{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Python: Choice of learners\n", "\n", "This notebooks contains some practical recommendations to choose the right learner and evaluate different learners for the corresponding nuisance components.\n", "\n", "For the example, we will work with a IRM, but all of the important components are directly usable for all other models too.\n", "\n", "To be able to compare the properties of different learners, we will start by setting the true treatment parameter to zero, fix some other parameters of the data generating process and generate several datasets \n", "to obtain some information about the distribution of the estimators." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import doubleml as dml\n", "\n", "from doubleml.datasets import make_irm_data\n", "\n", "theta = 0\n", "n_obs = 500\n", "dim_x = 5\n", "n_rep = 200\n", "\n", "np.random.seed(42)\n", "datasets = []\n", "for i in range(n_rep):\n", " data = make_irm_data(theta=theta, n_obs=n_obs, dim_x=dim_x, \n", " R2_d=0.8, R2_y=0.8, return_type='DataFrame')\n", " datasets.append(dml.DoubleMLData(data, 'y', 'd'))" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Comparing different learners\n", "For simplicity, we will restrict ourselves to the comparison of two different types and evaluate a learner of linear type and a tree based estimator for each nuisance component (with default hyperparameters)." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "from sklearn.linear_model import LinearRegression, LogisticRegressionCV\n", "from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier\n", "from sklearn.base import clone\n", "\n", "reg_learner_1 = LinearRegression()\n", "reg_learner_2 = GradientBoostingRegressor()\n", "class_learner_1 = LogisticRegressionCV()\n", "class_learner_2 = GradientBoostingClassifier()\n", "\n", "learner_list = [{'ml_g': reg_learner_1, 'ml_m': class_learner_1},\n", " {'ml_g': reg_learner_2, 'ml_m': class_learner_1},\n", " {'ml_g': reg_learner_1, 'ml_m': class_learner_2},\n", " {'ml_g': reg_learner_2, 'ml_m': class_learner_2}]" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "In all combinations, we now can try to evaluate four different IRM models. To make the comparison fair, we will apply all different models to the same cross-fitting samples (usually this should not matter, we only consider this here to get slightly cleaner comparison)." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Standard approach\n", "\n", "At first, we will look at the most straightforward approach using the inbuild nuisance losses. The `nuisance_loss` attribute contains the out-of-sample RMSE or Log Loss for the nuisance functions. We will save all RMSEs and the corresponding treatment estimates for all combinations of learners over all repetitions." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Processing: 100.0 %\n", "Coverage: [0.935 0.66 0.975 0.95 ]\n" ] } ], "source": [ "from doubleml.utils import DoubleMLResampling\n", "\n", "coefs = np.full(shape=(n_rep, len(learner_list)), fill_value=np.nan)\n", "loss_ml_m = np.full(shape=(n_rep, len(learner_list)), fill_value=np.nan)\n", "loss_ml_g0 = np.full(shape=(n_rep, len(learner_list)), fill_value=np.nan)\n", "loss_ml_g1 = np.full(shape=(n_rep, len(learner_list)), fill_value=np.nan)\n", "\n", "coverage = np.full(shape=(n_rep, len(learner_list)), fill_value=np.nan)\n", "\n", "for i_rep in range(n_rep):\n", " print(f\"\\rProcessing: {round((i_rep+1)/n_rep*100, 3)} %\", end=\"\")\n", " dml_data = datasets[i_rep]\n", " # define the sample splitting\n", " smpls = DoubleMLResampling(n_folds=5, n_rep=1, n_obs=n_obs, stratify=dml_data.d).split_samples()\n", " \n", " for i_learners, learners in enumerate(learner_list):\n", " np.random.seed(42)\n", " dml_irm = dml.DoubleMLIRM(dml_data,\n", " ml_g=clone(learners['ml_g']),\n", " ml_m=clone(learners['ml_m']),\n", " draw_sample_splitting=False)\n", " dml_irm.set_sample_splitting(smpls)\n", " dml_irm.fit(n_jobs_cv=5)\n", "\n", " coefs[i_rep, i_learners] = dml_irm.coef[0]\n", " loss_ml_m[i_rep, i_learners] = dml_irm.nuisance_loss['ml_m'][0][0]\n", " loss_ml_g0[i_rep, i_learners] = dml_irm.nuisance_loss['ml_g0'][0][0]\n", " loss_ml_g1[i_rep, i_learners] = dml_irm.nuisance_loss['ml_g1'][0][0]\n", "\n", " confint = dml_irm.confint()\n", " coverage[i_rep, i_learners] = (confint['2.5 %'].iloc[0] <= theta) & (confint['97.5 %'].iloc[0] >= theta)\n", "\n", "print(f'\\nCoverage: {coverage.mean(0)}')" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Next, let us take a look at the corresponding results" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n", "colnames = ['Linear + Logit','Boost + Logit', 'Linear + Boost', 'Boost + Boost']\n", "\n", "df_coefs = pd.DataFrame(coefs, columns=colnames)\n", "df_ml_m = pd.DataFrame(loss_ml_m, columns=colnames)\n", "df_ml_g0 = pd.DataFrame(loss_ml_g0, columns=colnames)\n", "df_ml_g1 = pd.DataFrame(loss_ml_g1, columns=colnames)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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2towcOZLc3Nxi8SxdupSuXbvi6uqKhYUFQUFBLFy4sFiZ2rVrc+rUKfbs2WOI6d9xpKamMnHiRHx8fLCwsCAgIIA5c+ag1+uL1ZOamsqIESOwt7fHwcGB4cOHk5qael/vZ9euXQGIioq65/7c7FPv3r3ZunUrLVq0wMrKikWLFpWpjt27dxvqaNy4seHva926dTRu3BhLS0tCQ0M5evToLXWcOXOGxx57DCcnJywtLWnRogUbN240vH63nwmAzZs306FDB2xsbLCzs6NXr16cOnWqWDsjRozA1taWCxcu0LNnT+zs7HjiiSdK8U4LIYQQQlQuR48epUePHuh0OmxtbXnwwQdL3Fbn+PHjdOrUCSsrK7y9vXnnnXdYunSpUfew27lzp2E85uDgQL9+/YiIiChW5saNG0ycOJHatWtjYWGBq6srDz30EEeOHDGUOXfuHAMGDMDd3R1LS0u8vb0ZPHgwaWlpd2y/c+fONGrUyNBXa2trAgIC+OGHHwDYs2cPrVu3xsrKisDAQH777bf77vPNpc9nz57lySefxN7eHhcXF958800URSEmJoZ+/fqh0+lwd3dn3rx5992mEDWVzPQTohJIS0u7Zb8PlUqFs7MzANu3b2fIkCE8+OCDzJkzB4CIiAj27dvHhAkT6NixI+PHj+fjjz/mtddeo2HDhgCG/97OrFmzsLKyYsqUKZw/f55PPvkEc3Nz1Go1KSkpzJgxg4MHD7Js2TL8/f2ZNm2a4d6FCxcSHBxM3759MTMz4+eff2bMmDHo9XpefPFFABYsWMC4ceOwtbXl9ddfB8DNzQ2ArKwsOnXqRGxsLM8//zy+vr7s37+fqVOnEhcXx4IFC4CimXT9+vVj7969jB49moYNG7J+/XqGDx9+X+/5hQsXAAzv8b3056bIyEiGDBnC888/z6hRowgMDCx1HefPn2fo0KE8//zzPPnkk8ydO5c+ffrwxRdf8NprrzFmzBjD39HAgQOJjIxErS76nubUqVO0a9cOLy8vpkyZgo2NDd9//z39+/fnxx9/5JFHHrnrz8Q333zD8OHDCQsLY86cOWRlZbFw4ULat2/P0aNHiy0HLigoICwsjPbt2zN37lysra3v670XQgghhDCVU6dO0aFDB3Q6Ha+88grm5uYsWrSIzp07GxJcALGxsYYvTqdOnYqNjQ2LFy/GwsLCaLH89ttv9OjRgzp16jBjxgyys7P55JNPaNeuHUeOHDGMx0aPHs0PP/zA2LFjCQoKIikpib179xIREUHz5s3Jy8sjLCyM3Nxcxo0bh7u7O7Gxsfzyyy+kpqZib29/xzhSUlLo3bs3gwcP5vHHH2fhwoUMHjyYVatWMXHiREaPHs3QoUP54IMPeOyxx4iJicHOzu6++z9o0CAaNmzI7Nmz+fXXX3nnnXdwcnJi0aJFdO3alTlz5rBq1SomT55My5Yt6dix4323KUSNowghTGbp0qUKUOLDwsLCUG7ChAmKTqdTCgoKblvX2rVrFUDZtWvXLa916tRJ6dSpk+H5rl27FEBp1KiRkpeXZ7g+ZMgQRaVSKT169Ch2f5s2bRQ/P79i17Kysm5pJywsTKlTp06xa8HBwcXavuntt99WbGxslLNnzxa7PmXKFEWj0SiXL19WFEVRNmzYoADK+++/byhTUFCgdOjQQQGUpUuX3lL3v93s65IlS5TExETl6tWryq+//qrUrl1bUalUyqFDh0rVHz8/PwVQtmzZckv50taxf/9+w7WtW7cqgGJlZaVcunTJcH3RokW3/L0++OCDSuPGjZWcnBzDNb1er7Rt21apV6+e4drtfiZu3LihODg4KKNGjSp2PT4+XrG3ty92ffjw4QqgTJky5Za+CSGEEEJUJjfH1jfHdyXp37+/otVqlQsXLhiuXb16VbGzs1M6duxouDZu3DhFpVIpR48eNVxLSkpSnJycFECJioq6YyzTp09XACUxMfG2ZUJCQhRXV1clKSnJcO3YsWOKWq1Whg0bZrhmb2+vvPjii7et5+jRowqgrF279o4xlaRTp04KoKxevdpw7cyZMwqgqNVq5eDBg4brN8er/x5/33zP7/Z+/NvN9+a5554zXCsoKFC8vb0VlUqlzJ4923A9JSVFsbKyUoYPH17qvgkhFEWW9wpRCXz22Wds37692GPz5s2G1x0cHMjMzGT79u1GbXfYsGGYm5sbnrdu3RpFUXj66aeLlWvdujUxMTEUFBQYrv17D7ubMxU7derExYsX77qMAGDt2rV06NABR0dHrl+/bnh069aNwsJCfv/9dwA2bdqEmZkZL7zwguFejUbDuHHjStXXp59+GhcXFzw9PenVqxeZmZksX77csJdiafrj7+9PWFjYLW2Upo6goCDatGljeH7zW+WuXbvi6+t7y/WbJw0nJyezc+dOBg4cyI0bNwzvW1JSEmFhYZw7d47Y2Ng7vhfbt28nNTWVIUOGFHvvNRoNrVu3ZteuXbfc8+/3XwghhBCiKiosLGTbtm3079+fOnXqGK57eHgwdOhQ9u7dS3p6OgBbtmyhTZs2hISEGMo5OTkZbZuTuLg4wsPDGTFiBE5OTobrTZo04aGHHmLTpk2Gaw4ODvz5559cvXq1xLpuzuTbunUrWVlZpY7F1taWwYMHG54HBgbi4OBAw4YNDWNRuHVcer+effZZw581Gg0tWrRAURSeeeYZw3UHBwcCAwON1qYQNY0s7xWiEmjVqtUdD/IYM2YM33//PT169MDLy4uHH36YgQMH0r179/tq99/JJfhnwODj43PLdb1eT1pammE57L59+5g+fToHDhy4ZXCRlpZ212UE586d4/jx47i4uJT4ekJCAgCXLl3Cw8MDW1vbYq/fXFJ7r6ZNm0aHDh3QaDTUqlWLhg0bYmb2zz+BpelPSSctl7aO0rz3ULTsAoqWBSuKwptvvsmbb75ZYhwJCQl4eXmV+BoUvffwz76G/6XT6Yo9NzMzw9vb+7b1CSGEEEJUBYmJiWRlZZU4jmzYsCF6vZ6YmBiCg4O5dOlSsS9obwoICDBKLJcuXQJKHtM2bNiQrVu3Gg5Pe//99xk+fDg+Pj6EhobSs2dPhg0bZkhc+vv7M2nSJObPn8+qVavo0KEDffv2NeyXdzfe3t6oVKpi1+zt7e86Lr1fJY2HLS0tqVWr1i3Xk5KSjNKmEDWNJP2EqAJcXV0JDw9n69atbN68mc2bN7N06VKGDRvG8uXLy1yvRqMp1XVFUYCi/fAefPBBGjRowPz58/Hx8UGr1bJp0yY+/PDDWw7iKIler+ehhx7ilVdeKfH1+vXr32Mv7k3jxo3p1q1bia+Vtj8lndRb2jrK+t7frGfy5MklzjaEuw9Gb9bxzTff4O7ufsvr/06GAlhYWBj2ExRCCCGEEBVr4MCBdOjQgfXr17Nt2zY++OAD5syZw7p16+jRowcA8+bNY8SIEfz0009s27aN8ePHM2vWLA4ePHjXL2/LOi69XyXVX95tClHTSNJPiCpCq9XSp08f+vTpg16vZ8yYMSxatIg333yTgICAW76dK08///wzubm5bNy4sdg3dCUtC71dXHXr1iUjI+O2ibib/Pz82LFjBxkZGcVm+0VGRpYx+luVpj/lWce9uPmNrrm5+V3fuzu991CUTL5bHUIIIYQQ1YWLiwvW1tYljiPPnDmDWq02zG7z8/Pj/Pnzt5Qr6VpZ+Pn5ASWPac+cOUOtWrWwsbExXPPw8GDMmDGMGTOGhIQEmjdvzrvvvmtI+kHRl9yNGzfmjTfeYP/+/bRr144vvviCd955xygxCyGqHpm6IUQV8N/p7Gq1miZNmgCQm5sLYBgUpKamlns8N7+B+/c3bmlpaSxduvSWsjY2NiXGNHDgQA4cOMDWrVtveS01NdWwf2DPnj0pKChg4cKFhtcLCwv55JNP7rcbBqXpT3nWcS9cXV3p3LkzixYtIi4u7pbXExMTDX++3c9EWFgYOp2O9957j/z8/DvWIYQQQghRXWg0Gh5++GF++uknoqOjDdevXbvG6tWrad++vWGbk7CwMA4cOEB4eLihXHJyMqtWrTJKLB4eHoSEhLB8+fJiY7WTJ0+ybds2evbsCRSNe/+7N7Srqyuenp6G3wPS09OL7b0NRQlAtVptKCOEqJlkpp8QlcDmzZs5c+bMLdfbtm1LnTp1ePbZZ0lOTqZr1654e3tz6dIlPvnkE0JCQmjYsCEAISEhaDQa5syZQ1paGhYWFnTt2hVXV1ejx/vwww8bZh4+//zzZGRk8NVXX+Hq6npLIio0NJSFCxfyzjvvEBAQgKurK127duXll19m48aN9O7dmxEjRhAaGkpmZiYnTpzghx9+IDo6mlq1atGnTx/atWvHlClTiI6OJigoiHXr1t3TYSHl0Z/yrONeffbZZ7Rv357GjRszatQo6tSpw7Vr1zhw4ABXrlzh2LFjwJ1/JhYuXMhTTz1F8+bNGTx4MC4uLly+fJlff/2Vdu3a8emnnxo1ZiGEEEKIirJkyRK2bNlyy/UJEybwzjvvsH37dtq3b8+YMWMwMzNj0aJF5Obm8v777xvKvvLKK6xcuZKHHnqIcePGYWNjw+LFi/H19SU5OfmeV9nMnz8fa2vrYtfUajWvvfYaH3zwAT169KBNmzY888wzZGdn88knn2Bvb8+MGTMAuHHjBt7e3jz22GM0bdoUW1tbfvvtNw4dOsS8efMA2LlzJ2PHjuXxxx+nfv36FBQU8M0336DRaBgwYEAZ30UhRHUgST8hKoFp06aVeH3p0qXUqVOHJ598ki+//JLPP/+c1NRU3N3dGTRoEDNmzDDstebu7s4XX3zBrFmzeOaZZygsLGTXrl3lkvQLDAzkhx9+4I033mDy5Mm4u7vzwgsv4OLicsvJv9OmTePSpUu8//773Lhxg06dOtG1a1esra3Zs2cP7733HmvXrmXFihXodDrq16/PzJkzDRsFq9VqNm7cyMSJE1m5ciUqlYq+ffsyb948mjVrVuH9Kc867lVQUBB///03M2fOZNmyZSQlJeHq6kqzZs2K/Szd6Wdi6NCheHp6Mnv2bD744ANyc3Px8vKiQ4cOjBw50qjxCiGEEEJUpH+vEPm3ESNGEBwczB9//MHUqVOZNWsWer2e1q1bs3LlymIn1fr4+LBr1y7Gjx/Pe++9h4uLCy+++CI2NjaMHz8eS0vLe4pl1qxZt1zTaDS89tprdOvWjS1btjB9+nSmTZuGubk5nTp1Ys6cOYaD46ytrRkzZgzbtm1j3bp16PV6AgIC+Pzzz3nhhRcAaNq0KWFhYfz888/ExsZibW1N06ZN2bx5Mw888EBp3z4hRDWiUmRHTCGEEEIIIYQQ4q4mTpzIokWLyMjIuO2hE0IIUVnInn5CCCGEEEIIIcR/ZGdnF3uelJTEN998Q/v27SXhJ4SoEmR5rxBCCCGEEEII8R9t2rShc+fONGzYkGvXrvH111+Tnp7Om2++aerQKqWMjAwyMjLuWMbFxUUSpkJUIEn6CSGEEEIIIYQQ/9GzZ09++OEHvvzyS1QqFc2bN+frr7+mY8eOpg6tUpo7dy4zZ868Y5moqChq165dMQEJIWRPPyGEEEIIIYQQQtyfixcvcvHixTuWad++/T0fgiKEuH+S9BNCCCGEEEIIIYQQopqpcct79Xo9V69exc7ODpVKZepwhBBCCFEGiqJw48YNPD09UavlXDIh/k3Gu0IIIUTVZ4zxbo1L+l29ehUfHx9ThyGEEEIII4iJicHb29vUYQhRqch4VwghhKg+7me8W+OSfnZ2dkDRm6bT6UwcjRBCCCHKIj09HR8fH8PnuhDiHzLeFUIIIao+Y4x3a1zS7+YSB51OJ4MgIYQQooqTpYtC3ErGu0IIIUT1cT/jXdkERwghhBBCCCGEEEKIakaSfkIIIYQQQgghhBBCVDOS9BNCCCGEEEIIIYQQopqpcXv6CSGEML7CwkLy8/NNHYaoZrRaLWq1fD8phBBCCCFEWUjSTwghRJkpikJ8fDypqammDkVUQ2q1Gn9/f7RaralDEUIIIYQQosqRpJ8Qolq7lJTJO79EcCg6mVA/R6b0aEA9t7IfeS6Ku5nwc3V1xdraWk5SFUaj1+u5evUqcXFx+Pr6ys+WEEKIMlP0ev48+DvrDpzhVJo5sfl2qFDw096giVMhg7u2IKhxqKnDFEIIo5OknxCi2oq6nsnARQcAaFPXmaOXU3n8iwN8+9wDNPTQmTi6qq+wsNCQ8HN2djZ1OKIacnFx4erVqxQUFGBubm7qcIQQQlRBu3dtY+aOOKIKauGq1lHf6gb17JJQFEjIVvNzvAMrVsXTyupL3h34APUaNjF1yEIIYTSS9BNCVEt6vcKk78NRq+CVsAbYW5kTFuzOvG2RjF19hE0TOmBhpjF1mFXazT38rK2tTRyJqK5uLustLCyUpJ8QQohSSU26xhuL1/NLig8NzQt5OTCB+l6uqFQOxcoVFBYSfvEqG69Y02v5RV5pdIynBw9FLeNEIUQ1ILtjCyGqpe8OxXD0cirD29TG3qooWWBrYcYz7f2JTsri810XTBxh9SHLLkV5kZ8tIYQQZXH+zEn6zd/K7pRaPOMdy6QObgR6u5X4uWKm0dCinhdvtNPRSRfPOyedeGXBVxTk5ZkgciGEMC5J+gkhqp38Qj0f7zxHa38n6v9n/z5vR2u6NXTlqz8ukpIpgzkhhBBCiOrk4P7dPLLsDAWKmtdD82kT6H1PXyJptWYMaunLKJ9Y1l33ZNy8JeTl5FRAxEIIUX4qxfLezz77jA8++ID4+HiaNm3KJ598QqtWrUosu2zZMkaOHFnsmoWFBTnyD7IQ4v/9cvwq8Wk5jO5Yp8TXuwe7sysykaX7opj0cGAFR1czxKZmV2hS1dFGi5eDVYW1J4QQQojK5+D+3YzYmEwd83RGt3LE2tKi1HW0ru+N1uwqi6I8mPzRMj56+TlUapkrI4Somkye9FuzZg2TJk3iiy++oHXr1ixYsICwsDAiIyNxdXUt8R6dTkdkZKThuSz/EUL829d7o2nkqcPbseS95uwszelYrxbL9kczpksAluayZ4sxxaZm8+C83eTk6yusTUtzNTte6my0xJ9KpWL9+vX079/fKPXVNLt376ZLly6kpKTg4OBg6nCEEELUAIcO/s7IjcnUMU9h7AMuaLVl/1W3WR1Pni2M5YvLPtRZsoyJzz5txEiFEKLimDzpN3/+fEaNGmWYvffFF1/w66+/smTJEqZMmVLiPSqVCnd394oMUwhRRZy9doOTsWm80KnuHct1CXTlt4gEfj0ex4BQ7wqKrmZIycwjJ1/Ps+398bC3LPf24tJyWLw3ipTMvHtO+o0YMYLU1FQ2bNhQcp1xcTg6OhoxysqlvJOabdu2JS4uDnt7e6Bolv7EiRNJTU0tl/aEEELUbOfPnODpDdfwM7/Biw/Uuq+E300t6nnRP/MSC877UveX9fTp/YgRIhVCiIpl0qRfXl4ehw8fZurUqYZrarWabt26ceDAgdvel5GRgZ+fH3q9nubNm/Pee+8RHBxcYtnc3Fxyc3MNz9PT043XASFEpfPjkSvYWpjRxNv+juXcdJYEeehY9eclSfqVEw97S/ycbUwdRplUhi+WFEWhsLAQM7M7f1RHR0fj7++PoigVFNndabXaSvEeCiGEqP4S42MZvuIY9mqFMa0csdAa77T3Xk19iTsQy6t7nWncIILaAQ2NVrcQQlQEk25OcP36dQoLC3Fzcyt23c3Njfj4+BLvCQwMZMmSJfz000+sXLkSvV5P27ZtuXLlSonlZ82ahb29veHh4+Nj9H4IISoHvV5hw9FYWtZ2xFxz93/eOtarxZHLqVxIzKiA6ERVolKpDLMAo6OjUalUrFu3ji5dumBtbU3Tpk1v+XJq7969dOjQASsrK3x8fBg/fjyZmZmG17/55htatGiBnZ0d7u7uDB06lISEBMPru3fvRqVSsXnzZkJDQ7GwsGDv3r0V0t9/0+v1vPXWW3h7e2NhYUFISAhbtmwpVmb//v2EhIRgaWlJixYt2LBhAyqVivDw8GJ9SU1NZffu3YwcOZK0tDRUKhUqlYoZM2ZUeL+EEEJUPzlZmTyzcDOZijnjmmvLtIffnahUKp4KdcVWncv4FfvIz8u9+01CCFGJVLkdSdu0acOwYcMICQmhU6dOrFu3DhcXFxYtWlRi+alTp5KWlmZ4xMTEVHDEQoiKEn4llWvpubSs7XRP5Zv6OGCt1fDT0dhyjkxUB6+//jqTJ08mPDyc+vXrM2TIEAoKCgC4cOEC3bt3Z8CAARw/fpw1a9awd+9exo4da7g/Pz+ft99+m2PHjrFhwwaio6MZMWLELe1MmTKF2bNnExERQZMmTSqqewYfffQR8+bNY+7cuRw/fpywsDD69u3LuXPngKIZ83369KFx48YcOXKEt99+m1dfffW29bVt25YFCxag0+mIi4sjLi6OyZMnV1R3hBBCVFOKXs+bC78hIteFsQ1zcLa3K5d2LC20PNuggFN5rny45JtyaUMIIcqLSZf31qpVC41Gw7Vr14pdv3bt2j0vCzI3N6dZs2acP3++xNctLCywsDDuNz5CiMpp26lr2FmaEeBie0/lzTVqmvs6su5oLP97qL4cCiTuaPLkyfTq1QuAmTNnEhwczPnz52nQoAGzZs3iiSeeYOLEiQDUq1ePjz/+mE6dOrFw4UIsLS15+ul/NgGvU6cOH3/8MS1btiQjIwNb239+Zt966y0eeuihCu3bv82dO5dXX32VwYMHAzBnzhx27drFggUL+Oyzz1i9ejUqlYqvvvoKS0tLgoKCiI2NZdSoUSXWp9Vqsbe3l/14hRBCGNXK779jbaIPT3vFUtujfLdqqeNRi77XLvFFtDfdjxykSfMHyrU9IYQwFpPO9NNqtYSGhrJjxw7DNb1ez44dO2jTps091VFYWMiJEyfw8PAorzCFEFWAoihsORlHU28H1Op7T949UMeJKynZHLmcWn7BiWrh37Pubn7m3Fyee+zYMZYtW4atra3hERYWhl6vJyoqCoDDhw/Tp08ffH19sbOzo1OnTgBcvny5WDstWrS4ayzBwcGGdm7uafvvtnv06FGmPqanp3P16lXatWtX7Hq7du2IiIgAIDIykiZNmmBp+c8hLa1atSpTe0IIIURZHP37ADPDbehqF0PbBhWzN3NYY2+8NSm8sv60LPMVQlQZJj+9d9KkSQwfPpwWLVrQqlUrFixYQGZmpuE032HDhuHl5cWsWbOAohkQDzzwAAEBAaSmpvLBBx9w6dIlnn32WVN2QwhhYlHXM4lOyqJ3E89S3Vff1Q57K3M2nYgj1K/6ntYq7p+5+T8bg9+cFarX64GiA6aef/55xo8ff8t9vr6+ZGZmEhYWRlhYGKtWrcLFxYXLly8TFhZGXl5esfI2Nnc//GTTpk3k5+cDEBsbS+fOnQ376QFYWd3bKcZCCCFEVZOadI0x6y7gZ1bAwFCvCmvXTKNhWEN476QLi1d/ywslbNEhhBCVjcmTfoMGDSIxMZFp06YRHx9v2DD85uEely9fRq3+Z0JiSkoKo0aNIj4+HkdHR0JDQ9m/fz9BQUGm6oIQohLYczYRM7WKBu6l289FrVbR3NeBX0/E8UavhrLEV5RJ8+bNOX36NAEBASW+fuLECZKSkpg9e7bhQKm///67zO35+fkZ/nzzdN/btV0aOp0OT09P9u3bZ5iJCLBv3z7DbL7AwEBWrlxJbm6uYfuMQ4cO3bFerVZLYWHhfccnhBCiZlP0el76Yj039LWY2LwQM42mQtuv7VaLbpcvs+CMG72jzuLjX79C268I+txcsg4eJOP3P8g5E0FBQiLo9WgcHNDWro11i1BsOnRE611xCVchRNmZPOkHMHbs2GKbnf/b7t27iz3/8MMP+fDDDysgKiFEVbLnbCL13eywNC/94C/Uz5FdkYmEx6TSzFdm+xlLXFpOpW4nLS2t2Ow4AGdn5zKd8v7qq6/ywAMPMHbsWJ599llsbGw4ffo027dv59NPP8XX1xetVssnn3zC6NGjOXnyJG+//XaZ4jaWqKioW/pfr149Xn75ZaZPn07dunUJCQlh6dKlhIeHs2rVKgCGDh3K66+/znPPPceUKVO4fPkyc+fOBbht0rx27dpkZGSwY8cOmjZtirW1NdbW1uXaPyGEENXP16tWsuOGD+P943C2L93qDmPp28Sdv/fl8vZ3O/lyavVJ+umzskj5bg1JX39NYVISmlq10Pr7YxEYiEqtRp+ZSc7p06Rv2QL6t7F+oDVOTz2FbZcu8qW5EJVYpUj6CSHE/cjJL+TAhST6lHJp7031Xe3QWZqx5WS8JP2MwNFGi6W5msV7oyqsTUtzNY422lLds3v3bpo1a1bs2jPPPMPixYtL3X6TJk3Ys2cPr7/+Oh06dEBRFOrWrcugQYMAcHFxYdmyZbz22mt8/PHHNG/enLlz59K3b99St2UskyZNuuXaH3/8wfjx40lLS+Oll14iISGBoKAgNm7cSL169YCi2YA///wzL7zwAiEhITRu3Jhp06YxdOjQYvv8/Vvbtm0ZPXo0gwYNIikpienTpzNjxozy7J4QQohq5tiRg8w+ZU+YfQxN6viaLA5LCy0DvBP4KsaHP/Zsp0Mn0x2+ZSxZR45w9dUp5F+9inWrVtg9+CA5bg4kFKaSor+BBjW2amu8zGphmaeQHR5O5r59XBnzIpaNGuE25VWs72FPYiFExVMpiqKYOoiKlJ6ejr29PWlpaeh0OlOHI4Qwgn3nr/PE4j+Z3icIH8eyzR5avj+aS8lZ7Hm5s3xbeY9ycnKIiorC39//lmRPbGo2KZl5t7nT+BxttHg5yD52prJq1SpGjhxJWlqaUfcTvNPPmHyeC3F78v+HqG5upCbT84NfMUPPq+2dMDOr2GW9/6UoCnP/SCBfUbN12mDMtRYmjaesFEUh6cuvSFywAG2dOmQ/0YtNlmfZn3Oai/lxKBRPFahQ4WvmygOWDels1ZT60Xnc2PgzedHR2D82ALeXX0Zjb2+i3ghR/Rjj81xm+gkhqrz9F66jszS7r6RPM18H/jh/nbPXMggs5b6A4lZeDlaShKvGVqxYQZ06dfDy8uLYsWO8+uqrDBw4UA4QEUIIYXSKXs/ri77neqEr00JzTZ7wg6LtLAYHmvH2SQdW/bCWEUOfNHVIpaYUFhL/zjukfvsdN/p1YkXzdH7PWYhlgZYgrS+P2XbAVeOArdoKPXqy9XlcK0zhckECm7P+Yk3Gbvzs3Rj0Qic6Hm1J+k+/kLl3L17z5mEdGmrq7gkh/p8k/YQQVd7+C0nUd7NDfR8z9Bp66LAy17DlZLwk/YS4i/j4eMMBXB4eHjz++OO8++67pg5LCCFENbR2/Q9sTPHhOd9YXBy8TR2OgY+bM+2iYlhwwpFHUq5j71jL1CHdM0Wv5+prr5P6y89seiGE1Q4HscuzYoBte0IsAtCqSkgTaMDX3JWWBKJXFC7mx7Ev5xQfpH7P8gBHRr3am+Yrj3DpqWG4TvofTs88I6tnhKgE1HcvIoQQlVdGbgHHY9JKfWrvf5lr1DT2smfb6XgjRSZE9fXKK68QHR1tWH774YcfysEcQgghjO78mZNMO2RGe5srtKpXeRJ+N/Vr5ES2Ys7n3643dSilkvDBXGJ+28jsCZ5843CKjlaNecnxcVpZNig54fcfapWKAK0nw3UP8ZLD49TS2PNO7nqmD1WR3KcNCXPnETvpJfTZ2RXQGyHEnUjSTwhRpR2KTqZQUWjgcf97FoX4OHDqajpXUrKMEJkQQghRNgsXLqRJkybodDp0Oh1t2rRh8+bNpg5LiAqVk5XJ2FV/4ajOZkhzN1OHUyIHWxvCnOJZctmFmKizpg7nniSvXEXE+qW88YIdFyzTGKXrQXeblveU7CuJq5kDw3UP8byuF/H6VMYG/c22ca1J27WDS08No+D6dSP3QAhRGpL0E0JUaX9eTMbByhw3u/vfQLmxlz1mahXbT18zQmRCCCFE2Xh7ezN79mwOHz7M33//TdeuXenXrx+nTp0ydWhCVJhZi1dyId+Z54L0WGi1pg7ntsKCPbBR5TF37U5Th3JXWUePcvjLWUx7xgq9pTljHfoToPUySt11tZ5MdHiE9lbBfG17mPcmehKXfInogYPIvXjRKG0IIUpPkn5CiCrtr6gkAlxtjbJniJVWQwMPO7aclCW+QgghTKdPnz707NmTevXqUb9+fd59911sbW05ePCgqUMTokJs3foLy696M9AtDh83Z1OHc0cWWi193JPZmOzF6ROHTR3ObRUkJXH09XG8NVSDhZUdo+1746ixNWob5iozetq05jn73lzWpPLycDjqkUf0kKFknzhh1LaEEPdGkn5CiCorJ7+Q41fSqO9mvIM3mvk48nd0CimZeUarUwghhCirwsJCvvvuOzIzM2nTpk2JZXJzc0lPTy/2EKKqiok+x+Rd2TS3uErnYB9Th3NP2gV64aZOZ85Ph0wdSokUReHszNd4+6F01NbWjLLvgZ26/PbirWvuwQSHR3A3d+bth1P5qb0Z0cOGkXngQLm1KYQomST9hBBV1tHLqRToFeq5Gu9byqbe9ugVhR1nEoxWpxBCCFFaJ06cwNbWFgsLC0aPHs369esJCgoqseysWbOwt7c3PHx8qkaiRIj/ysvJYezSPViqChge6lxlTn8102jo753FngwfDuzbZepwbpHy80bect5LqoMZT5dzwu8mG7UlT+vC6GIVwjdN0vjkMUvOv/g8N3ZW/mXQQlQnZdutUwghKoFD0clYazV4OVgZrU4Hay11XWzYejKex0Ir3ylxVUZqDGQlVVx71s7gIL/kmsru3bvp0qULKSkpODg4mDocIaqFwMBAwsPDSUtL44cffmD48OHs2bOnxMTf1KlTmTRpkuF5enq6JP5ElfT+199wKteVV4LSsLGyN3U4pRIa4Il/7HVmb8tnQ5tOqNSVY35NfkICH2+fwelQNaPsH8bVzKHC2lar1HS3aYmnmTNrvPcQN8KSl6eMJ3jaHOx796qwOISoySTpJ4Sosg5fSqGuiw1qtXG/BQ7xceTn41fJyivAWiv/TJZaagx81hLysyuuTXMrePHQPSf+RowYwfLlyw3PnZycaNmyJe+//z5NmjQpryhvoVKpWL9+Pf3796/S7bRt25a4uDjs7Yt+QVu2bBkTJ04kNTW1XNoToibQarUEBAQAEBoayqFDh/joo49YtGjRLWUtLCywsLj/A62EMKUtW35mcYwnA11iqOPha+pwSk2lUvGIfyHzz3uw/bfNPPxw5Uhq/fr5y/zYooDu5s0I0HqaJIYmFnVw0tixXLWd157RMuX9l3kgJxuHxx4zSTxC1CTy26wQokrS6xUOX0qhW0NXo9fdzNeBH45c4feziXRv5GH0+qu9rKSihF+bsaAzzolwd5QeCwc+LWq3FLP9unfvztKlSwGIj4/njTfeoHfv3ly+fLm8IjWK6Oho/P39URTF1KEYaLVa3N3dTR2GENWaXq8nNzfX1GEIUS4uXTjD5N25hFqm8FDjqjtLNcjPnYaX4vngdxUPdi1AY2baX7dj927nA9fD1M9zprNzqElj8TZzYaxDP5aptzFteCGTvp7Gw1nZOA17yqRxCVHdVY45x0IIUUrnEzPIyC2grotxTx0DcNNZ4uNoxWY5xff+6LzAyb/8H2VMLFpYWODu7o67uzshISFMmTKFmJgYEhMTDWVOnDhB165dsbKywtnZmeeee46MjAzD63q9nrfeegtvb28sLCwICQlhy5Ythtfz8vIYO3YsHh4eWFpa4ufnx6xZswCoXbs2AI888ggqlcrwvKLdrQ8A+/fvJyQkBEtLS1q0aMGGDRtQqVSEh4cDRct7VSoVqamp7N69m5EjR5KWloZKpUKlUjFjxoyK75gQVdjUqVP5/fffiY6O5sSJE0ydOpXdu3fzxBNPmDo0IYwuJyuD0cv2Y6POY3horSqzj9/tPFLPjHMFrvz0ywaTxqHPzWXm7tfJ16oZ6NoddSV4X+01Nox26E1tK29mDdKw+tdZXP/yK1OHJUS1Jkk/IUSVdPhSCmoV+NeyKZf6Q3wc2BGRQF6BvlzqF5VLRkYGK1euJCAgAGdnZwAyMzMJCwvD0dGRQ4cOsXbtWn777TfGjh1ruO+jjz5i3rx5zJ07l+PHjxMWFkbfvn05d+4cAB9//DEbN27k+++/JzIyklWrVhmSe4cOFZ3wt3TpUuLi4gzPK9rd+pCenk6fPn1o3LgxR44c4e233+bVV1+9bX1t27ZlwYIF6HQ64uLiiIuLY/LkyRXVHSGqhYSEBIYNG0ZgYCAPPvgghw4dYuvWrTz00EOmDk0Io1L0el7/fCUX8p0YHVSItWXVX6Zex6MWzS2uMu+vHPJyckwWx7rvZnLAJ5t+5i3RmZXPeLksLFTmDNM9RCvLhizqqeGTvxdwbcGHlWoFgxDViSzvFUJUSYcvpeDrZI2luaZc6m/u58jPx+PYd+E6XQKNv4RYmN4vv/yCrW3RTNHMzEw8PDz45ZdfUP//xturV68mJyeHFStWYGNTNFj+9NNP6dOnD3PmzMHNzY25c+fy6quvMnjwYADmzJnDrl27WLBgAZ999hmXL1+mXr16tG/fHpVKhZ+fn6F9FxcXABwcHEy6NPZufVi9ejUqlYqvvvoKS0tLgoKCiI2NZdSoUSXWp9Vqsbe3R6VSyZJfIcro66+/NnUIQlSIld9/x4/XfXjGOxYft+pzgFr/BtbMOKbju/U/MmxIxc/QTbl+hQ+zfiYo35bmdZtWePt3o1GpecS2HU4aO9a1+4trpxbz2nsZ+Ex9vdIcgCJEdSH/RwkhqqTDl1LKbZYfgLeDFW46C7ackCW+1VWXLl0IDw8nPDycv/76i7CwMHr06MGlS5cAiIiIoGnTpoaEH0C7du3Q6/VERkaSnp7O1atXadeuXbF627VrR0REBFB0YEh4eDiBgYGMHz+ebdu2lSnW4OBgbG1tsbW1JTg4GMDw3NbWlh49epSp3nvpQ2RkJE2aNMHS0tLweqtWrcrUnhBCCHHT4UP7mBluQ1e7GNoEVp+EH4BnLUcesL7KR8c1ZGWkV3j7c3+YSK459HfrVuFt3yuVSkVn66Y8YdeVP4PMeEm/hsg3XkYpKDB1aEJUK5L0E0JUOWlZ+URdz6ROLePv53eTSqWiua8jW0/FU1AoS3yrIxsbGwICAggICKBly5YsXryYzMxMvvrKeHvLNG/enKioKN5++22ys7MZOHAgj5XhpLpNmzYZEpSbNm0CMDwPDw9n8eLFRotZCCGEKG/XYi/x/LrL+JslM7B5BRz6ZQJ9G9qTplix9PsfK7Td8DO7+dkyks5pXjjYulRo22XR1KIuzzn04rK3lnEuWzn08nPo5dAiIYxGkn5CiCon/EoqAHVcynd/klBfR1Kz8zl4Mblc2xGVg0qlQq1Wk52dDUDDhg05duwYmZmZhjL79u1DrVYTGBiITqfD09OTffv2Fatn3759BAUFGZ7rdDoGDRrEV199xZo1a/jxxx9JTi76mTI3N6ewsPCusfn5+RkSlDeXCN98HhAQgJdX2X5hupc+BAYGcuLEiWKnht5t/0GtVntP/RJCCFHz5OZkM/rLbehRMzrUFjOz8tmqxdRqOejoaBfHF2ftSE26ViFtKorCO3um4ZquoqP/gxXSpjHUNndnrPMjFDjYMiHwEJtfGkRhesXPkBSiOqoUSb/PPvuM2rVrY2lpSevWrfnrr7/u6b7vvvsOlUpF//79yzdAIUSlcvRyCrYWZrjale9mz37O1rjYWvDribhybUeYRm5uLvHx8cTHxxMREcG4cePIyMigT58+ADzxxBNYWloyfPhwTp48ya5duxg3bhxPPfUUbm5uALz88svMmTOHNWvWEBkZyZQpUwgPD2fChAkAzJ8/n2+//ZYzZ85w9uxZ1q5di7u7Ow4ODkDRCb47duwgPj6elJSUcu1vVFRUsdmB4eHhZGZm3rUPQ4cORa/X89xzzxEREcHWrVuZO3cuwG1PWKxduzYZGRns2LGD69evk5WVVa59E0IIUTUoej0zFi7nRK4roxvkYG9beQ6YKA+9gpzJR8PC7zdWSHu/HF5FpGUKPfLqY25RtQ5FqaWxZ2ytR3G1qMXrzS+w5LV+5MfLNjtC3C+TH+SxZs0aJk2axBdffEHr1q1ZsGABYWFhREZG4up6+83zo6OjmTx5Mh06dKjAaIUQlcHRy6n417K+bcLBWFQqFc39HNhyMo63+wVjpqkU35NUHemxlbqdLVu24OHhAYCdnR0NGjRg7dq1dO7cGQBra2u2bt3KhAkTaNmyJdbW1gwYMID58+cb6hg/fjxpaWm89NJLJCQkEBQUxMaNG6lXr56h3vfff59z586h0Who2bIlmzZtMhwWMm/ePCZNmsRXX32Fl5cX0dHRZX8f7mLSpEm3XPvjjz/u2gedTsfPP//MCy+8QEhICI0bN2batGkMHTq02D5//9a2bVtGjx7NoEGDSEpKYvr06cyYMaPc+iaEEKJqWPn9d3x7zYcRnleo4+lj6nDKnb2tDd0cL7PskhsjYqLw8PEvt7ZyCnL4MPwj6iWpadCgbbm1U56s1ZY869KX9Um7+LhlFJc+6MPUUcuxaRB095uFECVSKSY+G7t169a0bNmSTz/9FAC9Xo+Pjw/jxo1jypQpJd5TWFhIx44defrpp/njjz9ITU1lw4YN99Reeno69vb2pKWlodPpjNUNIUQFURSFkLe206m+C32bepZ7exevZ/DepjOsfrY1bQNqlXt7VUlOTg5RUVH4+/sXT/6kxsBnLSE/u+KCMbeCFw+BQ/X/BaIyWLVqFSNHjiQtLQ0rK6tya+e2P2PI57kQdyL/f4jK6M/9exi6MY1OdlcZ2srX1OFUmOzcXF7bW0hvt+vM+t/ocmvnyz/m8dn5pbyY2BSf4Nbl1k5FUBSFP9KP8mveYZpeUjGn0wd4dg4zdVhCVDhjfJ6bdKZfXl4ehw8fZurUqYZrarWabt26ceDAgdve99Zbb+Hq6sozzzzDH3/8ccc2cnNzi+1DlC57AwhRpV1OziItO5865Xhy77/5O9tQy1bLryfiJOl3rxx8ihJwWUkV16a1syT8ytGKFSuoU6cOXl5eHDt2jFdffZWBAweWa8JPCCFE9RF76QIv/BxPPfNMBoZWz4M7bsfKwoIeLpf5/ponz5w5SUCDRkZvIz0vnSXnVhISo8E7JNTo9Vc0lUpFR/vmuGY58a3XDp4Kf4k5l08T+tTEcl/pI0R1Y9Kk3/Xr1yksLDTsjXSTm5sbZ86cKfGevXv38vXXXxMeHn5PbcyaNYuZM2feb6hCiEoiPCYVgNrOFZP0U6lUhPo6sulEHDP7yhLfe+bgI0m4aiQ+Pp5p06YRHx+Ph4cHjz/+OO+++66pwxJCCFEFZGfcYNTXu1FjzfMtdJhpqufBHXfSJciTnb9n8v66vXz5mvGTfl8f/IQ8pYCu2hBUZibfwctoGljXZpz5Yywv3Mjo3K956f2TDP7fQlRaralDE6LKqFK/vd64cYOnnnqKr776ilq17m3GzdSpU0lLSzM8YmJiyjlKIUR5OhaThpvOAlvLihvQtKjtREpWPn9FySm+omZ65ZVXiI6ONiy3/fDDD7G2tjZ1WEIIISo5Ra9nyuerOJ/nzJjgAuxq6GeHuZkZ/b1usC3dh7//vPNKtdK6nn2dVRfWEhqlolaDEKPWXRnUMrdnnNdgAvWuvOf+F2/MCiM7QQ74EOJemTTpV6tWLTQaDdeuFT/C/Nq1a7i7u99S/sKFC0RHR9OnTx/MzMwwMzNjxYoVbNy4ETMzMy5cuHDLPRYWFuh0umIPIUTVFR6Tgp9TxZ70VltO8RVCCCGEKLWvV6/ip2QfRvgm4OvmbOpwTKpVfS/8NEm8u+kMil5vtHoXH/oMVUEhnSwao9aaG63eykSrMucJr370LmzEL77XeWpZd6IP7TR1WEJUCSZN+mm1WkJDQ9mxY4fhml6vZ8eOHbRp0+aW8g0aNODEiROEh4cbHn379qVLly6Eh4fj4yNLyYSozvIL9Zy6mk7tWhX7LbFKpaK5rwObT8ZTqDfp2UdCCCGEEFXC/r07mXXSnjD7GFrV8zZ1OCanVqkY4F/A0VxPtmz9xSh1JmYlsvbiekIvqnAMDjFKnZWVSqWio1sbnrd8iDh7haGHJ7B19XuY+FxSISo9ky/vnTRpEl999RXLly8nIiKCF154gczMTEaOHAnAsGHDDAd9WFpa0qhRo2IPBwcH7OzsaNSoEVpZ2y9EtXb22g1yC/T4V9AhHv/WorYTyZl5/BlVgYdTCCGEEEJUQVdjLvLir4kEml/n0WaS8LspyM+dxtp4Zu1NJTcn+77rW3L0S9T5hbQ3C0RtYWGECCs/f11tJroOxq3QhpfzVjNn3gDyMzNMHZYQlZbJk36DBg1i7ty5TJs2jZCQEMLDw9myZYvhcI/Lly8TFydL6oQQcPxKGmoV+DpW/H4whiW+x+XfIyGEEEKI28nLyeGFxTvRoGdUCx0aOQStmMcbWnKl0IFv1q69r3quZ1/n+3NraX5ewaFRcyNFVzXYam141m8IXbP9We18juGfd+Hq2aOmDkuISqlS/As8duxYLl26RG5uLn/++SetW7c2vLZ7926WLVt223uXLVvGhg0byj9IIYTJHb+ShpeDFRbmFX/qm0qlopmvA1tkia8QQgghxG29++VyTuW68HzDvBp7cMedeNZypIPtVT46ZUXK9Wt3v+E2lp1YgqqgkLZKHTS2Fb8KxtTUKhVhvg/xjLojUbpcBv42jD2/LDR1WEJUOpUi6SeEEPfiWEwqvk6mGzyG+jmSlJnH39Fyiq8QQgghxH/9/Mt6ll/1ZrB7PP4eLqYOp9Lq18iZAjQsWL2hTPen5aaxJuI7mp3X49S4Zs3y+6/6tQKZ4Pw4joUWjEv8jAWfDkOfn2/qsISoNMxMHYAQQtyLnPxCIq/dYHBL0x3Y41/LBicbLZtPxtO6Ts0+ge5u4jLiSMlNqbD2HC0c8bD1MFp9KpWK9evX079/f6PVKYQQQlRn0ecjmLJXT2urWDoFyT5+d6KzsaGXy2VWXvVk6KlwAkt5CMeqiFUUFhbQJtMDMyen8gmyCrG3sue52k+y+dJmvrY7yskFnfngyVU4etQ2dWhCmJwk/YQQVUJEXDqFegU/Z9PN9FOrVIT4FC3xnd4nCJVKZbJYKrO4jDj6buhLTmFOhbVpqbFkY/+N95z4GzFiBKmpqbfdHiIuLg5HR0cjRli5/PtnV6PR4OnpyWOPPcasWbOwqKCNwJctW8bEiRNJTU2tkPaEEEKUn9ycbF5csR9btRVPhbrKGOkedAvyYu8f6cz88U9WNWyCSn1vi/Cy8rNYeWI5TS4W4hxcs2f5/ZuZWk0f/174xYXzg8MhBq7tx0etZxHUuqepQxPCpCTpJ4SoEk7GpmGmVuFjgkM8/q25rwM7zyRw/EoaTX0cTBpLZZWSm0JOYQ7DgobhbuNe7u3FZ8az4vQKUnJTjDbbz929/OO+G0VRKCwsxMzszh/V0dHR+Pv7oyil22ty6dKldO/enfz8fI4dO8bIkSOxsbHh7bffvp+whRBC1EBzFn9DZJ4brzVKx9JCa+pwqgQzMw0D/bL5OMqbrdt+pXv3Pvd039qza8kszKJNogPmD8iMyv9q4hGC+w13ludvYtjxV5kedYQ+g98wdVhCmIzs6SeEqBKOX0nDy9EKcxOfAFfP1Q5bCzO2noo3aRxVgbuNOz52PuX+KI/EokqlMswCjI6ORqVSsW7dOrp06YK1tTVNmzblwIEDxe7Zu3cvHTp0wMrKCh8fH8aPH09mZqbh9W+++YYWLVpgZ2eHu7s7Q4cOJSEhwfD67t27UalUbN68mdDQUCwsLNi7d6/R+3aTg4MD7u7u+Pj40Lt3b/r168eRI0eKlVm4cCF169ZFq9USGBjIN998U+z1y5cv069fP2xtbdHpdAwcOJBr1/7ZlPzYsWN06dIFOzs7dDodoaGh/P333+zevZuRI0eSlpaGSqVCpVIxY8aMcuurEEKI8vP77m0sueLFAJc4fN1k+5PSaOzvQWNtPG//nkZOVsZdy+cX5rP8+BKCovW41g9BJlSWzNXOnXFeT1An25bXctcw/6Ohss+fqLEk6SeEqBKOXUnFz4SHeNykUato4m3PlpOS9KtpXn/9dSZPnkx4eDj169dnyJAhFBQUAHDhwgW6d+/OgAEDOH78OGvWrGHv3r2MHTvWcH9+fj5vv/02x44dY8OGDURHRzNixIhb2pkyZQqzZ88mIiKCJk2aVEjfzp49y86dO2ndurXh2vr165kwYQIvvfQSJ0+e5Pnnn2fkyJHs2rULAL1eT79+/UhOTmbPnj1s376dixcvMmjQIEMdTzzxBN7e3hw6dIjDhw8zZcoUzM3Nadu2LQsWLECn0xEXF0dcXByTJ0+ukL4KIYQwnuTEeF7amkSweTwPNjbdvstVlUqlYnCwFdf0dny+as1dy/8a9SuJeck8cNkCi4CACoiw6rI0s2B4ncF0zvBmqcMJJszvRnbKdVOHJUSFk+W9QohKLzuvkAsJmbT2rxzfHjfzceCzCxeIup6Jfy0bU4cjKsjkyZPp1asXADNnziQ4OJjz58/ToEEDZs2axRNPPMHEiRMBqFevHh9//DGdOnVi4cKFWFpa8vTTTxvqqlOnDh9//DEtW7YkIyMDW1tbw2tvvfUWDz30ULn3Z8iQIWg0GgoKCsjNzaV3795MnTrV8PrcuXMZMWIEY8aMAWDSpEkcPHiQuXPn0qVLF3bs2MGJEyeIiorCx6foF70VK1YQHBzMoUOHaNmyJZcvX+bll1+mQYMGhvflJnt7e1QqVaVYSi2EEKL0FL2e1xZvIEdxZmSIHWqZdlYmbk4OhDlc4osLHjx6PoLaAQ1LLKdX9Cw5tpiAOPD2a4zKTFPBkVY9apWKnrV74Bb3Nz+6HGXE4jA+HfANLnWCTB2aEBWmTDP9Ll68aOw4hBDitk7HpVOomPYQj38L8tBhrlGxI+La3QuLauPfs+48PIr2Dry5PPfYsWMsW7YMW1tbwyMsLAy9Xk9UVBQAhw8fpk+fPvj6+mJnZ0enTp2AoiWy/9aiRYu7xhIcHGxoJzg4GKBY2z169LhrHR9++CHh4eEcO3aMX375hbNnz/LUU08ZXo+IiKBdu3bF7mnXrh0RERGG1318fAwJP4CgoCAcHBwMZSZNmsSzzz5Lt27dmD17NhcuXLhrXEJUdTJOFjXFz7/+xJY0H57wTcVBZ3v3G8Rt9WzsiZ0qh+nf7UHR60ss8/uV34nKuETrSLAMlqRVaYR6tOAZbReiHfJ5auMQog/vNnVIQlSYMiX9AgIC6NKlCytXriQnp+JOZxRC1Ew3D/HwcrAydSgAWJhraOihY/tpSfrVJObm5oY/3zyVUP//A/OMjAyef/55wsPDDY9jx45x7tw56tatS2ZmJmFhYeh0OlatWsWhQ4dYv349AHl5ecXasbG5++zRTZs2GdrZtGkTQLG2Fy9efNc63N3dCQgIIDAwkF69ejFz5kzWrFnD+fPn7+0NuQczZszg1KlT9OrVi507dxIUFGTotxDVlYyTRU2QEHeFN/fn09Iylhb1vEwdTpVnoTVnsF8GezJ82LL1lxLLLDnxNd4paurWqo/aqnKMiauSuo4BvGDfh0xLGHZwHMe2fWvqkISoEGVK+h05coQmTZowadIk3N3def755/nrr7+MHZsQQgBwIjYN70pwiMe/NfV24O/oFNKyZFNgAc2bN+f06dMEBATc8tBqtZw5c4akpCRmz55Nhw4daNCgQbFDPErLz8/PUL+fnx9AsTa9vEr/C5hGU7RMKDs7G4CGDRuyb9++YmX27dtHUFCQ4fWYmBhiYmIMr58+fZrU1FRDGYD69evzv//9j23btvHoo4+ydOlSALRaLYWFhaWOU4jKTsbJorpT9HreWPYLCiqGhjiZOpxqo1ldT0Is4pj+ewY3UpOLvXY88ThHE8NpcTofywra77c6crNx40W3x9GqzXn+4rscXPe5qUMSotyV6TfokJAQPvroI65evcqSJUuIi4ujffv2NGrUiPnz55OYmGjsOIUQNdjxK6n4VoJDPP6tsZc9hYrC7+fk37uqKi0trdjsuPDw8GIJrNJ49dVX2b9/P2PHjiU8PJxz587x008/GQ7y8PX1RavV8sknn3Dx4kU2btzI22+/bczulFpqairx8fFcvXqVPXv28NZbb1G/fn0aNizaS+jll19m2bJlLFy4kHPnzjF//nzWrVtnOHCjW7duNG7cmCeeeIIjR47w119/MWzYMDp16kSLFi3Izs5m7Nix7N69m0uXLrFv3z4OHTpkqL927dpkZGSwY8cOrl+/TlZWlsneCyGMScbJorrbuvUXtqX5MNQnFTubyjU+q+qGNLYjXbFi3oq1xa4vO7kMp2wNQRpvzJwcTRRd9WCv1THacxCOeivGJn3OzhXvmTokIcrVfU2bMTMz49FHH2Xt2rXMmTOH8+fPM3nyZHx8fBg2bBhxcXHGilMIUUPl5Bcd4uHnXLkOzHCy0eLjZMWuyLLP1qru4jPjibkRU+6P+MyynaS8e/dumjVrVuwxc+bMMtXVpEkT9uzZw9mzZ+nQoQPNmjVj2rRpeHp6AuDi4sKyZctYu3YtQUFBzJ49m7lz55apLWMZOXIkHh4eeHt7M2TIEIKDg9m8eTNmZkVnfPXv35+PPvqIuXPnEhwczKJFi1i6dCmdO3cGipY4//TTTzg6OtKxY0e6detGnTp1WLOm6PRBjUZDUlISw4YNo379+gwcOJAePXoY3uO2bdsyevRoBg0ahIuLC++//75J3gchyouMk0V1lJZynTd/zyTEIo7QAE9Th1PtONvb0dflGsuvenL07wMAxKTHsOPyb4Sezse6SVMTR1g92JhZ8bzPYLzy7Xg5bzVbv3rT1CEJUW5UiqIoZb3577//ZsmSJXz33XfY2NgwfPhwnnnmGa5cucLMmTNJT0+vdMsZ0tPTsbe3Jy0tDZ1OZ+pwhBB3ceRyCo9+vp83ejakdiU7KffHI1c4eDGJw288hFpd806sy8nJISoqCn9/fywtLQ3X4zLi6LuhLzmFFbeXlaXGko39N+Jh61FhbYryd7ufMZDPc1H5mXKcLP9/iPLy+oJF/BjvwlstC3CSwzvKRWGhnll/JKFV6fnljSHMPTafjSe/54XfrXF5fDBySLLx5OkLWH7lR6K0abyb24MeL7xv2LdZiMrAGJ/nZmW5af78+SxdupTIyEh69uzJihUr6NmzJ2p10cRBf39/li1bRu3atcsUlBBC3GQ4xMOx8m1Y3NjLns0n4zn+9x+EJG+G2MOQegn0hWDlBK4NwL8TNOgNdm6mDrfCeNh6sLH/RlJyUyqsTUcLR0n4CSEqBRkni+rqyKH9rIr3ZqhbDE46X1OHU21pNGqGNVTzzklHPv1mBest19H8bD52jZpKws/ItGozRvo8xrIrP/K6xWb4FHqMlcSfqF7KlPRbuHAhTz/9NCNGjMDDo+RfslxdXfn666/vKzghhDgZm4ZXJTvE46Y6+WexVuXx+8YlhNjtKUry+bYFtQZy0iAxEiJ+hs2vQMO+0OkVcG1o6rArhIethyThhBA1koyTRXVUWFDAGz9HUFtjRucgH1OHU+35ujnT/colFl7woJ63LaFX9Fi2rWfqsKolM5WGEd4DWHplHW9Yb0bzCTw8ThJ/ovooU9Jv+/bt+Pr6Gr6xvElRFGJiYgwblg8fPtwoQQohaq7jV9Lwdaxkm0Tn3oC/l2B2+QANzAfzu3VPxvd7HFQlJCbzMiDqDzi7GT5fD82fgm4zwVpOuxNCiOpIxsmiOlq9dg0Rea5MDUqpkVuamEKPxu78vTcdfeyjONQ9hMqsTL+6i3tQlPh7hKWx63jdajPahZZ0GWPaA9eEMJYyTZ2pW7cu169fv+V6cnIy/v7+9x2UEEJA0SEe5xIy8HWuREm/1Muw9TW4ehQa9CLY35ujaTak599mAKy1hcAe0HM+hA6HEz/Apy3h3PaKjVsIIUSFkHGyqG6SEuJ4/5iWDjZXqeNRy9Th1BiXMqKoZ7ubSMWfSGv5t6O8mavMGOH1KB56Ha+arWff4ndNHZIQRlGmpN/tzv7IyMi4ZaNtIYQoq7PXblCoV/BzqiRJv8QzsH160Z+bDwPXIIKcoVCB/Xc7hFFjBvW7Q8+5YO8Dqx6D7dOK9v8TQghRbcg4WVQ3c1f9hB4VjzRxNnUoNYaCwrGEY9gWptCOk3yT2py05BumDqva06rMeNrzUZwVG17O/5ajKz82dUhC3LdSzRGeNGkSACqVimnTpmFt/c8v4oWFhfz555+EhIQYNUAhRM11IjYNtQq8K8Py3oTTsHsW2HpAo0dAowXAxUqFm7XCvqsK3f3uYbmLtRN0ehnO/Ar7P4Frp+DxZWBhV77xCyGEKFcyThbV0ekTh1lzzYuBrrHYWcvhHRXlcnoMqXmpNErQ4+SXwrnkDFadcWT0A7mo1RpTh1etWaq1POMxgC/ivmdC6pd8tV5H4CMjTB2WEGVWqpl+R48e5ejRoyiKwokTJwzPjx49ypkzZ2jatCnLli0rdRCfffYZtWvXxtLSktatW/PXX3/dtuy6deto0aIFDg4O2NjYEBISwjfffFPqNoUQld/J2HQ8HazQmpn4EI/kC7BnDug8odEAQ8LvpgaO8MfVUtSnUkPDPtBpClzaD0t7wI1rxo1ZCCFEhSqvcbIQpqLo9cxcdxh3dRqdg7xMHU6NciwxHF2uGgdzHea21vRyOs+pQh8On0k3dWg1go3Gkmc9HgOtlrHRc7m8faOpQxKizEo102/Xrl0AjBw5ko8++gidTnffAaxZs4ZJkybxxRdf0Lp1axYsWEBYWBiRkZG4urreUt7JyYnXX3+dBg0aoNVq+eWXXxg5ciSurq6EhYXddzxCiMrjRGwqvqZe2puZALtng7UzBPUvWqb7H0FOsCcWrmYoeNqWYnNrjybw4PSihOLSHjDiV9DJibdCCFEVGXOcPGvWLNatW8eZM2ewsrKibdu2zJkzh8DAQGOFK8Rdbd32K39mezGhTjxmGpldVlESshKIy4wjMFGPmZsbAK620CkjkhVJIQQkncDR+f5/Dxd3Zq+xZpRrfz5P/JGxR1/nazsHXB7oaOqwhCi1Mk2fWbp0qVESfgDz589n1KhRjBw5kqCgIL744gusra1ZsmRJieU7d+7MI488QsOGDalbty4TJkygSZMm7N271yjxCCEqh7wCPZHxN0y7n19+JuyeA2oNBD9yywy/mwIdQQXsvdu+fiVx9IMHp0FOGizrKTP+hBCiijPGOHnPnj28+OKLHDx4kO3bt5Ofn8/DDz9MZmamkaIU4s7y83KZ9UcKjbTxNPaXLyQr0rHEY1gVanDOt0Lzr39LWrjewE2VyrIzrhQWyJ7QFaGWuQPPOPXhqrOKidvHciPilKlDEqLU7nmm36OPPsqyZcvQ6XQ8+uijdyy7bt26e6ozLy+Pw4cPM3XqVMM1tVpNt27dOHDgwF3vVxSFnTt3EhkZyZw5c0osk5ubS25uruF5erpMiRaiKjiXcIP8QsV0J/cqejjwOWRdh5Anwfz2cdiaq/DTKRyIUxhYrxQz/W6yc4cH34TfZsI3j8DIX8HK8T6CN738q1cpSEmpsPbMHB0x9/SssPaEEOLfjD1O3rJlS7Hny5Ytw9XVlcOHD9Oxo8w0EeVv9Q8/cLnQkenBaaYOpUZJy00jKu0i/gkK5m5eRd8q/z+NWkUv5yiWXG/G3lPH6dTU3nSB1iBelq4Ms3uYJd5beWX1Uyx4/icsvH1MHZYQ9+yek3729vaoVCrDn43h+vXrFBYW4vb/05ZvcnNz48yZM7e9Ly0tDS8vL3Jzc9FoNHz++ec89NBDJZadNWsWM2fONEq8QoiKczI2DRXga6pDPE5tgNjDEPxo0eEbdxHoAPuuFn0ZcfPfylKxdYMuU2HHW/DdE/DUBjAreWZhZZd/9SoXevZCycmpsDZVlpbU3fTrPSf+RowYwfLlyw3PnZycaNmyJe+//z5NmjQprzBvoVKpWL9+Pf379y/3dm7SaDR4enry2GOPMWvWLCwsLMq17ZuWLVvGxIkTSU1NrZD2hKhI5TFO/re0tKLEi5NTyZ9H8iW3MKb01CQWnDCjrfVVvF0kuVGRjiUex1yvxiVbjVmdW/9/d7KBbpkRrEkPITDuKO4eDhUfZA1Uz8aXwYUdWF33D975dBAzpmxC4+Bg6rCEuCf3nPRbunRpiX82BTs7O8LDw8nIyGDHjh1MmjSJOnXq0Llz51vKTp061XCaGhQNgnx85MNLiMruZGw6Hg6WWJibYA+Za6fg5FrwawvOde/ploZOsPUyXEyHumX9fc/eB9pPgl3vwS8ToN/nUJYEookVpKSg5OTgNGIEZu7u5d9efDzJy5ZRkJJSqtl+3bt3N3yexcfH88Ybb9C7d28uX75cXqEaRXR0NP7+/iiKUqr7li5dSvfu3cnPz+fYsWOMHDkSGxsb3n777XKKVIiaozzHyXq9nokTJ9KuXTsaNWpUYhn5klsY06I168lUatE/uGqvOqhqsgqyiUw+g3eSHgsXj9uOAZvUyuZc7DW+ulCbVx0T0FpWzS+Jq5qmugakJ2ewoeFRas0eyLgZG1FbWpo6LCHuqkx7+mVnZ5OVlWV4funSJRYsWMC2bdtKVU+tWrXQaDRcu1Z8D6tr167hfodfFNVqNQEBAYSEhPDSSy8ZZiuUxMLCAp1OV+whhKj8jsem4mOKWX45abD/46IEnG/be74twB40KjhQln39/s21IbR6DsJXw19f3mdlpmXm7o7W17fcH2VNLFpYWODu7o67uzshISFMmTKFmJgYEhMTDWVOnDhB165dsbKywtnZmeeee46MjAzD63q9nrfeegtvb28sLCwICQkptiwvLy+PsWPH4uHhgaWlJX5+fobPq9q1awPwyCOPoFKpDM/Li4ODA+7u7vj4+NC7d2/69evHkSNHipVZuHAhdevWRavVEhgYyDfffFPs9cuXL9OvXz9sbW3R6XQMHDiw2Gf4sWPH6NKlC3Z2duh0OkJDQ/n777/ZvXs3I0eOJC0tDZVKhUqlYsaMGeXaXyFMxVjj5JtefPFFTp48yXfffXfbMlOnTiUtLc3wiImJKVNbQiRcjWFxlDPdHOJw1NmaOpwa5cT146CAe7oaM2fn25ZTq1X0cIkhRbFl00nZ268idXBqQUd9AIsbxvHdrBEohfL+i8qvTEm/fv36sWLFCgBSU1Np1aoV8+bNo1+/fixcuPCe69FqtYSGhrJjxw7DNb1ez44dO2jTps0916PX64staRBCVG0FhXrOxN3Ar8L381Pg4OegL4AGvUo1y87STIW/DvbHlW72VYn8O0BgT9j6Gly6+/6m4v5lZGSwcuVKAgICcP7/gXZmZiZhYWE4Ojpy6NAh1q5dy2+//cbYsWMN93300UfMmzePuXPncvz4ccLCwujbty/nzp0D4OOPP2bjxo18//33REZGsmrVKkNy79ChQ0DRrKC4uDjD84pw9uxZdu7cSevWrQ3X1q9fz4QJE3jppZc4efIkzz//PCNHjjScSKrX6+nXrx/Jycns2bOH7du3c/HiRQYNGmSo44knnsDb25tDhw5x+PBhpkyZgrm5OW3btmXBggXodDri4uKIi4tj8uTJFdZfISqSscbJAGPHjuWXX35h165deHt737acfMktjOXj7zdhTiHdg8t/pr74R25hHqeun8QtRY+lkwsqszuvdLG1VNHL7jRbsxtx/mJSBUUpAHq5dKZpvjsf+J3ktwUylhGVX5mSfkeOHKFDhw4A/PDDD7i7u3Pp0iVWrFjBxx9/XKq6Jk2axFdffcXy5cuJiIjghRdeIDMzk5EjRwIwbNiwYgd9zJo1y/CLRkREBPPmzeObb77hySefLEtXhBCV0PnEDHIL9NR2tqnYhiO3QNwxqN8dtKX/djvQsWimX2mXXZYoZCg414Mfn4as5PuvT9zil19+wdbWFltbW+zs7Ni4cSNr1qxBrS76aFy9ejU5OTmsWLGCRo0a0bVrVz799FO++eYbw+y2uXPn8uqrrzJ48GACAwOZM2cOISEhLFiwACiaGVevXj3at2+Pn58f7du3Z8iQIQC4uLgA/8zAu/m8vAwZMgRbW1ssLS0JDAwkODi42Ofr3LlzGTFiBGPGjKF+/fpMmjSJRx99lLlz5wKwY8cOTpw4werVqwkNDaV169asWLGCPXv2GBKWly9fplu3bjRo0IB69erx+OOP07RpU7RarWHPs5uzK21tZQaJqJ6MMU5WFIWxY8eyfv16du7cib+/f3mGLAQA0ecj+Dbegx4u17G2rJj9XkWR00mnKCwsxDMFzFxd7+meug75NDOLYlFsIzLSs+5+gzAKlUrFIPee+BY48Ibtdo6sWGDqkIS4ozIl/bKysrCzswNg27ZtPProo6jVah544AEuXbpUqroGDRrE3LlzmTZtGiEhIYSHh7NlyxbD4R6XL18mLu6f9XKZmZmMGTOG4OBg2rVrx48//sjKlSt59tlny9IVIUQldOJK0YblFbq8Ny0GwleBV3NwqlOmKgIdISUXzqUaIR61GbR5EXLSYeN4MEYiURTTpUsXwsPDCQ8P56+//iIsLIwePXoYPsciIiJo2rQpNjb/JJ/btWuHXq8nMjKS9PR0rl69Srt27YrV265dOyIiIoCiA0PCw8MJDAxk/PjxZV7eFxwcbEhQBgcHAxie29ra0qNHj7vW8eGHHxIeHs6xY8f45ZdfOHv2LE899ZTh9YiIiDv2JSIiAh8fn2L74gYFBeHg4GAoM2nSJJ599lm6devG7NmzuXDhQpn6K0RVZoxx8osvvsjKlStZvXo1dnZ2xMfHEx8fT3Z2dnmGLmq4eT/uwV6VRZcG974/rrh/BfoCjicexyVdwcahFirze9t2X6VW0cUtEQsKWHnKDr1elppWFDOVhuEe/bHDkpdSFxO1fb2pQxLitsqU9AsICGDDhg3ExMSwdetWHn74YQASEhLKtJxg7NixXLp0idzcXP78889iy412797NsmXLDM/feecdzp07R3Z2NsnJyezfv7/Y0iIhRNV36mo6HvaWWGkr6BAPfQHs/wSsHKB2pzJXU9cezFRwMN5IcdnUKtrf78zPcHyNkSoVN9nY2BAQEEBAQAAtW7Zk8eLFZGZm8tVXXxmtjebNmxMVFcXbb79NdnY2AwcO5LHHHit1PZs2bTIkKDdt2gRgeB4eHs7ixYvvWoe7uzsBAQEEBgbSq1cvZs6cyZo1azh//nyp47mdGTNmcOrUKXr16sXOnTsJCgpi/XoZCIuaxRjj5IULF5KWlkbnzp3x8PAwPNaskc8CUT5OnzjMzyk+9PZIQ6u957MehRFEJEeQU5CD53UwcyvdsmqtmYq+zpGcKPDlz9M3yilCURIrtZan3R4h18qMCUenkXTssKlDEqJEZUr6TZs2jcmTJ1O7dm1at25t2H9v27ZtNGvWzKgBCiFqnuNX7u8QDwWFzPxMYjNjic2IJSErgXx9/u1vOPkDpF0p2kdPU/aBroVGhb89HDDGvn43+bSC2h1g02RIizVeveIWKpUKtVptmEnTsGFDjh07RmZmpqHMvn37UKvVBAYGotPp8PT0ZN++fcXq2bdvH0FBQYbnOp2OQYMG8dVXX7FmzRp+/PFHkpOLlmybm5tTeA+bQPv5+RkSlH5+fgCG5wEBAXh5eZW6vxpNUVL93/29U18aNmxITExMsQMCTp8+TWpqarH+1q9fn//973+GGU43TzLVarX31FchqjpjjJMVRSnxMWLEiHKMXNRkczcewl2dSrv6pf88EWVXoBQSnnAUlxsqbHXOqLTmpa7DzRa6WkawMqUZ1+JSjR+kuC1HcztGOvfmqrOKSRufJfuqjNVF5VOm324fe+wx2rdvT1xcHE2bNjVcf/DBB3nkkUeMFpwQouYp1CucjkunT5PSLS3JKcwhPCGcE4knuJB2gcz8zFvKOFs5E+AQQLBzMMG1gtGqtXD9HJz+Cfzaga3bfcdf3wH2//++fqpSHARyR6HDYdMp+PUlGPJtqQ4YEbeXm5tLfHzRtMyUlBQ+/fRTMjIy6NOnD1B0KMX06dMZPnw4M2bMIDExkXHjxvHUU08ZtqB4+eWXmT59OnXr1iUkJISlS5cSHh7OqlWrAJg/fz4eHh40a9YMtVrN2rVrcXd3x8HBASg6wXfHjh20a9cOCwsLHB0dy62/qampxMfHo9frOXfuHG+99Rb169enYcOGhr4MHDiQZs2a0a1bN37++WfWrVvHb7/9BkC3bt1o3LgxTzzxBAsWLKCgoIAxY8bQqVMnWrRoQXZ2Ni+//DKPPfYY/v7+XLlyhUOHDjFgwABDXzMyMtixYwdNmzbF2toaa2sTnNAtRDmTcbKoao4c2s/OGz6M8olFoym/zyFxq8jkSLLys6l/HczqeZS5nuYumUTFXmPhBX+mOsRjYSV7MlYUbwtXnrDuzDLvXUxfNJBZL29FI/sWi0qkzFNabm7E/W+tWrW674CEEDXbhcQMcvL193xy7438G+y4vIM/rvxBXmEe7jbuBDoG4mTlhI25DWrU5OvzSc9LJzknmXOp5/gz7k+0Gi0PuDbnkXMHMbN1B5/Wd2/sHgQ6wq/RRfv61TfWuFlrW5T42/shRPwMQX2NVHH5Kog31jrn8mlny5YteHgUDbDt7Oxo0KABa9eupXPnzgBYW1uzdetWJkyYQMuWLbG2tmbAgAHMnz/fUMf48eNJS0vjpZdeIiEhgaCgIDZu3Ei9evUM9b7//vucO3cOjUZDy5Yt2bRpk+GwkHnz5hkOtPLy8iI6Orrsb8Rd3Dwg6+ZhGh07duS9997DzKxoKNC/f38++ugj5s6dy4QJE/D392fp0qWG90OlUvHTTz8xbtw4OnbsiFqtpnv37nzyySdA0czBpKQkhg0bxrVr16hVqxaPPvooM2fOBKBt27aMHj2aQYMGkZSUxPTp05kxY0a59VcIU5JxsqhKPth8Eh+NFS3rySy/ilSoFBKecIRaGSrs7JxQW5R+lt9NarWKni6XWXKtCetPqBnUQkGlli+JK0oDXQD989JYV+cInvOHMuH19ag0FbRNkRB3oVLKcMxkZmYms2fPZseOHSQkJKDX64u9fvHiRaMFaGzp6enY29uTlpZWpv0HhRDl68fDV3hp7TE+HhyC9R32lNErev6I/YOfL/6MXq8nuFYwQc5B2Jrf/Zu1tNw0zqWco27Uftpm3OCARz0CvdvhanX/p6fmFipM2APTWqsY1tCIgy1FgT/mFi3xHfc3WNgZr+4yysnJISoqCn9/fywtLQ3X869e5ULPXig5ORUWi8rSkrqbfsXcUzYfr05u9zMG8nkuKq/KME6W/z/Evdq/dydDf8nmxdpxNKsrn6EVKSIpgt+v/E7TaHCq1wh1GZb2/tflVDWr00IZ7XKIpg2c7j9IUSq/XN3B79qLTEvpyOMTPzN1OKIaMMbneZlm+j377LPs2bOHp556Cg8PD+MtYRNC1HgnYtNw11neMeGXnJPMslPLiEqLoqFTQ1p5tMJCc+/LGOwt7Olk4YZ/xg2ia9XmUkEGp8+tw9++Dq3dW2FvYV/m+Iv29VM4GK8YN+mnUkHoCPh1Mvz+ATz0lvHqNjJzT0/qbvqVgpSUCmvTzNFREn5CiEpBxsmiqlD0eub9dgF/jZaQOmVfWipKr0Ap5PC1v6mVoUJn52yUhB+Ar4Oe9tmRfJ0YwnTnEzi7lH1MK0qvp0dXkmPSmGW3B8/vF9Bu4ERThyRE2ZJ+mzdv5tdff6Vdu3bGjkcIUcOdiE3D1+n2S3sjkiNYenIpGrWGPnX74GFT+kGqujAfr9M/k2vjjNq7Be1QcTUjlgupF/j+7Pc0cm5Ec7dQLDTaMvWhngMcMPa+fgA2LkVLew98Ds2GQa0A49VtZOaenpKEE0LUSDJOFlXFnj2/cTjHk4l14yU5XcHOJJ8hMz+LetfBvL5xE65t3NK4HJvCF5E+vKK7jrlF2cazovTUKhWDvfvyZcx3vJL3Ncv31SegXU9ThyVquDKd3uvo6IiTk0wXFkIYV6Fe4dTVtNvu57f7ym4Whi/ExcqFAfUGlCnhB+B2YQ/a7DSSfFsCKtSAt60X7b3aU9e+LqeST/HdmW85k3wGhdKfxBvoACm5cD6tTOHdWYM+YOUI294oh8qFEELcLxkni6pA0euZtyuGALMEgv3c736DMJoCfQFH4g/jkg46BzdU5mXeZr9EGrWKvi7RJCl2bDiuoOhLP5YVZadVmzHC61HMVBrGHppCUtQZU4ckargyJf3efvttpk2bRlZWlrHjEULUYLc7xENBYeOFjfx49kca1WpEmH9YqZbz/pt1WizOl/4kxaMRBf/ZF0+jUlPXoQ4dvDrgaOnInit72HB+A9ezr5eqjQAH0KjgYFyZQrwzMy00HQxnN0PUH+XQgBBCiPsh42RRFfz222ZO5LnTz9/IqxLEXZ1MOklOQTY+yWrM3NzKpQ1bSxX9dKfZmRPE8bMVt92KKGJrZs1Ip14k2yhMWPsUOenlMRNAiHtTpq8V5s2bx4ULF3Bzc6N27dqYmxffg+DIkSNGCU4IUbMcv5KGCvBzsjFcU9DzfeRa9sbu5QGPB2ji0qTM9asLC/A6tZE8GyduuNa/bTlLjQVNXZrgbedNRNIZ1p1bR5BzMC3dW9xTstFCo8Jfp3AgXuEpY+7rd5NvG4jcDFtfg+f2gLpM398YTRnOgxLinsjPlqiKZJwsKjt9QSHz/rhGA3No6Cuz/CpSTmEOR68dxjUFbJ3dUZmV3wmvtR0L6ZATyeLEZkxzPI6Lm+zvV5HcbNwYmtORpS6/M23R48yetBm1nOgrTKBMSb/+/fsbOQwhhIDjV1LxsLfESlv0gaigGBJ+Hb070sCpwX3V7xK1F4usVK4GdgPunoxztnSirWcbLt24xJmUCC6mXeABjweo51gP1V3ur+9YTvv6QdGhHiFDYcdMOL0eGg0wbv336OYvsllZWVhZWZkkBlG95eXlAaCRQbKoQmScLCq7Ldt+4Uy+G682SDR1KDVOeEI4BQWF+N4wwzzQtdzba+OWxpXY63x+rjav2sVhaW1Z7m2KfwQ6B9L3ShIbXE9RZ/EYRj+/yNQhiRqoTEm/6dOnGzsOIYTgWEwqvv+/tFdBYd25deyN3Usn704EOgXeV91WN+JxiT5AmltDCizv/ZtOtUqFv6427tbuRKZEsitmF2eSz9Deuz1OFrffsynQATZFF+3rV8/hvkIvmWtD8GwOO9+Bhn1BY5xT30pDo9Hg4OBAQkICANbW1rJESBiNXq8nMTERa2trzMyMu9+REOVJxsmiMissKGD+/hSCtYXU85ITeyvSjfwbnEg8gWeSHht3P1CX/5hJrVbR2+USy6415rvjFgxrpUdt4hUiNU1b77YkXLjO53b78N/4EWF9J5g6JFHDlHkUnZqayg8//MCFCxd4+eWXcXJy4siRI7i5ueHl5WXMGIUQNUBegZ6IuBs82rzo34/NUZvZHbOb9l7t7zvhp1L0eJ36lXwLO9LcyjZb0MrMkhCXply39SYiOYIfIn+kqUsTmrs2x7yEhFuAA5ipimb7lUvSD6DJINgyBY6uhBYjy6mRO3N3L1oWdDPxJ4QxqdVqfH19JZksqhwZJ4vK6pfNP3O+wIXXgpJMHUqN82fsQcwKFbxzrdE4OFZYuzaWah51PM03Kc0JOHWU9o0dKqxtUaSPf2+uX1zNGwWL8T3WmIZNu5o6JFGDlCnpd/z4cbp164a9vT3R0dGMGjUKJycn1q1bx+XLl1mxYoWx4xRCVHNnr90gr7DoEI/fY39nc9RmWrq3JMg56L7rdr58CMsb8cTXfxBU9/ftZi0rZ9p6tiU6LZrj149zPvU87b3a46fzK1bOQqPC315hf5zCsPLY1w/A0a9of7/f3y9a7mtWtsNN7odKpcLDwwNXV1fy8/MrvH1RvWm1WpmRIKocGSeLyqogL4/5B2/Q1CKPOh6epg6nRonPiudC+kXqXlOw8va7l11mjMpDp/BwzilWpYbifekQtf3khPGKpFGrecJ3AJ/FrmbcH5NY47kRZxdfU4claogyJf0mTZrEiBEjeP/997Gz++f0y549ezJ06FCjBSeEqDmOXUlFrYI0/QXWRq6lUa1GNHMNue96tdmpuJ3fzQ2XeuRZG2eAc/OUX3cbdyKSI9gSvQV/+zq082yLjfk/h5AEOsDeONArCurymqnU+DHYNBkOL4fWz5VPG/dAo9HIvmtCCIGMk0Xlte7nDVwqdGZ4w2RTh1KjKCjsv7wXmxzwtKiF2to0+yA3rZVNXFw0n15uxJt2kdg72d39JmE0Vlorhjv14LOMXxm3egjLXtyJ1gRf2Iuap0xfnx86dIjnn3/+luteXl7Ex8ffd1BCiJrneEwarjozVp1ZTh37OrTxbMP9fw2q4BmxGb2ZllT3RsYIsxgbc2tC3UJpUqsJVzNi+T5yLZEpkSgUnTga6AipuRCZYvSm/6HzBL/28MdcyM8ux4aEEELcCxkni8ooLyeHBYfzCLW8iq+bs6nDqVHOJJ0hMS8J/+tqzD1Mt7xfpVbRzT0Be1UWCyM8yMvJM1ksNZWLvSdDlFac0qXxzpLhpg5H1BBlSvpZWFiQnp5+y/WzZ8/i4uJy30EJIWqeQ5cSSdWfxdnKmS4+Xe56Ou69cIg/jV3SRZK9m6FoyucgABXgaetBO8921LJyZnfMbrZEbSGrIIu69mCuhv1x5dL0Pxo9ApmJcESWjAkhhKnJOFlURms2rCNOb0/fQGtTh1Kj5BTkcDB2Py6pCrVq+aAyM+2qCHONiv6uF0jU2/P9MQ16vd6k8dREgd5N6ZHiy3qLU6z++T1ThyNqgDIl/fr27ctbb71l2L9JpVJx+fJlXn31VQYMGGDUAIUQ1d+1jFQuJmahtUjhYb+H0ajvf0Ckyc/BI3IbmfbeZOvKf98arcacJi6NaebanGtZ1/g+8ntiMy8RYA/7rirl27idB/i1gz/mQ35O+bYlhBDijmScLCqbnKwMPj6mopVVHF61Ku4ACQEHY/ejLyzAP8cWjWPl2EdPZ6ligMNJDuQF8MeJG6YOp0bqEPAwzRJt+SDxWw6f2GbqcEQ1V6ak37x588jIyMDFxYXs7Gw6depEQEAAdnZ2vPvuu8aOUQhRjRXoCxi36X1ATae6QViZGWefE7cLu1EX5JHsHYI+P5/8pGRyY2LIiYwk6/hxMg/9TcbBP8k4cICMAwfJOHSIrCPhZJ+OIPdiFPkJiRRm51DadJ2btQttPdtib+HAtuitWGku8We8QoG+nBN/wY9AZgIc/aZ82xFCCHFHMk4Wlc2KH34kWbGhbwPZw60ixWZcJTLtHL7XwcbLv8IP77gTL3uFHtYn+S69OZEXZI/HiqbWqBng3x/3dDX/2/cKCckxpg5JVGNlWu9mb2/P9u3b2bdvH8eOHSMjI4PmzZvTrVs3Y8cnhKjm5v09j+NX0tCoFWo72hulTov48zjFHCYBF9L2/oWSnVX0ggrQWqAyN0dlZgZqNajNAAUKCtHn5qGkpaLk5UFh4f9XpsXM2RmNszPmbm6otdq7t6/R0sw1hMvplzmcEE5mgR9/XE2ni7dx+lcinWfRSb57P4Tmw8Hs7nEKIYQwPhkni8rkRmoyn0VY0d42DjcnH1OHU2Pk6wvYHf0bukzwtvVBbWFu6pBu0cg5h8T883x2NYTXrY/j5uFg6pBqFK2lFU86hvFp9mbGr3mCFc/9hlYj43dhfKVO+un1epYtW8a6deuIjo5GpVLh7++Pu7s7iqKgKsMJlZ999hkffPAB8fHxNG3alE8++YRWrVqVWParr75ixYoVnDx5EoDQ0FDee++925YXQlRe68+tZ2XEStw0r1DooEatLvtXoIWpqeSeO09e1EVCrC6So1GTmKVCo7NF7eGGysoatYUF3EsbCujz8lGys9BnZFKYnk5BXDy5J0+hdnRE6+2NmYc76jucVqsC/HS+2GntiUzNZd7hg/jZ1aOOfZ0y9/GugvvDppfh+HfQfFj5tSOEEKJE5TFOFuJ+LF67gWzFid4NZVlvRTp05QBZBdk0y7DBvHYtU4dTIpVaRWf3ZJJjrfnofH1es4nCVmdj6rBqFKda3gy+EMJS26PM+u4Fpj/xtalDEtVQqZb3KopC3759efbZZ4mNjaVx48YEBwdz6dIlRowYwSOPPFLqANasWcOkSZOYPn06R44coWnTpoSFhZGQkFBi+d27dzNkyBB27drFgQMH8PHx4eGHHyY2NrbUbQshTOdowlHeOvgW7TzbkZbmhJtD6XcbUAoKyDl9mtQf15Hy7bdkhx/F0zoDW8tCrrs2wrJhQ8x9fNA4OaG2sry3hB+ACtQW5mgc7DH39sSyQQMsmzTB3M8XCgrIOX6cjB07yTkdQWH2nffQc7K0p44un8TsBnx85GP+ij9U6n7eM3sf8GkFv8+FwoLya0cIIcQtymOcLMT9uH7tKl9dsKezLh5Hna2pw6kxYjOuciLlNL5JKuwr2bLe/1KrVfR2v4K5qpDPT7iSl5Nr6pBqnPp1W/Jwghs/FPzF+j++NHU4ohoq1W/Zy5Yt4/fff2fHjh0cPXqUb7/9lu+++45jx47x22+/sXPnTlasKN3pkfPnz2fUqFGMHDmSoKAgvvjiC6ytrVmyZEmJ5VetWsWYMWMICQmhQYMGLF68GL1ez44dO0rVrhDCdOIy4piwawK1dbXp6jmA1CzwcLj3EVFhZiaZBw6QvGIFGXv2gKLHqmkIDp3a4meVSLq1B7lWxv1GW2VuhlmtWljUr4dF40aYubiQd+UKmXv2kHXsOIVZ2be9N8Ahl+s53njbNmDF6eVsi96GopTTHn9Bj0DqJTi1rnzqF0IIUaLyGCcLcT8+XfMzAD2D5dToipJbmMuuC9vQZYGvvT8qbeVb1vtfluYqBricJUFvz4pwSwoL5IvjitapQS8axWt55+wnnLkSbupwRDVTqqTft99+y2uvvUaXLl1uea1r165MmTKFVatW3XN9eXl5HD58uNgeJ2q1mm7dunHgwIF7qiMrK4v8/HycnEo+DSk3N5f09PRiDyGE6WTlZzF+53jUqHm60dNcSihK9rnfw0y/whs3yNizh9SVq8g5dQpzT09sO3XCukULzD098E4+Bgok6fzLtQ9qCwvMvTyxbNQIMy8vChISyPz9d7JPnUafl39L+Tq6HAoVFTrLMEJdQ9l4cSPrzq8rn8Sfkz94hMAf80CvN379QgghSmTscbIQ9+PyxUhWXnWne61EbK2Nc0iauDMFhd8vbCdHn0uDPCfMHcpxL2cjs7dSMdDpJMfyffklPA+lvA+gE8Wozc143LsPDpkw4dfnuJErpyoL4ylV0u/48eN07979tq/36NGDY8eO3XN9169fp7CwEDc3t2LX3dzciI+Pv6c6Xn31VTw9PW+7OfKsWbOwt7c3PHx8ZANbIUxFURTe3PcmUelRjGoyCjutHReuFaKzAhvL28/002dnk7F3H6mrvyX3/AW09eph26kzlg0aoLa2BsAmO4FaN6JI1vmjV1fMt6oqMw3mbq5FyT8PD/JjY8nYvYfc6EvFEnqOlgU4WeRzNNGOFu4taO/Znl0xu1h95lv0Sjkk5oL7Q+IZiNxk/LqFEEKUyNjjZCHux7wfdmGnyuHBhh6mDqXGOB13jIs5sdRN1WLn4WvqcErNzQ4e0Z1gS3Zj9p1KM3U4NY6VgxNDNW1IUmcx5bsR5bcqSNQ4pUr6JScn35Kg+zc3NzdSUlLuO6h7NXv2bL777jvWr1+PpaVliWWmTp1KWlqa4RETI8dhC2EqX534im2XtvFkwyfxsvUC4Gyc/vaz/AoLyT52jJRVq8mNiEBbtw62nTphUccflfk/5xCpFD2+CYfIMbcj3briB7cqjRpzD3csg4PRODqQG3GazH37KEz9Z8BUxz6bvxNsURQIrhVMF+8uHIw7wKqI1RQae0aeSwNwDYLfPwAZMAghRIWobONkUXMdP3KQn5J96OOeikUVWF5aHSRmJLD/2p+4panxdqt/7/tIVzJ1HQvoYXWCVamhnDybbOpwahzP2o3ol+DN75xl+a55pg5HVBOlSvoVFhZiZnb7A381Gg0FpdgDoFatWmg0Gq5du1bs+rVr13B3d7/jvXPnzmX27Nls27aNJk2a3LachYUFOp2u2EMIUfF2Xt7JJ0c/oUftHoS4hgCQV6BwKVGPp+Ot/xTlx14l5fvvydx/AHMPd2w7dcQiIACV2a0n5tZKO4dVXiqJ9gFgwpMRVeZmaP18sWjQEKVQT+aB/eScjkBfqCfAPpvEbC1XMrQA1HeqTxefrvwV/yffnllt/Bl/Qf0gLhwu7DRuvUIIIUpk7HGyEGWh6PW8/fNxvNTJtAv0MnU4NUJ2QTZbz/2MVR4E2tWtEvv43UmTWtm000ay8Foo0Zck8VfRQhuF0eqKBQsuLSf80kFThyOqgduPTEqgKAojRozAwsKixNdzc0t32o9WqyU0NJQdO3bQv39/AMOhHGPHjr3tfe+//z7vvvsuW7dupUWLFqVqUwhR8c6mnGXKH1MIcQmhu/8/S5+iE/QU6sHD8Z9EnZKbS+b+/eScOYPG0RGbdu3Q6OxuW7dZYQ6eScdIt3YnV1s5kvpqG2ssGwSSn5BI3uXL5Cck4N2oKWZqF/5OsMPHLgmAeo4BgMKumF1oNVoer/84KmMlLd2bgFPdopN8Ax40Tp1CCCFuy9jjZCHKYtv2TRzK9mJi3Xg0mlLN7xBlUKgUsuXkOnKVAkLxwty26p+SrFKraOeWRmbcJRZcbspU7QncPBxMHVaNoTLT0Me/D7HxPzBp21jWPfUbDpYOpg5LVGGlSvoNHz78rmWGDRtWqgAmTZrE8OHDadGiBa1atWLBggVkZmYycuRIQ31eXl7MmjULgDlz5jBt2jRWr15N7dq1DXv/2draYlsN/pEVorpJzklm7I6xOFs682TDJ1Gr/hmAno8vxFwDteyKEl150dHc2L0HCvKxbNQIc2/vuybBvK6Ho1IUknR1yrUfpaZSYe7misZeR170JfIPHcDXzZO/4m15pG6SoVg9x3oU6Av5PXYPVmZW9Knbx2jtE9y/6ECPSwfAr41x6hVCCFGi8hgnC1EaeTk5vPd7MsHafBrVlr38ytv/sXff4VFV6QPHv3f6TJJJ742E3kNVQAUUBAuKBeva1rK6YFlX17Yrlt8uFlx7WXUVC6iLCioiilSRogKhg7RACOm9Tb3390ckGkkgZZJJeT/PMw/kzrn3vufOnbln3jn3HA2N1Tu+JJ8KBjnCCIyK8ndIPqPTKUyMzafqqIln9vXlAdMeQsPbx4/rXYE5OJQrC8fwsraGv/3vBl675pM636GEaIomJf3efvttnwdw+eWXk5+fz8MPP0xOTg5paWksWbKkdkyUw4cPo9P9eoK/+uqruFwuLr300jrbmTlzJo888ojP4xNCNJ/b6+YvK/5CpbuSvw77K2ZD3d4PNeP5KSgeDxXfr8GxezeGyEisAwagNDBO52/ZHIWEl+2nILhHm03e0VQ6iwVL7164c3JJLtrNKs8YSnLLCIn+teHUN7wPLtXJ14e+JtAUyPjE42d+bJb4YRCSVDO23zWf+mabQggh6tUa7WQhmuLd+f8j0xvKw/1lEoa28OPuZfysZtOrMoCIqGR/h+Nzep3C+TFZzD9q4t+7Urlv0CEC7QH+DqvLiErtx4WbMvgwaR//XfE0N595n79DEh1Uk5J+rWXGjBkN3s67cuXKOn9nZGS0fkBCiBbTNI3H1z/O1oKt3J52O2HWsDrPq5rG3mwvfUOrKZn/JWpVFZaBgzDGxzduWD5NIynvB5zGAEptca1TCV9RFIyxMfQJ9LCiSMd3X+9lwhATlgEDansyDo4cjMPt4JO9n2A32RkWPcwH+9VBv6mw9gXI2gTxQ1u+TSGEEEK0O3nZR3h2RwBjg7JIiOx4M8d2NNv3fc9m136Syy0kRvWEjjlvx0mZDAoXxxzkg5yePL81lruH5GENOPkP88I3hgyaRMaWubykvs/ww2MZknSqv0MSHZD0ERVCtIp3d77Lgn0LuKL3FaSGHH/rbVaBl0onBG9ZBXodAWPGYEpoZMIPiCjbR4CziILgnn6dvKMpgoMMxJkqSA8dSeV3ayj/6ivUakft8yNjR9IzpCfv7XyPn4v3+maniaeCPa6mt58QQgghOqWn5y1CQePCAZ3nFtP2ate+dXxfuZ3YChPdI/t0mHZoc1lNCpdF7aVCs/KfLSG4HC5/h9RlKAY956WeT1wx3P3N7ZQ6pRevaDpJ+gkhfG754eU889MzTEyeyCmxpxz3vLe8nE0frkCneYmLC8B2yqnobLZGb1/vdRBfkE6ZNRqHKdiXobe6HrYStup6oB86EvfRbIo/+ghXZiYAiqIwNmEs0bYY3tj6BjmVOS3foU4HfS+EPYshZ1vLtyeEEEKIdiV94zrm5ycyNaaQQJv0wmpNe/au47vKrURXG+kT0Q9F17kTfscEWhSuiNzFEW8Yb2+24HG5/R1Sl2EODeMy3SlUqtX8bf6NaJrm75BEByNJPyGET+0q3MV9q+9jcORgzk89/7jnnfv3k/vPf7GvMpBIo4PA3j2aPGNtQkE6iqZSaO/uq7DbTG9bMQ7VwHZrPwJOG4POZqNs0SIq161H83rR6/Sc3W0iVoOFV7a8SpmrvOU77TYGAmNg1VMt35YQQggh2g2Py8WDn+0kWV/IGX0T/B1Op7Zj5ypWVm0lwmGkX1g/dF0k4XdMiA2uCN/OLk88H27WoXq8/g6py4jpOYgLsuNY693D22ue83c4ooORpJ8QwmdyKnOYvmw60QHRXNPvmjqzTGmaRvnyFeT9+1mw2TgU2pfY4Kb/UhXgyCeibD9F9m549e1z8o4TCTc6iDJWsbY0Fp3Fgm34cMy9e1O9ZQulnyzAU1yCSW9icrdzcHiqeWPr67jVFv6aqjNAvwtg1+eQu8M3FRFCCCGE373zv4/Y5Yri6t50uSRUW9HQSE//mjWe3UQ7zAwI7V9nosmuJDIQLgvZxgZXKgs3u1FV1d8hdRlDBk9m5CEjL+x7iy1ZG/0djuhAuuanlRDC5yrdlUxfNh2v5uWWgbdg0ptqn1OdTorefpuS+fOxDhyAY8LFlHmMxAc4m7QPRVNJyv0BhzGo/U/ecQK9bUX8VB6NU9Wh6BTMqakEnHoqanU1JfPnU71jJ4GmAM5OnkRmeSbzds1reVf+lDMgMApWPumbSgghhBDCr7IzD/LMdhvjgrJIjY3wdzidkub1svaHT9mgyyDeYaVfWN8un1yND1a5xL6Fbx19+XZLJZoqt5u2BZ3RwPnJ5xNdDHd/9WfKXGX+Dkl0EJL0E0K0mFt1c/fKu8ksz+RPg/6E3Wyvfc6Tn0/uU09Tnb6FoIkTCRg9mp/LAtChEWtrWg+2yJKfsbpKyO9Ak3fUp29AEQ7VwMbyXwfb1ocEYxszGmNcHJWrVlH21VdEEsjYxLH8mPsjXx/6pmU71RlqZvLd9RnkbG/ZtoQQQrSq1atXM2XKFOLi4lAUhYULF/o7JNHOaKrKP979GpPi5aJB0f4Op1NyO6r4ZsNctlsKSHGF0Ce0N7oO3P70pZRQlamBW1lQkcb6nTK5RFuxRERwmTacMrWS+z++Wcb3E40iST8hRItomsbj6x5nQ/YGbhxwI3GBv/bAq96xg9xZT6BVVhJ88UWYe9SMwbenxEq0zYVR1/gLlcldSVxhOmUBcThNQT6vR1sKMzqJN1Wwsrju2Ds6gx7rgP5Yhw3Dk51D8YcfklioMCx6GIsOfEF6XnrLdpxyBgRGw4p/tWw7QgghWlVlZSWDBw/m5Zdf9ncoop36fNFCvi1P5KrkUmwWs7/D6XTKC3NYmD6Pw7Zq+nqjSQ1J6dA/OLeG3uFuJlu38W7xMLbsLvJ3OF1GbO8hTMmK5jv3Tt75/gV/hyM6AIO/AxBCdGyvbnmVBfsWcE3fa+gd1hv4Zfy+b76h9LPPMCUlEXjWWejMNQ1SVYM9pVb6hlQ1fieaRlL+D2g6A4VBqa1RjTbXL6CQ5cWJlHmM2A11ezwao6PQh5yGY+dOyr/+hp6pKRT37Ma7O98lwhpBQlAzB+rWGWDAJbD+FcjaBPFDfVATIYQQvnbOOedwzjnnNLq80+nE6fx1yIyyMrntqzPLz8ni4XUeRlgKGNpdJu/wtaz9W1hWtB6vGYYYuhFiCfV3SO3W4IhqqvJ285/8Ydxt2kiP1DB/h9TpKQoMSzuXjG1zeU59k7SUMaTFD/d3WKIdk55+Qohm+/jnj3l1y6tMSZ3CyNiRAKgOB4Vvvknpws+wDh1G0ORzahN+AJkVJqo8ehICXI3eT2jFIYIrj5If3ANVp/d5Pfyhb0DNL6KrS+LrfV5nNmEbkoZ1yBA8R44yaPUR7KqZ17b+p2VjeCSfBvZ4WP5487chhBCiXZk1axbBwcG1j8TERH+HJFqJpqo89PYXaChclSYJFl/SVJX0jYtZXL4eg6JnhKWvJPxOQtEpjIoqY5jxIM9nDSHrSLG/Q+oSdCYjU5LPI+aX8f1KnXKLtWiYJP2EEM2y7PAyHl/3OKfHn87E5InAr+P3ObZtJ2jS2QSMHIHyu8GOdxbbMOlUom2NS/oZvA6S8n+i3BJJpaXzDFJt03voaSthaVESJxqOwxgbQ8Dpp2EOi2DED6U4K8p4/aeXcaue5u1Yp4OB02D/cjj4XfO2IYQQol154IEHKC0trX1kZmb6OyTRSuZ/Op9vShO5OqmEoACbv8PpNJwVZXyz/n02GDOJdlsZbh+AxWjxd1gdgqJTGB9TRC99Ns8cHEBBniSg2oIlIpLLlZFUqFXcM/8GVE1mUhb1k6SfEKLJNuZu5G+r/8bgqMFc2utSFEWhesdOcp94Aq2qqmb8vtT6b8PdWWwjPsCFvpHDoiTm/Qiql4LgHj6sQfswODCfI84gfq4KOWE5ndmEbfBgwtNGMuqAgczKLN75ehbe6urm7TjxFAjrDt/O5IQZRyGEEB2C2WzGbrfXeYjO5+C+Xcz8ychpAUcY1qP+OwVE0+Ue3sUnOz+sHb+vX2gfdDr5mtwUeh2cG5tNjK6Uf+/pSXlJhb9D6hJieg1manY8Gzx7eXX5LH+HI9op+TQTQjTJ7qLdTF82nW72blzT7xoUDcq+WkLByy9hiIwk+JKLMYTVf7uJw6NwoNxCQoCz3ud/L7T8EGEVhykI6YlXb/JlNdqFbpYyQgwOlhQmN6q8MSKcpKFnMLIimnRzLgvefpCK79agqU38ZU9RYPAVkLURdn3RjMiFEEII0ZZcDgd3vvs9dp2DK4bIbL2+oHm9bN64mM+LVqPqFUYYexIXFHfyFUW9DHqFC2MOYVI8vLA9jupKh79D6hLS0iYxJsPCfzI/5Lu9S/0djmiHJOknhGi0Q2WH+NPSPxFuCefmgTejd3oofP11Sj//HOuw48fv+71dJTZUTSEp8ORJP6OniqS8Hyi3RFJhjfJlNdoNRYGhQXl8XxpHsbtxM+8peh19EtMYpEtiVW83P6ycR87j/0fV1q1oTem1FzMQYtNg6cPgafz4ikIIIYRoe/96/R12uiK5qY8bi7nz/RDa1irLCvnyh/f44ZfbeUcEDSDQHOjvsDo8i1Hhsqi9lGtW3txix+Nyn3wl0SKKwcA5vaaQkqfwt1X3kll62N8hiXZGkn5CiEbJqczhpq9vwqQ3cevgW9HlFpI76wkcu3YTdM5kAkYcP37f720rshFmdmM3eU+8M02jW+46APJDevqqCu3SoMACdIrGN0VJTVpvSEAfkozRLBptJNeuUvjqa+TNfgbHnj2N30ja1VByCH58s4lRCyGEaE0VFRWkp6eTnp4OwMGDB0lPT+fwYfky1xV9/sUC5hxN4LLobFJiI/0dTod3cN9PfLz3Y3ItTgaosfQL7YO+k0wU1x4EWBQuj9jFAW8UH27WoaonafeLFjMGh3BF0DgMDg+3f3IN1Z5mDgEkOiVJ+gkhTqqwupCbvrkJt+rmz4P/jG7zTvKefApUleBLLsHcrdtJt6FqsK0wgORG9PKLKtmNvSqH3NA+qDqjD2rQfll0XgYFFrC4sBsOtfENTp2icLp1AHZ9IPPTqlHOPwtvRTn5zz1P3rPP4vj555NvJCQRUsfDqiegsrAFtRBCCOFLP/30E0OGDGHIkCEA3H333QwZMoSHH37Yz5GJtrZnRzr3fa9xijWL8f1lVuaW8LhdrP7xY76p2ojFq+cUcx+iA2P8HVanFBYA00K2sc7VnSXpDjRVxpBubSFJPbiivB+HtUIe/ORPTbsDSHRqkvQTQpxQqbOUW5beQomjhNsG3IL2yVcUzXkHU0oK9osuwhAS3KjtZJSbqfDoSQ468fgeAY4CEgo2UxyYQLU51BdVaPdG2nOo8hr5tqhpjXmjYuBMaxoevLwXuA3rRRcQNHkynqIi8p99jtxnnqF6164TX/QHXQaqB1b8s4W1EEII4Svjxo1D07TjHnPmzPF3aKINFeZl88e52wjXV3LNsCgUpZGzoInjFOQf4uPN77LbWEiqM5ghoQOxmKz+DqtTiw/WmBq4jS8qB7Fxd4m/w+kSUgaexpTMKL51bOY1mdhD/EKSfkKIBlW4Krh16a1kVWRxS8LlaC+8ReW6dQSOG0vgmWeiMxoava30wgCsei/R1obH9tB7naRmf4fTFERhUP2z/3ZGwQYX/QIKWZjfHZfatI/lAL2Fs2xDyfOW8EH5cgzdkgm5dBpB50zGW1ZGwQsvkvvkk1Slp9c/4YclGPpfAhvfhuytPqqREEIIIVrC5XBw6ytfUKGZuX2IUcbxayZN00jfsZwFWUtw6VSG67qREpqKThKobaJXuJuzLDv5b+Fw9h+Uu0pam6LAyCHnMybDwquZH7B0+0J/hyTaAUn6CSHqVeWu4s/L/syB0gP80TAW3XNvoVZWEXLxxVj69qUpbSVNg035gaTYHTQ47J+mkZqzBp3qJiekL03aQScwOvgopR5zk8f2AwjXBzHWOohd7sN8UVkzFqKpWzdCLr4E+3nngdtN4X9eJ+fxx6ncsAHN+7uxVXqdDfZ4+PJuaOpMwEIIIYTwKdXj5Z7n57DZEc1tvasID7b7O6QOqbKqlC9+eo8N3r1EO8yMDByA3dY17iJpT4ZHVjDUcIAXjgwhL7fE3+F0ejqDgXN7X0SvHB0PbHiY7Vkb/R2S8DNJ+gkhjlPtqWbGshnsKtzJlUeSsL77BabkJIIvuQRDRHiTt5dZaaLQaaT7CW7tjStMJ6gqh9zQvngMlpaE3yGFGZ0MCCjgk7weVHubPph0ojGSU819+d6xndXVv/TYU8CYlIj9wgsJnnohitlM0Zx3yJ75CBWrv0P1eGrK6Qww/I9w5EfY/J4PayWEEEKIptBUlcde/S9fFMdzU1IePeKj/B1Sh3TwyDbm7/qIfH01A5xR9Ivoh8HQ+DtUhO8oOoWzYgtI1BXw3M+9KC+p9HdInZ4xKJArY88jvEzjtsU3k1Wa6e+QhB/5Pen38ssv061bNywWC6eccgo//PBDg2V37NjBJZdcQrdu3VAUheeee67tAhWii3B4HNyx/A625KVz6Y9GIjb8TOCZZxJ01lnoTM2bVGNjfiAWvZfYAFe9z4eVHyS2eCeF9tQuM45ffU4LOUqVamBhfvdmrd/HnMggUyqLqtaT7thX5zlDbCz2c88leNql6ENDKP7wA3L+/g/KV6xEdbkgqi+kjIWl/4DyXF9URwghhBBN9MJbc5iTFc/VMUcZ3jPe3+F0OB7Vw+qtn/NN0VqsboVTTD2JDpXj6G96ncKUmEyMiodXdkTjcpx8Yj/RMraIGK4xjwWXi5v+dxlF1UX+Dkn4iV+Tfh999BF33303M2fOZNOmTQwePJhJkyaRl5dXb/mqqipSU1N54okniImRmZaE8DWn18mdy+5g49EfuWiVi6RyM8GXXIqld69mb1PVYENeEN3tDvT13LEbUJ1Pcu56yqzRlAQktCD6js9ucDEiKJfPC1LJczVvcOmh5h70MMTzYcVK9riOHPe8ISKCoIkTCbniCgzR0ZTMn0/2Px6mfOVK1IFX1NyL/dXfWloVIYQQQjTRS2+9zbP7orko/DDjZKbeJisqy+OTze+yW80mtSKgZrIOS6C/wxK/sBgVLo3cS75qZ266CdXjPflKokXCk3txnWskRWo5f5p3KVXuKn+HJPzAr0m/f//739x8883ccMMN9OvXj9deew2bzcZbb71Vb/kRI0bw9NNPc8UVV2A2m9s4WiE6N4fHwYwvb+bHrPVcvNpNn/g0gi+a2ujZeRvyc4mVUpeBXsHVxz1ndpfT4+hKHMYg8kN6dblx/OpzanA2Zp2XOdl9m7W+oiiMtvYnXh/Ou2XfcMhdf689fUgIgWedSegVV2CMjaXkf/PJ+efTOIJGwc6FsPPzFtRCCCGEEI2lqSovvfU2s3+O4sKww5yXluzvkDoUDY3tGRv45MACnKqboZ54UqJ6odP7/aY28Tt2q8JlYTvY5O7Gkq0ONFXzd0idXnyvNK4p7c8BLY9b378Eh6fh4ZZE5+S3T0KXy8XGjRuZMGHCr8HodEyYMIF169b5bD9Op5OysrI6DyFEXdWuKm6dN42N+Zu5dLOZQaMvJOCUkSi6ln9ErM8LItjkOW7WXoPXQc8jy1B1enLC+qMp0jADMOtUxodmsqEslo1lkc3ahl5RGGsbTLjOzlulX5Htabg7vy4kmMAzxxNyxeXow8PJ//QHHOVBqAumo1XU3+taCCGEEL6herz889X/1ib8pgyRhF9TOLwOlmz9hO/L0oms0jPC2peQEBkHsT2LDtK4MGgbX1QOYvOeYn+H0yV07z+Gq/J7st2byfS503B56x9ySXROfvuWXVBQgNfrJTo6us7y6OhocnJyfLafWbNmERwcXPtITJSu8kL8Vunh/dz4ygS2ug5y5ZFEBp95BcZY39w+X+nWsTE/gD4hVXU68elVFz2ylqNX3WSHDULVNW+swM6qr62IFEsp/zk6sFmTegAYFT1n2oYQoLPweuki8jwlJyyvDwkhaOJEgi+9lLLCBLTKcqofPR3H7t3N2r8QQgghTszlcHDvc2/w38wYrorOlIRfEx0tyeR/W9/nqLeQvuV2BkQMxGjpepPBdUQ9wzyMt+zizYJhZBySsebaQt+B47kiJ4VN3oPc9t4lVHuOvwtLdE6dvmvNAw88QGlpae0jM1NmrhECQNM0Mj98lxvnXsTPgeVc5z2VAcMnN3uyjvqszwtCRaFPyK8XFUXz0j1rFRZXOdnhA3F3wZl6T0ZRYFJYBmUeE+/lNO82XwCzzsBE2zDMipH/lC4i31N60nUMkZEEnnsR1UGnYwvIofjeSeQ8/n94S0++rhBCCCEap6Qwl2uefJfPCmK5KTGbMwck+TukDkPVNH48sJpFhxZjdHoZ5kkkLro76GSYmI5kZGQ5gw2HeP7wIArzpZ3Z2hQFBqRN5MrcVDZ7DnLzOxdS4arwd1iiDfgt6RcREYFeryc3t+54U7m5uT6dpMNsNmO32+s8hOjqXEeOsO1P1/DnQ09xOErhxuBz6ZUwyKf7UDVYddROSpADm0EFfkn4Za8mwFlAdvgAnEYZXLkhIUYX40Mz+boomc3lEc3ejkVnYqJtGHpFx2ulX5B/kh5/ACig63Ea7pChxAwtp3rpB+yffA4lCxeiaTL2ihBCCNESe3dt5cJ/f83O6jDu7l3EKb269kRmTVHuLuezbR+yqXwX8WUGhgb2JTC0+e0k4T+KTmFCTD7RuhKe29OdyjKZZKK1KQoMHDSBPxT2Zrd6lD/MOYf8ivrH/xadh9+SfiaTiWHDhrFs2bLaZaqqsmzZMkaNGuWvsITo1DRVpei99/nxivP5W79tFESb+VPEVLrZ4n2+r62FAeQ7TAwOqwRA0VS65azFXplNTmh/HKaWTRDSFQwJzCfFUsqLRwZT6jE1ezs2nZlJtuG/JP4Wketp3Pgp7tiz0KwRJE12Ye6RRPb9D3D42utwHjzY7FiEEEKIrmzRlwu54J19eDSFB4a66ZUQffKVBAD7CvYwf8eHlLrLGFQWTO/oAejldt4OzaBXmBp9CE2D/2wLx+1yn3wl0WJ9B4zlxooh5HmLuWLeefx8dJu/QxKtyK+3995999288cYbvPPOO+zatYvbbruNyspKbrjhBgCuvfZaHnjggdryLpeL9PR00tPTcblcZGVlkZ6ezr59+/xVBSE6DOe+fRy68io2v/pP/n6tnqowG7eGXkicIdzn+9I0+PpICHE2J9E2N2gaybnrCK04TE5YP6osYT7fZ2ekKHBe+EHcqp4XMwfTkgnObDozk20jMCp6Xi39nCxPwclX0hlxJlyEzlNK3MhiIqb/GVdGBgcvnErBf15Hc0vDTAghhGgMR1UlDz//H2Z8Z2SQtYAHxwQRFSo/gDaG2+tm+c9fsezoSuyVKsPVJCJjU+V23k7CalKYFrmHI2oY8zbpUT1ef4fUJXTrNYI/MRYcTq5efDVLN833d0iilfg16Xf55Zcze/ZsHn74YdLS0khPT2fJkiW1k3scPnyY7Ozs2vJHjx5lyJAhDBkyhOzsbGbPns2QIUO46aab/FUFIdo9zeUi/+WXOXjRxezwZvLwLYHobQHcFjKFKENIq+xzR7GNjHILQyIqf0n4rSesPIPc0L5UWuQWjKYINLg5L+Igmyui+LwgtUXbsupMTLYNx6ZYeK3kCw64sk+6jmYOwxU/BWPheuyBO4h66CECzjiD/Oef5+C0y3Ds2dOimIQQQojO7uedW7hw1v/4IDuGq6MzuWlUPGZT83vwdyW5lTn8b/tc9lcepkehiUHB/bCE+v4Ha+FfITaFy0K386M7la+2ONBa8ku3aLSopN7cFjKVpCIdd297jKc+vQu3Kj/qdzaK1sUGaCorKyM4OJjS0lIZ3090elWbNpH9j3/gyjjE9ksG82T3XcQawrnePhGbrnVuh1A1mLU5Aa8GFyYV0i1/PeFlB8gN6UOFTW5haa6VxQn8UBbNI6kb6B/QslnOXKqHFdXp5HlLuDLoTAaaU066jiF/Daa81VT3vQdP5Gm4Dh2i+P33cefmEnHbrUTccguKUWZhFm1HrudCNEzeH+2D1+PhrXnzeHqnnQhdBTf310iMkoRVY3g1L5uO/MDmoq0EVENfdzj2mMSa2yBEp7WvyMDH5UO4NvQnRg0I9Xc4XYbH4eDb3V+wMq6YPu4InrroDZIje/o7LIFvruedfvZeIboib2kp2Q/P5NBVV6OpXlb/7Swe776NXqYEbg4+p9USfgBrc4M4UmnmlMhyuuXVJPzyJOHXYmeEHCHBXMEzh4ZS6Da3aFsmnYEJtqEkGaJ4v3wp31Vvg5P8/OOJGIMnuD+W3c+hL92BKTmZqL/9jaAJEyh4+RUyLr8C5969LYpLCCGE6Cz27d7OZf/3Nv/aGcpYew5/H2OXhF8jFTmL+HTHR2wu2kpCsY6h5u7YY5Mk4dcF9AjzcLZ1O+8WD2fH3pb9yC0az2CxMGnwNP5YNJCj7kIu/vxi3ln6NKqm+js04QPS00+ITkTTNMq+XEzurFmoVZUEXDCF/wzI4bOqdZxhHcS5tpHoWrHBVO7S8cjGJBIDnPxRv7jmll5J+PlMpdfAnOx+xJiqeDx1PUZdyy7EqqaxybmXba6DnGrux9TA0eiUE/wWpHowH/4InSOfqsH/Qg3sBoArI4Oi997DW1BA5J13EHbDDSh6fYtiE+Jk5HouRMPk/eE/LoeD1+d9wPM/hxKmq+K6nk6ZrKORVE1lS/YmfsrfhNmp0bsykLDYVBSDtCm6Ek3VWJMbwjpXD+5N2ki3ZBkLvC1VFhfwReZXbIpz0NsRyiOTnmZAt1P8HVaX5YvruST9hOgknAcPkvPYY1StW4916FDUiyfzsPsTtrsOcnHgaYyw9G7V/WsavLozhr2lFu4NXERS1V5yQ/tSYY1q1f12NUedAczL7cPpwVlMT9jqkx+9f3YdYZ1jF90MMVxjP4sAnbXhwl4HlowPwFtF1eB/of0y87PmdlP6xRdULFuGdfBg4p6Yhalbt5YHJ0QD5HouRMPk/eEfP6xbxYNfHuSAJ4yzg7O4YFA8JpPB32F1CAXVhaw88A2FnjLiiiHVkog5PAKkc1+XpKoa32RHstOTwH2pW4iLl1t925KmauzZu45Fhh3k22GyMoC7L3yamJAEf4fW5UjSrxmkESQ6G7W6moLX/kPhf/+LPjSEkMsuZ18PGw8VvIVTc/MH+1mkGGNaPY5vjwTzycEI/hiwilGun8gJ6yeTdrSSbRXhfFmYyg2xOzg/IsMn28zxFLOyagtmnZFrgiaSaIxsuLCnEkvGPFDdVA3+Z23iD2pmiS5+/328paVE3XMPoVdfhaKTkSSE78n1XIiGyfujbeVlH2HW+4tYUJhID0M+V/fVy628jeRRPWw8+gNbCrdhcULPchvhsanoTDJOcFfnVTUWHY0lwxvJ/b12EBUd4u+Quhx3RQXfHfiW1WF5uA0KF1lHceuUx4gMav3vlqKGJP2aQRpBorPQNI3yr78m94kn8BQWETRxIoETJ/CRcy2vln5OoiGSq4POIlgf0OqxbCoI4M1d0Yw37eAKviEntD9VFumK35pWFCfwQ1kMDyT/yDB7vk+2WeF1sLJ6C0VqOecHnMIYy4CGf2H3VNT0+FOdVA98BDXw15mFVaeT0oULqVy1CuuI4cT961+YEhN9EqMQx8j1XIiGyfujbbgcDt6d/z+e22FDAS6KK+S0PgmtOpRKZ3Ko7DDfZaygSnWQUKzQzZqASXr3id/weDUWHk0gRw3hb712Exkd7O+QuqSKwlxWZK3kx4hSvHqF84zDuOmcf5Ac0d3foXV6kvRrBmkEic7AsXs3uf+aRdUPP2AZNJCQiy+hNNzC44Xv84NzN2dYB3GObQT6E43P5iM/5gUyZ08UaYYD3Kx8QW7EABwmuSC3NlWDBfk9yHQG8c/UtXSzlvtku15N5SfHz+x0H6K/MZlLg8YS0NDEL54qzIc+Qucupbr/A3hDBtZ52rFnD8Vz56JWVBB1992E/uFq6fUnfEau50I0TN4frW/F8q95bHkOhzxhjA3M4sKBUQTaWm+itM6k1FnK94dWkunIIbgCejjsBMcmoxjlVmhxPLcHFmQnkqsGc1+vnURKjz+/Kc8/ynfZ3/NDWDHVZoVTPclcO2YGo/ucfeJxwUWzSdKvGaQRJDoyd14eBS++SMnHn2CIjib4kkuw9u/P8qrNPFX8EZoGlwWdQW9T6/eqcqsKn2eE8W1WCKfod3Otfil5EQNwGgNbfd+ihkvVMTe3Dx5Nx5PdvyfU6PTZtg+581hbvQOjYuCyoLENn1NeJ+bMT9FVHcbRczqemDPrPK06HDW9/lavxpqWRuw//w9zd/lVULScXM+FaJi8P1rPzzu38H+frGd1ZQJ9jLlc3scot/I2ksPrYGPWD+wo3oXJA92KDMSGJ6OXc1SchNursfBoIjlqCPf02ElMbIi/Q+rSqosL+fHIWn6w5ZAXDHFOK5cmTeHisX8m3Cqfh74kSb9mkEaQ6Ii8FZUUvfUWhW+9haLXE3TuuQSecQaFVPDv4o9ZWb2FgaYULgocQ+CJJmHwAU2D7UU2Pj4YTmG1gan6NZxp3E5O+EDcBvmFu62Ve4y8m9OXCKODx1PXYdV7fbbtSq+D7x07yPIWMNLch/MCTsGqMx9fUPNiOroEQ8kWXPHn40y5HnR1f6137t1L8bx5eIuKCL/5JsJvuQWdRc4X0XxyPReiYfL+8L38nCye/WARH+bGEaErZ1pSFWmpsShyK+9JuVUP23LTSc9Lx6t6SShRSDLHYI6MxiczkokuwePV+Dw7ngxvFH9JSpdZfdsBr8PB3oyfWO/ey54oNygKpym9uGzULYzpOQG9TmbebilJ+jWDNIJER6I6HBTPnUfB66+jVlUROG4c9kmT0KwWFlas4bXSRehQmBI4isGm1FZteLpVhY35ASzLCuFIpZlupmKuVz8jzOwhJ6w/qk5uyfCXXJeVeTl96BdQxAPdfkKv+O5jXdM09riPsNGxF7Ni5KLA0xhg6nb8eDuahqFoI8acb1GDelDd969olui6RdxuypYsofybbzDGxRHz94cIHDvWZ7GKrkWu50I0TN4fvlNdUc6bH33Mq3vtKMB5kfmM7xeH0SDtnpNxqx525G0jPXcTLjxElUA3wgiITkAxSDJANJ1X1ViaHclWTzK3Rm9kYC9J/LUHmqpRkp3BT/kbSQ8qIj9YIdJlZkrMBC49488kBif5O8QOS5J+zSCNINERqJWVFH/4EYVvvYW3pISAUaMImjwZQ1gYGx0/83zJAva7sxhh7s25ASOxNTTmmg9kVZr4PieI9blBVHv1JAVUM0n3IyOrf6A0MIECe6r8StsOHKy2Mz+vJ+NCsvhzwlafvyQVXgfrHTvJ9ObT15jEBYGjCdcf/xmqq8rCdOQzFNWJo/tNeKLPPO78cOfkUPLRRzj37CHgjDOIvv9+zKkpvg1YdHpyPReiYfL+aDmPy8XHn33KvzepFGkBjLdnc17/SAJtrXtHRWfg9DrZkbOFrQXbcOEhshSS1RACoxJkVl7RYqqqsTY3mDWu3lwUuIUJA63oJIncbnirqjiQsZkfXXvZFeXCaVIY4o1n2tDrOXvgRZj19dw1JBokSb9mkEaQaM88hYUUz51L0dx5qBUVBJx6KkGTJmGIiGC/6yivlX7B944dJBmiuCBgFEnGqNaJQ4WNBYGsOhrMwXILNoOXXsHVDLEeZVjRCkyeSvKDe1Fuiz75xkSb2V4RzqLCVC6O3MfVMXt8vn1N0zjsyecHx26qNRdjrYMYb03DrPtdA97rwJTzLYaSrXhCBuPoeSuaNfa4bTnS0ylZsABvcTEh0y4lcvp0DJGRPo9bdE5yPReiYfL+aD5NVVny9SKe/r6YA54ITrFmMbVfEJEhMknZyZQ6y9h65Ad+rjiIqqlElkGSFkpgRDw6syT7hG/tKjCyqHIggwyH+cOACmxBNn+HJH5D06Aq5wibszey2ZpHZiQEeAycF3Y6V4ydQc+wXv4OsUOQpF8zSCNItEfV27ZTPG8eZV9+CYpCwKhRBJ51FobwcDLcObxduoRvqzcTrgvi7IDhrXYrb5VHx+psO8uzgil3G0gIcNIvtIqUgAoSircRU7wTpzGI3NA+uA1yYW2PfiiLZnlxEtfG7OLCyAOtsg+35mWb8yA7XBmYFRNn24Yx0tLnuNmideX7MGV/g+KpwJVwAa7ES8AQUKeM5nZTsXIl5d98g+b1EvaHqwm78UYMoaGtErvoPOR6LkTD5P3RdJqqsnr1t8xensk2Vwz9TTlc3MtEcrQMSn8iqqZyqOggO7I3keUtwuCFmDId8YZIAiKi5TZe0aryKhQWFPZGReG2pF0kyzh/7ZLqdJJ1YCs/Vu1ia7SDKotCfzWWK4bewOT+F2GRceEbJEm/ZpBGkGgvvGVllH35JSUff4Jjxw704eEEnHYagaedhi4ggO3Og7xftozvHNsI0QUw3prGCEvv4xIrvlDu0rHsaAgrjwbjURV6BVczKLySMJOb0MrDxOdvxuStpigwieLAJLmdt51bVRzPurI4bo7bxuTww622nwqvg83Ovez3HCVUZ2eibRhDzN3R/fYcVV0YCjZgLFgPehOuhItwxZ1zXPJPraqifOlSKlatAkUh9LLLCLvuWoxxca0Wv+jY5HouRMPk/dF4mqqy9vsV/HvZATY64uhhyGdqqkafRLmb4USKq4vYnbmRvZWHqNZ7CaiGOIeFGFsMppAQaSuKNuNwa3ybG812bzITLDs5r7+GxSZJpPZI08CRm82WIz+w0ZbHoSgI9Bi4IGYCV542nW7B3fwdYrsjSb9mkEaQ8Ce1spKK776jdNGXVK5aheb1YhnQn4BRo7EMHIhHUVlRtYX5FSvZ4TpEpD6YM6yDGGbuiUHx/S+lJU4932aFsDq75r3QL7SKwWGVBBi8hFRmElO4jQBXCRWWcArtqdK7r4PQNFhWnMhP5THcGr+ViWGZrbq/Ym85m5z7OezJJVxn50zrEIZaetZNULsrMOavwVCyBXRm3LETccWei2at+6XKW1FBxYoVVK5ejepwEDRpEmFXX4V16FCZIVHUIddzIRom74+TO9az74WVh9joiCNFX8AFyR4GdIuR600DKpxl/Hw4nf3lBykyODB4IbxCR7wulOCQWLmFV/iNpmocKDHyVXlvFOCy8J0M6RWEXibcabfUagdH96ez3rGLbbFuqs0Kw3UpXHXqrYzvfjYGmSQSkKRfs0gjSLQ199GjVHy3hoqVK6j8fi2ay4UxORnb8OHYhg9HHxxMhjuXLyvX82XlBkrUCnoY4xhjGUBfU2LdXlM+cqTCxPKjwfyQF4RBpzEgtJJBYZXYtQrCyg8SUboPs6eSKlMIxUHJVJtDfB6DaF2aBkuLk9hUHs1Ncds5J/xQq++zwFvGFucBDntysSsBjLH251RLX6y6XwfsVdzlGAp/xFCSjuJ14AkZjDv6TDwRI0H/6+DoqsNB5dq1VH73HZ7cXEw9ehByySUETzkfQ0REq9dFtH9yPReiYfL+aJjH5WLJ0sW8uqGQHa4YUg0FnJ/kYaAk++pVWlHA/sytHKw6TIHRiaJCaJWOaAKJDIjBaAsAOWyinXC4NH4oDGG9qzuRSikXhu9nQPcgjDKBTLulqRqVRzJIz97IxuAisiIUwr1WLkm5kGkjbyQmIMbfIfqVJP2aQRpBorW5c3Op3rSJyg0bqFq/AVdGBuh0mFJTsQ4ahHXwYAyRkeR7S1letZmvK39it/swAYqFNHN3TrX0Jdrg+/HMnF6FzQUBrMmxs7/MSqDRy6CQckZYDxPuPEpw5VECnEWoio4KSySlAfE4TUE+j0O0HU2D5cWJ/Fgew+VRe5gWta9N7rYp9laww3mIA55sdCgMMffgVGtfEvSRv34xUF3oS3djKNmCvioTTWfCEzoUT8QpeMOGoRlrPp81VcW5ezeVa9dSvXUraBq2kSOwn3MOQb+Meym6JrmeC9EweX8cr6ykkI+//Ir/7oAsNZR+xlzOSYY+SdGS7PsN1eslO/tnMvL3kunJo9TsRadCsENHFIFE2qIxWwMl0SfatbJqjfVFEaR7krHhZELgXobEeoiMCkbRycnbXnnLyjm4byMbtP3sivPiNiiMNvXhilG3cnryOPS6rjdGqCT9mkEaQcKXPMXFOHftwrFzJ9XbtlG9ZSuenBwADDExmHv0wNynD5Y+fVCsVg578vi+ejurqrey3ZWBDoXepkSGmnvQ15SEUfFdN2ZF9eJyuthdbGFTYRCbSsNxaQZSjQWMNu1hhLoTm6cUBfAqBqrMoVRawqmyRKB2wQ/UzkrTYF1ZLKtLEjgzJJNb4rdh1LXNx36V6mSvO4s9riNUatXE6sMZbunFYHN37LpfbxVXXCXoy3ahL9uDvvooGgpqYHc8YUPwhgzGa+8NOiNqZSVVGzdSvXkzzr17QdOwDBxA4NixBIwajXXgABSj/JLbVcj1XIiGyfvjVzu2/sS85Rv5NCcSFwZGWHOY2N0qE3T8QkOjojCbQ9m7OVJxlGxjJS4jGLwQ5jQQrg8mMiAKo0nGSBMdT6VDZXtpEOmORIoJIlIpYYQ1k94h1cSEmQgMDkCn8/1dVaJlNI+XsoN72ViUzuawMnJDFcI9FqYkTObiU/9ISnCKv0NsM5L0awZpBImm0jQNb0EBrowMnAcP4tp/AOe+vTh+3os3Px8AxWzGlJSEMSkJc2oqptRU9MHBFHvL2ezcx0bHz6x37CLbW4QRPT1NCQwwdaOfKRnbb259PBGd6sbmLMbiLMHqKsXsLsfkrsTkqcToqcborUbxONnjjmW9uxffeQewUeuFFz1J5DBc9zMjdLsJMbhw6y24jTachkAcpiBchgAZcLmT21YRzpLCbvSwlfDXpM2EGx1ttm9V08jyFLDXncURTwEqKimGGAaYU+hvSiZU/5sepe4K9BX70VccQF95CMVbhaYY8dr74A0ZgDd4AF57T7yVThw7duDYtg3Hnj1oVVUoNhvWwYOwDR2GdfAgLAMHyizAnZhcz4VoWFd/fxTmZbPo2+XM3+1guyuGEKWC00OKGdsjjBB7oL/D8ysNjcqCHLJyfuZoxVGydeWUW2q+DgY6dYRqViJMYYTYwiQZIjoNVdXIq9BxoCqQ/a4IsrSaoWKsOEnUFxBvLCPM4CTY7MFm0LAYNUwGBaNeQa9X0Ot16HQKep3u178NevQGvbxPWpm7uJiMjHQ2eg+wM86Lw6TQR43i/D4Xc+7gy4i0Rfo7xFYlSb9m6OqNIHE8TdNQy8pw5+TgPnq05pF1FHdmJq7Dh3FnZqJWVdUUVhQMUZEYYmIwxsRiTEjAmJCAITISj6JywJ3NLtdhdroOscW5n0xPTVIwUh9MT2M8vYwJ9DDFYzpBjz6jp4rA6jyCqnMJrM4noDqPQEcBFnf5rzGj4NZbcOkt7CeR7739WO/uyUZ3CpWaBbPiJtVYSHdzIT0tRQQZPXh1Jrw6oyT3urAsZwCf5XdHReFP8dsYHZzT5jE4VTeHPXkcdOeQ7S1CRSVCF0xvUyLdjXF0M0YTqPtlbD9NQ3Hkoa/MQF95GF1VJorqQFMMeIN64Q3pjze4P56AHriPFuDYswfXgQO4DhxArawEwBAdhbl3Hyy9e2FK7Y45pRvG5GT0ISFyO1cHJ9dzIRrWFd8fhXnZLF21iq92l7Cmsmbm94HmPE6Lg4HJMej1XfOLuep2UZJ7iKP5GeRV55Krq6Tsl872FrdCsNdMmDGYcGsEZoPJv8EK0UacHpXCKj0FTjNFHgvFXhvlqoVSzUYVZjQa/3lhwk0ADgJ11dgVB3a9g2C9ixCjC7vJi90CQTYdgQFmLDazJAmbSVNVKo8cYkfuVrZY8jgYpaHqFAZocUzscQ4TBl9Coj3R32H6XKdJ+r388ss8/fTT5OTkMHjwYF588UVGjhzZYPn58+fzj3/8g4yMDHr27MmTTz7Jueee26h9dcVGUFekeb14y8pQy8rwlpTgKS7GW1SMt6gQT0Ehnvx8PHl5uPPy8OTmojl+0+tJr8cQFoY+PBxDRASGyMiaR3Q0hogIVIOeHG8RGe5cMtw5HHRn87P7CBnuXDx40aEQawgnyRBFN0M0qcZYgvUBvwtQw+yuwOYsrEnsOfIJqs4jqCoXs6cCABUdDpMdpykIh7HmX6cxiAIllI2OBNIrotlSHkGhx4oelXhzBUmWcrpZyog1V6JX/P7WFu1QtVfPkqJu7KkKY2hQLjfE7iLOXOmXWFyqh6PeQrLcBeR4iynTauII1QWRaIgk1hBOjCGUKF0o4fogdFCTBKw6jK4yE31VZk1PQHSogSl4g/vjDe6LJ6gPnjIPrkOHcB85gjsrC3dODt7Cwtp964KCMMbFYoyPxxgTW/P+jorCEBGOPiwMQ2go+tBQdFZrA9ELf5PruegKmtpGPqYrvD9cDgdbt/zI99v2siJTZYszGgXoZcxnWJiL4akRBNlsJ91OZ6J6PJTnZ5FfeJj8ilzy1VKKzB6cv4x8YXXrsKtmQg12wqxhWA1yy64Qv6epGm4VPL88VBU8qoIXUFUFrwZeTcGj6fCqCi5Nwa3qcah6HKqBKs1EpWqiQrNQogXi4tehZ4y4idUVE6cvJdpURaTFTZhNIzjQQGCgBZNFEu+NobrclB3ex7aSXey0FJIRqeHVKyS4gxgVksbpA89neMrpBHWC8ek7RdLvo48+4tprr+W1117jlFNO4bnnnmP+/Pns2bOHqKio48qvXbuWM844g1mzZnH++eczb948nnzySTZt2sSAAQNOur+u0AhqKdXpRK2oQK2owFtRgVZVhXrsUe1AdVSjOV1oLhea213z8LjB40XzemsGEdNUNFWtu2FFQVF0NT3NdLpfBlFVav+u+W9NzxtN00CjZluqWrNd1Yvm9tTsz+VCc7tQHQ7U6mq0agdqZQVqVXVtzPXR2Wzo7Hb0QUE1/wYHow8Nrfk3LAxDWBg6u50qnOR4isnxFnHUU8hRTyFZngIyPflkeQrw4AXArBiJ1ocSpwsmSWcnWQkmQbFiVb0YvA6MnipMnmpM7grM7gosrlKsrprbc/Wqu6auKDiMQThMwThMdqrNIVSbQnCagtAUPS5Vx56qULZVhLOlIpL91cFoKEQaq0i2lJFiLSPRXI5Jp9ZbZyHqs6cqhOXFSZR5TJwenMV5ERl0t5a2aUdQt6pQ7LFQ5jFR4TVS5vVSqlZRoZbh0EpxKvko+kKMumr0io5gnY1QnZ0QfQB2nY1AxUKEx0OUs4Tg6gJsjnyM7jIAvOZIVHsfvEE9UYO64w1IRfUoePLy8BQU4MnPx1tUVPMoLcVbUoJaUXFcjIrJhC4wEF1QEPrAwJr/BwbWfJZYreisFhSLFZ3FjGK2oFjM6MwWFLO5dpnOYkaxHFtmQbFYata1WGQMwhaQ67no7JraRv6tzvb+0FSVI4f3s2P3brZm5LIpT2VLVQTVmLHhoK+liIFhKoOSwrAHBJx8gx2c6vFQUZRLcUk2xRX5FLtKKFErKTF7cf1yWTF4IchjIEhnI8QYRLAlDJPOd2NHCyFOTlM1XKpGtUuh3K2nzG2g1GOm2Gul2GujQLPj4NehnuxUEqsvIdpQTrjRQajZg92sEWhWsJp1mM1GTBYjBoOhyROSaKqGqqp43B48bi8ejxe3R8XjUXF7Ndxe8Hg13KqCx0ttclPVQP0lY6RTfnkAep2GUQcGPRj1YDQomA0KRqMes0mP0WzAYDC2+sQpqsdDxZFD/Fy8mz1KHhkhbkoDFRQNurnsDAroyaCEEQzocwY9I/pg1HestnenSPqdcsopjBgxgpdeegkAVVVJTEzk9ttv5/777z+u/OWXX05lZSWLFi2qXXbqqaeSlpbGa6+9dtL9dbZGUH00txtvRQVqeTnesnLU8rJf/y0tw1tWhresFPXY/0tLa59TKyrQ3O4T70CnQzGZUAyGmi+sej2KXl/z77EEHtR8IvzWsU8LTat9aL/7+zhKTVJQ0elAr0PR/bIfgwH0enQmU00sRuOvX6p/+TKts9lQbDZ0AQHoAwMhwEalzk2xWkGJWkGRt5wibxkFahnFnhIqnAW43AV43KWYvdWEeFWCVZUQr0YkBsJUHSEqBGsaAaoXi+rF5HWjV93oNU+Dh0tV9DVj6OkteAxWXIYAnMYAnMZAnCY7TkMg2i8TZ7hVhWxXAIccdvZXB7OnKoQD1SF4NB02nZtkSxndfkn02Q2uxp0QQjTAoymkl0fyY3kMpR4zCeZyTrXnMCiwgO62Uiw6b4v34VZ15Lqs5LgCOOoMINsVQJYzgBxnAEUeC1ojpv8zKW5CjWWEmgoJNORjM+RiNuRgNOSg6ErqJCpDvF5S3B5S3G6S3V4S3R5MNZ805BhMHDRbOWqykW2ykmu0UWC0UGq0oig6FA0Ur4riVdF5NXQeLzqvht6roXer6D0qRreK0a1hdGuYXCpGl4rZoWJxeDFXe7BUe7FWe7G6wOYEqwusTjA0lJPX62s+s2w2FKsVXUBATUIxIAB9QEDN38cegYHoAn79TPt1WUDteorZ3GVuW+4K13PRtTW1jfxbHfH9UV1RTkF+NkezszmaV0BmYRmHip3sKzew3xlCBTU9r0OUSlJNpXS3e+kdaSUpOgJdJ5qNU/W4qa4spaqylKqqUiocZVQ6y6h0V1GhOajQuak0qXh/mXNN0cDi0WFTjQTqzATpA7BbQrDqzTKkixDtnKZqOD1Q7lIocxsodxso8Voo9VooVa2UagFUcvxdJzpUrDixKi5MeDAoXnRoKGg1E+Kh4NV0uNHj1gw4MeLAhNrI25aNeDDgRc+v2wV++Z+CFx0e9LjRn/BWaAMe7FRh11UTrKvGrncSpHcRaPAQaPRiM2rYjGAx6TCbdJhNekwWU7OSmsd4SsvIyzvAgcrDZOiKORrgpNAOmqKgVyHGaaWbLoIkWwKJod2Ij+xOfGwvoiK6EWJpf8P/+OJ67tefe1wuFxs3buSBBx6oXabT6ZgwYQLr1q2rd51169Zx991311k2adIkFi5cWG95p9OJ0+ms/bu0tBSoOXi+pmkaFatW4di+45cE1q9JLk3Vfu21pqk1CTDVW7Nc9aJ5vGhezy+95Tw1fx/rRed2ozmdaC4XqssFDkdNb7yqKvA0nGxqDMVm+/XLZmgouri42l4otb1SzGZ0ZnNtcg29b2d23aw7wjbd0ZqOfWi1/6poaMovyzQvA6uOEOyp+qWMBqg1pbSaf0EFzYsOFUXzoqvwYqrQMGsaVlXDqmnYVI1QVSWJ5uW63ehxK3ocip4K9HgVIx7FglfR4VF0eBU9HkWPWzFQiYVvnUMpUoN++chUcKPHoxlwaCaqNRNVmoVy1UaJWn/XY6viIEG/nzh9AeGUoTg0cMCRkmYebCF+x0YBp7ObTC2an8sT+F9ZPP8jvvZ5I25C9RUEKZVYdTUNC6Pi+U2jQodb0+PUDDg0M5WalXLVSrXW8C1DVsVBsK6A7rpKApVqbIoDs+LGgLdmNmlqGikOzUyFaqVcs1LmCmBXeRwaCfVuU4eXIF0FNl0VFsWBWXFgUJwYFQ82zUmQ5iJI8xCkurhMv4qJuqP1bscNlCt6ynUKVTodlToFp6JQrSi4FXApCh6jgtcEasAvt3oooKKwIMDKAZMJGpHIBDB5FcxeBYvHicnjwugpweTRMLpU9B4No1vFUK6hLwW9F/Qq6LSah1LPv8fu6Fc0GLlHJangNzszGGo+x2sfJhTjLz+a/OZHHMVg+OWhB73hlx90jv3goqvpra3TgU759f+K8mtPbeU3PbiPLdcbCL74IoyRvh1o+dh1vB2MUiKEzzW1jdym7V1VZem3X7H1UD6qBl61pl3mVcGrabW3xHlUcKng8io4VQWHqsOh6qlSDVSqRio0C54TfhUJBAIJU8qJN+YQZ3GTEKTDbv21l0ZxeSXF5W07RIWmqeRV5eKoKEN1u2q/Ch/7JNJ+s0TTtF/atKCh1rRWFe1YC7bm/798bDaGolnQqzZMlTpM6DFgwKgYald3AYVAIZWAf4buEEI0nwmI+uXBL58NXk2PCyNOrxEnRpyaEadmwqPpcGOoTe4dS8gpv/xPh4YFL4GKih4PBkXD+Es73qB4MeCt+VdR0Ss131b1aOiUpv3or2q62vSgWzPgVnW4fxOnQzNSrZkp0ixUEoqb5vW0M+LCiguL4sKCG5PixoAHk+KtrY9eqckF6JVYdERjUiClRCXliAev5sajePHovBQqUKDAZg6hWn5gV8TP1Je7tLkULB4dVq8es6bHjB4TBozoMf7yrx4dBkVPQFI3/jDmdhLs9X9XaS5ftHf9mvQrKCjA6/USHR1dZ3l0dDS7d++ud52cnJx6y+fk1D8g/axZs3j00UePW56Y2PkGeRTt0Y8t3sLPPohCCPGr468IotX97d5W23R5eTnBwcGttn0h/KGpbeTO3N7N9HcAQgghRCPM4uR3njZXS9q7nX5ghwceeKBOz0BVVSkqKiI8PLzVu26WlZWRmJhIZmZmh7m1oqmkjp1DV6gjdI16Sh07B6njyWmaRnl5OXFxca0QnRAdiz/bu77SFT732gM5zq1PjnHrk2Pc+uQYt77GHGNftHf9mvSLiIhAr9eTm5tbZ3lubi4xMTH1rhMTE9Ok8mazGbPZXGdZSEhI84NuBrvd3unfKFLHzqEr1BG6Rj2ljp2D1PHEpIef6Kya2kZuD+1dX+kKn3vtgRzn1ifHuPXJMW59coxb38mOcUvbu40bybGVmEwmhg0bxrJly2qXqarKsmXLGDVqVL3rjBo1qk55gKVLlzZYXgghhBBCiI6kOW1kIYQQQojf8/vtvXfffTfXXXcdw4cPZ+TIkTz33HNUVlZyww03AHDttdcSHx/PrFmzALjzzjsZO3YszzzzDOeddx4ffvghP/30E6+//ro/qyGEEEIIIYTPnKyNLIQQQghxMn5P+l1++eXk5+fz8MMPk5OTQ1paGkuWLKkduPjw4cPodL92SBw9ejTz5s3j73//Ow8++CA9e/Zk4cKFDBgwwF9VaJDZbGbmzJnH3W7RmUgdO4euUEfoGvWUOnYOUkchxMnayJ2NfCa0DTnOrU+OceuTY9z65Bi3vrY6xorWkrl/hRBCCCGEEEIIIYQQ7Y5fx/QTQgghhBBCCCGEEEL4niT9hBBCCCGEEEIIIYToZCTpJ4QQQgghhBBCCCFEJyNJPyGEEEIIIYQQQgghOhlJ+vlIRkYGN954IykpKVitVrp3787MmTNxuVwnXM/hcDB9+nTCw8MJDAzkkksuITc3t42ibp5//vOfjB49GpvNRkhISKPWuf7661EUpc5j8uTJrRtoCzSnjpqm8fDDDxMbG4vVamXChAns3bu3dQNtgaKiIq6++mrsdjshISHceOONVFRUnHCdcePGHfc63nrrrW0UceO8/PLLdOvWDYvFwimnnMIPP/xwwvLz58+nT58+WCwWBg4cyOLFi9so0uZrSh3nzJlz3GtmsVjaMNqmW716NVOmTCEuLg5FUVi4cOFJ11m5ciVDhw7FbDbTo0cP5syZ0+pxtkRT67hy5crjXkdFUcjJyWmbgJth1qxZjBgxgqCgIKKiopg6dSp79uw56Xod8T0phKhfU65Xb7zxBqeffjqhoaGEhoYyYcKE48p3tPZkW2jKMf70008ZPnw4ISEhBAQEkJaWxnvvvVenTEdrz7YFXx9jOY/r19Q2/DEffvghiqIwderUOsvlXD6er4+xnMvH8/X3NF+cx5L085Hdu3ejqir/+c9/2LFjB88++yyvvfYaDz744AnX+8tf/sIXX3zB/PnzWbVqFUePHuXiiy9uo6ibx+VyMW3aNG677bYmrTd58mSys7NrHx988EErRdhyzanjU089xQsvvMBrr73Ghg0bCAgIYNKkSTgcjlaMtPmuvvpqduzYwdKlS1m0aBGrV6/mlltuOel6N998c53X8amnnmqDaBvno48+4u6772bmzJls2rSJwYMHM2nSJPLy8uotv3btWq688kpuvPFGNm/ezNSpU5k6dSrbt29v48gbr6l1BLDb7XVes0OHDrVhxE1XWVnJ4MGDefnllxtV/uDBg5x33nmMHz+e9PR07rrrLm666Sa+/vrrVo60+Zpax2P27NlT57WMiopqpQhbbtWqVUyfPp3169ezdOlS3G43Z599NpWVlQ2u0xHfk0KI+jX1erVy5UquvPJKVqxYwbp160hMTOTss88mKyurTrmO1J5sbU09xmFhYTz00EOsW7eOrVu3csMNN3DDDTfUuV52tPZsa2uNYwxyHv9ec9q3UNPx5p577uH0008/7jk5l+tqjWMMci7/Vmt8T/PJeayJVvPUU09pKSkpDT5fUlKiGY1Gbf78+bXLdu3apQHaunXr2iLEFnn77be14ODgRpW97rrrtAsvvLBV42kNja2jqqpaTEyM9vTTT9cuKykp0cxms/bBBx+0YoTNs3PnTg3Qfvzxx9plX331laYoipaVldXgemPHjtXuvPPONoiweUaOHKlNnz699m+v16vFxcVps2bNqrf8ZZddpp133nl1lp1yyinan/70p1aNsyWaWsemvE/bI0BbsGDBCcv87W9/0/r3719n2eWXX65NmjSpFSPzncbUccWKFRqgFRcXt0lMrSEvL08DtFWrVjVYpiO+J4UQ9Wvq9er3PB6PFhQUpL3zzju1yzpqe7K1tPQYa5qmDRkyRPv73/+uaVrHa8+2BV8fY02T87g+zTnOHo9HGz16tPbmm28ed0zlXD6er4+xpsm5/Hu+/p7mq/NYevq1otLSUsLCwhp8fuPGjbjdbiZMmFC7rE+fPiQlJbFu3bq2CLFNrVy5kqioKHr37s1tt91GYWGhv0PymYMHD5KTk1PntQwODuaUU05pl6/lunXrCAkJYfjw4bXLJkyYgE6nY8OGDSdcd+7cuURERDBgwAAeeOABqqqqWjvcRnG5XGzcuLHOa6DT6ZgwYUKDr8G6devqlAeYNGlSu3zNoHl1BKioqCA5OZnExEQuvPBCduzY0RbhtpmO9jq2RFpaGrGxsUycOJHvv//e3+E0SWlpKcAJr4td6bUUojNr7vXqt6qqqnC73cd9ZnTm9mRTtPQYa5rGsmXL2LNnD2eccQbQ8dqzra01jvExch7/qrnH+bHHHiMqKoobb7zxuOfkXK6rNY7xMXIu12iN72m+Oo8NTayLaKR9+/bx4osvMnv27AbL5OTkYDKZjhszLjo6ul2P09QckydP5uKLLyYlJYX9+/fz4IMPcs4557Bu3Tr0er2/w2uxY69XdHR0neXt9bXMyck57rZAg8FAWFjYCeO96qqrSE5OJi4ujq1bt3LfffexZ88ePv3009YO+aQKCgrwer31vga7d++ud52cnJwO85pB8+rYu3dv3nrrLQYNGkRpaSmzZ89m9OjR7Nixg4SEhLYIu9U19DqWlZVRXV2N1Wr1U2S+Exsby2uvvcbw4cNxOp28+eabjBs3jg0bNjB06FB/h3dSqqpy1113MWbMGAYMGNBguY72nhRC1K8516vfu++++4iLi6vzZaeztyebornHuLS0lPj4eJxOJ3q9nldeeYWJEycCHa8929pa4xiDnMe/15zjvGbNGv773/+Snp5e7/NyLtfVGscY5Fz+rdb4nuar81iSfidx//338+STT56wzK5du+jTp0/t31lZWUyePJlp06Zx8803t3aIPtGcejbFFVdcUfv/gQMHMmjQILp3787KlSs566yzmrXNpmrtOrYHja1jc/12zL+BAwcSGxvLWWedxf79++nevXuztytaz6hRoxg1alTt36NHj6Zv37785z//4fHHH/djZKIpevfuTe/evWv/Hj16NPv37+fZZ589boDw9mj69Ols376dNWvW+DsUIUQH8MQTT/Dhhx+ycuXKOoOat4f2ZEcXFBREeno6FRUVLFu2jLvvvpvU1FTGjRvn79A6jZMdYzmPW6a8vJxrrrmGN954g4iICH+H0yk19hjLudwybfU9TZJ+J/HXv/6V66+//oRlUlNTa/9/9OhRxo8fz+jRo3n99ddPuF5MTAwul4uSkpI6vf1yc3OJiYlpSdhN1tR6tlRqaioRERHs27evzT4QWrOOx16v3NxcYmNja5fn5uaSlpbWrG02R2PrGBMTc9yAoh6Ph6Kioiade6eccgpQ07PV30m/iIgI9Hr9cbNfn+j9FBMT06Ty/tacOv6e0WhkyJAh7Nu3rzVC9IuGXke73d4pevk1ZOTIkR0iiTZjxozayYJO1ru0o70nhRD1a8n1avbs2TzxxBN8++23DBo06IRl/dGebC+ae4x1Oh09evQAaoaM2LVrF7NmzWLcuHHtpj3bXrTGMa5PVz6PoenHef/+/WRkZDBlypTaZaqqAjV3Lu3Zs0fO5d9pjWNc33e/rnwut8b3NF+dxzKm30lERkbSp0+fEz5MJhNQ08Nv3LhxDBs2jLfffhud7sSHd9iwYRiNRpYtW1a7bM+ePRw+fLhOxrctNKWevnDkyBEKCwvrnLytrTXrmJKSQkxMTJ3XsqysjA0bNrTpa9nYOo4aNYqSkhI2btxYu+7y5ctRVbU2kdcYx7p7t+Xr2BCTycSwYcPqvAaqqrJs2bIGX4NRo0bVKQ+wdOnSNn//NVZz6vh7Xq+Xbdu2tYvXzFc62uvoK+np6e36ddQ0jRkzZrBgwQKWL19OSkrKSdfpqq+lEJ1Nc69XTz31FI8//jhLliypM+5wQ/zRnmwvfNEmOLaO0+kE2k97tr1ojWNcn658HkPTj3OfPn3Ytm0b6enptY8LLriA8ePHk56eTmJiopzLv9Max7g+Xflcbo3vaT47jxs95Yc4oSNHjmg9evTQzjrrLO3IkSNadnZ27eO3ZXr37q1t2LChdtmtt96qJSUlacuXL9d++uknbdSoUdqoUaP8UYVGO3TokLZ582bt0Ucf1QIDA7XNmzdrmzdv1srLy2vL9O7dW/v00081TdO08vJy7Z577tHWrVunHTx4UPv222+1oUOHaj179tQcDoe/qnFCTa2jpmnaE088oYWEhGifffaZtnXrVu3CCy/UUlJStOrqan9U4aQmT56sDRkyRNuwYYO2Zs0arWfPntqVV15Z+/zvz9d9+/Zpjz32mPbTTz9pBw8e1D777DMtNTVVO+OMM/xVheN8+OGHmtls1ubMmaPt3LlTu+WWW7SQkBAtJydH0zRNu+aaa7T777+/tvz333+vGQwGbfbs2dquXbu0mTNnakajUdu2bZu/qnBSTa3jo48+qn399dfa/v37tY0bN2pXXHGFZrFYtB07dvirCidVXl5e+54DtH//+9/a5s2btUOHDmmapmn333+/ds0119SWP3DggGaz2bR7771X27Vrl/byyy9rer1eW7Jkib+qcFJNreOzzz6rLVy4UNu7d6+2bds27c4779R0Op327bff+qsKJ3XbbbdpwcHB2sqVK+tcE6uqqmrLdIb3pBCifk29Xj3xxBOayWTSPv744zqfGcfaXh2xPdnamnqM//Wvf2nffPONtn//fm3nzp3a7NmzNYPBoL3xxhu1ZTpae7a1+foYy3lcv6Ye59+rbxZZOZfr8vUxlnP5eK3xPc0X57Ek/Xzk7bff1oB6H8ccPHhQA7QVK1bULquurtb+/Oc/a6GhoZrNZtMuuuiiOonC9ui6666rt56/rRegvf3225qmaVpVVZV29tlna5GRkZrRaNSSk5O1m2++ufbkb4+aWkdNq5lS+x//+IcWHR2tmc1m7ayzztL27NnT9sE3UmFhoXbllVdqgYGBmt1u12644YY6Sc3fn6+HDx/WzjjjDC0sLEwzm81ajx49tHvvvVcrLS31Uw3q9+KLL2pJSUmayWTSRo4cqa1fv772ubFjx2rXXXddnfL/+9//tF69emkmk0nr37+/9uWXX7ZxxE3XlDreddddtWWjo6O1c889V9u0aZMfom68FStW1Pv+O1av6667Ths7duxx66SlpWkmk0lLTU2t895sj5paxyeffFLr3r27ZrFYtLCwMG3cuHHa8uXL/RN8IzV0Tfzta9NZ3pNCiPo15XqVnJxc72fGzJkzNU3rmO3JttCUY/zQQw9pPXr00CwWixYaGqqNGjVK+/DDD+tsr6O1Z9uCL4+xnMcNa2ob/rfqS/rJuXw8Xx5jOZfr5+vvab44jxVN07TG9wsUQgghhBBCCCGEEEK0dzKmnxBCCCGEEEIIIYQQnYwk/YQQQgghhBBCCCGE6GQk6SeEEEIIIYQQQgghRCcjST8hhBBCCCGEEEIIIToZSfoJIYQQQgghhBBCCNHJSNJPCCGEEEIIIYQQQohORpJ+QgghhBBCCCGEEEJ0MpL0E0IIIYQQQgghhBCik5GknxBCCCGEEEIIIYQQnYwk/YQQQgghhBBCCCGE6GQk6SeEEEIIIYQQQgghRCcjST8hhBBCCCGEEEIIIToZSfoJIYQQQgghhBBCCNHJSNJPCCGEEEIIIYQQQohORpJ+QgghhBBCCCGEEEJ0MpL0E0IIIYQQQgghhBCik5GknxBCCCGEEEIIIYQQnYwk/YQQQgghhBBCCCGE6GQk6SeEEEIIIYQQQgghRCcjST8hhBBCCCGEEEIIIToZSfoJIYQQQgghhBBCCNHJSNJPCCGEEEIIIYQQQohORpJ+QgghhBBCCCGEEEJ0MpL0E0KIk1AUhUceecTfYQghhBBCCNEqpL0rROckST8hRB1z5sxBUZTah8FgID4+nuuvv56srKzjyo8bNw5FUejZs2e921u6dGnttj7++OM6z23bto1LL72U5ORkLBYL8fHxTJw4kRdffLFOuW7dutWJ6bePyZMn+67yflJSUsItt9xCZGQkAQEBjB8/nk2bNvk7LCGEEEKITknau20rOzub+++/n/HjxxMUFISiKKxcudLfYQnRJRj8HYAQon167LHHSElJweFwsH79eubMmcOaNWvYvn07FoulTlmLxcK+ffv44YcfGDlyZJ3n5s6di8ViweFw1Fm+du1axo8fT1JSEjfffDMxMTFkZmayfv16nn/+eW6//fY65dPS0vjrX/96XJxxcXE+qrF/qKrKeeedx5YtW7j33nuJiIjglVdeYdy4cWzcuLHBxqUQQgghhGgZae+2jT179vDkk0/Ss2dPBg4cyLp16/wdkhBdhiT9hBD1Oueccxg+fDgAN910ExERETz55JN8/vnnXHbZZXXKdu/eHY/HwwcffFCnEeRwOFiwYAHnnXcen3zySZ11/vnPfxIcHMyPP/5ISEhInefy8vKOiyc+Pp4//OEPPqpd+/Hxxx+zdu1a5s+fz6WXXgrAZZddRq9evZg5cybz5s3zc4RCCCGEEJ2TtHfbxrBhwygsLCQsLIyPP/6YadOm+TskIboMub1XCNEop59+OgD79++v9/krr7ySjz76CFVVa5d98cUXVFVVHddoOrad/v37H9cAAoiKivJN0Px6+8aaNWu44447iIyMJCQkhD/96U+4XC5KSkq49tprCQ0NJTQ0lL/97W9omtbi/RYWFnLNNddgt9sJCQnhuuuuY8uWLSiKwpw5c2rLffzxx0RHR3PxxRfXLouMjOSyyy7js88+w+l0tjgWIYQQQghxctLebZrGtneDgoIICwtr8f6EEE0nST8hRKNkZGQAEBoaWu/zV111FdnZ2XXG55g3bx5nnXVWvY2a5ORkNm7cyPbt2xu1f7fbTUFBwXGP6urqRq1/++23s3fvXh599FEuuOACXn/9df7xj38wZcoUvF4v//rXvzjttNN4+umnee+99xq1zYaoqsqUKVP44IMPuO666/jnP/9JdnY211133XFlN2/ezNChQ9Hp6n4cjxw5kqqqKn7++ecWxSKEEEIIIRpH2ruN15T2rhDCfyTpJ4SoV2lpKQUFBRw5coRPPvmERx99FLPZzPnnn19v+Z49ezJ8+PDa21FLSkpYvHgxV111Vb3l77nnHqqqqkhLS2P06NHcd999fPPNN7jd7nrLf/PNN0RGRh73eP755xtVn+joaBYvXsyf//xn3n33XUaNGsXTTz/NgAEDmDt3LrfddhsLFy4kISGBt956q1HbbMjChQtZt24dzzzzDC+++CLTp09nyZIlhIeHH1c2Ozub2NjY45YfW3b06NEWxSKEEEIIIeon7d3ma0p7VwjhP5L0E0LUa8KECURGRpKYmMill15KQEAAn3/+OQkJCQ2uc9VVV/Hpp5/icrn4+OOP0ev1XHTRRfWWnThxIuvWreOCCy5gy5YtPPXUU0yaNIn4+Hg+//zz48qfcsopLF269LjHlVde2aj63HjjjSiKUmd7mqZx44031i7T6/UMHz6cAwcONGqbDVmyZAlGo5Gbb765dplOp2P69OnHla2ursZsNh+3/Njg0Y39ZVcIIYQQQjSNtHebryntXSGE/8hEHkKIer388sv06tWL0tJS3nrrLVavXl1vcuq3rrjiCu655x6++uor5s6dy/nnn09QUFCD5UeMGFHbaNqyZQsLFizg2Wef5dJLLyU9PZ1+/frVlo2IiGDChAnNrk9SUlKdv4ODgwFITEw8bnlxcXGz9wNw6NAhYmNjsdlsdZb36NHjuLJWq7XecfuOzf5mtVpbFIsQQgghhKiftHebryntXSGE/0hPPyFEvUaOHMmECRO45JJL+PzzzxkwYABXXXUVFRUVDa4TGxvLuHHjeOaZZ1i9enWDtzr8nslkYsSIEfzrX//i1Vdfxe12M3/+fF9VBaj5VbOxy30xsHFjxcbGkp2dfdzyY8vi4uLaLBYhhBBCiK5E2rtCiM5Okn5CiJPS6/XMmjWLo0eP8tJLL52w7FVXXcV3332H3W7n3HPPbfK+hg8fDlBvIqyjSE5OJjs7m6qqqjrL9+3bd1zZtLQ0Nm3aVGcWOIANGzZgs9no1atXq8YqhBBCCCGkvdtUTWnvCiH8R5J+QohGGTduHCNHjuS5556rvfW0PpdeeikzZ87klVdewWQyNVhuxYoV9f7CuHjxYgB69+7d8qD9ZNKkSbjdbt54443aZaqq8vLLLx9X9tJLLyU3N5dPP/20dllBQQHz589nypQpJ73FRAghhBBC+Ia0dxuvKe1dIYT/yJh+QohGu/fee5k2bRpz5szh1ltvrbdMcHAwjzzyyEm3dfvtt1NVVcVFF11Enz59cLlcrF27lo8++ohu3bpxww031CmflZXF+++/f9x2AgMDmTp1anOq02qmTp3KyJEj+etf/8q+ffvo06cPn3/+OUVFRQB1Bli+9NJLOfXUU7nhhhvYuXMnERERvPLKK3i9Xh599FF/VUEIIYQQokuS9m7jNKW9C/B///d/AOzYsQOA9957jzVr1gDw97//vQ0jF6JrkaSfEKLRLr74Yrp3787s2bO5+eabGxw3pDFmz57N/PnzWbx4Ma+//joul4ukpCT+/Oc/8/e//52QkJA65dPT07nmmmuO205ycnK7awTp9Xq+/PJL7rzzTt555x10Oh0XXXQRM2fOZMyYMbUz8x4ru3jxYu69915eeOEFqqurGTFiBHPmzOnQv/4KIYQQQnRE0t5tnKa0dwH+8Y9/1Pn7rbfeqv2/JP2EaD2KJiN4CiFEm1i4cCEXXXQRa9asYcyYMf4ORwghhBBCCJ+S9q4Q7Ysk/YQQohVUV1djtVpr//Z6vZx99tn89NNP5OTk1HlOCCGEEEKIjkbau0K0f3J7rxBCNJLX6yU/P/+EZQIDAwkMDOT222+nurqaUaNG4XQ6+fTTT1m7di3/+te/pAEkhBBCCCHaJWnvCtG5SE8/IYRopIyMDFJSUk5YZubMmTzyyCPMmzePZ555hn379uFwOOjRowe33XYbM2bMaKNohRBCCCGEaBpp7wrRuUjSTwghGsnhcNTOMtaQ1NRUUlNT2ygiIYQQQgghfEfau0J0LpL0E0IIIYQQQgghhBCik9H5OwAhhBBCCCGEEEIIIYRvdbmJPFRV5ejRowQFBaEoir/DEUIIIUQzaJpGeXk5cXFx6HTyG6YQvyXtXSGEEKLj80V7t8sl/Y4ePUpiYqK/wxBCCCGED2RmZpKQkODvMIRoV6S9K4QQQnQeLWnvdrmkX1BQEFBz0Ox2u5+jEUIIIURzlJWVkZiYWHtdF0L8Stq7QgghRMfni/Zul0v6HbvFwW63SyNICCGE6ODk1kUhjiftXSGEEKLzaEl7VwbBEUIIIYQQQgghhBCik5GknxBCCCGEEEIIIYQQnYwk/YQQQgghhBBCCCGE6GS63Jh+QgghhBBCCCGEEI2laRoejwev1+vvUEQnYzQa0ev1rbZ9SfoJ0YGoqson2zfy+ba9ZBU7MegVEsMsXDakP5N6DfR3eEIIIYQQQtSRU1bCy2u/44eMQqpdGkEWHaNSI/nz6DMIswX6OzwhTsrlcpGdnU1VVZW/QxGdkKIoJCQkEBjYOp+HkvQTooN4de1Knl+aiaM6DEVnwWhSQVP4+bCFZZsPExryE49ekMYF/Yb4O1QhhBBCCNHFebxeZnz6CV+n69C8VgwmMwaDhyy3kR0HNN5a8RXjB3l58eKLsBnN/g5XiHqpqsrBgwfR6/XExcVhMplaNJOqEL+laRr5+fkcOXKEnj17tkqPP0n6CdHOlTmquPTtj/j5UBQWK5w2sIS0hEj0ih0Al8fDD4fy2HrQzB3vZrJg8G7+e/nl6HQyZKcQQgghhGh7R8uKufA/C8gvjCIiPI+z+hmICYqsfT6rpIxVe8pZtjmSYXs+4rU/jGRsah8/RixE/VwuF6qqkpiYiM1m83c4ohOKjIwkIyMDt9vdKkk/yQoI0Y4dKS1izL//x8+Hw+ifms8t46IZlhiNXvn1rWsyGDitexw3jQ8jPrqAFVvsnP3qOzjcLj9GLoQQQgiAWbNmMWLECIKCgoiKimLq1Kns2bPnpOvNnz+fPn36YLFYGDhwIIsXL26DaIVoubyKUs5+8XMKikMY1b+Eq09JIiao7m1r8SF2rjolicnDqnC7jVz/5g5eW7fKTxELcXLSoUK0ltbuOSpnrhDtVE5ZCWe/sIiKCjsThziY0CcBRWn4LWvSG7h0WBKDehSwLzOC81+fi6qqbRixEEIIIX5v1apVTJ8+nfXr17N06VLcbjdnn302lZWVDa6zdu1arrzySm688UY2b97M1KlTmTp1Ktu3b2/DyIVoOofbxaSXPqWiws7ZaW5GJsecsHzv6HCuPd2O1VbBE5+V8sTyr9soUiGE6BoUTdM0fwfRlsrKyggODqa0tBS73e7vcISoV6XTyWnPzqOkLJhzh3npERnapPVX/JzF1n0RjBlQwtw/XN1KUQohhP/I9Vx0VPn5+URFRbFq1SrOOOOMestcfvnlVFZWsmjRotplp556Kmlpabz22msn3Ye8P4S/XPTWu2z+OZRxg8oZnBDV6PXcXi9z12dTWhrOLRMNPHjWOa0YpRCN53A4OHjwICkpKVgsljrPZZVUU1zZdndXhQaYiA+xttn+RNs40Tnmi+u5jOknRDt06ZwPKC4JZ+ygCnpENr7BdMz4XvGUVWXy/fYoXliznDtOO7MVohRCCCFEU5WWlgIQFhbWYJl169Zx991311k2adIkFi5cWG95p9OJ0+ms/busrKzlgQrRRM+u/pbNP4fTMymPwQmJTVrXqNdzzeg43lt7lNeXhhFkXs7t0n4V7VhWSTVnPbMSh7vt7qyyGHUs++s4nyX+FEVhwYIFTJ061Sfb62pWrlzJ+PHjKS4uJiQkxN/hNEiSfkK0MzO/XsSug5H0S8knLSGh2duZMjiet8qyefarAMZ3z2RgbNMaX0IIIYTwLVVVueuuuxgzZgwDBgxosFxOTg7R0dF1lkVHR5OTk1Nv+VmzZvHoo4/6NFYhmuJwcQEvflNEQGA15/SPb9Y29IqOq0+N5d21OTzzpUqM/UemDRrh40iF8I3iShcOt8pNp6UQG2w5+QotlF3q4M01BymudDU66Xf99ddTUlLS4A9G2dnZhIY27Y6yjqS1k5qjR48mOzub4OBgAObMmcNdd91FSUlJq+yvuSTpJ0Q7suHwPt5d5SI0pJQJfZrXYDpGp+iYNiKCd1ZX88f3v2XDX6+TAWiFEEIIP5o+fTrbt29nzZo1Pt3uAw88UKdnYFlZGYmJ8mOfaDvXz/0CTQ3mgiEhJxyD+mSMej1XnxrFnO8Kue+jDOLtIYzu1tOHkQrhW7HBFpLDA/wdRrPExJx4zM22oGkaXq8Xg+HEqamMjAxSUlJoT6PTmUymdnEMT0YyAEK0Ey6Ph5vfX4Oic3PxsKgWNZiOCbaaGdnLQX5hFDO/+dIHUQohhBCiOWbMmMGiRYtYsWIFCSfpyR8TE0Nubm6dZbm5uQ1+uTCbzdjt9joPIdrKh+kbOHAkir7JZUQFtTz5YTEauXJUKIrOy/Vv/0BGUb4PohRC/J6iKLW9ADMyMlAUhU8//ZTx48djs9kYPHgw69atq7POmjVrOP3007FarSQmJnLHHXfUmZjqvffeY/jw4QQFBRETE8NVV11FXl5e7fMrV65EURS++uorhg0bhtls9vkPYY2hqiqPPfYYCQkJmM1m0tLSWLJkSZ0ya9euJS0tDYvFwvDhw1m4cCGKopCenl6nLiUlJaxcuZIbbriB0tJSFEVBURQeeeSRNq9XfSTpJ0Q78ZfPF1BWFsnYfiqBZrPPtntqSgzBwfnMXVPNkdIin21XCCGEECenaRozZsxgwYIFLF++nJSUlJOuM2rUKJYtW1Zn2dKlSxk1alRrhSlEs7g8Hh77YjdGcwln9onz2XaDrWamDjfhdlu48D9fUuF0+GzbQoiGPfTQQ9xzzz2kp6fTq1cvrrzySjweDwD79+9n8uTJXHLJJWzdupWPPvqINWvWMGPGjNr13W43jz/+OFu2bGHhwoVkZGRw/fXXH7ef+++/nyeeeIJdu3YxaNCgtqpereeff55nnnmG2bNns3XrViZNmsQFF1zA3r17gZoe81OmTGHgwIFs2rSJxx9/nPvuu6/B7Y0ePZrnnnsOu91OdnY22dnZ3HPPPW1VnROSpJ8Q7cDmrEN8+aOBiPBcBiVE+nz75w4KRfUaufV/X/h820IIIYRo2PTp03n//feZN28eQUFB5OTkkJOTQ3V1dW2Za6+9lgceeKD27zvvvJMlS5bwzDPPsHv3bh555BF++umnOl+shGgPHlv6FVWVEZzeV4feB3ep/FZCqJ0zB7soLQtlyhsfoKptN2GCEF3VPffcw3nnnUevXr149NFHOXToEPv27QNqxo+9+uqrueuuu+jZsyejR4/mhRde4N1338XhqEnM//GPf+Scc84hNTWVU089lRdeeIGvvvqKioqKOvt57LHHmDhxIt27dz/hxFatZfbs2dx3331cccUV9O7dmyeffJK0tDSee+45AObNm4eiKLzxxhv069ePc845h3vvvbfB7ZlMJoKDg1EUhZiYGGJiYggMDGyj2pyYJP2EaAdu+3AZOp2HCwZHn7xwM0QFBZASX8L2/aF8n/Fzq+xDCCGEEMd79dVXKS0tZdy4ccTGxtY+Pvroo9oyhw8fJjs7u/bv0aNHM2/ePF5//XUGDx7Mxx9/zMKFC084+YcQba3C6eDDdRUEBeUzMM73P1oDDIiNJK17KQePRHH9Bx+2yj6EEL/6ba+72NhYgNrbc7ds2cKcOXMIDAysfUyaNAlVVTl48CAAGzduZMqUKSQlJREUFMTYsWOBmuvcbw0fPvyksfTv3792P/379weos+9zzjmnWXUsKyvj6NGjjBkzps7yMWPGsGvXLgD27NnDoEGDsFh+naRl5MiRzdqfv8lEHkL42atrV5KTH83QXoUEWVpv9qSz+8bwRnY5f1uwhu//0qvV9iOEEEKIXzVm0PGVK1cet2zatGlMmzatFSISwjfu+/ILPK5Axg81tup+xvaKo7gqk9Xbonjkmy955OzzWnV/QnRlRuOv72dFUQBqe9lWVFTwpz/9iTvuuOO49ZKSkqisrGTSpElMmjSJuXPnEhkZyeHDh5k0aRIul6tO+YCAk4//uXjxYtxuNwBZWVmMGzeudjw9AKu1cbMYd3WS9BPCj6rcTp79JguLTeW07q0784/FaKRft2q2749m4fZNTB0wtFX3J4QQQgghOqeS6kq+2qQRFlpASljrzxR94eB43qvKYs7ycLqFfs/1I8acfCUhhE8NHTqUnTt30qNHj3qf37ZtG4WFhTzxxBO1M8j/9NNPzd5fcnJy7f+Pze7b0L6bwm63ExcXx/fff1/bExHg+++/r+3N17t3b95//32cTifmX8bb//HHH0+4XZPJhNfrbXF8viZJPyH86G9ffI7LEcx5I6t9MlvvyYzrFcuuw0X8a0muJP2EEEIIIUSzPLh4MarHypl92+brpKLouOKUWOasyePRBW5ig7cxqdfANtm3ECeSXdo2k8w0dz+lpaV1escBhIeH1yblmuK+++7j1FNPZcaMGdx0000EBASwc+dOli5dyksvvURSUhImk4kXX3yRW2+9le3bt/P44483K25fOXjw4HH179mzJ/feey8zZ86ke/fupKWl8fbbb5Oens7cuXMBuOqqq3jooYe45ZZbuP/++zl8+DCzZ88Gfu0B+XvdunWjoqKCZcuWMXjwYGw2GzabrVXr1xiS9BPCTzJLCvlyo0JYWB49IpLaZJ96RceAZCdb9kWzcMcmpvaXxJ8QQgghhGi8MkcVX29WCQ0tID6k9Xv5HWPSG7jq1HDeXVPKn9/dyUe3BjA8IbXN9i/Eb4UGmLAYdby55mCb7dNi1BEaYGrSOitXrmTIkCF1lt144428+eabTd7/oEGDWLVqFQ899BCnn346mqbRvXt3Lr/8cgAiIyOZM2cODz74IC+88AJDhw5l9uzZXHDBBU3el6/cfffdxy377rvvuOOOOygtLeWvf/0reXl59OvXj88//5yePXsCNb0Bv/jiC2677TbS0tIYOHAgDz/8MFdddVWdcf5+a/To0dx6661cfvnlFBYWMnPmTB555JHWrF6jKFpjBhppJbNmzeLTTz9l9+7dWK1WRo8ezZNPPknv3r1PuN78+fP5xz/+QUZGBj179uTJJ5/k3HPPbdQ+y8rKCA4OprS0FLvd7otqCNEsF7/1Hpv2BnH16ToiAtvuFwCP18try4uJDK1i/d1/bLP9CiGEL8n1XIiGyftDtKa7P/uUT9cZuXiUm8TQtj+/Ciqq+HBtNTq9l8//PJa+0fFtHoPoOhwOBwcPHiQlJeW4ZE9WSTXFla4G1vS90AAT8SEyjp2/zJ07lxtuuIHS0lKfjid4onPMF9dzv/b0W7VqFdOnT2fEiBF4PB4efPBBzj77bHbu3NngwI5r167lyiuvZNasWZx//vnMmzePqVOnsmnTJpnRTHQYm7MOsWmvncToIiIC2+4XUgCDXk+fRAc7DkSx6sBuxqb2adP9CyGEEEKIjsnhdvHFRgd2exmJoQl+iSEi0MZFp3j4dL2Oqa+tYPGMiXQPj/ZLLKJriw+xShKuE3v33XdJTU0lPj6eLVu2cN9993HZZZd1uAlEWn8QsRNYsmQJ119/Pf3792fw4MHMmTOHw4cPs3HjxgbXef7555k8eTL33nsvffv25fHHH2fo0KG89NJLbRi5EC3z1wXLUXRuJvX3TwPljB4xKHoH/7dkvV/2L4QQQgghOp7ZK7/F7QpmTC//jlMVH2xnyggFl8vCeS99w4HCPL/GI4TofHJycvjDH/5A3759+ctf/sK0adN4/fXX/R1Wk/k16fd7paWlAISFhTVYZt26dUyYMKHOskmTJrFu3bp6yzudTsrKyuo8hPCnZft2cuBIBD0TygkwN21MBl8xGQx0iy1nb2Yoe/Kz/RKDEEIIIYToOFRVZd76AmwBhfSKavj7WlvpFhbClOHgdFk596Wv2Z1/1N8hCSE6kb/97W9kZGTU3n777LPPtouJOZqq3ST9VFXlrrvuYsyYMSe8TTcnJ4fo6Lq9o6Kjo8nJyam3/KxZswgODq59NGeWGiF86aHP16MzVHFWn1i/xjGuVxSg8djXy/0ahxBCCCGEaP/e3biOqspwhqbo/R1KrZTwEC4YAS6XhQteWsHGIxn+DkkIIdqVdpP0mz59Otu3b+fDDz/06XYfeOABSktLax+ZmZk+3b4QTfHpto3k5EUzoJsDk96/k2fbLWYiwopYt9tIhbNtppoXQgghhBAd0yur9mA0lTI0MdLfodTRLSyEi0bq8XoNXPaf9azYt8vfIQkhRLvRLpJ+M2bMYNGiRaxYsYKEhBMPCBsTE0Nubm6dZbm5ucTExNRb3mw2Y7fb6zyE8Jd/Lt6KwVTGGT3928vvmDE97aieAJ5csdTfoQghhBBCiHZq1YHd5BVE0zvRjaK0i6+QdSSE2rl8lBVFUfnj2zv4MH2Dv0MSQoh2wa+f2JqmMWPGDBYsWMDy5ctJSUk56TqjRo1i2bJldZYtXbqUUaNGtVaYQvjEuz+tpbA4isEpbvTtpLHULSwEW0AhCzYW+TsUIYQQQgjRTj2xdD2KvprTu7ffWXIjg2xcc1owZlM193+UzdMrvvF3SEII4Xd+zTxMnz6d999/n3nz5hEUFEROTg45OTlUV1fXlrn22mt54IEHav++8847WbJkCc888wy7d+/mkUce4aeffmLGjBn+qIIQjaKqKrO/+RmjuYTRqfX3SvWX/kkKFeURfL5zs79DEUIIIYQQ7czh4gJ2ZQSTGFWGyeDf4WlOxm4xc/1pkdiDSnj5axczPp3v75CEEMKv/Pqp/eqrrwIwbty4Osvffvttrr/+egAOHz6MTvdrbnL06NHMmzePv//97zz44IP07NmThQsXnnDyDyH87T/rV1NWFsmofsXo2kkvv2NGJkWx8edSXly5hQv6DfF3OEIIIYQQoh155OtvAStje7WvsfwaYjYauG5MHPN/OsKiH6I5UvwuH19/NQZ9+5mARHQSJZlQVdh2+7OFQ4hMTCqaxq9JP03TTlpm5cqVxy2bNm0a06ZNa4WIhPA9VVV5cdkhzFY9I5Kj/B3OcQx6PYnRlezNDCGnrIQYe4i/QxJCCCGEEO2Aw+1i1Q4ICykiLKDjJBt0io7LRyTx1fZM0vdGMv7Fd/jqtqsINFv8HZroLEoy4eUR4K4+eVlfMVph+o+S+POTlStXMn78eIqLiwkJCfF3OI3WvvtnC9EJPLP6W6oqIxg7qLRdDnwMMLp7GIeO6nhqxXL+feHF/g5HCCGEEEK0A/9evQyvO4hTu3fMXnLnDEjEbs3mpz0RnPHcB3w9/RIiA2ViR+EDVYU1Cb9RM8Ae3/r7K8uCdS/V7LeRSb/rr7+ed955p/bvsLAwRowYwVNPPcWgQYNaK9LjKIrCggULmDp1aofez+jRo8nOziY4OBiAOXPmcNddd1FSUtIq+/MVSfoJ0YpcHg//XZWL1aaRlhDn73AaFBUUQGDQEZZs1fHvC/0dTefhPHCQ0gULqNq8GefevaBp6KwWLIMGEzh6FPYpF6APDPB3mEIIIYQQ9fpwQy4Wq56eUbH+DqXZxnSPJcCcx6ptIYx7bgFf3z6FhOAwf4clOgt7PISdfEJSf5k8eTJvv/02ADk5Ofz973/n/PPP5/Dhw36O7MQyMjJISUlp1N2hbcVkMhET077G52+M9tntSIhO4l/Lv8ZRHcaY3iZ/h3JSAxINVFWGs3DHJn+H0uE59+3j8E03c/CCybhXvkFE5EZSzi6g++Qskk/PJFRZTNW7D7J/wjjyX3kF1eXyd8hCCCGEEHV8vnMzZWWRDEjydyQtl5YQxaQhLqqqApn4/CIyivL9HZIQbcJsNhMTE0NMTAxpaWncf//9ZGZmkp//63tg27ZtnHnmmVitVsLDw7nllluoqKiofV5VVR577DESEhIwm82kpaWxZMmS2uddLhczZswgNjYWi8VCcnIys2bNAqBbt24AXHTRRSiKUvt3WztZHQDWrl1LWloaFouF4cOHs3DhQhRFIT09Hai5vVdRFEpKSli5ciU33HADpaWlKIqCoig88sgjbV+xRpCknxCtpMrtZO73pQQG5dM/NsLf4ZzUsMRIdPoq/rNmq79D6bA0j4f8F17k4CUXYnWsodelJcQPO4ot2oMuPBYtsj+6iASs8UbiTymix9kHUL99gozLL8W5b5+/wxdCCCGEqPXc8nR0hkpO6db+xqRujj4x4Zw7XMXhCOCcl74is6QNJ2AQoh2oqKjg/fffp0ePHoSHhwNQWVnJpEmTCA0N5ccff2T+/Pl8++23zJgxo3a9559/nmeeeYbZs2ezdetWJk2axAUXXMDevXsBeOGFF/j888/53//+x549e5g7d25tcu/HH38EaiZrzc7Orv27rZ2sDmVlZUyZMoWBAweyadMmHn/8ce67774Gtzd69Giee+457HY72dnZZGdnc88997RVdZpEbu8VopX846svcTvtnD24DQd3bQGDXk9sRDm7DwVTUl1JiFVuO20Kb2kpR/7yF9Q939H9AgcGfQWe0ME4I05FM4UeV15xFWMo/JGoQRtxVW4g64apRD/1BgGjRvkheiGEEEKIX+0tyOFAVhip8UUY9J3nVtgeEaGcO7yYxT8FMPmlRay86yIZ4090aosWLSIwMBCoSfDFxsayaNEidLqa/l/z5s3D4XDw7rvvEhBQ8/3vpZdeYsqUKTz55JNER0cze/Zs7rvvPq644goAnnzySVasWMFzzz3Hyy+/zOHDh+nZsyennXYaiqKQnJxcu//IyJpZv0NCQvx6a+zJ6jBv3jwUReGNN97AYrHQr18/srKyuPnmm+vdnslkIjg4GEVR2v0tv9LTT4hWUFRVwcIf3AQHF9Aj4viET3t1amoImmrh2dUr/B1Kh+LOzubgtMuwlK6m28QCdIGBOHrcjDvunHoTfgCaKRR37Nk4uv8RY2gg3cYdpfDv11L+7bdtHL0QQgghRF3/981yAM7o2Tl6+f1Wj4hQJg3xUFlpZ/LLn1DpdPo7JCFazfjx40lPTyc9PZ0ffviBSZMmcc4553Do0CEAdu3axeDBg2sTfgBjxoxBVVX27NlDWVkZR48eZcyYMXW2O2bMGHbt2gXUTBiSnp5O7969ueOOO/jmm2+aFWv//v0JDAwkMDCQ/v37A9T+HRgYyDnnnNOs7TamDnv27GHQoEFYLL/O8D1y5Mhm7a+9kaSfEK3gns8X4fVYmdi/Y/1ymBBqx2It4ot0GeeksdxHj3LommsIi9tH9MB8PGHDcKZcg2YOb9T6miUaR+p1aPZkEk/Lp+TJP1G54YdWjloIIYQQon5VbidrdukJCy0k2Gr2dzitond0OGcMqKawOJwL3pyHqqr+DkmIVhEQEECPHj3o0aMHI0aM4M0336SyspI33njDZ/sYOnQoBw8e5PHHH6e6uprLLruMSy+9tMnbWbx4cW2CcvHixQC1f6enp/Pmm2/6LOauRJJ+QvjY4eICVmw1EhleQHxIx0r6AfSIVSkqjmRz1iF/h9LueQoKOHTttUQkHyC0WwGumAm4YyeCom/ahvRmnMmX4bX3JGFUIUUzb8C5f3/rBC2EEEIIcQLPrV6O1x3IqB5B/g6lVQ1JjGJwj2L2Z0Zx0//+5+9whGgTiqKg0+morq4Zgqpv375s2bKFysrK2jLff/89Op2O3r17Y7fbiYuL4/vvv6+zne+//55+/frV/m2327n88st54403+Oijj/jkk08oKioCwGg04vV6TxpbcnJybYLy2C3Cx/7u0aMH8fHxzapzY+rQu3dvtm3bhvM3PX9PNv6gyWRqVL38Tcb0E8LH7vh0MZoWxNkD2v/kHfUZlRLJ9oMunl35Pe9enXzyFbootaqKzD/9ibD4/YQkFuGMOw9v6ODmb1DR4UqYisn7P+KHHybzL9eTMO8r9L+MwSGEEEII0RY+3JCLxaqnR0Ssv0NpdeN6xVNYcZjl6VG8nLSC6aPH+zsk0dGUZbXr/TidTnJycgAoLi7mpZdeoqKigilTpgBw9dVXM3PmTK677joeeeQR8vPzuf3227nmmmuIjo4G4N5772XmzJl0796dtLQ03n77bdLT05k7dy4A//73v4mNjWXIkCHodDrmz59PTEwMISEhQM0MvsuWLWPMmDGYzWZCQ1tv+KuDBw/WzrZ7TM+ePU9ah6uuuoqHHnqIW265hfvvv5/Dhw8ze/ZsoCZRWp9u3bpRUVHBsmXLGDx4MDabDZvN1mp1ay5J+gnhQz8dOUD6vmCSYgqJCEjydzjNYjObCA3OZd3PZlRVrR3kVfxKU1Wy7rmXAGUjYSnFuGLOblnC7xidAVfSJZj3vU1Mz13kPPwAcc+80OCFRgghhBDClxbv3kpZWSTDenedmW0vGpLAW99lM3uRkxGJ+xmZ2N3fIYmOwBYORiuse6nt9mm01uy3CZYsWUJsbE0CPygoiD59+jB//nzGjRsHgM1m4+uvv+bOO+9kxIgR2Gw2LrnkEv7973/XbuOOO+6gtLSUv/71r+Tl5dGvXz8+//xzevbsWbvdp556ir1796LX6xkxYgSLFy+u/R75zDPPcPfdd/PGG28QHx9PRkZGy49FA+6+++7jln333XcnrYPdbueLL77gtttuIy0tjYEDB/Lwww9z1VVX1Rnn77dGjx7NrbfeyuWXX05hYSEzZ87kkUceabW6NZeiaZrm7yDaUllZGcHBwZSWlmK3d7xbL0X7dvrzb3Ekz85N42zYzCZ/h9Ns6UfyWLU1mH9eFs7VQ0/1dzjtTv4rr+D89GniTyvCEzEad/Q4n25fcRZg2fc2ZYf0aOe+RMi0aT7dvhCdgVzPhWiYvD9Ec0185W32ZwVw24RgDPomDlfSgZU5nLy7ugKLxcEP915BgLlzjmUoms7hcHDw4EFSUlKOT/6UZEJVGybIbeEQkth2++vi5s6dyw033EBpaSlWq7XV9nOic8wX13Pp6SeEj8zf+iOZ2dH0T83HZg7xdzgtMig+gu92lvDOhjxJ+v1OxerVlL/7LN0mleG198EdNdbn+9DMEbgSziOYhWS9+xABp52GMbbz32IjhBBCCP/JKMpnb2Yo3eKKMejD/B1Om7JbzEwYVMHXm0K56v2P+OzGa/0dkugIQhIlCdeJvPvuu6SmphIfH8+WLVu47777uOyyy1o14dcW5L49IXxAVVUe+2IHRlMp43vH+TucFtMpOuIiKtibGUxJdeXJV+gi3Lm5ZD9wN4njy9EsYbjip0Ar3XrrDe6HJ7APMYPyyXvsPrpYp2whhBBCtLH/W7ocNB2n9+iY41K3VJ+YcLonFrJlbzivr1/t73CEEG0sJyeHP/zhD/Tt25e//OUvTJs2jddff93fYbWYJP2E8IHHvv2K8vJIRvUGvdI53lYjU0LQVDMvrFnl71DaBc3r5ei99xIzKAeD9f/bu+8oqer7/+PPQik4JgAAdkpJREFUe6fP7M723uks0pvYEMUgImKLxhhFDPlpEqOGqJHEWJOgCSq22AWxYgErFqoIglJcpElfdhe2953dqff+/kD3GyJ12d27u/N+nDNHZ+bO3NfcZWY+876fouHPuBRUS5vu0592PorVhjv4KXUfL2zTfQkhhGgbK1asYOLEiaSmpqIoCu+9995Rt1++fDmKovzk8uNE7EK0BX8wyPItGjHRlcS6OnevlpNxwSlpOJyVPPRRMUW1VUbHEUK0ozvuuIP8/Pzm4baPPvpoh1yY40R1jeqEEAaq8NTzypf1REaWMzgj0eg4rSYjxo3NUcUHeaVGR+kQKl98CVv1ciKT6vGnTUS3tcOwF7OTQPp4ItN8NLwwnVCD9LoUQojOxuPxMHDgQJ566qkTetz27dspLi5uviQmdp02huh4nli1jKDfzYhunf8H7slQFZWLhroJhaxMfuV9o+MIIcRJk6KfECfpt2+/RyjoYFz/rjdRdrekEBWV8Wwrbael6Dso7/ffUzdnJklDGgjEDiXk7tVu+w5F9iZozyKxVxGVz7bj6mBCCCFaxfjx4/n73//OJZdcckKPS0xMJDk5ufny4yqIQrSFV1cXYbNX0yf5xFYG7YqSIyPol1PL7qJEnvpqmdFxhBDipEjrQYiT8OWe7az9PoqMpCrSorte0e/UbgmgaDzyxUqjoxhG8/sp/vNtpJ1ei26PJZB0TvsGUBQC6eMxO3TUb57CX1jYvvsXQghhiEGDBpGSksJ5553HqlWrjrqtz+ejrq7ukIsQx2vZrm1U1yTSJz1kdJQO49zeqThdlTz6aQnlDfJ+EkJ0XlL0E6KFNE3j1re/QjU3cUH/ZKPjtAm33YY7spovv/cbHcUwFU/9B7cjD6vLjy99UpvP43c4ui2WQNwIYnvVUTnzvnbfvxBCiPaTkpLCM888w7vvvsu7775LRkYGZ599Nhs2bDjiY2bMmEFUVFTzJSNDVpMUx+/fS75GMTVxWvcko6N0GIqicsHACIIBJ1PfXGB0HCGEaDEp+gnRQv9c+imV1YmM6BXAbmn/QlB7yU03422MZeH33xkdpd01bdlC43tPEtu7nkDSWeh24+ZTCiaeDiYrjuqPaNq02bAcQggh2lbv3r254YYbGDp0KKeddhovvfQSp512Go8++ugRHzN9+nRqa2ubL4XSK1wcp5K6GrbtiyItoQ6ryWx0nA4lLdpNTloVG3fF8vG2jUbHEUKIFpFPdiFa4EBdNbOX1xMZ2cTI7HSj47SpIRkJfP19A8+u/JYL+gwwOk670QMBSu66k/RRdWjOdIJxI40NZLIRTDqLKH0RxU/ejePZ+cbmEUII0W5GjBjBypVHnmrDZrNhs9naMZHoKv6xeDG6ZuPMnjKX3+Gc3y+F58uquXP+t4y78xTMJpPRkUQHUtxQTLWvut32F2OLISUipd32J7oGKfoJ0QLXv/4+oVAUFwzq+g1si8lEYlwtm/Ij8Ab82C1WoyO1i8oXXyTKuQGzPYQ37UJQjO8YHYwbgqlsDZHaSjxr1uA69VSjIwkhhGgHeXl5pKTIDz3RujRN47PvvLjdDSRGdu2T2C1lNZk5vY/OF98lcNenH/HghElGRxIdRHFDMRe9dxHekLfd9mk32fng4g9arfCnKAoLFizg4osvbpXnEx2TFP2EOEGvbVjD9/kJ9MosIzkyPObMGZIVwSflLp77eiU3n9HOC1kYwLdnL555j5A1ugF/8lh0W6zRkQ5STARTzyFSf5/iZ+/DOXIhiqIYnUoIIcRRNDQ0sGvXrubre/fuJS8vj9jYWDIzM5k+fTr79+9n7ty5AMyaNYucnBz69euH1+vlhRdeYOnSpXz++edGvQTRRb24dhV+bwyn9ao1OkqHNig9kbx9hby12sXNZ1aT6o4xOpLoAKp91XhDXq7NvZZkV9vP717iKWHu1rlU+6qPu+h33XXXUVNTw3vvvXfY+4uLi4mJ6br/nv/7d5LJZCI1NZXLL7+cGTNmtFvv+Dlz5nDrrbdSU1PTLvs7HCn6CXECapo83Pf+bqx2nZ/1SzM6TrvpmRDNYmsF89aWcvMZRqdpW7qmUXLPX0gZXk3IkU4wdpjRkQ4RisolVPwFkbZ1NH79tfT2E0KIDm7dunWMGTOm+fq0adMAmDx5MnPmzKG4uJiCgoLm+/1+P3/605/Yv38/TqeTAQMGsHjx4kOeQ4jW8OLKnZitdgakxRsdpcMbPyCGN1fCb9/+kPd/fa3RcUQHkuxKJqOTdgRJTjZ+MUpd1wmFQpjNRy9N5efnk5OTg67rJ/T8s2fP5vzzzycQCLBx40amTJmCy+XigQceOJnYnYrx49WE6ER+/eZ8/L5Izh9ox9QBhnu2F0VRyUzysb8sjsKaSqPjtKmad98lwv8FFmcIf9qEDjGs9xCKQjD1XCKS/dS9ICv5CiFER3f22Wej6/pPLnPmzAEO9gJYvnx58/Z33HEHu3btoqmpicrKSpYtWyYFP9HqNh4ooKQ8gR6pfpSO1tbpgJIiI8hOrWLjzmi+2PO90XGEaBWKojT3AszPz0dRFObPn8+YMWNwOp0MHDiQ1atXH/KYlStXcuaZZ+JwOMjIyODmm2/G4/E03//KK68wbNgwIiMjSU5O5pe//CVlZWXN9y9fvhxFUfjkk08YOnQoNpvtqHPWnqzo6GiSk5PJyMjgwgsvZNKkSWzYsOGQbZ5++mm6d++O1Wqld+/evPLKK4fcX1BQwKRJk4iIiMDtdnPFFVdQWlrafP/GjRsZM2YMkZGRuN1uhg4dyrp161i+fDlTpkyhtrYWRVFQFIV77723zV7rkRj6Cb9ixQomTpxIamrqIf/gjuTHfyD/eykpKWmfwCKsvf3dWtZvjyY7tZKcuGij47S7U3PiQFd55IsvjI7SZoIVFdQ9ez+xfTwEEs9Ct3XMSa1D7l6E1BgizevwfPON0XGEEEII0cn8c9EKUIKc0T3R6Cidxvm5qajmJm6f/5XRUYRoM3/961+57bbbyMvLo1evXlx11VUEg0EAdu/ezfnnn89ll13Gd999x7x581i5ciU33XRT8+MDgQAPPPAAGzdu5L333iM/P5/rrrvuJ/u58847efDBB9m2bRsDBrTPYpE7duxg6dKljBz5fws0LliwgFtuuYU//elPbN68mRtuuIEpU6awbNky4ODcp5MmTaKqqoovvviCRYsWsWfPHq688srm57j66qtJT09n7dq1rF+/njvvvBOLxcJpp53GrFmzcLvdFBcXU1xczG233dYur/W/tWh47549e+jWrdtJ79zj8TBw4ECuv/56Lr300uN+3Pbt23G73c3XExPly0q0rTpvI3fN347VpjChf6rRcQwRH+HEFbGfRZuBLjqHcck//07ygBI0exLBeINX6z0aRSGYejYR2gJKZs/ANWKB0YmEEKLLaa32rhAdjcfnY+1OK4lx1bhsmUbH6TRsFjODu/tYvz2JZ1Z/wY2jRhsdSYhWd9tttzFhwgQA7rvvPvr168euXbvo06cPM2bM4Oqrr+bWW28FoGfPnjz++OOMHj2ap59+GrvdzvXXX9/8XN26dePxxx9n+PDhNDQ0EBER0Xzf/fffz3nnndfmr+eqq67CZDIRDAbx+XxceOGFTJ8+vfn+mTNnct111/G73/0OODgFx5o1a5g5cyZjxoxhyZIlbNq0ib1795KRcXAY99y5c+nXrx9r165l+PDhFBQUcPvtt9OnT5/m4/KjqKgoFEUxdCh1i3r69ejRgzFjxvDqq6/i9bZ8tZrx48fz97//nUsuueSEHpeYmEhycnLzRVWP/DJ8Ph91dXWHXIQ4Ude8+jY+XyTnD7RhNpmMjmOYPmkKDfXxLNu1zegora7hyy+x7XsHqzuAP71jrNZ7NCF3H0KKG1dwFd6tW42OI4QQXU5rtXeF6GgeXrEELeji1O7uY28sDnF6t2Tsjioe/bwQb8BvdBwhWt1/97r7cdX4H4fnbty4kTlz5hAREdF8GTduHJqmsXfvXgDWr1/PxIkTyczMJDIyktGjDxbH/3vuWoBhw449b3q/fv2a99OvXz+AQ/Y9fvz4Yz7Ho48+Sl5eHhs3buSjjz5ix44dXHPNNc33b9u2jdNPP/2Qx5x++uls27at+f6MjIzmgh9Abm4u0dHRzdtMmzaNqVOnMnbsWB588EF27959zFztqUW/ajds2MCAAQOYNm0aycnJ3HDDDXzTjkPMBg0aREpKCueddx6rVq066rYzZswgKiqq+fLffywhjsectavYuCue7ulV5MRHGx3HUCOyElFUH09+uc7oKK1Ka2yk8qE/E9+vgWD8KHR7ktGRjk1RCKaMJjLNR+2LDxqdRgghuhyj27tCtJV315Vhd1SF5XQ1J0tRVEb3teJriuEvCz8yOk7Hp4WgYids+xC+fBg+uBleuRSePh0e7g0z0uGBhIOXvyfBQzkwayC8OA7euR6W/gM2vQMVu0DTjH41YcFisTT//4+r32o/HPuGhgZuuOEG8vLymi8bN25k586ddO/eHY/Hw7hx43C73bz22musXbuWBQsOjkjy+w8tkrtcrmNmWbhwYfN+Fi5cCHDIvl944YVjPkdycjI9evSgd+/eTJgwgfvuu4958+axa9eu4zsgx+Hee+9ly5YtTJgwgaVLl5Kbm9v8ujuCFg3vHTRoEI899hgPP/wwH3zwAXPmzOGMM86gV69eXH/99VxzzTUkJCS0dlZSUlJ45plnGDZsGD6fjxdeeIGzzz6br7/+miFDhhz2MdOnT29eJQ2grq5OCn/iuBXVVvHAB4XYHQEuOCV8Vus9EqvZTHxMDXm7XXgDfuwWq9GRWkX5E0+Q2G0nmjWGQELnWZ44FN2P0IGl2Ks/x79vH9asLKMjCSFEl2FUe1eItrRk11ZqaxMY3KtrL8zWlvokx/FNdCHvfRPBHWNqSHZHGx3JUJrfT7C4mEBpKaHC71FL1qPWbsfsz8esl6MqB+eD0zQToaCNUMiKFrKgKzZ0NQHFbEOx2VCtVhSbCZNVQQ0GUEq3ouxeCk3VB3dkc0PW6dBtNPT8GcR1N/BVh6chQ4awdetWevTocdj7N23aRGVlJQ8++GBzzWXdupZ3Fsn6r982P67ue6R9Hy/TD6P2mpqaAOjbty+rVq1i8uTJzdusWrWK3Nzc5vsLCwspLCxsfk1bt26lpqameRuAXr160atXL/74xz9y1VVXMXv2bC655BKsViuhUOikMp+sFhX9mh9sNnPppZcyYcIE/vOf/zB9+nRuu+02/vKXv3DFFVfw0EMPNXcJbQ29e/emd+/ezddPO+00du/ezaOPPvqTFVZ+ZLPZsNlsrZZBhA9N07hqzntooWgmjbSjdvDhnu1lWE4En6xz8dzXK7n5jHOMjnPSvFu3wuqnsA/048u4ENST+lhsX4pKKHEUbn0xFXMeI+GeR4xOJIQQXU57t3eFaEuPLl2LokZwao7MiX4yxvWP4c2VCjfN/5B3rrvm2A/o5HRdJ1hSgnf7dvy79+Dbsxt/fj5a6R5s5gO4Ev04E324Ig4WN4I+MwGvA28gnmDIRUiLRFesoPwwTZKuoYc09GAAAgE0bwNaUxP6D4UYAMVswpSYii19ELYkO7YIP6aaApTP/waf3gnxvaH/5dD/5xCbY8RhAaDE0z6LirZ0P7W1teTl5R1yW1xcXIs6Qv35z3/m1FNP5aabbmLq1Km4XC62bt3KokWLePLJJ8nMzMRqtfLEE09w4403snnzZh544IEW5W4tNTU1lJSUoGkaO3fu5P7776dXr1707dsXgNtvv50rrriCwYMHM3bsWD788EPmz5/P4sWLARg7diz9+/fn6quvZtasWQSDQX73u98xevRohg0bRlNTE7fffjuXX345OTk5FBUVsXbtWi677DIAsrOzaWhoYMmSJQwcOBCn04nT6WzXY3BSv27XrVvHSy+9xJtvvonL5eK2227j17/+NUVFRdx3331MmjSpzYdBjBgxok2XeBbh6+7PPqawOIlBPStJdofn4h2H0zMhmsW2Ct74ppSbO0+nuMPSg0HK7ptGev86grFD0ZzpRkc6YcG4wZhLV2De/RbB6r9hjokxOpIQQnQpHaG9K0RrqGpsYEt+BCkJtVhNUUbH6dSSIiNITypg3fY41hflMzQ92+hIrSpYXU3Tt9/S9G0eTZs34d2yFa2uDtWs4UrXiMhSSMhqwNLTA0BIjSHk6I43MgfNnQ2Wgws2mDnBgkMwhNbQQKiujlBNDaGaanwFJTRuqEAPhlDMZmzdBuLqFoGNWtQvH0ZZ9g/IGQ3Droc+F4KpfU7gx9hisJvszN06t132B2A32YmxnVhbf/ny5QwePPiQ2379618f19DY/zVgwAC++OIL/vrXv3LmmWei6zrdu3dvXsk2ISGBOXPm8Je//IXHH3+cIUOGMHPmTC666KIT3ldrmTJlCkDzYhpnnXUW//znP5t7Dl588cU89thjzJw5k1tuuYWcnBxmz57N2Wef3fy4999/nz/84Q+cddZZqKrK+eefzxNPPAEc7DlYWVnJtddeS2lpKfHx8Vx66aXcd999wMGOajfeeCNXXnkllZWV3HPPPdx7773tegwUXdf1E33QI488wuzZs9m+fTsXXHABU6dO5YILLjhkQY2ioiKys7Obl3c+ZhBFYcGCBVx88cUnlOW8884jMjKS+fPnH9f2dXV1REVFUVtbe8gKwEL8t1X5O/jVc1uIdtdw7WkyHPx/fbK5kB0FcSy6bQQ9441biehkVb7wAvYN03Ek2/D2ugFMnXO4sunAciwVX1Gd/Dfift/+y8ALYQT5PhdtrS3au+1F3h/icP668ANeW2Hi8tN8pEXLv4uT1ejz88LyRtIT6/jyluuP/YAOTGtqovGbb2j4ciWeNWvw/zDfmSkmioheUUSkBrE7y7CEilDQ0CzRaBHZhFzZhFxZYD72/GwnF1AjWFlJoLiYwP4DBA/sR/cHMMe4cQ+IxxFRgVqfD1HpcOrvYeh1YG2d3lRer5e9e/eSk5OD3W4/5L7ihmKqfdWtsp/jEWOLISVCepZ3NUf7N9Ya3+ctKoM//fTTXH/99Vx33XVHHM6QmJjIiy++eNTnaWhoOGQCxb1795KXl0dsbCyZmZlMnz6d/fv3M3fuwer5rFmzyMnJoV+/fni9Xl544QWWLl3K559/3pKXIcRhNfi8/OaVNagmK5cN7QQLOhjgtO6J7CgI8e9lK3ju51cYHadF/AUFBD76J3GD/HgzLu+0BT+AUOJILFVrYM2zaL+5GdXaeV+LEEJ0FK3V3hWio/jg2yqcLpW0aBnB0hqcNiu5WWVs2ZPEWxu/4YqBI4yOdEJCDQ3UL1pM/eef41m1Ct3vxxQXhzM3nfhTB+BwlGNp3IYSakRX7YScWQQifkYoIgfd2s4jS1QVc0IC5oQEHAMGQEgjUFKMf+9eatbnU9XQgD09g6jeFiyf/xXly5lw+q0w4jdgcbRZrJSIFCnCiQ6vRUW/RYsWkZmZeciZTjg41r+wsLB5LPd/T4Z4OOvWrWPMmDHN139ccGPy5MnMmTOH4uLiQ5Z29vv9/OlPf2L//v04nU4GDBjA4sWLD3kOIU7WVXPn0eiJYfwwLy6bFE8OJ8phIyqqiOVbzGiXaT/5LOjodE2j7O5bSe1fRSBqEFpEttGRTo7ZQTCiH1Hpm6l7fz7RP/+F0YmEEKLTa632rhAdwec7NlNfn8DQ3rKAR2sa0zuVHfsruP+jci7vP6zDt4l1TcOzejU177xLw5Il6H4/1h45xF84gIgkDxbvNkxNm9CDCloojWDsEEIR3dAcqdCR5jc3qVjS0rCkpeE67XQCxcX4dmynbMUeVHMUsYPs2Bbdg7LmP3DOXTDwl9DB/zZCtJUWDe81mUwUFxeTmHjoBLCVlZUkJiYavjrJ0chwB3E0/172OU99FqBXZhnjT5FhvUezobCMLzdF8c8r4/jl4FONjnNCql57FfuqP2JLtuLrdQOYOv9iP4q/GvuOp6ko7EX8i9+gKIrRkYRoU/J9LtqatHdFVzLx+ZfZnO/ixrERWNtpzrNw8W1hGSs2RXH9uQp3n3eB0XEOK9TQQO38+VS98iqBwkKsmYnEDo/HFVeNpWELiuZFs7jRXN0IRXYj5MoGk/2Yz9vR6H4/vu078G7ejOKtJKa/CbujCj1lIMoFMyHjxHtjHm3opRCtoUMO7z1SnbChoUHeCKLTWrNvF/9ZVI/bXc/5/dKMjtPhDUqP56tt1Ty/qqxTFf38RUUE5t9NbH8/3syfd4mCH4BujSFoycIdvZvGr7/GdWrn+ZsIIURHJO1d0VXUeRvZnO8iOa4Oqyna6DhdzuCMRDbk72fuCis3n+Eh2tHG89udgGB1NdWvvELV3Fcg4CH2tESiznFj8W9C0UOEAqkE4kcSiuyBbkuETn7SWLFasfc/BXu/fvj37aN6wwZUb4BY/07ML/4MZeh1MPYecMjCdyJ8nFDR78fht4qicPfddx+y1HAoFOLrr79m0KBBrRpQiPZQ0+RhystfoZpsXD4sAaUjdV/voFRFJSPJw979sRTWVJIRHWd0pGPSQyHK//Y7UvpVEYgeiubKMjpSqwqlj8YenEvdmzNwnfq+0XGEEKJTkvau6GoeXbEMPeTg1O7Svm0rPzslkvmrzdw8/33mXv1Lo+OgNTZSNXculc8/j93dQPpYO05HGegH0EwZBJLOJeTujW6JNDpq21AVrDnZWLOz8RcWUP7NNzjMRUTpr6Bs/RDl4ieh93ijUwrRLk6o6Pftt98CB898btq0Cet/TRZvtVoZOHAgt90mK0eKzkXTNC576S2ammK5YKifSHvX6PnVHk7vEU/+fpixZCn/ueznRsc5pqo5LxEXtQrdHEUg5Ryj47Q6zZVOkFicvq/wFxRgzcw0OpIQQnQ60t4VXc17G8qwO8xkxsiCA20lI8ZNSmIBKzbH8F1xAQNSjGmD6bpO3YcfUv7wv3BFFtHt/BAWSx2aNZZA9GmEok5Bt0YZks0QClgzM7GmZ+DbtYvSb1cRnVqB441foPf/BcqFM8HWRQufQvzghIp+y5YtA2DKlCk89thjMkeI6BJu/+g9dhcm0r97BT0TZVjviYh3OXG7i1iyyYx2Scde0MO7fQcsuR9bzxDe7MtAtRgdqU2EUs7AxQdUvvIIcX+dZXQcIYTodKS9K7qSdUV7qK5JZECPCqOjdHnjT0li9hdN/P6txXx5y/Xtvn/fnj2U3HMnjoavyDnTi2oOEIrsjTf2woOjWzr50N2ToirYevXE2i0Hz/r1NO34mujQW+i7v0C9+jVIG2p0QiHaTIt+oc+ePVsaQKJLeDPva979ykx8XCnn9JaCX0sMyrLi88Ywd8Nqo6MckdbURNW91xHXq45A4tnojiSjI7WZUEwuIc2Oec87hBo8RscRQohOS9q7oit4ZNlqUPyMzE4wOkqXF2m30S/LQ2FxEnPXfdVu+9WDQSqefoLa28eQnrmYhIEetIT+eHv+Fn/mZWgR2eFd8PsvitmMc+RILKOvoaKgG4GSMvTnx6KtfBJOfH1TITqF4+7pd+mllzJnzhzcbjeXXnrpUbedP3/+SQcToq1tKS3ir+/kY3P4+fmwVKPjdFqD0uP56vtKnv+ylOuGnW50nMMqf+hvJGZvI2jLJJjQxRe4UFSCscNws5Kad14m5rrfGZ1ICCE6DWnviq4kGArxzQ6VuJganNYMo+OEhTG9U9hxoIx/LqziioHDsFusx37QSfDt3kPtPycTE7sF8ykaweiB+JPO7Lpz9bUSU2wMERf8nIZNG7EeWEbk4r8S2vEFpl/NAevxL8QSOHCAYHV12wX9H+aYGCyp8rtVnJjjLvpFRUWh/HCGICoqjOYBEF1SnbeRK55fgq47uXx4FFZTixayFoCiqHRL8bKjIJ4tpUX0S0o3OtIhaj/6gIiyl1ETrXizLwmLM52h5BHoVV+hr3wS/dobUTrwsGshhOhIpL0rupLZ674iGHAzOKvrt306ClVRGZNr4bMNLqZ98H6bzXmt6zr1bzyNadV9JGZ4CVgz8WaOR7d1/IX1OgxVwTFwEKHKTKrWvkd0aBGhh0dgunEhxBx7sb/AgQPsvmACutfbDmEPUux2ui/8WAp/4oQcd6Vj9uzZh/1/ITobTdOY+Nw8PJ5Yxg32Eh/hPPaDxFGd0SORHYUB/vH5F7x+zdVGx2nm27WL4Js34e7ux5f1KzAf/5m7Ts1kJ+DogzthG54vlhAx5jyjEwkhRKcg7V3Rlbz69S5MFge5ybFGRwkrfZLjWB9XwCfro9lyRuufENca6qj/52VEmr5Bi7HjTbscLbpXq+4jnJjiYjGNvZaa1Z8T6f8OZo1EufYd1O5nHPVxwepqdK+X2Ouuw5yc3OY5gyUlVM2ZQ7C6+riLftdddx0vv/xy8/XY2FiGDx/Ov/71LwYMGNBWUX9CURQWLFjAxRdf3Ob7+ZHJZCI1NZXLL7+cGTNmYLO1z2Kdc+bM4dZbb6WmpqZd9nc8WtS9qampCV3XcToPFkv27dvHggULyM3N5Wc/+1mrBhSitV0/bx77DiQyuFclfZLlLElriLTbSIgtZc32CDw+H652+lA9mlB9PdX3/JLknrX4Es5BiwivlWy1jNFYd27F996DUvQTQogWkPau6MxK6mrYVxxDdmoVihJvdJywM3FgEnOWe7nhjc9ZeWvrLerh37gc7dWrcTsb8Jl6o/W+qMsuTteuzGbsZ16AZ1s69urPsL08keD4WZhHTT72Q5OTsWZ23N8Z559/fvNJrJKSEu666y4uvPBCCgoKDE52dPn5+eTk5KCf4FyLs2fP5vzzzycQCLBx40amTJmCy+XigQceaKOkHV+LxnxNmjSJuXPnAlBTU8OIESN4+OGHmTRpEk8//XSrBhSiNf1zyScs3+gmI7mUs3pIwa81jerpRgu6+NfyRUZHQQ8GKbvjOhK77SDg6EkoYaTRkdqdboshoKQSoebh27PH6DhCCNHpSHtXdGazvvwCdDOndpNefkZw220M6NZAUUkST65aevJPqOt4592D+Z2LMZt8NMZfgtb3Min4tTJr3wH4u/2Kpmo7pk9vJvD+vUZHOmk2m43k5GSSk5MZNGgQd955J4WFhZSXlzdvs2nTJs455xwcDgdxcXH8v//3/2hoaGi+X9M07r//ftLT07HZbAwaNIhPP/20+X6/389NN91ESkoKdrudrKwsZsyYAUB2djYAl1xyCYqiNF9vK9HR0SQnJ5ORkcGFF17IpEmT2LBhwyHbPP3003Tv3h2r1Urv3r155ZVXDrm/oKCASZMmERERgdvt5oorrqC0tLT5/o0bNzJmzBgiIyNxu90MHTqUdevWsXz5cqZMmUJtbS2KoqAoCvfee2+bvt7j0aKi34YNGzjzzDMBeOedd0hOTmbfvn3MnTuXxx9/vFUDCtFa3tr4Dc8t9hEVVcYlgzvWvHNdQU5sNE5XJe+srUTTNEOzlD/0N+LdS9GscQSyLw6LefwOR8sYjS0qiOfVfxodRQghOh1p74rO7JPvqnG6qkiKjDA6Stga3TMFh6uSWZ+VUuGpb/Hz6L4GfDPHYt82C29jLL7c36Ik923FpOK/mZNT0IZMxVMVg+XbR/EvuNvoSK2moaGBV199lR49ehAXd3D+R4/Hw7hx44iJiWHt2rW8/fbbLF68mJtuuqn5cY899hgPP/wwM2fO5LvvvmPcuHFcdNFF7Ny5E4DHH3+cDz74gLfeeovt27fz2muvNRf31q5dCxzsgVdcXNx8vT3s2LGDpUuXMnLk/3UAWbBgAbfccgt/+tOf2Lx5MzfccANTpkxh2bJlwMEC56RJk6iqquKLL75g0aJF7NmzhyuvvLL5Oa6++mrS09NZu3Yt69ev584778RisXDaaacxa9Ys3G43xcXFFBcXc9ttt7Xb6z2SFg3vbWxsJDLy4IpAn3/+OZdeeimqqnLqqaeyb9++Vg0oRGtYs28Xd761D5vdy1UjUlAUWdigLQzMUlm9NYY3N37DLwcbs0pu1fNP4i5/DjXKhq/7L8P6DKgWmU0gFIm19CNCDQ2YIqThL4QQx0vau6KzWle0h9raBAb2qDA6SlhTFJULBkTw7hoTv35jPu9PPfZQ0f+ll2wl+PQELKEq6vy5mEdMQlHD82R2e1IjItBPnUrdN69h3b8QvfuFoGcbHatFPvroIyJ++A3g8XhISUnho48+Qv1hob/XX38dr9fL3LlzcbkOzn/+5JNPMnHiRB566CGSkpKYOXMmf/7zn/nFL34BwEMPPcSyZcuYNWsWTz31FAUFBfTs2ZMzzjgDRVHIyvq/hVASEhKA/+uB19auuuoqTCYTwWAQn8/HhRdeyPTp05vvnzlzJtdddx2/+93vAJg2bRpr1qxh5syZjBkzhiVLlrBp0yb27t1LRsbBVc/nzp1Lv379WLt2LcOHD6egoIDbb7+dPn36ANCzZ8/m5/9xQbD2eK3Hq0WVjx49evDee+9RWFjIZ5991jyvSVlZGW63u1UDCnGy8qvKufalr1HUEFeOjMFmkZV628qwrETMljqeWLbVkP3XvDMP+7f3YY1S8Pf4FVjCvMilKISSRhGR1ET9mzIUTQghToS0d0Vn9fgXa0AJMCI7wegoYS89xk23tCo27orj7e9OrIdTaON76P85Exprqbeci3n4xSAFv3ajWC2YT7uWhoYMlFATWkU+nOD8ch3BmDFjyMvLIy8vj2+++YZx48Yxfvz45pNX27ZtY+DAgc0FP4DTTz8dTdPYvn07dXV1HDhwgNNPP/2Q5z399NPZtm0bcHDBkLy8PHr37s3NN9/M559/3qKs/fr1IyIigoiICPr16wfQfD0iIoLx48cf8zkeffRR8vLy2LhxIx999BE7duzgmmuuab5/27ZtR30t27ZtIyMjo7ngB5Cbm0t0dHTzNtOmTWPq1KmMHTuWBx98kN27d7fo9baXFhX97r77bm677Tays7MZOXIko0aNAg6eBR08eHCrBhTiZNQ0eZj4zMcEAnYuHmYlxmk3OlKXpioqPdN8FJclsGbfrnbdd+1772Be9AfsCUH8OVeh26WhC6AlDCYUsqCsew7d4GHXQgjRmUh7V3RGmqaxZodOTFQ1TqvV6DgCGN8vFau9lrsWbKfO23jsB+g6oU8fQJ0/GW+NicaUK7Dkht/81B2CqmIaeAGabkHRmtBKdnS6wp/L5aJHjx706NGD4cOH88ILL+DxeHj++edbbR9Dhgxh7969PPDAAzQ1NXHFFVdw+eWXn/DzLFy4sLlAuXDhQoDm63l5ebzwwgvHfI7k5GR69OhB7969mTBhAvfddx/z5s1j167W+2167733smXLFiZMmMDSpUvJzc1lwYIFrfb8ra1FRb/LL7+cgoIC1q1bd8gEjueeey6PPvpoq4UT4mT4g0HO/8886utjGDsoSHqMnJVvD2f1TEJR/dz/6cp222fNO/MwffI7XMkB/FlXoLlkzsZmqolAxAAiE8rwLP7I6DRCCNFpSHtXdEYLNm/A74umX4YU/DoKs8nEzwZY8TW5uf7Nd4++cdBP6NVrMa2ZSX2Jm1Df67Bk5LRPUHF4CmB1oul2FK2xUxb+/puiKKiqSlNTEwB9+/Zl48aNeDye5m1WrVqFqqr07t0bt9tNamoqq1atOuR5Vq1aRW5ubvN1t9vNlVdeyfPPP8+8efN49913qaqqAsBisRAKhY6ZLSsrq7lA+eMQ4R+v9+jRg7S0tBN+vSaTCeCQ13u019K3b18KCwspLCxsvn/r1q3U1NQc8np79erFH//4x+bpP35cIdlqtR7Xa21PLR7n+OMKMP9txIgRJx1IiNagaRoXv/gqJeUJnJpbS24HGlPf1dktFjKSatmaH8ueyjK6xSW22b50Xafq6cewb/4HzuQgvqwr0SKlYfS/tIwz4fsNBBfOgJ9dZHQcIYToNKS9Kzqb2V9vQTU5GZQuIx46ku7xMWSnFbLu+3je/m4tPx8w/KcbNVUTevFi1LI8qvcnYTn9SlRZiKXjsEeghVRUrRGtZCf8UPcLlpS0y+5buh+fz0fJD4+trq7mySefpKGhgYkTJwIHF6W45557mDx5Mvfeey/l5eX84Q9/4JprriEpKQmA22+/nXvuuYfu3bszaNAgZs+eTV5eHq+99hoAjzzyCCkpKQwePBhVVXn77bdJTk4mOjoaOLiC75IlSzj99NOx2WzExMSc5NE4spqaGkpKStA0jZ07d3L//ffTq1cv+vbt2/xarrjiCgYPHszYsWP58MMPmT9/PosXLwZg7Nix9O/fn6uvvppZs2YRDAb53e9+x+jRoxk2bBhNTU3cfvvtXH755eTk5FBUVMTatWu57LLLml9rQ0MDS5YsYeDAgTidTpxOZ5u93uPRoqKfx+PhwQcfZMmSJZSVlf1kpc49e/a0SjghWmrqW2+xdW8CuTnljMyWXl/t7Zw+ScwpCXLXwkW8fs3VbbIPzeej/IE7iGp4FWuCgi/nV9LD70gsTgKmHCLs3+PbvhVb79xjP0YIIcJca7V3V6xYwb///W/Wr19PcXExCxYs4OKLLz7qY5YvX860adPYsmULGRkZ3HXXXVx33XUtfCUiXDQGfGzNd5IYW49Jabsf1aJlJpySyvOVFfx1fh3n9cwl2vF/c6hRvQ/t+Qugdj+VJdk4xlyGYrMZF1YcniMKrUnHpHtQQk0odjtVc+a02+4Vux3zCRbMPv30U1JSUgCIjIykT58+vP3225x99tkAOJ1OPvvsM2655RaGDx+O0+nksssu45FHHml+jptvvpna2lr+9Kc/UVZWRm5uLh988EHzAhaRkZH861//YufOnZhMJoYPH87ChQubFwt5+OGHmTZtGs8//zxpaWnk5+ef/ME4gilTpgA0L6Zx1lln8c9//hOz+WDp6+KLL+axxx5j5syZ3HLLLeTk5DB79uzm46EoCu+//z5/+MMfOOuss1BVlfPPP58nnngCONhzsLKykmuvvZbS0lLi4+O59NJLue+++wA47bTTuPHGG7nyyiuprKzknnvu4d57722z13s8FF0/8b6pV111FV988QXXXHMNKSkpKMqhE4recsstrRawtdXV1REVFUVtba1MwtxF3fPZR7y8TCE9uZTLhmQaHSdsvfFNIeXVkXwz/WckRLTue81fVETF9F+RmL4RxWbH3+NX6Lb4Vt1HV6M0luPY+zw12nlE3/+O0XGEOGnyfS7aWmu1dz/55BNWrVrF0KFDufTSS49Z9Nu7dy+nnHIKN954I1OnTmXJkiXceuutfPzxx4wbN+649invj/D05KqlzPywifHDPPRKjDU6jjiMvVU1fPC1hf7davjwNz+s5lu8Ee3FiYTq66mp7o1rzEQwy8KDHYXP4uRA+nCy01OxWy0Hb2ysxqT68Fb40U3t1xvTHBODJTW13fYn2ofX62Xv3r3k5ORgtx+6BkFrfJ+36NPkk08+4eOPP/7JqidCGO2lb1by8jKNmJhKLh0svb6MNKZPDPNWqtz1ySc8+/MrW+U5dV2n5s3XCX3wV1J6VROypuHr9nMwG9tlujPQnQn4Qgk4PMsJVlVhjpUfA0IIcTSt1d4dP378ca04+KNnnnmGnJwcHn74YeDg/EIrV67k0UcfPe6inwhPb6/fh9lqp2dCnNFRxBHkxEbTI72QTbsTefGbL/l1XAD91V8QqA1S5x2M69zzQG3RtPuiPTljCDVWYY+HkNWNKT7j2I8RwiAt+kSJiYkhVn4wig5m2a5tPPBeKQ5XDVeNSEVR5AvTSMnuCGKiK1m8keNbqewYvNu2UTx1IvbVtxDXq4ZA3Gn4e14jBb8TEEo/G5s7gOeVB4yOIoQQHZ5R7d3Vq1czduzYQ24bN24cq1evPuJjfD4fdXV1h1xEeKnw1LOvOJr0eK+0gTu48aekYXdW8cj7hdTOvQZvhUadNgrX6J9Jwa8zccaiaWZUXwWh6vaZ10+IlmjRp8oDDzzA3XffTWPjyf+QF6I17K4s5Tdzv8Vk9nPVyDgsP6zSI4w1uo+bUNDFPZ99euyNj8C3Zw/Ff/49Tf88h5T0L7HGR+DrPplgytkgjdoTE9ODQCASy+556H6/0WmEEKJDM6q9W1JS0jx5+o+SkpKoq6trXn3wf82YMYOoqKjmS0aG9DoJN//56kvQrQzLjjI6ijgGVVGZmrwPLWRjmn8yjRFjcJ12FqjKsR8sOhTdGY+um1Abi9HqqoyOI8RhtWh478MPP8zu3btJSkoiOzsbi8VyyP0bNmxolXBCHA+Pz8clz35CKBTB5aeaiLTLpLcdRVZsFFFRRXy43sY/LvDhtBzf30YPhfCsWkX9W89iq15GUrdG6GYhkDyWYNwwKfa1lKIQTBiF0/I59e88ReQv/2h0IiGE6LA6U3t3+vTpTJs2rfl6XV2dFP7CzEcbS7DZraRFJx97Y2EgnaQdS0gs/JrzKOMDfRyvZdiYKvW+Tkt3xEFTBUrdPjSLFdUhKy6LjqVFRb9jrTgmRHu65KU3qKuL5dzBHlKjoo2OI/7Hmb1dfPSNk3s/+4R/XXjxEbfTg0Ga8vJo+PxDtLx3iIwrIyXJjxZvIxh/BsH4EWCSgu7J0lOHECxfjvL1U+hX3fqTiemFEEIcZFR7Nzk5mdLS0kNuKy0txe1243A4DvsYm82GTVb6DFsF1RWUVcTTK7PS6CjiKBRdI3XLh8QWb+ZAnY2eCZAcKOTJ/X0ZG7WDbJvl2E8iDHPE1U8VFd0eh+KtgIrd6El9UKzyeSyOXwvW1j0hLSr63XPPPa2y8xUrVvDvf/+b9evXU1xcfMzVzACWL1/OtGnT2LJlCxkZGdx1111cd911rZJHdD5/+mA+O/Yl0K9bOaekyMIdHVH3+Bjc7iLmf2Pl3nH/19tP13X8+fk0rllFYN1HKAfW4oypIyHeD30hZE3FlzCUUFRfUGUFs1ajqAQiB+Myf03T4ndwnvdzoxMJIUSH1Frt3RM1atQoFi5ceMhtixYtYtSoUYbkER3f4yu/BMwMz5EFPDoqNRQgPe8d3JV7KKxz0JjaB0uUm4uDJbxYHMdNe9x82LtJBrN0QOagD7QQTb4ADusRCrOqCc0agxqoQivdgZqaiyLTTYnj5P9h2iVTG/2bafEv6ZqaGt555x12797N7bffTmxsLBs2bCApKYm0tLTjeg6Px8PAgQO5/vrrufTSS4+5/d69e5kwYQI33ngjr732GkuWLGHq1KmkpKTIamZhaP6m9by72kR8XClj+2QaHUccxVl9Dvb2+9ubr3G3rxhtx0qUqu+xOeuJjg6gRILWy0zIloo/NhfN3RPdEml07C5Lzx6Ntmkdoc/+CVL0E0KII2qN9m5DQwO7du1qvr53717y8vKIjY0lMzOT6dOns3//fubOnQvAjTfeyJNPPskdd9zB9ddfz9KlS3nrrbf4+OOP2+Q1is5v0eYanC4T8a5Uo6OIwzAFvGStfx1HXTH7al34M/tiinABEG2Gs6N3sKhqEI+UruNPKXaD04r/ZdJDRNYWUW4+WPBz2CwcfpyMApoLVWtAK9yOmpQjI2rEMWmaRnl5OU6nE7O5bTq6tOhZv/vuO8aOHUtUVBT5+fn85je/ITY2lvnz51NQUNDcaDmW8ePHM378+OPe7zPPPENOTg4PP/wwAH379mXlypU8+uijUvQLM0W1Vdzx9g6sthA/HyoNnI5Jx1xWgD1/M6NqishXTmP1llgctoexOEKEUiLQbBn4Y7qhRWai2xNlrr72YjLjs+XiUjfhzVuFfdDpRicSQogOp7Xau+vWrWPMmDHN13+ce2/y5MnMmTOH4uJiCgoKmu/Pycnh448/5o9//COPPfYY6enpvPDCC9LWFYe1qbiQ2toETukmQ3s7IrOvnpxvXsXcWM2eejdaTl9Ux6GFvaGRAb5vzOfl4lOYEL2VPg6rQWnFkcTV5ANQFgyAeozeWMEmVL0craQKNUp634pjU1WVzMzMNisSt6joN23aNK677jr+9a9/ERn5f71xLrjgAn75y1+2Wrj/tXr1asaOHXvIbePGjePWW2894mN8Ph8+n6/5el1dXVvFE+1E0zSuePE9QsFoLhmlYG2jirg4ceZAI87873Dt34LbV4ZV1dB18Glmxpq+ZVbwCv4V8RumZUaCKvOWGKrHubB1M4G37sQ+6Euj0wghRIfTWu3ds88++6jz9cyZM+ewj/n2229PKK8IT0+tXA3YGZ4db3QU8T9sngqy174CXi97GmKgWx/UI8zbd1F8Oc8fiOePe2NY2Mcj58E7GAWIr8knpraQoPkY8/XpOuqO97FQSlPvW3Gd/6t2ySg6L6vViqq23Zu+RdWStWvX8uyzz/7k9rS0NEpKSk461JGUlJSQlJR0yG1JSUnU1dXR1NR02MmNZ8yYwX333ddmmUT7u3PhBxwoS2JY7ypSo1KMjhP21KAP9/5NuPPX4/ZVoCjQGFCpVSLw2uPwRSWBxYYdSCst4NWaQfy/9L1ESGPGWBYnPrUHLjbh37UJa4/+RicSQogOxaj2rhAn4svtTURGNuG2H99wc9E+nDVFZK1/g6AvSL4vDlPP3ijmI/cQizTBmJidfFY5mEdK1vGnVBnm2xGZ9BCmQOOxN8w6B8t3T2FZfQfB9N44hp3Z9uGEOIIW/ey22WyH7TG3Y8cOEhISTjpUa5o+fTq1tbXNl8LCQqMjiZOwZt8u3lqlExtTxundpeBnJEd9CSnfzqfPskdI3/4Zan01xYFodtlzOZBxBrWZg/HFZ4Ll/86GjY0uxxuK4MHikIHJxY+0nuNAAf+rtxkdRQghOpzO1N4V4emr/J14GuLpmSLzhnUk7vId5Kx7BW9TkPxgMuYefY5a8PvR4IggGfZ8Xi7pxx5foB2SijZjMhPscy2qBfRXfkGwTE4UCeO0qOh30UUXcf/99xMIHPwwUhSFgoIC/vznP3PZZZe1asD/lpycTGlp6SG3lZaW4na7D9vLDw422Nxu9yEX0TkFQyFueH0lJpOfS4ckHfsBog3oRFTuJfurl+ix5kXcpd9T0Whjt5JDSdpIGrMHQmwCqIdvfKbYdLKd+XxY0YfyQLCds4v/pdjd+LQsHIGvCRbtNjqOEEJ0KEa1d4U4Xs9+tRaUAMOypAjdUcQWricz723qG1UKzVmYu/WAExi2NzGuHEXR+dPeiDZMKdqD7ojBl34JjqgGPP84H/2HFVqFaG8tKvo9/PDDNDQ0kJCQQFNTE6NHj6ZHjx5ERkbyj3/8o7UzNhs1ahRLliw55LZFixYxatSoNtun6Dj++P4CamsTOCM3hMsmE9y2N1f1PnK+epGcDa9jqimlsCGSPY5c6rsNh9RMlCMtYf8/zouqIqRZ+ft+OSvdEeg9z0dRwTvnFqOjCCFEh2JUe1eI47Vmp0aUuwaHReZJNp5O0o6lpH3/KZUeMwdcPbBkZMIJTszvNsPpUTvZ7unJnIqmNsoq2k1cL3wRpxIVs5eaf042Oo0IUy2a0y8qKopFixaxatUqNm7cSENDA0OGDPnJIhvH0tDQwK5du5qv7927l7y8PGJjY8nMzGT69Ons37+/eXW0G2+8kSeffJI77riD66+/nqVLl/LWW2/x8ccft+RliE5k44ECPlxrIj6ulEHpmUbHCSs2TyVJWz8lqiafxoDKPq8bb3wWpswYzC1YYSjOqtPDtZelVX3JT9lC9hEmNBbtxBWLL5SBo2klweJ8zCnZRicSQogOobXau0K0hc92bMLXFMPgnGqjo4Q9RQuR+t37xJZvo7jeTl1Cbyyx0S1+vpERPrY07ufJop5MiiogxiITYXdmWvYY/FuLiAp+Qt1rT+C++g9GRxJh5oSLfpqmMWfOHObPn09+fj6KopCTk0NycjK6rp/QMsPr1q1jzJgxzdenTZsGwOTJk5kzZw7FxcUUFBQ035+Tk8PHH3/MH//4Rx577DHS09N54YUXGDdu3Im+DNHJ/L83FqOqkVw0UIb1thc16CNx1xfEF67FH1Io8LjwxGZhyYzHdJLLiZ8XU8OexgzuLbQyp8eRVzQU7UPrMR5l73N45/wB8/QPjY4jhBCGa832rhBt4YWv8lBUF4MzZNVeI5kCXjLWv0FE3QEK6l14M3IxuZwn9ZyKqjAh9gBziwczvUjhmZxWCiuMoSgE+/wCdfN/sK2/l6bcETgGjzQ6lQgjiq7rx/2LW9d1Jk6cyMKFCxk4cCB9+vRB13W2bdvGpk2buOiii3jvvffaMO7Jq6urIyoqitraWpnfr5N4dMViHlvoY2jvSs7onmp0nLDgLvuelC0LMQeaKG2wUeNKx5ycclyTEB+vT6tdbKzrzdzeeQyNsB37AaJtbXgFu6kI7XfrMad2NzqNEMck3+eirUh7V3R0mqbR6955REV4+dWpGUbHCVsWbx1ZX7+MxVtHQWM0wexc1OOc7uZ4fFwVyeb6nrzUO4+R0lbu9BRPKbY9s2koj8L59/WY4+KMjiQ6gdb4Pj+hvsJz5sxhxYoVLFmyhG+//ZY33niDN998k40bN7J48WKWLl3aPBRXiNZQ523kP4vLcLoqOb1bstFxujyzr4HM9W+StfFdmhr87GpMoj5rEJb09FYt+AGcE9WAVW3kgSL5MdIRaL0mAOCf/TuDkwghhLGkvSs6ure+W0vQ7+aUdCkEGcVeV0y3lc9haqon359EqNsprVrwAzgvuh67qYG/7YtD11r1qYUBdFcS/sSf4U6qpvaeS9GDsqihaB8nVPR74403+Mtf/nLIkNwfnXPOOdx555289tprrRZOiD/Mf4+AP4Lz+jtRFJnPoi1FlWyl58r/4KrYQ0GtixJ3X9QefVDt9jbZn1VVGB61lx2ennxc422TfYjjp0bE0hTKwe5dQ3DPRqPjCCGEYaS9Kzq6V7/5HtXUSP80GdprhIiyHXT7eg4Bf4B8JRtyerf6yXEAqwqjo/dS5M3mxQppK3cFWtIQ/NY+xMTkUf3wn42OI8LECVVRvvvuO84///wj3j9+/Hg2bpQfi6J1bCvdzxebHCQnVJB9EpPhiqMzBbyk571D5qYFNHh0dvlT8fUYhCk+Dtp4yqLTIrxEmiv4d1GKnMHsCPpOQA+p+F+R3n5CiPAl7V3RkXkDfrYWOEiIrcckJ8TbXcye1WTnvU2DV2W/qy9q+omv0HsiBkUESLDt59kD3fGEpLHcFQS7X4SmRBFRPpu6hQuMjiPCwAl9U1RVVZGUdOSFFJKSkqiulhWkROv4wzufoyga4/vL4h1txVlTRI+VTxNZtp3COhelMbmYu/VAsbRoYe8TpqoKZ0cXUuZL4+lyOYNpNMURiVfphUPbTGDjYqPjCCGEIaS9Kzqyl9evRgu6GJThMjpKmNFJ3PgR6buXUtlkozRhIGp8+/S0HBdTTGPIzQP7Q+2yP9HGVDOBXr/E7ATlvd/j273H6ESiizuhol8oFMJsPnIxwGQyEZSx6aIVfLZjE7sKE+iZXofbLvOVtD6d+N1f0m3tywQavexpSsbbfRCm2Jh2T9LPFSTJVsRLxT1oCEpjxnD9JhD0mgm99Qc4/nWehBCiy5D2rujI5q3bg8lST++k9m+zhSs1FCTtq1dJLN1IcVMEVZlDUSMj2m3/6Tadbs69LKzoS4Ev0G77FW1Ht8XgT7uQyFQPdX+/HK2x0ehIogs7oe48uq5z3XXXYbMdvgjj8/laJZQQf/tgLSazi3P7phgdpcsxBZpI+/YdomoLKGuwUhndHUtCYpsP5T2acTElvFIymPsPaPwrs/XnRBHHT7HY8DmHEaGvwff5s9jG3Wh0JCGEaFfS3hUdVU2Th70HokhPrEFRZD6/9qAGGslcORtXoIaiQDzenL6oavsPqx4XU81zTencU2Rhdvd2371oA1psP/y1e4hL30TFPb8l4V9zUNpwqLgIXydU9Js8efIxt7n22mtbHEYIgNc2rKGsIonBvSqxmtpnmGm4cNSVkLH+DUz+RvY1RuHL6I3F4TA6Fqk2nR6uvXxSkcv/S9hCD4fV6EhhTe19Nt51GzF9cT/6uVNQzNLbVggRPqS9Kzqq/3y1Al2zMSwn2ugoYcHcUEXW6tlYNS+FagaBrG5tOX3fUbnN0D9yD9/U9GV9Qx5DI6Rt1hUEs8ajfr+faP1Daua+SMzkqUZHEl2QouvhNX6rrq6OqKgoamtrcbvdRscR/0PTNAY9OJcmn5Ubz4lHlQmKW03Uge9I3/IR3oBCkZ6KkpGDYuo4x7c+BM/tP4U+EfuY10uGTRktlL+JiPoP8WZehWPqM0bHEeIn5PtciCOT90fXNPLhl6iut/Hbc2S+67ZmrSgkZ8Nr6JrGfkdPtHjjRx/5NZ2n9vcl21HCgj7S47irUPzV2LY/T8MBC5bff4Jj8GCjI4kOpDW+zzvOL34hgOe/+ZK6ugSGddek4NdadI2kLZ+SueVDahpNFDp7o2Z171AFP4BIEwyN2s2m+j58UiOLehjNlN2fRk8itn3z0Ep3GR1HCCGECGsF1RWUlseTleg3OkqXZy/YSvcNrxAK6RRF9+8QBT8Aq6owPGov2z09+aymyeg4opXo1hgCGRNxpzdR/6+rCVZVGR1JdDEd61e/CGuapvH44nxs9mqGZyUaHadLUIM+Mr9+lYT96yn2OKlIHoTJ4Pn7jubMyEYizZX8szCNgBZWnZA7JL3PJPQQ+J+/xugoQgghRFibtWIFoDAiO87oKF2ac/vXdP9+Ad6gif2JQ9EjO9aCKaMivESYq5i5PxFdMzqNaC2h6L74IweR0HM/5X+egi6LRYlWJEU/0WH8Z/VyPA3xDO+hoEgvv5Nm8dWRs/I5ImoLKWiKoSF7MGqEy+hYR2VSFcbG7KPSn8LMYjmTbTQ1OgGPnos9uJXAsheMjiOEEEKErUWb63C6qoiPcBodpctybVxMt32LqQ/ZKEkbAfaOd6xNqsIodwH7vVm8VS29/bqSYPrPCFniiYv6kopZ/zI6juhCpLIiOgRN03h6WSE2ezVDMhKMjtPp2epK6Pbls5i99ewNpRLo1h/F0jkWRentDJHp2MubZX0p8gWMjhP2zP0vwFvnRFn8F/SmGqPjCCGEEGFnzb5d1Ncn0Culgw7V6AIiv/6QbmVfU6O5KE8bDmaL0ZGOaIjLT5SljCcPpEtvv65ENRPo9nPMLhXbtseoX7TI6ESii5Cin+gQmnv59ZRefifLVbGHbmtmE/QH2WfuBpk9Qe1cjcQLYivQdZU/F8jKZIazmAmkX4Cq+PA9c7XRaYQQQoiw8/iKr0HxMyJHToy3Ph33l2+RXfcdlXoUlWlDwWQyOtRRKarC6OgiKv0pvFgh82B3JQfn97sId4aXxmduxLdnr9GRRBcg1RVhOE3TeGb5D7380qUxczLcRRvJ3vAGTX6FoshclJT0Djt/39FEm2GIexff1uXysUxUbDhTRi883u7Ya1cSWPWa0XGEEEKIsBEMhVi7UyEmugaHpeP2PuuUdJ3oZa+R5d1JBbFUpw7sNCfKc50h4q3FvFiSJfNgdzGhqN4EooeS2K+C8juvRfN4jI4kOjkp+gnDvfDNShrq4xnSDenldxKid68hc+tH1PksFMcPRI3t3BM9j3Y3EmUp5x8FWXhD0pgxmjroErx1dpRP/ojeUGF0HCGEECIsvP7t1wT8UQzMkNEPrSoUInrJXDKC+yhTE6lJ7Q9K5yj4/Wh0dDG1gQSeKvMZHUW0skDKWDRbMkk5Wyj5yzR0XX4LiZaTCosw3FPL9mC11ciKvSch7vtlZOxZQqXXRlnyYNTISKMjnTSTqnB+bAE1gXju2S8rWBlNsVkJZF6EovvxPzkJpPEhhBBCtLk5a7ZjMjcwIC3e6ChdRyhE9JKXydCLKDMnU5fc1+hELdLTESLJVsRrpd3lBHlXo5rw51yOyWHG7Z1P1QvPG51IdGJS9BOGem3DGmprExjYTZNefi2ik7D5U1ILv6KsyUFV2mBUp8PoUK0mx67Ry7Wbj8tPYYNHzmIazZTWA0/wFGzezfjn32N0HCGEEKJLK2+oY8/+aNITG6Wd3FpCIaIXv0yGUkyZOYW6xN5GJzop50SX4AlG82iJ3+goopXpFjf+rEtxJfvRPr+fhi9XGh1JdFLy7SEM9ejibZittZyanWR0lE5IJ3HjxyQXr6ekyUVt5hBUe9cb+nFBTA02UyN35CcQkjlLDGceMgFPdSyWjY8T/H6F0XGEEEKILuvhL5aBbmFkTozRUbqGUAj3orlkqMWUW1KoS+xldKKTlmXXSbMX8HZ5TxqCIaPjiFamReQQSBxNQr96ah78f/j37TM6kuiEpOgnDPPelg1UVCVySlYQVc5eniCdpLwPSSrbSLE3gvrMQSgWs9Gh2oTdBOfG7OGAN5MZxXIW03Cqij7gFwQazfDqFei1JUYnEkIIIbqkj/KqcbqqSImKMDpK5xcKEbH4VbJMB6iwJFOb0PkLfj8aG12ONxTBv0qk6NcVBRNOI+jqQcrgYor/eD2hBlnYQ5wYqbQIwzz0WR4mSz1n9Eg2Okono5OU9wGJ5Zs40BRJQxcu+P3oFFeQbOce5pWewuZGKfwZTXVH402dhKJ5Ccw6D0IBoyMJIYQQXcriHVtoqE+gb7rRSboATcOx5A1y1CIqzYnUJHTuIb3/K8Wmk+XYxwflvakKSOGvy1EU/BkXgc1NcvZGiu+4FV3TjE4lOhEp+glDLNm1leKyJPpkeDFJL78ToJO08SMSyzezv8mNJ2sgitlkdKh2cVFsBRbFz61742WYbwdgzuiFxzQKS6gA3xMXy8IeQghxDE899RTZ2dnY7XZGjhzJN998c8Rt58yZg6Ioh1zsdns7phVGe+yLdSiqlxHZstDdSdF1bEvfpjv7qDLHU53Qx+hEbWJsdCUBzcaMA1IM6pJMdvzZP8fqBrfvA8ofnWV0ItGJSLVFGOL+hV+jmjyc1VN6+R0/nYTvFpJY9h0HmiJpzBoQNgU/AKdJYWzsLg54M3nggPQs6wjM/c+mvrEXtpqV+F7+vdFxhBCiw5o3bx7Tpk3jnnvuYcOGDQwcOJBx48ZRVlZ2xMe43W6Ki4ubL/tkLqewUdXYwOa9ESTH12I1de3RHG1Lx7z8PXpqu6gzR1OVkAuKYnSoNpFg1enuyuezyr4c8AeNjiPagG5PwJ9xEe4ML6x6hNoPPjA6kugkpOgn2t2afbvYdyCeHmkeacicgPiti0guzaO4yRVWPfz+2ymuID1cu3intD+r671GxxEKmIdfRkNdCrb81/DNv9/oREII0SE98sgj/OY3v2HKlCnk5ubyzDPP4HQ6eemll474GEVRSE5Obr4kJcmiZ+HiX8uWoGs2Tu8RbXSUTkxH/fJTegW20qC6KU8c0GULfj8aG12Njsr9+8PvN0K4CLl74084k8QB9dQ/NY3GDRuMjiQ6ASn6iXZ398IvUUw+xvSWXn7HK+775aTsX0tJk+vgHH5hWPD70cSYKhymBm7Lz8ATkiEMhlMV1GG/orE2FuvGh/F/9LDRiYQQokPx+/2sX7+esWPHNt+mqipjx45l9erVR3xcQ0MDWVlZZGRkMGnSJLZs2XLEbX0+H3V1dYdcROf1wYYanBGVpEW7jY7SeX2zgt6NG/CqTsqSB3b5gh9AtBl6u/aysqoP25tkVExXFUw4g2BkH1JHVFL+51/jLygwOpLo4DpE0U/mOAkfGw8UsGNfHNkp9dgtFqPjdArRu78itXAVZU0O6jMGdvlFO47FZlKYGL+HGn8ct+R3/QZcp2CxoA+9nqb6KCzf3I9PCn9CCNGsoqKCUCj0k556SUlJlJQcfgX03r1789JLL/H+++/z6quvomkap512GkVFRYfdfsaMGURFRTVfMjIyWv11iPbx7nfraPTE0z8jfE/wnix94zf0rlpFQLVRmjwEPYzmDx8bXYtJCXJfofw+7rIUBX/6RHRnImnDCjnw+ymEamqMTiU6MMM/AWWOk/Dyl4+WoagBzu0tQ1SOR1TBt2TsWUZFk42atIEoVimUAmTbNYZGbeermv68XNFkdBwBKDYr+pCpeBuisa69H+9b9xkdSQghOq1Ro0Zx7bXXMmjQIEaPHs38+fNJSEjg2WefPez206dPp7a2tvlSWFjYzolFa3ls2SZM5gaGywIeLRLavpme+5egqyaKE4eiKeFVPHWaFAa695BX35uvZCqcrku14Mu6AtVpI7nHJor+cCOaz2d0KtFBGV70kzlOwse20v1s2RtNZnIdLpvV6DgdXuSBrWR8v5Aqr5WqlIGodpvRkTqUc90ekmxFPFLYj21NfqPjCECx2dCH/YamhjjsWx+h6bkbjI4khBCGi4+Px2QyUVpaesjtpaWlJCcf31QnFouFwYMHs2vXrsPeb7PZcLvdh1xE57OpuJCC4ji6pTZiCqPeaa0llL+H7js/wmyG4sQhaObwPFk+2t2Iw1TP3wtjjY4i2pIlAl/2ldiiNeJcyzjw59vRNZn6SPyUod8mMsdJePnzh4tRlBDn9JEzl8fiqthN5uYF1PnMVCQNRHU6jI7U4SiqwuUJxZgIcePuJBplfr+OwWKB4VPxeNJwHHiTxhnnowelKCuECF9Wq5WhQ4eyZMmS5ts0TWPJkiWMGjXquJ4jFAqxadMmUlJS2iqm6ADu/2w5KCHO6ilt5RMVKj5A1qZ3cFg1ihMGE7SEb9vZosKoqHzym7oxr1JGxHRluj0JX+bluJJ9RFS9TemMGei6bnQs0cEYWvSTOU7Cx7bS/Xy3O4qMpBrc0mPtqBw1RWRteAuP30RpfH9Ul9PoSB1WpAkmxO+kwpfE7/PljHiHYTKhDJ9MQyAXR9NqvH/rT6j88J/RQggRDqZNm8bzzz/Pyy+/zLZt2/jtb3+Lx+NhypQpAFx77bVMnz69efv777+fzz//nD179rBhwwZ+9atfsW/fPqZOnWrUSxBtrKyhlnU7nCTFVxNhk7byiQhVVZL6zetE2QIUx/XHb40wOpLhhrt8xFhKeWx/Jn7p/dWlaRE5+NMuJLpbI+a8/1D53PNGRxIdTKf7lSxznHROd3ywGEXROLevDMU+GltDGdlrX8UbgOKofqgyPOeYejpCDI/6nq9rTmFWicxd0mEooA66GI/zHKxqKdrDQ/Ctes/oVEIIYYgrr7ySmTNncvfddzNo0CDy8vL49NNPm098FxQUUFxc3Lx9dXU1v/nNb+jbty8XXHABdXV1fPXVV+Tm5hr1EkQb+9snn6JrFs7uI0MyT0Soro7EL18hweGjJLovPnuM0ZE6BEVVODe2iJpAIv8qlpV8u7pQdH/8SecQ36+B4McPUP3mPKMjiQ7E0GVA22uOE5ucLTPUd8UFbNoTTVZKFW679LQ8EmtjNdlrXiYQ0Nkf0Rs1Vhotx2uMu5ES/15e2D+QAc6NnOOWFcs6CrX7qXgrUrHkv43l08l4vr0C52+fQTGF18TaQghx0003cdNNNx32vuXLlx9y/dFHH+XRRx9th1SiI/D4fCz+DmJiKkmOlLby8dKamohZ9gqpTg+lEd1pdMmw6P/Ww66R5cjn7bK+XJ+wg1SroT/9RRsLxp+KEmwkecgaDsy9A9XlImrihUbHEh2AoT39ZI6T8HDH+0tRlCBj+8oX8ZGYffVkrZmNFghQZO2GGi/H6kQoqsLl8eVEmGu4fU9P8n1yRrMjUeIzCQ64CV8wFVfFWzT9JZfAns1GxxJCCCE6hL99+jGhgIsze0caHaXT0P1+Ij5/hSxnDRWOTOrd6UZH6pDGx1ag6SbuLJBFFMNBIGkMgejBpAyvoeGpP1C/eLHRkUQHYPjwXpnjpGtbs28X3+fHkpNaJ/OTHIEp0Ej2V7NR/V6K1CyUlDSjI3VKVlXhyoR9hHQzU3Ym4JGFPToWqw19yHV4HGOwmctQXzgTz4uyypgQQojw5vH5eH+dn6ioCnJio42O0ynowSD2z16nu7OcKlsyNdHZRkfqsKLNMMS9i3W1uXxWI4t6dHmKQiD1fELRA0gdWUXdzP9H/bJlRqcSBjO86CdznHRtty9YgWryMbbv8Q3XDjdq0EfW6pex+Oop0NMgLcvoSJ1anFVnYvx2yn3JXL/bii71pA5H6TYKb68bCGgxuAqfo2l6H/xb1xgdSwghhDDEXZ9+TCgQwei+svjE8dBDISyfz6OnbT+1lniqYnuBohgdq0M72+0h0lzJ/YWZ+ORka9enKPjTLiAUfQqpIyup//dU6v9nCgkRXhQ9zNZ0rqurIyoqitraWtyySEKb+mDrt9w89wD9upUzto90uf9faihA5po5OBrK2BdIQs/uLY2WVrK6zsYX1QOYmPAtD2VajI4jjkDbvQp73UpUk0ZTzPk4f/sCikN+9IjjI9/nQhyZvD86hwpPPSNnLCTS1cjk02Uuv2PSNNRF75Cr7KTBEk1pwgBpOx+n3V6Vt0sHMzFho7SNw4WuYS36CFPtZorXxxL5xxeIPPdco1OJE9Qa3+eG9/QTXZOmafzt/Y2YrXWc3VPmW/xfihYi45tXcXrKKPAloGdJwa81jXL76Bf5PR+WD+bZMhnK0FGp3U/H1/cmvIE0nDWfELyvO76FT0B4nYsSQggRpqa99yGhoIOxp0QZHaXj0zSUJQvoo+zEY3ZTltBf2s4noLtdo5drNx+Xn8LaBp/RcUR7UFT86RMJxgwmdVgVnieup+6TT4xOJQwgRT/RJh7+YjG1tQmM6KlhllU6D6HoITLWvU5E/QEKm2IJ5fQBVRotre3C6DrS7Pt4onAQn9R4jY4jjkCxu2DotXgSfo4WMmH75i5895xCcMdXRkcTQggh2sz28mK+3GInKaGCtCjpjXlUuo6y9D36at/jNUVQmjgQXZGfsSdqQkw1dpOHP+1NkWG+4eKHOf4CcaeSPKgG/5zfUP3WW0anEu1MPi1Fq6vzNvLcsgqcrkqGZyUZHadDUfQQ6evm4a4poLApmkB2Looqb8O2oKgKVyaUEWWpYvrevnzrkbOaHZma3JPQ0D9Qz0hUXzGm18bjmzUBvabI6GhCCCFEq7tx3qeAzvhTpK18VJqGsngBfULb8JpdlCQNkoJfC9lMCuPj9lDhT+bOAin6hQ1FIZB8Dv7Ec4nvW4+68GYqn3+GMJvlLazJJ6Zodb97dwEBfwTn9XcaHaVDUXSNtA3vEFW9l4LGKPzZp6CYpRdkW7KqcFViAWYlwA27cijyBYyOJI7GpGLqdy6+3N/TUJ+NuWwV+iP9Cc67Bbx1RqcTQgghWsWbeV+ztyiRvln1RDlsRsfpuEIh1MXz6atvw2eOoCRpMLoibeeT0dMRol/kDj6rHMAH1TIFTjgJJozElzYJd5YPx7d/o+yfd6NLj8+wIEU/0arW7NvFys0uUhMryI6NNjpOh6HoIdI2vEV05S4KPG4p+LUjtxmuSNyJT7Pzq52J1AVDRkcSx6A6IzCN+iWexF/iqYxC3TwH7cFeaCseg6Df6HhCCCFEizX4vNzz/k6s9hrO6ZNqdJwOSw8GMX/2Jn3ZTpMp8oceftJ2bg0XRNcRYynnvn295IR4mAlF98PX7WrsiRBT/RSlf5qM1iTF365Oin6i1Wiaxm/f+BLV5GdC/2Sj43QYihYifd1bRFfuPljwy+mPYjEbHSusJFthUvw2KvwJXL3ThV/OanUK5rQsTGfdQK02hsYyHWXJ3Wj/7oe+6R2Qv6EQQohOaOq8d/A1uTlvgBWTDFM9LN3nw/7JK/Qx5+OxRFMqBb9WZVIVLo8vJKhZ+fXuGGkXhxnNmYGv568xRUaS6PqQ8t+PI1hebnQs0Ybkm0a0mr988iHVNYmM6hPAabMaHadDUENBMta9TlT1Hgo8UVLwM1APh8bPYreyu7EbU/eY0aV90zmoKrZBo2DY/6OypC++A9Uo7/4a7anTYO8Ko9MJIYQQx+3t79ayZqubjOQKesTHGB2nQwrV1eH+9EV62Q9QZ42nNGGAzOHXBuKsOuPivqewKZNb9hmdRrQ33RqDr/dUQq4ckjI3Uv/n02jamGd0LNFG5BNUtIp1RXuYtypETEwZwzJlQmIANegj85u5RNYUsM8TjT/nFCn4GWxQRIDTozezrjaXPxYEjY4jToAaGYFz7CX4Mn9O+Y4kAvk74eWJ6K9cBqVbjY4nhBBCHNWBumqmv7MTq72OiQPTjI7TIQUPHCBp2QtkO6upsKdTHp8LimJ0rC7rFFeQIe5tfFE1gJkHZMG7sGOyEuh+Jb6Ys4jOKEN/4WfUzXve6FSiDUjRT5w0b8DP9XNXoKhBLh2SaHScDsHsbyR79Us46orJb4wl0E0Kfh3FmVE+BkZu5fPKQfx9vzRwOhtrdjbOCddSp59BxbYIQttWoD99Grx/E9QVGx1PCCGE+IlgKMSlLywgGHBw0RAnFpMMVf1foS15dFv/CsmOJkoie1IT210Kfu3gvCgP2c49zC4exLxKmdst7CgKodQz8GX9Cls0uDbeTvW9v0DzyxzaXYkU/cRJu/b1t6iri2N0vxARNlmBzNZYTc6q57B6qsn3JhDq1k8W7ehgzo9uoKdrF6+XDOWpEq/RccQJUqwWXKNOw3r21ZTvzaFmlwNtwzz0xwfB0n+Ar97oiEIIIUSzyW+8SUlZEiP7eEiLdhsdp0PRg0GUpe/Tt2AhTptOUdwgGiJlgZP2oqgKl8VVEm8r4e/7BrCoVtrF4UiLzMSX+3uC1jRi+ITG2/vh/z7P6FiilUjRT5yUx75cwjfbYshOK2dAWoLRcQznqCmi21fPg7eR/FAKdOuLYpK3WUejqAqXxFaRYc/nP/sH80qFNHA6I3N8HFEXX4refSzF66Jp2G9F//Jh9McHw9oXISRDuIUQQhhr+sL3WbU5mqzUUk7NkYXu/luoohz3x8/TL7iZoMVJUfIIfPYoo2OFHYsKVyccINJcy217cllRL+3isGS2E+p3LY1R5+CIrEB9+Rw8s/+KrutGJxMnSaoRosUW79jCrE9qiYisYOKAdKPjGC56/0a6rZ2LzxeiwJSDktUTVBmW0FGpqsKVCWUk2op5aN9A3q2SBk6npCrYTzmFqJ9fRWOwN8VrIvBV6PDxNHh6FGz/BKSxIoQQwgAPLfuMN1aoxMeWMWmgtJWb6Tr6+q/IWf0SWY4qqhypFCcPJWSShQCNYjfB1Un7sKuN3LyrL1/USbs4bKWfiq/nDYR0N659T+L9a3+C+zYbnUqcBCn6iRb5rriAG17ZhNns5aoRCahhvKqWomskbf2MjK0fUdNooiiiL2paBki9r8MzqwpXJxYTa63g3vwBfFgjDZzOSnW5iPzZeTjPnUjl9ghKN0YTrK6DN34Bsy+AonVGRxRCCBFG7vv8Y57+zE9UVCW/GJmGEsZt5f8Wqign8qPn6Ve5DIcNiuIGUh3bU+bv6wAiTXBN0l7sahM37+rHpzUyx1/YcsYQGnIjDdbTsbAf9fkz8L54I3pAfit1RooeZv016+rqiIqKora2Frdb5tRoiZ0VJVz45GICQStXjrKRGOkyOpJhzP5G0te9SURDMSUeB/UpuaiREUbHEifIG4KXyzKoC8TwYLdNTIi2Gx1JnIxgkMYN39KUtwFnmpWY3iFUXxn0vQjOvRviexqdULQC+T4X4sjk/WEcTdOY+tZbLM2LJCa6jKtHpWGSgh+6z4dlzadkNmzFadGotiVRE9cTTZF5rzsaTwheLc2kNhjDHZkbuTbeYXQkYSCtoQZ18zs4XGUEtQiUCx7EPOpXUqhvJ63xfS5FP3FCtpXu5+Knl+EP2Jk0XCErNnzn3XBV5ZO+4R2UoI9CXwyhzL4oVovRsUQLNYV05pZmUReM5p/dNjFRCn+dXqimFs+qVQQK9hE1KI7IxCoUXw0M+iWMvhOiM4yOKE6CfJ8LcWTy/jDGgbpqrnhpAUUlSaQnl3LJ4PSwHg0DBxfqMG34kpTSdcTa/XhwUBmXi98mJ8k7Ml9I5/XyVEp9qVyZ9C13p9oI83/KYS+4awPW8mXY3T781u6Yr34GNWuE0bG6PCn6tYA0glpu2a5t/GZuHlrIzEXD1bAt+ClaiMTvF5NQtI4Gv0qxORM1PVPOdnQBTSGdV8oyqQnEck/WRn4eJ2c2Oz0d/PvyafxqNaGGWmJPT8VpK0AJNMGQa+HMaRAl8yx1RvJ9LsSRyfuj/T391XIe/rSYYMDO0F71nNE9vFeg1f1+zHkrSSrdQJzdh1e3UO3uhiciSdrMnURI03mvKoadnp4Mdm/luW5BXLJAYVjTfT6CeZ/jULZidYUIxI7EfOUslKRco6N1WVL0awFpBLXMv5d/zn8+b8Bk9nLZSCfJYTqE1V53gLQN7+Lw11Ha6KA2sTemqPAsfnZVvpDOq2XpVPgTuTnjW25IlMJflxAM0bR5M00bNqCoGrGnp2JnJ0rQB4OvhtNvhdgco1OKEyDf50Icmbw/2s/yPdu4c8FqSsqTcLgqmTAogrSo8D3menUVzu+Wk1i/E7cteLDYF5mJJzJNin2d1Je1Nr6qySXOWsZT3Yvp77QZHUkYLFRZibZ5IS7Xfkx2jVDqaMwXz4CkfkZH63Kk6NcC0gg6MRWeeq559V227U0gMrKcK0YkEGELvw96Negj4fvFJBzIwxtU2a8loqd3R7GYjY4m2kBAgzfLE9nvzeDnSXncm2qVIQ1dhNbkpenbDXi3bEF1WIg7LQWbthPF3wC5F8NpN0HaUKNjiuMg3+dCHJm8P9qWpmm8umENz6z4ngNliajmRgZkezmrZ3JYLtihBwKYt68nujCPBKUSiwkadAe1UVk0uRKl2NcF7PaqfFjRk6Bm5YbUzfw+WabBERAo3Ie+fTER0eWYHRrBxFMxT7gHMkfJ+76VSNGvBaQRdHw0TePhFYt5dmklwYCT3pnVjMtNDcOGjE5U0Xckf/85Zs1PaaOD+vieqDExRgcTbUzTdN6vimG7pyfDorbwXI6G3SRfXl2FVt9wsPi37XsUh5W409Kwm/JRGsshfQSMvOHgwh9mq9FRxRHI97kQRybvj9YXDIX4aNtG3tywlQ27Ffy+aEyWenqnezmrRzK2MDsRrAcDWHZtxF30HbH+UhwWjYCmUmeJpSEmi4A1PEcFdWUNIVhQkch+bxZ9I3bwWLaHdJvMZx72dPDv2Y2+azmumAqsrhChiO6o592B0u9SaUufJCn6tYA0go5O0zReXLuKJ5bspq4uAaergvEDIkmPCbdjpeMq30PSlk9wBWqp9Zopt6WjJ6WjmGWVsXCyotbO6pq+JNmKebp7Bb0d0rjpSrT6Bpo2bsT3/fega7hHZhIRW4NauxuccTDwKhh0NchcJR2OfJ8LcWTy/jg5mqaxs7KU5bt38HV+MVv3N1JW5UYLOVBUH9FRtZySbmNQenxYLdSh1Fdj37WByPJdxGiVWE06QU2hQY2kITINb0SC9O4JA6vrrKyq6YWqaFyXso1bEu0yIkaApuPfl4+2YyUOVzGOmCCaGgFDr0Ud9f9kGp0WkqJfC0gj6PDKG+p4+ItlfLChlkZPHFZbDcN7wtCMhDDr3afjKttJwrYlRPqr8PhVyvR4AqndUMNwWLM4aHujiYWVvdBQ+UPaVqYmypCGrkZr8uLbtg3v1i1o9Q3YuyUQ1duGJbAXxV8PibnQ/+eQOwniuhsdVyDf50Icjbw/jq3O28jGA4VsLSthV3k1+6oaKKkNUFmn0tjkQg/9MKev4sfuaCDBHaB7op3clDgspvA4Aaz4GrHnb8ZRshN3UzERqg9FAW/IhMfkpjEyCV+EDN8NRzVB+LDyYK+/ZFshd2WWco5b2sfioGBJKf5tX2ML7sKV6EM164RiBqCeeQNK7kVgl++l4yVFvxaQRtD/KW+oY87aNXz43X4KSqJBt+CKqGRQtjnsin1qyI973wbi8tfgDHnw+FUqiMOXkI3qchodT3QAdUF4tyKFUl86uRHbmZndSLYMaeh6NA1/QQHebdsIFBSCohN5Sjyu5BBmfyFKyAcJvaHX+dBjLGSMBLOcEDCCfJ8LcWTy/jiowefly707WF9YxPayGoqqfVTUKnianGhB139tqaOaG7FafDjsQaKcEB9pJjPGRVp0RNj05jN5arAXbMVRvpuIpjIiFC+KAoGQQiMOGm0xeN0phOyuYz+ZCAubGswsr83GE4whN2I7f0lvYIhL2kXiIM3TiG/7FtTSPByR1diig6CYCKWcjum0ySi9xoEt0uiYHZoU/VognBtB3oCfT7dv5sMtO/g2v4mqmljQzVistaTF+xiRE0NKVDjNv6HjrCzAvXsNMTV7MKFR5zNTbU7An5CJ6pCzVeJQuqbzVYOdNTU90VGYmLCNv6SacJnC48dAuNGamvDv2YN/z14C+/ejKBqubk6cqSaspgqUUCO6xYGSPgKyToeMEQcXAZGzl+0inL/PhTiWcHx/FNVWsXDbZr7ed4AdJU2UVVvweaOAg9/RiurFam3CYQsQ4dCJdpqIdVlIjLSTEOnEagqvOfnUUABr2V7sxTtx1B4gwl+FXQ0C4A+pNOo2mixR+CISCbqiQZXefOLwgprOynonG+qy8WtOciN2clNKPWdLzz/xIx2CpaUEdm/CVP89jigPNncIXVcJxQ5EHXI5ar8JMgT4MKTo1wLh1AjaXVnKwm1bWLXnADuKA1TXRqNrVlACOJ21pMVpDEiLCqv5+hQthLN8NxH7NhJduxcrAfwhhZqAgzpnKnp8sszZJ46pLgifVseypzEHu6mBifG7uDlJJS7MJvEOJ7rXh7+oiEBRIcHiYkI1NVhcIRzJKvYEBYu1AZUAOgpEZUH6UJSU/pDY72DPwKgMUKU43JrC6ftciBPV1d8fHp+Pz3ZsZunOPWwqaqC40orfF33wTiWAzV6P2xkgPlIlJdpGZoybKEf49j5SQ35s5fuwlu7FUXMAp68SJwd78Wk6NAXNNCkOfLYofK54NJdbhuyKE+bXdL6sc7GpPhOvFkmyrZBJ8aVcG2clxiJtIPEDTSdYUkJg31bM9TuxOeuwRQVRVAgqMegpIzENvAC1z7kQlW50WsN1maLfU089xb///W9KSkoYOHAgTzzxBCNGjDji9m+//TZ/+9vfyM/Pp2fPnjz00ENccMEFx7WvrtgICoZCbDiQz8o9e9hQVMbuUh/l1U6CgYNdZRXVi9PZQEJUiG4JDnonxYTN2Uw1FMBeWYD9wA5c1flEBqoxKTr+kEJdwI7HFocvOkWG8IoWOeBTWFoTz35vBiYlwCD3Hi6LbeSCaBsWOSPepWmeRoJlZQTLywlWVhKqqkQN1mCLDGKJCGKNCGGJCKGaDn7FarqZkBqLZktCd6aiRaRDVAZKbDZKXBZqVByqy4XqcqHY7SjyY+uYuuL3uejapL3bMt6An5V7d7J89x6+Lawivww8nmjQzUAIq72O6IgAKdEqWfEusmLcYTMc93+poQCWmhKs5YVYaw5gb6zEGazFrgQA0HVoCqp4seMzu/DZowlGxoE1fAuiovUFNZ31HhvfNSRS6U/BpPjo5crn3Oh6Lo0xk2wNj9+h4vhotXX4i/agVm7HHCrB5vJicYUACGlOgq4eKOlDMPU5C7XnaSiRyWF1UqJLFP3mzZvHtddeyzPPPMPIkSOZNWsWb7/9Ntu3bycxMfEn23/11VecddZZzJgxgwsvvJDXX3+dhx56iA0bNnDKKaccc3+dtRGkaRpFtVVsOFDA5uISdpbVsK/SR3mtSmOj+2APPkBRfdjtDURHBEmJNpMTF0FadESXn59PDfqw1JZjrj6AraYEm6ccp68aBwcnHA5p0Bgw06i68NjjCEYloNqly7loHZV+hVX1kexuTMWnRWJVG+jtKmJYRANnuhWGOq1SBAwHwRChujpC9fVoDfVoHg80VaKGajErDZhMTZjMPsy2ICabdkh7JehTCDaaCDSaCPrMhII2NN2JpkSgmd3giEF3xKFExGKKjEKNjMAU6cbkjkR1uzH9cPnx/8OhcNhZv89FeJL27rF5fD427M/n2wP72Vxcwe4yD8VVJhob3aAfnEPXZKkn0tlEYhRkxTvpER+F1Rw+BQQ1FMDkqcZcU465vgKLpwpLUw02fz12rQnbD8NzAfwhBZ9mwvdDgS9gdxNwxaLY7NC1vx5EB1Lih/UNkeQ3JVAfjAM04q0l9HFVMsjlY6RLYYC0k8WPdAjV1hIq2YdSsxuTvwyzuQ6rM4hqOVi2CoXMhIhDc6ahx+SgJPXClJ6LKesU1LhM6GKdm7pE0W/kyJEMHz6cJ598EjhY3MrIyOAPf/gDd95550+2v/LKK/F4PHz00UfNt5166qkMGjSIZ5555ifb+3w+fD5f8/Xa2loyMzMpLCxs10aQPxjEG/TT5PfjCfrx+Hx4/D7qfF5qmpqo8TZR4/FS1eSlptFHTWOA2iaN+kbw+CwE/c7mwh4cLO5ZrE047QGinZAYZSEjxkWCy9FJCnz6wdONmoYSCqGEgqAFUIIBCAZQAj7UoB814IOAD5PfixpswhTwYgp4MQe9mENNWDQ/ViWAWdGanzmogT9kwocVv+rEa4siEBF7cPXdLv4jWBhL03R2+0zsaIzggD+WhmA0oKIqfqIs1SRY6km0ekm0BIk3a8SbIdYCblUh0qQSoSo4VQWbqmBBpVO8lcWJ00EP+KCpCsVXg+KvRQnUowQbUEKNqDSh6F5U/CjKoV/Rug5awETIrxL0g+ZTCAVUtICCFlDRgsrB/9fNYHGAxYlic6E4IlDskShOF4rdheKM/OG/LhSHC9XhRLE7UWwOVLsDxe5AsVpQLRYUiwUsFhSTCcVsRjGZ4Mf/mkyGFRfr6urIyMigpqaGqKgoQzIIcbzCob0bDIXwBgN4A348fj8Nfi8NPh91viZqvV6qmhqp8nipbmyi2uOjqjFIbWOIukYVr89GKBBBczVKCWCxenA5AsS6ICXaSk5cFJF261EzdBw6hEIomgbaD+3cUODgf4MBCPhRQwHUoB8l6EMJ+FGDPkwB7w//9aGGfJg1H2bNj0UPYFaCmP/nOyEYgoBmIoAFv2IhaHYQsDgJOKLQ7S4UmVZCdCAVAfi+yU6RL4IqfzQ+7eBnk0qASEsNseYG4q1NxFsCxJpDxJoh1gTRZog0KUSoCi5VxaGo2E1IWzlcaDqhulr0qgPQsB/VV4mq12FSvZitQUzW//tc1HUIBcxomp2Q4kI3R4A5Et0eiWJ3gz3q4Nzb9ggURySK/Yf2sc0JdheKzYFidaBYbShmO1itKGYrWKyGfZ62RnvX0DKo3+9n/fr1TJ8+vfk2VVUZO3Ysq1evPuxjVq9ezbRp0w65bdy4cbz33nuH3X7GjBncd999P7k9IyOj5cGFEEII0SHU19dL0U90aNLeFUIIIcTJOJn2rqFFv4qKCkKhEElJSYfcnpSUxPfff3/Yx5SUlBx2+5KSksNuP3369EMaTZqmUVVVRVxcXJcf+nS8fqwet3fvx3Amx7z9yTFvf3LM2184HXNd16mvryc1NdXoKEIclbR3O59w+iw1ghzftifHuG3J8W1bcnz/T2u0d7vWgOfDsNls2GyHTk4bHR1tTJgOzu12h/2bqr3JMW9/cszbnxzz9hcux1x6+AlxkLR320a4fJYaRY5v25Nj3Lbk+LYtOb4HnWx719BR8PHx8ZhMJkpLSw+5vbS0lOTk5MM+Jjk5+YS2F0IIIYQQwijS3hVCCCGEUQwt+lmtVoYOHcqSJUuab9M0jSVLljBq1KjDPmbUqFGHbA+waNGiI24vhBBCCCGEUaS9K4QQQgijGD68d9q0aUyePJlhw4YxYsQIZs2ahcfjYcqUKQBce+21pKWlMWPGDABuueUWRo8ezcMPP8yECRN48803WbduHc8995yRL6NTs9ls3HPPPT8ZFiLajhzz9ifHvP3JMW9/csyF6Jikvdu5yGdp25Lj2/bkGLctOb5tS45v61J0XdePvVnbevLJJ/n3v/9NSUkJgwYN4vHHH2fkyJEAnH322WRnZzNnzpzm7d9++23uuusu8vPz6dmzJ//617+44IILDEovhBBCCCHE0Ul7VwghhBDtrUMU/YQQQgghhBBCCCGEEK3H0Dn9hBBCCCGEEEIIIYQQrU+KfkIIIYQQQgghhBBCdDFS9BNCCCGEEEIIIYQQoouRop8QQgghhBBCCCGEEF2MFP3CxFNPPUV2djZ2u52RI0fyzTffHHHbQCDA/fffT/fu3bHb7QwcOJBPP/20HdN2fitWrGDixImkpqaiKArvvffeMR+zfPlyhgwZgs1mo0ePHoes4CeO7kSPd3FxMb/85S/p1asXqqpy6623tkvOruREj/n8+fM577zzSEhIwO12M2rUKD777LP2CdtFnOgxX7lyJaeffjpxcXE4HA769OnDo48+2j5hhRCig5O2cduRdnDbknZv25I2btuTNm37kqJfGJg3bx7Tpk3jnnvuYcOGDQwcOJBx48ZRVlZ22O3vuusunn32WZ544gm2bt3KjTfeyCWXXMK3337bzsk7L4/Hw8CBA3nqqaeOa/u9e/cyYcIExowZQ15eHrfeeitTp06VL4zjdKLH2+fzkZCQwF133cXAgQPbOF3XdKLHfMWKFZx33nksXLiQ9evXM2bMGCZOnCifKyfgRI+5y+XipptuYsWKFWzbto277rqLu+66i+eee66NkwohRMcmbeO2Je3gtiXt3rYlbdy2J23adqaLLm/EiBH673//++broVBIT01N1WfMmHHY7VNSUvQnn3zykNsuvfRS/eqrr27TnF0VoC9YsOCo29xxxx16v379Drntyiuv1MeNG9eGybqm4zne/2306NH6Lbfc0mZ5wsGJHvMf5ebm6vfdd1/rBwoDLT3ml1xyif6rX/2q9QMJIUQnIm3j9iPt4LYl7d62JW3ctidt2rYnPf26OL/fz/r16xk7dmzzbaqqMnbsWFavXn3Yx/h8Pux2+yG3ORwOVq5c2aZZw9nq1asP+RsBjBs37oh/IyE6O03TqK+vJzY21ugoYePbb7/lq6++YvTo0UZHEUIIw0jbuOORdrDoSqSN2/akTXtipOjXxVVUVBAKhUhKSjrk9qSkJEpKSg77mHHjxvHII4+wc+dONE1j0aJFzJ8/n+Li4vaIHJZKSkoO+zeqq6ujqanJoFRCtJ2ZM2fS0NDAFVdcYXSULi89PR2bzcawYcP4/e9/z9SpU42OJIQQhpG2cccj7WDRlUgbt+1Im7ZlpOgnfuKxxx6jZ8+e9OnTB6vVyk033cSUKVNQVfnnIoQ4ea+//jr33Xcfb731FomJiUbH6fK+/PJL1q1bxzPPPMOsWbN44403jI4khBCdirSNhRDHQ9q4bUvatC1jNjqAaFvx8fGYTCZKS0sPub20tJTk5OTDPiYhIYH33nsPr9dLZWUlqamp3HnnnXTr1q09Ioel5OTkw/6N3G43DofDoFRCtL4333yTqVOn8vbbb/9kKI9oGzk5OQD079+f0tJS7r33Xq666iqDUwkhhDGkbdzxSDtYdAXSxm170qZtGTk91cVZrVaGDh3KkiVLmm/TNI0lS5YwatSooz7WbreTlpZGMBjk3XffZdKkSW0dN2yNGjXqkL8RwKJFi475NxKiM3njjTeYMmUKb7zxBhMmTDA6TljSNA2fz2d0DCGEMIy0jTseaQeLzk7auO1P2rTHT3r6hYFp06YxefJkhg0bxogRI5g1axYej4cpU6YAcO2115KWlsaMGTMA+Prrr9m/fz+DBg1i//793HvvvWiaxh133GHky+hUGhoa2LVrV/P1vXv3kpeXR2xsLJmZmUyfPp39+/czd+5cAG688UaefPJJ7rjjDq6//nqWLl3KW2+9xccff2zUS+hUTvR4A+Tl5TU/try8nLy8PKxWK7m5ue0dv1M60WP++uuvM3nyZB577DFGjhzZPG+Sw+EgKirKkNfQ2ZzoMX/qqafIzMykT58+AKxYsYKZM2dy8803G5JfCCE6Cmkbty1pB7ctafe2LWnjtj1p07Yzo5cPFu3jiSee0DMzM3Wr1aqPGDFCX7NmTfN9o0eP1idPntx8ffny5Xrfvn11m82mx8XF6ddcc42+f/9+A1J3XsuWLdOBn1x+PM6TJ0/WR48e/ZPHDBo0SLdarXq3bt302bNnt3vuzqolx/tw22dlZbV79s7qRI/56NGjj7q9OLYTPeaPP/643q9fP93pdOput1sfPHiw/p///EcPhULGvAAhhOhApG3cdqQd3Lak3du2pI3b9qRN274UXdf11ikfCiGEEEIIIYQQQgghOgKZ008IIYQQQgghhBBCiC5Gin5CCCGEEEIIIYQQQnQxUvQTQgghhBBCCCGEEKKLkaKfEEIIIYQQQgghhBBdjBT9hBBCCCGEEEIIIYToYqToJ4QQQgghhBBCCCFEFyNFPyGEEEIIIYQQQgghuhgp+gkhhBBCCCGEEEII0cVI0U8IIYQQQgghhBBCiC5Gin5CCCGEEEIIIYQQQnQxUvQTQgghhBBCCCGEEKKL+f9voNmXlpvvpgAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", "fig, axes = plt.subplots(2, 2, figsize=(15, 5))\n", "fig.suptitle('Learner Comparison')\n", "\n", "\n", "sns.kdeplot(data=df_coefs,ax=axes[0][0], fill=True, alpha=0.3)\n", "sns.kdeplot(data=df_ml_m, ax=axes[0][1], fill=True, alpha=0.3, legend=False)\n", "sns.kdeplot(data=df_ml_g0, ax=axes[1][0], fill=True, alpha=0.3, legend=False)\n", "sns.kdeplot(data=df_ml_g1, ax=axes[1][1], fill=True, alpha=0.3)\n", "\n", "axes[0][0].title.set_text('Estimated Parameter')\n", "axes[0][1].title.set_text('Log Loss ml_m')\n", "axes[1][0].title.set_text('RMSE ml_g0')\n", "axes[1][1].title.set_text('RMSE ml_g1')\n", "\n", "plt.subplots_adjust(left=0.1,\n", " bottom=0.1, \n", " right=0.9, \n", " top=0.9, \n", " wspace=0.4, \n", " hspace=0.4)\n", "plt.show()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "We can now easily observe that in this setting, the linear learners are able to approximate the corresponding nuisance functions better than the boosting algorithm (as should be expected since the data is generated accordingly).\n", "\n", "Let us take a look at what would have happend if a each repetition for each nuisance element, we would have selected the learner with smallest out-of-sample loss (in our example this corresponds to minimizing the product of losses). \n", "Remark that we cannot select different learners for `ml_g0` and `ml_g1`." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([0, 2], dtype=int64), array([194, 6], dtype=int64))" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "selected_learners = (loss_ml_m * (loss_ml_g0 + loss_ml_g1)).argmin(axis=1)\n", "np.unique(selected_learners, return_counts=True)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Most of the time, we will use linear learners for both nuisance elements. Sometimes the tree-based estimator is chosen for the propensity score `ml_m`. \n", "Let us compare which learners, how the estimated coefficients would have performed with the selected learners." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Coverage of selected learners: 0.94\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(f'Coverage of selected learners: {np.mean(np.array([coverage[i_rep, selected_learners[i_rep]] for i_rep in range(n_rep)]))}')\n", "\n", "selected_coefs = np.array([coefs[i_rep, selected_learners[i_rep]] for i_rep in range(n_rep)])\n", "df_coefs['Selected'] = selected_coefs\n", "sns.kdeplot(data=df_coefs, fill=True, alpha=0.3)\n", "plt.show()\n" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "This procedure will be generally valid as long as we do not compare a excessively large number of different learners." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Custom evaluation metrics\n", "\n", "If one wants to evaluate a learner based on some other metric/loss it is possible to use the inbuilt `evaluate_learners()` method.\n", "Without further arguments this will default to the RMSE for all nuisance components and result in the same output as the `nuisance_loss` attribute for regressors." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'ml_g0': array([[1.02528067]]), 'ml_g1': array([[1.060581]]), 'ml_m': array([[0.34943627]])}\n", "{'ml_g0': array([[1.02528067]]), 'ml_g1': array([[1.060581]]), 'ml_m': array([[0.39236801]])}\n" ] } ], "source": [ "print(dml_irm.evaluate_learners())\n", "print(dml_irm.nuisance_loss)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "To evaluate a self-defined metric, the user has to hand over a callable. In this example, we define the mean absolute deviation as an error metric.\n", "\n", "Remark that the metric should be able to handle `nan` values, since e.g. in the IRM model the learner `ml_g` is used to onto two different subsamples. As a result, we have two different nuisance components for\n", "\n", "$$\n", "\\begin{aligned}\n", "g_0(x) &= \\mathbb{E}[Y|X=x, D=0] \\\\\n", "g_1(x) &= \\mathbb{E}[Y|X=x, D=1]\n", "\\end{aligned}\n", "$$\n", "\n", "which are both fitted with the learner `ml_g`. Of course, we can only observe the target value for $g_0(x)$ if $D=0$ and vice versa, resulting in `nan` values for all other observations." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'ml_g0': array([[0.8173602]]),\n", " 'ml_g1': array([[0.85265193]]),\n", " 'ml_m': array([[0.20073763]])}" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.metrics import mean_absolute_error\n", "\n", "def mae(y_true, y_pred):\n", " subset = np.logical_not(np.isnan(y_true))\n", " return mean_absolute_error(y_true[subset], y_pred[subset])\n", "\n", "dml_irm.evaluate_learners(metric=mae)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Another option is to access the out-of-sample predictions and target values for the nuisance elements via the `nuisance_targets` and `predictions` attributes." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(500, 1, 1)\n", "(500, 1, 1)\n" ] } ], "source": [ "print(dml_irm.nuisance_targets['ml_g1'].shape)\n", "print(dml_irm.predictions['ml_g1'].shape)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "For most models minimizing the RMSE for each learner should result in improved performance as the theoretical backbone of the DML Framework is build on $\\ell_2$-convergence rates for the nuisance estimates ([Chernozhukov et al. (2018)](https://doi.org/10.1111/ectj.12097)). But for some models (e.g. classification learners) it might be helpful to further check other error metrics (e.g. as in [scikit-learn](https://scikit-learn.org/stable/modules/model_evaluation.html#)) to gain a overview whether the nuisance function can be approximated sufficiently well. Specifically, for binary classifications the log loss is a common and stable choice as it is also a calibrated metric. \n", "\n", "Of course, if one has some prior knowledge on functional form assumptions (e.g. linearity as in the IRM example above) using these learners will usually improve the performance of the estimator and might speed up computation time." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Computation time\n", "\n", "The choice of the learner has a huge impact on the computation time of the DoubleML models. As the largest part of the computation time is usually used to train the learners for the nuisance components, some clever choices of learners and hyperparameters can speed up the computation time. \n", "\n", "Resourcewise, most implementations support the `n_jobs_cv` argument, which can parallelize the k-fold estimation and might speed up the calculation nearly up to $k$-times if the resources are available." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Time without parallelization of crossfitting: 4.7799 seconds\n", "Time with parallelization of crossfitting: 0.9944 seconds\n", "Speedup of factor 4.81\n" ] } ], "source": [ "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n", "from time import perf_counter\n", "\n", "np.random.seed(42)\n", "n_obs = 1000\n", "dml_data = dml.DoubleMLData(make_irm_data(theta=0, n_obs=n_obs, dim_x=20, return_type='DataFrame'), 'y', 'd')\n", "\n", "# define the sample splitting\n", "smpls = DoubleMLResampling(n_folds=5, n_rep=1, n_obs=n_obs, stratify=dml_data.d).split_samples()\n", "\n", "dml_irm = dml.DoubleMLIRM(dml_data,\n", " ml_g=RandomForestRegressor(),\n", " ml_m=RandomForestClassifier(),\n", " draw_sample_splitting=False)\n", "dml_irm.set_sample_splitting(smpls)\n", "\n", "np.random.seed(42)\n", "t_1_start = perf_counter()\n", "dml_irm.fit()\n", "t_1_stop = perf_counter()\n", "print(f'Time without parallelization of crossfitting: {round(t_1_stop - t_1_start, 4)} seconds')\n", "\n", "np.random.seed(42)\n", "t_2_start = perf_counter()\n", "dml_irm.fit(n_jobs_cv=5)\n", "t_2_stop = perf_counter()\n", "print(f'Time with parallelization of crossfitting: {round(t_2_stop - t_2_start, 4)} seconds')\n", "print(f'Speedup of factor {round((t_1_stop - t_1_start) / (t_2_stop - t_2_start), 2)}')" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Other more helpful ways to improve computation time will largly depend on the implemented learner. Of course linear learners are quite fast, but if no functional form restrictions are known Boosting or Random Forest might be better default options to saveguard against wrong model assumptions. Especially Boosting performs very well as a default option for tabular data. As a general recommendation all popular Boosting frameworks (XGBoost, Lightgbm, Catboost, etc.) should improve computation time.\n", "But this might vary heavily with the number of features in your dataset.\n", "Let us compare the computation time with Boosting and Random Forest (we increase the sample size and the number of features)." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Time without RandomForest (Scikit-Learn): 9.6598 seconds\n", "Time with XGBoost: 1.5964 seconds\n", "Speedup of factor 6.05\n", "Time with LightGBM: 0.5232 seconds\n", "Speedup of factor 18.46\n" ] } ], "source": [ "from xgboost import XGBClassifier, XGBRegressor\n", "from lightgbm import LGBMClassifier, LGBMRegressor\n", "\n", "np.random.seed(42)\n", "n_obs = 1000\n", "dml_data = dml.DoubleMLData(make_irm_data(theta=0, n_obs=n_obs, dim_x=50, return_type='DataFrame'), 'y', 'd')\n", "\n", "# define the sample splitting\n", "smpls = DoubleMLResampling(n_folds=5, n_rep=1, n_obs=n_obs, stratify=dml_data.d).split_samples()\n", "\n", "np.random.seed(42)\n", "t_1_start = perf_counter()\n", "dml_irm = dml.DoubleMLIRM(dml_data,\n", " ml_g=RandomForestRegressor(),\n", " ml_m=RandomForestClassifier(),\n", " draw_sample_splitting=False)\n", "dml_irm.set_sample_splitting(smpls)\n", "dml_irm.fit()\n", "t_1_stop = perf_counter()\n", "print(f'Time without RandomForest (Scikit-Learn): {round(t_1_stop - t_1_start, 4)} seconds')\n", "\n", "np.random.seed(42)\n", "t_2_start = perf_counter()\n", "dml_irm = dml.DoubleMLIRM(dml_data,\n", " ml_g=XGBRegressor(),\n", " ml_m=XGBClassifier(),\n", " draw_sample_splitting=False)\n", "dml_irm.set_sample_splitting(smpls)\n", "dml_irm.fit()\n", "t_2_stop = perf_counter()\n", "print(f'Time with XGBoost: {round(t_2_stop - t_2_start, 4)} seconds')\n", "print(f'Speedup of factor {round((t_1_stop - t_1_start) / (t_2_stop - t_2_start), 2)}')\n", "\n", "np.random.seed(42)\n", "t_3_start = perf_counter()\n", "dml_irm = dml.DoubleMLIRM(dml_data,\n", " ml_g=LGBMRegressor(verbose=-1),\n", " ml_m=LGBMClassifier(verbose=-1),\n", " draw_sample_splitting=False)\n", "dml_irm.set_sample_splitting(smpls)\n", "dml_irm.fit()\n", "t_3_stop = perf_counter()\n", "print(f'Time with LightGBM: {round(t_3_stop - t_3_start, 4)} seconds')\n", "print(f'Speedup of factor {round((t_1_stop - t_1_start) / (t_3_stop - t_3_start), 2)}')" ] } ], "metadata": { "kernelspec": { "display_name": "doubleml", "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": "3.11.9" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }