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{
"cells": [
{
"cell_type": "markdown",
"id": "59f50187-f73f-471b-b668-9126e6f48501",
"metadata": {},
"source": [
"# Learning high-resolution data from low-resolution\n",
"\n",
"This is an example notebook showing how to use the `pes_to_spec` infrastructure in this package.\n",
"\n",
"We start by importing some modules. The key module here is called `pes_to_spec`."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d44af0b6-9c00-4e70-b49b-d74ed562e92f",
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"# add the pes_to_spec main directory\n",
"# (change this depending on where you started the notebook if needed, or comment it out if you have done pip install in pes_to_spec)\n",
"sys.path.append('..')\n",
"\n",
"# you meay need to do pip install matplotlib seaborn extra_data for this notebook, additionally\n",
"# for this notebook the following packages are needed:\n",
"# pip install \"numpy>=1.21\" \"scipy>=1.6\" \"scikit-learn>=1.2.0\" torch torchbnn matplotlib seaborn extra_data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "da002d3e-c0da-419b-922b-0ab5c6deece8",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.gridspec import GridSpec \n",
"import seaborn as sns\n",
"\n",
"from extra_data import open_run, by_id\n",
"from itertools import product\n",
"from pes_to_spec.model import Model, matching_ids\n",
"\n",
"from typing import Any, Dict"
]
},
{
"cell_type": "markdown",
"id": "494a729c-dff4-4501-b828-fba2aaae5a23",
"metadata": {},
"source": [
"# Input data\n",
"\n",
"Read data from two runs. One shall be used for training the model. The second one is used for testing it.\n",
"Note that the data in the training run must be large enough, compared to the number of model parameters.\n",
"\n",
"Only the SPEC, PES and XGM data is used for training, while only the PES and XGM data is needed for testing.\n",
"However, more data is collected here to validate the results."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "4a301f2a-dedb-46e4-b096-fc9c6cf5b23a",
"metadata": {},
"outputs": [],
"source": [
"run = open_run(proposal=3384, run=2)\n",
"run_test = open_run(proposal=3384, run=3)\n",
"\n",
"# useful names to avoid repeating it all over the notebook, in case they ever change\n",
"spec_name = \"SA3_XTD10_SPECT/MDL/SPECTROMETER_SQS_NAVITAR:output\"\n",
"pes_name = \"SA3_XTD10_PES/ADC/1:network\"\n",
"xgm_name = \"SA3_XTD10_XGM/XGM/DOOCS:output\"\n",
"\n",
"pres_name = \"SA3_XTD10_PES/GAUGE/G30310F\"\n",
"volt_name = \"SA3_XTD10_PES/MDL/DAQ_MPOD\"\n",
"\n",
"# PES channels\n",
"channels = [f\"channel_{i}_{l}\" for i, l in product(range(1, 5), [\"A\", \"B\", \"C\", \"D\"])]\n",
"\n",
"def get_gas(run) -> str:\n",
" \"\"\"Get gas in chamber for logging.\"\"\"\n",
" gas_sources = [\n",
" \"SA3_XTD10_PES/DCTRL/V30300S_NITROGEN\",\n",
" \"SA3_XTD10_PES/DCTRL/V30310S_NEON\",\n",
" \"SA3_XTD10_PES/DCTRL/V30320S_KRYPTON\",\n",
" \"SA3_XTD10_PES/DCTRL/V30330S_XENON\",\n",
" ]\n",
" gas_active = list()\n",
" for gas in gas_sources:\n",
" # check if this gas source is interlocked\n",
" if gas in run.all_sources and run[gas, \"interlock.AActionState.value\"].ndarray().sum() == 0:\n",
" # it is not, so this gas was used\n",
" gas_active += [gas.split(\"/\")[-1].split(\"_\")[-1]]\n",
" gas = \"_\".join(gas_active)\n",
" return gas\n",
"\n",
"def get_tids(run, need_spec:bool=True) -> np.ndarray:\n",
" \"\"\"Get which train IDs contain all necessary inputs for training.\"\"\"\n",
" spec_tid = run[spec_name, \"data.trainId\"].ndarray()\n",
" pes_tid = run[pes_name, \"digitizers.trainId\"].ndarray()\n",
" xgm_tid = run[xgm_name, \"data.trainId\"].ndarray()\n",
"\n",
" # match tids to be sure we have all inputs:\n",
" tids = matching_ids(spec_tid, pes_tid, xgm_tid)\n",
" return tids\n",
"\n",
"def get_data(run, tids) -> Dict[str, Any]:\n",
" \"\"\"Get all relevant data.\"\"\"\n",
" data = dict()\n",
" data[\"int\"] = run[xgm_name, \"data.intensitySa3TD\"].select_trains(by_id[tids]).ndarray()[:, 0][:, np.newaxis]\n",
" data[\"pressure\"] = run[pres_name, \"value\"].select_trains(by_id[tids]).ndarray()\n",
" data[\"voltage\"] = run[volt_name, \"u212.value\"].select_trains(by_id[tids]).ndarray()\n",
" data[\"energy\"] = run[spec_name, \"data.photonEnergy\"].select_trains(by_id[tids]).ndarray()\n",
" data[\"spec\"] = run[spec_name, \"data.intensityDistribution\"].select_trains(by_id[tids]).ndarray()\n",
" data[\"pes\"] = {ch: run[pes_name,\n",
" f\"digitizers.{ch}.raw.samples\"].select_trains(by_id[tids]).ndarray()\n",
" for ch in channels}\n",
" data[\"gas\"] = get_gas(run)\n",
" return data\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "210c0550-1abb-43a0-99a5-7c35d2766be0",
"metadata": {},
"outputs": [],
"source": [
"\n",
"# get the matched train IDs\n",
"tids = get_tids(run)\n",
"\n",
"# we don't need the spec for testing in reality,\n",
"# but it is nice to plot it in the test run too,\n",
"# to check that this works during validation\n",
"test_tids = get_tids(run_test, need_spec=True)\n",
"\n",
"# get the data\n",
"data = get_data(run, tids)\n",
"data_test = get_data(run_test, test_tids)\n"
]
},
{
"cell_type": "markdown",
"id": "017865a1-057f-48c7-8bef-a6e40490de2c",
"metadata": {},
"source": [
"Now the `data` and `data_test` dictionaries contain the necessary information about the training and test runs.\n",
"The code above also selected only entries with train IDs on which at least SPEC, PES and XGM were present.\n",
"\n",
"Note that for training, it is assumed that only one pulse is present. For testing there is no such requirement.\n",
"\n",
"First output some general information about the conditions of the measurement device."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "956105a6-d37e-453c-bfeb-2b1c876ee3f2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gas in training: NEON\n",
"Gas in testing: NEON\n"
]
}
],
"source": [
"print(f\"Gas in training: {data['gas']}\")\n",
"print(f\"Gas in testing: {data_test['gas']}\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "4654f205-edc6-45f7-97bd-0d088c38edb0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Voltage in training: -15.36 +/- 0.01\n",
"Voltage in testing: -15.36 +/- 0.00\n"
]
}
],
"source": [
"print(f\"Voltage in training: {np.mean(data['voltage']):0.2f} +/- {np.std(data['voltage']):0.2f}\")\n",
"print(f\"Voltage in testing: {np.mean(data_test['voltage']):0.2f} +/- {np.std(data_test['voltage']):0.2f}\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "fa662544-3caa-4404-bb61-fa41add82642",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pressure in training: 3.73e-07 +/- 1.11e-08\n",
"Pressure in testing: 3.74e-07 +/- 8.43e-09\n"
]
}
],
"source": [
"print(f\"Pressure in training: {np.mean(data['pressure']):0.2e} +/- {np.std(data['pressure']):0.2e}\")\n",
"print(f\"Pressure in testing: {np.mean(data_test['pressure']):0.2e} +/- {np.std(data_test['pressure']):0.2e}\")"
]
},
{
"cell_type": "markdown",
"id": "5962e483-60da-4c70-bb09-dce5fc9745e0",
"metadata": {},
"source": [
"Now we will actually train the model. We do that by creating a `Model` object (from `pes_to_spec`) and calling the `fit` function.\n",
"The `fit` function requires the PES intensity, the SPEC intensity, the energy axis from SPEC (stored as a reference only), as well as the energy measured in the XGM (which has better resolution than the integral of the PES)."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "a0adb57b-7496-4781-9511-ac2a8d05658d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting PCA on low-resolution data.\n",
"Fitting PCA on high-resolution data.\n",
"Fitting outlier detection\n",
"Fitting model.\n",
"Calculate PCA unc. on high-resolution data.\n",
"Calculate transfer function\n",
"Calculate PCA on channel_1_A\n",
"Calculate PCA on channel_1_B\n",
"Calculate PCA on channel_1_C\n",
"Calculate PCA on channel_1_D\n",
"Calculate PCA on channel_2_A\n",
"Calculate PCA on channel_2_B\n",
"Calculate PCA on channel_2_C\n",
"Calculate PCA on channel_2_D\n",
"Calculate PCA on channel_3_A\n",
"Calculate PCA on channel_3_B\n",
"Calculate PCA on channel_3_C\n",
"Calculate PCA on channel_3_D\n",
"Calculate PCA on channel_4_A\n",
"Calculate PCA on channel_4_B\n",
"Calculate PCA on channel_4_C\n",
"Calculate PCA on channel_4_D\n",
"End of fit.\n"
]
}
],
"source": [
"# this is the main object holding all\n",
"# information needed for training and prediction\n",
"# the default parameters should be sufficient in most times\n",
"model = Model()\n",
"\n",
"# this trains the model\n",
"# the first parameter is expected to be a dictionary with the channel name as a key\n",
"model.fit(data['pes'],\n",
" data['spec'],\n",
" data['energy'],\n",
" pulse_energy=data['int'])\n",
"\n",
"# save it for later usage:\n",
"model.save(\"model.joblib\")\n",
"\n",
"# load a model (you can start from here if working on an existing model)\n",
"model = Model.load(\"model.joblib\")\n",
"\n",
"# and use it to map a low-resolution spectrum to a high-resolution one\n",
"# as before, the low_resolution_raw_data refers to a dictionary mapping the channel name\n",
"# in the format \"channel_[1-4]_[A-D]\" to the 2D numpy array with shape (number_of_train_IDs, features)\n",
"# all names and shapes must match the format in training, except for the number_of_train_IDs, which may vary\n",
"pred = model.predict(data['pes'], pulse_energy=data['int'])\n"
]
},
{
"cell_type": "markdown",
"id": "e0286ae3-1a59-468f-ae40-c3ed94b7b301",
"metadata": {},
"source": [
"Now we can try it in the test dataset:"
]
},
{
"cell_type": "markdown",
"id": "ffc06362-3479-4cb9-b102-b438a83d2950",
"metadata": {},
"source": [
"We can predict it in the training data itself, but this is a bit biased, since we used the same information to fit the model."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "917156f3-9476-48e0-9121-5f75f185045f",
"metadata": {},
"outputs": [],
"source": [
"pred = model.predict(data_test['pes'], pulse_energy=data_test['int'])\n",
"\n",
"# add the references in this array in the same array format, so we can plot them later\n",
"pred[\"energy\"] = model.get_energy_values()\n",
"\n",
"# this provides a smoothened version of the high-resolution spectrum, filtering sources of noise\n",
"# caused by fluctuations below the spectrometer's resolution\n",
"pred['spec_smooth'] = model.preprocess_high_res(data_test['spec'])[:, np.newaxis, :]"
]
},
{
"cell_type": "markdown",
"id": "77866435-1cb6-40ac-a9a5-8f2ae37017b7",
"metadata": {},
"source": [
"Let's try to predict in the independent run in the test dataset. The performance of the model varies a lot if the beam intensity is very different from the training one. To ensure we take a train ID to visualize that is relatively high intensity, we sort the train IDs by XGM intensity and then choose the highest intensity one.\n",
"One could try other train IDs.\n",
"\n",
"For train IDs with close to zero beam intensity, there is a relatively larger error, since the training data did not contain any of those samples and the signal-to-noise ratio is relatively high."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "ed62606a-4ea7-4e0a-8b61-73e682cacf04",
"metadata": {},
"outputs": [],
"source": [
"# choose train ID of the test dataset by XGM intensity\n",
"test_intensity = np.argsort(data_test['int'][:,0])\n",
"example_tid = test_intensity[-1]"
]
},
{
"cell_type": "markdown",
"id": "f931e9e0-84a7-4e4f-bfad-588bfe77267c",
"metadata": {},
"source": [
"Now we can actually plot it."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "fd42984c-554c-4c69-bf8a-119eeb0cca62",
"metadata": {},
"outputs": [],
"source": [
"def plot(data):\n",
" \"\"\"Plot prediction and expectation.\"\"\"\n",
" fig = plt.figure(figsize=(12, 8))\n",
" gs = GridSpec(1, 1)\n",
" ax = fig.add_subplot(gs[0, 0])\n",
" ax.plot(data[\"energy\"], data[\"spec_smooth\"], c='b', lw=3, label=\"High-res. measurement (smoothened)\")\n",
" ax.plot(data[\"energy\"], data[\"expected\"], c='r', ls='--', lw=3, label=\"High-res. prediction\")\n",
" ax.fill_between(data[\"energy\"], data[\"expected\"] - data[\"total_unc\"], data[\"expected\"] + data[\"total_unc\"], facecolor='gold', alpha=0.5, label=\"68% unc.\")\n",
" ax.legend(frameon=False, borderaxespad=0, loc='upper left')\n",
" ax.spines['top'].set_visible(False)\n",
" ax.spines['right'].set_visible(False)\n",
" Y = np.amax(data[\"spec_smooth\"])\n",
" ax.set(\n",
" xlabel=\"Photon energy [eV]\",\n",
" ylabel=\"Intensity\",\n",
" ylim=(0, 1.3*Y))\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "bbbf77b5-f914-4b47-8ab6-fd3a89d0f983",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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XM2vWLAoKCrjkkkv85h8Hy7x583j11Vd58803OeKIIzj66KNZunSpN5SnpKTw2GOPMWfOHKZPn87bb7/NihUrSG+ZJ1BYWGiaRx4sl19+OY8//jjLli1j2rRpHH/88SxfvpzRo0cD8N3vfpdFixbxs5/9jEMPPZRVq1Zx6623eu93OByUlpZy8cUXM2HCBM4//3xOP/10Fi9eDHgqvA899BAPPvggM2bMYM2aNaag2J64uDjef/99Ro4cyTnnnMPkyZO57LLLqK+v71WFPFjPXbJkCTt37mTs2LEdDsWeP38+X3/9NZs3bwY6/3711LJlyzjssMP4zne+w+zZszEMg9dee807JH3WrFk8//zzPPfccxxyyCHcdtttLFmyhEsuuaTbX5NVZ79DXTF9+nTee+89tmzZwrHHHsvMmTO57bbbTFNOli1bRl5eHscffzznnHMOV155JVlZWabnrF69GqfTyfe///0uv7aIiIiIhD+bYZ10OwRVVlaSnJyM0+kM6nDkgeSUU04hJyeHv//97/3dFRlEbrjhBiorK/nrX//a310Z9C644AJmzJjBr3/96/7uioiIiIgEkea0D0G1tbU88sgjzJs3D4fDwbPPPutdBE4kmH7zm9/w0EMP4Xa7TSvdS3A1NjYybdo0Fi1a1N9dEREREZEgU6WdoVdpr6ur48wzz+Tzzz+nvr6eiRMncssttwRc3V1ERERERET6j0I7Qy+0i4iIiIiIyMCg8aoiIiIiIiIiYUqhXURERERERCRMKbSLiIiIiIiIhCmFdhEREREREZEwpdAuIiIiIiIiEqYU2oeA5cuXk5KS0q17LrnkEr73ve+FpD8D3bvvvovNZqOiogLo2ffXKhjPEBERERGRwUehfQBrL1hbQ+UFF1zAli1b+rZzQ0h3v7+jRo3ij3/8Y6+eISIiIiIiQ0NEf3dAQi82NpbY2Nh+ee2mpiYiIyP75bU7YhgGLpeLiIje/08gGN/f/vwZiYiIiIhI+FKlvSMHD/b8o66u/eeWlAS+J0QCDb2+6667yMrKIjExkcsvv5xf/epXHHrooX73/v73vyc3N5f09HQWLlxIU1NTh69ls9l4+OGH+e53v0t8fDx33303AP/+97+ZNWsWMTExjBkzhsWLF9Pc3Ax4AvQdd9zByJEjiY6OJi8vj2uuuabLX9/OnTux2Ww899xzHHPMMcTExHDIIYfw3nvvea9pHX3w+uuvc9hhhxEdHc2HH36I2+3m3nvvZfTo0cTGxjJjxgz+3//7f6bnv/baa0yYMIHY2FhOPPFEdu7caTof6Pu7YsUKjjjiCGJiYsjIyODss88G4IQTTmDXrl0sWrQIm82GzWZr9xkPP/wwY8eOJSoqiokTJ/L3v//d73v9+OOPc/bZZxMXF8f48eN55ZVXuvx9ExERERGR8KfQ3pGsrJ5//O1v7T938uTA9/SRf/zjH9x999389re/Zd26dYwcOZKHH37Y77p33nmH7du388477/Dkk0+yfPlyli9f3unz77jjDs4++2w2bNjApZdeygcffMDFF1/ML37xCzZu3Mhf//pXli9f7g30L774IkuXLuWvf/0rW7du5eWXX2batGnd/rpuuOEGrrvuOj7//HNmz57NmWeeSWlpqemaX/3qV9x3331s2rSJ6dOnc++99/LUU0/xyCOP8PXXX7No0SJ+9KMfeQP/nj17OOecczjzzDNZv3699w2OjvznP//h7LPP5tvf/jaff/45K1eu5MgjjwTgX//6F8OHD2fJkiUUFhZSWFgY8BkvvfQSv/jFL7juuuv46quvuOqqq/jxj3/MO++8Y7pu8eLFnH/++Xz55Zd8+9vfZv78+ZSVlXX7eyciIiIiImHKEMPpdBqA4XQ6zSeg5x9/+Uv7L5iREfieblqwYIHhcDiM+Ph400dMTIwBGOXl5YZhGMayZcuM5ORk731HHXWUsXDhQtOz5syZY8yYMcP07Pz8fKO5udnbdt555xkXXHBBh30CjF/+8pemtpNPPtm45557TG1///vfjdzcXMMwDOOBBx4wJkyYYDQ2Nnb1SzcpKCgwAOO+++7ztjU1NRnDhw83fvvb3xqGYRjvvPOOARgvv/yy95r6+nojLi7OWLVqlel5l112mXHhhRcahmEYN998szFlyhTT+ZtuuqnD7+/s2bON+fPnt9vf/Px8Y+nSpaY26zOOOeYY44orrjBdc9555xnf/va3vceAccstt3iPq6urDcB4/fXX231tEREREREZWFRpH+BOPPFE1q9fb/p4/PHHO7xn8+bN3spvK+sxwNSpU3E4HN7j3NxciouLAbjnnntISEjwfuzevdt73eGHH256zhdffMGSJUtM119xxRUUFhZSW1vLeeedR11dHWPGjOGKK67gpZde8g6d747Zs2d7P4+IiODwww9n06ZNpmt8+7Zt2zZqa2s55ZRTTH176qmn2L59OwCbNm3iqKOOavd1Alm/fj0nn3xyt/vva9OmTcyZM8fUNmfOHL+vZ/r06d7P4+PjSUpK8v6MRERERERk4NNCdANcfHw848aNM7Xt3bs3KM+2LiBns9lwu90AXH311Zx//vnec3l5eaY++aqurmbx4sWcc845fq8RExPDiBEj2Lx5M2+//TZvvfUWP/3pT/nd737He++9F/RF7Hz7Vl1dDXiGsw8bNsx0XXR0dI9foy8XlOvoZyQiIiIiIgOfQntHelOxTEho/9ymTZ4B8f1k4sSJrF27losvvtjbtnbt2m49Iy0tjbS0tC5dO2vWLDZv3uz35oKv2NhYzjzzTM4880wWLlzIpEmT2LBhA7Nmzepynz7++GOOO+44AJqbm1m3bh0/+9nP2r1+ypQpREdHs3v3bo4//viA10yePNlvcbePP/64w35Mnz6dlStX8uMf/zjg+aioKFwuV4fPmDx5Mh999BELFizwtn300UdMmTKlw/tERERERGRwUWjvSGZmaJ6bkRGa53bRz3/+c6644goOP/xwjjnmGP75z3/y5ZdfMmbMmJC83m233cZ3vvMdRo4cyfe//33sdjtffPEFX331FXfddRfLly/H5XJx1FFHERcXx9NPP01sbCz5+fkA3Hzzzezbt4+nnnqqw9d58MEHGT9+PJMnT2bp0qWUl5dz6aWXtnt9YmIi119/PYsWLcLtdvOtb30Lp9PJRx99RFJSEgsWLODqq6/mgQce4IYbbuDyyy9n3bp1nS7Gd/vtt3PyySczduxYfvCDH9Dc3Mxrr73GTTfdBHj2aX///ff5wQ9+QHR0NBkBfh9uuOEGzj//fGbOnMncuXNZsWIF//rXv3j77bc7+W6LiIiIiMhgojntQ9D8+fO5+eabuf7665k1axYFBQVccsklxMTEhOT15s2bx6uvvsqbb77JEUccwdFHH83SpUu9oTwlJYXHHnuMOXPmMH36dN5++21WrFhBeno6AIWFhaY58+257777uO+++5gxYwYffvghr7zySsBA7OvOO+/k1ltv5d5772Xy5Mmcdtpp/Oc//2H06NEAjBw5khdffJGXX36ZGTNm8Mgjj3DPPfd0+MwTTjiBF154gVdeeYVDDz2Uk046iTVr1njPL1myhJ07dzJ27Fgy23lj6Hvf+x5/+tOf+P3vf8/UqVP561//yrJlyzjhhBM6/T6IiIiIiMjgYTOMfhynHSYqKytJTk7G6XSSlJTU393pF6eccgo5OTl+e4EPBDt37mT06NF8/vnnAfeaFxERERERGag0PH4Iqq2t5ZFHHmHevHk4HA6effZZ7yJwIiIiIiIiEj4U2ocgm83Ga6+9xt133019fT0TJ07kxRdfZO7cuf3dNREREREREfGh4fFoeLyIiIiIiIiEJy1EJyIiIiIiIhKmFNpFREREREREwpRCu4iIiIiIiEiYUmgXERERERERCVMK7SIiIiIiIiJhSqFdREREREREJEwptIuIiIiIiIiEqYj+7kBYO3hH375eZvdfb9++fdx00028/vrr1NbWMm7cOJYtW8bhhx8OQHV1Nb/61a94+eWXKS0tZfTo0VxzzTVcffXV3mdce+21LF++nPj4eO677z7mz5/vPffCCy/w1FNPsWLFil5/eSIiIiIiItI9Cu0DWHl5OXPmzOHEE0/k9ddfJzMzk61bt5Kamuq95tprr+V///sfTz/9NKNGjeLNN9/kpz/9KXl5eXz3u99lxYoVPPPMM7z55pts3bqVSy+9lHnz5pGRkYHT6eQ3v/kNb7/9dj9+lSIiIiIiIkOXhscPYL/97W8ZMWIEy5Yt48gjj2T06NGceuqpjB071nvNqlWrWLBgASeccAKjRo3iyiuvZMaMGaxZswaATZs2ccIJJ3D44Ydz4YUXkpSUREFBAQA33ngjP/nJTxg5cmSnfbnkkkv43ve+Z2r75S9/yQknnOA9PuGEE7jmmmu48cYbSUtLIycnhzvuuMN0T0VFBVdddRXZ2dnExMRwyCGH8Oqrr/bsGyQiIiIiIjLAKbQPYK+88gqHH3445513HllZWcycOZPHHnvMdM0xxxzDK6+8wr59+zAMg3feeYctW7Zw6qmnAjBjxgw+/fRTysvLWbduHXV1dYwbN44PP/yQzz77jGuuuSaofX7yySeJj4/nk08+4f7772fJkiW89dZbALjdbk4//XQ++ugjnn76aTZu3Mh9992Hw+EIah9EREREREQGCg2PH8B27NjBww8/zLXXXsuvf/1r1q5dyzXXXENUVBQLFiwA4P/+7/+48sorGT58OBEREdjtdh577DGOO+44AObNm8ePfvQjjjjiCGJjY72h+ic/+QnLly/n4Ycf5v/+7//IyMjg0UcfZerUqb3q8/Tp07n99tsBGD9+PH/5y19YuXIlp5xyCm+//TZr1qxh06ZNTJgwAYAxY8b06vVEREREREQGMoX2AcztdnP44Ydzzz33ADBz5ky++uorHnnkEVNo//jjj3nllVfIz8/n/fffZ+HCheTl5TF37lwA7rjjDtMw9cWLFzN37lwiIyO566672LBhA6+++ioXX3wx69at61Wfp0+fbjrOzc2luLgYgPXr1zN8+HBvYBcRERERERnqFNoHsNzcXKZMmWJqmzx5Mi+++CIAdXV1/PrXv+all17ijDPOADyhef369fz+97/3hnZf33zzDU8//TSff/45f/vb3zjuuOPIzMzk/PPP59JLL6WqqorExES/++x2O4ZhmNqampr8rouMjDQd22w23G43ALGxsd346kVERERERAY/zWkfwObMmcPmzZtNbVu2bCE/Px/whOampibsdvOP2eFweIOyL8MwuOqqq/jDH/5AQkICLpfLG7xb/+tyuQL2JTMzk8LCQlPb+vXru/X1TJ8+nb1797Jly5Zu3SciIiIiIjJYKbQPYIsWLeLjjz/mnnvuYdu2bTzzzDM8+uijLFy4EICkpCSOP/54brjhBt59910KCgpYvnw5Tz31FGeffbbf8x5//HEyMzM588wzAc+bAv/73//4+OOPWbp0KVOmTCElJSVgX0466SQ+/fRTnnrqKbZu3crtt9/OV1991a2v5/jjj+e4447j3HPP5a233qKgoIDXX3+d//73v4BnT/pJkyZ5V74XEREREREZ7BTaB7AjjjiCl156iWeffZZDDjmEO++8kz/+8Y/Mnz/fe81zzz3HEUccwfz585kyZQr33Xcfd999N1dffbXpWQcOHODuu+/mz3/+s7ftyCOP5LrrruOMM87g+eefZ9myZe32Zd68edx6663ceOONHHHEEVRVVXHxxRd3+2t68cUXOeKII7jwwguZMmUKN954o7e639TUxObNm6mtre32c0VERERERAYim2GdiDwEVVZWkpycjNPpJCkpqb+7IyIiIiIiIgKo0i4iIiIiIiISthTaRURERERERMKUQruIiIiIiIhImFJoFxEREREREQlTCu0iIiIiIiIiYUqhXURERERERCRMKbSLiIiIiIiIhCmFdhEREREREZEwpdAuIiIiIiIiEqb6PbTv27ePH/3oR6SnpxMbG8u0adP49NNPvecNw+C2224jNzeX2NhY5s6dy9atW03PKCsrY/78+SQlJZGSksJll11GdXV1X38pIiIiIiIiIkHVr6G9vLycOXPmEBkZyeuvv87GjRt54IEHSE1N9V5z//338+c//5lHHnmETz75hPj4eObNm0d9fb33mvnz5/P111/z1ltv8eqrr/L+++9z5ZVX9seXJCIiIiIiIhI0NsMwjP568V/96ld89NFHfPDBBwHPG4ZBXl4e1113Hddffz0ATqeT7Oxsli9fzg9+8AM2bdrElClTWLt2LYcffjgA//3vf/n2t7/N3r17ycvL67QflZWVJCcn43Q6SUpKCt4XKCIiIiIiItIL/Vppf+WVVzj88MM577zzyMrKYubMmTz22GPe8wUFBRQVFTF37lxvW3JyMkcddRSrV68GYPXq1aSkpHgDO8DcuXOx2+188sknAV+3oaGByspK04eIiIiIiIhIuOnX0L5jxw4efvhhxo8fzxtvvMFPfvITrrnmGp588kkAioqKAMjOzjbdl52d7T1XVFREVlaW6XxERARpaWnea6zuvfdekpOTvR8jRowI9pcmIiIiIiIi0mv9GtrdbjezZs3innvuYebMmVx55ZVcccUVPPLIIyF93Ztvvhmn0+n92LNnT0hfT0RERERERKQn+jW05+bmMmXKFFPb5MmT2b17NwA5OTkAHDhwwHTNgQMHvOdycnIoLi42nW9ubqasrMx7jVV0dDRJSUmmDxEREREREZFw06+hfc6cOWzevNnUtmXLFvLz8wEYPXo0OTk5rFy50nu+srKSTz75hNmzZwMwe/ZsKioqWLdunfea//3vf7jdbo466qg++CpEREREREREQiOiP1980aJFHHPMMdxzzz2cf/75rFmzhkcffZRHH30UAJvNxi9/+Uvuuusuxo8fz+jRo7n11lvJy8vje9/7HuCpzJ922mneYfVNTU387Gc/4wc/+EGXVo4XERERERERCVf9uuUbwKuvvsrNN9/M1q1bGT16NNdeey1XXHGF97xhGNx+++08+uijVFRU8K1vfYuHHnqICRMmeK8pKyvjZz/7GStWrMBut3Puuefy5z//mYSEhC71QVu+iYiIiIiISDjq99AeDhTaRUREREREJBz165x2EREREREREWmfQruIiIiIiIhImFJoFxEREREREQlTCu0iIiIiIiIiYUqhXURERERERCRMKbSLiIiIiIiIhCmFdhEREREREZEwpdAuIiIiIiIiEqYU2kVERERERETClEK7iIiIiIiISJhSaBcREREREREJUwrtIiIiIiIiImFKoV1EREREREQkTCm0i4iIiIiIiIQphXYRERERERGRMKXQLiIiIiIiIhKmFNpFREREREREwpRCu4iIiIiIiEiYUmgXERERERERCVMK7SIiIiIiIiJhSqFdREREREREJEwptIuIiIiIiIiEKYV2ERERERERkTCl0C4iIiIiIiISphTaRURERERERMKUQruIiIiIiIhImFJoFxEREREREQlTCu0iIiIiIiIiYUqhXURERERERCRMKbSLiIiIiIiIhCmFdhEREREREZEwpdAuIiIiIiIiEqYU2kVERERERETClEK7iIiIiIiISJhSaBcREREREREJUwrtIiIiIiIiImFKoV1EREREREQkTCm0i4iIiIiIiIQphXYRERERERGRMKXQLiIiIiIiIhKmFNpFREREREREwpRCu4iIiIiIiEiYUmgXEZHBz1XV3z0QERER6RGFdhERGdwat0L5H6D2w/7uiYiIiEi3KbSLiMjgZRhQ84bnv3UfgNHY3z0SERER6RaFdhERGbyadkFziedzdwM0fNO//RERERHpJoV2EREZvBq/thxv7Z9+iIiIiPSQQruIiAxejVvMx00F/dMPERERkR5SaBcRkcGp+SC4nOY2dzW4yvunPyIiIiI9oNAuIiKDU9POwO3N+/q0GyIiIiK9odAuIiKDU7uhvbBPuyEiIiLSGwrtIiIyODXvaaf9QN/2Q0RERKQXFNpFRGTwcTnBVdnOuYN92xcRERGRXlBoFxGRwad5b/vnXE7Pnu0iIiIiA4BCu4iIDD5NHYR2AFdp3/RDREREpJcU2kVEZPDpbIV4d1nf9ENERESklxTaRURkcDHcna8Qr0q7iIiIDBAK7SIiMri4isFo6uSa8r7pi4iIiEgvKbSLiMjg0tl8dlBoFxERkQFDoV1ERAaXjlaOb+WuCHk3RERERIJBoV1ERAaXpj2dX+Ou8sx9Fwkhw4BvvoGSkv7uiYiIDGQK7SIiMni4q7u2yJzhBndl6PsjQ1ZJCcyaBZMnQ2Ym3Hdff/dIREQGKoV2EREZPJp2dv1ahXYJoeuvh/Xr245vvhk+/rjfuiMiIgOYQruIiAwejdu7fq3LGbp+yJBWWQnPPOPf/thjfd8XEREZ+BTaRURkcDAMaNza9etVaZcQeestaAqw6+Arr3h+TUVERLpDoV1ERAaHpl2eOe1dpdAuIbJqVeD2khLY3o3BICIiIqDQLiIig0XD5927XqFdQuTTT9s/98knfdcPEREZHBTaRURk4HPXQMPX3bynKjR9kSHv6w5+Fb/5pu/6ISIig4NCu4iIDHx1n4DR3L17FNolBCoqoLSDXQe3bOmzroiIyCCh0C4iIgOb0Qj1a7t/n7taq4JJ0HU2Z31rN9ZKFBERAYV2EREZ6Oq/BHdd9+8zXGD04D6RDmzb1vH5rVv1XpGIiHSPQruIiAxsDZ/1/N7urDYv0gXWSvv48ebj6mo4cKDv+iMiIgOfQruIiAxczSXQtL/tuKgKvirq+v0K7RJk1kr7CSdATIy5TfPaRUSkOxTaRURk4Gr0WYq70QU/ehZ+8A/Y6+za/QrtEmQFBebj8eNh9Ghz2+7dfdcfEREZ+BTaRURk4Gr0KVk+8zl8WQjF1TD/Gahq6Px+hXYJsn37zMcjR8KwYea2/fsRERHpMoV2EREZUP7zHxg3DrKyDK65YSpNTXZocsGfP2y7aFMx3P5G5w8zakLXURmSiiyzM3Jz/UO7NdiLiIh0JKK/OyAiItJVX34J3/seNDcD2Pi/x48iIb6Rew5/yH9I/NnTOn+gW6FdgqemBqqqzG25uZCXZ25TpV1ERLpDlXYRERkwfvvb1sDe5o+PHk3TC5vMjYcNg2+N8n+Ada8thXYJosJC/zZV2kVEpLcU2kVEZEBoaICXX/ZvN+qa4Y3N5sbvTwebDVxuWLsH7l4Jxz0EH+00X6c57RJE1tCekOD5UKVdRER6Q8PjRURkQFi1Cmpr/dvn8BGRDfVtDXYbfGeK5/ML/gHv72g79+YW+JbPUt5GgAeK9JA1tOfmev4baCE6txvsKp2IiEgX9OufizvuuAObzWb6mDRpkvd8fX09CxcuJD09nYSEBM4991wOHDhgesbu3bs544wziIuLIysrixtuuIFm69hJEREZ8FavDtw+l7fNDTPzIDvB8/mRI8znVu0yH2t4vARRe6HdWmlvaoLS0r7pk4iIDHz9/h7v1KlTKSws9H58+GHb6r+LFi1ixYoVvPDCC7z33nvs37+fc845x3ve5XJxxhln0NjYyKpVq3jyySdZvnw5t912W398KSIiEkKffhq4/WRWmhuO9amkW+e1f1UE1T5bwRnNYDQGpX8i7YX2nBz/qrqGyIuISFf1e2iPiIggJyfH+5GRkQGA0+nkiSee4A9/+AMnnXQShx12GMuWLWPVqlV8/PHHALz55pts3LiRp59+mkMPPZTTTz+dO++8kwcffJDGRv0jTERkMPnyS/+2BKo4jHXmxuPGtH1+6DCI8PlT5zZgnWUVMFXbJUgCbfcGEBEB6enmc8XFfdMnEREZ+Po9tG/dupW8vDzGjBnD/Pnz2b17NwDr1q2jqamJuXPneq+dNGkSI0eOZHXLGMnVq1czbdo0srOzvdfMmzePyspKvv7663Zfs6GhgcrKStOHiIiEr8ZGKCjwbz+MddjxWRE+wg6H+wyJj4uEGbnmm9bsNh+7Na9dguPgQfNxVlbb55mZHV8rIiLSnn4N7UcddRTLly/nv//9Lw8//DAFBQUce+yxVFVVUVRURFRUFCkpKaZ7srOzKWp5K7uoqMgU2FvPt55rz7333ktycrL3Y8SIEe1eKyIi/W/HDs/CXb7mnbiNI1hrbpySDTGWNVYPt/x//AbL3wdj4FTaP/4Yjj8eJk6EO+4Al6u/eyS+rPPUWwYPAgrtIiLSc/26evzpp5/u/Xz69OkcddRR5Ofn8/zzzxMbGxuy17355pu59tprvceVlZUK7iIiYWzLFvNxdmY1c47czcR3LKH9UMuKXwDTLZX2DZaJx+663newD6xdC8cd51nEDGDxYs8Q64ce6t9+SZuSEvOx75B43wAPCu0iItJ1/T483ldKSgoTJkxg27Zt5OTk0NjYSEVFhemaAwcOkJOTA0BOTo7favKtx63XBBIdHU1SUpLpQ0REwpc1tE8YW8qUCQc5kjXmEzMDhPZDLH8P9lVCmc+Q+AGy7duNN7YF9lYPPxx4rr/0D2to76jSbr1WRESkPWEV2qurq9m+fTu5ubkcdthhREZGsnJl26rAmzdvZvfu3cyePRuA2bNns2HDBop9VnN56623SEpKYsqUKX3efxERCY1AoX188h5GYd7CremQ4f43j0uHaIe57SufIfIDYE77tm3w7ruBzz3ySJ92RdrR1AROp7nNt9Ku4fEiItJT/Rrar7/+et577z127tzJqlWrOPvss3E4HFx44YUkJydz2WWXce211/LOO++wbt06fvzjHzN79myOPvpoAE499VSmTJnCRRddxBdffMEbb7zBLbfcwsKFC4mOju7PL01ERIJo+3bz8fjRpYyYUMd3WMEd3M5/+DY7yWd33Dj/myMdMNm8/glf+4zSGgCh/d//bv/cq6+CYbR/XvpGWZl/m+a0i4hIMPTrnPa9e/dy4YUXUlpaSmZmJt/61rf4+OOPyWz5y7Z06VLsdjvnnnsuDQ0NzJs3j4d8Ju85HA5effVVfvKTnzB79mzi4+NZsGABS5Ys6a8vSUREQmDPHvNx/ggnqTkuPkyay38qv+Ntf3PfU4wdH2BHkMlZsN5nY+xtPmOTB8Dw+Pfea//cnj3w9ddwyCF91x/xZ12EDiAtre1zhXYREempfg3tzz33XIfnY2JiePDBB3nwwQfbvSY/P5/XXnst2F0TEZEwYRiwd6+5bXiuJ5iPHlnO+q/aFpor2J0a+CHjLKuAbfUJ7WG+EJ1hwJo1HV/z2msK7f3NOkc9ORkiI9uOFdpFRKSnwmpOu4iIiFV5OdRZcvWIYZ7Jw6NHVpjaC/bkQNR4/4eMSzcfb/MpixrhHdr37AHLmqucfLL5ePXqvuuPBNbRdm/gH9rLyrRln4iIdI1Cu4iIhDXr0HibzSA3uxrwVNp9FeyfAInfB7tl21Brpb2kBpz1ns/DfE67tcqekgIXXWRu+/TTPuuOtKOj7d7AP8QbRuAh9SIiIlb9OjxeRESkM9ah8dmZ1URFeUqU+SPMy3XvK0wEux2ipkD9urYTo1IhJQbyU2FsBoxPB3fL6m1hXmlfv958fMQRng9fe/dCURF0sNuphFhH272BeX57q/JyyMoKXZ9ERGRwUGgXEZGwZg3tI4ZVQoUnaOflVJnO7d/fMoAsepI5tEc6YPONYLP5v4DRDEYT2CL9z4UB63Z306bBxImQkADV1W3tn34K3/kO0k+sq8dbK+0xMZ6P+vq2tnLzQBEREZGANDxeRETCmnV4/PDcSnhqHUy4nzNvuIo3OJWf4lmwdP/+lu3PIvPBZtmbPVBgbxXGi9H57VE/ARwOmDXL3P75533XJ/FXUWE+TknxvybVsk6iQruIiHSFQruIiIS1ffvMx8NyK+Ebz9Lb0WUVnMpbHMJXgKeKWVEB2KIgwrI3e0fCdNs3txu2bjW3TZjg+e+0aeb2TZv6pk8SmEK7iIiEikK7iIiEteJi83FudjXsMK/gtYnJ3s/3t27HHjGi6y/iru/8mn6wfz/UWt5PaA3tU6aY2zdu7Js+SWA9Ce3We0RERAJRaBcRkbBmDe1ZGTV+oX0LE7yfeyvzEXldf5EwXYzOOjQ+Lg7yWr4sa2jfvFlbiPUnp3lNRFXaRUQkaBTaRUQkrFlD+7DoIqgwV8a3Mc77eVulPbfrLxKmoX3bNvPx+PFtU/MnTzafq6+HnTv7pFsSgLVqnpzsf41Cu4iI9IRWjxcRkbBlGP6hfUTDTtNxsy2CncYo77E3tDsywBbhWR2+1QPvwY4y2FMBuyvgyQtgRl7YLkRnXYRv9Oi2z7OyPNuI+a5avmkTjB3bN30TM81pFxGRUFGlXUREwlZVlXmLLICsKnOSLUnIw+XzHrQ3tNvs4Mg03/zsenjhS/h4N+yv9AR3ACM857RbQ/sIn2n6Nptn6zdf27eHvk/izzAU2kVEJHQU2kVEJGxZq+wAKaX7TceV6eZh8Pt9T1tXkB+RYj7e1ZKawnR4fEehHfyr6jt2hLY/Elh9PTQ1mdsU2kVEJFgU2kVEJGxZQ3tcXCNRew+a2hqGZZmOTVvEWSvt1tC+p8Lz3wEyPN4a2seMMR8rtPePQKvAa067iIgEi0K7iIiErYArxxeYV45ndJrp0Fxpt4T2kSnm4zAeHm8YCu0DhUK7iIiEkkK7iIiErcChvczUFjMlyXRcWAhud8tBVyvtYRjaS0v95/NbQ7vvwnTgCe2GEdp+iT9raI+Ph8hI/+sU2kVEpCcU2kVEJGwdOGA+Hp2832+7t8QZGaZjl8tnRXV7imcF+Vb5KeYH7qnwpFx3+IV2a5Xdbm/bo72VtdJeXw9FRaHtl/iz7tEeqMoO/qG9qgqamwNfKyIi0kqhXUREwpa10j452rJxud1G2vQcv/u8Yd9mA4fP8Hlrpb2uGQ7WhOVCdKa5+UBODkRYNmrNy4OoKHObhsj3vYArxzfthpq3oLntl9ga2gPdKyIiYqXQLiIiYcsa2sc6CswNOYlEJaX6hSHTfY4M0/VEWP707a8My+Hx1oq5tcoOnup7oCHy0rf8QntyHTiXQ+1H4PwbNJcAgUO7hsiLiEhnFNpFRCRsWUP7CPduS0MKOJLJMi8gbx5W71tpd9ghO8F88f5KMNxgNPa2u0FlDe05/gMKAP8h8gUFga+T0LEOj09JKPT8ToFn6kX1qwDExPiPjFBoFxGRzkR0fomIiEj/sIb20uOPgEtqPXPR91RAbhLYk8jOhs2b27nPkW5+SG4S7KtsOy5s+dxdBw5LoupHPQ3t27eHpj/SPmulPTmxxtzQtBMad2KLGkVqqvlNJYV2ERHpjEK7iIiELWtoTxobBcePNTfau1FpB8gzrzbP/pbQbtQD7awg1g+soT07O/B1o0aZj3fvDniZhJDf8PikANMt6teAQruIiPSAhseLiEhYcrt9VoFvkZFW639hS6Xdlyns2y2hPdcS2gurPP8Ns3ntXa20W7eB27s3NP2R9vnPaQ/wu9S4Gdx12vZNRES6TaFdRETCktPps996i/R2QnvHlfZEsPlsmm2ttHuHxw+e0K692vuW35z2QJV2wwWNmz0ry/uorPS/VERExJdCu4iIhCVrlR0gLcWyNZvNAfaEjivtAA6f8mZuovmcd3h8Q4/6GSrWPerbC+3Dh5uP6+uhtDQ0fZLA/Oa0BwrtAI3fkGR5z0ihXUREOqM57SIiEpasoT0qqpm4uCZzoz0BbLaOK+3gCe2t+2WPToPZ+Z7wnpsE+S2BPoyGx1dXez58tRfa8/I8W7/5jkrYswcyMgJfL8HXpeHxAE0FJCe58a2ZWKv0IiIiVgrtIiISlqzV4vHJe7HtKodhSRDp8DTaPWXLTivtdp9K+4w8+Pcl/i8YRqHd700H2g/tERGQmwv79rW17dkDM2eGpm/iz1otT0psZ9SGu4GkxCp8FzxUpV1ERDqj0C4iImHJWmn/ke1pOPLPYLdBTiLMmwB/vBHAr9JeW+upVCe0bsluXUE+kDAK7QcPmo9jYny+lgCGDzeHdi1G17eqqw3A5j1OjG9s99qk+DIU2kVEpDs0p11ERMKSNbSPsRd4PnEbnnnolQ1g98xPD7Qdmnmv9pTOXzCMFqIrKTEfdzbU3boY3Z49we2PtM8woMayLXtCB6E9Od78joyGx4uISGcU2kVEJCxZh8ePNHZZGlK8w+MTEjzVaF+mIeZ2yz5bgYTRQnTWr72z0G5djE6V9r7T2AjNzTZTW3xHlfY489wHVdpFRKQzCu0iIhKWrJX2vEZLEh2e7K2022z+Q+S7XWkPo+HxqrQPHNYFA6HjSntSgnnbQoV2ERHpjEK7iIiEJWtoT6uzrM42rC20QyeL0dkiPSvNd2QAh3ZV2vtPdZXh19bh8HjLdnAaHi8iIp3RQnQiIhKWfEN7NPUk1FeYLxiWDI62Ta873/YtBdwtZdGVW+HZ9VBc7fk4NA8evypIPe896/D49PSOr7dW2vfu9cy1ttkCXy/BU+0sB8wLHcbFNgW+GP+V5VVpFxGRzii0i4hIWPINrsPY539BXlLXK+0A9hSgpQS91wmvbGw7lxI7oBeiGzbMfNzQ4HnTo7OwL71XU1mMb2iPi2vE4fCvvreyhvaGBs9HdHSoeigiIgOdhseLiEhY8q20D8cy3jshCpKTPcPeW3Sp0u692DJUvrh6QA+PD7SHe2Fh8Poj7at2mudxxMe1X2UHSA6wh7uq7SIi0hGFdhERCUsdhnZLlR26Wmlvvdh8L8XV4G4Cw9WjvgabNbR3VjGPivK/RqG9b1RXVpiOE+Lan88O/pV2UGgXEZGOKbSLiEjYcbuhvLzt2C+05/qH9s4r7ck+F1sq7Y0ucNaHzbZv3d3yDSA313xcVBS8/kg7jEaqq+pMTR0tQgcQG9uEw+E2tSm0i4hIRxTaRUQk7DidnuDeKuiV9sx4/xcNkyHybndwQrsq7X2guZCamkhTU2eh3WbzX0FeoV1ERDqi0C4iImHHut1bTyrtpaXQ5Du92HdOe3QEpMSYbzhQHRaVdqcTXJZR+l0J7dZ57QrtfaB5P9U1Uaam+E6Gx4P/EHlt+yYiIh1RaBcRkbBjDe0jbHvMDQEq7dbQDpa54bZIsMf53BBgMTp3/4d2a5UdurYKvCrt/aC50C+0d1ZpB//F6FRpFxGRjii0i4hI2PEP7YGGx5tDd0aG/77kfvPa7R3Maw+T4fHWRehiYyEuLvC1vjSnvR/0MLRrr3YREekOhXYREQk7FRVtn0fSSKbbMkE9wPB4h8N/GHnH274FWEE+DIbHd3e7t1aqtPcxowlcpZbQbjC95tNOb9XweBER6Q6FdhERCTu+IcaFg4unvAHP/hAe+A5cdxyMTPEL7eA/r7vjSrtlMbowqbRbh8d3ZWg8KLT3ueZiMNzU1LaF9qv4Kze8fjk8uKrDW5MTtRCdiIh0XUR/d0BERMTKN7S7cVCSPRpOHm++yDI8HjwryG/Y0HbcrW3fwmQhup5W2q1vWFRVQU0NxAdYKF+CwOX55WqttKdQzt38xnNu8VtQUgO3zfWfs4GGx4uISPeo0i4iImHHOlzYukUW9hjPwnIW1uDqN6+7ozntB8MjtAer0g6qtodUszm0X8OfScdnMYYHV8H6/QFv1fB4ERHpDoV2EREJO/6h3RKmAwyNB/+92v2Hx6e0fR5wIbr+D+2+8/kBUlO7dl9ion9VXYvRhZBPaI+giav4q/n8dybDzGGezw0DPt4NFz8H//7a7/dZlXYREemIhseLiEjY8QvtidZKu//QeOhCaHcktX1uDe2ltdBQA4Ef3WfKy83HKSldvzc3F7ZtaztWpT2EXJ7FEatrojiTFeRh+WYvOs7z3492wl1vw7p9nmObjaTjVGkXEZGuU2gXEZGw09NKe6fD423xYIsAo9mzAv3xYzzhvfWjua53HQ+CnlbaQaG9z7irwV0LQE1tJBfwT/P5I0bAtJZfxj0VbYEd4H9bSTvB/M5MdXUI+yoiIgOeQruIiIQd39A+nS8Y7toNTS6IdHgaezo83mYDexK4yiA1Fl64yHw+0t27jgdBbyrt1jctFNpDpLltC0JXdRPf4VXz+QtmtH1+2kSItENTy+9Wg4sxuz4F5nsvqaoKYV9FRGTA05x2EREJO76h/T+cwSW/+zEMvwumPQCrd7U7PN4aWktKoLnZcpHvCvJWA3hOO/gvRqc57SHiOuj99KjKD4in1nts2O3w7Ult16bEwpzRptuHbf3SdKzQLiIiHVFoFxGRsNMa2h00k9s6V9jAsy1bQlSXK+2GAQcPWi6yh3do7+2cdl+qtIdIS6Xd7bZxXMP/TKdqZoyDDMuKgMeZQ3vG11+ZjhXaRUSkIwrtIiISdlpDexbFOLAMWc9OBFvgSntGBtgtf9n8V5AP39BuGMGttCu0h0hLpb2uLoJTeMt0qvFbE/yvP36M6TB2fzEj2eU9rqkBd//PzBARkTCl0C4iImHFMNpCex6Wfa4j7J4qpiNwpd3hgMxMc5vfEHHfFeT9Xryxe50Nsro6aLR0oTuV9qws87HfKAMJjtbQvrmKcWw3nzthrP/1U3M8ayj4mM1q03FNTVB7KCIig4hCu4iIhJWaGnC5PJ/nWrfRyowHu63dSjt0Za/2jirtLjCaut7ZILNW2aF7lfZAoV0V3CBz14Dbs8uA8e4O06liMomZme5/j90Ghw83NVlDu4bIi4hIexTaRUQkrPguQucX2nMSwRYF9uh27+902zff0L5yKyz4J3znb3D0/8GV/69fh8hb57MDJHfwHoOVNbQ3Nwd+I0B6wVXi/TTyY3OV/S1OITbeFfi+w8yh/Wg+Nh0rtIuISHsU2kVEJKz4hna/4fHZie2uHO+9pLNKu+/q8YVV8Po3sGYP7CiDneX9GtqtATsxESK6sTmrdWoAQHGxf5v0QmtoNwxiNxSYTq2KPhabrZ37LJX2mXxONPXeY+3VLiIi7VFoFxGRsNJhpT0rod2V41tZK+3+e7VHgT3G83l6nPlcaQ24w6fS3p357AAxMZBkmbKvee1B1tzyDS2uJqqswnRqQ8IR7d83c5jpMIomprDRe6xKu4iItEehXUREwkqnw+O7WWkPuFd56xD5dMvWXKW1YVVp78589lbWIfKqtAeZq9Tz3+xE/vOXxziN11nCrfyb71KUOLL9+xKjYZT5BzqDL7yfK7SLiEh7ujHoTkREJPQ6Hh4fhEo7tIT2A/77adc2QXU5pI0OcFPoWUN7dyvt4Ant27a1HSu0B5nPnPZyI5U3OI03OA2AQxM62WNvSrZnCkaL6Xzp/VzD40VEpD2qtIuISFjpsNIejDnt0LbtW0ac/7mDgW7oG70dHg+qtIeU4QJ3hfewuibKdDohvpMtA6eafzlVaRcRka5QaBcRkbDSGtrtuMjBMrY9J7HTSrs1tJeUQJN1F7fW4fGJ0RBp+VPYjylXw+PDnKsMDMN72O3QPqXllzMznnWpc3iHE72nFNpFRKQ9Gh4vIiJhpTW0Z3IQB5ZNxrMTOq20Dxvm37Z/P+Tn+zS0hnabzTNEvtAnMQ3wSrt1BXmF9iBqnc/eoqa2m6H9hLHw1XWQlcA9Pz6ff/1niveUQruIiLRHlXYREQkrraHdb2i8vSVgdxLaU1MhNtbctnev5SK7zxLr1sXoDpbQX1RpD3OW0G6ttMfHWYd0WMRHeXZAABITzAG/uqqd/d1FRGTIU2gXEZGw0hra/Rahy4wHh73T4fE2Gww3b4nNnj2Wi3z3ardu+3bQHMz6kua0h7nW0P7GZnj9GyIPlAFtw+U7rbT7SEww71JQVdn1e0VEZGhRaBcRkbDSGtrf5QRmsJ5nz7sH/vhduOlEsDnAFtvxA/AP7QEr7Tab53PrCvL9GNq7VGlv+AZqPzDNrfal0B5CraH9D+/Dgn9y77/O5CCZnMfzQPdCu/XaqqrmoHVTREQGF81pFxGRsNIa2muJ50tmUDJzH/ywpSppT2gL2x0YMcJ87BfabQ6wxYNR7b+CfIml3N2HOq20u5xQ9f/AaAZbJMQe7fcMa2gvK4PmZojQX/zec5VBkws2tq17kEEppaQD3a20W0O7hseLiEhgqrSLiEhY8d3yDSA50WcYcSfz2Vt1Ojwe2obI+81pr+jSa4RCp5X2+rWewA5Q+y646/2eYQ3t4FlBX3rJaAR3FWwpgQZzwP6cmYAltNtj2rYWDMA6PL66KvDICREREYV2EREJK9bgmpzkE0w7mc/eqtPh8dC2GJ11eHyJ0//aPuBy+b9h4Vdpb/iq7XN3vSfEW6Sn+w9G0BD5IHC1DIPYYF4gsYBRlJMGQHxcS2iPyIHUayD1lxAzw/ychmZ4/guOe3cZz3Me65lBCuVUVYe4/yIiMmBpsJyIiIQVv0p7Uvcr7Z0Oj4e2bd+sC9GV9M/eW5WV/m2mSnvzQXBVmC+oWwOxx3iG+7dwOCAjAw4ebLtMoT0IWuezW0J7a5UdWirtNgckfh/sLb9XCd+BpgJwtfyAbTb4xb+Z4TJojfMT2EJF1aQQfwEiIjJQKbSLiEjYMAz/8BqMSnthITQ1QWSkT2Pr8PiRqTB3vKfinh4HwwKML+8D1vnsYKm0N+3wv8BdBQ0bIOZQU3NWlkJ70LnKPP/dUGRq/oxZ3s8T4hshZiZEZLRdYIuE2G9B9Wue4yiH53euoMx7yQS28L/qqSHruoiIDGwK7SIiEjZqajzDxG24OZI1FJJLcnRN2wU9nNNuGJ7gPnKkT2Pr8Pip2fDMD33ao3vW+V6yTguIiIB435H7TYEm5gN1H0L0DNOY+MxM8yUK7UHgKvf8In1j/mZ+Qdvw94SEJoid7X9vzKFQ87ZnXjzAuHRTaJ/IZv5dHel/n4iICJrTLiIiYaR1aHw6pXzMbHYxijHfuh4m3Q/F1V2utKenQ0yMuc1vMTp7MgEZje1upxZKgVaON81Nbw40xh9oLvFU231o27cQcJd5fgcrzIv/fcUh3s8TktLAke5/ry0Kon2Gv481XzORzVTXRGG4aoPaZRERGRwU2kVEJGy0hvY89ptPlNdBamyXQ7vN1oUV5O3trOxtGG0V0T7kN5ff9z0Fd63/fHZfte+A0baiuUJ7CLjKYJP5G1lNPLvI9x7Hp45t//6oyW2fW0L7OLZhGDZqqvpnPQUREQlvCu0iIhI2WoNrLubFvkiPh0gH2Lo2PB5g1Cjz8Q7rlHB7omkBN5N+CO1+c/l9Q3uz5fth5SqHuk+8hxkZ5tOlpb3r25BnuMBd6Tc0/mumYvj8UyohdUz7z4ga2/b7lm/ey28MOwCD6soa//tERGTIU2gXEZGw0W5oz07wlM/t8f43tWPcOPPxtm2WC2y29iv3RkPg9hDqsNLefKDzB9S976nIo9AedO6KgPPZv8a8eFxCkmVOhi9bFES0bGtgCe3JVJJKOVWVGh4vIiL+FNpFRCRstBvacxLBFg+2rv/Z6jS0QwdD5Ps+tFsr7Um+XXN1YXy7ux7qPgI8c/p9lZT0rm9DXuvK8d8cNDX7zmcHSOhsIEjkKM9/hydj2G2mU6MpoMpZ14tOiojIYKXV40VEJGy0O6c9O6HL89lbdS20t5Szn1oHb22B0lrP/PkfOeGOpd16vd7qsNLuG9prmzwV36RoGGcpqdethdg5ZGSY955XaO8lVzm4DdjcfmiPiDCIirJZ7zRrDe2RDmzDkmBP2w99NAVUVeYGq8ciIjKIKLSLiEjYaH94fGKvQ3thoWdLOdM2aq17tW8uhje2tLXvamd7tRBqt9JuGJ4V4gH+tw1+9hKUtAyjPnUCPHwOJLZsU2c0Qv06MjKONT2rtBTcbrBrfF3PuMphrxNqzGsd+A6PT0joJLADRA7zjBYx3J692n1C+xh2UF0VYOV5EREZ8vTnW0REwkbHob3ri9ABjAmwJphftb210p5qrkxT0veTwNsN7e5KTxj/phgu+WdbYAd4cwv87GXzFnX1n5KRbt6yzu32r+RLN7jLobwWxmeAwxPOm+Lj2E+e95JOh8YD2CLBke35fGSK6ZSn0t4cpA6LiMhgotAuIiJhI5jD42NjYcQIc9vGjZaLWivtaZbQXlrWrdcKhnaHx7taqux3rYR6S6izASkx0Ni23RsuJ+lJO/2eryHyveAqhxl58NFC2PlreOdqVl/5Szw/AI8uhXbwVNsh4AryVVWuADeIiMhQFzah/b777sNms/HLX/7S21ZfX8/ChQtJT08nISGBc889lwMHzCvo7t69mzPOOIO4uDiysrK44YYbaG7WO9UiIgORJ7gaQRkeDzBtmvn4iy8sF7RW2tNize1lfV+WbrfS7iqFLQc9VXVfEzLg7avgT2dBtHm2W6z9S+KsgwcU2nvOXdH2eXQETM1mx+gjTJd0ObRHtFTnA1Taq6sBQ/+GERERs7AI7WvXruWvf/0r06dPN7UvWrSIFStW8MILL/Dee++xf/9+zjnnHO95l8vFGWecQWNjI6tWreLJJ59k+fLl3HbbbX39JYiISBBUVEAq5URj2Sc9p/vD4wFmzDAf+4f2lmRsrbSXVHT7tXqr/Up7Kfx9nf8N/70CpuUEfljjN2RkmIfIK7T3kLsW3P67CVTXRJmO47u6G2FEy2Jzlkp7PruorowAd1VPeikiIoNYv4f26upq5s+fz2OPPUZqatsfMKfTyRNPPMEf/vAHTjrpJA477DCWLVvGqlWr+PjjjwF488032bhxI08//TSHHnoop59+OnfeeScPPvggjY2N7b2kiIiEKaczwHx2gKzuD48H/9C+fr3lAnss2KP957SXV3kmgvehdivtzSXw+mbzyWvmQII5NJq468hIN/8d1F7tPeQqD9hsDe1drrQ7ssDmgPxUiuOH8T7H8g9+yFIWUee0KbSLiIiffg/tCxcu5IwzzmDu3Lmm9nXr1tHU1GRqnzRpEiNHjmT16tUArF69mmnTppGdne29Zt68eVRWVvL111+3+5oNDQ1UVlaaPkREpP85nQHms6fFQpQjKKG9qMjzYWJP8h8e73Z7yv59qN1K+5dfwW5LX862jPsPICPNHP5Uae8hd5BDu80BjgzISmDx+f/ieN7nR/yDm7mP8sYkhXYREfHTr6H9ueee47PPPuPee+/1O1dUVERUVBQpKSmm9uzsbIpa/sVVVFRkCuyt51vPtefee+8lOTnZ+zHCulKRiIj0C6eznfnsNhvYuzr+uM348f5h6t13LRfZU/wr7dCnpWm3G6osWS0pCc/WYG9+Zj4xIhmmZHX6zPQU82J6Cu095KoI2Nzj0A6eajuQEG8eDVFdE6XQLiIifvottO/Zs4df/OIX/OMf/yAmJqZPX/vmm2/G6XR6P/bs6fv9eEVExF/A4fHZiWCL81Qou8nhgOOPN7e9/bblInsSxEVCrHkxt75MuTU15l3boCW0u53w8U7ziVMmeN7E6ERGqrlCrNDeQ65y+N82uO0NeGodrN4FJTXU1EaaLutWaI/wFBgSE8yhvao6WqFdRET8RHR+SWisW7eO4uJiZs2a5W1zuVy8//77/OUvf+GNN96gsbGRiooKU7X9wIED5OR4Ft7JyclhzZo1pue2ri7fek0g0dHRREdHB/GrERGR3jIMz7zuh/kJr3M6uRTy2A3PMHJ6Y4+GxreaOxf+85+24zff9LyWN/c6Ujz/TYuDfT7Tpfqw0h5oD/XkZKCxGNZa3liend/2eUMzfFUEn+2DdXvhyJFwqWdV84y0WtNtmtPeQ+4KeGc7/PXjtrbvTqHacbHpsi4vRAfgyATaq7RX97CjIiIyWPVbaD/55JPZsGGDqe3HP/4xkyZN4qabbmLEiBFERkaycuVKzj33XAA2b97M7t27mT17NgCzZ8/m7rvvpri4mKwsz1Czt956i6SkJKZMmdK3X5CIiPRKTQ24XDaqSGID09nAdFznb4L8il6F9lNOMR/v2QPPPw+rVsHLL4Or+SjOODmWB5MeJcI3tPdhaTrQ0iqJicBn66DSsnL5kT5Tuu75Hzy8uu3YWd9uaFelvYdcFbDd8o7HuHSqv+rF8PiIdobH12p4vIiI+Ou30J6YmMghhxxiaouPjyc9Pd3bftlll3HttdeSlpZGUlISP//5z5k9ezZHH300AKeeeipTpkzhoosu4v7776eoqIhbbrmFhQsXqpIuIjLABKw2J7UE1l6E9ilTYOJE2OyzAPsPfuB7RRSP/v1wLokfz2zfofn9WGmPj4eICODjj80nRqZAblLb8WHDzOc/2+cdRpCu0N57huGZorDV8s0bl0H1J70I7fYUsEWQmGB+Q6aqWqFdRET89Vto74qlS5dit9s599xzaWhoYN68eTz00EPe8w6Hg1dffZWf/OQnzJ49m/j4eBYsWMCSJUv6sdciItITgUJ7UmLvQ7vNBgsXwjXXdHzdrppcZvs29GOl3bvd2/qN5hOHDTcfz7Icl9fBjjIYm65KezC4q6CuHvZUmNvHZVBT24vQbrOBI53M2v38lhsZwR5GsIfEHbXgvqDX3RYRkcElrEL7u5YlfWNiYnjwwQd58MEH270nPz+f1157LcQ9ExGRUPOrNsc1EhHRsld6L0I7wJVXwkMPwTfftH9NCRnmhj6stFtDu3e7t2/lQ+0M+LIQthyEaZb1WoYlefawL/aZB/1VUcDQXlbmWaXe3u+bvQ4g7gooKAO3ZZXAcem9Wz0ewJFBEt9wI79ra2sC6r4NRjPYwuqfaCIi0o/0F0FERMKC3z7lSfVtB/buJiKz6Gh49VU480zYtMnTFhEBxx0H//uf5/gzZvFvvsvU2dWMm1UPJ53S/gODzPq1e7Z7M+C7o+HMljns9c3Q7DJfaLPB1GxzaN94AM6aSka6ObS3bj2flhb07g9eLidsswxRyEmEhGiqa8w733RrIToARzqRowL8XhdWQW512wKJIiIy5On9dhERCQtOJyRQxdGsJp+dZCb4JNleVtoBxo6FL7+ElSvhxRdh927P56ed5jm/jEv5Hv/m1pw/wu2nwFmn9fo1uyrg8Hh3pafi2iomAhJa1muxRUDU2LbQ7mujZxeV9NQ6v9fREPlucpf7h/bxnhEZ1TXmtXO6X2lPJzbLQRXmG5t2VWkFeRERMVFoFxGRsOB0wqGsZzXHsJPRrN82EmYu9ZwMQmgHT3X9pJPgnHMgN9fTds455mve/WiUZ890o8Hv/lDxG2WQDLjK2r8h8VxIvghiDoMpgUN7TEwz8XHm1ckV2rvJ5YRt/ivHA1TXmAcr9iS0JyQ0sYcRpuaGglotRiciIiYK7SIiEhacTsihyNwYFeGpJvdyeHxHTjzRfFxUnMiW7el9GtoDV9rbCe1R4yB6sufz2GP8Q/seJ1R6phZYh8hrr/ZuclcE2O4tg8ZGB01N5n9CdT+0p5GY0OAX2lVpFxERK4V2EREJC04n5PpuuQaQnQC2eLCF7s/V2LEwLM9ckX73o1FhUGlvJ2HHHdv2uSMNJk2GCMv3p2WLMq0g30uuCs9CdL7GplNTG+l3abdDuz2OuHiHX2g39im0i4iImUK7iIiEhYpyw7/SnpUQtKHx7bHZ4LhjzQH98w25YDS2c0fwBay0BwrtEZkQmW9ui58I+anmtpbqsEJ7LxgGlBZ6ttHzNSqN6rpkv8u7vRAd4IhKpSgyz9Rm21+h0C4iIiYK7SIiEhacTlfgSrsjKfANQTRzpvnP4Zcbs/t1eHxKogsm/RTOXAbXrYBHVkNFHUTP8L85cjiMTTe3tYT29FSF9h4zaqDgoLnNYYMRyVTX5fhd3pPQjj2V0mjzsyIOVICh0C4iIm205ZuIiIQFp9PVL5V2gGnTPdt3PczVDGMf2Z8dxJi2H9s/X4ZZs0L++tbh8cMatkNhhefjk92exvNmQPRU/5sj8mCMZR+31kq75rT3XKCh8SNSINJBdb15HYGYGM8ih93mSKUs3g4+GT2qtFyVdhERMVFoFxGRsOB0ugNU2hP7JLRPn+EA4ExWMIz94Aa2AUVFHd4XLNZKe2755+aG9DjIHg0OyzB4AHsKjLcsRrdNw+N7ze30D+2jPW+O1DRkmJq7PZ+9lSOV6oRIONDWFF1dDXUVkNLDZ4qIyKCj4fEiIhIWAq4e30eV9txcSE+rpwRzGOurlGuttGeVfmFuGJcBURMC32yzwYTx5raCUnAbfqH9oGW0t3TAVQE5iTA73/Nf8Ib26lrzyIYeDY0HsKdSmxLgjZg+erNIREQGBlXaRUQkLFRXuMii2NzYR6HdZoNDplRR+qFlbngfjSe3VtpTD2w0N0zI8Gz11p6Jk8Fug5EpnoA/Ng3qm0lPMy+iVtbB1u9i4XbC/FmeD4CaRmh0gc1GdV2K6dLeVNpJaqaOGGKpb2svrIAZdWCP7eGDRURkMFFoFxGRsBDpLMOOYW7so9AOMG5sIyUfWirtfRDam5uhpsbcFn9gh7lhbBZEDG//IXljYfdvIMphak5LUWjvMVeF+Tg+CuIBewrVNeZ/PvU4tNuTSEgoZD95jMXnZ17Usu2bQruIiKDh8SIiEgYMAxKqzFV2w273zOW2h371ePDs194fw+OrqvzbovfvMzeMmQA2h/+FrSIy/AI7QFqqf2g3DL/LJBC3M3C7I9PvTZYeh3abg8REN4XkmtsPaK92ERFpo0q7iIj0u5oayHSbQ7srLYGIiCiwx/VJH8aOdbCZvh8ebx0aH0UDjgPl5sZxAbZ682UPMC8a/0p7czNUV0Ni3wxeGNjaC+0RWVRb8nSPQzuQkGBnDUfSSBT7ySNzip150yI8W86JiIig0C4iImHA6aSdleN7kYa6adz4GD6yVNrdxSUhH5JmXYRuNAXYrOXwCUd2/BBHSsBma6UdPNV2hfZOuOs9H4E4/EN7jxeiAxISHVzHH7zHP5i4gXlHvahKu4iIeGl4vIiI9LtAK8fbc/puaDzA2PGJlFoq7U1FfV9pnxq7zdyQHgdpEzt+iD0O7NF+zYkJDUREuExtmtfeBe7K9s8FudKemGiun1TXRLX0QaFdREQ8VGkXEZF+F6jSbs/pu0XoAJJTHDQmJIJPVjL6YE67tdI+JXoL+BbI87PAFtn5g+zJYJliYLN5hsgXl7SlSoX2LnBXwAPvweaDnm3eRqfBkSNgbAY4MoI7PD4xynTcFto1PF5ERDwU2kVEpN85K5q5hbv4K1eRQxFTknfwp/M/7tPQDhCdGwdb244jK0s9K7fZbCF7TWulfZxtq7lh9IiuPag+Bv72MeyuaPt45RLSUs2hvY92sRvYXE54dwd8srut7Y5T4OfjwRYRvIXogIRE8wiJquqWY1XaRUSkhUK7iIj0O2dFA05ScJLCV0xjX/ZBmF3Yp8PjAWKHJ5lCu6O5MeQrt1kr7WOM7eaGseO79qDIFLj1DUy75u2q0LZvPeF2QoHlGzU6DSIyAYI7PD4pxnTsrbRrIToREWmhOe0iItLvKsobTcfJiS2LgPVxpT1ldIA3CUJcmrZW2oc37TI3jJvctQfFZUCupf97KgJu+yadcBZBsSWZj04DR+DQ3quF6JLMuyNoTruIiFgptIuISL9zVjSbjpOTGjyfOPq20p45JolGLPPH+zS0G+TW7zFf0Nl2b63sSTAixdy2u9y/0l6qjdo7tX27f1t+aruhvXfD4z2DHu/hZp7jAp4vPh2O+DOs+abnDxURkUGlR6F9wYIFvP/++8Hui4iIDFFOp3mF8+Sk/qm0j8iP9ltBnhAvRuc7PN6Bi5dn/hSuPhrOnAKzRsLYTlaOb2VPhJEp5rbdgSrtTb3r8FCwvcB8nJcEsZEhCe2tMy/O4wUu4HmOca2CXeWwrxzc/lv2iYjI0NOjOe1Op5O5c+eSn5/Pj3/8YxYsWMCwYcOC3TcRERkinE636dhbae/jOe0jRsbxJqeSRCWlpFPhSOe6ESMJ3TJ05kq7iwg+PeIcfrC4JQVGTYDkkV17kD3Rv9K+p4K0Q62V9ibAvGK5+DDcsGOfuW1MmmcxwogMgOAuRNdy737yGIdPhf9AtWcFeXtszx8uIiKDQo9C+8svv8zBgwf5+9//zpNPPsntt9/O3LlzueyyyzjrrLOIjOzC1jQiIiIt3AdKmM1OCsmliBzPnHZ7LNj6dr3UkaPj+BZPtjW4YEEmZIbwNa0L0XlHGYA3JHaJPRHyU8xtuytIO8FaaTe/QSIW7ioosEyJGJ3m2VLP5nmzI6jD431Cu0lRVctidN34HRARkUGpx3PaMzMzufbaa/niiy/45JNPGDduHBdddBF5eXksWrSIrVu3dv4QERERYHTBR6xiDgWMoY44Fv3rsj6vsgPk5oLdbg61e/eG9jWtC9ElJTa0HTi6E9pjYViaua2wkvQ0c2jXlm+dcDthh2W1vlFp3p+FYQR3IbrW4fF+ob2wSovRiYgIEISF6AoLC3nrrbd46623cDgcfPvb32bDhg1MmTKFpUuXBqOPIiIyyMU6i03H7tioPp/PDhARAVmZDaa2oqLQvqZ/pd3n9e2WEN6Z4bnm44p6MmMqTE1l5drttUPuSthZbm4b1bYIXX09uC2DFXpTaY+NBZvNoIgc84mDLcPjRURkyOtRaG9qauLFF1/kO9/5Dvn5+bzwwgv88pe/ZP/+/Tz55JO8/fbbPP/88yxZsiTY/RURkUEovtpc/m1KS+qXSjtATrZ5obbCwtC+nl+lPcG30t7d0O6/vkxW837TcVl5FIYWkG9fTTEUWn4oo9O8UxWsVXboXWi32SAhIUBoL1ZoFxERjx693Z6bm4vb7ebCCy9kzZo1HHrooX7XnHjiiaSkpPSyeyIiMhQk11oq7Rn9F9pzc9ys/7LtONSVduuWb9457baI7o82SEyHlBioaJsXn1Fn/gIaGx3U1riJT9CurwHt3AbWNzXyU73D462L0EHvQjt4hsgfqMo2Nx6saZnTLiIiQ12PQvvSpUs577zziImJafealJQUCgoK2j0vIiICgOEmtdG8rZotOwEcfT88HiAn17xWfKgr7b7D47cynuHXVMHYeBieBbefBxO7uOUbeEJ+XrIptCdXH/S7rKykkviElF70ehDbucN8nBYLidHtbvcGniHuvZGQYPOvtJfVQlNl4BtERGRI6dHb7O+88w5NTf77vNbU1HDppZf2ulMiIjJ0GO5aMpoPmNocefH9VmmfHrmd5SzgVc5gNUfz02ePDdlrNTR4PgAiaGIMO4jZXwwfFMCzn0BdN/fptid49hT3EVNW6re4XllJVW+6Pbjt2GU+zk8Fe5zng8CL0Nl7OWghIcHGASyVdrcBxSEe5iEiIgNCj/7MPPnkk9QF+IdEXV0dTz31VK87JSIiQ0dNVQ05mMNJ1PCYfgvteYn1LOApzuA1juYTxpR/GrLXqvLJzsPYh906LntkF/dob2WL9wvt9kInqSn1prayEg27bleBZY/2kammVfyDud1bq8REOEgmLus/yw4cCHyDiIgMKd0aHl9ZWYlhGBiGQVVVlWl4vMvl4rXXXiMrKyvonRQRkcHLecDJMMzD4+PyY/pl9XiAxDHmime0ux5qayEuLuiv5Ts0fiS7zSfjYyE1tXsPtPuHdvZXkpZSR2lZW//LSuuRAIwmmJEF506D3RWwq7xl5fi20G6d0x6M0J6QAG4clJBBNj7rOxwobv8mEREZMroV2lNSUrDZbNhsNiZMmOB33mazsXjx4qB1TkREBr+agv1+bfGjY73Dkftayjj/vdGNgyXY8rtZ9e6CyopawPN1jmCP+eSIXM/S4t1hT4AxaTAxE3KTYFgSzMgj7Z+WvdpL/Ke4CZ7t3s4+xPPRyjBCXmlvfcYBsi2hvQKMZs+ihCIiMmR166/AO++8g2EYnHTSSbz44oukpbVtRRMVFUV+fj55eXlB76SIiAxe9TvMob2RSKKy+2/UVta4JJqIIJJmb1vNrhISQhHaSwuAqUCASvuI4d1/oD0evneI58NH+pu1puOysmYkAJfTv81m8273BoHntPdWYsugkiJymM6GthOte7U7knv/IiIiMmB1K7Qff/zxABQUFDBy5Ehs3a0AiIiIWDTtMc/bLbFnkReR0j+dwbN6fCnp5NDWr4rtpSQcF/zXcpbspt3Qnj+q+w+0BU6QaSnmSntZWfcfPSS421mtvQ8r7SbFCu0iItKN0P7ll19yyCGHYLfbcTqdbNiwod1rp0+fHpTOiYjI4Ofeb563WxKZRV4/zWcHT+V0lz2DHHdbaK8sKOngjh5q2kdlRVuY9gvtI8d0/5n2aM9QasNcSU9LtYT28mhwVfXbtnphyx2o0h4B9hTvYShD+2fMIotiYoZHc/xZpTBnlPZqFxGRrof2Qw89lKKiIrKysjj00EOx2WwYhuF3nc1mw+VyBbWTIiIyiBWbA7EzJh0c/bNyfKvq6DTwybn1e0IQ2uvXUVkV7T0chmXV8uE9HI5vj/cb5u1XaS+PBXe5QruVK0Cl3ZEGtrZV3UOxEF3r8Pg/sog/sojTJ27l+Nv/4Wl0K7SLiAx1XQ7tBQUFZGZmej8XEREJhogScyCuik/vt+3eWtXFZ5hCe2NhaXBfwF0HDRtwVh3lbfIL7cOG9ezZtjjAEtqtlfaKWHCVQ2Tw5+kPaIGGxzvMCxOGstLufY2aKJ8+KbSLiAx1XQ7t+fn5AT8XERHpjehy8wTr2sTUfg/tTUkZ+O5CZxwMcqW9/nMwmryV9igayLRse9fj0G73n9cesNLu2tmz5w9mS/4OH2yE/FTIT4ETxsKJx5ouCcVCdNbQXlXtE9o1PF5EZMjr0R4iTz75JBkZGZxxxhkA3HjjjTz66KNMmTKFZ599VqFeRES6xnCz7OjbeG17DjkUkUMRE8fZ+j20u1PN1VVHeRBDu+GG+k8AvKE9l0L/63q6G4s9Ht7ZDm9uhv1VsN/J7JGzgHO9l5S2Do8Xs893wNo9ng+AKAfMDX2lPdEyS0GVdhER8WXv/BJ/99xzD7GxsQCsXr2av/zlL9x///1kZGSwaNGioHZQREQGMXc1RQ0ZbGQq/+NknmE+Vfn5/R7ayTAHtaiqIA6Pb/jKO+fcWRkDBBgaHxsNKSk9e74tDj7bC0+shde/gS8KST1g3gPeOzxe2rgbYJfl55yfGgbD483b9YmIyNDTo0r7nj17GDduHAAvv/wy3//+97nyyiuZM2cOJ5xwQjD7JyIig5lRg7MqxtSUnNQI9iAkoV6IyE43HcfVBqnSbhhQ96H3sLLaU2n3C+15mZ79wXvCHgfZ5tJtrNM8BaG+PpK66ipiU3r2EoNSUznsqTC3jUwFh/l3IRQL0fkPj29boFDD40VEpEeV9oSEBEpLPe9Gv/nmm5xyyikAxMTEUFdX19GtIiIibdxVOCujTU0pKUbPA2uQRA8zV1cTG4IU2hs3QXPbFnetw+Pz2G++LteyX3d32OMgxxzao8r8tzIrK3f5bQ03pO3ZCk1uc9vo4WA3v6kU6uHxd/Ebnqy9EOM7f4Oj/g/e+bz3LyAiIgNajyrtp5xyCpdffjkzZ85ky5YtfPvb3wbg66+/ZtSoUcHsn4iIDGbuapyV5rnbycmOfupMm7iR5tCe7ArC8HjDgNp3TU2tb1i8ywlcx++59MT3mJq8A6bP6fnr2OL9Ku220moiaKKZSG9bWVkMw1wVEJGBAAXfmI/jIiHbf42eUC9Edwb/4VC+gDUtDfuKA94jIiJDR49C+4MPPsgtt9zCnj17ePHFF0lP9wwdW7duHRdeeGFQOygiIoOYuxpnlbnSnpzS/6E9YXwu73MsJWRQSjolZHBtrYvouF70rWGDqcoObZX2LziULziU4y7JY+rpmyH1qp6/jj0Oss3lX5vbYHzSbjZVjvW2lVW0Lkan0A7A9m3m4/zUgG9ohHpO+wEsoyyKKz3z7e3m/52IiMjQ0aPQnpKSwl/+8he/9sWLF/e6QyIiMoTs2sGksmbs5FNEDrXEk5wS1fl9IZY6YyQTeN/UdlEZDI/r4QONZqj9n19zZbU5iCUlNng+sfWifGuLg4x4cNjAZXibJ8TvNIf2ci1GZ7Jjp/m4D0O77/D4InLMJ4urW+a1K7SLiAxVPQrtABUVFaxZs4bi4mLc7rY5YDabjYsuuigonRMRkcHNeHElr1c/7T1+lTNITrm7H3vkkZYGdruB2902t/7gQRg+vIcPrFsDrgpTk2G0rR7fyhvaA+y13mX2OLDbPEPk91d6m8fEBlhB3l2BtNi113w8MsVvETq3G2oti7kHI7RHRUFEBDQ3B6i0H6zxrCDvSOv9C4mIyIDUo9C+YsUK5s+fT3V1NUlJSdh8FgxSaBcRka5q2lOCb129ghSmpsT2W39a2e2QkWGj2Gc0+8EiJ5Dc/Ye5a6Dufb/mhoYImprMw+2TE1uGQdt6MQzfFuNZyC87wRTaR0WYQ3tpWRy4yqx3D10FhebjfP+V462BHYIT2m02z3MqKgKF9mrt1S4iMsT1aPX46667jksvvZTq6moqKiooLy/3fpSV6R8AIiLSNc17zH8z9pNHcmoQVvYKgsxM8/HBoh7+fat5G9z1fs2VVf7DnZMSG3o3NB7AZvcEd8tidMPs5m3lPHu1V/TutQaTXZYdAvLTwJ5qarIOjYfgLEQHbUPk/YbHH6zWtm8iIkNcj0L7vn37uOaaa4iL6+nkPhERETAKzXOq95NHUkr/7tHeyhrai4sqA1/YkaY90LA+4Km2BfiMlo+W0G4Pwt9Wm/+2bznuItNxeUVMy0J0Qk0FFFeZ20ble94A8REotAej0u77nHaHx4uIyJDVo9A+b948Pv3002D3RUREhhJ3A/ZicxAui8oiIiqxnRv6VlaW+fhgca1nInpXGS6ofrXde1or7afwFtUksIUJxFz4BNzwz552uU2AFeQzmg6Yjj1z2usDjgIYcgo2+beNHu/XVGMpeDscEB2k9eFaQ3sxll+8slpo6sEbRiIiMmj0aE77GWecwQ033MDGjRuZNm0akZGRpvPf/e53g9I5EREZxFyVRJZWmJoqEzL8qpv95dSK5zmDV8mghAxKKPrfTGjOg0j/vbsDqvsImg+0e7o1tA9jH/HUMp6t8BHgDELn7f6V9tT6g6bjsoqWtQPcFWC3DMkeaqyhPS0Wkof5XRZo5XifZX16pXV4/EEsQzwMoPRAj5ZTEBGRwaFHof2KK64AYMmSJX7nbDYbLperd70SEZHBz1lIREODqak2ObOdi/vexNrP+RZ/9x6/X5zp2Wu9K6G9+QDUvtfhJa0rx+ex33wiLwjfA1usX2hPqCk1HZeVt4R2VzlEDPHQvmu7+XhEit8idOAf2oM1nx3aKu0l+G8zR/EBGBO81xIRkYGlR6Hdd4s3ERGRHtm306+pMS1AYOkntkxzX2JqKqHhK4g/FWwd7CVvNEHVi57h8R3wrbSb5GUHuLqb7HGQZQ7tsVVOHDTjavnTb6q0D3XThsM1c2CvE3ZXwOQssHce2oM1n933Wc1EUkYqafisN+C7jYGIiAw5Pd6nvVV9fT0xMTGdXygiIuJr3y7TYRmpxKSGz9+TqFxzaEtocHrmf9d/BrFHt39j9X+gufOQVVndXmjP7XZf/dhiITcRpmZ7Ku7ZCex1DSfqn43UtYZ2b6W9ovevN9DNGg5T5prbulBpD2ZoT/R5j6WYLHNoP3jQ/wYRERkyejRx0OVyceeddzJs2DASEhLYsWMHALfeeitPPPFEUDsoIiKDVOFe0+F+8khODtIE4SCIGW6utCc2tYSo2g/AXRf4ptoPoX59l57vrPSEdv/h8cO7083A7HGQEQ/vXA3Pzoc/nkXNNfOoo21l+uqaaBobHZ7h8UOd27LQmz0aHP4LIloXogtFpR0CLEZ3UD8jEZGhrEeh/e6772b58uXcf//9REW1DRE85JBDePzxx4PWORERGcT2mcPqfvJISQmf0J6Qb660phulnpDrroGql8CwTBWrfd+zJ3sXtTs8fviIHvXXxBbr15Sa7P9Gg2fbt4rev95A5xfa0wJe1hfD4yHAYnQHq8BtXv9BRESGjh6F9qeeeopHH32U+fPn43A4vO0zZszgm2++CVrnRERkECs0r6zuCe29nrUVNMljzZX2OOoo2dvSv8Yt4HwSGjZ65rlXLIea/3Xr+ZVV0ThoJhvLCvN5I3vR6xY2/73eU1P8t3Yrq4jV8HjwD+0BhsZDaBei8x0ev4YjWZN2PFwwAxYeA0eNBKOm/ZtFRGRQ69G/jvbt28e4ceP82t1uN01NTb3ulIiIDAGF5nm6+8kjJbWDBd76mDW0A5QXGOS1ruLdtMvz0UPOqhiyOYADS8V+RBCWCbf7rw0QFeUiIb6B6pq2jcXLymPBKAF3rWdI/VBkNPlPd+hiaA9Vpf1+buKDMT9k1f/5TDl014Ij8AgAEREZ3HpUaZ8yZQoffPCBX/v/+3//j5kzZ/a6UyIiMgQUmufp7ieP5JTodi7ue460ZFyWP5NVu4O3pWllVbT/0PhIO2QGYU57gEo7QFqqOZx6V5AfytV2a5Ud+j20A1TXWN7AcqvSLiIyVPWo0n7bbbexYMEC9u3bh9vt5l//+hebN2/mqaee4tVXXw12H0VEZLBxN0CR09S0nzyOSe3Re8mhYbdT4Ugn3dU2IqBmXzMQnHn3AUN7dhI4Inv/cLv/nHaAtJQ6du9N8R6Xm7Z9y+v96w5Ey5fB7Us9e7OPSIHDh8OiKwJeGsqF6BIt6975hXajNngvJiIiA0qPQvtZZ53FihUrWLJkCfHx8dx2223MmjWLFStWcMoppwS7jyIiMti4q+Cdqzl+9rk4iivJYz+fcBSXp/R3x8yqojNIr20L7Y0HGoDgbEvnrIxmmnXl+NyUoDwbWyTYIuDLvfDUp3CgGg5U8dd973AU73kva6u0D+HVybdvhX2Vno+Pd0NVA1zf/5X2KlXaRUSkRY9X/Dn22GN56623gtkXEREZKtxVkBjN57XTqKJtSHxycj/2KYC62HTwKXA2H6wnWKG9sjpApT03iHOWbbFQXAVPrvM2jY9uNF3i3at9KK8gv9OyLsGI9IBrAkBoF6LrdHi8Ku0iIkNWj8YhjhkzhtLSUr/2iooKxowJwgI6IiIyuLmraG62U1VtnsOektI/3WlPQ6J5MTpbafCCU8Dh8bmBK7w9Yo+DLPOY6+TGMhw0e481px3YbfkZ5Le/pkAoK+3W4fH19ZE0N/v8M02VdhGRIatHlfadO3ficvkvxtPQ0MC+ffsC3CEiIuLDXeXdp9xXuIX25hRzaLc7gxPaDQOclTHkWYfH52UGvqEnbDGQbU6VdsNNFsUUtsxfL6tQpZ3dRebj/FHtXhrKOe3WZz3ETzAufh+cVVBSA8/cAEcH7/VERGTg6FZof+WVV7yfv/HGGyT7jGN0uVysXLmSUaNGBa1zIiIySLkrcVb6h/ZwGx5PurnyHVVVFZTH1tVF4nLZeYDreJ3TyWM/V5/6PxJmTAzK8wHPYnQZ8eCwgcvwNudS2BbaW4fHu5yBnjD4NTfD/jJz22j/LW1b9eWc9nN5kci3fbZFLDoQvBcTEZEBpVuh/Xvf+x4ANpuNBQsWmM5FRkYyatQoHnjggaB1TkREBil3FRWV5nnDDodBfHxwVmYPlubxk3lv5XGUkEEp6eyKnMpcer9oW+sogzc4jTc4DYBL/nQ/CSNm9frZXrZYsNsgMwGK2t5syKXQ+7m30m40eoZf24M4SXsg2LsXXG5z26jJ7V7el6G9mCyy8AntxQcREZGhqVuh3e32/GEbPXo0a9euJSMjo5M7REREAli3AVY1MY6t7CePWuJJSTGw2cIrtFeds4BTH2l7kzqzuYa7+V2vn+sMMDUgKbHBE7SDpXXbt+wOQnu5z+u5KoZeaN+5w3wcFwlZ7a/NE8qF6CIjIToaGho8xwexTJUoHsIr/IuIDHE9WoiuoKBAgV1ERHrugX8z89bfspUJ1JDArSwhOTm8AjtApiU3lZbF4nb3vp/W+fwxMU1ERbnaXbW8R1rfAMg2r3AWsNIOQ3Ne+84t5uMRKeAIvBhgc3NboG4VzEq79XnFZJlPllZ6RkSIiMiQ0+Mt31auXMnKlSspLi72VuBb/e1vf+t1x0REZJAyDCgyVw2LySIlJfxDu9ttp7wihvS0ul491xrakxJa0mBQK+1xnv9aFqPzDe0VzhhcLhsOhzE0V5Av2Go+HpEOdv9REOC/CB0EP7QnJkLr5jx+ob2kBty14Ijyv1FERAa1HlXaFy9ezKmnnsrKlSspKSmhvLzc9CEiItIuowaKKk1N+8kLu5XjAQINKjtY2vsx0U7LfP7kpNbQHsxKe8uzstoP7YZha+vLUKy07yowH4/MafdS69B4CO7weDC/CeA3PL6kVtu+iYgMUT2qtD/yyCMsX76ciy66KNj9ERGRwa6pAg6YE9B+8hgZbivH45ljnJjopqqq7T3uktI4GN+751ZWRxNBE81EADbPfHZom4ceDF0YHg9QXhFDWmrd0Ky079ptPh41st1LA20c0KfD40tqwAjOloMiIjKw9KjS3tjYyDHHHBPsvoiIyFBQvBuazdOqwrXSDpCZae7rwdK4Xj+zsiqa33EDNcSzhfE8XnAOLP80dAvR+cizhPYhvVf77v3m4/zR7V5qrbRHR3sWjwumRJ/3V/wr7TWqtIuIDFE9Cu2XX345zzzzTLD7IiIiQ8E+85BkF/aWOe39053O3NhwF09yMa9zGp9yGI41BZ3f1AlnZTTD2EccdYxnG4dWfgL7nKEZHm+ptGdTBLTt2962V3tF8F57IHC7YU+xuW30hHYvt4b2xMTA1/VGp5V2tyrtIiJDUY+Gx9fX1/Poo4/y9ttvM336dCItbzX/4Q9/CErnRERkENq/y3R4gGxcRJAchsPjAU6tfJHRfOU93rvvVCDwYmVdVVkVTR6WKm9uEth691wTW+BKexRNpFNKKZ4J+217tTcNrb3aCwuhyWVuGzWl3cutw+ODPTTe+ky/0F7dCHXl0PuBHiIiMsD0KLR/+eWXHHrooQB89dVXHV8sIiLia/9e8yF5AGFbaa9LyAKfwGYcrCEYoX0Y+8yNeekQzH3qbdFgs0Omf7rMY39baDft1V4+dEJ7XBz89lzYUwj7Kz172ed2vdIeitDe4fB4gOJCCLwjnYiIDGI9Cu3vvPNOsPshIiJDxT7znOpwD+0NyVn4TgN3lFYDab16ptMZ5V9pzwmwVH1v2GyeIfJRbjh2NMREQHYC/1x9NOXbU72X+e/VPjy4/QhXKSlw6aFgHOI5tsdBRPtlbGulPdTD4ytIodkWQYTR3NZYfAAmB/91RUQkvHUrtJ9zzjmdXmOz2XjxxRd73CERERnkCs3ziFtDe7gOj3elm4cpR1VWtnNl17lL6oiiydyYlxX44t6wxQC18OLF3qb/XnMWe7eP8B6bQvtQmtdu1IJvIHZ0/EZMX1TafZ9pYKcyKpW0hoNtjQcP+t8kIiKDXrdCe3K4/otKREQGjv2l5sMwr7TbssxhOq6motfPjCozP8Nts2PPye71c/3YY8EybTstpc50bBoeP5RWkHdbSudhENqt1fvyiHRzaC9WaBcRGYq6FdqXLVsWqn6IiMhQ4K6F/RWmpj14qr7hGtojLBXwhPqKXj8zrsL8xkVDUjKxkSGYSx5gC7m0VEtoH6qVdrdlxIQ9NfB1Lfp6eDzAurijGXuMCzLiPR8TUoL/oiIiEvZ6tOVbsDz88MNMnz6dpKQkkpKSmD17Nq+//rr3fH19PQsXLiQ9PZ2EhATOPfdcDhw4YHrG7t27OeOMM4iLiyMrK4sbbriB5uZm60uJiEg4cFdCoTks7W2ZQx2ug7liRppDe2pzaTtXdl1SdYnpuCkjJbjbvbUK8ExV2lu4LKE9DCrt1mcuTvst/OOH8Kez4Na5MCPTPKRfRESGhH4N7cOHD+e+++5j3bp1fPrpp5x00kmcddZZfP311wAsWrSIFStW8MILL/Dee++xf/9+07x6l8vFGWecQWNjI6tWreLJJ59k+fLl3Hbbbf31JYmISEdqiqHMHBpbQ3u4VtrjR5tDe6ZRTE1NZDtXd84wIKXWPMzZnZ0E9hCEdnsPKu2GwZDQ7DQfOzqutPfFPu3WZ1ZVB9ilQHu1i4gMOT1aPT5YzjzzTNPx3XffzcMPP8zHH3/M8OHDeeKJJ3jmmWc46aSTAM/w/MmTJ/Pxxx9z9NFH8+abb7Jx40befvttsrOzOfTQQ7nzzju56aabuOOOO4iKiuqPL0tERNqzd7t/U0toT0rq6850TfJ4c2iPp5bd+xzET2hq546O1ddHkO02b/dmy00IUaU9QGjvqNJuNIO7GhwhSKThpK4O0k+DnHgYlgx5yfDgAshp/5a+3qcdoLomwL9jjBogTP/HIiIiIdGvlXZfLpeL5557jpqaGmbPns26detoampi7ty53msmTZrEyJEjWb16NQCrV69m2rRpZGe3Ld4zb948KisrvdX6QBoaGqisrDR9iIhIHxiVCDt/zbq/3s2J/I/5PE01iSQmgsPR350LLGG0/37ZFdvdPX5eRWWM3x7tkSPiQzs8fp8TrlsBP3qWY35zK1sZB3gq6uXOWHNxfSgMkd+7F5qaYY8TPt4N/9oAcR1vgN4fw+MDhnZ3TfBfWEREwlq/VtoBNmzYwOzZs6mvrychIYGXXnqJKVOmsH79eqKiokixjJfMzs6mqKgIgKKiIlNgbz3feq499957L4sXLw7uFyIiIp1zV0JcJEUJ+bzLid7mcB0aD2BLSqSeaGJo8LZV7+5ZlR2gwukf2qPz40IT2luHxze54e+fARAPjAOSqKSSZJqaHNTURJGQ0Oi51lUBkSMCPW3w2L3bfJwU2+lQj75YiM76zKYmBw0NDqKjfbYA0PB4EZEhp98r7RMnTmT9+vV88skn/OQnP2HBggVs3LgxpK95880343Q6vR979uwJ6euJiEgLl2cecYXTHFDDObRjs1Eeaa621+9taOfizgUK7Y68hNDMaW8dHp/tXxbOpdD7uWle+1CotFv/7g/P6PSW/qi0Q4Bqu6FKu4jIUNPvlfaoqCjGjRsHwGGHHcbatWv505/+xAUXXEBjYyMVFRWmavuBAwfIyfFMOsvJyWHNmjWm57WuLt96TSDR0dFERwdY3EVERELL7QntzkpzQA3XleNbVUZnkNu013vcVFQP9GzdlMpSBxlYVqDPTQzt8PjYSEiKhsq2NxtyKWQzkwDPvPaRw1sWZnOVB78f4Wb3TvPx8A4ms7foj9CeShnRN/0L6iugpBZKamDN4eC/VIGIiAxi/V5pt3K73TQ0NHDYYYcRGRnJypUrvec2b97M7t27mT17NgCzZ89mw4YNFBcXe6956623SEpKYsqUKX3edxER6YBhePfGrqgcQJV2oDbBXIk1Dva82lnhjOE8nueXLOV3XM9/ks6BvKTQDo8HyDaPvR7SlfbdO8zHI4Z3ektfDI+Pjzcfu3CQ8O818MYWWLcXdpVDcWHgm0VEZNDq10r7zTffzOmnn87IkSOpqqrimWee4d133+WNN94gOTmZyy67jGuvvZa0tDSSkpL4+c9/zuzZszn66KMBOPXUU5kyZQoXXXQR999/P0VFRdxyyy0sXLhQlXQRkXDjrgLDs4Cbs9L8/9HhHtoP5k7l/aIGismimCwaYyZzmrVa3kVltYn8P77jPf7W5F2ckbQs9Pu0ZyfA1rb94dsN7a6K4Pcj3OzeZT7OH9XpLX1RaXc4IC4OalumrVeShDvCgb3ZZ077weLAN4uIyKDVr6G9uLiYiy++mMLCQpKTk5k+fTpvvPEGp5xyCgBLly7Fbrdz7rnn0tDQwLx583jooYe89zscDl599VV+8pOfMHv2bOLj41mwYAFLlizpry9JRETa43bCS19BRjxRew4QSy11xAHhH9pXn/Eb7vi8bV77WVHf8Eue69Gz/EYZJNd7Pgn1Pu1ZHVTafbd9czs9oyJstuD3J1zs2Ws+HjGuw8sbG6HJsvZgKCrt4HkzoNa71pyNxsREYsor2i5QaBcRGXL6NbQ/8cQTHZ6PiYnhwQcf5MEHH2z3mvz8fF577bVgd01ERIKt7iBc9SIAd/IUdwKHsIGvOSTs57RnZpoDbElZXI+f5bcIX1I92Bxg69kc+Q7ZosBm94xwsCxG126l3XB5pjE4wvyH0lOGAXstQ8xHTerwFuvQeAhNpR08bwb4zPqjPjHZHNqLezbCQ0REBq6wm9MuIiKD1L7t/k0MA8J/ITpraD9Y2vPQXu40ryKWklwPthBO6WpnBfk89ns/N1XaYXAvRldRAdV15raRozq8xTo0HkIX2q3PrY1LMTeUlIXmhUVEJGwptIuISN/YU2A6rCGOClIASE3th/50Q0amw3R8sDS+nSs751dpT64PzXz2Vq3PzuniQnQwuBejs273ZrPBsGEd3hKo0m5dNC5YrKG9OibF3HCwwjMaQkREhgyFdhER6Rt7d5sPGQ54Kthpaf3Qn27ItIT28opYmpt79ie0vswFGN7jlKT60Mxnb9U6r72j1eOHUqV9t/n3kKxk6GTxWmulPT4e7CH6F5R1rnxltOV/HKW14K5FRESGDoV2ERHpG3v3mQ9p22Yr3CvtmVmRfm2lZT3bLPsva8+ilji2MZb3OI7x5V/0TaXdMjw+mUpi8YS/ckv1f0hV2sNkj/b2nl0Rad5ukNJaMHq+5aCIiAw8Cu0iItI39plXvfYN7eFeaU9Lj+IebuYpLuIt5vIVU6lc6+z+gwyDzMYDxFLPWHZwHB+QFBvq4fGBK+3QVm0f0pX2ER0PjYe+2aO9lTW0lzmyzA0lNaq0i4gMMf26eryIiAwR7lrYbw6CA6nSHhll51Lb38g22t54+HL7PCCpew8qqyPWMC+CFjkqIbShvXXofUIUxEVCbdveZbkUsoOx/nPaB3VoN6+twMj8Tm/py0q79Q2BYptlJEBpDbhVaRcRGUoU2kVEJPRcFVBYaWoaSJV2gNLIbLIb20J7w946uhvajX1OfNehd2EndnRMiFePbwntNhtkJcDOtkDurbRbQ7tRDUYz2AbhPxOuvBCmVcL+StjnhNnHdnqLNbT3ZaW9mFxzg4bHi4gMOYPwr7GIiIQdd7knJPloDe0REaFbiTuYnLGZ0Nh27C6sBrK79YzGHTX4xvP95JGS2hTahehsPoH82NEwMZOKmAz++O9T+AbP/uS1tVHU10cQE9Psuc4wPG+0RGT4P2+gmz0Zphzp+dzmgPQFnd5iHR7fl3Pai1yW0F7bBFWl0LMlFUREZABSaBcRkdBrKIED5nJla2hPS/MUgcNdTWI6+ExjtxUH2AesE3UF5tC+hxFMCvWWb75vCDxwJgBNJXEs/veNpsvKnTHkxvj8jNzlwCAM7S6fN4/sSV365evP4fH7mvL8LyouhCz/ZhERGZy0EJ2IiIRe4U5wG6am1tAe7vPZW9WnpJuOo8squv2M5p3m9LeHESQlNvTNQnQ+UlPq/dqGzGJ0bp83Wxxd++Xrz4XoDtSnQaTln2sHD4SuAyIiEnZUaRcRkdDbY178q4EoSlqquANhPjuAK8sc2mMrux9qjX3mKQJFkcOIiHD3zZZvPiIi3CQl1lNZ1XZuyCxG5/attKd06Zb+3PKtujYKThrnOciIh/Q4SNM/30REhhL9v76IiISeZZutfQyDliXZBkql3ZZjfnchqba0289wFFWYjsvisoG60C5EZw88+Tkttc4c2q2VdvdgDe2+lfaULt3Sn8Pjq6od8PcLzY2OuNB1QEREwo6Gx4uISGgZBuzZb2raRds2WwOl0h4x3FxpT2s86PnauiH6YJnpuCKxZSG7kC5EF/jZaSnmreeGRKW9oQGaK9qO7clduq0/h8dXVzv8L9Lq8SIiQ4oq7SIiElruKig3hwzf0D5QKu2xo80djTIaoaIeUru4jHezm1inObRXp2QAO/t8eDwECO1DYU77bbfB0gcgJwFykuACN1w3o9Pb+nV4fLUNw7Csl+euB8PlWf1eREQGPYV2EREJLVcZ/OZkuO542O/kjl/M5JVPDvWeHiiV9qTx/h01iqqwdTW0H6jC7nabmpozW94ICGloj/YkPsOA+mb49WtQVM2jXzxPDOUcyRr2MoJyp3Wv9kZw14B9AOzH11X79kCTC/Y4PR/HdG2KQ38Oj3e7bdTVRRIX12Q5UQuOEJb8RUQkbCi0i4hIaLXOjY6JgDHpvBdxIp8z2nt6oFTa0/OiKSWNdNqq5TU7G0iY3MUHWBahqyeaiJwYT6AO5Zx2m83zpoBRB9EO+OcX0OT2/gRyKWQvI/wr7eB5w2Uwhfa9e8zHw0cHvs6iP4fHA1TXxvqHdqMGUGgXERkKNKddRERCyzLMutwyd3qgVNozM23sx7xnds0O/63T2rXPaTrcwwjSUuvBFhX6jepbK/k2G2SZU2EuhUCAOe0w+IbI79tnPh4xoku3WUN7Xw6PB6iuCzD33q157SIiQ4Uq7SIiElou8zxuazgcKJX2+IQIDthzmOb+ytvWsLvnoX03I0lPqwvt0PhW9ti2AJ6daKr6e0N7oEr7YFpB3jBgf6G5bdiwLt1aaR4kQVJSkPoUQFxc22yGVo1fV8Lv/w0lNVBaC5EOePfc0HVCRETCikK7iIiElttaaTeH1IES2rFFUxGdAT7rtzXvr+36/WdN5cZ/XED99lpGsIcCRjMztTa0Q+Nb+b4xkG0eUt1xpb3Mv22gcjqh1vImSxdCu8sFNZaidnLXFp3vEZvNU233re43lgDPrm9riI3QCvIiIkOIQruIiISWq22xr6YmO9U15pA6UIbHY4tiT8p4Pqj7FoXkUkguh4yIY0xX7x+Rwkvus9lG29Zx/y/ln31TaTeF9naGxwec0z6IKu3WofEAeXn+bRbWofEQ2ko7+If2iqhc8wV1zVBVAl1cA1FERAY2hXYREQkddy2s3QbfFMPwFCrjc4imngbaQuTAqbRH8caki7m2cIm36be5b3EyH3X5EaXlcabjPhseb/NJd+2F9sFead+713yckQLRnY9ysA6Nh9CH9sREKPQZyV8aEeDNheJCyAptP0REJDwotIuISOi4yuDVTfDgKgDSgSfZxQ/4p/eSgRPao8lIM1eeS8ri2rnYn8tlo8JpDuhpKXVg64MVwO3tD4/PYz8AzsoYmpvtRET4bEvnrvZs/WaLCn0fQ81aaR/WeZUdPKPqrUK5EB34vylQ5sqGKAc0utoaDxaFthMiIhI2tHq8iIiEjqsM9ppTzy7yvZ/HxXWp2BkebFFkZpjnER8s7Xpor3DGYBjmVeLT02rNgTpUulBpB/zeVAAGzxB5v9Des0XoEhPBHuJ/PVnnzDur4iDd8rtWfDC0nRARkbCh0C4iIqHjKoW9FaYm39A+YOazA9iiyEgzLzx3sLTre5hbh8ZDa6W9r+e0myvt2RzAjqeCO6iHyFuHxw/LD3ydhTW0h3IRuvZew1kZA+mW37WDJaHviIiIhAWFdhERCR1XKexpv9I+YIbGA9iiyc40V9oPHOxiaC+tpXprHTbahp7HxjYRG9vcN6vH29uvtDtwk4mnaht4MbpBEtr37TEfD+/aHu19ud1bK/9KezRkWEP7IBkBISIinVJoFxGR0KkpguJqU9NuRno/H1iVdgc52eZKe1FxFyc3/+MzZl18E3XE8g0TuYebSU9teVZfV9oz4sFuHqY/JBajs4b2Lg6Pt85p74/QXlkZ5T88vsQJRnPoOyMiIv1OoV1EREJnT4Ff04CttAM5WY38iL+zjEtYyUm8s/8w3A+u7vzGXZ6qaDSNTGQLGZSQntqy4XufzGn3eQ2H3a9q2xrayysC9MU9SEL7/kLzcQ/ntPdFaLe+hrMyEjIsob20Ftzaq11EZCjQ6vEiIhIarirYY14sqzYqgcrGtjLigKq0AznZjRzHJ1zCk962um8yO98ue1eF6bCA0aS1hva+Hh4PniHyPiMgBn2lvaEBikvNbcOHd+nWsBgeX2mHKZZRHSU1ntDu6INJ9iIi0q8U2kVEJDRcJX4rxx+MzYPGtuPMzD7uUy9lZzebhvcDNO+q6vzG3eb5xzsY03/D4wFyEmFD25Zh3tAeaE67u9IzDNs2gP/JYLPBs3fCzlVQWAkliTCiZ3Pa+2UhOqcNMizDUkprwVClXURkKBjAf4FFRCSsuUpgT4WpaX+EubqZkdGH/QmCyKhIyuOzwTcr7Quwkbcvl9vvzYsdjGGWt9LeR6HdZgPD8BwfOQIcdt7ddgjvbJvGu5wAtFNpNwxPtT0iK/T9DJWoKDhjBtS3zAFPOh+iu5a+w6LS7gQyLcNSSms0PF5EZIhQaBcRkdBwlcI+c+LZbZir1AMttGOLojY9zhTaow92Mnx8jxOa3aamAkYzN/Wblmf2wfB4mx1sUWA0eI5/cSwA/7njFH6/bY73soCVdhj4oR08IwZa2bteLg+Hheg8od0yLKVEoV1EZKjQQnQiIhIaroN+e7Rvax5jOh54oT0aV455mHJUfS1U1LV/z3bzftplpFJChmePduibhegAbP6B3DuvvkXASjt43oAZ6Nw+0xjsXU/e4VBpr6wEMrPNjXXNUD0Ifi4iItIpVdpFRCQ0XCV+e7RvqhtrOh54oT0K+4gkmtc4iMDV1l5QBjPbWY18qzm0b2YiYCMro6atAt4XAgzD975x0KLDSvtA11pptznA3sWt+giP0F5fD42puUQdMcKz9VtGnGcHgOYurKcgIiIDnkK7iIgEn9EIzRWw3xzatzYN/NCemdPATkYxju1t7dtL2w/t28zVUE9oh6zMmr4ZGt/KuoI8Q6jSbrjA3bLwnz3RM7+/i8JhIToAJ7lk/udSc2OU2/9CEREZdDQ8XkREgq+5BA5UQ5M5VFhXXh9oq8djiyYnq5qtjDe37+igEr3NXGn/hkkAZGdW980idK26UmkfrKG9bC+4Wxbh68Z8dgiPSjuAszrA6ADNaRcRGRIU2kVEJPhcB6GsFrLagoY7MoIDtM3LjYjomwAUVLYocrOr2MIEc3uHob2dSnvG/2fvvsPjqK4GDv9mV9Kq92rLvfeGbQzGgG2w6b13CCSETiCBUEPoJKEFQpKPFnqJKTEdY2PcjXHvTS6S1XvX7s73x6jsnS1a9ZV03ufZRzt37szO2mt5z5x7z63o5KC9uUy7TmFRGE6nhyy0s8wYPdFdTT0O+j0Kk5+DeU/CqlV+H9oVhehCQyE42HQdZRHuHSVoF0KIXkGGxwshhGh/jnwYnQJbfmcss5VVws/fxKA/1HSvODGxRaOUA0N9pn25OdO+30smurQacsuVpoagPSmhEiydOD/AXPDub0sZsSyH9bxCPw5xO8/xtvMKyspDiImucT/eUQhBqZ1zre1J1yErxxj1cbjEbfk9XxwOqDDFxZ0RtGua8ToFLh+rklIPQbteaby/bvcPSQghREtIpl0IIUT7c+Q2PQ8NgsEJ7EmepHTpdvPZAbQQj8Pj9b2FTWuguzJl2e1Y2csQ4mKrCAlxdO6cdnOmfdVBwpdtYyIbSaCQdA4DUOR1iHy+5/ZAV1AANaZRAn291B8wKfNQ562zRoe4VZAv9/D3ojtAr+6cCxJCCNFlJGgXQgjR/ux5bk35heHKdvcM2m30SXUfHq+VVkNBpXt/03z2/QyiFpsxNB66dk57XzX67MchoAfOa8/MVLc1DVL9GzFgns8OnVOIztPrlJR6+aw4yz23CyGE6DFkeLwQQoj2pdeBs8itOb+gJwTtIURH1VAYnkJNZQg2XDK4+wqNZbhc7fZRhA46N2g3V49P8xy0FxT2sKD98GF1OyXJfcK4F+b57ACR/q8W1ybuQXsILM+A9zcYN4gKKmBkMrxxNdDdKjoKIYRoCQnahRBCtC9Hvseh4vlFapDYPYN2G5oGfdMr2LdrMKPY0bRvbz5M66f2/+NsuGIKb/8pmV3/szcOq2/KtHfm8HjfmfaG4fHmERGNumvQbs60p6f7fag50x4ZCZZOGqPoHrRbIKMMPtjY1KgDuhSjE0KInk6CdiGEEO3Lw9B4gPzCnhC0hwCQnlbKT7uO4yD92cgERp5l5cwTPBRv0zToH8uPMfP4P6Y0Nqck1Qda5uJwHck8p72PGhU2ZNrzCjwUPIPuO6fdHLT37ee5nwdFpgEjcXHtcD1+cgvaS4DBCWpjfoVUkBdCiF5AgnYhhBDty5ELZTXw8groFwvpMZAeQ0GPGB5vZMbT+5Tya/7V2HxTwhrOTPvS62E5eeqY6i6Z024eHt/HdBOFAsKodJvG0MhZA44ysEZ10AV2ELeg3b8idBCAQXuS6R9NQYXMaRdCiF5AgnYhhBDty54LGYXw16VNbRoUjXpY6dY9g3Yj096vjzrZ+VCW75Liuflq9rppTnvgDI8H6Esmed6CdjCy7d0taD98UN3u1kF7stpYZYeyAvAyOEIIIUTPINXjhRBCtC9Hnvta2KlRZBeq2ebuGbQ3ZdpdHW4maM/JU6OqLqse77qed6QNotSbBv045H14PHTPIfKHD6nb3SRoN79WURGQ7KHqfe6RTrkeIYQQXUeCdiGEEO1HrwVnsVvQrqfHkl+gVuzunkG7BbRg+vVtWdDunmnviqDd4p7ZN2Xb+3PQeyE66J5Be5YpqO3OQXtMIoRY1R15OZ12TUIIIbqGBO1CCCHajz3PqBx/qFhtTo2jrk79L6dbBu0AWgjpaWrQnpsfSU2NSzDlcMLji+CrHVRk1FJZGaL0b8y0d2YhOnC/SZAeq2wO4EDzw+O7k6oqKDKN+mhD0B4b2/ZL8ld8vLpdWAhYIyDB9PeTm9tp1ySEEKJryJx2IYQQ7cdRn/UzZdrL493Xke6+QbvNbXg8QPZmnQHle+GEIbA1B55bBhjTjffwARPYSAXGFIGU5C6Y0w71FeSLm7b7qROnB7HfeyE66H5Bu7kIHbRoybfiYnW7yzPtWjgkRsCRsqYded10KT4hhBB+k6BdCCFE+7HXZ/1MQXtJVIqyHRoK4T5iw4CmhRATXUhkRA2RFYU8zh85jp8YcOpeCLLA7j/A97uVQ5xYGgP26KhqoiJr68/VyZl2cwX5/mpkOJAMCorCcTo1LBbd/XhnqTEFQgtx3xeIzEF7VCRE+V9IL5CGxxcXg0OPwJpgqjmQX2ZU9rd08g0gIYQQnUaGxwshhGg/jvo12k1Be27YQGU7MVGtidataDY0zShGV0YUV/AWQ9lr7LM7Ycle+E4N2r/k1MbnjVl6LRg00/zkjma+SdA/VtkcSAYOh4XiEi83E3Qd7N0o2374sLrdt0+LDu/KoN08PB6gpDTCyLS7yq8AXdZqF0KInkwy7UIIIdqPPQeq6oxAwsXh4GHKdrJp5apupX5Ie/++JezYncRSZjGbxU37/74CflGDRdegvW9q/dDmzs6yQ/3weBf9YtVNDhFEHXkF4cTHVXk+hyMPglsW/HaZ2bPh7d8Yy74dKYPUU5s/xkUgZdoBikoiiTfPaS+oNNZqt3qI8oUQQvQIErQLIYRoH8764CGzxG3XPudIZTslxa1L91E/NHzwACOi+4gL1KB9nRqwVwWH82Pd8Y3bjZn2zi5CB+7D4wfEwYlDePWnE9ltH8J+BqGhk5cfwYihXuZKN4ymCABbtsArrxgJ9UmT4KabTLUS0tJg/mBwpoA1GuLvbNH5uzJoDwuDkBCorW1qKywOY0iiOWivMP7dCSGE6LEkaBdCCNE+GuazHzIF7XFhHC5V15fu3kG7kWkfMtCI6BZwLn/nZqw4PXZf1PdcajKaAvS+DZXnO7sIHbhn2uPC4IPLefSo28g42BSR+l72LTCC9uefhzvvBGf9H/tnnxltCxbACSfUd9IdoFcazy2+l+Uzq62Fykq1rTODdk0zhshnZze1FRUHQaJaPJB8CdqFEKKnkzntQggh2oejPmg3Z9r7xpCdG6k0deug3dIQtBcCkEsKH3Kh574avBF2g9LUuFxcVwyP95LdT0pQo1Ofy77Zuz5o/+9/4fbbmwL2BkVFcMYZsHFjfYOzzJiHDy0O2s2V46Fzl3wD95sEhYVAkmkYfMPweCGEED2WBO1CCCHaR2MRumK1PT2GnFw1WExVE+/diynTDnAfj1FOhHvfa6aysmiC0tQ3LYDmtNdLjFeDdp+ZdmcR6HXteVUtUloKN97ofX95OVx9NdTVYVS7b2Dxv2o8uA+Nh87NtHt6vaIioH8fmNYPThkJV0yGyyZJ0C6EED2cDI8XQgjRPuz1a7Sbh8f3iyHnJ3UoeLfOtNcH7Q1z2gH2M5gz+ZxvEs8lOL/+/V80gdr7TuHI62qw2Jhp74o57V6C9qQEtXBgXr6HGxANdN1Yrz0orT2vzG9//SvkmZL9kZFGsN5gwwZjrvstN7isZ97CTLs5aA8LA1snz2gwV5AvLATGj4CF16o7JGgXQogeTTLtQggh2oeX5d5IjyUnV13arHsH7UYhusjIWlKSmoKlxczmu6efg0W/hl9uhxfP5kBeArqurm03qCHY75Lh8d6C9hZk2qHLhsjX1MDLL6ttp58O+fkwdqza/tcnaqnetx8c9WPo25hp7+wsu6fXLCoCLB5uqMiSb0II0aNJpl0IIUTbOUrBWb9EWFwYJEVAnhFI1KXGUVio3iPu3kF7U7p18IAicvKa5uvvzkqB05vG/u/LUKOu+LhKYqJr6s8TOJn2RJegPZoS33Paoal+QSf79FMjQHf1xBNGBvzll2HWrKb2xCObCB19BgRZICUSBn0JS1cbFd78EAhBuznTbgTtHv5umsm06zosXAjLlkH//nDVVcboBCGEEN2DBO1CCCHazrWi+BsXGT+r6iCrlFznYLfuPSVoHzKwkJU/92vc3puhRln7DqiRnuuQ+oDJtOeUcfFn9zOfuxhIBtGUcmzeXt/nsXdN0P6f/6jbJ5zQlGE/7jhjWfYffjC2+5JpPLE7IbMUtEy/A3YIjKDdYyE6zUOm3UfQrutw/fXw6qtNbS+/DEuXQkJC+1ynEEKIjiXD44UQQrSdp8xrWDAMSSC7doDSbLW6ZxC7FSVoVyO7PftbELR3yZz2ENDUqQqEBjNw82omspFYSrCgY8sr9H2eLsi0l5bC99+rbdddp27fcUfT88agvbEhvUWvZ64eHwhBu9dMu+4AZ6V7O/D++2rADrBtm1F9XwghRPcgQbsQQoi285F5zclX03nJyWDpzv/7uATtw4cUKLu27UpSts1B+6D+xS7n6YKgHdyHyMeEUhetjpWOLzrs+xzOEnDWtPOF+fb118ba6Q2Cg43l3Vydcgr0qx/40Nag3Zxp7+zl3sBLITpPc9rBY7Zd1+HJJz13f+cdOHCgbdcnhBCic3Tnr01CCCEChcN7YbKcPDVw7dZD40EJ2seOVG9WHDgUS2lZ0/6AGx4PHofI2/snKtv9a/ZTWRns/Ry67vPvvCN8+aW6PWcOxMSobVYrXHaZ8dwtaE9vWdBunjufmOi5X0fymGnXwmHBZrjpE7j4HTjpX/DcTx6D9uXLYdMmz+fWdXj33fa/ZiGEEO1PgnYhhBBt5zNoV5fa6klB+4ih+VitTmX3tp1Gtt3h0Ni5V430hg5yGXYeKJl2wDJMTekOZ1fzFeQdOe15Vc368Ud1+9RTPfe74ALjZzqm0QJ9+7bo9QI2aLdEwPos+GgT/LAHNh6BPQUeg/a33/Z9/ub2CyGECAwStAshhGgbh++h0tm56nDenhS022wOtyHyW3YkA7D/QBxVVWq2WsnMd8WcdvCYaQ8ZGats+xW02zsvaD9wADIy1LYTTvDcd9IkGDzY0/D4lgXt5rXguyJoNw+Pr6iA6ppwSDT93eRXuAXtuu4+OsF8o2PbNjh4sJ0uVgghRIeRoF0IIUTbuGbZX14BTy2Gd9fD0n1QWElOnhokdv+gXWtcqx3ch8hv2ma8wYbgvUFSYgXJSS7raQdQpl0bqtYdGM4u8vIDJ2g3Z9kTEmDMGM99Nc3Itrc1aDdn2pOSPPfrSMnJ7m15BTZIVEevUFABzjKlads2OHRI7fbkk+4V481/tkIIIQKPBO1CCCHaxrUI3Tvr4a9L4fbP4fy3YNEeMrNsSvcWxk6BySXbPm6UGryuWW+8wc3b1YhLCe61IOPRFTyt1T5YjeT6c4jCI81cXydWkDcHlrNm+S5mePacMqJRg9icoO43PD421pin7yo3F0gyjZsvqHTLtH/7rdqlXz9jeTzXtewBlixpjysVQgjRkSRoF0II0TYNmXZdh8PF6r5+MWSZgr8+fTrnsjqUpSloP3qKOnf6l01pVFcHuWXa1aC9i7Ls4Hmt9kHua/A59hT7Po+zypga0QmWLlW3jz/ed/+j0jLd2r7d6n/QXlNjLDHnqisy7RaL++vm5QGJpsaCCnCoF7xsmdrl5JONUQjmPzvzn60QQojAI0G7EEKItmkI2gsqocqu7NLTU8jM1JS2npZpnzY5E03TG7fr6qz8simNFWv7KYcoGfmums8ORvVxs/Bg8sNS1W778t37mXVCMbrCQtizR20zZ4vNgnLUoL2IWL5c0sxwfxcFBe5tXZFpB/ch8rm5QLIpaK+yQ3lTkUNdhxUr1C4zZxo/jztObd+zx31NeiGEEIFFgnYhhBBt0xC0Z5qyrlaN4sjBVFerzT0i0+4StMdE1zB6hFq17K2PxnM4S12P7NhpLhOMAy3TDhTEqUuihWb6EZB3wrz2X35Rt202Y5i3T5lq0J5JX779FpxOL/1NzEPjNc29kntn8ZhpT05175iX3fg0IwOys9Xdxxxj/Bw71ljj3tWGDW29SiGEEB1JgnYhhBCt5yhtqhx/yBS0p0WTmZ/mdkiae1P3o6nz9GdOU0twv/LmVGU7KbGCUcNdAvuuDNo9zWkHylPVv5iYvKzmz2XPbr5PG61bp25PmOAedLoxBe2HSaewELZu9e81zZXjExLc55Z3Fo+Z9uhECDFPdi8CvRaAVavUXYmJMGyY8TwkxP2mx/r17Xe9Qggh2p8E7UIIIVrP4ZKSNM9nT48hK0ctcJaYaGRKuz1T0H3m/J0+ux8/IwPNdZaAl8C5U3jJtNf1V1O6SSV+rAXWBUH7lCl+HHRYrTOQiTEnw9/524FQhK6Bx0y7NQKSIkw7mpZ9M2fOp01D+fxNmqTul6BdCCECmwTtQgghWs91ubfDpkx7eiyZ2eqY4h4xnx3cMu1zjttHVKT3ternztqnNgTanHbAMkwtRte/aq8xOdoXZ2FjdrejtC5oV284dOeg3WOm3RIBSZHqjrzyxmXfNm5Ud02cqG6bg3ZzfyGEEIFFgnYhhBCt5zNojyYrW11PukfMZwe3oN1mc3DW/B0euwYHOzjv9O2m4wNvTnvYuFhlO04vxnmk3GPfRrreofPai4pgn+l+h19B+6P3wH8uZvn5v+Yx/shiTgSMoL25+xDgPh/c03rpncWcac/NxbjxkmwK2nObgnZzpn3CBHXbPDx+1y7/5/sLIYTofBK0CyGEaD276/B4D5n2I+oQ3p6aaQe4+6YVShX5BtdcvJ7EhErT8V04PF4L8bhGfNxYG+9zEX/iQc7nI0awg8IgP9Y568Ah8ubg02aDMWP8OHBECswfQfJdo7mfx1hSH7RnZ7tXovfEHLR3ZR0G8w2DvDyMTHuyaXh8fdCekwM5pvso5qB95Eh1u7oaDvoxG0IIIUTXkKBdCCFE6zU3pz1bzSj3mKDd4h60jx+Tw5P3f6+0jR6RyxP3L/JwfBdm2sHjTYPk5Cou4X0e5k/8l/PZxQiyC6I9HGxiP9IBF2jYbhqgMGqUH0XoAJzGmuVDBxWSmqJOW/BniPwR01tK9VCsvbN4HR5vzrTnVYCzzG2oe3g4DB2qtqWkQLTpr3an77IMQgghupAE7UIIIVrHWdVY+IqKWiisUvf3iyUzS42wes7weM9B9+9vWc73H7/J7b9eyXOPfsXP3/6L+Lgq945dOTwewOI+rz0kxEFCvDoiICcvwq2fG0fHZdrNgaQ5Q+yVwwjaNQ1mzVTfkz9BeyBl2s3D4ysroaLSw5z23HJwuAft48a5V77XNPc/yx2eZ3cIIYQIAO7j44QQQgh/OHwMjQfoG0tWlnpvuMdk2j0Mj28wZ9Z+5sza38zxgZdpB0hJKqegsCmgz86N9NhPYc8F3QFa+6+JZg7aR4zw88D6TDvArOPsfPjfpl3dPdMOkFdgI2JQIkztZ2TckyJgRBI4y5qdz95g5EhYs6ZpW4J2IYQIXBK0CyGEaB3XoD3TFLQnhFMXmkh2tqY09/RMu9+8FIPrNB4y7QCpyeVs29kUJfoVtOt2oyBhUPtHtuZA0u9Mu0vQftxx6o2jjAzIyvL+WXQ63eeEd2WmPTramBJQV9fUlpcHA0+aCLMHq52dpc1Wjm9gvgEiw+OFECJwyfB4IYQQreMatB8qVvelx5CZk+JWkbp//w6/qs7R1qC9KwvR+Xj91CS1WnxOnh9BO3TIvPaqKvfiaH5l2nNzIfMAOIwP35hx4W7zt5cv9354fj44HGpbV2baNc09256TA2juUxeqKyvdbnT4yrS7kky7EEIEri4N2p944gmmTp1KVFQUycnJnH322ew03eqtrq7mpptuIiEhgcjISM477zxyTLfADx48yGmnnUZ4eDjJycncfffd2O32znwrQgjR+7gG7ZE2mNTHGKYLkB7LwUw10oiIgHh1KfDuy8fweP+OD7w57WBk2hskk0Pqhl9gqx9LunVA0L57t/vybMOH+3HgX/4CY+6H9Edh0nNYH3mcY45Ru/gK2s3z2T0FzZ0tJUXdzs7G49/h1h1xbjccxo3zfE7zDZAjR6C01HNfIYQQXatLg/Yff/yRm266iVWrVvHdd99RV1fHySefTEVFRWOfO+64g//973989NFH/Pjjj2RlZXHuuec27nc4HJx22mnU1tayYsUK3nzzTd544w0efPDBrnhLQgjRe7gu93beOPjmeth6Fxz4I/zldA4cTlC69+9vBEA9Qluqv2ta24P+tvIStKckVfAHnuQA/ckhlTuX3wrvb2j+fPas9r0+3DO//foZN36adeiA8dOhG9M2qqo49li1y7Jl3g83z2dPSoKgLp5MaK4FkZmJUUHeZONWdUjAkCEQFeX5nEOHgsX0LdCf5fCEEEJ0vi79b+jrr79Wtt944w2Sk5NZt24ds2bNoqSkhFdffZV3332X2bNnA/D6668zatQoVq1axdFHH823337Ltm3b+P7770lJSWHixIn8+c9/5g9/+AMPP/wwISEhXfHWhBCiZ9Md4CzyvC8sGMKCOZgZozQPGNAJ19VZtGDQLKA7m+/rdmxo19+98DY8Prmccmrpz6Gmxg1+BOT2HOPPQmu/XECri9BlHlK309OZOVFt2rAByssh0sPo/8OH1e2unM/ewBy0Hz6Mx+HxG7eqKXlv89nBWPO+b1845PLHtX8/TJ7c+usUQgjRMQJqTntJiVHIKL5+/OS6deuoq6tj7ty5jX1GjhxJ//79WblyJQArV65k3LhxpLiMHZs3bx6lpaVs3bq1E69eCCF6EUdRswHrwcNqiq/HzGdv0Noh7l09nx08ZmnBCNp/5ii1cfMRsDdzc0KvM4rRtaNWB+3mqLtvX6ZNU7PlDgesXu35cPM8+kC42ZSerm77m2n3Np+9waBB6vb+ZhY9EEII0TUCJmh3Op3cfvvtHHvssYwdOxaA7OxsQkJCiI2NVfqmpKSQXT/pLDs7WwnYG/Y37POkpqaG0tJS5SGEEKIFXOeze3HgoBqc9rygvZVD3NsytL69+FjybS1T1cbKOtjlR0DezkPkW1U5XtchyzQHPz2d8HD3DLK3ee0HDqjbgRC0e8y0W8Lhw41w86dw4dvoJ7zCceveVfpJ0C6EED1DwATtN910E1u2bOH999/v8Nd64okniImJaXz069evw19TCCF6FEdBs10OHlaD2kAIftpVt860ey9El08SGZj+svwaIt9+QbuutzLTXlAANbVqW32a2t957eagPRBuNnnMtGsRsO6wEbgv2Yu2LYd+NRlKP1/D40GCdiGE6C4CImi/+eabWbhwIYsXLybd5X+m1NRUamtrKS4uVvrn5OSQWr/+Smpqqls1+YbtVC9rtNx7772UlJQ0Pg4dOuSxnxBCCC+aybTrOhw4YFXaAiH4aVetzZgHQtCueQ7aExMqsVic7tn2dYc99le0Y9B+5Igx59yVX0G7eWi8pjWu1zZzprpr5UrwtNBMd8i0FxZCVU0EJKmT8lNpGmEYG2sU7/NFgnYhhOgeujRo13Wdm2++mU8++YQffviBQab/PaZMmUJwcDCLFi1qbNu5cycHDx5kxowZAMyYMYPNmzeTm5vb2Oe7774jOjqa0aNHe3xdm81GdHS08hBCCNECrpn2xXvh1s/g6SXw3nr4JZPC4kgqK9Via4EQ/LSr1mbaLQEQtFtCQbO6NVutOsmJFaziaHXHygNufd3Yc4wChe3APDQ+PNw92+xRZqa6nZoEwcGAe6a9vBw2b1bbHA61MBsExufWHLQDZB6JgmQ1aE+hKYkxYULz9Q7NQXtGhvsye0IIIbpel1aPv+mmm3j33Xf57LPPiIqKapyDHhMTQ1hYGDExMVx33XXceeedxMfHEx0dzS233MKMGTM4+mjjC8XJJ5/M6NGjueKKK3j66afJzs7m/vvv56abbsJm6+IldYQQoqdyzbSvO6wuCzZ3GAf+eILS3WKBPn065co6T3ceHg/Gdejlbs19Usv4Mfd4tXFPAeSUQ4qHcusNdDs4ciGo7eXWzUPjhw93X57MIw9F6BqkpBjLnLkua7Z8OUya1LSdne2efQ+EoD062li6raysqe3g4XCGJnvPtDc3NB7cg/bqauPPIBAq5gshhGjSpZn2f/zjH5SUlHDCCSeQlpbW+Pjggw8a+zz77LOcfvrpnHfeecyaNYvU1FQWLFjQuN9qtbJw4UKsViszZszg8ssv58orr+SRRx7pirckhBA9n7MKnJVN24eK1f3pMRzITFaa+vRpTHj2HK0O2gOgEB14rSCf3qeUDUykFNMC3yszmj9nOw2RN2fa/V/uzZRpT1fHhzc3r908PNxmg2T1o9xlBg9Wt/fuC4ZkdVlFI2g3UuX+LN3Wpw+YV8aVIfJCCBF4unx4vKfH1Vdf3dgnNDSUl156icLCQioqKliwYIHbXPUBAwbw5ZdfUllZSV5eHn/5y18ICurSQQRCCNFzmeezHy5Rt/vFsjcjSWkyBxw9QmvntAfC8HjwWowuPa0UB0EswzQJfIUfQ+TrMpvv4wdzpt2vyvEAhzLU7XS1kIJ5XvuyZepw8O3b1f1DhzY/xLyzDBmibu/ZA6SqdxRs1BKD8e/RdQSBNxaL+0gCCdqFECLwBEQhOiGEEN2IuXK8OdPeL4a9GXFKkzng6BG6/fB4z0F73zRjDPYSTlB3rMho/pztlGlv/RrtphsLponw5kx7Zqa6Lrs5aB81ys/X7QTmf0N79wIp7gV3U8kmNNTp97VLMTohhAh8ErQLIYRoGdeg3alDpinTnh7L3v1qgU8J2l14yXB3Om/D49NKAQ9B+658OFjs+5yOXNDr2nRZVVXuFdz9Hx5vumlgquA2ciQkJKhdXNdrD+SgfehQdXvvXiAsFmLUz2Eq2YwbU4W/Aw4laBdCiMAnQbsQQoiWcR0en1MGdU51f79Y9u5XA9MeGbS3dph7gGfa0/sYQfs6ppBHorrzu12+z6k7wX6kTZe1e7d7BfPhw/08ONP02qZMu6bBMceoXVzntQdy0O5peLyuRUCSevMllWwmTzTdSPNBgnYhhAh8ErQLIYRoGddM+yFTcGCzUhcbyYGDanWrHhm0d+cl38BnIToAJ1a+5FR15zfNBO0A9rbNazcPjU9Ph0gfResblZVBaYX7wSbmee2LFxs/KyrcM/x+z6XvBOZ/Q+XlkJMf53HZt0njTHUnfJCgXQghAp9UaxNCCOE/XQdHYdO2W+X4WA5kxuNwqNW7JGh3PS5QgnYvc9pTSxufL+R0ruI/6BYNbWo/mDPU4zGKukxow1tsdeX4sDBY+jQcXA9ZpZCf5HGB8xNOcH+9ffvcV4uzWFrw2p2gXz9jvfpKl4UbNm5JJCY6ljCa7jakks20SYeA8X6d1xy0HzpkLHsn9XyFECJwyK9kIYQQ/nOWGOtxNzhcrO5Pdy9CFxsL8fEdfmWdrzXBtxYCmrX9r6U1NM+Z9oiIOmJjqiguCeMb5nE5b3HDayXMOjXPv/O2c6bd72x3UBCMi4eR9WPpoy8Am/vf0VFHQVIS5Lm8nS++MDLXrsaPN4LkQGG1Gte0alVT2/qNcQypS2MoGxvb+gVlMn60H5X+65mrxzsckJUF/ft77i+EEKLzyfB4IYQQ/jNXjjcXJusfy94DaqWvHpllh9Yt+RYoRejA6/B4aBoiX0Y073A5GWV9/D+vowiclc3386LVlePBuKnUwBLlsYvFAqecorZ9/LH7mu3mYfSBwLz2+i8bo9hZNlBpGx59ECvFfp8zKckYpODKPE1ACCFE15KgXQghhP+aW6M9PYa9Gera0T02aNeCQWvhgLVAGRoPPoP2vqllynZmtucA2KtWZtt1vQ3D43UHOF2u2xLttetZZ6nbS5fCl1+qbebl4QKBee319RtDWXFArZbXLygT9FpwVvl1Tk1zz6pL0C6EEIFFhscLIYTwnznT7ha0x7J3Yy/JtIMxr10vb75fg0DKtGthoFmMiu8mDZn2BoezvAfAHtUdhpBhLb6kI0fch6n7PTzeWaqWnfeSaQc47TRjykZhodcu3SLTvmdPED8xkZ+YSTap5JDC6efU/xt1lvhd9HDgQHWEQ0ZGu1yuEEKIdiKZdiGEEP5zDdp13X1Oe79YtzntPTpob2kleC/LrHUJTfO+7FuaKWg/0sKgvZWZdvPQ+LAwjwXgPXO4Do0P91k7wGaDSy/1fqpp01rwup1o3DiIMt2L+IlZzOInLuQjHk16kv5/qr/L4Sj2+7zmee2SaRdCiMAiQbsQQgj/uQ6Pz6+EKruyW0+PYV+GugTVUD8KjndbLR3uHijLvTVoZtm3BpmuQXuNHT7cCKt8RHatDNrNQ+OHDzfmoPtl889wpBQcTp9Z9gZ33mkE75786ld+vmYnCw6GOXO87z9j3k4slvrRBs5iv88rQbsQQgQ2CdqFEEL4R68zhiA3qHPAmaNhUh9IjIBgC1n0obJSnXklmXYXgZRpB69Be19zpj0rGvIq4C8/wuTn4OZP4ZkfvZ/XWeU+lcIPra4cD3DKDTDhWej7KIy9D1au9Nl90CC45x739tGj4corW/C6neyii7zvu/z8TU0bjhLvHU0kaBdCiMAmc9qFEEL4x1GozhnuEw3/d0HTdrWd7atTlEMiIqBPCwqPdzvdPdPuZdk38/D4nLxI6r7cQ/DTS5oaf9pvZNuPNkV8DeoywZrgeZ8Xra4cX1MDeUXGc6cOWUXu48g9eOABKCmBl16CujpjObiPPvKegQ8E55xjBNnmwPqYqQeZNcOlsQWZ9oED1e0DB8DpbMEoByGEEB1Kfh0LIYTwj7lyvFloENt3JypNI0f28C/+LQ7avVds7xJerqdf31K3toPTZkKKOvWBJxerN3Jc2Q+3+HJaXTk+K8u9zY9J6VYrPPss5OZCcTGsXesewAYamw1efx1CXVYcTE4s5/UXPkPTXDq2YU57TY3xZyKEECIw9OSvUkIIIdqTH8Odt+9KUrZHjfLSsafo9sPjIz02x8ZUExujLhm2PzcJbjOVVF9xwMi4e9LCee1VVe7ZY7+Hx2eaXis8FGJi/H7t2NgWde9yJ54I69fDgw/C3/+6lR0r/s7wIaZ/ny3ItKelQZBp7KUMkRdCiMAhQbsQQgj/NJdpB7dMe48P2lucae8eQTvAwH7Fyvb+g7Fw+RRjWoSrx3/wnG23Z4Nud2/3Ys8e99MMH+7nwYcOqdt9U1HTzj3PyJHwpz/BTb8pJG7tJrjlUzj/LZj5Evz+C6OugLPar3NZrdCvn9omQbsQQgQOCdqFEEL4x49M+7advS3T3sIgPOAy7d6H6w/qX6xs7z8YB6FBcMdxasdfMuF/29xPoDvAfsTvSzEPje/bFyK931NQHdqrbgfiem0dxRIBO/Pgg42wdB/syoe9DWu1F/t9GvO0AFmrXQghAocE7UIIIfzTTKa9sCiM3Hw1yurxQXsvyrRnHIo1nlw6CQbHq50f+8FYTcCsBUPk21Q5/qApaO/bz3O/nsgSCWmm0Q/ZZcZPR5Hfp5EK8kIIEbgkaBdCCNE8Rxk4a5q2i6vg3Dfh1s/gmSXw/gZ2bFEnBQcH9/Dl3qBlQbjFBlqALdqieQ/aBw1QA779B2KNJ8FWuM+0WPj+QvjPOveT1PlfjK7VleMBDmSo2wMGt+Dgbk6LgDRTpfwj9YUEJWgXQogeIcC+PQghhAhI5iz7oWJYltG0rcGOp65QugwbZgTuPVpLMu2BNjQejKHVmuZxTrp5eHxjph3g9FEwpS+sc8mk//VHuGgCRLqsl9aCCvKtrhwPcMiU0Q/0EvDtyRIJqaagvbwWymsgTIJ2IYToCSTTLoQQonnm+eyHStTttGi27ktTmnr80HhoYaY9wJZ7A9AsXm8mmIfHZ+dGUVVVf69f0+DBk9QD8ivh5ZVqm6MYnBXNXoaut3V4vGnufP/+LTi4m7NEQGq0e3t2WYsy7Z7mtHtbzU8IIUTnkqBdCCFE8xx56vbhYnU7PYZtvW25NwAt2Hj4IxCDdgBLlMdmc9AOpmz7jAFwsqm8+8srIKdcbfNjiHx2NpSVqW1+Z9rLy6HI9Jq9KWjXgiAyCmJC1fYjZeAo9Ps05kx7eTkU+R/zCyGE6EAStAshhGiep+HxrtJj2b67Fwbt4H+2XQvUoN3zvPbIyFoSE9QsuRK0A9w/BywuS6tV1hnD5F35MUTePDQ+LMx9CTKvzMu9Qe8K2sH7vHZnCehOv06Rnu6+Sp4MkRdCiMAgQbsQQojmuQXt6vD42tR4DpgCutGjO/iaAoW/c9W7WaYd3Oe179lvqho/MhkunqC2vbVOvanjRwV589D44cPB4u83lAP71e3EWAgPwPoBHckSCSmmv8fsMmPZPWeJ52NMQkKMZfZcSdAuhBCBQYJ2IYQQvum14CxV2w6rgcCREHVdbE1rYSGx7szfYDxgg3bvFeSHDVJrGezel+De6fcnGuu3A0zsA38/Ry2MZs9sdnJ0m4rQZZgO7pfmuV9P5nPZt9YPkZe12oUQIjBI9XghhBC+2fPdgy7T8Pi99kHK9sCBxhDnXsHf4fE+guMuZfFQxKze8CFq0L5rr4egvU80PHkqDE6A6f3cx1g7a4yaCEHJXl9n2zZ1u0VTKw7sUbd729B4qA/azcPjG4L2AsC/tRcHDIDly5u2JdMuhBCBQYJ2IYQQvpmHxpfXQlGV0rSxVE2N9pr57OD/XPWAndPufXi8X0E7wKWTfL+G/bDPoH37dnW7RVMr7rwQ5uuQWWKMABl+WQsO7iE8LfuW3bBWe+sz7RK0CyFEYJCgXQghhG/NVY4HVueMUbZ7VdDe7YfH+x+0ZxyKpabGis3maNlr1GVC6GSPu0pL4bCpVl2LPj9hNTA6xXgAJF7QsmvrCSwRHoJ210y7f8zLvknQLoQQgUHmtAshhPDNHLSbK8cnRbBxnzqnvdcUoYMWBO3db3j8sMFqllbXNfZmxHvp7YOPCvLm+ewWi1GIzm9Ol2u0hPm/BF9P4mlOe045OPUWBe2SaRdCiMAkQbsQQgjf7OagXS1C50yPdasqLpl2E80KWoBO8rdEgOb560B0VA2pyeoC6l6HyPviyDUKGnpgns8+ZAjYbC05t2vQ7v0GRI+meZjTbndCXgU4i40q8n4wB+0FBcZ67UIIIbqWBO1CCCG80+1qJhPcMu3lscnY7ValrVcF7ZofGXRLpHuBtkChWXyOAhgx1M957a50HdYdhnu+NGog6LoxRN6DNs1n13VwFDVt99ag3RIFiREwqQ+cOhJ+PR3+PA9sVmOddj/ntXuq4SfZdiGE6Hoyp10IIYR3Dg+V403LvWWHqIs7p6ZCbGwHX1cg8SfTHqhD4xtYosFR6nHX8CEF/LhiYOP2Lk/Lvrl6ZSW8/Qvsqi9gOK0fnDvOGCIfMsituznT3qKg3Vlq3Fhq0GuD9ggIssI313ve78iHoKRmTxMeDsnJkJvb1HbgAIwZ4/0YIYQQHU8y7UIIIbyz57q3HShSNvfo6nJSvSrLDv5l0X0UewsIvpZ9G6xm2rftbCb4W7y3KWAHWFifSvcyr71Ny73t2wSrDxqV4x1OsPbSoF2zgOZj6UHzChA+yLx2IYQIPJJpF0II4Z3DQ9B+ygjoH2sE7weL2VShLvfWq4rQQf189VDQq7z3CfigPcbrrjEj1c/Alh3J6LqP+xRnjjYC9wY/7IGKWrC4D4+vqoL9+9W2Fn1+PvgQHnjdeG7V4Pyf4f3jW3CCHsQSCc4Kz/vMxSR9GDAA1q5t2s7IaNtlCSGEaDsJ2oUQQnjnKdN+xyxl88PZZynbvS7TDkZQ7uzGQbuPDPW4UepnoKzcxoFDsQzsX+z5gPkj4a6F4KifVlFZZwTuZ4w25p9b4xq77tzpPvti5MgWXPfBjKbnDh3CAnRZvc5giQRyPO8zF5P0QTLtQggReGR4vBBCCO8cXoKAek6nxo696nDp3hm0NzNnPdDnWvvItPdNKyU2Rr0hsXl7svdzJYTDsaa56w1D5OvUIfJbtqjdBgyAiJbE3QdNQ+77D27BwT2MrxtDnmpTeCFrtQshROCRoF0IIYRnzmpwlPjscuBQDFVV6rrYvTNobyaTHuiZdkus112aBuNHqzdvNm1L8X2+000fgm93QbXdbV77hg1qtwkTmrlOs/1H1O3BIzz36w083ThqCNT1OnAWue/3QDLtQggReCRoF0II4VkzWXaA7bvVLHtMjFE9vtdpLpMe6Jl2q/dMO7gPkd+8vZmg/dSR4DrnvaIWVh1oNmifONH3aRWOOjioFsljyPAWnKCHsURCcRXc/hmc/xYc/SIMeNxYcg88T3XxwBy0HzkC1dXtfK1CCCFaRIJ2IYQQntmzm+2yfVeisj16dOAuR96hmsukNxMUdzlLBGjBXnePG6XewPE5PB4gORImq0sBsmiP8ZnS6wAjCdymTHvmdiN772pwLx8ebwuCdzfA0n2wr9D488msHy3jx004cA/aAQ4dar/LFEII0XIStAshhPDMn6B9t8xnB3xn0i1hoIV03rW0ljXW6y5zpn3nnkSqq5upZTt7qLr9wx7QHWDPAiAzEwpMifIWZdp3b1S3Q4MhLa0FJ+hhLJEQFgyJpqXfDhcbP+3+Be0xMRAbq7bJEHkhhOhaErQLIYTwzFPQfv5bcM6bxhDcvy2leHOlsrvXBu2+MumBPjS+gY957eNG56BpTYXMHA4LG7Y0Mw9izjB1e3c+HCyGOiNta86yR0e7F0Hzaa9pgfeBqb10mEe9htEe6bFq++H6TLsfN+EayLx2IYQILBK0CyGEcKc73Ndod+qw+gAszzCG4D65mPK9tUqXXhu0+6i+7iuDHVBclmIzi4qsZdRwddmwNev7euldb2Ifo5K8q0W7we45aJ8wASwt+Vayb4+6PSi9BQf3QA1Be1/TZ7EhaHcWgbPGr1OZg3ZZq10IIbqWBO1CCCHcOXKNwN1VbjnUqG2bKtRFtXtv0B7ufU64xXswHFB8BO0A0ydnKtur1zUTtFs0ONE0RH7RnsZM+88/q7taXDl+nyn9O2hgC0/Qw2jBYLFBupegXddbPa9dMu1CCNG1JGgXQgjhrn7esSJDXTLKERJMNk1DpENDPRex6jW8ZdSbCYYDhiXe5+5pk9SgvdlMO8AcU9C+PANqytDr8lm1ynT+aX5cYwPdCRmm4d5Dhnnu25tYorwH7eD537UHsla7EEIEFgnahRBCuKvLdG87qAbtpTEp6C7/jYwcCVZrR19YAPOWUe8uQbvVd9A+fYq6XNue/QkUFIb5Pudxg9TtilrYkMWBfdnkmJK+Rx/t74UCjkLIKFTbhvTWYR4uPAbtxU3P/QzaJdMuhBCBRYJ2IYQQ7jx9uT+gBu1Ztv7Kdq8dGt/AW3DeTAY7YFjjfBZyGzsyl9DQOqWt2Wx7ciSMSoawIDhhCNw/B/pEs3J5ldItIQGGDvVyDk9K9kFehdo2dEwLTtBDWaKgf6zallUKNfVL43m6GeeBOWg/fBjsds99hRBCdDwJ2oUQQqj0WvcidGBU/naxyzFE2ZagPcG9TbN0n0y7FuSzgnxwsJMp448obT+uGNj8ed+8GHb9AT68HG6dCX1jWLVanf9/9NEtLPy+Z6N72+CWRP09lCUKBppuEunAoWLjubMQnNXNnsYctDscxhJ9QgghuoYE7UIIIVR1WcacYTNTpn1jmRqlS9DuIWi3xIHWjeYMWBN97j7h2Axle9FPgzx3dDUwDmzqmu4r1yYp2zNm+HV1TQ7uVrfTYiGsmaH6vYElCqJs7mu176+fSqDrYD/sfpxJYiKEm04hFeSFEKLrSNAuhBBCVb8klxtz0F4uleMV1iT3tqDkzr+OtmgmaJ9z3D5le93GPhQWtSxYLisPYf1mdY33Fs1nB5iTCvvvhR9vhP9cDI9d3cIT9FCWaOPnAFO23bWIZN3BZk+jaTB4sNq2e7fnvo10B1RvgrLPoPS/ULkCnBXNHCSEEMIfErQLIYRQefpSX1ELR8qUpj00DUe2WmFYby/ebY0GS6iprZsF7UEebjy4mHHUYcLCmua167rGkuUDW/QSS5YPxG5vGn0QHNzCoN1ZAY5SiAgx5svPHwEXntqia+ixGtZqH2iakuFatM+PoB1gxAh1e8cOH53rDkHRS1C2AKrXQ81mqPgWil4wtoUQQrSJBO1CCCGa6E6we/hSv7/Qrck1aB86FEJCOvLCugmrmkEmKK1rrqO1mrnJEBpqZ+Y09fPx/VKXlKwlFIIHgub9w/DtErUWwrHHQkREC67RU5FES4x7W2/UELQPMmXa97tk2u2HQW++qpw5aN+500vHmq1Q8oZR0d/MWWNk3it+aPb1hBBCeCdBuxBCiCb2I8YXbTNT0F4UmUI1TcOiR4/u6AvrJoL7qNtBfqxlHkisSc1WhDMPkf/8mxE4nRqEDIW42yH2aoi/1esNi++WDGY0WwmjEoCTT27hNdqPuLdZY1t4kh7KEmX8/fnKtOt2qGt+XrtfQXvNTij7rzE03pfKpVC1stnXFEII4ZkE7UIIIZrU7fPcvrdA2TwQrBYgGyOrbRmCXMpuW+PBGtV119IaFpv39ebrnTFvl7KdeSSa1RsnQPTFTdMDLJEQfSlYXKqZvb+Biis+Z8necWxlLMfzIwAnndTCa7R7KGPuo+p9r6JZQQuHUSlw0jC4YTo8Ph8eO0Xt5+3fuQtz0L5vH9TWujTYs6HsY89FKz2p+A5q9/vXVwghhEKCdiGEEE28fZnfp2bat9SqRegk014veEBTtfiQbroEWVCqz92jR+Qxanie0vbRl/OMJeNcWaMgwiUif3c9Ed+sJ5UcAOawiPi4GiZNauH1ecoSS6a9iSUaxqXCO5fCo/PhV9PhRHVKAnV7mj2NOWh3OIzAHTCWjSv9APQ6t+O80p1Q/olfS84JIYRQSdAuhBDC4KzxXqTKlGn/pWKcsi2Z9nqWUAgZbjy3je/aa2mtoD7NdrngjK3K9scLwnB4GiFtmwhB9RXpZ6qjM2bzA6edtAerRff/2hyF8Mtu+M86WHkAcstBC/M5h77XsfgxusN+BBxlPrvExkKyqcRBYzG68v+Bo8h8SPMcpUbGXQghRItI0C6EEMJQt9f73NTrpsLNx8CpI6kc2IdtNKXWLRad4cM76Rq7g4iTIGIuBKd39ZW0jh/z8M8/Y5uyfegQfP21h46aBmHHApAzYqyyayIbuOzEZZ4Ly3lTdxC+2gF3LYSz3oCxf4XrP/L/+N7AGt18H12HWm+V5ZqMVAfUGPPaqzcYxedaq+YXv+bUCyGEaCJBuxBCCEPNdu/7zh0HD54Eb1zEB3e8xDfMb9w1dIid0FDvh/Y61ngIn9nVV9F6wX1B8/31YOyoXCaOVQvCvfiil862cWAJ45XNZ1HpUrzQgs5s6xKobW4BcBd1GbA7X21L72YV+juaxY+gHaC2+cDbPER+29ZaqPiqFRflQtehwtMdHiGEEN5I0C6EEMKYm+pH5g1g2051Le/Ro1swvFkEPi2k2Xntmga33KAGfd98A9s93ffRgqjWJ/HK+0ezDPVmRvCqvVDrawFwE09B+4huWjugo/gbtNdlGMPVfRirDo5g4/pyz6tLtFTdYd83CYUQQigkaBdCCAE1O0Cvbb4fsHWnOtF1zBj5r6THMa0O4MkllyeTkKC23Xef576vvjud7NwofmC2umPZfqMKuT/zo+35UJkPe0xB++ixnvv3Vp6C9jqH27KN6DrUbPB5qokT1e1tO2OorbW26fIaVS42rkEIIUSz5JuWEEJ0UyUl8Kc/wZw5cMYZ8Pbb4PRz9SU31ev87rptlynTPibIS0/RbQU3k70OSiQsdhw33qg2f/IJfPGF2lZQAH96NAbAPWjflQ85Zf7Nka7bY/R3mAK98VOaP7Y3aQjas0rhmg9h5ksw4HE49iUjeHdV/bPPNdYnTFC36+qsbN+V2D7Xac+F2m3N9xNCCCFBuxBCdEfbtxtZsIcfhh9+gIUL4YorYP58qKho4cnsR4yhsn4oLw/hwKFYpU0qx/dAwQPAEuZ9f/hs0DTuugu3bPs118Ce+hXFHA649lrIq18h7hcmU4IpE7wsA2o2N39NNTtgW47a1j8W4vo3f2xv0lCILjwYvthu3OiwO42HOdvuKPWZbY+JgUGD1JskG7b4njrRIpVL2+9cQgjRg0nQLoQQ3UxhIZx2GmRkuO/77ju4+OIWZtwrl/jeX9E0bH77bjXLZrE4pXJ8T6RZIGS0533B/cBm7IuJgSeeUHfn5cExx8Ajj8DJJ8PnnzftcxDEziRTZnzZfrDnQJ2PKvLOcrAfcA/ax6SCJcbPN9VLaCHGDZfYMEiOVPftynfvX7HY+9rpus7EMWql9w1b2zFot+e0rBChEEL0UhK0CyFEN3PnnbB/v/f9CxfCK6/4ebLaPVDjowCdwwmjn4Hxf4ML3iLiif+RSlPV8MEDKwjzkZAV3VjYUe5tmgUiT1WafvUrOP10tVteHjz0kDEKxFVcbDXDrzFl2n+q/zBXr/V+LdUbjfnPbkF7f9DaaY51T9IwRH6EOpWF3XnufZ3lUL7QfX657oTyz5kwao/SvLE9g3aAyuXtez4hhOiBJGgXQohuZP16+M9/1LZBgyA6Wv3Cfc89OvkekmoKRwmUfeq7z8FiqLJDdhn8uI/RSxbioClIGjOq0u9rF91MUBrYRqlt4ccb7S40Dd55ByZN8n26kBBY8O5GYk81LdF2sBgOFEHNJs/VzHXdmHsN7kH7WBnm4VHD6INhpvnnO738UqjZAuWfgLP+37M9H0rfhur1TBybrXT9ZVMaTqfWftdal2FM0RFCCOGVBO1CCNGNPP64mhCLjKzjp8//yX9ffUvpV1am8ezja6D0Y6PInKNYPVHtfih5zciy+bJLzcyVBMeSR1P2bvSouta8DdFdRJ4OQclGZB52NITN8tgtOtrIqp91lufTpKYaUzdOmJMII5MhMVztsGy/URCtcrH7wTWbjOryueWQZyrYME4KKnhk9RK0e8q0N6jeBIV/gYJnoOjvULsPgKMmqtMWSkpD3ZZ9bLOqVe17PiGE6GGk5K8QQnQTBw7AggU60JTluueWpfRNOULfFLj03E28u2B8474X/z2Bu278gbjYLUaDJQqsUeCsMLLs/tipfsnfaRmlvP6Y0a0tVy+6BUsExP4G9BrfhemA2FijevyiRfDuu7B7tzHnfc4cuO46I7BHHwBWGxw7CD5zqRj/UwZcNtkoimYbDSHDjHZnBVR8ZzzfYJrzHhYEw2W5N48aMu3m4fF78o0pL1YvORvdCbp6Y6RvWhn904s5eDi2sW35mn6MHZXbftdbswUiTjY+b0IIIdxIpl0IIbqJ1151KsNSIyNquPm6NY3bD/xuKZrWlIYvK7fxypsu85KdZUaxL38DdnAL2tfVqGtAjR4j84l7PM3SbMDe2FWDuXPhtdfgp5+M+gp33FEfsANoQRA8EGYObDooLAgs9Z9rXYfSD6FqtVFroeTNptEg69SCaEzsC8HtnPHtKRrmtA83/flU2SGjqMWnO3baIWV7+Zp2rtivO1q07KQQQvQ2ErQLIUQ3oDsdvPO2mgG76qKNxETXNG6PHJbPhWep613/660pbZt/agrat9FUUVzTdEaOsrX+3KJ3ChkKJw6Fu46Hz66GXX+Al89p2q/XQflXUPqesZZ3g59NQftR6WA1rTcnDNZY42dypHsF+S3Zbt2bc+zUg8r24uUD3erWtVn1z0amXwghhBsJ2oUQohtYu3QVe/dHKW1XXbTBrd8dv16pbGccjOO7JYNb96IOp9scWNegffCAIsIiosxHCeFbyFBjffXfnwAzBoDNj5l6Dif8kqm2HdUPrHEdcYXdn+syeGNN1d5bEbSfODND2T6cFcPWHcmtuDAfHKVQu6t9zymEED2EBO1CCBHoarbz7nvqMPShgwrcCkQBTJucyYQx6pfyf/7Hw9Jd/thXaAyndbGFpjnEY0bmg0Uy7aKFrPEtD7bLa+GUkTDQ5bipY2W5N28sUca0BoBxpqB9c8uD9lHD8+ifXqy0fbVoaCsvzoeGVQKEEEIoJGgXQohA5qxCL1vIfxeqS29deu5mNA+j3jUNfn2l+sV34XfDKSxqxWLqm9VlmApCksklpXF7wtjilp9TCIDgFo7+iAk1htCvuRW23QXvXwZ9BnXMtfUEmqVpXns7ZNo1DU6Zra7XvuCLUV56t0HdXmOlACGEEAoJ2oUQIpBVfM/mLZEczopRms1z111det5mbLamDHldndUt6PfLJjVo/0VXF+KeMM60/JYQ/goZ0vpjEyNg9lAISmy+b29miTV+moP23HLIaWapRw/OmLdT2V61rh+798W38uK80HWo/qV9zymEED2ABO1CCBGo7DlQ8wsLvxuuNA/sX8ToEd7XW46JruH0k9S5oe99Mq7lr28aRruqbqqyPWG8rNEuWil4EB6HihRU+n8Oq1SO96mhGN2geAgPVvdtbXm2/eQT9pKUqN6o+/dbU1p5cT5Ur5eCdEIIYSJBuxBCBKqKRaDrLPxWDdpPP2mXx3jH1SXnbFa2lywfSOaRFhSN03W3YbTracq0R0bUMHiI/BciWskSBkFpxvPKOvh4E5z3Hxj/V/+zwFbJtPvUkGm3aO7Z9j35LT5dcLCTS89Vf6+8/PpU8vLDW3mBXjjLpSCdEEKYyDcuIYQIRHWHoXYXBYVhrFqXruwyZ9E9OXXubqKjqhu3dV3jg0/H+jjCJLMUiqqUJtegfdyoXCxB0eajhPBf8GCwO+HoF+G3n8BP+6HOCR9t9O94aztXL+9pGjLtABeMh9tnwjuXwI674YajW3XKm65dg8XSlAWvqAzhht+dgd3e9HXSbrdQV9fGr5cyRF4IIRQStAshRCCq/BEwMuS63pRWDw+v5fhjDjR7eFiYnXNP2660vbugBUPkN6qV6SuCo8hgYOP2hDHZRoVqIVoreAgEWeB4U1G69zfQuAi4rsP2XNwWBbfGyMoFzWnItANcdRT8cQ6cNBziW58ZHza4kMvP36S0ffrVKCZOuYo3R+dy4dBJhPR9gNB+9zPu+Bt57p9HU1PTigr/dXuMJeCEEEIAErQLIUTgsWdD7W4AFi9XK2TPnHaQ0FC7p6PcmIeyrtvYh117E/y7hvXqmtibgicCTTcPJozJaapOLURrBPcDLRgumai278qHH/cZz1cfhOP/ATNfhqeXwK76Wg6SZW9eB61h/8T9i0iIb6o9MJU1fHnkOK7K/wf3lj4Muo7TaWHL9hTueGA+x515LUdyIo3Oh0vcb8B4ojuhZkNHXL4QQnRLErQLIUSgqVrR+HTJioHKrhNnZvh9mhNnZpCcqM4P/uDTMf4d/Mc5sOQ38PRpOM4Zz/tV5yu7jUy7BO2iDbQgCO4PMwYYxdJcPfOjEdy9vNLY3p0Pf/kRrnjfaA9KcT+fUFmiO2Qd+z6pZbzz8n8JCbFzGgv5iePozyEAJrGBC/hI6b92fV9OveQyKtcWGDdgHvrWv8C9er1//YQQoheQoF0IIQKJowxqjOXccvMi2LpDzSiecEyG36cKCnJywZnblLb3Phnn3/dgiwajU+Dqo1h/4028oN/auEvTdMaNzgNLhN/XIoRHwUOMKvI3H6O2rz0EV74PX6vLjHHNUUZ/qwTtzdI0dYh8O5o3ey/r77ubBZyLjVpl34M8Aqi/ZIq21FB3/ntQVgOvrIJnf2r+RRxFUJfRfhcthBDdmATtQggRSKrXgO4A3LPskRE1TJmQ5eEg7y4+e4uyvX1XElu2t2xo8Zpf+irbwwYXEBltA03+CxFt1LBe+0UToV+Muu8bU8HFaBtcPtl43lB5XvjWQUPkOVDE6L+9Qgjqso+Fo0ZgfftCvv/4PwzqXwRACDV8zXxiqgqaOj65GD7b2vzr1Kxvz6sWQohuq0u/cS1dupQzzjiDPn36oGkan376qbJf13UefPBB0tLSCAsLY+7cuezevVvpU1hYyGWXXUZ0dDSxsbFcd911lJf7uVyMEEIEEt0O1esaN5csH6jsnjn9IMHBLVu/+Jhph0jvU6K0ffBZC6rIA6tNQfv0yZlgifHSW4gWCEoxChqGWOHp03z3vXc2RNqMefBWP2sz9HaegvaCSliwGW7+1Cjy11LVdvjVR1BSrbZfNIH4RRcy8mSdObP28/UHbxMRXkstNh7jPpyY1qm8839woMj3a9VsB2e17z5CCNELdGnQXlFRwYQJE3jppZc87n/66ad54YUXeOWVV1i9ejURERHMmzeP6uqmX+CXXXYZW7du5bvvvmPhwoUsXbqUG264obPeghBCtJ+areBsKvC02BS0n3hshvdjc8rh+Z/gordh3r+NocWvrsFSXsVFZ6kZrfc/GduiqaJr1puD9sMyn120n4Zs+5xhcOuxnvvMGQrXTDWeB6UZQ79F86ymWgG/+ghGPwO/WQAfboTvWrEe+kPfwMYjatuZo+H5s4zVAOoNH1LAn36/GIC3uYLbeU49pqzGWOrP6eOXkV4HNZu97xdCiF6iS4P2U045hUcffZRzzjnHbZ+u6zz33HPcf//9nHXWWYwfP57//Oc/ZGVlNWbkt2/fztdff83//d//MX36dGbOnMmLL77I+++/T1ZWy4aQCiFEl6te2/g0OyeSHbuTlN0nztzvfoyuwysrYdrz8NgPsHgvrM8y5gLf+xVMe4HfRr+qHLI3I551G/v4dUnFJaFu1zFtcqYE7aL9hAxten7fHHh8vjEUHiDYAldNgdcuNOosAAT599kVgMUUtCdGqNPNf9jTsvN9sgVe/1ltG5oAz53Z9Pfj4qZr15KWUgbAi9zKG1yldlh7CN5a53acQobICyFE4M5p379/P9nZ2cydO7exLSYmhunTp7NypVFNduXKlcTGxnLUUUc19pk7dy4Wi4XVq1d7PXdNTQ2lpaXKQwghupT9CNQdbtw0z2ePiqxh0rhs9Zhah5Exe/BbqPKyDFxhFYO+/JKRA9Rj3//EyxD5FRnw+CJjqS27k583qAFSSIjdWO7NKsPjRTsJHtJUH0HT4FfTYfvdsPoW2Pl7eOZ0CAt26Z/eNdfZHZkz7bOHqturDxrD5f2xOx/u+FxtCwuCVy80pi14EBpq5/YbVjVu38KL7NUGq50eXQS5PqY11mWBPce/axRCiB4qYIP27GzjC2ZKilohNiUlpXFfdnY2yclqQaWgoCDi4+Mb+3jyxBNPEBMT0/jo169fO1+9EEK0UJWavVq8bKCyPWvGAYKCXOazO5xw0ydG5suXpAi0/1zMeeeqw2A/+GwMTqeHIcbvbYDnlsEZr8OoZwh6YYmye9K4bEJCHJJpF+3HEgZBpv+Hg63GMnCegsEgCdr9Zo1TpxIcO9AItBs4dFi4ze0wN5V1xtD6SrXwHE+dBqN8F7a86qINWK3G765yorhB/5faoaQa/vy979evlmy7EKJ3C9igvSPde++9lJSUND4OHTrU1ZckhOjNnDVu8zbNmXa3pd4eXeRefVkDLppgFPSaNxxsVvjPxdA3xq2K/OGsGFasMQVKVXXwxfam7ZJqtmeqmfajp9SPBpBCdKI9hYzwr581GqyxHXopPYoWpP5bjQiBk4arff7bzJxxXYc/fOFetO6ySXDxxGYvISW5gtNParpp+ANz+DbhLLXThxthq49ses2mxlU1hBCiNwrYoD01NRWAnBz1l3hOTk7jvtTUVHJz1f9E7HY7hYWFjX08sdlsREdHKw8hhOgyNZtAb1rrOCs7il17E5Uuynz2b3fBSyvUc4QHw9uXwItnw9VHwVuXwPKbYYqRlRw7KpcxI9Xfl+8uGKee45udUN50Hbqm8Uzm9ep1HFt/HTI8XrQn20j/+gUP7NDL6JHMlfbPMU2NWXXQdxX5d9fDBxvVttEp8Pgpfl/CNZeomfKrCv6BM8JlFIUOPLbI+wmclVC70+/XE0KIniZgg/ZBgwaRmprKokVNv8RLS0tZvXo1M2bMAGDGjBkUFxezbl1TEZMffvgBp9PJ9OnTO/2ahRCiVarVofHmpd5ioquZOLZ+yk9WKdzyqXp8sAXevNg9g9Y/Vtk0Z9vfXTCOysr6ucK6Dv9cpewvHjOC/TUDGrctFifHH3PAyN5pEc2+LSH8Zo2HIO832xsFD+r4a+lpzEH73GFGQTpX/1b/7SsGxUN8WNN2ZAi8doFaZ6AZ82fvITKipnE7mzTWzzpX7fT9bqOmhjcyRF4I0Yt1adBeXl7Ohg0b2LBhA2AUn9uwYQMHDx5E0zRuv/12Hn30UT7//HM2b97MlVdeSZ8+fTj77LMBGDVqFPPnz+f6669nzZo1LF++nJtvvpmLL76YPn2kuqwQohuoO+xWZMm81NusGQewWutLPt//NRRVqed4ZB4cbyru5MEVF2xE04zz3MILHFu6iI8+H23sXLIP1mUq/b8dcKGyPXn8EWJjqo357LLklmhvNi/FEV0FD+n46+hprOqoHWxBcOVkte29DbC3wPPxxwyEb65vmrv+/FkwOMFzXy9sNgenzFEr1T9ZcxckmW4e/OVH7yep2wMOKRwshOidujRo//nnn5k0aRKTJk0C4M4772TSpEk8+OCDAPz+97/nlltu4YYbbmDq1KmUl5fz9ddfExoa2niOd955h5EjRzJnzhxOPfVUZs6cyb/+9S+PryeEEAHHlGUHWLxMzSY2Dkn/aT8s3K52PmM0XDvVr5ca0K+EeSfs5nHu5QVu4yMuYOfzuZBXAb//Qu2cHsPfs69WmmbPlKHxogPZxvm+GRSUZsxpFy1jzrQDXD0VQk0F6e78n1Hg0pMBcfDFtfDCWcbvnFY4a/4OZft/y8dTc8tstdOyDFjjpc6QrkPNhla9thBCdHearut68916ttLSUmJiYigpKZH57UKIzuOsgsK/gd5UkflQZjT9J92pdPtl0SvGcm/ltfD8T/CPlcZyb/FhsPIWiAszn9kzu5OMC39i4LIlzXYteeh84h75EF1vCqK+fv8t5s3eC6GTIOosH0cL0Uolb0Otl7XDI2ZD+KzOvZ6ewFFq/J4x+/P38OJytW3ecLhxhpFdb2dFxaEkjfo9DkdTvuirN99g/h9uhewyCLEahe1uPw7SvdwYtMZB3K0y0kcI0esE7Jx2IYTo8Wo2KAE7uM9nj4utMtZFB2Mu6X1zYOlv4aRh8Mc5/gfsABaN/qlFzfc7Kp13Q69QAvaoyBpOODbD2JDq3aKjhHoZNaJpRiZetJw1Giwels67bSb0MwXH3+yCvy7tkMuIi63mmKlqFv27lcPhzlnw6+mw9lb4y+neA3YARxHUZXTI9QkhRCCToF0IIbqCrrutzQ6weLk6NH7W0QewWEwDogbHwzuXwhWmeanNsWhYnj+D9aPmee/TNxr+fT7vfTZBaT795F3YbPVLLslyb6KjhAyHIA/rfgcPNbKsonXM89oBokPhlfOMDLerXXkddhlzjtunbH+/dLCx2sWf50OanyMdq3/pgCsTQojAJkG7EEJ0hbq94HAv/GQuQqcs9WbWmiGiwVb6fDib64P+jyzS1H0nDIHPr2Fr2VB+WjVA2XX+6duaNiyxLX9dIfyhaRAx1709/LjOv5aexOrhRgjA1H7GDcAol0x8TjkUV3nu30ZzjlN/n23alkpuXgtXoqjdbkwtEkKIXkSCdiGE6ApVa9ya9h+IJeOgmk2c7Stob6WUlErCrh7LAA4wnVWcxkKGazvZ+ODvoF8sf3n5GLV/Ujmnn7yrqUEynqIjhQyHsKOatsOmQnD/rruensCa5H3f8YNhxc1w0zEwqQ+MSDKKU3aA6VMOK0u/gfuNymbpdqjZ2Hw/IYToQYKa7yKEEKJdOQqgbrdbs3lofHJ8KWMidgPtPxz97puW8++3J7OmerrRoMMlv4njqQe+480PJip9r7vsF0JC6ofGa1ZjyTchOlLEaRDUF9DBNqmrr6b7C0rxvT8lEh46qcMvIzjYyawZB/jy++GNbd8vHcxFZ29t2Ymqf4Gwo9v56oQQInBJpl0IITpb1WpjTrvJDz+pQfsj/Z7FMvNFeHIxVNa59W+Lfn1L+cPNauXo7buSOPOKS5UCdJERNdxyncuoAEuMVG4WHU/TjFUKQifL5609eBse3wXMQ+QXLR3s3qmiFt74GT7d4vkk9lyo87I0nBBC9EAStAshRGdyVkL1erdmXVeHicZSxJV7/gY1DvjbUpj5EvzgZSmsVrrn1mVMHHvEZ58/3v4TqSnlTQ0yNF6I7scaBZYWzh3vIOZidPsPxnE4q370TnaZsRTdpGfh91/AE4vB6WVl4up1HXylQggROCRoF0KIzlS1xm2ZN4BdexPIym4adv4IDxJWUdrU4XAJWNv3V3ZoqJ3/vvYhaSllHvfPn72bu367Qm20xrfrNQghOklzQ+Q7ybjRucTGqIXklq/pZzzZk2+sHV9cbWzvL4Tv3acSAVCzVQrSCSF6DQnahRCis+i1UL3a467Fy5qGxo9jE7/lZbXDaaOMglHtbPDAIlZ//W9OOn6v0n7FBRv5+NUPCQ52qgdYJNMuRLcUlNZ8n05gsegcO00d2r5sdX2hwWMHwhjTzYV/rvJ8Ir1OCtIJIXoNKUQnhBCdpWqN18zQD41Bu86L3IIVl2A5NAj+dHKHXVa/vqV8+9FbHDwcw96MOIYPKaBvmufsu2TaheimAiRoB5g5/SBffNdUjK4xaNc0+PXRcOtnTZ1/2m+sHT/cQwX86p+lIJ0QoleQTLsQQnQGZw1ULfe8y6mxZMVAAC7nbY5nqdrhlmOhf2zHXh/QP72EE2dmeA/YQYJ2IbqrQArapx1UtjduTaWktH6t+HPGQmK4esCbP3s+kT0fatt/WUwhhAg0ErQLIURnqFruNcu+dUcSefkRxFLEX7hL3dk/Fm4+1v/XscaAbTRYY1t9qV5pmgTtQnRXlniwhHb1VQBw1MQsQkLsjdu6rrHy5/p57bYguNS0zN/7G42K8p5Ur+2gqxRCiMAhQbsQQnQ0RylUrfS6++sfhgLwGPeRQq6687H5EBbs3+uEToK4WyH6QuNn2IzWXrFnlmjQZFaVEN2SpkFQ366+CsAogjl1YpbS1jhEHuDKo8B1pb+yGliw2fPJancYv2OFEKIHk6BdCCE6WsV3HivGN/jqh2EcxVp+wyvqjvkjYN4I/14jZDhEngma1djWLBA5D8KOauVFe2BNbL9zCSE6X1B6J71OGoTPhMj5EH4sBKW6dZk5XR0irwTt/WPhpOHqAa//bKyNaaY7JdsuhOjxJGgXQoiOVLsfarxkiICy8hBWr0rl//gVFly+kIYHG1l2f1jCIOosI5NmFjEfgpJbeNFeWBPa5zxCiK4R3K9jzx+UBDFXQdyvIWKuUSQu4iSI+w3EXKFMrzEH7at/6UttrbWp4WrTDcct2bAu0/PrVq8D3e55nxBC9AAStAshREfR66B8oc8ui5YO5g/2J5jAJnXHnbOgX6x/rxN+IlgiPO/TgiDyDM8BfUtJpl2I7i2oX/v8LvDENhZib4CQQZ73hwyB2F8bo4KAY6aqy75VVwfzyyaXYnknDnEvwPm6l4y6sxJqNnneJ4QQPYAE7UII0VEqfgBHgc8uWz+s4488rjaOToHf+Dkf3RoHoc0MgQ/uByFj/Tufz9fysOSSEKL7sNjA6j5Uvc3CpkLUeaA1U3/DYoPoi8E2mvi4KsaMVGt4KEPkrRa4cop6/OdboaDS87mrvKznLoQQPYAE7UKI3sdZZRSGK//C+OmsaP/XqM2Aat9fInUdjlv0OkE4GtscFiu8eBaEWH0c6SJ8pjF/vTkRJ/rXz5cgCdqF6Pa8ZcJbK3QiRJzqfwZfsxgBfvAAt6XffnIN2sGoIu/6u7DGAe+t93xeey7U7vH/uoUQohuRoF0I0bvU7oGiF6H8G6haa/wsegGq23FopbMKyhd4LprkYtvOJM6u+Zi3uayxLf+qeTDOz/WULZFgm+BfX2s82Mb519fja4UbryeE6N6CB7ffuUIG1xfAbOGQe80K0Rcxc0ae0rx8TX+cTpdzJUbAmWPUY9/4GZxefrdWLW/ZdQghRDchQbsQoveo3Q2l7xnzH105a6BsAVT+1PbX0HUo/9SvJYi+/H4YRcRzBW9zNp+w2DaX5D9P9v+1wqa2bAm2sBas925mbadidkKIrhU8oPlh7P6wxkPUBa0fwWMJ57i545WmgsJwduw21c4wF6SLCIHsMs/nrN0PdV6K1QkhRDcmQbsQondwFEDZx6A7vPepWNT2eZFVy6Fmp19dP/lyVOPzzzib9y98As3fYfGatfm57GZByRAytGXHNB6b0rrjhBCBRQuG4DYOkdeCIfoiY+WKNug/pC/pfauVtp9WmYbIT02HSX3g3LHw+TWw5DfQJ9r7SauWtemahBAiEEnQLoTo+XSnkUl31jTft+Ibv4NuN7V7ofIHv7pmZUex8md1+aVzTtvh/2uFjPZeMd6XMD8L3Jl5WGdZCNFN2Ua27fjIU9vlRp6mwXGzQpS2ZeZ57ZoGX14Hr5wHR/dvfih+7Q5jfrsQQvQgErQLIXq+6tX+D5nUdSj7b8u/9DVm8p3Nnx/49Ev1S3N0VDWzZ+73//XCWphlbxA8GIJasXRbkJ/z7IUQgS9kZOuHtYdOhNBJ7XYpM2eq1/HT6gHunawtuFZdh8qlbbwqIYQILBK0CyF6NmcFVC5p2TF6ree5775eo+QdowCdL0v3wVlvwOESFnwxStl1xsm7CAnxMXTfVVCSMS+1NTQNQqe28JggWe5NiJ7EEg7BrZgqE5RkZNnb0XHHqdsHDsVyKNPH8Hd/1G6VbLsQokeRoF0I0bNV/ujfsHgzRxGUvg96ne9+zmoofQcchb775ZbDbz+BVQdxnvhPopZvVHafd/o2/68ttAXF6jyxTQQtpNlujYJSjTn0Qoieo6XZci0Eoi5s2e8OP4wZA7GxapvbEPmW0nW/pyoJIUR3IEG7EKLnchRD9brWH193EEo/8B64O8uh5E2oy2rmOpxw0ydG4A5YSqr4xHkus1kEQFhYHfNO3OvfNWlB/i/z5o3FBqHjm+/XICi9ba8nhAg8ISPAGut//6gzjUx7O7NY4FjTwhY/rfJjJJGuw+Zs70tr1uyAusNtv0AhhAgAErQLIXquyqW+q8X7o3YPlLwB9nxT+24o/hfYjzR/jr8uhR/3KU1fcgqLORGA0+buIjy8mYx+g5BRxtDWtmrJEPngNma9hBCBR7P4vwxk+LFgG9thl2IeIu8z015VB+9vgJP/DXP+Cb/4qFdS8W27XJ8QQnS1FizwK4QQ3YijBGo2Nt/PH3WZUPwyBA8ES5QRqPs7X3LxXvjrj0pTJn24ijfR6++bXnHBJv+vpbUF6MyCUox58XUHmu8rQbsQPVPoZKheA/Y8731sYyF8bodehjlo37IjmaLiUOJiq907n/k6bHS5WfrqWpjiZTRQ3UGo2Qq2Me13sUII0QUk0y6E6JmqVrY9y+5Kd0LtPqje6H/AnlUKv10ALqM3nZqFi/iAfIxhpgnxlcyfvce/87WlAJ0nYdP9eM1ksES232sKIQKHZoXIs73XrLCNhqhzml9mrY2mTAGbrWlb1zWWr/Fys/CM0er251sbpx55VPFd87VJhBAiwEnQLoToeZxVUP1L115DnQOu/xgK1Ar0z8Q9wHJmNm5fdNYW/6vGt7Tqe3NCRjY/pzWkFRWmhRDdR3BfiDpfLTCnWSH8BIi6oFOKUNpsMN10D9HrEPnLJoPN5ZpqHfCOj9/3jmJZAk4I0e1J0C6E6Hmq1xrLtnWlRxfB2kNKU97UidxT+JDS5vfQeIut7QXozDQLhM3w3SdkRPu+phAi8NhGQdzNxnJukadB3K0QcUKHZ9hdzZypbi9ZMchzx4RwOGec2vbGz2B3ej951QpZAk4I0a1J0C6E6Fl0O1St6dprWLgd/rFSbesfy+8TXgKavgSPGJrP9Cl+Vje2TTIC9/YWOtn78HdrNATJfHYhegVrNIRNg7CpYI3p9Jc//nh1e+36PhSXhHrufJ1p1NGRMvhqh/eT6w4o/8yY5iSEEN2QBO1CiJ6lZrOxFFtX2ZINN3+itoVYyXv6Kt7+Xh3/eePVa/1LZGkWCDu6/a5ROXcwhB/veZ9tYqdm2oQQvddxx6nz2p1OjSUrBnvuPKGPe/G519b6foG6TKha3raLFEKILiJBuxCiZ6la2XyfjpJXAVe+D5Wmokd/nscr68/Ebm+ahxkWVsdVF/lZ3T5kTMvWU26p0CkQlKa2acFG1k0IITpBWJj7EPnvfvJRx+Na077lGcZNU18ql0BdVmsuTwghupQE7UKInqN2T8vnLa4+CBe+DU/8AD8fBl1v/hhPKuvgyvfgcInafvFEqi46mpdeUwPgy87bRGyMh+WMzDQNwme17pr8pVkg6jywuAxFDT9BqsYLITrVXNPKct/92N/7aJ8zR0NihNr2cjM3bXUHlH0ETj9+9wohRACRoF0I0XNUrWj5MXsLYMleePYnOPVVmPMv+GCjUf29JcKC4Kh+atvUfvDMabz+/mRy8tQA+Marf/bvvLYJxlJvHS0oEWKuhdDxEDkPwo7p+NcUQggXJ52kbu/ebeVA9iTPnW1BcM1RatunWyCzxHP/Bo4iKP+k9TdohRCiC0jQLoToGezZxjrqLZVdpm5vyYZbPoUZf4e3fzGWE/KHpsEjJ8Pd9fPD+0bD6xdSZwnh6ZeOVbqedPxeJo8/4sc5gyF8tn+v3x6CkiHqXKOivMxlF0J0skmTICFBbftu+bGeOwNcO824YdrA7oR/rmr+hWp2QuUPrbtIIYToAhK0CyF6hkovBYZqHXCgCIDiklC270rkwKEYnM76oNQctDc4WAx3/g+OftFYTqjG3vw1aBrcfQI8eSp8eAUkR/LOf8dz4FCs0u2Pt//k11si/ESjorMQQvQCFgvMmaO2ffF1AgT38XxAQjhcYsrEv/ULlPgx/L3yJ6he17oLFUKITqbpuowPKi0tJSYmhpKSEqKj5QuyEN2OoxiKXnBfzkfX0W/6lJqv9nNj2tu8sefMxl3xcZWcf/o2/jD5bQbvXQv/294Y3HuUEgknD4dB8VBeAwPi3L8smlRVBTF8xi0czmpaPumYqQdZtvC15hPZwf0h5mpjvrkQQvQSb74JV1/dtB0eDvmHtxBm/9jzARlFxs1Vp8vX2fvnwK0zPfd3pVkg6nywjXbbVVwMO3dCbi4EB0P//jBsmPFcCCE6m3wbFEJ0f1XLPa6/W/TkBrSPNxFaUcbLey7iLD5t3FdYFM6/3jqKoXc+yzX5f6fwq7vhPxfDlL6eXyOn3MjgPPI9/O0neLWZ5YWAZ/85QwnYAe6/c2nzAbs1GqIukIBdCNHrnHaakXFvUFkJi5aN9j7qaGAcnGEKuj/b6t+cdd0JZf+FGmON96oqeOUVOOYYiI+Ho4+GM8+EU06BMWMgLg7OPx+++AKcsuS7EKITyTdCIUT35iiD6vVuzfu+qCbi2a8at8Oo5k2uIp4CpZ+ua7zx/iTGnngz34ScCl9eBx9eDtP7+37dTUcgx8vQemBfRhyPPXec0jb7uH3Mn73H93ktURB9BVijfPcTQogeKDERjjVNY//scwuE+liC8qb6wpnDEuHZM4zf4/7W5dAdUPYhH797gCFD4MYbYeVKzzF/RQX8979w+ukwcSIsWeLfSwghRFtJ0C6E6N6qloGuzjfP2BlO3a8+JYSm9dKdaFzKuxSSYD4DAEdyoph/8RXc8sdTqZo+Aj6/GhZcCccO9P7aizwH4E6nxg2/O4PKypDGNk3T+evD3/r+Hhk8EGKv75xq8UIIEaDOOkvd/vRTqLNOMYpzejKxj/E7+6ffwmWTjcryfiots3HhdedxwWUDOOJHfdAGmzfDiSfCNddAebn/xwkhRGvInHZkTrsQ3ZajtH4ue1PQXlJq463pRdxc8IzS9R/Rt9H379OZd+IesrKjeOP9ifztlRmUV9jcTjt2VA7v/uO/jBtdv+b79lxYtBu25kBuOUSEQP9YuGA8THAvkPT4c8dx3+NqNaXfXrOGl5760v09aBYjWA+dBraRLf4jEEKInmbfPhgyRG1buBBOO34hVPm5XKYfDhyK4fTLL2XL9hSP+4OCIC0NqqshL8/7ecaMgQULYPjwdrs0IYRQSNCOBO1CdFtl/3Or/nvr5dN56ttzCaOpevCO8HGkrLmQuGQ1I5+VHcW1t53FN4uHup3aZrPz1APfccuv1mCx+P9r8rOvRnDuNRfhdDYNZOrXt4QtS18mOqrGaNCCIGQ4hIyAkGFgCff7/EII0Rscc4wxTL3BxRfDe2/lQdFL7XL+PfvimXXWNRzJcZ+KNHvWYe78XRRzTo4hNNRoy8mBzz6D55+HbdvczxcXB19/DdN8jOIXQojWkuHxQojuyZ4HNepc9vcWjGX2t/9UAnYHFtI/PMEtYAfok1rGV++/zUtPfkFYWJ2yr6YmiNvvP4WjT/kVK9b08+uSPvliJBdef4ESsFssTl5//lMjYNeCIfx4iP8dRF8IoRMkYBdCCA8uv1zd/vRTKK1MgpAhHvu3xOGsaOZecKVbwB4fV8l7//yY7z/6P0475u+E6qsbJ7enpMANN8CmTfDSS2DO8RQVGcvVyTx3IURHkKBdCNE9VXyrVIw/lBnNa3cmcjafqd0uOpbIafFeT6Np8Ntr17Luu38yYUy22/616/ty7OnXMe/Cy/nfN8OpqbG69SksCuO2++Zz7jUXU1urzqV89N4fmDNrv7HOcNyNEHEiWMJa+m6FEKJXufBCY3h6g+pqYzk4wo5u2YkW74XSphu5efnhnHTBFRw4FKt0GzU8j7Xf/JuLz9li1B7R66D8Kyh5Dew5jf2sVvjtb2HdOhg/Xn2p8nI44wz4uf1G8AshBCDD4wEZHi9Et1O7C0rebdx0OjXmnn8lv192I/P5prG9Oiqa0F9uhJhQv05bU2Plvsfn8Nd/HOO1T2REDdMnZzJ0UCHBwQ727I9n8fJB1NS4Fz667rJf+Ndf/4clbCREnWcMixdCCOGX884z5oo3GDYMdmzXsZS+BPZ83wfvyYeHvoXvdhvV5R86ieKSUGafexXrN6cpXSeNO8IPC94kNqba87k0C4ROgfATldFRFRXGNX7zjdo9KQmWLZM57kKI9iNBOxK0C9Gt6HVQ9DI4ihqb/vaPGXz4UAyrmKH2/fM8+HULszLA9z8O5qZ7TmXX3sRWX+a1lxoBuzV8DESdK2uuCyFECy1ZYlRod7VwIZw2ey2Uf+H9wPc3wJ3/A3v9aKxgC5Xf3sbJf7id5WvU5TxHDstj6Wevk5RY2fwFWUIh7FgImw6asTpITQ1ccgl88onadeBAWLvWWMJOCCHaSr5FCiG6l8olSsC+ZXsy9z42h4f4k9LNmRgJV05p1UvMPX4fm3/8B889+hUJ8X58kXMREmLnhce/5P+e/Rxr+AgJ2IUQopWOPx7GjVPbHnwQnCETjADam2n9wHV5zTonG87f7BawD+hXzHcfveVfwA7grIaKRVD4AlStAt2OzQbvvWfMZ3eVkWEM8be7l1MRQogWk2+SQojuoy4TqprKCVdXB3Hpb85jYu06TuFrpavllmMgzMuavn4ICXFw2w2rObT+b/zfs58x46hDaJr3gUnBwQ4uPXcT25e9xC2/WoNmGwRR50vALoQQraRpcOedatsvv8BHH4cYw9W9GZwAv5quNB1T8D1n8HnjdmpyGd9/9B/S+5S2/MKc5VD+NRQ+D1WrsIXU8cknMHmy2m3xYrjrrpafXgghzGR4PDI8XohuQa+DolfAUdDYdMcD83junzP4glM5la+a+iZGwM+3QXjrg3ZPcvMi+HHlAHbsTuRQVgy6DsmJFYwblcu8E/cQF1s/HzK4H8Rc0Th8UgghROvY7Ua2fceOpra+fWHzhhLieF4pSKooqUaf/iJaYVMWPYs0RrMNS6yNpZ+9zthRue1zkZZICJvBkeKpHDU1hKwsdfcbb8BVV7XPSwkheicJ2pGgXYhuoewzqG5a4u2bH4Yw/+Ir0HDyMA9zO88RTZmx8+GT4Lfei8l1qOCBEH0JWGxd8/pCCNHDLFhgFHxzddFF8N4/P0ar3eLxmPLyEP7vTAu3b7lXaf9P0FWMXHgs0yZntv+FWsJYvXkOs+ZNoba2aXx+aCisWAGTJrX/SwohegcZtymECHzVG5SAff+BWC77rfENTsfCQzzCSNtucq+aD4Pj4aqjuuY6w44yMuwSsAshRLs55xw4+WS17YMP4J4/z8NT6mn3vnhmnXUNd2z5A18xX9l3pf1NpuX+0DEX6qxi+piFvPLMQqW5utq46VBY2DEvK4To+STTjmTahQhodVnGOrm6Uc2nvDyEY0+/lk3bUpVuf3/iC266bi04nGBtp/uRlnCwRINmA3TQq8BZCs6apj6aBYIHQdhxEDKwfV5XCCGE4vBhY5h8cbHafv5Z+/nTXV8yYmg++w7E8dq7k3j+30dTVWVMj+rHQbYwtmkkFhjLgH5/AwyI67DrvfmeU3nptWlK2/z58MUXYJGUmRCihSRoR4J2IQKWoxSK/w1O48tWVVUQZ1x+KYt+Gqx0O2v+Dj558300zdNJmmGxgTUJrAlgjTd+Whp+esmYO6vAWWE8t8aA1r5z54UQQrj7/HMj6+70Mo3dm9vD/86zlbeojRPS4NOrIaJjao/U1lo58ZyrWLFWrVj/0P2lPPxn+a4phGgZCdqRoF2IgOSshpLXwZ4DQGVlMOdfdyFfLRqmdBs9IpeVX75KdFSNp7N4FjwQbGOMDLk1gdZF+0IIITrbG2/ANdf433/4kHwWvv0Ow578N3y2Vd158nB44yII6pjUd1Z2FJPn/JqcvEilfeF7n3LaGeEQMgqC0uX/ICFEsyRoR4J2IQKOXgclb0PdAQCycyI588pLWLu+r9ItPq6S1V/9H0MH+zlRMGQ4RMyFoOT2vmIhhBCd5PPPjWrs5qHyriwWJ7+9Zi1P3v89ERF1UFYDJ/0L9pn+v7h4Ijx7RvtNqzL5ccUA5px3FQ5H0/ljY6r4+dt/MWRQkVF5PmQE2EYYN5Jl5JYQwgMJ2pGgXYiAotuh9H2o3QMYVeKvvvVssnOjGrtMZh1XhLzL7HfTGD+rvPlzaiEQeQaEjuuoqxZCCNGJ8vPh6afh7bfhyJGm9rSUMs44eSe/++1Khg8pUA/alQenvQYl1Wr7v8+Hs8Z02LX+7R8z+N1D85S2CWOyWfHFq4SH1zU1asFG4G4bAcHDwRqFEEKABO2ABO1CBAy9rj5g38uRnEj++Ngc3njffY2cL4NO5xT7F8Y67NdMNZZ3S4rwfE5LJMRcDkGpnvcLIYTotnQdMjOhNGcdUdYfSe9T6nu0+aoDcMFbUOMwtn89HR6Z16FD1HUdLr7hfD78bKzSftl5m3jr5QWeX1rTIKgPhIysH0af2GHXJ4QIfBK0I0G7EAHBWQWl71FdlsXfXpnB488dR0Wle4GgM1IX83n2bLXx/jlw60z3c1oiIOYa+bIjhBA9nbMaip5VV/fw5pudcN1HcOF4+OsZnTKnvLw8hGnzr2f7riSl/c7frOAvf/q2+UsISjZqsdjGGrVYhBC9igTtSNAuRJdzFKGXvMN/P03m7j+dRMZBz8vwnDJnN584zsK2ZHtTY3wY/Hw7RJoCfC0IYq6G4PQOu2whhBABpGIRVP7kX9/N2TA62ftc9rIaiPKygkgr7didyLR511NWrp73kT/8wAO/W+r/iYLSIHQ8hIyVIfRC9BIStCNBuxBdqnYv65f/xB33n8CPKwZ67BIZUcOT93/Pbyd8gnbqq+rO+2bDbce5HxR1JoRObv/rFUIIEZicFVD4nDHVqi10Heb/H9Q6jEJ1p42C9Jj2uEIWfjucs6+6WClMB0bG/ZmHv8NiacHXcs1CWfUwvlx8DD+uSGfvPiu5uRAUBDExMGoUHH+8sT58ZGTzpxNCBC4J2pGgXYguoTs5tGsl9z8YwVsfTUDX3ccGaprONZes57F7fyA1pRwufBuW7G3qkBAOa29zz7LbxkD0BR38BoQQQgSc8m+gamXbzrFoN1zyrto2LBGOGwQT+8D4NBie1Oql4t7+aDxX3HSuW/tpJ+3i1Wc/IyW5wufxBYVh/O/bEfx34Si++3EINTVBPvvbbHDRRXD33TB2rM+uQogAJUE7ErQL0dlKC3N58tFMnv3HWKqrPS9vM3P6AZ5/7Gsmj68vC7zqIJz5utrpoZPgpmPUNksExN0ElvAOuHIhhBABzVkOhc+3Ptuu63Dqq7Au03e/sCAYmghDEoxHw/N+scYN5Wa89OpUbr73NLf2mOhqfnfjCq6+eAP9+pYC4HRq7N4Xz7dLhvDZVyNZsmKgW6beX5dcAk89Bf36tepwIUQXkaAdCdqF6CzVldX83z/288iT/cnL91ztvX96Mc889B0XnLlVLcxzzpuwPKNpOzkS1txqVJB3FXUOhE5o92sXQgjRTVR8C5UrWnfskVI4/TU4VNK64yf3ha9/5Xnf1zvhYJExVz4ihO/XjeTRf82lyBFLOZGUE0kZUVQRBmgkJ5YTGVHLkdwoqqrab/32sDC49174/e+NLLwQIvBJ0I4E7UJ0tKrKOv798iGe+msSWdmei+ZERdbwh1uWcedvVhIWZld3LtlrDI139dh8uH662hbcD2Kva8crF0II0e04K6Hoef8qyXs8Xoef9sMHG42h8kVV/h972ih4/ULP+675EL7Y7nmfCwcWyomkkHgOk84h+rGBiTzD7z32Dw52MOe4fcycfpD0tFKcukbmkWiWrhzA4uUDsdutHo8bPtzJP/5hYfZsj7uFEAHE9yQYIYRog727K/nXP/J5/a0E8vIHe+xjtTr59ZU/89BdP5Kc5GEen8MJD32rtvWJhiumuPeNmNcOVy2EEKJbs4RD6AyoXNLK4zU4frDxcDiNSvNL98H6LNiU5TsLn+ajmntptV8vb8VJDKXEUMogMgDoxyElaA8Lq2P+iXs497TtnNnvR6KrCmFUMqREKkvY5eWH89Jr03j+39MpLglTXmfXLgtz5sDlF2XwlyeySembCEGpYJGqdUIEGgnahRDtRtdhz26dzxYU8smnOitWJwL9vfY/a/4Onnzge0YOy/d+0vc3wPZcte2u4yHU9OvLNkqWdxNCCGEIPwaq1xoV5dvCajGKz03s09RWUAnbcmBPPuwtMB57CuBQMaT5GLFZ1srMP3CYdNJSyjj5hL2ccfJO5s/eQ0RE/bz9P6yA1382nseFwchkGJkEI5NJGpXCwzcUcPN1a3jwqRP553+m4HSq8+Hf/mAgC79O4Yn7FnHDle9gCYo0lpVzfVjbp3q+EKJ1ZHg8MjxedF+6DpmZkJEB+flQUADV1cZNdosFoqMhKQkSEyE52XgEt9O0OLsd9u+HXbtg+zY7K1dUsWJlMNk5oc0eO/u4fTx014/MmnHAd8fyWjj6Rcgtb2oblQw//FpdW1fTIPZGCEpu5bsRQgjR41T9DOULO+/17E7jYb6p3OCeL2FXnvF/W3kNVNQazytqjSH5PpRdfQKRTx2v1nppcNYbsLKZ/09TIiE9hqLwZL7eOZm1uaPJog9bGcMWxjV2mzw+i/tu/4mzTtmB1epyTZZwIwsflArWVAhKAWsiaJ6H3gsh2pcE7UjQLgJfVRXs2QM7d8KOHU2PnTuhvLz54xtomhHAp6ZCWpr6MyrKWNvVajX6VVZCRUXTIz8fcnJ0crKdZOfoZGVZqKtrWfXaeSfu4YE7f+TY6Yf8O+DR7+GF5Wrbh5fDCUPUNlniTQghhJnuhOJ/gj2nq6/EN12HantTAF9eYzzPr4CsEsgsNYbqzx7q+dgRT0Oxf0PvzV7kZm7lRbf2wQMK+U/CbxgTuYeYARa0hDCICzcy+QnhxvOESEjqC3H9ITgFghLBmmA8tBAPryaEaC0ZHi9EO6qshMJCI+NdWgp1dUZGuq7OeDid7g+HQ90uLYWcHMjOhqwsI5N98KDx/3Jb6Trk5RmPzZtbcwYNaNld9bjYKq6+aAM3sZ11wAAAIjxJREFUXLnO9zB4s7IaeOsXtW3OUPeAXdMgfFaLrkkIIUQvoFkg8lQofr35vl1J0yAs2HgkeV5ZxauKWhicADtyobLly9z1HWUHD7Xx9h2IJ/LATmLZ1Ow5dE2jNioSe1Qk9ugInLERlB07mYrzzyAyOpKklAhCI+LAGg/WOOOmRHi4kSUQQvhFMu300Ey7rkNhIbW7D1CyK4fiYVMpCU6kstIIInW9/uHU6fvFv9A1DdDQLVZ0q5Fu1YKsTanXICtYg4w2qxUt2Giv7T8Ue1IaFosxHLthWLamgS3bGKplCW46RgsyzmsJMY63BNdvB1mUY3399LbP45CxVvyxNTwqKqCsrOlRWgq5uUYw3RBU5+QYj9zcpqHpAlKSyjlz/k7OPmUHc47bh83maN2Jskrhvq+NarvBFlj8GxiepPYJGQ4xl7b9ooUQQvRMZZ9C9YauvoqO5dSNOfXbc40Afkeu8Xx3vjFk35vH57No+MXcdM9p7NyTqOw6TF/6ktWqy3mBW7iNFxq3oyJrSEqoIDmxgn/uu4CxRWuotkVTExlLXUw8enwC1qQkgtJSCUtPJKRPIlpiAiQkGEMEE+qfh0gGX/ROErTTQ4J2u53cD5eQ++73hKxfRd+cX4hwlDXuPolv+Z6T3A6z4MDRhgEXN/MiL3Gzx305JJNMnl/ncaLhwIoDK3aCeJw/8gR/9Nj3f5zOYPYp/V1/Go8gt/0OrHxrmc/rlqYlwRoCdKcTfstLDGafcr4qwsglWXkcJh077bdeansIC6sjJamchLgqIsJr0XUNh0OjuDSU/IJw8gvD3QrPtJeE+EqGDy5g7Khcjpl6iGOnHWTooMJ2uYnS6JudcKAIbjjafV/sNRA8oB1fTAghRI/irIKil8DZgvlkPUWtwyiUd6DIuBHeMNw+q9QYfv/H2XDaKOx2Cx//bzRPvjCTjVtTAZ0qwgildcXzHuJhHuEhj/s2MY5xbGnVeb/rfw1vzHyN4GDcHsdtfpmYyiM4QiNwhkeih4ahhQRjsQWj2YKxNvwMDSEoLJigsGCCw4MJCbNiTU0ieHA/QkNpfNhs9Qmh7GyoqWnKHJkfVqv3fQ2PNmj4nupwNI3OdB2l2fBdtqFvS376o/H7nOsLNpxE19EwHo3PNWOfRWtqR9PQYqIbz9dwTk2DoLoqgqrLsVp0IxEXaoOICCNxKBrJn0Y3V7pqG/vv+jv9V39Esj0fb2W4IvBcPVWjbfdsHD6GSlvxP7tqQceCnWDsQA1B2L32HcJeRrGjJZfZKNuZ4vWG84V8yPEsbfYcdqzsZxC7GcZuhrGJ8axlKpsZ36praom+aaWMHJrPyGH1j/rnfdNKfQbJTqdGQWEYR3KiyM6NJDs3kiM5kWTnRTa2VdcEYbdbcDg0nE6NsDA7EeG1RITXERFeS2x0NanJ5aQml5OSVEFqcjnDBheQEN+C9Wtba94Iz+3BfSVgF0II4ZslDCJPh9L3u/pKOl+I1SjgOsp3odagICcXn7OFi87ewtr1fflwwUie+uhhtKJKEiggkXwSKGh8nkg+YXgfXphPotd9CRS0+u1sPRjNu+963ncBbzKdNa067yv8mht5xa3dZoNv7Zcyy7G4Vef9Nupcbkn7rxJoN/x8rvAKTq3+pPGbuPk7ueu263MLsIxZzOcbj6/5PLfya/7p1u7tO39D0K2hs4WxTGSjx35/4mEe5M8+3q13OSSTiufaEjfzf7zIrW7tNYRQpYVTZY2kxJZMSWRfahLTYfhwwo+ZyLALJhIzILZV19MdSdDeTeV+tY683z7EmIwvmOBH/44K2u0+PkK+Au/m+LoZ0NXnDcLBMPYwjD3AVwBsZySjXSaFaZpOdFQNISEOgoMcBAc7CQ5yYLXqWCz1D83lucsjvD5rnpxYQUpSBYMHFDFiaD7DBhUSGVnbqvdtsegkJVaSlFjJ+DEBXpCnJcJmdPUVCCGE6A5sIyF0ClSv6+orCWiaBtMmZzJtciY8ClnZMaxeN5qdexNZcjiWIzlRVFTaqKgMwVlRR3hVKZE1RUTUlBBdW0h0XTHR9kLWOqeBxySJ3qagvYAEr/siaf1ICiees+E1NeD0/Eb8UlJmYVeZt73VXr+fNycE798Hg7Bj87Hfl5Yk3FrCV7yh4znrZKMWm15LrL2YNPthqPgFcoCtwCfA3bA9bDJLzv87M+6cwYQJ7TNVNlBJ0N7NVOzOYtf5f2TSpje9ZtVdObCQQwp2gggJMUabNEwH0jQIQmNNwbH1w1l0rLoDCw6sut0YUK7bG9uCsGPR6wef63ZjaH1wGDZL09Ad159t+Yfv62ZAIJ43+th4Vt3/bxLiqoiPqyI2phqLpf4X1FvrjGFpk/rC5L6+13Dt7RxOdSk3X6wxEDK6Y69HCCFEzxF5CtizwH6kq6+kZTQLaGGghYLFBgQbbUqgqRvV8nEaz13yt40PzQpYQQsGLaj+PCEuD1v9IwQsoaCF0Cfexjlj6ts0/6cGPoCRUS4pMYrf5ubW/8yBl/bupyarAHtOPnpeHpaiAoJKCwkrzyfWWeiW2Y+nqPG8voL21gbA4D1oB7C0IWj3dd5A1Jrguq3nbYtRVb9w5luJ7HkLRo6EG2+Ea64xVkTqaSRo7yYcNXY2XPwkoz57gkl6pdd+661T2DtkHhx9NHHHjSV5cjp9BwbzTrS3qSFBwLJWX9dr9Q9P9Mo8nHYHzjoHut2Bs9aObneg1xk/nXVNz3WHA73WDg6j3+/S+nF7qvuNAKcTbItf5khFGdQZ/RvOicM4j2a3ozkdjZN/NIcdnA40h4Ozx89gzjH116c3zauxWCD5X6dTdHgUmsM4Pki3E1xbTlBhLpb8PKPiXI3nuV1950XSd0qm5z+Ijzer66emRTUF8JP6wsQ+EGVr9d9Bj7FwOzz3E7xxEaTHNN8/dFr9lxYhhBDCD1oQRF8Mxf8OzPnt1rj6ddCT65dOiwdLDFgiu2UK0WqF+HjjMaJxlpsG9Kl/qHTdWMY2L88o/ptdBnvKobzYTm1OEfacAgbrifyBplV5XFfp2b7qHDIrj2CrLSekrpwQexUWZx1WRx1WvQ6rs44gp/E8yOVhxeEzqSNBu6EtQbsvbTnvHoawh2GAsRTybbfB/ffD9dfD734Hfdw/Zt2WFKIj8AvRff89/O5OnWc3z2E27nNqColjycgbSfj9dcy4bLAU1uwoug5FRcaC6bt3w871sHklrN8OL54FR3uYW213wtAnfS/DomEs19Iw52xk/c+B8RDUvX7Zt9q2HDj1VePPKTEcXrvQ859nAy0Y4u805ikKIYQQLWE/AiVvgrMLl3zRrBBUX5cluL/x3BLeddfTy9ntRl6mutr9UVtSRU2lg5oqZ+ND051GgshZ/7z+4bpt1Zw4IyKpTezbWKvO9Wfkkd2EVBRhtRr3ZBp+WqyaUuPO3GaxgBYdhXPYCKXOXcN9HWvmQSyFTUvsGgtEaUrxN9dtqA+cNQ1CQ3EOHd7U7hIlagX5aIUFShW5htnwaJpRl67hOcZ243OLFWdqH7eieboOzvJKnKXl2B0adjs4K6vRyytwVlRSV1RBZU4pjkNH0DIPYzmYQVL2JgZXbiGEOrdVClzNDVrClMtGcuOfUhnQA8ofSdBO4Aft110Hr70GE9jAL0zGUn8XrJQovp/2R6a8fgsDRrdwXU/RfpzlULUKqteC05SJ35YDJ7gXNvGLzQoPnwzXTWv7NQayI6VwxutwsLipLdgC3//ae+GcsKOMokJCCCFEa9RlQenb4PQ+erFdaRpYUyBkMAQPNoL1Fgw3F0I0qS6tZecbK/l+UzJ/XzSKjAz3PrfxHM9zO7GxkJUFYd08z9Nj0ngvvfQSAwcOJDQ0lOnTp7NmTeuqRwaiP/8ZwsNhIxN5letwYOGr9OvJXLybc1ffIwF7V7NEQsRciLsdwo8z5n01iLLB7TNh1uCWD4GvcUCij7/bJ36Ad36B9Zm+M/mBrKASLnhLDdgBzhsPI5M8HgIYQ+OFEEKI1gruA7HXG8PRO4o1HkInQ/T5EH8XxP0GIk6GkKESsAvRBqHRIUy49Xh+93+j2LcPFi+Gs89uGgQwlTWsZSoAv/pV9w/YoYdk2j/44AOuvPJKXnnlFaZPn85zzz3HRx99xM6dO0lObr5cW6Bn2gH+9Cd4+GE4fmQOj9+azTE3+lMzXnQJZyVULYeqtaC7VO906sZaqb9kGoH2L5mwNRvqfMyV+um3MMJD8FpUBSOebtq2ajAqxZgnP6UvTE6HYYlgCeA5cDllcOHbsD1XbZ+SDp9eBTYv88tCBkPMlR1/fUIIIXo+3Q6VP0HVCtDbcANcs0JQCgSlQ1A/I5NuDczvlEL0VPv2wTPPQNi/X+BZx62EhsL+/ZDagffmOkuPCNqnT5/O1KlT+fvf/w6A0+mkX79+3HLLLdxzzz3NHt8dgvaKCvjoI7jiCmN+i+gGnBVQtdLzsPkG1XZjCP32HCN43Z4LO3Ihr8IYIp7xRwj28Be+IgPOftP360fZjCJ3R6UbgfDkvr4z951pbwFc/I5RVd/V0AT47BpI8nGdMZdCyHDv+4UQQoiWcpYby8HVbAV7ru++ligISjQKxgUlgzXN+KlJfWchAsHhg07+8jcLNhs89VRXX0376PZBe21tLeHh4Xz88cecffbZje1XXXUVxcXFfPbZZ82eozsE7aIbc1YZgXvVGv+r1eZXQEaREXB78uoauPerll/LwDgjgL/nRBgQ1/Lj28OXO+DmT6DctIZoegz87xro66NyvDUB4m7ullV0hRBCdBPOKnDkGzffcWAskxZqTIezxsjQdiFEp+v2twTz8/NxOBykpKQo7SkpKezYscPjMTU1NdS4LN1VUlICGMG7EB1jIgSNNe7gV/0Mjmbu4tuCjGHxZV4y9AkRMG847MqD/UWe+3iSUWQ87jre87mdev1yrh0QFBdVwmM/wCdb3PcNiIM3LoToUO/vGSBiDJSVtf+1CSGEEIqY+odZVf1DCCHaT1RUFJqP79/dPmhvjSeeeII//elPbu39+vXrgqsRogvM+HtXX4HqQBGc+E8/Oj7Z4ZcihBBCCCFEZ2puxHe3D9oTExOxWq3k5OQo7Tk5OaR6qTpw7733cueddzZuO51OCgsLSUhI8HmHQ4jOUlpaSr9+/Th06JBM2RABTT6rojuRz6voLuSzKroT+by2XVRUlM/93T5oDwkJYcqUKSxatKhxTrvT6WTRokXcfPPNHo+x2WzYbOryW7GxsR18pUK0XHR0tPzyE92CfFZFdyKfV9FdyGdVdCfyee043T5oB7jzzju56qqrOOqoo5g2bRrPPfccFRUVXHPNNV19aUIIIYQQQgghRKv1iKD9oosuIi8vjwcffJDs7GwmTpzI119/7VacTgghhBBCCCGE6E56RNAOcPPNN3sdDi9Ed2Oz2XjooYfcpnEIEWjksyq6E/m8iu5CPquiO5HPa8fr9uu0CyGEEEIIIYQQPZWlqy9ACCGEEEIIIYQQnknQLoQQQgghhBBCBCgJ2oUQQgghhBBCiAAlQbsQQgghhBBCCBGgJGgXohM4HA4eeOABBg0aRFhYGEOGDOHPf/4zrnUgc3JyuPrqq+nTpw/h4eHMnz+f3bt3K+eprq7mpptuIiEhgcjISM477zxycnI6++2IHq69Pq8nnHACmqYpj9/85jed/XZEL1BWVsbtt9/OgAEDCAsL45hjjmHt2rWN+3Vd58EHHyQtLY2wsDDmzp3r9nktLCzksssuIzo6mtjYWK677jrKy8s7+62IHq49PqsDBw50+9365JNPdvZbEb1Ac5/XBQsWcPLJJ5OQkICmaWzYsMHtHPLdtX1I0C5EJ3jqqaf4xz/+wd///ne2b9/OU089xdNPP82LL74IGP9Jn3322ezbt4/PPvuM9evXM2DAAObOnUtFRUXjee644w7+97//8dFHH/Hjjz+SlZXFueee21VvS/RQ7fV5Bbj++us5cuRI4+Ppp5/uirckerhf/epXfPfdd7z11lts3ryZk08+mblz55KZmQnA008/zQsvvMArr7zC6tWriYiIYN68eVRXVzee47LLLmPr1q189913LFy4kKVLl3LDDTd01VsSPVR7fFYBHnnkEeV36y233NIVb0f0cM19XisqKpg5cyZPPfWU13PId9d2ogshOtxpp52mX3vttUrbueeeq1922WW6ruv6zp07dUDfsmVL436Hw6EnJSXp//73v3Vd1/Xi4mI9ODhY/+ijjxr7bN++XQf0lStXdsK7EL1Fe3xedV3Xjz/+eP22227rlGsWvVdlZaVutVr1hQsXKu2TJ0/W77vvPt3pdOqpqan6M88807ivuLhYt9ls+nvvvafruq5v27ZNB/S1a9c29vnqq690TdP0zMzMznkjosdrj8+qruv6gAED9GeffbazLlv0Us19Xl3t379fB/T169cr7fLdtf1Ipl2ITnDMMcewaNEidu3aBcDGjRtZtmwZp5xyCgA1NTUAhIaGNh5jsViw2WwsW7YMgHXr1lFXV8fcuXMb+4wcOZL+/fuzcuXKznorohdoj89rg3feeYfExETGjh3LvffeS2VlZSe9C9Fb2O12HA6H8nkECAsLY9myZezfv5/s7Gzld2dMTAzTp09v/N25cuVKYmNjOeqooxr7zJ07F4vFwurVqzvnjYgerz0+qw2efPJJEhISmDRpEs888wx2u71T3oPoPZr7vPpDvru2n6CuvgAheoN77rmH0tJSRo4cidVqxeFw8Nhjj3HZZZcBTb/A7r33Xv75z38SERHBs88+y+HDhzly5AgA2dnZhISEEBsbq5w7JSWF7Ozszn5Logdrj88rwKWXXsqAAQPo06cPmzZt4g9/+AM7d+5kwYIFXfXWRA8UFRXFjBkz+POf/8yoUaNISUnhvffeY+XKlQwdOrTx92NKSopynOvvzuzsbJKTk5X9QUFBxMfHy+9X0W7a47MKcOuttzJ58mTi4+NZsWIF9957L0eOHOFvf/tbp74f0bM193n1h3x3bT8StAvRCT788EPeeecd3n33XcaMGcOGDRu4/fbb6dOnD1dddRXBwcEsWLCA6667jvj4eKxWK3PnzuWUU05Rin8J0Rna6/PqOh943LhxpKWlMWfOHPbu3cuQIUO64q2JHuqtt97i2muvpW/fvlitViZPnswll1zCunXruvrShFC0x2f1zjvvbHw+fvx4QkJC+PWvf80TTzyBzWbriMsWvZT8bg0cMjxeiE5w9913c88993DxxRczbtw4rrjiCu644w6eeOKJxj5Tpkxhw4YNFBcXc+TIEb7++msKCgoYPHgwAKmpqdTW1lJcXKycOycnh9TU1M58O6KHa4/PqyfTp08HYM+ePR3+HkTvMmTIEH788UfKy8s5dOgQa9asoa6ujsGDBzf+fjRXK3b93Zmamkpubq6y3263U1hYKL9fRbtq62fVk+nTp2O328nIyOjISxe9kK/Pqz/ku2v7kaBdiE5QWVmJxaL+c7NarTidTre+MTExJCUlsXv3bn7++WfOOusswAiSgoODWbRoUWPfnTt3cvDgQWbMmNGxb0D0Ku3xefWkYSmYtLS0dr1eIRpERESQlpZGUVER33zzDWeddRaDBg0iNTVV+d1ZWlrK6tWrG393zpgxg+LiYiV79MMPP+B0OhtvNgnRnlr7WfVkw4YNWCwWtykeQrQXT59Xf8h31/Yjw+OF6ARnnHEGjz32GP3792fMmDGsX7+ev/3tb1x77bWNfT766COSkpLo378/mzdv5rbbbuPss8/m5JNPBozg6LrrruPOO+8kPj6e6OhobrnlFmbMmMHRRx/dVW9N9EDt8Xndu3cv7777LqeeeioJCQls2rSJO+64g1mzZjF+/Piuemuih/rmm2/QdZ0RI0awZ88e7r77bkaOHMk111yDpmncfvvtPProowwbNoxBgwbxwAMP0KdPH84++2wARo0axfz587n++ut55ZVXqKur4+abb+biiy+mT58+XfvmRI/S1s/qypUrWb16NSeeeCJRUVGsXLmSO+64g8svv5y4uLiufXOix/H1eQUoLCzk4MGDZGVlAUZADkaGPTU1Vb67tqcurV0vRC9RWlqq33bbbXr//v310NBQffDgwfp9992n19TUNPZ5/vnn9fT0dD04OFjv37+/fv/99yv7dV3Xq6qq9N/+9rd6XFycHh4erp9zzjn6kSNHOvvtiB6uPT6vBw8e1GfNmqXHx8frNptNHzp0qH733XfrJSUlXfGWRA/3wQcf6IMHD9ZDQkL01NRU/aabbtKLi4sb9zudTv2BBx7QU1JSdJvNps+ZM0ffuXOnco6CggL9kksu0SMjI/Xo6Gj9mmuu0cvKyjr7rYgerq2f1XXr1unTp0/XY2Ji9NDQUH3UqFH6448/rldXV3fF2xE9XHOf19dff10H3B4PPfRQYx/57to+NF2XKldCCCGEEEIIIUQgkjntQgghhBBCCCFEgJKgXQghhBBCCCGECFAStAshhBBCCCGEEAFKgnYhhBBCCCGEECJASdAuhBBCCCGEEEIEKAnahRBCCCGEEEKIACVBuxBCCCGEEEIIEaAkaBdCCCE6wBtvvEFsbGxXX0avtmTJEjRNQ9M0zj777HY//9VXX914/k8//bTdzy+EEEKABO1CCCFEq7gGbCEhIQwdOpRHHnkEu93ebq+RkZGBpmls2LCh3c7ZG+3cuZM33njDr77r1q1D0zRWrVrlcf+cOXM499xzAXj++ec5cuRIe12mEEII4ZEE7UIIIUQrzZ8/nyNHjrB7925+97vf8fDDD/PMM8909WV1K7qut+uNDk+Sk5P9HvUwZcoUJkyYwGuvvea2LyMjg8WLF3PdddcBEBMTQ2pqanteqhBCCOFGgnYhhBCilWw2G6mpqQwYMIAbb7yRuXPn8vnnnyt9vvnmG0aNGkVkZGRjkN/A6XTyyCOPkJ6ejs1mY+LEiXz99deN+wcNGgTApEmT0DSNE044wa/jGjL0CxYs4MQTTyQ8PJwJEyawcuVKn++nuLiYX/3qVyQlJREdHc3s2bPZuHFj4/6HH36YiRMn8tZbbzFw4EBiYmK4+OKLKSsrU97TE088waBBgwgLC2PChAl8/PHHjfsbhqx/9dVXTJkyBZvNxrJlyygrK+Oyyy4jIiKCtLQ0nn32WU444QRuv/12AB555BHGjh3rds0TJ07kgQce8Pm+zJq7xuuuu44PPviAyspK5bg33niDtLQ05s+f36LXE0IIIdpCgnYhhBCinYSFhVFbW9u4XVlZyV/+8hfeeustli5dysGDB7nrrrsa9z///PP89a9/5S9/+QubNm1i3rx5nHnmmezevRv+v727DWmybeMA/p/pplNn9OYrIi2thKTsRcrSRCOCQoMCp8HaNDChQnMRChlGKEFGUZRQWUH1IVARrcgiRdaHJGeBSOUMikwmaA5LS/J4PkgX7snSqfedD8//92nn23EeJ36Q47qu7QLw/PlzAMDjx4/x6dMnVFVVTWndT0VFRSgoKEBbWxuioqJgMBj+eFd77969cDgcePDgAV68eIHY2FgkJyejr69PmWO321FTU4O6ujrU1dWhqakJZWVlynhpaSlu3bqFK1euoL29HXl5edi3bx+amppc9jp+/DjKysrQ0dGBmJgY5Ofnw2q1ora2Fg0NDWhubkZra6sy32w2o6OjAy0tLUqfzWbDq1evYDKZJv/jjDNZjpmZmfj27ZtLIS8iuHnzJvbv34958+a5tR8REdGMCBEREbnNaDRKamqqiIiMjo5KQ0ODaDQaKSgoEBGRyspKASCdnZ3KmkuXLklgYKDSDgkJkdOnT7vEXb9+veTm5oqIyLt37wSA2Gw2lzlTXXf16lVlvL29XQBIR0fHhOdpbm4WnU4nw8PDLv16vV4qKipERKS4uFi0Wq04nU5l3GKxSFxcnIiIDA8Pi1arlWfPnrnEyMrKEoPBICIiT58+FQBSU1OjjDudTvHy8pJ79+4pfZ8/fxatVitHjhxR+nbs2CEHDx5U2ocOHZKtW7dOeJ7xe/X39yt9U8lRRCQ9PV0SExOV9pMnTwSAvH379pd9AEh1dfVv8yAiIpoJz793uYCIiOh/W11dHfz8/DAyMoLR0VFkZGTg5MmTyrhWq4Ver1fawcHBcDgcAACn04nu7m7Ex8e7xIyPj3d5JP2/ubMuJibGZW8AcDgcWLFixS9xX758icHBQSxcuNClf2hoCHa7XWlHRETA399/wjN1dnbi69ev2LZtm0uM79+/Y82aNS5969atUz53dXVhZGQEGzZsUPoCAgKwfPlylzUHDhyA2WxGeXk5PDw8cOfOHZw7d+6Xs/zJVHM0m83Yvn077HY79Ho9rl+/jsTERCxbtsyt/YiIiGaKRTsREdE0JSUl4fLly1Cr1QgJCYGnp+u/VS8vL5e2SqWCiPxr+Y3fX6VSARj7PvdEBgcHERwcjMbGxl/Gxv+I20Rn+hlzcHAQAFBfX4/Q0FCXeRqNxqXt6+s7tUOMs2vXLmg0GlRXV0OtVmNkZAR79uxxK8ZUc0xOTkZ4eDhu3LgBi8WCqqoqVFRUuJ0zERHRTLFoJyIimiZfX99p33nV6XQICQmB1WpFYmKi0m+1WpU7zmq1GgDw48cPt9ZNR2xsLHp6euDp6YmIiIhpxYiOjoZGo8H79+9dcpvM0qVL4eXlhZaWFoSHhwMABgYG8ObNGyQkJCjzPD09YTQaUVlZCbVajfT0dPj4+PwjOXp4eMBkMuHatWsIDQ2FWq12+wIBERHRbGDRTkRE9JdYLBYUFxdDr9dj9erVqKysRFtbG27fvg1g7FVlPj4+ePjwIcLCwuDt7Y2AgIBJ101HSkoKNm7ciLS0NJw5cwZRUVHo7u5GfX09du/e7fI4++/4+/ujoKAAeXl5GB0dxebNmzEwMACr1QqdTgej0fjbdUajERaLBQsWLMCSJUtQXFwMDw8P5QmBn7Kzs7Fy5UoAYxcq3OVOjiaTCSUlJSgsLITBYHD7AgEREdFsYNFORET0lxw+fBgDAwM4evQoHA4HoqOjUVtbi8jISABjd5YvXLiAkpISnDhxAlu2bEFjY+Ok66ZDpVLh/v37KCoqgslkQm9vL4KCgpCQkIDAwMApxzl16hQWL16M0tJSdHV1Yf78+YiNjUVhYeEf15WXlyMnJwc7d+6ETqfDsWPH8OHDB3h7e7vMi4yMxKZNm9DX14e4uLhpnXWqOYaHhyMlJQWPHj2C2Wye1l5EREQzpZJ/88t1RERERFPw5csXhIaG4uzZs8jKylL6RQSRkZHIzc1Ffn7+H2M0NjYiKSkJ/f39Lt/Ln20qlQrV1dVIS0v7x/YgIqL/X3xPOxEREf11NpsNd+/ehd1uR2trKzIzMwEAqampypze3l5cvHgRPT09br2bPSwsDAaDYdZzzsnJgZ+f36zHJSIiGo932omIiOivs9lsyM7OxuvXr6FWq7F27VqUl5dj1apVyhyVSoVFixbh/PnzyMjImDTm0NAQPn78CADw8/NDUFDQrObscDjgdDoBjL36bjq/iE9ERDQZFu1EREREREREcxQfjyciIiIiIiKao1i0ExEREREREc1RLNqJiIiIiIiI5igW7URERERERERzFIt2IiIiIiIiojmKRTsRERERERHRHMWinYiIiIiIiGiOYtFORERERERENEexaCciIiIiIiKao/4DLjZCqUbeC3EAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 1200x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# select the correct train ID for the data to plot\n",
"# except for the energy axis, which is always the same\n",
"plot({k: v[example_tid, 0, :] if k != \"energy\" else v\n",
" for k, v in pred.items()\n",
" if k in [\"expected\", \"total_unc\", \"spec_smooth\", \"energy\"]})"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "04802efc-cb81-48d8-b437-7660aa9d85b2",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "e93e8617-0a26-4ca8-81da-85bd029e8b1d",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "pes_to_spec",
"language": "python",
"name": "pes_to_spec"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.16"
}
},
"nbformat": 4,
"nbformat_minor": 5
}