{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "7920fce9-f0d9-4b83-a019-1221e9bb748b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab ipympl \n",
"\n",
"#import sys\n",
"\n",
"#sys.path.insert(1, '/user/scratch14_csa/jissa/NuGridPy')\n",
"\n",
"from multizone import mppnp_reader\n",
"import multizone_plot as mzp"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "d063174f-3eee-4851-87bd-3ced9f0346a3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reading in data for cycle block 0000001. This may take a while.\n",
"Searching files, please wait.......\n",
"Writing preprocessor files\n",
"my_test_hif.0000001.out.h5\n",
"Warning this method will overwrite /data/niagara_project/projects/ocmerger_issa2025/RUNS/MLT_RUNS/hif7.95E+03/H5_out/h5Preproc.txt\n",
"Would you like to continue? (y)es or (n)no?\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"--> y\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Yes selected\n",
"Continuing as normal\n",
"Reading in data for cycle block 0010001. This may take a while.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Exception in thread Thread-3:\n",
"Traceback (most recent call last):\n",
" File \"/usr/lib/python3.6/threading.py\", line 916, in _bootstrap_inner\n",
" self.run()\n",
" File \"/usr/local/lib/python3.6/dist-packages/nugridpy/h5T.py\", line 458, in run\n",
" write(self.preprocName,header,dcols,data,sldir=self.filename)\n",
" File \"/usr/local/lib/python3.6/dist-packages/nugridpy/ascii_table.py\", line 480, in write\n",
" tmp1=data_fmt.format(data[i][j])\n",
"ValueError: Unknown format code 'f' for object of type 'str'\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Searching files, please wait.......\n",
"Writing preprocessor files\n",
"my_test_hif.0010001.out.h5\n",
"Warning this method will overwrite /data/niagara_project/projects/ocmerger_issa2025/RUNS/MLT_RUNS/hif7.95E+03/H5_out/h5Preproc.txt\n",
"Would you like to continue? (y)es or (n)no?\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"--> y\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Yes selected\n",
"Continuing as normal\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Exception in thread Thread-5:\n",
"Traceback (most recent call last):\n",
" File \"/usr/lib/python3.6/threading.py\", line 916, in _bootstrap_inner\n",
" self.run()\n",
" File \"/usr/local/lib/python3.6/dist-packages/nugridpy/h5T.py\", line 458, in run\n",
" write(self.preprocName,header,dcols,data,sldir=self.filename)\n",
" File \"/usr/local/lib/python3.6/dist-packages/nugridpy/ascii_table.py\", line 480, in write\n",
" tmp1=data_fmt.format(data[i][j])\n",
"ValueError: Unknown format code 'f' for object of type 'str'\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:\n",
"This initial abundance file uses an element name that does\n",
"not contain the mass number in the 3rd to 5th position.\n",
"It is assumed that this is the proton and we will change\n",
"the name to 'h 1' to be consistent with the notation used\n",
"in iniab.dat files\n",
"Reading reaction cross-section information. This may take a while.\n",
"Processed 0/404 files\n",
"Processed 50/404 files\n",
"Processed 100/404 files\n",
"Processed 150/404 files\n",
"Processed 200/404 files\n",
"Processed 250/404 files\n",
"Processed 300/404 files\n",
"Processed 350/404 files\n",
"Processed 400/404 files\n",
"WARNING:\n",
"This initial abundance file uses an element name that does\n",
"not contain the mass number in the 3rd to 5th position.\n",
"It is assumed that this is the proton and we will change\n",
"the name to 'h 1' to be consistent with the notation used\n",
"in iniab.dat files\n",
"Ingestion rate is understood to be in Msun/second\n"
]
}
],
"source": [
"MLT = mppnp_reader(initialpath = \"/data/niagara_project/projects/ocmerger_issa2025/CONDITIONS/initial_abund.dat\",\n",
" solarpath = \"/data/niagara_project/projects/ocmerger_issa2025/CONDITIONS/iniab2.0E-02GN93.ppn\",\n",
" multizonepath = \"/data/niagara_project/projects/ocmerger_issa2025/RUNS/MLT_RUNS/hif7.95E+03/H5_out\",\n",
" surfpath = \"/data/niagara_project/projects/ocmerger_issa2025/RUNS/MLT_RUNS/hif7.95E+03/H5_surf\",\n",
" ingestionpath = \"/data/niagara_project/projects/ocmerger_issa2025/CONDITIONS/ingested_abund.ppn\",\n",
" xsectionpath = \"/data/niagara_project/projects/ocmerger_issa2025/RUNS/MLT_RUNS/flux_run/fluxes_fixed\",\n",
" networksetuppath = \"/data/niagara_project/projects/ocmerger_issa2025/RUNS/MLT_RUNS/flux_run/networksetup.txt\")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "8795bdd2-85ed-4e03-87bc-b8387bc250ec",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" reading ['mass']...100%.100%"
]
},
{
"data": {
"text/plain": [
"(3.4975484774347e-12, 3.5762612217014007e-12)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dy156 = MLT.get('out', 11000, 'Dy-156')\n",
"mass = MLT.get('out', 11000, 'mass')\n",
"\n",
"(np.trapz(dy156, mass) / (mass[-1] - mass[0])).value , MLT.get('surf', 11000, 'Dy-156')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "0129aafb-b25f-46cd-92d8-e395214529d7",
"metadata": {},
"outputs": [],
"source": [
"light_oddZ = ['P-31', 'Cl-35', 'Cl-37', 'K-39', 'K-40', 'K-41', 'Sc-45']"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "e481e3a1-19bb-4ca1-9c5d-8fd3b6bddeb6",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3ae9347081d548e9a2141e3349e6384a",
"version_major": 2,
"version_minor": 0
},
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ndeLECb388ss6ceKE3n777XJ8S8Dx1q9fr7Fjx6pLly56/PHH7TqmMDiUtjKlRo0aki4GispITU1Vt27dtG/fPkkXQ87ixYvVokWLSo0LlKYi14UktWrVShMnTlTXrl0VERGh3Nxcbdu2Ta+99ppWrVql22+/XT/++GOJTwgZOXKk3njjDaMdEhKirl27qmnTpgoKClJ2drbS09O1adMmbd++XVarVfn5+Tp//nxlvzKcjIAOVDMvv/yyXn75ZeXn5ys1NVXffvut3nzzTbVo0UKzZ8/WbbfdVuKxFotFtQKKX+Zuz+x5//79i/1FUlBQoDfffFMjR46UJP322296/fXX9e6779r9vQBHqcw1Iknnzp3Tp59+Ksn+2fPhw4dr/PjxRjshIUHjxo0zlvZeKj8/X/Pnz9frr7+ubdu26cKFC3Z+O8A5zp49qwceeEC+vr7lXsLrCrt27VL37t31xx9/SJIaNWqkJUuWqFGjRm6uDNVZZa6Lp59+2qYdFBSkW265RV27dlXfvn31zTffaNiwYdqyZctlx27YsEFvvvmm0X722Wf12muvlfiHsEOHDmnu3Lk8KaGK4B50oJry9vbWX/7yFz377LP6/vvvlZubq0GDBpV5/1EN3+IDemV2bvfw8NArr7yi/v37G599+umnKigoqPCYQGVV9BqZN2+ejh8/rqCgIN19991lnmfOnDk24fwf//iHlixZUmI4L6ytb9++2rJli9566y15e7MxI9zrxRdf1O7duzVu3LgS7yUvTlBQkCQpOzu7xD5nzpyRJAUHB1eotvXr1+vmm282wnmLFi20evVqwjmcrqLXRWksFotGjRol6eKKr/T09Mv6fPzxx8Z/Q/Xr108TJkwodZVKWFiYhg4dqq1bt+rqq692SJ1wHmbQgSvAjTfeqJiYGO3YsUPr1q1T165dy3W8o3Zuf+ihhzR79mxJFzc1OXDggP7yl79UelygsspzjRQubx8wYECZG8rl5+fr2WefNdo9e/bUv//9b7tnWSwWi0aMGGEEGMBd5s6dK4vFoi+//NJ4vGChlJQUSdIPP/xgrKIq3Myt8H7c4kJGocKfVeRpBMuWLdMdd9xh/GGtQ4cOWrhwoV2PSgQqq6LXRVmaNm1qvP/jjz8uu/1q48aNxvsBAwaUq2Z7f/9s2rRJn332mVasWKH09HSdPHlSfn5+ioyMVNu2bdWrVy/16dNHvr6+5To/ykZAB64QhUHi2LFjdvX38/7fAhtHPff80r8uHz16lIAO07DnGklNTVVSUpIk+5a3f/7550pNTZV0cSXJf/7znwotDS68RxdwJ6vVqp9++qnEnxduclhUbGyspIt/lP3999+LnWXcsGGDJNk8I90e8+bN0z333GPseN2zZ0/NmTOHDRXhUhW5Lspy/Phx433hKpSiiq70cvTtJqdOndLgwYP19ddfX/azM2fOaMeOHdqxY4dmzJihQYMGGU80geOwxB24Ahw9elRbt26VJDVp0sSuY+oFXfz7XU2f8w577vmls4D8RxTMwt5rZNq0aSooKFDLli0VFxdX5rjffPON8b5bt242mygCVUlKSoqsVmuxr8LHFD744IPGZ4WuueYa41r5/PPPLxt31apVSk9Pl4+Pj3r16mV3PdOnT1e/fv2McH7PPfdo/vz5/F6BS1X0uijLrFmzJF287SM6Ovqyn4eGhhrvHfkIwaNHj6pdu3Y24Tw2Nlb/+Mc/9Oabb+qVV17RgAEDjBl99kdxDmbQgWpg48aNOnDggG6//fbLlhr98ccfuu+++5SXl6cbbrjBmM0oS01/T4X4eyrSP8dhdRZd2uXp6anIyEiHjQ2UxhHXSEFBgfEYNntmzwsKCmxmVRISEirxDYCq68UXX9Sdd96psWPH6tZbbzVmyo8fP64nnnhC0sUQExISYtd4//73v/Xss88ageexxx5TYmKiPDyYd0LVsGXLFqWlpalXr17y8vpfHCv8PfPiiy9Kkp566qli9yDp3r27fv75Z0nS5MmTFRERoaeeeqrY2XZ7Wa1W3XPPPdqzZ4+ki/etf/rpp8X+7rJarVq9erX2799f4fOhZAR0oBpIT0/X3XffrRo1aqh169Zq0KCBLBaLUlJStH79ep0/f15NmzbVl19+edmxmzZtMv4DSbq4w7okTZ06Vd99/4PO55+Tr6+vvvnmG4WFhVWqxqIbZbVv3141a9as8HhAeVTmGim0ZMkSpaWlyc/PT3/729/KPGdGRoZOnjxptMu7fBeoLu644w499dRTeu+999SuXTt17dpVgYGBSkpK0qlTp3TTTTfZ7NVQmrFjx2rx4sVG+6WXXtLo0aOdVTrgFCkpKbrzzjtVq1YttW7dWqGhoTp16pS2b9+utLQ0SRfvLS+chb/U3//+d3300UfGxogvv/yy3nzzTXXq1Mn4Q3NsbGy59nVYuHChli1bJunibVUrVqwocUWZxWJRx44d1bFjx3J8a9iLgA5UAzfccIPx3My9e/dqw4YNOn/+vK666iolJCTorrvu0sCBA4vdyCMrK0u//vrrZZ8fPHhQBw8eNNqFywjL4/z58zp48KC+++47vfbaazb3YBXuUAq4QmWukUKF99nddddddm1AVfQeQkmqU6dO5b4EUIVNnDhRN910kxITE7VmzRrl5+ercePGGj58uJ5++mkdOnTIrnGKhvMXX3yRcI4qqVWrVho2bJg2bNig3bt3a/Xq1bJarQoNDVW/fv300EMPlXrLR82aNbVkyRL17t3bmMXOzc3V4sWLba6RsLAwde3aVX/729/Uo0ePUmv64IMPjPcvvPCC3bdEwvEs1vLcEAE4QVZWlkJCQpSZmVnhR6zAeVJTU0tdih4VFWVsgmUPDw8Pvf322xo2bJgDqgPMp/CaWb16tc3swv79+x1+D3rh5kCRkZHGjsFAVVTa75qSfs80bNhQy5cv53YpXLH27dunlStX6pNPPtEvv/xS6n3uN954o2bMmFHsPe35+fmqVauW8TjEtLS0y3aOh/0qm224WQeAS3h4eKhbt2766aefCOe4Ilz6S5lHpQGV99ZbbxnX1u+//674+Hj+OIUrlo+Pjx555BGtWbNGR44c0fz58zVy5EjdfvvtNhvJSdKvv/6qG2+8UTt27LhsnNTUVCOch4WFEc7djCXuABzmscces5kh9PLyUnBwsCIiItS6dWvVrl3bjdUBrnXVVVfZtC9d8g6g/Nq3b68ff/xRPXr0UGZmplJSUhQfH6/ly5cX+wg34EpRp04d9e7dW7179zY+W79+vSZMmGDsr5KZmakHH3zQeLRhoaK/n+rVq+eaglEiAjoAh+nfv7/i4+PdXQZgCvXr11fNmjV16tQpSRc3ZGQnd6DybrzxRi1ZskTdu3fXqVOnlJqaqs6dO2v58uU8yhAoIi4uTrNnz1bLli318ssvS7r4VJP169fb9ahQuAdL3AEAcAIPDw917tzZaCclJbmxGqB6iYuL05IlS4wNG9PT0xUfH89jn4BivPDCCzYbla5fv97m50VXfB05csRldaF4BHQAAJzkzjvvNN7/+OOPOnDggBurAaqXtm3baunSpcbtU3/88Yfi4+O1b98+N1cGmIuXl5euvfZao52ZmWnz86ioKAUGBkqSDh06ZDy+De5BQAcAwEnuvfdeRURESJIKCgr05JNPVmgcNpgDite6dWslJSUZM4AHDx5UfHy89u7d6+bKAHMp+qjbS/dI8fLyUqdOnYz29OnTXVUWikFABwDASby9vfX2228b7e+++05PP/10qY/CKcpqtWrs2LF67bXXnFUiUOVdf/31WrZsmbGE988//1R8fLx2797t5soA51i2bJny8/PL1b/oCq4bb7zxsj5PPPGE8X7cuHGsRHEjAjoAAE7Ur18//fOf/zTa7777rrp163bZLrpF5efna+7cubr++us1YsSIcv2HGHAlatmypZYvX666detKurhMNz4+Xjt37nRzZYDjvfjii2rcuLH+85//6Lfffiu177fffqu//vWvRvvGG29Uq1atLuv3f//3f8ZGpmfOnDE2XiyO1WrVTz/9xEy7k7CLOwAATjZ27FgFBATotddek9VqVVJSkuLi4nTttdfq5ptvVlhYmAIDA3Xs2DH99ttvWr58ubKysozjvbz4dQ2U5brrrtPy5cvVtWtXHT58WIcPH1aXLl20bNkyNW/e3N3lAQ6Vnp6uCRMmaMKECWrcuLFuuOEGRUREqGbNmjp37pxSUlL0008/2QT4q666SlOnTi12PIvFoi+++EI33XST9u/fr0OHDikhIUGtW7dWp06dVK9ePZ09e1b79+/XTz/9pD/++EMPPvigBg4c6KJvfOXgNz4AAE5msVj06quvqmPHjnrppZf066+/SpL27dtX6jLCevXq6fnnn6/wvevAlaZ58+Zavny5EhISlJGRoSNHjqhLly5KSkpSixYt3F0e4BAdO3bUjh07jP1JfvvttzJn0jt27KiPP/5YTZs2LbFPvXr19Ouvv+rBBx/UwoULJV18ROimTZuK7e/t7V3Bb4DSENABAHCRrl27qmvXrlq1apV++OEHrVy5Uunp6Tp27Jjy8/NVs2ZNRUREKC4uTrfeeqtuvfVWZs+BcmrWrJlWrFihLl266NChQzp69KgSEhKUlJSkli1burs8oNImTJigN998U1999ZX27t2rTZs2ae/evTp8+LCys7Pl6+urkJAQNWnSRG3atFHfvn3VoUMHu8auXbu2FixYoLVr1+rTTz/VypUrdfDgQZ0+fVqBgYGKjIxUXFycbrvtNt12221O/qZXJovV3p1qACfJyspSSEiIMjMzFRwc7O5ycInU1FRFRka6uwygyuCaAcqP6wYoP64bc6pstmGTOAAAAAAATICADtOIi4tTTEyMEhMT3V0KAAAAANgtMTFRMTExiouLq9Q43NgG01i/fj1L3AEAAABUOUOHDtXQoUONJe4VxQw6AAAAAAAmQEAHAAAAAMAECOgAAAAAAJgAAR0AAAAAABMgoAMAAAAAYAIEdAAAAAAATICADgAAAACACRDQAQAAAAAwAQI6AAAAAAAmQEAHAAAAAMAECOgAAAAAAJgAAR0AAAAAABMgoAMAAAAAYAIEdAAAAAAATICADgAAAACACRDQAQAAAAAwAQI6AAAAAAAmQEAHAAAAAMAECOgAAAAAAJgAAR0AAAAAABMgoAMAAAAAYAIEdAAAAAAATICADgAAAACACRDQAQAAAAAwAQI6AAAAAAAmQEAHAAAAAMAECOgAAAAAAJgAAb0KyMnJ0ffff6/nn39et9xyi6655hr5+/srICBAV199tbp3764333xTf/75p9NqiIqKksViKddr+PDhTqsHAAAAAKobL3cXgJIdPnxYTz31lBYuXKicnJxi++Tm5urPP//UkiVL9Nprr+m5557TqFGj5OPj4+JqAQAAAACVQUA3sfT0dH355Zc2n1ksFjVq1Ej169eXp6en9u3bp0OHDkmS8vPzNWbMGG3ZskXz5s1zWkiPi4tT7dq1y+wXHR3tlPMDAAAAQHVEQK8CLBaLEhISNGjQIHXv3l116tSx+fnKlSv1xBNPaOfOnZKk77//Xq+88orGjRvnlHrGjx+v+Ph4p4wNAAAAAFcq7kE3MQ8PD/Xt21fbt2/X0qVLde+9914WziWpc+fOWrNmjWJiYozP3n33XR0+fNiV5QIAAAAAKoGAbmKtW7fW119/bRO8SxISEqJ///vfRvvcuXNauHChM8sDAAAAADgQAb0a6dq1q/z9/Y327t273VgNAAAAAKA8COjViKenp0JCQox2VlaWG6sBAAAAAJQHAb0ayc3N1ZEjR4x2vXr13FgNAAAAAKA8COjVyLfffquCggKj3a5dO6ecZ8KECYqNjVXNmjXl6+ursLAwdejQQcOHD1dycrJTzgkAAAAA1R0BvZo4f/683nrrLaNdr149de3a1SnnWrRokbZs2aLMzEydO3dOGRkZWrt2rcaNG6dWrVqpX79+OnHihFPODQAAAADVFc9BrybGjh1rM3v98ssvy8/PzynnCgkJUZMmTRQcHKwzZ85o3759RiC3Wq2aM2eONmzYoJ9//lnh4eF2j3vpPfO+vr7y9fV1aO0AAAAA4Ch5eXnKy8sz2pXdB4wZ9GpgyZIlGjVqlNHu0KGDnnjiCYeeIyoqSqNHj9b27dt16tQprVu3TkuXLtUvv/yiY8eO6aefflKnTp2M/qmpqerdu7fOnTtn9znCw8MVEhJivMaMGePQ7wAAAAAAjjRmzBibDFOeCcriWKxWq9VBtcENdu/erZtuusmYwa5Vq5Y2b96syMhIl9dSUFCgxx57TB9//LHx2Xvvvacnn3yy1OOysrIUEhKi9PR0BQcHG58zg24Oqampbvn3CaiquGaA8uO6AcqP68YciptBDw8PV2Zmpk22sRcz6OU0ffp0WSwWh7+mT59e7lrS09PVvXt3I5wHBARo4cKFbrtQPTw89MEHH6hFixbGZ++//77dxwcHB9u8COcAAAAAzMzX1/eyHFMZBPQq6vDhw7rllluUnp4u6eK/GPPmzVOHDh3cWpeXl5eeffZZo71v3z6lpqa6sSIAAAAAqBrYJK6c/P39FRoa6pRx7XXixAl169ZNe/fulXQxFM+ePVvdunVzeF0VER8fb9Peu3cvy28AAAAAoAwE9HLq37+/+vfv77bzZ2VlqUePHsaO7R4eHpo5c6b69OnjtpouFRYWZtM+duyYmyoBAAAAgKqDJe5VSHZ2tnr16qUNGzZIkiwWi6ZMmaJ77rnHzZXZysnJsWkHBAS4qRIAAAAAqDoI6FXE2bNndfvtt2v16tXGZ4mJiXrooYfcWFXxdu7cadOuV6+emyoBAAAAgKqDgF4F5Ofnq1+/flq2bJnx2TvvvKPHH3/cjVWVbNasWcZ7f39/xcbGurEaAAAAAKgaCOgmd+HCBd17771atGiR8dmbb76pp59+2o1VlWzjxo2aPHmy0e7Ro4f8/PzcWBEAAAAAVA0EdBOzWq16+OGH9fXXXxufjRw5Ui+++KJDxi/6HPaBAweW2K9fv35avny5rFZrqeMlJSXp1ltvVV5enjH+q6++6pBaAQAAAKC6Yxd3E/vqq680Y8YMo+3n56dff/1VPXv2tOv4li1bavz48ZWuY+nSpZozZ44iIiLUq1cvxcbGKjw8XEFBQcrOztaePXv07bff2izBl6Rx48bp+uuvr/T5AQAAAOBKQEA3sUt3Qz979qwWL15s9/Fnz551aD1paWn68MMPy+zn4+OjMWPG6JlnnnHo+QEAAACgOmOJO8o0aNAgRUdHl9nP399fAwcO1ObNmwnnAAAAAFBOFmtZNxYD/9/Jkye1bds2paSk6OjRo8rNzVVAQIBq1aqlmJgYtW7dWj4+PuUeNysrSyEhIcrMzFRwcLATKkdlpKamKjIy0t1lAFUG1wxQflw3QPlx3ZhTZbMNS9xht1q1aqlz587q3Lmzu0sBAAAAgGqHJe4AAAAAAJgAAR0AAAAAABMgoAMAAAAAYAIEdAAAAAAATICADgAAAACACRDQAQAAAAAwAQI6AAAAAAAmQEAHAAAAAMAECOgAAAAAAJgAAR0AAAAAABMgoAMAAAAAYAIEdAAAAAAATICADgAAAACACRDQAQAAAAAwAQI6AAAAAAAmQEAHAAAAAMAECOgAAAAAAJgAAR0AAAAAABMgoAMAAAAAYAIEdAAAAAAATICADgAAAACACRDQAQAAAAAwAQI6AAAAAAAmQEAHAAAAAMAECOgAAAAAAJgAAR2mERcXp5iYGCUmJrq7FAAAAACwW2JiomJiYhQXF1epcbwcVA9QaevXr1dwcLC7ywAAAACAchk6dKiGDh2qrKwshYSEVHgcZtABAAAAADABAjoAAAAAACbAEncAMKH0E+eUd76gwsdbLFKjOr6yWCwOrAoAAADOREAHABPKPHtBm9NzKnx8ZG0fNa7r58CKAAAA4GwscQcAE4oO9ZOvV8Vmvy2SWl7t79iCAAAA4HQEdAAwIW9Pi2LCKhayI2r7qGYAC6QAAACqGgI6AJhURWbRmT0HAACougjoAGBSFZlFZ/YcAACg6iKgA4CJlWcWndlzAACAqo2ADgAmVp5ZdGbPAQAAqjYCOgCYnD2z6MyeAwAAVH0EdAAwOXtm0Zk9BwAAqPoI6ABQBZQ2i87sOQAAQPVAQAeAKqC0WXRmzwEAAKoHAjoAVBHFzaIzew4AAFB9ENABoIoobhad2XMAAIDqg4AOAFVI0Vl0Zs8BAACqFwI6AFQhRWfRmT0HAACoXgjoAFDFRIf6yc/Lwuw5AABANUNAB4AqxtvTooSmwcyeAwAAVDMEdACogq4KJJwDAABUNwR0AAAAAABMgIAOAAAAAIAJENABAAAAADABAjoAAAAAACZAQAcAAAAAwAQI6AAAAAAAmADP6TG5goICJScna/Xq1dqyZYt27dql1NRUHT9+XHl5eQoKClKDBg3UunVr9enTR3369JG3t7dTa8rIyNCMGTM0b948paSk6OTJk6pfv76aNm2qAQMG6O6775a/v79TawAAAACA6sZitVqt7i4CJUtMTNTf//53u/s3atRIH3/8sRISEpxSz6xZs/TYY48pMzOzxD5NmzbV559/rtjYWLvGzMrKUkhIiDIzMxUcHOyoUuEgqampioyMdHcZQJXBNQOUH9cNUH5cN+ZU2WzDEneTu/TvJ35+fmrevLk6deqkhIQEtWjRwmbG/MCBA+rRo4fmzJnj8FpmzpypAQMG2ITzJk2aqHPnzjb/57B7927Fx8dr586dDq8BAAAAAKorArrJeXt7KyEhQRMnTtSWLVuUnZ2t7du3a+XKlUpKStK2bdt07Ngxvf322woICJAknT9/XoMGDdLBgwcdVkdycrIGDx5stKOjo7Vhwwbt2bNHK1asUEpKin788UeFhoZKuviXo969e+vs2bMOqwEAAAAAqjMCusk9+uijSkpK0lNPPaVWrVrJw+Py/8mCg4P1zDPPaN68ebJYLJIuBuTJkyc7rI6XXnpJeXl5kqQ6depo5cqVatOmjU2fbt26KSkpSb6+vpIuzuZPmjTJYTUAAAAAQHVGQK9GunXrpi5duhjtFStWOGTcnTt3asGCBUZ79OjRxkz5pZo3b65hw4YZ7fHjx6ugoMAhdQAAAABAdUZAr2aKzmpnZGQ4ZMy5c+ca72vUqKH77ruv1P5DhgyxqWHt2rUOqQMAAAAAqjMCejVz/vx5472jdkRftGiR8b5jx46qUaNGqf0bNWqk6OjoYo8HAAAAABSPgF7N/PTTT8b79u3bV3o8q9Wq5OTkco9ZtN/WrVsrXQcAAAAAVHcE9GokMTFRGzdulCT5+Pho6NChlR4zLS1N2dnZRrtx48Z2HVe0365duypdBwAAAABUd17uLgAVd+HCBZ08eVKbNm3S1KlT9eWXX0qSPDw89OGHH9osM6+o1NRUm3ZERIRdxxXtl5qaKqvVauwwDwAAAAC4HAG9imnbtq0xS16cuLg4TZgwQZ06dXLI+bKysmzaISEhdh1X9P73goIC5eTkKDAwsFzn8vX1NR7ZBgAAAABmk5eXZzyOWro805QXS9yrkeuuu05PPvmkQ+49L1R0ebsk+fn52XWcv79/qeMUJzw8XCEhIcZrzJgx9hcKAAAAAC42ZswYmwwTHh5eqfGYQa9iOnTooDp16kiS8vPzdfToUe3atUvnz5/X9u3b9cADD2j06NGaPXu2rr/++kqfLz8/36bt5WXfvzKX9jt37lyZx6Snp9vMvDN7DgAAAMDMRowYoWeeecZoZ2VlVSqkE9DLafr06XrooYccPu60adM0cODAMvu99957l32WnZ2tzz77TC+99JKOHTumvXv3qkuXLlq7dq2aNm1aqboCAgJs2mfPnrXruEv7lbW8Xbq4LN5Rj4YDAAAAAGdz9G25LHGvBgIDAzVkyBBt2LBBDRo0kCSdOnVKgwcPrvTYlz7zPDc3167jcnJySh0HAAAAAGCLGfRy8vf3V2hoqFPGrazIyEhNmDBB9957ryRp1apVSk5OVosWLSo8ZuFy+kKHDh2y67iMjAzjfVBQkLy9vStcAwAAAABcCQjo5dS/f3/179/f3WWU6I477pC3t7dx7/jatWsrFdCbNGli005LS7PruPT0dON9ZZfZAwAAAMCVgCXu1Yy/v7/NrPexY8cqNV6NGjVsNjnYsmWLXcdt3rzZeN+sWbNK1QAAAAAAVwICejVU9Nl7NWvWrPR4RZ+pvmrVqjL75+fn69dffy32eAAAAABA8Qjo1cymTZtsnjneuHHjSo/Zp08f4/2uXbtsZseLM3/+fJ0+fVqS5OHhod69e1e6BgAAAACo7gjo1YjVatXIkSONdmBgoDp37lzpcXv16qW6desa7dGjR5fY98KFCxo7dqzRvvXWW1WvXr1K1wAAAAAA1R0B3cR27NihwYMHa9++fWX2zczM1AMPPKBFixYZnw0bNkx+fn4lHmOxWIxXac9gDwwM1IgRI4z23Llz9e67717Wz2q16rnnntOGDRuM8d94440yawcAAAAAsIu7qeXn52vKlCmaMmWKYmNj1aVLF7Vq1UqhoaGqUaOGcnJylJaWprVr12rOnDk6deqUcewNN9ygF1980WG1DB06VHPmzNHq1aslSU8//bSSkpJ03333qX79+kpJSdHUqVNt7lF/9tlnFRsb67AaAAAAAKA6I6BXEZs3by7z3u9Ct912m2bOnKmAgACHnd/Hx0fffPONunbtquTkZEnSwoULtXDhwmL733vvvRo3bpzDzg8AAAAA1R1L3E0sKipKL730klq3bi1PT89S+3p4eKhHjx769ttvtWDBAofs3n6punXrat26dXr++ecVEhJSYs1Tp07VZ599Jg8P/vUCAAAAAHtZrFar1d1FoGzZ2dnaunWrfvvtNx0+fFi5ubkKCAhQzZo1de211yo2NlZBQUEuq+fs2bNasWKFUlJSdPLkSYWGhqpZs2Zq166dLBZLucbKyspSSEiIMjMzFRwc7KSKUVGpqamKjIx0dxlAlcE1A5Qf1w1Qflw35lTZbMMS9yoiMDBQHTp0UIcOHdxdiiTJz89PPXv2dHcZAAAAAFBtsAYZAAAAAAATIKADAAAAAGACBHQAAAAAAEyAgA4AAAAAgAkQ0AEAAAAAMAECOgAAAAAAJkBABwAAAADABAjoAAAAAACYAAEdAAAAAAATIKADAAAAAGACBHQAAAAAAEyAgA4AAAAAgAkQ0AEAAAAAMAECOgAAAAAAJkBABwAAAADABAjoAAAAAACYAAEdAAAAAAATIKADAAAAAGACBHQAAAAAAEyAgA4AAAAAgAkQ0AEAAAAAMAECOgAAAAAAJkBABwAAAADABAjoAAAAAACYAAEdAAAAAAATIKDDNOLi4hQTE6PExER3lwIAAAAAdktMTFRMTIzi4uIqNY6Xg+oBKm39+vUKDg52dxkAAAAAUC5Dhw7V0KFDlZWVpZCQkAqPwww6AAAAAAAmQEAHAAAAAMAECOgAAAAAAJgAAR0AAAAAABMgoAMAAAAAYAIEdAAAAAAATICADgAAAACACRDQAQAAAAAwAQI6AAAAAAAmQEAHAAAAAMAECOgAAAAAAJgAAR0AAAAAABMgoAMAAAAAYAIEdAAAAAAATICADgAAAACACRDQAQAAAAAwAQI6AAAAAAAmQEAHAAAAAMAECOgAAAAAAJgAAR0AAAAAABMgoAMAAAAAYAIEdAAAAAAATICADgAAAACACRDQAQAAAAAwAQI6AAAAAAAmQEAHAAAAAMAECOgAAAAAAJgAAd3kCgoKtHXrVn3wwQcaMmSIbr75ZkVERCgwMFBeXl6qVauWmjdvrvvvv19ff/218vPznVJHVFSULBZLuV7Dhw93Si0AAAAAUB15ubsAlG7SpEn6+9//XuLPT506pVOnTmnnzp369NNP1ahRI3388cdKSEhwYZUAAAAAgMoioJuc1Wq1afv5+alx48a66qqr5OXlpaNHj2r37t3GzPmBAwfUo0cPzZo1S3379nVKTXFxcapdu3aZ/aKjo51yfgAAAACojgjoJuft7a2EhAT16dNHnTt3VosWLeThYXtnQlZWlqZMmaJXXnlFOTk5On/+vAYNGqR27drp6quvdnhN48ePV3x8vMPHBQAAAIArGfegm9yjjz6qpKQkPfXUU2rVqtVl4VySgoOD9cwzz2jevHmyWCySLob2yZMnu7pcAAAAAEAFEdCrkW7duqlLly5Ge8WKFe4rBgAAAABQLgT0aqZNmzbG+4yMDDdWAgAAAAAoDwJ6NXP+/HnjfXBwsBsrAQAAAACUBwG9mvnpp5+M9+3bt3djJQAAAACA8iCgVyOJiYnauHGjJMnHx0dDhw51ynkmTJig2NhY1axZU76+vgoLC1OHDh00fPhwJScnO+WcAAAAAFDd8Zi1KuzChQs6efKkNm3apKlTp+rLL7+UJHl4eOjDDz902nPIFy1aZNPOyMhQRkaG1q5dq/Hjx+uuu+7S5MmT7XpWOgAAAADgIgJ6FdO2bVtjlrw4cXFxmjBhgjp16uS0GkJCQtSkSRMFBwfrzJkz2rdvn06cOCFJslqtmjNnjjZs2KCff/5Z4eHhdo+blZVl0/b19ZWvr69DawcAAAAAR8nLy1NeXp7RvjTTlBdL3KuR6667Tk8++aRT7j2PiorS6NGjtX37dp06dUrr1q3T0qVL9csvv+jYsWP66aefbP4okJqaqt69e+vcuXN2nyM8PFwhISHGa8yYMQ7/HgAAAADgKGPGjLHJMOWZoCyOxWq1Wh1UG1zgqaee0t69eyVJ+fn5Onr0qHbt2mWze3uTJk00e/ZsXX/99S6traCgQI899pg+/vhj47P33ntPTz75ZKnHZWVlKSQkROnp6TY7zzODbg6pqamKjIx0dxlAlcE1A5Qf1w1Qflw35lDcDHp4eLgyMzMr9FQtZtDLafr06bJYLA5/TZ8+3a7zv/fee/rhhx/0ww8/KCkpSdu2bdOpU6f00UcfqU6dOpKkvXv3qkuXLtq9e7cT/0lczsPDQx988IFatGhhfPb+++/bfXxwcLDNi3AOAAAAwMx8fX0vyzGVQUCvBgIDAzVkyBBt2LBBDRo0kCSdOnVKgwcPdnktXl5eevbZZ432vn37lJqa6vI6AAAAAKCqYZO4cvL391doaKhTxq2syMhITZgwQffee68kadWqVUpOTraZ0XaF+Ph4m/bevXtZfgMAAAAAZSCgl1P//v3Vv39/d5dRojvuuEPe3t7Kz8+XJK1du9blAT0sLMymfezYMZeeHwAAAACqIpa4VzP+/v7GveiSe8JxTk6OTTsgIMDlNQAAAABAVUNAr4aKPnuvZs2aLj//zp07bdr16tVzeQ0AAAAAUNUQ0KuZTZs2KTs722g3btzY5TXMmjXLeO/v76/Y2FiX1wAAAAAAVQ0BvRqxWq0aOXKk0Q4MDFTnzp1dWsPGjRs1efJko92jRw/5+fm5tAYAAAAAqIoI6Ca2Y8cODR48WPv27Suzb2Zmph544AEtWrTI+GzYsGGlhuOiz2EfOHBgif369eun5cuXy2q1llpDUlKSbr31VuXl5Rnjv/rqq2XWDgAAAABgF3dTy8/P15QpUzRlyhTFxsaqS5cuatWqlUJDQ1WjRg3l5OQoLS1Na9eu1Zw5c3Tq1Cnj2BtuuEEvvviiQ+pYunSp5syZo4iICPXq1UuxsbEKDw9XUFCQsrOztWfPHn377bdatmyZzXHjxo3T9ddf75AaAAAAAKC6I6BXEZs3b9bmzZvt6nvbbbdp5syZDt89PS0tTR9++GGZ/Xx8fDRmzBg988wzDj0/AAAAAFRnLHE3saioKL300ktq3bq1PD09S+3r4eGhHj166Ntvv9WCBQscunv7oEGDFB0dXWY/f39/DRw4UJs3byacAwAAAEA5Waxl3VgMU8jOztbWrVv122+/6fDhw8rNzVVAQIBq1qypa6+9VrGxsQoKCnJqDSdPntS2bduUkpKio0ePGjXUqlVLMTExat26tXx8fMo9blZWlkJCQpSZmang4GAnVI7KSE1NVWRkpLvLAKoMrhmg/LhugPLjujGnymYblrhXEYGBgerQoYM6dOjgthpq1aqlzp07u3xneAAAAAC4ErDEHQAAAAAAEyCgAwAAAABgAgR0AAAAAABMgIAOAAAAAIAJENABAAAAADABAjoAAAAAACZAQAcAAAAAwAQI6AAAAAAAmAABHQAAAAAAEyCgAwAAAABgAgR0AAAAAABMgIAOAAAAAIAJENABAAAAADABAjoAAAAAACZAQAcAAAAAwAS83Hny3Nxcbd68Wfv27VNqaqr++OMPnT59WtnZ2Tp//rwCAgIUGBiounXrKjIyUpGRkWrVqpUiIyPdWTYAAAAAAA7n0oCenZ2tpKQkLV68WD/99JN2796tgoKCco9Tu3ZtxcXFqVu3burZs6eaNWvmhGoBAAAAAHAdpwf0c+fOacGCBfr000/1/fffKz8/X5JktVorPObx48e1ePFiLV68WM8995waNmyov/3tb7r33nvVpEkTR5UOAACqiAKrVVvSc3Sh4v95oSBfDzWt7++4ogAAKCenBfQ//vhD//nPfzRlyhSdPHlSkm0ot1gslT5H4XgHDhzQG2+8oTfeeEOdOnXSP/7xD/Xp08ch5wAAAObnYbHofIFVew7nVXiMDo1qOLAiAADKz+GbxO3du1f33XefGjVqpH/96186ceKEEaQtFovxslqtl728vb0VEhKi+vXr65prrtFVV12lgIAASSq2f3Hj/fTTT+rbt68aNWqkjz/+WBcuXHD0VwQAACZ0XQN/eVTwb/NBfh5qWMfHsQUBAFBODptBT0lJ0auvvqrPP/9cBQUFNqFcsp09b9q0qdq0aaOWLVvquuuuU0REhBo0aKBatWoVO3Z+fr7+/PNPHTx4UHv27FFycrK2bdumX3/9VdnZ2Ua/oudKTU3VY489pjFjxujVV1/Vgw8+6KivCgAATCjAx1PX1vOt0Cx6iwYB8mDlHQDAzSod0HNycvTWW2/pnXfeUV5ens3MduGsdq1atdS7d2/93//9nzp37qx69eqV6xze3t7GLu4dOnQwPr9w4YI2bNigZcuWad68edqwYYNxfuliUE9JSdGgQYP0/vvv67333rM5HgAAVC/XNfDXviN5KijHvejMngMAzKLSAb1JkyY6dOjQZcHcz89Pffv21cCBAxUfHy9PT09H1GvD09NTN954o2688UaNGDFCBw8e1OzZs/XJJ59o586dkmTUs2nTJt1888366KOP9Mgjjzi8FgAA4H4VmUVn9hwAYBaVvgf9zz//lPS/IBwVFaWJEyfq0KFDmjlzprp27eqUcF6cq6++Ws8884y2b9+uNWvWqH///vLw8DDqK1ovAAConspzLzqz5wAAM3HIJnFWq1UtW7bUF198oX379unJJ59USEiII4ausHbt2umLL77Q/v379fe//10+PvzyBQDgSlA4i24PZs8BAGZS6YD+l7/8RV988YU2b95sM2NtFpGRkXrvvfe0d+9ePfTQQ/Lycvqj31FBcXFxiomJUWJiortLAQBUcfbMojN7DgBwlMTERMXExCguLq5S41isRbdXr4ALFy64bAm7I1S1eq8EWVlZCgkJUWZmpoKDg91dDi6RmpqqyMhId5cBVBlcM+axLuVMqfeid2hUQ43r2jfTDufiugHKj+vGnCqbbSo93V3Vwm5VqxcAAFRMabPozJ4DAMzIYevRL1y44KihAAAAKq20e9G59xwAYEYOC+g9e/ZUVlaWo4YDAACotOJm0Zk9BwCYlcMCelJSktq3b6+UlBRHDQkAAFApxc2iM3sOADArh265vnv3bt1www1as2aNI4cFAACosKKz6MyeAwDMzOHPRDt27Ji6du2qzz77zNFDAwAAlFvRWXRmzwEAZubwgG6xWJSXl6cHHnhAr776qqOHBwAAKLfrGvirpr8ns+cAAFNzWEBv27atCh+pbrFYZLVaNXr0aN177706d+6co05jlz179rj0fAAAwNwCfDzVtWkws+cAAFNzWED/6aef1Ldv38tC+uzZs9WlSxcdPXrUUacq0fr163XXXXfpuuuuc/q5AABA1RLg4/CFgwAAOJTDflP5+fnpq6++0vDhwy8L6WvXrlW7du20a9cuR53OxuLFi5WQkKB27dpp3rx5KigocMp5AAAAAABwFof/Kfmtt97S9OnT5e3tLeliSJek33//XR06dNCPP/7okPMUzs63bt1avXr10sqVK40/DAAAAAAAUNU4Za3XAw88oCVLlqh27dqSLoZ0i8WizMxM3Xbbbfrwww8rPPa5c+f04Ycf6tprr9W9996rLVu2yGq1ymq1Gn8MAAAAAACgqnHazVg333yzfvnlFzVp0sRmyfv58+c1dOhQPf300+Wa8c7KytLYsWMVGRmpoUOH6sCBA0YoL3xZrVZ5enqqf//+zvpaAAAAAAA4hVN3S2ncuLF++eUXJSQkXHZf+nvvvac+ffooOzu71DEyMjL0wgsvKCIiQi+99JIOHz5sE8yli8vdfXx8NGTIEO3evVuff/65M78WAAAAAAAO5/TtTENCQrR48WINHjz4spC+aNEidezYUQcPHrzsuP3792vIkCFq2LChJkyYoKysrGKDeY0aNfT888/r999/14cffqjGjRs7+ysBAAAAAOBwLnneiKenpz766CNNmDDBCNeFIX3r1q2Ki4vTxo0bJUmbNm3S3XffrWbNmmnq1KnKy8srNpjXqVNHo0ePVlpamsaNG6f69eu74qsAAAAAAOAUXq482TPPPKNrr71W9913n7Kzs42QnpGRoc6dO6tNmzZatWqVJNnMthdtR0ZG6rnnntPDDz8sPz8/V5YPAAAAAIDTuGQGvajevXvr559/1jXXXCPpfzu85+TkaNWqVTY7shcGeKvVqubNm+u///2v9u/fr6FDhxLOAQAAAADVissDuiS1aNFCTz31lM0u7oVh/NJg3q5dO3377bdKTk7W3/72N3l6erqjZAAAAAAAnMqlS9zz8vI0depUvf3220pJSbEJ5ZLtcnZvb29988036tWrlytLBAAAAADALVwS0DMzM/Wf//xH77//vo4ePWpzf3nRUF74XpLOnz+vV155RbGxsQoLC3NFmQAAAAAAuI1Tl7gfOnRIzz//vCIiIjRy5EgdOXKk2B3Z/fz89Ne//tVYvl74s82bN+uGG27Q1q1bnVkmAAAAAABu55SAvnfvXj3yyCNq1KiR3nnnHZ0+fbrYYB4cHKwRI0YoJSVFs2fP1qJFixQcHGwz1sGDB3XzzTdr4cKFzigVAAAAAABTcGhAX79+vfr27auYmBhNmzatxGeY169fX+PGjVNaWprefPNN1a1bV5LUrVs3rVq1SuHh4TbHnTlzRnfeeacmTpzoyHIBAAAAADANh92D3rVrV61YsUJSyc8wb9y4sf75z3/qwQcflI+PT7HjNG/eXOvWrdPtt9+udevWGSH9woULeuaZZ7Rv3z6999578vBwywb0AAAAAAA4hcNS7vLly0t8hnlsbKxmzZqlPXv2aPDgwSWG80L16tXTihUr1LdvX5uwb7VaNWnSJN122206c+aMo0oHAAAAAMDtHDoNfWkwj4+P1w8//KCNGzfq7rvvLtest5+fn7766is9//zzl4X0xYsX66abbtIff/zhyPIBAAAAAHAbhwb0wiDdp08frV27VsuWLVP37t0rNea4ceM0efJkmx3erVarkpOTdcMNN2jDhg2Vrrsqy8zMVIMGDYw/jlgsFg0cONCp58zIyNC4cePUvn17hYWFyc/PT1FRUerZs6dmzJih3Nxcp54fAAAAAKojhwV0b29vPfjgg9qxY4e++eYb3XjjjY4aWo888oi+++47Y4f3wiCakZGh+Ph4ffPNNw47V1Xzz3/+U4cOHXLZ+WbNmqWmTZtq+PDh+uWXX5SRkaG8vDylpqZq8eLFGjhwoFq3bq3Nmze7rCYAAAAAqA4cFtB/++03TZs2TU2bNnXUkDZuueUWrVmzRhERETZL3nNycvTXv/5V//rXv5xyXjP7+eef9fHHH7vsfDNnztSAAQOUmZlpfNakSRN17txZkZGRxme7d+9WfHy8du7c6bLaAAAAAKCqc1hAv+aaaxw1VImaNWumdevW6YYbbrAJ6QUFBRo+fLiGDBmiCxcuOL0OM8jLy9PgwYNltVpVt25dtWzZ0qnnS05O1uDBg412dHS0NmzYoD179mjFihVKSUnRjz/+qNDQUElSVlaWevfurbNnzzq1LgAAAACoLqrcs8rq1q2rlStXql+/fpdtHjd16lT17NlTWVlZbq7S+UaPHq09e/ZIkt5++23VqlXLqed76aWXlJeXJ0mqU6eOVq5cqTZt2tj06datm5KSkuTr6ytJOnDggCZNmuTUugAAAACguqhyAV2SfH199eWXX+qFF164LKQnJSWpffv2bq7QubZv365x48ZJkhISEnT//fc79Xw7d+7UggULjPbo0aONmfJLNW/eXMOGDTPa48ePV0FBgVPrAwAAAIDqoEoG9EJjxozRlClT5OXlJeliSJcu3gNdXRUUFGjw4MHKz8+Xr6+vS2ao586da7yvUaOG7rvvvlL7DxkyxHifkZGhtWvXOq02AAAAAKguqnRAl6RBgwbpu+++U0hIiLtLcYnExET98ssvkqQRI0aoSZMmTj/nokWLjPcdO3ZUjRo1Su3fqFEjRUdHF3s8AAAAAKB4VT6gS1LXrl21evVqm53Eq6P09HS99NJLki7unj58+HCnn7PwmfOF7L19oGi/rVu3OrwuAAAAAKhuqkVAl/63w3u7du3cXYrTPPHEEzp9+rQkadKkScZmbM6Ulpam7Oxso924cWO7jivab9euXQ6vCwAAAACqm2oT0KWLu4svX75cf/3rX91disPNnj1bCxculCTdf//9SkhIcMl5U1NTbdoRERF2HVe0X2pqqrGZHwAAAACgeF7uLsDRfH19NWvWLHeX4VAnT57UP/7xD0lS7dq19fbbb7vs3Jc+ss7ee/2Dg4ON9wUFBcrJyVFgYGC5zuXr6+uSVQIAAAAAUBF5eXnG46ilyzNNeVWrGfTq6tlnn9Xhw4clXXxsWd26dV127qLL2yXJz8/PruP8/f1LHac44eHhCgkJMV5jxoyxv1AAAAAAcLExY8bYZJjw8PBKjVfpgN6jRw9t2bKlssM4XV5ensaPH6/333/f3aWUy7JlyzRt2jRJF3dQHzRokEvPn5+fb9MufKRdWS7td+7cuTKPSU9PV2ZmpvEaMWKE/YUCAAAAgIuNGDHCJsOkp6dXarxKB/QlS5aobdu2GjBggLZv317Z4Rzu7Nmz+uCDD/SXv/xFI0aM0MmTJys13vTp02WxWBz+mj59erG1P/roo5Ikb29vffjhh8az3l0lICDgsprscWm/spa3SxeXxRd9sbwdAAAAgJn5+vpelmMqwyFL3K1Wq7788ku1atVK//d//6cVK1Y4YthKOXLkiF577TVFREToySef1MGDB91dUrmNGjVK+/fvlyQ999xzat68uctruPSZ57m5uXYdl5OTU+o4AAAAAABbld4kzs/Pz7gp3mq16ocfftAPP/ygv/zlL3rkkUd0//33q379+pUu1B4FBQVavHixpkyZooULF+r8+fM2u4dbrdbL7o0uL39/f4WGhla21GLHLSo9Pd3YDK5hw4Z65ZVXHH5Oe9SpU8emfejQIbuOy8jIMN4HBQXJ29vboXUBAAAAQHVT6YC+e/duPf300/rmm2+M5ddWq1X79u3T8OHDNWLECLVv31533nmnevXqpaZNm1a66KIyMzO1fPlyffvtt1qwYIGxhL0wmFssFlmtVoWGhuqtt97SQw89VKnz9e/fX/3796903WU5fvy4zp8/L0n6/fffL1tqXpoZM2ZoxowZRnv58uWKj4+vUB1NmjSxaaelpdl1XNF7Lxz9vzkAAAAAVEeVDugRERGaM2eOli1bphdeeEEbN260CepWq1Vr1qzRmjVr9Pzzz6tu3bq6+eab1bp1a1133XVq3ry5IiIi7Np87NixY9q7d6+2b9+u5ORkrV69WsnJySooKDDOJ8m4r7twxvzJJ5/Uiy++qKCgoMp+3StOjRo1FB4ebgRuezcE3Lx5s/G+WbNmzigNAAAAAKoVhz0HPSEhQevXr9e3336rUaNGaevWrZJkE9ali/eGz507V3PnzrU5vk6dOgoNDVVAQID8/f3l6emps2fPKjc3VydPntShQ4cu2wm86PL1S8/l7++vxx57TMOHD3fpY8kcxcvLS1dddZXd/TMzM40Zd19fX5t7viu7vLxTp0767LPPJEmrVq0qs39+fr5+/fVXm+MBAAAAAKVzWEAv1KdPH/Xp00dLlizRxIkT9f3338tqtdrsPn5psJako0eP6ujRo5JUZt9Cl/azWq265ppr9MQTT2jIkCGqXbu2I76SW1x33XU6duyY3f3j4+O1cuVKSdI999xT7K7wFdWnTx8joO/atUubN29WbGxsif3nz5+v06dPS5I8PDzUu3dvh9UCAAAAANWVQ3ZxL063bt20cOFC7d+/X6+++qr+8pe/GCFaUqmPHJMuX65e3KtwPD8/P919992aP3++fv/9dw0fPrxKh3Oz6dWrl80qhNGjR5fY98KFCxo7dqzRvvXWW1WvXj2n1gcAAAAA1YHTAnqhhg0b6tVXX9WePXu0efNmjRkzRvHx8fL19TUC9qUvSZfdx37pq2HDhnr00Uc1d+5cHTlyRLNmzdJtt90mT09PZ3+laqPoHzsGDhxYYr/AwECNGDHCaM+dO1fvvvvuZf2sVquee+45bdiwwRj/jTfecHTZAAAAAFAtOXyJe2latWqlVq1a6YUXXtD58+e1Y8cObdq0Sfv27VNqaqoOHjyorKws5eTk6Pz58woICFBgYKDq1q2ryMhIRUVFqUWLFmrbti0z5C42dOhQzZkzR6tXr5YkPf3000pKStJ9992n+vXrKyUlRVOnTrW5R/3ZZ58tdSk8AAAAAOB/XBrQbU7s5WUEdpifj4+PvvnmG3Xt2lXJycmSpIULF2rhwoXF9r/33ns1btw4V5YIAAAAAFWa05e4o/qoW7eu1q1bp+eff14hISHF9omKitLUqVP12WefycODf70AAAAAwF5um0GHY61YsaLcx5S2Q35J/Pz8NH78eL3++utasWKFUlJSdPLkSYWGhqpZs2Zq166dze76AAAAAAD7ENBRIX5+furZs6e7ywAAl0s7cU4FpfyB82iul6zH80r8uafFovDaPs4oDQAAVHEEdAAAyuHPzHPad6TkAC75aXfmmRJ/Gh3qS0AHAADF4iZhAADKoUUDf3lU8E4eD4t0XQN/xxYEAACqDQI6AADlEOjrqcZ1fSt07LX1fBXg4+ngigAAQHVRJZe45+TkaPfu3Tpx4oROnz6t3Nxc+fv7KygoSLVr11Z0dLQCAwPdXSYAoJpq0cBfvx3NU0E59tpk9hwAAJSlSgT0goICLV68WHPmzFFSUpLS0tLKPCYiIkJdu3bVXXfdpZ49e/LILwCAwxTOopd+L7otZs8BAEBZTJ9a582bp+bNm+u2227TtGnTlJqaKqvVWuIjwgp/lpqaqmnTpql3796KiYnRvHnzXFs4AKBaK8+96MyeAwAAe5g2oFutVj333HPq27ev9u7dW2woL/ys6OvSn0vS3r171bdvXz333HMuqx8AUL2V5150Zs8BAIA9TLvE/fXXX9c777wji8Uiq9UqDw8PdenSRV26dFHr1q0VGRmp+vXry9/fX76+vsrLy1Nubq4yMjKUlpamjRs3asWKFVq2bJkR3v/9738rKChIr776qru/HgCgGrDnXnRmzwEAgL0s1pLWirvRrl271KpVK124cEFWq1V33XWXJkyYoKioqHKPlZaWpueee05ff/21JMnb21tbtmxRs2bNHFw1KiorK0shISHKzMxUcHCwu8vBJVJTUxUZGenuMgDT+uX3M6Xeix4d6qsbomq4sCKg6uF3DVB+XDfmVNlsY8ol7tOnT9f58+clScOGDdPXX39doXAuXdws7ssvv9SwYcMkSefPn9e0adMcVCkA4EpX2r3ozJ4DAIDyMGVAX7x4sSSpXr16GjdunEPGHDt2rOrVqydJ+vHHHx0yJgAApd2Lzr3nAACgPEwZ0P/44w9ZLBZ16tRJ3t7eDhnTx8dHnTt3ltVqVXp6ukPGBABAKn4WndlzAABQXqYM6Lm5uZKkGjUce89eYGCgJOns2bMOHRcAcGUrbhad2XMAAFBepgzoYWFhkqRt27Y5dNzk5GSb8QEAcJSis+jMngMAgIowZUCPjY2V1WrVpk2btHTpUoeMuWTJEm3cuFEWi0WxsbEOGRMAgEJFZ9GZPQcAABVhyoA+YMAA4/3dd9+t77//vlLjLV68WPfcc4/Rvvfeeys1HgAAxWnRwF+eFiuz5wAAoEJMGdDvvPNOdezYUVarVadOndJtt92mbt266YsvvlBGRoZdY2RkZGjWrFnq3r27evXqpZMnT8pisahjx4668847nfwNAABXokBfT7WolcvsOQAAqBAvdxdQHIvFok8//VRdunTR77//LqvVqmXLlmnZsmWSpNDQUEVFRSk0NFT+/v7y8fHRuXPnlJubq8OHDyslJUWHDx82xrNarZKkhg0baubMmW75TgCAK0OwT4G7SwAAAFWUKQO6JEVEROjXX3/V/fffr8WLFxshW7o4O140gF+qsK/FYjHe9+zZUzNmzFDdunWdWzgAAAAAABVgyiXuherUqaPvv/9eS5cuVZ8+feTv/797+qxWa4mvQn5+frrjjjuUlJSk7777jnAOAAAAADAt086gF5WQkKCEhATl5uZq7dq12rFjh/bs2aPjx4/rzJkzys3Nlb+/v2rUqKGrrrpK0dHRat68udq3b28T6gEAAAAAMKsqEdAL+fv7G2EdAAAAAIDqxNRL3AEAAAAAuFIQ0AEAAAAAMAECOgAAAAAAJkBAh2nExcUpJiZGiYmJ7i4FAAAAAOyWmJiomJgYxcXFVWqcKrVJXGVNnjxZGRkZkqSRI0e6uRpcav369QoODnZ3GQAAAABQLkOHDtXQoUOVlZWlkJCQCo9zRQX0SZMmadu2bZII6AAAAAAAc7nilrhbrVZ3lwAAAAAAwGWuuIAOAAAAAIAZEdABAAAAADABU96DnpaW5pRxz50755RxAQAAAACoLFMG9KioKFksFneXAQAAAACAy5gyoEvO2cyN0A8AAAAAMCvT3oNusVgcHqjZwR0AAAAAYFamnEEPCgrS6dOnZbFYNHPmTF1zzTUOGfeRRx7Rb7/95pCxAAAAAABwJFMG9NatW2vlypWSJD8/P3Xu3Nkh49aoUcMh4wAAAAAA4GimXOLepk0b4/2GDRvcWAkAAAAAAK5hyoDetm1b4z0BHQAAAABwJTBlQC86g75p0yY3VgIAAAAAgGuY8h70a6+9VsHBwcrKytKpU6f022+/qXHjxpUet23btqpZs2blCwQAAAAAwMFMGdAlKSkpSWfOnJEk1alTxyFjfvzxxw4ZBwAAAAAARzNtQC+6zB0AAAAAgOrOlPegAwAAAABwpSGgAwAAAABgAgR0AAAAAABMgIAOAAAAAIAJENABAAAAADABAjoAAAAAACbg0ses/fe//3Xa2J6engoKClLNmjVVq1YtRUdHy8fHx2nnAwAAAADAkVwa0AcOHCiLxeKSc3l5eal58+bq2LGjBg0apOuvv94l5wUAAAAAoCLcssTdarU6/ZWfn68tW7YoMTFRbdq00U033aStW7e64+sCAAAAAFAmtwR0i8XisldhYF+7dq1uvPFGvfvuu+74ygAAAAAAlMrlAf3Sme7SflbSqzzHFA3r586d07PPPqspU6a48is7VWZmpho0aGDzPQcOHOjw80RFRZX7jyPDhw93eB0AAAAAUF25NKAXFBQYr7Fjx8rPz0/SxZDt5eWl22+/XZMmTdKaNWv0559/6syZM8rPz9fx48e1b98+ffPNNxoxYoQaNmxoBHWLxaJ27dpp586dys/P19GjR5WcnKypU6eqX79+8vDwsOlrtVo1dOhQbdmyxZVf3Wn++c9/6tChQ+4uAwAAAABQSS7dJE6SLly4oLvvvlvz5s0zPhs0aJDGjBmjunXrFntMrVq1VKtWLTVu3Fh9+vTRm2++qfnz5+uZZ57RgQMH9Ouvv6p9+/aaM2eOEhISdNVVV6l58+Z66KGH9Pvvv+uBBx7Q6tWrjQ3q8vPz9dprr+mbb75xxVd2mp9//lkff/yxy88bFxen2rVrl9kvOjraBdUAAAAAQPXg8oA+ePBgIxh7e3vrk08+0X333VfucW6//XbFx8erV69eWrNmjTIzM9WnTx/98ssvat68udGvYcOGWrlypf7v//5PixcvNmbR58+fr71796pJkyYO+26ulJeXp8GDB8tqtapu3boKCwvTtm3bXHLu8ePHKz4+3iXnAgAAAIArhUuXuH///feaPn26pIvLzUeNGlWhcF4oODhYCxcuVP369WWxWJSdna2HHnrosn4eHh764osvLpv1/fHHHyt8bncbPXq09uzZI0l6++23VatWLTdXBAAAAACoDJcG9Lfeest436BBA/3zn/+s9Jg1a9bUyJEjjfvMN27cWGzwrlmzpp544glj4zhJWrlyZaXP7w7bt2/XuHHjJEkJCQm6//773VwRAAAAAKCyXBbQDx48aNwHbrFYdNddd8nT09MhY//1r3+Vh4eHEby/+uqrYvvdeeedxnur1ardu3c75PyuVFBQoMGDBys/P1++vr6aNGmSu0sCAAAAADiAywL6+vXrJcmY6S56n3hlXXXVVapXr54x/rp164rt16pVK/n7+xvtEydOOKwGV0lMTNQvv/wiSRoxYkSVvYceAAAAAGDLZQE9JSXFpm3PLuDlUfQe7NTU1GL7eHh4qE6dOka7qgX09PR0vfTSS5KkJk2a8JxxAAAAAKhGXBbQc3NzbdpHjx516PjHjx8v8VxFhYSEGO8LZ/OriieeeEKnT5+WJE2aNEm+vr5uqWPChAmKjY1VzZo15evrq7CwMHXo0EHDhw9XcnKyW2oCAAAAgKrOZQG98BnnhfeJb9682WFj//nnnzaBv+gs+aWys7ON9wEBAQ6rwdlmz56thQsXSpLuv/9+JSQkuK2WRYsWacuWLcrMzNS5c+eUkZGhtWvXaty4cWrVqpX69etX5VYnAAAAAIC7uSygX3311cZ7q9WqefPmlTrTXR6fffaZzWx40XNdquhMe2lB3kxOnjypf/zjH5Iu3hrw9ttvu7WekJAQxcXFqWvXrrrxxhttblewWq2aM2eOWrdurfT09HKNm5WVZfPKy8tzdOkAAAAA4DB5eXmX5ZjKcFlAv+mmm+Tj42O0jx8/btxPXRkHDx7Um2++KYvFYjxCraTZ5fT0dOMfmMViUVRUVKXP7wrPPvusDh8+LEkaP368sRrBlaKiojR69Ght375dp06d0rp167R06VL98ssvOnbsmH766Sd16tTJ6J+amqrevXvr3Llzdp8jPDxcISEhxmvMmDHO+CoAAAAA4BBjxoyxyTDh4eGVGs9lAT04OFjdu3c3QrTVatXEiRON53lXxMGDB9WtW7fL/krRv3//Yvtfurt7TExMhc/tKsuWLdO0adMkSR07dtSgQYPcUseKFSv00ksvFbv7vsVi0c0336zly5dr8ODBxudbt27VRx99ZPc50tPTlZmZabxGjBjhkNoBAAAAwBlGjBhhk2HKu4r4Ui4L6JI0cuRI49nnhSH9xRdfVM+ePbV9+3a7x8nLy1NiYqJatmypPXv22Mye33777YqNjS32uG+//VbS/zaH69ChQ7m/w/Tp041nuTvyNX369MvOdfbsWT366KOSJG9vb3344YfGPfxm5OHhoQ8++EAtWrQwPnv//fftPj44ONjm5a5N8AAAAADAHr6+vpflmMrwclBddmnbtq2GDRumt99+2wimVqtVS5YsUatWrdSqVSv17NlT119/vaKiohQcHCwfHx+dPn1aJ06c0Pbt27Vu3TotWLBAp0+fNoJ2YWitWbOmEhMTiz33mTNnNH/+fOOcHh4ebt1ozR6jRo3S/v37JUnPPfecQ58d7yxeXl569tlnNXDgQEnSvn37lJqaqsjISPcWBgAAAAAm59KALl28h/rIkSOaOXOmTUiXpC1btmjr1q1ljnFpMLdarapZs6YWL16sBg0aFHvMlClTbJbCd+7cuUKbxPn7+ys0NLTcx9kzblHp6enGZnANGzbUK6+84vBzOkt8fLxNe+/evQR0AAAAACiDywN64XLusLAwvf322yooKLBZtm3Ps8kv7d+sWTN9/vnnatWqVYnH3HXXXerWrZvRruhGa/379y/xHndHOn78uM6fPy9J+v3338v1SLgZM2ZoxowZRnv58uWXhWZnCgsLs2kfO3bMZecGAAAAgKrK5QFduhiwx44dq7/+9a966qmntHbtWpuflaUwxIeEhGjYsGEaMWKEzQ7xxYmIiKhc0bBbTk6OTbsqPW8eAAAAANzFLQG9UJs2bbR69Wpt375dn3zyiZYvX64dO3YYM8fFqVOnjm688Ub169dPd99992VLw6sLLy8vXXXVVXb3z8zMNP65+fr6qkaNGsbPvL29HV5faXbu3GnTrlevnkvPDwAAAABVkVsDeqHrrrtO77zzjiQpNzdXu3fv1okTJ3Tq1Cnl5eUpODhYNWvWVHh4+BVzL/N1111XrqXh8fHxWrlypSTpnnvuKXZXeFeZNWuW8d7f37/EXfUBAAAAAP9jioBeFIGuatu4caMmT55stHv06CE/Pz83VgQAAAAAVYNLn4MOcyn6HPbCx6IVp1+/flq+fHmZG/glJSXp1ltvVV5enjH+q6++6siSAQAAAKDaMt0MOsxn6dKlmjNnjiIiItSrVy/FxsYqPDxcQUFBys7O1p49e/Ttt99q2bJlNseNGzdO119/vXuKBgAAAIAqhoAOu6WlpenDDz8ss5+Pj4/GjBmjZ555xgVVAQAAAED1wBJ3lGnQoEGKjo4us5+/v78GDhyozZs3E84BAAAAoJxMM4Oenp6u5cuXa+3atdq8ebOOHTumU6dO6fTp0woKClLNmjVVp04dtW7dWu3bt1eXLl10zTXXuLts01ixYkW5jynrnvJC77zzjt555x2dPHlS27ZtU0pKio4eParc3FwFBASoVq1aiomJUevWrct8Hj0AAAAAoHgWq70pzUmWLVumiRMn6rvvvlNBQYGk0oOjxWKRJHl4eOi2227TP/7xD8XHx7uiVDhJVlaWQkJClJmZqeDgYHeXg0ukpqZeMY83BByBawYoP64boPy4bsypstnGbUvcs7KydP/996tbt25auHChLly4IKvVKqvVarO7+KWvwj4XLlzQ/Pnz1bVrVz344IM6ffq0u74KAAAAAACV5paA/scffyg2Nlaff/55saFckvF50ZekYsP6p59+qtjYWB08eNAdXwcAAAAAgEpz+T3ox48fV/fu3fX7779Lkk0gL3T11VerWbNmqlmzpgIDA5Wdna1Tp05p9+7d+uOPP4x+RY89cOCAunfvrp9//lm1a9d24TcCAAAAAKDyXB7QH3/8ce3evdsI19LFgH399dfrkUceUb9+/VSvXr0Sjz969Ki+/vprTZ06VZs2bbKZTd+9e7cef/xxzZ492xVfBQAAAAAAh3HpEvc1a9bo66+/tpn5DggI0OTJk7Vx40Y98cQTpYZzSapbt64ef/xxbdiwQVOmTFFgYKAkGSH966+/1i+//OL07wIAAAAAgCO5NKC//fbbxnur1aqaNWtq6dKleuSRR2xm1O01aNAgLV26VCEhITafT5gwodK1AgAAAADgSi4L6Pn5+VqyZIkx022xWPTee+/pxhtvrNS4N9xwg9577z1jTKvVqiVLluj8+fMOqhwAAAAAAOdzWUD/9ddfdebMGaPdtGlT/e1vf3PI2H/729/UrFkzo33mzBmWuQMAAAAAqhSXBfT09HTjvcVi0V133eXQ8fv27WuzE3zR8wEAAAAAYHYuC+hHjhyR9L/HqTVu3Nih4zdq1KjY8wEAAAAAUBW4LKDn5eXZtP39/R06vp+fn6T/PRv90vMBAAAAAGBmLgvodevWtWn/+eefDh3/0KFDkv43Q1+nTh2Hjg8AAAAAgDO5LKAXPt+8cIY7KSnJoeMvW7bMph0aGurQ8QEAAAAAcCaXBfS2bdsa4dxqtWrp0qU6cOCAQ8b+7bff9OOPPxrjWywWtW3b1iFjAwAAAADgCi4L6KGhoWrdurWkiwH63LlzGjRokPLz8ys1bn5+vs04FotFsbGxzKADAAAAAKoUlwV0SbrvvvtsHoX2888/q3fv3srMzKzQeJmZmbrtttv0888/y2KxGGM76vnqAAAAAAC4iksD+tChQ9WwYUNJMgL1kiVLFB0drcmTJ+vMmTN2jZOdna2PPvpI0dHRWrp0qfG5xWJRw4YN9cQTTzilfgAAAAAAnMXLlSfz9vbWBx98oN69e+vChQtGSD9y5Igef/xxPfPMM+ratavatGmj6OhohYSEKDAwUNnZ2crMzNSePXu0ceNGJSUlKTc315gxLxzHy8tLH3zwgby9vV35tQAAAAAAqDSXBnRJ6tGjhyZPnqyHH35Ykmw2jsvJydHChQu1cOHCUscoGswL2xaLRR9//LG6d+/uxOoBAAAAAHAOly5xLzRw4EDNmjVLNWvWtAnbhTPhZb0K+0oXw3nt2rX15Zdf6sEHH3TH1wEAAAAAoNLcEtAl6a9//auSk5PVv39/eXp6Xha+S3sV9vXy8tKAAQOUnJysvn37uuurAAAAAABQaS5f4l5UgwYN9MUXX+jQoUOaMmWKli5dqg0bNig3N7fEYwICAtS2bVvdcsstevjhhxUWFubCigEAAAAAcA63BvRCYWFheuWVV/TKK6/owoUL2rNnj44dO6aTJ0/qzJkzqlGjhmrVqqU6deooOjpanp6e7i4ZAAAAAACHMkVAL8rT01MxMTHuLgMAAAAAAJdy2z3oAAAAAADgfwjoMI24uDjFxMQoMTHR3aUAAAAAgN0SExMVExOjuLi4So1juiXuuHKtX79ewcHB7i4DAAAAAMpl6NChGjp0qLKyshQSElLhcZhBBwAAAADABAjoAAAAAACYQKWXuJvxkWcWi0Xnz593dxkAAAAAANit0gHdarU6og4AAAAAAK5oDtkkzmKxOGIYh+APBgAAAACAqsghAZ1QDAAAAABA5VQ6oE+bNs0RdQAAAAAAcEWrdEB/8MEHHVEHAAAAAABXNB6zBgAAAACACRDQAQAAAAAwAQI6AAAAAAAmQEAHAAAAAMAECOgAAAAAAJgAAR0AAAAAABMgoAMAAAAAYAIEdAAAAAAATICADgAAAACACRDQAQAAAAAwAQI6AAAAAAAmQEAHAAAAAMAECOgAAAAAAJgAAR0AAAAAABMgoAMAAAAAYAIEdAAAAAAATICADgAAAACACRDQAQAAAAAwAQI6AAAAAAAmQEA3uZSUFFkslnK/fvjhB6fVdODAAY0cOVJt2rRR3bp15e/vr8aNG+vOO+/U119/rQsXLjjt3AAAAABQXXm5uwBULe+++66GDx+uvLw8m88PHDigAwcOaN68eWrfvr0+++wzNWzY0E1VAgAAAEDVQ0CvYnr06GFXv7p16zr83G+88YZGjhxptD08PBQTE6PatWtr3759OnTokCRp7dq16tSpk9avX6/69es7vA4AAAAAqI4I6FWMM5eul2bx4sV69dVXjXb79u01ffp0NWnSRJJUUFCgL7/8UoMHD9aZM2f0xx9/qF+/flq1apVb6gUAAACAqoZ70FEmq9WqF154QVarVZIUHR2tpUuXGuFcujibfs899+ibb74xPlu9erVNGwAAAABQMgI6yvTDDz9o69atRnvixIkKCAgotu8tt9yi/v37G+2xY8c6vT4AAAAAqA4I6CjTnDlzjPcNGzZU9+7dS+3/6KOPGu/Xr1+vP/74w2m1AQAAAEB1QUBHmRYtWmS879GjhywWS6n9b775ZgUGBkq6uDz+u+++c2p9AAAAAFAdENBRqiNHjigjI8Not2/fvsxjvLy8FBcXZ7SLLo8HAAAAABSPgF7FPPDAA7r22msVGBiowMBARUREqGfPnho/fryOHDni8PPt2rXLpt24cWO7jiva79IxAAAAAACXI6BXMTNnztT+/fuVk5OjnJwcpaena/HixXrhhRcUGRmpV155RRcuXHDY+VJTU23aERERdh1XtF9KSorD6gEAAACA6ornoFcxYWFhioqKkr+/v06ePKldu3bp7NmzkqSzZ89q9OjRWr9+vRYsWCBvb+9Kny8rK8umHRISYtdxwcHBxvvTp09X6Fy+vr7y9fW161gAAAAAcLW8vDzl5eUZ7UszTXkxg25yFotFN9xwgz7++GP9+eef+vPPP7VmzRolJSVp06ZNOnXqlD7//HNFRUUZxyxevFhPPfWUQ86fnZ1t0/bz87PrOH9//xLHKEl4eLhCQkKM15gxY+wvFAAAAABcbMyYMTYZJjw8vFLjEdBNLjIyUr/++qseeeQRhYWFXfZzX19fDRgwQJs2bVKbNm2Mzz/66CNt27at0ufPz8+3aXt52bfoomi/c+fO2XVMenq6MjMzjdeIESPsLxQAAAAAXGzEiBE2GSY9Pb1S4xHQy2n69OmyWCwOf02fPr1SddWqVUtz5841ZritVqv+85//VPr7BgQE2LQLl9OXpWi/wkeulSU4ONjmxfJ2AAAAAGbm6+t7WY6pDAJ6NRIREaF77rnHaC9ZsqTSY9aoUcOmnZuba9dxOTk5JY4BAAAAALgcm8SVk7+/v0JDQ50yriPEx8cbs/EpKSk6d+6cfHx8KjxenTp1bNqHDh3SVVddVeZxRZ+dbk9/AAAAALjSEdDLqX///urfv7+7yyjRpfepHz9+vNh71+0VHR1t005LS9N1111X5nFF771o2rRphc8PAAAAAFcKlrhXM0WXlkuX30NeXtdee63Nhm9btmyx67jNmzcb75s1a1apGgAAAADgSkBAr2Z27txpvPf19bX7ueUl8fHx0Y033mi0V61aVeYxGRkZ2r9/v9Hu1KlTpWoAAAAAgCsBAb0asVqtmj17ttFu3769Q8bt06eP8X7p0qU6cuRIqf0/++wz433NmjXVuXNnh9QBAAAAANUZAb0aSUxMtHn2+R133OGQcQcMGGA88iw/P1/jxo0rse+ZM2f03nvvGe377rvP7menAwAAAMCVjIBuYjt27NDDDz+sPXv2lNrParVq4sSJGjZsmPFZgwYNNGTIkBKPSUlJsXkO+6hRo0rse8011+jRRx812hMnTtScOXMu65efn6+HHnpIaWlpki7uTP/SSy+VWjsAAAAA4CKmNk0sPz9fn3zyiT755BO1adNGCQkJatWqlerVqyd/f3+dPHlSmzdv1hdffKHdu3cbx/n6+mr27NkOe3SbJI0aNUrff/+99u3bpwsXLujuu+/WvffeqzvuuEO1a9fWnj17NGnSJJsZ/H/961+V2kEeAAAAAK4kBPQqYuPGjdq4cWOZ/erXr6+ZM2eqY8eODj1/rVq1tHDhQt1yyy1KT09XQUGBPv30U3366afF9n/hhRc0dOhQh9YAAAAAANUZS9xNLCwsTAMGDFD9+vXL7BsaGqqXX35ZycnJuuWWW5xST5MmTbRt2zY9/PDDJc7Ox8TEaMGCBRo7dqxTagAAAACA6ooZdBMLDQ3V559/LklKT0/Xjh07lJaWppMnTyo/P19BQUGqU6eOYmNj1axZM1ksFrvHjoqKktVqLXdNNWvW1JQpU/Tvf/9by5YtU3p6urKzsxUWFqYWLVooNja23GMCAAAAAAjoVUZ4eLjCw8PdXYYhKCjI5vFrAAAAAIDKYYk7AAAAAAAmQEAHAAAAAMAECOgAAAAAAJgAAR0AAAAAABMgoAMAAAAAYAIEdAAAAAAATICADgAAAACACRDQAQAAAAAwAQI6AAAAAAAmQEAHAAAAAMAECOgAAAAAAJgAAR0AAAAAABMgoAMAAAAAYAIEdAAAAAAATICADgAAAACACRDQAQAAAAAwAQI6AAAAAAAmQEAHAAAAAMAECOgAAAAAAJgAAR0AAAAAABMgoAMAAAAAYAIEdAAAAAAATICADgAAAACACRDQAQAAAAAwAQI6AAAAAAAmQECHacTFxSkmJkaJiYnuLgUAAAAA7JaYmKiYmBjFxcVVahwvB9UDVNr69esVHBzs7jIAAAAAoFyGDh2qoUOHKisrSyEhIRUehxl0AAAAAABMgIAOAAAAAIAJENABAAAAADABAjoAAAAAACZAQAcAAAAAwAQI6AAAAAAAmAABHQAAAAAAEyCgAwAAAABgAgR0AAAAAABMgIAOAAAAAIAJENABAAAAADABAjoAAAAAACZAQAcAAAAAwAQI6AAAAAAAmAABHQAAAAAAEyCgAwAAAABgAgR0AAAAAABMgIAOAAAAAIAJENABAAAAADABAjoAAAAAACZAQAcAAAAAwAQI6AAAAAAAmAABHQAAAAAAEyCgAwAAAABgAgR0AAAAAABMgIAOAAAAAIAJENABAAAAADABArrJpaSkyGKxlPv1ww8/OLSOitTw4YcfOrQGAAAAAKjOCOgAAAAAAJiAl7sLQPn06NHDrn5169Z1Wg2dOnWSv79/mf0iIiKcVgMAAAAAVDcE9CrG0UvXK2LGjBmKiopydxkAAAAAUK2wxB0AAAAAABMgoAMAAAAAYAIEdAAAAAAATICADgAAAACACRDQAQAAAAAwAQJ6FfPAAw/o2muvVWBgoAIDAxUREaGePXtq/PjxOnLkiEtqeP7559W8eXMFBwfL399f11xzjbp06aJRo0bp999/d0kNAAAAAFDdWKxWq9XdRaBkKSkpatiwoV19/fz89Nxzz2nUqFHy9PR0aB0Wi8Wufl5eXnr44Yf173//265npUtSVlaWQkJClJmZqeDg4MqUCSdITU1VZGSku8sAqgyuGaD8uG6A8uO6MafKZhueg17FhIWFKSoqSv7+/jp58qR27dqls2fPSpLOnj2r0aNHa/369VqwYIG8vb2dUkPdunXVqFEj1ahRQ5mZmdq9e7fOnDkjSTp//rw++ugjrVu3TsuXL1dISIjd42ZlZdm0fX195evr69DaAQAAAMBR8vLylJeXZ7QvzTTlxRJ3k7NYLLrhhhv08ccf688//9Sff/6pNWvWKCkpSZs2bdKpU6f0+eefKyoqyjhm8eLFeuqppxxaR0xMjN5991399ttvOnLkiH755RctXbpU69ev18mTJ7Vw4UK1bNnS6L9582bdc8895TpHeHi4QkJCjNeYMWMc+h0AAAAAwJHGjBljk2HCw8MrNR5L3KuJkydPqlu3btq4caOki8F+y5YtNqHZ2c6ePat+/fpp0aJFxmfz589X7969Sz2ucBlIenq6zTIQZtDNgeVTQPlwzQDlx3UDlB/XjTkUN4MeHh5e4SXuzKCX0/Tp02WxWBz+mj59eqXqqlWrlubOnSs/Pz9JktVq1X/+8x8HfGP7+fn56YsvvlBoaKjx2fvvv2/38cHBwTYvwjkAAAAAM/P19b0sx1QGAb0aiYiIsFlWvmTJEpfXEBQUpMcff9xo//zzz8Y98gAAAACAkrFJXDn5+/vbzBA7clxHiI+PN2bjU1JSdO7cOfn4+Dhk7PLUUOjs2bNKT0/Xtdde69IaAAAAAKCqIaCXU//+/dW/f393l1GisLAwm/bx48cv+8zVNRw7doyADgAAAABlYIl7NZOTk2PTDggIuCJrAAAAAICqhoBezezcudN47+vrW67nkDujBkmqV6+ey2sAAAAAgKqGgF6NWK1WzZ4922i3b9/eLXXMmjXLeB8VFeXyJfYAAAAAUBUR0KuRxMREbdu2zWjfcccdLq9hwYIFWrBggVtrAAAAAICqiIBuYjt27NDDDz+sPXv2lNrParVq4sSJGjZsmPFZgwYNNGTIkBKPSUlJsXkO+6hRo4rtl5mZqb59+2rjxo1l1vvFF19owIABRjsgIEAvvPBCmccBAAAAANjF3dTy8/P1ySef6JNPPlGbNm2UkJCgVq1aqV69evL399fJkye1efNmffHFF9q9e7dxnK+vr2bPnu2QR7dZrVbNnTtXc+fOVdOmTdWjRw9df/31CgsLU2BgoE6fPq3k5GR9/fXXWr9+vXGcxWLRtGnTVL9+/UrXAAAAAABXAgJ6FbFx40a7ZrHr16+vmTNnqmPHjg6vYffu3TZ/CChJUFCQPvroI919990OrwEAAAAAqiuWuJtYWFiYBgwYYNcsdGhoqF5++WUlJyfrlltucVgN/v7+euihhxQZGVlm35CQED311FPavn27zVJ3AAAAAEDZmEE3sdDQUH3++eeSpPT0dO3YsUNpaWk6efKk8vPzFRQUpDp16ig2NlbNmjWTxWKxe+yoqChZrdYy+/n6+uqTTz6RJB05ckTbtm1TWlqajh07pry8PNWoUUO1a9dWy5Yt1bJlS3l6elbsywIAAADAFY6AXkWEh4crPDzcrTXUq1fPobPzAAAAAID/YYk7AAAAAAAmQEAHAAAAAMAECOgAAAAAAJgAAR0AAAAAABMgoAMAAAAAYAIEdAAAAAAATICADgAAAACACRDQAQAAAAAwAQI6AAAAAAAmQEAHAAAAAMAECOgAAAAAAJgAAR0AAAAAABMgoAMAAAAAYAIEdAAAAAAATICADgAAAACACRDQAQAAAAAwAQI6AAAAAAAmQEAHAAAAAMAECOgAAAAAAJgAAR0AAAAAABMgoAMAAAAAYAIEdAAAAAAATICADgAAAACACRDQAQAAAAAwAQI6AAAAAAAmQECHacTFxSkmJkaJiYnuLgUAAAAA7JaYmKiYmBjFxcVVahwvB9UDVNr69esVHBzs7jIAAAAAoFyGDh2qoUOHKisrSyEhIRUehxl0AAAAAABMgIAOAAAAAIAJsMQdAAAATnP+glWLd2bqfIG1xD75+QHacupkiT+PuspXra4JcEZ5AGAqBHQAAAA4jZenRWEh3tpx6GwpvTyUe6Gg2J9YJDWs4+uU2gDAbFjiDgAAAKeKCfOXVwX/q7NhHV8F+3k6tiAAMCkCOgAAAJzKz9tD0aF+5T7OIqnF1f6OLwgATIqADgAAAKeryCw6s+cArjQEdAAAADhdeWfRmT0HcCUioAMAAMAlyjOLzuw5gCsRAR0AAAAuYe8sOrPnAK5UBHQAAAC4jD2z6MyeA7hSEdABAADgMmXNojN7DuBKRkAHAACAS5U2i87sOYArGQEdAAAALlXSLDqz5wCudAR0AAAAuFxxs+jMngO40hHQAQAA4HKXzqIzew4ABHQAAAC4SdFZdGbPAYCADgAAADf53yy6ldlzABABHQAAAG4UE+avBgH5zJ4DgAjoAAAAcCM/bw81Djrn7jIAwBQI6AAAAHAri8XdFQCAORDQAQAAAAAwAQI6AAAAAAAmQEAHAAAAAMAECOgAAAAAAJgAAR0AAAAAABMgoAMAAAAAYAIE9CooKytL//3vf9WnTx9FR0crKChIvr6+atCggeLj4/Xyyy9r2bJlysvLc8r5MzIyNG7cOLVv315hYWHy8/NTVFSUevbsqRkzZig3N9cp5wUAAACA6sxitVqt7i4C9vvss8/09NNP6+jRo2X2/eqrr9SvXz+Hnn/WrFl67LHHlJmZWWKfpk2b6vPPP1dsbKxdY2ZlZSkkJESZmZkKDg52VKlwkNTUVEVGRrq7DKDK4JoByo/rBig/rhtzqmy28XJCTXCSf/zjH3rvvfdsPgsPD1d4eLh8fHx05MgR7d27V+fPn3fK+WfOnKkHHnjA5rMmTZooLCxMKSkpSk1NlSTt3r1b8fHxWrt2rWJiYpxSCwAAAABUNwT0KuLFF180wrnFYtGDDz6oF154QU2bNrXpl5ubq+XLl+vTTz+Vj4+Pw86fnJyswYMHG+3o6Gh99tlnatOmjfHZkiVLdP/99+vw4cPKyspS7969tWPHDvn5+TmsDgAAAACorgjoVcDq1as1duxYSZKHh4emT5+u+++/v9i+/v7+6tWrl3r16uXQGl566SXjnvY6depo5cqVCg0NtenTrVs3JSUlqU2bNsrLy9OBAwc0adIkPf300w6tBQAAAACqIzaJMzmr1aohQ4aocKuA5557rsRw7iw7d+7UggULjPbo0aMvC+eFmjdvrmHDhhnt8ePHq6CgwNklAgAAAECVR0A3uaVLl2rnzp2SpJCQEI0cOdLlNcydO9d4X6NGDd13332l9h8yZIjxPiMjQ2vXrnVabQAAAABQXRDQTW7KlCnG+759+yowMNDlNSxatMh437FjR9WoUaPU/o0aNVJ0dHSxxwMAAAAAikdAN7mlS5ca7xMSElx+fqvVquTkZKPdvn17u44r2m/r1q0OrwsAAAAAqhsCuont379fJ06cMNotW7aUdHFH9b///e+Kjo5WYGCgatasqWbNmunRRx/VTz/95NAa0tLSlJ2dbbQbN25s13FF++3atcuhNQEAAABAdcQu7ia2bds2m3b9+vU1atQojR49WhcuXLD5WWZmpnbv3q3Jkyfr9ttv13//+1+FhIRUuobCZ5sXioiIsOu4ov1SU1NltVplsVgqXQ8AAAAAVFcEdBM7fvy4TXvcuHF6++23JV18FnpMTIzq1aunI0eOaOfOncZO7/Pnz9fNN9+sNWvWlHm/eFmysrJs2vaG/uDgYON9QUGBcnJyyrx//tJz+fr6ytfX185KAQAAAMC18vLyjMdRS5dnmvJiibuJZWZm2rQLw3m3bt20b98+bd++XcuWLdP27du1f/9+de/e3eibnJysoUOHVrqGosvbJcnPz8+u4/z9/Usdpzjh4eEKCQkxXmPGjLG/UAAAAABwsTFjxthkmPDw8EqNxwy6iZ09e/ayzzp37qxFixbJ29vb5vNGjRpp0aJF6t69u5YvXy5JmjlzpkaMGKGmTZtWuIb8/HybtpeXff/KXNrv3LlzZR6Tnp5uM/PO7DkAAIDznC+wKvdcQaXG8PK0yN+bOT9cuUaMGKFnnnnGaGdlZVUqpBPQy2n69Ol66KGHHD7utGnTNHDgQJvPilsS/sEHH1wWzgt5eXlp0qRJatasmaxWq6xWq6ZPn66xY8dWuK6AgACbdnF/NCjOpf3seTxccHCwTUAHAACA8xQUWPXd9kydu2Ct8BjtGgbq2nr2rbAEqiNH35bLn7tM7NL7x1u3bq2YmJhSj4mOjlbbtm2NdmV3db+0htzcXLuOy8nJKXUcAAAAuJePl4ea1a94uK7h66HGdVnxCDgSM+jl5O/vr9DQUKeMe6k6derYtFu3bm3XWK1bt9b69eslSQcOHKhUXZfWcOjQIbuOy8jIMN4HBQWVOOsPAP+vvTsPjqLM/zj+mRASICThDlAFCQKGJIIyGkVFM0nMEtiSu8TFXUHKY5VdFVExwIIosoi6HivFgqKouGXBYlhgV0RzmAqrEDBIlNuQEK6QQExAOQKZ3x8U/WPINZPM0YPvV1VX9TP9PN3fkXqQ73y7nwYA+E6/rq208+iZJlXRr+veWgG8pQdwKxJ0F40bN07jxo3zyrViYmIc2h07dnRq3OX9KioqmhXDtdde69A+cOCAU+NKSkqM/eY8Aw8AAADPuVRF/+6Qc3dJXkL1HPAMbnE3sT59+igoKMhoX758f0Muf/7b2VXX69O2bVuHRQ62bdvm1Lj8/Hxj/8ofGgAAAGAe/bq2UlAL1yrhVM8BzyBBN7HAwEDdfvvtRnv//v1OjSsqKjL23XE7/p133mns5+bmNtq/urpamzZtqnM8AAAAzMXVZ9GpngOeQ4JucqNHjzb2c3JyGq2inzt3zmFhuEGDBjU7hhEjRhj7O3fudKiO12XNmjU6efKkJCkgIEB33313s2MAAACA57hSRad6DngOz6Cb3Lhx45SWlqZTp06poqJCixcv1uOPP15v/3feeUfl5eVG+/LkuqmGDRumzp07q6ysTJI0d+5crVq1qs6+Fy5ccHit29ChQ9WlS5dmxwAAAADPcfZZdKrn3lF15oL2HWv49cZVJ4N0/MDP9R7v3i5IXcNYqNnfUEE3uc6dOzu8+D4tLU2ZmZl19s3Ozta0adOMdkxMjEaNGlXvuS0Wi7Fd+Q72y4WEhCgtLc1of/rpp3rjjTdq9bPb7Xr66ae1ZcsW4/wvvvhivecFAACAeThTRad67h1tgwJUdOKcfjhypt6t5Oegeo/tPHpGocGkev6ICrofmDZtmj777DPl5eXpl19+UUpKiu677z4NHz5cERERKi0t1dq1a7V8+XLV1NRIurg43PLlyxUQ4J6JOXnyZK1atUobN26UJE2ZMkUZGRm677771LVrVxUVFWnp0qUOz6hPnTpVAwcOdMv1AQAA4FmNVdGpnntPQIBF/bu31jf766+QN6RP52CFBLdwc1TwBhJ0P9CmTRutXbtWKSkpKigoUE1NjT766CN99NFHdfYPCwvTihUrnH5vujOCgoKUnp6u5ORkFRQUSJLWrVundevW1dl//Pjxevnll912fQAAAHheQ+9Fp3ruXb07Bavg8Gn9fLbGpXEBlot/VvBP3PfgJyIiIpSXl6cZM2bU+z70wMBAjR8/Xvn5+RoyZIjbY+jcubM2b96sZ555RuHh4XX2iYqK0tKlS/Xxxx+7rXoPAAAA76hvRXeq5953qYruKqrn/s1it9tr/zwGU6uurlZOTo4KCwtVVlamsLAwRUZGKiEhQWFhYV6J4cyZM8rOzlZRUZEqKioUERGhmJgYDRo0SBYXf1mtqqpSeHi4KisrvRY/nFdcXKzIyEhfhwH4DeYM4DrmjbmcO1+j9G0/OVTRB/UKUd8uzr+KDe5RU2PX6u0/OV1FD7BII69vR4LuQ83NbbjF3Q+1bNlSycnJSk5O9lkMrVq1Umpqqs+uDwAAAM+48ll0que+4+qz6FTP/R/3IAMAAABwcPmK7jx77lu9OwUrxIkV2Xn2/OpAgg4AAADAwaUqOtVz33P2WXSq51cHEnQAAAAAtfTr2krWnm2onptAY1V0qudXDxJ0AAAAALUEBQYosgPVczNorIpO9fzqQYIOAAAAACZXXxWd6vnVhQQdAAAAAEyuvio61fOrCwk6AAAAAPiBK6voVM+vPiToAAAAAOAHrqyiUz2/+pCgAwAAAICfuFRFt8hO9fwqRIIOAAAAAH7iUhW9a+vzVM+vQoG+DgAAAAAA4LzenYKlU+d8HQY8gAo6AAAAAPiRgACLglrYfR0GPIAEHQAAAAAAEyBBBwAAAADABEjQAQAAAAAwARJ0AAAAAABMgAQdAAAAAAATIEEHAAAAAMAESNABAAAAADABEnQAAAAAAEyABB0AAAAAABMgQQcAAAAAwARI0AEAAAAAMAESdAAAAAAATIAEHQAAAAAAEyBBh2nEx8crNjZWCxcu9HUoAAAAAOC0hQsXKjY2VvHx8c06T6Cb4gGaLS8vT2FhYb4OAwAAAABcMnnyZE2ePFlVVVUKDw9v8nmooAMAAAAAYAIk6AAAAAAAmAAJOgC40fvvv6/f//73GjBggCIiIhQUFKSwsDBZrVbNmjVLJ06cqHPcxIkTZbFYGtzOnDnj5W8DeF9T55AkHT58WE888YT69u2rVq1aqUOHDkpMTNQnn3zixW8AmMPChQuN/39MnDixzj67d+/W3//+d02cOFH9+/dXYGCgLBaLnn/+ea/GCuD/8Qw6ALjRm2++qYKCAsXGxspqtap9+/YqLS3V//73P+Xn52vJkiXKyMhQXFxcneNvv/129enTp85jLVq08GTogCk0dQ5t3bpVqampKi8vV0REhIYMGaLKykp98803ys7OVkZGht555x0ffSvAu/bt26dnn31WFotFdru93n6LFi3Sm2++6cXIADSGBB0A3GjRokWKjY2ttTjI8ePHNXLkSOXm5mrSpEnatGlTneMffPDBeisdwK9BU+bQ2bNnNWbMGJWXl2vs2LH64IMP1KZNG0nSzp07NXToUL377ru69dZbNWnSJK9+H8DbampqdP/996tFixaaMGGCli1bVm/f6667Tk8//bQGDhwoq9WqefPm6aOPPvJesABq4RZ3AHCjW2+9tc6VOzt27Kh58+ZJkjZv3qyKigpvhwb4habMofT0dBUXFys8PFxLliwxknNJiomJ0auvvipJeuGFFxqsJgJXg1deeUVff/21FixYoMjIyAb7Pvjgg3rllVc0fvx49evXTwEBpAaArzELAcBLLt2ibrFYFBQU5ONonHfpGcaoqChfh4Jfufrm0ObNmyVJN954o9q3b19r3G9+8xtJUnFxsfLy8rwQKeAb33//vWbNmqWkpCQ98sgjvg4HQBOQoAOAF1RVVWn27NmSpKSkJIWEhNTZLysrS1OnTtXDDz+stLQ0paen6+zZs026Zm5urv7yl78oISFBvXr1UmhoqIKDg9W1a1fFx8frscce0+rVq3Xu3Lkmfy/AWxqaQ6dOnZIkderUqc6xoaGhRkK/devWOvtERUUZP0ZlZ2c3Gk9lZaXuvPNOY0xISIjWr1/vylcC3Kq6ulr333+/goKCtHTpUlksFo9fk3kDuB/PoAOAB6xdu1arVq1STU2NscDVqVOndOONN+r999+vd9yHH35Y67Nu3brpvffeU2pqqlPXzszM1IwZM/TNN9/Ueby0tFSlpaXasmWLFi1apA4dOuipp57SlClTHG4NBnzJlTnUpUsXSVJhYWGd5zp48KDxQ1R9fVxx7NgxpaamKj8/X5LUvn17rVu3Trfddluzzw001QsvvKD8/HwtXLjQlHc8MW8A55CgA4AHfPfdd/rggw8cPrvrrru0ePFi9ejRo1b/66+/Xm+++aaSk5PVs2dPnT59Wtu3b9ecOXOUm5ur4cOHa8OGDbLZbPVe0263a+7cuZo9e7bDc7a9e/fWnXfeqW7duikkJETl5eUqLCxUZmamTp48qRMnTmjmzJk6ceKEXnvtNbf9NwCaw5U5lJycrJdeeklbt27Vli1bdNNNNzkcX7RokbFfVVXVrLiKi4uVkpKivXv3Srr4A9rnn3+u/v37N+u8QHPk5eVp/vz5SkxM1KOPPurrcGph3gDO4xZ3APCAmTNnym6369y5c9q7d69effVVbd26Vf3799e6detq9Z8yZYoef/xxxcXFKTQ0VF26dNFdd92lnJwcjRo1StXV1XryyScbvOZzzz2nWbNmGcl5UlKS8vLytG/fPr333nt66aWXNH36dP3tb3/T6tWrdfz4cf3rX//SgAEDJEkXLlxw+38HoKlcmUOJiYmy2Wyy2+0aMWKE0tPTVVFRoQMHDujFF1/UK6+8opYtW0pSsxbB2rlzpwYPHmwkGddcc41yc3NJMuBTZ86c0f3336/g4GCv3druCuYN4BoSdADwoJYtW6pPnz6aOnWqPvvsM50+fVqTJk3SyZMnnRpvsVj0/PPPS7pYUSwpKamz36pVq7RgwQKj/cQTT+iLL76oVUm8MrYxY8Zo27ZtmjdvnpHAAGbi7BxauXKlEhISdPjwYY0ePVodOnRQZGSkZs2apTFjxui3v/2tpIurwTdFXl6e7rjjDh08eFCS1L9/f23cuFHXXHNN874g0EzTp0/Xrl279PLLL6tXr16+DscB8wZwHbe4A4CX3HLLLYqNjdUPP/ygzZs3Kzk52alx/fr1M/YPHjxY6/be6upqTZ061Winpqbq9ddfd7qKYrFYlJaWZiy0BZhVQ3OoU6dOys7OVmZmpjIzM3Xs2DF17NhRqampSkhIMJ5zbUrVLjMzUyNHjjR+FLjtttu0bt26OleMB7zt008/lcVi0YoVK7Ry5UqHY0VFRZKk9evXG49IObOYmzswb4CmIUEHAC+6tPJ0eXm502OOHz9u7IeGhtY6/s9//lPFxcWSLt6++/bbbzfpFse2bdu6PAbwtsbmUFJSkpKSkhw+q6qq0rZt29SyZcsG13Goy+rVq3Xvvfcab1NITU3VqlWrWFARpmK325WTk1Pv8UuLg3oL8wZoOm5xBwAvKSsr03fffSdJuvbaa50e98knn0iSwsLCFB0dXet4enq6sZ+SkqLevXs3M1LAnJo6h95++22dPn1a99xzjyIiIpwet2zZMo0dO9ZIMu69916tWbOGJAOmUlRUJLvdXud26dWEEyZMMD7zNOYN0Dwk6ADgJlu3btXKlSvrfG/5wYMHjX+w3HzzzRo4cKBxbNu2bVqzZo3Onz/vMKampkZLly7V9OnTJUmPP/54refEa2pqHKomV1YOAX/S1DkkSTt27Ki1QrvdbtfixYs1e/ZsderUyaW3FLz++uuaNGmSsXjiH//4R3388ces1QA0gHkDNB+3uAOAm5SUlOiee+5R27ZtZbVa1b17d1ksFhUVFSkvL0/nz59Xv379tGLFCodxRUVFGjVqlNq3by+r1aqIiAj99NNP+v7773XgwAFJ0u9+9zujEnK5o0ePqqKiwmhbrVbPfknAg5o6hyRpyZIl+sc//iGr1aoePXqourpaeXl5OnjwoLp3767PPvvM6er5/Pnz9fnnnxvtGTNmaO7cuW77noBZfPvtt3rssceM9o8//ihJevfdd7V+/Xrj8/T0dHXr1q3BczFvAPcgQQcAN7n55puN95bv2bNHW7Zs0fnz59WxY0clJSVp9OjRmjhxooKDgx3GXX/99XryySe1ZcsW7dq1Sxs3bpTdbldERITGjh2rBx54QMOGDavzmpc/ny5dXCgL8FdNnUOSNGzYMBUVFSk/P1/5+fnG6u8PP/ywnnzyyTrXb6jP5UnG9OnTSTJw1aqqqtKmTZtqfX7o0CEdOnTIaNd1V8uVmDeAe1js3ngYBWhAVVWVwsPDVVlZqbCwMF+HgysUFxcrMjLS12GgHhs3btTgwYON9r59+9z+DPqlBeciIyONFYFRP+aMf4qKijIWW7xcr169lJWVxZ+phzFv/BPzxreYN+bU3NyGZ9ABwI9d+Rc/r0oDmm/evHnG3Nq/f79sNhs/TgGNYN4A7kGCDgB+rGPHjg7tK295B+C6W2+9VRs2bFB4eLiki+tE2Gw27d+/38eRAebFvAHcgwQdAPxY165d1a5dO6P97bff+i4Y4Cpyyy236IsvvjDmV3FxsRISEoxFtADUxrwBmo8EHQD8WEBAgBISEox2RkaGD6MBri7x8fH64osv1L59e0kXV5m32Wzat2+fjyMDzIt5AzQPCToA+LlRo0YZ+xs2bFBhYaEPowGuLjfddJO+/PJLdejQQdLF97HbbDbt3bvXx5EB5sW8AZqOBB0A/Nz48ePVs2dPSVJNTY3+/Oc/N+k8LDAH1M1qtSojI8NY8+HQoUOy2Wzas2ePjyMDzIt5AzQNCToA+LmWLVvqtddeM9r//e9/NWXKFDn7Fk273a758+drzpw5ngoR8Hs33HCDMjMz1alTJ0nS4cOHZbPZtGvXLh9HBpgX8wZwHQm6H6qqqtKHH36oESNGKDo6WqGhoQoODlb37t1ls9k0c+ZMZWZm6uzZs267ZlRUlCwWi0vbc88957brA2jY2LFj9eyzzxrtN954QykpKdqyZUu9Y6qrq/Xpp5/qhhtuUFpamqqrq70RKuC3BgwYoKysLHXu3FmSdOTIEdlsNu3YscPHkQHmxbwBXBPo6wDgmo8//lhTpkxRWVlZrWNHjhzRkSNH9NVXX+mll17SypUrNXbsWB9ECcAX5s+frzZt2mjOnDmy2+3KyMhQfHy8+vbtqzvuuEPdunVTSEiIysvL9eOPPyorK0tVVVXG+MBA/pcANOa6665TVlaWkpOTVVpaqtLSUiUmJiozM1NxcXG+Dg8wJeYN4Dz+NeZHnnjiCb311lsOn/Xo0UM9evRQUFCQjh07pj179uj8+fMejSM+Pt5Y9KMh0dHRHo0DgCOLxaLZs2dr8ODBmjFjhjZt2iRJ2rt3b4ML83Tp0kXPPPNMk59dB35t4uLilJWVpaSkJB09elTHjh1TYmKiMjIy1L9/f1+HB5gS8wZwDgm6n5g+fbqRnFssFk2YMEHTpk1Tv379HPqdPn1aWVlZWr58uYKCgjwSy4IFC2Sz2TxybgDNl5ycrOTkZOXm5mr9+vX66quvVFJSovLyclVXV6tdu3bq2bOn4uPjNXToUA0dOpTqOeCimJgYZWdnKzExUUeOHFFZWZmSkpKUkZGhAQMG+Do8wJSYN0Dj+BeZH9i4caPmz58v6eI7j5ctW6Y//OEPdfZt3bq1hg0bpmHDhnkzRAAmNHjwYA0ePLjZ53F2sTnAnxUVFbk8Jjo6WocPH3Z/MICfYN4A7scicSZnt9v18MMPG/9Afvrpp+tNzgEAAAAA/osE3eS+/PJLY5XL8PBwzZo1y8cRAQAAAAA8gQTd5N59911jf8yYMQoJCfFhNAAAAAAATyFBN7kvv/zS2E9KSvJhJAAAAAAATyJBN7F9+/bpxIkTRvvS6pYFBQX605/+pOjoaIWEhKhdu3aKiYnRI488opycHI/H9eqrr2rgwIFq166dgoOD1a1bN91222167rnnVFBQ4PHrAwAAAMDViFXcTWz79u0O7a5du+r555/X3LlzdeHCBYdjlZWV2rVrl5YsWaLhw4frww8/VHh4uEfi+s9//uPQPnr0qI4ePaqvv/5aCxYs0OjRo7VkyRKn3pUOAAAAALiICrqJHT9+3KH98ssva86cObpw4YIsFovi4uKUmJiouLg4WSwWo9+aNWt0xx136NSpUx6JKzw8XPHx8UpOTtYtt9zikIjb7XatWrVKVqtVJSUlHrk+AAAAAFyNqKCbWGVlpUP7tddekySlpKRo0aJF6t27t3GssLBQjz76qDZs2CDp4m3wkydP1gcffOCWWKKiovTQQw9p5MiRiouLczhmt9uVm5urmTNnGrfYFxcX6+6779bmzZs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