{
 "cells": [
  {
   "attachments": {},
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   "source": [
    "# Python: Conditional Value at Risk of potential outcomes\n",
    "In this example, we illustrate how the [DoubleML](https://docs.doubleml.org/stable/index.html) package can be used to estimate the conditional Value at Risk of potential outcomes. The estimation is based on [Kallus  et al. (2019)](https://arxiv.org/abs/1912.12945)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Data\n",
    "We define a data generating process to create synthetic data to compare the estimates to the true effect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import doubleml as dml\n",
    "import multiprocessing\n",
    "\n",
    "from lightgbm import LGBMClassifier, LGBMRegressor"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "The data is generated as a location-scale model with\n",
    "\n",
    "$$Y_i = \\text{loc}(D_i,X_i) + \\text{scale}(D_i,X_i)\\cdot\\varepsilon_i,$$\n",
    "\n",
    "where $X_i\\sim\\mathcal{U}[-1,1]^{p}$ and $\\varepsilon_i \\sim \\mathcal{N}(0,1)$.\n",
    "Further, the location and scale are determined according to the following functions\n",
    "\n",
    "$$\\begin{aligned}\n",
    "\\text{loc}(d,x) &:= 0.5d + 2dx_5 + 2\\cdot 1\\{x_2 > 0.1\\} - 1.7\\cdot 1\\{x_1x_3 > 0\\} - 3x_4 \\\\\n",
    "\\text{scale}(d,x) &:= \\sqrt{0.5d + 0.3dx_1 + 2},\n",
    "\\end{aligned}$$\n",
    "\n",
    "and the treatment takes the following form\n",
    "\n",
    "$$D_i = 1_{\\{(X_2 - X_4 + 1.5\\cdot 1\\{x_1 > 0\\} + \\epsilon_i > 0)\\}}$$\n",
    "\n",
    "with $\\epsilon_i \\sim \\mathcal{N}(0,1)$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def f_loc(D, X):\n",
    "  loc = 0.5*D + 2*D*X[:,4] + 2.0*(X[:,1] > 0.1) - 1.7*(X[:,0] * X[:,2] > 0) - 3*X[:,3]\n",
    "  return loc\n",
    "\n",
    "def f_scale(D, X):\n",
    "  scale = np.sqrt(0.5*D + 0.3*D*X[:,1] + 2)\n",
    "  return scale\n",
    "\n",
    "def dgp(n=200, p=5):\n",
    "    X = np.random.uniform(-1,1,size=[n,p])\n",
    "    D = ((X[:,1 ] - X[:,3] + 1.5*(X[:,0] > 0) + np.random.normal(size=n)) > 0)*1.0\n",
    "    epsilon = np.random.normal(size=n)\n",
    "\n",
    "    Y = f_loc(D, X) + f_scale(D, X)*epsilon\n",
    "\n",
    "    return Y, X, D, epsilon"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "We can calculate the true conditional value at risk through simulations. Here, we will just approximate the true conditional value at risk for the potential outcomes for a range of quantiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conditional Value at Risk Y(0): [np.float64(0.5473064979425033), np.float64(0.756585519864526), np.float64(0.9574793755564219), np.float64(1.1539831483813536), np.float64(1.3486970271639334), np.float64(1.5436876323961168), np.float64(1.7407627053044026), np.float64(1.9417090334740552), np.float64(2.1484554868601506), np.float64(2.3631031251500065), np.float64(2.5883964619856044), np.float64(2.827874854703913), np.float64(3.086401570476133), np.float64(3.3711317415516624), np.float64(3.6934117220290754), np.float64(4.0735183279373635), np.float64(4.554986206618521)]\n",
      "Conditional Value at Risk Y(1): [np.float64(1.1121351274811793), np.float64(1.3466832975109777), np.float64(1.5714294154804167), np.float64(1.7910908091075142), np.float64(2.008825189994473), np.float64(2.227018245501943), np.float64(2.447863428811719), np.float64(2.6734628878523097), np.float64(2.906051023766621), np.float64(3.1482102407485826), np.float64(3.403113188429014), np.float64(3.675011328023803), np.float64(3.969702563412915), np.float64(4.295855642811191), np.float64(4.667285944503316), np.float64(5.108783087402629), np.float64(5.673581815999206)]\n"
     ]
    }
   ],
   "source": [
    "tau_vec = np.arange(0.1,0.95,0.05)\n",
    "p = 5\n",
    "n_true = int(10e+6)\n",
    "\n",
    "_, X_true, _, epsilon_true = dgp(n=n_true, p = p)\n",
    "D1 = np.ones(n_true)\n",
    "D0 = np.zeros(n_true)\n",
    "\n",
    "Y1 = f_loc(D1, X_true) + f_scale(D1, X_true)*epsilon_true\n",
    "Y0 = f_loc(D0, X_true) + f_scale(D0, X_true)*epsilon_true\n",
    "\n",
    "Y1_quant = np.quantile(Y1, q=tau_vec)\n",
    "Y0_quant = np.quantile(Y0, q=tau_vec)\n",
    "\n",
    "Y1_cvar = [Y1[Y1 >= quant].mean() for quant in Y1_quant]\n",
    "Y0_cvar = [Y0[Y0 >= quant].mean() for quant in Y0_quant]\n",
    "\n",
    "print(f'Conditional Value at Risk Y(0): {Y0_cvar}')\n",
    "print(f'Conditional Value at Risk Y(1): {Y1_cvar}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Let us generate $n=5000$ observations and convert them to a [DoubleMLData](https://docs.doubleml.org/stable/api/generated/doubleml.data.DoubleMLData.html) object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "n = 5000\n",
    "np.random.seed(42)\n",
    "Y, X, D, _ = dgp(n=n,p=p)\n",
    "obj_dml_data = dml.DoubleMLData.from_arrays(X, Y, D)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Conditional Value at Risk (CVaR)\n",
    "Next, we can initialize our two machine learning algorithms to train the different nuisance elements (remark that in contrast to potential quantile estimation `ml_g` is a regressor). Then we can initialize the `DoubleMLCVAR` objects and call `fit()` to estimate the relevant parameters. To obtain confidence intervals, we can use the `confint()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quantile: 0.1\n",
      "Quantile: 0.15000000000000002\n",
      "Quantile: 0.20000000000000004\n",
      "Quantile: 0.25000000000000006\n",
      "Quantile: 0.30000000000000004\n",
      "Quantile: 0.3500000000000001\n",
      "Quantile: 0.40000000000000013\n",
      "Quantile: 0.45000000000000007\n",
      "Quantile: 0.5000000000000001\n",
      "Quantile: 0.5500000000000002\n",
      "Quantile: 0.6000000000000002\n",
      "Quantile: 0.6500000000000001\n",
      "Quantile: 0.7000000000000002\n",
      "Quantile: 0.7500000000000002\n",
      "Quantile: 0.8000000000000002\n",
      "Quantile: 0.8500000000000002\n",
      "Quantile: 0.9000000000000002\n"
     ]
    }
   ],
   "source": [
    "ml_g = LGBMRegressor(n_estimators=300, learning_rate=0.05, num_leaves=10, verbose=-1, n_jobs=1)\n",
    "ml_m = LGBMClassifier(n_estimators=300, learning_rate=0.05, num_leaves=10, verbose=-1, n_jobs=1)\n",
    "\n",
    "CVAR_0 = np.full((len(tau_vec)), np.nan)\n",
    "CVAR_1 = np.full((len(tau_vec)), np.nan)\n",
    "\n",
    "ci_CVAR_0 = np.full((len(tau_vec),2), np.nan)\n",
    "ci_CVAR_1 = np.full((len(tau_vec),2), np.nan)\n",
    "\n",
    "for idx_tau, tau in enumerate(tau_vec):\n",
    "    print(f'Quantile: {tau}')\n",
    "    dml_CVAR_0 = dml.DoubleMLCVAR(obj_dml_data,\n",
    "                                ml_g, ml_m,\n",
    "                                quantile=tau,\n",
    "                                treatment=0,\n",
    "                                n_folds=5)\n",
    "    dml_CVAR_1 = dml.DoubleMLCVAR(obj_dml_data,\n",
    "                                ml_g, ml_m,\n",
    "                                quantile=tau,\n",
    "                                treatment=1,\n",
    "                                n_folds=5)\n",
    "\n",
    "    dml_CVAR_0.fit()\n",
    "    dml_CVAR_1.fit()\n",
    "\n",
    "    ci_CVAR_0[idx_tau, :] = dml_CVAR_0.confint(level=0.95).to_numpy()\n",
    "    ci_CVAR_1[idx_tau, :] = dml_CVAR_1.confint(level=0.95).to_numpy()\n",
    "\n",
    "    CVAR_0[idx_tau] = dml_CVAR_0.coef.squeeze()\n",
    "    CVAR_1[idx_tau] = dml_CVAR_1.coef.squeeze()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Finally, let us take a look at the estimated values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Quantile  CVaR Y(0)  CVaR Y(1)  DML CVaR Y(0)  DML CVaR Y(1)  \\\n",
      "0       0.10   0.547306   1.112135       0.360683       1.057962   \n",
      "1       0.15   0.756586   1.346683       0.590911       1.273356   \n",
      "2       0.20   0.957479   1.571429       0.829543       1.489699   \n",
      "3       0.25   1.153983   1.791091       1.015038       1.697000   \n",
      "4       0.30   1.348697   2.008825       1.203284       1.925736   \n",
      "5       0.35   1.543688   2.227018       1.502494       2.144084   \n",
      "6       0.40   1.740763   2.447863       1.678826       2.338775   \n",
      "7       0.45   1.941709   2.673463       1.822482       2.559144   \n",
      "8       0.50   2.148455   2.906051       2.153119       2.824701   \n",
      "9       0.55   2.363103   3.148210       2.156969       3.041831   \n",
      "10      0.60   2.588396   3.403113       2.495657       3.298120   \n",
      "11      0.65   2.827875   3.675011       2.653846       3.582761   \n",
      "12      0.70   3.086402   3.969703       2.847948       3.842405   \n",
      "13      0.75   3.371132   4.295856       3.076347       4.163895   \n",
      "14      0.80   3.693412   4.667286       3.523163       4.543075   \n",
      "15      0.85   4.073518   5.108783       3.869020       4.913774   \n",
      "16      0.90   4.554986   5.673582       4.372097       5.482038   \n",
      "\n",
      "    DML CVaR Y(0) lower  DML CVaR Y(0) upper  DML CVaR Y(1) lower  \\\n",
      "0              0.162710             0.558655             0.957745   \n",
      "1              0.360801             0.821021             1.175284   \n",
      "2              0.606342             1.052745             1.393604   \n",
      "3              0.824889             1.205187             1.601061   \n",
      "4              1.009428             1.397140             1.824750   \n",
      "5              1.292028             1.712960             2.041147   \n",
      "6              1.455078             1.902573             2.234534   \n",
      "7              1.579238             2.065725             2.455107   \n",
      "8              1.883914             2.422325             2.715407   \n",
      "9              1.907491             2.406446             2.932027   \n",
      "10             2.250210             2.741104             3.183526   \n",
      "11             2.382872             2.924821             3.466440   \n",
      "12             2.554076             3.141820             3.722848   \n",
      "13             2.727976             3.424717             4.041284   \n",
      "14             3.140833             3.905494             4.409746   \n",
      "15             3.483717             4.254324             4.773177   \n",
      "16             3.921372             4.822822             5.313209   \n",
      "\n",
      "    DML CVaR Y(1) upper  \n",
      "0              1.158178  \n",
      "1              1.371429  \n",
      "2              1.585793  \n",
      "3              1.792939  \n",
      "4              2.026723  \n",
      "5              2.247020  \n",
      "6              2.443016  \n",
      "7              2.663182  \n",
      "8              2.933996  \n",
      "9              3.151636  \n",
      "10             3.412714  \n",
      "11             3.699082  \n",
      "12             3.961962  \n",
      "13             4.286507  \n",
      "14             4.676405  \n",
      "15             5.054370  \n",
      "16             5.650867  \n"
     ]
    }
   ],
   "source": [
    "data = {\"Quantile\": tau_vec, \"CVaR Y(0)\": Y0_cvar, \"CVaR Y(1)\": Y1_cvar,\n",
    "        \"DML CVaR Y(0)\": CVAR_0, \"DML CVaR Y(1)\": CVAR_1,\n",
    "        \"DML CVaR Y(0) lower\": ci_CVAR_0[:, 0], \"DML CVaR Y(0) upper\": ci_CVAR_0[:, 1],\n",
    "        \"DML CVaR Y(1) lower\": ci_CVAR_1[:, 0], \"DML CVaR Y(1) upper\": ci_CVAR_1[:, 1]}\n",
    "df = pd.DataFrame(data)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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djz/+OPr06YNx48YhLS0NzzzzTJHeFxF5Js99LUxEVMpq1KiBBx98EAAwceLEXNfqA+zdSbPHpVWtWtXRFdJdS4EQwrE9ez2/wmjfvj0kScLu3btx5MgRl/379u3LdxfR7IAje1xbXmm1WscEHDm1kmRPslIU9579e4mMjHQJBAHk2JpTWD4+Phg6dCgA+/1k39OwYcNgNBrdptFda1tCQgLWrFmTr8/ODqLd/a6Bm2MDb3fvvfcCuBnAFYXc7m316tW4du2a2/MK+v3KC6PRiHfffReA/Tt44sSJAl/Lz88PU6dORUBAADIyMnJcH7R9+/ZYu3YtAgMD8eyzz+Ktt94q8GcSEWVjMEhEVIo+++wz1KxZE6dPn8Y999yDv//+2+UYi8WCWbNmoWnTpjh8+LBj+wsvvAAAeOONN7Bv3z7HdiEE3nzzTezduxcBAQF45JFHCp3O6OhoDBw4EKqq4oknnnAan3Xjxg08+eST+e6mV7VqVQD2lp3cxrm5M3HiRADAl19+ibVr1zrty+7Op9Pp8Oyzz+bruu7Url0bsixj//79WL9+vdO+33//HR999FGhPyMn2d1BFy1a5Bgzd3sXUcDe/RAAvvnmG1gsFsf25ORkjBo1CsnJyfn63JYtW8LPzw+HDh3C/PnznfYtXrwYn376qdvzHn30UVSrVg2LFy/Gyy+/7LZl8fLlyznOcOtO9r1lz9qa7ejRo3j88cdzPC/7+5XbOM/CGDhwIO6++24oiuJ2gpnbZWRk4MMPP3Q7VnLTpk1ISkqCLMuOdLtz1113Yf369YiIiMB//vOffE9gQ0R0OwaDRESlKDAwEJs3b0bHjh1x+PBhtGvXDtWrV8eAAQMwbNgwdOnSBcHBwRg7dizS0tIQGRnpOPexxx7DiBEjcO3aNbRo0QJdu3bFsGHDEBcXh9dffx1eXl5YsGCBY6Kawvr8889Ro0YNrF+/HrGxsbjvvvswaNAgVK9eHVeuXMnXRBqAPcBs0aIFEhIS0LBhQzz00EMYN25cnh5w7733XkyaNAkmkwndunVDu3btMHz4cDRv3hwPP/wwZFnGV199hfr16xf0dh1CQkLw9NNPQ1EUdOnSBR07dsSwYcPQvHlz9OvXDy+++GKhPyMnrVq1Qr169ZCWlgaTyYQmTZqgWbNmLsdNmDABAQEBWLFiBapXr477778f/fv3R7Vq1bBv3z6MGTMmX5/r5eXlCHBGjhyJNm3aYPDgwWjQoAGGDBmS4++oUqVKWL58OWJiYvDee+8hOjoaHTp0wPDhwzFw4EDUr18fkZGRmDx5cp7TMmXKFEiShMmTJ6NRo0Z48MEH0aVLFzRs2BDVq1dHmzZt3J43YMAAaDQafPrpp+jWrRvGjBmDcePGYdmyZfnKi9y8/fbbAIAff/zxjrPJWiwWTJw4EREREWjSpAkGDx6MYcOGoU2bNujQoQMA4D//+c8d/14bNmyITZs2ITo6Gu+++y6eeuqpYhsvSUQVH4NBIqJSFhYWhnXr1mHlypUYOXIkZFnG2rVr8fPPP+PQoUNo3bo1Pv74Y5w+fRotW7Z0nCdJEubNm4cFCxbgnnvuwa5du/Dzzz8jIyMDo0ePxp49exzd9opCREQEtm3bhvHjx8Pb2xt//PEHduzYgaFDh2Lr1q1u17i7kyVLlmDYsGFISUnBTz/9hJkzZ2LhwoV5OveNN97AypUrce+99+Lw4cNYtGgRLl26hMGDB2PLli35DoBy89FHH2HmzJlo2rQpdu3ahRUrVsDb2xsLFy7EG2+8UWSf486tLYE53VNsbCz27NmD4cOHQ5Zl/PHHH9i3bx8efPBB7NmzB1FRUfn+3AkTJmDu3Llo1qwZ9uzZgz///BPh4eH4888/c83b+vXr499//8V7772HuLg4/Pvvv1i8eDG2bduGSpUq4YUXXrjjzKC3GjRoEDZs2IAuXbogPj4ey5YtQ0JCAqZOnYqVK1dCp9O5Pa9Ro0ZYsmQJWrdujW3btmHOnDmYOXMmdu/ene+8yEmnTp0ca1NOnTo112N9fHzw1VdfYciQITCbzVizZg1+/fVXJCQkYNCgQVi7dm2eWhgBoGbNmvj7779Ru3ZtfPHFFxg9enSpTEJFROWfJPg6iYiIiIiIyOOwZZCIiIiIiMgDMRgkIiIiIiLyQAwGiYiIiIiIPBCDQSIiIiIiIg/EYJCIiIiIiMgDMRgkIiIiIiLyQAwGiYiIiIiIPBCDQSIiIiIiIg/EYJCIiIiIiMgDMRgkIiIiIiLyQAwGiYiIiIiIPBCDQSIiIiIiIg/EYJCIiIiIiMgDMRgkIiIiIiLyQAwGiYiIiIiIPBCDQSIiIiIiIg/EYJCIiIiIiMgDMRgkIiIiIiLyQAwGiYiIiIiIPBCDQSIiIiIiIg/EYJCIiIiIiMgDMRgkIiIiIiLyQAwGiYiIiIiIPBCDQSIiIiIiIg/EYJCIiIiIiMgDMRgkIiIiIiLyQAwGiYiIiIiIPBCDQSIiIiIiIg/EYJCIiIiIiMgDMRgkIiIiIiLyQAwGiYiIiIiIPBCDQSIiIiIiIg/EYJCIiIiIiMgDMRgkIiIiIiLyQAwGiYiIiIiIPBCDQSIiIiIiIg/EYJCIiIiIiMgDMRgkIiIiIiLyQAwGiYiIiIiIPBCDQSIiIiIiIg/EYJCIiIiIiMgDMRgkIiIiIiLyQAwGiYiIiIiIPBCDQSIiIiIiIg/EYJCIiIiIiMgDMRgkIiIiIiLyQAwGiYiIiIiIPBCDQSIiIiIiIg/EYJCIiIiIiMgDMRgkIiIiIiLyQAwGiYiIiIiIPBCDQSIiIiIiIg/EYJCIiIiIiMgDMRgsIy5evIiHHnoIwcHB8PLyQsOGDbFz587SThYREXkw1k1ERBWbtrQTQMCNGzfQtm1bdOrUCStXrkRoaCiOHz+OwMDA0k4aERF5KNZNREQVnySEEKWdCE/3yiuvYPPmzdi0aVNpJ4WIiAgA6yYiIk/AbqJlwLJly9CiRQsMHjwYYWFhaNq0Kb799tvSThYREXkw1k1ERBUfWwbLAKPRCAB4/vnnMXjwYOzYsQPPPvssvvrqK4waNcrtOWazGWaz2fFvVVWRmJiI4OBgSJJUIukmIiJACIHU1FRERkZCo6k471hZNxERlV95rpsElTqdTidat27ttG38+PGiVatWOZ4zZcoUAYA//OEPf/hTRn7Onz9f3NVFiWLdxB/+8Ic/5f/nTnUTJ5ApAypXrox69eo5bYuLi8OSJUtyPOfVV1/F888/7/h3cnIyoqOjcfr0afj6+hZbWkuL1WrFunXr0KlTJ+h0utJOTrnD/Csc5l/hVPT8S01NRWxsbIUre1k33VlF/24XN+Zf4TD/Cqei519e6yYGg2VA27ZtcfToUadtx44dQ7Vq1XI8x2AwwGAwuGwPCgqCn59fkaextFmtVnh7eyM4OLhC/sEWN+Zf4TD/Cqei51/2PVW0bpCsm+6son+3ixvzr3CYf4VT0fMvr3VTxRncUI4999xz2Lp1K9566y2cOHECCxYswDfffIOnnnqqtJNGREQeinUTEVHFx2CwDLjrrruwdOlS/Pjjj2jQoAHeeOMNfPzxxxg+fHhpJ42IiDwU6yYiooqP3UTLiD59+qBPnz6lnQyiO1IUBVartbSTUaKsViu0Wi1MJhMURSnt5JQ7FSH/9Hp9hZopNK9Kom5SVRUWi6VYP6O4VITvdmny5PzT6XSQZbm0k0HEYJCI8kYIgcuXLyMpKam0k1LihBCIiIjA+fPnK9y4sJJQEfJPo9EgNjYWer2+tJNSoVgsFpw+fRqqqpZ2UgqkIny3S5On519AQAAiIiI88t6p7GAwSER5kh0IhoWFwdvb26MqL1VVkZaWBh8fH49sHSqs8p5/qqri0qVLiI+PR3R0tEd994uTEALx8fGQZRlRUVHl9rtRnr/bpc1T808IgYyMDCQkJACwz9xLVFoYDBLRHSmK4ggEg4ODSzs5JS67G5vRaPSoB5aiUhHyLzQ0FJcuXYLNZquQs86VBpvNhoyMDERGRsLb27u0k1MgFeG7XZo8Of+8vLwAAAkJCQgLC2OXUSo1nvWXR0QFkj1GsLw+sBEVVnb3UE8b11ScsvOSXW/JU2XXqZ42Dp/KFgaDRJRn7B5Hnorf/eLDvCVPxe8+lQUMBomIiIiIiDwQg0EiIiIiIiIPxGCQiCgf5syZg4CAgNJORr6UxzQX1Nq1axEXF5evsX1Dhw7FjBkzijFVRLkrj3+j5THNBVWQcuWVV17B+PHjizFVREWDwSARVVijR4+GJEkuPz179szT+TExMfj444+dtg0ZMgTHjh0rhtQ6K40HrXXr1qFXr14IDg6Gt7c36tWrh4kTJ+LixYtYsmQJZFnGxYsX3Z5bq1YtPP/883f8jPXr1zv9LkJDQ9GrVy/s378/x3Pmz5+PSpUq4cSJE07bL126hMDAQPz3v/91bHvppZcwadIkp5n51q9fj2bNmsFgMKBmzZqYM2eO03UmTZqE6dOnIzk5+Y7pJ7q1XJFlGYGBgZBlmeVKDu5UrgQHB5e7ciU+Ph7Dhg1D7dq1odFoMGHCBJfrv/DCC5g7dy5OnTp1x/QTlSYGg0RUofXs2RPx8fFOPz/++GOBr+fl5YWwsLAiTGHZ8PXXX6Nr166IiIjAkiVLcOjQIXz11VdITk7GjBkz0K9fPwQHB2Pu3Lku527cuBEnTpzA2LFj8/x5R48eRXx8PFavXg2z2YzevXvDYrG4PXbEiBHo0aMHRo8e7bQ4+SOPPILmzZvjqaeeAgD8/fffOHnyJO677z7HMadPn0bv3r3RqVMn7N27FxMmTMC4ceOwevVqxzENGjRAjRo18P333+c5/eTZssuVixcv4siRI7h48SLLFTfyUq4EBQVh3rx5LueW5XLFbDYjNDQUkyZNQuPGjd1ePyQkBD169MCXX36Z5/QTlQpBFUJycrIAIJKTk0s7KcXCYrGIX3/9VVgsltJOSrlU2PzLzMwUhw4dEpmZmY5tqqoK1VwKP6qa53SPGjVK9O/fP8f9qqqKKVOmiKioKKHX60XlypXF+PHjhRBCdOjQQQBw+lEURcyePVv4+/s7rjFlyhTRuHFjMXPmTBEVFSUqVaoknnjiCWGz2cS7774rwsPDRWhoqHjzzTedPnvGjBmiQYMGwtvbW1StWlU88cQTIjU1VQghxLp161w+e8qUKUIIIUwmk5g4caKIjIwU3t7eomXLlmLdunVO1549e7aIiooSXl5eYsCAAeKDDz5wSvPtzp8/L/R6vZgwYYLb/Tdu3BBCCPH888+LWrVquc3nu+++O8f7Sk5OFjdu3BCKojjuLfuaQgixbNkyAUDs27cvxzQmJCSI0NBQ8f777zvu0d/fX5w7d85xzFNPPSXuv/9+p/NeeuklUb9+fadtQ4YMET169HDaNm3aNHHPPffk+Pnu/gayVfTytzByy5vb87TUypRClCuKoji+29nyW64IITy2XFEURTz11FMFKley7+vWeyupcuVWHTp0EM8++6zbfXPnzhVVq1bN8dzcypW84LNR4VT0/Mtr3cRF54moYKxA0rtJJf6xAS8HAEW0LNmSJUvw0UcfYeHChahfvz4uX76Mffv2AQB++eUXNG7cGI8++ijGjh2L1NTUHK9z8uRJrFy5EqtWrcLJkydx//3349SpU6hduzY2bNiALVu2YMyYMejatSvuvvtuAIBGo8Gnn36K2NhYnDp1Ck8++SReeuklfPHFF2jTpg0+/vhjvP766zh69CgAwMfHBwDw9NNP49ChQ1i4cCEiIyOxdOlS9OzZE/v370etWrWwbds2jB07Fm+//TYGDBiAVatWYcqUKbnmw+LFi2GxWPDSSy+53Z/drWzs2LH48MMPsXHjRrRv3x4AkJaWhp9//hkfffRRjvf18ssv4+2333Z77eTkZCxcuBBA7uvNhYaG4ptvvsGDDz6Ixo0b47nnnsMnn3yCqKgoxzGbNm3CsGHDnM77559/0LVrV6dtPXr0cOnW1bJlS0yfPh1msxkGgyHHdFAxKqUyBSidcuWRRx7J9TqeUK6oqoqHHnoIn3/+eb7Llez7cqe4y5W8atmyJS5cuIAzZ84gJiamQNcgKm4MBomoQvvjjz8cDzzZXnvtNbz22ms4d+4cIiIi0LVrV+h0OkRHR6Nly5YAgKCgIMiyDF9fX0RERDgWB3ZHVVXMmjULvr6+qFevHjp16oSjR49ixYoV0Gg0qFOnDt59912sW7fO8dB2azASExODN998E48//ji++OIL6PV6+Pv7Q5IkREREOI47d+4cZs+ejXPnziEyMhKAfVzKqlWrMHv2bLz11lv45JNP0LNnT8cDWO3atbFlyxasWrUqx/QfP34cfn5+qFy5cq55Wa9ePbRq1QqzZs1yPLQtWrQIQggMHTo01/u6PRisWrUqACA9PR0A0K9fP9StWzfXzx8wYAAeeOAB9OzZE3379sWoUaOc9p89e9aRL9kuX76M8PBwp23h4eFISUlBZmYmvLy8AACRkZGwWCy4fPkyqlWrlms6iIqqXMmNp5QrdevWLXC5cnswWFLlSl5ln3f27FkGg1RmMRgkooLRZb1NL4XPzY9OnTq5jNkICgoCAAwePBgff/wxqlevjp49e6JXr17o27cvtNr8FY0xMTHw9fV1/Ds8PByyLEOj0ThtS0hIcPz7r7/+wttvv40jR44gJSUFNpsNJpMJGRkZOQae+/fvh6IoqF27ttN2s9mM4OBgAMDhw4cxcOBAp/2tW7fO9aFNCJHnxY/HjBmD5557Dp999hl8fX0xa9YsDB482HH/ud2Xn5+f4zqbNm2Ct7c3tm7dirfeegtfffVVnj5/8uTJmDdvHiZNmuSyLzMzE0ajMU/XuV12UJiRkVGg86kIlFaZkvXZ+ZFdrqiqirS0NPj4+CAkJAQAy5Vs+SlXRo8ejYkTJxaoXLn1vliuEOUfJ5AhogKRJAmSvhR+8vhwka1SpUqoWbOm0092MBgVFYWjR4/iiy++gJeXF5588km0b98eVqs1X5+h0zk/SUqS5HZb9iQFZ86cQZ8+fdCoUSMsWbIEu3btwueffw4AOU52ANi7TsmyjF27dmHv3r2On8OHD+OTTz7JV5pvVbt2bSQnJyM+Pv6Ox2a/qV+0aBGOHz+OzZs3OyZ4yO2+bs/T2NhY1KlTB6NGjcK4ceMwZMiQPKU1+4Ha3YN1SEgIbty44bQtIiICV65ccdp25coV+Pn5OR7UACAxMRGAvdsYlY5SK1MKWa5Ur16d5YobJVGu3H5fJVWu5BXLFSoPGAwSkUfz8vJC37598emnn2L9+vX4559/HNOR6/X6fK0rlVe7du2CqqqYMWMGWrVqhdq1a+PSpUtOx7j77KZNm0JRFCQkJLgEuNndvuLi4rBt2zan87Zu3Zpreu6//37o9Xq89957bvcnJSU5/t/X1xeDBw/GrFmzMHv2bNSuXRvt2rXL832589RTT+HAgQNYunTpHY/NTdOmTXHo0CGnba1bt8batWudtq1ZswatW7d22nbgwAFUrVrV0bpDVBgsVyp2uZJXBw4cgE6nQ/369QuVBvJMp26cgqIWfVlxO3YTJaIKzWw24/Lly07btFotQkJCMGfOHCiKgrvvvhve3t74/vvv4eXl5RgzFhMTg40bN+KBBx6AxWJx6uZYGDVr1oTVasVnn32Gvn37YvPmzS7dmWJiYpCWloa1a9eicePG8Pb2Ru3atTF8+HCMHDkSM2bMQNOmTXH16lWsXbsWjRo1Qu/evfHMM8+gbdu2+OCDD9C/f3+sXr06165cgL0l46OPPsLTTz+NlJQUjBw5EjExMbhw4QLmzZsHHx8fp0XZx44di3bt2uHw4cN4+eWX83Vf7nh7e+ORRx7BlClTMGDAgHy30mTr0aOHy9IXjz/+OP773//ipZdewpgxY/C///0PixYtwvLly52O27RpE7p3716gzyXPk12uqKqK1NRUZGRkQK/X56tcGTp0KAwGQ5G9gCiP5cr777/vOL48lSsAsHfvXgD2ltWrV69i79690Ov1qFevnuOYTZs2oV27dk69EIjywqJY0GluJ3jrvPHrkF9RJ6RO8X1YCcxsSiWgok9tXtGn/y1uxbG0RHkwatQol6nUAYg6deoIIYRYunSpuPvuu4Wfn5+oVKmSaNWqlfjrr78c5//zzz+iUaNGwmAw3HFpids/9/YlLW6ffvzDDz8UlStXFl5eXqJHjx5i3rx5LlOjP/744yI4ONhpCniLxSJef/11ERMTI3Q6nahcubIYOHCg+Pfffx3nzZw5U1StWlV4eXmJvn373nEK+Gxr1qwRPXr0EIGBgcJoNIq6deuKF154QVy6dMnl2Dp16ghZll325XRfZ86cyXFpCSGEOHfunNBqteKnn37KNY2nT58WAMSePXtc9l2/fl0YjUZx5MgRp+3r1q0TTZo0EXq9XlSvXl3Mnj3baX9mZqbw9/cX//zzT46fy6UlCiY/S0uUF0VdrgiR89ISt39uRStXbl+aI7/lSvZ9lUa54u47UK1aNadj6tSpI3788cccP5dLS5Suspx/3+76VmAqRMQHESLDklGga+S1bpKEEKL4Qk0qKSkpKfD390dycnKRtV6UJVarFStWrECvXr1cxkzQnRU2/0wmE06fPo3Y2NgCD6Qvz1RVRUpKCvz8/Jwmb6C8Kcn8e/HFF5GSkoKvv/46z+d8+eWXWLp0Kf78888cj8ntb6Cil7+FkVveVIRyhWVD4ZSX/CtIubJy5UpMnDgR//77b46TBxX2b4DPRoVTVvPPqlhR5791cDrpNGa0n4HnOj5XoJbtvNZNZfcvj4iIKJ/+85//oFq1ao5JNfJCp9Phs88+K8ZUEVF5VpByJT09HbNnz873LLJEC/YvwOmk0wjzCsOY4DFAzvM/FQl+Q4mIqMIICAjAa6+9lq9zxo0bV0ypIaKKoCDlyv33319MqaGKzKbaMH3TdADAs3WehdFa/L0m2DJIRERERERUyn468BOOJx5HsDEYj4Y+WiKfyWCQiIiIiIioFCmqcrNVsO6zqGSrVCKfy26iREREREREpWjJ4SU4fO0wAgwBeCzsMUj6gi2Hkl9sGSQiIiIiIiolqlDx5sY3AQDj48bDT/hBMpRMMMiWQSIiIiIiolLy25HfsD9hP/z0fngi/AnAq+Q+my2DREREREREpUAIgTc2vgEAeLLekwhQAyAZS6ZVEGDLIBERERERUalYfnw59lzeg0q6ShhfeTwknQRJkiAgSuTz2TJIRER5cv36dYSFheHMmTN5Puerr75Cv379ii9RRFSuJSYmIiIiIl/lyqpVq9CkSZN8LQJPVBYJIfB/G/4PAPBk/ScRaAuE5F1yrYIAg0EiqqAkScr1Z+rUqSWanhMnTuDhhx9G1apVYTAYEBsbiwcffBA7d+7ElStXoNPpsHDhQrfnjh07Fs2aNcvT59x6j35+frjrrrvw22+/5Xj8sWPH4O3tjQULFjhtV1UVbdq0cVo4efr06ejfvz9iYmIc286dO4fevXvD29sbYWFhePHFF2Gz2Rz7x4wZgz179mDLli15Sj9RWXZ7OSLLMgIDAyHLMsuVW+SnXJkxYwb69evnVK4888wzaN68OQwGA5o0aeJy/Z49e0Kn0+GHH37IU/qJyqrVJ1djx6Ud8NJ6YXzkeEgG+98ZAChXFQhL8bcOMhgkogopPj7e8fPxxx/Dz8/PadsLL7zgOFYI4RTAFLWdO3eiefPmOHbsGL7++mscOnQIS5cuRd26dTFx4kSEh4ejd+/emDVrlsu56enpWLRoEcaOHZvnz5s9ezbi4+Oxc+dOtG3bFvfffz/279/v9tjatWvjnXfewfjx4xEfH+/YPmPGDJw6dQpfffUVACAjIwMzZ850SoeiKOjduzcsFgu2bNmCuXPnYs6cOXj99dcdx+j1ejz44IP45ptv8px+orLKXbly5MgRXLx4keXKLfJTrsyfPx9jxoxxucaYMWMwZMiQHNMzevRofPrpp3lOP1FZc2ur4GP1H0OoLRRSJXsgKKwC5m1mmNaZoFxVij0hVAEkJycLACI5Obm0k1IsLBaL+PXXX4XFYintpJRLhc2/zMxMcejQIZGZmenYpqqqSDOnlfiPqqr5Tv/s2bOFv7+/49/r1q0TAMSKFStEs2bNhE6nE+vWrROjRo0S/fv3dzr32WefFR06dBA3btwQiqIIRVHEW2+9JWJiYoTRaBSNGjUSixcvzvGzVVUV9evXF82bNxeKorjsv3HjhhBCiGXLlgmNRiPOnj3rknaj0Shu3LghVq5cKdq2bSv8/f1FUFCQ6N27tzhx4oTT8QDE0qVLHf9OSUkRAMQnn3ySaxo7deokevfuLYQQ4vDhw8JoNIrffvvNcczixYtFaGio03krVqwQGo1GXL582bHtyy+/FH5+fsJsNju2rVu3Tuj1epGWlpZjGso6d38D2Sp6+VsYueXN7XlaWmVKYcuV7LKhIOVKNk8tV3766ScREhLi9h6EEGLKlCmicePGbvedPXtWAHBJa3mSW7mSF3w2KpzSzr+/Tv4lMBXC+KZRnN5xWmTuyBTmo2ZhPmoWqUtTReL/JYobM24IJcP938ed5LVu4gQyRFQgGdYM+LztU+Kfm/ZqGirpKxXJtV555RV88MEHqF69OgIDA/N0zttvv43vv/8eX331FWrVqoWNGzfioYceQmhoKDp06OBy/N69e3Hw4EEsWLAAGo1rZ4yAgAAAQK9evRAeHu7SsjZ79mwMGjQIAQEBSE9Px/PPP49GjRohLS0Nr7/+OgYOHIi9e/e6vbbNZsPMmTMB2FvociJJEmbPno1GjRrh22+/xcyZMzF06FCnsX6bNm1C8+bNnc77559/0LBhQ4SHhzu29ejRA0888QQOHjyIpk2bAgBatGgBm82Gbdu2oXPnzjmmgzxbaZUpAMuV0ipX/v77bzRu3DhPeXS76OhohIeHY9OmTahRo0aBrkFUmv5vo71V8JH6jyDCFgHJP6tV0CZgPWYFAGhraiFpincMIYNBIvJY//d//4du3brl+Xiz2Yy33noLf/31F1q3bg0AqF69Ov7++298/fXXbh/ajh8/DgCoW7durteWZRmjRo3CnDlzMHnyZEiShJMnT2LTpk1Ys2YNAOC+++5zOmfWrFkIDQ3FoUOH0KBBA8f2Bx98ELIsIzMzE6qqIiYmBg888ECun1+tWjV8/PHHGDduHKpWrYo///zTaf/Zs2cRGRnptO3y5ctOgSAAx78vX77s2Obt7Q0/Pz+cPXs21zQQVQQsV27KS7lSuXLlXK+Rm8jISJYrVC5tOLMBG89uhF7W47lqz0FYBDSy/eWL7YQNMANSJQlyZbnY08JgkIgKxFvnjbRX00rlc4tKixYt8nX8iRMnkJGR4fKgZ7FYHK1gtxMi74O/x4wZg3feeQfr1q1D586dMXv2bMTExDha044fP47XX38d27Ztw7Vr1xwz6Z07d87poe2jjz5C165dcerUKTz33HP49NNPERQUdMfPf/jhhzF58mSMHz8efn5+TvsyMzNhNBrzfC+38/LyQkZGRoHPp4qvtMqU7M8uKixXnN2pXAkNDc3zvdyO5QqVV9nrCo6pPwaVLZUh+d7SKnjU3iqoq60r9lZBgMEgERWQJElF1q2qtFSq5Jx+jUbj8pBltVod/5+WZn9QXb58OapUqeJ0nMFgcPsZtWvXBgAcOXIkxwe7bLVq1UK7du0we/ZsdOzYEfPmzcMjjzzimFmsb9++qFatGr799ltERkZCVVU0aNAAFovF6ToRERGoWbMmatasidmzZ6NXr144dOgQwsLCcv18ANBqtdBqXauGkJAQ3Lhxw+Vztm/f7rTtypUrjn23unHjRqEe+qjiqwhlCsByxZ3cypWkpKQ7np+TxMRElitU7mw5vwVrT6+FTqPDxOoTARMgae1/j7ZTNgiTgOQtQY6WAesdLlYEOJsoEVGW0NBQp5nvAPvYnGz16tWDwWDAuXPnHA9F2T9RUVFur9mkSRPUq1cPM2bMcLsm1u0PQmPHjsWSJUuwZMkSXLx4EaNHjwZgX+Pv6NGjmDRpErp06YK4uDiX4Mydli1bonnz5pg+ffodj81N06ZNcejQIadtrVu3xv79+5GQkODYtmbNGvj5+aFevXqObSdPnoTJZLrjQytRRcRyJWdNmzbF0aNHC3SuyWTCyZMnWa5QuZPdKjiy3khUNVe9OYOoImA9ktUqGFcyrYIAg0EiIofOnTtj586dmDdvHo4fP44pU6bgwIEDjv2+vr544YUX8Nxzz2Hu3Lk4efIkdu/ejc8++wxz5851e83sSRSOHTuGdu3aYcWKFTh16hT+/fdfx7p9txo8eDB0Oh0ee+wxdO/e3fEwGBgYiODgYHzzzTc4ceIE/ve//+H555/P031NmDABX3/9NS5evFjAnLFPDHPw4EGnB8Xu3bujXr16GDFiBPbt24fVq1dj0qRJeOqpp5xaNDZt2oSYmBhO8kAeieVKzrp3744jR464BKAnTpzA3r17cfnyZWRmZmLv3r3Yu3evU2vl1q1bYTAYHOMsicqD7Re3Y9WJVZAlGS/WehFQAEmf1Sp42gaRKSB5SdDGllznTQaDRERZevTogcmTJ+Oll17CXXfdhdTUVIwcOdLpmDfeeAOTJ0/G22+/jbi4OPTs2RPLly9HbGxsjtdt2bIldu7ciZo1a+KRRx5BXFwc+vXrh4MHD+Ljjz92Otbb2xtDhw7FjRs3nNbe0mg0WLhwIXbt2oUGDRrgueeew/vvv5+n++rZsydiY2ML9Ra/YcOGaNasGRYtWuTYJssy/vjjD8iyjNatW+Ohhx7CyJEj8X//939O5y5cuNAlH4k8BcuVnDVs2BCNGzd2KlcAYNy4cWjatCm+/vprHDt2DE2bNkXTpk1x6dIlxzE//vgjhg8fDm/vohvvSVTcslsFH6r/EKpZq91sFVRvaRWsq4Mkl0yrIABIIj+jkKnMSklJgb+/P5KTk10GaFcEVqsVK1asQK9evaDT6Uo7OeVOYfPPZDLh9OnTiI2NLdQkIuWVqqpISUmBn5+f26nWPcXy5cvx4osv4sCBA3nOh4MHD6Jz587Yvn07oqKiym3+5fY3UNHL38LILW8qQrnCsqFwVFXF4sWLMW3atHyVK9euXUOdOnWwc+fOXAPmsq6wfwN8Niqcks6/PfF70OybZtBIGuy/bz+qJ1eHJsT+nbeessKywwIYAO8+3pC0EoRFQJgFDA0MkAz5Dw7zWjdxAhkiIsqT3r174/jx47h48WKOY5luFx8fjzlz5sDf37+YU0dE5VGPHj1w6dKlfJUrZ86cwRdffFGuA0HyPNmtgg/WexDVLdWBrEZtoQpYD9/SKqgtuVZBgMEgERHlw4QJE/J1fNeuXR2tJ0RE7jz77LP5allt0aJFvpfwICpN/175F0uPLIUECS/FvQSRLKAJtn/nlfMKRJoA9ICuRsm38LJPAxERERERUTGZvsk+tnZw3GDUsdWBZJQgSRKEELAcsk+MpKujg6Qr2VZBgMEgERERERFRsTh09RAWH1wMAHi14atQ01VI3vagT7mgQKQIQAfoat7WKqgCkLJ+ihGDQSIiIiIiomIwfdN0CAgMqjsIcbY4SAYJksbeKmg9lDVWsLbOscRENpEuIAfKLtuLGoNBIiIiIiKiInbs+jEsPLAQAPBqo1ch0sTNVsFLCtQkFdACulrOrYLCZG8tlMPkYk8jg0EiIiIiIqIi9tamt6AKFX1r9UUjNAK0gCRntQoezGoVrKVzWTpCpAnIoTI0lYo/VGMwSEREREREVIRO3TiF7//9HgDwWrPXoKaojkXmlcsK1BsqINu7iN5KzVQBAyCHFn+rIMBgkIgKSVgFhKmEfqyitG83R0IIPProowgKCoIkSdi7dy86dux4x6UYYmJi8PHHH5dIGj0d87p8KNEyheUKFRLzmnLy9qa3oQgF99a8F800zSAg7IvJ39IqqK2hhWS82SoohADS7YGgxrtkwjSuM0hEBSasApajFnvf9hIgGSXo6+jzNfXy5cuXMX36dCxfvhwXL15EWFgYmjRpggkTJqBLly5FlrZVq1Zhzpw5WL9+PapXr46QkBD88ssv0OlKfs2gonbmzBnExsZiz549aNKkSZ7OmTp1Kn799Vfs3bu3WNNGFUtJlykAy5XSkl2ubNy4EW3bts3TOSxXqLw4m3QWc/bNAQD8p/l/oCap0PjYgzs1QYV6XQU09kXmbyVMAjCWXKsgwGCQiApDySq4tICkLd7ZroTN/hYfCoA8PgedOXMGbdu2RUBAAN5//300bNgQVqsVq1evxlNPPYUjR44UWfpOnjyJypUro02bNo5tQUFBRXZ9T2WxWKDX60s7GVRSSrBMAQpfrrz77ruIjY2FwWDAmjVrWK6UEyxXqLi98/c7sKk2dI3tirt0d0FVVccLp+x1BbXVtdB43Wz9E0JApAtoo523Fzd2EyWiQpO0EiR9Mf8U4MHwySefhCRJ2L59O+677z7Url0b9evXx/PPP4+tW7c6jjt37hz69+8PHx8f+Pn54YEHHsCVK1cc+6dNm4Z27dph/vz5iImJgb+/P4YOHYrU1FQAwOjRozF+/HicO3cOkiQhJiYGAFy6cyUkJKBv377w8vJCbGwsfvjhB5c0JyUlYdy4cQgNDYWfnx86d+6Mffv2OfZPnToVTZo0yTEtAKCqKt577z3UrFkTBoMB0dHRmD59umP/+fPn8cADDyAgIABBQUHo378/zpw5k+d8Xb9+PSRJwtq1a9GiRQt4e3ujTZs2OHr0KABgzpw5mDZtGvbt2wdJkiDLMhYsWJCv+/vuu+8QGxsLo9GIb775BpGRkVBV1Skd/fv3x5gxYwDYH5r79++P8PBw+Pj44K677sJff/2V53uisqVEypQiKldq1qxZoHLlTn/LLFdyL1ckScKcOXPydX8sV6gkXEi5gFl7ZwEAJrWcBPXGLWMFrypQE7JaBeNuaxXMFJC8JMghJdcqCDAYJKIKKjExEatWrcJTTz2FSpUquewPCAgAYH/A6d+/PxITE7FhwwasWbMGp06dwpAhQ5yOP3PmDH777Tf88ccf+OOPP7Bhwwa88847AIBPPvkE//d//4eqVasiPj4eO3bscJum0aNH4/z581i3bh1+/vlnfPHFF0hISHA6ZvDgwUhISMDKlSuxa9cuNGvWDF26dEFiYqLjmJMnT+LXX391mxYAePXVV/HOO+9g8uTJOHToEBYsWIDw8HAAgNVqRY8ePeDr64tNmzZh8+bN8PHxQc+ePWGxWPKVx//5z38wY8YM7Ny5E1qt1vEANWTIEEycOBH169dHfHw8Ll68iIEDB+b5/k6cOIElS5bgl19+wd69ezF48GBcv34d69atcxyT/fsdPnw4ACAtLQ29evXC2rVrsWfPHvTs2RN9+/bFuXPn8nVPRLkp6nIlt79lliu5lyvx8fGO/GS5QmXJe5vfg0WxoEO1Dmjj1QawwjFbaPa6gtoYrdOYQCEERIaAHCZDYyzZ8IzdRImoQjpx4gSEEKhbt26ux61duxb79+/H6dOnERUVBQCYN28e6tevjx07duCuu+4CYH+4mz17Nvz9/QEAI0aMwNq1azF9+nT4+/vD19cXsiwjIiLC7eccO3YMK1euxPbt2x3XnDlzJuLi4hzH/P3339i+fTsSEhJgMBgAAB988AF+/fVX/Pzzz3j00UcdaZkzZw58fX1d0pKamopPPvkE//3vfzFq1CgAQI0aNXDPPfcAAH766SeoqorvvvsOkmSvnGbPno2AgACsX78e3bt3z3MeT58+HR06dAAAvPLKK+jduzdMJhO8vLzg4+MDrVaLiIgIqKqKlJSUPN+fxWLBvHnzEBoa6vise++9FwsWLHCMx/r5558REhKCTp06AQAaN26Mxo0bO45/4403sHTpUixbtgxPP/10nu+JKDfFUa7k9LfMciX3ciW/98dyhUpCfGo8vtn1DQBgUqtJUK4pgLd9n3JdgXJZASQ3rYIZAhovDbQhJR+asWWQiCokIfI2AcXhw4cRFRXleGADgHr16iEgIACHDx92bIuOjnY8JAFA5cqVXd6+3+lztFotmjdv7thWt25dR0sCAOzbtw9paWkIDg6Gj4+P4+f06dM4efKk47iYmJgc03L48GGYzeYcJ7HYt28fTpw4AV9fX8f1g4KCYDKZnD4jLxo1auSUBgC55sm///6bp/urVq2a0wMbAAwfPhxLliyB2WwGAPzwww8YOnQoNBp7NZaWloYXXngBcXFxCAgIgI+PDw4fPsw3+FSkirpcye1vOa+f4+nlSl7vj+UKlYQPtnwAs2JG26i2aO/THsIsHLOFOloFq2kdk8kAWa2CmQKacI3LeoMlgS2DRFQh1apVC5IkFdlkDlqtc3EpSZLLWJPCSktLQ+XKlbF+/XqXfbc+3N0+k+CtafHy8rrjZzRv3tztuKLbH5Tu5NZ0ZLcG5JYneb0/d93v+vbtCyEEli9fjrvuugubNm3CRx995Nj/wgsvYM2aNfjggw9Qs2ZNeHl54f777893FzWi3BR1uZLb33JRYblix3KFiltCegK+3PklAGBS60kQVwUkb/v4VuWGAuWSAgDQ1XPTKuhdOq2CAINBIqqggoKC0KNHD3z++ed45plnXB4EkpKSEBAQgLi4OJw/fx7nz593vMU/dOgQkpKSUK9evSJLT926dWGz2bBr1y5Hd66jR48iKSnJcUyzZs1w+fJlaLVax2QR+VWrVi14eXlh7dq1GDdunMv+Zs2a4aeffkJYWBj8/PwK9Bl5odfroSiK07amTZsW+P6MRiMGDRqEH374ASdOnECdOnXQrFkzx/7Nmzdj9OjRjrGJaWlp+Zq8gigvbi9Xbg+SWK6UfLlSmPtjuUJFacaWGci0ZaJllZboEtAFttM2aILtLYDZrYJytAyN7y2tgqq9VVCOlSHpS75VEGA3USKqwD7//HMoioKWLVtiyZIlOH78OA4fPoxPP/0UrVu3BgB07doVDRs2xPDhw7F7925s374dI0eORIcOHdCiRYsiS0udOnXQs2dPPPbYY9i2bRt27dqFcePGOT1Mdu3aFa1bt8aAAQPw559/4syZM9iyZQv+85//YOfOnXn6HKPRiJdffhkvvfQS5s2bh5MnT2Lr1q2YOXMmAHu3qJCQEPTv3x+bNm3C6dOnsX79ejzzzDO4cOFCkd1vTEwMTp8+jb179+LatWswm82Fvr/hw4dj+fLlmDVrlmOCh2y1atVyTAyxb98+DBs2rMhbWIgA13Ll5MmTLFdYrpCHu5hyEZ/v+BwAMLntZKhXVUhGe6ugmqxCuWB/iaGPc17SRGQIaCppoA0uvfY5BoNEVGjCJiAsxfxjy/8i1NWrV8fu3bvRqVMnTJw4EQ0aNEC3bt2wdu1afPmlvSuHJEn47bffEBgYiPbt26Nr166oXr06fvrpp6LOJsyePRuRkZHo0KEDBg0ahEcffRRhYWGO/ZIkYcWKFWjfvj0efvhh1K5dG0OHDsXZs2cds/blxeTJkzFx4kS8/vrriIuLw5AhQxxjbry9vbFx40ZER0dj0KBBiIuLw9ixY2EymYr0jf59992Hnj17olOnTggPD8eSJUsKfX+dO3dGUFAQjh49imHDhjnt+/DDDxEYGIg2bdqgb9++6NGjh9MbfipfSqRMKYJy5cUXX0SbNm3Qo0cPlislXK6Ehobixx9/ZLlCZcIzq55BujUdraq2Qs+gnhDp9i6iwM11BeWqMjQBt7UKmgTk8NJrFQQASeR1NDSVaSkpKfD390dycnKxdtEoLVarFStWrECvXr1cxjXQnRU2/0wmE06fPu1YnymbsApYjlrsizaXAMkoQV9H71i4taRkz4bp5+fnmFiA8q4i5F9OfwNAxS9/CyO3vHGXpyVdpgCFK1cqwne7NHl6/uVWruQFn40Kp6jyb9nRZei/sD+0Gi12jt2Jujfq2ieE8dNATVWRuTITEICxuxFy4M01BNVU+0L0+rjiea7Ja93EMYNEVGCSzv4QBeXOxxYJGSUeCBJRySnxMgVguUJEBZZqTsVTK54CAExsPRENDA1gTbVC8r9lBlEByJGyUyAoVHvvBF1VXamXPwwGiahQJJ0E8IUkERURlilEVF5MXjcZF1IuIDYgFpPbTYZyWgE0gCRLUNNU2M7aALiZQTRNQOOjcUwwU5pKPwUEAJg6dSokSXL6udOitkRERMWF9RIRUc52XtqJz7Z/BgD4qs9XMCYboSapkHyyWgUPZ7UKRsiQg51bBWEFtOFaSNrS75XAlsEypH79+vjrr78c/759XTMiIqKSxHqJiMiVTbXh0d8fhSpUDGs4DF3Du8JyzALoAUkrQc1QYTuTc6ug5CtBE1Q22uRYqpchWq0WERERpZ0MIiIiAKyXiIjc+XTbp9hzeQ8CjYH4sMuHsJ23ARY4AjzrYSugApowDeTQW1oFlaxWwZiy0SoIMBgsU44fP47IyEgYjUa0bt0ab7/9NqKjo90eazabYTabHf9OSUkBYJ8ZyWq1lkh6S1L2PVXEeysJhc0/m80GIQRsNptHrrGUPemyEMIj77+wKkL+KYri+Bu4/e+oIpdL+amXgPzVTdnliqIo5fZ7URG+26XJ0/Pv1rq1IOUIn40Kp6D5dzb5LCavmwwAeKvTW/C97gvzdTM0gRp7XZEpYDtlbxXU1NHAqty8vkgRkHwkaHw0UK3F+53P631xaYkyYuXKlUhLS0OdOnUQHx+PadOm4eLFizhw4AB8fX1djp86dSqmTZvmsn3BggXw9vYuiSSThwkPD4ePjw+CgoLYVYw8ihACKSkpSExMxJUrV3B7tZmRkYFhw4ZVuKUl8lsvAfmrmyRJQnh4OIKCguDn5wdJKhtvyYlKgs1mQ2JiItLS0nDlypXSTg7lkRAC009Px86UnahXqR7erPkmNJJzd8/ql6qj6rWqSPZOxr4a+4BSKtryWjcxGCyjkpKSUK1aNXz44YcYO3asy353b1+joqJw7dq1CvUwks1qtWLNmjXo1q0b19IpgKLIP6vViitXriAzM7OIU1f2CSFgMplgNBr5wFoAFSH/JElC5cqVUalSJZd9KSkpCAkJqXDB4O3uVC8B+a+b0tPTER8f7xJglxcV4btdmjw9/7y8vBAeHl6oepnPRgVXkPxbcngJHlz6IHQaHXY8tAM1b9SEUIRj0hhhFrCusAIKoG2rhabyLYvMJ9uP09XUQZKL//ue17qJr/fLqICAANSuXRsnTpxwu99gMMBgMLhs1+l0FbpAqOj3V9wKk386nQ4xMTGw2WxQlJJcBKz0Wa1WbNy4Ee3bt+f3rwAqQv7pdDrIspzjPk9wp3oJyH/dFBAQAF9f33Lbza0ifLdLkyfnnyzL0Gq1RRIE89mocPKaf8mmZDy/5nkAwCttX0GcJQ6qWYUUJDl+j5YTFkABNIEa6KvoHduFTUBAQFdZB9novi4pann9TjAYLKPS0tJw8uRJjBgxorSTQuQgSZJHVjqyLMNms8FoNHrcvRcF5l/FUFz1kizLOQbaZR2/24XD/KPy5NW1ryI+LR61g2vjxeovQrmkQBOouRnwmQWsx+0vtnT1dU6BvpqqQg6QoQksGzOI3qrspchDvfDCC9iwYQPOnDmDLVu2YODAgZBlGQ8++GBpJ42IiDwQ6yUiIrt/zv+Dr3Z+BQD4vP3n0CXooPHROHX3tB63AjZA46+BHHnLDKI2AQkS5HAZkqbsdYdmy2AZceHCBTz44IO4fv06QkNDcc8992Dr1q0IDQ0t7aQREZEHYr1ERARYFSse/eNRCAiMbjAa7dAOQhKQjLe0/GWosB67Q6ugf9lsg2MwWEYsXLiwtJNARETkwHqJiAiY8c8MHEg4gBDvELxV9y37RDDBN4M9oQqY/zEDVvtYQbnqLa2C1rLdKgiwmygREREREZGLk4knMW2Dfbmc91q+h8CUQEgBklPLn/WAFeo1FdAChtYGl1ZBTaCmzLYKAmwZJCIiIiIiciKEwBPLn4DJZkKXqC4Y6j0U0AGS9mawZ4u3wXrY3j3UcJcBGt9blpKwCkiSBG140cwaW1zKbphKRERERERUChbsX4A1p9bAIBvwSdwngApovG+GTmqGCvM2+7qq2hpaaKOd29hEqoAmSAPJr+wGggCDQSIiIiIiIofEzEQ8t/o5AMBrDV9DdVHdqaunUAXMW82AGdAEaKBvqnc6X1gEoEGZbxUEGAwSERERERE5vLTmJVzNuIr6gfUxIXQCNP4apwlgrAetUK9mjRNsY3BaYgIARJqAJlgDybdsB4IAg0EiIiIoVxUIkyjtZBARUSnbeHYjZu6ZCQD4tN6n0HvpIeluBnXKZQXWQ+7HCQL2xeehAbRhZb9VEGAwSEREHk5NV2G7bIOaoZZ2UoiIqBSZbWY8+vujAIBx1cehjU8baCrdMk4wU4VpqwmA+3GCgL1VUA6RIfmU/UAQYDBIREQeTrmmQKSzVZCIyNO9u/ldHL1+FOHGcEyLngYp4Lb1BLPHCfproG+idzlfmAWgBeQwuVy0CgIMBomIyIOpGSqUawrARkEiIo929NpRTN80HQDwfp33ERwU7DxO8JAVasIt4wS1rsFedqugxqf8hFjlJ6VERERFTLmmAGYA5eMFLhERFQMhBB5f/jgsigU9wnrg/qr3QzLcNk7wYNY4wRYGaPxcQyg1QwX09lbB8oTBIBEReSQ1M6tVsFJpp4SIiErTnL1zsP7MenjL3vikzidOk8KombesJ1hdC201N+MErQLIBORI2WmMYXlQvlJLRERURJTr9hlEJSObBYmIPNXV9Kt4Yc0LAIBJ1SchpnKMY7xf9jhBYRKQ/CWX9QSzj1GTVWjCNNCGuwaKZR2DQSIi8jiqSYWSoEDylsrNIH8iIip6L619CYmZiWjk2wjj6413WjPw1nGCxjZGt+ME1SQVGn8NdFE6pzGG5QWDQSIi8jiOVkGv8ldxExFR0diXug8/HPgBEiT8t+F/ofe+2fKnXLllnGDzHMYJpquQdJI9ENSXz/qEwSAREXkUYRZQE1S2ChIRebBMaya+PP8lAODxqMfRMrqlY5+aqdqXkQCgjdVCG+NmnKBFAGZAV1XnNlAsL8pvyomIiArAdt0GNVNlqyARkQd7a/NbuGy5jEhDJKY1n+Y8TnBb1jhBPwn6ZjmME0xRIYfL0ISV73CqfKeeiIgoH4RFQL1iDwTZKkhE5Jm2XdiGGVtnAAA+aPgB/L38Hfush61Qr6iADBjbuo4TFEJAJAloAjXQVtWW+7qEwSAREXkMR6ugd/muvImIqGBuZN7AkJ+HwKbacE/APehXvZ9jn5Jwc5ygvrnebfdPkSYAA+zjBHXlvy5hMEhERB7B0SpoZKsgEZEnEkJgzG9jcDb5LGK9YvFk1JM395kEzP+YAQFoY7TQxepczzcLwJY1TtCnYoRRFeMuiIiI7kBJVKBmsFWQiMhTfbrtU/x69FfoJT3mt5oPb9kbgD1ING013Rwn2NzNOEFFQE1VIVeWoQmpOCFUxbkTIiKiHAirgHJFsbcKlsN1oIiIqHB2XNyBF9e8CAB4p947aBLaxLHPaZygm/UEs8cJykEytJHlf5zgrRgMEhFRhacmqvb1oNgqSETkcZJMSRjy8xBYVSv6h/XHEw2ecOxTr6qwHrhlnKC/m3GCqfZ1abXRWrcLz5dnDAaJiKhCEzYB2xUbJANbBYmIPI0QAuOWjcPppNOI8YrB162/hkZjD4F0Nh1s22y5jxM0CUAFtFFaaLwrXuhU8e6IiIjoFo5WwUruA8H9N/ZjQ+KGEk4VERGVhC92fIElh5dAJ+kw/675CPQOBGAPEuucqwOYAMk3h/UEbQJqmgo5UoYmqGKGTRXzroiIiHBLq6DOfaugEALP7noWPXf2xKd7Py2FFBIRUXHZHb8bz//5PABgeu3paFmlpWOfelRFUFrQzXGCOjfjBJMF5FAZ2soVa5zgrRgMEhFRhaXeUCHSBCQf95X4z+d+xuarm+Gt8cagmoNKOHVERFRcUswpeGDxA7AoFvQJ7YPxjcc79ilXFSgHFQCA3ESGJsDNOMFkAamSBG2UFpJcMQNBgMEgERFVUEKxtwpCB7etghm2DLy29zUAwMTYiajqU7Wkk0hERMVACIFHfn8EJ2+cRJQxCt/c/Q00sj3sERYB81b7eoIJAQnQxLiGQ2qmCkhZ4wSNFTtcqth3R0REHku9odpngMthrOBHRz7CuYxziPaOxoSYCSWbOCIiKjZf7/oaiw4uglbSYn6z+Qj2DQZgDxLNO8wQGQKoBByvctyl+6ewCSADkKvIkAPl0kh+iWIwSEREFY6jVVALt917zqefx/uH3gcAvNXkLcfCw0REVL7tvbwXE1ZNAAD8X83/Q6voVo59tpM2KBcUQANo79ZCkRWnc4VqX09QE6qBNkJbkskuNQwGiYiowlGTsloFcxgrOGnfJGQqmWgb2hb3R99fwqkjIqLikGpOxQOLH4BZMePekHsxofEER8ufmqTCsscCANA31LudHVRNViH5StBF6TxmKSIGg0REVKEIVcCWYAM07lsFt17bioVnF0KChBnNZlTYGeKIiDyJEAKPL38cxxOPo4qxCr5t8S1knb2bp7AJmP4xASogV5ahrePa6qdmqJC0EnTROkgGz6kXGAwSEVGFoiap9lngfF0rc1WomLhrIgBgdPXRaBrUtKSTR0RExWDmnplYsH8BZEnGvMbzEBoY6thn2WOBSBGQjBIMLQ2u4wStAsgEtFW00Ph7VnjkWXdLREQVmlAFlAQlx1bBH07/gJ2JO+Gr9cW0RtNKIYVERFTU9l/Zj/Er7UtHTK0xFW1j2jr22c7bYDtlAwAYWhkgGW+rG1R791A5XIYcVvEnjLkdg0EiIqow1GTVPubDzVjBVGsqJv87GQDwaoNXEe4VXtLJIyKiIpZmScMDPz8Ak82EHiE9MLHhRMd4PzVNhXmHGQCgi9NBDncN9kSKgMZfA21VrceME7wVg0EiIqoQhLC3CgoISFrXCv29Q+8hPjMe1X2q4+naT5dCComIqCgJIfDk8idx5NoRRBoj8W3jbyEbs8YJqgLmf8yAFdAEa6BroHN/ER3s4wT1nhcIAgwGiYioglCTVahJKjS+rlXbqbRT+OTIJwCAd5u+C4NsKOnkERFREZuzdw7m/zsfGkmDuQ3mIiw0zLHPesAKNVEFdIChtcG11c9q/482Uuu23vAUnnvnRERUYThaBYX7VsFX97wKs2pG5/DO6Fulr9M+NVOFMIuSSioRERWBgwkH8dSKpwAAr9d4He1i2jkmhlEuK7Aetkd7hrsM0FRyDnmEKqCmqAAATYhnh0OeffdERFQhiBQB9Yb7VsENVzbg1wu/QiNp8EGzD1xmkbPstcC0xQTrMWtJJZeIiAoh3ZKOB35+AJm2THQJ6YIX416EpLOX7cIkYN5mHyeora6FNsp1GQmRJKAJsNcXnr68EINBIiIq14TIWlcQcDwMZFNUBRN325eSeLTmo6gfUN95f6IC5ZwCKIAmkFUiEVF5MH7leBy6eggRxgjMbDATWh97wCeEPRAUJgHJT4K+qd7lXDVNBQyAtqprkOiJWPMREVG5JlLtrYLuZhCddWoW9iftR6A+EK83fN35PCFg2WsBAMgRMuRQz5tSnIiovJm/bz5m750NjaTB7PqzEREW4dhnO2qDclkBZMDY2ugybEBYBGAFdFV1Ll1HPRVzgYiIyi1Hq6Dq2iqYZEnC1H+nAgAmN5yMYEOw037lkgL1qgpoAG1NviEmIirrDl89jMeXPw4AeK3Ga+gU3cmxpqxyXYHlX/sLPn0TvaMbaDah2McJyhGyx48TvBVzgoiIyi2RKqAmum8VnH5gOq6Zr6GuX108WvNR5/NUAcs++0ODro4OGiOrQyKisizDmoEHfn4AGdYMdAztiFdqveJYQF5Ys5aREIBcVYa2hvMLPiEERLKAHChDG6n1+HGCt2LtR0RE5ZIQArarNkCBy/pQR1OO4otjXwAA3m/2PnQa5/WlbKdsEKkCMAC6ujmsPUVERGXGsyufxYGEAwgzhmFW3Cxo/W8ZJ7jTDJEuIHlLMNxlcAn2RJq9vNdGa116kXg6BoNERFQuifScWwVf2vMSbMKGXpG90L1yd+fzrAKWA1ldierpPXahYSKi8uLz7Z/juz3fQYKE2Q1mIzIs0hHw2c7Y7BOBSVnrCd5WpguzAGwcJ5gT5ggREZVLylX7LKCSwbniX31pNVZdWgWdRod3m77rcp71iBUwA5KP5NKViIiIypZVJ1bhmVXPAACm1Z2GzpU7O1r31BQVll1ZXf4b6CCHOE8EJhQBNZXjBHPDXCEionJHTVOhXFcgVXIOBK2qFS/ueREA8FTtp1Dbr7bzeZkqrEft6wnqG+kdEw8QEVHZcyDhAB5Y/ABUoeKh6IcwMXoiND728EUoWeMEFUATpnHp8u8YJxgkQ1uF4wRzwmCQiIjKFSGEvVXQ6toq+NXxr3A05ShCDaF4rf5rLuda91vtDw7BGshVuZQEEVFZdTntMnov6I1USyrah7bH57U/hxx4s9y27LNATbKvGWhoZYCkyWGcYJTWZYkJuonBIBERlSvqNRVKguIyVvCa+Rre3P8mAGBao2nw1/s7n5ekwnbGvji9vomeb4mJiMqoTGsmBiwcgHPJ51DTtyZ+qPcDDEE3Az7bRRtsx+3luaGlARqv25aRyB4nGMVxgnfC3CEionJDTVVhPW8FdK6tgtP+nYYkaxIaBTTC6OqjXc61/GtxTDt++7gSIiIqG1ShYtSvo7Dt4jYE6gPxS6NfEBoa6ujWr2aoMG83AwC0tbXQRt62jET2OMHKMjTBDHXuhDlERETlgjALWM9aISzCMWYk24GkA/ju5HcAgBnNZ0DWOAd7ymUFSrx9tjl9I32JpZmIiPLn9XWvY/GhxdBpdPipyU+oHVbbMWGMUAXMW82ABdAEalzKcyEERFLWOEGuJ5gnDAaJiKjME4qA9bwVIkVAE3BbdyAhMHH3RKhCxaCoQWgf1t5lf/YC89qaWmh8WfUREZVFc/fOxfRN0wEAnzf4HO0i2jkWlgcA62Er1KsqoM1aRkJ2HScoGSWOE8wH1ohERFSmCSFgi7dBuapACpBcJglYdnEZ1l9ZD4PGgLebvO1yvu2MzT7JgA7Q12erIBFRWbTx7EY88vsjAIAXa72IEVVHOI33U64qsB60zwZtaG5webGXPU5QG6XlOMF8YE4REVGZpl5XoVxUoKmkcXnTa1bMeHnPywCA5+KeQ4xPjNN+YROwHrA/POjidC7jDImIqPQdv34cA38aCKtqxaAqgzC1+lRIfjfLazVTtS8jIQBtjBbaGI4TLCrMLSIiKrPUtKwJY7Rw6iqU7bOjn+F02mlU9qqMF+NedNlvPW6FyBCQvCXoaulc9hMRUelKzExE7wW9kZiZiBZBLfBtnW8hB8iO8X7CJmDeZIbIFJB8JeibcZxgUWIwSEREZZKw2CeMgRlux/ldzryMtw/au4VObzwdPjof5/NNAtbDWa2CDXUcP0JEVMZYFAvuW3QfjiceR3SlaCxqsAiVQio5hgNkTxij3lABPWBsZ3RMJpPNMU4wmuMEC4LBIBERlTlCtU8YoyarkALcV+6v73sdabY03BV8Fx6MedBlv+WQBbACmgANtNW0bq5ARESlRQiBx/94HOvPrIevzhdLGi1BZFik06Qwln0WKBcVQAMY7zHmPE4wWguNN8OagmCuERFRmZI9YYyaoELjr3GZMAYAdl3fhXmn5wEAZjSbAY3kXJ2pqSpsJ7jAPBFRWfXe5vcwe+9saCQN5jeaj4YRDZ1a/azHrbAdy1pY/m4D5FDnJYOcxgkGMaQpKOYcERGVKWqifcIYeMOlOxBgDxZf2P0CBASGxQzD3SF3uxzjWGC+sgw5nAvMExGVJT8f+hmvrH0FAPBBvQ/Qo0oPp3Hhtks2WPbYlwTSNdRBG33bhDEcJ1hkGAwSEVGZoaarsJ2zARpA4+W+ilp8bjG2XNsCb9kbbzZ+02W/ck2BcoELzBMRlUU7Lu7AiKUjAABPxD6BJ6o94byERKJyc+bQWC10ca6Tf4lUAcmL4wSLAoNBIiIqE4RFwHrOCmG2zxjnzpXMK3hh9wsAgJfqvYQq3lWcryEELHuzFpiP1bosUE9ERKXnXPI59FvYDyabCT0ieuC9mu85LyGRocK8yQzYAE24BvoWrt38hVkAStZ6ghwnWGjMQSIiKnVCFbBdsEG9YZ8wxl2XH1WoGLN1DK6YrqC+f308F/ecyzHKBQXqdRWQAV0DLiVBRFRWpJhT0GdBH1xOu4wG/g0wr9486IJ0N5eQsAqYNpogTAKSnwRjG6PLmHHHOMFIjhMsKszFMuidd96BJEmYMGFCaSeFiKhE2C7boFxRcpwwBgA+OvIR/rr8F7xkL/zQ9gcYZaPTfqEI+1hBALq6uhy7mVLBsG4iooKyqTY8uORB7E/Yj3BjOJY0XAL/EH/nJSS2mCGS7ctEGNsbIelvCwSzxwkGy9BW5jjBosKasozZsWMHvv76azRq1Ki0k0JEVCKUxKwxfjlMGAMAO6/vxOv7Xgdgnz00zj/O5RjbSZtjvSldHbYKFiXWTURUGBNXT8SK4ytglI1Y3HgxqkVUcywhIYSAZbcFymUFkAFDO4PTGMJsIjlrnGAUxwkWJQaDZUhaWhqGDx+Ob7/9FoGBgaWdHCKiYpeXCWNSrCl4aMtDsAkb7ou6D2NqjHE5RlgELAezWgXr63IMKin/WDcRUWF8vv1zfLr9UwDAzIYz0TKypfMSEketsJ3MWkKitQFykOsM0Gqavfu/NobjBIsaV+EtQ5566in07t0bXbt2xZtvus6Qdyuz2Qyz2ez4d0pKCgDAarXCarUWazpLQ/Y9VcR7KwnMv8Jh/hVOTvknrALW01aIdAEpUIKiKC7nCiHw1PancDrtNKK9o/FJ809gU20ux9kO2QALAF9AVBOwKvn7XQnVvnCxalXzdR5Q8b8XrJtyxrKhcJh/hVMe8m/1ydV4ZtUzAIAptaegX5V+sOlsQFZxr15QYdtnL9PlxjJEhGv5LUwCMNsDQbWSWqBy2p3ykH+Fkdf7YjBYRixcuBC7d+/Gjh078nT822+/jWnTprls//PPP+Ht7V3UySsz1qxZU9pJKNeYf4XD/CucXPPvmvvNa6+vxaLzi6CBBk9WeRI7z+x0OcZgMeCuY3dBAw0OBB9A4onEgiUwhzTcSUZGRsFOLAdYN+UNy4bCYf4VTlnNvzOZZ/Dq8VehChWdgzqjiVcTrL241rHfN90XjU41ggwZF4Mv4qR6EjiWywUTiiedZTX/CiuvdZMkhBDFnBa6g/Pnz6NFixZYs2aNYzxGx44d0aRJE3z88cduz3H39jUqKgrXrl2Dn59fSSS7RFmtVqxZswbdunWDTsexQPnF/Csc5l/huMs/6xUr1LOqfUrxHF5LHks5hnZ/tUO6LR1TGkzBi/VedHucbbsN6jkVUogEbYeCTSogEgW0tbSQA/O/QH1KSgpCQkKQnJxcocpf1k13xrKhcJh/hVOW8+9CygV0nNcR51LO4Z6Qe/Bb499gCDIAWcWzSBOwrrMCZkCqLEHbxk3ZbQPUZPvModqqRT9hTFnOv6KQ17qJLYNlwK5du5CQkIBmzZo5timKgo0bN+K///0vzGYzZNn5AcVgMMBgMLhcS6fTVcgvdLaKfn/FjflXOMy/wsnOP+WGAk28BppKGmgM7sd+mBUzxmwbg3RbOjqGdcTL9V+GrHEN1JQbCtRz9i5DhqYGyNr8B3MAoGpU6LQ6yLr8n19RvxOsm/Kuot9fcWP+FU5Zy7+r6VfRa2EvnEs5h5q+NfFTg59QKaTSzZlDLQKZmzMBM6AJ0MDY2ugyIUz2EhK6CB101XSOyWaKQ1nLv6KS13tiMFgGdOnSBfv373fa9vDDD6Nu3bp4+eWXXSpbIqLySs3ImjAGyHUSgNf2voa9N/YixBCC2a1nuw0EhRCw7LNPGiNHy24nHaCCY91ERPmVZEpCj+974Mi1I4jyjsIfjf9AcGjwzUBQETBtNkGk2mcGNbQ3uEz45VhCIkgu9kCQGAyWCb6+vmjQoIHTtkqVKiE4ONhlOxFReSVsArZzNogMASko58p9+cXl+O+x/wIAvr37W0R6R7o9TrmsQL2iAhpA30hfLGn2ZKybiCg/0i3p6LOgD/Zc3oNQQyh+b/I7YirHOFr9hBCw7LBATVABLWBsb3SZRVoIAfWGCo2PBtpqWs4MXQIYDBIRUYmwXbRBSpSgCdTkOPbjUsYlPLL1EQDA+Drj0atKL7fHCfVmq6Culs7tmlRERFQyzDYzBi0ahM3nN8Nf549lTZehbtW6Tq161oNW2M7aAAkwtDFAE+BmLcEUAY1BA12sLsflhqhoMRgso9avX1/aSSAiKlJqggqdX85dfhRVwcP/PIzrlutoEtgE0xtPz/FatjM2iGQB6AFdvcKN9VAzVEAPSAa+gb4T1k1EdDubasPwX4bjz5N/wlv2xtImS9E0uqlzIHjGCutB+1IH+uZ6aCu7hiBqugpIWWsJ+jAQLCnMaSIiKlZqZtaaUAZA0ucccL1/+H2sT1iPStpKmN9mPgyy60QkgL27qXV/1kNFPX2u17wToQiIDAG5sszWRSKifFKFikd+fwRLDi+BXqPHoiaL0KZ6G6dAUElQYNmR1ZOjrg66Gq4v8IRJABZAF60r0KzOVHCs+YiIqNgIIaBcsa8uLHnnHLT9c/Uf/N/+/wMAfNL8E9T2q53jsdYjVgiTgFRJgrZm4Tq4qCkqNAEaaMPYUYaIKD+EEHhu1XOYs3cOZEnGvIbz0LVmV8dkMYC9jDVtNgEqIFeVoWvkJhC0CIh0AbmqDE0oQ5OSxhwnIqJioyarUK+puR6TZEnCyC0joQgFQ6sNxUOxD+V4rJKgwHooq1Wwkb5Qs8wJs4AkSdBV0blMa05ERLmbsn4KPt3+KQDg6/pfY0DcAKdAUJgETBtNgAXQBGtguNvgMl5c2ATUFBVyZRnaykW/liDdGYNBIiIqFkIRUC4rEBA5HyMEntj+BM5lnEN1n+r47K7PcnwYUNNVmLaYAAHI1WTIUQXvSiRU+xpWcrhsX/ieiIjybMaWGXhj4xsAgA/jPsRDDR5yKruFVcD0twki3d6Lw3iPm7UEVQE1SYUcmrWovIZlcWlgMEhERMVCva7apwj3zbmqmXlyJn45/wu0khbz28yHn87P7XHCJmDebHYsUmxo4fqGOT9EmrBPXc430URE+fLtrm/xwpoXAADTak/Dk42fdA4EzQKmdSao11VAZ19CQjK6WUvwhoAcmLWWIHtnlBoOkiAioiInLAK2eBugB5BDA96h5EOYuHsiAOCNxm+gRXAL99cSAuadZqg3VMAAGO4xFOrBQVgFYAO0sdpCTT5DRORpFh5YiMf+eAwA8Hz15/Fys5edAkE1Q4VpgwkiRQCGrLUE/VxfCKpJKjSVNNDGsBwubQwGiYioyNkSbFDTVWiCNYCbIYOZtkw8tPkhmBQTukV0w4S6E3K+1jEblLMKIAHG1sZCzfophLCvYxWmgSaInWOIiPJq+bHlGLF0BAQEHqn2CN5q+ZZzIJimwrQ+q2uolwRjxxwCwRQVkl6CLoZrCZYFDAaJiKhIqekqlCsKpEpSjl0wX9rzEg4mH0S4MRwzW82ERnL/QKBcURyLy+ub6CGHF27KcZEhACOgjWT3UCKivFp3eh3uX3Q/bKoNQ6oMwaetP3UOBJOyWgRNApJPViDo5sWdmq4CAtBV07kNFKnk8bdARERFRggB22Wbffa4HN74/nr+V3xz4hsAwMxWMxHuFe72ODVdhekf+4Qx2hgttLUK9/5SKAIw2QNBvo0mIsqb7Re2o9+P/WBSTOgd3hsz73F+gadcV5C5LtMeCPpLMHZ2HwhmryWojdZCDuJagmUFWwaJiKjIqEn2pSRymqHzXPo5PLbNPt5kYtxEdKvcze1xwiZg/jtrwphADfTN9YVuyRPJAppADeQQPoQQEeXF/iv7ce/39yLNmoYOIR2woMMC6DQ31wpUrigw/W0CbPblI4ztjW7HAApr1lqCUTLkMJbBZQmDQSIiKhJCyZo0RgIknevDgE21YfQ/o5FkTUKLoBaY2nCq++sIAfMOM9SkrAlj2hZuwhgAUDNVQAtoq2gLtTYhEZGnOHHtBLrP7Y5EcyJaBrbEko5LYJSNjv22izaYt5gBFdCEa2Bsa3Rb9gtFQE1Woa2sZRf9MojBIBERFQnlmgI1WYUmwH0XzHcOvYPNVzfDV+uL+W3nQy/r3R5nO2aDci5rwpg2hZswBrCvZSXSBbTR2lyXuSAiIrvzSefRbW43XM68jAZ+DfBbp9/gq/N17LeescKy3WJf97WKDENrg9sXbULNWkIiWIY2imsJlkUMBomIqNCEWUCJVyAZJLcPBAfSDuC9k+8BAD6/63NU96nu9jouE8YUQXcikSKg8ddAG84qj4joThJSEtBtTjecSTuDGpVqYHnn5QgyBDn2W49bYdltL6e1MVro79K7DfKEEBBJ9nGEuhiuJVhWsWYkIqJCs12xQc3IWkriNtfN1/HR2Y+gChUjY0diSMwQt9dQ01SYthTdhDGAPUiFyJo91E33JSIiuikpLQk95vTA0eSjqOJVBSu7rESEVwQAe3BnPWyFdb8VAKCtpYW+qfvx3ELNCgS9swJBA8vfsorBIBERFYqaqkJJUKDx0bg8FCiqgke3P4rr1uuo5VsLHzX/yO01hE3AvNlsn4U0UAN9iyKYMEYIqKn2cSo5dV0lIiK79Ix09JnXB3tv7EWoIRQrO69EtUrVANjLU8s+C2xHbQAAXX0ddPV1OQeCNwQkXwm6WF2hu/pT8WIwSEREBeZYSsIGtzOIvrDnBayOXw29pMecVnPgo/Nxew2nCWPucT/2JN9pSxPQVNJwwgIiojvINGVi4PcDsfnqZvjr/LG803LU8asDwB7cWXZaYDttDwT1TfTQ1dG5vY5QsloEA7iofHnBYJCIiApMTVShXlch+boGW/89+l98cewLAMCEahPQOLCx22vYjt4yYUxbIzTehX94EFYBWO3rWbF7EhFRzjIyMzBg/gCsiV8Db9kbv3b41VFeC0XAvM0M5by9jNa30ENXPYdA0CagJqmQg2V2DS1HGAwSEVGBCFvWUhKy61ISv1/4HS/sfgEA8EajN9BQ09DtNZTLCiz/Zk0Y01QPObQIJowRwj5pTIjG7RhGIiKySzelo9+8fvjf5f/BW/bG0g5L0Sa0DYCb3feVywqgAQytDNBGuQ8dhNW+fIQcmhUIcox2ucFakoiICsR21QY1xbVVcHfibozcMhICAmNrjMWEOhPcnq+mqTD9kzVhTKwW2ppF835SZAjAkLWmIKcxJyJyKzUzFb3m9ML/Lv8PleRKWNZxGTqGdwQACIuAaYPJHgjK9u77OQaClqx1BCO00MUyECxv2DJIRET5pmaqUC4rkLwkp4DrXPo5DNwwEBlKBrpGdMUnLT4BhOv5wiZg+ttknzAmSAN988JPGANkjVcxCWhjtEXS3ZSIqCJKyUxBrzm9sDnBvvbr7x1/R+vQ1gAAYbIHgmqSCugAYztjjr02hClrHdcqWmiraotkvDeVLAaDRESUL0IIKFcUIBOQgm9W/CnWFAzcMBCXTZdR378+FrRdAJ1GB6tidTnfvN0MkSwgGSUY2hbNhDEA7N2UAmVow1i9ERG5k5SRhJ5zemLb1W3w1/njj45/oGVISwCAmqHCtN4EkWrvYWHsYIQc6D4QVDNVIBOQo2T7RF3siVEusbYkIqJ8EakCSoICyVdytOZZVSuG/T0MB5IPIMIYgV87/Ap/vb/b861HrI7JCAxtDEXWgidMApIs2R9K+HaaiMhFYkYieszugZ3XdiJQF4jlnZajeXBzAPZlgkzrTRAZ9vUBjR2N0Pi6L5/VdBWwAHK0DG1lzthcnjEYJCKiPBNq1qQxAo6Z4oQQmLBrAtZcXuOYgCC6UrTb823xNseCxfpmRTNhTHa61DQV2igtNH7sHkpEdLtr6dfQfXZ37Lm+B8H6YKzotAJNgpoAANQUFaZ1JvtLNV8Jxg7GHNcHVFNVQIF96Ygw1/VlqXxhMEhERHmmJqpQb6hOawp+dOQjfHfiO0iQMLfNXDQLaub2XJEmYPnHcnPCmBpFVwWJVAGNrwbaCFZrRES3S0hLQLfZ3fBv4r8I0YdgVedVaBhon+VZTb0ZCGoCNDB2MEIyug/w1GQV0AC66jrIIUXzMo9KF2tNIiLKE2HNahXUApLW/qCw9PxSvLr3VQDAe03fQ7+q/dyeq1E0sG2xAVZAE1x0E8YA9pnsoGbNHspZ7IiInFxJu4Ius7rg4I2DCDeEY1WXVajnXw9A1qzO2S2C/vauoe7WBxTCvoagpLcvJp/TOEIqfxgMEhFRntgSbFBTVWiC7F2Htl/bjtH/jAYAPF7rcYyvM97teUII1LlQByKl6CeMyV5TUI6QoQlk91AioltdSrmELrO74EjSEUQYI7C6y2rU9asLwD7uz7TOBJEpIPlJ8OrolWMgKJIENEYNdLE6dsWvYBgMEhHRHakZKpQrCiRv+1ISp9NO476N98GkmHBv5L2Y0WxGji196jEVocmh9glj2hqg8Sq6BwmRbp/oQI6UOW6FiOgWF1MuovOszjiWfAxVjFWwuutq1PKtBeCWWUMzBCSfrBZBN11DhSogbtiP0cXqoPFhIFjR8DdKRES5EkLYFx42A5KXhCRLEgZsGIAEcwIaBzTG922+h1bj/t2imqJCOaAAAOSmcpGOMRE2AZgBbaQWGiOrMyKibOeTz6PDzA44lnwMUV5R+KvbXzcDwcysQDBNQKokwdjJ6PYlnVAERKK91VBXg4FgRcXfKhER5UpNVqFcsy8lYVWtGPL3EBxJOYIqXlWwtMNS+Oh83J4nhIB5pxkQwHXf69DEFm2VI5IFNMEaaEJYlRERZTuTdAYdZnXAyZSTiPGOwV9d/0J1n+oAshaUz1pHUPLOCgTdLO8jbALqDfuwAH0NfZEtAURlD3+zRESUI6HYWwWFEIAOeHrH01h/ZT18tD5Y2mEpqnhXyfFc2xkb1KsqIAMnq5ws0m6caoYK6LMmjeFCx0REAIBTN06hw8wOOJ1yGtW9q+Ovrn8hxicGACDMWYFgioDklRUIulk+Qljtk8XIoTJ01XU5zixKFQODQSIiypF63b6UhMZPg/cOvYe5p+dCI2nwfdvv0TiwcY7nCbOAZZ8FACDHyTDpTUWWJmETEBkCcmU5x3WwiIg8zfHrx9FhVgecSzuHWpVq4a9ufyGqUhQA+6zLpg0mqMkqJGPWgvJuun0Ki4CarEIOl6GL1UHSMxCs6FiLEhGRW8KStZSEHlh8YTFe//d1AMBHzT/CvZH35nquZZ/FPsbQT4KmdhFOGKMKiGQBOVSGNpxzoBERAcDRa0fRYXYHXEi7gLo+dbGm2xpHzw1hzQoEb6iAAfZA0M2MoMIsIFIFtJW10MXoHEsIUcXGmpSIiNyyJdigpqvYqm7FuK3jAADP1HkGj9d6PNfzlKsKbKdtAABDCwNUjVpkaVKTVGh8NdBF64pseQoiovLs0NVD6PFDD1zJuIL6vvWxsstKhHuFA8gKBDeaoCbau9Z7dfSCxt99i6BIE5CryNBWZfd7T8KWQSIicqGm25eSOCVOYfCmwTCrZvSt0hfvNHkn1/OEkjVpDABtdS3k0KKbPVRNzVrwuJrO7VpYRESe5kzmGXT7vhuuZFxBQ9+GWN119c1A0CZg+tsE9ZoK6ABjByM0ATkEgikCciQDQU/ElkEiInIiFHv30Otp1zFg1wBct1xH86DmmNtmLmRN7sGd9agVIkUABkDfSF90aTIJwAb79Oa+fI9JRLT3yl5MPjEZqUoqmvg1wYquKxBsCAZgL8dNf5ugJqiA1h4IykGu5bewCKipKrRVtNBGMRD0RKxRiYjIQSgCtnM2ZFzOwNADQ3Ei9QSivaPxS/tfUElbKddz1TQV1kNWAIC+ib7IWu+ETUBNVyFXkaEJZrVFRLT1wlb0+L4HUpVUNPNrhpVdVzoFgubNZqhXsgLB9kbIwW4CQauAmqJCG6Fli6AHY61KREQA7JOz2M7bYI234snjT+Lva3/DT+eHXzv8igiviNzPFQKWXRZAATRhGmirFU3HE6FmTXEeJkNbWVuky1MQEZVHq06sQpe5XXDDfAN1vOvg986/I8gQBMBeZpr/MUOJVwAZMLYzuu2uL6z2WUO1lbXQRms5BtuDsZsoERE5AkHlkoI3zr+BH8/9CFmSsaDtAtQPqH/H85ULCpTLCqABDM0NRRK0CSEgkgQ0/hroojhhDBHRj/t/xMhfR8Km2tA1uCvGRY6Dv94fQFYguNUM5aK9LDbeY4QclksgGMFAkNgySETk8YQqYLtgDwQ/iP8A7xyxTxLzWYvP0K1ytzufbxGw7LavKaiL07mdsrxA6UoVkAxZE8ZwrSsi8nCfb/8cw38ZDptqw+DIwVjUcRGMshFAViC43QzlfNZLuXsMkCPcBIK2m+sIaqsxECQGg0REHs0RCF5U8Fn8Z5h8YDIA4I1Gb2BszbF5uoZlvwXCJCD5SNDF6YokXWqmCqiANlrrdmFkIiJPIYTAlHVT8PTKpyEg8FjMY5jXfh70st6x37LDAuWsAkiAoY0B2squnf+ELavbfbhsf8nGQJDAbqJERB5LCAHbRXsg+PWVr/HS/pcAAJMaTMJL9V/K0zWU6wpsJ7LWFGxuKJKHC2EVQAYgV5PdTnpAROQpVKFi/Mrx+GLHFwCASXUmYVLTSZAkCQoUQADKHgXqGdUeCLY2QFsll0AwLCsQ5ILylIXBIBGRB3IEghcUzLo6CxP2TQAAvBj3IiY1mJS3a6gClp327qFyNdltl6R8p0u9ZSxLBKsoIvJcFsWCUUtHYeHBhZAg4cOGH+LJBk869gshUONSDajXswLBuw3QRuUSCIbK0MUwECRnrGmJiDyMEAK2S/ZA8Ptr3+PpPU8DAJ6t8yzeaPxGnid/sR23QU2yL2ZsaGIoknSJGwJyoMz1rojIo6Vb0nHfovuw+uRqaCUtZjafiaG1hjr2CyGg/KugyvUqAAD9XXq3szgLhYEg5Y7BIBGRB3EEgucVLLq+CI/teQwCAk/UegLvNn03z4GgmqHCcsDeKqhvrIdkLILuoSkCkpdkn9RAxwcWIvJM1zOuo/eC3th2cRu8Nd5Y2GohelTr4dgvVPukXepJFQAgN5Ohi3Udry0UAfWGCjkkKxBkuUpucFQ+EZGHEELAFm8PBH+98SvG7BoDVagYU2MMPmz+Yb6Wg7DssQA2QBOsgbZ64d8rqhn2hxptNS003qyaiMgzXUi5gPaz22PbxW0I1AZiRfsVzoGgYl8+wnbSPlb7WJVjkKu7mTU0OxAMZiBIuWONS0TkAW4NBP9I+gMjdoyAIhSMjB2Jz+/6HBop79VBdhdTSIChReHXFBQWAZgAuaoMOZATxhCRZzp2/RjazmyLQ9cOobKhMtZ2WovWlVs79gubgPnvm8tHyHfLuBx82eU6ToFgLJfmodwxGCQiquCEELBdtgeCq5NXY9iOYbAJG4ZWG4qvWn6Vr0BQ2AQsu7LWFKyjgyagcNWIUATUlKw1rzhhDBF5qF2XdqHtzLY4l3IONb1rYl2XdagfUt+xX5gFTOtNUC4rgJy1jmCUmxZBNWvsdRADQcob1rxERBWYEALKFQXKOQX/S/4fhmwbAqtqxaCoQZjZaiZkTf5a4qwHrRAZApK3BF39wq0pKISASLI/tGijtIVuYSQiKo/WnV6Hfgv7Ic2Shia+TbCs0zKEVwp37FczVZg2mCCSBaAHjO2MkENkWBWr03WEKiASBTRBGgaClGcMBomIKjA1QYXtrA2bUjfh/u33w6ya0bdKX8xrMw9aTf6qADVJhfWo/eFD31xf6FnpRLKAVClrwhjOcEdEHuiXw7/gwSUPwqJY0D6wPX7u+DP8jf6O/WpqViCYLiAZJRg7GN32yHAEgoFZgaCBZSrlDbuJEhFVUEqCAutZK7akbsHAbQORqWSiZ2RP/ND2B+g0+WvVE0LAvNMMCPvYPm1k4d4lqukqIAO6ajpovFgVEZHn+W73dxi8eDAsigX9wvphWZdlToGgckOB6X9ZgaCPBGMX94EgVEDcEJACJQaClG+sgYmIKiAlQYH1jBU70nZgwLYBSLelo0tEF/x0z08wyPlfE9B20mZf2FgL6JvqC5U2YRaAGdBV1UHjz2qIiDyLEALv/v0uHvn9EahCxegqo7GgwwJ46bwcxyhXFZjWmSBMApoADby6eEHj4768FEkCkl9WIFgEy/yQZ2E3USKiCka5ag8E96TtQb+t/ZBqS0WHsA74ud3PMMrGfF9PmAQs/2atKdhQX6ilH4RNQE1Voa2qhSaMgSAReRZVqHhpzUuY8c8MAMDE2Il4s+Wb0Ghuloe2SzaYt5gBBdCEaGBsZ3Q//s++Io89EKyug8bIMpXyj8EgEVEFolyzB4L7M/ajz7Y+SLImoU1IG/zS/hd4a70LdE3zXjNgBTQBGmhrFqLaUO3jBOUQezdTThhDRJ7Eptowbtk4zN03FwDwdp238VzT55zKQtsZG8zbs7rkV5ZhaGNwO6ZaKPYJuABAG6Nld3sqMAaDREQVhHLdHggeTj+M3lt7I9GSiJbBLfFbx9/go/Mp2DUvK1DOKgAAfQs9JE3BAzg1RYXWVwtdtI4TxhCRR8mwZmDoz0Px+7HfIUsyvmzwJUbWH+kUCFqPW2HZbe+FIVeTYWhpcFvmCpuAmqRCCpCAa2CLIBUKg0EiogogOxA8nn4cvbb1wlXzVTQNbIrfO/4OP51fga4pFAHzLjMAQFtTCzm4cAvCS1oJumoc00JEniUhPQF9F/TF9kvbYdAY8H2z79GvVj/HfiEErAetsB60z9asraWFvqnebe8JYRVQk1XIoTLkSBk4UWK3QRUUg0EionJMqAK2KzYoFxScyjyFe7fdi8umy2gY0BDLOy1HgD6gwNe2HrZCpNmnM9c3LMSkMVlLYWmraqHx4xtsIvIcR68dRa8feuFU0ikEagOx6O5FaB/d3rFfCAHLbgtsJ2wAAF0DHXT1dO4DQbOAmqZCG6GFNloLm7CV2H1QxcVgkIionBJWAdt5G5QrCs6Ks7h32724mHkRcX5xWNFpBYINwQW+tpqiwno4a03BZvoCL14shICaYp/lQBPMQJCIPMff5/5G/x/7I9GUiBivGCxrtwx1gus49gtVwLzNDOVcVlf8Znroarlf9keYBES6gLaKFtoqWkiy5HjRRlQYDAaJiMohNV2F9ZwV6g0Vl3SXcO/Ge3Eu4xxq+dbCqs6rEGYMK/C1hcjqHqraJzCQqxa8e6hIFfbp0K+DE8YQkcdYfHAxRiwdAbNiRgu/FljScQkiKkU49gubgHmLGUq8AkiA4W4DtNXcP5arGSpgBuRoTr5FRY+vaYmIyhEhhH184DErRLLAWf1ZdN/YHafTTiPWJxarO69GhFfEnS+UC9sZG9QE+6Lw+mbux63kKa0WASj2gJKIyBMIIfD+5vfxwM8PwKyY0Se8D/7s/qdzIGgRMG0w2QNBGTC0yyUQTFUBi33GUAaCVBzYMkhEVE4IRcAWb4NyUQG0wBHNEfT+X29cyryEmEoxWN15Nap4VynUZ1hP3pzNTldPl+Mix3dMa1b3UG1lLUSgKFSaiIjKA0VV8MzyZ/DF7i8AAE9UfwIz7poBWXPzhZiaqcK8wQw1WQV0gLGdEXKo6wszIQREigA0gK66DnIIX6pR8WAwSERUDgizgPWcFco1BZpKGuxK34W+6/si0ZKIev71sLzjckR6Rxb8+qqAZa8FtuP2CQnkKBm6uu7HruTpein27qHaSC1sEic5IKKKLd2SjqE/DcUfp/6ABAnvNnoXz9R7xqklT01TYdpgckzMZexghCbA9YWbEALqDRUagwbaWC3kAAaCVHzYTbSM+PLLL9GoUSP4+fnBz88PrVu3xsqVK0s7WURUBqgpKizHLFCvqdD4a7AheQN6/K8HEi2JuCv4LvzV5a/CBYJmAdNGkyMQ1DXQwdDa/fpWeb0eFPvsoZKBXZrKK9ZLRHlzOfUyOs7siD9O/QGDxoAf2vyAZ+s/6xQIKtcUmNZmBYKVJBi75BAIqgIiUUDjrYGupo6BIBU7tgyWEVWrVsU777yDWrVqQQiBuXPnon///tizZw/q169f2skjolIghICaoMJ6wQphE9AEavD7pd/x0OaHYFbN6BTeCT+3+7nAC8oD9kDTtMn+gAJt1iQGVQteNQhVQE1VoY3UQhPI943lGeslojs7fOUwen3fC2fSziBYF4wlHZagdWhrx34hBGwnbLDssQACkPyzWgS93ASCioC4ISD5S9DF6qDxZhlKxY/BYBnRt29fp39Pnz4dX375JbZu3cpKl8gDCZuA7YINymUFMAByoIwfTv+AR7Y9AkUo6Fe1H+a3mQ+jbCzwZ9jibTD/YwasgOQtwXCPAXJg4d5Ci1QBja+GEx1UAKyXiHK34fgGDPx5IG5YbqB6pepY1mkZavnWcuwXNgHzTjOUs/alI+QoGYa7DJB0btYQtAmoSSrkIBm6GB0kI8tPKhl5DgZ3796d74s3a9Ys3+cQoCgKFi9ejPT0dLRu3drtMWazGWaz2fHvlJQUAIDVaoXVWvEWnsm+p4p4byWB+Vc4JZ1/aqYK23mbfcyInwbQAf898l+8uOdFAMDwmOH4vMXnkCHDquQ/TUIIqMdVKP/aH1CkEAnaVlqoRhWqohY84Rb7A402PGucYFbSKvr3r6Le163yUi8BrJsof8pz/i3cuRDj/hoHi2pBy6CW+OmenxBqDHWUySJNwPaPDSJZABIgN5ShqaWxl43KbRezAWqyCk2IBnKUDJtsy9MaguU5/8qCip5/eb0vSQiRp2neNBpNnt/yCiEgSRIU5fZvO+Vm//79aN26NUwmE3x8fLBgwQL06tXL7bFTp07FtGnTXLYvWLAA3t7exZ1UIiohQggsurIIP17+EQDQJ6QPxlQZA41UsO5Dkiqh1sVaiLhhn+Y8PigeJyJPQGg442dBZWRkYNiwYUhOToafn19pJ6dI5adeAlg3UcUnhMDShKWYFz8PANDKvxWeq/YcDBqD45iglCDUPVcXWlULi9aCw9GHkeyTXFpJJg+V17opz8Hg3Llz852IUaNG5fscT2axWHDu3DkkJyfj559/xnfffYcNGzagXr16Lse6e/saFRWFa9euVbiHEcD+dmPNmjXo1q0bdLqCz3DoqZh/hVMS+SdUAVuCDepFFdAAko8EFSpe2fsKvjhun6b8P/X/g1fqvVLwdf9MArYtNojErDfVjWVoauT9RV+u104WkLwk6GrqIOmdr1fRv38pKSkICQmpkMFgfuolgHUT5U95yz+rxYpnf3sW3x3/DgDwVK2n8FbjtxxLRwghoBxSoB6297CQgiRoW2shebkvY4VJAJmApnJW1/p8TtpV3vKvrKno+ZfXuinP3UQZ2BU/vV6PmjVrAgCaN2+OHTt24JNPPsHXX3/tcqzBYIDBYHDZrtPpKuQXOltFv7/ixvwrnOLKP2ERsF6wQpOggcZbA42XBjbVhqe3P415p+1vn2c0m4Gn6zxd4M9QEhWY/zZDZAr72lZtjJAjimaWOmEWEBoBXbQOcqWcr1lRv38V8Z6y5adeAlg3UcGUh/xLSUrB0EVDsTJ+JSRIeL/Z+xhfZ7xjvzALmLeaoV62B4Lamlrom+ghye4DPDXdvpi8HCNDW7lwY6zLQ/6VZRU1//J6T/maQObSpUsAgMjInKcwv3TpEiRJQuXKlfNzaXJDVVWnN6xEVPGoqSqs56z28SL+Gkg6CSbFhBFbRmDZhWWQJRnf3v0thscOL/Bn2M7ZYN5uBhRA8pVgbGeExrdoZqlzzB5aRet2mnSqWFgvkSe6eP4i+i7piz3Je2CUjZjXeh76R/V37FduKDBvNkOkC0AGDC0M0Mbk/IitpqqACvuMoaFF0zuDqKDyXHPv2rUL0dHRWLhwYa7HLVy4ENHR0di/f3+hE+dJXn31VWzcuBFnzpzB/v378eqrr2L9+vUYPrzgD4BEVHYJIaBcVWA5boGaqkITZA8EU62pGLBhAJZdWAaDxoCF9ywscCAohIBlv8U+Y6gCyBEyvLp6FVkgCGQtLu+nKfSbbSp7WC+RpxOqwL6D+9DmxzbYk7wHIYYQ/Nn5T6dA0Hraal8/MP3m+oE5BYJC2GcMBQBddR3kMJnlJpW6PLcMfv7556hduzaee+65XI977rnnMGvWLHz66af49ttvC51AT5GQkICRI0ciPj4e/v7+aNSoEVavXo1u3bqVdtKIqIgJq4At3gYlXgF0gBxk71qZaE5Evw39sOP6DvhofbCk/RJ0DO9Y4M8wbzNDuWifyEtbRwt9I32BF5J3+xlm+5BzbVWtyzhBKv9YL5EnE6rA/3b8D/f/dT+SbEmo6VsTyzosQw3fGvb9ioBljwW2kzYAgFxZhqGVIceyUCgCIilrbHWsDhp/9qSgsiHPweC6deswatSoO77BkCQJgwcPLtCEM55s5syZpZ0EIioBSpIC20WbvVuorwaSwV6mXsq4hN7re+NQ8iEE6YPwe8ff0SK4RYE+Q01XYd5khppsn4xG30IPXWzRjodwdA+tquVDTQXFeok8lVAEZv0/e/cdZUV9/3/8OeWWBZay9F4VRAUFpIOo2JKvsWui2Hv9RVQsiaImtqiJMbEXQKOgSSzR2AsqihWxURSR3tvusrv33rkzn98fs6xu2IWtbLmvxzl7dPfOnf3cOcuded35fN7vdx7jgg8vwDMeI9qM4F9j/0WbWBsAgsKA5AdJgk3Fd/n2ihDpHyn3GtmkDEFe2EPQ7epiN9V7ptQfFQ6Dq1evpkePHhXatlu3biXrC0VE5Gd3A9f4GGPCaaHFd+l+yP+BX7zzC5YULKFTVidePuBl9mixR5V+j7/eJ/FBApJgxS1io2I4bWqmUMzPaXqoiDRGftrnquev4q5v7wLg2K7H8ujwR8lys8LH1/okZofvsUQhNjyG23EH6wMLAkiC29ENZ1GU0XBepC5VOAw2bdqUTZs2VWjbzZs3q5+QiAjFa0RyA9Ir0gR5AXYzGzv+06fC3275ll+88wvWJNbQq1kvXj7gZXo261ml3+X94JGak4IA7JY2sTEx7CY1/wm0SfxseqgubESkkcgvyufkp07mxRUvAnDtntdy3d7XYVs2xhi8hR7eVx6Y4vfYUTHsZmW/x25bH2i5Fm5PV+sDpd6q8FXCgAEDePHFFyu07UsvvcSAAQOqPCgRkcbAeIb08jTedx6msPhuYPyni4GPN3zMQW8dxJrEGvZqsRdvj3+7SkHQBIbknCSpz8Ig6HR1iB8Ur50gGBiCrQFORwenZc3fcRQRqQtLNy5l9MOjeXHFi8TsGNNGTGPygMlhEPQMyQ+TeF+GQdDt4YbvseUFwbTBbDTYTWyifaK47TWDQuqvCl8pnHrqqbz77rv87W9/2+F2f//733n33XfVl1BEMpYxBn+zT2phivSKNFaWhd3SLlW85a01b3H4O4ezObWZ4W2G8+b4N+mYVfmWPEFhQOLdBOnvwyIGkb0ixEbEsNzaufDY1gLD7VCpzkQiIvXWR0s/Ytgjw/hq81e0i7Xj9YNe59c9fg1AkBdQ9EYR/go/XIM9OEp0aLTc91iTCO8I2m1tortFtaZa6r1KNZ1/5pln+O1vf8vLL7/MhAkT2HvvvcnOziY/P5+vv/6af/zjH7z++uscfPDBnH766bU4bBGR+smkDOk14dpAoNTawG2eX/48p3x4CqkgxfgO43lmzDM0dZtW+nell6ZJfp4ED3AhNiyG26X2QppJGCzLItIloumhItIoTP9yOmf85wySQZK9WuzFs/s/S/em3QFILy/u0ZoGK6t4DXbrsmdEGGMwWw2kwe3mhuupy2k4L1KfVPiqwbZtnnvuOa644goeeughXn/99VKPG2NwHIfzzjuPu+66S7fDRSSjbFsfkl6RDvsG/qxS6M+3uWfhPVz1xVUYDMd0PYapI6YSc2KV+10pQ/LzJP6ynwJnbHisRvsHbvc7i6eHul1VPVREGj5jDJPfnswfZv0BgF92/CXTRk0jO5INgPedR+qLFAB2O5v4iHipaf6l9hWEbSOIQaR3BLu1GslLw1Gpj5Dj8Th///vfueaaa3jllVeYP38+eXl5NG/enH79+nH44YfTpUuX2hqriEi9ZFI/VQrFKvtuYDpIc/mcy3ng+wcAOKfPOfx18F9x7Mqtu/PX+iQ/TmKKDFgQ6V9c0rwG+weWJcgNsFvaO6yaJyLSEBR5RZz+3Ok8M/8ZACb2ncgf9/ljyftxan4qLBQDuLu5RPcpv0er8cIiYXYrm0jXSLnrCEXqqyqd1Tt37szZZ59d02MREWlQjDEEm4Owb2A5dwMBtnpbOeXDU3h51ctYWNy6z638tt9vK/XJsfENqa9SpL8L1wZazSxiw8ufslSTgqIAy7aIdI7U2lpEEZFdYXX+ao6cfiSfrv6UiBXhb4P/xhm7nQGE7+neNx7evDAIRvaMENmz/P6BQWEAReB2KG4bUU7DeZH6rEY+vti8eTMHHnggX3zxRU3sTkSk3jMpQ3ppGu97D5MorhRaRhBcVbiKg946iJdXvUzciTN99HQu2+OySgXBYEtYwGBbEHR7u2QdmrVLgqAJDKbA4HR0ND1URBq0L1Z/wX4P78enqz8lJ5LDf/f/b6kgmJqb+ikIDogQ3Sta5nv1tmUBeOD0cHB7KAhKw1Uj831SqRQzZ85k8+bNNbE7EZF6q+Ru4Io0wdby7wYCfLPlG45890hWFK6gbawt/x77b4a1GVap35VemCb1ddgyghjE9ovhdt51UzWDLeH0J1UPFZGG7Ln5zzHhuQkUeoX0bdqXZ/d/lj4t+gDFQfDzFOkfwg/cooOiRHaLlLkf44frA62mFm43Vy12pMHT2V1EpIJMypBekcZfF5YYL2tt4DZvrH6D38z6DfnpfPo278vz+z9Pr2a9Kvy7gsKA5MdJgnUBAE4nh9h+sXILGNSGoCjAcjQ9VEQaLmMMt39wO9e8dQ0A41uP5x9j/kGrrFbh44Eh9WmK9JLiILhflEivcoJg0hDkBzitHdxuLnaWZktIw6cwKCJSQd73Hnahjd3c3uGUoEcXPcoln12Cb3z2b7c/T495mlbRVhX+PaVaRjgQ3TeK22vXNi02fjg91O3uYjfXBY+INDzJdJJzXzqXx798HIDzu57PncPvJOKGYc/4huTHSfzlYfGv2LAYbveyL42DreG0ULeLi9vJ1Qdk0mjUSBjMysritNNOo1OnTjWxOxGResXfErZwMEmzw5LhgQm47svruHP+nQCc3ONkHhj6AFEnWqHfUxctI8ocR/F6GKeVg9tenxmKSMOzvmA9Rz99NB8s/wDHcrir712cP/D8ktkcxjckP0zirwpnesRGlN2n1QQGk2sgApFeEew2ahshjUuNnOWbN2/OlClTamJXIiL1SlAQhJ8aA1Zzq9yLgISf4KyPzuJfy/4FwHV7Xcfv9vpdhS8a6qplxP/aNg3KbmaH1fH06beINDDfrPuGI6YfwZItS2gRacGTA55kfJ/xPwXBtCExK0GwNgAHYqNiZbbNMenwgzG7hU2kW2SXfzAnsivUSBh89913efLJJ1m5ciUdOnTgxBNP5JBDDqmJXYuI1BmTMnhLwmqhO7I+sZ7j3z+e2RtmE7EjPDD0ASb0nFCx31GHLSNKjSMIe2VZloXbycXt6JZbGEdEpL56+fuX+fW/fk1+Kp9eTXvx7D7P0q9Lv5+CoGdIvJcg2BCAC/ExcZx227/fbusf6LRziHSLqFqoNFrV/ojjwQcf5Je//CWpVIqBAweyadMmfvnLX3LXXXfVxPhEROqE8Q3eUi8MSC3Kvwj4Lu87xr4xltkbZtMy0pL/jvtvhYPgdi0jerlkHbJrWkaUGkdhQLAprIwa2T2C211BUEQaFmMMd390N0dMP4L8VD5j247lvf3eKx0Ek4bEO8VBMALx/csJgqkwCLodXCI9FASlcavwncH8/Hyys7O3+/ndd9/N9OnTOeKII0p+du211/KXv/yFyy+/vGZGKSKyCxljSK9M46/3sVvaBFZQ5naz1s3i+PePZ1NqEz2a9uD5/Z9njxZ7VGz/ddwyAsIpUCbXQBTcHi5uO00LFZGGJ5lOcuF/L+SxuY8BcEaPM7i7z93EWsd+CoIJQ2JmgiA3gGhxEMwpIwgmDWarwe1c3Eje0XuiNG4VvjPYp08fHn300Qpta1nlr6sREanvgnUB/io/7CFYTjiasWQGh79zOJtSmxjaeijvH/J+hYKgv8kn8XaC1JdhEHQ6OTQ5rMkuDYLGhOsCTa7BbmMT7Rcl0kntI0Sk4Vmdv5px08bx2NzHsC2bP+39J+7d/V5ibX4KgkFhQNHbReFMj7hF1oFZZQbBoCjAFBicLg5uVwVByQwVvvq4//77ufLKK7nvvvu45557GDVqFACXXnopJ510EscffzydOnViwYIFPP/889xyyy21NmgRkdrib/HxlnsQo8ypksYYbvv2Nm74+gYAju56NI8Nf4wmbpMd7jcoCvC+8kp6WdVZy4htBWKa2Lh93LA66i4uUiMiUhM+XfkpRz19FKvyV9Ey1pIn9nmCg1oehN3qp4qfwdaAxMwEpsBgNbGIj4uXWQgmKAwgCU5XJ2wdoZsakiEqfGfwmGOOYd68eRx99NEcdthh/PrXv2bFihVccMEFPPfcc9i2zZw5c8jOzuaFF15g0qRJtTluEZEaFxQGYVgzYDfd/u0x5ac47+PzSoLgZf0u46lRT+0wCJq0ITUvRdHLRSVB0OnukPWLLCK9I7vsgsMEYVU8U2hwO7pE94jitHUUBEWkQXriyycYM2UMq/JX0a9FP2YNncX4duNxcpyfgmB+QOLt4iDYzCJ+YDlBsKA4CHZTEJTMU6l5SbFYjN///vecccYZTJo0iX79+nHFFVdw1VVXMX78+Noao4hIrTOpsGCMKTJYOdtfCGxNb+Xo94/m3XXvYls2dw++m/N2O6/8/RmDv9wn9WUKUxhWI7Vb20T3je76AjHFU5/sFjZuJxe7pfpkiUjDlA7SXP3m1dw1OyxU+H+d/49Hdn+Elq1alprNEWwJSLybwCQMVvPiO4JZZQTB/AACiPSMYLfVe6NknipVE+3cuTNPPvkkr7/+Ov/973/p27cv06dPr+mxiYjsEsY3eMs9gs0BVqvt1zwvK1jGNYuu4d1179LMbcZzY5/bYRD0N4brApOzk5hCg5UVtouIHxTfpUHQpA3BpgDS4HZ3ifaN4rRydLEjIg3S5qLN/PKpX5YEwav7Xs3TezxNyzalg6C/yafonSJMwmC3tMk6IKvsIJgXFgeL9IrgtNN7o2SmSt0ZXLZsGa+99hqFhYUMGzaMkSNH8sknn/DYY48xceJE/v73v3PPPfcwePDg2hqviEiNMsaQXpXGXxdWDv3faZOz1s3ixFknsiG5gY5ZHXlh/xcY2GpgmfsKCgO8r0uvC4zsESHSd9cWZzHGYAoMJMHOsXE7u9jN1CxZRBqueevnceSMI1m0aRFN3CY8NOAhjml3zHYzHfz1Pon3E+CF73/x/ePbtYYwpriSshveEXRa7drZGiL1SYWvDl566SX69evHbbfdxuOPP87o0aO54oorsCyLs846i++++44RI0YwevRozjzzTNatW1eb4xYRqRHB+uLKoU23rxz68KKHOfTtQ9mQ3EDPrJ7MPGhmmUHQpA2pb0uvC3R7uGT9IovontFdGwRTBrPRYNkWkd4RIrtFFARFpEF7ceGLDH9kOIs2LaJ70+68s987HNvl2O1mOvhrfRLvFgfBtjbxcWUHwWBzgBW1iPaOKghKxqvwFcJVV13Fcccdxw8//MDnn3/OtGnT+Mtf/sLKlSsByM7O5s477+TLL79k7dq17LbbbrU2aBGRmhDkBmHl0ChY8Z8uGFJ+ios/vZiLP72YtElzbNdjuW232+jcpHOp5xtjSC9LU/RKEd43HvjhusD4+DixYTHsJrsuhJUUiCkwOB0dIv2Kpz2pQIyINFDGGG55/xaOnHHkT43kh7zHwI4Dtyvy5f3okXgvAT44HRziY+NYkTLuCG422E1sIr0j2C30QZlIhaeJrlixgksvvbTk+1GjRmGMYdWqVXTu/NMF0u67785///tfXnnllZodqYhIDQoKA7ylxQHuZ9Xl1iXW8ZtZv2HW+llYWPxh4B/4f7v/P976/q1Sz/c3+qS+SBFsDNecWE0sogOjOF13/bqTkgIxzYunhKpAjIg0cAWpAs544Qz+Oe+fAJzf63z+1OtPRHOipfr/Gd+Qmpsivai4WnNnh9iI2HY9Ak0QBkGrmUWkV6TMitEimajCYXD06NHcc8897LXXXrRs2ZKbb76ZnJwc9txzzzK3P/zww2tskCIiNcl44R09U1C6cujcTXM57v3jWF64nOaR5kwbMY1fdP4Fnu+VbBMUFvcLXFq36wIhnJ5q8gxEwgIxbjt3u0/CRUQamqVblnLU00cxd81cInaEu/e8mzO7nInVonSBr6AoIPlhkmBDcSGYvSJE+m/fsscEBrPJYLUoDoJlFJMRyVQVDoMPP/wwp556KmPHjsUYQ+/evfnnP/9JkyY7brQsIlKfmMDgLfPwN/mlGhM/s/QZzv34XIr8InbL3o1/jf0X/Zr3K3meHdj483xSC1Pghz9ze7hEBuz6CwtjDGargVQ4LdXtpAIxItI4vLvkXY7753FsKNxAu3g7nhrwFKM6jtpu2r2/wSf5QRKTCD8Qiw2P4Xba/rLW+OEaQSfHIdIjUmpJgIhUIgx26tSJN998k0QiQSKRoGXLlrU4LBGRmretcmiwLsBuYWM5Fn7gM/mrydwx/w4ADu14KI+PfJyW0ZYlzwtWBwxZOATfC1Og3aa4X2DOri88YJKGID/AbmLj9nGxW29fAVVEpCG6/9P7ufTVS0kHafZttS9P7/U03dp1K1UExhhD+oc0qS9SEBD2EBxddjN5kw7XUjutHSI9I9sVkxGRSraWAIjH48Tj8doYi4hIrQo2BPgrfaymFlbEIjeVy6mzT+XVVa8CcMUeV3DTgJtw7J9Cnr/eJ/1hmriJQxOIDYzVybpA4xuCvADLsnA7ubgd3VJ9tUREGqqUn+KS1y7hwc8fBODELidyX9/7aNq66fbrAz9Pkf6xeH1gF4fY0FiZ0+ONZwhyA5y2xXcENYVepEyVDoP/65NPPuHNN99k06ZNtG3blkMPPZR99tmnBoYmIlJzgrwAb5kHkbBy6MK8hRz33nF8l/8dcSfOQ0Mf4sQeJ5Z+TkFA4oMEGFjfYj2dDuyEG63222alGGMwhQZTZLBb2UQ6RbCaWyoQIyKNwhZvC4c+eSgfrPggLNrV9w9M7DUxnL3x8/WBhQHJD5IEmwKwILJ3hEi/7dcHQthiJ8gLcDu4uN3cXb6eW6QhqfBVzS9+8QsmTZrEuHHjAEin05xyyik888wzGGNKtrv22ms577zzuO+++2p8sCIiVREUhZVDTdrgtHJ4ZdUrnPrhqeR5eXRp0oV/jvkng3IGlXqOSRuSs5KQBKulxcIuC+nsdC7nN9QO4xUXiImB29PFbauLGhFpPL5Y8wVXfHcFG7wNtIi0YOpeUzms22Hbrw9c55P4MAFJIAqxETHcDmVfwppkuKba7eTidnW3qyoqIqVVuOLAq6++yqpVq0q+v+mmm3j66ac5++yzWbBgAQUFBXz11Vccf/zxPPjggzzyyCO1MmARkcowniG9NI3ZGlaSu3PenRz97tHkeXmMajuKDw/5cPsgaAzJT5IEW4IwiI10Cexg1405CD/VNlsNdjubaL8okY67vlqpiEhtefKrJxn3+Dg2eBvYrdluvLvfuxze+/BSQdAYg/edR2JmGATtljZZB2eVGwSDovB90+nshHcEFQRFdqrK850efvhhjjzySB588MGSn+21117MmDGDFStW8NBDD3H22WfXyCBFRKrCBIb0ijT+Jp9EdoLzZ5/PM8ueAeCs3mdx9+C7iTrR7Z7nzffwl/tgQ3xUnKDJLgyCCUOwNcDOLq4SmqOegSLSeKSDNFe+fiV3f3w3AIObD+aFQS/Qpn2bUsWwTNqQ+ixV0sbH6eYQ2y9W5odixhhMvoEAnO5OuKZa75siFVKlMFhQUMDatWs57rjjynz8mGOOYfLkydUamIhkliA/gIBwvkLxl+VYP/1/JStmGmNIr07jr/FZEVnBCe+cwNzNc3Etl78M/gvn7nZumc9Lr0zjfR32FYwOiuK0dQj82g+DJT0DXXC7urjtXVW+E5FGZX3Bek741wnMXDITgEm9JjEsexgt27Ys9R4fFAQkZxXPzrAgOjCKu3vZAa+kmXyWhdvNrZMqzyINWaXC4LZ/hPF4nGg0StOmTcvcLisriyDYdZ+ki0jD5m/28RZ7sK23+7ZAaIX/tSwLHMABy7XCkOgAbhgYLbs4NDqUBEeTMPgrfWYVzuKkT09ifXI9bWJtmDF6BmPajSlzHMGWgORHSQDcPi6R3pHafunhJ9oFJpwClVN8N7CMEukiIg3Z56s+5+inj2Z53nKauc14eM+HOaLXEbz545vhe30xf41PYnYCUkAM4iPiOO3LDnglFUNbhdNC7aZ67xSprEqFwauvvppbb7215Puvv/6ao446arvtFi1aRIcOHao9OBFp/IKigPSydNgvqpUFhvAO4c/+awIDaSAVhieCn/5rFV9FGEwYGg0lFxaPrHqEy765jLRJM7DlQP459p90b9q9zHGYpCExKwFpwnV6+24/fbSmmVR4N9BqYuH2drHbqGegiDQ+0+ZO47yXziPpJ9mt6W48ve/T9O/cn7RJl2xjjMFb6OF95YEBu5VNbFSs3IAXFAZQRFgxtKur1hEiVVThMDh27NhSt+dHjBjBsmXLttsulUoxffp0xo4dWzMjFJFGy6QN6WVpTKHByilul1B8N/DnLCp3kk96SS7/4nIe/uFhAI7rdhwPDXuIpm7ZsxlMYEh8mMAUGKymFvGR8VoNZSYdNo63sHA6OjgdHey4PtEWkcbF8z0mvjaRv3/6dwB+0fYXPDb0MVo1bxVu4If/MWlD8vNkuFYbcHu4RAdHy18fmBdWsXd6OOGUen2IJlJlFQ6DM2fOrNB2yWSS6dOn07172Z++i4hA8Zq+VWn8jT52q5orkrKqcBUTPpzAB+vDnlU3DbiJK/tfucP9p75IEawLwIX46HitNXM3XlgcxjIWTgsHp4OD3VIFYkSk8Vm7dS3H//N43l/2PgC/7/17rh10LY5bespnPBkn/XY6DHhWuFbb7V3O+kDfYLYUrw/s7uK00vpAkeqq8e7J2dnZ7L///jW9WxFpZIINAf5qHzvbrrHy3++te48JH0xgbWItzSPNmTpiKr/s/MsdPsf7wSO9KJyqFBsew25Z83fojPezO4GtHJx2TthQWZ9mi0gj9PGKjzn2mWNZmb+S5m5zHtv3Mf6v9/9tF/CC1QH7LtoX4xusuEVsZAynbTnrA4sbyTs5xesDm2g2hUhNqFIYLCws5JtvvmHlypUUFRXRrFkzdt99d/r161fT4xORRijID/CWexChRu7CGWP484I/c92X1+Ebnz1b7MmM0TPYvfnuO3yev94nNScFQGTvCG7nmv18zCSLi8PY4LR2cNoWh0DdCRSRRurROY9y4X8vJBWk6Nu0L/8c/U/65vQttY3xDamvU6QXpokQwcqxiI+OY2ftZH1gRxe3i9YHitSkSl35fP/991x77bW89NJLpFKp7R7v0qULl112GZdeeim2rU9sRGR7JmXwlnmYlKmREuC5qVzO+fgcXljxAgAn9TiJv+/393LXB24TFAQkPkiEfam6OkT2qLnKoSZRHAJdsNvauG1drGxLIVBEGq2Un+LSly/lwTlh/+lftf8Vj4x6hBaxFqW221a1OcgNq86var2K7mO7Y0e3v240xmByiz9Q6+HgdlD/QJGaVuEw+OWXXzJu3Dh832f8+PE0adKEjz/+mDVr1jBp0iR83+eNN95g4sSJvPnmmzz//PO4bo3PQhWRBswEBm+5R5AbYOdU/wOjb7Z8w4mzTmRR/iKidpS7Bt3FOX3O2enFgkkbkrOSYTuHljaxobFqX2AYYzAJAwVAFJwO4Z1Aq6lCoIg0bqvyV3H8jOP5cNWHWFjc0P8GJg2YhG399D5vjCH9XZrUV6mwUnQM3MEui7YuoofTY7t9lqwPbFrcP7Cl1geK1IYKp7VJkyaRk5PDBx98UNI2wvM8TjnlFF577TU+/vhjbr75Zp5++mkmTJjAX/7yF6688spaG7iINDzpNWmCdUGNrJd76senuPDTCynyi+japCvTR09nv9b77fR5xhiSHxc3M45BbHSszIp1FWWMwRSZsCJqzMLp4uC0dtTvSkQywgdLP+C4Z45jTeEaWrgtmDZiGod3ObzUNkFhQPKTJMHa8G6g09EhNjRGOpKG77bfZ6n1gd3dcqePikj1Vfhf1+zZs7nwwgtL9Q+MRCL8/ve/57PPPuPbb78F4MQTT+T0009n6tSpNT5YEWm4/M0+/kofmlCt9R5JP8kln17CGR+dQZFfxPgO4/nosI8qFAQBvHke/gofbIiPilc5tBljCAoCgo0BGHC7uUT7R4l0iygIikijZ4zh/o/u54DHD2BN4Rr2zN6TDw/7cLsgmF6Wpui1ojAIOhAdHCU2JoYVL/s8EBQEmK0Gt5NLpE9EQVCkllX4zqBlWTjO9rfoHcfBGENubm7Jz0aMGMGTTz5ZMyMUkQavpLE8VOvEvqxgGSd9cBKfbvwUgN/t9Tt+t+fvcOyKTR9Kr0jjfeMB4QVJeVXrdsQE4V1AkzDYWXa4jiXHLffCRkSksSnyirj4+Yt5bN5jABzb5VgeGv4QzSLNSrYxKUNqTor00uL3/lZ2WLG5ednngJ+vD3R7ujjtHE2xF9kFKhwGR4wYwQMPPMDpp59Oy5YtgfAf7p/+9Cei0Sh77rlnybYbN24kOzu7xgcrIg3P/zaWr6o3Vr/BaR+exsbURlpFWzFlxBQO73T4zp9YLNgSkPw4CYC7m0ukV+ULxpithsALsJvaOD0d3NYuVlQXKyKSOZZvXM6xM47l0w2fYmPzh4F/4PI9Li8V3Pz1PsmPkpjCsHdgZI8IkT0j5S8P8ClZHxjpHsFuobuBIrtKhcPgzTffzJgxY+jTpw/jx48nKyuLjz76iO+++45rr72WFi1+qhb1+uuvM2jQoFoZsIg0HDXRWD4wAbd9exs3fX0TBsOgnEFMHzWdHs16VHwcSUNiVgLSYLe3ie4Trdwgwg+2saIW0W5R7Bxbpc1FJOO8O/9dTnjhBNYl19Eq0oonRj3BwR0PLnnc+AbvWw9vfjgDw2pqERsew2mz41kYwZaASNtI2D9Q00JFdqkKh8HBgwcza9YsrrvuOl555RWSySR9+/blvvvu47zzziu17fXXX0/nzp1rfLAi0rBUt7H8puQmTp99Oq+tfg2As3qfxZ8H/5m4E6/wPkxgSHyYwBSEnzrHR8QrVbzGBKakBHpk9whOliraiUhmCYKAe965hys/uJK0SbN3i715Zuwz9GrW66dt8gKSs4uLcxFO9YzuGy33gzNjilvwAE4nh0i3SLWKeYlI1VSq98OgQYP473//u9PtxowZU+UBiUjjUN3G8nM2zeHXs37N0oKlxJ04fxvyN07tdWql95P6IkWwLgAX4mPilRqLMWFpc7uFDRvRhYqIZJyCRAFn//NsZiyeAcCJ3U/k/qH3l/RyNcaQXpQm9WUKfCAKsf1iuF3Kv8Q06eL1gcWTNNwurt5fReqIGgGKSI3b1lgeLywaUKnnGsOUxVP47We/JRkk6dmsJ0+PfpqBrQZWehzeIo/0onCOZ2x4rNLrUEx+2C7C7erC4kr/ehGRBm3RmkUc8/QxfL3laxzL4bZ9buOSvpeUTPkPigJSn6Tw1/hA2F81OjS6w6meQWEARWC3tol0iMAKVChGpA4pDIpIjapOY/midBH/77P/x7QfpwHwy86/5LHhj9Ey2rLS4/DX+aTmpACI7B3B7Vy5t7ugKIAgnOoUNA0q/ftFRBqyl758iVP+ewpbvC20i7XjydFPMrbd2JLH0yvSJD9NQoqwZcSAKO5ubrnBzgSGYEuAFbHCKsztXNJBehe9GhEpj8KgiNSoqjaW/yH/B34z6zd8ueVLbMvmpgE3cfkel2Nb2wdKYwwYICD878++TGDCgjEfJsCA09UhskflKocaz0AhON3DBvKBpzAoIpnBD3xueuUm/vDZHzAYhrUexvTR0+ncJKwFYTxD6osU6R+LW0a0tHc688IkDMHWALuVTaRLBDu7eFu9tYrUOYVBEakxVWksb4zhhQ9f4Jxl55BHHm2sNjwae5Sx34+laGHRdmGvMhcPdiub2NBYpaYgbSsY43ZwcTvoLVJEMsem/E2c8swpvLziZQDO2+087tz3TqJOuLjP31DcMqK48EukX4TIXpFyC4SZwGDywm3dri5uR60NFKlvdKUjIjWiKo3lU36Ka965hr+v/zsA+7EfU5hCp1QnDKZqA7HCL7uFTWx0rFIXHsYYzGaD08rB7epW6s6miEhDNnfZXI7957Es3rqYmB3j7/v9vaRolzGG9HfFRWIMWE0sYsNiOO3Kr65sUoYgL8DOtnG7uNgtq9ZeSERql8KgiFRbVRrLL9m6hJPfOZnPtn4GwEVtL+LmATcTdaNgFRcUsACbkoC37cuyre1+VvJYNS42TJ7ByrJwu7vqIygiGeMfH/+Dc984lyK/iG5NuvHMmGfYN2dfIJwWmvw0ib+8uEhMN4fY4BhWdActI7Ya8MDt6OJ2cqtUUVpEdo0KhcH33nuvSjsfO3bszjcSkQatKo3lX1j+Aud+dC5b0ltoQQse6PQAx+x/zC4YbfmCwuLeWN1d7CZqeiwijV8qneLK/1zJPV/fA8D4DuN5fOTjtI61BsIWQYlZiXCqpwXRfaO4fXZQJKa4ZcS2D9Xs1robKFLfVSgMjhs3rtQ/ZmNMhf5x+75f9ZGJSINQmcbyST/J1XOv5r7v7gNgCEOY0m4Ku43ZbVcMtVwmZSBRXDCmlZrKi0jjt3rLak6YcQKz1s4C4Kr+VzF578k4dvgemF6ZJvlxEjyw4haxkTGctuW/PwYFASTAblM8LbSCywVEpG5VKAy+8847pb5PJpNMmjSJwsJCzj33XPr27QvAggULePjhh2natCl/+tOfan60IlKvVKax/A/5PzDhwwnM2TQHgIu5mOtbXE/zMc3rdG2e8cN1LW5HFYwRkcwwa9EsTnz2RFYVrSLbzebREY9yZJcjgeL2QN96ePM8IAx3sZGxcsOd8YsbyEfCVjxOO0frrUUakApd+ey///6lvp84cSLRaJSPPvqIeDxe8vMjjjiCiy66iP33359XX32Vgw8+uGZHKyL1RmUay/972b85/5PzyfPyaGW34n5zP4fEDyFrbFa56052BWMMZovBySkuGKPpTCLSiBljuPe9e5n47kQ847FH8z14eszT9G0efqhvkobkx0n81eHMLnc3l+jAaPnVQotbRjg5Tng3sJnuBoo0NFX6V/vkk09yyimnlAqC2zRp0oRTTjmFf/zjH9UenIjUTyb5U2N5q0X5ASrhJ7jk00s46YOTyPPyGN5kOO+adznEOYT4qHidXziYXIPVtLhgjMqdi0gjVpgq5NQZp3LJzEvwjMcxXY/h/UPeLwmC/mafojeKwiDoQGxYjNigWJlBcFsDeZMwuN1cIn0idf5+LiJVU6V/uQUFBaxevbrcx1evXk1hYWGVB5WJbr31Vvbbbz+ys7Np164dRx11FAsXLqzrYYmUYnyDv84ntSCFv9bfYWP57/O/Z+zrY3lo0UMAXNH5Cv5T9B+6WF2IDonucO3JrhAUBOBApHtEa1tEyqDzUuOxeMNiRj4wkn989w9sbG7d51aeGvUU2ZFsANJL0iTeSmAKwg/I4gfFcXuUPXnMpAzBpgCriUVk9wiRLhF9mCbSgFXpCmj8+PH89a9/5dlnn93usX//+9/89a9/Zfz48dUeXCZ59913ueiii/joo49444038DyPQw45hIKCgroemgjGhI3Yve89vB88TNqEVeLKab/w9JKnGf7qcL7c8iVtYm14Yd8XuHbttbiWGzYp7hnZxa+gNJM0kIRI1wh2CwVBkbLovNQ4vPLtKwx5eAhfbv6SNtE2vHzAy0zcYyKWZWECQ3JOMiwU44PTwSHr4KxyC2kF+QGmwOB2dInuHsVpqYJbIg1dlaol3HvvvRx44IEcf/zxdOzYkT59+gDwww8/sGrVKnr37s3f/va3Gh1oY/fqq6+W+n7q1Km0a9eOzz//XC06pE4FibBaqL/exwQmbBxczvqRonQRE+dM5LEfHgNgbLuxTNlnCq3ebwUBOJ0dIgPqOAimDUF+EK5vaasgKFIenZcatsAE3PzGzUyePRmDYUjOEGaMnkHXpl3Dx4sCkh8mCTaEbXUi/SNE9oyUOdvDBOH6amIQ6R1RywiRRqRKYbBz5858+eWXPPjgg7zyyissXboUgD333JMrr7ySc845h6ysrBodaKbJzc0FICcnp8zHk8kkyWSy5Pu8vDwAPM/D87zaH+Autu01NcbXtitU5fiZtCG9MY1ZazBFBivbgigEBFBG15iFeQs5ZfYpzMudh4XFpP6TuHq3q+G98E6c1cLC3s8mHaRr6mVVXgBms8FqY2HaGdLpio1Ff3/V09iPX2N9Xf9rZ+cl0LmpvthStIUznz2Tl5a+BMDpPU/nzkF3EnfieL5HsCEg/VEaEoAL7lAXq5NF2qS3f39PQ5AbYLewcbu6BE0DgnRQI+Osr8evodDxq57Gfvwq+rosY4yp5bFIJQVBwK9+9Su2bNnCrFmzytzmhhtu4MYbb9zu50899RRNmjSp7SGKlPLOpnd4YMUDJIMkLd2WXNb9MgY2G8ieS/akdX5rkm6SuX3mkowmd74zkQamsLCQk046idzcXJo3b17Xw6kVFTkvgc5N9cHSoqXcvuR2ViVX4Vou53U5j4NbF1d3N9BxY0d6r+qNjU1BrIB5PeZRFCuq20GLSI2r6LlJYbAeuuCCC3jllVeYNWsWXbp0KXObsj597dq1Kxs2bGiUFyOe5/HGG29w8MEHE4nU7TTDhqiixy8oCkivSWM2GLDBambBDpaEFKQLuHzO5fxjSVg9eP92+/PYsMdon9We9Fdpgu8CsMEd52Ln1HHl0ILwrS7Su/JV7/T3Vz2N/fjl5eXRpk2bRh0GK3JeAp2b6tozXz/DuS+fS6FfSJesLjw56kkG5wwGiguAzfEJloZ39ewuNs4Qp+ziLwZMfvieaXeycdu5tdI7sL4dv4ZGx696Gvvxq+i5qcodll977TUeffRRFi9ezObNm/nfTGlZFj/88ENVd5+xLr74Yl566SXee++9HZ5wY7EYsVhsu59HIpFG+Qe9TWN/fbWtvONnPEN6XRqzxuCkHKzm1k77/83LncdJs05ift58bMvmur2u46r+V+HYDt5iLwyChOXJ3bZ128zdJAzGN0R6RcotjFAR+vurnsZ6/Brja/q5ip6XQOemuuL5HpNencTdn90NwAHtDuCJUU/QNt4WgGBrQPKDJMGWACyIDoji9i27t+q2JvJWk7Dtzq4oElPXx6+h0/GrnsZ6/Cr6mqp0hXbHHXdw9dVX0759e4YOHcree+9dld3IzxhjuOSSS3juueeYOXMmPXv2rOshSQYwgSHYHJBelSbID0uF2613fNfMGMPUxVO57PPLKPKL6JjVkWkjprF/+/0BwtYTn6UAiOwZwe1Wx0EwbQgKAtyuLnYbFYwRqSidlxqGtVvXcsKME3hv5XsAXNHvCm4ceCOuHb73plenSX6UhBQQg/iIOE77sgOeSYYFtpzWDm43V213RDJAla7S/vrXv3LggQfy8ssvN8okXRcuuuginnrqKV544QWys7NZs2YNAC1atFAxHqkVQX44JTTYGIALdk75PQO32ZLawkWfXsS/lv0LgPEdxjNlxBTaxduF+9wakPggAQacrg6RPeu4cmhxY2SnnYPbsexPwUWkbDov1X8fLvuQ4585nlUFq2jmNOOR4Y9wdLejgTDMe/M9vK/DIhJ2jk1sVAy7SdkBL9gagAduFxe3k6vegSIZokphcPPmzRx33HEKgjXo/vvvB2DcuHGlfj5lyhROP/30XT8gabRMMpwS6q/1IU04JbQCJ/3Z62dz2uzTWFqwFNdyuWHADVy+x+XYVnhhYVKGxHsJSBVfdAyN1Wn4MiYshW63sIl0i5TbDkNEyqbzUv1ljOG+T+/jstcuwws8+jbryzP7P0O/5v3Cx9OG5CdJ/OVhaVC3l0t0ULTM98FtH5pZUYtIrwh2G7WNEMkkVQqDQ4cOZeHChTU9loymOj6yK/jrfYL1AUFBgN3Mxmq+8xO+H/j8ad6f+MM3f8A3Pj2a9uCJkU8wtM3Qkm1MYEjOTmLyDVaWRWx0rE4+VTbGQEDYQqKwuCdW98hO1z+KyPZ0Xqqfirwizn/xfB7/+nEAju50NA+PfJjsSDYAQWFA8v3i9YE2RAdFifQu+8N745mSthGRbhHsbE0LFck0VQqD9913H4cffjhDhgzhpJNOqukxiUgNCwrCYi7pH9O4cbfCDYNXFK7gjNln8N66cC3Kb7r/hnv2u4fmkdJVqVJfpPDX+OBAbEysRteZmMCEfa+C4v8vDnv44feWsTAYLIpfjw04YLlWuOalkpVDRUTqqx83/8gxM45h7rq52NjcvPfNXLbnZSXv5/4Gn+QHSUwi/DAsPiqO07bs9YFBUQCF4LR3iHTVh2YimapKYfDEE08knU5zyimncMEFF9ClSxccp/SbjWVZfPnllzUySBGpGuOHU0K9FeGaEauFhR2tWDh6YcULnP/x+WxKbaKZ24x7htzDyT1P3m4773uP9KKweXtseKxK1TqNMZitBuOF4Q74KeBZhO0tbMI1jS7hGseoDRGwIlY49ak4AOJQ8v9WRBc3ItI4vLroVU7690lsTmymbaQtT4x6ggM6HlDyuPejFxbvCsBuYYcfzDXd/v3eGIPJM+Ha7u4ObofaaRshIg1DlcJgTk4OrVu3Zrfddqvp8YhIDQkKA9IrigvEbKv0XoGcVpQuYtIXk3ho0UMADMoZxOMjH2e37O3/vftrfFJfFFcOHRDB7VL5txRjwoqmdpaN0zbsebUt3OGyfdCz0XoWEckYgQm4+b2bmTxzMgbDkBZDmD52Ot2adQPCGRKpr1KkF4YfyjmdHWLDYmV+GGb8cC21lVXcNqIarXZEpHGoUhicOXNmDQ9DRGqKCQzBhgBvpQeJ8G5gRcPTt1u+ZcKHE5iXOw+Aif0mcuOAG4k60e22DfICEh+GlUPdHi6RflUrKGXyDHbMDgsXaL2KiEiJLYktnPrcqbz43YsAnN3tbO4adhdxNw6EhbuSHyXxV4eFYiL9I0T2ipTdPzBlCPICnJzithHlVBUVkcxStw3ARKRGBUUB6ZVpgvXh3UArpzgI+jt+njGGBxc9yFVfXEXCT9A+3p5Hhz/KwR0PLnv7pCHxfgI8sNvYRIdEq3S3LsgPCxy4PV0FQRGRn/l67dcc8/QxLNq8iJgd454B93D6HqeXPB7kByRmJcIpnw7EhsbK7esabA0gBW5nF7ez2kaIyE+qFQY9z2PBggXk5uYSBMF2j48dO7Y6uxeRCjLGEGwMp4WaQhPeDazgermNyY2c9/F5vLgy/OT50I6H8sjwR0p6B/7v7/HX+qTmpjBbDVZTi/ioeJXaNgSFAfgQ6RXBaampSiIi20z/ejpnv3g2hV4h3eLdmDFiBoM7DC553F/rhzMzUpRUcHZytn8fNUFYLdRyi9tGtFXbCBEprUphMAgCrrnmGu677z4KCwvL3c73d3I7QkSqzSQN6ZVp/HV+WFCldcWnhb679l3OmH0GK4tWErEj3DLwFi7ue3FJ78Cf89f7pL5OhXcdASIQHx3Hilf+wsIkDCTD6aVOGwVBEREAz/e48o0r+evHfwXgoJyDmDZ6Gm2btgXCD+TSi9LhWm1T3NN1dNkVnE26uNdq8+K2Ec01+0JEtlelMHjLLbdwxx13cN555zF69GhOOeUUbr/9dlq2bMl9992HZVn86U9/qumxisjPbCu8kl6RJtgaYDe3K1waPB2k+eM3f+S2b2/DYNg9e3eeGPkE++Tss922/kYf7xsvbB0B4bTOPi7RPaJVC4JJgykwON0cnHYKgiIiAGu2ruGEf57A+8veB2BSz0lMHjIZ1w0v1YxvSH2RIv1DWCjG7e4S3a+cRvJJQ5Af4LR1iHSLYMV0N1BEylalMDh16lROOOEE7r//fjZu3AjA4MGDOfDAAznttNMYMWIEb7/9NuPHj6/RwYpIyKQM6dXpkt5+Fe0bCLBk6xJOm30aH234CIAzep3BXYPvoqnbtNR2wZaA1Dcp/JXFIdACt5dLpH+kyoUHjGcItgbhupVOrqYriYgAH634iGOfOZZV+avIdrN5ZMAjHLn7kSXvkSZpSHyQKJmZERkYIdK37EIxwdYAPHC7FK8PrMI0fhHJHFW6oluxYgUHHnggALFYWLM+kUgAEI1GmTBhAk888UQNDVFEfs7f4pP6LkV6ZRqriRXeEaxgqPrXsn+x36v78dGGj2geac4/Rv6DB4Y9UCoIBvkBidkJil4rKgmCbneXrF9kERsSq3oQTIdrV9z2xRcoCoIiIjz0+UOMnTKWVfmr6Ne0H7PGzuKovkeVvEcGWwKK3igKg6ALsTExov22L9pljMHf7IOBSM8IblcFQRHZuSrdGWzdujVbt24FoFmzZjRv3pzFixeX2mbz5s3VH52IlDCeIb0mXVJC3M6xK9wouCBdwN+W/Y23Nr0FwPA2w5k2Yho9mvUo2SYoCPC+9UgvSYMJf+Z0cYjuFcVuUb21JsY3BFvCKUtuN12giIgk0gkuefkSHvniEQCOancUD494mOZNmpdsk16ZJvlREtJgNbOIj46X+X5sfIPZbLCzbSLdtT5QRCquSmFw33335dNPPy35/oADDuDuu+9m3333JQgC7rnnHgYOHFhjgxTJdEFegLfCI9gSYDezK7VWb86mOZz64al8n/89FhbX7HkNv9vrd7h2+M8/KArw5nmkF6ehuDaM09EhsnekRhoSmyC8SHFyHCLdIyppLiIZb3nuco575jg+WfUJNjY37HYDV+57JbYThjhjDN58D+9rDwC7nU18ZLzMtX8l6wNbO7jdXey4gqCIVFyVwuC5557L1KlTSSaTxGIxbr75ZsaOHcvYsWMxxtCqVSumT59e02MVyTjG/+luoPFNpe4G+oHPnxf8mRu+uoG0SdM60ponRj3BQR0PCvedNHgLPLzvvZI+hHY7m+je0Rqr8GlMGAStFhaRHpEKF7gREWmsZi6ZyQn/PIH1hevJieQwbfA0Dul5SMnjJm1IfprEX1Y8Tb+PS3TfaJnv/UFBEFZmVv9AEamiKoXBX/3qV/zqV78q+b5///788MMPzJw5E8dxGDlyJDk5OTU2SJFMFGwNK4X6m3ysphZO84oHtOUFyznzozN5b917ABzV5SiObXksY9uNxaQM3nce3kIPwqJ02K2LQ2D7mqvuua3aqd3UJtIzUqXKoyIijYUxhrs/upsr37gS3/gMyB7A06OfplfLXiXbBEUByVlJgk0BWBAdFCXSJ1Lmvkxu2Gw+0jOC3U79A0WkaqrVdP7nWrRowZFHHllTuxPJaEFugLfYwyQNdiu7Umvs/rXsX1z0yUVs8bbQ1G3K3YPv5tfdfs07C9/BXxAWnyEVbmu3tMPpoB2dGr+QMHkGOxYGwaoWnRERaQwKvULO+c85PPXNUwD8ptNvuHfEvTSN/lS8y1/vk5ydxBQZiEJ8ZLzMD+iMH/YPtJpa4frAaq7pFpHMVmNhUERqRlAQ4C3xCFIBTk7F79Tle/lc9vllPPFjWMl3v9b7MXXEVHo37U1yUZKhC4bip8NpR1Zzi+heUZwuNR8CIaxIig1uTxc7WxcqIpK5Fm9ezNEzjuardV/hWA6373k7F+918U9tI7atD/zGAxO+P8fHxLGblVEoJmUI8sJzg9vdLbPZvIhIZSgMitQjJmnwlniYwnB9YEV9vOFjTpt9Gj9u/RHbsrm6/9Vcu9e1OHkOibcTBBsDokShKUT3jOJ2dyu89rCygoIAfIj0juC0VFN5Eclcry56lZP+dRKbk5tpF23HkyOeZGynsSWPm4Qh+XEy7BkLON0dYoNjWJEy1gcWBpAAt6Mbto3Q+kARqQEKgyL1hEkXB8E8g9XKqtAdu3SQ5vZ5t3PzNzfjG5/uTbszZcQURuaMxPvWo2hBUVgh1IVF7Raxx/A9iES2X39SU4KiAFIQ6RHBaa0gKCKZyRjDLe/fwnXvXIfBsF+L/ZgxdgZdmnUp2cZfVzwtNBGu/YsOiuL23L4HqzEGkxf2+3G6O7gd1KdVRGqOwqBIPWB8Q3pZGn+jH64RrMBdux+3/sgZs89g9obZAPy6+6+5Z8g9NNvSjKLXijD5xRcPnRzsfWxWrVhFf7t/7b2GpIEicLo62O00dUlEMlNeMo/Tnz2d5757DoCzup/FX4b9hZgTA8J2O958D+/bn00LHRHHbllO/8AtBquJhdvNrZF2PyIiP6cwKFLHjDGkV6ZJr01jt6hYsZinfnyKSz+7lPx0Ps0jzblnyD38utOvSX2ZIrE4AYAVt4gOCtcFpoN07b4Gz2C2GpzODm4nfWotIplpwYYFHP3U0SzYvICoFeXuQXdz1u5nlTxuEobERwmCtWFTV7eHS3RwtMwpn8YzBLnF6wO7uSrEJSK1olbeWRYuXMiZZ55ZG7sWaXTSa9L4q/ywmXwZ60R+bktqC6d+eCpnfHQG+el8RrYZySeHfsLxzvEUvVIUNo4H3F4uWYdnhetKajmYmXTxBUsHB7eLgqCIZKbn5j3H0IeGsmDzAjrFOvHmQW+WCoL+Wp+i14rCIOhAdGiU2LBYmUEwKAoweQa3g0uktyoyi0jtqfSdwfXr17N48WJatWrF7rvvXuqxTz75hNtuu43//Oc/2LbNY489VmMDFWmM/A0+/nIf4mDFdhyiZq2bxRmzz2BZ4TIcy+G6va7j8h6XE3wRkFyVBMDKtogNieG02zVTiYxvCLYEOG2dMHjWUlEaEZH6yjc+1799Pbd9dBsAo1uP5qkxT9E+qz1QzrTQkfEyW0IYY8Ip/sHP1gfqfVVEalGFw2AymeTss89m+vTpGBOuRdp77715/vnnycrK4rzzzuPFF18kKyuLCy64gIkTJ9baoEUagyA3wFvqgcMOP/X1Ao8/fvNH/jTvTwQmoFezXkwdPpVBmweRei0VNo63IbJHhMgekUr1JKwOExjMZoOT4xDpEVFlOxHJOJuLNnPz4puZkz8HgEv6XMKtg28lYoeFuoKigORHSYJ1xdNCe7pEB5UzLXTb+sCs4vWBlWgtJCJSVRUOg7fccgtPPvkkw4cPZ/To0fz44488++yznHbaaaxbt47Vq1dz/fXXc8kll5CTk1ObYxZp8IKCAO9HD3zKLBqwzff533P6h6fz2abPADit52nc0ecOonOjpDaGnePt1jax/WK7tPGwSRtMrsFqYRHpGdnp9FYRkcbmq9VfcdT0o/gx/0ey7Czu2+8+Tup1Usnj/lqf5Ec/qxY6JEqkR9nVnE3SEOQXrw/s6mI31bRQEdk1KhwGZ8yYwSGHHMKrr75a8rO77rqLK6+8kv79+7NgwQI6dOhQK4MUaUxMwuD96GESYQuJMrcxhmmLpzFxzkQK0gW0jLTk3iH38qv8X+G97RGYAFyIDozi9t516/RM0mAKDFhgt7Jxu7k7nd4qItLY/Pvrf3Paf06jIF1A+2h7ntv/OQa3GQwUTwudVzwtFLBaFE8LbV7OtNCtBtLgdnHDAlyaZSEiu1CFP3paunQpRx55ZKmfHX300QBMmjRJQVCkAoxn8JYW9xJsWXYvwY3Jjfzmg99w3ifnUZAuYP92+/PJ0E/4xbxf4M0L15w4nR2yDs8i0idS+wVijCEoDAg2BJikwW5rE+0XJdJXRQ1EJLMEJuC6V6/juGePoyBdwLi247hj9zsY0GpA+HhRQOLdREkQdHu5ZI3PKjsI+gazyWDZFpHeETWSF5E6UeE7g6lUihYtWpT62bbvu3TpUtZTRORnjG/wlnk77CX4xuo3OOfjc1hdtBrXcrlxzxu5sPBCzGyDwYTtIgZHcbvUflcYExhMoQnvYGZZOF0dnBxH05dEJCPlJnI55elTeHHJiwBcuvul3DTgJmYumgmAv8Yn8VECkoALscEx3B5lv1eXmhaqthEiUocqdUVZ3h0IlZIX2bFtvQT9tX6ZvQSL0kX8/svf8/fv/g5A3+Z9ebTno+z5/Z7hehPA7e0SHRDFitZ+qwhTEE5bsppaOD0d3BxNBxWRzPX9uu85cvqRzN8yn5gd476h9zGh5wQ8P5ytkf4mTbAgLBJjt7CJjYxpWqiINAiVCoNnnXUW55133nY//7//+z8cp3TVK8uyyM3Nrd7oRBoBYwzp1cW9BLO37yX41eavOG32aczLnQfA+X3OZ3JyMrGvY+HdwGyL2H4xnLa1W1nOpAzB1gDLsrCaW7htXeyWti5URCSjvfLtK5z0wkls8bbQKd6JZ8Y+w36t9wPAFBkGLB5AUFBcLbS3S3SfClQL7eFi59j6MF1E6lyFw+Bpp51Wm+MQabSCDUHYSzCrdC/BwATcveBuJn81mVSQon28PQ8OfZADlh+Av9LfJe0ijAmngVIIuOC0dnDaOtjNy57GKiKSKYwx3PHWHVzzwTUEBAxvPZwZY2bQMasjEFYL9WZ7tEy2DKeF7hfD7VbOtNBE+GGb07q4WqimhYpIPVHhMDhlypTaHIdIo+Rv8fGWeRABO+unk//yguWc/dHZzFw3E4AjOh/BffvdR4t5LUgvTYMFsVEx3E61szaw1HrAuIXTycFp7WA1LbuojYhIJilMFnL2P89m+g/TATi91+ncM+QeYk4MYwzeAg/v63CK6Nb4Vlrt3wq35fbv1yVN5H1wu7q4HTUtVETql9qvQiGSoYKtAeklaQgo1QPwmaXPcMmnl7DF20ITpwl3Db6LM3qdgfeth7corEAXG1ZLQdAPexwaz2A3sXF6FBeFietTahERgGXrl3H0jKOZs2kOjuVw56A7uWC3C7AsC5MyJD9JhrM3ALuHzdxmczkw+8Dt9lNqWmhPTQsVkfqpWlebnuexYMECcnNzCYJgu8fHjh1bnd2LNFhBIsBbUrqX4JbUFn772W+ZvjT8pHlo66E8NuIxdsveDe+7n3pSRQdFcbvXcBBMh/8xecWN4rtFwoqmahYvIlLivfnvcfwLx7MuuY420TY8Nfop9m+/PwDBloDEB4mwAIwdvleb7obg++2vf0zCEBT8rIm8poWKSD1VpSvOIAi45ppruO+++ygsLCx3O9/3qzwwkYbKeIb0kjQmPwyClmXx3rr3OGv2WSwrXIZjOVyz5zVcs+c1uLZLekma1BcpACJ7RYjsFqm5sRRXriupSLqbS7R1VOsBRUR+xhjDAzMf4P+9///wjMeAlgP455h/0qNZDwDSS9MkP02CD1YTi9jIGE5rJ6wm+j/7KZkW2kXTQkWk/qtSGLzlllu44447OO+88xg9ejSnnHIKt99+Oy1btuS+++7Dsiz+9Kc/1fRYReo944dN5f1NYS9Bz3jcOPdG7pp/FwZDr2a9mDJiCsPbDAcgvTJN8pMkEAa1SP+aC4JBUYApMNjN7HDK6QZwWjoKgiIiP5NMJLn0uUt56LuHADiu23E8NOwhmrpNMb4h9WWK9Pfh9Aq7vU18RLzMVjuaFioiDVGVwuDUqVM54YQTuP/++9m4cSMAgwcP5sADD+S0005jxIgRvP3224wfP75GBytSn5nAkF6Rxl/nY7e0WbB1AafPPp25m+cCcEavM7hj0B1kR7IB8Nf5JGcnwYDb3SW6b7RGLhyMZzB5BqLFBQvau6StdLX3KyLS2KxZt4bj/nkcH2z4AAuLmwbcxJX9r8SyLIKigOSHSYIN4TTQSP8IkT0jZX+gloSgUNVCRaThqdK71YoVKzjwwHCxdCwWAyCRSAAQjUaZMGECTzzxRA0NUaT++3kvQSvb4oHFDzD8teHM3TyX1tHWPDP6GR4Y9sBPQXCzT2JWAnxwOjlEh1Y/CJrAEOQGmHyD3cYm2jdKpGuk1pvUi4g0NMYYPp3/KftN3Y8PNnxA80hznh37LJP2nIRlWfjrfBKvJ8IgGIHY6BjRvcuYYm+K/1NkcLu6RPpEFARFpEGp0p3B1q1bs3XrVgCaNWtG8+bNWbx4caltNm/eXP3RiTQAJmlIrwzvCK6x13De7PN4ffXrABzc4WAeHv5wSV8qgCA/IPFuAjyw29rERsSqNXXTGIMpCltF2M1t3I6aniQiUh7jG/4x6x+c+965JIIEu2Xvxr/G/ot+zfuFH+x9lyb1ZQoMWC0s4qPi2NnbBzzjG8zm4vXYvV3ctq7ed0WkwalSGNx333359NNPS74/4IADuPvuu9l3330JgoB77rmHgQMH1tggReojYwzB5oD0ijTB1oCXtr7EhXMuZENyA3Enzq373FpSjnyboDAgMTMBSbBb2sRHx6tVXMCkiosVxMDtUXwxogqhIiJl8go9rn7pav48/88AHNbpMKaNmEbLaEuMZ0h+msRfHha/c7o7xIbEynyPNilDkBdgtbTC9ditHAVBEWmQqhQGzznnHKZNm0YymSQWi3HzzTczduxYxo4dizGGVq1aMX369Joeq0i9YbziaaGrfbYGW5n0wySmLJ4CwMCWA5k2chp7tNij9HOShsS7CUyhwcq2iO8fr/IUTuMXX4hg4bR3cDo4mpokIrIDG9du5KTnTuL1teHMjUn9J3HD3jfg2A5BXnHbiDwDFkT3jeL2KftOX1AQQBLcTi6mvYFFu/qViIjUnAqHwSuuuIJTTjmFgQMHcuSRR3LkkUeWPNa/f39++OEHZs6cieM4jBw5kpycnFoZsEhdC3IDvBUeQW7Ap96nnPHZGSzeuhgLi8v3uJzJe08m6kRLPcd4xUEwL6w0F98/jhWvfBA0xmAKTMmdRbeDi91SU0JFRMpjAsN333/HUS8dxYKtC8hysnh42MMc3/14ANLLi6s6p8HKKm4b0cbZfj/GEGwJsFyLSM8IdjubdFrFuUSkYatwGPzzn//MX/7yF/r168eECRM46aST6N69e8njLVq0KBUQRRob4xvSa8IiMV7a4/ZVt3PbvNvwjU+3Jt14dMSjjG03tsznJWYlCDYHEIX4uDh208rfxTNJQ5AfYGfZOL0cnDYOlqMQKCJSHpMyzPx8Jie8cwIbvA10zurMs2OfZZ+cfTCBwfvKw1sY9gq029rER5b9QZ1Jh20j7GybSLcIdgvNxBCRxqHC72bfffcd119/PUEQ8Lvf/Y5evXoxevRoHnjggZL2EiKNVbA1wPveI700zaLUIsZ/Pp6bv70Z3/j8pvtv+PTwT8sOgoEhOTtJsC4AF+L7x7GbV+4iwqTDtYmmyOB2donuEcVt7yoIiojsQJAfMOXNKRz2xmFs8DYwKGcQsw6ZFQbBRDhbY1sQjPSNEB9XThBMhncE7bY20d2iCoIi0qhU+B2tT58+TJ48mfnz5/PZZ5/x29/+lqVLl3LhhRfSqVMnfvWrX/H0009TVFRUm+MV2aVMEN4NTC1M4W/2mbJpCsPeGcYnGz+hZaQlT4x8gqkjp9Iy2nL75xpD6rMU/kofbIiPjuPkbD/1qNzfbcI7gSbXYLe0ifaLEukeqdL0UhGRTGGMwVvrce1/ruWsT88iZVIc1eUo3jzoTTo16YS/wafo9aKSD+liI2NE9ymjbQThB4GmsLhtRC+9/4pI41OlAjKDBg1i0KBB3HnnncycOZOnnnqKZ599lpdeeolmzZpx1FFHcfLJJ3PooYfW9HhFdpmgqLhS6IaA9dZ6Lvz2Ql5a+RIA49qN45Hhj9C1adcyn2uMIfVlivSPabAgNiKG074SQTBd/El0Mxu3m4vd2q5W+wkRkUxg0ob8pfmc+eaZ/HvNvwG4co8ruWngTVhYeN97pOamIACreXHbiDJma5jAYHINRCDSOxK+B2tttog0QtWa62BZFgcccAAPP/wwa9as4fnnn2fUqFH84x//4Je//GVNjVFklzLG4K/38RZ4BBsCXit8jf3e3Y+XVr5E1I5y2z638cqBr5QbBAG8+R7phWFhgeiQKG6Xin/usi0IOu0dov2iOG0dBUERkZ0ICgKWf72cg/5zEP9e828idoSHhj3EH/f5I5Zvkfw4SWpOGASdrg5Z47PKDoKeIdgUYDWziO4WDddnKwiKSCNVpTuD/yuVSvHSSy/x1FNPMXPmTADat29fE7sW2aV+3kC+0C7kmsXX8NCihwDo36I/00ZMY0CrATvch7fIw/s6XIcS3SdKpFek4r9/WxBs54RTQqvRg1BEJBMYE4a3L77+gqM/PpoViRXkRHN4eszTjG03liC/uG1EbnHbiIFR3N3LaRtRFEAhOO0dIl0jVW7/IyLSUFQ5DBpjeOutt3jqqad47rnnyM3NJTs7mxNPPJEJEyZw4IEH1uQ4RWrV/zaQ/8L/gjM+PYPv8r8D4NK+l/KHgX8g7sR3uJ/0sjSpz1MARPaIEOlbhSDY1iHSQ0FQRGRnjB/2fH3pi5c49ctT2epvZbfs3Xhu/+fYLXs30iuK20Z4YMWL20a0LbtthMk34V3Dbg5uR1czMkQkI1Q6DH7yySc89dRTPPPMM6xduxbXdTn00EOZMGECv/rVr4jHd3yxLFLf/LyBvG/53LnmTv74zR9JmzSdsjrxyPBHOKjDQTvdT3p1muTHSQDc3i6RvSsRBH0FQRGRyggSAd4yj3s+v4erv7uagIBx7ccxY/QMWrotSX2ZwlvwU9uI2IgYdlYZ00L9sG2ElWXhdnMrVehLRKShq3AYvP7665k+fTqLFy/GGMPIkSO57rrrOPHEE9VgXhqsnzeQX2ov5azPzuLDDR8CcEzXY7h3v3vJie3879vf4JP8IFmyFiU6KFrhNSbGD+9KOm2Kg2BEQVBEZEf8LT5FS4qY+PlEHl7xMABn9j6Te4bcg5tySbybCKuFAu7uLtGBZVcLNSlDkBfg5Dhhsa4mahshIpmlwmHwj3/8I/369eOmm27i5JNPpkePHrU4LJHaZTxDel3YQD7wA57KfYqJcyaSn84n283m7iF3c3KPk3ca6IwxpL9Pk/oqBT44HRxiw2IVnl5UEgRbKwiKiOyMCQzptWk2/riRCXMm8NbGt7CwuHWfW/ltv98SbAwo+rAIU2TCthFDY7hdy77UCQoDSIDb0cXt4ur9V0QyUoXD4Oeff86+++5bm2MRqXXGDwsNpFeHawM3RTZxybxLeG75cwCMajuKx4Y/Ro9mPXa6r6AgIPlJsuTTZ6eDQ2xUrMLN4EsFwZ4qVCAisiMmZfCWeyxasohj5h7Dwq0LaeI0YdrIaRzR+Yjwg7m5KTA7aRthittG2OD2cHHaq1qoiGSuCofBPfbYg/PPP58999yTSy65pNzt7rnnHubPn88999xDJFLxNVMitcmYcE2ev8Yn2BJABN5KvcW5s85lddFqXMtl8t6TuXyPy3HsHa8XMcaQXlx80ZEGnLBqqNu77Op0Ze7DN5jNBidHQVBEZGeC/HB94KwlszjxqxPZmNpI56zOPDv2WQZmDyT5URJ/mQ+EU/Vj+8XKvNNXsj6wafH6wJZaHygima3CYfChhx5i6tSpzJs3b4fb/fKXv2TSpEkMGDCACy64oNoDFKmuID8gvSZNsCnAYEhlp/jd17/j3u/uBWD37N2ZNnIag3IG7XxfhQGpT1P4a8KLDruNTWxYDLtZxdeZmCC8GLFzbAVBEZEdMMYQrA/wlns8ufRJLvz2QlJBikE5g/j3mH/TId2BojeLMHnFbSP2ieLuVvYHcyZpCPLD2RhuN7fMYjIiIpmmwu+EzzzzDMceeyy9evXa4Xa9e/fm+OOPZ/r06dUenEh1BIUB3o8eqQUpgo1hA+H51nxGvTmqJAiev9v5fHzYxzsNgsYY0kvSFL1aFAZBO7zoiB8Qr3wQ3GSwWxYHwZiCoIhIeUyeIfljkhsW3sDZX59NKkhxVJejeOugt2i3sR1Fb4RB0IpbxA+IE9k9Unb/wK0BptDgdnGJ9I4oCIqIFKvwncGvv/6ak08+uULbjhw5khdffLHKgxKpDpMypNen8df6kAAr28JEDfcsvIfff/l7UkGKdrF2PDT8IQ7vdPjO95cwJD9L4q8svhvYyiY2PFbmWpQd7icIp4ZarSwFQRGRCihIFHD6F6fz7NpnAbhyjyu5ce8b8b/2SS4MW/nssG1EEC4RsKIWkV4R7Da21geKiPxMhcNgKpUiGo1WaNtoNEoymazyoESqwqQN/kYff7VPUBhgNbGwWlusLlrN2TPP5q01bwHwi06/4MFhD9Iu3m6n+0yvSJP8LAlJwIZI/wiRPSKVbkZcEgRbFAfBuC5GRER2ZM3WNRzx7BF8tu4zInaE+/a7jwkdJ5B8N0mwPizcFekbITKg7Pdkkw6DoN3cJtI9gp2tu4EiIv+rwmGwU6dOfPPNNxXa9ptvvqFTp05VHpRIZZggrMqZXp0myA+wYhZ26/DT3xeWv8AFn1zAxtRGspwsbt/3ds7tc+7OW0akDMk5SfylxXcDW9hEh0VxWlW+2EBJEGweBkE7rgsSEZGdeerrp/hs3WfkRHJ4ZuwzjLRGkng9gUkUt40YFsPtUk7biKIACsBp7xDpopkYIiLlqXAYHD9+PI8//jjXXHMN7dqVf0dl3bp1PP744xx//PE1MkCR8hhjMHkmLA6zOQAnnMJp2RZbva1c+cWVPPbDYwDs02ofpo6Yyh4t9tjpftOr06Q+TYV9qiyI9IsQ2TNS4ZYRpcb48yDYS+tUREQq6rLhl7F241pOiZ9Cz809SXyZqFjbiHwDATjdHdyObqVncoiIZJIKX5leddVVJBIJDjzwQD7++OMyt/n444856KCDSCQSXHnllTU2SJH/FWwN8BZ7pBam8Df7WM0t7BZhEPxs42cMe20Yj/3wGBYWl+9xOe8f/P5Og6DxDMlPkyTfS2KKDFa2RfygONEB0aoFQVMcBLMVBEVEKsuyLG4ecjPdFncr6R/odHPIGp9VdhD0wwJdlmsR6RMh0rnyU/pFRDJNhe8M9urVi2eeeYbf/OY3jBw5kl69erH33nuTnZ1Nfn4+33zzDT/88ANNmjRhxowZ9O7duzbHLRkqSAZ4qz38dT54YXEYOxpeFPiBz53z7+Smr28ibdJ0adKFR4c/yrj243a6X3+tT/KTJKbQAODu7hLdO4rlVu1CoiQINlMQFBGpCn+9T8G/CsKZHztrG5EyBHkBTo6D29XFbqr3XBGRiqjUu+Uvf/lLvvrqK84991wSiQTPP/88TzzxBM8//zyFhYWcc845fPnllxxxxBG1Nd5G67333uOII46gU6dOWJbF888/X9dDqleMH4Y0b6FHekUaK1q8LrC4R9/SgqUc8vYhXP/V9aRNmmO7Hstnh3+20yBo0uHawMTMBKYwbEQcPyBObN9Y9YNg0+Ig2EQXJSLSMNXluSm9ongJQAziB+6gbURBgNlqcDsWt41QEBQRqbAK3xncpkePHtx///3cf//95Ofnk5eXR/PmzcnOzq6N8WWMgoICBg4cyJlnnskxxxxT18OpV0zK4C3zSr7fVhxmmxlLZnDpZ5eS6+XSzG3G3YPvZkLPCTstEuNv8El+nMRsLb4b2NslOjCKFan6tKKSINikOAjqokREGrC6PDdF94mGywBsC6fN9sW7jDGYXAM2uD1cnPaO2kaIiFRSpcPgz2VnZysE1pDDDz+cww/fec+7TBMUBnhLPYKNYRlxq4lVcrLPTeVy6WeXMmPpDACGtR7GlBFT6J294ynKxjd433h4C72wGEGWRXS/KG7Hav1zwJiwqqndxFYQFJFGoS7PTZZlEdsnFr5X/w+TNpgtxVPxu0ewW+j9VkSkKqp39St1JplMlurlmJeXB4DneXje9ifOhsjP9fGX+5hCQ9AigI2Q9tMAfLD+A875+ByWFS7Dtmyu7n81k/aYhGu7eH75rz/YFOB/5mPywruBdncbZ6CDiZodPm+nDGE/qywbu5uNH/PxPb/q+6th2/4mGsvfxq6m41c9jf34NdbXVRU1fW7y0z7pII3l/+yOXwqC/AC7tY3b1a3T99vG/rdd23T8qkfHr3oa+/Gr6OuyjDGmlscilWRZFs899xxHHXVUudvccMMN3Hjjjdv9/KmnnqJJkya1OLq6lTZpnl7zNP9e+28CAtpH23NZ98vo17TfDp9nBRbd13an6/quWFik3BTfd/6ejS027qKRi0hjVVhYyEknnURubi7Nmzev6+HUGp2bREQajoqemxQG66GKnHDL+vS1a9eubNiwoUFfjBjfkF6bJlgVQDScFgrhHcGn5j3FI2seYc7mOQBM6DGBO/a9g+zIjqcqb3c3sKuNs49T/SbEhnC9oQ92WzvsZxWtn+tVPM/jjTfe4OCDDyYSidT1cBocHb/qaezHLy8vjzZt2igMUvPnJn+zT/r7NFZLK3wPj4Db2d1u7Xhdaex/27VNx696dPyqp7Efv4qemzRNtIGKxWLEYrHtfh6JRBrsH7RJGbyVHtY6i0jTCFY8PNEbY5i2eBqXL7ycRJCgZaQl9w29j2O7Hbvj/fkG71uP9II0GCAGsSEx3C7V/7PfVsbcbmaHFyY59ePCZGca8t9HfaDjVz2N9fg1xtdUVTV9brJdGywwuQa7hR2uD2xW/9YHNta/7V1Fx696dPyqp7Eev4q+JoVBqReCwoD00jT+Zj9sHl9c0XNLagsXfnIh/17+bwDGtB3DlBFT6Nq06w73528q7huYG94NdLo5xAbFqn030BgTfjodgNvRxe3kVv8Oo4iIlC8KTiuHSNdIvZ19ISLSUCkM1hNbt25l0aJFJd//+OOPzJ07l5ycHLp161aHI6t9QW5YMdQUmPAOmx2e7D9c/yGnfXgaywqX4VouJ3U4ib+N+hvxSLzcfRnf4M3z8OZ7P90NHBzD7VrDdwO7uNitGsbdQBGRqqrrc5PdzCbSI1Lq3CAiIjVHYbCe+OyzzzjggANKvp84cSIAp512GlOnTq2jUdUuYwzBhiDsIeiDlRO2jfADn9vn3c4fvvkDgQno1awXjw17jC0bt+DY2/ea2ma7u4Fdi+8Gxqt5NzAwmHzdDRSRzFPX5yYrWnaPQRERqRkKg/XEuHHjyKRaPiYwpFel8Vf6EAG7ZbgGZHnBcs6YfQbvr38fgJN7nMxfh/yVuB3nzY1vlr0v3+DN9/Dm6W6giEhNyrRzk4hIplEYlF3OeAZvmUewNsBqZpXcuXt++fOc/8n5bE5tppnbjL8N+Rsn9TwJoNwegP5mn9QnKYItYVN6p4tDbLDuBoqIiIiI7IzCoOxSQVFxoZhNPxWKKUwXcuUXV/LIokcAGJIzhGkjp9Enu0+5+zFB8drAbXcDo8V3A7vpbqCIiIiISEUoDMouU6pQTCsby7H4Zss3TPhgAvPz5gNw+R6Xc8PeNxB1ouXuZ5fcDexUfDdQletEREREpJFSGJRa9/NCMSYdVgwFuP+7+7nqi6tIBkk6xDvw2IjHOKjDQeXuxzIW/jyf1PxUqbuBTlen2nfuTNIQ5AfY2cV9A3U3UEREREQaOYVBqVUmMKRXp/FXhIVinFYOG5IbOO/j83hp5UsAHNbpMB4Z9ght423L3U+QG7DP9/vgJ3wAnM4O0cFR7KzqNR/W3UARERERyVQKg1JrjGdIL0/jr/WxmoaFYmaunckZs89gVdEqonaUW/a5hYt3v3iHd+HSK9KkP0qT7WdDpPhuYDfdDRQRERERqQ6FQakVJmHwlnglhWLSTpo/fPkH/jTvTxgMfZv35YmRTzCw1cDy92EM6YVpUl+mANjUbBPt92+P26z6f7ZBfgCe7gaKiIiISOZSGJQaFxQFeD96mC1hoZglRUs49cNT+WTjJwCc2ftM7hx0J03dpuXuwwSG1JwU6R/SANi9bL5p+g0dsjpUa2wmMJgtBmIQ6RPBbq27gSIiIiKSmRQGpUYFBcVBMN9g5Vg8s+wZLv7sYvK8PFpEWnD/0Ps5ttuxO9yH8QzJ2Un81eH6wOg+UUxvA99Xb2zGMwS5AXYLm0j3CHaz6q03FBERERFpyBQGpcYEWwO8xR6m0FDQrICJn0zk8R8fB2BEmxFMGzmN7k2773gfhQHJ95IEuQE4EBsew+3iltt0vsJjKwqgEJz2DpGuEU0LFREREZGMpzAoNSLIK74jmDB8Y33DSW+cxPf532NbNtfseQ3X7nktrr3jPzd/s0/y/SSmyGDFLWKjYzitnWqNy5ifqoU63R3cDi6WrSAoIiIiIqIwKNXmb/FJ/5jGT/pM2zSNiXMmkvATdGnShakjpjKm3Zid7iO9Kk1ydhLSYDW3iI+NYzetZtsIP1wfaGVZuN1cnJzqBUsRERERkcZEYVCqxd/k4y3x2JrYyqXfXcr0pdMBOLTjoTw24jHaxNrsdB/edx6puWEjebu9TXxkvNrTOE3KEOQFODkObjcXu4nWB4qIiIiI/JzCoFSZv8HHW+oxL28eJ809iYV5C3EshxsH3Mjle1yObe04gJnAkJqbIv19WDHU7ekSHRKt9jTOoCCAJLgdXdwuLlZE00JFRERERP6XwqBUib8uDIL/WPkPLv3mUor8IjpldeKJkU8wut3onT7fpIsrhq4KK4ZGBkSI9ItUq82DMQaTa8AOg6XTrvqN6UVEREREGiuFQakUYwz+Wp+8H/O4bP5lPL48rBY6vsN4poyYQrt4u53uIygKSL6fJNgcgA2xYTHcbtX7UyxZH9jUCttGtNC0UBERERGRHVEYlAozxpBenWbe/Hmc/OXJfJv/LbZlc/1e13PVnlftdFooQLAlIPF+AlMYNn6Pj47jtKlmxdCkIcgPcFo7uN1d7LiCoIiIiIjIzigMSoWYwJBelebJT57k4nkXU+AX0D7ensdHPs649uMqtI/06jTJD4srhmYXVwytZuP3YGsAHrhdXNxOLparaaEiIiIiIhWhMCg7ZQJD/pJ8Jr4zkUdXPArAuHbjmDZyGh2yOlRoH94ij9Sc4oqhbW3io+JYsWqsDwwMwZYAK2oR6RXBbmNrfaCIiIiISCUoDMoOGd8w/9v5/ObN3/BV/ldYWFy717X8bs/f4dg7n95pjMH7ysNb4AHgdneJ7hfFcqoRBNNhELSb2+H6wGxNCxURERERqSyFQSmXSRumvzed8z88n3w/n7axtkwdMZXxHcdX+PnJj5P4K4orhu4VIdK/ehVDSYIpMjjtHCJdI9W6uygiIiIikskUBqVMiaIElz1/GQ989wAAY9qO4fGRj9OpSacKPd8kDIn3EwSbiiuG7hfD7VGNPzfz036dbg5uB7dadxdFRERERDKdwqBs54e1P3DCMycwZ9McACb1n8TkvSfj2hX7cwm2BCRmJTAFBqIQHxXHaVf1iqHGhNNCIewf6LZztT5QRERERKSaFAallGe/epYzXzqTXC+X1tHWPDbiMQ7rdFiFn59emSb5UXHF0GbFFUOrsabPBAaz2WA3tWE9ODlqJC8iIiIiUhMUBgWAlJ9i0quT+OtnfwVgeOvh/GPUP+jatGuFnm+MwVvg4X0VFoqx29nER1azYui2QjGtbNwuLiyp8q5EREREROR/KAwKy3OXc/w/j+fjlR8DcFnfy/jDPn8gYkcq9HzjG1KfpUgvSQPg9naJDopi2dUIgp4hyA1w2jpEukdIW+kq70tERERERLanMCi4tsuPm3+kVaQVjwx5hP/r8X8Vfq5JGBKzEgQbA7Agum8Ut0/11vSZpMHkG9wOLm634kbyXpV3JyIiIiIiZVAYFDpmd+S5Y5+j9crW9GjVo8LP8zf7JGclMYUGIhAfGcfpUPVCMQBBYQAJcLo6uJ3dat1dFBERERGR8ikMCgAjOo8guTlZ4e3TK4oLxfhgZVvEx1SvUAxAsDWANDjdi1tHqFCMiIiIiEitURiUSjHG4M338L4uLhTTvrhQTLQa00KNweQacCDSK4LTpnp3F0VEREREZOcUBqXCTNqQ/DSJv8wHwN3NJbpPNQvFBGEQtGIWbg8Xp6WCoIiIiIjIrqAwKBUSFAUkZyUJNhUXihkUJdKnYtVGy2N8g9lisJpZRHpGsJtVb5qpiIiIiIhUnMKg7JS/qbhQTJGBaHGhmPbVu4O3rYegk+PgdnexsxQERURERER2JYVB2aH08jTJj4sLxTS3iI+ufqEYkzIEeT/1EKzOekMREREREakahUEpkzEG71sP79uwUIzT0SE2PFbt4GYSBlNgcDu6uF2LewiKiIiIiMgupzAo2zFpQ/KTJP7y4kIxu7tEB1avUAwU9xBMFvcQ7KQegiIiIiIidUlhUEoJigJSH6cINgdgQ3RwlEiv6hWKAQjyA/DB7e7itHfUQ1BEREREpI4pDEqJYEtAak4KkzAQg/ioOE7bahaKMWGhGCtiEekdwWmt1hEiIiIiIvWBwqAAkJqXIvlREgKwWhQXiqlmqwcThK0j7CybSI8IdgtVDBURERERqS8UBoX0yjSFLxUC4HRwiI2MYUWqWSgmXdxDsIUVBsGmCoIiIiIiIvWJwqDgdnaJ7hMlyAuIDIhUOwgGRQGm0OC0Lm4dEdf6QBERERGR+kZhUADIOjiL1LepahV2MYEhyA2wHAu3u4vb3sVyFARFREREROojhUEBqHZ1z22N5O3mNpGuWh8oIiIiIlLfKQxKtRhjMFsNpAkbyXd2q92YXkREREREap/CoFSZSRtMrsGKW7i9XezWtvoHioiIiIg0EAqDUiUlRWJyHNyuLnYTTQsVEREREWlIFAalUkoVienm4nZQkRgRERERkYZIYVAqTEViREREREQaD4VB2amSIjGeisSIiIiIiDQWCoOyQ6WKxPRRkRgRERERkcZCYVDKpSIxIiIiIiKNl8KgbEdFYkREREREGj+FQSnFeAaTZ1QkRkRERESkkVMYlNKSKhIjIiIiIpIJFAYl5IDdzMbJcbDbqEiMiIiIiEhjpzAoAFiuRbRvtK6HISIiIiIiu4gWhImIiIiIiGQghUEREREREZEMpDAoIiIiIiKSgRQGRUREREREMpDCoIiIiIiISAZSGBQREREREclACoP1yL333kuPHj2Ix+MMGzaMTz75pK6HJCIiGU7nJhGRxkthsJ54+umnmThxIpMnT2bOnDkMHDiQQw89lHXr1tX10EREJEPp3CQi0rgpDNYTf/7znznnnHM444wz6N+/Pw888ABNmjThscceq+uhiYhIhtK5SUSkcXPregACqVSKzz//nGuuuabkZ7ZtM378eGbPnl3mc5LJJMlksuT7vLw8ADzPw/O82h1wHdj2mhrja9sVdPyqR8evehr78Wusr0vnpp1r7H/btU3Hr3p0/KqnsR+/ir4uhcF6YMOGDfi+T/v27Uv9vH379ixYsKDM59x6663ceOON2/389ddfp0mTJrUyzvrgjTfeqOshNGg6ftWj41c9jfX4FRYW1vUQaoXOTRXXWP+2dxUdv+rR8auexnr8KnpuUhhsoK655homTpxY8n1eXh5du3blkEMOoXnz5nU4strheR5vvPEGBx98MJFIpK6H0+Do+FWPjl/1NPbjt+3ul+jcJJWj41c9On7V09iPX0XPTQqD9UCbNm1wHIe1a9eW+vnatWvp0KFDmc+JxWLEYrHtfh6JRBrlH/Q2jf311TYdv+rR8auexnr8GuNrAp2bKqOxv77apuNXPTp+1dNYj19FX5MKyNQD0WiUwYMH89Zbb5X8LAgC3nrrLUaMGFGHIxMRkUylc5OISOOnO4P1xMSJEznttNMYMmQIQ4cO5e6776agoIAzzjijrocmIiIZSucmEZHGTWGwnjjxxBNZv349119/PWvWrGGfffbh1Vdf3W7hvoiIyK6ic5OISOOmMFiPXHzxxVx88cV1PQwREZESOjeJiDReWjMoIiIiIiKSgRQGRUREREREMpDCoIiIiIiISAZSGBQREREREclACoMiIiIiIiIZSGFQREREREQkAykMioiIiIiIZCCFQRERERERkQykMCgiIiIiIpKBFAZFREREREQykMKgiIiIiIhIBlIYFBERERERyUAKgyIiIiIiIhlIYVBERERERCQDKQyKiIiIiIhkIIVBERERERGRDKQwKCIiIiIikoEUBkVERERERDKQwqCIiIiIiEgGUhgUERERERHJQAqDIiIiIiIiGUhhUEREREREJAMpDIqIiIiIiGQghUEREREREZEMpDAoIiIiIiKSgRQGRUREREREMpDCoIiIiIiISAZSGBQREREREclACoMiIiIiIiIZSGFQREREREQkAykMioiIiIiIZCCFQRERERERkQykMCgiIiIiIpKBFAZFREREREQykMKgiIiIiIhIBlIYFBERERERyUAKgyIiIiIiIhlIYVBERERERCQDKQyKiIiIiIhkIIVBERERERGRDKQwKCIiIiIikoEUBkVERERERDKQwqCIiIiIiEgGUhgUERERERHJQAqDIiIiIiIiGUhhUEREREREJAMpDIqIiIiIiGQghUEREREREZEMpDAoIiIiIiKSgRQGRUREREREMpDCoIiIiIiISAZSGBQREREREclACoMiIiIiIiIZSGFQREREREQkAykMioiIiIiIZCCFQRERERERkQykMCgiIiIiIpKBFAZFREREREQykMKgiIiIiIhIBlIYFBERERERyUAKgyIiIiIiIhlIYbAeuPnmmxk5ciRNmjShZcuWdT0cERERnZtERDKAwmA9kEqlOP7447ngggvqeigiIiKAzk0iIpnAresBCNx4440ATJ06tW4HIiIiUkznJhGRxk9hsIFKJpMkk8mS7/Py8gDwPA/P8+pqWLVm22tqjK9tV9Dxqx4dv+pp7Mevsb6uqtC5SSpDx696dPyqp7Efv4q+LoXBBurWW28t+dT2515//XWaNGlSByPaNd544426HkKDpuNXPTp+1dNYj19hYWFdD6He0LlJqkLHr3p0/KqnsR6/ip6bFAZrydVXX83tt9++w23mz59Pv379qrT/a665hokTJ5Z8n5eXR9euXTnkkENo3rx5lfZZn3mexxtvvMHBBx9MJBKp6+E0ODp+1aPjVz2N/fhtu/vVEOjcVLMa+992bdPxqx4dv+pp7MevoucmhcFacvnll3P66afvcJtevXpVef+xWIxYLLbdzyORSKP8g96msb++2qbjVz06ftXTWI9fQ3pNOjfVjsb++mqbjl/16PhVT2M9fhV9TQqDtaRt27a0bdu2rochIiJSQucmERH5OYXBemDZsmVs2rSJZcuW4fs+c+fOBaBPnz40a9asbgcnIiIZSecmEZHGT2GwHrj++uuZNm1ayff77rsvAO+88w7jxo2ro1GJiEgm07lJRKTxU9P5emDq1KkYY7b70slWRETqis5NIiKNn8KgiIiIiIhIBlIYFBERERERyUAKgyIiIiIiIhlIYVBERERERCQDKQyKiIiIiIhkIIVBERERERGRDKQwKCIiIiIikoEUBkVERERERDKQwqCIiIiIiEgGUhgUERERERHJQAqDIiIiIiIiGUhhUEREREREJAMpDIqIiIiIiGQghUEREREREZEMpDAoIiIiIiKSgRQGRUREREREMpDCoIiIiIiISAZSGBQREREREclACoMiIiIiIiIZSGFQREREREQkAykMioiIiIiIZCCFQRERERERkQykMCgiIiIiIpKBFAZFREREREQykMKgiIiIiIhIBlIYFBERERERyUAKgyIiIiIiIhlIYVBERERERCQDKQyKiIiIiIhkIIVBERERERGRDKQwKCIiIiIikoEUBkVERERERDKQwqCIiIiIiEgGUhgUERERERHJQAqDIiIiIiIiGUhhUEREREREJAMpDIqIiIiIiGQghUEREREREZEMpDAoIiIiIiKSgRQGRUREREREMpDCoIiIiIiISAZSGBQREREREclACoMiIiIiIiIZSGFQREREREQkAykMioiIiIiIZCCFQRERERERkQykMCgiIiIiIpKBFAZFREREREQykMKgiIiIiIhIBlIYFBERERERyUAKgyIiIiIiIhlIYVBERERERCQDKQyKiIiIiIhkIIVBERERERGRDKQwKCIiIiIikoEUBkVERERERDKQwqCIiIiIiEgGUhgUERERERHJQAqDIiIiIiIiGUhhUEREREREJAMpDIqIiIiIiGQghcE6tmTJEs466yx69uxJVlYWvXv3ZvLkyaRSqboemoiIZCidm0REMoNb1wPIdAsWLCAIAh588EH69OnDN998wznnnENBQQF33nlnXQ9PREQykM5NIiKZQWGwjh122GEcdthhJd/36tWLhQsXcv/99+uEKyIidULnJhGRzKAwWA/l5uaSk5Ozw22SySTJZLLUcwA2bdqE53m1Or664HkehYWFbNy4kUgkUtfDaXB0/KpHx696Gvvxy8/PB8AYU8cjqV06N22vsf9t1zYdv+rR8auexn78KnxuMlKvfP/996Z58+bmoYce2uF2kydPNoC+9KUvfemrnnwtX758F50pdj2dm/SlL33pq2F+7ezcZBnTyD/KrCNXX301t99++w63mT9/Pv369Sv5fuXKley///6MGzeORx55ZIfP/d9PX4MgYNOmTbRu3RrLsqo3+HooLy+Prl27snz5cpo3b17Xw2lwdPyqR8evehr78TPGkJ+fT6dOnbDt+l2XTeemmtXY/7Zrm45f9ej4VU9jP34VPTcpDNaS9evXs3Hjxh1u06tXL6LRKACrVq1i3LhxDB8+nKlTp9b7C4pdLS8vjxYtWpCbm9so/8HWNh2/6tHxqx4dv/pD56aapb/t6tHxqx4dv+rR8QtpzWAtadu2LW3btq3QtitXruSAAw5g8ODBTJkyRSdbERGpFTo3iYjIzykM1rGVK1cybtw4unfvzp133sn69etLHuvQoUMdjkxERDKVzk0iIplBYbCOvfHGGyxatIhFixbRpUuXUo9pBu9PYrEYkydPJhaL1fVQGiQdv+rR8aseHb+GR+emitHfdvXo+FWPjl/16PiFtGZQREREREQkA2kBgIiIiIiISAZSGBQREREREclACoMiIiIiIiIZSGFQREREREQkAykMSr1x77330qNHD+LxOMOGDeOTTz4pd9uHH36YMWPG0KpVK1q1asX48eN3uH0mqMzx+7kZM2ZgWRZHHXVU7Q6wnqvs8duyZQsXXXQRHTt2JBaLsfvuu/Pyyy/votHWP5U9fnfffTd9+/YlKyuLrl27ctlll5FIJHbRaEUqTuem6tG5qXp0bqoenZsqwIjUAzNmzDDRaNQ89thj5ttvvzXnnHOOadmypVm7dm2Z25900knm3nvvNV988YWZP3++Of30002LFi3MihUrdvHI64fKHr9tfvzxR9O5c2czZswYc+SRR+6awdZDlT1+yWTSDBkyxPziF78ws2bNMj/++KOZOXOmmTt37i4eef1Q2eP35JNPmlgsZp588knz448/mtdee8107NjRXHbZZbt45CI7pnNT9ejcVD06N1WPzk0VozAo9cLQoUPNRRddVPK97/umU6dO5tZbb63Q89PptMnOzjbTpk2rrSHWa1U5ful02owcOdI88sgj5rTTTsvoE25lj9/9999vevXqZVKp1K4aYr1W2eN30UUXmQMPPLDUzyZOnGhGjRpVq+MUqSydm6pH56bq0bmpenRuqhhNE5U6l0ql+Pzzzxk/fnzJz2zbZvz48cyePbtC+ygsLMTzPHJycmprmPVWVY/fTTfdRLt27TjrrLN2xTDrraocv//85z+MGDGCiy66iPbt27PXXntxyy234Pv+rhp2vVGV4zdy5Eg+//zzkuk6ixcv5uWXX+YXv/jFLhmzSEXo3FQ9OjdVj85N1aNzU8W5dT0AkQ0bNuD7Pu3bty/18/bt27NgwYIK7eOqq66iU6dOpf7RZ4qqHL9Zs2bx6KOPMnfu3F0wwvqtKsdv8eLFvP3225x88sm8/PLLLFq0iAsvvBDP85g8efKuGHa9UZXjd9JJJ7FhwwZGjx6NMYZ0Os3555/PtddeuyuGLFIhOjdVj85N1aNzU/Xo3FRxujMoDd5tt93GjBkzeO6554jH43U9nHovPz+fU045hYcffpg2bdrU9XAapCAIaNeuHQ899BCDBw/mxBNP5He/+x0PPPBAXQ+tQZg5cya33HIL9913H3PmzOHZZ5/lv//9L3/4wx/qemgiNUbnpsrRuan6dG6qnkw9N+nOoNS5Nm3a4DgOa9euLfXztWvX0qFDhx0+98477+S2227jzTffZMCAAbU5zHqrssfvhx9+YMmSJRxxxBElPwuCAADXdVm4cCG9e/eu3UHXI1X5++vYsSORSATHcUp+tscee7BmzRpSqRTRaLRWx1yfVOX4XXfddZxyyimcffbZAOy9994UFBRw7rnn8rvf/Q7b1ueUUvd0bqoenZuqR+em6tG5qeIa56uSBiUajTJ48GDeeuutkp8FQcBbb73FiBEjyn3en/70J/7whz/w6quvMmTIkF0x1HqpssevX79+fP3118ydO7fk61e/+hUHHHAAc+fOpWvXrrty+HWuKn9/o0aNYtGiRSUXKgDfffcdHTt2zKiTLVTt+BUWFm53Ut128WKMqb3BilSCzk3Vo3NT9ejcVD06N1VC3davEQnNmDHDxGIxM3XqVDNv3jxz7rnnmpYtW5o1a9YYY4w55ZRTzNVXX12y/W233Wai0aj517/+ZVavXl3ylZ+fX1cvoU5V9vj9r0yv2FbZ47ds2TKTnZ1tLr74YrNw4ULz0ksvmXbt2pk//vGPdfUS6lRlj9/kyZNNdna2mT59ulm8eLF5/fXXTe/evc0JJ5xQVy9BpEw6N1WPzk3Vo3NT9ejcVDEKg1Jv/O1vfzPdunUz0WjUDB061Hz00Uclj+2///7mtNNOK/m+e/fuBtjua/Lkybt+4PVEZY7f/8r0E64xlT9+H374oRk2bJiJxWKmV69e5uabbzbpdHoXj7r+qMzx8zzP3HDDDaZ3794mHo+brl27mgsvvNBs3rx51w9cZCd0bqoenZuqR+em6tG5aecsYxrzfU8REREREREpi9YMioiIiIiIZCCFQRERERERkQykMCgiIiIiIpKBFAZFREREREQykMKgiIiIiIhIBlIYFBERERERyUAKgyIiIiIiIhlIYVBERERERCQDKQyKiIiIiIhkIIVBERERERGRDKQwKCIiIiIikoEUBkVERERERDKQwqCIiIiIiEgGUhgUERERERHJQAqDIiIiIiIiGUhhUEREREREJAMpDIqIiIiIiGQghUEREREREZEMpDAoIiIiIiKSgRQGRUREREREMpDCoIiIiIiISAZSGBQREREREclACoMiIiIiIiIZSGFQREREREQkAykMioiIiIiIZCCFQRERERERkQykMCgiIiIiIpKBFAZFREREREQykMKgiIiIiIhIBlIYFBERERERyUAKgyIiIiIiIhlIYVBERERERCQDKQyKiIiIiIhkIIVBERERERGRDKQwKCIiIiIikoEUBkVERERERDKQwqCIiEgjN27cOMaNG1fy/ZIlS7Asi6lTp9bZmEREpO4pDIqIiOzEt99+y4QJE+jcuTOxWIxOnToxYcIE5s2bV9dDKzFv3jxuuOEGlixZUtdDERGRBkJhUEREZAeeffZZBg0axFtvvcUZZ5zBfffdx1lnncXbb7/NoEGDeOGFF+p6iEAYBm+88cYyw+Drr7/O66+/vusHJSIi9Zpb1wMQERGpr3744QdOOeUUevXqxXvvvUfbtm1LHvt//+//MWbMGCZMmMBXX31Fz54963CkOxaNRut6CCIiUg/pzqCIiEg57rjjDgoLC3nooYdKBUGANm3a8OCDD7J161buuOMOAE4//XR69Oix3X5uuOEGLMsq9bMpU6Zw4IEH0q5dO2KxGP379+f+++/f7rk9evTg//7v/5g1axZDhw4lHo/Tq1cvHn/88ZJtpk6dyvHHHw/AAQccgGVZWJbFzJkzge3XDJZnwYIFHHfcceTk5BCPxxkyZAj/+c9/dvo8ERFpmBQGRUREyvHiiy/So0cPxowZU+bjY8eOpUePHrz44ouV3vf9999P9+7dufbaa7nrrrvo2rUrF154Iffee+922y5atIjjjjuOgw8+mLvuuotWrVpx+umn8+2335aM49JLLwXg2muv5YknnuCJJ55gjz32qPB4vv32W4YPH878+fO5+uqrueuuu2jatClHHXUUzz33XKVfn4iI1H+aJioiIlKG3NxcVq1axZFHHrnD7QYMGMB//vMf8uLpVnMAAASDSURBVPPzK7X/d999l6ysrJLvL774Yg477DD+/Oc/c9FFF5XaduHChbz33nslofSEE06ga9euTJkyhTvvvJNevXoxZswY7rnnHg4++OAK3QX8X//v//0/unXrxqeffkosFgPgwgsvZPTo0Vx11VUcffTRld6niIjUb7ozKCIiUoZt4S47O3uH2217vLJh8OdBMDc3lw0bNrD//vuzePFicnNzS23bv3//Uncn27ZtS9++fVm8eHGlfmd5Nm3axNtvv80JJ5xAfn4+GzZsYMOGDWzcuJFDDz2U77//npUrV9bI7xIRkfpDdwZFRETKUNGQl5+fj2VZtGnTplL7/+CDD5g8eTKzZ8+msLCw1GO5ubm0aNGi5Ptu3bpt9/xWrVqxefPmSv3O8ixatAhjDNdddx3XXXddmdusW7eOzp0718jvExGR+kFhUEREpAwtWrSgU6dOfPXVVzvc7quvvqJLly5Eo9HtisRs4/t+qe9/+OEHDjroIPr168ef//xnunbtSjQa5eWXX+Yvf/kLQRCU2t5xnDL3a4ypxCsq37bfd8UVV3DooYeWuU2fPn1q5HeJiEj9oTAoIiJSjiOOOIIHH3yQWbNmMXr06O0ef//991myZAkTJ04Ewrt1W7Zs2W67pUuXlvr+xRdfJJlM8p///KfUXb933nmnymMtL4hWRK9evQCIRCKMHz++yvsREZGGRWsGRUREynHFFVfQpEkTzjvvPDZu3FjqsU2bNnH++efTvHlzLr74YgB69+5Nbm5uqbuJq1ev3q4a57Y7fT+/s5ebm8uUKVOqPNamTZsClBlGd6Zdu3aMGzeOBx98kNWrV2/3+Pr166s8LhERqb90Z1BERKQcffr04fHHH+c3v/kNe++9N2eddRY9e/ZkyZIlPProo2zevJkZM2aUNJz/9a9/XVJ589JLL6WwsJD777+f3XffnTlz5pTs95BDDiEajXLEEUdw3nnnsXXrVh5++GHatWtXZhiriH322QfHcbj99tvJzc0lFouV9DGsiHvvvZfRo0ez9957c84559CrVy/Wrl3L7NmzWbFiBV9++WWVxiUiIvWXwqCIiMgOHHvsscyZM4dbb72VRx55hHXr1hEEAfF4nM8//5z+/fuXbNu6dWuee+45Jk6cyKRJk+jZsye33nor33//fakw2LdvX/71r3/x+9//niuuuIIOHTpwwQUX0LZtW84888wqjbNDhw488MAD3HrrrZx11ln4vs8777xT4TDYv39/PvvsM2688UamTp3Kxo0badeuHfvuuy/XX399lcYkIiL1m2VqavW5iIhIhnj88cc5/fTTmTBhAo8//nhdD0dERKRKdGdQRESkkk499VRWr17N1VdfTZcuXbjlllvqekgiIiKVpjuDIiIiIiIiGUjVREVERERERDKQwqCIiIiIiEgGUhgUERERERHJQAqDIiIiIiIiGUhhUEREREREJAMpDIqIiIiIiGQghUEREREREZEMpDAoIiIiIiKSgRQGRUT+f/t1IAAAAAAgyN96kMsiAIAhGQQAABgK/YbfHgbDblsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x750 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = 10., 7.5\n",
    "fig, (ax1, ax2) = plt.subplots(1 ,2)\n",
    "ax1.grid(); ax2.grid()\n",
    "\n",
    "ax1.plot(df['Quantile'],df['DML CVaR Y(0)'], color='violet', label='Estimated CVaR Y(0)')\n",
    "ax1.plot(df['Quantile'],df['CVaR Y(0)'], color='green', label='True CVaR Y(0)')\n",
    "ax1.fill_between(df['Quantile'], df['DML CVaR Y(0) lower'], df['DML CVaR Y(0) upper'], color='violet', alpha=.3, label='Confidence Interval')\n",
    "ax1.legend()\n",
    "ax1.set_ylim(-2, 6)\n",
    "\n",
    "ax2.plot(df['Quantile'],df['DML CVaR Y(1)'], color='violet', label='Estimated CVaR Y(1)')\n",
    "ax2.plot(df['Quantile'],df['CVaR Y(1)'], color='green', label='True CVaR Y(1)')\n",
    "ax2.fill_between(df['Quantile'], df['DML CVaR Y(1) lower'], df['DML CVaR Y(1) upper'], color='violet', alpha=.3, label='Confidence Interval')\n",
    "ax2.legend()\n",
    "ax2.set_ylim(-2, 6)\n",
    "\n",
    "fig.suptitle('Conditional Value at Risk', fontsize=16)\n",
    "fig.supxlabel('Quantile')\n",
    "_ = fig.supylabel('CVaR and 95%-CI')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CVaR Treatment Effects\n",
    "In most cases, we want to evaluate the treatment effect on the CVaR as the difference between potential CVaRs.\n",
    "To estimate the treatment effect, we can use the `DoubleMLQTE` object and specify `CVaR` as the score. \n",
    "\n",
    "As for quantile treatment effects, different quantiles can be estimated in parallel with `n_jobs_models`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Cores used: 5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================== DoubleMLQTE Object ==================\n",
      "\n",
      "------------------ Fit summary       ------------------\n",
      "          coef   std err         t         P>|t|     2.5 %    97.5 %\n",
      "0.10  0.627564  0.103806  6.045553  1.488982e-09  0.424108  0.831019\n",
      "0.15  0.677980  0.102616  6.606954  3.923084e-11  0.476856  0.879103\n",
      "0.20  0.706645  0.100356  7.041387  1.903366e-12  0.509951  0.903339\n",
      "0.25  0.716793  0.102775  6.974414  3.071543e-12  0.515358  0.918227\n",
      "0.30  0.716762  0.107073  6.694154  2.169220e-11  0.506903  0.926621\n",
      "0.35  0.740869  0.112216  6.602168  4.051870e-11  0.520930  0.960808\n",
      "0.40  0.756969  0.114647  6.602628  4.039302e-11  0.532266  0.981672\n",
      "0.45  0.751710  0.117710  6.386102  1.701672e-10  0.521002  0.982417\n",
      "0.50  0.779682  0.122408  6.369556  1.895768e-10  0.539767  1.019596\n",
      "0.55  0.786744  0.130370  6.034690  1.592681e-09  0.531223  1.042265\n",
      "0.60  0.814351  0.138378  5.884996  3.980643e-09  0.543136  1.085566\n",
      "0.65  0.848868  0.144800  5.862359  4.563374e-09  0.565066  1.132671\n",
      "0.70  0.946968  0.154828  6.116274  9.578847e-10  0.643512  1.250425\n",
      "0.75  0.997621  0.164805  6.053331  1.418806e-09  0.674609  1.320633\n",
      "0.80  1.073520  0.190915  5.623024  1.876431e-08  0.699333  1.447706\n",
      "0.85  1.053558  0.236008  4.464076  8.041491e-06  0.590991  1.516125\n",
      "0.90  1.097468  0.338908  3.238251  1.202650e-03  0.433221  1.761714\n"
     ]
    }
   ],
   "source": [
    "n_cores = multiprocessing.cpu_count()\n",
    "cores_used = np.min([5, n_cores - 1])\n",
    "print(f\"Number of Cores used: {cores_used}\")\n",
    "\n",
    "dml_CVAR = dml.DoubleMLQTE(obj_dml_data,\n",
    "                           ml_g,\n",
    "                           ml_m,\n",
    "                           score='CVaR',\n",
    "                           quantiles=tau_vec,\n",
    "                           n_folds=5)\n",
    "dml_CVAR.fit(n_jobs_models=cores_used)\n",
    "print(dml_CVAR)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As for standard `DoubleMLCVAR` objects, we can construct valid confidencebands with the `confint()` method. Additionally, it might be helpful to construct uniformly valid confidence regions via boostrap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         2.5 %    97.5 %\n",
      "0.10  0.367571  0.887556\n",
      "0.15  0.420967  0.934992\n",
      "0.20  0.455293  0.957996\n",
      "0.25  0.459383  0.974202\n",
      "0.30  0.448587  0.984937\n",
      "0.35  0.459812  1.021926\n",
      "0.40  0.469825  1.044113\n",
      "0.45  0.456892  1.046527\n",
      "0.50  0.473099  1.086264\n",
      "0.55  0.460218  1.113270\n",
      "0.60  0.467770  1.160932\n",
      "0.65  0.486202  1.211534\n",
      "0.70  0.559186  1.334750\n",
      "0.75  0.584849  1.410393\n",
      "0.80  0.595353  1.551686\n",
      "0.85  0.462451  1.644665\n",
      "0.90  0.248638  1.946297\n"
     ]
    }
   ],
   "source": [
    "ci_CVAR = dml_CVAR.confint(level=0.95, joint=False)\n",
    "\n",
    "dml_CVAR.bootstrap(n_rep_boot=2000)\n",
    "ci_joint_CVAR = dml_CVAR.confint(level=0.95, joint=True)\n",
    "print(ci_joint_CVAR)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can compare the predicted treatment effect with the true treatment effect on the CVaR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Quantile      CVaR  DML CVaR  DML CVaR pointwise lower  \\\n",
      "0.10      0.10  0.564829  0.627564                  0.424108   \n",
      "0.15      0.15  0.590098  0.677980                  0.476856   \n",
      "0.20      0.20  0.613950  0.706645                  0.509951   \n",
      "0.25      0.25  0.637108  0.716793                  0.515358   \n",
      "0.30      0.30  0.660128  0.716762                  0.506903   \n",
      "0.35      0.35  0.683331  0.740869                  0.520930   \n",
      "0.40      0.40  0.707101  0.756969                  0.532266   \n",
      "0.45      0.45  0.731754  0.751710                  0.521002   \n",
      "0.50      0.50  0.757596  0.779682                  0.539767   \n",
      "0.55      0.55  0.785107  0.786744                  0.531223   \n",
      "0.60      0.60  0.814717  0.814351                  0.543136   \n",
      "0.65      0.65  0.847136  0.848868                  0.565066   \n",
      "0.70      0.70  0.883301  0.946968                  0.643512   \n",
      "0.75      0.75  0.924724  0.997621                  0.674609   \n",
      "0.80      0.80  0.973874  1.073520                  0.699333   \n",
      "0.85      0.85  1.035265  1.053558                  0.590991   \n",
      "0.90      0.90  1.118596  1.097468                  0.433221   \n",
      "\n",
      "      DML CVaR pointwise upper  DML CVaR joint lower  DML CVaR joint upper  \n",
      "0.10                  0.831019              0.367571              0.887556  \n",
      "0.15                  0.879103              0.420967              0.934992  \n",
      "0.20                  0.903339              0.455293              0.957996  \n",
      "0.25                  0.918227              0.459383              0.974202  \n",
      "0.30                  0.926621              0.448587              0.984937  \n",
      "0.35                  0.960808              0.459812              1.021926  \n",
      "0.40                  0.981672              0.469825              1.044113  \n",
      "0.45                  0.982417              0.456892              1.046527  \n",
      "0.50                  1.019596              0.473099              1.086264  \n",
      "0.55                  1.042265              0.460218              1.113270  \n",
      "0.60                  1.085566              0.467770              1.160932  \n",
      "0.65                  1.132671              0.486202              1.211534  \n",
      "0.70                  1.250425              0.559186              1.334750  \n",
      "0.75                  1.320633              0.584849              1.410393  \n",
      "0.80                  1.447706              0.595353              1.551686  \n",
      "0.85                  1.516125              0.462451              1.644665  \n",
      "0.90                  1.761714              0.248638              1.946297  \n"
     ]
    }
   ],
   "source": [
    "CVAR = np.array(Y1_cvar) - np.array(Y0_cvar)\n",
    "data = {\"Quantile\": tau_vec, \"CVaR\": CVAR, \"DML CVaR\": dml_CVAR.coef,\n",
    "        \"DML CVaR pointwise lower\": ci_CVAR['2.5 %'], \"DML CVaR pointwise upper\": ci_CVAR['97.5 %'],\n",
    "        \"DML CVaR joint lower\": ci_joint_CVAR['2.5 %'], \"DML CVaR joint upper\": ci_joint_CVAR['97.5 %']}\n",
    "df = pd.DataFrame(data)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x750 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = 10., 7.5\n",
    "fig, ax = plt.subplots()\n",
    "ax.grid()\n",
    "\n",
    "ax.plot(df['Quantile'],df['DML CVaR'], color='violet', label='Estimated CVaR')\n",
    "ax.plot(df['Quantile'],df['CVaR'], color='green', label='True CVaR')\n",
    "ax.fill_between(df['Quantile'], df['DML CVaR pointwise lower'], df['DML CVaR pointwise upper'], color='violet', alpha=.3, label='Pointwise Confidence Interval')\n",
    "ax.fill_between(df['Quantile'], df['DML CVaR joint lower'], df['DML CVaR joint upper'], color='violet', alpha=.2, label='Joint Confidence Interval')\n",
    "\n",
    "plt.legend()\n",
    "plt.title('Conditional Value at Risk', fontsize=16)\n",
    "plt.xlabel('Quantile')\n",
    "_ = plt.ylabel('QTE and 95%-CI')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}