454545454546]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5636363636363636]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5727272727272728]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5818181818181818]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5909090909090909]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.609090909090909]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6181818181818182]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6272727272727272]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6363636363636364]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6454545454545454]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6545454545454545]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6636363636363636]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6727272727272727]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6818181818181818]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6909090909090908]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.709090909090909]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7181818181818181]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7272727272727272]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7363636363636363]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7454545454545454]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7545454545454545]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7636363636363636]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7727272727272727]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7818181818181817]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7909090909090909]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7999999999999999]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8090909090909091]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8181818181818181]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8272727272727273]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8363636363636363]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8454545454545453]} ...</p><p style=\"line-height:120%\">00:26 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8545454545454545]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8636363636363635]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8727272727272727]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8818181818181817]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8909090909090909]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8999999999999999]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9090909090909091]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9181818181818181]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9272727272727272]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9363636363636363]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9454545454545454]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9545454545454545]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9636363636363636]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9727272727272727]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9818181818181817]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9909090909090909]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [1.0]} ...</p><p style=\"line-height:120%\">00:27 <bold>NeuralNetworkStatefreeOutputReductor</bold>: Computing validation snapshots ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.796560443700367]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.4949905957768471]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8727381279202442]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7276312261534275]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.18475961309888458]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9780601164730803]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7850257317913176]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8074578747492585]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.2153022694079913]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5053473441060105]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.4337182218093232]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9340884899637416]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6794786080725981]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.840485451943747]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.499072778944598]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.3045148496062992]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5991263083142513]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.1574355304937578]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8448680547933238]} ...</p><p style=\"line-height:120%\">00:27 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6684979592098583]} ...</p><p style=\"line-height:120%\">00:27 <bold>NeuralNetworkStatefreeOutputReductor</bold>: Training of neural network ...</p><p style=\"line-height:120%\">00:27 |   <bold>NeuralNetworkStatefreeOutputReductor</bold>: Initializing neural network ...</p><p style=\"line-height:120%\">00:27 |   <bold>FullyConnectedNN</bold>: Architecture of the neural network:\nFullyConnectedNN(\n  (layers): ModuleList(\n    (0): Linear(in_features=1, out_features=9, bias=True)\n    (1): Linear(in_features=9, out_features=9, bias=True)\n    (2): Linear(in_features=9, out_features=2, bias=True)\n  )\n)</p><p style=\"line-height:120%\">00:27 |   <bold>multiple_restarts_training</bold>: Performing up to 100 restarts to train a neural network with a loss below 1.000e-05 ...</p><p style=\"line-height:120%\">00:27 |   <bold>multiple_restarts_training</bold>: Training neural network #0 ...</p><p style=\"line-height:120%\">00:27 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:28 |   |   <bold>train_neural_network</bold>: Stopping training process early after 19 epochs with validation loss of 3.277e-06 ...</p><p style=\"line-height:120%\">00:28 |   <bold>multiple_restarts_training</bold>: Finished training after 0 restarts, found neural network with loss of 5.467e-06 ...</p><p style=\"line-height:120%\">00:28 <bold>NeuralNetworkStatefreeOutputReductor</bold>: Using neural network with smallest validation error ...</p><p style=\"line-height:120%\">00:28 <bold>NeuralNetworkStatefreeOutputReductor</bold>: Finished training with a validation loss of 3.277409657316228e-06 ...</p><p style=\"line-height:120%\">00:28 <bold>NeuralNetworkStatefreeOutputReductor</bold>: Building ROM ...</p><p style=\"line-height:120%\">00:28 <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.4190733713168815]} ...</p><p style=\"line-height:120%\">00:28 <bold>NeuralNetworkStatefreeOutputModel</bold>: Solving 2DProblem_CG_output_reduced for {input: , mu: [0.4190733713168815]} ...</p><p style=\"line-height:120%\">00:28 <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.973628221955413]} ...</p><p style=\"line-height:120%\">00:28 <bold>NeuralNetworkStatefreeOutputModel</bold>: Solving 2DProblem_CG_output_reduced for {input: , mu: [0.973628221955413]} ...</p><p style=\"line-height:120%\">00:28 <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9038090091899779]} ...</p><p style=\"line-height:120%\">00:28 <bold>NeuralNetworkStatefreeOutputModel</bold>: Solving 2DProblem_CG_output_reduced for {input: , mu: [0.9038090091899779]} ...</p><p style=\"line-height:120%\">00:28 <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8005451473663857]} ...</p><p style=\"line-height:120%\">00:28 <bold>NeuralNetworkStatefreeOutputModel</bold>: Solving 2DProblem_CG_output_reduced for {input: , mu: [0.8005451473663857]} ...</p><p style=\"line-height:120%\">00:28 <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.2751748370667708]} ...</p><p style=\"line-height:120%\">00:28 <bold>NeuralNetworkStatefreeOutputModel</bold>: Solving 2DProblem_CG_output_reduced for {input: , mu: [0.2751748370667708]} ...</p><p style=\"line-height:120%\">00:28 <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5200489033543307]} ...</p><p style=\"line-height:120%\">00:28 <bold>NeuralNetworkStatefreeOutputModel</bold>: Solving 2DProblem_CG_output_reduced for {input: , mu: [0.5200489033543307]} ...</p><p style=\"line-height:120%\">00:28 <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.13942338920850592]} ...</p><p style=\"line-height:120%\">00:28 <bold>NeuralNetworkStatefreeOutputModel</bold>: Solving 2DProblem_CG_output_reduced for {input: , mu: [0.13942338920850592]} ...</p><p style=\"line-height:120%\">00:28 <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.23886054286079306]} ...</p><p style=\"line-height:120%\">00:28 <bold>NeuralNetworkStatefreeOutputModel</bold>: Solving 2DProblem_CG_output_reduced for {input: , mu: [0.23886054286079306]} ...</p><p style=\"line-height:120%\">00:28 <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7147440579182092]} ...</p><p style=\"line-height:120%\">00:28 <bold>NeuralNetworkStatefreeOutputModel</bold>: Solving 2DProblem_CG_output_reduced for {input: , mu: [0.7147440579182092]} ...</p><p style=\"line-height:120%\">00:28 <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7702859403170355]} ...</p><p style=\"line-height:120%\">00:28 <bold>NeuralNetworkStatefreeOutputModel</bold>: Solving 2DProblem_CG_output_reduced for {input: , mu: [0.7702859403170355]} ...</p>"
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<bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.15454545454545454]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.16363636363636364]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.17272727272727273]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.18181818181818182]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.19090909090909092]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.2]} ...</p><p style=\"line-height:120%\">00:03 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.2090909090909091]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.2181818181818182]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.22727272727272727]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.23636363636363636]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.24545454545454545]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.2545454545454545]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.26363636363636367]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.2727272727272727]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.28181818181818186]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.2909090909090909]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.3]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.3090909090909091]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.3181818181818182]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.32727272727272727]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.33636363636363636]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.34545454545454546]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.3545454545454545]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.36363636363636365]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.3727272727272727]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.38181818181818183]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.3909090909090909]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.4]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.40909090909090906]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.4181818181818182]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.42727272727272725]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.4363636363636364]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.44545454545454544]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.4545454545454546]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.4636363636363636]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.47272727272727266]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.4818181818181818]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.49090909090909085]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.509090909090909]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5181818181818182]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5272727272727272]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5363636363636364]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5454545454545454]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5545454545454546]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5636363636363636]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5727272727272728]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5818181818181818]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5909090909090909]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.609090909090909]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6181818181818182]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6272727272727272]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6363636363636364]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6454545454545454]} ...</p><p style=\"line-height:120%\">00:04 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6545454545454545]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6636363636363636]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6727272727272727]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6818181818181818]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6909090909090908]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.709090909090909]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7181818181818181]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7272727272727272]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7363636363636363]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7454545454545454]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7545454545454545]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7636363636363636]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7727272727272727]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7818181818181817]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7909090909090909]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7999999999999999]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8090909090909091]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8181818181818181]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8272727272727273]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8363636363636363]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8454545454545453]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8545454545454545]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8636363636363635]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8727272727272727]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8818181818181817]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8909090909090909]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8999999999999999]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9090909090909091]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9181818181818181]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9272727272727272]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9363636363636363]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9454545454545454]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9545454545454545]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9636363636363636]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9727272727272727]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9818181818181817]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9909090909090909]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [1.0]} ...</p><p style=\"line-height:120%\">00:05 <bold>NeuralNetworkReductor</bold>: Building reduced basis ...</p><p style=\"line-height:120%\">00:05 |   <bold>pod</bold>: Computing SVD ...</p><p style=\"line-height:120%\">00:05 |   |   <bold>method_of_snapshots</bold>: Computing Gramian (100 vectors) ...</p><p style=\"line-height:120%\">00:05 |   |   <bold>method_of_snapshots</bold>: Computing eigenvalue decomposition ...</p><p style=\"line-height:120%\">00:05 |   |   <bold>method_of_snapshots</bold>: Computing left-singular vectors (8 vectors) ...</p><p style=\"line-height:120%\">00:05 |   <bold>pod</bold>: Checking orthonormality ...</p><p style=\"line-height:120%\">00:05 |   <bold>pod</bold>: Reorthogonalizing POD modes ...</p><p style=\"line-height:120%\">00:05 <bold>NeuralNetworkReductor</bold>: Computing training samples ...</p><p style=\"line-height:120%\">00:05 <bold>NeuralNetworkReductor</bold>: Computing validation snapshots ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.796560443700367]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.4949905957768471]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8727381279202442]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7276312261534275]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.18475961309888458]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9780601164730803]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.7850257317913176]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8074578747492585]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.2153022694079913]} ...</p><p style=\"line-height:120%\">00:05 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5053473441060105]} ...</p><p style=\"line-height:120%\">00:06 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.4337182218093232]} ...</p><p style=\"line-height:120%\">00:06 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.9340884899637416]} ...</p><p style=\"line-height:120%\">00:06 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6794786080725981]} ...</p><p style=\"line-height:120%\">00:06 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.840485451943747]} ...</p><p style=\"line-height:120%\">00:06 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.499072778944598]} ...</p><p style=\"line-height:120%\">00:06 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.3045148496062992]} ...</p><p style=\"line-height:120%\">00:06 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.5991263083142513]} ...</p><p style=\"line-height:120%\">00:06 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.1574355304937578]} ...</p><p style=\"line-height:120%\">00:06 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.8448680547933238]} ...</p><p style=\"line-height:120%\">00:06 |   <bold>StationaryModel</bold>: Solving 2DProblem_CG for {input: , mu: [0.6684979592098583]} ...</p><p style=\"line-height:120%\">00:06 <bold>NeuralNetworkReductor</bold>: Training of neural network ...</p><p style=\"line-height:120%\">00:06 |   <bold>NeuralNetworkReductor</bold>: Initializing neural network ...</p><p style=\"line-height:120%\">00:06 |   <bold>FullyConnectedNN</bold>: Architecture of the neural network:\nFullyConnectedNN(\n  (layers): ModuleList(\n    (0): Linear(in_features=1, out_features=27, bias=True)\n    (1): Linear(in_features=27, out_features=27, bias=True)\n    (2): Linear(in_features=27, out_features=8, bias=True)\n  )\n)</p><p style=\"line-height:120%\">00:06 |   <bold>multiple_restarts_training</bold>: Performing up to 100 restarts to train a neural network with a loss below 1.000e-05 ...</p><p style=\"line-height:120%\">00:06 |   <bold>multiple_restarts_training</bold>: Training neural network #0 ...</p><p style=\"line-height:120%\">00:06 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:09 |   |   <bold>train_neural_network</bold>: Stopping training process early after 79 epochs with validation loss of 5.311e-05 ...</p><p style=\"line-height:120%\">00:09 |   <bold>multiple_restarts_training</bold>: Training neural network #1 ...</p><p style=\"line-height:120%\">00:09 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:10 |   |   <bold>train_neural_network</bold>: Stopping training process early after 14 epochs with validation loss of 3.313e-01 ...</p><p style=\"line-height:120%\">00:10 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 4.869e-01 (instead of 6.059e-05) ...</p><p style=\"line-height:120%\">00:10 |   <bold>multiple_restarts_training</bold>: Training neural network #2 ...</p><p style=\"line-height:120%\">00:10 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:13 |   |   <bold>train_neural_network</bold>: Stopping training process early after 69 epochs with validation loss of 3.712e-04 ...</p><p style=\"line-height:120%\">00:13 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 5.347e-04 (instead of 6.059e-05) ...</p><p style=\"line-height:120%\">00:13 |   <bold>multiple_restarts_training</bold>: Training neural network #3 ...</p><p style=\"line-height:120%\">00:13 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:18 |   |   <bold>train_neural_network</bold>: Stopping training process early after 383 epochs with validation loss of 7.222e-05 ...</p><p style=\"line-height:120%\">00:18 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 7.815e-05 (instead of 6.059e-05) ...</p><p style=\"line-height:120%\">00:18 |   <bold>multiple_restarts_training</bold>: Training neural network #4 ...</p><p style=\"line-height:120%\">00:18 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:18 |   |   <bold>train_neural_network</bold>: Stopping training process early after 13 epochs with validation loss of 8.460e+01 ...</p><p style=\"line-height:120%\">00:18 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 8.742e+01 (instead of 6.059e-05) ...</p><p style=\"line-height:120%\">00:18 |   <bold>multiple_restarts_training</bold>: Training neural network #5 ...</p><p style=\"line-height:120%\">00:18 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:21 |   |   <bold>train_neural_network</bold>: Stopping training process early after 59 epochs with validation loss of 1.308e-04 ...</p><p style=\"line-height:120%\">00:21 |   <bold>multiple_restarts_training</bold>: Rejecting neural network with loss of 1.735e-04 (instead of 6.059e-05) ...</p><p style=\"line-height:120%\">00:21 |   <bold>multiple_restarts_training</bold>: Training neural network #6 ...</p><p style=\"line-height:120%\">00:21 |   |   <bold>train_neural_network</bold>: Starting optimization procedure ...</p><p style=\"line-height:120%\">00:23 |   |   <bold>train_neural_network</bold>: Stopping training process early after 66 epochs with validation loss of 2.242e-06 ...</p><p style=\"line-height:120%\">00:23 |   <bold>multiple_restarts_training</bold>: Found better neural network (loss of 3.064e-06 instead of 6.059e-05) ...</p><p style=\"line-height:120%\">00:23 |   <bold>multiple_restarts_training</bold>: Finished training after 6 restarts, found neural network with loss of 3.064e-06 ...</p><p style=\"line-height:120%\">00:23 <bold>NeuralNetworkReductor</bold>: Checking tolerances for error of neural network ...</p><p style=\"line-height:120%\">00:23 <bold>NeuralNetworkReductor</bold>: Building ROM ...</p>"
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