{ "cells": [ { "cell_type": "raw", "id": "bcf23fc8-ae63-4efe-84bb-c67acdffb973", "metadata": {}, "source": [ "---\n", "title: \"Data subsetting and plotting with earthaccess and xarray\"\n", "author: Luis Lopez (NASA) and adapted by Eli Holmes (NOAA)\n", "---" ] }, { "cell_type": "markdown", "id": "7cf2da01-6c4d-49c6-8728-7939a5c982ce", "metadata": {}, "source": [ "[![Colab Badge](https://img.shields.io/badge/Open_in_Colab-blue?style=for-the-badge)][colab-link]\n", "\n", " \"JupyterHub\n", " [![Download Badge](https://img.shields.io/badge/Download-grey?style=for-the-badge)][download-link]\n", "\n", "[download-link]: https://nmfs-opensci.github.io/NMFSHackDays-2025/topics-2025/2025-02-14-earthdata/2-subset-and-plot.ipynb\n", "[colab-link]: https://colab.research.google.com/github/nmfs-opensci/nmfshackdays-2025/blob/main/topics-2025/2025-02-14-earthdata/2-subset-and-plot.ipynb\n", "[jupyter-link]: https://nmfs-openscapes.2i2c.cloud/hub/user-redirect/lab?fromURL=https://raw.githubusercontent.com/nmfs-opensci/nmfshackdays-2025/main/topics-2025/2025-02-14-earthdata/2-subset-and-plot.ipynb" ] }, { "cell_type": "markdown", "id": "45bbcecf-537b-40a7-92a3-e040f2c64c2f", "metadata": {}, "source": [ ">📘 Learning Objectives\n", ">\n", "> 1. How to crop a single data file\n", "> 2. How to create a data cube (DataSet) with `xarray`\n", "> 3. Extract variables, temporal slices, and spatial slices from an `xarray` dataset\n", ">\n" ] }, { "cell_type": "markdown", "id": "a28d9430-1a3e-480c-bf15-c35f938b4210", "metadata": {}, "source": [ "## Summary\n", "\n", "In this examples we will use the [xarray](https://xarray.dev/) and [earthaccess](https://nsidc.github.io/earthaccess/) to subset data and make figures.\n", "\n", "For this tutorial we will use the [GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis](https://cmr.earthdata.nasa.gov/search/concepts/C1996881146-POCLOUD.html) (v4.1) data. This is much higher resolution data than the AVHRR data and we will do spatially subsetting to a small area of interest.\n", "\n", "#### For those not working in the JupyterHub\n", "\n", "Create a code cell and run `pip install earthaccess`" ] }, { "cell_type": "markdown", "id": "66d78efc-2d62-428a-a813-e58f949ee1bf", "metadata": {}, "source": [ "### Import Required Packages" ] }, { "cell_type": "code", "execution_count": 1, "id": "f04a653d-b9e5-4cfe-a198-b5b612389742", "metadata": { "tags": [] }, "outputs": [], "source": [ "# Suppress warnings\n", "import warnings\n", "warnings.simplefilter('ignore')\n", "warnings.filterwarnings('ignore')\n", "from pprint import pprint\n", "\n", "import earthaccess\n", "import xarray as xr" ] }, { "cell_type": "markdown", "id": "442bd92a-8f2d-4448-a59e-da4567710730", "metadata": {}, "source": [ "## Authenticate to NASA Earthdata\n", "\n", "We will authenticate our Earthaccess session, and then open the results like we did in the Search & Discovery section." ] }, { "cell_type": "code", "execution_count": 2, "id": "0fe0002f-c759-4611-8dd7-861b8bd38971", "metadata": { "tags": [] }, "outputs": [], "source": [ "auth = earthaccess.login()\n", "# are we authenticated?\n", "if not auth.authenticated:\n", " # ask for credentials and persist them in a .netrc file\n", " auth.login(strategy=\"interactive\", persist=True)" ] }, { "cell_type": "markdown", "id": "a6a3cb10-6988-401e-a618-59e2f5ac3228", "metadata": {}, "source": [ "## Get a vector of urls to our nc files" ] }, { "cell_type": "code", "execution_count": 3, "id": "3dbe9828-37e9-4949-846f-297057e5b0d5", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "93" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "short_name = 'MUR-JPL-L4-GLOB-v4.1'\n", "version = \"4.1\"\n", "date_start = \"2020-01-01\"\n", "date_end = \"2020-04-01\"\n", "date_range = (date_start, date_end)\n", "# min lon, min lat, max lon, max lat\n", "bbox = (-75.5, 33.5, -73.5, 35.5) \n", "\n", "results = earthaccess.search_data(\n", " short_name = short_name,\n", " version = version,\n", " cloud_hosted = True,\n", " temporal = date_range,\n", " bounding_box = bbox,\n", ")\n", "len(results)" ] }, { "cell_type": "code", "execution_count": 5, "id": "70973325-f862-4ad2-932f-46a4b9c24217", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "
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\n", " " ], "text/plain": [ "Collection: {'Version': '4.1', 'ShortName': 'MUR-JPL-L4-GLOB-v4.1'}\n", "Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'BoundingRectangles': [{'WestBoundingCoordinate': -180, 'SouthBoundingCoordinate': -90, 'EastBoundingCoordinate': 180, 'NorthBoundingCoordinate': 90}]}}}\n", "Temporal coverage: {'RangeDateTime': {'EndingDateTime': '2020-01-01T21:00:00.000Z', 'BeginningDateTime': '2019-12-31T21:00:00.000Z'}}\n", "Size(MB): 679.0388813018799\n", "Data: ['https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20200101090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc']" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results[0]" ] }, { "cell_type": "markdown", "id": "f03eea04-435c-4ae9-aca1-9f6e3fc4fee0", "metadata": {}, "source": [ "## Crop and plot one netCDF file\n", "\n", "Each MUR SST `netCDF` file is large so I do not want to download. Instead we will subset the data on the server side. We will start with one file.\n", "\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "36985cec-3b0f-4422-8bd5-cbbc8a01a0a5", "metadata": { "tags": [] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8e85d3547f7648f39cdae6b2d1268e2d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "QUEUEING TASKS | : 0%| | 0/1 [00:00\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset> Size: 29GB\n",
       "Dimensions:           (time: 1, lat: 17999, lon: 36000)\n",
       "Coordinates:\n",
       "  * time              (time) datetime64[ns] 8B 2020-01-01T09:00:00\n",
       "  * lat               (lat) float32 72kB -89.99 -89.98 -89.97 ... 89.98 89.99\n",
       "  * lon               (lon) float32 144kB -180.0 -180.0 -180.0 ... 180.0 180.0\n",
       "Data variables:\n",
       "    analysed_sst      (time, lat, lon) float64 5GB ...\n",
       "    analysis_error    (time, lat, lon) float64 5GB ...\n",
       "    mask              (time, lat, lon) float32 3GB ...\n",
       "    sea_ice_fraction  (time, lat, lon) float64 5GB ...\n",
       "    dt_1km_data       (time, lat, lon) timedelta64[ns] 5GB ...\n",
       "    sst_anomaly       (time, lat, lon) float64 5GB ...\n",
       "Attributes: (12/47)\n",
       "    Conventions:                CF-1.7\n",
       "    title:                      Daily MUR SST, Final product\n",
       "    summary:                    A merged, multi-sensor L4 Foundation SST anal...\n",
       "    references:                 http://podaac.jpl.nasa.gov/Multi-scale_Ultra-...\n",
       "    institution:                Jet Propulsion Laboratory\n",
       "    history:                    created at nominal 4-day latency; replaced nr...\n",
       "    ...                         ...\n",
       "    project:                    NASA Making Earth Science Data Records for Us...\n",
       "    publisher_name:             GHRSST Project Office\n",
       "    publisher_url:              http://www.ghrsst.org\n",
       "    publisher_email:            ghrsst-po@nceo.ac.uk\n",
       "    processing_level:           L4\n",
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" ], "text/plain": [ " Size: 29GB\n", "Dimensions: (time: 1, lat: 17999, lon: 36000)\n", "Coordinates:\n", " * time (time) datetime64[ns] 8B 2020-01-01T09:00:00\n", " * lat (lat) float32 72kB -89.99 -89.98 -89.97 ... 89.98 89.99\n", " * lon (lon) float32 144kB -180.0 -180.0 -180.0 ... 180.0 180.0\n", "Data variables:\n", " analysed_sst (time, lat, lon) float64 5GB ...\n", " analysis_error (time, lat, lon) float64 5GB ...\n", " mask (time, lat, lon) float32 3GB ...\n", " sea_ice_fraction (time, lat, lon) float64 5GB ...\n", " dt_1km_data (time, lat, lon) timedelta64[ns] 5GB ...\n", " sst_anomaly (time, lat, lon) float64 5GB ...\n", "Attributes: (12/47)\n", " Conventions: CF-1.7\n", " title: Daily MUR SST, Final product\n", " summary: A merged, multi-sensor L4 Foundation SST anal...\n", " references: http://podaac.jpl.nasa.gov/Multi-scale_Ultra-...\n", " institution: Jet Propulsion Laboratory\n", " history: created at nominal 4-day latency; replaced nr...\n", " ... ...\n", " project: NASA Making Earth Science Data Records for Us...\n", " publisher_name: GHRSST Project Office\n", " publisher_url: http://www.ghrsst.org\n", " publisher_email: ghrsst-po@nceo.ac.uk\n", " processing_level: L4\n", " cdm_data_type: grid" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds" ] }, { "cell_type": "markdown", "id": "7de0326f-8c8e-4ae1-b8b2-ae0a73f594cb", "metadata": {}, "source": [ "Note that `xarray` works with \"lazy\" computation whenever possible. In this case, the metadata are loaded into JupyterHub memory, but the data arrays and their values are not — until there is a need for them." ] }, { "cell_type": "markdown", "id": "f72eb32d-8421-4f54-a2bd-7b8bc3dc531a", "metadata": { "tags": [] }, "source": [ "Let's print out all the variable names." ] }, { "cell_type": "code", "execution_count": 5, "id": "0f8beea9-bc30-4d02-9401-5b8605ad6847", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "time\n", "lat\n", "lon\n", "analysed_sst\n", "analysis_error\n", "mask\n", "sea_ice_fraction\n", "dt_1km_data\n", "sst_anomaly\n" ] } ], "source": [ "for v in ds.variables:\n", " print(v)" ] }, { "cell_type": "markdown", "id": "383e376f-149f-4d5f-aa2e-f292e951170f", "metadata": {}, "source": [ "Of the variables listed above, we are interested in `analysed_sst`." ] }, { "cell_type": "code", "execution_count": 6, "id": "f0241c11-ac86-418a-92e4-bb289468cc16", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "{'long_name': 'analysed sea surface temperature',\n", " 'standard_name': 'sea_surface_foundation_temperature',\n", " 'units': 'kelvin',\n", " 'valid_min': -32767,\n", " 'valid_max': 32767,\n", " 'comment': '\"Final\" version using Multi-Resolution Variational Analysis (MRVA) method for interpolation',\n", " 'source': 'MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, AVHRRMTB_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAF'}" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds.variables['analysed_sst'].attrs" ] }, { "cell_type": "markdown", "id": "f480119f-777f-42b6-ae2c-d9a5de1cb8a0", "metadata": {}, "source": [ "### Subsetting\n", "\n", "In addition to directly accessing the files archived and distributed by each of the NASA DAACs, many datasets also support services that allow us to customize the data via subsetting, reformatting, reprojection/regridding, and file aggregation. What does subsetting mean? To **subset** means to extract only the portions of a dataset that are needed for a given purpose. \n", "\n", "There are three primary types of subsetting that we will walk through: \n", "1. Temporal\n", "2. Spatial\n", "3. Variable\n", "\n", "In each case, we will be excluding parts of the dataset that are not wanted using `xarray`. Note that \"subsetting\" is also called a data \"transformation\"." ] }, { "cell_type": "code", "execution_count": 7, "id": "29355579-79f2-4ebf-b7d7-5a5c60a59d2e", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 29GB\n",
       "Dimensions:           (time: 1, lat: 17999, lon: 36000)\n",
       "Coordinates:\n",
       "  * time              (time) datetime64[ns] 8B 2020-01-01T09:00:00\n",
       "  * lat               (lat) float32 72kB -89.99 -89.98 -89.97 ... 89.98 89.99\n",
       "  * lon               (lon) float32 144kB -180.0 -180.0 -180.0 ... 180.0 180.0\n",
       "Data variables:\n",
       "    analysed_sst      (time, lat, lon) float64 5GB ...\n",
       "    analysis_error    (time, lat, lon) float64 5GB ...\n",
       "    mask              (time, lat, lon) float32 3GB ...\n",
       "    sea_ice_fraction  (time, lat, lon) float64 5GB ...\n",
       "    dt_1km_data       (time, lat, lon) timedelta64[ns] 5GB ...\n",
       "    sst_anomaly       (time, lat, lon) float64 5GB ...\n",
       "Attributes: (12/47)\n",
       "    Conventions:                CF-1.7\n",
       "    title:                      Daily MUR SST, Final product\n",
       "    summary:                    A merged, multi-sensor L4 Foundation SST anal...\n",
       "    references:                 http://podaac.jpl.nasa.gov/Multi-scale_Ultra-...\n",
       "    institution:                Jet Propulsion Laboratory\n",
       "    history:                    created at nominal 4-day latency; replaced nr...\n",
       "    ...                         ...\n",
       "    project:                    NASA Making Earth Science Data Records for Us...\n",
       "    publisher_name:             GHRSST Project Office\n",
       "    publisher_url:              http://www.ghrsst.org\n",
       "    publisher_email:            ghrsst-po@nceo.ac.uk\n",
       "    processing_level:           L4\n",
       "    cdm_data_type:              grid
" ], "text/plain": [ " Size: 29GB\n", "Dimensions: (time: 1, lat: 17999, lon: 36000)\n", "Coordinates:\n", " * time (time) datetime64[ns] 8B 2020-01-01T09:00:00\n", " * lat (lat) float32 72kB -89.99 -89.98 -89.97 ... 89.98 89.99\n", " * lon (lon) float32 144kB -180.0 -180.0 -180.0 ... 180.0 180.0\n", "Data variables:\n", " analysed_sst (time, lat, lon) float64 5GB ...\n", " analysis_error (time, lat, lon) float64 5GB ...\n", " mask (time, lat, lon) float32 3GB ...\n", " sea_ice_fraction (time, lat, lon) float64 5GB ...\n", " dt_1km_data (time, lat, lon) timedelta64[ns] 5GB ...\n", " sst_anomaly (time, lat, lon) float64 5GB ...\n", "Attributes: (12/47)\n", " Conventions: CF-1.7\n", " title: Daily MUR SST, Final product\n", " summary: A merged, multi-sensor L4 Foundation SST anal...\n", " references: http://podaac.jpl.nasa.gov/Multi-scale_Ultra-...\n", " institution: Jet Propulsion Laboratory\n", " history: created at nominal 4-day latency; replaced nr...\n", " ... ...\n", " project: NASA Making Earth Science Data Records for Us...\n", " publisher_name: GHRSST Project Office\n", " publisher_url: http://www.ghrsst.org\n", " publisher_email: ghrsst-po@nceo.ac.uk\n", " processing_level: L4\n", " cdm_data_type: grid" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Display the full dataset's metadata\n", "ds" ] }, { "cell_type": "markdown", "id": "8641b5ae-043e-4233-bdfe-dada8370dfd5", "metadata": {}, "source": [ "Now we will prepare a subset. We're using essentially the same spatial bounds as above; however, as opposed to the `earthaccess` inputs above, here we must provide inputs in the formats expected by `xarray`. Instead of a single, four-element, bounding box, we use Python `slice` objects, which are defined by starting and ending numbers." ] }, { "cell_type": "code", "execution_count": 7, "id": "e3b5b217-246c-4549-8e7d-773dec50bf40", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 2MB\n",
       "Dimensions:           (time: 1, lat: 201, lon: 201)\n",
       "Coordinates:\n",
       "  * time              (time) datetime64[ns] 8B 2020-01-01T09:00:00\n",
       "  * lat               (lat) float32 804B 33.5 33.51 33.52 ... 35.48 35.49 35.5\n",
       "  * lon               (lon) float32 804B -75.5 -75.49 -75.48 ... -73.51 -73.5\n",
       "Data variables:\n",
       "    analysed_sst      (time, lat, lon) float64 323kB ...\n",
       "    analysis_error    (time, lat, lon) float64 323kB ...\n",
       "    mask              (time, lat, lon) float32 162kB ...\n",
       "    sea_ice_fraction  (time, lat, lon) float64 323kB ...\n",
       "    dt_1km_data       (time, lat, lon) timedelta64[ns] 323kB ...\n",
       "    sst_anomaly       (time, lat, lon) float64 323kB ...\n",
       "Attributes: (12/47)\n",
       "    Conventions:                CF-1.7\n",
       "    title:                      Daily MUR SST, Final product\n",
       "    summary:                    A merged, multi-sensor L4 Foundation SST anal...\n",
       "    references:                 http://podaac.jpl.nasa.gov/Multi-scale_Ultra-...\n",
       "    institution:                Jet Propulsion Laboratory\n",
       "    history:                    created at nominal 4-day latency; replaced nr...\n",
       "    ...                         ...\n",
       "    project:                    NASA Making Earth Science Data Records for Us...\n",
       "    publisher_name:             GHRSST Project Office\n",
       "    publisher_url:              http://www.ghrsst.org\n",
       "    publisher_email:            ghrsst-po@nceo.ac.uk\n",
       "    processing_level:           L4\n",
       "    cdm_data_type:              grid
" ], "text/plain": [ " Size: 2MB\n", "Dimensions: (time: 1, lat: 201, lon: 201)\n", "Coordinates:\n", " * time (time) datetime64[ns] 8B 2020-01-01T09:00:00\n", " * lat (lat) float32 804B 33.5 33.51 33.52 ... 35.48 35.49 35.5\n", " * lon (lon) float32 804B -75.5 -75.49 -75.48 ... -73.51 -73.5\n", "Data variables:\n", " analysed_sst (time, lat, lon) float64 323kB ...\n", " analysis_error (time, lat, lon) float64 323kB ...\n", " mask (time, lat, lon) float32 162kB ...\n", " sea_ice_fraction (time, lat, lon) float64 323kB ...\n", " dt_1km_data (time, lat, lon) timedelta64[ns] 323kB ...\n", " sst_anomaly (time, lat, lon) float64 323kB ...\n", "Attributes: (12/47)\n", " Conventions: CF-1.7\n", " title: Daily MUR SST, Final product\n", " summary: A merged, multi-sensor L4 Foundation SST anal...\n", " references: http://podaac.jpl.nasa.gov/Multi-scale_Ultra-...\n", " institution: Jet Propulsion Laboratory\n", " history: created at nominal 4-day latency; replaced nr...\n", " ... ...\n", " project: NASA Making Earth Science Data Records for Us...\n", " publisher_name: GHRSST Project Office\n", " publisher_url: http://www.ghrsst.org\n", " publisher_email: ghrsst-po@nceo.ac.uk\n", " processing_level: L4\n", " cdm_data_type: grid" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_subset = ds.sel(time=date_start, lat=slice(33.5, 35.5), lon=slice(-75.5, -73.5)) \n", "ds_subset" ] }, { "cell_type": "markdown", "id": "210377ad-4dd0-401d-aad8-0d1760941daf", "metadata": {}, "source": [ "### Plotting\n", "\n", "We will first plot using the methods built-in to the `xarray` package.\n", "\n", "Note that, as opposed to the \"lazy\" loading of metadata previously, this will now perform \"eager\" computation, pulling the required data chunks." ] }, { "cell_type": "code", "execution_count": 8, "id": "d97b5757-eb9e-4816-b2c8-5516823080ae", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ds_subset['analysed_sst'].plot(figsize=(10,6), x='lon', y='lat');" ] }, { "cell_type": "markdown", "id": "566d6593-7d61-4a8a-8873-e313b2474b5a", "metadata": {}, "source": [ "## Create a data cube by combining multiple netCDF files\n", "\n", "When we open multiple files, we use `open_mfdataset()`. Once again, we are doing lazy loading. Note this method works best if you are in the same Amazon Web Services (AWS) region as the data (us-west-2) and can use S3 connection. For the EDM workshop, we are on an Azure JupyterHub and are using https connection so this is much much slower. If we had spun up this JupyterHub on AWS us-west-2 where the NASA data are hosted, we could load a whole year of data instantly. We will load just a few days so it doesn't take so long." ] }, { "cell_type": "code", "execution_count": 9, "id": "5c8222e6-df93-496b-ac47-0dcff427fb33", "metadata": { "tags": [] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bfcdd8b150b84f27a2e385be2611fcc2", "version_major": 2, "version_minor": 0 }, "text/plain": [ "QUEUEING TASKS | : 0%| | 0/6 [00:00\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset> Size: 143GB\n",
       "Dimensions:           (time: 5, lat: 17999, lon: 36000)\n",
       "Coordinates:\n",
       "  * time              (time) datetime64[ns] 40B 2020-01-01T09:00:00 ... 2020-...\n",
       "  * lat               (lat) float32 72kB -89.99 -89.98 -89.97 ... 89.98 89.99\n",
       "  * lon               (lon) float32 144kB -180.0 -180.0 -180.0 ... 180.0 180.0\n",
       "Data variables:\n",
       "    analysed_sst      (time, lat, lon) float64 26GB dask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>\n",
       "    analysis_error    (time, lat, lon) float64 26GB dask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>\n",
       "    mask              (time, lat, lon) float32 13GB dask.array<chunksize=(1, 1447, 2895), meta=np.ndarray>\n",
       "    sea_ice_fraction  (time, lat, lon) float64 26GB dask.array<chunksize=(1, 1447, 2895), meta=np.ndarray>\n",
       "    dt_1km_data       (time, lat, lon) timedelta64[ns] 26GB dask.array<chunksize=(1, 1447, 2895), meta=np.ndarray>\n",
       "    sst_anomaly       (time, lat, lon) float64 26GB dask.array<chunksize=(1, 1023, 2047), meta=np.ndarray>\n",
       "Attributes: (12/47)\n",
       "    Conventions:                CF-1.7\n",
       "    title:                      Daily MUR SST, Final product\n",
       "    summary:                    A merged, multi-sensor L4 Foundation SST anal...\n",
       "    references:                 http://podaac.jpl.nasa.gov/Multi-scale_Ultra-...\n",
       "    institution:                Jet Propulsion Laboratory\n",
       "    history:                    created at nominal 4-day latency; replaced nr...\n",
       "    ...                         ...\n",
       "    project:                    NASA Making Earth Science Data Records for Us...\n",
       "    publisher_name:             GHRSST Project Office\n",
       "    publisher_url:              http://www.ghrsst.org\n",
       "    publisher_email:            ghrsst-po@nceo.ac.uk\n",
       "    processing_level:           L4\n",
       "    cdm_data_type:              grid
" ], "text/plain": [ " Size: 143GB\n", "Dimensions: (time: 5, lat: 17999, lon: 36000)\n", "Coordinates:\n", " * time (time) datetime64[ns] 40B 2020-01-01T09:00:00 ... 2020-...\n", " * lat (lat) float32 72kB -89.99 -89.98 -89.97 ... 89.98 89.99\n", " * lon (lon) float32 144kB -180.0 -180.0 -180.0 ... 180.0 180.0\n", "Data variables:\n", " analysed_sst (time, lat, lon) float64 26GB dask.array\n", " analysis_error (time, lat, lon) float64 26GB dask.array\n", " mask (time, lat, lon) float32 13GB dask.array\n", " sea_ice_fraction (time, lat, lon) float64 26GB dask.array\n", " dt_1km_data (time, lat, lon) timedelta64[ns] 26GB dask.array\n", " sst_anomaly (time, lat, lon) float64 26GB dask.array\n", "Attributes: (12/47)\n", " Conventions: CF-1.7\n", " title: Daily MUR SST, Final product\n", " summary: A merged, multi-sensor L4 Foundation SST anal...\n", " references: http://podaac.jpl.nasa.gov/Multi-scale_Ultra-...\n", " institution: Jet Propulsion Laboratory\n", " history: created at nominal 4-day latency; replaced nr...\n", " ... ...\n", " project: NASA Making Earth Science Data Records for Us...\n", " publisher_name: GHRSST Project Office\n", " publisher_url: http://www.ghrsst.org\n", " publisher_email: ghrsst-po@nceo.ac.uk\n", " processing_level: L4\n", " cdm_data_type: grid" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds" ] }, { "cell_type": "markdown", "id": "c5287526-b0c3-4f88-a745-969adc5ebc0f", "metadata": {}, "source": [ "We can subset a spatial box as we did with a single file." ] }, { "cell_type": "code", "execution_count": 11, "id": "a01c2fda-d3bb-4648-ba05-fef223c9d56d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset> Size: 9MB\n",
       "Dimensions:           (time: 5, lat: 201, lon: 201)\n",
       "Coordinates:\n",
       "  * time              (time) datetime64[ns] 40B 2020-01-01T09:00:00 ... 2020-...\n",
       "  * lat               (lat) float32 804B 33.5 33.51 33.52 ... 35.48 35.49 35.5\n",
       "  * lon               (lon) float32 804B -75.5 -75.49 -75.48 ... -73.51 -73.5\n",
       "Data variables:\n",
       "    analysed_sst      (time, lat, lon) float64 2MB dask.array<chunksize=(1, 201, 201), meta=np.ndarray>\n",
       "    analysis_error    (time, lat, lon) float64 2MB dask.array<chunksize=(1, 201, 201), meta=np.ndarray>\n",
       "    mask              (time, lat, lon) float32 808kB dask.array<chunksize=(1, 201, 201), meta=np.ndarray>\n",
       "    sea_ice_fraction  (time, lat, lon) float64 2MB dask.array<chunksize=(1, 201, 201), meta=np.ndarray>\n",
       "    dt_1km_data       (time, lat, lon) timedelta64[ns] 2MB dask.array<chunksize=(1, 201, 201), meta=np.ndarray>\n",
       "    sst_anomaly       (time, lat, lon) float64 2MB dask.array<chunksize=(1, 201, 201), meta=np.ndarray>\n",
       "Attributes: (12/47)\n",
       "    Conventions:                CF-1.7\n",
       "    title:                      Daily MUR SST, Final product\n",
       "    summary:                    A merged, multi-sensor L4 Foundation SST anal...\n",
       "    references:                 http://podaac.jpl.nasa.gov/Multi-scale_Ultra-...\n",
       "    institution:                Jet Propulsion Laboratory\n",
       "    history:                    created at nominal 4-day latency; replaced nr...\n",
       "    ...                         ...\n",
       "    project:                    NASA Making Earth Science Data Records for Us...\n",
       "    publisher_name:             GHRSST Project Office\n",
       "    publisher_url:              http://www.ghrsst.org\n",
       "    publisher_email:            ghrsst-po@nceo.ac.uk\n",
       "    processing_level:           L4\n",
       "    cdm_data_type:              grid
" ], "text/plain": [ " Size: 9MB\n", "Dimensions: (time: 5, lat: 201, lon: 201)\n", "Coordinates:\n", " * time (time) datetime64[ns] 40B 2020-01-01T09:00:00 ... 2020-...\n", " * lat (lat) float32 804B 33.5 33.51 33.52 ... 35.48 35.49 35.5\n", " * lon (lon) float32 804B -75.5 -75.49 -75.48 ... -73.51 -73.5\n", "Data variables:\n", " analysed_sst (time, lat, lon) float64 2MB dask.array\n", " analysis_error (time, lat, lon) float64 2MB dask.array\n", " mask (time, lat, lon) float32 808kB dask.array\n", " sea_ice_fraction (time, lat, lon) float64 2MB dask.array\n", " dt_1km_data (time, lat, lon) timedelta64[ns] 2MB dask.array\n", " sst_anomaly (time, lat, lon) float64 2MB dask.array\n", "Attributes: (12/47)\n", " Conventions: CF-1.7\n", " title: Daily MUR SST, Final product\n", " summary: A merged, multi-sensor L4 Foundation SST anal...\n", " references: http://podaac.jpl.nasa.gov/Multi-scale_Ultra-...\n", " institution: Jet Propulsion Laboratory\n", " history: created at nominal 4-day latency; replaced nr...\n", " ... ...\n", " project: NASA Making Earth Science Data Records for Us...\n", " publisher_name: GHRSST Project Office\n", " publisher_url: http://www.ghrsst.org\n", " publisher_email: ghrsst-po@nceo.ac.uk\n", " processing_level: L4\n", " cdm_data_type: grid" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_subset = ds.sel(lat=slice(33.5, 35.5), lon=slice(-75.5, -73.5)) \n", "ds_subset" ] }, { "cell_type": "markdown", "id": "bf91e50d-b206-48a2-9f16-b56aaad5f6f2", "metadata": {}, "source": [ "We can subset a slice of days also." ] }, { "cell_type": "code", "execution_count": 12, "id": "3e1fe9a2-2330-42e8-bc2e-a057adfd7a8a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 7MB\n",
       "Dimensions:           (time: 4, lat: 201, lon: 201)\n",
       "Coordinates:\n",
       "  * time              (time) datetime64[ns] 32B 2020-01-01T09:00:00 ... 2020-...\n",
       "  * lat               (lat) float32 804B 33.5 33.51 33.52 ... 35.48 35.49 35.5\n",
       "  * lon               (lon) float32 804B -75.5 -75.49 -75.48 ... -73.51 -73.5\n",
       "Data variables:\n",
       "    analysed_sst      (time, lat, lon) float64 1MB dask.array<chunksize=(1, 201, 201), meta=np.ndarray>\n",
       "    analysis_error    (time, lat, lon) float64 1MB dask.array<chunksize=(1, 201, 201), meta=np.ndarray>\n",
       "    mask              (time, lat, lon) float32 646kB dask.array<chunksize=(1, 201, 201), meta=np.ndarray>\n",
       "    sea_ice_fraction  (time, lat, lon) float64 1MB dask.array<chunksize=(1, 201, 201), meta=np.ndarray>\n",
       "    dt_1km_data       (time, lat, lon) timedelta64[ns] 1MB dask.array<chunksize=(1, 201, 201), meta=np.ndarray>\n",
       "    sst_anomaly       (time, lat, lon) float64 1MB dask.array<chunksize=(1, 201, 201), meta=np.ndarray>\n",
       "Attributes: (12/47)\n",
       "    Conventions:                CF-1.7\n",
       "    title:                      Daily MUR SST, Final product\n",
       "    summary:                    A merged, multi-sensor L4 Foundation SST anal...\n",
       "    references:                 http://podaac.jpl.nasa.gov/Multi-scale_Ultra-...\n",
       "    institution:                Jet Propulsion Laboratory\n",
       "    history:                    created at nominal 4-day latency; replaced nr...\n",
       "    ...                         ...\n",
       "    project:                    NASA Making Earth Science Data Records for Us...\n",
       "    publisher_name:             GHRSST Project Office\n",
       "    publisher_url:              http://www.ghrsst.org\n",
       "    publisher_email:            ghrsst-po@nceo.ac.uk\n",
       "    processing_level:           L4\n",
       "    cdm_data_type:              grid
" ], "text/plain": [ " Size: 7MB\n", "Dimensions: (time: 4, lat: 201, lon: 201)\n", "Coordinates:\n", " * time (time) datetime64[ns] 32B 2020-01-01T09:00:00 ... 2020-...\n", " * lat (lat) float32 804B 33.5 33.51 33.52 ... 35.48 35.49 35.5\n", " * lon (lon) float32 804B -75.5 -75.49 -75.48 ... -73.51 -73.5\n", "Data variables:\n", " analysed_sst (time, lat, lon) float64 1MB dask.array\n", " analysis_error (time, lat, lon) float64 1MB dask.array\n", " mask (time, lat, lon) float32 646kB dask.array\n", " sea_ice_fraction (time, lat, lon) float64 1MB dask.array\n", " dt_1km_data (time, lat, lon) timedelta64[ns] 1MB dask.array\n", " sst_anomaly (time, lat, lon) float64 1MB dask.array\n", "Attributes: (12/47)\n", " Conventions: CF-1.7\n", " title: Daily MUR SST, Final product\n", " summary: A merged, multi-sensor L4 Foundation SST anal...\n", " references: http://podaac.jpl.nasa.gov/Multi-scale_Ultra-...\n", " institution: Jet Propulsion Laboratory\n", " history: created at nominal 4-day latency; replaced nr...\n", " ... ...\n", " project: NASA Making Earth Science Data Records for Us...\n", " publisher_name: GHRSST Project Office\n", " publisher_url: http://www.ghrsst.org\n", " publisher_email: ghrsst-po@nceo.ac.uk\n", " processing_level: L4\n", " cdm_data_type: grid" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_subset_time = ds_subset.sel(time=slice(\"2020-01-01\", \"2020-01-04\"))\n", "ds_subset_time" ] }, { "cell_type": "code", "execution_count": 13, "id": "1990f6ce-28cc-4617-a837-c31abbf53cc9", "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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3PH3DDTf0/v62VwzjwJNnHKC/q/S69957W+0N48CTZxwYMDdQWjfe1wMGDBgwYMCAAQMGzCMGH9MBAwYMGDBgwIABCwIDMR0wYMCAAQMGDBiwIDAQ0wEDBgwYMGDAgAELAvNKTC+77DIceuihWLZsGZYtW4a1a9fiC1/4gt1/xhlnQCnlvY466qhkm1SuLXw98cQTc307AwYMGDBgwIABA2aBeU2wv8cee+DCCy/EfvvtBwD4u7/7O5x88sm47bbb8OxnPxsAcOKJJ3pVL6ampjrbXbZsGb73ve9520bJOzdgwIABAwYMGDBg22FeienLX/5y7/MHP/hBXHbZZbjpppssMV2yZElnPeoQSqne5wwYMGDAgAEDBgyYXyyYkqRVVeHTn/40HnvsMaxdu9Zu37BhA1asWIGnPOUpeNGLXoQPfvCDthpIDI8++ijWrFmDqqrw3Oc+Fx/4wAdw2GGHZfelrmv86Ec/wtKlS4d6vwMGjAFaazzyyCNYvXp1qxrQQsUwDgwYMF7M5zjwxBNPYOvWrXN6jampqcE6OwbMex7TO++8E2vXrsUTTzyBXXbZBZ/61KfwK7/yKwCAq666CrvssgvWrFmDe++9F+973/swMzODW265BUuWLBHbu+mmm/Cf//mfOOSQQ7B582Zceuml+PznP49vfetbeOYznymes2XLFmzZssV+/uEPf4iDDjpo/Dc7YMCTHD/4wQ+wxx57zHc3RAzjwIAB2wbbehx44oknsM+aXbDpwfGXEeZYuXIl7r333oGczhLzTky3bt2K+++/Hw899BCuvvpq/O3f/i1uuOEGcULYuHEj1qxZgyuvvBKvetWrstqv6xrPe97zcMwxx+DDH/6weMz555+P97///a3txz71NExM7ABMNMJyocz7sgTKAnqiAArzry7NC4VCPaHM5wmFulBAqVCXgC6N6lKXCnoC0IWCLmH36RLQRfO5aF6le9UlAGW2A3yfBuhYpe1nlOa9KjRQmH9VoVGUtdkGoCg0ikJjoqxRFBqTRYVCaZRFjcmiQlnUmChqlEpjqqwwoWpMFBVKVWNCVZhUNQCYY6BRFhUmlXkVSmNCmWMnVY1CmWOK5pwCNQpl+lHCbCvtPo1S1eaYZn+hakypCgVqTKoZTKrKtNccW8K0pcI24f/E3TX9f4tGFCuC42so1BqooDCtC2zRE3hCT2CrLrFFT6JGgUqrpv8aBWrbd34vdC3e34nmHibt56Yv4i8VTYuuX/Z90+WKbaP3tW5vq1BAa4UKCpUuMI0ClS5QQ2FaT2Bal6hQoNaF+Rdo7rOw16b35jiFGT1hr+G+U9PjJx6dwfnH/RseeughLF++PHJ384vYOPA/ipdjcnJHoCygJiehyglgojSvsjBjQlFCT5TARAE9WTbPM40NCvWE+a7Mdjce8LGgnqDnXLlnuwDqYBww7bAxwjwg0KV2x0njQKkBpd2zX2qoooYqzfNeFMBEWaEsNCZUjaKoMVk2z3pRY7IZCyaK2o4DgHnOJouqGQ/cGFAojUlVRccA+3wo97yVzbMD+M+Ne5aa69Hzr2pMoMKUquw5U6rqHAM4/Ov7x/HP7tlxz1UF5T1LMVA7dIx5vhSmdYlpPdEaQybVTDPOVfb8cJwD3FgHAFq7/tGzXLHnlD/b03oCFQrMoMC0LrFVT2BGl6h10fQNqOn5bv7VTd9Vc33NxxptxgDTdomqNvc3U/PxQmHLozO4+hX/Z5uPA5s3b8by5cvx/Vv2xrKlc6PUbn6kxprD78PDDz+MZcuWzck1niyYd1P+1NSUDX464ogj8M1vfhOXXnopPvaxj7WOXbVqFdasWYO77747u/2iKHDkkUcmzzn33HNxzjnn2M+bN2/GnnvuiQk1hYliCiiImBbmPRHTsiGmZQFdlg1RDYhpGSemNAGpgJgqNinxCUmVMASUEdM6JKZFgpiWjpgWHjGtUTbbysJMTIaQOmI6ocwkRRMS/euIqSFYE0WBSWVeZmIqmkmpahHTMpOYlkqjhEKhFKYUUEBhSmlMKhqwa5SKE0xqE82//YhpeHwVENMJXaDUJSZ1iVKXAjFVKJVC2Qzc5l6UbZv3d1Lp5l/32ZwjgxPTapbEtPaIaWkns600uTBiWmnVSUynE8SUsJBN4vFxYBITagpQBZSahCoMEUUx4Y0J2o4LpVusSsR0ok1M61JBTdBzrvznPkZMWwtYRkylcYCIaekT08ISU42ydM9/WdSGqFpiWonEdEJxYlrbMYCe/9gYUI5ATGksmKTnXylMApiyi8oaU0p3jgEc4yCm9SyI6VZhDJm09+nOD8e5sO91i5gWLWJKzzY939O6xIQuUTaEtW7GNjPm+cS0ZuMZ/0zH1Fqh0CUKXWKmIaZFXdpjK+3anK9xYJelCrssnZtr14m//4B+mHdiGkJr7ZnTOH7605/iBz/4AVatWtWrvdtvvx2HHHJI9JglS5bIrgETJTA5aVQRoFFGCmDCKCKgCYhU0wnVqKCOlOoJppA0zIeTUq6W1mzy0aqtltJERKylZpMRSt2co81oXLQnIz4R2YG7pAnITAhloTHRENCyqJlKWouklCanSVVhsqjtKn+yqFBAY7KYYRORT0r7gCYyM/HMmEkLTp00bfsTnPnXJ6tuf5uUxiavEhpQCpWmvmgUuoab9kYD9cn922xnx8S+JTc5+p/9fUrY5xNSPnkROeWKChFSwE1Q8fupzcQWkNK+f+v5QmwcUA3hVJOTUBMTZjwomVpaMrWUxoPCEFKjhBZ2URojpc5a4pPSUC1tKab2X4GUhuOAtZgQMTWWEyKlVi1tSClXS6cEUjpVzACAVUIn7bjgK6bSGMB/E/Q7LZRGhQLQboFa6cK+J/DFziQqzzox1Vy36/kPEY4HsWNo0ZUiobMBWVyAtuUmhERKJdR8MYpmoYnCEtStjJSG9xWSUu/60HZ7qWrUujR90n6fBsI2oC/mlZi+5z3vwUknnYQ999wTjzzyCK688kps2LAB69evx6OPPorzzz8fr371q7Fq1Srcd999eM973oOnPe1peOUrX2nbOP300/GMZzwDF1xwAQDg/e9/P4466ig885nPxObNm/HhD38Yt99+O/7qr/6qfwcnyWTHFNPQhG+JqSOkuhBIaWlMdUBDRIsEKS3cZxQ+KfUmp4KR0sJ8JpMeKaR8MuKktCybgZuR0gkiqaSO2kkoTkrJ1DRZ1I1ZuhZJqTNJdZOUtlpaN0oKbef7ZVLaRUj5+xQpDc+t2MAL0IDcbRqSJthaK5TCmF3DkFPpm0qppDFCWjGqS6TUEE9u8ivMNq1apFSanHJIKrB4SGkSEyUULVCJjNK4EJJSWqx644EhpACipNRaVNgzLpFS9+yPTkqL5tknUmrceGgc0IaQFubZ8hamRY2pYka0lnhEVFiYOrXUV0k5oaq18sgpmmcdMG4kBfst0VhQqtqS0xA5pFQaD8w57phaON0sVtGMBXUja5t9IblLjS2VXfSNTt5Ci0i44KRrTDfuR86c3yal3r/sGY+RZK6eFkqj0uZvMxMcs1DIaaVrKzDMRdsDxoN5JaYPPPAA1q1bh40bN2L58uU49NBDsX79erz0pS/F448/jjvvvBOf/OQn8dBDD2HVqlU47rjjcNVVV2Hp0qW2jfvvv9+L7nvooYfwe7/3e9i0aROWL1+Oww47DF/96lfx/Oc/v38HpyaBCaeY6qIwExD9S35kZaCSshcnpc6UH/iS8QmoCEiqqKA2Pj40+RTcnO8mIlh/UlkdAeApJKVoriO1pGqZ7Apl/M4ANNsqz2RnfUADhSQXREoLVVu/q5KR0XAySl0jZqLjpDSkWrHWXB/qhug1ExNDpQu4sbhOqh+GoIZ+rT66CGnMXE/t831ESsmcZyeyhpQ60+LohBTYTkgp4CulnJQ2C1WUyowJ3HISjgfW/SZOSu17wa+Ujw+mncbnvJBJabg45eMAEdNC6WxSyseAmLWEL0xjY4BEEvnCCVo+JlykFrRYDfwuc0lon32Fcs8cN8lL5LTWKtkW4J4h93wVbJ/ySHEKoUqasoJQ2zmkNAaJ/PP74DA+xApA3sJ9wACOeSWmH//4x6P7dtxxR3zxi1/sbGPDhg3e54svvhgXX3zxbLtmUJTGbN/4h6Exz9kJqFDWh8xOONaHrFFKGCmtmRkuabILJyVBHTHtmIHRTEa68U8176HSpLRsfExjpNSa7phS6vuRVSib4CYAnaQ0PWG0/cpo4qGAJyKlLuCJBRNF2g59ScP34QRASmUKJTTqxo1gEhUqNEqo9icYCYasNlexilCBEpWZ6LRxGZD82gCfkHaZ68N9fBJ0vm0u6IL7k/rfSZyUSsTXfU/bBykFYIKdOCktSufS06ikpJiiaNx5JtrjAYBmX4YrT4SU0rM/G1JKz0WMlE4WVXRhmmMtCceASTXjmdcJ4W8thGQ5oXanmuCngllHQnBf53HDazdUTgWkTO2ECoVZ7Db/9gF/xj1LSOOaA4BZRGRSmgInpXzxWTeWoJp+mB2Yb+W0bsbvuWp7wHiw4HxMFxL0VGkibEmRJWXEU0obH7KGhPIJqZ6AI6cln1S6/ciigQ2kjqJRT5rJxymlMJG2zcRUULCToI4AyPIlk0gpN90BMWLaDm6SwAc9moimmnZipDQ04fP2k76iDWKqRGo6KBQAbcz5k0QmG9XUTU6xdhUK6BY5Ne4BCtBo/jXktHW+lhXSmLmewFUU+swnppCQ5poUOSntIhiLHqSQEimdKEVSqsvCBT4WqjUeAECdQ0ql8aBgKim4haRNSmMWEz4OAM6/fFJanHqKqUxKY9YSaQzgi09CTH23FgmBlE4xUjrZROO33XX6L4pCEkuLQGmc8LZRUhtOTlttm0Cw8Lkk4lixZ6iihWpD+CS6J6ma3sKTueeQTykAa8YnstqllKZ8e8nPlvzKc2COm9t0TQO2DwzENAUbad+Y8hti6gU5MVNdi5QyQkqTEeBPPElSGjPX0ThglVLfj4yi70umlJZN9D1XRwBYs11fUtoipoIvWWxCCsHN3DQRhaQ0FezUP5Aq87jmXxPm5AIeuGpqtuvkcOuUkObCjJxupVtXVWP6AqC17WPKbJ8y2dN+btoD4MhoM2FJKqn/HcT9w3JJqblO52ELF2Xpm++7SCkLfOTjAQA7ViT9yyfiY4HvxpNBStnilMYB7l8+yQhpmbCWSKRUspaEpJT20TPuBTVZ30yuxpFbDR3vk9LJJvCRk9LY89+llvZRU6Uxg4IiQ3IqkTz7zFq3maL1HBIq1PZ6RDjL5ruWfNXDhSf3GScyChjSSmb8XPO9u//2wp+PiYUwBpZs7JiND+04UaOeM1vO3LX85MN2LnUMGDBgwIABAwYMWCwYFNMErCrC/cNKQS2lFDBFWi3l/mGSya62KoivkEjqCABnwo/kKeVqKZntuC8ZgKzI266oWwCeWkpqiaSUAG0THk/3IgU4eAnqM9RSybcszFXKt3XB9dYpJGTO32rSIjTqT9w8ScoiKae1LlFBY0o1CgmKxseULuWCLPh9he9z1VKec9QGOAlqaam0CcBg90Oqaanq6P2R4kPKStjuXKXX2SaYKGatloaBj13ZOGJqqXXlC9LDWbWU+5dH1FJ69merltpMGxlqKX/GAbDfuR1eLVpJ9ZU/Hlgzv5AWKnxGSmgW7a88ta8PulLJQfsWFdHcDuV8vFGKz2GJurGwmOwERaNDmvZMZoLwGQzddCigkfKGklpp96FbLQ3zD6e+gz7Q8zwOVFqjmqOaQnPV7pMRAzFNQDdVm1z+0cZ3rAlucEEOZKJLk1Ie/CSR0tb2VDooIJ4SqqnmRKb7gghqEHULICvyNpWjlFcm4UEO0QkJkUGPEU2Kwvf2s4CnXNCE5K7r2kqBD/ulNanR9iZASRtz/pQytNIQ1Bpd4y6fgMxnhaLJ/Um+plIbYVJv3pYYkR8EQkzDD4DI8Qsr+P00fmshaCKN+ZrRtnoxD9oSKS0Ve+bl3MXOLC8HPo5ESsmUn0FKQ1eesiQSyvzLG1Iq5SueKirrxuNyktaeTymAFinlC1MboBQsJCvmlhNbqJaceApJ8Hm6NSn4z/w2fXI6CqTxwu+xGxOgwufRzxtsySMnjnDkEcqlxfLM9sz9h/ZzQkrHEymlXMTclE9J8Gvtnzfqd5CC8alF008/UHPAgC4MxDQFS0IbShJE3PKKTo6Axkmp5GMaJaVWKWXpoHjkPRCNvqc8pbaaSxDgQCopgGxSmoq6BWBJqU1+zyYXIN8P1FNHIqt2PsFIPo58QpLO967X/FsHnwFfySkVvPx3lNO0gEm2Xypl/oANUeuCOaYGdGFUykY1NdsgktNwwuPbvIlZCoRgKWN4oEWrX8HkUTRRwiHIP5D3KVxw+H5sM2ETiwdKIKWsshN4FH6Qu1gMfJxIkNIJJEmpTRdVtEkpD3rkpJSXG6YCGgDEIhqx1HA8Cwf3KQUgklKnlvrBSwSjfppgmEqIZLcLVB7UqNxibrLZ1soiwfOiokn4Tm00z1VoPZH8n8Wgp/YmEVQNivoTpm4KE91TvmBLOHVh/UypnVA59a7V/Chc9TbXtvkLuIUsV0tz4AeMtp/vXLgF6hCVP6AbAzFNgJvu7ecYKaV61wlS6uUglAIcwgh8moiEyHsgHn1fsAmJqjlJUbcAXLBThtlOSgc1qQzhiJFSaYLhCBVEAC21tC8o2CiFUZ2rSTW1FU8aAl03igalTeEqkOTGUKramuq4agplonJjCo+fPqqDlLIJiZPL2IRCpLS2k2GblPLgFQ6pTW4unK4X8VBDKeMoI4dQblQsqDHpjwdAMB70VEp5Rg4UAillQY+hxYSTUnr2wyIaKVLKXXm46R6A776TIKVUJINQNc+oRDJDpTSshmTUxKodCBiqr43rjLHk+OE5uYQ0B1Yd1L462KqqBl/J9IpYKAC6blTTogmuJHVUNYvYOvK8O9cd6x6gKerfkcJaF4hG4Y+RWEkEtO5BiAc8ubGIZ4ttAKWsSgrAmu97k9IJNwEBwSRUSoQULvqelFLuS9qY4lLR92WhLSnlJUb5RASg5VO6pJhJTkZSfkIAlpROqpnkZBSa42Uzmew3Kg1p0gBI1WP4RMQnnNTQyHOZVoL/mwTjD2bOlCL0Jb9MMtO5CYdUU98XzvZLUEvN+/aEzP1KuVoanuv3p116dNQ0UM7twJHSmUVcFaVV6S0gpVoipUwV9TJypEjpBLpJKUsVl0NKY1XdAETTQnVl4TD+nU4BJQI1yRawPOUbjQFtlx7/e55sngBAHgNC8N8+N2v7JnAYE7hNxwbjOx3xN5XSRFl3AMhFOMLKa97zyaLweXooqyDCEUdOTksUzIqCxoJSO3XDXt9ck+ckrnXRqvBE/YqZ7+ci36urIKUaUqwWhGJaDYrpgsdATBMwyfRdDsLQp9RVboqQ0on2BGTazSClHTXvAXglRiV/UlJJJVI6VdDkZCajJeVM52TkUkE5UjpFimmClLrAJsk031bgUumlKjSTC/9s2xotPUx4NW7W71O+LjboRxPvN5OkpJpak34Enq8pm+SoHzyXYWe/A1KaSoKdqvzECSl9nq5pclzE/mUjkFIX3BQEPgpjQh9Squ2zD4+UFpaQOjeekJTyAhoAsopoxEgppUoDmio/vESozV/qjwE2gDFBOrt+rzWUjf3kv/utzRfsUiW5gEQip9xVhgdD8aAlex0dJ6d+f5XNMUzPJH82rYIJ5cgjM+G3nreGnBof3ML1ScMtZIXvi1du4yZ8upa5p7haKmHcBQpqbWwwAwZ0YSCmAwYMGDBgwIDtHoOP6eLAQEwTMJGvrM41+YFSEFQq2CmijAD5aikppWTC50FOALwyo/Qv+ZLxQKeJoqn4EpjtACTVUh5978qLaquQkPkegFVLXT37uEpC5/DVvwtsiCsmNQpnmmvgKXjMfEf+b+ba3JUgH12muxgoUp1DTDCtCquEcHO+lEIHcEEVPKDC9ElWSv0gKD9ISfQJ5Sb/Dl8w1w+nwnA1ptbOfDmtS8wsYsW0Sy2tJ9pqKa/wROMBMHu11AY+FhhZLXXWEh6B77vwxLJwcJWU4NK7Nc88XEJ8bi2hMaBITOAU5MMDdgA/Qp1M3tSXrbq0WSd8s37Tx0Y1Da0RYQqpENwHtVBoKatcLeVmfBN5P+GOYWop9y2d1hMtVxs0YwEFh9E98EAol9mgCXpq2iN1lMz3vOyoOa5sWTWkDCnUb/rXZUHo79pDfqVDZP6APhiIaQpF28e0q7JTipR6xLSg4yKTUOhDFgQ5me65MqPcpzQkpdyEz812ADxS6t53172niYiCn2KkVJqMvBQoDXgwEZAOYAr9KU37QTWUwMc0BjorFgwQM91tZcTM5icMzOdRE34E1pyvKtio/c7++3lKOSklQtqOXE6Rf9Vhrpf3eeSUTYTkVzZdL96gB00liEclpfzZny0pJZdDTkpLl4ljomyCnCjYMVJuGDCL0qliJjsLB1+cAo7UcDM+LyUcGwO6KsHRF+WqBjWmaW2I2pSagUuf5IOb9acaMldo7flw22OD/Ka5JBVKeRXZWmZ8tkDjldZipNR7pjRQqPa4RYtyCpqk78X8yxaILOCJgp2cj+lowUex78ULxAzGz1r7GTsWgn8pgCGP6SLBQEwTqCcKqMavFCCllCaS7gj8VrQtI6a5k1AssAFAK3F+yqdU8iUDIJJSMzm1VVJOSom8UmATJ6X0HjCTUWwi8rYzYgXIfmgh+fQizUlNBRp/su5BIkVK+Xbuh8ZJ6VaUdiLKhZdjUSsx4b/1o1U+OReDKgSVlBNS5+8ZD3rq6isHXzgUSrd8cHmQR62VVW9mFvGgLZHSelL2KxVJ6YRbg82OlPqBj7mkVCqgAQC5WTg4MQXgjQUA2CJVJqUhIU35LaZ+z+bGC2zVE46cwl9M2cwSusBWalQ1fWx8uEuVR7aifQx83K0FI1ikmv3tZPdRUip+HzReuJKkBKcMF2LAE70PrRiAP/bOReATh81UgIVBTgcsfAzENAFdoInEN5+9SajoT0ptAETuJMQrObEJiCq3pEz3nJSSGT8McACQJKWxuvdENinIAUCLlHrVmjoGvgrKU1H56pvaKYPJrWrMXX1VSYJESsP0MbZeNSOlNBFZUipE5HJzoumrEK0rKMJh5oFaq8b06G8L08WEkzivJiMRz9mm4wKcu0Ip/A1apLQusVXKzbNIUE8WUBPtBPp9SGkrI8copLR0xJTGBHLhmSirxnzvF9GI5SgFkCSlUsAjT/vGqy5NNjXsvYptjJRK44AUdR97lj3Vv0mnRBaFcAFassWSzQcKk6aJ5wu2aZiEqlD2uh0klRNSem+rOTG1N0x2z0kpETU+vrrr82T77bHAU0qFgCen5voKK73n1+Im/VpT5al8ElkI2Uhsv+GsNPNNTGvk2KFGb3vAeDAQ0wRctL1TTLkJvxcp9SanvEnIVnIKJiBbuUWl00GFhDSciAC0JiOq5iSppBIplUz5IZnsTv3iTF/kb0mwJUiDQdtLBwNfAZhkE2gIyW8UaJNS2hYm4fYrzCiPhPJcgiEhTCkjvPQg96Ptq2SEpJRMeBxFo9T2Iady2Vf/fgpVo2LXsxOlVpjWBaYX8agtVXXy0kJNtCPwkxk5RiSlNB4AsGMCufKEpJTnKw7TQZF/eQ4pDRenBE6iyHJi3X5UjUm4cqXmeD9dVKq8rQSe11QaT+g6hlTVtkCFWTyZ9HFbddn4bjpf07BkaYgUkeKklPxcyXRPPqb0PKZIaexe6H4AuByn3vULbwEamvBtFH5gjSo85bt7HBiXojrfpHTA4sFATBNwk1Cz6pv0Tfj+5OI+i6S08ScFIE9CE24S4kFOlDSf+5KSj1gf072ZNPyJCID1JSMfMUoDkyovWlpz/kzLlC+R0k6fMuc6hxrkY6labYiVnBTPVepMi1QhSjqnlR4qMu52JdsumUrgVXTp8Ov01AnmO8bLDvL8izkISxxS2cMWlOwSIaWJ6lOtq9KlmeSVSWljrk+BTyXmKnfgtkA9qaA4KZ2I+5h7hTP4IjUIfhqVlFKquJCU8spuREpTde8BRM33MVLqyKFTSgGq4mRM+ERKQ5VUShkXK9YQIsyDzFPUuX8p0GkGwASgnaJfaWVUUlUb5RS1dZWx5/X4eXqFKwJSutWaz11gEj2LfLtfMc0JDSkXJGlMIVIqmfDpui3/TygvODQWADUKIS1RYwalaXMEf9a5RjWHeUwX8xi30DAQ0wTqCdVUejGfxYkog5RqPgEB3aSUBTnxpPmkipApnwc2xAKc/FKCDUFtJiIAXoADn4xySalVTANSKk5E7MH18o96x9SNmd7tC8uQ0nH+ZzZ5WYIqE9MupAipCYhSjQnbKYQ8eTb37QTa0bQSbLATI6dUWpXfX6jY0rlSfkTqF5COhLb3TfcWTFQx4sAnV6NC1SigrDzoAp9KzCxiU74uFPRkR+BjrMwoI6qmrdFIKZUbpmdfIqVU914ipbzuPRFTPhZIKimRUtoH+AFM9IxPNaZ8Iqgxa4lk0s8xF/OxhJPe4I/UvKkBNQMvB2gAqhrlbUv0Q+pvTCml55AU05CURlVSySrBzffCrdCzHjPhx5AzFojnMVXa9Fm3xvG6UaVJaiiV9ooRD6rpgBwMxHTAgAEDBgwYsN2j0n7w2rjbHjAeDMQ0gXpCAZPOx1SKwu+llrJygpI6QmppWGK0tLkInbkOQJZaSub7ErpVZpTa4AoJr3vdVfNair6X/EGBtlkopWSGaoZVb4VzfL+39ntSP3OV05haWihn8jfR6Mr2q9aFqEK4tC4uojgMFmsf71TTgiuVgdmT+7XSNWz5v2BfCOM+UHvt2PtsVFMOLyDDc0nQnQoIpY1ZzHlM60kFZX1MQ2tJYDnxXHf8bcDs1dKJJlVcOCaQWhoLdgorOgHwxgI5RRwfAzRTTZ1SCjS+iiyvccxiMgp4HuQC2suNav8+cAqoSbmmbVolHuRTCBaUMAgxNJeT1YKrgzzYiUfgA/5zaD7nV2BzbRTWR5ae06oJ3uLXoXu3JnvBtzRVxY1Q6yJqzid07ZfP0b1cJAYMIAzENIF6AlCNyQ7II6Vt074/AZl2gklI+SZ8PgnFIu8BtNLASJH3duJhCbMp7yCAYCIiAuh8TmOk1CbSZqa9kJR2pWORzO8hOCFtm/NlUmqOFa7B/7bBsZKlOWVyJP/SEhrTQr9jEfEhYoFhYRJxfnth7lLyb41Nfpw000SdymYgkWzJ/zRZDIEmySbB+CK25KOeIB/TBCmdAOqOBSpAx2o3NhQwC9YiTkq5n3lJxLQhpRT4OFnGg52kMqOAT0y7SCn3J3fR+H7gI3f5MdvdsyuV/bSfAxNxCNuX6KLXBVIVTX84QTXbWXaAwHWIkA5QDIMZ3fNH/uGVvXbt/uC8j0L74ZhWQ6GAdmSXXTZ8hnhqNnIVGNWMzxPuhyb78L0EMuMvdAxR+YsDAzFNgKeAMZ8d+UTRTESFQEqVT0rd5EQh5FIaGHh+pRIp5T5kADpJqVVLGx9T8iflgzRPB2MDnmiSgkuYHUbFTrGJCIA3aUR9u4TBvT1RyaRWUkZ9Ytu+3rjz8xXKN9eQn6m5b/I7LVomHZOgJphMVVxV8iYnxRQTNsnTpEQVZWw6FqbgxCYhyWc0poraew/8T6VjfKXGqcVGVV28zNTkLZV9zO2zL5BSnlbOReULpJTGg6KblFL+YZ6NI0VKl9gE+r46CqBV0YkHO3lklY0DgFP7Jd9PKfApfJ8LzxrTNb7wZ0ibs0xmCwSqaR59cJH6cqEPHvgIEDFTqFCjhMt6UTMLSKWLpJXBkVEelZ8me3xRSgFPfdRSL8g0GIdiFeJC5B5njl2848CAbYeBmCZQTwBoUsEAQZQtJ6Xh9pCUllRmlKLy48qIC3ZypFRKBwVATAPDA53CCSnMSwjAU0iozKhESsNqTrHo+5i6WWuFmHriRfx2qKiclErJ6ZN/z84j/BRRfNBuVYZR8Mz509pE+0rqqQQKbIhNlHZSiZjzyTzIU1PFFNqu1DwtBSo4PjXBuZQ1rkwqTbx+jtjFbcqHlBIq22riFqVRUsoS6EvjAZFSShUXktJ2sFO7xChfnAJta4mklvK0cES2pIpOnJTyYEWuFHb9BgrUqJqAwrBSnERKvfGBP/7KRN0bx5h2oJMEqY8hOXXXrp1bTkOCq2YxSdaI0prgFaAMwZRUU34NwFd/pf7ZY1h/6Rnk+UpTz+yo7hWxQEyekmqho+5QlGfb9oDxYCCmCZgJqR1RaxVRroYU7QnKHONIKU1OKXNdOAlxUspN9wBapFQio6ma9wC8tFDcpzRFSgHfdM8/Az7RtFBuIE2RyNSKWlJKHTEW/n7RloLjQoXT+pJK/XOqaaiaxNBKkq3bCqVVItkEZe6BdSKItOdZAJJmyGDS6BOpHw62vKINJ6Ck1FiTYqOUuglz8Rq6rPrJTfeFRD7jpNRTTCOkNEwTJ5HSidKpna6SE1NMEyVGuS8pEBJTtzDlpnvyI7eppAIyCrStJeFCs0TVEDSf/JF66IiOb9ondPmWtz5r46duK6ih7UtN4IooJ6W0CK3t4pMtuLVp0fmBwhLqUcC/D/4cx4g8zwYSLgxHKTlqrt1+Pl3VuLalKwdh/we1dEAuBmKagFFMGTFVMCZ8IqJsAvJLDWqRlCoy5QuBDdxcZycklqPU8ydVLg9hqqwgTwUlKSMAmeWcyS7qT8qUUkLoTxrmESVUTXolAK3qJdLxUgWWmA+p6YeMkA6lBtSY76vfT7+utj23UQy2NnWww8nBmxSbyjUFBSA1qiknjxS4Ya7JzHpeqqh8/69RwUs9StflSg2R0toS0u1HPagnADXBTPeSpSQR9EhjAdAopw0xlQIfY+48EzbYqSklzAlpU2a0q8Qor+oGtIlpOA5MqhmvvGjMTC+RUk7kHPk0v+uoP7lyCiS1y/eF1pho+jVVuGuSCT2xMOoKTPKrXekm2b0j0rQ45SnjvPbZYi4Ft9DLe56JkNL7UZDrYpHTPs/lvFBRazmeYFxtDxgPFu4vaMCAAQMGDBgwYMCTCoNimoBuom1tNH3hXigiZrtQLZ3wlREA0TQwOSlgeEnBHIUkFW0LwDPbcaUUgKiWcsUkpmKkU0PJgQSxpPihWhq+p5VVGQh0YQCS7+8Y39cXNUtwbVK3THiJr+PnFbbMYJgKxveXM/+E0cO29GnPtWVWQETCx42rOqFvKVdMuM/bYkc9oVBMBEGPMZU0opa6wEfnY07BTn3UUnLj6VJLlzSlhT23HlZuGHBuPFNBRD258UhqqZQKKmYtoW084Cf0s/T36SZgKW7KF92EApSND2iomrb+ro2ZPgV+D1KgJo2M/Lc/rSewtTGz2eT32j0/6WwW/j4pQp5ApntJNZXSvo2aWL8LKZ9Wrh4vBCtKV9aC2bY9YDwYiGkC1pTPiCkYOfUj8ANSOiEENjSTk0RKi0ImpbEcpQCwpJwRKzrF6l3LxDQeeW/et034fHLiiKV2CisEhWZzPuiHQU0FdItIFiogqD3GA95WWEWpb+RwFZjWt3L/ymDy8dI0NSZ8aLia3tqRTyKqRUBGwwk5Jx0Voa9jPp9Mw0AL875o+bSRCb/Sik2Wzn9usYK79HjPe9F+L+YoLTTILdCOB0GauLI1HsikdKplynfuPEvCgKfAv9yOC3A+pmE6KFqcpkipFIQUBi+KPqDaufWE5BTw/c/lwMd4Gjrhr2bJKeAC+mKBP1LAnruufD1Kz2Suphp3HlcBCnCVn1zu07QLTjg2po6RnkGOFBEdJS+pvX7GgtMbFwbCNqAnBmKaAE1IYMTUKqYqoo6Ugl8p8ykFIJLSsN51rLTghKqxpDT5A6WUUKEv2SSVCwwCnLiPaehPCnA1pO3nFfqUhcdJk5JETjkpjamsFVQrCCkV8JSCRErHQU5zVsrtMp8uEMr2D4X9W4T7ud8pT8dk+pDOmer5pgaTc1eaKImUttpnikmNwhLU7WVC8lLEsWDHlsWkEEhpGPjY+JeqwowBREoLVnbYlB6uRVLqK6bdpJQvSuPE1C+YwS0lMVLqp2+TF6Sh5SMkp2a7T1BjqZk4uhY53KeVnqGKpToL+5ompcFzC+WS62ufjLpUTe0cw+GiTkJL+UyQx5Q1IpXSTSLmsyGpKSxEi8mgmC4ODMQ0AT3RNuVbQmono34VXAB4uQklUjrpkdF2NSeqc58ipWSyC811XBkBYNURwA9mAoI8gh2Tk4RQHQ33SWipoRGzvmTCzy0JN+oAUjWJ4t3k5A+8xrRnTPMF0knsTX9VOxiMqahWWYUZ5EOVmszpMfDoff+6RXTyip2TQqHaCcVNG9vHQF2XQDERElBBQZVylBZh4CMMKW1M+X1JqVNM80hpSExpcQrASwclVXRLPffegjVwxZFAYwEnp2Z7/wj81LEAvGh5Tk5TbVbC7z5MM8VJ6bQusVWXmEaJGsq58DSmfPscjUpKE8eG58TyiHalheqzCO/KVVo0LhO1VphpzANmXFh45HTAwsdATBOwiilLKel8TLWvktBE1JECBkArAr8PKTVme1rx11GltIuU8qotMZWUV3WKIZygpPd0z1w1DX1Kc0qHtlTVDt5DPZOrNuWRJuk4l5rF+Vaa/piqL2WjYOYk826RNz6JeqmpfF/ULtIXEsxWHkRdiIqMV14x8Cs179NmQ3PP7vuYSfZycYBbTmxGDr44LTvGgsL3L1fNuFA0+YpL5lfaRUq5fzl35ckhpXwcABA13Yek1B4fkNK2f3mcnIbZLLhFpA8ZlSC5B5kvGz459c5xim1Ils13wxXd2uUKbogmkVJDUE02jmk9YVTTiE9pH1LKt+egX5L7PIWU2mx9vx3jRtH8pgqoZn5ZOG48c5kxZCH40G4vGIhpAjTxeMTU+o3BTDyKVNJ4UAP3HwNgcxN2kdLQXNcqKSj4lIbme54wPzTXAUDoS0oISWmOagK0JxQ+qIVmvlxCGoKGwEo7ctquttRsByfDzpwfS/2UQqiWUq3sME1MKUyEpo+M4AVqKs91SgS00izHIpnx2Xde67z8peEx3CWgqyRjn8GWJvhCmd9HoZt/2WJqMULzuvd2YUokVQhyorGBFqjK9y9XRW0sKEompZOleZYlUuqnihuNlNKzL5nupfKfkstO25zPc32abdx1hj9vUO53lUNKpd9grDxx3EJTe37a7rq+v6vn2tLkQfXzhpI6qiwpNRWXJqwJv+p4Ljli/R3FqpMKlAqPiX2WjidymiodS2VZi4aIThY16qpAAT4GDOrpgDwMxHTAgAEDBgwYsN1j8DFdHBiIaQJWKbE+pppVeYr4kXX4kAHwyo1GI/CLeFWnSVv5KR6Bz034sUpOQNx87++T1dIwMteeFwlkCrdxf1JpLZ1jcJL8SlPncTUnppbabbrthxaa8cMoXIJUftCooPLgFZ5fa6Oi8ooyoYrqlE8XeBTzDc2pIpNCW3WV2+PBbPUcBVVsa5gE+74rT8u/PLCc2CwcrMwogJZaOlE2FZ3IatKhlpIpv49fqVTNCYA3FvCUUSnrSCzIKVRLw/cAAO2rpkB3yiYpMMlEqytAdbvlSAFXbZcjP0gK4NYG1pfGSkLpkbhaurX515YKFlXeuLk9rLIUSxuVQtTNRji3b5Bn2B5dK1RpS+ZnOldBVQO2fwzENAFTilD7UfmlbgKghElIad+HTDDXAY3ZPCg3mmvCJ/M94KJqea5Sr8Qgm4ykSk5ANyE1+2RSGjOndW0LzfcxAw/fXgvbQoTDYHrCauczpUhyui/u/8YDHyg11DTIz2zClgblk4MUeWz66UzpSbQm9uYOBZMYJ7bjjoaP+cil0tVQ4IOZnEYv17gQoEPfUhvoCLMoLfzxwOYsDnzMAViXnqJ5/vuS0iWFIZWpCPwuUsrTwuUGOqWi7jkpjadWarJraN+Fpg8pdc9R3SKnXUhG9jd+qDVK5xuKokVSzfbGjUez8rvNwlSqlGau7RapXXlJ+T4epFhrytihW8enMFtS2iey3pDSJvCp8TVfaOS0q3zz7NoeMC4MxDQBPaGboAfzIFtVxAY/aZv+xeYkLPx0UHwCokGGghsmlJmMuoKdpOT5AFoqqS0tiJqRVjkNTCrAiRNS2hcjpV1kNJbqybTPtgfHhUroXHsnTfOgH+b/5pcBVU3ULSXSLq1vKUXl0vllYsIlhLkVpeh5fmwNKl8a8UtL+paZ/vQte0iI1c1OHc+T7C/mwAA/kb55/q1i2owFlJWjlaOUjQcAepFSyWoyyXxMw1ylUlqoWN17wPmV55BS6XkPn3Nzfhs1P1apliWCEA80DH5DygQkcXIa9sn3LZcDL/3rOJ9xANhKQUzg4wIjrZrGAudbyhE+b7FFahdcYKVMMOOWC5kQ5pDSrvyqfTIHtNtevOPAgG2HgZgmwNNAAWhUEEElLbRnuleFbqWDKpULfuKT0CiklAYpTkqNOY0TUp+ocpMd4BNSnyyGJn6nlHSldmmrEnEi6p0njFWxoKYYUmpprX2CTEopmeWnvVyDTCUVgjAo+IGb8GkVzhPrkyne3Y98I1JOUTGfqE4kB28FUhlCTcRXvK7gahCDREq5WkqmzTDBvttWoNYLSznpA5M2TnuBT6IrT7hIZdWciKi4YhrdpFSq6ESEo6QAMxts4ifQjy1O+fjRh5SmIu47C140uyxBVSrpShOCnjuvvYCcciuHhJgyy3OS8opNZAnhqIJFHeUsTVlA2jmc6yYTQNus71TkJuAoQ230g0vHY7LnfYu5FkjwleVwYSq7N2xr6DmMytcL4P62FwzENAHdKCHWEllo6ML5knJVRBUu/QtNPGTCL5U2k0/hSJ+bfNoJ9CeDiFueEoonxw+V0kJpT0WltFCSHxmBJiTqF98O8FQwfjRu14An+Y/2qdAkYRRqQyRUKkPKI+ynLcEqXOS7DqJ07X7lReLzCcqbnPj9JogZJ7A1CpFQpiYpypnKz+MlCX1TYrw8ZApdE5RfjtQR1BldYEYXqCMq2WIATwflme4p+r4I/MspdzEjpc6/3F+oRpVSVWf7l/O8xV2L09BakktKc6wgnQtPnU9OudsM+XUSKpjfbg2FihbMmvomE+isa0BhuhnsOUltn0ekrf2MhpkuALBcxO3njpNRwHyHdSSVG6Hl19k5Fo9nUdgntzGHLbih1Sw8Wwc8mTAQ0wT0hIaacIqpTQUVqKRFE+BAZjr6lwc4UZATgHaQU4KUerWuA2LKldJWaqgEKQ0V0lAdNW1zk52cIiaEq+bkm/VoYioz1JGK+1CxS6UU0ZxJKNwvVXCxwQ3MJ7L2JkQX+BCqpeJ1eECAcEwqz6lUQjF1vN1nlWZ37TBgyi8BKbfZRVhDtdRu1yaNzowuMVOXVilZCGrJqPDSQZHpnltOCvdvynICIJ+UNr6lKf/yWN5i63PejAOU79h+DqwlOUop4BPSGBmKPesVI6/WXzzw85ZIKv1uuM9nCd2oprWnnpr2I/0K2uYuLaSW8pzEtOCkY0wb/kIudKmhJPMtUJd4iVTUrBBA4ZHNUlUoWt9D219zFMLZd6yka+eCp8qyVhO7qJ3/cWCIyl8cGIhpCmW7aguRUpuPkKJulW75k3JfUgpwAjBrUlowIhkm0fci8INAJ47QfxSQyaj7LA9oXav1UrlJKuZ/5l9HeeQ0J6ApNiBwk314fKiWhBG3hGnvPGeyIz+zaJQtlJ0kY35i5DMaQ0hKu/xWKak/qaduRcWuYb8Tn6C2ri2oqWS+b1+38FQRUktrNIppXaBazKb8ZhywKmnB/ElpodoQUk5Ky8ZywjNyZJPSxp805V/O8xZLmTh40KO3AGXjR05gIy026b2ELupSQgFKs4IR8WT7YbATL+8JAFAmW4X5PdaYQmVVyVJ3V6QjuOsw1xP4ZNQjooiX/qUFYEiMuUsN5TcOlVP6G5l7M//YMC87drjQmniu1v56ZGxcDyPvpXGutuN6QqywCwszLszUcx0tMGB7wEBMBwwYMGDAgAHbPbgqPv6256TZJyUGYpqALjWKCaOIALCKCI+0NUqpU0Z4UANXS6dKXrUlVmo0UEmYYsrVUquawFdJwtRQsRKDEkJf0lxIyimZ8blPaWwo4Ka9nOsQQp/R1DmyCc/lG6y0U0un4aeMabXpmQDTA1wrbZOgnErXIOQET3FQntTQ59TzN02Y9r2+N2b+mEk/NOM7/1L3Mqb8xa+UUIoop5b6gU40LvAyo5KfOQDra96plhYuqLFgZngp8NGmguPPuVU/fd/SUN3qisDPUUv7ImbOD83txo+07edZeZaAAls1jGoKoFKqMe/7fQ7HgPD5TyGVe5gSRVkrRdBHKRAx5UrT9k8NrqmL5N9hFPO+n+LOfV+U8o37wAJtX1MpPZ1X5Y4sKZh/U/6AxYGBmCagJpgPGRwx9c33vrmOTHVeiVHmUwogGX3PTff2feNHxgOdABcpH5uMJMRqUwNpUkrnhQNLmPOzHRjhbygV90ds+53ROVVi8OWkNKekXywfovUxpc9wvqZAkzImFuxjzYzMB067dsy+4Fxl/lY5gU3O1aIfGUiRU94P6p/0NyeyaidOBVSRQBBuxm9H4zekdJFH5du69w0xVUUQ6ETBj6U/JoTBjwCySWms3Cj9HR0h9QOeAPfbKYLP9nasv2d7e0hKw3Nm/VUqpyy5wB/zmRNU85tTLhuINmMHLYbsc6TM0+qKYpSAqmxp3JjPagzuO/F/77EFJG2n8+hzKsOGvVbHsx0SVzL7m31SBgAevJoay4MsHnyhzAKqQnJq2xYWvLH+u4VqE/w0z8SUu2uMv+1BMh0XBmKagJlsapvPXPIp5QFPXaTUr3MtR9wWzSRDkxCRUpcWqgZXN8nXjAc6xJBKOZSDFKkl1BomV6ENUNBZQU/Wp4ptK5tmeB5EPkDG3pt+xMkoP4f7r9mAJiKb2qWBit5v4AfHk2y31EYK2IDvlxXmK50tJHLKr8VVnPD6APd9ay9EeDtttdSPxq9hlNKFMCHNCiVLB8WDH1n0PY0JREZpTJhk1hMAI5NSnhYKcMSRfMwBFmXPUrvlLmw4kclVSqNR+FEfSLfgjFlK2r6nPEgI5n1LsStYgJHRMYF2pL5URSqGEnLAIiA/r+GxqZzEuYilmpKPdSnAuhCSXakdUma9v4UXoNUmp7RIlQIfuWo6YEAXFq99bRtAKaeGjIOUUqRtV57SLlLqVFEeYZuKlm/yTMKZrWu0y2jGzuWR52Hi6nHDRvJb03NjQW31yw9cAPy0JG6fy1HqTHjK5CFlkfh+2+x+mzRQ03pCfJHPkv/9+td0126rqiFCN4KqIXqjf5+BKiYQ49mQYS/YiZnxed7CxT4h2QBIlr+YXmVpFq9lUXukdLKoPFJKC8ipshqJlLYj8F3AEzfxe+4fEZLSJzKZp4IKX61jM//E/Dgp6t/do3FtMoqwCeqcRIUplg5LIs0hAc2JBjfuR93jmUQ4/Wh+fzxILlQBN5b3dKHqgs1xG7xGuX4sG0ArmLPJyEGWk2n+PSyAiHzA/fbn6tUHF1xwAY488kgsXboUK1aswCmnnILvfe973jEPPPAAzjjjDKxevRo77bQTTjzxRNx9993eMZs2bcK6deuwcuVK7Lzzznje856Hz3zmM7P+ruYTAzFNQJWstGiZR0rbEfdu8plqXkvKGSwpZlqktETt6l2zhNktUqrkSHsJNFBubSoThWXz/PyAXSmC2uTU30/pl8aTOoOTU6BJGxmZPMJBL2nKZ+RPisSPgU82/EUVoLay6k9RpRFt5/uwPcnHtQ85baexGY2cOmJZsG3Ot5b307lFKJGQLoRJaVS4mvc1Gw/qVkqoibLCRFmLaeKmysr6mffJyMF9zMmdh/uVArALVC+LQ8b4EFuYhinfosqo8l/m3PC3579KKPNSfJtzG5BUWj8lXoVJNYNJGJJKPp6cqBoTft5Y1r4naksnv8PYOOAtUDlhzZhqQ3KYembkZPrOxSt+Xp6YEEvW7/5O/n5ucZqpnWJq/cwXEDldKLjhhhvw5je/GTfddBOuv/56zMzM4IQTTsBjjz0GANBa45RTTsF//dd/4dprr8Vtt92GNWvW4CUveYk9BgDWrVuH733ve7juuutw55134lWvehVOPfVU3HbbbfN1a7PGYMpPgCukgFFQiYwqVnWlaIipVPeek1Kbx1SxYKegopOfNN/3Kw3Jg28OVi3FIpx4TOLmGQC1SwuChrBZnyw/MMacxxpWbr84yFgSqRsTfD/E0kdx3zReczuGMJCii5RKhDGGGJnvGnR5hRrTh4BMNyayGoqlejL/8FKkff1O+/pUhSly5DZDFwkKJBHI6WKfjAo/HVTMxzyVJs4LfJxFmrgw8LEVPCNsS6Fq8maa576yxEPKQ9wFiZS6filUmpcsVTao1A1jzRuWfJ8ONW9NuqWiIUipXMZd34GX5F7J6ZBiiPqQM+QXrogFF8bcIXixDME3PWNsSLnouOu3++Uv9p0yXOnC5i6ergurlM4EpvyFgLmNyu83Lq9fv977fPnll2PFihW45ZZbcMwxx+Duu+/GTTfdhG9/+9t49rOfDQD4yEc+ghUrVuCKK67AG97wBgDAjTfeiMsuuwzPf/7zAQDvfe97cfHFF+PWW2/FYYcdNoY72/YYFNMBAwYMGDBgwIB5xMMPPwwA2HXXXQEAW7ZsAQDssMMO9piyLDE1NYWvfe1rdtsLX/hCXHXVVfjZz36Guq5x5ZVXYsuWLTj22GO3XefHjHklppdddhkOPfRQLFu2DMuWLcPatWvxhS98we4/44wzoJTyXkcddVRnu1dffTUOOuggLFmyBAcddBCuueaakfqnrM8Yq3lvTfhNoAGZ9Uk1LYxZLaaKxNTS8EWBDaFaWjS+ULx6CyGsSiK/hMCBAKlqRtZXMqJWumpK1I/498sj9Ami71pO8JTgS8VNgDlRxTzwyfXRJSEPfdC4opyjCCSrZgk+av7n7r8t7QvvaRSE90PJ9bni0IrG5xG4WDgqyWxRFL75nvuUTpTu34kirpZapXQWain9nkO3HjLjp4o1OFMrMzMzP236N3yueYGMFGJqaamUfc75+9CsT6Z905af3YNXoZqCK788ab8T2Y+SFw4Jx4VcxDKccBM9vaZ1aV+toJ/glUJqf8pPdTZIKbyhehqqpXwcmG4ymUwLJny9AIIgY+5Y43oBwObNm70XEcwUtNY455xz8MIXvhAHH3wwAODAAw/EmjVrcO655+LnP/85tm7digsvvBCbNm3Cxo0b7blXXXUVZmZmsNtuu2HJkiV405vehGuuuQb77rvv3HyJ2wDzSkz32GMPXHjhhbj55ptx88034/jjj8fJJ5+M73znO/aYE088ERs3brSvz3/+88k2b7zxRpx66qlYt24dvvWtb2HdunX49V//dXzjG9/o3T/nT6qtua6g6FtVQylj3ucpYFrBTvbfyjrzTxRVi5SG5jpbXpBNPlIOwhAUSU7gA6dXRjNw2DfntgcNP5DImWd5felYNaas6NdMcmqOdT5p5AOXysvq6nw7cmoj0Uc04xNSvqQ5gQcxP1++zS/12R2sNkqQVIxs03te7akO+hxG429PZJTD8zVvFqLkUxr6mU+WxmVnsvEpnSrYv0WFqWLGvvqSUhoTJFKaQup3U0d+xy2C2pDIUVFA2RcRVB7oyP1O7TnsGadgqJCguoV8HbwcKeVtZPtYBmTPuNL4Pvo8wHFal62xtmuBn0NaU22FhDT33qRr+PvbsQR84RmSUiLj5E86bcnq4i9HPAr23HNPLF++3L4uuOCCznPOPPNM3HHHHbjiiivstsnJSVx99dX4j//4D+y6667YaaedsGHDBpx00kkoS+ck9973vhc///nP8aUvfQk333wzzjnnHLz2ta/FnXfeOSf3ty0wrz6mL3/5y73PH/zgB3HZZZfhpptusj4VS5YswcqVK7PbvOSSS/DSl74U5557LgDg3HPPxQ033IBLLrnE+6PnYKI0JUaJOxVKG99SaCgFG9xACmmhNFNG/Oh7UkkBo8KlSKmkiBCyg556+xV2p4Lqc5x/jkZY+5k+19DW/8zfj2a/QSq3abI/zD9NStY9W5C/VjgpdPq4sb8jT74dIiz711XG1D+3O91NLiw5ZYsTHo1v2m0TWAAiMV9MKBqVlAc+mgWpbzXxFqjCIhWANybkFtTgpDQMeLJ9jCp7yttuxgVKLUTHMD9TrZsk9Rq1MsdU0MnnD80xps18ElLSdcLvGwD3NzUfeR7e5l+yyrRItE9KaZsduyJjgvE19X2nY7DJ+ZmPtXcPEUuW62N+ntFxIkVKvYUnW4wSKfXOY6SUBz/ONIrpTPPyVNMFsHCtE4uF2bdtfms/+MEPsGzZMrt9yZIlyfPe8pa34LrrrsNXv/pV7LHHHt6+ww8/HLfffjsefvhhbN26Fbvvvjte8IIX4IgjjgAA3HPPPfjLv/xLzw/1Oc95Dv71X/8Vf/VXf4WPfvSj47zFbYYF42NaVRWuvPJKPPbYY1i7dq3dvmHDBqxYsQL7778/3vjGN+LBBx9MtnPjjTfihBNO8La97GUvw9e//vXefTLpXyj1C6UoMaSUK2FiwBObgCaaKi6TRdt8TxMQX/lLpLRPJH4fR34p/6eUdkk+d7SfDyep9J5UFG7uM/v9FFKSapp9XWnihjOVV5qqwXRXdYq1mzKruYhfIeI12C4d40XB9/jueYRwrGoL316z7yFUSqQ2+MS1PUThhwhV0onAdD9BKilTR0XLSTAmeJk5Mkmp9DvJXaiIqY6YGZ/IxbR21pBpbdxxtmpT5z72sm3aBZSPnOTj9HwDvlkfaKeSMtt0SxGVxgNOTgl0nv1X+A5jkfmclNpnI7AehanTpDb6KKw5iFm8UmqsO66blHqpsOj+UTQBT0YxtTmMg8CnJ5NySq6J9IoRU601zjzzTHz2s5/Fl7/8Zeyzzz7RNpcvX47dd98dd999N26++WacfPLJAIBf/OIXAICiCNT0skRdL96iJvMelX/nnXdi7dq1eOKJJ7DLLrvgmmuuwUEHHQQAOOmkk/Da174Wa9aswb333ov3ve99OP7443HLLbdE/9ibNm3C05/+dG/b05/+dGzatCnahy1btnh+IJs3bwYAm4+UEw+l2Go8MOFTOpgpO+G085QCEEkppX/xK7h0m+kIJsl07UXNh5C288FaUkP7pn0KJ4qsc6BaExeR07A6VEnRvBoAtFFUdDv5fgq80lMKYXL91PFegvLMv5nYTse5vEhCSj0NE//nQJoUPVO+Z9pzZnzaJ1ejccnfFzpi40BZmnRQNk0ctKeSms+CPykjpURwcgtqjGo9CZOn2wwaXb8VDauamuT05t8ajhT61wn+npr65BTWGrDPZqnaz3gMlIHD/Zr8aH26vs0sIkSZxxT6sFCHqWxmvifzW3dCgJcmLXieJDcX+htL2QJSimoMXdWdbFYB9nfNIX85hJQ+c1Lq+b0TOecme6goISXf0vn2MV1IUflvfvOb8alPfQrXXnstli5dajnK8uXLseOOOwIAPv3pT2P33XfHXnvthTvvvBNnn302TjnlFCu+HXjggdhvv/3wpje9CX/2Z3+G3XbbDZ/73Odw/fXX4x//8R/He4PbEPNOTA844ADcfvvteOihh3D11Vfjda97HW644QYcdNBBOPXUU+1xBx98MI444gisWbMG//RP/4RXvepV0TZV4LeotW5t47jgggvw/ve/v7W9UK6UoGnX/fAmlDHneQmzBVK6pMlHSmoIAC9ZtkRKWzkJEa9Ckgub4458sCIDYza5C8zXIan1gxcyAoPACSCffNtm/lyQCmTe+3WxK2/A9dXSls8V80GthP0c46jcxNFF6PqY9tvndqulZptTSnkaKD4pee0KJEFFiMJCQmwcMCppac325FdOY4BkujduPX6AEwDnQxrkLu4sqBEhpaOWrpVAaeNMiaumFGWzCAxVR75IKRRQ0T7qhu1P2xQfLjo5eHo4c0y7ShQ3y6eCMMNyyf51nEmf+l4hneCO/NHJfzI0cdN7nzwSAfav38ciI50XcxVIVXVKXV8ipXZf4FvrfMpZ/mJdeGopmfG5OX++SelCw2WXXQYArej5yy+/HGeccQYAYOPGjTjnnHPwwAMPYNWqVTj99NPxvve9zx47OTmJz3/+83j3u9+Nl7/85Xj00Uex33774e/+7u/wK7/yK9vqVsaOeSemU1NT2G+//QAARxxxBL75zW/i0ksvxcc+9rHWsatWrcKaNWtalQ84Vq5c2VJHH3zwwZaKynHuuefinHPOsZ83b96MPffcExNlhbJwAxVNDNJkRIENREpjZjoAWaQ0JBthLeYYKq3MxKJZhY7E5FUIJDW2ouSBQ+OqnS1BUlC7IOUspUGzYgMo3y8H+oQqqe/sHwY9zYfvZFhadpRFS0hK6TvyjhHUUnv/wqS02M118XHAPOM8+0ZpyacbAwqlW0GPzp+c8pi6bbyghjPbx0lpuFgl0HPdymkaWbC4332jjKoaaFRTqNqS0wpAqWncaUieEPVeEXFVypK8mhTPgKAW6FaWvIWs0tZVgK5R93jksgIwO/zPKZuJH4mvvOfDtaU90iepqHTcbCBdg/qWc47Up5CUSoFOdA0y4XO11CbTj5jwuXI6X+jrBtWv7X5/U50huJx11lk466yzksc885nPxNVXX93r2gsdC8bHlKC1jqZX+OlPf4of/OAHWLVqVfT8tWvX4vrrr/e2/fM//zOOPvro6DlLlixp+YUMGDDgyYVhHBgwYMCA+ce8Kqbvec97cNJJJ2HPPffEI488giuvvBIbNmzA+vXr8eijj+L888/Hq1/9aqxatQr33Xcf3vOe9+BpT3saXvnKV9o2Tj/9dDzjGc+wKRnOPvtsHHPMMfjQhz6Ek08+Gddeey2+9KUveQlpc0GVnbhfKb24L9lUMWMVEmO69yNtuakOQLSqU6iWhgpnpVXSrB+adbnfE0HKx+lfQ4ggHcE8XWu0KsZIqaFimK1qKqmlPOWSn4rJKQFVY6Iy292xklrqX9v4qYVq5lwh5zrS3zJMQQXElGPZjG9Vk57Kx2KOyp9QJg2Uap57Uk5TaimPuC+hbVR+LMgpNSYAcpBT6amX/cqRxlA1CmqljVWkRglTEa6pOqSZtUSF5zpV08A37ZPfafirDDNwhCgbk3tsfzTVFf/JseCpXPCME/xavapECaZ3r489IfW/r1tAWMEp3B6WHHb73fjnxkzzfUzXpfUxDaPx9QKypPCcz3PR9oDxYF6J6QMPPIB169Zh48aNWL58OQ499FCsX78eL33pS/H444/jzjvvxCc/+Uk89NBDWLVqFY477jhcddVVWLp0qW3j/vvv9yLSjj76aFx55ZV473vfi/e9733Yd999cdVVV+EFL3hB7/6VbBICYCcfPilNlTOeH9mSYqblP9YuJ9iOtOWkNDTN5UZq8omLBz6Ye9HWv5T6ECLXKTx3cK+gUEBDShflt8fN8LMnMJyUTltzlEsQL5/jSGlISE2/ZFIqpYoaJzkN00XlXicWfR/u9yYbRkpjZny+KGoFPbB/tydIqaBsEQ37vrJjA40F3J+Uns0+0ffcfB9brIbm+hxSGjPx08KWflctgqfoN+NIqmlP+ySVTJTKMEpjvodHTs09+eCfc54eyX1H2m/M683+xE/TL2LhBz7ZfMcJM34KMdN7X/RxCYj738ZN+iEpDc34Zlthj/XzlkZecMn1F+/ydMC2xLwS049//OPRfTvuuCO++MUvdraxYcOG1rbXvOY1eM1rXjObrgHw61oDCFQSP09pGH2fU+d6Ss2IKqnkK1ahQKm0VU0BfyDNCYChSW5bKHp9IRFSUk1zAqD8QCemlOoC0yhtAmxAnphpnxR9CqSjXYmc8vZnE/k527/PbEip+cwmX0Etdec4/9IQi93flMOkgiqCwKZ2kJOkknJ/UmorN/pesp6k8l9ywhP6lXeND+Q3zsmpOV8gqIAlqQY1I690feNnysmpuY7OCoacDcJgIO4TT89qOFZImSfCtvKuLQc7cfRLRdf9d0shdq2oauoR8nhQI3/++aKUq6UzdWHVUk5S5xOzTcmVbnug3ePCvAc/LWRMNkFNEwUjpoyUTjYBT6nUL6GJDkCWSmqu1xBQXVhyyhHLvRe2w9XSAn7evnGR1Nk48/d5oPsdaxTQaV1iq2Y/9cjYaJWRREBQDDwYajYDXxmQAtc3FVVNk/3KJKV2vxCNy4/nSfTDiWp7zVdoqjgVotm+CAgoqaG8ghNVigMgmu5TwY9dhFQiLqk8ut65kWeJFrwxgkoKpIt6N3HzVpkMzOYVhb8L7j0xUCR+V2L/URBbwHpkjRGYMCOFIfB+yqoQ0vZRxshRo/e7+iIlzLf7wjEgHCNaKaLMa4YtYGdqPxrfvGZ9KwOeJBiIaQJTbEICzCQgJclOlRIM1RAAnaQ0nGwK5XLqkWrKkfI1a+pyiJG9IVIViFz78dGlXf0oNHFrm8/QJta3xCczx2FkojKTRYd6EJgq/fNrTAeRuTwKNRs6pWg51SYK5auuITkF8nKCSr6kQNslgSul3ETp5S8kMsqIOleYuGJir88icRc7+AJVMttbciqopLQ/JKZ93Xm6lLOWlYU9i14Ef5aR3CBFULl53C3Gau+ZN+mn/Lr3Zvvcq6YAU417LWZ9V5UqGAO4Cb+vn2jf40chsqlr9CGj7hzJhO/UUvIt5RH5RFAltXTeo/J1v+Ip/doemPe4MBDTBKaKCjuUjgiE6V/CAKdRAhm46T5n8iGTfrg9PL9sJjcipancpaOAE22/L1RRxd8uBT1kXScSBEUBERU/lr6XcDsRA0o7E/keioa68nND4mavH5s0UuOuTvsMmyTfzi0gpZ7mQCKkQB4pDdVSnrfUtdNOE8XLDvJ+LmaCanIVq6aCmxbN9lTZjawm3I809C/n6aC4Ow/gSovGxoTuUrcRQqqcf3ksSX8sDRwPquT+p6F53B7fqKZ9iVWsqIa7H/+55oolV2c9axFcfIDXPwayrHjPC1+Msecgx790lGT6ErrGmz59ANIuPjEyKgU8SWopveeqaaiWLqQgqAELGwMxHTBgwIABAwZs9xh8TBcHBmKawJJiGlOFtsmxSSUhdSRW874rQXZoquvyE6vA0j4Jv/2CqSGAH/BASomn2PQw50nwa8O3FQkANvAhtxRll0m/bEqPhn5nNj2N1oHCUBt/UdTNuWC+b+z+FfmT1YCWk2yHaVSARgGWlJCUmShUkYNIXVJJQ9UUgKicin1NpHhpbwvSQgVqaeWpH4JPaRCFn4rMXcyYKipMlfCU0nAsKJrnnfuK8gAnek5yzfdSIJOEWNEMt5/M2W21tNVWKmAHo1cYa18ndEVqfw6DHalMaex5l68jj01SCVMJXkCk9avsp5bybSnltEtl7XtN6rd3XMTf3LQhuy3Rs1+x5zhmxrd+pY1KWtW+eloPfqYDMjEQ0wSWFDPYsdRsgJPNdTzIKTXxELrM98kKLko+lsz25r2bjOj69h5GmFxiPplimb9Zoq/PqXh9MvM333OtC1fRhsH5bBq6WyqNGjWmGUENI9X5tXIqvMSOD0E5ZyVyas73CWoK0uQS7sslpZQiisz4fn5TXtWlgGTG5xPVYsVkMYOpQkcDnEJCGqaB4gvHrmwcYbU22teFdo7TNrENSelCzM4hoSsAihNNXq6U9nW3Hy9HSpk5eOT+uDEbn8dYzlFAdvnpIqOSTyk95zQuztSlTRE1Y8mpe/6r2kXnV7UZJ7g5fz5Rw7lpzEXbA8aDgZgmsEM5jSXNhASgpYpwn1LyJe2KrgUQnYAktP1Jq9YxnJDyScgRVA1JRekDTk79ACt54C8SPmapnKajgKsohLrxNauhADUD6Imm9CKDNl5sRAanE9cIJ6XQH0+aXHJqZucizEtL22KQyCjvpxfglCKl1reUAh6koKf0S+v5TxMzGywpZrCk0CIhDQOcpIj7rsDHXELataiUFo/uXC0eY46b3YJSUiS72qRfZKzoRk6KuBgkUupnCIicp/xFKQdXS6XCJQQeqDrXkEhpShU158TJqJ/H1V90TrOgoWldYKYuLfnkSikRVMm3lLYNGNCFgZgmYCYkHlykEZrpwuh7S1gDJYSDKyhSdSdCl5nOHCOb6mIqaV9Syk14ZrJlJnTeD2Zap8CnAsYEV0I17zODdjjhy5jgpDsqlG7M95Ux0asZTIInjjYqah1MRGHAECdwHDmTeY4Jz/U3+DsKf/sYEZXMkhIZNdt9ldS+j5BSMuHbgAftqyh0vhgIQXkMGaFdjJgqKktMYxH3KUIqBT6GVhNpPOhLRO12gZDGjs8lpa1xp/X8y23TApXGgr6QAqAQLEQrtItcxPo5CmhRxseCvuS0TwBUzrFSInyOnGAmu58po7TPX7QqzOjS7idS6pFTXUTzltZ64QQ/NU43c9b2gPFgIKYJLClmsEOh/UmFmed4KqjQPBcqIRwUMS8h12eMt2WO8Seg2ZjupWtLk1qojEgTEZHSGFImOk5KJfWE/M68yQpglWdgyWnREKxSVU0aG6ASTHemT27yCdMncX9Bnmc2B+EkGYu47lO+M6aM8n6JlV2YSgogSkq5Cd/3J42opXAKCpHSqi5Q1Yt30J4qZjBZTFhCGkbc8wVqn0T5EiFNmeRjkEhhjtl+FFLK25HSw9GYEGubjwd9ShSb483z3m5T9jeV+kDH2oUratQok2ppZ7/CFFkYPxHlSI03uaZ6iYzytiv7bJvnfLrxIwXgkdIZZrbnkfhVXVgyqjVQ2zGh160OeJJiIKYJLCmmsaTwU4/EchCSSsoJaTzRdT/SKPmLmfPlySdFSkPTTovokq8kO660ZFxb31J5UpJJKZ+Acsz4oUo6K5MekVPUgIonzK+IhAnqXpeZPma+8wNYdGub2S5P9LHfTiztk9RXKWhLUkkBJEipM+Eb8hoGOiXM+XABEIvZhLekmMESpUcmpJIbT8o3PERu7lEpsKm9kM1/luLWGd36rUpjgjQejArJz7RQsEFQqfsKiatEZFO+24XSKLU241LH95njMjBX6ENKJULqxgb3XPNk+jNN++RXGj7vlS7sIlRrN6Y4Uz5Q1/M7DlRsMT4XbQ8YD4ZvcsCAAQMGDBgwYMCCwKCYJmCqOvlqV0wZcWZ92Twn1bXvMtN1mejGEcgQKqPhex5IMIWq5WPq9o9fLQ2V0ti3JW3nqkhotjPvChtxO63LRiFw2wBYvzLTFykzQTxlTUotTQWP9anMY/ro3A6oz4TYttB8b7bLaqn5bsjsz3zs4JSU0J+M1BP7vo4r1YsBOxQUBDmaUioFN8WyaPRBMvXTOP1Jg99ywe6rvc0V2JB8S0c14/v9A2BN8vCCHmPqaepZTaFUNUpdY1LNNNeWfc6l6/E+jQtSXftYTfvQnzSMsufthaZ7ANZ873zGaew07jpkxp9pVNKUb6n9d2zfxGioMXeBmIs5wHOhYSCmCUyqCjvwYITEBMRJaWiq51WH7GeGHB8x6TxzbizyvX2sRI5j4ISUm+pCQtruo+xXGiOlseCmFCntk0qKT0he7WuQaZqlg2ne8wAHTuQsiVc1oOPfYBGQen+b70cqBo3waP7IJOgFLwQENPaZ2pPM99QmJ6U84IkTUX59yaSnGSHlZr3FiglVYbJAZ3BTKrBJCmoSs1zMYuqOuwKMRkh5m2GkO08Xl0NKZ+Nb6vfH+ZmWjJzWbFv0XMmETwGOaJt4TaYO439aURCoLjxzfi5J3VamfYmUStH2ISE1x7bN9165UVDwU+Glh+K+pVogozwifzG79AzYdhiIaQImgb6vUKYIqTlG9hmTSWUQrd8RsCANunQtXtc6B+HxKVUkRkp52VE7EfWcgGIlR/2+ypACIahfdWQfn4QoR6dJh+L8pngtbE7kAADkm6nCJOjxe+CkVCKko+aT5P5jEimNpZNJqaS0naeIcZOVm5xCtVQCTU7G92zxTkhLihnsoGZHSPskvR9XftFxEFLejpf2quVn2ialbl/8bx8uWLvGAneeGRc4OfXaiTRDx3Pf6fR1TF6SEjWgYFLKsWdMClzaVumiOKTyw+Fzbbcl/EnNNh7Q5PxLbfBTQ0qJoEqR+AQaAwCxNsw2x+BjujgwENMEptQMSjWBWKqXnMkH6DbRpczzrUjuSFtdhDRMZRE73o+yT5NSTxGEb7Lra8LnqLx8pMG+jOGNT0pVQDZdu41a2pAzGrDEnJ/eoE8KeGHN+yYxvgq+u7aJvmUCHZGAhGppLAUU9T2sic1VFUklDc16FXvPFdMwoX6onvD+LmalZLJZoOZYTAC0xoSuZPfbkojmLFzHRUp5wJOULi53XHAk1ARAhdH5kiIZW5x67jkRCwoHFd2om0weJi9ys1NwlVkoCJVSuy0jyKnmzz8jpdKzrtnzzkkot5wALLH+QmCnAxY8BmKagJmQVEA426Q012dUQg4pzU0h4ytj+as3yVyXItO5prr8iadbNQVGqwbFYSNHaWC2JnxO1Jwp3xzjm66px5ygAk4VDaP1gUAdDX4rkuk25qvkK6RBP0OCKviR2u8AzCSfS0oDEyD/TlKq6fbgd0WWk1EJaW5e0RixzE2FFMMo2T/4NUISKvmZ9yGl4yqwQYQ1F/x75FaRaPuqts9O0UTkVygA3Zj2m2wcYST+XCXZ7/Iv5W457jjfVJ9juo+RUorKN/6m4fE+CeWeWAtpUUpuW3PV9oDxYCCmCTSGXjH3YEwR6de+P7mEATakwnlJ7kciv3LwFUeOMiJBmnzGXdlJQsyMb/cjMMkHvmRerlL4aiAPeAr9MAEAmr4/428WZj/kfoiEsPqPOTvmH6xbhI6b50ITfixZPt0DJ41cJTX7M0lpQND5hESQyGkBDa00VI/crAsNO6hpTKqJ3i48OepoDsGcCxIa6094PcmnlI6JWU9mQ0pzF6l9wceCtp+04F+qtB1jCtT2mTMm/TY5Nfu0R07NNdr75hqSFQRoB0H2IaWeYsrV0sB3lI+jIiFdOBx1wALGQEwTKABMKVcCNEVKOQlprVr5oMZQQ4nklFAyEzEnp3l9D/1XI6Z7VUdJqT0GgkICMwH1UUm5r1mWSZ6958dzUlrb/RHFLpiEOLjvVSUM3lJbVL/e+3vreBUYQCalKXBSGV7fvS88UiqZ7iVSHU5aflsZpj1BQU6pokoBChqziHeZd0yqJjtHRCHtIqSzySWai9TY0GcxGyOlfFs84LJNSv39o/0I3DPeKJMZXx+Z8UOVlNx6uBk/hRI1KpTOOkKBrBnkFEByXx+k1NI+vo255vtWUGNA6HmZYUdE5WsqpReMapqy7oyj7QHjwaA9DxgwYMCAAQMGDFgQGBTTBIw6orzPXWopfZb8TSTlNOmDRythq3gUybRT0ftgvlLhuSm1NBWJL1Vy6VJEKuhkhG7qPKCtlPR1aqgE1SH02wScOmEVCe6TyVRT42naBo/eBuRgp9y0QJL63qWWSlH3BMmnlI7NUVI4UuqDasz3BYySUhZ9/1oLB2Q5yTHdx8z2qYwaHH3rbXdF1Lf25QRICRXIpIAnOxagO19prlrax4wfu8tQLeW/cVJLeUBkGAzVBzTWz5VqGvqqptLHpYIZ6ZjZqKUtFwhuwk+Z72HGA2g17y49FFcwV20PGA8GYpqAmYgYMY2UEm2Zdsf0A7Wk1SOozqQf+qQmJ6SEOTFGSsV2WIDDKH6loQk/Vn60y3znm/lpcIxdM/DJ5H9TmFyF4fsUiJzWWnmTsWkj8r1lktKUGT9GSnMS5hNiPqXu/O4Ji9oP++fdnwJQ1KjqAmXRj3AsNBhTvrm/LkIaI6O5bjihP3gfzNZlIFbsIZVtotWHWQRBjgrfd9LfFpJS77zGv7wLpdLWbG+3Nb6mtVZRcsqvPwo5TZHSmA+52dZ+xqm9PqSUrhMuQEMzPvcv5ccMGDAqBmKagFFI20FDXQnyJcU0lrM0FuTCzyFf1Ip8nECRou2JIwUiU/Q+vE5XO37eUnngKe2EkONDSqSznR4qJKSxaX0Uny2KrK5RYFo79XtalzZwCQAK3UTfs0ko+xo9gp0IISnlamcYhc+VUjo3lQrKHieQUnfdHhOW4HtG910DKDQsOdVaoVrEiumUqlCqMouQ5pBRaQHJSUcuOZXGo1Z6uREVqtZ4JailLmsIWgu02aJrgRr6lsdSQ5l93M/UV0sJrXFYeNa5n6mN1qcFqkBOgXZAVIy49oFESsVF6IgLz1iwUxjoyMlnzL80xHzT1TDryrjbHjAeDMQ0gUlVNYNumogSeDR1bjWXMAI7pywhn5BSCqdcVUhuUwp6iCHeRg8TVUBK+QQkkdGuATzMXSr2r5kYuAHeqOIFCm3KTFYoMNkcUSuK1o+rqCbwqbJtAXl/wy7EVPeQkHLzfYyUykFTslkPQOeEFQNf+ABMOVV6kZvya1toIxbYJKV26xV0FLjbdJHTNkmWn9/Z/hb7qK6+dWn8ammMlLaOg0+gaBsnpaSW0vtREHPZyiWntE+6l5ha2kVKw0j8EH1IabjolJRR02b778ADnsxPYfFaTAZsewzENIFJNYMppTuVLl+xMJBqq0uodNFST2PX40SUI8w7miKZOaofmaoBl7JqnJBIKVdEanSb57tQBWSfJ3UyBFWhgnHVqGFqYddQmERlB/VJyshACRN7qqZ9wCccKTVUzIQf5int8jOjdrtSQnWRUmniIpBqCjTKKUZX7hYCJtUMSpRJc33cmtJ93/S3oXNd7kyymPifbduCn7j5nPZtjV1/HJiNZpRarCbPExal3ITNo/DHhTKhmppt8XRRffKcdpnvc4tkmPP6CAeyeuodo9OFM8jHnJ873z6mPI3gXLQ9YDwYiGkCJsF+f18tACiVHBhD4JORl8Q52h4pNbU3IfFa9kDb5Mb/raBMfj7hAYr5DBbs+IKRR0k17QpukvwMiZROM9+wWuc95F3msNDEXGkqllCggsaUmgn60pBAFvBWQaHQhfletTHpjxuSXyn3Kc1qQ6eDH0LVNGXWo/YkUspVVX7tEDRZ00Q0mxrw840C2i5SYqZ6SbkE0oScvrfwdyyppyFyghe7rk994Mf7C7r5/5vlmvCl5Pnh+1At9a9TiNslhJYxY8Jvk1Mg7luaUlWlinOzIaW81GgfEz5HzK+0D+ablA5YPBiI6YABAwYMGDBgu8fgY7o4MBDTBCZRYxJt85zkkxQzt4X7c9BVjSVUScIygb5i4tqwpkitW6pC6B9o+83M+q5/2bdi2hCUl0prTy2l/kw3qkZMhUuhS2UtoQEqI9gEMlAQVIXa+pZWipvGlTXlT6NsRed696mLTqW8dU4i4Cm8t5gZf5xqaduk347SpX3Svxz09+LK6WLElKoaU77sP9qlkoZ+ns7c674faidm1g+vya+byqqRUj3puQ/7sK0Rs6TEtuWopTzAKfVM8YIUXShQAyqesokwqmrahZjLThdkK0i3b2mOXzlgAp9q8EpQ2bc0YICIgZgmMKUqTElBRaryB0OdLhlK+/NSk8RJKZnxwzKBlG80zC0YhVKtMMqQnNoJcsSJqoQSfcNCv1IipdOaiGnhTShem9qffK1PbsKPViKrZM5H42uKwKTvE1My79MEVrCSpPya3aSUV/qqg98PXcs30wWlRkckpe3o/bzIXPouQ7IafscxNxCOxZw+xpjy3W+kLxENEaaW48+dZNb3z22b6SVXnj4pnlIL09mihu4VAFUJrEaq9BRLC2X28ee/8Ma0XHN9iLJxBfK3GZeL0JwPuL8x9zntky4qDHbi99bKbZxhxp8twlKjZM7P/c0IU868oMLc+YL2kyQGpDAQ0wSUkmvFh35Z6Ap4aAYwHswQqiOxVbjJG1rbwCeplj0npTzPaByaQiURS7PiBr8a0LA5XLuQ8jMNSWnFSOm0JWftBNh0rxTAVAVBHl2Db4vgNmUFKxvQUqNCjammmUqp1rm151saV7Hk+3aBcO20NI6U8muGyfNni74poTxiCv+zbTOhlnIsZlIKkGLKFbBuIppDBulcIjVAt3LZlfw+9Ct356Fp3+8jBTd2kVPJciIeh/wAqK7ctuLCVhhbUlH4sfP6qKVS9H2YTcVunyXBjz3vYb7SXN/SPtk2YttGeX4X6jM/mPIXBwZi2gEpcX7XJMIHcEtiOYGxuwt0KSMhKZ1C1TLbhxVYbBvB2EDKQ8E7wZaypFaOO4K1gEpOQr4pzk/p4rY78PyuwGxyNTZ/F2vaN0rvFFM+rVLYI+gpVRXKOy4gpU79bF9rNmrpqAEQoUmf3vN/ge5JqIbKIgALFXyBGlNDY9kyJPCFrSGGTl3rTNXG3Qk8U36bkBbCV8631bpNTmcDviittLYpoyTVNObeY/a1wbdJJvzQSuI/UyxwSBjb+o53XEgIVdPWsfAj9aPEU/jupWcmlp94rsEJKldLyYwvYaGS0wELHwMxTaAM1v9t84w8iXiTFxEa5kdZegqqUwrDiN9QJeUKqblOO9G1ab/ffUopNOxArwqUPY0UsfQupVKiqa6MqA+A75tL35GNWrWKUf8UPe0+GP8x8jc120xO00oXnikXgCWIsWhz4yJAprvaU01DXzE6vlWhKjTlz4KUcj+zGfs+Tzmh82JkNCev4VyYibcVSrhkY6nfVu4CiS9sJYKa7otkyvf/9QthtF123Plti8mo7jyxTB2E5MI0YeOVzPjefkEJtdfkZFX7z1ZooRo1l6mE2aqmUunklJLdpZbmpIFLqaU5i8ou/9KFYMYHzPc5zr912PaA8WAgpiOg24eMP4XNsarwcwwzckqmfaCbkMb8SPs8ErE8odyMzlFBoYBCqfXIFV64alqg7Y9jJ1lKyaTRqJlEFtu+ZFVDF1rJqBP+vkB6ACngytDWqFFB2VRBRglVTv1hqgnlLTTflYzWZCiQUilfKZ07G1Lqm/O6/Uklk75ERnOV06pevIN2O6Awb5aNEToX/CIT1FgbbReCfELKt0vXyFVL7XHauQMhsNTQcpurptH2AsaSyl3alQbK7Wv7lZq2g3y+LRU3J1WUixUgc34f1TQXOWrptlzscX/SLrXUGycW8YJ0wPxhIKYDBgwYMGDAgO0eeg7divQidldaaBiIaQKhUgL0UzT8IKkgOXujCPKgKACiP2lMKZXW4GLie9atmNabu7I1Cefbymkq+lYy41HUvjHjh8e2VVPu8hAGLYTqSqmcyT83fZcLgipsoFeFGlP2eGXTSQHAtNiqDFJLQlcQIK6W9kEfc95MXWb7lIbBD7EShXQOR6ieLmZ/s0IIgpxNWqVQHZWUU37t2DX7KKWp63fBudB0B0CFwY85qumokAOh5IAnzyVIcKVx+8bT1zAOIYSkUsfKkKaQ89w7q0d+5Tpuxu86RzLja2+caG8bMCCFgZgmQBMSfzB5sAAdkzof4BNOm5hIQU+TqopG3ANtQspJIpFQ2iaR0pRfVohaK5SqIVBKNeYrIoMucKpU3FTPJwTXgZgvWaHgRi9VW7cBcz3KYFA35TpdadHKktXmnikoSpdmglQ1TAbKtEnV9LMd9EV+p+RrSt9HjQJQFaYhR2LWujA14pu0Uq68q09I+bawbCgPdrL9i5jxeZvdk1OefxkdG05OEhlNE1R5+2KCtEAdV7tAm6CG+2Ofw+Cm1v7getISjfuZzjYAivuZcu/8GDmNmfFTCH1iQzN+uI0HPPlmffe8j+IbGMuk0mW275u7lI8TduyZJXKf/7AffvCTaSe1AF2Iz/zgY7o4MBDTESBNUum8oRH1VPvkiJPRXFIKyIEHYdBALDk1XTdl3qhgAqAq5mdaaQCqIacAIEThxkgp9aVUaNRQ5q9mCSrtq1EqoPAIHL9PWf0oKUcpnKIayzGbAzO9FdbfFHrC3F9GuqhQaZJIqdvXzldK/ezyLQUQJaU1m5Bzgx748XxSAiB8ZveL9mS1mAftvsQ0Re54OznBRV1R9rHzpW+7QB754+3mENWqeVhTf+FUgFMXcvrM/bXdNWPqafsYqURpDkI/0xhy/UxznpNcn/Lw2e8DviDtzLrREFXNxoZwXNDBuDFgQAoDMR0DuiYtk5YovYKUcpRyhFH7IWLRq7kTkakF3w5KEq+FeDR6Llrmd/rM0leVqiG4WsGEIilUqI06q51yKqkIZtCuG9XUTAy1Llv9tsQvMWmYSaVsqkSpJgjKKLsUAJUyK5prhyq1T0rDKPwupBLpx1JCmX35kbhtlcRXQ6wayvocElazDajqxT0hxUhhCJ6CiaNrMRsjqX3IKNAvCNLLpQxekaid8F8CLW7DPpI5v09O0/B8ID6mSVlEwv22jzxwMrKItYpqZHyeTfq8USL058oHMnzexf3BPimZfqrSU8qEvxBIaezex9X2gPFgIKYjgitgXaQxBX5eWF6U2oZ2/qX9EljL1zPJtJtjmBkPykXF++4L/dIxpSq9iObEYLtn2kd6oE5OKsrkBC2s72idJN5JxUNp1NooJGVDkqFgzfnhd1Lrwkbqt64TIaX8XB6Fb7b5aqlrS1JM2qR0hrXTh5RKJjzbT7SVVECekOpFHJWf68PJn6uu/DhhqqaYMtpnXMn5hiXVlPrCXQskQjVqvuAu9FFxvfN0k+9YuPMwZ2muCd93txntNztKFH6rH8xCYvoiW0lmo5aG4wAh5lvKF6Jh1ScdGScWEikdsHgwENMEcvxIY+d0KV8lfAKYuobxjWpIqQJ43qnU8McnITquRqiOICCBxheWMjfGgi9KZQKYwut3lR/sMidy4uwUmXY5wAIaFfz0LQQ7KWjy6S0bYhkPhgK6lROf4BaAbq4dENBwoSJNUhIpjSXRpzakyYnakvKVRn1Me5JSSR2NkdVwIrL9X8SKaSGY8iX/Te+YjICflAobuu50kbc+NEgaCyRf07kqUzoKusZT95yElok8E/44gp66zPkxzLawQRfss5/hR9qlloYgE34OKdXAiPLN+FChmPWiIdX2gPFgIKYd6KNYpIJsYg+2VPeezgHcoGIJIssfSBHt/HGw/p/Ufng9+685qKZzGmWWKiFBVY1vqd+vLpNmLDo/TK5PUfk5yJ0cWxG2/DMjqaY/kb8Vu5aYk5AqRTULiwLms1i5pZmkQuWUE1F3rXxfsFAxoX77RDSRq1QgpUSCU/kKY6b8GBnl+xdKgu1RUKCb+LX3pxeaElJ5iXv7h0YemXBsCMkp/0MROY32cRtz1lrnkThJLeUuOzFSKqmlOWQjVp50W6Kr1DDQHkel8UEKduRjQLitDykdMCAXAzEdMGDAgAEDBmz3GHxMFwcGYprAbNLE5ES0SnWvCaFpqtLKRYHbyHXZt1VKFRWt2KTZGwo8YimbYgij8rmukMppKvVVCnIgdaRCW83IQUt9DAM0dNt31u7L8DPj1V5MUHI8cElqwys1GpgYw9RQ5jhnxnftOv8yd0zchxRom/Oqukia7yWVhBCqJWab28f/XcyDdqkSz08MwW+a/9K6fbRnh1Rfw9Ry7lq+SX/Uca+rNGlfSE8oWQpi/qX8uNyAJylDhpcTdYH9fkP/0lx0+ZiHY4FkMRlVLeXHDxiQwkBME5hQumWuateWls/l0blhVH40ojYwm/ESoWZf7RE+bo6XhmdpgiiDQcyY8bVN22RSM8HmEa3Y4GWCo8iVQLOMok1/O1JGuX4LARjNv6MGQnBEc4uGGGGMLBoiWuvSmvOhHTnNASelvH9h4YDWedpNRmJ6qCDgIZxwWtuEiUgy33PSKRHOVKADn5QWK0qo1nOTQgWdIIAGsSDAUTDqeXxRaNpwC94wGEpCLCo/fU1aaLfdFmLPfo5/qZS31O4PAp5mQ0pHySNKvrqzDYoKfctj6PQvj5DS9hjgxhhpHIiNAQv5+a/R/n2Ms+0B48FATDvQIpFBUENUWQjzcsL5NaZ8zKSaz1VDPgz5gQ22IZJoa2YLzeYoGCWUUT81C+JQylMyjQ8qsFUVmNI1oEwFKFgyLdeI5+R0VD9Tp5r2IQfpQcKkcPKPIV/QHF/PolGvuSLTlTzbr+8tR+QDfkSuUzrlSHxqy00+bd/Slo9pYiKSSGlfQrrQJqNxoE8mDK/yEdpV0iSimtNuDvi1YmSaP3PcusJ9Tmn8kghqbMwbNT2U2MdQce4p4IZqqWmzTTbbGTLCc0YjpVKVt1EgPf/ycb4/aaq9VOAjHdMKehSed2m75FPqjQODs+mADAzENAE56KEdnStVXqIKSRRYVEC3BtcwuT79G1coVC8zGfUlJKdyRLFPTqW+bEWJKV1ZcloTUQacWT9QTXMQM+cDeSbgypI4f3JJmt9YeVhXMUoe/LtynJJqWqGIktMYKXX34KeHap0vqCWkltL7qBoiqCY0EeWSUslkzwlplIzq4N9FCD+4MKVo+0nmQ5IKyGrquJBDSsN9FVvUhuRUitafXf+U975v0n1y7enjFkJqKQ94GgcprYLncNzgbjzxYwpvDAjPlwIfvf3BWEDvU0GPhJC4SqR0IRLSSsvf17jaHjAeDMQ0Acm3rISbTGgfH+itAtGYus1g70gq0Az+GQ+qi/zW9nOu32ufCY+b1nk1prpxQ/Dr0hvl1JQLBR3smfXnoj52qBzY1X1k8DY+uYmBvYnSL5W2E4Dkc5pdAUbpKDlNkVLZ7UDOWcr3x9RSt79NUmmflKewi5RKPqReGhhqK/a7W8SDdqlU1u+ZH8NJqu9fSqSobeXoS1LDbBzhNXLA+5NDTmNop0jTvfrRF9zNyS81WtjPIWHMIaUhuZCev9lWMeu0rGS0HyPnrcVraC1BfCyI+ZVKVpOYJWUhk9IBiwcjPWG/8zu/g0ceeaS1/bHHHsPv/M7vzLpTCwWSb1kJ5RFW2l8ELzqXjp1UfJ9fZrR9XZOeaVLVmEJlX5OqxqRy5DRsIzaxhX2NmQXDe6X2XXlUd6ZRLpyKUWkzGdnSp1pnKyKjqkYVm1SM32Xzaggd1UWudIFpXWJal+zzhJ2o+GQVvty1ZCJcqjoIYuvvJZtSS6l/Md9Sei+qpQmTneRLBsipYCTfsi5Syo/bXkz6BVTni8DJbDg2AGx8CBa1MYtI2EYOKZXOibZBZvqmD66f5o+6rVNDAePxNye11HeFKZoFnT9mhAqppJLORWldPobnqK9yajrfjC8lyA/Hg9hYkFqgxoIggUxSOs9jQTgejvs1YDwY6Sn7u7/7Ozz++OOt7Y8//jg++clPZrdz2WWX4dBDD8WyZcuwbNkyrF27Fl/4whfEY9/0pjdBKYVLLrkk2eYnPvEJKKVaryeeeCK7XwMGDBgwYMCAAQO2PXqZ8jdv3gytNbTWeOSRR7DDDjvYfVVV4fOf/zxWrFiR3d4ee+yBCy+8EPvttx8AQ3hPPvlk3HbbbXj2s59tj/vc5z6Hb3zjG1i9enVWu8uWLcP3vvc9bxvva19IqimHHPRDYMfaJPnN/1hdeMA3h5EZbYolZyfVIpWI2/ZxjIs3yioAppA6c77pizH9wb6nfnGzfl+fshhiQQxc1fSSantm9eaeVI1alyhQNO+biHt+30LFqRS6KuWMw4wfnieZ8enfUB0BICok3HQXQ+hrCqTV0u0JoRradSzg/KtTUejmuLh5v8uSkFJKc8HdDUKzPg+GlMoYbwvkBjzaSHtmxue+pUC3L/ooZvtc/9JxVdHKyTgi+ZKGn1vmfG7KH9Gdh7ZZBL+ThTAuaF2I3+G42h4wHvQipk95ylOsArn//vu39iul8P73vz+7vZe//OXe5w9+8IO47LLLcNNNN1li+sMf/hBnnnkmvvjFL+JXf/VXs9pVSmHlypXZ/YhhHD8zaeCnICNKz2TLg7YCq5w/acx0l0LMxyucGFvnsP555JmRU3s+87el3KYlVIucjhsVM7WZVEuOyHHTOM8DGp5v7tfUvS+IfMMR1Ji/WY45r8uPzPYjMw9hrNKT2edIJ33m7ylXKX3uE/AkBUABCVfSBTD5zDWk5yrMLhEjqPZ47T/XOdH8OX0Jf3FdvrG8Hy7oUUXJKQDrc7qtzPtS0KjkXxo9n0z1ASmVCGmWf2cvH978FFGd9yE8W60xSlq0smef/u2KwgcgklK3b/GR0gGLB72I6Ve+8hVorXH88cfj6quvxq677mr3TU1NYc2aNdmqZoiqqvDpT38ajz32GNauXQsAqOsa69atwzvf+U5PQe3Co48+ijVr1qCqKjz3uc/FBz7wARx22GEj9YsP2LMhquHA78gfwJ/i9sTCzokgnBS532uffkkoA3JKAVHmun7AQ6n6XbsvKiGJPfmIkUpKfdyqJxyJQ3uVTOSTlBVOUC1pVePwcpPRlSKKJlNpMgp9yUx7vlLK38slBvOi8EPwX5qkliqlt9tJqCvSXUp9FsvlGwuUAmSSmuqD9KzlBh9yRTckyV3k1GtnTBEusfRxfSLyuVqaS0pDUjhXJUYloppTjliMvA+DuwJlVNrGyapnSRHGghCLWSkl8KItc9H2gPGgFzF90YteBAC49957seeee6IoZk8/7rzzTqxduxZPPPEEdtllF1xzzTU46KCDAAAf+tCHMDExgbPOOiu7vQMPPBCf+MQncMghh2Dz5s249NJL8cu//Mv41re+hWc+85niOVu2bMGWLVvs582bN3v7x0WyWuqIake0E3LcBXhbbcWmaUeYoEIFM4wellJH2WMzTFLjJKWFYE63amijGprABacKEil1QVGC6VyXRtWEqXfPCaolpInJQnITyEGYIqovcsz49F4KcEqR0va1/MjbKBryYj8uUnKaGgdyIsxT5JQjJKopkmqQZ6aXnvUcF4QaukVQJXJq4BalCwUUiQ/Iz5QXxIi2dcW0sXDuJwct833HYtWdx0hlODZkWE04hgj8AXOFkdJFrVmzBgDwi1/8Avfffz+2bt3q7T/00EOz2zrggANw++2346GHHsLVV1+N173udbjhhhvw+OOP49JLL8Wtt94K1SP10FFHHYWjjjrKfv7lX/5lPO95z8Nf/MVf4MMf/rB4zgUXXCC6IOSkiRnFTM0H/tQxYV+ka+e0FSLVloOyVaEKrYOcpU07I45CNnLfi0bNh1VIm5eJsDcqKQBM65KZviPEFE7NMv8WmMRMk5O0OaBDMeVZAUz7fAII/b4i/qKZf7dQLYq2FyGEbcXUTUQEafIxP5UMorkdkNPYONAHOUUjJLIYM/kD8PzQc5VQ6RqhL2t4LBHUGDltJeFvXbONufI3j5nxJbXUHN/O2EHHE0ZZKHKrylxE7PdF2sfdV0uBdj7SmF8px2J7rjlqnf6OZtv2gPFgJGL64x//GK9//eujEfRVVYnbJUxNTdngpyOOOALf/OY3cemll+JZz3oWHnzwQey1115eu29/+9txySWX4L777stqvygKHHnkkbj77rujx5x77rk455xz7OfNmzdjzz33zL6HURCa0Ecxx4WTSJ/zw4mrUIHJUTMW2qink577gUt7tS1hyWCjlk7r0pLSaW20Z9puiWlEFSl0gVo1ZV5VBegJR06ba1H6p9CsH02En6EX9zHji8n6JZPdLBSfmCIiqeP0ScOQT4Cdx3dK+xc4YuNAbuATIbeiGUdvspo4L+xL1zbqq23L/h3dGCCR03Ghb1uSKbxNUH0TfkhKYxaP2PMbSwM312Q0p9pUzNphzg9UTmHsSCXSp/0xtXTAgLnASMT0rW99K37+85/jpptuwnHHHYdrrrkGDzzwAP74j/8Yf/7nfz6rDmmtsWXLFqxbtw4veclLvH0ve9nLsG7dOrz+9a/v1d7tt9+OQw45JHrMkiVLsGTJkpH6O0oFEwIN8Cl1NDVh8eunhq+UehKFNzk5clooeAoOB1WYCu9pNt8RBze/VSiwtclLSqS0RUwjE1CtlXERgBmQJ9F8p6p25NR+D4acdhFRSc1s+8POTp0B8gho1zGp5Nlmv3w+V0E9AV0iqIF66krXLlzMZhwIMQo5DdGXEPNrj3p8ZS0IOhhb2uSU0MvakTEOpKrAtaultYmlmN84IKV+po6MIEV2zCi5iseJcSh+3LWHt9sV8DQKFpL1hPLXzlXbA8aDkYjpl7/8ZVx77bU48sgjURQF1qxZg5e+9KVYtmwZLrjgguzo+fe85z046aSTsOeee+KRRx7BlVdeiQ0bNmD9+vXYbbfdsNtuu3nHT05OYuXKlTjggAPsttNPPx3PeMYzcMEFFwAA3v/+9+Ooo47CM5/5TGzevBkf/vCHcfvtt+Ov/uqvRrnVAQMGDBgwYMCAAdsIIxHTxx57zOYr3XXXXfHjH/8Y+++/Pw455BDceuut2e088MADWLduHTZu3Ijly5fj0EMPxfr16/HSl740u43777/fC8J66KGH8Hu/93vYtGkTli9fjsMOOwxf/epX8fznPz//BhvkqnxdimCf9XWXWipuZx+pH5L5z891KAdK1J7fZ+MT1qimVZO+ynO0d7JZE9Gely6K1KRUrkbyq6ya4IYKTYR5o4TUcBWdYoqp+SybugpFfTBlRKELlKryUkmZfrTVEr6NR/9L8AOW2HvBBJlCKuCBowj+RgDYvXapqb6SEv7puDrKW2qZ9gOz/mKGFFWfA0k1ncsynTHkBkABrs85qqlrf3TwsTH0OQ+ry9l9onLK9jPf0phaKj2/KRRM8acgybnGtqwklDLj90JgLbGbF4hqWkN1WpRm0/aA8WAkYnrAAQfge9/7Hvbee28897nPxcc+9jHsvffe+OhHP4pVq1Zlt/Pxj3+813Ulv9INGzZ4ny+++GJcfPHFvdpNoRICD/qYpbuGr9Dkbba5H3hOmqgweMFvX3ntiGZ9LyLX309t1tpMSJS/UHL0noY5zuVgVL0nLRtdy/6tNUXhG7P9Vl1iKyOjRESnax78lCaLUZ+xoM+cwErthdcZxYxP5DuFUScppbRZMLDzVWTyiJFSaUJR7DshkspN+y2/0yfpmL1QiWjsnBp+jftccspBLj3SIrULXQtVj1DyaPwgyt6a8SOktO/zJI0DOeS07Q+78My93a4/6f0t0hlZlC4Wl54B84+RnpK3vvWt2LhxIwDgvPPOw/r167Hnnnvi0ksvxZ/8yZ+MtYPziXDYofrvdbOPv6RzY8NWwV6EkJSGdbTFetzCMf514qSUMg5wItv6zK5HfQ1/MKRmTGunbtQwE0wF3foOPIVEt7cBjvRytdTUrvbVUppkiJQ6oqrs9rDySQlT254mmQLaC2zi0cax5Pxd4FkAUmppCCnwKZZ3r4/KU0BDKffi26R2jZ+Z8zWT8prytuy/wf4Bo4E/66O8YuDPfPji1w77QufOBrExEjDjRJdayn/vPNgpppbWTBWNkVLK6tH1sm0Hz0CKZKbOzSq8MUcEVnouw8IuOcpmeAQfD6IHLQCY1IJz9+qDCy64AEceeSSWLl2KFStW4JRTTmlVrHzggQdwxhlnYPXq1dhpp51w4oknioHcN954I44//njsvPPOeMpTnoJjjz1WLBu/WDCSYnraaafZ94cddhjuu+8+fPe738Vee+2Fpz3taWPr3EJAi5xGzXrpH2VX+qeQcKYmmMIbiHUkj2mi/YxJJpYY3PRPA0p5qilNIGVD3G0Ev2qCpzrgyJxry1NLQWlgXDoYS0qbyHwa/K1Jv/nWKZk+KRyFMmSUSGmh6ibDQFCSlP31Q8WEtvmfZRLKE2j7JRL7Kze5oIh6ygNLfddKA0WNqi5QaA2tdBMI1Y7O50m0VfM5nHxILbH/oh0U9WSFLTHaOTbM3QzeJ9G+qwKlvHElHAu8/KaQVc5QNQX8oEiJqPJ2QlLKqz3Z4yNWCF5qNAyYlKwT9GyGzz9vj8aCmAVFcvFx7betN6k8sKkxYVz5Y5XSQEaQY7KN5l/+5xfV0yf3MBDFDTfcgDe/+c048sgjMTMzgz/8wz/ECSecgLvuugs777wztNY45ZRTMDk5iWuvvRbLli3DRRddhJe85CX2GMCQ0hNPPBHnnnsu/uIv/gJTU1P41re+NZY88/OFbGLK06h04aKLLhqpMwsdMb/IcADmkFRRs11+8MM2UhMLmd75pJHjBiC5J+SAEu8TIZV8v+ijuW9OTuU8ijmeWlYd9SJs/XRKLb+xiOIQktJJVdkJSSKnOQgnnKg5P5K2apzqiJjiiU1CxrQPS0gV5AmqVdkFjpx67SJNTsNjt2fEqxbJYwPQn5TOVrnsajskpyFGSRUVktMUSC0FIJJSUkttOWLax8mopJYGpDSn5nxsLJAWqXH/8vTilae06+tznoJdmDJ/c2lsoIVrDlGNpX8LCepCJqcLKSp//fr13ufLL78cK1aswC233IJjjjkGd999N2666SZ8+9vftpUvP/KRj2DFihW44oor8IY3vAEA8La3vQ1nnXUW3v3ud9u2YsWEFguyieltt92WdVyfZPgLHdLPLO0DpQPTuoHkOwqkatnnmc/4RBKqpl2BTrbPiWCpPvCqjCig1NoFRDH1JAdh0BNATuttM765h3YOQwmclE4WM5aIhoRU8h0LJ6L2/fvlUn3C3s6lKvmZzgbSBMQnImuuU7ATUQFjsudZh7vKkcZ6KZFTft5iRhd5nG1aqFzMJSmNXpONK6nxRgI998ljhDZILe1CJVkhArVUInvhwjFWKKSmoEj0D3hKmf3HXTXLe84j5JP3icYKR0rRpAJ01hPbFvtMiCmqobVkoZLTuUZYOTI3Dd3DDz8MALbUO1Wh22GHHewxZVliamoKX/va1/CGN7wBDz74IL7xjW/gtNNOw9FHH4177rkHBx54ID74wQ/ihS984bhuaZsjm5h+5Stfmct+DBgwYMCAAQMGzBm4y9dctA2gVZznvPPOw/nnn588V2uNc845By984Qtx8MEHAzDl1desWYNzzz0XH/vYx7DzzjvjoosuwqZNm2yMz3/9138BAM4//3z82Z/9GZ773Ofik5/8JF784hfj29/+9qJVTkfyMX0yITRdpaJR0+10q6UpP9DCmq4ENY/5hHVdNwYqF0iKyKgpcmx7IDNSt/JMgQ70Pq/9wpnCrFmf+5wplBGVs2Qq6aQyemHf9C+xqH+pBGpMLW2Z+XqY9CVlJHacuaZvgi/YtkJpa8ULzXXhFbj5vi3gySY/JbSzvSDfCpDnbzqXKITfVziejFIMI2dMTLkz+Mf5rkExM347Gp9bK5hVJfAtDS0b/JqSchpTTSVzPm1v31PbfWAuk7FzRTRmug9N+AWo7LTw/CqAy52Sr3ms8EZLNd1eBwKGH/zgB1i2bJn9nKOWnnnmmbjjjjvwta99zW6bnJzE1Vdfjd/93d/FrrvuirIs8ZKXvAQnnXSSPaauze/xTW96ky08dNhhh+Ff/uVf8L//9/+2+d0XGwZimoAzwRNo9o4FBbWfutnUtnb74oOYS+Ui+4RRLfgchBOSiaqPzzjeQD7mASdmFgfaZnBzjO9nRv+G5LRoCCmZ8IE8Upo74bjjC6+fMVLaSiejR1/Rt0z3yl3HTiSJtssmKCq0ufGJyP3ruzfUZBKEbp2zEPIXLhSEBI0/n7JP53i+u9gYUqAQF7tSP6h/o/iZ9kUfczdf0FGKKLPduRfFSGnsmnxsS5HT7vuIp5Hj/ug5SB0b5i4W3Xma90RGQ39zbs4H/1s3zzZND5IpP1UVbiE9/xpzl8dUN+0uW7bMI6ZdeMtb3oLrrrsOX/3qV7HHHnt4+w4//HDcfvvtePjhh7F161bsvvvueMELXoAjjjgCAGx6zoMOOsg771nPehbuv//+2dzOvGLxhm3NA6zPKFSLhIafu77YPn5pNHHEJhDuwxorYdpHAQ2PpRRZKZTQrVcMsTRRYXvSe68dFjjQhblOiJ2aZMKBsJ37dEQiKhGZkIgLxJxDKad+KqVRFrUhnYVLL1UUdfOvRmlftZ9+ihFW869m15BTU23PoGcu57lLHcf3jVLWt2j+mwvwcTDmS9rXuuQWi+0Tw221R/L84EeulgJttTJ8tfrdembbfqwxhKmmQlIaXi9MeSWhbxEAbzu0fe55CjlCmEKuYOMBfTbH+c93+Fw/2Z7x2UJrjTPPPBOf/exn8eUvfxn77LNP9Njly5dj9913x913342bb74ZJ598MgBg7733xurVq1tppv7jP/4Da9asmdP+zyUGxbQneLqTePBS+Ll7UAmVTTKtu/1pYhUmyQ8nuj7VX4A0cbYR93wZjdk59RfNqXwiK1GjhsJ0xvk5A3c5QsQ94FQSORVMMMkkyLK0zQaNCS4BOShYlgR/W1s94Z9DM55VTWBIJ1Vv4moLIE8+pIo4Rcb8LAbF1KD1bGeYtWOBjDmYKyI6CrqCnyQUCoDWqCjlHGpAGXN8qerks8EtJ4BTS82+xOJRSBkVmvj5Md0FMdrjAt8ujZWUPo5cD3gFq1wUqkbdpMuLjQPcnF8DZgxonv9CG5O+UcV95dTeW9P3pIKKhamahi5f4267D9785jfjU5/6FK699losXboUmzZtAmBI6I477ggA+PSnP43dd98de+21F+68806cffbZOOWUU3DCCScAMMHm73znO3HeeefhOc95Dp773Ofi7/7u7/Dd734Xn/nMZ8Z7g9sQAzEdEbF8fFJ6KMmcnuVvFZBTuR+y72kfE+DoJRe1W0IDHkkloikN3+STRibBEro9CUT6H5sQJILG94WodQE0ZUjpUqGq6kfSxklpbMLzFJ2ECT90QegLPhm5bWZS55H6/BqWlDYTUtl4A9NkRJNJzE+3VflJIKdmX8wfdfFjrqLxxUj1ZgyJZdGYCzIqLXBTPqij+t/HUDbuKEROa5QdZzhQiijAT8tESJntCc58LxNUfky4nSNGSnOtPX2RitIPzfl2G+D5nXOEC8xwAUrH8HEAWLjkdKHgsssuAwAce+yx3vbLL78cZ5xxBgBg48aNOOecc/DAAw9g1apVOP300/G+973PO/6tb30rnnjiCbztbW/Dz372MzznOc/B9ddfj3333Xdb3MacYCCmCVRNjfgUYsNK39QuMSIZTgJ+LtTCe1+jjk4cqXKq4gQUpIlBYM7npNIiSLqfMueH7cwVCqF1n5Saf3NUkFYb8ElpTC2NkVJphR1LIN6FkIjy7bxNpTSK5jg+IRE5Vcqki6rtJJPdBY+c0r0EovqABinVlOf7zVnAzpaUdvmZ5qSGyoF0P2HbkjWGL64qrVCgRpVJUqVnOlUuWCKpIUHlx+W4AUjHhVXgxk1Qw/Rx5po+sSQ/9LBccaiacusHANE6whFNJ7UATP0LKY+pzhgYzzrrLJx11lmdx7373e/28pgudiwcm8+AAQMGDBgwYMCAJzUGxXSMiKmkMTN+V13rvsEOXDXt225Y5pRvb5vzfFO8106myJeXYqYdRcvN7TZBvqptGdJke7pAoar2diijnsI3zUmImQNDU2GogvhVZ9p+paOa8UOkEmzza/tqqVNHuFpC98Eh/SS5qY+/56rK9mjKn2vkFslYyMhJsk+IjQlkDahAbj/NdvY+uz+dvr1pVVRST1OIjRdA2+dcKpc6VyD1GXDp42LjQJhkP+VTnvI1pXPnEwvJx3RAHAMxzcBsKq7YKk6tqP00eUy3yU34zo81F2H7IVGNpZ4C/DrZRE7N9du+onM1xJaqblURoUApAIAqknlMJVRQKKG91DAhYtG1njlfKC2YIqWckI5rYAtNd+G+GrDm/PY+MymBfE/ZZMN9Rzm4n5n5rFpm/b4EYrGDnqFk1bVsU338mIUU6DQuhOZ8N6YYP1MKgJrNbypmdpWCn8y146b7LoRV4AA/t7FEUPvkNM5FmFIqVYY0hJQKLmauT/maDhiQg4GYJlAqlUESux/sHIWUX5O/T9W15+25ibDtL5aavLjC2qXQ5viD0gBuU2vN0SKyRJOLVLdTodQa2aTUz1HoyKlprx3YkENK3TmyT2kXKR2XD1TM75Tv56qpVUHo90o+fs3pYa9cjlTzOSSoBE5Qn0yYrco5yvmjLFTnG55PraCalswXkit9oyL1fIX7+iqkOe2GBTfM/rZaOtv7jFlPvGAoyneMtPUk9FOfTSDUfMKUtp4jxXSRWjUWIgZi2hPSZDGq6pFDCLdF9G3Yh3FNarMlpTxdlMnFZ8hooWtPNS1V3UxY5rOh5v0CmUJySts5wkAnfgwnpTzti7uGahHSURRSmkjsFNLE58UyEgCRrAQwqmk4KQFuIqLJhpQWKQCC31+YoIFPUIsZKesB0B2dP0q1p5b7j7BAliwn/HPucxwLfLL5UzPbGWdEPuCyflC7ZMY3/5oFtXnOavsd16gxzQKjysRY0BVw1JWaqi9iBTfqyAJV+jwO5FaNa53T9DkkoLmBUANtG5CLgZh2oE+apz4KR6iMdvejyLpGThWX9PmxVDC+eptKkj+OoZwrI0XjKFCqGqWujRqqnWoKTeY9Z8qHjk22yvqToiGjXCXl273zEoTUtos0Kc0hpKNOhIZYFyNPZNbPTLuKMACLiA4ipPNJqm4dvxjRFUU/VyVHcxamsaIaMaTGiC6ryShJ/kcBXyjxFHJkzk+fW6NAkTw2Jwp+lEh5ypUcOzenApxEpMcdSZ5d0hhGNeVmezLrx9LEhf6mC4mcDj6miwMDMU1A8g+djXluVF/VXFK6rcGnN+5bSgpcH9g0LcyvrIRJsl2iNpNM4zda6tooqEobU35DTtv9a3qha0ZWTYqoWpcodGEIbXAe/56532yMjHrbBPUjTPg9G/LZdW5YijTelvM1peNDcipNOLxiTBdJlZLtL2bkpHgaJ2Kp4dw2+ZohKc1drG4L0pnjV5sKgiJzvhkPisb871TR6Z63MHbza+gvmgh4SqWPk8z4s3UnCNEaA9A25/OfROi7Hprsc8npgAFdGIhpB0rIZT7FY+cg9LgvKc2tfR0/v5+P2rgHSwL5lZXamPMnUaFq7q5QNSbVTDQSv1U1pSGlNUqjADD/qOlGbaUJo2Qkt4D2qk6Fage/HgdVbvG2NSv1ucqhF8Iz3zcTD4foZ5ZBTm2TAUkF4Jn7Y+c9mZD7zHZVaUuZ7FNt8XGgL+lsk9v2+dzEP24zPsEW8eCL1cTxhTJPed24AdGYUYkLV/l5nhUEa0tsvAitKqZP7W3SuTkYWzBlRDWVIvG7yOl8Y1BMFwe2v5DOAQMGDBgwYMCAAYsSg2KawKg+o9sLuO/cbMx83HTn1QBXcj5UWytbMOdPYaZROXUT9OSCoKxJrFFLrQqhFQpl3ABqFqVOih9XXqd12SpNyjGuPINhmcP5ghxh66umAEsLFkTiAy7gwTPzq0E1zUUqMLKPWspBamllfYPl31mXf6pcHnV0pMz4UhWo2tvvyucaNdQPgKI2pv1mbQCU1G/JtD47RHxLBatKuF3O1jH3/qYpSAFNkn+qmMM0oqrOJwbFdHFgIKYZyBnUU6VDc5FKDTUb0CSViugfJaE/4IhVrkm/K5I5zIUqmfMnVYXpxs/UEFZh8gzM5rUG6sbMVqg0KZwBmfJZuqjEdydPefI55Cc634TU9IW5NTSTfkhOgbZZn0My8dv2GDldzHlMQ6I229yjKcRLDne3R/3kpLSGTjxr+ZH71N58IyddVIkaFVQrAKpQskkfkM3r4b5YfzgqjejCNmw7RUq7SI7Jfxo/Zra+s3xxyrcBYKb5dvYNvhgN29seMnQM2DYYiGkCqUGdQ5o0aBDflkpqKpcp4PucOd9Vd2wr8r7lZ2a2VVpWTcIcpimkEvUDcdV0ChVqKEyrEqWaFFMhVXaSCf08yyYCnwii7xsVYlr7j4dUAcaQrtLmTY2RVDrWqLfpIKa58tuV+gKwtC9CAm5f9fSDnKgdoK2kSillBvhIjQ1908KFBJOT0ho6OR7FiHZliW7avzQFnjIuh9CH40IMUgCU6WvRpI+S/UwpOJMj5fNJbcZQa+GZV6nj/e8gt/pbbEyomywgNZwP+2yUO4mQAmiNDTFf0lQKqYVASgfFdHFgIKY9kaswEFmttO5NTqXju6rIxBCbkHJUVLpue1vTdmZ/clUmQko1rdAopcrkNaWUUdyMXDLS1Rosmo9cOSGy2u43N6kF7ejCZgUAjBpbKJ2clDhi5HRbk9KuJNwhseTbgThBBeT8p4sRozx3Xc9rvHxxfzNtNOCJkVK71astGfQp4nLjX6uNuQp8IvQNgGqfb8YOMufTorKWAqIihDRNGt3C1LSRXqBKRFcqthGOD3NNfGIppEJzfq5pPpXfdMCAFAZi2gNSpGpBK3yExE9nk9NcE1lX9G4KMTcBKXI3VIppYiO1FKBk842yCbdtrkzUoa9p0eQzLZUx5Zt/zb2QKV6CGOmaUCNS+01llIaQat8jjk9UfNLxSSFNkPkKi3f9Ju1VH8QnH181lcgpnc/7JRFUKTn/9oK5SAtFSFdo84muvGD0TfhyOwzBuCT7k47uYz6Oqm+hn6nXfpBoH4BNtl8zcz6RWLOAVYBuCCq0TR0Xw6jFMKpmDOqqQBcS3pCUhgvVufIvjT2nobsPwHz0O1RT+Zg56X42NGbv5pBqe8B4MBDTHkhVU8khp4Suik9c5Uwpm9QXKcVTKm2URJSlIAmutnATPpFSAJ7SWESed2kyl9wE3L68gaME5TNtVFMypzf5SkltjSkQ5rqpBUP8PIBS0yhHUKFcXtUeyMlPGqJkOR1HVVJi58bIKZAy4XeXKVysKKB6E9K5yDk8F2VGK22ekVQQlBsbmnPs9vzrjIvQ+wFQbUJJOU0lc77x/9RWNaXAJ+k5kNI2pReQ6P3ct9uYH9bWVffevJdV0xxy6s6Z09sYsB1hIKYDBgwYMGDAgO0eg4/p4sBATHuijxISOzZUSiUlpIDylE1J/SQVNaWkUECTr6bKEfi8NnaXWhqqmqEJn8x4XUpJl+oSliOchkuEX6jamvDJnA8YBaRQzm80VERcYu20EiKdw1FohVoZhXSyOQoKLbM+9YG/jw1ifqWpbTPQ8eAmq3oy1ZT3JaWcSqoptbWYo/L7IGd8GHdAZCqRfgHV/CZ91bPdRrerAMds/Ur5Ezer9FPKBUABaP3O/PKkxs+UovNNMY0i+tuMpW2KWnTE1E6ykhqW8PXPaZvxu8aicSM1RuW656Si9AcMSGEgpgnUDUHLJaN9g3zoGvz8Vls6PpHVzaArbbftJAhoCB6F24eUotkXVgKakzKN0Jaslk04w6Sq7AsApptghNpOPDIptT6kHYN+NDhIoXEsKlBzP1PlMgCY410GAOtuMMJgLec0HO93LJFToN3fuG+pb8J7MgQ+jMtsnxuQKJ0jgSLciZxWWrdajgVaEmJm/D4Y9zjAS5OGPpomVRT3MyVfz9pG5xsfU+NfWjVlidGY+kN4ZUK7FtkZhNX5lUfG9EyXnq6UWbnIGYc6/dIj5vzYMfOJQTFdHBiI6QjI8ZkEuiNzQ1Iaqpo5yC0/mvKPpeuHffL3x0kpkE4UH6aC6oP49SjFlUu0T4P+ZFGhqgtUSpmr6qLxNW3aFEgpETC5DKA/sXgR/6pRBC1RLZxKQk1ZX7a6SRcVJ6cxtTScsPg9zCVS5BSIJ+gPlZJw0fJkRlcwZPhMc6Kaet6lRWhITlPoVEkTvqVUXz0H4/7VFk0AFOBH2nvlSZvtlGyfq6Z8n82qod372ZQGNenkVNO+ts/sKP6o7Sj9uXn+YzXuw1RwHClyyo95slhOBswOAzFNoIBqEcTY6j+2PSdgIYyAB+BPIgnVtA+6yCkPdqi97XE4VdC8L2DM+PR98KEzFWGbgkROKWVUyRLtO8V0wn6eRgnATEJFoKByUuqppskgh6I1qZhznVoKwDfnq9ErPLUmozky48Wi8IE2OXV9ax8vKafzrZIsRPQpyJGz+ExFzXvktAMptTR+jnDNyKXGRaUoZZSUOsoGQJErj2fOLx0ppUCojOh8jq5FI9BWRnMIquQu0A7KCq89+sM1q8BJlgquq+KTlNVjvjAoposDAzHtiVHM9aGaKuULpAlh3P5nYQYAKYLf62uQYD/lS1ZC20h8IqUhQiWF6FpYftBdkwbk+HWLpgXKFVg2EfmUU3SymDEDeDPmT6M0+xrV1KRwMT2TCGnUt8r6U7bJqe2/bkqfNmlp7LeguidlPhnFUlVxv1gpsbb7PLsBOEVO7bYEmZWOGRDHOCrHpdBVcW0UzC4GfbxwFegcUTVEtG3OD6PzAaO0mrRvtJgsgowjbbcgc17MP71tZaHxg6fVG4fqGS6sZ4NYjmP/WfdTweVWfFooZUkHLHwMxHQE0ODeGdjT4cvJEaqZlKJmNv5rPO1UjKACbZKao5ACTiX19vfsbleFly4UqslpCmfKL7XGpKpQqaJRU42/KammKWUk5m9aIBykC2fWV7C+pikTXYzQ1oGKK5HSMFCLSGgX2upOnFxKfqQhObXtCOZ9qVKUVU07e7qwkXrm+xa/yMlH3IeoSmppzDrS5YbU6fKT+axWWh4L+pj8u0AKKAVA8e2VR0i1NedTEJRNkZXIaVow075XOKLnYouPFaMqhp6aOkv/1LlELFXcQilHOiimiwPz/0seMGDAgAEDBgwYMACDYjorcLN+l4k8B1w5masKM13J/ctmqSvpfoWi9CfUR660+BjF5SFEn/KcpapRakoX1SS7b1QKk/bIJL+vm+wBqbbFyHyWdaC2qmBcIY0F+9RBKqmUWiqlq5GCtUIz/jgRNdFHgqLEwKdFHvRgnmlnCp/N71qu2NQd9BgrLZzyLc0dk3LM/KmCGHybpHSE3xk/bhT/1RTKUPXkifQbM7+J5o8n2wfggp9YEBQFUs6mbDCNHakqeTnp4nJS3PUFf6ZjkfV0jZg5H5BVU8J8q6ZaqzlzKRhcFcaHgZj2RFhtKYWYH2Uu6Bo0Gbm8pXJO03B7Xz+10IxXQgFKN2ZqM5jGqjulME7zHQeR0VJpFFrbgbPUlObLbOPm/KLJLkB+pn1KeloiBm7KY0FPjLRzIgu4CH4AXvqYipHJWhf2GmHUveRiwEmp18/AXDXbgKm+5DTn3O0NfTJwdLUDdBPUPoi1NReVpHIRI5x9x0spZRQgB0BVMKmhKk2BUO2cpmA+p13XNSSzf9U2Qt8SzrnP8bYuAZxDTp8s48CA8WAgpiOCJiJSA+izSO4gKQ6yXxg/B2iT0vC9f36cuHLkTG45EfR9BtZccsoTzLevl26BUkiZQIXG94yCgbhqylSVLn8xcR/f1PiWEjmdqUugqPzjtK8P8Ykj9CeVfEntsWE2AUZKedBTsu8dSBFMoK0KSORUUk3nWymZDfJTt8nkdBQC2GcBPCq6/EljSI0L/DnnfqZ9lea+fucUCEl+pRxl429KaqoUnQ+mmgJwgVAJ1bSPrym3roSBUPLx/jiQOqbvvj4Qi290KKKpKP35himyMEc+povek37hYPAxnQV4pST+OSfNVNeEM5sJqWj+k5BTdSqZukb7rwrKqH4wk1Kl3aQi53aN9Tn9Y6wswTfVW8y9sIAHZlKnQChj0hcWAI2K6q7db8DkUe81FGZ0gZm6dJ/r0pjotTHH02BoiKR7zegSNRSmm2Onm/OsaZ6f60Xbu2OAdlRuXF2d/cCplG5NMN532ezjxyyECWmcSKl67UDC2d37XKuafdyHcskif8ZnWyFqFBSeMODyHfN91roC7YImm/HCpKKL02+TH1V7n/ti1GcxVIfHRYb4M9onQIumCv6dU1tSO9vbWDBgbjAopj0RNbEFyml4bB0oBrG8grONxI+hjwnQSxmlG8KJSIJ9m+uQzPxy2ihzTH/4pu7090KTyXSjgkw320NzPkXnl8090TH2Pdw92b53TSSNWloojYmiYkqqaYnuJYRkro9dN3as9P3kqinScTlKUE46qe0F3MeUECqA/PO4yWTfqH+OLp/y7mvHP0u/Z1dCuL/feTyLyWj3XqCWFdTAnG/7nama0rNhziEFNG3Sl7NxtKP0o8/7CN9B6hke5RnnVhPp/FRKuYVSAW6Iyl8cGIjpNsKoEwtViYmVK+Rm+1iFmJxJKfSHpXKkISnlRDFMGt3YawDopihA37vtRhWYvs216yaRPmy/QMENjTl/mpnuKQjKfOZm/XZu0xiiJn5tCKojp8ZfLTUBhfcTm5z4vrZZv62MpqpZjQNPdr+xFDkdN/qS09DqMRuCmso3LG0PyemoJn2vyEek2zm/bQpuIj9TAJ45n1JGAczyYv1Ni6hJ3yXJd/6mpk/+M5tOITcK4dy2hs4uEstJZ5e/6YABORiI6Rwgxz8slkc0ldOQE1S+XdoX7s/tl39+cx4jpdNBPj1r0lL9qhvlRPn6x7eronBQ0n37ufEzJVD+Qq6gUHR+/7yEPon0zlcsOErJ58TaMp9lImo/CwQ2V1HOPQbon6vxyQDp+Rlnwvqc68+lz2kK3CQfK4JRKLc/NRZIhD52Lf88tzA27/MJWuhnavrrCoiEBLULBRXpEPxNRzHth+hDPsf5nOZYPmQf8jx/0/nGEJW/ODD4mA4YMGDAgAEDBgxYEBgU0zGina/PVzhiOQiz07no+P5a50cE5/jAkYpBKoVTKvwKK/45fookaifpVzZGwSm8NuUzLQTTGk8bVeu4UhAqh5I6EfqKmWPSeQ0kRURsG6GiGjP5B+rpHJvx+2B7UBK8Z20elcu5jNbnyqVk8u8yrfMcx6SaSub88FrpPs3uPkvVLqtM+UyBZgxTQOW58zRImPMrro7Ss5xhIcnBbH1L+0L0F01k2IidG+Y2NdvCanBzfjtJDD6miwMDMR0zwtKFfBILk2SXSkV9vsKI/9S15gr+RKS8xM99Ije7THejgCJnaxSoIJvtQ79TGwAVGUDIPGdNfQln/vZ5beSQz66JJ0aEU+8lUtrVzrgRktHFTE6l4KcYOZ1LP9Oc66fQ179UWlZ1EUVOTkeBdR8K2uxCHRj/SuZParexAMcyCHRywU/deY3DUqUU+MRT3XX5lEfvI2HCj5UinQ9IPqQc23Mw5IC5x0BME6ihe0XYSr5nYaQ+0D1BcFLadf3cqi5doGtVQdUnKRI/l5SGQQ/jQol2JSGqhw3ABjqVzT1Now0bGNVE6PvVYprgDR51nlAM6N8wDVWMhOZPUvHjutJD5bYzyrW3ZRsLCbHqS63jvHRF8+UT6vqaGm+60ltVaCuOOURR7tPoAZGzUU559H0YAGXUT34s+czHg6BsVD4bJzg5lforjSkLDV0+oTn3kPI1XQgV4AYf08WBgZiOEeGgTgn4Q+RUiJFzgBqCy5P2d6kmOQqORIDDwZoIDykRJeomB6C2x/QJfuoLiqItMyqtlKq25JQ+h4n2R51cgTQxp4pPOblE8yKK05NBjJR2tT3bCTIchGOkW8+h6Wy+kaNahs/xtiaquSppHzP+XCEkwCG5m61ZnxCWLQ3N+clz4Uz6NQpLTg1cVD4fC3m/t2VQYY75XTyvGe+l5PocMdVUMunzaw8Y0IWBmCZQad0apLla0jnoN4fyClFA3E8slrCfEEvan1IjYxOhnxrK3Wcd7CuUmTAoBZNps2r2OTJqSGq0G2OHmVwUKjT5SCMpo7qQE5nv0sH0jN4XUjr1iaBPXS9XjR33JChNUOG1JTK6PUxHkvrY16Qeuvr03T+ba0vn82v6frRCtPwYiKGkmrYU2cT50u+5yohgLwLTPfczJZA5H4BXnpSrpjxVHienFRsfwpymYTW7cZPT2bQ3yrlhRbcck/5CidCfy4XyQri/7QXzGpV/2WWX4dBDD8WyZcuwbNkyrF27Fl/4whfEY9/0pjdBKYVLLrmks92rr74aBx10EJYsWYKDDjoI11xzzUj9o2pGHERWc5QIR/bcBBASQv6iY/qirwpTBdcl8700GZn2tVVH+Yu2c1LaK2VUz1u1ZvPI1FWg9vZ1pW0JV+9ilage98N9PMMSofS+1qZSFK/oNKOL1ja/0lP7Za+JeIm9UQfg2Hm5pJQfv72qpRzSc9wFGgvCV7h/LvsbbgPQcuWhAhvjhJd6quMWJcsGJ3mzMvGruhXA2XW8Gfeo9LG2442tLGX/ddXngLZFaRTlsJxDtTHWH6mSW+p4guTtMlR9GpCLeSWme+yxBy688ELcfPPNuPnmm3H88cfj5JNPxne+8x3vuM997nP4xje+gdWrV3e2eeONN+LUU0/FunXr8K1vfQvr1q3Dr//6r+Mb3/jGXN3GgAEDBgwYMGCBQ8O4GczJa75vbjvCvBLTl7/85fiVX/kV7L///th///3xwQ9+ELvssgtuuukme8wPf/hDnHnmmfiHf/gHTE5OdrZ5ySWX4KUvfSnOPfdcHHjggTj33HPx4he/OEtpjaGGrJ52HQ+0VVNAVkMkNTUXfZQVHuREL1JKw3YKOBM9V0f5i4M+z+WPynMdgInMD5UEv3Z791+Nn2/UYFI7BAW1Y9WfUjnJxzV23Exd+CorU0/Fa2WmkhoHJJ/SmFpaBwEGC6EU4ThAz3KXxWQUBTV6zTFPd1Jwk32vtbjdO39M3am0rJbmjrFVRoW2sN59TB0lFdSUKSYLUO3+VXUwpuSppu54d90u1XQcyfljbQPdFiBb5144Tiktqp5KyfdEqilva1BNB+RgwSTYr6oKV155JR577DGsXbsWAFDXNdatW4d3vvOdePazn53Vzo033ogTTjjB2/ayl70MX//616PnbNmyBZs3b/ZeQCwAKR8SOZUIqmTeDxFG/Ifnxs7rmthi5vsYKKep7ZeaXXqYURBOOKYf/Qb0/senJ5SQFHZ9lraH+7ztoZl/lqS0jymRk8zw2hIpFfu0CFLHxMYBQPYnz3XpmUtyOpu2+dgh+ZhzM74UiMRf48a4zPUhSkYk+51X24Ww3cbdhgRy6o8Z+eSUo4BukUTeVh9Xo1bbmWNAl5kfiJNTu22B6InSODrO14DxYN6J6Z133olddtkFS5Yswe///u/jmmuuwUEHHQQA+NCHPoSJiQmcddZZ2e1t2rQJT3/6071tT3/607Fp06boORdccAGWL19uX3vuuedoNwOfuNKX25Vexp0bW+XGzw8JacpvNTbBxXxUzSSlwEuSElzSfV9F4T+oUVPD5EIiqH1Bg3yKqNKgmkNOk4Q09XcUAqMWgn9mSEo5YqR0saqlo44D3PqQyks8W4IaQ067seuHAU+EVAlSiSjOBTmdS0hEE3C+oaFqavc357hsJO59SE5Ne46g5pLT2aqmfUlgX3U1rpC2t/Opb6GQ0wELH/NOTA844ADcfvvtuOmmm/AHf/AHeN3rXoe77roLt9xyCy699FJ84hOfgOpZLiI8XmudbOPcc8/Fww8/bF8/+MEPku3XwXvJ1C99sQVUkmT2jbDlAQvSyzumY1BIVmeCq/wkBeCI7W2DeYpH2cZSSEnbY0EElIfUBXjJ5nx+fAyjKJizSaUyyrmzuV4XKTXm/OZYzF3uwHEiNg70TUyfq6SOEzHSKRHSVqBVxIRPC1OgTUrngojORUqqHHWUyGUqEIpM+vYcgZyGZv2QoFI77rr55JRIndSmf95o4xXfL5nbw3MXKzmlPKZz9RowHsx7uqipqSnst99+AIAjjjgC3/zmN3HppZfiWc96Fh588EHstdde9tiqqvD2t78dl1xyCe677z6xvZUrV7bU0QcffLClonIsWbIES5YsEffFkmWnhjtfNWxSaWQO5AWUGDGbcz71ia5faS2qtQWUK2QTROGa89ykNK15OVKalEweU96nUQacUvWPzK+gWulhwooozvcxb91FKaO6juG5/ez2HulWwjZiOUp50n6pDY5YGdUQXamwutDHTLUQlN5RkBoHCLnPPSA/f6OmeMrJR9yn1HB4fCoSP0ZK+TNDKZHmMpfxqJDKknbBlhpt/qV7LZvPNVRDVI2vK+V3pjRSFk2FqFg6KalalEtJ1Z2zOeyzdHyfgiGtc+FykYbtxPOYtrfPdynSAYsL866YhtBaY8uWLVi3bh3uuOMO3H777fa1evVqvPOd78QXv/jF6Plr167F9ddf723753/+Zxx99NGz7lvfQIQ+pDRn0hHN9BnJsHsFUjFSSib8kJSaz0WgCjbHZl8pcv0Oczc/rmYktbLJoorWsTHwSVRSUUPVVDLpS5+ldux71karalSwzWtD8DULt6dU17D9nP6L6aGYWp5jwt+e/K66ftux9HLt40Yjb7MJhIr5o4dqaWjC3xZKaQx10JfZwAU3Na47qD1zPldNQ+U0NOnzYKhQOTXH8+c9nU7KnBdPJcWPSwV1jmr9iJ2nhDFLOqePcgqEhX23PTqDU2f5GjAezKti+p73vAcnnXQS9txzTzzyyCO48sorsWHDBqxfvx677bYbdtttN+/4yclJrFy5EgcccIDddvrpp+MZz3gGLrjgAgDA2WefjWOOOQYf+tCHcPLJJ+Paa6/Fl770JXzta18bqY9hQuhRktlnXWeESYdPKl1uBBxhsEPs+u2Ah6asHzN7WSUhNBfq8ZnzjSsBM9ujQKULS0bDmtg5CbclFLb0IF0nrjD0VU75fkmFEM+J+RxLCoglHarVZkyRzR1IpeT9KRN+iO3BxOUpih2+2jmWi9kop/xaOceG17X7I0U1uAkfaJNSp+hp7xgaA2qNWQVEzjX5pYT4yWNgqkF5aik0QKVHc5RTmO+qQDsRv92XUE9jz2fY/1Rd+lTSe+m4qPIZnJOTZL8r+f6AATHMKzF94IEHsG7dOmzcuBHLly/HoYceivXr1+OlL31pdhv3338/isIRkaOPPhpXXnkl3vve9+J973sf9t13X1x11VV4wQteMBe3MGDAgAEDBgxYBKCco3PV9oDxYF6J6cc//vFex0t+pRs2bGhte81rXoPXvOY1I/aqDck/idRA2lcq5wfW6G5junb6156fW1UOdgB8Ex4348fQVS2F7n9U1ZRnAQhX2RVTMNzxBSrt+55WKFw6G11EVVSjXvi1s+0+6Mb2ZFQN4z9G95j2NxWVikA1zUFuMIPUrqTK9unvbLE9mfGBbqtGqGbycYCeOa6cxkoTj6MvIWLPv5Qeigc8xdTSvD4aFXWc4+FcwSigsD6h3M+TfE1rXfRSTc35uqWaAm2fU3PNIqqakrKaUi5DjPuZ5laZsP1c1TQsAztggIR5D35ayEiW4wvmBSJhPEiBTHhdZruYz1k89UxwXCRAK9ZeaJbkpDQFIqQ2kX5gyoPWgMoPgMi9rncN0DlEOP1t5ric2tkafporR0JpGvVNlo6cmonGBTKFx0qmTmkb3877leqzBB5I5V0rmEh4G+OcsNpm/bE1vSAgBQdKsItVNgaEpCxm1ieMQlJTaEXkj5GUdpn08/uYedyYF04FM9dLpm1oRzA9QtpBTgHYAChu1qdtoVk/JJ+pgCi/j4lgJx0PjOxajMaIpfve9MjkdL4T7M9l9Pz24LK0ULDQF7LzCp5cOszjKaWJ4hNWLLm+lHM0ty9hcIVNsq/dtSvooE9+haean9ec67a73KSSYsmd9J3Df7sClH//WbfXmajb5vZEISijLuiJjq11YV8xuKCGJsgJJsULr/wkBRyEwVB8G72XAqS6tseCoVJBTanjeT/D4KlU5H8KfGB/MvmMcQIXPSZ4Dl2wUThOdOc7jQU7ppA6JxwD5lIplfuWh66cqbP1P6UAKKlaHAVB8SAmCpoCqAqe8D4IhqJzw2Aq9zzGk/Hz9qW+E+Ip7/qHn44yFsTGvpxgqAELD//5n/+JL37xi3j88ccBmCD0+cSgmHYgZ2Dk5qpKA7APIQ2yGgWcGS8rrUsksMn0gU84aL9XzqVAOi8k0LEJiSYjroLwwcaLaud9ylBNYmppbsnDkIyaNiO5TGcxmVnzvfKDFbhyStcIVcuUCT3ruiMgTEPVUnQjakdOu092SAvPEAU7jiwogBwYJZHTVDGOftk1EovFYByYC1JKAVBeUBTaSkgOjYoFYfZBGSigBFJNaeywuUo1Uzt13Tz/RadyCgBQtW2PnjEeFEWmfXNsPJ1U1KwfCXbqE4DZB5Jqmmo7pZzONwbF1MdPf/pTnHrqqfjyl78MpRTuvvtu/NIv/RLe8IY34ClPeQr+/M//fF76NRDTHohFm7qBU3v/VNDMx1Kh1rRijiO3SpTYP0jkmPVT+yqwPS9BSMGO53Xq+b9d/elClwpCk0kq2r6V2zSiwLqJho4nH7LwvflMiiuZ+iVyavcj7tc5CtHsc05oVk0R1D5+atubn+g44FstfIuCTzyb7YygmuP8RWNo6pfQNS7kuP1IhJTugSuUEiEN77MPQnKaOi4XuQSLfEfDRayJuneuQNwkz88LySkAl980fA8zDhUNYXXwzfvcj1WK2A/JKQBGUCPm+47IfLpezvF0Ttd33NWG1NZCIKcDHN72trdhYmIC999/P571rGfZ7aeeeire9ra3icT0nHPO6X2d9773vdh1112zjx+IaU+E5NRT+Ozkof3jgYAoCoSp+TecYPKDm4Ljhec/VEYI3Gwf5isFnEmLwymocn/4ZNRKuRVRS8eFLB9TdJBTwPM5teqoQE6BPL/OUZATJCWliLL7BFIc+sjOZQAUsH1NRtLCDuI2twjlSmsXSSVIyfr79M/vl0xIqc+xhSl9breX50fKx8rUOe1gxvb2UX6XPAiJbyP1E3BKqiGH7tm3PqMBOTXva5tGqgiUUgtOHhsFNaWepsgpIccntM++PpiNajrOfswWtVZQc9SPhXB/ffHP//zP+OIXv4g99tjD2/7MZz4T3//+98VzLrnkEqxduxZTU1NZ1/ja176GM888cyCmAwYMGDBgwIABA+J47LHHsNNOO7W2/+QnP0lWwbvmmmuwYsWKrGssXbq0d7+G4KcEYl9Ord1L2s6DpagaEgVGVNoPPhoFJZQYfR9eM3yFfmTe8ZreF5jWJaZ1Eexrp2nqk0Sb3z+QVp7Ce+oLE3DAArWawAYXzND+3luBUK1gKNdWGKhg3vsBSjzgKDs1VHCOVHFFDKASzkkFTs1Gwe2D+a6LvS3AxwI+JsSeQ28cCMaCWMBUn5e5dtCmdm48PMgxDHQM3XhoPOAv2Gu01UypOpRkmQlf4fcZayt3LODBSIQiCHjiVaBKxWvc161gpjAYylWR0mJAVNG0YQOhKLCKAiq9dppnVAiI8u5JeJZobOqLMFAztm+2CC0l22rcSYHymM7Va7HhmGOOwSc/+Un7WSmFuq7xp3/6pzjuuOPEcy6//HIsX748+xof+9jHkiXhJQyK6RjQ8jNrfqCeyR/cXN8c25j1YrlPY0NOSGhpspHdCoI22UTktrlJyL4PTFGjkozwvmL+eQi2SzlMUyhVbc3n001OUkMumzasL1jhfEyZr6nNJxj4mHom/ohJ35zvgh3s2YFPVy5GDY6S3AmcWbDdr1wfsb5Qyg3SdI3tyZwvucNw8Oe//RvXyY+hqd/bl/hbheeIGUKCfktBTtyVR74OpU+q7bESafL8SrU/FnJI3+FsIu95wJDdxoKReBBUwUz13KRP53hpoKifyuUqjfmcWhM/RxAYJZn1nUuBq+wUji+EnGe3z/O9rUztC4GcDnD40z/9Uxx77LG4+eabsXXrVrzrXe/Cd77zHfzsZz/Dv/3bv4nnvO51r+t1jd/6rd/q3a+BmGagT81maUCmbTwwAnC+l5ycttuLP8hSXXu+z6zg/fvIJaU0QBdRepyecPhklSKj+VH4kl9u3XxvNEkSIdWYxAygJ0wkcytQoU1O3WQg+JgK5JSuUycmkHEMwl2KiHS9kHhKk85cTES+j5lPTrfXxNrJ8YBJKNzXMjgo+ZGQIqyAT0QB+ZlLEVL6TMQz5bMogRY/nm95MBZ2IZYBJed3SuMUD3IyCzSW7B6MeCJOTqkdKdKegqYAtHxO6bjQv5Si9tuBUUJCftbHuE+pHADlH9PfH3UukOObui1hlM256c9iVEwPOugg3HHHHbjssstQliUee+wxvOpVr8Kb3/xmrFq1KrudrVu34sEHH0Rd+/PVXnvtNVK/BmLaA5J5qTMyPSCnADyCylM8Ra8bU2bgk9KwXxWU97TEgpxMP9ukNISYrzRQh7sCHGITVGiy44OnNJBKgQ2kFE6pGWzFBArUmARQwBQ5AOCR0wIVaihUaAZrHgxlj20+B+TURPpK1Z/mKqRLBr9eqNrGIvBp37gQTw/jk9PFCulpCH/H0pjgEa3YrNWybLSPCwOoYogtAHMIqfm38H4XLeUR7LeuCmvitgvvCDmlc0OkLCau7+0+5sBTSRt10xBDF40fJacAi6BHm1BqPyCKjuMppCyCqH3aJlWLIjWW99G7J2apSSGHuAL9FNVcMicRX7KWLCSCOsBh5cqVeP/73z/SuXfffTd+53d+B1//+te97VprKKVQVaNJEgMx7YnWKl4gZOFgbCexxCRkCZ5wzbhJP146VPb3kpURbrqvmS+pp3pqhUopm9OTEFOHpb7EkmZLx4b9jKFUGhVqlCgAzAAApvWEi75FDegJQDUktVXaTzbtN3edTU63JeJqlhzF66pabZvI0ZCcbg8omwUMh+QjSdtzQZYNgvk9hUfl/b5Svp383xQhbamWlKMT/rjmL+RMz0NySsdK7Xb1n/ezC5yE0jWpLU6+nGoZJ6cWzX5bHQoIVFF3nNnnH2evH6ipqVKm5vh2mePm28gjmwsk+l2CUnreXXqGPKY+Lr/8cuyyyy547Wtf623/9Kc/jV/84hedZvszzjgDExMT+Md//EesWrUKakwD/kBMEygVrVKbQbt5prxB3A5Q7DxhMOa+pwSX89Jtq9jxvA2pTSkZNu+fa5OTPWeyt9uYStoi1Q0BM36mBQxB8+/XQmthUnV9DfsifZb6H6JUprZ9aXP91YaAkr8onLIBVVizvjWzZZj2WW86yan5tO0GpZgfq9mXN4F1IWeQ7UoPw/u3+IbsfHQrWG2/xyRCdTVzsO9DRs17f3Fqt4c5gTnpU7WnkAJ0f83YANhnIrxvafEev5e4cpuCZ67n5vOGgIbk1PTXkdPaU1nD/KP56qlozm/OC8kpfVe8AEBMNZ0Nxk1aU3lTFyo5HuDjwgsvxEc/+tHW9hUrVuD3fu/3Oonp7bffjltuuQUHHnjgWPs1ENMBAwYMGDBgwHYPjVz7w2htLzZ8//vfxz777NPavmbNGtx///2d5x900EH4yU9+MvZ+DcS0J3iwULPF/OOZgfxzpEhRjtCcZ9poK4sxvzYpSEDyIaXtff1ITR9d4ulKaZQ2SMAcb1U8bRTmnATcKbW0SyVxKkijLMCZ1yooZlbDaKopkFEdiuUcmONRaVuoE7NtR/Ils8rpdhCNW6p4IGQ6CCrdbugOErrDiOODAKk/KR/NmFIaGxsKMJO550fZ/Nv4nUqKati/GMb1W6Yxt0s1pSh4T+1k90V+p5JZPytivzk2Wca05Q9b9P4eqHTyOC03nSVOEz6qc+HLPmD8WLFiBe644w7svffe3vZvfetb2G233TrP/9CHPoR3vetd+JM/+RMccsghmJyc9PYvW7ZspH4NeUwTKKFaXxCPYK+1wrQuUaHAVpRecBG93Dn5L8BMgOGL9m9Fga0oWtfz2ylsPykn6VZdYlpPYBoltmrz4pOFoavtF72b1hNNGwWe0BPNvYd5UMOJcHwTjckvWNv3ANi22uYQnFKV2aZqTKrK5gucVDOYVFWzTdt8g1KeU8otWCqX29TLcwqe47TGRFF5rzCPaJ+XhD75UAn0+6uD32RqshhpQgxAvmQLwadsHJDKgXLiF5rHw7yfOc98+PcJx4Ou8/l4II8Jbjzw9unCvug55/fouQixUYGf538XdO9+adOu31zsmK7xw5ZH9vKG1q19ZrvLH9o6tvmd0n7A5Totm3EgzHdKzyRdm48d7Xba+U4JfPxJITUGLVTkjm/bCuRjOlevPrjgggtw5JFHYunSpVixYgVOOeUUfO973/OOeeCBB3DGGWdg9erV2GmnnXDiiSfi7rvvjtybxkknnQSlFD73uc9l9eE3fuM3cNZZZ+ErX/kKqqpCVVX48pe/jLPPPhu/8Ru/0Xn+S17yEtx000148YtfjBUrVuCpT30qnvrUp+IpT3kKnvrUp2b1QcKgmHagVMB06BvKCKfv8O+rqF4UK+QoV4uO33TMh7Tts9lWRGjSofcSCjZYe+3pcKIqnQ+jmgGanKFQFaDNAM8DocaBaOqTRgWBIl9Yp3qUVgFtVIRGOaVtPFrfC4hSCoUm/1oT8FJAYRoluFpaqKoJLHJ+Yhyl8qMRc3zFunKhunuOKyPUBiel8nHKEtZofzL/fl3KyvZAUAsY/2/6V7KCSIF6OVHkPCco0B43cv4MMb/ymMXEnhcQUQnSuFU3v3ygGSOYz6Xps6+eSn3sQmr8iP3mWmqooJzWCPOQuj5btKLtSW0V1NVAPbU+p007XkqpQDkF2j691r818HefT/VxoaV92h5www034M1vfjOOPPJIzMzM4A//8A9xwgkn4K677sLOO+8MrTVOOeUUTE5O4tprr8WyZctw0UUX4SUveYk9huOSSy7pHXz0x3/8x/j+97+PF7/4xZiYMHSwrmucfvrp+JM/+ZPO87/yla/0ul4uBmKaAA0JPM1TCY1KaUxrP1gojOiEPQPNcdKkBbuqTrkCAHEyKrUrme3DCankq3dGSmMBCvz8is7XE6jYPUJVcAnt44FQs4EznRXWLG8Igx+sULPBfTIw69sIWu3aaJv2m2s0k8MkKtSqQKU1Cq0MAWQEFXCkUEKh0qlZTI3seOons60rYEQmpVyNkpSpcFs4AfF9sdQv3WrP4iWm9JuWIvMJXfk/k1Dh7yZML9TdRIwkt0z5uq2Keud1BM5R8JMBJ3LNv8pF54fjYI47wiiIuUrlkFPJtG/uU9uo/SiBtQnx/cj71lwQIae2/94Y1N+MP270cQ+aqyIdc4YF5GS6fv167/Pll1+OFStW4JZbbsExxxyDu+++GzfddBO+/e1v49nPfjYA4CMf+QhWrFiBK664Am94wxvsud/61rdw0UUX4Zvf/Gav/KNTU1O46qqr8IEPfADf+ta3sOOOO+KQQw7BmjVrss5/0YtelH2tPhiIaQKlUphkk1GhGrOLNlWGtpJaCHg+V2ZA06hRdvvaCIN3yqQVEtFYu30nHEoLI0XrO4LL2uS+mQCqRkMptfZK/4Xgk0jK99ZOJnCRqrHj7eRhfUxLl/6lmRgKS2SRR06BaMS+mbQcQS2b6OOiUUmjf+vI38CoLdynsF1RCggS+ndMCBIpDfeH++Z7QlzIKJVCrXXjZ0rEJv43yLEWSEqib3khpE21XYtTwPcflfxK/avJfS+gWxXh+F4ANoMHvaf76aoU1b4naQzM8zzj6aOylFOgTT6BaNR+TD3lEfuhzyiPuLdqqUBSOXikf4VSPKYP5ur5TvqaBn/rxZzPOBebN2/2Pi9ZsiRZd57w8MMPAwB23XVXAMCWLVsAADvssIM9pixLTE1N4Wtf+5olpr/4xS/wm7/5m/jLv/xLrFy5cqQ+77///th///2zjr3jjjtw8MEHoygK3HHHHcljDz300JH6MxDTBAoolEphqvlca40pGFI6TduagT4cXCpLNOVJhU9KkhuAhDABttkWmRiFicimJuETjKrtoDety1ZaKk5I/VrZ8CahQheY3Ea+TpJqCk5gaUAnsqrM9ORIKKLktFSVRwq5eT9Mxk8E1Zj0HYkkotpG5PvxTKuOpEoENaZmhGopvedKqd2GNiGlz6SEdqkgKdOeZLZfzKb8wj43zpxfKBOUVChXPjI3cI8gmsgFJbqLkCUXskFAU9uUn0cAS9TtYwOSSiZ+49LTENGAnPK+jVNBbaXX6yCnvP+hegrAU1Bl8z3dt5/vNEwn5VWLItci+yynAzH538EtXJ07UW7VvG2BRUM4R/AF7dM2AOy5557e5vPOOw/nn39++lStcc455+CFL3whDj74YADAgQceiDVr1uDcc8/Fxz72Mey888646KKLsGnTJmzcuNGe+7a3vQ1HH300Tj755JG6/d///d+47rrrcP/992Pr1q3evosuuqh1/HOf+1xs2rQJK1aswHOf+1wopaCFAiJDgv05QgHVvMyXPqloYmoCUbQb6KfZoG2Od3k2Q//NcEAO8wFyMhv6jJptXYpH4BcaqJ3e+yZ4K4aY0kq16fk63hC2aFO9MIp/asmUGj4RuLyFSJLTUlXNZMoVU3gThlQtykbtMrM+J6quf23CWgcmfG+yZKQnlp9UUjxDNdR7j7hZXyKlXb6jOVgIQQ+zRdH8zUv7rykx7Hwo29HUXVYKQpjA3m6PWE9CiOqiQEhpe0gww+e/5fOo2lXW0LRu0XKBkcmpuS/5fseNFDnl+0PTvr/Pr1/PE/LXnHQCwXjT/N55H7wFczHr+ydiO+7vMfabG0cFp8W8QM3FD37wAy8aPUctPfPMM3HHHXfga1/7mt02OTmJq6++Gr/7u7+LXXfdFWVZ4iUveQlOOukke8x1112HL3/5y7jttttG6uu//Mu/4BWveAX22WcffO9738PBBx+M++67D1prPO95zxPPuffee7H77rvb93OBgZgOGDBgwIABA7Z7aI05q2lP7S5btqxXmqS3vOUtuO666/DVr34Ve+yxh7fv8MMPx+23346HH34YW7duxe67744XvOAFOOKIIwAAX/7yl3HPPffgKU95infeq1/9avyP//E/sGHDhuS1zz33XLz97W/HH/3RH2Hp0qW4+uqrsWLFCpx22mk48cQTxXO4/+nuu++OnXbaKfteczEQ054oG7VkChWmVYFpXTb+phNWnSCF1ETFa5NuRvvKiFTZqW3Wd+iKqs1ZNdv0NWEAhBdYJZvx7HsbbQoT/KNJTa5sP3gAVLsP/VbbWVHNzJwPwJrwyUUhNKVJqqlVhQTTWqyMaalgldNCSfkfBdN9qKKxr8NTT61q23wPzb6uqk4xMz33OfW2J0z4s/FJW+wKqQTzHJOfqTHnl1o3Kpn7rrryFoeQFEqg37MStiFZTcz29rMei9K3EP6UbRWVXGHIl7StmgKyVSjr/jJ968V+CqqpaTPuc8rN+lLQk/kMu9/zOVVg19R2rOH34JnxE+Z8X4117l4Vi28wY7KphtcH2zJoaaEopQupJKnWGm95y1twzTXXYMOGDWKie8Ly5csBmNr0N998Mz7wgQ8AAN797nd7QVAAcMghh+Diiy/Gy1/+8s4+/L//9/9wxRVXAAAmJibw+OOPY5dddsEf/dEf4eSTT8Yf/MEfJM+nNFfr1q3DS1/6UhRFnpWoCwMxzQDlMTS5RDUmlTb/osa01piGGdBdMFSJUpvcd5NqBpRGyXesD1KFBAEDhFRUbehHloNKFzb3qmm/yTuYeKhKm9+zZoNyfHKptbJm61itbMmkHdsXbs83kbZ9TaPkFPD3AYaUojHJBxNHSZNPMHnIBNXdYxh5L6UMqxkJdSZBMLO+Dshn2/fYy4sZI6tzQEolQjpKDtaFhAIKpfWzduZ8NOZ8AKgFc35Ywz0Xo5wT/uYkd5/QV1xKqC+NAxXKdkAj++hIahCdHpDTcPGdS077kFJpUSCRU36sn4TfXsVeO8esL5FTwH3HJR9rtOwaUcTIKdD8rvz7DP1MF2Ip0IVCSBci3vzmN+NTn/oUrr32WixduhSbNm0CYEjojjvuCMDUrN99992x11574c4778TZZ5+NU045BSeccAIAYOXKlWLA01577ZUkuoSdd97ZBlmtXr0a99xzj80AkFPR6ZOf/CSuuOIKvPKVr8SyZctw6qmn4rd/+7dx5JFH5n0JEQzEtAcKUPUX3SinNSbVDLbq0vgwshyfZvCbATDRDCDKqopmgA4GJpZuhaL6gXRUreQvFoMhnxRlX9jrb9Ularuv3ZZREmhiaYZmVaOE79RcN96XBZugig5ympt9gPKxuntOT9xelL5HRsPPjpx69a6tEsImWUZOy6Yf0uRhzgs+RxEhqcrtt4ESzUTEt7Vbaydqj0Xf10w5mEtCur2BR+ejUU2hTQo5c7ukzvuKXIiu33BfpLJwxAgp9aPLYgLA/aYJXs5P2kYt6uD5iJNTQA4QjfVj1N9nGNREPQ3JKUFKKeXfq3vWo+SUvhNhbHVjlFNNuxRyLyVeo5qGfqad2Tq2IXGVSOm8W1K0skFKc9J2D1x22WUAgGOPPdbbfvnll+OMM84AAGzcuBHnnHMOHnjgAaxatQqnn3463ve+942jtwCAo446Cv/2b/+Ggw46CL/6q7+Kt7/97bjzzjvx2c9+FkcddVTn+a961avwqle9Co888gg+85nP4IorrsDRRx+NffbZB7/927+N//k//+dI/RqIaU+QWmJUU1MSc0qZgJgaBbbCBb1MszyfFXesbwZvr10q49ls5rQvppCaIJy8Cc6S0ibFkVVM0SiofJt2JRIpGrcgom0n3SY9FDM7FrxfyqTViqXUyUlO3spAIKjFHF3J58MUUklyCvgENTS58e1wk5Q5L/hMYJN5pVUwSLvoe5osi8ZER9u7MBtSOm6zPf99L/YE+6Vy7ik1dPPXdkFQUMqa9PuQ075IEZeutE9dbjz+QlAm0n6QHntvf+/oTU7Da8eQWsT2QU4qKULMvC9F7IfkNLxmaKFpZ19ImPQBZ5nRTpXnqikVNjFt9Tftj5uwxkhpPweO7RtSJHuIs846C2edddbY2yVcdNFFePTRRwEA559/Ph599FFcddVV2G+//XDxxRdnt7N06VK8/vWvx+tf/3rcddddOO200/D+979/IKZzhZIPfqSWMtXU+JrOGD8uzACAR05rbchq2RBXAOBJ6YnYcX9UKY2K5CfmpW/iJiN+HldatWITlE9yKRWV2w97LW52I/VUhCLXgNoqAl4RAe9+8sxzo7grEHLMZ0DbDyxVtYWrE2WLrLpa2V47zf0VECZzhCTVTUyOnDbbddEy35EZv0s5576lWvA5TflHpUhlSErDBddiJqQSCihvHDC/Gfhqd4ScAmiRIo4sP/HEc5CKtk+Z7blbj3SuBf/dcaLKF2Q9ySlHTjGJcUSgSwuFMJUU9xGl7zWpngbkNEzCHyIWoc8rQsWKfRiXIQWumtbs3y6kchePI6/xglRKG2yL4KeFjg9/+MP4vd/7Peywww6YmJjAIYccAgDYaaed8JGPfGSkNp944glcd911+NSnPoX169djxYoVeMc73jFyHwdi2hMFzERUwFWBmkQNNJlNjRpq6tFP66bEV0MoiOxxsjuty8Yfs2ZEikzGbRN4qJKGA2yX2ZCrpfwcIqWhWkKlB+m6U5hp1NM2KpgKScbcaRTWsNCA6UNC+ZHUVWFSlfZz8NQvfJtk0g/9wLz8gZ7SrcGV01q7qi+2JGGongK9SKpDt3kv5jcaqqWhPynty3XW11plTTbbOynlkFRTOzNFyCkQ93dM/e45ktXDhOdDDJQUTPfhuCD6nIe/U8+ED0acYMmp2e6TU3OOP8aZPoyfjMYq2UnVnrzzvL/T/8/el8dJUdztP929ByiyiqKgIKB4oeCtYAxqFPE2Go+oLxqTePxebyQmGs8YRHOIqAmaaFCTKMYgahKDkldBTcR4oajReKAYBI0Hhyjs7nT9/uiu7m9VV1VXz8zuziz18JkPM93V1d2zU1VPP98rtcTo1NO8h19ZNdUFQgk5TgGFz24Y+/fHFq04v7JPrCw0/WBXm+75dTjUDsaPH49vfvOb6NGjB4YMGYIlS5Zg4403LquvRx99FL///e/xwAMPIAgCHHPMMXjkkUcqrgjliKmDg4ODg4ND9wcD0FE8uU7496abbooZM2bgkEMOAWMM//nPf7B69Wpl280339zY19e//nUceuihuPPOO3HooYeisbGxKtfoiGkZ4H6mPF1ME0qxQtCWMcOrfLeyJrwQTbHKwp+saXlQnU9pnnlbVkiEazH4dWUUitgvjat8OtM8/x64WwK/t7RSk+EcdF+O6ltEQaHmfF3mAap6AuT+iC+YnKqJq6c0OImrpqU48CuJziYqS9SXrXpKo5yjz7KpTk4FpVNLqU9pYs43qClFVY48tbS7qCY0Oh9AYs5HrJqWmNyeqIgcOT6n2dRPKv/KIscb5gHiykPb0OMS6HymqXIqqaZJxSiimvog9+9l5xLb+6KQXZiUbTRBmHS/SlXlUCmn3OJCVdNUMUeyzWTOV1lkIvVT488el8SG5yP0QvjwBD9T6n6gC4aqdqoop5bWDy699FKcc845OPvss+F5njKCnjFmVblp6dKlhXK22sIR0wKQ/ctCHvCAMJmIgXZxcgMTfDsBxaISBxPB4+Y0QkggElJTvkJKcFSLkSo1lG6CEkz6xKzUpjmmCZFvbXSuEE1xtgJu1k8IqgTdYqOayE2Rx+WABy7Qc6n9z7KLh2xyE3IhEnIKQEtQo+OzJDUJqoi2Rv9Jkb0RwfST40wkU/YppaRUNud75JpNi4ppn4qUdoegBx8ewoSQctN9+hAGZH1NS5zEeOnfKjpebUrWlQvNq84kwyZPsY6U6q4nM+wzwX5QklN+RXJAj0BQK4QpF2wlpU9ln1NAQVC9YnOR7DLE+0gyc3hk/teQ06jyYORrGjCG0NK/VAXqfy5vs0ERl52uJqq1lMe0q3D66afjhBNOwHvvvYcRI0bgb3/7GzbccEPr41esWGH8TFEuaXXEtAxQP1MgLk0YR6G3IZpc2kh7TgpbycRBFUh4fuS3yeJ0S54YlW+TIon3SaFSSMpBSCfJ2F+Wl2Sl4OS0yQNaWYPkMyuqRjIBLKKIFvW9U0Hlg6pSF0Sfraxqqi1vSH3UNAQ1uR9ZSdUoLBRadVTjW6qCaiLV+ZOaYEoL1dULUWdAVk1lX1P6e/bjtFJ5v3FTiqe0jUk11QdCqYKcKCnVkazMdupbCohklJLTpG02WLDa4HNqiflVI6eAOqhUiNpX+K4Dim3SA6ZqfqEPv+mDjuivGzIv8TUN43nZB7MOgAKqE4lfKDCymzygdgest9562G677fCb3/wG2223Hfr372997Prrrw/PM/92bBVXHRwxLRMBPICbTqjpBnEEPSIy2cqiICiekkmAl06kPEAKzE7tUF9TfvR7UYiVqfyEnLbF168ip1z95YtUNImmVJ4qJargD1v1wSYQpFqKjAxfUk1t0jnpghHo303Mp5pNK1NKCGe0wLWHvpKUdhSKBDx1V1KaBC96SFRTJHlMDYFQkALrtDlOU1JqqspkKoohnwvIpoGipDSUxkveg14ScQ/og5+E4hMQySlQyIxvghBAJc0xuipayiwFBV2EKDmtFDRZv0xOo/1MIKe+xwS3EZ+ki1J97gjUbXBjnV52tREEAc4880z861//KnTc448/3kFXlMIR0zIgm/HkZ4ISixaUVhZgddiINtYQmeOZuCghIaMhSgiSBPZtOQNHGW0OMikrJnwb4mQ+p6cnp4BisLdLixQArxRH6csLpLlkoq5CTB7pzovmz4NKVTW2l4iGygxYBOJDSUxGmY/2OIsCJ6X8MyCa5cohqKrFxnYBqtuFqgCoOb8S5JUtpaRUFU3P25igGvOqyHsdKTX+bmUTMyCQ0cjKE+V5FvxsOTmlxxFQZVJ3/ox6KX2PwhzD9P2WS0rpdVTy4Gszv/C5vNL5u5qwGefd9aG0u2H48OF45513rKpEcVQacW8DR0wLQrcwcfM9nfTC2AQfwkMraxCIAs8NmiavR0RKwatI6SH7lSY14XnCala8FrWcVw8KFVBLTgGleqoip1GuVtUioVaJ+LZyUe6iLkP2M6VKsryoVaqihLGCxZVRvq0tIaWEnDJfIKEyGc3LT0r3d6Ta2R0XKu5zrlJNTX9/nXKuqr5Gf795CmimP81vXBXglEdK5YdE0feUmpkRB+Bo1FPZ71SGBQHLJYMCceXjR6z4lN6Xnd+6TXBVZ6BItb9aQq2Mf+djKmLixImYMGECrr76auy6665Yd911hf02PqJPPvkkbr31Vrzzzju47777sNlmm+G3v/0thgwZgr333rus66qdxzAHBwcHBwcHB4dOwUEHHYSXXnoJRxxxBAYMGIANNtgAG2ywAdZff31ssMEGucfPmDEDY8eORc+ePfHCCy9gzZo1AICVK1fimmuuKfu6nGJqATlFjLDPi6Lz26SI6ai9n5j1QyaneoqSSwUshO8xBAjjZPshANF53VRqk6utgRemSgWQUVHLQZL4GVllgaqmALTR+jQVVsbHDGnGAZ3Zklad4ih6T3JmAnl7V0JVAYuWjm2Lr7edBWgP4xfz0R76yf86tdQErtp1lPm9VhSSaqDEBJkPQJoyTgc/UVIhWRPyzbd8PKjU0tzqTMJ15wVYmdVSZcJ+1TmTAKe4AhGYWiVN/KZ5eiXFNUPvumMDwfc1vqa0b31QUJ4KW00XHWX/0t+Xz7f0c/KeWEn4PNGZifTzUNNjn6HjfExr+LZ1qNRf9Mc//jFuueUWnHzyyZg+fXqyfa+99sKPfvSjsvt1xLRMcD9Tm5izEvwsKeITTxyd34YgIacUNoM8yRUIkagCEMhq4LEk0ME0qXJCynPpCdGhBJGrQnwMPKh8OXmkPi/JKqeNUtXupqmtACTuAmm6pfR4m0UsO+mrA0qMJR015j+Vb5o+BZc+kC0hlzEpbQ8DlOChPf49tMWElPuWUlKaV1ZUvh5uwleZlOXfm8m8b4rGl/usDUNo1yMt/eslv/30fdZcT8cDP57uU8HWp1qVmkpHSo3EJ5PPVPSfz5DT+BhqXheuC34FpnPpuILkUf3wL/7O5e+tXFcj/ltI+lKQUuF9/BtJqvTJx2vmHTkriA6dUTWqpknrWohK/UXfeOMNjB49OrO9d+/eWLZsWdn9OmJaBlR+pkmJOC+ET4rmhhoSkybbj33VECmocn+mtT9xjCeTYxoIxYkqS5QJmyhNlS+lHHWuLp8YINQEdDQBca6/lKCKx4qKEM+1Krfj/rcBuVehZrcBpkTi1YKpPKyOlMqEFEBKSsMgDVCJlRHqU6pKnl8URfKRmrA2BD5xyOPf5iG1SHBNQlgVpNSUAL8cBU+Yn1QPbhbjJGkjROBnySkPiIoefKUcqDLK/TllLrfyRyLb7B8h9EU8TJAferUqafwAkzzcJJagMsZ9vC6EmgfUcqGaT2qLjHpQSvVV67u+8MQTTxj3q0gnRf/+/fHWW29h8ODBwvannnoKW2yxRdnX5YhpDlTmOl1Uri5XHg1iiT6LNdBLirxzNpG/nNCGiBQJ30uV18QETs1rQv/qQaR7+telRMok4mc+2hQ/K04meaRutC2ESs2kqm4mryh4SvI4k4HHrIO9dInE6YJe1uJe4Jj0fuVrSNNAcVIaxv8DUBJSQEyez/uVFwLVoiMHPqn2O+RDSLqvMe/rFn2VWppHSrXJ71Heg4ncj46U2qSqEwgqi8zyPLiTj1m+jz9QRg+rirGrI6w5kFXYytTXfKgj/NV/N9Pcwse9TEqFqn90DpDmTdt0den58n8rRQlr7ZNSBxn77rtvZhvNUZqXh/SMM87Aeeedh9/85jfwPA8ffPABnn76aUyYMAGXX3552dfliKkBoRhCSrZFfmeqNNEBjzpnfB+dDNT+Q1F/kglec02+YAaPzbzxecGCiEAmS4GXENQS6ZtCeALX+DnpoJsMSzzTQHzrITz4jCVlVgP4idqZHkMIGiGl2dyvRIkB8pWXzP3ambvTe8yq3PJ7VT8qtdSWlPKcpG0xOeXnC8m+7LWp3xdFuYTUlJi/lvzfqoEi6aKU1gXF71pHSlXjIr2OrNpZLsolpHK7hKB68bxDfdGpaR+Qku9LkMufFoE8F1SBG5nSWMm+wCaSmFE9k7+zaFmjpnt+TjpH8zkj03/B3wEnn/L/3RbOx1TAZ599Jnxua2vDiy++iMsuuwwTJ07MPf6iiy7C8uXLsd9++2H16tUYPXo0mpubMWHCBJx99tllX5cjplWC74nVkCKTcxjvY5kfLTXRUNg44asIpujryZCmslEsBLS/HD+nItAl4BcCmKRr0uXyoxO4gNhMKARZAIUXMpVaypHx87PwL1WprqoFXmW6l0lpOwsIWaVt1P6seT6mJtgSUZXyEalhejK6NqgllKTKaqn4W1H9JjzhAUxFSuWKTDoyWmkhDfnaVH3mkR7fSy01JnIqpJMi9yEop4qfMj2/0X1HngsMD615qfk4bAodqHyBbdTSpF+imsoP5imJzbo46YhkXhUom7R23Z6oruVoaWnJbBszZgyam5txwQUX4Pnnnzce39raiokTJ+KHP/whXnvtNYRhiGHDhqFXr174+OOPsdFGG5V1XY6YFgBVSzlk853vMQSxOuh7UaBPFHkfJqZ3rnTq1A8d/OT85kkuBNAIHinvoxHt0Q7FBC2redo+C5mrPYmcUhOeh0bl4iQuwPq+o34CLw744m2lr0+34KhMoTYJxXVqqWxuS7eXR0qj/tLKTiHp21RqtCNJqS1k1ZSS0+6yuNmqpdSfW5UrU7AKQE9K6XiQLQgqslsN6MqX5iGUx2Km8lMojFcamBlts3epUZJZfh2Q/M4VD608s0e5xTdUFfl0vsDpdenVUhMp1UFWWlXQEUtVoNNaoZo6xdQKffv2xRtvvJHb7rjjjsP999+PddZZB7vttluy/cMPP8T++++PV155pazzO2Lq4ODg4ODg4LCW4eWXXxY+M8awZMkSXHvttdhxxx1zj1+yZAm+853vYNq0acK2r33ta9h+++3Lvi5HTKsAnss0QJSPlJvx+ftqwSq4IX7KTnKQeiHAGsRI2KLnFVSafL9TP/Yl1ammAI0KFY+Xo0+jPtNz+h5LVFMAYtWZGIEX5iohdqY12bSZ9QNL2kq+YFGbrN9enlrKVVKbkqK1UGlENuebVNN6RQhmVEqpv3nmWPK3BuLfDVKlK811rFdL6W9L9ZtSusIwu2wVOqjLmZp/bzz4M50DfCDO05yMWXm80i7LmJ94AGQGdJN02ZF7QWVjR6UqqzJ+yP7ysutUHujfWMxxXCAoiX8Z5JBych7bXKsq8LJo0FWHgnnRq6P6rjPstNNO8DwPjIl/t5EjR+I3v/lN7vEPP/wwRo8ejQsuuACTJ0/G4sWL8bWvfQ077rijkNe0KBwxtYCt+S4ipixJGcUnTG7Op+mgxP6r94PmREFIgC8HDEGf/1OOvtf5nOp8ZAN6fHzeUEq4neRdVSx+sglLnsj4RJcEdcU+t5QgqZJhU+S6CkifzRkVROKg6kcOduLb5MU/uncveS8cW3AxqjZMBFNITYaUMHvdzIxPYUqun7bJEscSGTeCb6mBlOoe0lRJ94Vza37nOsJaDhlVteVliyk5BSA8mKoeKMuOoFcRWuq2RP5UyTUpQOfEIq5Numwf0Ta1Wd7GjF/OuMnzK8201/iZlmvO7w4PomsTFi5cKHz2fR99+/ZFjx49rI7fcMMN8cgjjySlR//yl79gl112we9//3v4fvl+746Y5kAmpSXpyUJeoKiPaeQLmQ18skW5NddF30uRnPJFSc57akLRHIwAIajct5YEbVEyqTueKgt0keCBFYkKA2QXOxTxx82SStWEnFFIiW8pPV7MbiAqJ1E7TyIXXoakpmTUfO2ex5Kn9Lz0T8pjCyJv0TER1O5ETvmY1z2wqgLkaEGNSDX10MoaiAJmR0p1/p+66H8l8vw3FcfZ+pom1hJ6Hp3PKSDVtUdCqmyLN0TXqyC0jBQVIXOdKYBKR0bVhJ2OfZGQZhLwS3OajpTq7i26huornCY/03L75Ncqq6S1QlYZSwuydUTf9YZBgwZlti1btsyamALAgAEDMHv2bOy9994YM2YMfvvb3wopp8qBI6YGyOmiOCmVqVyJm6c9iE/nGtIXgCUTcon5SgLKJ+ZyySnvG4CWnKogVxvRtdEeTxaltPqUj8AroQQfPkrJtalK+8kTsUrBpDkTZfUUgEB6iyxwMlQLlS6/IL9mW1JKTfhJ3woVVYdkISGJsjnZzCOolQQ9yYuTLlofyBLU7oBM5L20GoVJOzEQLiUnxKRLAp5MpFQ056rJaFnEMQdFFFRqxs+cR6eeAtqHdlP2RKWqKl8rJcPIElR6rbZQkVFArZDSz/xYXXoo+Vjdw3SRBP6VrBtJHxU8UNL5mX+uFXLqkOK6667D4MGDcfzxxwOIgpn++Mc/on///nj44YeVfqYbbLCBknh+8cUX+NOf/oQNN9ww2fbpp5+WdV1dSkynTp2KqVOn4t133wUAbL/99rj88stx8MEHAwCuvPJKTJ8+He+//z6ampqw6667YuLEidhzzz21fd5xxx049dRTM9u//PLLQk8BOqQLD1fksm24agog9jPl77PENvDChJzq4CfHVX+BlxUY+endRFJNCb4jX8kAjUCimvqIJqjAyx4vm751ZkpqipPV06RkKSXF0Kc0soFJ2ZBNbro8pdE16UlEKC1mtA8KeaHgpT598vxECWpnwERUKUHtDuSUktJQeB/tk+cC4XdM1LSEdMb721iDkZSqxoIyF3IBH3T7e7b7u9F23JQPEIKqUk+BshLpKwtqSNaS1CrDCbCeoBrPJT8YKx84xTmBbpPVbpVbkGx94e0rRTRfmPYVO0c5GTZqLjOHi8oXcOutt+J3v/sdAGD27NmYPXs2Zs2ahT/84Q/43ve+h0cffTRzzA033NDh19WlxHTAgAG49tprMXToUADAnXfeiSOPPBIvvvgitt9+e2y99da4+eabscUWW+DLL7/E5MmTceCBB+Ktt95C3759tf327t07k+qgElKqM9/zhYhPb/IkQFNG+R5LFFRu2pbNWPrzx5N8zi9fJoYAck360XVnq47w7bYwPek3AkkASUByHSb3pyCl8rXIgRX8OJVpn/qe8murhJzKUPmB2ZBS2YRfLuSJ3vMY/DgVV1cvAsrzeh3zUNVZiErtZkmpPC8k++WAPuneE7WUq2eWpFSlkOp8v1XQqaW0Yl0lZnzlOej8I3zm++3JIZ0zQgTJb51aS1Tn4gTVj4OeuO+/ql+llcSgjkbb1AopIFp+MlWdFA+6qvuuBCqf08wDbqyuqsz51UgdVXPk1CHBkiVLMHDgQADAn//8Zxx33HE48MADMXjwYK0AeMopp3T4dVX+y68Ahx9+OA455BBsvfXW2HrrrTFx4kT06tUL8+bNAwCceOKJOOCAA7DFFltg++23x/XXX48VK1ZkUhzI8DwP/fr1E14ODg4ODg4OazF4VH5HveoMG2ywAd5//30AwKxZs3DAAQcAiNJG6cqRrlixotA5Vq5cWfi6asbHtFQq4b777sOqVaswatSozP7W1lb86le/QktLS25+rc8//xyDBg1CqVTCTjvthKuvvho777yztv2aNWuwZs2a5DP94qkqQp/tZbVUVhrklFFNXruopgnJpkX1sgi0Ne9pgJFGNU3aKp7gVeqnqiyfbLrKPhFn+2lEexKdn9yHpCxQ1ZaqMDTil7enqindVk3V1BQ1S/0A9eb7rApdTkADryImq6NcNY2uISdIqZMn0HpRSUzzAIesllIzfvSe/ya8xIe4RH/PxIwfMh+tLCiklpbrZiNkyyBQqahF5yDdsepAKEO6KENfQDq2EmuPwh1AlamDmvVl1RQwK5Mm072NUsq3qdRS+Vj5fbWQzAWxy09HjUfZXae7pY3rjjj66KNx4oknYquttsInn3ySuFHOnz8/sWTL2GCDDbBkyRJsvPHGVufYbLPNMH/+fGyxxRbW19XlxHTBggUYNWoUVq9ejV69emHmzJkYNmxYsv/Pf/4zvvnNb+KLL75A//79MXv2bGOZq2233RZ33HEHhg8fjhUrVmDKlCn4yle+gpdeeglbbbWV8phJkybhqquuymxfvrIk+Q9FoP5kIbg/JYsXoxBtrB1rGPBl6GMNC7AmDLCGAWviY1pDnY+YCBNJBABmiGqnvQVeCA+kGhXSyT06B0MpjljlC66cmdGTSB3fT88nRw0DsY+tx9DgldDot6PRC9HoRWQ9nahCgdiFzEdbYn4ivmPxNXPTI3VH8IV2qYmP33/aTg5WyfqEqa6Ffz9RuVAfIQvJIhQm7fn3riKlAMAEkpH+jXmqKO5vyuChPQzItcTlSsPo+ljcL31F/Usko6AJvZqLFo3Kb/+iNd5Wu4uTbh5Y+Tn/+2ZJKSAS0xAe2hifB4B25qGVeVgd/53bwdDGGFpZ9H87wvhYFpNSIGQs/u2l/afERvUQW1LMEabwoQj61Eyq/opBGHPkfSBtCwwPi7qUXPL4l8e+PO4TH39PzCudd4eJi1ZCSNM92Xkj3pP8NugDaHovESlN597ot1RK5nJTHmQ5iFI1l7QxX5hDaMaPqI/snMHnE/4e0nvVnKDapvMjFwjql107D3gsenVU3/WGyZMnY/DgwXj//ffxk5/8BL169QIQmfj/93//V3kMYwy33XZb0jYPbW1tha/LY128UrS2tmLRokVYtmwZZsyYgdtuuw1z585NyOmqVauwZMkSfPzxx/j1r3+Nxx57DM8884w1Ww/DELvssgtGjx6NG2+8UdlGVkoWL14skGMHB4fq4O233y705NyZcPOAg0PnoLPngRUrVqClpQUDp/wIfs/Kg6BVCL9cjffPuxzLly9H7969O+QctYDBgwcXTgf1xBNPJL6sNuhyxbSpqSmRjHfbbTc8++yzmDJlCm699VYAwLrrrouhQ4di6NChGDlyJLbaaivcfvvtuPjii636930fu+++O958801tm+bmZjQ3Nyefe/Xqhddeew3Dhg3D+++/321+ZCtWrMDAgQPdPdU4uuM9LV++HJtvvjn69OnT1ZeihZsH6hfunuoDXT4PuKj8isGzKHUkupyYymCMCapF0f2q9vPnz8fw4cOtj/F9H5ttthmAKMK/u0wKHO6e6gPd8Z4qqQbS2XDzQP3B3VN9oJ7mAYfOR5cS00suuQQHH3wwBg4ciJUrV2L69OmYM2cOZs2ahVWrVmHixIk44ogj0L9/f3zyySf45S9/if/85z849thjkz5OPvlkbLbZZpg0aRIA4KqrrkqU1RUrVuDGG2/E/Pnz8Ytf/KKrbtPBwcHBwcGhq9GR0fN1EuRZD+hSYvrhhx9i3LhxWLJkCVpaWjBixAjMmjULY8aMwerVq/H666/jzjvvxMcff4wNN9wQu+++O5588klsv/32SR+LFi0Snr6WLVuG008/HUuXLkVLSwt23nlnPPHEE9hjjz264hYdHBwcHBwcHBws0aXE9Pbbb9fu69GjB+6///7cPubMmSN8njx5MiZPnlzppaG5uRlXXHGF4HNW73D3VB9w91Q7qNfrNsHdU33A3VMHwPmY1gW6PCrfwcHBwcHBwaGjkETlX391x0blj7+srqLyS6USJk+ejD/84Q9YtGgRWltbhf3l1rqvFM4D2cHBwcHBwaH7g3Xwq85w1VVX4frrr8dxxx2H5cuXY/z48Tj66KPh+z6uvPJKqz6efPJJ/M///A9GjRqFxYsXAwB++9vf4qmnnir7uhwxdXBwcHBwcHBYy/D73/8ev/71rzFhwgQ0NDTghBNOwG233YbLL788KQ1vwowZMzB27Fj07NkTL774YpIxaeXKlbjmmmvKvi5HTB0cHBwcHBy6P5xiKmDp0qVJKs1evXph+fLlAIDDDjsMf/nLX3KP//GPf4xbbrkFv/71r9HY2Jhs32uvvfDCCy+UfV2OmDo4ODg4ODg4rGUYMGAAlixZAgAYOnQoHn30UQDAs88+axWg9sYbb2D06NGZ7b1798ayZcvKvi5HTB0cHBwcHBy6P3ge04561RmOOuoo/N///R8A4LzzzsNll12GrbbaCieffDK+/e1v5x7fv39/vPXWW5ntTz31VEUlZ2uu8pODg4ODg4ODg0PH4tprr03eH3PMMRgwYAD+8Y9/YOjQoTjiiCNyjz/jjDNw3nnn4Te/+Q08z8MHH3yAp59+GhMmTMDll19e9nU5Yurg4ODg4ODQ7eGx6NVRfdc7Ro4ciZEjR1q3v+iii7B8+XLst99+WL16NUaPHo3m5mZMmDABZ599dtnX4Uz5Dg4ODg4ODg5rIX7729/iK1/5CjbddFO89957AIAbbrgBDz74oPG4UqmEuXPn4sILL8THH3+Mf/7zn5g3bx7++9//4uqrr67omhwxdXBwcHBwcOj+cFH5AqZOnYrx48fjkEMOwbJly1AqlQAA66+/Pm644QbjsUEQYOzYsVi+fDnWWWcd7Lbbbthjjz3Qq1eviq/LEVMHBwcHBwcHh7UMN910E37961/jhz/8IYIgSLbvtttuWLBgQe7xw4cPxzvvvFP163LE1MHBwcHBwcFhLcPChQux8847Z7Y3Nzdj1apVucdPnDgREyZMwJ///GcsWbIEK1asEF7lwgU/OTg4ODg4ODisZRgyZAjmz5+PQYMGCdv/+te/YtiwYbnHH3TQQQCAI444Ap6XpstijMHzvMQ1oCgcMXVwcHBwcHDo9vDQgVH5HdNth+J73/sezjrrLKxevRqMMfzzn//EPffcg0mTJuG2227LPf7xxx/vkOtyxNTBwcHBwcHBYS3Dqaeeivb2dlx00UX44osvcOKJJ2KzzTbDlClT8M1vfjP3+H322adDrssRUwcHBwcHB4fuj46s0FSHlZ8A4LTTTsNpp52Gjz/+GGEYYuONN7Y+9oknnjDuV5UrtYEjpg4ODg4ODg4Oaxm+/PJLMMawzjrrYKONNsJ7772HG264AcOGDcOBBx6Ye/y+++6b2UZ9Tcv1MXVR+VXAnDlz4Hkeli1b1tWX4uDg0EVw84CDQ43D5TEVcOSRR+Kuu+4CACxbtgx77LEHfv7zn+PII4/E1KlTc4//7LPPhNdHH32EWbNmYffdd8ejjz5a9nU5YloQ++67L84//3xh21577YUlS5agpaWlay6qTLS1teH73/8+hg8fjnXXXRebbropTj75ZHzwwQdCuzVr1uCcc87BRhtthHXXXRdHHHEE/vOf/yT73333XXznO9/BkCFD0LNnT2y55Za44oor0NraKvSzaNEiHH744Vh33XWx0UYb4dxzz820UWHu3LnYdddd0aNHD2yxxRa45ZZbhP2vvvoqvvGNb2Dw4MHwPC83MTAHYwxXXnklNt10U/Ts2RP77rsvXn31VaHNGWecgS233BI9e/ZE3759ceSRR+L1118HANxxxx3wPM/4mjNnjtU9tLW14Uc/+hG23HJL9OjRAzvuuCNmzZqVew+fffYZxo0bh5aWFrS0tGDcuHEZYlTu9/7LX/4SQ4YMQY8ePbDrrrviySefLPz9dVe4eWDtmgdo24MPPhie5+GBBx4A4OaBtXkeqHe88MIL+OpXvwoA+OMf/4h+/frhvffew1133YUbb7wx93j+e+OvjTbaCGPGjMFPfvITXHTRRWVflyOmVUBTUxP69esnSNj1gC+++AIvvPACLrvsMrzwwgu4//778e9//xtHHHGE0O7888/HzJkzMX36dDz11FP4/PPPcdhhhyUy/euvv44wDHHrrbfi1VdfxeTJk3HLLbfgkksuSfoolUo49NBDsWrVKjz11FOYPn06ZsyYgQsvvNB4jQsXLsQhhxyCr371q3jxxRdxySWX4Nxzz8WMGTOE+9hiiy1w7bXXol+/ftb3/5Of/ATXX389br75Zjz77LPo168fxowZg5UrVyZtdt11V0ybNg3/+te/8Mgjj4AxhgMPPBClUgnHH388lixZkrxGjRqF0047Tdi21157Wd3DpZdeiltvvRU33XQTXnvtNZx55pk46qij8OKLLxrv4cQTT8T8+fMxa9YszJo1C/Pnz8e4ceMq/t7vvfdenH/++fjhD3+IF198EV/96ldx8MEHY9GiRYW+v7UJbh7ovvMAxw033JD5+7p5oI7mAaeYCvjiiy+w3nrrAQAeffRRHH300fB9HyNHjkzKk5aDvn374o033ij/wpiDNU455ZTMT3HhwoXs8ccfZwDYZ599xhhjbNq0aaylpYX96U9/YltvvTXr2bMn+8Y3vsE+//xzdscdd7BBgwax9ddfn5199tmsvb096X/NmjXse9/7Htt0003ZOuusw/bYYw/2+OOPd+o9/vOf/2QA2HvvvccYY2zZsmWssbGRTZ8+PWmzePFi5vs+mzVrlrafn/zkJ2zIkCHJ54cffpj5vs8WL16cbLvnnntYc3MzW758ubafiy66iG277bbCtjPOOIONHDlS2X7QoEFs8uTJxntkjLEwDFm/fv3Ytddem2xbvXo1a2lpYbfccov2uJdeeokBYG+99VZm3z777MPOO++8su6hf//+7OabbxbaHHnkkeykk07SXstrr73GALB58+Yl255++mkGgL3++uuMsfK/9z322IOdeeaZwrZtt92W/eAHP2CMlf/9dQe4eSDC2jYPzJ8/nw0YMIAtWbKEAWAzZ85U9unmgdqbB5YvX84AsEHXTGRDrv95h7wGXTORATB+n7WG4cOHsylTprBFixax3r17s3/84x+MMcaee+45tskmm+Qe/9JLLwmv+fPns7/+9a9sn332YXvttVfZ1+UU0wKYMmVK5ml44MCByrZffPEFbrzxRkyfPh2zZs3CnDlzcPTRR+Phhx/Gww8/jN/+9rf41a9+hT/+8Y/JMaeeeir+/ve/Y/r06Xj55Zdx7LHH4qCDDsKbb76pvaaDDz4YvXr1Mr6KYPny5fA8D+uvvz4A4Pnnn0dbW5vgCL3ppptihx12wD/+8Q9jP3369Ek+P/3009hhhx2w6aabJtvGjh2LNWvW4Pnnn9f28/TTT2ecsMeOHYvnnnY0S40AAN1oSURBVHsObW1the6NYuHChVi6dKnQd3NzM/bZZx/tfa1atQrTpk3DkCFDtH93FWzuYc2aNejRo4fQpmfPnnjqqaeSz9xkSPttaWnBnnvumWwbOXIkWlpaknuw/d49z8Mdd9wBAGhtbcXzzz+fueYDDzww6bec76+7wM0DEdameeCLL77ACSecgJtvvrmQGkvh5oGuh8c69lVvuPzyyzFhwgQMHjwYe+65J0aNGgUgUk9VFaFk7LTTTth5552x0047Je8POeQQtLa24vbbby/7ulxUfgG0tLSgqakJ66yzTu7k1NbWhqlTp2LLLbcEABxzzDH47W9/iw8//BC9evXCsGHDsN9+++Hxxx/H8ccfj7fffhv33HMP/vOf/ySTx4QJEzBr1ixMmzYN11xzjfI8t912G7788suq3N/q1avxgx/8ACeeeCJ69+4NAFi6dCmampqwwQYbCG032WQTLF26VNnP22+/jZtuugk///nPk21Lly7FJptsIrTbYIMN0NTUpO1Hd9wmm2yC9vZ2fPzxx+jfv3+he6T98r7kvmUTxi9/+UtcdNFFWLVqFbbddlvMnj0bTU1Nhc6Vdw9jx47F9ddfj9GjR2PLLbfE//3f/+HBBx8UohpbWlqwzTbbCP2qUntsvPHGyf3Zfu/bbLNN4hv58ccfo1QqKa+Z9su3yW0qMQHVA9w8kGJtmQcuuOAC7LXXXjjyyCPLOg8/l5sHHGoJxxxzDPbee28sWbIEO+64Y7J9//33x1FHHZV7/MKFC4XPvu+jb9++mYeronDEtIOwzjrrJIsREA3UwYMHC8rFJptsgo8++ghA5ITMGMPWW28t9LNmzRpsuOGG2vNsttlmVbnetrY2fPOb30QYhvjlL3+Z257FJcdkfPDBBzjooINw7LHH4rvf/a6wT9We9kO/m//5n/9JAgPk4xhj2v5U+P3vf48zzjgj+fzXv/4VQRBo+5a3nXTSSRgzZgyWLFmCn/3sZzjuuOPw97//vdDgy7uHKVOm4LTTTsO2224Lz/Ow5ZZb4tRTT8W0adOSY4466qjMZJH3ndq24QFdedcsb7NpszbDzQP1Pw889NBDeOyxx3L9PG3g5oEuRkf6gtahYgoA/fr1yzxg77HHHlbHzp07F8cffzyam5uF7a2trZg+fTpOPvnksq7JEdMOQmNjo/DZ8zzltjAMAQBhGCIIAjz//PPJZMlhMsMdfPDBmShJGZ9//rlxf1tbG4477jgsXLgQjz32WKKSANGPtrW1FZ999pmglnz00UfYa6+9hH4++OAD7Lfffhg1ahR+9atfCfv69euHZ555Rtj22Wefoa2tLXnanj9/frKPX0O/fv0ySspHH32EhoYG40JNccQRRwhmrs022wxLliwBED3xU7Xlo48+yjz984jDrbbaCiNHjsQGG2yAmTNn4oQTTrA6v8099O3bFw888ABWr16NTz75BJtuuil+8IMfYMiQIcZ+P/zww8z2//73v8k92HzvMjbaaCMEQaC8ZtovYPf9rc1w80D9zwOPPfYY3n777cStgeMb3/gGvvrVryYR93lw84BDd8Opp56Kgw46KKPYr1y5EqeeemrZxNT5mBZEU1NT2UljTdh5551RKpXw0UcfYejQocLLZC687bbbMH/+fOPLBL4Yvfnmm/jb3/6WmeR33XVXNDY2Yvbs2cm2JUuW4JVXXhEWpMWLF2PffffFLrvsgmnTpsH3xZ/WqFGj8MorryQLARD5sTQ3N2PXXXcFAOGe+Q991KhRwrn5cbvttltmgddhvfXWE/ru2bMnhgwZgn79+gl9t7a2Yu7cuZmFVgZjDGvWrLE6d9F76NGjBzbbbDO0t7djxowZRtPhqFGjsHz5cvzzn/9Mtj3zzDNYvnx5cg8237uMpqYm7Lrrrplrnj17dtJvJd9fd4CbB9aeeeAHP/gBXn755cx3OXnyZEHJzIObB2oALiq/qtAp4//5z38qSpvnFNOCGDx4MJ555hm8++676NWrl+DYXwm23nprnHTSSTj55JPx85//HDvvvDM+/vhjPPbYYxg+fDgOOeQQ5XGVmPDa29txzDHH4IUXXsCf//xnlEql5Om4T58+aGpqQktLC77zne/gwgsvxIYbbog+ffpgwoQJGD58OA444AAAkUKy7777YvPNN8fPfvYz/Pe//03OwRfTAw88EMOGDcO4cePw05/+FJ9++ikmTJiA0047TVBmZJx55pm4+eabMX78eJx22ml4+umncfvtt+Oee+5J2rS2tuK1115L3i9evBjz589Hr169MHToUGW/nufh/PPPxzXXXIOtttoKW221Fa655hqss846OPHEEwEA77zzDu69914ceOCB6Nu3LxYvXozrrrsOPXv21P49yr2HZ555BosXL8ZOO+2ExYsX48orr0QYhkIuuJkzZ+Liiy9OzG3bbbcdDjroIJx22mm49dZbAQCnn346DjvssMQHzfZ733bbbTFp0qTERDh+/HiMGzcOu+22W6J8LVq0CGeeeab199ed4eaBtWceUJk6AWDzzTc3Kpnl3IObBxzqATvvvHOSo3f//fdHQ0NKJUulEhYuXIiDDjqo/BOUHc+/luKNN95gI0eOZD179sxNE0NxxRVXsB133FHYdsopp7Ajjzwy+dza2souv/xyNnjwYNbY2Mj69evHjjrqKPbyyy93yL0sXLhQ++xH09N8+eWX7Oyzz2Z9+vRhPXv2ZIcddhhbtGhRsn/atGnafijee+89duihh7KePXuyPn36sLPPPputXr069zrnzJnDdt55Z9bU1MQGDx7Mpk6danUf++yzj7HfMAzZFVdcwfr168eam5vZ6NGj2YIFC5L9ixcvZgcffDDbeOONWWNjIxswYAA78cQTkxQsMnRpYmzuYc6cOWy77bZjzc3NbMMNN2Tjxo0TUrswln7PFJ988gk76aST2HrrrcfWW289dtJJJyW/Qw6b7x0AmzZtmrDtF7/4BRs0aBBrampiu+yyC5s7d26h7687w80Da888oALKSBdlcw9uHugY8HRRQ340kW35k593yGvIj+ovXVS5uPLKK9mVV17JPM9jEyZMSD5feeWV7JprrmF33303W7NmTdn9e4yxtVCAdnBwcHBwcFgbsGLFCrS0tGDIjybCrzBiXIdw9WosvPyHWL58uVH955g0aRLuv/9+vP766+jZsyf22msvXHfddULGhQ8//BDf//738eijj2LZsmUYPXo0brrpJmy11VYAgE8//RRXXHEFHn30Ubz//vvYaKON8PWvfx1XX3211pT+0EMPWd+TXGRDxp133onjjz++4ih8Gc6U7+Dg4ODg4ND9wbzo1VF9F8DcuXNx1llnYffdd0d7ezt++MMf4sADD8Rrr72GddddF4wxfP3rX0djYyMefPBB9O7dG9dffz0OOOCApM0HH3yADz74AD/72c8wbNgwvPfeezjzzDPxwQcfCLmRKb7+9a8Lnz3PA9Unqc9onh/9KaecUuiebeEUUwcHBwcHB4dui0QxveqajlVMr7jEWjGV8d///hcbb7wx5s6di9GjR+Pf//43ttlmG7zyyivYfvvtAUREceONN8Z1112XScPGcd999+F//ud/sGrVKsH3U4W//e1v+P73v49rrrkGo0aNgud5+Mc//oFLL70U11xzDcaMGWM8vlQqYfLkyfjDH/6ARYsWobW1Vdj/6aefFvgGUriofAcHBwcHB4fujxqOyl++fDkAJIGUPPMLNZMHQYCmpiahEpiqn969e+eSUgA4//zzMWXKFIwdOxa9e/fGeuutlxR5OPfcc3OPv+qqq3D99dfjuOOOw/LlyzF+/HgcffTR8H0fV155Ze7xOnQpMZ06dSpGjBiB3r17o3fv3hg1ahT++te/Jvu/9a1vJZFf/DVy5Ehjn7xcm/xavXp1R9+Og4ODg4ODw1qMFStWCC+b1IKMMYwfPx577703dthhBwBRhoRBgwbh4osvxmeffYbW1lZce+21WLp0qZD2i+KTTz7B1VdfLRSSMOHtt99W+qK2tLTg3XffzT3+97//PX79619jwoQJaGhowAknnIDbbrsNl19+OebNm2d1DSp0KTEdMGAArr32Wjz33HN47rnn8LWvfQ1HHnkkXn311aTNQQcdlNSjXrJkCR5++OHcfnv37i0cs2TJkqo75zo4ODg4ODjUD4rUvS/nBQADBw5MirK0tLRg0qRJudd19tln4+WXXxZShzU2NmLGjBn497//jT59+mCdddbBnDlzcPDBB2eKbwARIT700EMxbNgwXHHFFVbfx+67747zzz9fILpLly7FhRdeaFX9aenSpRg+fDiAqAAIV30PO+ww/OUvf7G6BhW6NPjp8MMPFz5PnDgRU6dOxbx58xKfiubm5tx61DI8zyt8jIODg4ODg4NDJXj//fcFH1O5XKeMc845Bw899BCeeOIJDBgwQNi36667Yv78+Vi+fDlaW1vRt29f7Lnnnthtt92EditXrsRBBx2EXr16YebMmdZFJ37zm9/gqKOOwqBBg7D55psDABYtWoStt94aDzzwQO7xAwYMwJIlS7D55ptj6NChePTRR7HLLrvg2Wefzb1vE2omKr9UKuG+++7DqlWrMGrUqGT7nDlzsPHGG2P99dfHPvvsg4kTJ2bKX8n4/PPPMWjQIJRKJey00064+uqrsfPOO1tfSxiG+OCDD7DeeuvVZr1fB4c6A2MMK1euxKabbpqpBlSrcPOAg0N10eXzQEdWaIr75a6Juc0ZwznnnIOZM2dizpw5xmIN3Nz+5ptv4rnnnsPVV1+d7FuxYgXGjh2L5uZmPPTQQ4Wsw0OHDsXLL7+M2bNn4/XXXwdjDMOGDcMBBxxgNecdddRR+L//+z/sueeeOO+883DCCSfg9ttvx6JFi3DBBRdYX4eMLo/KX7BgAUaNGoXVq1ejV69euPvuu5PqJvfeey969eqFQYMGYeHChbjsssvQ3t6O559/XsvG582bh7feegvDhw/HihUrMGXKFDz88MN46aWXktxfMtasWSP4gSxevBjDhg2r/s06OKzleP/99zOqQK3AzQMODp2Dzp4HeFT+Fpd3bFT+Oz+yj8r/3//9X9x999148MEHhdylLS0t6NmzJ4Aowr5v377YfPPNsWDBApx33nnYddddMWPGDACRUjpmzBh88cUXmDlzJtZdd92kn759+ypN/jqsXr0azc3NFT2EP/PMM/j73/+OoUOH5uZANaHLiWlraysWLVqEZcuWYcaMGbjtttswd+5c5YKwZMkSDBo0CNOnT8fRRx9t1X8Yhthll10wevRo3Hjjjco2V155Ja666qrM9n03+w4amtcF4j8uawzAggCs0QcL/Ph/D2GDjzDwwBq86HOAeDsAn38GmB/9wVmAaJsfvzy+P/6f7EO8Dz7AApbsAwDms3g/S/bDZ/CCyOHF89OXH4TR/x5DQ8Dg+yEAIPAZAi+E7zEEfohGv4TAD9Hgx9s8hgY/RIMXosEvRW3B0OiX0OCVEN9S/J6hMf4/QNSXB95/iACI/2fwvRA+wqRt4IXJ9+7zYxDCBxCAHx8i8Bh8hGj02uGDockrJe2D+JHV433Gn/3Y+SfQPCrrtssoITtg5W1hnMuObi/BB2MeSvBQYj7a4KONBQjho401oMQ8lOAjZD5KxO07RBah5BZein8MYXw+/jnqz0vah8l2j7xX9M/UKobvhZl2JeaD8XtiAULmoY0FKIUB2lh0nhAeSszDl5+XMPOIe7Fs2bKKaih3JHTzwMiv/gBBUw8wzwMCLxqvPgDPi8dpNNbFbUj28XEMQNgHD1LbeLsfzQ90P/jc4Ee/VRYACFgkAMXjHwEDC6LxDl89/oP4BQBBECLw4/HoR+8bvBAeH/N+9Kvi732wzBwAROPV90I0egyeR+YNrxSP3Wh8quYCAGScinMCgORcdD6IjsnOCRxpW5bMKwFY5jMQzRW8P7q9COSxDgCMecL+zDiVtvE7KzGfvM/2S8dnCeqxqpo3ou3Z9iXNeBeulcwpyb54bonmrGist8VzQjsL0MaC6P8w+tweRvvXfN6OGUf8odPngYSYXnYNgg4ipqXVq/HO1fbEVEcAp02bhm9961sAgBtvvBE//elP8eGHH6J///44+eSTcdlll6GpqQlAZFHeb7/9lP0sXLgQgwcPNl5DGIaYOHEibrnlFnz44Yf497//jS222AKXXXYZBg8ejO985zvaY9va2nD66afjsssuwxZbbJF7v0XQ5ab8pqampI7xbrvthmeffRZTpkxJav5S9O/fH4MGDcKbb75p3b/v+9h9992Nx1x88cUYP3588nnFihUYOHAgGprWQUNDT7CG+KmjMQBr8GOC6iNs9MEaPHgNPvyGlJB6gQfWgOj/AEC8ncXdMJ9vM5BSvjAlnxkQE1QEfFJFvBAhWZASUhqki1IgkNIwXoTiCdmL3jcEJTR4IQLfJ0Q0WiQ4KW3wvHhhYmjwAN/z0ejFi5PnxaQ0+j/wAB8eAi9Eowf4Xrw4eB4AL16YooFJiamP9Lr8eNEJ4MH3PDR5iLd5aIQf9Q2PtEHcn/yZw0v2iyj2hJhHUGVyykliCR7aWAOaEBG4VtaARvhoZQEhi9lFKek3Z2EJyWKXntOXFjNPIKkq6Mip6tz8nH68ECGMfrwe89DOApSYB4/5aGRtAPSTcS1ANw8EzT0jYhp4GULKx6oXvw+DeFz6APM8gYACEtHkD6XytgDxmCaENSai/CfgBSzK5x0weAEAPyKlPiWlgTj+g3jcBwF/KI3HFyGlAR/z8WfxfYgGz4seQP0S/PhiGvwSAvgRsYznhIhcRnNENJbj/z0vaRuN0RDpuES8P0QQ/0z4HOIDCMh8EbXzEHh+TEqjUR7wfuPzAUFMSqN7Bfx4/HvxNfK5IN3OwR9oVQgl4pm+95X7UxLqp/NCPH74cUIb5gn9RX3mk9JkvybZexFyKs8n6XWI80obC6J7ChsQwIOXKDFUgUnnBKC254HOgo0meO655xrTNu27775W/ejw4x//GHfeeSd+8pOf4LTTTku2Dx8+HJMnTzYS08bGRsycOROXXXZZ2efXoeacvRhj2vQKn3zyCd5//33079+/UH/z5883HtPc3Jz4hdj6hzg4OHQvuHnAwaGbo4bzmHYF7rrrLvzqV7/CSSedJJj9R4wYgddffz33+KOOOsoqSKooulQxveSSS3DwwQdj4MCBWLlyJaZPn445c+Zg1qxZ+Pzzz3HllVfiG9/4Bvr37493330Xl1xyCTbaaCMcddRRSR8nn3wyNttssyQlw1VXXYWRI0diq622wooVK3DjjTdi/vz5+MUvflH8Aj0fCHygITbPBF6kmPoewgYvVkyo6S7+3IDItM8fGIO0DYDUtF9ALRWUU24W1JnwLdRSrlA2BKJaQtVSar5LTfmRia4xVlAafa50hqlS4vH3qdk+/T9uL5nQKES1NBSOCxLzXdRnkPRNzH9khtC9p/AND++h4pAALKM2qrbZQGWus1VLVQi8UKmAFLk23wsFdYaa8VVqaqi6XqkdK+O7qRVE49Qj4zhVSsXxT+aByDAQjWNJMc1TS4X/k7mBxS49LOmH+Sy2vOSPf66WcpM9AKVaKlpJRItJaspnyXsg+u2n21NTPJ8LElO8YT4AILj1qC0nfO7ImvBTE316LL82wcwvuffkbdfB91jyuzeNfZtxJ7rvqFVR2aKStCFjLyDXHnhMq5raIDSowCXhGj3x+mJ3pJBYifi+6H9ziUuHzsXixYsTizVFGIZoa2vLPX7o0KG4+uqr8Y9//AO77rqr4OMKwCpJvwpdSkw//PBDjBs3DkuWLEFLSwtGjBiBWbNmYcyYMfjyyy+xYMEC3HXXXVi2bBn69++P/fbbD/feey/WW2+9pI9FixYJ0X3Lli3D6aefjqVLl6KlpQU777wznnjiCaucXBkEfrwYxf37PpgfbYPPFyryv4/Y10wmq6lVI+onS0rpwpRHSllsyq+ElDYQc56OlKbmu2hhSsioRFIBJAtRZMbXL0J04g+kRcX4p4gXIb7wJOY5iZRy2CwwJkJK29iSU3puFVnj4JO3eh9xCTCQURX59MGEBUWFPDN+0pfmb8K368z9grmR+5l2VG3qToLgOx4/eBoJaTy+OSmlpvzEb9SDQEAzxNRXzAEFHkr9IITP5wFCShuCMCEwKlKaNd+zDCltSEz1UT8yKU3mAAUhBWCcD+hxAIykNDmOPvASQis8lJLP8kNxUVJaLgTSKY2fkjRukmMU5nsd4SwxrzA5NfmYCm5CUj/Ut5S7KoTwIt9T5gljn/uZh8zr+gfUTojKrydsv/32ePLJJzFo0CBh+3333WeVyei2227D+uuvj+effx7PP/+8sM/zvPokprfffrt2X8+ePfHII4/k9jFnzhzh8+TJkzF58uRKLw0AIkIaBOAOT6zBjwipT9STBrJABarFSlRFgMgNTyCifqqiWpFSrr4UVkqiBScgqomJlNIFKSGj0gLFJ/dGvyQsRI1eSbkAyQTS7FuaqqW+tGjxz0JfhplB3mdDSuW2MkGVyWmeaiovAjQwSSaqRQMVagGVKDS1jDDw4JGAxmj8S4TUh0A6KSkVfEzJZ7GP/PEfzSHxjzD+7MVzAPcr9QMGz49IqU/GPSWliWJqSUobvPSB1PdS8pmMaUWgk0klBdIHWTofFCWlfA7QzSm0jYwiwU5FrSHGh1Lqiw7PqJaaSKk8P3BVuAg5Vc0n8oNtCdlrkEkpVUt1sPVbd+hcXHHFFRg3bhwWL16MMAxx//3344033sBdd92FP//5z7nHL1y4sEOuq8uDn2oaDaJimhDSwJfM+B5ZrES1FL64+ABIo2596b28KPlQktJUMS2flDZ4ZHHSkFK6IOlIKY3MbfTbtSqpSR3VLUZ0IbIx4Xc0VATVdtFKA6G4gpA1zakWnVogo3TxNt1r4DGEXogSj/LrJuAPnWEDiHKaPojKKigkYpoQVEhzgURKmTT+kZDXmJRyEgokQY+Ix38UdY+IlAbRuEgsJDz6npjuAfVDqWC6j99TUiqTUyA1wRcx3edl4wBgRUrpsRS2JvzkeMV22Q0oz1yvt4Lo1VH5M8/QkfYtWSB0c0MSLBYK5LTIw6IuCl8VRKk9VmHG52opV1S7ErRCU0f0XW84/PDDce+99+Kaa66B53m4/PLLscsuu+BPf/oTxowZY91Pa2srFi5ciC233BINDZXTSkdMDWC+H5nvqY8pNd03GEz4go9puvBE/YIsOlD4lzHBt0yVEgpARaQ0WZwKktJGkhaK+oJRUtpI1RMp7YsKJlM+9SOjJvyiKMGzUkdsoFNQOWRfrJAQTmrG5+auUhyFm0dI88z0RUAX2nK+l9SkH8D3GEo5XXT1glQJMqQ0iFRUgVhSZdSLrXr0M/9zEr9SQSVVjH/EkfdCOig69mMiGo19CCnhuELq87lA8icFsg+lKlLaSM34fHz7aVonwOzGE+3XP6QK/qBlkNKMT6lkwq8WivqPZ9LIwc9VS2UTvo6UyqmnAKQ/MC8lpzLk4+m1pOdRk1L53gTVlKVtuRnfoX4wduxYjB07tqxjv/jiC5xzzjm48847ASBJN3Xuuedi0003xQ9+8IOy+u16OaaWEQc7wfdSE36DxreMfA5VJn2uhkh5SrPmvJSUwpCntFJS2uCbldJGYr5v9EpKUtroldDotyektNErZUhpEgjFVU7FK37GVqql0Z8hzKgrKpTgEWf8VKFUQUcqi4Cmp5LPJb8v8Yk88ccSCapwH9S3Kz7GhpSWWEp8bVSOAKzwIl7OMfUObgmRSWkoZsNJVU9fGus0e05D/BDLXz7ZFjCwhth83xDPAw1xgFNDnKO0IXpFpvswmQt8OvYDMylN3hvGv4qUNvglgZTy8d4Yzw0qlTRq056ZE6iySueBIM6ByueATICUoIKKbgBKk30FZnxq5ZChe9CSt9uopUYTvkT0El9OPqfEL7qtjTVAnFVpbtTs3KK7tjBDlD0lKU2uhZyH+pc61C7ef/99/Oc//0k+//Of/8T555+PX/3qV1bHX3zxxXjppZcwZ84coeLUAQccgHvvvbfs63LE1MHBwcHBwcFhLcOJJ56Ixx9/HACwdOlSHHDAAfjnP/+JSy65BD/60Y9yj3/ggQdw8803Y++99xZy0w4bNgxvv/122dfliKkBgule9i2l6aKoGV9SQHmEbkZBVQQ+COY9nyUKqir6tmK11DOrpb7CdC+rpdxsz19c3Wj0Smjy2gWlNKrUFD2702Oi/qIKTk2J2ppNB6NzA+DKoDqFiah4lJPOCUhVQp1aKCfXL8FLVMvItOULZnxutqPmumQ/MdNlAhGIQqJ8IVVLdddXDnQR+raod9VEDHZK1VLBJSdjjs958bHekLrshOR9qpYysIZUGU0CnYhSGgRh9L+fqqQmtVRlLaFp4dIE+qJamlZdSxVSG59S32OCWuojrcIkW0uAtKoTfw+ILj0m39J0u8KXvQwnwLyxo/IvLWrGB9S+pXKGDtEXNau8UuW0jQXKOUJngRH6Nii48jGJBYil/qPqNFE1AtbBrzrDK6+8kmQs+sMf/oDhw4fjH//4B+6++27ccccducf/97//xcYbb5zZvmrVqoqKKDgfUwNY7F/KeFR+jm8pTyeTCXii75Fvxk9Iqa9PCQPASEppBK68KDX5aUStipTK5UVT4skypJSWE+Rm/Gz0vDoin4MSH3khUkXdcnC/0YSIeXQCjI9h0YLE21Jf05AVi85PrzEbCFGKHfspKU0IMkQzfomQae47pvQbg9rXVFuKMOPb6luRUhviKec2XZugctmBByEfqRCRT/+n7ZG2Sf3Lmfiej3tSzcnzRX9SANpAJ5oSLojnBC/2F6UPpUC21Kicq1goMayJvAegDHoExMh7mn2DBjoCyMwPNqRUNyeYzPMh8wRyWq7fuaqyG90OUD9QkUzKpvM831I5+l4XOEn75N9NNTOGyhXj6FxA/VD5A3d6PfX9UNrd0dbWhubmZgDA3/72t6S+/bbbboslS5bkHr/77rvjL3/5C8455xwAaUWvX//61xg1alTZ1+WIqQEJAaXEtIGQUOpLqkq2HS84NAAKIPsFlYUEPEiLE08FQ1VSAIVJaVNQSlUSvjiQlDAyKeX+ZJmAJ09MBwXASEpVCogONMDBhBKLSg4KxIuuMQlJDSsmp/ICZkwJRUhpG2tI2kdl+1Lf0iRaVUFKdZH4mZRSGWVUVChEJTfbZ6VK6FoDGlkfv09efL9EQmWLSUJMhYdRnjSfjH+fCamg5HHPxz7PUZpHSiPVVEwD1SAFPtJcxSpSmpYYzQY5AciQUjk9nCphfvQ++8BahJTSPougBD8zD8mkVQVdGVK6j5YfpW1lRdLGt9REanXXBQAhxKwYNkqx6vtI5iniVxrtE31LeXseja+6plqBi8oXsf322+OWW27BoYceitmzZ+Pqq68GAHzwwQfYcMMNc4+fNGkSDjroILz22mtob2/HlClT8Oqrr+Lpp5/G3Llzy76utVMCsQRXR8PAEwOaEhM/xPQxMckUKjvFi5Ogomaqu8QLFI/CVSom6cJUjlIqk9IGv5S+CCltTIKa7EgpDXhKAqJiM35T8j4KfmiKzfb8JZv0I9N/mqNUp5ZSR376amUBWuO6zW0sDQDiaiagNuvbBEJxx3/5uDQgQPyfBzul76OgBGrCDyGa16gpPzqnL7yic6ZJrOU2UcCBGKTAXyaY3BQciFoqEVSZqCafFZYSoXR4wOJAJ0RjPn4hYEAS5BQm6aH8hlgZjc321HTPA534/w1+lpQqAx0NpJTPATIpVQU5mdLDNXntmSAnOeqeBjklLjwkCKqcDBxFILvh0LElb1cdozPhcwcFlQk/Lz2UHOykMuErVVppvuCvNhYIL3qfqnnEBtSELwdxVuo65NB5uO6663Drrbdi3333xQknnIAdd9wRAPDQQw9ZFSXaa6+98Pe//x1ffPEFttxySzz66KPYZJNN8PTTT2PXXXct+7qcYmpA4lOaJNjnBJWUHaVmPup3pvArTcY8NfMFRDUhEfhKUhqkhBSA0qdUJqWNQUlclIjZHkDiTyaXGaVmOxMpTRLsJ+mjspH1Ov+wcpCY76UFgZKqkPnR+bxILY3U08qVU96Onlc24XMi2oYgWUTaECSktI01JIsFVUFM5rnovGZ1lF+PbhtHHvkst7RqdwbzAS8es1Q9FVJCKcZ6ZvwjHd+ClUTnskPGPFVIAWhVUi8e/9zcLprrZVO+WEBDyFcqRd9TgknLDQNpOqfovb6saPRebban21RWE51aWi6oaqoy6esUPxUppW47shKablOnhxLdblJSKvuUqkz4KtcdG/DrS757lp2TVWqpLpE+V0uLXEOXwj1/J9h3333x8ccfY8WKFdhggw2S7aeffjrWWWcdqz6GDx+epIuqFhwxNSAhopyYxiQ141cayJVgUoVEDngARP+yVE1lKXmN8xXqSGlizvOYMdDJREr5AmcipbSaE12MKCnlSbYpKeUKaRHfMBsozffx9hJk0uvHpLQYObW6DqK6UlLaFqsbnJS2xSY17lfKSWkpCYhS+4vpF0V5QRBN9iZCSrebyGm1SWmtmvSKgHle6npDVFHhf51SSkgogKzpPiajLAluQjZhvmC2j4mdFODkkwdSHyk5VVV04jXudcnzZWsJJ5mV+JLaEFLAjpTaIET0cErnDO6Pzue+PHJKoSOkdB8lpeK28v1KdZWYdKQ062KgJoq+FxXC4A/5sluPipRSlyOVWkrN+KHB9cChdnDllVfi1FNPzZQkHTx4sHUfpVIJM2fOxL/+9S94noftttsORx55ZEWJ9uvg8cbBwcHBwcHBoUK4qHwBf/rTn7Dlllti//33x913343Vq1cXOv6VV17B1ltvjVNOOQUzZ87E/fffj1NOOQVbbbUVFixYUPZ1OWJqQMgj8GPVlJYiTPxIfS+rkirMeJkXrYMtqyc+I5VdsmopT78SJdEurpZGibJLglrKk2fLammj365VS+Xk2NynlKqlgRf7miFEI6I+TS/t34IoELoUKNyHKk1m76fvc/xNzQFNXubVxiKFtBXcdyurlvLri/anammUADv1PaWppUw+Y8l3IfmS8m30Wql/qexrqsookPcdFIFKJa0LE58GXBUVFFJADIqS2mQCG3nwIx/vsT8p4qT6NGm+H6eH8oPYtzRWRhuCEA1BKX7FPp98zAfReA7I2G/yU7/yJjL2eao4WtmNzgGyWsoLaaj8SWW1VJUKivqTAmKAk0ktpUnzxST75Sn+chq5aJtoZdC95PZ5aimdm/hxslqamsuJ6pi8zyqttmqparxn569AmENkP1S6nwY7yWopNePL3z/3p6flSOt5HuiOeP755/HCCy9gxIgRuOCCC9C/f3/8v//3//Dss89aHf/d734X22+/Pf7zn//ghRdewAsvvID3338fI0aMwOmnn172dblfiQGclCYVXhIzPmIzvpcsOAlplQOepO1KgpqY9UWTXlpuUPQxa0gqu5RHSnmQAyWlaeBDSSClYpBTeybQiS8+TSSPqZzqiS5WuheHvOgIgT+KRUe3TSanYp9Zcsrf6xYlSkhVaaHaWEPsSxokPqXpJK8npYC40OiCEOhiIxNSHUlVQUzz0nHDP0Q3WoSIqV7wMZWCn5IxTn3ISeS9QEp5ueGGND+p3xDCb8jmJs0EOMVBTg3x/kZOSnmQIyeico5iWtWNkFGap5TPAWKauIiMpkGK7WIeYkJKac5iWsUJQCbAiYO3A0RSmuwv0/2HkrzMPg051UEOQIy26UkpzRVaCSmlOYrFc1PfVPFhNLmOnIdUSkjp9rYkgDQQSKnxuyakk5rxKSmtBfCo/I561SNGjBiByZMnY/HixfjNb36DxYsX4ytf+QqGDx+OKVOmYPny5dpjX3rpJUyaNEnwT91ggw0wceJEzJ8/v+xr6iYrR8cgVUaJb6mglsp+ZKJPqRytn774YsVitYUl0blevIAlpDRWTPwgFFTSSklpI1FL6IIkKySqMqM0gp4nxqfJ9Ru99kQlpYQU0Cerp21UioiKnAqKgkToKDmN9qeqaXI8E0mn6ZUky4cnqKStMfFs5Wot0uj7iJzqSWnap5mIyosN3c+3Z7ZJqnK1UI6yWiuLUrlIxrBESDMvWVnN+JCLpNSLI/G9QFRJg4aUhDY2ZBXSxpiMBn5KMhtI5g1KSpsIIW3y2xMrSQMpM5z8T6wlwlwgFdCQS4vShPsyIZVLi9IAJ0pIATtSahP0pPp9lqSxAthFtKsyYgjZNjjhJNuS6Hv+qoSUKgmxSEr5e35/qvkg6Y+82sIGgYzKJDWj+vLPkloq+suKvrMUXD11qE2EYYjW1lasWbMGjDH06dMHU6dOxcCBA7XlRbfZZht8+OGHme0fffQRhg4dWva1uOAnA8IA8GJCCoiklKqlQuUX8koWKqKeAOmixRXTJG8hN+eTiFxKSn0S6ASguPleMt8BMFZ0oiY7OTKXqqQAhDackAK8zr1+McmLAjctTD6Y8kmeBjUAsXKiCISiyflprj9d5SSqkAoLEktN9yEiskoXHhqBT0kp7zM9b5ZAmoIZlEFPNaBS+h5DFGQWIuTJe+sZhHyqVFKlaiq8YkIKZEmpL5ntpbykngehglOSH5SkgvI9lgQ48SAnHm2f7hODnACuYGaruukCnVRme0AMfIxuUZ8oX4acDqpSUiqDB0EB2eBJoTgH9Pk+VamiaAJ9OdBJcA8gpFSOwI/2m0mpfA2c1NJr0X6WiaHKuhSPT/m7ld0I5GtQQTbj02uvGetJR/qC1qli+vzzz2PatGm455570NzcjJNPPhm/+MUvEmL585//HOeeey6OP/74zLHXXHMNzj33XFx55ZUYOXIkAGDevHn40Y9+hOuuuw4rVqxI2vbu3dv6mhwxNYCqpAC0pDTMKKKighpSlRQQk+jHi1dSbpBE5MrRuA1BKUkNA0BfatSClPJoehMpbfTaMwuRTEqbeD+S6Z4qpHy/DD4hU3IaeFEkrUw69aUHo3bqfKcRSwg8dQ0UMSI/u/iknymR9EQCCh6R35AsPiHj+8QFR0dK5UlbRdR1Efe6RSgPaZRyNiK3GvDJ37ScUpC1BJmAykqpchtJBZU8pAJpOigFKZVLinIfT7mCEwAh6p6ngkqIqZfmJ1WVF02KYmj8SeXSorp5gIP6kQJmQmrKS6qLvs8jpJxwCtso+Yy/fBqlL7cBzMq+ipDy7UK0OuQ0T/ZpoVSkVFZ1VSRUR0htqsilfeuhfohO1dKaIp4OhTBixAj861//woEHHojbb78dhx9+OIJAFBNOPvlkfO9731Mef9hhhwEAjjvuuKTqE2PReDr88MOTz57noVSyr0XmiKkBYUOsmPKKTZSUSspoViWh+1OTHsBVFeJn5qevyKcUMTlNFylKShuCdHEyJtA3kNIkXZRkwlctRrocpU2xOR9AhpSqCGmyILB0oZBz/lHIC5IqByonsUB2IubbS/AyZ+GqqtLslzGrq1XSEB5aWYOw+CRJ9KUFx4aU5qV6URFS/h0Ix8ErpC7ZkNNyFx7f4yl66rfKFB3jVB0VzPZe2jb5zM3/sasOgCQ3sY6UNgQlpUpKKzgByCilskqaEFEvreREfUkBaHMVcxcenUoqK6SAvr49YFZFZZSrkvIxoCKoCfFEmkKKtpUJKj1WdQ6VShq1T8326TGeRCj1pFSVp1RlUeF+o/QadaRUIMkaxTPPh1c1H5rmbRnRb7Y2Shp3pC9oPT5/H3vssfj2t7+NzTbbTNumb9++CEP1b+Txxx/vkOtyxNQEPzXdAxIpJapoaFBLk+h7IZchE8uNBikp9bmS4osKCiWlgR8nyK6AlHLFVK2WRotRk9eeWYhkUson90aUBJVU5S/GQQkqJ6cBIZG6hUvpEiD0ld2vciXguQypyV9lnlKppEVIqayA2JJSW7IqJ+AW2pVBTstBpIyqEXgMtrlhaxqcjMbvqVLKCDlNCKnHRBM+ceNJxrpESmkwo5wsv8GL1VJiyhcIKVFJdaZ72WwPQMi+oVZLRb9yOS+pMspeo44q5wEDISo3gb6KoKrIqaqtzqVIpZLy7dR0rzLbA/am+6hvvZuPynSfR0hVQV6qEqYmq0a2fXovOujGfr1bT7obLrvssoqO32effap0JSIcMXVwcHBwcHDo/nA+phg/frx12+uvvz63zerVq/Hyyy/jo48+yiirRxxxROHrAxwxNSIMAD823wMKNbSgWpqIUtz3jAdBeKJamgZBpGoK9TWjfmZF/UppGhgAVmppuj2bDqoRaT8mpZSqlrI/qQn0ONkkSJVWKBQZG6gCDOTrVKmlPFGWbKpL26urOpWrluaZ7ssBVZIqRYAwUmk9DwFjCL008KnS8pFdjUQZ9SUzfqKQxg1J8FOyzyfBTkCUFipOBWVSS3VVnPh3KVd1oj6lpmpO1FpiCnRSpYGyUUpNKmmeybiavxPZ71Tnc8rb5sHGp1Q7H5ThU5rtw86n1BSoJETMZ8zzEL4T3f0n18Pvx6CaRr8PPw44jXpx6Hq8+OKLVu24z6gJs2bNwsknn4yPP/5YeXwRv1IKR0wNCBs8IDbfAxLxlEkp9yklZr30RRLoAyQ1VExKid8ZJ6VBEGZIaUNssk9M+RWQ0sQEL5FSITLXaxfM96rIe9l8Lwc9qSBH4lNfU9VxWrcAJrbSwRRwwaELftKRUtViVjL4XtnU3s6LuOfXk7TPIaTUnE/NltSntGjqJ9W1dncIkffE31SIxM/4nbI4uJG8EPuOByxDSqNxrvYpTQKb4iAnAJJ/KQluitvllRgFkDHdy2nhdKSUzwUcqtLDQOWmepMvqg2ZtDHrR9eiP4/KfB9tN5NSMTI/33wPiD6lNpH3eeZ7lele9n+VUUKgzJ4gtJFN+/U0FzjFtKp+oWeffTaOPfZYXH755dhkk02q1q8jpgawAGBxQn3+OYwXpgwpzeQxZen/nJQm6aJiUkqqOvkBicSNSalPFBRKSvniVAkpTaPy80kpf6+KvKdBTplIfEMKpqROvcej8MX0Tnn+aZntOZOCaZFTJcym1yIjo1hKC5EOuih8us1GJa1GDWo54El1TXkBSzbkoFukjCIPmTQKX1BGBUKKjFrqcR9TH+JDJ0+i76UqqexT6idkkyVjKk0NlSWlNPpeF3kf9ZEf6ETngfSYUCCjgDqivhwF1DRO5Xa2mShM6imgHuPC8QZSSv0tKSmlCmnmWAUhFT9nVVF+HUVU0lC6Bn6dFOoqbZ6VL6jLSerw0UcfYfz48VUlpYAjpkaEDYgj56PPzFMQ0BxSCh6VS3IZ0mAngZQmeUqjz1E1l9R8T033ANLKLjEp9eOE1TakNEkZYyClqvKiMikVo/KzhJSDb6OTmUxOk7ZK9SVnolQsaLpIXQobUqpM36QIckj3eXaKjkYVpdtszPZyG3q/chAUXaTzlA4bJcREkrtLyqhEKZUU0yToiQY/+QAvK4ykYEZqyudjnZLSxjgVlA0pFYOfQoGUqqLvKSmlkfcAH/v6QCeVSkpN9ip1lP7WMhHyht9pR0OXUso0r8iWE8CelKoIKf1sMt2rzPb8M+/DpJKqCCm/TtqWgo716GGSWLQUY9eGlAZxND4153d1dg4XlZ/Fs88+i/vuuw+LFi1Ca2ursO/+++83HnvMMcdgzpw52HLLLat6TY6YGkBJp/BZJqQyKY0XI3g8Ap/4kwKAjySBvpcQUpaY9GRSyssNUtM9AMnHjCVVXWxIaeJnRhYiXs1JRUqpz6nKl1QmpVQ5oeSNpw/ioORUBcHHVDPyQ5ZdYFR90jZ5SbNN4ObwzHVIJn4bFVW+5ujaNO4AmvQ1mX4kQi7ngy2a87QzCUQtIiGjXAWlY95D9oHUR/RAK0Tfx8SNE1JCSmmO0jxSmvqYhsLDaAMZ63SbKh1UOvbNBTRUpFS0jIhkNO93Uu3fURHVFNCb9m2OkVNCpfvVpFSlaPLjAbVKSq9HRUpVJDdPJTUppNp5Jt7OSWQ5yqiu+IlDbWH69Ok4+eSTceCBB2L27Nk48MAD8eabb2Lp0qU46qijco+/+eabceyxx+LJJ5/E8OHD0djYKOw/99xzy7ouR0wNoCQ0+SwrpPLCFJvtmS+RUq6mAEkOQ8+PzPaeREqT9FC0shP1JxVM+Wn9a0pKA05WNaSULyp0QSpCSm0IKUeUL9RMTnWQyajSb82TTevFg3p0pFSlpMqLITXdqaBaDPKqtqjOo7peE1QLsc1iYVX2UfEddFvICqlu7DfEpvyGeMw30Hyl8fjwwzgncTS+vPjlk/91pJQTUSA15fNxL1dwStRPkg6KFs+I+sgPclKRUh0htRlztj7Nqr5UxxYlp4BaPdW1A0RSyqHyKTWlikuOKyMVlHwtJtO9TEjz1FF+Loq04EB5+Y19zYN7TcD5mAq45pprMHnyZJx11llYb731MGXKFAwZMgRnnHEG+vfvn3v83XffjUceeQQ9e/bEnDlzhIApz/PKJqY1+utxcHBwcHBwcHDoKLz99ts49NBDAQDNzc1YtWoVPM/DBRdcgF/96le5x1966aX40Y9+hOXLl+Pdd9/FwoULk9c777xT9nU5xdSAMIDgYwofahO+n5rxeAnCjFoap4ICkPiWBqS6U+Cn6aEaiVIaeFm1NPUx1aulDUQpVamlmZQxJMjBRi1V+ZMmFV9UZvUc1VSGql8dMvu9rEKhg2AuM/iURvuzPqW6NFFyu8JmfQvfUh34PfPvUOfbp9tuSs5fRJ0KBVNk/Zr1krFO/EvVJvxILQV324lN+QEtihHwtFAkJZRgouc+nJJ6mrxCsj9M2uWppXIEPoCy1VK5JCl17TFBSO8G/TjTKa+0CIewvUqqqaoPWS2Vk+jr1FKdL6m4TW89MSXOtzXfZxLjK+4vT8HuqJLFXQanmAro06cPVq5cCQDYbLPN8Morr2D48OFYtmwZvvjii9zjW1tbcfzxx8P3q6txOsXUANYQv+IFKAyigKhsoJMYga8jpZyQ8sWKklJVdK6OlDb4JSUp9b0wQ0r5gpRJB+OpI29pBL6JlPKFMiGyiLarFhW+LVNukJBPeQHm7YW0NHH/ulfajpgblYu7RJxpIINFcFGyXTLtyQjjOtI6mMz45UA+H/9MX0BsXmSKBZP7lhkCWNZGJO48sn+pnH2DjHskuUppYCNLMm3Iv0OPfJbN9bSaU4PkW+qT8RiQMWkipYl7j4aUNnrtaPTa0eSVtKSUjrtkvxdVhOMvvi2da0KBwKrmCxt3AOUcQ85jCz4O6Hig0JFSFWRSWkJa7Y2ngUq36QOdZFIawquIlPJzqcz3StcI6bs1kdKaNdc7WOOrX/0qZs+eDSCqd3/eeefhtNNOwwknnID9998/9/hTTjkF9957b9WvyymmBih9TOMAiJCro1Qx9ZHUxVaSUh6ZyxVTBSltTPzPCBmVSSlXPOLFJ0mq7YdJwENWJeUqSJo8H+ATOlFIyEJkIqU6dVQXwZkEOSmUUxVkQipDPk72K+Xnit7bTaC62thANhq3JBA6OzVQlRJKeR3See36zvqucdDvskh6GNUir1VbESJEnaeF0oAT0cTHVLKSJKS0IVJMvYY4sDH2LfVjv1IAcaATBN9S+iCgJKcQE+gD2bGfjFsLUprWsleTUl2gk87PPNqftaD4kKwiXpoaLi/AMA869ZTfF0dRJZVDRUrTPtWpoZL9BmtJ0fykwlwg5DnVBzlRUirDNPeIgablPZDmWcK6Gi4qX8TNN9+M1atXAwAuvvhiNDY24qmnnsLRRx9tVa60VCrhJz/5CR555BGMGDEiE/xkUzlKBUdMDQgbEH1D8TgTzffcdA8xgb6vJ6U+SbIdJdFO00MplVIadS+Y7lJzHo++5/kK6cJESWmj154QSmqKk6s6UXXEhpQqCY20aPDJSkdO8yCndTGlowJEkkoJavTZF64pcy6NCT0TjUsWJtPx9Jy6e6sUpoTavseE85vcIgSikbMwmcyn0e/Ug89YVe6vqyG77yQPpMk8wOIofCRFM3xiwufmeyAas4n5Ph7XNOiJt6H7aTq4Rr+UtEnzlYppofJIaUDmD9l8bwp00rnzAIbgRxqYyJCmdYsPlc37RSEX68jstzDXU9AHQZmUmlLE8WNVlohkfxVJqYmQ0r5p/zYol5B2h3G+NqJPnz7Je9/3cdFFF+Giiy6yPn7BggXYeeedAQCvvPKKsM+mcpQOjpgaQP3KAKKS8u0Bi33NWNQmYEmKGPjIkNIkZYzHlBVfGoNShpTqEugDEFJC8YWJk1JeZpAvSqochQAyvmTUZMdBSWmyTfFZBiWH1SSneQmgac5UmSSrzhsirewUfc5fnDLXRyJx8+4juja1r2pRhUeVv5Bfp48w812V4JtdKshDS14Cft4eXpREn+fQrGXFpBywhvghlY59XyKlxK/UD8Jk7HPzvceJG0+0r3m4ks33DX4p+czHdtQ2GpM0A4fgosNde5CmhKKmewAZUqojJRmfUuV8oLagBCilpMXzUxW1gF+oqhSxeI58v1V67brzmsavfE5deijt8WWS0rzI+7yo+0qyINjcT3Z7jY9952OaQRiGeOutt5S17kePHm08tppVpCgcMTUgU/nJZyQAiiWVXpAQVjMp5RO5WBtbr5TqSGmDT/MQpimhqFpCK7pQpYQuRhxyJRdVvlKhLVmURN9OdYqXtJ692awvHucn51P1ZVOdhKqisoJqVD3IPjmNlCmZdrmwzVuauaacpNrJexYqvyuBWOQoJWlOQ6K+kkXe9xhKdTox5yFsZPAbmEBIQR9ISdlRL97neXFtDaKCcpj+FqqgJ+5LTq0lsk85JZ2ckJrylEbnKhbopAty0pHSdD/fHh/r+al6Sg5RjSPV+fg2VXur4CoFOc3kCDaY8CtFEVIqp5rSkdJq+Ht2qyAnDZwpX8S8efNw4okn4r333gNj4g0UqXX/1ltv4e2338bo0aPRs2dPMMYqUkyd97KDg4ODg4ODw1qGM888E7vtthteeeUVfPrpp/jss8+S16effpp7/CeffIL9998fW2+9NQ455BAsWbIEAPDd734XF154YdnX5YipAWLUPfErE6LvkfqVJqVG1WppQxAKamkDTaAfm9tNaimNwuVmvEQp9dLqLjQSXzbhyf5kcsBTXmomijy1lG9Po/JpNL8YeS9H4HPQaNbos0eUBotk8Z5sclRfZxJRCzFSl8cSl5gnqKUm5KkquiCocoKeVJkBkmslKqocsZ8o3iSSWWfGLwKbtEH1BtYAsMY0cX6UTD8NckzGvkfUUo8pzfUU1L8UkEz5xLdU9BkPE7WU+pXL1hI6/rmJn/qU2qqltlAFQcqvNJI/TD972ah9+jJ/f+Z2uiwhJghzjcZ9Rxf0pOsvfe9l3perllJUqpbShPo2qOR8XR7Jzzr4VWd48803cc0112C77bbD+uuvj5aWFuGVhwsuuACNjY1YtGgR1llnnWT78ccfj1mzZpV9Xc6Ub4BQYhCICKhkwuPBEHxhypQajUkpLzkKIDbhi6SUV3fS5S9M00ClvmC0upPKt0y1KMn+ZGL0e34kvsm31NfM0yFLgxT4/77HkoFs8oMSo3pT8z7vy8akH11bfrCTXAM72i/7ePmkrb0ZP5fMFiSlmbRQmkWrxDzBbUMFuYJPR5j0unxBqgAs8S0lpvvYjcfzkdgHPe5Lavl7pO/TAEcmfU7N83Tsc1JK85dqxz83tUtuPKZyoxxFqjrZ3HPq9x2KPqdAGhhlAdkUT8mpyge13NynwlygGOs2/qVCeyGgST3mVdH3yTUo0kF1FnTnkr/buvU/XQux55574q233sLQoUPLOv7RRx/FI488ggEDBgjbt9pqK7z33ntlX5cjpgZEeUwjMgqkEbkpKeWlRuOFKSakKlIalSEkKlWcLoqSUmW+Ui8Uyg5ypQSA0reMR+HTgCeqlABIFqPoWtQLUh6yCom+bbKP+LCoAqKifsNMIE8y8fHDk0jfsBA51aEU5wpUpoJCdiGxicS3RdGFJZsZIJu2KhOVz7z0O2OcEKQ+h4BdvlKba1UFidR7zexEIfWQjHuujCZjn+co9uPAJotbzpQgldRS/kAKIBn7qS9qGtlPH0pVAY/i3zklqjpSWlQtlWETmBh1T3xOk20pbKLtVWNPlY7KFL2vKjkq+5YmbQ3+4NqgKmm7oJyS8UHzlEbHicGM5ZBS+b7l4ht511oE4vV6sR9sbK0pYOXqULjgJwHnnHMOLrzwQixdulRZ637EiBHG41etWiUopRwff/wxmpuby74uR0wNEJQSICKkZHHigU7iwpQlpUm+Uo8opj5PGaUnpbrqLmJEfbSQyVH41IQfXXooLEoBWeBUybNltVQFUyS+jIiIpuppuo2qKGpzVzLhc/IkENTyyWlESPWKh6yS0m22uUurCV0qGtWileyz+F4oKdWppeUsWHQx6vIFqRIESDNu+EgCnABOUAkp9cMkCl9nzvcU6qjsviOrpTSJPoAM6aQR+OKDZ/bBNL0tMeiRb5MfVpIcpRYPsKrfmiogiVpMMgQVYkYNGXSO0BFUVYBUXmopE0STvN6Mrw/m9CCrpSL5FZPn21Zz6moUUUujfXbBNA6dg2984xsAgG9/+9vJNs/zkuClvOCn0aNH46677sLVV1+dHBuGIX76059iv/32K/u6HDE1IPEj5WPNI6Z7X01KeUUnSkp5tZdEMSVR+Cq1hC9INAo/kMgpANGMn+NXJvuTFQU14+vM9yZzX2rC15NTFaifV8iiFEgCQZXIKf9edCjByyiN0Xax1CCHSiUtSYtG9D5LCFXnzkMmOjiTVkpcvIRrKLBohcxD4KXnMBEOrQmP6c8dMkpKIw2rXsGT5wvuOvw3xpXTeLynYz8a71788MkDVPlxXC2Vfcpl9x0/2SaSSNmvFEhTSJlM+Lq0ULJfqYqUJm0141wed7oHV1V+4wjkujz17572a0NQK4FK/TftT9vpo/czBFVSWnUZNni/umsxQZ5rVddUjquGSokF9Gop39eV8GBlzCi773rDwoULKzr+pz/9Kfbdd18899xzaG1txUUXXYRXX30Vn376Kf7+97+X3a8jpibEJQbTBPuS6d5XKCUqUsoV08TH1C41FA12ovkKaZ17k1+ZSimRVRIbtVRFZKN94mcO1ZQZwkxOTcikQGKKxNk5k6tNMv3sMfmk1NRHJYn1lSUSNTlLTeqtkaQzHyBm3ugcYm3svAUwE7CRLEjcB1csh1qP8HiJ0YYwCWgUlFCumgIJIRWIqSeOfVWgY6KcJsn0U0sJTaJPH0p1JnyAj3Pxbys/mOqCnWjyfDpPRH2k59chj+RoiRLxOZertvF2MkHN8x0tWmVKHne5VZ1yXFV4qVHVeXR+pYBZKbUhpaoxXIliLENFSnVuOyElqXU8D3RHDBo0qKLjhw0bhpdffhlTp05FEARYtWoVjj76aJx11lno379/2f06YmoCLzXI1Q5ZJfUsSalk1ovUULVKIgc7UbWkQVG5xbQoRW1ylBIDKRXbZdVSvp3DNF1GumaWnCZ9JH6oAJAtb5n6oLKEnCZ5ED0xaXwRs34uQcxRSoW2Fj5mqvdyYn+dyV5VjrAaJr4wiuRJfjM2C5/+Xr3EZ5cqJu1h/QY/oSFE0BQiaCglQYw0RZ9Pxjclo9xVh0beqx5IU1N+SkobCeGkD6WyiR5AMvaptQTIVnaygZy/WBX8lNxLhSq4TJRUbj15BFWnnurIaTnkLEtG1UTT9CDKCWrG31ThV0pJadYSUYyUytDdf4bgK9wvVFCRUlktFd87H9NaxGuvvYZFixahtbVV2H7EEUcYj1u0aBEGDhyIq666Srlv8803L+t6HDF1cHBwcHBwcFjL8M477+Coo47CggULEt9SIC0nmudjOmTIECxZsgQbb7yxsP2TTz7BkCFDrBP0y6hjGaPjwWvee6ocpTxPaRDC5/lKNWopTQvFo/BpehjZjK8LeKJ+ZbY5CzmUuQotAhl0KaJ8T62WBp76JbRJzIFmfzWbPKml+Ck8yj9YLMdpEajUUl0JU51/Kc1fqlJLk9ypjN6TmMdV53+mQqrS6SJwPeX5bZFkMoivlfsBc5N+O0s/d7lSUgGChhBBYwkNDSU0N7WhR1M7mhvb0aOpDT2a2tDU2I4eje3o2diO5oZ29GxsQ4/GNvRoaENT0I6eDW3Jq0dDO5oSd510TlCppbS6kzD+iW8pdeMBxKAo4R6IGT/giq4h8BGwV0uL+CeqLC4036hP1WWyXc5zXCSgrxzY+Jfq3G1KyTjQW1ZMvqVRG5VJvDrLtU1+Vz6XajMZkDFtVku9zPuuhMc69lVvOO+88zBkyBB8+OGHWGeddfDqq6/iiSeewG677YY5c+bkHq+r8PT555+jR48eZV+XU0wN8HzuY5oGLFCfUs9DJtiBm/lUpJT7mclR+Kp8haqAJ+pXBkDrWwpkAx5MsM1bmroM0KAI2k/8vxzAA5bsAxNN+rzf9EtHmkqGm+iT3VkzWKULkA1sSKlsytMl4pdJqSpVjJxYm0K8hvJ8S/k5AoRx/9H3mBBsct0ZX17lPfloZwHawujhoJ0FyeLUzny0hz5KrH6Dn/zGEhobfDQEJTQEYWJWB9IgJi82x9OgJjk3KQDhf5oWSkdKqW8pH//JdQmEVCJuZZjxKUw+paoSpHSOUPaneMhUmY/lwCh5Oy0nLLdR9WkLlRk+z78UMKeJ4tej8zMF1L6leX6lRYKVKiWzJiIZCg+1hKQKRNQXCWodP6B2Rzz99NN47LHH0LdvX/i+D9/3sffee2PSpEk499xz8eKLLyqPGz9+PIBIWb3sssuElFGlUgnPPPMMdtppp7KvyxFTE2KFlPqYct9S6lPKfctUAQ9yrlJAX+FFDnhK1BKigtB62TYBDxSqCk+2+Qp1pDTwaBv1pMO3JwSVkFN4nhAIFX3RIOSULEzSpeaRUqtUSWCFE5jofEL5OTM+aYrFyURK5fyF2fPLfmfi57x75t+LTE4BCATVB9P7ksYLchsLhKCn9jBI1NL2MOgWC1JjQ4jGhhICP8ohTMkoH8Oex4QHTRrQxMkpRUo6+VwgkVGPWz+y4x8gD5MF1FIbiPlMs78jEylVIS9TByD5hyoCo1R+pypyyo9PSKxnl0TfBJV/qXJMaiwlFDxFFLU0pPvUGTZkQkrf675bGsDoe2Ehcqq7B1Ufskqa3kdKStuJxSRkXte7YTofUwGlUgm9evUCAGy00Ub44IMPsM0222DQoEF44403tMdxwsoYw4IFC9DU1JTsa2pqwo477ogJEyaUfV2OmBpAo+6jz6JKygkqD3DguUoTxdSXTPfxRJvJWZioIWLAk6nkIEAVU33AQyUpoqJzqM0+OlKqmwLDuI2JnAJkIZOU0yTYSYLq2spNf+IjREkKukqvX61yRucjUenJ+5SIZt4rSKnOVF80X2omZY/BjJ8lp4DwF1R833LJxJB5aGMB2sMAbfF9JYQ0DnpqZ/mLdi2jsaGERt9LMmuoCGkawJS+pwSTw5cIXWqqT9tyCwkNeKTBU9GxKUE05S2lf/8A5amntoGEpsBI2+OoFcWknprIqfIekB+dbwpetI3G55YS1dhXntPChN8ZyIv6V96rgZBmTPmJa0/9zgPdETvssANefvllbLHFFthzzz3xk5/8BE1NTfjVr36FLbbYQnvc448/DgA49dRTMWXKFPTu3buq1+WIqQG03j1ATPmAYLLn/qU8VynPXUhJKSeiAEQ1hZjxVH6lsgmfk1FANLOplJJKIZvfuFpKSSlVSVM/U8Xkk1R94vIzI0+YDDzMOROpT8ipUL6QIEPGFOZH5f0RNYEudDrICbEBcWGRSWnGL01STKJtYl5VG0Kqm9xtCEQIP/EhTPqhOSOJyT1kWZJOCXQIH21htBC3MW7ODxK1tD0k/rJ1HJXf6JcQ+EHysElr3MtENM20ISqiFLSkMJBm16CKqexXqrKWUEsJdePh0LnxmGrQ69KxcfAHmbyHP73vODmXoons4qMip2lb9ZgtEnmfyT9sGH8qtZS78JSjluaZ8OX5w5bo26jjunRP6fVIbgya7AA0T6mOlLaHojm/y1GHymZH4dJLL8WqVasAAD/+8Y9x2GGH4atf/So23HBD3HvvvbnHT5s2rUOuyxFTA/wgRBCkpnyqknK/0oScggmKioqUUv8yucILTQ9DTXhJDWxitlP5lamUEjngodqwJqXx9hKLSC1XT+ExlBg/liulnlC6VCCnQCbxdnot+YFcukkxIqhB/D66Jh2SICFJ4aQ+pTIpTUz5mtyFNqS03Am9xLzM356fJ0NQAYQIlARXdY3tYRD5lIYBQngiKSU+pnTxqkcEfhj5lxIf0oSUIktOfU8e7yRQiJBRvo9bJWRSSlVSdTAjk/oW5wCxbbGHVp7jFoCwkFOVnSNV3/VkVVWymG+TCWoeOVURT2r2t74//l7zQKr1E1cEL9H2qodSHWQTfmdDJqXUPJ+2Uaevo+mfdCqpiqQ61A7Gjh2bvN9iiy3w2muv4dNPP8UGG2ygDGrqLHTpr2Tq1KkYMWIEevfujd69e2PUqFH461//muz/1re+Bc/zhNfIkSNz+50xYwaGDRuG5uZmDBs2DDNnzuzI23BwcHBwcHCocXgWkfWVvLoD+vTp06WkFOhixXTAgAG49tprMXToUADAnXfeiSOPPBIvvvgitt9+ewDAQQcdJMjF1MlWhaeffhrHH388rr76ahx11FGYOXMmjjvuODz11FPYc889C12fF6QBTgCEBNq6ZNqpeppVS3nwEzXd04Anmh6GJtNXpYYC1CY8UxSuyYSnQgkeUXayZvyozxRULfUzJjKW7qdmfUE1BbhZXwiI8kDM+Obk0cn5Nd9B1CZyDSgxz+hXml67aIaz8SkFABu/UqpE5gU1lQuVasrPqYLKxCqbGXnQUzsLEvU0BP0/9S0thfXtW9YYlNDgBUkAYxq0mLraqMz3sskeyCqlALRqqexbTgOdeB+mOUBl2heqwGlMviH5TUfjIw22ogGFyqwaCqjUUt1+6sqjUk2Ta1f4mqogB0DpTPx5Fdzygp7ySpCqzPgq39JsIKPmb6S5Xrm9LjMIPZec7okel85PetO/SikFkAl8CpkHVsfzgEPnoUuJ6eGHHy58njhxIqZOnYp58+YlxLS5uRn9+vWz7vOGG27AmDFjcPHFFwMALr74YsydOxc33HAD7rnnnkLXx8km51Mq833yPm5rIqV0UWogC5FNwJOcrzC5Pom8cujM+Hl+qFEKpvKS4prgw0OYRBRHZv3oOlNyGrUDqM8pJ7FJlK60BhYJdFJH8PLzMsHnNA/p4pIlofyznKuUv88jpeWQuDz/0nKCKuSAiJJsuovN96nJ3k/SQ8n+ZfW8IDX6pYicKiLubQipL5FJmZBG29WklAY7iX6lWf9yeQ5Ij9PPATJ5lcdTQsbi4MM0Ot7PHFsNyBXhZHJqE+hkC51/qQmqB9Tk+IJmfH5O3dikJDM7vrNV2mwzL0THqUkpN8lHZxB9RzPHCvvp3KA26bN4nuhSMLio/DpAzTh8lEolTJ8+HatWrcKoUaOS7XPmzMHGG2+MrbfeGqeddho++ugjYz9PP/00DjzwQGHb2LFj8Y9//KPwNQVB6jca+JG/aSAkzif7/DgNk5/mJ5VJaYNfIom0GRoTH9P8gCeaGiqTbFrhWypDUEo0C0riP5jjD2jywTRBVlETv1R4kBPxy0n40/vPJvvPnIcs/KoE3jzvK5AqQRT0+y2RSZgGOtiSUg5T0IMNbCOjbZHxA4tVT/oK43RQ0Yv7jkavNuanPqXEr7Q99BO1tD30waRFqx7R4IdJUvymmKA2+SU0+e1pqjfuM+7RBPY02p4ENpGxTDNyqEgpJZuqeYBC5V9OkWcxKZHfb16C9c5COdk1VMfZjrMSi1Kfpf6iqVqal1Bf6Cf+7kxqaSUPpNR/mVrRcu8P4sMmvW+hhHASwBikY1t+heKLP6DK+0ph9B20M7+uH1AdOg9dHvy0YMECjBo1CqtXr0avXr0wc+ZMDBs2DABw8MEH49hjj8WgQYOwcOFCXHbZZfja176G559/Hs3Nzcr+li5dik022UTYtskmm2Dp0qXaa1izZg3WrFmTfF6xYgUAHnEfkVEgTqYtqaS6xNoqUkqDnzgppYn0dTlLqQlfSB0DfSR+OWppUvFFsRiEDLGKyYQpnvfoAyix1FwfginN+em1KIKhgExAVKSSAGBpcIWc4zC955wofMkk6HssWRgitdRDyWDal0mpyXQPZPOdCt8FMQ3aJsqn7/l92xJW1aKny5GoSh3Dk2irzHdCsFMoqaaxWloPC5JuHmj0I0KamvDT9zTlE5BVR6P/sw9SgnKaQ0rlRPpRP2FmrlChaOBjZxWtMIGqpur9YhCUKXVUXh7TvEwcMlQPqGlf5f3Gy31oKxrwpTofHdei0pkNZpKPp8qpoJYShZTv4w+oXZ2doyN9QbuLj2ktoMuJ6TbbbIP58+dj2bJlmDFjBk455RTMnTsXw4YNw/HHH5+022GHHbDbbrth0KBB+Mtf/oKjjz5a26fsuKsrm8UxadIkXHXVVZntDUGIhiCd9D2PmvfVuQxNpLSBKB8y6ZRzFsp+pbLpDtBH4stQKSX6JPyyj5LaP7HEAHgsIZQhUnIa9eMJRDR7vH5f4CFDTgHqVyZG6laCqC+znykt1ZlHSukx8meVL5qKlNrcUzUIqap8qy46ly5U/Bha3YmSUr4wlVisloT1oZTo5oGmoIQGz4/HdMlISFXmV9kvVNhnSUplN56o31A7B+jU0kwlr9gkHxJ/cvEcesLbkVCR0yJpoExQjU0byKQ02S6YufXmfJNaqkMl85tKxaX7VKSU5iBWRdfTe5H/5+9lQsr318sDqkPXo8tN+U1NTRg6dCh22203TJo0CTvuuCOmTJmibNu/f38MGjQIb775pra/fv36ZdTRjz76KKOiUlx88cVYvnx58nr//fcBxGbexGSfmu2TdFDEdJ+kgJJIqU8ILFc95bKDsl9pkiIqszAR87O0IFFUklBfhdQsFZup+XYWVXPirxAg+5iRfArXS95zoism749yqFYL3JxaFJl0KQpSKucC1KmlnQF58eSv9O9J1RE/NdHHZvq22JzfLpvsZfOeZL5vj013JeJrWg9igm4eSGrZkwptjTxoUTLXA0jGPCArpGZSKkMmpdSNx4awVJomLq3wVv0cyeVcRxEUJbCcKJaQ+o7mmfFV7jyZNsSMrzxvFeaCXP9y4pKR9RdVpHniwYt0XCtM9ypzPTfZ8wdSrpJGrxqYB1gHvxyqgi5XTGUwxgRzGsUnn3yC999/H/3799ceP2rUKMyePRsXXHBBsu3RRx/FXnvtpT2mublZ6RqQBDR5xJRPVFJquqcklJLSBmqyF/yCiI+ZpV+pbgEDoFRKVIuSyi+tCGSTPvc3DbyIpAaEuFIFtQh4hSjeRyjsy6qmOpRbASo93kdE+7NBQMbjLBQZW5+3cqFTdFQLlMpMz7dn2rJsxC1VTKlSyhclxuxVqa6Ebh6ICKkvqKQmhZRDNr/TNnKEPN+nDmQU54Bom71aWgRyOVLRN119f2Wfq0oKqC2EB8ic8+oCmExR+JXWpa8m8q5FqG9PzffwBH9ROq45qOpJ5whZHU3fUwtM7XxHDrWLLiWml1xyCQ4++GAMHDgQK1euxPTp0zFnzhzMmjULn3/+Oa688kp84xvfQP/+/fHuu+/ikksuwUYbbYSjjjoq6ePkk0/GZptthkmTJgEAzjvvPIwePRrXXXcdjjzySDz44IP429/+hqeeeqqrbtPBwcHBwcGhi+F8TOsDXUpMP/zwQ4wbNw5LlixBS0sLRowYgVmzZmHMmDH48ssvsWDBAtx1111YtmwZ+vfvj/322w/33nsv1ltvvaSPRYsWwffTp7C99toL06dPx6WXXorLLrsMW265Je69997COUwBoCGI0sRwUKVUeOWopWnULQl+iqPwk7rYFgFPcqCDzrdUzmnI2xZFieuVDEn1qyiXIRLVNAF/myg6onJaDrivKUdeUIQtIsVDrZiExJRHoUoNk+23+J0GJACrWlCppXJwE/Ux4+2oSU8+Xt5HU0G1E3U0hJdRS+U+6w2e5FNKS4MCalOqsVynFNCW51sa9SdaTPLUUmrGp6qnLrhJ5V+qmjuqnR2i2ijyOysa+NQRqEb6K52LEWAe9yGxeojvRbWUj2vV+agyyj+n71OltDtk53DoPHQpMb399tu1+3r27IlHHnkkt485c+Zkth1zzDE45phjKrk0AEj8SDlMhDS7LSWoNAIf4KY5MQqft5EDnlSkNEkfI/mQiX6olS0gIfMyixANQuLklC/AafBT3FhBUIGOc2rWRedTc76KjEY+s74yL2G2bfWu3keYmPMr/VvpiK0NKVVF4/J2yTFSIETSjyUp5e/rFQ1eCY3xw29RUqqKzI/6UfuJqgKeRLLJx74+6LGIf7lwnGTG125TuCDIbWoJ1G2m3MAnuW1eYn8b8IfSapBTFVTBjRSZYEZpnNPMGtQ8T/vMElORjPJ9fPyHYRcT0470BS3Y76RJk3D//ffj9ddfR8+ePbHXXnvhuuuuwzbbbJO0+fDDD/H9738fjz76KJYtW4bRo0fjpptuwlZbbZW0WbNmDSZMmIB77rkHX375Jfbff3/88pe/xIABA6p1Z52Orn9krGEIOUnJy4aU0rrZcp37VHnJJtKPFFSqmGZJKfd8DJIFQe1bqstbKKsjeQsKTawuOtKLOQ/F4Cd+LEv8RRHvV73yUJmvqFohrcZiYOMjly18kP59ilbjUvdvyH4g+Inmk1IaAMHzGWaic5knBjqFYsAD9Svl7+s5GlcOcspDEb9JWS3lkAOe0t8My5BS8Rg5uX/+9foC6dT7lqrSXqnviV6T/rzV8i/VVl3SBRxl0tjZRcgD4vgtgoDM5TyuINqefreqVx5Maqm8Xx77fD/froq+50onk8Y/Y/Qh1IvHe+RfLu8LwzQiv57ngWpj7ty5OOusszBv3jzMnj0b7e3tOPDAA7Fq1SoAUbzN17/+dbzzzjt48MEH8eKLL2LQoEE44IADkjYAcP7552PmzJmYPn06nnrqKXz++ec47LDDUCpVv1BOZ6Hmgp9qCZyIclBTvC9PKFQp9dL3gbCNRO0iTM34JOBJFYFrisC3Md8FZL8NRJUxrfDCVdSMcopoAeLHJWeRAqOiY4pPTLoAqHKQEuw012gpJmFRVGk6afOJtloO+2n+xRDw0oWhHHIqB0/JLgFypRZVoJOSlEoqi5wOpp0saDyBfru8GJGFjJPSel6QdITURBySscB8KzKry1kqW0xU1hJa5Q3IklKdG49KLdUdR4Ouah06y4j4OS/KXozIzwuWMsEHUx7PrSbluvPYm/D1Y5/2k7GIKEgqPbfKVB+9F9szRf9dhhpSTGfNmiV8njZtGjbeeGM8//zzGD16NN58803MmzcPr7zySlIJ85e//CU23nhj3HPPPfjud7+L5cuX4/bbb8dvf/tbHHDAAQCA3/3udxg4cCD+9re/YezYsVW5tc6GU0wNiHKLhtk0UNSfVEVKBVWUm+vTp+CkypOXVUkzUfgKUsory+SRUrqQ8QWF+qeqFiMdZLOQXB0mZKKCCojqqU5BrQRyeVZblaEcUltkQqXmVvp98+sEIKT9KXLtHDZqa959lkNKqZmPk1IhNYyGlHb5glQBZNM9f8kqclHIZUejbWY3HnUfaTGNJAWVRGRpe1rZjLoG2ZrwqYLalWZ8m3FcLTO+DuJvIzseVQ8FstUkascKufSYfm95Jnzah6iciuOdEktReU1JKX0gZQyJOhqGWeU0mg+sb3Gtw/LlywEAffr0AYAkO1GPHj2SNkEQoKmpKQnmfv7559HW1iZUu9x0002xww47lFXtslbgiKkBSX5SyYwvEFIlKSUlCeNFhqeLMlV3SgkqMfvIqWP4sRakNDlGQUrTffqZIqMuKsgpBzXvC5+RNe9H+/UE1URcq5nPVF7YeOATf/FUUbbVXUR3iqzSTsmpTFDTPooR1TxyKpjqJMVE1z49Th0MIZNSFi88lJSGYbo4UR+zeoTvwervIZNUWoRBJq+Z1FIeHc9Zv1KVG49MSgH9+JfJKHUJ4Ofkx2e2FSBMtmb8PITCXCErg2q3HLq9pFMPFWO+I6AqE8rN+YDepacoQeXQmfB1Y1/lWwoovh+JnMqkVCakOjKamvIL31pV4bGOfQFRxTj60qW/pGCMYfz48dh7772xww47AAC23XZbDBo0CBdffDE+++wztLa24tprr8XSpUuxZMkSAFGly6amJmywwQZCf3nVLmsdjpg6ODg4ODg4OFQBAwcOREtLS/LiqSxNOPvss/Hyyy/jnnvuSbY1NjZixowZ+Pe//40+ffpgnXXWwZw5c3DwwQcjCPSVCoH8ape1DudjaoDsYwqIgQKpn2nWpC9sJ8FOQPq07IOh0W/XpoZKKkDRp2mIT+ImtURMG1XdR1WVv2nqV5f6nPJ9ctQ+r+xUqVm/iL+pUmmxjMiPjs+2MZ2f/x1KzE/8zIRt8d8t8TuNQa/FpFbx+6ER/jbQ+ZYq1VISFMGVUgCJXykj7ViikngZpbSrlZJqQ63a8XrtYbrfyzdzJ4q6ah6Q1FKVXykd/ypLCT0HhU0yfdm3tGggVDVRTjWn5FhBQc0vC6y/hvxxppoTqJ+pyddcztRR1PdUV4VOBb3KanZNkdVS6kMujvmsf2qX+5p3go/p+++/j969eyebVUU7KM455xw89NBDeOKJJzKR9Lvuuivmz5+P5cuXo7W1FX379sWee+6J3XbbDUBU6bK1tRWfffaZoJp+9NFHxqJCtQ6nmBpA/UupnykNbvKJeYZD6Vsq+RNy0skXo0avZE1KqWmuCCkVfU5ZZkEy5TqVzfmAuDjz7dSkrzLrJ+3LmBxEfzfzwkjvjyI/hUpcmpCJgU+2qWFMfmayz6nKtB9du2h61Z8r+7vTgd+Trhxhus1MSnl5Qn2wk4qU1nfwk4xMpDPzFd+r3e8l/Q1kiR915aHme5mUUp/SqI8wOVb1m0temjmgCCkV7qVL/Ez19eDlz/R3nvE3LWMpFFN06e9ddMXK+prTz5ngVgvTvjESH54w9uWH0Ex7lv2+aFBTkvaJZX3I5W1yJH5HcsJaQu/evYWXjpgyxnD22Wfj/vvvx2OPPYYhQ4Zo+2xpaUHfvn3x5ptv4rnnnsORRx4JICKujY2NmD17dtJ2yZIleOWVV+qamDrF1ACVn1C2DZ9Qsmop/z8gZBRIFwjuU2oipbrI26gfpiSVsk9pdn9l0wON2k9UO08sFSqfg0bs82lUVk+F9gUvscg96dPL2JOnSBXzhXOXEkWEZzEgeQ8V6mlmuyd+p1HfaiXVFtzHLLs960umI6WccHKlhLbnCxH3K5WVFKAGVJIKofttCWRUpeaxOJ+Ex98DvldKxmxCTsgDqpyNA4BASmW/cpFQmse96l6yD7RqUqqCacx1lH9p3nZAHeSUlMQE/Zvlle0sHpFP5wX5M7Wa8PEu+x3LymkRFFFLozbZgEf5vWqbbjyrxnytEVGPMXgdZL4p2u9ZZ52Fu+++Gw8++CDWW2+9xCe0paUFPXv2BADcd9996Nu3LzbffHMsWLAA5513Hr7+9a8nwU4tLS34zne+gwsvvBAbbrgh+vTpgwkTJmD48OFJlH49whFTA4JYJTVNTL48kWvUUrrQcEKqir6VSWn6RC0qpNG57Uz3JlKqCp7RLTg0dZQOMjnlZn0KSk6BfBIqn5FXgOrsWttAjule2kcfahISyxclTuwJQS0l5EWdcJt/97KpPy+QKbPdJpqZkFKePF8I7JGqOslKCketLlCVgqbg4cg82MSE1Pf0+QRpKipBLSUmfNlaAqQPpaasGxx5Y94m2ClLWJlyf9RGe7tlw2Tp0LnoZPtIU8QVPr/hGJmMAtmHVTru5fGnStYvk1NdSqk8tVQHOZ2crj0z9CdbR/JIaXeznFSKqVOnAgD23XdfYfu0adPwrW99C0Ckfo4fPx4ffvgh+vfvj5NPPhmXXXaZ0H7y5MloaGjAcccdlyTYv+OOO3L9UGsZjpgakKigkjqYaUd8Tfn/QiJ9T+wnVUkZGr12IyktmqPUVi3RLU4yuC+pDFkVVbVT+ZzSSlHRdZghmP8VE2ceOa2UvPIFJYiVL1kNASRlVFqQaNs8FcOGnMrQ+TpqS65qFnZZLRW2IyWo6eKT+pqpcpXKJrzuAtXCT8u6cuRZWuiDKm9P1VIA6Vwgme4BOXo+zBDSomNd7TuqU1E7jpTaqKXpfnO2jFKcm7gaoPOAznKhm2uUxFVSTXm/tuNehlZZJu47KjO+Dir/UHG/+Tim2FYT6AQfU+vmFgrrueeei3PPPdfYpkePHrjppptw0003FbuAGoYjpgY0+iVhgfG97BNquk/MW+p7IRr9MCpnyF9+O4DU/5GT0iavXelLplJIovf5AQ7lKCbVgsqUryKngJ6gFrkilduA1XVqCBxf/EOECFmAACFCpE+fOmVUJqimoKiSZN5ToegiVWLqRUf2LaXIps9RmPCZaMLnEJNqq4MhGGlbz9D9PVWktFC/xNdTDniSXXlUc4CcUk5HSPVJ9impzCelumOjNpY3XRAmv9Fqosjf0DQ2dQ+hJtVU2Y/GpJ83JxS5j3JIcJFj6n3cO3QNHDF1cHBwcHBw6Pag+UY7om+H6sARUwN8MDRKPqaBV8r4+qTmtTRZvuxbSqNdIwXVTi0tkjhf3h+1yfcpNX4HFY42UyAURd5VFas/nm++V0XrBh5DCaFg/vPBUEL8PbDUnK9MCSMpJTqzvvHavbBq5kdA72emSrKvCoCgZn2aIDstRWgIhuD/dyPVRKWU2ShUiUuO5NqTZvbIKqCq9HAUOrXUlGVD5T+uUkqF/hSpoWzV0iLWjDwzvo1/adGxI8/lgv82CVzTjaPUBK9RSeN5oiPM+Wn5VEUAUxyNrz82m40j04dgHdFfR3dLBefQ9XDE1ICowlNqTAmp/x+PwCZ+o2nAE4nE94kpPw6C4G0pKU0j8/VRt3w7RzUIad5x1QQNhKI5TvNQDdNdErQDn5AufblCIPrOSvCj74ghyjkokVP5+ky+pDZ+ptljzP7N8n0I2xXnUvmY0aAm+l5V65pDV6awO5FQDo8QP50PoQxa+U0FOWUTNePrfEtVfqVCeiflw6spcl72Fa0eKe3o1FG2GSrog57JP7RIX+X4maqupyOQDYai812au1hsU9mY9bw6Iqc15GPqoIcjpgYEsWKapgGKiGWU0gPxtnSy5oQzirqP/wdLSClfpBq9UkJKG712o0paKSHl/WTurcp+pbaQo/TLq1tv3m+TYsa0OKTR70FMSn2AqBuUnEbtOy47gJGQMno/6aJTyuQpzC9BCsCooDCyuFGoIm27GznlPuPJw4gHAPx3oTmGRtgjzVObEE5JLaV5S3W+pXJKKBMpzVNGKeRMG7aktBxCqhor8nhWlzr2Mvs6Eyq/0Dw/U8AQkwAxdVSlsI3GL9qXg0NXwBFTAwK/BN8Lss/GMSlJo/B5RCxDQ6x8NvoxKfXbk8WHK6Z5pFSnksrVW/KUEVuV1NRHuaDm+mwEf/mBEipSmkdEQ+Yp3wNZghpAqr7CUnWEKiWqKP38a89vm7dIFVF8dOY8XWJ9+b0qwTZF3agkVUA0xuNxkxBSc7ZJ2YLCtzX6pYxaKrj80DlBIqVCsnxCSpXBTzluODpCKh9rQ0q7Irk+RzXJqiolm6C6KohknhLbEQ+ushmfbgPSgMe8B9u8/KWVwvNYTT2oOh/T+oAjpgYEHkOjr1MW0+TXAFLFw0urOqUm/NSfFIjzmMYktNFrz1VJq6GQ6o43wWTqqxRFyKlJIbUhpek2c5UYHahqyk36aWGBVEkrkg2Aw5xrMH9flAQ8q5ZmzHWaPJC6xclkxs+7tu4GXn0nzSUZEdK8JOiJEho/sAJIrCgZtTSeL2QTfnT+lJTK41dFSm2VUXFf7ZHS/Cptmu0VmOtl0Mh4oTCGgozmueqY3XwqSQ9n9i0V079lH0ppGxv4mpyqQEpEPaSW7Vojpw61D0dMDWjwSmjwdL5ENI2UmBBbruhEiSoQTXZNcRud/1jUzi79U3IdFRDSjvcJU6eQqqS/vG3U/Md9S3luQ9PixZXwPPUyL/ChKATCqkr7VIZaqgtyypQlhOhnqrsGG3S3hajBi0pLAHHKOI8natcQQKkIBq36xtPGUb9z2YSvsp5kzgF5zlCZ3/MfJLP+onaktKPJqGlfJX6iMnSJ622hIpTlKKR5Y011jbJaGsqfLe/LxlpiA9O49+Lflad5aOo0OB/TuoAjpgZEC0c2kpKDmtWEpPrI5ijlZnsA2qCGjiCkch/VQleZ7ooQUr7PtJCFhv0R8cxWz4jM+NmJX16Q8hYnXe1uEykVyWS+WiqTUFXgQ545r1ySyVWTLl+MKgR/uOQoxb/9BsPXQmugCw+lgiVFfHDlJnzVvKDyKS2HlGrnEAUhld/nkVLVKOoaT3YzdH6dmawahLTmmfM7AjZBjfJ4l3MWy2OfzgdFTPkelUAhBjzxYGD+WaWaOjgUgSOmDg4ODg4ODt0ezse0PuCIqQENkmIqlxAEIKikXAVVlRhtIlH5eUqpbQqnavmS2kTwVsO/VGXOtz3OZruslEb/p0ojNePblivkAVA8nyEktcTW1ywJViigtMj9ymmutIFNxIyf3S8qJsL2KkT26sx59aya8oAljkaUlH/HPGsKAKObjyngiaulmaptklpaVCmlx8rt0r7N/RgDwOL/6VVRy4LOnSfPvzTTvhMUTBMqTUVFYeNTm8m8IfmWqvxIteeT+rK1kPAxTdsnailRTQGnnDoUgyOmBjT67Wj0s2QqS0pTMkp9TeWE+U3Ex9RU55r3LcMmOX5XpYGyTcRvQ06LJMcHshO5jpRWG+XWtQbyzfi2pFSVIor6lupyF6bHZhcwm4WJBkB4HouDgbqnOS/yMfWFsUVN+xR0buCf+TwRbTfnMG5KAiPNpFQw6xtIabE8pvaktLNpoGpM0+31itwsHBn/cLGMcp5vKR37KjO+KaixqAuPKZ+pJ/3fZXA+pnUBR0wNaEwWDepnJS4w/L2JkKrSQKkIablVmioho+WopTaqp6lNuYtJXjUY0b8z65NJ1dJKISsk5QZA5ZFSlU8p3S77lgoRuHQ7CWpQLUjJMQWi7z0PALIKqayadvliVCEa/Xb0UPxp9RWZKCmVUspJlpQgsa605/qVAtmxro/AtyektL0u+t6GlAYeUNKcNnpwqX/kReZXAmHcax5MdZ91vqU2c1JRf1MV6EO6rJo6OBSFI6YGREFMpWwEPiGYlIxG29SEVGeyFyf86pjmOxOVlizNg45E6lTS6H1WVZGViRI3ecPvcjMgkCWlskIKZFXSqL1ISjMLlJQeRlZLTES1nEWFqiZ0Yarn5Yk/oHIoKz1JcwTdxueJ9L3+wdUm2CnquzxXGxuVNGqn2Cbfs+KPSrfpSKoOeZaQrgJNGVUJij642qqlKhO+LtJeNf4z11lgtMrmfD7+KTml+2sBdexVtNbAEVMDGrx2NHqBMOnbLDLRPvVCk/RDFhzad2fB1tezHLW0GqiEkNJ2wkRuUEupGpmHakXlllhW1ZWvKW2bNd3Tz3J6KFXpURuwCo7trqpJFEHPci0JQDqGZWuI7sGVbysagQ/oTfg2kfeZ6y5ASlWEVNmnpKDaqqbyGO2INFF5qLYaKiOvuIXuM9+mKz2qMuvbpH7iRNU0VvlvOvT439ITTPeya5PwYFor6aIc6gKOmBoQlQ5lmfRNeeoobycTUlNNa7pfhc7ypbJdvExtKyGvtoRUbmsipXLe0pLklyX2WXwxKrqI5Z07WkyyAU4qf1ITKVWRVJ1aqlNNbaBTTeT99Ypmrw2NXkOGdFKIgU/0YZNBVFPNhJT3RX1Ki5BSHWzM9yrTfTmEVDiHwbzPz1lJPuPuApMZH8iqpVE7da5i2X1H7MfOWlIUIgmt4apwjHXcxdXsTdcfut6G6eDg4ODg4ODg4ACnmBrBTXgc1GwPoJBSqjLZF1EWdW3LUVJtTX3VLEGahyJKqdxeGehE1FLdOXQqp7GsIAl+KAqV+V/nWxq1r55aKium1YKsjqjM+fx9vaLRK6GHn3W9ASBUeUq2kfmBfubt5bki6jdrvk/bZJVSud90W7HAxHLV0iA3cwa1MmVVU1V1pI6oKW+LjjbdcwjzkaLYRbrPFKCotvioAp5UFd2qDTrO+finvqZA7fiYujym9QFHTA2IzPbp4sPN9vy9qpSoipBqo+4L/JJ1k0pnTeadYcaXkWe+jz7r/TQTH07oF4OuCnwykVRT9H3aNp+UmqAy4xddPEzpsuQgiHpGo8LXXB+RL5JQQO1HrsvKoTPfV/LQqMtTqr5+kZQGAmG1/zsG8ARymlwLukd0frWQ8SnNROOnQY4y5LEv96skrwY3Hupfzpj4f6XoDvOAQ+fBEVMDGhBGPqZC0JK9QlqNxYUTL36sapIoQk6roZZWm5TaXrut4plXtjQziWv76RzSqvMvUwc56IOdZKhKD+rayseUF5Hf/ZLrA0Cz145GIRuHIRWTJphRILUWfuflzhlFiljIamkRUmpTfpSS0zxfU/m6SvAqyhNcS1D5hor75YdlvfVIp5bS/dltHaeYqrJwyKpp2pbFKea6EAwuj2kdwBFTAyKF1MsEK0Tv8wMW8hJg2ySa58fIBBUQJxobclouKa00b2k5MEXfR5/NEfh5aqntYpC5rgrVaZtoXJUZT5UWKtO3wYSvbpvfphyoFqYuX5AqQJNXQpOnD1qUIZNQ+RhTEGSRuvdFYcpVqiOllJDmPaapqjxVA3xu6yxze6Uox4KlisY3uRTZqKVaV4Aygp5cXI9DZ8IRUwMavXY0kZKkqnykqrQuOkJqaw7nEwZvryKo/HhbclotUmrKg9hZKEpKdWppxqyvWPRsCasJxgVGUktFv1IxLZSqXxsTvqCaaBYspljg+Pa1HT5CQTHN7jdH6atyFZdDRvOydpiKZdiSUpVKWpQKUnO9SjXl+1NlNGrbkdH5Kt/wauUntYE8ByhdeVTzj5QWTt+/evznEdBy3XhsUWsR+l4YvTqqb4fqwBFTA2QfUkDvRxq1F0mpTEhNKVs4uAmLI2RehqDmkVPeVtU/PS57LbVPSimKPPHr1NLOWph01wLYqyWVmPCzfekVFdU21eLSHcysNojGtadUPzNtNQUzdKnhTGS0GsGRKugCnfJU0iBH9i4xeu9qcpqHVB0VzfldGRhVLeSN+6hNDpHUpIiS25jUUlPfa8uYdqhtOGLq4ODg4ODg0P3hfEzrAo6YGhCppR5UgQu6qizUfJ816Rfz1aTqKVUNVGZ9Uz+Z+6oxtTRPDdEl0pf3C3Xnc8z4OpRT794GuSqpovKUKjWUCrJvqXK/4VjZjFetSNzugqg8g59J3aRqx2EqH1rE5zzP3cd43Qozvq1aWkQpldtx5bQjIvC5xajWlFR5XrKdR0rQud+IqeF0UKWISvswpZzS9+vcdxy6Go6YGiCb8NJtlZHSoqROZ9KikzSQv1jpo+n1C6SN+4Ht/mouIOWa8VXb8vxIZXN/nm9qkUUpc66CQQ+mhafIomTjX6puo73UbgNdjlEKG1N9tD2fsFpdk9SW/k1V6aF084+JlMqE1Deagekc6WXIKTfn6/xMo+su38+Uf/9dlf7NFtoHRMWDqbDfEPSYttE/mGrbFwh8rDTwsxbg8pjWBxwxNYAqJRxFIu9tAg90CJmC4Hlmn1MVQbUlo/L1qY41EVJbNbja6oatWpq2z1+4Oiryt8iiUTIoIdk+iqulqkVJpZ7K/qVro5oSjfN4jGki822T4Jse+sp5YNX1RfujaaHKIaUmQiq34QSVktOikP1Mi6Izg5ryoM4/mh/EZIJpvPM+lNs17dfGMe1Q23DE1AAa5ATYk1JRPTWTUu1iRNUKlk7UnJzyY3UBUSrooorLJaTlmPMrIaeVEEZ1kJA+fVSl0N2jLho3X7XND3oA8oOa5H2qaHwlQVW0oX3Q/8PkMz2P9pJrHqrE93w7herh1DS25H1FxpNNvlJVnlJVNafIKsS36QmpnMtUnUDfy5DTPNU0D9U029dKyqmQecbrsDHj836qsr3MjBx541re3+XzAGMddxFdfnPdB46YGhAtQkHGhGebEkqlWMj7KGgeQKqK+h4y5BRQ+51a3Zd16qjqEVL5+EoWGP3kmi0/mmfGt0W1zPdRW7OvqM13Y/ItS/sqYKaz2Jb1Rc3u06WiqmdErjxeoRRPelO+2aXHxpKiO1Z57QpSmqeSUkJqqvakI6qUnNpAnuvyzPm25LKceYYXU7EZz7ZzQF6aJxP4scZoekOauCLntO2j3PFc7/OAQ+fBEdMcULUEEBUTHSkt5CuWu42pySmQEFS6BlSax1TXtqtTQ6lgIp/q9uY8pdlcg5VNpLb5S9XH2pnxeVvtvgqDnhgT+8gjq/Q40/56g6qssIqUqgmrxr/T8NWo0sjlHWPqJy8dlExKs5WeZAVdNUd4GXKap5qarjuPVFarMlTgMe1YN+0rAqP53sJiEvWRk8u0oH9ptWG2mNTGPOB8TOsDjpgaoCOlHUXSAmnscnOXipzy61BF7utgIszlkFFbdacSVMMnNc+MX0k/FEXM94Dd4hAm5Dt/USpnEdKZ8CpRS1WktFYWpnIQjXvxuzeZ7U1uPNF+qX9L/+yiyDPbR9v1hNTkW6ojqkVylvJrNNl65HympjbaPryw04KiTNeRN3/Y+5QXG0tFfFFV4zQvh3He2O4u84BD58ERUwcHBwcHB4fuDwaXx7QO4IipATa+Zbpyo0Uhq6WZ/ZJqCogBEHLFqLy+ytlX1IxoKjXYkfkHKzHxZ/zGDMpqNcxhtmY823OWpZoqzPqA2owvK6XdXS2lsBn7OrU0TyXtCD0vr7yoTi1VKaU0KEoVbS8EPcWqaZ45P3MOMsfprC3VUkc7U0WlsDmnrY+5cns15qQKUkWtDfOAQ8fDEdMc2JBSiiL+pRQlliWnebWlVQTVFjbtixJRUz+VmvU5YeQTn0w+bU3zlS5GecdrAyCqZMaz7ct8HrPfqTJy32Da1y1GwqJkvOLah43/uA0ptSGkeQ+pNrApK5pnvtcl1pe3pzlLs0FPpkCoSvxMayW6XoX8dE8WxNMqbVTlP5RKyKLOh9RESrt6HnA+pvUBR0wN4L5llSact4VMTlWTtiohter8RchqtQhoLaNI4FOmAlMFQUy2/aT9FVMmyoUqTZS4P0uAs6lf9ItRdyKlKuiCnXSkNI+QmsioKTJehzwyKvebR0j9ODuo8vqEhPqxSiqporJqqrpWna9pWdH1ilymPljNJIm3nTe66nrVFhTzNt2cVCuk1KF+UNYj57e//W2sXLkys33VqlX49re/XfFF1RJsE2cXBZ9o5V5KLH1RCAsNmLDw8ZfuuuV28ksG77/clwpFyG81I0arFfhUBMYAiApyExaBbR86lZRD3sfN99SEb0NKu4MZLy8LRzmkNPAUlhJ4wks+Nu/V6HlRKeX45SN9yf3y7dG1eMS8L/7TbUvvQ2H+N7kGFMweAqhFgXItVB2NErxcMh0lya9N1dcEk/VENxfUzDzA85h21MuhKihrVNx555348ssvM9u//PJL3HXXXRVfVK2jKClNSChTm7Rte8uoLBIZtCGf5RLLIqiF1FK2+UvLMQdWksPUFrYVn4z7C6otnITq1FJVVSh6rPA/amgxqhA2ZKlcUiqeJ5+Ipsd6ylej5wtEVCajmX3k2Oh8WdKpvXeprSoPqnB/KvKq+WmUkxarK+adajxE2qaK6mxUwyoDdJ95wKHzUGhFXbFiBZYvXw7GGFauXIkVK1Ykr88++wwPP/wwNt54Y+v+pk6dihEjRqB3797o3bs3Ro0ahb/+9a/KtmeccQY8z8MNN9xg7POOO+6A53mZ1+rVq4vcKgBREdFXU9JPhnlPzHR/EXIqL1L8OsohnTYKjOqVh2osEtZm7fiKbFQK8/n0gU9FSV4lOUxrBTrfUrpd5WfGkN3WXVCkkIZgbUE+KaXII6JRGwXRVLzofrmvqJ+sCqrqm/YhXmv296zLh8rvTQWlv20ZaeyKtBXSAVZoBVOhGrmIq9G/sY8clx6b7UXSR3UluI9pR70cqoNCPqbrr79+QvS23nrrzH7P83DVVVdZ9zdgwABce+21GDp0KIBIiT3yyCPx4osvYvvtt0/aPfDAA3jmmWew6aabWvXbu3dvvPHGG8K2Hj16WF+Xg4ODg4ODg4ND56MQMX388cfBGMPXvvY1zJgxA3369En2NTU1YdCgQdbkEQAOP/xw4fPEiRMxdepUzJs3LyGmixcvxtlnn41HHnkEhx56qFW/nuehX79+1tdhQrakoL4Oti1CVp2Ao67U3eRzq7QGOWih3Oh8ncLYURWfbNARlVMy56hC4EN1InfF1FB0e/S/OeihQ3MHdhJs/Rt1ammhc5H3ptr10XlkpVVlLpfbqK/IlFBfbqePtPdy00klbXPSRuVtKxe1GtGfH4XfNdesm4dsim5kzPhdPQ905FzU1ffWjVCImO6zzz4AgIULF2LgwIHw/eoNlFKphPvuuw+rVq3CqFGjAABhGGLcuHH43ve+Jyioefj8888xaNAglEol7LTTTrj66qux8847a9uvWbMGa9asST6vWLFC2S6PlFbqiJ+tL1MdVCP9DGBO7ZIXWWvst2Akur2Jv3jgkyki3xydX7vmK5oiKgl2ggca+CT7l+rIpi7woWYXogKwnwf0ZnwZRdNBmfKMRucpTkSjdsXJaODFLjJMkRZPIpqmqH352mTiyr8P0/yi7EtDMLsqR6kOtXQt1YaqrLG2TR3NBQ5di7LSRQ0aNAgA8MUXX2DRokVobW0V9o8YMcK6rwULFmDUqFFYvXo1evXqhZkzZ2LYsGEAgOuuuw4NDQ0499xzrfvbdtttcccdd2D48OFYsWIFpkyZgq985St46aWXsNVWWymPmTRpktIFwfMYAC/je2RLSqvljF8tYlkp8lJZKY+potJhi0qUVIcUnKjmlSnsDqQU0M8DHHQeEDN0qH1LK/l1Faldb0tEbUioaZ+KoKrb89RQemVVlTYq00ZhYTGVJjVfU3GyWqvKalfA5DeqzejRURdTAVwe0/pAWcT0v//9L0499VRtoFKpVLLua5tttsH8+fOxbNkyzJgxA6eccgrmzp2LL7/8ElOmTMELL7wAT5PoWYWRI0di5MiRyeevfOUr2GWXXXDTTTfhxhtvVB5z8cUXY/z48cnnFStWYODAgQCyDvEdoZSaUA4pLSfvoQlKc5unJqc2C05HIDfQLGdRoguQrKpS5dVUJao7Iy/Jfm7kbQ0HRHCY5gEOlVoK2JPSIuM5ryKTaOa3J6ImEmpC4PkCOeX9h8k9q1VTnclehXLmDx1ZVeUy7SrYuuXUuuXFBso5IJkgavf+HGoHZRHT888/H5999hnmzZuH/fbbDzNnzsSHH36IH//4x/j5z39eqK+mpqYk+Gm33XbDs88+iylTpmC77bbDRx99hM033zxpWyqVcOGFF+KGG27Au+++a9W/7/vYfffd8eabb2rbNDc3o7m5ObevbG7Q4pH60XGa7XI/OWO4KAE1TdGmxYCfR15c8iq3lIOiakiuab4D/EvXZpiS7Is7LNrUGHTzALeccKjUUvreFH1vA0o4ZVKa5y+qNu3bp3+iUJJMiZwWQRE/U7FNx/qc2qLWXARs4cffr44cV6PwgD56v8bGvi5nY7X6dqgKyiKmjz32GB588EHsvvvu8H0fgwYNwpgxY9C7d29MmjTJOkhJBcYY1qxZg3HjxuGAAw4Q9o0dOxbjxo3DqaeeWqi/+fPnY/jw4YWvJUC28hNHEdN9OSZ91YJmS0TLmTrtgpmyi4jKP8xG9SiSSku5v5yApYL+pWsjuH+pyYwP5KildUhK8xAgzJBQasKvFinloARTRUrzCKmJjBbJUwqoCWpHo1zimXccJWGUaAYec+PfEjZ55B1Fc6gEZRHTVatWJflK+/Tpg//+97/YeuutMXz4cLzwwgvW/VxyySU4+OCDMXDgQKxcuRLTp0/HnDlzMGvWLGy44YbYcMMNhfaNjY3o168fttlmm2TbySefjM022wyTJk0CAFx11VUYOXIkttpqK6xYsQI33ngj5s+fj1/84hfl3KqDg4ODg4NDd4CLyq8LlEVMt9lmG7zxxhsYPHgwdtppJ9x6660YPHgwbrnlFvTv39+6nw8//BDjxo3DkiVL0NLSghEjRmDWrFkYM2aMdR+LFi0SsgMsW7YMp59+OpYuXYqWlhbsvPPOeOKJJ7DHHnsUukcOG/O9rVJqKhuap7SY1NKOMC7pouyL+ItVA9wUX42IfFW/xjaWEflFUatBFUXNcd1FDS0C3xMLVdAxXalami2a4WXU0nKUUluFtNahNunnj6WuMPt3FnwvrBl3pDy3nrVxvnAoD2X7mC5ZsgQAcMUVV2Ds2LH43e9+h6amJtx5553W/dx+++2FzqvyK50zZ47wefLkyZg8eXKhfnWIFqB837LsMSJ09eiT/cZrKJ+QqqJ1TVDnH+zYYKZqLhg68lgk8KmWUA3fL1vk+faWEwndXRaiyIyf9XHUlR0Nku15D0cs047Xtpe3ASLBrGdCqst1qvJZLzf3ca35g3bWWC6atcDzWLcZpzbw0IFR+R3T7VqJsojpSSedlLzfeeed8e677+L111/H5ptvjo022qhqF1cLyCbYt1NM88ho0o7uzyiqcqBDPoqSUflYG3LaEappJYngdeS2nMCnakXx1pKSUS3Y+JZ1xLG1AlktBbK+pbaklLeR85bKvqUyKa1nQlotlJsyqiNQ7WuptrpbS9+Vg4MtrIkpTaOSh+uvv76si6k1+FI0ri5VTNo+24dt4u1KSGklZFTVl4qcVgO2ygefmGUzfqa/AgtwrQQ+uYWi/iBbTqhaCoikNBCIpUXfUmUnGoVflJR2NSHtiiApDi4g1OLYqjX1lmOtnIuiiM6O69uhKrAmpi+++KJVuyI5R+sBOoUEqIyIJu1rhJDWAxLCmle6rwr+pbZ9cXRnPzaHVC2VH05VpFRXUjTTZ8Y9oPZIqUw2aaooXfL8SqFyHyo3ZRTNZSpE4XeSn3cRy0le2+5ohXFwUMGamD7++OMdeR01Ca6UFAliAmx8P1Xnyiek5RBRuqBVayHJM+PTRUW1cHQmgesK/1LdgllEOanFRcjziokC3cV/Tbac0PRQgJqUZvONKn4PmmpOJlLamab7chXQPItLR1lkOKrxkFjp+OO/D9vr6Gz1spLz6eaBehjvrvJTfaAsH9O1CTqF1IaMlpMgvxqE1FR6MA+qRaOjDHR80qYTpGzGt+7LYhGxTaxvSx6LqC6dGchUDvycPI6qhazIQlTv4j5XS2UTfiB9BrLmebEfPWFV5SnNI6UdAR0hNamlqmPChJwx5THloNzI/EqhqiKlO29RUmuTQ1XVRh6THT3H1LK7hEP3giOmDg4ODg4ODt0fLo9pXcARUwMihSTr18RhW0I0L0JX73+a/2Rqq47aqBU2aqlsxpfTu+jU1XKrteU9nReJyM89V41EM3dVUILnQTm55imjmcM0/dQrlMFPECPwZbVUUDoNKik9JtqnTwnVkTCZ7eUSpKa5pJpm+u7ut+17DGBZJbaa/avmkXLml9w5QDLvC1NAN5sPHDoejpgaoMtTWEkyfPl4FYoETOTB1nxWjglfJqWZ/dXMUSr1VW5Efl4KqXJATXcqM14R39Iii3FH+KH6YAg9HoASrSiqBUm1UNluq0dwM342GDJrik98RRVlReW26f7yS4xG1Ma8vxzIhDTqSzVP2LWrJmxIlxjspB9XKjN9ubAKyIoJqc3Dc16d+85AnouALdH1asAJ02MMXgf5OHdUv2sjHDE1wJeicIvkHJXbm5CnjJarnJSrkkbHKtoWCHrSnq/CCdZE8GrNh9P3om+kWlG5tQgt8exGKkmm0AayvqU6UlopIY2Oyf9NVJqqSUVCxf7zCalqLunMKnGdCdXDZm5UPRhQpQDIzvBZ973oHEX5Fp0TusuDqUPnwhHTHJjM9UUT4HdmEFO55jZbQlpELS3XjF8pxJKi2e+y2gETRU2PXDmBZ87XWkvgP2H5J8R5qE41rXdwtVR4OJUS5JtIaTmENDqu8t9oHulUwTR/qEgwnVPUJLb834Cp+lOtmPtNAVj8AbVILtOi5vZMIFQFZvyIkNpVbpPHe80/qIbouGjerkvj2+3giKkBcjUXjs5I7VQJVItAUTIKFCOkeSmisn13/WLSkTCaDguY8moFnheRstBmEUoaIF2M6picZtLDeWoTPlCclJrGfDmk1ERCKyGHZh9Udb/q+aN+fwfloPjDaggw33hMR/qgF8u0UdD3HKjrecCh8+CIqQGBp1dIa42McsiLT1HfUZ3pTUVI1eqq5AvK9PuqhaJVVaphLueLA1dK8vxMgeKmvGqBltUtZ0FTq6Cpasr3K1XTWlFKKoSslgJQqqWZ4xR17vOOiY6z+43oiKiOhFarOpPeBcjuj13OVZhIXhD3yB/2ihDCjgg+snXjqYbVxNYFyGT+j+Ymu/PrTPyqOUF+39VwPqb1gfqRbBwcHBwcHBwcHLo1nGKagzyzvU4lrYYSmhfdL6ubeWppEZN92od6e1G1tLug3GTeXVUvW+l7BsBn+cFiNsEPOjWkllSSakJlxtf5lpabXUOnlhYNUMrzBa026PnpnNLREfpdDXmMmTMARFYTwC/LncdGHS1q6rcdq9xXXNXW6py1MB24PKZ1AaeYGkDLDPJXss/zIFd4oa9Kz5tHSvMgBiOkRLIElnmlx2RfFKHUl3C+HFKqm6g7wleKTvadnZtUTicEpGa9WoPvsch3VPg/Wjx1AUt+3E4Gb6/8a9bCglQBfClvabRNTTxVpNQ20ImjxELlS4UQLHlFn9N/JcaEVxsLhfa2Lx1s2xnvtYzjVGOs2ijX3Uc31n0vRFDBPOCDIVCMO9VYVLXRtbM5ns8J6n3ZuYJ+rvOh36GYNGkSdt99d6y33nrYeOON8fWvfx1vvPGG0Obzzz/H2WefjQEDBqBnz57YbrvtMHXqVKHN0qVLMW7cOPTr1w/rrrsudtllF/zxj3/szFupOhwxNUAmo4CekAptYmIpv2xQLiFNyv/Fi1C0LUtIozbFSKiJjPKXcGwn5jctApM6UQ6B5ZN6oPx20jKWXQXdYlIusguQuF0mp0L7brJCaevbaxWy6k6xKjKoI6O0XRtC5UNp3kt1ThMRlR92BRW1G/rg0bGvmgd8L6xoDvC9sNCDbZExb0tqdVAZBOg2gaDy9109DzDWsa8CmDt3Ls466yzMmzcPs2fPRnt7Ow488ECsWrUqaXPBBRdg1qxZ+N3vfod//etfuOCCC3DOOefgwQcfTNqMGzcOb7zxBh566CEsWLAARx99NI4//ni8+OKLVfvaOhuOmFqAk1ETIbUhoEVJqg66yV9WSWlbmYCqiKeJhKbnVpPR5LyKsVnLEfi2yfVt1A66gHS0SmqzqFQTMim1PX+3SBWV87mjoSODJjLKiWgbQrQhVUrbWKh9qcinycJCodrXGUn2Owv0wdWG/JX7UBqpm2Fc0MF8vM11lKuU+golNO94X0VE0fVctFYxa9YsfOtb38L222+PHXfcEdOmTcOiRYvw/PPPJ22efvppnHLKKdh3330xePBgnH766dhxxx3x3HPPCW3OOecc7LHHHthiiy1w6aWXYv3118cLL7zQFbdVFThiaoBMRgE9IVW1MZn1KyWnHDIp5cRSJqR0n5zaqchLex2s/kipCjZ+oDqFNN3f9WSsWos2N+9zyKa7PNWUbqtnFEkRlZedI4+w6ZTJUPpnIqOchKZm/OhlehjlbXSqK4ctWaVQPTR3JWTrSaV5jul4yzPnR2b5EL7HECD630eoNNXbnE+3rdw5IG+8qkz3qjlBblsL84DHOvYFACtWrBBea9assbq25cuXAwD69OmTbNt7773x0EMPYfHixWCM4fHHH8e///1vjB07Vmhz77334tNPP0UYhpg+fTrWrFmDfffdt2rfW2fDBT9ZQFmNRbFNR0L5dnlBCuAV9rEyKaWh1IYS0mh7en2dEZikIqW1QlSrlUPUlDaqo9AZVV+A/KAIXcCDnD7KIQsbNTGvslIy/onJPW8uMIH70JZA5je5ADqBbr6r12An2znBNPbk4MgALAp2yslNWgSqOabIvKNNvg+mTRnleYCf/LY8YTv9idC+hZRRSFPJdXcMHDhQ+HzFFVfgyiuvNB7DGMP48eOx9957Y4cddki233jjjTjttNMwYMAANDQ0wPd93Hbbbdh7772TNvfeey+OP/54bLjhhmhoaMA666yDmTNnYsstt6zqfXUmHDHNgQ0ptQ12UhFUmZyWwJSk1+S3ZUtIORm1nSDLVf6qST47Mpl0PcK6ckuF35mK/Ooj8KPFSZnvFN1rQbLJXVpiLFFN8+rYUxSpqETJKG0XQpwHVA+l2vPHffhxH4GnJqnpvdn9TWvBt1T5kGxB5GxcfdKH0vQhlZ+umg+pnDjK16Qa7ybyqSLWpodQ2ypSYu5SdZ7jLkcZvqCF+gbw/vvvo3fv3snm5ubm3EPPPvtsvPzyy3jqqaeE7TfeeCPmzZuHhx56CIMGDcITTzyB//3f/0X//v1xwAEHAAAuvfRSfPbZZ/jb3/6GjTbaCA888ACOPfZYPPnkkxg+fHgVb7Dz4Iipg4ODg4ODg0MV0Lt3b4GY5uGcc87BQw89hCeeeAIDBgxItn/55Ze45JJLMHPmTBx66KEAgBEjRmD+/Pn42c9+hgMOOABvv/02br75ZrzyyivYfvvtAQA77rgjnnzySfziF7/ALbfcUt2b6yQ4YmqAKto+r41tv3mqqQq6ICeTUiqrpEXUUn2Vlez1dVQZ0lpVS6nJTjbndxQ6Ioeh8nhF7lLZZJeXv1Q249Uz5JLERVGk2pLOXA9krSYqsz2dA1TjX34fQJxL/EQlZYpysl6kpkrKaZH7qTXI2ThM48uksMqqabVhqiYnK6A6VdRW+RS2Q523VB7/9LPKnF8rqqkXxmlkO6jvImCM4ZxzzsHMmTMxZ84cDBkyRNjf1taGtrY2+L74dw+CAGEYneyLL74AAGObeoQjphaoFiEtB6p0K2LwErMmpLYTpu8x7eJF+ywC1TGdRTqp6auIaa2IH2dHuxwEHss1K5Z7DZ4X5enULUKmfvk+mbjyfrsLOVUhBIMfP1QG8cMmnxeoOd+EIkRUbq8y21O3HRqwyP9+8jhsQzq+Sx5DQOYY30sJamTWZ+B/SUpQOeT7LVoOuZooYrrPC3yyTSVHySl43lkPAPMTP9Mi9x/EpntAPVfamvB5EJTtdiDHtC8RX30pUtHFp7u481QLZ511Fu6++248+OCDWG+99bB06VIAQEtLC3r27InevXtjn332wfe+9z307NkTgwYNwty5c3HXXXfh+uuvBwBsu+22GDp0KM444wz87Gc/w4YbbogHHngAs2fPxp///OeuvL2K4IhpDsohpXSCLkcx0FVwsvElpYRUtyipVD0h0lxyZpcn+aK+p7US8KQDX3hMiohqX0crpECaEia3SlMZAVE29bFVygiAjCoa7cv6mnbXClA6yOTU9hgOm+BG3s6kjvIHUTrmdX/nNkTjPIhTRiXR3NEfE5DU04iEp+AjoNbVUUA/jqLvRx+pz8d/pUGHvqECnI8QSKp+ma0jtgFQhVVSzZzALSmAXbCT/MBaM+S0E3xMbcET5cvR89OmTcO3vvUtAMD06dNx8cUX46STTsKnn36KQYMGYeLEiTjzzDMBAI2NjXj44Yfxgx/8AIcffjg+//xzDB06FHfeeScOOeSQim+pq+CIqQEqAlqElNqiUE5S8tu3IaQyGdWSEKEMYtRzgHTSoalHTGpq9t7svo9qqbDJvgoCG2wXH0FV6UTzndwGBtKRbZ+SnDzCaFJG5D7l88vktJ4hjznVX4QGLVJyKkMVMCQHMkXnzH8YNamj0WdfeChNyVXsgkLoZYAQIbxYNY0IasljaGJhQk59fn5PDNDUfSfZe68uyh1zJfh69VQ27WvuTHW87UNq4IUIWQAAqbrq2Y3hrgiAstmuUk1V5LTLwVAzJUmZBZHt168fpk2bZmyz1VZbYcaMGcVOXuNwxNQClZDRwPOMSoJt+idZHY222xFSnRlPuA7mpSTT82M1MGqvI6h5fXYXyItQZvFi9Lv247+LT/4e0UJYbdcAW9M9/ZvZLuY6EpruF33HVCb97kROZXBTvaCQSuRUe6whw0a0TWynI6NAVh2lD6AyGZXHagkROYrGt48SwoSgwisBDGj1fGtyCugJqrpyXPV+EypSmPdwGsJHiXmaY72MJYWPX9qv6ti8McaPDwzqqQod5WeqM+knLj6Z7VHqKN6/yac0S06tb9dhLYYjpjkwkdJy1FFAnX8QsCOklIzyzzaENG8CDLyIiCYkNFZQOUHlpFVHUIuiK4OaTIuYPJkXIaVW52Z+/Hcq3wUgUlkszPuW7VTH2aovKujIab0j9SVNfSxV5DQPlSqj6fWIhFQY/8xPiZWCUAnwCK1k8WcWAF4JPg+CJGb9xHQfX2seQa2HEIyImPsZJdLWhC+PjfQBQXxQLTd9lHpMZq0lJgtKIVXVksCafEpl1AIp9RiD10Gm/I7qd22Eq/zk4ODg4ODg4OBQE3CKqQGdEXlfJEG2TjHh76P/s6Z72bdMho/IpCSrplHgU9asD1RPOa0Wyg1KMEXcFjXhR+/t1RGdAmlK1WWbMsrUv+k4le+prIoA2YAGarbj7ahqWu8osTQynSqEVDUFsnOG0qfUoJKaimKIlduySmmeSqo07YIJY5+rpoFXisY+i3waM5k5GK0UlS0KUisqqW1Eveo4kwkfUPvtU6U0D3lWLD+O5I/6Fb9fVZYOk8opX6euvecx+JKVRTcvUHcBkytPTRVJqaHgJwc9HDE1wOQnVo4ZXxd9K1dvUhFS/l5O+yT7lfFtUb9mQppel29FTnnfQu7DMghqV0xSNqbzvAUoaSctRCpSKhwf+5eqFqIiC2deyiibKHvlcYZ8harFJ7cvJTktdEk1BRXJogE/YqUnPRGlfRUlo7oHUEpIRZ/S6n7hJXipWZ/6mkJN2OsNKjM+YO9XSreHQtvyvhNjijZLn1KTn6nKxScviFFohyjXsQ+G0IPiwVTva+rgkAdHTDsYqsAnuba1Lh+hjpCqIu0FJVVBSIsky1feh4Gc8msAakdBzYMqFYz8HhAXIZMyIiukPOAJEBeAckm5cZFBsch8DpU6Iu7X+4qq0sCoE2x3HyFBVk2pP6UpwDEvzRMg+o/aZtVQkdIQ+f7LgZJqR3OBjxCBJlN4yKLcpjrUKjlV5lAmgU/CuCUBT/RhUvedyj7moULhVD2slgM+1un16nxKK/UdVT3k0lRx0Wf7/KWcnJbKu/XqgU+XHdW3Q1XgiGkOdKlfbBNo037kz5SUqnIS6lLAJH1IwQ7R8eU9reeppjJU5JRfk4mc1oxJR4KKnOlSbJlIaaKaZnIi5isuQNZU78ckQZWfsNzAJg7d30KOxpVVU35sEXJa70gDnrIKoRzwI697qjRvsmuO/OCpI6LJe8lsT0mpzkQsE06eIzfwwsxY9j2mJbAm2JJTVZBYqQoLu+43bfP96JCbas94Pb7yvQkqy4jKvUdnzs+S1+LmfDl3sRydT4/j8wNtZyKnDg55cMS0A5H6kqmj8Pk2WSWVFytVGpj0HJURUhuoTPr8PDpyCmTVU+2i0UUqi2xGl9URlUIabRdVEVVqqKh/6aGB9Geb5kmteuT7mfLjAcSR1nbnlc9ZNMJeR07rGSEhoDI5BZAhqIBoAaH7bB46VQ+cUR+ymp+2yVNKOSmVySYlq1wttbGiiK4MYtnWWlVO8yD7lZbiSH1V5o3UeqSfF8qBj1D4O2sfTDvAnC8rp7JqKudAzpj0Nf6mtZSZw0Xl1wccMe0g6Egp3R8ia7qX8xJGx2ZTwXDoCKmsCujMczpyqdsnk1MTKkmA3RFQkbk8f1LVwhNttyOl6bHZxS29BrvvSZdcO9kfLza2JFQ+VvYzVaki8r68ak/dxbcssSBAJKfRPs340fiP8jHeFhOfaLs61RM9Ntu/NMahrkQmk1KqlEbbmUBKuVqavmdlVHvTk9Nq5i+tFrTFNjTjNut3nn1YTdvq52UT9Gb3fHN+pWmjVKqp7jOfA1T+pjWVXN+hbuCIqYODg4ODg0P3B0MHRuV3TLdrIxwxtYAuFYzKz1SMwFWb8Lla2pr4mKrV0rySgny7eH5NxCipNpIH7mdqAzmNVC2h7FQxqujcnKAGGxO+7CJguj7Zp8xU9SVqZmfel6ELgDKVGwTMqojK37TLgx6qAJVqCkhmbIUvKQC0kXHMx3cbC5TjOjchvgT5b8fHLh3HVC2lY1ulllYLqgT8taiW6lBK/h7ZaHvVeDSZ8G1db0xWjiJ+pjqlNLoWMq/ozPUKcz4f36kfadanFNCXJOX7HBzy4IhphdBF41JSygOd0n1ZUtpK07/kEFJbMlpN0KAmlTnf5BJQ69AFEOkibZU+pjk5D8uFLiepyVRXSb+ZwCeNOd+mFKlMTusVoeLSZR9LeR+g9ydtRRD9zwItGS033RM156uCnUyklPZBzfjJ8YZLkv1MhX1VnhcE03gH+rJG5VkDpYuEiZRWI12UTDiVQZFMLHOsTQOVCCNmAmsy55va0jmAlypVp4mqgTnA5TGtCzhiWgC6CH1VO0AkpbqUUG1MrZJ2FiGtJpmsZ3Ka912WQ0rV6aX0vx8+iZuCm/L8THX9Jn8Wg/+paWGifmQADItPNhq3O4CnSqK/cUpO+WcOlRUEQIaU6nKP6vysbfy7ZSIVnYkJPqVANtiJ+paK/dXX37CaZFUkYOpAJAobZTS3lDAJgFKqnEof0eycYUolRfs0qabUoqIOfspaT3weX6HwN+0u84FDx8IR04IwkVM52t6UEqotIaJpEIQuAKKoaa8zUCQIytxP50bFVCOwqlxSqoMucb6tOT/aFwVFAL4QACVfiwqyOV800anNdTRFDE0DYzLt1zt05FQGzUEcHZeNwJdzj6bHFv99JgopqBpKo+2pGhqppACMpFRWS7s7Ao8BTB085kv75HFYjTlMF7iWbSfNCYoHVV2hDdV2GyWUHytE3UMkp1EbcfzzYCh5X5ciBDpsyamVcmfdAI6YlgFTRShd8nwaoctJaVvso1ikrGDUd94Td9ctKJ2hmpbrO1rWuTR5CMWk3Gaf0vSY/BlRp5rKC0ggEdFyTL95aozOXAeQhQcedH6nvJ3nsbompirioc5Y4WU+ywnyAcRpiERSmon6LuATriI1AhlVRN/zz4CalKZ9823iZxVM5nwTqpG/tFpIvm/mA14IMJH42z44mMiqLoUTII45G3O+Kr9xnkKqaktJK20vkFBiLVGrpSz5duQ0Unwfq+N5wKHz4IhplUD9qHSklAZAUFLaJihvYpWQvFrXKoTwlORU9i+j8Mnjnm3gU0ejFnNg6v421SClKvC/UylRK7K+ZUWhWqhUqqnOXBftV5v2gdS8XRMKSZVBqx/pyAfNRZzJOxyrpUD0nWlN9tIYzLNOyKmgaB8m0z0/Nrut6xTT2ph9UgQIAS8SDzg5paRQFZRk1a8Xaq1g2XymhLAqlE/+oJpNrK8mr4DGj1zxXk4lx8mmWi2l80bcnpj2uxouj2l9wBFTC9g678tR93LyfJo4XyalJkIa9V3ZqK4VskmhVqJqx13BBqaKUUVA1RFb1dQWQuCRKjhCo5zoyCkH7yePoAL1X/1JMN8TciqDmvDFgBhJTVVUBtONUe4jagsdIeXbREVUTUopTEFPlaIj1FITUYxIJmJ3Fx9gis9AvC1STfnfio8TFTlVXQNgZ+aX+xX64QGnxM+13GT78naVr6ngS0oeRikJ5eppQk4zaik/F/+cEtR6tpw4dB4cMXVwcHBwcHDo/nBR+XUBR0wNCMFy1VIh4Cmp9pRVS7PmvdivVFJLq6GU5qkrtmb8ck15tRKdL5vDgGJ+YjroVBKdmqnbLl+fSjVN9mmCHrIBT9kAKO35JcVEXbGJZYIcAFE9zVNO15Ya2VwtLWrWTUrhKqq1WUXhZ8z+qVKa9iP6k9K2slKqSxFlM6bL9TPtDJh8O00IiK9pntXCtlywCYlKaTDn889Z872uChSE7bLJHogVUjnISVJNAaKI8p8Dn2eSs+g+q13MHBxkOGJaBuTgJzlHqYmUItkmmvajfor7k8roKN/SahOLataTrgZM/l7lwirYKf7++f2oAh6ivmj6mOotfvJnOT1MhpwSEguoCSqQ+pUBkW9ZdzPhmcz5Ngi8ECELrNrKJUSV/VEySQgp36fyJwWy5nvbvKXVQJ4Zv5oZO4qObz8x+af+3KkJnyVR+pyI2vqZ8uvgBDD5bDDnR8eJ80IlyfZV5JO+lwOhQmnsZ+YF2QUgOQ8yn7v8mcUppnUBR0wtoIvClwkpIKaFEtrCUxKVahIsmZTqlJSobV4wRZ7qWns+q7UGXTSssi0h23yByVNNy74m4meq8ieLrifrQxZtF0F/BWJOT5Gksm6ulKiS8FMkxSliJayEIB6vYZzuJ6uWqj7rxqXqAVMmpPI2uT8TKa22BURHSk2zStnJ6k0+pzHJFPxMkfp0BnGwIfc1FQoYQE1OVYFRRZVUqpoC4jphk2wf0Pie2gQ6EXKaB1k9zSOo3e0B1aFj4IhpDlSkVK72pCKlOrU0akcmldj0KrcpYnbKU0mBfFJqVFVrSC3Vweb7MpX11KdvUSeuLrrIyFAmuNeQ0/gI5WKkMt9TkiSfOy81lBBpK5vqmJiPNBsMRe+FnBNe1yslHYA8MqpCFFEd/y353zAOTpKT4gPRb1NFSuUxrEoBRfsxpYKqtkqaZ84vGvBUzvecB26e1wkDnLAmie7jv5suEKrouctRTel5dZ+jbbrE+ukckmeyDyUlVJVGiiIzZwBKE3+XE1OnmNYFujQEeurUqRgxYgR69+6N3r17Y9SoUfjrX/+qbHvGGWfA8zzccMMNuf3OmDEDw4YNQ3NzM4YNG4aZM2eWdX15pDQEMd1rSGnSlqUJ9XVQlRDkL9U2eR/vQyaZeQtaUVLandRSs3lUQeAzrhAqs2j+9+N7THil/eUr2XkPCr4XRv0aVC7Vdcv/pypp/FuTrtmTXrp9/HM9oxLFMBqDkb9o5OMZ5xONSWejV4IPhkaUkhf/zuUcpPwFpGO3ySsl45x///x82e3ZFxARUh0pVd173sKhI58mUlrOrJI3XykfwjXbfC9Mvjd6vDA/Er9fYdxmHgxC5bYikO8lkM6n+rsEinGWmWMU77X/kwca1ZxlmhPkecPz8ucuBwegi4npgAEDcO211+K5557Dc889h6997Ws48sgj8eqrrwrtHnjgATzzzDPYdNNNc/t8+umncfzxx2PcuHF46aWXMG7cOBx33HF45plnKr5enek+2iduT47JIaMAkkUKyBLFtI2ZaNgQUtncZ1JVgfJJaWcFPpnOUzQPJIU8kafb1SpVpeeTz5kSDybtr84Dge3CpF1oLIiqavvaCN1CHBHSdjSilBBU+SUQTomM8oAmSjobvVIhQsrJKE2eryOu5YCS0BIrppRWpZqSZh4VP4dKMhdI2wNPTNmlqrIlE1EVkjZeqogLnzUPt3Tu1s1NqvUhc79emNlnQ07lB1mbl4qgdvk8EHbwy6Eq6FJievjhh+OQQw7B1ltvja233hoTJ05Er169MG/evKTN4sWLcfbZZ+P3v/89Ghsbc/u84YYbMGbMGFx88cXYdtttcfHFF2P//fe3UlodHBwcHBwcHBy6DjXjY1oqlXDfffdh1apVGDVqFAAgDEOMGzcO3/ve97D99ttb9fP000/jggsuELaNHTvWSEzXrFmDNWvWJJ9XrFghXpvBpzTaL2/3rPyiuL9ZSXKUB8Sn/bzAiEyfBDpfNOMxOpWnCmqpTgnpisT6RdJJmerW65JW23xfmVKSXhr0EMLPJNiuFKYAJ5MvmexfCkj+ZIAYLCGcp/p+yh0B3TwQcOddS/DfShr4wpAOixDwYv9C5iVlL9MAEf6bUgczUZTjR9rRQU0ybFTSzhKa5Mh8VaS+nAUDEOcJ6muqqgglwxS1n+drGh2fjr/oGLvIe9N2E3RzAQBkfiqKrlXnpP6nXT0PuMpP9YEuL7OzYMEC9OrVC83NzTjzzDMxc+ZMDBs2DABw3XXXoaGhAeeee651f0uXLsUmm2wibNtkk02wdOlS7TGTJk1CS0tL8ho4cGCyL4+UypDTQukgLB4e8RtTmM9kE58MldnNxnTPj9Vdl9iufu0USlOdcpv6HjMmfIh/O9V7lf+o8tqg8GuTTPrytaQm2jD2QzSb+ZW+rDLJlEz48nEmU12eqb9eTPmmeUAGNYObAoYyY1nhb9rklZLx2ei1o9FrN/qPVmK215npfcVLed+GfbWM7N9BfmBn2W0Kc77qvc7XVPYtLeprajLnp23U40p1DqULj+JhRjX+5c++PN4tTfsuj6mDDbpcMd1mm20wf/58LFu2DDNmzMApp5yCuXPn4ssvv8SUKVPwwgsvwCtYz1Buzxgz9nHxxRdj/PjxyecVK1YoFyXVdKLyLdWVJeQQU47E6kqsnER9+WUpGXnR9vx8ymMrJKSd5VeqPLeshOREyqr26yLz5baCUqpQTfn3XRIi6PXfjZgOKj2XrDzwBdKc3JspE+2bshWYchrmQb5G+ZdCo/q7Wimxge08oCKivpQNQbB+gInqkqSeAkgVVKFPtU9j0memjaiO0muVSagNOoOAKudUoZRref3S7z+TqJ785uXo/OR79lLVlEbo8/RRNrlN6XXoUkrZqKZAdqxFf3d1dhCVqmoDk1Irj1+jmmo4dZc/oLqo/LpAlxPTpqYmDB06FACw22674dlnn8WUKVOw3Xbb4aOPPsLmm2+etC2VSrjwwgtxww034N1331X2169fv4w6+tFHH2VUVIrm5mY0NzdbX7PJhF8EWfLBJ6l0ujblOdVFcNsoo/QalH3UASG1QbZ6SnYyVxFbAImZjh8H5gtkgy8wCeI+KEGlUCX4zpjqZCIcX0tV893K5jqFK0KlEHKfsmwQRi0ibx7IS6XE93MyxccGrYTGTfuR+ZchQIm47+j71pvxs8RUVa1J9eupZkqnrgYfs3kuQXSsy2NNReiEh0WDSV+V2xSAmjjmkNO8+zIRT2Xu0vg3kI7x6Ly68Z8lwPZIcqACWlO/g0MeupyYymCMYc2aNRg3bhwOOOAAYd/YsWMxbtw4nHrqqdrjR40ahdmzZwt+po8++ij22muvwtcSmfHL8+1TK6VMuU9YwCTFLS99EKA3QSvTvFiYlm1QDiHtiPylRaBaiGiuwmhb1u9MVrhhoX5QCOqMBJoXEUh9U23KH5YDrT8auWaVwlrugqLyTa035JnrVe2N6imgUVB1/WXJp+pznjpapFSo3LYjiGpnOAeJRJSqqCKBy5QHRphRTWnS/aRPi9ym8jxR5JplmKreVVIO1ebhtAhh1eVB7XJiGjKgo66hIxLurqXoUmJ6ySWX4OCDD8bAgQOxcuVKTJ8+HXPmzMGs/9/euQdLUVx//Duzl5foRYUgCAhICC9FMDEK+gOi+EpU8odWNEZjUvERxVeMFZ9BtCySCqURE/GFqIkprBAfVIwkoiIxUcpHRIzvSIIQwBJFovK4d6d/f8z2bHdPd0/P7Ozu7OV8qrbu3Zmenp6dne7vnnP69NKl6Nu3L/r27SuV79atGwYMGIBRo0ZF284880wMGjQIc+bMAQBcfPHFmDJlCn7+859jxowZePTRR7Fs2TI8++yzmdspdqBJ1lLX76ZOfOgEahrSWkXFttR6jiKgWgbkAURd0k8VqUFs9RRxcgMgT2SwiVNAceUbhGp0XiBxcPMFty8QSMn1XZe2lD4rxWoq7UtzjyMroX7ZQv631cVpWkzWU0C2oAKw/va1CVHxPOo+kyAtOfw41MXP60RtUa2qaX/Qqf0EUJ1s6Fdc/Sarqc6lr65Tr+0nHKymaa4jzwlQtnPoUN36tiT9BOFCU4Xppk2bcMYZZ2DDhg3o06cPxo8fj6VLl+Loo492rmPt2rXw/Wo3PHnyZCxatAjXXHMNrr32WowYMQIPPvggDj300HpcAkEQBEEQrQDFmLYETRWmCxYsSFVeF1e6fPny2LaTTz4ZJ598csZWNQ4pcB6IWU6zYrOQpp1dX2QLaVZia02rbjyNFVOd2KBaTQGEqX8AyXLKMVlQddZd2yQE7fXAvBZ49ZrN9Witpq4WTmGCk81ququiWk6BBOupgs1Cqu6vxUoq1ylmFrG0rQ7u/jzDfUwToExxpjp3vhg7Cs8PlwmuuPmjsB1DrGl4Pb6z1VRquzaVVXOsjiYPii5FXlI/syv3BYQ7hYsx7aqoLvuy4gLiZThpXDg2uoqbXiXK82kQZaYBxzbhweTON8265eIUQGygEbEJVTH8IG12Ael6PVZTiJPpe5SUUUB02enEqUs9rYLr86H7PqqCUufi15GUd1T9dtgEadI3SddT8DpsAlU8t6s4bWTyOZc403iOUya587PEmobni/cZJvFtiy219QW2OmvFZVnjpNADU8aT5lBHi2kLjZ9Fh4RpnTELJ1mccnQi1f1ctSfAbwZpkuubOm+X7epgY4sdA5A461YrRoU2RAOgIEoDYSADZIGqE6fNRJfbkKMm4e7K8WM+0i3NqVpEtXU6flxJQhSIWy5FQZrmmySWVXuSErzcxWkzsVlNAWh/yNpm6EeZOBigesG06eksVtM8hVw9nstYHG2lvaZzFUeUEq0CCVMLpZT5U5MwuezLioUuD4omQBs5I1+fMkVvNTW56Hg96iovAABWzXUYvg+3iwJVcvFXUPcDZkGd66QFw0Dneg7dj6S07evKwtWG6TmUn/kEq6muXosY1R2T1JepC4n4aB1xqvNA6Sf1xWe012I1VV366kSo8DgxtCXZha/iEsqTR99qEo8BhPRPSrt4m8RjA+ZFL3Ff9L7Z/QDFmLYExTDNFBwf6T8oXX7BqD5loNetxJLmPLqXDdPKHF2NaKlGacUW0Qqo3y6ukhWurMRXbqlsi3JIBlE9fEUmcXUX/qq2J75fbB8fALJkZEhD3vc6tgQhK9hgVCNZnv8kbM+qr7yAUOyJr7AOL3qpx4XHeNErsT2aclpBXCDrl8v3WPf5ljTPmWmVJXFFqPAZr26PjtWsCKWu5ibWYWtj1mc/yzNmtHDGlhRNrlsVpPw4cbu6nyBMkMU0JUlWAzWHobmc/pdwPSc+1XJsozsUJ0ueEmcqusfTWk3L8GN5TaW8pJGrvlKZYj0Vk2qLFlSgOhipq8HwfWK6mCJhXDMb+sFLGpS6iCgVyRKjWUudSVZR3fE2EepDvWe6tFCeZD2txXJaJOy5QZWVoMD7EHt/ER4bXxFKPZcuBVza3KYumOpR+0G1nGrVVP8HYMy7qxOf6naxvqb3BwFD3WJBKY9pbpAwTUDtqKvbQ3cV77h1saRqYm39pIh0E56aZdl0cSmZsLma0sSXZqEqOuOxpmISbVWcAogGHN2SgWpGBZtADbfH3fza70MTJwroJiwlWVUC5sVceer/jHlgzR6QGoD6TQ4s+0zo8oVmFaSqEDXtVwVqHuK0qLGmtkmTknC0xKbrXPq63KZinTqXfqxtyvYsP1YDeJmWI5Xq0BwvzbhX8pJKwhTxbWKdu0I/QNQOCVOCIAiCILo+LKjOEahH3UQukDBNgc5yoC0nuPO1yxHClFKmNhND1jCANMHzRZiBrV82VPicLWtPV11vcSuIZDUFjJOcVLd+kuVUnAjlmscwLVmsJFbXncN+QHbhqxYUoGohaeWZuWJcZxorYJo7msVtHx6nfr/Tf846y6nJU9TVsPUVgFv4j5o+Sgz9MWX1CP9P9qhlRdefaENtDJOTkvoD3Xad+17sBwJ4Ld0PEI2DhKkFUyfP3Vgu7nwYtutm5Nuo5yx7l/Q2ImnEab3d+La1ozmqO18Up/HcpKI7T3bVSQMPYBSoPJ4sovIR2JJsh+fLFmeaZ2efpi7TYNaVRKlKFoHqUp+0zSH/aBpB6pow34brj/IiUX0WqzHoauyoipgY3zX8B0Csj4jqMy6ckV9sqQm9uDSfUycoReFqW5LU9LdwfQDNym8JSJg6wAeBMmOZrKaAXbTmha2utAK4kemd6kEsNjSlOI0wpIhS407DMvIxSUm2pdyFGQcql2OSBoVaU7uY4kpdzt2q5CFQ04rSNHGkptnzNpHqw9NOiDKRJvm+SL0FruvCFHHBmmw1jbY5xJoC1e+/yWqq1pU3rjPqtf/DPpveND9CtZKK25rtbSNaAxKmCZg6axerqU6cctIKv1oFrC6nZlL5WsVpVmtp1s7LJKpdxSkAafap6t6PcheK5SoDim1ilDgjV2spzfhZ55bnVGMBd03toivT1UQpT8mkE2B5TvLJQ5SmSeeUPIEpuzs/y2eiPgPNnOScNGnSxaUf1WWwmobHm599nfckQJhzOXw+fZRR/Z9PfIrNutc822FdZtc8F6WdgS9ZPqVyBguq+vyL52/65Ceald8SkDC1oHb8vKNOspraxKlYRiRpzey8cRHJtYjTervwpc5esWiolhDb8WrMqZwcW7GgViyhqnsfgDH+VDxOtcyK50hLHhZtl5AMF3GqHbS6iCgVyVOc6qylIknfiFjf1ITPO0tYQFZrab08OC59BeAWmy6Wqx6nT7wvHm+DP39l+Kl+iKrZMsJtyf2Mrk9gglAVy4F/DprvgSpKmy5IiZaChKkDxrQqBqtpuE8Wp4D9B5WLKHVdxlBH0rnz7PhrqStvV4/JpS/vi4vTiOjexQWqS1opwOzGy24prY/bz+TKpEGlSjNyeIrW0lqtpGlolLW0EWFDrnGmogteZzWt1hcPF1CtpgAEa6tiRXUMN3DFlCbKZC0VUZcWDkWn/cel6vJvmQVaKMa0JShWRm+CIAiCIAhil4UspgmoFhLVne9qNQVki2dSOEot1lFbfabzprHe1WLVrFdCfZM7H0hvNY3aqsSGAvG4UwCJaaV43Ck8vzpj38tm+TSnbPGl2DDXulVriQ7PY4lxo+pkj65ELJm9xmqa1p1fZsnufBeKtERoEmnc+EUM18vDnR8ek0/KvbLmWVf7Ab5NJZZGT7PCm4/KUtV+AAS+4D2Kx5wm3VuxD2kqDHW0mNan2l0RsphaUN1mfiR07A+YNGnBtB62Z3+5Iq657RoOYKo/jxjXWiY85e7G165Dzir7xHWv+Vr1QXVyE0LXHF8rW1zrmq+Zra6b7cfqY/DBpPWvc1+n3uDCA+IzZU2fL2+T77FoMBJfru3oiqKUoxOnjW+D+zl9eKnKJ6EKD1M4g0mcm4RLkbJ/SM+pQ19Y0lxVyVN/sMhldMekxfTDk4tSdZt4HGD5cak89z4Y2vwAvsfg8b6h0j94reK6J1oSspg6YEqLYrKaivuqdTRmRn7a2fcu9bnWUa9Z+MbJWeqa1KjGfNrK6iYkqGtki/Wosafa1FJC3KkpnyGY/Dn4lfW1If4FELCS/nOwDDomdINQUpwZGKAeFiCMNU2yjJoGdJeBvugkTnpssNXUJI6zCtI0qaJ05CVKVWtpvcSrbXlSjimTh1SP4wTGrHmKRXR9gPrjVPWaqPGgSfAfo21+gM4gzC7C/wcAv9I/pJXYhRCzFGPaEpDFNAFXq2kp2i6W1aOzcqrb0lhBbZiOd7XMxlO46A8ypjyBX5fUUK6I12+yiIhWDLlM1YLKrae8fAlBZD0V6yh5TLKclrwgdi6/YoU14SLiklZhyYLNOqpaSSLrifLq6sjekHy/u3lMqkojSm3nEyc+uVhLdaI00BwbHu9lFqXi+2bmxFQto4Dcj6jelzwQBTF340v7NbPn04hS6VkWvSjKK7KepvCocAohTonCQxZTCzrxabKaysfJllNoysj11vdhTWP1TDtTvJ65SrNaSlziTcNy+lQuojhVLah8QFLjT2Oxp0xYylCwiNZqMTEhpodRXXouS4uq+8JlFeP7+MBSiHixBpEUulMLecWa6jBZQHXCUixrm42fRpTqj9dZ8I2ny0RSHHselsuoLsfZ9aaMF67Iz7B8Pn3u0mofEFjEvJQk3xR3Wmm7XDa0nPLtLZM4PzD9XMqrbiIPyGLqgBqvpVpN+YcoWlDUwcYXXunP7/ayYbOcitjEoNr5ZLGSusaS1uq+U60VrpZT1ZKpWlCr26vWUwAxyykvb7KaJFlN02ByI6oDkmn9a5d7olpHvBRxZmnKFhU5ZZOwvU5u5qzfjAAseqU5xqUdqigtM3dRarKSmkSprrzJWlqEOFXfi/cNrmQ1THA3fvRecOHr1q83Pf+6bWp5m+VU3E8QeUAW05QkrpYi7DfFnNXr14CLdTYvbKJUR9Y4UusxKa0eNsspUBVyYiwpL8vPp1pPTZZTeL4U91WS4kmrdfuSNbUklTfGmgqflWniEyeNy9/k9pPX+GZSWZMFtdWFaN7wZ1L3jRWtpmWwuk6sUvsuVZSaXPg6UapiEqQ6bII0zfYs1MtrYcJmoeV9hq88/xy1P9U/t/rk+za3fnhupt2XZvlQz2OR5ZTXWWjrKcWYtgRkMSUIgiAIgiAKAVlMEzCtF+3DQwCmXaZUtZoC+a2nnTe2+K60Ew101lLbcZmXO3XN0amZQSuSFGumxo/plkE1WU3D8tU8p7Z2VM/HJKuT7zHt96aUEK+mLkeYFGMam0RhSScjWk7F43YFC6nYFyTN0BcJlP+bZQ2ol7XU1VKa1kqq25cUTiQ+r4W23KG21Z+ipUpjKeF0eUzjz78uf7Hovrd9djpLqxhDW2irKVlMWwISpingYtS8Xy9OgfSpZIqM5EpO6bpPNbEqB5ebbSJUvGxc8Onc+knilB+XZdDxLWK5lNDhx5Nt2wcn6VjHtFJZaPX4s6SZ7i7hO3kQgEltSeP2F/siXR/mIkrzFKRJ/YC+HrMoTXrWtPV1IYeh6cekLraUb9MJSPVHralOEZ48v2XEKVF4SJjmgMmqqhOnIo0Uqi6CUO7okycauFpIEwehBsd8icRzoeqtkbrVosLy5tVgRHyEqz2JcabwfK01lcejllEVxgH08aYuaOPHHNOA2fYlDT6tLEZNZLGaine30fN2k6ykQHpR6iJI87COVutyt5LqjpFjsuNl+X5xX9oZ9LY8pq79m+oxCbeZn/9qFg7+V139LSF1FDPXxVFjytNQy7F1I2Co2xJNRVyurEUhYZoBLjh1FtQ0Lj5dmphGiVVbvsC8RKlxIkODhWiSS98Vkzg1lYm2CaJV2z6HCVChCIz/dcU0sSlp0EiytOrEaVcUpOKzrvsh6mo1zfubb7OaZnXdq8eaRGkt1tGkH6uuP3DThg9FZRzvBK+fP+tB9N4znj9pQqJadx64/rDUzeI3lVWfbZe0c7of9oUUqEShIWFaZ5Jm8cfK1yhWa7Fm2DCJUucBxCJGs4rGvBNYV+uVZ+mL6MSpzmrKywLpBqASmJTvlHfqZRZebxklaQZvSRCoLmLVZBmpV2L+NGVakTQ/RLNSZsyaS9Wlf7HlKU1rJXURpGnEaJY4dJfZ6jHLqoO1tJZco9a8zcZrj68ipWun7gepnzLntHzeZJGZpk/g7nzeLt31FiGkh7EAjNXnia1XvbsiJExzwubiSytO43Vnt6S6iNIka6mrKHUZCPKM64otDWiQCLZz6tvY/F/2UnopVD5rz6+uFMYYgopFNcyh6AMM0f9pBpV6WjIia0ndztA48rKa1gI/v+sKTzYrabhf2JdSlGYRo7WkjUtjHbX1RZHVMzHO1c1aql23voZ+TgzlERF/mPrwpM8/fM6q/QC/tLTPtpoWzlaGIOpF14n+JgiCIAiCMMFY+IuqHq+Us/LnzJmDQw45BHvssQf69++Pb37zm3jrrbekMp9++ilmzpyJwYMHo1evXhgzZgzmz58fq+u5557DkUceid69e2PPPffEtGnTsG3btpo+qmZCFtMGUavV1IUkq1SjrKWqFdJqscxgnTStlCKex2Q9TZoI4WItrSXNi4pfWb5Unhjlx5Yv5eUChJbToGI1aatMkeLWkjYE6ET4YHdGFh0hbkxYXrDeVtVmu+26MuoMfd1+kTzjSdOsZx+WTx9jmnU54ySvja4v0rnxTdbSpPaq8aXi+U3hTKbPosQ9IajeF56Zo4TQY1KCB/hldAYlwAN8rxxl5wjgAT7QGVT7gaTnP69nttYlWHcFnnnmGVxwwQU45JBD0NnZiauvvhrHHHMMXn/9dfTu3RsAcOmll+Lpp5/Gb3/7WwwbNgx/+ctfcP7552PffffFjBkzAISi9LjjjsOVV16JW2+9Fd27d8eqVavg+61rdyRhmoDcodf20PJJCmkFqotLMCm2NM1gkkaUurjMTGWzotajE6raAcgqnhvfiSYJ3MidD1Rc+n4Y/8oC+J6HbiijAyW0oQwfPgIe4xpAEqdq3kF1cHKZnJBGyHY1QaouR+w64dHVna+LK3dBdeub+qdaRKmpD0krRl0nLunIFIOaIEhNsaVim7QxqRY3Pt+nnksneNPCV5Pjzz4QoFtlnw8fpVL47AfMBzwgYAE6mQ+fVeM6O4NKiA9D9PzrSHrOTfmMxTjTsF0FFKesjrPyU1pMly5dKr1fuHAh+vfvj5deeglTpkwBEIrO7373u5g2bRoA4JxzzsEdd9yBF198MRKml156KS666CJcccUVUV0jR46s4UKaT+tK6oKT1webRZSq60zXQ5Sq5ygzX7IuiHFcprK1vHTXa1qPO3Y9SlqYvDrPmLUEfqaByPcClMDCv5WXDxb+jwC+x9DNK0d/u3kB2vwy2qT/A/hegDYvQJsfSGtcA9UJXuLglMfkBNPxrS5WdTPfdROS8lxOVLJYWga9AMyYBspFlIpr3gewi1LxGVOfOTVFkfxc+rGXeozpBUBTn71PqV6zLwlFqf2OojQptlTqHwVLq8laql/kIsUEyehZDqRnv4ffWX2VOtGz1IHufqfwKqPND/uDtkqf4oPF+wYvvs0VdaENv85ewiKydetW6bVjxw6n4z755BMAwN577x1tO+KII7BkyRKsX78ejDE8/fTTePvtt3HssccCAD744AOsXLkS/fv3x+TJk7HPPvtg6tSpePbZZ/O/sAZCFlMLYaeu+6XvMgs2j/Mnl9GJUmm/wcphs3DoRKlLXkB1AJDaZeh404hCX7LuaFI18TXtHawo6nmzCEidm6+W9DF+NOnBbrkMXf8+uqGMwPNRZgw+8xDAR9lj8FkQDl5BCTsDwGce2vzws+FuPQAxy4lpVSfp3BXUtDL264rnZ2xlak0Tl5Wk2fliORHXVZxcraRp+w5dOakNGcR82iwfth+m4X5fsy9uDTWJUvEZN/Uz5YR+QsWvZOjg7/hzH1pNET37bYh/tkGlH+n0AnRU2rOz0id08nynUf7T6t/wWZUNFTqPStIqcIW1nAaB8JnmTGVW/pAhQ6TNs2bNwnXXXWc/lDH86Ec/whFHHIEDDjgg2j5v3jycffbZGDx4MNra2uD7Pu6++24cccQRAID33nsPAHDddddh7ty5mDBhAu6//34cddRReO2111rWckrC1IFaXfhZsIlS02OVpyhNct3bXGVJg0BWjEtlRpaf5M4+D0Eqnku0poiDVmxQhs7Saz93SRqYIMzSDwenkhcKDR9e6L6DjzLzwngzhMUCxuPMwm1tfiBboiJLmRcbbMJrMXzmjpaUVreUiujixE2La1T3u/3ANKEuYSqeSxSpujbY3PbhMfFyVetnfJspv7Fcxi0OXW5Hdv9Smvj1JMFos5KK5XSiNFDqCJQ+UPcDP42VFJB/lAKIBCoAyX7Cn+MAPjq8MvwgHOZLHsMOMPjMhy4Zf4Rjn2ATp7G2F0mc1pn3338f7e3t0fsePXokHjNz5ky8+uqrMUvnvHnz8Pzzz2PJkiUYOnQoVqxYgfPPPx8DBw7E9OnTEQTh03vuuefie9/7HgBg4sSJePLJJ3HPPfdgzpw5OV5Z4yBhmhLT0n62wUl3rLFMQhEXUeqaPD8s6+66r7Yx2Uqat3XSRAlBqg5Pb9HM1h6TKK3WG485c11xSbd6lJhCKhQ8okD1Kw5STzLflX1PUjd8kkSMlNZTF7qSKFWxLU/ciImOQJJ7XyjnsKyoyW0PZb+LdTSpz5DPn09f4B5rarBg6rwfjlbStKK0DF+oS/95qgt4RDmTuThFPDaZP2+8bMA8+H51wQ6fpzMMSuhkTJokFXhmgcqblSRIbfGmQEHc+g2IMW1vb5eEaRIXXnghlixZghUrVmDw4MHR9m3btuGqq67Cww8/jG984xsAgPHjx+OVV17B3LlzMX36dAwcOBAAMHbsWKnOMWPGYO3atbVeUdOgGFOCIAiCIIgGwhjDzJkz8dBDD+Gpp57C8OHDpf0dHR3o6OiIza4vlUqRpXTYsGHYd999Y2mm3n77bQwdOrS+F1BHyGKaQBY3vn6GfG3W0jzd99ZUT44xpUmzXMMy5ngztWwWSl62tE15WW90FhVb2phA+Iz0ycLN5+cWVG41DeBVJkaF9fkI08Tw2FP4QDnw0M0LEHi+PNlJCZ2O4sgyuvX17S2AdaQONMIaqi4zqrrzTST1OzrXfVgmnfs+PnlStQjq+gJ73KeJUsrVjVxSwCVNcgq3Z7eWSpOeNNbS6j7+uVuee2EVupIYbyrAt/OyfLJk4MneEv6/X3HndwYlBJXnXPSkiM++msUj2ubgxtdZTZsNCwKwOsWYpl356YILLsDvfvc7PProo9hjjz2wceNGAECfPn3Qq1cvtLe3Y+rUqbj88svRq1cvDB06FM888wzuv/9+3HTTTQAAz/Nw+eWXY9asWTjooIMwYcIE3HfffXjzzTexePHi3K+xUZAwtRAoI3hWN37SQJaH+x5IJ0pNg4tYRiznsnKKLR+gWkce1OqCT3eueAfrEnMWvpdFadQOzWBV3Wduo5RGCtBOkCiBRbkOu/llBOXwXG0856F+Xp82nRTH1a2vE6WtLFRdV1kSIihyJ229LrlJw3KyKLW5711d9/pJgclufU5JEA7aNFMJx+tCe9RnLCmeVDzGJkpjM/t1M/U1oT6qKE0SqWKMZkkRVqIgBfjqb2F2Y/FLGfh+VahW/gbMRyd8tPnlqltf931n1WsU08yp4tRlIhQRwhPl81RQnIULF+Kss84CACxatAhXXnklTj/9dHz00UcYOnQobrzxRpx33nlR+UsuuQTbt2/HpZdeio8++ggHHXQQnnjiCYwYMaJRl5I7JEwdUIWlzYqaNOFA2pdBkOo66rRW0rT5SZMGGrmMLLbSJNtvFK6z5KVjLOLRNebMFG8m12Vumxh3Ksag8UGLJ+Qus3CBAb50oc8Y2vxwgcPOoKQVp+rsW52VJGyD8iykmJ3f1eBxpkkToJIos3i8oGo1da4r5ax7IC5KbT9kXX/ABvA12xyvx/EHp60+Fw+NzUIK5PN8i8cnidIkRHEqCry4IA0nTQYs9JTw57Kt4kkRxWngsfAvPHRWak7jUdH9WK0lLr3uFCiPKXMoP2DAACxcuDCx3BVXXCHlMW11SJjWiGvyavkYe51ZBKlaJstkhViZhElOpokFLqumZBGHeZI2BEC1ZJQ1g5ptwBKPySJKdUTpsSoDUGRRQYAAJfgeQ4kJK8QA0SoxacQp4JZCykRXSxdVD0ziFHDLjeoiSMNyouiMb3exktomN0n5ixPyhKqWPyCfyYxie1RiIUgaAQnUZiUVLaRyXZ7TNlv7VYujVpQCUkaP2OcpiNPOoFQ5lof78KwfiIlTsQ06cZpmIhRB2CBhmoDNWmoSpTpBmtVdH9Zndtmr+5Pc9qbcgrXMug/L2UVpXlZKue1u4tK3xBS51hF3BcpiNNymd9knCdJwm92VZyOasRtZSiqitOLK51ZTAGFaqYr1gw9ItYrTXQHbLPy80IlTwO510dUhUksqKJuV1EWUin2C7kdqaNFTns2UX6+0+UzDY+wue3GbSyypbdZ9+D7+bJsEadI12VBXwOPi1Of1eZVUcfDge170o7UbgA6IyxjrxWm1vXK/IFL4PiJgQL08OzV4TQgZEqYW1BjTLKK0noJULJPkts/DQhovo+/gJXeeJY7SBZtQc62zzPxwST9bGUtd6kCmitFwm956ArhZSHXXmSXxOBAODlV3fjgAQXDlc0HahkDrvksSp+p17arw/oD3BVVXuTk9UxImcepynNw2YV9KK6m8T+eajv/4ShKlvC/g9fHnsYwSSqJQUK5D9ArocBWfsf2xH+jx51YnSnXPue75TmMdFetLi2h5LsPTLs8cWaYr6aZ8Mf4coSgNrfIBfOZV+oHwB66I7tl3demT1ZRIAwlTB1RLiToQAW6C1MWdmdZlr75Pa+0I26p3ubtYSKtlze77PN3m0vY0HZ3n1gZdnTohGm53c9WH2+orRlW4O7/khQNQuTLI+JFbv2pZ1VlIYgOPxkLiYh3Z1eJORbIm1ufHuQjUpH7GZiWV92dz3Yfni7vv+fGqKA2E/sGvLLHLn7mSx6K6uCU1KUbdZP10RWclFetVXfc2t734jCdaTHOcCBrFmmtDI6oufS5iRatpwCpLkLIw+wGPJQ2E/9U+ITyXXM7k0i8cjPd39aqbyIP8ng6CIAiCIAiCqAGymCaQ1n1vc6mZMFnJarGUhv+ns3jYylTLZrdQpImfNMV0ym1J8bsqRY65uFXGbCUFzJZS10kPadCtCKVSEuJMuUWkJMSY+oxJsabw5O+a6rJLsprycl0Z1/hSUzyo6dtnu5NiX8Ktp665jpPc9+L/abwsLv2G6+SlgPlyDGO0ohGzPtu2ZUKz4DqR0eS+18221z7zljAoHS6plaJ6hDhSIB5vClT7BQBSDHqZlYyeFNGdr3XlJyw1qrrzmw0LGFid2uEyy55wg4SpBR5jqqaCcYklSxM7GjtvBkEqbk8zezZ2bILLvlrefSAoIUyEbxNVWYRoKndYprg9ffwoUJsYTRpEbZO1TIgToLh7jceZllGK6uSDUMCqsaah+Ky688V7HnPTCQm/5Tbr3XdivGpXoBxdvz6+NCpnmBWvok+ZrjmvoyAN2xgXpa4TJNPGlJr2u/YPAfOr3034kmvfhm5Ri2ifcHxJ+e6JseY2QRpuk0WpyX2vS/+kc9vr+hEdPCY0gOckTtXzJPUfvG+IzqeIy9jiGtGPV/Mz31LufKLwkDBNSZIotQ0SxjoNfY9NkIbHpbd0JCXHD8tks4qKdfgIEMBHyWPWgcY2qOjTTFnaKewTU9H4YMZ9puPF+tOme7IJUdugFA1IwmCtwxfKqcdzcVqGEmfK4/i4cAWLLKX8f1OsqTYdTArracC8Lj1IqX2CaYUllzrS2v0aLUqTvCtJlkDdj9NAEFRqf6BOWtQlvVcXtJAQPCUlj2n7Mu3iGRpR6molDZgf66ei+hxisk2ptMJrkGNvdflM08C9Krwufg1pLKFpyjYdFqB+MaaUFC8vmhpjOn/+fIwfPx7t7e1ob2/HpEmT8Pjjj0f7r7vuOowePRq9e/fGXnvthenTp2PlypXWOu+99154nhd7bd++PVMbde57cQASByExgTV/Rccy8yt2TuVY8T0f5G2DSviL3osmI/AJCDzptbi9ut+LXmX40qv6WfjWl4qYCkY3K15MycJfvJ2q5UI8h7g6ien8pmsT96llo3ullkV1YOLWEj448QGJW0k6WAlleOhgper+yvYOVkJQ2Se+jz538RoNAlZ00fleIL2A6uBUzW/Ior/i/2IdNmzWTpM1pytZSAHBOmqwlor7bMt+qs+1/lxuw6ZaTu4j4qJU14fI++NelrSiVEe4PGaAkseiPsD0nePPizohSe2LdKJUfN47WCnW74Xt1AlQ8w9hmygVn3mdKFWfa7Xv0L3Ez9/0WYr/qy/+2Yp9hNRGB3FckvoGVulbwpn8vA/xlTLq/7p+hiDS0FSL6eDBg/Gzn/0MX/ziFwEA9913H2bMmIF//OMfGDduHL70pS/hV7/6Ffbff39s27YNN998M4455hi8++67+MIXvmCst729HW+99Za0rWfPnqnbFw48njaeVDcAmeK6rOewDFRJLvvwf73b3mQhNVk2VAGaJzqrqSlPoKkNNuuoWJ90XsizUXk9fnQP48cEyoCbNPuWv49ZS1k8tYze1V3JOeoFVtedONjo4sfCGbWyNUp154fbqjFlYrxZ1UoqW011rjrxWpKsJV0V0YVv6hNMz7a4XXcvxTqc2mLod9KmknMN/Ukb9sMpeQxgyWE9YVv0bmldWiddG/izFM1ER5gz1eS9UWNKdYii1DTrXnzueX287wg0fZ2Ux9Uzp7VTE+nH9is/WF1jbuXn2+kQ4TzuVlNOs+NMKca0NWiqMD3xxBOl9zfeeCPmz5+P559/HuPGjcO3v/1taf9NN92EBQsW4NVXX8VRRx1lrNfzPAwYMCCXNppEaVJKFnWfK/p0UfEBI3yfLEpNgjTuQtfHj7kSS5RdA67uehEexyqVrbwP49bM7jH1XKaUMDaXfZIg1VlEIosCC8WgfiUc+UulG5gC5qMkuOGS0LnzbSSt7LIrIU6CyiJKVXg5k0B1PR5wF6VpJ0jmIUp5aA8gx5yH504WqDpEa6kqBpN+6CWhWkt1otSW+k199oOorcrn5HGXfPy5VtsuWkOTPR16cWr6kSz3S/EcpvH6zSvB7Yr9ApEvhYkxLZfL+P3vf4/PPvsMkyZNiu3fuXMn7rzzTvTp0wcHHXSQta5PP/0UQ4cORblcxoQJE3DDDTdg4sSJxvI7duzAjh07ovdbt27NfiEEQbQk1A8QRBeHYkxbgqYL09WrV2PSpEnYvn07dt99dzz88MMYO3ZstP+Pf/wjTj31VHz++ecYOHAgnnjiCfTr189Y3+jRo3HvvffiwAMPxNatW3HLLbfg8MMPx6pVqzBy5EjtMXPmzMHs2bNj2z/9NJCsIoBsLY3cRlKsmfpr0bZPh24CCbeGycH73HVQjtrGXUV8e9WVHYC7srllT800IF6j2wOmzngF4r/8A1Td+GWw6LOqxuvxcvFQAtUKAumI9ETuMKP7NG4x5ZaOgAltjvaLFp8yyix0aFWtEoFQTwDVEh2uzBTG3nF3vofwb3Xt60pZMb5M852qtin8v7MSZxewzkosa/gt6Qh8MOajg1Viz1CJ7RWsQ4FgVQkgxzSL30vxf/UTVa0mnZ/vDMsV2N1l6gf+92kQPR/h95lJ8eZJfYIO39AVpLHwBZU+ITxfdXu1rxD3x7eFfUlQWZFH2cfCfeE5yhpLajl6r1pLw+95WSgvfnbVvkBscdhGN09NuVJ/ALE/i/cZ/LOsPveWpYk19YgW0wB+FDde1tRTjeEsg0Ge7BQ+fz7KLNB6TEoes/YFAFD2ApQQrtKks5waQwuEfWGfEFSug//1UvUH/LNR5zoEzIvuqNpXiH+b3Q90oiPeWeVZN5ELHmvySLFz506sXbsWW7ZswR/+8AfcfffdeOaZZyJx+tlnn2HDhg348MMPcdddd+Gpp57CypUr0b9/f6f6gyDAwQcfjClTpmDevHnaMqqlZP369ZI4JggiH/71r39h//33b3YztFA/QBCNodH9wPbt2zF8+HBs3LixrucZMGAA1qxZk2lOC1Gl6cJUZfr06RgxYgTuuOMO7f6RI0fi+9//Pq688krnOs8++2ysW7dOmvFvIwgCvPXWWxg7dizef/99tLe3O5+ryGzduhVDhgyhayo4XfGaPvnkE+y33374+OOPseeeeza7OU5QP9A60DW1Bs3sB7Zv346dO3fW9Rzdu3cnUZoDTXflqzDGJKtF2v268q+88goOPPBA52N838egQYMAIEpl1ZWga2oNuuI1+X5TM9SlgvqB1oOuqTVoRj/Qs2dPEo0tQlOF6VVXXYXjjz8eQ4YMwf/+9z8sWrQIy5cvx9KlS/HZZ5/hxhtvxEknnYSBAwdi8+bNuO2227Bu3TqccsopUR1nnnkmBg0ahDlz5gAAZs+ejcMOOwwjR47E1q1bMW/ePLzyyiv49a9/3azLJAiCIAiCIBxoqjDdtGkTzjjjDGzYsAF9+vTB+PHjsXTpUhx99NHYvn073nzzTdx333348MMP0bdvXxxyyCH461//inHjxkV1rF27Vvr1tWXLFpxzzjnYuHEj+vTpg4kTJ2LFihX46le/2oxLJAiCIAiCIBxpqjBdsGCBcV/Pnj3x0EMPJdaxfPly6f3NN9+Mm2++udamoUePHpg1axZ69OhRc11Fga6pNaBrKg6t2m4bdE2tAV0TsatSuMlPBEEQBEEQxK5J68xEIAiCIAiCILo0JEwJgiAIgiCIQkDClCAIgiAIgigEu6QwXb58OTzP075eeOGFqJxu/+23326te9q0abFjTj311HpfUl2vaceOHbjwwgvRr18/9O7dGyeddBLWrVtX70tyvibO5s2bMXjwYHiehy1btljrLvp94qS5piLfp82bN+O4447Dvvvuix49emDIkCGYOXNm4nr09bxP1A9QP1D0+8ShfqA594loEmwXZMeOHWzDhg3S6wc/+AEbNmwYC4IgKgeALVy4UCr3+eefW+ueOnUqO/vss6VjtmzZUu9Lqus1nXfeeWzQoEHsiSeeYC+//DL72te+xg466CDW2dlZiGvizJgxgx1//PEMAPv444+tdRf9PnHSXFOR79NHH33EbrvtNvbCCy+wf//732zZsmVs1KhR7LTTTrPWXc/7RP0A9QNFv08c6geac5+I5rBLClOVnTt3sv79+7Prr79e2g6APfzww6nqmjp1Krv44ovza1xG8rqmLVu2sG7durFFixZF29avX89832dLly7Nq7lOmK6JMcZuu+02NnXqVPbkk086D0hFvk+MpbumVrlPIrfccgsbPHiwtUwj7xP1A2Za5ftF/UBr3CeRovUDRPMhYcoYW7x4MfN9n61du1baDoANGjSI9e3bl33lK19h8+fPZ+Vy2VrX1KlTWb9+/Vjfvn3Z2LFj2WWXXca2bt1az+ZryeuaeGf40UcfSdvHjx/PfvrTn9al7SZM1/TPf/6TDRgwgP3nP/9hTz/9tPOAVOT7lPaaWuE+iaxfv55NnTqVnX766da6GnmfqB+gfqBo94n6gZCi3CeiMTQ1wX5RWLBgAY499lgMGTJE2n7DDTfgqKOOQq9evfDkk0/isssuw4cffohrrrnGWNfpp5+O4cOHY8CAAXjttddw5ZVXYtWqVXjiiSfqfRkSeV3Txo0b0b17d+y1117S9n322QcbN26sW/t16K5px44dOO200/CLX/wC++23H9577z2nuop8n7JcU9HvE+e0007Do48+im3btuHEE0/E3Xffba2rkfeJ+gHqB4p0n6gfqFKU+0Q0iGYr4zyZNWsWA2B9vfDCC9Ix77//PvN9ny1evDix/rlz57L29vZUbXrxxRcZAPbSSy+lOo7T7Gt64IEHWPfu3WPbp0+fzs4999z0F8TyvaZLL72Ufetb34reu1pKVIp0n7JcU9HvE2fDhg3sjTfeYI888ggbO3Ys++EPf5iqTS73qdnPTNZ222j2NRX9+0X9QEjR7xOnEf0A0bp0KYvpzJkzE2fqDRs2THq/cOFC9O3bFyeddFJi/Ycddhi2bt2KTZs2YZ999nFq08EHH4xu3brhnXfewcEHH+x0jEizr2nAgAHYuXMnPv74Y+lX+AcffIDJkye7XYRCntf01FNPYfXq1Vi8eDEAgFUWMuvXrx+uvvpqzJ4926lNRbpPWa6p6PdJbOeAAQMwevRo9O3bF//3f/+Ha6+9FgMHDnRqk8t9avYzk7XdNpp9TUX/flE/EFL0+yS2s979ANHCNFcXN5cgCNjw4cPZZZdd5lT+1ltvZT179mTbt293Psfq1asZAPbMM89kbWYq8r4mHkz/4IMPRtv++9//NjSY3nZN7777Llu9enX0uueeexgA9ve//51t2rTJ+RxFuk9Zrqno90nHihUrGAC2Zs0a53PU4z5RP0D9AKdI94n6ATONvk9EY9mlhemyZcsYAPb666/H9i1ZsoTdeeedbPXq1ezdd99ld911F2tvb2cXXXRRVGbdunVs1KhRbOXKlYyxsCOZPXs2e+GFF9iaNWvYY489xkaPHs0mTpxY91Qd9bomxsL0I4MHD2bLli1jL7/8MjvyyCMbkn7E5ZpUdO6uVrtPKi7XxFix79Njjz3G7rnnHrZ69eroMx83bhw7/PDDjdfUqPtE/QD1A0W8TyrUDzTvPhGNZZcWpqeddhqbPHmydt/jjz/OJkyYwHbffXe22267sQMOOID98pe/ZB0dHVGZNWvWMADs6aefZowxtnbtWjZlyhS29957s+7du7MRI0awiy66iG3evLkRl8MYy/+aGGNs27ZtbObMmWzvvfdmvXr1YieccIJ1pmXe2K5JRdd5t9p9UnG5JsaKfZ+eeuopNmnSJNanTx/Ws2dPNnLkSPaTn/ykEPeJ+gHqB4p4n1SoH2jefSIai8dYJXCFIAiCIAiCIJrILrkkKUEQBEEQBFE8SJgSBEEQBEEQhYCEKUEQBEEQBFEISJgSBEEQBEEQhYCEKUEQBEEQBFEISJgSBEEQBEEQhYCEKUEQBEEQBFEISJgSBEEQBEEQhYCEKVFYpk2bhksuuaTZzSAIoslQX0AQuw4kTAmCIAiCIIhCQMKUIAiCIAiCKAQkTImW4OOPP8aZZ56JvfbaC7vtthuOP/54vPPOO9H+e++9F3vuuSf+/Oc/Y8yYMdh9991x3HHHYcOGDU1sNUEQeUN9AUF0bUiYEi3BWWedhRdffBFLlizBc889B8YYvv71r6OjoyMq8/nnn2Pu3Ln4zW9+gxUrVmDt2rX48Y9/3MRWEwSRN9QXEETXpq3ZDSCIJN555x0sWbIEf/vb3zB58mQAwAMPPIAhQ4bgkUcewSmnnAIA6OjowO23344RI0YAAGbOnInrr7++ae0mCCJfqC8giK4PWUyJwvPGG2+gra0Nhx56aLStb9++GDVqFN54441o22677RYNRAAwcOBAfPDBBw1tK0EQ9YP6AoLo+pAwJQoPY8y43fO86H23bt2k/Z7nGY8lCKL1oL6AILo+JEyJwjN27Fh0dnZi5cqV0bbNmzfj7bffxpgxY5rYMoIgGgn1BQTR9SFhShSekSNHYsaMGTj77LPx7LPPYtWqVfjOd76DQYMGYcaMGc1uHkEQDYL6AoLo+pAwJVqChQsX4stf/jJOOOEETJo0CYwx/OlPf4q57AiC6NpQX0AQXRuPUeANQRAEQRAEUQDIYkoQBEEQBEEUAhKmBEEQBEEQRCEgYUoQBEEQBEEUAhKmBEEQBEEQRCEgYUoQBEEQBEEUAhKmBEEQBEEQRCEgYUoQBEEQBEEUAhKmBEEQBEEQRCEgYUoQBEEQBEEUAhKmBEEQBEEQRCEgYUoQBEEQBEEUAhKmBEEQBEEQRCH4f1UYjDzNX+oYAAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ds_subset_time['analysed_sst'].plot(x='lon', y='lat', col=\"time\", col_wrap=2);" ] }, { "cell_type": "markdown", "id": "a44f1dfd-9bc2-4086-affe-260b3232642b", "metadata": {}, "source": [ "## Summary\n", "\n", "We learned how to subset `xarray` data cubes by time and space using `sel()` and `slice()`. Next we will show how to select via a shapefile. If you want to jump instead to creating monthly and seasonal means from a data cube, you can look at the `4-data-cube.ipynb` tutorial or explore the [gallery](https://docs.xarray.dev/en/latest/gallery.html) of `xarray` examples." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.9" } }, "nbformat": 4, "nbformat_minor": 5 }