{ "cells": [ { "cell_type": "markdown", "id": "f4273c8e", "metadata": { "papermill": { "duration": 0.003689, "end_time": "2023-12-28T10:56:56.409463", "exception": false, "start_time": "2023-12-28T10:56:56.405774", "status": "completed" }, "tags": [] }, "source": [ "# Accessing evolutionary Coeval data" ] }, { "cell_type": "markdown", "id": "37cece10", "metadata": { "papermill": { "duration": 0.003545, "end_time": "2023-12-28T10:56:56.416646", "exception": false, "start_time": "2023-12-28T10:56:56.413101", "status": "completed" }, "tags": [] }, "source": [ "When you run `run_coeval` with non-saturated spin temperature fluctuations (or various other options, such as inhomogeneous recombinations), *or* you run `run_lightcone`, many coeval simulation cubes at higher redshifts are computed, to generate the evolution up to the redshift you requested (or in the case of lightcones, to be interpolated over). By default, with `write=True`, all these boxes are saved to the cache (or a folder of your choice). What is the best way to access that data?\n", "\n", "Looking at the output datafiles themselves (a bunch of `.h5` files) is fairly confusing -- the files are saved with names based on inscrutable hashes. You could just run `run_coeval` at the redshift you care about, with the same input parameters -- this will just return the cached object. But this is a bit clunky, and requires you to know the redshifts that were calculated during the evolution.\n", "\n", "Never fear though -- in v4 we provide a `RunCache` object that assists with understanding what's in your cache." ] }, { "cell_type": "code", "execution_count": 1, "id": "d5560e99", "metadata": { "ExecuteTime": { "end_time": "2020-02-29T22:10:11.054977Z", "start_time": "2020-02-29T22:10:10.248058Z" }, "execution": { "iopub.execute_input": "2026-09-18T12:53:45.919737Z", "iopub.status.busy": "2026-09-18T12:53:45.919625Z", "iopub.status.idle": "2026-09-18T12:53:47.473104Z", "shell.execute_reply": "2026-09-18T12:53:47.472697Z" }, "papermill": { "duration": 1.043591, "end_time": "2023-12-28T10:56:57.463259", "exception": false, "start_time": "2023-12-28T10:56:56.419668", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "\n", "import py21cmfast as p21c\n", "from tempfile import mkdtemp\n", "import numpy as np\n", "\n", "# For plotting the cubes, we use the plotting submodule:\n", "# For interacting with the cache\n", "from py21cmfast import plotting" ] }, { "cell_type": "markdown", "id": "835b8eb1", "metadata": { "papermill": { "duration": 0.005969, "end_time": "2023-12-28T10:56:57.522457", "exception": false, "start_time": "2023-12-28T10:56:57.516488", "status": "completed" }, "tags": [] }, "source": [ "We're going to make a temporary cache, so that this notebook produces the same output every time. \n", "\n", "If you want to hold on to each computed OutputStruct, you can specify your own folder in `OutputCache` and read/copy/write/delete the files as needed:" ] }, { "cell_type": "code", "execution_count": 2, "id": "48d2c6d9", "metadata": { "ExecuteTime": { "end_time": "2020-02-29T22:10:11.367976Z", "start_time": "2020-02-29T22:10:11.062517Z" }, "execution": { "iopub.execute_input": "2026-09-18T12:53:47.474674Z", "iopub.status.busy": "2026-09-18T12:53:47.474414Z", "iopub.status.idle": "2026-09-18T12:53:47.476432Z", "shell.execute_reply": "2026-09-18T12:53:47.476135Z" }, "papermill": { "duration": 0.235508, "end_time": "2023-12-28T10:56:57.764316", "exception": false, "start_time": "2023-12-28T10:56:57.528808", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "cache = p21c.OutputCache(mkdtemp())" ] }, { "cell_type": "markdown", "id": "dca25bfe", "metadata": {}, "source": [ "To control which outputs are cached, one can pass a `CacheConfig` object to the `write` arguments of the lightcone and coeval functions." ] }, { "cell_type": "code", "execution_count": 3, "id": "895d5d58", "metadata": { "execution": { "iopub.execute_input": "2026-09-18T12:53:47.477378Z", "iopub.status.busy": "2026-09-18T12:53:47.477283Z", "iopub.status.idle": "2026-09-18T12:53:47.504092Z", "shell.execute_reply": "2026-09-18T12:53:47.503598Z" } }, "outputs": [ { "data": { "text/plain": [ "CacheConfig(initial_conditions=True, perturbed_field=True, spin_temp=True, ionized_box=True, brightness_temp=True, halobox=True, halo_catalog=True, xray_source_box=True)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cacheconfig = p21c.CacheConfig.on()\n", "cacheconfig" ] }, { "cell_type": "markdown", "id": "f8fa627b", "metadata": {}, "source": [ "### Note on cached cosmology tables\n", "\n", "`InputParameters` may contain derived `cosmo_tables` (transfer functions and normalization metadata).\n", "\n", "For cache-file writes, these tables are only saved **if they were already computed** in memory before serialization. This keeps files compact and avoids triggering expensive CLASS work while writing. If they were not already computed, they are omitted from the file and will be lazily recomputed when first needed after loading.\n", "\n", "In normal workflows, you do not need to manage this manually: the cache layer handles it automatically." ] }, { "cell_type": "markdown", "id": "857387c9", "metadata": {}, "source": [ "Passing `True` or `False` to the `write` argument turns caching for all objects on and off respectively" ] }, { "cell_type": "markdown", "id": "eb99ec62", "metadata": { "papermill": { "duration": 0.003407, "end_time": "2023-12-28T10:56:57.771250", "exception": false, "start_time": "2023-12-28T10:56:57.767843", "status": "completed" }, "tags": [] }, "source": [ "## Create an example box" ] }, { "cell_type": "markdown", "id": "a7e79a05", "metadata": { "papermill": { "duration": 0.07619, "end_time": "2023-12-28T10:56:57.850581", "exception": false, "start_time": "2023-12-28T10:56:57.774391", "status": "completed" }, "tags": [] }, "source": [ "We first run a coeval box to have something to read later:" ] }, { "cell_type": "code", "execution_count": 4, "id": "6f3fe3ce", "metadata": { "ExecuteTime": { "end_time": "2020-02-29T22:10:27.413255Z", "start_time": "2020-02-29T22:10:11.369635Z" }, "execution": { "iopub.execute_input": "2026-09-18T12:53:47.505210Z", "iopub.status.busy": "2026-09-18T12:53:47.505100Z", "iopub.status.idle": "2026-09-18T12:54:04.507643Z", "shell.execute_reply": "2026-09-18T12:54:04.507090Z" }, "papermill": { "duration": 44.741332, "end_time": "2023-12-28T10:57:42.595499", "exception": false, "start_time": "2023-12-28T10:56:57.854167", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
/home/steven/Documents/work/21cmfast/21cmfast/.claude/worktrees/issue-565-discussion-46670d/.venv/lib/python3.13/si\n",
"te-packages/rich/live.py:260: UserWarning: install \"ipywidgets\" for Jupyter support\n",
" warnings.warn('install \"ipywidgets\" for Jupyter support')\n",
"\n"
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"/home/steven/Documents/work/21cmfast/21cmfast/.claude/worktrees/issue-565-discussion-46670d/.venv/lib/python3.13/si\n",
"te-packages/rich/live.py:260: UserWarning: install \"ipywidgets\" for Jupyter support\n",
" warnings.warn('install \"ipywidgets\" for Jupyter support')\n"
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"source": [
"inputs = p21c.InputParameters.from_template(\n",
" ['simple', 'small'],\n",
" random_seed=1234,\n",
" node_redshifts=np.arange(6, 21),\n",
")\n",
"\n",
"coevals = p21c.run_coeval(\n",
" out_redshifts=(6,16),\n",
" inputs=inputs,\n",
" write=cacheconfig,\n",
" cache=cache,\n",
" progressbar=True\n",
")"
]
},
{
"cell_type": "markdown",
"id": "398ecd06",
"metadata": {
"papermill": {
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"source": [
"## Accessing cached data from `Coeval`"
]
},
{
"cell_type": "markdown",
"id": "ccd6621d",
"metadata": {
"papermill": {
"duration": 0.003737,
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"source": [
"If you have the exact object you want to load, you can simply pass it in to the `find_existing` method of the OutputCache to get the path, then read it using our hdf5 submodule"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "d5075357",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-18T12:54:04.508979Z",
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"status": "completed"
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"tags": []
},
"outputs": [],
"source": [
"# Create an *empty* brightness temperature object\n",
"brightness_temp_z16 = p21c.BrightnessTemp.new(redshift=inputs.node_redshifts[10],inputs=inputs)\n",
"\n",
"# Get the path to such an object within the cache.\n",
"path = cache.find_existing(brightness_temp_z16)\n",
"\n",
"# Read the output struct (this is lazy -- it doesn't actually read any simulation data until you ask for it)\n",
"brightness_temp_z16 = p21c.read_output_struct(path)"
]
},
{
"cell_type": "markdown",
"id": "c0df9439",
"metadata": {
"papermill": {
"duration": 0.004361,
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"source": [
"The output is an `OutputStruct` of whatever kind you requested:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e82193c9",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-18T12:54:04.518971Z",
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"status": "completed"
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"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"py21cmfast.wrapper.outputs.BrightnessTemp"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(brightness_temp_z16)"
]
},
{
"cell_type": "markdown",
"id": "a62fedea",
"metadata": {},
"source": [
"You can also Construct a `RunCache` instance, which contains the filenames of every box needed for the run, and find it there"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "5e1ef257",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-18T12:54:04.549053Z",
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"shell.execute_reply": "2026-09-18T12:54:04.580400Z"
}
},
"outputs": [],
"source": [
"runcache = p21c.RunCache.from_inputs(cache=cache, inputs=inputs)\n",
"brightness_temp_z16 = runcache.get_output_struct_at_z(\n",
" kind=\"BrightnessTemp\",\n",
" z=16.0,\n",
" match_z_within=0.01,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "ba08d4f4",
"metadata": {},
"source": [
"To list all objects currently stored in your cache, use `list_datasets()` with filters for output type, input parameters, redshift and seed. Pass None for each filter to output everything"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "21a1c683",
"metadata": {
"execution": {
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"shell.execute_reply": "2026-09-18T12:54:04.608686Z"
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{
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"[PosixPath('/tmp/tmppywonmv1/88f2ee9c0ff5b47851dfe9555afa682e/1234/01a01b1fe697e22e1682177d07c45412/6.0000/PerturbedField.h5'),\n",
" PosixPath('/tmp/tmppywonmv1/88f2ee9c0ff5b47851dfe9555afa682e/1234/01a01b1fe697e22e1682177d07c45412/7.0000/PerturbedField.h5'),\n",
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]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cache.list_datasets(\n",
" kind=\"PerturbedField\",\n",
" inputs=None,\n",
" redshift=None,\n",
" all_seeds=True,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "f76d815c",
"metadata": {},
"source": [
"Note that the directory structure in the cache is split up into several hashes which represent various parts of the input structure and redshift. This ensures we don't re-calculate the same boxes twice unless we explicitly pass `regenerate=True` to our functions"
]
},
{
"cell_type": "markdown",
"id": "c790bcda",
"metadata": {
"papermill": {
"duration": 0.003781,
"end_time": "2023-12-28T10:57:42.667178",
"exception": false,
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"status": "completed"
},
"tags": []
},
"source": [
"We can proceed to plot the brightness temperature that we read from cache"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "ff032bb6",
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"execution": {
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"tags": []
},
"outputs": [
{
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jyI6diIhCF6/BU8zOGobxGiO1XOMyYl6SJEnk2LFj2vlrbEehuIjr8UaVu86dO0uuXLkkZ86c+qYw7Bxj6wH3MXTN0/AEzPs2O/wBrfFERBQ6ZoKNp2FqnlSuXFmLn7hat26dTj8qVqyYhJuiRYtqjZKEZM+eXcJV6dKldToYGg5wnjGcvHDhwjEawT0N83edAoDGdneM10REkRezzcZrVP9GL/GGDRuclcAxWmrTpk3a+Bxu8J7c47Wn2OVto0FIE+/hw4fLqlWrZNSoUXphgmFqffr00RZ0vOn4hifEN5zQ0/AHIiIKHX8GG8yR/uijj3Te1fPPP68t1J9//rlu97bgSSBhmBpukezo0aPSpUsXHYH2zDPPaC82KtWiQBzWZ/V2CgDjNRFR+PFXJE2fPr022H788cdaIwT1ThC/ERNQvCzcoJI5CqsFSsiuUNALjXXSxo8fLx06dNBtGGKORckxPwAt694OT8BQB9dCLngtlK0nIqLIX04MhdWmTp2qSTfW8cYcLCzVhSSOAmP06NE63A4j1TDkHBBXsfb4q6++qo3liMsHDhxI1BQAxmsiImvHbBTGbtOmjY6AQuKN9btnz57tHBFlJyFLvK9du6YnHkPM3ddPw5IlgCFr+KFgeEKzZs2c+/z+++9Sr169xA9/+AdNL4ksJOKpCETJBJ6D4sfzF96iw7wYymGTx5fWAkVZzBY2ct/vilg+2KDoJqqN7t+/X5M6xI5wh6lT77//vi4hhqlUd+7cifE4CpcZDdHhBvP3MBTeSLrBOOd4zJgCgDXWUZ8FvRzGFAB8D5aOCXi8BsZs/2K8Dn9mrltDxarxGhizTcP0pCVLlujoNMQLjHL2pTJ4sCAfxUhs1CjB9CpXBQsWlN27d0dO4o153SVLlpQxY8boUiooNLNnzx75/vvvdZ1QA9btRgVzDHHDkmMzZszQNeHc5/cREZG9IGErVaqURAok3CNHjtRKqbjwcE1ioUKFChKu0Ng9ceJEra+CSuworoahgxkyZNCEG1Cf5Y033tClxDD6AAn4sGHDtOHcvZGdiIjsJXfu3GG53GdcELswqgtDz9OlSxfjMeSt3gjpZDgMM8CcPCThuB06dEhatWqlS4gZMPEePRolSpTQwI15ZegVwIkgIqLIEH4zr4MPq3S8/vrrOjQ70mCEAVr/H330UY3XmMKFCxHM78aa5MbcuLlz5+p89smTJ2sPAaq/ImYTEVHksHvMPnXqlJw7d05zVaxEYonziup26L7H/DzMA8N8MfcWBAxFwNC1Tz75RM6cOaO93mnSpAnZMRMRUeKF/6CywMubN6+uhR2psB43GsYPHz6scRg9F5j37d4zjsf37dun8TxfvnwhO14iIvKO3WN25syZtVgrpkVbJvE2oLXcaDH3ZR8iIgpPYRFsQgxTpurXr69DszHFKnXq1DEeR3B3T2TDDRrDMUw+PngfKIBHRESRye4xO2XKlNKjRw9dxQO1WdBw7gqx2puE3F7ndYtbAYczHvYp6HafI9r9LxKLaNjJYZNFRDwp4Odj8VQ8KZw/U9d8ODZP+3lqa1xj4md23eRrUtBb0BG8XYuFusLwbEy/Ii/jNTBm+1e4/G2luLn//We8No8xm+JRrVo1+eCDD7S+mDsUVzt48KAklr0SbyIiCgm7D1sDrDW+fft2XUbTU3E1FBwlIiIKNbvH7Nu3b8vTTz+t9U2wFJp7cTX3EWtmMfEmIqKAY7ARXbmjb9++uloHERFRuLJ7zD579qwu+Yk6Y/6cAmb380pEREHAYCNSpEgROX/+PD9vREQU1uwes3PkyKFzuK9fv+710mGe2Ou8ZhWRdImcB7PL5DxW1n0jq/D098XsnDF/y2bRc+fLvO+SJs7TFQk7dh+2Bk888YQ0aNBAV/RAkTX3oWq4jyqq5GW8BsZsshv3OMF47f25i+v8MWbbUqdOnaRDhw7y3nvvxVqhA1PFvEnIGeGJiIiCYMiQIVqMBXPGPGFxNSIiotA7dOiQLmUN3333XazHWVyNiIjCFlt5RQYOHCjPP/98nOcooWW6iIiIgsHuMTt37tyybt26OB9PlSqVV89r9/NKRERBwGDzX2LN5JqIiMKd3WN2qlSppHr16n5/3phrmRAREQVojndCNyvC0HJUR/X3vkRERIFix3h969Yt2bp1q9/3tW+DRg0Ryehy/7CHfcwUpQhV4QryzJdCGGSueBfPsff8/VksYGLbJQk79go2/zNo0CB54IEHtEiLP/e1PH/F68TsR4HHWBL4mM1z7BvGbNvG7OPHj0uzZs20Edyf+9r9vBIREQXNjBkzZMuWLQnut2HDBk28iYiIKPjOnTsnPXv2THC/S5e86+Fg4k1ERAFn12BToUIFiY6Olr1795pa5zt//vxBOS4iIqK42DFmp02bVu6//35T8RqwPGhi2fG8EhFRkFl1TlhC0HJupvWciIgoXNgxZufIkUMWLlwY0Ndg4k1ERAHHYENERBQZGLMDI7ntCiakTaBI0TUTRRbS+lC8hUW//I/nNPD4+fZetMn9svnwGkRW4028Nr7PzDb+TQsNxuvA42fbN4zZFED2SryJiCgkGGyIiIgiA2N2YPC8EhFRwNlxvhgREVEkYswODCbeREQUcAw2REREkYExOzCSBuh5iYiIYgTxhG52cuLECV22pGrVqrJ+/XpZuXKlfPzxx3L48OFQHxoREdkc43VMI0eOlJIlS8rLL78sFy5c0Hj93XffSWLZ7VrHuyIs3j5XJAqnohyeClx4uiat5OfXNVNgz8qifShiRObOp6dCUWQrU6dOlcyZM8vrr78uffv2lVOnTkn9+vWldu3asmvXLl1PlAIUr3393kiL2XaK11b62Xp77nlO/Hs+gTHb1hwOh/Tv318T7RUrVkitWrWkUqVKGsePHDkir7zyiunnYo83EREFZb5YQjc7OXPmjCbZNWvWlDJlykiHDh1k7NixUqVKFVm6dGmoD4+IiGyM8fp/rl37rzWrSZMm0qdPH9mzZ498/fXXMmXKFPnyyy8lMezd401EREER6mBz7NgxWbJkiZw8eVKKFy8ujz32mKRIkSJWq/bChQtl48aNki1bNnnyySclZ86cATkevNaVK1f0a/Rup06dWr8uXLiwtqATERHZMWY7HA5Zvny5/Pnnn5I+fXpp2LChlCpVKtZ+R48elW+//VYuXrwo1atX18Q4UMcDiNmI1ylTppSkSZN6Fa/Z401ERJae4425WXXr1pXff/9dbty4Ie+++672MkdHxxxT2LZtW3n++ec1uC5YsEDuvfde2b59e0COqUSJEjJp0iRN8GfOnKkXGYsXL5arV6/Kv//+G5DXJCIiMiNU8To6OlrKlSsnH330kVy+fFk2b96sw7oRx1398ccfGqN//vlnTbzbt2+v8TsQkidPrtcMuXLl0gT/1q1bMmbMGNm9e3ei43USh5HGW9ilS5ckKipK5OJFkYwZQ3044Y1zvDnH2+wc72wettltbp0Zu0zOF/Pnubt0SSQqSoNRxjD5mzcqSZIE93k5QOEIPdhly5bVVmq4fv26tp6jR3v48OG6DUnvI488Ilu3bpXSpUtrC3fjxo21VXvZsmU+vf4333yjFxK4SHB3+vRpvYBAyz5uONZevXrpcDY7YrxOJM7xjs1uccjMHG/Ga/MYs03F7EDF64sXL8rx48dj9HB//vnnWtTs3LlzzmsaFCZFj/OsWbP0/q+//qoFS9euXSs1atTw+vXPnj2rc7d79uwZ67Hbt2/rNYIRr3E7ePCgfk+kjP6jcBOJAcvfhUTMFFfbZTKwedoW7iLxmP3N/TOwyWTy7GlbST8dE3mtcuXKMe6nSZNGq5O6DhFD0RTMr0bSDUmSJJGOHTvqDcmgLw0YmLONnnZPiXeOHDnk4Ycf1puBPd5k2Zgd6Hgd1/MxZluXp88AY3bEioqK+q+z1AV6vO/cuaMJOWIxpo6hwXrQoEHOferUqSNFixbVWO5L4o1edvSue0q80fNdsWJFvT333HNexWsONSciorAYan7z5k1Ncl1v2OZvWLLrl19+0cqkhn/++UeKFSsWYz/cv3v3ruzbt0+CyX3uORERUTCFS7yGadOmaSP1Pffco/cxxBs8xWzE8nCO1+zxJiKigDMTbIYOHSqDBw+Ose3tt9+O0aoN+/fvl/Hjx8f7XFiaC0PFPVUnbdmypfZsd+vWzbkdc6vde7WNVnc8RkREZBfJ/RSvAfEacTs+77//vk7tcjd79mxd8QP/J0uWLEZM9hSzEzPsOxSYeBMRUcCZaRPu16+fzm92lSpVKo/DvTJlyhTvc2E4uTvM7W7evLkm3yjIYsz5BlROxdwyVxcuXHA+5qtRo0ZpKz2GqGHYHIbEYTg7ERFRpMVss/HaiKEJxewkHuIhVhnBUpuIn0888USM5wPEbBQ8c43Z/ojXp06dkk6dOmmsRsyuUKGCZMiQQfyBiTcREYUFBO24ArerAgUKyBtvvJGo58Yc60cffVTniK1atUqyZ88e43EUctmwYUOMbUiUkeQbw9t8NW/ePBk2bJgOX0fLPIK5EdjxP+adGy36REREkR6v4amnnkr08y9evFhHp6G6eY8ePWI8hlhpxGisEGLAMPM2bdqIrxCHUTTt+++/1+QejQK4DnCN17hlyZIl0c/NxNvO/F3kxN/SmixUFYxiLWaKaB02WSGcxbbCn/vPco3Jz0AlD9v48w55sMG8MyTdKMiCnm7MFXOHCufjxo3TKqUosoZCLl9++aU8+OCDki5dOp+PARVZ0YKOpcq2bNkimzZt0tuKFStk9OjRWi118uTJug9RxMXscI7XwJhtXZ6uvRizIzpmL126VHu4serISy+9FOvx3LlzS+3atWXixInSrFkzTYyx+giSZSTrvsIyn2igB9R4MeI1ljb7+OOPdTWSggUL6uslFhNvIiIKuFCWC3vllVfkxx9/lM6dO8uIESOc2wsVKuRc97NBgwbStWtXadq0qbRo0ULX7zaKsPkThsHhggE314aBv//+O1YvPBERkZ1i9oEDB+Sxxx6T/Pnza2O56+i2Z5991llQDUuM1atXT29FihSRuXPnSt++fWOtYuIrTAvDDY3zhqNHj8qePXu8ej4m3kREFHChDDYNGzbUJNud+5wt9Hi3a9dO19LGeqDoJXdf1iQQMFzvvvvuC/jrEBERhXPMTpMmjRZpS6iCeNmyZXWo+YIFC3Q4OIqlVqtWLSjHmC9fPr15g4k3EREFXCiDTWKGniHhxs2fMJSca3MTEVGkCFXMzpUrl+kaLlmzZtWRbP6EIeSrV6+WQOE63kRERAGEHnSjQroxlA6FY7AdhdaIiIgo9C5fvhyj0OqtW7c0Ef/pp590brev2ONtxaISZouNSJgXgjJbcMXfxWUKePmanr5vlx+Oh4Iv2svfqWiTn4FsJrdZSCjneIdaly5dZM6cOfo1CqhhnpqRcOfJk0f69OkjPXv25PJidmDVmB3O8Tqu12XMtgZPcZcx22d2jdnz58/XeI053SiGWqtWLa3BYlQ7x9S1MWPG6Lxvb7DHm4iIAi65iZtVoUgbliJBETUk2K+++qocOXJE56dhSN0HH3zAauZERBQ27BqvDx065FxC9LPPPpPz58/Lr7/+qoXeUDkdtWFQwA2rk3jDyueOiIjChJ2DTcqUKXW5sLVr10ratGl1iRQsfwLFixeXxx9/XNf0xnC2unXrhvpwiYjI5uwas1OlSqXx2uj9fvPNN52rkGCEGlZAwTY0oGN50sRijzcREVEAIameMmWKrguO6upG0m3ImzevtGrVSpYvX86fAxERUQjjNZYfxZzuP/74QwoXLhxrHyTdWGoU878Ti4k3EREFZb5YQjerevfdd2XAgAEyb948XSrl/fffj1VU7cyZM14FcSIiIn+za7xu0KCBDjVv3LixVK1aVQYOHBhrzW7Ea8Rwo2c8Mew6ksB+hVrSmtzPU5GK/0ZY2Mc1PxaNqeSH46HgizZRhKeADz9vT79nFmfnYFOvXj05fvy4VkvNlCmTPPLII/L111/r/1gLdNOmTVrMBS3sZHGBjtl2j9fAmG0vnuIpY7bP7BqzkyZNqmuD79+/X/Lnzy8//PCDrg+OaWD33XefJtyTJk3SomuYOpZYdj2vREQURHYPNijIghsgwR43bpxMnz5dW9KxFukXX3yh1VKJiIhCze4xu0iRIvo/arCUKFFCPvnkE5kwYYJcu3ZNatasKaNGjfLqee1+XomIKAisPDQtsbAkyQsvvKA3IiKicMOY/T+lS5fWGi3+wDneRERERERERAHEHm8iIgo4BhsiIqLIwJgdGDyvkV6UxdO8/mw+FCXx9Brfe9jWQuxdWMuXIi/RPvzMKDiumSia5ulnZrbOhtnPlIUw2JDthCJm2z1eA2O2vXj6vWDM9hljdmDwvBIRUcBxvhgREVFkYMwODCbeREQUcAw2REREkYExOzBYXI2IiCiEevXqpeuD/vjjj/w5EBERhamjR4/q+t6vvvqqnD9/PtHfz8SbiIiC0nqe0M2uqlWrJpUrV5YePXrInDlzQn04RERkc4zXnqVLl05at24tu3btkkaNGkli2flaJ/JkC8LzeSpUYjfZvCyac9iHc1zJZMGQaxFYqOtwBB5zWhPHy4J4icL5YnFDEMcN/v3338SdWApfjNnhc44Zs+0Tr4Ex22eM2Z5lzpxZPvroI6/jNXu8iYgo4NjjHXu42sWLF2OdpxQpeLlDREShxR7v/7l27ZocOHBA/BGvmXgTEREFybRp0yR79uw6R2zevHm6DcPLBwwYwJ8BERFRmDh16pQ0a9ZM0qdPL/Xq1dNtd+7ckYYNG8rx48e9ek4m3kREFHDs8Rb5888/5eWXX5ZRo0bJ448/7jw3LVq0kOnTp2uQJyIiCjX2eIt07dpVUqZMKT/88IPzvCRLlkxj9rBhw7w6r0y8iYgo4FKYuFnd999/rwXU2rZtKxkzZowxXK1MmTKyZs2akB4fERER2D1e37x5U5YvXy5Tp07V+OyqRo0asmLFCq+eN+TF1dBlP2vWLFm5cqWkTZtWOnfuLBUrVoyxDyrHTZgwQXsDypYtqxcu6PanOHgqwGVWAZOFK8y+rtnvDSdmjjna5DazxdDSmtzmy882VApE4GfF/Zh3ediHBV0iK9iEgejoaGcAT5IkSYzHbty4IQ6HQ8Ld3r175auvvpLDhw/rEmjPP/98jHluiOl4fPXq1Vr99amnnpI6deqE9JhtFbMZrz1jzPbu8xSJ8RoYs31m95h9/vx5SZMmjeam/ozXSUPdmtCkSRN5++23pXz58np78cUXZcuWLc59Nm3apMusnDt3ToP3t99+q//je4mIKDJwqLlojFu6dKmeD9dA/vfff2tvd5UqVSScLVq0SMqVK6eN4IjdKBCHxnJX7du3lyFDhmhSjuqv9evXl2+++SZkx0xERIln96HmOXPm1EZlTBFzjddIuMeMGaMxzhshPXcffvihbN68WXbs2KFvEDp27ChXr1517tOvXz954IEHZPLkyXq/ZcuWWpQG99HSTkREFAmefvppnd/98MMPy5kzZ3Qpkj/++EN7iDt06CCFChWScIUK7DjGV199Vd577z3d1q5dOzl79qxzn3Xr1mmSvWHDBudFCXrAe/fuLa1atZKkSTm7jYiIwh+S7XfffVcefPBBadOmjeamH3zwgRZFRd6KhDygifcXX3yRqCc2kxR/+eWX2jpuJN16QMmTS1RUlH6NXm0MQR8/frzz8axZs0qDBg1k8eLFTLyJiCJEOOVc6F1GQli3bl2Pw8u2b98u2bJlk5IlS/r1dTH0Gq89ePBg2b17txZsyZcvnwwaNEh69erlt9fBBUFiLgrQ055Qb/vcuXM1+XY/TsRkA+JygQIFYvQEIOFGERqMZKtUqVKi3gcREdk7Zh8/flwT3RIlSmjHq7tt27ZpbMJUZNfaKf7QpUsXXYVkxIgRen/kyJFSs2ZNzV9xPAFNvLt37y5FixY1te++ffsSTIpxcYM5Ygj2eCMbN26UPHnyaCKOkwd4/Pbt2xrIXeE+5o/FBQm761D0S5cumTpuIiIKkGThcWZRPRwjq1An5MKFCzEeGzdunPboFi9eXA4dOqRDw+fPn+9sDPYVpk4hoUevN26BsnDhQn3+LFmyJLgvpnGh0npCifdff/0lxYoV0+HlAwcOlFu3bmmC/cwzzzjneO/fv99jvDYe85R4M14TEYWhMIjZN27c0B5nJNcff/yx9OzZ0/kYRo1h9BjW186dO7f+jxiOuiL+WrsbDeXNmzfXm78kT2xRFTPcJ6F7Ygwnf/PNN/WkNmrUSNavX6+BecGCBbrNSJ4xsd0VLpjww4jL0KFDtUfBcg6bLHCRzeR+/i5K4ul1fRGM1/CWp46bYHTmhFNBE1/48tnz5bMsJgvHlDRxHIdNPle4F6axURBHDHv99de1QOfXX38d47GtW7fKCy+8IDNmzJDWrVtrUl6tWjXp27dvjFFXvhg7dqy2lrvPiw4EJNPoSU+ImX2MmI1h5RhejoZ4DBvHhRAaMn7++WddYgUx21O8hrhitmXjdahiNuO1Z4zZ9onXcR0LY3bExeyePXvK/fffrw3h7hCvMXINjyHufP755xpbEWP9MW0LHcTPPfecx9f2hemBBCiA5s99M2XKpP8j0cZFDea+YbI61jbFmHowehnw5l0h+Bvf7wnmhWPYgXE7cuSI6WMnIqIABfGEbgGEHlrM00KiV7hw4ViPT5kyRYM1km5AjDEScXyvP6DHGMPYAw11UXDz5744H4i9WFoFDRdIvjEa4Ndff9UpYUbM9hSvje/3hPGaiCgMhTBeG9Ob0OM8fPhwcYfcDstzIjE3Gnux5jaGmiNm+wOmQWPEtPvIuKD1eJttFTe7L1rBixQpokP6XN1zzz1abAYw9w1VUVHx9aGHHnLug/uorBqXVKlS6Y2IiCKH+7Bjf/49R083pkuhQBimN7nzNAcZ99HTi57ye++91+djMJbWwvDrxo0bx0pGkbhi+RJfmU26E7Mvht2Da8xGQwJGuJ04cULvIy5j5RE0VKRMmdIZr8GYQuaO8ZqIKPIEMl4fOnRIG3iXLVsmqVOnjvU4hp5jKrJrzEaNMMQg15WxfIHYhpFjjz32mOa1uH7AaxgwygvzvyOqqnmnTp1k5syZ+oZQdOb69es6Nw3DBIw3jWFtmMSOOeO4SMHcbiTmqCxHREQRwkQLuadhxxhB5d6Yi15V1AWJD3q1jbokWAbru+++03nKccFzojHYU+Ew915cb2FuNC4oXnnlFY+PY7UOxMVwhDluaAifPXu2c6j8nDlzdMh51apVnauOvPHGGzJhwgS9aMIwQBSlQWNDwYIFQ/wOiIjIXzHbbLwGxOuE4miDBg0070NC3bZtW53mZTT4ujOey72OCWK2v+I1Rktjacy4GqgR0w4ePBicxBvd/yinPm3atBjbURgNQ8VxM+O1117Tyqvo5a5QoYK2jKOX+5NPPnHug2VLUJCmVKlSesM8cMwLx9qgRERkncQbw47dq2Z7aj1H4c2EGl8Rj5B4o/cVySwab41K3//8848G9+XLl2tPNgp7oocWjb/uxVXA6L31FeJZnz594nwc8c/fsGRZvXr1dP6b60gxxFskxxgmbhRHiw964zGED732aCDABRIupkaPHu2s/p43b15NunGu0ah++vRpPc/otSAiIuvEbLPxGhA74mv4BuR1iCuIKRhFhXiFGA2II4jbGHpeu3ZtZ0z2FLP9Fa8Rz1D7JS7evk4SB1YCT6TSpUvrcDIkwq527typ8+OMoWVm4Y2hFwDD7zAczb04Gw4RvdynTp2SMmXKeJyfFx+M0df54hcvivi51HzIBaMA2Y8etsUsXPufkn5+DU/vg6vRkC8CXejssMnflUDDSg5RUToPyt/La3gte8JFN+VMosNRglDU65FHHonVko0q21hODGtMo5gnEsqTJ0865ysD5pBhmBkSSG+GlIUD9EojXs+aNSvWY4jXuJltLIfLly/ret0YcofeCNflxFyrzSJmY+4dRrAl5gLF0vE6GDGb8ZqsIhiFSRmzvY/ZAYjXgLpfGFnlCqOdMSINQ8vRqIskHMt5YTuKrxnQiVurVi2tGRauvOrxxnJhKN3uDttwMhILyXZc878AibgxlI2IiMgszA8zWs0NmOON4XCu2zHnulu3bhIdHa1LfgFGdiGQ+yvpRmt9fKuDoCUf86b9Ka547W3MzpAhg56r+OB8udZlISIiMgNF0nBzhanGKOhpLCeGWiMohorGcSPxRmM6On79tUrGlStXtNE6vlplmF4VlMQbbxhv1n0uGubQ+fuigYiILCCkFUUShh5vrH2N3nEEdwyLw1JZmB/uL4iR7i35qJiKAm64sEBLv79jKOL1V199pT3/rkVqMERvyZIlOvydiIgokmL2sGHDNG6j9gimlWH6GRqvmzVr5pfnR2weMGBAjG2oW4LR10aHcNASb8zNRmvEjh07tKUBQ8F/+eUX+eyzz/y23ikREVlIGKwJasC0JvdiKRgOjfWosTY1lszCEOpVq1ZpIPcX1C9xrWECiJ+Y/4b4iWHt/oaGBFw8IFZjeTTMIz969Ki+HqqyomgaERFRuMbsBx54QOO2qyeffFKTbtQcQf0vTJtCozkKfvqDESvdIfHGa8dXr8Xvc7wBbxTrbaNrHzD2HsHdqHYaTiw9Z4xzvInM4xzv0CloYo73ocDMGYsEDRs21JhavXp1vz/38ePHdVkUrBqC5V9QAAe9Ap9++qkWlgsnlo7XwDneROZwjnd4x2wbx+s1a9ZovF66dGnwBhIgwcYNxXvQ5R42BXzsJlsQCk2InwtcmH0NCu8AuMlkIbECIQqou/xcANCMUBRSixRh1HoejjAM/NixYwF5biTXmKuGyrCI2UhsXdcjJYvHbMZre2G8No8xO26M2QGJ18l9bZlG4RbAvDQm30RERJ4hXqLityssd/brr7/KTz/9pEXfAuXu3btazR2930jEsQapv4bkERERWcnNmzdl8+bNsbZj9ZP3339fqlSpErzEG4VgsATLpEmTdI1QwDqgzz77rHz00Ue6hAgREZETW8/lnXfeka+//jrGhwIjxjB3beLEiQErTop1tDH3bdeu/w0DwXKgKCaHIe5EREQx2DxmnzhxQmrUqBFrO3LcRo0ayYcffujV83rV3I0CLVjrFNVZMfEcN3yNpVl69Ojh1YEQEZHFg3hCN4sbN26croPtekO18YMHD0qHDh0C8pq7d+/WAmpNmzaVrVu36nJp+L9Jkyby8MMPe7UEKBERWZzN43XBggVjxWssMYbOZ6zslSNHjuD1eM+dO1fWrl0r5cqVc27LmzevFC5cWCvAovAaERGRkw0CdULQOI3e7bJly8Z6DMuWxfWYL3744QdNsEeMGOHchortuI+EH49jBBsREZGTzWP2pUuXdMURrB/uDrVS4nosIIk3CrMg0XaHbVqNlCK3uqonlfxcIMZsMYu0fi6slS0IhW6sytPPwpdVloIxG4U/Wwozc+bM0WVRPCXXeKxevXp+T7zjitfAmG3BmM14TYzXRD47f/68Dif3lFzjMawj7k3i7dVQcwxbe/vtt53zuwFfDxo0iGuCEhFRbBxqHm/hMyzNmS2b/1uLGjRoIAsWLJC///47xnbcR293/fr1+WklIqKYbD7UPD579uzxOl571eN96NAhWbJkiS5PUr58ecFS4AjiWFT8wQcflJYtWzr3xT5ERGRzNg7Uffr00ViI+dVYSxuN1K4uXLggKVOmlFq1avn9tf/8808t4FahQgWpXLmy5MqVS6uybty4UYe2v/baa859W7VqpTciIrI5m8bso0eP6rRpLL+JWFmoUKEYjxvbhwwZErzEG8G6W7duMbYVKVLEqwMgIiIbsGkQN0aJlSxZUuuflChRQmrWrOl8DElxlixZpG7dupIpUya/vzYqsKKwmvsQcyThnvYlIiKya8zOlCmTDBgwQM6ePSvDhw/Xr12lSpVKihcvLtWqVfPq+ZM40F1tgwnyOvf84kWRjBlDfTjhPV/smodtnq7F/D0i8rCfX5dzvCnaxvO+L13C5F4tAJIxXP7mVUuS8D7rrR2ONm3apMPT0HhNnjFe+xizGa8pEtk5XkdqzLZ4vL527ZqsWbNGGjdu7Nfn9arHmywimw+Jt7/5ch26y+Qx26kzJ1Q/x3Bip6BNEaFSpf9VvsIFFpYncYWeb/Y6k08xm/E6Mtk9ZjNeU5hBLDaS7jt37sjp06f1f0Py5Ml16lZAE+9ixYqZ2m/v3r2JPhAiIrIwmw5bc/fFF1/I4MGDdY6YOwxF79Spk19eZ9SoUXpLyMsvv6w3IiIiJ8ZsOXz4sDz//PO6FKhrQXFjnW8syRnQxHvfvn36Qiielj59en46iYgoANHGmn755Rfp37+/jB07VmbOnCn33Xef9oJjHtmNGzfkscce89trnTt3TguhYg1vFFaLS7ly5fz2mkREZBGM2YKG8Ny5c2txVCwdNn/+fFm1apW88847upxYwE/ruHHjZOLEifp/69atpUuXLlKjRg2vXpiIiGyEreeydOlSeeGFF7RyOFYGwTA1FD7D+t333nuvHDhwIN4kOTGeeuop7VVHgn/kyBF55plnpF27dgEp4EZERBZj85h948YN+f3333U1kjNnzkiyZMmkSpUqekMcnTFjhlergCRqHe+uXbvKhg0bZO3atTr2/ZFHHtGLhY8//ljHvhMREXnEdbzlxIkTzhVAUPATVVONKqmoMr5z506/fXhQdRXD2vGaL730kibgefLk0eR75cqVugwoERGRVzHb4k6dOiXZs2fXfBfxGqPIjLiJlUm8jdeJSrwNZcuW1bljx48fl4EDB8r06dMlX758Xh2A7VzzcAtlFUn32yYPt8MebqGyy8PN0zlNa/JmBWbfP4XmZxFOv/MUUgjaWD4M0Gj9/fffy82bN7VFfd26dZIzZ06/vyYuGjBcDtVZN2/e7OxlHzp0qN9fy5IiLWYzXoc/xuzwwZhNJuI1erhRbX7RokV6f8GCBV7Ha69H8GMBcQyb++abb2THjh3SsGFDb5+KiIiszgYt5AkpXLiwLidmDAX/9NNPJXPmzFoptWrVqrqWd6Bgvjd6vTFXDRcMZcqUCdhrERFRhLN5zE6VKlWMGihDhgyRRx99VBNwLDU2d+7c4CTeu3btkkmTJsmUKVO0wFrnzp3l888/l7x583p1AEREZAM2D+Lw9ttvO79G/Pzjjz/kt99+01b1Bx54QOeQ+RN60+fNmydffvml/Prrr1poDYXdmjRp4vfXIiIiC7F5iMidO7f2bBueffZZqVOnjg4xL1++vDakBzzxrlWrlg5Ve/zxx7XlHBcKRjc8ERFRnGwexOHNN9/USuZG9XIMA2/UqFFAXmv27Nm6DAouHlAIFYVgMF+NiIgoQTaP2cePH5cOHTrIihUrnNtKlCihN18kKvFGUbUcOXJolTfc4sJ1vImIiGK6fv26rgsaDJgCdvnyZcmSJYuOSsPNE67jTUREFBMaxtHZ7G+JSryxbpntoHiJO18KVR02+XwFJPDS+vDezF47/jed0bvzbPb5zL5GOIv24X2xcFrgXfPz748d2bz1HNDT3bt3bx2yli5duoC+Vv369SV58oRDvL+WLwsbjNmxMV4H53PGmB0+GLN9Z/OYnSlTJqlUqZLWMmvTpk1oEu8BAwb47YWJiMhGbB7E4Z9//tEeb8wNw3Ik7mtqIyGvXbu2X17r/vvv1xsREVGi2TxmR0dHay2Utm3baiHUokWLxmjMRqHUjz76KHCJ96pVq3ROt7/3JSIiG7B5EIe7d+/GmNON1UFc+Wtt7YMHD+r/hQoV8uu+RERkE4zZgroo7dq18xizsRqJN0wn3vXq1TN9UZCYfYmIyAYYxKVr1656C7SvvvpK/x80aJBf9yUiIpuweczOli2bTJs2ze/Pm6ih5hgGR0RERN7bsmWL3qpXry4lS5aUc+fOSYoUKSRDhgx+O61YBuXo0aMJ7rdp0yZp3ry5316XiIjIKi5evKjLcZ4/f16rnGPk2rFjxyR//vyBTbxbt24tV65cMb2vZRw2WVSjpMlCGwVMFoE4HISCa54KQVUy+b27TG5L6+cCaVYopHbY5DaznymiSBDi1vN///1Xhg4dKlOnTpUzZ87oyKyRI0dKwYIFnfsgGX7llVdk48aN2tqN5bjeeOMNvx4HlvbCMaRMmVI+++wzTbw3bNigXy9cuNAvr1GmTBnZtWuXqZhdvHhx3d8yGLNjY7z2/2eKMZusLsQxe+PGjVpbbN26dTrku0+fPtKtWzfn40iA3377bZk8ebImx2jIHj16tMZUf0FsfvTRR+XGjRsSFRWliXfSpEk1z8W8bywPGrDEG1XdiIiIIjGIY54WencxtLpixYqyZs0a/RqB2yik0rBhQ3nyySdl1qxZ8tdff0nLli0lderU0rNnT78cw3fffaevi3nV/fv3d25v2rSp9OrVS5cAu/fee31+HRw3bkRERJEWs//8808tDoqG8K+//lrnU7/33nty6NAhZ2P54MGD5YsvvpC5c+dKsWLFNDFHDEejc/r06f1yHBjpjedFPK1bt65zO64Jhg8fLrNnzw7sUHMiIiKvhDDaLF++XObMmSPr16+XqlWr6rYmTZrozTBp0iQN7qNGjdJh37ly5ZIXX3xRq5Yi+CdJksTn40DSjRb7PHnyxHoMCTeSfX8k3kRERJEas3v27KlFujFKzYBRYYZbt25pjzN6xI3VO5CEo2d8xowZfqmlcunSJV2FBEuAIuH3FK+9kdTnIyMiIjLTep7QLUDmzZunLeJG0u3Jb7/9pkt5Iek2NGjQQOdyuQddb6EiKobHgXsif+TIEb/O8SYiIvJaiOL1mTNnNB67VhN3t337dh1ejiljhowZM+rQ77Vr1/otXqMxHsXC/RmvmXgTEVFYuHnzprYyu96wzVf79u3TeV99+/bVudu5c+fWIeXGUlpw4sQJyZEjR4zvQ+s5nDx5Uvyhfv36OrwdFwyugRwt9biQqFWrll9eh4iIKFLjNWBedeXKlTXBLVu2rIwbN05c4zV4itn+itdZsmTRtbu//PLLGPEar/3mm2/GGDGXGBxqnpC0Puzny/deM7nN7GuYfT6zxbsKmCzgYvaY05osXpLNh3MgJgvlZTPxHsTPx+HvwnnkX778bOk/JlrIMawM87ZcYQ62+1JXq1ev1t7o+GAOtfFc6GVevHixFClSROd/Xb58WYeiPfjggzpcDIXOPEERFfDX8pgo0oIh7/fcc4++Joq54T3v3btXxo8fL5kzZ/bL69gaY7a94zUwZhNjdsBjttl4DYjXiNvxQdKeLFky56gwPPfMmTO1+OeSJUu0sFnatGn1/7ggZvtzOWsk+7hOwDB3ow7M77//rgn5a6+9FrzE+/r16zpnrlmzZnp/xYoVWkkOB/Luu+9KmjRpvDoYIiKyb+Ldr18/LTLmKlWqVLH2w5wutIbHx0iaAT3ciEuffPKJBnb0emPudoUKFeTvv/+WKlWqSM6cOXWIm6vTp0/r/3jMH9BqPn36dF3qCw0BZ8+elXz58umQOhxDoPzxxx/6ngsXLizXrl2TgQMHyoEDB+SFF17QCwkiIqLExGyz8Rp+/PHHBBPiZMmSOeM19OjRwzkKrE2bNlr0FDck3kZMRsx2XdYLMRtxzl+qVasm27Zt08rp+B/HOGzYMOnUqZM2AgQt8X7rrbecxWEwZO6JJ57QFgGcWCzZguI0REREiUm8EbTjCtzuCWzy5ObDF4I3Koq7Dhczvjb+r1mzpibjmNdlPPfKlSv1IsCfgRywbnaw1s4+fvy4XiSglR5w0YA576jQih549LYbFzpERERmYrbZeO2aVJtRqFAhzTFdG8+NWG3Ea/SCYwg6etErVfpvHWSMZEM19PjmhnsDx+K6ComvvJrjjRaHtm3b6tfLli3TsfcYDoALG9yIiIjCxVNPPSWZMmXSNbkRnDFHC631qExarlw53eeZZ57RIW5YOuTChQsa0MeMGaPVVf1R0dyA58YQOgy9w2s/8sgj2pqOIi6BsHTpUqlTp46zEAyWP5kwYYK+ZosWLWTRokUBeV0iIqLESpIkiQ7jxkhqrOWNIejz58+XhQsXas83IOHHqiMffvihjug6d+6cvPzyyxrnEe/96eeff9Z1u5Hgo4G+e/fuznnoQevxxhs0hpPjgLAGqdEqgIsKIiKicFkTFGt6YnoUhq5hyDXuoxoqkk6jijmGrqEhGcEc+6CwCpYRQ0E2fzl16pRWXUWPOpJevAYqpiO5//bbb/XCwp9Jvnu8RtEZLI9iLL/CmE1EROEWs1955RWd1oyRYRhOjvosmGft2ps9ZMgQHWWNHBSF3apXr64jr1Hd3F8wjx1TszA6DDdMcfvll1+kdOnS+lpGLE2MJA4vZqFjyZUaNWro8HIMM8ccb7QErFu3TnsLUAY+nOAHEhUVhXHxqDfv+xMGo3CHL68rJgupZPNzkS9fCsJdM1lIpaQPx5LWj8XVdvnw/NdMnnez7zVUQvV7QAm7dEkkKkqnAvkzCPmkr4mEcrj/iqKEI/R0I17i5rpsGZYmQQydO3euV4E8Pmhw6Ny5s0ydOlVHpqG3H3PMAVVZEbMbNWoklo3XwJhtn3gd1/PZPWYzXoe3SIzZFo/XN27c0MZxjOQ2OpgNr7/+uva0YzpaUIaajxgxQlvnEbQ7duzoHF+P+XEI4kREROGyjne4QFXUli1bxki6AcVh0KDtXtzNH1A8DY3kiNdooX///fd1O6q5G1VaiYiIYrB5vL569aqkTp06VtINGM7ubbz2aqg5hsqhIirmpLlOmP/888/9Vv2ViIgsxAaBOiGY040eaMxFc4VK46iubsw39zcsVTZ27NgY8bpYsWI6j93fQ9uJiMgCbB6zs2bNqpXLd+zYofVgXK1du1bKly8f/HW83avUMekmIiLyrGrVqjpfDPO7MU0Lw9gw5xprhaKSK+Z74waYQ+bPauPu8TpdunT8MREREcUx1PzJJ5/UQqio/YJK6rdu3ZJff/1VJk6cqHPO0ZAOqKNiLH3mt8Tb6GpHhVRP3e6usA8REZGTzVvPjWlaKLCGCq24uXOdL4aq41gGzBvTpk3TW/v27Z3344J9jP2IiIiUzWP2yZMnZeTIkfr1gAEDYj3epUsX59cFCxaUgwcP+jfxRqbv6WtbSuvngiG+vK6nQiCHTR6LL4XUzB5f2iB87y6T56CxycIsZpgtTLfJ5Pvy9jhCiUXTKDFsHsThq6++0lug5ciRQ+M0/k8oZhv7WBpjtr3jdVzfa6eYzXhNiWXzmF2oUCHxov64/xJvFE7z9DUREVGCbB7Eg6lx48Z6c71PRERkGmN2QCT3dl0zlFJPmjRmUfQrV67oeqQY+05EROTEIO6sJj59+nQ5duyYFih11b17d6lbt65fPzRYogzzxUuWjL3WEeanoVDqc889xw8qERExZru4efOmTvvauHGjXL582fUhyZ49u4wePVqCtpxY/fr1de1Rw/r166VixYq6ljcRERHF9Pvvv0uVKlU0XqZKlUrSp08f4+a+zJg/YE555cqV5YsvvohxMdG7d29dZgyvS0RERDE9/vjj8vbbb2vHsnu89rZAqVc93lj2BOt3Y+kTLCG2d+9eGTJkiLaaf/zxx14dCBERWZhPa2hYw6xZs+SFF16QTz/9NGividfDBcJLL70kS5YskV69eulyZhcvXtRibnXq1AnasRARUYSwecy+ePGijgrDyiP+XLXLq9OaK1curVzep08fXUQcy5R8++23ukSKLZgtmhaMYhbZTB7fYZPb/F1wzRe+FHoJ9HGYPU8FLFCUhcgfONRcp2ehYEuwoaEcS53UrFlTHnjgAR2x9ssvv0hUVJTYAmO2feJ1XK/BmE2UODaP2UmSJNGebX8vle3VUHOYOXOmfPnll9KoUSM9qA8//FD27dvn14MjIiILBfGEbhaHNUGnTJkily5dCurrnj59Wl555RVdg/TRRx+V3377TSZNmhSQiq1ERGQBNo/XGTNmlKpVq8rUqVP9+rxe9Xh36NBBC7YMGzZMFxU/e/asDjOvUKGCjBo1Sjp37uzXgyQiIop01atXl+LFi0vhwoW1Jkrq1KljPP7qq69KgwYN/Pqaa9eu1Xlq99xzjxZ2w3qjGKHWrVs3HXqO5c3y5Mnj19ckIiKKdMOGDdMaKZ988onkzZs3xmPodEYHdNDmeP/xxx9aKRWyZs0q3333nVYzxzwyJt5ERBSDDVrIE7JgwQKZPXu2NG3aVIoWLRprZZAMGTL4/TV//vlnnefdv39/nRYGLVu2lBo1augQ9PHjx8ugQYP8/rpERBTBGLMFMTJ37txaFNW9mFqWLFm8Oq1eJd4bNmzQiqzunn32Wb8vhUJERBbAIK61UdCr/dFHHwXttKMWi6d4jdb7n376Sfbv3x+0YyEioghh85h9+vRp2b59uxw/flw7mP3Fq8TbUxA3YDib5QWjOIi/lbRwka+0YfLerpncRpHH7M82W4iKQkUCmwdxQPD2ZwD3NV6jeAx63i0vEn9nrBqzwyVeA2O2dTFm+87mMTtTpkw6Cs3fMdvrYvHnz5/XIWwos3779u1YLeyJhblnc+bM0aqrDz30UKzXwpw0rEdatmxZad68uV4wEBFRhLB5EAesm929e3etkxLsBBwj1bZu3arx1BViLm6JNWLECK3vMmDAgFhz1desWaNV0zE077HHHpMCBcJpuQwiIkqQzWN2ypQpdYj5uHHjtCZKSBPv9evXy8MPP6xl1g8dOiQlSpSQAwcOaMVUzPtObOJ9+fJladWqlZw8eVIXKXdNvPH8WAYFxWhwAjCHHJPZv//++1jz44iIiMIVqomjsRrxrFy5crES1tdee00aN27s99dFoo86LEiEETfv3r0rZ86c0eT/3XffTXTiPWbMGHnnnXc0iUe8d30fmEv+2WefSfv27eXEiRPy5ptvyuLFizkNjYiIIgY6exEnn3/+eW1ozpcvX6yltadNmxacxBuBtnfv3tKvXz/ted61a5ceHAJtpUqVEv18aElAsZdFixbFegwXIvnz59fe9eTJk2sV9ZIlS2qBmjZt2nhz+EREFGw2bz0HrOEdX8t5IHrBV6xYIatXr9a53GPHjtVtKKaGlUlwQYGK54ktrjp06FB57733tGibqx07duhjP/zwgzbOA4qt4nV27tzpx3dFREQBZfOYnTJlSl0yGzdPMmfO7NXzepV4Y1g4Aiug9fzmzZuSPXt2+eKLL6R27doaeM3CWqJ79uyRr7/+OlbijSHseB0UokHSDZiPdv/992vrPRNvIqIIYfMgbqzjjVswIV4jucbSJ6hqfu3af5Mfn3jiCa2yPn/+fF0O1Ax8b+vWrWX06NEep3vNmzdPrwVcR60988wzumQZkvJ7773Xj++MiIgCxuYxO3PmzPLBBx/4/Xm9SrwxNByTziFHjhw6dA5F1XCQ586dM/086Cl/4403dD5YihQpYj2O571+/boUK1Ysxna81rp16+J8XjQE4Ga4dOmS2IrdptNl86Gwhj+L7lipEJadRHvYdjhMCh1Z6fNj8yBucDgc2si8ZcsWnfONNUIxVQtF0AKxnjbin2u8xtxrQ2JjNkacoXEd87Yx3cvd7t27tXHcNSk3Cq7iMU+Jt+3jtd1idrjE62C9BgU/XgNjtu8Ys525KKZLIfft27ev3LlzR5fUrl69unjD50nS9erV0wPBMikIyuXLlzf1fTdu3NCWc8wvK168uMd9rl69qv9nzJgxxvaoqCjnY56gxx37GDcMVSciIgolBGwk2506ddJeYxQ7A9Q3CcYILiz3iVFkkydPlpkzZ8qUKVNMx2zsj0bykSNHxrkP4rKneG085gnjNRERhaNly5Zp7bJPP/1Ua5sARo5hutbKlSuDl3gPHz7c+fWwYcPk4sWL2gK+bds2rf5mBiako3AaWhJQFRU3XHygJxtfo/gLircBnt/VhQsXnI95grnn+B7jduTIEW/eJhER+UtSEzeLmzFjhlYCP3jwYIzh2DVq1NCe6Y0bN/r9NVGszSjYhvooaOxGjMS86y5dukjTpk1NPQ8a2PH9SJQRo/FeAHO9ly9frl8jLnuK18ZjnjBeExGFIZvHa0AdkwkTJmjnsiuj4Jo3vC6uZkCVNxQ+S6wKFSrEqn6O4WloSUCFVHyNJUjSpk0r//zzjzRp0sS5H+7jAiAuGLIX39qlREQUZDYJ1PH5888/5emnn9Yk1H2ONIZkI7Zh6Lk/uVcsf/nll/WWWL169XLODwdjehhirfE14vKSJUu04dxYdQTvyXjME8ZrIqIwZPOYff78eb1hNBo6iT3F66Cu4+0Krd9oRU8MLA2Gmyus1V21alV9PkAS/uijj+pwOLQuILhjntivv/4q33zzjT8OnYiIgiF2LS7bQUzDPDFPENuyZMkS8GMwhsfVr18/0Ym3K8zxRq83GtCNOeQY+Yb4jaJtLVq00G3oLcBQvfgay4mIKMzYPGYn/f/i4Sj07alGmbfx2i+JN4aaJTbxNuvDDz+UOnXq6FreSNQR7BHQUZGVLMTfRU4KmHwNf/J3sa1dHrbx2jU4P7dgFE6zG5u3ngOGlz/77LPa6230eP/7778yePBgOXr0qMa5QDOKqyU28TYDyfXbb7+t64ajhsvx48d1Xrj7MD2KcFaI18CYHXkYr4PH5jE7KipKKlasqJXNO3bs6NyOadVY6hpTtUKWePsLirMVLFgwxjYURkMBGixTgsXMsfwYhp17WsqEiIjIEyxbOXHiRK0gjgJgDRs2lFatWjmHRBsFwEaNGqVzrbNly6aB9b777vPbCTVeE5W/MY0K07R69uypreqzZs2Kt3ZJuEGF8nfeeUfSpEkTY/tbb72lBeQwMg2N5TjngajWTkRE1nT79m0dUbVq1SqNy1jdCsteFipUKMZ+iDNYjhq1RVBlvEePHjpd2V++/PJLXcf7s88+09dA4zKuJR544AHp3bu3V8+ZxIG1TXyEJNgPTxMwKFqjlVVR9MWt4iqFCS4dEht7vMlbWEIxKkoDhXuV6ZCZZ6Kx9LHAxBG0UCMoY7RU8+bN5dixY5o0omcW1UqNQI8eZ/z/0ksv6frXCLaoaupr7/D777+vLedISGHTpk26PAkKraFOCtb2Rk2TYEA1Vtf/ww3jdQRgvPaMMZvsFLMDFK8BPcw//fST9O/fXxvBsarG6tWrNXYWLlxY98EIaMROFP5EYv7RRx9pIy++z5fOWRT6xjXAnDlz9D4Sf0yFxnUEpouhbsojjzwSo9E+4D3e6HnOmTOn876xJAoREZFHIRykhACNxtepU6c6A/KtW7d0hQ4j8UZg37x5s66CYcS306dP65AyFEXzBYqwuPb6VqpUSW/BcObMGb1wMd43qrQSERGFY8y+e/eu1vHCkptdu3bVbS1bttRaIosWLdLR0YD6IohnaNiG2rVrS4kSJXQfJMbewnLXWKfbkC5duhhDzX3lVbqOFnr0HKCACnoHypQp47cDIps67OEW7eHmi7Qebtc83Pz5mr7wdGxEkSqEy4lheBiWtTpx4oRz244dO3S4tAHzkNHj7dqojGCPYefR0aH8Q+AbrD1apEgRnUeOJTxz5MihNyJLxmvGbCL/CFG8Tpo0qVYN37lzp3Mbpohdv37dGbMx3Hvfvn3y+OOPO/cpXry4lCtXLuxrinjV440hcphrjeIpaIFAoZhnnnlGWxqIiIhiMRGoMdcZN38vN4UEGj2/mHNcqlQpLfqFZNQYSmYEdsy9dq8xYjyGXmNfoPd879698e6DpD9DhgziT+gRwHzyyZMny5AhQ3TYPOauowI5l90kIiJvYnag4jWg17pTp06aSCP2ooo45nIb074Qk42OYPeYbTzmC3QqJxSvsdKWe12ygCXemGiOG9Y3w+R3JOHDhg3Tce9IwJGQR1KRGCIiCr2hQ4dqz6wrVMl2n4+M6U39+vWL97mw9mb79u31a7SMY1g5CqJgiUpUEEfMQvx69dVXnUPPkaC6Mu7jMV+9/vrreosPkmNcbPgTercxXB63tWvXarzG8D0k5E899ZTG7GANeyciInvFa0C8Tmha8oIFC5zzplHUDFO0EDOzZ88us2fP1iHlSLxz587tjMmeYjZyU1+hDgx63eODpPvgwYOhKa4GKEKDCm84GUi6sWQKfiDhUCSAxVoiwK4QLRtxzcS2UC0ttcnDNl4fU6QWalmU8ISxmw1vmGpBR1GydevWxftcCJrGKCzMz8LQctd5W9OmTZPOnTvr8HO0qKPieObMmWP0gqNi6v33369FVbAWtbeQTOfKlUuaNm2a4JB47BdoGHKOBvL169frfVRuR7E5rBgSaozXESCc43UgXtcsxmyyUcw2G68B8RpxOz4PP/yw1iLZv3+/Fkv74YcfdJsx7xuj1bAk54gRI3SZSiw17R6bGzRooOtru8bxxEIyjWKsmGceH1RPx35BXU4MLQLo+kcrPXoQUF0OCTeG1GH9bQxlW7FihS8vQURENhlqbnaYWtasWRNVPAXJtfswcgR2DCdDvELiXb58eQ30rrZs2aIt6NjXV0iq0eMeKnfu3JEff/xRe7zRs4D39PHHH+sxoecfdVswN65u3bohO0YiIoqMmJ2YYeU1atRIVFVxh8MRI2ajJxzVzJF3ApJtVBjH6iNG4o0Yh0QcFcl9haQ6UPHaq8QbZdURvBHEUVjt5Zdf1iF96C0wNG7cWIcHWJKnFlZPBT2CszqMvc6pv1uy05rcFgrs3SYrCWAxloRUrVpVxo8fr0E7b968GtSnTJmiLeNGUo0Yhpb0efPmaaPx5cuXZezYsdozHMlzoQ8fPizjxo3TRnIUmEMDOdYPx9QwA4aaoxECvQiWTLwZs0NzPu0Ur4Exm6wkRDG7dOnSWkkcMdqoWI751pgqhaHsgHyzWbNmMmrUKC2whkQZncAYZt62bVsJZ14l3pgThvlzGDqAIWqeoOga1l8jIiIKpTfffFOXCsNQtcqVK2txNQxpnj59uqRMmVL3wTrbSLzbtWunBV1QoAUt7Fgb1FfoUQ9V3RNcjGAt8gEDBuic7riGMaLHm4iIKJSioqK0obhbt24yf/587cTFNDEMO0dHr+Hzzz/X6VvoGUeD+vbt2+WLL76INbotsZInTx6raJs/eTXHG4uJozUiUvh9zhhbz4MzN8pTS3bJALw2kdWE43yxH00sCtrYLyVH4p27hfljaBjGsiRoJXd36tQp+fvvv3U4O5JxY/3rSGX7eA2M2f7FeE3kX5EYswMcr69du6a1WTD6DCPTjFVGXGHuN5b8xHlDvEbcDnde9XhHUtJNRET2HmpuKFSokN4SWtILq3ZYBeM1ERFFWsxOmzatLgEaH8z9jmvkdbjyqbgaERFRpCTeREREZAJjdkAw8fZGWpND2TaZ3K+22MvhEC1FQuZcM/kzEx9+jsEoxEPhJbJHbFMkY8z2HuN15MVsxmvyB8bsgGB7BhERUYhgHVTMYSMiIqLwdu7cOV0ZxVtMvImIKPCSmrhZHIJ1165dtao6YFkvVDxHsbfBgweH+vCIiIj+Y/N4DStXrnSubIL4jSXMUMDtnnvukUOHDok3bHLqiIgopJh4yzfffCPXr1+XPHnyOJc5e+GFF2ThwoW6lBnW3CYiIgo5Jt7SvXt3eeyxx/R0/Pjjj7os6apVq6RGjRry7rvvenVaOcebiIgCj/PFZN26dVK9enU9HRcuXJAtW7bI8uXLtfJ4vXr1ZNOmTVKgQAF+GomIKLRsHrPPnTsn58+fd64LvmzZMunUqZPUrVtXR6l17NjRq+dl4u2v4lOeird4ssvDNk+dHFa+9rLye7OCtCYLn6U1+VmO9qGAW0kfftcovHB8laRJk0ZOnDihp2Pp0qW6DIqx3NeNGzc8ritOfsCY7T3G6/DnHhMZr8kfbB6zU6dOrfVXrl69qnF6yZIlMmbMGJ/jNRNvIiKiIMD8sCZNmsjp06dl0aJFMnDgQN1+5coV2bFjh7M3nIiIiEK7jnitWrWkUaNGkjNnTo3TderU0ccwUg3bvWHz9gwiIgoKzvGW2rVry/Tp0+Xff/+V3r17a6E1WLFihfTr10+HrxEREYUc53jLrFmzNG6jCCp6vFOkSCF37tyRXbt2aQz3RhKHLzXRI8SlS5ckKipK5OJFkYwZAzNsLdqHtb09rePN4V0UTqJ9GGouJn83POFQc+9cuiQSFSUXL16UjP74m+cP601MGKtm+XBEwY7XwJhNdsJ4HXkiMWYzXnuFPd5ERBR47PFWkydPdi4ntn//fqlfv75UrlxZK5sTERGFBfZ4y759+3Q1EsMHH3wg9957rxZWw9xvb3COtzfSmuyhvmayuBqLRZG/efqcme1R9iSbD8932IfPfFqTv1fXfDjmQDPbu8+/A5a3evVqHWreuXNnvd+rVy9JmTKlNG3aVNq3by8HDx7kcPNAYMwmO8VsxmvfMGbT/3vuuedk8ODB+jWWEsMSYpgW9v3332sS/s4770hiscebiIgCjz3esnjxYnn44YedVVGxLuhXX30l7733nlY4x3JjREREIWfzHu+rV6/qkp+Y4w0LFiyQp59+Wvr37y8jR47U+O0NG5w6IiIKOSbemmzfvn1bT8dvv/0mxYoVk1y5cun9DBky6ONEREQhZ/PE+9atWxqv796966xk3rBhQ5/jNYeaExFR4JmorWZ1WJrktdde06VJxo4dK82bN9ftCOwYxvbpp5+G+hCJiIhsH7MzZ84s+fLlk549e0rBggU1RhuJ94YNG6RatWpefUqYeBMRUeDZoIU8IU888YQGbMwLK1u2rCbhxpIljRs3lvz584f6EImIiBizRWTq1Kny0ksvaW/3pEmTtOI8lhMbN25cjKJricHlxILN03JiXDKJzBb4MFuAy2xBs3ApQObr75DZ4mqe9qsk4ftztNLSJH+b6PIux+XE7C4gy4n5gjGbzGLMjvx4HdfrMmbHxnjtFfZBEBFR4HGOt/r333+1oBqGr61du1a3bd++Xfbu3ctPIRERhQebz/E2bNu2zVnNHNDjvWjRIvGWjU4dERGFTBITNxsk3aiQOmTIEPnuu+/kn3/+0e0o4PLUU0+Jw8EefyIiCgM2j9fw7bffStWqVWXJkiUyc+ZM3ZYsWTIddv7DDz+IN5h4ExFR4LHHW3u6U6dOLbt27ZIGDRo4T0358uU16V6/fj0/iUREFHrs8RaMTEMj+YwZM2KcmmeeeUbGjBnj9WklIiKiAPvrr7/kySeflJQpU0qSJDG7DAoVKiT79+/nz4CIiCjEoqOjdcmwpk2b+jVe27uqeSgKKBQIwWtSZPLlc5HNwp8zT79DnkSb3Obt34FrYfI3JVKwmVfSpEkjZ8+ejXVqsJzY1q1bpVu3biH50USEUP1uMWaTWYzZ5n5/QhGv4/pexuy42Txmp0qVSq5duyY3b9702IieI0cOr57X5qeViIiCgnO8pUWLFroMyY4dO5wt6JcvX5YXX3xRLly4oPO/iYiIQs7mc7wzZMigMfnNN9/UgmqGX375Rfr27SstW7b06nnt3eNNRETBwWZeqVWrlrzyyitSrlw5SZEihSxYsECeffZZDfDz5s3T+d9EREQhx5gtEydOlAcffFDGjh2rRVCzZcumo9aeeOIJbTD3BhNvIiKyRRA/cOCALF26VIoXLx6juJmrzZs3y8aNGzXANm7cWNKmTevVPnF5/fXXpVWrVrJs2TIN4Pny5ZNmzZpJlixZfHpvREREVonZt2/f1mW7jhw5Ih07dtQGancYMYZYevHiRalWrZqUKVPGq33iUqBAAR1Wju/HsmKoaF6zZk29eSuJwwbrl1y6dEmioqJELl4UyZgxtHM7ok3OxyXyhZXnLUX7sN9hD9tqh2COd6B/FpcuiURFaaDJ6Po3L5T2mxibViQw4ejkyZPSpUsX2blzp1y/fl2aNGmiFcbd9e7d29nCjX0RsFetWqXBNzH7kEXiNTBmUzBYNWaHS7wGxmz/xuwAxWuYMGGCrp2NedR//vmnNpqjoJmr3bt3S/369SVPnjxSuHBhWbx4sbz22mvy1ltvJWqfYLN34m3mF9MKf/iIrM5TQN3lYVuBADd87fLhgiqbye81UzgvHBPvAyYS78KBCUfHjh2TLVu2aLKMHmr0Mrsn3pi3VbduXVmzZo0OCcea23Xq1JG8efPK3LlzTe+TkD179uh6oDgm13ljxhIlvrSki93jNTBmE4W3cInXcb0uY7a5mB2geA2YhlW5cmVNuBFjPSXeSKixQgiS6aRJk+r3oI4KYj2mc5ndJz6I0YjXmzZt0tjkCiPePvjgA0ksDjUnIiJLD1tDYoxbfGbNmqWBGAk1YA72c889J927d9fKphhObmaf+Pz9999y3333SalSpXS4Oy4EXCGRJyIisnPMbt68uf6PhNuT06dP60gzNHgbcRTfg0b12bNna5w2s09C2rdvr8Pd77//fkmfPn2sVUq8wcSbiIjCIohj2Q73pTuwpAdugYZK40iIXeE+kuG9e/dqkDazT3ymT58uTz31lEyePDkg74GIiCgYMTuU8Xrnzp2CAdvu8bhkyZIap83uEx80piNp37VrlxQpUsRvx87Em4iIAs/ESPOhQ4fK4MGDY2x7++23ZdCgQTG2YZg2qoDHBz3LKKRiFuZqlyhRIsa2zJkz6//GEDMz+yQ0bK1KlSqmj4mIiCgcY7bZeA2I14jb8enRo4dzmc2EIBZDpkyZYsVjFC01u09C8Rrf68+kG5h4ExFRWOjXr5/06tUrxjZPredoiUYrdHzc54MlBMPGjEBtMJJpYwi5mX3ig/nhKBqDCwwiIiKrx2s4fPiw1jeJD3qnzSbexjBvxONcuXLFiMeu8TqhfeKDKur58+fX+d2VKlUSf2Hi7c79Z8HiLUSh40uVcP/9nfRcXdXTsa0x+XwlTb6HAvYaam52mNo999wjn332mX+Oy+U53ZP5/fv364VA0aJFTe8TH8wJR4VWFHzBzX3dbhR/K126tF/ejy14+p1hzCayd7wGxuyAx+zEDCt/5ZVXxJ/uuece5xxw42sjHj/yyCOm94kPEvSyZcvq0qNPP/20JuGuUAQUNV4Si4k3ERHZYh3v+KDoytdff62t8kaQnjZtmvZSa5Vtk/vEZ+3atbo8yrlz5+Tbb7+N9TgKrjHxJiKikAvjmF2gQAEpX768xl+sVAIbNmyQf/75x1mYzcw+8bly5YpWPy9YsKCsXr061uNYooyJNxERhSdzI8gCxughP3r0qAZU3MdSa2jJhkcffVQefvhhDdAdO3bUCuS//vprjIBrZp/4DBkyRG9ERERhLYQxe8OGDXpD7zRMmTJFsmTJIg0bNtTiaDB69GiNxZiLjRFnEydO1CrkaAg3mNknLkiskXjbqD2DiIjIPzBEHDcEbhQ4w9f79u1zPo7h4igA89577+mcMOyzbds2qVixYqL2ic/48eM1UU/sY0RERHYRHR2tMfrWrVtaEwVLg+H+xYsXnftgfe+tW7dqIo66L0iykaC7MrNPfMfw6quvJvqxhCRxYDa7xWGcvg4DxA8sY8bEfTPnixGFTrj8/gVjvlgBP87xRsGvqCgNUujVDQtnTDSfZ7d2OOrUqZM88MAD+r879KAbc8nszKd4HU5/M4jsJpx+9xizAx+zLR6vDx48qPEa/7vDvHHEa6NHPjE4x9ubPySefqGzmdxGRJ7t8iFoFwhwgPa0zyaT76Giyb8r0R622ay4mlVhrVOs9X379m39GkPdXWHO9+bNm6Vdu3YhO0bLYMwmsne8jms/xuzEsWnMdjgccvXqVe0hx9fu8Rpx/KeffopRKT0xmHgTEZHl53iHUrdu3bQoG0yfPl2ef/75WPtguRIz886IiIgCzqYx+9ChQ1K4cOEYy4q5S5kypUyaNMmr52fiTUREgWfT1nMYOHCgJtvvvPOOJtgo0OY6bxxFY1D4JWlSG58kIiIKHzYNR7lz55Z169bJiRMn5IUXXtC6Lq6whFqhQoUkc+bMXj0/E28iIqIAQlKN2+TJkyVNmjQeW9CJiIgotJBYV69eXYeUo6Ecy4n5ExNvIiIKPJu2nrvCGt6Y6/3LL7/IsWPHdIkTVzVr1pQiRYqE7PiIiIiUzWN28uTJNenG0PO//vpLC3+6Sp8+vbRo0SLxz+vHY7SmbCaLNuwyWbk4VAXXwqnapFmeik2JyfcR7u+NzP2+mC1CtsuHgodmCr0UMHlsaTxsq+xhWyWT72GTyfMUCZ93m84Xc1+CpFatWrJ3717JmjVrrKHln376KRNvXzFmhwbjtb2Ec7yOaz/G7MRhzJYRI0ZI3759JXXq1Jpou8qfPz8TbyIiClM2bz2HL774QnLmzKnzxzCvm4iIKCzZPGbfvn1b3nzzTZk5c6Y8+eSTfnte9ngTEVHg2TyIw6lTp6RNmzZMuomIKLzZPGZfvHhR67H4M+kOi8T75MmT8scff+hY+sqVK+scOE+tDr/++qtetJQtW1ZKly4dkmMlIiLyFuLX33//HbEnEHPSN27cqHPeUNW1SpUqWpXd3ZEjR7RXP126dPLAAw/o/0RERJEC08FQaA25J0aqRXzijUXJn3nmGVmxYoVUqFBBFypHoP7oo4+ke/fuzv3OnTsnjRo10v/LlCkjq1ev1u8bOXJkqA6diIgSy6at5wcPHtS53YAY9t5772kDc/369XXemCusHYpgH45+/vlnXVolY8aMUqBAAY3XWHZlyZIlki3b/yZjTpgwQXr27Cm1a9fWhnW89x9//JEN5kREkcSGMfvWrVsxGsfbt28vzZo1k379+umcbldIytGYnlhJHMiAQ+Du3bsyZcoUfVPo7YYvv/xSunXrJnv27HEuXo61T1EBdsOGDTqxff369VKjRg0N9k2aNDH1WqhEFxUVhXEDIhkzBuYNHTZZ8ChUxdUiseCa2ePbZLJ4lZ2E+89WTBZcER+KqwT6/a7xoWhMAT8XCXR/PlTfjIrSoVJIlMLCLROVWlKGJBwFVKdOneTrr782tS+WG8P+4WjlypWacBcrVkzvX716VXu8UYkdsRsOHz4s99xzj3z++efSpUsXbWDHRcvZs2c1UQ+beK0H62EbY7b3GK/tFbMjLV4DY7Z/Y7YF4/XBgwed+WdCUPEc+0dMjzequbpfYCBAP/vss7Jz50594wja33zzjQwYMMBZTa5atWp6mzFjhunEm4iIQsyGreeAJNTsCK20acP3Shs99K4wfLxevXry559/Ord9++23+h6efvppvY9h6C+99JI0bdpU9u/fz4rtRESRwoYxu0CBAnL+/HlT+7qvShIxc7xdLV26VN8IhuMZ88TQY+M+pxuPb9rkqZvzPzdv3tSbwX3tNSIiomBAIhrOCbW3sB45esHr1Knj3LZt2zYpXry4pEiRwrnNiOfbt2/3mHgzXhMRUThADpopU6aAvkbYJN5Y1/TVV1+Vl19+WVscAEk3uJ8ELMNiPObJ0KFDZfDgwQE+YiIiMs2GrefuFi1aJFu3bvX4WLJkyXSuNHqRUbgs3PXu3VuLzvTv39+5DXHZU7w2HvOE8ZqIKAzZPGZfvHhRxo4d6/ExjOZCjZaSJUtKgwYNnFOmIybxxrywhg0bavXT4cOHO7enSZPGOZfM1eXLl52PeYJJ8L169YrR4+0+KZ6IiILIxBRvq8NIrU8++UQuXLigDcxISo8ePaoFyPLly6fTq06cOCGjR4/WQmbhasiQITJp0iStteLaSIC4jGTcPV4bj3nCeE1EFIZsHrOvXbsm06dP15FcmFqFKdAYoXXgwAGtU3bvvfdqpzG2//bbb5I5c+bISLwxnBwJd8WKFXU+t2urAS5MMGTNffI67hctWjTO50SlOdyCymwBiXAS7sU8zB6L3QupReLP9nCYFx709nOX1uR7NVuELa3Jn20k/P1JkkzsrkWLFpqwLl++XJfPBATwadOmac8vlupauHCh1j957LHHtGp4uHn33Xflww8/lMWLF8cYZg6Iy1h5xJURv+OK2SGJ15HyOxNJf9cZr637s7VCvAbG7MSxeczOnTu3Vi1HxzBWIzGmjB07dkzatm2rxcGfeOIJvQ0aNEg+/fTT8B9IgJZ+JN3ly5eX2bNnx5gXBilTptSlxGbNmuXchtZ0zCt75JFHQnDERETkneQmbtY2d+5cXanDSLqNOWUoRoYKqX/88Ye0atVKC4hu3rxZws37778vH3zwgQ6Zr1u3bqzHEZcR19H6b5g5c6aOOCtXrlyQj5aIiLxn73j977//au0xLHPtWqclb968MmzYMI1tWP4TU5vNrtohoTxzN27c0CqpV65ckYceeki78w21atXSJUkALeu4j4uR6tWr63IrWPe7Y8eOoTp0IiKiREPDcfbs2T0+hilVxjBtLAHnbcXUQMGSYZjP3blzZ+3F/uqrr3Q7huA9+eST+vV9992nj+M+6rUcP35c58jNmTMn7N4PERFRXM6dO6dxGWt7u0+V8iVehyzxvn37tq7/Ca6t44DKp0bijYqof/31lw7P2717t67zjSXH3HvHiYgonPFvNkZwoXe7RIkS8vjjj2swP3PmjIwYMUKX5apdu7bOiUYBNjQ0hxMMB0eDN4bGr1q1yrkd89SNxNtI0DFK7ZdfftFegt9//z1GDz8REUUCe8fsnDlzaqxu06aN1h9DXnrnzh1Zu3at1mDBMpmAWieJWd46iQPVXCwOxdWioqJQog5NE6E+nPARbZF5OxR5P9vDPsxn8/d+/pyTt8vke73m5znejd3uYwnFqCityonW2PDwX3Xr+J0Tq8NcMcznRos5qqJi9BeGYo8ZM0aaNWumBdjwmPv8abtgvI7gv+tkzZ+tFeI1MGb7OWZbP17v3r1bG8s3bNig05/RaYy0GXO8x48fr43naGzu0KGDxnMzmHgH+4+Vp+XHW0ho/uCY/UMfLgU+KDJFm/xMRdrn7LDJ93rN5DZfRETindPEPjErYlt5CBvmcKOaOZLuKlWqaFCnECfeVojZkfZ3lMKLVeM1MGb7OWbbI14DRl6jgjmSa4zERk0Wb1l/djwREYUBhhvX4dlY+5OIiCg8MWYbUAQcN3/gWSUiIgoQLBWGec4YirZnzx79Oi7YBxXNiYiIKPij0d566y2tVv7KK6/o13ExKponFhNvIiIKghS2DeRYYgvzto2v44J9iIiIQs9+Mfv27dsao1HJ3Pg6LtjHG5zjHUhWmC8WqXN5KHxYdc4Y54sl0n8rVcRvjwQSiqJgnlb69Okld+7cHvdBsTPMvc6TJ0+cS4SgsumRI0ckc+bM/81HTsS6oHjOZMmSef0erI5zvP8f53hTKFg1XgNjtp9jdmDjNaCx+vTp01K0aNE4V7M6ceKExvQMGTJIXM6fP6+xBTVVzC79hesFJNdY0cOf2OMdSKH6Y+UpYK8x+b0FPGwr6ePxUOS75sNnOVsQiouFQgGTgT2byW1mL3g8vW5ECF3r+bVr12TcuHF6O3DggFYkNdahNqCaOIaVrV69WpNpBOk+ffrEGmr2/fff67KWWFYLxetatWqlVU3NBGd83/333y+dOnXy+3skP7BCzGa8JsZrc78rwJgdljH7jz/+kJEjR8rChQs1FiNuFypUyPk4EmIswzlq1ChNkLFPuXLltNI4ip8Zrly5olO4Fi9erMk5qpBPnjxZl/ZMCBrXEa8PHjzo1/dmfsVvIiKiCHTo0CE5fPiwJs1xLdOFdad79OghFy5c0OFlixYt0mW/Jk6c6Nxn//790rp1a3nzzTd1/W30nmNN6wEDBpheF/TUKftUgiUiIkqsJUuWyIMPPijfffedx8cRR9GL/eeff8rx48c1HufNm1cefvhhHZFm6Nmzp+zYsUOTaIxk69Klizz++OOm4jDmcOM10MjuT0y8iYgoCJKbuAVGqVKltHW8ZMm4uwMRoBHojWHgSNDR2r18+XLnPmgpz549u7z88st6v0CBApqsIznHfLCEdOzYUZ9j1y5PC74TERGFi9DEa8BIs/bt28c5kgxDxj/44APnlDH0ZPfq1Usb2FHE1KiZguKmvXv3lhw5ckiSJEm00RxDzbE9IenSpZMnnnhCBg4cqNPE/IVDzYmIKAgiq1ALEmkEcLSOuw5/q1q1qgZwQ82aNbWXHL3hxYsXj/c5Z8yYofPRSpcurRcOmTJlivH4kCFDpHnz5gF4N0RERNaN2Tt27NCk2kjGt27dKjdv3pTq1as790EiX6lSJdm4cWOCz4ee9N9++03++ecf+eyzzzRmJ0/+v7QZdWAwhD2xmHgTEVFYBHEESdxcIVC6t3pjzjZatuOTLVs2vXlr0KBBOnzthRdecG47e/as9nK7vw5gGFtCiTeS9IwZM8b5OArIEBERhXvMNhuvAfEacTs+8Y1IS8ixY8ekf//+WkfFKHiKeA3u1wG4j3idEMwJf+655+J8PDGFVV0x8fam6FNaH57L++tA88wWZDJb4Mrsfrt8KKxVwCJVM61qkw8/s2smPyuNg1A4JtBqe9gWf34YvwJenpNrkZl4Y061+7qYb7/9tibBrrZt2yZPP/10vM/VvXt3XYfTG2PHjpXhw4fLt99+K0WKFHFuxzB09yVEjAsP15bwuDRt2lRv5CdmP+d2itmM18R4bR5jttcx22y8BtRB2bBhQ7zPt337dq9W/EAS3aRJE51S9sknnzi3G8/lKWabiddoJEeBVX9j4k1ERGGhX79+Ok/LlafWcwz3DtQ86QkTJuh872+++UaaNWsW4zEMNcNQcVcnT57U//Ply5fo18JQuN27d+vQc1w0EBERWSlew5QpUwJyDGfPnpWGDRtqITRUQE+dOnWMeA2I2a4V0RGzK1SokOjXwpzxlStX6nvE6DX0iHuDxdWIiChIrefx3xDQ0MrsevP3GprxQZG0F198UWbOnCmPPfZYrMcfeOABWbNmjQZgA6qf33PPPTrfKz4o0IILAcwv27x5s97H8idPPvmkJt4oFENERBQewjtenzt3TpNuDPnGXGsUQ3OFxmysJLJ06dIYSTeWDkUsj8/OnTs1uc6VK5c2LmBllBIlSmgNFvSuV6xYUYe3e4OJNxERWbqqOaCHHDfMM8P62/h63759zsdR5RTzwzBU7t5773Xu7zqXHOtvIxBj7W4k4GPGjNF1Q1EULT7Lli3TIXBYT7R8+fLyzDPP6DriP/74owb0L774QofnufemExERhUbo4nV0dHSM+ItYjfuI3XD58mVdixvxHLEVy4W5xnhAoTXE1WHDhulqIlgytE2bNhrf0eAdH8TolClT6qol8+fP1yKrSLqxhCgazrG6ibeN5RxqTkRElq+Q2qJFixgt5biPdT9XrFih21C9FD3XGBLnOiyucuXKMn36dP0aLeqrV6/WpU4QkDG8DZXKEwriGJ6G+ebvv/++3q9Ro4Z07dpVLxwAX2No+5YtW5wVWYmIiOwYs7/55hutJA7oaUa8NeaVYzQaEnKMPMMKI+3atYvxvUiyEWPh+eef16XGJk2apEk7tmNeeooUcb+369eva3KN4qoZMmTQqW24Xvj999+d34eku2/fvl69N3sl3kdEJEMii6b4UrSpgA/FkvzNU7FAT0X9fJk2abboVbYwKo4VzoW6QsVTMbCEC0Am7nyaLbgW7UMhomAURTIjm8lz7EtBNPfnu+LDc1lUQnPCUVDNDMzlRhBPDATw2rX/V8UHCb77nHAMQz99+nSintfSvInXwJhtDuO1dbj//We89g1jdsi9+OKLeosLpmeZrfPSsWNHvSUmXmPdbyTdRrzGfddk3Zd4ba/Em4iIQiSy1gT1p7t37+qwNwO+dl0LHHDf4XCE4OiIiIjc2TNm3w1wvGbiTUREQWDPIG747rvvdH4YYBgbhrsb941tCRV8ISIiCg77xuwLFy7oEmiAIequ941t3mLiTUREQWDfcIPKqijGtmPHDuc2fO16H1AVloiIKPTsGbNTpEgh2bJl03nmBvf74LpEWWLY86wSEVGQ2bf1/MMPP9QbERFRZLBnzM6bN2+M0Wj+Zq/EG2ups0MhcEVoPBVwC/cCaXYvpOZLUUBP532ND+d9lw8/n00+FFzztghbtJ+3mT2OayZ+9y6ZfC6icMV47b94xXhtbe4/X8ZrzxizKQzYK/EmIqIQsWfrORERUeRhzA4EJt5ERBQEDOJERESRgTE7EJh4ExFREDDcEBERRQbG7EDgWSUioiBg6zkREVFkYMwOBCbeduapSJPZohzhVJQsnI7FTsye98M+vEYBk8/nqTCbWd4WVzvs59+VayZfw9M5ISLrMxOzGa/JE7vH67iOhTGbgoyJNxERBQFbz4mIiCIDY3YgMPEmIqIgYBAnIiKKDIzZgcDEm4iIgoDhhoiIKDIwZgdC0oA8KxEREREREREpNmfYWTYfCjwRRZvcZpbZz56n10hjsphMWpOve83L34HaPvzumX2vno63pEQADlsjCnjMZrwmu8fruI7FE8bseDBmBwITbyIiCgIGcSIiosjAmB0ITLyJiCgIGMSJiIgiA2N2IDDxJiKiIGC4ISIiigyM2YHA4mpEREREREREAcTmDEqY2QIXnvajyBPtw8+2tsnv9fQaazxsO+RhW3YfCgWaLVZ2zctzYvb3ooDJ/Sz1O8Vha0QBx3htP+7xye7xOq7vZcxOJMbsQGDiTUREQcAgTkREFBkYswOBiTcREQUBgzgREVFkYMwOBCbeREQUBAziREREkYExOxBYXI2IiIiIiIgogNjjTd4VgjKLRdgij9nCJ562ickiJ+LD56yAD0VYPH2+S4p3dkngz7u/i9+FFMMNkd+5/+4zXtuPe+xgvPaMMTuRGLMDgT3eREQUpGFrCd0CZ8eOHfLyyy9L0aJFpU+fPvHue+DAASlZsqSUK1cu1mMHDx6Udu3a6eO1a9eWqVOnBvCoiYiIQiF08frGjRsaW+vUqSOFChWSo0ePxrv/a6+9pvtNmjQp1mPjxo2T6tWrS6lSpaRz585y4sQJCSUm3kREZOnE+6+//pKWLVtKkSJFJHv27BIdHfcwjH///Vfatm0rWbJkkcOHD8d47MqVK1K3bl25fv26TJs2TYN4ly5dmHwTEZHFhC7x7ty5syxbtkxatGghhw4dktu3b8e574IFC3Tfs2fPyqVLl2I8NmrUKOnVq5c2un/99deawDdo0EBu3bolocJxBEREZGmlS5fWHm9YuHBhvPv2799fihUrJlWqVJFBgwbFeGzy5Mka3JF0p02bVvf5+++/ZfDgwdKhQ4eAvgciIiI7mDp1qiRPnlzWrPG0YPz/IJHu3r27Jt4Ygebqzp078t5778nrr78uTz31lG6bMWOG5M6dW2bPni3t27eXUGCPNxERWbrHGwHcDATvOXPmyJgxYzw+vmrVKh36hqTb0LRpU9m3b1+CQ+GIiIgiR+h6vJObiNlIrJFQY5h5mTJlYj2OxvbTp09LkyZNnNsw4q1y5coay0OFPd7hKtqH4kvhxJeiT2aLd0VEYakIl9aHImzRPhRcM/saZh02+RpmfteyheizGIl/B0yGm5s3b+rNVapUqfQWaKdOndLhbbNmzZKoqCiP+yC5vvfee2Nsy5Url/5/7NgxyZcvX8CPk8KUFWI247U1MF57xpjt15gdyngNQ4YM0UZwDCP3xGgMN2K0AfcRr0OFPd5ERBQWPd5Dhw7VpNf1hm3ufv/9dy2kEt/t448/Nn1kDodDh4o/88wz2qMdl7t378ZqiU+RIoWz9Z2IiMga/BOvoU2bNgnG7DuJiKG//PKLFk3D9K8kSZLEGa/BU8wOZbxmjzcREQVBwkPT+vXrp4VQXHlqPa9QoUKCQ8UyZcpk+siuXr0qP/30k+zcuVPnbwOKtOCGC4IPP/xQWrduLdmyZdM53q6MQm0YwkZERGSHmG02XsPIkSO1Unl8kiVLJmahoBrido0aNZzbEK/RC45Ra+vWrdN4DYjZefPmjRGzXe8HGxNvIiIKC2aHqaVOnVoTYn9Jly6dLiHmCsuS4GIBCb4RwKtWrSoTJkzQHnKjlR3FX1ABHRXTiYiI7CAxw8rdh3v7CkVQX3zxxRjbsPwntnXt2lXvly1bVq8V1q5d61waFMn/xo0btVp6qHCoORERWX4d7/ggiXYf9oZkOmnSpPp1+vTpdT/MAT9//rwMGzZMh7Ht3btXC7Eh0CemtZ6IiCi8hWe8hsyZM8eK2YjXiNtGrRXM/+7UqZMMHz5c53tjeDlWKsF+7dq1k1Bhj3e4CqeCYaEqGhNO54DM/XzMFnU5bLJomtnv9aSxyf28LdaWzUZFlywQbowechRRQ5KMnmwE6ISWK3F/jrlz52qijWVKUFgGS5JgeBvZXLjEK8Zr8sTu8RoYsyMmZo8cOVJvRvE2LBWGudofffSRtGzZ0vTzoNZLly5ddEQaeucxJWz+/PkhnRqWxIExcxaHcf9apfbiRZGMGSUiXAujwM7EgfzN34Hc0/fGXNIxdL9rofj9uXRJJCpKLl68KBnD5m/edhP7lA7Yqx88eDDWNgTyuCqRI25cuHBBChQoEOsx9HYjgUdccV1ajGwar8MpZjNek7/ZKV4DY7bJmB24eH3hwgW9ucO0L2MEmrvDhw9rbRdP1zxXrlzRW86cOeMsxhYs7PEmIiLLS+yccATvuBotMFQtd+7cfjoyIiIiMiCBTkyBVPDUSG5Ash5Xwh5sTLyJiCgIQjsnjIiIiMxizLZ14n3kyBEd2le8ePEwGjpJRETmMIjbBeblbd++XavFlyhRItSHQ0REicaYbcvEG6XfUX1uyZIlUrBgQTl06JCuqfrSSy+JpYXTfG7OGSN/8zQiaJOHbbs8bCvp59+XQP+uHTb5vszOcYtYYR9uyA8Qqzt06KDztFEBHkVtfvjhB+sPzQ+XmM14Tf5mp3gNjNn/jzHblsuJDR48WDZs2CD79u2TnTt3yowZM+Tll1+W9evXh/rQiIjIAsuJkX9ER0dL69at5ZVXXtGYffz4cUmRIoVWlSUiokjCeG3LxHvy5Mny7LPPOlvLseh5mTJldDsRERGFhzlz5uhaqb1799b7qVOnlj59+sjSpUvlxIkToT48IiKikArrcQRoLce87sqVK8fYXrVqVdm8eXO888uMtd8AS+o4l9ih+F32sO2Kh208leRvnj5n101+Rm+b3BbO78ufv1P//7cuvFaLZI+21SEulypVKsYSa4jX+Bxu2bLF43Bzxmsfuf89ZLymYLBqvAbG7P/HmG27xPvcuXP6f9asWWNsx33jMU+GDh2qQ9RjyZ/f/wdJRBSmLl++/N+ayGGBQdzqEJc9xWvjMU8Yr4mI/sOYbX1hnXhjbphRYM3V9evXJWXKlHF+X79+/aRXr17O+1iEHYXZsLh6+FyEBt+lS5ckf/78WiHezpXheR54Hqz+eUAPIwJ4njx5JHyEdbghP8VsT/Ea4orZjNf2+bvkDZ4Hngs7fCYYs+0jrK+E8IuVNGlSOXbsWIztuB/fQumpUqXSmzsk3Vb5JfUFzgHPA88DPw/W/r0Iv0ZG9nhbHRq4//jjjxjbjPgdV8xmvLbX3yVv8TzwXFj9M8GYbQ9hXVwN88Rq1qwpCxYscG67evWqLF++XBo1ahTSYyMiIqL/QVxGNfMdO3Y4t82fP1+yZMkilSpV4qkiIiJbC+seb3j33Xc1mGM4Wo0aNWT06NGSI0cO6dq1a6gPjYiITGOPt9U1aNBAGjduLE8++aTWWUGB1Pfee09GjBjhnDpGRESRgH+zbdfjDXXr1pWff/5ZDh06JJ9++qmULl1a1qxZI+nTpzf9HBjK9vbbb3scfm4nPA88D/w88PeCKJDmzZsnbdu2lfHjx8vKlStl6tSp0r17d9PfzzjF88DPA383+DeCrCqJI7zWmyEiIiIiIiKylLDv8SYiIiIiIiKKZEy8iYiIiIiIiAKIiTcRERERERFRAFk+8T579qyuK3ry5Emxi1u3bsnWrVvlwIEDcvfu3Tj327t3r2zcuFGuX78uVnbp0iUtyLd//36Pjx89elT+/PNPuXjxoljZnj17dJmfuMo6nDlzRn9XTp8+LVaF94bPPD4LcZ0HfA7weThy5EjQj4/IzvA7uX37dvnrr7/k9u3bYhe4PtmyZYvGqrhcuXJF/y4dPHhQrG7btm0asz19BnB9s2nTJtm9e7dYGT4LeJ8XLlzw+Diu7XCd9/fff8udO3fEiv7991/ZtWuX/m7Ed32GzwLO1c2bN4N6fERecVjYW2+95UiVKpXj3nvv1f+7dOniuHPnjsOqrl275ujTp48jS5YsjrJlyzpy587tKFGihGPdunUx9jt79qyjdu3ajowZMzruueceR1RUlGPOnDkOq2revLkjadKkjh49esTYfuPGDUfLli0dadKkcZQqVcqROnVqx4gRIxxWs379ekfJkiX181CpUiVH+fLlHTt37oyxDz43rr8rL730kuPu3bsOK/1uPP7444506dLpOciRI4eek7/++ivGfqNGjXJ+HvA/vgefEyIKrN27d+vvJH438+fP78ibN69j7dq1lj7tq1atclSrVs2RM2dO/buMvzn42+t+nTJ58mT924V4jv+bNGniuHz5ssOKEK8Qg3B5eubMmRiPLVu2zJEtWzZH4cKF9TqnYsWKjqNHjzqs5Pbt246ePXvqZwHvr0CBAo633347xj5///23o2jRohrT8XtSsGBBx6ZNmxxWMn/+fEeuXLn0Z238buA6xdXx48cdlStXdmTOnNlRpEgRR9asWR2LFy8O2TETmWHZxPv77793pEiRwvHbb7/p/V27dmmC+emnnzqs6tixY47hw4c7rly54vwD/swzz+iFzK1bt5z7tWnTRv+QGYEbySYC3aFDhxxWg593w4YNNdlyT7wHDBjgyJMnjzNw//DDDxrsjc+MFeBnigaW3r17Oy/mkHSvWLHCuc+0adM0qG3cuFHvIxlNmzat48svv3RYxbBhw/T3/8iRI3ofvw+NGzd21KpVy7nP77//7kiSJIljwYIFzt8nfD769esXsuMmsgskGc2aNdO4Bd26ddPfv+vXrzusauLEiY4NGzbESKjSp08f4zpl+/btjmTJkjmmTJmi96OjozXp6t69u8NqLly44ChWrJgmWO6JNzoM8DccHSqABlH8/W7UqJHDSpB0I6HGNSsgbo8dO9b5OO6jgap169bOxvH27dtr4vnvv/86rAB/A4zrFtdGKnwmfvrpJ+e2pk2bOmrWrOn8G4EGCnyfe4MNUTgRK/dy4pfS1bPPPqsJp50gmcAfKwRvuHjxojZIfPXVV8598McarcdDhw51WMnmzZv1wg0JFFpF3RNvPIbk21WFChV0ZIRVoPcELebxBeT69etrz78rNM64JqWRrm/fvo4yZcrE2Na/f39H8eLFnfe7du2qP39X+HygN4qIAge9dYhTiFcGNJKhIWzevHm2OvW4bnnyySed91977TVHoUKFYuzz0UcfaYLu2qBuBa1atXK88cYbzkZw1wRq/PjxOirNtacfHSzY7/Dhww4rOHnypF6f4b3G5ZdfftH3vG3bthijRdyT0kiGnzF+97/99lvnNjS0YOTijBkz9D6u67APPgMGdDqhE8G1oYIo3Fh2jvfmzZulcuXKMbZVrVpV5w5h3ohdYM5uihQppECBAnrfeP+u5yZ58uRSoUIFPWdWcfXqVWndurWMHj1a8uTJ43Gu7/Hjxz1+Rqx0HlasWCEPPvigzgfDHCjMD3Sf2xzX74qVzkP37t11juQbb7why5cvl4kTJ8rkyZPl3XffTfA8nDp1Sk6cOBGCoyayB+NvTaVKlZzb8uXLJ7lz57bU36GEYI4q5u0WK1Yswb9L+HuGOi1WMWHCBNm3b58MGTLE4+M4DyVKlJD06dPHOA+AOcBW8Msvv+j1WbNmzbTGCN7X5cuXY52HVKlSSenSpZ3bihcvLhkzZrTM7wp+xgMGDJC33npL5syZI8uWLZMOHTpIjRo1pEWLFroPzg2uZVx/N9KlSyelSpWyzHkga0ouFnXu3DnJmjVrjG24jyIUKFrh/phVi2kNHDhQXn31VWewwnkBT+fGeMwKXnjhBalTp448/vjjHh+3y3lA48L58+c1GGXIkEGOHTsmefPmlZkzZ+o2BC4Ub/F0Hq5du6YXggjyka5gwYLStWtX+fDDD+XHH3/Ugnr4fDzwwAMJ/s0wHkMSQET+h98vJA5oJLby3+OE9O7dW4ud9ujRw7kN779w4cJx/l2yAhT9fPPNN7WgmvtnwOzfZ6vEa8Tbd955R+bPny9ZsmTRxpXXXntNBg0aFOd5sOLvSqtWrTRW9+nTR6KiorQBfNSoUZImTRpbXcOR9Vi2xxt/vG/cuBFjm1G9O2XKlGJ1SCyaNGkidevWlffee8+53Qhqns6NVc7L4sWL5fvvv5eWLVtqIMcNvQPotcTXSDbtcB4A73PhwoUaxNFCjFb0nDlzytNPP62PJ0mSREc8xPW7EtdFUKTBhQyCNnqT0POP3w98Dh566CHnPnb/m0EUKp5+96z49zg+iNMYhfPdd99p46id/i516tRJezKxsgZiNBJxWL9+vRw6dMg25wHvEY3deF9436hY/sMPP2j8WrRokW1+V9BZgGvXhg0bOs/DvHnztNcb5wPscg1H1mPZHm/0cKF3zxXuZ8qUSXv+rAxJBXryypQpI7Nnz9bEyvW8GOfCtRUd96tVqyZWgKFaZcuWjTGMGO8PIx3Qarpy5Uq9sEmWLJnHz4gxLN8KChUqJJkzZ9bPAqROnVovctq1a6c92mnTptX36+k8YKhn0qTWaJtD40Pz5s0lf/78eh+B+bnnntPEG70MmI4Q198MnAOcCyIKDPzuYZmo6OhoyZYtm27D6DT8vbbS3+O4fPDBB5p44+8UEg5Xcf1dAqucG4wm2rlzp04FMhIvQMKJePX888/refj1118tfR4QrwGxCdcn0KBBA516gPf+8MMP63nA+THiNyBZx9K5VjkPa9eu1V5r15EfNWvWlIoVK2rijaH4rteyuMYx4H7t2rVDctxEZljjqtqDRo0aac+n6/qG6PXDdivDH5169erpMOJvv/02Vssf5kgh+ViwYIFzG1oU0RtqlXPz6KOPOnu6jRveN4ad42ucEySgGGrseh7QUvrTTz9Z5jwARj0gsXRvmMHUA2PIFt4vLviMud/4H+fFSuche/bs+r5dofcfFzcYzgd4v5j/jQsa178ZtWrVcp4rIvK/+++/X/8uu/49RgMp5rda6e+QJ8OGDdN5zUgo6tevH+txvH/ELSMZNf4uoXEZo5esAO/HNV5jShAgLiHpNs6D0fvp+n3oTLnvvvvECnBNgmTataEFSTUapBDDAJ8RNAbj3BhwrYs1z5GkW4HxXl1jNq7lMWrReAxzuxG7Xf9mYETbgQMHLP83gyKcw6Kwvh+W0UK1ZiwPhKVJsETS1q1bHVZ17tw5rdKMG5aL+vXXX503LNPhunxU8uTJdYmluXPn6lJb1atXt/Qa556qmq9Zs0YriKLiNdaMxPJSqB6Lyu9WgaVnsCYulpVbunSpY8yYMbokyzvvvOPc58CBA7oOZrt27fR3pWPHjrokx549exxWgbU9UQEVy5NgLVhUPcV6sPi7YLh06ZIuyYLPAT4Pr7/+uv6erF69OqTHTmQHAwcOdGTKlMkxYcIEx/Tp0x358uVzPP300w4rw99jXIYNHjw4RrzGsmKu1ZyxIkPt2rW1wvugQYN0eTFU/rYqT1XN4cEHH9SltGbPnu0YPXq0VjkfNWqUw0o+/PBDXZsbK88sWrRIV+jB8mKnT5927vPqq69q/ML67lhmDitvvPDCCw6rwHJiWCYM69ajijniN67lUcnf9boEvz/4DGD5PXwmSpUqpfGbKJwlwT9iUWj5Qmvy7t27dQgOioyVL19erArvs0uXLh4fGzlypFSpUsV5H62lmE928eJFrRTZt29fLW5jVc8++6xWAcVnwNW6devks88+k5MnT+pwbAx1s1oRLfR44/cAFe1z5Mihc9/di879888/Mnz4cK0qiykIKGiCURNW8vvvv2s1c/SaoAALRgNgrrsxpA/wOcCwT7ScozcJQ93Q401EgYVLka+++krmzp2rvXf4/XzxxRctU2fCk9dff11+++23WNvLlSsnn3/+ufM+ht3i79LGjRv1b1e3bt0s07sZ11BjFBTDvGYU1nIdlfbJJ5/I6tWrtWe4bdu2unqJ1cyaNUu++eYbnb+Mz0KvXr1ijG7AKiXjx4/X3l783mAIOlbucI1lVliZZsyYMRq38XPHqMWXXnpJihYtGmM/VD2fPn26jlTDyBmcK2MIPlE4snTiTURERERERBRqlp3jTURERERERBQOmHgTERERERERBRATbyIiIiIiIqIAYuJNREREREREFEBMvImIiIiIiIgCiIk3ERERERERUQAx8SYiIiIiIiIKoOSBfHIiKzp27Jj8/vvv8sQTTyT6e/fs2SMbN27Ur8uVKyf33nuvhMKpU6fk559/1q8LFSok1atXD8lxEBERBdKyZcs0zpUoUSLR3ztv3jy5efOmJE2aVFq1aiWhsmLFCjlz5ox+/cgjj0j69OlDdixE5D32eBMl0h9//CEdO3b0+gKga9eu8v3338vu3bt12+nTp+Wbb76RH374Idb+d+7ckVmzZunjN27c8NvPCgEcx9CvXz/57LPP/Pa8RERE4eT111/3GF/NeO6552TUqFEyf/5857aVK1dqTD5w4ECs/f/66y997M8//xR/+vXXX2X69OnStm1bOXnypF+fm4iChz3eRImUL18+admypdfnLU+ePBqYDTt27NBgmiRJEu0RL1q0qPOxxYsXS5s2bfTrEydOSK5cufzy8ypTpoweQ6dOneT27dt+eU4iIqJw07RpUylZsqTX3//yyy874zAMGTJEVq9eLR06dJApU6bE2PeZZ56RTZs2Sbdu3aRKlSriL4MGDZKDBw/KwoUL/facRBR87PGmiIWkFEO+3Vubv/vuuzi/5+zZs5pw4oYe3+3btyf6eXPnzi3NmjWL8fiFCxdk+fLlesPX3qhTp45MmjQpxrYvv/xS6tatG2PboUOHtPUdveEI8Pj66NGjHp8TLeMI1L/99pv8+++/Xh0XERGRLw4fPqxx98qVK85taPSdPXu2/PPPP3F+HxJcfB9Gfq1atSpWfDXzvPXq1ZN77rknxvf9/fffsmDBAtm2bZtX7+f++++XuXPnysWLF53btmzZInv37pWKFSvG2HfRokWyb98+neKFUW94T4jf7rAN1x44LkxpIyLrYeJNESs6OloaNGggu3bt0vvHjx/X+wh8cTl//rwm3LhNnjxZA3Lz5s1j9Pom9LzuQ80x9wrzxwYPHiwff/yxVKpUSQNnYnXp0kW+/vprZ0BG0rx06VLtlXYfcvb0009r4O/evbu+ZrFixXQYmqv+/ftLkSJFZNiwYfLGG29I7dq1nXPEiIiIgiV79uzyzjvvyCuvvOLc9tZbb0mvXr0kW7ZscX7funXrNF6j4RtToxDTEBcT87yuQ80RXx999FHtBUfDNmLpgw8+qPO4E6NUqVJSvnx5mTlzpnPbxIkTtWc8bdq0MfbFsWGKGa4NPv30U90H8fjy5cvOfXC9Ubp0aWndurWMGzdOG+LHjh2bqGMiogjgIIpgbdq0cVSsWNFx48YNR8OGDR316tVz3Llzx/T3X7x40VG0aFHHxIkTTT/vvHnzHOnSpXPui8dfe+015/1Lly45fvrpJ4+vN3r0aEeJEiVibPv5558d+FU8duyYo0CBAo5Fixbp9qFDhzoefPBBfS48fuLECd0+depUvd+/f3/nc4wYMcIRFRXlOHfunN6fPn26I0WKFI61a9c699m8ebPj0KFDMV67Y8eOjnbt2pk+X0RERN7YsmWLI1WqVI45c+Y4Vq1a5UiePLljxYoViXqOMWPGOPLnz++4e/eu6ectX768Y/jw4fr1mjVrdF8jVsKPP/7ouHz5ssfXy5o1q2PmzJkxttWtW9fRrVs3vW647777dNv169cdmTJlcqxfv95Rq1YtfdyAa4zs2bNrjIezZ886Chcu7Hjrrbf0Pq4tSpYs6Xj88ccdN2/e1G23bt1yXgsYDhw4oLF/z549iTpnRBQ+2ONNEQ0twhg+ft9992m1cMy3QvXR+KDFe8OGDVqtFMPKCxQooPe9fd40adLoMLKrV6/q/QwZMkjDhg0T/V7w/J07d9ZWeMCwc/SCx6VPnz7Or3v06KHv68cff9T76DlH1fUaNWo496lQoYK+VyIiomBDD/HQoUO197d9+/by6quvSv369RP8Pow6++mnn3S4ORw5ciRGgbHEPC/iNUa4uU4za9SokVdVwtE7vXPnTtm6dat8++23Wv+latWqHvdt166d1neBLFmyaNE2DIeH9evXa4/3+++/LylTptRtKVKkkIceeijRx0RE4Y3F1SiiZcqUSRNQFD9B0ELgM2Bo2q1bt/TrHDlyaCBGgty4cWOM9NClvBBsMT86KirK9PO6++ijjzRBxmtgWS4s9YHCKu7DzcxA4o0lT+bMmaNz2TAMHvPBPL1v3AwI0nnz5tX538a8Ny4RRkRE4eSll16SDz74QC5duqRFygyYj42aJQbEa8RUTJkaOXKkFirDsPK7d+86VwNBvZWEntcdhntjWhiGmyOG4nWeffZZqVatWqLfC64fsMQYGsk3b94cb0M5pqO5Kly4cIx4jeKqmDJGRNbGHm+KaJiPjZZuJNFYFuvcuXMxCpoY87nXrFmj2xBwUdF7//79WnQMRVkQiJGIm31ed8WLF9d516g4ihZ3BGFvq54XLFhQ526jNRwVU5FQe4K5Ye7VyDF/3ZjThgsK9NgTERGFiw8//FCTTMQ2JNQGNIob8Ro3xGDEaTR8I36jARq9yrgP7jE7ruf1BMk8Enf0oKP3uVatWrEKqpqFZBsxH9+P3va4ID7HF6/xftz3ISLrYeJNEQ0t1WhJxnBwDONCwmrAkG2jgjmKrQCGp6FH2YBeZRRHS8zzujOqj6I1HkPPXnvtNa+DOOD7UfgFSXxcMKzcdVkRVC1H4TRjaDl69dHj71rpFcVj0BtAREQUbChMisZvJKoTJkzQuIwYCyhwZsRr3NDojXiNZBqN2wYk34l5XndIuNFonSxZMu1FR8KOBm/36WZm1axZU5cDHThwYLxF4lBw1bWxAPEZCT8gbqP33H1pMhZDJbIeDjWniDV+/HhZuXKlLuGROnVqreqN3msEX6yl6UmLFi20wnfWrFk10H3xxRexeo4T+7yocI71tRFEkRCjaumTTz7p9fvC/PCE5oinSpVKXnzxRZ0XhjlhqFyOY0OlVcAweSwzhuFzaERAwJ8xY4ZWYM2YMaPXx0ZERJRYqIGCec5oUDbmLiN2YhuGmHuammXUJcEIMsRUxGTEMV+eF0uD9uzZU58Pw73RSI4EF43d3sJ1REIwLx3T0DDEHY39eF00GABi8ujRo/U9YPUUXG/8+eefGrdR4ZyIrIM93hSRbty4oS3UWBIMy4sAWsWRHGP5kbiWBnnhhRe0RRzBDQVR0Er+ySef6NIdZp8X871dh5JjXU4EfKwLikIrWNrk888/j/PYMUwcLfo7duzQ+5jHhp5yFH3xBEm9++MYmobGAVx04HXxPlwDNAq84XiRnONxrB+K1nRjLVPcxzEcOHAgUeediIgosVD4E0toDR8+3LkNQ8LR24vCaZ4gacZoLqyLjTW8MSwc9xEPM2fObPp5kVSXLFnSWUgNjdLoScfw9Zw5c2pC79qr7g6x1CiEBpgXjsKrcUHDufvjmHeOZB9zwTE3HdcZxjEBlg3F6+A943/0xrteRyBZN5ZEI6LIlQSlzUN9EER2gYsEJPGAi4fHHnss0c8xbdo07dF2reqaWNu2bZN3331Xv0ajA6qiExER0f+gFxpTtDA0HaPfvIGiaYjZzz//vNendtCgQTrCDTCqDg0GRBR5mHgTRRh/JN5EREQUeP5IvInIGjjUnCjCoOgb5qoTERFReMPcbi4VRkTAHm8iIiIiIiKiAGKPNxEREREREVEAMfEmIiIiIiIiCiAm3kREREREREQBxMSbiIiIiIiIKICYeBMREREREREFEBNvIiIiIiIiogBi4k1EREREREQUQEy8iYiIiIiIiAKIiTcRERERERGRBM7/AQ0WNU+t62E3AAAAAElFTkSuQmCC",
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