survival_analysis_cox.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# author: Jing\n",
    "# date: May 24 2019\n",
    "# function: survival analysis based on cox model, \n",
    "# plot results with seaborn ( plt.clf() to prevent plot accumulated), seaborn is slow "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 669,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "from sksurv.datasets import load_veterans_lung_cancer\n",
    "from sksurv.column import encode_categorical\n",
    "from sksurv.metrics import concordance_index_censored\n",
    "from sksurv.linear_model import CoxPHSurvivalAnalysis\n",
    "\n",
    "from sksurv.preprocessing import OneHotEncoder\n",
    "import matplotlib.pyplot as plt\n",
    "from sksurv.nonparametric import kaplan_meier_estimator\n",
    "sns.set_style(\"whitegrid\")\n",
    "# generate the response variable for cox model\n",
    "from sksurv.util import Surv\n",
    "from sksurv.linear_model import CoxnetSurvivalAnalysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 670,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = \"gene_mutations_clinical_mskcc.txt\"\n",
    "data_ = pd.read_csv(filename, sep = '\\t')#, dtype = 'object')\n",
    "\n",
    "d = {'LIVING': False, 'DECEASED': True}\n",
    "data_[\"OS_STATUS\"] = data_[\"OS_STATUS\"].map(d)\n",
    "\n",
    "data_y = data_[['OS_STATUS','OS_MONTHS']]\n",
    "\n",
    "data_x = data_\n",
    "data_x = data_x.drop(columns = ['Tumor_Sample_Barcode','PATIENT_ID','OS_MONTHS','OS_STATUS','ONCOTREE_CODE'])\n",
    "\n",
    "#a = OneHotEncoder().fit_transform(data_x[\"SEX\"])\n",
    "x = encode_categorical(data_x)\n",
    "y = Surv.from_dataframe(event='OS_STATUS', time='OS_MONTHS', data=data_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 640,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1610, 480)"
      ]
     },
     "execution_count": 640,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 671,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1610, 485)"
      ]
     },
     "execution_count": 671,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fn = \"gene_mutations_mskcc.txt\"\n",
    "td = pd.read_csv(filename, sep = '\\t')\n",
    "td.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 651,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0]),)"
      ]
     },
     "execution_count": 651,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(data_x.columns=='ABL1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 672,
   "metadata": {},
   "outputs": [],
   "source": [
    "# uncomment to plot features\n",
    "# for x in data_x.columns:\n",
    "#     print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 691,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nsns.set(rc={\\'figure.figsize\\':(30,85)})\\n\\nax = sns.barplot(y = x.columns, x = estimator.coef_)\\nplt.savefig(\\'./out/mskcc/Cox/cox_overall_linear.pdf\\')\\n\\nind = np.argsort(abs(estimator.coef_))\\nind = ind[::-1]#reverse the order\\nsns.set(rc={\\'figure.figsize\\':(45,155)})\\nsns.set_context(\"paper\", font_scale=3)\\nax = sns.barplot(y = x.columns[ind], x = estimator.coef_[ind])\\nplt.savefig(\\'./out/mskcc/Cox/cox_overall_linear_ordered.pdf\\')\\n'"
      ]
     },
     "execution_count": 691,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cox model on pan-cancer\n",
    "estimator = CoxPHSurvivalAnalysis()\n",
    "estimator.fit(x, y)\n",
    "\n",
    "pd.Series(estimator.coef_, index=x.columns)\n",
    "'''\n",
    "sns.set(rc={'figure.figsize':(30,85)})\n",
    "\n",
    "ax = sns.barplot(y = x.columns, x = estimator.coef_)\n",
    "plt.savefig('./out/mskcc/Cox/cox_overall_linear.pdf')\n",
    "\n",
    "ind = np.argsort(abs(estimator.coef_))\n",
    "ind = ind[::-1]#reverse the order\n",
    "sns.set(rc={'figure.figsize':(45,155)})\n",
    "sns.set_context(\"paper\", font_scale=3)\n",
    "ax = sns.barplot(y = x.columns[ind], x = estimator.coef_[ind])\n",
    "plt.savefig('./out/mskcc/Cox/cox_overall_linear_ordered.pdf')\n",
    "'''\n",
    "# REF: https://www.ncbi.nlm.nih.gov/pubmed/23539594 Cancer genome landscapes\n",
    "#      the human protein atlas https://www.proteinatlas.org/ENSG00000105173-CCNE1/pathology\n",
    "# GTF21 renal; HIST1H3H renal pancreatic(stomach) cervical; CCNE1 liver endometrial ovarian; knstrn renal pancreatic\n",
    "# rragc liver; lyn liver; RRAS renal glioma lung; rac2 renal, breast;nthl1 colorectal breast prostate lung liver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 667,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run COXPHSurvival within cancer subtypes leading to error that matrix is singular\n",
    "# mu, sigma = 0, 0.001\n",
    "# noise = np.random.normal(mu, sigma, (x.shape))\n",
    "# f = open(\"./out/mskcc/mskcc_gene_coxphsurvival.txt\",\"w+\")\n",
    "# tpx = x + noise\n",
    "# for cancer_type in ('Breast Cancer', 'Esophagogastric Cancer', 'Bladder Cancer',\n",
    "#        'Non-Small Cell Lung Cancer', 'Glioma', 'Head and Neck Cancer',\n",
    "#        'Melanoma', 'Colorectal Cancer', 'Renal Cell Carcinoma',\n",
    "#        'Cancer of Unknown Primary'):#data_x[\"CANCER_TYPE\"].unique():\n",
    "#     mask = data_[\"CANCER_TYPE\"] == cancer_type\n",
    "#     ind = np.where(mask)[0].tolist()\n",
    "#     tmpt = tpx.loc[mask]\n",
    "#     tmpy = y[ind]\n",
    "#     noise = np.random.normal(mu, sigma, (tmpt.shape))\n",
    "#     tmpx  = tmpt + 2*noise\n",
    "    \n",
    "#     #print(tmpx.shape, tmpy.shape)\n",
    "#     net = CoxPHSurvivalAnalysis() # L1\n",
    "#     net.fit(tmpx, tmpy)\n",
    "#     order = np.argsort((net.coef_))\n",
    "#     order = order[::-1][0:50]\n",
    "#     f.write(cancer_type + \"\\r\" + str(np.count_nonzero(tmpt))+\"\\r\" + str(x.columns[order].values)+'\\n')\n",
    "    \n",
    "#     sns.set(rc={'figure.figsize':(15.7,20)})\n",
    "\n",
    "#     ax = sns.barplot(y = x.columns[order], x = net.coef_[order])\n",
    "#     plt.savefig('./out/mskcc/Cox/cox_' + cancer_type + '_addnoise2.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 688,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "Exception",
     "evalue": "Data must be 1-dimensional",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-688-c70dcc736546>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mcoxnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoxnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, dtype, name, copy, fastpath)\u001b[0m\n\u001b[1;32m    260\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m                 data = sanitize_array(data, index, dtype, copy,\n\u001b[0;32m--> 262\u001b[0;31m                                       raise_cast_failure=True)\n\u001b[0m\u001b[1;32m    263\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    264\u001b[0m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSingleBlockManager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfastpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36msanitize_array\u001b[0;34m(data, index, dtype, copy, raise_cast_failure)\u001b[0m\n\u001b[1;32m    656\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0msubarr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    657\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 658\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Data must be 1-dimensional'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    659\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    660\u001b[0m             \u001b[0msubarr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray_tuplesafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mException\u001b[0m: Data must be 1-dimensional"
     ]
    }
   ],
   "source": [
    "coxnet = CoxnetSurvivalAnalysis(n_alphas=100)#l1_ratio = 0.000001) # L1\n",
    "coxnet.fit(x, y)\n",
    "\n",
    "coxnet.score(x,y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 699,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.08073438, 3.71821319, 3.38789737, 3.08692591, 2.81269192,\n",
       "       2.56282012])"
      ]
     },
     "execution_count": 699,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\n",
    "#plt.plot(coxnet.coef_[:,1])\n",
    "coxnet.alphas_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 708,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6,)\n",
      "[-0.0060588  -0.00554036 -0.00507385 -0.00465585 -0.00428286 -0.00395138]\n",
      "[4.08073438 3.71821319 3.38789737 3.08692591 2.81269192 2.56282012]\n",
      "0.0001\n",
      "1e-07\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"\\n# uncomment to plot the above result into figures\\n\\nplt.clf()\\nsns.set(rc={'figure.figsize':(15.7,105)})\\n\\nax = sns.barplot(y = x.columns[ind], x = coxnet.coef_[:,-1][ind])\\nplt.savefig('./out/mskcc/Cox/coxnet_overall_ordered.pdf')\\n\""
      ]
     },
     "execution_count": 708,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(coxnet.alphas_.shape)\n",
    "print(coxnet.deviance_ratio_)\n",
    "print(coxnet.alphas_)\n",
    "print(coxnet.alpha_min_ratio)\n",
    "print(coxnet.tol)\n",
    "ind = np.argsort((coxnet.coef_[:, -1]))\n",
    "ind = ind[::-1] #reverse the order\n",
    "# save the result\n",
    "\n",
    "result=pd.DataFrame(data = coxnet.coef_, columns = coxnet.alphas_, index = x.columns)\n",
    "\n",
    "result.to_csv('./out/mskcc/Cox/result_coxnet_overall_ordered.txt', sep='\\t')\n",
    "'''\n",
    "# uncomment to plot the above result into figures\n",
    "\n",
    "plt.clf()\n",
    "sns.set(rc={'figure.figsize':(15.7,105)})\n",
    "\n",
    "ax = sns.barplot(y = x.columns[ind], x = coxnet.coef_[:,-1][ind])\n",
    "plt.savefig('./out/mskcc/Cox/coxnet_overall_ordered.pdf')\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 707,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 505,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# write log file to recrod result\n",
    "f = open(\"./out/mskcc/Cox/log_coxnetL2.txt\",\"w+\")\n",
    "f.write(\"cancer\\t\\n\")\n",
    "f.write(\"---process %s seconds ---\\n\" % (23))\n",
    "f.write(\"---process %s seconds ---\\n\" % (23))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 710,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ABL1', 'ACVR1', 'AGO2', 'AKT1', 'AKT2', 'AKT3', 'ALK', 'ALOX12B',\n",
       "       'AMER1', 'ANKRD11',\n",
       "       ...\n",
       "       'CANCER_TYPE=Breast Cancer', 'CANCER_TYPE=Cancer of Unknown Primary',\n",
       "       'CANCER_TYPE=Colorectal Cancer', 'CANCER_TYPE=Esophagogastric Cancer',\n",
       "       'CANCER_TYPE=Glioma', 'CANCER_TYPE=Head and Neck Cancer',\n",
       "       'CANCER_TYPE=Melanoma', 'CANCER_TYPE=Non-Small Cell Lung Cancer',\n",
       "       'CANCER_TYPE=Renal Cell Carcinoma',\n",
       "       'CANCER_TYPE=Skin Cancer, Non-Melanoma'],\n",
       "      dtype='object', length=492)"
      ]
     },
     "execution_count": 710,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_time[0]=1\n",
    "new_x.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 712,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1610,)\n",
      "(41, 492)\n",
      "---plot 2.64599609375 seconds ---\n",
      "(1610,)\n",
      "(118, 492)\n",
      "---plot 2.7666709423065186 seconds ---\n",
      "(1610,)\n",
      "(211, 492)\n",
      "---plot 2.735240936279297 seconds ---\n",
      "(1610,)\n",
      "(344, 492)\n",
      "---plot 2.649031162261963 seconds ---\n",
      "(1610,)\n",
      "(116, 492)\n",
      "---plot 2.809062957763672 seconds ---\n",
      "(1610,)\n",
      "(129, 492)\n",
      "---plot 2.7574820518493652 seconds ---\n",
      "(1610,)\n",
      "(313, 492)\n",
      "---plot 2.7979447841644287 seconds ---\n",
      "(1610,)\n",
      "(109, 492)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/scikit_survival-0.9.dev5+g9fdc0f7-py3.7-macosx-10.14-x86_64.egg/sksurv/linear_model/coxph.py:80: RuntimeWarning: overflow encountered in exp\n",
      "  numpy.exp(- self.cum_baseline_hazard_.y))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---plot 2.6443910598754883 seconds ---\n",
      "(1610,)\n",
      "(143, 492)\n",
      "---plot 2.7502028942108154 seconds ---\n",
      "(1610,)\n",
      "(85, 492)\n",
      "---plot 2.8005998134613037 seconds ---\n"
     ]
    },
    {
     "data": {
      "image/png": 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l5eXprbfeUl1dna8tOjpakvTggw/qzTff9BVtduzYoc2bN6u2tla1tbXavXu32traenzdCgAAAADw9Td4oAMAQplhGMrPz/drs1qtKisr069+9St9+OGHslgsioyM1EUXXaTf/OY3io+PP+NcCQkJuvvuu7V69WpNmjTJ79oll1yiX//61/rFL34ht9utyy67TNdee63vemxsrH784x/LbrdrwoQJgV8oAAAAACBkRXi9Xu9ABwEg8NzuTnV39+7xTkoapra2jvN3REghb+GL3IUn8ha+yF34InfhibyFL3LXPyIjI5SQENu3MUGKBQAAAAAAAAFA8QYAAAAAACCEUbwBAAAAAAAIYRRvgF5obm5WamqqqqurfW02m63H159WrFghwzAkSSaTSVlZWbJYLLJYLDKZTFqwYIFOnDghSUpNTfUbu3LlSt155506fvy4bDabpk6dKovFopycHFmtVn3wwQfBXSQAAAAAICRRvAF6wTAMmc1m2e32Po176qmn5HA45HA4tGvXLh08eFDbt2/v0a+wsFAtLS3auHGjLr74Yl+bw+HQjh07dPPNN2vNmjUBWQsAAAAAILxQvAHOw+PxyOl0Kj8/X01NTWptbf1S83R0dKijo0PDhw/3a3/sscfU0tKi3//+9xo6dOgZx/4/9u4+Oqry3P//Z08eiBgSyJCIiERKVodgaLRSEwHJIYCQEB0rxSrWopZ4sJjhiwWNFgJWNAZMqoRDbYpIodggOjBBEVCjJiCJqXapnAhSSAuhSB6KkkHDJJn5/cGPOU3DQ5A8zMj7tZZ/zN73vfd15Vr7Dy/2fW+n06m+fft+q/sCAAAAAPxbYHcHAPi6d999V/3799egQYM0btw4FRYW6uGHH27X3Pvvv18BAQGqr69Xv3799LOf/UwpKSne80uWLNGLL76oP/7xjwoJCWk1d968eerZs6caGhr01Vdfac2aNR2aFwAAAADAP/DmDXAOdrtdaWlpkqTU1FRt2LBBLpdLhmG0GevxeGQy/d9jVVBQoE2bNmnBggX617/+peTk5Fbz/va3vyknJ0ePPfaYGhoaWl3r1LKp4uJiPfPMM7r33nvldDo7KUsAAAAAgK+ieQOcRX19vUpKSrRy5UolJydr3rx5OnbsmLZt26bw8PA2DZf6+nqFhYW1uc6ECRM0evRoZWVltTqen5+vW2+9Vddee60WLFhwxjjGjBkjt9utqqqqjkkMAAAAAOA3aN4AZ1FUVKTExESVlJSouLhY77zzjmbMmKF169YpMTFRGzduVHNzsyRp37592rVrl6655prTXmvWrFn661//qnfeecd7LDg4WJK0YMECffTRR3r11VdPO3fXrl1qbm7WoEGDOjhDAAAAAICvY88b4Czsdrtmz57d6tjUqVO1YsUKzZ8/XwcPHpTVapXJZFKPHj2Um5uriIiI017LbDYrPT1dixcv1o033tjqXFhYmLKzszVz5kz98Ic/lPR/e954PB653W4988wzCg0N7ZxEAQAAAAA+y/B4PJ7uDgJAx6uvd8rtbt/jHRnZS7W1DeceCJ9C3fwXtfNP1M1/UTv/Re38E3XzX9Sua5hMhszm8/uHeZZNAQAAAAAA+DCaNwAAAAAAAD6M5g1wAT7//HNZLBZt3bpVknTixAlNmjRJdrvdO+bgwYNKTk7WoUOHVF5errvvvlvSyf10EhISVFdX5x1bXV2t5OTkVvfYs2ePJk2a1AXZAAAAAAB8Ec0b4ALY7XZNmDBBhYWFkqQePXpoyZIlWrx4sQ4dOiSXy6VZs2bp0Ucf1RVXXNFm/vHjx8/6ifCNGzdq+vTp+uabbzotBwAAAACAb6N5A3xLzc3NKioq0uzZs1VZWakDBw5IkoYOHar77rtPmZmZysvLU3x8vMaPH3/aa0yYMEH/+Mc/tGnTpjbnGhoa9PbbbysvL69T8wAAAAAA+DaaN8C39O6776p///4aNGiQxo0b5337RpKmT5+upqYmFRcXKzMz84zXCAoKUnZ2trKzs1stn5KkXr16KT8/X5dffnmn5QAAAAAA8H00b4BvyW63Ky0tTZKUmpqqDRs2yOVySZJqa2v1xRdf6NixY9q3b99ZrzNs2DBNmTLlrMunAAAAAAAXr8DuDgDwR/X19SopKdGuXbu0evVqeTweHTt2TNu2bVNqaqrmzp2re+65R6GhoZozZ47sdrtCQkLOeL2ZM2fqtttu02uvvdaFWQAAAAAA/AHNG+BbKCoqUmJiolasWOE9lp+fr3Xr1unAgQMKDAzUtGnTZBiGiouLlZOTc9Y3a4KDg5Wdna27775bERERXZECAAAAAMBPsGwK+BbsdrumTp3a6tjUqVNVUVGhtWvXKjs7W4ZhSJIWLVqkN998U++9995Zrzls2DBNmzat02IGAAAAAPgnw+PxeLo7CAAdr77eKbe7fY93ZGQv1dY2dHJE6GjUzX9RO/9E3fwXtfNf1M4/UTf/Re26hslkyGwOPb85nRQLAAAAAAAAOgDNGwAAAAAAAB9G8wYAAAAAAMCH0bwBLsDnn38ui8WirVu3SpJOnDihSZMmyW63e8ccPHhQycnJOnTokMrLy3X33XdLOrnpcUJCgurq6rxjq6urlZycLEk6fvy4Zs2apZtvvlk333yzXn/99S7MDAAAAADgK2jeABfAbrdrwoQJKiwslCT16NFDS5Ys0eLFi3Xo0CG5XC7NmjVLjz76qK644oo2848fP37GT4gXFBSof//+2rRpk1atWqXs7OxWjR4AAAAAwMWB5g3wLTU3N6uoqEizZ89WZWWlDhw4IEkaOnSo7rvvPmVmZiovL0/x8fEaP378aa8xYcIE/eMf/9CmTZvanLv++uu9b+mYzWb17t2b5g0AAAAAXIRo3gDf0rvvvqv+/ftr0KBBGjdunPftG0maPn26mpqaVFxcrMzMzDNeIygoSNnZ2ad9q2bkyJHq37+/JGnz5s1yuVyKiYnpnGQAAAAAAD6L5g3wLdntdqWlpUmSUlNTtWHDBrlcLklSbW2tvvjiCx07dkz79u0763WGDRumKVOmnHH51BtvvKGnnnpKS5cuVWBgYMcmAQAAAADwefyfIPAt1NfXq6SkRLt27dLq1avl8Xh07Ngxbdu2TampqZo7d67uuecehYaGas6cObLb7QoJCTnj9WbOnKnbbrtNr732Wqvja9as0QsvvKAXXnhBFouls9MCAAAAAPggmjfAt1BUVKTExEStWLHCeyw/P1/r1q3TgQMHFBgYqGnTpskwDBUXFysnJ+eMb9ZIUnBwsLKzs3X33XcrIiJCkvTWW29p1apV+vOf/6zLL7+803MCAAAAAPgmlk0B34LdbtfUqVNbHZs6daoqKiq0du1aZWdnyzAMSdKiRYv05ptv6r333jvrNYcNG6Zp06Z5fy9dulQnTpzQjBkzZLVaZbVa9emnn3Z8MgAAAAAAn2Z4PB5PdwcBoOPV1zvldrfv8Y6M7KXa2oZOjggdjbr5L2rnn6ib/6J2/ova+Sfq5r+oXdcwmQyZzaHnN6eTYgEAAAAAAEAHoHkDAAAAAADgw2jeAF3k888/l8Vi0datWyVJJ06c0KRJk2S3271jDh48qOTkZB06dEj5+fnKz8/vrnABAAAAAD6C5g3QRex2uyZMmKDCwkJJUo8ePbRkyRItXrxYhw4dksvl0qxZs/Too4/qiiuu6OZoAQAAAAC+gk+FA12gublZRUVFWrt2re644w4dOHBAAwcO1NChQ3XfffcpMzNTV199teLj4zV+/PjuDhcAAAAA4EN48wboAu+++6769++vQYMGady4cd63byRp+vTpampqUnFxsTIzM7sxSgAAAACAL6J5A3QBu92utLQ0SVJqaqo2bNggl8slSaqtrdUXX3yhY8eOad++fd0ZJgAAAADAB7FsCuhk9fX1Kikp0a5du7R69Wp5PB4dO3ZM27ZtU2pqqubOnat77rlHoaGhmjNnjux2u0JCQro7bAAAAACAj6B5A3SyoqIiJSYmasWKFd5j+fn5WrdunQ4cOKDAwEBNmzZNhmGouLhYOTk5WrBgQTdGDAAAAADwJSybAjqZ3W7X1KlTWx2bOnWqKioqtHbtWmVnZ8swDEnSokWL9Oabb+q9996TJP3+97/Xtdde6/2vqKioy+MHAAAAAHQvw+PxeLo7CAAdr77eKbe7fY93ZGQv1dY2dHJE6GjUzX9RO/9E3fwXtfNf1M4/UTf/Re26hslkyGwOPb85nRQLAAAAAAAAOgDNGwAAAAAAAB9G8wYAAAAAAMCH0bwBOkl5ebmuvfZaWa1W3XLLLUpJSdHvfvc7SdKHH36oyZMny2q1atq0aTp06JAkKTMzU0OGDNGRI0daXeuXv/ylkpOTuzwHAAAAAED3o3kDdKK4uDg5HA4VFRXplVdeUWFhof72t79p7ty5evLJJ+VwOHTzzTdr0aJF3jmXXXaZtm3b5v3tdDpVWVnZHeEDAAAAAHwAzRugizQ2NiogIECXXHKJZs2apSFDhkiSLBaLDh8+7B130003aevWrd7fb731lv7rv/6rq8MFAAAAAPgImjdAJ9q1a5esVqtuvvlmJScn6/rrr1f//v1ltVolSW63W8uWLdO4ceO8c2JjY1VfX6+6ujpJ0htvvKGUlJRuiR8AAAAA0P1o3gCd6NSyqU2bNun999/XoUOHVFBQIElyuVyaM2eOmpub9d///d+t5t10003atm2bvvrqKzmdTl1xxRXdET4AAAAAwAfQvAG6yKWXXqpx48bpo48+0vHjxzV9+nQ1Nzfrd7/7nYKCglqNTUlJ0datW/Xmm29q/Pjx3RQxAAAAAMAX0LwBukhLS4s++OADDR06VHPnzlV0dLSee+45BQcHtxk7ZMgQ1dXVaf369Zo4cWI3RAsAAAAA8BWB3R0A8F12as8bSfrmm280bNgwjRo1SsuXL1dMTIxuvfVWSVJUVJT+8Ic/tJo7fvx4ffDBB+rXr5+qq6u7PHYAAAAAgG8wPB6Pp7uDANDx6uudcrvb93hHRvZSbW1DJ0eEjkbd/Be180/UzX9RO/9F7fwTdfNf1K5rmEyGzObQ85vTSbEAAAAAAACgA9C8AQAAAAAA8GE0bwAAAAAAAHwYGxYD7eR0OpWbm6uKigoFBAQoLCxMmZmZCg8P18SJEzV48GBJUmNjoywWi7KystS3b1+tX79ef/rTn7zXqa6ultVqVVZWlux2u1asWKGAgAAlJCQoMzNTgYH/91hOnjxZkZGRev7557s8XwAAAACAb+DNG6Ad3G630tPTFR4ero0bN8rhcGjmzJlKT0/Xl19+qaioKDkcDjkcDm3ZskXR0dGy2WySpClTpnjPPfPMMzKbzXrwwQe1f/9+Pfvss1q1apU2bdqk5uZmrVmzxnvPPXv2KCgoSLt379bhw4e7K3UAAAAAQDejeQO0Q3l5uWpqamSz2bxvxiQmJio7O1tut7vVWMMwlJGRob1792r37t2tzi1cuFCzZ89WRESE9uzZo2uuuUZRUVGSpDFjxuitt97yjrXb7Ro5cqTGjh2rl19+uZMzBAAAAAD4Kpo3QDtUVlZq2LBhMplaPzJJSUmKiIhoMz44OFjR0dHav3+/99j777+vxsZGpaSkSJKGDBmijz/+WIcPH1ZLS4u2bNmiuro6SVJTU5OKioqUkpKilJQUvfLKK2pubu7EDAEAAAAAvormDdAOJpNJHo/nvOYYhqGQkBDv78LCQt17773e34MGDdKvfvUrPfDAA7rrrrtksVgUFBQkSXrvvfcUGRmpmJgYXXfddTKZTHrnnXc6JhkAAAAAgF+heQO0Q1xcnCorK9s0cPLy8lReXt5mvMvlUlVVlWJiYry/KyoqlJyc7B1z4sQJ/eAHP9DGjRtVWFio/v3768orr5Qkvfrqqzp8+LCSk5M1duxYOZ1OFRYWdmKGAAAAAABfRfMGaIfhw4fLbDZr2bJlamlpkSSVlpbKbrd7GzSnuN1u5efnKz4+XgMHDpR0cvPhq666Sj179vSO+/rrrzVt2jQ5nU65XC6tWbNGqampqqur044dO/Taa6+puLhYxcXF2rhxo8rKynTw4MGuSxoAAAAA4BP4VDjQDoZhaPny5crOzlZaWpoCAwPVp08fFRQUKCwsTDU1Ne4PSqEAACAASURBVLJarZJONm9iY2OVm5vrnX/w4EH169ev1TX79OmjBx98UD/96U/V3NystLQ03XzzzVq5cqWSkpJ02WWXecdeeeWVSk5O1rp16zRnzpyuSRoAAAAA4BMMz/lu5AHAL9TXO+V2t+/xjozspdrahk6OCB2NuvkvauefqJv/onb+i9r5J+rmv6hd1zCZDJnNoec3p5NiAQAAAAAAQAegeQMAAAAAAODD2PMGaCen06nc3FxVVFQoICBAYWFhyszMVHh4uCZOnKjBgwdLkhobG2WxWJSVlaW+fftq/fr1+tOf/uS9TnV1taxWq7KysmS327VixQoFBAQoISFBmZmZCgwMVHJyskJCQhQUFKTm5mb1799fOTk5ioiI6K70AQAAAADdhDdvgHZwu91KT09XeHi4Nm7cKIfDoZkzZyo9PV1ffvmloqKi5HA45HA4tGXLFkVHR8tms0mSpkyZ4j33zDPPyGw268EHH9T+/fv17LPPatWqVdq0aZOam5u1Zs0a7z0LCgrkcDj0+uuv66qrrtKKFSu6K30AAAAAQDeieQO0Q3l5uWpqamSz2RQYePKFtcTERGVnZ8vtdrcaaxiGMjIytHfvXu3evbvVuYULF2r27NmKiIjQnj17dM011ygqKkqSNGbMGL311ltt7u12u3X8+HH17du3k7IDAAAAAPgymjdAO1RWVmrYsGEymVo/MklJSaddyhQcHKzo6Gjt37/fe+z9999XY2OjUlJSJElDhgzRxx9/rMOHD6ulpUVbtmxRXV2dd/z9998vq9Wq0aNHa8eOHZo4cWInZQcAAAAA8GXseQO0g8lkksfTvs9un2IYhkJCQry/CwsLde+993p/Dxo0SL/61a/0wAMPKCQkRBMnTtSnn37qPV9QUKABAwZIktauXatf/OIX2rx5swzDuMBsAAAAAAD+hDdvgHaIi4tTZWVlmwZOXl6eysvL24x3uVyqqqpSTEyM93dFRYWSk5O9Y06cOKEf/OAH2rhxowoLC9W/f39deeWVp73/Lbfcov379+vo0aMdmBUAAAAAwB/QvAHaYfjw4TKbzVq2bJlaWlokSaWlpbLb7d4GzSlut1v5+fmKj4/XwIEDJUl79uzRVVddpZ49e3rHff3115o2bZqcTqdcLpfWrFmj1NTU095/586duvzyy/naFAAAAABchFg2BbSDYRhavny5srOzlZaWpsDAQPXp00cFBQUKCwtTTU2NrFarpJPNm9jYWOXm5nrnHzx4UP369Wt1zT59+ujBBx/UT3/6UzU3NystLU0333yz9/z999+voKAgud1uBQUFKS8vr2uSBQAAAAD4FMNzvht5APAL9fVOud3te7wjI3uptrahkyNCR6Nu/ova+Sfq5r+onf+idv6Juvkvatc1TCZDZnPo+c3ppFgAAAAAAADQAWjeAAAAAAAA+DCaNwAAAAAAAD6MDYuBdnA6ncrNzVVFRYUCAgIUFhamzMxMhYeHa+LEiRo8eLAkqbGxURaLRVlZWerbt+9Z51599dWt7pGTk6OjR4/q6aefliS99dZbys/Pl8fj0YABA5Sdna3w8PCuTRwAAAAA0O148wY4B7fbrfT0dIWHh2vjxo1yOByaOXOm0tPT9eWXXyoqKkoOh0MOh0NbtmxRdHS0bDbbOecePXrUe4+dO3dqw4YN3t9Op1MLFy5UQUGBioqKZLFYlJ+f3+W5AwAAAAC6H80b4BzKy8tVU1Mjm82mwMCTL6slJiYqOztbbre71VjDMJSRkaG9e/dq9+7d7Zr75Zdf6re//a1mzJjhvU5TU5MWLlyoyy67TJJksVh0+PDhrkgXAAAAAOBjaN4A51BZWalhw4bJZGr9uCQlJSkiIqLN+ODgYEVHR2v//v1nnWs2myVJWVlZmj17tsLCwrzn+/Tpo3Hjxkk6uRSroKDA+xsAAAAAcHGheQOcg8lkksfjOa85hmEoJCTknHPXr1+vyy+/XDfccMNpzzc0NCg9PV1DhgzRj3/84/OKAQAAAADw3UDzBjiHuLg4VVZWtmnC5OXlqby8vM14l8ulqqoqxcTEnHVuWVmZNm/erB07dshqtWrp0qUqLi7WU089JUmqqanR1KlTNWTIED355JOdlyAAAAAAwKfRvAHOYfjw4TKbzVq2bJlaWlokSaWlpbLb7YqJiWk11u12Kz8/X/Hx8Ro4cOA557744ot67bXX5HA4ZLPZlJycrMcee0wtLS2aMWOGUlJS9Otf/1qGYXR53gAAAAAA38CnwoFzMAxDy5cvV3Z2ttLS0hQYGKg+ffqooKBAYWFhqqmpkdVqlXSyeRMbG6vc3Nxzzj31KfHTKS4uVmVlpVpaWrR161ZJJ98A4g0cAAAAALj4GJ7z3cwDgF+or3fK7W7f4x0Z2Uu1tQ2dHBE6GnXzX9TOP1E3/0Xt/Be180/UzX9Ru65hMhkym0PPb04nxQIAAAAAAIAOQPMGAAAAAADAh7HnDdAOTqdTubm5qqioUEBAgMLCwpSZmanw8HBNnDhRgwcPliQ1NjbKYrEoKyvLu6fNmeZeffXVWr9+vf70pz9571NdXS2r1aqsrCy99NJLWrt2rTwej5KSkvTwww+zcTEAAAAAXIRo3gDn4Ha7lZ6eroSEBG3cuFGBgYEqKytTenq6CgoKFBUVJYfDIUnyeDzKy8uTzWbTSy+9dNa5r7/+uqZMmaIpU6ZIkvbu3auZM2fqwQcf1MGDB7Vq1Spt3LhRPXr00F133aUdO3Zo1KhR3fmnAAAAAAB0A5o3wDmUl5erpqZGNptNJtPJlYaJiYnKzs6W2+1uNdYwDGVkZGjkyJHavXu3jh492u65Cxcu1OzZsxUREaGIiAi9/vrrCgoK0tGjR+V0OhUWFtY1CQMAAAAAfAp73gDnUFlZqWHDhnmbL6ckJSUpIiKizfjg4GBFR0dr//79Z51rNpu9v99//301NjYqJSXFeywoKEgvv/yyxo0bp8jISA0ZMqSDMwMAAAAA+AOaN8A5mEwmeTzt++T2KYZhKCQkpN1zCwsLde+997Y5fvvtt6u8vFx9+/bVsmXLzisGAAAAAMB3A80b4Bzi4uJUWVnZpgmTl5en8vLyNuNdLpeqqqoUExNz1rllZWXe8RUVFUpOTvaeP3z4sD788ENJUmBgoCZNmqQ9e/Z0dGoAAAAAAD9A8wY4h+HDh8tsNmvZsmVqaWmRJJWWlsputysmJqbVWLfbrfz8fMXHx2vgwIHtmrtnzx5dddVV6tmzp/c6DQ0Nmjt3ro4dOyaPx6OtW7fquuuu66KMAQAAAAC+hA2LgXMwDEPLly9Xdna20tLSFBgYqD59+qigoEBhYWGqqamR1WqVdLJ5Exsbq9zc3HPOPfUp8YMHD6pfv36t7vn9739f999/v+644w4FBARo+PDhp11WBQAAAAD47jM857uZBwC/UF/vlNvdvsc7MrKXamsbOjkidDTq5r+onX+ibv6L2vkvauefqJv/onZdw2QyZDaHnt+cTooFAAAAAAAAHYDmDQAAAAAAgA+jeQMAAAAAAODD2LAYFx2n06nc3FxVVFQoICBAYWFhyszM1Kuvvqq6ujotXbrUO3b79u1asGCBHA6Hjh07pt/85jc6dOiQPB6PBg8erKysLJnNZuXn50uSMjIyWt1rypQpcrlc+uqrr/T111/r8ssvlyQtXrxYL774osrKyhQeHi5J+uabb9S7d29lZ2dr8ODB2rdvn+bPn6/jx48rJCRECxcuVGxsbBf9lQAAAAAAvoLmDS4qbrdb6enpSkhI0MaNGxUYGKiysjKlp6dr/fr1uvvuu1VcXKzk5GR9/fXXWrhwoZ566imFhobq//2//6dbb71VaWlpkqTf//73WrBggZYtW3bG+61fv16SZLfb9cEHH+jpp59udd5ms+m2227z/n7yySeVn5+vZ599VvPmzdP999+vMWPGaOfOnXrkkUdUVFTUCX8VAAAAAIAvo3mDi0p5eblqampks9lkMp1cNZiYmKjs7GyFhIRo0aJFeuyxx5SYmKilS5cqOTlZCQkJkqS6ujp988033mvddddd+vTTTzssNpfLpdraWu+bOFOmTNHo0aMlSRaLRYcPH+6wewEAAAAA/AfNG1xUKisrNWzYMG/j5pSkpCRJ0ogRIzRq1Cg9+uij2r9/v/fNGUl66KGHNHfuXOXn5+uGG27Q6NGjlZKSckHxLF26VKtWrdKXX36pHj16aNy4cZo5c6YktXojZ+nSpRo3btwF3QsAAAAA4J/YsBgXFZPJJI/Hc9YxmZmZ2rFjh+bNm6eQkBDv8dGjR6ukpESLFi1SRESElixZogcffPCC4rHZbCoqKtKqVavU1NSkhIQEhYaGes97PB7l5OTo448/1mOPPXZB9wIAAAAA+CeaN7ioxMXFqbKysk0DJy8vT2VlZZKk0NBQhYWF6YorrvCe//LLL/XUU0+pR48eGj16tB555BFt2rRJO3bs0L/+9a8Ljut73/ue5syZo8cee0wNDQ2SpObmZs2ZM0effvqpVq9erV69el3wfQAAAAAA/ofmDS4qw4cPl9ls1rJly9TS0iJJKi0tld1uV0xMzBnn9erVS8XFxdq4caP32IEDB2Q2m7171FyotLQ0XXnllVq+fLkkKScnR06nUytXrqRxAwAAAAAXMfa8wUXFMAwtX75c2dnZSktLU2BgoPr06aOCggL17dv3jPMCAgJUUFCgp59+Ws8995xCQkIUFRWl559/XgEBAZJOfn1q5cqV3jmPP/64brnllvOK7+GHH9Y999yjiRMnau3atRowYICmTJniPe9wOM4zYwAAAACAvzM859oABIBfqq93yu1u3+MdGdlLtbUNnRwROhp181/Uzj9RN/9F7fwXtfNP1M1/UbuuYTIZMptDzz3w3+d0UiwAAAAAAADoADRvAAAAAAAAfBjNGwAAAAAAAB9G8wa4ANXV1YqLi5PVapXVatWECRNks9lUV1fnHfP555/LYrFo69at3mMnTpzQpEmTZLfbvccOHjyo5ORkHTp0SMuWLdPPf/5z7yfNPR6PZsyYoTVr1nRdcgAAAAAAn0DzBrhAUVFRcjgccjgc2rJli6Kjo2Wz2bzn7Xa7JkyYoMLCQu+xHj16aMmSJVq8eLEOHTokl8ulWbNm6dFHH9UVV1yhBx54QCdOnNCqVaskSStXrpTJZNLdd9/d1ekBAAAAALoZnwoHOpBhGMrIyNDIkSO1e/duxcTEqKioSGvXrtUdd9yhAwcOaODAgZKkoUOH6r777lNmZqauvvpqxcfHa/z48ZJOfpp8yZIluv3222U2m/XnP/9Zr7zySnemBgAAAADoJrx5A3Sw4OBgRUdHa//+/Xr33XfVv39/DRo0SOPGjWv19o0kTZ8+XU1NTSouLlZmZmarcwMHDtSsWbM0d+5cPfHEE+rdu3dXpgEAAAAA8BE0b4BOYBiGQkJCZLfblZaWJklKTU3Vhg0b5HK5vONqa2v1xRdf6NixY9q3b1+b6/zlL39R3759VVJS0mWxAwAAAAB8C8umgA7mcrlUVVUls9mskpIS7dq1S6tXr5bH49GxY8e0bds2paWlye12a+7cubrnnnsUGhqqOXPmyG63KyQkRJL06quvqqqqSq+88oomT56s0aNH64Ybbujm7AAAAAAAXY03b4AO5Ha7lZ+fr/j4eH300UdKTExUSUmJiouL9c4772jGjBlat26dJOn5559XYGCgpk2bpp/85Ce66qqrlJOTI0nav3+/cnNz9cwzz+jyyy/X448/rszMTH311VfdmR4AAAAAoBvQvAEuUE1NjfdT4VarVUeOHFFubq7sdrumTp3aauzUqVP1ySef6KOPPtLatWuVnZ0twzAkSYsWLdKbb76pt956Sw899JBmzZql733ve5Kk8ePHa+TIkVqwYEGX5wcAAAAA6F6Gx+PxdHcQADpefb1Tbnf7Hu/IyF6qrW3o5IjQ0aib/6J2/om6+S9q57+onX+ibv6L2nUNk8mQ2Rx6fnM6KRYAAAAAAAB0AJo3AAAAAAAAPozmDS4K1dXViouL8+5LM2HCBNlsNtXV1UmSnE6nHn/8caWlpclqteruu+/W//7v/3rnJicnt7mmxWKRJJWXlysuLk579+497flTjhw5olGjRrU7plOcTqfS0tJUXV194X8IAAAAAIDfoXmDi0ZUVJQcDoccDoe2bNmi6Oho2Ww2ud1upaenKzw8XBs3bpTD4dDMmTOVnp6uo0ePtvv6mZmZamlpOe259957Tz//+c9VW1vbrphO+fjjj3XnnXfq73//+7fKGQAAAADg/2je4KJkGIYyMjK0d+9erVmzRjU1NbLZbAoMDJQkJSYmKjs7W263u13Xu/baaxUeHq4//OEPpz3/yiuvKD8/v90x7d69W5L08ssva8GCBYqKijqP7AAAAAAA3yU0b3DRCg4OVnR0tFwul4YNGyaTqfXjkJSUJLPZ3O7rLVq0SKtWrWqzfEqS8vPz9f3vf7/dMe3fv1+S9OSTT2r48OHtjgEAAAAA8N1D8wYXNcMwdOLECXk8Z/6k9n82dSTJ4/HIMIxWx/r376+HHnrorMun2htTSEjIt54PAAAAAPhuoXmDi5bL5VJVVZWuv/56VVZWtmng5OXlqaysTGFhYWpoaGh1rr6+XuHh4W2uefvtt591+VR7Y4qJiflW8wEAAAAA3z00b3BRcrvdys/PV3x8vH70ox/JbDZr2bJl3jdmSktLZbfbFRMTo9DQUEVHR2vr1q3e+evWrdMNN9xw2mufWj51ITENHDjwW+UFAAAAAPjuCezuAICuUlNTI6vVKulkoyQ2Nla5ubkyDEPLly9Xdna20tLSFBgYqD59+qigoEB9+/aVJC1ZskQLFy7U//zP/6ipqUkWi0VZWVmnvc+p5VPz58//1jEBAAAAAHCK4TnbZh8A/FZ9vVNud/se78jIXqqtbTj3QPgU6ua/qJ1/om7+i9r5L2rnn6ib/6J2XcNkMmQ2h57fnE6KBQAAAAAAAB2A5g0AAAAAAIAPo3kDAAAAAADgw9iwGLgA1dXVmjhxogYPHixJamxs9G5m3NjYeMZzffv2bTPX7Xbr+PHjuvXWW2Wz2SRJy5Yt0xtvvCFJSkpK0sMPP9wNWQIAAAAAuhPNG+ACRUVFyeFwSJI8Ho/y8vJks9m0ePHiM5576aWX2syVpCNHjmjChAmaNGmSjhw5ou3bt2vDhg0yDEPTp0/Xm2++qfHjx3d9kgAAAACAbsOyKaADGYahjIwM7d27V06n84zndu/efdr5tbW18ng8uvTSSxUZGanMzEwFBwcrKChIgwcP1j//+c+uSAMAAAAA4EN48wboYMHBwYqOjlZpaekZz+3fv18/+MEPVFNTI6vVqhMnTujo0aMaNmyYli1bpn79+qlfv37eeX//+9+1efNmFRYWdmUqAAAAAAAfwJs3QCcwDEMhISHnPHdq2dTmzZtltVrV1NSkxMTEVuP37t2r++67T4888oiuuuqqzg4dAAAAAOBjaN4AHczlcqmqqkpJSUlnPBcTE9PquMlk0sMPP6z6+nqtXLnSe/zDDz/UPffco1/96lf68Y9/3OmxAwAAAAB8D80boAO53W7l5+crPj5eJpPpjOcGDhzYZm5gYKAefvhhPf/886qtrdXhw4c1c+ZMPfPMM5o0aVJXpQAAAAAA8DHseQNcoFP71kgnGzSxsbHKzc2V0+k847kzGT16tK655ho9++yzuuSSS3TixAk9/fTT3vN33HGH7rzzzs5NCAAAAADgUwyPx+Pp7iAAdLz6eqfc7vY93pGRvVRb29DJEaGjUTf/Re38E3XzX9TOf1E7/0Td/Be16xomkyGzOfT85nRSLAAAAAAAAOgANG8AAAAAAAB8GHveAP+hurpaEydO1ODBgyVJjY2NslgsysrKUt++fWWxWLRnzx5JktPpVG5urioqKhQQEKCwsDBlZmbq6quvbnOdU26//XbdddddSk5OVkhIiIKCgiRJDQ0NiouL09NPP62ePXt6xz/yyCNKSEjQbbfd1kV/AQAAAACAL6F5A5xGVFSUHA6HJMnj8SgvL082m00vvfSSd4zb7VZ6eroSEhK0ceNGBQYGqqysTOnp6Xr99dfbXOd0CgoKNGDAAEknPyM+depUbdy4UVOnTtWRI0e0YMEC7dy5UwkJCZ2YLQAAAADAl9G8Ac7BMAxlZGRo5MiR2r17t/d4eXm5ampqZLPZvJ8FT0xMVHZ2ttxu93nfp6GhQQ0NDerdu7ckadOmTRo7dqz3NwAAAADg4kTzBmiH4OBgRUdHa//+/d5jlZWVGjZsmLdxc0pSUpKkk8uv/v1T4acsXrxYFotFknT//fcrICBA9fX16tevn372s58pJSVFkjR9+nRJ0ocffthpeQEAAAAAfB/NG6CdDMNQSEiI97fJZJLHc/ZPcbd32dTWrVuVnZ2t5ORkGYbRYTEDAAAAAPwfX5sC2sHlcqmqqkoxMTHeY3FxcaqsrGzTwMnLy1NZWdl5XX/ChAkaPXq0srKyOiReAAAAAMB3B80b4Bzcbrfy8/MVHx+vgQMHeo8PHz5cZrNZy5YtU0tLiySptLRUdru9VZOnvWbNmqW//vWveueddzosdgAAAACA/2PZFHAa/75XjdvtVmxsrHJzc1uNMQxDy5cvV3Z2ttLS0hQYGKg+ffqooKBAffv2PeOeNz/60Y80b968Nvc0m81KT0/X4sWLdeONNyowkMcTAAAAACAZnnNt2gHAL9XXO+V2t+/xjozspdrahk6OCB2NuvkvauefqJv/onb+i9r5J+rmv6hd1zCZDJnNoec3p5NiAQAAAAAAQAegeQMAAAAAAODDaN4AAAAAAAD4MHZEBTpYdXW1Jk6cqMGDB0uSGhsbZbFYlJWVpb59+8pisWjPnj2SpC1btqigoEDNzc3yeDyyWq2aPn26JOkvf/mLnnrqKTU1NemKK65QTk6OwsPDuy0vAAAAAED34M0boBNERUXJ4XDI4XBoy5Ytio6Ols1mazXmyJEjysnJ0QsvvKCioiIVFhZq8+bNevvttyVJjz76qBYvXqxNmzYpJiZGL7zwQnekAgAAAADoZrx5A3QywzCUkZGhkSNHavfu3d7jR48eVVNTkxobGyVJl156qZ5++mn16NFDkrR582YFBQWpqalJR44ckcVi6Zb4AQAAAADdi+YN0AWCg4MVHR2t/fv3e48NGTJEY8eO1bhx4xQbG6uEhATdfPPNio6OliQFBQVpz549uvfeexUYGKiHHnqou8IHAAAAAHQjlk0BXcQwDIWEhLQ69vjjj6u4uFh33nmn/vnPf+r222/Xtm3bvOctFovef/99/fKXv9Ts2bO7OmQAAAAAgA+geQN0AZfLpaqqKsXExHiPvfvuu9q8ebMuu+wyTZ48Wb/97W81b948vfLKKzpx4oTeeust79hbbrnFu8kxAAAAAODiQvMG6GRut1v5+fmKj4/XwIEDvcdDQkKUm5ur6upqSZLH49Hf/vY3xcbGKjAwUI8//rh27dolSXrjjTf0wx/+sFviBwAAAAB0L/a8ATpBTU2NrFarpJPNm9jYWOXm5rYak5iYqAcffFAzZsxQU1OTJOnGG2/UzJkzFRAQoN/+9rfKyspSS0uLLrvsMj355JNdngcAAAAAoPsZHo/H091BAOh49fVOud3te7wjI3uptrahkyNCR6Nu/ova+Sfq5r+onf+idv6Juvkvatc1TCZDZnPo+c3ppFgAAAAAAADQAWjeAAAAAAAA+DCaNwAAAAAAAD6M5g3w/6uurlZcXJysVqusVqsmTJggm82muro6VVdXy2KxaMeOHa3mJCcne78WJUmff/65LBaLtm7desb7WCwWSZLdbtf1118vq9WqW265RampqXrllVfajH/kkUdkt9s7KEsAAAAAgL+heQP8m6ioKDkcDjkcDm3ZskXR0dGy2WySpKCgIM2fP19Op/OM8+12uyZMmKDCwsJ23S85OVkOh0NFRUX64x//qKeeekoNDSc3CDty5IhmzJihLVu2XHhiAAAAAAC/RfMGOAPDMJSRkaG9e/fK6XQqKipKI0aMUE5OzmnHNzc3q6ioSLNnz1ZlZaUOHDhwXvc7fvy4evbsqR49ekiSNm3apLFjxyolJeWCcwEAAAAA+C+aN8BZBAcHKzo6WqWlpZKkzMxMbd++vc3yKUl699131b9/fw0aNEjjxo1r19s3xcXFslqtSktLU1pamqZMmaLg4GBJ0vTp0zVlypSOTQgAAAAA4Hdo3gDnYBiGQkJCJEmhoaF64oknTrt8ym63Ky0tTZKUmpqqDRs2yOVynfXap5ZNvfbaayopKdGWLVv02muvdU4iAAAAAAC/RPMGOAuXy6WqqiolJSV5j40aNarN8qn6+nqVlJRo5cqVSk5O1rx583Ts2DFt27ZNR44c0V/+8hdJksfjUUBAwGnvFRERoRtvvFEfffRR5yYFAAAAAPArNG+AM3C73crPz1d8fLxMptaPyqnlUzU1NZKkoqIiJSYmqqSkRMXFxXrnnXc0Y8YMrVu3Tv/4xz+Uk5Mjj8ejPXv26Morrzzt/Vwulz766CMNHTq003MDAAAAAPiPwO4OAPAlNTU1slqtkk42b2JjY5Wbm9tmidSp5VO/+MUvJJ1cMjV79uxWY6ZOnaoVK1bIbDbLYrEoLS1Nzc3N+vWvf+0dc2rPG8MwdPz4cSUlJem2227r5CwBAAAAAP7E8Hg8nu4OAkDHq693yu1u3+MdGdlLtbUNnRwROhp181/Uzj9RN/9F7fwXtfNP1M1/UbuuYTIZMptDz29OJ8UCAAAAAACADkDzBgAAAAAAwIfRvAHOorq6WnFxcbJarbJarZowYYJsNpvq6upUXV0ti8WiHTt2tJqTnJys6upq7/msrKxW5z/77DNZLBbZ7Xa9+uqr3mvHxcUpNTVVVqtVjz/+Kd6maAAAIABJREFUuHe80+lUWlqaqquruyRnAAAAAIBvYcNi4ByioqLkcDgknfzUd15enmw2mxYvXqygoCDNnz9fRUVFCg1tu2axd+/eKi0tVUtLi/cT4Zs3b1ZERIQkafLkyZo8ebKkk02fgoICDRgwwDv/448/1rx58/T3v/+9k7MEAAAAAPgq3rwBzoNhGMrIyNDevXvldDoVFRWlESNGKCcn57TjL730UsXGxqqiosJ7bMeOHRoxYkS77vfyyy9rwYIFioqK6pD4AQAAAAD+h+YNcJ6Cg4MVHR2t0tJSSVJmZqa2b9/eZvnUKSkpKdq6dask6ZNPPpHFYlFQUFC77vXkk09q+PDhHRM4AAAAAMAv0bwBvgXDMBQSEiJJCg0N1RNPPKH58+fL6XS2GTtmzBiVlJTI7XbrjTfeUEpKSleHCwAAAADwYzRvgPPkcrlUVVWlpKQk77FRo0adcflUaGiohgwZog8//FBlZWXtXjIFAAAAAIBE8wY4L263W/n5+YqPj5fJ1PrxObV8qqamps28lJQU5ebmKi4uToGB7BMOAAAAAGg/mjfAOdTU1Hg/5221WnXkyBHl5ua2GXdq+VRTU1Obc2PGjNFnn32m1NTUrggZAAAAAPAdYng8Hk93BwGg49XXO+V2t+/xjozspdrahk6OCB2NuvkvauefqJv/onb+i9r5J+rmv6hd1zCZDJnNoec3p5NiAQAAAAAAQAegeQMAAAAAAODDaN4AAAAAAAD4MJo3uKhVV1crLi7OuxnxhAkTZLPZVFdXp+rqalksFu3YsaPVnOTkZFVXV3vPZ2VltTr/2WefyWKxyG6369VXX/VeOy4uTqmpqbJarXr88cclSc8995xSU1M1adIkvfjii62uc/ToUQ0bNkwrV67s3D8CAAAAAMCn8c1iXPSioqLkcDgkSR6PR3l5ebLZbFq8eLGCgoI0f/58FRUVKTS07YZSvXv3VmlpqVpaWhQQECBJ2rx5syIiIiRJkydP1uTJkyWdbPoUFBRowIABkqQPPvhAZWVlKioqUnNzs1JTU5WUlKTvfe97kqTXXntNY8aM0bp163TvvffKMIxO/1sAAAAAAHwPb94A/8YwDGVkZGjv3r1yOp2KiorSiBEjlJOTc9rxl156qWJjY1VRUeE9tmPHDo0YMeKc97r++uu1evVqBQYGqr6+Xi0tLerZs6f3vN1u11133aXg4GCVlZVdeHIAAAAAAL9E8wb4D8HBwYqOjlZpaakkKTMzU9u3b2+zfOqUlJQUbd26VZL0ySefyGKxKCgoqF33CgoK0tKlSzVp0iTdcMMNuuyyyyRJu3fvVm1trYYPH66UlBQVFhZ2QGYAAAAAAH9E8wY4DcMwFBISIkkKDQ3VE088ofnz58vpdLYZO2bMGJWUlMjtduuNN95QSkrKed3LZrNp586dOnz4sF5++WVJ0quvvqqJEycqICBAqampevvtt1VXV3fhiQEAAAAA/A7NG+A/uFwuVVVVKSkpyXts1KhRZ1w+FRoaqiFDhujDDz9UWVlZu5ZMSdK+ffv02WefSZIuueQS3XTTTdqzZ4+ampq0adMmbdmyRcnJybrvvvsknWzoAAAAAAAuPjRvgH/jdruVn5+v+Ph4mUytH49Ty6dqamrazEtJSVFubq7i4uIUGNi+fcCrq6s1b948uVwuuVwuvf3227ruuuv0zjvvKCIiQtu3b1dxcbGKi4v1m9/8Ri+//LI8Hk+H5AkAAAAA8B80b3DRq6mp8X7O22q16siRI8rNzW0z7tTyqaampjbnxowZo88++0ypqantvm9SUpKSkpJ06623avLkybr22ms1adIk2e123Xnnna3GpqWl6cSJE959eAAAAAAAFw/Dwz/lA99J9fVOud3te7wjI3uptrahkyNCR6Nu/ova+Sfq5r+onf+idv6Juvkvatc1TCZDZnPo+c3ppFgAAAAAAADQAWjeAAAAAAAA+LD27awKfEdVV1dr4sSJGjx4sCSpsbFRFotFWVlZamxs1NixY7Vy5UqNHDnSOyc5OVmrV6+WJI0dO1Y//elP9Zvf/MZ7/rPPPtOtt96q7OxseTwe79h9+/Zp4MCBCgoK0g9/+EMtWLBAzz33nLZu3SrDMPSTn/xE9957b6uYPB6PmpqaNHLkSD322GNtNlEGAAAAAHz30bzBRS8qKkoOh0OS5PF4lJeXJ5vNpsWLFysoKEjz589XUVGRQkPbrkns3bu3SktL1dLSooCAAEnS5s2bFRERIUmaPHmyJk+eLOlk06egoEADBgyQJH3wwQcqKytTUVGRmpublZqaqqSkJAUHB7eKqampSXfeeadKS0tbfb4cAAAAAHBx4J/xgX9jGIYyMjK0d+9eOZ1ORUVFacSIEcrJyTnt+EsvvVSxsbGqqKjwHtuxY4dGjBhxzntdf/31Wr16tQIDA1VfX6+Wlhb17NmzzbgTJ06oqalJffr0+faJAQAAAAD8Fs0b4D8EBwcrOjra+1nuzMxMbd++XTt27Djt+JSUFG3dulWS9Mknn8hisSgoKKhd9woKCtLSpUs1adIk3XDDDbrssssk/d/ny2+55RYlJSXJbDZryJAhHZAdAAAAAMDf0LwBTsMwDIWEhEiSQkND9cQTT2j+/PlyOp1txo4ZM0YlJSVyu9164403lJKScl73stls2rlzpw4fPqyXX35Z0v8t5SoqKtLOnTvVp08fLVq06MITAwAAAAD4HZo3wH9wuVyqqqpqtb/MqFGjzrh8KjQ0VEOGDNGHH36osrKydi2Zkk5uYPzZZ59Jki655BLddNNN2rNnT5txwcHBSklJ0UcfffQtMwIAAAAA+DOaN8C/cbvdys/PV3x8fJsvO51aPlVTU9NmXkpKinJzcxUXF6fAwPbtA15dXa158+bJ5XLJ5XLp7bff1nXXXXfasWVlZRo6dOj5JwQAAAAA8Ht8bQoXvVP7y0gnmzexsbHKzc1ts0Tq1PKpX/ziF22uMWbMGP3617/WrFmz2n3fpKQkffzxx7r11lsVEBCgm266SZMmTVJ1dXWrmE6cOKHo6GiWTQEAAADARcrweDye7g4CQMerr3fK7W7f4x0Z2Uu1tQ2dHBE6GnXzX9TOP1E3/0Xt/Be180/UzX9Ru65hMhkym0PPb04nxQIAAAAAAIAOQPMGAAAAAADAh9G8AQAAAAAA8GFsWAy0Q3V1tSZOnKjBgwdLkhobG2WxWJSVlaW+ffvq+PHjeuaZZ7R9+3ZdcsklCg0NVUZGhm644Qbt2bNHDz/8sCTp8OHD6tmzp8LDwxUcHKz169d77/HII48oISFBt912myQpOTlZq1ev1oABA7o+YQAAAACAz6B5A7RTVFSUHA6HJMnj8SgvL082m01r167VjBkzFBsbq9dff13BwcGqrKzU/fffr9zcXCUkJHjnZWZm6vrrr/c2aCTpyJEjWrBggXbu3KmEhIRuyQ0AAAAA4Lto3gDfgmEYysjI0MiRI7VmzRr985//1OrVq2UYhiRp6NCheuCBB7R8+fJzNmQ2bdqksWPHqnfv3l0ROgAAAADAz7DnDfAtBQcHKzo6Wo2NjYqLi/M2bk750Y9+pP+PvbuPqrrK+///OkcOYuItgmNiaphIC2M0EeyqTNNEQ9FUunEssyCnlOIaM2+o0dJAFCNAnYt10VU2NXh39EilNkUpXNeITfcM5qhZEzMmCFqgOdyc8/ujb+fXCTTUc+B88vlYy7Xm7M/e+/PevNfnj3n32fvz6aef/uw8Dz74oKZPn+6pMAEAAAAABkfxBrgEJpNJ9fX1amxsbHKtvr6+DSICAAAAAPzSULwBLlJdXZ2OHj2qyMhIlZaWNinWfPTRRxo8eHAbRQcAAAAA+KWgeANcBLvdruzsbEVERGj48OEaMGCAnn32WWcBp7S0VOvXr9fDDz/cxpECAAAAAIyOA4uBFqqoqFBcXJyk74s3YWFhysjIkCTl5OToueeeU2xsrNq1a6cuXbpo1apVl/z1qNjYWJezdD788MNLmg8AAAAAYDwmh8PhaOsgALhfVVWt7PaWPd6BgZ1UWVnj4YjgbuTNuMidMZE34yJ3xkXujIm8GRe5ax1ms0kBAf4XNsZDsQAAAAAAAMANKN4AAAAAAAB4MYo3AAAAAAAAXowDi3HZKi8vV0xMjEJCQiRJZ8+eVWhoqJ566imdPXvW5Zrdbtfp06c1efJkJSUlSZJOnz6t1atXq7i4WB06dJC/v7/mzZunESNGSJK+/fZbLVu2TH//+98lSUFBQXryySfVr18/lZSUKCcnRy+//LJLPPfee68KCwudbX/84x+Vlpamd955R4GBga3ydwEAAAAAeBeKN7isBQUFyWazSZIcDofWrFmjpKQkpaenu1yTpOPHj2vcuHG6/fbbdfXVV2vOnDkKCwvT66+/Ll9fX5WVlSkxMVEZGRmKiopSRkaGBg4c6Pwi1Wuvvabk5GRt27atxfFZrVaNHj1aW7Zs0W9/+1v3Lh4AAAAAYAhsmwL+H5PJpHnz5unQoUOqra1tcr2yslIOh0MdO3bU/v379a9//UuLFi2Sr6+vJOnaa6/Vb3/7W61bt06SdOLECf373/+W3W6XJE2YMEHz5s1rcTyfffaZTp06pcTERG3evNk5DwAAAADg8sKbN8CP+Pr6qm/fvioqKlJFRYXi4uL073//WydPntTgwYOVk5OjX/3qV3rttdcUHh4uk8nkMj4yMtL5ps1vf/tbPfLII3r11VcVHR2t//iP/9CkSZOcfUtLSxUXF+f8XV9f7zKX1WpVTEyMwsPD1a5dOxUVFWnkyJEeXD0AAAAAwBvx5g3wEyaTSX5+fs5tU2+88Ybi4uJUX1+v6OhoZ5/GxsYmY39cgAkPD9fbb7+trKws9evXTy+88ILuueceNTQ0OK/bbDbnv9zcXJd5CgoKFBsbK+n7t3by8/M9uWwAAAAAgJeieAP8SF1dnY4ePeryhovZbNaCBQtUVVWlF154QZIUERGh0tLSJm/LfPTRRxo8eLAcDod+//vfq7GxUcOHD9djjz2mHTt26OTJkyorK/vZON599119++23mjt3rkaPHi2r1ao9e/bo66+/du+CAQAAAABej+IN8P/Y7XZlZ2crIiJCZrPro+Hj46MFCxboD3/4gyorKzVs2DANGDBAzz77rLOAU1paqvXr1+vhhx+WyWTSkSNHlJeX5zyrpqKiQg0NDbrqqqt+NpatW7fq0UcfVWFhoQoLC1VUVKTrr79emzdvdv/CAQAAAABejeINLms/nGvzw7/jx487z6z5qZtvvlm//vWvlZmZKUnKycmRr6+vYmNjNWHCBK1YsUKrVq1SVFSUJGnNmjU6dOiQbr31Vk2YMEH/+Z//qYyMDHXt2vW8MZ04cUIlJSWaNm2aS/v999+vzZs3N7tdCwAAAADwy2VyOByOtg4CgPtVVdXKbm/Z4x0Y2EmVlTUejgjuRt6Mi9wZE3kzLnJnXOTOmMibcZG71mE2mxQQ4H9hYzwUCwAAAAAAANyA4g0AAAAAAIAX82nrAABvV15erpiYGIWEhEiSzp49q9DQUD311FM6e/as7r33XhUWFrqMCQ0N1cGDByVJr7zyijZt2iSHwyGTyaT7779fkydPliSNHj1afn5+slgscjgczoORf/gkuSTV1tZq8uTJWrFihfM8HQAAAADA5YPiDdACQUFBstlskiSHw6E1a9YoKSlJ6enp5x338ccfa/Pmzdq4caP8/PxUVVWlqVOnatCgQRo0aJAkKTc3V8HBwZKkoqIiJScna+/evbJYLJKkZ555Rt9++60HVwcAAAAA8GZsmwIukMlk0rx583To0CHV1taet29lZaUcDoe+++47SVJAQICysrLUrVu3ZvtHRkaqurpaNTXfHxL2xhtvqGPHjgoNDXXvIgAAAAAAhkHxBrgIvr6+6tu3r4qKis7b7+abb1bv3r1100036Te/+Y2ys7PVtWtX9ezZs9n+NptN/fr1U/fu3fWvf/1LL730khYsWOCJJQAAAAAADIJtU8BFMplM6tGjR5P2H862kb4v8qxbt05ffvmliouLVVRUpLy8PL344ov69a9/LUlKTEyUxWJRfX29evXqpczMTNntdi1ZskRPPvmk/Pz8WnVdAAAAAADvQvEGuAh1dXU6evSowsLCnFucflBVVaUuXbpIkrZv366ePXtqxIgR6tu3r2bMmKHnnntONpvNWbz58Zk3Pzh8+LA+//xzLVmyRJL0j3/8QykpKXrmmWdcDjMGAAAAAPzysW0KuEB2u13Z2dmKiIjQoEGD1LdvX+3evdt5fePGjRoxYoQkqbGxURkZGaqurpYkNTQ06OjRo7r22mvPe48BAwZoz549stlsstlsCg8P1/LlyyncAAAAAMBliDdvgBaoqKhQXFycpO+LN2FhYcrIyJAkrVq1SkuXLtXatWtVX1/v/Iy4JE2dOlUnT57U3XffLbP5+1rp7bffrmnTprXNQgAAAAAAhmNyOByOtg4CgPtVVdXKbm/Z4x0Y2EmVlTU/3xFehbwZF7kzJvJmXOTOuMidMZE34yJ3rcNsNikgwP/CxngoFgAAAAAAALgBxRsAAAAAAAAvRvEGAAAAAADAi3FgMeBG5eXliomJUUhIiEt7fHy8evbsqezsbDkcDgUHBys1NVVdunTRzJkz9fXXX+uKK65w6T9jxgw9//zz2r17t0wmk6ZNm6b777+/tZcEAAAAAGhjFG8ANwsKCpLNZnNpq62tVUxMjLZu3aqePXvq+eefV3Z2tlJSUiRJy5cvV1RUlMuY/fv3a9++fdqxY4caGho0YcIEjRw5UldffXWrrQUAAAAA0PbYNgW0gvr6ei1dulQ9e/aUJIWGhurYsWPnHTN8+HBt2LBBPj4+qqqqUmNjo8vbOQAAAACAywNv3gBuVlFRobi4OJe29PR0jRkzRpJ09uxZ5ebmaubMmc7rKSkpzsJMx44d9eqrr0qSLBaLsrKy9MILLygmJsZZ/AEAAAAAXD4o3gBu1ty2qR/U1NTo4Ycf1qBBgzRlyhRne3Pbpn6QlJSkhIQEzZkzR5s2bdKdd97pkbgBAAAAAN6JbVNAK6moqNA999yjQYMGacWKFT/b/8iRIzpw4IAkqUOHDrrtttt08OBBT4cJAAAAAPAyFG+AVtDY2Kg5c+Zo/PjxWrJkiUwm08+OKS8vV0pKiurq6lRXV6e3335b119/fStECwAAAADwJmybAtysuTNv7Ha7Dh06pMbGRu3evVuSFB4eft43cEaOHKmPP/5YkydPVrt27XTbbbfp9ttv92jsAAAAAADvY3I4HI62DgKA+1VV1cpub9njHRjYSZWVNR6OCO5G3oyL3BkTeTMucmdc5M6YyJtxkbvWYTabFBDgf2FjPBQLAAAAAAAA3IDiDQAAAAAAgBfjzBvAQ8rLyxUTE6OQkBCX9vj4eOXl5cnPz08Wi8XZfu211yo1NVULFy7Uvn371KVLF9ntdknS7NmzXT4tDgAAAAC4fFC8ATwoKChINputSXteXp5yc3MVHBzc7LikpCTdcccdkqSvvvpK99xzj3r27KkbbrjBo/ECAAAAALwP26YAL9enTx/de++9evXVV9s6FAAAAABAG+DNG8CDmvtseHp6uiQpMTHRZdvUvffeq6lTpzY7z8CBA7Vt2zbPBQoAAAAA8FoUbwAPOte2KUnn3TbVHD8/P3eFBQAAAAAwELZNAQZw8ODBJgcfAwAAAAAuDxRvAC/3xRdf6NVXX9Xdd9/d1qEAAAAAANoA26YAD2ruzJvIyEhJTc+86dChg/Lz8yVJWVlZeumll2QymdSuXTs98cQTGjp0aOsFDgAAAADwGhRvAA8JDg5WaWlps9dSUlLOOS4tLc1TIQEAAAAADIhtUwAAAAAAAF6M4g0AAAAAAIAXo3gDAAAAAADgxTjzBmih8vJyxcTENPlkd3x8vGbMmKGdO3cqLy9Pp0+fVn19vYYPH65FixapU6dOKikp0Zw5c3TVVVe5jLVarZo1a5bmzp2rqKgol2t33HGHGhsbJUlnz57VV199pb1796pHjx6eXSgAAAAAwKtQvAEuQFBQkGw2W5P2goIC5eTkaN26dQoJCZHD4dCqVau0ZMkSZWVlSZLCw8P18ssvt/heVqvV+b8XLFigKVOmULgBAAAAgMsQ26YAN8jJydHixYudb+WYTCYlJydr8ODBlzz3X/7yF3322WdKSEi45LkAAAAAAMbDmzfABaioqFBcXJxL25IlS/TFF19o2LBhLu0Wi8Wl4FJaWuoy9oEHHtCkSZN+9p5ZWVlKTk5Wu3btLjF6AAAAAIARUbwBLkBz26ZOnTol6fu3baTvz8Z55JFHJEnV1dXatGmTpAvfNiVJhw4d0smTJzVq1KhLDR0AAAAAYFBsmwIuUdeuXdWnTx998MEHkqTg4GDZbDbZbDZZLBbnocMX46233tKECRPcFSoAAAAAwIAo3gBu8Nhjj2n58uU6cuSIs+2vf/2rTp06dUnbnT766KMm27EAAAAAAJcXtk0BF6C5M28iIyOVkpKiK664QikpKTpz5ozq6+vVu3dv5eTkqFevXvrHP/5x3nkTEhJcijyvv/66rrzySn311Vfq2bOnR9YCAAAAADAGk8PhcLR1EADcr6qqVnZ7yx7vwMBOqqys8XBEcDfyZlzkzpjIm3GRO+Mid8ZE3oyL3LUOs9mkgAD/CxvjoVgAAAAAAADgBhRvAAAAAAAAvBjFGwAAAAAAAC/GgcXARSovL1dMTIxCQkJc2uPj4zVjxgzt3LlTeXl5On36tOrr6zV8+HAtWrRInTp1UklJiebMmaOrrrrKZazVatWSJUu0b98+denSRZL03XffqWvXrkpNTW1yLwAAAADALx/FG+ASBAUFyWazNWkvKChQTk6O1q1bp5CQEDkcDq1atUpLlixRVlaWJCk8PFwvv/xys/MmJSXpjjvucP5esWKFsrOzlZmZ6ZmFAAAAAAC8FtumAA/IycnR4sWLnW/KmEwmJScna/DgwRc8V11dnSorK51v4gAAAAAALi+8eQNcgoqKCsXFxbm0LVmyRF988YWGDRvm0m6xWJSQkOD8XVpa6jL2gQce0KRJkyRJWVlZevHFF3Xq1Cm1b99eY8aM0SOPPOLBlQAAAAAAvBXFG+ASNLdt6tSpU5K+f9tG+v5snB8KL9XV1dq0aZOklm2b+vzzzzV79mxFRUXJ39/fU8sAAAAAAHgxtk0Bbta1a1f16dNHH3zwgSQpODhYNptNNptNFotFjY2NLZ7r6quv1vz587V48WLV1NR4KmQAAAAAgBejeAN4wGOPPably5fryJEjzra//vWvOnXqlNq1a3dBc8XGxqpPnz5at26du8MEAAAAABgA26aAS9DcmTeRkZFKSUnRFVdcoZSUFJ05c0b19fXq3bu3cnJy1KtXL/3jH/+4oPssWLBAs2bN0j333KM+ffq4cwkAAAAAAC9ncjgcjrYOAoD7VVXVym5v2eMdGNhJlZVsyzIa8mZc5M6YyJtxkTvjInfGRN6Mi9y1DrPZpICACzvTlG1TAAAAAAAAXoziDQAAAAAAgBejeAOcR3l5ucLDwxUXF+fy75VXXtHMmTNVUlIiSRo9erQmTJjg0ufPf/6zc57jx4/rxhtvbDL/3//+d4WGhmr37t3Otn//+9+6/fbbZbVanW1fffWVRo8erX/+858eXC0AAAAAwBtxYDHwM4KCgmSz2Zq079q1y+V3bm6ugoODm/Tbs2ePnn32WVVWVja5ZrVaNW7cOOXn52vcuHGSpPbt22vVqlWaPXu2oqKiFBgYqEcffVSLFi1S79693bQqAAAAAIBR8OYN4GFbtmxRdnZ2k/aGhgbt2LFDycnJKisrc/kC1bXXXqvZs2dr4cKFWrNmjSIiIjR27NjWDBsAAAAA4CV48wb4Gc19Djw9Pb1Jv8TERFksFklS//79lZmZKUnNFm4k6d1339WVV16p/v37a8yYMcrPz9eCBQuc1x988EEVFhaqsLBQBQUF7loOAAAAAMBgKN4AP+Nc26Z+6lzbps7FarUqNjZWkjRhwgTNnz9fjz32mHx9fSVJlZWV+vrrr3X27FkdOXJE11577cUtAAAAAABgaBRvgDZQVVWlvXv3qrS0VBs2bJDD4dC3336rN998U7GxsbLb7Xr88cc1a9Ys+fv7a/78+bJarfLz82vr0AEAAAAArYwzb4A2sGPHDkVHR2vv3r0qLCzUO++8ozlz5mjjxo2SpD/84Q/y8fHRfffdp2nTpqlfv35auXJlG0cNAAAAAGgLvHkD/IzmzryJjIy8pDmtVquSk5Nd2u655x7993//tz744AO98sorslqtMplMkqTly5dr0qRJuuWWWzRy5MhLujcAAAAAwFhMDofD0dZBAHC/qqpa2e0te7wDAzupsrLGwxHB3cibcZE7YyJvxkXujIvcGRN5My5y1zrMZpMCAvwvbIyHYgEAAAAAAIAbULwBAAAAAADwYhRvAAAAAAAAvBgHFgM/o7y8XDExMQoJCXFpj4+P165duzR37lxFRUVp9OjR8vPzk8VicfaZO3euxo4d6/ydlJSka665RvPmzXO2nTx5UjfffLOSk5M1e/ZsZ/uRI0f05JNP6vTp0/Lz89PSpUsVFhbmwZUCAAAAALwRxRugBYKCgmSz2Zq079q1y+V3bm6ugoODm51jy5YtKikp0TXXXOPS/tprr2nUqFHauHGj7r//fucXplJSUpSYmKhRo0bpL3/5i5544gnt2LHDTSsCAAAAABgF26aAVvDll19q27Ztuuuuu5pcs1qtmjFjhnx9fbVv3z5n+/Tp03XzzTdLkkJDQ3Xs2LFWixcAAAAA4D148wZogYqKCsXFxbm0paenN+mXmJjo3DbVv39/ZWZmqqGhQSkpKVq2bJl27tzp0v+zzz5TZWWlhg0bpvHjxys/P18jRoyhpaoJAAAgAElEQVSQJN1xxx3OfllZWRozZoy7lwUAAAAAMACKN0ALnGvb1E81t20qOztbY8eO1YABA5r037p1q2JiYtSuXTtNmDBB69at04kTJ9SjRw9JksPhUHp6uj7++GNt2LDBPYsBAAAAABgKxRvAw3bv3i1fX19t3bpVJ06ckCR16NBB9913nwoKCuTj46PCwkJn/61bt+qhhx5SQ0ODnnjiCR0/flwbNmxQp06d2moJAAAAAIA2RPEG8LAfH2qcnZ0tSXrwwQf15ptvqnv37nrjjTec161Wq9auXavExEStXLlStbW1euGFF+Tr69vqcQMAAAAAvAPFG6AFmjvzJjIy8pLmtFqtuvvuu13aYmNjtWbNGtlsNr3yyisKDg7W9OnTnddbsnULAAAAAPDLYnI4HI62DgKA+1VV1cpub9njHRjYSZWVNR6OCO5G3oyL3BkTeTMucmdc5M6YyJtxkbvWYTabFBDgf2FjPBQLAAAAAAAA3IDiDQAAAAAAgBfjzBvgIpWXlysmJkYhISEu7fHx8dq1a5fmzp2rqKgojR49Wn5+frJYLM4+c+fO1dixY52/k5KSdM0112jevHmSpNDQUA0aNMhl3ltuuUXJyckeXBEAAAAAwBtRvAEuQVBQULOHCP/4C1OSlJubq+Dg4Gbn2LJli0pKSnTNNde4tHM4MQAAAABAYtsU0Ka+/PJLbdu2TXfddVdbhwIAAAAA8FK8eQNcguY+IZ6ent6kX2JionPbVP/+/ZWZmamGhgalpKRo2bJl2rlzZ5MxP513/vz5uummm9wYPQAAAADACCjeAJfgXNumfqq5bVPZ2dkaO3asBgwY0OwYtk0BAAAAACSKN0Cb2b17t3x9fbV161adOHFCktShQwc9+OCDbRwZAAAAAMCbULwB2siPDzXOzs6WJAo3AAAAAIAmKN4Al6C5M28iIyPdMvdP5+3bt6+ysrLcMjcAAAAAwDgo3gAXKTg4WKWlpT/br7Cw8Gf7zJs3z+X3wYMHLzouAAAAAMAvC58KBwAAAAAA8GIUbwAAAAAAALwYxRsAAAAAAAAvxpk3gBuVl5crJiZGISEhLu3x8fF6+umnmz3LZvTo0fLz85PFYnG2zZ07V6NGjdKyZcv04YcfymQyKTExURMnTvT4GgAAAAAA3oXiDeBmQUFBstlsTdqffvrpc47Jzc1VcHCwS9u2bdt0+vRpvfbaa6qurtb48eM1atQo+fv7uz1mAAAAAID3ongDeKkpU6Y437SpqKiQxWJxeTsHAAAAAHB5oHgDuFlFRYXi4uJc2tLT0887JjEx0VmY6d+/vzIzMyVJPj4+WrJkiWw2mxITE9W+fXvPBA0AAAAA8FoUbwA3O9e2qfNpbtvUD1asWKH58+dr5syZGjp0qG688UZ3hAkAAAAAMAi+NgV4qdLSUn3xxReSpG7duummm25q9sBjAAAAAMAvG8UbwEt9/PHHWrVqlex2u2pra1VcXKyhQ4e2dVgAAAAAgFbGtinAzZo78yYyMlKSNGTIEGfblVdeqddff/2c89x11106ePCgJk6cKLPZrBkzZriMBwAAAABcHkwOh8PR1kEAcL+qqlrZ7S17vAMDO6myssbDEcHdyJtxkTtjIm/GRe6Mi9wZE3kzLnLXOsxmkwIC/C9sjIdiAQAAAAAAgBtQvAEAAAAAAPBiFG8AAAAAAAC8GAcWAxeovLxcMTExCgkJcWmPj49XXl6e/Pz8ZLFY5HA45OPjowULFig6OloVFRVasGCBqqur5efnp2XLliksLEwOh0Pp6el65513ZDab9cwzz+j666+XJL3wwgvatGmTHA6Hfve73+m2225riyUDAAAAANoQxRvgIgQFBclmszVpz8vLU25uroKDgyVJRUVFSk5O1t69e/Xcc89p3Lhxuvvuu7V3714tW7ZM+fn52r17t44cOaI33nhDX375pRITE7Vz506VlZVpx44dstlsqq2t1Z133qnhw4era9eurb1cAAAAAEAbongDeFBkZKSqq6tVU1OjFStWONvLy8vVuXNnSdKePXs0YcIEmc1m9e/fX1deeaU+/PBDlZSUaOzYsWrfvr3at2+v4cOH691339XkyZPbajkAAAAAgDZA8Qa4CBUVFYqLi3NpS09Pb9LPZrOpX79+6t69u7MtJiZG//znP7Vu3TrnXEFBQc7rgYGB+vrrr1VRUaHBgwc3aQcAAAAAXF4o3gAX4VzbpiQpMTFRFotF9fX16tWrlzIzM12u79q1SwcOHNDs2bO1c+dOORyOJnOYzeZztgMAAAAALi8UbwA3+/GZNz/27rvvKjIyUh07dlRYWJiuvPJKffXVV+rZs6cqKyud/SorKxUUFNRse//+/VtlDQAAAAAA78F/xgdaybZt27Rp0yZJ0uHDh3XixAldffXVuvnmm1VQUKDGxkZ9+eWX+uKLLzR48GDdfPPNevPNN/Xdd9+purpa+/bt04gRI9p4FQAAAACA1sabN8BFaO7Mm8jIyPOOWbx4sRYvXqxt27apffv2ysjIUMeOHRUTE6NPPvlEkyZNkiStWLFCfn5+uu666zRp0iRNmzZNDQ0NSkpKUs+ePT22JgAAAACAdzI5mjtYA4DhVVXVym5v2eMdGNhJlZU1Ho4I7kbejIvcGRN5My5yZ1zkzpjIm3GRu9ZhNpsUEOB/YWM8FAsAAAAAAADcgOINAAAAAACAF+PMG6AFysvLFRMTo5CQEJf2+Ph45eXlyc/PTxaLRQ6HQz4+PlqwYIGio6Od/VauXKlt27Zp79698vX1dbbv2rVLubm5amhokMPhUFxcnB588EGXezz//PMym82aN2+eZxcJAAAAAPBKFG+AFgoKCpLNZmvSnpeX5/J58KKiIiUnJ2vv3r2yWCxqaGjQzp07NWTIEO3atct5MPHx48e1cuVKWa1WdevWTadPn9bMmTPVv39/3XrrraqpqVFqaqpef/31JgUdAAAAAMDlg21TgJtFRkaqurpaNTXfH/S1Z88e9enTR5MnT9bGjRud/U6ePKn6+nqdPXtWktSxY0elpaVpwIABkqS3335b/fr10/3339/6iwAAAAAAeA3evAFaqLnPg6enpzfpZ7PZ1K9fP3Xv3l2SZLVaFRMTo5EjR2rRokU6fPiwBgwYoEGDBunWW2/VmDFjFBYWpqioKE2cOFF9+/aVJE2ePFmSlJ2d7eGVAQAAAAC8GcUboIXOtW1KkhITE2WxWFRfX69evXopMzNTklRdXa3i4mI988wz8vPz06hRo5Sfn6+UlBRJ0rJly/Twww+ruLhYxcXFio+P1+rVq3Xbbbe12roAAAAAAN6N4g3gBj8+8+bHduzYIYfDoWnTpkmSzp49q/r6es2fP1/79u3TmTNnNGHCBE2dOlVTp07Vpk2btGXLFoo3AAAAAAAnzrwBPGjr1q1KS0tTYWGhCgsLVVxcrC5duuiNN96Qn5+fMjIyVF5eLklyOBw6fPiwwsLC2jhqAAAAAIA34c0boIWaO/MmMjLynP1LS0t18uRJjR071tlmNpt13333KT8/X5s2bdLcuXM1Z84c1dfXS5JuuukmPfLII55ZAAAAAADAkEwOh8PR1kEAcL+qqlrZ7S17vAMDO6myssbDEcHdyJtxkTtjIm/GRe6Mi9wZE3kzLnLXOsxmkwIC/C9sjIdiAQAAAAAAgBtQvAEAAAAAAPBiFG8AAAAAAAC8GAcWA+dQXl6umJgYhYSEuLTHx8crLy9Pfn5+slgscjgc8vHx0YIFCxQdHS1J+uijj/Tcc8/p5MmTstvtGjZsmBYuXCg/Pz/nPCtXrtS2bdu0d+9e+fr6Ou9566236s4779TTTz/t7HvgwAFNnjxZqampuuOOO1ph9QAAAAAAb0HxBjiPoKAg2Wy2Ju15eXnKzc1VcHCwJKmoqEjJycnau3evjhw5orlz52rt2rWKiIhQQ0ODnnnmGT355JNatWqVJKmhoUE7d+7UkCFDtGvXLk2aNMk5d9euXVVUVKTGxka1a9dOkvTGG2+oe/furbBiAAAAAIC3YdsU4AaRkZGqrq5WTU2N8vLydOeddyoiIkKS5OPjo8cff1xjxoxx9t+zZ4/69OmjyZMna+PGjS5zdezYUWFhYXrvvfecbf/7v/+rG264oXUWAwAAAADwKhRvgPOoqKhQXFycy7+DBw826Wez2dSvXz91795dBw4c0HXXXedy3d/fX+PGjXP+tlqtiomJ0ciRI3XgwAEdPnzYpf/48eO1e/duSdInn3yi0NBQWSwWD6wQAAAAAODt2DYFnMe5tk1JUmJioiwWi+rr69WrVy9lZmZKkkwm03nnrK6uVnFxsZ555hn5+flp1KhRys/PV0pKirPPqFGjlJmZKbvdrp07d2r8+PF644033LcwAAAAAIBhULwBLtKPz7z5sfDwcH366acaOXKks622tlbz589XVlaWduzYIYfDoWnTpkmSzp49q/r6es2fP9/Z39/fX4MGDdL777+vffv26Xe/+x3FGwAAAAC4TLFtCnCzWbNm6U9/+pM++eQTSVJ9fb3S0tLk7+8vX19fbd26VWlpaSosLFRhYaGKi4vVpUuXJsWZ8ePHKyMjQ+Hh4fLxoc4KAAAAAJcr/h8hcB4/nHnzY5GRkecdExoaqlWrVmnFihX67rvv1NDQoBEjRiglJUWlpaU6efKkxo4d6+xvNpt13333KT8/X8OHD3e2jxo1SkuWLNGjjz7q3kUBAAAAAAzF5HA4HG0dBAD3q6qqld3essc7MLCTKitrPBwR3I28GRe5MybyZlzkzrjInTGRN+Mid63DbDYpIMD/wsZ4KBYAAAAAAAC4AcUbAAAAAAAAL8aZN0ALlJeXKyYmRiEhIS7t8fHxysvLk5+fnywWixwOh3x8fLRgwQJFR0crOztbkjRv3jznGKvVqv379ystLc3ZlpSUpKNHj6qgoMDZ1tDQoGXLlunDDz+UyWRSYmKiJk6c6OGVAgAAAAC8DcUboIWCgoJks9matOfl5bl8NryoqEjJycnau3dvi+Y9efKkysrK1KNHD73//vu6/vrrJUkFBQU6ffq0XnvtNVVXV2v8+PEaNWqU/P0vbG8kAAAAAMDYKN4AbhYZGanq6mrV1LTsoK+CggINGzZMAwcO1MaNG53FmylTpjjftKmoqJDFYpHFYvFY3AAAAAAA70TxBmih5j4bnp6e3qSfzWZTv3791L17d0lSfn6+3nrrLef1b775RtHR0c7fVqtVycnJGjhwoJ5//nktXrxYXbt2lST5+PhoyZIlstlsSkxMVPv27T2xNAAAAACAF6N4A7TQubZNSVJiYqIsFovq6+vVq1cvZWZmOq/dddddzZ55I0kHDhzQsWPHdMMNN8hisSgsLEzbt2/XrFmznP1XrFih+fPna+bMmRo6dKhuvPFGzywQAAAAAOCVKN4AbvDjM28uxNatW1VXV6dx48ZJkk6fPq38/HzNmjVLpaWl8vf3V79+/dStWzfddNNNOnjwIMUbAAAAALjM8KlwoI3U1dWpoKBAL774ogoLC1VYWKi3335blZWVKikp0ccff6xVq1bJbrertrZWxcXFGjp0aFuHDQAAAABoZbx5A7RQc2feREZGXvR877zzjnr37q2IiAhnm7+/v6ZPn678/HytXr1aBw8e1MSJE2U2mzVjxgwNGTLkou8HAAAAADAmk8PhcLR1EADcr6qqVnZ7yx7vwMBOqqxs2dex4D3Im3GRO2Mib8ZF7oyL3BkTeTMuctc6zGaTAgL8L2yMh2IBAAAAAACAG1C8AQAAAAAA8GIUbwAAAAAAALwYBxYDLVBeXq6YmBiFhIS4tMfHxysvL09+fn6yWCxyOBzy8fHRggULFB0drezsbEnSvHnznGOsVqv279+vtLQ0Z1tSUpKOHj2qgoICl/lvvfVW+fv//3sh//CHP6hXr16eWCIAAAAAwEtRvAFaKCgoSDabrUl7Xl6ecnNzFRwcLEkqKipScnKy9u7d26J5T548qbKyMvXo0UPvv/++rr/+eme7xWJp9p4AAAAAgMsH26YAN4uMjFR1dbVqalp2SntBQYGGDRum2267TRs3bnS2f/rpp3I4HJoxY4amTJminTt3eipkAAAAAIAX480boIUqKioUFxfn0paent6kn81mU79+/dS9e3dJUn5+vt566y3n9W+++UbR0dHO31arVcnJyRo4cKCef/55LV68WF27dlVdXZ1uuukmPfHEEzp+/LhmzJihgQMHNtm6BQAAAAD4ZaN4A7TQubZNSVJiYqIsFovq6+vVq1cvZWZmOq/dddddzZ55I0kHDhzQsWPHdMMNN8hisSgsLEzbt2/XrFmzNGbMGI0ZM0aSFBwcrLFjx6q4uJjiDQAAAABcZijeAG7w4zNvLsTWrVtVV1encePGSZJOnz6t/Px8zZo1S++884569OihwYMHO/v7+PDIAgAAAMDlhjNvgDZSV1engoICvfjiiyosLFRhYaHefvttVVZWqqSkRP/85z+1du1a2e12nThxQoWFhbrlllvaOmwAAAAAQCvjP+MDLdTcmTeRkZEXPd8777yj3r17KyIiwtnm7++v6dOnKz8/X6tWrdLBgwcVGxsru92u+fPnq3fv3hd9PwAAAACAMZkcDoejrYMA4H5VVbWy21v2eAcGdlJlZcu+jgXvQd6Mi9wZE3kzLnJnXOTOmMibcZG71mE2mxQQ4H9hYzwUCwAAAAAAANyA4g0AAAAAAIAXo3gDAAAAAADgxTiwGGiB8vJyxcTEKCQkxKU9Pj5eeXl58vPzk8VikcPhkI+PjxYsWKDo6GhlZ2dLkubNm+ccY7VatX//fqWlpTnbkpKSdPToURUUFDjbcnJy9Oc//9n5++jRo3r00Uf1wAMPeGqZAAAAAAAvRPEGaKGgoCDZbLYm7Xl5ecrNzVVwcLAkqaioSMnJydq7d2+L5j158qTKysrUo0cPvf/++7r++uslSXPnztXcuXMlSf/3f/+n9PR0/eY3v3HTagAAAAAARsG2KcDNIiMjVV1drZqalp3SXlBQoGHDhum2227Txo0bm1yvq6vTsmXLtGzZMrVv397d4QIAAAAAvBxv3gAtVFFRobi4OJe29PT0Jv1sNpv69eun7t27S5Ly8/P11ltvOa9/8803io6Odv62Wq1KTk7WwIED9fzzz2vx4sXq2rWry3yhoaGKiIhw95IAAAAAAAZA8QZooXNtm5KkxMREWSwW1dfXq1evXsrMzHReu+uuu5o980aSDhw4oGPHjumGG26QxWJRWFiYtm/frlmzZjn75+fnKyUlxTOLAgAAAAB4PYo3gBv8+MybC7F161bV1dVp3LhxkqTTp08rPz/fWbw5fvy4Tp48qSFDhrgzXAAAAACAgVC8AdpIXV2dCgoK9OKLLzq3RNXW1mrkyJEqKSlRVFSUPvzwQw0dOrSNIwUAAAAAtCWKN0ALNXfmTWRk5EXP984776h3794uZ9n4+/tr+vTpys/PV1RUlL766iv96le/uuh7AAAAAACMz+RwOBxtHQQA96uqqpXd3rLHOzCwkyorW/Z1LHgP8mZc5M6YyJtxkTvjInfGRN6Mi9y1DrPZpIAA/wsb46FYAAAAAAAA4AYUbwAAAAAAALwYZ94Al6i2tlYZGRl677331K5dO3Xu3FkLFy7U1q1bdeLECWVlZTn7FhcX6/e//71sNpsmTZqkDRs2NPlK1cyZM/X111/riiuuUGNjo3x9ffXoo49q5MiRrb00AAAAAIAXoHgDXAK73a6EhARFRUVp+/bt8vHx0b59+5SQkKDNmzdr5syZKiws1OjRo3XmzBktXbpUzz77rPz9z7+/cfny5YqKipIkffrpp3rwwQf1yiuvaMCAAa2xLAAAAACAF2HbFHAJSkpKVFFRoaSkJPn4fF8LjY6OVmpqqvz8/LR8+XI9/fTTOnPmjLKysjR69GhnUaalBg8erPHjx2vz5s2eWAIAAAAAwMvx5g1wCcrKyjR48GCZza510B+2ON1www268cYbtWjRIn3++ecXXYC55ppr9O67715quAAAAAAAA6J4A1wCs9ksh+P8n+NeuHChbrnlFq1du1Z+fn4XdR+TyXTRYwEAAAAAxsa2KeAShIeHq6ysrEkBZ82aNdq3b58kyd/fX507d1bv3r0v+j4HDx5USEjIJcUKAAAAADAmijfAJRg2bJgCAgKUk5OjxsZGSVJRUZGsVqvbDhf+5JNPtHv3bk2bNs0t8wEAAAAAjIVtU8AlMJlMWrdunVJTUxUbGysfHx9169ZNubm56tGjx8+Oj42Nlclkcv7+8MMPJUkpKSm64oorZDKZ1KFDBz333HNNPikOAAAAALg8mBw/d2AHAEOqqqqV3d6yxzswsJMqK2s8HBHcjbwZF7kzJvJmXOTOuMidMZE34yJ3rcNsNikgwP/CxngoFgAAAAAAALgBxRsAAAAAAAAvRvEGAAAAAADAi3FgMfAzamtrlZGRoffee0/t2rVT586dtXDhQtXW1up3v/udbDabAgICJEl5eXn66KOPlJ2dLUnavn27Xn75ZTU0NMhut2v69Om69957JUkzZ87U119/rSuuuMJ5nz59+mj16tUuhx2XlZUpPj5epaWlrbxyAAAAAIA3oHgDnIfdbldCQoKioqK0fft2+fj4aN++fUpISNDrr7+uiRMnKiUlRevXr9dHH32kjRs3asuWLZKkjRs3Kj8/X//1X/+loKAgffvtt5o9e7Y6dOig6dOnS5KWL1+uqKgo572SkpL0P//zP3r88cclSd99952efvpp1dfXt80fAAAAAADQ5tg2BZxHSUmJKioqlJSUJB+f72ud0dHRSk1Nld1uV3Jysv71r39pw4YNWrBggVauXKnOnTtLktavX6+FCxcqKChIktS5c2etXLlSAwcObPZeZ86c0cmTJ9WlSxdnW1pammbNmuXZRQIAAAAAvBpv3gDnUVZWpsGDB8tsdq1zjhw50vm/V69erbi4OCUmJmrIkCGSpOrqah07dkwREREu40JCQlx+p6SkqEOHDqqurlaXLl00YcIEZ7Hm7bff1tmzZxUTE+OBlQEAAAAAjII3b4DzMJvNcjgc5+3zwQcfqFu3bvrLX/6ihoYG5zhJPzt2+fLl2rFjh7KysvTNN99o5MiR8vX1VWVlpdavX68nn3zSPQsBAAAAABgWxRvgPMLDw1VWVtakCLNmzRrt27dPhw8fVnZ2tvLz8+Xr66v169dLkrp27ao+ffo0OWR4//79Wr16dZP7DB06VDNnztQTTzyhhoYGvfvuuzp16pRmzJihuLg4SVJcXJxqa2s9tFIAAAAAgLeieAOcx7BhwxQQEKCcnBw1NjZKkoqKimS1WtWnTx8lJyfr8ccfV58+fZSWlqY//vGP+uijjyRJDzzwgNLS0lRZWSnp+61UaWlp6tu3b7P3uv/++/Xdd98pPz9f06dP11tvvSWbzSabzSZJstls8vf3b4VVAwAAAAC8CWfeAOdhMpm0bt06paamKjY2Vj4+PurWrZtyc3OVm5urgQMHOt+M6d27txYtWqTHH39c27dv19133636+nrNnj1bJpNJDodDd955p/NLUz/l6+urxx57TM8++6zi4uLUqVOn1lwqAAAAAMBLmRw/dygHAEOqqqqV3d6yxzswsJMqK2s8HBHcjbwZF7kzJvJmXOTOuMidMZE34yJ3rcNsNikg4MJ2VbBtCgAAAAAAwItRvAEAAAAAAPBiFG8AN6itrdWyZcsUGxuruLg4zZw5U3/7299UXl6u0aNHN+kfGhoqSbJarVq4cGGT6w0NDXryyScVGxuriRMnqqCgwONrAAAAAAB4Jw4sBi6R3W5XQkKCoqKitH37dvn4+Gjfvn1KSEhQbm7uRc1ZUFCg06dP67XXXlN1dbXGjx+vUaNG8bUpAAAAALgMUbwBLlFJSYkqKiqUlJQks/n7l9mio6OVmpoqu91+UXNOmTJFEydOlCRVVFTIYrHIYrG4LWYAAAAAgHGwbQq4RGVlZRo8eLCzcPODkSNHqnv37hc9r4+Pj5YsWaJp06YpPj5e7du3v9RQAQAAAAAGRPEGuERms1kOR/Of5P5pQUeSHA6HTCZTi+ZesWKFioqK9Oabb6q4uPiS4gQAAAAAGBPFG+AShYeHq6ysrEkBZ82aNfrss89UU1Pj0l5VVaUuXbqcd87S0lJ98cUXkqRu3brppptu0sGDB90aNwAAAADAGCjeAJdo2LBhCggIUE5OjhobGyVJRUVFslqtuu6669S3b1/t3r3b2X/jxo0aMWLEeef8+OOPtWrVKtntdtXW1qq4uFhDhw716DoAAAAAAN6JA4uBS2QymbRu3TqlpqYqNjZWPj4+6tatm3Jzc9WjRw+tWrVKS5cu1dq1a1VfX6/Q0FA99dRTzvEFBQUuxZ2HHnpICQkJOnjwoCZOnCiz2awZM2ZoyJAhbbE8AAAAAEAbMznOdVgHAEOrqqqV3d6yxzswsJMqK2t+viO8CnkzLnJnTOTNuMidcZE7YyJvxkXuWofZbFJAgP+FjfFQLAAAAAAAAHADijcAAAAAAABejOINAAAAAACAF6N4g8tebW2tli1bptjYWMXFxWnmzJn629/+pvLycoWHhysuLk5xcXEaN26ckpKSdOLECUlSSUmJZs6c6TLPnXfeqbS0NEnS6NGj9dxzz7nca+HChbJarZKkt956S3FxcZo0aZIefvhhffPNN85+27dv19SpUxUXF6eJEydqw4YNnv4zAAAAAAC8FMUbXNbsdrsSEhLUpUsXbd++XTabTY888ogSEhJ06tQpBQUFyWazyWazadeuXerbt6+SkpKazHP69Gk9+OCDioyM1MKFC53tL730kkpLS5v0r62t1dKlS5Wbm6sdO3YoNDRU2dnZkr7/lPhLL72k9evXy2az6ZVXXtGOHTu0efNmz/0hAAAAAABei+INLtONsBwAACAASURBVGslJSWqqKhQUlKSfHx8JEnR0dFKTU2V3W536WsymTRv3jwdOnRIn332mbP9zJkzSkxMVHR0tObPn+8y5qGHHtKiRYtUV1fn0l5fX6+lS5eqZ8+ekqTQ0FAdO3ZMkrR+/XotXLhQQUFBkqTOnTtr5cqVGjhwoHsXDwAAAAAwBIo3uKyVlZVp8ODBMptdH4WRI0eqe/fuTfr7+vqqb9+++vzzzyVJ3333nR566CEdOnRIs2bNatJ/4sSJ6tOnj9auXevS3q1bN40ZM0aSdPbsWeXm5mrMmDGqrq7WsWPHFBER4dI/JCSkSRsAAAAA4PJA8QaXNbPZLIfDcUFjTCaT/Pz8JEmffvqpRowYofHjxyslJaXZ/suWLdPmzZub3T5VU1OjhIQEDRo0SFOmTHEWkS40JgAAAADALxfFG1zWwsPDVVZW1qRYsmbNGpWUlDTpX1dXp6NHj2rAgAGSpF//+td6+OGHtXDhQh06dEh/+tOfmowJDAzUwoULtWjRItXX1zvbKyoqdM8992jQoEFasWKFJKlr167q06dPk0LP/v37tXr16kteLwAAAADAeCje4LI2bNgwBQQEKCcnR42NjZKkoqIiWa1WZ4HmB3a7XdnZ2YqIiNBVV10l6fttVJLUoUMHpaena9WqVTp8+HCT+0yaNEl9+vTR7t27JUmNjY2aM2eOxo8fryVLlshkMjn7PvDAA0pLS1NlZaUkqbq6Wmlpaerbt6/7/wAAAAAAAK/n09YBAG3JZDJp3bp1Sk1NVWxsrHx8fNStWzfl5uaqc+fOqqioUFxcnKTvizdhYWHKyMhodq6IiAjNmjVLycnJ2rJlS5PrP3yOXJIKCwtVVlamxsZGZ0EnPDxcK1as0N133636+nrNnj1bJpNJDodDd955p6ZPn+6hvwIAAAAAwJuZHByuAfwiVVXVym5v2eMdGNhJlZU1Ho4I7kbejIvcGRN5My5yZ1zkzpjIm3GRu9ZhNpsUEOB/YWM8FAsAAAAAAADcgOINAAAAAACAF6N4AwAAAAAA4MU4sBi4BLW1tcrIyNB7772ndu3aqXPnzlq4cKG6dOmimJgYhYSESJLOnj2r0NBQPfXUU+rRo4fKy8t17733qrCw0GW+0NBQHTx40Pn7+PHjmjp1qoqLi1t1XQAAAAAA78GbN8BFstvtSkhIUJcuXbR9+3bZbDY98sgjSkhI0KlTpxQUFCSbzSabzaZdu3apb9++SkpKavH8e/bs0b333uv8ZDgAAAAA4PJE8Qa4SCUlJaqoqFBSUpJ8fL5/iS06Olqpqamy2+0ufU0mk+bNm6dDhw7ps88+a9H8W7ZsUXZ2ttvjBgAAAAAYC9umgItUVlamwYMHy2x2rYGOHDlS5eXlTfr7+vqqb9+++vzzz3XdddepoqJCcXFx55yfwg0AAAAAQKJ4A1w0s9ksh8NxQWNMJpP8/Pwkybmt6sdCQ0PdFh8AAAAA4JeBbVPARQoPD1dZWVmTAs6aNWtUUlLSpH9dXZ2OHj2qAQMGtFaIAAAAAIBfAIo3wEUaNmyYAgIClJOTo8bGRklSUVGRrFZrkwKN3W5Xdna2IiIidNVVV7VFuAAAAAAAg2LbFHCRTCaT1q1bp9TUVMXGxsrHx0fdunVTbm6uOnfu7HKmjd1uV1hYmDIyMto4agAAAACA0ZgcF3poBwBDqKqqld3essc7MLCTKitrPBwR3I28GRe5MybyZlzkzrjInTGRN+Mid63D/P+xd/fRUZd3/v+fM5mE4E3SEJLWriG65BjQpIEVJDl2m68DHoibMAq1erBodskkKZjRWNRJZZG4xeGmibYJtDu1p6WcI0HCmAnehBaDCixMaU93C0zRsLBWuugAijIBNjef/P7IYX5OJ0giCWbI63GO55Dr7nNd8/bzz/tc1/Uxm0hOvmZgfYZoLiIiIiIiIiIiMgiUvBERERERERERGcaUvBHpp2AwSHV1NYWFhdhsNubPn8+BAwc4evQoWVlZ2Gw2bDYbM2fOxOFwcOLECQB8Ph/z588PG+e+++5jxYoVYeM/+eSTeDye0N9/+MMfmDt3LjabjYceeoi//vWvl2ehIiIiIiIiMqwoeSPSD4ZhYLfbSUxMpKmpCa/Xy6JFi7Db7Zw6dYrU1FS8Xi9er5eWlhbS09NxOBwR47S3t1NSUsLUqVNxOp0AfPjhh5SXl9PS0hLW9vHHH2f58uV4vV6Kior44Q9/eFnWKiIiIiIiIsOLkjci/eDz+QgEAjgcDiyW3o+05ebm4nK5MAwjrK3JZKKiooK2tjYOHjwYKj9z5gylpaXk5uayePHiUPmWLVuYPn06BQUFobKOjg4eeeQRJkyYAEBmZibHjh0byiWKiIiIiIjIMKXkjUg/+P1+srOzMZvDX5n8/HzGjBkT0T4uLo709HQOHz4MwNmzZykrK6OtrY3i4uKwtiUlJdx7770R/T/7mfH6+npmzJgxiCsSERERERGRaKHkjUg/mM1menr699nt80wmE/Hx8QDs27ePvLw8CgoKWLJkSb/H6OjoYPHixXR1dVFWVjag54uIiIiIiMiVQckbkX7IysrC7/dHJHBqa2vx+XwR7Ts6Ojhy5AgZGRkATJo0iYULF+J0Omlra2PDhg0Xfeb5+3G6urr46U9/Smxs7OAsRkRERERERKKKkjci/TBlyhSSk5Opr6+nu7sbgB07duDxeEIJmvMMw6Curo6cnBzGjRsH9B6DAhg9ejSrVq1i9erVHDp06HOf+fjjj5Oens6Pf/zjUH8REREREREZeSxf9gREooHJZGLt2rW4XC4KCwuxWCwkJSXhdrtJSEggEAiE3VEzceJEampq+hwrJyeH4uJiKisraWxsZNSoURFt/H4/b7zxBhkZGdx9990ApKam8vOf/3zoFikiIiIiIiLDkqlnoBd5iEhUOHkyiGH07/VOSbmW48dPD/GMZLApbtFLsYtOilv0Uuyil2IXnRS36KXYXR5ms4nk5GsG1meI5iIiIiIiIiIiIoNAyRsRERERERERkWFMyRsRERERERERkWFMyRuRSxQMBqmurqawsBCbzcb8+fM5cOAAR48eJSsrC5vNhs1mY+bMmTgcDk6cOAGAz+dj/vz5YePcd999rFixImz8N998E6vVelnXJCIiIiIiIsOHkjcil8AwDOx2O4mJiTQ1NeH1elm0aBF2u51Tp06RmpqK1+vF6/XS0tJCeno6DocjYpz29nZKSkqYOnUqTqczVH7ixAlWrlx5OZckIiIiIiIiw4ySNyKXwOfzEQgEcDgcWCwWAHJzc3G5XBiGEdbWZDJRUVFBW1sbBw8eDJWfOXOG0tJScnNzWbx4cVifJUuW8PDDDw/9QkRERERERGTYUvJG5BL4/X6ys7Mxm8Nfpfz8fMaMGRPRPi4ujvT0dA4fPgzA2bNnKSsro62tjeLi4rC2v/71r7n55pvJyckZsvmLiIiIiIjI8KfkjcglMJvN9PT0DKiPyWQiPj4egH379pGXl0dBQQFLliwJtXn33Xf5zW9+w8KFCwd1viIiIiIiIhJ9lLwRuQRZWVn4/f6IBE5tbS0+ny+ifUdHB0eOHCEjIwOASZMmsXDhQpxOJ21tbWzYsAGAlpYWjh8/zty5cyktLSUQCDBv3ryhX5CIiIiIiIgMO0reiFyCKVOmkJycTH19Pd3d3QDs2LEDj8cTStCcZxgGdXV15OTkMG7cOKD3GBXA6NGjWbVqFatXr+bQoUM4HA62bt2K1+vF7XaTmprKiy++eHkXJyIiIiIiIsOC5cuegEg0M5lMrF27FpfLRWFhIRaLhaSkJNxuNwkJCQQCAWw2G9CbvJk4cSI1NTV9jpWTk0NxcTGVlZU0NjYyatSoy7kUERERERERGaZMPQO9sENEosLJk0EMo3+vd0rKtRw/fnqIZySDTXGLXopddFLcopdiF70Uu+ikuEUvxe7yMJtNJCdfM7A+QzQXEREREREREREZBEreiIiIiIiIiIgMY7rzRuQSBYNBampq2Lt3LzExMSQkJOB0OklMTGTWrFmMHz8egHPnzpGZmcnSpUsZO3YsPp+P+vp61q9fHxpnwYIFTJ48GafTyeHDh3n66af55JNPSElJoba2lsTExC9zqSIiIiIiIvIl0M4bkUtgGAZ2u53ExESamprwer0sWrQIu93OqVOnSE1Nxev14vV6aWlpIT09HYfDETFOe3s7JSUlTJ06FafTSU9PD9/73vew2+00NzczceJE3G73l7BCERERERER+bJp543IJfD5fAQCARwOB2Zzby40NzcXl8uFYRhhbU0mExUVFdx+++0cPHgwVH7mzBlKS0vJzc3l0UcfBeDAgQNcddVVfOtb3wKgvLycTz/99DKtSkRERERERIYT7bwRuQR+v5/s7OxQ4ua8/Px8xowZE9E+Li6O9PR0Dh8+DMDZs2cpKyujra2N4uLiULu//OUvjB07lieffJKioiKefvpprrrqqiFdi4iIiIiIiAxPSt6IXAKz2UxPT/8+x32eyWQiPj4egH379pGXl0dBQQFLliwJtenq6uJ3v/sd3/3ud9myZQtpaWmsWLFiUOcuIiIiIiIi0UHJG5FLkJWVhd/vj0jg1NbW4vP5Itp3dHRw5MgRMjIyAJg0aRILFy7E6XTS1tbGhg0bAEhJSSE9PZ3s7GwACgsL+dOf/jTEqxEREREREZHhSMkbkUswZcoUkpOTqa+vp7u7G4AdO3bg8XhCCZrzDMOgrq6OnJwcxo0bB/QeowIYPXo0q1atYvXq1Rw6dIjJkyfz0Ucfhe7GaW1t5ZZbbrmMKxMREREREZHhQhcWi1wCk8nE2rVrcblcFBYWYrFYSEpKwu12k5CQQCAQwGazAb3Jm4kTJ1JTU9PnWDk5ORQXF1NZWUljYyNr1qxhyZIlnD17lq997WusWrXqci5NREREREREhglTz0Av7BCRqHDyZBDD6N/rnZJyLcePnx7iGclgU9yil2IXnRS36KXYRS/FLjopbtFLsbs8zGYTycnXDKzPEM1FREREREREREQGgZI3IiIiIiIiIiLDmJI3IiIiIiIiIiLDmC4sFrlEwWCQmpoa9u7dS0xMDAkJCTidThITE5k1axbjx48H4Ny5c2RmZrJ06VLGjh2Lz+ejvr6e9evXh8ZZsGABkydPxul0cuDAAZYuXUpnZyfXXXcdq1evJiEh4ctcqoiIiIiIiHwJtPNG5BIYhoHdbicxMZGmpia8Xi+LFi3Cbrdz6tQpUlNT8Xq9eL1eWlpaSE9Px+FwRIzT3t5OSUkJU6dOxel0ArB8+XIcDgfNzc3ceOON/OIXv7jcyxMREREREZFhQMkbkUvg8/kIBAI4HA4slt6NbLm5ubhcLgzDCGtrMpmoqKigra2NgwcPhsrPnDlDaWkpubm5LF68OFRuGAbt7e0AnD17lvj4+MuwIhERERERERlulLwRuQR+v5/s7GzM5vBXKT8/nzFjxkS0j4uLIz09ncOHDwO9SZmysjLa2tooLi4Oa+t0Onnqqaf45je/yX/8x39w//33D9k6REREREREZPhS8kbkEpjNZnp6egbUx2QyhXbR7Nu3j7y8PAoKCliyZEmozblz53jqqadYt24dO3fuZN68eTz55JODOncRERERERGJDkreiFyCrKws/H5/RAKntrYWn88X0b6jo4MjR46QkZEBwKRJk1i4cCFOp5O2tjY2bNgAwLvvvsuoUaP4xje+AcB9993H7373uyFejYiIiIiIiAxHSt6IXIIpU6aQnJxMfX093d3dAOzYsQOPxxNK0JxnGAZ1dXXk5OQwbtw4oPcYFcDo0aNZtWoVq1ev5tChQ6Snp/PBBx+Ejle98cYbZGdnX8aViYiIiIiIyHChT4WLXAKTycTatWtxuVwUFhZisVhISkrC7XaTkJBAIBDAZrMBvcmbiRMnUlNT0+dYOTk5FBcXU1lZSWNjIy6Xi0cffZSenh6Sk5N59tlnL+fSREREREREZJgw9Qz0wg4RiQonTwYxjP693ikp13L8+OkhnpEMNsUteil20Ulxi16KXfRS7KKT4ha9FLvLw2w2kZx8zcD6DNFcRERERERERERkECh5IyIiIiIiIiIyjCl5IyIiIiIiIiIyjCl5IwIEg0Gqq6spLCzEZrMxf/58Dhw4wNGjR8nKysJms2Gz2Zg5cyYOh4MTJ04A4PP5mD9/ftg49913HytWrADAarXy3HPPhT3L6XTi8XjCyp588smIMoC5c+dSXl4+2MsVERERERGRKKLkjYx4hmFgt9tJTEykqakJr9fLokWLsNvtnDp1itTUVLxeL16vl5aWFtLT03E4HBHjtLe3U1JSwtSpU3E6naHydevWsX///j6f/eGHH1JeXk5LS0tE3TvvvENsbCwHDx7k2LFjg7dgERERERERiSpK3siI5/P5CAQCOBwOLBYLALm5ubhcLgzDCGtrMpmoqKigra2NgwcPhsrPnDlDaWkpubm5LF68OKxPWVkZVVVVdHR0RDx7y5YtTJ8+nYKCgog6j8fD7bffzvTp03nppZcGY6kiIiIiIiIShZS8kRHP7/eTnZ2N2Rz+OuTn5zNmzJiI9nFxcaSnp3P48GEAzp49S1lZGW1tbRQXF0e0LyoqIi0tjTVr1kTUlZSUcO+990aUd3Z20tzcTEFBAQUFBTQ2NtLV1fUFVygiIiIiIiLRTMkbGfHMZjM9PT0D6mMymYiPjwdg37595OXlUVBQwJIlS/psX11dzaZNmy54fOpvvfXWW6SkpJCRkcGtt96K2Wxm+/btA5qjiIiIiIiIXBmUvJERLysrC7/fH5HAqa2txefzRbTv6OjgyJEjZGRkADBp0iQWLlyI0+mkra2NDRs2RPRJSUnB6XRSVVVFZ2fnRee0efNmjh07htVqZfr06QSDQRoaGr7gCkVERERERCSaKXkjI96UKVNITk6mvr6e7u5uAHbs2IHH4wklaM4zDIO6ujpycnIYN24c0HuMCmD06NGsWrWK1atXc+jQoYjnzJ49m7S0NLZu3fq58zlx4gS7du3ilVdeobW1ldbWVpqamtizZw/vv//+YCxZREREREREooiSNzLimUwm1q5dy1/+8hcKCwspKiri5z//OW63m+TkZAKBQOhT4TabjQ8//JCampo+x8rJyaG4uJjKykr+7//+L6K+urqaq6+++nPn09zcTH5+Pl/96ldDZWlpaVitVjZu3HhpixUREREREZGoY+oZ6GUfIhIVTp4MYhj9e71TUq7l+PHTQzwjGWyKW/RS7KKT4ha9FLvopdhFJ8Uteil2l4fZbCI5+ZqB9RmiuYiIiIiIiIiIyCBQ8kZEREREREREZBizfNkTEBkOgsEgNTU17N27l5iYGBISEnA6nSQmJjJr1izGjx8PwLlz58jMzGTp0qWMHTsWn89HfX0969evD42zYMECJk+ejNPpxGq1UlRURGVlZehZTqeT2267jTlz5oTKnnzySaZNmxYqs1qtxMfHExsbS1dXF1//+tdZuXIlY8aMuYy/ioiIiIiIiAwH2nkjI55hGNjtdhITE2lqasLr9bJo0SLsdjunTp0iNTUVr9eL1+ulpaWF9PR0HA5HxDjt7e2UlJQwdepUnE5nqHzdunXs37+/z2d/+OGHlJeX09LSElHndrvxer28+uqr3HDDDbzwwguDt2gRERERERGJGkreyIjn8/kIBAI4HA4slt7NaLm5ubhcLgzDCGtrMpmoqKigra2NgwcPhsrPnDlDaWkpubm5LF68OKxPWVkZVVVVdHR0RDx7y5YtTJ8+nYKCggvOzzAM2tvbGTt27KUsU0RERERERKKUjk3JiOf3+8nOzsZsDs9l5ufnc/To0Yj2cXFxpKenc/jwYZKTkzl79ixlZWW0tbWxZs2aiPZFRUXs27ePNWvWhB2fAigpKQHgD3/4Q0S/0tJSYmNjOXnyJDExMTz88MOXskwRERERERGJUtp5IyOe2Wymp6d/n9Q+z2QyER8fD8C+ffvIy8ujoKCAJUuW9Nm+urqaTZs2XfD4VF/OH5vauXMnpaWlLFiwYMDzFBERERERkein5I2MeFlZWfj9/ojESG1tLT6fL6J9R0cHR44cISMjA4BJkyaxcOFCnE4nbW1tbNiwIaJPSkoKTqeTqqoqOjs7BzzH2bNnc/jwYT7++OMB9xUREREREZHopuSNjHhTpkwhOTmZ+vp6uru7AdixYwcejyeUoDnPMAzq6urIyclh3LhxQO8xKoDRo0ezatUqVq9ezaFDhyKeM3v2bNLS0ti6deuA57h7926uu+46fW1KRERERERkBNKdNzLimUwm1q5di8vlorCwEIvFQlJSEm63m4SEBAKBADabDehN3kycOJGampo+x8rJyaG4uJjKykoaGxsj6qurqyksLOzXvM7feWMYBrGxsdTW1n7xRYqIiIiIiEjUMvXoEg2RK9LJk0EMo3+vd0rKtRw/fnqIZySDTXGLXopddFLcopdiF70Uu+ikuEUvxe7yMJtNJCdfM7A+QzQXEREREREREREZBEreiIiIiIiIiIgMY0reiIiIiIiIiIgMY0reiADBYDB0mbDNZmP+/PkcOHCAo0ePkpWVhc1mw2azMXPmTBwOBydOnADA5/Mxf/78sHHuu+8+VqxYAYDVauW5554Le5bT6cTj8QCwbds2bDYbs2fPZuHChXzyySdhbefOnUt5eflQLl1ERERERESGOSVvZMQzDAO73U5iYiJNTU14vV4WLVqE3W7n1KlTpKam4vV68Xq9tLS0kJ6ejsPhiBinvb2dkpISpk6ditPpDJWvW7eO/fv3R7QPBoMsW7YMt9tNc3MzmZmZ1NXVherfeecdYmNjOXjwIMeOHRuaxYuIiIiIiMiwp+SNjHg+n49AIIDD4cBisQCQm5uLy+XCMIywtiaTiYqKCtra2jh48GCo/MyZM5SWlpKbm8vixYvD+pSVlVFVVUVHR0dYeWdnJ8uWLeOrX/0qAJmZmWFJGo/Hw+2338706dN56aWXBnXNIiIiIiIiEj2UvJERz+/3k52djdkc/jrk5+czZsyYiPZxcXGkp6dz+PBhAM6ePUtZWRltbW0UFxdHtC8qKiItLY01a9aElSclJTFjxgwAzp07h9vtDv3d2dlJc3MzBQUFFBQU0NjYSFdX12AsV0RERERERKKMkjcy4pnNZnp6egbUx2QyER8fD8C+ffvIy8ujoKCAJUuW9Nm+urqaTZs29Xl86vTp09jtdiZMmMA999wDwFtvvUVKSgoZGRnceuutmM1mtm/fPsCViYiIiIiIyJVAyRsZ8bKysvD7/REJnNraWnw+X0T7jo4Ojhw5QkZGBgCTJk1i4cKFOJ1O2tra2LBhQ0SflJQUnE4nVVVVdHZ2hsoDgQDz5s1jwoQJLF++PFS+efNmjh07htVqZfr06QSDQRoaGgZrySIiIiIiIhJFlLyREW/KlCkkJydTX19Pd3c3ADt27MDj8YQSNOcZhkFdXR05OTmMGzcO6D1GBTB69GhWrVrF6tWrOXToUMRzZs+eTVpaGlu3bgWgu7ub8vJyCgoKeOqppzCZTACcOHGCXbt28corr9Da2kpraytNTU3s2bOH999/f8h+BxERERERERmeLF/2BES+bCaTibVr1+JyuSgsLMRisZCUlITb7SYhIYFAIIDNZgN6kzcTJ06kpqamz7FycnIoLi6msrKSxsbGiPrznyMHaG1txe/3093dHUroZGVlMX78ePLz80MXGQOkpaVhtVrZuHFjxIXIIiIiIiIicmUz9Qz0sg8RiQonTwYxjP693ikp13L8+OkhnpEMNsUteil20Ulxi16KXfRS7KKT4ha9FLvLw2w2kZx8zcD6DNFcRERERERERERkECh5IyIiIiIiIiIyjOnOGxlywWCQmpoa9u7dS0xMDAkJCTidThITE5k1axbjx48H4Ny5c2RmZrJ06VLGjh2Lz+ejvr6e9evXh8ZZsGABkydPxul0YrVaKSoqorKyMvQsp9PJbbfdxpw5c9i2bRt1dXX09PRw/fXX43K5SExMDLWdO3cuKSkp/OxnP+v3Wj788EPmzp3Lzp07I+pWrlzJxx9/zIoVK0JlXV1d/L//9/+YOXMm//qv/xoqr6uro6GhgbFjxwK9X7CyWCwsW7aMW2+9NaIe4Oabb8blcvV7riIiIiIiInJl0M4bGVKGYWC320lMTKSpqQmv18uiRYuw2+2cOnWK1NRUvF4vXq+XlpYW0tPTcTgcEeO0t7dTUlLC1KlTcTqdofJ169axf//+iPbBYJBly5bhdrtpbm4mMzOTurq6UP0777xDbGwsBw8e5NixY/1ay1tvvcWDDz7I8ePHI+p2797Nyy+/HFH+9ttvk52dzeuvv87Zs2fD6u6///7Q2l9//XXmzp0blvj5bL3X61XiRkREREREZIRS8kaGlM/nIxAI4HA4sFh6N3rl5ubicrkwDCOsrclkoqKigra2Ng4ePBgqP3PmDKWlpeTm5kZ8aamsrIyqqio6OjrCyjs7O1m2bFnoi02ZmZlhSRqPx8Ptt9/O9OnTeemll0LldXV1fP/73+fee+/lzjvv5IUXXgjVNTY2hiWAzjt16hTPPfcc5eXlEXUej4c777yTb3zjG7z66qsX/J0Mw+CDDz4I2xkkIiIiIiIiAkreyBDz+/1kZ2djNof/r5afn8+YMWMi2sfFxZGens7hw4cBOHv2LGVlZbS1tVFcXBzRvqioiLS0NNasWRNWnpSUxIwZM4De41hutzv0d2dnJ83NzRQUFFBQUEBjYyNdXV2hvu+++y6/+tWv8Hg8bNy4kQMHDgC9iZ2bbropYg5Lly6lsrKShISEsPKPPvqIXbt2MX36dAoKCmhoaAirb2howGazcccdd3DHHXdw9uxZnn322Yj68/+d/01ERERERERkZFHyRoaU2WxmoF+jN5lMxMfHA7Bvd6ZlvwAAIABJREFU3z7y8vIoKChgyZIlfbavrq5m06ZNfR6fOn36NHa7nQkTJnDPPfcAvcefUlJSyMjI4NZbb8VsNrN9+/ZQn8LCQq6++mquvfZarFYre/bsueBcN23axHXXXUdeXl5EXXNzM7m5uSQmJjJ9+nTeffdd/H5/qP78saiXXnqJq6++mltuuYXU1NSI+vP//f3f//1FfjkRERERERG5Eil5I0MqKysLv98fkcCpra3F5/NFtO/o6ODIkSNkZGQAMGnSJBYuXIjT6aStrY0NGzZE9ElJScHpdFJVVUVnZ2eoPBAIMG/ePCZMmMDy5ctD5Zs3b+bYsWNYrVamT59OMBgM2xUTExMT+rdhGGF//63XXnuNXbt2YbPZ+MlPfkJra2to94zH4+GPf/wjVquV2bNnYzabI3bfnJ//D3/4Q5YvX877779/wWeJiIiIiIjIyKTkjQypKVOmkJycTH19Pd3d3QDs2LEDj8cTStCcZxgGdXV15OTkMG7cOKD3GBXA6NGjWbVqFatXr+bQoUMRz5k9ezZpaWls3boVgO7ubsrLyykoKOCpp57CZDIBcOLECXbt2sUrr7xCa2srra2tNDU1sWfPnlDiZNu2bXR0dPDJJ5+wfft2vvnNb15wfb/85S955ZVX8Hq9OBwOrFYrP/jBDzhw4AAffPABb775Zug5//7v/86WLVsIBoMR4/zDP/wDVquV1atXD/QnFhERERERkSucPhUuQ8pkMrF27VpcLheFhYVYLBaSkpJwu90kJCQQCASw2WxAb/Jm4sSJ1NTU9DlWTk4OxcXFVFZW0tjYGFFfXV1NYWEhAK2trfj9frq7u0MJnaysLMaPH09+fn7oImOAtLQ0rFYrGzduZNSoUYwaNYp58+YRDAYpKyuLSDL1h8fjYc6cOaHjXwDTpk3jxhtvZMuWLX32eeyxx7jrrrv4/e9/P+DniYiIiIiIyJXL1DPQC0lErmDnvyZVUVHxJc/k0p08GcQw+vd6p6Rcy/Hjp4d4RjLYFLfopdhFJ8Uteil20Uuxi06KW/RS7C4Ps9lEcvI1A+szRHMREREREREREZFBoGNTIp9xJey4ERERERERkSuLdt6IiIiIiIiIiAxj2nkjX4pgMEhNTQ179+4lJiaGhIQEnE4niYmJzJo1i/HjxwNw7tw5MjMzWbp0KWPHjsXn81FfX8/69etD4yxYsIDJkyfjdDqxWq0UFRVRWVkZepbT6eS2225jzpw5bNu2jbq6Onp6erj++utxuVwkJiaG2s6dO5eUlBR+9rOf9XstH374IXPnzmXnzp0RdStXruTjjz9mxYoVAMyfP5+HH36YadOmYbVaiY+PJzY2FoDTp0+TlZXFihUruOqqqyLqAR5++GHuvPPOAfzSIiIiIiIiEu2080YuO8MwsNvtJCYm0tTUhNfrZdGiRdjtdk6dOkVqaiperxev10tLSwvp6ek4HI6Icdrb2ykpKWHq1Kk4nc5Q+bp169i/f39E+2AwyLJly3C73TQ3N5OZmRm6oBjgnXfeITY2loMHD3Ls2LF+reWtt97iwQcf5Pjx4xF1u3fv5uWXX/7c/m63O2yt//u//0tTU1Of9V6vV4kbERERERGREUjJG7nsfD4fgUAAh8OBxdK7+Ss3NxeXy4VhGGFtTSYTFRUVtLW1cfDgwVD5mTNnKC0tJTc3l8WLF4f1KSsro6qqio6OjrDyzs5Oli1bFvpMeGZmZliSxuPxcPvttzN9+nReeumlUHldXR3f//73uffee7nzzjt54YUXQnWNjY1hCaDzTp06xXPPPUd5eXm/f5fTp09z+vRpvvKVr/S7j4iIiIiIiFz5dGxKLju/3092djZmc3juMD8/n6NHj0a0j4uLIz09ncOHD5OcnMzZs2cpKyujra2NNWvWRLQvKipi3759rFmzJuz4VFJSEjNmzAB6j2O53W7mz58P9CZ2mpubWb9+PadOnaKyspJFixaFkkvvvvsuDQ0NGIbBnDlzyMvL45ZbbukzcQOwdOlSKisrL7qDp7S0lJiYGE6ePMnXvvY1vvvd71JQUBBWf/7Y1I033sjzzz//ueOJiIiIiIjIlUc7b+SyM5vN9PT0DKiPyWQiPj4egH379pGXl0dBQQFLlizps311dTWbNm3q8/jU6dOnsdvtTJgwgXvuuQfoPf6UkpJCRkYGt956K2azme3bt4f6FBYWcvXVV3PttdditVrZs2fPBee6adMmrrvuOvLy8i66LrfbzZYtW3j66af56KOPsFqtmEymsPrzR6aUuBERERERERmZlLyRyy4rKwu/3x+RwKmtrcXn80W07+jo4MiRI2RkZAAwadIkFi5ciNPppK2tjQ0bNkT0SUlJwel0UlVVRWdnZ6g8EAgwb948JkyYwPLly0Plmzdv5tixY1itVqZPn04wGKShoSFUHxMTE/q3YRhhf/+t1157jV27dmGz2fjJT35Ca2srzz777Of+JjNnzuRb3/oWS5cu/dx2IiIiIiIiMvIoeSOX3ZQpU0hOTqa+vp7u7m4AduzYgcfjCSVozjMMg7q6OnJychg3bhzQe4wKYPTo0axatYrVq1dz6NChiOfMnj2btLQ0tm7dCkB3dzfl5eUUFBTw1FNPhXa4nDhxgl27dvHKK6/Q2tpKa2srTU1N7Nmzh/fffx+Abdu20dHRwSeffML27dv55je/ecH1/fKXv+SVV17B6/XicDiwWq384Ac/uOjv8sgjj/DHP/4xbMePiIiIiIiIiO68kcvOZDKxdu1aXC4XhYWFWCwWkpKScLvdJCQkEAgEsNlsQG/yZuLEidTU1PQ5Vk5ODsXFxVRWVtLY2BhRX11dTWFhIQCtra34/X66u7tDCZ2srCzGjx9Pfn5+6CJjgLS0NKxWKxs3bmTUqFGMGjWKefPmEQwGKSsri0gyDYbk5GTsdjurVq3iH//xHwd9fBEREREREYlOpp6BXj4iMsKcv5S4oqLiS57JwJw8GcQw+vd6p6Rcy/Hjp4d4RjLYFLfopdhFJ8Uteil20Uuxi06KW/RS7C4Ps9lEcvI1A+szRHMREREREREREZFBoGNTIhcRbTtuRERERERE5MqinTciIiIiIiIiIsOYkjfyhQWDwdCFwDabjfnz53PgwAGOHj1KVlYWNpsNm83GzJkzcTgcnDhxAgCfz8f8+fPDxrnvvvtYsWIFAFarleeeey7sWU6nE4/HA/R++clmszF79mwWLlzIJ598EtZ27ty5lJeXD2gtH374YcQXpFpbW5kzZw6zZs3ihz/8YajcarVy9OhRADIzM3nppZfC+s2fPx+fz8fRo0exWq2h8s7OThYuXMijjz5KV1dXqNzv95OVlRUxp5UrV5Kbm0tHR8eA1iIiIiIiIiJXFiVv5AsxDAO73U5iYiJNTU14vV4WLVqE3W7n1KlTpKam4vV68Xq9tLS0kJ6ejsPhiBinvb2dkpISpk6ditPpDJWvW7eO/fv3R7QPBoMsW7YMt9tNc3MzmZmZoQuFAd555x1iY2M5ePAgx44d69da3nrrLR588EGOHz8eKnv//fd5+umnWbt2LVu2bMHv9/PWW2/12f+555676LO6urp47LHHGD16NDU1NVgsvScWz549yzPPPENnZ2dE+9dff53JkyfT0tLSr3WIiIiIiIjIlUnJG/lCfD4fgUAAh8MRSkTk5ubicrkwDCOsrclkoqKigra2Ng4ePBgqP3PmDKWlpeTm5rJ48eKwPmVlZVRVVUXsOuns7GTZsmWhz3pnZmaGJU48Hg+3334706dPD9sRU1dXx/e//33uvfde7rzzTl544YVQXWNjY1gCCOC3v/0td911F1/72teIjY3lueeeIycnp8/f4sEHH2TJkiUX/K26u7tZvHgxV111FatXryYmJiZUt2LFCoqLiyP6vPXWW6SlpXH33XezcePGC44tIiIiIiIiVz4lb+QL8fv9ZGdnYzaH/y+Un5/PmDFjItrHxcWRnp7O4cOHgd4dJ2VlZbS1tfWZvCgqKiItLY01a9aElSclJTFjxgwAzp07h9vtDv3d2dlJc3MzBQUFFBQU0NjYGHY86d133+VXv/oVHo+HjRs3cuDAAaA3sXPTTTeFPee9996ju7ubBQsWMHv2bF588UUSExP7/C3sdjsff/xxxPEp6E3cPPHEE/z2t7/le9/7Xtjv9cYbb3Du3DlmzZoV0c/j8TBr1izy8/P585//zKFDh/p8toiIiIiIiFz5lLyRL8RsNtPT0zOgPiaTifj4eAD27dtHXl4eBQUFF9y1Ul1dzaZNm/o8PnX69GnsdjsTJkzgnnvuAXp3q6SkpJCRkcGtt96K2Wxm+/btoT6FhYVcffXVXHvttVitVvbs2XPBuXZ3d7N7925Wr17NSy+9xL59+3j55Zf7bGuxWFixYkWfx6c++OADrrrqKh555BEef/zxUDLp+PHj/PSnP+Vf//VfI8b76KOP2LlzJwUFBcTHx3PHHXfQ0NBwwbmKiIiIiIjIlU3JG/lCsrKy8Pv9EQmc2tpafD5fRPuOjg6OHDlCRkYGAJMmTWLhwoU4nU7a2trYsGFDRJ+UlBScTidVVVVhd8IEAgHmzZvHhAkTWL58eah88+bNHDt2DKvVyvTp0wkGg2FJj88eVzIMI+zvvzV27Fjy8vIYM2YM8fHxTJ8+nT/96U8XbH/TTTf1eXwqNTWVZ555hpKSEuLi4nj++ecBePPNNzl16hQPPPAANpsNAJvNRjAYpLm5mZ6eHr797W9jtVrZvXs3Xq+Xc+fOXfD5IiIiIiIicuVS8ka+kClTppCcnEx9fT3d3d0A7NixA4/HE0rQnGcYBnV1deTk5DBu3Dig9xgVwOjRo1m1ahWrV6/u82jQ7NmzSUtLY+vWrUDvjpjy8nIKCgp46qmnMJlMAJw4cYJdu3bxyiuv0NraSmtrK01NTezZs4f3338f6P1KVUdHB5988gnbt2+P+LrUZ91xxx3s3LmTTz/9lO7ubnbs2MEtt9zyub/J+eNTf/zjH0NlsbGxmEwmzGYzK1eupKGhgd27d3Pvvfeybdu20KXOAF6vl2uuuYbNmzezYsWK0Dp27txJYmIir7322uc+X0RERERERK5Mli97AhKdTCYTa9euxeVyUVhYiMViISkpCbfbTUJCAoFAILSjxDAMJk6cSE1NTZ9j5eTkUFxcTGVlJY2NjRH15z9HDr2f7/b7/XR3d4cSOllZWYwfP578/PzQRcYAaWlpWK1WNm7cyKhRoxg1ahTz5s0jGAxSVlYWkWT62zmVlJQwb948Ojs7uf3225k7d+7n/ibnj0/NmTOnz/rrr7+eqqoqHn/8cZqbm/u8G2j//v18/PHH3HnnnaEys9nMQw89RENDwwXHFhERERERkSuXqWegF5eIRKHzX5OqqKj4kmdy+Zw8GcQw+vd6p6Rcy/Hjp4d4RjLYFLfopdhFJ8Uteil20Uuxi06KW/RS7C4Ps9lEcvI1A+szRHMREREREREREZFBoGNTMiKMpB03IiIiIiIicmVR8kYECAaD1NTUsHfvXmJiYkhISMDpdJKYmMisWbMYP348AOfOnSMzM5OlS5cyduxYfD4f9fX1rF+/PjTOggULmDx5Mk6nE6vVSlFREZWVlaFnOZ1ObrvtNubMmcO2bduoq6ujp6eH66+/HpfLRWJiIlarlfj4eGJjY+nq6uLrX/86K1eu7POeHBEREREREbmy6diUjHiGYWC320lMTKSpqQmv18uiRYuw2+2cOnWK1NTU0FehWlpaSE9Px+FwRIzT3t5OSUkJU6dOxel0hsrXrVvH/v37I9oHg0GWLVuG2+2mubmZzMzM0N08AG63G6/Xy6uvvsoNN9zACy+8MDQ/gIiIiIiIiAxrSt7IiOfz+QgEAjgcDiyW3s1oubm5uFwuDMMIa2symaioqKCtrY2DBw+Gys+cOUNpaSm5ubksXrw4rE9ZWRlVVVV0dHSElXd2drJs2bLQF7IyMzM5duxYxPwMw6C9vZ2xY8cOynpFREREREQkuih5IyOe3+8nOzsbszn8dcjPz+/zmFJcXBzp6ekcPnwYgLNnz1JWVkZbWxvFxcUR7YuKikhLS2PNmjVh5UlJScyYMQPoPY7ldrtDfwOUlpZis9n41re+xa5du5g1a9alLlVERERERESikJI3MuKZzWZ6evr3Se3zTCYT8fHxAOzbt4+8vDwKCgpYsmRJn+2rq6vZtGlTn8enTp8+jd1uZ8KECdxzzz2h8vPHpnbu3ElpaSkLFiwY8DxFREREREQk+il5IyNeVlYWfr8/IjFSW1uLz+eLaN/R0cGRI0fIyMgAYNKkSSxcuBCn00lbWxsbNmyI6JOSkoLT6aSqqorOzs5QeSAQYN68eUyYMIHly5dfcI6zZ8/m8OHDfPzxx190mSIiIiIiIhKllLyREW/KlCkkJydTX19Pd3c3ADt27MDj8YQSNOcZhkFdXR05OTmMGzcO6D1GBTB69GhWrVrF6tWrOXToUMRzZs+eTVpaGlu3bgWgu7ub8vJyCgoKeOqppzCZTBec4+7du7nuuuv0tSkREREREZERSJ8KlxHPZDKxdu1aXC4XhYWFWCwWkpKScLvdJCQkEAgEsNlsQG/yZuLEidTU1PQ5Vk5ODsXFxVRWVtLY2BhRX11dTWFhIQCtra34/X66u7tDCZ2srKzQDpzS0lJiY2MxDIPY2Fhqa2uHYvkiIiIiIiIyzJl6dImGyBXp5MkghtG/1zsl5VqOHz89xDOSwaa4RS/FLjopbtFLsYteil10Utyil2J3eZjNJpKTrxlYnyGai4iIiIiIiIiIDAIlb0REREREREREhjElb0REREREREREhjFdWCxyEcFgkJqaGvbu3UtMTAwJCQk4nU4SExOZNWsW48ePD2v/ne98hwceeIDMzEwmTJgAQGdnJxMnTmT58uXEx8dTV1dHQ0MDY8eOBeDcuXPMmjWLysrK0DjvvvsuRUVF/OQnP2HmzJmXb8EiIiIiIiIyrCh5I/I5DMPAbrczbdo0mpqasFgs7NmzB7vdjtvtJjU1Fa/Xe8H+5+t6enqoqKhg8+bNPPDAAwDcf//9VFRUAHDmzBlsNhvZ2dnMmDEDAI/Hw8yZM2loaFDyRkREREREZATTsSmRz+Hz+QgEAjgcDiyW3lxnbm4uLpcLwzD6PU5nZydnz54N7bT5W1dddRW33HIL//M//wNAV1cXzc3NVFZW4vf7+ctf/nLJaxEREREREZHopJ03Ip/D7/eTnZ2N2Rye58zPz+fo0aMEAgFsNltY3apVq8jMzAQI1X3wwQd89atfJS8vr8/n/PWvf+X3v/898+fPB+DNN9/k61//OjfeeCMzZsygoaGBJ554YrCXJyIiIiIiIlFAyRuRz2E2m+np6blgfX+PTRmGQU1NDZWVlfziF78AoKGhgW3btmEYBjExMZSXl3PrrbcCvUemCgsLAbjrrrtYvHgxjz76KHFxcYO1NBEREREREYkSSt6IfI6srCxefPFFenp6MJlMofLa2lrS09P7PY7ZbKaoqIgXX3wxVPbZO28+6+TJk7z99tvs37+fX//61/T09PDpp5/ym9/8JpTQERERERERkZFDd96IfI4pU6aQnJxMfX093d3dAOzYsQOPx0NGRsaAxtq9ezc333zzRds1NzeTm5vL22+/TWtrK9u3b6e8vJyNGzd+oTWIiIiIiIhIdNPOG5HPYTKZWLt2LS6Xi8LCQiwWC0lJSbjdbhISEvq882bq1KksWbIE+P/vvOnq6iIpKYlnnnnmos/0eDxhnwwHmDdvHi+88AL//d//HfFpchEREREREbmymXo+70IPEYlaJ08GMYz+vd4pKddy/PjpIZ6RDDbFLXopdtFJcYteil30Uuyik+IWvRS7y8NsNpGcfM3A+gzRXEREREREREREZBAoeSMiIiIiIiIiMowpeSMCBINBqqurKSwsxGazMX/+fA4cOEB1dTU2m4277rqLrKwsbDYbNpuNzZs34/F4mDZtGidOnAiNc/ToUaxWK8BF6+vq6rj99tux2WzMnj2boqIitm/fHjavzs5OHnroIXw+32X4FURERERERGQ40oXFMuIZhoHdbmfatGk0NTVhsVjYs2cPdrudV199laSkJI4ePcqDDz6I1+sN9fN4PLS3t/P000+zZs2aPse+WP1nPxf+5z//mX/5l39h9+7dABw+fJgf/OAH+P3+QV6xiIiIiIiIRBPtvJERz+fzEQgEcDgcWCy9+czc3FxcLheGYXxu35kzZ/Lee++xZcuWL1T/WadPn2bs2LGhvxsbGykpKSEnJ2cAqxEREREREZErjXbeyIjn9/vJzs7GbA7PZebn51+0b2xsLC6Xi7KyMvLy8gZc39DQwLZt2+jo6OC9994L+5T4E088AcC6desGuiQRERERERG5gmjnjYx4ZrOZnp7+fVK7L9nZ2dx77708/fTTA66///778Xq9vP766/zmN7+htraWP/zhD194LiIiIiIiInLlUfJGRrysrCz8fn9EAqe2tpY9e/b0a4xFixbx3nvv8corr3yheoDrr7+eyZMn85//+Z/9n7yIiIiIiIhc8ZS8kRFvypQpJCcnU19fT3d3NwA7duzA4/GQkZHRrzHi4uJwuVz87Gc/+0L1AJ9++il+v5+bb7554IsQERERERGRK5buvJERz2QysXbtWlwuF4WFhVgsFpKSknC73WEXCF9MdnY2Dz300AUvJ+6r/vydNyaTifb2du69994+78YRERERERGRkcvUcymXfYjIsHXyZBDD6N/rnZJyLcePnx7iGclgU9yil2IXnRS36KXYRS/FLjopbtFLsbs8zGYTycnXDKzPEM1FREREREREREQGgZI3IiIiIiIiIiLDmJI3IiIiIiIiIiLDmC4sFvkcwWCQmpoa9u7dS0xMDAkJCTidThITE5k1axbjx48Pa/+d73yHBx54AKvVSlFREZWVlaE6p9PJbbfdxpw5c7BarcTHxxMbG0tPTw8Wi4UnnniC3NzcUPuVK1fy8ssv8/bbbxMXF3fZ1iwiIiIiIiLDi5I3IhdgGAZ2u51p06bR1NSExWJhz5492O123G43qampeL3eC/Zft24dd955J1lZWX3Wu91urr/+eqD30+SVlZW8/fbbxMbG0tXVxeuvv87kyZNpaWlh9uzZQ7JGERERERERGf50bErkAnw+H4FAAIfDgcXSm+fMzc3F5XJhGMZF+5eVlVFVVUVHR8dF206dOpWPPvqI06d7b3Z/6623SEtL4+6772bjxo2XthARERERERGJakreiFyA3+8nOzsbszn8NcnPz2fMmDEEAgFsNlvYf++8806oXVFREWlpaaxZs+aiz/J6vdxwww2MGTMGAI/Hw6xZs8jPz+fPf/4zhw4dGtzFiYiIiIiISNTQsSmRCzCbzfT09Fyw/mLHpgCqq6ux2WzceeedEXWlpaXExsbS2dnJddddx/PPPw/ARx99xM6dO/m3f/s34uPjueOOO2hoaGDJkiWXtiARERERERGJSkreiFxAVlYWL774Ij09PZhMplB5bW0t6enp/RojJSUFp9NJVVUVN910U1jdZ++8+azm5mZ6enr49re/DcC5c+fo7Oxk8eLFxMfHX8KKREREREREJBrp2JTIBUyZMoXk5GTq6+vp7u4Gei8W9ng8ZGRk9Huc2bNnk5aWxtatW/vVfvPmzaxYsYLW1lZaW1vZuXMniYmJvPbaa19oHSIiIiIiIhLdtPNG5AJMJhNr167F5XJRWFiIxWIhKSkJt9tNQkJC6M6bz5o6dWqfx5uqq6spLCy86DP379/Pxx9/HHbMymw289BDD9HQ0MCcOXMufWEiIiIiIiISVUw9n3eph4hErZMngxhG/17vlJRrOX789BDPSAab4ha9FLvopLhFL8Uueil20Ulxi16K3eVhNptITr5mYH2GaC4iIiIiIiIiIjIIlLwRERERERERERnGlLwRERERERERERnGdGGxSD8Eg0FqamrYu3cvMTExJCQk4HQ6SUxM5MEHH6S1tTWsfWZmJu+88w4+n4/y8nLGjRsXVv/www8zceLEsL6dnZ088sgjxMXF8aMf/QiLpff1/PGPf4zZbKaiouLyLFZERERERESGFSVvRC7CMAzsdjvTpk2jqakJi8XCnj17sNvtuN3ui/bPyspi/fr1EeVHjx4N/burq4vHHnuM0aNHs2rVKmJiYjh9+jQul4tXX32VkpKSQV2TiIiIiIiIRA8dmxK5CJ/PRyAQwOFwhHbD5Obm4nK5MAzjksfv7u5m8eLFXHXVVaxevZqYmBgA3njjDW644Qb++Z//+ZKfISIiIiIiItFLO29ELsLv95OdnY3ZHJ7rzM/P5+jRowQCAWw22wX779+/P6L+V7/6FdCbuHniiSf47W9/y6uvvhr2jLvvvhuAurq6QVqJiIiIiIiIRCMlb0Quwmw209PTc8H61NRUvF5vWFlmZmbo3xc6NtXe3s4HH3zAVVddxSOPPMLjjz/Ohg0bQrt7REREREREREDHpkQuKisrC7/fH5HAqa2txefzXdLYqampPPPMM5SUlBAXF8fzzz9/SeOJiIiIiIjIlUfJG5GLmDJlCsnJydTX19Pd3Q3Ajh078Hg8ZGRkXNLYsbGxmEwmzGYzK1eupKGhgd27dw/GtEVEREREROQKofMZIhdhMplYu3YtLpeLwsJCLBYLSUlJuN1uEhISLtq/rztv/umf/om77rorrOz666+nqqqKxx9/nObmZsaMGTOo6xAREREREZHoZOr5vMs8RCRqnTwZxDD693qnpFzL8eOnh3hGMtgUt+il2EUnxS16KXbRS7GLTopb9FLsLg+z2URy8jUD6zNEcxERERERERERkUGg5I2IiIiIiIiIyDCmO29E+iEYDFJTU8PevXuJiYkhISEBp9NJYmIiDz74IK2trWHtMzMzeefbNLmCAAAgAElEQVSdd/D5fJSXlzNu3Liw+ocffpiJEyeG9e3s7OSRRx4hLi6OH/3oR/zXf/0Xzz77LF1dXXzlK1/h2Wef5e/+7u8u25pFRERERERkeFDyRuQiDMPAbrczbdo0mpqasFgs7NmzB7vdjtvtvmj/rKws1q9fH1F+9OjR0L+7urp47LHHGD16NKtW/X/s3X1UlWW+//HP3mxMQTFEpGY8xxwpQCFzMjeVyiimouBmtLE6hcuOYIhE05l0qPhVPnRAC00lJ3ej2TipU7YVGVGndEhxhKxmTTWY+XC0hakQOY2g4Gbv/fvDcZ/hqDwkIPf4fq3lWu37erivL991/zHfua/rXigfHx/NmjVLy5cvV3h4uDZs2KD58+frV7/6VavGBgAAAADo+Ng2BTShtLRUFRUVysjIkMVyod4ZHR2t7Oxsud3uq57f5XLpqaeekp+fn1566SX5+Pjo/PnzeuKJJxQeHi7pwps8J06cuOp7AQAAAACMhzdvgCaUlZUpKipKZnPDWmdMTIzKy8tVUVFxyafA/9nlPhW+evVqSRcKN7Nnz9Z7772nLVu2eO/RqVMn7xi32628vDyNGjWqFaMCAAAAABgFxRugCWazWR7PlT+53atXL+Xn5ze4FhYW5v3vK22bqqmp0cmTJ+Xn56cnnnhCs2bN0rp167xv90jS+fPnlZmZqfr6ej322GOtEA0AAAAAwGjYNgU0ITIyUmVlZZcUcBYtWqTS0tKrmrtXr16aO3eukpOT1alTJ73yyivetpqaGiUnJ6u+vl6/+tWv5Ovre1X3AgAAAAAYE8UboAmDBw9WUFCQ8vLy5HK5JEm7d++Ww+FQaGjoVc3t6+srk8kks9msBQsWaP369dq7d68kadasWerTp4+WLFmiTp06XXUcAAAAAABjYtsU0ASTyaTly5crOztb8fHxslgsCgwMlN1uV0BAQJPjL3fmzfjx4zVu3LgG13r37q2nn37a+5WpHTt2KDQ0VImJiZIuvKXz+uuvt15gAAAAAABDMHkaO8wDgGFVVVXL7W7e4x0c3E2VlWfaeEVobeTNuMidMZE34yJ3xkXujIm8GRe5ax9ms0lBQV1bNqaN1gIAAAAAAIBWQPEGAAAAAACgA6N4AwAAAAAA0IFxYDHQTqqrq5Wbm6t9+/bJx8dHAQEByszM1IABAxQWFqYDBw402Q8AAAAAcP2heAO0A7fbrZSUFFmtVm3atEkWi0UlJSVKSUnRli1bmt0vMDDwGkYBAAAAALgWKN4A7aC0tFQVFRXKyMiQ2Xxht2J0dLSys7Pldrtb3A8AAAAAcP2geAO0g7KyMkVFRXkLMhfFxMR8r34AAAAAgOsHBxYD7cBsNsvj8bRaPwAAAADA9YPiDdAOIiMjVVZWdklhZtGiRSopKWlxPwAAAADA9YPiDdAOBg8erKCgIOXl5cnlckmSdu/eLYfDodDQ0Bb3AwAAAABcPzjzBmgHJpNJy5cvV3Z2tuLj42WxWBQYGCi73a6ePXu2uB8AAAAA4PpB8QZoJz169NBLL7102bYDBw40qx8AAAAA4PrDtikAAAAAAIAOjOINAAAAAABAB8a2KVz3qqurlZubq3379snHx0cBAQHKzMxU9+7dNXbsWPXr10+S5Ha7VVNTo8TERGVkZKi8vLxB+0WTJ0/Www8/rLCwMIWHh0uSnE6nIiIi9OKLL6pz587KzMxUSUmJunfvLrfbLV9fXz3//PMaOHBgg3lNJpOcTqd69eql7Oxs3XTTTe3+9wEAAAAAXFsUb3Bdc7vdSklJkdVq1aZNm2SxWFRSUqKUlBTZ7Xb16tVL+fn53v6nTp3SmDFjNH78eN1www2XtP9fF9s8Ho8ef/xxvfvuu3r44YclSRkZGZo4caIk6f3339e8efO0YcMGSbpk3tzcXM2bN0+vvvpqq/8NAAAAAAAdG9umcF0rLS1VRUWFMjIyZLFcqGVGR0crOztbbrf7kv6VlZXyeDzy9/dv0X2cTqfOnTt3xS9GnTlzptGvSQ0ePFhHjx5t0T0BAAAAAP8aePMG17WysjJFRUXJbG5Yx4yJiVF5ebkqKipks9lUV1en06dPKyoqSnl5ebrpppsatP+zhQsXKiwsTJK8bSdPnlRISIjuvvtub7+lS5fqzTff1Llz5/T111/rV7/61WXX6HQ6tXXrVv34xz9uzdABAAAAAAZB8QbXNbPZLI/Hc8X2i9uX3G63cnJydODAAUVHR1/SfiUX29xut3Jzc/Xkk09q5cqVkhpumyorK9Mjjzyi/Px8mUymBkWh8+fP6/bbb9cvfvGLq44XAAAAAGA8FG9wXYuMjNTatWvl8XhkMpm81xctWqQ+ffp4f5vNZs2ePVuJiYlatWqVHnvssRbdx2w2KyEhQWvXrr1se//+/fXv//7v+utf/6rIyMgmi0IAAAAAgOsHZ97gujZ48GAFBQUpLy9PLpdLkrR79245HA6FhoY26GuxWDR79my99tprqqysbPG99u7dq/79+1+27fjx4yovL/d+nQoAAAAAgIt48wbXNZPJpOXLlys7O1vx8fGyWCwKDAyU3W5XQEDAJf2HDx+uO+64Q6+88opmzJhx2TNv7rrrLmVlZUn63zNv6uvrFRgYqLlz53r7XTzzRpJqa2v1y1/+UrfccovKy8vbKlwAAAAAgAGZPI0d+AHAsKqqquV2N+/xDg7upsrKM228IrQ28mZc5M6YyJtxkTvjInfGRN6Mi9y1D7PZpKCgri0b00ZrAQAAAAAAQCugeAMAAAAAANCBUbwBAAAAAADowDiwGLhK1dXVys3N1b59++Tj46OAgABlZmaqe/fuGjt2rPr16ydJcrvdqqmpUWJiojIyMlRaWqq8vDytWbPGO8+0adM0aNAgZWZmKiwsTAcOHLiWoQEAAAAAOgCKN8BVcLvdSklJkdVq1aZNm2SxWFRSUqKUlBTZ7Xb16tVL+fn53v6nTp3SmDFjNH78+Abz1NTUKDk5WXfddZeeeuqp9g4DAAAAANCBsW0KuAqlpaWqqKhQRkaGLJYLtdDo6GhlZ2fL7XZf0r+yslIej0f+/v7ea2fPntX06dMVHR1N4QYAAAAAcAnevAGuQllZmaKiomQ2N6yDxsTEqLy8XBUVFbLZbKqrq9Pp06cVFRWlvLw83XTTTTp27JjOnTunxx57TAcPHtSrr756jaIAAAAAAHRkvHkDXAWz2SyPx3PF9ovbpgoLC2Wz2eR0OhUdHe1t/+yzz3T33XcrLi5OWVlZ7bFkAAAAAIDBULwBrkJkZKTKysouKeAsWrRIpaWl3t9ms1mzZ89WVVWVVq1a5b1+xx13KC0tTZmZmTp48KDWrVvXbmsHAAAAABgDxRvgKgwePFhBQUHKy8uTy+WSJO3evVsOh0OhoaEN+losFs2ePVuvvfaaKisrJUmdOnWSJHXp0kULFy7USy+9pEOHDrVvEAAAAACADo3iDXAVTCaTli9frq+++krx8fFKSEjQ66+/LrvdrqCgoEv6Dx8+XHfccYdeeeWVS9oGDhyoqVOn6sknn1RdXZ0kadCgQd5///cLVQAAAACA64PJ09iBHQAMq6qqWm538x7v4OBuqqw808YrQmsjb8ZF7oyJvBkXuTMucmdM5M24yF37MJtNCgrq2rIxbbQWAAAAAAAAtAKKNwAAAAAAAB0YxRsAAAAAAIAOzHKtFwAYSXV1tXJzc7Vv3z75+PgoICBAmZmZ6t69u8aOHat+/fpJktxut2pqapSYmKiMjAyVlpYqLy9Pa9asaTBfeXl5g3EXvfbaa/Lx8dHTTz+tb775xvup8bvvvrvdYgUAAAAAdAwUb4BmcrvdSklJkdVq1aZNm2SxWFRSUqKUlBTZ7Xb16tVL+fn53v6nTp3SmDFjmvxK1P8dd9FTTz2lESNG6JFHHtGRI0eUlJSkXbt2ycfHp9VjAwAAAAB0XGybApqptLRUFRUVysjIkMVyoe4ZHR2t7Oxsud3uS/pXVlbK4/HI39//e91v9OjRSkhIkCT16dNHdXV1Onv27PcPAAAAAABgSLx5AzRTWVmZoqKiZDY3rHnGxMSovLxcFRUVstlsqqur0+nTpxUVFaW8vDzddNNNOnbs2BXnvTjuooSEBCUnJ2v06NHeaytXrlRERIS6devW+oEBAAAAADo0ijdAM5nNZnk8niu2X9z+5Ha7lZOTowMHDig6OrrJea+0beqi1atX63e/+51++9vffq91AwAAAACMjW1TQDNFRkaqrKzskgLOokWLVFpa6v198XDhqqoqrVq16qruuXDhQr3zzjt66623dPPNN1/VXAAAAAAAY6J4AzTT4MGDFRQUpLy8PLlcLknS7t275XA4FBoa2qCvxWLR7Nmz9dprr6mysvJ73W/16tUqLS3VunXrdNNNN131+gEAAAAAxsS2KaCZTCaTli9fruzsbMXHx8tisSgwMFB2u10BAQGX9B8+fLjuuOMOvfLKK5owYYI++ugjDRo0yNuekJCg6dOnX/ZeHo9Hr776qrp27aqkpCTvdbvdrpCQkNYPDgAAAADQYZk8jR3iAcCwqqqq5XY37/EODu6mysozbbwitDbyZlzkzpjIm3GRO+Mid8ZE3oyL3LUPs9mkoKCuLRvTRmsBAAAAAABAK6B4AwAAAAAA0IFx5g3QDNXV1crNzdW+ffvk4+OjgIAAZWZmasCAAdq2bZvsdrvq6+vl8Xhks9mUnJwsSUpKStLJkyfl5+cnl8ulTp066YknnlBMTMwl7Rf17NlTK1eu9P7es2eP7Ha73nzzzfYNGgAAAADQIVC8AZrgdruVkpIiq9WqTZs2yWKxqKSkRCkpKdq4caMWLFggh8OhwMBA1dTUKCkpSX379lVsbKwkaf78+bJarZKkzz77TMnJyXrrrbe8X6j65/b/e9/Vq1drxYoVuu2229ovYAAAAABAh8K2KaAJpaWlqqioUEZGhiyWC/XO6OhoZWdn69ixY3I6naqtrZUk+fv7Kycn55JPh18UFRWluLg4vfPOO03e9/Dhwzp8+LDmzZvXesEAAAAAAAyHN2+AJpSVlSkqKkpmc8Na58WtT7GxsRo1apQiIiJktVqVkJCgPn36XHG+W2+9VUVFRd7fWVlZDbZNjR07VjNmzNCtt96qF198UaWlpa0bEAAAAADAUCjeAE0wm83yeK78ye05c+YoLS1NxcXFKi4u1uTJk/Xyyy9r9OjRl+1vMpnUuXNn7+8rbZsCAAAAAEBi2xTQpMjISJWVlV1SwFm0aJGKiopUWFiokJAQTZo0SYsXL1ZWVpY2bNhwxfkOHDigfv36tfWyAQAAAAD/IijeAE0YPHiwgoKClJeXJ5fLJUnavXu3HA6Hzp49q9zcXJWXl0uSPB6PDh06pIiIiMvO9emnn2r79u26//772239AAAAAABjY9sU0ASTyaTly5crOztb8fHxslgsCgwMlN1uV//+/VVXV6fU1FQ5nU5J0rBhwzRz5kzv+Itn2phMJnXp0kWLFy9W7969L2n/Z2vWrFFAQED7BAgAAAAA6NBMnsYO8wBgWFVV1XK7m/d4Bwd3U2XlmTZeEVobeTMucmdM5M24yJ1xkTtjIm/GRe7ah9lsUlBQ15aNaaO1AAAAAAAAoBVQvAEAAAAAAOjAKN4AAAAAAAB0YBxYDDRDdXW1cnNztW/fPvn4+CggIECZmZkaMGCAtm3bJrvdrvr6enk8HtlsNiUnJ0uSkpKSdPLkyQYHEk+ePFkPP/ywd94HH3xQr732mvcQ47CwMIWHh0u68PWqM2fOaNiwYXr++efl4+PTzpEDAAAAAK41ijdAE9xut1JSUmS1WrVp0yZZLBaVlJQoJSVFGzdu1IIFC+RwOBQYGKiamholJSWpb9++io2NlSTNnz9fVqv1knn/8pe/KCsrS0ePHr2kLT8/3/vf1dXVio+PV3FxsWJiYtosTgAAAABAx8S2KaAJpaWlqqioUEZGhiyWC/XO6OhoZWdn69ixY3I6naqtrZUk+fv7KycnR6GhoU3O+/bbb+v5559Xr169Gu13+vRpnTt3TjfeeOPVBwMAAAAAMBzevAGaUFZWpqioKJnNDWudF9+CiY2N1ahRoxQRESGr1aqEhAT16dPH2y8rK8u7bcrf319r166VJL344otXvKfNZlN9fb2qqqrUr18/ZWVlaeDAga0dGgAAAADAACjeAE0wm83yeDxXbJ8zZ47S0tJUXFys4uJiTZ48WS+//LJGjx4t6crbphpzcdvU6tWr9e677+onP/nJ914/AAAAAMDY2DYFNCEyMlJlZWWXFHAWLVqkoqIiFRYWKiQkRJMmTdLixYuVlZWlDRs2tMq9p06dql69eumll15qlfkAAAAAAMZD8QZowuDBgxUUFKS8vDy5XC5J0u7du+VwOHT27Fnl5uaqvLxc0oWvQx06dEgRERGtdv/MzEw5HA598cUXrTYnAAAAAMA42DYFNMFkMmn58uXKzs5WfHy8LBaLAgMDZbfb1b9/f9XV1Sk1NVVOp1OSNGzYMM2cObPV7n/rrbcqMTFRCxYs0BtvvNFq8wIAAAAAjMHkaewwDwCGVVVVLbe7eY93cHA3VVaeaeMVobWRN+Mid8ZE3oyL3BkXuTMm8mZc5K59mM0mBQV1bdmYNloLAAAAAAAAWgHFGwAAAAAAgA6MM2+AZqiurlZubq727dsnHx8fBQQEKDMzUwMGDNC2bdtkt9tVX18vj8cjm82m5ORkSVJSUpJOnjwpPz8/71yTJ0/Www8/rCVLlmj79u0ymUy6//779eijj0qSwsLCFB4eLunCAchnzpzRsGHD9Pzzz8vHx6f9gwcAAAAAXFMUb4AmuN1upaSkyGq1atOmTbJYLCopKVFKSoo2btyoBQsWyOFwKDAwUDU1NUpKSlLfvn0VGxsrSZo/f76sVmuDOT/88EOVlJRo8+bNqq+v17hx4xQTE6Mf/ehHkqT8/Hxv3+rqasXHx6u4uFgxMTHtFzgAAAAAoEOgeAM0obS0VBUVFcrIyJDZfGGnYXR0tLKzs3Xs2DE5nU7V1tZKkvz9/ZWTk6Mbbrih0TmHDBmi3/zmN7JYLDp16pRcLleDt3P+2enTp3Xu3DndeOONrRsYAAAAAMAQKN4ATSgrK1NUVJS3cHPRxbdgYmNjNWrUKEVERMhqtSohIUF9+vTx9svKyvIWZvz9/bV27VpJkq+vr5YuXapVq1Zp7NixCgkJ8Y6x2Wyqr69XVVWV+vXrp6ysLA0cOLCtQwUAAAAAdEAcWAw0wWw2y+O58ie358yZo507d+qhhx7S119/rcmTJ+sPf/iDt33+/PnKz89Xfn6+t3BzUUZGhvbu3asTJ07o7bff9l7Pz8/Xli1blJqaqr///e/6yU9+0upxAQAAAACMgeIN0ITIyEiVlZVdUsBZtGiRioqKVFhYqJCQEE2aNEmLFy9WVlaWNmzY0Oichw8f1v79+yVJXbp00ejRo3XgwIFL+k2dOlW9evXSSy+91HoBAQAAAAAMheIN0ITBgwcrKChIeXl5crlckqTdu3fL4XDo7Nmzys3NVXl5uaQLX4c6dOiQIiIiGp2zvLxcWVlZOn/+vM6fP68dO3bozjvvvGzfzMxMORwOffHFF60bGAAAAADAEDjzBmiCyWTS8uXLlZ2drfj4eFksFgUGBsput6t///6qq6tTamqqnE6nJGnYsGGaOXNmo3PGxMToL3/5ixITE+Xj46PRo0dr/Pjxl+176623KjExUQsWLNAbb7zR6vEBAAAAADo2k6exwzwAGFZVVbXc7uY93sHB3VRZeaaNV4TWRt6Mi9wZE3kzLnJnXOTOmMibcZG79mE2mxQU1LVlY9poLQAAAAAAAGgFFG8AAAAAAAA6MIo3AAAAAAAAHRgHFgNXqbq6Wrm5udq3b598fHwUEBCgzMxMbdiwQZ988omcTqe++uor9evXT5I0ZcoUTZo0SUeOHNHChQt1/PhxSdJtt92mZ599Vj169JDD4VBOTo5uvvlm73169uyplStXXpMYAQAAAADXDsUb4Cq43W6lpKTIarVq06ZNslgsKikpUUpKirZs2aLAwECVl5drypQpys/P9447deqUpkyZorlz52rkyJHyeDxasWKF0tPTtXbtWknSyJEjlZOTc61CAwAAAAB0EBRvgKtQWlqqiooKZWRkyGy+sAsxOjpa2dnZcrvdVxy3bt06DR06VCNHjpR04XPkKSkp6t27t+rr69tl7QAAAAAAY6B4A1yFsrIyRUVFeQs3F8XExDQ6bv/+/Zf08fHxUXx8vPf3zp07ZbPZvL+ffvppRUdHt8KqAQAAAABGQvEGuApms1kej6fF40wmU5Pj2DYFAAAAAJD42hRwVSIjI1VWVnZJIWbRokUqKSlpdNznn3/e4Jrb7VZ6erq++eabNlkrAAAAAMCYKN4AV2Hw4MEKCgpSXl6eXC6XJGn37t1yOBwKDQ294rgHHnhAH3zwgT744ANJksfj0fLly1VVVaWePXu2y9oBAAAAAMbAtingKphMJi1fvlzZ2dmKj4+XxWJRYGCg7HZ7o0WY4OBgvf7661q4cKFefvlluVwu9e/fX6+++mo7rh4AAAAAYAQmz/c5sANAh1dVVS23u3mPd3BwN1VWnmnjFaG1kTfjInfGRN6Mi9wZF7kzJvJmXOSufZjNJgUFdW3ZmDZaCwAAAAAAAFoBxRsAAAAAAIAOjOINAAAAAABAB8aBxUALVFdXKzc3V/v27ZOPj48CAgKUmZmpDRs26JNPPpHT6dRXX32lfv36SZKmTJmir7/+WpL0+OOPX3bO3/72t8rJydEf//hHBQcHX9L+1ltvadu2bVqzZk3bBQYAAAAA6LAo3gDN5Ha7lZKSIqvVqk2bNslisaikpEQpKSnasmWLAgMDVV5erilTpig/P987btmyZY3O63A4NHLkSG3YsEEzZsxo0Hbo0CGtWLFCffr0aZOYAAAAAAAdH9umgGYqLS1VRUWFMjIyZLFcqHtGR0crOztbbrf7e835xRdf6G9/+5umT5+ud955p8E858+f13PPPacnnniiVdYPAAAAADAmijdAM5WVlSkqKkpmc8PHJiYmRkFBQd9rTofDobFjxyoyMlI+Pj7avXu3ty03N1eTJk1S7969r2rdAAAAAABjo3gDNJPZbJbH42m1+ZxOpwoKChQfHy9JGjdunNavXy9J2rNnj06cOKFJkya12v0AAAAAAMbEmTdAM0VGRmrt2rXyeDwymUze64sWLdI999yj6OjoFs1XVFSkv//970pPT5d0oZhTVVWlkydP6ve//70OHjwom82ms2fP6ptvvtHPf/5zvfLKK60aEwAAAACg46N4AzTT4MGDFRQUpLy8PKWlpXm3OTkcDk2ZMqXF87377rt64oknNH36dO+1pKQkvfPOO8rOzvZeKy0tVV5eHoUbAAAAALhOsW0KaCaTyaTly5frq6++Unx8vBISEvT666/LbrerZ8+ejY5dsWKFBg0a5P23ceNGlZaW6v7772/Q79FHH9U777wjl8vVlqEAAAAAAAzE5GnNQzwAdBhVVdVyu5v3eAcHd1Nl5Zk2XhFaG3kzLnJnTOTNuMidcZE7YyJvxkXu2ofZbFJQUNeWjWmjtQAAAAAAAKAVULwBAAAAAADowCje4LpSXl6usLAwPffccw2u79+/X2FhYXI4HJKk+vp6DR06VPPmzWvQb9myZbr33ntls9lks9k0ZswYLV682Nu+ZMkSjRs3TuPHj9cbb7zhvefIkSO9fZxOp9LS0vTzn/9c9fX1DcYuW7bM+/vQoUN68MEHNWHCBCUlJen48eOt94cAAAAAABgGxRtcd2688Ubt3r27waHAhYWF6tGjh/f3rl27FBUVpa1bt+rcuXMNxj/44IPKz89Xfn6+Nm7cqMLCQr3//vv68MMPVVJSos2bN+vdd9/VmjVrdOTIkQZj6+vr9V//9V/q0qWLcnNzZbFYdObMGT3zzDNatWpVg75z5sxRWlqaNm/erHHjxmnRokVt8NcAAAAAAHR0FG9w3fH391dERIT27dvnvbZnzx7dc8893t8Oh0P33Xefbr/9dm3ZsuWKc/n5+WnAgAE6evSohgwZot/85jeyWCyqqqqSy+WSn5+ft6/L5dJTTz0lPz8/vfTSS/Lx8ZEk7dixQ7fccoseffTRBnO/8cYbGj58uNxut77++msFBAS01p8AAAAAAGAgFG9wXYqLi9P27dslSZ9++qnCwsLk6+srSfr222+1Z88excbGKi4uTuvXr7/iPMePH9dHH32kQYMGSZJ8fX21dOlSjR8/XnfffbdCQkIkXSjczJ49W++9955mzJghs/l/H73ExERNnz7dW8y5yGKx6O9//7uGDx+udevWafLkya36NwAAAAAAGAPFG1yXRowYoV27dsntdmvr1q2Ki4vztm3evFnR0dHq3r27YmNj9eWXX6qsrMzbvn79etlsNiUkJGjmzJlKTU3VnXfe6W3PyMjQ3r17deLECb399tuSpJMnT8rPz09PPPGEZs2a1eCsm8YEBASouLhYixYt0owZMxps9QIAAAAAXB8o3uC61LVrV4WHh+vjjz9WSUnJJVum/vznP2vkyJGaMGGCzGZzg7dvLp55U1BQoE2bNumRRx6RJB0+fFj79++XJHXp0kWjR4/WgQMHJEm9evXS3LlzlZycrE6dOumVV15pco2FhYXyeDySpOHDh6u2tlbfffddq/0NAAAAAADGQPEG1624uDjl5uYqMjJSFotFkvTdd9/p5MmTKioq0s6dO7Vz506tWLFCBQUFqq6ubnS+8vJyZWVl6fz58zp//rx27NjhfSPH19dXJpNJZrNZCxYs0Pr167V3795G51u1apXee+89SVJJSYkCAwMbHKoMAAAAALg+ULzBdWvEiBHav3+/xo0b5722dOlSTZw4UZ07d/Zes1qt6tu3rwoKChqdL3mBiOIAACAASURBVCYmRjExMUpMTNSkSZM0aNAgjR8//pJ+vXv31tNPP61Zs2bp22+/veJ8OTk5euONN2Sz2ZSXl6elS5d+jygBAAAAAEZn8lzclwHgX0pVVbXc7uY93sHB3VRZeaaNV4TWRt6Mi9wZE3kzLnJnXOTOmMibcZG79mE2mxQU1LVlY9poLQAAAAAAAGgFFG8AAAAAAAA6MIo3AAAAAAAAHRjFG6AR5eXlCgsL03PPPdfg+v79+xUWFiaHwyFJqq+v19ChQzVv3rwG/ZYtW6Z7771XNptNNptNY8aM0eLFiyVJpaWlSkpK8vatrq7WAw88oJycnAZz/PKXv/TeBwAAAABw/aF4AzThxhtv1O7du+VyubzXCgsLG3y2e9euXYqKitLWrVt17ty5BuMffPBB5efnKz8/Xxs3blRhYaHef//9Bn1qamqUnJysu+66S5mZmZKkU6dOKTU1Vdu2bWvD6AAAAAAAHR3FG6AJ/v7+ioiI0L59+7zX9uzZo3vuucf72+Fw6L777tPtt9+uLVu2XHEuPz8/DRgwQEePHvVeO3v2rKZPn67o6Gg99dRT3usFBQWKjY1VXFxc6wYEAAAAADAUijdAM8TFxWn79u2SpE8//VRhYWHy9fWVJH377bfas2ePt9Cyfv36K85z/PhxffTRRxo0aJAk6dy5c3rsscd08OBBTZ06tUHf5ORk/exnP2ubgAAAAAAAhkHxBmiGESNGaNeuXXK73dq6dWuDt2E2b96s6Ohode/eXbGxsfryyy9VVlbmbV+/fr1sNpsSEhI0c+ZMpaam6s4775QkffbZZ7r77rsVFxenrKysdo8LAAAAANDxWa71AgAj6Nq1q8LDw/Xxxx+rpKREv/jFL1RYWCjpwpapiooKjRw5UpJkNpu1fv16zZ07V9KFM28ef/zxy857xx13KC0tTefOnVNiYqLWrVunhx56qH2CAgAAAAAYAsUboJni4uKUm5uryMhIWSwXHp3vvvtOJ0+e1K5du9S5c2dJF74ilZqaqtmzZzc5Z6dOnSRJXbp00cKFC/Xoo4/qrrvuUmhoaNsFAgAAAAAwFLZNAc00YsQI7d+/X+PGjfNeW7p0qSZOnOgt3EiS1WpV3759VVBQ0KL5Bw4cqKlTp+rJJ59UXV1dq60bAAAAAGBsJo/H47nWiwDQ+qqqquV2N+/xDg7upsrKM228IrQ28mZc5M6YyJtxkTvjInfGRN6Mi9y1D7PZpKCgri0b00ZrAQAAAAAAQCugeAMAAAAAANCBUbwBAAAAAADowCjeAI0oLy9XWFiYnnvuuQbX9+/fr7CwMDkcDklSfX29hg4dqnnz5jXot2zZMt17772y2Wyy2WwaM2aMFi9eLOnCV6mSkpK8faurq/XAAw8oJydHkvTee+8pISFB48ePV2Zmps6fP9+WoQIAAAAAOiiKN0ATbrzxRu3evVsul8t7rbCwUD169PD+3rVrl6KiorR161adO3euwfgHH3xQ+fn5ys/P18aNG1VYWKj333+/QZ+amholJyfrrrvuUmZmps6ePau5c+fqjTfe0JYtW1RXV6eNGze2baAAAAAAgA6J4g3QBH9/f0VERGjfvn3ea3v27NE999zj/e1wOHTffffp9ttv15YtW644l5+fnwYMGKCjR496r509e1bTp09XdHS0nnrqKW+/nTt3qmfPnjp79qyqqqoUEBDQ+sEBAAAAADo8ijdAM8TFxWn79u2SpE8//VRhYWHy9fWVJH377bfas2ePYmNjFRcXp/Xr119xnuPHj+ujjz7SoEGDJEnnzp3TY489poMHD2rq1KkN+vr6+uqDDz7QiBEjdPr0aQ0dOrRtggMAAAAAdGgUb4BmGDFihHbt2iW3262tW7cqLi7O27Z582ZFR0ere/fuio2N1ZdffqmysjJv+/r162Wz2ZSQkKCZM2cqNTVVd955pyTps88+09133624uDhlZWVdct+YmBiVlpZqxIgReuGFF9o8TgAAAABAx0PxBmiGrl27Kjw8XB9//LFKSkou2TL15z//WSNHjtSECRNkNpsbvH1z8cybgoICbdq0SY888oi37Y477lBaWpoyMzN18OBBrVu3TpL0t7/9TcXFxd5+CQkJOnDgQDtECgAAAADoaCjeAM0UFxen3NxcRUZGymKxSJK+++47nTx5UkVFRdq5c6d27typFStWqKCgQNXV1U3O2alTJ0lSly5dtHDhQr300ks6dOiQPB6PZs2apa+//lqStHXrVv34xz9uu+AAAAAAAB0WxRugmUaMGKH9+/dr3Lhx3mtLly7VxIkT1blzZ+81q9Wqvn37qqCgoEXzDxw4UFOnTtWTTz4pPz8/zZs3T4899pgmTJigo0ePatasWa0WCwAAAADAOEwej8dzrRcBoPVVVVXL7W7e4x0c3E2VlWfaeEVobeTNuMidMZE34yJ3xkXujIm8GRe5ax9ms0lBQV1bNqaN1gIAAAAAAIBWQPEGAAAAAACgA7Nc6wUAHV15ebnGjh2rfv36SZLcbrdqamqUmJgoq9Wq1NRU/fu//7s8Ho+cTqcmTJigGTNmSJKSkpL017/+VSUlJd7DiSXJZrMpICBAa9as8V7bs2eP7Ha73nzzTUmSx+PRwoUL9cc//lFms1nz5s3zfmIcAAAAAHD9oHgDNEOvXr2Un5/v/X3q1CmNGTNGPXv2VGRkpLcIU1NTo3Hjxum+++5TaGioJKlbt24qLi7WyJEjJUlHjhxRRUWFAgICJF0oBq1evVorVqzQbbfd5r3H9u3bdfjwYRUWFurYsWOaPn26tm7d6v3SFQAAAADg+sC2KeB7qKyslMfjkb+/f4PrtbW18vHxUbdu3bzXRo8ere3bt3t/FxYWasyYMd7fhw8f1uHDhzVv3rwGc33wwQcaN26czGaz+vbtqx/84Af685//3EYRAQAAAAA6Kv4vfKAZKioqZLPZVFdXp9OnTysqKkp5eXnq1KmTPv/8c9lsNrndbn311VeKi4tTr169vGOHDRum559/Xk6nU76+vioqKtLjjz+uw4cPS5JuvfVWvfjiiyotLb3knv88T3BwsE6ePNk+AQMAAAAAOgzevAGa4eK2qcLCQtlsNjmdTkVHR0uSIiMjlZ+fr4KCAv3pT3/S8ePHZbfbvWNvuOEG3XnnnfrTn/6kL7/8Uv/2b/+mzp07N3lPj+fSz3ybzTyyAAAAAHC94X8JAi1gNps1e/ZsVVVVadWqVZe0+/v7a9SoUfrkk08aXB87dqy2b9+urVu3aty4cc26V0hIiCorK72/KysrG7yJAwAAAAC4PlC8AVrIYrFo9uzZeu211/TNN980aHO5XPrwww/Vv3//BteHDx+u0tJS7dq1S8OHD2/WfYYPH66CggK5XC4dO3ZMR48eVVRUVKvFAQAAAAAwBs68Ab6H4cOH64477tArr7yib775RjabTZJ07tw5RUVFKSUlpUH/Tp066cc//rGkC9uommPs2LH69NNPNWHCBEnSiy++2KztVgAAAACAfy0mz+UO1gBgeFVV1XK7m/d4Bwd3U2XlmTZeEVobeTMucmdM5M24yJ1xkTtjIm/GRe7ah9lsUlBQ15aNaaO1AAAAAAAAoBVQvAEAAAAAAOjAKN4AAAAAAAB0YBxYDLTQtm3bZLfbVV9fL4/HI5vNpuTkZCUlJenkyZPy8/Pz9p08ebIefvhhLVmyRNu3b5fJZNL999+vRx99VOXl5Ro7dqz69esnj8cjp9Ope++9V88884zM5v+tq27YsEEfffSRcnJyrkW4AAAAAIBrjOIN0AKnTp3SggUL5HA4FBgYqJqaGiUlJalv376SpPnz58tqtTYY8+GHH6qkpESbN29WfX29xo0bp5iYGHXq1Em9evVSfn6+JMnpdOqhhx7S7t27FRMTo7q6Oi1btkxvvfWWxowZ0+6xAgAAAAA6BrZNAS1w+vRpOZ1O1dbWSpL8/f2Vk5Oj0NDQK44ZMmSIfvOb38hisaiqqkoul6vB2zkX1dXVyel0KjAwUJK0b98+ud1uzZo1q22CAQAAAAAYAm/eAC0QHh6u2NhYjRo1ShEREbJarUpISFCfPn0kSVlZWd7CjL+/v9auXStJ8vX11dKlS7Vq1SqNHTtWISEhOn78uCoqKmSz2eTxeHT8+HENHDhQ4eHhkqShQ4dq6NChcjgc1yZYAAAAAECHwJs3QAvNmTNHO3fu1EMPPaSvv/5akydP1h/+8AdJF7ZN5efnKz8/31u4uSgjI0N79+7ViRMn9Pbbb0uSd9vU5s2btXfvXgUGBmr+/PntHhMAAAAAoOOieAO0QFFRkQoLCxUSEqJJkyZp8eLFysrK0oYNG6445vDhw9q/f78kqUuXLho9erQOHDhwSb9OnTopLi5On3zySZutHwAAAABgPBRvgBbo3LmzcnNzVV5eLknyeDw6dOiQIiIirjimvLxcWVlZOn/+vM6fP68dO3bozjvvvGzfkpIS9e/fv03WDgAAAAAwJs68AVogOjpa6enpSk1NldPplCQNGzZMM2fO1LRp0y47JiYmRn/5y1+UmJgoHx8fjR49WuPHj1d5ebn3zBvpwoHFffr0YdsUAAAAAKABk8fj8VzrRQBofVVV1XK7m/d4Bwd3U2XlmTZeEVobeTMucmdM5M24yJ1xkTtjIm/GRe7ah9lsUlBQ15aNaaO1AAAAAAAAoBVQvAEAAAAAAOjAOPMGaIFt27bJbrervr5eHo9HNptNycnJSkpKUnp6uqxWq7dvZmamhgwZookTJ+r999/XsmXL5PF41Lt3b2VnZ6t79+4aOXKkOnfuLF9fX9XX1+sHP/iBFixYoB49eqimpkaZmZk6evSofHx8NHv2bN1zzz3XMHoAAAAAwLXAmzdAM506dUoLFizQypUrtXnzZq1fv16FhYXasWNHo+Oqq6v1wgsvyG63a/PmzQoLC9OyZcu87Xa7Xfn5+dqyZYtuueUW/frXv5YkvfHGG+rTp48KCgqUm5ur2bNnt2l8AAAAAICOiTdvgGY6ffq0nE6namtrJUn+/v7KycnRDTfc0Og4p9OpF154QSEhIZKksLAwFRQUXNLP7XarpqZGoaGhkqT09HTV19dLuvC58e7du7dmOAAAAAAAg6B4AzRTeHi4YmNjNWrUKEVERMhqtSohIUF9+vSRJGVlZcnPz8/b/8SJExoyZIgCAwM1atQoSVJtba3sdruSkpK8/aZPny5fX19VVVXJx8dH6enp3jaLxaJp06Zp7969mjt3bjtFCgAAAADoSCjeAC0wZ84cpaWlqbi4WMXFxZo8ebJefvllSdL8+fMvOfPmn505c0ZpaWkKDw/XT3/6U+91u92u3r17S5LeeustTZs2TYWFhTKZTJKklStX6vjx43rwwQc1aNAg9evXr63DBAAAAAB0IJx5AzRTUVGRCgsLFRISokmTJmnx4sXKysrShg0bmhxbUVGh//iP/1B4eLhefPHFK/abMGGCjhw5otOnT+vDDz9URUWFJOmHP/yhBg0apIMHD7ZaPAAAAAAAY6B4AzRT586dlZubq/LyckmSx+PRoUOHFBER0eg4l8ul1NRUxcXF6dlnn/W+UXM5e/fu1c0336wePXqoqKhIdrtd0oXiz+eff66oqKjWCwgAAAAAYAhsmwKaKTo6Wunp6UpNTZXT6ZQkDRs2TDNnztS0adOuOG7nzp0qKyuTy+XS9u3bJUmRkZHeN3Aunnnjdrvl6+urRYsWSZLS0tL07LPPKiEhQT4+PnrmmWf0wx/+sI2jBAAAAAB0NCaPx+O51osA0Pqqqqrldjfv8Q4O7qbKyjNtvCK0NvJmXOTOmMibcZE74yJ3xkTejIvctQ+z2aSgoK4tG9NGawEAAAAAAEAroHgDAAAAAADQgVG8AQAAAAAA6MA4sBj4J9u2bZPdbld9fb08Ho9sNpuSk5OVlJSk9PR0Wa1Wb9/MzEwNGTJEEydO1Pvvv69ly5bJ4/God+/eys7OVvfu3ZWUlKSTJ0/Kz89PLpdLnTp10hNPPKGYmJgG912yZInMZrMef/zxBtczMjL0P//zPyooKGiX+AEAAAAAHQ/FG+AfTp06pQULFsjhcCgwMFA1NTVKSkpS3759Gx1XXV2tF154Qe+++65CQkK0ZMkSLVu2TFlZWZKk+fPne4s+n332mZKTk/XWW28pNDRUZ86cUXZ2trZs2aLk5OQG854+fVplZWXq2bOnPv74Y915551tEzgAAAAAoENj2xTwD6dPn5bT6VRtba0kyd/fXzk5OQoNDW10nNPp1AsvvKCQkBBJUlhYmE6cOHHZvlFRUYqLi9M777wjSdqxY4duueUWPfroo5f0LSgo0ODBgzV69Gj97ne/u5rQAAAAAAAGxps3wD+Eh4crNjZWo0aNUkREhKxWqxISEtSnTx9JUlZWlvz8/Lz9T5w4oSFDhigwMFCjRo2SJNXW1sputyspKemK97n11ltVVFQkSUpMTJQkLVu27JJ+DodDTz75pG677TYtWbJEzzzzjG688cbWChcAAAAAYBC8eQP8kzlz5mjnzp166KGH9PXXX2vy5Mn6wx/+IOnC9qf8/Hzvv5EjRzYYe+bMGaWkpCg8PFw//elPr3gPk8mkzp07N7qO/fv368SJE7rnnnt08803KyIiQps2bbr6AAEAAAAAhsObN8A/FBUV6ezZsxo3bpwmTZqkSZMm6e2339aGDRuaHFtRUaFp06YpOjpazzzzTKN9Dxw4oH79+jXa591339X58+c1ZswYSVJNTY3Wr1+vqVOnNjseAAAAAMC/Bt68Af6hc+fOys3NVXl5uSTJ4/Ho0KFDioiIaHScy+VSamqq4uLi9Oyzz8pkMl2x76effqrt27fr/vvvv2Kf8+fPq6CgQKtXr9bOnTu1c+dO7dixQ5WVlSotLf1+wQEAAAAADIs3b4B/iI6OVnp6ulJTU+V0OiVJw4YN08yZMzVt2rQrjtu5c6fKysrkcrm0fft2SVJkZKRefPFFSf97Vo7JZFKXLl20ePFi9e7d+4rz/fGPf9QPf/hDDRw40Huta9eu+tnPfqb169c3+Fw5AAAAAOBfn8nj8Xiu9SIAtL6qqmq53c17vIODu6my8kwbrwitjbwZF7kzJvJmXOTOuMidMZE34yJ37cNsNikoqGvLxrTRWgAAAAAAANAKKN4AAAAAAAB0YBRvAAAAAAAAOjAOLAaasG3bNtntdtXX18vj8chmsyk5OVlJSUlKT09vcIBwZmamhgwZookTJ0qSVq9erd/97nfy8fGRj4+PJk+erIcfftjb/8iRI1q4cKGOHz8uSbrtttv07LPPqkePHqqpqdEzzzyjI0eOSJJSU1M1fvz4dowcAAAAANARULwBGnHq1CktWLBADodDgYGBqqmpUVJSkvr27dvk2GXLlmnfvn1as2aNevbsqW+//VZpaWn629/+ppkzZ+rUqVOaMmWK5s6dq5EjR8rj8WjFihVKT0/X2rVrZbfb9YMf/EBLlixRVVWVbDabrFarevbs2Q6RAwAAAAA6Coo3QCNOnz4tp9Op2tpaSZK/v79ycnJ0ww03NDru3LlzWrlypX7/+997iy09evTQ/Pnz9bOf/Uz/+Z//qXXr1mno0KEaOXKkJMlkMiklJUW9e/dWfX29hgwZ4i0SBQUF6cYbb9Q333xD8QYAAAAArjMUb4BGhIeHKzY2VqNGjVJERISsVqsSEhLUp08fSVJWVpb8/Py8/U+cOKEhQ4bo4MGD6tKli3r37t1gvtDQUHXq1ElHjhzR/v37FRMT06Ddx8dH8fHxkqR7773Xe72wsFDnz59XaGhoW4UKAAAAAOigKN4ATZgzZ47S0tJUXFys4uJiTZ48WS+//LIkaf78+ZeceSNdeIvG5XJddr76+npvH4/H0+T9t27dqv/+7//Wr3/9a1ksPLIAAAAAcL3ha1NAI4qKilRYWKiQkBBNmjRJixcvVlZWljZs2NDouNDQUDmdTu9hwxcdPHhQbrdbP/rRjxQZGanPP/+8Qbvb7VZ6erq++eYbSdKaNWu0YMECrVy5UuHh4a0bHAAAAADAECjeAI3o3LmzcnNzVV5eLknyeDw6dOiQIiIiGh3XpUsXzZgxQ88++6yqqqokSVVVVfp//+//KTk5WV26dNEDDzygDz74QB988IF37uXLl6uqqko9e/bU+++/r9WrV2vdunUKCwtr20ABAAAAAB0WezCARkRHRys9PV2pqalyOp2SpGHDhmnmzJmaNm1ao2OnT5+ubt26aerUqfJ4PDKZTHrwwQe9nwoPDg7W66+/roULF+rll1+Wy+VS//799eqrr0qSli5dqrq6OqWmpnrnnD9/vqKiotooWgAAAABAR2TyNOfQDQCGU1VVLbe7eY93cHA3VVaeaeMVobWRN+Mid8ZE3oyL3BkXuTMm8mZc5K59mM0mBQV1bdmYNloLAAAAAAAAWgHFGwAAAAAAgA6M4g0AAAAAAEAHRvEGuErbtm3TxIkTNWHCBCUkJOjXv/61JCkpKUmlpaUN+mZmZsrhcHh/v/XWW0pISFBCQoISExO1bt26S+Y/cOCAxo8f37ZBAAAAAAA6LL42BVyFU6dOacGCBXI4HAoMDFRNTY2SkpLUt2/fJscuX75ce/bs0ZtvvqkePXro22+/VVpamr777jvvF6Y2bdqk3Nxc+fr6tnUoAAAAAIAOijdvgKtw+vRpOZ1O1dbWSpL8/f2Vk5Oj0NDQRsfV1tbq9ddf17x589SjRw9JUo8ePTR//ny9/vrrqq2t1ZkzZ7Rjxw4tWrSozeMAAAAAAHRcvHkDXIXw8HDFxsZq1KhRioiIkNVqVUJCgvr06SNJysrKkp+fn7f/iRMnNGTIEB0+fFhms1k/+tGPGswXGhoqX19fHT58WAMGDNCyZctUXl7erjEBAAAAADoWijfAVZozZ47S0tJUXFys4uJiTZ48WS+//LIkaf78+bJard6+mZmZ3v82mUyXna+urk4ul6ttFw0AAAAAMAyKN8BVKCoq0tmzZzVu3DhNmjRJkyZN0ttvv60NGzY0Oq5fv35yuVw6fPiw+vXrp+rqavn6+urYsWNyu9269dZb2ykCAAAAAEBHx5k3wFXo3LmzcnNzvVubPB6PDh06pIiIiCbHTZ8+Xc8995y+/fZbff7555o4caJmzZql5ORkdenSpT2WDwAAAAAwAN68Aa5CdHS00tPTlZqaKqfTKUkaNmyYZs6cqWnTpjU6dsaMGerWrZumTJkiSTKbzbr55pv1xRdf6MSJE7r55pvbfP0AAAAA8P/Zu/+4quv7//+3AwhnIhojpSwgii5BkZmWUO83OH4oeAgObw9vNtqFOUs3SqO6VMY2XG8s9ZwR6sxab7Lauy4Vkh6FEsFNRsoU3Vo/5o8ysi4TLSBgTTHGgfP6/uGl8x0f01mCcOx+/e+8Xs9fj9fjcv55XF7P50tGPpNhGMZwL0JE/n9vv/02l112GePHjz+ncTo6juN2n93fe/z4INrbj53TfHL+KW/eS7nzTsqb91LuvJdy552UN++l3J0fPj4mQkLGfK0+evNGZISZPHnycC9BRERERERERhCdeSMiIiIiIiIiMoKpeCMySGpra5k9ezZZWVlkZmaydu1aAJKTkz0HGn/pn//8JxkZGTidTs+1w4cPk5yczJEjRzzX+vr6+P73vz+gnYiIiIiIiHy7aNuUyCBobW3F4XDgdDoJDg6mu7ub/Px8IiMjv7J9QEAApaWl3HHHHcTFxTF+/Hjuvfdefvazn3HZZZd52j355JN8/PHH5ykKERERERERGYlUvBEZBF1dXbhcLnp6egAIDAzEbrcTEBBw2j7XXnstd9xxB0VFRVx33XXccMMNzJgxw3P/zTff5P333ycpKWnI1y8iIiIiIiIjl7ZNiQyC6OhoUlJSSE1NJScnh9LSUtxuNxEREWfsN2/ePFwuF/X19RQVFXmuHz9+HLvdzpIlS4Z66SIiIiIiIjLCqXgjMkhKSkqor68nLy+Po0ePkpuby9atW8/Yp729nU8//ZR//OMffPjhhwPGKigo4OKLLx7qZYuIiIiIiMgIp21TIoOgoaGBEydOYLFYsNls2Gw2KisrWb9+/Wn7uN1uHnroIX784x8zZswYHnzwQZxOJ319fezatYuDBw+yevVqPvnkE5qamvDz8yMrK+s8RiUiIiIiIiIjgYo3IoPAbDbz6KOPMmnSJC6//HIMw6C5uZmYmBiam5u/ss/TTz+Nn58fc+bMwWQyUV9fj8Ph4JFHHqGxsdHTrqioiGnTpqlwIyIiIiIi8i2l4o3IIIiPj2fhwoUUFBTgcrkASEhIYMGCBbz22mvcdtttmEwmT/tnn32Wl156CafT6bn+2GOPkZWVxfe+9z2mT58+LHGIiIiIiIjIyGMyDMMY7kWIyODr6DiO2312f+/x44Nobz82xCuSwaa8eS/lzjspb95LufNeyp13Ut68l3J3fvj4mAgJGfP1+gzRWkREREREREREZBCoeCMiIiIiIiIiMoLpzBuRM6itraW8vJy+vj4Mw8BqtTJv3jzy8/PZt28fTU1N+Pv7e9pbrVbGjh3Liy++6LnmcDjYuHEj27dv97RtaWkhPT2dq666CoCenh6uueYafvnLX3o+D75mzRq2bNkCwPTp01m0aNH5CltERERERERGEL15I3Iara2tOBwOnn32Waqrq6moqKCmpoZt27YBEBQUNOCrUIcOHaKtrW3AGH19fWzZsoUbb7yR2traAfcmTJhAVVUVVVVV1NbWEhERQWFhIQA7d+6ksbGRjRs3smnTJvbt28fvfve7IY5YRERERERERiIVb0ROo6urC5fLRU9PDwCBgYHY7XaioqIAmDlzJnV1dZ72NTU1pKWlDRjjjTfeICwsjOzsbNatW3fauUwmE/fccw8ffPAB7733HuPHj6eoqAh/f39GjRrFVVddxdGjR4cg9d7+MQAAIABJREFUShERERERERnpVLwROY3o6GhSUlJITU0lJyeH0tJS3G43ERERwMlPge/Zs8fzafCGhgaSkpIGjOF0OklPT2f69OkcOHCA5ubm087n7+9PREQEhw4d4uqrr2by5MkAfPzxx9TU1Ojz4SIiIiIiIt9SKt6InEFJSQn19fXk5eVx9OhRcnNz2bp1KwABAQFMnTqVnTt3cvDgQcLCwjCbzZ6+nZ2dNDY2MmvWLMxmM0lJSVRUVJxxPpPJNGCMDz74gDvuuIOHH36YK664YkhiFBERERERkZFNBxaLnEZDQwMnTpzAYrFgs9mw2WxUVlayfv16T5v09HTq6uoIDQ3FYrEM6F9dXY1hGOTk5AAnDyV2uVw8+OCDXzlfb28vH330kWdb1ptvvklhYSE///nPycjIGKIoRUREREREZKTTmzcip2E2mykrK6OlpQUAwzBobm4mJibG0yYxMZHdu3ezfft2EhMTB/TfsGEDdrud+vp66uvraWxsZNy4cdTU1Jwyl9vt5oknnuCGG24gPDycTz75hAULFvD444+rcCMiIiIiIvItpzdvRE4jPj6ehQsXUlBQ4DnXJiEhgQULFnDnnXcCJ8+pmTJlCnByG9WX9u7dS1dXFzNmzPBc8/HxYc6cOVRUVDBt2jTa2tqwWq3AyeJNTEwMZWVlADz77LP885//xG63e/r/4Ac/IC8vb2iDFhERERERkRHHZBiGMdyLEJHB19FxHLf77P7e48cH0d5+bIhXJINNefNeyp13Ut68l3LnvZQ776S8eS/l7vzw8TEREjLm6/UZorWIiIiIiIiIiMggUPFGRERERERERGQEU/FGRERERERERGQEU/FG5Axqa2uZPXs2WVlZZGZmsnbtWgDy8/OZMmUKvb29A9pbrVby8/MHXHM4HMTHxw9o29LSQmxsLFarFavVSlpaGoWFhXz22WeeNr/+9a+xWCxkZGTw/PPPD2GUIiIiIiIiMpLpa1Mip9Ha2orD4cDpdBIcHEx3dzf5+flERkYCEBQURGNjI8nJyQAcOnSItrY2xo4d6xmjr6+PLVu2cOONN1JbW0tWVpbn3oQJE6iqqgJOfoZ8xYoVFBYW8vLLL7Nnzx6ampqorq6mr68Pi8XC9OnTufLKK8/jExAREREREZGRQG/eiJxGV1cXLpeLnp4eAAIDA7Hb7URFRQEwc+ZM6urqPO1rampIS0sbMMYbb7xBWFgY2dnZrFu37rRzmUwm7rnnHj744APee+89pk2bxgsvvICfnx8dHR309/czevToIYhSRERERERERjoVb0ROIzo6mpSUFFJTU8nJyaG0tBS3201ERAQACQkJ7NmzB5fLBUBDQwNJSUkDxnA6naSnpzN9+nQOHDhAc3Pzaefz9/cnIiKCQ4cOATBq1ChWr15NRkYGt9xyC6GhoUMUqYiIiIiIiIxkKt6InEFJSQn19fXk5eVx9OhRcnNz2bp1KwABAQFMnTqVnTt3cvDgQcLCwjCbzZ6+nZ2dNDY2MmvWLMxmM0lJSVRUVJxxPpPJNGCMwsJCdu3axSeffEJlZeXQBCkiIiIiIiIjms68ETmNhoYGTpw4gcViwWazYbPZqKysZP369Z426enp1NXVERoaisViGdC/uroawzDIyckBoKenB5fLxYMPPviV8/X29vLRRx8RFRXFhx9+SG9vLzExMXznO99h5syZvP/++0MXrIiIiIiIiIxYevNG5DTMZjNlZWW0tLQAJw8Vbm5uJiYmxtMmMTGR3bt3s337dhITEwf037BhA3a7nfr6eurr62lsbGTcuHHU1NScMpfb7eaJJ57ghhtuIDw8nJaWFoqLi+nt7aW3t5dt27YxderUoQ1YRERERERERiS9eSNyGvHx8SxcuJCCggLPuTYJCQksWLCAO++8Ezh5Ts2UKVOAk9uovrR37166urqYMWOG55qPjw9z5syhoqKCadOm0dbWhtVqBU4Wb2JiYigrKwNg+vTpvPPOO2RnZ+Pr68vMmTPJyMg4L3GLiIiIiIjIyGIyDMMY7kWIyODr6DiO2312f+/x44Nobz82xCuSwaa8eS/lzjspb95LufNeyp13Ut68l3J3fvj4mAgJGfP1+gzRWkREREREREREZBCoeCMiIiIiIiIiMoKpeCMiIiIiIiIiMoKpeCNyBrW1tcyePZusrCwyMzNZu3YtAPn5+UyZMoXe3t4B7a1WK/n5+QOuORwO4uPjB7RtaWkhNjYWq9WK1WolLS2NwsJCPvvss1PW4HA4KCoqGoLoRERERERExBuoeCNyGq2trTgcDp599lmqq6upqKigpqaGbdu2ARAUFERjY6On/aFDh2hraxswRl9fH1u2bOHGG2+ktrZ2wL0JEyZQVVVFVVUVtbW1REREUFhYOKDNrl272Lhx4xBFKCIiIiIiIt5AxRuR0+jq6sLlctHT0wNAYGAgdrudqKgoAGbOnEldXZ2nfU1NDWlpaQPGeOONNwgLCyM7O5t169addi6TycQ999zDBx98wHvvvQfA3//+d1auXElBQcFghyYiIiIiIiJeRMUbkdOIjo4mJSWF1NRUcnJyKC0txe12ExERAUBCQgJ79uzB5XIB0NDQQFJS0oAxnE4n6enpTJ8+nQMHDtDc3Hza+fz9/YmIiODQoUMA/PKXv+T+++9n7NixQxShiIiIiIiIeAMVb0TOoKSkhPr6evLy8jh69Ci5ubls3boVgICAAKZOncrOnTs5ePAgYWFhmM1mT9/Ozk4aGxuZNWsWZrOZpKQkKioqzjifyWTCbDbz6quvcumll3LLLbcMaXwiIiIiIiIy8vkN9wJERqqGhgZOnDiBxWLBZrNhs9morKxk/fr1njbp6enU1dURGhqKxWIZ0L+6uhrDMMjJyQGgp6cHl8vFgw8++JXz9fb28tFHHxEVFcWLL75Ie3s7VquVzz//nBMnTrBs2TJ+/vOfD13AIiIiIiIiMiLpzRuR0zCbzZSVldHS0gKAYRg0NzcTExPjaZOYmMju3bvZvn07iYmJA/pv2LABu91OfX099fX1NDY2Mm7cOGpqak6Zy+1288QTT3DDDTcQHh7O888/z+uvv05VVRWFhYUkJyercCMiIiIiIvItpTdvRE4jPj6ehQsXUlBQ4DnXJiEhgQULFnDnnXcCJ8+pmTJlCnByG9WX9u7dS1dXFzNmzPBc8/HxYc6cOVRUVDBt2jTa2tqwWq3AyeJNTEwMZWVl5ys8ERERERER8RImwzCM4V6EiAy+jo7juN1n9/cePz6I9vZjQ7wiGWzKm/dS7ryT8ua9lDvvpdx5J+XNeyl354ePj4mQkDFfr88QrUVERERERERERAaBijciIiIiIiIiIiOYijdywautrWX27NlkZWWRmZnJ2rVrAcjPz2fKlCn09vYOaG+1WsnPzx9wzeFwEB8fP6BtS0sLsbGxWK1WrFYrmZmZJCcns3r1ak+b48ePU1JSwm233eYZd9++fQPGPnjwINdccw11dXVfuf79+/cTGxt7Ts9AREREREREvJcOLJYLWmtrKw6HA6fTSXBwMN3d3eTn5xMZGQlAUFAQjY2NJCcnA3Do0CHa2toYO3asZ4y+vj62bNnCjTfeSG1tLVlZWZ57EyZMoKqqasB8aWlpZGRkEBkZyfz584mLi2PTpk34+fnR1NTE/Pnz2bx5M8HBwQA4nU7S0tKoqKggLS1twPq/+OILlixZ4jkwWURERERERL599OaNXNC6urpwuVz09PQAEBgYiN1uJyoqCoCZM2cOeOOlpqbmlALKG2+8QVhYGNnZ2axbt+6M87W3t2MYBoGBgezevZu2tjYKCwvx8ztZJ42Pj2f58uW43W7gZGGourqa+++/n/379/O3v/1twHh2u50f//jH5/QMRERERERExLupeCMXtOjoaFJSUkhNTSUnJ4fS0lLcbjcRERHAyU9/79mzx/NmS0NDA0lJSQPGcDqdpKenM336dA4cOEBzc7Pn3pef+05PTycuLo5Vq1axZs0aLrnkEvbv38/111+Pj8/Av9n06dMJCQnxzDdx4kQiIyNJTU2loqLC027btm309PSQnp4+JM9GREREREREvIOKN3LBKykpob6+nry8PI4ePUpubi5bt24FICAggKlTp7Jz504OHjxIWFgYZrPZ07ezs5PGxkZmzZqF2WwmKSlpQIHly21TNTU1WK1WXC4X8fHxAPj4+GAYZ/5Ut9Pp5LbbbgPAYrGwceNGent7aW9v5ze/+Q2LFy8e7MchIiIiIiIiXkZn3sgFraGhgRMnTmCxWLDZbNhsNiorK1m/fr2nTXp6OnV1dYSGhmKxWAb0r66uxjAMcnJyAOjp6cHlcvHggw8OaOfj48OiRYvIzs7mueee46c//SmxsbG8/PLLGIaByWTytF2xYgW33norV199Ndu3b2fv3r288MILGIbBP/7xD7Zu3coXX3zB3//+d374wx96+lmtVl566SXGjBkzFI9KRERERERERii9eSMXNLPZTFlZGS0tLQAYhkFzczMxMTGeNomJiezevZvt27eTmJg4oP+GDRuw2+3U19dTX19PY2Mj48aNo6am5pS5/Pz8WLRoEU8//TTt7e3cdNNNhISEsGbNGvr7+wHYsWMHTqeTqKgoqquriY+PZ/v27dTX1/OHP/yBgoIC1q1bx3//93/z+9//nqqqKs+ByFVVVSrciIiIiIiIfAupeCMXtPj4eBYuXEhBQQFpaWmkp6fjdrtZsGCBp42/vz9TpkzhyiuvJCAgwHN97969dHV1MWPGDM81Hx8f5syZM2Dr1L9KTExk8uTJrFq1CpPJxFNPPcXf/vY3brvtNjIzM3nmmWcoLy/n4osvxul0cvvttw/of/vtt/Puu+/y4YcfDvKTEBEREREREW9lMv7doRwi4pU6Oo7jdp/d33v8+CDa248N8YpksClv3ku5807Km/dS7ryXcuedlDfvpdydHz4+JkJCvt6uCr15IyIiIiIiIiIygql4IyIiIiIiIiIygql4IyIiIiIiIiIygulT4XLBq62tpby8nL6+PgzDwGq1Mm/ePPLz89m3bx9NTU34+/t72lutVsaOHcuLL77oueZwONi4cSPbt2/3tG1paSE9PZ2rrroKALfbTXd3N9nZ2RQWFgJw/PhxysrK+NOf/oSvry9jx46lqKiI6667jt27d1NQUEB4eDiGYeByucjKyuKuu+4asP5f//rX+Pj4cM899wz1oxIREREREZERSMUbuaC1trbicDhwOp0EBwfT3d1Nfn4+kZGRAAQFBdHY2EhycjIAhw4doq2tjbFjx3rG6OvrY8uWLdx4443U1taSlZXluTdhwgTPp7y/nC8tLY2MjAwiIyOZP38+cXFxbNq0CT8/P5qampg/fz6bN28GIDY21lMk6u7uxmKxMGPGDKKiojh27BjLly9n8+bNzJs3b8iflYiIiIiIiIxM2jYlF7Suri5cLhc9PT0ABAYGYrfbiYqKAmDmzJnU1dV52tfU1JCWljZgjDfeeIOwsDCys7NZt27dGedrb2/HMAwCAwPZvXs3bW1tFBYW4ud3sk4aHx/P8uXLcbvdp/Tt6enB19eXoKAgALZt28YVV1zB3Llzv/kDEBEREREREa+n4o1c0KKjo0lJSSE1NZWcnBxKS0txu91EREQAkJCQwJ49e3C5XAA0NDSQlJQ0YAyn00l6ejrTp0/nwIEDNDc3e+61tbVhtVpJT08nLi6OVatWsWbNGi655BL279/P9ddfj4/PwL/Z9OnTCQkJAWDv3r1YrVYyMzNJTk5m2rRpTJgwAYDs7Gx+8pOf4OvrO2TPR0REREREREY+FW/kgldSUkJ9fT15eXkcPXqU3Nxctm7dCkBAQABTp05l586dHDx4kLCwMMxms6dvZ2cnjY2NzJo1C7PZTFJSEhUVFZ77X26bqqmpwWq14nK5iI+PB8DHxwfDMM64ttjYWKqqqnjttdfYuXMnR44coby8fAiegoiIiIiIiHgrFW/kgtbQ0EBNTQ2hoaHYbDZWrlxJcXEx69ev97RJT0+nrq6OLVu2YLFYBvSvrq7GMAxycnJITk5m165dVFVVebZhfcnHx4dFixbR0dHBc889B5wszOzfv/+UAs6KFStoamo6Za2BgYGkpqbyl7/8ZbDCFxERERERkQuAijdyQTObzZSVldHS0gKAYRg0NzcTExPjaZOYmMju3bvZvn07iYmJA/pv2LABu91OfX099fX1NDY2Mm7cOGpqak6Zy8/Pj0WLFvH000/T3t7OTTfdREhICGvWrKG/vx+AHTt24HQ6PWfu/Kv+/n727NnDtddeO5iPQERERERERLycijdyQYuPj2fhwoUUFBSQlpZGeno6brebBQsWeNr4+/szZcoUrrzySgICAjzX9+7dS1dXFzNmzPBc8/HxYc6cOQO2Tv2rxMREJk+ezKpVqzCZTDz11FP87W9/47bbbiMzM5NnnnmG8vJyLr74Ys8cVqsVq9Xq2Zo1f/78IXoaIiIiIiIi4o1Mxr87lENEvFJHx3Hc7rP7e48fH0R7+7EhXpEMNuXNeyl33kl5817KnfdS7ryT8ua9lLvzw8fHREjImK/XZ4jWIiIiIiIiIiIig0DFGxERERERERGREcxvuBcgMpRqa2spLy+nr68PwzCwWq3MmzeP/Px89u3bR1NTE/7+/p72VquVsWPH8uKLL3quORwONm7cyPbt2z1tW1paSE9P56qrrgLA7XbT3d1NdnY2hYWFABw/fpyysjL+9Kc/4evry9ixYykqKuK66647pX9PTw/XXHMNv/zlLz3n4Xw5xg9+8AOefvppLr/88iF/XiIiIiIiIjLyqHgjF6zW1lYcDgdOp5Pg4GC6u7vJz88nMjISgKCgIBobG0lOTgbg0KFDtLW1MXbsWM8YfX19bNmyhRtvvJHa2lqysrI89yZMmEBVVdWA+dLS0sjIyCAyMpL58+cTFxfHpk2b8PPzo6mpifnz57N58+ZT+huGwYoVKygsLOTll18G4J133qG4uJiPP/54SJ+TiIiIiIiIjGzaNiUXrK6uLlwuFz09PQAEBgZit9s9n+meOXMmdXV1nvY1NTWkpaUNGOONN94gLCyM7Oxs1q1bd8b52tvbMQyDwMBAdu/eTVtbG4WFhfj5nayRxsfHs3z5ctxu9yl9TSYT99xzDx988AHvvfceAJWVlTzyyCNMmDDhmz8EERERERER8Xoq3sgFKzo6mpSUFFJTU8nJyaG0tBS3201ERAQACQkJ7NmzB5fLBUBDQwNJSUkDxnA6naSnpzN9+nQOHDhAc3Oz515bWxtWq5X09HTi4uJYtWoVa9as4ZJLLmH//v1cf/31+PgM/ItNnz6dkJCQr1yvv78/ERERHDp0CIClS5dy0003DdrzEBEREREREe+k4o1c0EpKSqivrycvL4+jR4+Sm5vL1q1bAQgICGDq1Kns3LmTgwcPEhYWhtls9vTt7OyksbGRWbNmYTabSUpKoqKiwnP/y21PNTU1WK1WXC4X8fHxAPj4+GAYZ/eZ7n9lMpkGrEFERERERERExRu5YDU0NFBTU0NoaCg2m42VK1dSXFzM+vXrPW3S09Opq6tjy5YtWCyWAf2rq6sxDIOcnBySk5PZtWsXVVVVnm1YX/Lx8WHRokV0dHTw3HPPARAbG8v+/ftPKeCsWLGCpqamr1xvb28vH330kWdbl4iIiIiIiAioeCMXMLPZTFlZGS0tLcDJQ4Gbm5uJiYnxtElMTGT37t1s376dxMTEAf03bNiA3W6nvr6e+vp6GhsbGTduHDU1NafM5efnx6JFi3j66adpb2/npptuIiQkhDVr1tDf3w/Ajh07cDqdX1mccbvdPPHEE9xwww2Eh4cP5mMQERERERERL6evTckFKz4+noULF1JQUOA51yYhIYEFCxZw5513AifPmZkyZQpwchvVl/bu3UtXVxczZszwXPPx8WHOnDlUVFQwbdq0U+ZLTExk8uTJrFq1iqVLl/LUU0+xfPlybrvtNvz8/AgODqa8vJyLL76YlpYWz5k5cLJ4ExMTQ1lZ2ZA9DxEREREREfFOJuObHMwhIiNeR8dx3O6z+3uPHx9Ee/uxIV6RDDblzXspd95JefNeyp33Uu68k/LmvZS788PHx0RIyJiv12eI1iIiIiIiIiIiIoNAxRsRERERERERkRFMxRsRERERERERkRFMBxaLDLLa2lrKy8vp6+vDMAysVivz5s0jPz+fffv20dTUhL+/v6e91Wpl7NixvPjiiwC8++67PP7447S2tuLn58ekSZN46KGH+O53vztcIYmIiIiIiMgwUvFGZBC1trbicDhwOp0EBwfT3d1Nfn4+kZGRAAQFBdHY2EhycjIAhw4doq2tjbFjxwLQ3NzM3Xffza9+9StuvfVW3G43a9eu5Uc/+hEbNmwY8EUsERERERER+XbQtimRQdTV1YXL5aKnpweAwMBA7HY7UVFRAMycOZO6ujpP+5qaGtLS0jy/165dy/e//31uvfVW4OTnyX/yk59gNpvZsmXLeYxERERERERERgoVb0QGUXR0NCkpKaSmppKTk0NpaSlut5uIiAgAEhIS2LNnDy6XC4CGhgaSkpI8/f/6178yadKkU8a9+eab2bt37/kJQkREREREREYUFW9EBllJSQn19fXk5eVx9OhRcnNz2bp1KwABAQFMnTqVnTt3cvDgQcLCwjCbzZ6+JpOJvr6+U8b8stgjIiIiIiIi3z4680ZkEDU0NHDixAksFgs2mw2bzUZlZSXr16/3tElPT6euro7Q0FAsFsuA/pMmTeLtt98mJSVlwPW33nqL/Pz88xKDiIiIiIiIjCx680ZkEJnNZsrKymhpaQHAMAyam5uJiYnxtElMTGT37t1s376dxMTEAf1/+tOfsmHDBv74xz96+j/11FP09PQwa9as8xeIiIiIiIiIjBgq3ogMovj4eBYuXEhBQQFpaWmkp6fjdrtZsGCBp42/vz9TpkzhyiuvPOXrURERETz77LM899xzzJo1i7S0NA4fPsyLL76oL02JiIiIiIh8S5kMwzCGexEiMvg6Oo7jdp/d33v8+CDa248N8YpksClv3ku5807Km/dS7ryXcuedlDfvpdydHz4+JkJCxny9PkO0FhERERERERERGQQq3oiIiIiIiIiIjGAq3oiIiIiIiIiIjGAq3oicQW1tLbNnzyYrK4vMzEzWrl0LQH5+PlOmTKG3t3dAe6vVSn5+PseOHSMxMZE//OEPA+4/8MADOBwOWlpaiI2NxWq1YrVaSUtLo7CwkM8++8zT9te//jUWi4WMjAyef/75oQ9WRERERERERiS/4V6AyEjV2tqKw+HA6XQSHBxMd3c3+fn5REZGAhAUFERjYyPJyckAHDp0iLa2NsaOHUtQUBAlJSUsWbKEadOmERgYSENDA++99x7Lli2jvb2dCRMmUFVVBZz8JPiKFSsoLCzk5ZdfZs+ePTQ1NVFdXU1fXx8Wi4Xp06dz5ZVXDtvzEBERERERkeGhN29ETqOrqwuXy0VPTw8AgYGB2O12oqKiAJg5cyZ1dXWe9jU1NaSlpXl+JyUlcdNNN7F69WpOnDjBY489ht1u/8pPfptMJu655x4++OAD3nvvPaZNm8YLL7yAn58fHR0d9Pf3M3r06CGOWEREREREREYiFW9ETiM6OpqUlBRSU1PJycmhtLQUt9tNREQEAAkJCezZsweXywVAQ0MDSUlJA8b4xS9+wZYtW/jZz35GVlYW119//Wnn8/f3JyIigkOHDgEwatQoVq9eTUZGBrfccguhoaFDFKmIiIiIiIiMZCreiJxBSUkJ9fX15OXlcfToUXJzc9m6dSsAAQEBTJ06lZ07d3Lw4EHCwsIwm80D+l900UXcf//9HDhwgLvuuuvfzmcymQaMUVhYyK5du/jkk0+orKwc3OBERERERETEK+jMG5HTaGho4MSJE1gsFmw2GzabjcrKStavX+9pk56eTl1dHaGhoVgslq8cZ+LEiYSGhjJq1Kgzztfb28tHH31EVFQUH374Ib29vcTExPCd73yHmTNn8v777w9qfCIiIiIiIuId9OaNyGmYzWbKyspoaWkBTh4q3NzcTExMjKdNYmIiu3fvZvv27SQmJn7judxuN0888QQ33HAD4eHhtLS0UFxcTG9vL729vWzbto2pU6eec0wiIiIiIiLiffTmjchpxMfHs3DhQgoKCjzn2iQkJLBgwQLuvPNO4OQ5NVOmTAH4yoOIz6StrQ2r1QqcLN7ExMRQVlYGwPTp03nnnXfIzs7G19eXmTNnkpGRMVihiYiIiIiIiBcxGYZhDPciRGTwdXQcx+0+u7/3+PFBtLcfG+IVyWBT3ryXcuedlDfvpdx5L+XOOylv3ku5Oz98fEyEhIz5en2GaC0iIiIiIiIiIjIIVLwRERERERERERnBdOaNyL9RW1tLeXk5fX19GIaB1Wpl3rx5AFRXV7N27Vr6+/vx8fEhPT2dgoICfH19gZPn2vzqV7/iwIED+Pr6cumll1JcXExYWBgAL730EpWVlRiGgclkYu7cuWRnZw+Y/+GHHyYuLo7Zs2ef38BFRERERERkRFDxRuQMWltbcTgcOJ1OgoOD6e7uJj8/n8jISD7//HOef/55nnzyScLDwzl+/DhFRUUsXryYZcuWceLECfLz87njjjsoLS3FZDJRXV3N3Llz2bJlC/v37+fVV19l3bp1mM1mOjo6sNlsREdHEx0dTWtrK4888gi7du0iLi5uuB+FiIiIiIiIDBMVb0TOoKurC5fLRU9PDwCBgYHY7XYCAgKYO3cuy5cvJzw8HIAxY8awdOlSzxepdu7cyYQJE/j+97/vGS8rKwt/f396e3tpb2/HMAy++OILzGYzISEhrF69muDgYABee+01UlJSuOiii85/4CIiIiIiIjJiqHgjcgbR0dGkpKSQmppUpDRMAAAgAElEQVRKTEwMcXFxZGZmEhQUxJEjR5g0adKA9uPGjSMqKop9+/Zx4MCBU+4DpKenA5CYmIjT6SQhIYHJkycTFxeH1WolNDQUwLM168033xziKEVERERERGQk04HFIv9GSUkJ9fX15OXlcfToUXJzc2lqagKgv7//lPYulwsAHx8fDOP0n+r29/fnqaeeYvPmzcyaNYt9+/aRlZXF22+/PTSBiIiIiIiIiFfSmzciZ9DQ0MCJEyewWCzYbDZsNhuVlZVs2rSJ8PBw3nrrLRISEjztOzs7OXz4MLGxsZw4cQKn03nKmL/4xS/48Y9/zL59+wgNDeWWW24hIiKCH/7wh6xcuZKqqiomT558PsMUERERERGREUxv3oicgdlspqysjJaWFgAMw6C5uZmYmBjuu+8+li1bxuHDhwHo7u6muLgYi8XCxIkTSU9P58iRI7z66que8TZs2MCePXuIiIigv7+fsrIyOjs7Aejr6+Ojjz7i2muvPf+BioiIiIiIyIilN29EziA+Pp6FCxdSUFDg2Q715YHE/v7++Pr6cu+999Lb20t/fz8ZGRkUFBQAJws/v/3tb1m2bBm//e1vMZlMXH755Tz33HP4+/tjs9no6uoiLy8PH5+TddSMjAxycnKGLV4REREREREZeUzGmQ7lEBGv1dFxHLf77P7e48cH0d5+bIhXJINNefNeyp13Ut68l3LnvZQ776S8eS/l7vzw8TEREjLm6/UZorWIiIiIiIiIiMggUPFGRERERERERGQEU/FGRERERERERGQE04HFIueotraW8vJy+vr6MAwDq9XKvHnzAKiurmbt2rX09/fj4+NDeno6BQUF+Pr6AvDSSy9RWVmJYRiYTCbmzp1Ldnb2gPEffvhh4uLimD179nmPTURERERERIafijci56C1tRWHw4HT6SQ4OJju7m7y8/OJjIzk888/5/nnn+fJJ58kPDyc48ePU1RUxOLFi1m2bBnvvPMOr776KuvWrcNsNtPR0YHNZiM6Opro6GhaW1t55JFH2LVrF3FxccMdqoiIiIiIiAwTFW9EzkFXVxcul4uenh4AAgMDsdvtBAQEMHfuXJYvX054eDgAY8aMYenSpZ5Pjbe3t2MYBl988QVms5mQkBBWr15NcHAwAK+99hopKSlcdNFFwxafiIiIiIiIDD8Vb0TOQXR0NCkpKaSmphITE0NcXByZmZkEBQVx5MgRJk2aNKD9uHHjiIqKYt++fXzve9/D6XSSkJDA5MmTiYuLw2q1EhoaCuDZevXmm2+e97hERERERERk5NCBxSLnqKSkhPr6evLy8jh69Ci5ubk0NTUB0N/ff0p7l8sFgL+/P0899RSbN29m1qxZ7Nu3j6ysLN5+++3zun4REREREREZ2fTmjcg5aGho4MSJE1gsFmw2GzabjcrKSjZt2kR4eDhvvfUWCQkJnvadnZ0cPnyY2NhYNm3aRGhoKLfccgsRERH88Ic/ZOXKlVRVVTF58uRhjEpERERERERGEr15I3IOzGYzZWVltLS0AGAYBs3NzcTExHDfffexbNkyDh8+DEB3dzfFxcVYLBYmTpxIf38/ZWVldHZ2AtDX18dHH33EtddeO2zxiIiIiIiIyMijN29EzkF8fDwLFy6koKDAsx3qywOJ/f398fX15d5776W3t5f+/n4yMjIoKCgAwGaz0dXVRV5eHj4+J+uoGRkZ5OTkDFs8IiIiIiIiMvKYDMMwhnsRIjL4OjqO43af3d97/Pgg2tuPDfGKZLApb95LufNOypv3Uu68l3LnnZQ376XcnR8+PiZCQsZ8vT5DtBYRERERERERERkEKt6IiIiIiIiIiIxgOvNG5CzV1tZSXl5OX18fhmFgtVqZN28eANXV1axdu5b+/n58fHxIT0+noKAAX19fNm3axP/+7/+yceNGzGYzAEuWLMHf35+ioiLP+A8//DBxcXHMnj2bjo4O7rjjDs+9Y8eO0dXVxVtvvXV+gxYREREREZFhp+KNyFlobW3F4XDgdDoJDg6mu7ub/Px8IiMj+fzzz3n++ed58sknCQ8P5/jx4xQVFbF48WKWLVtGdnY29fX1PP744xQXF1NbW8u7777LK6+84hn7kUceYdeuXcTFxQEQEhJCVVUVAG63mzlz5nD//fcPW/wiIiIiIiIyfFS8ETkLXV1duFwuenp6AAgMDMRutxMQEMDcuXNZvnw54eHhAIwZM4alS5d6vjp12WWXsWTJEqxWK9dddx2rV6/m//7v/xg1ahQAr732GikpKVx00UVfOfeGDRv4zne+Q2Zm5vkJVkREREREREYUFW9EzkJ0dDQpKSmkpqYSExNDXFwcmZmZBAUFceTIESZNmjSg/bhx44iKimLfvn1cdtllXHTRRTz66KPMnz+fxx57zFPoATxbr958881T5u3v7+c3v/kNv/nNb4Y2QBERERERERmxdGCxyFkqKSmhvr6evLw8jh49Sm5uLk1NTcDJIsv/y+VyDfj95z//mYsvvpgdO3ac9Zw7duwgMjKSa6655twWLyIiIiIiIl5LxRuRs9DQ0EBNTQ2hoaHYbDZWrlxJcXExmzZtIjw8/JSDhDs7Ozl8+DCxsbEA7Nq1i9dffx2n08n777+P0+k8q3l///vfY7FYBj0eERERERER8R4q3oicBbPZTFlZGS0tLQAYhkFzczMxMTHcd999LFu2jMOHDwPQ3d1NcXExFouFiRMn0tnZycMPP4zD4SA0NJTS0lLsdrun/Zm8/fbb3HTTTUMam4iIiIiIiIxsOvNG5CzEx8ezcOFCCgoKPNuhvjyQ2N/fH19fX+699156e3vp7+8nIyODgoICDMOgqKiI7Oxsbr75ZgAmTZrEnDlzeOihh3jppZfw9fU97byHDx/mkksuOS8xioiIiIiIyMhkMgzDGO5FiMjg6+g4jtt9dn/v8eODaG8/NsQrksGmvHkv5c47KW/eS7nzXsqdd1LevJdyd374+JgICRnz9foM0VpERERERERERGQQqHgjIiIiIiIiIjKCqXgjIiIiIiIiIjKCqXgjco5qa2uZPXs2WVlZZGZmsnbtWs+96upqsrKyyMjIIDMzkyeffJL+/n4ANm3axKxZs+jp6fG0X7JkCXa7HTj5mXCr1UpWVhZ33303n3/++fkNTEREREREREYEFW9EzkFraysOh4Nnn32W6upqKioqqKmpYdu2bTidTp555hnWrFnD5s2beeWVVzhw4ACLFy8GIDs7m6uvvprHH38cOFkEevfdd3nggQc4fvw4//M//0N5eTnV1dVcc801PPHEE8MZqoiIiIiIiAwTFW9EzkFXVxcul8vz9kxgYCB2u52oqCjWrFlDcXEx4eHhAIwZM4alS5fy+uuvc+TIEeDkmza/+93v2LhxIw6HgxUrVjBq1ChcLhf/8z//Q2hoKADXXHMNn3zyyfAEKSIiIiIiIsNKxRuRcxAdHU1KSgqpqank5ORQWlqK2+0mKCiII0eOMGnSpAHtx40bR1RUFPv27QPgoosu4tFHH6WoqIi7777bU+gJDg4mNTUVgJ6eHsrLyz2/RURERERE5NtFxRuRc1RSUkJ9fT15eXkcPXqU3NxcmpqaADzn2/wrl8s14Pef//xnLr74Ynbs2HFK22PHjjF//nyio6P5r//6r6EJQEREREREREY0FW9EzkFDQwM1NTWEhoZis9lYuXIlxcXFbNq0ifDwcN56660B7Ts7Ozl8+DCxsbEA7Nq1i9dffx2n08n777+P0+n0tG1ra+P2228nOjqapUuXnte4REREREREZORQ8UbkHJjNZsrKymhpaQHAMAyam5uJiYnhvvvuY9myZRw+fBiA7u5uiouLsVgsTJw4kc7OTh5++GEcDgehoaGUlpZit9s5fPgw/f39FBQUMGvWLH7xi19gMpmGM0wREREREREZRn7DvQARbxYfH8/ChQspKCjwbIdKSEhgwYIF+Pv74+vry7333ktvby/9/f1kZGRQUFCAYRgUFRWRnZ3NzTffDMCkSZOYM2cODz30EHPnzmX//v309/dTV1cHQGxsrN7AERERERER+RYyGYZhDPciRGTwdXQcx+0+u7/3+PFBtLcfG+IVyWBT3ryXcuedlDfvpdx5L+XOOylv3ku5Oz98fEyEhIz5en2GaC0iIiIiIiIiIjIIVLwRERERERERERnBVLwRERERERERERnBVLwROUe1tbXMnj2brKwsMjMzWbt2rededXU1WVlZZGRkkJmZyZNPPkl/fz8AmzZtYtasWfT09HjaL1myBLvdDsAbb7xBZmYmmZmZPPDAA3R3d5/fwERERERERGREUPFG5By0trbicDh49tlnqa6upqKigpqaGrZt24bT6eSZZ55hzZo1bN68mVdeeYUDBw6wePFiALKzs7n66qt5/PHHgZNFoHfffZcHHniAf/zjHxQVFbFy5Upee+01oqOjWbly5XCGKiIiIiIiIsNExRuRc9DV1YXL5fK8PRMYGIjdbicqKoo1a9ZQXFxMeHg4AGPGjGHp0qW8/vrrHDlyBDj5ps3vfvc7Nm7ciMPhYMWKFYwaNYqPP/6YiRMnEhUVBUBSUhK///3vhydIERERERERGVYq3oicg+joaFJSUkhNTSUnJ4fS0lLcbjdBQUEcOXKESZMmDWg/btw4oqKi2LdvHwAXXXQRjz76KEVFRdx9992eQs8VV1zBp59+ynvvvQfAli1b+Oyzz85vcCIiIiIiIjIiqHgjco5KSkqor68nLy+Po0ePkpubS1NTE4DnfJt/5XK5Bvz+85//zMUXX8yOHTs818aOHYvD4WDx4sXYbDYmTJjAqFGjhjYQERERERERGZFUvBE5Bw0NDdTU1BAaGorNZmPlypUUFxezadMmwsPDeeuttwa07+zs5PDhw8TGxgKwa9cuXn/9dZxOJ++//z5OpxM4WfS55JJLePXVV9mwYQOxsbGEhYWd9/hERERERERk+Kl4I3IOzGYzZWVltLS0AGAYBs3NzcTExHDfffexbNkyDh8+DEB3dzfFxcVYLBYmTpxIZ2cnDz/8MA6Hg9DQUEpLS7Hb7Rw+fBiTycQdd9xBa2srhmHw3HPPYbFYhjNUERERERERGSZ+w70AEW8WHx/PwoULKSgo8GyHSkhIYMGCBfj7++Pr68u9995Lb28v/f39ZGRkUFBQgGEYFBUVkZ2dzc033wzApEmTmDNnDg899BAvvfQSS5YsYd68efT29nLLLbdw5513DmeoIiIiIiIiMkxMhmEYw70IERl8HR3HcbvP7u89fnwQ7e3HhnhFMtiUN++l3Hkn5c17KXfeS7nzTsqb91Luzg8fHxMhIWO+Xp8hWouIiIiIiIiIiAwCFW9EREREREREREYwnXkjcg5qa2spLy+nr68PwzCwWq3MmzeP/Px8Pv30U0aPHo1hGBiGwV133YXFYsHpdGK327n00ksHjLVkyRJCQkJISUnhueee4z/+4z8895KTk3nhhRe4/PLLz3eIIiIiIiIiMsxUvBH5hlpbW3E4HDidToKDg+nu7iY/P5/IyEgAHnvsMeLi4gBobm4mJyfH8zs5ORm73X7KmC0tLYwaNYrFixdTXV3NmDFfbx+kiIiIiIiIXHi0bUrkG+rq6sLlctHT0wNAYGAgdrudqKioU9pGRUUxevRojhw58m/HnTBhArfeeisOh2PQ1ywiIiIiIiLeR2/eiHxD0dHRpKSkkJqaSkxMDHFxcWRmZhIREXFK2x07dtDf389VV11Fc3Mz9fX1WK1Wz31/f39effVVz++ioiIyMzP54x//OGD7lIiIiIiIiHz7qHgjcg5KSkq4++67aWxspLGxkdzcXB5//HEAiouLGT16NP39/YwbN45Vq1YRGBgInH7b1JfGjBnDo48+6tk+JSIiIiIiIt9eKt6IfEMNDQ2cOHECi8WCzWbDZrNRWVnJ+vXrgYFn3nwT//mf/6ntUyIiIiIiIqIzb0S+KbPZTFlZGS0tLQAYhkFzczMxMTGDNkdRURGNjY20tbUN2pgiIiIiIiLiXfTmjcg3FB8fz8KFCykoKMDlcgGQkJDAggULuPPOO8/Y9/898wZg7ty53HTTTQOufbl96t+NJyIiIiIiIhcuk2EYxnAvQkQGX0fHcdzus/t7jx8fRHv7sSFekQw25c17KXfeSXnzXsqd91LuvJPy5r2Uu/PDx8dESMiYr9dniNYiIiIiIiIiIiKDQMUbEREREREREZERTMUbEREREREREZERTAcWi5yD2tpaysvL6evrwzAMrFYr8+bNIz8/n08//ZTRo0djGAaGYXDXXXdhsVgA+Pjjj3E4HDQ3NxMQEEBkZCSLFi0iLCwMgOTkZMxmM6NGjcIwDPz8/Fi0aBHx8fHDGa6IiIiIiIgMAxVvRL6h1tZWHA4HTqeT4OBguru7yc/PJzIyEoDHHnuMuLg4AJqbm8nJySEuLg7DMPjRj37Egw8+SFZWFgBVVVXk5eVRXV3Nd7/7XQDKy8u5/PLLAdixYwf3338/27dvZ9SoUcMQrYiIiIiIiAwXbZsS+Ya6urpwuVz09PQAEBgYiN1uJyoq6pS2UVFRjB49miNHjvDKK69w6623ego3AFarlalTp/LKK6985Vw333wznZ2dHDumk99FRERERES+bVS8EfmGoqOjSUlJITU1lZycHEpLS3G73URERJzSdseOHfT393PVVVfx17/+leuvv/6UNjfffDN//etfv3KuqqoqrrjiCs9bOSIiIiIiIvLtoW1TIuegpKSEu+++m8bGRhobG8nNzeXxxx8HoLi4mNGjR9Pf38+4ceNYtWoVgYGBmEwm+vv7TxnL5XIN+P2Tn/yEUaNG4XK5uPTSS1m1atV5iUlERERERERGFhVvRL6hhoYGTpw4gcViwWazYbPZqKysZP369cDAM2/+1aRJk3j77bf50Y9+NOD6W2+9NeCNnH8980ZERERERES+vbRtSuQbMpvNlJWV0dLSAoBhGDQ3NxMTE3PGfrfffjtvvvkmVVVVnmubNm3iL3/5C3l5eUO6ZhEREREREfE+Kt6IfEPx8f8fe/cfVlWZ7n/8s7eICKghP7RJcwwKmBEzsyBNnIACMYLAscjB0TTTRD1NI9oZrDCzTYJakpmdyPJUZkrir6xJshFPNjP9mMk8MlnjFKnbDe4mocCNe33/8Li/MShibWDv8f26rq6rvda6n3U/3tf6w9v1PCtOubm5mjZtmpKTk5WSkiKn06kZM2a0GhcUFKQXX3xRO3bsUHJyspKTk/XWW2/p5ZdfZk8bAAAAAEALJsMwjM5OAoD71dbWyels2+MdGtpDNhtfsvI21M17UTvvRN28F7XzXtTOO1E370XtOobZbFJwcOD5xbRTLgAAAAAAAHADmjcAAAAAAAAejK9NAZK2b9+uVatWqampSYZhKD09XVOmTFFOTo6OHDkif39/GYYhwzA0ffp0paamqqysTBaLRRdffLEk6eTJkzpx4oTy8vKUlJTU4vxpCxYsUHBwsFJSUhQeHi5JamhoUGRkpB544AGFhIS4rq2rq9Ptt9+ulStX8uUpAAAAALhA0bzBBc9qtaqwsFBlZWUKCgpSfX29cnJyNHDgQEnNP/l94MABjR071vU7ISFBFovFNdZbb72lBx54QElJSWc8f1p1dbXCwsJcX5wyDENLlizRrFmz9NJLL0mS/vKXvyg/P18HDx5st7kDAAAAADwfy6ZwwbPb7XI4HGpoaJAkBQQEyGKxKCIiosW1ERER8vf311dffXXGsb766iv16tXrvHMwmUyaOXOmPv30U+3fv1+StG7dOj344IMKCws77/EAAAAAAP8+ePMGF7yoqCglJiYqKSlJ0dHRio2NVVpamgYMGNDi2l27dunkyZMKDw/XgQMHVFFRofT0dNXV1amhoUEjRozQihUrXNefPn+ar6+vXn311TPm4evrqwEDBujzzz9XVFSUHnnkEfdPFgAAAADgdWjeAJIKCgp0zz33qLKyUpWVlRo3bpyKiookSfn5+fL399fJkyfVq1cvLVu2TAEBAZL+/7Kouro6TZ06VT/5yU9cy62+f76tTCaT/Pz83Ds5AAAAAIBXo3mDC97OnTv17bffKjU1VVlZWcrKytK6deu0fv16Sc33vDmbwMBAFRYW6uabb9bIkSN19dVXn3ceJ06c0N///vczLtcCAAAAAFy42PMGFzw/Pz8VFxerurpa0qnNgw8cOKDo6OjzGqd///7KycnRo48+KsMwzivW6XRq+fLluvLKK3XppZeeVywAAAAA4N8bb97gghcXF6fc3FxNmzZNDodDkjRy5EjNmDFDkydPPq+x7r77bq1fv16bNm2S1HLPG0maNGmShg0bpqNHj7rOOZ1ORUdHq7i42A0zAgAAAAD8OzEZ5/uKAACvUFtbJ6ezbY93aGgP2WzH2zkjuBt1817UzjtRN+9F7bwXtfNO1M17UbuOYTabFBwceH4x7ZQLAAAAAAAA3IDmDQAAAAAAgAejeQMAAAAAAODB2LAYaCfbt2/XqlWr1NTUJMMwlJ6erilTpignJ0dHjhyRv7+/JKmurk79+/dXUVGRQkJCJEkbN27UmjVr1NTUJKfTqV/+8peaMGFCZ04HAAAAANBJaN4A7cBqtaqwsFBlZWUKCgpSfX29cnJyNHDgQEnSwoULFRsbK+nUl6ZmzZql5557TnPmzNErr7yitWvX6umnn1ZYWJi++eYb3Xnnnerevbt++ctfdua0AAAAAACdgOYN0A7sdrscDocaGhokSQEBAbJYLOrWrVuLa7/99lvZ7XYNHjxYkvTUU0+psLBQYWFhkqSePXuqsLBQdXV1HTcBAAAAAIDHoHkDtIOoqCglJiYqKSlJ0dHRio2NVVpamgYMGCBJys/PV/fu3XXs2DH16tVLqampmjhxoo4dO6bDhw/ryiuvbDZeeHh4Z0wDAAAAAOAB2LAYaCcFBQWqqKhQdna2Dh06pHHjxunNN9+UdGrZ1KZNm/TEE0/on//8p0aNGiVfX1+ZzaceScMwOjN1AAAAAIAHoXkDtIOdO3dq27Zt6tOnj7KysrR06VLl5+dr/fr1za4bOnSocnJyNHfuXDU1Nemiiy5S//79tXfv3mbX/fGPf1RRUVFHTgEAAAAA4CFo3gDtwM/PT8XFxaqurpZ06k2aAwcOKDo6usW1kyZN0nfffae1a9dKkiZPniyLxSKbzSZJOnbsmCwWi2vJFQAAAADgwsKeN0A7iIuLU25urqZNmyaHwyFJGjlypGbMmKHJkyc3u9bX11f/8R//oUWLFik9PV3Z2dlyOBy68847ZTKZZBiGbrvtNr40BQAAAAAXKJPB5hrAv6Xa2jo5nW17vENDe8hmO97OGcHdqJv3onbeibp5L2rnvaidd6Ju3ovadQyz2aTg4MDzi2mnXAAAAAAAAOAGNG8AAAAAAAA8GM0bAAAAAAAAD8aGxUAbbd++XatWrVJTU5MMw1B6erqmTJminJwcHTlyRP7+/pKkuro69e/fX0VFRQoJCdGhQ4e0YMECffXVVzIMQ+Hh4XrggQcUHByssrIyWSwWXXzxxa77hISE6Nlnn9X777+vRYsWuT4hvmjRIl1yySWdNX0AAAAAQCeheQO0gdVqVWFhocrKyhQUFKT6+nrl5ORo4MCBkqSFCxcqNjZWkuR0OjVr1iw999xzmjNnjh544AFlZGTo5ptvliQ9/fTTevDBB1VSUiJJSkhIkMViaXHPOXPmaMWKFYqKitL69eu1cOFCPfXUUx00YwAAAACAp2DZFNAGdrtdDodDDQ0NkqSAgABZLBZFRES0uPbbb7+V3W5Xr169JEk1NTX67rvvXOfHjx+v8ePHt3q/EydOaPbs2YqKipIkRUZG6vDhw+6aDgAAAADAi/DmDdAGUVFRSkxMVFJSkqKjoxUbG6u0tDQNGDBAkpSfn6/u3bvr2LFj6tWrl1JTUzVx4kRJ0m9+8xvNmTNHy5cv13XXXaf4+HiNHj3aNXZFRYXS09Ndv++//37FxcW5jjmdTpWUlCgpKanjJgwAAAAA8Bg0b4A2Kigo0D333KPKykpVVlZq3LhxKioqkvT/l0198MEHmjVrlkaNGiVfX19JUnx8vP7whz/ovffe07vvvqvFixdr69atWrFihaSzL5uSTr2BM2/ePDU1Nenuu+/umIkCAAAAADwKy6aANti5c6e2bdumPn36KCsrS0uXLlV+fr7Wr1/f7LqhQ4cqJydHc+fOVVNTk77++mstWrRI3bp1U3x8vObOnavNmzdr9+7dOnbsWKv3rK+v15QpU9TU1KSnnnpKXbt2bc8pAgAAAAA8FM0boA38/PxUXFys6upqSZJhGDpw4ICio6NbXDtp0iR99913Wrt2rXr06KGKigpt3LjRdf6LL75QcHCwa0+cs5kzZ44GDBigxx9/3PUWDwAAAADgwsOyKaAN4uLilJubq2nTpsnhcEiSRo4cqRkzZmjy5MnNrvX19dV//Md/aNGiRUpPT9eqVatksVj0+OOPy8/PT2FhYVq5cqW6dOly1vvt27dPO3bsUEREhDIyMiRJYWFheuaZZ9pvkgAAAAAAj2QyDMPo7CQAuF9tbZ2czrY93qGhPWSzHW/njOBu1M17UTvvRN28F7XzXtTOO1E370XtOobZbFJwcOD5xbRTLgAAAAAAAHADmjcAAAAAAAAejD1vgDbYvn27Vq1apaamJhmGofT0dEVGRro+Ff7FF18oJCRE/v7+6tevn5588klJkt1uV3x8vO69917deeedzcbct2+fli5dqn/84x+SpP79++v+++9XRESEJOmVV17RmjVrZDKZNGjQIBUUFLBxMQAAAABcgGjeAOdgtVpVWFiosrIyBQUFqb6+Xjk5ORo4cKDKy8slSTk5OcrNzVVsbGyz2C1btuiGG27QK6+8okmTJslkMkmSDh48qDvvvFOFhYUaNZXkDycAACAASURBVGqUJOmtt97S1KlTtX37dn311Vd69tlnVVZWpoCAAM2bN08vvfSSJk6c2KFzBwAAAAB0PpZNAedgt9vlcDjU0NAgSQoICJDFYnG9IdOasrIyjR8/Xr6+vtqzZ4/r+LPPPqvMzExX40aSkpKSNHXqVNXV1cnX11cPPfSQAgMDZTKZdMUVV+jQoUPunxwAAAAAwOPRvAHOISoqSomJiUpKStLYsWO1ePFiOZ1ODRgwoNW4/fv3y2azadiwYRo9erTWrl3rOvfRRx/pmmuuaRFz++23q3fv3rrkkks0fPhwSdKxY8f04osvKjEx0b0TAwAAAAB4BZo3QBsUFBSooqJC2dnZOnTokMaNG6c333yz1ZgNGzYoJSVFXbp0UWpqqnbs2KGamhrX+dNLqCR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\n",
      "text/plain": [
       "<Figure size 1130.4x7560 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# within cancer COXNET without TMB_SCORE\n",
    "import time\n",
    "l1ratio = 0.95# 0 is L2; 1 is L1\n",
    "f = open(\"./out/mskcc/Cox/log_coxnetL1_\"+ str(l1ratio) + \".txt\",\"w+\")\n",
    "proc_time = np.zeros((3,1))\n",
    "\n",
    "new_x = x.drop(columns = \"TMB_SCORE\")\n",
    "for cancer_type in ('Breast Cancer', 'Esophagogastric Cancer', 'Bladder Cancer',\n",
    "       'Non-Small Cell Lung Cancer', 'Glioma', 'Head and Neck Cancer',\n",
    "       'Melanoma', 'Colorectal Cancer', 'Renal Cell Carcinoma',\n",
    "       'Cancer of Unknown Primary'): #data_x[\"CANCER_TYPE\"].unique():\n",
    "    processT = time.time()\n",
    "    mask = data_[\"CANCER_TYPE\"] == cancer_type\n",
    "    print(mask.shape)\n",
    "    ind= np.where(mask)[0].tolist()\n",
    "    tmpx = new_x.loc[mask]\n",
    "    print(tmpx.shape)\n",
    "    tmpy = y[ind]\n",
    "    f.write(cancer_type + \"\\t\" + \"sample size %d \\t\\n\" % len(ind))\n",
    "    proc_time[0] = time.time() - processT\n",
    "    #print(tmpx.shape, tmpy.shape)\n",
    "    \n",
    "    modT = time.time()\n",
    "    coxnet = CoxnetSurvivalAnalysis(l1_ratio=l1ratio,fit_baseline_model = True)#l1_ratio = 0.000001) # L1\n",
    "    coxnet.fit(tmpx, tmpy)\n",
    "    proc_time[1] = (time.time()-modT)\n",
    "   \n",
    "    \n",
    "    sortT = time.time()\n",
    "    coef = coxnet.coef_[:,-1]#pd.DataFrame(, index = new_x.columns, dtype=float)\n",
    "    \n",
    "    #print(coxnet.coef_[474,0],coxnet.coef_[474,1],coxnet.coef_[474,2],coxnet.coef_[474,3],coxnet.coef_[4,0])\n",
    "    order = np.argsort(coef)\n",
    "    order = order[::-1]\n",
    "    proc_time[2] = (time.time()-sortT)\n",
    "\n",
    "    result=pd.DataFrame(data = coxnet.coef_, columns = coxnet.alphas_, index = new_x.columns)\n",
    "\n",
    "    result.to_csv('./out/mskcc/Cox/result_coxnet_' + cancer_type + '_L1ratio_'+str(l1ratio)+'.txt', sep='\\t')\n",
    "    pltT = time.time()\n",
    "    plt.clf()\n",
    "    #print(caner_type, x.columns[474])\n",
    "    sns.set(rc={'figure.figsize':(30,105)})\n",
    "    #sns.set_context(\"paper\", font_scale=3)   \n",
    "    ax = sns.barplot(y = new_x.columns[order], x = coef[order])\n",
    "    ax.set(title=str(cancer_type))\n",
    "    plt.savefig('./out/mskcc/Cox/coxnet_' + cancer_type + '_L1ratio_'+str(l1ratio)+'.pdf')\n",
    "    print(\"---plot %s seconds ---\" % (time.time() - pltT))\n",
    "    f.write(\"---plot %6s seconds ---\\n\" % (time.time()-pltT))\n",
    "    f.write(\"# nonzero \"+ str(np.nonzero(coef)[0])+\"\\t\\n\")\n",
    "    \n",
    "    m = CoxnetSurvivalAnalysis()\n",
    "    m.fit(tmpx.iloc[:, np.nonzero(coef)[0]],tmpy) # select  features \n",
    "    score = m.score(tmpx.iloc[:, np.nonzero(coef)[0]],tmpy)\n",
    "    f.write(\"score %.6s \\t\\n\" % score )\n",
    "    f.write(\"nonzero %d \\t\\n\" % np.count_nonzero(abs(coef)))\n",
    "    for t in new_x.columns[np.nonzero(coef)[0]]:\n",
    "          f.write(t + '\\t')\n",
    "    f.write(\"\\n\")\n",
    "    f.write(\"---order %.6f, preprocess %.6f, coxnet %.6f ---\\n\" % (proc_time[0], proc_time[1], proc_time[2]))\n",
    "\n",
    "    f.write('\\n')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 728,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cross validation to select number of features\n",
    "\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sksurv.datasets import load_veterans_lung_cancer\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "\n",
    "pipe = Pipeline([#('encode', OneHotEncoder()),\n",
    "                 ('select', SelectKBest(fit_and_score_features, k=2)),\n",
    "                 ('model', CoxnetSurvivalAnalysis())])# CoxnetSurvivalAnalysis())])CoxPHSurvivalAnalysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 731,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Non-Small Cell Lung Cancer\n",
      "(344, 492)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:542: FutureWarning: From version 0.22, errors during fit will result in a cross validation score of NaN by default. Use error_score='raise' if you want an exception raised or error_score=np.nan to adopt the behavior from version 0.22.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "ename": "LinAlgError",
     "evalue": "Matrix is singular.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mLinAlgError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-731-d9789df7378e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0mparam_grid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'select__k'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtmpx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0mgcv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpipe\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_grid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_train_score\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m     \u001b[0mgcv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtmpx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtmpy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m     \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgcv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcv_results_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'mean_test_score'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mascending\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m    720\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresults_container\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    721\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 722\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    723\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    724\u001b[0m         \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresults_container\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[0;34m(self, evaluate_candidates)\u001b[0m\n\u001b[1;32m   1189\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1190\u001b[0m         \u001b[0;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1191\u001b[0;31m         \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1192\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1193\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[0;34m(candidate_params)\u001b[0m\n\u001b[1;32m    709\u001b[0m                                \u001b[0;32mfor\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    710\u001b[0m                                in product(candidate_params,\n\u001b[0;32m--> 711\u001b[0;31m                                           cv.split(X, y, groups)))\n\u001b[0m\u001b[1;32m    712\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    713\u001b[0m                 \u001b[0mall_candidate_params\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcandidate_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    915\u001b[0m             \u001b[0;31m# remaining jobs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    916\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 917\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    918\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    919\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    757\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    758\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 759\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    760\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    761\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    714\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    715\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 716\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    717\u001b[0m             \u001b[0;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    718\u001b[0m             \u001b[0;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/sklearn/externals/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    180\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    183\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/sklearn/externals/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    547\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    548\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 549\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    550\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    551\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    223\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 225\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    223\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 225\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)\u001b[0m\n\u001b[1;32m    526\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    527\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 528\u001b[0;31m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    529\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    530\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    263\u001b[0m             \u001b[0mThis\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    264\u001b[0m         \"\"\"\n\u001b[0;32m--> 265\u001b[0;31m         \u001b[0mXt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    266\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_final_estimator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    267\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_final_estimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    228\u001b[0m                 Xt, fitted_transformer = fit_transform_one_cached(\n\u001b[1;32m    229\u001b[0m                     \u001b[0mcloned_transformer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mXt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 230\u001b[0;31m                     **fit_params_steps[name])\n\u001b[0m\u001b[1;32m    231\u001b[0m                 \u001b[0;31m# Replace the transformer of the step with the fitted\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m                 \u001b[0;31m# transformer. This is necessary when loading the transformer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/sklearn/externals/joblib/memory.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    340\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    341\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 342\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    343\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    344\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcall_and_shelve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    465\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    466\u001b[0m             \u001b[0;31m# fit method of arity 2 (supervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 467\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    468\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    469\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/sklearn/feature_selection/univariate_selection.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    347\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    348\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 349\u001b[0;31m         \u001b[0mscore_func_ret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    350\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscore_func_ret\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    351\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpvalues_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscore_func_ret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-591-09c42172d743>\u001b[0m in \u001b[0;36mfit_and_score_features\u001b[0;34m(X, y)\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0mXj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m         \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m         \u001b[0mscores\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mscores\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/scikit_survival-0.9.dev5+g9fdc0f7-py3.7-macosx-10.14-x86_64.egg/sksurv/linear_model/coxph.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    328\u001b[0m             \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    329\u001b[0m             delta = solve(optimizer.hessian, optimizer.gradient,\n\u001b[0;32m--> 330\u001b[0;31m                           overwrite_a=False, overwrite_b=False, check_finite=False)\n\u001b[0m\u001b[1;32m    331\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    332\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdelta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/scipy/linalg/basic.py\u001b[0m in \u001b[0;36msolve\u001b[0;34m(a, b, sym_pos, lower, overwrite_a, overwrite_b, debug, check_finite, assume_a, transposed)\u001b[0m\n\u001b[1;32m    214\u001b[0m                                                (a1, b1))\n\u001b[1;32m    215\u001b[0m         \u001b[0mlu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mipvt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetrf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverwrite_a\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moverwrite_a\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 216\u001b[0;31m         \u001b[0m_solve_check\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    217\u001b[0m         x, info = getrs(lu, ipvt, b1,\n\u001b[1;32m    218\u001b[0m                         trans=trans, overwrite_b=overwrite_b)\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/scipy/linalg/basic.py\u001b[0m in \u001b[0;36m_solve_check\u001b[0;34m(n, info, lamch, rcond)\u001b[0m\n\u001b[1;32m     29\u001b[0m                          '.'.format(-info))\n\u001b[1;32m     30\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mLinAlgError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Matrix is singular.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlamch\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mLinAlgError\u001b[0m: Matrix is singular."
     ]
    }
   ],
   "source": [
    "new_x = x.drop(columns = \"TMB_SCORE\")\n",
    "# 'Breast Cancer', \n",
    "for cancer_type in ('Non-Small Cell Lung Cancer', 'Esophagogastric Cancer', 'Bladder Cancer',\n",
    "       'Glioma', 'Head and Neck Cancer',\n",
    "       'Melanoma', 'Colorectal Cancer', 'Renal Cell Carcinoma',\n",
    "       'Cancer of Unknown Primary'):\n",
    "    print(cancer_type)\n",
    "    mask = data_[\"CANCER_TYPE\"] == cancer_type\n",
    "    ind= np.where(mask)[0].tolist()\n",
    "    tmpx = new_x.loc[mask]\n",
    "    print(tmpx.shape)\n",
    "\n",
    "    tmpy = y[ind]\n",
    "    param_grid = {'select__k': np.arange(1, tmpx.shape[1] + 1)}\n",
    "    gcv = GridSearchCV(pipe, param_grid, return_train_score=True, cv=2, iid=True)\n",
    "    gcv.fit(tmpx, tmpy)\n",
    "\n",
    "    pd.DataFrame(gcv.cv_results_).sort_values(by='mean_test_score', ascending=False)\n",
    "    pd.to_csv('./out/mskcc/Cox_Cross/CV_' + cancer_type + \".txt\", sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 591,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/ipykernel_launcher.py:15: FutureWarning: The signature of `Series.to_csv` was aligned to that of `DataFrame.to_csv`, and argument 'header' will change its default value from False to True: please pass an explicit value to suppress this warning.\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    }
   ],
   "source": [
    "# Feature Selection: Which Variable is Most Predictive\n",
    "\n",
    "def fit_and_score_features(X, y):\n",
    "    n_features = X.shape[1]\n",
    "    scores = np.empty(n_features)\n",
    "    m = CoxPHSurvivalAnalysis()\n",
    "    for j in range(n_features):\n",
    "        Xj = X[:, j:j+1]\n",
    "        m.fit(Xj, y)\n",
    "        scores[j] = m.score(Xj, y)\n",
    "    return scores\n",
    "\n",
    "scores = fit_and_score_features(x.values, y)\n",
    "fea_select = pd.Series(scores, index=x.columns).sort_values(ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 597,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: The signature of `Series.to_csv` was aligned to that of `DataFrame.to_csv`, and argument 'header' will change its default value from False to True: please pass an explicit value to suppress this warning.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "fea_select.to_csv('./out/mskcc/Cox/fit_and_score_features.txt', sep='\\t')"
   ]
  }
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