{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f930b79f",
   "metadata": {},
   "source": [
    "# Getting Started With `pspec_likelihood`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "199cb650",
   "metadata": {},
   "source": [
    "In this tutorial, we will introduce how to use `pspec_likelihood` as a library. We will\n",
    "cover the different classes you will interact with, how to create a data interface from\n",
    "a `UVPSpec` object, and how to calculate likelihoods. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "48d7f268",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from astropy import cosmology\n",
    "from astropy import units as un\n",
    "from hera_pspec import UVPSpec\n",
    "\n",
    "import pspec_likelihood as pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b81e928",
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "path_to_uvp = \"../../dvlpt/\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d0f943a",
   "metadata": {},
   "source": [
    "## The layout of the package"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f329590",
   "metadata": {},
   "source": [
    "In `pspec_likelihood`, the idea is to be able to define a data likelihood given some\n",
    "measured (or mock) power spectra. To do this, you will need to define two objects:\n",
    "\n",
    "1. A `DataModelInterface`: An object that holds the data and theory models and knows\n",
    "   how to do basic operations on them (like spherically average and apply window \n",
    "   functions).\n",
    "2. A `PSpecLikelihood`: this defines the statistical likelihood based on the model\n",
    "   and data. There are a number of these classes, each corresponding to a different\n",
    "   set of statistical models.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7703fbb5",
   "metadata": {},
   "source": [
    "## Defining the theoretical model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "088eb541",
   "metadata": {},
   "source": [
    "Our first goal is to define the theoretical model. In this tutorial, our data comes from\n",
    "the H1C IDR3 *Validation* simulation, for which we know the input EoR power spectrum is\n",
    "a simply power-law. We simply define a function that computes the model in the following\n",
    "format:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "34dd2ab8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def theory_model(z: float, k: np.ndarray, params: list[float]) -> np.ndarray:\n",
    "    amplitude, index = params\n",
    "    return k**3 / (2 * np.pi**2) * amplitude * un.mK**2 * (1.0 + z) / k**index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03acc21b",
   "metadata": {},
   "source": [
    "Notice that the function must take exactly three parameters: the redshift, the wavenumbers\n",
    "and a list of parameters. It should output an array of the same shape as `k`, representing\n",
    "the $\\Delta^2(k)$ values in units of ${\\rm mK}^2$.\n",
    "\n",
    "For us, this is simple. For more complicated theoretical models, for instance those that\n",
    "compute entire simulations, this function may itself need to call some other functions,\n",
    "whose output might not actually depend on the `k` given (i.e. the output wavenumbers \n",
    "might be set by the size of the simulation). In these cases, the theory model function\n",
    "should do some kind of interpolation to yield the model at the input `k`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "745d52a8",
   "metadata": {},
   "source": [
    "## Define the `DataModelInterface`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a99a6af",
   "metadata": {},
   "source": [
    "There are two main ways to define the main `DataModelInterface` object. You can use the \n",
    "basic class constructor, to which you need to pass all of the relevant data (power spectrum,\n",
    "window function, k-bins, covariance, etc.). However, if your data is in the format of \n",
    "a PSpec object, you can simply use the `DataModelInterface.from_uvpspec` method.\n",
    "\n",
    "First, read in the data with `UVPSpec`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f8da36af",
   "metadata": {},
   "outputs": [],
   "source": [
    "field = \"C\"\n",
    "spw = 0\n",
    "\n",
    "filename = f\"{path_to_uvp}sph_xtk_idr3_field{field}_band{spw + 1}.hdf5\"\n",
    "\n",
    "# spherically averaged power spectrum\n",
    "sph_pk = UVPSpec()\n",
    "sph_pk.read_hdf5(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7631ef82",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Had to normalize window_function.             See utils.normalize_wf.\n"
     ]
    }
   ],
   "source": [
    "dmi = pl.DataModelInterface.from_uvpspec(\n",
    "    uvp=sph_pk,\n",
    "    theory_uses_spherical_k=True,\n",
    "    theory_model=theory_model,\n",
    "    theory_uses_little_h=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59fc5d8f",
   "metadata": {},
   "source": [
    "In the above, we do two main things: set the data by giving the `UVPSpec` object, and \n",
    "set the theory via the `theory_model` parameter. There are many other parameters that\n",
    "can be given, and we will talk about some of them later. A few deal with how the theory\n",
    "function behaves (eg. whether the theory function expects wavenumbers to be given in \n",
    "units with little h, and whether it *requires* spherical k values). We can also pass a \n",
    "cosmology to use.\n",
    "\n",
    "Another parameter that can be passed is a `sys_model` that computes bias from systematics,\n",
    "given some parameters. Here, we do not use an explicit systematic model. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c89ac8d3",
   "metadata": {},
   "source": [
    "Now that we have the object, we can plot both the data itself and a model: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fcd0cc08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100.0, 10000000.0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.errorbar(\n",
    "    dmi.kpar_bins_obs.value,\n",
    "    dmi.power_spectrum.value,\n",
    "    yerr=np.sqrt(np.diag(dmi.covariance.value)),\n",
    "    lw=0,\n",
    "    marker=\"o\",\n",
    "    ms=5,\n",
    "    capsize=1,\n",
    "    capthick=1,\n",
    "    elinewidth=1,\n",
    ")\n",
    "plt.plot(dmi.kpar_bins_obs.value, dmi.compute_model([2e5, 2.7], []).value)\n",
    "plt.yscale(\"log\")\n",
    "plt.ylim(1e2, 1e7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfd05600",
   "metadata": {},
   "source": [
    "Notice here that the model is computed by giving two lists of float parameters. The first\n",
    "is the theory parameters, and the second are the systematics parameters (of which we have\n",
    "none)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "058af6c0",
   "metadata": {},
   "source": [
    "## Creating the Likelihood"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a059b2d",
   "metadata": {},
   "source": [
    "Even though we have specified a model for the power spectrum itself, we have not yet\n",
    "specified a statistical likelihood of the data given that model. There are a number of\n",
    "different choices that can be made here. The simplest idea would be to say that the model\n",
    "residuals (to both theory and systematics) are drawn from a multivariate normal \n",
    "distribution. However, if there are many different systematics parameters, sampling over\n",
    "this space can become inefficient. If some of the systematics parameters are linear, then\n",
    "we can do a bit better by analytically marginalizing over them. Further performance \n",
    "benefits can be gained if the covariance is assumed to be diagonal. Given all these \n",
    "considerations, we provide *different* classes for different kinds of likelihood.\n",
    "\n",
    "Here, we will use two such likelihoods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7fa04a1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Your covariance matrix is not diagonal. The MarginalizedLinearPositiveSystematics class requires diagonal covariance. Forcing it...\n"
     ]
    }
   ],
   "source": [
    "like = pl.MarginalizedLinearPositiveSystematics(model=dmi)\n",
    "likeg = pl.Gaussian(model=dmi)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89efb148",
   "metadata": {},
   "source": [
    "Each likelihood must be given the `DataModelInterface` object via the `model` parameter.\n",
    "Some likelihoods also take other parameters, which you can find out in their documentation.\n",
    "\n",
    "The first likelihood we defined here is the same as that used for H1C (both IDR2.1 and IDR3).\n",
    "It is a likelihood in which each k-bin is assumed to have a positive systematic component\n",
    "that is equally likely to be any positive number, and is not correlated with other\n",
    "k-bins.\n",
    "\n",
    "The second likelihood is the simplest multivariate Gaussian, assuming no systematics\n",
    "at all (note that the assumption of no systematics is not intrinsic to the \n",
    "`Gaussian` likelihood, but rather is because we gave no `sys_model` to the `DataModelInterface`)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0e623d4",
   "metadata": {},
   "source": [
    "These likelihood classes expose one useful method: `loglike(theory_params, sys_params) -> float`, which computes the log-likelihood the data given the theory and systematic parameters.\n",
    "This function is intended to be used in either Bayesian sampling or maximum-likelihood/posterior techniques to either sample the posterior or find the a point-estimate of the parameters. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0717762a",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "<strong>Note</strong>\n",
    "<p>\n",
    "We do *not* provide a sampler in `pspec_likelihood`. That is up to the user to provide.\n",
    "We simply provide a method for calculating the likelihood.\n",
    "</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "795d64ac",
   "metadata": {},
   "source": [
    "In this case, we know that the \"correct\" values for the theory parameters are about\n",
    "$A = 2e5 {\\rm mK}^2$ and $\\gamma=2.7$. We build an array around each of these values,\n",
    "and compute the log-likelihood at each:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "255860ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Ignoring data in positions (array([0, 1, 2]),) as the variance is zero\n"
     ]
    }
   ],
   "source": [
    "amp = np.logspace(3, 7, 100)\n",
    "\n",
    "likes = np.zeros(amp.shape)\n",
    "likes_g = np.zeros(amp.shape)\n",
    "\n",
    "for i, a in enumerate(amp):\n",
    "    likes[i] = like.loglike([a, 2.7], [])\n",
    "    likes_g[i] = likeg.loglike([a, 2.7], [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b389c704",
   "metadata": {},
   "outputs": [],
   "source": [
    "indx = np.linspace(1, 3, 100)\n",
    "\n",
    "likes_indx = np.zeros(indx.shape)\n",
    "likes_g_indx = np.zeros(indx.shape)\n",
    "\n",
    "for i, a in enumerate(indx):\n",
    "    likes_indx[i] = like.loglike([2e5, a], [])\n",
    "    likes_g_indx[i] = likeg.loglike([2e5, a], [])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2894f2c9",
   "metadata": {},
   "source": [
    "Now we can plot the resulting log-likelihoods as a function of these parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b7e35dee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb195368590>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAACGQAAAY4CAYAAADSvYmnAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAuIwAALiMBeKU/dgABAABJREFUeJzs3Qd8nHX9B/Bf9mrT0pYCZS/ZG5S9BBygIMstuP4qblHBiSjuvVBRFMHJkuViKENQkA2Csim0hQ5o0yZp9v/1e0rSu+QuuUvuMu7e79frIXn27567C2mez32/FX19fX0BAAAAAAAAAICCqSzcoQAAAAAAAAAAiAQyAAAAAAAAAAAKTCADAAAAAAAAAKDABDIAAAAAAAAAAApMIAMAAAAAAAAAoMAEMgAAAAAAAAAACkwgAwAAAAAAAACgwAQyAAAAAAAAAAAKTCADAAAAAAAAAKDABDIAAAAAAAAAAApMIAMAAAAAAAAAoMAEMgAAAAAAAAAACkwgAwAAAAAAAACgwAQyAAAAAAAAAAAKTCADAAAAAAAAAKDABDIAAAAAAAAAAApMIAMAAAAAAAAAoMAEMgAAAAAAAAAACkwgAwAAAAAAAACgwAQyAAAAAAAAAAAKTCADAAAAAAAAAKDABDIAAAAAAAAAAApMIAMAAAAAAAAAoMAEMgAAAAAAAAAACkwgAwAAAAAAAACgwAQyAAAAAAAAAAAKTCADAAAAAAAAAKDABDIAAAAAAAAAAApMIAMAAAAAAAAAoMAEMgAAAAAAAAAACqy60AcEQnj00UfDbbfdFp5++unQ2dkZ1llnnbDtttuGfffdN9TX17tEAAAAAAAAACVOIAMK6LLLLgtf+MIXwp133plx/bRp08LJJ58czjjjjDBnzhzXHgAAAAAAAKBEVfT19fVN9CBgquvo6Ahvf/vbw69//euctl933XXDxRdfHA488MCijw0AAAAAAACA8SeQAWPU29sbjj322HD55ZenLa+qqgqbbLJJmDFjRnj88cfDihUr0tY3NjaGa6+9Nuyzzz6eAwAAAAAAAIASUznRA4Cp7utf//qQMMa73/3uMH/+/PDYY4+Fu+66Kzz33HPh0ksvTQIa/dra2sKJJ544JKgBAAAAAAAAwNSnQgaMwbJly8Lmm28eVq5cObDsy1/+cjj99NMzbr9gwYKw//77hyeeeGJg2Wc/+9lw5plneh4AAAAAAAAASohABozBaaedFr72ta8NzB944IHh+uuvDxUVFVn3ue6668Jhhx02MD99+vSkpcns2bM9FwAAAAAAAAAlQssSGKXe3t7wi1/8Im3Z5z73uWHDGNFLX/rScMABBwzMx+oaF154oecBAAAAAAAAoIQIZMAo3XLLLWHJkiUD81tssUU4+OCDc9r37W9/e9r8ZZdd5nkAAAAAAAAAKCECGTBKf/zjH9PmDz/88BGrY6Rumyq2OWltbfVcAAAAAAAAAJQIgQwYpbvvvjttft99981533nz5oXNNttsYL6zszM88MADngsAAAAAAACAEiGQAaP04IMPps1vv/32ee0/ePvBxwMAAAAAAABg6hLIgFFob28P8+fPT1u28cYb53WMwdv/73//81wAAAAAAAAAlIjqiR4ATEVLly4NfX19A/M1NTVh7ty5eR1jww03TJtfvHjxmMcVj7FkyZK89mlpaQm33357aG5uDjNnzkyCInV1dWMeCwAAAAAAAMBE6+joCE899dTA/EEHHZTcFx0PAhkwCqtWrUqbb2xsDBUVFXkdo6mpadhjjsbZZ58dzjzzzDEfBwAAAAAAAKAUXXbZZeHoo48el3NpWQKjMDg8UV9fn/cxGhoahj0mAAAAAAAAAFOXQAaMwurVq9Pma2tr8z7G4LYg7e3tngsAAAAAAACAEqFlCYzC4IoYnZ2do+pVNNwxR+OUU04JJ5xwQl77PPDAA+HEE09MK9Gz1VZbjXks5eiMy+8Py1rzey309Q2zLvTlvE+24/SN5sQ5HiPT+PIdR18e4xh8kL4cjjd4m8Gny+XsIw0x9ZzDnS/teg0zjqHny3z89H1Gfq1kHWdfLmPIdP6U4/UNd9yhK/uGGVM+LwkAoPRUVVaE2qrKUFdTGeqqq0JtdZyvCnXVFaGuJs5Xrlnfv666KtTXVIb66qqBfeqr4/5xnxfm4/qalPUvfI3nAqaQG78Zwn0XZl//si+GsNVh4zkiAABginjkkUfCMcccMzC/8cYbj9u5BTJgFKZNmzZsxYxcDK6IMfiYozF37txkGosYxthhhx3GPJZydLHrBkXRH9xID7ikr0tf1j/fNzSEMygoMjhMMnCuQdvHBfnuMziMkrp+xGOlhWmGPp6+Qeddu/3a/Qefb2DfTOcf5hzpj2vw+qHjSH3cg5f1j2vguBnHtnZ96jlTH2f/PmnXIeVYw50ndUwD1ynlfKnnznT83oHjp447/di9A+dNPdbabeL6tddnzfLeDMdMXZa6beo50h93/z6p5+gLvb3pYw8vfJ8cY8g4Ur4OXp4cd81J+/dfc8w1g06dT32sA+cZdPzkWKEv9PS+cOwXvq5dv/Z4qeMFSl9PCKHthWlA9wtTVn0v7Bmn3MRgRwxnNNRWhcba6tCYfF37fVzeNLD8ha916dv0f022q1vzNR6zokLYAwqqa3UIV14Xwtyq7NvMrQnBv8sBAIBRdDIoJoEMGIXB4Ym2trbkZkE+f3RrbW0d9pgArNH/szXzj1g3O6DcpAY0+kMaA6GOON87NNCxNhzyQvAjQ+ijJ37/wr7JMQeOH0MjQ883+PvUMfWfJ36fjC3leGuWh6zrUs/VM2jbNduvGWuyXf85+vftH8ML2w7s98J41ny/9lipx009X6b1yX496ft3r0nVwJTV2dObTC2rY9IjvYLhWMTKGzGkMa2uOjS9ME17IawxZFn8vrZ/WVWYXl8dptfXJF+TbWurQ6VKHhDCf68KYfXy4a/EyoWuFAAAMOkIZMAozJkzJ7lB2P+J0K6urrB48eKw3nrr5XyMBQsWpM2PtbIFAEA5iL+DVVWEUCWQNSmsCYIMH9xIDXCkz/cmoY/unrX7Jtuk7L9mn96BbfqPEb929/SmzSfbJcvXHj/52vPC9i/s09WzdtuunjXbrFm2Zr7/mMn3A/uu+T51PWQTXx8rV3cn01jFQGoMZkyvWxvUSMIaKd8319es2SYlzDGjoWZgiuEQFTuY8u785cjbtCwaj5EAAADkRSADRqGhoSFssskm4cknnxxYNn/+/LwCGXH7VNtuu63nAgCAKSV+cr8yVISaYSrIl2oQpT/QkQQ1BgU2ul+ovLAm0LEmBNK/fs20Znlnd4bvX9imM+v3a7Zbs33/fmu26ezuSdb1L+9M+arlz9QUn7eBcMeK/FtlRtWVFaH5hXBG/No8KLDRv27wNLNxTdBDmIMJ99zjITx+48jbrRTIAAAAJh+BDBilGKBIDWQ88MADYa+99sp5/wcffHDI8QAAgKkRRKmNU3VlmEpVGwaHNJKvL0wd3T1rvg5aHrfr6OpJ2z5u09HVv27Nvh3JMdZsO/B9XJ6sX7tN3J/xFUNCz7V2JlO+aqoqwszG2rBOYwxo1IZZ8fummoFl6yRf1yzr/z4GPGLbFiiYu36V23Yt6ZVIAQAAJgOBDBilXXfdNfz1r38dmL/lllvCSSedlNO+ixYtCk888cTAfE1NTdh+++09FwAAQFHEG+QNtVWhIVRNeHWRJMjxQnhjdVdvWN0dv77wffK1J6zuXvP9wDbJsjUBjzXbp2zb1Rvau3pCe+ea+bbOnjXzMUgiADImseLKkpUdyZRPm5VYYSOGN2ZPqw1zptUlX2c31YU58eu0uoFlc5rqQnODKhwMo6c7hLt/ndslii1LYlmZ+CIEAACYJAQyYJSOOuqo8NWvfnVg/tprrw19fX05lXO9+uqr0+YPOeSQMG3aNM8FAABQ8tVF6iurQn3sc9NQMy6VQWJII1tgY3Xnmvm2ZH13aO1Ys7ytszu0daxZ19rZnezbGveLy/v36ewOvX1FfwhTTrwfvrytK5keW9qaUxWOWU39wY26MKdpTZCjP7gxd3pdWK+5Pvka26hooVJmHr0u91YkPR0htD8fQuOsYo8KAAAgZwIZMEr77rtvmDNnTli6dGky/9hjj4Xrr78+CVeM5Nxzz02bP/rooz0PAAAARagM0lRXnUyFFgP5sdJHEtroWBPUWNURQx1rpoHvU5avXZ9h286eJEBSjlU4nm3pSKaRxDZBqQGN5GtzDG3Uh/Wa1y6PFToEN0rEnefnt33LQoEMAABgUhHIgFGqrKwMJ598cvjGN74xsOzMM88MBx988LB/+LnuuuvCTTfdNDA/ffr0cOKJJ3oeAAAAppD4775Y6SNOscJDIQIesf3Kyo6usHJ19wtTV1j1wvct8fuOtcvj1zjfMmi7WOGjVMUWNE8/355MIwU31ksJasyb0RA2mNkQNpxZH+bNbEim2U21QhuT3cpnQ/jfn/PcZ1EI6+9YrBEBAADkTSADxuC0004LP/7xj8OqVauS+RtuuCFpY3L66adn3H7BggXhHe94R9qyD37wg0mlDQAAAMo74NFQW5VMc6ePLbQQwxsr2tdOLS9MqcvWruse2GZlR3coBfEaPPVcezJlU1dd+UI4oz4JbMTvN3whrJEsm9mwprUOE+ee34bQly1gFD8I05e5QgYAAMAkIpABYxCDFJ/85CeTqd8nPvGJMH/+/PDpT386zJs3L1nW29sbrrjiiiR8Edf1i+tPPfVUzwEAAAAFEatDzJlWl0z56u7pTapsLG/vCs+3dYbnWzvD821dYXn8Ppm6XljWGZa39W/TFTp7eqfcsxfbzTy+tDWZsolVNPoDGhuv0xg2nd0YNpndFDad1Rg2XKch1FRVjuuYy0pfX/Z2JbO2CKF+RggL7xq6TiADAACYZAQyoABVMm655ZZw1VVXDSz70Y9+FM4555yw6aabhhkzZoTHH388LF++PG2/hoaGcOGFF4aZM2d6DgAAAJhw1VWVYZ2m2mTaPDTl3GqlrbNnIKTx3AuBjfh12arOsKy1IyyNX1d1hGWtnWHpyo7Q2jk12qrE8cbpvgUrhqyrqqxIKmokIY1ZL4Q1ZjUlX+PUWOtPbmPy5C0hPPdo5nW7vyWEp2/PHMhYqUIGAAAwufjXIYxRZWVluOiii8Jb3/rW8Lvf/W5geU9PT3jssccy7jN79uxw8cUXh/3228/1BwAAYEq3Wmmqq06mjdbJbZ/2zp4kqDEQ2FjZGZb2z78Q3FiyMgY51nwfiyVMNj29fWH+c23JlMm60+uSShqbxIDGC0GNLdZtCluuOy25VozgrgsyL6+oCmGXN4SwYkHm9S2LXFoAAGBS8S9AKID6+vrw29/+Nhx//PHhrLPOCnfffXfG7ZqamsJJJ50UzjjjjDB37lzXHgAAgLLTUFsVNqptDBut0zjitl09vUkw49mWjrC4ZXV4duULX1tWh8Ur1y6PwY3JJAZK4nT7k88PWRcra2w1d1rYOk7rTUu+32ru9DCjoWZCxjrptC8P4T+XZV73opeHMH29EJo3yLx+pUAGAAAwuQhkQAEdd9xxyfTII4+EW2+9NSxYsCB0dnYmbUm22267pCJGDG8AAAAAI6upqgwbzGhIpuF0dvcHN2JYI4YhVodnWlaHZ1Z0hIXL28PCFe1h0fLVobOnd8Iv+4Ll7cl0w0NL0pbPnV63JqCx7rSw1XrTk8BGDGvMbqpNKpGUjfsvDqG7PfO63d+85uv0eZnXt2hZAgAATC4CGVAEW221VTIBAAAAxVdbXRnmzWxIpmx6e/uSShpJQOOFUMTC5asHAhvx69JVE1dpI1b8iNPNjyxLW75OY03Yeu70sOXcaWHb9aeHHTecEbbfoDmpNFKS7szSrmTa+iFsdfia77NVyGh/LoSu1SHU+DAMZNPX1xd6e3uTrwAAE6mioiJUVlaWfABdIAMAAACAkldZWRHWnV6XTLtsPDPjNqu7esIzK1anBTaeer4tzF/WFp58rjWpvjHenm/rCrc98Vwy9ausCEn1jBjO2GnDGQMhjaa6Kf6nvkX3hLAocxvYsNsbQ6h64fE1b5j9GLFtyazNizM+mIJi8GL16tVh5cqVyRSr+QIATCa1tbVh+vTpyRQ7DZRaQGOK/ysNAAAAAAqjvqYqbDanKZkyae/sCfOfawtPLmt94WsMasTARmt4+vn20N07Pp84j6d56NlVyXTpnQuSZfFvlluuOy3sOK95IKix/bzmML2+Jkz56hjRbm9a+/30LBUy+tuWCGRAoq2tLSxcuDB0dXW5IgDApNXZ2RmWLVuWTDU1NWHevHmhsbExlAqBDAAAAADIQWwTss3605NpsO6e3qSiRqykEYMa/cGN+P0Ty1rD6q7eol7j2H3gkcWrkumyuxcOLN9iTlMS0NhxwzVBjTg1T8aQRld7CPddmHndZgeEMGuLtfN100Koaw6hoyVzhQwgCWPMnz9faxIAYErp6upKfofZZJNNSiaUIZABAAAAAGP9I1tVZdhkdmMyHbB1+rre3r6kBcrDi1eGh59dE5p4+IXwxKqO7qJe+8eWtibTFfcsHGh3EkMZ+2wxO+y95eyw12azwrTJ0OrkwStDWL0i87rdTxq6LFbJyBTIiBUyoMwJYwAAU73l2vwSCmVMgn9tAQAAAEDpqqysCBvPakymQ7ddL+0Pjc+2dKwNaixZFR55NoY1Vobn27qK1u7k3qdXJNNPbnwsVFVWJO1N9tlydhLS2HOzdUJj7QT8yfDO8zMvr58RwnZHDV3evEEIS/83dLkKGZS5+HMltimJX1PF8t/Nzc1h2rRpyfel1psdAJh6+vr6kooYq1atCi0tLWlt1vp/p9lyyy2n/O8tAhkAAAAAMAHiHxbXn1GfTAdsvW7aumWrYlBjTSWNRxevCg8sagkPLGwpeEWNnt6+cPdTy5PpR9c/GmqqKsIuG80cCGjsvuk6ob6mKhTVskdDeOKmzOt2fm0INQ1Dl0+fl3l7FTIoc6tXr067mRFNnz49bLjhhlP+ZgYAUHpqamqSKhjrrrtuWLBgQVi5cuXAuvg7TUdHR6ivrw9TmUAGAAAAAEwys6fVJdPeW8xOa33yxLLWcN+CFeE/C1vCfU+vCPcvXBFWri5cSKOrpy/c/uTzyfT9vz0Saqsqw26brA1o7LrJzFBXXeCAxl2/yr5u97dkXh4rZGSiQgZlLvUmRv9NDmEMAGCyq6ioSH5nefTRR9PCpbFyhkAGAAAAADAurU+2WHdaMh2964YDIY2nnm9LQhpxuj+ZWsKK9sK0POns6Q23Pv5cMn0nPBzqqivDXpvNCq/aZYPwip02CM31NWM7QU93CHf/JvO6DXYNYf2dMq9rViEDcglkxDYlKmMAAFNBRUVF8rvLsmXL0n63mTt3bpjKVMgAAAAAgCkc0th0dlMyHbXzvIF+y08/3z4Q0Oj/+nzb2EMaHd294R+PLE2mz17+n3DEDuuHY3fbMByw9ZxQXVWZ/wEfuSaEVc/kVx1juJYlsUJGb2+8MPmPBaa4+N7v7OxMWzZt2rQJGw8AQL7i7y6pgYz4u038HWcqB0wFMgAAAACghMQ/Vm48qzGZXrnTmtYe8Y+Yjy9tDf98bFn456PLwr8eWxaWrkq/cTuacMaV9yxMpjnT6sLRu84Lx+6+Ydh+gzw+kX/n+ZmXVzeEsNPx2ffL1rKktzuEtqUhTJvan6KD0eiNYaRBYssSAICporq6OuPvOFVVBW6bOI4EMgAAAACgxMWARH+7kze+ZNMkoPHI4lVpAY2xVNBYuqojnPuPx5Np2/WnJ8GM2FZlveb67DutfCaEh/6aed0Ox4RQPyP/ChlRy0KBDMpSfF8PNpU/TQoAlJ/KDJXuMv2OM5UIZAAAAABAmYk3abdeb3oyvWWfzUJvb1/437Mrk3BGDGnc+tiy0LK6e1TH/u8zK8OX/vTf8JU//zfsv/W6SUuTI3ZYLzTWDvpT5N2/CaGvJ/92JVHTuiFUVq+piJGpbUnYdVRjBwAAKCSBDAAAAAAoc5WVFWG7DZqT6W37bx56evvCg4taksoZMaRx2+PPhZUd+QU0evtCuPGhJcnUVFsVXrHTBknljL03nx0q44f277og846ztwphk31GGnAI09YPoeXpzBUyAAAAJgGBDAAAAAAgTVVlRdhxwxnJ9I4DtgjdPb3h/oUt4c/3LQp/uGtBWLyyI68r1trZEy6+4+lkmjejPrx/y2fD6597LPPGu705lvAY+aDNG2QOZCQVMgAAACaeQAYAAAAAMPwfEasqw64bz0ymj79823DzI0uTYMZf7n8mtHdlaTuSxcIVq0Pdfb8OoSrDytiGZJfX53ag5nmZl6uQAQAATBICGQAAAABAXtUzDnzRusn0hWO6k1DGpXc+Hf752LLQ1zfy/s2hNbyy8tbMK1/08hCmr5fbQKYLZAAAAJObQAYAAAAAMCrT6qrD8XtslEwLlreHy+5akIQzHl3SmnWfV1fdEuorujKv3P0tuZ88tizJRMsSAABgkhDIAAAAAADGbMOZDeG9h2wVTjl4y3DfghXh0jsXhMvvXhCeb0sPX7yu6u/ZK15s+dLcT5i1QsaifIYNAABQNAIZAAAAAEDBVFRUhJ03mplMn3zlduGGh5YkVTOue3Bx2Lr30bBj5ROZd9z1DSFUVY+9QkbHihA6W0OobRrdAwAAACgQgQwAAAAAoChqqyvD4duvl0zn//OJEP54bvaNd3tTfgefniWQ0V8lY85W+R0PAACgwCoLfUAAAAAAgMFevf3McEzVzRkvzP8adgth1ub5XbTmLC1LopYFngAAGKXPfe5zScWr/un666/Puu0TTzyRtu3JJ5885a77wQcfnPYYhhOvReq28VoNZ7PNNhvYNn5PccXXX+rzE1+fMNFUyAAAAAAAim7mE38JoaIt47ofr9w/nNHWGWY21uZ+wJqGEBrWCaH9+aHrVi4aw0gBYKhFixaFhx56KMyfPz8sXbo0tLW1hdra2jBjxozkRvuee+4ZZs2a5dIBkEYgAwAAAAAovjsvyLh4eV9T+FP3HmHXuxeGk/bN85Oj0+dlDmS0LBzlIAHKQwwQPPnkkwPzf//735MqAdnEKgBnnnnmsMesrq4OdXV1obm5OcydOzdsuummYbvttgsvfvGLw4EHHhjmzJkz6vGed9554a1vfeuI2zU0NCTnX2+99cKuu+4a9t133/Da1742zJw5M+9zxuvz61//Otx0003h9ttvT0IYI4mP9f/+7//CSSedlFyPYhr8nMRzxusEwOSiZQkAAAAAUFzLnwrhyX9kXPWHnv1DR6gNF97+VP7Hbd4g83IVMgDGXXd3d2htbU0qSdxzzz3hiiuuCF/96lfDcccdlwQkDj300CTgELcrlvb29vDss8+Ge++9N5x//vnh3e9+d9hggw3CRz7ykWRdPq655prwqU99KvzlL3/JKYwR3XbbbeEd73hH2GuvvcL9998/ykcBQCkRyAAAAAAAimvhXVlX/b7nkOTrfxa2hPsXrMjvuNOzBDJUyACYVHp7e5MqHG9605vCNttsE/70pz+N27lXr14dvv3tbychiWXLlo35eDHgsdNOO4W999477LjjjmHatGlDtrn77rvDQQcdlHwFoLxpWQIAAAAAFFfr4oyLn+6bE/7bt8nA/MV3PB123HBG7sdtnpd5uQoZAEV1xBFHhI997GNpy/r6+sKKFSvC8uXLw5IlS5I2H//617/CwoXpbaQee+yxcOSRR4aPfvSj4Wtf+1qoqKjI+/w777xz+OY3vzlkeazQsWDBgnDDDTeEP/zhD6Grq2tg3X/+859w9NFHJy1Icj1nVVVV0m7lpS99afJ1l112SVqiDA6b3HHHHeF73/te+NWvfjWw/LnnnkvapcRqHbGVy1QS26HEiaFia5/4WgfIlUAGAAAAAFBcrZlLvT/bt07a/GV3LwifeOW2oa66aowVMhblPUQA8qsScdhhh+W07c0335xUqIgBiRhe6PeNb3wjCVCcffbZeV/6ddZZZ9jzn3LKKeHRRx8Nxx57bBKISB3LRRddFE488cQRz/Hyl788aX8ye/bsYberrKxMqm9ccMEFyZhOPvnkgXUPPfRQ+PnPfx7e85735PzYACgtWpYAAAAAAMW1KnOFjGV96Z8yXt7WFa59IPO2GTVvmOV8z4TQ053XEAEojv322y9cfPHF4c9//nOYM2dO2rof/ehH4dxzzy3KebfccsvknDNnzkxbfs455+S0/0YbbTRiGGOwk046Kbzuda9LWxYfOwDlSyADAAAAACiu1iUZFy8dFMiILrz9qdyP25ylQkZfb9Y2KQBMXJuT2MIkVrdIdeqpp4alSzNXUhqrefPmhf/7v/9LWxarZKxevToUy5ve9Ka0+QcffLBo5wJg8tOyBAAAAACYkJYly8KMIctufHhJWLi8Pcyb2TDycafPy74uti1pHmY9AOMuVq0477zzwtFHHz2wbMWKFeFb3/pW+NKXvlSUc8Y2Il/72tcG5mMYY8GCBclYimHwcYsVNilnPT094fbbbw+PPPJIWLx4cejo6Ajrrrtu2HzzzZOKLHV1dWGqiOO/6aabwuOPPx66urqSKjLbb7992HvvvUNVVY4t3IYxf/78cNtttyXtd+J7bdasWWH99ddPrlO8ZoXQ1tYW/vGPf4SnnnoqLFmyJNTX14e5c+eGHXbYIeyyyy4FOUcc/4033pi8d9vb25Ox77rrrmGPPfYIFRUVBTkHFItABgAAAAAwIRUyBrcsifr6Qrj0zqfD+w7deuTjNs4KoaouhJ6OoetWLgwh7DGq4QJQPK9+9avDwQcfHK6//vqBZT/72c/C5z//+VBdXfjbVrH1yGAxJFGsQMbg6huDW6ZMBZ/73OfCmWeeOTD/97//PXnOCmnlypXhuOOOC9dcc83Asvic/OUvfwlbbbVVxn2eeOKJ8IUvfCFcdtll4bnnnsu4TWNjYzj22GOT11MMaBRafN0ecsghA/NnnHFGcr3y9dBDD4XTTz89XH755aG3t3fI+tgu55Of/GR4//vfH2pqavI6djxeDD59+9vfDvfff3/GbSorK8OLX/zi5ByvetWrwmjcc889yeP/61//mrXqzIYbbhje9ra3hY9//ONh2rRpeZ8jVpj5yEc+krxOYhBnsE033TR86lOfCu94xzsEM5i0tCwBAAAAACYkkFHTPDfj8ovueDr0xWTGSOInIqevn71CBgCT0gc/+MG0+fip+ltuuaUo58r0/5NifqL+1ltvTZuPn+An3TPPPBMOOuigtDDGXnvtFf75z39mDWOcddZZYZtttgk///nPs4Yx+qs1/OpXvwrbbrttOPfccyflpb/44ouT6g5/+MMfMoYxomXLliXtfF7zmtfk1WInVpCI1/Ltb3971jBGFM8bWwjFgNQrX/nKJCCTz3vqtNNOC7vvvnsSKBlufHE8MUSz9dZbJ1U08hGfv9122y0J6WQKY0RPPvlk0pbo+OOPD52dnXkdH8aLQAYAAAAAUDzdnSGsXp5x1a7bZK6C8eSytnDb49lvtqTJ1pYkqZABwGQUbwAPbisRqzAUw9NPPz1k2XrrrVeUc8VWCrH9SqqTTjqpKOeaqv773/+GffbZJ9x1110Dy17xilckz3+mFhrxRvzJJ58cPvOZzwy54R6rSOy8885hzz33TCoxpIrbxqoJsUrEZPLHP/4xvO51r0teK1GsfvGiF70oqVax2WabZdw+VpfIRWx7su+++4Y777xzSDWMLbbYIrlOsaLEYH/+85+Tqh/PP/98TmGM+HzENkCDwySx3UoMaWy33XZJ25LBIZwjjjgiqaaRi1//+tfhne98Z9KOJlVzc3PynO+0005h+vTpA8svvfTScMopp+R0bBhvWpYAAAAAAMXTtizrqn122TZU37YwdPcO/fTyhbc/HV6yxezRBzJaBDIgH909vWHRitw/hc3obDCjPlRX+axsbW1t8sn3+An9fqk36Avp2muvHXLTONNN6bGKlQDe/OY3J60o+sU2H6997WsLfq6pKlZBie0xUitcvPWtbw3nnHNO1nY1sfXIL3/5y4H5GGCIN95jVYTtt98+bdtHH300fP3rX0+O118ZJYYZXvKSlyRBhYm2YsWK5DUSQyaxlU58bLGyQ2qw4OGHHw4f/vCHkyBGvx/+8IfhXe96V9hhhx2yHru7uzu8/vWvD/Pnzx9YFq/pRz/60aTtybx589Ku01e+8pWkVVC/O+64I7znPe8Jv/vd74Z9DD/4wQ/C+eefn7bsgAMOSI4Xgzb91WdWrVqVHCtW0uh/vmMI5Q1veEO477770sYz2GOPPZY8v6nVbWI7mxh2imGu/tdKDN1cddVVSUuTWCkjVtTIFGqBiSaQAQAAAACMe7uSaNa6G4ZDt+0OVz/w7JB1f7pvUfjcq7cP0+tH6Js+fYPMywUyIC8xjHHA14pToYC1bvr4IWHjWY0uyQutPFIDGfEmbKEtXLgwuTmf6oQTThjVseIN7+uvvz5tWbzpHCtwxFYMV1xxxUDVg2jvvfdOPrVfzPYoU0lszxFvxqe2t/j0pz+dtLMYLsARW5WkhmliNYdY6SGTeNP+xz/+cVLtIZ4rVnCIz1sMGtxzzz1hoi1fvqZiWKwiEdtwZKoIElt7xDYgRx11VLJNFB9HDE8MV+3ju9/9blq7nBh6isd5+ctfnvE6/fSnP02qcsTgQ7/f//73SfWOY445JuM54ms9BiwGV4D5xS9+MeR1Pm3atKRCyWGHHRb233//JLAUxXBGDNRcdtllWR/L+973vqT1TL94vWIFlVgdI1V8jMcee2zyfB944IFJi5Ynnngi63FhoohhAgAAAAATEMioCKFxVjhxz40zrm3v6gl/vHfRGFqW5LAvABMm3lxPtWhRYX9ux4BHbIURqxL0a2hoGHJDOVcxfHH44YenTa95zWuS6gPxRnZ/GCO2Q4ntHG688cawzjrrFOzxTGVnn312UgmiP4xRVVUVfvKTnwwbxohiBYn+thix7UYMGGQLY6SKVUlOPfXUgfl77713SKWUiRJDBTGokymM0S9en8HhixhEySZW3IiBjFRf+tKXMoYxUsWWIO9+97vTln3zm98c9nlMDR3tsssuSVBkuNBRrFhx0UUXpW0Tw0uxEki2ljapj7WxsTEJ8wwOY6SK77MY8BjcBgkmC4EMAAAAAKB4WpdmXt44O4TKqnDwNuuGdadn/gP6RXc8PfLxs1bIWBQbneczUgDG0cyZM4cEHnL1/PPPJzfYB09XXnll+NGPfpR8yn+77bZLbsT3i20OYquFYrQr6RfDGJ/61KeSG92xtQYhfPKTnwzvfe97B4IVMRQTAwmplRkyefDBB8Nf//rXtJBFPm1HYvAmtQ3KJZdcMimejhiAyOU1uO2224add955YD4GGLK9R+J1euqppwbm4/E/+MEP5jSeL37xi0nooV+s9hKv/WCxfUhsCZLqG9/4RtZWM6liK5PU1j3xWKntUlLFahup4mtnk002GfEcsepHrIQCk5FABgAAAAAw/hUymtZ8MrS6qjIcu/uGGTe548nnwyOLV42uQkZXawgdLfmNFYBxE1sapOrs7Mx53xi0GFytIk6vfvWrk3YIsWJF6vG22WabcM011yRVGorp2WefDR/4wAeSG8ix0kC88Vyuurq6knYWX/7ylweWzZ49O1x33XXJ8zSSwRUh3vzmN+d1/niu2Ban30033RQmg9Rgwkh23XXXge9joKW/7cdgN9xwQ9r8W97ylpyCEtGsWbOGtCiJ1V0yVa5YvHjxwHx8jb/0pS8NuXrb29424jmiwW2B4mPJ1cknn5zztjCeBDIAAAAAgAkIZKwtVX/CHpnblkQX3bH2E595Vcjor5IBwKS0cuXKtPlitRuIN4Lvv//+cPDBB4+5okcMWPRPsU3Ec889F+68887wve99L+y0005pj+2jH/1oEkjorwxRTuLjP+qoo5KKJKmtK26++eakWkIuBgcocmlVMlhqZYUYKJjogEysmhLbfORq7ty5afOp7XdS3XrrrWnzhx56aF7jGhys+Ne//jXiOQ455JBhW5UMduCBB6aFRO66664hIaw4f/fdd6eFanbccceczxGv7eDKOzAZCGQAAAAAAOPfsuSFChnRVnOnhd03yfwH9EvuWBC6enpHF8hYuTD3cQIwrgbfXB5cMaNQfv7znydVCTo6Ogp63MrKyrDOOuuE3XbbLbz//e8P99xzT9LCIfUm9QUXXJBUyignsUrIQQcdFK6++uqBZfEa/fOf/0wqleRqcNuMGE6I1zaf6aKLLhrYPwZoWlomtnJWrEZRVVWV8/ZNTU1p8+3t7Rm3e/LJJ9PmU1ud5GJwSGT+/PkFP0cMXMU2LP3i+zG+VlItXLgwLaSRTxijX2owCiaL3OrVAAAAAAAUskLGtPRPfZ6458bhzvnLh2y2dFVHuOF/S8Jh26+X+TjVtWvCHZnO0yKQAbnaYEZ9uOnjh7hg43CdWSO1/UE0b16WFlQZxBv+g1sbdHd3hyVLloTbb789/OxnPwtXXHHFwLpLL700aXlx4YUXFu3yxwDAqaeemozj9NNPH1h+5plnhne84x1JeKMc/OUvf0mbf/GLXxyuvfbaMH369LyOs2zZsqKEgGbMmBEmSn392N7/2Sp8PP/882lBoRj8yMecOXOyHi/bssH7jPY8G2+8tkra8uXpvwfGChn5Gs0+UGwCGQAAAADAhLYsiY7ceYNw5pUPhPauniGbXnj7U9kDGf1VMjIGMrQsgVxVV1WGjWc1umCMmzvuuCNtfssttxzT8WI7hA022CC86lWvSqaf/OQn4d3vfvfA+lgt4eyzzw6nnHJKKKYYyjjnnHPCY489lsy3traGiy++OLzzne8M5SBWQkitRnLfffclLTAOP/zwvI4z+OZ8IZRq+5hVq1YNfN/YmP/P8cGVOAa3Exp8jkz7FOI8g89RiMcCk4FABgAAAAAwoS1Loun1NeGVO20QLrnz6SGb/u2/i5NKGXOm1WU+VvO8EJ65d+hyLUsAJqXYliC2+EgV21oU0rve9a7w6KOPhq9//esDy2LlimOPPTasv/76oVhiMOTVr351+M53vjOw7JZbbimbQMaJJ54Yamtrw7nnnjvQZiMGZC655JJw5JFH5nyceDM+tcXIn//85+TajkUxn/eJFNv99LcAamtry3v/GBpKlamayeCWQoP3KcR5BocpCvFYYDIQyAAAAAAAiiOW1m5NL0mfLZARnbjnRhkDGd29feGyuxaEdxywRfYKGZmokAEwKV155ZVpVRSiQw4pfMucL3zhC+Gyyy4LDz/88MAn8j/72c8mFSyKaXC1j2eeeSaUi9gy46c//WkSyvjRj36ULIvPdQzC/O53vwuvec1rcm5vkRrI2H333cPcuentzlgjtsPpD2TEKiCxFUg+LXKWLk0Pz2bad/Cy0bSUGek8M2fOHHb7iWp1A2NVOeYjAAAAAABk0rkqhO7VOQcyXrz5rLDp7MzlqX//76ey9k5PKmRkokIGwKT0/e9/f0jlgr333rso7TO+/OUvpy37xS9+ER5//PEwnmpqakI5qaioSNrDfOhDH0qrihKrZ1x44YU5HWPzzTdPm3/kkUcKPs5Ssemmm6bND64+M5LB2w8+XiHOEUM5//vf/9Lem+utl96ObsMNN0yCPP3uv//+kK/YIgcmG4EMAAAAAKA4WpdkX9c0J+MNnBP22Cjj5g8vXhXueXrNpz+HUCEDYMq4/PLLww033JC2LLbzqKqqKsr5jjvuuLR2KN3d3eFLX/pSKKYnn3wybX7wjedy8e1vfzucdtppadf+DW94Q/jVr3414r6DK6b87W9/K8oYS8HgMFO+12rw9pnCUYOXXX/99dmDshncdNNNoaurK63iSWr4Iorzu+yyy8D8c889l1coI4ZEli9fnvP2MF4EMgAAAACA4mgdptR0hgoZ0XF7bBQqKjLvcuHtT2Ve0ZylZUlsl9LdOeIwARgfjz76aHjrW9+atmzWrFnhwx/+cFHP++lPfzpt/pe//GWYP39+Uc4Vb1JfddVVact23nnnUK6+8pWvJG1i+vX09ISTTjop/PznPx92v5e//OVp87HNTOoNfdY66KCD0i5HDLzE8EsuYnuTP/zhD2nLDjzwwCHbbbPNNmnBohg6+vvf/57z0zD4+R485n4HH3xw2vz555+f8znOO++8nLeF8SSQAQAAAACMb4WM6voQaqdlXLXBjIZw4NaZwxpX3r0wtHf2DF3RvGH2Max6JrexAlBU11xzTfIp+3gDONV3vvOdsM466xT13K95zWvCTjvtNDAfb+wPbmVSKLElyn//+9+0Za9+9atDOTvzzDPDF7/4xYH53t7e8I53vCP86Ec/yrrPHnvskVYl46mnnhoSrGGNI444ImyyySYDlyO25PnBD36Q0+X5zGc+E9ra2gbmDzjggLDttttmrGL29re/PW3Zxz72sSRgM5Lbbrst/O53v0s7Vnz+Mxkc2PrhD3+YPPe5hL2Gez3BRBLIAAAAAADGN5ARq2NkK4MRQjhxz40zLl/Z0R3++p9ncm9ZErUsGnmcABTNLbfcEk444YSk4sHSpemVkz74wQ+GN7/5zUW/+vEG8OCb+TE4sWDBgozbx5YqX/va19JuVOfioosuCu95z3vSlsXHvummm4Zy98lPfjJ885vfTKskcsoppySBnGy+8IUvhMrKtbcy43Py+c9/Pq9WGU8//XQSHPj3v/8dSlVs9xPfS6lOP/30cN11141YteLss89OW3bqqadm3T6+thsaGgbm77zzzvDud7972OcjVqI5/vjj07Y5+uijw5Zbbplx++222y687GUvG5iP78EYqFq5cmXWc8SQV9ymo6Mj6zYwkaon9OwAAAAAQBkGMuYMu9th288NMxtrwvK2roxtS47ZbVBFjPoZIdQ0htCV4cbZyoX5jRmAES1atChce+21acviDdeWlpawfPnysGTJknD77beHf/3rXxlDDzEgEW/Qn3XWWeN2teNN4Xiz98EHH0zm483b2E7j+9//fsYbvKeddlpSRePYY49NbvbutddeaS0b+sUbxbF1w09+8pPwpz/9aUg7ltQQwng/J7naYYcdwgYbDBNuLJCPfOQjoa6uLrz//e8fuEEf29V0dnaGj3/840O232+//ZLKGp/4xCcGlp1xxhnhiiuuCB/96EeTG/eDq6vEig0PPfRQuOmmm8Ill1wS/va3vyXtO4488shQymIg48ILLwy33nrrwOv7Fa94RRJGed/73pf2/D722GPhq1/9avjpT3+aFpR47Wtfm4Qlstloo42SUEx8/vr97Gc/C4888kjyXokVcPq1trYmVTFiMCQ1iBXfE7HqxXBidY9Y0Wb16tXJ/B133BF222238O1vfzt5TNXV1QOVbq688srkdRVbqESbbbZZeOKJJ/K6dlBsAhkAAAAAQHG0pn8SOq1CxjDqqqvCMbtuGM67Zegf1G95dFl46rm2sPGsxrULY7WNWCXjuUeHHkyFDICCu/rqq5NpNF70ohclN1wPP/zwMJ5ipYVPfepT4U1velPazeQYDMkWRojhklhFIE7RuuuuG+bMmROam5uTEMFzzz2XVADIVCFg5syZSZuWjTfOXPVpMj0nsVrIySefHMbDe9/73lBbW5tUVoitS6IYfokBgtg+Y7B4Q3/x4sXJzfh+8Qb961//+uQ5ja06Zs+ePfB8xWBKvpVNSqVKxm9+85ukzUt8TfYHFr70pS8lwaPNN988CUPEsFSmwMLuu++eU8uP+PzFFiQXXHDBwLLrr78+7LPPPsn7I77eY5Aitk1pb29P2zdW14hjnDdv3rDn2GqrrcKPf/zjpH1J/3srtiSJrX9mzJiRhC7i8niO1MoZsQ1KfMwCGUw2WpYAAAAAAOPfsmQEJ+y5UdZ1F93x9NCFzVn+uK9CBsCkuFl86KGHht/+9rfhgQceGPcwRr/Xve51SSCkX7xxHD/xn6t4MztW2IhVCO66667kU/mZwhjx8cV2DvEmN0O9853vTEIgqe1IPvvZz2YMZETf+ta3wi9/+csk5JIqBjrizfcY0IhTvGmfKYwxffr0IfuWoi222CLcfPPNQ1538TrFaxPbtmQKK8SqEzFUMbjaSCaxuk18LmLljdTnr//9EV/38T0+OIyx/vrrJ4Gh1HYkwznppJOSqjMxvJNqxYoV4Z577gn33ntvWhjjuOOOG7HyBkwUgQwAAAAAYFK1LIl2mDcj7DCvOeO6S+54OvT2DroBFitkZKJCBsC4hS4aGxuTG6+77LJL8mn2WN0gto149tlnw3XXXZcEIuJ2EyWeO1bESBVv+sYKDKmOOOKIcOmllybBgW222Sa5CT2SWDXjDW94Q/I4443nWJGA7N7ylreEX//61wPtJ6LYwibe6M+2fQwTfOELX0gL1WQTwwWxTc35558fnnnmmbDrrruWxdMR24rE4EWs/hJb0WQTX9MveclLwuWXX5602omhlVzFfWOQKYYv4vs8tqHJJlbDiEGbhx9+OOy///55PZb4/ovniO/HweGPfrFCyjnnnBMuuuiiIeENmCwq+jJF94Cy8Z///CfsuOOOA/P333//sP+TBgAAAMjZ2fuEsPiBocuPOCuEfdf2H8/ml7c8Ec644j8Z1/3q7S8J+2+dEuy45owQbv7O0A032TeEt/3Zk0bJ6+7uTm54pdp6663TbnYCoxNbYcRP/ccWCTG80dramtz8je0TYruMnXbaKQkJ5BLcoDAWLFiQBA/i87Fs2bLkhn0MxWy44YZhu+22C1tuuWXWm/jlJLYviRVdYiiqpaUlCarEFj377rtvmDt3bkHOEauS/OMf/0jOtXTp0iSgEY8d7zUVKggTW9HcdNNNyfMeq2/E9ijx2Hvuuaf3XYnpLtLvMxN5P9RvYgAAAADApGtZEh2967zwxT8+GDp71vSYT3Xh7U+lBzK0LAGgSGK7i3gDO05MDjF4ESeGFytIxKmYYmWcWMWimGKI5MQTTyzqOaBYRMMAAAAAgMLr7QmhbdmoW5ZEMxtrwxE7rJdx3V/+80xY0daVW8sSRYIBAIAJIJABAAAAABRe+/Mh9A2tbJFoyr1E9ol7bpxxeWd3b7jingUjV8jo6VgzFgAAgHEmkAEAAAAAjF+7kjxalkT7bTUnbDCjPuO6C29/euQKGVHLwpzPBwAAUCgCGQAAAADA+AYyGmfnfJiqyopw/B4bZVx334IV4cFFLWtmpq0XQkWWP3euXJTz+QAAAApFIAMAAAAAGL9ARv3MEKpr8zpUtkBGdFF/lYyq6uytUFpSWpsAAACME4EMAAAAAKDwVi0Zc7uSfpvObgp7bzEr47q/3J9S/aJ5XuYDtKiQAQAAjD+BDAAAAABg/CpkjCKQEZ2wx8YZly9csTqs7uoZPpCxcuGozgkAADAWAhkAAAAAwDgGMuaM6nA7bzQj67qW9q4130zfIMsGKmQAAADjTyADAAAAACi81qUFrZAxo6Em67oV/YGM5iyBjJUCGQAAwPgTyAAAAAAAJn3LkuZcAhnTs7QsadGyBAAAGH8CGQAAAADApG9ZUl9TFWqrK0dXIaP9uRC6Vo/qvAAAAKMlkAEAAAAATPqWJcO1LVkbyNgw+84rVckAAADGl0AGAAAAAFBYXe0hdK4c/0DG9CwVMqKWRaM+LwAAwGgIZAAAAAAA41Mdo0iBjJb27jXf1E0Loa45884rBTIAAIDxJZABAAAAABRW65Ls65rmFK9CxnBVMlq0LAEAAMaXQAYAAAAAMD4VMiqrQ6ifOerDNtdXjxzIaM4SyFAhAwAAGGcCGQAAAADA+FTIaJwTQmVlkStkzMu8swoZAADAOBPIAAAAAADGJ5Axbd0xHTZbIKMllwoZAhkAAMA4E8gAAAAAAMYnkNE0tkBGcy4VMpqzVMjQsgQAABhnAhkAAAAAQGG1Li1KICNrhYzVObQsiYGM3t4xnR8AACAfAhkAAAAAwJSokDEjpwoZWVqW9HaH0JYlKAIAAFAEAhkAAAAAQGG1Ls68vGlOUVqWtHX2hK6e3uErZEQtC8d0fgAAgHwIZAAAAAAAU7plSVqVjHiOyursbUsAAADGiUAGAAAAAFA4fX3j3rIkLZBRWRnCtPUzb6RCBgAAMI4EMgAAAACAwlm9PITe7qK0LMkpkBE1Z2lbIpABAACMI4EMAAAAAKD47UoKUCGjsbYqVFdW5BDI2CDzAbQsAQAAxpFABgAAAABQONnalUSNY6uQUVFRkbVKRktqIGO6ChkAAMDEE8gAAAAAAIofyKidFkJt45gP35xLIEOFDADI6HOf+1wScOyfrr/+eldqAp188slpz8cTTzyRddv4XKVuG5/LqWazzTYbGH/8fjjnnXde2uON88NJ3fbggw8u8MgZLF7j1GtOdtXDrAMAAAAAKEwgo2ls1TFGCmSsyKlCxqKCjAGgHDz77LPhnnvuCU8++WRYvnx56OjoCNOmTQszZ84M6667bthll13CRhttNNHDBIBJTSADAAAAACic1qWZlzetW5DDz8glkJGtQkbHihA6VoVQN60gYwEoNQ8//HD46U9/Gv7whz+ERx55ZMTtYzDjwAMPDK997WvDUUcdFRoaGsZlnDDeVSx++ctfDsyfccYZU7I6BTAxtCwBAAAAAMahQsY4BjKmZwlkRCtVyQAYbMGCBeGNb3xj2HbbbcPXv/71nMIY0ZIlS8Ill1wSTjzxxLDeeuuFz3zmM0k1DQBgDYEMAAAAAGDKtCyZ0VCdQ4WMLC1LopaFBRkHQKn44x//GHbeeefwm9/8JvT29g5Z39TUFDbddNOwxx57hB133DGpilFZOfT20sqVK8NZZ50VNt9889DS0jJOoweAyU0gAwAAAACY8i1LWtq7187UNITQsE7mA6iQATDgggsuCEcffXR47rnn0q7KDjvsEL797W+He++9N6xatSo88cQT4fbbbw/33XdfWLx4cWhvbw/XXHNN+MhHPpKENVLFChmdnZ2u8iQVW2309fUNTAcffPBED6msnXfeeWnPx2abbTbRQ5pUrWJSr02ch6lIIAMAAAAAmDItS5rrc2hZEk3PUiVDhQyAxG233Rbe/va3h56enoErMnPmzHD++ecnQYwPfehDYaeddsp4tWpra8Nhhx0WvvnNb4aHH344nHPOOUOCGQCAQAYAAAAAMIUCGdkqZAwJZDRvkPkAKmQAJBUxTjzxxNDVtfZn50YbbRT+8Y9/hDe/+c0ZW5JkU1NTE975zneG//3vf+E973mPqwsAKTI3XAQAAAAAyFdPVwjtz09Qy5LBFTKyBDJUyABI2lY8+eSTaaGKK664ImlVMlp1dXXh7LPPDi996UuTChoAgEAGAAAAAFAobcuyrytyIGNlR3fo6e0LVZUVaxY0Z2lZokIGUOaWLVsWzj333LRln/zkJ8Nuu+1WkOMfd9xxee+zePHicP/994dHH300LF++PHR3d4dZs2aF9ddfP7zkJS9Jvk4V7e3t4Z577gkPPPBAeP7555P5hoaG0NzcHDbbbLOw7bbbho033nhUVU3uvPPO8Mgjj4QVK1Yk16ixsTHMmTMnbL755kmYZp111gnF9vTTT4f//Oc/4fHHH0/GEcXnasMNNwz77LNPUcYQr+GNN94Y/vvf/4ZVq1Yl54jX8qCDDgpNTU0FPx9rdXZ2hn/961/hiSeeCEuWLAm9vb1h3XXXDVtvvXXYe++9Q1VV1ZS5XPPnzw+33HJL8rWvry95HLvuumvys6+i4oXfH8cgVgm66667kp9nra2tyXtz3rx5Yf/99w8zZswoyGOIPx9vvvnmsHDhwrB06dIwbdq0MHfu3OQxvOhFLyrIOeJ7+9Zbbw0LFixIqijFn7977rln2HHHHQty/HKkQgYAAAAAUNx2JQUMZDRnCWT0V8lYp+mFT2WrkAGQ0Y9+9KPQ1tY2MB9v6n/4wx8e16sVb4bG9igXXnhhuOaaa5IbmcPZaaedwqmnnhre+MY3hurq3G5tHXzwweGGG25IO2euzjvvvPDWt751YP4Xv/hFOPnkk4fdJwYlPv/5z4dLL700uRk7nHiT9mUve1n4v//7v+Sm9nCuu+668NWvfjX5Gm+GZxNvKG+zzTbh6KOPDu9973uzhj5idZQzzzxzYP7vf/97cq2yicGPa6+9Nlx88cXJ19TKKpnGEB/Pxz/+8WQcud7kjtf2l7/8ZdoN4Ri4iIGPONZzzjkn4zWNlVje/va3J9c93vyeirI99kJ65plnwitf+cokLNBvr732Cn/84x+TUEImMSAVr+uf//znJASTycyZM8Ob3vSm8JnPfCYJBRTaaN6Hmfz73/8Op59+evjb3/6WcX18r8THOppjd3R0hO9///vhxz/+cRIoyyT+zIrhofjei+GM0YiBpPheiF/jezKTrbbaKpxyyinJ+380VYr++c9/ho9+9KNJaCWTGPqK1+nYY4/N+9jlLvcmYAAAAAAAw1m1OMuKihAaZxW1QkbUsjqlbUnzhpk3WvVsCD2Z/5ANUA4uv/zytPkTTjihYJ/eztXHPvaxcOCBB4Yf/OAHI4Yxovvuuy+5WXrIIYcknz6fbC644ILk0+Px60hhjCh+uj3eXI6PP5sYIHn/+98fDjvssCS0MlwYo3/7WEEihjf++te/hkJ53eteF17xilckVVWGC2P0jyHe1H3Na14Tjj/++JyuRTaxwsguu+wSvv3tb2c9TqzeEANGMQQSKzgwVHxNxMolqWGMGM6IQZxMYYx4sz++7uK1v+iii7KGMfqrNcTXcAwCxHDHZPS9730vefzZwhjRU089lQQ/YkBqpPdZqlgpZvvtt09+nmULY/Rf0xioOuCAA8Lb3va2pOpEruJr/C1veUsS6IiPIVsYoz8U9pGPfCT5WRSf93zEoEUMi2QLY/Q/3lgB6QMf+EBeATdUyAAAAAAACqV1aebljbNDqKwqeoWMFe2pgYwNMm/U1xtC6+LsLU0ASli8uZp6YzY68sgjx30cq1evHrIstqGIpfFja4/4qfNnn302LFq0KG2bWFXj0EMPTT7xHtuATAYxLHHSSScNuUEZK4/ESgf9jye2L4mtEnK94fvZz342Y2AjtgeJn+iPjz8GFWLbgsHXqdjPVbyRH6fp06cn62NrgziOVLFSSKxwcfXVV4fKyvw+Hx7DFa9//euTyg79Nt1006QKQ3wNP/TQQ6Gnp2dgXbwZHj+1f9ttt+VcQaUcxJvrr3rVq5J2N/1iIOAnP/lJxusUK+fE65gp0BPfm3GKz2VsW5MajFq5cmVSEeW3v/1tEvCaLGLVig9+8IMD8/E9E9+Tsc1HfM3GYFSqn/70p0lLoRhqGMntt98ejjjiiOR9naqmpiY5Rwy5xeMPPkcMYsX3awzGjVTFIv7cOOqoo5LKNINtsMEGSZuglpaWpKpKasjj4YcfTsIV8WdTLq2ovvSlL4Uzzjgj48+a+L6L44jniK2DolgRpBgVUUqZn0oAAAAAQHFblhSoXUk0va46xAromT6YlxbImD5M4KJlkUAGDBYrx7QscF2KLVbvqZq4WzOxekHqjexozz33nJCxxJv5sYpCDITsu+++yQ3GweJN01/96lfhK1/5SvJp/P5Pacf2A9/97nfDZPChD30oLYwRq3jEm5vxhmhVVXoYMd7QvPvuu8Of/vSn8Jvf/CbrMePjjpUuUr3rXe9Kbi5vt912Q7aPN4XjzferrroquSleaLEdyIknnpg8Vy9+8YsztgeJn87/+c9/nlS06A9xxKoA8XnKtyVObEMSwxjxBnqsPhAfe2zzkvp44/X52te+NnDtY9Ao3lB/z3veM+bHWwr+8Ic/hDe84Q1pgZpPf/rT4Qtf+ELWfeK1Sw1jxOBCfO5i9YjNN988bdt77rknnHXWWUkrmyj+XInPWwwAxIoZEy2+HvvDGPE9E8caK4PU19cPbHPnnXeG973vfcnPxX6x/UoMWM2ePTvrsWMAJQZPUsMYMYAVW5LEaxCDDP3uvffe5OfBZZddNrDsL3/5SxK4ij/XhvPJT35ySBjjmGOOSVqX7LzzzgPLYuAmVrCJ5+kPTSxbtiwZY/x5E5/HbOLPjfi6SLX77ruHb37zm0lVjv62QzGs87vf/S5pRxSPHcew3nrrDTt+1hLIAAAAAACKHMgoXF/3ysqK0Fxfkx6+eEHastgipaouhJ6OoQdZGT+tuEfBxgQlIYYxvrv2Bg9F8sF7Q1hn0wm7vLGyQKpYvWHwjdbxEG/wxk9lx/MPJ34C/LTTTkvaZsSgQ/yUdhRvvMebj6k3PidCbKsRp35xjPEGaraKEDFgENsnxCne0Iw3jTOJn55P/cR7vHkbt88mVheJYYk4ff3rXx/yqf2x+NSnPpXcZE+9kZ1JvAkfn9MY3HjpS186UJUh3tiNLTDyqVzx2GOPJc9tvHG91157ZXy88WZ2DPWk3kw+55xzBDJCCGeffXZyzfurscRgUFwWW3Jk8/vf/z6cf/75A/NbbrllEs6IXzPpb2kSX2/xJn1/UOHUU08d0hZpIsRQU/Syl70sqdYSAxODxeBBfL/GQFgMmPQHD2IILLWyRqagRGqLnFgNI7aAyVSNIgYnYjgmvk6/+MUvDiyP1y2+V+IYMolVgGK4KVW2nwPxvRKDS7F6UJxi1Yz+yjHxvN/5zncyniO+PuJrIjVQFkMrMTwSK32kitcvVleJ7+399tsvub7915iR5VcjCAAAAAAg35YlBayQEc3I0rYkLZARP9E3ff3sFTIAylBq64IoU6WD8bDHHnuMGMZIFcvmxxBGv/gp8Php7Yk2OOASKznk2p4jbveiF70op+OecsopOY8pfho+tjQplBgeGSmMkWrXXXdNKlf0izdtY9uSfMVP/GcKY6SKYZ2NNtpoYD5WA4itbspZDAu8973vHQhjxBBQDAQMF8aIN+RjdYfUm+/DhTFSxSBAapuSK6+8csjrd6JssskmSdAkUxijX1w3uFLFn//856zbx0o9sRLM4NfqSK1BYoWOV7ziFQPz8fkZHLhIFdelBiVi65LhQln9P1djKCnVz372s6R1UCaxpUmsONQvVimKP1cHhzEG/yyeDD97pxqBDAAAAABgyrQsyTmQETVnaVuiLQNQpgYHMuInu/Nx4403Jp8oH2m6+eabCzzykHwyO7WtSSy1P9H62wP0G+5G5mQ47niJFU1S27Xk+1zFtiixNcNIYtWNY489Nm3ZHXfcEcpRrKjylre8JXz5y18eWBbbbvztb38Lr3rVq4bdN4Yv/vvf/w7Mx+oQuYQx+qVWKYkhghgAmQxia6NcfsYdfvjhSdWV1FYm2cRWQ7GKRr9YLeK4447LaTzf+ta30uYvvPDCjGGJGPq45JJLBuZj25BYaSYXr33ta8Pee+89MN/a2pq1PdLgYEl8HmPVmZHEdky5vD9ZSyADAAAAACiJQEZLe3dugYyVKmQA5Sm2FEjV1NSU1/7x5ne8eTnS9MY3vjEUw2abbTbw/V133RUm2rx56f+f+fWvf12U48YWClNJfF3NnTt31M9VvKmcT0WOVE899VQox/d1rKBwwQUXDCyLrYhiECb15nw2f/rTn9Lm3/zmN+d1/tiWY/3111Ylu+mmm8JEiyGG2BIkFzE8tNNOOw3ML1myJHR0ZGh5F0K44YYb0uZjG49cbbvttkl7lH6dnZ3hX//615Dt/vnPfybrUgMQ2arpZDJ4TDFIl8n111+fFvqKQapcnXzyyTlvi0AGAAAAAFD0liWFLYnf3FCdW4WM6Ws/SZ2mZWFBxwMwVQz+9HP89PREe+KJJ5IWF7HtwXbbbRfWXXfdUFdXl9xQHTzFG5X9li7N8v+ccfSSl7wkrfXKpZdemtwEvu+++8Z03BhqSXXqqacmn15/5plnwkSK7Q1i24Sjjz46bL311knLm9ra2ozP1aJFi0b9XO255545b5sa/IiytWcoVfE1cdBBB6W1hYntM2IYI9eb+KkBihimicGBfKW2yXnwwQfDRIvhrVghpNCvo1tvvTVt/tBDD8270k+qTIGM8TjHk08+GRYvXpwWqpk1a1bO54ivOXKX+V8uAAAAAAD5iH2uJ7xCRo4tS1TIAMrU4BtuE3nzOt4QjK0RrrjiiqTNQb5iWf+JVl9fH0477bTwqU99amDZRRddlEwxXHLEEUckNy732WeftAoCI4mfoo+hjGuuuSaZ7+7uDl/84heTdhRxXbzhesABByStPXJpMTBWMWDyvve9L+sn7Qv9XA2+OT6cwVVeBrd7KWUPPfRQ8tqKoaZ+8XUT213k87pIDVDEkFZlZWVBWyNNhHxeQ7m+juLPqdQKLDGMlVq1Jxe77LJL2vz8+fMz/mxMFcMS+dhiiy2S57+/IlIccxx7DEr1e/zxx9P22XHHHfM6x8yZM5MQTjlWpBkNgQwAAAAAYOw6W0Pobh+XQEZzlkBG7hUyFq0JkKT8YRrKXvOGIXzw3rK/DEUXr/MkCmQsW7Ysr/2zVTqIpe8POeSQnI9z2223JWGFsQRCUkv6T6RPfOITyQ3Uc845Z8hN7jh997vfTea32Wab8LKXvSy84Q1vSCprjOQ3v/lNeNWrXpX26fbe3t7wj3/8I5mi6urqpJpEbFcR28Tke3M4F1dddVU47rjjxnS9s7V/GC7oMlqjCfdMVb/97W/T5l/96leHiy++OGk/kasYwMj3+RnJZKhSMpbXULbXUXxc8T3YL58KHP1iVZlUzz///JBtBi8bvE8u4tj6Axk9PT3J96nVfAaHpEbzWOI+Ahm5EcgAAAAAAMYuW3WMIrQsmZFrICNbhYyu1hA6WkKon1HQccGUVlUdwjqbTvQoKLLBLQziDcb46fpi3MjPJoZAXvnKVw65aRs/BR6rPmy11VZh3rx5oaGhIbmpmvqp7ti64957J1dwKI7vJz/5STj22GPDWWedNRCWGOx///tfMn3ve98L++23X/jOd74zbGuOeBM2VqT42c9+Fr71rW+FRx55ZMg2sXJGDGzE6bOf/WwS9vjGN74R1ltvvYJVYDj++OPTwhjx8cbKHLFSR/wkfqz8EZ+nwTfA3/SmN4Vnn322IOMgs9jaJzVMEduUxLYyu+6664RWminVUMyqVauGraqRi8H79IcmxuM8qYGMwedobGwc8znITiADAAAAABi71mH6w49Ty5KcK2T0V8kQyADKTGxvUFVVlXxiut/tt98+roGM2HojtTLH1ltvHX71q18lN/lHMpqbhuMlVr+IU2wFcPXVVydVQ2KgYuHChUO2vfnmm5NQRnzcJ5xwQtZjxkoH73nPe5IpPk/XXXddctx4472lpSVt2/jJ/Xi8a6+9NtkmVuQYq9NPPz3thn98jn75y1+GbbfddsR9U4M0FMeHPvShJKD05z//eaCCzaGHHhr++te/hr322iunYwx+T8UqOr///e+LMt6pbtq0aUOqi+Rr8D6ZWsuMx3kGhyna2trGfA6yE8gAAAAAAIpXIaO6PoS66ZMwkLEghLkj31ACKCXxRt9uu+2W3Nzv96c//SmpgjBeUm/2xqoKf/nLX5JKC7l47rnncj7PWAIBo7k52W/zzTcP73rXu5Ipeuyxx5IgxaWXXpoENfpbHsSqE295y1uS9iWbbLLJiMeN1TTidNpppyXHuOeee5JrF69n/L7fM888kzyfcVllZeWoH0f8BP0f//jHgflYdSOeb5111slp/0ytGCis+P657LLLklDPFVdcMXDdDzvssCSkEauYjGTmzJlJ65tYbSVqb29P9meoGTNmJO+p/vdwvi2fMrV9yvR+GrxsNOdJ3SeG8AYHMuLzPty48j0Hwxv9T2IAAAAAgJECGbE6RoE/JZstkLFydVfo7U0pk11dm706x8pFBR0TwFRx9NFHp81feOGFQ6otFMv8+fPTKka8/OUvzzmMEW8Ux+oTuRrcQiPun6slS4Zpw5Wn+Pje+c53JjfIY0gi9fGuXr06/PCHP8z7mPGmcAzWfOITnwh33313uOSSS5IWL/3uv//+pErCWNx5551prUpe//rX5xzGiO1VUitrUDy1tbXh4osvTgtVxfdzrNYSK7TkElzadNNN094nmaq6sOZabbzxxmnXObZ8ykdqeCpKvfbZlg3eZyQxBJbaCiUGvgYH1Ab/3I0/M/IRW9089dRTee1TzgQyAAAAAIAiBjLmFPzqNtdnDmTELMaqzjWf8ByxSkZsWQJQhmL7i9Q2BbHs/He/+91xOfezzz6bNp9PW42bbropdHUNqoQ0jObm5mHPPZx///vfoRh23HHHcM4556Qt+8c//jHm4x577LHh1FNPLehxx/Jc/e1vfxvTuclPbG3zu9/9LrzxjW9Mq3Dyile8IqnOMpJDDjkkbd7zl93ee+89pms1ePvBxxuvc8TQx9y5cwfm77vvvrwqEN1www15jancCWQAAAAAAGPXmqXUcbYKFUWokBGtaBt0s655XuYNV/r0J1CeZs+eHd72trelLTvrrLPCvffeW/Rz9/WlVDF6oW1Hrs4+++y8zjX4U+Z33XVXTvvF0v3FvCG93377DTnfZDzuaJ+ruN+PfvSjMZ2b/MW2FOeff35461vfmtZ656ijjkpazQwnVqpJ9YMf/MBTkMVBBx2UNn/eeeflfK3+97//hZtvvnlgvq6uLmlZNFgMUMTKJ6nhqlh1Jlc///nPhx1zpuUx7BZDPbnK53EjkAEAAAAAFELr4skRyGgfFMhQIQNgiM997nNppffjzfZXv/rV4b///W9Rr9b6668/qioOf/rTn8Lll1+e17l23333Ia1ZcvH5z38+r/Ym+RoclMi1Dch4H3e0z1UMY8Q2Koy/2Mrm3HPPDe9617vS2uLENkVXXHFF1v2OOeaYsNVWWw3M33rrrUI1WcTWPU1NTWmVey677LKcnp/BVWxOPPHEMGPGjCHbzZw5M60FTQw5ffSjH83pHLF9zT//+c+B+WnTpiVjziRTMC+11Uk28WdBro+ZNVTIAAAAAACmVsuSYQIZLYMDGSpkAGSskvH73/8+aXXQ78knn0yqLMRPSQ+ujjCShx56KKftNtlkk7DhhhumtQaJ4xjObbfdFt70pjeFfB122GFpjy8GMkYKFfzsZz/LqzpAbPXywx/+MKlEkKuvf/3rafN77LHHkG3e+973hiuvvDLn56GjoyN873vfG/G4+Yj7p35K/9JLLw233HLLsPtcddVV4SMf+ciYzsvYVFRUhB//+MfhAx/4QFrgKt7gjzfrs1XX+MIXvpC27IMf/GD46U9/mvfPgf/7v/8LCxYsCKUqhiUGBxni/EgVhs4444zwxz/+MS088+EPfzjr9nFd3KZfDKTFwMRwYhDqHe94R9qyOD+4fVO/I444Imy33XYD84sWLQqve93rhm0NFf8/EbchPwIZAAAAAMCUallSVVkRptdVj7FChpYlQHnbZ599wjnnnJN20++5555LPk292267he9///vhwQcfzLhvDAo8/vjjyY3f/fffP+0T+SN5y1veMmT+y1/+cmhpaUlb/vTTT4dPf/rT4cADDwzPP/98qK+vD5tttlnO55kzZ054zWteMzDf29ubtG+IpfYHt9+47777ktDHO9/5zuSxbbnlljmdI16D973vfWHevHnhpJNOCn/4wx+Sm5rZbpbGG5mpwYl47Qff3I1iW4NYsWTzzTdPPhl//fXXD7k+UbxxGttRxCBNDLekVreIj3UsYhWA4447bmC+p6cnvOIVr0heM7HqQqqHH344nHLKKUklhhgOmTt3bhL6KVWPPfZYuPbaa0c1xdfyeIhhoY997GNpr5X4+vvNb36Tcfu4LvV9HLeP4YqXvvSlSdCmtbV1yD5xm3vuuSd85zvfCQcccEDYdtttkxDHcDf0S8EXv/jFtJ9F8Tndd999wze/+c0hz+/999+fvI9i5Z1U8bmJP2ez2XPPPYcENj7zmc8kwZp4zFTxnN/4xjeSnwMrVqwYWB5/jg0X4og/f37yk58kIZ7UakTx/w3xZ05qICyGzn7xi18k44qBm+rq6rRwHcPL/K8WAAAAAICCVMgofCCjv0rGyo7uIctbVudYISOOt7szhOq1n/4FKDcnn3xy0toifl2+fPnA8niTtf8T9rHk/brrrpsEHOINuljSPoYlMt2gjeJ2n/3sZ7OeMwYMLrjgguQYUQxHfPKTn0z22WabbZIgwJIlS8ITTzyRdkMwBhl+/etfJ8tz9bWvfS25wbhq1apkPt6sfOtb3xre//73JzcrY2WAOI7Fi9e23YoBkBjOiDejcxWPe/755ydTFK9XDCVMnz49CS/EMade334f//jHh61kET+NHm/yxineNI03QGPQoaGhIQloxGDA4HBEfEyx0kfcZqxi1YT4qf7+MEj8Gm/ax+oJL3rRi0JdXV0SQOl/LvvPH0Mv73nPe8KyZctCKYqv3ziNxt///vdw8MEHh/EQX//xOeq/KR9DNW9+85uT91x8zw8WQ1jx5n5qe5+//e1vyRRvwG+66aZh1qxZobu7O3k9xxvzg8NN5SC+ry+66KKkwkR/ACP+PIw/2z7xiU8kQapYlSK+NzJVC3n5y18+JKCRLfgRfxbHIE+/Sy65JJliCCxO8edx/DkwOAQTf07E5zG1vUomMUhz5plnpv3MvuOOO8IhhxySHCM+5zFkFc+R2soptr265pprSroaSiEJZAAAAAAAY9PbE0LbsnFrWdIfyFiwfO0fhrNWyMgWyIhWPRPCzE2KMDqAqSNWNYjl9uMntuMNvMFtMmKYIU6xGsRwYrDj7W9/e/jUpz6VlPXPJt7QveKKK5JqC88+++zA8niT9z//+U/GT3HHT3/H6hUxkJGPeDMxtmk49thj09qKxMcTb3QOduihhyatOWKli7GIgZI4ZRNDC/E6xRuhuYrPSww+pIYfMj0H8VPsRx55ZCiEGFqJN57jJ/z7Qy1RDIFkatEQq5jEMEZ8bpkcYqgmhjJidYX+SjGxKku8gR/fU6lii5/YQiiGhOIN99Qb8PH9+eijjybTcGIgqxBhoMkuVoq48cYbk5+fMazQL17X4Vo4xSBMrDKT2k4pm/i8xUBU/Ln6q1/9Km3dwoULkymTrbfeOvkZGyuW5CK+NuLzG18rqT//Y6AqU6gqhvXiz68YyCA3WpYAAAAAAGPT/nwIfb3jWiFjRsMYW5ZELZnLygOUm4033jj87ne/S1qUnHrqqWGLLbbIab/11lsvHHPMMcm+8dPgX//614cNY/SLpfrjp7BjJYoYTsgkVoQ4/PDDw7/+9a8hpfvz8bKXvSzcdtttyafSU0vzp4otPmLbhauvvjrMmDEj52PHT7nHxx4fR7yGI4nVRuK2d91117BhjCuvvDL84Ac/SIIVuVzP+En5GKiJN4LjDeJCilUAYjuUV73qVVm3idUTYiuFGHJ57WtfW9DzM3ax9U98b/aLN91jpZP4GsskVm6JAaxY8WGTTUYOrsb3T3xdxzBTDAnEnwvlYMcddwwPPPBAcm2H+5kZ3x+x9ctNN92UBKZyCWP0q62tTaqxxBYiMTAWjzVcgCpW04ktTXINY/SLP4/i+GK7kmy22267pDpHbIdDfir6BkcdgbISE8fxfxr94g/qHXbYYULHBAAAAEwxix8M4ey9M6/7yIPDV6kYpXddcHv463/WfrK635v23iScdcxOaxfEP39+aV4IXWs/GT3ghPNC2OE1BR8bTIT46daHH354yKdkh7t5A8OJAYtYBSG2zYhl+WNrgliqP1ZhiKXsd9ppp6QCxVg999xzySfN43li+f1YYj+W/N93332Tth+FFCty3HDDDclN49hiIFbr2HnnncPee++dNRiSj1i+/7///W9yMztes1jqv7GxMble8e/u8ZrFT73nI97Gi0GL+P6eP39+0jYktp6Iz0W8ER7HH9uHxEoi4/GaiDdtY5WOWHEktmXYaqutkucql+AIU9MjjzwS7r777qTqS3xdx/+vxOBSDGvEm/SbbbbZRA9xUojv/Ri2ii2Q4vsjvu9jm6H9998/r6DXcGKrmH/84x/Jz7BYvSL+vIwBmF133TVp+VQIseJHDMLFc8SKH/HnTKwIEn9+TeXfZ/4zgfdD/SYGAAAAAIxNa/ay7KGxOC1LZjRk/nThivbu9AXx09CxSsZzGUpsq5ABkNUGG2yQTMUWQxGxysZ4iDcuTzzxxKIdP958jVMhxaoe8UZroW62jkV8PRTz+jE5xdBNnBherEqRb2WKfMXg01FHHVXUc8RqH7lWSSI3WpYAAAAAAMUJZNTPCKG6dpwDGYNalkTZKnSszNx7GwAAoBAEMgAAAACAsWldmnl507pFu7LZAhktmQIZsUJGxo0FMgAAgOIRyAAAAAAAilMho4iBjOZ8AhnZKmRoWQIAABSRQAYAAAAAUKRAxpyiXVktSwAAgMlOIAMAAAAAKFLLkrnjXiFjRXtX6Ovry7FlyaIQBm8LAABQIAIZAAAAAMCUa1mSrUJGd29faOvsya1lSU9HCO3PF2F0AAAAAhkAAAAAQAm1LOmvkpFThYyoZWEBRwUAALCWChkAAAAAQJFalox/hYyoZfWgQMa09UKoyPKnUIEMAACgSAQyAAAAAIDR61odQkfLuAcymuuHqZDRNiiQUVW9JpSRyUoVMgAAgOIQyAAAAAAACt+upMiBjNrqytBQU5Vby5Lh2pa0LCrwyAAAANYQyAAAAAAAihTImFPUK5utbUnGQEbzvMwHUSEDAAAoEoEMAAAAAGD0Wpdm+ctjdQj1MydPIEOFDAAAYJwJZAAAAAAAha+Q0TgnhMrKCQlktGSskJGlZclKLUsAAIDiEMgAAAAAAAofyGhat+hXtTlbIGN199CF07O0LGlZWOBRAQAArCGQAQAAAAAUIZAxp+hXtbmhOveWJdkqZLQ/F0JXe4FHBuOvoqJiyLK+vj5PBQAwZfRl+N0l0+84U4lABgAAAAAweq1LJ6xCRraWJZkDGRtmP5C2JZSAygwtgnp6eiZkLAAAo9GT4XeXTL/jTCVTe/QAAAAAQNm2LMkrkDE9S4WMqGVRAUcFEyN+erSqqiptWXu76i8AwNTR1taWNh9/t1EhAwAAAAAoXxPYsiSvQEbdtBDqmjMfSIUMSkRTU1Pa/MqVKydsLAAA+Vq1alXa/LRp08JUp0IGAAAAAFD6LUuGq5LRsrCAo4KJM3369CGfMu3s7Jyw8QAA5Cr+zjK4QoZABgAAAABQvvr6pk7Lkqg5SyBDhQxKtEJGX19feOqpp0J3d/eEjQkAYCTxd5X4O0v83WW4322mouqJHgAAAAAAMEWtXhFCb9eEBTKaswQyOrt7w+qunlBfU5W+Yvq8zAdqWVCE0cH4i33WY5WM1FYl8dOmjz76aGhubk6mmpqaUFmpeDYAMLF6e3tDV1dXaGlpSaY4nyr+ThN/t5nqBDIAAAAAgMK2K4mmTVyFjKilvWtoICNbhYyWRQUeGUycDTbYIAlhdHR0DCyLNziWL1+eTAAAk11dXV3yO00pEIMFAAAAAEYnW7uSqHHOhAYyMrYtac5SIUPLEkpI/CTpxhtvHKqrfR4TAJh6ampqkt9lSqE6RiSQAQAAAAAUNpBROy2E2sbJF8iYPkwgY1CJZJjqNzI22WSTkui7DgCUj6ampiSMEX+XKRUisgAAAABAYQMZTcWvjhHFliS11ZWhs7s3xwoZWcoe93aH0P7cuI0bxqvUdwxlxN7sK1asSKb4fV9fnycAAJgUKioqkvDFjBkzkqmUghj9BDIAAAAAgAIHMtYdtysaq2QsWdmRWyCjfmb2A3W2CmRQkuKNjTlz5iRTDGPEqVdFGABgglVWViaBjDiVMoEMAAAAAGDKBjKa66szBjJaMgUyaodp39DVXuCRweTTf9Mj3gABAKD4/NYFAAAAAEzJliX9FTIyWdHePXRhTUP2A3W1FnBUAAAAAhkAAAAAwGi1Lp0ULUsyydiypHq4QIYKGQAAQGGpkAEAAAAATNmWJXkFMqqqQ6iqzXwggQwAAKDABDIAAAAAgPIIZAzXtqSrrYCjAgAAEMgAAAAAAEajpyuE9uczr2uaM+GBjJasgYymzMtVyAAAAApMhQwAAAAAIH9ty7KvG8cKGc3ZAhmr86yQ0dlawFEBAAAIZAAAAAAAhWxXMkkCGdlbljRmXq5CBgAAUGAqZAAAAAAABQxkVITQMGvCW5ZkD2RkqZAhkAEAABSYQAYAAAAAkL/WpZmXN84Koap6wgMZbZ09oaunN49ARluBRwYAAJQ7gQwAAAAAoHAVMsaxXclwgYysVTJqmzJvrEIGAABQYAIZAAAAAED5BDKyVshoLeCoAAAABDIAAAAAgCkcyGgeJpDRklcgo72AowIAABDIAAAAAABGo3XppAhkNNVWharKijwqZDRmPpBABgAAUGBalgAAAAAAU7ZCRkVFRda2Jfm1LGkr8MgAAIByJ5ABAAAAAORvVbZAxpxxv5rZAhmZW5Y0ZT6IChkAAECBCWQAAAAAAPnp65s0FTKi5kJUyOhsLfCoAACAcieQAQAAAADkJ4YXutsnTSCjMC1LsjweAACAURLIAAAAAADyk606xgS1LGmur84jkNGY+SACGQAAQIEJZAAAAAAA+Wldmn3dJKqQ0dLenUeFjLYCjwoAACh3AhkAAAAAQGEqZFTVhVA3fXK3LKltynwQFTIAAIACE8gAAAAAAAoTyIjVMSoqJncgI1uFjO72EHp7CzwyAACgnAlkAAAAAAAFCmTMmZArmV8gozH7gWIoAwAAoEAEMgAAAACA/LQuzV4hYxIFMlryqZARaVsCAAAUkEAGAAAAAFC4liUToDlLIGNlR3fo6e3LI5DRVuCRAQAA5UwgAwAAAAAoyZYl0crVg6pk1DRlP5AKGQAAQAEJZAAAAAAAJdmyJFoxuG3JcBUyOlsLOCoAAKDcCWQAAAAAAIWpkDFt7qRqWZJ3IEOFDAAAoIAEMgAAAACA3PX2htC2dFK1LJleVx0qKnIMZFTVhFCZJcAhkAEAABSQQAYAAAAAkLv250Po651ULUsqKytCc31NboGMqKYx84G62go8MgAAoJwJZAAAAAAAY29XMoGBjKi5oTr3QEZttkBGe4FHBQAAlDOBDAAAAACgMIGMxolpWRLNaMhcIaOlvXvowpqGzAfpai3wqAAAgHImkAEAAAAA5K51cebl9TNCqK6ddIGM/FqWqJABAAAUjkAGAAAAAJC71qWTrl1J/oGMbBUy2go8KgAAoJwJZAAAAAAAY29ZMkkDGS15BTJUyAAAAApHIAMAAAAAKEAgY86EXsXmvCpkNGU+iEAGAABQQAIZAAAAAMCUb1nSXF+AliWdrQUeFQAAUM4EMgAAAACA0m1ZsjpTIKMx80FUyAAAAApIIAMAAAAAKNlARl4VMrraCjwqAACgnAlkAAAAAAAFaFkyZ3JWyGjvCr29fTkGMtqLMDIAAKBcCWQAAAAAALnpWh1CR8uUqpARsxirOrvTF9Y2ZT6IChkAAEABCWQAAAAAALlpy1IdYxIHMqIVbYPalmhZAgAAjAOBDAAAAAAgN61LpmYgo31wIKMx84ZalgAAAAUkkAEAAAAA5KY1S4WMiqoQ6mdO6FWcXl+ddV3LkEBGQ+YNBTIAAIACEsgAAAAAAMZWISNWx6ic2D81VldVhml1mUMZLau1LAEAAMafQAYAAAAAMPZAxiSQrW3J0JYlTZkP0NlWhFEBAADlSiADAAAAABhjIGPOpLiCzTkHMrK0LOluD6G3twgjAwAAypFABgAAAACQm9alk7xCRnWOgYzG7AfpXl3gUQEAAOVKIAMAAAAAyM2qxZM8kDHGChlRV3uBRwUAAJQrgQwAAAAAoDRaltRnC2R05xHIaCvwqAAAgHIlkAEAAAAAlEjLksyBjJbBFTJqm7IfRCADAAAoEIEMAAAAAGBkfX3DVMgopZYlKmQAAACFIZABAAAAAIxs9YoQegcFGyZbIKMxxwoZNY3ZD9LVXuBRAQAA5UogAwAAAAAYfbuSqGnO1KqQUVUTQmV15oOokAEAABSIQAYAAAAAMLJs7UomUSCjeZhARl9suZJLlQwVMgAAgAIRyAAAAAAARh/IqGkKobZpUlzB5vrMgYzu3r7Q1tmTWyCjs60IIwMAAMqRQAYAAAAAMPpAxiSpjjFcy5KoZfWgtiU1DZk31LIEAAAoEIEMAAAAAGBkrUszL29ad0oEMmLbkjRalgAAAEUmkAEAAAAAjKFCxhQJZLSpkAEAAIwvgQwAAAAAoCRaltRWV4aGmqocK2Rka1nSXoSRAQAA5UggAwAAAAAYfcuSaXMn1dXLViVjSCCjtinzAbraijAqAACgHAlkAAAAAAAl0bIkam6oHmOFDIEMAACgMAQyAAAAAICSCWRkq5DRsro7fUFNY+YDaFkCAAAUiEAGAAAAADC8nu4Q2p/LvK5pztQIZKiQAQAAjDOBDAAAAABgeG3Lsq+bZBUymrMEMnJvWdJehFEBAADlSCADAAAAABhe6+IpE8iYkXMgoynzATrbijAqAACgHAlkAAAAAADDa12SZUVFCA2zpmggI1uFDIEMAACgMAQyAAAAAIDhtS7NvLxxVghV1ZPq6jXX5xrIaMx8AC1LAACAAhHIAAAAAABGVyFjkrUrKUyFjPYijAoAAChHAhkAAAAAQMkHMlq0LAEAAMaZQAYAAAAAMMpAxpxJd+VmNGYOZHR094bVXT1rF9Q2ZT5AV1uRRgYAAJQbgQwAAAAAYHitS6d8hYwhVTKytixpC6GvrwgjAwAAyo1ABgAAAABQ8i1LohVpgYzG7AfpXl3gUQEAAOVIIAMAAAAAKJmWJc31uQYyslTIiLraCzwqAACgHAlkAAAAAAAl07KkvqYy1FZVjq1CRmxbAgAAMEYCGQAAAABAdp2t2QMKkzCQUVFREZqztC1pWZ1jIKNTIAMAABg7gQwAAAAAIP92JZM0kBHNaKjOuHxFW64tSwQyAACAsRPIAAAAAADyb1cyqQMZmStkrGjvzrFlSXsRRgUAAJQbgQwAAAAAIP8KGVV1IdRNn2KBjJQKGVU1IVRUZT6AChkAAEABCGQAAAAAAPkHMprmhFBRMSmvXHMugYw49mxVMlTIAAAACkAgAwAAAADIbnVL5uUNsybtVcupQkZUmy2Q0VaEUQEAAOVGIAMAAAAAyK5jZeblk7RdyXCBjJbVgwIZNQ2ZDyCQAQAAFIBABgAAAABQHoGMwRUytCwBAACKSCADAAAAAMiuo2XKBTKac21ZokIGAABQRAIZAAAAAEBZVMgYGshozHyArvYijAoAACg3AhkAAAAAQEkFMprrMwcy2jp7QldP78iBjM62Io0MAAAoJwIZAAAAAMAoAhnNU65CxpAqGVqWAAAARSSQAQAAAACUVIWMGY3ZAxktaYEMLUsAAIDiEcgAAAAAAEorkKFCBgAAMAkIZAAAAAAAJRXIaKqtClWVFWNoWdJepJEBAADlRCADAAAAAMisry+EjpYpF8ioqKjIWiUjLZBR25T5AF1tRRoZAABQTgQyAAAAAIDMOltjKiPzurrmSX3VmuurMy5vyalChkAGAAAwdgIZAAAAAEB+7UomeYWMKKcKGTWNmXfWsgQAACgAgQwAAAAAoOQCGc05BTJUyAAAAIpHIAMAAAAAKJsKGS3t3WtnVMgAAACKSCADAAAAAMisoyXLiooQaptKt2VJZ1uRRgUAAJQTgQwAAAAAIL8KGXXNIVRUlEAgY5iWJX19RRoZAABQLgQyAAAAAIA8AxmTu11J1DyWChmhL4TujuIMDAAAKBsCGQAAAABAyQUyxlQho79KBgAAwBgIZAAAAAAAZRPIaMmpQkYMZLQXYVQAAEA5qZ7oAVB+ent7wyOPPBLuu+++sGjRotDS0hIaGhrCrFmzwnbbbRd22223UFOT+R/Mo7Vs2bJw8803h0cffTS0traGpqamsOWWW4b99tsvzJ49u2Dn6evrC3feeWe4++67w+LFi5Nl6623Xthll13C7rvvHiomeW9VAAAAgDQdLSUXyFjZ0R16evtCVWVFCLXDBTJUyAAAAMZGIINx8cwzz4RLLrkkXH311eH6669PQhjZxHDG8ccfHz784Q8n4YyxuOeee8JnP/vZcNVVVyVBkMGqqqrCkUceGb7whS+EnXfeedTn6erqCt/97nfDd77znbBgwYKM22y00UbhQx/6UPjABz5Q8MAJAAAAQFGUYIWMaOXqrjCzsVbLEgAAoKi0LKHojj766LDhhhuG973vfeGKK64YNowRtbe3hwsuuCDsueee4WMf+1jo7Owc1XljQCIeI54zUxgj6unpSdbvscce4fvf//6ozvPUU0+Fl7zkJclYs4Uxoqeffjp89KMfDfvss8+w2wEAAABMGiUayFjR37ZEyxIAAKCIBDIoutgqJFMgIlaJ2HTTTZPQxI477hgaG9NLRMZ9vvGNb4QTTjghdHd353XOb33rW0k1isH7bbDBBkn4In5NFbeLlSu+973v5XWe2JbkkEMOCXfdddeQKh877LBD0oKlvr4+bd0dd9yR7LN06dK8zgUAAAAweQIZzWGya67PIZBRVRtCRZY/kWpZAgAAjJFABuNqvfXWSypJXHfddUmljCeeeCL8+9//Dvfdd19Yvnx5Uq1ip512StsnLjv99NNzPsctt9wSPv7xj6ctO/jgg5MgxMKFC8Ptt9+efI3nPeigg9K2O/XUU8Ntt92W87lOPvnk8Oijjw7Mx/BFbFsSwxb3339/eOCBB5LvY0AkNZjx8MMPh7e97W05nwcAAABgQkzhChnT66tDRcUIgYy4QbYqGV3txRscAABQFgQyGBexAsZFF12UtO342te+Fg499NAhlSNixYxXvepVSSDiqKOOSlsXK1c89NBDOZ0rBj5iK5J+8Zh//etfw+677562XazMcfXVV4cjjzwyrVJG3D8Xcd8///nPaeOP5/ngBz+YVu2jqakpfPjDHw5/+ctfkm36XXnlleHvf/97TucCAAAAmBBTOJBRWVkRptdVDx/IiLIFMjrbijQyAACgXAhkUHTnnntuuOeee8Lxxx8fqqsz/yM4VQxq/O53vwsbbbTRwLKurq7wy1/+csR9Y0AiVsjoN3v27OT8tbW1GbePy3/+858n2/W78cYbwzXXXDPiuT7zmc+kzccqHgceeGDW7WM1jtNOOy1t2ac//ekRzwMAAAAwYTpapmwgI5rRmLltSUt7SpvbmobMO2tZAgAAjJFABkV39NFHh8rK/F5qsarEBz7wgbRlsfrESH72s5+lzb/3ve8N66677rD7zJ07N5xyyinDHmew2GIltbVJHG8ulTViK5W4bb8YHnnwwQdH3A8AAABgQkzhChnRjIaa0VfI0LIEAAAYI4EMJq0DDjggbX7+/PnDbt/R0TEktPG2t70tp3MN3i5W2ujs7My6/eWXX542f+KJJ4bp00f+Q0Tc5oQTTkhbdtlll+U0RgAAAIBx1dc3TCCjuYQCGSpkAAAAxSGQwaS1zjrrpM2vWLFi2O2vv/760NraOjC/zTbbhE033TSnc2222WZh6623HphfuXJluOGGG7Ju/8c//jFt/ogjjgi5Ovzww9Pmr7rqqpz3BQAAABg33R0h9KYEF6ZghYzmehUyAACAiSOQwaS1YMGCtPnZs2cPu/3dd9+dNr/vvvvmdb799ttv2OP16+vrC/fee++ozzX4PPfcc09yTAAAAIBJJVt1jCkUyMhWIaMltUJGbbaWJW1FGhUAAFAuBDKYtG666aa0+Re96EXDbv/ggw+mzW+//fZ5nW/w9oOP1+/JJ58MbW1r/0He1NQUNtlkk5zPE6t2NDau/Yd+rOrx1FNP5TVWAAAAgKLraCnZQIaWJQAAwHgQyGBS6unpCeeff37asle+8pXD7vO///0vbX7jjTfO65yDtx98vEKdJ59zAQAAAEyYEqiQ0ZytQsbqlAoZNdkqZLQXaVQAAEC5qJ7oAUAmP/nJT8Jjjz02MF9TUxPe8IY3DHuxFi9enDa/0UYb5XVxN9xww2GPV6jz9J8rNYSR7Vz5isdZsmRJXvs88sgjBTk3AAAAUGJKIJChQgYAADCRBDKYdB599NFw+umnpy1773vfO2LwYdWqVWnzsZVIPgZvP/h4hTpPPufK19lnnx3OPPPMghwLAAAAKHPZAhk1TSFUVoXSCWSokAEAABSHliVMKm1tbeH4448PK1eu/Qf/pptuGj7/+c+PuO/gUEN9fX1e525oaBj2eIU6Tz7nAgAAAJh0gYwpUh1j2JYl7V2ht7dv+EBGZ1sRRwYAAJQDgQwmjb6+vnDSSSeFu+++e2BZdXV1+PWvfx2mTx/5H/qrV69Om6+trc3r/HV1dWnz7e3tRTlPPucCAAAAmDAdLVM+kJGtQkbMYqzq7F4zU5P+wZkBXQIZAADA2GhZUuI+9KEPhe9+97tFP88ZZ5wRPve5z43pGKeeemq4+OKL05bFse+333457R8rVcQKG/06OzvzOn9HR8eQ42U7T6p8z5PPufJ1yimnhBNOOCGvfR555JFwzDHHFOT8AAAAQAkpgQoZ2QIZ0Yq2rtBcX6NlCQAAUDQCGUwKX/nKV8K3v/3tISGPGDDI1bRp09ICGYMrWYxkcJWKeLxs50mV73nyOVe+5s6dm0wAAAAAY1bqgYz2rrBx/EaFDAAAoEi0LGHC/eQnPwmf+MQn0pZ94AMfyLvixuBQQ2tra177D94+10BGvufJ51wAAAAAE6YEAhnN9dk/j9ayumvNNzWNmTfo0mIWAAAYGxUyStyRRx4Z5syZU/TzHHjggaPa7ze/+c2QKhgnnXRS+M53vpP3sWJliMcee2xg/umnn85r/wULFgw5XrbzpMr3PPmcCwAAAGDyBTKaw1RRXVUZptVVh1Ud3UPWtbS/EMiozRbIWFuJFQAAYDQEMkrc4YcfnkyT0eWXX56EL3p7eweWHXfcceHcc88NFRUVeR9vm222Cf/6178G5ufPn5/X/oO333bbbbOeJ9VTTz2V13ky7ZPtXAAAAAATpgQqZPS3LckUyIgtS8JILUv6+kIYxd+pAAAAIi1LmBDXXntteO1rXxu6u9f+Y/hlL3tZUjGjqqpqVMccHGp44IEH8tr/wQcfHPZ4/TbddNPQ0NCQ1n7kySefzPk8cdu2trWfsGhqagobb5x0LAUAAACYPDpaSiKQMT1L25K1gYwsFTL6ekPo6SziyAAAgFInkMG4u/nmm8PRRx8dOjo6BpYdcMAB4Q9/+EOora0d9XF33XXXtPlbbrkl73ENd7x+sXrHzjvvPOpzDT5PPNZoKoIAAAAAFFUJVcjIZMQKGZG2JQAAwBgIZDCu7rzzznDkkUemVYjYc889w1VXXZVWdWI0Dj744KTaRL+HHnoo58oVTzzxRHj44YcH5qdPn54cL5ujjjoqbf6aa67JeZyDt33Vq16V874AAAAA46ZsAhlZKmREXe1FGhUAAFAOBDIYN7GFSGxLsmLFioFlO+64Y/jLX/4Smpubx3z8+vr6cMQRR6Qt+/nPf57TvoO3e/nLXz5stY5Xv/rVafMXXXRRWLVq1YjnWblyZbJtqlgtBAAAAGDqBDLG/necyRDIaGnvHjmQ0bn2Q0UAAAD5EshgXDz++OPh8MMPD0uXLh1YttVWWyXVImbPnl2w87z97W9Pm//hD38YlixZMuw+ixcvDmefffawxxksthnZa6+9BuZjGONrX/vaiOOL27S2tg7M77333mH77bcfcT8AAACAcVc2FTK0LAEAAIpDIIOiW7hwYTjssMOSr/022WSTcN1114X111+/oOeK7VBiyKHfsmXLknBFV9cL/8AepLOzM1kft+t3wAEHJJU8RvL5z38+bf4rX/lKuPHGG7Nuf8MNN4SvfvWracvOOuusEc8DAAAAMO56ukLobi+TQIaWJQAAQHFUF+m4kGhra0vaiDz22GMDV6Sqqiqcfvrp4aGHHkqmfOy///5Ja5LhfP3rXw8HHXRQ6O3tTeavvPLKZAzf/OY3w+677z6w3R133BFOPfXUJCiROrZcKl30tzWJx7366quT+Rj6iEGOGMx45zvfGRob1/xjPlbE+OlPfxo+8YlPpAVDXvnKV4aXvvSleT1+AAAAgAmtjjEFAxnNWVuWvPB3muq6EEJFCKFv6EZdWpYAAACjJ5BBUcV2IP/5z3/SlvX09IRTTjll1K1PNttssxFDG1/+8pfDaaedNrDs+uuvD3vssUeYN29e2GCDDZJqHYsWLRqybwxjpFbYGMn5558f9tlnn2Rc0erVq8OHPvShJHyxxRZbhL6+viSMEpen2nLLLcN5552X83kAAAAAxlUJBTJGrJBRUbGmSkbX2jazA7qyVAkBAADIgZYllKSPf/zj4Rvf+EZS8SJVDGLEyhiDwxhxu29/+9vhIx/5SF7nWW+99cLf//73sMsuu6Qtb29vT4IoDzzwwJAwxq677prss+666+b9uAAAAADGRUdL9nV1zSUTyIgfpknUZmlbokIGAAAwBgIZlKzYjuT2228PRx55ZKiszPxSj8uPOuqoJKQRK1uMxqabbhpuu+228NWvfjWpwJFNXBcrcNx6661h4403HtW5AAAAACa+Qsa0kmhZ0t3bF9q7etbM1DRk3lkgAwAAGAMtSyiq2F5k4JMGEyBWo7jqqqvC0qVLwz/+8Y+kfUhra2toampK2obst99+Yc6cOWM+T21tbVKV46Mf/WgS7rjnnnuSdi3R3Llzk3HsvvvuWYMhAAAAAFMikFFVF0J1XSiFChn9VTIaa6vXtCzJRMsSAABgDAQyKAsxdHHMMccU/TwxcLHXXnslEwAAAEDJBTLqpoepZqRAxgYzGlTIAAAAisLH9QEAAACAdB0tJRPIaG7I/pm0FW1da75RIQMAACgCgQwAAAAAoGQrZNRVV4X6msqsFTKGDWR0thZxZAAAQKkTyAAAAAAAcgxkNE/JK5WtbcnaQEZD5h272os4KgAAoNQJZAAAAAAAJVshY7hARsvq7jXfaFkCAAAUgUAGAAAAAFCWgYyRK2S0FXFUAABAqRPIAAAAAADSdbSURSCjZSCQ0Zh5Ry1LAACAMRDIAAAAAABKukJGc/0IFTJqswUyVMgAAABGTyADAAAAACjtQIaWJQAAwAQQyAAAAAAAcgxkNJdUy5KBChlalgAAAEUgkAEAAAAA5BbIqC+tQEbLQCCjIfOOWpYAAABjIJABAAAAAJR0yxIVMgAAgIkgkAEAAAAArNXbE0LnqsxXpNwCGZ1tRRwVAABQ6gQyAAAAAIC1soUxpnAgozlLIKOjuzes7uoZvmVJX19xBwcAAJQsgQwAAAAAYOR2JVFdc0lVyIhaYpWMbBUy+npC6HmhigYAAECeBDIAAAAAgBwDGdNLLpCRtC3JViGjv0oGAADAKAhkAAAAAABlHsjIUiEj6movzqAAAICSJ5ABAAAAAKzV0ZLlL4nVIVTXT8krVV9TGWqrMv8ptGV1Vwi1wwUyVMgAAABGRyADAAAAABi5QkasjlFRMSWvVEVFRWjOUiVj5AoZAhkAAMDoCGQAAAAAALkFMqaw5obqjMtXtMVARkP2HbUsAQAARkkgAwAAAADIIZDRPKWv0oysFTK6h2/FokIGAAAwSgIZAAAAAEDJV8jIHsjoWtOKJVvbEhUyAACAURLIAAAAAADKO5ARZQtkdLYWcVQAAEApE8gAAAAAANbqaCmrQEbL6hECGSpkAAAAoySQAQAAAACsVbYVMhoy7yiQAQAAjJJABgAAAABQ8oGM5vosFTJGDGS0FXFUAABAKRPIAAAAAAByCGQ0l3iFDC1LAACAwhLIAAAAAABKv0LGSIGM2myBjNYijgoAAChlAhkAAAAAQMkHMrJVyGjr7AldPb3DtCxpL+7AAACAkiWQAQAAAACs1dFSVoGMqCVWydCyBAAAKDCBDAAAAABgjb6+0q2Q0Zg9kJG0LclaIaOteIMCAABKmkAGAAAAALA2fNDXm/lq1DVP6avUXF89QiCjMfNKLUsAAIBREsgAAAAAANbIVh2jBCpkTKurDlWVFfkHMjpbizswAACgZAlkAAAAAAAlH8ioqKjIWiVj+JYl7cUdGAAAULIEMgAAAACANTpaSjaQEc1oqMm4vEXLEgAAoAgEMgAAAACAESpkVIRQ01S6gYzV3cNUyGgr7qAAAICSJZABAAAAAAwfyIjVMSqn/p8Sm7MEMta0LGnMvJOWJQAAwChN/X9FAQAAAADFD2SUgKyBjLauEGqzBTJUyAAAAEZHIAMAAAAAKItAxoxhK2RoWQIAABSWQAYAAAAAsEZHSxkHMrJUyOjtDqGnq7gDAwAASpJABgAAAACwhgoZmWlbAgAAjIJABgAAAACwRlkHMrJUyIi62os3KAAAoGQJZAAAAAAAZR3IaFk9QiCjs7V4gwIAAEqWQAYAAAAAMEIgo7kkrlBzfeZAxsrV3aGnuiH7jipkAAAAoyCQAQAAAACUdYWMaGVPdfYdBTIAAIBREMgAAAAAANboaCnpQEZTXVXWde192cMaoautOAMCAABKmkAGAAAAAFAWFTJqq7P/ObSzJ4SQrW2JChkAAMAoCGQAAAAAAGuUcyCjuzeE2sbMK7taizcoAACgZAlkAAAAAAAjBDKaS+IK1VVlb1nSEQMZNdkCGe3FGxQAAFCyBDIAAAAAgBC6O0Lo6SzfChk9MZChZQkAAFA4AhkAAAAAQPbqGOUSyOgeLpDRVrxBAQAAJUsgAwAAAAAIoaOl5AMZVZUVyZQ9kKFlCQAAUDgCGQAAAABAWVTIiGqrKvMPZHS2FndQAABASRLIAAAAAACGD2TUllAgI0vbks6e4VqWtBd3UAAAQEkSyAAAAAAAsgcyYtWIqurSD2RoWQIAABSYQAYAAAAAkD2QUULtSkZuWZKtQkZbcQcFAACUJIEMAAAAACCEjpayCGTUZamQ0dHds6YaSCZalgAAAKMgkAEAAAAAlE+FjKyBjN4QarMFMlqLOygAAKAkCWQAAAAAAGUTyMhWIaOzZ7iWJe3FHRQAAFCSBDIAAAAAgGECGc1lUSGjM1bI0LIEAAAoIIEMAAAAAKBsKmQMH8jIViGjrbiDAgAASpJABgAAAABQPoGMKhUyAACA8SGQAQAAAACE0NFS3hUyeoZpWdKpQgYAAJA/gQwAAAAAoHwqZFRXDdOyJEsgQ8sSAABgFAQyAAAAAIDyCWQM27KkIfNOvV0h9HQVd2AAAEDJEcgAAAAAAIYJZDSXRcuSjp5hAhlRV3vxBgUAAJQkgQwAAAAAoGwqZNRlCWQM27IkEsgAAADyJJABAAAAAOWupzuErrayCGTUDhfIqB0ukNFavEEBAAAlSSADAAAAAMpdZ5bqGKUYyKhSIQMAABgfAhkAAAAAUO5Wt5RPICNbhYye2LKkIfuOWpYAAAB5EsgAAAAAgHLXMVyFjOZQNi1LqocLZGRp6QIAAJCFQAYAAAAAlLthAxll1LKksjKE6vrMO6qQAQAA5EkgAwAAAADKXbZARlVtCNV1oWxalkQ1jZl37Gwt4qgAAIBSJJABAAAAAOWuo6UsqmOM2LJkuECGChkAAECeBDIAAAAAoNxlq5BRgoGMuiyBjI6BQEZD5h272oo4KgAAoBQJZAAAAABAuSujQEZtVbYKGT0jBDLaizgqAACgFAlkAAAAAEC5yxrIaA5l07KkR8sSAACgsAQyAAAAAKDclVOFjGyBjO7e0NfXF0JtY+Ydu1qLOzAAAKDkCGQAAAAAQLkro0BGXXVVxuW9fSF0x//UZAtkaFkCAADkRyADAAAAAMpdR0so9woZ/VUyQk1D5pVdbcUbFAAAUJIEMgAAAACg3JVRhYzaqtEGMlTIAAAA8iOQAQAAAADlrpwCGcNVyOiJgQwtSwAAgMIQyAAAAACAcpc1kNEcSk3diC1LsgQyOluLNygAAKAkCWQAAAAAQLlTIWPNZRgukKFlCQAAkCeBDAAAAAAod2VUIaO2aqQKGQ2ZVwpkAAAAeRLIAAAAAIBy1tsbQme2QMb0UGpqh2tZ0jNcIKOteIMCAABKkkAGAAAAAJSzzlXZ15VbIEPLEgAAoIAEMgAAAACgnGVrV1KiLUuqKytCRcUwgYzaxswru1qLOi4AAKD0CGQAAAAAQDkbNpBRehUyKioqQm1V5j+Ldvb0hFCTLZDRXtyBAQAAJUcgAwAAAADKWZkFMoZrW7KmZUlD5p16OkPo6S7uwAAAgJIikAEAAAAA5ayjJfPyiqrs4YQpri5LIKNjuEBG1K1KBgAAkDuBDAAAAAAoZ9kqZMTqGBUVoRRlbVmSBDKytCyJtC0BAADyIJABAAAAAOUsayCjOZSqrC1LekYIZHS2Fm9QAABAyRHIAAAAAIByNlyFjHILZKiQAQAAFJBABgAAAACUM4GMQYGMhuzXSssSAAAgDwIZAAAAAFDOOlrKr0JG1WgDGW3FGxQAAFByBDIAAAAAoJypkDGgs6c3hMqqEKrqMl8rFTIAAIA8CGQAAAAAQDkry0BGVfYKGckGjZl37Got4qgAAIBSI5ABAAAAAOWsHAMZWVqWdPQHMmqyBTLaizgqAACg1AhkAAAAAEA5yxrIaA6lqq5mpEBGQ+Ydu9qKOCoAAKDUCGQAAAAAQDkrwwoZdVkqZHSOGMhQIQMAAMidQAYAAAAAlLMyDGTUVmcJZPRoWQIAABSOQAYAAAAAlLOOlszLyzGQ0d2z5puaxsw7drYWcVQAAECpEcgAAAAAgHLV11eeFTJGbFmSJZChZQkAAJAHgQwAAAAAKFcxYND3QlWIweqaQ/m2LGnIvGNXWxFHBQAAlBqBDAAAAAAoV9mqY5Rty5KRAhntRRwVAABQagQyAAAAAKBcCWSk0bIEAAAoJIEMAAAAAChXHS3lWSGjKvOfRTv6K2TUNmbesau1iKMCAABKjUAGAAAAAJSr4Spk1E4LpaouW8uSnv6WJdkCGVqWAAAAuRPIAAAAAIBylS2QUTs9hMrS/dNhbbZARn+FjJqGzDt2tRVxVAAAQKkp3X9VAQAAAACjC2SUcLuSsQUyVMgAAID/Z+/O4+Ou6/yBv5NJJm3TFkpb5C4gotzsgsIilEMRxQN0F8UTpILH/lB0UVEWBYtyrID3Cgoo4Cqiq8t6wqLlEJFDBOQ+Wi6BchRKDzLJJL/H9ysTkjLfZHJMZibzfD4e88h8v/M9PpmUPzK88n5ROYEMAAAAAGhWzRrIyOXK7ldZAgAAjCeBDAAAAABoVl3LmzOQMeyEjGnlTyysrOKqAACAyUYgAwAAAACaVbNOyBhtIENlCQAAMAICGQAAAADQrJo1kJEr/7FoT29f9Pb2RbRPLX9isSuit1jdxQEAAJOGQAYAAAAANKvMQMbMaMYJGYlCsTd7QkbClAwAAKBCAhkAAAAA0KyadEJGxxCBjK6ktiRrQkZCIAMAAKiQQAYAAAAANKsmDWQMOSEjCWTkh5qQsbI6iwIAACYdgQwAAAAAaFZdy5szkJFTWQIAAFSfQAYAAAAANCsTMspPyBiysmRVVX8kAADA5CGQAQAAAADNqkkDGR1DVJZ09RQj2oYKZKyuzqIAAIBJRyADAAAAAJpVZiBjZkxm+eEmZOTaInL58gcIZAAAABUSyAAAAACAZtTTFVHsasoJGcMGMhLt0zIOWFmlVQEAAJONQAYAAAAANKOuFdmvTfZARm4MgQwTMgAAgAoJZAAAAABAM+pa3rSBjJaWlsxQRlexFMiYWv7k7lVVXBkAADCZCGQAAAAAQDPqerZpAxlD1ZaYkAEAAIwXgQwAAAAAaEYCGcMEMrImZKyu4g8FAACYTAQyAAAAAKAZZQUy2qZG5NpjssuqLOkPZOSnlT+xe2UVVwUAAEwmAhkAAAAA0IyyAhlNUFcyZGVJsTQhIyuQYUIGAABQGYEMAAAAAGhGXcvL72/2QMawlSWrqrgqAABgMhHIAAAAAIBm1OwTMnKjDWSYkAEAAFRGIAMAAAAAmlGzBzJUlgAAAFUmkAEAAAAAzSgzkDEzmjmQ0dU/IWNa+RMLK6u4KgAAYDIRyAAAAACAZtTkEzI6siZkDBfIUFkCAABUSCADAAAAAJpR1/KmDmTkc8MFMqaWP7F7VRVXBQAATCYCGQAAAADQjJp8QkZWZUmhWBwmkLG6iqsCAAAmE4EMAAAAAGhGAhll3xaVJQAAwHgRyAAAAACAZtTsgYzhKkvy08qf2L2yiqsCAAAmE4EMAAAAAGhGmYGMmdHclSXPBzLaswIZKksAAIDKCGQAAAAAQDNq9gkZbcNMyGifWv7Enuciep8/BgAAYAgCGQAAAADQbHqL2dUbTR7I6OoZZkJGoseUDAAAYHgCGQAAAADQbLqWZ7/WJIGMjrbcMIGMjAkZCbUlAABABQQyAAAAAKDZZNWVNFUgY7jKkiEmZBQyposAAAAMIJABAAAAAM1myEDGzGgG+dwYAhkmZAAAABUQyAAAAACAZmNCRuSzJmQUK6ksWVWlHwwAADCZCGQAAAAAQLPJCmS0tke0dURTBzJ6KglkrK7SqgAAgMlEIAMAAAAAmk3X8vL7O2ZEtLREMxi2siTX/veASjkCGQAAQAUEMgAAAACg2WRNyEgCGU1i2MqS9KBp5U/uXlmlVQEAAJOJQAYAAAAANJvMQMbMaBbDVpYk2rMCGSpLAACA4QlkAAAAAECzMSGjwkDG1PLvX/eqKv1gAACAyUQgAwAAAACajUBGdOSyK0v6+vr+vmFCBgAAMAYCGQAAAADQbLqWl9/fMSOafUJGKZSRMiEDAAAYA4EMAAAAAGg2JmQMHcgo1ZZkTcgoqCwBAACGJ5ABAAAAAM1GIGNsgYzu1VX6wQAAAJOJQAYAAAAANJvMQMbMaBb5nMoSAACgugQyAAAAAKDZmJBhQgYAAFB1AhkAAAAA0GwEMioMZEwtf0D3qir9YAAAgMlEIAMAAAAAmo1ARnTkctlvTymQkZ9W/gCBDAAAoAICGQAAAADQTHp7swMZU2ZGsxhyQkaxNCEjK5CxukqrAgAAJhOBDAAAAABoJt0rI6Kv/GsdM6JZqCwBAACqTSADAAAAAJpJ1nSMREfzTMjItbakj3IKpcoSEzIAAIAxEMgAAAAAgGYyZCCjeSZkJPK51mECGVPLn6iyBAAAqIBABgAAAAA0E4GMfh3t5T8e7RpuQkYhqX0BAAAYmkAGAAAAADSTruXl97e0ZgcQmm1CRrH49ycqSwAAgDEQyAAAAACAZpI1ISOpK2lpiWaSbxtlZUnP6oje548BAADIIJABAAAAAM0kM5AxM5rN8IGMISaG9DxXpVUBAACThUAGAAAAADSToSZkNJmsypKu4SZkJLpXV2lVAADAZCGQAQAAAADNRCCjX0fWhIzi84GM/BATMrpXjvdPBgAAmGQEMgAAAACgmXQtL7+/GSdkjKWyxIQMAABgGAIZAAAAANBMTMgYQSBjqMqSVeP9kwEAACYZgQwAAAAAaCYCGf3yORMyAACA6hHIAAAAAIBmkhnImBnNJnNCRvH5CRm59ojWtvInm5ABAAAMQyADAAAAAJqJCRn98m25oStLEu2d5d/HgsoSAABgaAIZAAAAANBMBDIqryxJtE8t/z52rx7vnwwAADDJCGQAAAAAQDPpWl5+f8eMaDZZlSVdpcqSIQMZJmQAAABDE8gAAAAAgGZiQka/jrZKJmRMK/8+mpABAAAMQyADAAAAAJpFX59ARgUTMiqrLDEhAwAAGFpbNIDly5fH9ddfHzfddFMsWbIkHnzwwXjmmWdi5cqV6eudnZ2x1lprxSabbBKbbrppbL/99rHzzjvHzJkza710AAAAAKgfPc9F9PaUf62j+T5Ly+cqCGTksyZkCGQAAAANGsi46qqr4he/+EX85je/iVtuuaXsMX1Joj8iWlpayr6+3XbbxX777RdvfOMbY/78+VVdLwAAAAA0bF1JomNGNJvMCRlFlSUAAMAkC2Tcf//9ceaZZ8Z//dd/pVMwBoYuSgaGL8oFMQYef/PNN6dhji9/+cux0UYbxbve9a744Ac/mE7RAAAAAICmI5AxiMoSAACgmspHwCfYH//4x3jb294WW2yxRZxyyinxwAMPZE6/SPYP9RiodG6yPwl4nHrqqfGyl70svVdyTwAAAABoKl3Ls19rxgkZlVSWtGdVlqyu0qoAAIDJoqYTMm644YY47rjj4re//W1/cGJgACPZnjFjRuy4445p/cgrXvGK2GCDDWL99deP6dOnx7Rp09JjVq9eHStWrIi//e1v6eOOO+5IJ2PcdNNN8eyzg8cwFovF+J//+Z/0kdSZLFy4MHbaaacJ/94BAAAAoK4mZOSnR7OprLJkavmTu1dVaVUAAMBkUZNAxqOPPhrHHHNMXHDBBS+abJEEMnbbbbc44IADYu+9945/+Id/iNbW0Q3y6O3tjT//+c+xaNGiNICRTMUYeL8kCHLJJZfEe97znjj55JNjvfXWG7fvEQAAAAAaJpCRhDFac9FsKqssyZiQURDIAAAA6jCQseWWW8bKlSsHBTG22mqrWLBgQbz73e+Ol7zkJeNynyTIsfPOO6ePo48+OpYuXZqGQM4555y47bbb+sMZ559/fvz85z+Pp59+elzuCwAAAAANFchowrqSREdGIKOrp/jChsoSAABglEY3emKMknqRUj3J/vvvn06wuPXWW+MTn/jEuIUxyll33XXTe/z1r39N75ncO1lDspY1q00AAAAAYNIRyKgwkKGyBAAAaNBARiKpJLnpppviF7/4RcyfP3/C75/cM7n3zTffHG95y1sm/P4AAAAAMOG6lpff36QTMsZUWdK9ukqrAgAAJouaVJZcffXVseuuu0Y92GabbdK6kj/96U+1XgoAAAAAVJcJGYPkc7myb1Oh2Ns/4Tfap5Z/LwUyAACAepyQUS9hjIF22WWXWi8BAAAAAKpLIKOiCRl9fRE9vX3PH5Q1IWPleP90AACASaZmlSUAAAAAQL0EMmZGM8oKZAyqLVFZAgAAjJJABgAAAAA0CxMyBsnnKglkZFWWrPr7KA0AAIAMAhkAAAAA0CwEMiqfkFEcZkJGoue58frJAAAAk5BABgAAAAA0i67l5fd3zIhm1NE2hgkZie7VVVgVAAAwWQhkAAAAAECzMCGj4gkZXT0VTMgorByvnwwAADAJtUUDKRaLcf3118eNN94YDzzwQCxfvjxWr14dfSPsamxpaYmzzz67ausEAAAAgMYKZMyMZpTPVTIhY4hAhgkZAABAowcynnnmmTjxxBPj/PPPj8cff3xM10rCGwIZAAAAADQlEzIqnpBRKFZSWbJqvH4yAADAJFT3gYw//vGP8da3vjUNYgychJGEKgAAAACACvUUInqeK/9ax4ymfBuHDGSYkAEAAEzmQMatt94a++23X6xYsaI/hFEKZYy0pgQAAAAAmlrh75+xldWkgYy21pZI/u6r3EeN/YGMXHtESy6ir/jig0zIAAAAGjWQ8ZGPfCQNY5SmYSQhjNe97nVxwAEHxHbbbRezZ8+OadOG6HAEAAAAAP6ua3n2O9GkgYzkc8d8rjW6SuGLAQrF5wMYyWeT+c7y759ABgAA0IiBjHvvvTeuvPLK/qkYs2bNip/+9Kex11571XppAAAAANB4up7Nfq1jZjSrpLakbCBj4L72qRmBjNVVXh0AANDIsksSa+wPf/hD+jUJYyShjDPPPFMYAwAAAACqEcjIT2/a97WjrfxHpF1rBjLKMSEDAABoxEDGo48+2v88mY7xz//8zzVdDwAAAABMykBG25SItnw0q6SypJzBEzIyapNNyAAAABoxkJHL5dKvyXSMzTffPP0KAAAAAIxzIKNjRlO/pUllSTmFogkZAADAJA1kbLLJJv3Pn3vuuZquBQAAAAAaXtfy8vsFMkY/IaOwajx+MgAAwCRVt4GM3XbbLZ2K0dfXF0uWLImenp5aLwkAAAAAGpcJGSObkKGyBAAAmKyBjA033DBe+9rXps9XrlwZv/rVr2q9JAAAAACYhIGMmdHMOtr+Xp28pq5BgYyp5U/uNiEDAABowEBG4pRTTon29vb0+ac//ek0mAEAAAAAjIIJGWXlcyZkAAAATRjI2HHHHeNb3/pW+vyuu+6KN73pTbF06dJaLwsAAAAAGo9AxsgqS4omZAAAAJM4kJFYsGBBXHDBBTF16tS4/PLLY5tttonjjz8+brnllujr66v18gAAAACgMXQtL7+/Y0Y0s8xAxsDKkvy08ierLAEAAIbQFg3gne98Z+yyyy7x5je/OW6//fZYuHBh+kjqTNZZZ52YMmXKiK7X0tIS9957b9XWCwAAAAB1x4SMEQUyugYGMtqzAhmrx+MnAwAATFINEcj4wx/+EJ/4xCfijjvuSMMUpckYhUIhHn300RFfL7kGAAAAADQVgYyyOnIVTMhon1r+ZBMyAACARg5kfOMb34iPf/zj0dvbmwYxkjDFWAIVak4AAAAAaEqZgYyZ0cwyK0uKJmQAAACTOJDxy1/+Mj72sY8NCmKUAhX5fD7WWmutmDYtY1wgAAAAAPACEzJGFsjoKb6wYUIGAAAw2QIZRx11VH8YI/m67rrrptUlb3nLW2LLLbeM1tbyvywBAAAAAGsQyCgrX1FlScYfhRVW+WcGAAA0XiDjmmuuiXvvvbe/nmT77bePyy67LGbPnl3rpQEAAABAY+ktRhRWlH+tY0Y0M5UlAABAtdTtiIk///nP6ddSRcl3v/tdYQwAAAAAGM/pGAmBjAomZEwt/951r0o+wPRvEgAAaKxAxjPPPNP/fMMNN4ydd965pusBAAAAgMkZyJgZzSxzQkYllSXRF9HTVZ2FAQAADa9uAxlz585NvyaVJRtssEGtlwMAAAAAjcuEjEz5XPmPSLsqmZBRmpIBAADQSIGMjTfeuP/58uXLa7oWAAAAAGhoAhmZOrImZBQHBDLyndkXEMgAAAAaLZCx++67x/Tp06Ovry/uvfdeoQwAAAAAGO9ARmtbRNuUpn5fK6ssGWpCxuoqrAoAAJgM6jaQ0dnZGQcddFD6vKenJ84777xaLwkAAAAAGlNXxgTajhlJZ3A0s7EHMlSWAAAADRbISCxcuDBmz56dPv/85z8fd955Z62XBAAAAACTZ0JGEshocvlcbvjKkvZp2RcwIQMAAGjEQMYGG2wQF198cay11lqxbNmy2HvvvePSSy+t9bIAAAAAYJIEMmZGs6toQkYuH9GS8VGqCRkAAECGtqhjDzzwQGy44Ybx4x//OI444ohYsmRJvP71r4/ddtstrTPZaaedYu7cuTFlysh7LjfZZJOqrBkAAAAA6o4JGWMLZCS1Lu2dEYUywZaCyhIAAKABAxmbbrpptAzosEye9/X1xdVXX50+Riu5Tk9PzzitEgAAAADqnEBGpnyufCCjp7cvenv7orX1+c8n26eWD2SoLAEAABoxkFGShDBKwYzS12QfAAAAAFCBruXl93fMaPq3L2tCRqJQ7I0prbkXAhnlqCwBAAAyZP+2UWeSAMbABwAAAABQIRMyMnUMEcjoGlhb0j6t/EEmZAAAAI04IeOQQw6p9RIAAAAAoPEJZIwykFFMkhh/3zAhAwAAmEyBjHPPPbfWSwAAAACASRzImBnNbsjKkoETMvKd5Q9SWQIAADR6ZQkAAAAAMEomZIw9kJE5IWO1f5YAAEBjTch49NFH49prr+3f3n777WPTTTet6ZoAAAAAoCEJZGTK54YIZBQrCWSsGstPBgAAmMTqNpDx3//933HkkUf2b99yyy01XQ8AAAAANCyBjHGYkDGt/EEmZAAAAI1WWfL0009HX19f+lhvvfVi6623rvWSAAAAAKDx9PVFdC0v/1rHjGh2Y68sMSEDAABosEDG7Nmz068tLS2x4YYb1no5AAAAANCYCiuTVEb51zrWimY3ZGVJJRMyCgIZAABAgwUyNthgg/7nK1cmvzQCAAAAAONWV5IwISP9g7CsUEZXUWUJAAAwCQMZO+20U7S2tqaVJffff38UCoVaLwkAAAAAGo9AxqhrS1SWAAAAk3ZCxl577ZU+X716dfzyl7+s9ZIAAAAAoPEIZIxTICOjsqR79Wh/MgAAwCRXt4GMxMc//vH+55/5zGdi1Sp9jAAAAAAwIl3LM15oich3ejOTQEZGZYkJGQAAwKQNZLzxjW+Mf/3Xf01rS+6+++50e+nSpbVeFgAAAAA0/oSMjpkRLS0TvZrGmpBRHDAhIyu80u2PyAAAgAYMZCS+/vWvxzHHHBMtLS1xxRVXxNZbbx3HH3983HHHHbVeGgAAAAA0cCBjxkSvpMErS6aWP1llCQAAkKEt6tg+++zT/3z27Nnx+OOPx1NPPRULFy5MHzNmzIh58+bFzJkzo729veLrJuGOyy67rEqrBgAAAIA6IpBR/cqSvj7TRgAAgMYKZCxatCgNT5SUnicVJonly5fHLbfcMuiY4STnjuR4AAAAAGhoAhnjU1nSPq38yX29EcVCRFvHaH9CAADAJFX3lSXlJIGKgQ8AAAAAIEPX8vL7VZYMG8joqmRCRmlKBgAAQCNNyBg4DQMAAAAAGAUTMobVkTUhY1AgozP7AoVVEVNnjeanAwAATGJ1Hcjo7R3wCw8AAAAAMHICGcPK5yoJZAw1IWO1f5kAAEBjBTJgvDz77LPxhz/8Ie66665Yvnx5TJ06NebNmxe77bZbbLDBBuP6Rt96661xww03xCOPPBLFYjFmz54d2267beyyyy7R1uY/OQAAAKBeAhkzJ3olDVdZUigWX9hQWQIAAIyQ/ztMXU1E2WOPPeLqq68etH/PPfeMRYsWjeqaixcvjs997nPx4x//OAqFwoteb2lpSa9/wgknxPz588dUrXPuuefGKaeckoY+ykmCGR/+8IfjmGOOic7OIUZcAgAAAIwnEzJGH8gYNCFjWvYFTMgAAADKKP+bBtTAN77xjReFMcYiCWEkkykuuOCCsmGMUpAiCXvstddeaVAi2R6pp59+Ovbbb79YsGBBZhgj8eSTT8aJJ54Y22+/fTpFAwAAAGBCCGQMq6OSQEZbR/LnPeUv0L1qtD8dAABgEhPIoC7cf//9ceyxx47b9S666KJ45zvfGatWDf5leO7cufGP//iPsdFGG6XTMUqSIEYy3eITn/jEiO6zevXqNIxx6aWXDtqfz+djyy23jO222+5F0zDuu+++2HvvveOee+4Z1fcGAAAAMCJdy8vv75jhjRxmQkbXwEBG8llSPmPqqUAGAABQhkAGdeGII46IFStWpM/HWudx7733xvvf//60AqVkhx12iN/97nexdOnSuOGGG+LBBx+M22+/Pd72trcNOvcrX/lK/Pd//3fF90oCHNdee23/dmtraxx33HHx6KOPxp133hk333xzPPXUU2mdyaxZs/qPe/zxx+Ptb397FAf2kAIAAABUgwkZw8rncsNPyEi0Ty1/AZUlAADAZAhkJJMMfvvb36bTFF7zmtfEy172spgzZ050dHSkX5Pt1772tfHv//7vL5paQH363ve+F5dcckn6fPr06fGpT31qTNdLAhErV67s337lK18ZV1xxRTqVYqCXv/zl8ZOf/CQNgwyU3L+np2fY+9xxxx3xne98Z9C+pB7lC1/4wqDwRTIt49BDD40rr7wy1l577f79N954Y5x33nmj+h4BAAAAKpLUs2YGMmZ6E4eZkFEoVhrIUFkCAAA0cCAjCWJ87Wtfi5e+9KWx//77x8knnxyLFi1KpyEkEwi6u7vTr8n273//+zjppJPi9a9/fXr8N77xjfR86s9jjz02qCZk4cKFsckmm4z6erfeemtceOGFg8IQ3//+92PmzPIfMCS1JV/96lfTIE9J8m8omWgxnM9//vODJly8973vTWtSsmyzzTbx5S9/edC+E044If23CwAAAFAVPV0RvRmfPagsGVllSaJ9Wvn30oQMAACgUQMZSb3E/Pnz4+Mf/3gsWbIkDVeUAhbJ/1Bf85EoHbN48eL42Mc+FnvuuWd6HerLv/7rv8ayZcvS5zvvvHN89KMfHdP1zjnnnEFVJQcffHBstdVWQ54zZcqUOOaYYwbt++53vzvkOcmaB1abJP/ujj/++GHXl1SpzJs3r3/7/vvvj//7v/8b9jwAAACAUcmajpEQyHjhrciakFFxZYkJGQAAQAMGMh599NHYa6+94uqrr04DFuVCF8n2tGnT0q/lwhrJ9lVXXRX77LNPOpGB+vCzn/0sfvrTn6bP29ra0vqP1tax/ZO8+OKLB20vWLCgovPe8Y53RGdnZ//2ddddF3/7298yj//lL385qNYk+Te6+eabD3uf5PtLQhkD/fznP69ojQAAAAAj1rU8+zWBjH75XKWBjBc+Pxp8oEAGAADQYIGMJEhxwAEHpFMuEqVwxUYbbRSf/exn47LLLosnnngi/R/jzz77bPo12U72H3vssbHxxhv3BzZKVRQHHnhgjb8rEk8//XQ6HaMkmX6y4447junNufPOO+Oee+7p304CFrvttltF5655bPLvJgldZFnztde97nUVr3PfffcdtP2LX/yi4nMBAAAARsSEjDFVlhSKJmQAAACTNJBx/vnnp5MKSkGMZIrCySefnAYrTjzxxNh7771jnXXWGXROsp3sX7hwYfo/50899dRob2/vD2Vce+216XWprU984hPxyCOPpM8322yziuo+hvOXv/xl0ParXvWq9N9MpV796lcPeb2hXqs0+JHYaaedoqOjo387mcTx+OOPV3w+AAAAQMUEMsYWyKi4smS1f5QAAEBjBTJOO+20/jBGEqpIqh0+9alPVfw/2ZPjjj766Pif//mfyOVy/df68pe/XPW1k+3//u//4txzz+3f/va3v51WzozV7bffPmh76623HtH5ax6/5vVKuru7B03iGOm9kjDGS1/60oruBQAAAFCVQEZSvdGa8+aOuLIk4zMsgQwAAKCRAhkPPfRQ3HLLLenzJEiRBCve8IY3jOpa++23X3p+EsZI/PWvf02vz8RbuXJlHHHEEf3b7373u0dU9zFcZclASWXNSKx5/JrXK7nvvvvSepySqVOnxpw5c6pyLwAAAICqBDI6ZnhjB1BZAgAAVEPlfQ4T7E9/+lP6NQlRJNMtjjzyyDFd76Mf/WhaX1IKZSTX32ijjcZlrVTu2GOPjcWLF6fPZ8+eHWeccca4vX1Lly4dtD3Sn++GG244aDurRmTN+6x53mjuteY1Ryu5zkjrT9ac9gEAAABMIl3Ly+8XyBhdZUm+s/z72b1qVD8eAABgcqvbQMZjjz3WPx1j0003jfXWW29M10vO32yzzeLee+9Nr1m6PhPnmmuuia9//ev920l1zNy5c8ft+itWrBi03dmZ8QtyhjWPT6pJurq60oqR8bxPuXPWvOZofetb34oTTjhhXK4FAAAATOYJGdMneiWTI5DRPrX8BVSWAAAAjVRZ8swzz/Q/X2eddcblmgOvs3x5xl8HUBWFQiEWLFgQvb1//yV2n332iUMPPXRc77FmqGHKlCkjOj+pHhnumuNxn3L3Gq9ABgAAAMAghZXl35C8QMZAHbmMQEaxt3/i7tCBDBMyAACABgpklMITyS8841XnMLDKYdasWeNyTSqzcOHCuO222/oDDGeeeea4v3XPPffcoO18Pj+i89echJFYvXr1uN+n3L3K3QcAAABgzLKCAlnVG00qa0JGKZTRr31a+YNMyAAAABqpsmRgRckDDzwQixcvTitHRis5f8mSJWldyZrXn8yOOuqo+OpXv1r1+3z+85+P448/vuxrN998c5xyyin928cdd1xsscUW476GNSdVJFM5RiKpJxnumuNxn3L3Gs2UjXI+8pGPxEEHHTSic+6555448MADx+X+AAAAQINMyMgKFjSpIQMZPb3R0Zb7+4YJGQAAwGQIZOy222794YnEqaeeGv/5n/856usl55cmbrS2tqbXp/qKxWJaVdLd3Z1ub7fddvHJT36yKveaPn36kJMshlNuSsWa1xyP+5S7V7n7jMa6666bPgAAAACGnpAhkDGSQEa/9ozJIgWVJQAAQANVlsydOzde9apX9YcozjrrrPje9743qmudf/756flJwCN5vPKVr0yvT/Wdfvrpcf3116fPkyBM8nNob2+vyr3WDDWsXJnxFyAZ1jy+ra2t7OSKsd6n3DnjFcgAAAAAqCgokBUsaFL5XKWVJVPLH6SyBAAAaKQJGYlPf/rT8ba3vS0NUSShjGTSwl/+8pf4whe+EDNnzhz2/GeffTat0vja176WbifXSK51zDHHRLN44xvfGHPmzKn6febPn192CkTy/pd8+MMfjl133bVqa1hzMsRDDz00ovMffvjhQdtZoZ0177PmeaO5l6kWAAAAQFUUVpTfb0LGKCdkZAUyViUfPkYMmPgLAABQ14GMAw88MPbdd9+49NJL+0MZX//61+Occ85Jgxr77LNPbL/99mngoLOzM5068OSTT8ZNN90Uv/vd7+JnP/tZrFixoj+IkTyS6x1wwAHRLJLvN3nUQldX16Bqjm9+85vpY6Quv/zyQfU1iWXLlsXaa689aN/LX/7yQdsPPPDAiO6z5vGveMUryh63+eabp9Mzenp60u3ke3z88cdHNHWl0nsBAAAAVKWyxISMUQYyMqpe+ooRxe6ItvyofkwAAMDkVNeBjMRFF10Ue+65ZxqyKIUykpBFUkOSPIaSHJsonbfjjjum12NyWjPUcNttt43o/Ntvv33I65UklSsvfelL48477xx0r+TfaaVBlfvuu6+iewEAAABUpbIkr7JkoI5cLvMt7KpkQkYp/CKQAQAADJAd/a4TSTVJMu0imYgxcNJFItnOeiQGHvfP//zPcdlll8WMGTNq+v1QPUngZqDrrruuf4pFJf7whz8Meb2hXrv66qsrvs8NN9yQhjJK1l9/fZUlAAAAQHV0ryy/X2XJIB3tQ0zIKPZWFmTJmkYCAAA0rbqfkJGYNWtW/OQnP4mf/vSncfrpp8cf//jHQa8PrLMohTFKX3fbbbf4t3/7t3jrW986watm+vTpad3MSF1yySXxH//xH/3bSS3Naaed9qJrrymZMpFMrrj33nvT7aTCJglKzJ8/f9h7JscO/HeV/Jt605velHl88tqFF17Yv518n5/5zGcq+O7+fuxAb37zmys6DwAAAGDcJmSoLBkkn8sOZHR1Vzoh44XqXgAAgIYJZJQkUy6SR1Itcfnll8ef/vSnuP/++2PZsmVpjUnyP+mT8Ma8efNi1113TSskVEHUTltbW7z2ta8d8XkPPfTQoO3kZ1rpdd7ylrfEGWec0b999tlnVxTISMIVyb+hkp133jk22GCDzOP333//9PsrTeBYtGhRWkOy+eabD3mfJCj0ve99b9C+Aw44YNj1AQAAAIxK1tQGEzIGaW1tibbWlujp/fsfeWVOyGifNvL3GgAAaFoNFcgo2WqrrdLHhz70oVovhTpz2GGHxVe+8pX+CSk/+tGP4phjjkn/vWR57rnn4uSTTx60b8GCBUPeZ5111okDDzwwndySSO53/PHHx3nnnTfkeeecc04sWbKkfzsJD40mtAIAAAAwrOTzkUJGZclQwYImlW9rjZ5C8UX7Cz0mZAAAAKOTPYsPGtC2224bb3/72/u3C4VCHHLIIbF8+fKyxydBiqOOOiruvvvu/n3JlIsk2DGcE044IVpbX/hP6Pzzz48f/vCHmcffdtttcfTRRw/ad9xxx0U+nx/2XgAAAAAj1vNc8ulH+dfynd7QNd+StvIflQ4KZLRNScpuy793JmQAAABrEMhg0jnxxBNj2rQX/srjuuuuS2tLklqRge666674l3/5lzjzzDMH7U+mZbS3tw97n6233jo+8IEPDNr3nve8Jz73uc+lNTol3d3daU3J7rvvHk8//XT//u233z4NiwAAAABURWGICg0TMl4kn8sIZBQHTM1oacl+74Z6vwEAgKbUkJUlMJQtttgizj777HjXu97VX11y0003xd577x1z586NTTbZJJYuXRoPPfRQ/+slRx55ZBx00EEVv8FnnHFG/PnPf47rr78+3e7t7Y2FCxfGKaecEptttll0dHTEfffdFytWrBh03pw5c+Kiiy6Ktjb/CQIAAABVUhj8ecQgeZUlo5qQkWifGtFdpgrGhAwAAGAN/m8wk9LBBx+chi0WLFgQq1ev7t//+OOPp49ykjqRU089dUT3SSZx/Pa3v01DHL/73e8GVaXceeedZc/ZdNNN4+KLL44tt9xyRPcCAAAAGJGhAgLtKktGH8jICLN0v/AZFAAAQMMGMu6444548MEH45lnnkn/Z/uaUw4q8b73va8qa6N+vPOd74xddtkljjvuuHQaRVIdUk5SZ/KFL3wh9txzz1HdZ5111olLL700ncqRBDruueeezOM+9KEPxWc+85mYPn36qO4FAAAAULGhKjTyAhmVVpZ0lZuQUY5ABgAA0KiBjGQKwVlnnRWXXHJJrFo19j5GgYz6deihh6aP8bD55pvHD37wg/jP//zPuOqqq+Luu++OZ599NqZMmZJWl7z61a+ODTfccMz3aW1tjcMPPzx93HLLLWmNySOPPBLFYjFmz54d2267bRoOaW9vH5fvCwAAAGBY5Wo1hpvy0MQ6siZkFCsNZAzxfgMAAE2p7gMZTz/9dPo/5//3f/833R7NNIySlpaW9PzkK81l5syZsf/++0/Ivbbbbrv0AQAAAFCXEzLapiZ/XTLRq5k8lSVZ00VMyAAAABopkJFMwthvv/3i+uuv7w9SlEIVozGWMAcAAAAANJSsiQ150zHGFMjInJAx9qm+AADA5FLXgYyTTjoprrvuukFBjKRq4nWve13suOOOse6660Znp75LAAAAAKh4Qka7z9PKyefGGshY7R8hAADQGIGMQqEQX/nKVwZNxDj88MPjlFNOibXXXrvWywMAAACA+pY1scGEjJFNyCiuGcjImDAikAEAADRKIOOqq66KlStX9k/HOPTQQ+PMM8+s9bIAAAAAoDEUVpTfnxUoaHL5ttzYJmQUMipiAACAplU+9l0H7rnnnvRrMh0jCWR88YtfrPWSAAAAAKDxK0vyKkvGVlmS8f6ZkAEAADRKIOPJJ59MvyZhjC222CLWW2+9Wi8JAAAAABq/ssSEjBFVlnQVK5yQkfV+AwAATatuAxlTp77wi82sWbNquhYAAAAAaDhZFRomZJTVkRHIqLiyxIQMAACgUQIZyVSMNadlAAAAAAAVyprYkJ/mLSz3tlQcyMh4/wQyAACARglkzJ8/P/L5fPT19cXixYtj2bJltV4SAAAAADSOQlZlSedEr6Qh5HNjnZCRMZEEAABoWnUbyJg5c2a8613vSp/39vbG+eefX+slAQAAAEDjyAoImJAxsgkZxd7KKl9MyAAAABolkJH40pe+FOuss076fOHChbFkyZJaLwkAAAAAGoMJGVWqLMmakLF6ZDcEAAAmvboOZKy33nrxs5/9LKZOnRpPPvlkvOY1r4kbb7yx1ssCAAAAgPrXnVFZYkJGWR0ZgYyunmKFgYyM9xsAAGhadR3ISOyxxx5xxRVXxOabbx6LFy+OXXbZJa0y+fnPfx4PPfRQFAqFWi8RAAAAAOpPYUX5/e3TJnolk2xCRsb719sTUeyuwsoAAIBG1RZ1LJfLDdpuaWmJnp6euPDCC9PHaJWuAwAAAABNV1mS75zolTSEfG6MlSXpwSsjpq49zisDAAAaVV0HMvr6+gaFKEpfB+4HAAAAAEZSWSKQMaIJGcU1AxlDvH/dqwUyAACAxqksKQUxkhBG6THWawEAAADApJZ8hpZMayhHZUlZHRmBjK6RTMjICsEAAABNqa4nZMyfP1+IAgAAAABGque5JJVR/jUTMkY2IWNEgYzVFf+IAACAya+uAxmLFi2q9RIAAAAAoPEUhpjUYEJGWflcrvxbWexNp/b2T9/NtWe/t73dI/oxAQAAk1vdV5YAAAAAACPUnVFXkshP83aOYEJG0v7S0ztg2kgun/3+FXu8twAAQD+BDAAAAABoqgkZnRO5koYPZLyotmTIQEZhnFcFAAA0MoEMAAAAAJhsCiZkjFQ+V2EgozWpNnm+vmRNAhkAAMAAAhkAAAAA0EyVJe0qS0Y8IaM4IJAx1JSMXpUlAADACwQyAAAAAKBZKkvapjw/4YE1dVRaWTJUIMOEDAAAoNaBjEKh/roU63FNAAAAADCuEzLynd7QUUzI6HpRIKOt/IECGQAAQK0DGS972cvi+9//fvT19UWtJWs499xzY8stt6z1UgAAAACguhMy2gUysuRz4zEhQ2UJAABQ40DGgw8+GIcddlhstdVWcfbZZ0d3d/eEryG553e/+910DR/4wAfSNQEAAADApNCdEcjIT5volUyKCRmF4hqBjNb28geakAEAANQ6kFFy9913xxFHHBHz5s2Lf//3f4/FixdX/Z733XdfHHvssbHJJpvEBz/4wXQN9TCpAwAAAADGTSGjsqRdIGNUgYwXTcgQyAAAAOo0kHH++efH+uuv3x+EePTRR+Okk05Kq0z22GOPOOOMM9LgxHhJrnX66afH7rvvnlaTnHzyyfHYY4+l908eG2ywQbomAAAAAJjcEzJUlmRpa22JlpYxVpb0qiwBAABe0BY18O53vzsOPPDA+NKXvhRf+9rXYuXKvyf2e3t74+qrr04fRx99dGy88cYxf/782HnnnWPbbbeNl7/85WmQo7W1fI4kOf9vf/tb3HnnnXHLLbfEDTfcEFdccUU89NBD/cckAYyW53+z6uzsjI997GPxmc98Jn0OAAAAAJNCISOQYUJGpuQzw3yuNbrWDF+klSXFwTtMyAAAAOo1kJFIAhBf/OIX4+Mf/3g6HeM73/lOrFixYlBw4oEHHogf/OAH6aMkCWPMnTs3pk+fHlOnTk2Pe+655+LZZ5+NJ554Ig1lDFSawlEKYSSmTZsWH/rQh+JTn/pUei0AAAAAmFQKL3zONkheZclwtSVlAxkVV5Z0V/4zAgAAJr2aBTJK5syZE6eddlp87nOfi+9+97vx7W9/O+69995BIYpSqCJRLBbTipNKJOcnj1I1yeabbx4f/vCH4wMf+ECstdZaVfqOAAAAAKBOK0vaTYkdSkdbazxbZn9XpZUlAhkAAMAA5bs/aiAJSPzbv/1b3H333Wllyf/7f/8vNt1000FhjIEhi6zHQMm58+bNi3/913+NP/zhD3HPPfek9xDGAAAAAKApK0vyAhlDSSpLyr6dawYyWrMmZBQq+/kAAABNoeYTMsrZdddd08fXvva1NKBx5ZVXxg033BA333xzLF68OJ2QsWY1SVJlsv7666chju233z522mmn2GOPPeJlL3tZzb4PAAAAAKiJ7pXl96ssGVJHe67s/hdPyMgIZPSqLAEAAOo8kDFQEqhIHocddtig2pJnn302Vq36e9J/2rRpMWPGjMjlyv/CBAAAAABNJWtChsqS8ZmQobIEAACYDIGMcpLgxdprr50+AAAAAIA1dGdVlkzzVg0h35YRyChWOCFDZQkAADBA+d8wAAAAAIDGVcioLGkXyBhVIKPSypKiyhIAAOAFAhkAAAAA0DQTMjoneiUNRWUJAAAwngQyAAAAAGCyMSGjupUlrSpLAACA4QlkAAAAAMBk0teXHcjIqyypamVJr8oSAADgBQIZAAAAADCZ9DyXpDLKv9ausmQ0gYyuFwUy8uUvUBTIAAAAXiCQAQAAAACTSWFV9mt5gYyhdOTGOCGjWBj+5wMAADQNgQwAAAAAmEy6M+pKEipLRldZUqw0kGFCBgAA8AKBDAAAAABolgkZKktGF8joKQ7eobIEAACogEAGAAAAAEwmJmSMWr7SypJWlSUAAMDwBDIAAAAAoGkmZEybyJU0X2VJr8oSAADgBQIZAAAAADCZdGcEMtqmRLTmJno1k6SyZM1ARr78BYoCGQAAwAsEMgAAAABgMimsKL/fdIxxDGSoLAEAAIYnkAEAAAAAzVBZku+c6JU0nHyu/MelXRUHMkzIAAAAXiCQAQAAAADNUFliQsawOrImZBTXCGS0CmQAAADDE8gAAAAAgMmksLL8fhMyxrGyJF/+Ar0mZAAAAC8QyAAAAACAZpiQIZAxjoGMrAkZheFvAgAANA2BDAAAAACYTAoqS0Yrn8tVVlmSGcgwIQMAAHiBQAYAAAAATCbdWZUl0yZ6Jc1XWSKQAQAADNAWNXDFFVdErc2fP7/WSwAAAACACZyQ0endHkZHRiCjaySVJX19ES0t3msAAKA2gYy99torWmr4S0ly756enprdHwAAAACqpmBCxnhPyCj29qWPXOvzn2m2ZgQyoi+itxiRq8nHrgAAQJ2p6W8GfUlaHAAAAACofmVJu8qS0QYySrUlU/O5oStLEr3dAhkAAEAq+zeMOgxjJJMt1nyM5TgAAAAAaJrKkrzKkuHkc0MHMoatLCnVlgAAANRqQsbnP//5EYc3zjvvvLj//vv7t9va2mLbbbeN7bbbLmbPnh2dnZ2xcuXKePLJJ+Pmm2+OW2+9Na0lKYUxNttss3jf+95Xle8HAAAAAOpGt0DGaHUMMSGjq1hMxoxUEMjoHvX9AQCAyaXuAxnLly+Pgw46KA1jJEGMuXPnxrHHHhvvfve70yBGliSYccEFF8SXvvSlePzxx2PJkiVxzTXXxI9//OOYMWPGOH0nAAAAAFBnCipLqlVZ0m+oyhKBDAAAoNaVJZUoFovx5je/Of7v//4v3d5nn33irrvuio9+9KNDhjESyesf+9jH0uOT85IwxyWXXJJer7d3wC9PAAAAADCZmJAxAYEMlSUAAECDBzJOO+20uPLKK9PnSTXJr3/961hrrbVGdI3k+F/96lex/fbbp6GM5HrJdQEAAABgUipkVJa0T5volTScfG6IQEZxQCCjVWUJAADQwIGMZDrGwODE6aefHu3tQ/yiM4R8Pt9/rSSU8eUvf9mUDAAAAAAmn76+iO6MypK8QMaEVJb0dg97HwAAoDnUbSDjmmuuiccffzx9PmvWrLR2ZCyS89dZZ530+RNPPBF//OMfx2WdAAAAAFA3eroi+jLqets7J3o1DUdlCQAA0BSBjL/+9a/p15aWlpg3b96Yr7fmdUrXBwAAAIBJo5AxHSNhQsbYKksGTchQWQIAADRwIOOpp57qf97dPT5j/gZeZ+D1AQAAAGBSyKorSbSrLKnkj7qyQhldxQorS4oqSwAAgDoPZMycOTP92tfXF4sXL46urq4xXS85/7777kt/qUrMmDFjXNYJAAAAAHWjsCr7tbzKkrHUlgyakNHaln2BYqGi+wAAAJNf3QYyNtpoo/7nq1atip/85Cdjut5FF12UXicJeCQ23njjMa8RAAAAABpmQoZAxvgFMpI/+mrNqC0xIQMAAKj3QMZee+0V+Xw+nWiRhCiOPvroePTRR0d1rUceeSQ++clP9k/HSK6bXB8AAAAAmmZChsqSimRVlgwKZAxVW9KrsgQAAKjzQMZaa60VBx54YBrGSIIUjz32WLz61a+OP//5zyO6zg033BC77757en7pWgcccEB6fQAAAACYVLozAhltUyJacxO9msk1IaO4ZiAjo7ZEZQkAAFDvgYzEaaedFjNnzkyfJ0GKxYsXx6te9ap45zvfGb/61a9i5cryIxhXrFiRvn7wwQfHLrvskp5Xmo4xY8aM9LoAAAAAMOkUMipLTMcY38qSoSZkqCwBAACelxHjrg8bbrhh/Nd//Vf8y7/8S3R1daWhit7e3vjxj3+cPpLtTTbZJGbPnh3Tpk2LVatWxRNPPBEPPvhgOg0jUZqKkXzt6OhIr5dcFwAAAACaZkJGvnOiV9K8lSUCGQAAQCMEMhL7779//OIXv4j3vOc98eijj/ZPukgCFsljyZIl6aMUuhgo2Vfav+6668YPfvCDeM1rXlOj7wQAAAAAqsyEjImrLGlVWQIAADRwZUnJPvvsE7fffnt86EMfiilTpvQHL0qBi1JIY83t0lSMD37wg+n5whgAAAAANGUgIz9tolcy6QIZXZVOyOjtrsKqAACARlT3EzJK1lprrfjWt74VJ510Uvzwhz+MRYsWxbXXXhv333//oMkYSRhj3rx58cpXvjL22muveOc73xlrr712TdcOAAAAADWtLGlXWVKpjqwJGSpLAACAyRrIGBjMSCZlJI9EEsZYvnx5rFixIjo7O9PXSxMyAAAAAKCpFDICGSZkjDmQ0dVTHLwjp7IEAAAYWsMFMtaUhC+SEEbyAAAAAICm1p1VWWJCxlgrS0zIAAAARqr8bxcAAAAAwOSZkKGypGL5nEAGAAAwPgQyAAAAAGCy6FZZUrUJGcXewTtaVZYAAACTuLLkwQcfjAceeCCWLVsWzz77bMyYMSNmzZoV8+bNi4022qjWywMAAACAiVXIqCxpn+YnMVGVJb3d3msAAKAxAxm///3v46yzzoorr7wyHnnkkczj1l9//dhzzz3j8MMPj7322mtC1wgAAAAA9TUho3OiV9Kw8rnc2AIZRYEMAACgwSpL/vrXv8ZOO+0Ur33ta+PHP/5x/O1vf4u+vr7MR/L6j370o3jNa14Tr3zlK+PWW2+t9bcAAAAAANVlQsbEVZbkVJYAAACTIJDx/e9/Pw1V/OUvf0nDFomWlpb0Uc7A15Ljb7jhhth5553jvPPOm9B1AwAAAEBdBDLyKkvGGsjoMiEDAACYbJUlP/vZz+IDH/hAFIvF/qBFKZSx8cYbxw477BBz5syJzs7OWLlyZTzxxBNx0003xYMPPpgeUwpmdHV1xYIFC2KttdaKAw44oKbfEwAAAABMaGVJu8qSSnVkTcgQyAAAACZTIOPJJ5+M97///f1hjCSIMXXq1PjYxz4Whx12WGyxxRaZ5957771x9tlnx9e+9rVYvXp1en5ynUMPPTTuueeemD179oR+LwAAAABQdYWMQEZeIKNS+VyFgYxWlSUAAEADV5Z84QtfiOXLl/eHMbbbbru47bbb4ktf+tKQYYzES1/60vS4W2+9NT2vNFUjud6JJ544Qd8BAAAAAEyQ5POvbpUl1aosKRTXCGTk8uUv0Ns95jUAAACTQ90GMpIAxQ9/+MP+MMYmm2wSV1xxRcybN29E10mOX7RoUfq1dK0f/OAHVVs3AAAAANRET1dE3xqhgRKVJWMPZKgsAQAAJksg49prr40nnngiDVAkQYrTTjst1lprrVFda+21144vf/nL/VMykiqU5PoAAAAAMGl0Z9SVJPLTJnIlzVFZksuqLDEhAwAAqPNAxt13393/fPr06XHAAQeM6XpvectbYsaMGf3bd91115iuBwAAAAB1pZBRV5JoF8iYsMqSYqHiewEAAJNb3QYyli5dmn5NpmNsuumm0daWkTivUHt7e3qdNa8PAAAAAJM+kJHvnMiVNEdlSWt7+QuYkAEAANR7IKNYLPY/z+Vy43LNgdfp7c3o0wQAAACARtRtQsaEBjJyGYGMXpUlAABAnQcy1l133fRrX19f3H///enXsShdp2Tu3LljXiMAAAAA1I3CquzXTMioWEcuu7Jk0GeUKksAAIBGDWRsttlm/c+ffvrpuOyyy8Z0veT8ZcuWlb0+AAAAADS87oxARq4jonV8JtA284SMUihj2AkZKksAAIB6D2T80z/9U8yYMSNaWlrS5PnRRx8d3d2jG/eXnPfJT36yf3v69Omx2267jeNqAQAAAKDGChmVJaZjjF8gY2BtiUAGAADQqIGM9vb2eMtb3pKGMZJQxi233JJur1ixYkTXWbVqVbz1rW+Nm266Kd1OrnXggQdGW1tblVYOAAAAAHU0IUMgo0qBjHz5g0zIAAAA6j2QkVi4cGHk83//xSYJZlxyySWx1VZbxbnnnpsGLYaSvP69730vXvGKV8Svf/3rNIiRSK53wgknTMj6AQAAAGDCFDI+L2uf5ocwAvlchZUlrVmVJQXvNwAAkKrrMRGbbrppnHzyyfGJT3yiv7rk4Ycfjg984APx0Y9+NF75ylfG9ttvH3PmzInOzs5YuXJlPPnkk+k0jOuuuy4NZZQmbCSSr6ecckp6XQAAAACYVLqzKksEMkaioz03tsqS3tHVLgMAAJNPXQcyEkcddVQasvjiF7/YH6xIQhZJ+OLyyy9PH+UkxyRKQY7EcccdlwY5AAAAAKB5JmR0TvRKJu2EjC6VJQAAwGSpLBlYXfI///M/se666/ZPvCiFMxLJvtKjZGB44yUveUlcfPHFqkoAAAAAmLwKJmSMh3xb69gmZKgsAQAAGimQkXjzm98c9913X3z729+OnXfeOVpbW18UwkiU9iWvv+pVr4qzzjorPe9Nb3pTzdYOAAAAADWrLGlXWTISHW2VTsjICmSoLAEAABqksmSgqVOnxhFHHJE+ksqS66+/Pu6///5YtmxZrFixIqZPnx6zZs2KefPmpaGNzk7jGAEAAABo8sqSvM/IxquyZPCEjHz5g/qKEb3FiNbciO4LAABMPg0VyBgoCVvsueeetV4GAAAAANSHboGM8dDa2hJtrS3R0zt4Mm+iUBwQyGjNmJBRmpIhkAEAAE2vYSpLAAAAAIAhFFSWjJd8Rm3J4AkZQwQyetWWAAAAIZABAAAAAJOCCRkTHMjIqCwpTcgAAACangkZAAAAADAZFDIqS9qnTfRKGl4+lxHIKBYrm5BRLFRhVQAAQKNpiwbT19cXl1xySVxxxRVxzTXXxAMPPBDLli2LZ599NmbMmBGzZs2KefPmxa677hp77rln7LvvvrVeMgAAAABUX3dGZUleIGPCK0tMyAAAABopkJEEMb7+9a/HV77ylbj//vsH7S956qmn0sd9990Xv//97+Okk06KTTfdND7+8Y/Hv/7rv0ZLS0uNVg8AAAAAVVbICGS0d3rrJ7yyxIQMAAAgGqOy5MEHH4z58+enwYolS5akIYxSECMJWaz5SJSOWbx4cXzsYx9Lp2Uk1wEAAACApqosMSFj3CpLugYGMlqHmJDR2zPymwIAAJNO3QcyHn300dhrr73i6quvTgMW5UIXyfa0adPSr+XCGsn2VVddFfvss0889thjNf6OAAAAAGCcJZ+HZVWWtKssGamOrAkZxUorS0zIAAAA6jyQkQQpDjjggHTKRaIUrthoo43is5/9bFx22WXxxBNPRE9PTzz77LPp12Q72X/sscfGxhtv3B/YSNx7771x4IEH1vi7AgAAAIBx1tMV0TcgLDBQXmVJdSpLBDIAAIAGDmScf/75cd111/UHMdra2uLkk09OgxUnnnhi7L333rHOOusMOifZTvYvXLgw7rnnnjj11FOjvb29P5Rx7bXXptcFAAAAgEmjO6OuJCGQUaVARj77AkWVJQAAQJ0HMk477bT+MEYSqvj5z38en/rUp9JgRiWS444++uj4n//5n8jlcv3X+vKXv1z1tQMAAADAhClk1JUkVJaMWD5XQSCj1YQMAACgQQMZDz30UNxyyy3p8yRIkQQr3vCGN4zqWvvtt196fhLGSPz1r39Nrw8AAAAAk4IJGRMzIaM4MJDRGtGSK3+BYmF8FwQAADSkug1k/OlPf0q/JiGK1tbWOPLII8d0vY9+9KPpdUrVJaXrAwAAAEDDMyFjXOXbcsNPyBiqtqRXZQkAAFDHgYzHHnss/ZoEKDbddNNYb731xnS95PzNNtusf0pG6foAAAAA0PBMyJj4ypKhAhkmZAAAAPUcyHjmmWf6n6+zzjrjcs2B11m+fPm4XBMAAAAA6nZCRq4jojWjVoMRV5Z0DawsSd/ftvIXEMgAAADqOZBRCk8kEy2WLl06Ltd8/PHH+5/PmjVrXK4JAAAAAHUbyMhPm+iVTAodbWOdkKGyBAAAqONAxsCKkgceeCAWL148pusl5y9ZsiStQFnz+gAAAAAwKStL2jsneiVNFshoL38BEzIAAIB6DmTstttuaXiiFKA49dRTx3S90vnJxI3kmsn1AQAAAGBSKGQEMkzIGN/Kkp7i4B2tAhkAAEADBjLmzp0br3rVq/pDFGeddVZ873vfG9W1zj///PT8UsDjla98ZXp9AAAAAJgUurMqS0zIGI18boyVJb0qSwAAgDoOZCQ+/elP90+0SL4uWLAgjjrqqFi+fHlF5z/77LPxiU98It7//ven28k1Esccc0xV1w0AAAAAdTEhQ2XJuE7IKBRVlgAAAJVrizp24IEHxr777huXXnppfyjj61//epxzzjnxtre9LfbZZ5/YfvvtY86cOdHZ2RkrV66MJ598Mm666ab43e9+Fz/72c9ixYoV/aGO5JFc74ADDqj1twYAAAAA46dbZcmEBDJeNCFDZQkAANCggYzERRddFHvuuWcasiiFMpKQRVJDkjyGUpqIUTpvxx13TK8HAAAAAJNKIaOypH3aRK+kyQIZGZUlRZUlAABAnVeWJGbOnJlOu0gmYgycdJFItrMeiYHH/fM//3NcdtllMWPGjJp+PwAAAAAwcRMyOr3Zo5DPmZABAAA0QSAjMWvWrPjJT36STrfYddddB4UuEqWQRimAkSgds9tuu8VPf/rT9NzkOgAAAAAw6ZiQMTETMoprTMhozags6e0e3wUBAAANqe4rSwZKplwkj9tvvz0uv/zy+NOf/hT3339/LFu2LK0xmT59ehq6mDdvXhrcSKpOXvGKV9R62QAAAABQm0BGXmXJaHRkBDK6Kq4sEcgAAAAaLJBRstVWW6WPD33oQ7VeCgAAAADUb2VJu8qScZ2Q8aJARsaEjGJhVPcFAAAml4aoLAEAAAAAhlDICGTkBTJGI5/LlX+bi72DqpSzAxkmZAAAAAIZAAAAAND4ulWWTMSEjCSL0dM7MJChsgQAAMhmQgYAAAAATNYJGSpLxjWQ8aLaktaMRmiVJQAAgEAGAAAAAEwC3VmVJdMmeiWTQj5XYSAja0JGr8oSAAAgIiPCXd/uuOOOePDBB+OZZ56J1atXD+5trND73ve+qqwNAAAAACZU8tlYIaOypF0gY9wnZBQrCGQUBTIAAIAGCmT89re/jbPOOisuueSSWLUqI/E/AgIZAAAAAEwKPV0RfcXyr+U7J3o1k0JHpZUlOZUlAABAAwcynn766Tj00EPjf//3f9Pt0UzDKGlpaUnPT74CAAAAwKSuK0mYkDHuEzK6KqksMSEDAACo90BGMgljv/32i+uvv74/SFEKVYzGWMIcAAAAAFCXsupKEiZkjEo+V+mEDIEMAACgQQMZJ510Ulx33XWDghhTpkyJ173udbHjjjvGuuuuG52dxi4CAAAA0MRMyJjQCRmF4oBARqvKEgAAoAEDGYVCIb7yla8Mmohx+OGHxymnnBJrr712rZcHAAAAAPXBhIyJDWRUMiGjt3v8FwUAADScug1kXHXVVbFy5cr+6RiHHnponHnmmbVeFgAAAAA0zoQMlSWj0taafCaZVCC/+DWVJQAAQKWyo941ds8996Rfk+kYSSDji1/8Yq2XBAAAAAD1p5ARyMh1RLTmJno1k0LyeWRHxpSMQrH4wkZOZQkAANCAgYwnn3yy/5efLbbYItZbb71aLwkAAAAA6k/3yvL789MmeiWTSj5X/qPTru4KKkuKKksAAIA6DmRMnTq1//msWbNquhYAAAAAaLgJGe2dE72SSSXfVn66SKEokAEAADR4ICOZirHmtAwAAAAAYA0FEzKqIauypKtnQCCjVWUJAADQgIGM+fPnRz6fj76+vli8eHEsW7as1ksCAAAAgMapLGlXWTIW+YxARqGngsqSXpUlAABAHQcyZs6cGe9617vS5729vXH++efXekkAAAAA0DiVJXmVJWORz40hkFEUyAAAAOo4kJH40pe+FOuss076fOHChbFkyZJaLwkAAAAA6ku3QMaETsgoDgxkZFWWCGQAAAB1HshYb7314mc/+1lMnTo1nnzyyXjNa14TN954Y62XBQAAAAD1o6CypC4rS/r6qrIuAACgcdR1ICOxxx57xBVXXBGbb755LF68OHbZZZe0yuTnP/95PPTQQ1EoFGq9RAAAAACoHRMy6q+yJGFKBgAANL2MmXrVlcvlRnVeS0tL9PT0xIUXXpg+Rqt0HQAAAABoeCZk1K6ypLU9+wLJlIwYIrABAABMejUJZPSNYlxfEqIofR3N+QAAAADQXBMypk30SpqwsmSIQEYxmezbWYWVAQAAjaJmlSWlgEWlkhBG6TFR9wQAAACAxp2QIQxQjUBGl8oSAACgnidkzJ8/XzgCAAAAAMZDwYSMaujIjXVCRlJZAgAANLOaBDIWLVpUi9sCAAAAwOTTnTUhQ2VJVSpLiiOpLAEAAJpZzSpLAAAAAIBqTshQWVKVQEZP8YWNVhMyAACAbAIZAAAAANDIugUyqiFfUWVJPvsCvSpLAACg2QlkAAAAAECj6uuLKKgsqQaVJQAAwFgJZAAAAABAoyoWIvoGVGgMpLKkSpUlAydkqCwBAACyCWQAAAAAQKPKmo6RaJ82kStp0kDGEJUlRZUlAADQ7AQyAAAAAKBRda/Kfs2EjDHJ58p/dNo1MJDRmotoac2eXgIAADS1tlrc9LDDDhu03dLSEmefffawx42XrPsBAAAAQEMxIaNqOrImZBQHBDISre0Rxa4XH2hCBgAANL2aBDK+973vpaGIRF9fX2ZAYuBx42Wo+wEAAADApAlk5FWWjEVHW678Wz5wQkaptqRcIKNXZQkAADQ7lSUAAAAAMBkrS9o7J3Ilk06+rYLKkkSuvfwFVJYAAEDTq8mEjNKkivE8DgAAAACaTiEjkJFMbcjV7KO/SR3IKFQcyOipwqoAAIBGUpPfyhYvXjyuxwEAAABAU+rOqCzJm44xVvlcpYGMfPkLmJABAABNryaBjHnz5o3rcQAAAADQlLImZKgrqd6EjKLKEgAAoDLlf6sAAAAAAOpfd0YgIz9tolfSNIGMYm9f+ujXmlFZ0quyBAAAmp1ABgAAAAA0qkJGZUm7QEa1Ahkvqi1RWQIAAGQQyAAAAACAyRbIyHdO9EomnXyu0kBGxoSMYqEKqwIAABqJQAYAAAAATLbKEhMyxqxjiAkZXcViBYEMlSUAANDsBDIAAAAAYNJNyFBZMlYqSwAAgLFqixp44IEHotY22WSTWi8BAAAAAKo0IUNlycQFMlSWAAAAdRTI2HTTTaOlpSVqJbl3T4+RgQAAAABM1gkZAhljlc8NEcgoDghktGYEMnp9/ggAAM2uJoGMkr6+vlreHgAAAAAm54QMlSVjZkIGAAAwVtkxbwAAAACgvhVUllSLQAYAANCQEzIOOeSQWtwWAAAAACaX7qzKkmkTvZLmqizpGVBZksuXP6iosgQAAJpdTQIZ5557bi1uCwAAAABNMiFDIGOsWlpa0lBGoTggfPG8roH7cu3lL1AsjHkNAABAY1NZAgAAAACNqpA1IaNzolfSVLUlgyZktApkAAAA5QlkAAAAAECj6jYho+aBjKzKkl6VJQAA0OwEMgAAAACgEfX1DTEhQ2XJeEgqS4YPZJiQAQAAlCeQAQAAAACNqFiI6CuWf61dZUlVJ2QUBTIAAIDhtUUDe/jhh+Opp56KZ555Jnp7e2OHHXaItdZaq9bLAgAAAIDqy5qOkcgLZNS8sqSosgQAAJpdQwUyCoVCXHDBBfHTn/40rr766li+fPmg1y+99NLYZ599XnTeL3/5y3jyySfT5+uuu268/vWvn7A1AwAAAEBVdK/Kfk0gY1x0VBTIUFkCAAA0eCDjJz/5SRx55JGxdOnSdLsv6cgcoKWlJfPcv/zlL/G5z30ufd7R0RGPPPKISRoAAAAANLbCEIGM9mkTuZKmm5DR1TOgKqY1I5DR212lVQEAAI2i/G8UdebjH/94vOMd74jHHntsREGMko985CMxZcqU9Nyurq648MILq7haAAAAAJgA3UNVlghkjId8biyVJQIZAADQ7Oo+kLFw4cL46le/moYpSuGLuXPnpgGNT37yky8KaJQza9asQTUlv/jFL6q6ZgAAAACousIQgYz2Tj+Aak7IKKosAQAAGjyQccstt8QJJ5yQBjGSRzLl4pvf/GY89NBD8cMf/jBOOeWUiqdkvPWtb02/JgGOyy+/PIrFAWMFAQAAAGCyVJYkExtyDdNUXNc62iqZkJFRWWJCBgAANL26/s3suOOOi97ev/9yk8/n4ze/+U3sscceo7rWbrvt1v98xYoVcffdd8crXvGKcVsrAAAAANRFZUm7upJqT8hQWQIAADT0hIyVK1emAYzSdIyjjz561GGMxOabbx4zZszo37799tvHaaUAAAAAUANZEzLy6krGSz43lkBGYdzWAQAANKa6DWRceeWVUSgU0oqRxP/7f/9vzNdcb731+p8/+uijY74eAAAAANRMd0Ygw4SM6k/IKA4IZLRmDCHu7R6/hQAAAA2pbgMZDz30UPo1mY6x6aabDgpTjNbaa6/d/3z58uVjvh4AAAAA1Ewho7LEhIxxo7IEAACYlIGMJ554ov/5uuuuOy7X7Onp6X/e2lq33zoAAAAAjH5ChkDGuMnncmX3qywBAAAqUbephM7OF7ouV67MSPuPIeQxe/bscbkmAAAAANTVhAyVJRNbWZLLqCwpdkc8X8cMAAA0p7oNZJSmYvT19cWDDz445ustXbo0vU5SgZIYjwoUAAAAAKi/CRnTJnolTRfI6OoZGMjIZ5zdF9FbrM7CAACAhlC3gYytttqq//ny5cvjz3/+85iud/HFF/cHPJJQxi677DLmNQIAAABA/U3IeGHyLGPTkTUhY2Ago7U9+wLFgh8BAAA0sboNZGy//fax/vrr90+0+MY3vjHqa3V3d8eXv/zl9FrJY7vttlNZAgAAAMDkDGSYkDFu8rkKAhm5IQIZvd3jtxgAAKDh1G0gI/GOd7wjnWiRPM4777y45JJLRnWdI488Mu666670OokPfOAD47xSAAAAAKiTypJ2lSXVriwpFCupLEkmZAhkAABAM6vrQMZnP/vZmDFjRjrVore3N972trfFRRddVPH5y5Yti3e9613xne98p3/SRjJ14/DDD6/iqgEAAABgAhQyAhl5lSVVD2RUOiFDZQkAADS1ug5kzJkzJ04//fR0skUSqFi1alUcfPDBsfvuu8dZZ50VN9xwQ3pcafLFQw89FNdff3386Ec/iiOOOCLmzZsXF154Yf+UjVwuF+eee27k80Ok1gEAAACgEXRnVZYIZNRNZYkJGQAA0NTaos4tWLAglixZEl/84hfTUEYSrPjjH/+YPgZK9r///e9/0b7knNJ5//Ef/xH77rvvBH8HAAAAADCBEzJUlowblSUAAMCknZBRsnDhwjj77LNj6tSp/ftKUy9KgYtS6KL0SJT2Jef98Ic/jKOOOqqG3wUAAAAAjKNulSV1UVnSOsSEjN7uKqwKAABoFA0RyEgk0y9uueWWtIpkypQp/fvXDGAM3J9UlJTOe8c73lGTdQMAAABAVZiQUR+BjCErSwpVWBUAANAo6r6yZKDNNtssvv3tb8dJJ50UixYtiiuvvDJuv/32ePLJJ+Ppp5+OadOmxZw5c9Lj9t5773jta18b6667bq2XDQAAAADjK/kDpcKK8q/lp3m3x0lHLiOQUeztn94buXz2BYomZAAAQDNrqEBGyaxZs+Ktb31r+hiN3t7eaG1tmOEgAAAAAPDiyQt9xfLvSnund2ucdLRnf4aYhDI62nLDTMgQyAAAgGZW16mEG264YdyvmSTX3/ve9477dQEAAABgwhRWZr9mQsa4yedyma91lWpLWof4mzeVJQAA0NTqOpDxxje+MRYvXjyu13z/+98fP/rRj8b1mgAAAAAwobpXZb9mQsa4ybcNMSGjFMgYqrbEhAwAAGhqdR3IWLp0abz+9a+PJ554Ylyu98EPfjDOO++8cbkWAAAAANRMYYhAhgkZExvISLRm1Jb0qiwBAIBmVteBjMQ999wTb3rTm2L16tVjus6RRx4Z3/nOd8ZtXQAAAABQM91DVZZ0TuRKJrWKAxm5jECGyhIAAGhqdR/ISFx33XVx0EEHRW/vgF9yRuDoo4+Ob37zm9GSjA+MiOnTp4/zCgEAAACgTiZkqCwZN/ncEIGM4sBAhsoSAACgwQIZ73nPe6Kvry99/utf/zqtHBmpY489Nk4//fQ0jJFca9q0aXHxxRdXYbUAAAAAMEG6MwIZSTAg1+bHUDcTMlSWAABAM6vrQMY555wTr33ta9MgRfJIto8//viKz//CF74QJ510Un8YY8qUKfHzn/889txzz6quGwAAAACqqpBRWdI+zRs/jjqGCGR0qSwBAAAaOZDR1tYW//3f/x077rhjf6hi4cKF8d3vfnfYc08++eQ0vFE6r6OjI37yk5+kAQ8AAAAAmJSBjHznRK+keStLeiqpLClUYVUAAECjqOtARmL69Onxq1/9KjbZZJP+cMVHPvKR+MUvfpF5zhlnnBGf/exn+49vb2+PH/3oR7H//vtP6NoBAAAAYEIrS0zIGFetrS3R1tpS9rVCcUAgozWjsqS3Z3wXBAAANJS6D2Qk1ltvvfj1r38ds2bNSkMWPT09cfDBB8e11177omO/+c1vxr/927/1hzGSKRsXXHBBHHDAATVZOwAAAABM3IQMlSXjLZ9RWzJ4QkZGIMOEDAAAaGoNEchIvOIVr4iLL744rR5JwharVq2KN73pTXHPPff0H5NUmXz0ox/tD2Pkcrk455xz4qCDDqrp2gEAAABgYiZkqCwZbwIZAADApA9kJHbbbbf4wQ9+kAYukscTTzwRr3/962Pp0qVx3nnnxQc/+MH0uCSM0draGmeeeWa85z3vqfWyAQAAAGCCJmQIZIy3fC5jQkax+MJGLl/+5KLKEgAAaGYNFchIvPWtb42vfe1raegiCWUsXrw4DWosWLAg3Vfa//Wvfz0OO+ywWi8XAAAAACZuQobKknFnQgYAANA0gYzERz7ykTjmmGPS8EXivvvui+LzifQkjHH66afHhz/84RqvEgAAAACqpKCypK4CGa3t5U8uFqq0KgAAoBE0ZCAj8aUvfSne+9739k/EGLj/Yx/7WE3XBgAAAABV1Z1VWTLNGz9BlSVdAwMZWZUlvSpLAACgmTVsICNx9tlnx+te97r+UMYJJ5wQn/70p2u9LAAAAACorkJGIKNdIGO8dWRNyCgODGSYkAEAALxYW9TAPvvsM27XWrFiRfq1o6Mjfv/736eP4SThjcsuu2zc1gAAAAAAdVFZku/0g6hFZUlmIKPbzwMAAJpYTQIZixYtGlQzMh66urri8ssvH/a4NStOAAAAAGDSVJaYkFGjQEZGZYlABgAANLWGriwpEbAAAAAAoKlkTshQWTLe8rmxTMgojPt6AACAxlGTCRmlSRUAAAAAwCh0ZwQy2lWWTNiEjOKAQEZrRiCjV2UJAAA0s5oEMnp7B/yyAgAAAACM04QMgYzx1tGWK/8jUFkCAAA0Q2UJAAAAADSV7pXl96ssmbgJGSpLAACAYQhkAAAAAEAj6SlE9PaUf01lyYQFMroqCmSoLAEAgGYmkAEAAAAAk2E6RsKEjHGXz1USyMiXP1kgAwAAmppABgAAAAA0ksIQgQwTMsZdR1ZlSbGSCRmF8V8QAADQMAQyAAAAAKCRFFZlv2ZCxoRVlhR6ii9stGYEMnpVlgAAQDMTyAAAAACAyVJZ0j5tIlfS1JUlBZUlAADAMNqiBs4777wX7Xvf+95X0XHjpdz9AAAAAKCxJ2R0TuRKmntChsoSAACgHgMZhx56aLS0tAwbkCh33HgRyAAAAACgIXVnBDKS2oxcRnUGVags6X1hI+t9L/Z45wEAoInVJJAxUF9f37Chi+SY8ZDcp5L7AQAAAEDdKmRUlpiOUcNARr78ycVCdRYFAAA0hJoFMioNWYxXGGO8rwUAAAAAdTUhQyCjKvK5sUzIEMgAAIBmVpNAxrnnnjuuxwEAAABANPuEjPZpE72S5p6QUewdXBdTTq/KEgAAaGY1CWQccsgh43ocAAAAADSNzMoSgYxq6MgIZHSpLAEAAIZR/rcJAAAAAKCxKkvaOyd6Jc09IWNQICPj796K3VVaFQAA0AgEMgAAAACgkZiQMaHyuVz5H0OxN/r6+v6+kcuXP7mvGNFbrOLqAACAelaTypJaWLx4cZx//vn925/73Odquh4AAAAAGN8JGSpLJnJCRpLF6Onti/ZcS3YgozQlo7V8qAMAAJjcmiaQcd9998Xxxx8fLS0t6bZABgAAAAANqZARyMirLJnIQEb6o+jpjfZca0TrEB+z9ia1JVOqsjYAAKC+NV1lSf8YQQAAAABoRN0ry+8XyKiKfBK4GCKQkRpuQgYAANCUmi6QAQAAAACTckKGypKJn5BRrCSQUajCqgAAgEYgkAEAAAAAjaRbZclE6himsiSVG6KyxIQMAABoWgIZAAAAANBIChmVJSZkTPiEjK6KKktMyAAAgGYlkAEAAAAAkyGQkZ820StpCpVNyBgqkNFdhVUBAACNYIhZejAx+vr64pZbbkkfjzzySDz33HPR2dkZG2ywQbz85S+PbbfdNtraxvZP9dlnn40//OEPcdddd8Xy5ctj6tSpMW/evNhtt93S+4ynW2+9NW644Yb0eykWizF79uz0e9hll13G/H0AAAAAZFaWtHd6cyZ4Qkah+Hwgo3WIz3x6BTIAAKBZ+b/D1MzSpUvj9NNPj+9///vx6KOPZh43bdq02H333eN973tfvPvd7x7RPRYvXhyf+9zn4sc//nEUCi8eD9nS0hJ77rlnnHDCCTF//vwYS6jk3HPPjVNOOSUNfZSTBDM+/OEPxzHHHJMGTgAAAABGpZARyDAhoyryuSEqS7qLf3+isgQAAChDZQk1cc4558QWW2yRBhiGCmMkVq1aFZdcckl8/etfH9E9khBGMpniggsuKBvGKAUpFi1aFHvttVcalEi2R+rpp5+O/fbbLxYsWJAZxkg8+eSTceKJJ8b222+fTtEAAAAAGJXujMoSEzKqoi3XGq0tw0zIUFkCAACUYUIGE+6oo46Kr371qy/aX6opWWeddWLFihXx0EMPxTPPPDOqe1x00UXxzne+M3p7n/+l+Hlz586NjTfeOJ3O8fDDD/cHMJKvSTikq6srzjjjjIrvs3r16jSMce211w7an8/nY9NNN42Ojo647777YuXKFz4oSbb33nvvuPrqq9NQCgAAAEDFegoRvT3lXzMho6q1Jc91D/6cKVHoKQUy2rNPLqosAQCAZmVCBhPquOOOe1EYI6khufzyy9PwRTJh4pprrom//vWv6eSJpHLkrLPOSkMPra2V/XO999574/3vf/+gMMYOO+wQv/vd79Igxg033BAPPvhg3H777fG2t71t0Llf+cpX4r//+78r/n4+8YlPDApjJGtMvsdk6sedd94ZN998czz11FNpncmsWbP6j3v88cfj7W9/exSLz4+1BAAAABjLdIxEXkXqRNeW9AcyWloiWjP+9q1YfnIrAAAw+QlkMGGuvPLK+OIXv9i/PXPmzLjsssvSSpH58+dHLpd70TnJlInDDz88fvOb38Qvf/nLiu6TBCIGTqR45StfGVdccUU6lWKgl7/85fGTn/wkjjjiiEH7P/WpT0VPT8Zfmgxwxx13xHe+851B+5Lv5Qtf+MKg8EUyLePQQw9Nv/+11167f/+NN94Y5513XkXfEwAAAECqsCr7jVBZUjX5ttzQlSVD1ZaYkAEAAE1LIIMJkVSBJMGHUkVIW1tbGrLYZ599Kr7GwJBDlltvvTUuvPDCQWGI73//+2n4o5yWlpZ0YsfLXvayQRM2kokWw/n85z8/aMLFe9/73rQmJcs222wTX/7ylwftO+GEE6K729hKAAAAoELdQwQyVJZUTUdb+Y9Ru0oTMhKtGbUlvT77AQCAZiWQwYT49re/nU6UKPn4xz8e//RP/zTu9znnnHMGVZUcfPDBsdVWWw15zpQpU+KYY44ZtO+73/3ukOcsW7ZsULVJEuw4/vjjh11fUqUyb968/u37778//u///m/Y8wAAAABShSEqS9qneZOqJN82TGVJIpcRyFBZAgAATSuj2HBijGQ6wlgl/wOd2hlY7ZFMrVgzADFeLr744kHbCxYsqOi8d7zjHfHRj360v+rkuuuui7/97W+xwQYblD0+qU8ZWGuy1157xeabbz7sfVpbW9NQxsDwxs9//vN4wxveUNE6AQAAgCY3VCAj3zmRK2kq+VwlgQyVJQAAQB0FMhYtWpROFpgoyb1KlRlMnGuuuSatEinZf//9Y5111hn3+9x5551xzz339G93dnbGbrvtVtG5pWMvvfTSdDv5d5KELg4//PCyxyevDfS6172u4nXuu+++gwIZv/jFLyo+FwAAAGhyWZUlSV1G1oQGqjchozgwkJHxUWtRZQkAADQrlSVU3Zrhhb333rsq9/nLX/4yaPtVr3pVtLVVnjl69atfPeT1hnqt0uBHYqeddoqOjo7+7WQSx+OPP17x+QAAAEATy5qQkVdXUvvKkqwJGYUqrQoAAKh3NQ9kJJMIJvLBxEvqPwbaYYcd+p9feeWVcdhhh8U222wTM2fOjBkzZsRLX/rSePOb3xzf/OY346mnnqr4Prfffvug7a233npE61zz+DWvV9Ld3T1oEsdI75WEMZLvsZJ7AQAAAFQ0IaNdXUnNK0uSKSXl9L5QewsAADSXmlaWnHvuubW8PRPk+uuvH7S9+eabx9KlS2PBggVl6zpWrFgR9913X/rav//7v8dxxx0Xn/jEJyqqLBlo4403HtE61zx+zeuVJGvr6XnhF+mpU6fGnDlzRnyv2267bdC95s+fP6JrAAAAAE0oc0KGQEbtK0syAhkmZAAAQNOqaSDjkEMOqeXtmQDLli2LJ598ctC+lStXxu677x4PPPDAsOc//fTT8W//9m9x4403xjnnnBPt7dldqEnIY6CNNtpoRGvdcMMNB21n1YiseZ81zxvNvda85mgl1xlp/cma0z4AAACABpyQobKkqlSWAAAADRfIYPJLAhVreve7390fxmhra4uDDjooXvva18Z6660XzzzzTFx99dXxgx/8IA1zlFxwwQWx7rrrxmmnnZZ5r2SyxkCdnSP7y5A1j0+qSbq6utKKkfG8T7lz1rzmaH3rW9+KE044YVyuBQAAANShgsqSegpkdPVUMiFDZQkAADQrgQwmPJDx5z//Of262Wabxf/+7//GNttsM+j1d77znfG5z30u/XrZZZf17z/jjDPiLW95S+y5555l77VmqGHKlCkjWmtSPVLumsMFMkZ6n3L3Gq9ABgAAADDJdWdVlkyb6JU0lY5cRmVJRYGMQpVWBQAA1Lvyv0nAOMkKGsyYMSMNW6wZxiiZO3duGtbYdttt+/f19fXFF7/4xcx7Pffcc4O28/n8iNa6ZvAisXr16nG/T7l7lbsPAAAAwIsUMgIZ7QIZNaksKQ4MZGR8RiSQAQAATcuEjEnuqKOOiq9+9atVv8/nP//5OP7441+0P2t6xGc/+9l0QsZwUyS+8Y1vxF577dW/79JLL43FixeXPXfNexUKI/vrg6SeZLhrjsd9yt1rNFM2yvnIRz6SVsCMxD333BMHHnjguNwfAAAAqFFlSX7klaqMQyCjp/jCRmvGhIxelSUAANCsBDKoqunTp79oX2trayxYsKCi85N6kpe//OVx55139u+74oorygYy1rzXmpMshlNuSkW59Y/1PuXuVe4+o7HuuuumDwAAAKDJKktMyKiqvMoSAABgFFSWUFXlggZbbbVVWklSqfnz5w/avv766yu618qVGR9QZFjz+La2trKTK8Z6n3LnjFcgAwAAAGjWCRkqS6pJZQkAADAaJmRMcm984xtjzpw5Vb/PmqGJkiR4kUzE6O19oU9zyy23HNG1kwkZAy1durTscWtOhnjooYdGdJ+HH3540HZWaGTN+6x53mjuZaoFAAAAUJHujEBGu8qS2lSWvPCZV+QyKkuKKksAAKBZCWRMcvvuu2/6qJVkwsSmm24a9913X/++mTNnjugaax6/bNmyioIbDzzwwIjus+bxr3jFK8oet/nmm6fTM3p6evrrRx5//PERTf2o9F4AAAAAgxQyJnXmBTKqqaMtN4ZARqFKqwIAAOqdyhKqbuuttx603dXVNaLzn3vuuUHb06aVH8G5ZqjhtttuG9F9br/99iGvV9Le3h4vfelLR32v5PsfGFAZ6l4AAAAAFU3IUFlSkwkZXYMCGfnyJwtkAABA0xLIoOr+8R//cdD2Y489NqLz16womT17dtnjdtxxx0Hb1113Xf8Ui0r84Q9/GPJ6Q7129dVXV3yfG264YVAoZf3111dZAgAAAFSmoLKkbitLWjMmZPSqLAEAgGYlkEHVveUtbxm0feONN0ZfX9+IAgxDVZMMnDIxcHLFypUrKw5KJMf+8Y9/7N9uaWmJN73pTZnHr/napZdeWtF9yh375je/ueJzAQAAgCbXnVVZUn6iKOOjI1fJhAyVJQAAwGACGVTdTjvtFJtsskn/9tNPPx2XXXZZRec++eST8bvf/W7Qvr322qvi8MfZZ59d0X0uvPDCWLFiRf/2zjvvHBtssEHm8fvvv3+0tbX1by9atOhFNSTlJEGU733ve4P2HXDAARWtEQAAACAKGYGM9k5vTi0mZBRVlgAAANkEMpgQRxxxxKDtE088saIpGaeeeuqgeo/NN988DXhkOeyww9LpFiU/+tGP4vbbbx/yHs8991ycfPLJg/YtWLBgyHPWWWedOPDAA/u3k+/l+OOPj+Gcc845sWTJkv7tefPmxWtf+9phzwMAAACInkJ2/YUJGbWvLMmckKGyBAAAmpVABhPiqKOOipe85CX925dffnl8/vOfH/Kciy++OE477bRB+z71qU9FLpfLPGfbbbeNt7/97f3bhUIhDjnkkFi+fHnZ45MgRbK2u+++e1DoIwl2DOeEE06I1tYX/hM6//zz44c//GHm8bfddlscffTRg/Ydd9xxkc/nh70XAAAAQGZdSaJdZUk15XNjCWQUqrQqAACg3glkMCE6OztfNIVi4cKF8c53vjPuvPPOQfsff/zxNKjwL//yL1EsFvv377HHHsNOrihN35g27YUPIa677rqYP39+Wisy0F133ZXe48wzzxy0P1lne3vGL9ADbL311vGBD3xg0L73vOc98bnPfS6WLVvWv6+7uzutKdl9993TupaS7bffPg2LAAAAAFSksCr7tbzKkrqtLOntrtKqAACAetdW6wXQPA499ND405/+FN/+9rcHVYokj0022SSdoPHMM8/EvffeOyiIkdh0003T49rahv8nu8UWW8TZZ58d73rXu/prUW666abYe++9Y+7cuem9li5dGg899NCLalOOPPLIOOiggyr+ns4444z485//HNdff3263dvbmwZNTjnllNhss82io6Mj7rvvvlixYsWg8+bMmRMXXXRRRd8PAAAAQKp7iECGCRk1CWQUe/vSR661JaI1a0KGQAYAADQr/zeYCfWtb30r1l577TSwMDAM8cADD6SPcv7pn/4pfvaznw2qPBnOwQcfnF4/maixevXqQdM3kkc5SZ3IqaeeOqLvJ5nE8dvf/jYNcfzud78bVJWy5uSPgeGSpI5lyy23HNG9AAAAgCZXGKKyxISMmgQySrUlU/M5lSUAAMCLqCxhQrW0tMRJJ50Uf/zjH+MNb3hDtLZm/xPcdttt47zzzourrrpqRGGMkqQO5a9//Ws6KWOoCpJSncl//Md/pOsbqXXWWScuvfTSOOuss9LpHEMd99nPfjZuueWW2G677UZ8HwAAAKDJDTUhQyCjqvK5oQMZqZwJGQAAwGAmZFATu+yyS/zqV7+KJ554Iq6++ur+Wo+11lorDV/suuuuabXIWG2++ebxgx/8IP7zP/8zDXbcfffd8eyzz8aUKVPS67/61a+ODTfccMz3SYIlhx9+ePpIAhdJjckjjzySVq/Mnj07DZck3/NQwRAAAACAIRUyAhlJVUZWGIBx0THEhIyutHo3+Rnkyx+gsgQAAJpWXQcyvvCFL4zr9dra2tL/4Z885s2bF//4j/8YnZ2d43oPRmbOnDnxlre8pepv28yZM2P//fePiZBMvzABAwAAABh3hRXl9+enebNrXFky9ISMQpVWBQAA1Lu6DmQcf/zxo6qQGMlUg+R/nC9YsCDe+973pv/THgAAAAAaqrKk3R8c1UUgI5lUUk5vd5VWBQAA1Lvs3yTqTF9fX9nHaI4tbSd1En/5y1/iox/9aGyxxRbxs5/9bAK/IwAAAAAYgcLK8vtNyKi6fG6IQEaxNCFDZQkAANBggYyBYYpkWkbpsebrawYvBh5bOn7NYEbptWTfE088Ef/yL/8Sp59++oR/jwAAAAAw+gkZKkuqTWUJAAAw6SpLfv/736dfly5dGsccc0wsWbIkDU8kVSN77bVX7LbbbrHVVlvF2muvHR0dHbF8+fL429/+lk69+OUvfxmPPvpoen5y/Ic+9KF4+9vfHitWrIinnnoqbr755rjsssvSYwcGNj71qU+l13zDG95Q0+8dAAAAAAYpZAQy8ipL6jqQ0duTfPCY/HVYlVYHAADUq7oOZOy5555x2223xfve97546KGH0sDEe97znvjSl74UG2200ZDn9vb2xk9+8pP45Cc/GQ8++GB8+9vfjnw+H2ecccag46688so4/PDD46677kqDGcl5SShDIAMAAACAutKdVVkikFHTypKeYSpLEsXuiLYhXgcAACaluq4sSSZZ7LfffmmgIvGtb30rzjvvvGHDGKWpGMlEjOuvvz522GGHNMzxta99LU466aRBx+2xxx5x7bXXxjbbbNO/LwmB/OpXv6rCdwQAAAAA4zwhQ2VJ1SV/yJUVyugqDjMhI1EsVGllAABAPavrQMaxxx4bDz/8cPoLz2GHHZbWjozU3Llz46KLLoopU6akoYzjjz8+7r///kHHzJw5M84///z09VJ9yaWXXjpu3wcAAAAAjFm3ypJ6rC3pn5DROkQgo7e7SqsCAADqWd0GMlauXBnf//73+7c/85nPjPpaW2yxRRx00EHp856envjOd77zomN23HHH2GeffdJQRuIPf/jDqO8HAAAAAOOukFFZYkJGfQQyhqssAQAAmk7dBjKuvPLKeO6559KJFRtvvHFsvvnmY7peErYoyZp+8ZrXvCb9moQyHnnkkTHdDwAAAAAmJJCRn+aNngAdwwYyVJYAAAANEsi46667+p+/5CUvGfP11l133f6wxd133132mJe+9KX9z5966qkx3xMAAAAAql5Z0t7pTa7hhIyuigIZJmQAAEAzqttAxooVK/qfP/PMM2O+3vLlywfVoZSz1lpr9T9Pqk0AAAAAoG6YkFFT+VzWhIzi35+oLAEAABolkDFnzpz+iRaLFy+Op59+ekzXu/766/ufz549u+wxq1a98FcGnZ3+sgAAAACARpiQobKklhMyCsXShIx89snFQpVWBQAA1LO6DWRsttlm6deWlpZ0WsU555wzpmkb//Vf/5VeK3mUrr2mhx9+uP+e41GTAgAAAADjppARyMj7w6KaBjJKlSWtbdkn96osAQCAZlS3gYz58+fHjBkz+qdkHH/88XHjjTeO6lof+chH4pFHHkmvk3jTm95U9rgbbrih//lLX/rSUd0LAAAAAKqiu3wNr0BGrStLKpmQIZABAADNqG4DGR0dHfG+970vDVEkEyuSKRf77LNPXHDBBRVfIwlhvPWtb40f/OAH6TUSU6dOTa+7pt7e3vjNb37Tv73TTjuN03cCAAAAAFWckNFuQkYtJ2R09VeWtGefLJABAABNqW4DGYkvfvGLsd5666XPk0DFM888E4ccckhsu+228aUvfSkWLVoUjz32WDz33HNpoGL58uVxxx13xA9/+MN497vfHS972cvi4osvTkMdpWBHMmljww03fNG9Lr300vRapeDGXnvtNeHfLwAAAACU1VPIrr3IT/OmTYCOYStLchEtGR+3FgtVXBkAAFCvhig2rL2ZM2fGr3/969h3333jySefTMMSSbDitttui+OOO27Y80shjNJ5H/jAB+Loo48ue+xJJ53Uf86cOXNizz33HPfvBwAAAADGta4k0S6QUcsJGf2BjFJtSc9zLz7IhAwAAGhKdT0hI7HDDjvE5ZdfHjvuuOOLAhZDPRKl49rb22PhwoVx5plnZt7n+9//fixevDh93HLLLdHaWvdvDQAAAADNXleSyKssmQj5XAWBjNaM2pKs6SYAAMCk1hCpg6222iquvfba+OpXvxpbbLFFf+CipBTSKNWNJJJj8vl8HHzwwXHjjTfGscceO+Q95s2b1/94yUteUrXvBQAAAABGrHuIQIYJGbWdkFEcOCEjI5ChsgQAAJpSXVeWDJTL5eLII49MH9dcc01cddVVcf3118fDDz8cTz/9dHR1dcVaa60V66yzTmy99dbxqle9Kl73utfF7Nmza710AAAAABibwhCVJXmVJXVVWVKOyhIAAGhKDRPIGGjXXXdNHwAAAADQFIackKGyZCLkc7kKAhlZEzJUlgAAQDNqiMoSAAAAAGhqhYxARmtbRFvGVAbGlcoSAABgpAQyAAAAAKDedWdUluRNx6h1IKNLZQkAAJBBIAMAAAAAGnVChrqSCdOREcgYVFnSmlFZ0quyBAAAmlFbNKi77747rrnmmnjggQdi2bJl8eyzz8aMGTNi1qxZMW/evNh1111jiy22qPUyAQAAAKCKEzKmeXcnSD5XQSAjlxHIKBaqtCoAAKCeNVQg49FHH43//M//jO985zvx2GOPDXv8+uuvH4cffnh88IMfjPXWW29C1ggAAAAA466QEchoF8iodWVJoTgwkJEvf3LRhAwAAGhGDVNZctZZZ8XLXvayOPHEE9NgRl9fX/9joIH7//a3v8UXvvCF2HLLLdMQBwAAAABMqsqSfOdEr6RpZQYyKpqQIZABAADNqCECGcmUiw9/+MOxcuXKNGjR0tKSPkrKhTNKxyT7VqxYER/60IfiiCOOqNF3AAAAAABVqCwxIWPCqCwBAAAmXWXJv//7v8fZZ5+dPi8FLJLHRhttFHvvvXfssMMOMWfOnOjs7EwDG0888UTcdNNNsWjRonjwwQf7gxvJOcl1XvKSl8TChQtr/F0BAAAAwHhMyFBZMlFUlgAAAJMqkHHrrbfGKaecMihUsd1228V//Md/xL777jtoSkY5l1xySXzqU5+Km2++uT/MkVzv4IMPjm222WaCvgsAAAAAGKPujEBGu8qSidJRSWVJa8bHrb0qSwAAoBnVdWXJcccdF8VisX/7fe97X/z5z3+O173udcOGMRLJccnxhxxySH/VSXK9z3/+81VeOQAAAACMo0JGZUleIKPWEzK6el74/DJy+fInFwtVWhUAAFDP6jaQ8dxzz8VvfvOb/uDF7rvvHt/73vcil8uN6Dqtra1x7rnnxh577NFfd/LrX/86vT4AAAAANPSEDJUlNQ9kdBf7ore3b5hARk8VVwYAANSrug1kXHXVVWloIglQJE499dQxXS+pKilJrptcHwAAAAAaQkFlSb1WliQKxedrS3IZlSUmZAAAQFOq20DGgw8+2P987ty5scsuu4zpervuumusu+66Za8PAAAAAHWtsKL8fhMyJkx+iMm9LwQyVJYAAAANEMhYunRp+jWpLNl4443H5ZoDr/P444+PyzUBAAAAoNnK3WMAAIb1SURBVGaVJe3TvPk1rixJFHqGCWT0qiwBAIBmVLeBjClTpvQ/X7Uq4xfOEVq9enX/846OjnG5JgAAAADUrLIk3+nNr6dARqvKEgAAoAECGaV6kb6+vliyZMmYQxnJ+YsXL37R9QEAAACg7nWvLL/fhIzGmJBRLFRpVQAAQD2r20DG1ltv3V9Z8txzz8WFF144pusl5w+ckLHNNtuMeY0AAAAAMCFMyKi5fG6IQEaxFMhoL39AUWUJAAA0o7oNZOywww6xwQYb9E/J+PSnPx0PPfTQqK718MMPp+cn4Y5Ect3tt99+XNcLAAAAAFVR7I7o7S7/mgkZdTYhIyuQYUIGAAA0o7oNZCQOPfTQNIyRBCmeeOKJ2H333eOGG24Y0TVuvPHG2GOPPdLzS9dKrgsAAAAADaGQUVeSyHdO5EqaWscQgYwulSUAAECjBTKOOeaYmDt3bvo8CVI88MADseuuu8YhhxwSixYtip6e8qP+kv3J60nw4lWvelUsWbKkfzrGuuuum14XAAAAABpC96rs1wQy6qOypBTIaM2YkNGrsgQAAJpRW9Sx6dOnx49//ON4wxveEF1dXWmoolgsxgUXXJA+2tvb4xWveEXMmTMnOjs7Y+XKlfHkk0/GHXfcEYXC38cAlqZiJF+nTJmSXi85FgAAAAAaQmGIQIbKkgnT2toSba0t0dPb96LXCkWVJQAAQIMFMhJ77rlnXHTRRfHe9743nn766f5JF0nAIgld3Hzzzf37SvtLkv2lMMbaa6+dhjiS+hIAAAAAaBiFFdmv5adN5EqaXr6tNXoKxewJGbl8+feo+Pc/HgMAAJpLXVeWlLzxjW9MgxfJ14Ghi1LgYqCB+0rHvfnNb45bbrkl9t9//wlfOwAAAABUrbKk3STYiQ5klPNCICOjsqSosgQAAJpR3U/IKNloo43if//3f+Ouu+6Ks88+Oy6//PL4y1/+0l9NMlA+n49/+Id/SKdrLFiwIF72spfVZM0AAAAAULXKkta2iLaMiQxURT6XEcgoPj81w4QMAACgEQMZJVtuuWWccsop6fMkjPHoo4/GsmXLYsWKFTF9+vSYNWtWrL/++tHenpFGBwAAAIBG0r2y/H7TMRpoQobKEgAAaEYNF8hYcxLGJptskj4AAAAAoKkmZOSnTfRKmt6wgYzWjEBGr8oSAABoRuV/g5iELrvsssjlcumjra2hcygAAAAANJOsCRn5zoleSdPLqizp6p+QkVEhY0IGAAA0paZKJvT19dV6CQAAAAAwPhMy2k3ImGgdWRMyihVUliSfTba0VHF1AABAvWmaCRkAAAAA0JC6sypLTMiou8qSrEBGordYpVUBAAD1SiADAAAAAOpZYUX5/SZkTLiOttwwgYyMypKE2hIAAGg6AhkAAAAA0IiVJXmVJfUyIaOrogkZ3VVaFQAAUK8EMgAAAACgEStL2lWWTLR8bpjKktYhAhlFgQwAAGg2AhkAAAAAUM8KK8vvNyGjbiZkqCwBAADKEcgAAAAAgIackKGypG4CGcUKKktMyAAAgKYjkAEAAAAA9ayQEcjIqyypvwkZAhkAAMALBDIAAAAAoJ51Z1WWCGRMtHyu/MepXf2BjHz2ycVClVYFAADUK4EMAAAAAGjECRkqSyZcx1gqS3q7q7QqAACgXglkAAAAAEA961ZZUv+VJcW/P2lVWQIAALxAIAMAAAAA6llhRfn9JmTUTWVJQWUJAABQhkAGAAAAADRiZUm+c6JX0vTyw1WWtOay36OiyhIAAGg2bbW8+RVXXDFh97rpppsm7F4AAAAAMC6S/4nfm/E/8k3IqKPKkucDGS0tEbl8RLHw4oMEMgAAoOnUNJCx1157RUvyS8oESe7V19c3YfcDAAAAgDEprMx+LT/Nm1tvgYxEZiCjzD4AAGBSq2kgo2SiQhITGf4AAAAAgDHrzqgrSbSrLJlo+VwFgYzWjI9csyadAAAAk1ZdBDIEJQAAAACgjMIQgQwTMupnQkZxjQkZ5agsAQCAplPTQMYmm2wijAEAAAAAWbqHqiwxIWOidWQEMrrWrCwpR2UJAAA0nZoGMpYsWVLL2wMAAABA407IUFlSPxMyBgUyMj5yNSEDAACaTvnfIAAAAACA2itkTMhobYtoy5jEQNXkc7nMypK+vr6/b5iQAQAAPE8gAwAAAAAarbLEdIy6mpCRZDF6eocJZPT2VHFlAABAPRLIAAAAAIBGqyzJT5volTBEIGNQbUkyvaScYsF7CAAATUYgAwAAAAAabkKGQEYt5HMVBDJUlgAAAM8TyAAAAACAemVCRl3paB8ikFEcLpChsgQAAJqNQAYAAAAA/5+9+wCPrKz3B/5mZrKbZOlderNQRKwoXOlFkSYqIleleb3i1T9eO14RsBe8F+xcaRYuVUSKlSqCAiIWWEQXpCMdKbtLpuT/vAcTMtk5ySSbmTlz5vN5njzunJk5552ThHFmvvn+AhlVThlZ0j+v3SthioaMp8ujgQwjSwAAgGcIZAAAAABAVg2njCyZI5DRCXNLkzVkVJ/5h5ElAADAPwlkAAAAAEC3NWTMGWr3SoinfZJAxtOVKUaW1IwsAQCAXiOQAQAAAABZNWxkSbcEMoZHAxkFI0sAAIBnCGQAAAAAQFYNP9l4u4aMjphTbCKQYWQJAADwTwIZAAAAANBtI0v6jSzphFKxEAp9ja8brk4VyDCyBAAAeo1ABgAAAAB028iSOfPavRKmGFvybEOGkSUAAMAzBDIAAAAAIKvKTzXeriEjc2NLjCwBAAAmEsgAAAAAgKzSkJE5c0rFyUeWFPob37FmZAkAAPQagQwAAAAAyKpyysgSDRkdMzdlZMnTYyNLUgIZ1eEWrgoAAMgigQwAAAAAyKrhlJElc+a1eyWMnvrSTEeWlJ1DAADoMQIZAAAAANBtDRkCGR0zpzhVICOtIUMgAwAAeo1ABgAAAABkUfwAP23MhZEl2WvIqBpZAgAA1BPIAAAAAIBuGlcSzRlq50oYf+qNLAEAAJokkAEAAAAA3TSuJOqf186VMJ2RJYWUkSU1I0sAAKDXCGQAAAAAQBYNTxLI0JDRMUaWAAAAzRLIAAAAAIAsKk8ysqTfyJLuG1lSaeGqAACALBLIAAAAAICua8gwsiRrgYynxwIZKSNLqsMtXBUAAJBFAhkAAAAA0E0NGYVSegsDLTe3OFVDhkAGAADwDIEMAAAAAOimhoz+eSH09bV7NUw1sqQ6xciSmpElAADQawQyAAAAACCLyimBjDlD7V4J48xNC2RUqs/8o6AhAwAAeIZABgAAAABk0fCTjbf3C2RksiHDyBIAAGACgQwAAAAA6KaRJRoyMhnIeLoyxciSkVoItX+2aAAAAD1BIAMAAAAAumlkSf+8dq+EceYUizNryIiqZecSAAB6iEAGAAAAAGTR8FONt2vIyObIkmozgYzhFq0KAADIIoEMAAAAAMii8qLG2/uH2r0SmglkTDWyJKpVnEsAAOghAhkAAAAAkEUCGd0ZyChoyAAAAJ4hkAEAAAAAWVRJa8gYbPdKGGdusfFbqk+PNWQIZAAAAM8QyAAAAACArmrIEMjIZENGtZlARrlFqwIAALJIIAMAAAAAsqi8sPF2gYxsjywpzkm/s0AGAAD0FIEMAAAAAOiqhoyhdq+EceYUpwpkGFkCAAA8QyADAAAAALLIyJLuHFlSmCSQUTOyBAAAeolABgAAAAB0UyCjNNDuldBEIKNaG0m+jCwBAABGCWQAAAAAQBYZWdJVgYyxsSWTjizRkAEAAL1EIAMAAAAAsqi8sPH2/sF2r4Rx5hSnCGT09YVQKDW+QXXYuQQAgB4ikAEAAAAAWVRZ3Hh7/1C7V8I4cydpyHi6Wn3mH8U5jW+gIQMAAHqKQAYAAAAAZM3IyCQNGQPtXg3TGVkSpY0tqRlZAgAAvUQgAwAAAAC6pR0j0pCR/UBGISWQYWQJAAD0FIEMAAAAAMia8qL06/oH27kSJphTnCSQUR1tyDCyBAAAEMgAAAAAgC4LZAy1cyVMUJokkFGtjUw+sqRqZAkAAPQSDRkAAAAA0E2BjNJAO1fCxNNf6FuKQMaw8wkAAD1EIAMAAAAAsqa8MP06I0s6qjhJIKMyFsgwsgQAABDIAAAAAIDsMbIknw0ZNSNLAACgl2jIAAAAAICsqaSNLOkLoTS3zYuh6YaM6j8DGQUjSwAAAIEMAAAAAOiehow4rqQvPRBA6/X19aWGMp5tyDCyBAAAEMgAAAAAgOwpL0wPZNBxaYGMSq32zxukNWQYWQIAAL3EyBIAAAAAyJry4sbb+4favRIaKE3ZkGFkCQAAIJABAAAAANmjIaNLGzKMLAEAAJ6lIQMAAAAAsqa8qPH20kC7V8JMGjIKpcbnrWZkCQAA9BKBDAAAAADolkCGkSWZUCwUZtiQMdzCVQEAAFkjkAEAAAAAWVNJC2QMtnslTKshozZFIENDBgAA9BKBDAAAAADomoYMgYwsKKYEMirV0YaMlJElAhkAANBTBDIAAAAAIGvKCxtvF8jIhFIxrSHDyBIAAOBZAhkAAAAAkDUaMrqzIWOqQEat0sJVAQAAWSOQAQAAAABdE8gYavdKaKBUmKIho5A2smTY+QQAgB4ikAEAAAAA3RLIKA20eyU0UOibYUOGQAYAAPQUgQwAAAAAyBoNGZlWKqY1ZNSmCGQYWQIAAL1EIAMAAAAAsqa8sPH2/sF2r4QGioXCFA0ZRpYAAAACGQAAAACQPZXFjbf3D7V7JTRQKqQ0ZFSNLAEAAJ6lIQMAAAAAuqYhY6DdK6GBYkog49mGjJSRJTUjSwAAoJcIZAAAAABA1pQXNd6uISPbDRmjgYyCkSUAAIBABgAAAABkT2ogY7DdK2E2GzKqw84nAAD0EA0ZAAAAAJAlIyMCGV3bkFGbIpBhZAkAAPQSgQwAAAAAyJJqOYSRauPrShoysqBYKEzRkGFkCQAAIJABAAAAANlSXph+nZElGW/ImGpkSbmFqwIAALJGQwYAAAAAZEl5Ufp1/UPtXAkpisW+KRoyUgIZNYEMAADoJQIZAAAAAJAllckCGUaWZLoho/rPQEbByBIAAEAgAwAAAAC6qCFDICMLioWZNmRUQhj5520AAIDc05ABAAAAAFkikNG9DRm12uSBjORGxpYAAECvEMgAAAAAgG4JZJQ0ZGRBsVCYoiEjZWRJVB1u0aoAAICsEcgAAAAAgG4IZJQGQkgJApCVhowpRpZENQ0ZAADQK7yCAwAAAIAsKS9MD2SQCcWUQMZYQ0ahP/3ORpYAAEDPEMgAAAAAgG5oyOgfavdKmHFDxmSBDCNLAACgVwhkAAAAAECWVNICGYPtXgkpisUpGjImG1miIQMAAHqGQAYAAAAAZImGjC5uyKg10ZBRbtGqAACArBHIAAAAAIAsKS9svL1/oN0rIUWx0Pht1Uq1mYYMI0sAAKBXCGQAAAAAQFc0ZBhZkv2GjJGpGzJqGjIAAKBXCGQAAAAAQJaUFzfe3j/U7pWQopgSyKiMBjIKRpYAAAACGQAAAADQHSNLSkaW5KIhw8gSAADoGRoyAAAAAKArRpZoyOiehoxiCH3FxneuGlkCAAC9QiADAAAAALqhIaN/sN0rYdoNGbVnL6S1ZAhkAABAzxDIAAAAAIAsqSxuvF0gIzOKxcLkDRnJjeY0vrORJQAA0DMEMgAAAACgK0aWaMjIfkPGyNQNGTUjSwAAoFcIZAAAAABAlhhZknnFlEBGpToukFEwsgQAAHqdQAYAAAAAdEVDxlC7V8JSNWQYWQIAAL1OIAMAAAAAssTIku5tyGhmZEnVyBIAAOgVAhkAAAAA0A2BjNJgu1dCilKh8duq1Vrt2QsCGQAA0PMEMgAAAAAgSzRk5LwhY7hFqwIAALJGIAMAAAAAsqS8sPH2/qF2r4QUpZRARrUukDGn8Z1rRpYAAECvEMgAAAAAgKyoVtI/sO83siQrisUmGjIKaQ0ZAhkAANArBDIAAAAAICsqi9Kv6x9o50pY6oYMgQwAAOh1AhkAAAAAkBXlyQIZRpZkRXGSQMbIyMgUgYzhFq4MAADIEoEMAAAAAOiKQIaRJVlRKqS/rTrWklGck3IDI0sAAKBXCGQAAAAAQFZoyOjqhoyoUpuiIaMmkAEAAL1CIAMAAAAAsqK8MP260kA7V8IkSpMEMmqjI0sKRpYAAECvE8gAAAAAgKwwsiRHDRlGlgAAQK8TyAAAAACArKgsarw9ti2kjcCg7UrF9EBGtTrFyJKqkSUAANArBDIAAAAAIOsNGf1D7V4JMxxZ8mxDhpElAADQ6wQyAAAAACDzgYyBdq+ESRQL6W+rVo0sAQAA/kkgAwAAAACyoryw8fb+wXavhBk3ZNQmb8ioGVkCAAC9QiADAAAAALKivLjxdiNLMqU4SSBjrCGjYGQJAAD0OoEMAAAAAMh6Q0bJyJLuacj4ZyCjOKfxDaoaMgAAoFcIZAAAAABAVpQXNd6uIaP7GjLSRpYIZAAAQM8QyAAAAACArDdk9A+2eyVMolRIf1u1Up0qkDHs3AIAQI8QyAAAAACArKgsbrxdICNTisVmGjKMLAEAgF4nkAEAAAAAmR9ZoiEjS0qTjCyp1GqTN2TUyi1aFQAAkDUCGQAAAACQFUaWdIVioYmGjIKRJQAA0OsEMgAAAAAg8w0ZQ+1eCZMo9k3WkDHVyJKKcwsAAD1CIAMAAAAAssLIkq5QKPSFtJKMsYaMtJEl1eHWLQwAAMgUgQwAAAAAyHogozTY7pUwhVKhMEVDhkAGAAD0OoEMAAAAAMgKDRldo5hSkVGt1SYfWVIzsgQAAHqFQAYAAAAAZEV5YePt/RoysqaUEsioVP/ZkFHQkAEAAL1OIAMAAAAAsqKyuPH2/qF2r4QpFItpDRlNjCwZ+edtAACAXBPIAAAAAIDMN2QMtHslzLQhYyyQkTKyJKpVnV8AAOgBAhkAAAAAkBXlRY23a8jInGJhhg0ZyY2GW7QqAAAgSwQyAAAAACALarVJRpYMtns1TKFUKEzRkCGQAQAAvU4gAwAAAACyIC2MEWnI6KKGjFoTI0sqLVoVAACQJQIZAAAAAJDlcSVRaaCdK6EJpZRAxlhDRkFDBgAA9DqBDAAAAADIgvLC9OuMLOmihgwjSwAAgGcIZAAAAABAFhhZkotARqU6MvXIkqqRJQAA0AsEMgAAAAAgCzRkdJVSUUMGAAAwOYEMAAAAAMiC8qL064wsyZxiofFbqxUjSwAAgH8SyAAAAACALEhryOgrTD7+go4opYwsqdZqz/xjsu9ZzcgSAADoBQIZAAAAAJAF5cWNt/cPhdDX+MN/OqeYEsgYa8go9KffuTrcolUBAABZIpABAAAAAFluyDCupMsaMkYDGcVYb9L4zgIZAADQEwQyAAAAACALyosaby8NtnslzEZDRmw1SRtbUjWyBAAAeoFABgAAAABkOZChIaM7GzKiYsrYEg0ZAADQEwQyAAAAACALKgIZ3aRYaPzWaqXaRCCjVm7RqgAAgCwRyAAAAACALNCQkZOGjNqzF1JHlghkAABALxDIAAAAAIAsKC9svN3IkkwqFhsHMirjR5YUjCwBAIBeJpABAAAAAJluyBhq90pYqoaMJkaWaMgAAICeIJABAAAAAFlgZElXKRaaaMhIDWQMt2hVAABAlghkAAAAAECWAxmlgXavhFlryJjT+M4aMgAAoCcIZAAAAABAFhhZ0lWKhcLMGzJq5RatCgAAyBKBDAAAAADIgvLCxtv7B9u9EpaqIaP27IWCkSUAANDLBDIAAAAAIAsqixtv7x9q90poQjElkFGpGlkCAAA8QyADAAAAADLdkDHQ7pWwFIGMajMjS6pGlgAAQC8QyAAAAACALCgvarxdQ0ZXjSypNBXIGG7RqgAAgCwRyAAAAACALCinjSwZbPdKmLWGjDmN76whAwAAeoJABgAAAABkemSJQEZ3NWTUpm7IqBlZAgAAvUAgAwAAAACyPLKkJJCRRcVCYeqGjIKRJQAA0MsEMgAAAACg00ZGNGR0mVIxrSHDyBIAAOAZAhkAAAAA0GnV4ZjKaHxd/1C7V0MTiikjS+oaMtJGllSNLAEAgF4gkAEAAAAAnVZemH5dv5ElWVRKCWRUqs0EMmIABwAAyDuBDAAAAADotPKi9OsEMrq4IWNO4zvXKi1aFQAAkCUCGQAAAADQaQIZ+WnIqNWevaAhAwAAeppABgAAAABkOpAx1M6V0KRioTB1Q0YhbWRJ2XkGAIAeIJABAAAAAJ2mISNHDRlNjCwRyAAAgJ4gkAEAAAAAnVZemH5daaCdK6FJxZRARl1DhpElAADQ0wQyAAAAACCrDRmlwRD6Gn/wT2eVis00ZBhZAgAAvUwgAwAAAAA6rZISyOgfbPdKmNWGjJSRJbWy8wwAAD1AIAMAAAAAstqQ0T/U7pXQpFJKIKNSrT17wcgSAADoaQIZAAAAANBp5YWNt/cPtHslNKlYKEzdkFEwsgQAAHqZQAYAAAAAZLYhw8iSrmvIaGZkSdXIEgAA6AUCGQAAAADQaeXFjbcbWZJZxZRARl1DRrHU+M7V4RatCgAAyBKBDAAAAADI6siSkpEl3diQMTLyz1CGhgwAAOhpAhkAAAAAkNmRJUPtXglL2ZARjZVkpAUyakaWAABALxDIAAAAAICsNmT0D7Z7JTSpVEwPZFRqtWf+UTCyBAAAeplABgAAAAB0WmVx4+0aMjKrWEh/a7U6WpGR1pAxUguhVm3RygAAgKwQyAAAAACAzDZkDLR7JTSpNMnIkspUgYyoamwJAADknUAGAAAAAHRaeVHj7UaWZFZxkkBGtToayEgZWZLcaLgFqwIAALJEIAMAAAAAOq1sZEm30ZABAABMRSADAAAAADI7smSw3SthNhoymhlZUjOyBAAA8k4gAwAAAACyOrKkJJCRVaVC+lurlVrtmX8UjCwBAIBeJpABAAAAAJ2mIaPrFItL2ZBR1ZABAAB5J5ABAAAAAJ1WWdx4e/9Qu1dCk0qTjCypCGQAAAACGQAAAACQ4ZEl/UaWZFWx0ExDhpElAADQyzRkAAAAAEBmR5YMtHslzEZDRrWJkSU1I0sAACDvBDIAAAAAoJOq5RBqlcbXGVnSlQ0ZtZEmAhnx+w4AAOSaQAYAAAAAZHFcSWRkSWaVCulvrVZGR5YUjCwBAIBeJpABAAAAAJkNZAy1cyXMUkNGtVZ75h99fSEU+lNupCEDAADybpKINrTOU089FX7729+Gv/71r+Gxxx4Lw8PDYfnllw/Pec5zwste9rKw7rrrztqxFi9eHK6++urw5z//OTz66KNhzpw5Ye211w5bbbVV2HDDDcNsuvXWW8O1114b7r777uQxrbjiiuEFL3hB2HrrrcPAgJmvAAAAQAPlhemnpeT9hKwqTRLIqFT/2ZAxOrak1iB8IZABAAC5J5BBW1122WXhuOOOCxdddFGoVqupt3vuc58b3vnOd4b/+I//CIODgzM61oMPPhiOOeaYcOqppyYBkEZe+tKXhiOPPDLsvffeYWmcd9554VOf+lT43e9+1/D6ZZZZJhx00EHhqKOOCqussspSHQsAAADIGQ0ZXalQ6EsKMEbGZS9GVUdHlkTFUgiNyjCqwy1dHwAA0HlGltAWsaXi4IMPDjvuuGM4//zzJw1jRLE540Mf+lDYYostwnXXXTft411++eVh0003DV//+tdTwxjR9ddfH/bZZ59w4IEHJo0W0/X000+Ht771reH1r399ahgjevLJJ8PXvva1ZE2//OUvp30cAAAAIMcqk40smdkfqtDZloxKXSBjTuM7N2rNAAAAckUgg5arVCph3333TZoqJlphhRXClltuGV7+8peHddZZZ4nrFyxYEHbZZZckONGsX/3qV2H33XcPDz300BLHevGLXxzWX3/9UCwW66777ne/G97ylreEkUZ/0pCiVquFN7/5zeG0006r2x73vcEGGySPK45hmdja8drXvjb8+te/bvo4AAAAQC83ZAhkZFkxJZBR15BR6G98ZyNLAAAg9wQyaLk4ouQnP/lJ3bZtttkmXHHFFeGRRx4JN9xwQ7j22mvDnXfeGe64445w+OGHh0Lh2R/Nf/zjH+Htb397KJen/quBRx99NAlJLFr07BsZ6623XjJSJB4rtlj87W9/C7fffnv493//97r7nnvuueF//ud/mn5cX/rSl8KPfvSjum3vete7ksdx2223JY8rHjPud9111x27zcKFC8N+++2XPC4AAACA1EBGbFYo1P9RCdlSGvceVnpDRlogw8gSAADIO4EMWiqGKD772c/Wbdtjjz2SMMa2224b+uKgzXFicCEGOCa2acyfPz+cc845TYUk7r333rHLsani6quvDnvvvXfdsdZee+3wrW99K3zmM5+pu/8nP/nJJNQxlYcffniJ+37uc58L3/zmN8Oaa645ti0GS+I4k7iG2Mwx6u677w7//d//PeVxAAAAgB5QXth4u3aMLm7IqE09skRDBgAA5J5ABi115ZVX1gUc+vv7wwknnLDEyJCJ3va2t4XddtutbtsFF1ww6X3iOJCvfvWrddu+/e1v1wUkJjriiCOSYMio2Fpx7LHHhql88YtfDE888cTY5biPj3zkI6m3X2uttcKJJ55Yty22ccRgBwAAANDjyosbb+8favdKmKZSSiCjuYaMqdtgAQCA7iaQQUvdcsstdZe32mqrSQMS473hDW+ou7xgwYJJb3/GGWeEJ598si4ksdNOO016n9iacdRRR9VtO/nkk8PIyLgXzRPUarVwyimn1G07+uijl2j7mCiu5dWvfvXY5RjoOOussya9DwAAANDDDRmlgXavhFlryDCyBAAAEMigxR555JG6y+uss07T943jS8Z77LHHJr39j370o7rLhx56aFPH2WGHHZLRJqP+/ve/h9/85jept4/jR2Ibx6gNN9wwbL/99k0da+KazjvvvKbuBwAAAORYeVHj7Royurchozoy9ciSmoYMAADIOw0ZtNTyyy9fd3nRopQ3GBqYeNtVVlkl9baxGeOXv/xl3bZdd921qePEZoudd965btuFF16YevuLLrqo7vIuu+wyZTvG+NuOd/nll4ennnqqqfsCAAAAPdaQ0T/Y7pUwTcViEw0ZBSNLAACgVwlk0FJbbrll3eXf/va3k44DGe/aa6+tu/yKV7wi9bY33XRTKJef/auC2HixxhprNL3ObbbZpu7y73//+9TbTrxu6623bvo4cVzL+uuvP3Z5eHg4zJ8/v+n7AwAAADlUWdx4u0BG5pUKjd9erTQ1skRDBgAA5J1ABi31qle9qm70yN133x3OOOOMpkadfPvb3x67HBsoDj744NTb33zzzXWXN91002mtc+LtJ+6vU8cCAAAAenlkiYaMrCumjCyp1mpTjyypDrdoVQAAQFYIZNBSxWIxfO5zn6vb9s53vjP89Kc/Tb3PPffcE3bffffw0EMPjW37j//4j/CiF70o9T633HJL3eV11llnWuucePs77rgjLF68uOEYlTvvvHNWjzVx7QAAAECPMbKka5VSAhkaMgAAgOQ1g9NAqx1wwAHhj3/8Y/jCF76QXH7yySfDa1/72rDjjjuGPfbYI2y00Uahv78//P3vfw+/+tWvwplnnhmeeuqpsfu/7W1vC8cdd9ykx3jggQfqLq+99trTWuPqq68eSqVSqFQqyeVarRYefvjhsNZaa9XdLoZExo9cietebbXVpnWsifucuPalEff14IMPTus+CxYsmLXjAwAAALPZkDHkdHZtQ0YTI0tqRpYAAEDeCWTQFp///OfDS17ykvDhD384aZ+ILr300uQrzRZbbBE+/vGPhze96U1T7j+GPMabN2/etNYXR6IMDg6GJ554InWfjbYNDQ0l952OiWtrdJyZ+sY3vhGOOeaYWdsfAAAA0AZGluS8IcPIEgAA6FVGltA2++23X7jhhhvCQQcdNOVtN9988/DBD34w7L333k3te2KoYWBgYNrri4GMyfbZzuMAAAAAPSQtkFGqfw+BLm3IKKQ0ZFQ1ZAAAQN4JZNAWcRTIZz7zmbDhhhuGU089dcrb33jjjeHtb397cvsLL7xwytsvXry47vKcOSl/eTCJuXPn1l1etGhRx44DAAAA9JDywsbb+wUysq5UaPz2aqXaxMgSgQwAAMg9I0ty7H3ve184/vjjW36co446Khx99NGp1z/++ONh9913D1ddddXYtrXXXjscfvjhYddddw0bbLBBElK4//77w69//etwwgknjI0yueeee8Kee+4ZvvrVr4b3vOc9qceY2FQxPDw87cfx9NNPT7rPdh5npt797nc3NeJlvAULFoR99tln1tYAAAAATFOl/g9AxghkdHFDRm3cjYwsAQCAXiWQQUuNjIyEN7zhDXVhjNe97nXh9NNPD8suu2zdbddZZ53kK442+da3vpWEC+L9o//3//5f2HTTTcOOO+7Y8DjLLLPMpE0WzZjYVDFxn+08zkytttpqyRcAAACQg5El/UPtXgnTVCo2DmRUxo8s0ZABAAA9y8gSWuq73/1uuPjii8cub7LJJuHss89eIowx0bve9a7w8Y9/fOxyDGa8973vTb39xFDDU089Na11xv3PJJCxcOHCsdBIsyaubTYDGQAAAECeRpbMXqsm7W7IaCKQUSu3aFUAAEBWaMjIsdhEscoqq7T8ONtuu23qdRNHphx55JFhcLC5+acf+chHklEljz32WHJ5/vz5SdPGNttss8RtJ7ZC3H333WE64riUSqUydrlQKDQ8d3FbX1/fWAijXC6HBx54IKy++upNHyuOYZls7QAAAECP0ZDRtUqFZhoyjCwBAIBeJZCRY7vsskvy1SmPPPJI+P3vf18Xcth9992bvv+8efPCDjvsEH74wx+ObfvlL3/ZMJDx/Oc/v+7ynXfeOa21Trz9euutFwYGlvwrlBgmWXfddcMdd9xRd9/pBDImHusFL3jBtNYKAAAA9Eogo7k/aiHjDRmFlIaMqoYMAADIOyNLaJnbb7+9bpzHSiutFJZffvlp7WODDTaYtF0iLdQQ2zSm4+abb550f506FgAAANADNGR0rVKh8durlVpt6pElAhkAAJB7Ahm0zNNPP113uVSafiFLf3/9C9ZqtdrwdptttlndbWMY5L777mv6OHEUynhbbrll6m0nXnf11Vc3fZy4pri2UXHNm266adP3BwAAAHKmVg2hWv8eypjSku2ddGFDRmogY7hFqwIAALJCIIOWWXnllesuP/zww2F4eHovNCc2Yqy66qoNb7fsssuGbbfdtm7bL37xi6aOEVs8Lr744rpte+65Z+rt99hjj7rL8b7jm0Am8/Of/7zuchzJsswyyzR1XwAAAKCH2jGi/qF2roQZKKUEMirV8YGMOY3vrCEDAAByTyCDlllrrbXqWjHK5XK48sorm75/rVYLl19+ed22jTbaKPX2e+21V93lk046qanjXHbZZeFvf/vb2OXVV189bLXVVqm333rrrcMqq6wydvm2225bYp1pJq5p7733bup+AAAAQE5VFqdf1z/YzpXQ7oaMWtk5BwCAnBPIoGXmzZsXXvnKV9ZtO/bYY5u+//e+971w9913123baaedUm+///77J8cc9ctf/jJceumlkx4jNlscc8wxddsOPvjgUEiZ/xnF6w466KC6bXEfU7VkXHLJJXWBlNjqsd9++016HwAAACDnygvTrxPIyLxSMaUhY3wgo2BkCQAA9CqBDFrqrW99a93ln/70p+FTn/rUlPe79tprw3vf+966bTvuuGNYe+21U++z2mqrhfe85z11297xjneEe++9N/U+n/vc55Lgxqjll18+fOhDH5pyfR/5yEfqRo1cccUV4Qtf+MKko1fiWsY7/PDD65o2AAAAgB406cgSDRn5aMgwsgQAAHqVQAYtdcghh4TnPe95dds+8YlPJONFrrnmmiVaJWJwIV6/7bbbhieeeOLZH9RCIXz2s5+d8ngf/vCHwxprrDF2OY4iiSNGzj///LpjxeaNd73rXeG//uu/6u4fL6+00kpTHicGKT72sY/VbTviiCPCu9/97roASBy7ct555yVruP3228e2r7nmmuEDH/jAlMcBAAAAck5DRlcrpbSsVmq1qUeWVI0sAQCAvCt1egHkW39/fxJI+Jd/+ZfwyCOPjG2/4IILkq8VV1wxrLvuumHOnDnh/vvvD3feeWfD/Rx//PFhq622mvJ4MUxx5plnht122y0sXvzMDNY77rgj7L333mGFFVYIG2ywQXjssceS41Sr1br7xtt88IMfbPqxxZaMq6++Olx44YVj2775zW+G//3f/w3rrbde0rYRAyHxeOMNDg6Gs846K1kPAAAA0OPKz7x/0VD/UDtXQssaMgQyAACgV2nIoOU22WSTZCzIFltsscR1jz76aPjDH/4QrrvuuoZhjGWXXTaccsopS4wimUxs17jooouWaLqIwYgbbrghCUlMDGMccMABSZCjr6/xi+hGYmvH2WefHfbff/+67XHft912W3KsiWGMlVdeOfz4xz8O22yzTdPHAQAAAHqwIaNQSv8gn8wopQQyKs2MLKlpyAAAgLwTyKAtNttssyR0ceKJJ4aXvvSlU95+9dVXTxoo5s+fHw466KBpH2/HHXdM7nvYYYeFoaH0vyZ58YtfHH7wgx+E0047LcydO3faxxkYGAinn356OOecc8KWW26Zert58+Yl40zimrbffvtpHwcAAADIqfKixttLg+1eCW1vyBh2zgEAIOeMLKFt4liSQw89NPmK40tGWzFii0S5XE5GfKy66qpJSOK5z33uUh8vhjq+8Y1vhC9/+cvJaJGbb745OVZcx1prrZWMQNl4441n5bG94Q1vSL4WLFgQrrnmmnDPPfeE4eHhZCxJbAiJjRgxvAEAAADQVCCjXyCjqxsyquMCGYWUQEatEsLISAjTaGwFAAC6i0AGHRHHiey2225tOdbg4GDYaaedkq9WiwGP2Qp5AAAAAD2gIpDRzYqFQhMNGSkjS5IblkMoTXI9AADQ1YwsAQAAAIBO0ZDR1UrFlIaMWm3qkSWRsSUAAJBrAhkAAAAA0CnlhY23G1nSFYopI0vqGzIEMgAAoFcJZAAAAABA5hoyhtq9EmaglBLIqDQ7sqRWcd4BACDHBDIAAAAAoFOMLOlqGjIAAIDJCGQAAAAAQNYCGaWBdq+EVjVkFIwsAQCAXiWQAQAAAACdYmRJVysWGr+9Wm12ZEnVyBIAAMgzgQwAAAAA6JRKSkNG/2C7V8KsNmTUnr1Q1JABAAC9SiADAAAAADpFQ0ZXK6YEMqrV8Q0ZAhkAANCrBDIAAAAAoFPKCxtv7x9o90qYgVIxrSGjyZElNSNLAAAgzwQyAAAAAKBTNGTksyFjfCCjoCEDAAB6lUAGAAAAAGQukDHY7pUwA6VCEw0ZhUIIfcXGO6gOO+8AAJBjAhkAAAAA0CkCGV2tGMMWUzVkTDa2pGpkCQAA5JlABgAAAABkLZBR0pDR3Q0ZtfoNxZSxJRoyAAAg1wQyAAAAAKBTygsbbzeypCsUUgIZSzZkpAQyauUWrAoAAMgKgQwAAAAA6JTK4sbb+4favRJmtSGj2ZElAhkAAJBnAhkAAAAA0AkjIxoyulwxJZARv7W18aGMgpElAADQiwQyAAAAACBL7RiRkSVd3ZCxREtG2sgSDRkAAJBrAhkAAAAA0AnlRenXCWR0dUNGVK0LZBhZAgAAvUggAwAAAAAyF8gYaudKmKFSIf3t1Uqt9uyFYqnxjarDzj0AAOSYQAYAAAAAdIKGjK631A0ZtXILVgUAAGSFQAYAAAAAdEJ5Yfp1pYF2roQZKhXTAxkVI0sAAKDnCWQAAAAAQCcYWdI7DRkFI0sAAKAXCWQAAAAAQCdUFqVc0RdCaW6bF8NMlCYJZGjIAAAABDIAAAAAIEsNGf1DIfSlf9BPlzRkVMc1ZBTnpNyo3IJVAQAAWSGQAQAAAACdUF7YeHv/QLtXwgyVCulvr1ZqtWcvFI0sAQCAXiSQAQAAAABZa8ig+xsyak00ZNQ0ZAAAQJ4JZAAAAABApgIZg+1eCTNUmiSQUWkmkGFkCQAA5JpABgAAAABkKZBRMrKkWxSLTTZkFIwsAQCAXiSQAQAAAACdYGRJ19OQAQAATEYgAwAAAAA6oWJkSbcrTjKypFqrjbthf8qNyi1YFQAAkBUCGQAAAADQCRoyul6pkP72aqU60kQgY7gFqwIAALJCIAMAAAAAOqG8sPH2/oF2r4QZmqQgI1Rr4wMZcxrfqKYhAwAA8kwgAwAAAAAy1ZAx2O6VMEN9fX2hlJLKqIwPZBSMLAEAgF4kkAEAAAAAnWBkSS4UUwIZ9Q0ZAhkAANCLBDIAAAAAoBM0ZORCUw0ZaSNLqsMtWhUAAJAFAhkAAAAAkKVARsnIknw0ZNTG3UhDBgAA9CKBDAAAAADohPLCxtv7BTK6SalYaKIhIyWQUSu3aFUAAEAWCGQAAAAAQCdUFjfe3j/U7pXQkoYMI0sAAKDXCWQAAAAAQCdoyMiFUjOBjIKRJQAA0IsEMgAAAACgE8qLGm83siQXDRlNjSypGlkCAAB5JpABAAAAAJ0gkNE7DRnFOY3vXB1u0aoAAIAsEMgAAAAAgHYbGRHIyAkNGQAAQBqBDAAAAABotziqYqTa+Lr+oXavhqVQKjR+i7VarU09sqRmZAkAAOSZQAYAAAAAtFt5Yfp1pYF2roS2NGRMMrIktqUAAAC5JJABAAAAAO1WXpR+nYaMrlIqNg5kVMcHMgopDRlRrdKCVQEAAFkgkAEAAAAA7VaZLJAx2M6V0MmGjNHxNQAAQC4JZAAAAABAu2nIyI1SoYmGjOIkDRlxbAkAAJBLAhkAAAAAkKlAxkA7V0JbGjImC2RoyAAAgLwSyAAAAACAdisvTL+uZGRJNykVGr/FWq3VmhtZUhPIAACAvBLIAAAAAIB2Ky9uvL00EELKB/zktSHDyBIAAMgrr+4AAAAAICsNGf3aMbpNKSWQUa2OC2QUJgtkVFqwKgAAIAsEMgAAAACg3cqLGm83riSnDRmTjCzRkAEAALklkAEAAAAA7VZJCWRoyOg6pWJKQ0ZdIKOUvgOBDAAAyC2BDAAAAADISkNG/1C7V8JSKhYKS9eQUTOyBAAA8kogAwAAAADarbyw8fb+gXavhKVUShlZUq3Vnr1gZAkAAPQkgQwAAAAAyExDxmC7V8JSKqYEMuoaMgrFEELj2xlZAgAA+SWQAQAAAADtVl7ceLuRJTlqyBgXyJisJaNqZAkAAOSVQAYAAAAAZGZkiYaMXDZkTBrIGG7BqgAAgCwQyAAAAACArIwsKQlk5KYhozoxkFFqvAOBDAAAyC2BDAAAAABoNw0ZuVEsFJauIaNmZAkAAOSVQAYAAAAAtFtlcePtRpZ0nVIxpSGjVqvfYGQJAAD0HIEMAAAAAMjKyJL+oXavhKVUTBlZskRDRsHIEgAA6DUCGQAAAACQmZElA+1eCUuplBLIqDY7sqRa9j0AAICcEsgAAAAAgHbTkNF7DRkCGQAA0HMEMgAAAAAgM4GMwXavhLY1ZBhZAgAAvUYgAwAAAADaTUNGbhQLhaVryKgZWQIAAHklkAEAAAAAWQlklAbavRJa1pBRq99gZAkAAPQcgQwAAAAAaLfywsbb+4favRKWUjElkFGpTmjIKBhZAgAAvUYgAwAAAADaqVpJH1PRP+h70WVKxbSGjCZHllSNLAEAgLwSyAAAAACAdqqkjCuJBDJy05CxZCCjv/EOBDIAACC3BDIAAAAAoJ3KAhl5UkobWdJsICOtLQUAAOh6AhkAAAAA0E7lhenXacjoOsVCYSlHlgy3YFUAAEAWCGQAAAAAQDuVF6df1z/UzpXQ0oaMWv2GgpElAADQawQyAAAAAKCdNGTkSjElkLFkQ4ZABgAA9BqBDAAAAABop/Ki9OtKg+1cCS1tyDCyBAAAep1ABgAAAAC0UyUlkBFHWhRLvhd5acioasgAAIBeJ5ABAAAAAFloyOgf8n3oQqVisw0ZKSNLauUWrAoAAMgCgQwAAAAAyEQgY8D3oQsVC43fYq02PbJEIAMAAPJKIAMAAAAA2qm8sPH2/kHfhy5UShlZUqnVlhxJ00h1uAWrAgAAskAgAwAAAADaqby48XYjS7pSMSWQEQsyauNbMtJGlmjIAACA3BLIAAAAAIB20pDREw0ZUXVkfCDDyBIAAOg1AhkAAAAA0E7lRY23l4wsyVNDRlRtqiHDyBIAAMgrgQwAAAAAaCcNGblSKqS/xVppJpBRK7dgVQAAQBYIZAAAAABAO1UWN97eryEjdw0ZVSNLAACglwlkAAAAAEAWRpb0D/k+dKFSMT2QUanVnr1QMLIEAAB6jUAGAAAAAGRiZMmA70PeGjKaGVlSNbIEAADySiADAAAAANpJQ0aulCYJZFTqAhlzGt9IIAMAAHJLIAMAAAAAMhHIGPR96MWGjJqGDAAAyCuBDAAAAABoJ4GMXCkVCk02ZKSNLBluwaoAAIAsEMgAAAAAgCwEMkoaMvLXkFGbemTJSC2EWrUFKwMAADpNIAMAAAAA2qm8sPF2I0u6UmmSQEZdQ0YhpSEjqhpbAgAAeSSQAQAAAADtVFnceHv/kO9DFyoWJwlkVJsYWRIZWwIAALkkkAEAAAAA7aQho2caMqq1ZgMZGjIAACCPBDIAAAAAoJ3KixpvN7KkKxWbHVlSnJO+k5pABgAA5JFABgAAAAC0S602yciSQd+HLlQqFGahIWN4llcFAABkgUAGAAAAALRLWhgj6h/yfehCkxRkhEoM4Izd0MgSAADoNQIZAAAAANDpcSWRhoyu1NfXlzq2pNrsyJKqkSUAAJBHAhkAAAAA0C7lhenXlQZ8H7pUWiCjYmQJAAD0NIEMAAAAAGgXI0tyqZTWkFEd35BhZAkAAPQagQwAAAAAyEJDhpElOW/ImGRkSc3IEgAAyCOBDAAAAABol/Ki9Ov6h3wfulRqQ8b4QEahlL6D6nALVgUAAHSaQAYAAAAAdLoho68w+UgLMq1YaPw2a6VWe/ZCX18IhZTvsUAGAADkkkAGAAAAALRLeXF6O0b8wJ78NmRMNrakWmnBqgAAgE4TyAAAAACATjdk9A/6HnSxYkogo7JEIENDBgAA9BKBDAAAAABol/KixttLAhndrFRstiFDIAMAAHqJQAYAAAAAdDqQoSGjRxoyUkaW1IwsAQCAPBLIAAAAAIB2qQhk5FEpJZBRrdbqN2jIAACAniKQAQAAAAAdb8gY8j3oYsVCobmGjELayJJyC1YFAAB0mkAGAAAAALRLeWHj7f0Dvgd5bMhodmSJQAYAAOSSQAYAAAAAtIuGjFwqpgQylmjIMLIEAAB6ikAGAAAAAHQ8kDHoe9ATDRkpI0tqRpYAAEAeCWQAAAAAQLsIZPRUQ4aRJQAA0NsEMgAAAACg04GMkoaMblYqNhnIKJQa76A63IJVAQAAnSaQAQAAAADtUl7YeLuRJV2tWGj8NmtliZElcxrvoGpkCQAA5JFABgAAAAC0S2Vx4+39Q74HXayUOrKkVr9BIAMAAHqKQAYAAAAAtIuGjFwqpgQylmzIMLIEAAB6iUAGAAAAALRLeVHj7UaW5LQho8mRJTUjSwAAII8EMgAAAACgXQQyerwhIyWQURXIAACAPBLIAAAAAIB2EcjorYaM6oRARsHIEgAA6CUCGQAAAADQ8UDGkO9BFysWGr/NqiEDAAB6m0AGAAAAALTDyEgI5YWNrysN+B7ksSGjVqvfYGQJAAD0FIEMAAAAAGiH6nBMZTS+TkNGVysW+5psyDCyBAAAeolABgAAAAC0Q1o7RtQ/6HuQy4aMiYGMOY13UCu3YFUAAECnCWQAAAAAQDuUF6VfpyGjqxULzTZkpAQyqgIZAACQRwIZAAAAANDxQMaA70EvNGQUjCwBAIBeIpABAAAAAO2gISO3ioXGb7NqyAAAgN4mkAEAAAAAHQ9kDPoe5LIho1a/wcgSAADoKQIZAAAAANAO5YXp15WMLOlmxZRARqU6YWRJMWVkSa3cglUBAACdJpABAAAAAJ1syCgNhtDX+AN9ur0hY2IgY07jHVSHW7AqAACg0wQyAAAAAKAdKimBDONKul6xmNKQ0XQgQ0MGAADkkUAGAAAAAHSyIaN/yPnvlYaMQsrIEoEMAADIJYEMAAAAAGiH8sLG2/sHnP8uVyw0fptVQwYAAPQ2gQwAAAAA6GhDxqDzn9uGjFqTI0uGW7AqAACg0wQyAAAAAKAdyosbbzeypOsVUwIZSzZkpIwsqZVbsCoAAKDTBDIAAAAAoKMjSzRk5LchY6S5hoxaJYSJbRoAAEDXE8gAAAAAgE6OLCkJZOS2IaM6IZBR6E/fiZYMAADIHYEMAAAAAGgHDRm5VSo225AxSSCjamwJAADkjUAGAAAAALRDZXHj7f1Dzn+XKxYav81aaXZkSVQdnuVVAQAAnSaQAQAAAADtoCEjt0opI0uqtVr9Bg0ZAADQUwQyAAAAAKAdyosab+8fcP67XDElkFGZzsiSmpElAACQNwIZAAAAANDRQIaRJfltyDCyBAAAeplABgAAAAB0NJAx6Pz3SkNGYZKGjKqGDAAAyBuBDAAAAABoBw0ZuVUqFJpsyBDIAACAXiKQAQAAAADtUF7YeHtpwPnPaUNGDGSMjIwLZRTnpO+kOtyClQEAAJ0kkAEAAAAA7VBZ3Hh7/5Dz3+VKxcaBjCVaMiZryKhVZnlVAABApwlkAAAAAEBHR5YMOv85bciIKuMDGYXJRpZoyAAAgLwRyAAAAACATo4sEcjoeqVCkw0ZhUIIhVLKDQUyAAAgbwQyAAAAAKDVquX0kRQCGb3TkDFZS0bVyBIAAMgbgQwAAAAA6NS4kkggo+uVYvNFMw0ZUXFOyg01ZAAAQN4IZAAAAABARwMZQ85/rhsyahNunNaQIZABAAB5I5ABAAAAAK1WXph+nYaMrleaJJCxZENGSiAjbaQNAADQtQQyAAAAAKCTDRmlQec/zw0Z1SYDGRoyAAAgdwQyAAAAAKDVKpONLBHI6Hal4nQaMuak3NDIEgAAyBuBDAAAAADoZEOGQEa+GzImBjIKaQ0ZRpYAAEDeCGQAAAAAQKcCGcW5IRSKzn+XKxUK02jIMLIEAAB6hUAGAAAAALRaeWHj7f0Dzn3uGzJqE24skAEAAL1CIAMAAAAAWq28uPH2/iHnPgdKkwQylmzImNP4hjUjSwAAIG8EMgAAAACgYw0Zg8597hsyjCwBAIBeJZABAAAAAK1WXtR4e0kgo+caMgpGlgAAQK8QyAAAAACAVtOQ0bsNGdUmR5ZUjSwBAIC8EcgAAAAAgFarLG683ciSXOjr60sNZSzRkFHUkAEAAL1CIAMAAAAAOjWypH/Iuc+JtEBGpVabcEOBDAAA6BUCGQAAAADQsZElA859TpSabshIGVlSM7IEAADyRiADAAAAAFpNQ0YPN2QYWQIAAL1KIAMAAAAAOhbIGHTue60ho5A2sqTcglUBAACdJJABAAAAAK2mISP3ioVCkw0ZKSNLBDIAACB3BDIAAAAAoNXKCxtvLw0497lvyKjVbyimNWQMt2BVAABAJwlkAAAAAECrVRY33m5kSW4UUwIZlepIc4GMmpElAACQNwIZAAAAANBqRpbkXqmY1pBhZAkAAPQqgQwAAAAA6NTIEg0Z+W/IWCKQYWQJAAD0CoEMAAAAAOhYQ8agc58TpUKTDRmFtECGkSUAAJA3AhkAAAAA0GoCGblXLBSabMiY03gHAhkAAJA7AhkAAAAA0LFAxpBzn/uGjFr9BiNLAACgZwhkAAAAAEAr1aohVJ9ufJ2RJblRTAlkLNmQkTKypGZkCQAA5I1ABgAAAAB0oh0jKg0693lvyKgaWQIAAL1KIAMAAAAAWqmyOP06DRm915BRKDXeQXW4BasCAAA6SSADAAAAAFqpvDD9OoGM3CgVUxoylhhZMqfxDqpGlgAAQN4IZAAAAABAp0aW9A859zlR6GuyIUMgAwAAeoZABgAAAAB0rCFjwLnPiVLKyJJqrVa/oWhkCQAA9AqBDAAAAABopfLi9Os0ZORGsVBYuoaMWjmEkQm3BQAAuppABgAAAAB0oiGjUAqh2O/c574ho8lARlSrzPKqAACAThLIAAAAAIBWKi9qvL006LznSLHY11xDRmGSEE61PMurAgAAOkkgAwAAAAA6EcjoF8joiYaM6sSGjMkCGcOzvCoAAKCTBDIAAAAAoJUqAhm9oFhosiFjspElGjIAACBXBDIAAAAAoCMNGUPOey80ZNRqzTdk1IwsAQCAPBHIAAAAAIBWKi9svL1/wHnPkWKh8VutEyeWGFkCAAC9QyADAAAAAFpJQ0ZPaL4hw8gSAADoFQIZAAAAANCRQMag854jxZRARmViRUZhkpElVSNLAAAgTwQyAAAAAKCVBDJ6vCFjQiCjOFkgY3iWVwUAAHSSQAYAAAAAdCKQUdKQkSfFYkpDxhKBDCNLAACgVwhkAAAAAEArVYws6QWz0pBRM7IEAADyRCADAAAAADoysmTIec+RYqHxW62VWq1+Q6EYQl/K27JGlgAAQK4IZAAAAABAK5UXNt7eb2RJTzZkTDa2pKohAwAA8kQgAwAAAAA60pAhkJEnxZRARqVRIKOQMrZEIAMAAHJFIAMAAAAAWkkgoydMryEjLZAxPMurAgAAOkkgAwAAAABaSSCjtxsyqtMJZBhZAgAAeSKQAQAAAAAdCWQMOe8925Axp/FOagIZAACQJwIZAAAAANBK5YWNt5cGnPccKRYbv9VaqdUa3NjIEgAA6AUCGQAAAADQSpXFjbdryOjdhoyCkSUAANALBDIAAAAAoFVGRtIbMvoHnfccKaYEMirTGVlSNbIEAADyRCADAAAAANrdjhFpyOjdhgwjSwAAoCcIZAAAAABAq5QXpV/XP+C892xDRsrIkpqGDAAAyBOBDAAAAADoSCBjyHnPkVKhMI2GDCNLAACgFwhkAAAAAEBHAhmDznsvNGRUa803ZFSHZ3lVAABAJwlkAAAAAECrlBemXyeQkSullEBGw4aMQlogw8gSAADIE4EMAAAAAOhEQ0ZJQ0aeFIspDRkNR5YIZAAAQC8QyAAAAACAVqmkBTL6QijNdd57tSGjOKfxTowsAQCAXBHIAAAAAIB2N2T0D4XQ1/gDfLpTsZDekDEyMtJcQ0bNyBIAAMgTgQwAAAAAaJXywsbb+wec85wpFdLfal2iJMPIEgAA6AkCGQAAAADQiYYMeqIhI6rUahNubGQJAAD0AoEMAAAAAGh7IGPQOc+Z0iSBjOrEioxCysiSqpElAACQJwIZAAAAANAqAhk9Y/KGjAmBDCNLAACgJwhkAAAAAEC7AxklDRl5UypO0pBRnRjIMLIEAAB6gUAGAAAAALRKxciSXlGajYaMWmWWVwUAAHSSQAYAAAAAtH1kyZBznjPFQvpbrdWmR5YMz/KqAACAThLIAAAAAIBWKS9svL3fyJLeasio1W8wsgQAAHqCQAYAAAAAtL0hY8A5z5niJIGMJRoyCmkNGUaWAABAnghkAAAAAECrGFnSMyZvyDCyBAAAepFABgAAAAC0PZBhZElPN2QUUxoyauVZXhUAANBJAhkAAAAA0CoaMnpGqZD+VmulOjGQMafxDasCGQAAkCcCGQAAAADQKuWFjbeXBpzznCkWp9OQkRbIGJ7lVQEAAJ0kkAEAAAAArVJZ3Hh7/5BznjOlSUaWVGq1+g2FUuMbasgAAIBcEcgAAAAAgHY3ZPQPOuc5UyzMRkOGkSUAAJAnAhkAAAAA0CrlRY23C2TkTrFvsoYMI0sAAKAXCWQAAAAAQKsIZPSMQqEvpJVkLNmQkTKypKYhAwAA8kQgAwAAAABaYWREIKPHlAqFpWvIGKmFUKu2YGUAAEAnCGQAAAAAQCtUyyGMpHy43j/knOdQMaUio1qrNRfISG48PMurAgAAOkUgAwAAAABaobww/br+Qec8h0opgYxKdUJDRiFlZMlokAcAAMgFgQwAAAAAaIXyovTrSgIZeVQspjVkNDmyJLmxQAYAAOSFQAYAAAAAtEJlkkCGhozeasiYViDDyBIAAMgLgQwAAAAAaHdDRv+Qc55DxUKzDRmTjCypacgAAIC8EMgAAAAAgLYHMowsyaNSoTALDRkCGQAAkBcCGQAAAADQCuWF6deVBpzznmrIqE24oZElAADQCwQyAAAAAKAVyovTwxgpTQp0t1JKIGOJhozCJCNLNGQAAEBueOUHAAAAAO1syDCupAcbMowsAQCAXiSQAQAAAACtUF7UeHtp0PnusUBGpTqdQMbwLK8KAADoFIEMAAAAAGiFSkogQ0NGbpWKTTZkFIrpO6mVZ3lVAABApwhkAAAAAEA7GzL6h5zvnCoWGr/dWpkYyOjrC6EvJZRRq7ZgZQAAQCcIZAAAAABAK5QXNt6uISO3SikjS6q12pIbC6XGOxHIAACA3BDIAAAAAIC2NmQMON85VUwJZCzRkDFZIGNEQwYAAOSFQAYAAAAAtEJ5cePtRpb0YENGo0BG2siSyiyvCgAA6BSBDAAAAABoBSNLes70GjIEMgAAIO8EMgAAAACgrSNLhpzvnJpWQ0ZfWiDDyBIAAMgLgQwAAAAAaGdDRmnA+c6pYqHx262VaqOGjFLjnQhkAABAbqT8v35ovSeeeCJcddVV4S9/+Ut4/PHHw+DgYFhvvfXC1ltvHdZcc81ZPdZNN90Urr/++nDfffeFarUaVl555bD55puHrbbaKpRKpa58TAAAAEDGVRY33t4/2O6V0PGGjFrzI0tGNGQAAEBeCGSQuO2228K1114brrnmmuR/b7jhhrBo0bO1mtttt124/PLLZ+Vs/e1vfwuf+MQnwllnnRWGh4eXuL6vry853jHHHBO23XbbGR9nZGQknHLKKeELX/hCEpBoJAYzDjvssPDRj340zJs3L/OPCQAAAOgiRpb0nGKxcSCj0mhkSVogo1aZ5VUBAACdYmRJDzv//PPD6173urDqqquGjTbaKLzlLW8Jxx13XLj66qvrwhizKQYWYjPF97///YbBhdEgRQx/bL/99klQIl6ersceeyzstttu4dBDD00NY0QPP/xw+PSnPx222GKLpEUjy48JAAAAyMnIEg0ZPdiQMZ2RJQIZAACQFwIZPezSSy8NP/7xj8NDDz3UluOdffbZSehj4cL6NyNiIOQlL3lJWHvttZMmiVExtBDbLd7//vdP6zgxTBLDGL/4xS/qts+ZMyc873nPCy984QuXaMOIDSE77LBDWLBgQSYfEwAAAJCnhgwjS/KqWJhGQ0ZfWkOGkSUAAJAXAhk0tDTjOxq59dZbw8EHHxxq4+ZlvuhFL0pCIQ888EC4/vrrw1133RVuvvnmsO+++9bdN7Z2nHvuuU0fK4Yd4tiVUYVCIRx55JHh73//e7jlllvCH//4x/DII48k40xWXHHFsds9+OCDYb/99gvVajVzjwkAAADoQgIZPWd2GjIEMgAAIC8EMggrrbRS0ijx8Y9/PPzoRz8K9913X/ja1742q2cmBiKeeuqpscsvf/nLwy9/+cuklWK85z//+eGcc84J73znO+u2f/jDHw6VytR1jX/+85/Dt7/97bptcZTIJz/5ybrwRWzLOOigg8KVV14ZVlhhhbHtN9xwQ/jud7+bqccEAAAA5C2QMdTuldAmxUKh+YaMQkpDxohABgAA5IVARg/7t3/7t2REx8MPPxx++tOfhk996lNhr732CmusscasHuemm24KZ555Zl0Y4jvf+U5YbrnlGt4+jvg4/vjjw3Of+9y6NorYaDGVo446qq7h4m1ve1syUiTNZpttFo499ti6bcccc0wol8uZeUwAAABAl9KQ0XPSGzKebVidMpBR8wc8AACQFwIZPSyGETbaaKOWH+fkk0+uG+ux//77h0022WTS+wwMDISPfvSjddtOPPHESe/z6KOP1o0BiSGIo48+esr1xbEj66233tjlO+64I1x88cWZeEwAAABAFysvbLy9NNjuldAmxZRARqXaoCGjTyADAADyTiCDljv//PPrLh966KFN3e/Nb35zmDdv3tjl6667Ltx7772pt7/ooovqRoBsv/32YcMNN5zyOIVCIQlljHfeeedl4jEBAAAAXapaCaGW0sDZL5DRew0ZjUaWlBrvpGZkCQAA5IVABi11yy23JGNRRsUwwtZbb93UfSfedmRkJAldpJl43a677tr0OnfZZZe6yxdeeGEmHhMAAADQpSqL0q8TyMitYjGlIUMgAwAAepJABi31+9//vu7yK17xilAqpaT/G9hmm20m3d9k1zUbkohe+tKXhrlz545djq0VDz74YMcfEwAAANClypMFMobauRIy25CRMrJkREMGAADkhUAGLXXzzTfXXd50002ndf+Jt5+4v1HlcrmutWK6x4phjI022qipY7XrMQEAAABdrLww/br+gXauhDYqFhq/3Vqp1ZoPZNSeHckLAAB0N4EMWiqO9xhvnXXWmdb9J95+4v5G3XbbbaFSefbF6uDgYFhllVVacqx2PSYAAACgi5UXp1+nISO3ptWQ0SeQAQAAedf8nAWYgQceeKDu8tprrz2t+6+11lp1l9PGiEw8zsT7zeRYE/fZ7sc0E3Ft093f/Pnz6y5PbBoBAAAAZuD++SE8kDJ64q+3hzBn9t4PIDseuOPOMPzgHUtsf6T0SLjppuXrN977VOOfkdv+HsJNN7VwlQAA0FsWTPj88+mnn27bsQUyaKknn3yy7vK8efOmdf+Jt4+jSeIvSBwxMpvHaXSfifts92OaiW984xvhmGOOWap97LPPPku9DgAAAGAS39zK6ekx94UQNv9Ms7f+9j+/AACAVrjrrrvCS17yktAORpbQUhPDCwMD05uRGkePTLXP2ThOo2M1G8ho1WMCAAAAAAAAoHsJZNBSixfXz0udM2fOtO7fqDVi0aJFs36cRsdqdJx2PiYAAAAAAAAAupeRJR3wvve9Lxx//PEtP85RRx0Vjj766NBJE9sjhoeHp3X/RvN7GjVSLO1xGh0rrfmiXY9pJt797neHN73pTdO6z+OPPx5++9vfhuWWWy6ssMIKYZ111lmq8SkHHXRQsr+Xvexl4dRTT53xftq9/9nab5xBNX7sy3nnnRc23njjWVol3aDVvwN5kcfz1A2PKQtrbPca2nE8z01kWRZ+77tF3s5VNzyerKzRc1P7z5PXTb0tK7/7WZfH89QNjykra8zbc5PXTGRdVn73sy6P56kbHlMW1tiJNXhu8lnTbIufz8YxJaO222670C4CGbTUMsssM2m7xFQaNUdM3OdsHKfRsRodp52PaSZWW2215Gu6XvWqV4XZMm/evLH/3WyzzWZtv63ef6v2G8MYrTgPZFerfwfyIo/nqRseUxbW2O41tON4npvIsiz83neLvJ2rbng8WVmj56bOnyevm3pLVn73sy6P56kbHlNW1pi35yavmci6rPzuZ10ez1M3PKYsrLETa/DcVM9rptnxkpe8JHSCkSW01MSgwVNPPTWt+0+8falUatgmsbTHaXSfZgMZrXpMAAAAAAAAAHQvDRkd8LrXvS6sssoqLT/OtttuGzptYlvD3XffPa3733PPPXWXV1111aaOM/F+MzlWWtNEux4TAAAAAAAAAN1LIKMDdtlll+SrFzz/+c+vu3znnXdO6/4Tb/+CF7yg4e023HDDpGmiUqmMjQV58MEHpxV2aPZY7XpMAAAAAAAAAHQvI0toqYlhg/nz50/r/jfffPOk+xvV398fNtpooxkf6+mnnw633XZbU8dq12MCAAAAAAAAoHtpyKClttxyy7rL1113XdJiEdssmnHVVVdNur+J191yyy1jl6+++uqw3XbbNXWc66+/PglljHrOc56TOrKknY+pGx100EFh++23D+uvv35X7b/V66Z3+Fnq3fPUDY8pC2ts9xracTzPTWRZFn7vu0XezlU3PJ6srNFzUzbPE/nlZ6l3z1M3PKasrDFvz01eM5F1Wfndz7o8nqdueExZWGMn1uC5iTzpGxkZGen0IsieU089NRx88MFjl2Ow4fLLL5/RvjbeeONw6623jl2+4oorwrbbbjvl/Z566qmwxhprhCeffDK53NfXF+6+++6w5pprNrz997///fC2t71t7PIOO+wQLr300qbWeMwxx4Sjjz567PI73/nOcMIJJ3T8MdF9brrpprD55puPXb7xxhvDZptt1tE1AdDbPDcBkDWemwDIEs9LAGSN56Z8MbKElttrr73qLp900klN3e/MM88cCy5EL3vZyyYNLuy+++51LRUxQDJxDEkjMZMUAyjj7b333pl4TAAAAAAAAAB0J4EMWu6QQw5JmiBGnXHGGeHmm2+e9D6LFy8On//85+u2HXrooZPeZ6WVVgr77LNPXdBifOtFmpNPPjncfvvtY5fXW2+9sPPOO2fiMQEAAAAAAADQnQQyaLk4vmG//fYbuzw8PBwOPPDA8Pjjjze8fQxSvO997wt//etfx7ZtuOGGSQiimdEjhcKzP9bf+973wumnn556+/nz54cPfvCDdduOPPLIMGfOnMw8JgAAAAAAAAC6z7PzHehJF198cWpQYbxHH3009bYxWBC/JvPpT386XHDBBWHhwoXJ5euuuy5su+224bjjjgvbb7/92O3+8pe/hCOOOCKce+65dfePzRL9/f1TPp5NN900vOMd7wj/+7//O7btrW99a9Je8Z//+Z9hxRVXTLaVy+Vw2mmnhfe///3hscceG7vtFltskQQrmtGuxwQAAAAAAABA9xHI6HG77LJLU7f74x//mHrbo446asrRIBtvvHE46aSTwgEHHJC0RUR/+MMfwg477BBWXXXVsO6664YHHngg3H333WPXj3rve98b3vSmNzX9mP7nf/4n/O53vwu//e1vk8u1Wi186lOfCl/4whfCBhtsEObOnRtuu+228OSTT9bdb5VVVglnn312KJWa+7Vo52MCAAAAAAAAoLsIZNA2+++/fxJMOPTQQ8OiRYvGtj/44IPJVyNxnMgXv/jFaR1naGgo/OxnP0sCD5deemndWJFbbrml4X3WX3/9cP7554fnPe95mXxMdI8YxokhpfGXAaCTPDcBkDWemwDIEs9LAGSN56Z86RuZ+Kf79JS+vr6l3kczDRnjxXaKI488MmmjiKNDGomjPz75yU+G7bbbbsbris0YscEihh8WLFjQ8DYrrbRSeNe73pWMFFlmmWVmfKx2PSYAAAAAAAAAuoNABh3z+OOPh1/96lfhr3/9a3jiiSfCwMBAMuZjm222CWuttdasHutPf/pTMsbkvvvuC9VqNay88sph8803D1tttVXo7+/vyscEAAAAAAAAQHYJZAAAAAAAAAAAzLLCbO8QAAAAAAAAAKDXCWQAAAAAAAAAAMwygQwAAAAAAAAAgFkmkAEAAAAAAAAAMMsEMgAAAAAAAAAAZplABgAAAAAAAADALBPIAAAAZtXtt98ell122dDX15d8rb/++s4wAG0Rn3NGn3+m+jruuON8VwAAgJ5x0EEHNf16afwXS6e0lPcHoA3K5XL4yU9+Eq6//vrk67bbbgsPP/xwePTRR8PcuXPDmmuuGV72speFN7/5zWHPPff0BAlAx4yMjIRDDz00PPnkk74LAAAA//T000+H0047Lfzwhz8Mf/zjH8P9998fBgYGwuqrrx5e+MIXhu233z7867/+a1hxxRWdMwAy4wUveEGnl9D1+kbiO6YAZNrdd98d1llnnaZuu/XWW4dzzjknPOc5z2n5ugBgom9+85vh3e9+d/KmYnyDMVpvvfWS1gwAaEdDxh133JEE1k855ZRJbxtfM6288sq+KQC03JVXXhkOOeSQsGDBgklvd9lllyXBDABohXvuuSf5Q9+pfOITn0gChNGXvvSl8MEPftA3ZCloyADoEiuttFLYbrvtkjcWN9hgg7DGGmskifl//OMf4Q9/+EM48cQTw5/+9Kdw9dVXh5122in87ne/S1L2ANAu8QOwD3/4w8m/v/rVr4b99tvPyQegI+bNmxc233xzZx+AjvvpT38aXv/614fFixcn79UdfPDBYddddw1rr7120jAYX0f96le/GvvgCwBaZa211kq+JhOfry6//PLk3/39/eHtb3+7b8hS0pAB0AVqtVryv4VCIfU2lUol7LvvvuGCCy5ILn/9619P/kIZANpl5513DpdccknYZ599kjcTR2dMasgAoN0NGTHMPvomIgB0yl133ZUEBB9//PHw3Oc+NwlnbLjhhpOOLY4ffgFAp5x++unhgAMOSP4dA4Xnnnuub8ZS0pAB0AUmC2KMKpVK4YgjjhgLZMSKQ4EMAMa79dZbw7XXXpuMwhoeHk6aluIcyDjuamlblU444YQkjLHCCiuEb3zjG048AB1/bgKATjv88MOTMMbcuXPD+eefP2kYIxLGAKDTr5lOOumksX8feuihviGzQCADIEdvLC633HJj/44v9gDI9szG+DxxzTXXJP/729/+NjzxxBNj189mq8R5550XPvWpTyXjrBpZZpllwkEHHRSOOuqosMoqq0x7/3feeWf40Ic+NDZX8jnPec5SrxmA9svTc9No0+B9990XnnrqqeT12KqrrrqUqwYg72bz/by//e1v4Uc/+lHy7/333z/ZDwDdLW+vmSaKbYOXXnpp8u8111wzvOY1r5mV/fY6gQygJ+T9SXLU97///bF/e5EHkD1XXXVV+PKXv5w8H917770tP97TTz+dJNlPO+20SW/35JNPhq997WvhzDPPDOecc07Ydtttp3Wcf/u3f0ueV3fcccfwjne8YylXDUA75fW56frrrw8rrbRS+Mc//jG2LQYydtlll/C+970vvPzlL5/xYwCgPbr9/bz4HDY6hjhWvo9/LozPubHtdvXVVw9z5syZlccAQGvk9TVTI6ecckoYGRlJ/n3ggQeGYrG41PskhL6R0bMK0MNPkrPxAq7ZJ8nxbwYu7ZNkfFH3wAMPhD//+c/h29/+dvi///u/ZHt8Iff73/8+bLLJJjPeNwCz77jjjgv/+Z//2dRtl/a5KT5H7LvvvmN/kTUqvpBad911w/LLL5/8xdb4D6qioaGhcPHFF4dXvepVTR3nxBNPTAIZ8X5//OMfw0YbbTR2XV9f36w8FgBaJ2/PTeuvv37yV12Tic9P73//+8MXv/jFpsZDAtA+eXo/b++9907GlERxnTFM8vGPfzz8+Mc/DuVyOdkeWzd22GGH8JGPfCRst912S/VYAGiNvL1mmuzYcbTW6Oupv/71r2HjjTee8f54lledQG5dd9114Yc//GFbEovxierNb37zEi/e4pPkBhtsELbccsvkiXK8Bx98MLz2ta8Nv/71r6d1rIceeih5AzF+xf3HWvj4wm00jBGPEx+3MAZAd4l/cTWb4uiQiS/e3vWudyXjRW677bZwww03hEceeSSce+65yQu6UQsXLgz77bffEi/sGok1vh/4wAeSf3/yk5+sC2MA0P268bkpvj6Kz00/+9nPknElsW4+3u83v/lN+H//7/+F/v7+5C++4od9H/zgB2f18QGw9PL0ft6NN95YFzR52cteljwPjoYxosWLF4ef/OQnYfvttw9HH330rDwuANqnG18zpbnkkkvGwhgxJCiMMXsEMoCelKcnyYl/5XXLLbeE3Xfffan3B0DrLLvssskbbh/60IfC2WefnSTnL7jgglnb/8MPPxw+85nP1G373Oc+F775zW8m8x9Hxb8KjtW5V199dfIXxeODFv/93/895XFiM8bjjz+e1L7H+ncAuldenpvi/Y499tiw6667hjXWWCMJYCy33HJhq622Cscff3y47LLLkr8eG/1Lt/jBHwDdodvez4v3HRVbOGJIMIYG//KXvyTNHHfddVeyhtHnpWOOOSaceuqps/oYAZg9eXnNlOakk06qe95i9hhZAuS+Rio+Sb70pS9NPix6xStekfxvrHSKrRKzUSMVnyRjan78DMv4JPnRj340df7lv/zLv9Qd7xOf+ETyoqsZ1Wo13HzzzWP/jsePqfz//d//Tfb9mte8JpxwwglhrbXWmtHjAaB1br311uSNtxe84AVLVKRffvnls/bcFOtuYw37qFinG/c/OkIkLQW/8847j12Oz5/x+XLllVduePuTTz45eXEWP+i6/vrrwwtf+MIlbmNkCUD25em5qVn/8z//k4TZo4MPPjh5TgMgG/L0fl6pVEreuxv/2A4//PAlbveLX/wi7LbbbkmDUwwTxscZR5kAkA298Jrp0UcfTdoG4+OM7VCxbXBwcHBGj4MlacgAcmvPPfcMN910U3jssceSv4KKT2RvfOMbkyfE2RT3O/7FW3ySjE+caWJQ4sQTT1ziDcH4QrAZsTZx8803T75e9KIXhR133DH813/9V/JYd9ppp3DRRRclL1hHQxsAZEcc6bHpppu2dF59rN095ZRT6rbF6tvJXrxF8Tnk1a9+9djl+Nx21llnpb4ZOfpBVnzOaxTGAKA75OW5aToOPPDAscd7xRVXLPX+AJg9eXo/b/wHWc997nOT0VmN7LLLLmGvvfZK/v33v/89+XANgOzohddMcXxXDGNEb3nLW4QxZplABpBbvfAkObG28Xvf+17yRHn//fcnFYsA9J5YVxjnGo/acMMNkzrFZkysIzzvvPMa3u4rX/lKUs8bq3XjPMkzzjij4deop556amzbbFY5AtAd2vHcNB0rrbRSWGWVVZJ/x7/8AiA78vR+Xvwr5VFxvPBk+3/d61439u9rr712yscAQL50+jXT+HElhxxyyLTvz+RKU1wPQAufJK+88sq6J8nDDjtsqc73aqutltQnxqrDX/7yl8mbi7FmCoDeEZuSJv611VRvLI6/7XixFjGGKebNm1e3fTQxH2cnH3TQQVPu96GHHkrS9VH8y7b4V28A9I52PDdN12iFfKyTB6C3tOv9vPjaZzT4t+6660663/HXP/DAA02tBYD86ORrphtuuCH8/ve/T/4dW3DjmDBml4YMgIw9SS6t0b/0imY6qwyA7jX6AmrU1ltv3fR911xzzbD++uuPXR4eHg7z58+f1fUB0Huy9tx09913j1XMr7322ku1LwC6T7vez4vjhicGAdOMv15YEKD3dPI108knn5zatsHsEMgAyNEbi6NvLjaqRgSgN9x88811l2Pd73RMvP3E/UXHHXdcGBkZmfJr/F+GjW4TFgToPe14bpqOr371q3X18wD0lna9n7fDDjuM/fuvf/3rpPtdsGDB2L+FBQF6T6deM8UW3NNOOy3595w5c8Jb3/rWaR2X5ghkAOTojcX4IddvfvOb5N+xjirO3QSgdyxatCjceeedddvWWWedae1j4u1vueWWWVkbAL2pnc9NF154YXjyyScn3dd3v/vdcOyxxyb/7u/vD+9973untRYAul+73s/ba6+9wuDgYPLv888/f2z0YyNnnXXW2L+bHZ8CQD508v28H/7wh+HRRx9N/r3PPvuElVdeeVrHpTkCGQBd8CQZE4oPPfTQpPuKsy/322+/UC6Xk8tvectbxl70AdAb4nPF+GaK+EHTaqutNq19rLXWWnWXzS8GoFuem2LQIt42/lXXCSeckNTIx7+CjqH1U045Jey6667hwAMPDLVaLbn9F7/4xfC85z1vRo8LgO7UzvfzlllmmfC+970v+ff9998fPvCBDzS83Te/+c1w1VVXJf9++ctfHrbaaqtprQeA7tbJ9/OMK2mPUpuOA5A77XyS/Pa3vx3e8Y53hN133z2pO4xJ/BVXXDFUKpVwzz33hCuuuCJ85zvfGUsybrzxxuHzn//8jB4XAN1r4l8FDw0NNT0LeVRsWJpsnwCQ5eemxx9/PAm0j9bupn1Advzxx4dDDjlkWusAoPu1+0Ovj33sY+GCCy4IN954Y/j6178ebr311vDOd74zGXsS73f66acn7U2jz3fxPUAAekun3s+LAcVLLrkk+fe6664bdt5552kdk+YJZAB0yZPk4sWLw7nnnpt8TSaGNk466STVUgA9aOLzyMDAwLT3MbFdSSADgG55bvryl78cLrvssnDttdcm9fEPP/xw8lUsFpPXR1tssUXYZZddkpaMGHAHoPe0+/28GAL82c9+Fl7/+tcnz08//elPk6+JVl999XDOOeeEF73oRdNaCwDdr1Pv58UWwdH2wIMOOigUCgZrtIpABkAXPEl+73vfCz/5yU/C1VdfHebPn5/UHMYRJdVqNSy//PJJI0asM9x///3VGgL0sBjeG2/OnDnT3sfcuXOXqPSdqfF/eQZAb2rnc9NLX/rS5AsAsvSh15prrpm8p/f9738/nHHGGeEPf/hD0tSx7LLLhk022STsueee4bDDDgvLLbecbxxAD+rU+3lHHXVU8kXrCWQAdMGTZJxNGesM4xcApJn4ZuLw8PC0T9bTTz896T4BYDo8NwGQJZ360Cu2NcWGpvgFAON5zZR/ukcAZsiTJABZE+twJ3uzsRkT30ycuE8AmA7PTQBkiffzAMgar5nyTyADYIY8SQKQ9eemhQsXTntsyFNPPTXpPgHAcxMA3cr7eQBkjffz8k8gA2CGPEkCkDWrrLJK6OvrG7tcLpfDAw88MK193HPPPXWXV1tttVlbHwC9x3MTAFni/TwAssZrpvwTyACYIU+SAGTN4OBgWHfddeu23XnnndPax8Tbv+AFL5iVtQHQmzw3AZAl3s8DIGu8Zso/gQyAGfIkCUAWTQxQzJ8/f1r3v/nmmyfdHwBMl+cmALLC+3kAZJHXTPkmkAGwFDxJApA1W265Zd3lq6++uun73nfffeH2228fu9zf3x823XTTWV0fAL3HcxMAWeL9PACyxmumfBPIAFgKniQByJo99tij7vLFF18cRkZGmrrvz3/+87rLO+ywwxIzlgHAcxMA3cz7eQBkjffz8k0gA2ApeJIEIGu23nrrZC7yqNtuuy1cfvnlTd33pJNOqru89957z/r6AOg9npsAyBLv5wGQNV4z5ZtABsBS8CQJQNYUCoVw0EEH1W075phjpmzJuOSSS8KVV145dnnZZZcN++23X8vWCUDv8NwEQJZ4Pw+ArPGaKd8EMgCW5j+iPvQCIIM+8pGP1I0aueKKK8IXvvCF1Nvfc8894R3veEfdtsMPP7yuaQMAPDcBkAfezwMgi7yfl1+lTi8AIA9Pkt/61rfCk08+Wfeh10c/+tGGt/ehF0Bvu+qqq8KiRYuW2P6HP/yh7vLixYvDxRdf3HAfa665Zth0001TjxGDFB/72MeSr1FHHHFEuPPOO8PHP/7x5P5RrVYL559/fhK+iNeN3/8HPvCBGT0+ALqP5yYAeo338wCYDq+ZWBp9I1N1FwPk9Enygx/84Njl1VdfPXz/+9+f0Yde0ec+97m6D72iww47rOkPvW666aawwgorTPvxAdB91l9//XDHHXcs1T4OPPDAcOqpp056m/i8s/fee4cLL7ywbnuxWAzrrbdeWH755cPf/va38Nhjj9VdPzg4GH7xi1+EbbbZZqnWCED38NwEQJZ4Pw+ArPGaiaUhkAHkmidJAHr1uWm0ZePggw8OZ5xxRlP7XXnllcM555wTtt9++6VaHwDdxXMTAFni/TwAssZrJpZGYanuDcAz/zEtFMLZZ58d9t9//7ozUq1Ww2233RZuuOGGJf4COX7o9eMf/9hfIAPQMgMDA+H0009PQhZbbrll6u3mzZsX3v3ud4f58+cLYwDQUp6bAMgK7+cBkEVeM+WPhgwg19qZWhz1gx/8IHz6058Ov//971M/9Ir7POqoo8Jqq622VGsDgOlYsGBBuOaaa8I999wThoeHk3FZm2yySRIOjC/2AKDdPDcBMJH38wDAa6Y8EcgAaBFvLAIAAABA9/B+HgAw2wQyAAAAAAAAAABmWWG2dwgAAAAAAAAA0OsEMgAAAAAAAAAAZplABgAAAAAAAADALBPIAAAAAAAAAACYZQIZAAAAAAAAAACzTCADAAAAAAAAAGCWCWQAAAAAAAAAAMwygQwAAAAAAAAAgFkmkAEAAAAAAAAAMMsEMgAAAAAAAAAAZplABgAAAAAAAADALBPIAAAAAAAAAACYZQIZAAAAAAAAAACzTCADAAAAAAAAAGCWCWQAAAAAAAAAAMwygQwAAAAAAAAAgFkmkAEAAAAAAAAAMMsEMgAAAAAAAAAAZplABgAAAAAAAADALBPIAAAAAAAAAACYZQIZAAAAAAAAAACzTCADAAAAAAAAAGCWCWQAAAAAAAAAAMwygQwAAAAAAAAAgFkmkAEAAAD0rO233z709fWNfU3m8ssvr7vt0Ucf3bZ1Ztn4cxLPZx4eR6Ovgw46qNNLJOMm/jei0depp57a6WUCAABtVGrnwQAAAAAAxluwYEH4/ve/P3Y5BhcOO+ywsNpqq7XkRD322GPhT3/6U/jLX/4SHn300VAul8OKK64Y1lhjjbDVVluF5zznOb5BAADArBDIAAAAgAz76U9/Gl772tfWbYstBJdddlnH1gR0p/jfjiuuuGLs8imnnDLt5o//+q//Cp/97Gfrtu26667hvPPOC4ODg9Ne0x133BF23HHHcNddd9Vt/8EPfpA0Tqy00kphadVqtWRfF1xwQbj44ovDjTfeOOntN9100/Af//EfybkZGhpa6uMDAAC9SyADAAAAMqxRvX38QDV+iLneeut1ZE3M7MPvkZERp65LbLHFFuHLX/5y3bY111wz9Lr//M//DMcdd1zdtr333juceeaZYe7cudPe37333ht22mmnJcIYUWyw2G233cIll1wSlltuuRmv+Wc/+1k4+OCDw3333df0febPn58EMuJjPe2008LLX/7ypu73ohe9KPziF7+o2/bzn/88fOlLX5r2ugEAgHwQyAAAAICM+sc//hF+9KMfLbE9frD/ne98J3ziE5/oyLog7+L4ip133rnTy8iM+N+cd7/73eFb3/pW3fb9998/fO973wul0vTfYnzggQeSMMatt946tq2/vz9UKpWx8NJvf/vbsPvuuyehinnz5s1o7X/9619TwxixfSOOKYn7vv/++8Odd965xH232267pKlo2223ndHPzd133z2jdQMAAPlQ6PQCAAAAgMbOOOOMsHjx4obXffe733XaOtB4ET8oHv06+uijfQ/IvWq1mjRMTAxjxG2xPWImYYxHHnkk7LLLLuHPf/7z2LZlllkm/PjHP07CZuP3edVVV4W99tor9b+F09HX15cc96STTkqCIA8//HC46aabwrXXXpu0DsWvD3zgA6FYLI7dZ9GiRWHPPfdM2jwAAACmSyADAAAAumBcSfwgcZttthm7HD9MvPLKKzu0MqAXxLaKAw44IAlJjBfbMmKooVCY/luLjz/+eDKK5I9//OPYtlVXXTVcdtllSbvE2972tqQZaGhoaOz6Sy+9NLzhDW8I5XJ5Ro9jzpw54b3vfW+47bbbkhEihxxySNhwww2XuN26664bjj322KSRY/wIlrjmI444YkbHBgAAeptABgAAAGTQX/7yl/Cb3/xm7HIMY3z0ox+tu83ED0kBZsvw8HB44xvfGM4666y67bFB4utf/3oSEpuup556KhlBEkeRjFpvvfXCr371q/Cyl71sbFu8zcUXX5yMFBkV2zPe8pa3JI0d07H11lsn/z39yle+EtZff/2m7hNHqXzxi1+s23bmmWeGJ598clrHBgAAEMgAAACAjLdjRG9961vDa17zmrDKKquMbTv77LPDwoULO7A6IM/imI44JiQ2VYx35JFHJg0SMxFHjsR9xhEkozbffPNw9dVXh+c973lL3P5Vr3pV0gK01lprjW37wQ9+EA488MBQq9WaPu5LXvKSJPQxXYcddlhYfvnlxy4//fTT4fLLL5/2fgAAgN42/SGPAAAAQEvFDxu/973v1dXt77fffqFUKoU3v/nNyV+nj9bo//CHPwz/+q//OqvHf/jhh5MPSe+5557wyCOPJCGQV7ziFWHLLbec8r4LFixImj3uvffe5C/o11xzzbD99tvXfag6G2MU4jFuvPHGZH3LLbdcWGeddZLjjP8AlaUTWwxuuummcN999yU/e/FD7dg2MJvfy1ExWBQ/qI8/cw888EAoFothtdVWC5tuumnygfpM2hiyJrYrxCaIu+66Kzz44IPJz2p8bK985SunfHx333138jt55513Jg0Ra6yxRtKas/HGG7dknXvuuecS4YPPf/7z4SMf+ciM2zbiyJE4emRUXP8FF1wQVlxxxdT7xe9/fNy77rpruOWWW5Jtp512WjLO5IQTTmjpz0V/f3/yvYnjS0bF8w8AADAtIwAAAECm/PznPx+JL9lHv/bZZ5+x637961/XXbfzzjtPe//bbbdd3T5G3XzzzSNvetObRubMmVN3/ejXi1/84pGrr7664T4vvfTSkVe+8pUN79fX1zey1157jdxxxx1Nre+yyy6ru/9RRx2VbB8eHh75/Oc/P7Lqqqs2PM7cuXNH9t9//5G77757qc/FdNY1XtzWaG1TfcV1LM3aJjrllFPq7hsvN+vkk08e2WijjVK/l7vuuuvIH/7wh7HbT/U4JvOrX/1q5LWvfW3yvUs7N6utttrIkUceOfL444+PtNLSPI7owAMPrNvH3/72t2R7/Hk85JBDRpZZZpmGjy+e6wsuuKDhPm+44YbkfMfz3ui+r371q0f+9Kc/Nb3GiT9TE38uHnvssZFXvepVS3zPv/KVr4zMVLlcHtl3333r9rnHHnuMLFy4sOl9PPjggyOveMUr6vZx+OGHj7Ra/O/J+GN+9rOfnfY+luZ3EQAA6H5GlgAAAEAXjCsZFf9ie6ONNhq7HP/iPP71/NKKowniX+vHMSjxr9kbueGGG8J2222XtHKMd8wxx4Sddtopaa1oJH7Wff7554etttpq7K/cp+uxxx4LO+ywQ/joRz+atAs0EkcKnHHGGclf1f/kJz+Z0XF6WRwpEZsRDjnkkHDrrbemfi9//vOfh5e//OXh9NNPn/GxnnrqqaT15V/+5V+S71X83qWJjRmf+tSnkrEW1113Xegmv/71r8OLXvSicPLJJyfNE43Ecx1HeXz1q1+t237SSSclzTTxfD+TF1lSHOkRR3uMHwOyNM048fc4rnlUoVAI3/72t8N73/veGbf9xBEj55577ti2eDn+N2RwcLDp/cSWnksuuSRpyhh1/PHHh4997GOhlWJjy3grr7xyS48HAADkj0AGAAAAZMjoGJJRcazBHnvsUXeb8SNK4gee3/3ud5fqmPHD3Pjh+KJFi5LLAwMDYZNNNgkve9nLwqqrrlp323K5nARE/vrXv46NMTj66KPHPjCO691iiy2S8SbLLLNM3X3//ve/h3333TfZx3TEx/imN72p7kPn+MHoi1/84mSdcb0Tz2E8zmWXXTbNM9G74hiYOFLiwgsvXOK6tddeO/lZiEGg+AF9FEM7b3/725cYa9GMGLCIwZ4Y/ml0rJe+9KXJz8/EURbx5yeOpYljP7pB/B3Zfffdk6BDFMe+PP/5z0/CLBPHvsTfn/e9731JwCKKYZd/+7d/G/tdmTdvXthss82SczPxvMSgx+tf//rw6KOPznit8XsSA0/XX3/92La43jg66dBDD53RPuNjeuc73xn+7//+b2zbBz/4wXDKKack+56u+N+T+PP5lre8ZWzb5z73uSSs0woxNPS73/2ublsMBQEAAEyHQAYAAABkyFlnnTUWjIje+MY3hrlz56Y2ZkTf+c53luqYcX/xA/Y111wzaeeIHyDPnz8/aSO4//77w89+9rOw7rrrjt1+4cKFyV+mx4DEf/3XfyXb4ofMF198cXLfP/zhD0mbxkMPPRS+8Y1v1K0/7vdb3/rWtNYXAydx31Fs8YhBi9iSET8sjfuL/z7hhBPCCiusUNf2cMABByTNGu0UQwq/+MUvkq8YTBlvdHujry9/+cuhk4499tjw4x//uG7b/vvvH26++eZw1113JT8LCxYsSBoD4vc+fqAeQxwHH3zwtMM1cb/jP/iPoZ8vfelL4b777kuO9dvf/nbs5yeGL3bccce6n734gfxoyCHLYhgh/vzFn8vjjjsu+Tn985//HK699tqk1eaaa64JL3zhC+vOzfvf//6kMeMd73hHEmiIAYDYLvHII4+EG2+8MTk3cT9nnnlm3c973PbpT396RuuM39Ntt902/OlPfxrbNmfOnOQY8Xdopvr6+sKJJ56YPI7Rr/h9jttnqr+/Pwl4jN/nkUceGVr13+IYyhi13HLLhW222aYlxwIAAPJr+nF0AAAAoGUmhismhi+i5z73uckog/jBbvSXv/wlGTMQRxfMxO2335785X4cfxJDGePFD0/jmIDRgMHoaInY4hGDF/FD5BgaiR+Sxg9Lx4tBjMMOOywZTTD+g/s4imE6IxDuuOOO5H9j28B55523xHHiX87HD7/jX/jHERjxr/1HGxViYOTrX/96aJcNN9ww+YomNhnsvPPOIYvi+Y1jZ8aLH+6Phm3GW2ONNcJnPvOZ5Dzvvffeyc/OdMQP5Mc3l8QxNnGczWqrrbbEbWMbR/wAPP7sHX744eFrX/tasj2GGT75yU8mIyuyLJ6b1VdfPfm9imN0Joq/w/G62Hwx+jMbAxdxbEwMnrz61a8OF110UVh22WXr7lcsFpNGmxhkGR9WiW0WsbFm4u/HVGuMYYzbbrttbFtsnIkhkNe+9rWhV8XzH3/GJjYTTefcAgAARBoyAAAAICPiX8aPH8ewzjrrJKMdGpnNloz4IWP8a/CJYYzx4l/qjw9VVKvVZCTDxhtvnBx7sg8qDzzwwCTwMSoGOWIbwnQ85znPCWecccakx4lBlYnjW+J4hH/84x/TOlaviY0lsVFkVByR0yiMMV78sP6oo46a9ofcX/ziF+u+p7GVo1EYY2IwIzZMvPKVrxzbdvLJJ7e9/WQmYuNMozDGqFVWWSVpxRgvtpKstNJKye/kxDDGeDGAFMNS41syYqBjOmLoYHwYI45Gid+TXg5jjI5WGR82iufl4x//eEfXBAAAdCeBDAAAAMjQh7fjxXEBafX+cexDHBsxKo4XGP+h+nS86U1vWmK8RiN77bXXEts++tGPhqGhoUnvFx/DxPvGkRTTET8MnezD6VG77bZbXWtAHP9y+umnT+tYvSSOfJj4c/eFL3yh6Q+tY2NGs2JYJo7eGHX00UcnwYNmxFaII444Yuzyk08+mYzSybKtt946vOY1r5nR79W73/3ups5tbClZmt+r+P2f+HsWgx69LP734pvf/Gbdts997nOTBtYAAADSCGQAAABABsQPRuPIganGlYyK4wrG/3V8bAv40Y9+NONARjM233zzJYIW++67b1P3feELX1h3+c4772x6fbEVIwZQmhUbOca7/PLLm75vr/nzn/+cjHYZ9dKXvnTSRoeJI2mm832JzQujYphoOveNdtppp6QtY9SVV14ZsqzZ36vYPjNnzpy6bXEMUKt/rxqJo2viiJheFcdAHXrooXXb4qik97znPR1bEwAA0N0EMgAAACADLrvssnDHHXeMXY6NFRMDEBP967/+a93liU0HzYofwjdj5ZVXrru8wQYbhBVXXHFG93388cebXl88F802KUTbb7/9Eh+y0tjEczPx3E33XE8WOLrqqqvqQgjLLbfctI4Vx0aM/zmKoz2yrNnfq9j+sfzyy9cFkKb63Z+N36vR8NLg4ODY5diyE1s3ejHEtGDBgrDnnnsmrTqjXvCCFyRBubSmIgAAgKkIZAAAAEAGTAxTTNaOMWqfffYJyyyzzNjl+Jft991337SPHds2mjFxNEmz92t03/Efek6l2Q+nR6277rp1H/bHoMvE0Qw8429/+9tSneuJDQ1p7r///rpxJfPnz08+5J7u14MPPji2j/H7y6KZ/n7E8FEMaUz3ftP9vRoN1MRmnYGBgbp97LHHHnUBmry79957k8ahBx54YGzbOuusE37+859PKwwGAAAwkUAGAAAAdNiTTz4Zzj333LHLcSzDAQcc0NSHsa9//evHLler1SXGnjRj/Iex7bhfNJ2AxMQWgGaM/xC1VqtNuzmgV8RRN0tzrpu9/cMPPxxm2z/+8Y+QZVn/vRq1yy67JP/9GT825amnngqvfe1rwzXXXBPyLgZ7YhhjfDgphmliwC2GMgAAAJZGaanuDQAAACy1s88+O/kAdPw4hziOoZmRDBtuuGHd5e985zvhwx/+cK6+KxNbAJodbzEx9DJ+LATPnpelOdcTz3OzwY/ZEIM2zI4Yvoj/HXrjG98YyuVysu2JJ54Iu+22W7jkkkuaHr/SbeJjfM1rXhNuuummsW3xvxM/+9nPwvOf//yOrg0AAMgHgQwAAADosBiiGO/Pf/5z8lfrMxFHQVx33XXh5S9/eciLhQsXTvs+4wMu0fjRLqQHKqZ7riee5zQTgx6bbrppOP7445fqWzE4OLhU96feXnvtFU4//fSw//77h0qlMtZCEtsjLr300vCiF70oV6dsdDRL/O/l+J/Tiy66KLz4xS/u6NoAAID8EMgAAACADoo1+b/85S9nPeCRp0DGQw89NKMxBONHwCy33HKh2/T19c34vs0GK1ZYYYWlOtfNjiJZZZVVlhitsfPOO0/rWLTeG97whmTs0Vvf+tZkBNLo71L8Xl1++eVhs802y8W3YXh4OHms4//bO3fu3HDeeeeFbbbZpqNrAwAA8qXQ6QUAAABAL4vhifjh9GyKf+UeP3DMixtvvHFat7/jjjvC448/PnZ5vfXWW6pwQ6cMDAws8Rf9zXrwwQebut3EkTfTPdd//OMfm7rdGmusUddoEb9Ho6MxyJbYkHHqqacmQabxQZ2ddtopae/pdjFocsABB4Sf/OQnY9tKpVI444wzZtxMBAAAkEYgAwAAADokBjG++93v1m2Lf4Uet0/3K35YOir+RfsFF1wQ8uJPf/pTXePFVK644oq6y694xStCJ4z/QDuabvBmYqvH/fff3/R9x49hmMzEJpWJ524qzd6+v7+/rnkgNnhcc8010zoW7RMbMk488cS6IFP8+Yv/nVmwYEHXfivi7+AhhxwSfvCDH9T9np5yyilhn3326ejaAACAfBLIAAAAgA6JdflxZMmoNddcM7z61a+e0b7e8pa31F2Of+GeF7FJIf71+nRaR8bbbrvtQifMmzdvRmNExjd7jHfDDTc0db/YZnDppZc2ddtNNtkkrL766mOXr7/++jB//vym7vv0008nbSzNes1rXlN3+atf/WrT96X9Dj744PDNb36zLpRx7733hh133LHuv1vd5D3vec8SIbhvfOMbSQAFAACgFQQyAAAAoEMmBgfe/OY3L9Gq0Kx99903zJkzZ+zyT3/60/DAAw+EvPj0pz8dnnjiiSlv97Of/awujBDHZEwMq7TLSiutVHd5uh9iv+QlL6m7fNZZZzV1v09+8pNNjzeJH7YfdNBBdds+8pGPNHXfY489dlqtHe94xzvCCiusMHb5nHPOCRdddFHT96f9/v3f/z185Stfqdt21113JaGMO++8s6u+JUcccUQSvpj4MxwfIwAAQKsIZAAAAEAHxLaE+IH0ePvvv/+M97fiiiuG3XbbbexypVIJ3//+90Ne3Hfffcn5iW0ZaeIohbe//e112w488MC6EEA7bbbZZnWXJ36/p7Lzzjsnoz7GBzJ+9atfTXqfOGbia1/72rSO8653vSvMnTt37PKFF14YPvvZz056nxj4OeaYY6Z1nOWXX74u7FGr1ZKwzPnnnz+t/cQWjxheon2tEl/+8pfrtt1+++1JKOOee+7pim/DF77whfD5z3++bttRRx0VPvCBD3RsTQAAQG8QyAAAAIAOiB/Oj2982GijjcIrXvGKpdrnxEDHxAaObjU6uuPHP/5xeNWrXhWuuOKKMDIyMnb9U089Fb797W8n5298K0gcxTFVsKCVdtlll7rLn/rUp5IxEN/73veSQMPFF1889hVDBhOtssoq4fWvf31dgGGPPfZIxtH8//bu5cXGP44D+HcyKUQuuTQuYWlEbmXhGuVSYmVhZcPOxkLKJQullPIHKGXBwsLeLJCFWxFDoZRbGkUoSqL59fnW8TvnzOCM+f7OmfnN61VPzXnObZ7nOc8s5nmf9+fbt281j+3u7s5jF/bs2ZP3TXyeGjV37tx05MiRmnWHDh1Ku3btSo8fP65ZH40Yhw8fTtu2bcvhmHjuQBw4cCBt3br15+04B3bs2JEbXqLZJMag1Pv69Wu6fft2PpbLli1Ly5cvb7gthDL279+fTpw4UbPu2bNnOZTR09MzpHdz/B08ePBgzbpVq1blpfocbGRpdJwPAABARfvPnwAAAICWjisZrO3bt6exY8fm9o3w4MGDdO/evbRkyZI0nEXrxc2bN1NXV1cOLqxbty5NmTIlBzXiYn2MAqkf0RGND9EQEs0hrRL7PS5YV0aoRKAiwhSx1Fu7dm26evVqn/UnT57MQZTPnz/n258+fcqhjn379uXQxahRo9Lr169rgihr1qzJ4Yy9e/cOKChx/fr1PPKl4sKFC3mZPXt2mjFjRvrw4UPe1z9+/Mj3t7e3p7Nnz6b169c3/D4xkuf8+fM5hFHZ3giQXLp0KS9x3OK4xnGLY/vx48e8fZX3pHUi1BBBoGiWqHj69GnasGFDPpZTp04dkofnypUrfdZF00x9YKoR0bjT3/kLAADwKxoyAAAAoMlevnzZ5yJhjG4YrHHjxuUGhf9bS0ZcxL948WL+RnvF+/fv0927d/M31uvDGOPHj88NJDHyo9WiDWPp0qV//fwIJ8S2RNCmWgQ07t+/n/dBdRgjAiAxAqR61Ekj4vERiNiyZUuf+169epXu3LmTR8JUghGjR4/O2xbhmIGK0SURronWhQh1VIuGjLjIf+vWrbx9L1686DeMESERmu/o0aO5PaVanINxrsU5CQAAQC2BDAAAAGiyc+fO1Yzc6OzsTAsXLizy2vXBjmgjiNESw11cxI+miRibEKM8+hMhgZ07d+YLxPXBlFbp6OjI7R4RKIkRIHGsJ06c2CeI8DubNm3KIzs2b96c2tra+n1MNFicPn06Xb58Oe+rvzFmzJjcxnHmzJk0f/78Xz4umgUioFE/ImcgYvtPnTqVnjx5kps8pk2b9sfnxHiUeGxs4/Pnz//6vRmc48eP50aVatHGE5+LaDQBAADgX2291f8BAgAAAGixGH9QPQYjRiQcO3bs5+3v37+nGzdupO7u7jxGY8KECWnWrFn5ORF2+D97+/ZtunbtWnrz5k368uVLmjx5clq0aFFauXJlHl9SUoQuHj58mHp6enKAYs6cObmlZObMmam0+PfUo0eP8vLu3bt8YT/Gl0S4ZN68eWnBggU53PJfqg67/GqEDAxUjDiJMUMVMeZn9+7ddiQAAIwQjX8dAwAAAGAIiHDA6tWr8zLSTJ8+PbeANMOKFSvy0gwRhoiWmFJNMQAAADAUGFkCAAAAAFWihSRCItWLVgP+JFpV6j831e0YAADAyCOQAQAAAAAAAABQmEAGAAAAAAAAAEBh7aVfEAAAAACGm66urt/e39HR0bTfheFp8eLFf/wcdXZ2Nu33AQAAWk8gAwAAAIARb+PGjSN+HzA4kyZN8jkCAABqGFkCAAAAAAAAAFCYQAYAAAAAAAAAQGFtvb29vaVfFAAAAAAAAABgJNOQAQAAAAAAAABQmEAGAAAAAAAAAEBhAhkAAAAAAAAAAIUJZAAAAAAAAAAAFCaQAQAAAAAAAABQmEAGAAAAAAAAAEBhAhkAAAAAAAAAAIUJZAAAAAAAAAAAFCaQAQAAAAAAAABQmEAGAAAAAAAAAEBhAhkAAAAAAAAAAIUJZAAAAAAAAAAAFCaQAQAAAAAAAABQmEAGAAAAAAAAAEBhAhkAAAAAAAAAAIUJZAAAAAAAAAAAFCaQAQAAAAAAAABQmEAGAAAAAAAAAEBhAhkAAAAAAAAAAIUJZAAAAAAAAAAAFCaQAQAAAAAAAABQmEAGAAAAAAAAAEBhAhkAAAAAAAAAAIUJZAAAAAAAAAAAFCaQAQAAAAAAAABQmEAGAAAAAAAAAEBhAhkAAAAAAAAAAIUJZAAAAAAAAAAApLL+AV1NYNTcA1knAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 2400x1800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6), dpi=300)\n",
    "plt.plot(amp, likes - likes.max(), label=\"IDR3 Likelihood\")\n",
    "plt.plot(amp, likes_g - likes_g.max(), label=\"Gaussian Likelihood\")\n",
    "plt.xscale(\"log\")\n",
    "plt.ylim(-1000, 0)\n",
    "plt.xlabel(\"Amplitude [mK^2]\")\n",
    "plt.ylabel(\"Log Likelihood (norm)\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7dbece71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb191f6d450>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2400x1800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6), dpi=300)\n",
    "plt.plot(indx, likes_indx - likes_indx.max(), label=\"IDR3 Likelihood\")\n",
    "plt.plot(indx, likes_g_indx - likes_g_indx.max(), label=\"Gaussian Likelihood\")\n",
    "plt.xscale(\"log\")\n",
    "plt.ylim(-1000, 0)\n",
    "plt.xlabel(\"Power-Law Index\")\n",
    "plt.ylabel(\"Log Likelihood (norm)\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "932312a5",
   "metadata": {},
   "source": [
    "Clearly, the maximum likelihood is at the known correct parameters, for the `Gaussian` likelihood. For the H1C likelihood, any model which gives a power significantly lower than the data estimate has essentially equivalent likelihood value. This is because this likelihood is essentially treating the data as an upper limit by assuming the systematics may make up any portion of the power."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b67b19a",
   "metadata": {},
   "source": [
    "## Creating a `DataModelInterface` without a UVPSpec object."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7eef2648",
   "metadata": {},
   "source": [
    "If you don't have a `UVPSpec` object, you can create a `DataModelInterface` from scratch.\n",
    "This makes it easier to mock up data for testing, or to read data from a different source.\n",
    "\n",
    "In the examples below, we create `DataModelInterface` objects of three different types\n",
    "with the standard constructor. Each has a different mix of whether the data is in \n",
    "spherical/cylindrical space, and whether the theory model is computed in spherical/cylindrical\n",
    "space."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e26fa00e",
   "metadata": {},
   "source": [
    "### Spherical Data, Spherical Theory"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb17f1b2",
   "metadata": {},
   "source": [
    "Let's create a mock power spectrum that is already in spherical coordinates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ea14a96f",
   "metadata": {},
   "outputs": [],
   "source": [
    "k = np.linspace(0.01, 0.5, 100)\n",
    "z = 9.0\n",
    "amp, indx = 1e5, 2.7\n",
    "power = k**3 / (2 * np.pi**2) * amp * un.mK**2 * (1.0 + z) / k**indx\n",
    "covariance = np.diag(k**3 * 1e5)\n",
    "window_function = np.eye(len(k))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d25153a3",
   "metadata": {},
   "source": [
    "Then we can define our `DataModelInterface` like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "27bfc376",
   "metadata": {},
   "outputs": [],
   "source": [
    "dmi = pl.DataModelInterface(\n",
    "    cosmology=cosmology.Planck18,\n",
    "    redshift=z,\n",
    "    power_spectrum=power,\n",
    "    window_function=window_function,\n",
    "    covariance=covariance * un.mK**4,\n",
    "    kpar_bins_obs=k * cosmology.units.littleh / un.Mpc,\n",
    "    theory_uses_little_h=True,\n",
    "    theory_uses_spherical_k=True,\n",
    "    theory_model=theory_model,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bcd8f74",
   "metadata": {},
   "source": [
    "Notice here that we define `kpar_bins_obs` and not `kperp_bins_obs`. If you pass only\n",
    "`kpar`, then it will be interpreted as spherical `k`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9e573a8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Log Likelihood (norm)')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkIAAAHACAYAAABONwdOAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAARhtJREFUeJzt3QmYU+X1+PEz+zAsAyiCIMqmLLKKlYK49C/K9hMQF6BUVFCsihu4QBE3pCgiqEhri1ahFUVrsaiIoqgIpSCLCoioiEBZRMVhgNkn+T/nJTcks5FkstybfD/Pk2dmbm5u7iTMzeF9zzlvktvtdgsAAEACSo71CQAAAMQKgRAAAEhYBEIAACBhEQgBAICERSAEAAASFoEQAABIWARCAAAgYREIAQCAhEUgBAAAEhaBEAAASFgEQgFavny5XHrppdK4cWNJSkqSN954I+gXW1czmT59upxxxhmSkZEhTZo0kSlTpgR9HAAAEB6pYTpO3Dty5Ih06tRJRo4cKYMHDw7pGLfffru89957Jhjq0KGDHDhwwNwAAEBsJLHoaggvWlKSLFy4UAYNGuTdVlhYKBMnTpSXX35ZcnJypH379vLYY4/JhRdeaO7fsmWLdOzYUTZt2iStW7cO41sIAABCxdRYmIwZM0ZWrVolr7zyinzxxRdy5ZVXSp8+feSbb74x97/55pvSokULeeutt6R58+bSrFkzuf766xkRAgAghgiEwmDnzp3ywgsvyGuvvSbnnXeetGzZUu666y7p2bOn2a6+++472bFjh9ln3rx58uKLL8q6devkiiuuCMcpAACAEJAjFAYbN26U0tJSkwTtS6fLTjjhBPO9y+UyP2sQZO33/PPPS9euXWXr1q1MlwEAEAMEQmFw+PBhSUlJMSM8+tVXrVq1zNeTTz5ZUlNT/YKltm3bekeUyBsCACD6CITCoEuXLmZEaP/+/WZqrCLnnnuulJSUyLZt28zUmfr666/N19NOOy0cpwEAAIJE1VgQoz7ffvutN/CZMWOG/OY3v5H69evLqaeeKr/73e9k5cqV8sQTT5j7f/zxR/nggw9MpVj//v3N1NivfvUrM0L05JNPmp9vueUWqVOnjimpBwAA0UcgFKCPPvrIBD5lXXPNNSbxubi4WB555BGTA7R792458cQT5de//rU89NBDpmeQ2rNnj9x6660m8KlZs6b07dvXBE4aTAEAgOhzXCA0e/Zsefzxx2Xfvn2mweGsWbPknHPOqXR/rdKaNGmSfP/993L66aeb3j79+vWL6jkDAAB7clT5/IIFC2Ts2LHywAMPyPr1600g1Lt3b5ObU5H//Oc/MmzYMBk1apRs2LDBNEDUmzY1BAAAcNSIULdu3UyezTPPPGN+1jybpk2bmumm8ePHl9t/yJAhZmkMbWJo0emqzp07y7PPPhvVcwcAAPbjmKqxoqIiU54+YcIE77bk5GTp1auX6ehcEd2uI0i+dASpqgVTtdeP3iwabOl6YNoPSJfWAAAA9qfjPIcOHTKLpWu84PhA6KeffjIl6g0bNvTbrj9/9dVXFT5G84gq2l+3V2bq1KkmwRkAADjfrl275JRTTnF+IBQtOuLkO4p08OBBUx6vL6SWugOIrCeXfi3Prdhuvu/foZE8dkUnXnIAQcvNzTXpM7Vr165yP8cEQlqOrl2bf/jhB7/t+nOjRo0qfIxuD2Z/lZGRYW5laRBEIAREfih76bZcSc7IMj8v//6IpGXWlBrp/h3bASBQx0trcUzVWHp6ulmXS5sU+ubv6M/du3ev8DG63Xd/tXTp0kr3BxBbG3blyK4D+ZKVniKNszPlSFGpLPuq4qpQAAgHxwRCSqes5syZI3PnzpUtW7bITTfdZKrCrrvuOnP/iBEj/JKpb7/9dlmyZIlpWqh5RA8++KCsXbtWxowZE8PfAkBlFn22x3y9pF1DGdC5ifn+zc+PbgOASHDM1JhVDq9LV9x///0m4VnL4DXQsRKidfFS38zwHj16yPz58+W+++6TP/zhD6aholaMtW/fPoa/BYCKlLrc8vbGveb7AZ0bS6M6NeTZj7fJsq37JbegWOpkpvHCAUjsPkKxSrbKzs42SdPkCAGRs/Lbn2T4c6ulblaarPlDL0lLSZKLZy6Xb/cflieu7CSXd6286gMAQv38dtTUGID4nxbr2/5kSU9NNgmOl3ZsfPQ+pscARAiBEICYKywplXc2eabFOh0NftSlnU42X1d8+5McOFIUs/MDEL8IhADE3PKvf5LcghJpWCdDzmle37u9RYNa0r5JHZM/tNiTPwQA4UQgBCDmrKmv/+vYWFKS/Xt+WCNEVI8BiAQCIQAxlVdUIu9/+UO5aTFLf0+e0JrvD8i+gwVRPz8A8Y1ACEBMacPE/OJSOe2ELOl4Sna5+5vUrSG/alZPtL71rS/oKQQgvAiEAMTU1n2HzNeerU6stBX+ha1PMl8378mN6rkBiH8EQgBiandOvvnapF6NSvc5xXPfHs++ABAuBEIAYsoKbnQKrDLWfXsOEggBCC8CIQAxtSfnaAJ04yoCoZM992mytMtFM3wA4UMgBCBmNKixKsFOzs6sdL+GtTNEq+qLS93y0+HCKJ4hgHhHIAQgZn46UihFpS4T5DSsU3kglJqS7L1/DyX0AMKIQAhAzKfFNMhJS6n6cmRNnZEwDSCcCIQAxMxeT6J0VflBFmvqjEAIQDgRCAGIeel8VflB5SrHPKNIABAOBEIAYsYKaqoqnbdYwdJeSugBhBGBEICY2RPE1Bg5QgAigUAIQMxYozuBTI15AyGqxgCEEYEQgJjZHUAzRYu1z4+HCqWwpDTi5wYgMRAIAYgJDWas5oiB5AjVy0qTjNSjl6wfDtJUEUB4EAgBiAmro3RmWrLUzUo77v66Mr0VMFnVZgBQXQRCAGLCCmZ0ykuDnECcXJfKMQDhRSAEwPal85bG2XSXBhBeBEIAYtpVOpCKsbKr0FM5BiBcCIQAxMQeT+l8IBVjliaeqTGW2QAQLgRCAGxfOm852TM1tpdlNgCECYEQgJiwRnWCyhFiRAhAmBEIAYg6t9sdWo6QZ0ToUGGJHCoojtj5AUgcBEIAoi43v0SOFJUGPTVWMyNVsmsc7Tm0l6U2AIQBgRCAmPUQOqFmumSmpQT1WCtwoqkigHAgEAIQu8VWPTk/wWjsmUojYRpAOBAIAYhZorTVIDEY3lXoWWYDQBgQCAFwROm8xRpFsvoQAUB1EAgBiNnUmFUOHwyr3J4RIQDhQCAEIHZTY6GMCFlNFakaAxAGBEIAYrbgaiiBkDWKpMnSLpc77OcGILEQCAGIqlKXW/blBr/yvKVhnUxJShIpKnXJz0eKInCGABIJgRCAqNp/qMAEQ6nJSXJirYygH5+WkiwNa7P4KoDwIBACEJP8oEbZmZKSnBTSMazKMSvpGgBCRSAEwDGl8+W7Sx89FgCEikAIQFRZi61aHaJDcay7NCNCAKqHQAiAY0rny3WXZmoMQDURCAFw3NSY1UvIKsMHgFARCAFwTFdpC92lAYQLgRCAqNp/qNDbDyhUVtXYj4cLpajEFbZzA5B4CIQARI3b7ZaDecXm+3pZ6SEf54Sa6aYPkdst8vORo4EVAISCQAhA1OQXl5qO0KpuVlrIx0lKSpLsGkcffzD/aGAFAKEgEAIQNTme0aC0lCSpkZZSrWNlewIp65gAEAoCIQBRYwUt2TXSzahOddRlRAhAGBAIAYianPyiak+LWbxTY4wIAagGAiEAUWMFLdZoTnXU9SRbW8EVAISCQAhA1OR4EpvDOiJEsjSAaiAQAhA1VtCiOULhCoRIlgZQHQRCAKLGClrCMSJkHcMaZQKAUBAIAYiag558Hms0JxyBUC6BEIBqIBAC4MgRIabGAIQDgRCAGPQRCkcgRNUYgOojEAIQg6qx8CVL00cIQHUQCAGImoN5noaK4cwRKiiRUpe72scDkJgIhABEvXw+nDlC6lABlWMAQkMgBCAqikpccqSo1HxfNwx9hNJSkqVm+tGFW+klBCBUBEIAojoapGut1s5MDcsxjy2zwYgQgNAQCAGIag+hOplpkpxcvZXnLSyzASBhAqEDBw7I8OHDpU6dOlK3bl0ZNWqUHD58uMr9b731VmndurXUqFFDTj31VLntttvk4MGDUT1vAOHvIVS+lxALrwKI80BIg6DNmzfL0qVL5a233pLly5fL6NGjK91/z5495jZ9+nTZtGmTvPjii7JkyRITQAGIYSAUhooxixVUsfAqgFCFZ6I+wrZs2WKCmE8//VTOPvtss23WrFnSr18/E+g0bty43GPat28vr7/+uvfnli1bypQpU+R3v/udlJSUSGqqI351IG5YeTzZYeghVC4Q8gRZABCXI0KrVq0y02FWEKR69eolycnJsnr16oCPo9NiOrVWVRBUWFgoubm5fjcA1WdNX4VzRKiONTVGsjSAeA6E9u3bJyeddJLfNg1m6tevb+4LxE8//SSTJ0+ucjpNTZ06VbKzs723pk2bVuvcAYjf4qjhzBGyyvApnwfgyEBo/PjxkpSUVOXtq6++qvbz6KhO//79pV27dvLggw9Wue+ECRPMyJF127VrV7WfH4DP1Bg5QgBsJKaJMuPGjZNrr722yn1atGghjRo1kv379/tt1zwfrQzT+6py6NAh6dOnj9SuXVsWLlwoaWlV/280IyPD3ADYd8HV8uXzVI0BcGAg1KBBA3M7nu7du0tOTo6sW7dOunbtarYtW7ZMXC6XdOvWrcqRoN69e5vAZtGiRZKZmRnW8wcQmwVXLVa+EVNjAOI6R6ht27ZmVOeGG26QNWvWyMqVK2XMmDEydOhQb8XY7t27pU2bNuZ+Kwi65JJL5MiRI/L888+bnzWfSG+lpUfb/ANw5oKrlmzK5wFUk2NqyF966SUT/Fx00UWmWuzyyy+Xp59+2nt/cXGxbN26VfLy8szP69ev91aUtWrVyu9Y27dvl2bNmkX5NwAS27ERoQg0VMwvFrfbbfIKASAuAyGtEJs/f36l92tgoxdCy4UXXuj3M4D46yxtTbPpgq4FxS6p4VmEFQDiamoMgLO5XG7JLSj26/0TDrr6fKpn3TK6SwMIBYEQgIg7VFAi1gBtOKvGdCrs2PQYlWMAgkcgBCDirCAlKz1FMlLDO33lTZhmmQ0AISAQAuDIBVcrSpgGgGARCAFw5IKrFiu4YkQIQCgIhAA4csHVspVjJEsDCAWBEICIs4KUcJbOW0iWBlAdBEIAIs6atgpnxVi5QIhkaQAhIBACEMUcoUhMjVkLr5IsDSB4BEIAolg1Fv5k6WMr0BMIAQgegRCAiDvo6SMUiRwh65hMjQEIBYEQAIf3EaJqDEDoCIQAODpH6FiyNEtsAAgegRAAR+cIWVNjuQUlUuryLGgGAAEiEAIQUW6325sjFMkRIXXIs8I9AASKQAhAROUXl0pxqTtiOUJpKclSM/3oQq4kTAMIFoEQgIiygpO0lCSz+nwkWMtssPAqgGARCAGISiCk1V1JSUkReQ56CQEIFYEQgIjKiWAPIQuVYwBCRSAEICrrjEUiP6hc5RjdpQEEiUAIQERZeTvRGRGiagxAcAiEAESUFZzUieCIkFWWT7I0gGARCAGIKGsx1Eg0U7RYx2bhVQDBIhAC4NgFVy1MjQEIFYEQgOgsrxHBQMg6thV0AUCgCIQARKmPUAQDIc+xmRoDECwCIQBRqhqLXI6QlYhN1RiAYBEIAYiog3meBVej0EeIqjEAwSIQAhCdEaGIBkJHR5uKSlxSUFwasecBEH8IhABEjAYmeUWlEU+W1tXnU5KPrmPG9BiAYKQGs7PL5ZKPP/5YPvnkE9mxY4fk5eVJgwYNpEuXLtKrVy9p2rRpUE8OIL5Zycu61mrtzMgFQrqYq444/XykyKxt1ig7M2LPBSABR4Ty8/PlkUceMYFOv3795J133pGcnBxJSUmRb7/9Vh544AFp3ry5ue+///1v5M8agCNY5ex1MtO8IzaR7i5trW0GAGEbETrjjDOke/fuMmfOHLn44oslLa38/+x0hGj+/PkydOhQmThxotxwww0BnQCA+BWNHkLlmiqy8CqAcAdC7733nrRt27bKfU477TSZMGGC3HXXXbJz585gzgFAnE+NRbJizEIvIQARmxo7XhDkS0eLWrZsGdLJAIgvhwtLzNdaGUGlI1arcoypMQDBCOnqVFBQIF988YXs37/fJFD7GjBgQCiHBBDHgVBWemoUp8ZYZgNA4IK+Oi1ZskRGjBghP/30U4WVG6Wl9PAAcNQR74hQSsRfEhZeBRCVPkK33nqrXHnllbJ3714zGuR7IwgC4OtI4dH/GNWMwtRY7cxUv+ALACISCP3www8yduxYadiwYbAPBZCwI0KRD4Ss6bfDnuALACISCF1xxRXy0UcfBfswAAnoSFFJ1EaEanqm3/I8zwkAgQj66vTMM8+YqTHtLt2hQ4dyPYVuu+22YA8JIE5ZozNZ6ZHPEbJGnZgaAxDRQOjll182fYUyMzPNyJAmSFv0ewIhAJa8GEyNHfGsbQYAgQj66qRdox966CEZP368JCezZiuA45fPR2NqjBEhAKEIOpIpKiqSIUOGEAQBCDhHKCojQp4cISv4AoCIBELXXHONLFiwINiHAUjg8vlo5gjlFZWK2+2O+PMBiA9B/zdNewVNmzZN3n33XenYsWO5ZOkZM2aE8/wAOFg0p8asYKvU5ZbCEpdkpkU++ALgfEFfnTZu3ChdunQx32/atMnvPt/EaQCIZrJ0TZ9lPLRyjEAIQCBSgx0N0kRpLZuvV69eMA8FkGBcLre3gisaI0LJyUlmVEinxnRK7oRaEX9KAImWI5SSkiKXXHKJ5OTkRO6MAMSFvOJjZezRGBHyL6EnYRpAhJKl27dvL999912wDwOQYKzGhslJIplp0Wm1YS3uSlNFAIEK+ur0yCOPyF133SVvvfWWWXg1NzfX7wYAfonS6alRyx88tt4YI0IAAhP0eHW/fv3M1wEDBvhd3LRcVX9mBXoAKi+KK89XVEIPAIEI+gr14YcfBvsQAAldOh+9MnaaKgKIeCB0wQUXBP0kABKPlacTrURp39Enq2wfAI4npCuUVo09//zzsmXLFvPzmWeeKSNHjpTs7OxQDgcgDlmVW1beTjTUYuFVAJFOll67dq20bNlSZs6cKQcOHDA37Sat29avXx/s4QDE+fIa0cwRsqbGqBoDEKigr1B33nmnSZSeM2eOpKYefXhJSYlcf/31cscdd8jy5cuDPSSAuJ4ai16OECvQA4h4IKQjQr5BkDlIaqrcc889cvbZZwd9AgDiUzTXGStfPk/VGIAITY3VqVNHdu7cWW77rl27pHbt2sEeDkCcikWytDX6lEdnaQCRCoSGDBkio0aNkgULFpjgR2+vvPKKmRobNmxYsIcDEKdikSxNQ0UAwQr6CjV9+nTTOHHEiBEmN0ilpaXJTTfdJI8++mjQJwAg3pOlU6JfPk9DRQCRCoTS09PlqaeekqlTp8q2bdvMNq0Yy8rKCvZQAOJYbKbGPIuu0kcIQIBCvkJp4NOhQ4dQHw4gzsUkWdoqnydHCECkcoSOHDkikyZNkh49ekirVq2kRYsWfrdI0X5Fw4cPN8nadevWNXlKhw8fDuixug5a3759zZTeG2+8EbFzBHCMFYxEc2rs2IgQVWMAAhP0f9U0Kfrjjz+Wq6++Wk4++eSorSqtQZCudr906VIpLi6W6667TkaPHi3z588/7mOffPLJqJ0ngDKLrkY1Wfpo0MXq8wACFfQV6p133pG3335bzj33XIkWXcpjyZIl8umnn3p7Fc2aNUv69etnkrcbN25c6WM/++wzeeKJJ0z/Iw3cAMTv1Jg1IlRU4pLiUpekpQQ96A0gwQR9lahXr57Ur19fomnVqlVmOsy3YWOvXr0kOTlZVq9eXenj8vLy5Le//a3Mnj1bGjVqFNBzFRYWSm5urt8NgDOSpX1L9a0RKQAIayA0efJkuf/++02QES379u2Tk046yW+bdrPWgEzvq2o5EM1lGjhwYMDPpdVwunisdWvatGm1zh1IRC6XW454StijOSKUnpos6Z5RIBKmAQQi6CuUTjNp2XzDhg2lWbNmpoeQr2AWXh0/frw89thjVe5jrXAfrEWLFsmyZctkw4YNQT1uwoQJMnbsWO/POiJEMAQEJ6/42GhMNJOlrecrynNRQg8gMoHQoEGDJFzGjRsn1157bZX7aCWaTmvt37/fb7s2c9RKssqmvDQI0oBNp9R8XX755XLeeefJRx99VOHjMjIyzA1A6PI802LJSSI10qIbCOn02C95xSRMA4hMIPTAAw9IuDRo0MDcjqd79+6Sk5Mj69atk65du3oDHZfLJd26dat0tEkr3Hxp36OZM2fKpZdeGqbfAECVidLpqVGv2LRykuguDSBsgZD24Yll+Xnbtm2lT58+csMNN8izzz5ryufHjBkjQ4cO9VaM7d69Wy666CKZN2+enHPOOWakqKLRolNPPVWaN28eg98CSMTlNaKXH1S2qSIl9ADClix95plnmoVVi4qKqtzvm2++idiaYy+99JK0adPGBDtaNt+zZ0/561//6r1fg6OtW7dGNYkbwPFK56M7LeY/InT0HACgKgH9d0179tx7771y8803y8UXX2zK2HUkJjMzU3755Rf58ssvZcWKFbJ582YzUqPBULhphVhVzRM1cVtHrqpyvPsBhLd0PiYjQt6mipTPAzi+gK5SOgqjDQk12FmwYIEZndmxY4fk5+fLiSeeKF26dDGr0Wv3Z+0zBCCxeZfXiGJX6XIr0LPwKoAABHWV0ukovQGAXXOEWIEeQDDoPw8ggl2lo58jZHWXZmoMQCAIhADExTpjFiv4IlkaQCAIhACEnRWExCZZ2hoRomoMwPERCAEIO2taqmYMkqVpqAggGARCACJYPh+DHCEaKgIIQkD/XdOFRwNVp06dYJ4fQFwnS8ewfJ6GigACENBVShcuDXSJjdJSmpgBic7Kz8mKafk81yIAxxfQVerDDz/0fv/999+bBU111XhdDFWtWrVK5s6dK1OnTg3kcADinLXgaWzK51lrDECYA6ELLrjA+/3DDz8sM2bMkGHDhnm3DRgwwKzsrmt/XXPNNUE8PYC4zhGKZbI0VWMAIpEsraM/utZYWbptzZo1wR4OQByKZR8hq3z+SFGpuFysLwggzIFQ06ZNZc6cOeW2P/fcc+Y+AIhlsrTvc+YXkycEoGpBX6Vmzpwpl19+ubzzzjvSrVs3s01Hgr755ht5/fXXgz0cgDijozA6GuNbyh5NmWnJkpwkooNBGpDFYlQKQByPCPXr188EPZdeeqkcOHDA3PT7r7/+2twHILH5jsLEYkRIK1xr+kyPAUBVQrpKnXLKKfLHP/4xlIcCSJBpMR2VqZEW/REhpaNAhwpLvOcCAGENhHJycuT555+XLVu2mJ/PPPNMGTlypGRnZ4dyOADxmCidnhpw/7Fwo7s0gIhNja1du1ZatmxpcoWsqTEtp9dt69evD/ZwAOKM1cgwlrk5x9YbY0QIQNWCvlLdeeedpm+QVo6lph59eElJiVx//fVyxx13yPLly4M9JIA4csQTfMQiUbp8U0VyhACEORDSESHfIMgcJDVV7rnnngr7CwFILLEsnbfQVBFAxKbGdFHVnTt3ltu+a9cuqV27drCHAxDHOUKxYjVVtM4FAMIWCA0ZMkRGjRolCxYsMMGP3l555RUzNea77AaAxGSHHCHruVl4FcDxBH2lmj59uqkEGTFihMkNUmlpaXLTTTfJo48+GuzhAMTrOmMxzBGyFnslWRpA2AOh9PR0eeqpp8xK89u2bTPbtGIsKysr2EMBiONk6ViOCDE1BiBQIV+pNPCpV6+e93sAsF2yNJ2lAYQ7R8jlcsnDDz9smieedtpp5la3bl2ZPHmyuQ9AYrNK1mvGMlnaMzVGsjSA4wn6SjVx4kTTVVrzgc4991yzbcWKFfLggw9KQUGBTJkyJdhDAogj9sgRoqEigAgFQnPnzpXnnnvONFW0dOzYUZo0aSI333wzgRCQ4I4FQnbIEaKhIoAwT43pkhpt2rQpt1236X0AEpsdkqWt0SgWXQUQ9kCoU6dO8swzz5Tbrtv0PgCJzerdY5WwxwKdpQEEKuj/sk2bNk369+8v77//vnTv3t1sW7VqlWmsuHjx4mAPByBep8boLA0gHkeELrjgAvn666/lsssuk5ycHHMbPHiwbN26Vc4777zInCUA5y2xYZPyebfbHbPzAGB/IV2pGjduTFI0gApZvXtimiztmZYrcbmlsMQlmWmxm6YDYG8hXal0FGjNmjWyf//+cr2DdOkNAIlJR1+OJUvHLvio6TMtp4EZgRCAsAVCb775pgwfPlwOHz5sVqLXdccs1hpkABLT0akoiXln6ZTkJMlMS5aCYpfJWapfMz1m5wIgznKExo0bJyNHjjSBkI4M/fLLL94b5fNAYrMSpZOTRGrEeDrKCsToLg0grIHQ7t275bbbbmN9MQCVJ0qnp/qNFseClaPECvQAwhoI9e7dW9auXRvswwAkUKK0lawcS3SXBhCIgCbxFy1a5P1eewjdfffd8uWXX0qHDh0kLS3Nb1/fpTcAJBY7lM5brIaOeZ5zAoCKBHS1GjRoULltugJ9WToUXlrK2j5AoucIxTJRuvyIEIEQgMoFdLUqWyIPAMfLEYo1KxhjvTEAYc0RAoDjN1O0Q46QZ+FVzzkBQEUC+m/b008/LaNHj5bMzEzzfVW0ogxAgq8zZoOpMescGBECUJWArlYzZ840TRQ1ENLvK6M5QgRCQOKyV7L0sfXGAKAyAV2ttm/fXuH3AGDbZGnP9BzJ0gCqQo4QgLA5XFhqu2RpGioCqEpAV6uxY8dKoGbMmBHwvgDiixV02CNZ2iqfZ2oMQDUDoQ0bNgSyW8xb6gOIrSM2bKhIsjSAqgR0tfrwww8D2Q1AgssvLvUrXbfDiBCBEICI5Ah9++238u6770p+fr752e12h3ooAHEi31OhlRnjlef9yuc903UAEJZA6Oeff5aLLrpIzjjjDOnXr5/s3bvXbB81apSMGzcu2MMBiCP5xUe70NewQSDkTZYmRwhAOAOhO++80yy0unPnTsnKyvJuHzJkiCxZsiTYwwGIIwWeqbEatpgao3wewPEFndH43nvvmSmxU045xW/76aefLjt27Aj2cADicGrMTiNChSUuKSl1SWoK3UIAlBf0leHIkSN+I0GWAwcOSEZGRrCHAxCHydJ2yBGyGioq1hsDELZA6LzzzpN58+b5lczr6vTTpk2T3/zmN8EeDkAcBkJ2mBrLSE2RtJSjLT2oHAMQtqkxDXg0WXrt2rVSVFQk99xzj2zevNmMCK1cuTLYwwGIE6UutxSV2CdZ2iqhP5hfTHdpAOEbEWrfvr18/fXX0rNnTxk4cKCZKhs8eLBputiyZctgDwcgzhKl7RQIWedR4KlmA4Bqjwhpc0WdAps4cWK5+2bPni233HJLsIcEEEfTYioj1R6JyZlpyeXODQB8BX210tGfdevWldv+1FNPyYQJE4I9HIC4a6aYLMnJ9lhux0ra9h2tAoBqBUKPP/649O3bV7766ivvtieeeELuv/9+efvtt4M9HIB46yFkk2kx36RtK0gDgGpPjV1//fUmMbpXr16yYsUKWbBggfzxj3+UxYsXy7nnnhvs4QDEW8WYnQIhz7kwNQagMiEtEa2VYrrUxtlnny2lpaWmweKvf/3rUA4FIN6mxmxQOm9hagxAWAKhp59+uty2Jk2amMaK559/vqxZs8bc1G233SaRoKNQt956q7z55puSnJwsl19+uclLqlWrVpWPW7VqlUnsXr16taSkpEjnzp1N4FajRo2InCeQqApsVjqvqBoDEJZAaObMmRVu18BCewdZ/YO0uWKkAqHhw4ebBV6XLl0qxcXFct1118no0aNl/vz5VQZBffr0MUncs2bNktTUVPn8889NIAUgfpfXKDsixNQYgGoFQtu3b5dY2rJli1nQ9dNPPzXTcUoDm379+sn06dOlcePGlS4Qq4HZ+PHjvdtat24dtfMGEomdFly11Ej3lM+TLA2gEo4YGtGRnbp163qDIKXJ2jqyo1NeFdm/f7+576STTpIePXpIw4YN5YILLjAJ3gDCzxp10aUt7CLTcy6UzwOo1ojQ2LFjZfLkyVKzZk3zfVVmzJgh4bZv3z4T0PjSaa769eub+yry3Xffma8PPvigGTXS3CBdI02XB9m0aZOcfvrpFT6usLDQ3Cy5ublh/V2AuJ8as9WIEIEQgDAEQrp8hublWN+Hi05ZPfbYY8edFguFLgSrbrzxRpNPpLp06SIffPCB/O1vf5OpU6dW+Djd/tBDD4X0nEAiO1Y+b5+BZnKEAIQlENJlNSr6vrrGjRsn1157bZX7tGjRQho1amSmunyVlJSYSjK9ryInn3yy+dquXTu/7W3btpWdO3dW+nyaWO076qUjQk2bNg3o9wESmS0bKnqTpVlrDEAY+whVRDtNDxgwwCzIGqgGDRqY2/F0795dcnJyzNIeXbt2NduWLVtmRn26detW4WOaNWtmkqi3bt3qt13PTztjVyYjI8PcAMRPHyGSpQFUJmxj2JpXs23bNokEHcXRMvgbbrjB9CvScv0xY8bI0KFDvRVju3fvljZt2nj7GWkp/9133216IP3zn/+Ub7/9ViZNmmQCtlGjRkXkPIFEZsvO0p6qscISltgAEOERoUh76aWXTPCjyc5WQ0XfRo+aw6SjP3l5ed5td9xxhxQUFJgyep1G69Spk+lD1LJlyxj9FkD8smUgxIgQgHgJhLRCrKrmiToV5na7K0zI9u0jBCBx+giRLA3geOxT3gEgPnKE7Dgi5AnSACDkEaF69eqZvJvKaBUXgMRlx6kxKygrpGoMQHUDoSeffJIXEUClrBJ1OwVC1jQdI0IAqh0IXXPNNYHuCiABFdixszTJ0gCOgxwhAGFhjbrYKUfIN1m6omIKACAQAhDHOULHLnGFJXSXBlAegRCAuJ0a8x2dYgV6ABUhEAIQtyNCaSnJkpZytNqVhGkAFSEQAlBtxaUuKXG5bRcIKdYbAxDWztK+K7P70h5DmZmZ0qpVKxk4cKDpBA0gMfiOtmR61veyUyB0qKBECuglBCAcgdCGDRtk/fr1UlpaKq1bt/au6J6SkmIWPf3Tn/4k48aNkxUrVki7du2CPTwAB+cHJSeJpKfYKxCiuzSAqgR9xdLRnl69esmePXtk3bp15va///1PLr74Yhk2bJhZBf788883C50CSLz8oKo60McyECJZGkBYAqHHH39cJk+eLHXq1PFuy87OlgcffFCmTZsmWVlZcv/995sACUCCBUI2qhizZFrdpT2jVgBQrUDo4MGDsn///nLbf/zxR8nNzTXf161bV4qKioI9NACHsuOCq5bM1KOXOarGAIRtamzkyJGycOFCMyWmN/1+1KhRMmjQILPPmjVr5Iwzzgj20AAcyo6l8xZrlIqpMQBhSZb+y1/+YvJ/hg4d6l1xPjU11axFNnPmTPOzJk0/99xzwR4agEMV2HhqjBwhAGENhGrVqiVz5swxQc93331ntrVo0cJst3Tu3DnYwwJwsPwil22nxqgaAxDWQMiigY/VK8g3CAKQeOw8NZbhXYGetcYAhCFHyOVyycMPP2wqxU477TRz0+RorSTT+wAkHjsHQt6psRKqxgCEYURo4sSJ8vzzz8ujjz4q5557rtmmzRO1fL6goECmTJkS7CEBOJwdF1y11PB0uqZ8HkBYAqG5c+eaROgBAwZ4t3Xs2FGaNGkiN998M4EQkMAjQnbOEaJqDEBYpsYOHDhgqsLK0m16H4AErhqzYSDkXXTVZz00AAg5EOrUqZM888wz5bbrNr0PQCJ3lrbXOmO+gRAjQgDCMjWmy2j0799f3n//fenevbvZtmrVKtm1a5csXrw42MMBiAN2HhE6Vj5PMQeA8oL+79sFF1xgVpu/7LLLJCcnx9wGDx4sW7dulfPOOy/YwwGIA3ZeYsPbWZq1xgCEq49Q48aNyyVF61Ibo0ePlr/+9a+hHBKAgzkhWZocIQAVCduE/s8//2zK6gEkHmvayY5TYxlpRy9z5AgBqIj9MhsBOI6t+wgxIgSgCgRCAOK7szSrzwOoAoEQgMTIESJZGkB1kqW1MqwqWj0GIDHl23hqzNtHqMQlbrdbkpKSYn1KAJwYCOkiq8e7f8SIEeE4JwAOY+c+QlYgVOpyS3GpW9JTCYQAhBAIvfDCC4HuCiDB2DpHyOec9DzTU8kIAHAMVwQA1aLTTd4cIRsusZGWkiQpyUdHgSihB1CW/a5aAByl0OTeiG1HhDQnKNMzCkQgBKAsAiEA1eIbXNixasw3iZvu0gDKIhACUC1WcKFTUGkp9rykWAEaJfQAyrLnVQuAY9h5wVUL3aUBVIZACEDcVoxZrCCt0LMmGgBYCIQAhKeHkA2bKVoYEQJQGQIhANWSX2TflectmVayNMtsACiDQAhA3K4zZqmRdvRSR9UYgLIIhAAkTI4QfYQAlEUgBKBaCmy84KrFCtIIhACURSAEIGFGhJgaA1AWgRCA+M8R8iZLUz4PwB+BEIBqsSqxathwwVVLZiojQgAqZt8rFwBn9RGy9YjQ0Utdoc+6aACgCIQAxH2OEA0VAVSGQAhAeNYas3HVGMnSACpDIAQg7keEWH0eQGUIhADEf46Q1UeohKoxAP4IhACEZ0TIxlNj1rlZzR8BwEIgBKBaCopdtu8jRI4QgMoQCAEITx8hGwdCVI0BqAyBEIDw5AjZeGos07P6PGuNASiLQAhA3FeNeXOEaKgIoAwCIQDxv9aY59yKS91SXErlGIBjCIQAhGmtMfsGQr5BGqNCAHwRCAEImcvllkJPb57MVPteTjJSkyUpyb/KDQCUfa9cAGyvoORYXx47jwglJSV5V6BnRAiALwIhANWeFlNWoGFXVqBm5TQBgCIQAhAyK6jQqafkZM/ck917CdFdGoATA6EDBw7I8OHDpU6dOlK3bl0ZNWqUHD58uMrH7Nu3T66++mpp1KiR1KxZU8466yx5/fXXo3bOQLxzQg8hSwa9hAA4ORDSIGjz5s2ydOlSeeutt2T58uUyevToKh8zYsQI2bp1qyxatEg2btwogwcPlquuuko2bNgQtfMG4ll+kcv2PYQsdJcG4NhAaMuWLbJkyRJ57rnnpFu3btKzZ0+ZNWuWvPLKK7Jnz55KH/ef//xHbr31VjnnnHOkRYsWct9995nRpHXr1kX1/IF45YRmiuVWoCdHCIDTAqFVq1aZAObss8/2buvVq5ckJyfL6tWrK31cjx49ZMGCBWZazeVymcCpoKBALrzwwiidORDfnNBM0UKyNICKpIoDaK7PSSed5LctNTVV6tevb+6rzKuvvipDhgyRE044weyflZUlCxculFatWlX6mMLCQnOz5Obmhum3AOKPE5opWjK85fP0EQJgkxGh8ePHm/4eVd2++uqrkI8/adIkycnJkffff1/Wrl0rY8eONTlCmi9UmalTp0p2drb31rRp05CfH0iYZGknjQhRNQbALiNC48aNk2uvvbbKfTS3R6u+9u/f77e9pKTETHnpfRXZtm2bPPPMM7Jp0yY588wzzbZOnTrJJ598IrNnz5Znn322wsdNmDDBBEy+I0IEQ0AcTI15qsboIwTANoFQgwYNzO14unfvbkZ2NMm5a9euZtuyZctM3o8mT1ckLy/PfNU8Il8pKSnmcZXJyMgwNwDxNTVGsjQAxyZLt23bVvr06SM33HCDrFmzRlauXCljxoyRoUOHSuPGjc0+u3fvljZt2pj7lX6vuUA33nij2aYjRE888YQpvx80aFCMfyMg3qrG7H8psUatqBoD4Mv+Vy+Pl156yQQ3F110kfTr18+U0P/1r3/13l9cXGx6BlkjQWlpabJ48WIz4nTppZdKx44dZd68eTJ37lzzeACJlSNkBUJMjQFwXNWY0gqx+fPnV3p/s2bNxO12+207/fTT6SQNRGFqLDPdScnSVI0BcOCIEAD7oaEiAKcjEAKQEIFQJmuNAagAgRCAhFh0lRwhABUhEAJQ/RwhB4wIsegqgIoQCAFIiKkxOksDqAiBEICQ5XvW7XJEIEQfIQAVIBACELICB3WWPtZQkfJ5AMcQCAFIiLXGSJYGUBECIQCJlSPkOWcAUARCABJiaswK1opKXFLq8u9CDyBxEQgBSKiGiqqwhFEhAEcRCAEISXGpS0o8IyuOCIRSU8r1PwIAAiEA1eoqrTLT7X8pSU5OkozUo+dJnhAA77WBlwJAKKxgIilJJD3FGZcSK5fJN4gDkNiccfUCYDuFnn48OsqSpNGQA1jTY/QSAmAhEAIQksISKxCyf36QhRJ6AGURCAEIiVV5ZeXdOIG3qSLJ0gA8nHMFA2DPESGfsnS7q+E5V5KlAViccwUDYMscId+ydOesN0ayNICjCIQAhKTAmhpz1IgQgRAAf865ggGwadWYg0aErPXGyBEC4EEgBCBhkqWtEaF8TxAHAM65ggGwafm8cy4j1npj5AgBsDjnCgbAVhzZR4gcIQBlEAgBCEmhp/LKd1V350yNUTUG4CjnXMEA2IoTR4RIlgZQFoEQgGqNCDmpfN671pgniAMA51zBANiKE5OlvWuNFZXE+lQA2IRzrmAAbMWRU2Oe0Svr3AGAQAhAwvQRsoI2qxkkADjnCgbAnmuNeSqxnMAK2qwgDgAIhAAkzFpj3hEhpsYAeDjnCgbApmuNOecyYgVtBEIALM65ggGwFScmS3unxmioCMCDQAhA4iVLMzUGwMM5VzAA9hwRclSO0NFzLSIQAuDhnCsYAJvmCDloaowcIQBlEAgBqFbVmJMWXbWCtqJSl7hc7lifDgAbcM4VDICtOHJEyCefSYMhACAQApBAydLHzpXu0gCUc65gAGzFieXzqSnJkpKcZL6nuzQARSAEIGGqxvyX2WBqDACBEIAQlJS6pNSTbOykqTHFemMAfDnrCgbAFgp8RlOctOiq71ReASvQAyAQAhAK3yUq0lOc9f8pegkB8OWsKxgAW7DyazQISvYkHzsFU2MAfBEIAahGxZjzLiGsNwbAl/OuYgDs00PIYRVj/ivQUzUGgEAIQIJ0lS6fI3QszwlA4nLef+cAxFxBsZNHhI4Gb/QRAqCcdxUDEHNO7CptoaEiAF8EQgASLFnayhFiagwAgRCABFlw1cLUGABfzruKAbBPsrTDukorGioC8EUgBCAxp8aoGgNAIASgWlVjqQ6uGqOPEAACIQDVGRFy2oKriqoxAL6c9985ADHn6GRpGioC8OG8qxiAmHN2HyEaKgI4hkAIQDWqxpx3CWGtMQC+nHcVAxBzTI0BiBcEQgCCVuDkRVeZGgPgg0AIQMgjQplOnhrz5DkBSGyOuYpNmTJFevToIVlZWVK3bt2AHuN2u+X++++Xk08+WWrUqCG9evWSb775JuLnCsS7uEiWZq0xAE4KhIqKiuTKK6+Um266KeDHTJs2TZ5++ml59tlnZfXq1VKzZk3p3bu3FBQURPRcgXjn6M7SnlGsIkaEAIhIqlNehYceesh8ffHFFwMeDXryySflvvvuk4EDB5pt8+bNk4YNG8obb7whQ4cOjej5AvHMGk1xdNUYgRAAJ40IBWv79u2yb98+Mx1myc7Olm7dusmqVatiem6A08XF1BhrjQFw0ohQsDQIUjoC5Et/tu6rSGFhoblZDh48aL7m5uZG7FwBpzly+JC4CvOkpOCI4/42ivKPmHPPk0LHnTuAwFl/3zpDZNtAaPz48fLYY49Vuc+WLVukTZs2UTunqVOneqfhfDVt2jRq5wA4Rb8nxdGyq778AIgDhw4dMjNCtgyExo0bJ9dee22V+7Ro0SKkYzdq1Mh8/eGHH0zVmEV/7ty5c6WPmzBhgowdO9b7s8vlkgMHDsgJJ5wgSUlJEo0IVoOuXbt2SZ06dSL+fOA9sCP+DmKP9yD2eA+qR0eCNAhq3LhxlfvFNBBq0KCBuUVC8+bNTTD0wQcfeAMf/Uel1WNVVZ5lZGSYm69Ay/XDSYMgAqHY4j2IPd6D2OM9iD3eg9BVNRLkuGTpnTt3ymeffWa+lpaWmu/1dvjwYe8+OoW2cOFC872O3txxxx3yyCOPyKJFi2Tjxo0yYsQIExkOGjQohr8JAACwC8ckS2tjxLlz53p/7tKli/n64YcfyoUXXmi+37p1qze5Wd1zzz1y5MgRGT16tOTk5EjPnj1lyZIlkpmZGYPfAAAA2I1jAiHtH3S8HkJlM8N1VOjhhx82N6fQabkHHnig3PQceA8SCX8Hscd7EHu8B9GR5D5eXRkAAECcckyOEAAAQLgRCAEAgIRFIAQAABIWgVA1aSn/pEmTTN+iGjVqSMuWLWXy5Ml+idv6vVa9aWNH3UfXP/vmm2/8jqNNG4cPH276RWjfolGjRvm1BlBffPGFnHfeeabqTZsuTps2rdz5vPbaa6aNgO7ToUMHWbx4sSQCbZql7RJOO+008xr36NFDPv30U+/9vAfhtXz5crn00ktNOwotStCFjH3Z6fUO5Fzi8T3417/+JZdccom3Gay2GymroKBAbrnlFrNPrVq15PLLLzdNZ31py5L+/ftLVlaWnHTSSXL33XdLSUmJ3z4fffSRnHXWWSa5t1WrVhUWtsyePVuaNWtm3idd83HNmjUSz+9BcXGx3HvvvebfZM2aNc0+2sJlz549fsfg78AGNFkaoZsyZYr7hBNOcL/11lvu7du3u1977TV3rVq13E899ZR3n0cffdSdnZ3tfuONN9yff/65e8CAAe7mzZu78/Pzvfv06dPH3alTJ/d///tf9yeffOJu1aqVe9iwYd77Dx486G7YsKF7+PDh7k2bNrlffvlld40aNdx/+ctfvPusXLnSnZKS4p42bZr7yy+/dN93333utLQ098aNG+P+Lb7qqqvc7dq1c3/88cfub775xv3AAw+469Sp4/7f//5n7uc9CK/Fixe7J06c6P7Xv/6lEb974cKFfvfb6fUO5Fzi8T2YN2+e+6GHHnLPmTPH3L9hw4Zyx/j973/vbtq0qfuDDz5wr1271v3rX//a3aNHD+/9JSUl7vbt27t79eplHq/PeeKJJ7onTJjg3ee7775zZ2VluceOHWveg1mzZpn3ZMmSJd59XnnlFXd6err7b3/7m3vz5s3uG264wV23bl33Dz/84I7X9yAnJ8e8bgsWLHB/9dVX7lWrVrnPOeccd9euXf2Owd9B7BEIVVP//v3dI0eO9Ns2ePBgc/FWLpfL3ahRI/fjjz/u9weSkZFhLuxKLx76R/Tpp59693nnnXfcSUlJ7t27d5uf//SnP7nr1avnLiws9O5z7733ulu3bu0XDOj5+OrWrZv7xhtvdMezvLw8c+HVYNTXWWedZS5SvAeRVfYDwE6vdyDnEg8qCoQs+h+0igIhfR00aNT/vFm2bNli9tUPbeuDPjk52b1v3z7vPn/+85/NfzKs9+Wee+5xn3nmmX7HHjJkiLt3797enzUAuOWWW7w/l5aWuhs3buyeOnWqOxHeA8uaNWvMfjt27DA/83dgD0yNVZNOwegyHl9//bX5+fPPP5cVK1ZI3759zc/bt283q93rcLxvy28dGl61apX5Wb/q1MDZZ5/t3Uf3T05ONkuCWPucf/75kp6e7t2nd+/eponkL7/84t3H93msfazniVc6TK9TlGUbZeo0iL4XvAfRZafXO5BzSVTr1q0z0ze+r41OMZ566ql+75NO7TRs2NDv9dXlijZv3hzQe1BUVGSey3cffZ/150R7D7Thr06hWcs28XdgDwRC1TR+/HgZOnSouYCkpaWZjteaq6K5D0ovwsr3QmL9bN2nX3Xu3VdqaqrUr1/fb5+KjuH7HJXtY90fr2rXri3du3c3uVk6/65B0T/+8Q9zkdm7dy/vQZTZ6d98IOeSqPT31yCz7FqKZV+/UN8DDZby8/Plp59+Mn+Tif4eaD6W5gwNGzbMu44kfwf2QCBUTa+++qq89NJLMn/+fFm/fr1ZBmT69Ol+y4Eg8v7+97+bpNgmTZqYhM2nn37aXHD0f54AEEs68nbVVVeZa9Sf//xn3gyb4VOimrSCwhoV0iHkq6++Wu68806ZOnWqub9Ro0bma9lKDP3Zuk+/7t+/v9x0j1YT+O5T0TF8n6Oyfaz745lW63388cem6mjXrl2mIkUvPi1atOA9iDI7/ZsP5FwSlf7+Om2l6zBW9fqF+h7oqIdOT5944omSkpKSsO+BFQTt2LFDli5d6h0NUvwd2AOBUDXl5eWVG3XQP3qXy2W+17J6/ceueUQWHTLWPAidzlH6VS9GOo9uWbZsmTmG5jJY+2ippv5RWfSPqnXr1lKvXj3vPr7PY+1jPU8i0DJVLZPWHJJ3331XBg4cyHsQZXb6Nx/IuSSqrl27mul839dG86+0XN73fdq4caNf0Gp9mLdr1y6g90Cn3/S5fPfR91l/jvf3wAqCtF3D+++/b9oU+OLvwCZina3tdNdcc427SZMm3vJ5LaPU8lKtpPAt39VS0X//+9/uL774wj1w4MAKS4m7dOniXr16tXvFihXu008/3a+UWCs8tJT46quvNqXEWo6qJatlS4lTU1Pd06dPN9UfWkKeKOXzWqqrVUdayvvee++ZsmytHioqKjL38x6E16FDh0wVkt70MjJjxgzzvVUNY6fXO5Bzicf34OeffzY/v/322+Z+ff3057179/qVz5966qnuZcuWmfL57t27m1vZ8vlLLrnE/dlnn5m/swYNGlRYPn/33Xeb92D27NkVls9rpd6LL75oKqVGjx5t3hPfarR4ew/02qOtGk455RTz2unrbt18KyH5O4g9AqFqys3Ndd9+++3mYpKZmelu0aKFKdn2/YeuJbyTJk0yF3W9GFx00UXurVu3+h1HL1r6IaA9iLQ09brrrjN/ZL60B0rPnj3NMTT40gt8Wa+++qr7jDPOMD07tKRVL4KJQHt16Guvv7eWS2uprn6QWngPwuvDDz80F/6yN/2Pgd1e70DOJR7fgxdeeKHC+zVYtGgwePPNN5s2BRrMXHbZZX6Bkvr+++/dffv2NT2c9D9548aNcxcXF5c7l86dO5v3QP8O9bnL0v5Cep3UfbScXvtHxfN7YLUtqOimj7PwdxB7rD4PAAASFjlCAAAgYREIAQCAhEUgBAAAEhaBEAAASFgEQgAAIGERCAEAgIRFIAQAABIWgRAAAEhYBEIAHO3aa6+VQYMGeX++8MIL5Y477ojKc4WiWbNmkpSUZG5lFzyNtFmzZpnn7dGjh1knMRQvvvii9/wj9ToD0UQgBMQB/YC2Ppx8b3369AnpA04XEtbFa4cMGWIW4QxEfn6+1K9f36w2XlhYKLHyr3/9SyZPnuwXeDz55JNiJw8//LDs3btXsrOzq3WcioK+p556SjIyMuSVV17x2/7SSy/JXXfdJU8//bQcOHBALr/8cr8FbdXnn38uw4YNk6ZNm5qV49u2bWuO50v/Tei5x/uCqUgcBEJAnNCgRz+gfG8vv/xyUMfQVcX1cbt375bXX3/drEZ+5ZVXBvRY3f/MM8+UNm3ayBtvvCGxosFY7dq1xc70/Bo1amSCznB64IEH5A9/+IP8+9//lqFDh3q3L168WH7/+9/La6+9JrfeeqssX75c9uzZYwJoXXPSsm7dOjnppJPkH//4h2zevFkmTpwoEyZMkGeeeca7jwZIeu66qjwQDwiEgDihowD6AeV7q1evnvd+HdkZOHCg1KpVywQ8V111lfzwww9+x9APZn2cjgbp9MmoUaNkzZo1kpube9znf/755+V3v/uduen3Zemx//KXv8j//d//SVZWlhltWLVqlXz77bdmZKNmzZrmObdt2+Z9zIMPPiidO3c2j9NRCn2cnvfBgwcDGiXR73fs2CF33nmnd7TL97i+dNRIR48spaWlMnbsWKlbt66ccMIJcs899/gFDcrlcsnUqVOlefPmJkDo1KmT/POf/5Rg6WicPs9bb70lrVu3Nr/nFVdcYaav5s6da85L38vbbrvNnFdZel4a4Ohoz9KlS/1GAleuXCnXXHONGSkbMGCA2abBzkcffWRe+9tvv92778iRI80I0AUXXCAtWrQw7+V1111nHgvEKwIhIAHoB7YGQTol8vHHH5sPy++++85Mc1Rm//79snDhQklJSTG3qmjwokGNBil6++STT0wAUpZOWY0YMUI+++wzM3L029/+Vm688UYz6rB27VrzgT5mzBi/x+iH9auvvipvvvmmLFmyRDZs2CA333xzQL+3foCfcsop3qkovQXqiSeeMAHK3/72N1mxYoV57fT18KVB0Lx58+TZZ581IygacGnwoK9xsDTo0UBGp7T099RA5bLLLjOjOXr7+9//bgLCsoFWSUmJeU7drs+rwaSvc889V3788Ue5+OKL/bZrYLV69WrznFXRoFNH2YB4lRrrEwAQHjqaoKM9vnSaRG8ffPCBbNy4UbZv325GVpR+gOtU1qeffiq/+tWvvB96egwNSKxkWh2F0NGaqmiw0LdvX+8IVO/eveWFF14wIy++dHRBAyV17733mjyTSZMmmf2Vjk7oPr4KCgrMuTZp0sSb8Nu/f38TqOjoVVX0A1yDOGsqKhg6QqQB2uDBg83PGuy8++673vs1D+qPf/yjvP/++958GR1F0aBJAxYdVQmG5uv8+c9/lpYtW5qfdURIgx8dtdP3pF27dvKb3/xGPvzwQ78Ads6cOd78Hg0uw+k///mPLFiwQN5+++2wHhewE0aEgDihH5I60uJ707wQtWXLFhMAWUGQ0g9WnY7R+ywaMOjjdHRGA42zzjpLpkyZUuXz6lSNTt/oqIRFv9fRFB2J8tWxY0fv9w0bNjRfO3To4LdNAx/fqbhTTz3VGwQpDTr0uJq/FCkaEOroUbdu3bzbUlNT5eyzz/YbqdJgUUdaNFCxbhq0+U7vBUqnw6wgyHotdErMN7jVbTpS56tnz55mHw0odXQoXDZt2mRGETXv6JJLLgnbcQG7YUQIiBM6atOqVatqHUOrxaxjaA6PfqDfdNNNZmSiMjpKosnVZafZNEDSkSjfKZm0tDTv91a+TkXbygZQ4aa/Z9l8n7IVVMdz+PBh81VHS3wDNStfK1i+r4P1WlS0rexro4GkBq29evUy74GO4GjQVh1ffvmlXHTRRTJ69Gi57777qnUswO4YEQISgAY1u3btMjffDzvtY6MjQ5UZP368+WBdv359pftoYrRWKJUdjdJtFSVNB0uTvLXCyfLf//7XBDKaVBwIrW4qm2DcoEED2bdvn18wpOds0bJ2TRjXHBqLjrZoVZVFXzcNePT8NHj0vfmOvEWDJn5r0KnVYDr1GGxQ50tznXR0UROsjzcaCMQDRoSAOKE5K/rh7ktHBrSvj44W6MjB8OHDTe6LfqhrwrHmsfhO95SlH+iasHv//febHKSyNAlXk5gXLVok7du397tPk6L1sZpkXJ1k28zMTPOhPH36dDNlpjlL+mEfaM6PTi9pgKCBmQYu+npoNZme+7Rp00wujiYnv/POO6aazqL5So8++qicfvrpJvdmxowZfg0QdRpR+/JogrSO0ugUlU6paZWWHkfPOZq0Ym3ZsmVmJEdfH00wLzuiFMh02P/7f//P5GxpxZz170nzrDR4BOIRI0JAnNAPcx3F8L3ph7M1paK9ZTSZ+fzzzzeBkSb26mjP8egHvU7/aBl9WZoPo1Ny+uFblm7TknLtSVMdOsKiCcv9+vUzuSqaZ/SnP/0p4Mdrxdj3339v8m+sD3MdIdNjzJ492wQQ+rtpUONr3LhxcvXVV5uARvOSNPDRwK5sFZzm5mj1mB5Ty9b1tdJy+ljQYFeDIU1y1v5PRUVFQT1eK880QNT3zPffkZVMD8SjJHfZiXIAsAmtOtPmjL7TVk6nI1Ta58jpy1PoqJpOydmtazcQLEaEACDKtHWAVnpV1RjSrnSpDj137RUFxANyhAAgirTpoZXMbPelQCqi3amttgLafgFwOqbGAABAwmJqDAAAJCwCIQAAkLAIhAAAQMIiEAIAAAmLQAgAACQsAiEAAJCwCIQAAEDCIhACAAAJi0AIAABIovr/EY+ye69IW6AAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "like = pl.Gaussian(model=dmi)\n",
    "amps = np.logspace(4.9, 5.1, 101)\n",
    "likes = np.array([like.loglike([a, indx], []) for a in amps])\n",
    "plt.plot(amps, likes - likes.max())\n",
    "plt.ylim(-1000000, 0)\n",
    "plt.xlabel(\"EoR Amplitude [mK^2]\")\n",
    "plt.ylabel(\"Log Likelihood (norm)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7825bdd1",
   "metadata": {},
   "source": [
    "### Cylindrical Data, Spherical Theory"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cfdb0b4",
   "metadata": {},
   "source": [
    "Suppose we have data that is in cylindrical co-ordinates. We can use this data, even\n",
    "if our theory model is purely spherical. Let's create some mock power spectrum data\n",
    "whose power is intrinsically only dependent on $|k|$, but which is in cylindrical \n",
    "coordinates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1ce0ae78",
   "metadata": {},
   "outputs": [],
   "source": [
    "kperp = np.linspace(0.01, 0.5, 15)\n",
    "kpar = np.linspace(0.01, 0.5, 100)\n",
    "\n",
    "KPERP, KPAR = np.meshgrid(kperp, kpar)\n",
    "KPERP = KPERP.flatten()\n",
    "KPAR = KPAR.flatten()\n",
    "k = np.sqrt(KPAR**2 + KPERP**2)\n",
    "\n",
    "z = 9.0\n",
    "amp, indx = 1e5, 2.7\n",
    "power = theory_model(z, k, [amp, indx])\n",
    "\n",
    "covariance = np.diag(k**3 * 1e5)\n",
    "window_function = np.eye(len(k))\n",
    "\n",
    "\n",
    "dmi = pl.DataModelInterface(\n",
    "    cosmology=cosmology.Planck18,\n",
    "    redshift=z,\n",
    "    power_spectrum=power,\n",
    "    window_function=window_function,\n",
    "    covariance=covariance * un.mK**4,\n",
    "    kpar_bins_obs=KPAR * cosmology.units.littleh / un.Mpc,\n",
    "    kperp_bins_obs=KPERP * cosmology.units.littleh / un.Mpc,\n",
    "    theory_uses_little_h=True,\n",
    "    theory_uses_spherical_k=True,\n",
    "    theory_model=theory_model,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c8cc79d",
   "metadata": {},
   "source": [
    "Note here that the theory model given is still the spherical one. However, we give both\n",
    "`kperp` and `kpar`, which are arrays of the *same length* (i.e. they are not assumed\n",
    "to be a rectilinear grid). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c16dae32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Log Likelihood (norm)')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "like = pl.Gaussian(model=dmi)\n",
    "amps = np.logspace(4.9, 5.1, 101)\n",
    "likes = np.array([like.loglike([a, indx], []) for a in amps])\n",
    "plt.plot(amps, likes - likes.max())\n",
    "plt.ylim(-1000000, 0)\n",
    "plt.xlabel(\"EoR Amplitude [mK^2]\")\n",
    "plt.ylabel(\"Log Likelihood (norm)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9e22955",
   "metadata": {},
   "source": [
    "### Cylindrical Data, Cylindrical Theory"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6befa83f",
   "metadata": {},
   "source": [
    "Now, if both data and theory are defined in cylindrical space, we can manage that too.\n",
    "The only difference is that the theory model needs to assume that the `k` argument given\n",
    "to it is really a tuple, `(kperp, kpar)`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a6c57460",
   "metadata": {},
   "outputs": [],
   "source": [
    "def powerlaw_eor_cylindrical(\n",
    "    z: float, kcyl: tuple[np.ndarray, np.ndarray], params: list[float]\n",
    ") -> np.ndarray:\n",
    "    amplitude, index = params\n",
    "    kperp, kpar = kcyl\n",
    "    k = np.sqrt(kperp**2 + kpar**2)\n",
    "    return k**3 / (2 * np.pi**2) * amplitude * un.mK**2 * (1.0 + z) / k**index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "938e54ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "dmi = pl.DataModelInterface(\n",
    "    cosmology=cosmology.Planck18,\n",
    "    redshift=z,\n",
    "    power_spectrum=power,\n",
    "    window_function=window_function,\n",
    "    covariance=covariance * un.mK**4,\n",
    "    kpar_bins_obs=KPAR * cosmology.units.littleh / un.Mpc,\n",
    "    kperp_bins_obs=KPERP * cosmology.units.littleh / un.Mpc,\n",
    "    theory_uses_little_h=True,\n",
    "    theory_uses_spherical_k=False,  # <-- this is the important bit\n",
    "    theory_model=powerlaw_eor_cylindrical,  # <-- this is the important bit\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4e4c7011",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Log Likelihood (norm)')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "like = pl.Gaussian(model=dmi)\n",
    "amps = np.logspace(4.9, 5.1, 101)\n",
    "likes = np.array([like.loglike([a, indx], []) for a in amps])\n",
    "plt.plot(amps, likes - likes.max())\n",
    "plt.ylim(-1000000, 0)\n",
    "plt.xlabel(\"EoR Amplitude [mK^2]\")\n",
    "plt.ylabel(\"Log Likelihood (norm)\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pspec-likelihood (3.13.11)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}