{
 "cells": [
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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": {
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   },
   "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": {},
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   "metadata": {
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   "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": {
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   },
   "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): [0.5467606094959261, 0.7559417564883749, 0.9567242535070148, 1.1530959776797396, 1.34769649731686, 1.5425843074324594, 1.7395359436844482, 1.940354721701296, 2.1469734445741286, 2.361518457569366, 2.586719493648897, 2.8259803249536914, 3.0841842065698133, 3.3684990272106954, 3.6903344145051182, 4.0701961897676835, 4.551586928482123]\n",
      "Conditional Value at Risk Y(1): [1.110902411746278, 1.3453813031813522, 1.5700384030890744, 1.789671060840732, 2.007332393760465, 2.225459760731946, 2.4461928741399595, 2.6716717587835648, 2.9041560442482157, 3.146142808990006, 3.400855956463958, 3.6723684718264447, 3.9666592590622916, 4.292302995303554, 4.663081975281988, 5.103951906910721, 5.667614205604159]\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": {
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   },
   "source": [
    "Let us generate $n=5000$ observations and convert them to a [DoubleMLData](https://docs.doubleml.org/stable/api/generated/doubleml.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)\n",
    "ml_m = LGBMClassifier(n_estimators=300, learning_rate=0.05, num_leaves=10)\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   0.546761   1.110902       0.360683       1.057962   \n",
      "1       0.15   0.755942   1.345381       0.590911       1.273356   \n",
      "2       0.20   0.956724   1.570038       0.829543       1.489699   \n",
      "3       0.25   1.153096   1.789671       1.015038       1.697000   \n",
      "4       0.30   1.347696   2.007332       1.203284       1.925736   \n",
      "5       0.35   1.542584   2.225460       1.502494       2.144084   \n",
      "6       0.40   1.739536   2.446193       1.678826       2.338775   \n",
      "7       0.45   1.940355   2.671672       1.822482       2.559144   \n",
      "8       0.50   2.146973   2.904156       2.153119       2.824701   \n",
      "9       0.55   2.361518   3.146143       2.156969       3.041831   \n",
      "10      0.60   2.586719   3.400856       2.495657       3.298120   \n",
      "11      0.65   2.825980   3.672368       2.653846       3.582761   \n",
      "12      0.70   3.084184   3.966659       2.847948       3.842405   \n",
      "13      0.75   3.368499   4.292303       3.076347       4.163895   \n",
      "14      0.80   3.690334   4.663082       3.523163       4.543075   \n",
      "15      0.85   4.070196   5.103952       3.869020       4.913774   \n",
      "16      0.90   4.551587   5.667614       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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",
      "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",
      "================== 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  3.923074e-11  0.476856  0.879103\n",
      "0.20  0.706645  0.100356  7.041387  1.903351e-12  0.509951  0.903339\n",
      "0.25  0.716793  0.102775  6.974414  3.071488e-12  0.515358  0.918227\n",
      "0.30  0.716762  0.107073  6.694154  2.169230e-11  0.506903  0.926621\n",
      "0.35  0.740869  0.112216  6.602168  4.051867e-11  0.520930  0.960808\n",
      "0.40  0.756969  0.114647  6.602628  4.039310e-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  9.578846e-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  0.564142  0.627564                  0.424108   \n",
      "0.15      0.15  0.589440  0.677980                  0.476856   \n",
      "0.20      0.20  0.613314  0.706645                  0.509951   \n",
      "0.25      0.25  0.636575  0.716793                  0.515358   \n",
      "0.30      0.30  0.659636  0.716762                  0.506903   \n",
      "0.35      0.35  0.682875  0.740869                  0.520930   \n",
      "0.40      0.40  0.706657  0.756969                  0.532266   \n",
      "0.45      0.45  0.731317  0.751710                  0.521002   \n",
      "0.50      0.50  0.757183  0.779682                  0.539767   \n",
      "0.55      0.55  0.784624  0.786744                  0.531223   \n",
      "0.60      0.60  0.814136  0.814351                  0.543136   \n",
      "0.65      0.65  0.846388  0.848868                  0.565066   \n",
      "0.70      0.70  0.882475  0.946968                  0.643512   \n",
      "0.75      0.75  0.923804  0.997621                  0.674609   \n",
      "0.80      0.80  0.972748  1.073520                  0.699333   \n",
      "0.85      0.85  1.033756  1.053558                  0.590991   \n",
      "0.90      0.90  1.116027  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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mcnIyFi9ejLlz5+Kpp57CoEGD4OfnZ/WcHj164Omnn0bfvn3xxBNPIDU1Fc8//3yxXhcRuYfS+7EpEZV6tWrVwvDhwwEA06ZNK3CvKECdPmhZ11OtWjVt6lten2QLIbT7LftJ3Y9OnTpBkiT8+uuvOHPmTK7HT548WegpgZY38JZ1Qbby8PDQCgLk9ym+pehDcVy75ftSpUqVXMEKQL6jDffL398fw4YNA6Bej+WaRowYAW9v7zzbmNdoUEJCAnbv3l2oc1tCaV7fa+DO2qq79e7dG8CdQFQcCrq2H374ATdu3MjzeUX9+bKFt7c3Fi5cCED9GTx//nyRXyswMBBz5sxBcHAw0tPT892frlOnToiKikKZMmXwwgsv4K233iryOYnIfTFcEVGp9v7776N27dqIiYnBQw89hAMHDuQ6Jjs7G6tWrUKLFi1w+vRp7f7p06cDAN58802cPHlSu18IgXnz5uHEiRMIDg7GxIkT77ud1atXx6BBg6AoCp555hmr9S23b9/Gs88+W+hpWdWqVQOgjjwUtE4oL9OmTQMAfPzxx4iKirJ6zDJ9y2Aw4IUXXijU6+albt260Ov1+OOPPxAdHW312Pfff4/33nvvvs+RH8v0vw0bNmhrju6eEgio080A4NNPP0V2drZ2f1JSEsaMGYOkpKRCnbdNmzYIDAzEqVOn8Pnnn1s99vXXX2PZsmV5Pu/JJ59EjRo18PXXX+Pll1/Oc+QrLi4u3wqWebFcm6Uqo8XZs2fx9NNP5/s8y89XQevk7segQYPQtm1byLKcZ8GLu6Wnp2Px4sV5rjX76aefkJiYCL1er7U7Lw888ACio6MREhKCV199tdAFNYjI/TFcEVGpVqZMGfz888+IiIjA6dOn0bFjR9SsWRMDBw7EiBEj8PDDD6NcuXKYMGECUlNTUaVKFe25Tz31FEaNGoUbN26gdevW6NatG0aMGIEGDRrg9ddfh4+PD9atW6cVzrhfH374IWrVqoXo6GiEh4fjsccew6OPPoqaNWsiPj6+0BshV69eHa1bt0ZCQgKaNGmC//znP3jiiSdsesPYu3dvvPbaa8jMzET37t3RsWNHjBw5Eq1atcK4ceOg1+uxfPnyQm1snJ/y5ctj8uTJkGUZDz/8MCIiIjBixAi0atUK/fv3x0svvXTf58jPgw8+iIYNGyI1NRWZmZlo3rw5WrZsmeu4KVOmIDg4GNu3b0fNmjXx+OOPY8CAAahRowZOnjyZ72bK+fHx8dECw+jRo9G+fXsMHjwYjRs3xtChQ/P9Hvn5+WHbtm0ICwvDokWLUL16dXTu3BkjR47EoEGD0KhRI1SpUgWzZs2yuS2zZ8+GJEmYNWsWmjZtiuHDh+Phhx9GkyZNULNmTbRv3z7P5w0cOBA6nQ7Lli1D9+7dMX78eDzxxBP47rvvCtUXBVmwYAEA4P/+7//uWS0yOzsb06ZNQ0hICJo3b47BgwdjxIgRaN++PTp37gwAePXVV+/577VJkyb46aefUL16dSxcuBCTJk2y23ozInI9DFdEVOpVrFgRe/fuxY4dOzB69Gjo9XpERUVh48aNOHXqFNq1a4clS5YgJiYGbdq00Z4nSRLWrl2LdevW4aGHHsLx48exceNGpKenY+zYsfjtt9+0aVrFISQkBIcPH8Zzzz0HX19fbN26FUePHsWwYcPwyy+/5LnH0r1s2rQJI0aMQHJyMr766iusXLkS69evt+m5b775Jnbs2IHevXvj9OnT2LBhA65du4bBgwfj4MGDhQ4UBXnvvfewcuVKtGjRAsePH8f27dvh6+uL9evX48033yy28+Ql50hVftcUHh6O3377DSNHjoRer8fWrVtx8uRJDB8+HL/99htCQ0MLfd4pU6ZgzZo1aNmyJX777Tfs2rULlSpVwq5duwrs20aNGuH333/HokWL0KBBA/z+++/4+uuvcfjwYfj5+WH69On3rPyX06OPPop9+/bh4YcfRmxsLL777jskJCRgzpw52LFjBwwGQ57Pa9q0KTZt2oR27drh8OHDiIyMxMqVK/Hrr78Wui/y06VLF21vtDlz5hR4rL+/P5YvX46hQ4ciKysLu3fvxpYtW5CQkIBHH30UUVFRNo2AAUDt2rVx4MAB1K1bFx999BHGjh3rkKI4ROR8JMGPW4iIiIiIiO4bR66IiIiIiIiKAcMVERERERFRMWC4IiIiIiIiKgYMV0RERERERMWA4YqIiIiIiKgYMFwREREREREVAw9HN8AZKYqCa9euISAgAJIkObo5RERERETkIEIIpKSkoEqVKtDpCh6bYrjKw7Vr14q04SMREREREbmny5cvo1q1agUew3CVh4CAAABqBwYGBjq0LUajEbt27UKPHj1gMBgc2hZ3xP61L/avfbF/7Y99bF/sX/ti/9oX+9e+nKl/k5OTERoaqmWEgjBc5cEyFTAwMNApwpWvry8CAwMd/oPljti/9sX+tS/2r/2xj+2L/Wtf7F/7Yv/alzP2ry3LhVjQgoiIiIiIqBgwXBERERERERUDh4arBQsW4IEHHkBAQAAqVqyIgQMH4uzZs/d83tdff4369evD29sbTZo0wfbt260eF0Lg9ddfR+XKleHj44Nu3brh3Llz9roMIiIiIiIix6652rdvHyZNmoQHHngAJpMJ//3vf9GjRw+cOnUKfn5+eT7n4MGDGD58OBYsWIC+ffti3bp1GDhwIH799Vc0btwYALBo0SIsW7YMa9asQXh4OGbNmoWePXvi1KlT8Pb2LslLJCIiIhekKAqys7Md3Yz7YjQa4eHhgczMTMiy7OjmuB32r32VZP8aDAbo9fpieS2HhqudO3dafR0ZGYmKFSvi+PHj6NSpU57PWbp0KXr16oWXXnoJAPDmm29i9+7d+OCDD7B8+XIIIbBkyRK89tprGDBgAABg7dq1qFSpErZs2YJhw4bZ96KIiIjIpWVnZyMmJgaKoji6KfdFCIGQkBBcvnyZ+3baAfvXvkq6f4ODgxESEnLf53KqaoFJSUkAgLJly+Z7zKFDhzB16lSr+3r27IktW7YAAGJiYhAXF4du3bppjwcFBaFt27Y4dOgQwxURERHlSwiB2NhY6PV6hIaG3nPDUGemKApSU1Ph7+/v0tfhrNi/9lVS/SuEQHp6OhISEgAAlStXvq/Xc5pwpSgKpkyZgg4dOmjT+/ISFxeHSpUqWd1XqVIlxMXFaY9b7svvmLtlZWUhKytL+zo5ORmAOhxpNBoLfzHFyHJ+R7fDXbF/7Yv9a1/sX/tjH9uXM/avyWRCWloaqlSp4vJLCYQQyM7OhpeXF0dW7ID9a18l2b9eXl5QFAXXr19HmTJlck0RLMzvKKcJV5MmTcKff/6JAwcOlPi5FyxYgLlz5+a6f9euXfD19S3x9uRl9+7djm6CW2P/2hf7177Yv/bHPrYvZ+pfDw8PhISEIDs7W/uw1dWlpKQ4uglujf1rXyXVv4qiICMjA1FRUTCZTFaPpaen2/w6ThGuJk+ejK1bt2L//v2oVq1agceGhIQgPj7e6r74+HiEhIRoj1vuyzmsFx8fj+bNm+f5mjNnzrSaamjZhblHjx5OsYnw7t270b17d6fZQM2dsH/ti/1rX+xf+2Mf25cz9m9mZiYuX74Mf39/txi5SklJQUBAAEdW7ID9a18l3b+ZmZnw8fFBp06dcv3bL8wHLQ4NV0IIPPfcc9i8eTOio6MRHh5+z+e0a9cOUVFRmDJlinbf7t270a5dOwBAeHg4QkJCEBUVpYWp5ORkHD58GM8880yer+nl5QUvL69c9xsMBqf5Ze9MbXFH7F/7Yv/aF/vX/tjH9uVM/SvLMiRJgk6nc/l1NJaCHJbroeLF/rWvku5fnU4HSZLy/H1UmN9PDv1JmDRpEr744gusW7cOAQEBiIuLQ1xcHDIyMrRjRo8ejZkzZ2pfv/DCC9i5cyfeffddnDlzBnPmzMGxY8cwefJkAOo3YMqUKZg3bx6+++47/PHHHxg9ejSqVKmCgQMHlvQlEhEREbmkyMhIBAcHO7oZheKKbSb34tBw9fHHHyMpKQkRERGoXLmydvvqq6+0Yy5duoTY2Fjt6/bt22PdunX49NNP0axZM2zcuBFbtmyxKoIxY8YMPPfcc3jyySfxwAMPIDU1FTt37nT54X0iIiKivIwdOxaSJFnd9Ho9Hn/8cZueHxYWhiVLlljdN3ToUPz99992aK01RwSivXv3ok+fPihXrhx8fX3RsGFDTJs2DVevXsWmTZug1+tx9erVPJ9bp06dXJWr8xIdHW31/ahQoQL69OmDP/74o7gvh5yIw6cF3kt0dHSu+wYPHozBgwfn+xxJkvDGG2/gjTfeuJ/mEREREbmMXr16YfXq1drX97sRso+PD3x8fIqjaU7lk08+wbPPPosxY8Zg06ZNCAsLw6VLl7B27Vq8++67WLhwIcqVK4c1a9bgv//9r9Vz9+/fj/Pnz2PChAk2n+/s2bMIDAzEtWvX8NJLL+GRRx7B+fPn4enpWdyXRk6AE0SJiIiI3ICXlxdCQkKsbpYRISEE5syZg+rVq8PLywtVqlTB888/DwCIiIjAv//+ixdffFEbZQFyjyjNmTMHzZs3x6pVq1C9enX4+/vj2WefhSzLWLRoEUJCQlCxYkXMnz/fql2LFy9GkyZN4Ofnh9DQUDz77LNITU0FoH6IPm7cOCQlJWnnnjNnDgB1q5zp06ejatWq8PPzQ9u2bXN96B4ZGYnq1avD19cXgwYNws2bNwvsoytXruD555/H888/j1WrViEiIgJhYWHo1KkTVqxYgddffx0GgwGjRo1CZGRkruevWrUKbdu2RaNGjfDee++hffv2CAgIyHVdOVWsWBEhISFo2bIlpkyZgsuXL+PMmTMFtpNcF8MVERERUT6EEBDZDrrZMMPHVps2bcJ7772HTz75BOfOncOWLVvQpEkTAMA333yDatWq4Y033kBsbKzVcoy7XbhwATt27MDOnTvxf//3f1i5ciUeeeQRXLlyBfv27cPChQvx2muv4fDhw9pzdDodli1bhr/++gtr1qzBjz/+iBkzZgBQl3ssWbIEgYGB2rmnT58OQK0mfejQIaxfvx6///47Bg8ejF69euHcuXMAgMOHD2PChAmYPHkyTpw4gS5dumDevHkF9sPXX3+N7Oxs7fx3s4TJCRMm4Ny5c9i/f7/2WGpqKjZu3KiNWul0OixcuBB//PFHruvKS1JSEtavXw8AHLVyY05Rip2IiIjIKRmBxIWJDjl18MvBQCHeg2/duhX+/v5W97344ouYO3cuLl26hJCQEHTr1g0GgwHVq1dHmzZtAABly5aFXq9HQECAtqVNfhRFwapVqxAQEICGDRuiS5cuOHv2LLZv3w6dTod69eph4cKF2Lt3L9q2bQsAVhWew8LCMG/ePDz99NP46KOP4OnpiaCgIEiSZHXuS5cuYfXq1bh06RKqVKkCAJg+fTp27tyJ1atX46233sLSpUvRq1cvLdDUrVsXBw8exM6dO/Nt/7lz5xAYGGi1XU9eGjZsiAcffBCrVq1Cp06dAAAbNmyAEALDhg0DoBZZS05ORmBgIGrWrGl1XTlZthlKS0sDAPTv3x/169cv8PzkuhiuiIiIiNxAly5d8PHHH2tfK4qilZAePHgwlixZgpo1a6JXr17o06cP+vXrBw+Pwr0VDAsLQ0BAgPZ1pUqVoNfrrUplV6pUCQkJCdrXe/bswYIFC3DmzBkkJyfDZDIhMzMT6enp8PX1zfM8f/zxB2RZRt26da3uz8rKQrly5QAAp0+fxqBBg6web9euXYHhSghh855J48ePx4svvoj3338fAQEBWLVqFQYPHqxd/549ezB//nycP3++wOv66aef4Ovri19++QVvvfUWli9fbtP5yTUxXBERERHlx2AeQXLQuQvDz88PtWvX1r5WFEXb/DQ0NBRnz57Fnj17sHv3bjz77LN4++23sW/fvkLt4XP3sZZ9ge6+z7JH0cWLF9G3b18888wzmD9/PsqWLYsDBw5gwoQJyM7OzjdcpaamQq/X4/jx49Dr9VaP3T06Vxh169ZFUlISYmNj7zl6NWzYMLz44ovYsGEDOnXqhJ9//hkLFizQrqt///4YP348FixYgPLly+d7XeHh4QgODka9evWQkJCAoUOHWk03JPfCNVdERERE+ZAkCZKng242jrDYysfHB/369cOyZcsQHR2NQ4cOaWXBPT09IctysZ4PAI4fPw5FUfDuu+/iwQcfRN26dXHt2jWrY/I6d4sWLSDLMhISElC7dm2rm2X6YIMGDazWdgHAL7/8UmB7Hn/8cXh6emLRokV5Pp6YmKj9PSAgAIMHD8aqVauwevVq1K1bFx07drS6rnnz5uV7XXmZNGkS/vzzT2zevPmex5Jr4sgVERERkRvIyspCXFyc9rWiKMjIyEBgYCAiIyMhyzLatm0LX19ffPHFF/Dx8UGNGjUAqNP99u/fj2HDhsHLywvly5cvljbVrl0bRqMR77//Pvr164eff/4517S4sLAwpKamIioqCs2aNYOvry/q1q2LkSNHYvTo0Xj33XfRokULXL9+HVFRUWjatCkeeeQRPP/88+jQoQPeeecdDBgwAD/88EOBUwIBdQTvvffew+TJk5GcnIzRo0cjLCwMV65cwdq1a+Hv7493331XO37ChAno2LEjTp8+jZdffjnXdX366ad4/PHHcejQIZum+/n6+mLixImYPXs2Bg4cWOwBmhyPI1dEREREbmDnzp2oXLmydqtatSp69+4NQK2C99lnn6FDhw5o2rQp9uzZg++//15bv/TGG2/g4sWLqFWrFipUqFBsbWrWrBkWL16MhQsXonHjxvjyyy+1qXUW7du3x9NPP42hQ4eiQoUK2qjS6tWrMXr0aEybNg316tXDwIEDcfToUVSvXh0A8OCDD+Kzzz7D0qVL0axZM+zatQuvvfbaPdv07LPPYteuXbh69SoGDRqE+vXr44knnkBgYKBWqdDioYceQr169bQglvO63n33XSxduhRNmzbN87ryM3nyZJw+fRpff/21TceTa5FEcdb5dBPJyckICgpCUlISAgMDHdoWo9GI7du3o0+fPoWaE022Yf/aF/vXvti/9sc+ti9n7N/MzEzExMQgPDwc3t7ejm7OfbGsuQoMDLQqOEHFg/1rXyXdvwX92y9MNuBPAhERERERUTFguCIiIiIiIufionPrGK6IiIiIiMi5KI5uQNEwXBERERERkdMQioCQXXPoiuGKiIiIiIichwKOXBEREREREd0PIVx31ApguCIiIiIiImchQx21ctGU4qLNJiIiIiIid6KNWkmObknRMVwREREREZHjWdZauXBCceGmExERERGROxBCQJjUUSvJhYeuGK6IiIiIXJgkSXne9Ho9ypQpg7lz55Zoe86fP49x48ahWrVq8PLyQnh4OIYPH45jx44hPj4eBoMB69evz/O5EyZMQMuWLW06T85rDQwMxAMPPIBvv/22OC+FSpIbjFoBLt98IiIiotItNjZWuy1ZsgSBgYGIjY3F1atXcebMGUybNk07VggBk8lkt7YcO3YMrVq1wt9//41PPvkEp06dwubNm1G/fn1MmzYNlSpVwiOPPIJVq1blem5aWho2bNiACRMm2Hy+1atXIzY2FseOHUOHDh3w+OOP448//ijOS6ISIIRQC1m4+KgVwHBFRERE5NJCQkK0W1BQECRJ0r4+d+4cgoKCsGPHDrRq1QpeXl44cOAAxo4di4EDB1q9zpQpUxAREaF9rSgKFixYgPDwcPj4+KBZs2bYuHFjvu0QQmDs2LGoU6cOfvrpJzzyyCOoVasWmjdvjtmzZ2ujShMmTEBUVBQuXbpk9fyvv/4aJpMJI0eOxM6dO/HQQw8hODgY5cqVQ9++fXHhwoVc5wwODkZISAjq1q2LN998EyaTCXv37i16Z5JjKOrGwe6QTDwc3QAiIiIiZyWEQLox3SHn9jX4QpKK51P8V155Be+88w5q1qyJMmXK2PScBQsW4IsvvsDy5ctRp04d7N+/H//5z39QoUIFdO7cOdfxJ06cwF9//YV169ZBp8v9Ljk4OBgA0KdPH1SqVAmRkZF4/fXXtcdXr16NRx99FMHBwUhLS8PUqVPRtGlTpKam4vXXX8egQYNw4sSJPF/bZDJh5cqVAABPT0+bro+cgzZqBdcftQIYroiIiIjylW5Mh/8Cf4ecO3VmKvw8/Yrltd544w10797d5uOzsrLw1ltvYc+ePWjXrh0AoGbNmjhw4AA++eSTPMPVuXPnAAD169cv8LX1ej3GjBmDyMhIzJo1C5Ik4cKFC/jpp5+we/duAMBjjz1m9ZxVq1ahQoUKOHXqFBo3bqzdP3z4cOj1emRkZEBRFISFhWHIkCE2Xyc5ATcatQLc5jKIiIiIKD+tW7cu1PHnz59Heno6unfvDn9/f+22du3aPKfnAeYRCBuNHz8eMTEx2hS+1atXIywsDF27dgWgBrXhw4ejZs2aCAwMRFhYGADkmkr43nvv4cSJE9ixYwcaNmyIFStWoGzZsoW6VnIcdxu1AjhyRURERJQvX4MvUmemOuzcxcXPz3oETKfT5QpDRqNR+3tqqnrN27ZtQ9WqVa2O8/LyyvMcdevWBQCcOXMGLVq0KLA9derUQceOHbF69WpERERg7dq1mDhxojYNsl+/fqhRowY+++wzVKlSBYqioHHjxsjOzrZ6nZCQENSuXRu1a9fG6tWr0adPH5w6dQoVK1Ys8PzkJIR51Mo9chUAhisiIiKifEmSVGxT85xJhQoV8Oeff1rdd+LECRgMBgBAw4YN4eXlhUuXLuU5BTAvzZs3R8OGDfHuu+9i6NChudZGJSYmauuuALWwxTPPPIP+/fvj6tWrGDt2LADg5s2bOHv2LD777DN07NgRAHDgwIF7nr9NmzZo1aoV5s+fj6VLl9rUZnIwGYAAJJ37pCtOCyQiIiIqZbp27Ypjx45h7dq1OHfuHGbPnm0VtgICAjB9+nS8+OKLWLNmDS5cuIBff/0V77//PtasWZPna0qShNWrV+Pvv/9Gx44dsX37dvzzzz/4/fffMX/+fAwYMMDq+MGDB8NgMOCpp55Cjx49EBoaCgAoU6YMypUrh08//RTnz5/Hjz/+iKlTp9p0XVOmTMEnn3yCq1evFrFnqKQIRUDI7rPWysLNLoeIiIiI7qVnz56YNWsWZsyYgQceeAApKSkYPXq01TFvvvkmZs2ahQULFqBBgwbo1asXtm3bhvDw8Hxft02bNjh27Bhq166NiRMnokGDBujfvz/++usvLFmyxOpYX19fDBs2DLdv38b48eO1+3U6HdavX4/jx4+jcePGePHFF/H222/bdF29evVCeHg45s+fb3tnkGNYRq2KqSKms+C0QCIiIiI3MXbsWG16HQA89NBDkGU5z/Llc+fOxdy5c/N9LUmS8MILL+CFF14oVBvq1q2b7+jW3T755BN88sknue7v1q0bTp06ZXXf3WvE8iqgIUkSTp8+XYjWkiO466gV4JaXRERERERETkuBW45aAQxXRERERERUQtx51Apw28siIiIiIiKno5hv7jdoBYDhioiIiIiISoAQd0at3GXT4LsxXBERERERkf3JcOtRK4DhioiIiIiI7EwbtZLcd9QKYLgiIiIiIiJ7s6y1cvP04eaXR0REREREjlRaRq0AhisiIiIiIrKnUjJqBZSKSyQiIiK6P8IoIDJL8GYUJXp9kZGRCA4OLtFzAkBYWBiWLFlS4ue1h7i4OHTv3h1+fn5aX0qShC1btuT7nIsXL0KSJJw4caJE2ugIQgi1kAUcO2pVUn3tYddXJyIiInJxwiiQfTYbIrPkAo/kLcGznickg21vRseOHYs1a9YAAAwGA6pXr45Ro0Zh0qRJNj1/6NCh6NOnT6HaGBERgebNm99XODp69Cj8/PyK/Hxb/Pbbb3jrrbewf/9+JCUlITQ0FBEREXjppZdQt27dYjvPe++9h9jYWJw4cQJBQUEAgNjYWJQpU6bYzuEokZGRmDJlChITE21+juXn471334NQ3HfT4LuVksskIiIiKiIZarDyUEOPvW/wMJ9PLlwze/XqhdjYWJw7dw7Tpk3D3LlzsWzZMpue6+Pjg4oVKxahc+5PhQoV4Ovra7fX37p1Kx588EFkZWXhyy+/xOnTp/HFF18gKCgIs2bNKtZzXbhwAa1atUKdOnW0vgwJCYGXl1exnseVFMeoVXZ2djG2yP4YroiIiIhsIHlIkDxL4OZRtDehXl5eCAkJQY0aNfDMM8/g4Ycfxs6dOwEAt2/fxujRo1GmTBn4+vqid+/eOHfunPbcu6cFzpkzB82bN8fnn3+OsLAwBAUFYdiwYUhJSQGgjpTt27cPS5cuhSRJkCQJFy9eROvWrfHOO+9orzNw4EAYDAakpqYCAK5cuQJJknD+/HkA1tMChRCYM2cOqlevDi8vL1SpUgXPP/+89lpZWVmYPn06qlatCj8/P7Rt2xbR0dH59kd6ejrGjRuHPn364LvvvkO3bt0QHh6Otm3b4p133sEnn3yiHbtv3z60adMGXl5eqFy5Ml555RWYTCbt8YiICDz//POYMWMGypYti5CQEMydO1d7PCwsDJs2bcLatWshSRLGjh0LIPe0wCNHjqBFixbw9vZG69at8dtvv+Vq959//onevXvD398flSpVwqhRo3Djxo0C2zJnzhyr10hMTMRTTz2FSpUqwdvbG40bN8bWrVu1xw8cOICOHTvCx8cHoaGheP7555GWlpZvX97N1p+PZcuWQeelg95Hj4sXL6rX99ef6NOvDwLKBiAkNASjx422ur4u3btg8guTMXX6VNSqVQu9e/fGiBEjMHToUKs2GI1GlC9fHmvXrgUA7Ny5Ew899BCCg4NRrlw59O3bFxcuXLD5mooLwxURERGRG/Lx8dE+9R87diyOHTuG7777DocOHYIQAn369IHRaMz3+RcuXMCWLVuwdetWbN26Ffv27cP//vc/AMDSpUvRrl07TJw4EbGxsYiNjUVoaCg6d+6sBR4hBH766ScEBwfjwIEDANQQU7VqVdSuXTvX+TZt2oT33nsPn3zyCc6dO4ctW7agSZMm2uOTJ0/GoUOHsH79evz+++8YPHgwevXqZRUSc/rhhx9w48YNzJgxI8/HLWHy6tWr6NOnDx544AGcPHkSH3/8MVauXIl58+ZZHb9mzRr4+fnh8OHDWLRoEd58803s3bsXgDq9sVevXhgyZAhiY2OxdOnSXOdLTU1F37590bBhQxw/fhxz5szB9OnTrY5JTExE165d0aJFCxw7dgw7d+5EfHw8hgwZUmBb3njjDezevRsAoCgKevfujZ9//hlffPEFTp06hf/973/Q6/UA1O9rr1698Nhjj+H333/HV199hQMHDmDy5Ml59lN+bPn5eGLCE7j6z1Vc+/caQkNDkZiYiId7PozmzZvj6MGj2PH9DsTHx2PoSOvgtPaLtTB4GrBz50589NFHGDlyJL7//nstpAPq9zc9PR2DBg0CAKSlpWHq1Kk4duwYoqKioNPpMGjQICiKUqjrul9cc0VERETkRoQQiIqKwq5duzBx4kScO3cO3333HX7++We0b98eAPDll18iNDQUW7ZsweDBg/N8HUVREBkZiYCAAADAqFGjEBUVhfnz5yMoKAienp7w9fVFSEiI9pyIiAisXLkSsizjzz//hKenJ4YOHYro6Gj06tUL0dHR6Ny5c57nu3TpEkJCQtCtWzdt3VibNm20x1avXo1Lly6hSpUqAIDp06dj586dWL16Nd56661cr2cJXfXr1y+wvz766COEhobigw8+gCRJqF+/Pq5du4aXX34Zr7/+OnQ6dSyiadOmmD17NgCgTp06+OCDD7Bv3z4MGDAAFSpUgJeXF3x8fKz6I6d169ZBURSsXLkS3t7eaNSoEa5cuYJnnnlGO+aDDz5AixYtrK5n1apVCA0Nxd9//62tEcurLVFRUejevTv27NmDI0eO4PTp09rxNWvW1F5vwYIFGDlyJKZMmaI9f9myZejcuTM+/vhjeHt7F9hfFjb9fHj7IqRKCCRJHY394OMP0KJZC7z15p3rW/npSlSvVd3q+urUroOFby1ESmYKAgMDUadOHfj5+WHz5s0YNWqU1p/9+/fXzv/YY49ZtW/VqlWoUKECTp06hcaNG9t0TcWBI1dEREREbmDr1q3w9/eHt7c3evfujSFDhuCVV17B6dOn4eHhgbZt22rHlitXDvXq1cPp06fzfb2wsDDtjSsAVK5cGQkJCQW2oWPHjkhJScFvv/2Gffv2oXPnzoiIiNBGs/bt24eIiIg8nzt48GBkZGSgZs2amDhxIjZv3qxNzfvjjz8gyzLq1q0Lf39/7bZv3758p34JYVsBktOnT6Ndu3ZaAACADh06IDU1FVeuXNHua9q0qdXzQkJCrKaz2XKepk2bWoWXdu3aWR1z8uRJ7N271+oaLeEw53Xe3Zac35sTJ06gWrVq+RbrOHnyJCIjI63O0bNnTyiKgpiYGJuv554/H+buz9mvv//+O/bu24uAsgHarUHTBur1/XPn+lq2bGl1Lg8PDwwZMgRffvklAHWU6ttvv8XIkSO1Y86dO4fhw4ejZs2aCAwMRFhYGAA1mJckjlwRERERuYEuXbrg448/hqenJ6pUqQKdTofk5OQiv57BYLD6WpKke06xCg4ORrNmzRAdHY1Dhw6he/fu6NSpE4YOHYq///4b586dy3fkKjQ0FGfPnsWePXuwe/duPPvss3j77bexb98+pKamQq/X4/jx49r0Ngt/f/88X88SLs6cOZMrxBRFUfqjsFJTU9GvXz8sXLgw12OVK1e2qS0+Pj73PMdTTz1ltZ7Nonr16ja3taA2CEWo4equ5YOpqano90g//G/+/3K9Xs7r8/PNXUFy5MiR6Ny5MxISErB79274+PigV69e2uP9+vVDjRo18Nlnn6FKlSpQFAWNGzcu8YIYDFdEREREbsDPz89qLZPljW6DBg1gMplw+PBhbVrgzZs3cfbsWTRs2LDI5/P09IQs5y5p2LlzZ+zduxdHjhzB/PnzUbZsWTRo0ADz589H5cqVCyx/7uPjg379+qFfv36YNGkS6tevjz/++AMtWrSALMtISEhAx44dbWpfjx49UL58eSxatAibN2/O9XhiYiKCg4PRoEEDbNq0CUIIbZTl559/RkBAAKpVq2Zjb9xbgwYN8PnnnyMzM1Mbvfrll1+sjmnZsiU2bdqEsLAweHgU7W1606ZNceXKFatpdnef49SpU3mueys2CmDwNOT6+WjRogW+2fxNka6vffv2CA0NxVdffYUdO3Zg8ODBWsCz/Dx/9tln2s+HZZ1fSeO0QCIiIiI3VqdOHQwYMAATJ07EgQMHcPLkSfznP/9B1apVMWDAgCK/blhYGA4fPoyLFy/ixo0bWpiLiIjADz/8AA8PD21KW0REBL788st8R60AtWLhypUr8eeff+Kff/7BF198AR8fH9SoUQN169bFyJEjMXr0aHzzzTeIiYnBkSNHsGDBAmzbti3P1/Pz88OKFSuwbds29O/fH3v27MHFixdx7NgxzJgxA08//TQA4Nlnn8Xly5fx3HPP4cyZM/j2228xe/ZsTJ06VVtvVRxGjBgBSZIwceJEnDp1Ctu3b7eqrAgAkyZNwq1btzB8+HAcPXoUFy5cwA8//IBx48blGWTz0rlzZ3Tq1AmPPfYYdu/ejZiYGOzYsUOrHPnyyy/j4MGDmDx5Mk6cOIFz587h22+/LXRBi/wIRUDIAmE1wnDk6BGrn49JT0/Crdu3MGLUCBw9Zr6+XT9g/MTxNl3fiBEjsHz5cuzevdtqSmCZMmVQrlw5fPrppzh//jx+/PFHTJ06tViup7AYroiIiIhsIEwCIrsEbqbi36x49erVaNWqFfr27Yt27dpBCIHt27fnmtpVGNOnT4der0fDhg1RoUIFbW1Lx44doSiKVZCKiIiALMv5rrcC1CmFn332GTp06ICmTZtiz549+P7771GuXDntGkaPHo1p06ahXr16GDhwII4ePVrgVLYBAwbg4MGDMBgMGDFiBOrXr4/hw4cjKSlJqwZYtWpVbN++HUeOHEGzZs3w9NNPY8KECXjttdeK3Dd58ff3x/fff6+NxL366qu5pv9VqVIFP//8M2RZRo8ePdCkSRNMmTIFwcHBhQp6mzZtwgMPPIDhw4ejYcOGmDFjhhZemjZtin379uHvv/9Gx44d0aJFC7z++utaoZD7pqi3aS9Og16vR6PmjVCxakWtGMmBvQcgyzJ6PtITTVs1xYvTX0RQUJBN1zdy5EicOnUKVatWRYcOHbT7dTod1q9fj+PHj6Nx48Z48cUX8fbbbxfP9RSSJGxd7VeKJCcnIygoCElJSQgMDHRoW4xGI7Zv344+ffrc1y9Ayhv7177Yv/bF/rU/9rF9OWP/ZmZmIiYmBuHh4drULWEUyD6brW7sW0Ikbwme9TwhGYq25xWgTgtMTk5GYGBgsY7AkIr9m5sQ6gcEgHUhi6JQFEWrFlgS/ZvXv32LwmQDrrkiIiIiKoBkUIMObJuVVTz0uK9gReQQMtSRK/29DnRfDFdERERE9yAZJMA5BteInJIQ6lorSIB0d5nAUoRjmEREREREdH/Ma61Ke7oo5ZdPRERERET3QwhzIZZSPmoFMFwREREREdH94KiVhl1ARERERERFIoRQC1lw1AoAwxURERERERWVom4czFShYjcQEREREVGhaaNW4KiVBcMVEREREREVHketcmFXEBEREd2DkAWEsQRvsrDbtURHR0OSJCQmJtrtHCVBCIEnn3wSZcuWhSRJOHHiBCIiIjBlypQCnxcWFoYlS5aUSBvdmRBCLWKB/EetwuuGY8myJSXXKCfATYSJiIiICiBkAdNVE5Bdgif1BDyqekDS2zbVauzYsUhMTMSWLVvueWz79u0RGxuLoKAgm5tTmNePi4vD/PnzsW3bNly9ehUVK1ZE8+bNMWXKFDz88MM2n/Nedu7cicjISERHR6NmzZooX748vvnmGxgMrr/b88WLFxEeHo7ffvsNzZs3t+k5c+bMwZYtW3DixAm7tk0joG0aTHcwXBEREREVRIEarPQomXdOJvP5FPM5i5mnpydCQkKK/4WhhoIOHTogODgYb7/9Npo0aQKj0YgffvgBkyZNwpkzZ4rtXBcuXEDlypXRvn177b6yZcsW2+uXVtnZ2fD09Lz3gTIAAUg6pqucOC2QiIiIyBYegOQh2f12vwEuKysLzz//PEJCQhASEoJOnTrh6NGj2uN3TwuMjIxEcHAwfvjhBzRo0AD+/v7o1asXYmNjAagjImvWrMG3334LSZIgSRKio6PzPPezzz4LSZJw5MgRPPbYY6hbty4aNWqEqVOn4pdfftGOu3TpEgYMGAB/f38EBgZiyJAhiI+P1x6fM2cOmjdvjs8//xxhYWEICgrCsGHDkJKSAkAdSXvuuedw6dIlSJKEsLAwAMg1LTAhIQH9+vWDj48PwsPD8eWXX+Zqc2JiIp544glUqFABgYGB6Nq1K06ePGlzWwBAURQsWrQItWvXhpeXF6pXr4758+drj1++fBlDhgxBcHAwypYtiwEDBuDixYsFfyNzsHzPoqKi0Lp1a/j6+qJ9+/Y4e/YsAPV7OHfuXJw8eVL7HkVGRhbq+lasWIHw8HB4e3vj008/RZUqVaAoilU7BgwYgPHjx0MoAufPncfAIQMREhqCgLIBaNO+DfZE7bH5mtwVwxURERGRG5kxYwY2bdqE1atXIzo6GrVq1ULPnj1x69atfJ+Tnp6Od955B59//jn279+PS5cuYfr06QCA6dOnY8iQIVrgio2NtRotsrh16xZ27tyJSZMmwc/PL9fjwcHBANQgMmDAANy6dQv79u3D7t278c8//2Do0KFWx1+4cAFbtmzB1q1bsXXrVuzbtw//+9//AABLly7FG2+8gWrVqiE2NtYqPOY0duxYXL58GXv37sXGjRvx0UcfISEhweqYwYMHIyEhATt27MDx48fRsmVLPPzww1b9lVdbFi5cqD0+c+ZM/O9//8OsWbNw6tQprFu3DpUqVQIAGI1G9OzZEwEBAfjpp5/w888/awE2O7twc01fffVVvPvuuzh27Bg8PDwwfvx4AMDQoUMxbdo0NGrUSPseWfrTlus7f/48Nm3ahG+++QYnTpzA4MGDcfPmTezdu1c7xvL9HTlyJCADqSmp6NOrD/bs3INfD/+Knj16ov+j/XHp0qVCXZO74bRAIiIiIjeRlpaGjz/+GJGRkejduzeSk5Px6aefombNmli5ciVeeumlPJ9nNBqxfPly1KpVCwAwefJkvPHGGwAAf39/+Pj4ICsrq8DphOfPn4cQAvXr1y+wjVFRUfjjjz8QExOD0NBQAMDatWvRqFEjHD16FA888AAANYRFRkYiICAAADBq1ChERUVh/vz5CAoKQkBAAPR6fb5t+vvvv7Fjxw4cOXJEe82VK1eiQYMG2jEHDhzAkSNHkJCQAC8vLwDAO++8gy1btmDjxo148skn823Ljz/+iBkzZiAlJQVLly7FBx98gDFjxgAAatWqhYceeggA8NVXX0FRFKxYsQKSpE6hW716NYKDgxEdHY0ePXoU2F85zZ8/H507dwYAvPLKK3jkkUeQmZkJHx8f+Pv7w8PDw6o/bL2+7OxsrF27FhUqVNCe27t3b6xbt05bJ7dx40aUL18eEZ0jIEwCzZo3Q/MWzbXj35zzJrZ8uwXfbf0Ok5+dbPM1uRuOXBERERG5iQsXLsBoNKJDhw7afQaDAW3atMHp06fzfZ6vr68WrACgcuXKuUZ47kUI2yocnj59GqGhoVqwAoCGDRsiODjYqo1hYWFamClKm06fPg0PDw+0atVKu69+/fraCBoAnDx5EqmpqShXrhz8/f21W0xMDC5cuGBTW06fPo2srKx8i3WcPHkS58+fR0BAgPb6ZcuWRWZmptU5bNG0aVOrNgAosE9svb4aNWpYBSsAGDlyJDZt2oSsrCwAwJdffolhw4ZBBx0ggNS0VEx/eToaNm2IMhXLIKBsAE6fOY1Llzly5TD79+/H22+/jePHjyM2NhabN2/GwIED8z1+7NixWLNmTa77GzZsiL/++guAOm907ty5Vo/Xq1evWBdQEhEREbmTuyvsSZJkc1iyqFOnDiRJKrb3XHm16e41QPcrNTUVlStXznMNWc4QVlBbfHx87nmOVq1a5bne6+5Acy8522EZBSuoT2y9vrymcfbr1w9CCGzbtg0PPPAAfvrpJyxevFitEKgDXnr5JeyJ2oO3F76N2rVqw8fbB4OHDy70VMd82W83Arty6MhVWloamjVrhg8//NCm45cuXarNI42NjcXly5dRtmxZDB482Oq4nPNNY2NjceDAAXs0n4iIiMip1KpVC56envj555+1+4xGI44ePYqGDRsW+XU9PT0hy3KBx5QtWxY9e/bEhx9+iLS0tFyPWwpoNGjQAJcvX8bly5e1x06dOoXExMT7auPd6tevD5PJhOPHj2v3nT171mp/r5YtWyIuLg4eHh6oXbu21a18+fI2nadOnTrw8fFBVFRUno+3bNkS586dQ8WKFXOdozDl8O8lr+/R/Vyft7c3Hn30UXz55Zf4v//7P9SrVw8tm7VUq1hKwMFDBzFm9BgMGjAITRo3QUhICC7+e7HYrgcF/7g5LYeGq969e2PevHkYNGiQTccHBQVplW9CQkJw7Ngx3L59G+PGjbM6zjLf1HKz9R8HERERkSvz8/PDM888g5deegk7d+7EmTNn8OSTTyI9PR0TJkwo8uuGhYXh999/x9mzZ3Hjxg0YjcY8j/vwww8hyzLatGmDTZs24dy5czh9+jSWLVuGdu3aAQC6deuGJk2aYOTIkfj1119x5MgRjB49Gp07d0br1q2L3Ma71atXD7169cJTTz2Fw4cP4/jx43jiiSesRpq6deuGdu3aYeDAgdi1axcuXryIgwcP4tVXX8WxY8dsOo+3tzdefvllzJgxA2vXrsWFCxfwyy+/YOXKlQDU6XXly5fHgAED8NNPPyEmJgbR0dF4/vnnceXKlWK73rCwMMTExODEiRO4ceMGsrKy7vv6Ro4ciW3btmHVqlUYMWKENmolQUKd2nWwectmnDh5Aid/P4mRo0cW28iiUOy7kbY9uXRBi5UrV6Jbt26oUaOG1f3nzp1DlSpV4O3tjXbt2mHBggWoXr16vq+TlZWlzScFgOTkZADqJz35/fIoKZbzO7od7or9a1/sX/ti/9of+9i+nLF/jUYjhBBQFEV7oygUAUUokIwShFICb/hkaG2QFNv2EJJlGXq9Hoqi4K233oIsyxgzZgxSUlLQqlUr7NixA0FBQVbXZfl7zq8t7r5vwoQJ2Lt3L1q3bo3U1FRERUUhIiIiVzvCwsJw7NgxvPXWW5g2bRpiY2NRoUIFtGzZEh9++KH2eps3b8bzzz+PTp06QafToWfPnli2bNmdPjdPSczZprvvy+sYy/2W+1auXImJEyeic+fOqFSpEt544w1cvnzZ6pitW7fitddew7hx43D9+nWEhISgY8eOqFChAhRFKbAtlr+/+uqr0Ov1eP3113Ht2jVUrlwZTz31FBRFgbe3N6Kjo/HKK6/g0UcfRUpKCqpWrYquXbvC398/z0BS0Pfo7u+N5b5BgwZh06ZN6NKlCxITE7Fy5UqMHTu2SNdnERERgbJly+Ls2bMYPnS4+vNv3nvtnUXv4ImnnkCHzh1Qvnx5zJg2A8kp6ntocdecvru/LogQAjCp/+4gWX8/7cnSF0ajEXq99QZzhfkdJYnCTqi1E0mS7rnmKqdr166hevXqWLduHYYMGaLdv2PHDqSmpqJevXqIjY3F3LlzcfXqVfz5559WCxFzymudFgCsW7cOvr6+RboeIiIicj2W2S+hoaHaRqpCFkA81I19S4ongEqApLctXD3++OMIDw/H22+/bd92Ebmp7OxsXL58GXFxcTCZTFaPpaenY8SIEUhKSkJgYGCBr+Oy4WrBggV49913ce3atQJ3kU5MTESNGjWwePHifIfD8xq5Cg0NxY0bN+7ZgfZmNBqxe/dudO/ePddiSrp/7F/7Yv/aF/vX/tjH9uWM/ZuZmYnLly8jLCwM3t7e2v1CFupak5Kisy1Y3b59Gz///DOGDBmCdevWWb2PEkIgJSUFAQEBWvEDKj6lqn8VQBjNUwLteK1CCPVDDKH+PVWkllj/ZmZm4uLFiwgNDbX6tw+o2aB8+fI2hSuXnBYohMCqVaswatSoAoMVoFZCqVu3Ls6fP5/vMV5eXlrt/5wMBoPT/LJ3pra4I/avfbF/7Yv9a3/sY/typv6VZRmSJEGn00Gny7E03Uk3r3niiSdw9OhRTJs2DYMGDbJ6E2qZSmW5HipepaV/hRAQioAkSfYPObI6HVDSS9qaq5LqX51OB0mS8vx9VJjfTy4Zrvbt24fz58/btDAzNTUVFy5cwKhRo0qgZUREREQlZ/PmzY5uArk7xXyzc74RQkCYhNN+kGErhzY/NTUVJ06cwIkTJwBAq3By6ZK6+djMmTMxevToXM9buXIl2rZti8aNG+d6bPr06di3b59WDWXQoEHQ6/UYPny4Xa+FiIiIiMidCCG0kugS7DgdEGoRCwj7TjssCQ4duTp27Bi6dOmifT116lQAwJgxYxAZGYnY2FgtaFkkJSVh06ZNWLp0aZ6veeXKFQwfPhw3b95EhQoV8NBDD+GXX34p9CZtREREVHo5yZJ0IsdSzFX77D0co6jrGiWd44JVcf2bd2i4ioiIKPBCIiMjc90XFBSE9PT0fJ+zfv364mgaERERlUKWEszZ2dlW+yERlTYlPmqlnshhLPniftd/uuSaKyIiIiJ78PDwgK+vL65fvw6DweDShQoURUF2djYyMzNd+jqclbv3r1CEWiFQsnO4Mp/n7lErRVGQLezfv0IIpKenIyEhAcHBwbn2uCoshisiIiIiM0mSULlyZcTExODff/91dHPuixACGRkZ8PHxcfl1LM7I3ftX237AnrlRQN0wGCJXHwpFIFNkllj/BgcHIyQk5L5fh+GKiIiIKAdPT0/UqVMH2dkluWtw8TMajdi/fz86derkNKXu3Yk796/IEjDFmgADIBnsF2zkGzLkmzJ0/rpcUwKNqUYcyTpSIv1rMBjue8TKguGKiIiI6C46nS7XRqKuRq/Xw2Qywdvb2+3e/DsDd+5fU4oJOuig9y2ewJEXkS5gTDTC4GnIc2RKkiWX7F/3myBKRERERERFIrIFRLKAzse+65xMN0yAEZC83GtKJcMVEREREREBAOQUGYpRgeRpxyIWyWqAk/zcK1gBDFdERERERARAGAVEkoDOy46jViYB+Ya5xrsbLlBiuCIiIiIiIsgpMkSWALzseI5EGUqqAsnf/UatAIYrIiIiIqJST5jUUSt4wW6lz0WWgLgh1HVW7pmtGK6IiIiIiEo7JVWByBSQvO0UrISAfFMdGbPXOZwBwxURERERUSkmZAElSQE87TdqhTRAuaWoRSzcN1sxXBERERERlWYiTUBk2HHUSjGXXgcA19myqkgYroiIiIiISimhCMhJMuABSDo7hatkAZEiIPm68ZCVGcMVEREREVEpJdIERJqA5GOnYGUSkK/LgB7qzc0xXBERERERlUJCmNda6e03aiXfkqGkK6Vi1ApguCIiIiIiKpVEuoCSpthv1CpTQLmpqGu5Ske2YrgiIiIiIipttFErCZD0xZ98hDAXsTDCrUuv343hioiIiIiolBEZAkqqHUetUgVEYukoYpETwxURERERUSmjJCuAACQPO4xayQLyDRkCwu1Lr9+N4YqIiIiIqBRRMhW1NLq9Rq0S1dLrOr/SFzVK3xUTEREREZViSrICIQtIBjuMWmWro1bwRKlMGqXwkomIiIiISieRJSCS7TdqJd+SoWQqpaqIRU4MV0REREREpYScIkMxKpA87TBqlS6g3FKg89WVmtLrd2O4IiIiIiIqBUS2Omql8yn+CKCVXpehTgkspRiuiIiIiIhKATlFhsgSdgk/IklAJJW+0ut3Y7giIiIiInJzwqiOWsEbkKTiDUDCZC5ioQPgUawv7XIYroiIiIiI3JiSpsAUa1ILTXgV/8iSnChDSVMg+ZXuUSug1GdLIiIiIiL3JEwC8m0Zym0FkABdoK74R60yBZQb5tDGbMVwRURERETkToQQEGkC8k0ZIl1A8pPss6eVUM+BbEAKZLICGK6IiIiIiNyGMOYYrdIDUpBU7KNVmjRAua2oRSyYrQAwXBERERERuTwhBESqOpKkZCjQ+ensMlqlnU9RS68LIex6HlfDcEVERERE5MJEtoB8S4aSpAAegC6o+NdW5TpnknnPLH/Wx8uJ4YqIiIiIyAUJISBSzKNVWebRKg/7jyIJo7n0ugGA3u6ncykMV0RERERELkZk5RitMtinEmB+5FsylHQFuiCOWt2N4YqIiIiIyEUI5c5olcgSkAIkSPqSW/MkMgSUWwokHxaxyAvDFRERERGRCxCZOUarvABdcMmOHAkhYLppAoxQKwRSLgxXREREREROTCgCcpIMcUtAGEt+tEprR4qASFT3zaK8MVwRERERETkpJUOBclOBkqpA8pIcts5JyOYiFgATRAHYNURERERETkbIAnKiDHFbQMjm0Sqd40aMlEQFIlVtB+WP4YqIiIiIyIko6QrkWzJEioDkI0Hn59iqfCJbQLmhAJ4AWCCwQAxXREREREROQButuiUghIAU6NjRKgv5lgwlk6XXbcFwRURERETkYEqaebQqVUDylaDzdI4gI9IElJsKdL46ll63AcMVEREREZGDCFOOtVVONFoFqFUKTTdMgAJ1SiDdE8MVEREREVEJE0JApKn7Vok05xqtshDJAiKZpdcLg+GKiIiIiKgECaOAfFuGkqgAOkAKkiBJzhVghMlcel0P9UY2YbgiIiIiIioBQgiIVHW0SklXoPPTQTI4V6iykG/LUNJYxKKwGK6IiIiIiOxMGM2hKlEB9IAuSOd0o1UWIlMtYiF5SyxiUUgMV0REREREdiKEgEgxB6sMBTp/HSQP500sQgjIN2UgS52uSIXDcEVEREREZAci2xyqkhTAw7lHqzRpgHJbUYtYOHlTnRHDFRERERFRMRJCrbIn35KhZJnXVjnxaJWFkNXS6wLCadeCOTuGKyIiIiKiYiKy1Gl1SrICeAK6QBcYrTITSWoo1AWwiEVRMVwREREREd0noQgoyQqUWwpEtoDkL0HSu0aoAswFN27IgAEAs1WRMVwREREREd0HJVMNVUqyAnjBJcuXawU3XLDtzoThioiIiIioiEy3TUCyuumuq41WWYgMAeWWAsmHRSzuF6MpEREREVEhiWwBAFAScuxb5YrBSqhFLGAEJC/Xa7+z4cgVEREREVEhiEwBU7wJACAFSi5dWU8kC4gkoZZep/vGkSsiIiIiIhtZgpXIVEeuJJ3rhhJhMhexADjkUkwYroiIiIiIbJAzWEkBrhuqLJREBUqqAsnf9a/FWTCjEhERERHdg5KpQI6XtWAlKa4dSESWgHJDUddZufalOBWOXBERERERFSBXsHKRTYELIt+SoWQpkLxd/1qcCUeuiIiIiIjyoWQqkONkiCz3CVYiVS29rvPTcdSqmDFcERERERHlQckwj1i5U7BSzKXXFQAGR7fG/XBaIBERERHRXbRgle0+wQowl15PYel1e2G4IiIiIiLKwSpY+btRsLKUXtdDvVGxY7giIiIiIjJz12AFmItYpCmQfN3nmpwN11wREREREcEcrOJkCKN7BSsh1KmAyi1zdUD3uCyn5NCRq/3796Nfv36oUqUKJEnCli1bCjw+OjoakiTlusXFxVkd9+GHHyIsLAze3t5o27Ytjhw5YserICIiIiJX547BSigCSrIC078mmP41QZiEuq8V2Y1Dw1VaWhqaNWuGDz/8sFDPO3v2LGJjY7VbxYoVtce++uorTJ06FbNnz8avv/6KZs2aoWfPnkhISCju5hMRERGRG1DS3StYCVlASVRguqSGKiVVnQqoC2DpdXtz6LTA3r17o3fv3oV+XsWKFREcHJznY4sXL8bEiRMxbtw4AMDy5cuxbds2rFq1Cq+88sr9NJeIiIiI3IySbl5jZRRq+HBhQlan/8k3ZYh0AUiA5CdB0jNRlRSXXHPVvHlzZGVloXHjxpgzZw46dOgAAMjOzsbx48cxc+ZM7VidTodu3brh0KFD+b5eVlYWsrKytK+Tk5MBAEajEUaj0U5XYRvL+R3dDnfF/rUv9q99sX/tj31sX+xf+2L/3puSoUBOuBOsZFm2+bkm2WT1pyMJk3lN1W1FDVV6qEUrLFlRcWjzisQk1H51hp/fwrRBEkIIO7bFZpIkYfPmzRg4cGC+x5w9exbR0dFo3bo1srKysGLFCnz++ec4fPgwWrZsiWvXrqFq1ao4ePAg2rVrpz1vxowZ2LdvHw4fPpzn686ZMwdz587Ndf+6devg6+t739dGRERERESuKT09HSNGjEBSUhICAwMLPNalRq7q1auHevXqaV+3b98eFy5cwHvvvYfPP/+8yK87c+ZMTJ06Vfs6OTkZoaGh6NGjxz070N6MRiN2796N7t27w2DgNtrFjf1rX+xf+2L/2h/72L7Yv/bF/s2fNmJlEtD5F20qoEk24ccTP6Jr867w0JfsW2qRrRaqELcFlCwFkkFyuyqA2cnZOJB1wCl+fi2z2mzhUuEqL23atMGBAwcAAOXLl4der0d8fLzVMfHx8QgJCcn3Nby8vODl5ZXrfoPB4PBvpoUztcUdsX/ti/1rX+xf+2Mf2xf7177Yv9aUdAW4AUhCgi7o/tdYeeg9YNCXTP+KLAE5SQZuAVKWBJ2XDh5BHm4VqiwUSZ3L6Aw/v4U5v2uv2gNw4sQJVK5cGQDg6emJVq1aISoqSntcURRERUVZTRMkIiIiotJHSTNXBZSLPmLlCCJTwBRvgjHGCDlWhoCALkgHyce9RqvcgUNHrlJTU3H+/Hnt65iYGJw4cQJly5ZF9erVMXPmTFy9ehVr164FACxZsgTh4eFo1KgRMjMzsWLFCvz444/YtWuX9hpTp07FmDFj0Lp1a7Rp0wZLlixBWlqaVj2QiIiIiEofJc1cFdCFgpXIEJATZSiJCkS2gORtHm1joHJaDg1Xx44dQ5cuXbSvLeuexowZg8jISMTGxuLSpUva49nZ2Zg2bRquXr0KX19fNG3aFHv27LF6jaFDh+L69et4/fXXERcXh+bNm2Pnzp2oVKlSyV0YERERETkNVwpWQgggA5Bvy1CSFMAIwAcMVS7CoeEqIiICBRUrjIyMtPp6xowZmDFjxj1fd/LkyZg8efL9No+IiIiIXJwWrBTnDlZCCCANMCWaIJIEIEOd9ufn6JZRYbh8QQsiIiIiorxoa6yEgM7POYOVFqpumSCSBSDMoYo1SFwSwxURERERuR0l1Txi5aTBSigCIlVAviVDpKozuSRvhipXx3BFRERERG7FmYOVUAREslqoQqQKtfKfj47vyt0Ev41ERERE5DacNVgJWUCkmEeq0gQgAZKvBEnPKhXuhOGKiIiIiNyCMwYrYTKHqpsyRLoA9IDkL7nBbrOUF4YrIiIiInJ5Sqq5eAWcI1gJk4BIMo9UpQvAAEgBDFXujuGKiIiIiFyaMwUrYRSQk2SIWwJKhgLJU4IUJHGPqlKC4YqIiIiIXJaSYp4KKAnofB0XrES2uUjFLQElS4HkJXHj31KI4YqIiIiIXJIzBCuRKWBKNUG5rUBkCYaqUo7hioiIiIhcjqODlchW96Yy/WuC3qiH5M1QRQxXRERERORiHB6sMgXkK7L6hR5qGxiqCAxXRERERORClBRz8Qqdg4JVmoDpmkmtAAhA8mSxCrqD4YqIiIiIXIKjg5WSrKjByiTUsupZJd4EcnIMV0RERETk9JRk81RABwQrIQTEbQFTnAkAoAvQQVGUEm0DuQaGKyIiIiJyao4OVvINGUq8AngAkg/nAFL+GK6IiIiIyGlpwUovoPMp4WAlC8jXZSjXFcALkLwYrKhgDFdERERE5JQcGqxMAnKcDPmWrI6WGUr09OSiGK6IiIiIyOk4NFgZ1YqASqICnb+O75jJZvxRISIiIiKnoiQpkBMcFKwyBUyxJijJCnSBOqDkixKSC2O4IiIiIiKn4dBglX5nDytdEDcGpsJjuCIiIiIip6AFKw8BnXfJBislRYF8TYaSbR6xYrCiImC4IiIiIiKHEkYBOVmGclMBDCj5YJWowBRrAhR1DysGKyoqhisiIiIicghhUkOVSBJQMhXofHWQPEsu2QghIN+UocSZ97DyZ6qi+8NwRUREREQlSsgCIlVAvi1DZAjAG9AF6SBJJRisFAE5wbyHlScgeTNY0f1juCIiIiKiEiEUc6hKlCHShBpqgqQSDVWAecQsXp2GKPlK3MOKig3DFRERERHZlRACIs08UpUm1Cl4gRIkXcmPFgmjudT6be5hRcWPP05EREREZBdCCIh0ASVJgZKiADpACnBMqAIAkWUOVkmKWrhC75BmkBtjuCIiIiKiYqdkKFASzaEKgOQnQdI7bl2TyBAwXTXvYcXNgclOGK6IiIiIqNiILAE5Sa0AKGShhioPxxaLEKnmYJUtIAVKLLVOdsNwRURERET3TWSby6onCgiTgOQrQWdw/PCQtoeVrE5JZLAie2K4IiIiIqIis2wALJIElCx1ryqdn+NDlRACyi0FcqwM6M3BisjOGK6IiIiIqNCELKCkKFBuKxCZApK3BH2wc1SIEIqAfEOGkqAABu5hRSWH4YqIiIiIbCYUAZFi3qsqw3F7VeVHyOY9rG4okHwkwNPRLaLShOGKiIiIiO5JKOa9qiwbABvMe1U5SagC1M2BTXEmKDcVdWoiNwemEsZwRURERET50vaquq1ASVXUDYAduFdVfkS2gOka97Aix2K4IiIiIqJchBAQGeLOXlUSIPk7dq+q/IhMc6n1VO5hRY7FcEVEREREVpRMdQNgkSIgFLWsuqP3qsqPSBUwxZrUohpBLLVOjsVwRUREREQA8t4AWOfhvMNASpK6h5UwCXUqIIMVORjDFREREVEpp+1VlSigGBXofHTQeTpvqBJCQNxWi1cAUIMVkRNguCIiIiIqpYQpxwbAmWqo0vs6dyUIIcx7WMWbi2v4cLiKnAfDFREREVEpI2QBkSog3zbvVeUN6IJ0TlVWPS9CFpCvmzcH9gYkL+duL5U+DFdEREREpYRQzKHKsleVk20AXBDuYUWugOGKiIiIyM0JYd4A+LY5VHmYNwB2sr2q8mO1h5W/ju9gyWnxR5OIiIjIjSnpCuQ0Wd2rSuecGwAXRGSaS60ncw8rcn4MV0RERERuSGQLAIDpmgmSXoLk55wbABdEpJmDVTr3sCLXwHBFRERE5GaUdEUrUy75SNB5ud5wj5KsQL4mq6XhA7mHFbkGhisiIiIiN6IkK5CvyxBGdeRK8nCtVCKEgEg072GlgJsDk0thuCIiIiJyA0KoBSuUGwqgh1r4wcUIISDflKHEmfew8meqItfCcEVERETk4oRiDiU3FUjeEiQvCbIsO7pZhaLtYXVdUUvEezNYkethuCIiIiJyYcJkDiWJilq0wuBaoUTIAiLFXCY+WUDyk7iHFbkshisiIiIiFyWyBeQEGXKKDF2AzqWqAWqh6pZ57y1J3XuLpdbJlTFcEREREbkgJUOBnCBDZKj7P7nK3lVaqLppDlV689oqhipyAwxXRERERC5GSTUHK5OAFChBkpw/WOUZqgIYqsi9MFwRERERuQghBJQkBcp1BUISaplyJydMOUJVulCrADJUkZtiuCIiIiJyAUK5U2pd8pSg83budCJMAiLZvKaKoYpKCYYrIiIiIicnZAH5hgzltgLJR4Lk6bzTAPMMVYESNwKmUoHhioiIiMiJCaO51HqSAslfguThnClFmAREkhqqlAwFkkFiqKJSh+GKiIiIyEmJTAHTdROUVMVpKwIKk3kd2C0FSrp5ymKgjqGKSiWGKyIiIiInpKSZKwJmC+iCdE5XEVCYBJREc6jKMIeqIIYqKt0YroiIiIicjJJsDlZCQApwrlLrwiggJ8kQt4QaqrwYqogsGK6IiIiInIQQdyoCwgPQ+ThPaT0tVN0UUDIZqojywnBFRERE5ASEYq4IeEuB5C1B8nKO1CKyc4xUMVQRFYjhioiIiMjBhMlcETDReSoCaqHqpoCSxVBFZAubw9Xvv/9u03FNmzYtcmOIiIiIShuRZa4ImKJAF6CDpHdsehHZAnKieaSKoYqoUGwOV82bN4ckSRBC5HrMcr8kSZBluVgbSEREROSulAxz4YoM4fBS6yJLHalSbikQWQKSN0MVUWHZHK5iYmLs2Q4iIiKiUkVJUSBflyFMAlKg4yoCikxzqLrNUEV0v2wuQVOjRg2bboWxf/9+9OvXD1WqVIEkSdiyZUuBx3/zzTfo3r07KlSogMDAQLRr1w4//PCD1TFz5syBJElWt/r16xeqXURERET2IoQ67U6OU0ut6wIcs4eVyBQwxZlgvGiEHCcDEtT9tLwlBiuiIrI5XJ07dw7Dhw9HcnJyrseSkpIwYsQI/PPPP4U6eVpaGpo1a4YPP/zQpuP379+P7t27Y/v27Th+/Di6dOmCfv364bfffrM6rlGjRoiNjdVuBw4cKFS7iIiIiOxBKALyTRlyvKyWWvct+VLrWqiKMart0DFUERUXm6cFvv322wgNDUVgYGCux4KCghAaGoq3334bH3/8sc0n7927N3r37m3z8UuWLLH6+q233sK3336L77//Hi1atNDu9/DwQEhIiM2vS0RERGRvQjZXBLytQPKVIHmWbJIRmeqImXJbAbIB+IDT/4iKmc0fl+zbtw+DBw/O9/EhQ4bgxx9/LJZG2UpRFKSkpKBs2bJW9587dw5VqlRBzZo1MXLkSFy6dKlE20VERESUkzAKyPHmYOVfssFKZAiYYtWRKiVBAfSAFGTeR4vBiqhY2TxydenSJVSsWDHfx8uXL4/Lly8XS6Ns9c477yA1NRVDhgzR7mvbti0iIyNRr149xMbGYu7cuejYsSP+/PNPBAQE5Pk6WVlZyMrK0r62TH00Go0wGo32vYh7sJzf0e1wV+xf+2L/2hf71/7Yx/ZVWvpXyTIXrkgXkALMhStKoLiyMV3t18x/M+Fh9IDkKwHe5geF+UZFZlJMVn9S8TIJtV+d4fdDYdogibxqq+chJCQE69atQ9euXfN8PCoqCiNHjkRcXJzNJ7dqiCRh8+bNGDhwoE3Hr1u3DhMnTsS3336Lbt265XtcYmIiatSogcWLF2PChAl5HjNnzhzMnTs3z3P4+vra1B4iIiIiInI/6enpGDFiBJKSkvJcIpWTzSNXnTp1wvvvv59vuFq2bBk6duxYuJYW0fr16/HEE0/g66+/LjBYAUBwcDDq1q2L8+fP53vMzJkzMXXqVO3r5ORkhIaGokePHvfsQHszGo3YvXs3unfvDoPB4NC2uCP2r32xf+2L/Wt/7GP7cuf+FUJApAjIN2QICOj8SqZwhRACIlFATpBhNBlxIPMAOpXrBA+dzW/5yEYmxYT9N/ezf+0kOzkbB7IOOMXvh7wK+uXH5p+EmTNnol27dnj88ccxY8YM1KtXDwBw5swZLFq0CD/88AMOHjxY+NYW0v/93/9h/PjxWL9+PR555JF7Hp+amooLFy5g1KhR+R7j5eUFLy+vXPcbDAaHfzMtnKkt7oj9a1/sX/ti/9of+9i+3K1/hRCQb8lQbijwMHhA51NCwUpRw5xyXYGHhwekAAnIBDx0Hnzzb0fsX/tQJAWAc/x+KMz5bf5JaNGiBTZu3Ijx48dj8+bNVo+VK1cOGzZsQMuWLW1vJdTgk3NEKSYmBidOnEDZsmVRvXp1zJw5E1evXsXatWsBqNP0xowZg6VLl6Jt27baFEQfHx8EBQUBAKZPn45+/fqhRo0auHbtGmbPng29Xo/hw4cXqm1EREREhSVktdS6crNkKwIKk1peXdwU6toqTwBKiZyaiHIoVMzu27cv/v33X+zcuRPnz5+HEAJ169ZFjx49irQ26dixY+jSpYv2tWVq3pgxYxAZGYnY2FirSn+ffvopTCYTJk2ahEmTJmn3W44HgCtXrmD48OG4efMmKlSogIceegi//PILKlSoUOj2EREREdlKGM2l1pPMFQE9SihYmfetUpIU6Px1hXx3R0TFqdD//Hx8fDBo0CAAapCpUqUKdLqiDXdHRESgoHoalsBkER0dfc/XXL9+fZHaQkRERFRUIkvAlGCCkqpAF6CDpC+hYJUqYLpmgsgU3LOKyAnc1yTghg0b4uLFi8XUFCIiIiLXo6QrMMWaINIEdIElE6yEEFBuKTBeNkJkC0iB3LOKyBnc18CxjVXciYiIiNySkqJATpAhZDXgSFIJBCvZPP3whgJ4QC1cQUROgbNyiYiIiApJCAH5tgxxU0DoBHQBJVQR0GheX3VLUcu7u0+RRSK3cF/h6r///S/Kli1bXG0hIiIicnpCMZdav6lA8pKg8yqhYJUhYIo1QUlR13VBXyKnJaJCuK/fBjNnzoTBYCjUxlpERERErkqY1A16lesKJB8JklfJTMlTkhWYLpnXdQUxWBE5qyKHq1OnTqF169YICAhAmTJl0KRJExw/frw420ZERETkFIQQUDIUyHEylNsKpAAJkqFkClfIN2SYLpsgTEJdX8UlVkROq8jh6qmnnsLkyZORmpqKmzdv4tFHH8Xo0aOLs21EREREDiWyBOREGaYrJshXZCipilq4oiQqAsoCcpwMOVZWC1f4M1gROTubw9WAAQNw9epV7evr16+jf//+8PX1RXBwMPr06YP4+Hi7NJKIiIiopAijUKfhXTPBdNkEOU6GyBKAN6AL0kHSlUCwyhYwXTVBTpDV6YfeTFVErsDmghb/+c9/0LVrV0yaNAnPPfccJk+ejEaNGqFz584wGo348ccfMW3aNHu2lYiIiMguhElAZAgoaQpEmoDIFupokZekVuUrybakq4UrRKpg4QoiF2Pzb4vBgwfjyJEjOHXqFB588EF06NABu3btQocOHdCxY0fs2rULr732mj3bSkRERFRshKKGKdN1dYTKdNUEJdm8d1SQBJ2/rkTWVeWkJJoLV2QISEESgxWRiylUKfagoCAsX74cBw4cwJgxY9C9e3e8+eab8PX1tVf7iIiIiIqNEAIiU0BJVyBS1b9DqCNUJbUJcJ7tUgTkmzKUeAXQcX0Vkasq1Dj3rVu3cPz4ca0yYGBgIFq0aIHt27fbq31ERERE90UIoRamuK1W3TNdNkG+LkPIApK/pK6j8nZgsDIJyLEylFgF8AQkPwYrIldlc7hat24dqlWrhkceeQQ1atTAjh07MHv2bHz77bdYtGgRhgwZwoIWRERE5DREtoCSpEC+KsN0yVyYwigg+UrQB+mh8ymZ4hQFtjFLqJUIb8iQfEtu3ywisg+bw9XMmTOxatUqxMXFISoqCrNmzQIA1K9fH9HR0ejevTvatWtnt4YSERER3YswCSgpCkyx5nVUsSYomYpa6S9YB52vrkTKqNtCpAl1/6okAV2gDjA4ukVEdL9sXnOVmpqKevXqAQBq1aqF9PR0q8cnTpyIAQMGFG/riIiIiO5ByOZ1VGnmdVTZQl235CWpZcwdNN0vP0IIiCQBU5wJwiigC9JxGiCRm7A5XI0ZMwaPPPIIIiIicOzYMYwaNSrXMRUrVizWxhERERHlRSjmQJWhQCSbAxUcX5jiXoQiIN+QoSQogB7qiBURuQ2bw9XixYvRpUsXnDlzBmPHjkWPHj3s2S4iIiIiK5bCFFaV/hQAXmp1PUevn7oXYRKQ42Qot9RpilxfReR+ClWKvV+/fujXr5+92kJERESUi8g2b/Cboqh/ygokg6QWgHCS9VP3IjLVaYBKkgKdv66Q78CIyFUU+Z92WloaNmzYgPPnz6Ny5coYPnw4ypUrV5xtIyIiolJKGM2BKlWBSBcQRgEYAMlbgt7DtXbWFakCplh1Y2BdoK6QG+EQkSuxOVw1bNgQBw4cQNmyZXH58mV06tQJt2/fRt26dXHhwgW8+eab+OWXXxAeHm7P9hIREZGbErI5UKUpEGk5ClN4S9D5uV4iEUJAJJoLV8jmYOUaA21EVEQ2/6Y6c+YMTCYTALUse5UqVfDvv//iyJEj+Pfff9G0aVO8+uqrdmsoERERuSclQ4HphgmmSyaYrpigJCpqqAqUoAvQQTK4XiIRsoCcIMN01QRIgC6AwYqoNCjStMBDhw5h+fLlCAoKAgD4+/tj7ty5GDZsWLE2joiIiNyPUNTCFKZk9UNb0xUTJJ26ga4U4PyFKe5FGAVM8SYotxTofHSAp6NbREQlpVDhylLWNDMzE5UrV7Z6rGrVqrh+/XrxtYyIiIjchiVQaZX+sgQUowJArfSnM7jetL+8iEx1fZWSzMIVRKVRof7JP/zww/Dw8EBycjLOnj2Lxo0ba4/9+++/LGhBREREmrwClVCEVulPZ16d4OojVRZKigI5VobI5MbARKWVzeFq9uzZVl/7+/tbff3999+jY8eOxdMqIiIicklCmDf3TTcXpci8K1Dpc4xQyY5rZ3ESQkDcUqcCQqhrxRisiEqnIoeru7399tv33RgiIiJyPYUKVG5GyALydRnKdUUtFe/DVEVUmnEmMBERERWaFqgyzFP+MgWELCB5un+gshBG8/qq24paKt7g6BYRkaMV6jffyZMnMXr0aNSsWRM+Pj7w8/NDkyZNMGvWLCQnJ9urjUREROQEhFDDlOmWCabL6k2OlyGMApKvBH2wHjpfHSS9+4/eiAwB0yVzsApgsCIilc3h6ocffkC7du2Qnp6ODh06QKfTYfz48XjkkUewfv16tGzZEnFxcfZsKxEREZWwuwOVfEUutYHKQklSYPrXBJFuLlyhd3SLiMhZ2ByuXnnlFSxevBgbN27E559/ji1btmDPnj343//+h1OnTiEsLAwzZ860Z1uJiIioBBQUqOCDUhmoALVfTDfUPhGKgBTAwhVEZM3mNVdnzpxBr169tK+7deuGCxcuIDY2FpUrV8bs2bPx2GOP2aWRREREZF9CmEulZwgoKQqQBSiyohal8JGg15fO4RkhBJAJKGkKlBR1fRk8AcmbqYqIcrM5XFWtWhVnz55FWFgYAODChQtQFEXb26patWpITU21SyOJiIio+DFQ5c0qUCUrEBnmYh0earEOlgMjovzY/Oth9OjReOKJJ/Dqq6/Cy8sLixcvRv/+/eHp6QkAOHHiBMLDw+3WUCIiIrp/BQYqbwl6j1IcqDIAJd0cqNLNgcrcL5IHR6qI6N5sDlf//e9/kZaWhjfffBNZWVno2bMnli5dqj1etWpVfPzxx3ZpJBERERWdVaBKVdRRGQaqggOVDwMVkSMpQkGWkuXoZhSazeHKw8MDCxcuxMKFC/N8vE2bNsXWKCIiIro/Qgh1VCpDYaDKwRKo5DQZIlncmfLHQEXkNH65/Qum/jUVVXyqYBAGObo5hcJZw0RERG5CC1SZavEFBiqVUNQ1VFaBSmGgInI2VzKvYNbfs/BV7FcAgPOZ55GUmYTyhvIObpntGK6IiIhckFAEYASEUai3LAGRab7PdKeiXakOVBmAnC5DJJn7RobaLwxURE4lQ87AkotL8G7Mu0iX0yFBwqiKo9C1QlcEeQc5unmFwnBFRETk5ISsBigYASVbHZES2QIwmR+DUPec8lADlc7D5m0s3cq9AhXf9RA5FyEENsdvxsyzM3E58zIAoH1we7zd4G00Eo0QnRnt2AYWAX/NEBEROQkhzIHJqP6pZKtlwLXRKBnqprUeUMOUt/qnTiqdYQrIEagsU/4yBaAAMEAtm146B+6InN6J5BN46cxL+Pn2zwCAat7V8Fbdt/BYyGOQJAnZSdkObmHRMFwRERE5gBB3TevLVtcCwWQOUgJ3gpSHBMlLgqTjVDbAHKjSzSNUDFRELiUhKwFzz89F5JVICAj46HwwNXwqXgx/Eb56X+04IQt4Z3k7sKVFY1O4WrZsmc0v+Pzzzxe5MURERO4o1/qozDtT1oQsIIRQg5MH7uyrxCBlJc9AJQPwYqAicgXZSjaWX1qOty68hWRTMgBgcMhgzKs7D6E+oQAAkSkgX5ehXFcgbgnU9a3ryCYXiU3h6r333rP6+vr160hPT0dwcDAAIDExEb6+vqhYsSLDFRERlWrCJKym9d29PgqAGgQ8oI5GlfJpfQURco41VDlHqDwZqIhcyc7rO/HymZdxLv0cAKB5YHO8U/8dtAtuB5EqYPrHBDlBhkgRVs8zyAb194DBEa0uGpvCVUxMjPb3devW4aOPPsLKlStRr149AMDZs2cxceJEPPXUU/ZpJRERkZOxWh9lLjRhVa1PMR9ontYHH0CnZ4i6Fy1Q5VxDJcBAReSCzqaexctnX8auG7sAABU9K2JO7TkY4TsCUoKE7D+z1X/jOUhBEvQV9ZC9ZRyXjqOPvo8jml5khV5zNWvWLGzcuFELVgBQr149vPfee3j88ccxcuTIYm0gERGRownFvCYqr7LnMtdH3S8hq+vN8gxUfgxURK4m0ZiIBRcW4ONLH8MkTDBIBjxb4VlM002D/3l/KCblzsE6QFdWB11FHfTl9ZC81N+dSpI68u9qCh2uYmNjYTKZct0vyzLi4+OLpVFERESOJBQ1QMmpMgDAdNkESUh31kdZyp57qvslSRKDVGEJk4BIUz+xli/KQBbuBCp/CeAgH5HLkYWMyCuRmHtuLm4YbwAAenn2wpumN1HrRq07BxoAfQU9dBV00JXTqb9T3UShw9XDDz+Mp556CitWrEDLli0BAMePH8czzzyDbt26FXsDiYiI7E2r3JcpoGQqEGnqSJVslO8cxLLn901kC4h0ASVNUddZZKkf1gpZMFARubj9N/fjpdMv4Y+0PwAAdVEX86X5eNj0MAB1rzldRR30FfSQgt33Q6lCh6tVq1ZhzJgxaN26NQwGdXWZyWRCz549sWLFimJvIBERkT0Ikzo6pWQqEOlCLTxhEoDeXLHPX4JOqO/2JW/JrT5ZLSlCmPs1Q0BOkdV+NgJCEuqon58EZKr9y2BF5HqEInAx/iL+e/6/+Db9WwBAEILwivQKxmM8PIM9tREqyc99A1VOhQ5XFSpUwPbt2/H333/jzJkzAID69eujbl3XK5VIRESlhxACyIIaqDLUQCWM5rU9BvUNvs7jrnf4cp4vRQXQClJk3Fk/JWS11LzkpRb20N5gKQW/FpGzUlIVyJdl1MqoBSVTgewnQ/KR1Jubr7cUJgHlpoLk+GQsTliMD+QPkIUs6KDDWGksXi33KipUqqCOUHm5d1/kpcibCNetW5eBioiInJql+IRlqh+yzaNTHndGp9z9jVBJEEa1IIWSrqiBKss6tEoe7GNyDyLDXDb8mvrJS1VUhXJTgZLjkwItZPmabz45/nTREXCRdWf/KfmmjI3KRswRcxCLWABAR8+OWBS+CE2rNS31/94LHa5kWUZkZCSioqKQkJAARbH+2OnHH38stsYREREVhqUQhchSiyWITPPoFKAWSshrdIoKTRsFtFT4S1Wn+wFQAxXXT5GbEdkCphgT5CuyNuIqVZBwCZdQHdWBDKjTXhX134XIEMCtPF7IG9D56KxDl+XvThRKhFB/hyrXFcjXZYgk9ffor+JXzBQzcRRHAQA1PGtgQf0FGBAyoFRM+bNFocPVCy+8gMjISDzyyCNo3LgxO5KIiBxGK0RhmeqXZi6Vrgi1ip/B/KaF/1fdN6Go66eUdAVKiqK+gTSa16h5SpACJLUcPZEbESYB+ZIM078mwFwsW1dGB4/aHlACFcRcj0F4hXB46DzU30fZashS0s3/RtLNtwx1XzxkAkqmAtzO42Tmvdx0vjrr0S5f9XeZ3a9VCIjEOyNUIv3O/lNxIg7zDPOwLnsdAMBP74cZNWfguRrPwVvvbfe2uZJCh6v169djw4YN6NPHtTb0IiIi9yDkO6NTSqqijqCYhPrG3vLmhJv1FgthyjHdLyXH+imDpAYqXwYqck9CEZCvyjD9YwKy1fukAAketT3U0uGSlGv2liRJgBcgeUnQlbH+HaR9EJSe49+U+e+WQi/IVkfI5MQ8FnsaYBW2cgYwGFDkD5CErK6fsoxQaSPQACAB2WWysVy3HO/cfAep2akAgJFVRmJunbmo4l2lSOd0d4UOV56enqhdu7Y92kJERJSL9mlwVo5Pg7PN+0153HmTz9Gp4mFVLj0lR9EPc3B11TUjRLYQQkCJU2C6YFJHm6CuofKo7QFdJV2Rf89IkqT+G/KUgGBAf9fO2JZ1izlHuiwBDNlQg1mS0KbnyTmr7Xgg1xRDS/iCV+7gJbLNo1MJCpRbinVhGQ9AV14HXQUdtovtmHluJmIyYgAArYNa453676BNcJsi9UFpUehwNW3aNCxduhQffPAB/yMjIiK7sCqTnmZe3yMLQGeeguYvQafj6FRxuGe5dE73o1JACAHlhgLTeZO6hhAAPAGPmh7QV9XbvfCNZDBP/QvMo22mHMErRwBTMhQgE4AJ6gchKSL3k3W4E7q8JShJihbQNN7mDX0r6qAL1uFU2inMODMDe2/tBQCEeIVgXt15GFZ5GPf5s0Ghw9WBAwewd+9e7NixA40aNdL2urL45ptviq1xRERUOghFHY1CFtQRk0xx7zLpVGR5lks3CUj6PMqlE7k5JVGB8ZwRItEcOjwAjzAP6KvrnWKkVvIwf8gRkPsxIQutgEbOUS9tnZcCiFRxJzBaXjNAgr6ief8pf3Xk/2b2Tcw7Mw+fXf4MChR46bzwfNjzeCn8Jfh7+JfQ1bq+Qoer4OBgDBo0yB5tISKiUkQrk55xZ+qLkHMUSPDmVL/iJIx3Pu1muXQida8q03kTlOvmeXE6QB+qh0eYhzp9zwVIenUkH3lkH6HcFbwyBCRfSd1/yvvO9ZkUEz679BnmnZ+H2ya10saAigPwVr23EO4bXlKX4jYKHa5Wr15tj3YQEZEDCWH+VNPy4WbODznvvi+fxwSETc8XRnOZ9CzzaJUO6ht8HxaiKE5W5dJTZW2fL0hguXQq1e7eqwoA9FX08KjlYRU6XJ2kkyD5SYBf/sf8ePNHzDgzA6dSTwEAGvs3xqL6ixBRLqJkGumGiryJMBERlTwh1HK+wqSuRwIA003TnakrloXJAndCTc77LK8h7jomj6n6NgWtnO26xzGQ1PuEENr6ApZJL15CNpdLz8inXHog109R6aXtVXVZ1n4/6Srq4FHLAzr/0vVJw4W0C5j590xsTdgKAChnKIdZtWdhfLXx8NAxHtyPIvXexo0bsWHDBly6dAnZ2dlWj/3666/F0jAiotJMyHdCFEyAYlIXLotsAcjq4yaTuumKckuB4pGj3JN0159335/HMdKdvxT83LxeA3etz8nvfioWQqg/AzCaRwGN5uqJmeaplTn3+WK5dKI7e1VdNMFSZE9XRgePOh7QBZWuUJViSsGifxbh/YvvI1tkQy/p8VToU/hvrf+irGdZRzfPLRQ6XC1btgyvvvoqxo4di2+//Rbjxo3DhQsXcPToUUyaNMkebSQicks5R6Esf4ps81Q5E9T7FPMokwR19EFvLuerl6AT6psCXaCO0+nckGVfHC1EmcyFJ8zFJ2CCNiopJDVMQc9y6UQWQhGQr8gwxdy1V1UdD+jKFr2suivKVrLxf9f+D3POzUF8djwAoFu5blhYfyEa+DdwcOvcS6HD1UcffYRPP/0Uw4cPR2RkJGbMmIGaNWvi9ddfx61bt+zRRiIilyaUu0ahjIq2b5NlFEqbuqeD+gbZw1yxTVfAtLk89pkk1yMU6xClGNVqidrmyDLuhCideUTKHLKhAyDlGHkkIrvtVeWK4rPiserKKnx2+TPEZcUBAGr51sLCegvRu0LvUtUXJaXQ4erSpUto3749AMDHxwcpKSkAgFGjRuHBBx/EBx98ULwtJCJyAdpULcsIlMm81iVLfcMMkzlEWXhADVEGc1U8O++hQo6njVAa1TClZOcIUbI5aAuhBiUd1I1BDRLgDa3wBEMUUf4cvVeVMzmWdAzLLy3HxtiNyBbqsF0lz0p4IewFPFPjGXjpvBzcQvdV6HAVEhKCW7duoUaNGqhevTp++eUXNGvWDDExMdYLmomI3NDdo1CWzW4tU/kso1ACQv2P3DKVz9tcMpefEro1y1TPfNdDWUKUeTW9NpXPU1LDFH8+iIrE2feqKgnZSja2xG/BR/9+hCNJR7T72wS1wbM1nsXASgPhqfN0YAtLh0KHq65du+K7775DixYtMG7cOLz44ovYuHEjjh07hkcffdQebSQiKnG5CkoYFXWEIUdBCa0anh7aVD7Ji+tdSgNLyEZ2jpHK/NZD6dTNeS0jldAzRBEVl3z3qgr3UP+9lQLxWfFYeXklVlxZoU39M0gGDK48GE9Xfxqtg1o7uIWlS6HD1aeffgpFUX+AJ02ahHLlyuHgwYPo378/nnrqqWJvIBGRvQkhtDVQSqZ5qpYJd0IUYF1Qwsu8FqoUTTEprYSifv9FqoCiKLmn8pnymMpnWQ+lV1+DU/mIip/IEDBeMEKJvVMpVV9VD4+a7rVXVUHym/o3MXQixoeOR4hXiINbWDoVOlzpdDrodHeqUg0bNgzDhg0r1kYREdmTpQqbyBJQshRtc1VhEnfeIHuYq/IVVFCC3I628W6mgJKuQE5Vq4aY/jWpUz0l6U7REYO56Ah/PohKTL57VdX2gM7P/aumcuqf8+MuYURUKgijuBOm0nNUYgMAAyB5S9B5uP9/zGRNG7XMFFAyFIhU8xopkwAkQHiY10b5Syx3T+RAwiQg/yvD9G+OvarKmkNVKdiryjL177PLn2ml1Dn1zzk59Kdx//796NevH6pUqQJJkrBly5Z7Pic6OhotW7aEl5cXateujcjIyFzHfPjhhwgLC4O3tzfatm2LI0eO5H4hInJrwiSgpCmQb8swXTHBdMkE0xV1Xr7IFuqoVKAEXZAOOl+dOlJFpYIwCigpCkwJJphiTDD+Y4TpX/PPhklA8pTUvcMCdXemF/HHg8ghhCJgumRC1oEsmP5Rg5UUIMHQ0gBDS4PbB6tjSccw4fcJqLevHuZdmIf47HiEeIXgtVqv4Wzns1jRZAWDlZNx6MhVWloamjVrhvHjx9tUDCMmJgaPPPIInn76aXz55ZeIiorCE088gcqVK6Nnz54AgK+++gpTp07F8uXL0bZtWyxZsgQ9e/bE2bNnUbFiRXtfEhE5iJDvVO0T6ebCAkahrofxkNSS5z6c4lcaCZMAMgElU4GSal4zZYQ6pchwZ3ofAxSR89D2qjpvUv/NovTsVcWpf67NoeGqd+/e6N27t83HL1++HOHh4Xj33XcBAA0aNMCBAwfw3nvvaeFq8eLFmDhxIsaNG6c9Z9u2bVi1ahVeeeWV4r8IInIIoZjLn2dBnc6VYQ5WQq3MJnmqlftyrhGl0kHIOcJUmgKkm/eWErhT9twbDFNETijfvapqeUBfxb33quLUP/dgc7hKSEgocOTHZDLh119/RZs2bYqlYXk5dOgQunXrZnVfz549MWXKFABAdnY2jh8/jpkzZ2qP63Q6dOvWDYcOHcr3dbOyspCVlaV9nZycDAAwGo0wGo3FeAWFZzm/o9vhrti/9lWc/WtVhCJTXTcljOqeUtCZ3zD7wPo/XgFtbr47Mskmqz9LK6HcKUIh0oX2syEU815jnoDkK1lPhBe4U0q/ACbFZPUnFS/2r325Uv8KIYAkQD4vA0nmOz0AXQ0dpFB1CwMZsrbFgTMorv49nnQcyy8vxzdx38Ao1P8vQzxDMCF0AsZVHYeKXhWL5TyuxiTU63WG92iFaYPN4apy5cqIjY3VAlaTJk2wfft2hIaGAgBu3ryJdu3aQZbt904mLi4OlSpVsrqvUqVKSE5ORkZGBm7fvg1ZlvM85syZM/m+7oIFCzB37txc9+/atQu+vr7F0/j7tHv3bkc3wa2xf+2L/WtfP5740dFNcG4Z9/8S+2/uv/8XoXyxf+3LmfvXK9sLFRMrouLtivDL8gMAyJKMa+Wv4XKFyzB5mIBbDm7kPRSlf42KEQeTDmL79e04m35Wu7+ebz08UuERtAtqB4POgD+T/yzOprokZ3gPkZ6ebvOxNocrIaw/4rt48WKuFHf3Ma5i5syZmDp1qvZ1cnIyQkND0aNHDwQGBjqwZWpS3r17N7p37w6DweDQtrgj9q99FaZ/hTDvGZR9V0U/Wa3aZlkbw8ITd5hkE3488SO6Nu8KD737Fn/NWdFPZAqINPOUUBMgJHHn58IOXWBSTNh/cz86lesED5379rGjsH/ty1n7V2QLiAR1TZU2SgWosxBCJHiGeyLcOxzhCHdYG21RlP5NyErAqiursPLKSm3qn6fkiUdDHsVToU+hVVArezbZpWQnZ+NA1gGneI9mmdVmi2L9l2bvxYUhISGIj4+3ui8+Ph6BgYHw8fGBXq+HXq/P85iQkPw3UvPy8oKXl1eu+w0Gg8O/mRbO1BZ3xP61r/z6V5iENs0P6QCyAJgAPfRqmPJTp4K488Ll4uCh94BB714/v8IotPLoSFWng0pGSd2Q1xOQvCTADyW2bspD5+FUb07dDfvXvpyhf4UsoFxXIMfKUG4qVtNydWV10IXooK+oVwvMuBhb+vdY0jF8/O/H2Bi38c7UP68QdcPfauNRyatSgc8vjRRJnQPqDO/RCnN+l/pN1q5dO2zfvt3qvt27d6Ndu3YAAE9PT7Rq1QpRUVEYOHAgAEBRFERFRWHy5Mkl3VwiuotW0S8rR0U/k7hTtc1TguTLin6lUZ4V/bLND3rcWVPHIhRErkMoAsotBXKcDCVBsVoDKwVI0IfooQ/R39nywM1kK9nYHLcZH136CEeTjmr3tw1ui2erP4sBlQaw6p8bsjlcSZKElJQUeHt7q9W4JAmpqanaMFlhhsssUlNTcf78ee3rmJgYnDhxAmXLlkX16tUxc+ZMXL16FWvXrgUAPP300/jggw8wY8YMjB8/Hj/++CM2bNiAbdu2aa8xdepUjBkzBq1bt0abNm2wZMkSpKWladUDich+hDAXmDDfFKP6qZOcJEMySmpFP0uhAQPLo5dmlop+IlNATpeBNNwpUGIJU4FgmCJyMUIIiGQBOVaGHC/f+ZAEail1XYgO+hA9dP7uW8k1LisOKy+vxIrLK6ym/j1e+XFW/SsFCrXmqm7dulZft2jRwurrwr5BOnbsGLp06aJ9bVn3NGbMGERGRiI2NhaXLl3SHg8PD8e2bdvw4osvYunSpahWrRpWrFihlWEHgKFDh+L69et4/fXXERcXh+bNm2Pnzp25ilwQUcHuDkpCyf21UNR1L5DNIw8K1FEo8zEmk1rpR46TofPSqWHKn+XRi4NlFBAAlGQFij6PElp3L4MtzLJYW58rLH+Igo/L+RTLuqm7K/r5Sw7e2p6IikpJM49QxZnXzFoYAH0lPfSV9ZCC3PvDNE79I6AQ4Wrv3r3FfvKIiIgCi2BERkbm+ZzffvutwNedPHkypwES5ZArGOURnIScIyjJQp2+YQlKwvwaln+ukvkxyXzTqX9KOvObY735a6H+J6oL1kGn57vmwtLCq9G8Bslk3ssrS50yp4XXS3Kh3rCIQqWsvEn3MaQkdOaRS18Jkt5932gRuTuRJSDHy5BjZYjknIuoAF1F8whVOZ1b701lVIzYELsBn1z+hFP/CEAhwtW///6LoUOH5ln4gYhKTs6glNdoEhTkDko5R5TuDkoWOUISdOYiEnoABvU+SZIK/R+kJLvvf6jFxVIl0SpAmYs5IDtH0DUPTAmdukmytrdXGiAFFu57cz/BqDg4+vxEVHTCJKAkmEepbilWH7rpyuqgr6yHroLO7Su7Xki7gC+vfYlPLn2C26bbADj1j1Q2h6tx48ahV69eBW4kTET3RwjzG+kco0dCNo8qZZvXpFjeaOccgbI8XxLqaFGOkGQ1omS487U7T81wNtr31RygYAKUbEUbgRKmuwKUZA5QerX8PLyhTZezCiZOtJkmEbkvoQgoN82V/q4rVr97pEAJ+sp66Cvp1SqebuxKxhVsit+EDbEb8FvynVlUIZ4hmFidU/9IVeR9roiocHIFJ0X9u2JSrN50a8FJVv/NCZjXM+qgjlboJauQZBlVIscTJvPeS5YAZbxT9c7yGJQ70/JsClBERA4ghIBIylGYIsfWppKvWulPF6KDzs+9p3zHZ8Vjc/xmbIzdiIOJB7X79ZIenct2RnP/5vhv7f/C18PXga0kZ1KoUux8A0eUN20EKed6JcuokzHHG27LMXdPy9NDDU46c3DScXTJWQlZqG8yTLgzhS8rR1n5HAFKktTwCw9zkPLEnSmXREROSEk1T/mLNX84ZOGJO6XTA937/6db2bfwXcJ32Bi3EdE3o6GYh+okSOhQpgMeD3kcAysNRFlDWfx4/UeuqSIrhQpXDz/8MDw8Cn7Kr7/+el8NInJGQhF5ByfTXdO65NxT9bQCDzlGnSS9e//H5Oq0NWtGc4Ay3dmfC9k5fh6EUEeZdLgToAzgxsdE5FJEpoAcJ0OOkyFScgQqvbkwRWU9dGXcuzBFiikFWxO24uu4rxF1I0qr9gcArYNaY3DIYAwKGYRq3tW0+02KyRFNJSdXqHDVs2dP+Pv726stRA4jjOLO2ibzyJIwqSMUIvuuEae7g9Nd0/UkXeELP5BjWQpIiCwBJVUBsu5U6rMEKK2QhGUaHwMUEbkwYRSQE9TS6cqtnIuoAF25HIUp3LiiZ4acgZ3Xd+LruK+x8/pOZCqZ2mNNAppgcMhgPBbyGMJ9w/+/vTuPjqu68wT+ve/VJqm0WrIkL3jHkjcMdrDNmtAG08wkYSbQYaAJYabJmQamO/HJRggQlgQmzQBZoGFoCHSfdMyEkHQPZGzAxNCAF9rYBkvesQ0YS14llfZ67/3mj/vqqUoq2ZJcpVr0/fjUkVT1Sr51VVK9b917fzeDraRcM6xw9Z3vfIcFLShniSPeSAQsvdbJ6tDvOlmfWlBKeeucAOjpXPHT9YIMTvlAxB196hE43Y7eb6nHneon0D9znxugCvoCFNdBEVGuE0fgHHMLUxzrV5iiTK+jMqtNXYk0T/U6vXj92Ot4selFvHzkZbTb7d5tswpn4Zraa3BtzbWoC9dlsJWUy4YcrvgOLeWK2JQubz1MVPRIRGztU2y9k4I3jxp+9I068bmeV0Tcn393XJjqlb7F2b64ghL80RNRvhHAOekg2hzVhSniZrKpIl3pz6gxYBTkb2EKy7Hw1sm38NvDv8W/Nv+rVzodACaHJuPammtxTe01OKf4HJ4D0BljtUDKSbHKe151tvjNVaPoWxMF0SNNpjttL5S43smw9YuJ8nMz03whjhumegROV1yYstBXXMIdlWKYIqJ8JVGBvd/Gks+WwInGDVEF3cIUtSZUOH/fUHTEwYaWDfjt4d/i982/x5HeI95t1YFqfKXmK7i29lqcX3p+3vYBZcaQw9X+/ftRWVnpfX3s2DEEAgGUlJSkpWFEQN8GqwkhKlZYIBauYpXZYiHKp6uyKUPBUPn7ThxpYgvQ7YapTgfS6QZtR3SZer/SU1wKwTBFRGOCfdxGtCEK9ABBBAEfYI43YdQaujBFnoYJEcH7be/jxaYX8WLTizjUfci7bZx/HK6uvhrX1l6LC8svhKnMDLaU8tmQw9WUKVPQ0tKCO++8Ey+88AJOntRDqlVVVbj55ptx1113obCQNf5pZET6rYeKOt66mIR9oQC9JsYdiVJBroEaa8TSYcrpceB0OkCnW5DCEb0+zqegCpQO2UREY4jYAmuPBfsTW19RADSOb8T8afPh9/sz27g0aog04LdNv8WLh1/ER10fedcXm8X4UvWXcG3NtfjCuC/Ab+RvH1D2GHK4OnHiBJYtW4ZDhw7hhhtuQH19PQCgsbERv/jFL/Daa6/h7bffxgcffIANGzbgb/7mb9LWaMpdXgU2txKfYzl61KE3LkTFZqDGFRZQIYaosap/JT/pdoO4iLcvmCrktE4iGtucVgfR7VFIp34RNSebwAzg2Iljefn3cW/HXm+EqrG90bu+wCjAVeOvwjU112BF5QqEzFAGW0lj0ZDD1X333YdAIIB9+/ahurp6wG1XXHEFbrzxRrz66qv4+c9/nvKGUm6JLyrhjUR1xxWViFXlcyvyxTZYVSZD1Fg2pEp+fuh1AnyeEBFBHIH1kQX7gK3/TgYB/1w/zHFm3u3D9EnXJ/hd0+/w26bfYkvbFu/6gArgiqorcE3NNbiq6iqEfdw2iDJnyOHqD3/4A5566qkBwQoAampq8NOf/hRXXXUV7rnnHtx0000pbSRlP++kuNsdXehNEqJ8yYtK0NjFSn5ERCPntLujVe7Gv0aNAX+dX//dzBPNPc14qeklvNj0Ita3rPeuN5WJL1R8AdfWXosvjv8iyvxlmWskUZwhh6vDhw9j7ty5g94+b948GIaBe+65JyUNo+yXEKgiDqTL3XjX7xaVKGBpc0rESn5ERGdORGB/bMPaa+n1yH7AX+eHWZMfRRpO9J7Avxz5F7x4+EW8eeJNb9sUBYWLyi/CNbXX4Orqq1EVqMpwS4kGGnK4qqysxIEDBzBp0qSkt+/fv58bDI8BpwxUQQUjzOp8Y5WIW3REANj6o0T1u6nOSQdWj+WFKVbyIyIaGekSRBuicE7qwGGMM+Cf44cK5fYf0eO9x7Hm2Bq82PQiXj/2Oizpm9L4udLP4dqaa/Gfav4TJoYmZrCVRKc35HC1YsUK3HnnnXjttdcQCAQSbuvp6cFdd92FK6+8MuUNpMxjoMp/yYIRHH0Rp++22DRPrwS++xFW3PEQ7/vEXhytQ5ZXJp+V/IiIhk9EYB+2Ye209N9pA/DN9sGcaObkDBERwbbINqw+uhprjq3Bey3veSNUADC/eD6urbkWX6n5CqYVTstgS4mGZ1gFLRYvXoxZs2bhtttuQ11dHUQEO3bswBNPPIGenh784z/+YzrbSqOIgSp3JAQjJ/FyJsFIH+ze1y3hqNzhJVHuyJNSgAE96qSgS6HHCk0EASUK6AKMEgOGwecMEdFISK8g2hiFc9SdHleq4J/nh1GYW39X26w2vHH8Daw+uhqvHnsVTT1NCbfPC8/DF6u/iGtrrkVduC5DrSQ6M0MOV5MmTcL69etx66234o477tAndNAnV5dffjl++ctf4qyzzkpbQyn9GKiyh4guTQ8LfYVBbB2WpFdSF4zcQJQsGHmBSfXdN6b/14NyTn8IERENzj5iI9oY1YV+FOCb4YM5NTdGq0QEuzp2Yc2xNVh9dDXeOflOwnS/IrMIXxj3BayoXIEVlSswqSD50hOiXDLkcAUA06ZNw//7f/8PJ0+exJ49ewAAM2fOREVFRVoaR+nHQDW6vNDkXrzQZIn+PIq+SouOe2xs5AkCBQUxJDEYQX/0rktVMCIioowRS2DtsmB/pt8xU2F3tKo4u1+XO+1OvHXiLaw5ugarj63Gwa6DCbfPLJyJK6uuxIrKFbio4iIEjWCGWkqUHsMKVzHl5eU4//zzU90WGiUMVOnhhSF3xMkLTrbojXBjVfGsuJDlxIUmuJvimtBhydQb5MJEQkhiOCIiym/2CRvRhijQrb82p5rwzfBl7f5+BzoPYPWx1VhzdA3ePPEmup1u77aACuCSiktwZdWVuKLyCswsmpnBlhKl34jCFeUeBqqRO+Vok6WDE3rhTduDhYQped5ok+GuT4pNv3P3bopN7WBoIiIa28QWWHst2B+7o1UFCv65fhjl2fUa3ev04t2T73rT/XZ17Eq4fVJokjc69fmKz6PIV5ShlhKNPoarPMZAdXqxtYPSJRC4/RO/tinqTtWzJbFQRCw0KXeKXmy0yaeAALx1TBxtIiKioXDa3A2BO/TrkjnRhO9sX9ZUV/2s+zO8euxVrDm6Bm8cfwMRO+LdZioTy8qWeYFqTnhOTqwJI0oHhqs8w0CVnDf61Kv3XpKoQLoFdpd+d9Da7y6wFfcOShd+iI02KVPp35bYFD2ONhERUQqII7AP2LA+svRrUADwz/HDrMrshsC22Hiv5T1vut+2yLaE28cHxuOKyitwZdWVuGzcZSjzl2WmoURZhuEqD8QCldPlQNplzAcqsdzRJnedk/TE9Umsuh4AMQRiupX0YnsvxWUlBiciIkonp8NBtCEKadWvRcZ4A/56v95cPQOO9R7D68dex5pja/DasddwInrCu01BYVHpIlxZeSVWVK3AuSXnwlBj6/yCaCgYrnLUKQNVSMHw5f8fvPjqel6Q6oorHBEXopRP6dGnAPToE/QLheM4QATeiBQREVG6iQjsT21Yuy39WuUD/HV+GDXGqE6nExFsjWzFmqNrsObYGmxq2eRt3wEAZb4yLK9cjiurrsTyccsxPjh+1NpGlKsYrnLIWA1UXohy1z85vXq6I3ri1kKJO43Pp9c/qUI1YN0TERFRpkm3INoQhXNCvwNoVBjwz/VDhUbntarNasMbx97A6mPJN/KdH56PFVUrcGXVlTi/9Hz4DJ4qEg0Hf2NyhN1qQ3WrvA5U4ujwFBuNcnodSHdciLLcDXFV3xoo5VcDSpUTERFlGxGB0+QgujOqZ1cYgG+WD+bk9G4ILCLY2b4Trx/X0/1OuZFv1QpMCnEjX6IzwXCVI+wmG0bIyItAlRCiov1CVKxan1vFzxuJCiqgEKw+REREOUd6BdGdUTjNerRKlbgbAhel5/X8WO8xvH3ibaw9vhb/98j/xZHeIwm3zyqc5Y1OXVh+ITfyJUohhqtcEUDa/gini8hpQpS7ma5S7hQ+H/QiXh9DFBER5Qf7qI1oY1Tvh6gA3zQfzGlmSjcEPhk9iX878W9468RbeOvEW9jevj3h9qARxMXlF3ul0mcUzUjZ/01EiRiuKGVEBOgCnE4HTpcDdPcLUXCn8JnudL4ChigiIspPYgms3RbsQ+6GwEXuhsClZ/5GaWu0Fe+cfAdvnXgLb554Ex9EPkgoRAEAc8JzcFH5Raj0VeJ/TPsfKPWXnvH/S0Snx3BFZ0REgG5dTtZpcyCdAnHc/aFia6JCSOk7dERERNnMaXE3BO5yNwQ+y4Rvpk+vFx6BiBXBuyff9UamtrRtgRMries6u+hsXFJxCS6tuBQXl1+M8cHxsBwLbxx9A0Vm0Rk/JiIaGoYrGrakgcoWKL/q2y+KiIhojBFHYO2zYB/Qo1UIAf65fpgVw9sQuNPuxPqT6/HmiTfx1om3sLltM2yxE46ZUTjDC1MXlV+ECaEJqXoYRHQGGK5oSET0WqmEQGUxUBEREQGAE3FHq9rdDYFrDfhn+/UMjtPosruwsWWjNzL1Xut7iEo04ZgpBVO8MHVJ+SWYVMCqfkTZiOGKBhULVNIhsNvsxEAVVFBhBioiIhrbRAT2QRvWXgsQAH7AX++HWT34aFWP04P3Wt7zRqY2tW5Cj9OTcMyk0CQ9xa/iYlxacSmmFExJ8yMholRguKIEXqDqjAtUUb05LwMVERFRH6fTQbQhCmlxR6sqDfjn+PX2IXF6nV5sbt3shakNLRvQ7XQnHFMTrNGjUu7o1LSCaSz6RJSDGK4IgN4xftBAVajA/XmJiIg0EYF9yIa12wJsACbgm+2DOUFvCGw5Ft5ve9+b5vduy7votDsTvsf4wHhcUnGJNzI1q3AWwxRRHmC4GsOkp1+g6hUoU0GFGKiIiIiSkR5BtDEK55i7IXCZgjHXwLboNrx1wA1TJ99FxI4k3G+cfxwurrjYG5mqK6pjmCLKQwxXY8wpA1UBAxUREVEyIgKn2UF0ZxROr4MG1YB3xr2Dt/E23ln/Dlqt1oTjy3xluKjiIm+q39zwXBjqzPe4IqLsxnA1BkhvXKDqYKAiIiI6HXEE0iZwTjroPdmLD09+iA32Brwj7+Ad9Q5OOieBo33Hl/hKcGH5hV6Yml88H6YaXgl2Isp9DFd5ygtUERvSrtdQwQCMoMFARURE1I/YAqfVgZwUHD9xHJtaNmGTswmbsAmbsRmdiFszJUCRWYQLyi/wwtTC4oXwGTytIhrr+Fcgj0hUdNn0dh2o0AuIIQxURERE/YglcFocWCcs7D6+GxsjG7FJdJjajd0Dji81S/G5ss95BSjOLTkXfsOfgZYTUTZjuMpxsUDltDtw2p2EQIUScLEsERER9IwOp8VB5HgEm49vxsZOHabew3s4iZMDjp8VmoUlFUuwtGwplpYvRV1RHddMEdFpMVzlILHiAlXEDVRKdNn0EsVARUREY550C+yTNj4+9jE2nNiATd16VOpDfAgbdsKxIRXCouJFWDpuKZaWLcX5ZeejKlCVoZYTUS5juMoVNuC06tEpieiiFAxUREREupKfdAm6T3Rj25Ft2NC6ARt7N+I9vIfDODzg+An+CVhWtgxLxy3FkrIlWFC8AAEjkIGWE1G+YbjKEfZBG0opBioiIhrzRPQMjuajzdhwdINeL2VtwlZsRTe6E441YeKcwnOwpGIJllUsw9KypZhUMClDLSeifMdwlSMcy4Eax0BFRERjj4jAarPQ2NyIDcc3YGP7RmxyNuEjfDTg2AqjAkuKl2DpuKVYNm4Zzis9D4VmYQZaTURjEcNVjlA+VvsjIqKxQRxB64lWbGrahA0tG7CxcyP+Xf4dEUQGHFsfqMeS0iVYWrUUyyqWYWbhTL4RSUQZw3BFREREGeVYDvYc24MNzRuwsW0jNnZvRCMaIZCE44pUET5X8DksKV+CpeOX4vzy81HuL89Qq4mIBmK4IiIiolEjImiLtGHLkS1478R7eKftHdxi34KjODrg2CnmFCwp0lP8llYvxbziedyol4iyGv9CERERUVqICLoj3fjg6AfYfGIzNndsxvu972O37B4wKhVAAAv9C/V6qfFLsaR6CSaEJmSo5UREI8NwRURERGdMHIEVsdB4rFEHqfbN2NK7BQ1oQBTRAcdPVBNxXug8TAlOwZfO+hIWVS1Cga8gAy0nIkodhisiIiIaFrEFTpuDvSf24v0T72Nzux6R+gAfoBOdA44fp8bhvOB5OK/4PCwetxiLxi9CTUENLMfCG0ffwNKqpZzuR0R5gX/JiIiIaFASFTgRB5+d+Az/fuLf8X7H+3i/931swRa0oGXA8WEVxrnBc3Fe8XlYNG4RFlctxpSCKazgR0RjAsMVERERAQCkRwep4y3HvRGpLdEt2IItOIzDA44PIID5wflYVLwIiyoWYVHlIswOz4apzAy0nogo8xiuiIiIxhgRAboBJ+Ig0hLBtpPb8O/t/44tlg5S+7F/wH0MGKgP1OupfRWLsWjcIswrnoeAEcjAIyAiyk4MV0RERHlMRCCdAokIulu70dDSoNdIWXpq307shANnwP2m+6d7I1KLKxbjnOJzUOQrysAjICLKHQxXREREeUIcgXToqX1Wq4Vdrbvwfvv72GJvwft4Hw1oQA96Btyv1lerg1S5vpxXch4qAhUZeARERLmN4YqIiChDRARwANiAWALYACxdjc/7OITrbMvGAesAtlnbsEX01L5t2IZ2tA/4P8vMMiwKu2ukyhZhUcki7idFRJQiDFdERERDJOIGm/gwFPf5gIB0uuNsoN9euqdliYXd2I0P8AG2yTZ86P6LIDLg2EJViHOKz8GiskX4XNnnsKh0EaYVTGPlPiKiNGG4IiIicomt1yY5EQfSJrDbbSzuXgxrpwXLtnQYShdTX5Sp9KuzCXSb3Wh0GrHN2YYPoh/gg+gHaOhtQI8MnNoXVEHMDc/FeaXnYVHpIiwqXYS6ojruH0VENIr4F5eIiMYksfqClNOmw5R0DBxGKkRh8m9gAvC5YcgElE8lXucGpNjnp7uu1WrFB5EPsK1tG7ZFtmFr21bs6tgFWwYmumKzGAtKFmBh8UKcU3IOzik5B3VFdfAb/hT2EBERDRfDFRER5b3YRrjS1vdROgeZjxcAjBIDqlhBwoItHVtwXuV5MH2mDlA+AAbOaGpdU08TtrXpALUtsg3b2rZhf9fA8ucAUBWowjnF52BhiQ5SC4sXYlrhNBjKGPH/T0RE6cFwRUREeUV644JUmwOJCKRrkCAV1EHKKDagSpT+GOoLTZZjofVoK1SxgmEMP8yICA52HcTWtq3YGtnqBarm3uakx58VOkuPRMWFqQnBCVwjRUSUIxiuiIgoZ0lPX4By2hw4EQfoTn6sKlA6JLmjUkaJARVIXWixHAu7O3fraX1uiPog8gFarJaBbYHC2UVnJ4SoBcULMC4wLmXtISKi0cdwRUREWU9EgB7oABUXptCb/HhVGBekYiNS/tQFqW67Gw3tDQnT+rZHtqPL6RpwrF/5MSc8xwtR55aci3nhedyQl4goD2VFuHr88cfxd3/3d2hqasI555yDX/ziFzj//POTHvv5z38eb7755oDrr7rqKrzyyisAgK9//et4/vnnE25fsWIFVq9enfrGExFRSonoaXyxAOUFqWjy41WR8gJUbFRK+VIXpDrtTrxz8h1sb9/ujUjt7NgJS6wBxxaZRVhQvCBhal99uB4BI5Cy9hARUfbKeLh64YUXsHLlSjz55JNYsmQJHnvsMaxYsQK7du3C+PHjBxz/0ksvobe3763K48eP45xzzsG1116bcNyVV16JX/3qV97XwWAwfQ+CiCiNYqM20i19lx6B0+XgnI5zYH9iwzEdXanOcCvQGeirShf73FDeMf2/jlW8g4EzLtYw3McmndJXaCIWpAbmFkDpIJUwGlWsdNtT1JaPuz/GB5EPsD2yHR9GPsQHbR/go66Pkh4/zj/OKzARq9g3o3AGTGWmpD1ERJR7Mh6uHnnkEdxyyy24+eabAQBPPvkkXnnlFTz77LP4/ve/P+D4ioqKhK9XrVqFwsLCAeEqGAyipqYmfQ0nIkoBEQF64QWmhADlXoceDLrRbClKdTgZ7k60p+MGsFOFtQG3DRbW4m8DEsufRyT53lEKelpffKGJcOqCVIfVgYb2BnwY+RAfRj7E9sh2bG/fjjarLenxE4MTdZCKq9g3KTSJhSaIiChBRsNVb28vNm/ejDvuuMO7zjAMLF++HOvXrx/S93jmmWdw3XXXoagoce76unXrMH78eJSXl+Oyyy7DAw88gHHjki8U7unpQU9P34aMbW36xTUajSIaHWQeyiiJ/f+WWDAclt1NNcuxEj5Sao31/hURPQLTrQsvoFuHqNgoFHr0bUPKRQpAAEAIUEEFhAAJChp7GjEnPEf/fXAA2IA44n3e/6M4kvR6OP3a4bgXICG4pTzEATqAhQFVotdJqWIFFLmBzGuO09euYRARHOw+iIaIDlKxQLW/a3/Sx+JXftQV1WFu8VzMD89HXVEd2q12fLH6iwM247XFHtrPjgY11v9GpBv7N73Yv+kVm3qd6XPx4bZBiUjGXho+++wzTJw4Ee+++y6WLVvmXf/d734Xb775JjZu3HjK+2/atAlLlizBxo0bE9ZoxUazpk2bhn379uEHP/gBwuEw1q9fD9McOF3jRz/6Ee69994B1//zP/8zCgsH2TySiMY80zYRjAb1pTfY93nc16acfoqYQNDr60WPvwc9gR79Mf4S6EGvr1cHrDRSomA4hr6IAdMxB37uGDDFTDjOcNzb+x3X/zbDMaBEoSvUhUhBBO0F7WgvaEdnsDMlj63b7sbB7oM40HUAB7oP4EDXARzsOohOpzPp8eW+ckwpmIKpBVMxNTQVUwumYmJwIjfiJSKiBJ2dnbj++uvR2tqKkpKSUx6b8WmBZ+KZZ57B/PnzBxS/uO6667zP58+fjwULFmDGjBlYt24d/uzP/mzA97njjjuwcuVK7+u2tjZMnjwZV1xxxWk7MN2i0Shee+01XBS4CIFSLohONcux8Nbxt3DJuEsGvCtNZy6X+1fsuBGmbiSMNnnXJZvOlowfCSNO/T8iCPgNP4owvOpxudq/hSjEOIy85PhIR6PmFc/DvPA8/bF4HqoCVaf9v3K1j3MF+ze92L/pxf5Nr962Xrzd8zYuv/xy+P2ZfdMrNqttKDL6TKisrIRpmmhuTtxMsbm5+bTrpTo6OrBq1Srcd999p/1/pk+fjsrKSuzduzdpuAoGg0kLXvj9/oz/MGN8ysdf3DTyGezfdMrW/vWKKXQIpF3gtDv6624ZtDLdAD5AhZS+BJX3OULouy5F64QGbUKW9m8qtFvtaGxvTCgysT2yHRE7kvT46kA15hfP9y7ziudhdtHsMx6Nyuc+zgbs3/Ri/6YX+zc9HKXngWfD+fhw/v+MPhMCgQAWLVqEtWvX4uqrrwYAOI6DtWvX4vbbbz/lfX/729+ip6cHf/mXf3na/+fTTz/F8ePHUVtbm4pmE1GOEXGLQ7S7IarD0Z93yKnX8JgDg1NCaAqltuT3WCYiONh1MDFEtW/HR50fJR2NCqgA6sJ1CSFqXngexgcHVpklIiIaLRmP2StXrsRNN92ExYsX4/zzz8djjz2Gjo4Or3rg1772NUycOBEPPvhgwv2eeeYZXH311QOKVLS3t+Pee+/FV77yFdTU1GDfvn347ne/i5kzZ2LFihWj9riIaPR5G82294Unp93RIWqwKXyGu09SWMEIG/rz2OiTb/RKko8l7Vb7wEp9pxiNqgnWYF54HhYUL8C84nmYXzwfZxedzbVRRESUdTIerr761a/i6NGjuPvuu9HU1ISFCxdi9erVqK6uBgB8/PHHMIzEKnm7du3C22+/jVdffXXA9zNNEx988AGef/55tLS0YMKECbjiiitw//33c68rojwRK1+eNEQNVrRJJQlRYQVVoBig0iTqRLG3cy8a2xvREGlAQ3sDGtsbTzsaFR+ihro2ioiIKBtkPFwBwO233z7oNMB169YNuG727NkYrMhhQUEB1qxZk8rmEVEGSW9fcPLWRbWfJkQVuiGqyN0bKRaiDIaodIhtvtsQ0eGpob0BjZFG7OrYhV7pTXqfmmCNntIX7pvWx9EoIiLKdVkRroiIJNpvPZT7OZKfmwPoC1GqyB2NCit9HUNU2hzpOYLG9kY0tjdie2Q7GtsbsaN9x6BT+sJmGHPCczAnPAdzi+diTngOR6OIiChvMVwR0agSa5AQ1TP4fVSBSpzSFwtRaa7CN5ZFrAh2tO9AQ3uDNyLV2N6II71Hkh4fK3c+p9gNUuG5mFs8F5NDk2EoboBORERjA8MVURYTR09/k6guDS7RU0yHG4pRziKOOKhuqYbdYsPpcHSI6j7FHULwpvIlrItiiEqbHqcHuzt2ozGip/PF1kUd7DqY9HgFhemF073RqHnF8zAnPAczC2dySh8REY15DFdEoyBpSHI/xgJTstvOKEhlidmYDXH/eYJIWA/lfc6y5mlji40DnQcSAlRjpBF7OvfAkuRPtNpgrTcCFQtT9eF6FJqFo9x6IiKi3MBwRTQMQwpJvSkebQJ0SXCfAvzux3RkkOQ1Ys7wWwqOR49jXNk4mMVmX4jyM0Sli4jgcM9hr7BEbErfjvYd6HK6kt6nzFeWsCZqblh/rAhUjHLriYiIchvDFY1pEhUUdxTDUQ5sy+4LSXGhKOUhya/6PvoTPya7Dj7kbIEGy7Gw/eh2XFZ1GXevT4OT0ZO6tHn3R9jZsdNbF3UieiLp8SEjhLpwHeaF5yUUmZgQnMBy9ERERCnAsx0aMyQqcNocSMT92CaQLsG5OBeO+2/I+ocjX36HJMqsk9GT2NG+I+HS2N6I5t7mpMebysSswllegJpTPAfzwvMwrXAaTGWOcuuJiIjGDoYryksSFTgRHaDig1Qy3f5uhEIhGAFjQFhiSKLRdDJ6Ejvbd3rT+GKjUU09TYPep8pfhfNKz/MKS8wNz8XZRWcjZIZGseVEREQEMFxRHhCrL0DFRqakM3mQUgUKqkTBKDGgihWcsINNLZs4bY1GVUu0xRt92tm+Ezs6dpw2RE0OTUZ9uB5zwnNQF65DfVE9ZhbOxHsn3+Pzl4goE9KwVplyH1+NKaeIJd60Pm9EarAgFYoLUrGP/QopiMO/jJQ+LdGWhJGoHR16St/hnsOD3mdyaDLqwnVeZb76onrUhetQ7CsecKzl5EE5SSKiXGDri1gCsQRKFET0OYT0CsDJAuRiuKKslRCk3Cl+0jFIGAoBRonRF6SKDagAp+7R6GiNtvaNRHXs9D4/VYiaFJrkhaf6sL7UhetQ4isZxZYTEVECB16IguV+DQAG9LKAgIJRqs8xxBRgD4BefbwqSlM1X8opDFeUFcROMiI1WJAK9gtSJQxSNDpao63eOqj44hKf9Xw26H0mhiaivqi+bySKIYqIKPMEuiKwJX2jUhBdOTW27jqs9CyY2BrsABL2Y3RsnbzMSSbUUQVpE6iwAlg3aExjuKJRN+IgVewGqSCDFKVXfIja2d43EjWUEBULULG1UQxRREQZJNDByZa+MAX0hShTAYV6KYERMLwCVvBjyFtUGCUGfAU+WM0WnBYHRqEBBNL3kCi7MVxRWoktkPZ+BSc6JPki0ECSESkGKUqjE70nvGl8uzp2ecUlDnUfGvQ+E4ITEkah6sP1qCuqQ6m/dBRbTkREAyRZFwUFwHBHnAoBI2ToEBWIC1EpqACsQgq+ST7YIRvOUQeIAqqQ0wTHIoYrSimn04FzfOhBKhaijGIDKsS/QJR6IoIjvUe84LSzfSd2dezCjvYdONJ7ZND7TQhOSAhQc8JzMLtoNsr8ZaPXeCIiGihuXZRYAuW45w/910UFFVQgblsVM73nGcpUMMebMIIG7GZbTxMsVrpdNGYwXNEZk16B3WTDbrIhrUmSlL9fkCoxgODQh9uJhkJEcKj7EHZ27BwQpE5ETwx6v1h1vlhVvrqiOtSF6xiiiIgybbB1UYZe16T8CkbY6FsX5Y5Gxa+LGm1KKagyvVbLarLgtDowivQ+mjQ2MFzRiIglcI44sJtsOCechNEpo9yAKmOQovRwxMHBroPeVL7YZru72nchYkeS3kdBYXrhdMwumu1N46sL12F20WyEfeFRfgRERDSAA0h0GOuiAtC3Zen5RcI0wWPuNMECThMcCxiuaMjEETjHHdiH3fnETt9tqkTBrDFh1phcJ0UpYTkWPur6SIen9p3eiNTujt3ocrqS3senfJhZODNhBKquqA6zimahwCwY5UdARERJxY1ISVQvH1CGSlwXFexXXCIF66JGm/IpmNUmjJABq8mCRNxpgrn3UGgYGK7olEQE0uJO+2u2gWjfbapAwag1YNaYesibaAR6nB7s7djrFZaIBak9HXsQlWjS+wSNIM4uPFuHJ3dK3+zwbMwonIGAwRJNRERZxXZHpaLoe2PW707rKzVghNwCE8HMTulLh9g0QX/Qz2mCYwTDFSXlRBxvHRW6424IoG+EqkRl7XA8ZZ9OuxO7OnZhV/subz3Uzvad+KjrI9hiJ71PkVmE2UWzE0ai6ovqMbVwKkzFjUSIiLJObFQqNsVP9Bop5VdACWAU9hWaQCB7p/WlmipQ8E32wT5iwznuAJY7TZDyDsMVeaQrrjBFe9wiKhMwxhswa029nioHh+Zp9LRGW3VZ846daIw04t2T7+Kbu76Jg10HIUlLRwKlvtIBU/nqw/WYFJoEQ3FUlIgoK8X2kIrqqn1w4tZJBRVUudKjUkE3SKW5Wl+2Uz6lz6VCht4Tq82BUWxwmmCeYbga46RXYDe7gaol7sRXAUalG6gqjTH/B5ESiQiO9h71CkrE9oja2bETh3sOD3q/Sn9lwlS+WFGJ2mDtmHn3kogoZzlIGJUC0Fe1r8zQywWCupBVNhebyCSlFFRFv2mCYYNn5HmEP8oxSGyBc9QtTHE8sdKfKncLU1SbegifxrT+5c1jH3d17MLx6PFB71cbrEV9uB6zCmcBAlxdczXmFM9BVaBqFFtPREQjlqzohHJLnRcoqCIdpLxRKc5qGRZVpOA7ywe72a26HAT3+8wTDFdjhDgC54RbOv2Io/eLcKmwHqY2a0z+Yo9Rttg40HkgITztaN+B3R27T1nefGrBVMwOz/YKStQV6ZGoUn8pAF3x742jb+DC8gvhM/jnhogoa7mb8iKKvnOE2Ia8JXFFJ0L5V3QiU5RfwZygz73sozaciDuKxe7NaTzbyWMiAmmNq/TX23ebCsVV+gtzTctY0ev0Ym/nXq+YRGx/qN2du9Hj9CS9T6y8ef/CErOKZqHQLBzlR0BERGfsVKXQw4BRpIOUV8GP0/vSRhkKZmVcufZWgQornqHnMP7o8pDT4U75a3IgXXFz/vyAWW3CrDWhSlnpL591WB3Y3bl7wFS+fZ37Bq3MFzJCOLvobNSH63WQckPUjMIZ8BusGUtElLNsvcbaK4WuoEel3LVSCdP7OCqVESqspwlaRyzICdEjhNw3NCcxXOUJ6XYLUxy2IZG4QGW4lf5qTBjjWOkv37REWxKKSsQ+Huw6OOh9SnwlA0ah6orqcFbBWSxvTkSULxx4lX+lS/QoVClgFLil0IPu+im+0Zo1VEDBN8EHO6iXcEivO4rFH1FOYbjKYRIVvV9Ck6MXQ8YowBjnBqrxrPSX62KV+eI32Y0Fqebe5kHvVxWoGhCiZhfNxoTgBL6YEhHlK9FhClF3H6UuwDfNB1+Bj+cDOUAZCr4qH5yQXicvrQJVrAC+95kzGK5yjNgC55hbmOKY07fTOQBV6hamqDb1O1SUU0QEn/V85k3liw9Sp6rMNzE0UReUiAUpN0RVBipHsfVERJRRAkiPQLoFRoEBs9oEwgBOutX9GKxyilGs171ZzRacFgdGgQEEMt0qGgqGq1wggLQJooeisI/YfXtLQJfyNGtNGDWG/sWjrOeIg4+7PvbWQu3o6AtRbVZb0vsoKEwrmDZgFGp2eDZKfCWj/AiIiCir9ALSKUAQ+k3Wcr2dirIZqHKZCir4Jvpgh9xKz1FAFXKaYLZjuMpi4gh63ujBkh1LIJbAjtVGDULvRVVrQoVZmCJbWY6F/V37+/aIcoPU7o7d6LQ7k97HVCZmFs5MWAtVF67D2UVno8AsGOVHQEREWc0CpEMAn7u+upxbquQbZSr4xvvgBN1pgm3uNEG+n561GK6ymDIU7E9tBK0gYLqBqsaEKmegyibJypvvbN+J3R270Su9Se8TUAGcXXS2F6Dqw3qfqJmFMxEwOO5PRESn4OjKwEoUVJmCb5wPqojnBfnMKNWFSKwmC06rA6PIAFjINysxXGW5wEUBbN64GfMq58Ffwd+iTOqyu7C7Y7c3ArWrfRd2duzE3s69g5Y3LzQLdXnzonpvPVR9UT2mFkzlprpERDQ84k7/swCjxIBZYUIV8w3XsUKFFHyT3GmCx5y+oiX88WcVnt1lOd90H47vPM4S6qMoYkWwq2NXwlS+Xe27sL9rPwSS9D7x5c1jQao+XI/JockwFMfuiYjoDIjeckV6BEaRAXOCG6pYpGLMUT4Fs1pvOsxpgtmJ4YrGrNZoK7ZHtuO146/htROv6UDVsROfdn866H0q/BXeJrv14XpvTRTLmxMRUTpIj0C6BEbQgDHRgFlmcqPfMU4pPR1UBd1qgpwmmFUYrijvtVlt2Nm+E43tjdjRvgM72negsb0Rn/V8Nuh9qgPVXoiKjULVFdWhKlDFEEVEROkXdacA+gGz2tRTALnNCsVRBe40waA7TdBypwlSRjFcUd6IWBEvPMUC1I6OHTjUfWjQ+0wMTkRVoAoXll+I+nC9F6gqAhWj2HIiIiKX7RarUApGhQFznMkTZhqU8imYNXqaoNVs9U0T5FMmYxiuKOe0W+3eJrteiGrfgU+6Pxn0PrXBWswJz/EC1JzwHNQV1aHILMIbR9/AZVWXscAEERFljuOOVDl6A1lfpQ8oAmdL0GkppaDKFfxBP6sJZgGeTVLW6rA6sKtjV1+I6tAh6mDXwUHvUx2oHhiiwnUo95cnPd5yrKTXExERjYpYsYpet1jFOBOqRLGQFQ2bKlTwneWD3WzDOcFpgpnCcEUZ12V3JR2JOth1cNDqfOMD45OORHE6HxER5QRxi1V0C4wCA76JPqhSxWIVdEaUT8Gc4E4TPGLBaXNgFBucJjiKGK5o1HTb3d5IVHxxiVOVOK8KVKG+qD4hRNWH6zEuMG6UW09ERJQiUUA6BAgCZq0Js9yE8vPsl1JDKQU1rt80wbDBs/5Rwm6mlOu2u7G7Y7cOTx19BSY+6vwIDpyk96n0V3oBqi5c54WoqkDVKLeeiIgoTSx3XZUBGJXuFMAQQ1W2EBFAADhI+ChOv+vd94MdW5/TiC2AmYkWn5oKK/im+HShixM6zPP5ln4MVzRijjj4uOtjfNj+IRoiDWhob8D2yHbs7dwLW+yk94ntE1Ufrkd9Ud9I1Pjg+FFuPRER0Shx3JEqAKpUwTfOB1XEk9x08gJRLBQ5ycOTKIGSuJ+F4RYRMfTnUIAKuBs2mwB8gDL0mjhxBNgLSEQgpZKVUzqVX8E3wQc7ZMM54kAiAhVmNcF0YriiITkZPYmGSAO2t2/H9sh2NLQ3oCHSgHa7PenxZb6yAVP56sP1qA5Us/IRERGNDQJIlwBRQBUrPVJVrPg6OAxJA5HIgNElCHRgiK0yUDoEQaEvJJnumjYTgKm/huEeFwtT8V+79x3s52VEDf2x3IC0ia7umIXTO5Wh4Kv0wQk6sA/ZkA43YFFaMFxRgl6nF7s7dicEqe3t2wfdKyqgAqgL12Fe8TzMC8/D3OK5mBuei9pgLV88iIhobIpVAOwRGIUGzBq3AqDJ18X+xBHABsTSH2EDAtHnELGg1G80SSkFBHTxBhhIGE1KGpLi75cGZqUJw2/AOe4AhcjazZ6NYgOYCFifWpBOgSrMznbmOoarMUpEcKjnkB6FcoNUQ6QBuzp2ISrRpPc5K3QW5hXrADU/PB9zi+diZuFM+A1upEBERAQA6AWcTgcqqKu2meVmVk4XywSxRa87iwUpd7RJmQrKr6AKlQ4m/YJR/5AElV37fylDwawyARNwjul1WNkcsHy1Ph2wuoVrsNKA4WoMiFgRNLY36lEod0rf9sh2tFgtSY8v8ZV4o1CxEak54Tko9ZeObsOJiIhyRdQtVuEDzGo3VAXH5olrwmiUpb9WcEfufIARMoACwPDrCnbKr6/PpsA0XEopmBW6qoVzzAGc7C0eYZQZMC0TdpOtw2og0y3KLwxXecRyLOzr3JewLmp7ZDsOdB1IerypTMwumo25YR2i5obnYn7xfEwKTcrpP3BERESjxgacDkeXv65Q8FX4xsx0K5G4qXxukAKgR5Z8en2TKlJQQffzWIjK0+mRsYClTAX7iA0RvYdZNjLGGRBH4DTp5y44CSllGK5yVHNPc0KA2t6+HTvad6DH6Ul6fG2w1huFigWp2eHZCBrBUW45ERFRHhC3AqCjp1qZ40yocP4WqxDHHYWyBxmNKjCAkDsa5XfXQ+X4aNRIKKVglplQhoLdbMPpdGAUZl/AUkrBrDQBG7CP2NwHK4XYjVmuM9qJvZ178Wnbp9jZtNNbH3W092jS4wvNQswNz/VGoWIFJrjpLhERUQrEF6soMmBWusUqjPwIEd5oVFyQAqDXPsXWRoX12ijld6vv+ZE3jz9VjBIDUDq4OB0OjKIsDFiGgjnehNgC57ij25x9zcw5DFdZ7tynz8X+lv0DrldQmFk4U6+LctdHzS+ej6kFU2Eo/mYQERGlhEBPe7PddUS9AiNkwJxowigzcrpYhTca5RaYEEdX6VNGv9GoQNzaKHPsjUaNlFGsw4rdbMOJOFk5sqlMBV+ND5ZlwWl1YJQa3APrDDFcZbn6ynocjxzHwqKFWFC+wFsfVR+uR6FZmOnmERER5QcHiWuHnLiS4LE9kUJ6rYpZZmZtNbhkRKSvuET/0Sh3LZQqjhuNiq2N4mjUGTOKDKBWByyJCFCcfeFU+fRGw5ZYkDaBKuEmw2eC4SrL/eY//QZvvPoGvhD6AgJlLOdCREQ0Yv1HoSyBEvcsMrZfkk/pzWCDCobf8IIG/LlViEEcgdOhy4JLRHRFOFOf7KugG6D8fYUnKH2MAgOocQNWmwAlWRiwAkqXaLctSLu7yXB2NTFnMFxluQJ/Qdb9AhIREWW1oYxCBQGj1EgcrfEj54swiCOQbgGi8ErB+yb54Av5dJjK4ceWy4yQAVWjYB2x4LQ5MIqNrBsZVCF3BOsTN2AVZ1f7cgXDFREREeWeoY5CFeqTxlwehRoKEYF0CdALqEK3ElwQwAd65ET58+vx5iIVVPBV+2AfsWG32TBKsjBgFSr4JvoQ/TQK6ZQxs61AKjFcERERUfYaw6NQQyHijlT1AqpAhyoV1iXSVTS/H3suUgEFs8YEDMBpdfQarCwL+irsFrk4ZEG6BKogu9qX7RiuiIiIKLMEfcUWBhuFMsfOKNRQeKGqx+2TGgNGOLerF44VyqdLoEMBTosDhJF1PzejzIDP9sE6bEF6xJtiSqfHcEVERESpJ+7F0R/F0Rvuxi4ioq8DIO0C8YmeIjWGR6GGQkTvsYVuHapUtYJZYmbdyTmdmhewDMA54egiKlk2dVNVKBi2AafJ0W9y+DPdotzAcEVERAR4a3jg9I2gOJautuZEHDiGo0/uDegqWu7FWzNhoG8Dzuw6R0qNwcJSbO2T6NEmgUAhrk8U+vZG8qFvLZTPvf0A4Jvigz/oH7OjUEMhokeppFv6QlWxmXUn5DR0ylQwq9wRrOOOHpnNohL/Srntc/RmyEaRweQwBOwiIiIaG2LhIL4IgiNQjupbw2NABwG3EILpN4FPALPShCmmd79YCIPTb0Qm7v8R6FEZBf39oTB4OIt9HX/baPXHEMJS7HF47TPgbTQbH5aUz92ANtaPZt/nMAYGJ7EFOABvjRAlJz0Cp8uBETRgjDf0SBVDVV5QhhtgTMA5pv+IZGXAsgD7uK03RjYz3arsxnBFRJQr+p8M2+6Uqm6BmNJ3kj6aJ+jZJj48OdAFEMQNCuh30l8IGAF3+llsrx83LHgjLTZ0uKo24TMTXzJFEqe5eSNf/abAiSN9P6+4dUVeoQZbEn6u3gVIGAUSiA4osccRGyUz3LbGfvaCkYWl2MiSib5NZIcZlii1vFAVMGCON/VIVRadeFNqKKVgVujE4hxzAEdP+cwWytRFOMQROCcdGKXG2Hx9GSKGKyKiTImdUMePHLjXxU6MY6MeA6ZZxU6yod/5VI7qCxSx74u+k3PvxNzoO6FOCGOx75vtYo8tLkCJHVf8IDYFzXA3SA0rIAAYPj2dxSuCEDvmDMSq1SV7F1cNsTP7r0OKD0L9v/ZG2+JHz+LDXNzP3QtLJrzAaPiNgWHJiHsMRtz9KKOkV+B0uqGqytQjVQxVeS0WsJSpYB+xISJ68+EsoXzuJsOOBWkTqBJuMjwYhisiolQY4hQrfagblmKjSyb6wk7sRDgWBkzlnQB7owaxkQMBsBUwZ5jwKZ8XOrwRE0u8oOaNmsTKWcePmNh97U8YKVF95a4TpoKle3QsWXhy4kJmfPW4AsAIGt7Ik/LFFT/IgVEVL+j0v36o4Uz6BepYuGJYyknSq/eqUj5dUt0sMVmlbQxRSsEsM6EMBbvZ1gG7MIsClt8NWJYFibibDPPpOQDDFRFRMsMdVYpfM9NvilXCqEEsrBhJRg3M4Z0IK9sduVLuehUTCdWcTnWCLpI4NS22/sgLNiJeEEsIZJYkjrLEBTJvdCx+XVGyQAYMKBwhdr/RudioStCduhdyH2N8MQRWj+t7PlFOk6joDVtNBaPCgFnKUDWWGSV62p19xIbT4ehCEllCBfUmw9YnFpx2R6/BogQMV0Q0dsQXNOhfFtqR5GEpflTJHRExfIY3khQbTRowqpTl61G88Bd/3WneghQnSTGHuNEliHuSGAtjVuIxXmCNBa24whFGwIDhNwaGpyzuQ6IzlRCqyg0YpQaMEE9WCTq0GNAjWBFHF33JkjeTVIGCb4IP0U+jkA6BKsqOdmULhiuibCZxH+Mv/SuLjXVxo0wDCgnEjzDFFv2bce/4h+BNJVOmO60sfi1KXGDKlhe2TPGmsI1kdCw2IuZIQklu9iuNRWKJPik1FYwyN1Rl0foayg5GkQHU6BEsiQhQnD1/L1XYnSJ4yNJTWQuyo13ZICt+kx9//HFMnToVoVAIS5YswaZNmwY99rnnntNTYOIuoVAo4RgRwd13343a2loUFBRg+fLl2LNnT7ofBo0l8etrbOh36KPupVdXeJJuPXdeuvQ7k9IheqPMiOg9c9ocOK0OpFW8i9Pa77qIe+lwv0e36Dn5Pe7XkcT7JnyfiNN331g7egToddvpjip4AS4bxdYDRd0+7dKPx4k4iY854vZHVK/NUT4FVahglpvw1fpgTjThP8sP/zQ//NP1xTfTB99MH/xT/fBN8sFX7YNZaeoTnWIDqkjpqWixSnJZ8oKWS5Ry+y7g9mWR0n0bdr9mv9IYI5bAaXMgXQKj1IA5yYRZbTJY0aCMQgNmjQkVUpA20W9aZQmj1ICvxqenj/dkT7syLeMjVy+88AJWrlyJJ598EkuWLMFjjz2GFStWYNeuXRg/fnzS+5SUlGDXrl3e1/1fnH/605/i5z//OZ5//nlMmzYNd911F1asWIHGxsYBQYzGMIEOGIAOIO6/WHBKKH/s8qaL9V2hxY8gqYEljuMv3rv/sWljsSJn8XvduJeEzUnjb4sLd8kKJ8SXevbWyMRKUsdXFTvTss8jLWwQX7I6rqiCOP02IY2bmucVe/C765f8cVPyfHFrl2JT8njSTkRZQiz95hCgp3sZZQZUAd9coKExQgZUjYJ1xILTptc5nWm101RR5QqGbcBpcgbMbBirMh6uHnnkEdxyyy24+eabAQBPPvkkXnnlFTz77LP4/ve/n/Q+SinU1NQkvU1E8Nhjj+GHP/whvvzlLwMA/vEf/xHV1dX4wx/+gOuuuy49D4Syn6PntyMKfWIPQHzufi/FyltHk1CiGv2qecUW6PcPF8DAADRKJ/jDqioWX6QhWdnn2HS64ZR9PkVAc0SXLnMijhdeBw1NhXGhKb46Xlxo4vobIsolYutRdYgbqkoNqEKGKho+FVTwVftgH7Fht9kwSrIjYCmlK1vCBpxmvTYs8+kiszL68Ht7e7F582bccccd3nWGYWD58uVYv379oPdrb2/HlClT4DgOzjvvPPzkJz/B3LlzAQD79+9HU1MTli9f7h1fWlqKJUuWYP369QxXY4kVt7je0SfxyqeAsJ7HrILuH4APoaeFmfn91yBhk9H+t51J2ee4QNY/fDlRR2/AWmnCDJiJxR9ilfRSsN8QEVE2EdsdqXIAI+yOVDFU0RlSAQWz2gQMwGl19BqsLHjTUSkFc7wOWPYxWxfjGMNVTDN6Nnns2DHYto3q6uqE66urq7Fz586k95k9ezaeffZZLFiwAK2trXj44YdxwQUXoKGhAZMmTUJTU5P3Pfp/z9ht/fX09KCnp8f7uq2tDQAQjUYRjUZH/PhSIfb/W2LBcDgne1CxERhLr0kC3DAVUFAlev0NAoAE9f4hjrsZjG3rISzLtjLV8twUq6J3mj+eYgvwCSCVAsd0BjkI3kgiDU/secvnb/qwj9Mr3/pXHHekytFv4hmlBpxCB6LEm4Y+mmLnEJk+l8lXmepfKRfYjg2nxa0i6Mt8wALc1/qoA6vF8qodnglL9C9NNjx/h9OGnHurftmyZVi2bJn39QUXXID6+no89dRTuP/++0f0PR988EHce++9A65/9dVXUVhYOOK2ptLbvW8DRzPdihzUBaD19Ie9sfWNtDdlLGP/phf7N/3Yx+nF/k2v1157LdNNyGvs3ySOp+5bZUP/dnZ2DvnYjIaryspKmKaJ5ubmhOubm5sHXVPVn9/vx7nnnou9e/cCgHe/5uZm1NbWJnzPhQsXJv0ed9xxB1auXOl93dbWhsmTJ+OKK65ASUnJcB5SykWjUbz22mu4KHARAqWBjLYlo2Kbl0bjSjn79RxkVeSOUAX16NRwpl1YtoU3tr6ByxZelvfTAjOB/Zte7N/0Yx+nV673rzju9D8LUEVKb/5bqLJmqnPsHOLyyy+H389KA6mW6f4VR2Aft+GccPTzLpAdzzvpEdiHbL2HW3G/QmDD0NvWi7d73s6K529sVttQZPQvWSAQwKJFi7B27VpcffXVAADHcbB27VrcfvvtQ/oetm3jww8/xFVXXQUAmDZtGmpqarB27VovTLW1tWHjxo3467/+66TfIxgMIhgMDrje7/dn/IcZ41M++Izce+EZEbeKn1i6tDbEneLnV0CZLktqBI2+/YlSwGf64Dez42edj9i/6cX+TT/2cXrlWv+Ko7fGQBR624cyU0/PypJQ1V82nc/ko0z2r9QK7KAN55ij92rMhoBVCMgkgfWpBafTgRE2RhSwHKWXE2TD83c4/3/Gz9ZXrlyJm266CYsXL8b555+Pxx57DB0dHV71wK997WuYOHEiHnzwQQDAfffdh6VLl2LmzJloaWnB3/3d3+HgwYP4q7/6KwB61OKb3/wmHnjgAcyaNcsrxT5hwgQvwFGWEegRqWjcnHSfXrgZ261eBRUQzI6Fm0RENPbECvrE9gtUhbpKmgorvjZRxiilYFboBdDOMUcX8Apl/vmoCtxNhj+19IbZ4cy3abRkPFx99atfxdGjR3H33XejqakJCxcuxOrVq72CFB9//DEMo29F3MmTJ3HLLbegqakJ5eXlWLRoEd59913MmTPHO+a73/0uOjo68I1vfAMtLS246KKLsHr1au5xlS3suJLoDvS7GX79i6iKFIxgXJhiZSUiIhol4sTtERi3XyAAb5sOFWCoouwSC1jKVLCP2BCRrNiYWoUVzAkmrEMWpEugCsbG70vGwxUA3H777YNOA1y3bl3C148++igeffTRU34/pRTuu+8+3HfffalqIo1UrIpfNMkUvxJ3il/I0EHKPzZ+6YiIKDO8LSPsxCDl7RUf2+jd1LMnVEC/XsU2Klemu96XoYqyjFLu9FRDwW629XS8wswHLKPEgM/26YDVLVkxqpZuWRGuKM9E40amAO9FyigxYBQYfYUnsqR0KBER5QdvL7748ORulB7bwDy2cbnyKf1OegB9m8ib6AtSWbp+iuhUjBK9vsk+YsPpcGAUZUHAKjdg2Aacw44uz57n9dkYrig1bEC69eiU8iuokIKqcKf4hdwwxRcqIsox4ughDRE5zZE0WhJCk9Nv6h7cUSU3HKkCd4q5T3mbl3u3c/SJ8lRsjym72YYTcffCyvAyC3Oc3mTYaXa8is/5iuGKRk50oEIv9FSKAr1DtxE2uF6KaBTETvjFkdNu6EwDieOOcsTW17ifx5+oe33c1m8jbAN6vWhsGpmBvovi378z4f1c7LifS/xG44YbjAzoyrEBPa28f3iCyZ8DjV1GkQHU6BEsiQhQnNnfB6UUzCodsOyjtg6Aefq6xXBFw+NW9nO6HSjRe0up8Qpm2AQKOTpFNBLeVCaB9zHZdeg3eOKNqnQIHMPR94M7/Sn+RL/fyf9Y+D311tbEwlOSEQ6vb9xRjPgpy7ETdBEBdgO+ye52GO5oiVjibRkRG0VBFPpnYAsk/ocVF8QSwlgsnOVxABCRvudu7LkM/VFE4Fj6CqfNgW3Y3nPX+7n4Vd/ap/7hyZfffUd0poxCN2A125A2AUoyHLAMBbPahDgC57ijpzBmftZiyjFc0dBYuvysREWvnyo3YBabegNfrp0iAuCewMefREry6yDQJ9ax8+/YSb5C4gl3bG2iAX0iaaiEoCS2AHvcE3/lSxiFEds9+Y/Gnfz36hNa74Q3JseCmBc849fWxD5Hv7U1JvpGOPxxIxyxkQ2z75hkJx1GVL/yGwUGDH/ys4BTtsdxfxZR9G2GfqogFhfCvP6PD2ajcGI0IBDFPX8Fkvg8jr8k4z6nlUp8fgNuoHWfX8Y4A/6QP/Hn4svO5x9RLjFCBlSNgnXEgtPmwCg2Mvp7pUwFX40PlmVB2gSqZOSbDGcrhisaXNx+HjD07vO+Gp8OVME8+00gciWcWMaNHHnXx18XO5EHvBeHhJNh5Z5Axp3Me6MkKkmIiT+xHsLUMu/EPzj4ib/3mGLlpeNHcLI0iA0ohx1rq/T1d2xaGAx4Fd3gdwsTGEgMTmmeHqZU3M/Xj77nRLLHNpIgFgvqQw1iseDuXhJGQYHEcJT0AWFgIIq/LhD3s4/vX6PvY0KQUsmvV0rpx7oN8FX4TvkcJqKRU0EFX7UP9hEbdpsNo9TI7AiWT8E3wQfLyc+AxXBFiQSQXgF69JcqpKBq3Gl/BZyCES/hpDPZxySfC2TgbcmOR7/7YOAJW7LpX94JyzBP0vOZN3IUH4r6X9f/JLN/P8ZCkq/fiEeyKV6GGhhAMkwp5f21P9WJPzBKQQxIDBbx65xU3PGxaWF+94Q+LqgmjDhlQR8PVUqCWL+fhTc9MX5tUv9A5EdCIPWq4SV5rif8LUl2HRHlHBVwp+TZAukUqKLM/i6rgBuwPrXgRPSIWr4ELIYr0qLQ1f5sgQoqGBWGHjouyt9NEsURPTIHwIk43poV73aIPpE41bu77seEE474d3rjj0PciZUa/IQm4YQ8/uQo/ntL34lW/JoPsdyT29jJsXtyFgtoogRKEsNAwpS0LF8HMmAdkpPkuv4/rySPL37fmtgajgGjMP1HlrKwP9JhNIIYFE65zilhxCmHglOqjTiICRL+rozlPiSiPsqvYI5zN/V1qztntD0hBV+tDljSLlDF+fG3iuFqLHPQVz7dVFBhBV+pO+0vkB9P8Hix6TYSdU9C4t5FN8r1tKqEaTDAgK+TBqdk4af/8eh3XJokTF1zMGBkwAtksXe846txOehbB+JIX5W0+BGyZO9qjzCQJZ1mlyw89Zt6l3TaXfA0QSlZcKIzNtIgBoClsFMsIYgREQ1CFel18/bxzE8PjLXHnGDqgNUpUIW5/7rAcDXWiLuOqgf6JDOkYFQaetpfKL9OOsXSwREWAAfeAunYZsYIQJ+kNwC+cT74/Ln/65AwGobTn/DGDBbC4qfRJQSy+ClISQJZjOO4lcCSjAwCGBDSlBoYlPqHo1wouEADxQcxIiLKDKUUzHIT0pUd0wMBwCg2+kawukXvj5rD+FI3VsTKpztuSdsqBbPYXUeVB+8ex94Vl143AIg7ghHQI3JGyNCjcf02M1bR3H/sqeAFltjXQwhlCdOQBhkls3ttYA9glBp6Q+nTjSqNwugeERHRWJZt0wMBwCgzYNom7MO2Ph8IZLpFI8dwlc/suGl/fgWjxIBZ4pZPz4JfpDMhEreWI6qvUz43TJUqfSIfC1M8WU+L/tOQkgUyX1T/ifFV5cfIIBERUT7ItumBAGBUGBBb4DQ5WdGekeLZTr4RHai88ukFCuZ4E0bYAIK5GzTEiQtTlr7O21yyLC5M+XP3MRIRERGNhmycHqiUgllpAjZgH7ETN33PIQxX+UDQN+1P9JoVNd4tn16Ym2tSxHGLT1gCsXTVPuXT0/tQgL5pfj6GKSIiIqLhysbpgcrQgwJiC+Szwco1ZzeGq1xm6eIUEhWogB7eNYvdaX++zP+CDIc4erTNKyvulss2Cg2oAjcwBnLvcRERERFlq2ycHqhMBV+ND9HOKNCS6dYMH8NVrnHcan+xaX9FetdtFXarrOWIZGXRlV/BCLthKuAGqjwotkFERESUjbJxeiCg19Gb402GK0ofsQTSpodHVUhB1SiYRe60vyx4l+F0vLLobtluryx6qaGn+gWgAxXDFBEREdGoUX4Fs8KE9Vn2TA8EAPgz3YCRYbjKEcqnYFQYMIoNPe0vi0NIQiW/YZRFJyIiIqLRp8LZNz0wVzFc5QjfZB98Fdn94xJLdKVCm2XRiYiIiHJFtk4PzEXZfbZOfbJ0aFQc8fbSMkwDRpGh102FWBadiIiIKFdk7fTAHMNwRcMm4lb26xZd1S/kViosMjk6RURERJSjOD3wzDFc0ZAlTPsLuGvAitzqflw7RURERJTTOD3wzDFc0SkNOu2vkHtOEREREeWbhOmBlvB8b5gYrmgATvsjIiIiGru86YEnbBglnB44HAxX5OG0PyIiIiLi9MCRY7ga4zjtj4iIiIj64/TAkWG4GoM47Y+IiIiITofTA4eP4WoM4bQ/IiIiIhoqTg8cPoarPMdpf0REREQ0UpweODwMV3mI0/6IiIiIKFU4PXDoGK7yiEQF0sNpf0RERESUOpweOHQMVznOm/bXKzB8nPZHRERERKnH6YFDw3CVgzjtj4iIiIhGmzc98LgNo5TTA5NhuMohnPZHRERERJnC6YGnx3CVK3oAKHDaHxERERFlDKcHnhrDVY4wqg34Sn2c9kdEREREGcXpgYMzMt0AGhpfuQ8qqPjkJSIiIqKMik0PNAoNSKdkujlZheGKiIiIiIiGJTY9EDYgFgNWDMMVERERERENW2x6oNPu6GrWxHBFRERERETD500PLOD0wBiGKyIiIiIiGhHlVzDHcXpgDMMVERERERGNGKcH9mG4IiIiIiKiEeP0wD4MV0REREREdEY4PVBjuCIiIiIiojOmwgpG2dieHshwRUREREREZ0wpvffVWJ4eyHBFREREREQpMdanBzJcERERERFRyozl6YEMV0RERERElDJjeXogwxUREREREaXUWJ0eyHBFREREREQpNxanBzJcERERERFRyo3F6YEMV0RERERElBZjbXogwxUREREREaXNWJoeyHBFRERERERpkzA9sIvhioiIiIiIaMS86YFWfk8PZLgiIiIiIqK0GwvTAxmuiIiIiIgo7cbC9ECGKyIiIiIiGhX5Pj2Q4YqIiIiIiEZNPk8PZLgiIiIiIqJRk8/TAxmuiIiIiIhoVOXr9ECGKyIiIiIiGnX5OD2Q4YqIiIiIiEZdPk4PZLgiIiIiIqKMyLfpgQxXRERERESUMfk0PZDhioiIiIiIMiafpgdmRbh6/PHHMXXqVIRCISxZsgSbNm0a9Ninn34aF198McrLy1FeXo7ly5cPOP7rX/86lFIJlyuvvDLdD4OIiIiIiEYgX6YHZjxcvfDCC1i5ciXuuecevP/++zjnnHOwYsUKHDlyJOnx69atw3/5L/8Ff/rTn7B+/XpMnjwZV1xxBQ4dOpRw3JVXXonDhw97l9/85jej8XCIiIiIiGgEVFjBKNXTA5Gj+Srj4eqRRx7BLbfcgptvvhlz5szBk08+icLCQjz77LNJj//1r3+NW2+9FQsXLkRdXR3+4R/+AY7jYO3atQnHBYNB1NTUeJfy8vLReDhERERERDQCSunRK6PAALoz3ZqR8WXyP+/t7cXmzZtxxx13eNcZhoHly5dj/fr1Q/oenZ2diEajqKioSLh+3bp1GD9+PMrLy3HZZZfhgQcewLhx45J+j56eHvT09Hhft7W1AQCi0Sii0ehwH1ZKxf7/TLcjX7F/04v9m17s3/RjH6cX+ze92L/pxf5NH6fUgRWxAGRH/w6nDUoyWJLjs88+w8SJE/Huu+9i2bJl3vXf/e538eabb2Ljxo2n/R633nor1qxZg4aGBoRCIQDAqlWrUFhYiGnTpmHfvn34wQ9+gHA4jPXr18M0zQHf40c/+hHuvffeAdf/8z//MwoLC8/gERIRERERUS7r7OzE9ddfj9bWVpSUlJzy2IyOXJ2phx56CKtWrcK6deu8YAUA1113nff5/PnzsWDBAsyYMQPr1q3Dn/3Znw34PnfccQdWrlzpfd3W1uat5TpdB6ZbNBrFa6+9hssvvxx+vz+jbclH7N/0Yv+mF/s3/djH6cX+TS/2b3qxf9Ort6sXr//p9azo39istqHIaLiqrKyEaZpobm5OuL65uRk1NTWnvO/DDz+Mhx56CK+//joWLFhwymOnT5+OyspK7N27N2m4CgaDCAaDA673+/0Z/2HGZFNb8hH7N73Yv+nF/k0/9nF6sX/Ti/2bXuzf9MqG/h3O/5/RghaBQACLFi1KKEYRK04RP02wv5/+9Ke4//77sXr1aixevPi0/8+nn36K48ePo7a2NiXtJiIiIiIi6i/j1QJXrlyJp59+Gs8//zx27NiBv/7rv0ZHRwduvvlmAMDXvva1hIIX//N//k/cddddePbZZzF16lQ0NTWhqakJ7e3tAID29nZ85zvfwYYNG3DgwAGsXbsWX/7ylzFz5kysWLEiI4+RiIiIiIjyX8bXXH31q1/F0aNHcffdd6OpqQkLFy7E6tWrUV1dDQD4+OOPYRh9GfDv//7v0dvbi2uuuSbh+9xzzz340Y9+BNM08cEHH+D5559HS0sLJkyYgCuuuAL3339/0ql/REREREREqZDxcAUAt99+O26//fakt61bty7h6wMHDpzyexUUFGDNmjUpahkREREREdHQZHxaIBERERERUT5guCIiIiIiIkoBhisiIiIiIqIUYLgiIiIiIiJKAYYrIiIiIiKiFGC4IiIiIiIiSgGGKyIiIiIiohRguCIiIiIiIkoBhisiIiIiIqIUYLgiIiIiIiJKAYYrIiIiIiKiFGC4IiIiIiIiSgGGKyIiIiIiohRguCIiIiIiIkoBhisiIiIiIqIUYLgiIiIiIiJKAYYrIiIiIiKiFGC4IiIiIiIiSgGGKyIiIiIiohTwZboB2UhEAABtbW0ZbgkQjUbR2dmJtrY2+P3+TDcn77B/04v9m17s3/RjH6cX+ze92L/pxf5Nr2zq31gmiGWEU2G4SiISiQAAJk+enOGWEBERERFRNohEIigtLT3lMUqGEsHGGMdx8Nlnn6G4uBhKqYy2pa2tDZMnT8Ynn3yCkpKSjLYlH7F/04v9m17s3/RjH6cX+ze92L/pxf5Nr2zqXxFBJBLBhAkTYBinXlXFkaskDMPApEmTMt2MBCUlJRl/YuUz9m96sX/Ti/2bfuzj9GL/phf7N73Yv+mVLf17uhGrGBa0ICIiIiIiSgGGKyIiIiIiohRguMpywWAQ99xzD4LBYKabkpfYv+nF/k0v9m/6sY/Ti/2bXuzf9GL/pleu9i8LWhAREREREaUAR66IiIiIiIhSgOGKiIiIiIgoBRiuiIiIiIiIUoDhioiIiIiIKAUYrrLA448/jqlTpyIUCmHJkiXYtGnToMc2NDTgK1/5CqZOnQqlFB577LHRa2iOGk7/Pv3007j44otRXl6O8vJyLF++/JTH0/D696WXXsLixYtRVlaGoqIiLFy4EP/0T/80iq3NPcPp33irVq2CUgpXX311ehuY44bTv8899xyUUgmXUCg0iq3NPcN9/ra0tOC2225DbW0tgsEgzj77bPzxj38cpdbmpuH08ec///kBz2GlFP7Df/gPo9ji3DLc5/Bjjz2G2bNno6CgAJMnT8a3vvUtdHd3j1Jrc89w+jcajeK+++7DjBkzEAqFcM4552D16tWj2NohEsqoVatWSSAQkGeffVYaGhrklltukbKyMmlubk56/KZNm+Tb3/62/OY3v5Gamhp59NFHR7fBOWa4/Xv99dfL448/Llu2bJEdO3bI17/+dSktLZVPP/10lFueG4bbv3/605/kpZdeksbGRtm7d6889thjYpqmrF69epRbnhuG278x+/fvl4kTJ8rFF18sX/7yl0ensTlouP37q1/9SkpKSuTw4cPepampaZRbnTuG2789PT2yePFiueqqq+Ttt9+W/fv3y7p162Tr1q2j3PLcMdw+Pn78eMLzd/v27WKapvzqV78a3YbniOH2769//WsJBoPy61//Wvbv3y9r1qyR2tpa+da3vjXKLc8Nw+3f7373uzJhwgR55ZVXZN++ffLEE09IKBSS999/f5RbfmoMVxl2/vnny2233eZ9bdu2TJgwQR588MHT3nfKlCkMV6dxJv0rImJZlhQXF8vzzz+fribmtDPtXxGRc889V374wx+mo3k5byT9a1mWXHDBBfIP//APctNNNzFcncJw+/dXv/qVlJaWjlLrct9w+/fv//7vZfr06dLb2ztaTcx5Z/o3+NFHH5Xi4mJpb29PVxNz2nD797bbbpPLLrss4bqVK1fKhRdemNZ25qrh9m9tba388pe/TLjuP//n/yw33HBDWts5XJwWmEG9vb3YvHkzli9f7l1nGAaWL1+O9evXZ7Bl+SEV/dvZ2YloNIqKiop0NTNnnWn/igjWrl2LXbt24ZJLLklnU3PSSPv3vvvuw/jx4/Hf/tt/G41m5qyR9m97ezumTJmCyZMn48tf/jIaGhpGo7k5ZyT9+6//+q9YtmwZbrvtNlRXV2PevHn4yU9+Atu2R6vZOSUVr3HPPPMMrrvuOhQVFaWrmTlrJP17wQUXYPPmzd7Uto8++gh//OMfcdVVV41Km3PJSPq3p6dnwFTsgoICvP3222lt63D5Mt2AsezYsWOwbRvV1dUJ11dXV2Pnzp0ZalX+SEX/fu9738OECRMSfvlJG2n/tra2YuLEiejp6YFpmnjiiSdw+eWXp7u5OWck/fv222/jmWeewdatW0ehhbltJP07e/ZsPPvss1iwYAFaW1vx8MMP44ILLkBDQwMmTZo0Gs3OGSPp348++ghvvPEGbrjhBvzxj3/E3r17ceuttyIajeKee+4ZjWbnlDN9jdu0aRO2b9+OZ555Jl1NzGkj6d/rr78ex44dw0UXXQQRgWVZ+O///b/jBz/4wWg0OaeMpH9XrFiBRx55BJdccglmzJiBtWvX4qWXXsq6N2A4ckU0iIceegirVq3C73//ey5aT6Hi4mJs3boV7733Hn784x9j5cqVWLduXaablfMikQhuvPFGPP3006isrMx0c/LSsmXL8LWvfQ0LFy7EpZdeipdeeglVVVV46qmnMt20vOA4DsaPH4///b//NxYtWoSvfvWruPPOO/Hkk09muml56ZlnnsH8+fNx/vnnZ7opeWPdunX4yU9+gieeeALvv/8+XnrpJbzyyiu4//77M920vPCzn/0Ms2bNQl1dHQKBAG6//XbcfPPNMIzsijMcucqgyspKmKaJ5ubmhOubm5tRU1OToVbljzPp34cffhgPPfQQXn/9dSxYsCCdzcxZI+1fwzAwc+ZMAMDChQuxY8cOPPjgg/j85z+fzubmnOH27759+3DgwAF88Ytf9K5zHAcA4PP5sGvXLsyYMSO9jc4hqfj76/f7ce6552Lv3r3paGJOG0n/1tbWwu/3wzRN77r6+no0NTWht7cXgUAgrW3ONWfyHO7o6MCqVatw3333pbOJOW0k/XvXXXfhxhtvxF/91V8BAObPn4+Ojg584xvfwJ133pl1ISCTRtK/VVVV+MMf/oDu7m4cP34cEyZMwPe//31Mnz59NJo8ZPwpZ1AgEMCiRYuwdu1a7zrHcbB27VosW7Ysgy3LDyPt35/+9Ke4//77sXr1aixevHg0mpqTUvX8dRwHPT096WhiThtu/9bV1eHDDz/E1q1bvcuXvvQlfOELX8DWrVsxefLk0Wx+1kvF89e2bXz44Yeora1NVzNz1kj698ILL8TevXu9NwUAYPfu3aitrWWwSuJMnsO//e1v0dPTg7/8y79MdzNz1kj6t7Ozc0CAir1ZICLpa2wOOpPnbygUwsSJE2FZFn73u9/hy1/+crqbOzwZLqgx5q1atUqCwaA899xz0tjYKN/4xjekrKzMK+974403yve//33v+J6eHtmyZYts2bJFamtr5dvf/rZs2bJF9uzZk6mHkNWG278PPfSQBAIBefHFFxPK1UYikUw9hKw23P79yU9+Iq+++qrs27dPGhsb5eGHHxafzydPP/10ph5CVhtu//bHaoGnNtz+vffee2XNmjWyb98+2bx5s1x33XUSCoWkoaEhUw8hqw23fz/++GMpLi6W22+/XXbt2iUvv/yyjB8/Xh544IFMPYSsN9K/ERdddJF89atfHe3m5pzh9u8999wjxcXF8pvf/EY++ugjefXVV2XGjBnyF3/xF5l6CFltuP27YcMG+d3vfif79u2Tt956Sy677DKZNm2anDx5MkOPIDmGqyzwi1/8Qs466ywJBAJy/vnny4YNG7zbLr30Urnpppu8r/fv3y8ABlwuvfTS0W94jhhO/06ZMiVp/95zzz2j3/AcMZz+vfPOO2XmzJkSCoWkvLxcli1bJqtWrcpAq3PHcPq3P4ar0xtO/37zm9/0jq2urparrroq6/ZXyTbDff6+++67smTJEgkGgzJ9+nT58Y9/LJZljXKrc8tw+3jnzp0CQF599dVRbmluGk7/RqNR+dGPfiQzZsyQUCgkkydPlltvvTXrTv6zyXD6d926dVJfXy/BYFDGjRsnN954oxw6dCgDrT41JcJxSiIiIiIiojPFNVdEREREREQpwHBFRERERESUAgxXREREREREKcBwRURERERElAIMV0RERERERCnAcEVERERERJQCDFdEREREREQpwHBFRERERESUAgxXREREKbZu3ToopdDS0gIAeO6551BWVpbRNhERUfoxXBERUc755JNP8F//63/FhAkTEAgEMGXKFPzt3/4tjh8/Pupt+fznP49vfvObCdddcMEFOHz4MEpLS0e9PURElDkMV0RElFM++ugjLF68GHv27MFvfvMb7N27F08++STWrl2LZcuW4cSJE5luIgKBAGpqaqCUynRTiIhoFDFcERFRTrntttsQCATw6quv4tJLL8VZZ52FP//zP8frr7+OQ4cO4c477wQAKKXwhz/8IeG+ZWVleO6557yvv/e97+Hss89GYWEhpk+fjrvuugvRaNS7/Uc/+hEWLlyIf/qnf8LUqVNRWlqK6667DpFIBADw9a9/HW+++SZ+9rOfQSkFpRQOHDgwYFpgMv/yL/+C8847D6FQCNOnT8e9994Ly7JS1k9ERDT6GK6IiChnnDhxAmvWrMGtt96KgoKChNtqampwww034IUXXoCIDOn7FRcX47nnnkNjYyN+9rOf4emnn8ajjz6acMy+ffvwhz/8AS+//DJefvllvPnmm3jooYcAAD/72c+wbNky3HLLLTh8+DAOHz6MyZMnn/b//bd/+zd87Wtfw9/+7d+isbERTz31FJ577jn8+Mc/HmJPEBFRNmK4IiKinLFnzx6ICOrr65PeXl9fj5MnT+Lo0aND+n4//OEPccEFF2Dq1Kn44he/iG9/+9v4P//n/yQc4zgOnnvuOcybNw8XX3wxbrzxRqxduxYAUFpaikAggMLCQtTU1KCmpgamaZ72/7333nvx/e9/HzfddBOmT5+Oyy+/HPfffz+eeuqpIbWbiIiyky/TDSAiIhqu041MBQKBIX2fF154AT//+c+xb98+tLe3w7IslJSUJBwzdepUFBcXe1/X1tbiyJEjw290nG3btuGdd95JGKmybRvd3d3o7OxEYWHhGX1/IiLKDI5cERFRzpg5cyaUUtixY0fS23fs2IGqqiqUlZVBKTUghMWvp1q/fj1uuOEGXHXVVXj55ZexZcsW3Hnnnejt7U24j9/vT/haKQXHcc7ocbS3t+Pee+/F1q1bvcuHH36IPXv2IBQKndH3JiKizOHIFRER5Yxx48bh8ssvxxNPPIFvfetbCeuumpqa8Otf/xq33XYbAKCqqgqHDx/2bt+zZw86Ozu9r999911MmTLFK4ABAAcPHhx2mwKBAGzbHtZ9zjvvPOzatQszZ84c9v9HRETZi+GKiIhyyi9/+UtccMEFWLFiBR544AFMmzYNDQ0N+M53voOzzz4bd999NwDgsssuwy9/+UssW7YMtm3je9/7XsIo1KxZs/Dxxx9j1apV+NznPodXXnkFv//974fdnqlTp2Ljxo04cOAAwuEwKioqTnufu+++G//xP/5HnHXWWbjmmmtgGAa2bduG7du344EHHhh2G4iIKDtwWiAREeWUWbNm4b333sP06dPxF3/xF5gyZQr+/M//HGeffTbeeecdhMNhAMD/+l//C5MnT8bFF1+M66+/Ht/+9rcT1jJ96Utfwre+9S3cfvvtWLhwId59913cddddw27Pt7/9bZimiTlz5qCqqgoff/zxae+zYsUKvPzyy3j11Vfxuc99DkuXLsWjjz6KKVOmDPv/JyKi7KFkqPVqiYiIstQ999yDRx55BK+99hqWLl2a6eYQEdEYxXBFRER54Ve/+hVaW1vxN3/zNzAMTswgIqLRx3BFRERERESUAnxrj4iIiIiIKAUYroiIiIiIiFKA4YqIiIiIiCgFGK6IiIiIiIhSgOGKiIiIiIgoBRiuiIiIiIiIUoDhioiIiIiIKAUYroiIiIiIiFKA4YqIiIiIiCgF/j+yMwXClgdDoQAAAABJRU5ErkJggg==",
      "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": "dml_base",
   "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": "3.11.2"
  },
  "vscode": {
   "interpreter": {
    "hash": "04413e1efd13b5e2aaedccc5facec5686292d28983ef24fa021a12c73bd6369e"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}